(* Content-type: application/vnd.wolfram.mathematica *) (*** Wolfram Notebook File ***) (* http://www.wolfram.com/nb *) (* CreatedBy='Mathematica 12.3' *) (*CacheID: 234*) (* Internal cache information: NotebookFileLineBreakTest NotebookFileLineBreakTest NotebookDataPosition[ 158, 7] NotebookDataLength[ 2223428, 39392] NotebookOptionsPosition[ 2206412, 39020] NotebookOutlinePosition[ 2208376, 39078] CellTagsIndexPosition[ 2208295, 39073] WindowTitle->Job Training Efficacy | Example Notebook WindowFrame->Normal*) (* Beginning of Notebook Content *) Notebook[{ Cell[CellGroupData[{ Cell[TextData[{ "Basic Examples", "\[NonBreakingSpace]", Cell["(1)", "ExampleCount"], "\[NonBreakingSpace]" }], "Subsection", TaggingRules->{}, CellTags->"DefaultContent", CellID->619504543], Cell["Retrieve the data:", "Text", TaggingRules->{}, CellID->687106258], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"jobs", "=", RowBox[{"ResourceData", "[", TagBox["\"\\"", #& , BoxID -> "ResourceTag-Job Training Efficacy-Input", AutoDelete->True], "]"}]}]], "Input", TaggingRules->{}, CellChangeTimes->{{3.834227721397431*^9, 3.834227750498866*^9}, { 3.8342294780570097`*^9, 3.83422947854563*^9}}, CellLabel->"In[1]:=", CellID->257298660], Cell[BoxData[ GraphicsBox[ TagBox[RasterBox[CompressedData[" 1:eJzsvXm8VWP//39ouBs1KJGkFN0NNClRKClTIQqRPkIDGmkwDxlShkhCKSWV oQFNPvfdfVc0iz53xVESJbmbDKeSIr7P3349un7L2nuvvc45a+1z9tnX64/z 2Gfta1/rfb2v9/Raa13Xqn5zv6t6HJ2RkXFXMf5c1W1Iqzvv7HbP1WX5p1Pf u27r2bf7rZf0Hdi9Z/c7m91ciIOX03ZU4YyM/+/znxYWFhYWFhYWFhYWFhYW FhYWFhYWFhYWFhYWFhYWFhYWFhYWFhYWFhYWvnHgwIHPP/8808LCwsLCwsLC wsLCwiIXiMe5fv311//+97+HDh3KMWv7+eefP/jgg3/84x//m/b45z//uWDB goKnin9GkNdSFDQUVK3iAgVyXLkEYaFABofkwxpYSJCJWt2GAavb8GBDqx8U yHqjoE69Jise59q1a9eqVat+/PHHHLO2rKwslLZx48bdu3fvSGMw/E8//RQT 2rZt286dO/NanCCxePHi5cuXp/n8BguU+fHHH//73//+/vvv81qWIMFw/vWv fzE0ay1OEBC++eYbGydzD6IrMfaTTz6xagwWmKh0Sxazug0W6Hbr1q2UYf/3 f/9ndRss0O2WLVtsaPUGeZlio4DlZcjLl19+ydR//fXXfM5rcQIDc0S9zXzF JFwHDx7MzMxcsWIFBv/777/nmLXBDalJfvnll71pDIb/n//8h8iMzvft25fX 4gSJDz/8cOXKlWk+v8ECZVJ54pg//fRTXssSJBgOrI3Cz1qLEwSE//73v8RJ Cgyrmdxgz549qn6tGoMFJkrmQrdkMavbYIFuIReUl+vWrbO6DRbolkLXlqDe IC9TbFByFCQV7d+/f+vWrUz99u3b+ZzX4gQG5oh6Ox5rg5+uXr167dq1/M3x 7TaxNtguestKYzB8sTa0iubzWpwgIdaW5vMbLFCmWBt+l9eyBAmGI9ZmrcUJ AsL333+v0sJqJjcQs4C1WTUGC0yUzCXWZnUbLNAtzEKszeo2WKBbXRCzJagH yMtibQVJRRD2LVu2MPXfffcdn/NanMDAHMVjbb/99tv69eszMzMxe304fPiw ZW05hmVtFv5hWVtawbK2oGBZW0iwrC08WNYWHixr8wPL2lIIHqwNU2cSd+7c yWcqCq0UsKwtx7CszcI/LGtLK1jWFhQsawsJlrWFB8vawoNlbX5gWVsKIR5r +/3339euXZuZmanlbLrv9vnnn+fgdluWZW0RWNZm4R+WtaUVLGsLCpa1hQTL 2sKDZW3hwbI2P7CsLYUQj7Vh51RWRGnnkTVr1jiPWNaWLVjWZuEflrWlFSxr CwqWtYUEy9rCg2Vt4cGyNj+wrC2FEJO1mTtrf/zxhzmou28czO5mklmWtUVg WZuFf1jWllawrC0oWNYWEixrCw+WtYUHy9r8wLK2FEJM1rZ9+3amb8+ePS7+ tXPnTo4TXixrywEsa7PwD8va0gqWtQUFy9pCgmVt4cGytvBgWZsfWNaWQohm bQcPHvzss8/Wr18fvYTt0KFDWuyWrdVtWZa1RWBZm4V/WNaWVrCsLShY1hYS LGsLD5a1hQfL2vzAsrYUQjRr27p168cff/zTTz/FpGB79uxZtWrVDz/8YFlb dmFZm4V/WNaWVrCsLShY1hYSLGsLD5a1hQfL2vzAsrYUgou1HTx4cO3atRs3 boy3eO3QoUNffPEFDX777TfL2rIFy9os/MOytrSCZW1BwbK2kGBZW3iwrC08 WNbmB5a1pRBcrG3nzp38Cy/bEh+ZmZm08X+7Lcuytggsa7PwD8va0gqWtQUF y9pCgmVt4cGytvBgWZsfWNaWQnCytsOHD2/btm3t2rXrE4E21Bg+V7dlpRlr 2xdBNC+zrC27iKfJdEBC1oZapB/vfnw2yy5y3G06szYPpeUT1lYAPM6ytpBg WVt4sKwtPPhhbQUg7uUSlrWlEOK9ry1AZKUZa9uzZ8/OnTv56zpuWVt2EU+T eQuGeejQoQMHDoR9Fm/W9tNPP6EcatSff/7Zo5+QdMjZd0XgffZopBxr03T/ 8ssvue/KKC36q/zA2phKzAnxUvr2rmVtIcGytvBgWVt48MPaFJZ/+OEH734O RpCfi7dff/2VbJWzq6mWtaUKLGsLHC+88MIdd9zx2muv4eDO45a1ZRejRo2K qck8BGPcunXrwoULN27cGGoc8GZtBOdVq1b17t176NChVPvxzAm9TZw4kWbY ZIDSItvatWvvvvvuQYMGbdq0KVs9pxZrQ7EEfKab8JVLn2XKGHX//v0HDhxI hRZ9orxlbUwip37ooYf69eu3bNmylJidmAiPtaGi/fERzzz0qwJQM4TK2nzq Nl6zVM+nobK2bNktjYlUJI4DBw6kulaFhKwNsvbggw8S92bPns3Y43Xy008/ rV69+uOPP6Z9/tQMUhH3KMaIgdn9bSCszcPSPG5lmgY5Pq+HPLlnbYjt4T7O bmMOP4xx7c83rO2XX375/fffAw9ZCaHzBnItPSsyxeeee25GRkbHjh01doP9 lrVlEy1atECTnTp1cmkyOWCODkXgrBkIbjfeeGOFChXOOeecbdu2hTeP+z1Z 2+HDh6dOnYpyqlatunnz5niRAb117tyZZi1btgxwdkjo8+fPL1myZJEiRVas WJGtnlOItTG5O3fu7NatG9ONEX7xxRe5ucFKkJkxYwZzUbhwYfiRy3LynLXt jzDx448/Hglfe+21sG8lh4fwWNv27du//PLLr2Jh06ZN0X7KnP78888UDPyK 32b3rnR+Q3isDc146BZQMEsAmqFqP8pPLYTH2owFeutWgKxt3bqVKERyWbx4 Mb9N3ThgkJC1ETEU9+69914Sa7xO5syZc3IE06dPD6pcDBDM3bJly+rVq1ep UqXHH388u2QhENYGRYoXHslrMZ0UOSlgaINrB15NBcLaIOnxfIdxIbaa4Uec yxWd1CDwyJ8fWJvI7Ny5cymNqOiCHaA3mMoFCxZw3nnz5kmSXHZID23atCEC 3HDDDZa15RIXXnghmuzSpUvyWRtjIZg8/PDD9913H6Yrl+cvvtmsWTOkKlWq FF6Zh6ztrbfeQoxatWoZ8aLxxx9//M///A/NLrroomBZGx5dsWLFMmXKrFq1 qgCzNphUq1atUOAZZ5zBdOSStb377rt/+9vfjjnmGMJ7PmRtVIzVq1eHib/+ +uupW62FwdpEWO6///5To3DaEYwbN871KwT46KOP6tatSzMiyc6dO1M67IfE 2kRY7r77bg/dTpo0iWYEPZrpiKvla6+9ltKkOCTWRl4gqtx1113xdPv3v/8d gqb7IKS2UaNGnX766SeccEL58uWrVKlCJUOsTmmjzfLH2oh7hQsXfuihh+Kx NlQ0bdq0jAhefPHFfMjafvvtN8ZYsmRJJLz55puzO2u5Z22//vpru3bt4lka hQqhw/XQFD/BtYmQNHjggQdIkTk7dTzknrUx0atXr/YITS1btsR+SJeZmZlt 27aNDk3UD4Ff3s8nrI2/uAz29sorr3gMcG8E8XrwRnQbHRkxYgTnffbZZ+N1 nq3+g2JtHsIE3ib38M/afMqsD4GwtoRnjPktbvjxxx8rr61du9ZEacaIswwZ MuTtt9+OWSf417a3YDlgbdG9+WFtOfCdZLK2mFryM6c+J8LD5XWvZMmSJUz3 3LlzkTwHkpg2gbO2bLlSwn78sLaw1R5IQMsua/MjiYrq3r17l/grqJGKFi2q Wm7MmDGuHigVWrdurW9vu+02ekjpAjgHrM2nbrH8nj17euh2/PjxhDiC3jXX XMO/hQoVcjV+7rnn0o21+dEtSsMIqeGjdXv00UdLt5MnT6bZoUOHhg0bRmg6 6qijatSo0blzZ+gb3xIQmO5Ut9vssrboIMO/1N4jR46kUIx+uMVn3AuwWotu iUgM5KWXXoL+YEXZDbC5Z20wsiZNmpSIAt4qS5s3bx7U0rSnpvrss8+or/Tt rbfeSrmSs1PHQ+5ZG3lw+fLl0YMqXry4xK5Tpw5qZ+wUQpUrV+YIXzlbUkNu 3bo1WA/KD6wNxs2g7rrrLj2cQ3nD5HJQN+j5zPyic/F05/OxeyPPsNHyp59+ omX0MkwamB4wSz47F5PyL8cxcs47atSo3yNwPti8N7L+VP1rmWe08hXx6Ic2 WqwK484xa+M4J0JgGuuk0eWT2nAiGqAiPvx2BE7h90d2UdDtIT2mHtsCgoAf 1qYJynJozNXApUnGIv7rZG0apvNE0kbMZ1w19dKA9g9xWo6+5a8MwzW5nHH1 6tUQk9q1a2/cuPHPyJsKZV06neuq0d4jS5U5F5LQOFobmiltV6VRaPpiXrjz z9qISxqIsUAzkHiszZiZtC1zimfb0YOKx9qk0phjN0jI2qQlPRCuIK9TS/my amWf6BM5x8WJoselPhVw5FxMgToxfgf4lY7LTlxhwTlYnVT6jx6LggbaoytM aPbs2blhbXpelyEwOs6oHjxcyQRGWV10h1Ks+sGiMjMz47E2MxD1HC2hVI3S 5GvyU6ebOIU3FmXG7uzf5eDRbfg23oiyfLA2zi5JzEQ7FahQwEGXJByhbPvi iy82HAFh4csvvxw4cCD+1bx5c1dq5uePPfaYKAZ/77jjjnRgbX5mOaZuv/32 W6duUSzqHTBgAKpr1arV9u3b1eeVV17JEUqFzz//3DTmh/hOkrQQDvywNqNb lTE+PYgjWGa0bskIaJJahfPSD+VZ48aNOTJkyBBmmSP8CqvmyKBBg1KdEftn bURpE1tcoZWDKk2dkcfkI5N8zbcKU8o1Kj+iE7SR0DttmQaKWs4yySmhThS9 zkjFiXf1lZC1KS+7fuhMjpwFPrvhr8CesbSjjjrqsssuw66McrTY5LrrrsPA dHGmZ8+eyWdtchYXcZC6GJe4xp49e1yDwpDee++9448/nmw+ZcqUrIipUANT EZUuXXrp0qW4mGm8adMm50PIgSDPWRsBAcb9zjvvXHLJJcxdv3793n333Vmz ZlENYiEff/zxzJkziTk//PADMW348OHPPfecbjjyLX8XL148fvz4Rx55ZNy4 cYwCDTtvi6B8wvucOXNefvnlhx9+eOzYsbRXZKMB3XKiK664gvP26NHj3QjM SnzVbIsWLVL//F24cKEm2gjPuSTYSy+99OijjzKDZLR27drljLWJRTJwTAIW yUnhsArjpr1qftRCZcWIHn/8cU5KewnPVJqAQ9SdO3fu888/j2BvvPHG+vXr w1tfnJC1IQ814fTp00eMGDFy5EjmFHtwVsIxNYmnO1kbbs6ImE3nTiD8iil7 ++236d9Jyph65hfNoAHKJzpEk/uOgG9pT2+YBGp85ZVXmFxjGHzg32eeeQav rFq1Kt++//778+bNw/CMTX700UdGeFGMJUuWMCkIzxBoTCHhugKAfXJGjmMA eARTzPS9+eab+HU0KfDJ2urVq0eoxKqx/6FDh06ePJmJMBMdk7X9EgG8CRKB qPxqwoQJy5cvdwVk5QXsn0FhirREZtIfx2OyNg7iyDgUOvfYqsWbtfErxoJg qHrnzp2oC5d/8cUX0QPVBVZEdYeRMDVPPfUUk0I0NmLzATWiNH4+ZswYpvXV V19lUsxm+7r9gZusWLGCsMM8ojRsg7lGeIItkqNMzjt//vxhw4bRCZ/Xrl2L ddEt2th75H469oNDYc80e/rppxWjnJOoRf1r1qyZOnUqpxg9ejSS8LlUqVI5 Y23oDalogPF89dVXGNizzz775JNPzpgxg5+4eAdt0JgCFxZOzFEx4OyQbMVg GTLy0w9DwI9q1qzpYm0qGFAChopt4038hNlx9kZjGhBkGCltpk2bpnCEfnAT VE0nyIDMWr6EPuWSeyOvQuDn2AP9c5DwzrRiZk7aqDbMFwqkfxyHKWZqYjJl b9Zm3JAhIAx5Bx+cNGmSGSk/xLM4SJhiCE4v3uuA5pfRnXLKKRUqVMAmnafD PAiJ5cqVK1u2rG63pQlrW7Bggccs4w7xZtmlWxTINFFLE2QIQXJhbPjiiy9G mXjT3igkURPBIyFrw4n4lgiAQRJdMVHUiDKVelAjAQq/4yAeinfQm4duCSNY 5nHHHUfQ0xGyxoknnohuqTMlALmD4EZFTe4IvOxMJnyyNuIe46UlSsb3XaFV nZgywFxCxNRpRj4i0hJGSF6UoCrYcAHVsTQmxZBriF20IdVqInR25dPVq1eb tEXcdqUtihwmiBMR9BADH6QlvREPSSWmmdyTZozUjA5h6J+fYy0MSgGcBq50 783a6JysTTxUEWWO054QirQmMzqBXfEtRlWtWjUEE20U+IogQP1AkNSSk+Sz tr2RdbKoS25l1IiSmUfCl0nTzkGJWfft21f3B1XMMzTck+Reo0YNZfBQQ1Oe szYCQu/evTOioN08brrpJj7ffffd3bt3L1y4MJ8ppEnxZExYCamwRIkS5ifF ihWjPcdF6PAaLMHcgRWwk3vvvVcXbO+5557o87Zt2xbj4VtqaX7ORJivihcv zjSZy3q0oXK79tpr8XfThiK/SpUqOWBt2AwucOONN2phrMGpp56K/aiNfkV8 UICNBgxUF2qgNueff76rH9iHqTyDhTdrYzpgzXXq1HHKc9ZZZ+EsCh16Ouua a65xavLkk0+WJsXaEJuZpQGzAIMwtzkIoZdffjnNiJxiK3sjD7ahNBiN84zU V9T/BECcEctxycNEDxo0CH9kIgiM0brFunBMJOnatSv/ol5pEuGpom+//Xbi j7M91Ro1jImNyNagQQM6ueuuu6666ipnywsuuICK1FWC+mRt5cuXv+SSS3AK 0xv2Q+5WCIpmbcjDTN18880nnXSSUwYyOKnKFKj8HFvFtvWcvHD00UdjYNTn xCgXaxNla9q0aUZk5xPqinjG4M3amNYWLVrg6X369NG0CogxcOBATqGHZg0Y HfWJguTnn39+yy23yGYMmBQ4qYgDo8NQOYj+4aqmZb9+/ZhWXSIgFV5//fU6 zgCZWcIFn3v16kUw18U3WVetWrWcJ8LYKC813TTDzDgvNmwaHHXUUbVr18YA UFoOWBs2iTxM05AhQ6RnAyYFMmu4M/zxyiuv5ESmQaFChXAuJtRkLlQBTcMH nf2cccYZpUuXxpZcrA2CTDJytsS20aTaEG1eeOEFVwMDMjJFEbKddtppsBsY 4tlnn62vGjdujBgoCorkVFRGJAib+kFP/jz00EOuoHfppZcSfqNzsTdrQ2aG yRQwKN24yYg85SJSQFVDlHCNNOYzwLQn8oiRUQg5L6wpIDRp0iQj8mAkyslI D9aGTnCKY4891jnLjRo1QueaZYo3p27x5ZhVomot5S+qU+mWvzjIeeedx0FM V1fInbc2UhoJWRuJG+0R20lzxv1RMnqAJhDNXPEceosLRCtHxnnOOecQY199 9VXzOEFmZibRiR+Ss+TX5A66JeGmA2vD64l77dq10+1Fg/bt26ue1PomAikJ haSM4aloIXc721NFdOvWjZ/QAPVmROouykjzrG9GJK088MADeIR4GWmLdOwK bqT11157TcJzIkKNshJOpJsCzgC7efNmTSKRv2bNmhwkPZniBFcaMGBApUqV nL8688wzycJO8/BgbaqpCOD8kDhsHp8gD5IW5cjRdwToHHnIC+RuKk/ns5H0 sGbNGj1POGrUKA0t+axNl4gzIsUPRZGKHwbCtOIgHIfexnwUFuqNtVCobNiw Qc7y+++/05gCiYM4sqJTeHdJDGvTfXOf0HZqgbA2pp7xUqTJKggRcLT77rsP Xs9vKcM4SH2Fv5BbOU4YZwoIIw8++CCFEC7AB8I4CZfPHKFYQv/yqWOOOSYj UtqNHj2aHyoRUDyQiLEcWEP//v0VrCg1KYfon2KYodEDeZbjlDEYKv3DlVSW k6M1U5wCapYRqccoinSdxHhHdlkbZ+QrhskoMGDs/Nlnn6XOz4gQHO2vjiVQ GaqQRmPa60kGRjpDA5A1xkVYVj3Gz8eNGzdjxgyKBw2ckjvJrI1xffDBBwpK FMYI/MYbb2hzSCIkGUc3/bXVYUxNGtaGA1KLMqcmCIi1dejQgWbPPfec3IS/ Cxcu1BmpIqjDCYBMB5GTnjkdykTJ6pmait70KCa13NKlS1EyUjHpCiacrkeP HpTuw4YNw4aRhLCcESnnpElOx1xIVEg3CseKKF34l/RKAJfj04xJwa+xKDIm 42V+jf1ASVxLcX2yNlP/kM3RgIpnZRZ6iMnaiFTEHMhv9+7dX4xABLZ69eoE ATXDK0lYGZGCv1OnThMnToQJEmapRSEILtbG0IhUUDzakzXIbh5vakjI2lq1 aoVCpCVUjZaUSTly+umnM4mciJJb9I2BUGbomT2YL4MqW7Ysv2KicXm5Br+i gPk1go8++ogeiNKkVzIjGsBrsBamFZLIeQk1/G3YsCFRhbRLoXj//ffTCawc 1sZZUODy5cuVcSj7MWY4DgyCf+lB1zBRCL7MWXR2Ui1K1j19gIQ5Y20UZhRR uD+Sk+iZcTFN5mj27NmqYGkmZ2eYemsGnJRpyohcB+NbsVcmmnI6I0KHBw8e jNH27dtXz/KhQ8Pa+PvOO++YnxNJsASUo3pGDXAZ+Rp2gqfTXoSFQoiMTP2g W9jogclCbwiG2BSEb775JhNHyNKVN2aNI0ii4HDTTTdp+w7acF6RUIoE+n/5 5Zd1eZZJid6FOyFrQzzUiMw44yWXXEJgZxLRiVnLT1hAkvHjxytfEAeiX67B v++//74WAVF3OQ2ez6QVlPn3v/+dUINXZqQNa4MRM79mltGtZnnu3LmaZabV Ocu66hKt2+nTp6NbSgLqIukWt8I1RFiwagqnkSNHTps2TRVyklQQGvywNnKZ PIi/hBo8COciYFIpaZXNrbfeypGxY8cq7hHc8IVo3U6ZMkX5C+OUmytpKuAT bPEdkhEH9fgTHDxJWggHflgbXkw0wGcJrZgooVXXDDkya9YsPVJIOiYKEdXJ Fxwh8pDoMyK8jORIjKLSIKqQC7TmAis1iwfRNnGAEgIH4V98gUJO3ZJAlbYI 6aQtChJd8aAljkY/Ym3K49QV+AXFA8aPnIrY9JwViWw4i25SECQ174S1O++8 U2K0bduW/ilf6ZncTZp22oY3a6MewPw4NVbnZG0qki+++OJohoIJUUTxExii 82YBUYLATujOOHK9UZVDnrA2ppJTM2vMgpO16eoQEd71q32RhaLaoIy60ajC 1GOoghKXxPfSSy+hz6xIIRfsoLIcrI1J/Nof0ANFV1Zk7U/uWVtWxCqggap8 qLV+iACTYBIJRDJ7Agg2yXFd9qFUoO7C+IneZut+3EQVLLxSjwlhvVBCPbmN him3dLlPrwDjvBAc1fzDhw+nc0yX8+JNZBZxQBI6R5gUhCd8ETDr1q2Lm9OG 2gZfo82AAQP05Jje6KEaOAf32jBgZnzJkiV6+IdTEJApVnE6Cl0t7kPOjMh1 ZvTJqFERbVAFPrV582Y9L4FH8xPKJOpYTBHhEUzPllChJZO1yUO10I8wiMHr 4XwmCI5AsKI0Qjw0qXXQRBijSYQXoXayNn5CDelibbp1Jda2P/I+NSUgWBJV uspUmvF5+/btNICUEWOJxpxLj9GuWbOG2MtP8DXdx+c4lsMRwiChDBtQfYjC ia4yM5WUmIrqc3RLdaelUkyKiNtdd90lUTmuUpO8gKi6cYP5qXpp165d1l8f vvXJ2qg/CaR6mxh6wy9024JIiGwxn5CkQxSImUnVemAGqUhJCkSAsKPbmtBn 3EeLTJEHx3c9Ick00R7WSQbh3/nz53uvoEzI2nT1Es9CSzgaOmF2EE8cDQk1 aytWrKhatSoHqVLkyxxHJ2RD4+98hulgV8yRYW2qcGBnDIGWP0WAPg2rwnNx JeTkOJMu1kZ6EmvjLMoyhCbUrgVcSNiyZUs0NnPmTIaPIyvpk3/19Ih++MQT T+jiQM5Ym2YWyamBtfaEaKMbypQZeuqGcpd5RFEEUuxcS9vwMkYNC4ZzSRh0 S4igJQGHZrobQlRkTs29NmUoUbCuXbsyfK1TwwzKR4BWMbAHHniArnAT/I4G WB2pkKjF7MhaNDqVK7Tk1IxUeRzyKPaN7+CbWlm8ePFiRopsTDHyw4nEnmSK UjgmRPnKoKIvQyVkbeZWBXPKSZVQmErVqBAuYoUW5S1btkwX4RHJlb5Rl3bG wFqci1C0wKFChQqqnZCfrJE+rK1+/fq6DOWcZdiBLosNGjTIzDKeqFlGya5F JZxFt0GpS41uVZRqi4xKlSoRH6DY5cqVIx1Ti+bbV2j5hB/WpmxImMW1iQa6 GIJOFDBhEChcdosDEvfwDlzVqRYFGRVaRHgn20W9zIjUW7NmTeg2aYXZadSo EU6a6rr1w9oUWinU9WwAIV3GTMVlnkFFpdieWBuxUXEJLqbVZMwI4UiPmALC KY7AT/r160dsVG2GklUfUupg58oLBFJn2iLpM32ESji4i7URV1999VWVMQig i/NXX321toDg1DISsTak4oOufnfr1o2koxrys88+oxJ2KSEhayPCI5KLtekx OSKni7Xti2xeqguDKND5bCTNpk2bhlooWYmr/LBTp055xdpQu+bdxdo0s9Gs DeEXLFhAJoVHYCFmXEgOE9FVUAo/CnJm6vjjj2domETgzwMY1kaG3e4PWBeS ZAXH2lAU5auuG1OD6TEkVchibVQIqMg8/8ZPHnzwwYzIOmUNQSvKMzMzMWMa kwjMAkkVmUR1xKYAUDp4/PHHsyKX7/AmbD4jsoekbljrvHoyk0JOV6u0gJq6 hYmgsGGKGZeWUuLvmIR5kJU+c7OHpCgGnVBKcTqmRg9iYRJycGybf/EdJUSt rxHjoHN+iybFWajQzH0TgjmpDUM677zzksnaEG/27NnUV+QaKkzNlGacUMZB ihm0pDLYpUk+OPeQ9MnatO7s6AgmT55sNlvQHT3n2GUYBCu0CouUDJSgepqO /qmNOUJVgBc7V4o5WRv/6nYwNaqptzWJN954I8cbNmyoR6Z1r40w3qNHD90b 2htZwqwH8JgXmjkV6JO1MfXMuB4J3hvZqJD+MyI3GTFd9BbN2mTP2osDv/g+ ApTPdNx9992qqRS1IB2c3RTe+4+8MtKwNmonymNdNxAHT2gqfu61ZUTuWurm EeflJ3Ai+aPWCGtBgaL9Y489lnXk4WHjPvJ3wpRKFEhNVuTCo1gbtqEnr0yo MayNikWXoHXcxdromcyIoijLlbuNneuWigILOpEF4rZm7xRMhSyQ491IdK+N U1OeIa2UwEFFSOphIgYzrtnHmIls5n4ZX0mBHTp04AicVIPFks0KcZoRSZy7 kaAubIwpJqIyasYu5+Uvs4MlY7oYmMgLUdTcLOa35EH89I033lCKN6wNu0Kl Ui/9Y95oCR8hvJuIzU+UC4hgaA+l0QbySLGhSyL6rezEXFU28MnasG3Itbk7 T8rgLAipxzCyjizt1zW9l19+2TVZBFtqWioiVGTMABUxs8oLxDdGqmt9xn5S vfr1ydpcs4ynoNsGDRpoaUPWX2d5woQJrrMQwLHzE088cfr06aZEpDfoA7OG +zRv3nzAgAG9evVScsSnqHuToIHw4J+1tW7dWlsMSYcffPABum3cuDGxzukd ulKqRYXOsyxduhTdkq2MnQt6QrhPnz4KXHi3ngZ87733Qt3HLAnww9qIe0p/ Cq0q9lRrYWmYvVZMO1kbfap0JBh+8cUXmhRd7MqKpG/dRGDWmBpdfNBXjz76 KMdbtGhh1hG40hb+pStIFKXqyrA26l6mScWDeRQNp4jJ2kz9jMxIaIqE/bFe /Rwsa0M/FGm4KkWC8+Wk5rFJuqKBtmoxrO3PoF/wFDhrA5qIjh07SngdxGwI QRTY1LrYwz333ENw0/Mh0IHw9pD0z9ow12BZG+ZEpaQY7tz531gdJYdr1CrU KYNp/3wEo0ePxh10g1gvhpB9ct6ZM2fij2R589DdwIEDcVUlWbmeLlbLs3BA BT1Eohh7/gio9LBDpmbGjBlMrgohyiSnYHtzsfO/akiKk2nTppH0cUazVEr+ i5s8/PDDOqnuwmMtTB9cklpLD+pAjuS5FHIvvviikZwKDSsiXCeTtVHIIXnJ kiWp6tGw0SSTpcezVYdLk7it6+c5YG0cEZ0hDpsiwQUZBsEEukENzFnMWkLS lu6C6dktsba1jp3/naxNJFS8AJpPbWxOwVdkTHLfKaecsmTJEsVzsba+ffua l8JgaegnI/J0ui7HOXvIwfvaGC/KKVq0KBONscVjbXwmZZCR77rrrgsuuOCE E07QFXJFTiTR9UCMLaYCDWvDPseOHavbPcReP3cTfLI2HN+wAHIZQTLD8b51 JWJlVSKkrrTLebXpzeDBgwm8uuWREbkQTRu8Q09IMt26j+PUp4gMHMT5BH40 a5s3b15GZPHgI488ghnLntG59Ixj4t26SwvJ0o1IdZXLnf/F2goXLkzeNNtT E8QYacaRBzjpQbdutSOuOQVKI54wv3B8TA7l66I6gdG5d9O6v+78jx44F+oi bJqRCnJYhkzPVDX0fO6558rI9aJeiB62gQeZKp0zkqkJSsb4OThu3LiMyCKL YcOGmVNgUXoyHPOjGUcU6ocPH/7CCy+YNjJRXQl3KsrPE5JyQ3NpF0kmT56s ayzwbqckutU4ZMgQpxvSs94Xw7eGi+nvU089pToKaowC0cbIkSMVWLClmN6U KvDD2qC9et7bOcta4AOzeOKJJ5y61aO2FKLOLQr3Hdl7E2rm3HQu68iaXBxQ Gz6gTIKYdj4kBDHvYWsgPPhhbYR6PIhw53ynGJlddvjkk086vUMOjiZdrE07 Zutyt9Ht/siG1aq1yHdES9xNK/ophChIUnrxoE/W5tr5HxvTBVWyDD+PZm00 mzp1qiq0atWqoTQSPRFYAd+wNvSJAk2Y1ROtGUdeqaCbXyZtER7PO+88k7a0 /I02hrURJ83ThoiKo+FuGH80a3MWJ9SizuIkJoJlbcimi1fXXnut07s5S9eu XZGZn/BZz4jqgvltt91GNgn2RXiBszaiPbODa+BlziUtugI/a9as9evX7z8C PeKiy49pyNoUZJxwraM3oOSgLiL6abko9aF2uNUzPJQ0WmxOsRqPtWn9pmu9 v4EWxUCrzf1fijenYDlmbXJePFo1MNNN4EVgPRuGeFgFLk9ByFfIQNGOL6O3 /v37Z0QYvRYcUZfqJy7gKdoRMZmsjRmEL5unu50oVKgQyqRUJi5JkwQE189z wNoIEXqItH79+npQNlpa5h2BFdDAaaedhp61jJco5J+1cRCd6yl0hslAzCno AdtjjJopvXkhmrUhrYrSoFgbZ4EtQs8rV668ePHimKyN6ERU12IxJGSAjAWb 0X1AfqLHLJk1iuqYChRrY2i4m9KEQt/y5csT5vecsTYFdidro8jRs/Fibbr3 9MEHH8iWSMFU9VAJPRFNSiVtGdYGS0XUmKxNT5aa49FPSL700ksxIwPaQxvY BpEB0iTLIRcbBYbB2hiUtlQSa8MaKQYyIrfmnU+b0P7RRx9l4JydEZkQgYUY JUSzNn5FJj0qgmjnpZkcEzcpW7Zs6dKlqRUR4KuvvtL+Wgzfub+ZWBsc0ym8 ashoMExEpWpiUHojTDQQADtnXDljbdBY44bYpJhCTEnQ25133un0bgxJWYPI Y4K8ntrVBRA0oyUqQPcisWpi+OzZs1P3dptP1ubk5rpb7T3LRE7nZhdMkC7o de7cOfrauxaWOi9HwFbohDmCJoc29NDhk7XhdziF86LNkCFD4nkHaoERu86i 4kTXW8xx7FlLRykPCP70j2+SmHSV+8orr0xY8+dn5Iy1GbuNydr0HAhxg8qc SkMRUkvFtbGYB2vTdT9i5urVq9Vy/vz5SlvMmtKWHoYn9XizNmYfd4vJ2lSc aFGG66JTTATI2ugBY9biSoox0w9tYKZFI8C7qeIUIXXhBaLEEcrInMxxHATL 2hgXGRMfJBEvWLAg5suAnFdCsCsVhyREy9qASm6qTbT33l9BaYS5oj28Q9vE 4Ur4BeUEtqRXwHizNnKTSkeYF725+p87dy4FNl3JI0g6TsFyxtrkF1DyihUr YtLUNozr2whUXYu1ySREUojJWLuejcTltYUvhsRsqnIbOXIkGnAJT17IjbXE g8e9NrKqthAhL7iEmTVrFr9CG+Lg1J+un7tYGwWtWNvLL7/sca9Nl3YJMnrZ gavPvZGtvxUVb7rpJmYTcocMqn6zy9qwIglJVe9MbfTA6Ajm5EF0rscRk8Da iKjPPvssgQUbwKeiWRvjgotplx4GC83RAkkyRUbkVhE/+fzzz2GyiEpXHqzN 3LluHUFG5KkP/MLbVMJjbSicmpmxc5xxUcURqdQmKNbGjJN3MiIPxGJmGI/L nknE+OmECRM0NXq2Vl0lgbURnHVviCjqZG0oTfn9xBNPRDzIhXacc6aeaNaG wOiNf4mi0ZGEwVKfoBB+CAOSJTRo0EAPrfET2jj3zHextiyH8Z933nlvvPEG 7uYK4/gv0lKoyMamTp0K63GJga26FJUD1oYkhBQZMGN3ScJIidvmirGq5WrV qmFIyGbuzJobdh5o2LBhvCSY/5ED1pblY5ZVS5hTYOe4J3H+6aefdt71jglt LX788cczobh26t4SygFr00Hd223ZsuWUKVNc3iEPcnZCrjz22GNLlizJjBjd agsjPVlNmSEd8pcAAiUpX748J9XLF0LWQVgIibVlHXnQl0RDbUOCKF26dEZk +6kvvviCJBKTtdFeiZvjui/Db5W26EFCkgi0o1QuWRveqrzcq1cv50WnmPBm bZyUggove/jhh01+jMfaDkZeOa0UQ2VoLAeTk5l5gLBMUguK4/jcjYRsRSGq OfVgbXxF8s2I3BHQinvvs5N2zRNlBZu1jRkzxg9r0659bdu2xRJo9vtfsS+y 87YeoKIcRcO0we/4oB8a1kaprJp/xIgRWUeecqEHPZHVoUMHvW7PCb1qkBkU O6Meds4IY9TdvWyxNm1Foh0M8MS9kXVVeAfeKm4o1qbNXQkLhA7sgRqJMmPg wIHaDmhvZFWdWbyvvQKihc+dvcRGPNbGEMgmoqLErmh5tD+qiM/FF1/sdAQ+ u96yTTQrUaIESQdtqKTXoletVTTr2nT9BKqI1TnHKxXxQcy9adOmJEoZBiGL SsPF2rSujTCoJxnUiWtdG2Jo90uIs3NaMRtdBdX71PZH9sZPAmtDHhEcjIRx uXYj2RtZwiNadN111+EC9KNXNuviAEGVn3BEDy9dffXVTgqTdcRBzL22jMiu 6UQ5/tW2itRaudyNJGesjVCvNzKQ7/iXcTEFTKXSRICsDWPIiOzlBfdBGJc9 0ydzARvKiKwcJykYC+QUuV/X5s3asEA9l4KNmbSisCAfIYlzhOitZwlGjRql hTCyN0LHKaecYlgbehg7diyjYKKJTtHOqwf7iVEU5DQjZmI2GBJnd5V5MVkb p9CFEZIg9Ua0MvW8JXEsI/IUJUqLbhP9RE0OWBstNWWQDsJUTElMD7qnRjVb pkwZCJ3TishiEJPJR/BGBDJLghs1MGV5Wt1ry4okdwVSZpnk5a1bGi9ZsqRU qVIVKlSAg7iuZivtOo/w89GjR2N7ODXKT2nd5oC16f0dyj5fffVVQrslHBF/ iH7z5893Xq4hQ2ktvPNKC9r+8ssvVatDQIJ9dC2ZCI+1aUkLmuFXhF+cnZiA uqZPn85cGNZGXDVrw4nYqjS0oRz/Ut5kHNkOV2mL03Xv3j2XrE01pG70E3td ixeiPcX7fW0MXz1TbTqvUykFu1gbhofz4pI4JiZkEsG+yIuGKQgnO0CuUTKi 2ENsGgT4Sndv1sZIly1blhF5FwNhR3mfkZICdBHbxdpooCV4zJ1r4xTdMXGV rwxESwkoHgoea9MiIxlY//79zWsK47G2vUd2BqaGJ2rpzeZm4wJNOp2IPcG8 tPnDvsirMaRGsbb9kX14lFVxE22ptzeyNQHegYdS+XOifUfg7B/Z9K4KajC8 WP0D1KhbXdllbWYv7pdfflkCa428NsqDp2hzS23bC7lAnyiNUl8LdiQef01Y wFX1smMhK+KYAVqOE/FYG0fI1NqIAHasElHy8Bl5NMvSJBGPYs9okgAiTRrW xkhPPPFE5p3JYo50CQh1aU9ps4ckZ9RbJLp160a4Mx1ynOnmV6pgMQ89Rrsv 8i4ArWQ0rE07K3Iugg8ebV4M5GJtiPHUU08hAPmU+KPR0WdmZqa2pYL4mGs4 YbC2KlWqaN2B4gYy6/4y4VQXNAxrk3LQgMwM6moch1JB0sJx6Pn3I1uakKEI v7rvr1sM6yIv2TTr2mDQRFoGyKkpg8kgMAvE9kjxIbE2/tXFiieffJJO9GA5 7iY2GiBrg7+I0nIEnmUWJjCVetBrf+Q1jnq1BLIpmgG6euSRRzCA3Oz878Ha lJdffPHFjMiyO7rSxOmNz7oxqucJsQHtttS4cWOKPclPY1wJHzR7SOruW7Vq 1ZD5lltuMSPdF1n5q+IBHdKYUHnBBRcQ1ghKmLFUvd+x+U9M1kaHa9asqVmz JscpkBSQTf8Ks/TPTMEl8a8nnnhC+3u42riQM9aGE+kRIAohLadyjtTZg0pl ChKmmPjsNHWp0UCvznSua0vpjR1yzNqYi1NPPZXjuJL2zYs3g+jn7bffxpyI 8yQUo1tV0dg2eVCXIwDfYvPK8tTG9By2BsJDzljbgcir5+UdxBaFGg/dQiuK FCmCR69evdrolsacWlu4457GDQmD8+bNq1y5MjFnwYIF9l6ba10bDVAjRYVJ vkR1FR6ERMPaiG8kHYVi0QQtoicUZ0UuRGuT+eHDh5u0hb/o8cJcsjYaEDMp YmkzevRoE9NojJm5wpo3ayMXK5NefPHFqoUApEZXbl2sjfNOnTo1I7K/nHa3 Nv2orHWC0any17q2YB9FSMjaUDUegX6o8xkjR8jyVAvaSMTF2pggzQuFpXNt qd4KQVGqGlhFoB6F1UtzXn311fBY2xZ/oG5hsFnBsTZxjTvvvDMj8pYfiAnZ HPPmt/FYG9auq8o4EWUzRkid/O6771J64TLa71pFHfH/zTffJCkTsjBpPYFs WJt56QYVEbolH8nFiGPaKr98+fIjRoxQ/zNmzIBljx07Vhf9VqxYoVoXmekc F6bsMa+LzcG9Nu1XdvbZZ8+ZM4eW1PPat9zJ2hQK6tevP2vWLAYFD+Uvsum6 zb4jD0lqOQ/1OTohsCA/oyA+0DiZ69qyInedyLZa2szswMvgp6hOL5vWCn3+ 1XpD9Gk0KbrqZG3Mqe44k0poRvFPJ8UiyHA8IQn4uV7Y3bFjR1Ih5vrCCy8c e+yxykpitVjO5MmTUQh1QoMGDbT4zrA2pMICxYAocVdEoOuZTta2L7LZlKSi Gn/22WcxXYo6MSOmz7yiNyTWlhHZRnL8+PGclyNSI6pj0hW3tcjI3PJjOvTU BBaCVzJ8NEN7LV8Sa9t3ZKMnscKnn34a+5k/fz7DpH/qBCKSc+d/5Roig5Zk Gnoe01TCu9emeUHz5Cx404QJE9CM3CfAdW2cmv5lclgX9kyuxPgxKvK73tdm yBTVFOwYQ8IIMWPZWHisDRvbtGmT6ljqEKyCiSMeal7QjF77qBXxevErZoxs WClVn1KM631t1AnKa507d8YxN27ciLMz0q5du2ZFLj/ia/yEUgSzV5wkKPEX Z3He74tmbXIfgoP2rMP2cDGUuXTpUmZh4MCBjEjlAW6lTe2gWtgbbZAB5TM1 0StucsDaJMm4ceMkCUmZKZMk119/ff/+/bdv3+68jU4KKFGiBFbESD3KDHWb Vjv/R7M2KWHMmDHRs0wGJ4DrHYJqjEtiD1gmaVQ7qOu47nErGpMlJ06cSLjD 9fS+S+yTuUuOEkJCzlibdDtq1CgUi3qxMRwZvS1ZsoRK+O6773ZuW4pu9Uog oiJxwHkW7RZL53z74IMPEvyJQsQ03QSh2Ej43Ht+RhisTW97gbNAyqhXiQME HEJoRuRF2/gI/VCq6X1qGLMSNC21pIXT0Rt94lC6gEZwhjiQtmhJn0pbuWRt 2AaBSw8slSxZkiSIeSh6a3dQJyXxZm3EeWVbcgR8c/HixfyloFIejGZtr7zy SsaR99V6Bz0kz8Od/3EE7XwFHXs/AkKK1oRGszYiv57/h6o4bzgyR9okkGlF yXjx2rVriWOqoFCCcxfNQGBYG/OV6Rs0zgqOtWVFqjXKQtX2wplnnompyFZR abTYhHHqE/mFAa5HsNIbnSgOzd6AAjrXrc8+ffqootBLnVSumDZyAZILJQ0d OnvASilXdPcE8SgntPzcgMyiW0umwnTJHG9dG5g6dape8mWAL4u8PPnkk3gr 56Xc1QPDLnBSfFzVIMJjM9pewwnK7GnTpiWZtel05HGzDMopM7mboIQmqW1c mmSY2giLBGQ0SbVp9tUU4CDa/xbyrtChO0oENK3eMmCW4QKwY2ZWBYABZERv venevbtY277Is5e6kmAAw0ISPdKAIWlo2tkMU3GZIj5LBDaXNJFN22Lfdttt TtamPST5irnzz9qIb7qchYXrHccG5AjYqKINBgO9lR44hVSNVK65qFOnjowW d1Pk5OfkccUo15SREZD/gw8+IBHgHWbe6ZxqPyOy888TTzwR79W3CVmbFvAi iZO1iVJdfvnlTtamx0go/HQLZt68ebr0Z1C3bl1dHNPG+IhEusmI8CbzchCj T12l0a15c5yeiScuw8B/hw8frrtpTpAxtRQdkMfVoQGJgATBX5QWHcb9sDbt LUZZ5WRtsBsO4gLiOAci710STXOiYcOGZJN9RxatMATqZ92kNjjvvPNkKtQb B468fldvE4je4Kh58+YqPDZv3qwrzC5QnyCb2BP9KBzBYZ1XKeWq0G1mxPVz 4j+q0C0VUj9Op2UjBhQ2yBD9/uuErE1RhSLEJQldMcuu8JIRWaxn9uiWiVLd ZUSekmVSPO5BqHDS5kjoMB1Ym4pSeK7/WXY+4I1u5W6VK1d2biApO3zuuedc cTsjktSIADL+JCghJPhhbaoECGXO4nb/kW3hnTWM0KxZM5KdMTnCmjb2Idnp Aq/pZF9kRQmJSW6u61rqBNeGpKS63SZkbSoUKbmlW5XiuiAGqxJrI7ZnRJ57 J3+hTFIJGhZtMYDWEYqdb9nW5R1nG2yYQkK1CpqfM2dOvLQ1ePBgw9q0EAOu ZFgbopo7HSpZodu6XE/uNmsZqM0I7K4d4ZABb3Jqw5u1Iae5tO4MjFps0qpV K8PaVH/q7QYtW7ZE8oSsTcm9W7dufyZ35389rPXCCy9QyZhBMaHXXnutbqDM mjXLGYJIEJop4r+zSFCpdtlll7nyaUbkgbfod33mHoa1keZ8sjAARWW8AbK2 vZHtODCkNm3anHXWWZTE2AOxBc5+9dVXR+8umBW5QESgw9FQF0beokULZh9r IVKZh3NmzJhBpce3FNW9evWCI6BeOsShzG7hNIPjQAA5L50Qu7SZCc7CXD/w wAP0T3mA5V955ZVQA+fVA73HsGPHjudGQPBcsGDB3LlzOcWoUaNci8g8WFvW kRuOEydORFQERh5i7GeffSYNwFbQj5ZUEEgJ4HgKInFSqlxzW4qKTjSBpEnx RhGCPoktuM+NN9749ttv66HEXFlMLHiwNoHJ4uzXXXcd5Ah5GB1Vk3PxC5qk ASN1apKAxhHcyuw9ghJeffVV6mE64S8pHruCQdDs/fffN/OuZzyYVryP3pi+ 9u3bjx07VjGQBjTGWtAzpA8qh/2vX7+eTmhjFsMeiLwhiCBMD5zukksumTJl it58R0sszWiSlkSne++999JLL5WxEeoxNicvQCQawOX1hncdZDjz58+nNwai 17o523uwNnpYsmQJP4SUwVZuuOEG6e3666/nJ+ZBLDqk9CX7YNiQ3+3bt4t3 MEy0p+FTYq1du3bmzJnIxtCMbLq6SLXJZDF8DIkpI3Pp8V3CBWVY165dnQ+u ExP69et31VVXEdaMc7ngzdo46SOPPIJD6X3f5ieQegb7zDPPGEtgmrAEDkJR aaAHP958803kZFz8veOOO5AWfzFtDkReSUb669mzJwnOKQDnGjFiBC2VT81x RkSfHOdc5k2+IkcYA/aMG8pb8TXnq0vpkHzRp08fJMFDiSH0j/sT1lCa8/6C kJC1UVoQmkgo5iVWSjqQd8RDXXqzZFbElZg4Tt22bVtOjQCMNzMz0/V6JoaD SO3atUN+vJLJ5dTDhg1jUHpQ2QjGZ+IS5kHAkSNgM/J39MBUNmrUiKKRE5mI ZC4ZYZAyORgcFmje4GagEyE/k87PEYb+hwwZgoEZFe2P7ExOHMC05M60wZtc my0YRXmwNg7iDoyFEbkkkRmPHz9eI0USfITCSZconfcsGAVtSCjxjNyojj5n z57NBNFtgKvs8wQJWRsNvGeZsOOa5TVr1jh1gm6ZF9SF9e7/6+s19TguoR5j JmphY1gCMRz33HvkTVipi4SsjeCG0+H+06ZNi9Yt3yorGe/AyJ3bvGRFktS4 ceNoQ6nv2qc9K2L8uBi5SZ00bdoUJRPM4811CiEhayMS9u7dG91SgThDK9wH bbz00kuYPWXDxo0bu3TpQpzXHlOqD4cPH44Rkl4VGPlX75zdf2QPSep2MgUJ mnlBsXygqnHeqeFEzKlJW0iyevVqUgmn5odKbTt37hw9ejRH4BHmORZdkiUQ YRgqDilcmTKa8XNncUJgJ44hHl5DqGcU1AyuRaPerC0rclWWKpQKRzmF9Ipt qJymFjLeqr+MiOPkF493ExuMHDlSpZdrEX3u4c3asiK+g26pLlAL44IsUOHj icqqFA9GeD7QCcEHO6F6dD3rjjLph/oEY1AGREUkiA0bNoTxVHzOWBulWrCs TcCY90aWt6MBvXCNefwzssY2Zvt9kZ2OaEnBw0/4uZ7vMg34CmPjW6yalvI1 OnSZBz/kV5yX+TIrmNS/WR9KMaAVvq7Z13YlOyOg8a8RcAr6dD1Y7s3asiKG gWD8SgU8AvMT+qE3+twfWTWMMZQvX54am/b0o43jCCNar3fmmWc6X0tKDzI2 4hKfzSunA0dC1pYVKSaRgeCgsKbpcDbwqUmxBnVCs/2RF/dIRc7e9kZW46pD po9fybr0rTZ1RM+YsSShn2jDkP7pgdNJQj2nGm2TMhX1aYzNpQEa6BTO4dCM g1qm5zq1B2vTa+XVW7TeXILxLYaNYE4diiZo+HRFJzFl49t9kZVQeyMrEJ2v 95J3O91BbTwcNisRa+Ps/DZaEnlBtCVw0FnD6LNGKmmlJdMmptjO80Zv12M0 4zoue9ZryumNNq4ZlwvzAQ/VlowII/1Eu2FC1mZ0y/w6lWDEc/apUcv3pYro PvdG9tJR8GRS/oysKZASXANRXMK0CDiMRaPQEmDsRzepJ0yYoKpezkI1bt4y T9QyA5cHRUsiV+XnWlzstDRXG/rXs6DRbQRv1pZ1xA3jSaKR0oke2Ig+C7+S zv/0d2VY0SnwgiT5SMjashLpNuEsG93Ge1YKZXIKrA47xBIORZDSXFhIyNqy Il6cULdKdjF1m3UkYMbTrYyffpggXJizaK+q3I8ub5GQtWXF0a0rtCrI/+lY e6WkrwpBgdHUh/sdO/9TLfMrZIiZoPdGNqLM+mvaUlVg0pbm11XkRDuLyRGu WTPlFkIS6lUzuzSQkLVJIeok66/pNTrdS7DfHe8M9YCG5sr4gSAha1MbBGD4 jAv1agYlf3QOkgHEfO2mCi3RN6OikK545CvWprFrN2n/oVhXg+P9ZG+cd8FH nzdeJ979Oxt4nyIha3NKEi0w/rJq1apixYo1atQoMzPTrF2Vg2snzA4dOkQX 7X6Gn0v4YW1OeXKpSTXzYyEec+fTMEwPfnTos08/8GZtMdt76C36W/+GEawJ ebO23CM5Bu//dN4Gb5CQtYUhW1Y2LdblSgTq5cuXV6tWrXDhwiRHxe39kTdq EZS0Xrhly5aUoz5H5B1ms3yE4iwfrC0QSdIQflibH+Retx7JOkXhh7X56ST3 aslWvksJ+GFtuew/Wu37//q+Nl0W9jh7EtKWd6j3w9oSdpLf4Ie1mZaBxJPk uE9+Y20FGD5ZWzxQCxHSa9euXaRIkYEDBxIKIPV79uzZtm3b4MGDS5UqVbRo 0alTp+aJkv2zNgufyC5rSxWEzdpSFGGwtrBBYiKqmxWRa9euJbJBmkiRzz33 HBEJNnf//feH9LaReAiEtVlEIyjWZhGNQFibRUyEzdpiwrC2OnXqON/Xlm/h k7WlFvyzttSCZW1JQy5Z297Ieweef/55s8lG9erVGzduXK5cuUKFClWqVGnE iBF5VeFb1hY4LGtLK6Qia8uKPAnz1FNPmdX0lStXbtSoUfny5Y8++ugKFSr0 69fPLAZMGixrCwmWtYUHy9rCQ16xtkmTJlGYnXHGGeZ9bfkZlrWlECxrSxpy ydqyjiwZJhq0b9++SZMmdevWrV+/fvPmzXv37m12mM8TWNYWOCxrSyukKGvL ijy5PWPGjGuuuaZp06b16tWjSiEi3XTTTcxynryezLK2kGBZW3iwrC085Alr 46SUZH379n3yySed7w3Jt7CsLYVgWVvSkHvWJhw8ePC3334jyH/xxRfYpBZo 5+3FHMvaAodlbWmF1GVtWZE9In7//Xfo0oYNG4jz/BtzN57kwLK2kGBZW3iw rC085Alry4pkcKJiquyWY1lbCsGytqQhKNYm5KsV2Za1BQ7L2tIKKc3ahHwS kSxrCwmWtYUHy9rCQ16xttSCZW0pBMvakoZgWVu+gmVtgcOytrRCAWBt+QSW tYUEy9rCg2Vt4cGyNj+wrC2FYFlb0mBZm4V/WNaWVrCsLShY1hYSLGsLD5a1 hQfL2vzAsrYUgmVtSYNlbRb+YVlbWsGytqBgWVtIsKwtPFjWFh4sa/MDy9pS CJa1JQ2WtVn4h2VtaQXL2oKCZW0hwbK28GBZW3iwrM0PLGtLIVjWljRY1mbh H5a1pRUsawsKlrWFBMvawoNlbeHBsjY/sKwthZA01qY96velMRj+2rVryXp7 9uz55Zdf8lqcICHWlubzGyxQJtQGx/z555/zWpYgwXBgbWvWrLHW4gQBgbKN OLl161armdzghx9+ELOwagwWmCiZC92SxaxugwW6hRHD2tavX291GyzQ7c6d O20J6g3yMsUGJUdBUtGBAwe+/fZbpv7777/nc16LExiYoySwNkL90qVLOdGK NAbDh92g6uXLlxcwVSxatGjx4sUFbFB5C1nLwoUL81qQ4MGgRPPzWpB8BLSx bNkygoONk7kE0RU1WgMLHOjT6jYkGN1+9NFHVrfBwujWhlZvFLy8zFiYdKae 3FrAxkW9zXyFytoWLFiAPXyU9pCq81qK4MGgIG55LUVBAyq11pI+EEm3cTL3 QI1E2ryWomDC6jY8WN2GBBta/aBA5uWCOvUqDkNlbR988MH333/vv/+Cis8/ //wf//jHL7/8kteCBAys6OOPP85rKQoa/vOf//z73//+/fff81qQIPHbb7/9 61//Wrt2bV4Lku/w888/Eyf/+9//5rUgqY1ff/31n//852effZbXghRAkLn+ 93//lyyW14IUQOzbtw/dfvHFF3ktSAGELUETgjIju0/cpQR27tzJ1P/www95 LUjAoN4O+wlJ6zKCZW0W/mFZW1rBsrZAYFlbeLCsLTxY1hYebAmaEJa1pRYs a0saLGuz8A/L2tIKlrUFAsvawoNlbeHBsrbwYEvQhLCsLbVgWVvSYFmbhX9Y 1pZWsKwtEFjWFh4sawsPlrWFB1uCJoRlbakFy9qSBsvaLPzDsra0gmVtgcCy tvBgWVt4sKwtPNgSNCEsa0stWNaWNFjWZuEflrWlFSxrCwSWtYUHy9rCg2Vt 4cGWoAlhWVtqwbK2pMGyNgv/sKwtrWBZWyCwrC08WNYWHixrCw+2BE0Iy9pS C5a1JQ2WtVn4h2VtaQXL2gKBZW3hwbK28GBZW3iwJWhCWNaWWrCsLWmwrM3C PyxrSytY1hYILGsLD5a1hQfL2sKDLUETwrK21IJlbUmDZW0W/mFZW1rBsrZA YFlbeLCsLTxY1hYebAmaEJa1pRYsa0saLGuz8A/L2tIKlrUFAsvawoNlbeHB srbwYEvQhLCsLbVgWVvSYFmbhX9Y1pZWsKwtEFjWFh4sawsPlrWFB1uCJoRl bakFy9qSBsvaLPzDsra0gmVtgcCytvBgWVt4sKwtPNgSNCEsa0stWNaWNFjW ZuEflrWlFSxrCwSWtYUHy9rCg2Vt4cGWoAlhWVtqIb+xNuq6PXv2HDp0yL88 qQKfrI3QPWfOnKVLl+7evTtmgx9//HHRokXz58/PJ0HeP2vbu3cv9vDHH3/E /Hbz5s3z5s2jN1JYoAKmJHyyth07dlCmrly5MiVcJlusjWCLwcSzlgIG/6xt +fLlzLjldzHhn7XRBoVv2bIlCVIVDPhnbatWraLlt99+mwSpCgayxdoIjB5V xLp160ijpA+CrX8BiLSkEo/6loDj0eH27dvJVgxh48aN/k8KCO/0fPjw4Wz9 KlvwX4KSRokeacjvssXaqD9TJS/7Z20MB95x8ODBJEiVe+Qf1vb111+/8MIL LVq0aNCgwfPPP+9fnlSBN2vDbGbMmHHOOeeceuqplSpVqlq1av369ceOHXvg wAHTZtu2bddff/0ZZ5xx4oknHn/88bS87LLL1q9fn6wRxEZC1ka0X7ZsWb9+ /ZjZiy++GHbmavDmm2+2adOmVq1aDPykk04688wzJ06cGGokz/9IyNowpAED BtSrV++EE044+eSTmzRpsnDhwiQKmBP4YW0U3gy8R48e2HmHDh3iXbsoYPDD 2hYsWEB8IDIw48z7nXfemSpZJmnww9o+/fTTli1b1qxZkxBau3btrl27ovyk SZi68MPaqHvPO++8atWqods6derccsstFHhJkzB14Ye10Wbu3LlXXHEFgbFb t24u3ye0Dhs27Oyzz65RowZp9JRTTiGlfvjhh37O/t1339EtIaV9+/aubqnc Jk2adNFFF/HtK6+8Ep2P9u/f/9BDD9WtW7dy5cpM+mmnncak+yHsqvdatWpF zzNnzgyPBfgpQUmdzZs3J40SWhlLnz590iq0+mFthFa01KtXL+pSrAWOnzTx cgw/rA2yNm7cuNatW+NWDz/8cNJkyw3yCWt77bXXypcvX7Ro0YwIKEj8y5Mq 8GBtBOQhQ4aULFmSsR977LHYT7FixfhcokSJUaNGKVSSEMmDHCxcuDCxsUqV KkcffTT/nnXWWRs2bEj6aP5/eLM2onqzZs3KlClz1FFHIS2B3UkzGdrQoUP5 lq/Kli3btGlTCCmfUcXkyZOTIn4+hTdrO3ToEMkRAyhUqBBZBqqL0sib/CTJ cmYLCVkbuQDbLl26tOIAtcf27duTKWFeISFrQ28UY+gE1kYcYN6Z/Z49exa8 J65zg4SsDbc69dRTUSPphnJRYbZDhw6WuCVEQta2evXq6tWro09yE7r929/+ xufrrrvOEreESMjaMGncv1SpUgqMJEpM3XxLPda1a9ciRYqQZMkCjRo1Ui1B LF22bJn3qQ8fPjxixAgTb53djh07lqmUj4D77rvP9UQHv+3Xrx9lG+fVRTbc is+XXnqp97MfJH3qHFPvjRkzJg9Z2+LFiyFrCq0kU+orhkB6pXQJSaT8hoSs bffu3bVr1z7mmGM0X9WqVdu6dWsyJcwZErK29957r2LFiopUoEuXLskUL8fI J6xt2rRpWEWTJk2INhQkgwYN8i9PqsCDtR04cOChhx4i3r788svECpxo48aN xGEMqUaNGl9//TVtqHVR0ZVXXskHGhBdp06dyk9o8+yzz+bhnamErK19+/aN GzcmHhIM4RfOmmrTpk1E+3Llyg0bNgznYly7du266KKLaMnA0ydsRsObtZFo CKHFixeH29KG6HTrrbdiCW3atMnPz5cmZG0M5Nxzzz3zzDOpNxhOrVq1LGv7 MxIfzj//fJwC1yCBMuOTJk0i1+A4fEiyqPkZCVnbtddei101a9aMNqhx3rx5 VI8lSpR47rnnkilnKiIhayM3odsWLVqQ7tEtFRExioT+4osvJlPOVERC1paZ mdmwYUMKpOOPPx4ln3POOU56NXPmTIpPONebb77JNKF8MrKuTlx11VXetcGi RYvKli2rq0DEW2e3xJazzz6b3K2TPvjggy4utnLlSvgOtO7111/npJyII6R4 HIqKzuOkTz31FEGeEVWoUIGeKXvyirUdPHiwdevWhNYLL7yQ2oNRTJkyhRER WidMmBCSSPkNflhby5YtmTIshPnCtFLi+eeErG3+/Pl16tRp2rQp0824/ud/ /ieJ0uUc+YS1GTRv3hwPSjfW9mfkaWHXs46rVq3SRQBVuYS1LVu20Mw0IISi KBrk7YMoPte1rVu3juGceOKJrppqzpw57777rvPIggULyEGlSpVCAwHLmjrw YG3M+6OPPsq833TTTSbJ4sK6F/Ppp58mV9JswP+6toULFyo7WNb2ZyRK614q AURH0OQ111zDwc6dO2drAUvBhjdr27x5MxytdOnSb7/9tjnYr18/Xe5w1qsW 0fBmbRs3bkSxFD+zZs0yB3v16oVuL7vssrR63iwH8L+u7Z577kGlkCmXuU6c OHHFihXOI6NHj6Zlo0aNvvnmm3i9wVOoxgsXLtysWbPy5cvXrFkzphcMHjw4 JmsbP348abpGjRrOg927dy9SpMjdd9+dcCwHDhygdMlb1rZmzRoYK9yTAl5H yLk33HADUhFgU2K1eO7hf13bhx9+qFsJBYO1GXTs2NGyNoPssjbCUXqytmjA WUqWLIk2KOBjNqBaGzhwIMZ288035+EdFp+sjTYxWVs0KK6qVatWvHjx2bNn ByRj6sGDtREwL7zwQuad4tMkOwzg3HPP5eDw4cOTK2k24J+1ETQsazN45JFH 0Abzax7kY96nTZvGwQYNGqSJivzAm7VRHBYtWpTSlGLVHFy8eLHqEPJassRM SXiztlGjRulpbWeNRL7THfPotcwWTvhnbbpOG83aooEj0LJ27dqffPJJvDZP PPGEnrd87LHHKlSogBdEd8u833nnnTFZ2zvvvFOmTJmqVas6q5rbb78dGjhg wICEY8FUqJPzlrU9/vjjuvlualokYVwcPP3001OCm+Qe/lmbjKrgsbYOHTpY 1mZgWZtBtlgbTtGpUycMqXXr1thezDZTpkzR7ZW33nor3z4haeCftUHWjj32 2GOOOWbTpk0ByZh68GBtGBJ0BlbrovNdunTBGLp3754sGbMNy9riwZu16WHp m2++2XkQP+LgSSedZDdjN/BmbUOGDDn66KPbtm3rPEhOx5UoWV23Kixc8GZt VOkk7ssvv9x5EHuGJh933HGrV69OioypijBY29NPP62W8aq1OXPmFCtWDMsn Jr/++uulSpWKea/Ng7UhNpQcnyLp6ILSrl27OFKiRAnzVIAH8gNr080+14Km jRs3YszUKuvWrQtJqnwFy9osa3PCsjYDP6zt008/HTFiRI8ePQh9hQoVatOm zZo1a5yM7MCBA5C1wYMHX3rppRUrVixSpMgTTzyRtzsSBM7aevbsSSKoW7du Om8j6cHaMJJy5cpVrlzZVUH16dOHyOMqnPIVLGuLB2/WBtHAIwYOHOg8qGfS ypQpY0tiA2/W1q1bN4zqhhtucB4kl1WpUgXitmDBgqTImKrwZm16qMx1YYEa vlKlSiVLlly8eHFSZExVBM7aDh48SBta3njjjTEb7Nixo3HjxjQgqsCYxo4d mwPWBmbNmkUyKly4cIsWLd566y3oD3XLNddc4+eZ2PzA2i677DJCa//+/Z0H v/76a+LqMcccs3LlypCkylewrM2yNicsazPww9qeeeYZs90i+Q7Vuep27JDw SJDUpjft2rXL863Rg2Vtq1atOuGEE9DA+PHjg5Mx9eDB2pYtW4Z+6tSp47oX ef/995MxzzrrrGTJmG1Y1hYP3qytYcOGRYoUefTRR50HKS1OPvlkZhwHTIqM KQBv1ta+fXuM6o477nAeJEPVr1+f4++9915SZExVeLO2tm3bRm/+vGfPHsIU wWru3LlJkTFVEThre/rppykSyKQxN/8nDt9+++16MhD758grr7ySM9ZGKSi3 YpbJ7/ytVauW9k9LiPzA2po2bYqiHnroIefBrVu3Vq9eHTa3aNGikKTKV7Cs zbI2JyxrM/DD2oixRMhOnTrVrVu3RARPPvmkM1Tu379/1KhRWNcFF1ygnZ3O PffcdevW5eFLDwNkbWR5PebXunXr/LwXYhLgwdpWrlxJrX7aaae53vhw9913 4zvnn39+kkTMPixriwdv1kZxFV1awNmpyvCphJt7pw+8WZuWnPfs2dN5kAwF s6BCmzNnTlJkTFV4s7Z27dqh2759+zoP7t69GyJAsMK2kyJjqiJY1rZ8+fKq VavSLN7iskmTJlWoUKFs2bImcb/66quwNuJt9PMtHqyNfH355ZfjO23atBk8 eDBzrQvOtPezPVp+YG2UT9jnfffd5zy4ZcuWKlWqFClSxOcL71IdlrVZ1uaE ZW0G2VrX9uOPPz788MOwNqW8mDFt0aJFlOgYW/v27fPwfUNBsbYDBw6QZfS6 H7vGxIO18RXlesWKFV2bjt52221o79prr02WjNmGZW3x4M3arrjiCqKi6zEe OHuxYsUwg2xF74INb9Z2++23U2G6HATNH3fccaVLl06T6+o5hjdru+WWWzBR 1/N4u3btKleuXJkyZZYsWZIUGVMVAbI28+hj8+bNY66Ih0o3bdo0I/Km1wkT JkycOHHq1Kk33XQTwQRHGDdu3Pvvv+9850481ga/IyLhUBQheuBn06ZNffr0 KVmyJAddDwbERH5gbR07dsRue/fu7Ty4efPm4sWLly9f3mMjl4IEy9osa3PC sjaD7O4hSYmrx05IiPFCNIxJrz5MuFgsPATC2swzG4RK8lcIYqYYPFgbDtig QQPovIvb6n1Jrto+X8GytnjwZm3i4xQYzoPMvt380AVv1jZ06FC8plmzZs6D W7du1dbf+fmVGfkB3qzt/vvvJ3G3bNnSeZAyXrs6xNsG2UIIirVt27btkksu oQHELV5YWLlypXmtcExUqFAhMzPTtI/H2pC2Tp06HHdd7tCGt7Vr104YuvMD a+vbty8CXHHFFc6DhAIOVq9e3c+MFABY1mZZmxOWtRlkl7WBzp07Y0tXX331 gQMHYjagANaLtvPw1WaBsLYHHnigSJEiRYsWvffee+37p/70ZG27du3S/qIk O9OAg40aNeJgfl4PaFlbPHiztjFjxqgS++6773Tk8OHDI0eO1BV15zsc0xze rG369OnFixevUqWKcyP6t956CzXWq1fPf5JKT3iztmnTphHAKeecbwebPHmy Xk6xY8eOZImZkgiEtVGd6hngk08+mdwRrweKt/fee2/q1KnTjmDWrFndunUj O1eqVOn111+fN2+eFrsJ8VjbggULqlWrxnHXHT0oT6FChapWrZrwye38wNrG jh2LAPXr14fw6gih9cUXX9Sivz179oQkVb6CZW2WtTlhWZuBB2sjqXXv3n3i xInOg1RoDRs2xJbuu+8+oiWhtWvXrq4K7c033yxcuHCpUqV8rv8NAz5Z2+rV q8XaovP+o48+WqxYMb3kJc2Xsxl4sDbSinJNixYtTFrBy9Bh2bJlt27dmlxJ swH/rI0aRqwtTWppb9bGnOLjpUuXprzRESJwkyZNUFG/fv3ycE1rfoM3ayO2 UG9o310dwZW0l0KXLl3SecdaP/BmbSQm6DAR/qmnntIR86wIjMCaqDdywNpc mzTu3bv32muvpXYqU6ZMDvbVMevaomfqwIEDhrU5L6hu2LDhjDPO4Pg777zj bD9lyhTEqF27tmoSfkLZQ9iPzmXYjGFt2RXYP7xL0O3bt5M0S5YsCVPTETR5 zjnnIFXv3r3TxG79szaoulibIbn5GZa1GQTO2qg8586dO2/ePAIXzo72Lrvs Mtx8zpw5mzdvLjCOE4+1US289tprRx99NKED2oJf4BErV668+uqrUUXx4sUX L15MUScGd8EFF4wbN46utmzZMmbMGBIlBzt37pxv17XxFZPLwJ9//nnqJVID 4ZGJZpjITLgYOnRoiRIlVKJDQqdNmzY5gtdffx2imrb3ETxYG1i3bt3JJ5+M 0nr06PHVV1+hTC1VuP322xO+xycPkZC1LV26FGthOBB5PS6ra7+4gJ/l7akL b9Z26NChm2++WekS5VAOMe/8e8opp9j96p3wZm3gvvvuo6Q84YQTiKLfffcd /xYqVKhixYrvvvtuMuVMRXizNjBw4ECtSsaFSWHwC6nabvOSEN6sjRKIsEkY JCOoKqhWrRoWi2KXLVtGUIVYXX/99doJpFWrVjNnzoQ6KY1OmjSJ5Bu996ML 0XtI7t69WyclC+vZe07N5/nz5+tZYqTq3r07xytXrgxx27VrFz/B+whKHEQe VW4wsnLlylWoUMFclIYoMVj6efvtt1u3bk3jW2+9dfbs2ZwrjFdPepegZNie PXvqeUiE/+abb+644w5pmH8DFyZ/IiFrw8yUl/VadiaU2WS+Fi1alJ/zsjdr 279//7wIsHPx9BYtWjDpmCKemJ8v4uUH1sbUx3vEmrCQn6vQbMHjXhvWddNN N1E8uIZPPITj6ALX+++/L0rrwvnnn+/aSzDJ8GZtJJGYM1u4cGG8A1s67rjj 4s0+oT5td1rwZm1/Rp6acxnMRRddBJdPppDZRULWVqtWrZiWULZs2YK92tGb tf0Z2dbsggsucOoEx6HWSqaQ+R8JWRuZS/sPGDWWLl3a3Hqz8EBC1kbRfvnl l7vc9umnn06mkCkKb9ZGFoBSxQyMVatWhQRBzeLlUNCkSZOEF3XJJlqBaMot qllcI2aHp59+utoQr9q1a1esWLGMyBU2bWpdpEgRkr55gq5fv3761T333KMj em42Jtq3bx+ANv+KhCUoorZp08YpBokVhQQuSb5FQtZWr169mPN1zDHHwL6T KWq24M3acLd4dnj11Vfn5/sF+YG1kQiujoPx48cnvEyUKvBe1/bHH3/MmjWr U6dO5513XtOmTeFi11xzzcKFC51ttm7deuedd7Zt27ZZs2Znn302VfqgQYN2 7dqVDOnjw5u1Pf744zFntmvXrp9++imG0atXr3izf/vtt6fEjfgwkJC1galT p8pgyJI9e/bM/4+aJ2Rt2HNMS+jRo4ef5ypTFwlZ258R9+/fv/+FF17YokUL 5p3ZT5p4qYKErO3PyGqaIUOGXHLJJc2bN7/iiivSqjzLDRKytj8jq2txYWpg dNuhQ4exY8cmTbyUhjdrO3z48K233hozMMKJKC8XL158/fXXx0ujDz30ULx1 8Qbkmi5duvTt29c8A0lNGO+kd999t/khBR7E/NJLL6UgoSzBrYYOHepcDrZy 5Uq42JVXXml2+1myZMkNN9wQs+dnnnkmV3qMBT8lKGXGgAEDCK3YbceOHeGV gYuRn5GQtTHjMecLC8nP19W9Wdv27dvjuczzzz+fn5fq5AfWlibwuRsJARYz 877DiP7z8JFIF3yua7PIFvywNgGDSZX9W/yva0s3+GFtAiVcfk4oeQs/rM3A qjFb8MPaDKxuswX/69ryLfZFEPOrP/74Iw+fN/NfgiJnetqt/3VtqQX/69pS C5a1JQ052EMyJWBZWxjwz9pSCJa1xYN/1mbhgWyxNotsIVuszSJbKACsLd/C lqAJYVlbasGytqTBsjYL/7CsLa1gWVsgsKwtPFjWFh4sawsPtgRNCMvaUguW tSUNlrVZ+IdlbWkFy9oCgWVt4cGytvBgWVt4sCVoQljWllqwrC1psKzNwj8s a0srWNYWCCxrCw+WtYUHy9rCgy1BE8KyttSCZW1Jg2VtFv5hWVtawbK2QGBZ W3iwrC08WNYWHmwJmhCWtaUWLGtLGixrs/APy9rSCpa1BQLL2sKDZW3hwbK2 8GBL0ISwrC21YFlb0mBZm4V/WNaWVrCsLRBY1hYeLGsLD5a1hQdbgiaEZW2p BcvakgbL2iz8w7K2tIJlbYHAsrbwYFlbeLCsLTzYEjQhLGtLLVjWljRY1mbh H5a1pRUsawsElrWFB8vawoNlbeHBlqAJYVlbasGytqTBsjYL/7CsLa1gWVsg sKwtPFjWFh4sawsPtgRNCMvaUguWtSUNlrVZ+IdlbWkFy9oCgWVt4cGytvBg WVt4sCVoQljWllpIDmvbsWNHjqQrUMjMzIS1HTx4MK8FCRiwttWrV+e1FAUN UBscM6+lCBh//PEHrG3dunV5LUi+A2UbcZIsk9eCpDZ+++03WJtlFmGAzAWz IIvltSAFEAcOHEC3GzZsyGtBCiD2799vS9CEoNj4z3/+k9dSBIzdu3cz9T// /HNeCxIwqLfDZm2k0eXLl69OeyxZsgRVQ5PzWpCAsWjRog8//DCvpShogAsv XLgwr6UIHgxKNN/CiVWrVhEcVqxYkdeCpDZ0EZJIm9eCFEBY3YYHub/VbRiQ bm0J6o0CmZfJp0z9ypUr81qQgEG9zXyFytoWLFiwePHiD9MesBtUXfBUsTCC vJaioKFAWgvDYVCi+RZOSDMFbLqTD2tg4cHqNjxY3YYHG1oToqCaX0GdepXc dl1bEmDXtVn4h13Xllaw69oCgV3XFh7surbwYNe1hQdbgiaEXdeWWrC7kSQN lrVZ+IdlbWkFy9oCgWVt4cGytvBgWVt4sCVoQljWllqwrC1psKzNwj8sa0sr WNYWCCxrCw+WtYUHy9rCgy1BE8KyttSCZW1Jg2VtFv5hWVtawbK2QGBZW3iw rC08WNYWHmwJmhCWtaUWLGtLGixrs/APy9rSCpa1BQLL2sKDZW3hwbK28GBL 0ISwrC21YFlb0mBZm4V/WNaWVrCsLRBY1hYeLGsLD5a1hQdbgiaEZW2pBcva kgbL2iz8w7K2tIJlbYHAsrbwYFlbeLCsLTzYEjQhLGtLLVjWljRY1mbhH5a1 pRUsawsElrWFB8vawoNlbeHBlqAJYVlbasGytqTBsjYL/7CsLa1gWVsgsKwt PFjWFh4sawsPtgRNCMvaUguWtSUNlrVZ+IdlbWkFy9oCgWVt4cGytvBgWVt4 sCVoQljWllqwrC1psKzNwj8sa0srWNYWCCxrCw+WtYUHy9rCgy1BE8KyttSC ZW1Jg2VtFv5hWVtawbK2QGBZW3iwrC08WNYWHmwJmhCWtaUWLGtLGixrs/AP y9rSCpa1BQLL2sKDZW3hwbK28GBL0ISwrC21YFlb0mBZm4V/WNaWVrCsLRBY 1hYeLGsLD5a1hQdbgiaEZW2phfzG2rAflEx151+eVIFP1rZhw4b58+evWbOG SO7RBq1+/fXXAYuYI/hnbYxo7969f/zxR8xvGQ6Za+nSpfv37w9UwJSET9a2 a9cumqH/Q4cOJUew3CBbrO3HH3/EYOJZSwGDf9a2atUqZpx8lASpUg4+WRvO 8sknn8ybN48w6+E427Zto7fly5cTtbx72xOBR9qize7du7OV2jB++vRus3Hj Rsxm3bp1Sbi845O1HTx4kBiObikqPKRCt2TDlStXekd7P3rjjIRBwoV/JVCW JKzl4FAk4vXr1x8+fNhntzlGtlgbIz1w4IBHA75FIf7PbqzIdRwt0c/uvyKe TVITEpc+/PDDhEarxq5uOZH/qjJb8F+CkkZJT2l46cw/a8NUCIn5pPJMCJ+s jchGyMJ0MZXkCJZL5B/WtnXr1pdeeqlVq1ZNmjQZPXq0f3lSBQlZ28yZM88/ //zatWufcMIJNWvWbNmyJYbkakMJccUVV6hNvXr1unTpsmPHjpAFT4CErI2s R3YeOHAgM9uuXbtol58+ffpll11Wt25dBlWtWrWzzz77jTfeSJNyPR4SsjYK 1MGDBzds2LBKlSqnnHLKOeecE20t+Q1+WBttGMgdd9zRuHHjTp06kdCTJl4e wg9rW7RoEfGBuWbGGzRowOwXyKtbuYEf1jZ79myyTI0aNSpVqsTfNm3aLFiw wNWGoHrbbbedccYZJ554YvXq1Qlcjz/+OOwgZod9+vRp1KgR/cRLo4SyW265 hTZEOT/3AaneBw0a1LRpU5w6MzMzZhuK/IsvvrhOnTqVK1c+7bTTzj33XAae sOfcICFrY5izZs3CRInh6JYUhoQYravZd99916NHD+kWY2aYw4cPj8ede/Xq hd7oB/YUswER8sYbbyQMkhbj6coJ5L/rrruY0PPOO49KJmYbKGfbtm2VZGvV qkXLhQsXJuw5N/DD2uBitOnYsSOBsXv37jGt8auvvho5ciRmg0KWLFmS8Lwr VqzAbo0VUXLgHeZbpqlhFOjcle7h3cQipCIuVa1alfm68847vS90tG/fnmau ni+55JINGzYklDm78FOCUsMwdhNakT8lroIGBT+sbdOmTRRvOAVuS6l23XXX 5f8rh35Y27vvvksIImRhumeeeSaxKP9XnvmEtU2aNOn4448vVqxYRgR4jX95 UgUerI2DQ4cOPeaYYxh7+fLliRulSpXi88knn+yMvWi+fv36HC9RosTpp59e pEgRPpOz8pa4ebM2QjoVxbHHHnv00UcjLdncmX8hdMOGDWPIfFWyZElC93HH Hcfn0qVLT5s2LSni51N4szbKdeqZQoUKoSuyLb7DB8LO4sWLkyxntpCQtRFm yQjlypVTHCCNUuMlU8K8QkLWRvVLZYVOqCRPPfVUPjD7cFvvq+7phoSsDTVW qFAB7fGXOl+axIOWLl1q2mzcuLF58+ayQHxK7Qm2AwcOjO7w1VdfJXApbser k0ePHq3UxtytWrXKQ34KjK5duxIDjzrqKJ102bJl0c3mz58vl8cG8BElC2Ks n0I9x0jI2tB82bJlkYQS6Morr0QwPuPOK1euNG2YmrPOOku6JbshMx+KFi16 zz33RHf40ksvkemUNeAXMU8KSVEeZKZWr17tIf/u3btvuOGGihUrSrf0HLM9 tEU5qHDhwgxBk4sNeE9cLpGQtaF2CIXUC2CdmLqzAaF1wIABKOpvf/ub2syc OdP7pO+88w59ysyqV68uK+IIpFUNKGLV1VEO0AxRTSdE7Msvv1yZiN/KLGlG WRLvvCR9+ZSr54QzmDMkLEHxmpNOOknuSSjgA7UK6bXgLWaJh4Ss7euvv4ba qEirVauWotnFF1/s575qHiIha8MFVHxie7gAH3Cf+++/P58Tt3zC2qZMmVKj Rg0oCbGUCDBo0CD/8qQKPFjbunXriGOwtrFjx6KxgwcPQm0gO1jRtddeK6vD s5544gmOnHHGGXRFG+gScZJwh/by0MwSsraLLroIjkk8RFTCo7Om2rRpE18x 8EcffRQXY1CYyoUXXsgwKajS+VFJb9aGzsuUKUOEee2110jf27dv79atmwKp x4O1eY6ErG3Hjh0UdVi4Sj4SBENLpoR5BW/WBjW74IILUAiuQRtmHLJArUvR O3ny5CSLmp+RkLVJjddccw01PNb4448/tm/fniPXXXedadOlSxdxChyQDonY hGWicTSB+vbbbwlfuh5FveqkfgbQDcxYbejT+7EEQn337t3r1KlD9KOWJhsu X77c1WbLli34CB3q3gQS7tq166677qLODPXGdELWBtUlwl9//fXEbXSLMER+ Rg0PVQMOdurUSVdjPvzwQySnJZT2/PPPdzI74Ztvvqldu7b0RtaIbgBQOGWD 2tSsWfOTTz7xkB8tESTRLS2pMWBA0e2/+uorqAodYhXkJiQkIvXv3/+OO+7A VHypKUfww9qQnAIJ1slgzznnHBdrO3To0ODBg+HINCtevDht3n33XY8zkmfR A82Yo8zMTHpjvhgmjqAShVzcqFEjGowZM4a5+OoINm/e7KxhhgwZwqQjFc1+ jeC9995DPCezcwEjpz3V8rRp0zBm0zOfXYMKBN4lKMNs27Ytw2zVqhVtEGDS pEkkVsSbOHFi4MLkTyRkbRgGKsK0oNVojBh74oknFi5cmIo0mXJmF96sjdIC h8J6cRw9dfzYY48RGSiq8/nK6HzC2gwU+dONtR0+fJgo5yrA5s2bhwlVrVqV OPln5HIHNoanOJ9hIFSqVg81p3jD57o2aAjBEGd3eQRBfvr06c61A6iCqF6q VKl03uTEg7WRoAkvKofMrZY1a9ZUqlSJg/l5TbH/dW2MXczdsjZAeUlUrFy5 Mm10BBvo2LEjKqJIts9JGnizNuoNfKRcuXLz5883BwlfqJG8o0fOPv30U7hS mTJlCErO30YvbmIKHnzwQX7bpk2bYsWKxWRtyHP55ZerNoYLJGRtBrgzjQmY LtaGGOPHj6fDli1bbtu2zU9XQcGbtcG/UBpx2/mg5oIFCySqnjeDwEJFqYdd hCJat+jtnnvu4bdU1PyEOiqatRH6Lr30UqU/vCMhazOg8sSVSpcu7WpPsFU+ bd26dZIXN/lf1ya1nH322fEIDjVqgwYNErK2Bx54oGjRoqeddpqrpjVzAck9 /fTTqTc8nt+gJqlWrRqGjd6cx70XGFLP4IPRlUBI8C5BybOMEeedO3eujiB8 586ddcE8TUKrN2vbsWMHzoWWxo4daw4q9J111lnej8LmLTxY2x9//PH+++/r QYtvvvlGB7F5ghUHJ0yYkITVrDlGfmNthKM0ZG0xQXKhfiBxK5gTPDEnAvLG jRtNm2XLlsFuyGtidnkCn6yNNjFZWzQYC7mgePHiTn6abvBgbVRrFIoYw1tv vWXusZL3dXN2+PDhyZU0G/DP2ggalrUZDB06FG20aNGCZjpCTpk6daoCQpqo yA+8WRuxt0aNGq6nr1etWqVHznRnv3fv3rrwHm8VmwFl9rHHHgsNfO+99ypF EM3aqJwhdHAEQlnJkiX9szZIStWqVaNZGwYAwaROdlZQyYE3a6N40yN8TrYr RnzOOeco6/Xo0UMkK2ExjLogdyeccAKVFX1Gszbi3jvvvAPv4CvaUFL6Z20k TXqOZm179uxp2rQpuk3+TRb/rI3SyJu1UWtpGYUHa2OkGDxthg0bFq/NV199 VatWLTTsrDdcePzxx6k9aJatbRw+/fTTY445pnr16sl5vs67BNXzS82aNTPX vTGtt99+m4OQ1iRfGMkreLO2KVOmMF8VK1Z0aoNJZOqJbNGLgvMPPFjboUOH evXqxSxfddVVJhzx4emnn9ayo5D2xgkElrUlDdllbfPmzUMVhI6tW7fy7+TJ kzGnW265xal8vqpXrx7Hox+kSRoCZ21z5syhHCJQxFstng7wYG0YEnSGatDl tnqyq3v37smSMduwrC0evFlb165d0Ua3bt2cB/EjPTzmZxOGNEHCJyQHDBiA 0ho2bGgYlmgacVX/wokKFSr0zDPP6F/yfsyLrlu2bNGauPHjx+/YsaNChQrH HXeci7VRhFerVo26l7Lnm2++KV68eO5Z2+7duznIuYgPf0bIe9L2BEj4hOTt t98u/msuIRKLOEJ1pH8bN26MNkaNGqV/40lO2Ncak9dff51CkUQQzdrWrVv3 /9h772gpyjVveysHEUTJOQjC2YAkSZIzgqBklIyYQDIKkhFEVAQkiyCIREEl SD4zwwzhgAoH4XXBLD5YApJcsxcwrLOYPczGD/Z3fX2vU29NdXdV9aarunv3 /fuDtamurn7qCff9u6rqeUrqB3Zjf5rswant2rVr0B8fSf8h9vpWtz5TGylb HkEXHw6nBD9be/z4cVqBGqZX04Eh2UOHDllwu1evXimmVQiuX7/ueK0jM3AH 9rHHHqMAOH9czcaNG8NNWoyK7C0oA59TIHWaNwKqbMSrhFsDJ5vJntoAWwCt bt265o30hGLFijE2CYC+lDErsqE2ho/MxJkwYYJ5OxadjU2bNo1n46HU5psi orb09PSRI0eKnaAOiasYCf47ZswY8/oDxEmZJWr/OISnijq1DRky5OGHH65S pUo836T2WjbURrIrUKBAyZIlLQ5K/Gfnzp39KmPEUmoLJ3tqa9euHVHRshoG 1iJv3rz58uVzaVaTQY7UdvHiRVkNA56aN2/e4sWLGUrAlEAQw02mBa1fv37V qlVNmjSpWrUqrAF3gGnGQdhNbhs1b96ceI43KFSokIXaKEnfvn3Zh1HJ33z9 0UcffXBq+/XXX+UuAKeJ3wCRKGGDBg1mzpzpxZwgsxypjQ5JXVE8qGHBggXz 58+nf1aoUEGs7927dzHq2Dxc+sqVK6FjSo4VpH7M1/Cx/a+88oo8pkimg6Q4 iIXaYByZHweDsz+GhHzx4NQml0EgerACB0LZqlWr1qhRo48//tgNjDyIfKa2 zZs3013p+efPn8dmMCI4U3rR3LlzjcOSfUjWhB3YjSAj7IzRNS8KKjPCwPBv vvmmQ4cOHITa69Onjz3sANpQG/zOkTmsDMDu3bt79B5Pewv64osv0nlGjRpl 3kiUeCIgT5egiR/ZUxtUTjdo37695SuMXxpx9uzZvpQxK7KhNqKZPABseTbp 0KFDRN3KlSsbj03GoZTafFNE1EalkeWJG3wFZCNrkKOJkNOnTzcvPHL79u1m zZqxfcWKFbFakCS61EYmlbXyli9fHr0yJp5sqA0jxxjBXmLhzNunTJmSI0cO kq9fZYxYSm3hZE9teKGcOXN+8MEH5o1YC9wOLe7pyoGJJUdqI0iCDLJ+HZaD iESY3bRpk3wKPkAWVCm+F1dP+AXHyOwyg8O4p4ntZCOVL3O4IItgaluzZg3H oQPLYubY46hQm8z3LFy4MI43T548OF5ZV5DuQYLwdCUiNyv/f/bZZ+a6xZxv 2bJFPoVbsf2Us0aNGnK1oWDBglQRoQyCM5Z837BhgyzeSKCQbwVT2xdffMEX cVYSAIGdqFDb3r17KXmRIkUgekvdElo9XVHQZ2pbsmQJJ0iHhLNy585NW8iZ 0mQDBgyQpLN//35agU/BZ3w7LEaNyUoy0lg3btwg1wik8xFtzSig3WVlGBve oUuwJz8KMY0ZM6Zdu3ayJiEn5cV0QnsL+uyzz9Lfpk2bZt54+fJlxhedKvi9 FdlS9tQmT3r07dvX8hWZkTF+/HhfypgV2VAbI65YsWJ0WsuUTOIM2+mfNg8G x1xKbb7JPbWRqmRC8bhx42T/u3fvTp06lS38a3bymL0mTZqwPYbrHUWR2hhf AwcOlIvY8TzL1QfZUBuBBdOSmppqebsNto2xA8X7VcaIpdQWTvbUBjIEWwtA APOJTQq5OHxyyp7awIrPP/+cGsNVDhs2jFqVheWxH0Jkp06dkjXA2T5y5EjC EQMQOy3XkeRJP8iOoYeTNy4yp6WlCbUZD3qRIgENHO/ixYtli9xrAwdOnDjh 5kTCUZvMuEEVKlRYv3492eHOnTvz5s3jtzi+kI5Hsqc26nbBggUSl6jbevXq yUqGrVq1Ev9z8uRJWRYepwQFyEuxd+3aJcvsjxgxIvMfFyI4EQ4lhzXutRlr wlMAsVvGZT3YDYNNrHBpY8JRG/UpdcuhAHkqNj09/ZNPPskdEGkuS9XmSj5T 28yZM+VaBL2dsybVcrQPPviAXv3EE09s3749M9Cgfw2IZuLve/fuQbXypgBZ 1Z9qr1WrllAtWy5cuMCe9FuZstGlS5dwa5JwNIwQlc8x5cigOs1BI3qBAPYW FPtEp508ebJ5I9QmVxji/xWoUZE9tclDpC+//LLlKzA7fgNH6ksZsyIbamNo W0K0iHgrM2pJr34VM2Iptfkml9RGH+vatavkDsOWE9mWLFnCxtGjR5uPgGGQ h1LMq6L5rGhRG4lj7NixnAseSY2oDbXxEf4Ht2N5EGXIkCHB0TWupNQWTvbU 1rlzZ6IiY9+8keCAvy1cuHA8rxrqs+ypjYwMApCUZZkjdl66dCkkJXCBc75+ /brca3v77bfNXwTQ2KdZs2bkepmrRbWvWrXq64CIzBwWu/vee+9t27YNq9Cn Tx+ZF7NmzRrZZ/78+dAi35oxY8bu3bsdl/wNR22yJlW+fPlIJcZGkqxcD//0 00+9e6rcntrkySKCkmQidsYOCQK3a9eOusUDYJOABctEEllph/qnbuXZSILb 6tWrpd4WLlxIJ6fJ3n///T179tBA3bt3lwdc165dK/vMmTOH0QHKASPs47gy RjhqkyktBQoUIPAaG2kpWVGQ0/HuaRafqY1uD/ZSsRbb0KhRI2rS0vnNGj58 ODtwfHlrBvunBN6aYZ7vtm7dOvapUqWK+aFiR8lSqy+++KL7r7iUvQXt0aMH peW8zBshUPw8vc6L98fFoeypTa4Gt23b1vKV1NRUasm4wBKHsqG2O3fu1KtX 7+GHH7a8vODAgQPEKHq4rCYRn1Jq801uqI1PZb68PP9v/kiusvbv3988cVgW emK7y0u4Xiha1DZv3jyanlRCyo7ztxz6IBtqw67LI+WWnAKv0ROGDRvmVxkj llJbONlT2xtvvBH8jApDXuyr5ZZrMsue2gS4unXrZt64Y8cO4hIeXm6mPP30 04Qgud1g6PDhwzLj6fTp0xJvbUSglsU0bBTy7WNmhaM2/GRK4MVJV65cMW/H O7F91KhR3j3IZ09tgo0AjnmjLPMIY8pVOIiYeraQAgEhJbCGCXXrWG9btmwB uOz3cUyF4ajtzJkzKYHXoV67ds28HSpkO57Eu5mDPlPb1q1b5RUYloPQf/gi IBPui8uXL8fTVqhQQdZpkXltlrkMeJKcOXM++eSTET25LSvJN23aNOqL7dtb 0JCXOumKKYE3L9s8D5ydZE9t9H8CEePCvJFoANXmz59/7dq1vpQxK7JfjUTe G2KspSPauXNnSize/RGRlNp8kyO10ZEGDRoE/mMbNmzYYPmUlpLJFGbTS8pj QBUpUsSSxP1UVKht+vTpOQMaP358krwkxV421Hb9+nV5Vxfp0tgBlpe7ritX rvS3pBFIqS2c7KlNpgvRvkZt3Lt3b+HChSmBZdXDvUU0CWVPbc2bN6fG5s2b Z96YkZEhq6DL7DZ5+Zfl0oe8d6xu3br4eY6/1qT169dDTHKvbcqUKSAb++zZ s8eyz9y5c+Ve2/vvv49ndlzzPBy18UWiPQnCfD/o/v37cjdwwoQJ3pGFPbXJ mvl0VPPG9PR0IQh5HUCLFi3Yx2KTdu/enRJYeh2TwBCw1BuNxcnCF9OmTaPe 6P/YKvM+69at+/jjj+Ve24wZM9jH8T5mOGqjAPxQ7ty5zc/FMdA+/PBDSghW yFvnvJDP1IbfS01NZR/LY5+yxo48ABlSpGmqulq1arIkmjw7Z1n/itEnMxMj WiVJLky1b98+6jeL7S0oOTQl8P4UA9UpwOeff54SmMrq6Wvr40f21LZ3716Z h2ge+1hZxnK5cuXieS0sG2rjlCdNmpQSeBLA8OT0ankf4siRIz2dx/qAik9q GzdunPvyJIrsqY0gPGvWLMKdvLMy+GYTjo4ORo9atmyZ8RV8AluIeJ7OQ7eX S2o7fvy4UFtw3iftkitz5MjBYInozS/ZWDbURlqRXIMLNSISZlLmlcfz2kfu qQ0PI9TmPnQktOypjTZ97LHH8JkrVqyQLQwTWQqAIaM3pg3ZU5vcD+rZs6d5 Y1paGkGJ0ARq8V9AgEBkWepnxIgRKYEXjYW0lNevX5cXt9mswHD58mWZ12ZO tTT69u3bT548Gbw/hxJqsyyKDmOOHj06xTTlOTNg1OUFjhTeu85gT23UKgWg hs0br169SrVw4vI8J703JbB4xcWLF2UHYzXOjh07hiw5mCbz2mzuoJ0/f17m tZkDC+wGtsjSoBYBwkJtlmNygvLyAuycwURXrlxp1aqVAfUeyT210e5CbeGW tSSGCLVZ7hebxXdl0X7+NTbSG6tWrYoDwYfwXypBRoQhuEYmsnXt2lW2QNwF Cxak85v7MNk8JXBjWjwJQ4ZEdvDgQRk7cBAd2GIYOHGZOirTG6MrewtKFyVp 0scMW5Weni5rBdAZkiS02lMb46J27drYcvPsP1l/oEOHDt5dynhw2VBbZiAO pARWHzJ6L3FJHqXYsWOHj8WMWPFAbQRYojrZlpKQLqm0Tp06HTp0iDh26dKl bDNwbKgNxp85cyY5OiUwjfebb7759ttvN/1DxEbsLvWwatUqxk6BAgXYiNn4 7LPPHgnIPMfBf9lTGyOC4u3fv19KS3iEOCSM3759m0j+0UcfsTEl8JL67777 bvPmzcaJ79y5M57fdeipbKhNPpU5I8OGDcMQMlhkigEWyOsFwB9EjtR29OhR egt9Q1I/fmDjxo379u07cuRIDK9L+CB7aiMzCnFgTakcnAbtnhJ4PDK2Yz/e ZE9tW7ZseeKJJ4hCEyZMAAeoc/KLrM9P3pG3jHGEunXrsqVNmzbnzp3DZBKv 4I48efIsWbIk5GFDriFpUfAakgxtiBsYr1atmjzjSoQHFWlQBv7ixYs5oKx5 Qv9n1Bi3kEjWWGU+mjZtGj9NZ+A45IUaNWp4+uY+e2pjnFJFuXPnnjp16s2b N6lb6lPW569evbpMcSLg87fwL2dK3XKaYILZM1sUcg1Ji4LXkJRX6FK38IvQ N3VLJUvdLliwgAqkOT799FPqlgFlvLye1smfPz91++GHH2Ja+HV5gu6ZZ56x LNgbXdlTG4UnwtOxSbVSpeXLl8cV8BUKLI+mQJfioDAP8i62iRMnsoUTNBjZ LPYsXrx4jhw5xo8ff/36dXrRq6++KiGFocHPMUxoUCDxwIEDmA1+SFarxn4Y KyvSh2U+Wp06dej8NCgZHE6nOaZPny77gOqFCxdm4/r16/nvBx98wKf8l7yP bcZX45AFM2lBLxb/t7egnILcMaxQoQLnxbnLY6Jly5blW1EvTHzKntoyAyRO V6EdCYa08pw5cxhcDMx4fjwy04naCFPykGT79u2PHz9OtLfEq7hVPFAbgyXc M+rE3nh2oRHJhtow4eGf0///A4ikFRJ3jx49GD7GR4AeIyi2VWRPba1btw55 UpR8165dpEICeLgTx7ck7UoL9tSGFi1ahFc0VxdVbbzfNj7lSG1y0SZYnGn2 xhN7assMTGgS12SIHGp5IE3luPL/5MmTZdRg2nGJslA5HnjVqlXGPvjPqlWr mqsa+/rmm2+GG4ygE7RizIwLKahNAppxPw6ykKWziYQyA4jjy3JMISUrqMhu q1evxjxbBsjWrVuzUGPu5bjy/7vvvgsNURjAh7qVVQr5w2zt9u/fX7lyZXPJ sX/Dhg0LV7fYJ0w+Z2fzImZgJyWw9osxz/fOnTvyGlPaRXCP2pYbpiFlxBYC 1MqVK2UZfPNA8/rauz21UTn58uULWXIgS6Bs3rx54c7ujTfeCHlYvsKpmfcs UaLEV199lRlwGgMGDJDWNAvQmz9/vvkgZ86ckTtThmBeUM5YAlogKCVAkZmB SdkdO3aUlwiYBTQFzwqJihwtKH2sRYsW5sLQ34x3wSeDHKmNsd+/f3+L8xwz Zkyc31Kxp7bMwHumZF6JIVlV1c9CZkHxQG0k2Y5h9MUXX8TzHdiIZENtv/32 W7gaQMOHDze+hUOYMmUK/rx+/fpt27adPXu21y8AdZQ9tc2YMSPkSfXu3Zsh Q8cgp4Q7cZxSDOfrxVaO1JYZeJqra9eujRo1wv69/vrrcX6BKNMFtb399tsh e8Jrr72WvfndkdoyA1GCUNC8efMGDRrQ7uvWrfOteIkiR2rLDCzF0K1bN6xm rVq1AOFXXnklmAjOnTtHXGJY1a1b97nnnluwYIGNP6Ht+vbti8u1ecXPjRs3 evTowTGN6yoc8Ouvv5aRK9Nn7t27x4gOFwwtSw9t3LiRPtCwYUMSAX9s27bN sXIeUI7UlhlYMqtLly70T6lbhm3wKny0Dts58Xr16rVr187+ygOOq2fPnuxv c6uLUcOPDh482Hg4nLC5Zs0amnjQoEHi2dgCmIerW8uDlNg2OQtEV/HhcSl7 aqNj0EtDlhzgTUtLY59du3Z17tw55D7h7mNmBu4+d+/evXHjxnQkTtlyZWzz 5s2kaT5lFPBvr169zLMpDVH/EDGehN1atWo1ffr09PR049Mff/yxffv2L774 ovlOKGhMxfKjfIWANnDgwJDPCUdFbizo5cuXCa2wG6OJaozhe5RiIkdqywx0 0UmTJhEMqaLnn39+1qxZ3i1XGy05UltmYOWZPn36EKzojYQa4+JYPCseqC1J FNFbth0VP68zczmvTRWR3FCbiBSZKFc23M9rSza5oTYRuTJ+xn68yQ21iTIy MuR1bDb7MKy8fkKbYjzI9WrGvm+dwQ21GXtSt/amjhP3oW4f5OvUrW9PZbuf 1+aF5M104T5lTN26dUuWH7FXuHVg6AkhBxo/yle8Nv/uLWjShlY31GYogVYe cENtIkK9zRCINym1+aboUlv8SKnNC7mntgSSUls4uac2lY3cU5sqUrmnNlWk ii21ZW+pBXVURNSWQHJPbYklpTbfpNSmci+ltqSSUltUpNTmnZTavJNSm3dS C+oopbbEklKbb1JqU7mXUltSSaktKlJq805Kbd5Jqc07qQV1lFJbYkmpzTcp tancS6ktqaTUFhUptXknpTbvpNTmndSCOkqpLbGk1OablNpU7qXUllRSaouK lNq8k1Kbd1Jq805qQR2l1JZYUmrzTUptKvdSaksqKbVFRUpt3kmpzTsptXkn taCOUmpLLCm1+SalNpV7KbUllZTaoiKlNu+k1OadlNq8k1pQRym1JZaU2nyT UpvKvZTakkpKbVGRUpt3UmrzTkpt3kktqKOU2hJLSm2+SalN5V5KbUklpbao SKnNOym1eSelNu+kFtRRSm2JJaU236TUpnIvpbakklJbVKTU5p2U2ryTUpt3 UgvqKKW2xJJSm29SalO5l1JbUkmpLSpSavNOSm3eSanNO6kFdZRSW2LJH2pL S0vLUumylc6cOQO1ZWRkxLogURbUdvz48ViXIrsJtGFgxroUUdb9+/ehtlOn TsW6IHEnbBtxkiwT64Iktv744w+oTcnCC5G5IAuyWKwLkg11584d6vbs2bOx Lkg2VHp6ulpQR2E2fvnll1iXIsq6ceMGTf/3v/891gWJsvDbXlMbafSnn346 kfQ6fPgwVU2Fx7ogUdaBAwcOHToU61JkN8HC+/fv//nnn2NdkGiK0+GkOLVY FyTu9Le//Q2ePXr0aKwLktiSdEakjXVBsqG0br2TXDw/cuRIrAuSDSWhVS2o jbJrXiaf0vTHjh2LdUGiLPw27eUpte3bt++A6sCB/QHFuhTRV3Y9r9gqu9Zq dj2vB5dWS1SkHcw7ad16J61b76QV66js2v2y60l5TW1/+ctffv/99/tJL5nX lp6eHuuCRFkyry3Wpchuknltf/zxR6wLEk3dvXtX5rXFuiBxJyKqTL6IdUES W3fu3JF5bbEuSDYUmUvmtcW6INlQt2/flqdPY12QbCiZMqwW1EbYDHniLtYF ibLS0tJo+ps3b8a6IFGWrkbim3Q1EpV76WokSSVdjSQq0tVIvJOuRuKddDUS 76QW1FG6GkliSanNNym1qdxLqS2ppNQWFSm1eSelNu+k1Oad1II6SqktsaTU 5puU2lTupdSWVFJqi4qU2ryTUpt3UmrzTmpBHaXUllhSavNNSm0q91JqSyop tUVFSm3eSanNOym1eSe1oI5SakssKbX5JqU2lXsptSWVlNqiIqU276TU5p2U 2ryTWlBHKbUllpTafJNSm8q9lNqSSkptUZFSm3dSavNOSm3eSS2oo5TaEktK bb5JqU3lXkptSSWltqhIqc07KbV5J6U276QW1FFKbYklpTbfpNSmci+ltqSS UltUpNTmnZTavJNSm3dSC+oopbbEklKbb1JqU7mXUltSSaktKlJq805Kbd5J qc07qQV1lFJbYkmpzTcptancS6ktqaTUFhUptXknpTbvpNTmndSCOkqpLbGk 1OablNpU7qXUllRSaouKlNq8k1Kbd1Jq805qQR2l1JZYUmrzTUptKvdSaksq KbVFRUpt3kmpzTsptXkntaCOUmpLLCm1+SalNpV7KbUllZTaoiKlNu+k1Oad lNq8k1pQRym1JZbiitrY+fDhwz/99NOdO3fclydR5JLaqF4C+NGjR8N1Nrri gQMHfvjhB6rLg2JGLPfUlp6efvv27fv374f89NKlSziuH3/8MfuBbRbkntru 3r1769athOC7iKiNIEOHCddbspncUxtjjeF//fp1H0qVcIqI2uhg4UINvY4B uG/fvosXL9ocISMj48aNGzdDiehNYcw7s4WgTSufO3cu+FAExuBDufQbFy5c kMhJHHCzf9bknto48RMnTlAkQnq4fa5du0b10pnDNQGtww72zuTs2bMU6ciR I+REx1KZRW27qSt8CI3C2Lx3715Ex49UkVIblUPBOAvLdrLAyZMnibE2NU++ oB+SXK5evRpRIfEktEjI3svvBg8BYhS/JTtITYYcKWz3NHm5t6A///zz/v37 I+1L2UDuqQ13Su/67bfffCjVg8sltdE5CZ6gR5w4akfFD7XNmTOnYcOG5cuX r1ChQuPGjZctW+a+SAkhR2rbvXt3mzZtqlevXqpUqT//+c8NGjRYu3YtxsDY gRg4c+bMevXqlS1blopih48//jjmttYNtREPJ0yY0KhRo65duwYP+e+//75L ly7PPPNM6dKlaf2mTZtu2rTJs/ImhtxQ2/nz5+fPn9+sWbMmTZp8/fXXvpUt y3JDbRgkbNjo0aPp3r179yat+1a8GMoNtTHQiA9EhieffJIgMHny5IRAdT/l htrohGDviBEj6GCvvPJKsPUlmr344ouVKlUiHNWoUePll18Ox26YWBri2TCi D8tuhOgVK1aQ3Z566qkSJUpUq1aN37W43+HDhwcfgaFtH1qJnB06dKCQFLVi xYr169f/5JNPzCkjinJJbVu3biV9U3tlypQhpPfs2ROOMO9AJx82bFjt2rXZ gc7Mzp9++imNYv6hadOmcS7swHHatWuHl7b8yuXLl9966y1qsmTJkuXKlWPn hQsXWjA5pAA9yUStWrX69ddfbfZkSHbq1In2HThwoNeX6yOitoMHD1J4usd7 771n3k58oK5SU1PpDNT8gAEDgn0Xo4MYQj+kbmvVqvXmm28CTfY/l56e/v77 77do0YLUzLfwJ/369bN0gzNnzgT3Xqpu7ty5Yni++OKLcMOE3fjU3AGiKzcW FIxt27YtvRFnVbdu3fHjxydVaHVDbRcuXMC80fr0gZo1a9K7HHtOzOWG2nbt 2kUIom/jqAnRxCLfipdlxQm1rV69+tFHH01JSaFLFC9enD/y5s27fft296WK f9lQG4GRCFygQAFOPHfu3HShlICeeOIJAppc6APZ5s2b98gjj7CdbFW5cmX+ 4L8fffRRbG9O2VMbZSPFFCtWLEeOHBSYP06fPm18ip+B1osUKSLnUrVq1YIF C/J3vnz5vvvuO1+KH6dypDZMC3QvowbRDfwsXtbkSG3Xr1/HS9AfHnroIU6K QHrt2jU/SxgrOVIbowxHQZ1QOdhU/vjTn/4EF7hxqskjR2rD7VepUqVw4cIy avjbcs8FxMDT8lGhQoXq1Knz2GOP8TdGjvEYfLRvvvkmJbxAP9kNJ5A/f362 YKc7duwof2MVzGkU+xp8BELinj17Qp4IQ2nKlCmSMnLlykWXoD9I+oBKslJ3 TnKkNoo0e/ZsKRJxiVQuZwFSGVfqABNOXLYDXMR52XnSpEmyA8g5atSonDlz sh0XLTtAIqRO44du3ryJxyZEkFMAWzkgJz5jxgyb8mMygWV+9OGHH5b9jx8/ brP/xIkTpZx41LS0tAhqKnK5pzbOvXXr1lKwl19+2dh++PDhEiVKSHyg30pe aNq0qfmYDA3p+fRwqFkqmZq0oVecG15d9iT+VKxYUX66efPmZlNHdAo5BAYP HixXRaZOnWozUj744APvKMnRgv70008SUak6Mg5/0K+GDx+eLZ/4CilHart0 6VKjRo0k1NB5JNQQ3zy9uf/gcqS2bdu2iflk7BCcJRZNnz7dz0JmQfFAbWfO nCF70hOIurgXnNvIkSOJyUTjSG/ix7NsqI34QI4g38FlJBcgDpNATKYXQWeS 8mgpUhijZsWKFekBrVu37vHHH8dXEDN9P5v/K3tqIx+1bNmStEs8pE1J5WZP Rb6AQDkFoPX333+ncq5cucL+nHilSpWS+VFJR2obNGjQk08+SS0xcHB3n3zy iZ/Fy5ocqQ13BLXR58XycXb0Cj9LGCvZUxsw8txzz1EhDA1CImN/2bJltDse LCHusfomR2ojlj799NOkG8nRGHKz8WC4vfDCC2xv1aoVexJ/zp4926RJE3HI wbcDyG7/z/8W3/roo4/y5s1bsGDBH3/8MTMQ9mvUqCHWlP0pIdGyatWqbFm6 dKkc5+7du/Xq1WPLggULSK/G0c6dO0dbhzuXd955h5QB7zBq2I00isEmxuJA vJgh5UhtR48eJRDheMFJKRIjvV27dpRTLjxSvb179+Y0CVzEAVID3X7+/PkN GzY8cuSIHIRsAlXhnT7++GOOwHEABL7y/PPPG8Fw9erVbIEgqElSJwVbvHgx 36JlbZ7dwsL179+fb2HRKSTs/PPPP4fbmUzN4BK+o2m8fmrOJbVRjePHj08J XLHh3759+8p2eiaAxpaOHTuSQKmQU6dOEUjZ8tprr0m9UZmypUePHvhY9uH0 5dovhBLuiZ0lS5bQFoyX7777TlzHli1bBKW//PJLY7ddu3axpUuXLseOHTN3 YAKaHBlXYxkppP5Zs2alBK6chHzqMlqyt6AMvfbt21MM8J+q4wRXrlwJpRYq VAh/5V2p4kqO1IYhl3QM4VJFe/fuJcjQCePcddhTG9aidu3anBcBCui4ffv2 tGnTGPJ4jzifvRtzaiMQMfypug4dOhhXtM6fPy9ZbPfu3TF/AjBasn9CkrBm SSIMELmzRgTmvwsXLuRvvLrx8C0BBycgGwn7Xpc/nFzOazt58iTIWapUKYun Igts2rTJjCfEBDImzsf+Wmj2lst5bRkZGZgQskycx0+R+3lt+/btk4vtSm3o xIkT4saJh7KFdu/WrRtV1K9fP+8eLko4uZ/XtmPHDmqvevXqZmrji9DcY489 Zn4kjyiUJ0+ekiVLcmTHw2Jp5KK0ce+b+FasWLHcuXObQzRcw5h99dVX5b8E f/iRJg5+FNBG2AxLyqCfFClS5PHHH/fiQQV7aiNSvfTSS5x4p06dLCnbCGJY ejIaPLt582bzDuYOTJ3IQYwtmBP8YfHixeVkOfiwYcPYZ+DAgcY+mC6akhMn aTqeCMWgNdk5HLUxDOvXry/3oeKK2igwCZT+CefSWwxqo92B0KJFiwLOxs4H Dhygj5UvX/7gwYP8Fy6Gv8qWLWt+YJV4AsDixi9cuBDyFxlQCxYs4PjGFlpz 7NixVMuoUaMMM7N+/Xq2jB492v39qUuXLtWpUwdLYOkMUZe9BSUZ0bsYoTt3 7pQt9MaePXtyOr17906S0GpPbQwuEjG1ZJ61NHnyZKqIfhhD5+koG2ojjMil Bk7N6PzsD7yzcfXq1V5PZX0QxZzasCsS7efPn29UFGN/0qRJEgeyzSNAka4h SdPI8zkEFqLH0KFD+fuLL74w7yOhmIwWQ3PrktrYJyS1BYtBBIngc4xAmoRy SW3EzCeffDL7URtBQ6nN0IwZM6iNJk2aGIGXUClOqVatWklSRW7kntq2bt0a TG3bt2+HKQAfYxUFUcuWLTHJEydOtD8mjbJu3bqHH34YcKNBZePevXsJenCf uXE//PBD+IXEJ/8l4snTJg94j4yRReHJGt98882DHCek7Knt3LlzcFCBAgVs TPjgwYOp83bt2lmq11BGRgZ1RZrADBgbgVO5hCv3PrBb48aN4799+vQx9sGY sQ9fPHTokOOJ/PDDDyVKlAhHbTTi22+/LXfuIO74oTb26devn+AS3Gqmto0b N5IuK1asaOZlTFTdunU5EXlw9Msvv+RvwoX5mHR+TpPt9FKXReUnVq1aJXf6 ZGYTNbZkyRK2zJw50/0pT5kyRa7Ve70EhL0F/fjjj1MCD/EacYATlCefa9So kZ2e9bKRPbV9/fXX+fLlK1y48JUrV4yNjB1cB6OVhO5XMSOWDbURgt566y1a uWvXrgab88ecOXPYOGDAAPeM479iTm1UbJ06dagoopZ5O11FrrkFzxZPUEVE bTgxoqI8QJ6WlkYfkwuMy5cvN+/222+/paamEsDDXSvzQVGntt27dxMiyKr0 tCiVMfGk1KbUZogkQm0Yt2ZEp0+fTglMBD5z5owvZUwARYvaLJcK5RJi//79 7Y95/vx5fDLR2MwO6enpcv325ZdflpU/yWitWrViy5o1a2SfEydOVKhQgVF8 5MgRHCN4cvjw4Uhn+pBJ+Ql+/amnnvJiiVF7aqPYlL9q1apSbPK+wa2GqBxY ddGiRfLf4EJSgfjDYsWKAVbGRtrinXfeobrmzp0rW44fP54SmHhII8oW6o1f r1atmpt1z+yp7cCBA0888QSfktf2798fJ9RG5BS+aNmyJYUZM2aM+QlJqC1P njxQm7nP8PeIESPYDXfKf0GtYGqjbl9//XX2cf+gNVZEmoNBIfRN2d5//315 Bpiev3r16p07d9qv9MIIlavN7mkxy7K3oHL6Rk2Kzp07x0a8inkOfjaWPbV9 9NFHDC5cunkjjc44ZbSaH5SNN9lQGz2/TZs2tLJlFjAWXR6XjedXRcSc2q5d u1a2bFmCudzHN/T999/LNZB4Zt6I5IbaMLSLFy8ePXr0M888I1Otjx49ei8g cpZcYDQ64f3795ctW0a4ZrvMoYiJok5t8Kk8tpFU6zhZpNSm1Gbo+eefx41j 1cwbsRZ58+a1n56TbHpAamPQEaAsd/k5mkxDM1YXCacNGzYQuNq1a2dZXY2w L/M0mzRpgsnB9GJZSW1GJIcO2IEmxngXKFAAL8SgptHd3Dm6cuXKwoULx44d ywH59dTUVOMx2ujKntrmzZvHr7du3frixYskqToBdejQYdu2bbIDDh+cBIg2 bdqEsW/RogU5rmHDhm+//bZhD8hi4BJVYeYXgsaHH34omCBXxTMyMgRbgK8h Q4bs2rXr2WefJQbCDm7mU9hQGyWRCaQACIciWMUJtZE96RiUmSiR+Y95RgZr YBKIAwULFjTf+OArsq5Oz549+S99ifzLiR8+fNjYh40ywZMu5LKosLasSWJM +2LQ0YgpgeVlOBqFxM8//fTTkydPDjcrE6ssN9p8WPHD3oJ27NiRcTdq1Cjz RvowHZVheOzYMa+LFw+ypzbiFVXUvn17y1doYnz7nDlzfCljVmRDbUSzGjVq BE/NY0RwUlWqVInntxvEnNouXbpECiMiWZ5IJ4gRh8uVK5dtlv52Q22y2phM gib04UCMNEQAIYzQo4YPH47vpeHIXERIWYXJh2tW4RRdajt58qTkkc8++yx6 ZUw8KbUptRmSBd9wkuaNFy5cKFu2LFbZbMOSXA9IbfRPWEnWgAI34OK1a9eS wWVFU/y8zXgkT4F17MZXLOzAYQcNGiSBGiSkKYsXL27Od1Bb+fLlie1NmzYl vPfo0UOeja9QoYLj8wacb548eSRl8K2NGzd6NCPDntrw7XTFokWLVqtWDS9E QpdJ2aCEhKbLly8T2NlIZ8YSU2ZJXpQcgpNbMzt27KB+qAqzYeB0li5dKveM jAdvcCy0CxtpGo4mtzJdPpYTjtpknjjl6dy5sxRAZtfGnNqoeapIpvIJt1qo jZLLQpqAMHV49uzZlStXCrIhToc6hI/4lP9CuEeOHOG3qFVJtfJwo8sFBGRp TQaI8ToMBh1WhF7NL/bv359mkqscdINFixYFH5YRR86i/letWpW16opI9ha0 fv36lHPatGnmjfRVnCc94cCBAz6UMOayp7ZXXnnF8kCyfEWWafJoxdqoyIba GHEYbAKmsSSU6OjRowSxQoUKebpCzgMq5tTGAGH8wiOWu0VgiFw5zDZvNndD bXjawYMHd+jQgcwFzJLyiHvGBUaYTjJUSmBxWviuZcuW8pqAGL7XPorURr96 44035KJ0orzx0CMptSm1GRJrYVmR+Pz58yVLlsQGmx8nS3I9ILVlBsIUnlNe UyKQValSpU6dOskMCJsD8qMQBE7A8o4ARuiwYcMYofwWaMPRhLDefPNN8yxF wI2hQZBnyDNMdu7cKY66X79+9idy5syZ119/vWPHjhUrViTAkjXee+89LyaD 21Mbdl0YqlatWkABjEDS79WrV0pgkcCrV6+ePn1a3ulDlUKm169fp5CgsaxF Lzc7SPrQHLnPPJ+IyiEJpgSWOpR7NwQQ2ojjkCw4cXlZDCC2fv16N49nhKO2 gwcPSs411vSQJyTBHK8vHdtQG9Qja44Bj0a1jB492tI3KDzQJF2LVqAawWdZ ENXYjQhDpzL2wbLCcXKZwjLzIpzoojgQjmCZwoY93rx587Vr1/7fgPBsQ4YM kYcMZS01s7799lvq2aPneINlb0FxGrLqqXkjprRMmTKMWTc3u7OB7KlNHiKV O7bmr8iSOJaqiyvZUBuRhLFADFm8eLF5OxgCshEf7B/xja1iTm18JI/0Wy5r SFZt2rRp8OPxCaqI5rUR0CZOnEhwo2b27dtnXLAi4pEfgbW+ffvy98mTJ6k9 HB1xxsuy2yla1IZjkQcnSO56+0CpTanNENRAfjTe2iw6e/Ys8QHTG8MrNvGm B6e2zMAzhwSiFwKaNm0a6VseABs0aFC4oxGfN23alBJ4s5ulEWfNmkUz0ZNp r8zAuhkQiqydzmFtbnAAKTQ6JXS5kB0pY+rUqdh1fs6LySb21CaPUaWmppqh lZrMnz8/1ojkzoljk0hV7777rvmL06dPTwm8auHu3bukM6CA3cy/Ykyboi0g OJq4efPmHAcukB3IjzJPEK/l5mnhkNR2+/btBg0acJDatWtvDui7776Tt4yR YVesWAHBefT68kxbajt27BgJEVDq06fPjh07SPp0XVmsHuJgC71d7q6eP39+ zJgxdFpI9sMPP+S/cgmUbmYcDcYnjLRp04aQMnv2bPo2h2WfLVu2OBaSOEP7 ys07x2uqnIu8+8yyXkFm4LYsXaVZs2bO9RIN2VvQ7t27U5gRI0aYN164cIFx BL8nySrW9tRGMKSK2rZta/kKnYGhvWDBAl/KmBXZPyEpa/VYXnRLpCK21KxZ M4aO2lExpzZyDcyeEljk37xd3snSrVu3eF5ZNCJFuoYkeUTuQRNSwuWLn376 KW/evGXKlMkGa0gy/AkO7LNq1ap4XnbVHym1KbUZEvdlue2CxWVjuXLlvHg5 V4IqKtQWLBwL+R0PH24HiENuf2CGzZBlPNtmed578uTJjzzyCG7c5iXyX3zx hTx1ZvPu9WDJ7RWLxYqK7Klt/vz5OXLkgJ7MG8n+MuVfoIBzgZUsD/PL3LE6 deqkpaVxpvBs0aJFzZPcaVNZP1n84ZEjR4SkzIvwXL16FQ/GdsKgY8wMSW2y to+NKFhEDRGRbKjN/v3UctM2pKmgT+Ksgn2pWYQdmUEfzFYWGT6tWrVqbh4e w/JJV1y5cqV5+/3792VEWK5BeSd7Cyr3BC03kqQzlC1b1k0kyQayp7Y5c+bg ymrUqGHeSJeDavPnz7927VpfypgV2a9G0qFDB1rZMlt8586dchHJu8H+4Io5 taWnpxN2UgLzf40FgQEWWTJx+vTp2eaVGZFSG5LrYKBruLBMQE4JPHIcw5U2 o0JtM2fOxMbIldhw60InlZTalNoMLV682HIf5969e7IRK5VtniF/cHlBbSTH PHnyFClSZMeOHeH2IYs9++yzlvsamYFZ27jclKAn2C9cuCDgYDMfWeZYValS JaIVG+RxBQoT9fec2lPbnj17COBwmXl5cLK/XHjcvn07/23evDlnZLFJfDEl sOwYXECvrly5suVhYIYGjPbQQw8J+n3++efS7S0FgItTAjO/HM1JSGqjqOvW rfvSpFWrVsmaJ+XLlwcY6TDeLZ1hQ2105jVr1pgLhk8W8GnQoMGGDRuOHj0a 0iMdPnyYmixdurRNH+NHaRFOUG4Eh9Ply5cFwSpVqmR+3ZuN6PnC0ZZRI6te w4nuV618QNlb0GXLlqUE5gMaiUYWeZM+aVlWKLvKntp2795dqFChfPnyma+T 7Nu3j56D94jn25E21MYpywzN559/3hjXpA95D92IESPCLaQTD4o5tTFGtm3b Jk8mGPNbKU+5cuWIOUeOHHFfsDiXDbURHEaNGmV5yQ5dTuLe+PHjBWTIX2Y6 w9iTzhg7sX0XuUtqY3QLtQXn/blz58qE+qFDh2abB2IfUC6pje4k1DZ79mx/ CvYgck9t2Amhtni+5BVF2VMbgZEB8sQTTxhPvmHz5G3Ow4cPj+HYjze5pzZJ OlCbJeAADpaXNMlk/Hbt2tnkcdJc8eLF8aKW+xokSsEW8wtqMwMTpgoGxFjg F6dNmyZrAxpKS0uTl5SZF64kGx48eFBiAkem6Y0VGkWML/HzXbp0cayBSGVP bSQmAJNAZK6B06dP586dO3/+/DJpXcxwrVq1sPSyA+cuNzs4TXm+QqgTvjO8 llzAgQflFg//pZ6JDGYPmZGR8e6777LbuHHjZE4fzbpr166QK7dTGKE288uj Q4raFvfudVZy+ZZtQ3Jj1/wqCpre3G8xDF27dmWf7t27G5aDjeZ7u/RnerUc x+A+ejJ2wjwZjaAkyAbckZKCCwPHjR071uIJQWDSPSHL8swqyFymTBkA37eF Puwt6JUrV+RFDytWrJAtVJe8qoOemSSh1Z7a6CeM2Yceeui9994zNspkN5DH u8eGH1w21Jb5j7v2RYsWNR7qJi5VrlyZjd9//72PxYxYMae2zIAnkRWQ+vTp w8GJGHLvsnPnzv7MV/VH4aiNbPXVV1+RiQoUKDBlyhRyCt2MSujXrx8jRea1 ZQZqqWHDhr169QJ/qJYdO3bIIlq9e/eO7RUhe2rDlmBRDh8+vHz5cmI14RHn yVeI3qQqzn3OnDmymFhqaurWrVs5ry3/ED0naSHOntoIR3xK4qOKihUrRj95 9dVX6Tls8Xq5sweRI7WR4uktnAgQSpcoXLjw5s2b8U7Hjh2L52tfDy57asNu ydsbySlUDpYe20ajly1bNoaLx8ahHKmNjsQYoQ5lhYeSJUsScOhyBFUxrpMm TapQoQIxiraACzC0EpktfGQREZvd8ubNa3kSEtcns3gANHwsaEPQAxaEuPmX AS5LshMD+WniJIciHcuDhfhJCf5o06ZN2F1GhDxyNn/+/D/96U+PPfYYhpkz unnzJoQyYMCAhx9+GMD/4osvolCb/1v21IaWLl3KmRYpUmT79u3k/bNnzwpC tm7dWgYvG59++mlhNAwSfR6OIymY643jA3qcxWuvvcZJ0V4AmjzDJlh369Yt cVYc9tdff6U+OSx1wrf4dXmDG+MFpKX26tSpI28ypSHYmYYm9SxZsqRQoUKP PvrookWLqHwyUbhZWnGyhmSwLGtIcnacLwl0w4YNBAd6wksvvURtFC9e3Lga QO2RI6j/b7/9FsNAfqEV2OfJJ580WIzeSGCh3nDpshoDe4oZQ++88w5Ho4Yl O3/33Xf8EF9p27atdGZiNc36+++/L1y4UJaIGTVqlGWGC8MNn0zlu7l2FxXZ W1DKP3DgQLEf2HicFQOKaildurRHb9CIQ9lTW2bgUSiGJA23atUqzP+CBQuI VwQff1YBzbLsqY2+LV23Y8eODAd5ZQn/ZYzE87L/mfFBbZmBh9tlgSlD5cuX d3MHJ4Fkc6+NQEeYlSnqZpGj586dK1czSOiYNPOnxJYGDRrEfFaLPbXJuAgW Q56oeP78+RIlSoTcISUwu9yyIFvyyJ7a5B30IWVZ3Suu5Eht1atXD3lS5Avc uJ9F9Vn21IYwUWL1DeGL3L9lKUnkSG141JAdDJNGXqN/vvzyyzlz5jR/RBAG kex/F0JhT+gjeJYHzoHYLm/VLFasGMQhi/hVqlRJ7vVgKnAL+fPntxQJ/Jk1 a5ZxHHl0Bw0bNiwzMJML0x6cMtgydOhQL+4ROFIbTPrGG29ATymB8C7lqVat GlnP2AcOkrd9pQRWhk8JLCk5aNAg414PcLF8+XLLeTVr1sz8BgQoVd5/R0tR jZJBqHyoRG603blzR+5UcnB5wwIcB0GEbHoULrawnU9r1KgRb9QmE/3oq/Jf /Fj79u2NhU9FJUuWNO4fZQYmpjVv3lzeYWGI4WC8di0zUEsSZHLlyiWPOcnF jXB69913qXAgvUKFCpZf579NmjQJXoiPxqXVKIZvj747WlC4Xm6IGypQoMC8 efP8KV48yJHa6J+9e/eWwCV65JFHRo4cGefrD9hTW2bgIl7NmjUto2b16tV+ FjILihNqywxMNOjatWv9gHr27GkO9dlD9vPaSFvffPPNCy+88Oyzz9KRqISO HTta5gifPn2asUNcrVWrVosWLd555514uCZgT23vvffe86HUo0cPvkXofuWV V0LugAYOHBjPK/l4KntqO3jwYLhKszxnG1dypLYRI0aEPKn+/fu7nE+RoHKk tsyAuxg8eHDjxo3r1q374osvfvXVV36VLmHkSG2vv/56yA6G4ZcHzDjCzJkz n3vuudq1azds2LBbt2579uxx/F3cKQcBoyxvHRUR8xctWtSmTZs6deoQ22EQ DM/58+eNHYCsb7/9lpBI2H/mmWdIAfyuJfgTLWl0jDfx1ti4ceNGvsX+kAXf xbpjwj16rMuR2jIDTyouWLCgZcuWnGmDBg0YtsELv7OF7XxKFmvVqtXChQst BSboff3115ws/ZzeTp8PNv8UY8iQIXzKibMblb9y5UrjfQccYdWqVfyE3LDL DEQeECZczAznfygqn44ePdrr19BESm2cC209d+5cYwvIPGXKlNatW9NvcQgA 3f79+y3funXr1rhx46hzap59+vTpY3ndEg2xZs0a6o3MKw/w0BD8ULh6W7t2 reD2pUuXJk6c2LRp09oBYU4mTZqUlpYWXHIQr3Pnzjg9356sc2NB8VFvvfUW 3UleDW9ZQSXby5HaMgMdjM5Dy1JFdDNw3s1bNmIrR2rLDBitl156iVBfr149 om6cPxspih9qExFjs9NTkWa5XI2EnkaSsqkEqpQgEz9XOVzOa1NFJJfz2hJL 7ue1JZvcUJuI7Gmz8GCSy/28NkddvnzZfZpzqd9//53QbbO+Funv4sWL4YI/ uSMkPpAySMpe50031GYIBLY3SxCE4yVHqst+BWk+pbpC0kFm4CamF++t80KR UpuN6LeO0wqoNJvly2iarNUbIHY5oPgxJ5mRWFBawTKnNUnkhtpEcH0CVZEb ahMR6hNoBn28UVs2VhbWkEwIKbV5IaW2pJJ7alPZKIrUprIoImpTRaQoUpvK IrWgjnJPbYkl99SWWFJq801KbSr3UmpLKim1RUVKbd5Jqc07KbV5J7WgjlJq SywptfkmpTaVeym1JZWU2qIipTbvpNTmnZTavJNaUEcptSWWlNp8k1Kbyr2U 2pJKSm1RkVKbd1Jq805Kbd5JLaijlNoSS0ptvkmpTeVeSm1JJaW2qEipzTsp tXknpTbvpBbUUUptiSWlNt+k1KZyL6W2pJJSW1Sk1OadlNq8k1Kbd1IL6iil tsSSUptvUmpTuZdSW1JJqS0qUmrzTkpt3kmpzTupBXWUUltiSanNNym1qdxL qS2ppNQWFSm1eSelNu+k1Oad1II6SqktsaTU5puU2lTupdSWVFJqi4qU2ryT Upt3UmrzTmpBHaXUllhSavNNSm0q91JqSyoptUVFSm3eSanNOym1eSe1oI5S akssKbX5JqU2lXsptSWVlNqiIqU276TU5p2U2ryTWlBHKbUllvyhtrS0tCyV LlvpzJkzUFtGRkasCxJlQW3Hjx+PdSmym0AbBmasSxFl3b9/H2o7depUrAsS d8K2ESfJMrEuSGLrjz/+gNqULLwQmQuyIIvFuiDZUHfu3KFuz549G+uCZEOl p6erBXUUZuOXX36JdSmirBs3btD0f//732NdkCgLv+01tZFGjx49+n+SXkeO HKGqf/7551gXJMo6cODAoUOHYl2K7KbDhw/v37//5MmTsS5INMXpMAQ4tVgX JO5EHIZnjx07FuuCJLZOnDhBByPSxrog2VBkLrqo1q0X0rr1ThJa1YLaiLyM 2fjrX/8a64JEWeRTmv5vf/tbrAsSZeG3aa//4yW17du3b79KpVKpVCqVSqVS qR5AnlLbX/7yl2vXrt1Lev37v//7P//zP//Xf/1XrAsSZcm8tliXIruJIflv //Zvd+/ejXVBoqmMjIx//dd//eWXX2JdkLjTrVu3iJO///57rAuS2Prv//7v f/mXfzl9+nSsC5INJXOvyGKxLkg2FDZJnj6NdUGyoTCrakHthc3AbJw8eTLW BYmy/uM//oOmv3HjRqwLEmXpaiS+SVcjUbmXrkaSVNLVSKIiXY3EO+lqJN5J VyPxTmpBHaWrkSSWlNp8k1Kbyr2U2pJKSm1RkVKbd1Jq805Kbd5JLaijlNoS S0ptvkmpTeVeSm1JJaW2qEipzTsptXknpTbvpBbUUUptiSWlNt+k1KZyL6W2 pJJSW1Sk1OadlNq8k1Kbd1IL6iiltsSSUptvUmpTuZdSW1JJqS0qUmrzTkpt 3kmpzTupBXWUUltiSanNNym1qdxLqS2ppNQWFSm1eSelNu+k1Oad1II6Sqkt saTU5puU2lTupdSWVFJqi4qU2ryTUpt3UmrzTmpBHaXUllhSavNNSm0q91Jq SyoptUVFSm3eSanNOym1eSe1oI5SakssKbX5JqU2lXsptSWVlNqiIqU276TU 5p2U2ryTWlBHKbUllpTafJNSm8q9lNqSSkptUZFSm3dSavNOSm3eSS2oo5Ta EktKbb5JqU3lXkptSSWltqhIqc07KbV5J6U276QW1FFKbYklpTbfpNSmci+l tqSSUltUpNTmnZTavJNSm3dSC+oopbbEklKbb1JqU7mXUltSSaktKlJq805K bd5Jqc07qQV1lFJbYineqO3evXu3bt3C3bkvT6IoUmq7ceMG/S24Kq5fv37o 0KGffvqJuo12GbMil9SGldq3b9/x48fT09N9KFWiyyW10UnoDCdOnEiIIRMR tQEydJX79+97Xap4UETURvgFT7wuUiLKJbVRz4waopYlGt+9e5fsc/Pmzf8M EgMNax3yF/nIsWAcmYPQcO7H6W+//UbAPH36dJxcurGnNpJRyHpjI2dtOYWz Z886ntqVK1fY58iRI8GDQkyCywayiPbimLQ+ji74U8oTsulpPscjP4giojbH 4X/16lVyB0kh3G7EVXEXIZWRkRH8FVyHpbHoyZQkXKO7NyeUxNMg796CUmMH Dx4M2TGyt9xT26+//rp///5Lly75UKoHl0tqY5gcPXr0hx9+uH37tj8Fe0DF D7URnL/66qsOHTo0a9Zs+fLl7suTKHJPbQyiJUuWNGrUqGXLlkQSYztxctas WQ0bNixfvnzFihWbNm06Z84cL4vsSo7UdvLkyV69elWvXr106dKpqamtWrXa vHmzb8VLUDlSG51h2rRpdBI6Q5UqVdq0aQPI+1nCLMgltXEiY8eObdKkSf/+ /TEA/pQttnJDbbgpaq9Pnz6NGzemfnwrWwLJkdqw35988kn9+vUZNcSi5s2b r1y50viUjvfCCy80CqOBAweaAQ2sWLZsGeOOjwAB+4INHz6c3bp163bmzBnH s8Aavfzyy3Xq1HnyySerVav23HPPrV271sXZeyt7apMTDCm6K2ZYdiOzk+Jr 1qxZtmxZMkKXLl0wS5ZDpaWlvf322/Xq1StTpgzNRL6bP3++GUAgCIJD8A8N GjTIHha2b99OZVaoUIHDNmjQYMKECZZLiIBk8GEpwMKFC+/cuZOVWnMnN9RG 8Dxw4AAhkXOntkOCJEZr5MiRVF25cuVICviHzz77DMi17Pbpp5/aNBZpxdgT OiZZ0xuphNWrV5vzEWGc7eGO06NHD/uLJ+L3OnfuzJF37drlHbi5saCYdgY+ NSYdY/LkycGVlo3lhtogNZq7Vq1adC1C02uvvRb/97DcUNvevXtbtGhBLhBH zUj3rXhZVpxQ29dff/3UU089/vjjKQERtN2XJ1Hkntp+/vnnfPnyUQ8PPfQQ rSMbCdqLFi169NFH2V6pUiVSD3/wX8DN04TiKHtqw4HUrl2bov7pT38i3TA6 +Lto0aJ79uzxs5AJJ3tqI6cwRnLmzEllwsLSWwg7P/74o8/ljEiO1IYfe/bZ Z0uUKPHwww9zRiSIa9eu+VnCWMmR2k6cOMGoZ+BIhAQ3fCxdwsiR2rC7EkL/ /Oc/AwX88cQTTxg2laDEf1PCCMoAKDIDJgdfh8F77LHH5KPvvvvOplSAYe7c udmtePHi+EP7UwAAJWDK/jLGKdW+ffsiro6oyp7aMOrh6g19//332PIxY8bk zZuX/+bJk4f8RXbj76pVqx4/ftw4DhjVvXt3Gf6EAiw9f1B77733nrHPhQsX Qv4KlpIAErJ4/DptVKhQIYkqTz/9NH/kyJGjV69eDD1jN0JuyCMPHjzYzY28 LMuR2viocuXKxYoVk/LUrVs3+D4aTUOGlR0IFNLPqer333/fsid4a9NY1Ins tm7dOlqnQIECsn3SpElmVDx06JCMoJAqVaqUuVktmjt3rtnvgZYxpDbcC6mT YuTPn59kmhLwKqTXkPccs6Ucqe3KlSvNmjWTmilbtqyM3K5du7oHgZjIkdp2 7txJjOVcihQpIoOL8TJz5kw/C5kFxQm1ffXVV8QZIjmhhlj67rvvui9Posgl tbFDy5YtJaeQrQ4cOCDbQTniJ0n8888/J9FQsVQatoE86Hil11PZU9vQoUM5 F3zIuXPnyE2U/PXXX2dLgwYNkuTht6zJntqgMzoDIXTp0qVUKTamd+/e1Grn zp3j+QFUR2oDW/AJJHTALSVwdeL333/3s4SxkiO14fZLlixJhBRCf+655/ws XqLIntr27t0L/hAzlyxZQvykzj/55BMGEZ1Nwuzt27fPnDlzyiQOxQGJsY88 8shHH30kIQvvOmrUqCcDypUrF82xZcuWcEUCt+nS4nPY3/6xBKCjTZs27Nm4 cWNanDx79erV0aNHA+kxv3xhT20XL1489b91+vRpCUqcC5F/165dWKPChQsT soBf6h/bz0hnBzDNyAVbt26lrsCBb7/99r8CWr58ORAHZZ8/f172oUolOBAh wRnjF9khXMDkF+W3BgwYgIsTW4JP47egOePXoUv26dKlCwGWMzWOTBTy9OaL I7VRmbhlhj9kQQlhZAu1gRg9e/bkI3ajx9Jz6Ev4T8j38OHDlqNxOpbGwtRN njyZTg5t4TRkt2XLllHJ5cuXlx8FnM3URqIhp1sGy08//cT+KYEL7zYPigCS 5QOSiyT8UKyojTPq2LGj9FL6MDEBf0VMoKN+/fXXHhUp3uRIbbRmSuBKF2aP KtqxYwdjBy8KfftZzkhlT210iXr16nFeI0aM4O9bt24xBAgIROk4n2EaJ9Rm iHBEvSUztX3wwQcEDRxa9erViaJiJ0gZixcvpoO98cYbxrVBAs706dPZ+NZb b8XQq9tTW9OmTSUsG1voP4UKFSpdunSSGPKsyYba5EFZahVTZLQ7TYApwt6Q Pf0taQRyP6+NkSJpIkk6ift5bcOHD6dm8PY+lCrhZE9tgwcPpupefPFF88Ye PXqwsX///iFtOWZy2LBh7NCpU6fgRxpwBbVq1bKhNry0HL9ly5aMTXtq47c4 jhjvs2fPGtspmNfzqtwo0tVIYCs8OUFp586dsgU/zLg27wM+c741atQwZspM mDCBLVSasQ8ojbmCtY3HLIkh7NOwYUPzbTJ7wYx8pVq1akbfoLa//PJLzEa3 bt2McffVV1+x2zvvvOPzvFH389rGjRsn524p4Q8//EAHgzUssw9c3jAizNap U4faWLBggeUjjjBmzJhgagsp3AtmnvbCAzv+KM03cODA2FIbOEyBixYtCrDL Fs7xpZdeolR9+vRJiNniDy57artx40ZqamqOHDk+++wzY+PEiRPl6kE8XyW2 oTb62549e1ICTyj9+uuvsjEtLU3uVq9duzaeH5GNN2ojHCUztR0+fLhYsWLA 2ty5c1u0aAG+CbVhGOSmlWXGH/s/+uijJUqUiKG5taE2hsZzzz1Hsc3z7+gM pPLixYsbg0UVLBtqu3r1atu2banVjRs3GskOb9O4cWNLVceb3FMbQUOpLaQE IpTaQsqe2lq1akXVffLJJ+aNcn3gmWeeCbmoCEfLly9fyZIlzfOLDRHK+KIN tQELuXPnFk8Id9hTG/a4Xbt2pL/XXnvN7iRjpIioDS/XoUMHzgUCstnt0KFD MsyPHTsmW+Q6ZJcuXcy/27x5c6w1bSFbAJNIh8Ds2bP5yvPPP2/eeOHCBQAZ tJTHVgm2MAu7ffTRR+6PHBW5pzasUUhqe/XVV+WKRBYAX563B/pI1sGUR/3T iG6ojWqkh9PhjZayF3b6lVdeiS21yfXP+vXrG96ekmzatEkuJpBqPSpVXMme 2rAZxMDChQtfvnzZ2EgcY0iWKlWKhO5XMSOWDbXRk4cMGSKhxmBz/pBAQbd0 f0XIfym1+SZHaqOPNWjQgD4zbty4a9eu1atXz0xt4tYs1Pbbb7+lpqZSYyQg z08gjOzvtc2fP5+zKFeunHGVdeHChWBps2bN4mRttPiUDbWdOXOGRofWLcO2 b9++9JBBgwb5VcaIpdQWTkptUZEbasOnmTeS04mf5cuXD/bMJC/5yowZM0Ie 0J7aSJQVK1bE26xZs4ZAjZu1pzZSQ4GAMI2yJdwsrZgoImpbuXJlrly5nnrq KXvz89lnn8nz8wYy03ZsoRKMq09EQmIdocAwjZh89oFwwYR169bt2LHDcY0X UD2Y2m7dutW9e3e2C2UALFOnTuW/H3/8MUmNI+/Zs+fixYtuzvcB9eDUVq1a NWrp888/l/9G1HNoU5lBGfLShEtqI1XJI5qDBw92+bvxQG1vvPEGBSB1mjee O3cuJTBh/PTp0x6VKq5kT20MB/pGnTp1zBtJ5UWLFoXmVq1a5UsZsyIbamP4 yA2F8ePHm7czDNmIO43nV0UotfkmR2ojdgE4pDDMG2bVTG0MK7IY3alfv37m yl+xYoXMiI/h+oH21Eb6kOfGy5QpM2nSpA0bNjz99NOc165du/wsZMLJhtpO njxZsGDBEiVKWByUPDtnvkwdb1JqCyeltqjIntrkcay2bduaN77//vtknCJF igSH0G3bthUuXDhPnjwwV8gD2lAb/lZufwwaNIiYD3Fgqu2pjdanJKmpqcTG jz76qGXLlnXr1gUbwcx4mPXvntpohfbt2wc7omCJceratauxhRBBmkgJLMAy dOhQBgU18PDDD48bN854Zknuj+TPn79cuXKPP/54sWLFqlatOnbsWJtYIbfn KleuTPA0NoJ7shYBWUlOcOTIkZKqEAUoWbJkzZo16SFev2X1AakN3ixVqhR9 lX64du1aapWe07x5c2rSDb7hIuQZ4JArn7ukNmwh3ZvdghcFDad4oDbMCYNu 1KhR5o2gOv0KJDFuAWdv2VPbmDFjqCLLFQ++gpHLlStXPD/bY0Nt9OoaNWrk yJHDchHv0KFDjzzySJUqVeL57QZKbb7JntqOHj1K4GUUUF3813KvLTOwKjWR hB3efvttDkX2mTBhgixNgPbu3evfmfxvOa78TweT9awYI7Js1IcffhjPjw3H g2yo7ccff2SMEFgYiebtkydPpoYbNGjgVxkjllJbOCm1RUX21LZ169a8efPm zJmTEPrrr79iyYYMGSJXvbDoRqQ1NGDAAAYa/4Z7KsCG2jZt2sQPlS9fXgpz /vx5R2rjI/CkYMGCderUyZ07N+ZBljrh3169enm6hqEbuae2nTt3lihRgvKb Z+cF68svv4SICxQosHv3bvP2c+fOVa9ePSWwhDK1IQ/+me/ZffzxxwQ6qK17 9+5AsTQBW0CAcBORyKelS5emehk4pE746NNPPzVWZF26dGlm4IGWESNGsE/Z smWpcKCb+MOnnMjy5cs9XTvrAakNyuBc6DPPPvssPZmOZyRc8Nn+diEBVp63 pw+HPEeX1LZmzRqqjp9zvyB8PFBb/fr1cVnmFUrR5cuXy5Urx+kEx4RsKXtq kzbq06eP5SsyBQwX6ksZsyIbamPEFStWjOAvY98QPpyhVKhQIYu5iisptfkm G2qjd3Xq1IkoAc7IFmqMCEw8gYlkS0ZGxqxZs+hm1A/RmFQOsjVt2lSWxjJf QvRZ9tTGKeNVSM2Yn1atWskK2NWqVbNZFliVaUtt8DtdpVKlStgb8/aJEyfS N+gSfpUxYim1hZNSW1RkT2337t3D5Mvi84RQnC0WF3/OH8WLF7c8Hnb16tUa NWrYXxALR22XLl2C18wXon/77TehtpAPoYmAF0olCLl48eIbN27QK+bPn89x GNcxvC4nck9to0aNosDt2rWzeSUNbSTL7+MJzY79woULJD7qCv8PrMnqhfio tWvXGsGQavnmm29IeeREOOL27dtYR34RBjx06FDIn+MnwArMGLsRPAGxxx9/ XG5ImZuPaLNx40ZM+92AGI9yw9TrZeUekNqOHTsmLzWgBoYOHUqxOeD69etl hUb7O56STcDkcIjthtowxv369WOfFStWOJ6C+Vsxp7bGjRsz6KZMmWLeSAco U6YM8BuuO2Uz2VPba6+9Rhv17NnT8hXx6paqiyvZUFt6enqpUqWwo0Ra83aG A0MpzlddUGrzTeGozVgfsmDBgosWLSJ379ix48svv6xYsSLxZObMmXv27DEe +//666+xGYSal19+ecOGDXiAChUqAHcxvJ9rQ23nz5+X1Mx5yZavvvpKtvBv khjyrMmG2vioWEAWdyrr1bz00kt+lTFiKbWFk1JbVOT4vjZE2CR4yjvcCac4 NOozNTXVMjUYJwwyQEw26TsktdHJObLMiwEutgeEL4UUihQpMnv27P3794ec 7SX32jDe5vVSsN8CDiSC2E4EdkltnH6HDh3E5Ie784V/aNGihVy+M2cuoECu Xg4YMECe1jtw4IA8RQm42bzqDnsmacX+deQcYfDgwU2bNuVXSEmcC2CI37C5 pXLq1KmyZctyZFrN/sQfRA9+r42uBWWYAY3ewn9lAmC4JTFhJXk8sm/fvjdv 3gy5jxtqI9ETqyPFnHigtm7dutEBRowYYd5IKGAY4seS5NqyPbXRi6giy4Pl fEUm1wcvOho/sn9Csk6dOoQay9JDBw8exE7XqFHDvPRKvEmpzTeFo7Y7d+7I 6n82IvuHPCZp6PHHHy9Tpkx8riGJ0yCSV61a1fw8JGUuUaIE2y1PFKvMsqG2 s2fPUqWYQOPFOiKZDA67+VXGiKXUFk5KbVGRG2qziPCVEnhBsyW5y0yoevXq 2TRKSGq7fv26zJayUUgA4YdSAitRWz794osvUgKLPJBM3Z9X1OWS2nA78nyj sZR68HFkJflixYoR4swfnThxArdcsmRJop+xkfqUV3jPmDEjHAZSM3369EkJ LCTi/owARnkDms30patXr8ocvTVr1rg/cqR6QGrjbyoNyrC8in337t0pgZeV Y19DHi09PV2W48C7hrsm4IbafvjhB3aoUqWK+xdDZMYHtb311lsppheLi2RJ HGg9okiSuLKnttmzZ+fKlatmzZrmjbhW+htjx9Nx8YCyX41ExvXYsWPN2+UV Ia1atdLVSJTaMm3vtbF9uUkrVqyAaGCxHDlykOBWrVoV8tF0471dxJwYJnQb apOrxCNGjLBkBHkfSu/evX0pYELKhtqIRd26daMCV65caeDwzZs369aty0Y8 nr8ljUBKbeGk1BYVZYHaZOXVrl27mq8sGa/CHDJkiM2EspDUlpGR8e2335rj OUNS3mNVqFChiRMnbtiwIeQaEaSG0qVLY5Dmz59vbMTNLl26VKJobKe2uaQ2 kPOxxx7jLEKu6wgmyANXgG0w1uEAH3nkkWrVqlm2y2vU4LKQb2fIDAyf1q1b RxT9qFgaIiXwwnqb6+pkXomrlsl30dWDryEJmuEWJk+ebN4oLw2HecPdR6Mf Utsptq+Jd6Q2Bs7nn38uPiSitSvjgdpkcNWqVcvYgZLIdRKqNK4WcfVO9tQG yBC48uXLZ+6f5HHHF1DGXDbUxinLqyGff/5540FuxtSUKVPYOHz48Hh+D118 Utu4cePclydR5PIt2yLChaxGYl6R6fbt2+a+dOrUKWwt1bVz505P50rby4ba Jk2aRPHIGpYU06xZM4a8ZeEmlVk21EaWlBWzMSoGre/fvx+z9Pjjj8fwHRCO ck9tsgBvamqqG5DJBor0LdtYTR9KlXBypDYykTlUghgFChTInz8/nGXejWH1 5ptvyv0dmxUY0tLShNq2bdtmX7ArV648+uij5cqVM99FIpibOYjBLp78hRde MIzErVu3IEo2LlmyJIZBPtM1tckrsEuWLBkciBj+Q4cOBS5y5869bt264O9K EANdzb9CuHv//fc55siRI6kWapJastw82rdvX548eTiyrOWV+Q/jYSZH+ob5 W2TY+vXrCzJImD158iTUY1lHEZCEQJ944glzw0Vdkb5lm5Rq6ZaQPtsbNGhw 7do12UJtSx/u0qVLuLW/GA64cWyGzRvWqDe5Nzpt2rSQ+YiSUG9yYSGk1z1y 5EjINa6xkbKsq+V9RtGVvQW9dOkSSZP2/fLLL2UL5yuP7w4ePDi2I8432VMb nZMoh2GbPn26sVG6Vrt27Vy+xj0msqG2zMDLjuXyEUZathBb5EHrrVu3xnPT xwO14VioQBnacuWnW7dux48fBwey05X2iKjt6tWrljUkCS/Nmzfv168fjUX9 7927Vx5Eeemll2J7RciG2sgFZcuWBdyGDBmCIyUe0h8Y+4888kihQoWATZ+L mkCyobbMwKNE+CIC6dixY2/cuEH9t2zZMiUwtd9mBYCYy5HaOGtCAaczb948 TqdIkSLff//9Dz/8wPnG83k9uOypjW4AXxAhf/75Z3nDFHGS/x46dOj06dP6 3kND9tR27ty52rVr9+7dG3tMIPrmm29SU1MFgS0emIgqt7Mtc9VFpDP6JNlq x44dsszg1KlTaQ42kvdD/nTwGpK0Gg4cZqxbt64xdQ7KIDAS9qFFxjVxnmjJ fwsXLhzzKTYuqW379u0pgTcUW6qC8x01ahTnkhJ4HRLNtGvXrh0BMcYZ9QQH fqJmzZrsgG2mq9OajPrvvvtO1iRZv359ZuD1DcQ9Ku2rr766fPkyVLtx40Zw WO5Ayc04WvPtt9+mbuEyeUwF58nAefbZZ/fs2UPT0wryJr7KlSsLXcI1rVu3 5sgEUopEE2P5Fi1aRM0LMHp6o9Oe2jCQFFiGvzwIWrFiRQiXzIvhlOHPiT/1 1FOUv0ePHhQeevr8889l0RXLKnlmYSrIzjCLseiZIfqe/ChpSJ6956f5m05u WQWLVhMPzygIjkVr164tVaoUNsC4MMLgYuxwHCKePKUG7hHK2BLuFRsPInsL SoFlHZUqVaocO3aM7jRx4kSqkfSaPP7EntoQUYg6AXDWrVtH1/rss8+A/Tx5 8kS0+Iz/sqc2hozcoO/SpQthDcs9YMAAc0yIW8UDtTFgwz3/P3z48HATaRNO EVEbKU/yl/HkP3GV6CfVwgiSP0heMX/02obaSDfY72LFilFU0geYiQ9PCbxq Z/LkyfF8NSPmsqc2qu6TTz6RJcIMNW7c2JJP402O1CZ3LoJFDrVM2chmsqe2 27dvy8qHwWJMGVfXVfbUBqZZQiiWtXnz5sFuGRyQGzEhbwnJ4lEhFe4Vw1Bb SuC6rjGFCrKQ6cy5cuUyPDPjHRiRIInllvXbiZwTJkyIebR0SW2yukWjRo0s nXnDhg3yIoOQatCgAYY5M3BfRl77lSNHjqpVq5YvX56/qYeXXnpJ7uNAK2wX +pMFIY2BYDzECOvVq1dPvih3eah/vmXJnjC7+cnATZs2lSlTRj7lXw4uf3Au Xi8oZ09t9ArjJQUWVapUySAdKKN06dJspHJkrWb+pUPa3CwmcacE3mkefE2A 8kjiDhbwa94Tj/f888+zffbs2cE/AZHJt4yVUqBs6djB8mIpLUcLSuPS/aQA 0uggCek15iPONzlSGwmoR48eRszk35w5cw4dOjTOLxjaU1tm4CVKRlgQEaJX rlzpZyGzoHigtlOnTrUKoyVLltjEnMRSRNR28+ZNwh3B0GxxcfKMHfIRGYpU wg7xcE3A8X1tGPVu3bpR7KeffrpOnTrt27e3eYpeJbKnNhEGqW3btrVq1QLe e/XqhTPxrXhZkyO1vfXWWyHjAGdnWXolm8me2ggaHTt2DFkzgwYNSpLJF27k +ITkgQMHJIRWq1atadOm77zzjsCCRTQEdpfqDflk1/bt29u0aROyOcKtqEYb Eff69Olj+H884erVqxm5ffv2tTzvRyGJ/LVr165Ro0br1q3t10X0TS6pbdu2 bdTD1KlTLetkkvvC9WE0evRo4+G6s2fPEgcaNmyIoapZs2aLFi0WLlxozptp aWnvvfce2/mUVMieQ4YMMadCwuYXX3xB3Q4YMMCYCnfp0iUOCynT9Lh0Pgq2 PYTQMWPG8GmNgNgZXvbhIW17art37x44E7LeXn31VXPxgK+ePXuSZKmWZs2a UW/270Uly3AQumXwTS4OFe5Hhw8fbt6ThqPS2B5y6t+hQ4doKbqxMZTg7hde eCHkkWfMmOGmuiKSGwtK53n99dehUXrUc889t2zZsqgXI57lSG2ZgQz19ttv MyKoIrrWtGnT4t+ZO1JbZuC56C5duhAr8FEEqM2bN/tWvCwrHqgtSRQRtdmI 4XPx4sX4ucrhSG0iik1ODDctWmWRG2oTkbXdv9g0tnI/ry3Z5H5em8pGLlcj obbj4XpXZmBBhnCP/mI5wj1vGRO5f19bVIQnJM3ZT5Ggftgn3NM45Jrgj6AY mt4+C9MilwKyR54oyv28NjfixL141DDLun37dgxXS3NvQSlnXI043+SG2kT3 79+nihLlLqQbahPdunUrgSBFqc03RYva4k0uqU0VkdxTWwJJqS2clNqioiys IalyKZ+pLakUXWpTmaUW1FHuqS2x5J7aEktKbb5JqU3lXkptSSWltqhIqc07 KbV5J6U276QW1FFKbYklpTbfpNSmci+ltqSSUltUpNTmnZTavJNSm3dSC+oo pbbEklKbb1JqU7mXUltSSaktKlJq805Kbd5Jqc07qQV1lFJbYkmpzTcptanc S6ktqaTUFhUptXknpTbvpNTmndSCOkqpLbGk1OablNpU7qXUllRSaouKlNq8 k1Kbd1Jq805qQR2l1JZYUmrzTUptKvdSaksqKbVFRUpt3kmpzTsptXkntaCO UmpLLCm1+SalNpV7KbUllZTaoiKlNu+k1OadlNq8k1pQRym1JZaU2nyTUpvK vZTakkpKbVGRUpt3UmrzTkpt3kktqKOU2hJLSm2+SalN5V5KbUklpbaoSKnN Oym1eSelNu+kFtRRSm2JJaU236TUpnIvpbakklJbVKTU5p2U2ryTUpt3Ugvq KKW2xJI/1JaWlpal0mUrnTlzBmrLyMiIdUGiLKjt+PHjsS5FdhNow8CMdSmi rPv370Ntp06dinVB4k7YNuIkWSbWBUls/fHHH1CbkoUXInNBFmSxWBckG+rO nTvU7dmzZ2NdkGyo9PR0taCOwmz88ssvsS5FlHXjxg2a/u9//3usCxJl4be9 pjbS6LFjx35Jeh05coSqPnHiRKwLEmUdOHDg0KFDsS5FdtPhw4f379/PwIx1 QaIpTochwKnFuiBxp59//hme/dvf/hbrgiS2Tp48SQcj0sa6INlQZC66qNat F5K6/eGHH2JdkGwoCa1qQW1EXsZsZL+8TD6l6WGcWBckysJvizn0jtr27dtH Jv3XpBeVQFVnv6rYH1CsS5HdlC1rVYZA9juvB1d2DQ4+S6vRO2ndeietW++k detG2TIvZ9eml8by+gnJa9eu/ZH0+vd///d//ud/vn37dqwLEmX99a9/PXbs WKxLkd0kt6UyMjJiXZBo6n/+53+IOb/88kusCxJ3+s///E/i5O+//x7rgiS2 0tPT/+Vf/uX06dOxLkg2FJnrn/7pn8hisS5INtTf//53efo01gXJhrp165Za UHthMzAbJ0+ejHVBoqz/+I//oOmvX78e64JEWfhtXY3EH+lqJCr3+kVXI0km 6WokUZGuRuKddDUS76SrkXgntaCO0tVIEku6hqRvUmpTuZdSW1JJqS0qUmrz Tkpt3kmpzTupBXWUUltiSanNNym1qdxLqS2ppNQWFSm1eSelNu+k1Oad1II6 SqktsaTU5puU2lTupdSWVFJqi4qU2ryTUpt3UmrzTmpBHaXUllhSavNNSm0q 91JqSyoptUVFSm3eSanNOym1eSe1oI5SakssKbX5JqU2lXsptSWVlNqiIqU2 76TU5p2U2ryTWlBHKbUllpTafJNSm8q9lNqSSkptUZFSm3dSavNOSm3eSS2o o5TaEktKbb5JqU3lXkptSSWltqhIqc07KbV5J6U276QW1FFKbYklpTbfpNSm ci+ltqSSUltUpNTmnZTavJNSm3dSC+oopbbEklKbb1JqU7mXUltSSaktKlJq 805Kbd5Jqc07qQV1lFJbYkmpzTcptancS6ktqaTUFhUptXknpTbvpNTmndSC OkqpLbGk1OablNpU7qXUllRSaouKlNq8k1Kbd1Jq805qQR2l1JZYUmrzTUpt KvdSaksqKbVFRUpt3kmpzTsptXkntaCOUmpLLMUVtd27d+/ixYuXL192X5gE kktqu3nzJtGbGrt//77NbnTIs2fPsnNUy5gVuaS2tLQ0CnzlyhX781KJXFIb NvX8+fPXrl3zp1QPqIiojZ0JCF4XKU4UEbXdvXvX6/IkqFxSG13rt99++/XX Xy0JndBE3WaEEke2H4yMQeLb9evXHUvoeBZ37txx3McscsqFCxc4Hfd5OQty SW3U7a8B3bp1y2Y3OOXcuXOkA8fflcbCdbjp9uxMDdvEDTf1bxZnQbPiSThy RF+MSC6pjRqgHgj4Ng1NOanY33//3eVP0wScoJuew5EZCEbd2gwWZF9dcqiI vpJlubegDGH6baSjLxvIPbURMOmBZCsfSvXgck9tly5dAj0SxW/ED7UtXry4 VatWqampVapUadOmzerVq90XKSHkSG1UVOfOnevWrfvkk0/WqFGjY8eOIWlo 9+7d7FarVq3y5cvXqVOnX79+p06d8rLgDnKkNgCkf//+zzzzTNmyZWnctm3b btu2zbfiJagcqY2PPvjgg2bNmlWsWLFatWrt27eP/zueLqmNE5k4cWKLFi0G DhyY/S6UhZQbasMjHThwYMCAAdTMhAkTfCtbAsmR2jBmI0aMoAIrVarEwKlX r97w4cOxavLpsWPHOnXqxJhqHiQ2vv7668FXydLT02fNmtWuXbuqVauWK1eu du3aw4YNgzIsu2HLidu0XdOmTVesWBHSoNIHvv/++759+7LP2rVr3dxnp0vM mzevSZMmf/7zn0kHzz777Pjx42/cuOH4xSzIkdpwPkOGDKFuK1SoQHkozOjR o4OrguKNHTu2UaNGTz31FJVGrv/ss8/C+aXNmzc/99xzNBZnRysQ8Wywa//+ /fw6LRWcX3AsW7Zs6d27N3W7YcMGN3W7Y8eOrl270kP46aeffpojL1261CNf 50htkNqgQYOoAanb+vXrU4cW5qVTzZ49m3JSsdWrV6cnHzp0KNwB6WzTp08n F3NqnCAHpLFsfBrDSuqWbixbcB19+vQJN1h69epl01VoiOCvvPTSS8ZIjKLc WNDjx4/jqUijVC89hJpJqmvLbqiNzkZowp3Su+gtgwcP9vQaUVTkhtrw5ISg ypUrgx4ACLHIt+JlWXFCbZs2bXrsscdSUlIKFy6cP39+/uBfvui+VPEvG2rL yMhYtGhRsWLFOPE//elPjIuUgGAc4ol5zyVLlpQsWVI+LVKkiPxBqCGq+3Ue VtlT28WLF0l8Uk4GOwmCP0qUKEFV+FnIhJM9tWEe3n333UceeYTKLF68uIwd Ig+20+dyRiRHasPRYUHLlCmTI0cOzujJJ59MlNuIDyhHaiNE48QYODKUMDl+ Fi9RZE9tuHojFhUoUCBXrlzyN1yQlpaWGbgglidPnpQwAjEsDQTE4T9lGObL l69s2bKyJy7UfKdp69at/G7RokXl01GjRgWjx8aNG2vVqkX6k32gcje3lqZO nSoFLlWqFAOHUfPwww/3798/4opzIXtqI54/88wzUngiUs6cOeXvDh06mO8/ kqfwSA899JA0gVQd4eu9994LPubnn38uOZEDEtz4g/379esHKQfvfPnyZbBC fnThwoXmj0DgGjVqFCpUSD7lt+xv61Dz06ZNM9pLyoCeeOKJjz/+2FVlRSh7 aqNbAhRG3eIQ+IM67NKli0HoZIopU6Y8+uijfFS6dGnp2xAZfT74gOfOnQNI 5Ti0AvvLAWmskHeaLly4AMtIAZYtWyYbDx48aFiRYPFROFcAEBltYRYbvchf jhb0559/rlSpEgXImzcv1csf9F7Sa/I80uBIbWRhiEaaychBUDZ162c5I5Uj te3Zs4fIKaNA+iR9YNasWX4WMguKB2ojhhBUCRqTJk0iewL1b775JhXYsmVL 9zf641821IaPlRQ2Z86cq1ev0s1++OGHOnXqUAmvvPKKUdunT5/Gx7KxU6dO Z8+eJWLjT0jWb7311u3bt/09m/8re2obMWIEBa5ZsyanjzXF53BGbGncuHFS Xc6KVPbU9tNPPxUsWBCThj+hShlBhFBqtXv37iEtTZzIkdoIFKmpqbgIuSJB Ms1OEcBGjtRGi2Pp4dnHH388JQAafhYvUWRPbXxEv2rYsOGRI0cInvh8EjRG F/v60UcfZQay1alTp06YRGakXUjlGLkZM2ZYbrVs2rSJoE1M3rx5M2xC3P7u u+/kqiOR3Nht9erVf/7zn7EHkB0fjR07NpjaIJSnnnqKng8asA8O3NE0njx5 kmNSgPnz5xMEKDz2Hu/BEXbu3JmV6rOVPbXt3buXFNasWbMff/yRwvz2228z Z86k0iSpyT5Es1dffVWMHyWkCejwH374IdmfWGc5IDZAUGXo0KHULRS8atUq SAqfEPwcTkZGxuTJkyFW4UHLBfNFixaVK1fOqFva0Z7aSEw4EFhy3Lhxly5d 4nSOHz/OiOO75cuXB2EiqzgXsqe27du3U/LWrVsfPXqUwlAAwJOTpesuWLBA 9qGEkBo9GRPFPhSbDEuB27dvH9zZ6IHsSXSlw9AK7E//p5NTe8uXL7fsDMeB MEbdrly50igznQHkMQ8WRpZcmCXvh6tkmpJhSG/hUOQC4+vYGy+Sl70FZZQB vxS4QYMGOFuqgt7CyVJCRnfUCxOfcqQ2OgxVRIBiN6poy5Yt1A+j2+h+8Sl7 aiP40Oic15AhQ4g2BBnGO/8lVuCufS5qRIo5tZEH16xZQ121bdvWcGhUWq1a tdhIKMs23t6G2ohv5P2lS5eaN37//fdED0bKxYsXM//x/AN18vzzz5ufgeGA sV2zwp7a5Bqd+dRoXIiDHKoThG1kQ21GT+jZsyepUzaS0HH1cFw8L8Xgfl4b QYMTxOsqtVk0bNgwaqZNmzY+lCrh5PiE5IEDByyTMgAN6vPll18O95WRI0ey wwsvvBAcuhl9sAnG1byxb9++4pYtO+MPMdvhqE2EQxaD5IbaoBjIgjFi3tiv Xz9Aadq0afbfzYIcn5Ckbs3X3knc9evX51wokmwBMwENqHbDhg3GbhiAkF4d DBQiNt+qmzp1KiGudu3algqkYGwnp8hVzZCPOfEro0aNckNtmYH5OwCIGdL5 L9BN4Xfs2GH/3SzIntooxv79+41QnxnoS2KQXnvtNdkC24qJMlIGtQ2aAcjB z4uSeadPn25pyk6dOklOsezM+cJrZQMyU1tIQX+0Gg1kM+MeP4MBKFWqFJhm c6hoyd6CUgkUuGjRopCIbMnIyOjWrZv022y2Glg42VMbYwHAZ3zBs8ZGAZwm TZrE8/J6NtRGdBKPUaFChXPnzslGOkmjRo3YuH79+nie4xZzamMHAgUV9emn nxoVRfIaP348G995551Ipw/HrSJdQ/L48eMk5Vy5cp05cyYzEOuqV69OhDHC S5zIhtoYGthLaVxjI9aUIFm8eHEvHmLPNrKhtv+PvfcOsqLc9v6HIFFyEBAk OmSQjGQEyRlEkCAoYAJEEDyKgChZBBVERBCQrERBrHPu1G+AH7mAqXOH+1JQ 5HRfXuBSFofLD87V+X1qrzpd/e7Zu7v3TPeO6/sHtenp3fvp9TzPWuvT/YQb N2507twZq5L8GM806EfycHXhwoXhLWkIUmoLJqU2V5SFNSSlK/Xt2zfgX+mD JOo4K7+R6haCmHLmzFmnTh2/465TG6lFoUKFyDrMZ5K9k1+99957DkvrXFlY Q1KGLBogIGRB0yUxtv3umjVrOHnYsGHmMXvkHmT7Tz755NGjR42DYN1zzz0H ocDXvXv3doXaMgsIoiUULlzYi/ibhTUkn3/+ebFPhq9pValS5Yknnpg/f75x AvXVtGnTJN+IXCcXnDRpEnRGEDEfxP3Wrl0b2/LXrl27WlMbaUClSpUAc+7F 4oewJGasXLmyRxMw/WSdgmIxbgpDGZhJSN24caMMEEqQ8fnW1LZ58+YiRYqU KFHiypUrxsHjx4/T3uiMmd+SR48sqI0uI+4Ij2H4Tz7MmzePgyNGjIjmFVci Tm0YtnHjxhjKbxYbGakMBYzg2D93FSq1/fbbb0R/fKZ0FuqI/zZp0kT+S9yx XqQrbLJ+1wavkUUQU1JSUuTIkiVLQNHWrVsnyIOsrMmC2qD46tWrExz9uq08 5H/jjTfCVcaQpdQWTEptrihUalu3bl3BggXz5MmTeWAYIvR06NABa8+cOdN5 GaQb9uvXz++469RGFCb1lYxawgqZZ40aNbgjYofzAjtUqNT2ww8/5POJD3KE 4GUeVWW9ALJQ2/Dhw/1sVa9ePa5pflvHOfKa6datW927d/eC2shSBg4cKGO2 bZcJzYJCpbbly5fTaKnoH3/8kf9evny5pE/EYuMc2s/kyZMDvj4LKHnXRr5q HIFf5Il6jx49sICMEQ1GbYSqwYMHc8KYMWOsf4hMAO6uWrUqgeDnn3/esWOH 39tqd2Wdgo4aNYoy02fNB8+ePZvkmx4YnreBEZc1tc2dO5fG1qhRI/NBelDp 0qWhOaN3R6EsqA2vIu35gw8+MB+nGyb5JiZH80iwiFPbjRs3KlasSKvYt2+f +fjOnTtlsHE0M29IConaZDC5hC2xAPaRt/YHDx6UFfZatGiBO92zZ4/HBbeR NbXRcSSSVqpUiaRly5YtcCgct2vXrnAWMuZkQW1paWklSpQoW7asXwYl+Xyw twbRIKW2YFJqc0VOqA0PvHv37s8//5w8XGZnfPTRRwEjGj6KE/Lnz+98KhPg AFb4zWsTuU5taNWqVQUKFMibN2/v3r1pP2+99VaOHDl69uzpxerlTqgNMiJw z549e8CAAUAEBZsxY4bEL/o+iXrhwoV/+umnzZs3ExSef/55oHjq1KkBOQjj y6BHM9wRPooXL25+qbRx40YsgD+Ut29dunRxkdquX78OH1HCNm3aUJjk5GQv JgxmOKO2+/fvAzjYtl+/fhiBu541a5YMSSX+FitWjIhgbvnEDnlxQJJge7/g Cc6Wk82PLwBnTP3UU08JVcl6FMGo7fz584R4Tjh06JD1b23duhVqo1vVqlWL GyH5r1mz5siRI42Bau7KOgWFVeky48ePNx+kvxcqVAgkif5lmV2RNbXJS1h6 lt9XqDU6eDSP7bGgNrxZ/fr16dR+a4/s378fGKFlml8sRpsiTm0YJ1++fPgc 85iHDB/z4jEqV64cDVuSuaKQqI3Gg9/AMxMmZBTcunXrknzrmNWoUSN37tw5 c+aUlXwIjnjaCL63sl35PzU1Vda2otgyVZ9wE83DhqNBFtR25MgRah+fSU80 H5f5+ORC4SpjyFJqCyalNlfkhNpk8oJ4JFnsIlg4GzFiBOlKSNNbSPLxcuXK lZPJyGZ5QW1EE3kmJoGAnyY6eDTy3Am1Xb16tVmzZrIyJJowYYIx0w0CAsH4 Ew6KKIBhZZ1Y/u3atWvmDQK4i2rVqsmUQyqUE2bOnCnr18maGxm+UQeycKUs JpPhNrX9+uuvBQsWlHJ6NDZS5ITaLly40LhxY8O2U6ZMMeYDUk6yhQoVKmBk 43zShhUrVnBmu3btbB99v/7669wmWE2iK0ewOcmGMbuBq1lTG/kJ0adTp062 e7WA3rK8T6tWrV577TVZESLJt8KSF/u8WKegNFeak98SpjRjCJTb8XuVEK+y pjZZPu6VV17x+wrVRy+O5j1oLKiNHieLb/v5imPHjpUuXbpEiRJ+yVVUKeLU RgehC+PG/aiNb+FGSNviZsMm59RG8iaTIseOHSsDROkjixYtEucGyW7cuBGY vXbtmsyUf+qppyI4R8ya2mhdBN8iRYrIXjmyIjGhNqQml4CyoDZ6CgGlevXq fg8nP/zwQ7woRg5XGUOWUlswKbW5IifURvCaNm1ajx49kpOTCT3gW8+ePTM/ Wb1x40b9+vUxtbFBla0OHjxYpUoV+uaSJUsyP5VyndpI2ocPH075mzRpMnLk yOLFiyf51safN2+ek4ljocoJtRGV4KnOnTvTc8mI8ufP369fP9lWLC0tTZZV JwRQWuL+vXv3CGTGupp+lwIT5s6dy03h0/hKgQIFqCxq7ZlnnuGWv/76a3wj t89f+TnDnjL36ttvv81ctixQG0EKZoR6wHAog1wOhPFiM2gn1EYKSoYMFhFP xbYvv/yyYBrfhdqAYqxqnI8BsUOSby1u62kmKSkpMCntdu3atXKEeyRey3tb IwbJaOGAI+KoShmn+t1339kuH0fj3Lp1a2pqKjXCZ+6dcnI75HterKJjnYK2 bNmS36W7mQ9iRhAYmtu/f7/r5YlCWVMbZJ15nC1fkZmVfqaLKllQG23v6aef xqssXbrUfJzkim4e5asuRJza+JNMpPV7rLF9+/Yk3xo1iTZCEgc7dOhQGVJo bJ+NJ/zxxx8F2cgNjJNxL/JADDfoYdEtZUFtly5dktWbQc4MX0///vvvZW+U unXrOslRE1YW1EZvLV26NKjul53K7NqXXnopXGUMWUptwaTU5opCmtcGFm3b tk1WxiM596Ok48ePy24sDsM3Kbe8NYBTjBcWfj/nIrVBhR9++CGJZbNmzSR1 P3Xq1KBBgygwCbB5tTe3FNK8NrLxzZs3y15Ir7zyCvci79rIkEEnI7Hnw9Sp UzkHIgg4qpPafPXVVzEsDZ6bopsQULhBUFqGo/Dd+fPn792797ffftuzZ0/D hg2TfEt5c8TvoVZ25rUBnnyXLAW68WJB+JDmtdF41q1bJ7vIjRgxgnshIoC3 ZcuWNRKGDF+0nTNnjiwOYPGymGYjy1EOGTLEyLWwnuwBSuAW2+7evVuG/o4f Px5P5fcm4uLFi8nJydnBHMFtipq1r1vIOgWltwKn48aNMx/kdrh9ur/zNYhi WtbUNmXKFHk24vcVmVy/ePHisJQxK7IeIdmoUaOcOXP67cBIA6abk52aH4BE myJObbdv35Zgh3MwH5ftAPr27Wte8Dam5YTacMjE4iTfxqOrV682/yk1NVVc q9+Q0R49ekjkitQWCRbUNnv2bLpArVq1zFHjyJEjsg2recErlZ8sqO3s2bOY NG/evH6TuGXmOOwWrjKGLKW2YFJqc0VZWENSAk3lypX99ugB6DjepEkTJ9PS jXUwKlasGGyvH3epjaRCsujt27cbBwkBw4YN4yAJiesb4GZhDUkZoVetWrUL Fy5QtipVqsBZfsuOpaSkSIGdNH68AZQNOsHUgiQWGjlypPm72VxD0hgi6AVZ ZGENySVLliT5VkehJdy5c6dkyZIlSpQw76kNOMv9Dh48ONhFrly5IjfVqlUr 8yDVadOmWdv2zTffNF+HmM7BGjVqhNQ8zJJfbN26teuvMq1TUG6E3x00aJD5 IA6Eg7S0aN5Gx0VZU9uCBQtINurXr28+iKeivRUtWtR4PxuFsl6NRB4U+L3l 37Nnj7ye1tVILCyAv3r99dcxFBm+Eac4KGP/CHNxs0O9LbVxp7jZ3Llz58mT J/Po8fT09Pz58+NJ/J6nSdv78ssvo5DaZE/VcePG+dHHgAEDrKOJyoLa8EV9 +/bFgKtWrTIGYuGaZC3WgKvhRYmU2oJJqc0VZYHazp07hz1LlSp17Ngx4yCu +JNPPpG3NraPDa9duybTqSpVqkTzDnaau9TGbRILcuTI4bd8OnfBQSCURNq6 2KEqC9RGqEryDeCXdxbk5Dlz5pwyZYr5nN9++y3Jt/R6wBeUfpo/fz4JZNWq VakULr7UpGXLlgEyNWvWlDen33zzzYEDB8zfzSa1offee0/IIgvftVYWqA2T Uphy5coRKQj9sj6/eYQh94s1LGYegdKymyrox2fzn9LS0jLbNjk5WZ4bY1vz gB9i0PLly5N8g+icVGJAvfHGG1yhc+fOrk94t05BuRd+t0GDBobvxZjff/+9 PLHxYr3QKJQ1te3evbt48eIAmvnldWpqKn0ZF3T8+PFwFTNkWVAbHkA2FyN/ Nt7y45bl6QFB1osN391SxKmNPrJ161bpIzIAPsO3ohGemVbh53hjWtbURivC Pcpc48zrjyGis2z+iP80AA1nK1O2CeIeFt1SFtQmM61atWrlN8+ibdu2VK7f wk0qsyyojYM0lSTf9G1jwsL+/fuf9Cmax2M7pzZ6CjdInpAgw2idU9vYsWOl 6sNQqpiTNbX9+OOPc+bM8UsLJUMj3JgHfRG2xowZIxm+NT0R2uQRCmxiPQOO bktSzZmTJ08Odk38pCwdTPKQue+TqB89elScP8wihOLn+QmmuNwaNWq4Ppve mtp++OGHBQsW+B0UNwUUyCKc8nqocePG5kcx8ni2W7dume+XkGc21KVLl0gS OBmgDlZIrsMJK1asyPwncrMJEyYkBVkL67hP8hmnCjsbW9WIKJ4wzoABA4L9 epZlTW3cjkwxMOuLL75I8q1OJlMyQTP+26ZNG2OUI7kTLaFChQoBc4OrV692 6tRJ3g5bPGowS7ZezfxuhTqSYa7jxo0LmOvSaI0MgZ5FL/DroZcvX5bt0f1e 4bki6xSUnyZoFi5c2BjaRB+U9494gARZM82a2ugO9erVI2H77LPPjINC2YSh aN5P2YLaMnwdhFswr7x6/fp1mdGzbdu2SL0EcaKIU1uGDz3wxrSK119//caN G6SdgiddunTJ8qObKJQFteH3vv76a7xHkm/XP6I/Pvw3n/bu3UvrIt2lFe3a tYsTihYtum7dOnwLOR59B8/csGHDCL7PtaA27qJ8+fICaHfv3uUuuP3Zs2fn zZu3WLFiuvi/hSyoDZ08ebJMmTK5cuWCi+mM9F/ZfGTo0KFeLPrtlmyp7fTp 04R4buerr76SNyB79uwhm+Ir0Rwdsi9raqMZUOPHjh3DDi+99FKSb1oo/z1y 5AiZXoKkFk5kQW3nzp3DaHny5AGyaFQ4TFLlNWvWyLKEPXr0MPe127dv9+/f Xx6RWfwcXY+r4YFz5849ceJEOiyV+Nu/JFGVCEhfplUfOnRIRpVQg/v378dn GmBF7aelpVGhuHrZ7o1/xa8aL0G2b99erVq1p59+Guef4etK0hJgN65GBOEI WCdZB6VyfVVhC2qTHSSxLUUiYN27dw/bgsP0X3NhOM4tEA44jchOmUGAAj5l nh1D/k8VtG3bFrvh02j/7dq147vlypWzeP+eeQ1J6ujUqVP4EKwkY8hfffXV gwcPYltjnc8tW7ZUrlwZcJA5awsXLuSHKDzMzmn8OjnJqFGj8LcFCxb0YjCD BbXRmKtWrUrEHDx4MK2L26FFLVu2TBafGThwoNiWesmXLx/t8K233qKm+K+s pdOrV6/Mjwi4cRmig+U/+uij1NTU30wKuEmZxRqS/Jw84gj4qGHDhg3PPPNM lSpVZAVOoj+F5I6oIwpJh6VeWrZsydcBTPP7brdknYJS4EGDBgn/0k4glOnT p1PRuIWdO3e6XpjolDW1ZfjGr+Ll6Hp0EDLPFStWkIXmz59/+fLl4SxnqLKm Npw87kWew+CKCb7in5OTk/1ePUebooHacAi4+hIlSiT5FvVFSb4tDnHX0Qy8 ocqC2mQkiSE6iPm/tCJ5gEYge+211+R9nOwLIJmtF3uqOpcFtZFPLliwoGTJ kpSTKANd0vGTfKsoT5kyRbNNC1lTG/1i1qxZgvlPPPGErE3dtGlT0qcwlzMk 2VJb48aNc/jk1x2I+w4fCMeorKmNXEIWSzdbRj43aNDgxo0bYS5t1MqC2mT7 qtKlS4v18vokZsSGdDfzydeuXZPZ1rKLcTDJnLhgklXX9u3bR47q16Tld0kU 5TopKSnEu4DnNGvWTM6R+c5JpomrpBb8lb5PxCRGwGvcEf/FzXqx9ZUFtWFb AEf8PCKdE9vCPjglMwXAy9ypnEMUSPItejl06NDMz2TOnj0r3CFrSMpON7CV 9QtNWefQzNp0KzLwgLYlZ5NzZFQqeu+99zJ8i1H07NlT9obgd4WGknyTzcET LzbZsaA2fo6cWRjNbFsqGtgxqoNgunTpUllCBJNKRGjUqJF5PoUhWRUwmEaP Hp35K1xfXjVmfo9JjicMmPllKxo3bpxcVrYzpqO1adNGbgGrYlupmjJlyng0 tt82BaWlyeQC6tpIrqDLxMlPbKmN8NSnTx+pKaPuaCdRPn3JmtpIoshda9So Ib2JPCrJl06DolHOHdFAbaKNGzd26tSpnk89evTwaDvLCMqC2oi/rVq1au1T q0waPny4MfCAbvLFF19wWq1aterWrdulS5eIjyu23a+Nu6ZCn3vuuerVq1O5 HTt23LJlS9iKF6OypjbRsmXL2rdvT7ZGetO/f3+Pdil1UbbU9vrrrxvN3twd +vXrF9/LeVlTG06Dnh7QMiNHjoynAQnZlO28tiNHjpCywjU1fWrSpMmECRMy LxdGwOI0jHz48GGLn9u7d2/rfymz35YBRWlpaQMHDjTXnfEZWpHr0Lb79u0b 8BwjhabkIAkQhFswCnDr1q2//OUvHJTb4cOkSZM8onjbeW0EgldffRXb4ufF tpMnT85cmJMnT+KscFmcA3WC0sFyP+qRWiCjJrOCrImDtjMW+UWs98svvxhH jh071rt374C2feedd+ScgwcPQnAtWrQwZmTgqcCTzp07E2QlbLVr127NmjUe 7YtqO68tNTWV1iK2JfRT0VC8n7ugzGvXriUi1K5dG3MNGTIkmLlAkmDtliOy QVtmgbT8NfMjYgr/7rvv+pnd0L59+6BLSM3oSiQzX3755QsvvEDkkpql8Vt3 tOzISQpKTjts2DBKgumoaL/V4ONettSW4XtRPnbsWBoeza958+YfffSRF9uL uCtrahPhe7t160Z2Smukv3uxQqzrih5qE2FnIpHzwsSQQtpl21ZkGlGyyo0t tYloM+fPn/ebO68KJifUJrp582as7ETvfF5bosn5vDaVhRyuRkKeeeHChYsX L3qUhHskih0w8v75559XfPL0EbHD1Ugo5Llz5y5dumRtW1wW9ndSYJJDAofr S2L66f79+wH3GCLIAlNePxhxuBoJhcS2ly9ftrbbtWvXvDZXSKIwAW1L5ApD zTpPQTkzmtd7905OqE30xx9/eO1nXJQTahNxTgytexZt1BbHcpfaokcOqU0V kpxTWwxJqS2YlNpcURbWkFQ5VBbWkFQ5VBbWkFQ5lKagtnJObbEl59QWW1Jq C5uU2lTOpdSWUFJqc0VKbd5Jqc07KbV5J01BbaXUFltSagublNpUzqXUllBS anNFSm3eSanNOym1eSdNQW2l1BZbUmoLm5TaVM6l1JZQUmpzRUpt3kmpzTsp tXknTUFtpdQWW1JqC5uU2lTOpdSWUFJqc0VKbd5Jqc07KbV5J01BbaXUFltS agublNpUzqXUllBSanNFSm3eSanNOym1eSdNQW2l1BZbUmoLm5TaVM6l1JZQ UmpzRUpt3kmpzTsptXknTUFtpdQWW1JqC5uU2lTOpdSWUFJqc0VKbd5Jqc07 KbV5J01BbaXUFltSagublNpUzqXUllBSanNFSm3eSanNOym1eSdNQW2l1BZb UmoLm5TaVM6l1JZQUmpzRUpt3kmpzTsptXknTUFtpdQWW1JqC5uU2lTOpdSW UFJqc0VKbd5Jqc07KbV5J01BbaXUFlsKD7XdunUrS6WLK505cwZqe/ToUaQL 4rKgthMnTkS6FPEm0IaOGelSuKw///wTaktPT490QaJOpG34SaJMpAsS2/rn P/8JtSlZeCEiF2RBFIt0QeJQDx8+xLZnz56NdEHiUA8ePNAU1FYkG3//+98j XQqXdefOHar+999/j3RBXBb5ttfURhiFDf894XX48GFSVkwd6YK4rH379gFu kS5FvOngwYOpqak40kgXxE3JC8RDhw5FuiBRp1OnTuEc1E9mU3hXGhieNtIF iUNhW5qo2tYLpaWlYdsjR45EuiBxKHWttiIuk2yQckS6IC4LuqHqT548GemC uCzyberLU2pLSUnBdCkJL9IJTB1/puCmuLVIlyLeJK0l0qVwX9paAgq3EJfO IcwSM2oD80JqW++ktvVO6lqdKC6bX7xWvSSHXo+QvH79+uOE1+nTp//2t79h kEgXxGUB/sePH490KeJN8tbg4cOHkS6Im/rv//5vXOjf//73SBck6nT37t2/ /vWvN27ciHRBYlv/+Mc//u3f/i09PT3SBYlDEblookSxSBckDkVCJXMGI12Q ONR//dd/YVtNQS1EmkGykZaWFumCuKz//M//pOr/z//5P5EuiMsi39bVSMIj XY1E5Vy6GklCSVcjcUW6Gol30tVIvJOuRuKdNAW1la5GElvSNSTDJqU2lXMp tSWUlNpckVKbd1Jq805Kbd5JU1BbKbXFlpTawialNpVzKbUllJTaXJFSm3dS avNOSm3eSVNQWym1xZaU2sImpTaVcym1JZSU2lyRUpt3UmrzTkpt3klTUFsp tcWWlNrCJqU2lXMptSWUlNpckVKbd1Jq805Kbd5JU1BbKbXFlpTawialNpVz KbUllJTaXJFSm3dSavNOSm3eSVNQWym1xZaU2sImpTaVcym1JZSU2lyRUpt3 UmrzTkpt3klTUFsptcWWlNrCJqU2lXMptSWUlNpckVKbd1Jq805Kbd5JU1Bb KbXFlpTawialNpVzKbUllJTaXJFSm3dSavNOSm3eSVNQWym1xZaU2sImpTaV cym1JZSU2lyRUpt3UmrzTkpt3klTUFsptcWWlNrCJqU2lXMptSWUlNpckVKb d1Jq805Kbd5JU1BbKbXFlpTawialNpVzKbUllJTaXJFSm3dSavNOSm3eSVNQ Wym1xZaU2sImpTaVcym1JZSU2lyRUpt3UmrzTkpt3klTUFsptcWWoora/vzz zytXrly/ft15YWJIDqnt3r17586dwwikuMHOuXXrFudEiSMKidr++OMPi79y 1xcvXnSjUDGvkKjNoqlElUKiNu4dh+B1kaJEIVFbrFR3+BUStTlsYFj78ePH Ac/Emz169OhxIJnriNMCnpP5gjh/wmvW4J2SZOFbzhUStTm3LcqObQ3duHHj woULtr8YqpVu375NjVAvIX0rVIVKbZgXOwSMDnfv3rXNDa5evYqtHjx4YHHO zZs3uQ55r5PyUIOZqymYqfl1ojyZoZMrZ1/OU1DOoWD4kDCUKqrknNroCLSc sNVdNuWc2q5duwZ6xEq+ET3UtmzZsk6dOtWsWbNOnTpdu3Zdt26d8yLFhGyp jYro379/8+bNq1SpUq9evRdffHHbtm1+EYpzXnrppYYNG1asWJFzevfufezY Me/LbiUn1Hb69OnZs2d37tx56NChOO3MJ2zdupVKp+qTk5O7dOmyfv16bwob M3JCbUDuihUrevTogeloKmErW5blkNpOnjw5bdo02v+oUaPi70FZQDmhNhrD gQMHXn/99Y4dO06dOjVsZYshOaQ2fOaHH35IA3v77bdJmC3OnDVrVocOHXCz aWlpmf+6b9++F154oUMQffTRR3La9OnTM59GsDt69KhxKULku+++26xZM5z/ c889h5OnFzi5ZfIonOeQIUPat2+/fft268di2ZFDajty5Mibb75JEx0/fvzD hw8tzpwxYwZ26NevX0CHkJKSYmFbvmucuXv37p49exINn332WULMqlWrMl+N /rVz587hw4djpU2bNjl5Gnb+/PnXXnutUaNG1Aj1QuQi87H9VtYUErXRbHr1 6oVx5syZYz6+du1aGmqTJk2qVq1KE8ImtAe/7x48eBCD16pVq1q1aq1bt540 aRKWMZ9A+1m6dKnYk+tw+wMGDCBvsS7S2bNnM9cRpuZSRhsgaSQHILjz60T5 li1bvv/++wGTAXflJAU9deoUt1m/fn0KRrHp9V6XKqrkhNpINl599VWyU3oZ dffOO+/4tZwolBNqIyEhfapduzbogU/+7rvvwla8LCtKqO3nn39+8sknk5KS ihYtKh+KFy9O/HVequiXBbVxcN68eU8//XSST6VLl5YPpUqVgl+MJwBkI9Wr V5c/0XHKly/PB3xgenp6eG/l/5I1tXFrffv2xf/nyZOH0pYsWTJzabds2VKu XDn+mj9/fk6Q2icGeVzwqJYttU2ePBk/U6hQIWkPBMRwFi9rsqW2O3fuEDQr VaqUK1cubqpixYo3btwIZwkjJVtqoz00bNhQujxq06ZNOIsXK7KlNprT888/ T7vKmTMnZsSdWrxG2bVrl7ijfPnyBQxGGzZsSAouqFBOo0kHPGHz5s1yAvVO Hi4HKVKRIkX4QF6NB7C+3x9++IH8XAqJ5s6d692Yaltqu3z5MsjwzDPPSGHq 1KljQcTbtm0rVqwYpxHugd/MJ6xevdrCtmCFnAaL4S44kjdvXsIlH7Det99+ a74U1QR9yF/RtGnTbN9Wcy9EWE7OnTt3jRo1pLU0btw4ILxnX86pjYSqR48e ciOgvRy8f//+2LFjyZ3kuLQfREZhfvp96NAhqR1MQeuSc6AVA53Icl955ZWC BQtyvECBApKJIRL1HTt2WJSKHCBgNY0YMULey1y5cqVPnz5PPPFEki8HKFGi BB+w6ssvv+z1ixvbFJTsl6Q9yZR+UM4PP/wwcYY02FIb1oNopE6NfjR48GAa XjjLGapsqY1OJz2CPEp6DR8WLlwYzkJmQdFAbVyQ0IPFSERljNzIkSP5b4cO HW7evOm8YFEua2qbMGECvmL69Onc/q1btw4cONC9e3eMUL9+/WvXrnHOw4cP 8SQSsMh7STYuXbr06quvcmT8+PHWox08lTW10a/JNunp4g8rVKjgl1PRs6T2 +/fvTzshgRk+fDj/pTc5HJ4Rl7KlNsIrHuapp54i9kHE8+fPD2fxsiZbaqMB kINxU2C7ZLDx5AEsZEtthw8fLly4cJkyZSSnMohAZZYtteFdy/gkyIB3DUZt d+/eBY0lP+HkgADFOUePHj1mEpF05syZpLv0TZp6hi8jateuncACftJ8spFO /OUvf+GEcuXK8St37twBGXDyHAGCrEf0zZkzh3spXbo0OTbnf/755xGkNqAD J095JNuHcYJR2+3bt1u0aGEkgQS7zOdgh8y2xYbcKc5BvoL95Tpdu3aFx4mb Y8aMES6QoClavHgxtsVK0nc+/fRT64T8zz//HDVqlIDniRMnKAneuHXr1hwB rr14m+mQ2igYhc+VK1eOHDkozJAhQ+Q4jaRjx45Q2+zZs0EwvCiZRt26dTmn VatWmCXD1zUMW3EOVUBjq1ixouRd8mQYe9IjMNRXX331nz4B1wLFoKJFU/z1 1185p1u3bvQ+o5FTfaQomIs2ScVRbPy5eDmuTG6c06c9e/a4Z8gAsk5BaQkk HtLXaNuU7YsvvsC8GOHnn3/2tGDRI1tqEwdFS6CV0pw2b94Md5OvLlmyJJzl DFXW1MaNyJOZ0aNHExfINCZOnMh/q1Spcu7cuTAXNSRFnNro1OvWrcNWuB1j RtuZM2ckk8cJxMpYU1tZj5CkzRCJzBEBGpJnU6dPn87wPY+Vxx0pKSnGOQcP HsyXL1/Tpk1xwl6XP5hsR0j+j0/48Lx58z799NN+OdXy5cuJ8sRuCS4Zvphe s2bN3Llzf/311x6WO7plS200FSIO2T6Rl3YSH9SW8a/WsnfvXnnGq9Qmwg1i PSzz9ttvi7cMZ/FiRbbUhhmlgW3duhUzktkGpDY6FwkJJzRr1gz+Ih+2fe0l IrN94YUX+KIxPJJY+fzzz3Nk9+7dAb9y//59IQKzuyNtqFGjBj7QeB8XUJIS cwtCK5GlNsO2kuM1atQoILVxwqJFizihefPm+fPnD0ZtmUVcEEPBxXLkhx9+ oGpgsStXrsgRMjR+l3PmzZtnfFGsRCoyfvx4J9QGa8jLly1bthgH09LSKCrJ KkZwUtqQ5JDa8JxkzoRRiBiyMKgtwzcx59ChQ+aTKbykoPv378/wjQrG2uXL lzePywVM4CZu1njdduHCBfOcC+qUZsl16tWrZ7HggLx0xrwW6c2MGTNAYOMI /eKVV17hW5999pmnb7WsU1BsLm9pDUbDh/Tp04eCDR8+PM5WAwsma2rDveCL gO4vv/zSOPj+++9jIvqj9SjoyMqC2mjYZOPSQc6ePSsHb9y4gVPi4MaNG6OZ OyJObZwwaNAgDLVw4UKjj9D3p0yZwsFJkyZ5Pck6bAp1DUlcnDy0lNoBarp1 68Z/sadxzsmTJwlbycnJEVx9zuFqJJwTkNoGDBjATeHAzQflUWeHDh1cLmvs yOFqJIT7OKM2EY1cqS2g3nnnHaW2YHK+Gsn27dstqO348eN4qiJFimzatKlq 1aqFCxd2Qm0EeiAL1AIcjFSBfLhhw4b8ljllNevixYskRZzgB0RvvPEGmbkx Cs5CxFBOjji1GYJYLajt4MGD5cqVK1asGFiBkeEgJ9QGeXFZUkcQ2BiXJaMy evfubU77YQfsBm77XeHBgwfvvvuuE2qjCUE31OPdu3fNx3v16sWVx40bZ1va UOWE2rD/6NGjKT91/dZbb/lRW2bJgy+igzzmXb16NdZ77rnnzOdwg9WrV+e4 BYrKUNWaNWsaaOwnmv0333wjhrW6yf9bVChkzbfefPNNTwfaWaegCxYskBdt d+7ckSPczvr16+VFfIKMz7emNvopnpB+eunSJeMg7E/WQTdJTU0NTyGzIAtq e/z48dixY+XtubEwFB/mzp3LwZEjR0bzrL2IU9vt27flUeSvv/5qPr527VoZ NWc9WzyGFBK1EWJmzJiBBQjo8hyMQENmnuQbiG44E5IKjgwdOjSCA4yzQ224 C5n0gds3n7xq1SqiEjlV4owt95NSm1JbQCm1WcgVaqNPycP2KVOmXL9+vXLl yg6pjTDXtWtXvmheBeL06dN16tTh4MmTJ+nUuEFz8pPhe61GO+cEvyUQv//+ e/p18+bNbX83hqiNzEGePU6dOpW4Br45pDYqQtjWyBNI++VSJN7mB+MbNmzI ly9flSpV/FbpdE5t/ETZsmUxvl/5Z82aJTmJbWlDlS21cYMbN24Er6pVq8Zp 8qbDgtpo1YMHD5aXxZItLF++PGfOnFSK+bSHDx8OGzaM03766aeA18HsMsKn c+fOwWZhYMzZs2dLRdC2jx8/TiOxXXWT5gT/8q1PPvkk4Hqqbsk6BYV/k0wz BEWUX+ZoJMg+F9bUhlchc8NVmg/iaYsXL16sWLEff/wxLGXMiiyojfKL95g4 caL5uIz17dChQzTvwhNxasOlkHbmyZPHb0ryzp07ZRBFNDNvSHJCbWfOnPnh hx+mT5/eqlWr3LlzN23adP/+/UYgNh7b1q9f/6uvvtqxYwexifhy8ODBsNxB YGWH2uhQ3KNfnpPh6zs0icSZ1pRZSm1KbQGl1GYhV6hNHpfJlDfiF8mbQ2oj 9cVpt2vXzjwh99ChQ7LyA+xWunRpUh3cWt++fY3xbIYPXLp0qfEtDg4YMIA0 Ozk52fZBXwxR2wcffCB/osFzj0899ZRDalu/fn2OHDlo9kYOxhVkwKTfctPU FPVVoUIFv/UJnVMb94jZ+bmtW7caB0lU2rZtm+RbCMh1yrClNnwmtipQoMCu XbsyfO8TA1LbuXPnvv3220mTJsmTcNoV0Vn+hOMlpBJ/ze98ISxSCL/RuRBi eno6CQbmkokqL7zwgsWKpo8ePZIJQZUrV65atWrRokXpMvz0vHnzLFbRP3/+ vCy/FmzksFuyTkHl/Sn2NB+8ePHik08+yY0kyEa01tT2/vvvY6IuXbr4faVm zZpkdNG8docFteHN8PC5cuUyD6VG5NtkU7Vq1Qr2ZjkaFHFqwzj58+eH2c3D rTN8q7tgPfyA3yiF2JUTalu8eDFRTBbQw0ubp7AZJ8iUapkRwIeVK1dGdvR1 dqiNwCozCPzesx85ciRfvnyVKlWK7PKYEZRSm1JbQCm1WSj71IY3I1uDrTZt 2pThWxPDIbUJZ4lDNk9PJg2Q9RzIBIYOHUryI+4dlJM575wsI9/KlSu3efPm W7duHTt2rEePHnIaabBtq4gVasOGBC9jnQfuyyG13b59u3fv3gJohm3xDA0a NOCg33IWaWlpVB9X9gMN59QGhoDVMtDl119/Jffbu3evIJu8vXJ9+zZragMS ZWmyl156SSaMBKQ2aOu7776T9EBeFR0+fNj4Kx5GFsNs164dHhgIBYSNJaln zZplvK+kCc2dO5cEQ/5Ur149mVkfTHQ6eWtGQtKzZ89BgwbJunxE8IC7MGT4 AoHUBR3Q61GI1ilo8+bNc+fOPX36dPNB0hJZxzjg6qbxJ2tqkyXv/Oaw8JVW rVpBcx9++GFYypgVWVAbPU6W9vIb5YXvlaXzvNvmI/uKOLXRQTAdgdKP2viW jAeImw2bnFAbMWLgwIHPP/88zQbGISFfs2aNxCmcKo6dYFSoUKERI0bIcHR6 E/Hl8uXL4bqJAMoOtV2/fl2WuvJLig4dOiTUZh0v4lhKbUptAaXUZqFsUhu1 QCrCcVJKSWKJPkJttvnbmTNnSP+I+KdOnTIfJ+Um54dT7t69i/Onw/7000+y g4ORCPFdskcyajxkYZ/w/926dcuTJw+Ztu0o8ZigNnmlSJr3/vvvyxFyKqE2 27Ei5CeyAr85HNBZZOERv5c1wFqxYsW4st8q/c6pLcM3qZyfo7SEIakRLijD X9u3b+/6MpLW1LZ48WLaRp06dYyl7eRGhg4d6ncmjR/eb9iwIRYgKJA+bd68 2Sjt1q1bSbS4KTKuIkWKcFNVqlSRxXOWLVtmXISWT+/o1KlT/fr1OYdGyAeM HGx9Bo6TxQFoZ8+e5UZoJ/yXsiX5lnoI2GDoERRPlj72etkH6xS0RYsW5FHT pk0zH6T89HqanCzkEveypjZZ0R0Y9/uKLEkazTuHWlAb3qBcuXIFChQwj3DI 8M3Xw/fS2c+fPx+uYoasiFMbf6pcuTL916+D7NixI8m3K1lCjZA0dOXKlXHj xuHZcLAyyAGXLvtzbdiwIcPX6hYtWiRPcXv16hVBts0OtdGQmjVrlnmE5J49 e2SEpLGwZKJJqU2pLaCU2iyUHWojuf3ggw9I1chUt2zZQkNNSUkh0ZV19Rcs WICjs3jJIotSkjDbbk6K3nvvPfLwqlWrGvhw+/ZtUkdS6Oeee27w4MHHjh0j kcYHkhrZXi36qY1STZw4kfQYRsNQYlvoFbjA2l988QW2DZZUkNXLEoUDBw40 h4P79+/L1gx+IyS5uIyQ9FvzMCRqy/Cl7hMmTGjdunWDBg3odDDgJ598knkO lCuyoDYCAYwPao0YMeLAgQM079TUVJl3SWvBbhcvXswMPuAt5kryrUZi3iMb GiUJp2q4r48//jg9PX3IkCGcZh4LataRI0dkXlutWrVCejhMAaiCJN8y4H5/ ogzyMg4Q8Hqztgy7FLRv374BR0iSd9E4dYQkmjJlCibq3Lmz31dq1KiRL1++ xYsXh6WMWZH1CEl8NU547ty55uMyQpK4EIb937OsiFMb0Ury9r1795qPy2ok eKfEXI0kw7RkNLHj0aNHBPEk3/pR5oVHcIlEKI7L3kARUXaojegpmxktX77c fPKqVavk6aKuRmJ9mlJbfEipzRVlh9qognr16iVZyi9IGaJhT5o0Kcm3maaT SU+ABmnhU089Zd5WzE9cEB8YH2tIEstkLRELBdzHPMM0AG/GjBnmcABld+nS heNAnxlbNm3alD9//kqVKvlNqgqV2jJLVqr3y/BdkQW1TZ8+3dpuVH3A1dfv 3Lkjq9y89dZbwX6XlkM10cws1pA8e/ZsmTJluM7OnTud3xF+W1Z78Bskee7c OZkr17x58/CMEbJOQaXjDB482HxQViPJvLdsvMqa2hYsWEDm5rf6KE2uRIkS RYoUIVEPSxmzIuvVSMR7TJ482XxcViNp3769k4dvkVLEqQ1/9dprr2GoOXPm GL6UgzJye9q0aZ6uLxROhUptGf8KE/3796fhyT5NK1euNJ8AzXXo0EHidaQ2 mMjmyv/yrK9v377mg3LjPXr0cLmssSOlNqW2gFJqs1B2qI1As23bti9N+vrr r2fNmlWsWLF8+fJh9mXLlgWbog4RyEY/cIGTcnLxPHnylC9fPtjav0RMSW7N eyQFU/RTG7b96aef/GwLPRUqVAh6JdZ/++23wbYDo2vIGlwy09As2SCmU6dO 5rsePXp0jhw52rVr53dyNqnt0qVL5cqVo7Tr168P9bu2sqC2tLS0b775xmy6 pUuXyu7AWHj58uV+27waIjeQiZYDBw4M9rt441y5chFBzpw5E+wc6E9yjGCT 1ALq6tWrssaOmfXOnz8vD0YaNGhgbJLltaxTUNmzgAZmvMYljyLLSvJtBxDB bXDDKWtq2717d/HixYsWLWqe6rVv3z5g/5lnnjl+/Hi4ihmyLKgNDyCbi3Xr 1s14vMMHeUhCsh1sxdRoUMSpjT6CP0/yTfI1/DZhl4SNVhFP44otqA17wqd+ s6pJJyQTeP/992lCsuzw5MmTzY8QaZD4Fo6vWbPG8xsIIofUdvLkSaiNRMUv QKxevbpw4cIlSpQwXklfu3aN2odE/BA1oeSQ2mhOQm0kbOEpWHbknNpkB8zI bkQYTjmnNtll5sUXXwxDqWJOzqlN1igmh7QeowVV0b9wUNZzrziNlJ6ANXv2 bPNx0umvvvrK7y0SdU31JfnG/xsHje2iRAsXLsRbck1z6CSBD+hpyfnffPNN eetkUchsyjm1TZ06lcI0btzYegdeAlyZMmVKlizpN6XdT3fv3i1VqhRw4bc3 ENq4cSP5ZIECBYwJX5hLuACz+51M25gwYULS/73yhqGTPpmP+NXIBx98QP2C G86TH+dyuMu2IbmR4cOHy38xEUDqVzUQhyxeiseQI9y1eW03vLGs8fLyyy/L IidXrlyBoP3e/1Kq0qVLc5osX5nha+2pqakG5hDQAWG/xObnn3/Onz8/kGu0 WOK7vH0juFvXuLuyTkFlMGSRIkWMFewfP37csWNHyjlq1CjXJzBGp6ypDX9V t25dP+cmOyZgqBjdZTvDNxgyybcGlJGR3rx5U7wHrVd32bZ+23j+/HkZYvrG G2/gCi5fvixDsglt8TStKRi14RnWrl2bO3du2g+59+nTp3GnGAHeJxuXYTk0 IRlwW6hQoQULFgBxHMHVf/bZZxx88sknI7ixiDW1UblEQxL19evX58mTB/e4 ZcsWkCQ9PV3wE59Qv359ebNGO+GmBg0aRGOA71xfqiuGZE1tWObEiROnTp06 dOhQ2bJlc+TIQdpGssoRL5IKt2RLbWQItBa6wNKlS2kSZAt4D26K5i15RbzK mtpoBrQHsQMpluAG/6Xf4Y0TJLVwIltqkz7CvwAOZpRdYmlyNLyAfY0sV1Yj sd5Mlv6IAyf9W7Jkifn48ePHcc5AB46a3yUhxLFTg5wMhmzevFlO27BhQ506 dRYtWgQpkBKvXLkSGJExgcalyJlr1KhBYUAVOUKbkVaBB+7fv3+Sb8PiY8eO ccRi4GWWZUttdFtpojJ4pnLlyhQMN3X27NmAtiW4y2okxur0AcVphAPMmLkK sJVMIujQoQO3TAI5YsQIbIv1jK6ElXA44iplC7ORI0dCDSCw8Zxw27ZtoESV KlVkcUuE/6GLEZdJYPCoU6ZMoXK58rfffuvYYCEoVGrzW0NStm+rWbMmxSYF xdq0cGkStD2jmcF6zZs3x8/gS6EVvs4dAc4GDsvebeA2LfDGjRtch+YkL9oq VKggVU8XmzRpEhX3yiuvyGN2WIwkpGvXrqQ3ZMhUyurVq2W9HcBH1iXgap07 d5YhnSDewYMHU/4lbhxa9C5Jtk5BuceXXnpJPCo5CU2IropZuEG/ufZxLGtq y/C9PSfHoE7pKYQb6pculi9fPr8FGKNN1tR2+/ZtWXuKhPPSpUv4XgCE26xW rVo0L0WSER3URof95ZdfihYtmuRb0F6WnCUXPXDgQDQDb6iyeNdGm+nUqRO9 gBvPmzcvppDPRIrp06fLV4j48+fP57h0nxYtWhDB+UxGsXz58ghmbtbU1rt3 b1ktSla8RHzgCF7RWPuLFiJOXp7O8QGfkDg+M6CsqY3s4ol/yZjggJGj/KWb LbU1a9ZMWgt5mtwUAZQjJFTWaXOsy5raSIRKliwpljGqW2q/SZMmCTKI1Ils qa1WrVoBGxhAFHCpZ0I5kQivaz1xmCjPpXDFfnvOAn19+vQRp82PcoJEtwIF CshsZTlt7NixlIFzOA6e8IEoAICY840PP/xQCmxMU9qzZw9e1M8JyH979uzp 3GgOZUttZDsBmyhACiNkPp/WXqxYsUKFClmPqAEGuVTmVaZFuAXZcQwjS+zA gLJgl4gyQxwBrQSPyDkyJzHJN4U8w9eKQBLiFI2Ea3JBiU2cZrEBWXYUKrXJ MGnwX/578uTJRo0a0ZJl0csiRYpI/kB7+/jjj2U4KDkqOCYNHptLO4Tpli1b ZiQPRBwZ44RoilxHahOyJqzIaWR08j7C2Ppt3bp1pUuX5qe5MrbCYnxO8u14 KD2RKCZcafQ4eWphaNGiRd6N7LVNQeFcGY1Mp5O65hY++eSTyG6oFE7ZUtu9 e/e6d+9OtVJZ4qD4gIOK8kep1tQGXOzbt0/eR9NfaPDS1JcuXRrl3BEN1CYi 3rVr166mT507dw5p6mtMyHpe24MHD1auXNmyZUvyh6pVq2KEVq1abdu2zW/+ 9Zo1a9q2bctfOYcM5IUXXojgOiQia2p77733mv5LzXySzx07djQH65SUlK5d u3JH3D43RUIS5R3Ha1lTG6lpQKuiCI6VtZUttQ0fPjzgfZGFRvP4+ezLmtpw GnQKs1kMywwdOtS8p3OCy5baBgwYELCB9evXL+CiYaTTYBfpvd8y8n46e/Ys lyKxybz12OPHj4EIIhrOjQwB/0aY27Rpk/k5G938q6++at26dfXq1ZOTk8mu yRz8AOHgwYNt2rRp2LChMd6S38KLBnQCDqfXhSRbauvdu3fAJorNA85ZIzfo 1q0blrHelBNq41K9evUK9gCcAIR/wLxYD9sSNM2x48iRI0SWgAUbM2aMnEMk atGiRZMmTYz9HR4+fDhz5szmzZtTHVwWy69du9a7R6OhUhuthbJRQuMIefWs WbO4C0oruQFtwy+JIg174403aEK0Q1Aa42feEPbmzZt/+ctfMBH4xnU4Ddua cwzaM5BVu3ZtQMwYb0nJAUlo7lmfgKCxY8cak0Bp3gsXLvQzvlnm7Qlcl5MU lPIPGjSIm6W6saGTyaTxJFtqy/A9gKLxULOYqFGjRpMnT47msZEia2oTHT16 9MUXX6Q903EIsl7MWnVd0UNtolu3bsXrTBaHq5HgNs+cOWNtBFrjuXPnomT/ cYfz2pyI+/J6z81YkcN5bbEl5/PaEk3O57WpLOR8Xlv4JU7b2r9du3Yt2IIn Gb5XrhHc4cX5vLaI6M6dO8HWM3EiGQmZ+fjly5ezc1mHCpXaLHT16lXyB79J eWbBR7RD2xQORqY8wU6jMWeOTbSQixcvXrhwIapWkHOeguKEI7v1baTkhNqM MzFRrKQlTqhNRC4dhm7ulqKN2uJYWVhDMibkIrWpDCm1JZSU2lxRNFNbrCvK qS2m5SK1qfykKaitnFNbbMk5tcWWlNrCJqU2lXMptSWUlNpckVKbd1Jq805K bd5JU1BbKbXFlpTawialNpVzKbUllJTaXJFSm3dSavNOSm3eSVNQWym1xZaU 2sImpTaVcym1JZSU2lyRUpt3UmrzTkpt3klTUFsptcWWlNrCJqU2lXMptSWU lNpckVKbd1Jq805Kbd5JU1BbKbXFlpTawialNpVzKbUllJTaXJFSm3dSavNO Sm3eSVNQWym1xZaU2sImpTaVcym1JZSU2lyRUpt3UmrzTkpt3klTUFsptcWW lNrCJqU2lXMptSWUlNpckVKbd1Jq805Kbd5JU1BbKbXFlpTawialNpVzKbUl lJTaXJFSm3dSavNOSm3eSVNQWym1xZaU2sImpTaVcym1JZSU2lyRUpt3Umrz Tkpt3klTUFsptcWWlNrCJqU2lXMptSWUlNpckVKbd1Jq805Kbd5JU1BbKbXF lsJDbbdu3cpS6eJKZ86cgdoePXoU6YK4LKjtxIkTkS5FvAm0oWNGuhTuC2pL T0+PdCmiTqRt+EmiTKQLEtv65z//CbUpWXghIhdkQRSLdEHiUA8fPsS2Z8+e jXRB4lAPHjzQFNRWJBt///vfI10Kl3X37l2q/vfff490QVwW+bbX1EYY5VdO J7wOHz5MykrXiHRBXNa+ffsAt0iXIt506NCh1NRUACfSBXFTgqLcWqQLEnVK S0vDOaifzKbkDTWeNtIFiUNhW5qo2tYLkYBh2yNHjkS6IHEoda22Is0g2Th4 8GCkC+KyTp48SdWfOnUq0gVxWeTb1Jen1JaSkoLp/i3hRTqBqbFGpAvisrgp bi3SpYg3SWuJdCncl7aWgMItYBn1k9mUmFEbmBdS23onta13UtfqRHHZ/KTq 4y/lluTQ6xGS165de5TwgpH/9re//f7775EuiMsC/I8fPx7pUsSb0tLS6Jv/ /d//HemCuKkHDx4QPfE2kS5I1OnOnTt//etfr1+/HumCxLbu379PUEtPT490 QeJQRC7iF1Es0gWJQ927dw/b/q//9b8iXZA41N27d3GtmoJaiDSDZOPUqVOR LojLunnzJlV/69atSBfEZZFv62ok4ZGuRqJyLl2NJKGkq5G4Il2NxDvpaiTe SVcj8U6agtpKVyOJLekakmGTUpvKuZTaEkpKba5Iqc07KbV5J6U276QpqK2U 2mJLSm1hk1KbyrmU2hJKSm2uSKnNOym1eSelNu+kKaitlNpiS0ptYZNSm8q5 lNoSSkptrkipzTsptXknpTbvpCmorZTaYktKbWGTUpvKuZTaEkpKba5Iqc07 KbV5J6U276QpqK2U2mJLSm1hk1KbyrmU2hJKSm2uSKnNOym1eSelNu+kKait lNpiS0ptYZNSm8q5lNoSSkptrkipzTsptXknpTbvpCmorZTaYktKbWGTUpvK uZTaEkpKba5Iqc07KbV5J6U276QpqK2U2mJLSm1hk1KbyrmU2hJKSm2uSKnN Oym1eSelNu+kKaitlNpiS0ptYZNSm8q5lNoSSkptrkipzTsptXknpTbvpCmo rZTaYktKbWGTUpvKuZTaEkpKba5Iqc07KbV5J6U276QpqK2U2mJLSm1hk1Kb yrmU2hJKSm2uSKnNOym1eSelNu+kKaitlNpiS0ptYZNSm8q5lNoSSkptrkip zTsptXknpTbvpCmorZTaYktRRW1//vnntWvXbt686bwwMSSH1IZtL1y4QHuz OOf27dvnz5+/deuWqwXMokKiNqo42J+IXBcvXrxx44ZL5YptOaS2x48fX7ly JVayfefURi+4dOkSTSIMpYoGOac2zqHGqfcwlCrm5Jza7t27RwOLv2do3sk5 teHDCU8EqWAn/PHHHxjfurWTaxEHQ41xXJNvWfy0WXQiHGzAkEQSQjAKW74X ErVhPesTqClb82aWWMP4L5//GURyGnazOMe2kKLr1697ne85T0Gx2OXLlx89 euRpeaJQIVEbNWuRxUWVnFMbLgv0iJX7ih5qW7FiRbdu3erUqVOvXr2ePXtu 3LjReZFiQrbURkI7aNCgFi1aVKtWrXHjxi+99FJaWprfOfv27Rs8eHCTJk0q V67coEGD/v37nzhxwuOC28gJtXHv8+bN69Gjx4gRI+gdfn9NSUl5/fXX27Zt ++yzz1L7vXv33rNnj2fljQ3ZUhvO8/PPP+/UqVPNmjUbNmzYt2/fzK0l2uSE 2mger732WsuWLatXr06TmDhx4oMHD8JWwkjJCbWdPHny5ZdfptdT4y+++CK1 H7bixYqcUBtZ/fDhw59//nkaWPv27T/88ENFYCdyQm24LMIWPpzwRAjDzjRa 8wnkRd98803Xrl0xPl5rwIABBw4c8LvI2rVriWvNmzcnHDRq1Khfv367d++2 LhuXXb58OT6Q3sG3mjZtSiQlrAQ7nxofPXo0zvOtt966e/eucfzhw4eLFi0i TtWvXz85OZlG8uqrrx4+fNj617Mvh9R25MiRt99+u0uXLlOmTAnYaO/cufPR Rx/RqsW8vXr1Wr16te2vY4E33ngDaxCF5bLAstincyZxkAzkrE8jR44Mdg52 s76dH374oXv37nXr1sXURPzffvvNtpxZk5MUlASYBoPFcK0dO3acO3euR4WJ TjmkNk749NNP6bxjxoyhpYWnbNmRE2pLTU0FN3BZoAcAsnLlyrAVL8uKEmrb sWNH4cKFk5KSChUqlD9/fj6ULFmSgjkvVfTLgtpwld9//32FChWSfCpXrpx8 wKGRvRunnTlzplatWvKnZs2alS1blg/4vciOCLKmNuLgwIEDCSJ58+aVak1P TzefsGrVKkK83NTTTz+dK1cuPnBrRDHvyx69sqY2spRp06bly5cPWxUtWvSJ J57gA54nysHNltpwFER8aQzFixeXD0Bc3L8TsaU2arZ27dpYI0+ePEWKFOED tT9z5sw4G0ObTdlS2+XLl1u1aiXtqlixYvybI0eOsWPHJuAD9lBlS23Hjx9/ 5plnMGnBggXhHQnoLVu2NH/l888/f/LJJzleokSJnDlz8oFWvX//fvkr2QJ1 IfWCChQoIB8qVqy4ZcuWYL97+/btESNGkDlwJlFG8gcEdv3yyy8Bv/L1119T 73KO8a7n4sWLffr0EafKRehoRhSGULJiMseypbbz589jUiNQwhc0db9zLly4 QEYt90UVyJn4ClsGAaDkZKpPLospiMVJQcQ1qWtgtlSpUsHOeeqpp44dOxbs F7/77jvClthZghe35hG42aagJCSETnGtUio+EF4Tx7XaUtu9e/doWtWqVZPK Kl++/NWrV8NZwqzJltpSUlIqVaokrkb8Em37yy+/DGchs6BooDY8UqNGjbDY e++9R1TFQw4bNoz/kr/F02hkC2ojj8Vd4G9Jw86dO0ccoTnB/hhh1KhRZHQZ voTk448/5gjd5+TJk3fu3KEihgwZwpEJEyZE8H2ENbXdv38frqQviD+ETM05 FVUvHPr2229jBG6cpti4cWMJTImcSllT24kTJyRiEpGvX7+O0Xr16sV/Bw8e HM2AY0ttH3zwAXeBI927dy/3tWbNGloOeRRRPpzlDL+sqY2O0L9/fyxD18B6 WGbWrFn8t0yZMrt27QpzUaNZttT2zjvvYLfq1auTJGNG2hWtC7748ccfw1nO WJQttRGYJDxdunSJ8HTmzJmmTZty5K233pIT+K48cBg3bhzGJ9JBIvy3S5cu 4ur5CZga/vrss89gEDLD3bt3U1mc065du2Ce7e7du8TK4sWLg4RXfdq8ebM8 Au3Ro0dmujl16lSVKlUMIjNyDIJRuXLlypYt+/PPP3ORK1euLF++HLrktHnz 5nmaw9tSGx6erJJ7FCZt0aKF3309fvz4tdde40/EBcp/7do1bmfy5MnS1C1+ mkADFIs1OFkuy7/kGP+vSQcPHty3b1/NmjU5jayD+iK1IwyZzzl06BAxS9Lg 0aNHBwvfGJa0P8n3OA47k/J17tyZ/zZv3tyL4GWdghKSXn75Zck3MAXNcv78 +UKd27dvd70w0Slbart16xZEX6xYMem/zz77bOYRU1Eoa2rjr23atOF2Ro4c CXTQLPFL/Bc4pe+EuaghKeLU9ueff27YsAFbtW/f3mgJhN169epBMeR4sTLW 1FYW1IZ/I21YuHCheTQ4vjdnzpw0Idxahm/QtTg3LmKcc+DAAbKOZs2aORzJ 74VsR0hyd0QBXHrevHmffvppc07F/a5cuXLZsmVm6ty2bRvhidhtfs+YaLKg Ng4uWrSIltCvXz/6lxzEvBjtiSee8PqxcHZkTW337t0jHHALX331lXFw7Nix 3GmHDh3iG+GtqQ3XkTt3bsnH5Aj9hVxXMh+HU0gSQdbUhnlJywsUKLBixQo5 QnAZMWIEZuzVq1fiPFrPmqyp7eHDh/gfGMc8mnHXrl3Y9oUXXhDbyjOZli1b crKcQOKRK1cu88MHiI+gZm7SREZJFOG4YGXD6eEAjVSBD/iQJN/wA7/0EjfS s2dPKYYftVFIXJM5lnGyPCkdPHiwp0PCbKmNO/r/fJo4cSLlgXb9qA0UxXOS V69du9Y4CMqRqllkULidAQMGkGjVrVu3aNGiGDkz5Br69ttvIUeA1yJjJzCR kADRFhMS586dS6Vzzv379+UISXJRn8DtYN/KsqxT0HPnzlHgkiVLGj9N45QW 8uqrryaIa7WlNqP57d27F8tUrVo1DqgtJSVFHhEbPg1mlwdNNIZo5o6IUxsn vPLKKxhqwYIFRtwkQLz//vsc5N+4ydZCXUOSqilYsCCkI84cNygPM82PzvDV 4mwjuB6Fw9VIOCcztQXUxYsX6Ur58+e3nc4Qx7Kgths3bnTr1o2WQD5j+Bbi rzy4Xrx4cXhLGoKsqW3Tpk2FCxcuXbq0PKYQkcKRVFSsWJFmFq5iRkDW1Eae I6makTqSTsi4pkaNGsXKWjRhkDW1YTFcUOXKlc1LHmF2gQLcTriKGZOypjbi OPAFuJlf/h45ckTa7YMHD3BltWrVIl3/5JNPjBMI7uK13nnnnWC/SxSQEXQh PY9as2YN36pZs6bZmeAtf/rpJ9oAReWDH7UFFGkJp/Xt29d6fbBsyvlqJJMn Tw5IbWPGjOF4586dLbArswCxJN8kC+C0VKlSZOPBvo6Tofpy586Nlw52tbS0 tGrVqmFei8BNFcgQ5QkTJpiPS24zbNgw54V3KOsU9PPPP5cxDMZzb0ooDwqe e+65eF0Zz0/OVyPBu8YHtT1+/FjerEHoxhRRPsyePVuehcoIt+hUxKmNziJ+ 228BirVr13JwwIABcbOOXKjUlpqaSoyDXySdIOOdN28eNhk4cKDhTLZs2cIR sNd4bBV+uU5tXJCoCrHCpC6VMfZkQW1kL7Vr1yaA+s30p2HQGMaOHRuuMoYs a2qjeefJk4dYaT5I9wflSpQoEX/LE5llTW2jR4+Wbm4+iNPOQjYb37KmtmnT puFR27RpYz5IZKcrPfXUUxFf1inKZTtCkmAt4ckYADBz5kyOvPzyyxk+O5cv X75QoUK0c+Mr+DfBkJdeeingNe/duzdo0CDhFOd5FPFRKKBTp07m/IHUpXnz 5jlz5vzuu++OHj1qS21gVIsWLTgN0gyJhkJV9qmtUaNGOM+FCxfKf5086z5z 5gxxlqC8bNmydevWUTUwV7DbXL16Nb/bsWNH4z1pZr355pvy2triPQWpY506 dagCY9iASLJl6su22KHKOgV9++23Jc80H6SRJ/lmUybIPhcJSG208+7du8u0 LPPxX3/9Vcb2RPOz0IhTGw4WMMHh7Nu3z3x8586dofrqKFdI1Eaj+vDDD2UM uWHtK1eukNPKGOxvvvnml19+wc2Sb0T2NYTr1DZp0iTyqCpVqpDku1TG2JMF taWlpZUsWZKA62dJmbPTt2/fcJUxZFlT24QJE3LkyNGjRw/zQdIPGnn+/Pmj f45wdmRNbd26dcMyEydONB8E1goUKFCsWLFEfrjhJ2tqA34xIxRgPoh3LVeu XMGCBeNs8SvXZUtt+KUaNWrIkMj169f/+OOPNM6yZcvCRxm+hwyEqhIlSpiT DdJ7GezdpUsX86KI586d+/777z/44AOBpqZNm9rGOC5FvRMW33//fZkm37Zt 2+PHjxsn0MVkBtO4ceP4Lf4UkNq4Tmpq6pIlSzhN1v+h93k9vz6b1MbtQMTY dseOHVu3boVB2rdvDz3NnTs3WKpGYsY58lqBKIO1n3zyyWDURopOnXLy3r17 gxXs4sWLMlsw86KgZnGPsqjFwYMHzcdl5ECrVq1cp2PrFLR37974hPHjx5sP ci9Yo2jRogmyEW0CUhvejFw6V65cfmv17N+/n8ZJxyfZDlcxQ1bEqQ3jkJLR QcS3G8KJYb3KlSvHzR55IVHb4cOHCXlgzvbt282DqxcvXizLQ/EveTsfVqxY EVm6cZfa8Ori/BNt9V0/WVDbkSNHcubMWbNmTXqi+fjUqVM5Tp4TrjKGLGtq Gzp0KPU+fPhw80ESkmbNmhFYP/7447CUMTKypjay0Ny5c3/66afmg6QWFSpU 4Lhf/pPIsqY2MjRZ+Mh8kAhVt25dGliw9QZVIicr/8uIuyTfwrbELyL7hg0b 5E/US/HixQE385DFjH8NZSRdN5IK2R1AlnSTiScWqxEawlXOnz/fWHaSOj19 +rTxV665atUqMopnn30WJORIsHdtOJw+ffoYC1G2bt3aYj6dW8omtZFElS5d mpQAEDOW3k3yrYU4cODAgFugzpw5k2CRnJx8/fr1DN+ijhbUtnPnTi5FZLEY Mbhp0yYu2K5dO/NOCpmFs8Jr5cuXz6+TylP6hg0buj5/0DoFbd68OS50+vTp 5oNXr16tWLEiKb2xuml8KwGpjR4nA7pwNebjuJpSpUqVLFnSL7mKKkWc2ugg mC4ztfEteg1uJAGpDb8nz8FGjx5tvGok7qSkpEA9mGvIkCGgjazx+9JLL0W2 B7lIbQ8ePJD5jATTmNgQxDtZUBs9haqvUaOGpB+GPvroI46T/4SrjCHLmtpk 5Vi/qQ2PHj2SCcJ+gTXOZE1tTZo0IbUg0TIfJJmkN5GIHjp0KCxljAFZU1vf vn2TTEsaiohQderUoeMk8ixaJ7Klts2bN5PtlC1bFjsb68a//PLLMiOMjg9Q ABeXLl0yvkJQk5cswJF5UM2vv/7aqVMn6qVw4cLwAh927NhhvT4Af926dStx s1atWjIfvFGjRr/99pt8CyACGGGxRYsWyfnGuza/dTNwuZ999lnbtm0hGuCC r+BRI77yv6GA1Ma9yFqX2OqVV16htJDasmXLKD8Ne8aMGX4XwbzywMdo8ytW rBBqy7wNHGWTYYQLFy4MVgUkhLKCJTmwdTUdPnyYn6Z2zEyd4dv7SWbphpna QFHyzGnTppkPkpQ+88wz2EepzU9xQ22kmrIy1dKlS83HSa5wYviKaF5GMuLU dvPmzcqVK+Nt/DqI9GL6VKKNkKQ5jRo1Ksm3KYZ57BOumOBFPPrxxx//+OMP DLtgwQJZ37hfv34RZFu3qE0elib5tszwbs/NWJEFtdFbS5UqFWyEZP/+/cNV xpBlTW0TJ07MPEKSLIIYQe5kXlgy/mRNbd27d8+ZM2fAEZJkwlG+SV84ZU1t Y8aMoYENHjzYfBDLAxo6QtJW1tRGI5RlANevX094oiV//PHHsvPawIEDHz16 lJ6ennmEJGcaIyTNI0bI/P/wiQgoe5oAFLboZHzr4MGDHTp04FtEzCtXruBF 5TUrjMYtHDhwgGRDXgviW7Zt24azNU8Ek+tQnr179zZp0kQibDSvRkKGSUSA PsaOHWtgF3f07rvvJvn2YjDf3f3798U40GhKSsq+ffvIVCdNmoSPJTqTopw8 edLMbmRo2I2OA+sFKxUkXrNmTQrgN88ls6hEWSg4SkZI9unTR0dIJiC14c0a NGgQbIQkfiOaN6SLOLXdvn27WbNmtAS/RF2W8aFPJdRqJHjLGTNm4EZwocuX LzeOE0QkyowePdqY653hG0cqu4tGMOVwi9pWrVqVO3duuszSpUt1FW4LaiO4 EyIxpt+EJpm27/cqIapkTW1z5szJkydP06ZNzQcfPHhA9IRN1q1bF5YyRkbW 1CbPsf3GjtJCZMq8k2QvQWRNbVOnTiVMk7KaDxLZ6UqlS5c2z4FSZZY1tcmT RujGfJBUHJcOqZ04cYIWjvMvXLgwpGCcgH+T7QAsnjXJc13OMa9pbys6BXXK t3755Re6FcVICi5SdPMbQLOAC9mj6vDhw85/PVRlk9oePnxYtmxZnKTZthn/ Wn6T/MqMnLRzGryFNcqUKWMuyenTp+k1XNxv8SuzZLhpcnKy7aT1e/fu1apV K2fOnDt37jQfl2VyO3bsaGuBUGWdgr7xxhtJmRZ6ktVI/PaWjWMlILXRfWTr HDqU+bisRtK+ffto3io64tTGCTJHeMmSJcYErkePHhFhOThx4sSEWvl/2bJl Mp5/ypQp5uO0Mdm4yoxyGb6cVjYKjOBaDa5Q265du0qWLClxP27ermZH1iv/ d+rUCVtt3brVGI4iG9Ry8PPPPw9vSUOQNbWtX7+exk/OYM4xaDY5cuQggMb3 YBVrapM9tVu3bm08wjK2uWzQoEE0x5cwy5ravv/++zx58pBymOPXvn375FVO GKYvxbSsqa1Fixak4n7bjty/f//ZZ5/FvLIdQM2aNZ944on58+cbJ+Df2rZt a/2sid/t06cP53zxxRfOS3v37l0QgG+tWrWKzkVyPtgksvQXX3wxyTf/jou/ ++67wTY8vXz5cvPmzTnT0wG02V9Dsk6dOrTtBQsWmA/+9ttvSb61XMwelTvC 2mZrDBs2rHHjxoAt7nfgwIHvv/++ef7atm3b5K1lsLcP+CJZ8bt37962voiT ZYWZjz76yHxcqtiPnlyRdQoqw3uoYvMq3D///HOSb7O/gFMC408JSG2PHz+W cb8knMZbfuywcOHCJN9WfdGchUac2iA1Wb4eR2T4ijNnziQnJ5OtpaamOi9Y lMua2rDDihUr8uXLhylI0vz+Srv69NNP+dMHH3xgxth79+7JrJ/Vq1d7V3Jr OaS2kydPQm3ly5fPHJvIpoie8jYhwaezGbLeZVv2kO3atauxO/mhQ4cKFSpU oEABv8luUSXbXbZr1KiRK1cuM3ga+xBZrDgdB7KmtrNnz5KSFStWzNgsCScg q7phnwTZCtaJrKkN34L/wceaZ6DLbqHEbn2/by1rapOs2w++QKEKFSrQdGUg DYl6km97ayNDJigAC2XKlJHRd1TQpEmT/GaU3Lp1S9anMt610fgJOsZpJJAT J06UVTUMkaXIal3bt28PWOBTp04l+fbkMpK606dPw0R+AejIkSPEJoDU09mj zqltypQpSb6ZI35LkMm7qlatWpn3HZMnvT179rRt2ytXrgTZQGy/4/gWuXK7 du3Mg3zMIgGWvcj5uYAjozA14cz4L2gpy/QZlidmFSlSBDtv3brVupxZkHUK euHCBYKmjOyVIxhWHoq+/vrrCeJanVMb4VueccXETnbWu2wDF9zL008/bfQ7 Gkn9+vU5+NNPP+ku29ZPYLggtiJbe+edd/CZuF+JpKQl0bxpQqiyoDYchUEu vXr1SklJOXDgwD6faFoydIcPBDic26JFi+Q5G//iUUEh3I7f3N5wypraLl26 hMfm3kk4Cd+UH89MWsURwU/ihbxlI2QQ3A8fPmzc+NGjR+NmfGyosqC2DN8o l1KlShH7Zs6cSTPA7fTo0SPJtzRNNNONNbVl+Ka2Jfn2IJPdG8nTihcvTpq9 bNmyMBYzArKmNnoKqVeSb7Y+dc1/P/30U9llLFhSmpiypraMfz0EqF69uuwa tnz58oIFCxYqVCiCT71iRdbU9t133+GO6K1GVyX0CzVUrVpVFmSj6dKXiQLj x4/Hs129elWGBxDoJaK99957Sb7ZZytWrJCv8HOyDRzuTqIM2dScOXPItdq0 aSM7mY4YMUJel1CJ0oPABBn+xGnBIuOxY8fkt+Qr5Od8JUeOHBTml19+IQ/h CGFIXgxxcU9XAremNm4ZO+A2oRu5WRow1iBtAzokveQuypUrRxWMHDlSoHjD hg2AGLmBk3eUxmokfkObIDKplK5du2ZeqEREw5BxhrBb5oD1888/E9lr1qxp vKwkwStdujT5HlhEJknJZXO9hg0bOt/N1rmsU1AK3K9fPxm0gJGJUORUmJH2 5gVCRqdsqe38+fMkJLRP2ol0RporDfLs2bPBWkU0yJra+KtsFT106NBr167R FAEQPADpRzQvIJkRHdSGe9yxY0fhwoUxYGGf+EC/Jm+PZuANVRbURpqBE5NR 5XgMPG0+k+rWrYtXl33bCXlka7Srtm3b4mP5zMlLliyJ4INia2rDJRbwyRhL L5hJPCW/unz5MjHdGFFP4DDfOLEpPT09nPcSPbKmNo5PnToVE+XMmRPYl1G1 ZCAWQBQNsqU2HKnMlKeRkAHSvLlBEpW4GSYdTNbUluFrD7Vq1ZI+UqxYMcxC J5oxY0Yi72mYWbbURnSWSG00MPwtwTqac48okTW10UOHDRtGeCKQ1a5dm/BU oUIFUqASJUosWrRI3lkQzb/66isJBLRhmZGNkzeWjz5y5AjfpW1TL/yV5FA8 G/9+9NFH4gy5lEwKoOJkKA41XqlSJX6LI0WKFOFbsjkOn+G7YK9L+C3O4eeM 5GTz5s1kHVyHEuJUS5YsKUNf+MCNe/raxZrauPEqVarQ37kvjCOxMn/+/Bxp 1KiRMXBx48aNsvwLBRaCoy4GDRpkjMew0LfffstdY0a/gZdU68CBA5N8KwwE +y6QKM8M/cZnioTc/SZ9rFmzRmqWOpJ8r3z58h7NzbdNQeH6OnXqJPl2UxLX im0h0MTxCbbU1q5dO2l+dHCpTboGR0hBo3kRJ2tqwx2RjcicWbyNvDTh3y+/ /DLK37FGA7WJfvjhhxYtWlTzqX379vH3oMOC2s6dO/fcc8818Om5TBowYIC8 cpLdMMk6nn322YoVKyYnJ7du3RrzRpZtralt3Lhxxo2Yb5DIe/DgQZKobt26 NfiX/G6cWCBPUxNQ1tQm+uKLL5o0aQL20h66dOkSwfetDmVLbRm+h8avvvoq hII7rVev3qRJk7x4ABttsqU2hN369etHlksK17RpU2MNc5UhW2rL8G1uNXjw YGlgOBnSMyVfJ7Jd+Z8Mf9myZeKRCE801M6dO1Md5nPIhFeuXNmsWTPaMPEL D++3CMzt27epkcaNG3OCxDgAcMeOHeZzli9fzvHu3bsbw7SuX7+Oo2jYsCHc IT/dqVMneZ0aTGfOnCHi0BLM+4sRiN9++23ahlynRo0a0Ir1ttGuyJrayCE7 duwYMIziEMxj1fbv38+NEw4of926dWfNmuVwScZdu3a1atWqb9++fqhCnY4f P56fmzp1qkXhx4wZwznbtm3L/FdyHuqFVmGemEzGsmnTppYtW5LsUdoXX3zR dvHJLMtJCorH6N+/P80Gn0BRo3luuBeypbZhw4YFbH5U3JEjR8JZ1JBkTW2i Q4cOyTYfeC26wJo1a8JWvCwreqhNxJkxMWI2Cwppl21r3bp1i6ATJVPAHM5r U4UkJ9Qmgnw9XZXaRTmhNhHJw4ULFxJnqpETahPdu3cv2Hp3KifUZpxpjC5T OZGTXbZFeCTCEw3V4pzLly9bb8fMCdRjsEVC8HiZB89Tm2AXJczmPjgPHjwA oLiFYDO5XJfzeW1ORHqAHaLnfQEtIViNkOwFq2K35DwFpZyJ+ZTY+by22JIT ahPRCKN5qX8/RRu1xbFcpLaoklKbF3JObTEk59SWaHJObSoLOac2VahyTm2q UOUutanM0hTUVkptsSWltrBJqU3lXEptCSWlNlek1OadlNq8k1Kbd9IU1FZK bbElpbawSalN5VxKbQklpTZXpNTmnZTavJNSm3fSFNRWSm2xJaW2sEmpTeVc Sm0JJaU2V6TU5p2U2ryTUpt30hTUVkptsSWltrBJqU3lXEptCSWlNlek1Oad lNq8k1Kbd9IU1FZKbbElpbawSalN5VxKbQklpTZXpNTmnZTavJNSm3fSFNRW Sm2xJaW2sEmpTeVcSm0JJaU2V6TU5p2U2ryTUpt30hTUVkptsSWltrBJqU3l XEptCSWlNlek1OadlNq8k1Kbd9IU1FZKbbElpbawSalN5VxKbQklpTZXpNTm nZTavJNSm3fSFNRWSm2xJaW2sEmpTeVcSm0JJaU2V6TU5p2U2ryTUpt30hTU VkptsSWltrBJqU3lXEptCSWlNlek1OadlNq8k1Kbd9IU1FZKbbGl8FDbrVu3 slS6uNKZM2egtkePHkW6IC4Lajtx4kSkSxFvAm3omJEuhfuC2tLT0yNdiqgT aRt+kigT6YLEtv75z39CbUoWXojIBVkQxSJdkDjUw4cPse3Zs2cjXZA41IMH DzQFtRXJxt///vdIl8Jl3b17l6r//fffI10Ql0W+7TW1EUZPnjz5HwmvI0eO yIuGSBfEZe3btw9wi3Qp4k2HDh1KTU09ffp0pAviprgdvM3hw4cjXZCoEx4Y 56B+MptKT0+ngeFpI12QOBSRiyaqtvVCJMzY9ujRo5EuSBxKXautiMskG6Qc kS6Iyzp16hRVn5aWFumCuCzyberLU2pL8elvCS/aD6aGYSNdEJfFTZEpRboU 8SZMimEjXQqXRePX1hJQWGbfvn3qJ7MpaWB42kgXJA6ltvVOalvvpK7VieIy LlPpcVn1khx6PULy6tWrDxNe6enpOBBMF+mCuCzA//jx45EuRbwpLS2Nvvng wYNIF8RN/eMf/yAzwdtEuiBRp9u3b//1r3+9du1apAsS2/r999/xsf/+7/8e 6YLEoYhcJAxEsUgXJA71X//1X9j2P/7jPyJdkDjUnTt3cK2aglqININk49Sp U5EuiMu6ceMGVf+///f/jnRBXBb5tq5GEh7JaiTYPNIFcVm6GokXktVI/vjj j0gXxE39z//8j65GElC6GokrevToka5G4pEe+uZe6ZxBL6SrkXgnTUFtRZqh q5HEkHQNybBJ15BUOZeuIZlQUmpzRbqGpHfSNSS9k1Kbd9IU1Fa6hmRsSakt bFJqUzmXUltCSanNFSm1eSelNu+k1OadNAW1lVJbbEmpLWxSalM5l1JbQkmp zRUptXknpTbvpNTmnTQFtZVSW2xJqS1sUmpTOZdSW0JJqc0VKbV5J6U276TU 5p00BbWVUltsSaktbFJqUzmXUltCSanNFSm1eSelNu+k1OadNAW1lVJbbEmp LWxSalM5l1JbQkmpzRUptXknpTbvpNTmnTQFtZVSW2xJqS1sUmpTOZdSW0JJ qc0VKbV5J6U276TU5p00BbWVUltsSaktbFJqUzmXUltCSanNFSm1eSelNu+k 1OadNAW1lVJbbEmpLWxSalM5l1JbQkmpzRUptXknpTbvpNTmnTQFtZVSW2xJ qS1sUmpTOZdSW0JJqc0VKbV5J6U276TU5p00BbWVUltsSaktbFJqUzmXUltC SanNFSm1eSelNu+k1OadNAW1lVJbbEmpLWxSalM5l1JbQkmpzRUptXknpTbv pNTmnTQFtZVSW2wp2qjtxo0b8dq/HFIbFrt06dKdO3f8jv/xxx8kvf8TSBz3 rNT2covaHjx4cPny5Xit/VAVErXRNrwujysKidr+/PNPr8sTPXJObbdu3bp2 7Vpku3zUyjm1YfArV648fPgwDKWKD1lTW7DwxEGzd+Jz5hMC9vR79+4RDvg3 pEKSpxE97969a3EOOQY96PHjxyFd2VM5pzZu8OrVq48ePQpDqeJDzlNQbBtt DSM8ConaYiguO6c2mgduIQxFckXRQ22rVq3q1atXvXr1GjRo0Ldv3y1btjgv UkzIltr27ds3dOjQNm3aJCcnN2vWbPDgwaTu8ic8yffff98tuHbs2BGp3uSE 2ohHn3/+ee/evV9//XUco99faYFvvPHGCy+8UKNGDWq/f//+e/fu9ay8sSEn 1Ea3WrNmzYABA/r06bNz586wlS3Lckht3PusWbN69OjxzjvvhJq2xaicUFta WtqQIUMaN25cp06drl27Ll68OGzFixU5obaLFy++9tprrVq1qlWrVseOHadN m5aAeVoWZE1tf/nLX4LFJjry4cOHM3zdf9CgQZlPeOutt65fv25cavXq1bTz li1bEg74d/To0U4wnPg4cOBAegffev7554mk+E+/c1JSUjinbt26tWvX7tKl y9y5c6MEf5xQG0YYPnx4kyZNKHznzp3nzJkTQ/lzBOUkBSXi0GBoPNIwyFXC VrxokENqw0qzZ8/u2bMnHTbza4UolBNqO3DgALhB2gl6ACA4n3CVLuuKEmr7 5ZdfihQpkpSUVKBAgTx58vChdOnS+/fvd16q6JcFtRHOaC2VKlVK8umpp56S D40aNTp9+jQnPHz4EK5JCq4FCxZEJ7VRckIwzjBfvnyUs0SJEunp6eYT1q5d W61aNb8bL1++PBHW+7JHr2ypjWyzYcOGxYoVE4uBOeEsXtZkS2042O7du1ev Xv2JJ57gpp555pkYegKWHdlSG+2BsIJNcuXK9eSTT/Ihf/78xNBYec0aHtlS 29WrV9u1ayddpnDhwvybI0eOCRMmKLjZypraSHsswtPPP/+c4WMTrJ35r7j9 tLS0DN/DyY8//rho0aJyvGTJkvKBKiPrCFYwEshRo0ZJbebOnVvyB1SzZs1f f/3VOI3C4084Xrx48XLlyvGBM99++21K5bapQpYttZ09e7Zp06aUOWfOnIUK FeIDHhJbafe3lW0KiruQ1otrFdvmzZt35syZcTY9wUK21EbCD9HUqFFDOhfp GY40nCXMmmypLTU1tWrVqlLjxFM+4HyWLl0azkJmQdFAbRcvXmzSpAkWGzt2 LBeHUwYPHsx/u3btGk8TPSyojTxWQhV+GB9y5cqV3377jaDDEWCNjA4iwzJk vP+PSeDS5MmTOadKlSrnzp0L/x2JrKnt/v37dHZgXFLNChUqmHOq8+fP16pV i+OjR48+ceIEroBLiQtt3LhxIqdSttTWr18/ogy5CokQvnT+/PnhLF7WZEtt N2/eLF26NE2lYMGCtAHwjSPhLGGkZE1tjx49GjhwIAaB00+ePHnp0qUZM2bw X5LP3bt3h7mo0Sxbanv33Xex27PPPrtnzx7MuGTJEuI1nWjDhg3hLGcsypra Tp069f9kkjRa/LkkADdu3IA1ypQp88033xw4cEDOwSEcPXpU0Gnz5s2kzXT/ uXPnnjlzhji4ceNGIIuLvPnmm8EKBrURYooUKTJnzpwLPq1bt+7pp5/mW6Sa 8jbt7t27devWlSOcQKBZv349P4QLNZNdpGRNbUT/kSNHUvg6deocP36cdou3 57+lSpXavn17mIsac7JOQQlJr7zyCsasX78+GQi2nTVrFv+lle7atSvMRY2U bKnt1q1bZcuWNeIy/jPziKkolDW13b59u3379tzOsGHDCBm4hbfeekvujs9h LmpIiji14ZE2bdpE5tm2bVu8tBxMT0/HQeXMmRMWdl6wKJcFtZFsrFq1ilBl ztK3bNmCWZKTky9fvhzwgniYli1bknVEdjSpNbVRvw8ePCAqEaYpKsHUnFNx v99+++1XX30F3BkHf/rpp2LFihG+/d7KJZRsqe3hw4cYDXf6zDPPkAvFB7XR Wv7h086dO8V/KrVl+AYYU8UkaaS1coSq79SpEyYaNWqUPm83ZE1tBHH8T4EC BfA5cgTTEbIxY58+fRLn0XrWFOpqJOfOnStdujSeHD6SI6dPn4aSIKyzZ88G /AqENX369B07dpib9LRp06ig559/PthLMZwGNb5//36jBvn6okWLJBWXsZeU XJ4CQT1yDicvXryYg0OHDo34cC9raiMvKly4cNGiRdeuXStHMBQtVgqv3d9a 1ino+fPn8+fPX6JECeO5DelK9+7dsS2knCC2taU2Iy7v3r0by1StWjUOqI1b JsGuWLGijGfL8I3EaNy4MTdIRh3Nw48jTm2cMGTIEAw1b948w+vScSZNmsTB yZMnR8nI8+wr1DUkqZqCBQtCOgGdObb6+OOPMdH48eOxoaslDU0OVyPhnMzU FlAXLlyoVKkSvjSR3yM4XI0ER4rbiRtqM4TTUGozhG/EGs2bN799+7YcIZ1Y uXKlvJKOpwEJ2ZQ1ta1evRoXhG8xz6Lau3cvZkxOTr548WK4ihmTConaqIgB AwZg2DFjxhgHiRQ5c+Z87rnnnOcPGf+qoNq1a5trzVYAjgySlGeeX3zxBf/t 1KmT+Ry4smzZssWLF4/4GsjW1LZ06VIKX7duXaOnk1Ju3ryZg/Xq1QvJLAko 6xRUGgZelAxfjmBbaTwNGjRIkOjjfDUSvGt8UBupCJnz/8/em4dHUWX//xEZ ZJd9UUBARFZZRERFQAEBBQTFEVcUFXBBRRxRkU1AkH0XEEFBQPZdfo7xxyJL cEgymjiZsO9OhIg+DJNPcCDf19PnsZ6a7k5VddLVS/q8/8jTqa6uunXuveec V9Wte7mWrl27Gohx6dKl0aNHy73Q8CbV1go7tZ07d+6OO+7AUJs3bzZvl46D 54+EYedBUaDUtm3btquvvpqc3G86kZ6eLvffEhMTg1rMgBV0atu1a1eVKlUg 1rBfWhil1KbUZuiFF17AGo899ph5I06bjbVq1Qrj6OhIkzW1DR8+HI/apk0b 80Yie+HChStXrrx///6QlDFaFRC1Ec0JT5UqVTIPFJEb9bfddhvtnNbLoYxU 2ULz58+XX9FHHBaV48vjko4dO8oojvfff59/2WjeLTMzs1u3bmwP+5Aea2ob NGgQhaSo5o2HDx++6qqrqlevHssjUpzIOgV96aWXJM80b6RlspHAmpaWFpIy hlkxSG1ECnERdC7z9i1btrCxQ4cOkXwvNOzURlZWs2ZN0s7t27ebt69bt07G RTj31RGugKiNRvXuu+9igccff9yvteWNth49eoT95a+gU9ubb75JHkU6GsvT myu1KbUZuv/++8nQ3njjDfPG9PT0YsWKlS1bNikpKSRljAJZUxvwixl79+5t 3oh3rVq1aokSJXynHFSZ5ZzaCEkPPvigjAMxb1+0aBHUfO211zZv3rx8+fIQ R4sWLd5++22LGYeuXLki7548+uij1idlT8o2d+5cDigTd9x999179+6Vb+XJ VMOGDc15eHx8vMxPsnz5ctuLclXW1Ma1027NTy1zPC8JlilTBjSW+TlVuck6 BaWhYtuBAweaNx45cqRkyZKYN+wPYUOjGKQ2vFnTpk1xR+PGjTNv37FjB9kU jsJ4XSsCFXZqwzjkHnSQhIQE83acGNarXbt2gVkjLyBq27dvH3GtSJEiq1ev 9h1cnZmZSaZB+FuwYIELJQ1MwaW2AwcOyJSSUTEpontSalNqM3TrrbcWLlx4 1KhR5o2kFuS9bN+1a1dIyhgFsqY2QYkXX3zRvJEI1bhxYzK3jRs3hqSM0Srn 1AYNVatWrVChQl49/aOPPorzTNoJtfXq1atu3boyKeJTTz2VW1hcsmQJQZBQ aDs1BK5ywoQJMluCTNxhfgh17NixChUq4CcfeughOg5e5dNPPyW7kJ3nzJnj wAAuypra2rVrh5WGDBli3khORfLMdhp8SMoYrbJOQVu1aoULHTZsmHkjSSlR lZS+gE1jnptikNrocXKzbvbs2ebtJN4VK1bEV1hMWht2hZ3aTpw4Ubx4cV9q 41f0GppHDFLb+fPnO3bsSO/o27evX1PLnUOSjYyMDBdKGpiCSG0Yh9gkMdfJ 4JkCLKU2pTZDLVq08KW2w4cPkxtT9bt37w5JGaNA1tQmEzjkRm2x/BatEzmn tjfffBOaaNu2rVewI5bNnTv3yy+/PHv2LGYneMl4kqJFi/qdbduYYfjJJ5+0 HXdx5cqVFStWtG7dGqdR1KM77rjDIJrLly9PmzaNzkLBKlWqVKVKlWuvvRZq k7czwr44rDW13XPPPblRGzlSjC+RYyvrFJQGgA29qI2klKiKy1Vq81KBobaL Fy9CbaCHl+cBQ6A2XATOJ1TFDFhhpzYZIVmkSBGvDiIjJO+8885YGyGZlZU1 YMAAmdY7t1ctZIbSLl26uFDMgBUsasN1TJkyhfSpVKlSXi85xqCU2pTaDD3w wAOkbb4jJEuUKFGuXDlZ60qVY0dt/fr1w714vR6I5fG0OkLSVg6pDT6Sl8Xe fvttX9TympkN9yWTtvXp08drzwsXLnTq1ElmFHE4UQynvnTpUnZ29rZt29q0 aSNzSBoLS/Et5e/Zs2edOnVatmxJok5Ioqg0iQh/r613794Usn///uaNxghJ YxSoyq+sU9AePXpgW6+hvDpCMjcVGGrDmzVr1gxgHz9+vHm7jJBs1KiRjpC0 oLazZ8/KKPStW7eaty9evFje2zLPCR/VckJthLnRo0eToeV2+zHHE32IdLia d955x52SBqZgUdtnn30GvBcuXHj69Omx/EabSKlNqc2QrNbkldnSQmLqlXkn sqa2oUOHEqY7dOhg3kgAwtlWrFjRmBNe5VcOqe3UqVOwEi1z9erVtrNn4xBe fvlldr7//vvNO2dlZRHd2F6+fHk8Rh5KSzmpU46wfv363PYB6OrVqwf4hL3q ralt4MCBhPuHH37YvBGyYON1112HHwhJGaNV1ilov379ZPYA80Y8qqwta8wJ X7AVg9RGpJCbQm+99ZZ5u8xY265dO+s1psOrsFMb6YqsxQmkGC9wXbp0Saa1 HzRoUEzN/D937txSpUpx4YMHD85tn9OnT1erVq148eJhf4daFBRq27x5swRZ OD2geaELqpTalNoMyXTEd999tzGhLinusmXL2Ni0adNIji8hljW1zZ8/v0iR InXq1DGP39ixY4fkIRG+smrY5ZDaEhMTy5UrBx076eZE/L59+4rbN6I/XmLc uHHUlO9bJ86VmZkJnnNki1e/JQVt0aJF2N9hsaY2WXuuefPm5jvYMiFngwYN jIeJKr+yTkFlURWv1QBXr14tqypYzJNTkBSD1AZiyIi1hx9+2Miy+CArQfTp 0yeSx/iFndrw1dCHDIY0dsN91atX76qrripIo1asqY007JNPPilWrBimGDVq lMVdyu3bt1eoUKFMmTL79u1zrbAByCG1Ec2hNnjTNzYtWrSobNmyXPgTTzxh rEgV43JIbVlZWUJtEydODE3B8iPn1EZPkVW0IuHNzRDIdpVtkliSYWOV7ezs 7Pbt22OiF154IUaWgnUiJ6ts42ONVbbRk08+iRl79uypz/et5ZDa8FrYs0qV Kl7DGskEyJC9MgFZiZv9jdG/eLzXXnsN6KOaCIh+T0HGtWfPHuP4pNZDhgzx ur1z6NChqlWrcuS1a9fKFkKqeQ0mPrdr144dpk+fHvZ5mG1X2b722mtLly5t rLKNunfvLm/86erw1nKyyjbZ1LJly2QL9uzcubPMKhAjrtU5tRG+sUydOnWi 4m6q7SrbXAsRwVg6h/jbrFkzXWXbltpyPK67cePGhQsXxl1zZEz39NNPY7q2 bdtG8qIJgcqC2ug1kAtZmSzLQo3s3bt3l0cwEbxj9syrV6/Gh5cvXz5C1mmy pjYiTmpqanp6+sqVK8k8cY/r1q0jPFF4iZWffvqpBO6bbrqJpvLdd98ZF85h L168GMJLiSBZUxtJPlYlO92/f/9111131VVXvfLKKwQgtkTy+oa21EYmxnVx IbJIU+XKlbdt20bHOXz4cMFOqq2pDUa7//77MUjLli0xDq1i3LhxoHrFihXx BiEuaiTLmtrQc889J69KyRwOcEFJj3IDBJUhh9Qmz4AaNWrk9Qzovffew//f eeedBAJ5hAF53X333bLmoHEH8q233qJhy5JJxAL2kXCwY8cOafnsM2HChBo1 atx7771Hjx7N+WP8cOvWrT///PNz587leJxn165d2Qi4yUyS8rZ4ly5d5C0w DtWrV69ChQpxnNxYKZSypjYk45FIKWUfSPOaa64hYTBYQ5WbrFNQWpRMLXvr rbeSk5CrT5w4kYZaoUKFsM9REzLZUhsdTeIyfhJbkbDhP3EFbInkuGxNbRkZ GfJy1jPPPAOEXrhwAQDBJ9xwww0RklrnpkigtsuXL69atYrQicXoLPAIWSh/ aRiRDLyByoLa+AonLLMQFy1atHTp0iVNwlebbxLOmjWLuIahMjMzQ1j8XGVN bY888si1HhlzMvOBf2vWrEn9wnTAmmyn0suWLWu+8IYNG8bIwHJfWVPb2rVr aSRyA1asR5OAiNkybdq0EBfVuWyprW3btlwCFyIPnVGpUqXYQkvYuXNnKIsa YllTG0pKSpKZ0jEOMFu4cGEcxdChQwvMAPKgyJbajh07dtttt4kXqlKlCunZ 1Vdf3a9fP34YynJGoxxS26JFizDvXXfd5dWYt23bJq6erk1wh6fkXYCKFSvO mzdPAv3SpUuLFy9uODQ6vjkckFPJEEGZaYQdZBaRrVu3VqtWTUInYfH666+n j/AvPxk1apS4UJKW6tWriz/h1DK0g360YMGCSHieYkttaWlpgLDR/f/k0Rtv vKHd31a2KSihtl69embbkowNGTIkdmxrS20dOnSQuGx0TzoXW7Cb1zrLESVr asPn0OlwC2Se5cqVwxEBICRUkyZNigSfYKFIoDYRrpt4eoNH+PwIeWkriLKg tvT09AYNGjT0qIGPunfvbn6AQqBp0qTJPffcEyFIa01tAwYMMC7EfIGtWrXi hydOnOjYsWPDP+R14ffdd1/MvmxiTW0kKr5WrV+/Pn8//vjjEBfVuWyprVev Xn5by7333luwp0qzpTaUnJzctWvXWrVqEWiaNm06YcKECPEAkSNbasvxPM+l mdWpU4dUn6YVU+lZfuSQ2pYtW0a3feWVV86fP+/1FWkA1m7cuHGNGjVgq9q1 a3fu3Nm8bsXKlSubN2+eWxwcM2aMRM9Zs2bx206dOhmvHQHjAwcOhGuo0+uu u44+QnzcvHmz0UH4kJqa+tBDD0GOxqkj50aQLbXlePKHBx98kJJzjdhw5MiR EZ5bRoicpKBE227dumFbXCvJ1QcffBBTrtWW2nr37u03Lrdt2zaS152xpjbR jh077rjjjpo1a+KUWrZsGQkrINsqcqhNhB+Oivcc86CAVtmOIjl8r00VkBy+ 1xZdcv5eW6zJCbWJzp07F7O3MmzlhNpE+GECWQHrX67K+Xpt1iIfBrI4ji/W ORc9xTz+RERtAj4pKSkyTtKvaCGcOtImvHJCbSJS0LDPnRJdcp6CZmZmRvIq Xe7J+Xtt0SUn1CbKyMjAKYWgSEFRpFFbAZZSm8q5lNpiSs6pTWUh59SmClTB ojaVr5xTmypQaQpqK6W26JJSW8ik1KZyLqW2mJJSW1Ck1OaelNrck1Kbe9IU 1FZKbdElpbaQSalN5VxKbTElpbagSKnNPSm1uSelNvekKaitlNqiS0ptIZNS m8q5lNpiSkptQZFSm3tSanNPSm3uSVNQWym1RZeU2kImpTaVcym1xZSU2oIi pTb3pNTmnpTa3JOmoLZSaosuKbWFTEptKudSaospKbUFRUpt7kmpzT0ptbkn TUFtpdQWXVJqC5mU2lTOpdQWU1JqC4qU2tyTUpt7UmpzT5qC2kqpLbqk1BYy KbWpnEupLaak1BYUKbW5J6U296TU5p40BbWVUlt0SaktZFJqUzmXUltMSakt KFJqc09Kbe5Jqc09aQpqK6W26JJSW8ik1KZyLqW2mJJSW1Ck1OaelNrck1Kb e9IU1FZKbdElpbaQSalN5VxKbTElpbagSKnNPSm1uSelNvekKaitlNqiS6Gh toyMjDyVrkApLS0NasvOzg53QYIsqG3//v3hLkVBE2hDxwx3KYIvqC0lJSXc pYg4kbbhJ4ky4S5IdOv333+H2pQs3BCRC7IgioW7IAVQWVlZ2DY9PT3cBSmA unjxoqagtiLZ+P7778NdiiArMzOTqv/tt9/CXZAgi3zbbWojjCYmJv4j5rV3 715JWcNdkCBr+/btgFu4S1HQtGfPnm3btv3444/hLkgwxeXgbbi0cBck4kTE jI+PT0pKCndBolupqak0MDxtuAtSAEXkIn6pbd3QDz/8gG0TEhLCXZACKHGt moJaiLhMsrF79+5wFyTISk5Opuqhm3AXJMgi36a+XKW2eI++innhljH1X//6 13AXJMjiosiUwl2KgqYC2Vq4HC6KSwt3QSJOX3/99fbt2/kb7oJEt7SBuSds SxNV27ohta17EteqKaiFxG0WvCyuoEZVasptauMsx48f/0/MKyUlhfbzyy+/ hLsgQdbOnTv37dsX7lIUNCUlJdE3L1y4EO6CBFNcDplJcnJyuAsScfr555/x kydOnAh3QaJbBCbSsx9++CHcBSmAInKR3RHFwl2QAqjMzExsm5qaGu6CFECd PXtWU1BrEZdJNkg5wl2QIOvUqVNU/U8//RTuggRZ5Ns6G0lo9A/PbCRZWVnh LkiQpbORuCGZjeTy5cvhLkgw9d///ldnI/Erff83KMrOztbZSFySvHv1D31n 0AVdvHjxK52NxB2BJJqCWos0o0DORgKwU/Xnz58Pd0GCLJ1DMmT6h84hqXIs nUMypqRzSAZFOoeke/qPziHpmnQOSfekKaitdA7J6JJSW8ik1KZyLqW2mJJS W1Ck1OaelNrck1Kbe9IU1FZKbdElpbaQSalN5VxKbTElpbagSKnNPSm1uSel NvekKaitlNqiS0ptIZNSm8q5lNpiSkptQZFSm3tSanNPSm3uSVNQWym1RZeU 2kImpTaVcym1xZSU2oIipTb3pNTmnpTa3JOmoLZSaosuKbWFTEptKudSaosp KbUFRUpt7kmpzT0ptbknTUFtpdQWXVJqC5mU2lTOpdQWU1JqC4qU2tyTUpt7 UmpzT5qC2kqpLbqk1BYyKbWpnEupLaak1BYUKbW5J6U296TU5p40BbWVUlt0 SaktZFJqUzmXUltMSaktKFJqc09Kbe5Jqc09aQpqK6W26JJSW8ik1KZyLqW2 mJJSW1Ck1OaelNrck1Kbe9IU1FZKbdElpbaQSalN5VxKbTElpbagSKnNPSm1 uSelNvekKaitlNqiS0ptIZNSm8q5lNpiSkptQZFSm3tSanNPSm3uSVNQWym1 RZcikNouX77sfOcokkNqu3DhwvHjx2lvFnYguzt58iR7BruMeVGwqA3LcFEZ GRn5P1QBUEDUduXKFbfLExQFRG3RclFBkXNqO3v27OnTp7FkCEoVdXJObYSk M2fOsL/FPqdOncLaDk997ty5EydOUI9e23Hj9OLLuchrZ1w6B8nMzHR4UhHh mF+5HQ4CojbbzottsZjDQ9Ev2N+JQ2AfrG2xZ6AdB57CtkQlV91RQNSWW0l8 W5e1KXLsbCvGtG206OLFi3lot3IKzu5q0HeegtIgY9O1Oqc2nAz1FS2PHpxT Gy0wirg+cqgN265du7ZPnz49e/b8/PPPnZcnWmRLbbt27eLy77nnnvr167ds 2bJHjx6bN2825+2kBMOHD8c+zZo1a9So0b333jtw4EBIJyTFz1VOqC09PX3K lCkPP/xwv3796PVe327fvv3ll1/u2LFjgwYNWrRo8ec//5k241p5o0NOqI2A u2TJkt69e/fq1Wvjxo0hK1ue5ZDa2GHcuHG0/1dfffX8+fOhKVt45YTa8NL4 BzzDLbfc0rVr1xkzZoSseNEiW2ojEg0dOlRcaNOmTTt37jxr1ixfdlu4cCH7 NG7c+Pbbb3/sscd27NhhccbZs2fTB9kT99W2bdt3332X3E++Jai9/vrr3bp1 6+5Pjz/+ON1c9gQP+WG7du1w/nfcccezzz7r5OYGzn/QoEFt2rThVwQOvOuh Q4fszZQnOaS2/fv3Dx48mM47ZMgQX8OSpX/88cd8S/xq1arVk08+uWfPHouj ffnllzi35s2bN2zY8MEHH5w+ffqlS5dy25lDcWSsvWXLFq+vwKKtW7e+9NJL fLtmzRond4bj4+Ofe+45giynvu222zjy4sWLbX+VNzmkNuIs1U1J3nvvPS87 YOpHH33Uq4Fxsa+99tqZM2f8Ho2Q8dBDD0kugW3pCF5m4Sy+TZfonJqaauyD lbBqhw4daPy0WzoLHt7JJdOcPvzwQ85LLyPo093mzZvnBho7SUFTUlKeeeYZ ujCu9YEHHiBXCXoxIllOqI3Y9Morr+BkaC2kavTuCHlqYCEn1Eb6+sgjj9x6 6610BBrhZ599FrLi5VkRQm2bNm268847q1SpEucRrsZ5eaJFFtTGRnKwOnXq yOWXKlVKPlSrVo0QI/vgKnFxsr1IkSJxf4gml5tbDo2sqS0rK4tUE2dYrFgx Slu+fHk8pHkHCP3mm2+Wa6lYsaJ8qFGjxrZt21wvegTLltpGjRpFlClXrpxY bMyYMaEsXt5kS204WHIS0iRp4dWrVzcS4IItW2qj15C7YpOrrrqqePHifOAv aU9MPZG0lTW14YHBNOkvWO/qq6/mQ8mSJYcNG2bebe7cuWXLljX7YRIVvJzv AYlupKwlSpRgn0KFCpUpU0b2x1GTh+d4nijdcMMNcbnoT3/6E7k6u2VmZpLu ykbavDT+Jk2a7N271+JiaS1kmIbDlA+EUZdGINtSGwhJOlevXj0pCb3YN68j zF177bV8W7p0admNZCk3cFu6dGnNmjXZBwtfd911UmsDBw7Mzs723Rlr3H// /XLMadOmmb8ihpJtYlj5Fhixfp4CEJG316pVy2gq8qFChQoQusUP8yxbajty 5Ej79u2NQInRvIj4/PnzftvY9ddfb9wZMIvsVAxC+69ataoYGdw2RxxxOL4i WzMOYiQtRuy+8cYbd+/ebX29+HkoSdq5dEMpCVUTgNWcyTYFTUtLg8rNdV20 aNGxY8cW1EFfvrKltnPnzkE0RjXJhxdeeIHsLpTlDFS21LZz5866detyLYUL F5bWSEL10UcfhbKQeVCEUNusWbOkyxD76MV/+ctfnJcnWmRBbRcvXhwwYECc B1eTkpKw8IYNG9q1a8eWli1bSuJKBAd8GjduvGLFivT0dLK4kSNHYjGyuE8+ +SSMyZs1tRG4cez0CKE2IoU5pzp8+HCDBg3Yjg/nAvmXCA7isQUksbitWuBl S22C8BJlMC8JfCiLlzfZUhtNnYS5qEdcF1lKeO9IhEzW1EZHkKyeTD4hIeHA gQNDhw6VlOzLL78McVEjWdbU1qtXL4zWtGlT0k5siEN+4okn2FKlSpXt27fL PkePHpWbh3369MEP42ZxRPx73333+bojXDEBixZLoCf3O3To0Jw5c6RLSuin PDi0rVu3/n9/6CuPyLrlmHj+HM8dGNx4+fLl161bd+zYMTrIvffeyw5t2rSx 8ABvvvmm8BoH5Ff44VatWrGlR48ewbHm/8qW2vgKDsXPS/7TokULQVdD2FPu Mj333HN8JusQLujWrZsvRtEXbr31Vr7t3bs38Y56mTBhAqxHerB27Vqvnfn5 xIkTjfzfi63Gjx8PkuBSKB7fvv/++9bURjCV7ta3b9/9+/fTVKhBaQbkeL5j RfIvW2ojK6ClYVu5BNjci9qwD00ItoWL4+PjpbFRbADKl53pIHgSjvP0009T ESDhuHHjyMY5uEFkmEjsj6sxDojoXzK01ch4yd737dtH7MYXgerSbq2Rh9RF ghcH56ppOSNGjAAely1blkcL5i7rFJTLxAhxnjszZCDUNSXhXwpjmKLAy5ba 6DJik5UrV9Jg5s2bV6pUqWuuuWbBggWhLGegsqY2mnGHDh24Ljo71047pCVL 1kGPCHFRA1KEUBsuCNueP3/+tttuw/nEGrURJmgneAmzK8ZVFi5cGGvIgAS+ Ii6bHTuZXr9+/WhmL774okT/sMia2rg0yknNbtu2jW5OnmnOqfCZRJlJkyaZ 3wdZvnw5iRBuwTwSI9ZkS20EeroMmAMIE20LBrUR6/EttJY1a9bQsG+66Sal NkQuQSZcoUKFpUuXyhb8avv27TERHkAftxmyprbExMT+/fsfP37c2ELgrl27 Nm4WbpItY8aMkWcZWFi2JCcn47gqVarkOwiZn7/66qs4aiNH5cNDDz3EEbp3 755bIfFvVCX8IqMOcN1ygw64MPah+9epU4d+vWHDBr8HgR0kqZ47d66xEaKn nGXKlHE4Si0g2VIbzuq8R4MHD6ZgFM+L2oYNGya34wyOINsHRmAN35sP1COc BUEbrwBw/Ndff52A2Lp1ay/sAo3hssqVK8tjIy9qy8rKwqvgSeTWqC215Xie GxKOzcGa2A0zEpjcuE9iS22GbWlvXMIdd9zhRW20UkEPJ0Nkp0yZQpMmcBgv lHH8l156iSPce++9EnQILrfccgsgTBjye5Ds7OypHplrefHixdAfzRvQzu3s xCzSAAoAhhsVwdHIEt14vGWdgpJ3AY90RmP4K5cjT+Sfe+65GHncZk1tGIR2 RT/94IMPZAtmefnll6W1RPJrgNbUtn37dpxJjRo1jITk2LFjzZs3Z+OqVasi OapGCLUZwh3FILX5FTmGDNHJrXboO++++67cFg7jGGOHs5Hs37/fl9r86vDh wzVr1ixWrFjs3OzylcPZSEj5brjhhgJDbYbIYZTaDJHeSLpLGJIt9P358+ez 8bbbbtMJfAzlYQ7Jrl27SoaW47Gq3DZ86623jB3og2AC+5DzOzng8OHDyXDI c/x+S+LdqlUrTjFr1ixJDEgVZLyB19Dxvn37sttjjz3m9zg7duyQIXxej356 9OjBRnIqJ0UNSM5nI5Go5EVt2LZx48ZYxjwKjly9ZcuW7AyMeB2ELFpug5sj ZmpqKogHPZkjDn3nrrvu4sjPP/+81KbfcYwc57XXXnNIbb7Ca4FsnJqMLtDf 2sr5bCSkRn6pDb8q3sBJbiaARtpgfnxMmlHBI8k3jh49Wq9evSJFigQ0ayiw BjhjJRn661ejRo0CBmkMztPI/Mg6BQVgpa0aXpReuXDhQjaSwEfRDBX5kTW1 rVmzpqxHBw4cMDbu3LmzcOHCgL/foeMRIgtqwwO8/vrr1PIDDzxgjLjmA41T Hh8bd+0iUEptIVNA1Eb7+eCDD2SUuPnmsFlJSUkyHnvq1KlhvOPhkNrYxyG1 7d69u0qVKiVKlAD0glTG6JNDaiPcF0hqw2kotRmSR+peCbzcXa9du7Y5mMa4 8kBt5GbkkG+//XaO5/0y/G3RokXNj9WMm2MPPvigkwP279+fnTt06OD329Wr V8u4dyOXoPrk5SCvYTnz5s2jX995551+j7N169Zq1arxK6/pegTwu3Xr5qSo ASmf1EYGRYFLlSplflaFf5Nxno888ojXQRYsWMB2QMwLTxo2bFisWDHMaGyR x6NNmjSB6bp3754btV28eDHP1AbdDB06FDAkfrnR3fJPbTI4oXXr1tgZ/Iee LGZ0fOaZZwTtvexA46eC5PEuAQjfQsjmetPS0uhTTlzxjh07ypcvT4bv92U6 kdTR4MGD5V+3V7exTkHlmdHDDz9s3sjFspHAyoW7WrYIkTW1TZ48mWZAvzNv zMrKEpSL5JkDLaiN7iN3eGA38/bNmzeL947kVXjyQG2HDh1SasuDnFAbRPzF F18QeTt37lykSJGmTZvizM1uDZoj7/3oo49obPLeNxmdwymUXVLQqY3aL1y4 cM2aNfW9NqU2pTb0wAMP4BUHDRpk3khiRvpK3ExKSgpJGaNAgVJbQkJC8eLF saE81ueHQhZeDg0KoDW2b9/e1iORKsu7WsZoIrPOnj0rqcLSpUuNwVckFXLz DUwz9iRuPv7442BC3bp1/b7yTzSRJ3TmRz8EAjl+q1atgu4880ltOLTKlSuT 0icnJxsbr1y5Ig87iHdeBV63bh2XDziYk40tW7ZUrFiRyDhx4kTZsn79euqr dOnSMpBPxrYFi9rIW6gpzgUFc1JCkhsvXuUEg9poPLiIcuXK0ZYwEUGBfUaM GOH3QfywYcPYmfzB/GLFxo0b+XnRokWnT5+e4xk/Rl+gClq2bFm1atUqVao0 a9bsueees/beWPjqq6/myBaRiwNy2M8++wyPN2DAgC5duvTq1YtKcb7KRkCy TkF79OiBKQYOHGjeeOTIkRIlSpQpUyZGFqK1pjaaHCbq1KmT10+oZTI6mC4k ZcyLLKgNb0Z7pq2OGzfOvH3Hjh1kUyBqbs9KIkFCbfz9p2Pt37/f+dNDpTZD TqgNh0lokzeOK1WqRAbitQOejeRBZi1DeDyvdwdCr+BS26FDh8jVuTTjTZPY lFKbUpuhFi1amN+9EpFaVK9ene22M7bFjgKiNlrjgw8+SLhp06aNRDQyVXkv 7ODBg+Y9ly9fLihkuw7FkiVLyHvJfv1m4Byfb8kWzC8fgW99+vTh+LVq1QIe KUlqaupjjz0mc2vceOONflsFVyqDIRs1akSf4lf4Ydkig7uCvrZsPqktPj4e s2DeY8eOmXf+9NNP2fnuu+/2WuouLS1NJsbs37+/rAU2a9YsGRRKm3/nnXdy PKP4ZAKWIUOGyK+CS21bt26lMUg4hg1Xrlzp5Fd5UP6pbdq0aVL1TZo0oRmI oWhCWM93/QXyEAIxO0ArsnDejBkzxNpc7MiRI3M8Y9RlWkjAmW7CGeX4fMht SPbOnTvZmX0sghHNkhYrFCAvIYrguO7du7sx04t1Csrl0Jy8ZpElYyeqYj2L JT8KkqypTZ7Meo304Cd33XUX9Sg9MTJlQW30OJpf8eLFvXzFvn37KnrkFQIi SkJtWVlZFx0L7+38JU2lNkNOqG3NmjV4swYNGhDcixQpggdet26deQdCG6GH gEiTk7mmccvhHX0dRGojvsjkePXr14/xt3WU2pTaDAm1kW2aNx4+fLhatWpK bWYFRG3Tp0/HzRYrVswYswdVVa5cGafqNaXDsmXLnFAbDZuMNM6zDIdviMzO zibD4dvhw4d79evk5GT5YdmyZenOkk60a9eOfn3zzTf7neg+xzMFhyTn5cuX 51cAUYkSJe655x5JrYM+8MwNarty5cqiRYtkaJ8XtVF+WIyQIeMSYYqSJUty XTKRhayo9corr/Dt7bffbjyPE2qbM2eOb6nyQG2kcLJAFdVBR6MuYEw3pinI P7VBXrTnjRs34kNopeCPvLZDk/j444+9DnLp0qU33niD1gWY4EOwLbuRhFep UoXuIAtBYq4lS5ZQOxyKA5IAk9/K2hb81rdg5JAy9PGWW26xuGMAjMt4YE7d vn17Yhzp8YIFC2Q9iDfffNORvQKRLbVRErqkeeOJEyeE2oypZQu28kxtbKez h6SMeZEFtdG8/VJbQkICyIabcm/hy/xLqM294yu1GXJCbfQFvDH7kHjQWQhJ RLrvvvvOvA8uFw9JjMOjymOpSJ5D0pAttZHnEC9wlXSl9evXB7uYUSalNqU2 Q/fffz+uwHgTRJSenk6uhX/QEZKGnFMbqCtLgJH5G3l4amqqjJBMTEw07wwF yAjJ3AAqx3N/vmPHjrKb30cGkMXdd9/NDl988YXfn7/66qtNmzatXr16p06d uIqZM2cWKVIEVLS4CjLeF154AazACfTu3XvHjh1jx47lFF27drW1QKDKJ7WR E5ILAZjmN57w+VOnTvU7QjLHE+lWrFjBVzVr1uQahw0bhjfgA2QBnhAWZR0B okaiR4QYeSQE7vGvV9aRB2qjeBKOU1JSnnzySTIToH7z5s1OfhuQ8k9tUlrz vySrjRs3lqkVfI+DbZcuXXrffffRcthtxIgR2BaeovEbN4qveGQ+/lNPPRXn WdPB61zEqcmTJwskbt261aL8nKV+/frs2bNnT4PTOYvMt3/nnXc6n67NoaxT UHna7rVA8JEjR7ADiOqVehVUWVMbKI2JunTp4vUT6jGqR0jibMk2zTP35nhG SOJ1o2KEpHvHV2ozFOgckmfPnpX3Heg1ub2ksHbtWjJ2mtnhw4eDV9LAFCxq I4gULVqUfjRlypRYfqNNpNSm1Gbo2WefjfPM+WbeiBnjPMt1xcgr807kkNpI wuVGMXxkfm2KKF+7dm2ggLowNpJSyvJSFih0/vx5oCnOs/Sb+b0ts06ePMmR S5cu7XDWNdADZ9irVy8nOxvq168fAdRvop5P5ZPayAFw/tdee615VQL8mzx/ 9JoOIjdRQbg7bJiQkIDHi7NU//79zb/Nz2wkOZ5HpdJmHBY1IAWF2rxEgZ9/ /vk4zyw6Tp4Pnj59umrVqoCwxSRgs2fPLly4MHDn9Q7awoULiUHFixefOXOm dcwC92gYhQoV8hpuum/fvjjPyuzGQg/BknUKKhM9PfHEE+aNeNQ4z9qyMbL2 kDW1wTVkbs2bNzdvpPlVrFiR7rxo0aJQFDFPsp6NBOdvHlwt+vLLL9nYrl27 SJ4+VKktZAqU2pCs9UmYyG0NetIPuWMcxptCQaG2rVu3VqpUKc6zzpHtyyOx IKU2pTZDsshpmzZtjEfqpGFffPFFnGfN6BgxkRM5obajR4926dJFhnJ5rSpF +2zSpAmsNHbsWPN2GXcHO/s9IH1QVigrVaqULK7tV/Hx8XGeZZqdvDHBMWW5 Aa+xW9Yi2pL34g3cuAGeT2q7dOmSzCRvTCSS40kXZT1xL8LKTbIcGHCRkZGx efPmnv+rHj16SBChEvnXa2RgPqkNDRo0KM7zCl4efmstN6gNgz/55JPsDPg7 obYFCxYUK1aMaGIxK8jUqVPpHTfffLP5XseGDRvKly8vIyedzHggj6S9smWQ gY20EK/XHvMv6xR03Lhx8ozPPFpJJuRs3LixG+/ZRaCsqW3ZsmWlS5euUKGC se5Mjic/gd/piW4sDRksWVAbvUNWb6R3GFkWH2RypKefftprwHZEKdKoje4D tXn16IIhC2q7cOECWTdh3Wuj3Nx7/fXXaWMJCQmjR4/2SuNx9fIGXBhn/3ZI bUlJSVBbtWrVfGMTsVjGukCpMf46myGH1AbOC7WZc6GIlXNqo6dIihsj7cGa 2nAdVDGpkXGDGocgyc/zzz8fI0vBOpEttWHJFi1axHkm8fA7PgFKivPMzG8Q R1paGtksScvatWt998/MzCTE8xM82IoVKyzKJsMsObLfKdm9koSZM2fi2CtX rnzixAljI9fl1Xe8JnMGNkml6DVuTMfnnNree+89GUfndbPxrbfeEuoxMmQS RQrMZfquYJ7jSR7M/545c4b0gCOYV9PzkvC4eTZOQ7QNedVrzJgxvhSTkpJi PFih2Oyzd+9er31kXXvYMLez51nOqY3USCjDDJ40A/y/11TS1JTAlF9zebU3 8EQG9si8HNjno48+8nroRnW0adNGnlAbG41X0ugFud1bptGau6QsaVG/fn1z WijrHJHwBH2YjXUKSuIkM/EuX75cthBzpRU988wzMeJarakN6mnQoAG0bs4x 5AZIu3btwvhujq2sV9mW22hkpMZtNJINmQGYxhD5q2y7d3wn1EY4wF+lp6cf OnSoadOmGO3JJ588duwY4TKSgTdQ5UZteIYlS5bgx2rXro1DO3r0aI5neCTO lsCNNTZs2IAZb7/9dgLciy++uG3bNjkI+Qkb2aFt27ZBnzHMuaypjXBA5ZIg rVmzBrosU6YMAZqKZovEnc8//7xKlSpcRZ06dYhcwN3eP8TnSPYJrsqa2mgP 9A66DJ72uuuuu+qqq1599VW6DFsi2WK21Hb8+HFaC11AVmuiYezcuZOL4tLC uCJhCGRNbdnZ2TKco1WrVuIfCKD0JlDCvXntolHW1IbbkbSfdjVp0iT6juFq EhISxIWSxRUvXhzbypQL+GFJ4fC0vj2LLX379i1UqBCeedCgQYmJiRzHOKbX MzUZZomv9k1u161bR7768ccfy0xf0J9Mr2cec4JvvPXWW8l1jdeOli5dyq/Y +ZJH48ePlyzday7rYMmW2nDp0lVlxbobb7wRB46bOnnypKRAYBFhjqAmHEFr Fw6CBXzDIlX5/PPPP/zww1KbHBxc4udEEOkCfuU7GwlARL2LqxS+pngwGhVt PDvYsmVLkyZNGjduLAtATJ8+nRyVcDx79my5HSrh+JprrqHwMhFKcGVNbVjP sO1zzz0X55mtCy8qtmWHt99+m7JhTFIFuc21Z88eeVZLLdAmvQ54/vx5kKR3 797yrJmG2q1bN9ow/kSeLhHTS5YsyW9JSOTmBpX11FNPYX9OZPicRYsW8RMp DzkwaYC0fM6OtaVOKRKGJa8zRh1fuHCB/bHwE088IZkhKY1MrCTLJgZX1iko EVYWy2jZsqVc6dSpU7nGcuXKGRxX4GVNbTl/PGWmR8ji6atXr5apziP5pbYc O2qjp+BRuS76FJ9proMHD8aZ16hRw2sMRqTJTG3Ei9+dKbhzSOJVKvwhjIYZ CZoVK1YkBg0dOtTiBfDoksWzNtw1WQG5N0GhUqVKtWrVIq/AieHZXnvtNbwc BifN4Fvsc+211+LiZHi/jL7esWNHGO8MWFPbY489JjUrd+QQH/j3pptuouGd OHHi5ptvlu1cLNdetmzZMn/olltuiZGB5b6yprb169fTOzCj5GmItkGXYcvM mTNDXFTnsqU2Eg8ugQspVaqUXBftgS3kVA7fBopSWVNbjsdRk0RhEIxDly/i EZmk7UCpmJI1tT3wwAPSqGRlK2S4Gj6TustupOVXe4Sd5YbS9ddf73cgEMRk vEXFQeiMZUwyj6jEP7/wwgvs1rFjR9/jyFfFihXjRJxUFnbp2bOnuT2Q0MqJ BgwYkOMBeXmTjp0JB7IGBNfVp08fl2512lIbmbl0XrnZKM8fsUmrVq1k2BtR DKIkhGFbUiMSP/apWbOm3/nViYnS4GWGTJmFngMuWbLEopD33Xcfu82aNcvY ghdt0KCBuEqycb7lrzjP7t27yz4QuhRYZqXA98qCAuzJeSmhzNiMevXq5cZt MWtqIwo0bNhQbCuXIGZBYDuNhDYvS67L8FHMJZM98pP58+f7Zmvszz6yg2Fb /hoPi4FBmddFEhIsIB2BNvbqq6/KDTRqTSpItsuSGYbIh+V9W1nG2usWxMaN GzkdzZXS1q1bFycvD2HdWHnWNgVNSkqSWd3EtWJheIQEPnZcqy210cbkMSs5 p8w4Gud5cyfsy05Zy5ra6BdffvnlddddR7+meeN7acb0IHyU2yu/51MGtXEJ x48f599kO7HPqVOnHIKbE2qThER0k0d16tSRf99///1YoLYcz0gbsgXiC60I B8hfHPWnn35q7E/cB2+7dOmCY2EHQh7dp1u3bs6XlHVJ1tT2/PPPS1VSp+bK bd68OW6fJteuXbub/tCN/6t77rknkudfdVXW1EaGmVuXsXizJuyypbYHH3zQ q7XIv61bty7Y89vbUluOx0926tSJ9IwQg6MYO3ZsjAzgcS5ranvkkUcMV2P0 F9HNN99s3L28dOkSnahx48YSyklXchuOsnr16twOiMwTpOO9X3/9dXZ78cUX fY9D8jN69GgcvixnzG+pXC/4io+PB4s4izGYkB2GDBlSr149fsIP+TkRxL1H 7bbUhqH8dt4OHToY4zypoJkzZzZq1Igy05Lx8LndjaFt79mzhwBHmCPesXPn zp3ZYl1Iwg3nNQ9V3blzJwhmLphRU2Cv7EPXo0i0AWP+Q2w7fvx4DI5hSeYJ x/Xr18e8LqWpttQGQ/m1LQ5Bno7JHKQQEAXGXCQJACw+0+/tXA6I2cklxLbs jG29Xo3HAlwvYdpISPi8ePFiYwTjtm3bcMu5xe6OHTseOXKE3TZv3kwTxbxe i8+SzFD7nJpMBnKkX7iBbDnOUlDAzXCtVPSoUaMiPG8PrmypLcczaOrRRx+l pqiv2rVr9+vXz2t4dgTKmtpEJCQtWrTAz9NxmjVrhueP/KhqftbG1dFzDx48 mJGR8bM/sf3AgQP8xL1VtguwHM78f/ToUdDYYt5Rqom8NzU1NUKGjzp8r00V kBy+1xZdcv5eW6zJCbWJ8MNhfIk1whXQem22IhQ6qZEg6tChQ1RubmlDZmam 3/LwkxDc3XL+XpsTObctKURKSoobb+qZBTKYJ1swBHqQ0pvfLnRDzt9rsxYO lpaAuRy+MUHizc7WuMS1s09+Jgmh7vzaNsczVpODuzqq33kKSjnT09Mj+YUm l+SE2kTyNlNuLzBGmpxQm+jMmTNhnIY9UJmpjS5PvMMt55YrXrp0iR24OufJ pFKboTzMIRkVUmpzQ0ptMSXn1KayUHCpTWVWcKlNZVawqE3lK01BbeWc2qJL zqktuuQ1Gwlpw/79+/3O23blypXTp0/zrfMHbTnaZUxSalM5l1JbTEmpLShS anNPSm3uSanNPWkKaiultuiSF7WRVqWlpZEx+s7YRkBMSkoK9AmydhlDSm0q 51JqiykptQVFSm3uSanNPSm1uSdNQW2l1BZd8p35nytNTEz0zR9OnjwJtfld a8ZC2mUMKbWpnEupLaak1BYUKbW5J6U296TU5p40BbWVUlt0yZfaLl++jGcm szI/bsvOzk5OTs7Dq5raZQwptamcS6ktpqTUFhQptbknpTb3pNTmnjQFtZVS W3TJ7yrb586dS0pKOnPmjLHl5MmTUFsepvrULmNIqU3lXEptMSWltqBIqc09 KbW5J6U296QpqK2U2qJLfqnt8uXLP3oka3MQCkm08uZStMsYUmpTOZdSW0xJ qS0oUmpzT0pt7kmpzT1pCmorpbbokl9qy/Es5MFXJ0+e5POxY8f2799/4cKF PBxfu4whpTaVcym1xZSU2oIipTb3pNTmnpTa3JOmoLZSaosu5UZtly9fxoek pqaeO3cuJSXl4MGDeVt8ULuMIaU2lXMptcWUlNqCIqU296TU5p6U2tyTpqC2 UmqLLuVGbTmeZev5NjExMc8P2nK0y5ik1KZyLqW2mJJSW1Ck1OaelNrck1Kb e9IU1FZKbdElC2ojxTp06FBCQsKRI0cuX76ct+NrlzGk1KZyLqW2mJJSW1Ck 1OaelNrck1Kbe9IU1FZKbdElC2pDXG9iYiLNPs/Hly6TkZGR5yMUGKWlpUFt 2dnZ4S5IkAW17d+/P9ylKGgCbSw6ZvQKaktJSQl3KSJOpG34SaJMuAsS3fr9 99+hNiULN0TkgiyIYuEuSAFUVlYWtk1PTw93QQqgLl68qCmorUg2vv/++3CX IsjKzMyk6n/77bdwFyTIIt+2SA5hcNKJPD9oy/FQG6iSlJSUFvNKSEggZU1N TQ13QYKsHTt27Nq1K9ylKGjas2fPtm3byD/DXZBgisvB23Bp4S5IxAlIj4+P T05ODndBols//vgjPnbv3r3hLkgBFJEL2xLFwl2QAqiUlBS1rUsS16opqIWI yyQbu3fvDndBgiziKVUPjYa7IEEW+Tb1lWcoc0Jt2O3rr7/+KuaFW8bUMGy4 CxJkcVGk4uEuRUGTtJZwlyL44qK4tHCXIuKEW9i+fbv6yfxLG5hLkiaqtnVD alv3pK7ViQqk26TSqfqCl3KTb7tNbZzl2LFjF2NeP/zwA60oMzMz3AUJsnbu 3Llv375wl6KgKSkpib5J9wl3QYKp3377jdCQnJwc7oJEnDIyMvCTx48fD3dB olvnz5+Xm6vhLkgBFJGL/IcoFu6CFECdPXsW26ampoa7IAVQP//8s6ag1iLN INlITEwMd0GCrJMnT1L1Z86cCXdBgizybVdfn5H32vQt+5w/ZiPJysoKd0GC LJ2NxA3JbCT5GZwcgfrvf/+rs5H4lb7/GxRlZ2frbCQuSd690ncG3RCZ2Fc6 G4k7unDhgqag1iLNKJCzkZw9e5aqP3/+fLgLEmRZz0aSf+kEPoZ0DkmVc+kc kjElnUMyKNI5JN2TziHpnnQOSfekKaitdA7J6JJSW8ik1KZyLqW2mJJSW1Ck 1OaelNrck1Kbe9IU1FZKbdElpbaQSalN5VxKbTElpbagSKnNPSm1uSelNvek KaitlNqiS0ptIZNSm8q5lNpiSkptQZFSm3tSanNPSm3uSVNQWym1RZeU2kIm pTaVcym1xZSU2oIipTb3pNTmnpTa3JOmoLZSaosuKbWFTEptKudSaospKbUF RUpt7kmpzT0ptbknTUFtpdQWXVJqC5mU2lTOpdQWU1JqC4qU2tyTUpt7Umpz T5qC2kqpLbqk1BYyKbWpnEupLaak1BYUKbW5J6U296TU5p40BbWVUlt0Sakt ZFJqUzmXUltMSaktKFJqc09Kbe5Jqc09aQpqK6W26JJSW8ik1KZyLqW2mJJS W1Ck1OaelNrck1Kbe9IU1FZKbdElpbaQSalN5VxKbTElpbagSKnNPSm1uSel NvekKaitlNqiS0ptIZNSm8q5lNpiSkptQZFSm3tSanNPSm3uSVNQWym1RZci jdoyMjLOnj3rXnnCqECpja50+fLlK1euWO9jvUMI5ITaKCQ1q57TuQKitrC3 AYdSastNAVFbtFR36BVEartw4cLp06d//fVXh/uTG5w6dYp4Z73buXPnqGU6 gsUOgfpJjsYx+ZWrtwQDojbbJnrmzBnn2RRWpS6wjN8TXfYn2x1sY6vo4sWL nPr8+fMOi5o3BURt1sUWW+FPHJ46MzMzt3Zra1uvk3IVDk8qYv/cqjWIcp6C 0iDZzaJvFlQ5pzaqjJ6blZUVglLlX86pDeggQQ1BkYKiyKG2PXv2jBkz5uGH H+7du/f48eOTkpLcK1VYFBC1gULDhg0bPXr00aNH/e5A95k0adJ77723YcMG cpVgFjRA2VIb3mDkyJHUbNeuXUeNGrVt27ZQFS2K5YTayCgSEhJoBnSc7777 LmRly7McUhvuYtOmTe+///7cuXMDzQSiVA6p7fjx459++im96YsvvghNwaJL DqmNXBG3SQNbsGCBr/Mk9MyfP//FF1/s0qVL3759Z86cefLkSYujbd++fcqU KU8++WTnzp0HDBhA1UB8vrsR4D744IPHH3+8Z8+e77777rp167zyQwIEffmx xx7r1q0be+7fv9/JJe/cufOdd97hmA888MCrr75Kx8nOznbyw0DlkNqw1eef f46fX7Ro0aVLl/wWGMsTC5566qnp06fnFuBEZFxLlizp169fp06dSAzGjh2L YzTvwLmG+4jjHzt2jG+hLarPdwcRtUYKmtupaUULFy4cOHAgzeCZZ55h58OH D1tfe57lkNpOnDixePFibItNfEPDvn375syZg60o8AsvvIDztPYn8fHxH374 4RNPPEG77d+/P32BfmHeAVfjazRijbk70NQpDD/nIC+99BIdxwmF0Yoo3ssv v3z//ffTI8hz3ItfTlJQvsV0NMju3bsPHTqUPM2lwkSmnFAbTZT28Morr9Bz Bw0atGLFisgfCOSE2ujU4Majjz5Kgkrb3rt3b8iKl2dFCLWxT/ny5ePi4v70 pz9dddVVfLjuuusIc+4VLPRyTm0HDx7k8uM8Isv1uw+8Jjs8/fTTzm8IuyFr aqOnV69enXKWKFGiUqVKfChdujSOUZ8XWMuW2vAwd911V4UKFaQZ8G8oi5c3 2VIbWdYjjzxyyy23XHPNNVwULccrkSiosqU2vAeJa7169aS6W7duHcriRYts qQ0Lk5g1atSIQIMZb7rpJq/HKDNmzDB877XXXisf7rjjjrS0NN+jkbICSiVL lpTd8Gz8JX499NBDFy9eNHa7fPnyuHHjJMCZRb5qPLkgab/55ptle/Hixflb pUoVWzafOnVq2bJl2ZkyNGjQQALoa6+9FoDJHMuW2k6dOgUvNGzYUK6CD770 Sm5cpkwZCQeyW4sWLcANvwck/+/Ro4fsVqdOnXLlyvGhRo0aYLKxD1UT50+r V6/m20OHDhke0lfU765du/yeGl6rVauW7CbnRTQbl24m21IbbAsWSRWjZs2a me820IrIPCtWrOjVbtu3b++XNKnEPn36FCtWTHaTvoBatmxphujmzZv7tdum TZtkh7Nnz9LUZeP1118vH4hKKSkpuV0IID958mTDtuLnUdWqVcH8wC1nL9sU ND09vVWrVlKMokWL8hfLwLOxk6LYUltmZiZxWUwk3gm9+OKL4X1eYCtbaqP7 169fn2spVKhQ4cKF+YC7+Pjjj0NZyDwoEqjt+PHjd955JxZ7/vnnSVa/++67 nj178u+DDz6I2d0rW4jlnNr+/Oc/S7/Ae5gjlKGdO3dK7EMvvPCC8+EQbsiC 2kifyMDjPGhJ2nPgwIGRI0dyUfjGWLudFahsqa1r164YtkiRIvKXKBPK4uVN ttQGo5UqVerqq6+WRII81uJmeEGSLbXR5SW/kuDSsWPHUBYvWmRLbQcPHsSA f/IIMzZp0sRMbV9++WXlypXZPnjwYMIQWfTy5cvJJ9ny1FNP+R5txYoVhHtg DdajVePipk+fLsnw/Pnzjd2AL2ErUuXk5GT2nDdvXqVKlYYPHy6ZIVGyTZs2 7HD33XfT8YkUEgFJbsmXcrsWTscOFOCtt97iM2H0k08+gYYIDW6MZ7ClNsxu bqLgmNeDcoOhsAMFJmpIdCDQ+3V0Y8eOjfPcv92yZQs0QXb98MMPx5luWUAr kjlQX+yz6Q/xWboS7IwpNm7caHy1efNm/r3xxhv5Va9evcxwbYiYK1gxYMCA vXv3ct61a9fWrl2bLXjdfBkxF9lSG7QotsU38oGrNifMa9asgdQw+9ChQ/fv 389xICDJEEitfY+WkJBQrVo1msrs2bNpjampqaNGjZImOmjQINkHX33rrbey ZciQIfQLw4B8NrKyd999V4ALk1K5mBruYwusnduFUGVC4s899xy0TptZt25d 06ZN2UJjcOMdCusUlIb37LPPcnaIGB9L85aLotVxpUEvTGTKltqkJ+Ibly5d iolmzZpVsmRJiNsl0A6WrKnt3LlznTp14roAUnxRSkrKM888w7/16tWTJ/UR q7BTG2Fr1apVV1111V133WXcFyK0YTocFP3IvbKFWA6pjXBP3McJ33TTTWTj vtRGgnfHHXfgosXXRTK1TZ48mUu44YYbDD9/6dIl3DXFvv/++/2OkFeJbKnt 119/zcjIwL0Qf4nmBYPaaBJnPSIfjvM8ClFqE9Fx6ERYpm/fvlimQ4cOoSxe tMiW2uhQ0sA+/fRTzNi4cWMzteGc33vvvY8//tg8yJCeFed5cuTbFKkRsuKt W7carozjS1JqJK4k5FQWW0gODedP1Dtx4oSReC9btoycmSwaQJAtJ0+elJvA c+fOze1aPv/8c3InaMIYiEgx4BpcrhtP3m2pjWuXJvr6669TcnJ+L2obOXKk PNAxAtaePXsI/WTINH7fAz7wwAPsD1AYW8isYIRy5cpJXeADYUP2oQqcXwip JqBNVMptxGNWVhYGnDZtmjlYf/TRR6Sp1atXdyOjs6U2PKfY9pVXXonzPPw1 Uxvs+Ze//GXJkiVGS6CBQXBibd8Xdjga/EWWZbRbtjz55JNxnqd4soVcF4wi B8tttA9VIIw2Y8YMYyOQW6VKFcxrkVVidkjNbFugj5ynUqVKdN7cfpVnWaeg 1Cb9jhZlAMiFCxc6duwomVWMPG6zpjZaFy2hUKFCo0ePli00mwEDBkgYiuRx ktbUBlxwUfRo48KPHDlCUo1HWrNmTSRXfdipjT7y9NNP0wDAeaMB4MRee+01 Nr799tsujdIPvZxQW2pqKlEYInvnnXfatWvHBy9qw7u++eabtKsmTZrMmzcv wqmtf//+lJA809y12blMmTL4di42VGWMPjmcjQSPSvpRYKjNEDmMUptfSdqm 1OZXzmcjIW/0pTa/gizwwzVr1sxtIJ+Xhg8fTjLAkeXfzz77DCIjKbVwdxIB gRRzf3/++efZeM899+T2K/JMqK1u3brm218DBw4k04abnBQ1IDmfjUR4wYva KKTkfnxrbCS4C3b5HdUp1DZ+/HhjC5hWq1YtoEDG4B0/fhw7s4/z6e9I5ARG Fi9e7PAnIrwW9VixYkW/o1/yKeezkbz11lu+1OZX4kJh/8TERCdlGDRoEHkF zUn+PXr0aL169YgsufUmkl55ZOmFsQ8//DDH8fuMLzdRQkGn9evXO/+VQ1mn oLA5l9C8eXNjB9L1BQsWSAOOkfnTrKlt7dq1ZT0y7inleJ5H4xVr1KiR2xjj SJAFtZGKvPHGG/L4wOhKuKMRI0awsV+/frbzSoVRYae2c+fOySAHY7C0SO6F PvLIIwVmOgJbasvKyurVq5c0JKi/ZcuWvtS2ceNGeOeaa67ZsGHDqlWrIpza nnrqKYnIXvRBZwfctmzZEpICRqUcUhu9o0BSG05Dqc2vXn75ZaW23OSc2khF HFLbmjVryEJvvvlmh5NRyF3o9u3by7+SZnfu3Dm3m7fgjAzUmThxonkfIiD9 mvPm5gTIlqtWrQoPfv7557KF/nX77bcXKVKE3zopakByTm0yxsyL2s6ePVut WrVSpUqZB55xaYMHD5ZA73uc0aNHY3kyauNlK35L4MDjyRtzsBtkwT5paWkH Dx7kr20Pkrzi3nvvDfRuMO0KRsbgft9wzKecU9tf/vIXh9QmV9qsWTPY1vaw tCV5cPb444/LFgJQ7dq1yTQw7IEDB3xti78ijvMTr+lHJk2aJDmM7UlFly5d evvtt/lJzZo1OZfDXzmXdQoq7hTSNG+Usb40MzfqOgJlTW2TJ0+mGTRs2NC8 kWRVUM5wPhEoC2qj+8g7Jl43uDZv3iwvIETyKjxhpzayslq1ahGevPBE7oUC dOFFkiDKltpo//QOwjQuFLPcdtttXtR2+vTpRo0axf0x9cSyZcsinNreeecd QiplNodIeJPIW7x48VmzZoWqjNEnpTalNr9SarOQG9Qmbxk7NHhmZqbM4TB2 7FjZIs/Rhg0btnfvXgiuZ8+evXr1Ivc2UnSq/q677mKf5cuXmw9FGo+TJDha TMgzZcoUQgY00bdv3927d5P9Xn311QCj30ks86l8UhsOrXLlyuXLl09OTjY2 QqnkhEK1vjOunzp1iuRf3jRht61bt4qhjHcGCT2k+kIxIOF1113HSZ9//vnc 8s+TJ0/KaFUOFegIqP79+wtCujGw3w1q69KlC3s+9NBDue2ABfbt2wfcjRgx QuY5IY81bk2QeGDSQoUKGbZt0aIFRjBmGoFoJBtZsWKFcUwyZBkhTPZizcU4 dhIYGnD37t3JcyBE83GCKOsUlNJSrQMHDjRvPHLkSIkSJUCSGFmI1praaHKY 6L777vP6CZlq0aJFqcGQlDEvsqA2vFmzZs3wluPGjTNv37FjB9kUDdvJvY5w KezUduLECWITabzXlJv8SvpygVkjz5ra8NjyIsPSpUtzPIDmRW2XLl2SNyzu vfdeGake+dQmb/fT5d98800yUqpy9uzZcoOuSJEixjBpla+U2pTa/EqpzUJB pzbSe0CD5MThe/f4ZHYm3xO6IcMnKRWnbUwRKSL/lzSJFi5vKHu924VTLVWq 1PXXX+81171ZdChJzuM8sywWK1aMvxYz+OVH+aS2+Pj4cuXKVapUyWtAHYaN 80zD4jeKJSQkyIyIxIvq1asDEa+++qqxXBR1LbPH8NUDDzwgT4vkZS6/KceG DRuoHQJroCPfiMKcgkA2bdq0gH7oUEGntjVr1sAdmM4ChWicTzzxhEx8ikjL zU+TaY0ydQzxxWzb1q1by4q6FEDmzCHF3bZt28WLF0nhjJkGmzRpYjGRTo6n LugmMpsW5Vy3bp3ttedN1imoTBEwbNgw80YydrIUUnpyeJdKFVGypjaZraV3 795eP7nrrrvoEXT2kJQxL7KgNnpc1apVfZ8d4HBo9hUrVnTjsW+wFAnURqyh /2Iu83acWOxQGwFL3mIgJZOQZFCb4Td2796Nc6tSpYqxZfny5ZEwBNeC2rKz s7kivB8XUqtWrRtvvJFQcvvtt9Mp6C8QXIiLGkVSalNq8yulNgsFl9oIWzK1 Y/v27Z3Mcc15SVbZf+TIkfJE5sKFC/fccw9b8IE05lWrVhEF+AtbsbFbt245 HmqTX+VGbbllU1euXBkyZAihE9fauXNnyZZJwj/77DPbouZB+aQ26sWX2rgE GcgHC/iuX4M9aeck9m3btjWYl1zx0KFDsgPH52Lnzp179OhRGIH6IgeTifp9 X5SjLuSd0AkTJgT0vIwLl0d+LVq0cOMhZk6wqe3kyZMy/SNUZfHAC+NDK+BY tWrVSL/Lly/fvXt3A9y4UoB6/vz51JfYFmIVgqYMsg85iTzrpFpp3qTBQLF0 GbIX6+Wqd+7ceeedd1Kt1C/pAV0gLM/aWrVqRd8cPny4eSNJKVGV7W68wxiB sqY2mVnxscce8/qJPPh+5513QlLGvMiC2i5evEguLdOomrfv27evokdKbdYj JGvUqHHNNdd43daQEZL0qQI/QhLnuWzZMlwEbvOLL75IS0tLSUkhxjVs2BBv 9sknn6SnpwNxd999d5xnLmLy+R88GjdunAyBwHniZ8JyUTl267URXBYuXIgn p4/goocOHUpMwSWWKVNm48aNoSxndEmpTanNr5TaLBREart06dITTzzBPkRw JwOlZLUy9m/Xrp2xDDE5s2zEmZtzclijZMmSgjCkx/Jmt9fqbFxI8eLFyYr9 rvFN1Jg4cWKxYsXq1atHvBCkgm4KFSpEHHHjZZN8UltycjLXS9nMjw6hp6lT p7Jzp06dvJbk5nQymhHUIsUi9SLeyVJ60JPxHIcjmMc68q9MstqoUSMvNKOC mjdvDp4ENKM7Hhg7x3lWxMttNsX8K4jUlpWVJWuoERdsOwLtk1PjfJYvXy5T i5jf+POyLaaQHoH9xbZ8S5mfffbZunXrEt+BRBqtpCVUqPWpORrVSuPfv3+/ LOhQvXr10M8hCajSJLwY/8iRI6VLlwZR3Vv7O6JkTW3y5il+zOsnDRo0IG+f NGlSSMqYF1mPkGzSpAlZt3myoxzPCMkiRYpwaTpC0oLaMOxtt93me6dxyZIl cZ6VXFy6uxV65UZtuF+5H2shiYMWIv0I12Sb1tTmK6IniRAR3OHcVrEppTal Nr9SarNQsKiNfieTKpCrL1u2zPZoVJ9MnO7r1h577LE4z/T1ZoigoqtVq0Za KCAgM417jdXhvCRFdAFjQKBZhFR5nkKgNDaSBhMxJat3sjBoQMontdGRr7/+ ei7ZnG9gZ9nZ9/Wr+Ph4oBUWMFcQ5qpatWrhwoW9ci2z5s+fj91q1Kjh1Ztw OyRp5cqVC2il7Hnz5uFdKcncuXPdm+Q8WNQG+Y4ePVoeuXpN72arLVu28KtS pUpZTOQ4e/ZseWosgyTNMvjupZdegoNgZ+en5vKlMUOFAZXZiaxT0H79+nFe Oq95Y1paGhvpoTEyzbU1tdHX6FBUkHkj2SZZHN05kpdss56N5L777ovzLEdo 3r5161Y2tm3bNpKnDw07tRHvZOLEOXPmGB0f5zN8+HAZ5xDhy687V27UxsWO GTOmq0ndunUjK8OF4v3wzz169CCCs7Hr/6pZs2aYiPDUuXPnN954w3pAgnsK lNokqtauXdtr7imVWUptSm1+pdRmoWBR24IFC0jUcVMjR460vRsGK8lEkSCe 7wxLAwcO5Kvu3bub/fOvv/6K3yZDlnl0ZcKTPn36mH84ePBg/P/tt9/u96TG StBeHYT+xa9uvPHGoN8Tyye1YUYZDjd58mRjI/6tffv2cZ5x/l4HWbx4saTT XtAqtiJnyO3skAVnqVmzpvm9KlKLlStXSo37fXbpV/zEGBPo6lzWQaG2y5cv T58+vWjRosWKFZs0aVKgs6bQjKtXr47ppk6dmts+06ZNg9qox9yeUF+4cEEe YQQaj2C9ONPMq0GUdQr6wQcfxHlmvTOvt24sC3Lq1KmglycCZU1tS5cuJReF 0cyoThAvXLhw1apV3XsAnX9ZUBtZt6xLhScxsiw+yKP/p556KpLH+IWd2vAt tIo4z/vIRtJy4MABmdQoPj7evbKFWA5X2RbJI0j6xZ49e3LbR8LQgAEDwgu2 1tTm9c7d6dOn5a7aiBEjXC9ZNMshtZHSCLVNnDgxNAXLj5xTGz2FRlK3bl3f JWILpAJdr61jx44hKFXUyTm1rV+/HjPecsstvi8Fk8iR99Kn4AvbDkhKIG/r ly1b1u9TuU2bNlWuXBkGND/iIS0E2cqXLy8E8cknn0AHZcqUMYCCBImywV/m Kdpk8Lx8Tk5Olle9du/ebT7d5s2bZdWtoE9a7pza3nvvPRlH5wVcQhxt2rQx giCuADtXqlTJ9/nOqlWruBDSaa+IKTNgCOFiN69F9MAfuYXOWczbqccxY8bE eda/8zt650ePzFugP+qoUKFCffv2dXsci3NqGzJkiFCG703ad955B2Nec801 ti+M79ixA6zzykvJNMqVK1eyZEksn+O5ueoF/titbdu2cZ55S4yNXgeZMGEC 3Fe7dm2zK0tNTTWaDbU5duxY8zyiOR6mlrfhQHLrkudB1iloeno6nMuFG2/V 0VRkoUDaWCQvIR1EWVNbZmYm2Tgkbr7fMmjQIHkmZabdSJP1KttEChmXa7wn S7Ihy0fiyXWVbeunjXQcWc/xrbfeog3QSF544QXx2JH8mDJQBURtvrOR+Epm I8FWETsbya+//krIw/vJgjv0jh49enBRFStWjJFnKHmWNbUR5Q8cOIA9yeKu v/56Osvrr79OyseWoI+MCqJsqY1LOHjw4IkTJxYuXEjbrlKlCkkpF8WWgh1A rant8uXLR44cwQ7YR0biNW/enH/xnPSjSI4vIZYttR0/flzMOGfOHHn3h9SU Jnfq1Ckx49SpU0lc5XHG/v37Qa2/eZSQkIAD92qE9LV+/fqR2OPT6ICkPfzk b39IJnaAXAQ0yEvlDXd4Sl7k79q1qzwQIeTJJOpkrZIGw+Zkv6VLlzam/Y+P j2/ZsmXjxo3ldWCuVN6Yo5zGOC4aSevWrQXqvV4Ty79sqe3YsWNi2xdffDHO M6clTgw3xSWIbeX+PEQs885BpkJYlNk392NnGQw5dOhQ47bkkiVLSLCxDLGP zB/U5Szz5s3jwnM8a78SDcEWasRrEW2s8dprr8V5ljL3tQxd79Zbb23WrJnx yhs8KLOaNGnSZNu2bUYzABKxtt8xq/mRNbVhPQKo2FZmLatfvz6NHNsa3X/0 6NHQR5zntUpKS6s22i2HNbdb4AsjYKWHHnqIhETaG8h29913Yzd6xPnz5/fu 3Qux3nzzzdhBZo8hASaUk6RhfOMFzJUrV3I6/nJ8ijFlyhQAPM4zG49xus2b Nzdt2pRkWN5ZYx/yfyhgwYIFkhXQDEj8OCwNY8aMGcE1bI5dCkrJpR+1atVK rnTmzJkyDazM5h0LsqY2JH2HviZP1tavX09opm9G+L1ia2oj2srkvf3798d1 4GSGDBlCF6hWrZp5PfEIVCRQG7mcLFVGz8VpyJyrBKwtW7a4sTZKuBQQtZFF EEdoQoSM3PaRZ5S48Yid+Z9IJO+P81fmmOKzTLoS4kJGnaypbcOGDbhN7Mlf kA2rlihRAiOzJZJn5rSltk6dOnFFXAhBU17YJA3gougLXs8UCpisqY2krm7d utgBQie3ifPMSSi1H+HrgYZYttQGLmE3GliZMmWkgVWuXBkzwj7ABY1TZpJH 2Jk9K/6hChUq0Di9BobJ8yDZnx04TkWT8MyyGyl0w4YNxQ1CZ+IS2dk8L8ea NWtkonXC34033ljYI9DS2EFWIpbBFbIFQqxdu7YctkOHDvfffz/RU47g9QQq KLKltttuu02aaPHixSkGlhHbggPyaj/RXN66IvknA6xevbrc6/abgeArhg0b Rv7MzljvwQcfpI4Aag77yCOPwE3UF5k2R+B0WIAOwoVf5RF84TX+hMJbDK18 4403xLYyKwXlkakREQXgiszNgMsBl/Jrzf+VNbURBYBHs21JDKp4BDSRmm7a tMlozwQCr3aL6cz3dWV+FZnwHzfLlUJn0va4WJnHhvqS2QY4GifFtlJZ2BYk F+zNzs6WdzY5FO2QRksKx7+PPvqoeWyqjOiO+2PmSVIFmTHVXGt4M66ICnJj GKptCkqRZLAx5eEqKBhdj5YQdDaPWNlS25kzZ2TGpHLlypHIybBhr3YVgbKm NtzRxo0b8VFUN81blo2m9seMGRPht4gjgdpyPAacNm1a/fr1xdWAwAsXLixg 95ADoraMjIyHHnoIn2YRf6FawvTQoUNdHXJvKwtqkyz93nvvJb7Q3+kgZBcJ CQkFrGbdkDW1Eaar/6EbPCL2yb+RvHa5LbWRecpVcDlyXfLv7bffvmvXrlAW NcSypbYGDRr4tQwoUZAGJORTttTWtm1bv2Zs3bo1FLB169bGjRvf8Ieq/696 9OjhlaWsXLnS2NnogIZeffVVY88jR47gz0mncYM4wy5dunj1AlziunXrSJUr VapECk11f/LJJ+anQmT1wAsnWrt2rbExPT396aefJtuUuMmH3r17uzSFgi21 wVB+bYvNjQnZOMiUKVOIa+XLl+dKMbvFbUncxerVq1u0aEHgwCbsD+t9+OGH Ri2Qj3G0W265hWvHsPzFbnPnzvUNsmzp06cPRRo8eLDviYikkAsZ++bNm3M8 w7NvvfXW3JrBfffdJw+JgihbaqM8fm1LbCU1Bfnr1auXWzsEo7wyBA64d+/e xx9/nAZDRcButMn27dvTd4zQDHlharqDYdtGjRrRJs04jP3ffPNNkI2DUEHk 8+PHj/fqIxs2bKDWMK8x3dzZs2eHDx9OssdPODVH5ofUo0vzzjlJQb/77jss KavAU9r33nsv6I+qI1m21JbjAfmePXtKg6FRPfPMM9br8UWCrKktx+N16Xc4 EPG6ONgZM2aEa4II54oQajOEP5TRDgVPAVFbFMnJbCR08OTkZDpRaIpUAOTw vbbokvP32mJNzt9rU1nI+XttYRF5KamRtRs8efJkbo9ySHf9Diy/ePGivJbl 6jsmzt9rs5XMGO98kDz5A+HDa3lus/gKwxrvp+RBGRkZYbz74fy9tuAKtwPj JyYmWmTgpGTY1rwAt5eozbS0NItBZdjW17NdvnyZdk7mEHQE9pLzFJRC0rwj P2kPupxQm4iG6rafCaJsqc2QhdeNQEUatRVgxTK1qQKVUltMSaktKIpwaotq BZHaVF4KF7XFgjQFtZVzaosuOae26JJSW8ik1KZyLqW2mJJSW1Ck1OaelNrc k1Kbe9IU1FZKbdElpbaQSalN5VxKbTElpbagSKnNPSm1uSelNvekKaitlNqi S0ptIZNSm8q5lNpiSkptQZFSm3tSanNPSm3uSVNQWym1RZeU2kImpTaVcym1 xZSU2oIipTb3pNTmnpTa3JOmoLZSaosuKbWFTEptKudSaospKbUFRUpt7kmp zT0ptbknTUFtpdQWXVJqC5mU2lTOpdQWU1JqC4qU2tyTUpt7UmpzT5qC2kqp Lbqk1BYyKbWpnEupLaak1BYUKbW5J6U296TU5p40BbWVUlt0SaktZFJqUzmX UltMSaktKFJqc09Kbe5Jqc09aQpqK6W26JJSW8ik1KZyLqW2mJJSW1Ck1Oae lNrck1Kbe9IU1FZKbdElpbaQSalN5VxKbTElpbagSKnNPSm1uSelNvekKait lNqiS6GhtoyMDPdOES1KS0uD2rKzs8NdkCALatu/f3+4S1HQBNq42jHDoitX rkBtKSkp4S5IxIm0DT9JlAl3QaJbv//+O9SmZOGGiFyQBVEs3AUpgMrKysK2 6enp4S5IAdTFixc1BbUVycb3338f7lIEWefOnaPqf/vtt3AXJMgi33ab2kAV XP2ZmFdiYmJ8fPzx48fDXZAga8eOHXv27Al3KQqavvvuOzrmqVOnwl2QYIrL 4aL+9re/hbsgEacjR46AG//85z/DXZDo1okTJ7755huCWrgLUgBF5KKJEsXC XZACqKNHj2LbpKSkcBekAEpcq6agFpK4vG/fvnAXJMhKT0+n6g8dOhTuggRZ 5NtuUxuogulg3q9iWxiBjCLcpQi+4j0KdykKmmgtBdKq4g3CXYpIFM7hr3/9 a7hLEfXSBuaeaKJqW5ektnVP6lptVSDdJpVe8KoekgLZtm3b5iq1cSJo97ff fvtFpVKpVCqVSqVSqVSBCJJKSEgIwXttp06dunz58iWVSqVSqVQqlUqlUgUi SEpen3Gb2k6ePPnf//43W6VSqVQqlUqlUqlUgQiSUmpTqVQqlUqlUqlUqoiV UptKpVKpVCqVSqVSRbKU2lQqlUqlUqlUKpUqkqXUplKpVCqVSqVSqVSRLKU2 lUqlUqlUKpVKpYpkKbWpVCqVSqVSqVQqVSRLqU2lUqlUKpVKpVKpIllKbSqV SqVSqVQqlUoVyVJqU+VHly5d+v3336lZ/oa7LCqVSqVSqVQqVcFUVFDb//3f /wX3qguSLnmUz53ZKEZ2fijZ+eeff6ZyFy9eHB8f77CajFN4Fcb8b0DFyJtC cIqQnUWlUqlUKpVKVbAVUdT273//+8iRI+n/qwMHDmzbti0lJSXdRxkZGcF6 xEN2nZSUdPr06eh6ZpSVlfXbb79htNTU1IsXL9ruT13861//wpjnzp0zX+mZ M2e+/fbb3bt3nzp1yvnZr1y5sn79+tGjR8+dO3fTpk1G/Z4/f57a+ef/6tCh Q9keAMfImDohIeHgwYMXLlwQrqEw/Ip99u7dm5iYSM0GZgiPKTgL5bcFJYzw ww8/cLq8IRUN8tixY7a//c9//sNZMjMz83AKlUqlUqlUKpXKrMihNvL2s2fP kv9DAR94NHbs2A8//HDcuHF85u/48eP5y0b+HTNmDP9u374dcAiKEdLS0qZM mfLpp5+SzOcT3H73SA5rXDJbLl++zL/mx0lsNP41fmX8a+CMHMcXE9hy/Pjx jz/+eOTIkRgE61mjBFC8evVqbPjee+/9/e9/p2rkILDSFI8mT548ceJEqMSv BSgDl2B8xQfAhLMvXLgQFjNf6Y8//kiRhnrEuYZ7NGHCBGCHKhs1atQ777zD V+yzbt26X3/9lZ9Qhvnz57Oz/GTatGnUSEA2/+mnn+bMmUN5sIN1De7cuZMm RJvncpyfQs4CIy9YsGDmzJnWgM9Xf/vb32iiW7du1YHBKpVKpVKpVKp8KnKo DXxgz717937zzTf/v0c7duxYsWIFHDFp0iTS7Hnz5vHVtm3b+Eo+kOrnPyUm dT916hQHhxA5y+bNm4GR/Axs+80j8vZ//etfGRkZEA2n+OWXXzjLzz//bNAZ DMXGrKwsOdevHskR+AlfYQ2ujo0AAoeiVF6YwA9PnDixZs2aGTNmwGLW1MZX nJGrW7p06YgRI77//nuhtgsXLixatAhkAwCPHj3KoaASr7GOApjnzp3jEvgr CInOnDkzffr0VatWcV3Gkz72PHz4MPacPXs2GMUBAW0A7aOPPvrnP/9JbQKY gB7fCmx+++23lAR8GzZsGDtTGKobyuMIlM2hzeXCaRjYgUNRGL+moCL+8Y9/ UAYKkIfnqmKHPXv20FSWLVsmtez3LLRMLDN37lysGl1Pb1UqlUqlUqlUEajI obZsT1Z85coVfiJ/yYrJ4SdPngxigAakyqTcxmHZJ//IxhHOnz/PWcj2ExMT N27cCGJ8/fXXAb0s5qX4+HjIaOfOnRMmTFi5ciXH5zP8QvnJ5Dds2ACRcd4D Bw4AKUeOHCHJh8gWL168fPly6Ix/oSeK9Pe//53kH9iBMjACO7Ddq1QQAaag 2E6etSF2BiRhIqE29gde3n///a+++kqsCgtTbEplsIbQ9JYtWygtlzBr1izs A2xyui+++ILfjh8/nquDyIyfAE18C8px7eyMSSnejz/+yA4HDx7kAzjGeeWp HybikoFTgP2nn34CGKF1CG7UqFEUNaBX9igVLQ303rp1q4HDhjhLeno6VwEw cvY8Nx4Ou337dgq/du1aXzzkLFiPWps6dSr0qg/aVCqVSqVSqVT5V0RRmyHS exJ7SIFsf8+ePTL+7eOPP545c6YZKPIp8m2ybrCCs0ArbMnMzPz000/BFtL7 PJ+FZH7EiBEAGsckb9+3bx9cw2GhlfXr13MuIItTg2AjR47cvXu3XB2f2S05 ORmLQXl8BlGXLVsGYrARgluxYoVfisnxPKhySG2ci4owqI0taWlpAAhtANzg 5xQSSExKSjIqC4z65ptvRo8eDaNJkcCiXbt2QZr79++X51b8HEYzG5afg9Xy IA80g1U5jrCwPG0E6KA56AybsKcxppRSJSQkDB06lLME+lIYB4f+Pv/8c+gS w5rbG58hNQrDV1xjfmBKms3q1aupI6rYa5irFIDCYxydikSlUqlUKpVKFRRF JrVlZWV9++23kMhXX33173//WyjgwIEDkyZNWrBgwb/+9a+g5MNwBNcOO2za tIlCClAcP3581qxZ8+bNo8B5AzcYimNu375d5sPnUFOmTAFA+JczQj18C2H9 8ssvoNyqVas4NcUAnWbMmLF582Ysxj58Zh9AdcmSJYJjuU024pfaMOB//lds yfZHbQAalJqSkiLUBkvyL6RsvPP122+/UTZ4BwTjEviLfWBJysMl8Bki4/i+ BZPhiO+//z7Eh1W9po6kSYwaNQrwgW7kXFLLZ86coYoBPdiHqg/U+FwgdDZn zpypU6dyLdLkZHToypUrsRK8mf8pSTnguXPnKOfkyZNplsZZsAMcx/XSofza RKVSqVQqlUqlyoMikNpIvH/44QchBbjAPP0FKTF5/ooVKwSy8nPhHI18e+LE ieTe8I55Mg1Y48MPP1y8eLHXRIsOtXbtWpjr559/5pgcedq0aRRYjgOe7N69 m0v48ccfKf8333wDXLDn6tWrITjwh7/wHdDBQeCsffv2gXhAHwYERvyezova EJxFhXKE9X+Iz8nJyXAERfKitr///e9gGv8KtR0+fJh/9+7da5AU7PPBBx98 /fXXMvELW0BLisRxzp8/nxu1CSht2bLl3Xffhb8oktSXoDGoSP1SDCxjDGWU B3CYYtiwYXxLSfJWxVKzGJb2k5GRITWL2TER5TFmrcynZCqY6dOn035OnTol 7/pBoNgKg8sUK/k/i0qlUqlUKpVKlR151MY+JMOQzuzZs9PT082z/MnzJnn1 DN7J51X/61//+uijj8i6ASivs/CXApPkgye5zWthIRBp5syZMmsHZwEfIBG5 dsAnISEBJJQpHDn1iBEj/vnPf4Jp33777d/+9jfKs2fPHpgoMTFRECwlJWX5 8uX8ZPLkyYcOHfJlAV9qw+DYZ82aNev+EJ/hMvjIl9o4Jr8F0ygbv2U7JAs1 G8+PDh48KDMuyv4UAONQmGPHjsEmuVEbu0Gj8+fPHzp0KMQnE7zI0zQOzilG jhwJ72ClHM/rdexPsalckI3ygKvmGTgDkpwIA3Icrh1Mw84YEDNKpeThmL7H l3GeqampGOeLL76QZQswy7x587hwMWZ+3o5UqVQqlUqlUqkMRRS1kQb/8ssv ixcvJqs3Bs55lZbEe8mSJRMmTAB88paBCyCsXLmSTH7Xrl2+Z5FirFixgoQc yAr0oYlBbfzw//7v/+Aa8BBskSlWZIGzU6dOydyV5Pnk/JMmTeLz0aNHZfUB Lh/cy/YMdBQbwh0UBvrzWulAXgSDmyAUwEFgIduDh161IMM1+XDmzBmoDdyQ jRkZGfyWUslugCqFOX36tPFoLDMzkx0opLyqxsaFCxeC1djQYoSkDMWkzFCb EKhsl1UGYFV5l5B9gPR///vfHO3LL78E5fgKhgXY2S5GyIME8GFVAJ+2xwEp 8IkTJwKd6t+vqNMsj7I9865gMS5zy5YtQChnSU5O5tqz/pAuEK9SqVQqlUql yr8ih9rItOEO6IAceO/evbk9pJAHWPPmzSND/v777wMFN8nnASuogau2MIu8 dwbCYJ+AwM1MbRxn9+7d77//PgwIeW3dulWm9ZBp/MnqP/vsMxiK/Unv4aOl S5fyL+cFZEAn+GjHjh2HDx/es2ePrEpgpjYO8o9//ANM+OSTT+CdnTt38pli +zUdG48dOwZAcdWcAtRKSkoCFbHGunXrhE8xOzhM8czXS8E2bdrE8Tdu3Mgl sDOX89e//pWSUGALajtw4ICsvybPTIXjsCdcJi+7TZ06FXDm2qlQTiHb5WU3 WTmOI+f5WZVMDII9ORFHS0lJCcpTNuoFuqTWPvdo2bJlGJ+iyjKCtEnO+Pkf 4vO+ffuCgooqlUqlUqlUqlhWpFHbkiVLoAPrcYkk5IAAvACD5I3aAJNVq1YZ L1vldpYTJ07MmTMHlgnoLHDNjBkzDHridPHx8WT1ABEEAb79/PPP8hXG2bJl CwzFT+S3VMSQIUO++eYbWEle8hKEgQhAuYyMDPOE/JQfc8ma41AVH6ZPnw6a 5baIGIelDOPGjWPnsR4Bg3zFYeU4bIETjeIZJ+JahHP5OX/BFllLju0wV27v tf3www8gGyx26NAhWWEc/JQp/UV8xb+A208//QQH8a9slyduiDrKzwhDmdsE wId8gzJYUaiNGoQ3Z/4heZYKuIFstJaZJvHV9u3bldpUKtX/a++8v5rK2j3+ j9xf7rrlvW+b7ozOvDO2GXvBhl1RpAsCFhSV5lixYBcVUBQrFoqVItKRXqWE ThJKgJAEEkLA+805kktV0PGqw/ezsljhZJ+9n/3sE9b+cM7ZhxBCCHlPPh1r E4EIjGS9CEiNQqF452X6lErlCJ/g/A6toGbUb7o0TnyOWFNTU0VFBfyor5CK FgA7My2WKC7MaFouEk3DoSA7dXV1g9OCJiBuKN8qIL4Rr9wbLrAWAbEwMLWL N9XV1WhlyGVexNURETy6ICZELCMGMFwm0QsxMFNIeNM6CDFXaHfwRyN/yvZw 6IVHgb/DzYlvAFG19QfBS6VSBDx4+3ArfxJCCCGEEDJyPjVr+3MwpPgMd7pn wMbBv775PJG+P28NbLjCb919uL3eussbAuhbYLjtnyDDje9HCYYQQgghhPzp obURQgghhBBCyKcMrY0QQgghhBBCPmVobYQQQgghhBDyKUNrI4QQQgghhJBP GVobIYQQQgghhHzK0NoIIYQQQggh5FOG1kYIIYQQQgghnzL/b9YmlUrx3kAI IYQQQgghZDTApNLT0z+0tUVFRaWmpmZnZ2cRQgghhBBCCBkNMKn4+Phnz559 UGuDFT4jhBBCCCGEEPJOxMXFPX/+/MNZW09Pz8c+o0gIIYQQ8uehmxAyJjEI l0oSQgghhJDPAr1eryWEjCW6uro+9h8eQgghhBAyInp6etrb22tqasrKyiSE kLEBvu9SqVSn033sv0CEEEIIIeTtGAyGqqoqWFszIWTM0NTUhC9+bW3tx/4L RAghhBBC3k5XV5dEIpHL5VqttoMQMmaAslVUVHzsv0CEEEIIIeTtwNrKy8th bZjFtRNCxgaitVVWVn7sv0CEEEIIIeTt0NoIGYPQ2gghhBBCPiNobYSMQWht hBBCCCGfEbQ2QsYgtDZCCCGEkM8IWhshYxBaGyGEEELIZwStjZBPgXdeCvKd m6O1EUIIIYR8LtDaCPnoKJXK1NTU2NjYZ8+exY4GlJdIJGq1erQt0toIIYQQ Qj4jaG0jgakhHw589ZqbmyMiIkJDQ0NCQm7dunVvZNy9e/fGjRsoX1dXp9Vq R9sorY0QQggh5HPh/a0NO+p0us7el06n/ZMJoL6zs0uPH7qPHcjbQfJ7ug2I 9UNUjmF9hdq79BrNO9ag02qRSIMxm7o/2UHyh5CXlxcUFJSVlVU/MvC1TU1N DQwMrKqqwhdvVG3R2gghhBBCPiPe39qUbao6eUOdzPiqldU3NCowq8fkflT/ /IfufSDXeDPotdHHhg8VvSgolly7F1UiqdJ2jO50xvujFcIbYSa12o7qOllk dFJhcfmHMKKWVuW9R3EpmfmdoxSEdmOe26FqjYrmwtKK9JwiSWVNuzDopgLw zU6jyv2RAX9GqNXqhoaG9PR0KFhdXR0UbCR3tOn1enx5AwICaG2EEEIIIX9u 3tPaIBRp2YWrnPYssfVcaL0br2UO3hu2HvQLCK2slkKHRlIJpAkz+ZjEDJVq 1LfnvA/osryhCe0WlVYM131Dl977WNDfp649HnC7q0v//xxejVQenZAOx4GR vbU8/A6Ff1608eyV+yMpP9pgqmvlyMNmn1OvXnWPdt/2jvbAG5ErHPf8tnLz z4scZ63d5upzCjqM4wefqtTqnMLSpPTclhblGDwHhy4rFIrQ0NBr164FBwdD pvp6OnwWIztkUjo7OyUSCa2NEEIIIeRPz3taG4QrJinjh7k2S2w9fPwu7fa9 sMZ577g51t/OtnLYeayypk7XX9yGXPgOZrTjwPkFVruaW1q0/T8ayUJ5Rifo eEthoczAzzp1uvTcIrR7PiRcrx/6TB/mz/nFZUE3HxRLqsS5tKmOES7iN5Ly Q36E3EYnps+12H47MnawMA7eA3N7GOjU5S7ojiBDw0bX8bbgB39qVMg6+dcz N7jtO2uytpF0H3TpOyOjk76bbTV7rdvJwDs3wqPdD54fN9t6wYadldV1nZ26 llblobMh1m6+VTXSASfyhhw4U+t9+zKSXYau4U1NDJHn9j5javq17wZxr7c1 3g+VSlVRUZGQkBAUFNTX2vSdutjEjIAbkYrm1sFV0toIIYQQQsYI729tsUkZ Py9y9Lt4C6agVLY1t7TW1Ml2H7745fT1Th7HFc3N4hQUGtGmUjU2NUvlDQ1N CpVKLQoaNirb2uzcj05e6lxRXdekaNEI903hU7VajV9l8sb6RoWyTTVkhCis aG5pbm5VqdWNCqHyRmPlfVzJ+BNRyeqN9bQq20xzdUQbnZCOdn3PXUMlLa2t QzbR1qaSNzQJdbYLXVCIMdc3NqE57GhqZTBqjQZdQEOYlqPXYt81/VVOYyzT jPDwEQpqe2WkuUV552HcvxZuvHg9ornVWIW4EwqgdXQTu6BfprZM1nbhWoSm XYMC8v4FTC2isnph997g+9mZOExIO6JCVnvTZbS2b/pYG4YbXatvaDKm9I1H CPq189D5yebOOYWl7YhMSNfBM1e/nWV179FzjHZVrWznQX9zW8/colJFb8Bi Zl4PHFppVfbNMwITBxrbUQA9GbhLY5Oyd6yHBDuigxigFqG8XOhI33OUxkS1 KtG0Kc9iZWgUG9vaXg8HWkTrYiZFUCESONoTx2iusLAQCtbX2nq6DftPXf12 tjW+UIhhwLWytDZCCCGEkDHCH2VtJwJDDcL5oA5BKzBn3rjr2Lg5NskZed2G LlReXlV3/OLt1Zv2/Lxoo7mdJ8wCc2a9vjM5Pc9mu++E+Xbfz7Wdu267k+dx 2IG+U4eJceCNyA1bD05a4mhm6e579np1rWzwKQ9Mj/f4Xd554HzQzQdrnH+f YGa/0GoX3pvEDdP7sCfxjrv9Ji5xmm/p7u13KaegBILTbdBD1n5d7vL9XJsp y5zNLHf6X72PSfLADup0D2KS128+8Dw1Cx1JSs+13HIw5N7TA6euzlm3fYKZ nf3OY6mZBcMlp0Yq33nw/Nnge2eD7y+18xxvZrfU3jsqIR1TfYQHR4Aq3giP tnDZ98sSx8U2Hr7nrpdIqpCWLn0nTOfX5a7I4W8rXBds2JX0Itdg0Ot02rKK aszkF9nshqDBix/FpohZEa0Nuxy9cPPCtfAFVrvQL2evEzGJ6abB7ezUZea9 9PELmrd+x2TzTTbbDz+MTTapGTpbJ6+HgC+18/plseNal30YJvhIp3CbVV9r wwAVlZRbufkigKz84jfc6YYWETA6uNhmd3WdDDlEF/CCoYc/TUAweDPP0h2j g7Gbv97dyu2QUV2Fc4XhTxM3eR6fssxl1tptOw9dSEjLEde6QWLzisqW2Hri CNnte3HiYsdte892Cp89jEne5HkCmZm3bsfvxy/nFJQOuYwMSl65+9jO/Uj4 k/gte07jmJy2YvPeE8G1UrlozagtJSNv16ELs9ZsQxo37zkdFf8Co6bXo+MV S+297j+Oh0MZh7hOjkS5HzgPh0UIrUrV7ycuO3uekFTU6EZ2hXC7USFbY2Nj 79y5ExgYWFNTY7IzvKmV1SMJyLzH4YtNwlj0yS2tjRBCCCFkTPCHW5tpPhkd n/4f/1qOmTkm6vL6RgvX/V9OW++w6xgsBpPeb2dZnQ8Je9VtyHtZ5nfh5ozV W340s9++/9zJoDvwNdSA2fg/frWw2nYIk9W1znu/mmHp4XtR03/5Qq1wYxqM bPw8W0z4HT38nL1OTlu5ZdxsG0SFeDq0HbcjY7+fY4O5t/sBf0eP4+Pn20FJ isurENXN8GhoC9qFSXkcDgh7mjD4QkToCRwNThEZnfiqp/tJXCpED03AsGAK tjsOfzfHGjJYVSMdnEBMsCuqamF5k8yNVujifQqh4v2vK1zTcwq79Hq1Wu0f Evb1DEskBI5mrG22tbWbL/wUwQfciLDcenD8PLs1Lvu8jgTCU6ByEB+4EnqE mfyOA/5zLNyQ/NjEdLiJydpgZNAWpMLF++Qvi5xgZ2lZBcgVyqCSRda70GUM hPtBf+gw6r/3+DkSi2k/FNjrWNC3M60wWIhnxUafr6Zb+l+5r2xrg4D0sbZX L8sqljt4T13mev9pfNcwF5e+HiOttlHRvPvwxf+euNIv4FZ2QUlDowJ97xaX ktTpZPUN7geN18dOXeqClB45f0Ol1sDaIJs/zLOdtdZt294zcMNfFjtNW7m5 oFiCQw7BvMgp+sdUix/m2Cyy2oXEXrr1EHUlpGX/tNAB7o9jDLtMmG+PLkjl DYOlEvUfD7iF+pGrNc570e4SO8+vZ2zwPBKoFzKZkVs0e60bGoUDIs9Tlrr8 a+HGp/FpCLi8qhZ9R8zoF2qGyP9l4qrfViC2chw/DU0KWJ6Nm69GrRnhdwrF Wlpanj59+vDhw8jISHwZB9zXVierd9h1FF8fr6OBKpXK5KG0NkIIIYSQMcIH srYuPQSh9KcFG33PXlMq22T1jRevhz99noaJOhqtb1TMXLNtmYM3GsW8F1tc fU5j3tverunpNsAvWlqV1+5H3XkYh6rwKX5CVX5auBGO1jdO0dqcPPwgOw9j k3t6DHCBqPi0/5606nhAKARPVt80aYnT8o0+ksqaV6+64RcRUYnjZltv+f1M T3cXLAwSgXbPBt83pkLfOXhJe8zhEcnkpc4PYpJQHl2AO5htcJdU1oqnnFx9 Tv3XLyuiEzIGp0+0NuF04aakF7nisvk3I2L+/aflsAzjFXElFfAgmKOipfXV qx5suXz70T9/tdh38sqrHgPKI7eTFjvdiohBeJi9o7/HA27/uMDh7qM4fIqN FdV1c9ZuX2bvBZ3V6/WwtinLXOCVL7IL0cEeQ1dcStYk800QE/QOIFq0+DAm Gb+hxeo6+RJbDwwf1AZbwp4k/HXK2vNXwzTGtPcomlt8/C5BKlMy8xG6aG3w r4y8YjgypCY5I6+r6+0rf8IyCkvKMQr/9ctKuM9q572wKvEMoHjeDR3be/Ly Kqc9OE7QKZSvrJauddlnbucJFUKe0TrKT1yyyftYYHNLK0JNzyn6z59XbP39 dFWt1JiKHkOttN5yy4G1rvuQc+HQ7g5/mvDFtHU3wqNx1A04vNHiiYDbsHvY ulqjRuGqWhlsa+66HYgBn2JQJpjZx6dmGwx6pCIzr3jeuu0zV29VtqlguBiF WWu2lZRXo9ZDZ0JgvlOXuUREJ3Ub9BDk8fNsz14Z4rztcAhnfrtwbOi0HXjh DcYOW0wvdLCtrW3fqeCvpq/3PhoIMRTFjdZGCCGEEDJG+EDWholo/suyKUtd 9p4IFu/HwSQWBSA7KRn5z1OzzCzdF2zYiY/QLrZv8jzx63LXJkWzKQxsxGwd c2kICKwBs+jx8+0hX4Otzd79yHxL9+pamdFLdLrKGunM1du8j11qbVXClX5e uPHohZvirVjiTVuYma/Y6INPUR41o10EP9zj2AZbG+bne09eET/FlqCbD8bN sYGLDd5XtDYL131Ou483NDVDT6AAuUWl4+fbIaS2NlXYk/gf5tmiBtTTLl4O J62H19i5H4E4YB7+6FnKxMVOV+8+EU8CooYN2w5NX7X1WUom7ADSlJFbZOGy DwJVXFYlqE0GwoOaCZ5i7C+sap3r/ukrt0DBGpuaV270gQo1Kl7fbIi07D91 5Ytp67PyihHPEf/rP5o55BdLDF1d+BTSl5iW87cpa29FxiI/sLbv59osdfBa ven3Gau3YhBH/rAGNFdaXn07ItbV+9Qkc2fo228rXHcduiCvb0SWcBhAxzAo 5VW16DWCz84vQZbOh4QZlU5rVHscG277zqJ1HBIwUBwV//nLinuPn6MLHa/F sAKpg2aKB8yLnEIY+sQlTgfPXhtww1p777m2yebOj5+lIm/tQg0bdx8zZlJS hcNskfWu9VsOCNeOvh5omCa6XyKpwpGMvb6ctj45I1+t1ixz8LbfeRT+eOD0 VZVKdeFaBLweQ9M5suQgkidxqX4Xb/ldvA0ZxEt4P/B1MjDU62ggvmjfzrL2 9gvCUBr/40FrI4QQQggZG3woa9N3JqXn/WXiqpC7T3WdOrVaHfrgmeWWAzNX b52+astvKzZ/P8dmofUuqMSQ1gY9QbVOHn6z1m5DeUyDf1rg8OMCh7KKIawN joOZs0zeKJy10VbXyVY4+ngeCVC2tV25+3jSkk2hkbEmv1Br1A47j81b717w UoLp+jtY25RlzmeC76FH7YJaiguGXL8fNXhf0drWuuzb7Wu8I0l8+FpRacWU ZS6Hz11HeEcv3oTYPolLEy8yRNfqG5pWOPgsd/CWNzRi4wBr02jaZ61xgzvM ENIovpAZ5CcmMR3hiauRnAwKNUlKl16/+/BFqFzey7KyyhrkH+4jyjI+RQYu 3Xo4Yb7d3Ydx9Y0KRw8/+G9FdZ14SSECyC0sRe98z12D7dZK5T+a2UNRv5m5 Ye667Sj2hufcDQDxiCe8GpsUcKLbkbEQyX/+tu7c1bB2Ya0Yk7WJT+6LS8n8 9x+XRcYkiWesxGVGTgTemWBm97KsEmmHmv3PpNXGQREKYK/UzPy/T7WYZL7J lByI4XezrbfuOSOVNwwIVbS2Xr3SiU14HAkQre15ShYO0T1+l9BrsTwSdeby XaQahoX3+S8laAipKywtN14m+iQeY2Ru5wWxdfL0W2LroepdOuatIMlW2w5B USfMt3/Ty8z+pwUbx8+z+2q6JUakpKxSeMIdrY0QQgghZEzwIawNU2K1RnM2 +P7fpqzFp5h4J6fnYca72Gb3+athcSlZOYUl5naeC6wGWpv4q044LzN33Q5M vzG1higVlEhcvE7+uMB+VNbWqmx7GJM0cYlTwI1I8XxKu1F81Csd92BeXSeV w2hEa4PmfBRrCw599POijbcjn4nhoWvy+sZZa7ZBb5XKNoQkWlvIPdO5Np25 rSfMK/FFTlJGHrwYP1/kFGUXlCIPXcIVkhA0n2NBpu50d+mdPI5PW7G5plZW J6tfZL3b2s23SdEiphE9OhkY+u0sK1iSornV43AATAfaIlokepeeU/TNTKvA 65H4tUYqh2svsfXcfuAc7MnZ62RTc4tpOMSlOQYnQVyysrJG2tJqfBab1rgG ixY13wiPwWFju+MIzEi0tpWOr8+1ofW07IJ//GpxIyxa1G0cUa2tyr0ngmHx cE/09LW1RfdaW6cup6D0y+nrD5y+ioSIycHgIn4ITtsgh3qTtZVVZueXzFm3 3dXnlKrX2l71GA6cvgK3QoUoj4BXb9rr6n3SPyRsjsV2WGHii9yfFztiXLAj 6jGM+NF+aFdSUROXnAVVFF7ZccmZz/q/sCUhLRuSOHHJpgnzbYNvPxKskOfa CCGEEELGCn+UtZ25fA+1wT7gF61KZcTThHFzbcwsd8K/ug36vScuY04LwTE2 2dMNN4ErDbC2KUtdMPtFBSh/6dYDWB5kSihvUKs1Fq77vp9rMyprw9S6rLL6 RzN7F++T7cLpEvjjy7JK1GztdgiNGtcJzMyfusz54JkQNDPkin8fztra2lRp WQVfTFvnfSyo9w4mQ1J67he/rdux/1xPjwF9efwsRVz5H2kwXjOp1bofPI/d YcHi3V7ClagSWAYcEL+I97UttvVoaFKgs+LdXnMs3OavdzeePtMYbw+EJxaW lIvZgGg57Dz61XTLypo6NHflzuO/T137JC5VvEMQ1WPLXyauiklMx3vxvrYd B/whTTsPnv96huWxCzfF+T/2lTc01knrBx9FXcbzYgVwZ1//67An4xFivLex G0KKrKI2ZBIj5eMXZLZhZ1WNFN2CFkGd5q3fsXnP6abmZqEj3SWSqiV2njhO 6hua9MIVkn2tDc4iqaydb+m+de8Z6Cc63mPogvmiWGV13eCheYO1FRSXQzNX bdozfdWWqlqpmKjm5lYL1/0/mdk3CqeDscXzSODMNdtWOe2x3u6L5hA5Wocg G29wi0oY7p8AQyI+7a25ubmxsQFoNGrhstAO8SV8QTqRkJVOe5D/6/efajQa 8VwqrY0QQgghZIzwh1gbzGL7fv+UjLy4lKy7j+L2nbwCM5qy1DnsSTwm2Jhh Xgl99PVMK/eD/ll5xS9yCrfuOfPdbOuFfawNFjB+nt2Zy3chFIgjOuEFarDa digtqzCnoPT3E8H4dcL80Z1rEx+htXHXse/n2Bzxv5GR9zLxRY7lloMTzOyv h0V1GQPTorlfFjsusfWAB1XVyLSDzhZ9UGuDWyFUeGXI3aeZecXRCemLbTwn mTs9iE6E2yBvcIqvZ2zYsPUg7KNJYbwz7snztH8tdFjluOd5SlZ2fvG9R89h YagE9ZvOtU2Yb+e27xyENCWrAJrzzSyrE4Gh2Bc6HHgz8ptZG5CxuOSszLyX Z4Pv49Mtv5+GNyEhOYWlUJXFNrsjohLx6c3waHQctiKprME49l1DEu/XOO+F R99/Eo8kwKR8/C5t8jgOzRlwIAlLIDa4eJ0cN8fm3JX7GblF0KLnqdlrXPZ+ MW09GkJ5lUp19MKNf/627vKtR/gUWxDPYX/jFohhalZBQloO2v37VItbEbHt wpm1AdbWIaxgg8IwUL8LNzNyX6KA79lrf5285urdJ+JzBPpG9QZrgwVDKk9f voPOuvqcxjGDVOw6dOGbmVZeRwPUqtfjfvtB7CTzTTi0/K/eN/4DQKtz9Dj+ 5bT1yBiSM9pvE5Tt4cOHd+/evXPnjkzW7zhEWyWSShxFX8+EJt8Qgn/9Ka2N EEIIIWSM8P7WFpOQjsnq36as+cevFvj5P5NXw7BcfU5huqvvXRNeVt+448D5 L6at+3aWFab6jruN9/7MXL1NvMQOs9DUzHxzW4//+Gm5uZ1nZU2dcBNT6Lg5 1l/P3PDDPFvoG8xl3GzrUuOqff2sDTWv23zAzNJdKmt4bW21MvjgjgP+xodb abVSeQOkCSHBE1HbXIvtMCzTvFet1sAxIRRQgINnromrgvQF8/Hg0MeIITwq QTxDNH6+3fGA2yZruxURg+n91TuPByenU1gl3tzey23vWZO1FRaXw6r2n7pi XE5fq4WHIlffzbIaJ4SHtMBETGdqmhQt234/+9UMSyQ2JjG929AF3Yh4mjBv /XaUh+eKjzLPKyrTCbeDRT032q7X0cB1m/ejU7Cenxc7Qn+UyrZ24VF6AIY4 x8Lt6xmW42bbYCz2+F2WyRvEhCCB6dlFUGCoIj6C0CHzxZIqJAFpR2L/MmmV i9cJ5AFdK5FUzVu/A1mFOUKZ51u6fzvbqqBEMth8tcZuVttuP/zXyauRSXgN fG3qcpfgO49blUpxHdEXOUWz1rpBsmat2Wa8OlSng3R7Hwv60cwBxwyiRfkz l++Jp5kQJ1Tu375fHPYk/lXvUo3YCNfzEnZBZhAY9Pzo+RuKltbBZ1FRie+5 a9BtHKUma8MxM9ncGcmEDeHAuHAtfPrKLUgFqjKuanLmmunKUgwc/BRpxDFf VFKhFc6++V8NxxGOjL3DV0mpVObm5kZFRQUGBvZ9yjZig33jS4EkhNx7LB7z /3eA0doIIYQQQsYG72lt2Ke+oSklIz/pRW5CWg5eqZkFOYWlUrlxbUBTjZgk 1zcqMEO+/yQ+NjFDKqsvKq3IzH0puo9IcVllTGJGQmpOS6tSnIG/yC4Mf5rw MCa5ukZaXlmbllXQ2qocEIBKpc4zXiJYbLp3Sdmmys4vgWug8g6h6YYmBbZE RCU+S8pEu+reh0qLc1cElpZd+CQuteClZPCdWeLTpdG0qDaQRLyv6L3oDlPo Olk9lLNGKh9yYtyqbEM20DWxp8KTuVpRQ1llzesHbQvimZZdgJ4mpGZDS8Vi phpgnckZeQgPDQnXzXWIuXqWnAlnSUrPq5XKTd4hb2hC0iCwVbWyuOSsR7Ep WfklprVHxIFAol6WVcYmZUREJ2IIYCKmpUvEYjDNpPTcsKfxMCOYmvHT3sSi OSTQpHgQlvjUbASDHdFNlFep1O1DgQhRVeKL3Kt3n/hfDcOYFhRLBAXTiu3C klAzjo3nKVniU/k6dVoMTXZBCXoRFf+isKS8ude/EGaTojk+LVva65siOmGX nALjWKOJrPzi5qGUTURSWYtcNTYpTMnBMZOeUySmC9Vi7KDY0QnpkTFJeYWl xqVQ+39HEJuwjKRaPBIQDDIAmR2yubei1+uLi4uhYH2t7VVPt+eRANj3pVsP 1GrNACOmtRFCCCGEjBHe09rahflql76z70uPOfog/cEWbMe007g6v/Cz77rx 4uLtxh11OjES46qDQj3iI73ERSqGDHJgVR3GE2R9H6ysFZoW1tzTdXbqBl0s pxUeZaYbbkVEYwHhOs/23rUQ+4oAPhUvthxy3w7j+bV+wRi39K9BJ9Qv5MdY cuAVhr3h9VU5sSO63n71CaZDrArpEnKpFVLYr8J+uw+KvEM4RSiO1IDK0X6X sMm0Bb909fZFGAXdG44hFBPPvUJzjO87+40m3opR9b0jrO/A6ft3RHiogX5A 1/5vF+GwEXoxbERiPH1j6BQeOjBEno1VDUxju/B4C+Mx2afpAfkZOUqlMjMz 89GjR5cuXeprbXjzsrQiLTNfqVQN8Rh3WhshhBBCyNjg/a2NkFHB42wA+Oop FIqwsLDbt29fvny5r7UJn7YPvCuvF1obIYQQQsgYgdZGyKeAWq3Ozc2FgtXV 1en1et0IMBgM+PLS2gghhBBC/vTQ2gj56Gg0msbGxoyMjKCgIPysHhk1NTXJ ycmBgYG0NkIIIYSQPze0NkI+LuIVkqGhoSEhIVeuXAkODr42Yi5duhQeHl5f Xz/cnZVvaJTWRgghhBDyuUBrI+Sjo1KpiouLc3Nz8wRyR0x+fr5MJhNX3RwV tDZCCCGEkM8IWhshnwiad+Idn9lBayOEEEII+XygtREyBqG1EUIIIYR8RtDa CBmD0NoIIYQQQj4jaG2EjEFobYQQQgghnxGwNolEIpPJOgghYwaNRlNTU1NR UfGx/wIRQgghhJC3093dLZfLKysrawkhYwl86xsbGz/2XyBCCCGEEDIi9Hq9 TCarqKioJISMDfB9b2hoMBgM/wsLIE1O "], {{0, 295.}, {585., 0}}, {0, 255}, ColorFunction->RGBColor, ImageResolution->144.], BoxForm`ImageTag["Byte", ColorSpace -> "RGB", Interleaving -> True], Selectable->False], DefaultBaseStyle->"ImageGraphics", ImageSizeRaw->{585., 295.}, PlotRange->{{0, 585.}, {0, 295.}}]], "Output", TaggingRules->{}, CellChangeTimes->{3.834313636266211*^9, 3.834405217435068*^9}, CellLabel->"Out[1]=", CellID->403275520] }, Open ]] }, Open ]], Cell[CellGroupData[{ Cell[TextData[{ "Scope & Additional Elements", "\[NonBreakingSpace]", Cell["(3)", "ExampleCount"], "\[NonBreakingSpace]" }], "Subsection", TaggingRules->{}, CellID->979821957], Cell["Retrieve the \"Lelonde\" variant of the data:", "Text", TaggingRules->{}, CellChangeTimes->{{3.834253754469287*^9, 3.834253779230194*^9}, { 3.834253843757756*^9, 3.8342538534327707`*^9}, {3.834311105804686*^9, 3.834311106146619*^9}, {3.8343136448015203`*^9, 3.834313646387723*^9}}, CellID->851344267], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"ResourceData", "[", RowBox[{ TagBox["\"\\"", #& , BoxID -> "ResourceTag-Job Training Efficacy-Input", AutoDelete->True], ",", "\"\\""}], "]"}]], "Input", TaggingRules->{}, CellChangeTimes->{{3.8342537932861137`*^9, 3.834253836731948*^9}, { 3.834313650543797*^9, 3.8343136518924227`*^9}}, CellLabel->"In[1]:=", CellID->1035485242], Cell[BoxData[ GraphicsBox[ TagBox[RasterBox[CompressedData[" 1:eJzsnXeYFEUX7mfJQZLkqKgEQRAMgIhIRlBJAoKiJBUBAUXFQBIFBEEE5VP4 vBIUiSIiIJlLhgUlSxCJy5KWuPghIOA9d85jPW33TE/NTHdPzdb7+2Ofne7q 7nPeU1PhTHd1yU69WryUzufzvZ6F/rTo+Fbt3r07vvNUbvrQqufrXbv0fPGF Rj3feLHLi72rdUpPG+dT2QGZfL7////fAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAPOSmH6fO9n8BAAAAAAAAAAAA ANAG+1zZkSNHjh49iswbAAAAAAAAAAAAAADhYpMou3r16i9+rl275mDmLRWE ic666ey7l+iss86+OwuU9AborCaIi5ogLmqCuLiBzqrC91hbAQCwI2Tm7ciR Iz/7ceq2N7QMkaGzbjr77iU666yz784CJb0BOqsJ4qImiIuaIC5uoLOq8D3W VgAA7LDPvIkb3hj6iMxbrNBZN5199xKdddbZd2eBkt4AndUEcVETxEVNEBc3 0FlV+B5rKwAAdthn3viGtxN+6B/6iMxbrNBZN5199xKdddbZd2eBkt4AndUE cVETxEVNEBc30FlV+B5rKwAAdthk3viGt23btt24ceP69ev0jyO3vaFliAyd ddPZdy/RWWedfXcWKOkN0FlNEBc1QVzUBHFxA51Vhe+xtgIAYIdN5k3c8MYf nbrtDS1DZOism86+e4nOOuvsu7NASW+AzmqCuKgJ4qImiIsb6KwqfI+1FQAA O4Jl3ow3vPEWp257Q8sQGTrrprPvXqKzzjr77ixQ0hugs5ogLmqCuKgJ4uIG OqsK32NtBQDAjmCZN77h7eTJk8aNjtz2hpYhMnTWTWffvURnnXX23VmgpDdA ZzVBXNQEcVETxMUNdFYVvsfaCgCAHQEzb9Yb3hj6GP1tb2gZIkNn3XT23Ut0 1lln350FSnoDdFYTxEVNEBc1QVzcQGdV4XusrQAA2BEw83b48GHrDW8MbYzy tje0DJGhs246++4lOuuss+/OAiW9ATqrCeKiJoiLmiAubqCzqvA91lYAAOyw Zt6C3fDGRH/bG1qGyNBZN5199xKdddbZd2eBkt4AndUEcVETxEVNEBc30FlV +B5rKwAAdlgzbzY3vDF82xsVQ+bNS3TWTWffvURnnXX23VmgpDdAZzVBXNQE cVETxMUNdFYVvsfaCgCAHabM25UrV2xueGOivO0NLUNk6Kybzr57ic466+y7 s0BJb4DOaoK4qAnioiaIixvorCp8j7UVAAA7TJm3kDe8MdHc9oaWITJ01k1n 371EZ5119t1ZoKQ3QGc1QVzUBHFRE8TFDXRWFb7H2goAgB3GzBvf8Pbzzz8f OHDgkC1UgIpRYToEmTdv0Fk3nX33Ep111tl3Z4GS3gCd1QRxURPERU0QFzfQ WVX4HmsrAAB2GDNvp06d+jlM6BBk3rxBZ9109t1LdNZZZ9+dBUp6A3RWE8RF TRAXNUFc3EBnVeF7rK0AANhhzLx5A1qGyNBZN5199xKdddbZd2eBkt4AndUE cVETxEVNEBc30FlV+B5rKwAAdiDzpghr1qyZN2/e4sWLgxXQWTdvfA8ZgjSP jM5Lly4llVavXi1zwrAKR8z+/fvn+UlKSor4JDp/v+SR+Y6oqeTRo0e5kuzd uzfWtjiDmjoDxEVNEBc1cXzUAVL1ru3wPdZWAADsQOZNER555BGfz5c/f/5g BXTWzRvfQ4ZANcaOHduiRYuvvvrKqRPK6Hz77beTStWrV5c5YViFI2b8+PE+ PzQ+j/gkaen7tX379hdeeOHll1/es2ePs2eW+Y6oqeTChQu5knzyySextsUZ 1NQZIC5qgrioieOjDsfHRV7iVN+tc22H77G2AgBgBzJvioDMmw3IvFlZt24d ZxLSpUvnVIIFmTcHrYohjz/+OAvSoUMHZ8+MzJs6eKPzoUOH1obi9OnTwQ7f vXs3lzly5IjbpiqCmnHZuHFjsGJnz55121oVUDMuDEXniy++eOutt8aMGbNk yZKLFy+6bac6ODvqcGNc5CVO9d3O1vYNGzbYV+mdO3cGO5aaFy6zefNmp+yx J4a+23z9qSt0yiQbXG3lzp8/v379evqKadVAAeA4KmfeqBGr6WfHjh0uyxB7 Y5B5s0HnzNvgwYOp1r3zzjum7dTd09iSDM6QIcO+ffscuRYybw5aFUOee+45 FuTFF1909szIvKmDNzoPHz7cF4rPPvss4LEHDhy49dZbucyXX37ptqmKoGZc MmfOHKzY119/7ba1KqBmXGhg2aJFC1OBatWq/fzzz26bqgjOjjrcGBd5iVN9 t7O1PXfu3PZVunDhwsGOfeutt7jMHXfc4ZQ99sTQ90GDBgUrVrZsWadMssGl Vo6ao2HDhpUsWZJ9+emnnxy/BAD6oHLmbePGjfw1p3/c1iHmxiDzZoPOmbcm TZqQVfTXumvOnDm9e/d2sBNE5s1Bq2LIwYMHP/jgg6FDhzp+oxEyb+qgTiZh 0qRJAY/ltotB5s1ZworL6dOnIwhfGkPBuFy4cOHRRx/ljTTBb9OmTa1atfjj nXfeeerUKbetVQHHRx2Oj4u8xKm+2+PsU+nSpQMeuGbNmvTp03OZtJp5M/r+ 6quvhiuRs7jRyr344osmX+bNm+fsJQDQCpUzb9R18tdchcyb28Yg82aDzpm3 GjVq+IJk3hwHmTcHrUqTIPOmDt7onJycvCsQ69aty5IlC+lZtWrVCxcuWA+c OHGicayOzJuzhBWXffv2cRTef/996yEnT55021oVUDAu06ZN47j06tUrJSWF N/73v//ljUOGDHHbWhVwfNQBUp2u7bt37w5Yq1944QWuq99//731qDNnzpQv X150AXGaeQvL9/bt29OWQoUKWct7cwemG61cly5dcvnJmjUrMm8ARI/Kmbcp U6ZIJruSkpIWLVp06NAh6649e/bQnOvgwYMhL7djx46ffvrpt99+i8YYGj6t WLFi/fr1586ds7/ciRMnlixZsnXrVn5knn/rdDDzZu+OgKRbunQpWXL48OGQ 55QX01nC9V3SznBDEBZ0zm3btlG1PHr0qE2xkGEqV65cZJk3qoE02qfaKP/T OYlM82X7MjwGfvjhh/kjVfhly5b9/PPPAVd+CDlgphAsX7587dq1Yt4RDBs9 Y5V5O3v2LF1x//791l0bNmxYs2ZNSKckv6SMTSsXcq8J95qp1EjHfps2bSKT 6HL2xehLTV/tX3/9VeacZDYZf/z4cfqf/gmZeZNpN2jWvGXLlvnz55PBkqud BAuNfBQCloxA5+gtEXTu3JnEpKE4iWzd+/vvv/Nzpg888AAybyFxOy6JiYkc hRkzZoRlWFpCwbi89tprtDFbtmym8zz00EO0vWXLlmFZG6fIZ97EqCNVur8w kZycvHr1ahqxcKdgD3UHixcvlnlhelj9OJtB9Wrnzp0RrJclObD0IM9M9nMq pn379gEL9OnTh5ud+++/P34zbwEJ5nvTpk1pY6VKlVy9ug3h+s5jpGPHjskU Fj8KIPMGQDSomXn79NNPqZXOkSMHf83pn9x+qAHnAhUrVqSPX3zxxb59+554 4olMmTJRsXz58okzUPc0dOjQQoUKiR9cihYtas0qULHJkyc/8sgjuXLlEiXp qDlz5sgbw2zfvp2mpRkzZuRiZBIZFnAiTLPyBx98kBej8PmfMhgwYMBjjz0W feZNxh3BypUrq1atKoolJCTkNtCsWbNwxXQPyd5E3k7JEJw8eZLORnt79Ohh OkO/fv1YKJpjmnYdOHCAxsyiwhClSpX65ptvjHbKhKl8+fKFCxemuPj8i5aI 0Hz77bdcgL8FDRs2NBlAo4J69eqJhX3Izfvuu4+GqVYd+Az/+c9/aHj5/PPP 58mTh8rbi8xjYKrqP//8s/Eq5EuXLl1Mi3UHy7zt3bv3pZdeIlnYOyJ9+vSt W7cOOJAOqWewzFv37t1ZsQ8++MDeqVS5OibkoqC3aNHilltu4evec889HBSa QL3xxhvFixcXTvXt29c0upb/ktq3cjZ7qc6z4zRxMJ3T7WYqXCW54hUoUEC0 Qh07djxz5oz1kJEjR9LXWSh266239urVK2DJ8+fPv/nmm3nz5hXnrFatmhg0 WjNvku3G6dOnKZrZs2cXxWjsbWwzV61aZfTOpnuSj4JNSXmdHbHECE1d+Zs7 bNiwgAXoJLSXTjt37lw+MzJvRjyOC83T+TwK3ozqGQrGhdNx1JSZyrdr187n X+0tDPfiFvnMW506dai/6NChQ8GCBW36i4Djoq1btzZu3NhnoGzZssYVDrnT pKE+deIDBw687bbbRMkyZcosWbLEZJJ8P85nvuuuu+j/qVOnVqlSRTyAWaRI EdOZbfrukAOhsFSNbHBrpH79+mRDsWLFkpOTrXtXr17Nbrbz41Mp8+ae7zVr 1qTtDRo0iNj4KJGcK5nGSD5/dnTdunU0U+CBWcCjkHkDwBHUzLzRBMcXCOpo uAB3xH369KGGQuwVXe2pU6e4YWT4Jn/m7bffFlc5duwYTZkDXog69B9//FHS GGL69OmiQ8yQIYOxYzW90WbSpElGe0xEk3mTdIehobiYU5MjxiktQwKGJaar yNQZeTvlQ3D06FHeSCNk0+Vef/113mX6oZNmmmJRcRP8mnv5MImslAmREwiY 15o2bZpxbCagOml9eoXP0KRJkzvvvFOUtNeZDyECLtldo0YN4zA4oIVjxozh GY0Va44upJ6pQTJvNHjgjfXq1aNhhr1TqeGM/2mqJXJrgpw5c27evJlGXFY7 P/74Y3GGsL6k9q2czV4hyPz5840n9KCZCktJU8UT9OzZ01iYvtoUxICWlC9f 3vQCOxoJ8wPawTBl3iTbDZpn1a1bl7fTVIuua63/y5cvlwmcfBTsS8rrHL0l JipVquTz/7If8J4N+mLyeWgWQxNe/h+ZNyMex0U81Sh5s2iaRMG40An5DAsW LDBuv/vuu33+lEVEjsYZ8nGhbles8W7E1F9YRx2HDx82DnHFQ3MEjUa4jOg0 A3bQNGKZOnWqOGFY/bg4M9/iaIJ6HGPmNljfLTMQCkvVCAa3RkTVpbbFupcG gfywBg0VyLvWrVv7VMq8ued7xYoVfTH95sp8m4KNkahN43+CPReAzBsAjsDf 058jxaXM244dO6ijEW/5GTZs2Fw/4klPMf0nKlSoQL3nypUr165dy3vFTc4t WrTgeRmdkFcoorbF+HJSXt72qaeemjFjxv79+6mxFQ2vuLM9pDHUs/DNQnnz 5v3666+p00lJSRk7dixP4uhAcbnff/9d3CfToUOH9evX79u3jw4Rv69Fec+b jDup/jtzihUr5vPfv7do0SLeSI0tl3zzzTcpsmI+Ky+me8j4LmlnWCEIt4M+ cuRIvnz5eHurVq1WrVpFfRx1UtQd00hePHsoGSYav1E14+E9DfPm/oNYlsc6 wjxw4IBYD5YE2bZtG5189OjRXBVpTEhfE6MXxu/RY489NmjQoClTptjrbDyk U6dOS5cuJQEnT54shoVU802FTfk0vgGDZivDhw9fvXo1P88oRtTGOYikntbM 2/Llyzm5V6ZMGckb6eXH/0yzZs0oQImJicZl5AmybcKECb/88ss777zDW+go 40kko58aqpWz2Rtw9O5NMxWBklTxqNZt3bqVmlau7Tlz5jSuPSX0KV26NGme lJS0YsUKsRR5o0aNjGfu1asXby9UqBCdlr4Ra9asEUuy+CyjSsl2g+TlYjSJ YNuoME+oqeaTRNRmnj59OmRo5KMQsmS4OkdsiQlxAxWNw617xXOm999///nz 5+mLwIWReTPicVw+//xz3kVzRvo20cSQqj1N2DVZw59RMC7UYnAqj74yoq2m Ds7aeqdhHO8vrKOOgQMH8rHdunXbu3fvxYsXN2zY0LhxYwqxaLRFp0mUKFHi iy++2L17N/Udbdu25Y00ZjY+FCzfjxvPTMMzGmVRjSKXxZP4/fv3txY2Rl9y IBSWqlFmn/jmrjvuuCPgjy9vvPGGaHDoY8uWLX1pKPNm4zv/JksNLA1uO/uh f6gWRexLuMh8m8IdIwmQeQPAEUTm7Xj4uJd5Y0R3aV1aTXTE1L2a+p0tW7bw pJs6VuN2muDzjMD4tm7qvsUjQgIxpzMupGBjDN9KnS5dOlNa4/333/f57zUS K0GJ9N2rr75qso1/5Ywy8ybpDs0TeQtZaCz5+OOP+/ypA7ElLDHdI6Tv8naG FYJwO2hx8ldeecVY+OzZs8bVQuRrHcEPtgRc5806whSjxI8++shYcsGCBbzd 9LO7+B7RRIy2y4+Baaw7c+ZM43YayvKp7rzzTnGJYE+bTps2zTTpE8twde3a VWyU1NOUeaOI8JODFPrt27fbuyMIa/zft29fsZHG/OJ5k4YNGxofmOUEDmH8 UVs++jatnP3egKN3b5qp1DCV5Iontj/99NO8ndoo3kK9DH8FSpYsaRSH6oAQ Tfz0vG3bNi5MoTfdC0czKS5sHFXKtxv8+CRtNE7x1q1bZzLA5J01NPJRCFly 9uzZ8jpHY4mJ5s2b+/z5h4AzPu5EsmTJQoGjj8i8BcTjuAwdOtQXiKJFiwa8 ZSVNomBcUv15OfEzB313qOnmluepp54K0794xdn+IjXQqINX36KuwTTqMC6O ITrNJ5980rS2W7NmzayNmHw/Ls5MXRi3iuIMvJ06F2thY98tORAy4mr2adOm TVyAGhbrXvqCiOdMeUtayrzZ+x7weROCRuYyCwZGT0jfwx0jGUHmDQBHiOvM W0JCgrEjY3ggROzatcu0iyb1PonVMwYPHsxnoIlVSGNoGJAtWzba/uyzz5rO I5p3cfM5D7Hy5ctnXc/KvXebWt0RnfvChQuNJd977z2f/04P8XRe9GI6Qkjf 5e0MKwRhddBUE3gNKDq5zPq9JgLWutRwMm8XLlzgxyhKlChhXflZPFJnXOGZ z1C1alX+KD8GDvjShDp16vAlxBwkrFeSsXo0++CP8noaM29nzpypUqUKV2NT 9bZH3nchl0A8C2lacKZ///4BY2olYPRtWjn7vdbRu2fNVGp0SopnhGfNmsVb Bg0axFuM91IyK1as4F2tW7fmLUJG028KqUHebSrfbrDB5L6pGD9z+u6771q9 s4ZGPgoyJUeOHCmpczSWmNi3bx8/k0INoHUvzUz5WLGeFTJvAfE4LiLz9tBD D/Xq1atnz578VJTP/xidCu+O9wAF45LqT5489dRTvn9TqVIlTV44m+p0f5Ea aNTBjTlhXNjNRLDHPAkaV/CuTp062dsZsB8XZ547d66pPD97ct9999mYEdnA 0tXsE98fRaNN61seUlJSypYt6/PfIigeN0hLmTcb31P/ybzlyZOnXbt277zz Tps2bcT6FWKU4iohfQ93jGQEmTcAHCGuM2/Wjpigts7nX4pnmQX+1bJgwYLW oxITE8eMGUPDKhrziJ8gjW+LDmbM9u3beTuNZq1X5F28lMTu3bv5Y8A3ATmb ebN356effuItYq1+hlzw/Xu938jEdJyQvkvaGW4Iwuqgd+zYITk8E4Ssdanh ZN5EVezevbu1sBijGm9yMJ0hysybWBHRtOBVsMwbDdKmT59ORzVr1qx06dJ8 7IMPPsh75fUUg9UlS5aIX4cHDRpkf5SJaHx/5pln+KKmBeU+/fRT3k7fOOvZ QkbfppWz32sdvXvWTKVGpyRNoPjqYu0a/moTAZ8a5plLhQoV+KOIPk14TSUD jirl2zdeF6V8+fLGc1InyOc03WIaLDTyUZAp2bt374hnrPKWmBgxYgTvpdpr 2rV//35+HI+0EvelIPMWEC/jwtAXyjjxv3DhwptvvsmHSP4yEu8oGBdqQEh8 2kvt/0svvWR80cxbb70VsafxhbP9RcDCS5cuFYvy1a9ff8aMGdalX20yb/Rl 4cPr1atntS1kP25zZl5OkF++EKxwBAPLVJezT3xbZtOmTa27qFfiY43JmbSU ebPxPdX/7rChQ4cak3Jbt27lUUrACuA4IX0Pd4xkBJk3ABwhrjNvAUeMvPaO DdSHGstTlx3skO+++y6kMWLtYht4GQfqi/mj9beGVOcybzLupKSk8K0ajz76 qDgwKSmJB361a9eOWEyXCOm7pJ3hhiCsDlosu/rhhx+G9Eiy1qWGk3kTVdGU B2DEA6fGRUWczbyJtbAmT55sX5jGJ127duV5ugnxymB5PcVgtVWrVuI84b5e Khrfn3/+eb6oaTwv7t43Zd4ko2+ft7TZax29e9ZMpUan5A8//MBXFzOpe++9 1+f/ETngeTghlilTJlb+wQcf9Pl/j7aWDDiqlG/fxKp9xhspxf14pvfT2Txk LRkFmZI0EYtYZ3lLTLRo0cLnTxTQbNS0S7w6kOa5v/3D4sWLeeOIESPoY8C3 4KUxoqn/bsQlGFSyQoUKPv+7cmTeQRPvKBgXzkhQ48aGnTlzhr4m4oWDAwYM iNTXeMLZ/iJY4QkTJhhfQlq4cGFqvY3P/9rkxwh+m2rZsmWNGyX7cZszV61a 1Rcq8xbWwFLgXvaJ5n281/rKeLoiv7itadOmvxngJQhKlCjBH+W9iIyY+G7D t99+a9NEOEtI38MdIxlB5g0AR0h7mTd+1owGk9WDIN7amWp4CoPmbjQKot6Z nBLzZZnM25QpU3h7qVKlgl1x0qRJqYbB2/Dhw61m86KdBQoUsI+UvWLy7rz9 9tu8sUGDBuTCxIkTy5Qp4/P/2GqcWoYlpnuE9F3SznBDEFYH/c033/CWkSNH 2rsjH6bUcDJvoiqOGjXKWlhMgd95551gZ4gy80ajEb6E+ME3YOEDBw6IN1rS t4a+WQsWLDhy5Ai/ZEFk3uT1NK5gTPBjQQQJa3+gEc8yb/LRdzDz5lkzler0 TIq/2vny5Qt4Hl7mOkOGDPx4NT9Dlzt3bmtJsc756NGjxUb59m3fvn08+cqZ MyfNgGbOnNmzZ0+eZdSuXdu00nIw7+SjIFOSJuYR6yxviQmasfoCPXUrVr+x J9gNnGmJaOq/43Gxp0ePHny5gM+zpzFUi4tYGdWUUdm1axev054lS5bff/89 TC/jD28yb6n+gQeNe4sUKSKao7Jly9LAg/faZ974ec8HHnhAbJHvx6PMvMkP hIy4l3367LPPeK91NQ9eTy8kFDV5RyIgJr7bkJyczEd5sHhjSN/DHSMZQeYN AEdIe5k3/pExV65cAd+5Y0TcqU7zr4MHD4rtonmRybyJH0ECrrdpRCzH3atX L+ve6O95C8udEydOWO84otGF6YkJeTFdJaTvknaGGwLRQVufvLN20GLu2a1b NxsbwgpTajiZN1EVA3onRnTGpU6czbyJtyOJVbMCFuZXeqVPn56Gqcbtpsyb pJ6p/868lS9fnmYrJUqU8PnTNWJcHRJvMm9hRd/BzJtnzVSq0zMpsQiSddE5 cR5xKwL/tk5YV0kK+HtuWO2b6AKMkAvWldWDeScfBZmS0egsb4mRnTt3Bqsb 4oT2VK5cWf5ycYpScbFHPHAaciHKNIBqcaHpLe/99ddfTbvGjRvHu3R4/4Vn mTeG+uipU6fyD83EM888w9tt8mPUU5sKh9WPR5l5kx8IGZHPPskMbo3w2hoJ CQnWHtm6YmFATC/ncpyY+G7DmTNn+Ec6sYixe4T0PdwxkhFk3gBwBJUzb7zg P2F6mVSq3GJTpieArLz44otc8sCBA8btAXvPYMacO3eOcyO1atWyv1xSUhKf oVKlSta90WfewnKH3wlVo0aNfv36UXf53HPPffjhh9YH/+XFdJWQvkvaGW4I zp49S51swB6TRkGmDpoK82IgVDmN78wyEVaYCH73YsOGDa2nMn0LaEjJhWlU ac0kiPdzGW9vcDDzRi4XLVrU5/+l3ubdpocOHWIzrG/FNWXeJPVMNQxWCxYs uHv37lTDjVtUse3dEXiTeQsr+g5m3jxrplKdnkmJt1RMnDjRVDgxMZF3tWjR greIb+WMGTNMhQOOKuXbN5r+kIBUIakj6NixI32bevfubb2KvXfyUZApGY3O 8pYYEc89UXW17qXpzGELYhWs0aNH08eAi/WlMVSLiw0NGjTw+d/5aN/Apg1U i8uAAQN8/pelnj592rSLBpniWyN/uTjF48wbk5yczDfeFy5cmLfY5Meoy+Bd AwcO5C1h9eNRZt7kB0JGQqoa1uDWCK//QKM1666UlBRrF0A0atSIDrntttv4 o9utTUx8t7eHz2l83sQlQvoe7hjJCDJvADiCypk3sWyU9ek5m7519erV3Kje c8899i38E0884fP/fmG8aeHMmTNt27a19p42xogfEQI+bmDk7rvv5pJz5swx bl+1ahWv2xlN5k3enVOnTvFLMI0vhAqIvJiuEtJ3eTvDDQGvfVewYEHj20In T54sFuw1dtDi/aFDhgwxXXfz5s38T1i1LvWfBUYCVgzrt4BPTtD4zeQd61Oq VCljdijizFu5cuWMa6QQw4cP50u3bNnSxsL169dzMdN9/nRdXp1YZN5S5fRM NQxWaaYjNvJgL+BwNyDeZN7Cir6DmbdUr5qpVKdnUmvXruWqS1ZRwyVKXrx4 UXgkVu8XD+ZUq1bNGAv6v3379rzLOKqUbzd4nTfjS+gi8C41nCiELBnlHary lghEJ2iqGDbgDQsB8TIua9asKV++PH2PTNvFyp8Bc+xpD9XiItq6zz//3LSL +jvetXTpUslrxS8eZN5oqrJp0ybTsbxGaI4cOfij6DT/z//5P8ZiSUlJfAt9 unTpxEnC6sejzLylSg+EjMioGtbgVsAj0ipVqtif3Ihqb1hIdcf3Vq1a9erV y3jCVP9Ahdfo8AVKdjlOSN/DHSMZQeYNAEdQOfMmelWajNO4kVowcbuO/Zy0 S5cufOC9995L1+K1bfft2/f222/TIaK1ee2117hYp06dDh06RDOvxYsXV65c 2fcPxt7Txphff/2Vl5bKkCFD//79+b021AvPnTv30UcfNS42JRo9Kk8zEerT 9+7d+/HHH3MezBdd5k3eneTkZL75uXHjxrt27aKe4qKfaMR0FZk6I2lnuCGo V68eb+/cuTONsjZs2EAnZPWsHTRNM/mXcZ//ARPSlq5LE5/mzZvTII3vqwmr 1qX+Mzbz+dfdOnny5CE/vMv6LRBVkcYPH374IZU8ceIEzRRy5szJJ6HZlvHk EWfeiDvvvHPatGlUl0gBce9QpkyZxKOmqf+sYF+yZEmxvvSpU6c410Ej3hUr VlCtoxgNHDiQvjvi+xWWnqmGwapxnrJjxw5+nzvZab2pwIo3mbewou9s5s2b ZirVhZmU+Grfd999q1evJpG3bNnSpEkT3kgTKFGSqlP58uV5e8OGDWlWkpKS QvWkTp06QmHTqFKy3aDqx1+rWbNmUZ23aTBtvEsNJwohS0aZSZC3RPDuu++y VhQF++sKkHkLiJdx4QfrMmfO/NZbby1atIgaw2PHjo0ZM4ZaYD5q9uzZETka Z6gWFxpC8ypwdFqa0or25Ntvv+WWtkiRIqaft9Ikbmfe+PY2GkhQ/d+zZw9t oXEUlecDa9asycVEp0lDixdffJEaLgou9R1ly5bl7R06dBCXCKsfjz7zJjkQ ClfVsAa3DPWPXKBRo0b2JzeiYObNcd8nT57MB1arVo06O4oRld+4cWPdunV5 O8Xag+lSSN/DHSPRWGjJP/Tp04cL0IidtwR7lTYAwAaReYsMVzNv1Ezxj00M jUZoXs+TaPs5KQ0sqfUTB9IE3PhWI9FHU2PLT+dxb8sJAZ9/1SBr72ljDEEt LU/zmbx584o23NirUqPHz3dY4RdPR5N5C8sd8SOaEZpX5suXr1WrVsZ1GCTF dBWZOiNpZ7ghINGsJW+55ZZXX32V/zd10CNGjBC/mvn8v5OK/1966aXUMMNE jB07VpyBDqTyNELgXQG/BePGjRPnN/H666+bRIs488bvxjVBzpoWAe7atSvv ypMnjxh1GN9AKrSieN12222+f2feZPRMDZJ5I/r16xfMcSveZN7Cir6zmbdU T5qpVBdmUvTV5rUBrVCd2bJli/EM5LJ4xYYR0XqbMm+S7QZVLWPdE9C1Klas SDNx41ro9oGTjELIklFmEsKyhKFpJu+1rkwVDGTeAuJlXKZMmSJy5r5/N6G+ 8JeGi19Ui0uqv1URGZUCBQrQ3Fw0U7R92bJl4foYj7ideaOT07BWRI3+F0HM kCHDihUruJjpPU0mypUrZ3ywNKx+PPrMW6rcQChcVcMd3BLUzfGudu3a2Z/c iIKZN8d9p4GEMXnlMwxuff5at3PnTsc8DI6M78HGSALjGGnw4ME2JbNnz+6y QwCkQUTmLYIcmtuZt1T/00A8H2eou1mzZg1tL1WqlM//UrlgB164cIFaDH42 SkA9MjUpZ86cEcVoUMp3HTM0rxw2bJhYD9OUAwlmDENTjEceecTYIdLY6amn njLd5U6G9enTxzh+o253xowZnGAhA+wjZS+XvDtTp061aU4JsidcMd1Dss5I 2hluCEhD0YeSklWqVKG4ixeTWTvoVatW8XIQgiJFiowbN04UCKvWnTt37pVX XjGe7YknnuBdwb4FGzduNGUqaMwT8H1SpjPI6MyH/Oc//6GhYO7cucUlaMpg HVjSzIJf0+YzrC939OhRseicz59ObNCgwdatW/l9u6bMm4yeEydO5O1U0nhg SkoKRdbnH2CHzBXI+24VnDNvdBVxax8jflg3vttUPvr2rZzNXiGI9ddwt5up 1OiUFCuNmFL69C149913+XlkUW1I9uTkZOvJExMTxa+6Pv+vJG3btk1KSuJ1 CKnqmsrLtBv0D9/AGYwyZcocP37c3juBZBTsS0ajcwSWpP7zQgrC+NivPbt2 7eJDJk+eLHlIvKNgXHbv3v3cc8+Jm9yYkiVLevD0kzooGJdU/4OQYmEEAd+L Iu9aXON4f2EtTI1/9+7d8+bNaxS5QoUKxn5ZpLw+++wzXs5UxJe+O9YIyvfj Nt0xX8iYebMpHHIgZERyzBzu4Nb+NV7BePbZZ33+LlL+kGiIle8XL16kcJhi RJdo37794cOHHXBMAknfTWOkzJkzt27dmgbw/FE+85YtWzY3vQEgbaJ45i3V P+eixnDmzJnUS/Id/mFBY865c+fSseIZPRPUpa5YsYLaHJnf8UMaw2ebN28e DZxMz/ubzrN+/frZs2fv3btX0hFJ3WTcoT6d59Q0xti4ceOyZcuWLl26ePHi zz//vE2bNtyiWhMgqRJiukS4dUbGzrBCQBN8Eu3HH3+kIZykDceOHVu0aBGZ EfD8YdW6VP+LCehU33//PfWYku+ZJQPIZhLBtAiwDeHqfOHCBdJwzpw5/BBH sDKkA+lseuSTpuS0nSyUfKDGXs/oCdf3aAg3+i4Z4EYzleqmklTzaeZL9qxb ty7kspP0lSGFqYLJL1Bp0240btzY538ilXo98m6pn1mzZg0aNIjvA/RZcuYh kYxCsJIO6ixvCQiJsnGhlpa+ONSL0ffC+jalNI+ycUn1DzBWrlxJ/Tv9Dfhr QhrGy56XOrIf/dCIxTSOEpk3TsdRF0DtOY2NbcYnMenHJQdC8qpGMLhVnJj7 TrWLY7RmzRqPHxgP69tElZxG7zT1YyPpH2vmDQDgOOpn3gDjoG5873fTpk2t uy5cuMCvmLR/oMxjUGe8QWeddfbdWdKekuKRyYCrmowbNy4m49W0p3PaAHFR E8RFTRSJi80zofGIIqrGBPge2bEh320KAHAEZN7iBQd142dmO3bsaN11/Phx fn1P48aNHbmWI6DOeIPOOuvsu7OkPSVFbi3g7aPicQzj4gMekPZ0ThsgLmqC uKiJInFB5i3NAN8jOxaZNwC8AZm3eMFB3Zo2berzL5T0+eefG9cfWLt2rXiT 5rRp0xy5liOgzniDzjrr7LuzpD0lExMTuVWsVavWhg0bjC/q/fDDD3mtmMqV K0s+Bu4UaU/ntAHioiaIi5ooEhdk3tIM8D2yY5F5A8AbkHmLFxzUbcWKFcbF lkuVKlWnTh1elpb56KOPHLmQU6DOeIPOOuvsu7OkSSWfeuop0TzecsstNWrU qFKlSs6cOXlL6dKlDx486LFJaVLnNADioiaIi5ooEhdk3tIM8D2yY5F5A8Ab kHmLF5zVbd26dc2bNxdvtBczymeffVa8g1IdUGe8QWeddfbdWdKkkufPnx88 ePAdd9xhbDATEhLuvvvusWPHevOKZxNpUuc0AOKiJoiLmigSl4ULF9b0kzbe KquIqjEBvkd2LE39+CswZ84cZ60CABhB5i1ecEO3U6dObdmyhYYcK1as8Ph1 pWGBOuMNOuuss+/OkoaVvHjx4u+//7569er58+dTyxmThJsgDesc1yAuaoK4 qAni4gY6qwrfY20FAMAOZN7iBZ1109l3L9FZZ519dxYo6Q3QWU0QFzVBXNQE cXEDnVWF77G2AgBgBzJv8YLOuunsu5forLPOvjsLlPQG6KwmiIuaIC5qgri4 gc6qwvdYWwEAsAOZt3hBZ9109t1LdNZZZ9+dBUp6A3RWE8RFTRAXNUFc3EBn VeF7rK0AANiBzFu8oLNuOvvuJTrrrLPvzgIlvQE6qwnioiaIi5ogLm6gs6rw PdZWAADsQOYtXtBZN5199xKdddbZd2eBkt4AndUEcVETxEVNEBc30FlV+B5r KwAAdiDzFi/orJvOvnuJzjrr7LuzQElvgM5qgrioCeKiJoiLG+isKnyPtRUA ADuQeYsXdNZNZ9+9RGeddfbdWaCkN0BnNUFc1ARxURPExQ10VhW+x9oKAIAd yLzFCzrrprPvXqKzzjr77ixQ0hugs5ogLmqCuKgJ4uIGOqsK32NtBQDADmTe 4gWdddPZdy/RWWedfXcWKOkN0FlNEBc1QVzUBHFxA51Vhe+xtgIAYEesMm8A AAAAAAAAAAAAAOgAMm8AAAAAAAAAAAAAALiB95m3CA7UHJ1109l3L9FZZ519 dxYo6Q3QWU0QFzVBXNQEcXEDnVWF77G2AgBgBzJv8YLOuunsu5forLPOvjsL lPQG6KwmiIuaIC5qgri4gc6qwvdYWwEAsAOZt3hBZ9109t1LdNZZZ9+dBUp6 A3RWE8RFTRAXNUFc3EBnVeF7rK0AANiBzFu8oLNuOvvuJTrrrLPvzgIlvQE6 qwnioiaIi5ogLm6gs6rwPdZWAADsQOYtXtBZN5199xKdddbZd2eBkt4AndUE cVETxEVNEBc30FlV+B5rKwAAdiDzFi/orJvOvnuJzjrr7LuzQElvgM5qgrio CeKiJoiLG+isKnyPtRUAADuQeYsXdNZNZ9+9RGeddfbdWaCkN0BnNUFc1ARx URPExQ10VhW+x9oKAIAdyLzFCzrrprPvXqKzzjr77ixQ0hugs5ogLmqCuKgJ 4uIGOqsK32NtBQDADmTe4gWdddPZdy/RWWedfXcWKOkN0FlNEBc1QVzUBHFx A51Vhe+xtgIAYAcyb/GCzrrp7LuX6Kyzzr47C5T0BuisJoiLmiAuaoK4uIHO qsL3WFsBALADmbd4QWfddPbdS3TWWWffnQVKegN0VhPERU0QFzVBXNxAZ1Xh e6ytAADYgcxbvKCzbjr77iU666yz784CJb0BOqsJ4qImiIuaIC5uoLOq8D3W VgAA7EDmLV7QWTedffcSnXXW2XdngZLeAJ3VBHFRE8RFTRAXN9BZVfgeaysA AHYg8xYv6Kybzr57ic466+y7s0BJb4DOaoK4qAnioiaIixvorCp8j7UVAAA7 kHmLF3TWTWffvURnnXX23VmgpDdAZzVBXNQEcVETxMUNdFYVvsfaCgCAHci8 xQs666az716is846++4sUNIboLOaIC5qgrioCeLiBjqrCt8jO/b8+fPL/Rw7 dsxRowAA/wKZt3hBZ9109t1LdNZZZ9+dBUp6A3RWE8RFTRAXNUFc3EBnVeF7 ZMeuWrXK5+eLL74QGw8cONCtW7eePXsmJSU5YyIA2hMvmbfjx49v3bqV/kZw xbSBvG7UQs6cOXPgwIHUfi5fvvyvv/4KWOzKlSvr168fNWrUkCFDZsyYcejQ IQetdZZw60zI2kKabNy4cYSf+fPnp6SkOGBl/COv8/Xr1zdv3kyVZ8yYMST1 jRs3XDbNdRyvY9qicy3yEuisJoiLmig4irh27dpWP7t37w5WhirJ9u3bt23b dvPmTZtT7dy5c9KkSQMGDBg/fvy6devsC0dzIcfROU/iHjqPZ+B7ZMcGzLw1 a9aMN7700kvOmAiA9iieebt06dLChQvbtWuXIUMG+u7XqlUrgiumDWR0++WX X+655x7fv7nrrrvmzZtnLEbjvbfffjtTpkzGYunTp+/UqdPFixdd9CFSJOuM TG0h33v06JE9e3aj77ly5aLxqvN2xxuSOu/Zs6do0aJGAUuVKnX06FH3DXQR B+uY5uhci7wEOqsJ4qImCo4iBgwYIAZp1r179+4dPXr0nXfeyWVWrlwZ8CQp KSlPP/20adRXo0YNOlzSDMkLuQQyb26g83gGvkd2bMDMG80KeWP37t2dMREA 7VE583b8+HFuEgWPPPJIBFdMG4TU7bPPPsuYMSMLlTt37sqVK6dLl44/ZsqU aePGjVyMVL3vvvt4e0JCQrly5QoWLCgUbtasmce/eMogU2dkasuZM2eoe+W9 JM4DDzxQpkwZUf7rr792y4E4QUbn3bt3iwpTtmxZmjLw/7fffvuRI0c8MdMV nKpjQOda5CXQWU0QFzVRbRSxZcsWcS1r5o3mub5/s3z5cutJbty4UbduXS6Q J0+e5557rl69evyxVKlSly9fDmmG5IXcA5k3N9B5PAPfIzs2YObt9OnTI0aM GDVq1Llz55wxEQDtUTnzlpycnPsfOImUNtrGyAip26RJk0iiYsWKLV26lJ9b OXHixJtvvsltaYMGDbhYampqhQoVaEvXrl1pDPm3f/BGZy5ZsiSXXL9+vfve hIdMnZGpLX379mUf33nnHfFsyLx587Jly0Yb8+bN+8cff7hhf7wgo3Pz5s19 /pzt1KlTecvYsWNZ1bi+Hd2pOgZ0rkVeAp3VBHFRE6VGEVevXq1YsaKY9Vsz bz169GAz+LTBEmI//PAD7+3Tp8+VK1d44zfffMMbP/7445CWSF7IPZB5cwOd xzPwPbJjA2beAACOo3LmzQjfCZ822sbIkNHt22+/5WSa4ObNm2XLliXpbr31 VrHxyJEjNGAzHTtt2jRudceMGeOQyY4Rbp0JVluuX7/euXPnCRMmmLaLsbS4 M1BPQup84sQJ/qGwS5cuxu08kcyRI0f8pi6dqmNA51rkJdBZTRAXNVFqFNG/ f39OvVapUiVg5k0g0mgBE2Jvv/027cqePbtpOV8ym7a3bds2pCWSF3IPZN7c QOfxDHyXL79///5169ZdunSJ/l+7dm1YmbfU1NRffvllw4YNfLgNBw8eXLly ZciV9C5cuLB+/Xqy5+zZszIGUPnVq1cHXGAzKSlp1apVp0+ftj/DlStXEhMT t2/fHmw5dADcAJm3eCHi8Un9+vV9/mXcaMRoU4yaO251Bw4cGJmF7uF2T0qd Avs+ceLECMxLM4TUecSIESyUaSmYWbNm8fZJkya5a6Jr6Dxacxada5GXQGc1 QVzURJ1RBE1XOfXa0U80mbeuXbv6/DfambbzaWvUqCFpfMgLuQcyb26g83gG vocsRpPBfv365c+fn7/y6dKlo7ZCtADGzNv06dPz+FmzZo3YuH///iZNmvj+ ISEhoXz58tR/GS9x8+bNmTNn1q5dO3fu3KJkkSJFFi9ebLVn8+bN1atXF8XI njwGWrVqxcUqV65MH6l/TE5Obt68eebMmalwgQIFjBcdNWoUXUWcqnjx4uSC 9YoHDhyoV6+eWO2cTkUnNN24AoBLIPMWL0Sm28WLF7l1LV26tH3JoUOHchMk nn9RB7d70rlz57Lvpr5DN0Lq3L59e59/FUFTFvfSpUs8lXjzzTfdNdE1dB6t OYvOtchLoLOaIC5qosgo4urVq7zcR8GCBVNSUp599tloMm8//vgj7zW5xm/a 6tixo6TxIS/kHsi8uYHO4xn4bl8mNTVVrFQZEGPm7euvv+aNK1as4C1nz54t Xry4KCyeUifEK2Zo4nnvvfcGPHm6dOmWLVtmtGf16tXp06f3+TN4ZcqUMZ6c adSoEZe844476GP//v0ffPBBsffxxx/nvZcvX6aSYnvWrFnF/6b7SajZzJkz J+/KmDGjWPGvWLFieMkR8ABk3uKFCHQ7fPhww4YNuUkZO3ZssGI0/v/22285 +U+NnsyqvB7jdk/au3dvVikpKSkS+9IKIXXm6kQTB+suXjC8Xbt2bhnnMjqP 1pxF51rkJdBZTRAXNVFkFCEeSqXZH31s27atL4rM259//slTyLx584qpMZU0 TZZlQOYtLaHzeAa+25fp06cPf9OLFCkye/bsU6dObdmypVu3bgkJCbzdPvMm btJ49dVXjx07dvPmzR07djRt2rRSpUrUHIkD69atSyds06bNvHnzTpw4cfz4 8XfffZcPfPTRR0Wxv/76q0SJErSR2rHVq1fzRjKAS/br12/v3r2iReXMG0OX Gz9+/ObNm7du3cp7+RF+4umnn+ZDDh48WLt2bU6v0f9cLCUl5dZbb6WN+fPn nzVr1tWrV69cufLVV19xpq5z586Rqw+AHMi8xQvyuk2aNKlTp0516tTh3xGy Zcv26aefWt9YSm1mr169WrZsedttt3F7RfIeOHDAedOjxtWe9MKFC3xzcsmS JSO0L60QUmdeFDrg69fLlStHu+rVq+eWcS6j82jNWXSuRV4CndUEcVETFUYR NE8Uz5nyligzb3/77xjJkSMHl2nWrBnNlPPmzUv/05xXxnL5C7kEMm9uoPN4 Br7bFPj999/5Lot8+fKZfiPgl/SFzLzRnJE+0klMN2lcu3bN+HH//v3WFIF4 8/L58+d5y/bt23nLRx99ZCxJTRltrFy5snGjyLzVrFlTvFCG+e233/jh06ZN mxq3//HHH+Qpbe/evTtv4SfxaXZMrbGx5PDhw2k7tc8nTpyw6gaAgyDzFi/I 69a4cWOfgffee8/4S4Rg06ZNxmLFixfftWuXw0Y7hKs96csvv8wKTJkyJUL7 0gohdeZbMlq0aGHdxUs633PPPW4Z5zI6j9acReda5CXQWU0QFzWJ+SiCporl y5enMiVKlLh48SJvjD7zRhPeNm3a+P7N/fff/7///U/GcvkLuQQyb26g83gG vtsUGDlyZMBM199B3m1qzbz16tWLt0SwOI+4+rZt20znp6sbS3744Yc+/71q xjUZOPOWLl26PXv2mM7MeTPi8OHDpl1sMC96efPmzezZs9PHDh06mIqdP3+e z2B6GBYAx0HmLV6Q140at0aNGlWuXFmsHlm2bNndu3ebih06dKhVq1YkadGi RblYQkLCgAEDrHfHxRz3etKVK1fyLdbVqlVT0HGPCakzr8DQpEkT6y4SkAf8 bhnnMjqP1pxF51rkJdBZTRAXNYn5KOKdd97hUZYxtRVl5u3SpUs1a9b0+V+J +8orrxQqVIgL0+SUxnIhLZe/kHvYx+XatWtHo0DcWqMbOo9n4LtNgU6dOvHX PDk52bRLMvO2fv16sTAazTTnzZtn//K+Xbt2jR8/vkOHDtSvibtzFy1axHvF i2m+//5741FvvPGGz/LuGM68Va9e3XqV5557zudfPXWDBb7JrXDhwn/7X6zA l6PzW0vyLrFaHQAugcxbvBCBbidPnqTRF48JqckKtoAbjRUXL15cqVIlZZsd l3rS3377je9DzpYt2/bt26MyMU0QUmde1zTgG9NKlSrlMyx2GnfoPFpzFp1r kZdAZzVBXNQktqOITZs28eofLVu2PG6AH6q6/fbb+aP1QPuEGCfubr31Vjr/ 3/7XN3z22WfilYVDhgyR91fNzNv//ve//xsF+/fv99AVhdB5PAPfbQrwjzvU WFl3SWbeiGnTphnfWFq0aNFhw4aZHv8kpk6dev/99/sC8dNPP3EZOipLliy0 pW7duuLACxcu8I8I9evXN56QM281a9a0Gh/sQoIMGTL8bXgPjg2DBw+2ERCA 6EHmLV6ITDdi4MCB3J58+eWXNsVOnDjBT8FQKxqhia7hRk96+vRpLkbMmDEj WhPTBCF15jeJlytXzrqL1yyN3+VJdR6tOYvOtchLoLOaIC5qEttRBC+OFJIl S5aYDrRJiO3YsYN3ffLJJ8bthw8f5sV7s2bNevLkSUl/1cy8/fXXX9u2bdsS KRQgD11RCJ3HM/DdpkDlypXJ2Tx58lh3rV69mluAcePGiY0BM2/EqVOnaGpZ rFgx0XaVL1/+3LlzosCoUaN4e+bMmdu2bTtt2rQ9e/aIpeRE5u1vwxS1cePG s2fPnj59Oq93mi5dOtMjqDaZt4ceeoh2ZcmSpWYQ+AWpdH6+VtmyZYOVxHwQ uA0yb/FCxJk3cXvtK6+8Yl+S79clzpw5E4mJruF4T/rHH3/wTz/Eu+++64CJ aYKQOr/00kvcu5GAxu1JSUksZt++fd010TV0Hq05i861yEugs5ogLmoS21GE dSm2gMyfP990oE1CjCbIvOvIkSOmXZMnT+Zdc+fOlfRXzcwbiAydxzPw3aYA 32RLWNeBlL/nTXD9+vU5c+Zwooxo3749b1+wYAE/bPXQQw8Zs9+ikTFm3qgh 5Z+cjNxyyy3Wx69sMm9PP/20z/+0qf2qQXv27OHzjxo1yqYYAK6CzFu8EFK3 YA3OwYMHuanp2rWrfcnnn3+eS546dSo6Yx3G2Z708uXL/KZp4plnnrlx44Yz VsY/IXWeMmUK6zZ79mzj9s8//5y3W3+yjxd0Hq05i861yEugs5ogLmoS21HE lStXzgbiySef9PnfiMofTe8H/Ns2ITZkyBCf/yV91ldobd68mY8y3r5iDzJv aQmdxzPw3abAq6++yl/zefPmmXZFkHljUlNTeZ0E8cBU9+7dff41LU1zyYCZ t3bt2vn8L/v+4IMP2rRp07lz508++cS6DN3ftpm3999/n8+8du1aG/f/+usv Xv8cLxAHMQSZt3ghpG6tW7du0aIFtYGm7eJW3okTJ9LHX375pVChQtafVk+e PFmgQAEqdttttzlotiM42JPSGFW82JoGvdQOO2Zl/BNSZ5pu8PIO9evXF3MN miw88MADXHPiN42p82jNWXSuRV4CndUEcVETNUcR0bxhYcmSJcahnZGPP/6Y d61fv563UK2jSfTUqVMDvune/kKugsybG+g8noHvNgW+++47/prXqFHD+GYE +v/FF1+Uybzt3bv3119/NZ22Vq1aVCZnzpz8sXnz5j7/46InTpwQZa5evSru 7hCZN2qXsmXLRlsWLFgQ0jubzBtNbOlytPfee++1/n5hRNz1h6dKQaxQOfO2 cePGdf/A79+kFlJsEa9l1wR73YxPr48bN44axps3b6akpPTr14/X9c2VK9ex Y8eoZIUKFfjHiG7dulHrRzLSuJHOTO0Vn+H111/3zis5ZOqMTG25cuVKw4YN 2U3qI+bPnz9v3rw5/ybgKseaIKMzVRsWsGfPnhcuXDh37px4WdJ7773niZmu 4FQdAzrXIi+BzmqCuKiJmqOIgJm35ORkcdH+/fvztYYOHcpbdu3axcUuXbrE RmbPnv2bb74RzzJ8//33PJktVqyYWPa8d+/efB7jg7GSF3IVZN7cQOfxDHy3 KUCtRMWKFfmb/vjjj+/evZuaiLVr1zZo0MD3DzaZN769LVOmTAMGDEhKSvrb /w6UqVOncpk6derwUW+//TZvefnll8+cOXPt2rU1a9bwT0umzBudkKeoTZs2 PXz4MM1Gb/oJaLxN5o3o0aMHn/y+++7btGkT/4BFTdzAgQPpEJFmPHLkCDWY VCxjxoyDBw/m1x9fvXp16dKldevWnTZtWhhyAxARKmfebrnlFl9wRo4cGcHV 4xd73ajd4OfcBXxLLZOQkCCW+6CT5MmTR+xKly6deEM0Ua1aNTqVRy5JI1Nn ZGoLTWpsyjCmd1trhYzONE+sWrWqsWrxP/Xr1w/2Y3pc4FQdAzrXIi+BzmqC uKiJmqOIgJk3upDNyclIUXL9+vVipFeoUKGHH3749ttv54+0fcOGDaKkePcf lYngQu6BzJsb6Dyege/2ZVasWMGpJxOi6bDJvG3atImfjWLo/6xZs/L/GTNm TExM5KN27NiROXNm3p7OD/9fvnx5/sf4tGmjRo2sxtC0lE7+zDPPGJ/Pss+8 Xbx4sUaNGuIMZJjxBaxTp04VJadMmSLMJvLnz8/ZP6J06dJhiw5AmMRv5m3E iBERXD1+kdHtu+++E0v+Ch599FHRHjKnTp3q1q2baU3LnDlzDh482LrqpgpE 35Nybenbt69NGUbmnue0iuR3k6aNTzzxhBjwZ8mS5emnn473CaNTdQzoXIu8 BDqrCeKiJmqOIjp06OCzvOjWPiFGs2Zj4T179vBicUb4bhZjseHDh/Ou0aNH R3Yhl0DmzQ10Hs/A95DFdu3aJe58I7Jly/b8889Tl1S8eHH6OGHCBFFy+vTp XGbdunW8hYq99tpr+fPnNypWqVKllStXGi8xe/bsQoUKiQIlSpSglufq1auc 4zJm3ubMmWMTC+Krr77ikmXLlvX5f58K5teNGzeoTcubN6/x8DvuuOOLL74w 3VKyb9++2rVri4Sbz/9TRZs2bawP0gLgOCpn3oARed2OHz++du3a+fPnr1+/ /uzZs8GKXbt2bfv27dQALlmyhFohlVc8Q53xhrB0vnz58qZNm6imiedZ4hrU MafQuRZ5CXRWE8RFTdJ2C5+amrp58+ZFixbRX+tivwxNKnfu3OmxYSFJ23GJ FTqrCt8lC6ekpKxYsWLdunX2C6MF49ixY8v8JCUlBXw+lHq3xMREuoT15csC unrWrFkTEhK+/PJLap02bNhA89Y1a9ZMnDjxueee45xYlSpVwrXt6NGjS5cu XblyJfloU4wtXL58+e7du1WeAoM0BjJv8YLOuunsu5forLPOvjsLlPQG6Kwm iIuaIC5qgri4gc6qwvdYWxEG/Lh9y5Ytrbtu3LjBL78oWLCg94YB4B7IvMUL Ouums+9eorPOOvvuLFDSG6CzmiAuaoK4qAni4gY6qwrfY21FGJQsWdLn83Xp 0sW669KlS4ULF6a9TZo08d4wANwDmbd4QWfddPbdS3TWWWffnQVKegN0VhPE RU0QFzVBXNxAZ1Xhe6ytCIOWLVv6fL7MmTNPnDjRuDjS1q1bq1ev7vv3+wEB SBsg8xYv6Kybzr57ic466+y7s0BJb4DOaoK4qAnioiaIixvorCp8j7UVYZCY mJgzZ07xmoMyZco0aNCA/vLHhISETz/9NNY2AuAwyLzFCzrrprPvXqKzzjr7 7ixQ0hugs5ogLmqCuKgJ4uIGOqsK32NtRXhs27atdevW4jXfTI4cOTp06LBn z55YWweA8yDzFi/orJvOvnuJzjrr7LuzQElvgM5qgrioCeKiJoiLG+isKnyP tRWRcPny5d9++23VqlWJiYn2LyQFIN5B5i1e0Fk3nX33Ep111tl3Z4GS3gCd 1QRxURPERU0QFzfQWVX4HmsrAAB2IPMWL+ism86+e4nOOuvsu7NASW+AzmqC uKgJ4qImiIsb6KwqfI+1FQAAO5B5ixd01k1n371EZ5119t1ZoKQ3QGc1QVzU BHFRE8TFDXRWFb7H2goAgB3IvMULOuums+9eorPOOvvuLFDSG6CzmiAuaoK4 qAni4gY6qwrfY20FAMAOZN7iBZ1109l3L9FZZ519dxYo6Q3QWU0QFzVBXNQE cXEDnVWF77G2AgBgBzJv8YLOuunsu5forLPOvjsLlPQG6KwmiIuaIC5qgri4 gc6qwvdYWwEAsAOZt3hBZ9109t1LdNZZZ9+dBUp6A3RWE8RFTRAXNUFc3EBn VeF7rK0AANiBzFu8oLNuOvvuJTrrrLPvzgIlvQE6qwnioiaIi5ogLm6gs6rw PdZWAADsQOYtXtBZN5199xKdddbZd2eBkt4AndUEcVETxEVNEBc30FlV+B5r KwAAdsQq8wYAAAAAAAAAAAAAgA4g8wYAAAAAAAAAAAAAgBvgaVP10Vk3nX33 Ep111tl3Z4GS3gCd1QRxURPERU0QFzfQWVX4HmsrAAB2IPMWL+ism86+e4nO Ouvsu7NASW+AzmqCuKgJ4qImiIsb6KwqfI+1FQAAO5B5ixd01k1n371EZ511 9t1ZoKQ3QGc1QVzUBHFRE8TFDXRWFb7H2goAgB3IvMULOuums+9eorPOOvvu LFDSG6CzmiAuaoK4qAni4gY6qwrfY20FAMAOZN7iBZ1109l3L9FZZ519dxYo 6Q3QWU0QFzVBXNQEcXEDnVWF77G2AgBgBzJv8YLOuunsu5forLPOvjsLlPQG 6KwmiIuaIC5qgri4gc6qwvdYWwEAsAOZt3hBZ9109t1LdNZZZ9+dBUp6A3RW E8RFTRAXNUFc3EBnVeF7rK0AANiBzFu8oLNuOvvuJTrrrLPvzgIlvQE6qwni oiaIi5ogLm6gs6rwPdZWAADsQOYtXtBZN5199xKdddbZd2eBkt4AndUEcVET xEVNEBc30FlV+B5rKwAAdiDzFi/orJvOvnuJzjrr7LuzQElvgM5qgrioCeKi JoiLG+isKnyPtRUAADuQeYsXdNZNZ9+9RGeddfbdWaCkN0BnNUFc1ARxURPE xQ10VhW+x9oKAIAdyLzFCzrrprPvXqKzzjr77ixQ0hugs5ogLmqCuKgJ4uIG OqsK32NtBQDADmTe4gWdddPZdy/RWWedfXcWKOkN0FlNEBc1QVzUBHFxA51V he+xtgIAYAcyb/GCzrrp7LuX6Kyzzr47C5T0BuisJoiLmiAuaoK4uIHOqsL3 WFsBALADmbd4QWfddPbdS3TWWWffnQVKegN0VhPERU0QFzVBXNxAZ1Xhe6yt AADYgcxbvKCzbjr77iU666yz784CJb0BOqsJ4qImiIuaIC5uoLOq8D3WVsSY EydOLPdz7ty5WNsCQACQeYsXdNZNZ9+9RGeddfbdWaCkN0BnNUFc1ARxURPE xQ10VhW+x9qKGPP111/7/KxYsSLWtgAQgHjJvB0/fnzr1q30N4Irpg3kdTtx 4sT06dMHDhw4ceLEzZs325Q8derUd9999957782ZM+fs2bPOGOoC4daZkLXl r7/+2rhx4wg/8+fPT0lJccDK+Ede5+vXr1PVGjVq1JgxY0jqGzduuGya6zhe x7RF51rkJfI6X7p06aeffqK2bsiQId9//z11EDaFk5OTqTsYNGjQpEmT9uzZ c/PmTfuTHz16dKufkL8vy5c0QfVk+/bt27ZtC2lMuMa7AeISvfFu4FILLy+C katXr27atGns2LFDhw5dunTpxYsXTQV27ty5NQjXrl0LeM7IRI7gQs6C/sIN dFYVvsfaihiDzBtQHMUzbzQ0XbhwYbt27TJkyEDfo1q1akVwxbSBjG6JiYnl ypXz/ZvHHnvs8OHD1sKvv/66sVhCQgINAl0xPWok64xMbaHBZI8ePbJnz270 PVeuXOPHj3fe7nhDUmca1RctWtQoYKlSpWj65r6BLuJgHdMcnWuRl0jqPGPG jMKFCxt1zpYt20cffWQtefnyZarYpu6jUqVK+/fvD3byU6dO5cuXj0tOmTLF xgz5kkb27t07evToO++8kw9cuXJlsJIRGO8SiEuUxruE4y28vAgmaD4oNGfo WsOGDTPmyrJkyeILwqxZs0wnjEbksC7kBugv3EBnVeF79OcZOXJknTp13nvv vehP5T3IvAHFUTnzdvz4cR78CB555JEIrpg2CKnbt99+KwZRBQsWfOihh3Lm zMkf77777qtXrxoL9+jRg3dlz569WrVqWbNm5Y/vv/++u25EhEydkaktZ86c oYE0702XLt0DDzxQpkwZUZ6aa7cciBNkdN69ezfVLlasbNmyd911F/9/++23 HzlyxBMzXcGpOgZ0rkVeIqMzjTypoWNtq1ev3r59e5Ht+fLLL40lqW7fd999 om2koOTKlYs/5s6dOzU1NeD5n3rqKfFdsM/byJcUdO/e3fdvli9fHrBkZMa7 BOISpfEu4WwLLy+CibFjx4pL3HrrraVLl05ISOCPH3zwAZf5888/fcGZMWOG yeaIRQ7rQi6B/sINdFYVvkd/Hu4X6G/0p/IeZN6A4qiceUtOTs79DzxM1Xme a6/b2bNnOc9GPciaNWv4runz588/+eST3AQNGzZMFN6+fTtvvP/++y9dusSH 0wiQtqRPnz4pKcl9b8JDps7I1Ja+ffuy4++88454wnTevHnZsmWjjXnz5v3j jz/csD9ekNG5efPmPv8dklOnTuUtNJVgVV966SXXTXQNp+oY0LkWeYmMzpUq VfL5f4jZs2cPbzl37twDDzzAs3LjwzWdO3dm/Vu1asXPvtFeGsFmz5593Lhx AU8+ffp04zzdJm8jX9JIjx49+LvG7bMveHIjAuPdA3GJxnj3cLaFlxfByMGD BzntlitXroULF/JNbsePH69fv365cuXE075kBp/zo48+Omzhf//7n/Gc0Ygc 1oVcAv2FG+isKnyP/jx8iwIybwC4gcqZNyN8S7/O89yQuq1fv/7xxx8/ffq0 ceOZM2c4I/fYY4+Jja+88oo1ySbScQre9hZunQlWW65fv07D1AkTJpi2i4zc xo0bozQ1rgmp84kTJ3ji0KVLF+N2HsbkyJEjflOXTtUxoHMt8pKQOtPEmRp5 kvTDDz80bl++fDk3d/v27eMtNMvOmDEjbWndurVpeagLFy4EPPnJkyf5ibmq Vava523kSwbjm2++sUluRGC8qyAuERvvKi618PYimHjxxRd56EXDLeP2Gzdu GG9O27VrF59z3rx59ieMUmT5C7kH+gs30FlV+C5TkhqcX375ZcOGDXz3hYkK FSpIZt6onVm9erV1rey//vpr27ZtiYmJly9flrGH+fXXX+kQe/3pijTbXbdu XbDFyV3NvB08eHDlypUh1/8MaaS1fEAZr1y5QoJQf0F62p9BviSIOci8xQsR /5bBP17cdttt/PHatWt58uTxBVrApHz58j7/7dbRWeo8bmdFqCHlhnrixIkR mJdmCKnziBEjWCjTmjazZs3i7ZMmTXLXRNdA5s0pdK5FXhJS55MnT7Ken332 mXH7kSNHePuyZct4S9euXXkLjXslr86TlEyZMi1dutQ+byNfMhj2yY0IjHcV xCVi410l5pm3pKQkUtvnvznNviRNwficmzZtsi8ZpcjyF3IP9BduoLOq8N2+ zP79+5s0aeL7h4SEBJr6iUUdK1asWLRoUb7jN2PGjHn+4fvvv+cClStXpo8k UXJyMnUimTNnppIFChQQ5z906NBjjz0m1j5Knz79gw8+mJiYaLWETzVhwoQL Fy688MILhQoV4kPo6l26dDEtkURs3ry5evXqwnIqlseAaFdNmbcdO3ZwgX79 +lltmD9/Pu/94Ycfgil28+bNmTNn1q5dO3fu3OLqRYoUWbx4sbWwpJEhZTxw 4EC9evW4yyCoABU7c+aM9YryJYEiIPMWL0SceaMvOElHTSt/pFaRv54ff/yx qeRbb73Fu7x5ykAet7Mic+fOZce9WVJYWULq3L59e5//eajr168bt1+6dIl/ Q3zzzTfdNdE1kHlzCp1rkZfI1FheAKpOnTrGBxi/++47bu7EgjZ33303faTB m+Slp06dymf44IMPaBjP/wfM28iXtME+uRGu8W6DuERmvNvEPPM2Y8YMLrl2 7Vr7kmJAEnLJqShFlr+Qe6C/cAOdVYXvNgXOnj1bvHhxkRcSD8sT/Jo5sQCp ienTp/MZ7rjjDvrYv3//Bx98UOx9/PHHee8PP/wgFhg3QsJap5x8qqeeeqpU qVLWQ9544w1j4dWrV/O94gkJCWXKlDF6wTRq1IhLmjJv1MnefvvtPn+uzPr6 Wq4MWbNmDbYk5sWLF++9996AmpBW4meycI20l/HHH38UMmbMmFEsDVqsWDHT S0DkSwJ1QOYtXohMt/Pnz3ND2rlzZ96yYcMG/mLOnj3bVPiLL77gXb///nv0 BjuI21mR3r17s+MKrnHnJSF1btiwIalUoUIF6y5errZdu3ZuGecyyLw5hc61 yEtkauyQIUO4ZaPhJQ8saa5Rp04d2lKzZk1RjIff7777Ln88fvx4YmKizfOM efPmpfJVqlShs+3bt48vYc3byJe0xz65EZbxHoC4RGC8B8Q88zZ8+HCekV25 cuVv/9NYmzdvprGWdTI4ceJEPifNqkjAjh070gRt6tSp1ue2ohRZ/kLugf7C DXRWFb7bFBg6dCh/5V999dVjx47dvHlzx44dTZs2rVSp0p9//kkFli1btnTp Uk7g3HvvvUv/QdyPwSkjho4aP348tWNbt2792/+mbH6iilo5akmocaMWady4 cfwKP5qKUkmjMcZTPfHEEzQn3b9//+jRo8VimOKi1FqWKFGCNubMmXP16tW8 UcxY+/Xrt3fvXjF9sz5tOnjwYN5iuktNPALWsmVLG9Hq1q1LHrVp02bevHkn Tpwgp6i15BM++uijolhYRtrImJKScuutt9L2/Pnzz5o16+rVq9RlfPXVVyyj mMuHVRIoBTJv8UJkuonbqidPnsxbvv/+e95iutGamDlzJu8K+Zusx7iaFaFh apEiRah8yZIlI7QvrRBS54oVK/oCPadMlCtXzqfSHQ7hgsybU+hci7xEpsbS wJKGlNyqFy5c+KOPPuL12LNkycJjPOL06dNcgEbINGS95557xICQ5iZiAClo 1qyZz/8bMQ0j6aNN3ka+pD02yY1wjfcAxCUC4z0g5pm3bt26UbFChQqROK1b txY3KlDQe/fubVzaaNSoUb5AFC9efO7cuaJY9CJLXshV0F+4gc6qwnebAtzv ZMqUyZRdpy7J+JEfXQy4zptIGdWsWZN/RBA8//zzvOvTTz81WcXb77//fuNy lOJU/fv3N25v164dbxfrYYp1yKmvNJ6Z+7LKlSsbN1ozb+Kl1c8++6yx5JIl S7ik9UYUI/v377emO6iS8LHnz5+PwEgbGTt27OjzP6VrSlTybzfkyIkTJ8It CZQCmbd4IQLdDh48yD+J3n333WLRRZGB37Fjh6m8WOHZvhXyHlezIi+//DJ7 He68I+0RUmf+QbBFixbWXaQ27aLBv1vGuQwyb06hcy3yEska+8svv/Aa7EaM LTyN2cRsnf/JkSNH9uzZ+f+EhIT58+eLwtRI8vbRo0fzlmB5G/mSIbFJboRl vDcgLuEa7w0xz7w9/vjjPv/jXSVLluRDChQoQFLw/1WrVhU3v4mEGF29T58+ b7zxBq8Z4vOv4bNz504uFr3IkhdyFfQXbqCzqvDdpkCvXr34C26/tE7IzFu6 dOnEi7kZar54vnn77bdbF/lv1KgRX3f//v2mUz388MOmwhMmTODCCxYs4C0i mbZq1SpjyQ8//NDnf8rS+OBwwDcs8LqmZKHxqdIuXbr4/LeomXJfMowcOZKv sm3btgiMDCbjzZs3uQ3v0KGD6Yrnz5/n8/MjrvIlgWog8xYvhKsbNYMiJ79w 4UKx/auvvuKNW7ZsMR2yaNEi3hXDF10FxL2syMqVK3noW61aNdOrwTQkpM68 akGTJk2su0hAn/8nLbeMcxlk3pxC51rkJTI19rvvvuP0zmOPPdawYUMxzS9d urR4geaPP/7o+wfaTk0iDZupMZwyZQo/s0BDRB6Xnjhxgh9tqFWrlmgtA+Zt 5EvKYJPckDfeMxCXsIz3jJhn3nhxP6Z///7Jycl/+yMixmn//e9/ReGpU6cu XbpUfKThXL9+/biYeB7ZEZFlLuQq6C/cQGdV4btNgfXr14ulwBo1akRzPdNi d0zIzFv16tVN2w8cOMCnfe2116xHiWSa8UUGfCprO7NgwQIuTK0TbxFvwRMv emDeeOMN2pg3b17jxoCZN5oF80bxHj1q6zgH2759e6vBAdm1a9f48eM7dOhA NSRHjhx8Qpo4R2BkSBnpqA0WeBevyCdfEqgGMm/xQri69ezZk796piULRJtm TYZ/++23vMt052rMcWnM/Ntvv+XLl8/n/x1E3NWsMyF15rVAa9SoYd3FS6SK BULjDmTenELnWuQlIXWmUSKPsWmgyKNr2iJeala4cGF++5UYpN19992mZxP6 9u1r7BHEsTR6P/4Pa9as4Y2fffYZfeQflOVLymCT3JA33jMQl7CM94yYZ954 dSlizJgxxu1nz57lSZx9u0fzxEqVKvn8T6dytXFJZOuFXAX9hRvorCp8ty8z bdo04zs6ixYtOmzYMFOKPmTmzZouE69rMT1qKgzjvYMHDw55KvEQqMi8kXn8 stS6deuKYhcuXODXodavX994eMDMm3jPQu3atXnLqlWruFjAV5SaIEvuv/9+ XyB++umnCIwMKaMNrKF8SaAayLzFC2HpJh4ioLbC9Dj/li1beNfMmTNNR1GD ybtUe9GAG2Pm06dPczFixowZ0ZqYJgipM0/cypUrZ93Ft1LE75KeyLw5hc61 yEtC6kzDZp//l1bTox8DBw7kdq93795/+++64Y8TJkwwnUF0FjTs/PXXX0MO 83z+33DlS0p6apPckDRe8kKOgLjIGy95IUeIeeaNn2wqUaKEdRc3iaVLl7Y/ w+uvv87X4gX63BPZdCFXQX/hBjqrCt9DFjt16hR1N8WKFRPNfvny5c+dOycK RJB5mz17Np/q888/tx4lft957733Qp7Kmnn729A/Nm7cmK41ffp0XpQvXbp0 pqc7A2be/v7nPQsJCQn8Kmd+8DZ//vwhf18QE+rMmTO3bdt22rRpe/bsmTRp Em8UmbewjAwpY9myZWsGgWes8iWBaiDzFi/I6zZr1ix+n2nBggWtObTjx4/z t7V///6mXS+88AK3S6bFNmOO42PmP/74g+8q9xneCwZC6vzSSy/5/D+FG5eD JqiasZh9+/Z110TXQObNKXSuRV4SUmf+pbVTp07WXbzSFC/5e/PmTf6h9o03 3jAVExH55JNPaKjpk+CBBx6QLynpqU1yQ9J4yQs5AuIib7zkhRwh5pk3Xu2H xleXLl0y7eL3a+TKlcv+DOI5UF5ZyD2RTRdyFfQXbqCzqvBdsvD169fnzJnD qSHfvx+6jCDztnfvXj7Pm2++aT1KZMOM68uFlXmjYHFe1Mgtt9xifaAyWOZN vGdh+PDh4urdu3cPrtD/Z8GCBbwcxEMPPXT69GmxXbT8xsybvJHBfBfd9KhR o+wNky8JVAOZt3hBUre5c+fyAjI5cuQI9qAB31Bteqc2jeJ4RiD/k7dnODtm vnz5cu3atbnJeuaZZ8SyxiCkzmJ1btM7OD7//HPeTj2muya6BjJvTqFzLfIS e52pPc+cObMvyC8LvNg7Nfj8kR+juOeee0xrXYrfqXmp9vPnz5+1IB55Gzdu HH28ePFiWCVlsE9uSBrvGYhLWMZ7Rswzb/PmzeOS1rXN69at65PIeTZu3Njn fy+h+G3UJZGtF3IP9BduoLOq8D2sQ1JTU3lKWLRoUbGRO6mAT90GSxldv36d jypXrpx10exWrVqxtsYXCoSVeeMXntaqVeuDDz5o06ZN586dP/nkE14t00Sw zNvf/7xnoVKlSiJttXbt2qDS+OnevbvP/4vJqVOnjNsDZt7kjQzm+19//cVp z5Av2JUvCVQDmbd4QUY3GmXxNzFLliw2hfm3V4KGZ2LjnDlzeONXX33lkMmO 4eCY+c8//xQLGj/55JPWV/DoTEidL1++zAtE1K9fX2QsaXBOUwbaeNttt8Vv GhOZN6fQuRZ5SUidq1ev7vOvAWVacOCPP/4oXLgw7WrQoAFvEWNI6gWMJfmd 9cTRo0eDXUV+ff5gJck8GirTGJsa54AH2ic3ojHeDRCX6I13g5hn3qhZK126 NJW87777jE3c77//zr+W8m2QW7ZsqVix4tatW632860XxuXf5UW2RjOsC7kH +gs30FlV+G5TYO/evb/++qtpY61atXz+V3yKLdwTFSxY0HqGYCmjv//JaxHU 1Bi3//zzz/wcVtmyZa3v95TJvFHI+MWp4m2nNthk3sRrBPk24xIlSoR8sx47 RfYb19K8evXq888/z6cSmbewjLSRsVmzZnzmkM+KypcESqFy5m3jxo3r/qFo 0aJUu2gsJLbI/zKbNgip28KFC/kXB+Ltt9+mjz/88MMcA6IVEvfcFihQIDEx kVqeNWvW5MqVy+e/Ldb6KETMkakzMrXlypUrYpVj6mXmz58/b968Of+GxPHC JSWR0blbt24sYM+ePS9cuHDu3DmaL/AW4wIOcYdTdQzoXIu8JKTOYnGSxo0b i2UHTp8+LZbZ/89//sMbaTDMeYYcOXJQr0HzDtryySef8Gi5RYsWNleJPsPT u3dv3m68DSw5OVl8s/r3788Fhg4dylt27dolSkZjvBsgLtEb7wYOtvDyIpiY OHEiF27atCm/R+PgwYP8OoP06dPz6+b54a8sWbIMGDBg9erVf/75J113/Pjx NGLx+W+9ML6qXl5kazTDupB7oL9wA51Vhe/B9vLtbZkyZaKvPHc9//vf/6ZO ncqO16lTR5Tkn4d8/tfBUJkUP7zLJmV05MiR7Nmz016aYFIrRE3cH3/8MWPG DJ5dEibb5DNvZDm1kNxyHj58mF/iHCxpJu4unjx5smmXeM8C06dPn2BaCWg2 zYVffvll8ujatWs0X+YkLSMyb2EZKSNjxowZBw8efP78+b/9ub6lS5fWrVt3 2rRpEZQESqFy5u2WW27xBWfkyJERXD1+sddNvFTFhooVK4ryNMbmJoKHWPxP 5syZZXL13iNTZ2RqC3Wp9hL5LO+D1goZnWmUUrVqVSGXqDz169cPdmtEXOBU HQM61yIvCakzDflEMocG29T+03CRf5P1+QeHxjEhDSbz5s3Lu7L64f/z588f 8FkJQfQZHvHKsIcfflhspG+TzXeNvonGM0RsvBsgLtEb7wYOtvBhiWCE5mL8 OJLP3+gVLFhQHCWWlpo9e7aoDD5/Rk40j75AayhJimyNZrgXcgn0F26gs6rw PdjeTZs2FShQQHhN/4vmImPGjImJiaLkV199JYrRxDBdunQDBw7kXTYpI2Ly 5MniDhAjpLB1gYWwnjZt1KiR9bQZMmQgL5555hnjY/Vnz57lgNJeXtLNyJAh Q8Th1jt+rezYsUN4lM4P/1++fHn+x/i0qbyR9jJSXyxC4/M35mLCbnoRj3xJ oA7xm3kbMWJEBFePX0Jm3sTXLRgPPvig8RBq04oXLy72UjugZtrtbyfGzFxb aHBrL5FP7j7htIrkd5MGLU888QQ/1+zz/2j+9NNPx/Vw5W/n6hjQuRZ5iYzO NNMfO3YsDcaMFTVfvnyffvqpdQWnAwcOPPTQQ2JgSVCAjE9YBOTw4cNc2Pqy bMmSNDbm7aNHjxYb7ZMb2bNnd8R4N0BcojfeDRxs4cMVwcSwYcOMq3DT/6aH s44ePdqpUye+90xw5513zps3L+AJZUQOGM1wL+QG6C/cQGdV4btNAfL6tdde M3U9lSpVWrlypbEY9VDiLlmmefPmvKts2bI+f4oy2CV27txZtWpVYxr/rrvu Crh0XrBTrVq1ig80Zt7EgkjBMC6U1LFjR54Rt2nTxnRyahjZNrq6jVBGZs+e zaugMyVKlKBW9OrVq3wJY+ZN3siQMu7bt6927drGeT3VVXLH+rCwfEmgCCpn 3oARl3SjMduKFSv4JcvKgjrjDWHpfPny5U2bNq1du/bKlStuGuURqGNOoXMt 8hJ5nW/cuHHo0CFq55cuXXrw4EH7RWxSU1Np3Etn5icXvIGGiDRcj/48MTHe BOJiJb7i4gE3b96k6dLy5ctJ4WBxpyZx27Zty5Ytoxoic4tgSJGDRTPcCzkL +gs30FlV+C5T8tixY8v8JCUlBXsiMiUlhfqmRYsW7dq1K+R6aCYuXry4bt26 lStXml5MEBl0qqxZsyYkJHz55ZfUiG3YsGH9+vVr1qyZOHHic889x+mmKlWq GA8hv+bPn797927TqcQvTYMGDZI3gOpJYmKi/WQ5AiMlr0s9BTlivyy5fEkQ c5B5ixd01k1n371EZ5119t1ZoKQ3QGc1QVzUBHFRE8TFDXRWFb7H2grnadu2 rc/na9mypXXXjRs3eK3LgO+DsMIL+qVPn/7QoUPKGgnSNsi8xQs666az716i s846++4sUNIboLOaIC5qgrioCeLiBjqrCt9jbYXzlCxZ0ufzdenSxbrr0qVL /CbWJk2ahDzPL7/8wk/li4dnFTQSpHmQeYsXdNZNZ9+9RGeddfbdWaCkN0Bn NUFc1ARxURPExQ10VhW+x9oK52nZsqXP/66HiRMnnj17VmzfunUrv4Y1ISFh 7ty5wQ7fvHnz0KFD33rrrRw5clDhbNmy7du3TzUjgT4g8xYv6Kybzr57ic46 6+y7s0BJb4DOaoK4qAnioiaIixvorCp8j7UVzpOYmGh8EUyZMmUaNGhAf/lj QkLCp59+anO48W0RWbJkmT59uoJGAn1A5i1e0Fk3nX33Ep111tl3Z4GS3gCd 1QRxURPERU0QFzfQWVX4HmsrXGHbtm2tW7cW76JlcuTI0aFDhz179tgfO2jQ oIoVKz788MNdu3bdtWuXmkYCfUDmLV7QWTedffcSnXXW2XdngZLeAJ3VBHFR E8RFTRAXN9BZVfgeaytc5PLly7/99tuqVasSExNTUlJibU5g4sJIEEOQeYsX dNZNZ9+9RGeddfbdWaCkN0BnNUFc1ARxURPExQ10VhW+x9oKAIAdyLzFCzrr prPvXqKzzjr77ixQ0hugs5ogLmqCuKgJ4uIGOqsK32NtBQDADmTe4gWdddPZ dy/RWWedfXcWKOkN0FlNEBc1QVzUBHFxA51Vhe+xtgIAYAcyb/GCzrrp7LuX 6Kyzzr47C5T0BuisJoiLmiAuaoK4uIHOqsL3WFsBALADmbd4QWfddPbdS3TW WWffnQVKegN0VhPERU0QFzVBXNxAZ1Xhe6ytAADYgcxbvKCzbjr77iU666yz 784CJb0BOqsJ4qImiIuaIC5uoLOq8D3WVgAA7EDmLV7QWTedffcSnXXW2Xdn gZLeAJ3VBHFRE8RFTRAXN9BZVfgeaysAAHYg8xYv6Kybzr57ic466+y7s0BJ b4DOaoK4qAnioiaIixvorCp8j7UVAAA7kHmLF3TWTWffvURnnXX23VmgpDdA ZzVBXNQEcVETxMUNdFYVvsfaCgCAHbHKvAEAAAAAAAAAAAAAoAPIvAEAAAAA AAAAAAAA4AZ42lR9dNZNZ9+9RGeddfbdWaCkN0BnNUFc1ARxURPExQ10VhW+ x9oKAIAdyLzFCzrrprPvXqKzzjr77ixQ0hugs5ogLmqCuKgJ4uIGOqsK32Nt BQDADmTe4gWdddPZdy/RWWedfXcWKOkN0FlNEBc1QVzUBHFxA51Vhe+xtgIA YAcyb/GCzrrp7LuX6Kyzzr47C5T0BuisJoiLmiAuaoK4uIHOqsL3WFsBALAD mbd4QWfddPbdS3TWWWffnQVKegN0VhPERU0QFzVBXNxAZ1Xhe6ytAADYgcxb vKCzbjr77iU666yz784CJb0BOqsJ4qImiIuaIC5uoLOq8D3WVgAA7EDmLV7Q WTedffcSnXXW2XdngZLeAJ3VBHFRE8RFTRAXN9BZVfgeaysAAHYg8xYv6Kyb zr57ic466+y7s0BJb4DOaoK4qAnioiaIixvorCp8j7UVAAA7kHmLF3TWTWff vURnnXX23VmgpDdAZzVBXNQEcVETxMUNdFYVvsfaCgCAHci8xQs666az716i s846++4sUNIboLOaIC5qgrioCeLiBjqrCt9jbQUAwA5k3uIFnXXT2Xcv0Vln nX13FijpDdBZTRAXNUFc1ARxcQOdVYXvsbYCAGAHMm/xgs666ey7l+iss86+ OwuU9AborCaIi5ogLmqCuLiBzqrC91hbAQCwA5m3eEFn3XT23Ut01lln350F SnoDdFYTxEVNEBc1QVzcQGdV4XusrQAA2IHM2/9j77zDrCiyv38RGcI4ZBAU BFQQUFiigCDiCIIiK0GCK6wyBoLg7o8krhIVUFAkSRAFlLBkUHLyJcjAzOyQ owhDmhlyGgQGJrznueexnrb73k63um/11Pn8wcNUV1fX+Z6udG53tVeQWTeZ bXcTmXWW2Xa+kJLuQDqLCflFTMgvYkJ+cQKZVSXbw10LgiD0oMibV5BZN5lt dxOZdZbZdr6Qku5AOosJ+UVMyC9iQn5xAplVJdvDXQuCIPSgyJtXkFk3mW13 E5l1ltl2vpCS7kA6iwn5RUzIL2JCfnECmVUl28NdCyFITU3d5OfKlSvhrgtB /AWKvHkFmXWT2XY3kVlnmW3nCynpDqSzmJBfxIT8IibkFyeQWVWyPdy1EIIf f/zR5+eXX34Jd10I4i+IH3nLyMhISEgYO3bs+PHjd+/enZmZaeOiOQDzup05 c2bhwoVDhgyZMmXKpk2b7t27FzBbWlra+vXrR40aNWHChPj4+PT0dJ7V5YrV eyYlJQVuFfg3WAbQZOfOnWP8rFy58uLFixxq6X246+whZLadL+aVpL49FLjf sVZHhH379u3WJSkpSXXK/v37Z82aNXjw4GnTpm3fvj0rK0tbLOQJVuDdu3fN WHrnzp3Y2Fi4r0aMGLFgwQJtNRzFkl9AZJB60qRJI0eO3LBhw/Xr14PlTE5O XrZs2bBhw0DAw4cPB5QOOH/+/OLFi4cOHQqZL1++rHNpM77QB9rv3r179+zZ Y/Lc06dPox/D8hCCjTUp3G9Y4UOHDmmPmm8vIc61TE5XzDQcOFe/zQK3b9+2 VL0QofHCCWRWlWwPdy2EgCJvhLAIHnmDGebDDz/sU1CxYkWYv9m4rtcxo1ti YuJTTz3l+yuPP/74ihUrVDnXrVv30EMPKbOBzjAGOVX70DB5z8D8ds2aNZ07 d77//vvBoiZNmmjzwBS0d+/ekZGRStsLFSoEqw/+9fYaHHX2HDLbzheTSlLf HiJ871gbI0KRIkV8ukAJLDMs+Tt27KjK0KhRoyNHjqiKzZcvX7ACFy1apG8s dO8DBw6MiIhQnpU7d+6YmBidoBZfzM9wYEVQvHhxZVXBR59//rkqkHXr1i1w n0qKGjVqHDt2TFVg3759lXly5co1cuRI7XXN+yIYkHPcuHGPPfYYnrt582bD U86fP8+MnTNnjskLccTGmnTw4MFYYZhEqQ6Zby+hzLUsTVfMNJzx48cHy8OY Pn26JZVChMYLJ5BZVbI93LUwBsagaD8nTpxw6BIUeSOEReTI26FDhx588EFs O5UrV4bJD/6/fPnyp06dsnFpT2Oo28SJE/PkyYMSFS5cuGbNmvfddx/+CSuR nTt3spybNm2COTmmN27cuG7durgoy58//8qVKx23xDpm7pmUlBS0gvHss8+q 8ly6dAkWnngUxKlTp84TTzzB8kNH7ZQBHoGXzl5EZtv5YkZJ6ttDh+Mda29E MIy8ValSBXNmZma+8MILmAhndenSpWnTpmyxc+vWLVbm7du3dQpcsGCBvrG1 atXCnGBO1apV2T0GtG7d2sZjXTYwOcOZNGkSc03RokUrVaqELgA+/fRTlk1p FIxZ0FgKFSqEf8Iof+PGDZazd+/emB4ZGVm/fn3wHf45fPhw5XXN+yIY77// vsovcP8YntWuXTuW3xORt127djEHqSJv5ttLKHMtS9MVkw3HTORNv5Vxh8YL J5BZVbI93LUwZv/+/Sg4/MehS1DkjRAWkSNvbdq08fmn0PPmzcMUmKxiU3rv vfdsXNrTGOo2a9YsUKZMmTIbNmzAp6ZTU1P79++Pir344ouY7c6dOzDBhpQS JUokJiZiYkJCQqlSpSDx0UcfzcjIcNgUy5i5Z5KTkwv/CYYctWvMjz/+GNX4 6KOP2CsbK1asKFCgACQWK1bs5s2bTtTfK/DS2YvIbDtfzChJfXvo8LpjbY8I p0+fPhmInj17oivXrl2LOZcvX44pAwYMgMth4uzZszHxq6++UlYYE0ePHq0t +Y8//tAx9saNG9WqVYNze/TocenSpWx/lAkkqlChApYZGxurLxcXzPjlxIkT GIEpVKjQmjVrMCSYkpLSrFmzqlWrKl/GfPvtt7Hy7du3x8f2wChYU0RGRk6d OpVl27t3L2arXbt2WloapFy+fLlSpUo+/yN/Z86cYTnN+yIYvXv3xjsKx02f icjb/PnzlbEd8SNv6enp1atXZxVWRt7Mt5cQ51qWpismGw60kYBtds+ePRiq bdiwocvv3NF44QQyq0q2h7sWxmzevJkib4S0CBt5S01Nxalpt27dlOnYYUZF RckWJDGj29y5c3HFwYApfeXKlX3+X9UxZdu2bdgdTZ48WZnz559/DuOsWB+r owm+BaNdY8JEFxYyM2bMUKWzKa7yyUAJ4aWzF5HZdr4YKkl9Oxd43bF8R4Sk pCSMDLz77rssceDAgT7/s1iqTUehMpD++uuvs5QDBw7gRbU7JJjh1KlTy5cv VyX+97//xTLHjx9vo0yrmPELiIMxsb179yrTMzMzlY+xnTx5Ep9j79Chg+qB vWvXrin/7NWrlzbIxsJxysfezPvCEBav04+8nTt3Dt8zrVevnlcib4MGDcKF +dNPP62KvJlvLyG2LEvTlRAbTo8ePeBcaLnaV5idhsYLJ5BZVbLd6lkHDx6M i4sztBoGly1btly4cMFm5RQsWbLEfORt9+7dkE27YzlUGPpAOMp+QlJCkTdC WISNvI0ZMwZbjWoLkUWLFmH6rFmzbFzdu9j+LaNZs2Y4J8cfWL/55hsUEObD qpxPPvkkpD/33HMhV5YzTkdF2O8vM2fOtFG9HIPM0SeZbeeLoZLUt3OB1x3L d0R46aWX4JRHHnlEGUHCdX2xYsVUmbt27erz7zDGUrZu3YqViY+PN39RfbZv 345lDhkyhFeZOhj6BdYvuBNd+/bt9YtC3QBYGelku3v3Lr75q93EDz1Yvnx5 VZlmfGGIycgbrmfB5A0bNngi8paYmIgL865+VJE38+3FoblWwOlKKA1nx44d +EDsuHHjbNQnRGi8cAKZVSXb9fPUrFkTxosZM2Zcu3btnXfewUdwff5X2rt1 66b9/ktWVtbYsWOVm1WWLVt2/vz5yjx//PEHZIBi+/btqzr9008/LeIHu8Fv v/0WutOCBQtiUfAfPPr0009jfigZ/sStKqZNm8ZeBGa/X5w9e7ZXr15PPPEE 208Juus33nhDFTmkyBshLMJG3t58802ffycT1QP5aWlpOCnq37+/jat7F3uR t+vXr5coUQLkqlSpEqZ89NFHPv+PudpNb7p37+7767bYguB0VOSnn37CLtpw B++cjczRJ5lt54uhktS3c4HXHctxRGAP80CPGjBdVWH8HlDXrl1ZCuuKOW62 M3LkSCyTvVjkKIZ+WbBgAdbn119/1S8KVh+QrWnTpvrZkpKSsEDtu6Iffvgh HmLvG5r3hSFmIm+gOeaB9dexY8fw/yJH3mDhie8sP/jggxcvXoQFne+vkTfz 7cWhuVbA6UooDad27do+/3vK7myEqILGCyeQWVWyXT/Po48+Cja2a9cO34VX 0a9fP2XmW7du4a9pCNs+1PfXX7KuXr2KiT169FBd7j//+Q8ewu+qDx8+XHtR n3+3PcyPEbOIiIiFCxeyvU8B/CrNtGnT8ubNG7CExo0bK69LkTdCWISNvDVv 3hyaDEyBtIdwY8zOnTvbuLp3sRF5O3nyJMoITJo0CROnTJmCKcrXUhCYG/v8 P3ywb9ALgtNRkT59+gTTRCpkjj7JbDtfDJWkvp0LvO5YjiNCdHS0zx+mUC3h b9++jb9xFytWjE2DN23apJ0Yz5w5ExN//vlnmLF37dp10KBB8+bNM7PzvxZY WM2dOxcfMCtbtqy9Qqxi6JcvvvjC5w/I4Dsy9+7dgzXF77//rt1fC9/bBR3w T1i5xMXFqd4zzfY/s4SiLVmyRHWIORfKxxTzvjDEMPJ27tw5uApkePrpp8EX R48exfwiR97Yu5xwB8Kfr7/+uu+vkTfz7cWhuVbA6YrthsMelgNvWq0JF2i8 cAKZVSXb9fNg5A155ZVXYNQ4duzYuHHj2Najyv1U8b17oGPHjtjhnDhx4vnn n4eUPHnysC+Tmo+8wSkbNmyIiYnBRLjuBj/stVMWMYPy8+XL1717d+ijVq9e jW+bYn9VpkyZ8ePHJyYm3rx589dff2Vf2VbaTpE3QliEjbzh9rbatyeAqlWr +kz8EJzDML/OmjVrFnRrsAjKnTu3z793x4QJE9hSaOPGjdgdqV69Wb9+Pfs5 4/jx47yrHxKORkVgIYPPUVeoUMFm/XIKMkefZLadL4ZKUt/OBV53LK8R4eDB g5h57Nix2qMwYY6KisIMrVu3hlkxxmQ6deqkzAbn+gJRtmxZ1XN0Opw9e/Zf //rXa6+9Vq5cOTwdrHZtUDP0C36BolSpUhcuXOjQoQN77wZWGX369GGvzMBR TJ86dSrIhc+kIbBmBD1ZgUuXLsV01ctTwMKFC/GQ8vk6k74wxDDyBoX7/I9J HDlyBP4UP/KWkJDA3jPFFG3kzXx7cWKuFWy6YrvhwB3o838DIuBeSS5A44UT yKwq2a6fh0XeBg0apPyNrHPnzpjOdh/97bff8AGzV199VVkCDFK4def777+P KeYjbwh7EF27zxuLmN13330BPwANHZrqBwUY3fAUGPe15VDkjRANYSNv+NND 27ZttYdwK2CYiNq4uncxv856+eWXlVOvoUOH3r59mx1NT0/HZ4zz5MkDvR9M /Pbs2fPpp58qn+CFFKfMsIWjURF87yNcywGhkDn6JLPtfDFUkvp2LvC6Y3mN CBhQKlCgAEzCtUfv3r3bqVMnVVigdu3aqs+VsgAC1HPAgAH9+vWrWbMmpkB9 TH4HLT4+XhV8OHDggJkTuWDol5YtW6JQ7KOrJUuWZK/V1KtXDx9+S0hIwBR8 +dHn3/o7MjIS/w/52aqEPVu1b98+1bXYw2zKx+FM+sIQ/cgbjKd4lO0eJnjk 7c6dO7j92iOPPIKfkc0OFHkz316cmGsFm67YazjJycn4CQ/2XKX70HjhBDKr Srbr58HIW8OGDVXpM2bMwB5j1apVmIKPZwMnT55UZf7Xv/7lU+wL6kTk7R// +IexwX/ywAMP+Pw/JGnLocgbIRrCRt5gtgxN5u9//7v2UP369XGiaOPq3sX8 OuvLL7986aWXYN6Fb9n4/G/QHzp0iGXYsGFDvnz5VBPvIkWKsNn45cuXnTLD Fs5FRTZv3oxLHripwrLJiVDIHH2S2Xa+GCpJfTsXON6xXEYE/BH8tdde0x5K S0tr3Lixzx876tWrl3JL58GDB6syz5s3D+rD/szMzPzkk08wv2ojl2AkJSW1 b98eLH344YfxROjk4ULu9PCGfqlVqxYTedCgQcnJydn+z+01bdoUE7/99tts xYZsPv82rTBU3bt3D0yYM2cOPjEFCyh8Tun777/HbLt27VJda+3atXiIffLS ki/00Ym8gTlFixb1+R8sYbILHnlj27IpzdFG3rKttBe+cy396YqNhjNx4kTM 4GZoWgWNF04gs6pku34ejLxp+4RVq1Zhb8A2RO3SpYvPvyHeDg346ZnSpUtj Ticib+vXr9exAsY+GCKHDx8OYz1uiIodo7YcirwRoiFs5K1u3bq+IF/awp8R W7ZsaePq3sXqOivbv8sKzKVxqgadrfIB3d9++61t27ZlypSBQ+XKlXv33XeP HDmC8zSYkHOuesg4FBUBEXC1WKBAAfZ8tczIHH2S2Xa+GCpJfTsX+N6xIY4I hw8fxlnumDFjtEcxfFG0aFH88GJ6ejos+fHTP8CIESP0C8/MzKxRo4bP/z6m ak9sfbKystatW4fnAtOmTTN/rm0M/cI2Xx0/frwy/fLly/gSKN7/bPc2WFak pqYqc7K9yHDTabZi2rhxo+pac+fOVebMDtkXSnQib7CqxUOxsbEpf7Jt2zZM hCvCn8qv37qAvl9ADdyd47XXXktRgC/Mli9fHv9k+c23F15zLRvTFcOG07Fj R6yGdo9B16DxwglkVpVs188TLPK2fv16VeQNv72iw/333485nYi8BYuY4W4S +MuOCvaBVDPlEES4EDbyhjO3qlWrag9hi3v77bdtXN272Ii8IUOGDMH+Z/r0 6dqjyhdR//nPf/qEfNDaiajIhQsX2LacCxYsCLWKOQKZo08y284XQyWpb+eC Q3esvREBBhfsS7ds2aI6tG/fPjz09ddfK9NPnjyJ+7Dlz5//3Llz+uX37dsX C8EdwyyRmpqKrxe589luQ79069bN53+lUXsImwZ+iByqjSbPmDFDlW3Xrl3K JRL7c+HChaqcEyZMwEO4OTYXXzCCRd7Yjn/6PPPMMyYvxAV9v7z22mtm6qx9 DMN8ewllrmV7uqLfcPCh0Oeff958gdyh8cIJZFaVbNfPYz7y1qBBA58/bt84 CC+99BLmdC3ydv78efZJ1sqVK0M5YO+VK1ewe6TIG+EJhI28vffee9jk2YbD CEwgsTV9/PHHNq7uXWxH3o4fP46K9erVSydbRkYGrAV8Qv7cw32NCTcVPlXu C+sOJ6Ihc/RJZtv5Yqgk9e1ccPqOtTQivPnmmz7/G4sqnwJTp05Ft546dUp1 6IcffsBDhl9PYO/N2duDFN+aAS5dumTjdEsY+mXUqFE+/1uNaWlpqkOw4vP5 vy6X7X9gD19U7NevnyobaykYQIMVDf45aNAgVc533nkHr4Xf0OTiC0awyBt7 AFKfOnXqmLwQF/T9ot34LiABd/xGzLcXq3OtUKYrOg0nKSkJDw0YMMBSmXyh 8cIJZFaVbNfPYz7yhs/EFi5c2HCjBhZ5e/fdd1WH+Ebe6tWr5/M/azdr1ixl OkXeCA8hbOSNbc+r3BkYmDx5MqbrvwOe8zDULVjfeOLECVRM+2OEkkWLFmG2 7777LpR6OgHfNeatW7fwo9g+/x6eYXzPQjRkjj7JbDtfDJWkvp0LTt+xlkYE 3LsMLqE9NGLECDiUO3du5TM/CPuIwNSpU/XLx88GRUREYAQpGMEGQXzECDh/ /rz+hULH0C8rVqzAyoDCqkMvvPCCTxGSwpd9nnrqKZVd7LVNFgjCxwCqVaum zAZn4TZu7OkyLr5g6LxtCguxyxrY+7NwCfiTfcXAHfT9cufOHW2FgVatWvn8 HxLFP3VuP/PtxVLLCnG6otNw2EaC4EdLZfKFxgsnkFlVsl0/j/nI2/DhwzFF +WnsgED3ct999/n++o0D5N///jcWooy84c9PPsU2CAydiNnFixfxEPumKoMi b4SHEDbyBvONwoULQ6tp1qwZm2xA64ZJqc+/XYZsARND3Tp06NC2bVvt3ins bdOZM2diCkwyVT+AXrlyBT+0DcLqL23CAsc1Jqw42EbWMKm+d+8et1p6H5mj TzLbzhdDJalv5wLHOzb0EaF06dKQuUGDBtpDbD7PBiDGV199hYdiY2Oz/W9N wkV3796tygZm4m6lyo2v4S6CqTWsEVgQKTExsVSpUtqnks6dO1eyZEm0xdCQ 0DH0C9zelSpVgvrUqlVLeav//vvv+KHJmJgYTGGhrWXLlilLwN2tgdOnT2MK W8hs27aNZYOzMPH777/HFPO+yA6ksAr9b5tqEfwLCwEJ+IUF8+3FfM6Aapuc rlhqOIxp06ZhyevWrTNQwUlovHACmVUl2/XzmI+8wZCK8bS//e1vhtMA/JUH ZgLKbmrhwoX333+/NvLGOp/JkyerytGJmO3duxcPqR4jiY+Pxy1SKfJGeAJh I29Az549seF88MEH165dgxkLzEgxZejQoTYu7Wn0dVuyZAkqU7ly5alTpx48 eDArK+vixYuffPIJ7htcqFChs2fPZvt/B2/fvj10hjBXT01NhZkhzNXhLDx9 ypQp7plkGjP3zM6dO7f/Ce5eAitNloI/r4OxbHfrggULwhptxYoVy/6KcnSQ DV46exGZbeeLGSWpbw8dXnds6CMCrFNwlGnVqpX2aFpaGl46MjJy9uzZ7PGt pUuXFihQANLLlCmD3+isWrWqz/8S0ODBg7du3Xr79m2oIUzRoa/2+V+ZXLNm DSu2T58+WEP2/l21atUwG9xdq1evhnNhCQASwaoBc/bt29eUsqFhxi8zZ87E Kr366qv4AuyJEydwM3xQkn2iNCMjA+OlsKxYvnw56AwpX3/9Na6G2rZtywqE YQsXOCVLloyLiwORwYkw6EPKAw88wF5rNe+L7EAKA8nJyez+GTRoEGYYOXIk puh/IjNnRN7MtxdLLUurtvnpiqWGwxg2bBgWDutrq7JwhMYLJ5BZVbJdP4/5 yBvQu3dvTKxVq1Z8fDzGJGEUGDJkCJSg/G5LixYtMGePHj2gu9u3bx/kwYkB olxbsWs9/fTTu3fvhq6SbUSpEzG7desWjn3Qs+EwBzWB0Qd/sfL9NfLGni3/ 4YcfzAtIEC4gcuQN+kN8pxvBH+98/t8pgv0Im4PR1y09PR1fyWdEREQopWOb t5w6dQofAECUHeO///1vS1+Ocw0z9wysL3zB+fLLLyEPDKk6eRBYg7hhkpDw 0tmLyGw7X8woSX176PC6Y0MfEc6dO4f5u3btGjBDbGwsG49KlSrVsGHD8uXL 45+QvmPHDsy2ZMkSjP+wmrAbA+jfv7+yTPbZNSiNCVKkSBGWH6bo7Nd2oH79 +jBKGtoSOmb8cu/evc6dO7ObHz8Agai2GNq2bVuxYsXwUH4/+P8SJUrAokOZ c86cOcx3TLe8efOuWrVKmc2kL7IDKQzAPaNzR8H9pmN1zoi8mW8vllqWVm3z 0xVLDYfRvXt3zKDd9M9NaLxwAplVJdv181iKvF2/fr1Ro0ZMKBiA8HFBbc7V q1drO6ioqKgPP/wQ/6+MvEHvx8Ydn/97zTBUofL6z6r94x//YGex8R1qVaFC Bd9fI2+XL19Gt0K2L774woKIBOEwIkfesv3d4yuvvMImivny5evYsaOnO0bb mNFt8eLFbCdexnPPPRcXF6fMlpqaqlTV5/+lO7x7fegT+hpzzJgxkAcWNTp5 ENU6RSp46exFZLadLyZ7eOrbQ4TjHRviiMB21NfZqh3y4JZZSlq2bHno0CFl ttOnT8fExOCzOozHHntsxYoVqgJhLo1Hx40bxxLPnz/fs2dP/D4dA0r77LPP /vjjD5PmhIj5Gc7nn3+urCr8HxYd2mzHjx9v0KAB/taPgLPAZdqcsA4qW7Ys ywYrrIDDmUlfBFRYP/IWGRmpY+/Jkycxm/YbrC5gL/L21ltv+TSfQTTfXszn 1KptabpivuEw2G+1t27dsioLR2i8cAKZVSXb9fPgY7fNmjVTpW/ZsgWlUMbT sv3PtEO3z34AQmBwmTJliurHLOi7WDQsd+7cMGzt2rWLfVBb9T5RYmIihstY fnzYe/78+Ziyfft2beWvXr3avn17dlbevHlffvnlY8eO4a5Kyshbtn9bBvy9 o1OnTgbCEYSLCB55Q2BiEB8f/+uvv7L3ICTEvG7Qv4FWK1eujI2NvXz5crBs 0GfGxcWtX79e9dO5gNibMxNWkVlnmW3niyUlqW+3Dfc71oUR4caNGwkJCWvX roV/tVuSMnB3rI0bN/7yyy86lTl48KD2y2jZ/h179u7du3r1arDl6NGjLm/m ackvWVlZUMNNmzaBLfqbC4FcsDKCkmHpoV/m8ePHQTfDB5nM+CKYwl4kjO3F ZM7Q1TbZcISCxgsnkFlVst2hwk+fPr1hw4bNmzdfvHgxWB4YSrZv3w5d0LVr 1wwLhKF53759sFaFMg3HNSUnT57cunWrGa+dOXMGylf9qEQQ4cUTkTciW27d ZLbdTWTWWWbb+UJKugPpLCbkFzEhv4gJ+cUJZFaVbA93LQiC0IMib15BZt1k tt1NZNZZZtv5Qkq6A+ksJuQXMSG/iAn5xQlkVpVsD3ctCILQgyJvXkFm3WS2 3U1k1llm2/lCSroD6Swm5BcxIb+ICfnFCWRWlWwPdy0IgtCDIm9eQWbdZLbd TWTWWWbb+UJKugPpLCbkFzEhv4gJ+cUJZFaVbA93LQiC0IMib15BZt1ktt1N ZNZZZtv5Qkq6A+ksJuQXMSG/iAn5xQlkVpVsD3ctCILQgyJvXkFm3WS23U1k 1llm2/lCSroD6Swm5BcxIb+ICfnFCWRWlWwPdy0IgtCDIm9eQWbdZLbdTWTW WWbb+UJKugPpLCbkFzEhv4gJ+cUJZFaVbA93LQiC0IMib15BZt1ktt1NZNZZ Ztv5Qkq6A+ksJuQXMSG/iAn5xQlkVpVsD3ctCILQgyJvXkFm3WS23U1k1llm 2/lCSroD6Swm5BcxIb+ICfnFCWRWlWwPdy0IgtCDIm9eQWbdZLbdTWTWWWbb +UJKugPpLCbkFzEhv4gJ+cUJZFaVbA93LQiC0IMib15BZt1ktt1NZNZZZtv5 Qkq6A+ksJuQXMSG/iAn5xQlkVpVsD3ctCILQgyJvXkFm3WS23U1k1llm2/lC SroD6Swm5BcxIb+ICfnFCWRWlWwPdy0IgtCDIm9eQWbdZLbdTWTWWWbb+UJK ugPpLCbkFzEhv4gJ+cUJZFaVbA93LQiC0IMib15BZt1ktt1NZNZZZtv5Qkq6 A+ksJuQXMSG/iAn5xQlkVpVsD3ctCILQgyJvXkFm3WS23U1k1llm2/lCSroD 6Swm5BcxIb+ICfnFCWRWlWwPdy0IgtAjXJE3giAIgiAIgiAIgiAIgpABirwR BEEQBEEQBEEQBEEQhBPQ26biI7NuMtvuJjLrLLPtfCEl3YF0FhPyi5iQX8SE /OIEMqtKtoe7FgRB6EGRN68gs24y2+4mMusss+18ISXdgXQWE/KLmJBfxIT8 4gQyq0q2h7sWBEHoQZE3ryCzbjLb7iYy6yyz7XwhJd2BdBYT8ouYkF/EhPzi BDKrSraHuxYEQehBkTevILNuMtvuJjLrLLPtfCEl3YF0FhPyi5iQX8SE/OIE MqtKtoe7FgRB6EGRN68gs24y2+4mMusss+18ISXdgXQWE/KLmJBfxIT84gQy q0q2h7sWBEHoQZE3ryCzbjLb7iYy6yyz7XwhJd2BdBYT8ouYkF/EhPziBDKr SraHuxYEQehBkTevILNuMtvuJjLrLLPtfCEl3YF0FhPyi5iQX8SE/OIEMqtK toe7FgRB6EGRN68gs24y2+4mMusss+18ISXdgXQWE/KLmJBfxIT84gQyq0q2 h7sWBEHoQZE3ryCzbjLb7iYy6yyz7XwhJd2BdBYT8ouYkF/EhPziBDKrSraH uxYEQehBkTevILNuMtvuJjLrLLPtfCEl3YF0FhPyi5iQX8SE/OIEMqtKtoe7 FgRB6EGRN68gs24y2+4mMusss+18ISXdgXQWE/KLmJBfxIT84gQyq0q2h7sW BEHoQZE3ryCzbjLb7iYy6yyz7XwhJd2BdBYT8ouYkF/EhPziBDKrSraHuxYE QehBkTevILNuMtvuJjLrLLPtfCEl3YF0FhPyi5iQX8SE/OIEMqtKtoe7FgRB 6EGRN68gs24y2+4mMusss+18ISXdgXQWE/KLmJBfxIT84gQyq0q2h7sWBEHo QZE3ryCzbjLb7iYy6yyz7XwhJd2BdBYT8ouYkF/EhPziBDKrSraHuxYEQehB kTevILNuMtvuJjLrLLPtfCEl3YF0FhPyi5iQX8SE/OIEMqtKtoe7FgRB6EGR N68gs24y2+4mMusss+18ISXdgXQWE/KLmJBfxIT84gQyq0q2h7sWbnD8+PGe PXt+8MEHZ86cCVcJBGEPr0TeUlJSdu/eDf/auGLOwIZud+/e3e3n0KFD2qNp aWnr168fNWrUhAkT4uPj09PT+VTUAczbDkatXr16zJgxI0aMWLp0aWpqqsNV y1GY1zkjIyMhIWHs2LHjx4+HGywzM9PhqjmO1fZFPVIwSEl3MK8z9P8zZ84c OnQodIlanS9evLjbiNu3b2uLhU5g7969e/bsycrKMlON06dPY2lXrlwxk5+R nJy8bNmyYcOGzZo16/DhwyYvFy4M/bJv3z59tZOSkpT57927t3PnzjF+Vq5c Cf7SKXz//v2g0uDBg6dNm7Z9+/aAWkGeYJeGOYMZG01OHkK/EEcc6pfM35xW /Y7otDLbLTcgVpszL2SedTiHzKqS7eGuhRu0bt3a5+e9994LVwkEYQ/BI28w wVuzZk3nzp3vv/9+aCBNmjSxccWcgY0eFabf2LE8/vjjqkPr1q176KGHfAoe fvhhGIO4VZcrJm1fsGBB6dKllUYVKFBg9OjRzlcwh2BSZ1hfwN2i1LlixYqw rHa+gg5i0nbqkQwhJd3BjM43btzo2rWr76+8//779+7dY3lg0eEzYvr06cpi jxw5Mm7cuMceewyPbt682bC258+fL168OOafM2eOSRtv3boFd4iqMjVq1Dh2 7JjJEtzH0C9FihTRVxt6V8x59+7d3r17R0ZGKo8WKlRo2rRp2mIvXrzYsWNH VVGNGjUCZ6ly5suXL9ilFy1aZGig+clDiBfiC/d+yerNad7viGErs9FyA2Kj OXNE5lmHc8isKtke7lq4QUxMDHoN5jPhKoEg7CFy5C0lJQUnP4xnn33WxhVz BlZ71F27djH1VJG3TZs25cqVC9IjIiIaN25ct25dzJk/f/6VK1dyrjcPzNj+ yy+/3HfffWjvM8888+abb7IonJnJJ5FtTudDhw49+OCDKGzlypXh1sL/ly9f /tSpU65U0xHM2E49khlISXcw1DkrK+u5555DecuVK9e8efNSpUrhn9HR0eyh IzPr9wULFrBiYZqqOgoDimFt27Vrx/KbjLzBTVKrVi08Bfp26G0KFSqEfxYu XPjGjRtmCnGf0CNvVapUgWyXLl1q0qQJM79OnTpPPPEEy/Pjjz8qy8zMzHzh hRfwEJTfpUuXpk2b4p+wnLx16xbLefv2bZOODoj5yUOIF+IO337Jxs1p0u+I mVZmteUGxF5z5ojMsw7nkFlVsj3ctXCDCxcujBkzZuzYsVYfoedYAkHYQ+TI W3JycuE/waCKzKszSz1qenp69erV2VRKGXm7c+cOTMUhsUSJEomJiZiYkJCA i7JHH300IyODe+VDxIztNWrUgPrDYHr48GFMge4UVis4E84Bj5G7gBmd27Rp A5LC4mvevHmYMmnSJLzNPP3MthnbqUcyAynpDoY6z5gxAxtmt27d8CE3+Ld7 9+6YCEcx240bN04GYs+ePfnz54ecDRs2VPafvXv3RscVKFDA5FJ9/vz5yqW9 ycjb22+/jfnbt29//fr1bH986ccff4yMjJw6daqZEsKCoV9Onz4dUPCePXui vWvXroVsH3/8Mf750UcfsTdMV6xYgbIXK1bs5s2brMzly5dj5gEDBsAQj4mz Z8/GxK+++orlhKaHiaNHj9bW4Y8//tCpuaXJQygXcgK+/ZKNm9Ok3xEzrcxq yw2IjebMF5lnHc4hs6pke7hrQRCEHiJH3pTgk/Ayr84s6TZo0CAcVp5++mlV 5G3btm04vkyePFl5ys8//2xpWeQmhrbDND537txQ+VGjRinTYQ6JRh09etTZ KuYIDHVOTU3FRwJgLa9Mx2lMVFSUcjHoLaz2S9QjBYOUdAdDnfEhqLJly7JQ DFK7dm1Ir1Spkv6qvEePHj7/C/vB3p5jgR39pfq5c+fwPdN69eqZH2JOnjyZ J08eyNyhQwfVxlPXrl0zPD2M2JvhJCUlYejj3XffxZSMjIy3336bBUgZLCK3 c+dOljhw4EBIiYyMVL5HDECzgvTXX3+dpRw4cABPX7FihdVKWpo8hHIhJ+DY L3G8ObV+V2GylSkxbLm8LsQFmWcdziGzqmS71bMOHjwYFxdnaPWFCxe2bNli +5lAq6enp6fDGBfizq43btzYunUrdLM2CoHBdM+ePaCM8qFx5y5HyANF3ryC ed0SExNxWOnqRxV5++abb3B+BQsi1YlPPvkkpD/33HP8as0HQ9vBFjRq4sSJ ynTo5DF948aNzlYxR2Co85gxY1BP1VYwixYtwvRZs2Y5W0XHoHgRL0hJdzDU uWTJkiBs9+7dVekLFy7E1qpz+o4dO/CZn3HjxgXLY3KpjsuZiIiIDRs2mI+8 YfQAgEWBYWahsDfDeemll8DYRx55xPAtWuh7UZmZM2eyRJSrWLFiqsw4AWjU qBFLgXUBnh4fH2+1kpYmD6FcyAk49kscb05Dv1sNiJlpuVwuxAuZZx3OIbOq ZLt+npo1axYpUmTGjBnXrl1755132B4U0G9069Yt4OdyJk2aVLZsWd+fFC9e vH///ua/ymfydKzY9OnT9+/f37x5c3xwFyhYsCB0uar88+fPL+Jn27ZtqsRK lSrB/5ctW9agQQO2e0CZMmW2b99uWAKSlJTUokULtk9p7ty569atGxcXp8pm 6XIEwaDIm1cwqRv0TtWqVfP537u8ePHiG2+84ftr5O2jjz7y+R+H08bk8UUk 1Ta/ImDGdtx0JTo6Wvkgx+LFi7Eb9PTWDa5hqPObb77p87+9q3olOS0tDUcc GE+draJjULyIF6SkO+jrDAMBdn1DhgxRHWK/U+hsgInPxcG/Oj/dmlmqz5s3 D/N8+umnx44dw/+bibxVqVIFcjZt2tQwp2jYmOGwZ8Z++uknw8yQBzMrP1LA SlBd+qmnnvL5f4PTnm5jTLQ0eQjlQk7AsV/idXOa8bvVgJiZlsvlQryQedbh HDKrSrbr53n00UfBxnbt2uHWASr69eunzHzr1q0WLVposwHVq1c/c+aM/rUs nY4Vq1SpUkREhDY/dMXKp/d//PFHTP/ll1+0iQMHDsT9SJXkz59f+WXwgCVk +7duKFiwoLYCcG8o922wejmCYFDkzSuY1I29igKTOvjz9ddf9/018jZlyhTM oO0zYXHk8//wwTbfFgQzto8YMQLtglEVfz6GUTU6OhpSGjdu7EYtvY+hzs2b Nwc9q1Wrpj2E29V27tzZqco5DMWLeEFKuoOhztgk3377bVV6eno6/pr8ySef BDyRPa0Ei3Gd8g2X6ufOnStWrBhkePrpp6E3Pnr0KOY3E3nDV/D+85//4J8p KSlxcXGCv2eK2Jjh4DgFw7SZaEmfPn20I/jt27dxsQCCs3UE22xBubKYOXMm myGAvF27dh00aNC8efPMvFBjafIQyoWcgGO/xOvmNON3SwExky039AtxROZZ h3PIrCrZrp8HA1zIK6+8smTJkmPHjo0bNw6jjoUKFVJuwgm9HOasUqXKxo0b oZeDvo59vqdVq1b617J0urJi3bp1+/XXX5OTkxcuXMi+iv7tt9+yzPqRN5// S0Off/757t274+Pj2U4Xn332mX4J58+fx0/h5MqVCwas33//Hbr3qVOn4pQJ BjjlV7wtXY4gGBR58wpmdIM+gb1niinayBv0ftgnqJ6FWL9+PXu49/jx45xr HxpmbIcJ/2uvvYb1L1269OjRo3EP5Hz58kFn6Eo1PY+hzvjZjiZNmmgPVa1a 1efNZ1QQihfxgpR0B0OdGzVqhBPpq1evssQ7d+60bNkS+8kuXboEPLFDhw4+ /y76qg3iVBgu1Vu3bu3z//J75MgR+NN85O3ChQuYE2a8MLnFB7cQWC5t3bpV //TwYvX+P3jwIJo2duxYw8ywcnnooYcgc4UKFVSHQJaoqCgsCpQH3TDs2alT J2U2uIovEGXLljV84s7S5CGUCzkBr36J181p0u+WAmImW27oF+KIzLMO55BZ VbJdPw8LcA0aNEgZ8+/cuTOm7927F1Ng4MbHz6AzVM4iYLXFomc6nbnV07Fi MGNRbQ26b98+fKKsYsWKrML6kTe4Is46kGPHjmEJbdq00WZWlvDPf/4TEydM mKCsA6iK6crHiS1djiAYFHnzCoa6wVwL91p55JFH8GNb2YEib+np6fiMcZ48 eUaOHAnz5D179nz66ad58+ZlE0hIcdQWq5i8ZxITE3HfYyVLlixxvoI5BJNP 0bRt21Z7CLfyhmWIU5VzGIoX8YKUdAdDndlDR7Vq1dq6dWtycvLixYsbN27M +sbWrVtrz4Js2IuyR3qCob9UnzNnDh5l+02Zj7wlJCSwUAb+JyoqKjIyEv8P c9qVK1fqlxBGrN7/+GnLAgUKKNcmwWCfptVqCMuZTp06qYY/WCaoviLKAmLQ 4gYMGNCvX7+aNWtiCswB9u/fr3N1S5OHUC7kBLz6JV43p0m/mw+ImW+5IV6I LzLPOpxDZlXJdv08GOBq2LChKp19DH3VqlWY8vnnn2PKd999p8ocFxeHh954 441gF7J6OlYs4DtKL774IuZnW4zqR942bNigKgHWxZBet25dbWZWQmZmJj7P XL58edW3irL/3JMTYF+usXQ5gmBQ5M0rGOrG9mBRTpy0kTcAegm2dSSjSJEi bN5++fJlh6ywh5l7BhaVOO1s0aJF8+bN2Uv3lSpVog+bmsRQZ9wl9e9//7v2 UP369X3+hZ5TlXMYihfxgpR0B0Ods7KymjVr5tMA/Ty+T9G7d2/tWRMnTsRs Bw4c0K+AzlI9NTW1aNGiPv9zBewHYvORN7b/FXbgmzdvhmkwlAMn4rNVMEW3 8VSPO1i9//FVmtdee80wJ+iA4xp0tqr3E9PS0jCmGhUV1atXL+Wm2YMHD1aV M2/ePOVKAZYbn3zyCeY33JnB0uQhlAtxh1e/xOvmNOl38wEx8y03xAvxReZZ h3PIrCrZrp8nWIBr1apV2ANAv40pXbp0wRT2LIcSDC7VqFEj2IWsnq4TeRs+ fDgWxb7orR95U23dBtStWxd7bJ3Mx48fx5T/+7//09aBRSaXL19u43IEwaDI m1fQ1y0+Pj537tw4kUtRgO/7lC9fHv9k+X/77be2bduWKVMGjpYrV+7dd989 cuQIzoph6u6GPVYwvGdgqomv2b711lu4aSqkwMCKvWLp0qUvXbrkUl29jKHO OJoov5THwGchWrZs6VTlHIbiRbwgJd3BjM6ZmZnjx4+vWbNm/vz5IyIioqOj v/nmmzt37uDXD1XbBSMdO3bEUUD5qZqA6CzVWd8bGxvLBqNt27Zh4sSJE+FP nY947tixA3NWqVIlNTVVeYhtZKrcbkUoLN3/hw8fRnPGjBmjnxOGbIzVFChQ gL0NxMCf2IoWLYofEk1PTweRS5QogYWPGDFCv3DwNayAfP7NGVS7jgesie3J g6UL8YVXv8Tl5jTvd/MBMfMtN8QL8UXmWYdzyKwq2a6fJ1iAa/369arIG363 DoaVgOU0adLE53+AOVhPbvV0ncjbtGnTsG7su0JWI2/PPPOMYeSNfRVI9aop 8v/+fOGU7d5m6XIEwaDIm1fQ141tcaYPdK2qE2/fvs3+j2+4C/igteE9065d O59/c2nVE8JDhgxBw/v06eNsFXMEhjrjgrpq1araQ/iIi3Y7d69A8SJekJLu YElnWIynp6fj/0+cOIG94uLFi7U5H374YTj0/PPPG5YZbKnONrDSB6amwUpO TU3FPDNmzFAd2rVrFx5iqwPRsOSX6dOnozlbtmzRyXbhwgVsJsCCBQtUR/ft 24eHvv76a2X6yZMny5Ur5/Nvtcde0glG3759sRDlfjX62Js82LgQF3j1S1xu TpN+z7YSEDPfckO8EF9knnU4h8yqku36ecxH3ho0aAB/lixZMmA5L7zwgs+/ 84D2xUx7p+tE3saMGYN1W7NmDaY4EXlbsmQJpkyePFlbB/bT4dChQ21cjiAY FHnzCvq6aTd4CYjODiQZGRn49K+AP/cY3jP4ck1MTIz2UIUKFeBQzZo1napc DsJQ5/fee8/nf1zh5s2byvQzZ87gDfbxxx87W0XHoHgRL0hJd7A3kmYrVv3a J3OSkpLw0IABAwzLCbZUZ8/z6FOnTp1gJWdlZeErjf369VMdYl2NKsokDpb8 8uabb/r874SqelQlcAjfgfIF2cJr6tSpePTUqVOqQz/88AMeMvyoAXsP1MYu r5YmD6FcKBR49Utcbk4zfkdMBsQstdxQLsQdmWcdziGzqmS7fh7zkTe2rgzY TWE5Tz75ZLALWT1dJ/KGu2IC0NFhihORtyNHjmBK//79tXVg+fWfuwt2OYJg UOTNK+jrdufOncuBaNWqlc//HTT88+7du8FKgM4E+xDtZphhR992mAnjDs8B VyX4Ib9SpUo5WL+cgmHbZLumq75bMXnyZEzXPlTpFShexAtS0h1sR97wa4ww KGh/qmZ7WMEy3LAcnaX61atXtYMRe01v6tSp8GfAvV8YtWvX9vmfoVJtaMZ+ dxb2IwuW/ILv40ATCJbh1q1bzz//PJr8j3/8I+CLhCNGjICjuXPnVj6EhrDP AYDm+jV5+eWXIVtERITOJCEYliYPoVwoFDj2S6HfnIZ+Z5gMiFlquaFciDsy zzqcQ2ZVyXb9POYjb5999hmmzJ8/X5X5wIEDuOlox44dg13I6unBKgYjBW7N ly9fPjYCOhF5y8jIwLVk1apVVX070L59e8x/+PBhG5cjCAZF3ryCPd0CfmHh zp07qp+br1y5gh/aLleunMvzYTMY2o69XJUqVWCdoky/efNm6dKl4dCLL77o bBVzBIY6g7yFCxcGPZs1a8ZGQLhh6tSpgzePvR1mRIDiRbwgJd3BjM579+5V RWO+++47nCt+++232vxsN5V169YZVsDqUj3YFxagV4EZLMz2lVVlhS9btkyZ uWvXrph++vRpMxd1H0v3Pw5PDRo0CHgUBGnatCna26pVq2Av9bAV08yZM1WH vvrqKzwUGxub7X8dEgb63bt3a+uM6yDl1uIB/WJy8mDpQu7AsV8K/ebU93vA a+m3MsOWG9CbNi7EHZlnHc4hs6pku34e85E36MBxS9innnpKubbKysrCLcS1 o7kSq6djxapVq6bqo8aPH4+ZO3XqxBKdiLwBbdq0wUQ4qjz9f//7H9pSuXJl tjEdRd4Ie4gcedu5c+f2P8EtLGAuxFL0fzHPefCKvEGn1759+/vvv3/UqFGp qakwkd62bRt0JtiBTJkyhWelOWFo+9ixY7H+L7/88pkzZzDxwoULbKPvb775 xo2Kehwz9xh76vuDDz64du0aLLtiYmIwhe1+4EXM2E49khlISXcw1DkuLq5A gQK1atWKjY29d+8edIzQQjHuUbZsWbbtm5Jhw4ZhW05MTAxYZnJyMnPToEGD MPPIkSMxRf+jisEib3369MF05UPLMLnF0EdUVNTy5cthKQQpX3/9Nc5+27Zt q3Oh8GJ+pAaj8LtIrVq10h6Fobl58+aoTMGCBVeuXLlixYplfwW/mpSWloaN KDIycvbs2ezH+qVLl8INAOllypTBr21WrVrV539yYPDgwVu3boUFDrS1adOm Qfk+/4fR2S462YH8Yn7yYOlC7sCxXwrx5tT3e7atVmbYcgO2slCaMy9knnU4 h8yqku36ecxH3oDevXtjYt26daFvgY7ut99+w421ff5vl+tfy9LpWDGff8UK o9uNGzdggBs+fDhOWvLmzcteNc12LPJ26tQpGEYhEYY56NIvXbp08+bNBQsW FCpUCDMr5aXIG2EPkSNvDzzwgC84X375pY2rexdekTfoWEqWLMlkxBkg8u9/ /9vlz42ZxNB2WBGwIFtERET16tXr1KmDiw7g1Vdf1T45TGgxc4/BLKVevXrs nsEx0ef/9TDYj+mewIzt1COZgZR0B0OdBw4cGLCff+SRR4Itz7t37455tDuG IeAaHceBW3XqEyzyhu/uAQ0bNlSmb9u2rVixYngovx/8f4kSJZKTk3UuFF7M j9Tnzp1Di7p27ao9CgtAHamRpUuXYubY2FgY+DCxVKlSoGT58uXxT0jfsWMH ZluyZAkbFvGuYB24T7O5jdYv5icPli7kDnz7pVBuTn2/Z9tqZYYtN2ArC6U5 80LmWYdzyKwq2a6fx1Lk7fr160qhlFSoUOG3337Tv5al01nkTQuMIJMmTVJm dijylu3fHBXfOVUBN4lqRyOKvBH28G7kzfBr7DkMe5G3t956y6f5iE9qauor r7zCJuo+/2/itrcHcQEztt+7dw96Zpj3Km+S4sWLT5gwQcD3Z8XE5D0Gkxbl /ZMvX76OHTt6erqSzWNdJluPFAxS0h3M6Dxjxgx8dAeB+WSrVq0uXrwYLD80 ZMypem2fob9Uj4yM1KnMyZMnMdvChQuV6V988QWmjxs3TnXK8ePHGzRogI8S IdDzwPilb3V4MT9Ss09RBNwV/+OPP9aRGlm1apWyNNzWVUnLli0PHTqkLPb0 6dMxMTH47BnjscceW7FihaoCAf1ifvJg/kLuwL1fsn1z6vs921YrM2y5Ab0Z SnPmhcyzDueQWVWyXT8PPqXcrFkzVfqWLVtQCtWnmWFtNWzYsKioKNYzwETi nXfeuXHjhpkqmT8dI2/R0dHQL7FnzIDy5ctrQ1vz58/Ho9u3b9dPRHC7VGUo TCfz/v3769Wrp/y16PHHH9fu/mfpcgTBEDnyRijhrlt6enpcXBx0JiI/P4CY tz0zMzMpKQl66Q0bNpw4ccK72zWEBUv3GMzw4+Pjf/31V3yPyetQv8QLUtId zOt89uzZdevWQWsN+IapCBw8eBDmusGOwhQdFgVg7NWrV92slT3Ce/+DVgkJ CWvXroV/dVZGuF3bxo0bYazUmQAE84v5yYPJC7mAQ37x0M2p38rChcyzDueQ WVWy3YmSs7Kyjh49umbNGujPbTzMYOZ0jLzhK6gZGRm7d+/++eef2fZB7nP9 +vXt27dv3rz5/Pnz4aoDkSOhyJtXkFk3mW13E5l1ltl2vpCS7kA6iwn5RUzI L2JCfnECmVUl28NdC5sEew2WIHIYFHnzCjLrJrPtbiKzzjLbzhdS0h1IZzEh v4gJ+UVMyC9OILOqZHu4a2ETirwRkkCRN68gs24y2+4mMusss+18ISXdgXQW E/KLmJBfxIT84gQyq0q2h7sWNqHIGyEJFHnzCjLrJrPtbiKzzjLbzhdS0h1I ZzEhv4gJ+UVMyC9OILOqZHu4a2ETirwRkkCRN68gs24y2+4mMusss+18ISXd gXQWE/KLmJBfxIT84gQyq0q2h7sWNomJiYmOjv7www/DXRGCcBaKvHkFmXWT 2XY3kVlnmW3nCynpDqSzmJBfxIT8IibkFyeQWVWyPdy1IAhCD4q8eQWZdZPZ djeRWWeZbecLKekOpLOYkF/EhPwiJuQXJ5BZVbI93LUgCEIPirx5BZl1k9l2 N5FZZ5lt5wsp6Q6ks5iQX8SE/CIm5BcnkFlVsj3ctSAIQg+KvHkFmXWT2XY3 kVlnmW3nCynpDqSzmJBfxIT8IibkFyeQWVWyPdy1IAhCD4q8eQWZdZPZdjeR WWeZbecLKekOpLOYkF/EhPwiJuQXJ5BZVbI93LUgCEIPirx5BZl1k9l2N5FZ Z5lt5wsp6Q6ks5iQX8SE/CIm5BcnkFlVsj3ctSAIQg+KvHkFmXWT2XY3kVln mW3nCynpDqSzmJBfxIT8IibkFyeQWVWyPdy1IAhCD4q8eQWZdZPZdjeRWWeZ becLKekOpLOYkF/EhPwiJuQXJ5BZVbI93LUgCEIPirx5BZl1k9l2N5FZZ5lt 5wsp6Q6ks5iQX8SE/CIm5BcnkFlVsj3ctSAIQg+KvHkFmXWT2XY3kVlnmW3n CynpDqSzmJBfxIT8IibkFyeQWVWyPdy1IAhCj3BF3giCIAiCIAiCIAiCIAhC BijyRhAEQRAEQRAEQRAEQRBOQG+bio/Muslsu5vIrLPMtvOFlHQH0llMyC9i Qn4RE/KLE8isKtke7loQBKEHRd68gsy6yWy7m8iss8y284WUdAfSWUzIL2JC fhET8osTyKwq2R7uWhAEoQdF3ryCzLrJbLubyKyzzLbzhZR0B9JZTMgvYkJ+ ERPyixPIrCrZHu5aEAShB0XevILMuslsu5vIrLPMtvOFlHQH0llMyC9iQn4R E/KLE8isKtke7loQBKEHRd68gsy6yWy7m8iss8y284WUdAfSWUzIL2JCfhET 8osTyKwq2R7uWhAEoQdF3ryCzLrJbLubyKyzzLbzhZR0B9JZTMgvYkJ+ERPy ixPIrCrZHu5aEAShB0XevILMuslsu5vIrLPMtvOFlHQH0llMyC9iQn4RE/KL E8isKtke7loQBKEHRd68gsy6yWy7m8iss8y284WUdAfSWUzIL2JCfhET8osT yKwq2R7uWhAEoQdF3ryCzLrJbLubyKyzzLbzhZR0B9JZTMgvYkJ+ERPyixPI rCrZHu5aEAShB0XevILMuslsu5vIrLPMtvOFlHQH0llMyC9iQn4RE/KLE8is Ktke7loQBKEHRd68gsy6yWy7m8iss8y284WUdAfSWUzIL2JCfhET8osTyKwq 2R7uWhAEoQdF3ryCzLrJbLubyKyzzLbzhZR0B9JZTMgvYkJ+ERPyixPIrCrZ Hu5aEAShB0XevILMuslsu5vIrLPMtvOFlHQH0llMyC9iQn4RE/KLE8isKtke 7loQBKEHRd68gsy6yWy7m8iss8y284WUdAfSWUzIL2JCfhET8osTyKwq2R7u WhAEoQdF3ryCzLrJbLubyKyzzLbzhZR0B9JZTMgvYkJ+ERPyixPIrCrZHu5a EAShB0XevILMuslsu5vIrLPMtvOFlHQH0llMyC9iQn4RE/KLE8isKtke7loQ BKEHRd68gsy6yWy7m8iss8y284WUdAfSWUzIL2JCfhET8osTyKwq2R7uWrjB 8ePHe/bs+cEHH5w5cyZcJRCEPcSPvGVkZCQkJIwdO3b8+PG7d+/OzMy0cdEc gHndUlNT58+fP2TIkJkzZ4J0+pmTk5OXLVs2bNiwWbNmHT58OCsri0NdeWPe 9rS0tNWrV48ZM2bEiBFLly4FKRyuWo7CvM45r1Va7ZdSUlLAcPjXsRp5FVLS HczrDBPLhQsXwogwZcqUTZs23bt3T3n04sWLu424ffu2TvmWRhzoOvbu3btn zx7DscZ8Ti379++HEW3w4MHTpk3bvn27m+MaL7+osDFSHzp0CDwydOhQGApN tq/Tp0+jx69cuRIsj22/3LlzJzY2FkYNGJ0XLFiQlJRk6fQQccIv58+fX7x4 MSgMrrl8+bLJmlhqL0hAv4TecrP9LSXYuXfv3jVpUSjIPOtwDplVJdvDXQs3 aN26tc/Pe++9F64SCMIegkfeYIb58MMP+xRUrFgRJiE2rut1zOgWFxdXtWpV 319p0aLFyZMntZlv3brVuXNnVeYaNWocO3bMEQNCwOQ9A5P50qVLK80pUKDA 6NGjna9gDsGkzjmyVZq0PS0tbc2aNdBw7r//fjC8SZMmzlfNY5CS7mBG58TE xKeeekrVyT/++OMrVqxgeWDR4TNi+vTpAcu3NOIcOXJk3Lhxjz32GGbbvHlz sGqbz6nl4sWLHTt2VFWpUaNGUKb5QkKBl18YNkbqGzdudO3aVXXK+++/rx/c O3/+fPHixTHznDlztBls++Xu3bsDBw6MiIhQ1id37twxMTHXr183WUiIcPdL 3759ldly5co1cuRI/fIttRdGML+E0nIZ+fLlC3buokWL9M/lgsyzDueQWVWy Pdy1cAMYO9BrMK6FqwSCsIfIkbdDhw49+OCD2DQqV64M8x/8f/ny5U+dOmXj 0p7GULe5c+eySRTo1qBBg4IFC+KfVapUSU9PV2ZOSUmpVasWHr3vvvtA3kKF CuGfhQsXhnm7s8ZYxMw988svv4AhaMIzzzzz5ptvsiic4eSTQMzonFNbpRnb odVgmIjx7LPPulI7L0FKuoOhzhMnTsyTJw/r1WvWrMl6yIiIiJ07d2I2M+v3 BQsWaMu3NOLA5FZV5qZNmwJW23xOLZmZmS+88AKeVaRIkS5dujRt2hT/hFXV rVu3TJYTCrz8gtgYqbOysp577jnMU65cuebNm5cqVQr/jI6O1nmQqV27dkxz beTNtl+UJuTKlatq1apsBAFat27tzhOJfP3Su3dvPBQZGVm/fv38+fPjn8OH Dw9WvqX2oiSYX2y3XMbt27dtn8sLmWcdziGzqmR7uGvhBhcuXBgzZszYsWN1 Hs92ugSCsIfIkbc2bdrgVG3evHmYMmnSJOweJXw6VF+3y5cv4ywORpBt27bh U9NXr15t1aoVKvb5558r87/99tuY3r59e/zRGU758ccfYRo5depUh02xjJl7 pkaNGjihPXz4MKZAd1qnTh2cReeAx8hdwIzOObVVmrE9OTm58J/gooziRVpI SXcw1HnWrFkgbJkyZTZs2IAdYGpqav/+/bG1vvjii5jtxo0bJwOxZ88ejCc0 bNhQ239aHXF69+6N7i5QoABmCBa3MZ9Ty/Lly/GUAQMG3LlzBxNnz56NiV99 9ZXJckKBl18QGyP1jBkz8JRu3brhQ27wb/fu3TERjgY8a/78+cqoizbyZtsv cINVq1YN8vfo0ePSpUtoAkhUoUIFLCc2NtZMOSHC0S979+7FxNq1a6elpWX7 m0OlSpV8/gf5Au4aZLW9MHT8Yq/lKoF+GIsdPXq0tpw//vhD51xeyDzrcA6Z VSXbw10LgiD0EDbyBnMefCgCZo/KdOwwo6Kibt68aePq3sVQN5i+tmzZ8sKF C8pEmOjifK9FixYsEeZU+Ntuhw4dVD83X7t2jWut+WBoO0wRYcYLFo0aNUqZ DksDHEyPHj3qbBVzBIY65+BWaXXGgq9cUbxICynpDmZ0njt3LsY6GNDhV65c GQQvWrSo/rk9evTw+V/YD/ZWo/kRRwmLgxnGbcznZAwcONDnfwxJ9Vol3F2Q /vrrr5ssJxQ4+sXeSI1P/ZUtW5bFHpHatWtDeqVKlbTRmHPnzuH7jPXq1QsW eWPY8MupU6eWL1+uSvzvf/+L5YwfP95kOaHA0S+9evXSBtlYOC7YY2822osl vzAMWy7jwIEDWGzA12ndQeZZh3PIrCrZbvWsgwcPxsXFGVoNfdeWLVtsPxNo 9fT09PSdO3eGuP34jRs3tm7dmpSUZKMQmEXs2bMHlDH/tHwolyPkQdjI25gx Y3BKoNpLZNGiRZg+a9YsG1f3LrZ/y2jSpInP/9YJS8GJGQD9La/qOYqh7TBB RYsmTpyoTIdOHtM3btzobBVzBIY65+BWSfEiXpCS7mB7RGjWrBnGDTIyMoLl 2bFjBz6LOG7cOKvla0ccJY5G3nBoK1asmCodNz1r1KiRyXJCgaNf7I3UJUuW hFO6d++uSl+4cCGWpq0eLjkjIiI2bNjgROQtINu3b8dyhgwZEko5JuHll7t3 7xYpUsQXaGvKJ5980ud/Yc1S+TrtxZJfEEstFxaJWGx8fLylOnNE5lmHc8is Ktmun6dmzZrQg82YMePatWvvvPMO24sA+o1u3boFfO190qRJZcuW9f1J8eLF +/fvr/OCvL3TsWLTp0/fv39/8+bN2Sv8BQsWhKFQlX/+/PlF/Gzbtk2VWKlS Jfj/smXLGjRowHY1KVOmDIw4hiUgSUlJLVq0YJsDQOdft27duLg4VTZLlyMI hrCRtzfffNPnf09QtTpIS0vDextaro2rexfb80bozUAumBOylCpVqkBK06ZN uVXOYczYjjvJREdHK3/QX7x4MXaDnt66wTUMdc7BrZLiRbwgJd3B3ohw/fr1 EiVK+PzPPulkw+ej4F8bP91qRxwljkbefv7554DBJdw2v2vXribLCQWOfrEx UsPyJFg4i/0+pdr4dN68eZj+6aefHjt2zLXI28iRI7Ec9sKXo/DyCyzKsNra l5c//PBDPGTpPc1g7cWqXxBLLfenn34K+wRJ5lmHc8isKtmun+fRRx8FG9u1 a1exYkWfhn79+ikz37p1q0WLFtpsQPXq1QO+WW/7dKwY9LSqb/EgMEVUPsX9 448/Yvovv/yiTRw4cGCuXLlUJeTPn//ixYv6JWT796xgO3AqgXtD1edbuhxB MISNvDVv3hxu3WrVqmkP4caYnTt3tnF172Jv3nj16lX8AfTtt99mibhPy3/+ 8x/8MyUlJS4uTsz3TBEzto8YMQK7OxhVcd9pGFWjo6MhpXHjxm7U0vsY6pyD WyXFi3hBSrqDjRHh5MmT2ISBSZMmBcvGnoSZPXu21VoFHHGUOBp5u337Ns6Z ixUrxqbTbM8B1QTbITj6xd5IjV2xVv/09HR8iuCTTz5hiefOnQOtIPHpp5+G EfPo0aNYDUcjb3ChuXPn4vKqbNmygnz5QktAv+zYsQNTlixZoso/ZcoUPPT7 77+bvESw9mLDL9nWW+7MmTMx/88//wy3WdeuXQcNGjRv3jx3PILIPOtwDplV Jdv182CAC3nllVegHzt27Ni4ceMw6lioUCHlDwfQLWDOKlWqbNy4EUYfGIPY d4tatWqlfy1Lpysr1q1bt19//TU5OXnhwoXsy87ffvsty6wfefP5P7H0+eef 7969Oz4+nr2t/9lnn+mXcP78eXykOVeuXNAZQk8Ow+7UqVNx6IS+OiEhwd7l CIIhbOStevXqvkDP8wP4WXYPPbLFBXuRN/ZY9Q8//IApFy5cwBToTKDfwIcB EBiJYObGud48MGP73bt3X3vtNTSkdOnSo0ePxr2p8+XLB52hK9X0PIY65+BW SfEiXpCS7mBe51mzZsXExERHR+NmmAUKFJgwYYLOIzEdOnSAbCVKlFBtFGYG 7YijwtHIW7Y/+BAVFYUntm7dGsY4jGB06tTJghkhwMsvtkfqRo0a4QLq6tWr LBFc2bJlSzy9S5cuLB0k8vl/nT9y5Aj86Wjk7ezZs//6179gmC5XrhyWAK3+ +PHjlgqxDS+/LF26FCuvelUtW/E+L6wZTdYqWHux4Zds6y137NixvkCULVv2 p59+MmlCiMg863AOmVUl2/XzsADXoEGDlNOAzp07Y/revXsxBTof/H0EJmnK 0QRWWyx6ptNRWD0dKwYjl2rbyX379uETZRUrVmQV1o+8wRWx50SOHTuGJbRp 00abWVnCP//5T0yEPl9ZB1AV05WPE1u6HEEwhI284U8Pbdu21R7C3ZJhImrj 6t7FRuTtxIkT+KN5lSpV2I7TCQkJbPaO/4F1SmRkJP4fuouVK1fyr31omLQ9 MTER96NWov1hmgiGoc45uFVSvIgXpKQ7mNf55ZdfVnaJQ4cOvX37drDMycnJ 2IuyR63ME3DEUeF05A1m9Z06dVKNAjBbdudDjdn8/GJ7pGYPMtWqVWvr1q3g 0MWLFzdu3JhdqHXr1phzzpw5mML2BHM08hYfH6+0t2zZsgcOHLBUQijw8gt7 sA3Wg6oT2dOVJmcdwdqLPb/YaLks8gbd74ABA/r164evvgJ58+bdv3+/yXJC QeZZh3PIrCrZrp8HA1wNGzZUpbOPYq9atQpTPv/8c0z57rvvVJnj4uLw0Btv vBHsQlZPx4oFfEfpxRdfxPznzp3DFP3I24YNG1QlPPLII5Bet25dbWZWQmZm JnbI5cuX105gXnrpJczPvlxj6XIEwRA28ob7Mf7973/XHqpfv77PP5e2cXXv YnU9C30I+1lhzZo1LJ3thOPzv1C/efNm6GGysrJgRofP00LvZ+NRB0cxYzss LnDa2aJFi+bNm7OX7sFG+rCpSQx1zsGtkuJFvCAl3cG8zl9++SVMGmFNzbZP qVy58qFDhwJmnjhxIuaxGhUJNuKocDTylpaWhiGmqKioXr16KfeOHjx4sAVj QoCXX2yP1JAHPwqgolOnTvgeTe/evbP9H/grWrSoz//sB/sR39HIW1JSUvv2 7aGlP/zww1gCDNPgF3c+A8fLL99//z0m7tq1S3Xi2rVr8ZCZT4UGay+2/WKv 5c6bN0+5bIRaffLJJ1iOO9t0yDzrcA6ZVSXb9fMEC3CtWrUKGz7beLNLly6Y cv36dW05GFyqUaNGsAtZPV0n8jZ8+HAsaufOnZiiH3nT7ixRt25d31+3UdVm Pn78OKb83//9n7YOLDLJvtNt6XIEwRA28ob3bcCPkeG2kC1btrRxde9idT37 wQcfYJ+g2rKA7VJSpUoVmOMpD3388cd4SPkmuwgY2g5TTdyj4K233sJNUyEF BlY0p3Tp0pcuXXKprl7GUOcc3CopXsQLUtIdrOqc7d88avDgwfirBMxyA+7m 1LFjR58/cqX8VI0Zgo04KhyNvL3++uuQv2jRovitxvT09IkTJ+IO+cCIESNM lhMKvPwSykgNvhs/fnzNmjXz588fERERHR39zTff3LlzB7cUw22i2fgYGxub 8ifbtm3DRNAN/sQdU1WEvs9bVlbWunXrYM2F5UybNs1eOZbg5Re2PtV+MH3u 3LnmZ1DB2ottv9huuSrgdHRNvnz5dD5/zAuZZx3OIbOqZLt+nmABrvXr12Mn wyJv+N06GE8DloMfZc6bN2+wXsLq6TqRNxgjsG6LFi3CFKuRt2eeecZnFHlj X5xRvWqK/L8/Xzhlu7dZuhxBMISNvOH0o2rVqtpD+INgsA2ccyqW5o3sJYLa tWurllcwh8dDM2bMUJ21a9cuVccrCIa2t2vXzuffVVv1hPCQIUPQoj59+jhb xRyBoc45uFVSvIgXpKQ72IgkIKxXVH3jEsHnkZ5//nlLZeqMOCqci7zt27cP 83/99dfK9JMnT+LGYvnz52fvqjgHL79wGakzMzPT09Px/ydOnMCzFi9efPDg QZ8JYPmgLZPXt03BQHztC265UMoxCS+/MPEXLlyoygnrNTxk+Mm/YO0lFL/Y a7kB6du3L15IuXmRQ8g863AOmVUl2/XzmI+8NWjQAP4sWbJkwHJeeOEFOJon T55gO0tYPV0n8sb2w2SPBzsReVuyZAmmTJ48WVsH9vPH0KFDbVyOIBjCRt7e e+89n/9Ht5s3byrTYUqDt/rHH39s4+rexfy8cdGiRfjTNkxrtTPArKwsUNWn +Xh0tkJb1col7Bjajm8VxcTEaA9VqFABDtWsWdOpyuUgDHXOwa2S4kW8ICXd wXYkgb1S0atXL9WhpKQkPDRgwADzBeqPOCqci7xNnToV8586dUp16IcffsBD Luwbz8sv3Efq6dOn41kJCQmHDx8OGNJRUadOHW05vCJv2YrXkVx4KJ2XX1JS UvDPQYMGqXK+8847Pv8rtHfv3tUpUKe92PaLvZYbDPbC6Z49e0IvTR+ZZx3O IbOqZLt+HvORN7ZjqkooZTlPPvlksAtZPV0n8tazZ08sCjo6THEi8nbkyBFM 6d+/v7YOLL/+c3fBLkcQDGEjb2yPWdVetZMnT8Z06CVsXN27mNQNVha43VlU VFSwVx5q167t828iqtpfhYX0RfvIgr7tYEXevHl9QTYWxg+6lSpVysH65RQM 77Ec3CopXsQLUtIdDHUOtn0We/apR48eqkNsb7HZs2ebrIaZEUeJc5G3ESNG QObcuXNrvx/BvlYwdepUM0WFAke/8B2p8euoFSpUwGcMrl69elkDe8UVhII/ A+7PYyPyFsxk9iG58+fPmzfEHhz9gq+kVatWTXU6/gIY8IE0hmF7secXGy1X B/zGREREhH4IkQsyzzqcQ2ZVyXb9POYjb5999hmmzJ8/X5X5wIED+Bp+x44d g13I6unBKga9EG7Nly9fPvYqvRORt4yMDFxLVq1aVTsctG/fHvMfPnzYxuUI giFs5O3WrVuFCxeGW7dZs2asrUEDrFOnDiSWK1cuxL0sPIcZ3WAejnsCQwel k5nNnJctW6ZM79q1K6afPn2aR5W5YWg79nJVqlRRveh08+bN0qVLw6EXX3zR 2SrmCAx1zsGtkuJFvCAl3cFQ5w4dOrRt21a7JRR7e27mzJmqQ2w3lXXr1pmp g8kRRwmvyBv0RTDvhTUCi7OxhYPWrq+++goPxcbGmqlkKHD0i+2Reu/evarw 43fffYenfPvttzp1C/0LC1q/JCYmlipVShskPHfuXMmSJXHg0KkSLzj6ZdSo UZiybds2lg18hInff/99sEvYaC+IoV8MW67WL7t27apevfru3btVOaFiuCh2 Z595mWcdziGzqmS7fh7zkTfoHPDp3Keeekq5tsrKymrdurXhSGH1dKxYtWrV VIPX+PHjMXOnTp1YohORN6BNmzaYCEeVp//vf/9DWypXrsw2pqPIG2EPYSNv 2YrnSz/44INr165duXIlJiYGU9h71vJgqNuaNWswXA8MHDgQ/ly+fPkyBcrA Pq52o6KiIA+MMpDy9ddfY8cS8Fvb4cXQdrZryssvv8xe37hw4QLbrPibb75x o6Iex0zbzKmt0oztO3fu3P4nuKkOtCOWEvD5EAkhJd1BX2e2YwlMFKdOnXrw 4EGY7l68ePGTTz7JnTs3pBcqVOjs2bOqs4YNG4ZnJSYmGlbA/IiTnJzMnDto 0CA8ZeTIkZii/BSj+Zx9+vTBo+xR57S0NLyXIiMjZ8+ezX6zXrp0aYECBSC9 TJkyLny2m6Nf7I3UcXFxYG+tWrViY2Pv3bsHAyL0zBhLKVu2LNv2LSDBIjyh +AVWUj7/O5gwdqxevRpaN9QKJPrb3/6GOfv27WtO2pDg6JeUlBT8olPJkiVB bci5bds2yAApDzzwANyHAS9hvr1oMYy8GbZcrV+qVq3q88cABw8evHXrVljt gmumTZtWsGBB9JfOF4o5IvOswzlkVpVs189jPvIG9O7dGxPr1q0LfQsMQL/9 9hturO3zf39Z/1qWTseKAY8//jj0hzdu3ICedvjw4Th4QefJXjXNdizydurU KZg/QCL08DDUXrp06ebNmwsWLMDuHVDKS5E3wh4iR96gP6xXr57vT7D1+fy/ U2hfJ8nx6OsGCwrcE0aH6tWrs/wwUSxWrBim5/eD/y9RogTMsd2wxwqG9wxM fVmQLSIiAiytU6cOrraAV199NdiLJIQSM20zp7ZKM7bDqkqnfX355Zeu1FR0 SEl30Nc5PT0dv3XIwIdtWLMNuONZ9+7dMYN2qzQVlkYccKhONrgZWLHmc+Kb mEDDhg1ZYmxsLDOzVKlScKh8+fLM/B07dpiTNiT4+sXGSD1w4EBWIEaNkEce ecQwoBoswhOKX0CNIkWKsMz33Xcfhq2Q+vXr6wcDecHXL6AP05YNgrA2XLVq VcDyrc7QVBhG3gxbrtYvS5YsYXMkvFWYIb4gOx05gcyzDueQWVWyXT+Ppcjb 9evXlUIpqVChwm+//aZ/LUuns8ibFuidJk2apMzsUOQt278rLPuJRAncJKod jSjyRthD5Mhbtr97fOWVV9gUCKYuMDvydMdoG8PIm3KOHZC6desqTzl+/HiD Bg3w13MEpE5NTXXcEuuYuWfu3bsHPTOsR5QmFy9efMKECS7sVZIzMNk2c2Sr DD1eNGbMGFdqKjqkpDuY0Xnx4sX169dXyfvcc8/FxcUFzM+CD/rfJ822OOLo x20iIyNZseZzfvHFF5g4btw4ZcUOHz7cqlUr1YktW7Y8dOiQvkW84O4XGyP1 jBkz8PE/BNYRoMnFixcNK3/y5Ek8RfXhzhD9cv78+Z49e+J3AxkFCxb87LPP /vjjD8NacYG7X2B9irsPIbBsDBZ2y7Y1Q1MSzC8Mw5Yb0C+nT5+OiYnBh9wY jz322IoVK4JqxBuZZx3OIbOqZLt+nsqVK/v8MUZV+pYtW1AK1SezYW01bNiw qKgo1kXAgPLOO+9oX8wPiPnTMfIWHR0Nww17xgwoX768NrQ1f/58PLp9+3b9 ROT555/3/TUUppN5//799erVU/4S8fjjj2t3/7N0OYJgCB55Q2AuER8f/+uv v7rwqoiw2NDNDND7QX8LJV+9epV74bwwb3tmZmZSUhL00hs2bDhx4oR3t2sI C5busRzWKh1qXxJCSrqDeZ1TUlKgna5cuTI2Nvby5csO18s9Dh48CDPkgIdg XEtISFi7di38a3KBwAuH/GJjpD579uy6deugl3bnoTJGML/cvXt37969q1ev hiXM0aNH8UMPruGQX44fPw7zDcNnREUgmF9gBN+zZ8/GjRvBEPdfeZB51uEc MqtKtjtRclZWFnTaa9asgb7CxsMMZk7HyBu+gpqRkbF79+6ff/7Z8GvpznH9 +vXt27dv3rzZhQ8AEVLhicgbkS23bjLb7iYy6yyz7XwhJd2BdBYT8ouYkF/E hPziBDKrSraHuxY2CfYaLEHkMCjy5hVk1k1m291EZp1ltp0vpKQ7kM5iQn4R E/KLmJBfnEBmVcn2cNfCJhR5IySBIm9eQWbdZLbdTWTWWWbb+UJKugPpLCbk FzEhv4gJ+cUJZFaVbA93LWxCkTdCEijy5hVk1k1m291EZp1ltp0vpKQ7kM5i Qn4RE/KLmJBfnEBmVcn2cNfCJhR5IySBIm9eQWbdZLbdTWTWWWbb+UJKugPp LCbkFzEhv4gJ+cUJZFaVbA93LWwSExMTHR394YcfhrsiBOEsFHnzCjLrJrPt biKzzjLbzhdS0h1IZzEhv4gJ+UVMyC9OILOqZHu4a0EQhB4UefMKMusms+1u IrPOMtvOF1LSHUhnMSG/iAn5RUzIL04gs6pke7hrQRCEHhR58woy6yaz7W4i s84y284XUtIdSGcxIb+ICflFTMgvTiCzqmR7uGtBEIQeFHnzCjLrJrPtbiKz zjLbzhdS0h1IZzEhv4gJ+UVMyC9OILOqZHu4a0EQhB4UefMKMusms+1uIrPO MtvOF1LSHUhnMSG/iAn5RUzIL04gs6pke7hrQRCEHhR58woy6yaz7W4is84y 284XUtIdSGcxIb+ICflFTMgvTiCzqmR7uGtBEIQeFHnzCjLrJrPtbiKzzjLb zhdS0h1IZzEhv4gJ+UVMyC9OILOqZHu4a0EQhB4UefMKMusms+1uIrPOMtvO F1LSHUhnMSG/iAn5RUzIL04gs6pke7hrQRCEHhR58woy6yaz7W4is84y284X UtIdSGcxIb+ICflFTMgvTiCzqmR7uGtBEIQeFHnzCjLrJrPtbiKzzjLbzhdS 0h1IZzEhv4gJ+UVMyC9OILOqZHu4a0EQhB7hirwRBEEQBEEQBEEQBEEQhAxQ 5I0gCIIgCIIgCIIgCIIgnIDeNhUfmXWT2XY3kVlnmW3nCynpDqSzmJBfxIT8 IibkFyeQWVWyPdy1IAhCD4q8eQWZdZPZdjeRWWeZbecLKekOpLOYkF/EhPwi JuQXJ5BZVbI93LUgCEIPirx5BZl1k9l2N5FZZ5lt5wsp6Q6ks5iQX8SE/CIm 5BcnkFlVsj3ctSAIQg+KvHkFmXWT2XY3kVlnmW3nCynpDqSzmJBfxIT8Iibk FyeQWVWyPdy1IAhCD4q8eQWZdZPZdjeRWWeZbecLKekOpLOYkF/EhPwiJuQX J5BZVbI93LUgCEIPirx5BZl1k9l2N5FZZ5lt5wsp6Q6ks5iQX8SE/CIm5Bcn kFlVsj3ctSAIQg+KvHkFmXWT2XY3kVlnmW3nCynpDqSzmJBfxIT8IibkFyeQ WVWyPdy1IAhCD4q8eQWZdZPZdjeRWWeZbecLKekOpLOYkF/EhPwiJuQXJ5BZ VbI93LUgCEIPirx5BZl1k9l2N5FZZ5lt5wsp6Q6ks5iQX8SE/CIm5BcnkFlV sj3ctSAIQg+KvHkFmXWT2XY3kVlnmW3nCynpDqSzmJBfxIT8IibkFyeQWVWy Pdy1IAhCD4q8eQWZdZPZdjeRWWeZbecLKekOpLOYkF/EhPwiJuQXJ5BZVbI9 3LUgCEIPirx5BZl1k9l2N5FZZ5lt5wsp6Q6ks5iQX8SE/CIm5BcnkFlVsj3c tSAIQg+KvHkFmXWT2XY3kVlnmW3nCynpDqSzmJBfxIT8IibkFyeQWVWyPdy1 IAhCD4q8eQWZdZPZdjeRWWeZbecLKekOpLOYkF/EhPwiJuQXJ5BZVbI93LUg CEIPirx5BZl1k9l2N5FZZ5lt5wsp6Q6ks5iQX8SE/CIm5BcnkFlVsj3ctSAI Qg+KvHkFmXWT2XY3kVlnmW3nCynpDqSzmJBfxIT8IibkFyeQWVWyPdy1CDOp qamb/Fy5csXRC129ehUvdPbsWUcvROQwKPLmFWTWTWbb3URmnWW2nS+kpDuQ zmJCfhET8ouYkF+cQGZVyfZw1yLM/Pjjjz4/v/zyi6MX2rJlC15oypQpLPH4 8eM9e/b84IMPzpw54+jVCe8ifuQtIyMjISFh7Nix48eP3717d2Zmpo2L5gDM 6wbtfeHChUOGDIHeYNOmTffu3QuWc//+/bNmzRo8ePC0adO2b9+elZXFrbpc sXrPpKSkwK0C/+rkOX/+/OLFi4cOHbps2bLLly+HWsUcgRM6ewWZbecLKekO jup8+vTp3X50fjXet2/fbl2SkpJYZhhrgmW7e/euquS0tLT169ePGjVqwoQJ 8fHx6enp5s1kwMxh7969e/bscXlc85ZfEEujoW1hubjVNjbWpHBnomKHDh3S Hr1z505sbCxMTUeMGLFgwQKtqgzzhpsvU4ulJgbTwp07d47xs3LlyosXL5q/ EF/M+4XWAuaRWVWyPdy1CDPhjby1bt0aE9977z1Hr054F8Ejb4cPH3744Yd9 CipWrAiTTxvX9TpmdEtMTHzqqad8f+Xxxx9fsWKFKidMtDp27KjK2ahRoyNH jjhlQAiYvGdgfrtmzZrOnTvff//9YE6TJk2C5ezbt6/S8Fy5co0cOZJnjb0J d509hMy284WUdAfndD5//nzx4sWxb5wzZ06wbEWKFPHpAmM3y5wvX75g2RYt WqQsdt26dQ899JCqHFgfGVabAaPYuHHjHnvsMTx98+bN5s8NHW/5JdvKaBiK sKG7NURsrEkHDx6MVYVJlDL97t27AwcOjIiIUJqTO3fumJiY69evqwoxabil MgNisonBhXr37h0ZGanMUKhQoWnTplkShxcm/UJrAUvIrCrZHno5X375ZXR0 9NChQ0Mvyn3CG3mDHhsT33//fUevTngXkSNvhw4devDBB/Eerly5Mkx+8P/l y5c/deqUjUt7GkPdJk6cmCdPHpSocOHCNWvWvO+++/BPmM7t3LmT5czMzHzh hRfwEMzSu3Tp0rRpUzb03Lp1y3FjLGLmnklJScElDOPZZ58NmBOmnZgBJp/1 69fPnz8//jl8+HD+VfcUfHX2FjLbzhdS0h2c07ldu3YsfygRnipVqmDO27dv 62RbsGABK3PTpk25cuXCMatx48Z169bF+kMvvXLlSjOywHRXVT6UaeZEXnjI L9lWRsNQhA3draFjdU26a9cu5iNl5A18V6tWLUwHo6pWrcqmqUDr1q2VjwKa NNxSmQEx2cQuXbrUpEkTTIT5YZ06dZ544gmWDVas5vXhhRm/0FrAKjKrSraH Xg6ONfBv6EW5T3gjbxcuXBgzZszYsWOd3mWO8C4iR97atGmD85B58+ZhyqRJ k/A+l/AxTkPdZs2aBcqUKVNmw4YN+NR0ampq//79UbEXX3yR5Vy+fDkmDhgw 4M6dO5g4e/ZsTPzqq6+ctMMOZu6Z5OTkwn+CIceAa5m9e/eimbVr105LS4OU y5cvV6pUyef/fVnyF/M56uw5ZLadL6SkOzik8/z585Vrdp0Iz+nTp08GomfP nnju2rVrWTUwZfTo0dr8f/zxB2aDwahixYqQrUSJEomJiZiYkJBQqlQpSHz0 0UczMjIMZenduzfaW6BAAbyogJE3QfxiaTS0LSwXt4aOpTVpenp69erVmdrK yNuNGzeqVasGiT169Lh06VK2/6dMKLlChQqYOTY2FnOaN9x8mcEw2cQ+/vhj zPbRRx+xN0xXrFiBPi1WrNjNmzdNSsQLM36htYBVZFaVbA+9HIzPU+RNn4CR N4IwRNjIW2pqKv442K1bN2U6dphRUVHuzxDCixnd5s6di9M2RlZWVuXKlUGx okWLssSBAwf6/L9xq7aA+//svXd8FcX3/38TpEuTKh0LJQiCgDTfSBEBEUSk qShNpSjqGxBRqtJUlCYifHnTpfcm0vwBgUCSD70EwRCKKdTQhBSS/M7jnofz WHfv3bt37+ze2cx5/pFH7uzs7pzX2Z1ydncGOv+Q/uabb/IrNR/8bU3wcxiP Y5mPPvpIO6xgAxDJX3vjqLPjkNl2vpCS9mCFzklJSfg9Y/369X1GeDwSFxeH o/j333+fJZ48eRKPpp33QEl4eDhmmzlzpjJ948aNJgrDniUJGHlTEkS/mGsN /RWWr1tN45dfRo4ciQPz559/3qX52vTixYvr169X7bJs2TI0Z9q0aZjil+EG j+kNg7fYw4cP+/TpM2/ePFU6i8gpP46wB59+obGACWRWlWw3kvPOnTuHDh06 cOAAPnNRgQ8CjETebt26tXfvXu1EkTC6PHr0aGRkpF9fUZ06dQp20dcfzhgR EbF//35vU5JaGnk7d+4cnBpF27dvn1+RN33NlZw/f3737t0+Z3/1KYU2v0dn Zbnnh9+zZ8/Vq1f1j5CSkgIOgk6CzgTyhE+EjbxNmjQJL2nVXCKrVq3C9AUL Fpg4u3Mx/SyjZcuW2LtmD1j79+/vcj/fVOXs1auXyz3bW4BF5Q6vsUxaWhp+ iaOdVKd69eou96vmARbV0cgcM5HZdr6QkvZghc449MiVK9eOHTvMRUXatGkD e5UvXx46mSwROnt4tKioKJ19f/rpJ8yWlJSk2oT184svvmi8JNkp8maFX0y3 hv4Ky9etpjHuFxgc4cC8lxtt5M0jMPxBM0ePHo0pgRuuPaY3DN5i3oBuNu4+ f/58E7sHgk+/0FjABDKrSrbr5zl37lz79u1d/xASEgLVEZsKsmbNmmXKlME3 sXPmzFnkH9auXYsZateuDT9Bovj4eGiYcufODTlLlCjBjh8XF9e6dWs27SSM PevVqxcZGaktCR5q3rx5t27deu+99/BlYJf7Q/i+fftqV6KJjo5u1KgRKzlk K6Kgc+fOmE0VeTt+/DhmGDFihLYMmzdvxq3aBx9KYPgMuxcvXpydGkbKrClU Rt6WL1+OBwwPDzeoOZKZmbly5cpmzZoVLlyY5SxduvS2bdu05TEohU9nwUkn T56snIm0XLlyYIL2jLGxsS+99BKbiRQOBQdUvepDGETYyFuPHj1c7vnKVF8i 3L17FztFn332mYmzOxdzkbfbt29jXVG5cmWWyB65qg6IqzNAVzPgwnKG11gG WgQ0XPtF7eeff46b2HcZEiJzzERm2/lCStoDd52XLl2KdeDYsWOho4j/+xXh YS3Lhg0blOnwE9P1p9D54osvsFOqndWqX79+Ls3SAPpkm8ibRX4x3Rr6Kyxf t5rGoF9guIfve5QsWfLatWtvv/22y1jkbcKECSgL+34tcMO1x/SGwVvM5+6q waAN+PQLjQVMILOqZLtOhhs3bpQrV46FWdjUAQCuscKmB1fBojFPPPEE/Bw5 cmS9evXY1rZt2+LW9evXFyxYULs7CKttaPBQb7zxBn6Vr2LIkCHKzHv37s2R IwfWqFWqVFFagbRp0wZzqiJvGRkZFStWdLmjWNrla/FiyJs3r/JZoQrYxKbH 9Igy8qZ9486n5lnuofqzzz7r8eDgkZ07d5qTQt9Z9+/fx0dyCJvl1aV51gNd CObWnDlzshlQy5Yt6/RFSYKCsJG3Vq1agVuhC6TdhBNjdu/e3cTZnYuJyNuF CxdQRmDGjBks/cGDB3gTFS1alFUO0ItWVRfiwGssc+DAAbRxzZo1qk1Qc+Km P//8M8DSOheZYyYy284XUtIe+OqclJQEzQFkeP7552E88scff2B96FeEp3nz 5i53mEIVapg/fz4eDfpvX375Za9evaAruHTpUtV3KKwS1s63OXbsWOyCpqWl GSxM9oi8WecX062hv8LydatpDPqFfXcJ1yr8fPPNN12+Im/glyVLluDLADAI Yld1IIZ7O6Y3DN5i3hg0aJC3olqNT7/QWMAEMqtKtutkYMH8Tz/99K+//oIW 4fjx46+99lqtWrVgYAgZdu7cuWPHDgytPPvsszv+gT2FwWAOAnvNnj07Ojr6 yJEjWe7Vt/E96pCQEKh/oPlISEiYNWsWhnSgulOt6aw81Kuvvgot0blz56ZO nYpnL1SoEDtpenp6+fLlIRGGrnv37sVEVsGOGDHizJkzrO7Sxr7GjRuHKar3 x9iL3506ddIRbejQobh76dKloZBg5uHDhwcMGICr57h8Rd58ao60aNECDtit W7dNmzYlJiaCdFCT447Kt6P9kkLHWVn/TKoAdO3aFXc5f/58s2bNXO7wGvyP 2a5du/bYY4+53BOWrlq1KjU1NSUlZe7cuejWPn366EhHeETYyBtOb6v9DgII CwuDTS+99JKJszsX4/35BQsW9O7dGzrbGBXPly/f9OnTVUMhuGELFCiAN12H Dh2grsDuPdz1lpQ+MHiNZdauXYsmq14yB1auXImb9u3bF2BpnYvMMROZbecL KWkPfHWGVsDlfugJ3Tb4aSLCc+rUKdxl8uTJqk2Q4vJEuXLllG9hQZ8f01XP W7dv386exsbGxhosT/aIvFnnF9Otob/C8nWraYz4BUYl7DtTTNGJvMEY6pNP PoEhW4UKFdAKcKLSEBOG+zymNwzeYh65desWfm1UqVIlnyfijk+/0FjABDKr SrbrZIC6xeWeuEAVk1c9AsCYv8d53lgwp0mTJmyFPuTdd9/FTTDkVJUK0+vU qaMcirJDjRw5UpnevXt3TD927BimsNlHv/vuO+WRsX2sXbu2MlEb+2KLib/9 9tvKnFAVY07t4yfGn3/+iWoUK1ZM9WAClzV0+Yq8GdT83Llz2qAKXIp4tOTk ZBNS6Djr7Nmz+PHpa6+9pky/d+8eTir74YcfYgpOuZAjRw5V4PTbb791ud9m TExM1OpG6CBs5A0fPXTs2FG7CRcCeOaZZ0yc3bkY78+/8soryq7XmDFjlHF1 BG75bt26qTppUCuK+a0lr7EMey5w/Phx1Sb2yp9ODZztkTlmIrPtfCEl7YGj zr/88gvWflOnTsUUExEeXDozX758rIvIYGEBOPvQoUOHDBkC/UNMgb7fiRMn MFtqaip+dZIzZ84JEybExsYePXp07Nix2D9EIMVgebJB5M1Sv5huDf0Vlq9b TePTLzAwwenXypcvf/v2bUzUibxFRUUpu0/lypU7efKkMoMJw30e0xsGbzGP 4Kevfl1UHPHpFxoLmEBmVcl2nQyffPIJ3uz635X7jLyFhobGxMQo0zMyMvA7 yooVK2qn32dfNZ47d051qMaNG6syz5s3DzNv2bIFU1g4a8+ePcqcEydOxApW +eGwxxUWcK5UKKHyq9K+ffu63C+PqaJSSr7//ns8mirSleVlbVPt2Q1qrn92 1lL4JYU3Z2X9EzcDLly4oNqEBcYp3zMzM/Pnzw8/e/bsqcoGPQo8gupjWMIn wkbe8Mvl9u3bazc1aNDA5Q4TmTi7czHen4dbFWo56HexuRCrVq16+vRpluHu 3btNmjRxuRfx+eijj5TTWo4aNcoqAwKA11hm7ty5aOnhw4dVm3777TfcpL80 WPZG5piJzLbzhZS0B146JyYm4qcETZs2Zc+dTUR48Dmpt682li5dumPHDvYT eukjRozAU0BjxNIhD5uZmVGkSBH2nMjgGl5Zzo+8We0X062hCWE5utU0Pv3C pmVT2qUTeYuLi+vcuTM4rkyZMmgF7As9KOXLG/4abuSY3jB4i6nYvXs3fjMF /WojZ+GOT7/QWMAEMqtKtutkiIiIYJN0wTgRanjVZHeIz8hbo0aNVOmxsbF4 2P/+97/avVgwTbmQAR5KWztt2bIFM7P5LdkSMGyhB2TIkCEuzXKBHiNvW7du xUS2iAzUkBiD7dGjh7bAjN69e+OO8fHxqk0GI28GNWecPHly9uzZPXv2hOuQ fZsGjbIJKbw5C3jnnXdc7tkOD2jAl9wef/zxLIVb4fjanLiJzVZHGETYyBvO B+hxnU18jMgmCZQEf/vzWe4pYqDPht0quAHZm67YmYRePS6DlZqa+uOPP7JF W8aPH8+98AHCayzD6nNtiH7JkiW4SfU+rVTIHDOR2Xa+kJL2wEtntuQW9A8T /iE8PBwToWmAnzqTDyMxMTGYf9KkSQbLA/3eWrVqwS558uRRdkTPnj3bsWPH smXLwqYKFSq8//77Z86cwRgC9EKN2+v0yJvVfjHdGpoTlpdbTaPvF+gL4ewc nTp1SlCAn/BUrFgRf3rcNzMzc9u2bXgxa4ch5gzXP6ZBvN1iyrJhYDZfvnzs wy6b8Xm/0FjABDKrSrbr51m2bJly9cwyZcp88803qpe+fEbetOEytkqL6lNT VjDcOm7cOJ+HYh+BssgbFA8fYbRo0YJlu3XrFr430rJlS+XuHiNvbJ2FZs2a YQqLm3lcPJSBwVioIbWbDEbesoxpnuV+elKnTh2XJ3799VcTUnhTGPB2IsYj jzySpXCrDkqfEkYQNvKG3c6wsDDtJnwQLNu0fiYib8jo0aPx7pgzZ06We4Vl /DllyhRltgsXLuDUInnz5k1KSuJSZl7wGsscPnwYbV+5cqVqEzQWuMn+GYbF QeaYicy284WUtAcuOrNJwPTx+MxUCTQumFP1BYQ+gwcPxr1wEjMVykkScAIZ vz4CcnTkzQa/mG4NAxQ2QLeaRt8vOBWPT2BI6O0IiYmJ+AaFtxVLTRju85g+ 0bnFrl69itcesGLFCnPHDxyf9wuNBUwgs6pku89sV65cgYEhPg5AqlevfvPm TZbBRORtzZo1eKiZM2dq92LPjMaMGePzUNrIW5ZiJPvKK6/AuZYvX46T8oWG hqpaN4+xr6x/1lkICQnBBaDxm8rixYvrv4GGn+0XKVJEu2nv3r14olmzZvk8 u0/N2YwBuXPnfvPNN5ctWxYTE8OmkmORN7+k0Im8NWzY0OV+KNPEC7hAKnNr 1apVveUMYvPhUISNvH3wwQd4Vdy7d0+ZDl1BvAyGDx9u4uzOxXTkjb0s+tFH H8FPqCLwp3b1+YULF+Imn1Py2gyvsXxCQgIaOHLkSNWm9957D+tkGxZZExaZ YyYy284XUtIeuOjM3onSp27duvoH79GjB3b8VO21PuxrOP1pvqBjjIt5+fUq gqMjbzb4xXRryEtYc241jb5ftNPeemTz5s06p8Dvd4Dr16/rZPPLcIPH9Ia3 WwyuB3yXA/jyyy9NHJkXPu8XGguYQGZVyXaDmaEiWrduHQZtXP/+6NJE5O3M mTN4nM8++0y7F4tHKec68yvyBs7CuKiSRx99VPs+sLfYF1tn4dtvv2VnZ+sI eANfewa0s6Abf+eN4U3zLVu24OdpDRs2vHr1KsvPWltl5M24FDqRt65du7rc X5vqTzLAuiLalbMI0wgbeWNzC6vm+J05cyam6zx8zJb41M3b7XP+/HlUrH// /vBz/PjxLvcyJdplF6KjozGnMoAvAhzH8vgyuWo9cZAO39T1+QpB9kbmmInM tvOFlLQHXjonJyff0MBm8IC2AH6yCee98dxzz0FmOIVfJuBiQNDJ13/eAX11 LMz//vc/4wd3dOQtyxa/mGsNeQlrzq2m0fdLSkqKVm2gXbt2Lvein/gTL1Rv fS22tN+VK1d0SuLR8ACP6Q2Pt9j9+/ebNWuGh33rrbcyMjJMHJkXPu8XGguY QGZVyXa/drlz5w42BMoXa3EVGI+PBrwFcx4+fIh7hYWFaWuzzp07o7bKqf79 irzhgqdNmzYdO3Zst27d+vTpM2XKFO3ca1m6sS9cZ6FWrVosoORx/W4ln376 KebUznpqIvKGaDX/8MMPXe6nXap63mPkzbgUOpG3r7/+2ogC6enpGIZ17oK/ AiJs5A36BvhZdMuWLVnHADoPdevWdbmnywhub8F+fOrWpUuXjh07aid+YS+m 4sSSrE5j80wyfvjhB9wUERHBseSBw3Esj+u/AOHh4Sxx3bp1mDh37tzAS+tc ZI6ZyGw7X0hJe7BUZ39n8n/88cdd7se12k2HDx+uWbPmkSNHVOlQeHzIq5zO OiUlRfVyzs2bN2F3bPRV0QPo4kLPXPsICXF65M0jHP2SZbY11BfWo1+Mu9U6 TIxJszytsHDo0KFSpUppX35LSkoqUaIEWsQSDRru1zG1Cvt1i8FeMIZCD7Zr 1067CqHN+PQLjQVMILOqZLtOhjNnzpw6dUqV2LRpU5d7iU+Wgg1HyZIltUfQ CeZgXAuACkqZ/n//93+hoaEu9xeL2pU3jUTewGW4cCpb7VQHndgXWzyoT58+ Lvcy1j6XlVm9ejXu8sILLygLD/+///77RiJvRjRH6UClxMRElic1NZU9eWGR N7+k0HEWNDrolGeffVa/CWZv/dFXpbwQNvIGDBgwAN398ccf37p1C3osbJER 5afikqCvm/Jb7FmzZsFtDvXJtWvXRowYgfMGFypU6K+//spyL2yKy2blz58f utCs2lm7di3ezmXLltVZYTkoGLlmDh48uP8f0EAY0bAU9m4Ae98YOrSRkZFg Pgw6QByX+2VdEMdyYwSGo86OQ2bb+UJK2oOlOvsV4YFxCrYyMJDXbsUPK/Lk yTNq1Ki9e/fCwB/OO3v2bOhzutwPebdu3Yo5oTbu3Lkz1M8TJ06E/ic0Q1A5 Q4um7dwCgwYNwnTlt3Lx8fHMupEjR2KGCRMmYMrJkyd92hI4TvFLlj+toXFh tX7xy63WwSvyVqNGDbxuoYMKoyFwVnp6OhwZxi9o0eDBgzGnccONHzPLk8LG bzEoQ6tWrXB32Lp58+ZNmzat+zfeFpKwCCN+obGAv8isKtnubSu+apUrVy6o KHAOz7///nvp0qVoePPmzVnORo0aYeK0adMgzzU3uEknmHPx4kUYV7rck/NP mTLl+vXr9+7dW7FiBbYpgKpsxiNvUHJsy1577bULFy5A9ZjpxqOZUKfh7gsX LlRtYussIEOHDvWmFQPOgs9KXO6XAE+fPg216L59+15++WV2HJ3Im0HNhw0b hin9+vUD3dLS0qCxwFAwwiJvfkmh4yxg4MCBePDnnnsuKioKA87Q1o8ePRp2 YWFG5tacOXOOGzcuOTk5yx0V3LFjR4sWLZYtW+ZTQ0KFyJE3qA/r16/PLjx8 eOdyP6fw9pg7G6OvG9wF+NU2A18QZdIpp26LiIhgW0uVKtW4cWNWF0H6gQMH 7LDHH4xcMzBScHnn+++/ZzlhzIIVl/Kiyp07t5EnCNkbvjo7C5lt5wspaQ+W 6uxXhCcpKQkz9+rVS7t1zZo1+EwHgbqX1bquf88JAx08fMOH5WT/f/rpp6pp kNnKXNB+sUSwSMdeUMOnLYHjFL8gBltD48Jq/eKXW62DV+QNDlKkSBFmQmho KEYvkQYNGkBnDHMaN9z4MbM8KWz8FhszZoyOH5G1a9f6q1IgGPELjQX8RWZV yXZvW6OiopQ1EvyfN29e/D9nzpyRkZEs59y5c1k2aA6gRho9ejRu0g/mLFy4 EL85VQEKa+eT9Otr0zZt2mgPC/UkWPHWW28pXxi+ceMGOhS24pRuSnDCJUT7 nrBHfv/9dww9qWADZ53Im0HNjx8/znQLdYP/V69eHf9Rfm1qXAp9Z92+ffuF F15gR4CCKRdgVYoP/QRWbJd7WQrWnFWuXNmIhoQSkSNvWe7q8dVXX2Vhojx5 8nTt2tXRFaNpjOi2evVqNmsu48UXX1TWqEhMTAxOYKIE4/lWGRAAgY9lJk2a pMwMVUq5cuXYVqidKOyWZYHODkJm2/lCStqDpTpfuHAB82gXvtTCpkzx9gT5 0qVLvXv3xjdwGE8++aR24pTExERli+9yv4O9ePFi7TGhR40Zpk6dyhL1A0TQ efZpS+A4yC+IkdbQuLAe/WLcrdZhLvLWs2dPl2YZxCtXrgwYMEA1xzVc3uPG jVPNwm3ccOPH9KiwwVts+PDhOn5EbO4LGfQLjQX8QmZVyXadDGD1f//73+LF iytv+Vq1au3evVuZLT09nb1bi7z++uu4Cd/abdmypbdTnDhxon79+srg/1NP PeVx6jxvh2LzpymDP2waBG8op0fo1asXhoa6deumOjjUyVg2OLuOUCpOnjzJ 3nwD8uXL9+6774KY2HTOmzeP5Vy+fDnm2b9/P6YY1HzNmjU4wypSvnx5qOFT U1PREGXkzbgUPp2VkZEBjXvRokWVu0Mf4Oeff1Y+7slyP/Vr1qyZ8vkR3Dsg r/ZDWsIngkfekPv370dFRe3bt0+0ryDtxLhuCQkJoNXmzZsjIiJu3Lihk/PO nTvR0dG//fYb/NVOECcO5vrMPomNjf3999+1a7xKi0U6OwKZbecLKWkPjtMZ 57zauXMn1LoeZwNmQJcvMjIS+ur62aDLB5183sUMFMf5BeHYGnrzi0G3WgR3 v6SlpR07dgwGRGARjEp0JkwzbrjBY3pT2PgtJg5++YXGAgaRWVWy3UjOv/76 a6eby5cve/tW8dq1azt27IAR4smTJ33Oh6bi9u3b+/fv3717t7mlYVTAofLm zRsSEjJnzhyo+g4cOADD2/Dw8Pnz57PVn59//nnlLmAXjIK1L5Owp1dfffWV v8UAQaBqhcKYm5vUp+ZwNUJLod8Qm5DCCJcuXQJfg7/YZ8UewRLu2rULhA36 HKHOxRGRNyJLbt1ktt1OZNZZZtv5QkraA+ksJuQXMSG/iAn5xQpkVpVsD3Yp +INf/Xfq1Em7KSMjA1cp8rgehBac0C9HjhxxcXGcS2kLHKUgggVF3pyCzLrJ bLudyKyzzLbzhZS0B9JZTMgvYkJ+ERPyixXIrCrZHuxS8KdSpUoul6tv377a TXfv3sWVWNu3b+/zOGxBT/bxrOPgJQURRCjy5hRk1k1m2+1EZp1ltp0vpKQ9 kM5iQn4RE/KLmJBfrEBmVcn2YJeCP506dXK513qYP3++cg6lI0eO4DKsqmUE VURHR0+YMOHzzz8vUKAAztL2xx9/2FJw/gQoBSECFHlzCjLrJrPtdiKzzjLb zhdS0h5IZzEhv4gJ+UVMyC9WILOqZHuwS8GfyMhI5fIxVapUefnll+Ev/gwJ CZk+fbrO7srVIvLkybN8+XLbSs6dAKUgRIAib05BZt1ktt1OZNZZZtv5Qkra A+ksJuQXMSG/iAn5xQpkVpVsD3YpLOHo0aNdunRRrhMNFChQoGfPnjExMfr7 fvXVVzVr1mzcuHH//v1PnjxpT4GtIxApCBGgyJtTkFk3mW23E5l1ltl2vpCS 9kA6iwn5RUzIL2JCfrECmVUl24NdCgu5f//+2bNn9+zZExkZqb8KZ7aHpHAu FHlzCjLrJrPtdiKzzjLbzhdS0h5IZzEhv4gJ+UVMyC9WILOqZHuwS0EQhB4U eXMKMusms+12IrPOMtvOF1LSHkhnMSG/iAn5RUzIL1Ygs6pke7BLQRCEHhR5 cwoy6yaz7XYis84y284XUtIeSGcxIb+ICflFTMgvViCzqmR7sEtBEIQeFHlz CjLrJrPtdiKzzjLbzhdS0h5IZzEhv4gJ+UVMyC9WILOqZHuwS0EQhB4UeXMK Musms+12IrPOMtvOF1LSHkhnMSG/iAn5RUzIL1Ygs6pke7BLQRCEHhR5cwoy 6yaz7XYis84y284XUtIeSGcxIb+ICflFTMgvViCzqmR7sEtBEIQeFHlzCjLr JrPtdiKzzjLbzhdS0h5IZzEhv4gJ+UVMyC9WILOqZHuwS0EQhB4UeXMKMusm s+12IrPOMtvOF1LSHkhnMSG/iAn5RUzIL1Ygs6pke7BLQRCEHhR5cwoy6yaz 7XYis84y284XUtIeSGcxIb+ICflFTMgvViCzqmR7sEtBEIQewYq8EQRBEARB EARBEARBEIQMUOSNIAiCIAiCIAiCIAiCIKyAvjYVH5l1k9l2O5FZZ5lt5wsp aQ+ks5iQX8SE/CIm5BcrkFlVsj3YpSAIQg+KvDkFmXWT2XY7kVlnmW3nCylp D6SzmJBfxIT8IibkFyuQWVWyPdilIAhCD4q8OQWZdZPZdjuRWWeZbecLKWkP pLOYkF/EhPwiJuQXK5BZVbI92KUgCEIPirw5BZl1k9l2O5FZZ5lt5wspaQ+k s5iQX8SE/CIm5BcrkFlVsj3YpSAIQg+KvDkFmXWT2XY7kVlnmW3nCylpD6Sz mJBfxIT8IibkFyuQWVWyPdilIAhCD4q8OQWZdZPZdjuRWWeZbecLKWkPpLOY kF/EhPwiJuQXK5BZVbI92KUgCEIPirw5BZl1k9l2O5FZZ5lt5wspaQ+ks5iQ X8SE/CIm5BcrkFlVsj3YpSAIQg+KvDkFmXWT2XY7kVlnmW3nCylpD6SzmJBf xIT8IibkFyuQWVWyPdilIAhCD4q8OQWZdZPZdjuRWWeZbecLKWkPpLOYkF/E hPwiJuQXK5BZVbI92KUgCEIPirw5BZl1k9l2O5FZZ5lt5wspaQ+ks5iQX8SE /CIm5BcrkFlVsj3YpSAIQg+KvDkFmXWT2XY7kVlnmW3nCylpD6SzmJBfxIT8 IibkFyuQWVWyPdilIAhCD4q8OQWZdZPZdjuRWWeZbecLKWkPpLOYkF/EhPwi JuQXK5BZVbI92KUgCEIPirw5BZl1k9l2O5FZZ5lt5wspaQ+ks5iQX8SE/CIm 5BcrkFlVsj3YpSAIQg+KvDkFmXWT2XY7kVlnmW3nCylpD6SzmJBfxIT8Iibk FyuQWVWyPdilIAhCD4q8OQWZdZPZdjuRWWeZbecLKWkPpLOYkF/EhPwiJuQX K5BZVbI92KUgCEIPirw5BZl1k9l2O5FZZ5lt5wspaQ+ks5iQX8SE/CIm5Bcr kFlVsj3YpSAIQg+KvDkFmXWT2XY7kVlnmW3nCylpD6SzmJBfxIT8IibkFyuQ WVWyPdilIAhCD/Ejbw8fPoyOjp48efK0adOOHDmSkZFh4qTZAOO6JSYmLl++ fPTo0fPnzwfpdHKeOHFiwYIFo0aNmj179v79+zMzM/mUlTf+XjMJCQlwqcBf bxnS09MPHjw4yc3mzZuvXbvGoZTOx7jO2e+ulNl2vpCS9kA6i4lxv9y9e/fX X3+FNmj8+PFr166FhttbzitXrqxevXrMmDHr1q27ceOGt2wpKSkRERHgaDjg ihUr4uLi/Cq5ud0vXbp0xM3Nmze5F4kj3HsRSHx8PDjlq6++gq5UTEyMX52o tLQ0lO706dMsEXojR3zx4MEDv3L6xOAFZgVUj1mBRVe7I5D5iqLIG0GIj+CR N+jJlClTxqXg6aefhp6eifM6HSO6RUZGhoWFuf5N69atL1y4oMoJfbauXbuq cr7wwgtnzpyxyoAAMHjNwEBm69at3bt3f+SRR8Ccpk2bavNAX3fgwIH58+dX Gl6oUKHZs2fzL7fTMKhztrwrZbadL6SkPZDOYmLQLytWrHj88ceVfsmXL993 332nzTl48GBltpCQkAkTJqjyQLs2bNiwXLlyKXPmyJGjd+/et2/f9lkY07tf uXKlWLFimP+XX37hWCTucOxFIPfv34dsqk5UrVq1zp07Z7BIo0aNwr2eeuop ljht2jSXL+bMmeNXTn2MXGDWQfWYFXC/2h2EzFcURd4IQnxEjrydPn26ZMmS WCVWrVoVOif4f8WKFS9evGji1I7Gp25LlizJkycPSgS6NWzYsGDBgvizWrVq qampLGdGRkaLFi1wU5EiRd55552XXnqJNT3Qn7TcGD8xcs0kJCRg54Hxn//8 R5Xn+vXr0LXAraGhoXXr1q1SpQrLv2jRIqsMcAhGdM6ud6XMtvOFlLQH0llM jPjl999/hwYIfdGoUaMePXqwKJwqVDJw4EBMz58/f4MGDfLmzYs/v/76a5YH 2r7nnnuOhU3CwsKY04EOHTrov4gVyO5vvPEGy6mMvAVYJCvg1YtgOZmB4Eq4 uQoVKoQ/CxcufOfOHZ/lOXz4MDuXv5G3FStW+JVTByMXmKVQPWYFfK92ZyHz FUWRN4IQH5Ejb6+//jp225YuXYopM2bMwOrxgw8+MHFqR6Ov240bNzDOBi1I eHg4vjWdnJzcrl07VOybb75hmdevX4+JQ4cOTUlJwcTFixdj4g8//GCxKX5j 5JqJj48v/A84qNH2IoYPH442fvHFF+wL002bNuXLlw8SixYteu/ePSvK7xSM 6Jxd70qZbecLKWkPpLOYGPFLrVq1XO4HZDExMZhy8+bNunXrutxxG/bR07Fj x9BZderUuXv3bpa7oa9cubLL/fLY5cuXMdudO3dq1KgBif37979+/XqW++Ea lKFSpUq4e0REhE5hTO++fPly5bBdGXkLsEhWwKsXgfTp0wcN6dy5M77CBwYu WrQof/78s2bN8lmY1NTUmjVrMumUkTeQ7oInjh49imGxxo0b4xViPKc3DF5g lkL1mBXwvdqdhcxXFEXeCEJ8hI28JSYm4uOYvn37KtOxwixQoIBsQRKfukFX tm3btlevXlUmQqcXI3KtW7dmicOGDXO5H3Gmp6crM0OzC+lvvvkm14JzwN/W 5Mknn/TYi3j48CF0mOfNm6dKZxG5gwcPBlhUR+NT52x8V8psO19ISXsgncXE p1/+/vvvHDlygAsmTpyoTN+1axc2Q3/88QemfPTRR9oYCIuWKN9Kunjx4vr1 61UnWrZsGeacNm2afplN7J6UlITfmdavX18beQu8SNzh1YsALly4kDNnTtja pUsX1ct7t27dMnLwkSNH4sD/+eefV0XevNG/f3+X+5Nkn1+zGs9p/AKzDqrH rIDj1e44ZL6i/PX7+fPnd+/e7XN+P6jWYIC5f/9+g5NAQv69e/dqJ9BOSUmJ jIyEGkY19tRiPCdBOA5hI2+TJk3C1h+qBWX6qlWrMH3BggUmzu5cTD/LwO8r K1SowFKwY1a0aFFVzl69erncs70FUExLsLoXAdcYXlTz5883Ubxsg0+ds/Fd KbPtfCEl7YF0FhOffklKSkL9f/zxR2X6xYsXMX3nzp1Z7nnSihQp4vI0+VL1 6tVd7g+j9EsCAyU84OjRo00Yor87jlJz5cq1Y8cOj5E3K4oUCBx7EdiDAk6d OmWiJIcOHcKBfy83RiJvBw4cwLeSpk6dyitn4BcYF6geswKKvOlkyMZXlBG/ Z2Zmrly5slmzZoULF3b9Q+nSpbdt26bNHB0d3ahRI5YN6pYiCjp37ozZateu DT9Bt/j4eGgacufODZlLlCjBjhMbG/vSSy+xaT8hA2TD16FVGM9JEA5F2Mhb jx49XO4vLx4+fKhMv3v3LnZaPvvsMxNndy6mI29QJYJc0JViKRs3bsQ6TXXA Z555BnuDgZWUP1b3IjZs2ICCQMtrpnzZBZ86Z+O7Umbb+UJK2gPpLCZGWiuc Iqx58+bKjwFXr16NzRBONBQXF4c/tfM/fP7557jp77//1jnLhAkTMBv7osov dHaHFNw0duzYc+fOGY+8BVikQODYi6hWrRpsguGhiWKkpqbid7glS5a8du3a 22+/bSTyVqdOHZf7m1Cf8+MZzxn4BcYFqsesgCJvOhmy8RXl0/bbt28/++yz Lk+EhobiQx/G3r178fXskJCQKlWqlCtXTrVLmzZtMOcTTzwBP0eOHFmvXj22 tW3btrgVhpxs1vGcOXOy2QXLli2rWtLCeE6CcC7CRt5atWoFtxt0UbSbcGLM 7t27mzi7czEXeUtOTsYHoH369GGJDx48wMqtaNGiv//+OyayT11YijhY3YsY NGgQ2m7PxCbC4lPnbHxXymw7X0hJeyCdxcRIazV+/HhscWAMiLPxwxiwefPm kNKkSRPMc+DAAcyzZs0a1e4///wzbvrzzz89Hh+OtmTJEnxtAIZL/q6apL97 UlIS9Bxg0/PPPw85//jjDyORtwCLFDgcexE4MeyXX36JPxMSEiIjIw1+Z8qm toAxJvx88803fUbeYPyLuyxevFj/4MZzZgV2gXGE6jEroMibToZsfEUZ8XuL Fi1CQkK6deu2adOmxMREqL6gKsP7/cUXX2TZ0tPTy5cvD4kwWoSKBRNZzTBi xIgzZ86wERNG3pBatWrNnj07Ojr6yJEjsOnatWuPPfYYpBcvXnzVqlWpqakp KSlz587FuSiVI1PjOQnC0QgbecPpZz0uch0WFuYy+8DRuZiLvLHXqhcuXKhM h4q0QIECuKlDhw6LFi3CvjTUxtxKzA9LexHQYS5dujTkr1SpksnyZRd86pyN 70qZbecLKWkPpLOYGGmt0tLSOnXqhO3v448//t133+GM/Xny5MHRCrB27VrM oPokCli5ciVu2rdvnzL9r7/++uSTT+DIFSpUwAzQCMbGxhosucHdocMAm2A0 BCMv+KkfeQuwSBzh1Yu4evUqGjJr1izoOOGXAgiM5dkQ1SMwGmXfmWKKkchb ly5dcDTK1sMKPGeWqQvMCqgeswKKvOlkyMZXlBG/nzt3Tjt4B5Pxlk9OTsYU Nt8jNE/KnFj/165dW5nIIm9NmjRRVT74QX2OHDmg9lOmf/vtt5AO9WFiYqK/ OQnC0QgbecNHDx07dtRuwoUAoMNj4uzOxUTk7fz58/hwtlq1aqppKqHn361b N9e/qVOnjg0fF5jA0l5Ev379dAYOUuFT52x8V8psO19ISXsgncXEYGt16NAh nKVfifLtI/Z2wfHjx1X7shfUVW8rRUVFKY9Wrly5kydPGi+5kd2hlcStbBox /chbgEXiCK9eBIwKWZwN/ylQoED+/Pnx/5CQkM2bN3s8IAxIcf608uXL43Ko WQYib/Hx8XidsFfsAs+JmLjArIDqMSugyJtOhmx8RZl7QwP4/vvv8ZY/evQo pixatAhT9uzZo8w5ceJEl/tTUOW3uhh5Cw0NZat1I5mZmVg39uzZU3XG5ORk PD5+4mo8J0E4HWEjb/hFefv27bWbGjRogGEiE2d3Lv7WqBkZGewpxtatW5Wb 7t6926RJE+wxfvTRR6VKlcJsUG2OGjWKc7l5YF0vYvfu3dBVhsxwUfmcFyXb 41PnbHxXymw7X0hJeyCdxcRIa7V69WoMkrRu3bpVq1bYBgGVK1dmC5vOnTsX Ew8fPqza/bfffsNNmzZtUqbHxcV17twZGr4yZcqwQBC06QabNp+7JyYm4tdA TZs2ZYn6kbcAi8QRXr0INk0u+gu6EOnp6WAOmI9fRcEg1OMrZ1988QWav2vX LpboM/L2448/4rl8RiyN50RMXGBWQPWYFVDkTSdDNr6i/PI7VBSzZ8/u2bMn 2Mu+gYJ7H7eyhefWrl2r3GvIkCEuzQp9GHlr1KiR6hSxsbF4ENjrgAbcBGXw KydBOB1hI284T6PHdTaffvppl2LyRknwtyX9+OOPsbLSTlmAnT3oQkdFRWW5 p/yFPlvx4sUx//jx4zkWmwsW9SLOnj1brFgxyJkvX75jx44FVMRsgU+ds/Fd KbPtfCEl7YF0FhOffoHxDn5yCEMefG0AUmAYiO3v448/juu4bdmyBVO0D/qX LFmCm1Rf5TAyMzO3bdtWq1YtcwMWb7uzQkZERCT8Q3h4OCZCLwJ+4rR13IsU OLx6EWwYWK1aNdXXT2wON61foK+Fc5V36tQpQQF+ulWxYkX8qS1G165dXe6H pMrFODxiPCcSyAXGEarHrIAibzoZsvEVZdDvS5cuxaVYtPz666+YJyUlJU+e PJDSokULtuOtW7fwVY2WLVsqD4iRNzZJKYOtXqfDuHHj/MpJEE5H2Mgb9vHC wsK0m/Cpq2zTLfrVkk6ePBlrKqhdVVMZHz9+HDdNmTJFmX7hwgWciSVv3rxJ SUm8is0FK3oRV69exWzAihUrAi1itsCnztn4rpTZdr6QkvZAOouJT7+88cYb Lvc7A6opIEaPHo3t0aBBg+Dn4cOH8efKlStVR5g+fTpu0l8SKDExEb+rKlOm jAlDVLufOnXK58jI5em1B45FCgRevQgwAS2dN2+eahNzmXblVjatnz7bt29X 7YjvCjZr1sxngY3nVJXW9AXGBarHrIAibzoZsvEVZcTvbHiYO3fuN998c9my ZTExMQsWLMBEFnnLUjRJr7zyypo1a5YvX47z4IWGhqo+QfUWeYO98AhVq1Zt 4gUcfxnPSRBOR9jI2wcffOByTzh87949ZTr0BPD2HD58uImzOxfjLemqVatw PVPo4mo7TrNmzUIBL168qNq0cOFC3LRhwwYuZeYF914EXFT4VrnL8KQoMuBT 52x8V8psO19ISXsgncXEp1/wnYHevXtrN1WqVMn1z+TVCQkJ6KaRI0eqsr33 3nsu93eLaWlp+oV555138CD4Hp2/KHeH0ZmR2FHdunUtLZJpePUiMjMz8VWQ IUOGqDaxO0v1WBPQTqvrEdUccXFxcZg+dOhQ/dIaz8ngcoEFDtVjVkCRN50M 2fiK8mn7li1bcHKDhg0bXr16laUvXrwYbVdG3kAfDEUqefTRR7VvLHuLvLFW Y/LkyfolN56TIJyOsJE3NpGvaorXmTNnYrr24WD2xqBuGzZswAlkChQo4PFL gfHjx7vcy8c8ePBAtYlNHTxr1iwuZeYF317E/fv3mzVrhpa+9dZbBj/NkAGf Omfju1Jm2/lCStoD6Swm+n7JzMzMnTu3y8sTn7Zt28KmUqVK4U/89KlGjRqq I2DsTvl2mbdp095991309ZUrV3TKbHD35OTkGxrY15fQbYCfbO2AAIvEHY69 CPxQ65lnnlHZyD681S6ykJKSopUOaNeuncu9rjr+VEW62JxyMC7WL63xnEqM X2DWQfWYFVDkTSdDNr6ifNr+4YcfutxBdVX16zHy1r17d5d7Vs+xY8d269at T58+U6ZMiY+P1x7WW+QtPT09V65cLgPLxRrPSRBOR9jI2/379wsXLuxyf07O YiPQLalbty4kVqhQQbaAiRHdoL+HdVeePHm8ZYY2BSvY+fPnqzb98MMPuCki IoJDifnBsRfx4MEDtvAEdHpV3/tIjk+ds/FdKbPtfCEl7YF0FhOffmnUqJHL PUuYaiKIe/fuPf7447Dp5ZdfxhRcRQ4IDw9n2datW4eJc+fOxZRDhw6VKlVK G+1JSkoqUaIE+polwkkXLVq0dOlS9ujNr921eFxhIcBjWgHHXgQbpYIvlOm9 evXC9EuXLhk8i/4KC7Nnz8YDbtu2Tf84PnNq/Z5l+AKzFKrHrIAibzoZsvEV 5dP2119/3eX+XFQ5R2Vqaip7IMIib6BSvnz5IGXLli0+z+st8gbgVJYuA7P6 GM9JEI5G2MgbMGDAALwNP/7441u3bt28ebN3796YMmbMGBOndjQ+ddu6dSs+ TAeGDRsGP9evX79Owe+//57lXtgU5wPJnz8/dCDZQ9u1a9diNVu2bFmPK3MF ESPXzMGDB/f/AxoIfQmWgg/iwa5WrVqhRAULFoRxwaZNm9b9G4+zHEuCEZ2z 610ps+18ISXtgXQWE59+YdPsvPLKK2w6iKtXr7L1C3766SdMhMYI12IoUaJE ZGQkNNbh4eGFChVyuT/5gaYcs9WoUcPlfo0B3A3jJmjs0tPToQzPPvssHnDw 4MHs7IMGDcJE9tKdX7tr8Rh5C/CYVsCrFwE8fPgQIxUFChSAXhaM0yFlypQp OMtHx44djZdKP/L21VdfoVyHDh3SP47PnFq/Zxm+wCyF6jEr4Hi1Ow6Zryif tsPYEM3s16/f9evX09LS4JbHkCPCIm937tzBRWFee+21Cxcu4ArO3t5k1om8 Xbx4EQabsDVnzpzjxo1LTk7Ocsf6duzY0aJFi2XLlpnISRCORuTIG9SH9evX ZxUCfpzucj+n0H4pme3R140tQ6NDzZo1MXNERAS+Gudyf9jSuHHjihUr4k9I P3DggE0mGcbINQMdRR3bv//+e8gDTaq+RC7NCtpSYUTn7HpXymw7X0hJeyCd xcSnX2DwwoJs0OBCuwwDH3zs5XIPc5Sjm19++QWHP0r35c6dW/keApyuSJEi zMuhoaEYTkEaNGgAgxeWmS1pB+2+id21eIy8BXhMK+DVi0BguFq0aFFMz+sG /y9evLjHr7G8oR95g9ExHlY7K6+/ObV+R4xcYJZC9ZgV8L3anYXMV5RP248f P87e0Ah1g/9Xr14d/1F+bdqmTRvthQE1eYkSJd566y3lK806kbcsdyXDakis JFmdU7lyZXM5CcK5iBx5y3JXj6+++ioLE+XJk6dr166OrhhN4zPyxioob9Sr V4/lj4mJwQlGlLRt2/b06dN2GOMngfciJk2aBHmGDx+uL5HL2JvV2RWD92a2 vCtltp0vpKQ9kM5iYsQv6enpM2bMgGGFsukpVqzY9OnTtXPaL126tFy5ciwb jHG0jdSVK1cGDBigmg27YMGC48aN+/vvv5U5v/32W9w6depUE7truXDhAuZX LZEZyDGtgFcvghEbG9uwYUM2dAXgRlN+w2WEnj17urwsswjArYpHVn2YbCKn R78jRi4w66B6zAq4X+0OQuYryojta9aswYkckfLly0OdkJqaikNIZeSNfXju DfZBetWqVV3uuKW3k/7xxx/NmjVTjlJB+W7dup06dcp0ToJwKIJH3hDoS0RF Re3bt0+0ryDtxIRuPrlz5050dPRvv/0Gf+F/vgfniBW2E1r80jmb3ZUy284X UtIeSGcxMe6XjIyMuLi433//fceOHefPn9efXCg2NhZy6r/7lJaWduzYMRg6 bd++HcYv3mYxhSHMiRMnTO/uF1Yc0xwW9SKg47Rnzx44Mn4bJTLe/I4YucCs gOoxK5C5zyzzFWXQdrA6MjJS/37fv39/3rx5Q0JC5syZA/XGgQMHIiIiwsPD 58+fz9anfv755/0qHp53165dp0+f1m8LjOckCMfhiMgbkSW3bjLbbicy6yyz 7XwhJe2BdBYT8ouYkF/EhPxiBTKrSrZzORR+CN+pUyftpoyMDJzosmTJklzO RRBSQZE3pyCzbjLbbicy6yyz7XwhJe2BdBYT8ouYkF/EhPxiBTKrSrZzOVSl SpVcLlffvn21m+7evYvLcLdv357LuQhCKijy5hRk1k1m2+1EZp1ltp0vpKQ9 kM5iQn4RE/KLmJBfrEBmVcl2Lofq1KmTy73Yyvz582/cuMHSjxw50qhRI5d7 WYoNGzZwORdBSAVF3pyCzLrJbLudyKyzzLbzhZS0B9JZTMgvYkJ+ERPyixXI rCrZzuVQkZGRBQsWZMscVKlS5eWXX4a/+DMkJGT69OlcTkQQskGRN6cgs24y 224nMusss+18ISXtgXQWE/KLmJBfxIT8YgUyq0q28zra0aNHu3TpwpZ/RQoU KNCzZ8+YmBheZyEI2aDIm1OQWTeZbbcTmXWW2Xa+kJL2QDqLCflFTMgvYkJ+ sQKZVSXb+R7z/v37Z8+e3bNnT2Rk5LVr1/genCAkhCJvTkFm3WS23U5k1llm 2/lCStoD6Swm5BcxIb+ICfnFCmRWlWwPdikIgtCDIm9OQWbdZLbdTmTWWWbb +UJK2gPpLCbkFzEhv4gJ+cUKZFaVbA92KQiC0IMib05BZt1ktt1OZNZZZtv5 QkraA+ksJuQXMSG/iAn5xQpkVpVsD3YpCILQgyJvTkFm3WS23U5k1llm2/lC StoD6Swm5BcxIb+ICfnFCmRWlWwPdikIgtCDIm9OQWbdZLbdTmTWWWbb+UJK 2gPpLCbkFzEhv4gJ+cUKZFaVbA92KQiC0IMib05BZt1ktt1OZNZZZtv5Qkra A+ksJuQXMSG/iAn5xQpkVpVsD3YpCILQgyJvTkFm3WS23U5k1llm2/lCStoD 6Swm5BcxIb+ICfnFCmRWlWwPdikIgtCDIm9OQWbdZLbdTmTWWWbb+UJK2gPp LCbkFzEhv4gJ+cUKZFaVbA92KQiC0IMib05BZt1ktt1OZNZZZtv5QkraA+ks JuQXMSG/iAn5xQpkVpVsD3YpCILQI1iRN4IgCIIgCIIgCIIgCIKQAYq8EQRB EARBEARBEARBEIQV0Nem4iOzbjLbbicy6yyz7XwhJe2BdBYT8ouYkF/EhPxi BTKrSrYHuxQEQehBkTenILNuMttuJzLrLLPtfCEl7YF0FhPyi5iQX8SE/GIF MqtKtge7FARB6EGRN6cgs24y224nMusss+18ISXtgXQWE/KLmJBfxIT8YgUy q0q2B7sUBEHoQZE3pyCzbjLbbicy6yyz7XwhJe2BdBYT8ouYkF/EhPxiBTKr SrYHuxQEQehBkTenILNuMttuJzLrLLPtfCEl7YF0FhPyi5iQX8SE/GIFMqtK tge7FARB6EGRN6cgs24y224nMusss+18ISXtgXQWE/KLmJBfxIT8YgUyq0q2 B7sUBEHoQZE3pyCzbjLbbicy6yyz7XwhJe2BdBYT8ouYkF/EhPxiBTKrSrYH uxQEQehBkTenILNuMttuJzLrLLPtfCEl7YF0FhPyi5iQX8SE/GIFMqtKtge7 FARB6EGRN6cgs24y224nMusss+18ISXtgXQWE/KLmJBfxIT8YgUyq0q2B7sU BEHoQZE3pyCzbjLbbicy6yyz7XwhJe2BdBYT8ouYkF/EhPxiBTKrSrYHuxQE QehBkTenILNuMttuJzLrLLPtfCEl7YF0FhPyi5iQX8SE/GIFMqtKtge7FARB 6EGRN6cgs24y224nMusss+18ISXtgXQWE/KLmJBfxIT8YgUyq0q2B7sUBEHo QZE3pyCzbjLbbicy6yyz7XwhJe2BdBYT8ouYkF/EhPxiBTKrSrYHuxQEQehB kTenILNuMttuJzLrLLPtfCEl7YF0FhPyi5iQX8SE/GIFMqtKtge7FARB6EGR N6cgs24y224nMusss+18ISXtgXQWE/KLmJBfxIT8YgUyq0q2B7sUBEHoQZE3 pyCzbjLbbicy6yyz7XwhJe2BdBYT8ouYkF/EhPxiBTKrSrYHuxQEQehBkTen ILNuMttuJzLrLLPtfCEl7YF0FhPyi5iQX8SE/GIFMqtKtge7FHYQGxs7YMCA jz/++PLly8E6AkGYQ/zI28OHD6OjoydPnjxt2rQjR45kZGSYOGk2wLhuiYmJ y5cvHz169Pz580E61dZr164d8cWDBw/4GxAA/l4zCQkJYAX89ZYhPT394MGD k9xs3rwZNOFQSufDXWcHIbPtfDGuJNXtgUA6i4lPvxw/fly//Y2Li2OZT5w4 4S1bWlqa6sgBtmspKSkRERFwnYwfP37FihXKYqi4e/fu9u3bJ06cOH369Kio qNTUVL9OFBTofhET8osVyNyfkfmKkify1qFDB5ebDz74IFhHIAhzCB55i4mJ KVOmjEvB008/fenSJRPndTpGdIuMjAwLC3P9m9atW1+4cIHlgSbG5Ys5c+ZY a4yfGLxmYDiwdevW7t27P/LII2BF06ZNtXlgtDJw4MD8+fMr7S1UqNDs2bP5 l9tpcNTZcchsO18MKkl1e4CQzmLi0y9FihTRb3/BXyxznjx5vGVbtWoVyxZg uwa7Dxs2LFeuXMrdc+TI0bt379u3b6syb9u2rXTp0qoCa5/xiQbdL2JCfrEC mfszMl9R8kTeoG1Cr3344YfBOgJBmEPkyNvp06dLliyJt0bVqlWfeuop/L9i xYoXL140cWpH41O3JUuWsF466NawYcOCBQviz2rVqrGn0kYibytWrLDDJMMY uWYSEhKw88D4z3/+o8pz/fp16Frg1tDQ0Lp161apUoXlX7RokVUGOAReOjsR mW3nixElqW4PHNJZTAKPvEF7jTkfPHhgpJkOsF2Dau25557DzCEhIWFhYeya ATp06JCZmcky79q1C/JAeq5cuZo0aVKvXj2sEvPmzbt58+aAhLMYul/EhPxi BTL3Z2S+ouSJvF29enXSpEmTJ0++efNmsI5AEOYQOfL2+uuvY1dw6dKlmDJj xgysHiV8O1Rftxs3bmCcDVqQ8PBwfGs6OTm5Xbt2qNg333yDOe/cuXPBE0eP HoXOM+Rs3LixaC9dG7lm4uPjC/8DjD489iKGDx+OanzxxRfsS5xNmzbly5cP EosWLXrv3j0ryu8UeOnsRGS2nS9GlKS6PXBIZzHx6ZdLly55bIIHDBiArvnt t98wJ1Q4mPLdd99p8//999+YLcB2DboENWrUgJz9+/e/fv06pEAHAEyoVKkS HjYiIgJzpqSkPP3005BSvHjxQ4cOYWJ0dHSpUqUg8Yknnnj48KF54SyG7hcx Ib9Ygcz9GZmvKHkibwThXISNvCUmJuLjmL59+yrTscIsUKCAbEESn7pB97ht 27ZXr15VJkJHGiNyrVu31j8+9LohG/TVz507F3hp+eJva/Lkk0967EXAuKBP nz7z5s1TpbORy8GDBwMsqqPhpbMTkdl2vvhUkup2LpDOYmJu7BMXF4eBsvff f58lnjx5EhumTZs26ewbeLt28eLF9evXqxKXLVuGu0+bNg1TwsPDMWXmzJnK nBs3bsT0X375Rf9EQYTuFzEhv1iBzP0Zma8oc63PqVOnIiMjfVoNo8s9e/aY fifQ391TU1Oh5YqJiVG+dO0vd+7c2bt3LzSvJg6Snp5+9OhRUOb+/fs2nI6Q B2Ejb5MmTcK+3O7du5Xpq1atwvQFCxaYOLtzMf0sA79DqVChgk6eAwcO4DOv qVOnmiuepVjdi4BrDC+q+fPnmyhetoF6a8bzZyfb+eJTSarbuUA6i4m5lrpN mzbgkfLly0PXnSVCHx49FRUVZaIkAbZr+/fvx91Hjx6NKT/99BOmJCUlqTJX r14d0l988UUTJ7IHul/EhPxiBTL3Z2S+ooz4vXbt2kWKFJk3b96tW7fee+89 fGPZ5Z6poG/fvh6Xy5kxY0a5cuVc/1CsWLHPPvvM+MI6BnfHgs2ZM+fEiROt WrXCj7CAggUL9u/fX5V/+fLlRdyEh4erEitXrgz/r1u3rmHDhux76rJly0KL 5vMISFxcXOvWrdn0TTly5KhXr15kZKQqm1+nIwiGsJG3Hj16wNVbuHBh1fcL d+/exWsb7lwTZ3cupiNvUJuBXNAx1slTp04dyAN/xQzUW92L2LBhA9aWyjmr JYR6a8bzZyfb+eJTSarbuUA6i4mJlpq9MwYtkTKdNUzmXjMIsF2bMGEC7s4+ yPriiy9c7k+0tP2Efv36uf69NoRo0P0iJuQXK5C5PyPzFWXE70888QTY+MYb b+DUASqGDBmizHz//v3WrVtrswE1a9a8fPmy/rn82h0LVrlyZdVaPwhcnCkp KSzzokWLMP3333/XJg4bNgznI1WSN29e5XrfHo8ArF+/ns2RrgSujR9++EGZ 06/TEQRD2Mhbq1at4NKtUaOGdhNOjNm9e3cTZ3cu5iJvycnJ+DJbnz59vOVh D9YXL14cUBEtw+pexKBBg1ABn+1I9oZ6a8bzZyfb+eJTSarbuUA6i4mJlrp5 8+Yu9xytqojW/PnzsWHauHHjl19+2atXr5EjRy5dutTgxy+m2zUYkC5ZsgSH P+XKlWOn+/nnn70dcOzYsS73WxNpaWl+ncs26H4RE/KLFcjcn5H5ijIeeUNe ffXVNWvWnDt3burUqRh1LFSoEJtBFIB2B3NWq1Zt586dt27dioyMfOmllzCx Xbt2+ufya3dlwfr27btv3774+PiVK1cWK1YME//f//t/LLN+5A0oUqTIN998 c+TIkaioqPr162PiuHHj9I9w5coVXAIpJCQEWts///wzISFh1qxZ+AIeNHDK Vbz9Oh1BMISNvNWsWdPlZZHrsLAw2AQ3r4mzOxdzkTf2WvXChQu95enSpYvL PWey8oGCUFjai4C2oHTp0pC/UqVKJsuXXaDemvH82cl2vvhUkup2LpDOYuJv TXLq1ClsoydPnqzaBCkuT5QrV071dpwWE+3aX3/99cknn3Tq1KlChQp4Iqjf YmNjWQYYOmE6+/4U2b59O/sySJlfKOh+ERPyixXI3J+R+YryK/I2cuRI5bOe 7t27Y/qxY8cw5cyZM/j8BS6P5ORkljMtLY1Fz3RaIn93x4IVKlRINa/p8ePH 8Y2yp59+mhVYP/IGZ4Szs/Rz587hEV5//XVtZuUR3n33XUycPn26sgygKqYr Pw3z63QEwRA28oaPHjp27KjdBK0DbHrmmWdMnN25mIi8nT9/HidtrlatWnp6 usc88fHxOXPmhDxffvklh1Jag6W9CPxMxiX27ND2QL014/mzk+188akk1e1c IJ3FxN+aBJc0hZZaOTZBWOQN/DV06NAhQ4bg3BFA7ty5T5w4oXNYE+1aVFSU Kr538uRJZYbU1FT8QAn6DBMmTIiNjT169OjYsWOhMGwvSDFuu53Q/SIm5Bcr kLk/I/MVZTzy1rhxY1X6vHnzsA7fsmULpnzzzTeY8r///U+VOTIyEje9/fbb 3k7k7+5YsCZNmmgP9fLLL2N+NsWofuRtx44dqiOUL18e0uvVq6fNzI6QkZGB Q+aKFStqh8w4FyvAViH063QEwRA28obzMbZv3167qUGDBhh5NnF25+JvSwp1 CHussHXrVm/ZfvzxR8yj6mMLhXW9iN27d+OzCbioxJzjzk6ot2Y8f3aynS8+ laS6nQuks5j4W5PgpzSdOnXyuHXp0qXKXj006yNGjMAm2+MIBTHXrsXFxXXu 3BnqtDJlyuAp4CCjRo1SHgEKw+adZhQpUqRbt274/40bNwyezmbofhET8osV yNyfkfmKMh550zYfW7ZswTqcTez5zjvvYMrt27e1x8HgUq1atbydyN/ddSJv X3/9NR6KrdOtH3lTTd0G1KtXz+WeRE4nc2xsLKb897//1ZaBRSbZOuB+nY4g GMJG3vC6feGFF7Sb8Klr27ZtTZzdufjbkn788cdYJ+hPWdC1a1eXex1t6NIH WkTLsKgXcfbsWRz15MuXj71fLTPUWzOePzvZzhefSlLdzgXSWUz8qkliYmKw mZ40aZLBXaClhtEK7JInTx7VDOFI4O1aZmbmtm3b8CzA7NmzVcfv2LFj2bJl Xe41099///0zZ85gPBA6EiZOZw90v4gJ+cUKZO7PyHxFBRJ52759uyry9txz z8HPxx57zONxmjZt6nK/fe2xGTKxu07kDdogLBtbLcjfyFujRo18Rt7YmkSq T02R/++fD07Z7G1+nY4gGMJG3tq3bw/XbVhYmHYT3Mgu3SUDsiV+taTsK5U6 deroz8aMT7ebNWvGoYiWYUUv4urVq5gNWLFiRaBFzBZQb814/uxkO198Kkl1 OxdIZzHxqyaZM2cOtkF79uwxforBgwfjXsq5ZRCO7VpiYiJ+luVtxdIHDx6w /3F6HJG/0qL7RUzIL1Ygc39G5iuKb+StYcOG8LNEiRIej9OiRQuXe+YBb3MZ +bu7TuSNzVjOPuCyIvK2Zs0aTJk5c6a2DOHh4bh1zJgxJk5HEAxhI28ffPCB y/1U9969e8r0y5cv46U+fPhwE2d3LsZb0lWrVuF6ptBt1l/ULC4uDsUcOnQo n1JaA/deBFxU+Fa5S+wJ7myGemvG82cn2/niU0mq27lAOouJXzVJjx49XO4V 01Q+0od9cKqaVI17u8Y+F7p+/bpOtocPH+KnQyK/K0L3i5iQX6xA5v6MzFcU 38gbm0PAY/OEx6levbq3E/m7u07kDWdDBWDQiilWRN7OnDmDKZ999pm2DCy/ /nt33k5HEAxhI2+//PILXtJr1qxRps+cORPToZYwcXbnYlC3DRs24IoJBQoU UC5/7JGNGzeimIsXL+ZTSmvg24u4f/9+s2bN0PC33npL5M9sbYZ6a8bzZyfb +eJTSarbuUA6i4lfNQl+jwOViV+neOWVV2CvXLlypaWlscRA2jVvc8Gxhd6u XLmiszuMRDCbdiZtcaD7RUzIL1Ygc39G5iuKb+Rt3LhxmLJ8+XJV5pMnT+JU ol27dvV2In9391YwaOZwar48efKwds2KyNvDhw9xtaCwsDBtm9i5c2fMHxMT Y+J0BMEQNvIG3cjChQvDpduyZUt2r8ENWLduXZd7dhHZAiZGdNu8eTMu4gwV lBGR2bfz27Zt41JIi+DYi3jw4AFbeKJdu3beXpOWE+qtGc+fnWzni08lqW7n AuksJn7VJI8//jj4omHDhtpNhw8frlmz5pEjR7THxzGLchpw4+0aXBUwXoCx FftW9NChQ6VKlYLOgypnUlJSiRIl8FJhiSkpKaoX7W7evAnlxGzKSKBo0P0i JuQXK5C5PyPzFcU38gatD34/9cwzzyinLcrMzOzQoQNm1lk729/dsWA1atRQ zmMATJs2DTN369aNJVoReQNef/11TIStyt3/7//+D22pWrUqm5iOIm+EOYSN vGUp3i/9+OOPb926BR283r17Ywr7zloefOq2detWDNcDw4YNg5/r169fp0Bb OXz11VeYH/reFhY9YIxcMwcPHtz/Dzh5HfQlWAqurQOjhlatWqHJBQsWhLHG pk2b1v2bhIQEO0wSEl46OxGZbeeLESWpbg8c0llMjPdwYHyXI0cOjJVpt4aF heFDtFGjRu3duxcGI1DDzJ49G1oul3vVUTbjjV/t2qBBgzAn+xwVRjp4QLha fv31VzhLeno6mPDss89izsGDB2NOGDF17tz5kUcemThxYmJiIpw3PDwcRiKY 7eeffw5QOkuh+0VMyC9WIHN/RuYrim/kDRg4cCAm1qtXD8aJDx8+PHv27Btv vIGJTZs21T+XX7tjwYCnnnoK2qw7d+5As/X111/jkyYY3rJPTbMsi7xdvHgx f/78kAjN3JQpU65fv37v3r0VK1YUKlQIMyvlpcgbYQ6RI29QH9avX9/1D3j3 udzPKVQhcRnQ1w36wNBFd+lSs2ZN1V79+vXDTVDbWFv6wDByzTz66KM6tn// /feQB5pUfYmAtWvX2mGSkPDS2YnIbDtfjChJdXvgkM5iYryHk5SUhO7o1auX duuaNWvy5cvHfJcjRw7mPte/J6Lxq12rU6cOpjRu3JgVuEiRIixnaGgoDDrY zwYNGqSmpmJO6CfgW3CsSOz/Tz/91NsKd4JA94uYkF+sQOb+jMxXFPfI2+3b t5VCKalUqdLZs2f1z+XX7izypgXamhkzZigzWxR5AxYuXMheYlECF4lq9lSK vBHmEDnyluWuHl999VX8gtLlfv7btWtXR1eMpvEZeVN2gz1Sr1491V4gJm7S X/806ATei5g0aRLkGT58uL5EwJYtW+wwSUh46exEZLadLwZreKrbA4R0FhPj PZyYmBh0ircVji5dutS7d298yY3x5JNPbtq0SZnNr3bt22+/xZSpU6eyI1y5 cmXAgAG4rh8Dzjtu3Li///5bea7ExETl5QSULVtW8HliEbpfxIT8YgUy92dk vqKM2I5vKbds2VKVvmfPHpRCGXkD0tPTv/rqqwIFCrBrI3fu3O+9996dO3eM FMn47hh5a968+ffff8/eMQMqVqyoDW0tX74ct+7fv18/EcFJUJWhMJ3MJ06c qF+/vvJR11NPPaWd/c+v0xEEQ/DIG3L//v2oqKh9+/alpKSYOGP2wIRu2QaZ bbcTmXWW2Xa++KUk1e2mIZ3FhHtNglOr7dy5E0Yf8fHxgR/w1KlTMLLQpqel pR07duzXX3+FIcYff/yhM1lcampqZGQkZONSHnug+0VMyC9WIHN/RuYryjq/ Z2ZmQqOwdetWaIxMzOdpZHeMvOEnqA8fPjxy5MjGjRsvX74caNHNcvv27f37 9+/evVt/gSGC8BdHRN6ILLl1k9l2O5FZZ5lt5wspaQ+ks5iQX8SE/CIm5Bcr kFlVsj3YpTCJt89gCSKbQZE3pyCzbjLbbicy6yyz7XwhJe2BdBYT8ouYkF/E hPxiBTKrSrYHuxQmocgbIQkUeXMKMusms+12IrPOMtvOF1LSHkhnMSG/iAn5 RUzIL1Ygs6pke7BLYRKKvBGSQJE3pyCzbjLbbicy6yyz7XwhJe2BdBYT8ouY kF/EhPxiBTKrSrYHuxQmocgbIQkUeXMKMusms+12IrPOMtvOF1LSHkhnMSG/ iAn5RUzIL1Ygs6pke7BLYZLevXs3b978888/D3ZBCMJaKPLmFGTWTWbb7URm nWW2nS+kpD2QzmJCfhET8ouYkF+sQGZVyfZgl4IgCD0o8uYUZNZNZtvtRGad ZbadL6SkPZDOYkJ+ERPyi5iQX6xAZlXJ9mCXgiAIPSjy5hRk1k1m2+1EZp1l tp0vpKQ9kM5iQn4RE/KLmJBfrEBmVcn2YJeCIAg9KPLmFGTWTWbb7URmnWW2 nS+kpD2QzmJCfhET8ouYkF+sQGZVyfZgl4IgCD0o8uYUZNZNZtvtRGadZbad L6SkPZDOYkJ+ERPyi5iQX6xAZlXJ9mCXgiAIPSjy5hRk1k1m2+1EZp1ltp0v pKQ9kM5iQn4RE/KLmJBfrEBmVcn2YJeCIAg9KPLmFGTWTWbb7URmnWW2nS+k pD2QzmJCfhET8ouYkF+sQGZVyfZgl4IgCD0o8uYUZNZNZtvtRGadZbadL6Sk PZDOYkJ+ERPyi5iQX6xAZlXJ9mCXgiAIPSjy5hRk1k1m2+1EZp1ltp0vpKQ9 kM5iQn4RE/KLmJBfrEBmVcn2YJeCIAg9KPLmFGTWTWbb7URmnWW2nS+kpD2Q zmJCfhET8ouYkF+sQGZVyfZgl4IgCD2CFXkjCIIgCIIgCIIgCIIgCBmgyBtB EARBEARBEARBEARBWAF9bSo+Musms+12IrPOMtvOF1LSHkhnMSG/iAn5RUzI L1Ygs6pke7BLQRCEHhR5cwoy6yaz7XYis84y284XUtIeSGcxIb+ICflFTMgv ViCzqmR7sEtBEIQeFHlzCjLrJrPtdiKzzjLbzhdS0h5IZzEhv4gJ+UVMyC9W ILOqZHuwS0EQhB4UeXMKMusms+12IrPOMtvOF1LSHkhnMSG/iAn5RUzIL1Yg s6pke7BLQRCEHhR5cwoy6yaz7XYis84y284XUtIeSGcxIb+ICflFTMgvViCz qmR7sEtBEIQeFHlzCjLrJrPtdiKzzjLbzhdS0h5IZzEhv4gJ+UVMyC9WILOq ZHuwS0EQhB4UeXMKMusms+12IrPOMtvOF1LSHkhnMSG/iAn5RUzIL1Ygs6pk e7BLQRCEHhR5cwoy6yaz7XYis84y284XUtIeSGcxIb+ICflFTMgvViCzqmR7 sEtBEIQeFHlzCjLrJrPtdiKzzjLbzhdS0h5IZzEhv4gJ+UVMyC9WILOqZHuw S0EQhB4UeXMKMusms+12IrPOMtvOF1LSHkhnMSG/iAn5RUzIL1Ygs6pke7BL QRCEHhR5cwoy6yaz7XYis84y284XUtIeSGcxIb+ICflFTMgvViCzqmR7sEtB EIQeFHlzCjLrJrPtdiKzzjLbzhdS0h5IZzEhv4gJ+UVMyC9WILOqZHuwS0EQ hB4UeXMKMusms+12IrPOMtvOF1LSHkhnMSG/iAn5RUzIL1Ygs6pke7BLQRCE HhR5cwoy6yaz7XYis84y284XUtIeSGcxIb+ICflFTMgvViCzqmR7sEtBEIQe FHlzCjLrJrPtdiKzzjLbzhdS0h5IZzEhv4gJ+UVMyC9WILOqZHuwS0EQhB4U eXMKMusms+12IrPOMtvOF1LSHkhnMSG/iAn5RUzIL1Ygs6pke7BLQRCEHhR5 cwoy6yaz7XYis84y284XUtIeSGcxIb+ICflFTMgvViCzqmR7sEtBEIQeTom8 JSQkHDlyBP6aOGP2wLhuly9fXrly5ejRo3/++eddu3alp6d7zJaSkhIRETF5 8uTx48evWLEiLi6OY2n54u814/NqAU0OHjw4yc3mzZuvXbvGoZTOh7vODkJm 2/liXMmHDx9GR0dDFTRt2jQQMyMjw+KiZSuM65yYmLh8+XJoEebPnw+C6+S8 cuXK6tWrx4wZs27duhs3bhgsif7xoXY94osHDx4od7l79+727dsnTpw4ffr0 qKio1NRUgyXhsnuAWFGTmPPL6dOnwSOw19q1aw3WVJcuXUKP3Lx501seuG2P HTt29OjRzMxMgyVBgtvl4OuXEydOeLuY09LS9I9sxC9+HZ9LfyY+Ph6urq++ +mrBggUxMTH+Otc01F5Ygcyqku3BLgVBEHoIHnmDLvTWrVu7d+/+yCOPuFyu pk2bmjhj9sCIbocOHXrmmWdc/+app57atGmTMhv03IYNG5YrVy5lthw5cvTu 3fv27dsW2mAWg9eMkasFbB84cGD+/PmVthcqVGj27Nn8y+00OOrsOGS2nS8G lYTBXZkyZZS34dNPPw0Df+sLmE0wonNkZGRYWJiqRWjduvWFCxe0mQcPHqzM FhISMmHChMCPD4Maly/mzJnD8m/btq106dLKrXCd6AcMlQS4e+Bwr0lM+OXO nTu9evVSifzhhx96ewyHXLlypVixYpj5l19+0WY4c+bM1KlTn3zyScyze/du n2YiInQ5+PolT5483i7mVatWeTu4cb8YPD6X/sz9+/fBXtVZatWqde7cOeMH MQ21F1Ygs6pke7BLQRCEHiJH3hISErDzw/jPf/5j4ozZA5+6/fjjjzlz5kSh ChcuXLt27dDQUPwJPd6DBw9iNlD1ueeew3Tow8O4qWTJkkzhDh062Pas0zhG rhkjV8v169ehI41bQZy6detWqVKF5V+0aJFVBjgEXjo7EZlt54sRJU+fPs2q napVqz711FP4f8WKFS9evGhLMR2PT52XLFnCxu+gdsOGDQsWLIg/q1WrpnoT DMbvuAlG8Q0aNMibNy/+/PrrrwM8vpHI24oVKzDzrl27oFXCNqtJkyb16tXD Ow7Ks3nzZp+aBLg7F/jWJCb8Ai34iy++iNkqVKjQqlWrUqVK4c/mzZvrvJH1 xhtvsPJoI28ffvihymugtr6ZzFgRuhwc/fLgwQMjF7MK434xeHwu/Rmld+Ag UBsXKlQIf0I38s6dO0YOEgjUXliBzKqS7cEuBUEQeogceYuPjy/8DxhEknmc 61O3BQsWgERly5bdsWMHvjWdmJj42WefYYPy8ssvYzboStWoUQNS+vfvDz03 SIHMcORKlSphzoiICOut8Q8j14yRq2X48OFo4xdffMG+yNi0aVO+fPkgsWjR ovfu3bOi/E6Bl85ORGbb+WJEyddff93lHoYvXboUU2bMmIH35gcffGB5EbMF +jrfuHED42AwpggPD8cWITk5uV27dqjzN998wzIfO3YME+vUqXP37l3cvXLl yi73i0mXL18O5PjQ4lzwxNGjRzGO1LhxY9w9JSXl6aefhpTixYsfOnQId4+O jsYAxRNPPPHw4UMdQQLcnRccaxITfgHmzZuHe/Xt2xdfpoK//fr1w0TY6nGv 5cuXK8M72sjbwIEDscDYXLoMR94E6XJw9Atkw5J/99132gv777//9nhw434x eHwu/Zk+ffrgQTp37ozvH4J3Fi1alD9//lmzZvncPXCovbACmVUl24NdCoIg 9BA58qYEP3CQeZxrRLclS5Zgz5aRmZlZtWpVkO6xxx5jiRcvXly/fr1q32XL lmG7M23aNE5F5oa/14y3qwVGXtDP1A49WA+WvRkoJ7x0diIy284Xn0omJibi iyUwAlWmY2e4QIECkgfADeJT54iIiLZt2169elWZCA0ERsxat27NEj/66CNt MIeFfby9XmX8+B7p378/ZMuXLx/7qC08PBzPOHPmTGXOjRs3egsHKQlwd15w rEnM+aVFixawtVy5cikpKcr0OnXqQHrlypW10xklJSXhd6b169f3qdXixYv9 irxlidHl4OiXkydPYslV83joY9wvBo8feH/mwoUL+KFEly5dVG8e3rp1y6hh gUHthRXIrCrZbjz/+fPnd+/e7XMWUKgNoLnfv3+/wVlGIf/evXu1E05C1RcZ GQlNmP68B37lJAjHQZE3p2D6WUbLli2x967/xB8qVeytjR492lwJrcPqqAg0 PWj7/PnzTRQv2yBz9Elm2/niU8lJkybh7aaaJ2rVqlWYvmDBAmuLmC0w3SLg F2oVKlTAn2lpaUWKFHF5mtKqevXqLvcHOIEc3yMHDhzAd4qmTp3KEn/66Se8 AJKSkjyW5MUXX9Q5ZoC784JXTWLaLyVKlICt/fr1U6WvXLkS9dEWD4ecuXLl 2rFjhxWRN4/Y3OXgWMPDoBJLHhUVZfyAxv1i7vgM4/0ZjH4Dp06dMnEiLlB7 YQUyq0q26+fJzMyEOqdZs2aFCxd2/UPp0qW3bdumzRwdHd2oUSOWDVrtIgo6 d+6M2WrXrg0/Qbf4+HhoTXLnzg2ZocZjx4mNjX3ppZfYVJ+QAbKpXhTxNydB OBSKvDkFc7rdvn27ePHiLvcTVf2cEyZMwIqOvX0tDlZHRTZs2IC268yNLAMy R59ktp0vPpXs0aOHyz2JkOpZwN27d/FJ9GeffWZtEbMFpiNv0EkGkatXr44/ 4+LisPb74YcfVDk///xz3OTtAzojx/cIvucDf5Vv2nzxxRcu9xdA2om/8KO8 MmXK6BwzwN15wasmMeeX1NRUb+GspKQk3KRczwKAFh/Tx44de+7cOfzfhsib zV0OjjU86zAYnxLKL7+YOL7H4vnsz1SrVg2ywTjXxFl4Qe2FFcisKtmukwGG hM8++6zLE6GhoTt37lRm3rt3b44cObBVrVKlSrly5VS7tGnTBnM+8cQT8HPk yJH16tVjW9u2bYtbN27cyOaAzZkzJ5tLs2zZsqolLYznJAjnQpE3p2BCtwsX LrRq1QorrhkzZnjLBq3PkiVL8BEDVK33798PtKy8sToqMmjQIFTJ28w5kiBz 9Elm2/niU0mslGrUqKHdhJMed+/e3arCZSPMtaTJycn4slmfPn0w5cCBA1j7 rVmzRpX5559/xk1//vmn6eNrYa/0LF682OPptPXw2LFjcWigs0BAgLvzgldN YtoveBNp9U9NTcWJ9UaMGMESk5KSihYtConPP/889AT++OMPPLKlkbegdDk4 1vDz589HBWCc+OWXX/bq1QuGnEuXLtU3xLhfzB2fYbw/gzPCwSnwZ0JCQmRk pG3fmSLUXliBzKqS7fp5WrRoERIS0q1bt02bNiUmJsJdDzUA1hjK18LT09PL ly8PiQULFoQmGxNZ0wOV1ZkzZ1gNg5E3pFatWrNnz46Ojj5y5Ahsunbt2mOP PeZyz7+6atUqqO5SUlLmzp2LlZ6yPjSekyAcDUXenIJx3RYsWNC7d+/mzZvj 0wroXE2fPl37GsBff/31ySefdOrUqUKFClhhgryxsbH8ix4wlkZFoJ9ZunRp yF+pUiWT5csuyBx9ktl2vvhUsmbNmi5P39ABYWFhrmC/g+EUzLWk7EObhQsX YsratWsxRfXpTZbiO7h9+/aZPr6WLl26YO9aNeHVzp07cV/Ve0Hbt29na3rq tFAB7s4LXjWJab+88MILsKlQoULJycksEaRu27Yt7vXOO++w9A4dOrjca7/C MAp+Whp5C26Xg2MNP3nyZJcnypUrt2HDBm8HNO4Xc8dHjPdnrl69ioedNWvW okWLnnnmGXaiGjVqsLG21VB7YQUyq0q26+c5d+6cdvAOJuO9z2onNqHod999 p8yJTUbt2rWViSzy1qRJE1Wz3qtXL5d7vqPo6Ghl+rfffgvpjzzySGJior85 CcLRsMibOUyf0d+9aJxrXLdXXnlF2VsbM2bMgwcPtNmioqJUnbqTJ09yLjQn LI2KsMXF7Jl/W2Rkjj7JbDtffCqJj5U7duyo3QR6wiYYA1pVuGyEiZb0/Pnz +JZLtWrV2MTF7Cn28ePHVfl37dqFm7SvXRk/vor4+Hic1J29acNITU3FxUkh w4QJE2JjY48ePTp27FicNAaBFG9nD3B3XvCqSUz7hb0x9dxzz+3duxcEX716 NQyImAgwdMKc0ORhCpttz9LIW3C7HFZE3mDr0KFDhwwZgl9Yu9yzEp04ccLj AY37xdzxEeP9GRjeYk5cedblnls+f/78+H9ISMjmzZuNSRUQ1F5Ygcyqku0m dvz+++9VTeSiRYswZc+ePcqcEydOxEZW+a0uRt5CQ0NjYmKUmTMzM7FK6dmz p+qMycnJeHz8xNV4ToJwOqbvU5vPSONc47pBFdqmTRvoqrE5KqtWrXr69GlV tri4uM6dO4OkZcqUYX2tUaNGad+OCzrWRUV2794NVkPmBg0aCGi4zcgcfZLZ dr74VBJnC2nfvr12E9yGLvf0X1YVLhvh7xWbkZHBnmtv3bqVpc+dOxcTDx8+ rNrlt99+w01G1nD0dnwVP/74I+bxGHXZsWNHnjx5XP+mSJEi3bp1w//111YL cHcu8KpJTPsFGjJcVkkFiIBLNgwcODDLvcAfftrTtGlT1vZZGnkLbpeDbw2/ dOlSuNjYT7j4R4wYgUY1adLE4y4G/WL6+Fl+9mfYmr8u9zzAsG96ejrsBa7H d0RhNK16fcUKqL2wAplVJdsNZoYmePbs2T179gR7CxQogFUBNC64lS3Usnbt WuVeQ4YMgcSiRYsqEzHy1qhRI9UpYmNj8SCw1wENuAnK4FdOgnA6FHlzCiZ0 S0pKgm4t9sSgYvQ2SQj0tbZt21arVi1hKzeLoiJnz54tVqyYy/1B7rFjxwIq YrZA5uiTzLbzxaeSOAfvCy+8oN2E7yyxiXkJHfy9Yj/++GOs4VWT2GzZsgXT tQ+UlyxZgptUX3/4dXwVXbt2dbnfrsnIyPCYAarljh07li1b1uVeIPX9998/ c+YMhh1gL5/FCHD3wOFVkwTiF9B22rRptWvXzps3b65cuZo3b/7TTz+lpKTg FHy4ZAMMPPEgERERCf8QHh6OiT/++CP8vHPnjvbggc/zFpQuh9U1PGiOFuXJ k8fbOvJG/GL6+P72Z9h4tlq1aqrPuIYPH278xg8Qai+sQGZVyXaf2ZYuXYqL HGn59ddfMQ/US/gYq0WLFmzHW7dulSpVChJbtmypPCBG3rQPBdhqLzqMGzfO r5wE4XQo8uYUTHtq9OjRWGupVjRTAV0vfAfbngXg/MKKPvPVq1cxG7BixYpA i5gtkDn6JLPtfPGpJA75w8LCtJvwJRyaStcIfl2x7Ps16G+rHsEcPnwYN61c uVK11/Tp03GTz6nadY6vAt93atasmc8yKydJePfdd11+fgQU4O6m4VWTcPFL RkZGamoq/n/+/Hnca/Xq1adOnfI5zHF5eochi9/apjZ3OWyo4QcPHozK4KR5 Onjzi+njm+jPgP6Yf968eapN7NqzYdlZai+sQGZVyXb9PKyxzp0795tvvrls 2bKYmJgFCxZgIou8ZSkGj6+88sqaNWuWL1+O8+CFhoaqPkH1FnmDvfAIVatW beIFrK+M5yQIp0ORN6dg2lPsJd6PPvpIP+c777yDOa9fv26miJbBvc987949 fKvc5Wm6IWmROfoks+188ankBx984HK/uQG3oTL98uXLeEsOHz7c2iJmC4xf satWrcKXakqWLKmN1SQkJKDsI0eOVG167733XO5PAvWXBNU/vpK4uDg819Ch Q42UHHn48CEusmbuVYQAd/cXXjVJ4H5RMWfOHDxgdHQ0DLV0Am6MunXrao/D K/KWZW+Xw4Yann0Q6td0gkq/mDu+uf5MZmYmvtMyZMgQ1SZWFU+ZMsW4Ieag 9sIKZFaVbNfJsGXLFvwMqmHDhlevXmXprFZXRt5AHwxFKnn00Ue1byl7i7yx hmby5Mn6JTeekyCcDkXenIJP3bxN68GeqPbv318/5//f3n3AV1Gl/QO/riui LNZV11exrO+uZV9ddRGwi4oNEAWpi6vgKh0RBFGKEEroXSCAtEikhCY9FKmB ACEECD2kERJIrxQF/o/30fMfZ+6dO/dmZu5czu/74cMnmTkzc85z5s6deTJz hm8MIKdPn65YZU1m7jlzeXl57dq1uaUtWrTw9tCThGTOPsncdnP5jKQY1101 PvzEiRN5ekxMjLVVvCIY3GOXLl3KbzSoWrWqt0t7fsTm0UcfVU6krwl+rsTj jU9+rV8QQ0vReb7PmgsLFizgpaZNm2Z8KbMW95eJR5KK9IsWv7zy/vvv55df FBQU5GmIJxAnT55MvxYVFWnXE0DmzQmnHDYc4fnlVpUqVfIrI6rqF3/XX5Hz GX7ijCqg6iDx0LENL1nA94UVZI4q2q5ToEOHDi73X21Uh1yPmbeWLVu63AOB DhgwoFmzZh999NHo0aMzMzO1q/WWeaNjGo837vN1scZLAoQ6ZN5Chc+4NWnS pGHDhtqBWcQNwzNmzKBf4+Pj6bxde0KVnZ19++23u9xj45hYbVOYeM589uxZ MQx4/fr1fZ7rSkXm7JPMbTeXz0jSpeJNN93kcg8VIq4T6VqyevXqfPxBMtwI I3ssHef5bLZy5co6hfltZYSuuMXExYsX88Rvv/22gusXIiIieJ1r1qzxWODc uXOq+3ny8/Mfe+wx3jFUCYfZs2dHRUUpnyo1vrh1TDySBNwviYmJqheaT5s2 jZeaMmWKTmUq/oYFbb845JTDrH7Zs2cP7VEJCQna9fPNJDrDsxvpF7/Wb/x8 xuPnRXQl7VTKwq1ateLp6enp3lZoFnxfWEHmqKLtOgXeffddl/txUeXQjufP nxd/BBGZN4oSv6l8xYoVPrfrLfNG3nnnHV6zz2dFjZcECGlOzrzt2LFj2294 cBg6FxJTPP419gqmHzflM/KTJ09OSkq6dOlSTk5O7969r776app+4403njx5 kkryG+TpFK59+/Z0jKUw0tkarfmf//wnr6Fbt272tcoYI/uMkb2Frstef/11 buYNN9xA1wLLli1b/HunTp2yo0mOZFacQ5HMbTeXkUjSwYc/hp07dy4sLMzP z2/dujVP6devny3VDHk+47xq1aprr72Wo9qzZ0/6dcmSJcpj3YYNG7gkHfT+ +Mc/UrHbb789Li6Ovju2bNlCXxku96MlJSUlFVy/0L9/fy4fHx+vXSFtt3Hj xlST8PBwui6gwzVVg77ReJFJkyYpC3ft2pWni8fr/FrcOiYeSQLrFypJV0xP PvlkbGwsfblnZGTQZ4qTNtWqVRPDi3nkLfOWmZkpqtenTx8uM3jwYJ6ifE2t tl8ccsphVr/wSEeVK1fu27fv5s2bz549S9MjIiLojIKb6e3Fvgb7xfj6/Tqf 0fbLZfeD2JxgrFq1Kn14L168SFNGjx7ND483bNiwYiE3BN8XVpA5qmi7TgH6 puZmtm3bNjc398KFC/SdwilHJjJvxcXFfPHYoEGD1NRUfvGxt7uXdTJvaWlp VapUobnXXHPNwIEDCwoKLrtzfWvXrn3llVe+//77AEoChDTl55Q+Uxf9FMDr 4I1n3ujc0uXdiBEj/N10SNOPGx2d+J1xAt+KwOhsbenSpWI9/Ap7RqdYfG7P atWqpX9mHhRG9hkjewt9peqUYao3aEvFrDiHIpnbbi4jkaRz3Zo1ayoPUPxD nTp1VPeEgDf6cRYvJtPx2GOPifLfffcdn2Yru+Paa6/19vduf9fP6GyfZ9E5 tnadNJFvgmKiPqRLly6q9zmKV7M9++yzASxuHXOPJP72y2XFtZUqCPfcc4/H hKeSt8wbVUmnwtQcUVLbLw455TCrXxYuXMi3gogIi34h3bt397Zyg/1ifP1+ nc9o+4XRdfett97Ks65z459vu+02j4+VmQ7fF1aQOapou06Bffv2ib+X/cGN f/7HP/7BPyifNn3zzTe1hxQ6etP3bIsWLZS3Metk3i67v8XEgYWPLeIA+Pe/ /z2wkgChS/k5PX369G4/BTA6h1mZt+HDh/u76ZBmJG7R0dFioF3hxRdfjIuL UxajXmvfvr1q5Mwbbrhh4MCBZWVlFrYhUBU/Z+a9pVevXjplmJE7q69UZsU5 FMncdnMZPMLTqW+9evXEHwgqV67ctGnTkD7ptZnPzJvy6t6jp556SrlIVFRU tWrVxFw6l9Y5GAawfiL+POTt/adZWVnKvYLcfffdHgeFGzp0KBcYM2ZMAItb x/QjiV/9wqZPn863bDG61Kpfv35OTo7PyqempvIiqheq6mfeqlSpIkp67Bcn nHKY2C/p6emtW7fmm9CEBx54YNmyZfrrN9gvBtfv1/mMx35hycnJTz/9tLgG J/QhUj6MZil8X1hB5qii7fplFi5cyCOFsnvuuYeOCefPn+cvdGXmTYxs4I0Y 8YDvLa9Tp463jR45cqR27drKcwaKfLNmzZKSkgIuCRCilJ9T+ujFx8fv3r07 JSUlTRcVoGJUOIA/VhrPvIGS8bidOnVq69aty5cvj42NzcvL81bswoULiYmJ dJiNiYmhY52TRzzDPmMPmeMsc9vN5Vcky8vLd+7cScerc+fOWVmpK5BFeyxd hm/YsMHjPWm2ofOKuLg4+mLSv+uGTsX3798f8OIWcU6/nDx5cs2aNfT5svk+ dm/9EtxTDtP7hQcVXLduHfWLX3uawX4JeP3eeOsXVlxcvGnTJgoRP+RlG3xf WEHmqKLtPotRq+krUv8LZdu2bdddd91VV101depUOm5s376drii3bNkyY8YM 8U7qGjVq+FU93u769esPHjyof/w3XhIg5Kg+p/Qx3L17t8+/dlEBKhbYyTmu cAMjc9xkbrudZI6zzG03FyJpD8TZmdAvzoR+cSb0ixVkjirabsqqmjdv7nK5 3nvvPe2sixcv8viQd9xxhynbApCK6nPKt73t3btXZ1AUmkUFArvhTbtFMEjm uMncdjvJHGeZ224uRNIeiLMzoV+cCf3iTOgXK8gcVbTdlFXdf//9LperTZs2 2lklJSV33nknzX377bdN2RaAVLSfU5+3vVXkhjePWwQjZI6bzG23k8xxlrnt 5kIk7YE4OxP6xZnQL86EfrGCzFFF201Z1XvvvcdjUc6YMUM5bFFCQsIzzzzj +v2b+wDAOO3nlG97ow+Xx9veaCLNCviGN49bBCNkjpvMbbeTzHGWue3mQiTt gTg7E/rFmdAvzoR+sYLMUUXbTVlVXFyc8iUvDz744GuvvUb/869XXXXVuHHj TNkQgGw8fk75trdTp05py9PEitzw5m2L4JPMcZO57XaSOc4yt91ciKQ9EGdn Qr84E/rFmdAvVpA5qmi7WWvbu3dvkyZNlO8KJ1WrVv3www8PHTpk1lYAZOPx c+rttreK3/DmbYvgk8xxk7ntdpI5zjK33VyIpD0QZ2dCvzgT+sWZ0C9WkDmq aLu56ywvLz969OimTZvi4uJycnLMXTmAhLx9Tj3e9sY3vKWnp1uxRdAnc9xk brudZI6zzG03FyJpD8TZmdAvzoR+cSb0ixVkjiraHuxaAIAeb59T7W1v4oa3 CxcuWLFF0Cdz3GRuu51kjrPMbTcXImkPxNmZ0C/OhH5xJvSLFWSOKtoe7FoA gB6dz2l6errytjdTbnjT3yLokDluMrfdTjLHWea2mwuRtAfi7EzoF2dCvzgT +sUKMkcVbQ92LQBAj87n9MKFC+K2N7NueNPfIuiQOW4yt91OMsdZ5rabC5G0 B+LsTOgXZ0K/OBP6xQoyRxVtD3YtAECP/udU3PaWmZlpyg1vPrcI3sgcN5nb bieZ4yxz282FSNoDcXYm9IszoV+cCf1iBZmjirYHuxYAoEf/c8q3ve12M+WG N59bBG9kjpvMbbeTzHGWue3mQiTtgTg7E/rFmdAvzoR+sYLMUUXbg10LANDj 83PKt72ZdcObkS2CRzLHTea220nmOMvcdnMhkvZAnJ0J/eJM6BdnQr9YQeao ou3BrgUA6PH5OeXb3vbs2WPKDW9GtggeyRw3mdtuJ5njLHPbzYVI2gNxdib0 izOhX5wJ/WIFmaOKtge7FgCgx8jnND09PSMjw84tgpbMcZO57XaSOc4yt91c iKQ9EGdnQr84E/rFmdAvVpA5qmh7sGsBAHrs/5z+CAAAAAAAAAAAIA1k3gAA AAAAAAAAAKxgf+bNzi1eGWSOm8xtt5PMcZa57eZCJO2BODsT+sWZ0C/OhH6x gsxRRduDXQsA0IPMW6iQOW4yt91OMsdZ5rabC5G0B+LsTOgXZ0K/OBP6xQoy RxVtD3YtAEAPMm+hQua4ydx2O8kcZ5nbbi5E0h6IszOhX5wJ/eJM6BcryBxV tD3YtQAAPci8hQqZ4yZz2+0kc5xlbru5EEl7IM7OhH5xJvSLM6FfrCBzVNH2 YNcCAPQg8xYqZI6bzG23k8xxlrnt5kIk7YE4OxP6xZnQL86EfrGCzFFF24Nd CwDQg8xbqJA5bjK33U4yx1nmtpsLkbQH4uxM6BdnQr84E/rFCjJHFW0Pdi0A QA8yb6FC5rjJ3HY7yRxnmdtuLkTSHoizM6FfnAn94kzoFyvIHFW0Pdi1AAA9 yLyFCpnjJnPb7SRznGVuu7kQSXsgzs6EfnEm9IszoV+sIHNU0fZg1wIA9CDz FipkjpvMbbeTzHGWue3mQiTtgTg7E/rFmdAvzoR+sYLMUUXbg10LANCDzFuo kDluMrfdTjLHWea2mwuRtAfi7EzoF2dCvzgT+sUKMkcVbQ92LQBADzJvoULm uMncdjvJHGeZ224uRNIeiLMzoV+cCf3iTOgXK8gcVbQ92LUAAD3IvIUKmeMm c9vtJHOcZW67uRBJeyDOzoR+cSb0izOhX6wgc1TR9mDXAgD0IPMWKmSOm8xt t5PMcZa57eZCJO2BODsT+sWZ0C/OhH6xgsxRRduDXQsA0IPMW6iQOW4yt91O MsdZ5rabC5G0B+LsTOgXZ0K/OBP6xQoyRxVtD3YtAEAPMm+hQua4ydx2O8kc Z5nbbi5E0h6IszOhX5wJ/eJM6BcryBxVtD3YtQAAPci8hQqZ4yZz2+0kc5xl bru5EEl7IM7OhH5xJvSLM6FfrCBzVNH2YNfCEbKysta75efnB7suAL+DzFuo kDluMrfdTjLHWea2mwuRtAfi7EzoF2dCvzgT+sUKMkcVbQ92LRxh9uzZLrcN GzYEuy4Av+P8zNvPP/+8a9euUaNGjR07NiEh4eLFi5ZVzdEC6KkLFy4kuB08 eFC/ZHp6Opd05l8HjLedWjpjxox+/fotWrTo1KlTFtfrSuPvPkYRpn3myoiz zG03FyJpD9Pj/NNPP+3YsWO42/Lly3NycgyuOSsra+7cuV9//TUde+nLWr9w Zmbm4sWL+/fvP3PmzEOHDl26dMljsdOnT0dHR9ORnArn5eUZrImwf/9+Wn/f vn0jIiK2bdvmbStWsGL/DywaJSUlK1eupN4cNGgQfSFSN2nLUKASvKDzBy5D e4K3MsLZs2d91ifgHcwUzvm8ZGRkzJ8/nz4vkyZNWr9+Pa1HW8ZIvwjU0TEx MeHh4ePGjdu5c+f58+eNN5OcO3cuNjaWzrFpP5k3b15KSopfi1eQ8X7BtYBx MkcVbQ92LRwBmTdwLIdn3ujM/K677nIp/O1vf0tPT7eygg4VQE/RdQcH7X// 9391itFZ/Z///Gcu+d1331WoltYw0vbi4uJWrVq5fq9Dhw4eT2vBI4P7GJ3n r1q1qmXLln/84x8pyC+99JL1VbOczG03FyJpDxPjTNfynTp1qlKlivLgeeON N0ZEROivPC4u7pFHHlEddd94443U1FRt4fLycqqGqvDjjz9+7NgxVclu3bop y1x11VWDBw/22VKWk5PTtGlT1Vaee+65w4cPG1xDBZm+/wcWjXnz5t15553K Ba+//vphw4apilWuXNnlxYIFC7gMXZZ6KyNMnTpVpzIB72AmcsLnJT4+/v/+ 7/9UoaPTs2XLlqlKGukXtmbNmv/5n/9RFqBzZp8JcNGQnj17VqpUSbn41Vdf 3bp166KiIiNrqDiD/YJrAb/IHFW0Pdi18I2+pl92O3HihEWbQOYNHMvJmbeD Bw/ecccd/Nl56KGH6PyEf77vvvvS0tIsrqbj+NtTe/bs4VNHl6/MW6NGjcRX T4hm3i5duvTiiy9yE+69997XX3/9L3/5C/9Kx3bt34jBIyP72KlTp8R+xZ5/ /nlbamctmdtuLkTSHmbFOTc396WXXuK5f/jDH6pXr/7ggw+K8nT66m3lc+bM EfkB+qZ++umnb7jhBv714YcfVt14QzV58sknxVboC/3GG2/kX2+66abi4mJR slOnTjy9SpUqtWrVuu666/jXsLAwnzG5ePHiK6+8wuVvvvnm999//9VXXxVX VeXl5T7XUHHm7v+BRYOuNSjIXPKZZ5754IMPRBZOmSI7e/asy7t58+ZxMSOZ N1FYK+AdzFxB/7yMHz/+mmuu4WK0zz/xxBOijypVqrRjxw5R0mC/kPXr1191 1VW8hhdeeOGpp57i+tN+snz5cp+NFR9JWskjjzwizrfJO++8Y8+dokb6BdcC /pI5qmh7sGvh2/79+zng9INFm0DmDRzLyZm3d999l08JoqKieMqECRP4o/TJ J59YWEVH8qun6KrnscceEydROpm3uXPnKk/qQjTzNn36dK5/mzZt+CY3+r9t 27Y8kebaVNEQZ2Qfy8zMvOk3fOFwZeRMZG67uRBJe5gV5169evFx8ssvvxRP zC1btuz666+nibfeemtpaal2zXl5eZxnoy+XLVu28HM6BQUF9evX57UNGTJE Wf6jjz7i6Y0bN+bbaWgROjeuUqXK5MmTRbHExEQu9q9//aukpIQ39Pe//93l vhUnIyNDv71LlizhxXv06HHu3DmeGBkZyRNHjhypv7gpTNz/A47G448/7nKn Qw8dOsRT8vPzq1ev7nLnfMRDVVQNXv+wYcNSNcrKyrhYcXGxdi7Zu3cvZwKf ffZZnQe1AtvBTBfczwuZOXMmFbj77rvXrl3L4crKyurevTuv7bXXXlNWw0i/ 0B7+t7/9jYrddttt8fHxPHHXrl38Z8e//vWvP//8s05jqVsfffRRKtmuXbvc 3NzL7o8khej+++/nrcfGxuqHyxRG+gXXAv6SOapoe7Br4dvGjRuReQNpOTbz Ruck/Me7Nm3aKKfzAbNq1ar2nK05h1891adPH/5aqVGjhk7mLTs7m58zrVmz Zkhn3vg+h2rVqomrLUYXLDSdLlWugAEcbODv0eCBBx64YnImMrfdXIikPcyK M12ef/TRR9o/T4gMg/JuHCW6MK9bt+6ZM2eUE+kSnjNyb7zxhpiYmprKd/s0 adJEdSNNYWGh8teOHTtq00oiAeXztreePXu63LeHqQYZoFbT9ObNm+svbgoT 9//AolFWVkaLUIHw8HDl9PXr1/OCR44c4SkHDhzgKdqnHY1o166dy/0Qq/Z5 YaWAdzBzBf3zctl9myjnuAT6ODz00EO01C233CImGuyXLVu2cLGJEycqp//w ww8GT+fS0tKWLFmimvj999/z4mPHjtVf3BQ++wXXAgGQOapou79LJSUlxcXF +Ww1fQ1t2rRJ9Y0fmIULFxrPvCUkJFAx7cBBVGE62NJc1XUfQ+YNHMuxmbfh w4fzp2bjxo3K6QsWLODpM2fOtKqKjmS8p+Lj4/lrpZWbTuaNv2gqVaq0du3a kM683X777VT5tm3bqqbPnz+f2xUSfwYKOplzJjK33VyIpD2sjrP4q/SMGTP8 qhg/i3fvvfeKKZyiIXSGr7PghQsXbr75ZpenwbX+8Y9/uNyPAulvmjd06623 qqbz9+Bzzz3nRzMCZVa/BByN7Oxsjvb48eOV09PS0nj6unXreMrmzZt5ys6d O41XmG3fvp3vChszZoy/y7KAd7DAOPbzUqdOHc6vilvUDPbLN998w8Wox1Wz eA958cUX/aoJ27ZtG6/266+/DmBxf/nsF1wLBEDmqKLt+mWeeOIJ+maZPn16 YWHhf//7XzEyDx3P27Rpo30/y6VLl0aNGqUcTLJatWpz585VlikrK6MCtNpu 3bqpFh8wYMDNbnyYmjJlCl2QioEp6AeeW6NGDS5Pa6ZfH374Yfo5IiJCPAgs /r5w8uTJjh07Pvjgg+Jpfbrg/fe//63KHCLzBo7l2MzbBx984HI/GaG6Yb6k pITTSt27d7eqio5kMG502OQnCO64446cnBw6HHnLvEVFRfFxiQ6Mx44dC93M GzXZ24miuAbRH/8ZmMw5E5nbbi5E0h5Wx3np0qV88FSN6O4TndjTUnTtL6bQ WTRNefXVV/UXTElJ4S1qHwv94osveJZ41M4jcbePKjI8rH2rVq38akhgzOqX ikSDx+96+eWXlTd7R0dH81JiICPRxQEMbcT3k9P/AY8GFvAOFhhnfl6Kiopu u+02l/vOfO2q9Pvlyy+/dLkfbdB2AQ+1cddddxmviTB48GDeungQz1I++wXX AgGQOapou36Zv/71r9TGRo0a8bPqKp9//rmycHl5+ZtvvinmioFGVRdcBQUF PLFdu3aqzX311Vc8i98THRYWpt2oyz3aHpfnjFmlSpXmz5/Pg1gyfmtMRETE tdde63ENL7zwgnK7yLyBYzk28/b666/TR+bRRx/VzuKBMVu2bGl+5RzMYNzE Iw90DUK/Nm/e3OUp85adnX3rrbfSrBo1atC3z5EjR3ipUMy8Xf5tl/joo49U 08+fP8/fFL1797awflcKmXMmMrfdXIikPayOc9euXflLwefoakp0Bs5/iVYe jXkILDoD51/pDDwuLk71nOll921UvMWFCxeqZk2aNIlnHT9+XGfrZ8+e5T+m 07ebON8WT1nacwZuVr9UJBqDBg3iAnSNyW+voG/5l19+WXV5MmPGDHG2QL3T qlWrPn36REVF+XwVhbgpKzIy0nhLVQLbwQLmwM9Lamoqn+iSCRMmiOkG+0Xs BtotDhgwwOW+icWv10vRTjJnzhx+1Wm1atUc8kYSXAsEQOaoou36ZTjzxurV q0ffL8eOHRszZgxnHW+88UblH3R45CLStGlTPs6cOHGidu3aNOWaa64RbyY1 nnmjRdauXdu6dWueSNtd6yYeOxUZM1p/5cqV27ZtS183K1eu5KdN+avn7rvv Hjt2bHx8fGlp6datW/lY7fr9X9yQeQPHcmzmjV8Q4PGV7o888ojLwB/QrzBG 4rZr1y7xnClP8ZZ5e+edd1zuv18cPnyYfg31zNtzzz3HXxl0/BcTz507V7du XW7X+++/b3ktQ5/MOROZ224uRNIelsa5sLCQny65//77/aqVeJBn1qxZPOXM mTM8ZfLkyXQyzLefMbr2oRNpseyiRYt4uuohoMuKcQPoNFu/ArTCqlWrcmH6 mqMt8t+YmjVr5ldDAmZWv1QkGhcuXHjvvfe4zJ133jls2DB+wwVdyCQkJIhi o0aNcnlSrVq1pUuX6tS5SZMmLvfA/h4H2DEi4B0sYM75vMycOZMuPF9++WUe ju/6668fN26c8r41g/2ybt06nq662z8mJkbcmpKcnOyzPidPnvz0009ph7n3 3nt5KWq1kQVN4bNfcC0QAJmjirbrlxGZtz59+igPOy1btuTpiYmJPOXo0aN8 g1mDBg2UaygtLeURwjt06MBTjGfemLixVjvOm8iY/eEPf/D4gmY6Bqr+KEDf g7wIHce060HmDZzGsZk3/tNDw4YNtbN4tGQ6gTe/cg7mM250DsyDe9xzzz38 8rjLXjJv3333HR+RxAgtoZ55E38jfvLJJ+nKKzMzMzo6+oUXXhCnrHQJZldl Q5jMOROZ224uRNIelsZZvBjar2+EEydO8O1tDz/8sBgPedeuXbwqHgbB5R7C ukqVKvzzVVddJc6uxT08+/btU61Z3LemvQFM5cKFC82aNVOlLP71r3/pP6Zq IrP6pYLRiI+P57daKKnKiwwPbb1Hjx6ff/45PylM6ILL29jX9PXKaxY3MQYg sB2sIpzzeXnrrbeUndKvX7+zZ88qCxjsl/Pnz/PzYtQddCWbnJy8d+/eAQMG KJ/Goik+67Nz505lfapVq3bgwAEjrTaFz37BtUAAZI4q2q5fhjNvzz77rGr6 9OnT+QiwYsUKnjJ06FCekpqaqir86aefuhRDp1qReWvRooXvBv/mT3/6k+v3 F3rIvIFjOTbzRt/+9JF5++23tbNq1arF59LmV87BfMZNDPpBp+ViojbzlpWV dcstt7jcf/ERf+8I9cwbNYSHKVahSzAepLpTp052VTaEyZwzkbnt5kIk7WFd nDdu3Mjjq9BXrfFRvC5evPjqq6/ygXfVqlViuhh7zeUezIpW/tNPP9Fq6buG 78yhCwG+derbb7/lYnv27FGtfPXq1TxL/22PJSUl/AeXqlWrduzYUTl2dN++ fQ02pILM6peKRCM6OpqTY2+88cbrr78uRsuh+IsXm7KoqKi1a9eKX6kTe/fu zYVVw+YI48eP5wIB52cC28EqyDmflxEjRrz55ptPPPEEP9fpco9xdPDgQWUZ g/1CZSpXrqw67aFzHpF8zsvL81mflJSUxo0bU0vvuusuXoqaQ58Xe7rGZ7/g WiAAMkcVbdcvw5k37eF9xYoV/PEXAzy+//77LveAeNs1+KVFd955J5e0IvMW ExOj0wo6Z6BTi7CwMDp28UCyfATWrgeZN3Aax2bennrqKZeXl5Hxn/nq1q1r fuUcTD9uO3fu5CcX3nvvvVMK/FTpfffdx79SSfq64cNRbGysKCZeT08n1fQr jwzjHEb2GTo1HTt2LJ3N0tUcndC+/PLL33zzDR2cedAh7SDVoCVzzkTmtpsL kbSHRXE+evQoP0hy/fXXi6dOjOjcuTN/iagGyRHjldHpcVZWlnKWGJWUB08W Z/7i5ZvCnDlzlCW94b803XLLLfxSyPPnz9M3Go9gTwYNGmS8OQEzq18CjsaB Awd40IkPP/yQhxCnKeJ7n66VcnNzdepD36SPP/64y/1oqmoEcta0aVOXO7ep fH2DcQHvYBXktM/LZfdwu3379uWsHV0O64+r5q1fqAINGza8++67Xe4XCn/8 8ceHDx/mNB31kV/1uXTp0po1a3grJCIiwq/FA+OzX3AtEACZo4q265fxlnmL iYlRZd74NTo66IuGS1qRefOWMeOn4/kGEhXxglQj6wEIFsdm3vhE8ZFHHtHO 4k+cdjj9K5t+3MSgLvqmTp1qpNgzzzxjY8t882svpRNU8V7sEydOcIuio6Ot qtwVROacicxtNxciaQ8r4nzmzBkxWPG8efOMr1w8H0fn6qoEQlZWFs+aPn26 aqk9e/bwLD7VF7/Onz9fVXLcuHE8S2f4+n379nGZ0aNHK6enpqbyAFbXXXdd dna28UYFxqx+CTgajRo1crnfMSEe+GVff/01L9W1a1f9KnXr1o1L8jCwKnxn VO3atX23TSPgHaziHPV5URL94vMN7Pr9onxk9T//+Y8r0Ifm6APLj+MF9mpU f/nsF1wLBEDmqKLt+mWMZ96efvpplzvV/4IXb775Jpe0LfN2+vRp8UrWhx56 iNZD7c3Pz+fjMDJvEBIcm3n75JNP+CNfWlqqnE6nmvxp6tWrl1VVdCT9uGlH tvFIDB2jr3r16ja2zLeA91KRadS/UwKYzDkTmdtuLkTSHqbHmb5q+Vkbl59D eC1YsIBvLaYLdm0u6NKlS/xA3Oeff66aJb7NOVdGZ+b8a58+fVQl//vf/7rc D8HpvKtx8uTJvHhaWppq1qxZs3iW/osDTGFWvwQcDX7GtnXr1tpZ999/P816 4okn9KskHmzUjhKWkpLCs3r06OGjYRoB72CmcM7nRSU5OZlX0rFjR/2SOv2i 9PPPP99zzz2uCty6w0+ZEf3bI03hs19wLRAAmaOKtuuXMZ5549ubb7rpJp8P novM28cff6yaZW7mrWbNmi73vXYzZ85UTkfmDUKIYzNv4i0AqjGBJ06cyNP1 nwG/8ujH7dy5c3me1K9f3+V+3xb/SifqdITUFhMPBNHFC/0qXtDgEAHvpfwe PWq+6q//4JHMOROZ224uRNIe5sa5vLy8du3a/C3QokUL4w8SLl26lIcUq1q1 qrc/cPBDK3Q0Vp3Ai1EOxEsW+M/Zjz76qLIYLcXZJP2bsQcNGkRlrr76atV4 9ZcVb3mgLziD7QqYif0SQDRoLg+w7zEXxC/7psX1q8SvAKhUqZI2sydG7YuM jPTRsN8LeAczS9A/L96uXsWd+drbRVR0+kVpwYIFvMJp06YFViW+ZY6cPn1a fw0V57NfcC0QAJmjirbrlzGeeQsLC+MpPl8pTkck/uub9mV2Xbp00WbewsPD eaL2nEEnY5aTk8OzxDtVBWTeIIQ4NvNGJzY33XQTfWrq1Kkjzmro0129enWX ezgL+8/cgiuwnvL4blOtUH/DAklMTFRdcNFpJzdqypQpFlbuCiJzzkTmtpsL kbSHiXGmI6d4OUL9+vWN/51i+fLlPEp85cqVdSoTGRnJK1+8eLFyOo/STNLT 03mKOCHfsmWLKEZL8cRvv/1WTKQzBDq1pmsEcdgXFw4zZsxQVWDkyJE8KzY2 1mDTAmZivxiPhtIzzzzjco+qp3rst7S09M4776RZr7322mX306yPPfZYQkKC tv488pjHYcYjIiJ462vWrPG4dW2/XK7ADmaioH9emjRp0rBhQ+0guuJpU95v /eqXc+fOqe5/y8/Pp8X5JFmZoNP2S3x8/F/+8heR9Bays7Nvv/12XoORdlWQ z37BtUAAZI4q2q5fxnjmjQ4RnE/75z//qZ/tv/zbvdb0FaM8Hs6fP58HHVVl 3sT3yMSJE1Xr0cmY0VUez1L9kWLnzp1Vq1ZF5g1ChWMzb6R9+/b8wencuXNh YSGdUbRu3Zqn9OvXz+JqOg4ybzoF4uLirr/++ieffJIurOiwn5GRQXsIn6ZW q1ZNDPsG+ozsYzt27Nj2Gx7wh65QxBSn3S1pnMxtNxciaQ+z4kwX76+//jof /2+44Qa6El+2bNni31OeMwurVq3i26tIz5496dclS5YolxJnvD///DPnMej0 mMrQdQ1NGT16NJ/VN2zYUKyTNsQn6nTtT0f1S5cubdmy5cYbb6Qpf/rTn0pK SkTJrl278qbFzV00l9tYpUqVyMhIcT/PokWL6NuBpt999938ElVLmbj/G4+G khhz76233hIP/545c0a8ZOGbb76hKY888ojLnTLt27fv5s2bz549S9ulCyLa B1zuR1mVL6gV+vfvzyuhizKPW9f2S8A7mLmC+3lZuHAhL/LQQw9Nnjw5KSmJ ejMnJ6d37978bizq1pMnT172p19oDY0bN6Y9JDw8PCsriypGuwetnzc0adIk ZQW0/fLoo4/yCulMe+XKlbQVOneiENFVNpfs1q2bGYH3wUi/4FrAXzJHFW3X L2M880Y6derEE+nyaufOnZyTzMzM/Prrr2kNyle9vPHGGyItRoejffv2URk+ uDHlUVFsq0aNGgkJCXQoE2NX6mTMysvL+ZyBDob8hUg1GTx4MN917/p95o0O yzxx1qxZxgMIYAMnZ97oeMjPdDNOpLjcf6fQPk5yxUPmTacAXfeJ/UR5qL/n nnu8XSCAlpF9jK74XN6NGDHClpqaT+a2mwuRtIdZcaYLDZ0ybNGiRao102U+ j96m47HHHhPlt2zZcuutt/L069z459tuu41OnpVrpu8gcQwXX/rXXnvtihUr lMXEa9eeffZZMTE2NpbvwXO5n6mkWffddx//StO3b98eUKT9Y+7+bzAaSnQ9 IpJs1GrqherVq3PukTRo0IBzkgsXLhQTXe7vTbF+0r17d48rb9u2LRfQDqbH tP0S2A5muuB+Xs6fP8+DJgliR3W5e1aMQGi8X6gL+OY0UVL83KVLF9V7abX9 QtG4+eabxSJ0SSvuTiG1atWy50+WRvoF1wL+kjmqaLt+Gb8yb0VFRc8995wI FH1x8+2C2pIrV650aVStWvWLL77gn5WZNzo6ia9ml/vF0PSlxpHXv1etRYsW YilxvKJa8RCmysxbXl4edysVGzp0qB9BBLCYkzNvl92Hx3r16olTFDrVp7OX kD4wBiywnvrwww9dXl7io5SamsoR1r5GzQmMtH369On8R2pGh/H69evn5OTY UsErRMWvTYYPH25LTc0nc9vNhUjaw6w49+rVS6cM0+Z5zp07p7zS9+ipp55S LpKcnPz000/z36wZfblnZWVpq03n89WqVRPF6EpBWwE6l+a5Y8aMUU4/dOgQ j26qVLdu3YMHDxqLa0WZvv8biYbKTz/9NGHChNtuu0252j//+c/jxo1TPjSU np7eunVrvplKeOCBB5YtW+ZtzSJ9pHqUVdD2S2A7mOmC+3lh0dHR4qUMwosv vhgXF6csZrxf6OOjPEN2uW/s9DgEn8fPy+nTp9u3b8/vcxRouwMHDiwrKzMQ VBMYPLPFtYBfZI4q2q5fhm+LrVOnjmr6pk2bOBTKfBq5ePHiiBEjxB/OGH0N TZo0SZWcp2OLyIbR6QF93e/Zs0e8c1x1J3B8fDyny0R5KkzT586dy1O2bdum rXxBQUHjxo3FUnSh99Zbbx07doyf2Vdm3i67h7Pgs5RmzZr5CByAjRyeeWN0 jrdz586tW7fa8KiIY9nfU85hvO0nT55cs2YN7S14wjQA2MeCXYsrASJpjxCN c3FxMZ3hU83pFFq/ZHJy8oYNG7zdW0WSkpK0b0YTW9m1a9fq1avpf+3IWpay qF98RkOLrphSUlJoqbVr1544ccLb4EU8Vti6deuopOr+w8Do9EsQOefzQleg dDa7fPny2NjYvLw8b8WM9wud7cTFxcXExOgX89YvFy5cSExMXLlyJa3hyJEj No/C51e/4FrAIJmjirZbtPL09HT6Ktm4caPOLQ30bbtt2zY6ahUWFvpcIR1q 9u3bR0dCWqfP8wGl1NTUzZs3G+m1jIwMWr9tf3cDMCIkMm9wWe64ydx2O8kc Z5nbbi5E0h6IszOhX5wJ/eJM6BcryBxVtD3YtQAAPci8hQqZ4yZz2+0kc5xl bru5EEl7IM7OhH5xJvSLM6FfrCBzVNH2YNcCAPQg8xYqZI6bzG23k8xxlrnt 5kIk7YE4OxP6xZnQL86EfrGCzFFF24NdCwDQg8xbqJA5bjK33U4yx1nmtpsL kbQH4uxM6BdnQr84E/rFCjJHFW0Pdi0AQA8yb6FC5rjJ3HY7yRxnmdtuLkTS HoizM6FfnAn94kzoFyvIHFW0Pdi1AAA9yLyFCpnjJnPb7SRznGVuu7kQSXsg zs6EfnEm9IszoV+sIHNU0fZg1wIA9CDzFipkjpvMbbeTzHGWue3mQiTtgTg7 E/rFmdAvzoR+sYLMUUXbg10LANCDzFuokDluMrfdTjLHWea2mwuRtAfi7Ezo F2dCvzgT+sUKMkcVbQ92LQBADzJvoULmuMncdjvJHGeZ224uRNIeiLMzoV+c Cf3iTOgXK8gcVbQ92LUAAD3IvIUKmeMmc9vtJHOcZW67uRBJeyDOzoR+cSb0 izOhX6wgc1TR9mDXAgD0IPMWKmSOm8xtt5PMcZa57eZCJO2BODsT+sWZ0C/O hH6xgsxRRduDXQsA0IPMW6iQOW4yt91OMsdZ5rabC5G0B+LsTOgXZ0K/OBP6 xQoyRxVtD3YtAEAPMm+hQua4ydx2O8kcZ5nbbi5E0h6IszOhX5wJ/eJM6Bcr yBxVtD3YtQAAPci8hQqZ4yZz2+0kc5xlbru5EEl7IM7OhH5xJvSLM6FfrCBz VNH2YNcCAPQg8xYqZI6bzG23k8xxlrnt5kIk7YE4OxP6xZnQL86EfrGCzFFF 24NdCwDQE6zMGwAAAAAAAAAAgAyQeQMAAAAAAAAAALCC/Zm38wAAAAAAAAAA AFc0ZN4AAAAAAAAAAACsgMwbAAAAAAAAAACAFZB5AwAAAAAAAAAAsAIybwAA AAAAAAAAAFZA5g0AAAAAAAAAAMAKyLwBAAAAAAAAAABYAZk3AAAAAAAAAAAA KyDzBgAAAAAAAAAAYAVk3gAAAAAAAAAAAKyAzBsAAAAAAAAAAIAVkHkDAAAA AAAAAACwAjJvAAAAAAAAAAAAVkDmDQAAAAAAAAAAwArIvIGTpaamRkVFTZ48 ec6cOcXFxcGuDgAAAAAAAACAH5yfeSstLT3lxaZNm5KTkz3OOnPmjHVBi4+P LygosG79QZSRkZGenm7KUufOnUtLS9uxY8fhw4epEwOrz5gxY8LCwkaNGjVh wgRaoZheXFyc6Ql1vSiTn5+flJSUmJionGhwrl+OHz/u1/5w6NChsrKyCm40 iNsFAAAAAAAAAIOcn3lLTk4e6r85c+ZYFLEjR47w+s+ePWvRJuxXUlKyfv36 yZMnh4WFjR49uuJLUXDmzZsX9pvx48fn5OT4W6vMzExadubMmcqcG0tMTAzz IiUlpaCgYPbs2cqJc+fOFbfM6c/11759+4YNG2Z8f+D9Z/r06eXl5YFtMbjb BQAAAAAAAADjnJ95S09Pn6oRERExbNgwTrKNHTtWW2DZsmVWhOvMmTNjxozh 7a5Zs8aKTQRFQUEBtWjw4MF+Zd50ltq0aRMnzZKTk1evXs0/+1urPXv20ILr 1q3TztLJvB07dozvlFNZunTpefctlDpzA5Cfnz9hwgSD+4PYfyg+gW0u6NsF AAAAAAAAAOOcn3nTKi0tnTZt2tChQ2fPnj18+HD6ISEhwayABGu7x48fj4+P P3fu3MGDB1esWJGYmHje/cBmUlJSTEzM6tWr9+7dK+5uoprs3r378OHDYnH6 edeuXYWFhfxrQUEB/ZqcnEw/5+bmbtq0ac2aNfS/zydJw8PDjWfevC1F1R43 btzAgQNFfb799tuwsLCsrCztst7amJGR8cMPP/ANadQW1TOh/KyxMGfOHE6g 8Q1ycXFx9POSJUtOnDixbt06njVkyBC+d05/bgBSU1N5f6D66xQT+8+CBQsC 3pYTtgsAAAAAAAAABoVi5m3RokVDhw6dMmVKSUnJ7t276ecRI0YEMDqZo7ZL Kw8LC1uxYgUnglatWkVbiYyMVN6XNXXq1Ly8PCpcVFQ0YMCAUaNGicXpZyqw a9cu/pV+oF83b9588uTJ8PBw5Up27typUw1TMm9nzpyhDc2ePVtM2bJlC02J i4tTLajTRk67CdplhfLy8m+++YbKUI+IZ1qp4fzD2bNnRQTy8/ONzA2Akf1B uf8EvCGHbBcAAAAAAAAAjAi5zNvWrVuHDh06evRocQPVypUracrEiRMtfeuB 1dvlzBuJiYk5efJkXl7eqlWr6Nfp06fTFnNycubOnUu/zps3j8vzXWR8JxiP h0YWLFjAcxcuXEi/pqenL1u2jH5Yv359WVkZrWfp0qX6+SVTMm8nTpygjSof +D1w4ABXQ7WgThtzc3M3btxIvy5fvpwCIm6f09q8eTM3f9u2bdq51F6eS52l veNLf65f9PcH7f5jlmBtFwAAAAAAAAB8Cq3M29GjR3mMtUOHDomJ5eXls2fP tvStBzZslzNvIrFWVlY2cODAwYMHi0QZTRk5ciSV4de2btiwgX7esWMH/bxt 2za+W2zEiBGcPho9evTw4cPp58WLF9Os2NhYg9UwJfPGeTbl+GzJycmqXJyR NvI4bxs3btTZNEWex20bMmSIx7ckiJTmwoUL/Z3rF539weP+Y5ZgbRcAAAAA AAAAfAqhzJvOKPF+jTbvzO1yCohHZjvvHuVM9cAmWbJkCU1MSkqin1NSUkSm bs6cOePHj+cnOtPT07Ozs8X9b8ePHx84cCD9Om3atF27dpWVlelXQ5t5S01N XeOJ8vFG1VIHDx7km/fElGPHjvGztMo1+2yjkcwbleTUGS2onbt7926eO3jw YO3LVfXnBsDj/mDD2w2CtV0AAAAAAAAA0BcqmTefo8QbHG3esdvlzFtKSgr/ evjwYfp1/vz5yjL8klAeqI0qM2zYMNp0eXn5kCFDfvjhh7S0NB7bjQoox3NL T0///vvvBwwYQBPHjBmjelWBijbzlpSU9I0nR44c8bYUbTHs928L3bdvH01R pYB8ttFI5k2MjKcdv+7AgQPcahIfH+/X3ICp9gfb3m4QrO0CAAAAAAAAgI5Q ybwZGSXeirct2LZdVeaNX1IwefJkZZmoqCiaePz4cf51/vz59CvfuJWYmMi5 uNmzZ/OqVDdx5ebmRkdH0/TvvvtOpxqmPG1aUFBAG4qIiBBTYmJiaMqePXuU S/lso5HMG22Fs2eq4CclJQ0aNIhnrVq1SrWU/twKUu4Pdr7dIFjbBQAAAAAA AABvQiLzZnyUeHPfemDndlWZt/Pusdpoiri1LDMzc8CAAeHh4WI0M36B6YQJ E+h/fgHBvHnzqMDYsWPHjx/PZTIyMkSVioqKqKSY5ZEpmTcyffp02hZt/bz7 /iuq0qBBg6gCqgX122gk8yYSaMqVJyYm8jO24kHURW78Cgb9uabg/WHkyJHW vd2AonpKgxNuI0aM4K0fOnRIVYAH0AMAAAAAAAAAezg/8+bXKPEmvvXA5u1q M2/8hObgwYPXrFkTExMzbNgw+nXr1q2iQG5uLueOxG1jcXFxPIXfZVBcXDxi xAhacO3atTt37oyMjKRZtCHt1mnuMjd+AJN/TktL06+zzlKJiYn8cCv19axZ s1QPnxpso8/MG+cSmfKZyvDw8DBPoqKifM41hdgfrHu7QXJy8lD/0f5pRWUA AAAAAAAAwCPnZ95OnToVERFhfJR4Hm2eNlHB4a1s3u7SpUu1j0wmJCQMHz6c 80JDhgyJjY1VrXzixIk0a/Xq1fwrP79J9u/fL9Ywbtw4kVyKjIz0eFceP7iq sm/fPv066y+1detWkeOikuXl5R5XotPGvXv36mfe+F0SZNiwYcrpQ4cO9Zhb 4xdS6M81C+8P1r3dgHaVqZ5MmTKFokGd7nGu6vWyAAAAAAAAAGAp52feSElJ iV/pLPE8ZgUFa7tKVIFst4okEgsKCk6ePGnKE7h+KS8vz8zM9BkWU9roQKdP nw5Ki4K1XQAAAAAAAABQCYnMGwAAAAAAAAAAQMhB5g0AAAAAAAAAAMAKyLwB AAAAAAAAAABYAZk3AAAAAAAAAAAAKyDzBgAAAAAAAAAAYAVk3gAAAAAAAAAA AKyAzBsAAAAAAAAAAIAVkHkDAAAAAAAAAACwAjJvAAAAAAAAAAAAVkDmDQAA AAAAAAAAwArIvAEAAAAAAAAAAFghWJk3AAAAAAAAAAAAGSDzBgAAAAAAAAAA YAU7M28AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAPK4dOlSXl5eCgCAg9Fh ig5WwT5eAgAAAAAAAPgHaTcACAn5+fnBPl4CAAAAAAAA+IcvaQsLC8sAAByJ DlB8pAr28RIAAAAAAADAP3w9G+wLawAAPci8AQAAAAAAQChC5g0AnA+ZNwAA AAAAAAhFyLwBgPMh8wYAAAAAAAChCJk3AHA+ZN4AAAAAAAAgFCHzBgDOh8wb AAAAAAAAhCJk3gDA+ZB5AwAAAAAAgFCEzBsAOB8ybwAAAAAAABCKkHkDAOdD 5g0AAAAAAABCETJvAKAjPj5+bcVs3bo1JyengtVA5g0AAAAAAABCETJvAKAj Ojo6qsJWrlxZwWog8wYAAAAAAAChCJk3ANCRm5tLhwhOoMXHx6f4iRbhZStY DWTeAAAAAAAAIBQh82aFU9lngl0FADNx9qy0tNTfBWkRZN4AAAAAAABAWiZm 3tIzs1Zu2K78F7trX3JqhikrDyFDvvmuet1PJs1eHOyKVNT3S9a+2LjzzPkV fU7QoHofflGnRdeKrycnN2/dll2TIpf0GjZ1cuSSjbHxBYVFFV+ttHict4pk z5B5AwAAAAAAAGmZmHlb9eP26nU/0f5r3jFs3ZZdAaxw7tJ1kyKX5BcUmlK9 Cjp0LGWSO43js2T7XqOo1V8OibChVkYYr7nKzAUrqSER3y21olZaLzXuXKtB uwquJOlI8jsffaXaA1t2HpCSninKBBwQOSnHeQtsDci8AQAAAAAAgLRMz7w1 +qT3xFmL6N+oKXNbfz7k6QbtaGKtBu02bNvt7wrfa9OXls3MOm1K9Spoxfpt VJmB42b7LJmembVwxY/OeeDUeM1VQi7zlpOb93yjTlTnDz4bvHjVpu3x+6NX /Eg7JE2p+8EXJSUlXCzggMhJOc6bdu74GdGjps4TsfUImTcAAAAAAACQlumZ t24DvlFOLCgs6j96Bk1/5t0Osbv3+bXCEM28OY08mTduafOOYUXFxWJiXn5B 3Q++oOmbtu9RFgvFrgwib9mzV5t3pWB+NXSKTvINmTcAAAAAAACQltWZN1Jc UtK1/3ia1fbLEcrp67bsCp8Q2eiT3vU+/OKz/uNXro8Vs+YuXde625Bn3mlP S73/6UD6efWPO4wsqPX90rW0+M6EpKjFMVSBV5p/1rxj2KTIJUVFxcpix06k TZy16OMew177d7fOfcdOn7ci6/SvN61RydafD3n3415UGZpLa6MCOltcuHIj lVmzcYdfFQhKzX+I2dJr2NS3PujRsvOAUVPnHTh8XDlXZN52xO/vGR5Bxf7T ZRBtS/nkps86GC+jyrzl5Rf0DJ9MdZ4dvUo/UMLwSVFU4b4jpqmmb9y+h1oR t+eAkYD0GT6tfqsvG7ftO3h85OYdCcq5vHj/0TPi9x2iur35fvfuAycql9UJ plbF+5fQx41WciYnT0wJGzOTpuxOPCSmUD/SlABuOmX647zRvvF8o476yTdk 3gAAAAAAAEBaNmTeyPGUDJr1UpNPxZQpc37gMbjqtOjKV+7077uFq3nutO+X 1f3gixr129DE11t+Tj8vWrXRyIJao6bMdSf9RtL/zzfq9My7HXiR/qNn/P+L +vTMN9/vLlbLP/yny6C8/AKaW1hUVO/DL2o37UITn2vYkSrTvEN/nTh8M2vR L1VatMZ4BYJSc64n56B45a80++xocpoowJm39r1GPeue+7J7Pb88UNymT05u nsE6GC+jzLzR9I97DKMyHfuMoVboB0pVYdorDh9P9VhAPyATZi7kir3YuDMH 5Kl6bZR53fyCQo4SVfWX2zjfaS8Sdz6DqVXx/iXdB06kKWIcxZy8/Fru57vH TJsvVtKy8wCacvhYgB9zn+O8+Uy+IfMGAAAAAAAA0rIn80Y4b3Mk+ZeUyLET aTXqt3nmnfb8/GlhUdF3i9bQ3PqtvlQuon3a1OCCSpzfoH/zflhXXFKSk5s3 fd4KTqrwmktLSxu3/WVD3QdOPHkqm7fSolMYTfli8CSxHuOPKHrMvOlUICg1 X7nhl856pflnCfsPl7kfCuZXsjb6pLcow4ks+teh16i0jFM05WhyWpN2X9OU HoN+Xb+ROhisp8i85RcUfPLFcJrbyZ+0G6GVv9Tk0+rucQW79BsXveJH7e15 3gKybO1WfiZ6Y2w8RTu/oPD7JWtpytMN2u07eIzLcOaNo7Rn/2GRZTISTC1T +nf+sg30K62Kf43ZFFf9t9ea8BRaLX1k6n34hfEwqvA4b/oHCv3kGzJvAAAA AAAAIC3bMm9teo6gucvWbi1zP3+akZmdfSZXzKUpr7f8nApwhoFpM28GF1Ti /IbyqcCSkpJXmn9GE7fH7y9zv+mSc3fK+6+SUzOebtDu2Xc7FPs/LL/HzJtO BbyxtOY9Bk2iidRlYkppaWnDj395GcHxlAyewpk3Cq8y4EeSU2vWb/tcw46c YDFSB4P15MxbfkEB7yqd+471K+3G9h069u9OA6orXmzarEM/ZTP1AxK1OEY5 MWzMTJo4Zc6vI92JzJvq/jEjwdQypX9TMzL5LjieO3DsrF9uFOw9WnwiNmzb TT9TQ4xEryJ0km/IvAEAAAAAAIC0bMu8vf/pQJqrGmwq6Ujy6h93RK/4ceb8 lfxUnTJTofOGBf0FlTi/QcWUE/kZvZhNcfTzktWb6Ofew6eqFmzeoT9N33/o 19G6Kph506mAN5bWvMFHX9HEOYtjFq3aKP7xY4krN/yaQeLMW/dBE1Xr57eF HnJnn4zUwWA9X2rc+al6bdp++Uvard1XIwNIuwmJSUcnzFjYutsQWiHnyj7u MUysUCcgyam/24vWuG8h6xo2gX/lzFudFl1VmzMSTC2z+rdJu69r1m+bm5dP P9dv9WWLTmHcwMWrNv2ylanz6Gcx6qBfxs+IbtNzhPF//CYL+td35LfK9SDz BgAAAAAAANKyJ/NWVFzMr0s4lf3r4PALlm9o2r6f8t4k/ucz82ZkQSXOb0Sv +FE5se/Ib0V+g1MTkyOXqBbkHMgPMVv41wpm3nQq4I11NRf3bnn8J15mypm3 8TOiVev/9OuxNJ3feWGkDgbryYOn8b8WncKokjrBMejw8dSBY2dx/u3bucv1 A6J9s2rSkRM0/d2PeymL1f3gd09uGgymlln9O3zy9/Trxu17jiSn0g/jpkef zsmtWb9tz/AImvvBZ4Nqvt1WOS6fcfze0gD+qZKTyLwBAAAAAACAtOzJvO1M SKquGI0tMenoU/XaPPtuh74jpi1bu3V7/P4Dh49znk0/82ZwQSWf+Y2oxTEe U2qtuoXTdKo5/+rAzFtFas5JlU3b98QlJKn+8ZBuZb9l3rSvCuXHOfe4xzQz UgeD9eTM29CJc/jdo18NnaITHL/QPqm8dc1jQF77dzdtmnfzjoTq7hvw+FeP mbcyY8HUMqt/uZJjv50fuXA1/RDnnv5R96GvNP8sJy+/5ttt6WcjUdJKSTtJ jTL478dt8fxJfL5Rxx2/f4wamTcAAAAAAACQlg2Zt/STp+q16qnMpfQdMY1z LKJMYVERDxKlzbwpcxcGF1Tymd+I33eIfm7ZeYCyQF5+wYuNO9es3za/4Nch tjhd02/UdJ9xsC3zVpGad+g1iiZu3pGgUwHOvDVq00c5bFdObt6z73ag9RcU Fhmsg8F6ijcsHDx64vlGnaprnsT0qVmHfq8270qbU02fMmcpra3P8F9TiB4D 0rHPmOq/vCR0p3Li5Mgl1RXvCfWWeTMSTC2z+pc64rmGHVt1C+/UZ8zLTbtw Z02N+uUVwPwiYDFOnXVoo/Tp9ph2K0PmDQAAAAAAACRmaeYtM+s0TeS3YTZu 2zfr9K+Pmv46trxiALGREb++51GZQON7q1as3yamGFxQyWd+o7CoiF+IOTXq B1GgZ3gETfngs0FiyrotO2nKe236+oyDbZm3itScczKNPumdfTqHp5xIO9ng o6+ea9hx36FfX+Up3m06euo8sSDfP/bBZ4P5VyN1MFhPkXkjazbuoLk16rfZ EvdrOiu/oHDMtPkTZiws1rw6U+A3itI6ldnag0dPcOJ3+bptOgGZ9v0ymvjW Bz3EbW+JSUeffbcDTdwYGy/q4DHzZiSYWmb1b5k7bVjL/dqFnuGTecr+Q8ep GN9GmHDgiLc6GBEfH7/2Nx4L6KfdypB5AwAAAAAAAImZnnl7vlGnpu370T8e dp7/vd36y/TMLFEybs8BHnqrRacwumZv3PaXe9tebtpFlUDjO9yebtCuY+/R m7bvMb6gkpHEV+zufTwMXcOPe3/Wfzy/suG1f3+uHG+f1s+bfuejr/TfFGlb 5q0iNS8qLuY7tV5p9ln3gRM79x3LSR7lqzY588aJU+rBbgO+4dfIUskjyami mJE6GCmjzLyVuYf35+pxmWVrt/K+JPJgWoePpXANX2zc+dOvx4ZPiOzYZwxn z2iHEWOdeQxIcUkJj1/3XMOObb8c2frzITXfbqvKOnrLvBkJppZZ/Uv4OdPq v71Vgb31QY/q7iHXSktLdarhU3R0dNRvtHN9pt3KkHkDAAAAAAAAiZmYeVv9 447qvx9o/ZVmn7XoFDYpcsnJU9mqwis3bH/DnUbg5MDiVZs6uR/3U6YUUtIz u/YfX7P+LwmQyIWrjS+oxMPUL/x9fqPfqOmqRwvXb93d9ssRnKWp3bTLZ/3H JyYdVa1q7tJ1fPfUy0276MRhojvzNmdxjF8VsL/mOXn5/UfPqPfhr++jfKX5 Z5NmLy4qLhYFZi1YRdPn/bDu27nLOedTq0E72ta2XYmq9Rupg88yLzX59Jl3 2otfS0tLO/f9JRXWvEN/+vlochrtTq/9u5syhauVkZndsfdoTprxv+cbdaKg qd7X4DEg+QUFA8fNFhnjZh36UVcqlyooLKLpFDHtdn0GU8vE/uV3KzxVr41y nDqqD03sVeHh8nJzc+kQ4S17xqlCnbRbGTJvAAAAAAAAIDETM28BSM/MOpF2 ssT784Nl7nRH9ukc1X07RhYMQGFR0fGUdJ3VUjVO5+SKIbacoyI1T07N0KZG tVLSM3Py8itSB4NlvCkqLtbPZSm3knDgyPqtu48mp3m740snIBmZ2eLJaH8Z DKa/KhI3U3jLnr3avKt+2k1nWb8g8wYAAAAAAAChKLiZNwBwOB7nzVv2LCXt JP3TXwMybwAAAAAAACAtZN4AQIf+OG9GIPMGAAAAAAAA0kLmDQB06I/zZgQy bwAAAAAAACAtZN4AwCfOnuXn6w305xEtgswbAAAAAAAASAuZNwDQoRznbdGi RWv9RIvQgvPnz69gNZB5AwAAAAAAgFCEzBsA6FCO8xaw+Pj4ClYDmTcAAAAA AAAIRci8AYAOHuetIrKzsyteDWTeAAAAAAAAIBQh8wYAzofMGwAAAAAAAIQi ZN4AwPmQeQMAAAAAAIBQhMwbADgfMm8AAAAAAAAQipB5AwDnQ+YNAAAAAAAA QhEybwDgfMi8AQAAAAAAQCji69nCwsJgX1gDAHhGByhk3gAAAAAAACAU5eXl pQAAOF5+fn6wj5cAAAAAAAAA/rl06RKSbwDgcHSYooPV/wPOXLPi "], {{0, 346.}, {831., 0}}, {0, 255}, ColorFunction->RGBColor, ImageResolution->144.], BoxForm`ImageTag["Byte", ColorSpace -> "RGB", Interleaving -> True], Selectable->False], DefaultBaseStyle->"ImageGraphics", ImageSizeRaw->{831., 346.}, PlotRange->{{0, 831.}, {0, 346.}}]], "Output", TaggingRules->{}, CellChangeTimes->{3.834253822325581*^9, 3.834311052074154*^9, 3.834313659671905*^9, 3.834405268762541*^9}, CellLabel->"Out[1]=", CellID->965288770] }, Open ]], Cell["Retrieve the \"DW\" variant of the data:", "Text", TaggingRules->{}, CellChangeTimes->{{3.834313678293263*^9, 3.834313686155478*^9}}, CellID->1903528209], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"ResourceData", "[", RowBox[{ TagBox["\"\\"", #& , BoxID -> "ResourceTag-Job Training Efficacy-Input", AutoDelete->True], ",", "\"\\""}], "]"}]], "Input", TaggingRules->{}, CellChangeTimes->{{3.8342537932861137`*^9, 3.834253836731948*^9}, { 3.834313650543797*^9, 3.8343136518924227`*^9}, {3.834313695261826*^9, 3.834313695822826*^9}}, CellLabel->"In[2]:=", CellID->601946357], Cell[BoxData[ GraphicsBox[ TagBox[RasterBox[CompressedData[" 1:eJzs/XncTdX7x48fQxKZQoiMkSFFxkozKRokQ4pCpcjQRxHJUBmSlClDvcuQ IYRExvjKcBvuzHMyzzO3ysz3+p3r1/ru9j5nn3XOWfuc6z7X9fzDw7332nuv 63Wt8Tprr12kebu6LdL6fL53M8I/dZu9/2j79s06v5Ad/qjf9t2Wb7Z94/Wn 2r73xptvtK/aPB0c/AHSNr7B5/v//f+6IAiCIAiCIAiCIAiCIAiCIAiCIAiC IAiCn2t+4p0LQRAEQRAEQRAEQRBizd69e/ft2xfvXAiCIAiCIAiCIAiCEFMu Xry42s+lS5eM3PD/EQRBEARBEARBEASBDC5T+L179/7ux9RSgXjbKgiCIAiC IAiCIAjC/0ew+btaJIDAn6ZiAilCmHDWjbPtNOHsEc62m0WUjC+iP03ELzQR v9BE/OIFnFUV24PN33GRwGE/8B/4U2IC8YKzbpxtpwlnj3C23SyiZHwR/Wki fqGJ+IUm4hcv4Kyq2B5w8o6LBNatW3f16tUrV67Af4wsFeCsdjRw1o2z7TTh 7BHOtptFlIwvoj9NxC80Eb/QRPziBZxVFdsDTt7VIgH809RSAc5qRwNn3Tjb ThPOHuFsu1lEyfgi+tNE/EIT8QtNxC9ewFlVsd05c7cuEsAjppYKcFY7Gjjr xtl2mnD2CGfbzSJKxhfRnybiF5qIX2gifvECzqqK7c6ZOy4SOHLkiPWgkaUC nNWOBs66cbadJpw9wtl2s4iS8UX0p4n4hSbiF5qIX7yAs6piu23a7lwkgMCf 0S8V4Kx2NHDWjbPtNOHsEc62m0WUjC+iP03ELzQRv9BE/OIFnFUV223T9j17 9jgXCSBwMMqlApzVjgbOunG2nSacPcLZdrOIkvFF9KeJ+IUm4heaiF+8gLOq Yrt1zh5skQAS/VIBzmpHA2fdONtOE84e4Wy7WUTJ+CL600T8QhPxC03EL17A WVWx3Tpnd1kkgOBSAUgmMYFYwlk3zrbThLNHONtuFlEyvoj+NBG/0ET8QhPx ixdwVlVsVxP2CxcuuCwSQKJcKsBZ7WjgrBtn22nC2SOcbTeLKBlfRH+aiF9o In6hifjFCzirKrarCXvIRQJINEsFOKsdDZx142w7TTh7hLPtZhEl44voTxPx C03ELzQRv3gBZ1XFdpyt4yIBmOzv3LlztyuQAJJBYrhEYgKxgbNunG2nCWeP cLbdLKJkfBH9aSJ+oYn4hSbiFy/grKrYjrP1o0eP/h4mcInEBGIDZ904204T zh7hbLtZRMn4IvrTRPxCE/ELTcQvXsBZVbE93Hl9NHBWOxo468bZdppw9ghn 280iSsYX0Z8m4heaiF9oIn7xAs6qiu0SE4g7S5YsmTFjxty5c4Ml4KxbbGwP 6QJBoeOR+fPng56LFy/WuWFYiSNmx44dM/zs378/4ptwron66NQmmkru27cP C8m2bdvinRdvoam/IH6hifiFJsZHI0IK79IutktMIO48+OCDPp8vd+7cwRJw 1i02tod0ATWGDBlSt27db7/9NvaP1vFI4cKFQc/7779f54ZhJY6YESNG+PzA CCHimyRSTVy/fv3rr7/+1ltvbd261eyddWoTTSVnz56NheTLL7+Md168hab+ gviFJuIXmhgfjcRxZBU9pvp0zqVdbJeYQNyRmIALEhNwsmzZMpy5pE2b1viE LiQSEzCYqzhSu3ZtFKRp06Zm7ywxAfrERv/du3cvDcWxY8eCXb5lyxZMs3fv Xq+zSgSaflmxYkWwZCdPnvQ6txSg6RcEvDNs2LD3339/4MCB8+bNO3v2rNf5 pIPZ0Uh8R1bRY6pPN1valy9f7l6kN27cGOxaaF4wTXJysqn8uBNH212qP3SF prLkAuWYAIjzkJ8NGzZ4LEP8MyMxARc4xwR69uwJpa5z586249CMQJ8FGU6f Pv327dtjnCuJCRjMVRxp0qQJCvLGG2+YvbPEBOgTG/379u3rC8XgwYMDXrtz 585bbrkF03zzzTdeZ5UINP1y4403Bks2ZswYr3NLAZp+gaFp3bp1bQmqVq36 +++/e51VIpgdjcR3ZBU9pvp0s6U9e/bs7kU6X758wa59//33MU3RokVN5ced ONr+0UcfBUtWsmRJU1lygXJMYMWKFSgF/MdrHeKeGYkJuMA5JvDss89CruBf 56lp06a1b99+1qxZsc+VxAQM5iqO7Nq165NPPundu7fxH2ElJkAfOnOcUaNG BbwWWz9EYgJmCcsvx44di8B9CQZBv5w5c+bhhx/GgzD1ePHFFx955BH8s1ix YkePHvU6txQwPhqJ48gqekz16TGeF5coUSLghUuWLEmXLh2mSdSYgNX2d955 J1yJzEI5JgBVEqWgEBPwOjMSE3CBc0ygWrVqviAxgTgiMQGDuUpIJCZAn9jo f/DgwU2BWLZsWcaMGUHnKlWqwNTGeeHIkSOtIyKJCZglLL9s374dvfDxxx87 Lzly5IjXuaUAQb9MmDAB/dKuXbvjx4/jwa+//hoP9urVy+vcUsD4aERIMV3a t2zZErBUv/7661hWp06d6rzqxIkTZcqUUV1AKo0JhGX7q6++Ckfy5s3rTB+b VSuUYwJjx47VnIbv379/zpw5u3fvdp7aunUrjPF27doV8nEbNmyAif8ff/wR TWagWV64cGFSUtKpU6fcH3f48OF58+atXbsW3/zC6K7BmIC7OQqQDuZHkJM9 e/aEvKe+mGYJ13bNfIbrgrCAe65btw6K5b59+1yShXRT6dKlI4sJQAmEUQSU xrB+LFi1ahVcAsq4J9PvhR944AH8E6rGr7/++vvvvwd81TFklw1ZWrBgwdKl S9XIJxguyscrJnDy5El44o4dO5ynli9fvmTJkpBGaVZnxKU9DHnWhncNWkqk Pa9mEYXqD43A5s2bde4J2YbMHzp0CP4P/wkZE9BpYWDcvmbNmpkzZ0KGNV/v DeYafS/op0yJSP/oc6h47bXXQOSbbroJxHee/fPPP/GtgYoVK0pMICRe+2Xl ypXohYkTJ4aVsUSCoF/+7//+Dw5mypTJdp/77rsPjterVy+s3KZSIhiNpGj3 IzYOHjy4ePFiGMlgZ+EOdBNz587V+chRWP07ZgPK1caNGyPYOEJzaBqDCBjk H8oz+AUmwgETdOzYEZudChUqpN6YQECC2f7cc8/BwXLlynn6dBdoxgQGDRoE 3s+SJQuWB/hPdj9QMDDB3XffDX8OGzZs+/btTz/9dIYMGSBZrly51B2g2Pfu 3Ttv3rwqxJQ/f/6RI0faHgTJRo8e/eCDD2bLlk2lhKumTZumnxlk/fr1MAy+ 4YYbMBlkCTIWcOANs4BKlSrhK0s+/4qvbt26Pfnkk9HHBHTMUSxatKhKlSoq WZo0abJbqFOnTrhieodmDdXPp6YLjhw5AneDs23atLHd4cMPP0ShYOxqO7Vz 507oi1WBAYoXL/79999b86njpjJlyuTLlw/84vO/2qZcM27cOEyAtaBmzZq2 DEBrU716dfX6J5h57733Qvfn1AHv8NVXX0G39corr9x6662qMDRr1uzEiRPB pNbvhaFS/P7779b8gNVvvvmmbVuqYDGBbdu2tWjRAgREHYB06dI1aNAgYFce UvlgMYG3334btf3kk0/cjdK0XQkLxaNu3bo333wzPveuu+5C98EQ7r333rv9 9tuVUV26dLH17/rV2b09dDkLtQMNd3400OsGLVwl9Yvo559/DhVfKQZTy3bt 2gVMefr06Q4dOuTMmVPds2rVqupXNmdMQLOFOXbsGHgzc+bMKhn0/tbW9bff frNa59KR6XtBP6VCX38jObQCg2qs0Z9++mnABHATOAu3nT59Ot5ZYgJWYuwX mEHgfQgu7IkZBP2CgQJo4mzpGzdu7PPvKhCGeakW/dHIY489Bv1I06ZN8+TJ 49KPBBxZrV27tlatWj4LJUuWtO6kgZ0pTBagc+/evXuhQoVUyjvvvHPevHm2 LOn373jnO+64A/4/fvz4ypUrq+X0t912m+3OLn16yAFSWKpGNjy2UqNGDchD gQIFDh486Dy7ePFiNLOxHx+lmIB3tj/00ENw/Iknnog481FCMyYAAypfIKAA YwKs4B07doTJjjqrqvDRo0dRcAQXXCGdOnVSTzlw4AAM0QM+CBqKn3/+WTMz wA8//KAqGkzfrBXWtlfkqFGjrPmxEU1MQNMcBLp4NYYHQ6xDaAQEDEtMT9Ep M/r51HfBvn378CD0vLbHvfvuu3jKFtqFEazaFMsGftpG301qFmxDzUECzqMn TJhgbfMVUCadKwnxDs8++2yxYsWcl7Rt2zYaj+DNgYCbU1WrVs3aEQe0ZeDA gTimcuKMHoRUPiVITADmm3iwevXqMEl0Nyos22Gwp2b9iqxZsyYnJ0Ob78xn //791R3Cqs7u7aHLWSXIzJkzrTeMQYMWlpKaRRQaAXBiwJyUKVPGtok09MX4 Yk4wbDEBzRYGRnqPP/44HofBHjzXWf4XLFig4zh9L+injEz/6HNoo1y5cj7/ ryEBf+eCCov3gfEVDMXx/xITsBJjv6g16poLbxISgn6BG+IdfvnlF+vxUqVK +fyTqYgMTWXo+wW64yJFivgc2PoR52hkz5491kEy/sKLwCgF06jONGDHDSMZ mM6rG4bVv6s747IQG9ATWWNKwfp0nQFSWKpGMDy2oooutC3OszA4xCWyMIQA 6xo0aOCjFBPwzva7777bF9eaSzMmsGHDBijAav/MTz/9dLoftW5fTTeAsmXL Qq1ctGjR0qVL8axacFK3bl0cB8IN8f1WaIetHw7A7VleeOGFiRMn7tixA5yo HKpWGYXMDJTYHDlywKmcOXOOGTMGCvPx48eHDBmCg0a4UD3uzz//VL8YNm3a NCkpafv27XCJiihGuU5Ax5wU/2+UBQoU8PnXPMyZMwcPwgAYU3bo0OH3339X 42d9Mb1Dx3bNfIblgnAr/t69e3PlyoXH69ev/9tvv8G8Y8aMGVDNYYSg1odr ugn6BShmOGyA7mP6v6iXN509186dO9V+JiDIunXr4OYDBgzAogh9DVQTqxXW evTkk0+OHTsWRuBQwvGhMHsN9qJoWDEBoHnz5jANB6lHjx6tOiaoI7bEtpk+ /jgF46W+ffsuXrwYV6erPt06CtJU3hkTgNkZhh3uvPNO6KbdLYrM9jp16oAr V65cad0wDYC8fffdd6tXr+7cuTMegausN9EsJymh2kOXswHHD7Fp0CJQMmQR VfqUKFECNN+/f//ChQvVpltPPfWU9c7t2rXD43nz5oXbQt1ZsmSJesXP54gJ aLYwIC8mg2EM5g0S45AeSj5IBK2r+pSYi2v0vaCfMkr9I86hDfWj89dff+08 q94aqFChwunTp6GCYGKJCViJsV+GDh2Kp2A0C7UMhqxQHWAqwWQXO4SgX6Al wSADVBnVhkPH52zVExjj/YhzNNK9e3e8tlWrVtu2bTt79uzy5ctr1aoFLlaN uepMgYIFCw4bNmzLli3QpzRq1AgPwqjb+oqHfv9uvTMM8D766CMoUWCyeq+q a9euzsRW72sOkMJSNcp5Mf4gDtP8gGHh9957TzU48Ge9evV8CRQTcLEdf0WC BhYGva/5gf9AKYrYlnBB23+PFI9iAoiqhs5X+FUFh2prK89r1qzBQT5UWOtx mFDgSMP6hQ5oFtQyToUaQ1pft3HJDC5rSZs2rW3C9fHHH/v8v8+q94hVYOGd d96x5Q3julHGBDTNgXEpHoEcWlPih02hlVBHwhLTO0Larp/PsFwQbsVXN2/d urU18cmTJ63vlOmXOgAXGQbcT8DZc6ne57PPPrOmhOkzHrf90KDqEQzwrMcb NmyIx6GoOJ+bEk4vDL3tpEmTrMehM8WbFytWTD002LsDEyZMsA071eveLVu2 VAc1lbfFBMB3uA4cCsn69evdzYnAdqBLly7qIIw61CrBmjVrWl9/wKklYA34 65cTl/bQ/WzA8UNsGrSUMJUMWUShP8LKUqRIEas4UAaUaCosv27dOkwMrret H4CxHCa2xgT0Wxhc9A4HrYNM9c1r2+8CLq7R94J+Shth6R9NDm08//zzPv/M KOBYFLshmCjhx9QkJhCQGPuld+/evkDkz58/4M98CQlBv6T4IwYqMAt1B5p0 bJFgshmmfakVs/1ISqDRCL7lDV2GbTRifQVSdabPPPOMbQ+BOnXqOBsx/f5d 3Rm6NusnJtUaKuh0nImtfbrmAMmKp/PiVatWYQJoWJxnoYKotwbwSCLFBNxt D7jKF4Cxvc7GFNGjYgKHwifuMYE0adI4v8GKDSywadMm2ymYRPg03rHq2bMn 3gEGciEzA81LpkyZ4PjLL79su48qNmohEDbduXLlcr4N7d13B5zmqEZj9uzZ 1pQ9evTw+X/zUiuooxfTCCFt189nWC4Iq+JDScA3iOHmOvvP2AhY6lLCiQmc OXMGl7QVLFjQuXORWvZs3aEI71ClShVbYrWcfvLkyQFzq98LB9w28LHHHsP7 q1FQWNsCo84w/sE/9ZW3xgROnDhRuXJlLPC2iuCOvu1OYdXKdtsLjF27dg3o fScBy4lLe+h+1jl+iFmDlhKdks4iqj7sa11/gixcuBBPNWjQAI8oGW1x0ZQg 3x3Qb2Eww2C+LRm+QfDBBx84rXO6Rt8LYfnLhr7+0eTQxvbt26HGwVloQp1n YcyM16r3piUmEJAY+0XFBO6777527dq1bdsW17j6/IuiKXwZKgYQ9EuKf1oH 03/ffylXrhyTj0GkmO5HUgKNRrCRB6wbCNgItmgfgPEGnmrevLl7PgP27+rO 06dPt6XHFb/33nuvSzYiG5p6Oi/G9XgwXnXuc3j8+PGSJUv6/Msq1NLNRIoJ uNie8m9MIEeOHI0bN+7cufOLL76o3lJUoxdPSdUxAWcFB0BDn/9Fzl8dYJw2 T548zqtWrlw5cOBAaK6hLVVBV+sXIoJlZv369XgceknnE/EUvnC0ZcsW/DPg HptmYwLu5qjvKqrd6hAwwfff/WoiE9M4IW3XzGe4Lgir4m/YsEGz2VeELHUp 4cQEVFF8++23nYlV32f9WSfYTBz6R0wc8EWzlKhjAmqPDtuL1cFiAtBN/PDD D3BVnTp1SpQogddWqlQJz+orr7rLefPmqcg5zCXdr7IRje0vvfQSPtS2ccGg QYPweMBvIocsJy7toftZ5/ghZg1aSnRKOosoNgJAwHdAcOxUtmxZ/FN53/l9 n4AxAf2WEPcoKFOmjPWe0F3iPW0LeIK5Rt8L+imdRDOWjvi5/fr1w7NQqm2n duzYgYurQUP1W57EBAISS78gUNGsU5IzZ8506NABL2HyiTeCfoGGBcSHs9Av tGjRwroF6/vvvx+xpakLs/1IwMQwqVebP9SoUWPixInOrYdcYgJQWfDy6tWr O/MWsn93uTNuW4HbDwZLHMHQNMXjeTEuZXnuueecp9q3b4/XzpgxQx1MpJiA i+0p/l21e/fubQ0XrF27FkcvAQuAcVJ1TCBgT4RvbroAddOaHpqCYJf8+OOP ITOj9t5xAV/2gTqOfzp/mUoxFxPQMQdmWPij1cMPP6wu3L9/P3Yojz76aMRi ekRI2zXzGa4Lwqr4atuQPn36hLRIs9SlhBMTUEXRNu9A1OsD1lfPgtWjn376 CRN7FBNQ71yPHj3aPTG0kC1btsSZgg314Q995VV3Wb9+fXWfcLd4jcb2V155 BR9qG1Go9eq2mIBmOXGPqLicdY4fYtagpUSnpLOI3nPPPT5/gD3gfXCqniFD BlS+UqVKPn+s3pkyYExAvyVUu0NYF5+oNQy2PaJdXpnR9IJ+SifR6B/xc+vW revzT2HUN9YValtvGIH/8S9z587FgzA5gj8D7lCdYFDzSzAgZdmyZX3+XWR1 dmdN7RD0C86VoNHDjJ04cQKqifqQSrdu3SK1NTVhth8Jlvi7776zfiAgX758 0Kpb3+ZwmbkD+KWDkiVLWg9q9u8ud8Zvh7nHBMIamiq8mxfDzBHPOj/zBE/E zc9hyvyHBXyhrGDBgvinvhWRERfbXRg3bpxLE2GWxIsJ4Hpg6KTuD4LaUT/F siIOxorQukKtB6PU+FwnJjB27Fg8Xrx48WBPHDVqVIqlU+jbt68z27jpBMzK 3T3lrpi+OZ06dcKDMCECE0aOHHnnnXf6/OFl61A2LDG9I6TtmvkM1wVhVfzv v/8ej3z++efu5ui7KSWcmIAqil988YUzsRpaw5wl2B0UXscEoD3E+6tgeMDE O3fuVLvNQ/2COvjLL7/s3bsXtxlUMQF95a179fj8n3XG/4AL3C+0ErOYgH45 MRgTiFmDlmJ6LIeNgPWzX1ZwQ6f06dPjazW48jl79uzOlGpHrwEDBqiD+i3h 9u3bcfiXNWtWGINNmjSpbdu2OM559NFHbXsKBbNO3wv6KZ1Eo3/Ez4WxtC/Q uxXqLUt3gi2GSSRI+cWdNm3a4OMCvrWUYFDzi9qZxzbX27RpE+5UljFjRvcP oiUGsYkJpPgHJDByvu2221RzBHN8GJDgWfeYAK7er1ixojqi379HGRPQHyBZ 8W5ePHjwYDzrfGcT920ICXhN35AIiIvtLhw8eBCvisEmIYkXE8CwarZs2QLu ZmlFrRqC8d6uXbvUcfV9ap2YgAr7BNwvworaZqpdu3bOs9GvEwjLnMOHDzt/ e4VWy7Z6TV9MTwlpu2Y+w3WBqvjO1dHOiq/GtK1atXLJQ1huSgknJqCKYkDr VE9hfSEuXjEBtbu7ejs7YGLcVjddunTQUVqP22ICmsqn/DcmUKZMGRgvFSxY 0OefSKqePSSxiQmEVU4MxgRi1qClmB7LqZdqnZsbqPuon2nwdwfA+dZtwHUC YbWEqrOwAiY49xALZp2+F/RTOolG/8ieu3HjxmBlRt3QnfLly+s/LpVCyi/u qNcHQm6EkgBQ88uAAQPwrPMDkcOHD8dTHHaAjFlMAIG+e/z48fgjGvDSSy/h cZeZO/TgtsRh9e9RxgT0B0hW9OfFOsNjK/gGZZo0aZw9tXNnjIDYtq02Tlxs d+HEiRP4s4LaRMs7KMcEcMs7wLZNa4req8q2VZpO3njjDUy5c+dO6/GAtTJY Zk6dOoWztkceecT9cfv378c7lCtXznk2+phAWObgbqvVqlX78MMPoRo2adKk T58+zldr9cX0lJC2a+YzXBecPHkSKm/Amgitq63iQ2J8ZQwKp3U3WhthuQnA 3c7VB46t2GoBdFXqs3rOmYva+db6g05cYgIgDn7qN2PGjC7fHdi9ezfmwflt C1tMQFP5FEt3mSdPHvwetPqx2+VTUDZiExMIq5wYjAnErEFLMT2WU/s0jhw5 0pZ45cqVeAqm9nhE1d+JEyfaEgeMCei3hDAAAwGhQEKX0axZM6h37du3dz7F 3Tp9L+indBKN/pE9V61iDfgVQhho7XGg3raGGRD8qfnB0FQNNb+48MQTT/j8 +7G7N7yJATW/dOvWzef/kIH6HJ4Chqmq1ug/LpUS45gAcvDgQVzEmC9fPjzi MnNXX/ru3r07Hgmrf48yJqA/QLISUtWwhsdW8C0/GMU5Tx0/ftzZBQBPPfUU XFKoUCH80+vWJi62u+cH72ld5esRlGMC6qVj51polzq7ePFidNZdd93lXnLw o1GQ2PrzzYkTJ9QH3ay10iUz6ienYEs0Ffh9LmDatGnW47/99hvuOxFNTEDf nKNHj+IG9cF2lVfoi+kpIW3Xz2e4LsA9FmAKad3Jf/To0WrDGWvFV3v79+rV y/bc5ORk/E9YpS7l39fQAhYMZy3AmwPQL9isQ32KFy9unY3GICZQunRp2xeU +vbtizevV6+eS07UFzNtq7PgubgPj4oJpOgpn2LpLvGjtwh2NwE73IDEJiYQ VjkxGBNIiVWDlmJ6LLd06VIs5JAr6xejzp49qyxS+9Sp5ZRVq1a1+gL+/+qr r+Ipa0xAv4XB/QSsG0FHYF1KOF7QT2kjytU+ETxXdaO2AuOC7DEYkFj6ZcmS JWXKlIH6ZTtu/cSt5oNSNdT8otrAoUOH2k5BP4in8Ku7iU0MYgIw2Vm1apXt WtyjJkuWLPin6kz/97//WZPt378flyOmTZtW3SSs/j3KmECK9gDJio6qYQ2P FTimrVy5svvNrVDbYzDFG9vr16/frl0721fDYACDb2L6Av2EYRzKMQFVW2Hw D/0RKKN+4nQfA7/55pt44T333APPwr1Ztm/f3qlTJ7hEjQD/7//+D5M1b958 9+7dMNKbO3du+fLlff9irZUumdm8eTO+mJw+ffquXbvijpFQu6dPn/7www9b X1VWA1FIDyMcaCu2bdvWv39/nKH7oosJ6Jtz8OBBXIhSq1atTZs2QQk86yca MT1Fp8xo5jNcF6iPx8HMFFrv5cuXww1RPWfFh+Er/hbg8y/2A23huTCgev75 56Hxx18Ywyp1Kf+2+T7/W9tHjhzZ7QdPOWuBKorQLvXp0wdSHj58GEYgWbNm xZvAKM568xjEBIBixYpNmDABSh1opX5vzZAhg/WzbrjTTpEiRdROSjCtw1kY 9LkLFy6E8gne7N69O36YyfffmICO8in//RahunbDhg34tRfIp/MHl2hsjyYm EFY5MRsTiE2DluLBWE41AjAlh1k8iLxmzZpnn30WD8IQTqWE4gTTHDxes2ZN GBcdP34cyon6SqbvvzGBFO0WBoofVsDJkydDmXdpWl2sSwnHC/opbUQ5x4ng uR988AFqCN5xf65CYgIBiaVfcJn0jTfe+P7778+ZMwcayQMHDsB0VX1Ke8qU KREZmsqg5hcYhONuA3Dbr7/+WrUz48aNwxb4tttuswXkExKvYwK4JAAGGFD+ t27dCkdgJAbp8UKYr2Ey1ZnCkOONN96AhgucC30KflkPaNq0qXpEWP179DEB zQFSuKqGNTxGoN/EBE899ZT7za0QjAkYt3306NF4YdWqVaGzAx9B+hUrVjz+ +ON4HHwdgwkX5ZgAmI/hNQRaOZhH4KDdfQwMHRaoqi6EAb91v1BV98GJuNYa azFOQHz+d06dtdIlMyn+ryqrj0gCOXPmVGXDWluh0ca1dk7wYxPRxATCMkeF Da3AODZXrlz169e3vq2jKaan6JQZzXyG6wIQzZny5ptvfuedd/D/torfr18/ FSf0+SPD6v8tWrRICdNNwJAhQ9Qd4EJIDy0PngpYC4YPH67ub8P5geMYxATw Cxc2QBbbdjfq+785cuRQ7Z716wBKVfBsoUKFfP+NCegonxIkJgB8+OGHwSSK xvZoYgJhlROzMYGUmDRoKR6M5aARwD0onECZWbNmjfUOYLLaZNKKaudtMQHN FgaKlrXsKeBZd999N8wFrLt+uTtO0wthpbQS5RwngufCABjPOt+ADobEBAIS S7+MHTtWRfl8/21afeFvQZB6oeaXFH9ro+Z6t956K8waVPMFx3/99ddwbUyN eB0TgJvDwFh5Df6vnJg+ffqFCxdiMtsOxjZKly5tfU0grP49+phAit4AKVxV wx0eA9D94anGjRu739wKwZiAcdthgGH9ScJnGfT6/KVu48aNxiwMDuWYQIp/ xSaO/1UxXrJkCRwvXry4779fzbNx5syZnj174vpVBdR0GOadOHFCJYPOTn3R 1ecfx3766adqPwfb7CxYZhAYujz44IPWigZt8gsvvGBbcQQZ69ixo7VfgOo8 ceJEnPpBBtw95S6Xvjnjx493lmcrkJ9wxfQOzTKjmc9wXQAaqroJSlauXBn8 rrb8dVb83377DV8aUtx2220wVVcJwip1p06dat26tfVuTz/9NJ4KVgtWrFhh mxlBWxpwp9Zgd1CvVEcTE8Cbf/XVV9AZZc+eXWUGBi3Org3GNrhVss+y48G+ ffvUNgg+f0gEZqBr167Fr2bYYgIpGsqPHDkSj0NK64XHjx+HMuDzd/EhZyv6 tjuFxZgAPMX2YSn1o4P1uwP65cS9PXQ5qwRx/lLgdYOWEp2SwYoo1JcPPvhA feUZiw3IHvAbditXrlSrBXz+SG+jRo3279+P+11A0bWl12lh4D/uHy688847 ofd0t06h6YWwUiqi0T+y5+JWjYD15Q53Nm3ahJeo75YmPAT9smXLliZNmqiF AUiRIkVisJaVDgT9kuJf1q5ef1Pg2id901I1xvsRZ2LoFN5++231kUekbNmy 1v5aTcYHDx6M2+ko/0LdcXpQv3936abxQdaYgEvikAMkK5qj7nCHx+5bYQfj 5Zdf9vm7Tv1LoiFetp89exbcYfMRPOLVV1/ds2ePAcM0UDGByPA6JpDiH+OB yJMmTYLah6utwgL6sunTp8O1asW1DaiqCxcuhEmKzi8XITODd5sxYwY0yLa3 Qmz3SUpKmjJlyrZt2zQN0dRNxxxoK3AMD20XzB9//fVXmJHNnTt36NChL774 IpZD54QrRUNMjwi3zOjkMywXwIQCRPv555+ha9DMw4EDB+bMmQPZCHj/sEpd in/DPbjV1KlTYRaj+Q0IyADkGUSwbWJjhHA9ApMpUHvatGm49C5YGlAMPGJb wA+TAjgOtmgug3RXPnrCtT0awi0nHmXAiwYtxUsloY7A2Bvys2zZspAboUDl AoWhgOlvmeLSwtSqVcvnf78A+kewbr6fyZMnf/TRR7h2wueI+4VE0wthpUwx qn9YzxXcIesXaIGhQkE/CPXFuSNxwkPWLyn+IcqiRYtghAD/Box/JjCx7JGh g/vZD4xkbCMxFRPAQAF0DdDOw+jaZdwSl/5dc4Ckr2oEw2PixN12KF3ooyVL lsT49R8VE4hgdh+bmICAGNQN1+E899xzzlMwL8NN3d0X/cYYKTPU4OwRzrab JfGUVAvdV65c6TyrPhBmeyshXiSe/omB+IUm4heaEPGLywr/1AgRVeOC2C4x AfoY1A3fgGjWrJnz1KFDh3BjzFq1ahl5lhGkzFCDs0c4226WxFNSzfoDLs7p 2bMnnrW+dBZHEk//xED8QhPxC02I+EViAgmD2C4xAfoY1O25557z+V+zHTp0 qPUtlaVLl6pd7idMmGDkWUaQMkMNzh7hbLtZEk/JlStXYvv5yCOPLF++3PoR jT59+uC7h+XLl9d8/cdrEk//xED8QhPxC02I+EViAgmD2C4xAfoY1G3hwoXW zYKKFy/+2GOP4bYqyGeffWbkQaaQMkMNzh7hbLtZElLJF154QTWkN998c7Vq 1SpXrqw+BlqiRIldu3bFO4//fxJS/wRA/EIT8QtNiPhFYgIJg9guMQH6mNVt 2bJlzz//vPqKjRrBvvzyy2rXdzpImaEGZ49wtt0sCank6dOne/bsWbRoUWvT miZNmlKlSg0ZMiQ2H2rRJCH1TwDELzQRv9CEiF9mz579kJ/E+OIDEVXjgtgu MQH6eKHb0aNH16xZA03ZwoULY/wpgbCQMkMNzh7hbLtZEljJs2fP/vnnn4sX L545cya0saRCAYoE1j9VI36hifiFJuIXL+CsqtguMQH6cNaNs+004ewRzrab RZSML6I/TcQvNBG/0ET84gWcVRXbJSZAH866cbadJpw9wtl2s4iS8UX0p4n4 hSbiF5qIX7yAs6piu8QE6MNZN86204SzRzjbbhZRMr6I/jQRv9BE/EIT8YsX cFZVbJeYAH0468bZdppw9ghn280iSsYX0Z8m4heaiF9oIn7xAs6qiu0SE6AP Z904204Tzh7hbLtZRMn4IvrTRPxCE/ELTcQvXsBZVbFdYgL04awbZ9tpwtkj nG03iygZX0R/mohfaCJ+oYn4xQs4qyq2S0yAPpx142w7TTh7hLPtZhEl44vo TxPxC03ELzQRv3gBZ1XFdokJ0Iezbpxtpwlnj3C23SyiZHwR/WkifqGJ+IUm 4hcv4Kyq2C4xAfpw1o2z7TTh7BHOtptFlIwvoj9NxC80Eb/QRPziBZxVFdsl JkAfzrpxtp0mnD3C2XaziJLxRfSnifiFJuIXmohfvICzqmK7xATow1k3zrbT hLNHONtuFlEyvoj+NBG/0ET8QhPxixdwVlVsj31MQBAEQRAEQRAEQRAECkhM QBAEQRAEQRAEQRB4EvuYQAQXMoezbpxtpwlnj3C23SyiZHwR/WkifqGJ+IUm 4hcv4Kyq2C4xAfpw1o2z7TTh7BHOtptFlIwvoj9NxC80Eb/QRPziBZxVFdsl JkAfzrpxtp0mnD3C2XaziJLxRfSnifiFJuIXmohfvICzqmK7xATow1k3zrbT hLNHONtuFlEyvoj+NBG/0ET8QhPxixdwVlVsl5gAfTjrxtl2mnD2CGfbzSJK xhfRnybiF5qIX2gifvECzqqK7RIToA9n3TjbThPOHuFsu1lEyfgi+tNE/EIT 8QtNxC9ewFlVsV1iAvThrBtn22nC2SOcbTeLKBlfRH+aiF9oIn6hifjFCzir KrZLTIA+nHXjbDtNOHuEs+1mESXji+hPE/ELTcQvNBG/eAFnVcV2iQnQh7Nu nG2nCWePcLbdLKJkfBH9aSJ+oYn4hSbiFy/grKrYLjEB+nDWjbPtNOHsEc62 m0WUjC+iP03ELzQRv9BE/OIFnFUV2yUmQB/OunG2nSacPcLZdrOIkvFF9KeJ +IUm4heaiF+8gLOqYrvEBOjDWTfOttOEs0c4224WUTK+iP40Eb/QRPxCE/GL F3BWVWyXmAB9OOvG2XaacPYIZ9vNIkrGF9GfJuIXmohfaCJ+8QLOqortEhOg D2fdONtOE84e4Wy7WUTJ+CL600T8QhPxC03EL17AWVWxXWIC9OGsG2fbacLZ I5xtN4soGV9Ef5qIX2gifqGJ+MULOKsqttOPCRw6dGjt2rXwbwRPTAz0ddu/ f/+kSZO6d+8+bNiwBQsWXL58OWCyCxcuJCUlffHFF7169Zo4ceLu3bsN5tYs 4ZaZkKUFNFmxYkU/PzNnzjx+/LiBXHJC3yNXrlxJTk6GYjZw4EBwytWrVz3O mucYL41s4VyKKCD600T8QhOC45BLly6t9bNly5ZgaaCQrF+/ft26ddeuXXO5 1caNG0eNGtWtW7cRI0YsW7bMPXE0DzKO1Bcv4DzOEdvJxgTOnTs3e/bsxo0b p0+f3ufzPfLIIxE8MTHQ0W316tV33XWX77/ccccdM2bMsCaDfqRTp04ZMmSw JkuXLl3z5s3Pnj3roQ2RollmdEoL2N6mTZvMmTNbbc+WLRv0g+bznbhoemTr 1q358+e3Sl28ePF9+/Z5n0EPMVgamcO5FFFA9KeJ+IUmBMchMIVXwzzn2W3b tg0YMKBYsWKYZtGiRQFvcvz48YYNG9rGjdWqVYPLNbOh+SCPkPriBZzHOWI7 zZjAoUOHUGrFgw8+GMETE4OQug0ePPiGG25AobJnz16+fPm0adPinzD9X7Fi BSYDVe+99148niZNmtKlS+fJk0cpXKdOnRjHeHXQKTM6peXEiRNQbfEsiFOx YsU777xTpR8zZoxXBiQcOh7ZsmWLKlolS5aEQQv+v3Dhwnv37o1JNj3BVGkU OJciCoj+NBG/0ITaOGTNmjXqWc6YwNtvv+37LwsWLHDe5OrVq48//jgmyJEj R5MmTapXr45/wpT5n3/+CZkNzQd5h9QXL+A8zhHbacYEDh48mP1fcHqbGJpH RkjdRo0aBRIVKFBg/vz5uCbq8OHDHTp0wOL6xBNPYLKUlJSyZcvCkZYtW0Lf dN3fKcCdixQpgimTkpK8tyY8dMqMTmnp0qUL2ti5c2e1Tm/GjBmZMmWCgzlz 5vzrr7+8yH/ioeOR559/3uePO40fPx6PDBkyBPVv0aKF51n0DFOlUeBciigg +tNE/EITUuOQixcv3n333Wo+4owJtGnTBrOBtw02Vf/pp5/wbMeOHS9cuIAH v//+ezzYv3//kDnRfJB3SH3xAs7jHLGdZkzACq5KSgzNI0NHt3HjxuE0X3Ht 2rWSJUuCdLfccos6uHfvXugIbNdOmDABW8iBAwcayrIxwi0zwUrLlStXXnvt te+++852XPXRajWF4E5Ijxw+fBiDqG+++ab1OHbNWbJkSb3hF1OlUeBciigg +tNE/EITUuOQrl274iS3cuXKAWMCCjXBDzhV79SpE5zKnDmzbeMpyDYcb9So UcicaD7IO6S+eAHncY7YLjEB+kSmG1CjRg2ff7sA6Ilcki1btgzb8+7du0eW Q+/wuoYuWrQIbR85cmQE2WNISI/069cPJbW9Wjh58mQ8PmrUKG+z6Bmc+wuz cC5FFBD9aSJ+oQmdccjq1atxktvMTzQxgZYtW/r8ixNsx/G21apV08x8yAd5 h9QXL+A8zhHbJSZAn8h0O3v2bO7cuUG6EiVKuKfs3bs3No9qbRUdvK6h06dP R9uhj4gkf/wI6ZFXX33V59/XwhaJOnfuHA5mOnTo4G0WPYNzf2EWzqWIAqI/ TcQvNCEyDrl48SK+/pknT57jx4+//PLL0cQEfv75ZzxrMw13q27WrJlm5kM+ yDukvngB53GO2C4xAfpEoNuePXtq1qyJrfSQIUOCJYN2cty4cfgZgttvv11n V5kY43UNbd++Paq0f//+SPLHj5AewYIHQxfnKdzqp3Hjxl5lzmM49xdm4VyK KCD600T8QhMi4xD1igFM5+HPRo0aRRMTOH/+fNasWXGpwMKFC/EgpMRL1BEd yMYEpL5EAOdxjtguMQH66Os2atSo5s2bP/bYY+nSpQPRMmXKNGjQIOfXBA4c ONCuXbt69eoVKlQIW3KQd+fOneazHjWe1tAzZ87cdtttkL5IkSIR5o8fIT2C 2x8F/DhL6dKl4VT16tW9ypzHcO4vzMK5FFFA9KeJ+IUmFMYhycnJ6q0BPBJl TABYvHhxlixZME2dOnXGjBmTM2dO+P+LL76ok3P9B3mE1Bcv4DzOEdslJkAf fd1q1arls9CjR4/z5887k61atcqa7Pbbb9+0aZPhTBvC0xr61ltvoQJjx46N MH/8COkRDL/XrVvXeQo3L7rrrru8ypzHcO4vzMK5FFFA9KeJ+IUmcR+HXLhw oUyZMpCmYMGCZ8+exYPRxwQuXboE03/ff6lQocLff/+tk3P9B3mE1Bcv4DzO EdslJkAffd0+//zzp556qnz58vg6gM//PdYtW7bYku3evbt+/fogaf78+TFZ mjRpunXr5lxREHe8q6GLFi0CqyFx1apVCRpOlpAeuf3220HVZ5991nkKpMYh h1eZ8xjO/YVZOJciCoj+NBG/0CTu45DOnTvjOM066Y4yJnDu3LmHHnrI599+ v3Xr1nnz5sXEadOmhdFgyJzrP8g7pL54AedxjtguMQH6RKDbkSNHoFXHvqZo 0aLBNgqAPmju3LnlypXD9nzEiBEGsmsUj2roH3/8kStXLp//9Yr169dHlUVm hPRIpUqVfEF2LS5evDicql27tleZ8xjO/YVZOJciCoj+NBG/0CS+45BVq1bh 26D16tU7ZKFOnTpwsHDhwvin80L3qTqGFG655Ra4/3X/BoaDBw/GjamBXr16 6dtLNiYg9SUCOI9zxHaJCdAnMt2A7t27Y0P9zTffuCQ7fPgwrrDKnz9/hFn0 DC9q6LFjxzAZMHHixGizyIyQHnn22WdB2NKlSztPwfADTr322mteZc5jOPcX ZuFciigg+tNE/EKT+I5D6tWr59Ng3rx5tgtdpuobNmzAU19++aX1+J49e3Cb qZtuuunIkSOa9pKNCUh9iQDO4xyxXWIC9Ik4JrBz505sqFu3bu2eskmTJpjy xIkTkWTRM4zX0L/++gvXjAEffPCBgSwyI6RHWrRoAdpmzJgRpLYe379/P8re pUsXb7PoGZz7C7NwLkUUEP1pIn6hSXzHIc5X/gMyc+ZM24UuU/Xhw4fjqb17 99pOjR49Gk9Nnz5d016yMQGpLxHAeZwjtktMgD4hdQv2GtquXbuw3WvZsqV7 yldeeQVTHj16NLrMGsZsDf3nn38effRRtPSll166evWqmVxyIqRHxo4diwpP mTLFenzo0KF43PlzRmqBc39hFs6liAKiP03ELzSJ7zjkwoULJwPxzDPP+Pxf K8A/L126ZLvQZareq1cvOJ4uXTrnNtTJycl41fDhwzXtJRsTkPoSAZzHOWK7 xAToE1K3Bg0a1K1bNyUlxXZcvTswcuRI+HP16tV58+Z1BpOPHDly6623QrJC hQoZzLYRDNZQ6PuqV6+OgkBnevnyZWO55ERIj8CAJ3v27CByjRo11GgHhisV K1bEMpZ6QzGc+wuzcC5FFBD9aSJ+oQnNcUg0ewzCXNg6OLTSv39/PJWUlIRH oNSNGTNm/PjxAb9j5f4gT5H64gWcxzliO82YwIoVK5b9C+6ND8qrI+pTLExw 123KlCnYGpcsWXL48OGbN2++du3a8ePHP/zwQ9yXJlu2bAcOHICUZcuW9fm3 rm3VqtWsWbNARuiP4M733HMP3uHdd9+NnVV66JQZndJy4cKFmjVroplZs2ad OXPmjBkzpv2XgLv0CDZ0PAIFDKVu27btmTNnTp061bx5czzSo0ePmGTTE0yV RoFzKaKA6E8T8QtNaI5DAsYEDh48qB7atWtXfFbv3r3xiPrq9Llz5zCTmTNn hhm9WkE6derUTJkywfECBQpAbvFg+/bt8T7W1xw0H+QpUl+8gPM4R2ynGRO4 +eabfcH5/PPPI3h66sVdt4sXLzZs2NCqj/oQIUYA1EthcJMcOXKoU2nTpk2f Pr36s2rVqnCrGJmkjU6Z0Skt0Pi7pEGgN4yFSakcHY9Az1ulShVrIcT/1KhR I9gPDakCU6VR4FyKKCD600T8QhOa45CAMQF4kMvNIZMqZVJSkhor5s2b94EH HihcuDD+CceXL1+uUlaoUAGPQ5oIHuQdUl+8gPM4R2xPjTGBfv36RfD01IuO bj/++KPaskbx8MMPr1y50prs6NGjrVq1wg1XFVmzZu3Zs+fff//toQ2REn0N xdLSpUsXlzTIL7/8EguTUjmatRg64qeffloNOTJmzNiwYcPU3gWbKo0C51JE AdGfJuIXmtAchzRt2tTn2FTffaqeOXNma+KtW7fipgRWateuvWXLFmuyvn37 4qkBAwZE9iCPkPriBZzHOWI7zZiAYEVft0OHDi1dunTmzJlJSUknT54MluzS pUvr16+fNWvWvHnztm/fTvnNeikz1AjLI//888+qVaugTKpViKkaKY2m4FyK KCD600T8QpPEbvlTUlKSk5PnzJkD/zq3pUI2b968cePGGGcsJFJfvCCxS7s7 YrvEBOjDWTfOttOEs0c4224WUTK+iP40Eb/QRPxCE/GLF3BWVWyXmAB9OOvG 2XaacPYIZ9vNIkrGF9GfJuIXmohfaCJ+8QLOqortEhOgD2fdONtOE84e4Wy7 WUTJ+CL600T8QhPxC03EL17AWVWxXWIC9OGsG2fbacLZI5xtN4soGV9Ef5qI X2gifqGJ+MULOKsqtktMgD6cdeNsO004e4Sz7WYRJeOL6E8T8QtNxC80Eb94 AWdVxXaJCdCHs26cbacJZ49wtt0somR8Ef1pIn6hifiFJuIXL+CsqtguMQH6 cNaNs+004ewRzrabRZSML6I/TcQvNBG/0ET84gWcVRXbJSZAH866cbadJpw9 wtl2s4iS8UX0p4n4hSbiF5qIX7yAs6piu8QE6MNZN86204SzRzjbbhZRMr6I /jQRv9BE/EIT8YsXcFZVbJeYAH0468bZdppw9ghn280iSsYX0Z8m4heaiF9o In7xAs6qiu0SE6APZ904204Tzh7hbLtZRMn4IvrTRPxCE/ELTcQvXsBZVbFd YgL04awbZ9tpwtkjnG03iygZX0R/mohfaCJ+oYn4xQs4qyq2S0yAPpx142w7 TTh7hLPtZhEl44voTxPxC03ELzQRv3gBZ1XFdokJ0Iezbpxtpwlnj3C23Syi ZHwR/WkifqGJ+IUm4hcv4Kyq2C4xAfpw1o2z7TTh7BHOtptFlIwvoj9NxC80 Eb/QRPziBZxVFdslJkAfzrpxtp0mnD3C2XaziJLxRfSnifiFJuIXmohfvICz qmK7xATow1k3zrbThLNHONtuFlEyvoj+NBG/0ET8QhPxixdwVlVsj31MQBAE QRAEQRAEQRAECkhMQBAEQRAEQRAEQRB4Iu8O0Iezbpxtpwlnj3C23SyiZHwR /WkifqGJ+IUm4hcv4Kyq2C4xAfpw1o2z7TTh7BHOtptFlIwvoj9NxC80Eb/Q RPziBZxVFdslJkAfzrpxtp0mnD3C2XaziJLxRfSnifiFJuIXmohfvICzqmK7 xATow1k3zrbThLNHONtuFlEyvoj+NBG/0ET8QhPxixdwVlVsl5gAfTjrxtl2 mnD2CGfbzSJKxhfRnybiF5qIX2gifvECzqqK7RIToA9n3TjbThPOHuFsu1lE yfgi+tNE/EIT8QtNxC9ewFlVsV1iAvThrBtn22nC2SOcbTeLKBlfRH+aiF9o In6hifjFCzirKrZLTIA+nHXjbDtNOHuEs+1mESXji+hPE/ELTcQvNBG/eAFn VcV2iQnQh7NunG2nCWePcLbdLKJkfBH9aSJ+oYn4hSbiFy/grKrYLjEB+nDW jbPtNOHsEc62m0WUjC+iP03ELzQRv9BE/OIFnFUV2yUmQB/OunG2nSacPcLZ drOIkvFF9KeJ+IUm4heaiF+8gLOqYrvEBOjDWTfOttOEs0c4224WUTK+iP40 Eb/QRPxCE/GLF3BWVWyXmAB9OOvG2XaacPYIZ9vNIkrGF9GfJuIXmohfaCJ+ 8QLOqortEhOgD2fdONtOE84e4Wy7WUTJ+CL600T8QhPxC03EL17AWVWxXWIC 9OGsG2fbacLZI5xtN4soGV9Ef5qIX2gifqGJ+MULOKsqtlOOCVy5ciU5OfmL L74YOHDg2rVrr169GsFDEwB93fbv3z9p0qTu3bsPGzZswYIFly9fDpjs3Llz 8+bN69Onz6BBg1atWnXx4kWT2TVKuGXm0KFDUFTg32AJQJMVK1b08zNz5szj x48byCUnjHskFcHZdrPoKym9gBcYL8nh9ikbNmxY68ru3bttl2zcuHHUqFHd unUbMWLEsmXLrl275rwtpAl2w0uXLulYeuHChaSkJChvvXr1mjhxojMbnhKW X0BkkHrIkCG9e/eeP3/+2bNng6U8ePDgtGnTPvroIxBw69atAaUDjh49+uOP P/bo0QMSnzx50uXROr5wB+r1+vXr161bp3ntvn370I+nTp0K91nRE8FMAcob ZnjLli3Os/r1JcrRmuaAR6fiwLXudRY4f/58WNmLEulHvICzqmI72ZgA9Fz5 8+f3WShevDj0CxE8N7Wjo9vq1avvuusu33+54447ZsyYYUs5d+7c2267zZoM dIay7VXuo0OzzEC/OXv27MaNG6dPnx4seuSRR5xpoGtr06ZN5syZrbZny5YN RjXm8524GPRIqoOz7WbRVFJ6AY8wW5Ij6FNy5MjhcwXuoBLDZKRhw4a2BNWq Vdu2bZvtthkzZgx2w8mTJ7sbCx1Ep06dMmTIYL0qXbp0zZs3d5lum0V/jLRw 4cJcuXJZswo++vTTT21T7H/++QfcZ5OiXLlyO3bssN3w3XfftaZJkyZN7969 nc/V90UwIOWAAQOKFSuG1y5atCjkJUePHlXGjh07VvNBBokgJtCtWzfMMAzD bKf060s0o7WwBjw6FQfmPsHSKL755puwVIoS6Ue8gLOqYjvNmMCWLVvy5MmD UpcsWRIaVfx/4cKF9+7dG8GjUzUhdRs8ePANN9yAEmXPnr18+fJp06bFP2GE s2LFCpVywYIF0Nfj8YceeqhSpUo42LvppptmzpzpuSXho1NmDh06hFYoHnzw QVuaEydOwIAWz4I4FStWvPPOO1X6MWPGeGVAwmHKI6kRzrabRUdJ6QW8w2BJ jqxPCRkTKFWqFKa8evXq448/jgfhqiZNmlSvXl0Nw2DOq+55/vx5lxtOnDjR 3dh7770XU4I5pUuXVmUPqFOnTgQ/hUeA5hhpyJAhyjW33HJLiRIl0AXAJ598 opJZjYJeDyoRTAnxTxgnpKSkqJQwecTjMIWsWrUq+A7//Pjjj63P1fdFMN5+ +22bX6D8hLzqhRdeUOlTRUxgzZo1ykG2mIB+fYlmtBbWgEez4ujEBNxrmXGk H/ECzqqK7TRjAs8//7zP3zWPHz8ej0AniLK3aNEigkenakLqNmrUKFCmQIEC 8+fPxxUshw8f7tChAyr2xBNPYLILFy5Axw1HcufOvXr1ajyYnJycN29eOFi0 aNErV654bErY6JSZgwcPZv8XDIY4x65dunRBNTp37qyWz82YMSNTpkxwMGfO nH/99ZcX+U88THkkNcLZdrPoKCm9gHeYKskR9yn79u3bE4hWrVqhi+fMmYMp f/rpJzzSsWNHeBwe/P777/Fg//79rRnGg5999pnzzn///beLsTBBLlu2LFzb smVLmE9d989/QaIiRYrgPZOSktzlMoKOX3bt2oVzQ5jgz549G4MVMP2vUaNG 6dKlrUvrX3vtNcx8/fr1cakDGAXzQZj4Dx8+XCVbv349JqtQocK5c+fgyMmT J0uUKOHzL5PYv3+/Sqnvi2C0adMGSxT2vD6NmMAPP/xgnXXSjwlcvHjx7rvv Vhm2xgT060uUo7WwBjyaFQfqSMA6u27dOgwiPfDAAzFeQS39iBdwVlVsJxgT gPksdnlvvvmm9Tg6IkuWLNymbzq6jRs3DkcyChgqlCxZ0uf/HQGPLFmyBMvt 0KFDrSl//vnnOPa27oQbn8cVic6xK3SgMED67rvvbMdV12ldTSG4YMojqRHO tpslpJLSC3iKqZJstk/ZvXs3zlneeOMNdbBTp04+/+/Xtu1xIDNwvFGjRurI pk2b8KHON+Z02Lt3L8x5bQcnTJiA9xw4cGAE9wwXHb+AODhbh7m89ThMx6w/ /cNkDVcPNmjQwLbI4cyZM9Y/W7du7Zz+q0CBdamAvi9CoiIJ7jGBI0eO4FsD VapUSS0xga5du+KUoXLlyraYgH59ibJmhTXgibLitGzZEq6Fmut8IcVrpB/x As6qiu0EYwL9+vXDBsr2otnkyZPx+KhRoyJ4euol3PGbokaNGtjXY0j5q6++ QgGhn7WlLFOmDBx/+OGHo86sYbyehUEZQ01GjhwZQfYYwnlezNl2s4RUUnoB TzFVks32KU899RRcUrBgQevcFmccOXPmtCVu1qyZz/8muzqyePFizMyqVav0 H+rOsmXL8J7du3c3dU8XQvoFpu2440H9+vXdb4W6AZs3b3ZJdunSJXyPw7lZ BHqwcOHCtnvq+CIkmjEBHGmDyfPnz08VMYHVq1fjlKGZH1tMQL++eDRaCzjg iabiLF++HBcRDRgwIIL8RIn0I17AWVWxnWBM4NVXX/X533ezLY46d+4cNrYd OnSI4Ompl8hiAmfPns2dOzfIVaJECTzSuXNnnz987Xw18q233vL9d1snIng9 C5s+fTpW5JA7UAkI53kxZ9vNElJJ6QU8xVRJNtinqB9AoU0OeNyWYdxTF6Zd 6ohqzA2+1Nm7d2+8p1om6ikh/TJx4kTMz9KlS91vVapUKUhWvXp192S7d+/G GzpX/r///vt4Sq0e1/dFSHRiAqA5pvnkk0927NhBPyZw8eJFfAMlT548x48f f/nll20xAf364tFoLeCAJ5qKU6FCBZ//rZPYbLhhQ/oRL+CsqthOMCZQs2ZN EBaaVucp3NihcePGETw99RJBTGDPnj0oIzBkyBA8OGzYMDxiXSKIQJ/r8+9F o/nBppjh9Sysffv2wTQRAsJ5XszZdrOEVFJ6AU8xVZIN9imPPfYYTqBsk4vz 589nzZrV5/95euHChXgQJpL4XHUEGDlyJB6EqesHH3wAU9SuXbvCvFJn7zsn MOQbN24c/ih/++23R3aTcAnpl759++JUEV/nv3z5cnJy8p9//ul8jxvfwgAd 8M9Dhw6tXLnS9tbAdf/vvCjalClTbKeUc+H+eETfFyEJGRM4cuQIPAUSVK5c GXyxfft2+jEBtTIfSiD82ahRI1tMQL++eDRaCzjgibjiqAUG4M1wc2IE6Ue8 gLOqYjvBmABuzxLws0elS5f2aYS+Ewz98duoUaOaN28Og6t06dL5/G94DRo0 SA2xfv31V2zAbcsg582bp/YZ3rlzp+nsR4WnszAYIOGHfooUKRJh/vjBeV7M 2XazhFRSegFPMVWSTfUpmzdvxsRffPGF8yxMPbJkyYIJ6tSpM2bMGJwtvvji i9ZkcK0vEDCjt609cOHAgQPt2rWrV69eoUKF8HKwOmbdYki/4B6MefPmPXbs WIMGDXCGDmTMmBGme+pVVjiLx4cPHw5yWb9TDKNZ0FPdcOrUqXjc+U3ASZMm 4SnrmgRNX4QkZEwAbu7z77GPXzmkHxNITk5Wbw3gEWdMQL++eDFaCzbgibji QAn0+XdBVBtOxhjpR7yAs6piO8GYAAZb6tat6zyFW9lABxfB01Mv+uO3WrVq WZv0Hj16nD9/Xp29ePEi7mR7ww039O7dGzqUdevWffLJJzfeeKO6BI54ZUZE eDoLwzV48RpmpFI4z4s5226WkEpKL+AppkqyqT4Fp7qZMmU6ffq08+ylS5dg ymmbsFSoUMH2KQE1tYF8duzY8b333itfvjwegfxs3LhRx9JVq1bZpkWbNm3S udAIIf1Su3ZtFEp9EOHWW29VHyKsUqUKLhiA+SkewaXsPv/mV+pT9ZBefcxO /R69YcMG27PUAgDrEgJNX4TEPSYAPTKeVW+pE48JwKQYX/MvWLAgfuLheqCY gH598WK0FmzAE1nFOXjwIG5iqdaixB7pR7yAs6piO8GYAPTCIOyzzz7rPFW1 alXsgCJ4eupFf/z2+eefP/XUU9Ce44pHn//rmVu2bFEJ5s+fnzFjRluHniNH DtXLnzx50iszIsK7WdiiRYtwKAWFKi6vwqVSOM+LOdtulpBKSi/gKQZLspE+ BfeWr1evnvPUuXPnHnroIZzVtm7dGr/F5vOvne7WrZst8fjx4yE/6k+YIH/4 4YeYHm6iY+nu3bvr168PlubPnx8vhG4CHhSbPiKkX+69914lcteuXWFedt2/ FXb16tXx4Ndff33d8uK/z7+hEHR2ly9fBhNgMoi/MhctWhR/2/32228x2Zo1 a2zPmjNnDp5S29GH5Qt3XGICYM4tt9zi8/8Yp2QnHhNQr/9bzXHGBK6HU1/M jtbcBzwRVJzBgwdjglgGzWxIP+IFnFUV2wnGBCpVquQLsoctBk5r164dwdNT L+GO367738WDPhq7AOj9re+F/fHHH3Xr1i1QoACcKlSo0BtvvLFt2zZs/6Gj N5z1qPFoFgYi4Cg0U6ZMti86Ce5wnhdztt0sIZWUXsBTzJbkKPuUrVu34uSi X79+zrM4sYJJIm6KfvHiRZiM4Pa5QK9evdxvDrObcuXK+fyr690/6W4D5k1z 587Fa4ERI0boXxsxIf2itgmyfRsRpoe4pB/rhdoloFSpUjDFtqZU77wnJyfD n7/88gv++euvv9qeNW7cOGvK61H7wopLTADG23gqKSnp0L+ob/PBE+FP65cp YoC7X0ANfFuzXr16hyzg6w+FCxfGP1V6/fpiarQWwYAnZMVp2LAhZsO5l0XM kH7ECzirKrYTjAlgj1C6dGnnKYwev/baaxE8PfUSQUwA6d69O3aj33zzjfOs 9bWCV155xUdy0YsXs7Bjx45hMmDixInRZpEZnOfFnG03S0glpRfwFI9KcmR9 CnRP2Br/9ttvtlMbNmzAU19++aX1+J49e/B9/5tuusn5sTYb7777Lt4E30wP C5hQ42LR2HyUJ6Rf3nzzTZ9/gbrzFFYZ/MwQZBtNdn6hfs2aNXgKv6Sg/pw0 aZIt5aBBg/AU7kdnxBeKYDEBtbOEO/fff7/mg4zg7pd69erp5HnevHm2C/Xr SzSjtYgHPO4VBxfSPProo/o3NI70I17AWVWxnWBMoEWLFj5/cFJtmINAx4QN VJcuXSJ4euol4pjAzp07UbHWrVu7JLty5QqMMXwkA1zGx65QqHCFjy+u78Gl XjjPiznbbpaQSkov4Clel+Sw+hT8ulPatGltvgaGDx+O7nZ+JW306NF4KuT+ gWoVdGS75TRp0gQvP3HiRASXh0VIv/Tp08fnX6N+7tw52ykYi8KpbNmyXfcv csBl5++9954tmapBOLU/dOgQ/tm1a1dbytdffx2fhfvbG/GFIlhMQC0acadi xYqaDzKCu1+cGywERO3h4ES/voQ7WotmwONScdQnLDt27BjWPc0i/YgXcFZV bCcYE1Dby9g+jjN06FA87gy3JjYhdQv2quOuXbtQsZYtW7pcPnnyZEz2v//9 L5p8eoHZses///zz6KOPorEvvfRSHNe8pV44z4s5226WkEpKL+ApXpfksPoU fEceHuE81atXLziVLl066++kiNpGD+aq7vfHrXczZMjg/u22YN0o/iwLHD16 1P1B0RPSLzNmzMDMWL8vjzz++OPWyTJ+OP6uu+6y2aUW4aspKi55tX1dC67C 7QLUL/JGfKFweXfg9OnTJx2otyHgEfCn2scvNrj75cKFC84MA88884zPv8k/ /ulS/PTrS1g1K8oBj0vFURtWxOsrhIj0I17AWVWxnWBMANqx7Nmzg7Y1atRQ jRg0StDZ+fwvVXGbyoXUrUGDBnXr1nW+YafeHRg5ciQegc7LFvI9deoUflwD hI3gc7deY3DsCiMZtRETdNaXL182lktOcJ4Xc7bdLCGVlF7AUwyW5Oj7lHz5 8kHi++67z3kKxle2LkzRv39/PJWUlHTdvwYeHrp27VpbMjAT99Wxbv0EpWvM mDHjx49X09vVq1fDFNj5S+6RI0duvfVWtCWkIdET0i9Q7EuUKAH5uffee61V 4M8//8RN4Js3b45H1KR72rRp1js0a9YMj+/btw+P4NoDYMmSJSoZXIUHv/32 Wzyi74vrgRS2EfJbhDaI7zEYkIB7DOrXF/2UAdXWHPCEVXEUI0aMwDvPnTs3 hApeIv2IF3BWVWwnGBO4/u+XiYC2bdueOXMGWkLo6fBIjx49Inh0qsZdtylT pqAyJUuWHD58+ObNm69du3b8+PEPP/wQ973Jli3bgQMHrvsj//Xr10+fPj2M AQ4fPgw9DowB4Cq8fNiwYbEzSRudMrNixYpl/4LvuMEIVh3BHxTAWLU7U9as WWHsN2PGjGn/xboLkBAMUx5JjXC23Sw6Skov4B2mSnL0fQqMoLCfgmmL8+y5 c+fw0ZkzZ4ZZpPrJe+rUqZkyZYLjBQoUwP3z8dvQGTNm7Nat2+LFi2FCBDmE mQu09j7/AvjZs2er27Zv3x5zqFZT4zf7IBmUulmzZsG1MIcCie655x5M+e67 72opGx06foEpOWbpueeew9cZdu3ahdvBgZLq8wFXrlzBSE6WLFl++ukn0BmO fPnll2nTpvX991ta0PGBB33+zxquXLkSRAYnwrABjtx8883qJQV9X1wPpPB1 /9frVPnp2rUrJujduzcecd++PjFiAvr1Jaya5VRbf8ATVsVRfPTRR3jz1atX hyuLQaQf8QLOqortNGMCoHOVKlV8/6I+v1ujRo1gYecExl23ixcv4gawCvUh QpROveK3d+9e/MkDwZEY8s4774S1J3PM0CkzMG7xBefzzz+HNFBVXdIgMLaJ hUmpHFMeSY1wtt0sOkpKL+Adpkpy9H3KkSNHMH2zZs0CJkhKSlI9Wt68eR94 4IHChQvjn3B8+fLlmGzKlCk4M1U5UQUG6NChg/WeuK4egLspQXLkyKHSw8QZ p8lI1apVoZ8NaUv06Pjl8uXLjRs3VpUCt0BEbK+ywiwyZ86ceOomP/j/3Llz 40cMFTDRVr5Tut14442//PKLNZmmL64HUvi6/1vJLiUKypuL1YkRE9CvL2HV LKfa+gOesCqO4q233sIEzs0lYon0I17AWVWxnWZM4Lpf9qefflp1QBkzZoSZ b6oWPGJ0dPvxxx/VTjKKhx9+eOXKldZkhw8ftqrq88f24/tGmDvRj13x+1bq A0wu2MY/QkBMeSQ1wtl2s2j2BdILeITBkhxln6L2lHPZrAzS4KvZVmrXrr1l yxZrsn379jVv3hx/31QUK1ZsxowZthv27dsXzw4YMEAdPHr0aKtWrXDvaAXc rWfPnn///bemOVGiP0b69NNPrVmF/48ZM8aZbOfOnffddx+uDUDAWbavEyLj x4/HT28jRYsWDdghavoioMLuMYHMmTO72Ltnzx5M5vw+QgyILCbQtGlTn2OL cv36op/SqXZYAx79iqNQv0NZv3Mde6Qf8QLOqortZGMCCDQ4q1atWrp0qVqT xhB93Q4dOgRazZw5Mykp6eTJk8GSXbx4ceXKlfPmzbP9WECQyPpiwTs4e4Sz 7WYJS0npBYxjvCTHoE9JSUlJTk6eM2cO/OvyeXp8C/vXX39duHChS2Y2b968 ceNG5/FLly6tX79+1qxZYMv27dtjvO1MWH65du0a5HDBggVgi/tLrCDXb7/9 Bnc+ffq0+z137twJuoX88VfHF8EUTo3Esb5opoxebc2KQwrpR7yAs6piO/GY gHCdt26cbacJZ49wtt0somR8Ef1pIn6hifiFJuIXL+CsqtguMQH6cNaNs+00 4ewRzrabRZSML6I/TcQvNBG/0ET84gWcVRXbJSZAH866cbadJpw9wtl2s4iS 8UX0p4n4hSbiF5qIX7yAs6piu8QE6MNZN86204SzRzjbbhZRMr6I/jQRv9BE /EIT8YsXcFZVbJeYAH0468bZdppw9ghn280iSsYX0Z8m4heaiF9oIn7xAs6q iu0SE6APZ904204Tzh7hbLtZRMn4IvrTRPxCE/ELTcQvXsBZVbFdYgL04awb Z9tpwtkjnG03iygZX0R/mohfaCJ+oYn4xQs4qyq2S0yAPpx142w7TTh7hLPt ZhEl44voTxPxC03ELzQRv3gBZ1XFdokJ0Iezbpxtpwlnj3C23SyiZHwR/Wki fqGJ+IUm4hcv4Kyq2C4xAfpw1o2z7TTh7BHOtptFlIwvoj9NxC80Eb/QRPzi BZxVFdslJkAfzrpxtp0mnD3C2XaziJLxRfSnifiFJuIXmohfvICzqmK7xATo w1k3zrbThLNHONtuFlEyvoj+NBG/0ET8QhPxixdwVlVsl5gAfTjrxtl2mnD2 CGfbzSJKxhfRnybiF5qIX2gifvECzqqK7RIToA9n3TjbThPOHuFsu1lEyfgi +tNE/EIT8QtNxC9ewFlVsV1iAvThrBtn22nC2SOcbTeLKBlfRH+aiF9oIn6h ifjFCzirKrZLTIA+nHXjbDtNOHuEs+1mESXji+hPE/ELTcQvNBG/eAFnVcV2 iQnQh7NunG2nCWePcLbdLKJkfBH9aSJ+oYn4hSbiFy/grKrYHvuYgCAIgiAI giAIgiAIFJCYgCAIgiAIgiAIgiDwRN4doA9n3TjbThPOHuFsu1lEyfgi+tNE /EIT8QtNxC9ewFlVsV1iAvThrBtn22nC2SOcbTeLKBlfRH+aiF9oIn6hifjF CzirKrZLTIA+nHXjbDtNOHuEs+1mESXji+hPE/ELTcQvNBG/eAFnVcV2iQnQ h7NunG2nCWePcLbdLKJkfBH9aSJ+oYn4hSbiFy/grKrYLjEB+nDWjbPtNOHs Ec62m0WUjC+iP03ELzQRv9BE/OIFnFUV2yUmQB/OunG2nSacPcLZdrOIkvFF 9KeJ+IUm4heaiF+8gLOqYrvEBOjDWTfOttOEs0c4224WUTK+iP40Eb/QRPxC E/GLF3BWVWyXmAB9OOvG2XaacPYIZ9vNIkrGF9GfJuIXmohfaCJ+8QLOqort EhOgD2fdONtOE84e4Wy7WUTJ+CL600T8QhPxC03EL17AWVWxXWIC9OGsG2fb acLZI5xtN4soGV9Ef5qIX2gifqGJ+MULOKsqtktMgD6cdeNsO004e4Sz7WYR JeOL6E8T8QtNxC80Eb94AWdVxXaJCdCHs26cbacJZ49wtt0somR8Ef1pIn6h ifiFJuIXL+CsqtguMQH6cNaNs+004ewRzrabRZSML6I/TcQvNBG/0ET84gWc VRXbJSZAH866cbadJpw9wtl2s4iS8UX0p4n4hSbiF5qIX7yAs6piu8QE6MNZ N86204SzRzjbbhZRMr6I/jQRv9BE/EIT8YsXcFZVbKccE7hy5UpycvIXX3wx cODAtWvXXr16NYKHJgD6uh0+fPiHH37o3r37yJEjQTqXlBs3bhw1alS3bt1G jBixbNmya9eumcmracItM4cOHYKiAv8GS3D58uUVK1b08zNz5szjx48byCUn 9D2SePWXs+1mESXji+hPE32/nDt3btasWdCL9erVa+rUqdD1B0t59OjRH3/8 sUePHtOmTTt58mSwZBcuXEhKSgJHww0nTpy4e/fusHIe2eX79u1b6+fUqVPG s2QQ4+MQ5ODBg+CUjz76CAZjW7duDWsYdunSJZRuy5Yt6iCMZ9aG4vz582Gl DIlmAfMCace8wKPSnirgXKKIxwSghcyfP7/PQvHixaEHieC5qR0d3VauXFm6 dGnff3nyySf37NljSwl9QcOGDW0pq1Wrtm3bNq8MiALNMgMDpNmzZzdu3Dh9 +vRgziOPPOJMA31omzZtMmfObDU8W7ZsI0aMMJ/vxEXTIwlZfznbbhZRMr6I /jTR9AtMkPPly2f1S6ZMmT777DNnynfffdeaLE2aNL1797algZ6xU6dOGTJk sKZMly5d8+bNz549GzIzEV8Oc8lcuXJh+rFjxxrMknEMjkOQf/75B5LZhmHl ypXbsWOHZpa6deuGV91xxx3qIExPfKH45ptvwkrpjk4B8w5px7zAeGlPRXAu UZRjAlu2bMmTJw9KXbJkSWj08P+FCxfeu3dvBI9O1YTUbdy4cRkzZkSJQLf7 7rsva9as+GepUqUuXryoUl69evXxxx/HUzly5GjSpEn16tVVkYZ+ynNjwkSn zBw6dAgbJcWDDz5oS3PixAlosvBs2rRpK1aseOedd6r0Y8aM8cqAhEPHI4la fznbbhZRMr6I/jTR8cvChQuhC0Nf3H///a+++qqKD9gmcW3atMHjmTNnrlq1 6k033YR/fvzxxyoN9J733nuvmtCVLl1aOR2oU6eO+4/X0Vz+wgsvqJTWmECU WfICU+MQlVIZCK6EypUtWzb8M3v27CkpKSHzs2bNGvWscGMCEydODCulCzoF zFOkHfMCs6U9dcG5RFGOCTz//PPYHYwfPx6PDBkyBGVv0aJFBI9O1bjrdvLk SYwAQMlcsmQJrmA5ffr0M888g4p9+umnKvFPP/2EBzt27HjhwgU8+P333+PB /v37e2xK2OiUmYMHD2b/FxwsOVunLl26oI2dO3dW7wvMmDEjU6ZMcDBnzpx/ /fWXF/lPPHQ8kqj1l7PtZhEl44voTxMdv5QrV87nD/5v3boVj5w6dapixYo+ /4xSLWFdv349OqtChQrnzp277h8qlChRwuf/wX3//v2YDGagZcuWhYMtW7Y8 ceLEdf8PB5CHIkWK4OVJSUkumYn48h9++ME6obDGBKLMkheYGocgr732GhpS v359XPYABo4ZMwZm1sOHDw+ZmYsXL959991KOmtMAKTbE4h169bhhP2BBx7A EqKfMhiaBcxTpB3zArOlPXXBuUSRjQkcPnwYA1Bvvvmm9Tg6IkuWLNymbyF1 gy6ydu3ax44dsx6EzhRjBU8++aQ62KlTJ58/qHv58mVrYqjOcLxRo0ZGM24A /TgSUqxYsYCt05UrV6Aj/u6772zHVaxgxYoVUWaVCSE9ksD1l7PtZhEl44vo T5OQfvn7779hwgUu6NOnj/X4ggULsCPbvn07HmndurVzdqbmcdZfcvfu3fvT Tz/ZHjRhwgRMOXDgQPc8R3D5kSNH8K2BKlWqOGMC0WfJOKbGIQDMu2+44QY4 26BBA9uChzNnzujcvGvXrjglqVy5si0mEIyWLVv6/C+YhHw3QT+lfgHzDmnH vMBgaU91cC5RZGMC/fr1w1Zl0aJF1uOTJ0/G46NGjYrg6amXcGuoAlfLFypU SB3BBj9nzpy2lM2aNfP5dxWIIpue4HXrBGUMC9XIkSMjyB5DQnokgesvZ9vN IkrGF9GfJiH9ArNp1H/w4MHW4zCJxuO//vrrdf/7+Dly5PAFesm3TJkyPv8y V/ecLFu2DG/YvXv3CAxxvxzHzxkyZJg/f37AmIAXWYoGg+MQHIMBmzdvjiAn q1evxilJMz86MYHly5fjL7kDBgwwlTL6AmYEace8QGICLgkSuESRjQm8+uqr Pv8quCtXrliPnzt3DhvDDh06RPD01EvEMYHy5cuDXNBEqyM///wzllvbDe+6 6y7sZaLLqXm8bp2mT5+OgkCNjiR//AjpkQSuv5xtN4soGV9Ef5ro9Hf4Kvpj jz1mXdr9448/YkeGL7Tu3r0b/3S+D/j+++/jqb///tvlKb1798Zkan1sWLhc Dkfw1CeffLJjxw79mECUWYoGg+OQUqVKwanq1atHkI2LFy/iWxV58uQ5fvz4 yy+/rBMTqFChgs+/wj/kPgz6KaMvYEaQdswLJCbgkiCBSxTZmEDNmjVBWGj6 nKdwY4fGjRtH8PTUS2QxgdOnT2PI97XXXlMHz58/jy8U5MyZc+HChXhQLTtU R+jgdevUvn17tD02r78lACE9ksD1l7PtZhEl44voTxOd/q5Xr17YZ8HoFPej g9HpY489BkceeughTLN8+XJMM2XKFNvlw4YNw1N//vlnwPvD3caNG4d7/t9+ ++3h7jzsfvmRI0dg7AGnKleuDCm3b9+uExOIMkvRY3AcglsYffDBB/jnoUOH Vq5cqfnWgHrV8eeff4Y/GzVqFDImsHjxYrzk+++/d7+5fsrr0RUwg0g75gUS E3BJkMAlimxMALdPCfhhC/zcXmQh1tRLZDEBtcRl9OjR1uPQ8mfJkgVP1alT Z8yYMdhHv/jii8ZybA5PWyfoiG+77TZIX6RIkQjzx4+QHkng+svZdrOIkvFF 9KeJTn936dKlevXqYQ+eL1++zz77DPesy5gx49q1azHN1KlTMYFtgSswadIk PLV06VLr8QMHDrRr1w7uXKhQIUwA3ejOnTs1c655OQw54NRNN92E3z52jwlE mSWDmBqHHDt2DA0ZPnw4DL1wfSYCswwYm7ncMzk5Wb01gEd0YgINGjSANLlz 51Z7Skef8npEBcwLpB3zAokJuCRI4BJFNiaAwZa6des6T+FWeNCQRvD01EsE MYFdu3ZhOLpUqVK27QRhRAHTf99/qVChQgwWekWAp63TW2+95TIgEQIS0iMJ XH85224WUTK+iP400ezvVq9ejfvUWbH+Yqt+q92wYYPtWrUs0PYL76pVq6x3 u/322zdt2qSfc53LoZ/Fs+p1dfeYQJRZMoipcQjM61UEAP+TJUuWzJkz4//T pEkzc+bMgDeEeTq+p1+wYEH8VMF1jZjAwYMHsZyoZQnRp0QiKGBeIO2YF0hM wCVBApcosjEBaPlB2GeffdZ5qmrVqjiBjeDpqZdwa+jVq1erV6+OzfLs2bOt p86dO/fQQw9hT9S6deu8efNisrRp03br1s1wvk3gXeu0aNEi6IIhMRSq2H/v OPUS0iMJXH85224WUTK+iP400envfvzxR5y+PfnkkzVr1sReDChRooT66MC3 336LB9esWWO7fM6cOXhqxowZ1uO7d++uX78+dJ358+dXU1QYFWh2jiEvP3z4 8C233OLz/8SmDrrHBKLMkkFMjUPUhk7oLxiEXL58GcwB8/ELgEWLFg34M33n zp3RfJhxq4MhYwKDBw/GZ4WMpeinRCIoYF4g7ZgXSEzAJUEClyiyMYFKlSr5 guyBX7x4cThVu3btCJ6eegm3hrZt2xbbZOeLLdiJQNe8atWq6/4ta6AvyJ07 N6bv1auXwWwbwaPW6Y8//sBvIWXKlGn9+vVRZZEZIT2SwPWXs+1mESXji+hP k5B+gSkbLiBv2rQpbnIFR2CAij14vnz5Tpw4AQd/+eUXPIKfIbAybtw4PJWc nBzwETBFnTt3brly5TDZiBEjwjIh2OUqk0lJSYf+ZcmSJXgQxiHwJ26PYDxL 0WNqHKJewy9VqtThw4etp9ReAU6/wGgNP0BZr169QxbwRYzChQvjn85sNGzY 0Of/Aci6HWVA9FMi0RQwg0g75gUSE3BJkMAlimxMAPuO0qVLO09hnNm6aR4H wqqhX3zxBTbIFSpUsG3Fs2HDBjz15ZdfWo/v2bMH39e76aabjhw5YirbRvCi dTp27BgmAyZOnBhtFpkR0iMJXH85224WUTK+iP40CemXF154weffItj2SmD3 7t2xR2vfvj38uWbNGvxz0qRJtjsMGjQIT7lvqwszVlwlmz9//ggMsV2+efNm nwb333+/d1mKBlPjEDABLf3uu+9sp5TLnF9VUNtHuDNv3jzbhbi+4tFHHw2Z Yf2UttxGXMCMIO2YF0hMwCVBApcosjGBFi1a+Pwb5vz111/W49DCYFPTpUuX CJ6eetGvoZMnT8ZvDUDX6WyQhw8fjgLi54qsjB49Gk9Nnz7dSJ5NYbx1gkKF K3x82q/OCVZCeiSB6y9n280iSsYX0Z8mIf2Cr/s1b97ceapIkSJwqnz58tf9 u9mjm7p27WpL9vrrr/v8q9AvXbrknpkmTZrgTXDtQbhYL9+6davOrLZixYqe ZiliTI1Drl27BnUKTr333nu2U6pm2X6yAZwbQAXEtheB+lxgx44d3XOrn1Jh pIBFj7RjXiAxAZcECVyiyMYE1EY0ti1Khg4dised4dDERlM3mM7ja4ZZsmQJ uGoLv2GULl268+fP206prW+GDx9uJM+mMNs6/fPPP48++iha+tJLL2kukxOs hPRIAtdfzrabRZSML6I/Tdz9AjPKG2+80Rckml27dm04lTdvXvwTF7LavpkF d8CogvUX+WCv57/yyivo66NHj7rkWfPy06dPn3Sg1tLDwAP+VLvnRZkl4xgc h1SoUMHn34jMZqN6jcK5zeCFCxec0gHPPPOMz//VJPzTNgdXexeE/Lagfkor +gXMO6Qd8wKJCbgkSOASRTYmABO37Nmzg7Y1atRQszZo7ipWrAgHCxUqxG0q p6Mb9CP49d6MGTMGSwxlFQvtyJEjbaf69++Pp5KSkgzk2BwGW6fz58+rrReh M7WtvRQ0CemRBK6/nG03iygZX0R/moT0C0y1fP630W0vBv7111/58uWDU088 8QQe6dOnD3Z2MNlUyaZNm4YHv/32WzyyevVqmMQ556FHjhy59dZb0dfqIDx0 zJgx48ePVz8rhHW5k4B7DEZ5Ty8wOA6BeTeaDL6wHm/WrBke37dvn+ZT3PcY HDFiBN5w7ty57vcJmdLp9+vaBcxTpB3zAokJuCRI4BJFNiYAtGrVChuWtm3b njlz5tSpU82bN8cjPXr0iODRqZqQus2ePRt/PgA6deoEf/7000/TLCxcuPC6 /6MD+NZY5syZoWNSYeqpU6fihwsLFCig82naWKJTZlasWLHsX9BAaKPUEfzp AeyqWbMmSpQ1a1YYb8yYMWPafwm4S49gQ8cjiVp/OdtuFlEyvoj+NAnpF7Vf UK1atdTrgceOHVM7+H311Vd4ELoz3I0Q5tErV66E7h7mbtmyZYMjN998MwwG MBl+FC9NmjTg7lmzZkF3efnyZcjDPffcgzd899131dPbt2+PB9VChbAudxIw JhDlPb3A1DgEuHLlCs6hsmTJAuM0mEHAkS+//BLf+gz4jbNguMcEPvroI5Rr 9erV7vcJmdLp9+vaBcxTpB3zAoOlPdXBuURRjgmAzlWqVPH9i/raTo0aNZzr 3hMed91gtotvqLlw9913Y+KkpCRcTuDzLzJ84IEHChcujH/C8eXLl8fIJG10 ygx0QC62f/7555AGqqq7RMDUqVNjYVIqR8cjiVp/OdtuFlEyvoj+NAnpF5h5 qek/dNnQs1esWBFD+sBzzz1nXZEOE23cr97qvhtvvPGXX36xPjFHjhzKyzAz xYkeUrVq1YsXL6rEuO4dgJFDBJc7CRgTiPKeXmBqHILA3Dlnzpx4/CY/+P/c uXMfPHhQP1fuMYG33noLb+vcPyrclE6/IzoFzFOkHfMCs6U9dcG5RFGOCVz3 y/7000+rCSxMexs2bJiqBY+YkDEB1SwHo1KlSir91q1b8TU0K7Vr196yZUss jAmT6Funfv36Xbd86MeFmHVkqRrNWpyQ9Zez7WYRJeOL6E8THb9cvnx5yJAh 6gvCSK5cuQYNGuTc1W38+PH4QW2kaNGizm7u6NGjrVq1wk2zFVmzZu3Zs+ff f/9tTdm3b188O2DAgAgud7Jnzx5Mb9u+Ppp7eoGpcYhi586d9913H64NQKCi 2b5OGJKmTZv6gmyBfv3fzwsCttdMIkgZ0O+ITgHzDmnHvMB4aU9FcC5RxGMC CLRRq1atWrp0KbU17bEkAt1CkpKSkpycPGfOHPg32EeBKeCF7UI0hOWRBKu/ nG03iygZX0R/muj75erVq7t37164cOH8+fN37drl/hIrzEAhpfvvxZcuXVq/ fv2sWbPmzZu3ffv2YPvtbN68eePGjRFfHhZe3DMyPBqHwNDrt99+gzufPn3a +M3NEszviE4B8wJpx7yA86ibc4lKFTEB4Tpv3TjbThPOHuFsu1lEyfgi+tNE /EIT8QtNxC9ewFlVsV1iAvThrBtn22nC2SOcbTeLKBlfRH+aiF9oIn6hifjF CzirKrZLTIA+nHXjbDtNOHuEs+1mESXji+hPE/ELTcQvNBG/eAFnVcV2iQnQ h7NunG2nCWePcLbdLKJkfBH9aSJ+oYn4hSbiFy/grKrYLjEB+nDWjbPtNOHs Ec62m0WUjC+iP03ELzQRv9BE/OIFnFUV2yUmQB/OunG2nSacPcLZdrOIkvFF 9KeJ+IUm4heaiF+8gLOqYrvEBOjDWTfOttOEs0c4224WUTK+iP40Eb/QRPxC E/GLF3BWVWyXmAB9OOvG2XaacPYIZ9vNIkrGF9H//2XvvKOkqNL33wRxBEki ipIERQEFJSkmUBYWlMUfIqC7i7rAVxRWdA+ICxJdVnDFRZKE1QVEGSWDIMgQ DoKMMHNIIkkXiTNDzsIMYfi9p99jnbZ7uru6+tbUO/0+nz84zK1b4X2eW3Xv fbuCTOCLTOCLTOCLG2hWFbEjJyAfzbppjl0mmh3RHLtZoKS3QH+ZwBeZwBeZ wBc30KwqYkdOQD6addMcu0w0O6I5drNASW+B/jKBLzKBLzKBL26gWVXEjpyA fDTrpjl2mWh2RHPsZoGS3gL9ZQJfZAJfZAJf3ECzqogdOQH5aNZNc+wy0eyI 5tjNAiW9BfrLBL7IBL7IBL64gWZVETtyAvLRrJvm2GWi2RHNsZsFSnoL9JcJ fJEJfJEJfHEDzaoiduQE5KNZN82xy0SzI5pjNwuU9BboLxP4IhP4IhP44gaa VUXsyAnIR7NummOXiWZHNMduFijpLdBfJvBFJvBFJvDFDTSritiRE5CPZt00 xy4TzY5ojt0sUNJboL9M4ItM4ItM4IsbaFYVsSMnIB/NummOXSaaHdEcu1mg pLdAf5nAF5nAF5nAFzfQrCpiz/+cAAAAAAAAAAAAACSAnAAAAAAAAAAAAKAT PDsgH826aY5dJpod0Ry7WaCkt0B/mcAXmcAXmcAXN9CsKmJHTkA+mnXTHLtM NDuiOXazQElvgf4ygS8ygS8ygS9uoFlVxI6cgHw066Y5dplodkRz7GaBkt4C /WUCX2QCX2QCX9xAs6qIHTkB+WjWTXPsMtHsiObYzQIlvQX6ywS+yAS+yAS+ uIFmVRE7cgLy0ayb5thlotkRzbGbBUp6C/SXCXyRCXyRCXxxA82qInbkBOSj WTfNsctEsyOaYzcLlPQW6C8T+CIT+CIT+OIGmlVF7MgJyEezbppjl4lmRzTH bhYo6S3QXybwRSbwRSbwxQ00q4rYkROQj2bdNMcuE82OaI7dLFDSW6C/TOCL TOCLTOCLG2hWFbEjJyAfzbppjl0mmh3RHLtZoKS3QH+ZwBeZwBeZwBc30Kwq YkdOQD6addMcu0w0O6I5drNASW+B/jKBLzKBLzKBL26gWVXEjpyAfDTrpjl2 mWh2RHPsZoGS3gL9ZQJfZAJfZAJf3ECzqogdOQH5aNZNc+wy0eyI5tjNAiW9 BfrLBL7IBL7IBL64gWZVETtyAvLRrJvm2GWi2RHNsZsFSnoL9JcJfJEJfJEJ fHEDzaoiduQE5KNZN82xy0SzI5pjNwuU9BboLxP4IhP4IhP44gaaVUXsyAnI R7NummOXiWZHNMduFijpLdBfJvBFJvBFJvDFDTSritgl5wQuX76cnp4+cuTI 0aNHb9q06cqVKw52mgDY1y0rK+uLL74YPHjwlClTSLqgpUePHt0UjQsXLpgP IA5ibTOZmZkUBf0brsKlS5fWrVs3ws+iRYtIEwNHqQnjjhQgNMduFvtKohdw A+gvk6i+fP/995F78D179liVt27dGq7axYsXg7YcZ8+YnZ2dmppK7eSdd96Z MWNG4GEEcfbs2ZSUlOHDh48ZMyYtLS0nJyemHXkCzheZwBc30DzO0dyihOcE duzYUbFiRV8ANWrU2L9/v4P9FnTs6LZ+/fratWv7fkurVq327t1r1aGm64vG Rx995G4wMWKzzdAwY8mSJZ06dSpatChF8dhjj4XWoVFQz549S5QoERhv6dKl J02aZP64ExeDjhQ4NMduFptKohdwCegvk6i+lC1bNnIPTn5ZlZOSksJVmzVr llUtzp6RVu/bt2+xYsUCVy9SpEiXLl1Onz4dVHnp0qW33npr0AGH/n4hDZwv MoEvbqB5nKO5RUnOCWzfvv3mm29mqWvWrHnHHXfw/2+77bZ9+/Y52HWBJqpu 06dPt3p/0u3BBx8sVaoU/1mrVi0rD28nJzBjxoz8CMk2dtpMZmYmX5QsHn30 0aA6x44do0sWLy1cuHDDhg3vuusuq/60adPcCiDhMOVIQURz7GaxoyR6AfeA /jKJPydAPT7XvHDhgp2OPs6ekS539evX58qFChWqXbu21WaItm3b5ubmWpVX rFhBdai8WLFiTZo0adSoEV8qr7vuukWLFsUlnMvgfJEJfHEDzeMczS1Kck7g 6aef5i4mOTmZS8aNG8eyd+vWzcGuCzSRdTt+/DhnAKhlrlmzhu9gOXnyZJs2 bVixd999l2ueOXNmb15s3ryZOmWq+fDDD0u7AcZOm8nIyCjzKzSqyfPq1L9/ f1ajX79+1l2RCxcuLF68OBWWK1fu3Llzbhx/4mHKkYKI5tjNYkdJ9ALuAf1l EtWX/fv359mJ9+jRg635+uuvuSZdiLjkvffeC63/yy+/cLU4e0YaVNSpU4dq du/e/dixY1RCQwgKoVq1arzZ1NRUrpmdnV2jRg0qKV++/IYNG7gwPT29QoUK VFi9evXLly87F85lcL7IBL64geZxjuYWJTYnkJWVxQmol19+ObCcjShZsqS2 6VtU3ajbbd269ZEjRwILqYPmXEGrVq0ib596c6pGY4Cffvop/qM1i/08EnP7 7bfneXWi8UbXrl0nT54cVG6NiNatWxfnoSrBlCMFEc2xmyWqkugFXAX6yyTW KwyzZ88ensK/9NJLVuEPP/zAXRtN8COsG3/PuG/fvvnz5wcVfv7557z66NGj uWTNmjVcMn78+MCaX375JZd/9tlnkXfkIThfZAJf3EDzOEdzixKbExgxYgT3 EatWrQosnzVrFpdPnTrVwd4LLs7GCQTfE1i1atUIdb777jvO8o0aNcrZ4bmK 21cnamPcqKZMmeLg8BSC/sJ+/USK3SxRlUQv4CrQXybO+vonnniCHKlSpcqZ M2eswtWrV7NTaWlpDo4kzp5x7dq1vPrgwYO55MMPP+SSQ4cOBVW+++67qbxp 06YOdpQ/4HyRCXxxA83jHM0tSmxO4MUXXyRhy5QpE3Qv2dmzZzk/06dPHwd7 L7g4zgnUq1eP5KION0KdBg0aUB36N/C5Pzm4fXVasGABn8iB71wCEUB/Yb9+ IsVulqhKohdwFegvEwd9vfU7O/VlgeVW1+bsEdc4e8Zhw4bx6tbttf369fP5 b7gNHWm88sorvt++HVEaOF9kAl/cQPM4R3OLEpsTaNmyJQlbp06d0EX8YodO nTo52HvBxVlO4OTJk3wDQNeuXcPVsX5K+PTTT+M6RNdw++rUq1cvVuDAgQNO jk8f6C/s10+k2M0SVUn0Aq4C/WXioK9v1qyZz/82oaC59pQpU7hr+/LLL996 663OnTsPHDiQZujnz5+3s1nHPSMNladPn86fIahcubK1uwkTJoTb4NChQ33+ NxyGfiFRCDhfZAJf3EDzOEdzixKbE6hbt64vzIct+HN7zZs3d7D3gouznIB1 i8snn3wSrk7Hjh19/nf+ZGdnx3WIruHq1enUqVP8XaRq1ao5PD59oL+wXz+R YjdLVCXRC7gK9JdJrFeYbdu2cS8/cuTIoEVU4ssLmqcH3VEQioOe8eDBg6+/ /nr79u2rVq3KO6Lr3u7du60Ky5cv53LraQImJSWFX3FMBNYXBc4XmcAXN9A8 ztHcosTmBDjZ0q5du9BF1Opo0T333ONg7wUXBzmBn3/+mV86VKtWrUuXLuVZ JyMj45prrqE6b731loGjdAdXr058y6JP9tuNpIH+wn79RIrdLFGVRC/gKtBf JrFeYfhzA9TXnzx5MmiRlRMgv95888033niDnyUkrr322q1bt0bYrIOeMS0t LSjz8MMPPwRWyMnJ4e8O0Khj2LBhNP3fvHnz0KFD6WCstajEfuz5Cc4XmcAX N9A8ztHcosTmBKg3IWGfeuqp0EWNGzf2+R9+d7D3gkusZ+iVK1eaN2/OneyS JUvCVRs7dizXCeq7ReHe1WnVqlX8rWRqVDLfpSAT9Bf26ydS7GaJqiR6AVeB /jKJ9Qpz4403khft27fPc2lycvKyZcusP2lgMGDAAO70mzRpEm6bznrGPXv2 dOjQga51FStW5F3QRgYNGhS4BTqYpKSkoPsWypYt+9xzz/H/jx8/bnN3+QzO F5nAFzfQPM7R3KLE5gQaNWpEwj7yyCOhizjP3Lp1awd7L7jEeoa+9tpr3MNG frDl2Wef9fm/nUFDhXgP0TVcujr9+OOPPJoqXrz4li1b4jpEZaC/sF8/kWI3 S1Ql0Qu4CvSXSUxXmB07dnBHP2LECJurUF9/33330So0Nw96RxYTf8+Ym5u7 dOlS3gsxadKkoO23a9euUqVKPv8XkV566aWdO3dypoKGIg52lz/gfJEJfHED zeMczS1KbE7gqaeeImFr164duuiGG27wRXxpXkIS0xlq3THYoEGDyG8T4nz+ 448/buAQXcONq9ORI0e4GjFjxox4D1EZ6C/s10+k2M0SVUn0Aq4C/WUS0xXm o48+4l7sm2++sb+L3r1781o0GQ9aZLBnzMrK4ptsw31N4MKFC9b/X3jhBeH3 3OJ8kQl8cQPN4xzNLUpsTqBbt26cxz537lxg+YEDB7i36t+/v4O9F1zsn6Gz Zs3ibw1Qdxz5dcF79uxhMd98800zR+kOxq9O1Kj4Dh+f7BcpiAX9hf36iRS7 WaIqiV7AVaC/TGK6wvBXsajHD/IoMtbjA0EP7xvvGZ9//nne2rFjxyJUu3z5 cpUqVYT/vobzRSbwxQ00j3M0tyixOYHPPvuMtZ0zZ05g+fjx47k8JSXFwd4L LjZ1W7BgAb8zsGTJkunp6ZErWx81FvsVQsbs1en8+fOPP/44B/6nP/1J8kMT YkF/Yb9+IsVulqhKohdwFegvk5iuMPXr1ycj6CIT0y6efPJJWqtYsWKBH/6L p2cM984B/vWfOHz4cITVZ82axdU+/vhj+zvNZ3C+yAS+uIHmcY7mFiU2J0Dd U5kyZUjbFi1aWH0T9V8NGzb0+Z9B0zaVs6PbokWL+IvASUlJdkSeNGkSN+Cl S5caOUiXMHh1unDhgvXqxTZt2oT7HAOIDPoL+/UTKXazRFUSvYCrQH+ZxHSF ueWWW8iLBx98MHTRxo0b69atu2nTptDt8/sDA1+EZb9npFYxbdq05ORk687/ DRs2VKhQgYYfQTUPHTp00003cVOxCrOzs4NuTjhx4gR/24uqBeYopIHzRSbw xQ00j3M0tyixOYGrv35hh3jttddOnTpFHUeXLl24ZMiQIQ52XaCJqtuSJUus D/r07duX/pw/f/68AFauXBm0yttvv831qU938dDjxk6bWbdu3dpf4Zck0DXK Kjl9+vRV/2ikZcuWHHKpUqVoDLNw4cJ5vyUzMzM/QirgmHKkIKI5drPYURK9 gHtAf5nYHyPRyLNIkSI8iw9dyl/KTkpKGjRo0OrVq2kKT1eeSZMmUd/n838R wPogUUw9Y69evbim9XBBnTp1eIPUWhYvXkx7uXTpEoVw7733cs3evXtzzdzc 3A4dOhQtWnT48OFZWVm03zVr1tSsWZOrTZgwIU7pXAXni0zgixtoHudoblGS cwKk8wMPPOD7Fc5sc2Ym8NU0SoisG/Wtod/3CaJu3bpBa1kfIN63b5+7Rx8f dtrM9ddfHyH2999/n+rQqRpZImLu3Ln5EVIBx5QjBRHNsZvFjpLoBdwD+svE /hjp0KFDbEfnzp1Dl86ZM6d48eKWd0WKFLHsI/r06WPVjKlnbNCgAZc8/PDD 1gGXLVvWqlm4cGGa9Vt/Nm7cOCcnh2vSSIPvHLAOyfr/3/72tzw/giAHnC8y gS9uoHmco7lFSc4JXPXL/oc//IHvh/f5M97PPvtsgRbcMVFzAoHda540atQo aC3+ECER+dsEnhP/1Ym/09S/f//IEhFfffVVfoRUwDHlSEFEc+xmsdkXoBdw CegvE/tjJOtDhOHeErx///4uXbrwjQEWt99++8KFCwOrxdQz/utf/+KSUaNG WVs4fPhwjx49+J3bFrTff/7zn7/88kvgvrKysgKbE1GpUiXhbzRicL7IBL64 geZxjuYWJTwnwNCMNS0t7dtvv6WZr4M9JgYOdEsYNMcuE82OaI7dLDEpiV7A ONBfJsavMPwI//Lly1euXJmRkRH/Brdt27Z169bQ8osXL27ZsmXx4sUpKSm7 du2K8FKCnJyc9evXUzUjx5M/4HyRCXxxA83jHM0tqkDkBMBV3bppjl0mmh3R HLtZoKS3QH+ZwBeZwBeZwBc30KwqYkdOQD6addMcu0w0O6I5drNASW+B/jKB LzKBLzKBL26gWVXEjpyAfDTrpjl2mWh2RHPsZoGS3gL9ZQJfZAJfZAJf3ECz qogdOQH5aNZNc+wy0eyI5tjNAiW9BfrLBL7IBL7IBL64gWZVETtyAvLRrJvm 2GWi2RHNsZsFSnoL9JcJfJEJfJEJfHEDzaoiduQE5KNZN82xy0SzI5pjNwuU 9BboLxP4IhP4IhP44gaaVUXsyAnIR7NummOXiWZHNMduFijpLdBfJvBFJvBF JvDFDTSritiRE5CPZt00xy4TzY5ojt0sUNJboL9M4ItM4ItM4IsbaFYVsSMn IB/NummOXSaaHdEcu1mgpLdAf5nAF5nAF5nAFzfQrCpiR05APpp10xy7TDQ7 ojl2s0BJb4H+MoEvMoEvMoEvbqBZVcSOnIB8NOumOXaZaHZEc+xmgZLeAv1l Al9kAl9kAl/cQLOqiB05Aflo1k1z7DLR7Ijm2M0CJb0F+ssEvsgEvsgEvriB ZlURO3IC8tGsm+bYZaLZEc2xmwVKegv0lwl8kQl8kQl8cQPNqiJ25ATko1k3 zbHLRLMjmmM3C5T0FugvE/giE/giE/jiBppVRezICchHs26aY5eJZkc0x24W KOkt0F8m8EUm8EUm8MUNNKuK2JETkI9m3TTHLhPNjmiO3SxQ0lugv0zgi0zg i0zgixtoVhWxIycgH826aY5dJpod0Ry7WaCkt0B/mcAXmcAXmcAXN9CsKmLP /5wAAAAAAAAAAAAAJICcAAAAAAAAAAAAoBM8OyAfzbppjl0mmh3RHLtZoKS3 QH+ZwBeZwBeZwBc30KwqYkdOQD6addMcu0w0O6I5drNASW+B/jKBLzKBLzKB L26gWVXEjpyAfDTrpjl2mWh2RHPsZoGS3gL9ZQJfZAJfZAJf3ECzqogdOQH5 aNZNc+wy0eyI5tjNAiW9BfrLBL7IBL7IBL64gWZVETtyAvLRrJvm2GWi2RHN sZsFSnoL9JcJfJEJfJEJfHEDzaoiduQE5KNZN82xy0SzI5pjNwuU9BboLxP4 IhP4IhP44gaaVUXsyAnIR7NummOXiWZHNMduFijpLdBfJvBFJvBFJvDFDTSr itiRE5CPZt00xy4TzY5ojt0sUNJboL9M4ItM4ItM4IsbaFYVsSMnIB/NummO XSaaHdEcu1mgpLdAf5nAF5nAF5nAFzfQrCpiR05APpp10xy7TDQ7ojl2s0BJ b4H+MoEvMoEvMoEvbqBZVcSOnIB8NOumOXaZaHZEc+xmgZLeAv1lAl9kAl9k Al/cQLOqiB05Aflo1k1z7DLR7Ijm2M0CJb0F+ssEvsgEvsgEvriBZlURO3IC 8tGsm+bYZaLZEc2xmwVKegv0lwl8kQl8kQl8cQPNqiJ25ATko1k3zbHLRLMj mmM3C5T0FugvE/giE/giE/jiBppVRezICchHs26aY5eJZkc0x24WKOkt0F8m 8EUm8EUm8MUNNKuK2OXnBDIzMzdt2kT/OthjYmBftwMHDsycOXPw4METJkxY sWLFpUuX8qyWnZ2dmpo6cuTId955Z8aMGXv27DF4tGaJtc1EbS2kybp160b4 WbRo0dGjRw0cpSaMO1KA0By7Wewrefny5fT0dLpYjR49msS8cuWKy4emAvv6 Z2VlffHFF9SnTJkyhYyIUPPw4cOzZ88eMmTIvHnzjh8/bvNIIm+frs+bonHh woXAVc6ePZuSkjJ8+PAxY8akpaXl5OTYPBIjq8eJG1cYZ75s376dHKG15s6d a/MKtn//fnbkxIkT4erQ6bxly5bNmzfn5ubaPBLG20GLWV+2bt0arjFfvHgx 8pbt+BLT9o2MiDIyMqh1vf3221OnTt2xY0es5joG/YgbaFYVsYvNCVDXvGTJ kk6dOhUtWtTn8z322GMO9pgY2NFtw4YN99xzj++33HHHHQsXLgysRj1C3759 ixUrFlitSJEiXbp0OX36tIsxOMVmm7HTWij2nj17lihRIjD20qVLT5o0yfxx Jy4GHSlwaI7dLDaVpOFlxYoVA0/YGjVq0NTD/QNMcOzov379+tq1awf1Ka1a tdq7d29o5d69ewdWK1So0LBhw+LfPg23fNH46KOPrPpLly699dZbA5dS+4mc yggkztXjx/gVxoEvZ86c6dy5c5DIf/3rX8P9xMAcPnz4xhtv5MqfffZZaIWd O3eOGjXq9ttv5zqrVq2KGiYjYdBi1pekpKRwjXnWrFnhNm7fF5vbNzIiOn/+ PMUbtJf77rvvp59+sr8Rx6AfcQPNqiJ2mTmBzMxMvqhaPProow72mBhE1W3s 2LHXXHMNC1WmTJl69eoVLlyY/6SedN26dVyNVK1fvz6X09iAxmM333yzpXDb tm3zLbtrHzttxk5rOXbsGHXQvJTEadiw4V133WXVnzZtmlsBJBymHCmIaI7d LHaU3L59u3WBqlmz5h133MH/v+222/bt25cvh5mwRNV/+vTp1syCXHjwwQdL lSrFf9aqVSvo13OaWfAiml80btz4uuuu4z//8Y9/xLl9OzmBGTNmcOUVK1ZQ v8a9XpMmTRo1asRnIh3PokWLomoS5+pGMHuFceALjQGaNm3K1apWrdqyZcsK FSrwn82aNYvwK/YzzzxjHU9oToCmrkGukdqRw7SClTBoMejLhQsX7DTmIOz7 YnP7RkZEge7QRugqXbp0af6TBqJnzpyxs5F4QD/iBppVRewycwIZGRllfoWn t5rH1VF1mzp1KklUqVKlZcuW8R0sWVlZffr04Yb6+9//nqvRJbpOnTpU0r17 d+oRqIQq05arVavGNVNTU92PJjbstBk7raV///4cY79+/ay74xYuXFi8eHEq LFeu3Llz59w4/sTDlCMFEc2xm8WOkk8//bTPPxFITk7mknHjxvFZ3K1bN9cP MaGJrP/x48d5hk6jnTVr1nCfcvLkyTZt2rD+7777rlV5y5YtXNigQYOzZ8/y 6nfeeafP/2PugQMH4tk+9Vl782Lz5s08w3344Yd59ezs7Bo1alBJ+fLlN2zY wKunp6fz1Kl69eqXL1+OIEicq5vC4BXGgS/E5MmTea2XX36Zf4Cmf1955RUu pKV5rvXFF18ETjxDcwI9e/bkA+YO12c7JyBk0GLQF6rGR/7ee++FNuxffvkl z43b98Xm9o2MiLp27cob6dChA9+zQe5MmzatRIkSEydOjLp6/KAfcQPNqiJ2 mTmBQPhmM83jaju6TZ8+nXtMi9zc3Jo1a5J0N9xwg1W4b9+++fPnB637+eef c3sePXq0oUM2RqxtJlxroREd9V+hQxqrZ7TupgCRMeVIQURz7GaJqmRWVhb/ 6EZj4MBy7o5LliyJJF48RNWfplqtW7c+cuRIYCF1MTyXb9WqlVX46quvhk4z rQlpuJ+k7W8/T2iGSNVo/mLdorxmzRre4/jx4wNrfvnll+EmqoHEubopDF5h nPnyu9/9jpZWrlw5Ozs7sLxBgwZUfuedd4Y+Nnvo0CF+auCBBx6IqtWnn34a U07gqoxBi0FffvjhBz7yoOc6I2PfF5vbj39EtHfvXr49tWPHjkF3a5w6dcpu YPGBfsQNNKuK2JETkI8z3YgWLVrwqCDybxxr167lXmDw4MHOjtA93J6FrVq1 imOfMmWKg8NTiOZ5sebYzRJVyREjRvCJGfTc8axZs7h86tSp7h5iQuO4T+H7 jatWrcp/Xrx4sWzZsr68Hp2+++67ff7bKePZfp589913/DvsqFGjrMIPP/yQ GwZNUfM8kqZNm0bYZpyrm8LUFcaxLzfddBMtfeWVV4LKZ86cyfqEHh4PhosV K7Zs2TI3cgJ5ks+DFoNX/tWrV/ORp6Wl2d+gfV+cbd/C/oiI83LEtm3bHOzI COhH3ECzqogdOQH5ONPt9OnT5cuX5xxy5JrDhg3jxmzdCSMHt2dhCxYs4Ngj vNsHBKJ5Xqw5drNEVfLFF1/0+R9KDcpnnj17lrP0ffr0cfcQExrHOYF69eqR +DSv5D/37NnD189///vfQTX//ve/86Jwt0Pb2X6e8G+j9G/gr5P9+vXz+e/n DH3AnG+xrlixYoRtxrm6KUxdYZz5kpOTE26ifejQIV4U+EZHgsYMXD506NCf fvop33IC+TxoMXjlt4Yc9h89jskXB9vP8/Cijohq1apF1Zo3b+5gL6ZAP+IG mlVF7MgJyMeBbnv37m3ZsiVf28eNGxeuGrXq6dOn8xt9K1eufP78+XiP1TRu z8J69erFKoV7vhIEoXlerDl2s0RVki9fderUCV3Er/fp1KmTWwenAGd98cmT J/kH+q5du3LJd999x9fPOXPmBFWeMGECL/rf//7nePuhWD+D0uwyz92FXslp uurzvwMtwivy4lzdFKauMI594ZMrVH+alvILHAYMGGAV0oS0XLlyVHj//ffT WGLXrl35kBPwZNBi8Mo/ZcoUVuDLL7986623OnfuPHDgwOTk5MiB2PfF2fYt 7I+I+M0DtAv+MzMzc/369fn21ACDfsQNNKuK2JETkI993aZOndqlS5dmzZoV KVLE53/ccsyYMaE/fBw8ePD1119v37591apV+fpP8u7evdv8oceNq7Mw6r/4 y1PVqlVzeHz60Dwv1hy7WaIqWbduXV+Yj3nxB+y8/X2qoOOsL7Zum/zkk0+4 ZO7cuVwS+mk5667mb7/91vH2Q+nYsaPP/ybAoAerly9fzusG/ZaakpJivW8/ Qh8X5+qmMHWFcezLI4884vN/kO7kyZNWIUndunVrXuv555+3ytu2bevzf5dh 586d9KerOQFvBy0Gr/wjR4705UXlypUXLFgQboP2fXG2fcb+iOjIkSO82YkT J06bNi3wQ9g0XVq9enXk1U2BfsQNNKuK2JETkI993Z588snAXmDIkCEXLlwI rZaWlhbUWfzwww+GD9oQrs7CrNf25s/7oxIDzfNizbGbJaqSnHJv165d6CLS kxbRKNStg1OAg774559/5l8Ga9WqZX0S3frR+fvvvw+qT5M+XhT6U7X97QeR kZHBrzWzfp20yMnJ4Q8HUIVhw4bRbHHz5s1Dhw699tprrZ6OSsLtPc7VTWHq CuPYF+tX5vr169PMjgSfPXt2kyZNLBHatm3LNanT5BLrrQ6u5gS8HbS4kROg pW+++eYbb7zBz8sQ1Ni2bt2a5wbt++Js+4z9EVF6ejrX5K9C+PxvVytRogT/ v1ChQvnz+U70I26gWVXEjpyAfOzr9v777z/xxBPUBfCddT7/1zO3b98eVG3P nj0dOnQgSStWrGhdwwcNGpQ/n/qNCfdmYatWreKvUTdu3Fhg4GLRPC/WHLtZ oipJY36S7qmnngpdRCesz/84uVsHp4BYW/KVK1eaN2/OncWSJUus8v/+979c uHHjxqBVvv76a15k5/3q4bYfxNixY7lOnvPBZcuWJSUl+X5L2bJln3vuOf7/ 8ePHIxxDnKsbwdQVxrEv1BXyq4mDIBH4pYU9e/a86n/59g033ODz/15m9Z6u 5gS8HbSYvfInJydTY7P+pMY/YMAADoqm+XmuYtMXx9u/GuOIyPoeh8//xipa 99KlS7QWWc/31VSvXj3oTh43QD/iBppVRezICcjHgW6HDh2i7pKv8HRxDvco GV3Dly5det999/G1fdKkSQYO1yguzcJ+/PFH/nxS8eLFt2zZEtchKkPzvFhz 7GaJqmSjRo1IukceeSR0Ef+e27p1a7cOTgGxtuTXXnuN+4ighyW/+uorLl++ fHnQKtOnT+dF6enpjrcfxLPPPuvz/yIZ+kU8hi7s7dq1q1Spks//8YKXXnpp 586dPCGitaIeRpyrx4+pK0w8vpC2o0ePrlevHk3uihUr1qxZsw8//JDmd/yq B35pIQ2JeSOpqamZv2J9z3Hs2LH055kzZ0I3Hv/7BDwZtLh95SfNOaKkpKRw X4my44vj7cc6IrJeWFGrVq2srKzARdbXDO2c+HGCfsQNNKuK2JETkI8z3YjB gwfzxTnoXcFB0CWd74fJn1crx4QbffGRI0e4GjFjxox4D1EZmufFmmM3S1Ql edJRu3bt0EX8A2WE19CBqMTUkq27kRs0aBCUXt64cSMvmjlzZtBaY8aM4UVR X1YWYftB8G/Ejz/+eNRjDnxo7oUXXvDFeEtnnKs7xtQVxogvNJHMycnh///8 88+81uzZs7dt2+azwUMPPRS6TVPfHcjnQUs+XPl79+7NyvDLGSIQzhfH23cw IiL9uf7kyZODFlltLx8+CYF+xA00q4rYkROQj+OcwO7du/ni/Oqrr0au+fzz z3PNY8eOOTlE1zDeF587d47v8PHl9VAqiIrmebHm2M0SVclu3br5/L9q0Qkb WE4TGT55+/fv7+4hJjT2W/KsWbP4h0iagoXOIjMzM9mOgQMHBi36v//7P5// Bu/Ir+uPvP1ArO/rvfnmm3aOnLl8+XKVKlV8Tn++iXP1WDF1hYnflyA++ugj 3mB6evqOHTvs5AQaNmwYuh1TOYGr+TtoyYcrv3V7f0yvrQj0xdn2nY2IcnNz +UGbN954I2iRdYn+4IMP7AfiDPQjbqBZVcSOnIB8ouoW7uEvK4fcvXv3yDX5 pxDi8OHD8R2sYcz2xefPn3/88cc50j/96U/hbkAFEdA8L9Ycu1miKmm9wSzo TWjjx4/n8pSUFHcPMaGx2ZIXLFjA7/QrWbJkuEkH3zAZ9G0m6mgqVKjgC/Nj cUzbt7AeYQ76CmFkZs2axWt9/PHH9tcytXqsGLzCxONLKPxi+WrVqvHrH0+e PHk8BOt+8okTJ9Kfp0+fDt2Og5yAhEFLPlz5+QXRxYoViylXE+RLrNuPZ0TU oEEDn//+mSCDrEdI8uE1g+hH3ECzqogdOQH5RNWtY8eO7dq1C318z3p2YMqU KfTnhg0baDwQeqE+dOjQTTfd5PM/QWnwsI1gsC++cOGC9RqrNm3aRO1DQZ5o nhdrjt0sUZWkwWqZMmVIvRYtWlgjVRrNNmzYkK9USOjFg52WTD0Fv6s2KSkp QuXhw4fzRZXmAlbhvHnzuPC///1vnNu3mDRpEm9z6dKleVbIzs4O+g30xIkT /OkoajBBU6Fp06YlJycHPiNgf3X3MHiFcezLli1bgj5X9PHHH/Na//nPfyIc TPzvGAz1RcigxZQvGzdupBa1adOm0O3z258ivKDMji8xbd/+iCjP88WykhpV YOXOnTtz+f79+8Nt0BToR9xAs6qIXWZOYN26dWt/hR8hpGusVZJn/jmBiazb nDlz+Apcs2bNiRMnbtu2LTc39+jRowMGDChSpIjP/03bgwcPUk3+agx1DT16 9Fi8eDHJSL0Abfnee+/lLfTu3Tv/orKHnTZjp7XQeK9ly5YcZqlSpWiMsXDh wnm/JTMzMz9CKuCYcqQgojl2s9hRki5TfMK+9tprp06doglaly5duGTIkCH5 cpgJS1T9lyxZYn2Gr2/fvvTn/PnzA6+WK1eu5Jp02SxatChVozna+vXrqfeh SSh1OlRy/fXXnz17Ns7tW7z99ttcn+aJoRuk/Xbo0IGOhObCWVlZdMGnw6A+ kVeZMGFCYOVevXpxuXWzdEyru4fBK4wzX6hm8eLF69evn5qaSsODAwcO0LnG 08nKlStbj7HnSbicQEZGhnV4AwcO5DrDhg3jksBPSIT6ImTQYsoX/oJ5UlLS oEGDVq9eTVNsKp80aRKNSTjMcB/dsOmL/e3HNCIK9eWq/7EaTn2ULFmSTl6a ClHJBx98wI8C5fmxNuOgH3EDzaoidpk5AeqzfOF5//33Hey94BJZN+oO+G3M FtaHCLkXWLBggbUd/mwNQ5duHjMwjRs3jtzje4KdNmOntdCpGqEOM3fu3PwI qYBjypGCiObYzWJHSeptH3jggcBLGf+nRYsWQb+XgViJrD/NF0K/yhdE3bp1 rfo0B+T8c6BNNOX/6quvjGyfsT6evm/fvtBtUiH/cMxYx0P87W9/C3rXOt/2 TDz88MMOVncPs1eYWH0h+vbtm6cIVapUyTMVE0i4nAAdUoQDpnCsmqG+CBm0 mPJlzpw5NLUPVNjyhejTp0+4jdv0xf72YxoRhfrCrFmzply5crzoOj/8//Ll y2dkZMQuc8ygH3EDzaoi9oKYExgxYoSDvRdc7Og2e/Zs60UxFk2bNl2/fn1g tcOHD/fo0YNfj2lRqlSpf/7zn7/88ouLMTgl/r6YW4v1fZwIRBgpAQtTjhRE NMduFpt9AXW+f/jDH6wkJ00kn3322QLd7Qohak4gcN6RJ40aNQpcJTk5mT/c zFSvXj3C5dTB9q/++iFCIty3CbKysgJbC1GpUqU8Xz7wr3/9iyuMGjXKweru YfwKE5MvzOTJk/lnbubaa69t06bN0aNHox783r17eZWgjx1EzgmUKFHCqpmn LxIGLQZ92b9/f5cuXfiHe4vbb7994cKFkbdv0xeb249pRJSnL8zu3bsffPBB vjeAoZMo6OuE7oF+xA00q4rYZeYEQCD2dcvMzPz2228XLVqUmpp6/PjxcNUu Xry4ZcuWxYsXp6Sk7Nq1S/KT9Wgz0tDsiObYzRKTkjQHTEtLoysbzSXdPChF uNSSaYKwcuXKPH/HzzdycnLWr19PXVvkXyq3bdu2detWx6u7hBxfDh48uHTp Ujrv8vnuwXC+eDtoMe4Lv7xi+fLl5EtMLc2mL463H45wvjBnzpz55ptvSKKT J0/Gvy/7oB9xA82qInbkBOSjWTfNsctEsyOaYzcLlPQW6C8T+CIT+CIT+OIG mlVF7MgJyEezbppjl4lmRzTHbhYo6S3QXybwRSbwRSbwxQ00q4rYkROQj2bd NMcuE82OaI7dLFDSW6C/TOCLTOCLTOCLG2hWFbEjJyAfzbppjl0mmh3RHLtZ oKS3QH+ZwBeZwBeZwBc30KwqYkdOQD6addMcu0w0O6I5drNASW+B/jKBLzKB LzKBL26gWVXEjpyAfDTrpjl2mWh2RHPsZoGS3gL9ZQJfZAJfZAJf3ECzqogd OQH5aNZNc+wy0eyI5tjNAiW9BfrLBL7IBL7IBL64gWZVETtyAvLRrJvm2GWi 2RHNsZsFSnoL9JcJfJEJfJEJfHEDzaoiduQE5KNZN82xy0SzI5pjNwuU9Bbo LxP4IhP4IhP44gaaVUXsyAnIR7NummOXiWZHNMduFijpLdBfJvBFJvBFJvDF DTSritiRE5CPZt00xy4TzY5ojt0sUNJboL9M4ItM4ItM4IsbaFYVsSMnIB/N ummOXSaaHdEcu1mgpLdAf5nAF5nAF5nAFzfQrCpiR05APpp10xy7TDQ7ojl2 s0BJb4H+MoEvMoEvMoEvbqBZVcSOnIB8NOumOXaZaHZEc+xmgZLeAv1lAl9k Al9kAl/cQLOqiB05Aflo1k1z7DLR7Ijm2M0CJb0F+ssEvsgEvsgEvriBZlUR O3IC8tGsm+bYZaLZEc2xmwVKegv0lwl8kQl8kQl8cQPNqiJ25ATko1k3zbHL RLMjmmM3C5T0FugvE/giE/giE/jiBppVRez5nxMAAAAAAAAAAACABJATAAAA AAAAAAAAdIJnB+SjWTfNsctEsyOaYzcLlPQW6C8T+CIT+CIT+OIGmlVF7MgJ yEezbppjl4lmRzTHbhYo6S3QXybwRSbwRSbwxQ00q4rYkROQj2bdNMcuE82O aI7dLFDSW6C/TOCLTOCLTOCLG2hWFbEjJyAfzbppjl0mmh3RHLtZoKS3QH+Z wBeZwBeZwBc30KwqYkdOQD6addMcu0w0O6I5drNASW+B/jKBLzKBLzKBL26g WVXEjpyAfDTrpjl2mWh2RHPsZoGS3gL9ZQJfZAJfZAJf3ECzqogdOQH5aNZN c+wy0eyI5tjNAiW9BfrLBL7IBL7IBL64gWZVETtyAvLRrJvm2GWi2RHNsZsF SnoL9JcJfJEJfJEJfHEDzaoiduQE5KNZN82xy0SzI5pjNwuU9BboLxP4IhP4 IhP44gaaVUXsyAnIR7NummOXiWZHNMduFijpLdBfJvBFJvBFJvDFDTSritiR E5CPZt00xy4TzY5ojt0sUNJboL9M4ItM4ItM4IsbaFYVsSMnIB/NummOXSaa HdEcu1mgpLdAf5nAF5nAF5nAFzfQrCpiR05APpp10xy7TDQ7ojl2s0BJb4H+ MoEvMoEvMoEvbqBZVcSOnIB8NOumOXaZaHZEc+xmgZLeAv1lAl9kAl9kAl/c QLOqiB05Aflo1k1z7DLR7Ijm2M0CJb0F+ssEvsgEvsgEvriBZlURu+ScwOXL l9PT00eOHDl69OhNmzZduXLFwU4TAAet9OLFi5v8bN++PXLN/fv3c80TJ044 P0TXsB87RTplypQhQ4bMnTs3MzPT5ePSS6ytkbyg1pUYjmiO3SxQ0luM63/p 0qV169aN8LNo0aKjR4/a3HJWVtYXX3wxePBgunpTdx+5ckZGxrx5895+++2p U6fu2LEjNzc3z2qHDx+ePXs29QVU+fjx4zaPxGLr1q20/UGDBk2aNGnt2rXh 9uIGbpwXztQ4e/bs4sWLyc133nmHulSyKbQOCbUpDDQC4TrUEsLVsbhw4ULU 43HcwIwg53w5cODAzJkz6XyZMGHCihUraDuhdez4YkFGp6SkDB8+fMyYMWlp aTk5OfbDJLKzs1NTU2mUTu1kxowZe/bsiWn1OLHvC2YT9tGsKmIXmxOgHr9i xYq+AGrUqEETWAf7Leg4yAnQeIZFu+OOOyJUo9HCjTfeyDU/++yzuI7SHezE fubMmc6dO/t+y1//+tc8u0sQJzZbI400lixZ0qlTp6JFi5Idjz32mPuH5jqa YzcLlPQWg/rTLKNnz54lSpQIvPyWLl2aJtSRN75+/fratWsHXbdbtWq1d+/e 0Mrnz5+nwwiqfN999/30009BNXv37h1Yp1ChQsOGDYsaKUNTs2effTZoL488 8sjOnTttbiFOjJ8XztSgyd0tt9wSuGLx4sXfe++9oGpJSUm+MMyaNYvr0IA5 XB2Ljz76KMLBOG5gBpFwvmzYsOGee+4Jko4GeAsXLgyqaccXZunSpbfeemtg BRp1R03NWYH07du3WLFigasXKVKkS5cup0+ftrOF+LHpC2YTMaFZVcQuMyew ffv2m2++maWuWbMmXff4/7fddtu+ffsc7LpAE2tOYOPGjdwl+aLlBJ555hmr SRfQnEBubm7Tpk05hKpVq7Zs2bJChQr8Z7NmzUKz4iBO7LTGzMxMqwUyjz76 aL4cnbtojt0sUNJbTOl/7Ngxmvjw0sKFCzds2PCuu+6y6k+bNi3cxqdPn27N XKivf/DBB0uVKsV/1qpVK+jHSjqS+vXrW3uhIQHNofjPMmXKnDlzxqpJsy0u pzlX48aNr7vuOv7zH//4R1RNrly58rvf/Y7rly1b9vnnn2/evLk13jt//nzU LcSP2fPCmRorV64kkbnmQw899OKLL1r5gcDJ+4ULF3zhmTFjBlezkxOwKofi uIGZxfPzZezYsddccw1XozZfr149yyOala9bt86qadMXYsWKFYUKFeItNGnS pFGjRnz81E4WLVoUNVjrlKSN1K5d2xqxE23bts2fu2vs+ILZRKxoVhWxy8wJ PP3003ypSU5O5pJx48ax7N26dXOw6wJNTDkBGk3VrVvXujhHyAl88cUXgZ1F Ac0JTJ48mY//5Zdf5hsD6N9XXnmFC2lpPh2oGuy0xoyMjDK/wkOXxJjNaY7d LFDSW0zp379/f77S9uvXz7r/eeHChcWLF6fCcuXKnTt3LnTLx48f5wwAdU9r 1qzhuy5PnjzZpk0b3tq7774bWL9r165c3qFDB/4JklahCRRNdSdOnGhV27Jl C1dr0KDB2bNneUd33nmnz//z5YEDByLHO3/+fF79zTffzM7O5sJPP/2UC//9 739HXt0IBs8Lx2rcd999Pn+iZseOHVxy4sQJmr36/LNR6xZZOgze/nvvvbc3 hF9++YWrnTlzJnQpsXnzZs5RPPzwwxFuu3XWwIzj7flCTJ06lSpUqlRp2bJl LFdWVlafPn14a7///e8DD8OOL9TCa9SoQdXKly+/YcMGLkxPT+efVKpXr375 8uUIwZKtderUoZrdu3c/duzYVf8pSRJVq1aN956amhpZLiPY8QWziVjRrCpi F5gToGsdpytplhdYzkaULFkyf3oBOcSUExg4cCA31/vvvz9CTuDQoUP81MAD DzxQoHMC/MtO5cqVrVEcQwMhKqchUAI85iOKmFojcfvttyfMbE5z7GaBkt5i Sn+aONCEPTT1as19An/BDISmDK1btz5y5EhgIU0uOFfQqlUrq5AmMvwLaceO HYN+fDx16lTgn6+++mrohNeaGke9VaBv374+/0/qQQ+dUdRU/sc//jHy6kYw eF44U4PmjLQKVRg+fHhg+YoVK3jFXbt2cckPP/zAJaH3rtuB5pI+/yMJoU9/ BOK4gZnF8/Plqv/WGp59W9DpULNmTVrrhhtusApt+rJmzRquNn78+MDyL7/8 0uaAcN++ffPnzw8q/Pzzz3n10aNHR17dCFF9wWzCAZpVRewCcwIjRozgq8qq VasCy2fNmsXlU6dOdbD3gov9/mjDhg3cXDv7iZAT4AZcrFixZcuWFeicwE03 3UQH/8orrwSVz5w5k+OKqSsHUdE8m9Mcu1mgpLe4rT/13Xz5nTJlSkwHxndW V61a1SrhySOxbdu2CCtevHixbNmyvrwe4r777rt9/hs7I++ad1SuXLmgcu5J H3nkkRjCcIopXxyrcejQIVZ77NixgeU0AeTy5cuXc8nq1au5JC0tzf4BM999 9x3/kj5q1KhY12UcNzBniD1fWrRowZkf62d9m758+OGHXI0cD1rELaRp06Yx HQmzdu1a3uzgwYMdrB4rUX3BbMIBmlVF7AJzAi+++KLPf5da0M1LZ8+e5Qlv nz59HOy94GJTt5ycHL6b6+abbz569Oif//zncDmB5ORkbr1Dhw796aefCm5O gEIO1wFZY5vI7y8CsaJ5Nqc5drNASW9xW/8FCxbw5TfonWZRqVevHq1FsxKr pFatWlTSvHnzyCvu2bOH9xh6k//f//53XmTdOJ0n1i+kQcrwi906d+4cUyDO MOVLPGrwc+LNmjULvMVu9uzZvJb1wKxlsYNHaPkuPvrX8VPnjhuYM2SeL6dP ny5fvrzPfz9k6KYi+9KvXz+f/4bSUAv40cuKFSvaPxKLYcOG8d6t26pdJaov mE04QLOqiF1gTqBly5YkLE1vQxfxix06derkYO8FF5u6Wbef0diG/vzjH/+Y Z06AJsvlypWjRffffz+16l27dhXcnMDVX5tE165dg8pzcnL4ccUBAwa4eHz6 0Dyb0xy7WaCkt7itf69evbhbifoUfyAnT57kn48Dr+f8qPVbb73Ff2ZmZq5f vz7oqYGr/p+eeY9z5swJWjRhwgRe9L///S/C3i9cuMBPLlD/uHLlSi607pm3 SlzFlC/xqPHOO+9wBRr98vsbaZzQrFkzKmnSpIlVbcqUKdZ4g9zp3LnzwIED aSYY9WWM1g/Zn376qf1Ig3DWwBwj8HzZu3cvD5WJcePGWeU2fbGaQegehw4d 6vO/AjGmVzRTI5k+fTp/hqBy5cpC3smJ2YQDNKuK2AXmBPgVeXl+xoU/XRT1 J4MEw45u6enp1lMDXBIuJ9C2bVuf/9Wy/H2lgp4TeOSRR3z+T/nQeNIqzM7O bt26Ncf1/PPPu36UmtA8m9Mcu1mgpLe4qj9N2PkDZ9WqVYvpqKzbMj/55BMu OXLkCJdMnDhx2rRpgd9io1EZzS6tdefOncvlQbd0Xg14juzbb7+NfAC0wZIl S3Jl6ihpj5w/f+6552IKxDGmfIlHDZoJtm/fnuvccsst7733Hr/jMSkpadOm TVa1kSNH+vKC5oMLFiyIcMwdO3b0+V9tF/QKIPs4bmCOkXO+TJ06tUuXLs2a NePXPhQvXnzMmDGBv/Xb9GX58uVcHnSPZUpKivV9it27d0c9noMHD77++uvU YKpWrcprUdR2VjRCVF8wm3CAZlURu8CcACdb2rVrF7qI3/ZDAwMHey+4RNWN +lZ+BKxKlSrWl2HzzAnQxJ+v29ZzfAU9J2BlxevXr08juoyMjNmzZzdp0sTq Cmlol18HqwLNsznNsZsFSnqLq/pbn32JqU/5+eef+ZaAWrVqWW/5S09PtzIA /B+as1vfdi9UqJD10TTrd8/vv/8+aMvWb/2hP5oHQdNhmv4HTaYaNGgQ+aED g5jyJU41NmzYYH35ziKovjX3pL2/+eabb7zxBj/3QVx77bVbt27Nc8vUQfOW rRs/HOCsgcWDnPPlySefDDRlyJAhFy5cCKxg05ecnBz+7gDZMWzYMJrFb968 eejQoVTH2jiVRD2etLS0wOOpXLnyDz/8YCdqI0T1BbMJB2hWFbELzAnQVYWE feqpp0IXNW7cmPtoB3svuETVzXo0jLp7qzA0J5CVlXXDDTf4/DkuK7Fc0HMC FAi/ZicIGtrxS5Z69uyZXwerAs2zOc2xmwVKeot7+q9atYo/ek6dtf2nxa9c udK8eXO+dC9ZssQqt57x9/kfmqaNX7p0iTZLvRX/mlm9enX+ufm///0vV9u4 cWPQxr/++mteFPlN7GfPnuVkcsmSJV999VX+KJvPfxP1oEGDbAYSJ6Z8iUeN 2bNn87S9VatWLVu2ZDdZf+ujA0xycvKyZcusP8nEAQMGcOXApwwCGTt2LFdw PHN01sDiRM758v777z/xxBM0zee79H3+L6Rv3749sI5NX6hOUlJS0MCJRk1W Wuz48eNRj2fPnj0dOnSgSCtWrMhrUTh0vuSPNVF9wWzCAZpVRewCcwKNGjXy hXnNLyc2W7du7WDvBZfIuqWlpfFdZO3bt88MgJ8RuO222/hPqknNmC/aqamp VjXrkzTUWdOf/PygHOy0GeryRo8eTb0kjRKpo2zWrNmHH35IA0V+NDV/viut B82zOc2xmwVKeotL+v/444/8idvixYtv2bLF/vZfe+017oaCHsa0nouvVatW VlZW4CLr/Tnp6en051dffcV/Wi/Gt5g+fXpgzXBwFv2GG27gF7bn5ORQn8jv cCPeeecd++E4xpQvjtWgqTo/hPiXv/yFX6JFJdbI4ZZbbgn6HF4Q1Bffd999 Pv+DBnl+4P7ZZ5/lrIuzbwQ7bmBxIu18uep/MRRNvTmfUL169cjP74fzhQ6g Xbt2lSpV8vk/9vHSSy/t3LmTEwjkUUzHk5ubu3TpUt4LMWnSpJhWd0ZUXzCb cIBmVRG7wJwAd0C1a9cOXcQ/c4e+UC6xiayb9ehfZD766CM71R566KF8jCw6 MfXF1PHRQI7///PPP3NEs2fPduvgVKJ5Nqc5drNASW9xQ/8jR45wNWLGjBn2 N27d7dygQYOgqU1WVhYvCv2k+8aNG3kRv+Hc+nPmzJlBNceMGcOLIrzA7fvv v+c6H3zwQWD53r17+UHp6667LvSrbcYx5YtjNZ555hmf/y2L1uMbzODBg3mt Xr16RT6k3r17c01+YVEQ/Gvy448/Hj22EBw3sPgRdb4EYvkS9ftKkX0JfADh hRde8Dm9BZpOWL652tlnC2Ilqi+YTThAs6qIXWBOoFu3bj5/PvPcuXOB5dSF 8TWtf//+DvZecImsW+jzj3liPWAYmYYNG+ZjZNGJtS+2sHIgkX8bArGieTan OXazQElvMa4/ddZ856QvxkfFZ82axTd00VQidJaam5vLtze/8cYbQYus8QDP 4jMzM/nPgQMHBtX8v//7P5//luYI71GfOHEirx76BbdPPvmEF0V+dZ4RTPni WA1+YqJLly6hi6pVq0aL6tWrF/mQrNvUQ59Gt76Q+Oabb0YJLATHDcwIcs6X IHbv3s0befXVVyPXjOBLIJcvX65SpYovjp87n3/+ed5R5FtKjBDVF8wmHKBZ VcQuMCdgvQcv6J0248eP5/KUlBQHey+4RNYtOzv7eF60adPG53+TLf9JA4CT J0+GVrNuzqRBEf1pvaJQCI5zAvyGago/6PcOECeaZ3OaYzcLlPQWs/qfP3/+ 8ccf537kT3/6k/3bwmmWzY+ulyxZMlzylr9lT9fzoCeUraferNcM8q2bQV+J orV4nhv5Fjj+AF+RIkWC3th2NeA9h9RF2ozLMQZ9caAGLeVXzOU5S+VP+dDq kQ+JX4JXrFix0JyD9XaIWL9C6LiBmcLz8yXc4/nW/ZDdu3ePvIUIvgQya9Ys 3uDHH3/s7JD4NgPi8OHDkbcQP1F9wWzCAZpVRewCcwJ0wSxTpgxp26JFC+tq Sdexhg0b+vwPPeV/j+AtzubF4b5FGERBf8cgsWXLlqCBHHVnHNR//vMfFw9O JZpnc5pjNwuU9BaD+tO113o9YJs2beznYGkuz+9JS0pKinAwNH/kjc+bNy+w vHPnzly+f/9+Lhk+fDiXrFmzxqpGa3Hhf//7X6uQxhjTpk1LTk62Og4ayHG1 KVOmBB3Av//9b16UmppqMzTHGPTFvhqBPPTQQz7/2xuCHuI4d+7cLbfcQot+ //vfX/U/m1C3bt3ATxNax89PuOf5oq1Jkybx3pcuXZrn3kN9uRpHAzOI5+dL x44d27VrF/q6J+vZAW63MfmSnZ0ddM/AiRMn+FNrNMwOTB2E+rJhw4YKFSpY 6TiLQ4cO3XTTTbwFO3HFSVRfMJtwgGZVEbvAnADRo0cPvtC99tprp06doitV ly5duGTIkCEOdl2gQU4gQoX169cXL168fv36NGCj7vXAgQPUQrj7q1y5svV6 AWAKO61x3bp1a3+FHyClMZJVIu1eFPtojt0sUNJbTOlP04qWLVtyD1KqVCma IyxcuHDeb+E33AaxZMkS66tnffv2pT/nz58fuNbKlSu55uXLl3mGVbJkSapD Iy4q+eCDD/iJg8BvQtGO+P14NCuhfiE3N5emw6VLl6aS66+//uzZs1bNXr16 8a6tH8RpKcdYokSJTz/91PoNdO7cufyFxEqVKvEHDlzF4HlhX41ArHc7PPnk k9ajHEeOHLFeM/jhhx9e/fVL3ElJSYMGDVq9ejVNFWm/NOWnNuDzP5gQ+PEI i7fffps3QjPKPPce6ovjBmYWb8+XOXPm8Co1a9acOHHitm3byM2jR48OGDCA 3y9Nth48ePBqLL7QFjp06EAtZPjw4VlZWXRg1Dxo+7yjCRMmBB5AqC/8bVDa II3VFy9eTHuh0RdJdO+993LN3r17mxA+CnZ8wWwiVjSrithl5gRI5wceeMD3 K9bXcFq0aBF6a1/Cg5xAhAo0nrTaCfePTJUqVcINPEA82GmNNOb0hef999/P lyM1j+bYzQIlvcWU/jQEilCHoWl10JZpAhL6EbQg6tata9Wn2Uq5cuW4/Do/ /P/y5ctnZGQEbpl6MasXsIYN11577VdffRVYjZ9HIB5++GGrMDU11fq+W4UK FWjRbbfdxn9S+XfffedI6dgwe17YVCMQmipa03+Kmlxo2LAhZ0WI//f//h9n S2iWahX6/D2vtX2iT58+eW78lVde4QqhL21gQn1x1sCM4+35kpOTw99rsLAa qs/vrPWmC/u+kAX8g75V0/r/3/72t6BvRoT6Qmrwt56ZwoULcwKKady4cf78 HGPHF8wmYkWzqohdZk7gql/2P/zhD9alj4YQdFUs0II7xllO4C9/+YsvzOsx A9m7dy8rHPqCYgnYiX3y5MnWt3F9/jFPmzZtjh49mi8HqI74R0cjRozIlyM1 j+bYzQIlvcWU/tYHASMQOgPNzs4OnIPkSaNGjQJX2b1794MPPsj3BjA0PAj6 OiGTnJzMn5BmqlevHnoA//rXv3jpqFGjAst37NjB7+EJpHXr1kHff3cP4+eF HTWCuHTp0rhx46yPMDI33njjmDFjAu8n379/f5cuXfgHaIvbb7994cKF4bZs TWzDfTgv1BdnDcw43p4vzOzZs63XElo0bdp0/fr1gdXs+0KnT+AY2+e/GSbP Vz3keb4cPny4R48e/K51C9rvP//5z19++cWGqAawOTbGbCImNKuK2MXmBBjq O9LS0r799tt8uG1PLM5yAomB/dgPHjy4dOlSai14XsBV0Bq9PopEAEp6SwHV /8yZM9988w0d+cmTJyPX3L1798qVK8P9Hk1s27Zt69at4faSnp7+9ddf07+h T3C7iku+RFUjlCtXruzZs4fWWrZs2c8//xzuIVl+Jn358uVUM+ieDWdE8MVD 5JwvmZmZNB5etGhRamrq8ePHw1Wz7wuNl9avX5+SkhK5WjhfLl68uGXLlsWL F9MWdu3alc9ve4jJF8wmbKJZVcQuPCcArurWTXPsMtHsiObYzQIlvQX6ywS+ yAS+yAS+uIFmVRE7cgLy0ayb5thlotkRzbGbBUp6C/SXCXyRCXyRCXxxA82q InbkBOSjWTfNsctEsyOaYzcLlPQW6C8T+CIT+CIT+OIGmlVF7MgJyEezbppj l4lmRzTHbhYo6S3QXybwRSbwRSbwxQ00q4rYkROQj2bdNMcuE82OaI7dLFDS W6C/TOCLTOCLTOCLG2hWFbEjJyAfzbppjl0mmh3RHLtZoKS3QH+ZwBeZwBeZ wBc30KwqYkdOQD6addMcu0w0O6I5drNASW+B/jKBLzKBLzKBL26gWVXEjpyA fDTrpjl2mWh2RHPsZoGS3gL9ZQJfZAJfZAJf3ECzqogdOQH5aNZNc+wy0eyI 5tjNAiW9BfrLBL7IBL7IBL64gWZVETtyAvLRrJvm2GWi2RHNsZsFSnoL9JcJ fJEJfJEJfHEDzaoiduQE5KNZN82xy0SzI5pjNwuU9BboLxP4IhP4IhP44gaa VUXsyAnIR7NummOXiWZHNMduFijpLdBfJvBFJvBFJvDFDTSritiRE5CPZt00 xy4TzY5ojt0sUNJboL9M4ItM4ItM4IsbaFYVsSMnIB/NummOXSaaHdEcu1mg pLdAf5nAF5nAF5nAFzfQrCpiR05APpp10xy7TDQ7ojl2s0BJb4H+MoEvMoEv MoEvbqBZVcSOnIB8NOumOXaZaHZEc+xmgZLeAv1lAl9kAl9kAl/cQLOqiB05 Aflo1k1z7DLR7Ijm2M0CJb0F+ssEvsgEvsgEvriBZlURe/7nBAAAAAAAAAAA ACAB5AQAAAAAAAAAAACd4NkB+WjWTXPsMtHsiObYzQIlvQX6ywS+yAS+yAS+ uIFmVRE7cgLy0ayb5thlotkRzbGbBUp6C/SXCXyRCXyRCXxxA82qInbkBOSj WTfNsctEsyOaYzcLlPQW6C8T+CIT+CIT+OIGmlVF7MgJyEezbppjl4lmRzTH bhYo6S3QXybwRSbwRSbwxQ00q4rYkROQj2bdNMcuE82OaI7dLFDSW6C/TOCL TOCLTOCLG2hWFbEjJyAfzbppjl0mmh3RHLtZoKS3QH+ZwBeZwBeZwBc30Kwq YkdOQD6addMcu0w0O6I5drNASW+B/jKBLzKBLzKBL26gWVXEjpyAfDTrpjl2 mWh2RHPsZoGS3gL9ZQJfZAJfZAJf3ECzqogdOQH5aNZNc+wy0eyI5tjNAiW9 BfrLBL7IBL7IBL64gWZVETtyAvLRrJvm2GWi2RHNsZsFSnoL9JcJfJEJfJEJ fHEDzaoiduQE5KNZN82xy0SzI5pjNwuU9BboLxP4IhP4IhP44gaaVUXsyAnI R7NummOXiWZHNMduFijpLdBfJvBFJvBFJvDFDTSritiRE5CPZt00xy4TzY5o jt0sUNJboL9M4ItM4ItM4IsbaFYVsSMnIB/NummOXSaaHdEcu1mgpLdAf5nA F5nAF5nAFzfQrCpiR05APpp10xy7TDQ7ojl2s0BJb4H+MoEvMoEvMoEvbqBZ VcQuOSdw+fLl9PT0kSNHjh49etOmTVeuXHGw0wTAvm4HDhyYOXPm4MGDJ0yY sGLFikuXLkWoTPJu2bJl8+bNubm5Zg7UBWJtM5mZmdRU6N9wFUiTdevWjfCz aNGio0ePGjhKTdh3JPHOX82xmwVKegv0l4mDEenFixc3+dm+fXvo0rNnz6ak pAwfPnzMmDFpaWk5OTl2trl//37e5okTJ2I6mIyMjHnz5r399ttTp07dsWNH 1KFF5B1lZ2enpqZS23vnnXdmzJixZ8+emA7GIDhfZAJf3MD4qLsAoblFCc8J UIdSsWJFXwA1atSgHsTBfgs6dnTbsGHDPffc4/std9xxx8KFC0Mr79y5c9So UbfffjtXW7VqlSvHbQKbbYZGPkuWLOnUqVPRokUposceeyy0Do2devbsWaJE iUCJSpcuPWnSJPPHnbjYdCQhz1/NsZsFSnoL9JeJg5zAoEGDrO4+aNHSpUtv vfXWQPvITRrHRt7g4cOHb7zxRq7/2Wef2TyM8+fPU/8bNAK57777fvrpJwc7 os66b9++xYoVC9xakSJFunTpcvr0aZuHZBCcLzKBL25gcNRd4NDcoiTnBLZv 337zzTez1DVr1qTOjv9/22237du3z8GuCzRRdRs7duw111zDEpUpU6ZevXqF CxfmP6lXXbduXWDlv/71r0Ed94oVK9wNIA7stJnMzEy+KFk8+uijQXWOHTtG lyxeSuI0bNjwrrvusupPmzbNrQASDjuOJOr5qzl2s0BJb4H+Mok1J7Bx40ar 7wvKCVC3XqhQIR4DNGnSpFGjRlzzuuuuW7RoUYRtPvPMM1bPaDMnQF1w/fr1 re6VWkvp0qWtAcmZM2di2lHg1iiE2rVrW+2QaNu2bf7f2YjzRSbwxQ1MjboL IppblOScwNNPP83dQXJyMpeMGzeOZe/WrZuDXRdoouo2depUUqZSpUrLli3j O1iysrL69OnDiv3+978PrNyzZ88yfooXL84VCnpOICMjo8yvcDIk9OrUv39/ DrZfv37W8wILFy5kEcqVK3fu3Dk3jj/xsONIop6/mmM3C5T0Fugvk5hyAjk5 OXXr1rXG5IE5gezs7Bo1alBh+fLlN2zYwIXp6ekVKlSgwurVq1++fDnPbX7x xReB43ybOYGuXbty/Q4dOvDv+DQOmTZtWokSJSZOnBjrjs6cOVOnTh0q7N69 +7Fjx3hrJEu1atW4cmpqqj2FjIHzRSbwxQ1MjboLIppblNicAM1nOQH18ssv B5azESVLltQ2fbOj2/Tp07n3tMjNza1ZsyYpdsMNN+S5yqeffpoYOYFA+IGI 0KsTDYFo3DJ58uSgcitXEHQ3BQhHVEcS+PzVHLtZoKS3QH+ZxNTfDRw4kIem 999/f1BOYM2aNdyvjR8/PnCVL7/8MsJk/9ChQ3wz/wMPPGA/J7B3716+TbFj x45Bv+CfOnUqz1Wi7mjfvn3z588PWuvzzz/nyqNHj456VGbB+SIT+OIGpkbd BRHNLUpsTmDEiBF85Q96zn3WrFlcPnXqVAd7L7jEeoZatGjRwud/Ci/PHwVU 5QTCQW2MRZgyZYqDw1NIVEcS+PzVHLtZoKS3QH+Z2O/vNmzYwEPTzn6CcgIf fvgh20Sz76AV7777bipv2rRp6DZ5WFusWLFly5bZzwl0796dK2/bts3OkTve 0dq1a7ny4MGDbe7IFDhfZAJf3AA5gQgVErhFic0JvPjiiz7/Y2hBM9mzZ89y J9inTx8Hey+4OMsJnD59unz58iTXnXfemWcF5ASIBQsWsAh0Rjs5Pn1EdSSB z1/NsZsFSnoL9JeJzf4uJyeH766/+eabjx49+uc//zkoJ9CvXz+f/xaC0Efv X3nlFZ//ZYNB5cnJydwVDh069KeffrI/Va9VqxbVbN68efTw4tvRsGHDuLJ1 y26+gfNFJvDFDZATiFAhgVuU2JxAy5YtSVjq8kIX8YsdOnXq5GDvBRcHOYG9 e/eyjMS4cePyrIOcANGrVy8W4cCBA06OTx9RHUng81dz7GaBkt4C/WVis7+z Hnn78ssv6c8//vGPQTmBCRMmhOvXaCbu878J8OLFi1bhoUOHypUrR+X3338/ jXV37dplf6rO7+R56623+M/MzMz169dHeGrAwY6o5vTp0/kzBJUrVz5//nzU ozILzheZwBc3QE4gQoUEblFicwL82pw8P2xRu3btmDLSiYH9M3Tq1KldunRp 1qxZkSJFSCjqrMeMGRPuJb3ICdC4hT/VVK1aNYfHp4+ojiTw+as5drNASW+B /jKx09+lp6dbTw1wSWhOYPny5dy5B91mn5KSct111/Gi3bt3W+Vt27b1+T9J sHPnTvrTfk7gyJEjXHPixInTpk0L/CAyDZtXr14dVD+mHR08ePD1119v3759 1apVuRr17IGHnW/gfJEJfHED5AQiVEjgFiU2J8DJlnbt2oUuolZHi6jfcbD3 gov9M/TJJ5/0BTBkyJALFy6Eq4ycAN9FafPXEMBEdSSBz1/NsZsFSnoL9JdJ VF+ys7P5hQBVqlThN/xfzSsnkJOTw98duOaaa4YNG0bz6M2bNw8dOvTaa6+1 hgdUwpWp++OSUaNGcYn9nEB6erqVAeD/lCxZskSJEvz/QoUKBX73MNYdpaWl BY5nKleu/MMPP0Q+HpfA+SIT+OIGyAlEqJDALUpsToCu/CTsU089FbqocePG tKhBgwYO9l5wsX+Gvv/++0888US9evX4Ljuf/+uZ27dvz7Oy8pzAqlWr+PPN 1Kjy/3vHBZeojiTw+as5drNASW+B/jKJ6ov1ooDAXjs0J0AsW7YsKSnJ91vK li373HPP8f+PHz9+1f8a7RtuuIF/+bL6Qfs5AetDBj7/m4uoV7106RJth1bk GxKqV6+enZ3tbEd79uzp0KED9eYVK1a0kgyDBg3K//4a54tM4IsbICcQoUIC tyixOYFGjRqRsI888kjoIk59t27d2sHeCy6xnqFX/U/tUdfJc17qlPN8/k5z TuDHH3/kbyEVL158y5YtcR2iMqI6ksDnr+bYzQIlvQX6yySyL2lpafxUYPv2 7TMD4Bvyb7vtNv7Tqk/dXLt27SpVqkRLq1at+tJLL+3cuXPAgAH8az7XocEt DwNSU1OtDVqfMhw7diz9eebMmXCH9N1333HNWrVq0aw/cJH10oP09PQ4d5Sb m7t06dL77ruPK0+aNCkWUQ2A80Um8MUNkBOIUCGBW5TYnAD3HbVr1w5dxHnm rl27Oth7wcVBToAZPHgw96EfffRR6FK1OYEjR45wNWLGjBnxHqIyojqSwOev 5tjNAiW9BfrLJLIv7du399kgJSUlaMXARwhfeOEF3683uG7bts3OBh966KFw h5SVlcV1Jk+eHLRo48aNvCg5OTn+HfG++Mbd0I8muA3OF5nAFzdATiBChQRu UWJzAt26dSNhk5KSzp07F1h+4MAB7jj69+/vYO8FF8c5gd27d7Nir776auhS nTkBalR8h48v4FXJwD5RHUng81dz7GaBkt4C/WUS2Rfrtv/IBD7CH8Tly5er VKni+/XHrB07dtjZYMOGDcNtMDc3l59QeOONN4IWWU3lgw8+iH9HzPPPP881 jx07FrmmWXC+yAS+uAFyAhEqJHCLEpsTsF5EM2fOnMDy8ePHc3loGjyxiapb uMfrfv75Z1ase/fuoUsV5gTOnz//+OOPc9R/+tOfrly5YuYoNRHVkQQ+fzXH bhYo6S3QXyaRfcnOzj6eF23atPH5v57DfwZ+ZDCIWbNmsX0ff/wxl5w8eTJ0 g9YTARMnTqQ/rZcZ5kmDBg18/hsPgsYh1nMBnKOIaUfhhjR8kwNx+PDhCIdk HJwvMoEvboCcQIQKCdyixOYEaOJWpkwZ0rZFixbWrI26uYYNG/r8j8Vpm8pF 1a1jx47t2rULfRbPenZgypQpoWtpywlcuHChefPmHDINoi5dumTsKDUR1ZEE Pn81x24WKOkt0F8msfZ3TJ7vGMzOzra+LMCcOHGCP6RF9kXIG1wN/+o/ahXT pk1LTk4OfBjBGkjMmzcvsHLnzp25fP/+/THtaMOGDRUqVAi92+HQoUM33XQT H3+Eg3cDnC8ygS9ugJxAhAoJ3KLE5gSIHj16cE/x2muvnTp1ivqyLl26cMmQ IUMc7LpAE1m3OXPmsDI1a9acOHHitm3bcnNzjx49OmDAAH4fUenSpQ8ePMiV MzIy1v7KwIEDecVhw4ZxiVcf+omAnTazbt06Kyh+QTFdo6wS/umBBkgtW7bk eEuVKkXjjYULF877LYFvZwLhsONIop6/mmM3C5T0FugvE1M5ARoDdOjQoWjR osOHD8/KyqLub82aNTRCYPsmTJgQeYPhcgK9evXi8sDH7i5fvsyTgpIlS86f P5+GxFTywQcfFC5c2Bfmo12Rd8SfNSxUqBC1wMWLF1MPfunSJZLl3nvv5cq9 e/eOTaC4wfkiE/jiBqZG3QURzS1Kck6AdH7ggQd8v8Lvz+fMTGCCWgmRdcvJ yXn22Wd9AVgfImTpFixYYFV+//33feG5/vrr8yOeWLDTZuiwIwRFIVMdOlUj 1GHmzp2bHyEVcOw4kqjnr+bYzQIlvQX6y8RUTmDfvn38kzrDvw4wf/vb32jO HnmD4XIC/JgA8fDDDweWr1mzply5crzoOj/8//Lly2dkZMS6I1KgbNmy1gEX Lly4aNGi1p+NGzemMU8M6pgA54tM4IsbmBp1F0Q0tyjJOYGrftn/8Ic/WNPb pKQkmvkWaMEdY0e32bNnW6/Os2jatOn69esDq0XOCZQoUcLFMBwR/9VpxIgR VwO+ixSBr776Kj9CKuDYPIsT8vzVHLtZoKS3QH+ZOMsJ/OUvf/GFvAo7Kysr 0DuiUqVKn376qZ0N7t27l1eZOXNmYPm//vUvLh81alTQKrt3737wwQf53gCG 9h70dUL7Ozp8+HCPHj34Pd4WpUqV+uc///nLL7/YCcEsOF9kAl/cwNSouyCi uUUJzwkw58+fT0tL+/bbb7Ozsx3sMTGwr1tmZiZptWjRotTU1OPHj7t8XPmB szEScI+YHEmw81dz7GaBkt4C/WVivL/LyclZv359SkpK5N/r7bNt27atW7eG W3rmzJlvvvmGQjh58mT8+7p48eKWLVsWL15Mx79r1y4PXwGE80Um8MUNNI+6 NbeoApETAFd166Y5dplodkRz7GaBkt4C/WUCX2QCX2QCX9xAs6qIHTkB+WjW TXPsMtHsiObYzQIlvQX6ywS+yAS+yAS+uIFmVRE7cgLy0ayb5thlotkRzbGb BUp6C/SXCXyRCXyRCXxxA82qInbkBOSjWTfNsctEsyOaYzcLlPQW6C8T+CIT +CIT+OIGmlVF7MgJyEezbppjl4lmRzTHbhYo6S3QXybwRSbwRSbwxQ00q4rY kROQj2bdNMcuE82OaI7dLFDSW6C/TOCLTOCLTOCLG2hWFbEjJyAfzbppjl0m mh3RHLtZoKS3QH+ZwBeZwBeZwBc30KwqYkdOQD6addMcu0w0O6I5drNASW+B /jKBLzKBLzKBL26gWVXEjpyAfDTrpjl2mWh2RHPsZoGS3gL9ZQJfZAJfZAJf 3ECzqogdOQH5aNZNc+wy0eyI5tjNAiW9BfrLBL7IBL7IBL64gWZVETtyAvLR rJvm2GWi2RHNsZsFSnoL9JcJfJEJfJEJfHEDzaoiduQE5KNZN82xy0SzI5pj NwuU9BboLxP4IhP4IhP44gaaVUXsyAnIR7NummOXiWZHNMduFijpLdBfJvBF JvBFJvDFDTSritiRE5CPZt00xy4TzY5ojt0sUNJboL9M4ItM4ItM4IsbaFYV sSMnIB/NummOXSaaHdEcu1mgpLdAf5nAF5nAF5nAFzfQrCpiR05APpp10xy7 TDQ7ojl2s0BJb4H+MoEvMoEvMoEvbqBZVcSOnIB8NOumOXaZaHZEc+xmgZLe Av1lAl9kAl9kAl/cQLOqiD3/cwIAAAAAAAAAAACQAHICAAAAAAAAAACATvDs gHw066Y5dplodkRz7GaBkt4C/WUCX2QCX2QCX9xAs6qIHTkB+WjWTXPsMtHs iObYzQIlvQX6ywS+yAS+yAS+uIFmVRE7cgLy0ayb5thlotkRzbGbBUp6C/SX CXyRCXyRCXxxA82qInbkBOSjWTfNsctEsyOaYzcLlPQW6C8T+CIT+CIT+OIG mlVF7MgJyEezbppjl4lmRzTHbhYo6S3QXybwRSbwRSbwxQ00q4rYkROQj2bd NMcuE82OaI7dLFDSW6C/TOCLTOCLTOCLG2hWFbEjJyAfzbppjl0mmh3RHLtZ oKS3QH+ZwBeZwBeZwBc30KwqYkdOQD6addMcu0w0O6I5drNASW+B/jKBLzKB LzKBL26gWVXEjpyAfDTrpjl2mWh2RHPsZoGS3gL9ZQJfZAJfZAJf3ECzqogd OQH5aNZNc+wy0eyI5tjNAiW9BfrLBL7IBL7IBL64gWZVETtyAvLRrJvm2GWi 2RHNsZsFSnoL9JcJfJEJfJEJfHEDzaoiduQE5KNZN82xy0SzI5pjNwuU9Bbo LxP4IhP4IhP44gaaVUXsyAnIR7NummOXiWZHNMduFijpLdBfJvBFJvBFJvDF DTSritiRE5CPZt00xy4TzY5ojt0sUNJboL9M4ItM4ItM4IsbaFYVsSMnIB/N ummOXSaaHdEcu1mgpLdAf5nAF5nAF5nAFzfQrCpil5wTuHz5cnp6+siRI0eP Hr1p06YrV6442GkCYF+3rKysL774YvDgwVOmTCHpwlW7dOnSunXrRvhZtGjR 0aNHjR2raWJtM5mZmdRU6N8IdQ4fPjx79uwhQ4bMmzfv+PHj8R6iMuw7knjn rxutUSdQ0lvM9ilbt27dFIaLFy/mucr3338fbhVmz549gfVj6rPiucKfPXs2 JSVl+PDhY8aMSUtLy8nJiWn1ODHe12dnZ6emptJF+J133pkxY0aQqhGwefW2 v/34Rx0edtx2fKGIwjXm/fv3R1iRlnK1EydORN5F1Jp0Jk6dOnXQoEGTJk1a u3Ztbm5u5A2aXT3/0TwacQ/NqiJ2sTmBHTt2VKxY0RdAjRo1Il9aExU7uq1f v7527dq+39KqVau9e/cGVqMRWs+ePUuUKBFYrXTp0tQFuBhAHNhsMzSQW7Jk SadOnYoWLUoRPfbYY+Fq9u7dOzD2QoUKDRs2zOQRJzo2HUnI89d4a1QLlPQW g30KkZSU5AvDrFmz8tx42bJlw63C0NWDa8baZ8VzhV+6dOmtt94adBgRZtzG MdvX9+3bt1ixYoHVihQp0qVLl9OnT0fehZ2rt/3tGxl1eNtx2/Hl3XffDdeY 77777nBrHT58+MYbb+Rqn332WYTtR6559OjRZ599Nmi/jzzyyM6dO+0EGOfq XqF5NOIemlVF7DJzAtu3b7/55ptZ6po1a95xxx38/9tuu23fvn0Odl2giarb 9OnTrVEZ6fbggw+WKlWK/6xVq5b1S8exY8doUM3lhQsXbtiw4V133WU16WnT puVHMDFip81kZmbylMHi0UcfzbMmjUy4Ao1PGjdufN111/Gf//jHP8wfeoJi x5FEPX/NtkbNQElvMdWnEBcuXPCFZ8aMGXluP2pOgPZyNfY+K54r/IoVK2im SZVpntukSZNGjRpx86ONLFq0KOrqRjDlC5079evX53IKqnbt2tYFmWjbtm2E n4DtXL3tb9/IqMPzjtvO9ervf/975MacJ88884xVLXJOIELNK1eu/O53v+NF dGY9//zzzZs35z9pqnL+/PnIRx7n6h6ieTTiHppVRewycwJPP/009zXJyclc Mm7cOJa9W7duDnZdoIms2/Hjx3lUQC1zzZo1fAfLyZMn27Rpw4q9++67XLN/ //5c0q9fP+vOvYULFxYvXpwKy5Urd+7cOfejiQ07bSYjI6PMr9Cowxdm7rBl yxYOv0GDBmfPnr3ql+7OO+/0+X/dOHDggBvHn3jYcSRRz1+DrVE5UNJbTPUp V/02ceF77723N4Rffvklz13s378/tDLRo0cP3trXX399NcY+K54rfHZ2Nk1/ qGb58uU3bNjAhenp6RUqVKDC6tWrX7582Z60cWHKlzNnztSpU4dKunfvTrPy q/55H225WrVqXDM1NTXcXuxcve1vP/5Rh4SO28716qWXXqJDuvXWW0NbNZ0j ea7yxRdf+AKIkBOIXHP+/Plc/uabb1JL5sJPP/2UC//9739HPvI4V/cQzaMR 99CsKmIXmBPIysri/PzLL78cWM5GlCxZUuDU1VWi6kb9b+vWrY8cORJYSD01 jx9atWrFJTSq6dq16+TJk4NWt3rtdevWGT1wA9jPIzG33357uLnDq6++GjqK sMYbuFXAJlEdSeDz12BrVA6U9BZTfQrxww8/8CWUJnpxHtWePXt4qkjTKy6J qc+K5wpPU2yuNn78+MDyL7/8Mup8zSAGfdm3bx/N9YJW//zzzzmc0aNH57l9 +1dvm9uPf9QhoeO2c71q3749Jy5sbvPQoUP8LMADDzwQuY1Frdm3b1+f/yaK S5cuBZbTBZPK//jHP0Y+kjhX9xDNoxH30KwqYheYExgxYgRf91atWhVYPmvW LC6fOnWqg70XXGIdP1vwPXtVq1aNXI10ZmGnTJniYC+uYmrucPHiRb5bNfSR 5Lvvvtvnv+0nzkNVQlRHEvj8xUzWFFDSWwz2KatXr+bzOi0tLc6jeuKJJ2g7 VapUOXPmTOSaoX1WnFf4Dz/8kDdI8688V2/atGlMsTjD7b5+7dq1HObgwYPz rBDn1Tvq9i1sjjqEdNx2fGnWrBkdz5NPPmlzmzyJKFas2LJlyyLnBKLW7N69 u89/00XQip07d/b5XwsQ+UjiXN1DNI9G3EOzqohdYE7gxRdfJGHLlCkTdLfe 2bNnOT/Tp08fB3svuDgeJ9SrV88X8f02zIIFC7gxh3sflIeYmjvs2bOHYwy9 Ec56DDDcba4gkKiOJPD5i5msKaCktxjsU6zuI85HKa1f5GmDUSuH9llxXuH7 9evn898OGvqg/SuvvOILeOehq7jd1w8bNoylsO56DSLOq3fU7VvYHHUI6bjt +MIW0DzazgZJHz74oUOH/vTTTxFyAnZqWudO0EHec889dg4pztU9RPNoxD00 q4rYBeYEWrZsScLWqVMndBG/2KFTp04O9l5wcTZOOHnyJD+E27Vr18g1e/Xq xT2CwGfqTc0dvvvuO45xzpw5QYsmTJjAi/73v//FebQaiOpIAp+/mMmaAkp6 i8E+ZcqUKXz9pJnFW2+9RTOIgQMH0kQm1leT8c+sd9xxh50voIX2WXFe4a06 oZ0gzcV8/vfjhfuuokHc6+tpBDt9+nT+TEDlypXDueP46m1z+xY2Rx1COm47 vlStWpVn0KNHj+7uh/6zcePG0JqHDh0qV64cVb7//vtJt127doXLCdiseeHC BX54hCqvXLmSC1esWMGVrZJwxLm6h2gejbiHZlURu8CcQN26dX1hPjvFn+Bp 3ry5g70XXJyNE6xbXD755JMI1U6dOsVfX6pWrZrzQ3QNU3OHuXPnshpBN/wQ M2fO5EXffvttnEergaiOJPD5i5msKaCktxjsU0aOHOnLC5oY2vnFn9m2bRuv RVuLWjnPPivOK/zy5cu5TtBN7ykpKdZb7nfv3m0zHMcY7+sPHjz4+uuvt2/f nmesfBJFCCTWq3es22fsjzqEdNx2fLE+ABFIoUKFXnjhBYo3sGbbtm19/u9Z 8Jf+IuQE7NdcvXp1yZIleSmtNW3aNE4mPPfcc3YCjHN1r9A8GnEPzaoidoE5 AU62tPv/7d15eBRV9j/+VhQCiCIq4rApLoOMoCwKowyOKPIdEcdBcRvRAVcQ nXlAHJT1w6pssglBZBEkyg6yJ8CDLBESA4QdZQmBbEDYAiEJEH7n1+fxPj29 VFd3V6Vu+rxff/CQ6ttVdc6prqp7u7qqXTvfl/ieJw888EAYSy+9wjhPOHTo EN+s6f777/e6b4wXvjDS71FGB1b1HdR3Cjt27PB6SY2H+34TAb6CViSKP7/o yVoFmXSWhccUNSZA1fnkk08+/vhjvoialCtXbufOnWZmzo8boPmfPn06aGO/ x6wI9/CFhYX83IHrr79+yJAh1Kvdvn37wIEDKQTVv6MpZmKJhOXH+qSkJM8u as2aNXft2mUwt1D33qHOn5k/69DkwG1+TKBKlSodO3bs379/hw4d1GjSP//5 T9WMQuaJo0eP5imBevrmW15133iB+u+u/9W4cWOTP6yI8O1OkXw2Yh/JWUXs Go4J0GGFEvvcc8/5vtSsWTNXKHd2jQ6hnidcuXJFPV52xYoVBi3XrVvHT2Sm xJq5YrPkWdV3mDJlCifE91q+lStX8kuR3zdbgqAVieLPL3qyVkEmnWXtMSUu Li4hIcGzce/evblxixYtzMyfb6v+4osvBm0Z6JgV+R6eQoiJifHqFt18882q r5Sbm2smlkhYfqw/fPhw+/bt6YNTvXp1bkbZ69u3b6DDfah771DnfzXEsw5N Dtxm6nLs2LFRo0Z5Dmr99ttvtWrV4pXkK/CzsrKqVKnicn/PqGL329M33/Kq +7fM9EFzuW973rVrV36Apsv9gxeqRdDoIny7gySfjdhHclYRu4ZjAg8//LAr wM1OeSS/TZs2YSy99Ar1POGjjz7iXbrxD1t+/fVXPhOrUKFCampqpGtpD6v6 DsuWLeOcrF692uulWbNm8UvJyckRrq0EQSsSxZ9f9GStgkw6y6ZjikJ91Yce eojaUy/b615Mvvbu3cszHz58uHFLg2OWJXt4mn+7du1q1Kjhct/D/5133tm3 bx+Pb1B3yfi9lrCvLtSvXLVqFReFTJo0yW+zsPfeJucf6lmHJgfuUOuiqN8+ DBo0iP6krgT/mZiYmPk79RzMcePG0Z/80A3zLcmrr77qcl+iwM/+KCwspAa3 3XYbNx48eLDxSkb4dgdJPhuxj+SsInYNxwR4Z1ivXj3fl3jgNOhN86JMSMcj dSVn48aNDe7zc/z4cT7NJrNnz7ZmRW1gVd9h69atHOycOXO8Xho7diy/pOEt FjUUtCJR/PlFT9YqyKSz7DimeOnevTu/i38KbWDy5Mnc8qeffjJoZnzMsnYP f/HiRfX/N954w1VSV4TaXZesrCy+9jXQYxQi3Hsbzz+Msw5NDtxhjwlQt51X 8pVXXlE3zTD26KOPmm9Ji9ixYwf/+eWXX3ouOi0tjW/yUL58ed8nbCoRvt1Z ks9G7CM5q4hdwzGBd9991+X+fuH8+fOe02nPz/uuXr16hbH00sv88Wju3Ll8 /2E6LhscKCmxfJUL+eyzzyxbURtY1XfIzMzkePv06eP10ttvv+1yX/FYAreV jgJBKxLFn1/0ZK2CTDrL8mOKL/XzgaA/w+enO9FSvPYYnoIes2zaw1++fJkv /y6Zb39KoC4dOnTgRJ08edL31cj33oHmH95ZhyYH7rDHBAoLC8uUKeNy37hP XQ9jrEmTJuZb0iJiY2P5T9+HgX777bf8ksHdPiN8u7Mkn43YR3JWEbuGYwLq zipet46ZMGECT4+Pjw9j6aWXybzRfvv66693ua9yNLiaLj8//4knnuBMvvba a1euXLFyXa1mYd+BL+zxeoZIcXEx/3qOh9whqKAVieLPL3qyVkEmnWXtMcWv Z555ht5YtmzZoF22Ro0aUUsqcaAGJo9ZduzhqevNy/3mm2/CeHuoLKxLoJ/q 82UPJCcnx/dV83vvkOYfyVmHDgfusMcE1D0Y+/fvf9X91MhcH+p5i9Q9pz/P nj0bUsvBgwfTlDJlynhe3MJo21DtA61hhG93luSzEftIzipi13BMgA4flStX pty2atVKHTvovKJJkyYu96/8NO/GWs5M3pYuXcqPBo6JiTFoTLt9dUuitm3b Gj+SQAcW9h2GDh3KgW/YsEFNXLhwIU+cMmVK5GsrQdCKRPHnFz1ZqyCTzrLq mLJ169YGDRps27bNd/58HzkzN1y64447qOWf//xnv6+aP2aZ38PTPmrGjBlx cXGe/aCCggKvSxpOnTrFT56ivVbJXEVmVV1SUlKoy0wtvaZnZ2dXrVqVI/L7 RpN775Dmb76Cfuuiw4E7aF1ee+21Hj16eIVWXFzcsmVLXk+DGyEaPE3ATEvq g/DEadOmebUfOXIkv5SYmMhTfDMc0tt1I/lsxD6Ss4rYNRwTuPr7k4nIRx99 dObMGTo0d+rUiafwcKsoQfO2YsUK9ciknj170p+LFi1a6IHveUvnPK1bt+Zm N954Ix3Q6Ti18H9lZmaWUFTmmNlmNm/evOl3fPdj6kGoKTyWftV9FeJ1111H r9JJy5YtW+h4TecYN910E0254YYb8vLybA8mKpipSLR+fi3cGoVDJp1l1TGF n8hMndO+ffuuX7+eOhpUl0mTJvFz2a655hrjB99cdd+NkC+upt6i76shHbPM 7+G7devG81QXsVPj9u3b09upB5qVlUXLpffWrVuXm02cODGU7IbPqrrUr1+f 80+74uXLl1NRqLtKc37wwQf5vd27dw+0CDN7b/PzD6mCvnW5qseB27guc+bM 4dVu3rw59dbT0tJoq965c6cK/LHHHjO402aEYwKUAd49VqxYcebMmer6jQUL FvBDKmvUqEFV4Im+GQ7p7bqRfDZiH8lZRex6jglQnps2ber6HX/j4HKPzPhe 4BT1jPNGu2vfJyh5adCgAbWkzdW4GaEDQckFZoKZbYZODAwiGjFihGpJR1I+ //TcqOgUa9myZfaGEUXMVCRaP7/Wbo2SIZPOsuqYMn/+fO44MNq7qg876dGj R9A1yc7O5sYdO3b0fTXUY5bJPXzjxo35Jeqs8ZQjR47wF9wqEPX///znP0Ef nWAVq+pCM7n55pvVxGuvvZa71axZs2aFhYWBlmJm721+/iFV0LcuzPEDt3Fd zp49+/TTT3tG5JkN2q4OHz5sMPMIxwRIYmIiXzpCqlWrRtm78847+U+a/vPP P6uWfjNs/u26kXw2Yh/JWUXseo4JXHWn/dlnn1V7KjoUvvzyy6U64WELep7g eQLj18MPP0wte/XqZdyM6NY7jrzv4PV8q7i4OH7AKKtTp45uIWvO5Kc4Kj+/ lm+NYiGTzrLqmELS09M7derEFwYod999t8mnxqvbqX3yySe+r4ZxzDKzh//i iy/41dGjR6uJWVlZnrssl/sb0pkzZ5qJwioW1iUnJ6dLly58K2yFyjRo0KAL Fy4Yr4aZvbfJ+YdUQb91Yc4euIPur4qLi7/99lu+M4Zy3XXXvfPOO7m5ucYz T0tL4/a+z1Yw35I+R23btvVKbJs2bfbs2ePZLFCGTb5dN5LPRuwjOauIXdsx AZafn5+UlLRx40ZtL14qAWHkLWrYFPvBgwfXrl3re6NdCCqkikTZ51fyJ9Fa yKSzLM8//xh/9erVtF/NyMiwcM7hCbqH3717986dO32nFxYWbtmyJT4+3pEo LK9LUVFRamrq8uXLKaL9+/eHdAchM3vvSObvV6C6MKcO3ObrcvTo0fXr1yck JGzdurXkj3rnzp1LTk5euXIl/Uv/99vGIMNm3q4VyWcj9pGcVcSu+ZgAXJWd N8mx60lyRSTHbi1k0lnIv55QFz2hLnpCXewgOauIHWMC+pOcN8mx60lyRSTH bi1k0lnIv55QFz2hLnpCXewgOauIHWMC+pOcN8mx60lyRSTHbi1k0lnIv55Q Fz2hLnpCXewgOauIHWMC+pOcN8mx60lyRSTHbi1k0lnIv55QFz2hLnpCXewg OauIHWMC+pOcN8mx60lyRSTHbi1k0lnIv55QFz2hLnpCXewgOauIHWMC+pOc N8mx60lyRSTHbi1k0lnIv55QFz2hLnpCXewgOauIHWMC+pOcN8mx60lyRSTH bi1k0lnIv55QFz2hLnpCXewgOauIHWMC+pOcN8mx60lyRSTHbi1k0lnIv55Q Fz2hLnpCXewgOauIHWMC+pOcN8mx60lyRSTHbi1k0lnIv55QFz2hLnpCXewg OauIHWMC+pOcN8mx60lyRSTHbi1k0lnIv55QFz2hLnpCXewgOauIHWMC+pOc N8mx60lyRSTHbi1k0lnIv55QFz2hLnpCXewgOauIHWMC+pOcN8mx60lyRSTH bi1k0lnIv55QFz2hLnpCXewgOauIHWMC+pOcN8mx60lyRSTHbi1k0lnIv55Q Fz2hLnpCXewgOauIHWMC+pOcN8mx60lyRSTHbi1k0lnIv55QFz2hLnpCXewg OauIHWMC+pOcN8mx60lyRSTHbi1k0lnIv55QFz2hLnpCXewgOauIHWMC+pOc N8mx60lyRSTHbi1k0lnIv55QFz2hLnpCXewgOauIHWMC+pOcN8mx60lyRSTH bi1k0lnIv55QFz2hLnpCXewgOauIveTHBAAAAAAAAABABxgTAAAAAAAAAJAJ vx3Qn+S8SY5dT5IrIjl2ayGTzkL+9YS66Al10RPqYgfJWUXsGBPQn+S8SY5d T5IrIjl2ayGTzkL+9YS66Al10RPqYgfJWUXsGBPQn+S8SY5dT5IrIjl2ayGT zkL+9YS66Al10RPqYgfJWUXsGBPQn+S8SY5dT5IrIjl2ayGTzkL+9YS66Al1 0RPqYgfJWUXsGBPQn+S8SY5dT5IrIjl2ayGTzkL+9YS66Al10RPqYgfJWUXs GBPQn+S8SY5dT5IrIjl2ayGTzkL+9YS66Al10RPqYgfJWUXsGBPQn+S8SY5d T5IrIjl2ayGTzkL+9YS66Al10RPqYgfJWUXsGBPQn+S8SY5dT5IrIjl2ayGT zkL+9YS66Al10RPqYgfJWUXsGBPQn+S8SY5dT5IrIjl2ayGTzkL+9YS66Al1 0RPqYgfJWUXsGBPQn+S8SY5dT5IrIjl2ayGTzkL+9YS66Al10RPqYgfJWUXs GBPQn+S8SY5dT5IrIjl2ayGTzkL+9YS66Al10RPqYgfJWUXsGBPQn+S8SY5d T5IrIjl2ayGTzkL+9YS66Al10RPqYgfJWUXsGBPQn+S8SY5dT5IrIjl2ayGT zkL+9YS66Al10RPqYgfJWUXsGBPQn+S8SY5dT5IrIjl2ayGTzkL+9YS66Al1 0RPqYgfJWUXsGBPQn+S8SY5dT5IrIjl2ayGTzkL+9YS66Al10RPqYgfJWUXs +o8JZGZmbtu2jf4NY4nRwXzejh49OmfOnH79+k2cOHHNmjWXLl3ybbNz585t ARQVFVm86hEzH/uePXumTZvWv3//BQsWSN5a7BbqpziaPr+SY7cWMuksy/Nf UFCQmJg4atSowYMHz549+/Dhw+ZnXlhYmJSUNH78+CFDhiQkJJw9e9Zvs5yc nHnz5tEefuHChbm5uYHmRke9zZs3D3dbunTpiRMnzK+Jl/T0dD4ynjp1KuyZ hESfuuTl5S1fvpxySG+kQ2pWVlbk8zdZQQMZGRn03v/7v/+bPn363r17i4uL w5hJGCyvS9hbqcm6XA0r2+Ft8JGXNWzm63L58uXk5GTaUMeMGUMBXrlyxeZV K8UkZxWxazsmQLu+FStWvP7669ddd53L5frrX/8axhKjg5m8paSkPPDAA67/ dc899yxZssSrZUxMjCuAuXPn2hVDuMzEfu7cuY4dO3rF8sEHH/gdEoEImfwU R+XnV3Ls1kImnWVh/ouKinr27Fm2bFnP3W+ZMmU6deoUqHfvae3atbfeeqvn e2lZn3/+uVd3r3v37p5trrnmmiFDhviuyYcfflixYkXPljfddNOkSZOCroYv 6umoFfvuu+/CmEMYNKkL9e7vuOMOzzdWqFBh2LBhkczfTAUN5OfnU7xeR/mH Hnrot99+Mz+TsFlbl7C3UjN1YWFkO7wNPsKyRshkXfbu3Vu9enXP9bz33nvT 09PtX8FSSXJWEbueYwKZmZm8U1X+8pe/hLHE6BA0b+PGjbv++us5UZUrV27Y sOG1117Lf9Ihe/PmzarlxYsXXYHREcf2YEIUNHY6dXz88cd5/WvXrt26detq 1arxny1bttTwyofSzsynOFo/v5JjtxYy6Syr8k9tGjVqpLoD9erVu/3221X7 559/3vib3PHjx6tFVKlS5b777qOZ8J8DBw5UzagPxROpJ9WsWbPy5cvznwMG DFBtTp48SV0wnk6HvyZNmvzxj39UazJjxoxQU/TCCy+ot2s1JmB3XdauXavO Hx599NE333xT9UMnT54c3vzNVNA4ZLU4Wre6detSJ5r/pBOec+fOmZlJJKyq SyRbqZm6sPCyHcYGH2FZI2emLnv27FEbJ20599xzD///zjvvPHLkSImsZikj OauIXc8xgYyMjMq/492g5LPBoHmbPn06pahGjRoJCQl8BUtWVlaPHj14Q336 6adVS0osTxw2bFiajwsXLtgdS6iCxj516lSO6L333uMLA+jf999/nyfSqyW0 omKY+RRH6+dXcuzWQiadZVX+qTtWv359eqlz587U36EpdACiOd911128B05M TAw0/0OHDnEfivp3K1as4F4kdaxatWpFfUx19XJqairPqnHjxnl5eTQlNzf3 vvvuc7m/lT569Cg369WrFzf79NNP1ZXYS5YsqVChAk285ZZbzp8/bz4/P/zw g2fnTqsxAbvr8tBDD1EDOundu3cvT6FaUO/V5e6A8wlGSPM3WUEDb731Fs+h ffv2fBECLY560NQVjY2NDfr2yFlVl0i2UjN1uRputsPY4CMva+TM1OUf//iH yz1yFRcXx1PGjx/Pa/7uu+/avoqlkOSsInY9xwQ83X333cLPBs3kbdasWXxo VugUq27dui739y9q4q5du3jT9f1NgZ6Cxv7kk09SODVr1iwoKPCcTscpmk5H qCj4mY9WQv0UR9PnV3Ls1kImnWVh/o8cObJo0SKvid9//z0faMaMGRNonu+8 8w53H6hz4Tmd9tie3/x27drVt5eh+iPqG8nLly9Tz9F3EFj1wjyvlzOWnZ3N F1E3bdpUwzEBT5bX5cKFC5RqajB06FDP6WvWrOE37t+/P9T5m6xgIGlpaXwZ 5EsvveR1+cGZM2eM32sVq+oS9lZqvi5hZDu8DT7CsloiaF2ysrJ44PG9997z nM7dukqVKoU0VCiE5KwidowJ6C+8vJFWrVrxTpuORDxl/fr1vMdOSkqychVt EzT2qlWrUjjvv/++1/Q5c+ZwpOGlDgKR3JuTHLu1kEln2Z3/TZs28e63X79+ fhtQP4J/it6+fXuD+RQVFd18880ufz/N/tOf/uRyX65pvCbr1q3jNZk2bZrJ leezO1q9hISE0jsm4FfQulD3kBuMGzfOc/qRI0d4+urVq0Oaf+QV7Ny5M89z 9+7dxi3tY3ddgm6lJusSXrbD2OAjL6slgtZl+PDhHBFl2HP63Llzefr06dPt XcVSSHJWETvGBPQXXt7Onj172223udzflauJixcv5u22tPzsxTj2wsLCQGc4 6hjq9VM7iJDk3pzk2K2FTDrL7vwPGTKEd7/q6kovs2fP5gYbN240mM/hw4e5 2ciRI71e+u9//8svGf/kTR3yTN5Bl1aY2w8cOPC3336LsjGBoHUh/Mv9li1b el5iN2/ePDNnDr7zj7yC999/P7V56qmngodnG7vrYmYrNVOXMLId3gYfeVkt EbQub775psv92wr1vRjLy8vjb3t79Ohh7yqWQpKzitgxJqC/MPKWlpbWunVr 3jOPHz9eTZ82bRpP/PHHHz/77LOOHTv26dOHDgr5+fkWr7RFgsbO9/p46623 vKYXFhbyHW969+5t4/rJI7k3Jzl2ayGTzrIv/3SmNGvWLL4GoGbNmoGOLF98 8YXL/ZNM/s3XpUuXkpOTDxw44PVTr59//pkPWPPnz/eaw8SJE/klepfB+nTr 1o2bmfmBc3Z29i233EKNH3nkEQpk//79UTMmYLIuZPDgwRw1nf3yjzjovdQV pSktWrQIdf6RV5B/a0+nK/xnZmbmli1bSuxXA8zu/ZWZrdRMXULNdtgbfORl tUTQuvBpcP369X1f4lPH119/3a6VK7UkZxWxY0xAf+bzNn369E6dOtFhgn96 RgfTsWPHev4Eb9SoUS5/6CC+ePFiuwKIQNDYmzdv7nLfper06dNqIp1ntmnT hkPr0KGD7WspieTenOTYrYVMOsvy/B87duzf//73iy++WLt2bd7xUuODBw8G at+lSxdqU61atePHj7/00ks33ngjvysmJob6R+onmQsWLODpXhdqXvX4dZjB lQbUc/zDH/5Abe666y4zYT7//PPUuHz58vv27aM/o2BMINS6XHVfFk7tufEd d9wxbNgwvsUflWbbtm2hzj/CCtLmwW1iY2NnzJjh+cBlOi1fv369UXasY+v+ yuRWaqYuoWY77A0+wrJaJWhdGjRo4ArwUMh69eq5nL7+RE+Ss4rYMSagP/N5 e+aZZzx7+v3797948aJnAzUmQPn85JNPPv7444YNG/KUcuXK7dy505YAIhA0 dnXlQ6NGjegMISMjY968eS1atFBJoKNeSa2sCJJ7c5JjtxYy6SzL85+UlOR5 6KlZs+auXbsMZshjthUqVFB3qq9atap6EGHTpk35ggH1neOOHTu85qDurub7 TaWiHkBjplNPbbjx6NGjeUoUjAmEWheWkpKinm6s+M1z0PlHWMHk5GRuw485 cLnv4lWxYkX+P20wS5cuDRpO5GzdX5nfSoPWJaRsR7LBR1hWqwStC391265d O9+XqDr00gMPPGDXypVakrOK2H8JV9hLDPVdOBs0n7cRI0b87W9/o24+X8Ln cj89c8+ePZ5t4uLiEhIS1J906tW7d29ubHBloFOCxl5cXMy3UvTyyiuv8D1w Pvzww5JaWREk9+Ykx24tZNJZluf/8OHD7du3pwbVq1dX/bW+fft63SheUY+b J3369MnIyLjqvqXzU089xRO//vprmjJlyhT+c+vWrV5zWLlyJb8U6Bk669at 40GGZs2aBVoNhRZdpUoVl/sLINU4CsYEQq3LVfdP1Lnj+f/+3/9r3bq1Gqi5 77771M3tzc8/kgqSH3/8UW0ntAJU00uXLtHMqSL828A6dep4PXLIDvbtr8xv pWbqYj7bEW7wEZbVKkHrUrNmTVqT5557zvclSrjL/SBFu1au1JKcVcSu/xJx NhhG3rKzs+m4zEcNOmga3y7gypUr/OjbmJgYr/tmOM5M7LT+Y8aMadiwIZ0k lC1btmXLll999RWdJ/Azgn3vgQORkNybkxy7tZBJZ9mXf+pfrFq1ig8oZNKk SX6bqdvdeD0ULzc3t1KlSjS9TZs29OeyZcu4me/t7mfNmsUvJScn+87/119/ 5cerVahQwetZh37ROR7PLTExMfN3GzZs4Injxo2jPz2fkGgTx+uya9cuvlPW v/71Lz4ZoCkqOXfccYfXI4+Dzj/sCjL1u/X777+furGeL6nn9xm83So21cX8 VmqyLuazHeEGH2FZrRK0Lg8//DCtSfPmzX1fuvfee9V+BjxJzipi13+JOBsM u1L9+vXjnXPQe+93796dW/LPyvQRUuxXrlwpLCzk/x86dIgjmjdvnl0rJ5Lk 3pzk2K2FTDrL7vxT942vsaxevbrfBu+99x69WqtWLd+XuLfCj8vZunUr78bn zJnj1Wzs2LH8ku9t2Y4fP84rTGbPnh10bXfv3u0y4dFHHzUVfAQcr8sLL7xA r95yyy2XLl3ynK7OJbp16xbS/MOroOcMuc3UqVO9XlJzNniGglXsqEtIW6nJ upjMduQbfIRltUrQuvDOpF69er4v8WUSvrenBslZRez6LxFng2FX6uDBg7xz 7tq1q3FL9fOB7du3h7OKtgk79smTJ3NEJTBYLYrk3pzk2K2FTDqrBPLfoUMH 3gP7/WZ56NChLvd15nl5eV4v8Z3Tbrrppqvum8zzTPr06ePV7O233+Y5FBUV eU4/f/48X8Pp8rhZvbG9e/ea6SI1adLEfPjhcbwu1apVo5c6derk+xLf+aFh w4YhzT+MCnoqLi6OiYmhZh9//LHXS9Tl5Dl/+eWXxqsUOcvrEupWarIuJrMd +QYfYVmtErQu7777rst9Bay6bSlTG0+vXr3sXcVSSHJWEbv+S8TZYNC8BfoZ mvquvHPnzsaL4JsTli1btgR24yEJeyvlGxTT4dJrXB0iJLk3Jzl2ayGTzrIw /4GOPm+88QYffXJycnxfXbJkCb/q+0D2J5980rM/whdkej37iRbKvSSvrzLz 8/OfeOIJnvNrr73m9WRDA6dPn871oS5cj42NpT/Pnj1rcm5hc7Yu9JZy5coF 6qXybSEp7aHOP6QK+mrcuLHLfecuryWqC91L4DaD1u6vQt1KQ6qLyWxHvsFH WFZLBK2Luo+i1w0PJ0yYwNPj4+PtXcVSSHJWEbv+S8TZYNC8vfTSS+3atfP9 8Ze6rmzatGlX3Zd7NWjQwPdxQjRzvvOAhjfHMLPNpKamej1e4ZtvvuHA+UZV YCHJvTnJsVsLmXSWVflPSUmhLoBvpyw7O7tq1ar0ltq1a/udIfWD7rvvPpf7 eTGefaIDBw7wjdTUV6J8RQGhPqBqtnDhQp44ZcoUNZGOAuoWhW3btjUYDaZO 2YwZM+Li4rwOHF5K7z0Gw64LdeVc7h/ve92D6Pz583fccQe99PTTT4c6f/MV 9FuXmTNnckt6i+eyOnbsyNPT09P9xmIhC/dX5rdSTybrcjWUbPsKtMH7rUsk C7JK0LrQmleuXJnWp1WrVmo/U1RU1KRJE95KzQ8byiE5q4hdzyVu3rx50+/4 fra0j1VTSmC4XivGeZs/fz7vhOvWrRsbG7t79+7i4uITJ0707t27TJkyLvdF mMeOHbv6+9MzY2Ji+vbtu379etq9UyYnTZrEz4a+5pprVqxYUXJRmRN0m9my ZUuFChXoxDIxMZEOr0ePHu3fvz8PcdSsWVPdXgCsYuZTHK2fX8mxWwuZdJZV +ecnxNH+tkuXLsuXL6eJtBOmOT/44IN8VOrevXug+avHyP7973/n68wPHTrE N6mjI5e6n3lmZibfXY06m7S3p6Mb9UHooEZTbrjhBvXTg4KCAnXfQjqiUXd1 yZIlC/8XzYobd+vWjVsaX7at55iArXVRTyt+5pln1O/Bjx8/rm5J99VXX4U6 f5MVvBqgLpcvX+YudqVKlRYtWkSn3DTlyy+/5HsI+30omOWsqktIW6knk3W5 Gkq2fQXa4P3WJZIFWcVMXWgT5ZX/6KOPzpw5c+rUqU6dOvEUOlcsgZUsdSRn FbHz/+njfCVEQR/uY7xEY7RLcQU2YsSIUBddqhnnjbq9L7/8smd+1IMI+ai9 ePFibjl//nzqPquX6LxLPc6G9OjRo4TiCUXQbaZnz56eEan/16pVKyUlpaRW UxAzn+Jo/fxKjt1ayKSzrMo/zYQf+cqom8bdBNasWTODUVnqRb7++uvcko5E fG865vWTTOqhqH27OmaVK1du2bJlqg2djBmsLVuwYAE35svRyWOPPWaQAT3H BGytC53XqW4mnUg0aNCgSZMm6rTh73//O5/4hTp/MxW8Grgu1Nm85ZZb+KXy bvz/2267jZ9iaTer6hLSVurJZF2YyWz7CrTBB6pL2Auyipm6UK+tadOmKr1q PVu1amV8mZBYkrOK2Pn/OTk5v4TI748EzS/RmPHedfjw4aEuulQzk7d58+ap W9Yojz/++JYtWzybpaend+rUiS8MUO6+++4SeJJseMzEPnXqVPVwZD4ktW3b 9sSJEyWyguJEfnZUej+/kmO3FjLpLAvzT2cCXbp04VsuK3SIGTRo0IULF4Ku yeeff+75Xvr/jBkzfJvFxcXxg6FZnTp1vPod6sl0BtRbvvjiC54yevRog3VL S0vjZr43V7eJDnW5dOnS+PHjqbvt+cZbb7117NixnrcbCnX+QSt41bAuBw8e /POf/8zXBrBnn33W6+mE9rGqLiFtpV5M1oWZybavQBu8QV3CW5BVTPYpqBNH W4v6siwmJubll18u1d03W0nOKmLn/xcWFqakpFBP//Dhw0cMUQNqRo3DuCrb /JgAeDKft8zMzI0bNy5dujQxMTE3NzdQs4KCgu3bt69evXrt2rUlM8weNvOx Hzt2bNWqVUlJSfi9gK0kf4olx24tZNJZluefeiWpqanLly+Pj4/fv39/SHd2 LS4upresWbNm9+7dxj/GpI4hHbPoPCTi9f3/nz+4c+fOyOdjLX3qQoWgkz3K dkJCwqFDhwLVJdT5B62gcV3OnTv3008/UYpOnz5tMhBL6LO/MlkXVmKfFwsX FJKQ6pKfn0/nh3SGTCfAdq5UqSc5q4hd/UkfZ+rsBx13pQbULLzPvj771dJF ct4kx64nyRWRHLu1kElnIf96Ql30hLroCXWxg+SsInb1J18qsH379suXLwd6 C71EDcK7SMB3iWCS5LxJjl1PkisiOXZrIZPOQv71hLroCXXRE+piB8lZReye U4JeKhDJRQJ+lwhmSM6b5Nj1JLkikmO3FjLpLORfT6iLnlAXPaEudpCcVcTu OYUvFdi2bZvfSwVoIr0U9kUCfpcIZkjOm+TY9SS5IpJjtxYy6SzkX0+oi55Q Fz2hLnaQnFXE7jWRLxXw+4BUmhjJRQKBlghBSc6b5Nj1JLkikmO3FjLpLORf T6iLnlAXPaEudpCcVcTuNTHQpQKRXyQQaIkQlOS8SY5dT5IrIjl2ayGTzkL+ 9YS66Al10RPqYgfJWUXsvtP9XirAFwmkp6fbsUQwJjlvkmPXk+SKSI7dWsik s5B/PaEuekJd9IS62EFyVhG773TfSwXURQJFRUV2LBGMSc6b5Nj1JLkikmO3 FjLpLORfT6iLnlAXPaEudpCcVcTu96X09HTPSwUsuUjAeIlgQHLeJMeuJ8kV kRy7tZBJZyH/ekJd9IS66Al1sYPkrCJ2vy8VFRWpSwWsukjAeIlgQHLeJMeu J8kVkRy7tZBJZyH/ekJd9IS66Al1sYPkrCL2QK+qSwUyMjIsuUgg6BIhEMl5 kxy7niRXRHLs1kImnYX86wl10RPqoifUxQ6Ss4rYA73Klwr84mbJRQJBlwiB SM6b5Nj1JLkikmO3FjLpLORfT6iLnlAXPaEudpCcVcRu0IAvFbDqIgEzSwS/ JOdNcux6klwRybFbC5l0FvKvJ9RFT6iLnlAXO0jOKmI3aMCXCmzdutWSiwTM LBH8kpw3ybHrSXJFJMduLWTSWci/nlAXPaEuekJd7CA5q4jduE16evrRo0dL congS3LeJMeuJ8kVkRy7tZBJZyH/ekJd9IS66Al1sYPkrCL26F5idJCcN8mx 60lyRSTHbi1k0lnIv55QFz2hLnpCXewgOauIPbqXGB0k501y7HqSXBHJsVsL mXQW8q8n1EVPqIueUBc7SM4qYi/5JQIAAAAAAACADjAmAAAAAAAAACBTyY8J lOQSo4PkvEmOXU+SKyI5dmshk85C/vWEuugJddET6mIHyVlF7NG9xOggOW+S Y9eT5IpIjt1ayKSzkH89oS56Ql30hLrYQXJWEXt0LzE6SM6b5Nj1JLkikmO3 FjLpLORfT6iLnlAXPaEudpCcVcQe3UuMDpLzJjl2PUmuiOTYrYVMOgv51xPq oifURU+oix0kZxWxR/cSo4PkvEmOXU+SKyI5dmshk85C/vWEuugJddET6mIH yVlF7NG9xOggOW+SY9eT5IpIjt1ayKSzkH89oS56Ql30hLrYQXJWEXt0LzE6 SM6b5Nj1JLkikmO3FjLpLORfT6iLnlAXPaEudpCcVcQe3UuMDpLzJjl2PUmu iOTYrYVMOgv51xPqoifURU+oix0kZxWxR/cSo4PkvEmOXU+SKyI5dmshk85C /vWEuugJddET6mIHyVlF7NG9xOggOW+SY9eT5IpIjt1ayKSzkH89oS56Ql30 hLrYQXJWEXt0LzE6SM6b5Nj1JLkikmO3FjLpLORfT6iLnlAXPaEudpCcVcQe 3UuMDpLzJjl2PUmuiOTYrYVMOgv51xPqoifURU+oix0kZxWxR/cSo4PkvEmO XU+SKyI5dmshk85C/vWEuugJddET6mIHyVlF7NG9xOggOW+SY9eT5IpIjt1a yKSzkH89oS56Ql30hLrYQXJWEXt0LzE6SM6b5Nj1JLkikmO3FjLpLORfT6iL nlAXPaEudpCcVcSu/xIzMzO3bdtG/9qwRqVDGHkrKira5rZnzx7fVy9durR5 8+bhbkuXLj1x4oQ1K2oD87FTpNOmTevfv/+CBQskby12C3VrjKbPr+TYrYVM Ost8/i9fvpycnDxq1KgxY8ZQCa5cuWLzqolmvi5Hjx6dM2dOv379Jk6cuGbN GjqmB2qZk5Mzb948OjIuXLgwNzfXb5udO3duC4DOJfy2nz59et++fSdNmrRp 06bi4mJz8YW2oIKCgsTERNr2Bg8ePHv27MOHD5tciuUs31+ZPwfLy8uLj48f OnTo2LFjk5KSCgsLzaxAeno6Z/XUqVPmVzukt9OeITU1dfv27earbznsx+wg +egseYvSfEyA9oQrVqx4/fXXr7vuOpfL9de//tXOVdNaGJWig7XL7Z577vGc TofdDz/8sGLFii4PN910Ex3ZrVxj65iJ/dy5cx07dnT9rw8++MDgNAnCZnJr jMrPr+TYrYVMOstk/vfu3Vu9enXP/eq9995L/QX7V1AoM3VJSUl54IEHvI53 dKBfsmSJb+Pu3bt7NrvmmmuGDBni2ywmJsYVwNy5cz1bUu/15Zdf9mrTvHnz ffv2mQnQ5ILoRKVnz55ly5b1bFCmTJlOnTqdPXvWzIKsZeH+KqRzsFWrVv3h D3/wbEmfR+qJGK9GTk7Orbfeyu2/++47cyGG8Haq9ejRo++++25us27dulAX YRXsx+wg+egseYvSeUwgMzOTNzPlL3/5i81rp69QK7V161aVPc8xgZMnT9LH lqdfe+21TZo0+eMf/6gyPGPGDOtXPWJBYy8uLn788cc5hNq1a7du3bpatWr8 Z8uWLf1+xwGRMLM1RuvnV3Ls1kImnWUm/3v27Ln99ts583Xr1qVDCf//zjvv PHLkSImspjhB6zJu3Ljrr7+eC1G5cuWGDRvSoZz/pB705s2bPRtT35Nfoh5o s2bNypcvz38OGDDAs9nFixddgc2ePVu1vHLlypNPPsnTb7755g4dOjz11FPq lDg/P984OpMLog9+o0aNeOI111xTr149tR2S559/vuS/mLZqfxXSOdiaNWso fK5sixYtHn74YZ4/1XHp0qUGa/LCCy+oeYYxJmD89g8++MCrcLSeoS7CKtiP 2UHy0VnyFqXzmEBGRkbl3/EhLzq2t/CEVKnCwsIGDRqoz6nnmECvXr144qef fqquVVuyZEmFChVo4i233HL+/HnLVz5CQWOfOnUqB/Xee+/xhQH07/vvv88T 6dUSWlExzGyN0fr5lRy7tZBJZ5nJ/z/+8Q/ulMXFxfGU8ePH83713XfftX0V RQpal+nTp1P+a9SokZCQwFerZmVl9ejRg+vy9NNPq5apqak8sXHjxnl5eTQl Nzf3vvvuc7m/cD969KhqSR80bjls2LA0HxcuXFAtFy1axC0/+eSTgoICnjhz 5kyeOHLkSOPoTC7o3Llz9evXp2adO3emTvRV91gEpeWuu+7itycmJoaSVAtY tb8yfw5G6b333ntp4m233ZaSksITk5OT+SuPOnXqXL582e9q/PDDD549tVDH BIK+/cMPP+QYeZ31HxPAfixUko/OkrconccEPPEVStGxvYUnpLz16dOHN9dH HnnEa0yADiJvvfWWbzdZHae8vmXQQdDY+WuLmjVrqlMURidCNJ1OgaLgZz5a CfVTHE2fX8mxWwuZdFbQ/FNPk78Geu+99zyn8+lQpUqVNBxAjgJmPhezZs3i nrJSXFxct25dqkuVKlXUxK5du/p2/9VAgeelArt27eKJfn994Klnz54u91UH Xr/Low8mTX/11VeN325+QUeOHFm0aJHXxO+//57fPmbMGOO3W86q/ZX5c7AN GzbwlAkTJni2/PHHHw06+9nZ2XzZf9OmTcMYEwjp7WosSOcxAezHwiD56Cx5 i8KYQGlhPm8pKSm8uXZ0c/ncT8CvdevW8b592rRpEa6q5YLGXrVqVVrz999/ 32v6nDlzOKgS3sijHo4X5ttHU+zWQiadFTT/w4cP5/2n14+F586dy9OnT59u 7yqKFPZZWatWrXgEgL8+Lioquvnmm13+fuT7pz/9yeW+zFVNWb9+Pdc0KSnJ eCmdO3d2ub/O9prOJxvNmzc3frv5Bfm1adMmfnu/fv3CeHsk7N5f+Z6DffXV VzyF+ulejbmCjz/+uO98uGNStmzZhISEMMYEQnp7qRgTwH4sDJKPzpK3KIwJ lBYm81ZYWMhX3N1+++0nTpz45z//aXJMYPHixbwxe91NSAfGsVPIgU4S6EjK L02ePNnWNZQGxwvz7aMpdmshk84Kmv8333zT5f7Futclynl5eTzs3KNHD3tX UaTwzpHOnj172223udzXxfGUw4cP8+HP93r+//73v/ySulZfnQAE/TGs+pLa ayX5nocdO3Y0frv5Bfk1ZMgQfru6ZLfE2L2/8j0H+/TTT13uCz59b57AP42s Xr2613RKC89k4MCBv/32W6hjAqG+vVSMCWA/FgbJR2fJWxTGBEoLk3lTl5/R gZv+fPXVV02OCXTr1o3f6HmRoSaCxs73+njrrbe8phcWFvItlXr37m3j+smD 44X59tEUu7WQSWcFzX/r1q0p4fXr1/d9iXe5r7/+ul0rJ1gY50hpaWlcLDJ+ /Hie+PPPP/OU+fPne7WfOHEiv3TgwAGeMm3aNHXm8Nlnn1HXvk+fPtRD9L1n 4MWLF2+88UaX+1KBtWvX8kTqEvLb1ZRAzC/IC51+z5o1ix9DULNmzaDtLWf3 /sr3HEyVyfesjPrsLvctCj1voZydnU1FoemPPPIIpWv//v0hjQmE8fZSMSaA /VgYJB+dJW9RGBMoLczkLTk5Wf1qgKeYHBM4c+YMP+zmrrvusmRtrRU09ubN m7vcj/I5ffq0mlhQUNCmTRs+YHXo0MH2tZQExwvz7aMpdmshk84Kmn++Ua3f x0vVq1ePXnrqqafsWjnBzH8upk+f3qlTp5YtW5YpU4bKUaFChbFjx6rvlBcs WMCHP9/nxKlf1W3cuJGnjBo1yuUP9b4XL17s9fb169dXqlSJGzz//PMzZszg vuQrr7wSdJ1DWhA5duzYv//97xdffLF27drckvYABw8eNJMfa9m6v/J7DrZ6 9WoO2esayPj4ePX8CM9UUC1c7kcS8EMhQx0TCOPtpWJMAPuxMEg+OkveojAm UFoEzRt1gfknZrVq1VJP7zU5JqBu0R/GM2tKQNDY1VcPjRo1otOVjIyMefPm tWjRQp1v0MGupFZWBBwvzLePptithUw6K2j++SuPdu3a+b7EN5R74IEH7Fo5 wcx/Lp555hnPbnX//v0vXryoXlXfMu/YscPrjeprfXUJgeqqU2U/+eSTjz/+ uGHDhjylXLlyO3fu9Hx7UVERdf+9OvWNGzf2fDxBICEtiCQlJXkNHezatctM cixn6/7K7zlYYWEhP3fg+uuvHzJkCHX/t2/fPnDgQEqUSghN4cb0Rp4yevRo nhLSmEB4by8VYwLYj4VB8tFZ8haFMYHSImje1E/PPHfOZsYE1q1bxw/Abdas Wck/89eMoLHTavPtlbzQeQvfZOnDDz8sqZUVAccL8+2jKXZrIZPOCpp/6n9R wp977jnfl+hgwd1Au1ZOMPOfixEjRvztb3+jPjVfUe9yPyl7z549/OqUKVN4 4tatW73euHLlSn7J8+b/cXFxCQkJ6s8rV6707t2bm7Vo0UJNz8vL4/H2SpUq de3alZ+L53Jfx963b18zq21yQezw4cPt27enT3316tW5DZ2u0IJK/lzFvv2V wTkYJSomJsbrxIbOatSYTG5u7lX3jdCrVKnicn93qeZgfkwg7LeXijEB7MfC IPnoLHmLwphAaWGct6SkJL568MUXX8z0wBeD3Xnnnfyn7xt//fVXfu5MhQoV UlNTbQwgAma2GTqvGDNmDJ0dlS9fnk6QWrZs+dVXXxUUFPBTU4M+NBlCguOF +fbRFLu1kElnBc3/ww8/7ApwJ3n++rJNmzZ2rZxgYZwjZWdnUzeZ+5V16tTh 39ovW7aM+2urV6/2aj9r1ix+KTk52WC2dFR96KGHqBl1S9XdtPiLBupC8oMD CgsLx40bx7c3JIMHDw5pzQ0W5IX6qqtWreJmZNKkSWEsKBI27a+CnoNRg3bt 2tWoUYPa1K5d+5133tm3bx+PolSqVInbUPeE05KYmKhO/9SjDKlA9Oe5c+cC rUPYby8VYwLYj4VB8tFZ8haFMYHSwjhvL774osuE+Ph4z3cdP36cE0tmz55t ewzhCmmbobMLOkvh/x86dIijmzdvnl0rJxKOF+bbR1Ps1kImnRU0/9xTqFev nu9L/K2i721dIXJhn5X169ePj3f8nJ2tW7fyn3PmzPFqOXbsWH4p6C2Fu3fv zi35N+Y7duzgP7/88kvPZmlpafx7//Lly/s+OM8MrwUFkpWVxRfu+t5y3252 7K9COgfz/GHIG2+84fr9EuXdu3ebOf179NFH/c42kreXijEB7MfCIPnoLHmL wphAaWGcN98f9/m1dOlS9Zbz58/zVS7ks88+K4kYwhX2VkqnRhyg8bchECoc L8y3j6bYrYVMOito/t99912X+6tbOlh4TqeOJO9Xe/XqZe8qihT28e7gwYNc l65du9KfmZmZ/GefPn28Wr799tsu90X4nnet90td1c+/W4+NjeU/fZ8k+O23 3/JLfm8VGJTXggx06NCBW548eTKMBYXN8v1V2Odgly9frlWrluv3ryP37t1r 5vSvSZMmfucWydtLxZgA9mNhkHx0lrxFYUygtDDOW0FBQa4/bdu2dbnvZMt/ qhOA/Pz8J554grfe11577cqVKyUURljC3kr5ickU/qVLl6xeKdFwvDDfPppi txYy6ayg+Ve3HfN6mN2ECRN4uteFZ2CJoHUJ9FN6dV1c586deQpfyOr1zCx6 O98EINAXx574NoZly5blk4fBgwfTn2XKlPH8zpolJyfz0mNjY4PONuiCrgYO k78iJzk5OWEsKGzW7q8iOQebO3cuv/Gbb77hKadPn/Y9/VMPo6SK0J/q1tO+ wn57qRgTwH4sDJKPzpK3KIwJlBbh5c3vPQbpaP7UU0/xptu2bVv9+8tmYk9N TfU6S6HDJcf49ddf27hyIuF4Yb59NMVuLWTSWUHzT92WypUrU85btWql+izU ZWvSpInL/dNmzQeTS6mgdXnppZfatWvn+/tu9duBadOm8ZShQ4fylA0bNqhm Cxcu5IlTpkzhKVu3bm3QoMG2bdt814TvUaBumUXnul6LUEaOHMkvJSYm8hTa fmbMmBEXF6cOzeYXlJKSUq1aNc8rG1l2dnbVqlV58zNIkR0s3F+ZPwcrKCjw unDi1KlT/Cg0yoDxZR6BbhLoW5eQ3u6pVIwJYD8WBslHZ8lblM5jAps3b970 O77lLG11aorBmGdUsmpMgA4xrVu35t34jTfeSMfcJUuWLPxffu9G6KCgsW/Z sqVChQqNGjWisxE6vB49erR///58jlGzZk11ewGwipmtMVo/v5JjtxYy6Swz +e/SpQsfLD766KMzZ85Qf6RTp048hfaxJbKa4hjXZf78+Zz/unXrxsbG7t69 u7i4+MSJE7179+b7DN90003Hjh3jxnQov+6662gi9aPpKEktN2zYQA1oyg03 3JCXl8fN+JnaMTExffv2Xb9+PXUV6ZM1adIkOkNwuX9isGLFCm5Jb+GPYcWK Fak/qL7KX7BgAR2CaXqNGjXoHIMnduvWjVdVXRhvfkH169fnKbQFLl++nJrR kZ3S8uCDD/I8u3fvbnHeg7Fqf2X+HIzS2759e6rg0KFDs7Ky6I1UPqo7v3fi xInGKxOoU+9bl5DenpGRoSLq06cPtxkyZAhPKfknRWI/ZgfJR2fJW5TOYwJ0 zHIFNmLECJvXVC9WjQnQ5mqQVUbHdytXPWJBY+/Zs6daeT4vYrVq1UpJSSmp 1RTEzNYYrZ9fybFbC5l0lpn809lO06ZNVcJ5oNXl/n7E+EtGCJtxXQoLC19+ +WXPT4F6ECEXyOvn/NShU8dEVb5y5cotW7ZMtZk/fz736NUxVLUkPXr08Jxh YmKiWmK1atUee+yxO++8U63Jzz//rFo2btyYp1ObUBdEGeDnCLNrr72WBzdY s2bNSn6o36r9lflzsCNHjvBFESpd6v//+c9/Aj2gQQnUqfetS0hvpygM1pwy YLxWlsN+zA6Sj86St6jSOyYwfPhwm9dUL+FV6l//+pfrf2+P2atXL4OsMs+z BR2YiX3q1Knq+cUu9zlP27ZtT5w4USIrKE7kx4vS+/mVHLu1kElnmTym0MnP s88+q7qBMTEx1Cct1ac9mjNTl3nz5qnb0ymPP/74li1bfBvHxcXxA7VZnTp1 fA/x6enpnTp14u/rlbvvvnvJkiW+M9y7dy/fqshTmzZt9uzZ49nsiy++4JdG jx4dxoJycnK6dOnC9/FW6I2DBg26cOGCcX7sYNX+KqRzsKysLM9Pn8t9JcbM mTPNrHBaWhq/xevBE37rYv7txmMCFStWNLNuFsJ+zA6Sj86StyidxwTAk+S8 mY/92LFjq1atSkpKwu8FbIWt0em1iAbIpLNCyn9+fj7tVzdu3KiuDAebmK9L ZmYmVWTp0qWJiYm5ubnGjQ8ePLh27Vrf5wV44l+vr169mlpmZGQYz/DcuXPJ yckrV66kf/0+vP6q+zl3O3fujGRBRUVFqampy5cvj4+P379/v4O3P3Jwf0Xn M1u2bKEMBC2KSYHqUhphP2YHyUdnyVsUxgRKC8l5kxy7niRXRHLs1kImnYX8 6wl10RPqoifUxQ6Ss4rYo3uJ0UFy3iTHrifJFZEcu7WQSWch/3pCXfSEuugJ dbGD5Kwi9uheYnSQnDfJsetJckUkx24tZNJZyL+eUBc9oS56Ql3sIDmriD26 lxgdJOdNcux6klwRybFbC5l0FvKvJ9RFT6iLnlAXO0jOKmKP7iVGB8l5kxy7 niRXRHLs1kImnYX86wl10RPqoifUxQ6Ss4rYo3uJ0UFy3iTHrifJFZEcu7WQ SWch/3pCXfSEuugJdbGD5Kwi9uheYnSQnDfJsetJckUkx24tZNJZyL+eUBc9 oS56Ql3sIDmriD26lxgdJOdNcux6klwRybFbC5l0FvKvJ9RFT6iLnlAXO0jO KmKP7iVGB8l5kxy7niRXRHLs1kImnYX86wl10RPqoifUxQ6Ss4rYo3uJ0UFy 3iTHrifJFZEcu7WQSWch/3pCXfSEuugJdbGD5Kwi9uheYnSQnDfJsetJckUk x24tZNJZyL+eUBc9oS56Ql3sIDmriD26lxgdJOdNcux6klwRybFbC5l0FvKv J9RFT6iLnlAXO0jOKmKP7iVGB8l5kxy7niRXRHLs1kImnYX86wl10RPqoifU xQ6Ss4rYo3uJ0UFy3iTHrifJFZEcu7WQSWch/3pCXfSEuugJdbGD5Kwi9uhe YnSQnDfJsetJckUkx24tZNJZyL+eUBc9oS56Ql3sIDmriD26lxgdJOdNcux6 klwRybFbC5l0FvKvJ9RFT6iLnlAXO0jOKmKP7iVGB8l5kxy7niRXRHLs1kIm nYX86wl10RPqoifUxQ6Ss4rYS36JAAAAAAAAAKADjAkAAAAAAAAAyFTyYwKF AAAAAAAAAOAojAkAAAAAAAAAyIQxAQAAAAAAAACZMCYAAAAAAAAAIBPGBAAA AAAAAABkwpgAAAAAAAAAgEwYEwAAAAAAAACQCWMCAAAAAAAAADJhTAAAAAAA AABAJowJAAAAAAAAAMiEMQEAAAAAAAAAmTAmAAAAAAAAACATxgRAZ2lpaXFx cbGxsbNmzTp37pzTqwMAAAAAABBVMCYAno4ePZqenm7JuwoKCo4cObJ58+Z9 +/adP38+vPUZPXr0gAEDRo0aNX78eJqh3zanT5/OcMvMzPRdh4zfnTlzRk2n lhk+Ll68GN5KAgAAAAAAlFL6jwlQdzIzgJ9++ungwYN+Xzp+/Lh9SUtJSaF+ qH3zL3l5eXlr1qyJjY2lDviXX34Z+buofz179uwBvxs3btyJEydCXSvqp9N7 p0+fHmg0gOTn548fP14tyGto4pdfflEvxcXF8USa2wB/PAcNQnLgwIGQtoe9 e/deuHAhvGXpsFwAAAAAAIga+o8JUK//i9DNmjXLpozt37+f5x9NXytT15KC GjJkSEhjAgbv+umnn7g7T+VbuXIl/z/Utdq6dSu9cfXq1QZt1q1b59mvP3Lk iHrp7NmzI0aMUC+pTSIvL8/CMYEdO3YMGzbM/PbA28/UqVPz8/PDWJzjywUA AAAAgGii/5hAenr6ZB+TJk2iDhF3/8eMGePbYMmSJXak6/jx46NHj+blrlq1 ysI5HzhwICUlpaCgYM+ePcuWLUtNTS10f6O9e/fu+Ph46lZv375d9f7Onz// yy+/7Nu3T72d/p+cnKx6tdRbpz+pP07/P3nyJPXQaW3p36C/Cxg6dKj5MYFA 76LVHjt27KBBg9T6TJkyhTrdWVlZvu8NFOPRo0d//PFHetcPP/xAsfj+LqDQ XQ4ekfA7JkDbgOdLakyAEsJTvvrqq59+t2nTpvAGeU6dOjV+/HiT24PafmiJ YSxLh+UCAAAAAEA00X9MwBf1iL/55hvq4MyYMWP48OH0n23btlmVEKeWu2DB AuqlLlu2jLurK1asyMvLmzlzpmevdvLkybm5uYXub8AHDhw4atQo9Xb6PzWg vjP/Sf+hP9evX3/s2DHqsHvOJCkpyWA1LBkToB4oLYiypKZs2LCBpmzZssXr jQYx8oCA4vteEhcXp3r3XmMCaWlpPGXChAleYwL8kwRCOQ81Ur9oWbw9bN++ 3aCZ2n7mzp1r8GsI/ZcLAAAAAABRozSOCVBXjjo4X3/9NfUof/nlF/r/iBEj wrgznlbL5TEBEh8fTx156hevWLGC/pw6dWpWVtaJEyd++OEH+nP27Nncnr95 52/PVSeXOn386vz58we4f1zP35WvWbPmwoULNJ/FixefOnXKYDUsGRM4dOgQ LdTzUo1du3bxani90SDGkydP8u8Cli5dSgnxvbB/9+7dHHVcXNycOXM8xwSo 50tl4in8AwTPMYHDhw+roYZRo0bFxsbSaoR9MwFmZnvw3H4iWZYOywUAAAAA gOhQ6sYENm7cSB0c6oGqC9GXL19OUyZMmGDrff/sXi6PCaguP3XhBw0aNGTI ENWFpykjR46kNnz7xLVr19L/N2/eTP/ftGkTf8NOfUP+IpjWc/jw4fT/hQsX 0kuJiYkmV8OSMQEeAfC8D8DBgwe9RgnMxMjd+XXr1vku8fz58/xIgsGDB+fk 5Kj7GfKYAKWF/1y8eLEaMFFjAvv27Rvgg+oY4f33jLcH3+3HKk4tFwAAAAAA okDpGhP49ddf+bf8e/fuVRPz8/NnzJhh633/SmC5PCbAdwAodP+a3uvye7Jo 0SKauHv37sLfv+zmMQRagXHjxvH1+enp6dnZ2eqagQMHDlC/m/785ptvkpOT g3Z7fccE0tLSVvnj+cW017v27NnDFzyoKb/99tsA9y8jPOccNEaDMYGEhAR1 kQAlTV0VkJSURCtMReE/d+3atWXLFv7/lClTqCVV6uzZs/PmzZs6dSq9tHjx 4oEDB3ID9cuL8BhsD363H6s4tVwAAAAAAIgCpWhMwOA+aSHdb03P5fKYAPX0 +U/+LnvOnDmebfgG/nxDgIKCgmHDhg0fPpy6hJ9//vmPP/545MiRAe57CFCD AR73DaDO+/fff889XwrE7836FN8xAeqef+XP/v37A72Llsjf0aspO3bsoCle CQwao8GYwKRJk3y/61fXAwR6iZw9e9ZrVuqmBMuXLzfIjBl+t4cSuL+fU8sF AAAAAIDSrrSMCQS9T5rJ+61pu1yvMQG+TV9sbKxnG+69HjhwgP/kH9H/8ssv 9G9qaiqPEsyYMYNndeLECc/3njx5ct68eTT9u+++M1gNS347cPr0aVoQddvV lPj4+AHun/Z7vitojDaNCdDq7dy5U81Ktfe8sCFsXttDid3fz6nlAgAAAABA qVZaxgTM3CfNjvsNlthyvcYECt33BKAp6uv4jIyMgQMHUu/73LlzPIUfLjB+ /Hj6l2+RN3v2bGowZsyYcePGcZujR4+qn5lTd5haqpf8smRMgEydOpWWRUsv dPdPaZUGDx7s+x29cYwGYwI5OTkZHtTDC1JTU7Ozsz1fUrcinDZtGv1JHWQe dpg1a9amTZsWLVqkfjuwY8eOUAP3y3N7KMn7+zm1XAAAAAAAKL1KxZiA+fuk WXvfv5Jcru+YAF9vP2TIkFWrVsXHxw8bNoz+pFVSDU6ePMmdWfVVu/rtPN/N j3rW1EOkNyYkJCQlJXHH2e8D+OjVJW7cQeb/q+f6BWLwLuqbD3D/VIFq/e23 3w74358SmIzRYEzAi9c9Bj153WPw+PHjn3/+ue/1A1OnTrXwZhS8PYwcOdK+ +/udP38+0wcPBVDReel79+71asA3bwQAAAAAAGD6jwmEdJ80C+/7V8LLpS7z APcdAj0nbtu2bfjw4dxppZ5sYmKi13XgEyZMoJdWrlzJf/LV+ERdG09zGDt2 rOr5zpw50++ohXqWn6eg35sbv4u69kOHDuWJ1JJS5HcmBjFu377d8jEBzlJc XJy6D+HAgQMp+RE+i9CL2h7su7/fwYMHvwidSgIAAAAAAEBhaRgTyMzMnDRp kvn7pPH91mgREf6M2qnleqG5ZbtFMtvTp08fO3bM1mc1+kVdY+qSqx87BGJJ jKGiZeXk5FCVI3wEYSC8Pdh3f7/09PTJ/nz99dfDhg0bO3as31e9HgcJAAAA AADC6T8mQPLy8kLqLQbthGq+XIgOOTk5jtzfz6nlAgAAAABAqVMqxgQAAAAA AAAAwHIYEwAAAAAAAACQCWMCAAAAAAAAADJhTAAAAAAAAABAJowJAAAAAAAA AMiEMQEAAAAAAAAAmTAmAAAAAAAAACATxgQAAAAAAAAAZMKYAAAAAAAAAIBM GBMAAAAAAAAAkAljAgAAAAAAAAAyYUwAAAAAAAAAQCanxgQAAAAAAAAAQAcY EwAAAAAAAACQqSTHBAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAABAjv8Py22UJA== "], {{0, 340.}, {686., 0}}, {0, 255}, ColorFunction->RGBColor, ImageResolution->144.], BoxForm`ImageTag["Byte", ColorSpace -> "RGB", Interleaving -> True], Selectable->False], DefaultBaseStyle->"ImageGraphics", ImageSizeRaw->{686., 340.}, PlotRange->{{0, 686.}, {0, 340.}}]], "Output", TaggingRules->{}, CellChangeTimes->{3.8343137009162073`*^9, 3.834405316856949*^9}, CellLabel->"Out[2]=", CellID->144090406] }, Open ]], Cell["Retrieve the \"MatchIt Lalonde\" variant:", "Text", TaggingRules->{}, CellChangeTimes->{{3.834317687323732*^9, 3.834317698564961*^9}}, CellID->1251899111], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"ResourceData", "[", RowBox[{ TagBox["\"\\"", #& , BoxID -> "ResourceTag-Job Training Efficacy-Input", AutoDelete->True], ",", "\"\\""}], "]"}]], "Input", TaggingRules->{}, CellChangeTimes->{{3.834317705666854*^9, 3.8343177092323847`*^9}}, CellLabel->"In[3]:=", CellID->1955029757], Cell[BoxData[ GraphicsBox[ TagBox[RasterBox[CompressedData[" 1:eJzsvXecFMUW9j/kHFXgSpYgWYIIKAqiZPECioqCXkARJKkIIlnUBQWRjMgV UIJIlCRx+eGSl0vOImmX3WXJLEhQkN9557zW2/bMdtfM9HTXbD/fP/iw3TXd p87TXVVPh+qSHXu17pze4/H0zkr/tO7w4dPvv9/hoxfy0h9tevbu+nbPt95s 2vODt95+6/3aHTPQwrlUtnZGj+f//P8eAAAAAAAAAAAAAAAAAAAACIG/vDgd BQAAAAAAAAD8P06fPh0XF+d0FAAAAAAAAADwf7l9+/ZOL3/88YclG/z/AAAA AAAAAEACA1tx+vTp/3mx6paK03UFAAAAAAAARAapeQpxM4WhP63yKSkuxs0Z QN2djgKYA6WsApl0FuRfTaCLmkAXNTH2KXwzJckL/Yf+hE8JHTdnAHV3Ogpg DpSyCmTSWZB/NYEuagJd1MTAp/DNlD179ty9e/fOnTv0H0tuqeBIcHMGUHen owDmQCmrQCadBflXE+iiJtBFTQx8iriZwn9adUsFR4KbM4C6Ox0FMAdKWQUy 6SzIv5pAFzWBLmqSmk/R3kzhJVbdUsGR4OYMoO5ORwHMgVJWgUw6C/KvJtBF TaCLmqTmU/hmytmzZ7ULLbmlgiPBzRlA3Z2OApgDpawCmXQW5F9NoIuaQBc1 8etTfG+mMPRn6LdUcCS4OQOou9NRAHOglFUgk86C/KsJdFET6KImfn3KqVOn fG+mMLQwxFsqOBLcnAHU3ekogDlQyiqQSWdB/tUEuqgJdFETX5+S2s0UJvRb KjgS3JwB1N3pKIA5UMoqkElnQf7VBLqoCXRRE1+fYnAzheFbKlQMPiU43JwB 1N3pKIA5UMoqkElnQf7VBLqoCXRRE51PuXXrlsHNFCbEWyo4EtycAdTd6SiA OVDKKpBJZ0H+1QS6qAl0UROdTzG9mcKEcksFR4KbM4C6Ox0FMAdKWQUy6SzI v5pAFzWBLmqi9Sl8M4UMyPHjx08aQgWoGBWmn8CnBIqbM4C6Ox0FMAdKWQUy 6SzIv5pAFzWBLmqi9SnJycn/CxD6CXxKoLg5A6i701EAc6CUVSCTzoL8qwl0 URPooia6575sAEeCmzOAujsdBTAHSlkFMuksyL+aQBc1gS5qAp9iP27OgD11 37hx47Jly1avXh3uHQWEm3WPLKCUDDJnmZqZjIuLW+blyJEjTscSXtTMP4Au agJd1AQ+xX7cnAF76v7kk096PJ4HHngg3DsKCDfrHlmkJaX27t375ptvdunS 5fDhw9ZuWeYsUzOTK1eu9Hj56quvnI4lvKiZfwBd1AS6qAl8iv24OQPwKU5H AcxJS0o1b96cx+T/+c9/rN0yfIr62JP/kydPbjLj3Llzqf380KFDXOb06dPh DlUR1NRl27ZtqRW7ePFiuKNVATV1YUidyZMnf/jhh2PHjl2zZs3Vq1fDHac6 qOxTSM2nvOzbty/MabA1GDX7bnuAT3E6CmBOWlKqffv2PCZ/6623rN0yfIr6 2JP/zz//3GPG+PHj/f72+PHj+fPn5zJTp04Nd6iKoKYuWbJkSa3Y999/H+5o VUBNXWjw2bp1a12B2rVr/+9//wt3qIqgsk8h/8iK0H/CnQc7g1Gz77YH+BSn owDmpCWlTpw48cknn0RFRVl+sRo+RX3UGXfNmDHD72+ff/55UQY+xVoC0uXc uXNByJfGUFCXK1eu1KtXjxfmzZv3lVdeqV+/Pv9ZqlSp5OTkcEerAir7lJ9/ /pnlUMGnWBiMmn23PcCnOB0FMAdKyQCfoj725D8hIeGAPzZv3pw1a1bKc61a tWi45fvD6dOna8dm8CnWEpAuR48eZRWGDRvm+5OzZ8+GO1oVUFCXH374gXXp 1avX+fPneeE333zDCz/77LNwR6sCKvuUWbNmSVqD+Pj4VatWnTx50nfV4cOH qVc6ceKE6e727dtHZuTXX38NMRhTAj0XjAMTUBLWrl27Zs2aU6dOmW5TPi3W EmjdJeNMSkqiiu/evZsf2uQLDlb5FNrmnj176ACLi4sLZTtBtIGpHdhU3+jo 6E2bNomGKzUkg6ftrF+/fsuWLZcuXQoowjRJEEpdvHiRzr5jx475rtq6devG jRtNlZI8zRmDFs90rQ556YM4y4Lr92NjYykk2p1xMWoWqHE4ePCgzDYpbAo+ MTGR/k//MfUpMi0PjSV27dq1fPlyCljycfHUpJFXIaBT1cI2J4gmolOnTpTk bNmyUfJ91/7222/8xNejjz4Kn2JKuHXZvn07q/Djjz8GFFhaQkFd3nvvPVqY PXt23Xbq1KlDy1988cWAoo1Q1PQp48aNe+ihh3LlysUnDv0nr5caNWpwgSpV qtCfkydPPnr06HPPPZc5c2Yqdv/994stUK8RFRVVqFAhca2mcOHC06dP1+2I in333XdPPvlknjx5REn61eLFi+WDCS7nxmVkAhNs2LCBDLgoli5durwaWrZs GWhawodkOyAfJw0Ca9asmT59ei5G9R08eHCTJk10I6izZ8/S1mhtjx49dFsY OHAgJ4r6Td2q48ePUzsgpCfKlCkzc+bMwOv9f5Cpu/GBfeTIkc6dO1MMJDHH kyFDhpdeesnviE4y+L1799KAM1OmTFyG9kj7lRziplXklZo4cSIdNq1bt86Z MycnsFKlSrNnz6YC1K188MEHRYsWFUoNGDBAN5qVP82NDwyDtXTW8OHtO4Gw vPSSZ1mImaQe//XXXy9QoIBoxzp06HDhwgXfn4waNYoaBJExGu726tXLb8nL ly/36dPnvvvuE9usXbu2uBrp61MkW55z586Rmjly5BDFaHShbXV/+eUXbe0M uip5FYI4VUNvc4LbL7Fu3TpupkaMGOG3AG2E1tJmlyxZwluGT9Fisy402Obt KHgD1DYU1IXNCzVxuvLt2rXzeN9SCaB6EYuaPoW6AI8/aKDFBUqUKEF/9u3b t3r16mJt48aNeW1ycnLDhg3Fcr6VxvTr10/s5cyZMzSo8LsjOmCWLl0qGUxw OTcoIBkYQ82LGD9QSNrum6FUBJSWsCKjvnycM2bM0K7VoR1BxcXF8UI663W7 6927N6/SXcqm3lO84Knj22+/DVPdDQ7ssWPHcpPoy+OPP67bjmTwc+fOFUYm Y8aMNJbm/z/44IOHDh0Koo5pA3mlqAMSTkSQO3fuHTt2NGrUyDf5X375pdhC QKe5cYtnsHbKlCn85/Lly7UblJde/iwLJZPPP/98qVKlfLffs2dPbWFqHJ59 9lm/kVSsWFE393JCQkLdunVTi9zj41MkWx7yMs888wwvJ4NJ+/V9ATk6OlpG OHkVgjtVQ2xzgt4vUbVqVSpG//q900StEG9n4MCBu3fv5v/Dp2ixWRfxfJHk Dco0iYK60AZ5CytWrNAuL1++PC0ktxJURSMMNX3Kvn37aKAlZqohg7nEi3jm ig8VpnLlyjSE27Bhw6ZNm3gtHUK8qnXr1txz0Qb5aWo6crQTdvELSi+88MKP P/547NgxGqmKUesTTzwhGUxwOTcuIxNYiveabZEiRTzeuzxkWHghdb5csk+f Pv/73/9E3y2flvAhU3fJOH/77TdxEfs///nPli1bjh49+v333xcvXpwXhuJT Tp8+ff/99/PyNm3a/PLLLzTmWbZsWZUqVah1Mn2GJ+i6GxzYfL2LmrvPP/88 JiaGH8IpWbKkbyMmGfzJkyfz5ctHZe677z7K24ULF2jVhAkTeHhGB3wQdUwb BKpUy5YtyVZs375d+1IwQQmfNm3azp07P/roI15Cv9JuRPI0TzFr8QzW+vUp 8tIHdJaFnskmTZrMmjWLBq7UzHIXT6ZP+2y8yE/ZsmUp5/Hx8evXrxcvljZt 2lS75V69evHyQoUK0WaPHz++cePGN998U+xO51MkWx5KLxd76aWXODYqzMOM /PnzU4qo1RXTihpII69C0KdqiG1O0PsVF+e/+eYb37Xiia8aNWpcvnyZThAu DJ+ixWZdJk2axKtoYExnGQ2A6XQgO+mSN7UZBXWhloSND50yog2n3ty3VU/D sC7/C5Yw+RRmyJAhrIWvIxCHyuOPP64bNO7atYsvOzdr1ky7nMZ13DZq5+ek DlHcoBeIXk/7SL9BMIEikwHJwKhP5CXDhg3TluSPJtAwSSwJKC3hw7Tu8nEK 5/juu+/qSvKlhlB8ith49+7dtYUvXrxIQ6NAavz/CKgN9D2wU7yXvHS9hnjS vmvXroEGzzeO06dPT82sthgdSx7vc0p+37ZwAwEpNWDAALGQRq3iCa7GjRtr n8fj4S6hfQBAvv0xPjAM1vr1KfLSB3SW+RJQJmlcpL2W+PLLL/NyauV4CfU4 /DQF2XNtcujAFkmjc4QX7tmzhwtTu6G7zzJ58mQurPUp8i0PP7BEC7UGavPm zboAdLXzlUZehaBP1RDbnKD326pVK493tOb3kg53TzR444lV4VP8YrMuUVFR Hn8ULlxYd0inYRTUJcXrYsTFIjp3qEnnFumFF14IsH6RivApiYHjuE9Jly6d 7wzSfEgQBw4c0K2isZxH4om+Tz/9lLdAXY9MMIEinwHTwMQgZOXKldqSQ4cO 9XivAV6+fJmXhJ4WSzCtu3ycfObef//9vm9n+M5EFJBPocESP3lOG+cXby1B vg30e2CnBodKzRf/KRk8FcuePTsVe+2113SrRK50jx65B3mlatWqpVsunkrS vS4xaNAg31bFL37bH+MDw2Ctr08JSPqAzjJfQsnkxIkTOZj58+fzko8//piX TJgwQVd4/fr1vOqll17iJSKNums4KanM9yXf8nDAVH1dMX76q3///r6185VG XoVQTtVQ2pyg93v06FHqemgtNa2+a8mM8G/Fc/jwKX6xWRfhU+rUqdOrV6+e PXtWqVKFl5B/V2HOVRtQUJcU73UYsiSef1K1alWXTMKWEuE+xbdrI1555RWP 97HhdT6w2y1YsKDvr7Zv3z527Fg6wEh9YV0XLVokE0ygBORTjAMTsyXzq7sC amQ8/3z3Kri0WI5p3SXjPHToEFf8jTfe8N1IiD5l3759vKRjx46hVlhDKGM2 LefPn587d+6AAQNatmxZtmxZDrVmzZq8VjL4vXv3cjE6VHxTzavowAu0jmkD eaV83wx69dVXOXviEgEzbtw4Xk7nrO/WTNsf4wPDYK2vT5GXPtCzzJdQMkn2 hPcu3qjixoE4c+aM73b4CdjKlSvzn+JOEA0DdCX9+hT5FpLfealYsaJ2m9Qh 8ja/+OIL39r5SiOvQiinaihtTtD7HTlyJK+lo1q36tixY/xgDOVQ3DuDT/GL nbowdKItWbJE/HnlypU+ffrwT3xPzzSJgrpQw0LJp7XUL3Tu3Fk7zciHH34Y dE0ji4j2KX7PHX5O2IAMGTJoy9O5mdpPFixYIBNMoEhmQCYwGrLyRbx69eqJ H8bHx/PB/PTTTwedljBhWnfJOGkIx3/6XixNCdmniDfXhg8fHmqFNYQyZmOO HDnStWtX7uh1iNnnJIMXb00aMGjQoOBqGumEotTrr7/O2dP5FPGskc6nSLY/ xgeGwVpfnyIvfaBnmS+hZPKnn37ivQuf8sgjj9CfdPz73Q7bh8yZM3Pmybl7 vNNw+Zb061PkW0jxtpH2Lra417NmzRqZ2smrEMqpGkr+g94vfzubhlW+30xp 1qwZ/3Dt2rW//s3q1at5IQ3Y6M+EhATjgNMAqumSGlSSjD/9ioYZugYtTaKg Li+++KLH2+hxYBcuXKDTRExgOHjw4GDrGkmkPZ/y2GOP8Wn1eCqI+a9SNPc6 qXej42HatGlUKTGicNCnyAfWr18/XtioUaNZs2ZNnz794Ycf9njttrYbDSgt 4cO07pJxijbh888/993IU089RavIrIklAfmUmTNn8pJRo0aFWmENIfqU48eP iwmRypQpQwfkihUrTp8+za/SC58iGbz4HhBtKrVUu+QbxL7Y5lPkT3MLfYq8 9IGeZb5Y61O4cdBOAaqFJyXImDEjf2iAn1rJmzevb0nx1uqYMWPEQvkW8ujR owULFvR43/EfPnz4vHnzevbsyZMuPv3007rpelKrnbwKoZyqoeQ/6P3+61// 8vh7Li42NtYjgfHd5LSBUroY06NHD96d/KPIkYtqumzdupW3qbvqeODAAZ5n MmvWrL7fU0h7pD2fwuY0T548ph/eEs8VUA+l/Z6XmF3fKZ8SUGBJSUm+F9hz 5Mihu8Mon5awYlp3yTjFW6u9evXyXWtwP8X3CRZfnyL603feeUe2YhKE6FP4 a2gZMmSgcax2uc6nSAZPJy8Xo6FygPVI+9jjUwI6zS30KfLSB3qW+WKtTxEP afv9YBBvp1y5cvwnv6xN+D7F7fd+SkAtpOgOtFAVfN+TTa128iqEcqqGkv/g 9rt///7UjhmxQWOqVasmv7sIRSldjBGPfpm+WJcGUE2XMWPG8FrfyaK//vpr XuWGWQ5U9in8MjihmzwhxbBfFp870d1/9+Wtt97iksePH9cu9ztOMAgmUEwz EFBgPCtO3bp1Bw4cSP14+/btyXr7PpItn5awYlp3yTjj4+O5WNWqVX3X+o6g Ll68yB9REu+bC2g8z5sSPoUK85yodJjR/2XrZkYobeDJkyc5SN9p2XQ+RTL4 S5cu8WxI9evXD7wqaRx7fEpAp7mFPkVe+kDPMl+s9SliLgLfDy+Kr2mT3eAl 4rz2/b62X58i30LGxsZSAukso06hQ4cOLVu2fP/991P7indqtZNXIZRTNZT8 B7df8dyp3xmJ4+LiTvkgnt6nURn96fflozSGaroYwN+Boj1a2BUqi2q6DB48 2OOdQEzMcy6ggag4a+R3F6Go7FPENPWjR4/WrTLol2NiYnhEWqlSJeMzi6eX pMLai2AXLlxo27at7zjBIJhAMc2AfGDJycnZsmXzaKbESQ35tIQV07rLx8nT ohK6j3f/8ssvPGufbgTF7+wULFiQHwthvvvuO/ElJu28xOJzb5999pluvzt2 7DCtpl9CaQPFBNS659Zog/zatfAp8sGLC86ufb4rNezxKQG1Pxb6lJRApA/0 LNNhrU/ZtGkTNw4UlXaC7qtXr4oaiXexxQOQtWvX1mpB/3/jjTd4ldanyLc8 /H5K9erVjetlXLuUQFQI+lQN8R5uEPsVHaXugDEA79H7xU5dNm7cWLFiRfE9 JsGKFSv4V36vVKQ9VNNFtIGTJk3SraLOnVetXbtWcl+Ri8o+RWhEYzA6g6gz Ek9IGvfab7/9Nv/wkUceoX3x20lHjx7t168f/UT0We+99x4X69ix48mTJ6lv Wr16dbVq1Tx/ox0nGAQTXM4NCsgHlpCQwM9FN2vW7MCBAzQCv+ollLSEFRn1 JeMU45Ds2bNTBxcfH3/kyJEvv/ySjZvHZwQlJoyloT6NDLdu3Uob5OwxWp9C XSdfG/F4b8hSbmm/1Ji3atWKBjPB3ZMKpQ2kURkPonLlyrV+/XqSmBIyZMgQ ns/Q80+fIhn8wYMHeR5F2sigQYP4gxQ0Tl6yZEm9evWmTZsWRB3TBvb4lIDa H2t9irz0gZ5lOqz1KSmaxoFsAjkLSvKuXbvE5zXr1q0rStI5QkMvXt64cWNy 6OfPn6eDv0GDBiLDuu88SrY8/PnIDBkyzJ8/n1pggybXoHYpgagQ9Kka4rgr iP3279+fc0jqGO9XAJ/iFzt14Xdas2TJ8uGHH65atercuXNnzpyhIbT4tPrC hQuDqmiEoZouNMzmt1dos998841oZ2bPns0t8IMPPhjcV6cjC5V9CvULxYoV E30K6ZI5c2a+/2Xca9MpVrt2bfHDrFmzio+vaXs9Gqnyh7083quaPA70eJ9w 9h0nGAQTXM4NCgQUmLh4roX60Pvvv79Nmzbz5s0LNC1hRUZ9yTjpnOVb0r7w DKW6ERQlzbdkzpw53333Xf6/1qekeGcLFLdaPN57r+L/nTt3DlPdDQ5sUlOr r0gOfxlc61Pkg6eBAX8ql7nvvvtEydKlSwdRx7SBPT4loNPcWp+SIi19oGeZ Dst9CjUO/KKWL3QikGfRboGqzMMGHaIl1/kUyZZn7dq12hNKQPuqUqUKjU+0 b7YaCyd/AgZ3qobY5gSxXzLdvNb3ifrUgE/xi526zJo1S1x58Pyzv/AE/kpL 5KKaLine1kZcdSxQoECtWrVE80XL161bF2gdIxHhUxT8Hn2K9148D8PE6bNx 40ZaXqZMGc8/593VceXKlU8//ZSfTBDQAUYdk/b7a3R6ivmoPd6ed8SIEVSA Dy3tOMEgmOByblxGPrA5c+Z4DNF+E00yLeFDUn3JOKlY3759tc0CNQU//vgj VZmTptss5VCM3imTjz32GCkoptTQ+ZQU78MtPBWq4MEHH/z666/DV3eDAzsu Lq5ly5YiEhri0gBy9+7dPOGbzqfIB0+DhCeffFLbMVHr98ILL8TGxgZXzTRA KEqxT8mYMaNukkka5XJ6tfN9yZ/mxi2ewdrp06fzxn1vAkpKH+hZpiWUTIq3 SHSXUC5dutS/f3/xlRk+Fyjtfuez3b59u7ir4vFeX2rbtm18fHzhwoXpz4kT J+rKy7Q89B/jSYwffvjhxL8/sWraVcmfgEGcqiG2OUHsl6cjILQP5hlz4MAB /sl3330n+ZNIR0FdDh061L59e3EDhSlZsmRqL16lSRTUJcX7hn7Tpk11jQzf I5avWkQjfEoQjsMGn5Li7ZVoJDlv3jzq3/k+WkDQ2bdkyRL67cmTJ/0WoMNj /fr1y5cvl7n+E2IwjGQGZAKjsQePH8aPH79t2zYy1+S+V69ePWnSJPFNNN8R bIpEWsJEoOrLxEmibNmyZeHChUeOHDHdIA1mKGlLly6lsYpkDGfOnFm1ahWF IbN9AwKtu1+oT6dgqAqSd3slg+eDbdmyZdT0ad/fcSeWKCVJQO1P+AIwlT6g s0wQvkxevXqVxgMUz+bNm01fuKPWgzJMZ438q3kGLQ9/BOSRRx6hHpBqt9bL /PnzP/74Y77H5PG5xmWK/AkY0KlqYf7RRFiIsrpQt0InFPWPdL74zsaT5lFW lxTv0GXDhg2LFi2if93wjSEt6vuUtIeFGeBvAP373//2XXXlyhWeCcr4wQyb cbP6bq57ZAGlrCLtZVI8pOT349FislDdE2VOkfbynzaALmoCXdQEPsV+LMwA P4fWoUMH31WJiYn8MbJmzZpZsi9LcLP6bq57ZAGlrCLtZVI4Ed1s0synn37K a4N7JNhy0l7+0wbQRU2gi5rAp9iPhRn497//7fE+nj1p0qRTp06J5Zs2bapV qxb3mEp9BsjN6ru57pEFlLKKtJdJ8aGW+vXrb926VbyFlJycPHz4cH79rVq1 as5+TleQ9vKfNoAuagJd1AQ+xX4szMD69eu1L76VKVOmQYMG/J4X88UXX1iy I6tws/purntkAaWsIk1m8oUXXhANbM6cOevWrfvYY4/lzp2bl5QtW/bEiRNO x/h/SZP5TwNAFzWBLmoCn2I/1mZg8+bNrVq1EjPXid7ztddeC/oLL+HDzeq7 ue6RBZSyijSZycuXL3/66acPPfSQtslNly5d+fLlJ0yYYM/EiZKkyfynAaCL mkAXNYFPsZ9wZCA5OXnXrl0rV65cv369zVN4BYSb1Xdz3SMLKGUVaTiTV69e /e2332JiYpYvX05tr1L2RJCG8x/RQBc1gS5qAp9iP27OAOrudBTAHChlFcik syD/agJd1AS6qAl8iv24OQOou9NRAHOglFUgk86C/KsJdFET6KIm8Cn24+YM oO5ORwHMgVJWgUw6C/KvJtBFTaCLmsCn2I+bM4C6Ox0FMAdKWQUy6SzIv5pA FzWBLmoCn2I/bs4A6u50FMAcKGUVyKSzIP9qAl3UBLqoCXyK/bg5A6i701EA c6CUVSCTzoL8qwl0URPooibwKfbj5gyg7k5HAcyBUlaBTDoL8q8m0EVNoIua wKfYj5szgLo7HQUwB0pZBTLpLMi/mkAXNYEuagKfYj9uzgDq7nQUwBwoZRXI pLMg/2oCXdQEuqgJfIr9uDkDqLvTUQBzoJRVIJPOgvyrCXRRE+iiJvAp9uPm DKDuTkcBzIFSVoFMOgvyrybQRU2gi5o45VMAAAAAAAAAwBj4FAAAAAAAAIBq 2O9TgvhhmsHNGUDdnY4CmAOlrAKZdBbkX02gi5pAFzWBT7EfN2cAdXc6CmAO lLIKZNJZkH81gS5qAl3UBD7FftycAdTd6SiAOVDKKpBJZ0H+1QS6qAl0URP4 FPtxcwZQd6ejAOZAKatAJp0F+VcT6KIm0EVN4FPsx80ZQN2djgKYA6WsApl0 FuRfTaCLmkAXNYFPsR83ZwB1dzoKYA6Usgpk0lmQfzWBLmoCXdQEPsV+3JwB 1N3pKIA5UMoqkElnQf7VBLqoCXRRE/gU+3FzBlB3p6MA5kApq0AmnQX5VxPo oibQRU3gU+zHzRlA3Z2OApgDpawCmXQW5F9NoIuaQBc1gU+xHzdnAHV3Ogpg DpSyCmTSWZB/NYEuagJd1AQ+xX7cnAHU3ekogDlQyiqQSWdB/tUEuqgJdFET +BT7cXMGUHenowDmQCmrQCadBflXE+iiJtBFTeBT7MfNGUDdnY4CmAOlrAKZ dBbkX02gi5pAFzWBT7EfN2cAdTcus2XLlujo6J07d8psMKDCQZOUlBTt5cqV K2HdkTq4+Si1FmTSWZB/NYEuagJd1AQ+xX7cnAHU3bjMQw895PF4nnrqKZkN BlQ4aL7//nuPF7JFYd2ROrj5KLUWZNJZkH81gS5qAl3UJFJ8SmJi4u7du+nf IPaoGvIZiI+Pnzdv3pAhQyZPnhwdHf3nn39q154/f363GTdv3gxLHYIlUPUl db9z587evXv37Nnz119/hRRfOIFPiRTCdJS6EGTSWeTzT03ojh07Ro8ePXbs WJLg7t27YQ7N1UAXNZHX5dChQ9OnTx86dOiiRYsM2qtr166tWbNm+PDh48aN i42NvX37dmolb926RT0sCf3ZZ5/9+OOPJ0+eNNh7cnLyggULaO+LFy++ePGi TMCB/nz//v2pjSr/+OOPQPcYIor7FFJ55cqV7dq1y5gxI42U6tevH8QeVUMm Azt37qxUqZLnn5QuXXrZsmWiDDVcHjOmTp0a3soEiKT68rofOXJkzJgxpUqV 4vpu2LDBynAtBT4lUrD8KHUtyKSzSOb/8OHDhQsX1nYcZcqUiYuLC3+ALgW6 qImMLikpKR06dNANtLp166a7jEysXr36wQcf1BYjNcl16orRsL9fv36ZM2fW lsyQIUPHjh2vXr3qG0Dv3r21JdOlSxcVFSVfR8mfZ82aNbVR5fz58+V3Zwkq +xRyqdxtCZ588skg9qgaphkYP358pkyZuMp58+atVq1a+vTp+U86mLdt28bF ZHwKGXM7qiSNjPryulPjoKtvdHS09UFbBHxKpGDtUepmkElnkcn/oUOHChYs yJkvV65c6dKl+f8lSpQ4ffq0LWG6DuiiJqa6/PXXX/Xq1WMhihcv3rhx40KF CvGfDRo00N5ooKEIWQAes1EfXbNmTW7lsmXLtnz5clGMWr/q1avzFqh8hQoV hOhEy5YtdY+I9OjRg1flyJGjdu3atDX+c9iwYTIVlPz5zZs3lRpVquxTEhIS 8v4ND9TTRv9lmoEZM2ZQZYsUKbJ27Vq+z5uUlNSnTx8+SBo1asTFyNef8see PXv48HviiSdUu00so7687nTScbHs2bPDp4QJ+BS/pNXWyVqQSWeRyX+rVq14 jDRnzhxeMmHCBD7lO3fuHPYQXQl0URNTXaZNm8YSvP3223wDhf7t0qULL6S1 XOzWrVtlypShJQ888ICY6GbHjh1saqjjvnPnDi+kUVzlypVpYdeuXS9cuEBL aMxGMZQsWdK32927dy8vrFGjxrVr12jJxYsXy5Yt6/Hef4mPjzeunfzPqU3m kl988YXvCPP3338PLK0ho7JP0cIP9qSN/ksmA7Nnz+aDVkC2uly5cpSE/Pnz G/+WDngqRkP3Y8eOhRiq5QSqvqTuM2fOTEs+pV69evwnNXdbt249cuSI3/du TH3K9evXt23btnv3btqO8X5p+7/99ltMTMzly5d1q+BTTElLrZO1IJPOYpr/ pKQkvsxL4y7tch4k58qVi9qQ8IboSqCLmpjq8swzz1D+ixYtqutSaeRPy2nM z1eGN27cyJ3mpEmTtMWWLl3Ky2fNmiUWnj59+qefftLt6IcffuCSY8eOFQu7 d+/u6ymE+zC9pSL/8wMHDvBC7YsGDgKfYj/BZYBo2LAhH2bCjPtCw1q+Jjlm zJjgQwwb8CnGZdh6PPvss4cPH27SpIl4RjRv3rw9evTQvb+Wmk85c+YMtUgP P/yweFyQurzXXnvNb9eWnJzctm3b3Llzc8l06dLRDxcsWCAKpOZT3nvvvXxe Ro4cGVAe1Aeja6tAJp3FNP908vLZrXuzb/78+bx8xowZ4Q3RlUAXNTHVpUCB ApT8Ll266JbPmzePdeGfT5w4kf88e/asrmTFihW1lyJTY/PmzbyFIUOG8BLq /am39fh7fY+3WaJECYMNBvTzmJgY3ntsbKxxnPYAn2I/wWXg6tWrDzzwAHt2 g2Ls6+lfNWe+gk8xLsPWg/D7Fhu1MNoJQ/z6lClTpmTJksX3t34dzdq1a++/ /36/hcXDBn59irj9TWbKwDVHKBhdWwUy6Sym+X/jjTc83ssgurP42rVrfD2/ T58+4Q3RlUAXNTHWhTpfnXcQkB/hVTxz0UcffeTxXvTzHYbxQ2KFCxc2jiQq KkrXEZ88eZKXfPnll7rCH374Ia8yeCIroJ8vWbKElyjyJhR8iv0EkYFTp041 btyYj5wJEyakVky4YBq3hxpleIBPMS4jfIrHe9GGrEFCQsK8efOEm/jvf/+r K6xzH3wMFClSZOzYsTt37rx+/fqmTZvEfGjaAC5dusRXh4hXX32VTueUlBRK YLVq1WrWrCnua/v6lG3btrEVqlChgt8JSSIdjK6tApl0FtP8c7dSuXJl31X8 Pm+7du3CFZyLgS5qYqoLJ79Tp0665WRh+KXggQMH0p+TJ0/mTtP3nZFPPvmE lqdPnz612X3Jmc6ePZun/ypatOiNGzd4+datW3mbCxcu1P1E7O63335LLfKA fj59+nResnTp0v79+3fo0GHQoEHkmEQwNgOfYj/yGZgxY0bHjh0bNGiQIUMG j/eVk3HjxhncKHnppZc83le3TN9HcAr4FOMybD3y5MmjnRKE2LdvH08eUqZM GXEApPbc15IlS3TtCVkVzk+vXr3EQmpseeH777+vLUztp/bT8zqfkpiYyHMt knU6fvy4ZN0jC4yurQKZdBbT/FepUsWTykTQFSpU8HifQQ1XcC4GuqiJqS51 69blDlr7IicNt5o3b869ZPv27WnJunXr+E/dnZc1a9aIKbZ0veeZM2eod37x xReLFy/OBagZ1JZZtGgRL/f9+IJ46ow6+tQiD+jno0eP9viDfBONLgzyEybg U+xHPgPNmjXTHiRDhw41+G5jQkICz2ZM/teyWK0GPsW4jMGr8Y0aNeI6JiUl mRb2JWfOnB7vPIf8J5kdXlKgQAGe+iM1tD7l9u3bderUof/TkfbLL7/I7DcS wejaKpBJZ5G8Pty6dWvfVaQCrapUqVK4gnMx0EVNTHURNxqqV68eExNDg64F CxZQFywGadzDUkfJ831RRxkVFUV2Y8+ePZ988on2kWxaot1ybGyszhEcOHBA W0Dc9di3b58uKhr28CrfeyXB/Vz4FDrY+vbt+8EHH1SrVo2XUBX2799vkker gU+xH/kMjBo1qmnTpnSEiG8AlStX7tChQ34Ljx8/nsvoDm+lgE8xLmNgPYYN G8Z1FB/QMfYpt27dWrp0Kf2qTZs25cuX59/Wrl2b1544cYKX+L4SqEP4lM2b N4tbMCNGjDD+VUSD0bVVIJPOYpp/Gg5Rwp9//nnfVdRWeLyvOoYrOBcDXdTE VJe//vqLpzPS8corr/Bb6j169OCSa9eu9X3JlMpQSf6/7kPwJ0+epJ6amj7x Zc906dINHjxYPD7x7bff8vJdu3bpolq1ahWvMpieK9Cfz5kzh6og/rx79+7A gQO5WLg/heALfIr9BJGBs2fP0hHLT/7Q6NTvU4Ivv/yyxztjoWrfTNECn2Jc xsB6TJkyhes4b94848J8Bzl//vy+zeljjz3GZcQEiV999ZVxSMKnvPrqq2I7 zZo1k6lyhILRtVUgk85imv+aNWtSwuvWreu7ii8IN2/ePFzBuRjooiYy7RWN r8aOHVutWrVs2bJlzpy5QYMGEydOvHXrFs+uqX1L/ddff23dunWRIkU83o9C vvXWW0eOHOHRPo3TUts+GZPVq1dXrVqVu1rq93n5ihUreMm6det0P5k9ezav 8v3YvSDEn3PFOSryXzZPngOfYj/BZYAYMmQIH048p4QOtuFPP/10qPGFE/gU 4zIGPkXMVLlq1SqDwsnJydyRebx336Kiominly5d4kwKn7JgwQIuYzAtAyN8 CpMjRw7+zw8//CBZ8YgDo2urQCadxTT/zz//vMc7IYbvKr7Q4fvKMAgd6KIm AbVXNG4X02+K5xO0U/oLtI/rv/766x6Jx/aSkpL42T8xM9iuXbt0FyoF48aN 41UGn3oM8edM7969uSQZLuOS1gKfYj9B+5Tjx4/zQdK9e3fdKjHpXN++fS0I MWzApxiXMfAp77zzDtfx1KlTBoVr1arl8X4wRTfBvs6nHDx4kLf27rvvGoek 9SlVqlQ5e/ZsiRIlPN4XW8j+GP82QsHo2iqQSWcxzX/nzp35Aqnu40o0YuFT fsCAAeEN0ZVAFzUJemw2depUmVsSd+7cKVasmEfudlj79u15m/zJ78TERP5z 0KBBupJvvvmmx/ucWGpziIX+c0Y8+qV7uSbcwKfYj2kGUpvRS3j2rl276laJ x3iUnZGYgU8xLpOaT6EGhJ9YzpYtm8F8X+fPn+c8dOvWTbcFnU+hDfI8/LQR 49ZJ+JR//etfcXFx9zSTq3fs2NG82hEIRtdWgUw6i2n+Z82axeey7g3cSZMm 8fI1a9aEN0RXAl3UJGifUqlSJRKlZMmSf/75p0Ex8ZlO7fcFUhvv8Z0XIjk5 mZfwkxK62arp54UKFaLljz/+uHGQIf783t8zO2XOnNnU0VgLfIr9mGbgpZde at26dUpKim65eO5r+vTpulXi5YXVq1dbGqzFwKcYl2HrQS2JbmbpsWPHcgXb tm2rK6z1KXv37vXrZGNjY3PlyqX1KUTTpk25sO+Hn7RzNQifQl5YLGzRogUv XL9+vWnFIw6Mrq0CmXQW0/zfuHEjb968lPOGDRuKFxtpEPLoo496vA/Vq/y2 Y+QCXdREpr2iTlY37SqZDu4Nv/nmG7GQenDdTYdLly7xdNMknxjn79y5k2yC 7jME97yvJPPXzaiwWDh8+HDe0caNG8XCxYsX88Jvv/1WLKTjhzruOXPmaEOV /PmuXbsozt27d/smh1+Rtn8OB5V9yrZt2zb/Db98Qb2YWBK5H5gzzsDChQv5 sClXrtzXX3998OBBMrznz58fOHAgf0UlT548Z86c0f3q448/5l/RYR/e6END Rn1J3RMSEsTCQYMGcfWjoqJ4iYKTnsn7FI/3OylLliwhr5qYmDhs2DBuH7Jk ySIe+iKoueDkiD6LWid+my937tzbt2+nI4eyRDnhCat1PuXo0aNiHrm+ffvS lu/cuUNtFNlk2oiYSt3v9+hPnDjB88BTnAZzZUcoFh6lLgeZdBaZ/IsHSnv2 7HnlyhUaTXXs2JGXDB061JYwXQd0URNTXahXzZ49e/Xq1ak3/PPPP+Pj40kL 7p2LFi0qXlehnrdNmzYZM2Yka5CUlESehawBjehYvsmTJ4sNVq5c2eN95ork /vnnn6m5o81SDI888ggX7t27tyhMgwF+CoIsDPfvtFkaENKSnDlzar8v8P77 7/PPtV+pkPw5f6Ana9asgwcPjomJof6dopoyZQoNKjjUlStXWpHsAFDZp/D3 HVJj1KhRQexdBYwzQIc6z9wlEINJPkj8fmenS5cuXOD06dNhDD1kZNSX1J3+ Y1CMNhL2ygSIvE/xnc+QIJeqe+e9V69evCp//vxi/g3trFzcKHm8T4uVLFnS 80+fcs87l7Uow7sQ/xfvQPn1Kff+/q6uR+3v9QSHhUepy0EmnUUm/zQG5pfa GB50ebxX8tPeJQhFgC5qYqpLv379/HaXxYoV014ipmEY3w3xLfnuu+9qJ8ui 3fGExkz69Om1PXLt2rWF92FmzZoltiYOiSxZsqxYsUJbjK9hEk888USgP1+4 cCF5MW3woiTRp0+foFIbEpHrU0aOHBnE3lVAJgMLFizgadK11KtXj1yw3/LC 2vidslgdQh+3CN2NfUqOHDnCXpkAkak7X3KZNm0aOQht81WiRAnfJ6zIOIhv 14r5Ny5fvtymTRvxQ2qCmjVrduzYMX5oUOdT7nnP4urVq2sboiJFinz33Xei wNy5c3m57ny/detW2bJlPd5PWSnujgPFwqPU5SCTziLZ29KQ+LnnnhMXxLJm zUodCgbD4QO6qImMLtQ7i0+ccA/bokWL8+fP64olJSVpteOO1e/rw8nJye+8 847uOwK5c+f+9NNPf//9d9/yc+bM4ZdVmYceekhnUojPP/+c144ZMyaIn8fF xXXs2JFvoAhKlSpl8H2WsKKyT0mryGcgMTFx06ZNy5cvpxGp7qtAEYqb1Q+0 7nfv3t27d+/q1asNZgukMjExMStXrtR1XqdOnaLldPDo3nNJjatXr1L5tWvX +j5S6ELcfJRaCzLpLAHl/8aNG7GxsfKNBgga6KIm8rpQR0ldM+miu9+hg9Zu 3759zZo1CQkJxhv8448/qLv/+eefqfDRo0eN38e/5539df369QZXCA8ePGjw 4XjTn9/7+xWbdevWUUnT+MMKfIr9uDkDqLvTUQBzoJRVIJPOgvyrCXRRE+ii JvAp9uPmDKDuTkcBzIFSVoFMOgvyrybQRU2gi5rAp9iPmzOAujsdBTAHSlkF MuksyL+aQBc1gS5qAp9iP27OAOrudBTAHChlFciksyD/agJd1AS6qAl8iv24 OQOou9NRAHOglFUgk86C/KsJdFET6KIm8Cn24+YMoO5ORwHMgVJWgUw6C/Kv JtBFTaCLmsCn2I+bM4C6Ox0FMAdKWQUy6SzIv5pAFzWBLmoCn2I/bs4A6u50 FMAcKGUVyKSzIP9qAl3UBLqoCXyK/bg5A6i701EAc6CUVSCTzoL8qwl0URPo oibwKfbj5gyg7k5HAcyBUlaBTDoL8q8m0EVNoIuawKfYj5szgLo7HQUwB0pZ BTLpLMi/mkAXNYEuagKfYj9uzgDq7nQUwBwoZRXIpLMg/2oCXdQEuqgJfIr9 uDkDqLvTUQBzoJRVIJPOgvyrCXRRE+iiJvAp9uPmDKDuTkcBzIFSVoFMOgvy rybQRU2gi5rAp9iPmzOAujsdBTAHSlkFMuksyL+aQBc1gS5qAp9iP27OAOru dBTAHChlFciksyD/agJd1AS6qAl8iv24OQOou9NRAHOglFUgk86C/KsJdFET 6KImTvkUAAAAAAAAADAGPgUAAAAAAACgGnjuy07cnAHU3ekogDlQyiqQSWdB /tUEuqgJdFET+BT7cXMGUHenowDmQCmrQCadBflXE+iiJtBFTeBT7MfNGUDd nY4CmAOlrAKZdBbkX02gi5pAFzWBT7EfN2cAdXc6CmAOlLIKZNJZkH81gS5q Al3UBD7FftycAdTd6SiAOVDKKpBJZ0H+1QS6qAl0URP4FPtxcwZQd6ejAOZA KatAJp0F+VcT6KIm0EVN4FPsx80ZQN2djgKYA6WsApl0FuRfTaCLmkAXNYFP sR83ZwB1dzoKYA6Usgpk0lmQfzWBLmoCXdQEPsV+3JwB1N3pKIA5UMoqkEln Qf7VBLqoCXRRE/gU+3FzBlB3p6MA5kApq0AmnQX5VxPooibQRU3gU+zHzRlA 3Z2OApgDpawCmXQW5F9NoIuaQBc1gU+xHzdnAHV3OgpgDpSyCmTSWZB/NYEu agJd1AQ+xX7cnAHU3ekogDlQyiqQSWdB/tUEuqgJdFET+BT7cXMGUHfjMlu2 bImOjt65c6ctEQH/uPkotRZk0lmQfzWBLmoCXdQEPsV+3JwB1N24zEMPPeTx eJ566imZDX777bcvv/zynDlzLAgOaHDzUWotyKSzIP9qAl3UBLqoSaT4lMTE xN27d9O/QexRNeQzkJSUNHfu3CFDhkyfPn3Hjh2pFfvzzz+3bds20svy5cvP nz9vWaxWI1/3Q4cOUa2HDh26aNEi9+gu71P27Nnj8ZIhQ4b4+HhrQgRe5I/S O3fu0Ik5evTosWPHUgN19+7dMIcWYQTa2qeldl4FcCSrCXRRE3ldqM+dN28e jc0mT54cHR1NYzDfMvv379+dCn/88Yeu8LVr19asWTN8+PBx48bFxsbevn3b 734D2mZq0EZmzJgxePDgKVOmbN68+a+//vJbTDIkG1Dcp1CiVq5c2a5du4wZ M9KQrH79+kHsUTVkMrB9+/YKFSp4/kmTJk1OnTqlLUZHZo8ePXLkyKEtlidP Hjr8wliBEJCpe0pKSocOHXR179atm9+mIIKw1qecPHmSHAoVzpQpU0JCgjUh Ai+SbdThw4cLFy6sPUrLlCkTFxcX/gAjBslMpsl2XgVwJKsJdFETGV127txZ qVIl3fikdOnSy5Yt05XMmjWrJxXmz5+vLbl69eoHH3xQW4B093tpWn6bfjl/ /vzLL7+s+2HdunWPHDmiKykfkg2o7FMSExO52xI8+eSTQexRNUwzMHv2bHE0 FixYsE6dOrlz5+Y/y5cvL1zthQsXqEPn5enTp3/00Ucffvhhkavvv//ejsoE iGndydrXq1ePq1C8ePHGjRsXKlSI/2zQoIH8FQMFsdan3PO2JB999NGGDRss CA5okFHq0KFDdG7ykVmuXDnqp/j/JUqUOH36tC1hRgAymUyr7bwK4EhWE+ii Jqa6jB8/PlOmTCxE3rx5q1WrRkMv/jNz5szbtm0TJW/evJmaoSB+/PFHUTI6 OjpdunS8Ber6a9asye1htmzZli9frt27/Db9cvfu3WeeeYYL58uXr3379s8+ +yz/Sf73xo0bQYRkDyr7lISEhLx/wwdD2ui/jDNw8eJFdiXULm3cuJHv816+ fLlFixZ8RI0YMYJLDhgwgJfQYFU860WmPnv27LTwvvvuu379evhrExim6k+b No0r9fbbb/MNFPq3S5cuvJDW2hRoGLDcp4AwIaNUq1atSClqzMX7QRMmTOCj tHPnzmEPMUKQyWRabedVAEeymkAXNTHVZcaMGZT/IkWKrF27lsdmSUlJffr0 YV0aNWokSlKzxgu/+OKLUz78/vvvXOzWrVvkEajYAw88IObP2bFjB1+epfHA nTt3At1mavz000/88759+9J+eeHMmTN54ZdffhlESPagsk/RUqpUqTTTf5lm YMuWLc2bNz937px24YULF9i/NGnShJfQ0dKpUyffobvwL1p3rwimdWe/X7Ro UXEeMTVq1KDlZcuWjdwHdOV9Sr169cSSgwcPbt++PVDLmZKSQi3M1q1br127 Zlo4Pj6eHPGVK1dMS544cWLDhg3yrw9QGDExMSdPnkztCVg1MVWK+ia+vkRu Wruchxa5cuVS8BKBIwTa2qeldl4FcCSrCXRRE5n2avbs2TQY0y6h3q1cuXKk S/78+cXCAwcO8DDM93kwLdTzcrFJkyZply9dupSXz5o1K9Btpka/fv3otzly 5NA9Qk/tLS1v27ZtECHZA3yK/QSXAYKf8ipevLhxMRpJ8uE0ffr0IPYSVkzr XqBAAYq8S5cuuuXz5s3jSgWXOhWQ9ymNGjUi19C5c+d//etfXOv06dNTh6V7 ka1atWr58uUjS6tdeOzYseeff97zN+nSpatYsaL2ydW5c+fSr0qXLk2NVVRU VMmSJUXJChUqbN68WRcSNcKU/Keffjpv3rxisw8++ODq1at1JXnL5CXp/4sX L65Tp454nqdIkSK+W1YWU6VGjhzJ9dI9dEd55uUzZswIb4gRAnyKs+BIVhPo oiZBj80aNmzo8c5pI+41xMTEsFKxsbEGP5w4cSIXO3v2rG4Vddy6i5aS20yN rl27erxP2uiW8+vAdevWDSIke4BPsZ+gzwUal1IS6FAxLrZkyRI+zGTeq7IZ 47rTOJwjHzJkiG4VnTK8aurUqWGNMHzI+xSyonzA6/jggw98C2sfErt48WLR okVFeX4CkBFTK3z//fe85JFHHvHdRZYsWchiiA1evXrVbzGP1zqtW7dOG4/Y cr9+/fjpVi3ZsmVTeSY6LaZKvfHGGx7v88m6O+DXrl1ja9anT5/whhghwKc4 C45kNYEuahLc2Ix6yQceeMDjfd5DLBTDMOOXiT766COP9yKh7yMH/Lh74cKF A91maogbIro68rQA5FaCCMke4FPsJ7gMXL58mR/e7tSpk3HJ999/n49GBaer Na07vznoW0eyMDTQpVUDBw4MY3zhRN6nMM8999zChQuPHTs2ZswY7pvy5Mmj fQbV16dERUXxb999990zZ85QO7Nv375///vfVatWvXnzJpcRbsLjfSVzxowZ cXFxu3btev3113lhsWLFtPeFn3nmGWqyXnnllWXLliUlJSUmJvbv359L6q6r aLecL1++ESNG7N69OzY2tlatWrzw008/DTmLdmCqVOPGjak6lStX9l3FB3C7 du3CFVxEAZ/iLDiS1QS6qEkQY7NTp06xWMSECRPE8unTp/NCcgfUY5ILGDRo 0Jw5c7SvqxOTJ09ObbT2ySef8PVAMX2Q5DZTg8YA/O7Afffdt379el4YHR3N 2xRLAgrJHuBT7Ce4DIgbwd99951BsStXrvBsciVLlgw+xLBhWve6devygJx8 mVh469at5s2bc/Xbt28f9ijDQ0A+hdof7dUM6pV4+d69e3WFtT7lxRdf9Hjn 6NA1XNpWRbiJ1q1b695JadOmDa/SPoBKRsn3TBfzhGhlElums1U7zyFtgW+v tGrVyrj6imCqVJUqVTypTJ/L04lTfsIVXEQBn+IsOJLVBLqoiXx7NWPGjI4d OzZo0IC/DpA9e/Zx48Zpu+zRo0d7/FG0aNElS5aIYuvWrePlumdI1qxZwxdm iePHjwe0TQNiYmJy5crFv2rZsiV12eRZ6P+vvPJKcCHZA3yK/QSRgRMnTvAz POXLlzf+jIiYGsv+d51kMK27uGJQvXp1OqcSEhIWLFhAQ3FxStLJZVewFiPv U5544gndcjEN2ooVK3SFtT6lV69eXMzgkT/hJsT1E8GWLVt4le/7QTpGjRrF Jffs2eO75bVr1+rKFytWjJbXrFnTeLOKIHnXj4ye7yp+J7FSpUrhCi6igE9x FhzJagJd1ES+vWrWrJnWKQwdOlQ8scAIT0F69e3b94MPPuDn9j3eh6v379/P xW7fvs2Ta2XKlCkqKorG/9SlfvLJJ1RGbFx0spLbNOCPP/4gS6KzOTVq1NA+ pxFQSPYAn2I/gWbg7t274vL1ypUrDUpu2LCBL1zXrl1bzRmWTOtOYfMraTro 5MqXLx/9p0ePHnYFazHyPsV3XmKyJ5wHMUel38JkNMSr602bNl22bJnvFIIG PoWONP65mFNOy4EDB6ZMmfKf//yHmjVxTWbVqlUyWyaH4vnn47sqY6oUvwT0 /PPP+66iU49b/nAFF1HApzgLjmQ1gS5qIt9ejRo1inpYsgmZM2fmXq9cuXKH Dh3SlqHOWnvJjrrXgQMHcmFtr01lfL/eSKMdYSguXrwY6Db9cu3aNb7kS913 9+7dxZfp0qdPP3jwYG3JgEKyAfgU+wk0Az179uRjw/iR1F9//fX+++/3eG9B ap8OUgqZutOpN3bsWGoBsmXLRo1AgwYNJk6ceOvWLX49R8zyHXGE4lPWrFkj 41OIH374QTsxV+HChUeMGKGd5NnATRA8w5hurgbaKc8L7cvPP/8ss+XHH388 LfkUtl1ighQtfCVKNwmba4FPcRYcyWoCXdQkiNHp2bNnaZDP14epRzZ+VYTG NlWrVqWS5AK0lxBp8Na6desiRYp4vLPovPXWW0eOHGEDQp7COIDUtulL27Zt Pd7Jk3m6sNu3b48fP55nACA+++wzbeFQQrIc+BT7CSgD4k4fDRQNToFz586J GaJMP0vqIAHVnU5AMRPviRMnuHYLFiwIV3Bhxh6fQiQnJw8ZMoRbGIZ8x6VL l3itsU/JmTMnrapVq5ZYIo7ALFmyUENHPujw4cP8uSvX+hSe+blChQq+q6gX 8EhMduES4FOcBUeymkAXNQludEpQh8t9n+l8pL179+aS2lc4BdqHx3hmG5kH /Iy3yezbt4/LfPXVV9rlp06dIhvi8U7I6TsRcdAhWQt8iv3IZ2D+/Pl8E6Fg wYIGk3ddv36d7wUT/fv3tyzQMBB0O0CnP1dwx44dVgdlE7b5FObOnTuLFy/m ly6JN954g5cbuAlyu7rCK1as4CtFderU0X54VHzE1p0+pXPnznz9Sve1NTpJ OQMDBgwIb4gRAnyKs+BIVhPooiZBj0+OHz/OunTv3t24pHhMy/gVD+q++aVO mRtnMtv8+uuvuYzvnMbfffcdrzJ+GT+gkKwFPsV+JDNAx0ymTJk83rtsBoPz GzduPP3003yYvfrqq4p/rj3odoCn+C5ZsqTxNAIqY7NPYVJSUvg5ATHnuYGb EBMSRkVF8ZJu3bp5vFOpJycna0u63KfMmjWLa7pw4ULt8kmTJvFy0iu8IUYI 8CnOgiNZTaCLmpjqktprv+J5j65duxrvgl/Az5w5s/HUvuKDnv/973/Nopba 5meffebxfolS974/QcNL3hd5GatCshb4FPuRycDy5cv5/aysWbMaFKZDTrxi 36JFC/XH8DJ137t3r+5UovOC6/jNN9+EMbgwY4NPOXLkyMGDB3W/rV+/PhXL nTs3/yncxOzZs7XFrly5UqJECW7KxEZatWrl8b5nl5SUJErevn1bfGzFnT7l xo0b/BJQw4YNxZUB6iMeffRRj/dpXsUvF9gGfIqz4EhWE+iiJqa6vPTSS61b t05JSdEtF899TZ8+nf7ctWtXlSpVdu/e7bt9fj5BOw3CrVu3dPdBLl26xBNT k9DCegS0TTp+qDum0YIYSokhBEeo5csvv+RVW7ZsCSgk21DZp2zbtm3z3xQu XNjj/S6DWHL16tUg9q4CphlYuXKlmAKuX79+9OdPP/20WAOPA+lYEh8YolEo WZtly5Yt/ieJiYk21UoO07pv3749e/bs1atXp1OGbFd8fPzQoUP5NCxatKh4 XSUSCbdP4VsnZG8HDx7MTwn+/vvvVJ5/2KBBAy4m3AS5j27duh09epSyumnT pooVK/Lyzp07i13Q4ccLu3TpcuHCBWqgNm7cyH2lm30K8c4773Ble/bsSRaP mvGOHTvyEjpibQkzApDJZFpt51UAR7KaQBc1MdZl4cKFnP9y5cp9/fXXBw8e /Ouvv86fPz9w4ED+ikqePHnOnDlz7+9v3GTNmpX64piYGDIL1I5NmTKFP7NI 4xkxcSttoU2bNhkzZhw+fHhSUhIN6qiHpe3zjiZPniz2Lr/Ne5qPfYsXAa5d u8ata44cOWbOnCluDC1atIi/eVGkSBGeb0c+JNtQ2afwK72pMWrUqCD2rgLG GaCjwndGOB1kbKkkNVbGxQg6CO2rmASm6ouBscd7YV/8v1ixYjt37rQrzLAQ bp8SGxtboEABkTH6v/gqU6ZMmcgAcjHtV+N9qVy5svYRr3379gnLnN4L/1+Y Gtf6FBo51KpVS+SNrbTHe/3T98a6a5HJZFpt51UAR7KaQBc1Mdbl9u3bL7/8 srZ1EpMSs0Di/Q5yNDz4Z2gkI+Qj+vTpI7Z5+vRpba+tHfO8++672vm75LdJ iPk5td9i27Jliwi4UKFCtIqfoOCKbN26NdCQbCNyfcrIkSOD2LsKmPoU7YHh F/5e3oABA4yLef75WUAVkFF/2rRpbPwZGie3aNHi/PnztgQYRmTqzlctqCfS Lf/ll184G1qf4luYurb33ntPzDTIVK1adcOGDaKMcBNTp04VbzZ5vC1Vx44d feeUo+ZRTLTu8RrGMWPGUIvNR6nWp8ydO5fLbN68WbcR3lFa8in3vNl+7rnn RMufNWtW6sUwhNASuk+J3HZeBXAkqwl0URMZXRYsWCCmLRLUq1dPXAlk4uLi qD/lmx2CUqVKLVu2TLfBpKQkrcoe762NmTNn+u5afpuff/45r6XOWrv88OHD NJrSBd+8eXPdl1/kQ7IHlX1KWsXNGZCv+5kzZ1avXh0bGxvRz3ppsVN3yt46 L/Hx8bpX/4RPYfNCBpC8xtatW7XfWNFB5oUa4fXr1/tOFZImCUgpSg4dpZs2 bTJIoGtxc1unAjiS1QS6qIm8LomJiaTI8uXLt2zZYvDRQ37Rgzpi6j0TEhIM NkjjHOpk16xZY1xMfpsHDx5M7Qv1KSkpO3bsWLVqFf3r+65NECGFG/gU+3Fz BlB3p6Mw+X4KuKeMUmkAZNJZkH81gS5qAl3UBD7FftycAdTd6SjgU8xRRKk0 ADLpLMi/mkAXNYEuagKfYj9uzgDq7nQU8CnmKKJUGgCZdBbkX02gi5pAFzWB T7EfN2cAdXc6CvgUcxRRKg2ATDoL8q8m0EVNoIuawKfYj5szgLo7HcX/mTqs gRfdFB9AoIhSaQBk0lmQfzWBLmoCXdQEPsV+3JwB1N3pKIA5UMoqkElnQf7V BLqoCXRRE/gU+3FzBlB3p6MA5kApq0AmnQX5VxPooibQRU3gU+zHzRlA3Z2O ApgDpawCmXQW5F9NoIuaQBc1gU+xHzdnAHV3OgpgDpSyCmTSWZB/NYEuagJd 1AQ+xX7cnAHU3ekogDlQyiqQSWdB/tUEuqgJdFET+BT7cXMGUHenowDmQCmr QCadBflXE+iiJtBFTeBT7MfNGUDdnY4CmAOlrAKZdBbkX02gi5pAFzWBT7Ef N2cAdXc6CmAOlLIKZNJZkH81gS5qAl3UBD7FftycAdTd6SiAOVDKKpBJZ0H+ 1QS6qAl0URP4FPtxcwZQd6ejAOZAKatAJp0F+VcT6KIm0EVN4FPsx80ZQN2d jgKYA6WsApl0FuRfTaCLmkAXNYFPsR83ZwB1dzoKYA6Usgpk0lmQfzWBLmoC XdTEKZ8CAAAAAAAAAMbApwAAAAAAAABUA8992YmbM4C6Ox0FMAdKWQUy6SzI v5pAFzWBLmoCn2I/bs4A6u50FMAcKGUVyKSzIP9qAl3UBLqoCXyK/bg5A6i7 01EAc6CUVSCTzoL8qwl0URPooibwKfbj5gyg7k5HAcyBUlaBTDoL8q8m0EVN oIuawKfYj5szgLo7HQUwB0pZBTLpLMi/mkAXNYEuagKfYj9uzgDq7nQUwBwo ZRXIpLMg/2oCXdQEuqgJfIr9uDkDqLvTUQBzoJRVIJPOgvyrCXRRE+iiJvAp 9uPmDKDuTkcBzIFSVoFMOgvyrybQRU2gi5rAp9iPmzOAujsdBTAHSlkFMuks yL+aQBc1gS5qAp9iP27OAOrudBTAHChlFciksyD/agJd1AS6qAl8iv24OQOo u9NRAHOglFUgk86C/KsJdFET6KIm8Cn24+YMoO5ORwHMgVJWgUw6C/KvJtBF TaCLmsCn2I+bM4C6Ox0FMAdKWQUy6SzIv5pAFzWBLmoCn2I/bs4A6m5cZsuW LdHR0Tt37pTZYECFgyYpKSnay5UrV8K6I3Vw81FqLciksyD/agJd1AS6qAl8 iv24OQOou3GZhx56yOPxPPXUUzIbDKhw0Hz//fceL2SLwrojdXDzUWotyKSz IP9qAl3UBLqoSaT4lMTExN27d9O/QexRNeQzEB8fP2/evCFDhkyePDk6OvrP P/9MrWRycvKCBQuGDh26ePHiixcvWhar1QSqvqnulJNt27aN9LJ8+fLz589b EGV4gE+JFOSP0jt37uzYsWP06NFjx46lA/Xu3bthDi3CQCadBflXE+iiJvK6 JCUlzZ07l8Zm06dPJ4FkfhIXF7fby6VLl0wLHzp0iLZMI7pFixYZj3slx37X rl1bs2bN8OHDx40bFxsbe/v2bZmYmf379+9OhT/++EN+O0GjuE+h3K5cubJd u3YZM2akkVL9+vWD2KNqyGRg586dlSpV8vyT0qVLL1u2zLdw7969tcXSpUsX FRUVltBDRlJ9Gd3pBOnRo0eOHDm0dc+TJ8+UKVOsj9sK4FMiBcmj9PDhw4UL F9YefmXKlKHOKPwBRgzIpLMg/2oCXdRERpft27dXqFBBNzZr0qTJqVOnDH5F buL+++/nwrNmzTIomZKS0qFDB932u3Xr5vcyteTYb/Xq1Q8++KC2JB1XkvaK yJo1qycV5s+fL7mRUFDZp5CL5GGq4Mknnwxij6phmoHx48dnypSJq5w3b95q 1aqlT5+e/8ycOfO2bdu0hWmszqtoxF67du1s2bLxn8OGDQtvNYJCRn0Z3S9c uEDmhddSch599NGHH35YlKehdbgqEALwKZGCjFKHDh0qWLAgZ6ZcuXKlS5fm /5coUeL06dO2hBkBIJPOgvyrCXRRE1NdZs+eLQbtpE6dOnVy587Nf5YvX97g JsULL7wgxicGPuWvv/6qV68eFytevHjjxo0LFSrEfzZo0EB380Jy7BcdHU3+ hUePNFqoWbMmj6+o/PLly01zcvPmTU/q/Pjjj6ZbCB2VfUpCQkLev+GBukt8 yowZM6iyRYoUWbt2Ld/nTUpK6tOnDx8YjRo1EiX37t3LC2vUqHHt2jVacvHi xbJly9KSDBkyxMfHh7kqASOjvozuAwYM4Ip/9NFH4lmvZcuWZc+enRbed999 169fD0f8oQCfEinIKNWqVSuP9/rVnDlzeMmECRM4UZ07dw57iBECMuksyL+a QBc1MdaFBlfsSsgzbty4kcdmly9fbtGiBesyYsQIvz+cO3eudmxv4FOmTZvG Zd5++22+gUL/dunShRfSWlFScux369atMmXK0MIHHnhATLmzY8cOtj80hLhz 545xTmg8xjv64osvTvnw+++/G//cElT2KVpKlSrlHp9yz2vbL1y4oF1CRrtc uXKUhPz584uF3bt397Uk4gBW8JZKoOqnpjudXJ06ddKetozwL7q7Tiog71Pq 1avHf1Ijs3Xr1iNHjpD6qRU28Clk1igPu3fvpu0Y75e2/9tvv8XExFCrq1sF n+JLUlISX5Ki3kS7nIcWuXLlUtAmOwIy6SzIv5pAFzUx1YU6webNm587d067 kIZq7F+aNGni+5OzZ8/yE1+1atUy9SnPPPMMFShatKiuyyYzQsvJhogXlCTH fuSneMmkSZO0G1y6dKlpMMyBAwe4pN+XDuwBPsV+gssA0bBhQz4y2QL/8ccf +fLl8/h7faNixYoe7w3i0KO1Fqt8Smps2LCBz6np06cHEV5Ykfcpzz777OHD h6nRE7eY8+bN26NHD91t39R8ypkzZ6gRe/jhh8XjgtTlvfbaa367tuTk5LZt 24qb1+nSpaMfLliwQBRIzae89957+byMHDkyoDyoj6lSVGXOCR1v2uXz58/n 5TNmzAhviBECMuksyL+aQBc1CXpsxk+hFy9e3HcVW8vMmTOvXbvW1BoUKFCA CnTp0kW3fN68efxbDk9+7Ddx4kT+IdklvyXFRdHUiImJ4S3ExsYalwwf8Cn2 E1wGrl69+sADD7Cn5iUnT57k4+fLL7/UFf7www95lT135eQJt09ZsmQJV9ye 17sCQt6nEH7fXKNGSfsErF+fMmXKlCxZsvj+1q+joZZTvNynQzxs4NeniNvT ZKZMbxxHHKZKvfHGGx6vedTV/dq1a3wVtE+fPuENMUJAJp0F+VcT6KImQfuU atWqkSg08tctp26UO8pPPvnk2LFjxj6FOncuMGTIEN0qchm8aurUqfcCGft9 9NFHHu/lR99HMvhxssKFCxtXTYypHHwrCj7FfoLIwKlTpxo3bsxHy4QJE3jh 1q1becnChQt15SdPnsyrfvvtN0titopw+5T333+fKx6h7+YIn+LxXlQha5CQ kDBv3jzhJv773//qCuvcB1/9KFKkyNixY3fu3Hn9+vVNmzZxGj1/X41hLl26 xFdviFdffZVO55SUlOjoaGpya9asKe47+/qUbdu2sRWqUKEC2WcrcqMWpkrx yVi5cmXfVfzqa7t27cIVXESBTDoL8q8m0EVNghudXr58mR9d6NSpk3Y5mYv7 7ruPlj/22GPkN48ePWrsU+79La5uO/e8FoZfkx84cOC9QMZ+4k/fERFZJ493 GiLjuYWnT5/OW1i6dGn//v07dOgwaNAg8l83btwwS4xlwKfYj3wGZsyY0bFj xwYNGmTIkIGqnz179nHjxglfvGjRIj5+dLeG72nuEtIY1drgQySsPuXKlSs8 +V7JkiWDjC+cyPuUPHny6Cbi2LdvH0/ZUaZMGXEApPbc15IlS3RtCB0GfDz0 6tVLLKTGkBeSudMWplZL++l5nU9JTEzkJJN1On78uGTdIwtTpapUqeJJZbps nrLy2WefDVdwEQUy6SzIv5pAFzUJbnQqHtL77rvvtMtbtmzp8U6rdeTIEfpT xqfUrVuXBwDaF0Vv3brVvHlz/m379u3vBTL2W7duHf+pu0ezZs0aMT+YcT8+ evRojz+KFi1KIw3JFIUIfIr9yGegWbNm2gNj6NChN2/eFGuFU6ZBrO6H0dHR qdltZwmrTxHTYpi+GuYI8j7F76vxjRo14tolJSWZFvYlZ86cVJhaTv6TzA4v KVCgAM8Wkhpan3L79u06derQ/zNlyvTLL7/I7DcSMVWKr3q1bt3adxUdq7Sq UqVK4QouokAmnQX5VxPooiZBjE5PnDjBs4yWL19e+4kTGoRwvzlmzBheIuNT xM2L6tWrx8TEJCQkLFiwgLp4MQjkHlx+7EddNs/3RV12VFQUWZI9e/Z88skn 2ofDaYlBBYVPoQOvb9++H3zwAT/kRtBG9u/fH1C6ggM+xX7kMzBq1KimTZvS UZE5c2Y+MMqVK3fo0CFe++233/LCXbt26X64atUqXuXgFA1+CZ9P2bBhA99x qF27tt/ZsRwnRJ8ybNgw1lRMZWbsU27durV06VL6VZs2bagJ5d9Scngtta68 xPeVPR3Cp2zevFncgkltAsa0galSRYsWpSQ8//zzvqsowx7vXJHhCi6iQCad BflXE+iiJoGOT+7evfvss89yn7hy5UqxPCkpKX/+/B7vHTExGpHxKVSYp0vS 8corr/CL8z169LgX4Nhv7dq1vq+70tZom/x/g6/YM3PmzKGNaGs9cOBA/m24 P4vAwKfYTxAZOHv27ODBg3kcTqNTfqpnxYoVfKisW7dOV3727Nm8Sv6To/YQ Jp/y66+/8hsc2bNn37t3b0ghho0QfcqUKVNY03nz5hkXPnPmTK9evbid1PHY Y49xGTEt4VdffWUckvApr776qthOs2bNZKocoZgqVbNmTUpC3bp1fVfxxavm zZuHK7iIApl0FuRfTaCLmgQ6PunZsyd3iLrXhchg8vItW7Yk/o2YInj8+PH0 Z0pKit9tkgsYO3ZstWrVsmXLljlz5gYNGkycOPHWrVv8Cgy/OB/o2I8GSK1b ty5SpIjHOynZW2+9deTIEfYauXLlkq+vNsiqVat6vBP+2DCRDnyK/QSXAWLI kCF8BPKcD2SldQNXwbhx43iVaq+Th8OnnDt3Trwnbs/XUYMjRJ8iHoJdtWqV QeHk5GTuyDzeu29RUVG000uXLnGKhE9ZsGABlxHTMqSG8ClMjhw5+D8//PCD ZMUjDlOluBuqUKGC7yq2h74vQroTZNJZkH81gS5qEtD4RDwQVaNGDe0LoQcP HvS9QujL448/brx98gJiek/x/AN/MiDosZ/2xYHXX3/dE8IDhL179+Yd8ds3 YQU+xX6C9inHjx/nA6N79+73vG8085+DBg3SlXzzzTc93snojGdysB/Lfcr1 69f5PjjRv39/C0IMGyH6lHfeeYereerUKYPC/DGpjBkz6ibY1/kU0Za+++67 xiFpfUqVKlXOnj1bokQJj/fFFrI/xr+NUEyV6ty5s8d7KUn3SRrqGjhRAwYM CG+IEQIy6SzIv5pAFzWRH5/Mnz+fb3AULFhQ5wgOHz4s41MeffRR+cCmTp3K v+K7JKGP/e7cuVOsWDFPCDfmxKNfxq+3WAJ8iv2YZiC11yuEp+7atSsv4Svn utkL6eeFChXySBh2+7HWp9y4cePpp5/mnLz66qviU61qEopPoTaHn1jOli2b wXxf58+f52x069ZNtwWdT6EN8jz8tBHjBk34lH/9619xcXH3NBOqd+zY0bza EYipUuIdSd08FZMmTeLla9asCW+IEQIy6SzIv5pAFzWRHJ9QD5gpUyaP96Ep v4/WX758+aIPYjLhr7/+mv4MaEr/SpUqebwTmYpX9UMc+4kPhmq/dBAQPMtT 5syZbbgYDp9iP6YZeOmll1q3bu37+KJ47kt8bH348OG8ZOPGjaLY4sWLeeG3 335rceghY6FPuXnzpniFrUWLFtqpNtRE3qdQ4yM+X8KMHTuWa9q2bVtdYa1P 2bt3r87JMrGxsdSian0K0bRpUy7s+60oMVfDPY1PWbp0qVhICeeF69evN614 xGGqFBnkvHnzUvUbNmwo3DE1148++qjH+wCw4pbZNpBJZ0H+1QS6qIlMH718 +XKe1yhr1qwBDWZk3qO/5+3EtU9nEWQl+IfffPONWCg/9qOxhO6Wx6VLl3ji azqQtC6Djjrq7ufMmSMC2LVrF5XcvXu3LkiqOL8ubc98Dir7lG3btm3+m8KF C1NOaNQqlkTuB+aMM7Bw4UI+0sqVK0e+++DBg+SRz58/P3DgQP6KSp48ec6c OcOFExMT+ap4gQIFtm/fTiXpoKUCtCRnzpzG8806goz6MrrTqSc+fJk7d25q OpYtW7b4n1By7KiSNPI+xeP9TsqSJUvIq1Ithg0bxm1ClixZxENfBDURnBzR Z1E7wzejKSd8PCQkJERFRfHFH51PoWZTzCPXt29f2vKdO3eoXSKbTBsRX97x +z36EydO8OzrFKeuUU0DyCglHsPr2bPnlStXqOXv2LEjLxk6dKgtYUYAyKSz IP9qAl3UxFSXlStXihl9+/XrR3/+9NNP2lGHwYU7GZ9CvXb27NmrV69Ove2f f/4ZHx9PWnPvX7RoUfG6yj3psR8tb9OmDZUkX5OUlEQDJypGY0uOZPLkydq9 i89ki0fo+WM95MgGDx4cExNDfT0NwKZMmUIDDI/36TLtLGfhQ2Wfwt93SI1R o0YFsXcVMM4AHYovv/yytqZiMMkHhu7bOnTMs3/htfwfOpVWrFgR9poEjoz6 MrrTyWtQhlm0aJEdVZJG3qf4ziJIkMq6d9579erFq/Lnzy/m3NDOysXtmMf7 tFjJkiU9//QpxPjx40UZ3oX4P78DdS8Vn3Lv76/ZepR/LSgIZJSikQO/CqQ7 9Ro2bJj2jFvQIJPOgvyrCXRRE2NdaJDvt2vWUqVKldR+LuNTyPv47Y6LFSu2 c+dOXWGZsd/p06fJyPjd5rvvvqubqouvfBJPPPEEL1m4cCF/HUb8XOyI6NOn j2E6LSNyfcrIkSOD2LsKyGRgwYIF4vVwQb169cg4+xaeM2cOv7zA0FhXTZNy zwqfwroPGDDAoAyjWhJk6s4XOqZNm0YOgudLZ0qUKOF7oYaMQ/HixbmAmHPj 8uXLbdq0ET+kVqtZs2bHjh3jhwZ1PuWe9yyuXr26tvEpUqSI9ru6c+fO5eW6 850a7bJly3q8H5CixjC4nKiJZBtFA4nnnntOXEagLuzll1/GEEILMuksyL+a QBc1MfUp2nG+X2rWrJnaz0+dOsVlfCfp0kK9Pz9GInrwFi1anD9/3m9hmbFf UlKS9ijiLn7mzJm+W/v888+5gPg2JREXF9exY0e+gSIoVaqUnd/mU9mnpFXk M5CYmLhp06bly5fTiNT0WzzHjx+noaziI0Y3qx9o3e/evbt3797Vq1cbTC5N ZWJiYlauXKnrvKhJpOV08Ojec0mNq1evUvm1a9eKRwrdTEBK3bhxIzY2Vj7V rgKZdBbkX02gi5qoMz6hjpi6ftJd+6xXasiM/Wg727dvX7NmTUJCgkGxgwcP +v3EPL/ksm7dOtqR8RbCAXyK/bg5A6i701EAc6CUVSCTzoL8qwl0URPooibw Kfbj5gyg7k5HAcyBUlaBTDoL8q8m0EVNoIuawKfYj5szgLo7HQUwB0pZBTLp LMi/mkAXNYEuagKfYj9uzgDq7nQUwBwoZRXIpLMg/2oCXdQEuqgJfIr9uDkD qLvTUQBzoJRVIJPOgvyrCXRRE+iiJvAp9uPmDKDuTkcBzIFSVoFMOgvyrybQ RU2gi5rAp9iPmzOAujsdBTAHSlkFMuksyL+aQBc1gS5qAp9iP27OAOrudBTA HChlFciksyD/agJd1AS6qAl8iv24OQOou9NRAHOglFUgk86C/KsJdFET6KIm 8Cn24+YMoO5ORwHMgVJWgUw6C/KvJtBFTaCLmsCn2I+bM4C6Ox0FMAdKWQUy 6SzIv5pAFzWBLmoCn2I/bs4A6u50FMAcKGUVyKSzIP9qAl3UBLqoCXyK/bg5 A6i701EAc6CUVSCTzoL8qwl0URPooibwKfbj5gyg7k5HAcyBUlaBTDoL8q8m 0EVNoIuawKfYj5szgLo7HQUwB0pZBTLpLMi/mkAXNYEuagKfYj9uzgDq7nQU wBwoZRXIpLMg/2oCXdQEuqgJfIr9uDkDqLvTUQBzoJRVIJPOgvyrCXRRE+ii Jk75FAAAAAAAAAAwBj4FAAAAAAAAoBp47stO3JwB1N3pKIA5UMoqkElnQf7V BLqoCXRRE/gU+3FzBlB3p6MA5kApq0AmnQX5VxPooibQRU3gU+zHzRlA3Z2O ApgDpawCmXQW5F9NoIuaQBc1gU+xHzdnAHV3OgpgDpSyCmTSWZB/NYEuagJd 1AQ+xX7cnAHU3ekogDlQyiqQSWdB/tUEuqgJdFET+BT7cXMGUHenowDmQCmr QCadBflXE+iiJtBFTeBT7MfNGUDdnY4CmAOlrAKZdBbkX02gi5pAFzWBT7Ef N2cAdXc6CmAOlLIKZNJZkH81gS5qAl3UBD7FftycAdTd6SiAOVDKKpBJZ0H+ 1QS6qAl0URP4FPtxcwZQd6ejAOZAKatAJp0F+VcT6KIm0EVN4FPsx80ZQN2d jgKYA6WsApl0FuRfTaCLmkAXNYFPsR83ZwB1dzoKYA6Usgpk0lmQfzWBLmoC XdQEPsV+3JwB1N3pKIA5UMoqkElnQf7VBLqoCXRRE/gU+3FzBlB34zJbtmyJ jo7euXOnzAYDKhw0SUlJ0V6uXLkS1h2pg5uPUmtBJp0F+VcT6KIm0EVN4FPs x80ZQN2Nyzz00EMej+epp56S2WBAhYPm+++/93ghWxTWHamDm49Sa0EmnQX5 VxPooibQRU0ixackJibu3r2b/g1ij6ohn4GkpKS5c+cOGTJk+vTpO3bs0K09 f/78bjNu3rxpfQVCIFD1JXW/c+fO3r179+zZ89dff4UUXziBT4kU5I9SOvDo xBw9evTYsWPpQL17926YQ4swwnS+A0lwJKsJdFETU1327dtnPOI6efKkKLx/ //7Uiv3xxx++G09OTl6wYMHQoUMXL1588eJF41ATEhKo2McffzxjxozDhw8H OvIxHTIFGnxYUdynXLt2beXKle3atcuYMSONlOrXrx/EHlVDJgPbt2+vUKGC 5580adLk1KlTogw1XB4zpk6dGt7KBIik+vK6HzlyZMyYMaVKleL6btiwwcpw LQU+JVKQPEqpdyhcuLD2dCtTpkxcXFz4A4wYLD/fQUDgSFYT6KImprrky5fP eMRFeonCWbNmTa3Y/PnzdVvu3bu3tkC6dOmioqL8xnDjxg1qKnUbrFq16rFj x2TqKDlkCij4cKOyT0lMTORuS/Dkk08GsUfVMM3A7NmzxUFSsGDBOnXq5M6d m/8sX7787du3uZiMT/nxxx/tqJI0MurL696tWzddfaOjo60P2iLgUyIFGaUO HTpE5yZnply5cqVLl+b/lyhR4vTp07aEGQFYe76DQMGRrCbQRU1C9yk0QuOS N2/elB+Y9ejRg5fnyJGjdu3a2bJl4z+HDRumC4Bay+rVq/Pa9OnT04GRJ08e /jNv3rwpKSnGFZQcMgUUvA2o7FMSEhLy/g0pkmb6L+MMXLx4kV0JtUsbN27k +7yXL19u0aIFHyQjRozgknRMnvLHnj17+Dh/4oknVLtNLKO+vO50dnOx7Nmz G5x0igCfEinIKNWqVSuP96rXnDlzeMmECRM4UZ07dw57iBGCtec7CBQcyWoC XdTEVJe4uDi/g6533nmHpVm1ahWXpGaNl3zxxRe+5X///Xexzb1793LJGjVq XLt27Z53EFi2bFlakiFDhvj4eG0AnTp14sJt2rS5evUqLaExHvXRZHC+/vpr 0wpKDpnkg7cHlX2KFr5LlTb6L9MM0ICwefPm586d0y68cOEC+5cmTZoYb79r 165UjI5DyfuAdhKo+pK6z5w5My35lHr16vGft27d2rp165EjR/w+RGrqU65f v75t27bdu3fTdoz3S9v/7bffYmJiyBHrVsGn+JKUlMS3AN5++23tch5a5MqV izIf3hAjhDCd70ASHMlqAl3UJLjR6cmTJ3nY/9Zbb4mFBw4c4H5z2bJlxj/v 3r27ryUR5kV7S4U8QqZMmWjhSy+9pBsSBDobp/GQST54e4BPsZ/gMkDUr1+f klC8eHGDMjSs5WuSY8aMCS68sAKfYlyGrcezzz57+PBhMqTi8b+8efP26NFD 9/5aaj7lzJkz1PQ9/PDDfCQQ1OW99tprfru25OTktm3bigcL06VLRz9csGCB KJCaT3nvvffyeRk5cmRAeVAfU6WoypwT3cO98+fP5+UzZswIb4gRAnyKs+BI VhPooibBjc2aNm1KihQrVkz72FVMTAwrFRsba/Bb6tP5WTLfl/IqVqzo8T7m J5bwJWji4MGDgQapw3jIJBm8bcCn2E/QPqVatWqUBDp6DcrUqFGDbyCqOfMV fIpxGbYehN+32KgpE28n3UvFp0yZMiVLliy+v/XraNauXXv//ff7LSweNvDr U6ZNm8YLyUzduXMnlLQoiKlSb7zxhsdrHnV1v3btGl8F7dOnT3hDjBDgU5wF R7KaQBc1CWJstnTpUu4KlyxZol1Of/Jy45eJTp48ycW+/PJL3aoPP/yQV4nn rMqXL+/xXsYMKEK/GA+ZJIO3DfgU+wkuA5cvX+bL4506dUqtjHDBdBCGFGLY gE8xLiN8yv/P3plHR1Gl778jymoAQVEEBFRWhZFNwQ11ABHEURQcEMcBZxCU xQ1+KiqIIy4oGkGFr8qigMhqAI2A4WAACeEEjMomAmFJQkACJMgSQvJ7T7/H e8rqTlV1p7ru2/Tz+SMnfet21Xvf51bVfbqqbhGDBg0ia5CVlTV37lzlJj75 5BNTZZP74D5Qt27dhISE9PT048ePr1mzRk3uYQwgLy+vVq1aXN63b1/anfPz 8ymBZIfbtWunbhUL9CmpqalshZo3b863yJ5j2Cp15513UvNbtGgRuIgffe3X r1+kgosq4FP0gp4sE+gikzDGZnfccYfP/zSx6ZfhadOm8XmTjMwLL7zQv3// l156afbs2SdOnDBWW7duHVdbsGCBac0fffQRL/rtt9+4hO8uo7Xxx+zs7PXr 14f3/mXrIZPD4D0DPsV7wsuAuhA8Y8aM0ur07t2bKlxyySW2zyPoAj7Fug5b j2rVqi1dutRY/tNPP8XFxfn881Kq42Fp930lJiaajidkVTg/w4cPV4Xqibyn n37aWLmwsNB46DP5FDo2Xn755fSRrNPOnTsdtj26sFWqZcuWvlKmz+XpxF35 yescAD5FL+jJMoEuMgn1eLV582Y+OU6YMMG0iEp8wahXr57xysvChQu5PHB+ 4Llz5/IiOn3Tx4MHD/LHyZMn00n52muvVeskP5uSkhJSS62HTA6D9wz4FO8J IwO7du1iK92sWbMzZ84ErZOVlcXPWCm7LRD4FOs6Fo/Gd+nShduYk5NjWzmQ Cy+8kCrfe++9/JHMDpfUqlWL5xgpDaNPOX36dIcOHeh/6mnff/+9k+1GI7ZK 8U+aPXv2DFxEfZUW0UkkUsFFFfApekFPlgl0kUmoxyue5ovGZoHzz6ihPuk1 cuTIZ599lu/bJypUqPDzzz9zNXXR5KeffjKtgQYzvIgvtWzYsEG5Ev4nPj6+ SpUq/H9cXJzpt01rHPoU6+A9Az7Fe0LNwNmzZzt16sSdJCkpqbRqEydO5Dq/ /PKLC1FGBvgU6zoW1mPs2LHcxtTUVNvKJf65whYvXkzf6tWrF9/XSrRv356X kvPlkkGDBlmHpHzK2rVr1SUYNTn2OYmtUvXq1aMk3HPPPYGLKMM+/wNikQou qoBP0Qt6skygi0xCPV7x/dgPPPBA0KWzZ89esWKF+kgDuRdffJFPoOqs/emn n3LJxo0bTV//9ttveRFPuqUehCEaN268atWqM2fOFBcXz5w5k99DQeMB5zfS 2A6ZnATvGfAp3hNqBoYNG8bdw/qW1AcffJAttrR3phiBT7GuY2E9pkyZwm2c O3eudeX9+/cPHz68Ro0avgCuv/56rqMOeu+++651SMqn9O3bV62nW7duTpoc pdgq1a5dO0rCzTffHLioUaNGtKh79+6RCi6qgE/RC3qyTKCLTEI6Xm3dupXP hs5nvKSx2XXXXefzz5PDMyR8/fXXvJLvvvvOVHnWrFm8aMOGDSWGJ1maNWum 7qlgRo0aZazphDCGTIHBewZ8iveElAF1Aa5NmzbWDzHVqVOHqt1+++0uhBgx 4FOs61j4FPWAknqTVNDKubm5fCLz+d9iPG7cONpoXl4eZ1L5lPnz53OdSZMm WYekfAqjLjR/8cUXDhseddgqdc899/j80wgELmJ7aDHZRUwBn6IX9GSZQBeZ hHS8+vjjj/lUGNIt0M888wx/a9u2bfRx48aNpp8fFe+//z4v4veqkDfhj1On TjXVVCtRs3TaEt6QyRS8Z8CneI/zDMybN4/n+Lr00ktNryU1oWa3GzlypDtR Rgb4FOs6Fj5FvfE2MzPTovINN9zg878wxTTBvsmnqAcAn3zySeuQjD6lZcuW Bw4caNCggc//YAvZH+vvRim2Sg0cOJB/VjK9koZ2Uk7UqFGjIhtilACfohf0 ZJlAF5mEdLziuaNphBbSOzfV3VM//vhjiX9eGv740ksvmWr+5z//8fkfPOH3 phUXF/PbCp599llTTdUrbO+OUIQ3ZDIF7xnwKd7jMAOJiYn8XHx8fLzt5Tx1 G4/YGYkZ+BTrOqX5FDpS8R3LlSpVspjv69ChQ5yHJ554wrQGk0+hFfI8/LQS 0+sjTSifUrt27b1795YYJlcfMGCAfbOjEFulZs6cyRkwTSb54Ycfcvny5csj G2KUAJ+iF/RkmUAXmYR0vGrdujUJQYeskDbRrVs3+lb58uXVaZfvfzDNQU1n +csuu4zKb7zxRlXIb8e79tprTXMgr169mnuF80fpwxsyBQbvDfAp3uMkA9Tf qDPwLypO0qUeXli2bJkrQUYI+BTrOmw96JBleiAuISGBG9inTx9TZaNPycjI 4GqDBw82fj0tLY3crtGnlPz5Fl1fsDdMbdmyRf2vfAp5YVXYo0cPLly5cqVt w6MOW6VOnDhRvXp1an7nzp3V42B06G7bti0V1q9fX/IzYl4Cn6IX9GSZQBeZ hHS8ql27NmnRoUOHwEUbN25s2bLlpk2bAtfP7xcwToPw+uuv88mU7IYqXLRo ERd++umnqlCNc2ipcbX9+/fncv4hscTff+jEPXv27JMnTwYN3mLIFFLw3iDZ p6Smpq79E374gs5iqiR6XzBnm4GkpCT1SvHnnnuOPn711VeLDAQODl955RWu n56eHsHQy4wT9R3qnpWVpQpfeuklbv64ceO4ROCkZ859is//npTExMT8/Pzs 7OyxY8fy8YF6hbrpq+TPX1coOeqcRUcnvlGwatWq69evLy4upixRTvjCnMmn bN++nb2wz3+7IK25qKiIjlG9e/emlfCc7SWlvI9+165dPMcIxVnakTB6caKU ug1v2LBhR48ezcvLGzBgAJeMGTPGkzCjABf3dxAG6MkygS4ycT46pXNuuXLl SIsePXoELuV33FSsWPHll19OSUmhUyQdx6ZMmULnZZ//Vi7jxK10iud7G2rV qsVnbTIs1apVo5ILL7zQ+NYAOkHzLznx8fE0JqQYqOTdd9/lk75xFuunn36a u4rxLRUOh0whBe8Nkn0Kv9+hNN5+++0wti4B6wycOnWK70K0gNyu6VuDBg3i RXv27Ils9GXDifoOdad/LKrRSiLemBBx7lOCdgA6KpqeeR8+fDgvqlGjhpp/ wzgrFx/9fP67xRo2bOj7q08p8c9lrerwJtT/Q4YM4TpBfQrx6quvcrnk9/WE hxOlaOTAjwIxbCR9/t8/zz3jFjYu7u8gDNCTZQJdZOJ8dHrgwAGWo3///oFL FyxYwC+8UydWJR8xYsQIU/2ZM2eqk6+qWaFCha+//tpUkyxMzZo11Wmdfy30 +d/uTTZEVePfMImbbrpJFTocMoUavAdEr09xPhecNGx9inG4GJR27dqZvsWT EhPWc4Jpp+zjFqW79U5XpUqViDcmRJy0vWnTpj7/hB7kIC666CLVnAYNGgRe RCPjUL9+fa6g5t84cuRIr1691BfpWNetW7cdO3aMHj3aF+BTSvx7cevWrY0H orp1686YMUNVmDNnDpeb9nfqqI0bN/b53/ko3B2HisNjFA0k7r77bnVNitwl 7YYYQhhxcX8HYYCeLBPoIhPno1M1KXFpMxft3bt3wIABfA1CcdVVV/HLUAKZ PXs2P4LKXHnllYEmhdm5c2eHDh34GgpDncQ0U/Gbb77Ji9577z1V6HzIFGrw kUayTzlXieUMoO3O6589ezYjI2PZsmUWU71RnZSUlKSkJNPJKzMzk8rXrFnj 8MVPx44do/orVqzYv3+/8wjPVUJS6sSJE2lpac5THVPE8v4uAfRkmUAXmbh+ vCLJfvzxx++++27lypXG6x2lQR6Eajr53S8/P//777+naI8cORK0wubNm8v4 4vhQg48c8CneE8sZQNt1RwHsgVJugUzqBfmXCXSRCXSRCXyK98RyBtB23VEA e6CUWyCTekH+ZQJdZAJdZAKf4j2xnAG0XXcUwB4o5RbIpF6Qf5lAF5lAF5nA p3hPLGcAbdcdBbAHSrkFMqkX5F8m0EUm0EUm8CneE8sZQNt1RwHsgVJugUzq BfmXCXSRCXSRCXyK98RyBtB23VEAe6CUWyCTekH+ZQJdZAJdZAKf4j2xnAG0 XXcUwB4o5RbIpF6Qf5lAF5lAF5nAp3hPLGcAbdcdBbAHSrkFMqkX5F8m0EUm 0EUm8CneE8sZQNt1RwHsgVJugUzqBfmXCXSRCXSRCXyK98RyBtB23VEAe6CU WyCTekH+ZQJdZAJdZAKf4j2xnAG0XXcUwB4o5RbIpF6Qf5lAF5lAF5nAp3hP LGcAbdcdBbAHSrkFMqkX5F8m0EUm0EUm8CneE8sZQNt1RwHsgVJugUzqBfmX CXSRCXSRCXyK98RyBtB23VEAe6CUWyCTekH+ZQJdZAJdZAKf4j2xnAG0XXcU wB4o5RbIpF6Qf5lAF5lAF5nAp3hPLGcAbdcdBbAHSrkFMqkX5F8m0EUm0EUm 8CneE8sZQNt1RwHsgVJugUzqBfmXCXSRCXSRiS6fAgAAAAAAAADWwKcAAAAA AAAApIH7vrwkljOAtuuOAtgDpdwCmdQL8i8T6CIT6CIT+BTvieUMoO26owD2 QCm3QCb1gvzLBLrIBLrIBD7Fe2I5A2i77iiAPVDKLZBJvSD/MoEuMoEuMoFP 8Z5YzgDarjsKYA+UcgtkUi/Iv0ygi0ygi0zgU7wnljOAtuuOAtgDpdwCmdQL 8i8T6CIT6CIT+BTvieUMoO26owD2QCm3QCb1gvzLBLrIBLrIBD7Fe2I5A2i7 7iiAPVDKLZBJvSD/MoEuMoEuMoFP8Z5YzgDarjsKYA+UcgtkUi/Iv0ygi0yg i0zgU7wnljOAtuuOAtgDpdwCmdQL8i8T6CIT6CIT+BTvieUMoO26owD2QCm3 QCb1gvzLBLrIBLrIBD7Fe2I5A2i77iiAPVDKLZBJvSD/MoEuMoEuMoFP8Z5Y zgDarjsKYA+UcgtkUi/Iv0ygi0ygi0zgU7wnljOAtuuOAtgDpdwCmdQL8i8T 6CIT6CIT+BTvieUMoO3WdX744Yfk5OT09HQnKwypctjk5OQk+zl69GhENySH WO6l7oJM6gX5lwl0kQl0kQl8ivfEcgbQdus6V155pc/nu/XWW52sMKTKYfPZ Z5/5/JAtiuiG5BDLvdRdkEm9IP8ygS4ygS4yiRafkp2dvWnTJvobxhal4TwD +/btmzt37ujRoz/66KPk5OQzZ84ErXbq1CkaQ06YMOG111778ssvd+/e7WK0 7uK87QUFBd9888348eOpUQsXLszJyYlwaBEHPiVacN5Li4qKNmzYQLteQkIC HaDOnj0b4dCijFCP9ufScV4C6MkygS4yca4LDUjmzJlDY7Np06aRQBY1f/75 5+nTp7/88stTpkxZu3ZtcXFx0Go04Fm+fPnrr7/+/vvvp6WlnT592kkYhw4d 2mTHyZMny74hvQj3KZTSpKSkfv36nX/++TRSuu2228LYojScZCA9Pf3aa6/1 /ZWrr756yZIlxmqFhYXPPfdc+fLljdXKlSs3YMCAY8eORbAN4eJQfXJbtWvX NjaqcuXKb731VuQDjCDwKdGCw166devWOnXqGHtpo0aN9u7dG/kAowaHmTwn j/MSQE+WCXSRiRNd1q9f37x5c9PYrGvXrpmZmaaaZCIefPBBU82bb75527Zt pprLli27/PLLjdVId2v7w5B79dnx8ccfl31DepHsU7Kzs/m0pbjlllvC2KI0 bDMwceLECy64gJtcvXr1Vq1anXfeefyRLElqaipXo/y0bt2ay+Pi4mjfufTS S1Wu7r333tKcu0acqL9y5UrV3htvvPGRRx5RnsW4x0Ud8CnRghOltmzZona3 pk2bXn311fx/gwYN9uzZ40mYUYCTTJ6rx3kJoCfLBLrIxFaXWbNmVaxYkYUg dTp06FC1alX+2KxZM+O1ibNnz/7973/nRRdddNHDDz/cqVMn/khm88SJE6pm cnIyjd94dEdn83bt2vHxsFKlSkuXLrUO2IlP+fLLL8u+Ib1I9ilZWVnV/4QH rufG+cs2A9OnT6fG1q1bd8WKFXydNycnZ8SIEdzrunTpwtXy8/NbtGhBJYMH D/79999L/LsGrblhw4ZiB5ZO1L/uuuv4ILB161YuycvLa9u2Lbu26L3wDZ8S LThR6r777uPfB2bPns0lkyZN4kQNHDgw4iFGCU4yea4e5yWAniwT6CITa10O Hz7MroQ84+rVq3kocuTIkR49erAub7zxhqr81VdfceHIkSNPnTrFhZ9//jkX vvPOO1xCi8i2UMkll1yipsTZsGHDZZddRoV0ii8qKrIImAaBmcH48ccfyX3Q Gm666SaOs4wb0otkn2LkqquuOmfOX04yQLadrYeiuLi4adOmlIQaNWqowj17 9tDuYPruF198wfsCeW2XQnYN27b/8ccf5cqVo+Bff/11Y3lycjI3avv27ZEN MWI49ykdO3bkj3RsWbdu3bZt24JeGrP1KcePH09NTd20aZM6TpYGrf+3335L SUmho65pEXxKIDk5OfxL1GOPPWYs56FFfHw8ZT6yIUYJoR7tz6XjvATQk2UC XWRiqwudBLt3737w4EFjIQ3V2L907dpVFT733HNUUqVKFdNjxXRwo/I+ffrw R/I7fHr98MMPjdUWL17M5TNnzgyjIYMHD/b575bfsWNHRDfkDfAp3hNeBojO nTv7/I+fWDvftWvXcscbPXp0eBFGDtu2HzhwgIOfOHGisZwcGZd/9913kQ0x Yjj3KZ06ddq6dSsd9NQl5urVqw8dOrSwsDCwcqBP2b9//5AhQ5o0aaJun6NT 3kMPPRT01Jabm0vHTHXxOi4ujr44f/58VaE0n/LUU09d5Gf8+PEh5UE+tkpR kzknq1atMpbPmzePy6dPnx7ZEKME+BS9oCfLBLrIJOyx2W233Uai1K9fX5Ww U6hZs6apZv/+/X3+p1T44wcffMCC0sjHVPOaa64x/mjpnHXr1vGp/7333lOF kdiQZ8CneE94GTh27Ngll1xCSWjcuLF1zXHjxnGHVNeL5eCk7fzQzR133GG8 xYtGztyo6L0117lPIZRDMUIHQ+MdsEF9ypQpUypUqBD43aCOZsWKFRdffHHQ yqrzBPUpU6dO5UIyU5KvF4eHrVKPPPKIz28eTW0vKCjgX0FHjBgR2RCjBPgU vaAnywS6yCRsn9KqVSsShQb8qkRdpzCtkOdHIrfCH59//nmf/+fBwFsmBg0a 5PM/5x5qMG3atKEv0l/jOiOxIc+AT/GeMDKQmZl55513crefNGlSadXomDZr 1iye/qtevXrGZ7WE4KTtr732GreUjtX5+fkl/naRbQk60o4iQvIpBB09yBpk ZWXNnTtXuYlPPvnEVNmUk5SUFJ//4aaEhIT09PTjx4+vWbOGdx/TMTMvL69W rVpc3rdvX9qdKdvJycl0yG3Xrp26VSzQp6SmprIVat68ucxp5cqIrVK8M7Zo 0SJwET/62q9fv0gFF1XAp+gFPVkm0EUm4Y1Ojxw5wtcvHn30UVV48uRJvkuh Zs2aK1eu5EJ1+7oq+eijj7hk3759ptW++uqrVE5rNt1HYQ0PAIjPP//cWO76 hrwEPsV7nGdg+vTpAwYMoCE6P7JRuXLl999/P9AO79+/f/jw4Q888ED9+vW5 K1Kidu7c6X7oZcZJ22lnobZwQ2rXrv3WW2/R7u/zX2LYtGmTJ2FGBOc+pVq1 aqb5N3766SeeqaNRo0aqA5R231diYqLJopJV4XxSP1GFnFXi6aefNlam/Btf PW/yKdnZ2TyxIVknmX2s7Ngq1bJlS18p0+fylJWdOnWKVHBRBXyKXtCTZQJd ZBLe6FTdpDdjxgxjOVmG+Ph4XnTvvffSmZQ8C/3/z3/+U9X57rvvuILpLv3l y5fzg/BESOfZ3r17+/wPy5seSnV9Q14Cn+I9zjPQrVs3n4ExY8aYXtnDpKWl GavVq1fvl19+cTlol3DY9vT0dDUzs2LBggWRDzCCOPcpQS8bdenShfOgXnkZ 0nxfF154IR8t+SOZHS6pVatWQUGBxReNPuX06dMdOnSg/0md77//3sl2oxFb pfgnzZ49ewYu4sckr7322kgFF1XAp+gFPVkm0EUmYYxOd+3aVblyZZ9/XmLT I/OFhYVkSUzDmDZt2vzxxx+qDp1SeRouOqWOGzeOnMKPP/746quvGm/ephKH wWRlZfHA6YUXXjAtcndDHgOf4j3OM/D222/fddddrVq1Um9ybNq06ZYtW0zV du/e3atXL0qOeidUXFzcyy+/HKXvT5k/fz7va127dr3zzjv5OoLP/2BO9E72 VVJmnzJ27FjOg3qBjrVPOXXq1OLFi+lb1DfoEMrfbd++PS+loyuXDBo0yDok 5VPWrl2rLsEYJ2A897BVql69epSEe+65J3ARZZhPRpEKLqqAT9ELerJMoItM Qj1enT17Vr0VJSkpybiooKCATs0+/+RsQ4YM4el/ff7bq2hsZqy5YsWKwMdR L7roIuVxDh8+7DCeiRMn8leC/lLt4oY8Bj7Fe8LIwIEDB6hv84idRqelPXhC xmTZsmX8/hFiypQpLoTrKrZtp/2LnxP897//zY8QUgkdrrlFtWvXNk3XHEWU 0aeQmpyEuXPnWlfm+wBr1KjhC+D666/nOuopv3fffdc6JOVT+vbtq9bTrVs3 J02OUmyVateunc8wZ4sR/s2qe/fukQouqoBP0Qt6skygi0xCPV4NGzaMT4iB jwv16dPH53+LRFpaWon/cgaZCJ4KiXjttdeMlX/99deePXvWrVvX55807L// /e+2bdtefPFFtjnO43nwwQf5K6W9Zs6tDXkMfIr3hJcBYvTo0dzJrV/LnpOT w1eNBU7gYNv2+++/3+d/9Mx0CVW13fQwRRRRRp+iboL99ttvLSrn5ubyiczn v/o2btw42mheXh7vQcqnqPnTLKZlYJRPYapUqcL/fPHFFw4bHnXYKsXGuXnz 5oGL2B4aH6iMZeBT9IKeLBPoIpOQjlcTJkzgU2GbNm1MPx3/9NNPQX8GzMzM 5IeIK1WqFDg/cIn/6Xv1/7/+9S9fiDf48R01t99+u23NMm7IY+BTvCdsn7Jz 507u/EOGDLGu+fDDD3NNaVcfbNvOl0cHDBgQuKhhw4a0qFWrVpEKLsKU0ac8 /vjjrCkd6ywq33DDDT7/C1NME+ybfMrmzZt5bU8++aR1SEaf0rJlSzq6NmjQ wOd/sIXsj/V3oxRbpQYOHOjzT+xgeiXNvn37OFGjRo2KbIhRAnyKXtCTZQJd ZOL8eDVv3jye4+vSSy8NnEFr8uTJLFPgaxRmzJjBixITEy3WX1RUdMUVV/hC uXC2e/duXvPIkSMdfiW8DXkPfIr32GagtOdK1DMFgwcPtq7JBpnIzc0tW7Au Y912ag4/1RX4FBhB+xEtIiMTwfgiSVl8SmFhId+xXKlSJYv5vg4dOsS6P/HE E6Y1mHwKrZDvr6OVWM9GqHxK7dq19+7dW+KfT4xLgtrJcwBbpWbOnMkZMM3t 8OGHH3L58uXLIxtilACfohf0ZJlAF5k4PF7RGZAfoY2Pj9+wYUNgBX63Qrly 5QInPqL6rCB5GYtNqBd6Gt9EYI26l9s0I7E1YWzIe+BTvMc2A7179+7Zsye/ OsSIuvdp2rRpJf5JsWjQbprAtsT/MAu/F8P4dlQh2Lb9xhtv9PmnzjBdSD1+ /DiNk2lRly5dIhtixHDuU1q0aGGaVDAhIYGl79Onj6my0adkZGSYnCyTlpbG EyQqn0LcddddXPmdd94xhWGcq0H5FDoMqsIePXpwoZoH/lzCVinqnNWrV6fm d+7cWd0JTHavbdu2vN+VdntwrAGfohf0ZJlAF5k4OV7RiIvnNapYsWJplclF GodqRuhsy4vU+8joXG+aaCsvL48npiahjb8iUq+g0/Hs2bODzvuqnmBdtmxZ 0Kicb0gakn1Kamrq2j/h++7oLKZKovcFc9YZWLBgAXe2pk2bkuPevHlzcXHx oUOHXnzxRX6LSrVq1fbv3081aTTr80/t9fjjj3/zzTeUkDNnztCa//a3v/Ea nnnmGe9a5Qxb9dU9n926dVOXUw8ePKgepf/ggw+8CDQCOPcpPv97UhITE8mr Zmdnjx07lqdQqFChgrrpq+TP187STqHOWXQc44vRVatWXb9+PfWcrKyscePG qUmejT5l+/btah65kSNH0pqLioo2btxINplWsmbNGq4W9H30u3bt4knXKc6g x8yoxolS6ja8YcOGHT16lA74AwYM4JIxY8Z4EmYU4CST5+pxXgLoyTKBLjKx 1SUpKUlN5Pvcc8/Rx6+++mqRAf7hrqCggA9lVapU+fzzz9UtEAsXLuRJjOvW rcs/RdKiXr16nX/++a+//npOTg4Vrl69msZ+vImPPvrIuPWnn36ay4PecPLK K6/w0vT09MClIW1IGpJ9Cr/foTTefvvtMLYuAesMnD59midtUKjBJLsSdVsj reSiiy5Si2hsyXfyMO3bt6dVedQkx9iqT3uTsiTUcDL7bdu25V2b+Mc//iFw smWHOPcpgZMH+vwXkU3PvA8fPpwX1ahRg+dGI4yzcqn+QJ6Cn+4x+pQS/zSG xj7DRphRz0AF9Sklf77EtrRjZlTjRCkaOfCjQGrH5H86d+587hm3sHGSyXP1 OC8B9GSZQBeZWOtCY/ugp2YjNGLhynS6VCO3yy677KabbuLnOn3+gc26deu4 2p49e/jul8BT8JNPPqlO6wz/MknQ2gLDGzRoEC8NfCgm1A1JI3p9yvjx48PY ugScZGD+/Pk8TbqRjh07rl+/3lgtNzf38ccfN81AW7Vq1f/973/GdwnJwUnb z5w5QwNyNYMfc/HFF7///vuSL03a4qTt/PvG1KlTyUEYTSgd4gLvsKIjIU8e Qmzbto0Ljxw50qtXL/XFChUqdOvWbceOHXzToMmnlPj34tatW6uToM//U4/x vbpz5szhctP+Tgftxo0b+/zvjQp6YIxeHB6jaCBx9913q5MRncIefPBBDCGM lN2nRO9xXgLoyTKBLjKx9SnG4X1Q2rVrp+pv3bpV3SCt6N69u+kVeDk5OUaV +RQc9BmTN998kyu89957gUvV79ulvbfC+YakIdmnnKs4z0B2dvaaNWuWLl1K I1KLV/DQ6D0jI+Obb75Zvnz59u3bTTP6isJ528+ePbt7924anK9YsWLXrl3n wO24ofZ8ajLJumzZssDpRIx1UlJSkpKSTCevzMxMKqfOY3rOpTSOHTtG9SnV fEthjBOSUnRSSEtLc57qmAJHe72gJ8sEusgkEser/Pz8DRs2fPvtt/Q38KFj xenTp9evX09DuKysLIu1bd68+eeffy5LPA43JAr4FO+J5Qyg7bqjAPZAKbdA JvWC/MsEusgEusgEPsV7YjkDaLvuKIA9UMotkEm9IP8ygS4ygS4ygU/xnljO ANquOwpgD5RyC2RSL8i/TKCLTKCLTOBTvCeWM4C2644C2AOl3AKZ1AvyLxPo IhPoIhP4FO+J5Qyg7bqjAPZAKbdAJvWC/MsEusgEusgEPsV7YjkDaLvuKIA9 UMotkEm9IP8ygS4ygS4ygU/xnljOANquOwpgD5RyC2RSL8i/TKCLTKCLTOBT vCeWM4C2644C2AOl3AKZ1AvyLxPoIhPoIhP4FO+J5Qyg7bqjAPZAKbdAJvWC /MsEusgEusgEPsV7YjkDaLvuKIA9UMotkEm9IP8ygS4ygS4ygU/xnljOANqu OwpgD5RyC2RSL8i/TKCLTKCLTOBTvCeWM4C2644C2AOl3AKZ1AvyLxPoIhPo IhP4FO+J5Qyg7bqjAPZAKbdAJvWC/MsEusgEusgEPsV7YjkDaLvuKIA9UMot kEm9IP8ygS4ygS4ygU/xnljOANquOwpgD5RyC2RSL8i/TKCLTKCLTOBTvCeW M4C2644C2AOl3AKZ1AvyLxPoIhPoIhP4FO+J5Qyg7bqjAPZAKbdAJvWC/MsE usgEushEl08BAAAAAAAAAGvgUwAAAAAAAADSwH1fXhLLGUDbdUcB7IFSboFM 6gX5lwl0kQl0kQl8ivfEcgbQdt1RAHuglFsgk3pB/mUCXWQCXWQCn+I9sZwB tF13FMAeKOUWyKRekH+ZQBeZQBeZwKd4TyxnAG3XHQWwB0q5BTKpF+RfJtBF JtBFJvAp3hPLGUDbdUcB7IFSboFM6gX5lwl0kQl0kQl8ivfEcgbQdt1RAHug lFsgk3pB/mUCXWQCXWQCn+I9sZwBtF13FMAeKOUWyKRekH+ZQBeZQBeZwKd4 TyxnAG3XHQWwB0q5BTKpF+RfJtBFJtBFJvAp3hPLGUDbdUcB7IFSboFM6gX5 lwl0kQl0kQl8ivfEcgbQdt1RAHuglFsgk3pB/mUCXWQCXWQCn+I9sZwBtF13 FMAeKOUWyKRekH+ZQBeZQBeZwKd4TyxnAG3XHQWwB0q5BTKpF+RfJtBFJtBF JvAp3hPLGUDbdUcB7IFSboFM6gX5lwl0kQl0kQl8ivfEcgbQdus6P/zwQ3Jy cnp6upMVhlQ5bHJycpL9HD16NKIbkkMs91J3QSb1gvzLBLrIBLrIBD7Fe2I5 A2i7dZ0rr7zS5/PdeuutTlYYUuWw+eyzz3x+yBZFdENyiOVe6i7IpF6Qf5lA F5lAF5lEi0/Jzs7etGkT/Q1ji9JwnoGcnJw5c+aMHj162rRpGzZsKK3amTNn UlNTx/tZunTpoUOHXIvVbZy3fcuWLdTqMWPGLFy4MHZ0h0+RgPNeWlRURDvm hAkTEhIS6AB19uzZCIcWZYR6tD+XjvMSQE+WCXSRSRij08LCwk1+aMQStEJu bu78+fNpJLNo0aLDhw8HrfPzzz9vKgVaf9CvZGVl0QpfeeWV6dOnb926tbi4 OKSwmb179/JW8vLyApcWFBQsX7789ddff//999PS0k6fPh3GJlxBuE+hRCUl JfXr1+/888+nkdJtt90Wxhal4SQD69evb968ue+vdO3aNTMz01iN+vDQoUOr VKlirFatWrUpU6ZEsAFlwEnb8/Pz+/fvb2r7E088QXbMkxgjBXxKtODwGEVn hzp16hh7aaNGjejIH/kAowaHmTwnj/MSQE+WCXSRSRg+5eWXX2Zprr766sCl zzzzjFG+uLi4cePGBVarWLGirxTmzZtnqnzixAk6VJqqXXfddTt27AgpcjJQ F198MX995syZpqXLli27/PLLjZugrmjxa3lEkexTsrOz+bSluOWWW8LYojRs MzBr1izVby+99NIOHTpUrVqVPzZr1ky52t9//51O6Fx+3nnntW3btkmTJipX NLz0ojEhYtv24uLijh07chPq169/5513XnbZZfzxjjvuKO23hagAPiVacKLU li1baN/kzDRt2pROUvx/gwYN9uzZ40mYUYCTTJ6rx3kJoCfLBLrIJFSfsnHj RnXsCvQpQ4cO5UVVqlRp3759pUqV+OPYsWON1U6ePOkrnS+//NJYmY6WrVu3 VqM+6hjVqlXjj9WrV8/Pz3ce/P3336+2YvIpycnJZKmovHz58jTAaNeuHTeT mrB06VLnm3ALyT4lKyur+p+QIufM+cs6A4cPH2ZXQt1+9erVfJ33yJEjPXr0 4B71xhtvcM1Ro0ZxyfPPP6/u9VqyZEnlypWpsGbNmsePH498a0LDVv2pU6dy ox577DG+gEJ/Bw0axIW01KNAIwB8SrTgRKn77rvP5/99bPbs2VwyadIkTtTA gQMjHmKU4CST5+pxXgLoyTKBLjIJyaecPn26ZcuWaqhv8ikZGRlc3qZNm4KC ghL/0K5x48ZUUq5cuX379qmadADkmm+99VZmAH/88YdxtY8++ihX7tWr17Fj x6iEhoh0jiYrNHnyZOctnTNnjtENGX3KqVOnGjVqRIWXXHKJmqVnw4YN/Isx jTqKioqcb8gVJPsUI1ddddU5c/6yzQANCLt3737w4EFj4e+//87+pWvXrlxC vYU6beDQXfmX1NRUVwN3Adu2//3vf6fI69WrRzuLsZx2diqn3Tx6b9B17lM6 duzIHykJ69at27ZtW9C7T219ChlV6gObNm0yJTMQWv9vv/2WkpJCjti0CD4l kJycHP59idy0sZyHFvHx8QJ/ItBCqEf7c+k4LwH0ZJlAF5mEdLx66aWX2Ehe f/31gT5lyJAhgZZEmRfjJZVffvmFC5csWWK9RbItF1xwAdXs3bu3aUgQ0myc Bw4c4Du+brjhhkCfsnr1ai788MMPjd9avHhx0IsvHgCf4j3hZYDgu7zq169v XW3VqlXcnaZNmxbGViKKbdtr1apFkQ8aNMhUPnfuXG5UeKmTgHOf0qlTp61b t5IhVbf/Va9efejQoabb3krzKfv376eDZJMmTfjXaYJOeQ899FDQU1tubm6f Pn3UjYV01KUvzp8/X1Uozac89dRTF/kZP358SHmQj61S1GTOCe1rxvJ58+Zx +fTp0yMbYpQAn6IX9GSZQBeZOD9epaens5Hs78fkU+hMTWdGX7BH7a655hqf /+Y9VZKSksKapqWlWW908ODBXHPz5s1OmxQMdrvly5dfsWJFoPX44IMPuJDs TNDg1e+ongGf4j1h+5RWrVpREqirWFdLTEzkbhb4BJZ2rNt++vRpjnz06NGm RbTL8KKPP/44ohFGDuc+hQj6YB0d9IxzbgT1KVOmTKlQoULgd4M6GjpMqSfp TKibDYL6FHV7Hpkp768CRxpbpR555BGf3zya2l5QUMAnrxEjRkQ2xCgBPkUv 6MkygS4ycXi8orNwixYtfP7Hhw8dOvTQQw+ZfMru3bv5/PjOO++Yvvv//t// 40Xqhi41YLN97KhZs2Y+/8+YobXqr9CZnTf36quv7tixI9CnPP/88z7/L5aB d3HwHfh16tQpSwBhAJ/iPeFl4MiRI/zz+KOPPmpd8+mnn+a+Z7zgKATbtvOT g4FtpCMDP4b24osvRjC+SBKST/H5LyqRNcjKypo7d65yE5988ompssl98I8z devWTUhISE9PP378+Jo1a3j38f31alReXh5fvSL69u1Lu3N+fn5ycjLZ4Xbt 2qlbxQJ9SmpqKluh5s2b8y2y5xi2St15553UfDpVBS7iDtyvX79IBRdVwKfo BT1ZJtBFJg6PV+rW+sWLF9PHPn36mHzKunXruMKCBQtM3/3oo4940W+//cYl 06ZNU2t74YUX+vfv/9JLL5GbOHHihOm7/Ogx1eGP2dnZ69evD/WOr5o1a9JK rr/+erLA27dvD/QpKsLAASRZG5//+X2PZzSCT/Ge8DKgLgTPmDHDohp1Wp5N rmHDhuGHGDFs237zzTf7/FMrGx+UoDFz9+7dufkPP/xwxKOMDM59CjXfNKvG Tz/9xPNvNGrUSP3KUdp9X4mJiaZDHFkVzt7w4cNVoXoij4ytsTIdgoyHPpNP oWMjdzCyTjt37nTY9ujCVil+fDLo9Lk8nXgZf/I6Z4BP0Qt6skygi0ycHK82 bNig7vjikkCfsnDhQj5pmm7bKzHcwU4nZS6ZMGGCLxj16tWjU7n64sGDB7l8 8uTJdFK+9tprVU3ysykpKU4aeO+99/r803Zt27aNPgb1Kd999x0Xmm5rWb58 uZqyzONTP3yK94SRgV27drGVbtasmfVrRNTUWN4/6+QE27ar3xZat25Nu15W Vtb8+fNpKK52SdrRvArWZZz7lKCPxnfp0oUzkJOTY1s5kAsvvNCYPTI7XFKr Vi2ejaQ0jD7l9OnTHTp0oP8vuOCC77//3sl2oxGHV/169uwZuIiOUbSITiKR Ci6qgE/RC3qyTKCLTGx1OXXqFD+jccUVV6h7CQJ9irok8dNPP5nWkJyczIvU pRblU0jZkSNHPvvss3yHP1GhQoWff/6Zq5E/Uq6E/4mPj1fvzouLi7OdMZjG hFz5vffe45KgPoXO8jzfF53lx40bR5bkxx9/fPXVV433k1OJbTJdBD7Fe0LN wNmzZzt16sTdIykpyaImmXf+1b19+/bhvZ800ti2ncLu3LlzwE8Lvn/+85/8 YNrQoUO9CtZlyuhTxo4dy6lQ07hZ+xQ6oi5evJi+1atXL76vlTsGLyXnyyWB UxaYUD5l7dq16hKMmhz7nMRWqXr16lES7rnnnsBFlGGffy7KSAUXVcCn6AU9 WSbQRSa2uqhnN8huqMJAn/Lpp5/yiXLjxo2mNXz77be8yDi71+zZs1esWKE+ 0pDvxRdf5Grq/K6m2/L5Jz6lwd6ZM2dovEQWgy9z0HjAYmLPnJycGjVq+PwX 6dTgMKhPKfE/uBr4hCwNwGgYxv8fPnzYIkuuA5/iPaFmYNiwYdw3rG9J/fXX X/kphsqVK2dkZJQ1ysjgpO20kyYkJLRq1Yr2vvLly99xxx0ffPAB7YD8eE7g g2nRQhl9ypQpU7gbzJ0717ry/v37hw8fzgclE9dffz3XUQe9d9991zok5VP6 9u2r1tOtWzcnTY5SbJVq164dJeHmm28OXMS/RHXv3j1SwUUV8Cl6QU+WCXSR ibUuaWlp5cqVo+Q/8MAD2Qb4ZqoGDRrwR6r59ddf84nyu+++M61k1qxZvMj6 3e40Crruuut8/hl1eC4F9cxLs2bN1D0VjHpexmKd5Hm5zg8//KAiV1MQT5w4 kT4aXxNJ48mePXvWrVvX559j9r///e+2bdvYPcXHx1slMQLAp3hPSBlQ1wTb tGkT+FyV4uDBg+pZadMLTEURUttpV1XTW6nf/41T5kYXZfQp6gGlb7/91qJy bm4un8h8/rcYjxs3jjaal5fH3UP5FEoj15k0aZJ1SMqnMOpC8xdffOGw4VGH rVJ8zG/evHngIraHtpNdxAjwKXpBT5YJdJGJtS5kTwJ/+gtk+fLlGzdu5P/V j4qK999/nxfZTnP0zDPPcE1+loS8CX8MfGWe2pyapdPE5s2bnUR+4403Bn73 5MmT6v9//etfPh33HMKneI/zDMybN48vIlx66aUWvfr48eN8LdhnmAtCJuGp T3z88cfcQOtfISRTRp/y+OOPcwYyMzMtKvObm84//3zTBPsmn6IOXE8++aR1 SEaf0rJlywMHDjRo0MDnf7CF7I/1d6MUW6UGDhzo8//SZXolDe2knKhRo0ZF NsQoAT5FL+jJMoEuMrHWRd31ZM3SpUuzs7P5/5deesm0kv/85z8+/51jtlNm qVu/+GGQ4uJivhfr2WefNdVUvaK0uyO2bt3qJPK2bdtaxFNUVHTFFVf4dFzL g0/xHocZSExM5HePxsfHWwzOT5w4cfvtt3M369u3r/DXtYftU3h2i4YNG1pP IyCZsvgUOqbxHcuVKlWymO/r0KFD3BOeeOIJ0xpMPoVWyJOW0EqsD5jKp9Su XXvv3r0lhvneBwwYYN/sKMRWKfVAomnayQ8//JDLly9fHtkQowT4FL2gJ8sE usjEWpdTp04dDkaPHj14cMIf+XzKdzWYZpamc/dll13mK+XKhYlu3br5/G9j VCfoNm3a+PyXM0xPH6vbtywepT9y5Ehg5OpessmTJ9NH67cMqHeMGl+O4A3w Kd7jJAPU36h/+vy/qFhUPnnypHrEnnYW+WN4J23PyMgwXmokaL/gNv7f//1f BIOLMM59Ch3cTA/EJSQkcAb69Oljqmz0KZQ6rjZ48GDj19PS0sjtGn0Kcddd d3HlwEd+tmzZov5XPoXnimf4yEysXLnStuFRh61SJ06cqF69OjW/c+fO6pcB Opu0bdvW57+bV/jPBZ4Bn6IX9GSZQBeZhDc6DXyOnnj99df5FEkmQhUuWrSI Cz/99FMu2bhxY8uWLTdt2hQYCc+JZJww4fPPP+ev03qMlfv378/l/ENiib// 0Il79uzZpqGUidKeo6fhh2lGr7y8PJ4rm/qexy9PKZHtU1JTU9f+SZ06dShF dBZTJdH7gjnbDCQlJakp4J577jn6+NVXXy0ywIND6kv8NiiiatWqZG2WLFmy 6K/wU11ysG37+vXrK1eu3Lp16x9++IFs1759+8aMGcM7bL169YxvY486nPsU n/89KYmJifn5+aTg2LFjOQPUK9RNXyV//rpCO4U6Z9HRiW8UpP5AmSwuLs7K yho3bhxfmDP5FDpGsRcmRo4cSWsuKiqiw2bv3r1pJWp296Dvo9+1axfPMUJx Wh8JoxEnSqnb8IYNG3b06FE6jA8YMIBLqMd6EmYU4CST5+pxXgLoyTKBLjJx 0afQiZvvWKhVqxafi8mwVKtWjUouvPBC9S4AfhtOxYoVX3755ZSUFDqZ0hFv ypQpdAb3+W8PM07xSido/iUnPj6exoR03qeSd999l0/6xlms1cu+rR8ECOpT KNRevXpR8GS1cnJyaJxJkTdt2pRrfvTRR6Hmp+xI9in8fofSePvtt8PYugRs ry0Gzghngowt1aSDlXU1YuHChd41zAG26pMvU8Hz3BrMFVdckZ6e7lWYEcG5 TwnaASgbpmfehw8fzotq1KjBU4IQxlm5+Djp898t1rBhQ99ffQoxceJEVceU 8CFDhnCdoD6l5M9X0/rEPxIVBk6UopEDPwrEsJH0+X//PPeMW9g4yeS5epyX AHqyTKCLTFz0KSX+m/fUKVXJV6FCha+//lrVWbBgAb8aT52CVU1ixIgRpnWS ZeAXyvNpXb148ZJLLsnKylLV+DdM4qabbrKIPKhP2bNnD3mroKOCJ598Uo00 vCR6fcr48ePD2LoEbH2KsWMEpV27diWGyegsMO4REnCi/tSpU/lnVYb26x49 ehw6dMiTACOIk7bzrxaUAXIQ/L4YpkGDBoF3WJFxqF+/PlfgKUFK/Leh9urV y5i9bt267dixY/To0b4An1Li34tbt25tPDbWrVt3xowZqsKcOXO43LS/U0dt 3Lixz/82KDqyhZcTmTg8RtFA4u6771bXpMhdPvjggxhCGCm7T4ne47wE0JNl Al1kEp5P+fe//+0rZXK22bNn84OlzJVXXhk4JNu7d++AAQP4AoriqquuMr5g xcjOnTs7dOjA11AY6iSmmYrffPNNXqRe6RiUzMxMrmaal4zWZux4PCr4/PPP HaUjAkj2KecqsZwB523fv3//smXL0tLSovpeLyOh6n727NmMjAxKgsVUb1Qn JSUlKSnJdPKi4w+Vr1mzxuLFT0aOHTtG9VesWEFpdx7huUpISp04cYJ6qfNU xxSxfKyTAHqyTKCLTCJ0vCJnsXLlSutf8/iRkO+++45qGq+MlEZ+fv73339P 0R45ciRohc2bN6t32YcHDb3Wr1+/fPlyJ/FEFPgU74nlDKDtuqMA9kApt0Am 9YL8ywS6yAS6yAQ+xXtiOQNou+4ogD1Qyi2QSb0g/zKBLjKBLjKBT/GeWM4A 2q47CmAPlHILZFIvyL9MoItMoItM4FO8J5YzgLbrjgLYA6XcApnUC/IvE+gi E+giE/gU74nlDKDtuqMA9kApt0Am9YL8ywS6yAS6yAQ+xXtiOQNou+4ogD1Q yi2QSb0g/zKBLjKBLjKBT/GeWM4A2q47CmAPlHILZFIvyL9MoItMoItM4FO8 J5YzgLbrjgLYA6XcApnUC/IvE+giE+giE/gU74nlDKDtuqMA9kApt0Am9YL8 ywS6yAS6yAQ+xXtiOQNou+4ogD1Qyi2QSb0g/zKBLjKBLjKBT/GeWM4A2q47 CmAPlHILZFIvyL9MoItMoItM4FO8J5YzgLbrjgLYA6XcApnUC/IvE+giE+gi E/gU74nlDKDtuqMA9kApt0Am9YL8ywS6yAS6yAQ+xXtiOQNou+4ogD1Qyi2Q Sb0g/zKBLjKBLjKBT/GeWM4A2q47CmAPlHILZFIvyL9MoItMoItM4FO8J5Yz gLbrjgLYA6XcApnUC/IvE+giE+giE/gU74nlDKDtuqMA9kApt0Am9YL8ywS6 yAS6yESXTwEAAAAAAAAAa+BTAAAAAAAAANLAfV9eEssZQNt1RwHsgVJugUzq BfmXCXSRCXSRCXyK98RyBtB23VEAe6CUWyCTekH+ZQJdZAJdZAKf4j2xnAG0 XXcUwB4o5RbIpF6Qf5lAF5lAF5nAp3hPLGcAbdcdBbAHSrkFMqkX5F8m0EUm 0EUm8CneE8sZQNt1RwHsgVJugUzqBfmXCXSRCXSRCXyK98RyBtB23VEAe6CU WyCTekH+ZQJdZAJdZAKf4j2xnAG0XXcUwB4o5RbIpF6Qf5lAF5lAF5nAp3hP LGcAbdcdBbAHSrkFMqkX5F8m0EUm0EUm8CneE8sZQNt1RwHsgVJugUzqBfmX CXSRCXSRCXyK98RyBtB23VEAe6CUWyCTekH+ZQJdZAJdZAKf4j2xnAG0XXcU wB4o5RbIpF6Qf5lAF5lAF5nAp3hPLGcAbdcdBbAHSrkFMqkX5F8m0EUm0EUm 8CneE8sZQNt1RwHsgVJugUzqBfmXCXSRCXSRCXyK98RyBtB26zo//PBDcnJy enq6kxWGVDlscnJykv0cPXo0ohuSQyz3UndBJvWC/MsEusgEusgEPsV7YjkD aLt1nSuvvNLn8916661OVhhS5bD57LPPfH7IFkV0Q3KI5V7qLsikXpB/mUAX mUAXmUSLT8nOzt60aRP9DWOL0nCegZycnDlz5owePXratGkbNmwordqpU6do DDlhwoTXXnvtyy+/3L17t1uhuk6o6tvqfubMmdTU1PF+li5deujQIReijAzw KdGC815aVFREOybtegkJCdRRz549G+HQogxkUi/Iv0ygi0ygi0yE+5SCgoKk pKR+/fqdf/75NFK67bbbwtiiNJxkYP369c2bN/f9la5du2ZmZhqrFRYWPvfc c+XLlzdWK1eu3IABA44dOxbBNoSLQ/Wd6E5tHzp0aJUqVYxtr1at2pQpU9yP 2w3gU6IFh71069atderUMXa/Ro0a7d27N/IBRg3IpF6Qf5lAF5lAF5lI9inZ 2dk8TFXccsstYWxRGrYZmDVrVsWKFbnJl156aYcOHapWrcofmzVrdvr0aa5G +WndujWXx8XFka+hyipX9957b3FxsRftCQUn6jvR/ffffyfzwkvPO++8tm3b NmnSRNWnoXWkGlAG4FOiBSdKbdmyRe1uTZs2vfrqq/n/Bg0a7Nmzx5MwowBk Ui/Iv0ygi0ygi0wk+5SsrKzqf0Jj0RjxKYcPH2ZXQv1/9erVfD3xyJEjPXr0 4N3hjTfe4Jr5+fktWrSgksGDB9O4nUqoMq25YcOGYgeWTtR3ovuoUaO4jc8/ /7y612vJkiWVK1emwpo1ax4/fjwS8ZcF+JRowYlS9913H/8+MHv2bC6ZNGkS J2rgwIERDzFKQCb1gvzLBLrIBLrIRLJPMXLVVVfFiE8p8c/j1L1794MHDxoL yYmwf+natasqJP/+1Vdfmb7+xRdf8F6TkJDgXtTuEKr6peleVFT06KOPTp06 1VSu/EtqamoZQ3Ud5z6lY8eO/PHUqVPr1q3btm1b0Etjtj6FzBrlYdOmTbQe 6+3S+n/77beUlBRyxKZF8CmB5OTk8CW/xx57zFjOp7D4+HiBNlkLyKRekH+Z QBeZQBeZwKd4T3gZIPhOp/r161tXW7t2LQ8sR48eHcZWIopbPqU0Vq1axW2f Nm1aGOFFFOc+pVOnTlu3biVDqm7/q169+tChQwsLCwMrB/qU/fv3DxkypEmT Jnw1iqBD60MPPRT0EJqbm9unTx91Y2FcXBx9cf78+apCaT7lqaeeusjP+PHj Q8qDfGyVoiZzTqi/GcvnzZvH5dOnT49siFECMqkX5F8m0EUm0EUm8CneE7ZP adWqFSXhmmuusa42btw43mXUdUk5RNqnJCYmctvpuBFOfJHEuU8hlEMxQkZV PZ1UUopPmTJlSoUKFQK/G9TRrFix4uKLLw5aWXWeoD5l6tSpXEhmqqioqCxp EYitUo888ojPbx5NbS8oKOBf20aMGBHZEKMEZFIvyL9MoItMoItM4FO8J7wM HDlyhH8ef/TRR0urQ/vOrFmzePqvevXqnThxokyBRoBI+5Snn36ax8/79u0L J75IEpJPIQYNGkTWICsra+7cucpNfPLJJ6bKJveRkpJChXXr1k1ISEhPTz9+ /PiaNWs4jYQxgLy8vFq1anF53759aXfOz89PTk4mO9yuXTt1q1igT0lNTWUr 1Lx5c5nTypURW6XuvPNOan6LFi0CF/Ejlv369YtUcFEFMqkX5F8m0EUm0EUm 8CneE14G1AXHGTNmmBbt379/+PDhDzzwQP369bkOJWrnzp3uhOsqEfUpR48e vfzyy6l+w4YNw4wvkjj3KdWqVVu6dKmx/KeffoqLi/P55z9Uz6qUdt9XYmKi yaKSVeGOQf1EFZLh5UIyd8bKhYWFxlfPm3xKdnY2J5msk8w+VnZslWrZsqWv lOmyeTrxTp06RSq4qAKZ1AvyLxPoIhPoIhP4FO8JIwO7du3imayaNWt25swZ 09K0tDTjHTv16tX75ZdfXAvXVSLqUwYNGsQZmDlzZpjxRRLnPiXoo/FdunTh 1uXk5NhWDuTCCy/0+Wer5o9kdrikVq1aBQUFFl80+pTTp0936NCB/r/gggu+ //57J9uNRmyV4p/OevbsGbiI+iotuvbaayMVXFSBTOoF+ZcJdJEJdJEJfIr3 hJqBs2fPkknnsWJSUlJghd27d/fq1YuSo949FBcX9/LLL0fp+1OMONd91apV fMWhffv2AhteUmafMnbsWBZXTWVm7VNOnTq1ePFi+hb1DbK3/F1KDi8l58sl ZO6sQ1I+Ze3ateoSjJoc+5zEVql69epREu65557ARZRhWtSmTZtIBRdVIJN6 Qf5lAl1kAl1kAp/iPaFmYNiwYTw4tL31kcbny5Ytu+6667i+wDezR8in/Prr r/wER+XKlTMyMsoUYsQoo08hNVnWuXPnWlfm+wBr1KjhC+D666/nOmRhuOTd d9+1Dkn5lL59+6r1dOvWzUmToxRbpdq1a0dJuPnmmwMXNWrUiBZ17949UsFF FcikXpB/mUAXmUAXmcCneE9IGZgwYQKPDMmnO3wuPicnh69O1qlTJ/woI0Mk fMrBgwfVc+JffvllWUOMGGX0KeoBpW+//daicm5uLh8wff635Y4bN442mpeX xylSPmX+/PlcZ9KkSdYhKZ/CVKlShf/54osvHDY86rBV6p577vH5pxEIXMT2 0GKyi5gCmdQL8i8T6CIT6CIT+BTvcZ6BefPm8Rxf5DtCmsDq4Ycf5sEkv6de Dq77lOPHj/P1VuKFF15wIcSIUUaf8vjjj3MzMzMzLSrfcMMNPv8LU0wTuZt8 yubNm3ltTz75pHVIRp/SsmXLAwcONGjQwOd/sIXsj/V3oxRbpQYOHOjzzx1t eiUN7aScqFGjRkU2xCgBmdQL8i8T6CIT6CIT+BTvcZiBxMTECy64wOd/yemG DRuC1intQYx//etfvNfk5uaWJVTXcdennDhx4vbbb+eW9u3b9+zZs+5EGRnK 4lMKCwv5zthKlSpZzPd16NAhzsYTTzxhWoPJp9AKeb53Wonp9ZEmlE+pXbv2 3r17SwwvqRkwYIB9s6MQW6VmzpzJGViwYIGx/MMPP+Ty5cuXRzbEKAGZ1Avy LxPoIhPoIhP4FO9xkoGlS5fya1DIuZdWOT09/bLLLjNNYEscOHCA34th++Z6 73HRp5w8eVJNL9CjR4/AadCk4dyntGjRQr2+hElISOCW9unTx1TZ6FMyMjK4 2uDBg41fT0tLI7dr9CnEXXfdxZXfeecdUxhbtmxR/yufsnjxYlVICefClStX 2jY86rBVigxy9erVqfmdO3dW7pjsXtu2bXm/E26ZPQOZ1AvyLxPoIhPoIhPJ PiU1NXXtn/BMVjRqVSXR+4I52wwkJSWpV4o/99xz9PGrr75aZIAHhzSa9fmn 9nr88ce/+eYbSgiN1WnNf/vb3/i7zzzzjEdNcowT9Z3oTsN4fuMSUbVqVTJr S5YsWfRXsrOzvWiSY5z7FJ//PSmJiYn5+fnUirFjx/JUZtQr1E1fRJs2bTg5 6thIR1G+UZBysn79+uLi4qysrHHjxvGFOZNP2b59O3thYuTIkbTmoqKijRs3 9u7dm1ayZs0arhb0ffS7du2qVKkSx0mG0a0UCcGJUuo2vGHDhh09ejQvL2/A gAFcMmbMGE/CjAKQSb0g/zKBLjKBLjKR7FP4/Q6l8fbbb4exdQlYZ4BG4BUr VrRouM//mACv56KLLlKFNLbkO3mY9u3bnz592rtWOcOJ+k50pwOCdYqIhQsX etEkxzj3KUE7QLly5UzPvA8fPpwX1ahRgywGFxpn5VL9gTxFw4YNfX/1KcTE iRONfYY2of4fMmQI1wnqU4hXX32Vy4U/FhQGTpSiMxQ/CsSwkfT5f2c794xb 2CCTekH+ZQJdZAJdZBK9PmX8+PFhbF0Ctj7FOFwMSrt27bhybm4uuXvTDLRV q1b93//+98cff3jUnlAou09h3UeNGmWdIuLrr7/2okmOcdL2pk2bUuRTp04l B2E0oQ0aNAi8w4qMQ/369bnCtm3buPDIkSO9evVSX6xQoUK3bt127NgxevRo X4BPKfHvxa1bt1YHW6Ju3bozZsxQFebMmcPlpv2dOmrjxo19/nc+7tmzJ7yc yMThMYpOWHfffbe6JkXu8sEHH8SpyggyqRfkXybQRSbQRSaSfcq5iusZKCws zMjI+Oabb5YvX759+3bJT2rEsvqhtv3s2bMk67JlyyymeqM6KSkpSUlJpoNk ZmYmla9Zs8b0nEtpHDt2jOqvWLFi//79ziM8VwlJqRMnTqSlpTlPdUyBTOoF +ZcJdJEJdJEJfIr3xHIG0HbdUQB7oJRbIJN6Qf5lAl1kAl1kAp/iPbGcAbRd dxTAHijlFsikXpB/mUAXmUAXmcCneE8sZwBt1x0FsAdKuQUyqRfkXybQRSbQ RSbwKd4TyxlA23VHAeyBUm6BTOoF+ZcJdJEJdJEJfIr3xHIG0HbdUQB7oJRb IJN6Qf5lAl1kAl1kAp/iPbGcAbRddxTAHijlFsikXpB/mUAXmUAXmcCneE8s ZwBt1x0FsAdKuQUyqRfkXybQRSbQRSbwKd4TyxlA23VHAeyBUm6BTOoF+ZcJ dJEJdJEJfIr3xHIG0HbdUQB7oJRbIJN6Qf5lAl1kAl1kAp/iPbGcAbRddxTA HijlFsikXpB/mUAXmUAXmcCneE8sZwBt1x0FsAdKuQUyqRfkXybQRSbQRSbw Kd4TyxlA23VHAeyBUm6BTOoF+ZcJdJEJdJEJfIr3xHIG0HbdUQB7oJRbIJN6 Qf5lAl1kAl1kAp/iPbGcAbRddxTAHijlFsikXpB/mUAXmUAXmcCneE8sZwBt 1x0FsAdKuQUyqRfkXybQRSbQRSbwKd4TyxlA23VHAeyBUm6BTOoF+ZcJdJEJ dJEJfIr3xHIG0HbdUQB7oJRbIJN6Qf5lAl1kAl1kosunAAAAAAAAAIA18CkA AAAAAAAAaeC+Ly+J5Qyg7bqjAPZAKbdAJvWC/MsEusgEusgEPsV7YjkDaLvu KIA9UMotkEm9IP8ygS4ygS4ygU/xnljOANquOwpgD5RyC2RSL8i/TKCLTKCL TOBTvCeWM4C2644C2AOl3AKZ1AvyLxPoIhPoIhP4FO+J5Qyg7bqjAPZAKbdA JvWC/MsEusgEusgEPsV7YjkDaLvuKIA9UMotkEm9IP8ygS4ygS4ygU/xnljO ANquOwpgD5RyC2RSL8i/TKCLTKCLTOBTvCeWM4C2644C2AOl3AKZ1AvyLxPo IhPoIhP4FO+J5Qyg7bqjAPZAKbdAJvWC/MsEusgEusgEPsV7YjkDaLvuKIA9 UMotkEm9IP8ygS4ygS4ygU/xnljOANquOwpgD5RyC2RSL8i/TKCLTKCLTOBT vCeWM4C2644C2AOl3AKZ1AvyLxPoIhPoIhP4FO+J5Qyg7bqjAPZAKbdAJvWC /MsEusgEusgEPsV7YjkDaLt1nR9++CE5OTk9Pd3JCkOqHDY5OTnJfo4ePRrR DckhlnupuyCTekH+ZQJdZAJdZAKf4j2xnAG03brOlVde6fP5br31VicrDKly 2Hz22Wc+P2SLIrohOcRyL3UXZFIvyL9MoItMoItMosWnZGdnb9q0if6GsUVp OM/Avn375s6dO3r06I8++ig5OfnMmTNBq1F5amrqeD9Lly49dOiQm+G6Sqjq O9S9qKgoIyPjxx9/LC4uLlN8kQQ+JVpw3kup423YsGHChAkJCQnUUc+ePRvh 0KKMCO3vwCHoyTKBLjJxrktBQcE333xDI67XXntt4cKFOTk5tl/Zu3fvJj95 eXmqkEZrm+w4efKkcT2ujPecD5mysrIWLVr0yiuvTJ8+fevWrVqGWMJ9CnWG pKSkfv36nX/++TRSuu2228LYojScZCA9Pf3aa6/1/ZWrr756yZIlxmqFhYVD hw6tUqWKsVq1atWmTJkSwQaUAYfqO9d927Zt77333lVXXcVtX7VqlZvhugp8 SrTgsJfSQbtOnTrGXa9Ro0Z0Mop8gFGD6/s7CAn0ZJlAF5k41OXLL7+sXbu2 UZfKlSu/9dZbFl/Jzc29+OKLufLMmTNVOdlPnx0ff/wxV3ZlvOd8yHTixAk6 JpuCue6663bs2OF8c64g2adkZ2fzaUtxyy23hLFFadhmYOLEiRdccAE3uXr1 6q1atTrvvPP4Y/ny5clKc7Xff/+dTuhcThXatm3bpEkTlSsaXnrRmBBxor5z 3Z944gnTTpScnOx+0C4BnxItOFFqy5Ytl156KWemadOmV199Nf/foEGDPXv2 eBJmFODu/g5CBT1ZJtBFJk50WblypRqP3XjjjY888ojyLMpQBHL//fer41uo PoVsUYlL4z3nQyY6LLdu3VptjnogGSL+SIPS/Px8J5tzC8k+JSsrq/qfcMc4 N85fthmYPn06NbZu3borVqzg67w5OTkjRozgTtKlSxeuNmrUKC55/vnn1bW/ JUuWkLWnwpo1ax4/fjzCTQkZJ+o7133o0KFcjZtssdNJAD4lWnCi1H333Uc5 iYuLmz17NpdMmjSJEzVw4MCIhxgluLu/g1BBT5YJdJGJE12uu+46koAs5Nat W7kkLy+PXAMP4IPemDdnzhyjNTD6FBrwZwbjxx9/rFSpElW+6aabeJ2ujPec D5keffRRrtCrV69jx45RCYVBg4EqVapMnjzZdkPuItmnGOGrVOfG+ctJBmbN mkX22VhSXFxMlpaSUKNGDS4pKiqivjR16lTTd1V/Vlde5BCq+g51//zzz88l n9KxY0f+eOrUqXXr1m3bti3oTaG2PoUOXNQHNm3aROux3i6t/7fffktJSTly 5IhpEXxKIDk5OXwJ4LHHHjOW89AiPj5e4E8EWojQ/g4cgp4sE+giE1td/vjj j3LlypEEr7/+urGcBh58lty+fbvpKwcOHOA7vm644YZAn1IagwcP9vlvJ1M3 Wbk73rMeMpFR4lt6evfubRp7aJn2Ez7Fe8LLANG5c2dKAu0m1GMtqq1atYp7 4LRp08KLMHLAp1jXYevRqVOnrVu3du3atWLFiupK69ChQwsLCwMrB/qU/fv3 DxkypEmTJuryNJ3yHnrooaCnttzc3D59+lStWpVrxsXF0Rfnz5+vKpTmU556 6qmL/IwfPz6kPMjHVilqMufEdHPvvHnzuHz69OmRDTFKgE/RC3qyTKCLTGx1 IdPB+Z84caKxfM+ePVz+3Xffmb7C1rJ8+fIrVqxw6FPWrVvH5+733nvPNubw xnvWQyZ2ScTmzZudrzNywKd4T3gZOHbs2CWXXEJJaNy4sXXNxMRE7mN0TAsz xIgBn2Jdh60HoRyKkdtuu+306dOmyiafMmXKlAoVKgR+N6ijoSOnerjPhLrZ IKhPmTp1KheSmbJ2zdGIrVKPPPKIz28eTW0vKCjgX0FHjBgR2RCjBPgUvaAn ywS6yMTJ8Yqf2rjjjjuMt3jNnz+fT4imR4foNMrlr7766o4dOxz6lDZt2lA1 +utkcq3wxnvWQ6ZmzZr5/L+XOl9hRIFP8Z4wMpCZmXnnnXdyv5o0aZJ15aef fppr7tu3L/woIwN8inUd5VOIQYMGkTXIysqaO3euchOffPKJqbLJfaSkpPj8 DzclJCSkp6cfP358zZo1anIPYwB5eXm1atXi8r59+9LunJ+fTwls1apVu3bt 1K1igT4lNTWVrVDz5s35ztVzDFuleGds0aJF4CJ+9LVfv36RCi6qgE/RC3qy TKCLTJwcr1577TU+IZKX5MfJyUuSbQk8Fx84cKBmzZpUfv3111Od7du3O/Ep fAYnaFTjJObwxnvWQyZ+euWFF17gj9nZ2evXr9f4omf4FO9xnoHp06cPGDCA dgG+JZI6z/vvv29tsakvXX755VS5YcOG7oTrKvAp1nXYelSrVm3p0qXG8p9+ +ikuLs7nn5dSdYDS7vtKTEw8ceKEsYSsCudn+PDhqlA9KEcHOmPlwsJC4xHJ 5FPokMUdjKzTzp07HbY9urBVqmXLlr5Sps8l7ybqlyi9wKfoBT1ZJtBFJk6O V3R+fOCBB/icWLt27bfeeovPpBUrVty0aZOx5r333kvllSpV2rZtG3106FN6 9+5NdS655BLbp0pLyjDesxgyHTx4kBdNnjyZzv7GF2SQcSYbFdKGXAE+xXuc Z6Bbt24+A2PGjDG98SeQQYMGOdkXdAGfYl3H4tH4Ll26cBvVK6VCmu/rwgsv pMp05OSPZHa4pFatWgUFBRZfNPqU06dPd+jQgf6/4IILvv/+eyfbjUZsleKf NHv27Bm4iPoqLaJje6SCiyrgU/SCniwT6CITh8er9PR09eYIxYIFC4x1aADG 5eoZEyc+JSsri9esrmVYE/Z4z2LItGHDBuVK+J/4+Hj10pa4uDjTj6geAJ/i Pc4z8Pbbb991112tWrUqX748d5KmTZtu2bKltPqrVq3iX93bt28v883s8CnW dSysx9ixY7mNaloPa59y6tSpxYsX07d69erFt5tyx+Clu3bt4hI60FmHpHzK 2rVr1SWYN954w/pbUY2tUvXq1aMk3HPPPYGLKMM+/63FkQouqoBP0Qt6skyg i0ycHK/mz5/PVqJr16533nknj7h8/geH1WRfOTk5NWrU8PmviKmRmBOfMnHi RK7zyy+/2EZblvGexZCJhg3KfFGjaCtnzpyh9VPYPFUyDTycXOtxEfgU7wkj AwcOHHj55Ze5T1InMd3Vw/z666/8FEPlypUzMjLcidVt4FOs61hYjylTpnAb 586da115//79w4cP5+Okieuvv57rqGPRu+++ax2S8il9+/ZV6+nWrZuTJkcp tkq1a9eOknDzzTcHLmrUqBEt6t69e6SCiyrgU/SCniwT6CITW13IPvA8Bv/+ 9795igMqITvJp8XatWvz6yRUyQ8//JD9J6tXr+ZCMiP0MeirEh988EGf//pF 0PewGCnjeM9iyLRu3Tpe1KxZM3XzBqPmQN6wYUOoWywL8CneE14GiNGjR3Mn CXzt6cGDB9Wz0vz2UpnAp1jXsfApaqbKb7/91qJybm4un8h8/qtv48aNo43m 5eVxJpVPUfOT2E7LoHwKo67/fvHFFw4bHnXYKsWnoebNmwcuYnv46KOPRiq4 qAI+RS/oyTKBLjKx1YVfK1+zZs0zZ84Yy9XY7Omnn968eXPgL4SB3HjjjYHr r1OnDi26/fbbreMs+3jPYshE3oQXBb6rZePGjbxITQfqDfAp3hO2T9m5cyd3 kiFDhhjLjx8/zteCfY5va9QFfIp1HQuf8vjjj3MbMzMzLSrzy6TOP/980wT7 Jp+ijqVPPvmkdUhGn9KyZcsDBw40aNDA53+wheyP9XejFFulBg4c6PM/OGl6 Jc2+ffs4UaNGjYpsiFECfIpe0JNlAl1kYqvLZZddRskfMGBA4KKGDRvSolat Wm3dutWJT2nbtq1pDbt37+ZFI0eOtIjBlfGexZCpuLiYX4vw7LPPmhap7md7 G4a7wKd4j20GSrvVUD1TMHjwYFV44sQJct9c3rdvX9vLhXqBT7GuU5pPKSws 5DuWK1WqZDHf16FDhzgPTzzxhGkNJp9CK+Tr17QS0+sjTSifUrt27b1795YY JmwPerg+B7BVSj0jaXp28sMPP+Ty5cuXRzbEKAE+RS/oyTKBLjKx1oXOvDwh f1B30L17d1pERob+P3LkyOEA1P1UkydPpo+BU/qrm7EtZiR2a7xnPWTiF7hc e+21prGounXN40fp4VO8xzYDvXv37tmzZ+Dti+raonrx6MmTJzt16sSFPXr0 MF2LFAh8inUdth4tWrQwPaeWkJDADezTp4+pstGnZGRkBDpZIi0tLT4+3uhT iLvuuosrv/POO6YwjHM1KJ9CR1FVSJ2NC1euXGnb8KjDVik6WVSvXp2a37lz Z3WmILvXtm1bKqxfv77wnws8Az5FL+jJMoEuMrHV5cYbb/T5H9wwPSN8/Pjx 2rVr06IuXbqU9l3b5+jVI6jLli0LWsH5eI/CoxP37NmzS5sh1nrIpJYuWrTI WN6/f38u518sPUOyT0lNTV37J3zbHp3FVEn0vmDOOgMLFizgntC0aVPy3Zs3 byZLe+jQoRdffJHfolKtWrX9+/eX+Cd0Ui9/rFq1KjncJUuWLPor2dnZ3jXM AU7Ud6h7VlaWKnzppZc4D+PGjeMSJ9NleIxzn+LzvyclMTGRvCopOHbsWJ5C oUKFCuqmr5I/f/Sg5KhzFh2dzjvvPO4P69evp55DWaKcqEkUjT6FDptqHrmR I0fSmouKijZu3Eg2mVayZs0arhb0ffS7du3iqT8oTtu5sqMOJ0qp2/CGDRt2 9OjRvLy8AQMGcMmYMWM8CTMKcHF/B2GAniwT6CITW10mTJjAEnTr1k29V/Hg wYPqwfkPPvigtO/a+pRXXnmFK6SnpwcuDWm8p17+aLz043zIRCMB/skoPj7+ q6++ogEGlbz77rs8ugg6XXZEkexT+P0OpfH222+HsXUJWGfg9OnTPOeDQg0m ff7Jq2n4yjXpYGWRH2bhwoUetcoZTtR3qDv9Y1GNVhLxxoSIc5/Cd4eaIJdq euZ9+PDhvKhGjRo89whhnJWL7+zy+e8W47tnjT6lxD8LoqrDm1D/q2eggvoU 4tVXX+Vy4Y9EhYETpWjkwI8CMWp2ys6dO597xi1sXNzfQRigJ8sEusjEVpfi 4mJlSWhg1rJly7Zt2/Lb24l//OMfFvMD2/oU9TKUPXv2BC4NabzHv2ESN910 k1pDSEOm1atX16xZkxdV8sP/X3LJJeR3LFIUCaLXp4wfPz6MrUvASQbmz5+v HpVSdOzYcf369aqOmiPOgq+//jqyjQmRso9blO7WO12VKlUi3pgQcdL2pk2b +vzzbJCDuOiii1RzGjRoEHiHFRmH+vXrcwV+422J/87YXr16qS9WqFChW7du O3bs4JsGTT6lxL8Xt27dWp0Eibp1686YMUNVmDNnDpeb9vdTp041btzY53/n Y9DjavTi8BhFA4m7775b/YxA7vLBBx/EEMKIi/s7CAP0ZJlAF5k40eXMmTOT Jk2i4brxMHXxxRe///771k96ZmZmcmX1ZgET6gfqoC+eCGm89+abb3KJestk SehDpp07d3bo0IGvoTDUG00zFXuDZJ9yruI8A9nZ2WvWrFm6dCmNSA8fPhzh uLwgltUPte1nz57NyMhYtmyZur4ctE5KSkpSUpLp5EWHRCqnzuPwfUzHjh2j +itWrOBbCmOckJSic0paWprzVMcUsby/SwA9WSbQRSbOdaEz7+7du1euXEkn zV27dgl8XGjz5s0///xz2deTn5///fffU1qOHDlS9rWFB3yK98RyBtB23VEA e6CUWyCTekH+ZQJdZAJdZAKf4j2xnAG0XXcUwB4o5RbIpF6Qf5lAF5lAF5nA p3hPLGcAbdcdBbAHSrkFMqkX5F8m0EUm0EUm8CneE8sZQNt1RwHsgVJugUzq BfmXCXSRCXSRCXyK98RyBtB23VEAe6CUWyCTekH+ZQJdZAJdZAKf4j2xnAG0 XXcUwB4o5RbIpF6Qf5lAF5lAF5nAp3hPLGcAbdcdBbAHSrkFMqkX5F8m0EUm 0EUm8CneE8sZQNt1RwHsgVJugUzqBfmXCXSRCXSRCXyK98RyBtB23VEAe6CU WyCTekH+ZQJdZAJdZAKf4j2xnAG0XXcUwB4o5RbIpF6Qf5lAF5lAF5nAp3hP LGcAbdcdBbAHSrkFMqkX5F8m0EUm0EUm8CneE8sZQNt1RwHsgVJugUzqBfmX CXSRCXSRCXyK98RyBtB23VEAe6CUWyCTekH+ZQJdZAJdZAKf4j2xnAG0XXcU wB4o5RbIpF6Qf5lAF5lAF5nAp3hPLGcAbdcdBbAHSrkFMqkX5F8m0EUm0EUm 8CneE8sZQNt1RwHsgVJugUzqBfmXCXSRCXSRCXyK98RyBtB23VEAe6CUWyCT ekH+ZQJdZAJdZKLLpwAAAAAAAACANfApAAAAAAAAAGngvi8vieUMoO26owD2 QCm3QCb1gvzLBLrIBLrIBD7Fe2I5A2i77iiAPVDKLZBJvSD/MoEuMoEuMoFP 8Z5YzgDarjsKYA+UcgtkUi/Iv0ygi0ygi0zgU7wnljOAtuuOAtgDpdwCmdQL 8i8T6CIT6CIT+BTvieUMoO26owD2QCm3QCb1gvzLBLrIBLrIBD7Fe2I5A2i7 7iiAPVDKLZBJvSD/MoEuMoEuMoFP8Z5YzgDarjsKYA+UcgtkUi/Iv0ygi0yg i0zgU7wnljOAtuuOAtgDpdwCmdQL8i8T6CIT6CIT+BTvieUMoO26owD2QCm3 QCb1gvzLBLrIBLrIBD7Fe2I5A2i77iiAPVDKLZBJvSD/MoEuMoEuMoFP8Z5Y zgDarjsKYA+UcgtkUi/Iv0ygi0ygi0zgU7wnljOAtuuOAtgDpdwCmdQL8i8T 6CIT6CIT+BTvieUMoO26owD2QCm3QCb1gvzLBLrIBLrIBD7Fe2I5A2i7dZ0f fvghOTk5PT3dyQpDqhw2OTk5yX6OHj0a0Q3JIZZ7qbsgk3pB/mUCXWQCXWQC n+I9sZwBtN26zpVXXunz+W699VYnKwypcth89tlnPj9kiyK6ITnEci91F2RS L8i/TKCLTKCLTKLFp2RnZ2/atIn+hrFFaTjPQE5Ozpw5c0aPHj1t2rQNGzaU Vu3MmTOpqanj/SxduvTQoUOuxeo2oarvUPesrKxFixa98sor06dP37p1a3Fx cZmijAzwKdGC815aVFREO+aECRMSEhKoo549ezbCoUUZyKRekH+ZQBeZuD42 Y0jEjIyMH3/80XZkEupIZu/evZv85OXlOQmbyc3NnT9//pgxY2hbhw8fLq2a nIGlcJ9SUFCQlJTUr1+/888/n0ZKt912WxhblIaTDKxfv7558+a+v9K1a9fM zExjtcLCwqFDh1apUsVYrVq1alOmTIlgA8qAQ/Wd637ixAmqZkrUddddt2PH DjfjdgP4lGjBYS+l80idOnWMHa9Ro0Z04oh8gFEDMqkX5F8m0EUmLo7NmG3b tr333ntXXXUVV1u1alVpqw1jJEN24+KLL+aaM2fOdNjGZ555xriJuLi4cePG mepIG1hK9inZ2dk8TFXccsstYWxRGrYZmDVrVsWKFbnJl156aYcOHapWrcof mzVrdvr0aa72+++/0wCey88777y2bds2adJE5YqGl140JkScqO9cd6rZunVr lYGmTZvSrsQfq1evnp+f734DygB8SrTgRKktW7bQvsmZoY539dVX8/8NGjTY s2ePJ2FGAcikXpB/mUAXmbg1NmOeeOIJ319JTk4OutrwRjL333+/WrNDn0Lu g+uTB2nfvn2lSpX449ixY1UdgQNLyT4lKyur+p9QuizGq9GFdQYOHz7MPZ+O S6tXr+brvEeOHOnRowf3kzfeeINrjho1ikuef/55dUluyZIllStXpsKaNWse P3488q0JDSfqO9f90Ucf5Qz06tXr2LFjVELpov2I9sHJkye7HnwZgU+JFpwo dd999/n8P0bNnj2bSyZNmsSJGjhwYMRDjBKQSb0g/zKBLjJxa2zGkCngYQwP ySx8ShgjmTlz5hgdkBOfkpGRwZXbtGlTUFDALWrcuDGVlCtXbt++fVxN4MBS sk8xwhfOYsGnlPjncerevfvBgweNhWRyeR/p2rUrlxQVFVH3njp1qunrqpul pqa6GrgLhKq+he6ZmZkXXHABLe3du7fpNk6ZM1M59ykdO3bkj6dOnVq3bt22 bduC3qdq61PoeEJ9YNOmTbQe6+3S+n/77beUlBQ66poWwacEkpOTw5f8Hnvs MWM5Dy3i4+MF/kSgBWRSL8i/TKCLTNwam5n4/PPPLXxKGCOZAwcO8B1fN9xw g3OfMmTIEJMlKTGYF3VJReDAEj7Fe8LLAMEX4+rXr29dbdWqVdydpk2bFsZW IoqLPmXw4MHczM2bN7sWXyRx7lM6deq0detWOuipS8zVq1cfOnRoYWFhYOVA n7J//346IjVp0oSvRhF0ynvooYeCntpyc3P79OmjLl7HxcXRF+fPn68qlOZT nnrqqYv8jB8/PqQ8yMdWKWoy58R0v/G8efO4fPr06ZENMUpAJvWC/MsEusgk QmMza58SxkiG7Wr58uVXrFjh0KfQ4IFO1r5gT/tec801Pv/9hNZr0DiwhE/x nrD3hVatWlESqFNZV0tMTOTuRMe0cOKLJC76lGbNmvGQ3rXgIoxzn0Ioh2KE jjDGO2CD+pQpU6ZUqFAh8LtBHQ0d5dSDeCbUzQZBfcrUqVO5kMxUUVFRWdIi EFulHnnkEZ/fPJraXlBQwL+CjhgxIrIhRgnIpF6Qf5lAF5lEaGxm7VNCHcn8 //buPEqK6nrgeKMSRUQUFVHhB5hICFGissQtMSrqcT3oUVGiWTCHuJO4xSWI YVPZtyAQQUQBQfZ9EQ4gIIsDDMgm67AM+zZsMyjwu6fv8Z2ye6aqZqaq6830 9/MHZ7qquuu9e4vqd7s2+WrWT2vTps26det81imbNm3SJTt37pww61//+pfO Onr0qMsnRDiwpE5JvaJF4MCBA/rz+NNPP+2+5Msvv6ybk/PoniUCrFP0bMm3 3npLX2ZnZy9cuNDOM75UoeoU8cwzz0hpsH379uHDh5tq4qOPPkpYOKH6mDNn jkysWrVq9+7dMzIyjhw5MnfuXHO/EWcD9u/fX7lyZZ3etGlT+e+ck5MjO1LZ 5TZo0MCcKpZcpyxYsEBLoTp16ujJtKWMZ6buvvtu6f4111yTPEsvfX3yySfD alyJQiSjRfztRF7sFNLYzL1OKdRIZufOnRdddJEs37BhQ6lh165d67NO+frr r3XJkSNHJsz68MMPddb69etdPiHCgSV1SuoVLQLmQPAnn3zisphs3pdffrks VrNmzaI3MTRB1Sm7d+/WaPTp00cG0ldffbUZ3su+XcbqQTY6IP7rlIoVK06Y MME5ffny5WXKlInF70tpTmEt6LyvsWPHHjt2zDlFShUNTosWLcxEc+2e7H+c C584ccK5k0yoU2QvqhuYlE4bNmzw2feSxTNTdevWjRVwu2y9ZWUJOswXKiIZ LeJvJ/Jip5DGZi51SmFHMo0bN5ZZ5cqVW7Nmjbz0X6eMGjVKl0y+N/Lw4cN1 lowTCnp7tANL6pTUK0IENm7cqEX3r371q++//95lyWeeecbndhuJoOqUxYsX m//L+keFChXM7b5lSJ8wzreB/zol30vj77rrLu3djh07PBdOdt5558nCspfT l1Ls6JTKlSvrrT8K4qxT8vLybrzxRvm7bNmys2fP9rPeksgzU/qT5sMPP5w8 S7ZVmSVfN2E1rkQhktEi/nYiL3YKaWzmUqcUaiQjgzqd3q1bN53iv04xB02W L1+eMEtapbOSD7UY0Q4sTZ1SNEVeY2Hflc51ysmTJxs1aqQbyeTJk12WlEpZ f3W/4YYbSu4z2Z0Kyvu4cePMzw61atWSjssuQros/4n0luAyhve8yVWKFbNO ad26tfbX3G3DvU6R7kuU5F2PPvqongGrG4bOlb2rTpH9j3uTTJ0yb948cwgm 4QaMpYxnpqpVqyZBePDBB5NnSYRj8Rs/htW4EoVIRov424m82CmksZlLneJ/ JLNjx45KlSrF4kfZzOjOf53Sv39/XXLJkiUJs6ZMmaKzxo8fn+97Ix9YFq1q SP0a07lOeemll3Qrcj8l9bvvvtOrGKS6z8zMLG4rwxFUnWJOtpQRuDm+oMzd 8xYvXlz8BgeomHVK3759tV/Dhw93X3jbtm0tWrTQfVqChg0b6jJm99i1a1f3 Jpk6pWnTpuZz7r33Xj9dLqE8M9WgQQMJwi233JI866qrrpJZ9913X1iNK1GI ZLSIv53Ii51CGpu51Cn+RzJStOrL+fPnZ//oq6++0ok9e/aUly7Ptp44caIu +eWXXybMGjx4sMuQyYaBJXVK6hUqAl26dNFNqF69egkXHTjt3r3bXCs9bNiw YBoagqDqFPkfrZ1Nvsv3kiVLdJa5Y5UlilmnmJNgp0yZ4rLwrl279IssFn+K cfv27WWl+/fv10iaOmXEiBG6TK9evdybZOoUZQ5JDx061GfHSxzPTOlXRp06 dZJnaXnoebOLNEEko0X87URe7BTG2Oy0a53icySzcuXKmA833XRTQW0wn2Z+ 5zR69Oihs5IvkLdkYEmdknr+I/DFF1/ofSQuvfRSl3ssHDlyRI8Fxxx3jbBT UHXKqVOn9M69r776asIsCZSGwvNIQYoVs0557rnntF+bN292WVgf/HTWWWcl 3GA/oU4x+71//OMf7k1y1il169bduXNnjRo1YvELW6T8cX9vCeWZqebNm8fi 945OeCSN2fbefvvtcJtYQhDJaBF/O5EXOwU+NlMudYrPkczq1av91Cn169cv qA3Z2dm6TMuWLRNm/e1vf4vFr4VJeECbPQNL6pTU8xmBsWPH6lNKK1So4HIK kxTyt912m25LTZs2PXnyZJBtDVpQdYqoV69eLH45YcIJk+ZIqG2X0henTpEd iJ6xXK5cOZf7fe3Zs0f7/vzzzyd8QkKdIh+o9+GXD0nYOyUwdcpll122ZcuW 0477qDdr1sy72yWQZ6bM9YwJFx727t1bp0+bNi3cJpYQRDJaxN9O5MVOwY7N DPf7EvscyRw4cGBfEnPaWJ8+feSl+2MC9ESLhJtdy0qrVKkSSzoWY9XAkjol 9fxEQLbMn/3sZ7H4LyouCx8/ftxcxvXAAw+43wrMBgHWKeb//ujRo53T//rX v+p0HVTbw3+dInuShJsAdO/eXTv1xBNPJCzsrFMyMzN1sWeffdb59kWLFske 1VmniHvuuUcXTn7w06pVq8zfpk4ZN26cmSgbm06cOXOmZ8dLHM9MyT78ggsu kO7feeedZgcu5V79+vVj8acSW/5zQcoQyWgRfzuRFzsFODZzcq9TijOSKeg6 etl+5It7yJAhMkQ0E9977z1dWCogM1FWqhP79+9vJto2sLS5TlmwYMG8H11x xRUSMRm1mikl9wFznhGYPHmyeaT4G2+8IS/HjBkz2kEHhzKU1adBifPPP1/+ +4wfP370T2VnZ6eoV/74yb7PvP/www9axcgIXOIj+22Z0rVrVz0am+8dHaPl v06JxZ+TMnbs2JycHMlg69at9W4bslWYk75O//g7jATBfGfJ3km7L9vDwoUL T506tX379vbt2+uPPwl1iuzidH8rXn/9dflkCeCSJUsee+wx+RBzK/V8n0e/ ceNGvRuJtNO5Jywd/GTKnIb30ksvHTx4cP/+/c2aNdMp7777bkqaWQIQyWgR fzuRFzsFNTYT8s1rBi0tW7bUt8h3sU759ttvzWcWZyRTUJ1insnoPF9LxhJ6 EkXlypV1eCAFS8WKFWXKeeedZx5PYOHA0pkXafbJQirCPcr81yn6fIeCdOrU qbCrtoR7BGQj0fMVXdStW1eWlJ2V+2Ji1KhRqeuYD36y7z/v8r9Mn80ai58Q pSNncckll8heItyeFJ7/OiXfDeDMM89MuOa9RYsWOqtSpUqyZ9OJzrty6U5J g1OzZs3YT+sU0bNnT7OMrsL8/cILL+gy+dYpok2bNjrd8kuiisBPpmTkoJcC KS0kY/HfP0tf4VZkRDJaxN9O5MVOQY3NhAxUXBaTQY7zk4s8kimoTtHfMMXN N9/snC6LmW95s0VJ5TVx4kSzjIUDS2dedu3aVdjnp8hbirNGd+7j1Y4dOxZ2 1Zbw/L/gHC7mq0GDBqcdt61z4dz8bFD8OiUh7xs2bLjxxhv1lwd1//33J9zf zxJ++l67du1Y/NYfUkFceOGFplM1atRIPsNKCofq1avrAvp02tPxs1gfffRR 80bZBd17773r1q1r1apVLKlOOR1/Wuv1119vdlmiatWqzufqfv755zo94XlJ sqHWqlUrFn/mY1ZWVtFiYief+ygZSMjGZo5JyVdYkyZNGEI4EcloEX87kRc7 BTU2O+1Vp5QvXz7hw4s2ktm8ebMunHAXrw8++ECnmydCGkOGDNFrXdWVV16Z MEq0cGDpzEteXl5GRoaMRjZt2pTlShaQxWRheUtx1pie0jkCIfU9Jydn9uzZ 8skySg/8w4NS2L6fPHkyMzNz6tSpLrcTkWXmzJkzefLkhC8v2X3J9Llz5/p8 2OWhQ4dk+enTp2/bts1/C0urQmXq2LFjixYt8h/qtEIko0X87URe7BT52CzA kczKlStXrFhR0Fwpi2bOnFlSfmBMyIs0WwoQzyJOFpDFitbHyLeEyKVzBOh7 1K2ANzIVFCIZLeJvJ/JiJ/Jip4S86CGVZcuWmdPdk8ksWaBoB1OS15iG0jkC 9D3qVsAbmQoKkYwW8bcTebETebFTcl48D6kU52BKvmtMN+kcAfoedSvgjUwF hUhGi/jbibzYibzYKTkvekhl6dKl+R5SkYkyq8gHU/JdY7pJ5wjQ96hbAW9k KihEMlrE307kxU7kxU755kUPqeR7h2SZWJyDKQWtMa2kcwToe9StgDcyFRQi GS3ibyfyYifyYqd881LQIZXiH0wpaI1pJZ0jQN+jbgW8kamgEMloEX87kRc7 kRc7FZSXfA+p6MGULVu2hLHG9JHOEaDvUbcC3shUUIhktIi/nciLnciLnQrK S/IhFXMw5cSJE2GsMX2kcwToe9StgDcyFRQiGS3ibyfyYifyYieXvGzZssV5 SCWQgynua0wT6RwB+h51K+CNTAWFSEaL+NuJvNiJvNjJJS8nTpwwh1SCOpji vsY0kc4RoO9RtwLeyFRQiGS0iL+dyIudyIud3PNiDqls3749kIMpnmtMB+kc AfoedSvgjUwFhUhGi/jbibzYibzYyT0vekjlm7hADqZ4rjEdpHME6HvUrYA3 MhUUIhkt4m8n8mIn8mInz7zoIZWgDqb4WWOpl84RoO9RtwLeyFRQiGS0iL+d yIudyIudPPOih1SWLFkSyMEUP2ss9dI5AvQ96lbAG5kKCpGMFvG3E3mxE3mx k5+8bNmyZevWralcY+mWzhGg71G3At7IVFCIZLSIv53Ii53Ii51Snxe2hHSO AH2PuhXwRqaCQiSjRfztRF7sRF7sRJ2SeukcAfoedSvgjUwFhUhGi/jbibzY ibzYKao6BQAAAADcUacAAAAAsE3q65RUrtE26RwB+h51K+CNTAWFSEaL+NuJ vNiJvNiJOiX10jkC9D3qVsAbmQoKkYwW8bcTebETebETdUrqpXME6HvUrYA3 MhUUIhkt4m8n8mIn8mIn6pTUS+cI0PeoWwFvZCooRDJaxN9O5MVO5MVO1Cmp l84RoO9RtwLeyFRQiGS0iL+dyIudyIudqFNSL50jQN+jbgW8kamgEMloEX87 kRc7kRc7UaekXjpHgL5H3Qp4I1NBIZLRIv52Ii92Ii92ok5JvXSOAH2PuhXw RqaCQiSjRfztRF7sRF7sRJ2SeukcAfoedSvgjUwFhUhGi/jbibzYibzYiTol 9dI5AvQ96lbAG5kKCpGMFvG3E3mxE3mxE3VK6qVzBOh71K2ANzIVFCIZLeJv J/JiJ/JiJ+qU1EvnCND3qFsBb2QqKEQyWsTfTuTFTuTFTtQpqZfOEaDvUbcC 3shUUIhktIi/nciLnciLnahTUi+dI0Dfw/v8+fPnz5gxIyMjI7xVpIl03kqD RSSjRfztRF7sRF7sRJ2SeukcAfoe3udfeeWVsVjs97//vZ+F+/fv36RJkyFD hoTXnpIrnbfSYBHJaBF/O5EXO5EXO5WUOiU7O3vp0qXybwgtSjX/Edi6devw 4cNbtWr14Ycfzpgx4/vvv893sdzc3Pnz53fp0qVdu3bDhg3btGlTgK0NVmGz 75l3icmCBQs6xk2YMGHPnj0BtDIc9tQpy5Yti8WdeeaZso2F16QSKvCtNG35 j+QPP/ywePFi2Yl1795dgnny5MmQm5YWiL+dyIudLByfnDhxYmncqlWrClpG NpLMzEz5Wj916pTLR61YsWLgwIHvvPNO3759582b575wcVYUOMvrlMOHD0+e PPnJJ58866yzZFj1hz/8IcympYifCGRkZFx99dWxn/rFL34xfvx452KyDb/x xhs/+9nPnIvJ4LNZs2aHDh0KsQ9F5TP7fvIufX/xxRfLly/v7HvFihXl/2Dw 7Q6CPXWKVLKykcjCZcuW3b59u5kue9Hb4zZu3BheO+0X4Faa5nxGcvXq1Vdc cYXzP/JVV121ZcuW8BtYyhF/O5EXO1k4PpGywgz/kueuWbOmW7duP//5z3WZ WbNm5fsh8s3epEmThPHkLbfcIm/32QyfKwqJzXWKVKm6GRi/+93vQm5dKnhG oGfPnjKA1C5fcMEF11133RlnnKEvpSSR8lwXk/hcf/31Or1MmTJ16tS59NJL TawaN26c4prXDz/Z95P3vXv3ys5B50pw6tev/8tf/tIsP2jQoLA6UAz21Cli 6tSpb775ZsLeZsWKFRpA+SOcNpYMQW2l8BPJVatWmR1X7dq15etY/65Ro0ZW VlZKmllqEX87kRc72TY+WbJkiVlXcp3y/PPPx35qxowZyR9y8uTJO+64Qxe4 8MILn3rqqUaNGulLKXuPHTvm2QyfKwqPzXXK9u3bL/iRDtRLx0jAMwIDBw6U zlatWnX69Ol6nHfHjh2vvfaabiF33XWXLpaTk3PNNdfIlGeffVb+X5yOb5Dy yTVr1tQl58+fH35vCsdP9v3k/e2339Y+ymDbHEsdP378ueeeKxMvuuiiI0eO hNH+4rCqTsmXlC3UKaeD20rhJ5IPPfRQLP5Li7laqlevXrodNm/ePPQmlmrE 307kxU5WjU/y8vLq1q1rSoPkOuXFF1/UZujHFlQ+jBkzRue+/vrrubm5OvHT Tz/ViZ07d/Zsic8VhcfmOsVJjzeVjpGAnwgMHjxYSw/j1KlTtWvXliBUqlTJ TMzKypKNMOG9Q4cO1W2pe/fuATU5MIXNfkF5/+GHH55++ukBAwYkTDf7B3PU yR6pqVNuvfVWM2XlypULFy70X7KNHDnST50i+zr52MzMzIIumCrpgtpK4RnJ HTt26A+Gf//7353TdZBWoUIFC39wKEGIv53Ii52sGp+0bNlSC9WGDRvmW6cY pujIt3x44403ZFb58uUTvq+l2TL9iSee8GyJzxWFhzol9Yoc8zvvvDMWv/xE /he4LDZv3jzdllq1alW0FoYn7BGgOSLw8ccfF6F5oXLvu1SmF8ZNmjTJOX3K lCk6vW3bts7p8j118cUXy/R33nlHp2idctdddx08eLB58+aXXXaZhuKMM86Q L7u8vDzn26+77jp573333acv+/XrJ7vB888/X98if+hKZQ/pfNeGDRsaNWpk roc6++yz5XszoaAuBahTguIZyY4dO+q2lHAK4hdffKHTBw4cGG4TSzXibyfy Yid7xicZGRlaqP41rjh1yrPPPqsHcRKm68fecsstPhvvuaLwUKekXtEicOjQ oUsuuUSCUKtWLfcl27dvr9uShXedDXs/MHbsWO277M+L0r4wufc9MzNTW/7y yy87p//zn//U6TfccINz+tSpU3W6ubWC1inVq1c3F7s5vfrqq863J5wk1rp1 6+S3xOLnRZu3jBs3zhQyZcuWNefNVq1atZRd2kmdEhTPSP75z3+Oxa/CS/jt 5fDhw7qBvfbaa+E2sVQj/nYiL3ayZHySl5enp/Rfeumle/bs+eMf/1icOkW+ uHVuQtf0Tk1SrfhsvOeKwkOdknpFiMDmzZvvvvtu3UJ69epV0GKyTxs8eLD+ 3F2tWjU/V0ilWNj7ARnka5QsvN2uZ9/1CMi1117rnKj7q1j8ONrBgwfNdD2Y K/WCfHPpFC091P333z9y5Mh169Z169ZNv9cqVqx49OhR8/aEOmXjxo3Tp09v 1qyZvl3eNT3OnAAme8tKlSrJLCmWZR8rO9Lc3Nz+/fuXK1dOJj799NNBRMgW 1ClB8Yyk7tZkI0+epRcRP/nkk2E1Lg0QfzuRFztZMj4xp4dJiSEvn3jiieLU KcePH9cfGC+66KKZM2fqRFlS32Km+EGd4q40jQT8R2DgwIEydLz99tv1LrLn nntujx49ku/itW3bthYtWjzyyCPVq1fXrUgCtWHDhuCbXmyh7gdkGH/55ZfL 8jVr1ixi+8Lk8ze0MmXK7Nu3T6fs2LEjFj+9qnLlyvLHqFGjzMK//e1vYz89 aGvqlJYtWzo3EvlG0+mZmZkJCydcdG+OxCVfn6LHiGU7XLx4sXP6Bx98INOl FJKm+oyD/ahTguIZSb1QNN8be9apU0dmNWrUKKzGpQHibyfyYicbxifyDWvO +NIpxaxTxJw5cypUqKDLNG7ceNCgQVKzyN+PP/64n5b7X1FIqFNSz38E7r33 3pjDu+++K6Vx8mKLFi1yLlatWrVvv/024EYHJNT9wDPPPKMR+Oyzz4rYvjB5 9n3IkCHa/pEjR+oU6Yi8vOmmm2R/Eovf2E2n5+Tk6K7sP//5j3m7lh4333xz wscOGDBAP3bixIkJC/usU6Tq0fvA/+Uvf0n48AMHDuhbvvzySz9BKBGoU4Li GUn9cfjhhx9OnqWXeV599dVhNS4NEH87kRc7RT4+yc3N/fWvfy3L/N///Z95 BF7x65QTJ07oEMKpXr16zlMs/KBOcVeaRgL+I9CpU6d77rnnuuuuM1cu165d O/mxpJs2bXr00UclOOaZUGXKlHnnnXdK6PNTnPznfdasWdLrWPw6Dgs7ftpH 3/fs2aP3OXz++ed1itQF8vKtt97673//K39INHT6pEmTNNHz5s0zby/ovsRS nujCzuuVClWnbNiwQae/+uqrXyfRWdY+XrMIqFOC4hnJatWqSegefPDB5Fny H1m/TMNqXBog/nYiL3aKfHzy5ptv6vjNWQgUs045fPiwfNHH4reJe+GFF6pU qaILy2DD3ITHJ+oUd6VpJFCECOzcuVO2KN3OZYRZ0IUnsv1PnTr12muvtXbo GNJ+4Lvvvrv44otj8VPjnGc3WcVP3+vXry+9qFOnjr6sWrWqHqowT2DUJ8Xr w3TOP/98550GC6pTpk2bVsw6xVz95yLhdmQlGnVKUDwj2aBBg1gB95y56qqr ZJa5JR2KgPjbibzYKdrxyaJFi/QM/0ceeSTboXHjxrH48z31ZfIb3csHLXMq Vaokn386fpF+z5499aZMol27dv77S53irjSNBIoc81atWulG8r///c9lsR07 duhR4yuuuKKITQxNGPuB3bt3mztcDRs2rLhNDI2fvpsL6KQyXb16tfxxzjnn HD9+XCpQzanWnvotlvBrW3h1inmuSu3atX9fAJsjX1jUKUHxjKRsw87C3Env 21DKbtGQYsTfTuTFTtGOT6Q88fw9UMgXesIbXcqH5cuX66yuXbs6p2/evFkv Zy5XrpwMNnz2lzrFXWkaCRQ55ub0mxdeeMF9yaeeekqXtO3ZFoHvB44cOaLH wWPx86MCaGJo/PR9zpw52pcRI0bouV633367zmratKm8fOyxx6TLenFKjx49 nO8Nr07Rikl06dKlkJ0ukahTguIZyebNm2sxnvDcuq1bt+omJ5V7uE0s1Yi/ nciLnaIdnyRfQpKvCRMmJLzRpXzo06ePzsrKykqY9cknn+issWPH+uwvdYq7 0jQS8IxAQacvbty4UTcScz11QUv+6U9/0iV37dpVvMYGLNj9wLFjx2677Tbt qQzjT548GUwrw+Gn799//73eQvCVV17RX1fMYdmPP/44Fr8tsHyIdlnKB+d7 i1+nvPfee7pkwk29pFV6hVSa3GSGOiUonpHUO0XEHPeOUL1799bpyT8ewj/i byfyYqdoxye5ubn78vPAAw/E4ncJ05cnTpxIeKNL+SDjh1j8Rp3Jt2CSb3l9 l9QyPvtLneKuNI0EPCPw2GOPPfzwwzk5OQnTzXlf+jDTjIyMKlWqJBfXO3fu 1NvYVq9ePcBmByLA/YD8v5NhswZE/iM7r9Swk8++P/TQQ7H4mcl6oteCBQt0 uvklTZ/6VLVq1YQ3Fr9O6du3ry4p34YJH6KnyMbsPrMuKNQpQfGMpHyVX3DB BRK9O++803yPyxexXqglezDLf3ywHPG3E3mxk53jk+JcR2++/XXQ6NS5c2ed NX/+fJ0iW92gQYNknJDvfWXdVxQqm+sUGaHN+5HeyUq2CjPF3LStxHGPgPNa AKlzV65ceerUqT179vz73//Wa6wqVqy4bdu20z8+AbBMmTLPPffcpEmTJCDy f0E++Te/+Y1+wiuvvJK6XvnjJ/t+8p6bm2sefHn++edLsTZ+/PjRP5XvFWcR 8rnlmwO12jXn84hlkzCzkm8RXPw6xSzZsGHDpUuXyoa3Zs0anZWVlaW3Ji5b tmzbtm0PHDhwOn5F3vTp0++4446hQ4cWIhDWC2orhZ9Iyu5Lt7qXXnrp4MGD +/fvN88bfffdd1PSzFKL+NuJvNjJzvFJvnXK9u3bzUpbtmyp62rfvr1OMU+m OHz4sDZSvr6lyjBn4IwaNercc8/VHzyltTrRPIbSeYqazxWFyuY65bzzzosV rFOnTiG3NCzuEZCxX5MmTZw9NTcl1qrEnEwoH3LhhReaWWeccYZetqBuuOEG +agUdck3P9n3k3fZUbsso5xPRbSBzy3fnN0XS7pS/sUXXzSzku/BXvw6RWqi GjVqmFXIfuzss882P63IGvXp8+qSSy7RwlnUqlXLdxhKgKC2UviJpIzB9KGl Su9qGIv/klzQz3rwifjbibzYyc7xSb51iqzI5cOlkWbJ+fPnmzFklSpVbr75 ZvMtL9O//vprs2S9evV0uvMpbP5XFJ6SW6d07Ngx5JaGxU8ERowYYS6/Mm69 9daFCxc6F9u1a9dzzz2nNwAxpH5v27ZtYZ/gkxrF3w9o3s19sVw4H2toA/9b vgz7tQvdu3d3TnfeHzj5Hh16tEW+xRKmz549W9/irFMKWjgjI6NmzZpmLVKJ LFmyxMxdu3btbbfdZsqTWHxH9/jjj69cudJPv0qKoLZS+NzmZUh2//33my/T c845p0mTJgzGio/424m82MnO8Yk+Ri3h5m/u5UP58uWdC69evVovcnG67777 Eh7G98EHH+isbt26FW1FIbG5Timt/EcgOzt77ty5EyZMkIp43759BS124sSJ zMzMSZMmTZs2TUaSNl+pkc7ZLyl9l+1n+fLlstXNmjVLz+9KcOzYMamXZ8yY IXs5mze2IispmbJfoSIp29WiRYtkj2fOQ0AxEX87kRc7le49f05OzuLFi6dM mSL/Jl/+rFauXJlwt08bUKekXjpHgL5H3Qp4I1NBIZLRIv52Ii92Ii92ok5J vXSOAH2PuhXwRqaCQiSjRfztRF7sRF7sRJ2SeukcAfoedSvgjUwFhUhGi/jb ibzYibzYiTol9dI5AvQ96lbAG5kKCpGMFvG3E3mxE3mxE3VK6qVzBOh71K2A NzIVFCIZLeJvJ/JiJ/JiJ+qU1EvnCND3qFsBb2QqKEQyWsTfTuTFTuTFTtQp qZfOEaDvUbcC3shUUIhktIi/nciLnciLnahTUi+dI0Dfo24FvJGpoBDJaBF/ O5EXO5EXO1GnpF46R4C+R90KeCNTQSGS0SL+diIvdiIvdqJOSb10jgB9j7oV 8EamgkIko0X87URe7ERe7ESdknrpHAH6HnUr4I1MBYVIRov424m82Im82Ik6 JfXSOQL0PepWwBuZCgqRjBbxtxN5sRN5sRN1SuqlcwToe9StgDcyFRQiGS3i byfyYifyYifqlNRL5wjQ96hbAW9kKihEMlrE307kxU7kxU7UKamXzhGg71G3 At7IVFCIZLSIv53Ii53Ii52oU1IvnSNA36NuBbyRqaAQyWgRfzuRFzuRFztR p6ReOkeAvkfdCngjU0EhktEi/nYiL3YiL3aKqk4BAAAAAHfUKQAAAABsk/o6 JQ8AAAAACkCdAgAAAMA21CkAAAAAbEOdAgAAAMA21CkAAAAAbEOdAgAAAMA2 1CkAAAAAbEOdAgAAAMA21CkAAAAAbEOdAgAAAMA21CkAAAAAbEOdAgAAAMA2 1Cmw2ebNm4cMGdKnT5/Bgwfn5ORE3RwAAACkCHUKnLZu3bply5ZA3pWbm5uV lbVgwYI1a9YcOXKkaO3p1q1b69atu3Tp0qtXL/nA5AWOHz8uq16yZMnGjRuT 13L06NHtcfm+VyZu/9HBgweL1kIAAACEwf46RQaf2QWYPXv2hg0b8p21e/fu 8IKWkZFx4MCB8D4/9Q4fPjxjxow+ffpIUdC1a9fiv0vKh2HDhrX+Uc+ePffs 2VPYVkn5IO8dOHBgvlWGkAqlR48eZi3t2rWbO3euLrxp0yZpW6dOnXSW1ErJ b//mm2/Me4cMGVLY5hnr168v1PawevVqKaCKvLrI1wsAAJAC9tcpUol8UHiD Bw8OKWJr167Vz5eheEirSD0Z7kqn2rdvX6g6xeVdUkJqiSHpmzJliv5d2FYt WbJE3vjll1/mO3fXrl1SmLT+KSlM9MhIwnQZoie8/dChQ6aKEUXeYJYvX96h Qwf/24NuPwMGDDh27FjR1hjtegEAAFLD/jply5Yt/0vSt29fGaRpSdK9e/fk BcaPHx9GuHbv3t2tWzdd79SpUwP85PXr12dkZOTm5q5atWrixImZmZl58ROT Vq5cOW3aNBnqL1u2zIxIjxw58s033ziPEcjfixcvNicvSQUhL6VGkL/37t0r VYO0Vv71PKfrvffe81+nFPQuaXaPHj3atm1r2tO/f3+pBXbs2JH83oL6uHXr 1nHjxsm7Pv/8c+lLdnZ2whtHjBihJcZnn322bt26BQsWSGqysrJ0boe4Nm3a FFSnyBbiLGSKXKfs37+/V69ePrcHs/1ILoq2usjXCwAAkBr21ynJZJT+0Ucf yaBr0KBBHTt2lD+WLl0aVECiWu+oUaNktCwVig6bJ0+efPjw4U8//dQ5lpb6 a9++fXnxYwEyAu/SpYt5u/wtC8h4Xl/KH/Jyzpw527ZtkyLC+SGLFi1yaUYg dYqMimVFEiUz5auvvpIpCxcuTHijSx+1SDES3iu50OlSjLhc/KL1UXKdsnnz Zp3eu3fvYtYp+mm6PUid5bKY2X6++OKLgs5kKxHrBQAASIGSWKfIkF4GXf36 9ZNR7jfffCN/d+rUqQhXf1u1Xq1TxLRp06S4kLG6lCrycsCAATt27NizZ8/n n38uL4cNG6bL6whcjzLodRxCBqI6d+TIkfJS2qZHDWbMmHH06FH5nLFjx+7f v9+lGYHUKRs3bpSVOg9pffvtt9qMhDe69HHv3r2zZs2SlxMmTJCAJFznLlO0 y7Lw7NmzpcoYM2bMihUrEj4/3zpFxuqSRJ2up5YVs07Ji1/q4rk9OLef4qzL hvUCAACErcTVKXPnzpVBl4yKzUlEkyZNkim9e/cO9dr2sNerdYopQ6SsaNu2 bfv27U1ZIVM6d+4sy+gtAmbOnCl/L1iwQP6eN2+eHomQ8ar+YC7t7Nixo/w9 evRomTV//nyfzQikTtGqxHldyYYNGxIqFz991CJCqpXkNUpJ0jo/CSdB5Vun SNB0olRtpsQr/gVN7ttD8vYTlKjWCwAAEKqSVad89913em2Ic9h57NixQYMG hXptewrWq3WKXlGSF786I+HUKTFmzBiZuHLlyrz4/axMXSMN6Nmzp55btWXL lp07d5pjK+vXr5daQF5+9NFHixcv9rzdU3Kdsnnz5qn5cf6An/CuVatW6YEh M2XdunWt42e1OT/Zs48udYo5DiL69etnTh5r06aNnjamkusUvfZfJ0o9tXDh Qv1blpTgFyePLttDvttPUKJaLwAAQKhKUJ3ici1woa4ptnO9WqdI9aEv16xZ Iy+HDx/uXEZvnKUXmOTm5nbo0KFjx44yTH3//ffHjRuXlZXVOn5NiizQ2nEd ihQUQ4cO1SvKpSPJF6Q7JdcpUjL8Nz9r164t6F2yRj1aYaYsX75cpiQE0LOP LnWKHqDRc8Z0cG5KFWfDkusU6U6+B2LUoUOHXILjKd/tIQXXsEe1XgAAgPCU lDrF81pgn9cUW7vehDpFL0Xv06ePc5khQ4bIxPXr1+tLGeHLS30ISGZmplYu gwYN0o9KeF7J3r179QZZn332mUszAjnv68CBA7Kivn37minTpk1rHb8YxPku zz661CkyMjcHU3SKJEinrFq1yiyW4jolL2l7SNk17FGtFwAAICQlpU7xcy1w GNfUp2y9CXVKXvwaE+fRge3bt7dp00YqgpycHJ2iN/Xq1auX/KuXmQ8bNkwW 6N69e8+ePXWZrVu3mssWZBDeOv7IRZdmBFKniAEDBsi6ZO158TGzNKldu3bJ VYB7H13qFPHxxx9rcSGb09KlS83ZXPv27du5c6eenGYmSk0nL2VFksftDqZs kU8r6LH1heXcHlJ5DXtU6wUAAAhDiahT/F8LHOy17alcb3KdoudKtW/fXgbY 06ZN69Chg7yUJpkF9u7dq2Nsc0jCXG2hV6zLaF9GrfLG6dOnL1q0SM+MkhUl r13mjo/T08P0b/MskoK4vCszM7N1/DQzyfUnn3zS+qengfnso3udsnnzZvN4 FGP06NEyS8qWfA+XJN9wLMDr6J10e+jcuXN417BL9ZedRMsTSbquffXq1QkL 6A0KAAAA7Gd/nVKoa4EDvLY9xeuVYXzr+FXwzoky3u7YsaMOpN9///358+cn /OCvj/+YMmWKvtQzqYS5Q698Qo8ePcxAXUqVfCspPYUsgRQR7m12f5eUG+bR LbJkQY9Bd+njsmXLXOqUvPhVKs7eSZWkhw8ir1PM9hDeNezS9w8KL9huAgAA hMf+OiU7O7tv377+rwXWa4plFcU8hyeq9SaQT9sZV5yPldpk27Ztod63OV8y XJdCwJyoVpBi9nHv3r2yFpenPUZCt4fwrmGXkvZ/+enXr1+HDh2kfMt3bsKt oQEAAKxlf52SF39qeaFGsJ4DY8vXi9Jh165dkVzDHtV6AQAAAlQi6hQAAAAA aYU6BQAAAIBtqFMAAAAA2IY6BQAAAIBtqFMAAAAA2IY6BQAAAIBtqFMAAAAA 2IY6BQAAAIBtqFMAAAAA2IY6BQAAAIBtqFMAAAAA2IY6BQAAAIBtoqpTAAAA AMAddQoAAAAA26SyTgEAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AABQsvw/WqfeYg== "], {{0, 340.}, {540., 0}}, {0, 255}, ColorFunction->RGBColor, ImageResolution->144.], BoxForm`ImageTag["Byte", ColorSpace -> "RGB", Interleaving -> True], Selectable->False], DefaultBaseStyle->"ImageGraphics", ImageSizeRaw->{540., 340.}, PlotRange->{{0, 540.}, {0, 340.}}]], "Output", TaggingRules->{}, CellChangeTimes->{3.834317716543977*^9, 3.8344054059802027`*^9}, CellLabel->"Out[3]=", CellID->1547913447] }, Open ]] }, Closed]], Cell[CellGroupData[{ Cell[TextData[{ "Visualizations", "\[NonBreakingSpace]", Cell["(2)", "ExampleCount"], "\[NonBreakingSpace]" }], "Subsection", TaggingRules->{}, CellID->50804313], Cell["Show the distribution of wages based on race:", "Text", TaggingRules->{}, CellChangeTimes->{{3.834230123005165*^9, 3.8342301411257887`*^9}}, CellID->772534288], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{ RowBox[{ InterpretationBox[ TagBox[ DynamicModuleBox[{Typeset`open = False}, FrameBox[ PaneSelectorBox[{False->GridBox[{ { PaneBox[GridBox[{ { StyleBox[ StyleBox[ AdjustmentBox["\<\"[\[FilledSmallSquare]]\"\>", BoxBaselineShift->-0.25, BoxMargins->{{0, 0}, {-1, -1}}], "ResourceFunctionIcon", FontColor->RGBColor[ 0.8745098039215686, 0.2784313725490196, 0.03137254901960784]], ShowStringCharacters->False, FontFamily->"Source Sans Pro Black", FontSize->0.6538461538461539 Inherited, FontWeight->"Heavy", PrivateFontOptions->{"OperatorSubstitution"->False}], StyleBox[ RowBox[{ StyleBox["MapReduceOperator", "ResourceFunctionLabel"], " "}], ShowAutoStyles->False, ShowStringCharacters->False, FontSize->Rational[12, 13] Inherited, FontColor->GrayLevel[0.1]]} }, GridBoxSpacings->{"Columns" -> {{0.25}}}], Alignment->Left, BaseStyle->{LineSpacing -> {0, 0}, LineBreakWithin -> False}, BaselinePosition->Baseline, FrameMargins->{{3, 0}, {0, 0}}], ItemBox[ PaneBox[ TogglerBox[Dynamic[Typeset`open], {True-> DynamicBox[FEPrivate`FrontEndResource[ "FEBitmaps", "IconizeCloser"], ImageSizeCache->{11., {2., 9.}}], False-> DynamicBox[FEPrivate`FrontEndResource[ "FEBitmaps", "IconizeOpener"], ImageSizeCache->{11., {2., 9.}}]}, Appearance->None, BaselinePosition->Baseline, ContentPadding->False, FrameMargins->0], Alignment->Left, BaselinePosition->Baseline, FrameMargins->{{1, 1}, {0, 0}}], Frame->{{ RGBColor[ 0.8313725490196079, 0.8470588235294118, 0.8509803921568627, 0.5], False}, {False, False}}]} }, BaselinePosition->{1, 1}, GridBoxAlignment->{"Columns" -> {{Left}}, "Rows" -> {{Baseline}}}, GridBoxItemSize->{ "Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}, GridBoxSpacings->{"Columns" -> {{0}}, "Rows" -> {{0}}}], True-> GridBox[{ {GridBox[{ { PaneBox[GridBox[{ { StyleBox[ StyleBox[ AdjustmentBox["\<\"[\[FilledSmallSquare]]\"\>", BoxBaselineShift->-0.25, BoxMargins->{{0, 0}, {-1, -1}}], "ResourceFunctionIcon", FontColor->RGBColor[ 0.8745098039215686, 0.2784313725490196, 0.03137254901960784]], ShowStringCharacters->False, FontFamily->"Source Sans Pro Black", FontSize->0.6538461538461539 Inherited, FontWeight->"Heavy", PrivateFontOptions->{"OperatorSubstitution"->False}], StyleBox[ RowBox[{ StyleBox["MapReduceOperator", "ResourceFunctionLabel"], " "}], ShowAutoStyles->False, ShowStringCharacters->False, FontSize->Rational[12, 13] Inherited, FontColor->GrayLevel[0.1]]} }, GridBoxSpacings->{"Columns" -> {{0.25}}}], Alignment->Left, BaseStyle->{LineSpacing -> {0, 0}, LineBreakWithin -> False}, BaselinePosition->Baseline, FrameMargins->{{3, 0}, {0, 0}}], ItemBox[ PaneBox[ TogglerBox[Dynamic[Typeset`open], {True-> DynamicBox[FEPrivate`FrontEndResource[ "FEBitmaps", "IconizeCloser"]], False-> DynamicBox[FEPrivate`FrontEndResource[ "FEBitmaps", "IconizeOpener"]]}, Appearance->None, BaselinePosition->Baseline, ContentPadding->False, FrameMargins->0], Alignment->Left, BaselinePosition->Baseline, FrameMargins->{{1, 1}, {0, 0}}], Frame->{{ RGBColor[ 0.8313725490196079, 0.8470588235294118, 0.8509803921568627, 0.5], False}, {False, False}}]} }, BaselinePosition->{1, 1}, GridBoxAlignment->{"Columns" -> {{Left}}, "Rows" -> {{Baseline}}}, GridBoxItemSize->{ "Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}, GridBoxSpacings->{"Columns" -> {{0}}, "Rows" -> {{0}}}]}, { StyleBox[ PaneBox[GridBox[{ { RowBox[{ TagBox["\<\"Version (latest): \"\>", "IconizedLabel"], " ", TagBox["\<\"1.0.0\"\>", "IconizedItem"]}]}, { TagBox[ TemplateBox[{ "\"Documentation \[RightGuillemet]\"", "https://resources.wolframcloud.com/FunctionRepository/\ resources/MapReduceOperator"}, "HyperlinkURL"], "IconizedItem"]} }, DefaultBaseStyle->"Column", GridBoxAlignment->{"Columns" -> {{Left}}}, GridBoxItemSize->{ "Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}], Alignment->Left, BaselinePosition->Baseline, FrameMargins->{{5, 4}, {0, 4}}], "DialogStyle", FontFamily->"Roboto", FontSize->11]} }, BaselinePosition->{1, 1}, GridBoxAlignment->{"Columns" -> {{Left}}, "Rows" -> {{Baseline}}}, GridBoxDividers->{"Columns" -> {{None}}, "Rows" -> {False, { GrayLevel[0.8]}, False}}, GridBoxItemSize->{ "Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}]}, Dynamic[ Typeset`open], BaselinePosition->Baseline, ImageSize->Automatic], Background->RGBColor[ 0.9686274509803922, 0.9764705882352941, 0.984313725490196], BaselinePosition->Baseline, DefaultBaseStyle->{}, FrameMargins->{{0, 0}, {1, 0}}, FrameStyle->RGBColor[ 0.8313725490196079, 0.8470588235294118, 0.8509803921568627], RoundingRadius->4]], {"FunctionResourceBox", RGBColor[0.8745098039215686, 0.2784313725490196, 0.03137254901960784], "MapReduceOperator"}, TagBoxNote->"FunctionResourceBox"], ResourceFunction[ ResourceObject[ Association[ "Name" -> "MapReduceOperator", "ShortName" -> "MapReduceOperator", "UUID" -> "856f4937-9a4c-44a9-88ae-cfc2efd4698f", "ResourceType" -> "Function", "Version" -> "1.0.0", "Description" -> "Like an operator form of GroupBy, but where one also specifies a \ reducer function to be applied", "RepositoryLocation" -> URL["https://www.wolframcloud.com/obj/resourcesystem/api/1.0"], "SymbolName" -> "FunctionRepository`$ad7fe533436b4f8294edfa758a34ac26`\ MapReduceOperator", "FunctionLocation" -> CloudObject[ "https://www.wolframcloud.com/obj/6d981522-1eb3-4b54-84f6-\ 55667fb2e236"]], ResourceSystemBase -> Automatic]], Selectable->False], "[", RowBox[{ RowBox[{ RowBox[{"(", RowBox[{ RowBox[{"Switch", "[", RowBox[{ RowBox[{"{", RowBox[{"#black", ",", "#hispanic"}], "}"}], ",", RowBox[{"{", RowBox[{"1", ",", "0"}], "}"}], ",", "\"\\"", ",", RowBox[{"{", RowBox[{"0", ",", "1"}], "}"}], ",", "\"\\"", ",", "_", ",", "\"\\""}], "]"}], "&"}], ")"}], "->", RowBox[{"(", RowBox[{"#re78", "&"}], ")"}]}], ",", RowBox[{ RowBox[{"Histogram", "[", RowBox[{"#", ",", RowBox[{"PlotRange", "->", RowBox[{"{", RowBox[{ RowBox[{"{", RowBox[{"0", ",", "35000"}], "}"}], ",", "All"}], "}"}]}], ",", RowBox[{"ImageSize", "->", "500"}]}], "]"}], "&"}]}], "]"}], "[", RowBox[{"Normal", "@", "jobs"}], "]"}]], "Input", TaggingRules->{}, CellChangeTimes->{{3.834229932431963*^9, 3.834230113947672*^9}, { 3.834313738996232*^9, 3.8343137523332987`*^9}}, CellLabel->"In[1]:=", CellID->1338959095], Cell[BoxData[ GraphicsBox[ TagBox[RasterBox[CompressedData[" 1:eJzs3QuUlfV97/9hBrkp4gVWHLyQaNS2eCVoRJdtYowmRhtjE7R1qScYkhg1 qKc9hBidEttq8UZrYhop6qgx+hf5hwj+Q5N4waAYCSoiARSNMgIBRFHiyNz4 P4e9mLWzB4aR51G+P+f1Wp/lgj3PfuZh4KTzPnvP3h8bNeaMr1dXVVX97z7Z f8746thPX3bZV8f93W7Zb77y7f99wTe+Pfprn//2P47+xujLjhlVk924V3bY L3pWVf3fX28EAACA4tx2223Tp0/f0VcBAABAFH/1V3915pln7uirAAAAIAqd CAAAQDmdCAAAQDmdCAAAQDmdCAAAQDmdCAAAQDmdCAAAQDmdCAAAQDmdCAAA 0N0sX778pz/96dY+qhMBAAC6m+uuu+7YY4/d2kd1IgAAQHdz+OGH19bWbu2j 70cnLliw4PBNFi5cuB13f/bZZ7/4xS+efPLJjz/+eLEXRk4XX3xx9tea/XdH XwgAALD9suaqqqq65JJLtnbANjvxnnvumTx58uzZszv/RKXDXnjhhezXv/nN b6o2mTt37nZc86mnnlq6+4gRI7bj7rx/TjzxxOzvJfsL2tEXAgAAbL9//Md/ zL6x/93vfre1A7bZiZ/4xCeyM3zpS1/q5Ji2trY+ffpkh/3Xf/3Xxtyd+JWv fKV096OOOqrzI6+77roJEyY0NjZux2ehQle+mDoRAABS19raOnjw4KwEOzlm m534D//wD1kaHHHEEZ0cs3LlylLZPfTQQxtzd+LixYtHjRp17rnnzps3r5PD mpube/TokX2WF198cTs+C+W6+MXUiQAAkLr/+Z//yb6r/7d/+7dOjtlmJ44f Pz47yYABAzo5Zs6cOaUwbGho2Ji7E7uoPU51Yn5d/GLqRAAASN0555zTo0eP V155pZNjttmJ9957b6kg1q5d2/kxO++8c1tb28ayTmx/vmtzc/NTTz21ZMmS lpaW7fqjbMGCBQveaydml/fCCy88+eSTb731VlGXUW79+vW//e1vsz9ma2vr e7rjmjVrsq9PdmF//OMfd8j1dPGLWerE0047rf2W5cuXz5kzp5N/G+2yr3l2 ZHb+0j+SLmpqasr+yh577LHsCrN/RV2/IwAA0FHWCFm4fepTn+r8sG124jPP PFMRfb///e/79u1bW1vbHn0TJkzIDjj88MNLv23vxOeffz7rlL/927/ddddd S7dkd8wOriiFz3zmMwO25Jprrul4PV/4whc+/vGP77333tkBpXP279+/4o73 3Xdfxb2ynDnrrLPaLyPL5wMOOOCee+7p/IvTddOmTcu+ktXV1aXzZ1/5L3/5 y6+99toWDz7mmGOyiyy9NNAjjzzy13/911WbZRd21VVXfWDX816/mKVO/MpX vpIlW11d3SGHHNJ+5Z/85CezIN3ixdx1110HH3xw6XmtpYvJ/sltsYh/9rOf ZZ+x1KGNjY1jx47dfffd2z/FwIED33zzzfxfHAAA6LbuuOOO7FvryZMnd37Y NjvxT3/6U+k7/ClTppRuueGGG0rft7e/b8WFF15YyofSb9s78fvf/37p9W16 9uy50047tX/Df9NNN5V/iiOOOKJqS/75n/+54/VkXbPFg8v95Cc/Kb/Lz3/+ 89122630ocGDBx966KFZrpZ++81vfrPzr09XjBo1qv1T77HHHr169Sr9Okue X/ziFx2PP/DAA7OPZlc1ffr03r17V1x89hf3gV3Pe/1iljrxlFNO+dSnPlX6 aL9+/dqPzHrz3XffLT9/9tvs4NJHsz9p1pX77LNP6bdZ9D333HMVV559ruxD w4YNe+eddz772c9WXMn++++f8yvz4osv/vSnP815EgAASFf2bXbWaOvWrev8 sK68f+J+++2XfZd+7bXXtp+59H37FVdcUbql9E4Wl19+eem37Z2Y2Wuvve65 557mTaZOndq/f/9SUJSf/49//GPDnys98LfFTly5cmXpmPr6+tKnmD17dsXd s8poP37ZsmWlSDz44IMfffTR0o2NjY2lV4LN/OpXv+r8j9+52267rXSeESNG LFq0KLtlw4YNWe/ssssupT9+x+dkljpx5MiRWcFlBf2tb33rmWeeyZLq9ddf /9nPfpb99wO7nvf6xSx1Ysk555zz8ssvZzeuWLHii1/8YunGSZMmlV9M+xf5 n/7pn95+++3SjU888USpT48//viKiy91Ym1tbekTDR069K677souMvsjLFiw 4Je//GWer8zGTQ989+jR48c//nHO8wAAQIpee+216urqrES2eWRXOvGkk07K vmnPcmbjpqez9u7du9Rxw4cPLx1w6KGHZr+9/fbbS79t78Ts9iwiyk/1ne98 p/Shzn8Qr1R2W+zEdtOnTy+dqvMfqTvttNOyY/bcc8+OP6f5hS98IfvQNp+a 24ksw0tPjPzLv/zLrGXKP5TlZ+nyLrjggop7lToxk7XbFh9w/ICvZ2OXv5il fMtSq+IR4axtd95554qTP/XUU6UnvmaRWHGeJ598svQg9SOPPFJ+e6kTS7L2 /NOf/tTFP3jXXXrppdmnvuWWWwo/MwAABFf6gcEHHnhgm0d2pRMvvvjiqk3P Nsx+nZ0z+/WFF16YdUH2/XYp90rZ2P401PZOfOKJJypO9eCDD5Y+lEVEJ5+x qE5sbm4uPfF1/PjxHT9aevmdLCE7+SydyzKndA3ZqTp+9DOf+Uz2oYMOOqji 9vZOLPw5kNt3PRvfYyeefPLJHT909NFHZx/K0rv9lquuuqpq0xNTyx+RbJeV bFWHZyC3d+Jhhx1W8RTWAo0ZMyb7p1vx0CcAAHzoHXrooYMGDerK60N2pRN/ 8IMflB6iyn79rW99K/v1fffd9zd/8zdVm36Y7o033ih9b79mzZrS8Z28L8bv fve70ocqHkiqUFQnPvvss6Vjjj322HM6aP8hu/Yrf6/+8z//s3SGiodNS0pv KVJdXV0RSqVOfD/eXWL7rmfje+zELV556XHb8gdnv/SlL1VtetWajl/5TOlx z4suuqj8JO2d+L6+o8rGTf9fH1kq/vd///f7+lkAACCO0iuUXnjhhV05uCud WHrKYr9+/bJfH3DAAdk32KtWrcoiLrvx7LPPnj9/ftWm10tpP76TTmx/9dQP phN/9rOfVXXBFquqK9p//m6LH508eXLpowsWLCi/vdSJXfwL+gCuZ2MRnXj6 6adXdOLWXp6o3De+8Y3yk7R34urVq7v6Zy7z1ltvXdE13/ve92pqarJ/ydt8 oScAAPhw+PWvf11sJy5btqz03fvvf//7qk3PCcxufPzxx7Nf77vvvr/85S+r Nr0tQvvxH3AnvvDCC1s75qmnniodM2HChLlb917f7rDd9ddf30nX/Ou//mvV pp/mq3i7xvevE7fvejZ27Yu58T12YunHPz/+8Y938pXP/mmVnyRnJ2b3+nSX lT5RVtbb8YkAACA5WfXsvffeBT7vtK2trfQqJaUnLl566aXZjS0tLaU33Sv9 GNo555zTfvwH04ntP4v35JNPbu2YxsbGnj17Zsd85zvf6fzPuH1KjVy1lR8F Lb0MbMd3c3j/OnH7rmdj176YG99jJ37ve9/Lbtlpp506ZunW5OzErrvrrruq q6tLL80EAADdROllRadPn77NI7vSiZlhw4ZVbXoJlPLTnnHGGdlvszOUarH9 4A+mE1966aXSqX70ox91cljpXez79u3b8cmW+bW/zmf29al4UHLOnDmlyzvv vPMq7vX+deL2Xc/GLn8x31MnPvTQQ6VznnvuuV28/g+mE7PPUlNT881vfrOt re39+ywAABBN6QmiZ5111jaP7GIn/v3f/33pG/iePXu2vxFe1hRVm5W/wOYH 04nNzc377rtvdth+++337LPPtt++dOnS0vv6lSxatKj0XvC1tbV33XVXexo0 NTVNnjz5k5/8ZOfv0LFN//Ef/1H6E5188smln3PMPsWDDz44cODA7Mbdd9+9 4w8/vn+duH3Xs7HLX8z31ImZ888/v3Qx2T/F8qeYZn8pX/3qV+vq6ipO8gF0 4t13351F4ujRo0UiAADdUFZAWR9t8yl/XezE0qvWVG164dD2G7OIaO/EefPm td/+wXRi5oc//GHpbNl3/ll8DR06NMuc7LdZg5QfltVHKRUz/fv3Hz58eHZk 7969S7d8//vf3+YfvxOtra3t7zJftelZnaWLz/Tp0+e+++7reJf3tRO343pK uvLFfK+d+Pbbbx933HGl0/bo0eNjH/vYiBEj9tprr9Itu+yyS8Urr77fnThl ypTsTzdq1CiRCABA91R6sK++vr7zw7rYiffcc0/pG/grrrii/PYDDjigdHv7 g4yZJ554onTj/PnzK85TbCdmbrnlliw3qsrsscceY8aMqTjslVde+bu/+7vS eymWVFdXH3bYYXfccUdLS8s2P8s23X777YMHD24/eRYjn/nMZ5YsWbLFg//i L/7i/evE7biedtv8Yp588snZjWeccUbH+26xEzduejTz1ltvHTJkSPlpd911 16997WtLly6tOLj9n9n71IkzZ8686KKLtvtliwAAIHVvvPFG7969P/vZz3Z+ WBc7MbLm5ubnnnvu17/+9aOPPvriiy92UgFZEi5evHjGjBlPP/30Ft/8Pafl y5fPnj37t7/97ftx8u2wHdfT9S/me7V27drHHnvsV7/6VUNDQ1HnBAAA3quR I0dWV1dnsdDJMR+CTgQAAKCLHnzwwaqqquuvv76TY3QiAABA99HS0lJbW3vk kUd2coxOBAAA6Fb+6Z/+qaqq6vnnn9/aAToRAACgW1mwYEHWiePGjdvaAToR AACguxk+fPhHP/rRrX1UJwIAAHQ3P/jBD44++uitfVQnAgAAdDdr1qyZPn36 1j6qEwEAACinEwEAACinEwEAACinEwEAACinEwEAACinEwEAACinEwEAACin EwEAACinEwEAACinEwEAACj3jW9845prrtnRVwEAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAEC3sHTp 0jlz5jQ2Nu7oCwEAACCXxx9//Oqrr7744ov/5V/+5bnnnmtubq44YMmSJTfe eONFF110xRVXZAc3NTVt8Ty/+tWvfv7zn//pT396/y8ZAACA90VLS8u3vvWt fv36VW220047ff/733/rrbfaj5k8efKgQYPaD6iurr788svXrVvX8Ww6EQAA IHUTJ07cbbfdamtrb7vttt///vc//vGPBw4cmJXgAw88sGHDhuyAuXPnDh48 uFevXuPHj3/qqaduuumm7IAePXpMnz69dEA5nQgAAJC0tra2Y445pqam5q67 7mr/ocLvfOc7u+yyyze/+c21a9dmvx09enS/fv3Gjh3b/gDizTffnKXlyJEj 16xZU3FCnQgAAJC0d999d9KkSRdffPHq1avbb7z++usHDBhw9tlnZxnY2tp6 yCGH9OjRY86cOS0tLaUDVq1atd9++/Xt23fp0qUVJ9SJAAAAHzIrVqw45phj evbsmdXi+vXrX3rppb333nvgwIGvvvpq+WHDhw+vqal56KGHKl7xRicCAAB8 aEydOvWrX/1q6WcPTznllOXLl2c3zps3r7a2dujQoaXftssO6NWr15QpU959 993y23UiAADAh8ZZZ53Vp0+f0iuafulLX1q6dGlra+vs2bM/8pGPHHnkkRWd eMYZZ/Tu3fvOO++seKtEnQgAAPChsWzZslWrVj366KPHHnvsTjvtdMwxx6xc ubLUicOGDVuxYkX5waeffnrWiffcc49OBAAA+NB7+eWXP/rRj1ZVVc2cOfOp p56qra096KCDXnvttfJjTjzxxKwlZ8yYUfHWGFknTpw4cerUqT/vQDwCAADE 19ramgXgwoULm5qaym8/4YQTsgy85ZZbli1btu+++/bv3/8Pf/hD+0fb2tqG Dh3ao0ePuXPnZmcov6NOBAAASNqSJUsGDx48YMCA8ne4aG5uPuyww6qrqx94 4IENGzaMGDGipqbmwQcfbH/ocMGCBbW1tfvss09DQ0PFCT3vFAAAIGmtra2f +MQnsiT87ne/u27dutKN1157bVaOAwcOfOmll7LfXnXVVbvuuusJJ5xQqsKm pqbzzjuvb9++F1xwwdq1aytOqBMBAABSN3369EGDBvXo0ePzn//8uHHjsv/2 6dMn++2tt976zjvvZAesXr36iCOOyFoy+++YMWOOP/74Xr167bXXXk8//XTF k0436kQAAIAPhQceeODQQw+tqakpvSnGgQceeP/995e/MeKyZctOPfXU9nfN OPLIIx955JGKH2ks0YkAAAAfDm1tbVkMzpkz57XXXuv4KGHJG2+88fTTT7/8 8sstLS1bO49OBAAAoJxOBAAAoJxOBAAAoJxOBAAAoJxOBAAAoJxOBAAAoJxO BAAAoJxOBAAAoJxOBAAAoJxOBAAAoJxOBAAAoJxOBAAAoJxOBAAAoFzWiRMn Tpw6derPN3nssccaGxu7cscXX3zxgQcemEoAy5Yta21tfb//qQAAAN3Ednfi 6PPOvPWqv7vvupG2Y3fTuM+Pv/zStWvXvt//VAAAgG5iu593eum3zl3x0P9p mXel7dgtnnbRxKu/88YbOhEAACiGTkx9OhEAACiWTkx9OhEAACiWTkx9OhEA ACiWTkx9OhEAACiWTkx9OhEAACiWTkx9OhEAACiWTkx9OhEAACiWTkx9OhEA ACiWTkx9OhEAACiWTkx9OhEAACiWTkx9OhEAACiWTkx9OhEAACiWTkx9OhEA ACiWTkx9OhEAACiWTkx9OhEAACiWTkx9OhEAAChW1on19fWzZs2avVljY2NX 7qgTg0wnAgAAxco6sa6ubtKkSbdvkgWjTkxrOhEAACiW552mPp0IAAAUSyem Pp0IAAAUSyemPp0IAAAUSyemPp0IAAAUSyemPp0IAAAUSyemPp0IAAAUSyem Pp0IAAAUSyemPp0IAAAUSyemPp0IAAAUSyemPp0IAAAUSyemPp0IAAAUSyem Pp0IAAAUSyemPp0IAAAUSyemPp0IAAAUSyemPp0IAAAUSyemPp0IAAAUSyem Pp0IAAAUK+vE+vr6WbNmzd6ssbGxK3fUiUGmEwEAgGJlnVhXVzdp0qTbN8mC USemNZ0IAAAUy/NOU59OBAAAiqUTU59OBAAAiqUTU59OBAAAiqUTU59OBAAA iqUTU59OBAAAiqUTU59OBAAAiqUTU59OBAAAiqUTU59OBAAAiqUTU59OBAAA iqUTU59OBAAAiqUTU59OBAAAiqUTU59OBAAAiqUTU59OBAAAiqUTU59OBAAA iqUTU59OBAAAiqUTU59OBAAAiqUTU59OBAAAipV1Yn19/fz58xdt1tTU1JU7 6sQg04kAAECxsk6sq6ubNGnS7ZvMmjWrsbGxK3fUiUGmEwEAgGJ53mnq04kA AECxdGLq04kAAECxdGLq04kAAECxdGLq04kAAECxdGLq04kAAECxdGLq04kA AECxdGLq04kAAECxdGLq04kAAEDJk08+eeONN377298eP378tGnT3nnnnfKP zps379577/3Jn5s5c2ZjY2PFeXRi6tOJAADAhg0bRo8e3a9fv6oyxx577JIl S9ra2krHnHnmmX369Kn6c0cdddSKFSsqzqYTU59OBAAArr766l133bW2tvZH P/rR888/P2PGjCwAe/bsecYZZ6xevbp0zGGHHVZTUzNq1KhLLrnk0s0mTpz4 1ltvVZxNJ6Y+nQgAAN1cW1vbsGHDqqur77///nfffbd04+LFiwcPHty7d+8l S5Zkv122bNnee+89ZMiQhoaGbZ5QJ6Y+nQgAAN3cqlWrPv3pT++///4VDTh0 6NAePXrMmjWrubl55syZgwYNOvXUU7ODt3lCnZj6dCIAANDR888/P3jw4L59 +y5dujT77Y033jhgwIDzzz//yiuvPOuss84999zrr79+9erV7T+9WE4npj6d CAAAVGhqaho5cmTv3r1PPvnk0gOIo0ePzpqx4kVsDjzwwDlz5jQ3N1fcXSem Pp0IAACUa2lpufjii3feeecBAwbMnj27lIHHHXdcTU3NwQcffOutty5cuHDa tGlHHHFEdsuIESO83umHbzoRAABo984775x//vl9+/bdZZddpkyZ0v6yNg8/ /PAtt9zy6quvtra2lm5ZtGjRkCFDqqqqZsyYsWHDhvKT6MTUpxMBAICS1atX n3baab169dpzzz2nTZtWUX8dnX766dnBN9xww/r168tvzzqxvr5+/vz5izpo amrq5IQ6Mch0IgAAkHn55ZdHjBjRs2fPj33sY7/5zW8qfuowK8GVK1e2tLSU 33j22Wf36dPn2muvffvtt8tvzzqxrq5uwoQJ13Wwbt26Tq5BJwaZTgQAAJYv X3700UfX1NQMHz584cKF7U8uLXnuuedqa2t33nnnF154of3Gd999Nzu44l0X SzzvNPXpRAAA4Mwzz+zdu/ewYcNeeumljm910dLScvjhh2dJOHbs2DfffLN0 4zXXXDNgwIADDjjglVdeqTheJ6Y+nQgAAN3c3Llz99prr6qqqr59++65554D /9xDDz3U1NQ0ZcqU7NfZMSeddNK4ceM+97nP9enTp2fPnnfeeWdjY2PFCXVi 6tOJAADQzWWtt/vuu1dtxS9+8YvSi89MnTp16NChNTU1pdv333//u+++u2Mk btSJ6U8nAgAAXdTW1tbQ0DB37tzFixdXvNBNOZ2Y+nQiAABQLJ2Y+nQiAABQ LJ2Y+nQiAABQLJ2Y+nQiAABQLJ2Y+nQiAABQLJ2Y+nQiAABQLJ2Y+nQiAABQ LJ2Y+nQiAABQLJ2Y+nQiAABQLJ2Y+nQiAABQLJ2Y+nQiAABQLJ2Y+nQiAABQ LJ2Y+nQiAABQrKwT6+vr58+fv2izpqamrtxRJwaZTgQAAIqVdWJdXd2ECROu 22Tq1KldfGxRJwaZTgQAAIrleaepTycCAADF0ompTycCAADF0ompTycCAADF 0ompTycCAADF0ompTycCAADF0ompTycCAADF0ompTycCAADF0ompTycCAADF 0ompTycCAADF0ompTycCAADF0ompTycCAADF0ompTycCAADF0ompTycCAADF 0ompTycCAADF0ompTycCAADF0ompTycCAADF0ompTycCAADFyjqxvr6+oaFh zWYtLS1duaNODDKdCAAAFCvrxLq6unGbTZo0af369V25o04MMp0IAAAUy/NO U59OBAAAiqUTU59OBAAAiqUTU59OBAAAiqUTU59OBAAAiqUTU59OBAAAiqUT U59OBAAAiqUTU59OBAAAiqUTU59OBAAAiqUTU59OBAAAiqUTU59OBAAAiqUT U59OBAAAiqUTU59OBAAAiqUTU59OBAAAiqUTU59OBAAAiqUTU59OBAAAiqUT U59OBAAAiqUTU59OBAAAipV1Yn19fUNDw5rNWlpaunJHnRhkOhEAAChW1ol1 dXXjNps0adL69eu7ckedGGQ6EQAAKJbnnaY+nQgAABRLJ6Y+nQgAABRLJ6Y+ nQgAABRLJ6Y+nQgAABRLJ6Y+nQgAABRLJ6Y+nQgAABRLJ6Y+nQgAABRLJ6Y+ nQgAABRLJ6Y+nQgAABRLJ6Y+nQgAABRLJ6Y+nQgAABRLJ6Y+nQgAABRLJ6Y+ nQgAABRLJ6Y+nQgAABRLJ6Y+nQgAABRLJ6Y+nQgAABRLJ6Y+nQgAABQr68T6 +vqGhoY1m7W0tHTljjoxyHQiAABQrKwT6+rqxm02adKk9evXd+WOOjHIdCIA AFAszztNfToRAAAolk5MfToRAAAolk5MfToRAAAolk5MfToRAAAolk5MfToR AAAolk5MfToRAAAolk5MfToRAAAolk5MfToRAAAoefLJJ2+88cZvf/vb48eP nzZt2jvvvFNxwJIlS7IDLrrooiuuuOLxxx9vamra4nl0YurTiQAAwIYNG0aP Ht2vX7+qMscee2wWhm1tbaVjJk+ePGjQoPaPVldXX3755evWret4Np2Y+nQi AABw9dVX77rrrrW1tT/60Y+ef/75GTNmHHXUUT179jzjjDNWr16dHTB37tzB gwf36tVr/PjxTz311E033TRw4MAePXpMnz49a8yKs+nE1KcTAQCgm2traxs2 bFh1dfX999//7rvvlm5cvHhxFoa9e/desmRJ9tvSo41jx45tfwDx5ptv3m23 3UaOHLlmzZqKE+rE1KcTAQCgm1u1atWnP/3p/fffv6Ghofz2oUOH9ujRY9as WRs2bDjkkEOyX8+ZM6elpaX9Xvvtt1/fvn2XLl1acUKdmPp0IgAA0NHzzz8/ ePDgUga+9NJLe++998CBA1999dXyY4YPH15TU/PQQw81NzeX364TU59OBAAA KjQ1NY0cObJ3794nn3zyqlWr5s2bV1tbO3To0OXLl5cfdsopp/Tq1WvKlCnt z1Yt0YmpTycCAADlWlpaLr744p133nnAgAGzZ89ubm7O/vuRj3zkyCOPrOjE M844I2vJO++8s7Gxsfx2nZj6dCIAANDunXfeOf/88/v27bvLLru0P1BY6sRh w4atWLGi/ODTTz8968R77rlHJ37IphMBAICS1atXn3baab169dpzzz2nTZvW /oYXzzzzTG1t7UEHHfTaa6+VH3/iiSfutNNOM2bMqHhrDJ2Y+nQiAACQefnl l0eMGNGzZ8+Pfexjv/nNb8pfmmbNmjX77rtv//79//CHP7Tf2NbWVnpB1Llz 57a2tpafKuvEMWPGfH1LOr6JRjmdGGQ6EQAAWL58+dFHH11TUzN8+PCFCxdW dF8mS8jsow8++GD7Q4cLFiyora3dZ599Kt5NY6PHE9OfTgQAAM4888zevXsP GzbspZdeamtr63jAVVddteuuu55wwgmlKmxqajrvvPP69u17wQUXrF1bWRM6 MfXpRAAA6Obmzp271157VVVVZd235557DvxzDz30UFaFq1evPuKII6qrq7P/ jhkz5vjjj+/Vq1d2r6effrrjg486MfXpRAAA6ObuvPPO3XffvWorfvGLX2Sd mB22bNmyU089tU+fPqXbjzzyyEceeaT0oQo6MfXpRAAAoOveeOONp59++uWX X25padnaMTox9elEAACgWDox9elEAACgWDox9elEAACgWDox9elEAACgWDox 9elEAACgWDox9elEAACgWDox9elEAACgWDox9elEAACgWDox9elEAACgWDox 9elEAACgWDox9elEAACgWDox9elEAACgWDox9elEAACgWDox9elEAACgWFkn jhkz5uubXX311evWrevKHXVikOlEAACgWB5PTH06EQAAKJZOTH06EQAAKJZO TH06EQAAKJZOTH06EQAAKJZOTH06EQAAKJZOTH06EQAAKJZOTH06EQAAKJZO TH06EQAAKJZOTH06EQAAKJZOTH06EQAAKJZOTH06EQAAKJZOTH06EQAAKJZO TH06EQAAKJZOTH06EQAAKJZOTH06EQAAKJZOTH06EQAAKJZOTH06EQAAKJZO TH06EQAAKFbWiXV1deM2mzBhwrp167pyR50YZDoRAAAolscTU59OBAAAiqUT U59OBAAAiqUTU59OBAAAiqUTU59OBAAAiqUTU59OBAAAiqUTU59OBAAAiqUT U59OBAAAiqUTU59OBAAAiqUTU59OBAAAiqUTU59OBAAAiqUTU59OBAAAiqUT U59OBAAAiqUTU59OBAAAiqUTU59OBAAAiqUTU59OBAAAiqUTU59OBAAAiqUT U59OBAAAiqUTU59OBAAAipV1Yl1d3bjNJkyYsG7duq7cUScGWdaJ53z5pIu+ fvYl3zrPduy+cf7Z55971rcvOHeHX4l97by/X7x4UWtr6/v9P6EAAB9KWSfW 19c3NDSs2aSlpaWLd9SJQTb37q9f/A+f/P3PLlr50D/ajt1/XX7qzZd/YemD Y3b4ldidV5/5q5kPvPvuu+/r/34CAHxYed5p6ss6se6bn1r9qL+LHb//rvvb n/77l996fNwOvxL7yb+fpRMBALabTkx9OjHOdGKc6UQAgDx0YurTiXGmE+NM JwIA5KETU59OjDOdGGc6EQAgD52Y+nRinOnEONOJAAB56MTUpxPjTCfGmU4E AMhDJ6Y+nRhnOjHOdCIAQB46MfXpxDjTiXGmEwEA8tCJqU8nxplOjDOdCACQ h05MfToxznRinOlEAIA8dGLq04lxphPjTCcCAOShE1OfTowznRhnOhEAIA+d mPp0YpzpxDjTiQAAeejE1KcT40wnxplOBADIQyemPp0YZzoxznQiAEAeOjH1 6cQ404lxphMBAPLQialPJ8aZTowznQgAkEfWiXV1deM2mzBhwrp167pyR50Y ZDoxznRinOlEAIA8sk6sr69vaGhYs0lLS0sX76gTg0wnxplOjDOdCACQh+ed pj6dGGc6Mc50IgBAHjox9enEONOJcaYTAQDy0ImpTyfGmU6MM50IAJCHTkx9 OjHOdGKc6UQAgDx0YurTiXGmE+NMJwIA5KETU59OjDOdGGc6EQAgD52Y+nRi nOnEONOJAEB3ln0XdOWVV9bX11eE3rx58+69996f/LmZM2c2NjZWnEEnpj6d GGc6Mc50IgDQnX33u9/t37//qFGjXn/99fLbzzzzzD59+lT9uaOOOmrFihUV Z9CJqU8nxplOjDOdCAB0T+vWrbvssst22WWXLAA7duJhhx1WU1OT3X7JJZdc utnEiRPfeuutivPoxNSnE+NMJ8aZTgQAuqEs7kolWHqgsKITly1btvfeew8Z MqShoaErp9KJSU8nxplOjDOdCAB0Q4ceemh1dfWxxx572WWXDRgwoKITZ86c OWjQoFNPPXXVqlXbPJVOTH06Mc50YpzpRACgGzrnnHNuu+229evX33777Xvs sUdFJ954441ZPJ5//vlXXnnlWWedde65515//fWrV69ua2vreCqdmPp0Ypzp xDjTiQBAd7bFThw9enTfvn0rXsTmwAMPnDNnTnNzc8UZdGLq04lxphPjTCcC AN3ZFjvxuOOOq6mpOfjgg2+99daFCxdOmzbtiCOOyG4ZMWKE1zv98E0nxplO jDOdCAB0Z1vsxIcffviWW2559dVXW1tbS7csWrRoyJAhVVVVM2bM2LBhQ/kZ dGLq04lxphPjTCcCAN3ZFjtxi04//fRevXrdcMMN69evL79dJ6Y+nRhnOjHO dCIA0J1tsROzEly5cmVLS0v5kWeffXafPn2uvfbat99+u/z2rBPr6uomTJhw XQfr1q3r5FPrxCDTiXGmE+NMJwIA3VnHTnzuuedqa2t33nnnF154of2w7Jul 4cOHV1dX33///RXfOGWdWF9fP3/+/EUdNDU1dfKpdWKQ6cQ404lxphMBgO6s Yye2tLQcfvjhWRKOHTv2zTffLN14zTXXDBgw4IADDnjllVcqzuB5p6lPJ8aZ TowznQgAdGdbfN7plClTBg4cWFVVddJJJ40bN+5zn/tcnz59evbseeeddzY2 NlacQSemPp0YZzoxznQiANCd3XHHHaVOXLt2bfntU6dOHTp0aE1NTenNE/ff f/+77767YyRu1InpTyfGmU6MM50IALBFbW1tDQ0Nc+fOXbx4cXNz89YO04mp TyfGmU6MM50IAJCHTkx9OjHOdGKc6UQAgDx0YurTiXGmE+NMJwIA5KETU59O jDOdGGc6EQAgD52Y+nRinOnEONOJAAB56MTUpxPjTCfGmU4EAMhDJ6Y+nRhn OjHOdCIAMbW1tT3xxBOPPvpoU1PTjr4W6IxOTH06Mc50YpzpRAACam1tPeaY Y0rvb/75z39+R18OdEYnpj6dGGc6Mc50IgABzZo1q6rMH//4xx19RbBVOjH1 6cQ404lxphMBCGj16tW9e/du78QpU6bs6CuCrdKJqU8nxplOjDOdCEBMkyZN +sQnPlHqxJtvvnlHXw5slU5MfToxznRinOlEAMJatWpVTU1N1okzZ87c0dcC W6UTU59OjDOdGGc6EYCwfvzjH2eR2KtXr5UrV+7oa4Gt0ompTyfGmU6MM50I QExLly4dMmRI1okXXnjhjr4W6EzWiXV1dRMmTLhuk8mTJ69fv74rd9SJQaYT 40wnxplOBCCgH/7wh3vuuWcWiYcffviaNWt29OVAZ7JOrK+vnz9//qJNVq1a 1dLS0pU76sQg04lxphPjTCcCEE1bW9uQIUN69+591VVXNTU17ejLgW3wvNPU pxPjTCfGmU4EIKAZM2YsXLhwR18FdIlOTH06Mc50YpzpRACAPHRi6tOJcaYT 40wnAgDkoRNTn06MM50YZzoRACAPnZj6dGKc6cQ404kAAHnoxNSnE+NMJ8aZ TgQAyEMnpj6dGGc6Mc50IgBAHjox9enEONOJcaYTAQDy0ImpTyfGmU6MM50I AJCHTkx9OjHOdGKc6UQAgDx0YurTiXGmE+NMJwIA5KETU59OjDOdGGc6EQAg D52Y+nRinOnEONOJAAB56MTUpxPjTCfGmU4EAMhDJ6Y+nRhnOjHOdCIAQB46 MfXpxDjTiXGmEwEA8tCJqU8nxplOjDOdCACQR9aJdXV1EyZMuG6TyZMnr1+/ vit31IlBphPjTCfGmU4EAMgj68T6+vr58+cv2mTVqlUtLS1duaNODDKdGGc6 Mc50IgBAHp53mvp0YpzpxDjTiQAAeejE1KcT40wnxplOBADIQyemPp0YZzox znQiAEAeOjH16cQ404lxphMBAPLQialPJ8aZTowznQgAkIdOTH06Mc50Ypzp RACAPHRi6tOJcaYT40wnAgDkoRNTn06MM50YZzoRACAPnZj6dGKc6cQ404kA AHnoxNSnE+NMJ8aZTgQAyEMnpj6dGGc6Mc50IgBAHjox9enEONOJcaYTAQDy 0ImpTyfGmU6MM50IAJCHTkx9OjHOdGKc6UQAgDx0YurTiXGmE+NMJwIA5KET U59OjDOdGGc6EQAgD52Y+nRinOnEONOJAAB5ZJ04ceLEyZMn377JtGnTutiM OjHIdGKc6cQ404kAAHmUOvHhhx+evcnzzz/f1NTUlTvqxCDTiXGmE+NMJwIA 5OF5p6lPJ8aZTowznQgAkIdOTH06Mc50YpzpRACAPHRi6tOJcaYT40wnAgDk oRNTn06MM50YZzoRACAPnZj6dGKc6cQ404kAAHnoxNSnE+NMJ8aZTgQAyEMn pj6dGGc6Mc50IgBAHjox9enEONOJcaYTAQDy0ImpTyfGmU6MM50IAJCHTkx9 OjHOdGKc6UQAgDx0YurTiXGmE+NMJwIA5KETU59OjDOdGGc6EQAgD52Y+nRi nOnEONOJAAB56MTUpxPjTCfGmU4EAMhDJ6Y+nRhnOjHOdCIAQB46MfXpxDjT iXGmEwEA8tCJqU8nxplOjDOdCACQR9aJEydOnDx58u2bTJs2rYvNqBODTCfG mU6MM50IAJBHqRMffvjh2Zs8//zzTU1NXbmjTgwynRhnOjHOdCIAQB6ed5r6 dGKc6cQ404kAAHnoxNSnE+NMJ8aZTgQAyEMnpj6dGGc6Mc50IgBAHjox9enE ONOJcaYTAQDy0ImpTyfGmU6MM50IAJCHTkx9OjHOdGKc6UQAgDx0YurTiXGm E+NMJwIA3Vn2XdCVV15ZX1/fMfSWLFly4403XnTRRVdcccXjjz++tXdF1Imp TyfGmU6MM50IAHRn3/3ud/v37z9q1KjXX3+9/PbJkycPGjSoarPq6urLL798 3bp1Hc+gE1OfTowznRhnOhEA6J6y6Lvssst22WWXLAMrOnHu3LmDBw/u1avX +PHjn3rqqZtuumngwIE9evSYPn36hg0bKs6jE1OfTowznRhnOhEA6IayuDvs sMNqampKDxdWdOLo0aP79es3duzY9gcQb7755t12223kyJFr1qzpeCqdmPR0 YpzpxDjTiQBAN3TooYdWV1cfe+yxl1122YABA8o7sbW19ZBDDunRo8ecOXNa WlpKN65atWq//fbr27fv0qVLK06lE1OfTowznRhnOhEA6IbOOeec2267bf36 9bfffvsee+xR3okvvfTS3nvvPXDgwFdffbX8LsOHD6+pqXnooYeam5vLb9eJ qU8nxplOjDOdCAB0Zx07cd68ebW1tUOHDl2+fHn5kaecckqvXr2mTJlS8Y2T Tkx9OjHOdGKc6UQAoDvr2ImzZ8/+yEc+cuSRR1Z04hlnnNG7d+8777yzsbGx /HadmPp0YpzpxDjTiQBAd7a1Thw2bNiKFSvKjzz99NOzTrznnnt04odsOjHO dGKc6UQAoDvr2InPPPNMbW3tQQcd9Nprr5UfeeKJJ+60004zZsyoeGsMnZj6 dGKc6cQ404kAQHfWsRPXrFmz77779u/f/w9/+EP7YW1tbUOHDu3Ro8fcuXNb W1vLz5B14sSJEydPnnx7B+vXr+/kU+vEINOJcaYT40wnAgDdWcdOzIwYMaKm pubBBx9sf+hwwYIFtbW1++yzT0NDQ8UZSp348MMPz+6g4hmqFXRikOnEONOJ caYTAYDubIudeNVVV+26664nnHBCqQqbmprOO++8vn37XnDBBWvXrq04g+ed pj6dGGc6Mc50IgDQnW2xE1evXn3EEUdUV1dn/x0zZszxxx/fq1evvfba6+mn n6540ulGnZj+dGKc6cQ404kAQHd2xx13lDqx4oHCZcuWnXrqqX369Kna5Mgj j3zkkUeampo6nkEnpj6dGGc6Mc50IgDA1rzxxhtPP/30yy+/3NLSsrVjdGLq 04lxphPjTCcCAOShE1OfTowznRhnOhEAIA+dmPp0YpzpxDjTiQAAeejE1KcT 40wnxplOBADIQyemPp0YZzoxznQiAEAeOjH16cQ404lxphMBAPLQialPJ8aZ TowznQgAkIdOTH06Mc50YpzpRACAPHRi6tOJcaYT40wnAgDkoRNTn06MM50Y ZzoRACAPnZj6dGKc6cQ404kAAHnoxNSnE+NMJ8aZTgQAyEMnpj6dGGc6Mc50 IgBAHjox9enEONOJcaYTAQDyyDpx4sSJU6dO/fkmjz32WGNjY1fuqBODTCfG mU6MM50IAJCHTkx9OjHOdGKc6UQAgDw87zT16cQ404lxphMBAPLQialPJ8aZ TowznQgAkIdOTH06Mc50YpzpRACAPHRi6tOJcaYT40wnAgDkoRNTn06MM50Y ZzoRACAPnZj6dGKc6cQ404kAAHnoxNSnE+NMJ8aZTgQAyEMnpj6dGGc6Mc50 IgBAHjox9enEONOJcaYTAQDy0ImpTyfGmU6MM50IAJCHTkx9OjHOdGKc6UQA gDx0YurTiXGmE+NMJwIA5KETU59OjDOdGGc6EQAgD52Y+nRinOnEONOJAAB5 6MTUpxPjTCfGmU4EAMhDJ6Y+nRhnOjHOdCIAQB46MfXpxDjTiXGmEwEA8sg6 ceLEiVOnTv35Jo899lhjY2NX7qgTg0wnxplOjDOdCACQh05MfToxznRinOlE AIA8PO809enEONOJcaYTAQDy0ImpTyfGmU6MM50IAJCHTkx9OjHOdGKc6UQA gDx0YurTiXGmE+NMJwIA5KETU59OjDOdGGc6EQAgD52Y+nRinOnEONOJAAB5 6MTUpxPjTCfGmU4EAMhDJ6Y+nRhnOjHOdCIAQB46MfXpxDjTiXGmEwEA8tCJ qU8nxplOjDOdCACQh05MfToxznRinOlEAIA8dGLq04lxphPjTCcCAOShE1Of TowznRhnOhEAIA+dmPp0YpzpxDjTiQAAeejE1KcT40wnxplOBADIQyemPp0Y ZzoxznQiAEAeOjH16cQ404lxphMBAPLIOrG+vn7WrFmzN2tsbOzKHXVikOnE ONOJcaYTAQDyyDqxrq5u0qRJt2+SBaNOTGs6Mc50YpzpRACAPDzvNPXpxDjT iXGmEwEA8tCJqU8nxplOjDOdCACQh05MfToxznRinOlEAIA8dGLq04lxphPj TCcCAOShE1OfTowznRhnOhEAIA+dmPp0YpzpxDjTiQAAeejE1KcT40wnxplO BADIQyemPp0YZzoxznQiAEAeOjH16cQ404lxphMBAPLQialPJ8aZTowznQgA kIdOTH06Mc50YpzpRACAPHRi6tOJcaYT40wnAgDkoRNTn06MM50YZzoRACAP nZj6dGKc6cQ404kAAHnoxNSnE+NMJ8aZTgQAyEMnpj6dGGc6Mc50IgBAHjox 9enEONOJcaYTAQDyyDqxvr5+1qxZszdrbGzsyh11YpDpxDjTiXGmEwEA8sg6 sa6ubtKkSbdvkgWjTkxrOjHOdGKc6UQAgDw87zT16cQ404lxphMBAPLQialP J8aZTowznQgAkIdOTH06Mc50YpzpRACAPHRi6tOJcaYT40wnAgDkoRNTn06M M50YZzoRACAPnZj6dGKc6cQ404kAAHnoxNSnE+NMJ8aZTgQAyEMnpj6dGGc6 Mc50IgBAhXnz5t17770/+XMzZ85sbGzseLBOTH06Mc50YpzpRACACmeeeWaf Pn2q/txRRx21YsWKjgfrxNSnE+NMJ8aZTgQAqHDYYYfV1NSMGjXqkksuuXSz iRMnvvXWWx0P1ompTyfGmU6MM50IAFBu2bJle++995AhQxoaGrpyvE5MfTox znRinOlEAIByM2fOHDRo0Kmnnrpq1aquHK8TU59OjDOdGGc6EQCg3I033jhg wIDzzz//yiuvPOuss84999zrr79+9erVbW1tWzxeJ6Y+nRhnOjHOdCIAQLnR o0f37du34kVsDjzwwDlz5jQ3N3c8XiemPp0YZzoxznQiAEC54447rqam5uCD D7711lsXLlw4bdq0I444IrtlxIgRXu/0QzmdGGc6Mc50IgBAuYcffviWW255 9dVXW1tbS7csWrRoyJAhVVVVM2bM2LBhQ8XxOjH16cQ404lxphMBALbp9NNP 79Wr1w033LB+/fqKD2WdWF9fP2vWrNkdNDY2dnJOnRhkOjHOdGKc6UQAgHJZ Ca5cubKlpaX8xrPPPrtPnz7XXnvt22+/XXF81ol1dXWTJk26vYOOUVlOJwaZ TowznRhnOhEAoN1zzz1XW1u78847v/DCC+03Zt8pDR8+vLq6+v777+/4XZPn naY+nRhnOjHOdCIAQLuWlpbDDz88S8KxY8e++eabpRuvueaaAQMGHHDAAa+8 8krHu+jE1KcT40wnxplOBAAoN2XKlIEDB1ZVVZ100knjxo373Oc+16dPn549 e955551b/HlDnZj6dGKc6cQ404kAABWmTp06dOjQmpqa0psn7r///nfffffW XpRGJ6Y+nRhnOjHOdCIAQEdtbW0NDQ1z585dvHhxc3NzJ0fqxNSnE+NMJ8aZ TgQAyEMnpj6dGGc6Mc50IgBAHjox9enEONOJcaYTAQDy0ImpTyfGmU6MM50I AJCHTkx9OjHOdGKc6UQAgDx0YurTiXGmE+NMJwIA5KETU59OjDOdGGc6EQAg D52Y+nRinOnEONOJAAB56MTUpxPjTCfGmU4EAMhDJ6Y+nRhnOjHOdCIAQB46 MfXpxDjTiXGmEwEA8tCJqU8nxplOjDOdCACQh05MfToxznRinOlEAIA8sk6s r6+fP3/+os2ampq6ckedGGQ6Mc50YpxlnfiTO/772Wef/T072sqVK1tbW9/v /1sGABQr68S6uroJEyZct8nUqVO7+NiiTgwynRhnOjHO/vXiE0d9ecQ/X3zq VZecZjt2Xz/3S4sXL5KKAJAWzztNfToxznRinE249LP/381n/2nO5Tv8Suz7 l3zphcULdSIApEUnpj6dGGc6Mc50YpzpRABIkU5MfToxznRinOnEONOJAJAi nZj6dGKc6cQ404lxphMBIEU6MfXpxDjTiXGmE+NMJwJAinRi6tOJcaYT40wn xplOBIAU6cTUpxPjTCfGmU6MM50IACnSialPJ8aZTowznRhnOhEAUqQTU59O jDOdGGc6Mc50IgCkSCemPp0YZzoxznRinOlEAEiRTkx9OjHOdGKc6cQ404kA kCKdmPp0YpzpxDjTiXGmEwEgRTox9enEONOJcaYT40wnAkCKdGLq04lxphPj TCfGmU4EgBTpxNSnE+NMJ8aZTowznQgAKdKJqU8nxplOjDOdGGc6EQBSpBNT n06MM50YZzoxznQiAKQo68T6+vr58+cv2qypqakrd9SJQaYT40wnxplOjDOd CAApyjqxrq5uwoQJ120yderULj62qBODTCfGmU6MM50YZzoRAFLkeaepTyfG mU6MM50YZzoRAFKkE1OfTowznRhnOjHOdCIApEgnpj6dGGc6Mc50YpzpRABI kU5MfToxznRinOnEONOJAJAinZj6dGKc6cQ404lxphMBIEU6MfXpxDjTiXGm E+NMJwJAinRi6tOJcaYT40wnxplOBIAU6cTUpxPjTCfGmU6MM50IACnSialP J8aZTowznRhnOhEAUqQTU59OjDOdGGc6Mc50IgCkSCemPp0YZzoxznRinOlE AEiRTkx9OjHOdGKc6cQ404kAkCKdmPp0YpzpxDjTiXGmEwEgRTox9enEONOJ caYT40wnAkCKdGLq04lxphPjTCfGmU4EgBTpxNSnE+NMJ8aZTowznQgAKdKJ qU8nxplOjDOdGGc6EQBSlHVifX39/PnzF23W1NTUlTvqxCDTiXGmE+NMJ8aZ TgSAFGWdWFdXN2HChOs2mTp1ahcfW9SJQaYT40wnxplOjDOdCAAp8rzT1KcT 40wnxplOjDOdCAAp0ompTyfGmU6MM50YZzoRAFKkE1OfTowznRhnOjHOdCIA pEgnpj6dGGc6Mc50YpzpRABIkU5MfToxznRinOnEONOJAJAinZj6dGKc6cQ4 04lxphMBIEU6MfXpxDjTiXGmE+NMJwJAinRi6tOJcaYT40wnxplOBIAU6cTU pxPjTCfGmU6MM50IACnSialPJ8aZTowznRhnOhEAUqQTU59OjDOdGGc6Mc50 IgCkSCemPp0YZzoxznRinOlEAEiRTkx9OjHOdGKc6cQ404kAkCKdmPp0Ypzp xDjTiXGmEwEgRTox9enEONOJcaYT40wnAkCKdGLq04lxphPjTCfGmU4EgBTp xNSnE+NMJ8aZTowznQgAKco6sb6+vqGhYc1mLS0tXbmjTgwynRhnOjHOdGKc 6UTo6PXXX19NDP7XCbYm68S6urpxm02aNGn9+vVduaNODDKdGGc6Mc50Ypzp RKiQReI3v3bOxf/rlEtG2Q7eWX97/PQHpr377rs7+h8FROR5p6lPJ8aZTowz nRhnOhEqvPjiizdccd6aWWN3+P/ztBk/OHvq/3PndnwbDN2BTkx9OjHOdGKc 6cQ404lQQSfGmU6ETujE1KcT40wnxplOjDOdCBV0YpzpROiETkx9OjHOdGKc 6cQ404lQQSfGmU6ETujE1KcT40wnxplOjDOdCBV0YpzpROiETkx9OjHOdGKc 6cQ404lQQSfGmU6ETujE1KcT40wnxplOjDOdCBV0YpzpROiETkx9OjHOdGKc 6cQ404lQQSfGmU6kG1qyZMmNN9540UUXXXHFFY8//nhTU9PWjtSJqU8nxplO jDOdGGc6ESroxDjTiXQ3kydPHjRoUNVm1dXVl19++bp167Z4sE5MfToxznRi nOnEONOJUEEnxplOpFuZO3fu4MGDe/XqNX78+Keeeuqmm24aOHBgjx49pk+f vmHDho7Hb3cn/p/LLlr5iO+Hd/wW/L9j/qPuf639zXd3+JXYfRO/OvWHF7z9 xPd2+JVY/b9/9Ve3X/SnJ/1d7Pj923fOe+nFJS0tLUX8nzhyue+++9avX7+j r4KNS5cu/c9/udj/4Y6wX9zyjWlT79GJdBOjR4/u16/f2LFj2x9AvPnmm3fb bbeRI0euWbOm4/Hb3Ynj/n/27gU6qvJQ+DcY7hBIKQgIVKSLCohAhKNC6eqq pWipp3r8KljsV2nrBagi9thatM1fD7bVaq0ecyiXttJC2hyFoBah2CoGoSgE EEGPhJvEoICIXEVDDP992Mv53iYhiSQmQ/I8ay+X7Hn3zDt7syfzYyYzkyfv yvVv9XW/bFr048yfT9y73PPhul+envXvT8z4wcEXflrnM7E8+l+3LJlz6+EX HYu6X+7N+P62rVt0Yp179913f/7zn0f/reuJ8L+dmPnLyX5wJ8MS/eD+y+OP 6UQagg8//LBfv36NGzd+4YUXEj+Ud+/e/ZnPfKZly5bR41LZTXTiqb7oxORZ dGLyLDoxeRadmCR0YvLQicmz6EQajq1bt3bt2rVDhw4FBQXh+sGDB6ekpDz7 7LNHjx4ttUmSdGIUO28uuaNodc1c29tL79i48MdHVtXYw8iquT+qwSecNXtn a7wTa/bOJvmxqNlrq/FOTOa/eEl+ZGu8E5P5L17NHtkan17NdmKUOfn5+RV8 ONvHtXr16iNHjtTUtUXP/3fv3p2cd7bGOzGZ7+yx5D6yNd6JDeohpcZ/cOtE Gog1a9Z06dLlnHPOefPNN8P1I0eObNas2dy5c99///1SmyRJJ0ZP6tbO/1FN PeeMHkNmPzhp3z9q7BF48r+Pr9k7W4PPYGu8E6MndZsWTa6pHxA1m05Rm/zi p9/f87w7ezLL9Hsnrs5xlp3MUuNnWYO6szXbiSf9M6tcNZ5OM2bMWLt2bU3F ToO6s1HWzZ49uwZ/fTJ6llLur9ucnMcee2zp0qU1FZ413ok1+7Ms+R9SavYH t06kgVi+fHmnTp3S09NLdeIVV1zRvHnz6BG47EOcTqzK0tCe1DWcdGpQd1Yn nvSiE6uz6MST1qDurE6s5lmmE09u0Yk0HHEnnnfeeW+99Va4/vLLL486MTs7 Wyee3NLQntQ1nHRqUHdWJ570ohOrs+jEk9ag7qxOrOZZphNPbtGJNBwvvfRS ly5dPve5z+3YsSNcP3z48KZNmz711FNlvxoj+jH0hz/8obCwcM/H9KMf/ei1 p34cPdWpkSU7838flAqfub1Gru3Fx370n3fftGXx5Jqa3m0/GF+zdzZn2i0F f0/SOxu1SZQAby6pmWv76yP//odfT9r2dM1Mb+PCH999x//+gHBnT2L5zT0T n5ntLHNna/vORmfZ6rxVu3fv/rg/Zcq1ePHik/uZVa78/Py7775727ZtNXJt kd/85jdLlix56623auTaGtSdXbly5X/+539u3769Rq5tz/FnKZs3b66pa8vO zn7iiSeiJ1c1cm3RnX3oFz9K2h/cyf+QUrM/uP/4yIyTO8t8PBenlugvbffu 3VNTU19//fXEypKSknPOOadx48Z5eXllv+l4xowZd9xxx2QAAKBqVq9eXYOf vAS1YMiQISkpKQsXLky8dLhhw4YuXbp069atsLCw7PiafVsLAAAAyWbKlClt 27a96KKL4iosKiq65pprWrZsOX78+L1795YdrxMBAADqt7fffnvgwIGnnXZa 9N+bb775C1/4QrNmzTp37rx27dqybzo9phMBAAAagDfeeOPSSy9t0aJFo+PS 09Ofe+65E72DWicCAAA0EO++++7atWu3bdtW8ccx6UQAAABCOhEAAICQTgQA ACCkEwEAAAh93E7Mz8//9a9/feONN/70pz/9xz/+4QtGa8qaNWv++7//O+uf LV68+MiRI4kxle786g9osN5///2MjIw//OEPZc+Fqu80B6j6TnQgqnKCJNTI fm7Ix+LFF1+M7vvEiRPvuuuuJ5544r333is1wElRayo+FrV8XjTwYxHd5V/8 4hc33XTT3XffvX79+qNHj5Ya4LyoNRUfC+cF1JSP1Ym/+93vOnbs2Ogjp512 2h133LF///5PepINwejRoxMfUZvwL//yL2+99VY8oNKdX/0BDdntt9+empr6 3e9+95133gnXV32nOUA14kQHotITJKFG9nODPRYffPDBdddd16pVq3A/Dx06 NHoiVFJSEo9xUtSOqhyL2jwvGvKxKC4unjBhQngsmjZt+h//8R8HDhxIjHFe 1I6qHAvnBdSUqndiXl7eGWec0axZs7vuumvVqlUPP/xwhw4dGjduvGDBgujH WS1MtX7r379/SkpK9PR40qRJt3zkwQcfjB/6Kt351R/QYEWP5z/4wQ/atGkT PcKXypOq7zQHqPoqOBDHKjtBEmpkPzfkY/GLX/yibdu2Xbp0+c1vfvPKK688 9dRT0ZOrJk2aXHHFFW+//fYxJ0UtqvRYHKvF86KBH4tol6alpUXH4pFHHvmf //mf6dOnR3c/KoK//OUvH3f/OBbVVOmxOOa8gJrz7rvv7tmzp+LvzojF/7B5 2223Jf6dZOrUqdHZOmrUqOgaPuFp1nNvvPFG165dzzzzzMLCwnIHVLrzqz+g Yfr73/8e/0CJ/yWwVJ5Ufac5QNVU8YGo9ARJqJH93GCPRUlJyXnnnRc945o3 b977778fr9y4cWP0RKh58+b5+fnHnBS1pSrHojbPiwZ+LC688MLo0WnOnDmJ Ny7++Mc/btOmzbhx4/bu3XvMeVFbqnIsnBdQ+z788MN+/fo1btz4hRdeSETl 7t27P/OZz7Rs2XLLli11O71T3eLFizt27HjppZdGu7TspZXu/OoPqJ27mYTO Pffc6JnY0KFDf/CDH7Rr1y7Mk6rvNAeo+io4EMcqO0ESamQ/N+RjsXPnzi99 6Us9e/Ys9fzqnHPOiXbI0qVLP/jgAydF7aj0WBw9erTWzosGfiyiTp85c+ZN N92UeBk38qtf/Sp6pLr66qujHPDDotZUeiyO1eLPiwZ+LCC0devWrl27dujQ oaCgIFw/ePDglJSUZ599tuwvdFN1v/71r6NHue9973sZGRlXXXXVt7/97ehx L3oYjH8JpdKdn5+fX80BDfbw/d//+38feeSRQ4cOzZo1q3379mGeVP3vvANU fRUciGOVnSAJ1T8Q0X72WFfKK6+8csYZZ8RPe5wUdSs8Fsdq8bxwLEp56623 LrzwwiZNmkQ7PHrUcl7UoVLH4pjzAurCmjVrunTpcs4557z55pvh+pEjRzZr 1mzu3LmJN8ZwEq677rroR3+p37nu1avXCy+8ED3OVLrzV6xYUc0BDl/ZPKn6 33kHqAaV24kVnyCJYdU/ENF+9lgXKioqGjVqVPPmzS+++OLdu3c7KepQqWNx rBbPC8ciIScn5zvf+U78O2jR3Y93iPOiTpR7LI45L6AuLF++vFOnTunp6aVO hyuuuCL6sTV79uxyP22YKvr85z+fkpJy9tln//73v3/11VefeOKJgQMHRmuG DBny1ltvVbrzn3nmmWoOcPjK5knV/847QDWo3E6s+ARJDKv+gYj2s8e6hOLi 4ptuuql169bt2rWLdkv0FMtJUVfKHotjtXheOBYJV111VeKDNP/t3/4tfvOh 86JOlHssjjkvoC7EJ9R5551X6lOFL7/88uh0yM7OdjpUx5IlS2bMmFFQUBA/ ykVee+21M888M3r0e+qpp6JLK9758YNVdQY4fCfqxKrstEpHOkBVV24nVnyC JD5WrvoHItGJjsV77733ve99r2XLlm3atEn8w7iTok6UeyyO1eJ54VgkvPHG G7t3787NzR06dGjTpk0vvPDCnTt3Oi/qRLnH4pjzAurCSy+91KVLl8997nM7 duwI1w8fPjw6PcNTj5oSPdQ0a9bsgQceiB7NKt75K1eurOYAh69snlT973yl Ix2gqiu3E8uVOEHi30k5VhMHItrPHusib7/99r/+679Gu/fTn/70E088UfW/ 6k6KGneiY3Ein8R54ViUtW3bth49ekTpsXjx4lWrVjkv6lB4LE70HffOC/hE 7dmzp3v37qmpqa+//npiZUlJSfzZa3l5eYl/t+EkRA9cO3fuLPXtJFdffXWL Fi3uu+++aJ9XvPN3795dzQEOX9k8qfrf+UpHOkBVV24nVnyCHDx4MF5T/QMR 7WePddGTriFDhjRp0uSss85atmxZ+Bs9TopaVsGxOFaL50UDPxbRvYtC4NVX Xy3VIBdddFGUAzNmzHjjjTecF7Wj0mPx3nvvOS+gTkQ/rVJSUhYuXJj4F5IN GzZ06dKlW7dulX5JDRVYv359tBtbt269adOmxMr3339/8ODBiS/PqnTnV39A A1dunlR9pzlANaXsgajKCZJYXyP7uSEfizfffPP888+P7n60e6MnY2Wf5zgp ak3Fx6KWz4uGfCyinX/GGWe0a9cu/KaDqNn79++f+Hp350XtqPRYrF692nkB dWLKlClt27a96KKL4r/8RUVF11xzTcuWLcePHx9/tyknp7i4eMCAAdEj2G23 3bZv37545T333BM9En72s5/dvn37sSrs/OoPaODK7cSq7zQHqKaUPRBVOUES amQ/N+RjMXr06ObNm5933nlbt24t9THyMSdFran4WNTyedGQj0VU6IMGDYp2 9e233574OvX77rsv2tUdOnSIjs4x50VtqfRYOC+grrz99tsDBw6Mzr7ovzff fPMXvvCFZs2ade7cee3atV5br6a5c+dGD3GNGjUaMWLE5MmTL7nkkhYtWjRp 0iTxeVmV7vzqD2jgyu3Equ80B6imlHsgKj1BEmpkPzfYY7FixYrobkb7OXqe 8+lPf7rDP3v22WejZ0FOitpRlWNRm+dFQz4WkQULFnTs2LFx48Zf/epXo10d /Tfa1dEff//737/33nvH/LCoRZUeC+cF1JU33njj0ksvTXwQcXp6+nPPPXei 3xrmY8nJyTnnnHNSUlLifduzZ88//elP4WNapTu/+gMasj/+8Y9xnpT6N8Cq 7zQHqEac6EBUeoIk1Mh+bpjHYubMmZ/61KcancBf//rXeA84KWpBFY9FbZ4X DfZYxP7yl7+ce+65iV3dq1evUm9idF7UmkqPhfMC6tC77767du3abdu2lfo1 YaqppKSksLAwLy9v48aNpT6vIKHSnV/9AZRV9Z3mAH1yqnKCJNTIfnYsKuCk SBK1fF405GMR7eooCl544YUdO3ac6NUi50XtqPRYOC8AAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACA0Ntvv/3kk0/+4x//qOuJ AAAAkBR0IgAAACGdCAAAQEgnAgAAENKJAAAAhHQiAAAAIZ0IAABASCcCAAAQ 0okAAACEdCIAAAAhnQgAAEBIJwIAABDSiQAAAISiTpw/f/68efNe+0hBQUFd TwoAAIA6E3ViVlZWRkbG/cdlZmZ6bREAAKAh875TAAAAQjoRAACAkE4EAAAg pBMBAAAI6UQAAABCOhEAAICQTgQAACCkEwEAAAjpRAAAAEI6EQAAIPnt27dv wYIFd9555y233PLggw+++OKL4aX79+9fsmRJ1j977LHH8vLyEmMOHz6cm5ub kZExceLEzMzMzZs3n+i2dCIAAEAyKykpWbt27bBhwxoF0tLSotw7ePBgPOal l14aOHBgo3/Wrl27G2+8MR5QWFgY/X9KSkri0l69emVnZ5d7izoRAAAgmW3Z suWb3/xm8+bNv/jFLz711FPr1q27//77u3bt2qlTp5/97GfxmGXLlqWmpnbr 1m3SpEm3fOT222/Pyck5dvzVxszMzCgS+/TpM3v27Nzc3HHjxkV/HDx48MaN G8veok4EAABIWiUlJVEDpqWlpaenr1+/Pl55+PDhRx55pHHjxsOHDy8qKjpy 5Eh2dnaLFi3GjBlT7pVs2LBh2LBhPXv2zMrKitds3779+uuv79y587333lt2 /El34tGjR99JVlEsf9y7AwAAkIQOHTq0cOHC3r17X3XVVeH6559/vlWrVhdc cMGOHTuirLv77rs7dux45513lnslubm5rVu3HjJkyO7du+M1UV3m5OSkpKSM GDGiuLi41PiT68QoEletXDHxulF33XJlsi3/36Rv/Pz265/5++KPdY8AAABO FVE8/va3vz3ttNPiytu2bdvYsWO7dev2wx/+8NZbb73yyitvuummuXPnfvDB B9Hg9957b86cOU2bNr388svDK4kysH379ueff35BQUGp6z+5Toxu6MVlT897 6DvFazKSbTm44vYXsn+Q89icau55AACAJFRSUrJq1aoBAwZ06NDhJz/5SbRm /fr1pT7lJtKqVavRo0e/+eabBw4cmDp1aps2ba699trwevLy8nr16pWenv7y yy+XugmdCAAAcAqJqvDLX/5ykyZNhg8fHmVgtGbFihWdO3du3br1pZde+vTT T69du/aee+7p1KlTWlrapEmT9u7d+8ADD6Smpo4fPz68njVr1vTr169///5R dZa6CZ0IAABwSvjwww+jpvv85z+fkpIydOjQxOuA77zzTk5Ozty5cxMf1XLo 0KFZs2ZFLTlo0KCVK1dGndi2bdsJEyaE1xZ1Yt++fQcMGBB1Zakb0okAAADJ r6io6O9//3ufPn2aNm160UUXvfLKKxWPX716dZSBvXv3jvpx2rRprVq1Gjt2 bDgg6scePXpEIZmfn19qW50IAACQ5KIEmzdv3hlnnNGyZcsrrriisLAwvLS4 uHjfvn179uwJV65bty5qwLPPPvuJJ554/PHHU1JSRo4cWVJSkhiwdOnSKB6H Dh26d+/eUjcXdWJWVlZGRsb9ZWRnZ1cwSZ0IAABQC95///0nn3zy9NNPb9u2 7bhx46IkDC89ePDgjBkzogy8+OKLi4qK4pVRDy5ZsqR9+/bxty6uWLEi2jzK xk2bNiW2mjlzZosWLUaPHl32FqNOnD9/flSmr5VR9sNRE3QiAABA7Vi7du3A gQPjSDxy5EjZAbm5udGlZ511VlZWVrxm69atY8aMadWq1be+9a3i4uLNmzeP GjUqysbbbrvt6NGjUUWuWbPmggsu6N69+/Tp08teofedAgAAJK0dO3Y8+OCD jRo1Ou2001JTUzv8s+HDh7/++us7d+6cPHlyNCZac/XVV998883p6emNGzeO /ht/lmlRUdGCBQvS0tKiaxgxYsQNN9zQu3fvZs2aXXbZZWXfdHpMJwIAACSx devWTZgwodEJDB48eMuWLdGwKBXvuuuuqBPj9S1btvza1762cuXKxPUcOXJk 4cKFffv2jfoxGhAF4zXXXLN169Zyb1QnAgAA1A9RDG7atGn16tWlPugm4ejR o9FFUXvu2rWrguvRiQAAAIR0IgAAACGdCAAAQEgnAgAAENKJAAAAhHQiAAAA IZ0IAABASCcCAAAQ0okAAACEdCIAAAAhnQgAAEBIJwIAABDSiQAAAIR0IgAA ACGdCAAAQEgnAgAAEIo6cf78+VEq7vnI/v37K91KJwIAANRXUSdmZWVN/siU KVMWLVpU6VY6EQAAoL7yvlMAAABCOhEAAICQTgQAACCkEwEAAAjpRAAAAEI6 EQAAgJBOBAAAIKQTAQAACOlEAAAAQjoRAACAkE4EAAAgpBMBAAAI6UQAAABC OhEAAICQTgQAACCkEwEAAAjpRAAAAEI6EQAAgJBOBAAAIKQTAQAACEWd+Oij jz700EOzjps9e3ZVmlEnAgAA1FdxJ06fPn35cStXriwoKKh0K50IAABQX3nf KQAAACGdCAAAQEgnAgAAENKJAAAAhHQiAAAAIZ0IAABASCcCAAAQ0okAAACE dCIAAAAhnQgAAEBIJwIAABDSiQAAAIR0IgAAACGdCAAAQEgnAgAAENKJAAAA hHQiAAAAIZ0IAABASCcCAAAQ0okAAACEok6cNWvW9R+ZNGlSdnZ2pVvpRAAA gPrK64kAAACEdCIAAAAhnQgAAEBIJwIAABDSiQAAAIR0IgAAACGdCAAAQEgn AgAAENKJAAAAhHQiAAAAIZ0IAABASCcCAAAQ0okAAACEdCIAAAAhnQgAAEBI JwIAABDSiQAAAIR0IgAAACGdCAAAQEgnAgAAEIo68dFHH83MzHzyuEWLFm3Y sKHSrXQiAABAfaUTAQAACHnfKQAAACGdCAAAQEgnAgAAENKJAAAAhHQiAAAA IZ0IAABASCcCAAAQ0okAAACEdCIAAAAhnQgAAJD89u3bt2DBgjvvvPOWW255 8MEHX3zxxVIDDh8+nJubm5GRMXHixMzMzM2bN3/cAQk6EQAAIJmVlJSsXbt2 2LBhjQJpaWlR7h08eDAeU1hYeOONN6akpCQG9OrVKzs7O3EllQ4I6UQAAIBk tmXLlm9+85vNmzf/4he/+NRTT61bt+7+++/v2rVrp06dfvazn0UD9u/fn5mZ GTVgnz59Zs+enZubO27cuOiPgwcP3rhxY1UGlKITAQAAklZJScmyZcvS0tLS 09PXr18frzx8+PAjjzzSuHHj4cOHFxUVbdiwYdiwYT179szKyooHbN++/frr r+/cufO9994b/bHSAaXoRAAAgKR16NChhQsX9u7d+6qrrgrXP//8861atbrg ggt27NiRm5vbunXrIUOG7N69O740isecnJyUlJQRI0YUFxdXOqDUjepEAACA U0sUj7/97W9PO+20qPIOHjw4Z86cpk2bXn755eGYqPLat29//vnnb9y4seIB BQUFpa5fJwIAAJxCSkpKVq1aNWDAgA4dOvzkJz85cODA1KlT27Rpc+2114bD 8vLyevXqlZ6evnz58ooHvPzyy6VuQicCAACcQtavX//lL3+5SZMmw4cPf/PN N999990HHnggNTV1/Pjx4bA1a9b069evf//+f/vb3yoeEFVnqZvQiQAAAKeE Dz/8MGq6z3/+8ykpKUOHDo1fB4w7sW3bthMmTAgHRxnYt2/fAQMGLFmypOIB a9euLXVDOhEAACD5FRUV/f3vf+/Tp0/Tpk0vuuiiV155JV5/4MCBadOmtWrV auzYseH4lStX9ujRY9CgQatXr654QH5+fqnb0okAAABJLkqwefPmnXHGGS1b trziiisKCwsTF0X9+Pjjj6ekpIwcObKkpCSxfunSpVEbDh06dNeuXRUP2Lt3 b6mbizpx1qxZ15fn/vvvr2CSOhEAAKAWvP/++08++eTpp5/etm3bcePG7du3 r9SAFStWRJcOGjRo06ZN8ZqDBw/OnDmzRYsWo0ePrsqAUryeCAAAkMzWrl07 cODAOBKPHDlSdsDmzZtHjRrVvn3722677ejRoyUlJWvWrLngggu6d+8+ffr0 qgwoRScCAAAkrR07djz44IONGjU67bTTUlNTO/yz4cOHv/7660VFRQsWLEhL S4sGjBgx4oYbbujdu3ezZs0uu+yy+D2llQ4oRScCAAAkrXXr1k2YMKHRCQwe PHjLli3RsCNHjixcuLBv376NGzeO1kc9eM0112zdujVxPZUOCOlEAACA+uHo 0aOFhYVRWu7atevkBsR0IgAAACGdCAAAQEgnAgAAENKJAAAAhHQiAAAAIZ0I AABASCcCAAAQ0okAAACEdCIAAAAhnQgAAEBIJwIAABDSiQAAAIR0IgAAACGd CAAAQEgnAgAAENKJAAAAhKJOnD9/fnZ29vKPbNiwodKtdCIAAEB9FXViVlbW lClTZh0XBaNOBAAAaMi87xQAAICQTgQAACCkEwEAAAjpRAAAAEI6EQAAgJBO BAAAIKQTAQAACOlEAAAAQjoRAACAkE4EAAAgpBMBAAAI6UQAAABCOhEAAICQ TgQAACCkEwEAAAjpRAAAAEI6EQAAgJBOBAAAIKQTAQAACOlEAAAAQlEnZmVl Tf5IRkZGdnZ2pVvpRAAAgPoq6sT58+c/+eSTe47bv39/VbbSiQAAAPWV950C AAAQ0okAAACEdCIAAAAhnQgAAEBIJwIAABDSiQAAAIR0IgAAACGdCAAAQEgn AgAAENKJAAAAhHQiAAAAIZ0IAABASCcCAAAQ0okAAACEdCIAAAAhnQgAAEBI JwIAABDSiQAAAIR0IgAAAKGoE+fPnz9v3rzXPlJQUFDpVjoRAACgvoo6MSsr KyMj4/7jMjMzq/Laok4EAACor7zvFAAAgJBOBAAAIKQTAQAACOlEAAAAQjoR AACAkE4EAAAgpBMBAAAI6UQAAABCOhEAAICQTgQAACCkEwEAAAjpRAAAAEI6 EQAAgJBOBAAAIKQTAQAACOlEAAAAQjoRAACAkE4EAAAgpBMBAAAI6UQAAABC USdmZWVlZGTcf9xDDz20aNGiSrfSiQAAAPVV1Inz58+fN2/ea8dt2bJl//79 lW6lEwEAAOor7zsFAAAgpBMBAAAI6UQAAABCOhEAAICQTgQAACCkEwEAAAjp RAAAAEI6EQAAgJBOBAAAIKQTAQAAThUlJSWvvPLKHXfc8eyzz4br9+/fv2TJ kqx/9thjj+Xl5SXGHD58ODc3NyMjY+LEiZmZmZs3bz7RrehEAACAU0V+fv7X v/71rl27Pvzww+H6l156aeDAgY3+Wbt27W688cZ4QGFhYfT/KSkpiUt79eqV nZ1d7q3oRAAAgORXXFycl5d3ySWXRIlXthOXLVuWmprarVu3SZMm3fKR22+/ PScn59jxVxszMzOjSOzTp8/s2bNzc3PHjRsX/XHw4MEbN24se1s6EQAAIMnt 2bPnV7/6VceOHeOXAkt14pEjR7Kzs1u0aDFmzJhyN9+wYcOwYcN69uyZlZUV r9m+ffv111/fuXPne++9t+x4nQgAAJDkli1b1qZNm7S0tMsvv/zqq68u1YlR 1t19991RRd55553lbp6bm9u6deshQ4bs3r07XlNUVJSTk5OSkjJixIji4uJS 43UiAABAklu3bt3YsWOfe+65N9544+abby7Vidu2bYsu7dat2w9/+MNbb731 yiuvvOmmm+bOnfvBBx8cO55vc+bMadq0adSY4XVGGdi+ffvzzz+/oKCg1M3p RAAAgFNFYWFh2U5cv379sGHDSn2ITatWrUaPHv3mm28eOHBg6tSpbdq0ufba a8OrysvL69WrV3p6+ssvv1zqVnQiAADAqaLcTlyxYkXnzp1bt2596aWXPv30 02vXrr3nnns6deqUlpY2adKkvXv3PvDAA6mpqePHjw+vas2aNf369evfv/+q VatK3YpOBAAAOFWU24nvvPNOTk7O3Llz9+/fH685dOjQrFmzmjRpMmjQoJUr V0ad2LZt2wkTJoRXFXVi3759BwwYEHVlqVvRiQAAAKeKcjuxXKtXr44ysHfv 3lE/Tps2rVWrVmPHjg0HRP3Yo0ePKCTz8/NLbRt14vz586NU3FNGIkXL0okA AAC1r9xOLC4u3rdvXxRx4ch169ZFDXj22Wc/8cQTjz/+eEpKysiRI0tKShID li5dGsXj0KFD9+7dW+pWok7MysqaXJ4ZM2acaG46EQAAoPaV7cSDBw9G7RZl 4MUXX1xUVBSvjHpwyZIl7du3T09PX79+/YoVK04//fQoGzdt2pTYaubMmS1a tBg9enTZW/G+UwAAgFNFua8n5ubmtm3b9qyzzsrKyorXbN26dcyYMa1atfrW t75VXFy8efPmUaNGRdl42223HT16NKrINWvWXHDBBd27d58+fXrZW9GJAAAA p4pyO3Hnzp2TJ09u1KhRhw4drr766mhAenp648aNo//Gn2VaVFS0YMGCtLS0 1NTUESNG3HDDDb17927WrNlll11W9k2nx3QiAADAqWPHjh2TJk3q1q1bqc+x iVLxrrvuijox/vLEli1bfu1rX1u5cmViwJEjRxYuXNi3b9+oH6MBUTBec801 W7duLfdWdCIAAED9EMXgpk2bVq9eXVhYWO6Ao0ePRhetW7du165dFVyPTgQA ACCkEwEAAAjpRAAAAEI6EQAAgJBOBAAAIKQTAQAACOlEAAAAQjoRAACAkE4E AAAgpBMBAAAI6UQAAABCOhEAAICQTgQAACCkEwEAAAjpRAAAAEI6EQAAgFDU iY8++uhDDz0067jZs2dXpRl1IgAAQH0Vd+L06dOXH7dy5cqCgoJKt9KJAAAA 9ZX3nQIAABDSiQAAAIR0IgAAACGdCAAAQEgnAgAAENKJAAAAhHQiAAAAIZ0I AABASCcCAAAQ0okAAACEdCIAAAAhnQgAAEBIJwIAABDSiQAAAIR0IgAAACGd CAAA1K3MzMwBZbzyyit1Mpm//OUv8QSOHDlSJxNIBjoRAACocfn5+XMCf/7z n5955plXX331ww8/LDv41ltvbVTGqlWran/akRkzZsQTiKqnTiaQDHQiAABQ 437+85+XTb9Ijx497r333pKSknDwtm3bln9k2rRpOrHO6UQAAKDGJTrxyiuv HD169MiRIwcOHNi4ceN45aRJk060YZSKOrHORZ04f/78KBX3fGT//v2VbqUT AQCACiQ6sbi4OLHy9ddfT09Pj1ZGwfjOO++Uu6FOTAZRJ2ZlZU3+yJQpUxYt WlTpVjoRAACoQLmdGFm6dGm8fuHCheVu+HE7ce/evS+99NKKFSuitKni3Hbu 3PmPf/xjx44d5V6qE4953ykAAPAJOFEnrlu3Ll4f9WC5G1axE99999377rsv PT098V7WSJcuXbKysk60yfbt28eMGdO+fftw/Le//e3du3eHw07UiYcOHfrK V77Srl27Tp065eXlVb4LTmU6EQAAqHEn6sTJkyfHgVbqo2wSqtKJixYt+tSn PpXIvbS0tLZt2yb++Mtf/rLsJlOnTm3ZsmViTOfOnZs0aRL/f8eOHVevXp0Y WW4nHjhwYNiwYdHK5s2bn+iV0KrbvHnzn//852peySdKJwIAADUu0Yk7d+6M omPTpk3PPPPMd77zncaNG7do0SJqsRNtWJVO/J//+Z+o8s4666zp06dv2bIl Xvn888/36NEj2jA1NXXXrl3h+OiilJSU6KJ27dpNmzZt3759x45HTU5OTvfu 3Xv16hV+SEvZTozGX3jhhXEkVuXX9CoVlWy0H6LJV/+qPiE6EQAAqHEn+l6M tm3bvvjiixVsWMX3nb7wwgtFRUWlVv7tb3+Lt507d25iZTTsM5/5TLSyVatW +fn5pTaJCrGgoCBcU6oT9+7dO3jw4OiPUd7+9a9/rcp9r4pbbrklSsUKerlu 6UQAAKDGJTqxT58+ffv2/exnP5uamhqvSUlJueGGGw4ePFjuhif3eafFxcVb tmx55pln4m2jW09ctGbNmnjlPffcU5WrCjsxyqWBAwfGkbh48eKqz6cqbr75 5igVZ86cWbNXWyN0IgAAUOPK/f3E7du333777fEnz1x11VXlblj1Tjx06NCf /vSnsWPH9uvXr3nz5uGrlj/84Q8Tw6IQi1e++uqrVZl5ohO3bdt27rnnxv// pS99qSrbflw33XRTtDd++9vffhJXXh06EQAAqHEn+hybyE9/+tP4oigby25Y xU587LHH0tLS4pEtW7YcMmTImDFjfvKTn8QRGnZi/Mk5kaNHj1Zl5olO7Nmz Z9iemZmZVdn82PEPvflp1UQTTklJieb8u9/9ropXXjt0IgAAUOMq6MQXXngh vqjcX/erSidGKRf3YJSHOTk5hw8fTlzUtWvXUp348MMPx1eY+MSbiiU6sdHx 36Z8/vnnv/GNbzQ6/iE24ceiViCKrC9VWXxDt956a1WuudboRAAAoMZV0IlP PfVUfFEUjGU3jNokvvREX7AYiT9Y5itf+UrZj7Ip24lR6MVXOG3atKrMPNGJ n/rUp+KP3Nm3b1/8Saqf/exn489KrSlz5sw57bTTJkyYUIPXWSN0IgAAVFNJ ScmKFStyc3PLZkuDdaJOPHr0aNR30fpmzZq9//77ZTfcunVrvOFdd91V7jV/ +OGHTZs2jQbcd999pS7Ky8tr06ZNqU48cOBA/A7V9u3bv/XWW6U2iY7dsmXL wjWJTgxf0IySNr7Rb3zjG1W491WSlZWVkpIybty4E32VZB3SiQAAUB1RtsRf rhf56le/WtfTSRaJToxiMGrDQ4cO5efnZ2dnDxkyJF5/ohfRosEtWrSIBnzu c5977bXXjh0PvUWLFkXXkBgTv7p37rnn7ty5M16ze/fuH/3oR/GXJJbqxGPH iyxef8YZZ+Tk5MS/qBgF7MqVK7/4xS9G66MmSgwu+/2JsV/+8pfx+ocffrj6 ++dPf/pTNNvrrrsuCSPxmE4EAIDqWbp0afhpJ6W+4b3BOtH3J8a+/vWvV/Cp MrfeemtiZPfu3eP6mzPn/z3D/9nPfhZf2qxZs/79+yc+cObMM8/s27dv2U6M TJw4Mf6VxkbHf9Owd+/erVu3jv/Ypk2b2bNnJ0aeqBOjoLvkkkviG83Ly6vO zpk7d250p7773e8mZyQe04kAAFA90TPq8EsZwm94b8juvffeUm3Yvn37gQMH fvOb3yz1Ps+yDh48GDVUuO3pp58efs9glFd33XVX4gsZI2edddYdd9xx+PDh W265Jfpj9P9lr3b58uVDhgxJvOYYia7hyiuvLCgoCIf97ne/a3T8Sx7Lvi12 165dXbp0aXT8FxU//PDDk903xxYvXnzjjTdW5xo+aToRAACqKUqYQYMGxekx derUup5OPbF79+7c3Nynn376tddeK/thOMeOf39iXl7eokWLyv7WYQWi+nv5 5Zeja87Pz0/al/PqnE4EAIDqi6ImfqFq8eLFdT0XqC6dCAAA1Td9+vT4N9cS n6wCpy6dCAAA1bRly5Yzzzwz6sTvf//7dT0XqAE6EQAAquO//uu/Pv3pT0eR OGDAgD179tT1dKAG6EQAADhpJSUlZ555ZvPmzadMmVJUVFTX04GaoRMBAKA6 nnrqqVdffbWuZwE1KerERx99NDMz88njFi1atGHDhkq30okAAAD1lU4EAAAg 5H2nAAAAhHQiAAAAIZ0IAABASCcCAAAQ0okAAACEdCIAAAAhnQgAAEBIJwIA ABDSiQAAAIR0IgAAACGdCAAAQEgnAgAAENKJAAAAhHQiAAAAIZ0IAABASCcC AAAQ0okAAACEdCIAAAAhnQgAAEBIJwIAABCKOnHWrFnXf2TSpEnZ2dmVbqUT AQAA6iuvJwIAABDSiQAAAIR0IgAAACGdCAAAQEgnAgAAENKJAAAAhHQiAAAA IZ0IAABASCcCAAAQ0okAAACEdCIAAMCpoqSk5JVXXrnjjjueffbZUhcdPnw4 Nzc3IyNj4sSJmZmZmzdv/rgDEnQiAADAqSI/P//rX/96165dH3744XB9YWHh jTfemJKS0ugjvXr1ys7OrvqAkE4EAABIfsXFxXl5eZdcckmUeKU6cf/+/ZmZ mVED9unTZ/bs2bm5uePGjYv+OHjw4I0bN1ZlQCk6EQAAIMnt2bPnV7/6VceO HeOXAkt14oYNG4YNG9azZ8+srKx4zfbt26+//vrOnTvfe++9VRlQik4EAABI csuWLWvTpk1aWtrll19+9dVXl+rE3Nzc1q1bDxkyZPfu3fGaoqKinJyclJSU ESNGFBcXVzqg1M3pRAAAgCS3bt26sWPHPvfcc2+88cbNN98cdmJUZ3PmzGna tGmUkOEmUeW1b9/+/PPP37hxY8UDCgoKSt2cTgQAADhVFBYWlurEAwcOTJ06 tU2bNtdee204Mi8vr1evXunp6cuXL694wMsvv1zqVnQiAADAqaJsJ7777rsP PPBAamrq+PHjw5Fr1qzp169f//79//a3v1U8YNWqVaVuRScCAACcKk7UiW3b tp0wYUI4MsrAvn37DhgwYMmSJRUPWLt2balb0YkAAACninLfdzpt2rRWrVqN HTs2HLly5coePXoMGjRo9erVFQ/Iz88vdStRJ86fPz87O3t5GRs2bDjR3HQi AABA7SvbiUVFRY8//nhKSsrIkSNLSkoSI5cuXRq14dChQ3ft2lXxgL1795a6 lagTs7KypkyZMquMRYsWnWhuOhEAAKD2le3EyIoVK04//fRBgwZt2rQpXnPw 4MGZM2e2aNFi9OjRVRlQivedAgAAnCrK7cTNmzePGjWqffv2t91229GjR0tK StasWXPBBRd07959+vTpVRlQik4EAAA4VZTbiUVFRQsWLEhLS0tNTR0xYsQN N9zQu3fvZs2aXXbZZfF7SisdUIpOBAAAOFXs2LFj0qRJ3bp1CzsxcuTIkYUL F/bt27dx48aNGjWKevCaa67ZunVr1QeEdCIAAED9cPTo0cLCwnXr1u3atevk BsR0IgAAACGdCAAAQEgnAgAAENKJAAAAhHQiAAAAIZ0IAABASCcCAAAQ0okA AACEdCIAAAAhnQgAAEBIJwIAABDSiQAAAIR0IgAAACGdCAAAQEgnAgAAENKJ AAAAhKJOzMrKmvyRjIyM7OzsSrfSiQAAAPVV1Inz589/8skn9xy3f//+qmyl EwEAAOor7zsFAAAgpBMBAAAI6UQAAABCOhEAAICQTgQAACCkEwEAAAjpRAAA AEI6EQAAgJBOBAAAIKQTAQAACOlEAAAAQjoRAACAkE4EAAAgpBMBAAAI6UQA AABCOhEAAICQTgQAACCkEwEAAAjpRAAAAEJRJ86fP3/evHmvfaSgoKDSrXQi AABAfRV1YlZWVkZGxv3HZWZmVuW1RZ0IAABQX3nfKQAAACGdCAAAQEgnAgAA ENKJAAAAhHQiAAAAIZ0IAABASCcCAAAQ0okAAACEdCIAAAAhnQgAAEBIJwIA ABDSiQAAAIR0IgAAACGdCAAAQEgnAgAAENKJAAAAhHQiAAAAIZ0IAABASCcC AAAQ0okAAACEok7MysrKyMi4/7iHHnpo0aJFlW6lEwEAAOqrqBPnz58/b968 147bsmXL/v37K91KJwIAANRX3ncKAABASCcCAAAQ0okAAACEdCIAAAAhnQgA AEBIJwIAABDSiQAAAIR0IgAAACGdCAAAQEgnAgAAENKJAAAAhHQiAAAAIZ0I AABASCfWsnff3fu3xYv+NPuRJFzmz3v01Vdfres9BAAA1DGdWJui/Zb7zMKf /+BfV8z+XrIty//43Zz//O7MqQ/U9U4CAADqmE6sTcm8346s+slLOf/+h5kP 1fVOAgAA6phOrE3JvN90IgAAENOJtSmZ95tOBAAAYjqxNiXzftOJAABALOrE +fPnz5s377WPFBQUVLpVMveOTtSJAABAdUSdmJWVlZGRcf9xmZmZVXltMZl7 RyfqRAAAoDq877Q2JfN+04kAAECs/nXi/uWTcx785hUjh9583f9JtuX737n8 2qu+8siUf6vzvaQTAQCAE6l/nfjush8v+q+r/3j35XU+k1NrbjoRAACI6URz ixedCAAAxHSiucWLTgQAAGI60dziRScCAAAxnWhu8aITAQCAmE40t3jRiQAA QEwnmlu86EQAACCmE80tXnQiAACc0vbv379kyZKsf/bYY4/l5eUlxhw+fDg3 NzcjI2PixImZmZmbN28u96p0ornFi04EAIBT2ksvvTRw4MBG/6xdu3Y33nhj PKCwsDD6/5SUlMSlvXr1ys7OLntVOtHc4kUnAgDAKW3ZsmWpqandunWbNGnS LR+5/fbbc3Jyjh1/tTEzMzOKxD59+syePTs3N3fcuHHRHwcPHrxx48ZSV6UT zS1edCIAAJy6jhw5kp2d3aJFizFjxpQ7YMOGDcOGDevZs2dWVla8Zvv27ddf f33nzp3vvffeUoN1ornFi04EAIBTV1R2d999d8eOHe+8885yB+Tm5rZu3XrI kCG7d++O1xQVFeXk5KSkpIwYMaK4uLjUtelEcyvWiQAAcCrbtm3b2LFju3Xr 9sMf/vDWW2+98sorb7rpprlz537wwQfHjhfcnDlzmjZtevnll4dbRSXYvn37 888/v6CgIFyvE80tXnQiAACcutavXz9s2LBSH2LTqlWr0aNHv/nmmwcOHJg6 dWqbNm2uvfbacKu8vLxevXqlp6e//PLL4XqdaG7xEnXi0ke+929fveDm6/5P Ei4/mjj2v7P/XAPnDwAA1EcrVqzo3Llz69atL7300qeffnrt2rX33HNPp06d 0tLSJk2atHfv3gceeCA1NXX8+PHhVmvWrOnXr1///v1XrVoVrteJ5hYvh1+8 Y/ms7z5w64g6n0nZxWudAABQsXfeeScnJ2fu3Ln79++P1xw6dGjWrFlNmjQZ NGjQypUro05s27bthAkTwq2iTuzbt++AAQOirgzX60RzixedCAAA9czq1auj DOzdu3fUj9OmTWvVqtXYsWPDAVE/9ujRIwrJ/Pz8cL1ONLd40YkAAHDqKi4u 3rdv3549e8KV69atixrw7LPPfuKJJx5//PGUlJSRI0eWlJQkBixdujSKx6FD h+7duzfcMOrERx999KGHHppVxqJFi040B51Y/+amEwEA4BR18ODBGTNmRBl4 8cUXFxUVxSujHlyyZEn79u3T09PXr1+/YsWK008/PcrGTZs2JbaaOXNmixYt Ro8eXeoK406cPn368jI2bNhwomnoxPo3N50IAACnrtzc3LZt25511llZWVnx mq1bt44ZM6ZVq1bf+ta3iouLN2/ePGrUqCgbb7vttqNHj0YVuWbNmgsuuKB7 9+5RD5a6Nu87Nbd40YkAAHDq2rlz5+TJkxs1atShQ4err7765ptvTk9Pb9y4 cfTf+LNMi4qKFixYkJaWlpqaOmLEiBtuuKF3797NmjW77LLLSr3p9JhONLeP Fp0IAACntCgV77rrrqgT4y9PbNmy5de+9rWVK1cmBhw5cmThwoV9+/aN+jEa EAXjNddcs3Xr1rJXpRPNLV50IgAA1ANRDG7atGn16tWFhYXlDjh69Gh00bp1 63bt2nWiK9GJ5hYvOhEAAIjpRHOLF50IAADEdKK5xYtOBAAAYjrR3OJFJwIA ADGdaG7xohMBAICYTjS3eNGJAABATCeaW7zoRAAAIKYTzS1edCIAABDTieYW L0neif+YM378d0dl/vrnSbj8+r67fz4lo86nUe4y4ze/Xvb80k/o0QMAgPpK J5pbvCRzJ0Zzy/3dNT/+7rCNT9yYhMuCh785YdS/1Pk0yl2e+e11v/rFHZ/Q owcAAPWVTjS3eEnyTkzauUXLusfG3XHtF+p8GuUury3494fvy/iEHj0AAKiv dKK5xUsyt1gyz61YJwIAUO/oRHOLl2RusWSeW7FOBACg3tGJ5hYvydxiyTy3 Yp0IAEC9oxPNLV6SucWSeW7FOhEAgHpHJ5pbvCRziyXz3Ip1IgAA9U7UifPn z49Scc9H9u/fX+lWOrH+zS2ZWyyZ51asEwEAqHeiTszKypr8kSlTpixatKjS rXRi/ZtbMrdYMs+tWCcCAFDveN+pucVLMrdYMs+tWCcCAFDv6ERzi5dkbrFk nluxTgQAoN7RieYWL8ncYsk8t2KdCABAvaMTzS1ekrnFknluxToRAIB6Ryea W7wkc4sl89yKdSIAAPWOTjS3eEnmFkvmuRXrRAAA6h2daG7xkswtlsxzK9aJ AADUOzrR3OIlmVssmedWrBMBAKh3dKK5xUsyt1gyz61YJwIAUO/oRHOLl2Ru sWSeW7FOBACg3tGJ5hYvydxiyTy3Yp0IAMD/z97dQFdV3on+BwIKwWDKFcGK jnovq8BiQArV0TKrVRnsqKP8vaNUfKOtVaQo0NGxoGWwyqgd9eItYwWl0mJs rgKxiKBWRcSK8v7mXEHexGCRohDeCYfw35e9zDommJzCIXn2yeezfqtLdp6z fbK3J8m3OSQ5RyfaWzwht1jIe0vpRAAAco5OtLd4Qm6xkPeW0okAAOQcnWhv 8YTcYiHvLaUTAQDIOTrR3uIJucVC3ltKJwIAkHN0or3FE3KLhby3lE4EACDn 6ER7iyfkFgt5bymdCABAztGJ9hZPyC0W8t5SOhEAgJyjE+0tnpBbLOS9pXQi AAA5RyfaWzwht1jIe0vpRAAAck7Uic8999zYsWOnHTJz5swVK1bU+iidmHt7 C7nFQt5bSicCAJBzdKK9xRNyi4W8t5ROBAAg53jdqb3FE3KLhby3lE4EACDn 6ER7iyfkFgt5bymdCABAztGJ9hZPyC0W8t5SOhEAgJyjE+0tnpBbLOS9pXQi AAA5RyfaWzwht1jIe0vpRAAAco5OtLd4Qm6xkPeW0okAAOQcnWhv8YTcYiHv LaUTAQDIOTrR3uIJucVC3ltKJwIAkHN0or3FE3KLhby3lE4EACDn6ER7iyfk Fgt5bymdCABAztGJ9hZPyC0W8t5SOhEAgJyjE+0tnpBbLOS9pXQiAAA5Ryfa Wzwht1jIe0vpRAAAco5OtLd4Qm6xkPeW0okAAOQcnWhv8YTcYiHvLaUTAQDI OTrR3uIJucVC3ltKJwIAkHN0or3FE3KLhby3lE4EACDn6ER7iyfkFgt5bymd CABAztGJ9hZPyC0W8t5SOhEAgJyjE+0tnpBbLOS9pXQiAAA5J+rEiRMn3vyF oUOHFhcX1/oonZh7ewu5xULeW0onAgCQc3w/0d7iCbnFQt5bSicCAJBzdKK9 xRNyi4W8t5ROBAAg5+hEe4sn5BYLeW8pnQgAQM7RifYWT8gtFvLeUjoRAICc oxPtLZ6QWyzkvaV0IgAAOUcn2ls8IbdYyHtLhd2JK14Y8q+3/+DlIL355pul paXH6CMbAABHQyfaWzwht1jIe0uF3YkLi2+5/p+6vjS2f4AzcXS/B37xs2P0 kQ0AgKOhE+0tnpBbLOS9pcLuxJD35jWxAADB0on2Fk/ILRby3lJht1jIe9OJ AADB0on2Fk/ILRby3lJht1jIe9OJAADB0on2Fk/ILRby3lJht1jIe9OJAADB 0on2Fk/ILRby3lJht1jIe9OJAADB0on2Fk/ILRby3lJht1jIe9OJAADB0on2 Fk/ILRby3lJht1jIe9OJAADB0on2Fk/ILRby3lJht1jIe1v03KB+V1zwixG3 BTj33TPsd7/9zTH6qAsAED6daG/xhNxiIe8tFXaLhby3xf9n4NDr/27zm3cG OIufHzzhsXvKysqO0QdeAIDA6UR7iyfkFgt5b6mwW8zejmxWz/gXnQgANGQ6 0d7iCbnFQt5bKuzesbcjG50IADRwOtHe4gm5xULeWyrs3rG3I5uVLw655/Z+ EyeMe3bS06HN8//n2ffee+8YfUYAAIjpRHuLJ+QWC3lvqbB7x96ObJZPGTTs unNf+fV1cyf9KLT5w6+uf+yBf/W9TgDgmIo6saSkpLi4+E9fWLFiRa2P0om5 t7eQWyzkvaXC7h17O7J5v+Qn9w++YOucu+p9J9XHa2IBgDoQdWJRUdF99903 8ZAoGHViw9xbyC0W8t5SYfeOvR3Z6EQAoIHzulN7iyfkFgt5b6mwe8fejmx0 IgDQwOlEe4sn5BYLeW+psHvH3o5sdCIA0MDpRHuLJ+QWC3lvqbB7x96ObELu xFXTh/3vfx+2fv36z8KjXgEgZ+hEe4sn5BYLeW+psHvH3o5sQu7EFVN/csv3 z7978JX3DrsqtBk5rP9jjzygFgEgB+hEe4sn5BYLeW+psHvH3o5sQu7EkPfm NbEAkDN0or3FE3KLhby3VNi9Y29HNiG3WMh704kAkDN0or3FE3KLhby3VNi9 Y29HNiG3WMh704kAkDN0or3FE3KLhby3VNi9Y29HNiG3WMh704kAkDN0or3F E3KLhby3VNi9Y29HNiG3WMh704kAkDN0or3FE3KLhby3VNi9Y29HNiG3WMh7 04kAELhdu3bNnj175MiRt99++9ixY1evXv1VK3WivcUTcouFvLdU2L1jb0c2 IbdYyHvTiQAQstLS0sGDB+fl5TX6QocOHYqLiw+7WCfaWzwht1jIe0uF3Tv2 dmQTcouFvLcPpg35t3+54fnnn385PK+99tp//dd/ZeNzLAAkUllZ2dixY6NI 7NSp06RJk2bPnj1w4MDojz179ly5cmX19Ufcie++/drz//vWev+ypPqE3GJZ 39vnb9+z7A/Z+XIx5BYLeW/RrCgZcv+wEP97S4XdYtndWxafC6mwWyzkvf3X H4b+7JZLXhjz/ZfG9g9tnnngqv8Y9ZPS0tIsfbIN165du9asWeO7uvXLXQhB eXn5n//85w0bNtT3RiAUK1as6NWr11lnnVVUVBQf+eijj26++eZ27do99NBD 1dcfWSdGHwD/NOeN4l8NrfcvS6pPw+nEPfN/vnDKXeN/eXtWzhZyi4W8t2j+ a/rPHh41uN63cdhpOJ2Y3bsQcouFvLcPZw7/zwdui5q93ndSfT569Y7oo+WM GTPeC1IWv9f58ccf/+Y3v/G1cf3atGlTcXFx9CVZfW+kQTuyL3Ehh82ePbtl y5bnnXfe5s2b4yPl5eVTp07Ny8vr06dPKpWqsl4nJndvOjGQ0Ykh7E0nhjAh d2J03W7vf/5jw6945qFrQpun7r9m3KPDs/W9Tp0YAp0YAp0I6Xbv3v3MM880 a9asb9++6cej50jr1q3POeec6p84AunEKHmy9aXF/2uxx6/77f1X5fzest+J v/3RI3deGuB1C3lvqWwXSnb3tuT5QXfffEFD2Fu2O3HwL27rk60Wy+51C3lv 2e3ELO9t+pD/PfzyDS9n59NWdvf20av/Ouk/7wmzE8vLy7P44smGc7bsdmLI 72nIZ9OJkG779u2PP/74CSeccNNNN6UfX7BgQYcOHbp3775s2bIqDwmhE7e/ +/M5v7/T2f7ayW4nOtsRT3YLxdlCOFt2e8fZjmzW/XHEpDFDsni23zwyZP2r wwM923/+e7bKLrudmN2zZbeenC2Es2W3xUI+GyTd1q1bH3300YKCgltvvTX9 +KJFi7p06dK1a9f58+dXeYhOTO7ZQq6nhnO2VNi942xHNiHXU8M5m048MjrR 2erybCGXnU6EdHEntmrVatCgQenHo07s3Llzt27dFi9eXOUhOjG5Zwu5nhrO 2VJh946zHdmEXE8N52wNqhPvG37z7Tf3G/Lj/3n0c9uPr7715hvDPNvtP+73 k1sGDL7p6oZwtlEj7w6z7ALvxN9O/M3gm/tn6S5c/ZObr8/W2QYN+P+uveof f3TdP1Ueycq7DHVm+/btTzzxRH5+/oABA9KPz5s374wzzujRo8eqVauqPEQn JvdsIddTwzlbKuzecbYjm5DrqeGcrUF1YgM5W+kbI4p/NSxbv8Im8LP9fvz9 YZZd4J344pRJb//+zqzchU9n3/2H8T/N1tk2vDLsN7+4Yu7vflR5JCvvMtSZ 8vLyF154IS8v75JLLqmoqKg8/tZbb0XxeP7553/++edVHvLxxx8/8MADw/9K d9xxx09uvWX4v9yalblj6EBnc7aEni3w7Tmbszmbszlb/ZztJz/5a7+4qumL roZztjDu6Yif/ujeYVelz50Dr7im7wV33nJF5ZG6/RofsmDu3Lknn3xyjx49 Pvzww/jIjh07nnzyyebNm/fr16/6+r/85S8lJSXTpk3bAgAADd5nGajbL/Ah C1avXn311Ve3bt36rrvu2r9/f0VFxaJFi84999zTTjtt3Lhx1df7S74AAAC5 rby8fPr06YWFhQUFBX369Lnllls6dux43HHHXXHFFdVfdHpQJwIAADQAe/bs mTFjRufOnRs3btyoUaMoGG+88ca1a9cedrFOBAAAaCD2799fWlq6dOnSTz/9 tIZlOhEAAIB0OhEAAIB0OhEAAIB0OhEAAIB0f20n7tq1a/bs2SNHjrz99tvH jh27evXqY7q9hqCsrGzWrFlFX/b8888vWLCgck2tl/3oFzRAFRUV77///t13 3/3GG29UeVPml8utOUpfdRcyeV5UcheO2LZt26ZPnz5q1Khhw4aNGTPmvffe q7LAc6EO1HwX6vi5kOGa3LN169aSkpIRI0b89Kc/nTBhwsaNG6uvyfDKZOUi N8y7cLC2G+FTA9Slv6oTS0tLBw8enJeX1+gLHTp0KC4uPtabzG1Lliw5++yz G33ZiSeeGF3qeEGtl/3oFzRMq1atuvzyy0899dRf/epX6cczv1xuzdH7qrtQ 6/OikrtwZKJCX7x4ca9evdKvcGFhYfTl0I4dO+I1ngvHWiZ3oS6fCxmuyT3L li377ne/W/kuN27cuFOnTlOnTk1fk+GVycpFbph34WAGN8KnBqhLmXdiWVnZ 2LFjo+dL9JydNGnS7NmzBw4cGP2xZ8+eK1eurIOt5qq33367oKCgffv2Q4cO HfaFESNGxB8Ya73sR7+gAUqlUgsWLPje974XfdivUiiZXy635ijVcBcO1va8 qOQuHLE1a9Zcc801xx9//He+852XXnpp6dKlDz/8cHQj2rZtO3r06IOeC3Wi 1rtwsA6fCxmuyT3r16//wQ9+EN2FCy644PXXX3/33XfjOvjWt771wQcfxGsy vDJZucgN8y4czOxG+NQAdWnXrl3R56no6VDryhUrVvTq1euss84qKiqKj3z0 0Uc333xzu3btHnrooWO8zZy1Z8+e4uLi5s2b9+/f/7ALar3sR7+godmyZcsj jzzSpk2b+P8erFIomV8ut+Zo1HwXan1eVHIXjlj05VZhYWH37t2XL18eH4k+ HTz99NONGzfu3bt3eXm550IdqPUu1OVzIcM1ueedd96JPhZ17dq18rWLUQhc dtllp5122hNPPBEfyfDKZOUiN8y7cDCDG+FTAwRr9uzZLVu2PO+88zZv3hwf iT6FTZ06NS8vr0+fPqlUqn63l1B/+ctf7r///ugD46hRow67oNbLfvQL6uDd DEr0hdkJJ5wQfW3Wt2/fa6+9tkqhZH653JqjUfNdqPV5UcldODL79u2bMWNG x44dv//976cfnzNnTn5+/rnnnrtx40bPhWMtk7tQl8+FTE6S5UsQgAMHDqxZ s2b06NGPPvpo9M/xwXXr1t1www2nnHLKI488Eh/J8Mpk5SI3wLtwMLMb4VMD hGn37t3PPPNMs2bNoi/q0o+/8847rVu3PuecczZs2FBfe0u06GPggAED2rdv f+edd95xxx1XXXXVbbfdNnny5Ojrh4MZXPaVK1ce5YIGeOOWLl0aXfM333zz 448/HjJkSHqhZP7fuVtzlGq4Cwdre15Ucheya+fOnU899VSTJk2ir4J27Njh uVAv0u9C9LVonT0XoovsE30surYvvvhiu3btooR/+eWXD2b8qSGTZVm5U8fq PQ9M9RvhUwOEafv27Y8//vgJJ5xw0003pR9fsGBBhw4dunfvvmzZsvraW6It X768yk8wiOTn5/fr1++TTz6p9bL/6U9/OsoFDfnGlZaWVimUzP87d2uypfpd OFjb86JymbuQRRUVFfPnz+/WrdtJJ510zz33eC7Uiyp34WAdPheii+wT/aZN m8aMGdO7d+8WLVq0adPm5z//efxtowyvTCbLsnKnjtX7H4yvuhE+NUCYtm7d +uijjxYUFNx6663pxxctWtSlS5euXbtGn9rqa2+JNnfu3Hbt2rVs2fKyyy57 9dVXFy9e/OCDD7Zt27awsHDo0KGff/55zZf9j3/841EuaMg3rnqhZP7fea0r 3ZoMHbYTa35eRF9Lx8vchSyKvgC76KKLmjZtGn1tFn3F5blQL6rchYN1+FyI LrJP9EuWLIkSIK6P6CKPGjUq/qmzGV6ZTJZl5U4dq/c/GF91I3xqgDDFT7pW rVoNGjQo/Xj0lOncuXO3bt2iZ2t97S3RPvvss6lTp06ePLnyRwnt3Llz4sSJ 0dcJPXr0mDdvXs2XfdasWUe5oCHfuK/qxEwuV60r3ZoMHbYTa35erFq1Kj7o LmTFgQMHoq95vv3tb+fl5Z1//vnx/0/uuVDHDnsXDtbhcyG6yD7R7927d8OG DevWrfv1r399+umnR/URfXQ6mPHTIZNlWblTx+r9D8ZX3QifGiBM27dvf+KJ J/Lz8wcMGJB+PAqZM844I/3pydFbuHBh9IGoY8eO0QfDmi97tPIoFzTkG3fY 151meLlqXenWZOiwnXhYlc+LV155JT7iLhy98vLy1157rVOnTs2aNbvwwgvf f//9+LjnQl36qrvwVY7FcyG6yD7RV9q9e/ezzz4b1UfPnj3Xrl2b4ZXJZFlW 7tQxe7+DU+VGHHaNTw1Q76LPYi+88EJeXt4ll1xS+Z39yFtvvRU9j84///zP P/+8HreXXKlUatu2bVu2bEk/uHTp0uij0De+8Y0//OEPNV/2Tz/99CgXNOQb V71QMv/vvNaVbk2GDtuJNT8vZsyYER9xF45S9DXYlClTvv71r7do0eLKK6+M 7kXlmzwX6kwNd+FgHT4XoovcYD/RR7dg3bp1VTIkKoLTTz+9S5cuc+bMyfDK ZLIsK3fqGF6LelXrjfCpAYI1d+7ck08+OXoyfvjhh/GRHTt2PPnkk82bN+/X r1/97i2hogs4fvz46APRxRdfHH1Yiw9GH5FmzZrVunXr+Ddq1XrZj35Bg/VV fzMuw8vl1mRF9buQyfOi8uHuwhHbu3fvtGnTove9VatWAwcOjL76qrLAc6EO 1HwX6vi5kOGaHLNz584JEyY0bdr00ksv3b9/f3wwusizZ88uKCiILkX8G94z vDJZucgN8C4czOBGLFiwwKcGCNbq1auvvvrq6Ml41113RU/h6Lm5aNGic889 97TTThs3blx97y6pog+A0ZcHZ555ZuUvcl27dm3//v3z8/Ovu+66VCpV62U/ +gUN1mE7MfPL5dZkxWHvQq3Pi8qV7sIRW7x48dlnnx3nyZ49e6ov8FyoA7Xe hbp8LmS4JvfMmTPna1/72llnnVVcXBwfiS7y9ddf36xZsyuuuCK+yBlemaxc 5IZ5Fw5mcCN8aoBglZeXT58+vbCwsKCgoE+fPrfcckvHjh2PO+646Mnr++9H bNOmTcOHD2/UqNFJJ5107bXXRl8wd+/evXHjxtH/xj9Nq9bLfvQLGqzDFkrm l8utyYrD3oVanxeV3IUjs23btjFjxkRXuEmTJtE7ftKX9e7de/369Z4Lx1om d6EunwsZrsk9mzdvHjVqVHSR27Rpc8MNN0QX+Zvf/Gb0x65du77zzjvxmgyv TFYucsO8CwczuBE+NUDI9uzZM2PGjM6dO0fPyuh5Gj13brzxxq/6m8VkKPq4 d++990Yf9OIfAd2iRYtLL7103rx5lQtqvexHv6Bh2rhx49ChQ9u3b1/lJ6hk frncmqP3VXeh1udFJXfhCHz88ceDBg1q9BV69uy5Zs2ag54Lx1iGd6EunwsZ rsk9UaGMHj06ypPKX8nXt2/fKj/TMsMrk5WL3DDvwsEMboRPDRC4/fv3l5aW Ll269NNPP63vveSO6MPRhx9+uHDhwio/xKBSrZf96BeQLvPL5dYcO7U+Lyq5 C8eO50II6vK5kOGa3LN79+6VK1cuWrRo69atX7UmwyuTlYvcMO/CwQxuhE8N AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AMfaX/7yl2nTpr3zzjv1vREAAACCoBMBAABIpxMBAABIpxMBAABIpxMBAABI pxMBAABIpxMBAABIpxMBAABIpxMBAABIpxMBAABIpxMBAABIpxMBAABIpxMB AABIF3Xic88999hjj008ZNKkSZoRAACgIYs7cdy4cX86ZN68eRs2bKjvTQEA AFBvvO4UAACAdDoRAACAdDoRAACAdDoRAAAgN2zdurWkpGTEiBE//elPJ0yY sHHjxvS3lpWVzZo1q+jLnn/++QULFlQ5j04EAADIAcuWLfvud7/b6AuNGzfu 1KnT1KlTKxcsWbLk7LPPbvRlJ5544uDBg6ucSicCAAAk3fr163/wgx8cf/zx F1xwweuvv/7uu+9G9ZeXl/etb33rgw8+iNe8/fbbBQUF7du3Hzp06LAvjBgx Ir0lYzoRAAAg0SoqKqKma9OmTdeuXStfRLpy5crLLrvstNNOe+KJJ6I/7tmz p7i4uHnz5v3796/1hDoRAAAg0fbv379mzZrRo0c/+uijBw4ciA+uW7fuhhtu OOWUUx555JGDh9Lv/vvvj1py1KhRtZ5QJwIAAOSYffv2vfjii+3atevYsePL L7988FA2DhgwoH379nfeeecdd9xx1VVX3XbbbZMnT45WVn+4TgQAAMgZmzZt GjNmTO/evVu0aNGmTZuf//znqVQqOr58+fJevXpV+SE2+fn5/fr1++STT6qc RCcCAADkjCVLlnTv3j3OwLZt244aNWrHjh3R8blz57Zr165ly5aXXXbZq6++ unjx4gcffDBaUFhYOHTo0IqKivST6EQAAICcsXfv3g0bNqxbt+7Xv/716aef HmXgkCFDouOfffbZ1KlTJ0+eXFZWFq/cuXPnxIkTmzZt2qNHj1WrVqWfRCcC AADknt27dz/77LNRBvbs2XPt2rWHXbNw4cLOnTt37NjxlVdeST+uEwEAABKt oqIiqsJ169ZV6cF58+adfvrpXbp0mTNnTiqV2rZt25YtW9IXLF26tEePHt/4 xjdmzJiRfjzqxKKiopEjRz5cTXFxcQ07+XTTpglPjbvv335m6n0ee+TB+fPn ZfE/MwAAIEGi+pswYULTpk0vvfTS/fv3xwejeJw9e3ZBQUFUggsWLBg/fnxe Xt7FF19cXl5euWDWrFmtW7fu3r378uXL008YdWJJScmUKVM+qGbDhg017GTF ssV33HzZ8imDTP3Owt/f8tyYH/7u6fHH6D85AAAgcFHxzZkz52tf+9pZZ51V +f2+tWvXXn/99c2aNbviiitSqVTUjK1atTrzzDOLiooqF/Tv3z8/P/+6666L fyZqpSN+3el/rVhy/7D/mVo00tTvlL0zfNZvb9OJAADQkG3evHnUqFGNGjVq 06bNDTfcMGTIkG9+85vRH7t27Rrn3qZNm4YPHx4dOemkk6699tpoQffu3Rs3 bhz97/z586ucTScmfXQiAABw8FAqjh49OurEyt+N2Ldv38WLF1cuiFLx3nvv jToxXtCiRYtLL7103rzD/BU2nZj00YkAAECl3bt3r1y5ctGiRVu3bj3sgj17 9nz44YcLFy4sLS39qpPoxKSPTgQAALJLJyZ9dCIAAJBdOjHpoxMBAIDs0olJ H50IAABkl05M+uhEAAAgu3Ri0kcnAgAA2aUTkz46EQAAyC6dmPTRiQAAQHbp xKSPTgQAALJLJyZ9dCIAAJBdOjHpoxMBAIDsijpx4sSJN39h6NChxcXFmTxQ JwYyOhEAAMgu309M+uhEAAAgu3Ri0kcnAgAA2aUTkz46EQAAyC6dmPTRiQAA QHbpxKSPTgQAALJLJyZ9dCIAAJBdOjHpoxMBAIDs0olJH50IAABkl05M+uhE AAAgu3Ri0kcnAgAA2aUTkz46EQAAyC6dmPTRiQAAQHbpxKSPTgQAALJLJyZ9 dCIAAJBdUSeWlJRMmTLlgy9s2LAhkwfqxEBGJwIAANkVdWJRUdHIkSMfPmTs 2LEZfm9RJwYyOhEAAMgurztN+uhEAAAgu3Ri0kcnAgAA2aUTkz46EQAAyC6d mPTRiQAAQHbpxKSPTgQAALJLJyZ9dCIAAJBdOjHpoxMBAIDs0olJH50IAABk l05M+uhEAAAgu3Ri0kcnAgAA2aUTkz46EQAAyC6dmPTRiQAAQHbpxKSPTgQA ALJLJyZ9dCIAAJBdUSc+99xzY8eOnXbIzJkzV6xYkckDdWIgoxMBAIDs0olJ H50IAABkl9edJn10IgAAkF06MemjEwEAgOzSiUkfnQgAAGSXTkz66EQAACC7 dGLSRycCAADZpROTPjoRAADILp2Y9NGJAABAdunEpI9OBAAAsksnJn10IgAA kF06MemjEwEAgOzSiUkfnQgAAGSXTkz66EQAACC7dGLSRycCAADZpROTPjoR AADIrqgTn3vuuccee2ziIZMmTcqwGXViIKMTAQCA7Io7cdy4cX86ZN68eRs2 bMjkgToxkNGJAABAdnndadJHJwIAANmlE5M+OhEAAMgunZj00YkAAEB26cSk j04EAABiW7duLSkpGTFixE9/+tMJEyZs3LixyoJdu3bNnj175MiRt99++9ix Y1evXn3Y8+jEpI9OBAAAIsuWLfvud7/b6AuNGzfu1KnT1KlTKxeUlpYOHjw4 Ly+vck2HDh2Ki4urn0onJn10IgAAsH79+h/84AfHH3/8BRdc8Prrr7/77rtx En7rW9/64IMPogVlZWVjx46NjkTxOGnSpNmzZw8cODD6Y8+ePVeuXFnlbDox 6aMTAQCggauoqIiark2bNl27dl2wYEF8MKq/yy677LTTTnviiSeiP65YsaJX r15nnXVWUVFRvOCjjz66+eab27Vr99BDD1U5oU5M+uhEAABo4Pbv379mzZrR o0c/+uijBw4ciA+uW7fuhhtuOOWUUx555JHoj7Nnz27ZsuV55523efPmeEF5 efnUqVPz8vL69OmTSqXST6gTkz46EQAAqGLfvn0vvvhiu3btOnbs+PLLL+/e vfuZZ55p1qxZ375905dFJdi6detzzjlnw4YN6cd1YtJHJwIAAJU2bdo0ZsyY 3r17t2jRok2bNj//+c9TqdT27dsff/zxE0444aabbkpfvGDBgg4dOnTv3n3Z smXpx3Vi0kcnAgAAlZYsWRJ1X/zjTNu2bTtq1KgdO3Zs3br10UcfLSgouPXW W9MXL1q0qEuXLl27dp0/f376cZ2Y9NGJAABApb17927YsGHdunW//vWvTz/9 9MLCwiFDhsSd2KpVq0GDBqUvjjqxc+fO3bp1W7x4cfpxnZj00YkAAEB1u3fv fvbZZ5s2bdqzZ8+lS5c+8cQT+fn5AwYMSF8zb968M844o0ePHqtWrUo/rhOT PjoRAAAauIqKiqgK161bt3bt2vTjUQaefvrpXbp0eeONN1544YW8vLxLLrkk Wly54K233ori8fzzz//888/THxh1YlFR0fDDGT++pvTQiYGMTgQAgAZuy5Yt EyZMaNq06aWXXrp///74YNSDs2fPLigo6NGjxwcffDB37tyTTz45+ucPP/ww XrBjx44nn3yyefPm/fr1q3LCqBNLSkqmTZu2pZqysrIadqITAxmdCAAADVyU hHPmzPna17521llnFRcXxwfXrl17/fXXN2vW7IorrkilUqtXr7766qtbt259 1113RS0ZPWTRokXnnnvuaaedNm7cuCon9LrTpI9OBAAANm/ePGrUqEaNGrVp 0+aGG24YMmTIN7/5zeiPXbt2jXOvvLx8+vTphYWFBQUFffr0ueWWWzp27Hjc ccdFFVnlRacHdWLyRycCAAAHD6Xi6NGjo06MfylGfn5+375903+Q6Z49e2bM mNG5c+fGjRtHC6JgvPHGG6v8lcaYTkz66EQAAKDS7t27V65cuWjRoq1btx52 wf79+0tLS5cuXfrpp59+1Ul0YtJHJwIAANmlE5M+OhEAAMgunZj00YkAAEB2 6cSkj04EAACySycmfXQiAACQXTox6aMTAQCA7NKJSR+dCAAAZJdOTProRAAA ILt0YtJHJwIAANmlE5M+OhEAAMiuqBNLSkqiVNzyhbKyskweqBMDGZ0IAABk V9SJRUVFw79w3333zZw5M5MH6sRARicCAADZ5XWnSR+dCAAAZJdOTProRAAA ILt0YtJHJwIAANmlE5M+OhEAAMgunZj00YkAAEB26cSkj04EAACySycmfXQi AACQXTox6aMTAQCA7NKJSR+dCAAAZJdOTProRAAAILt0YtJHJwIAANmlE5M+ OhEAAMgunZj00YkAAEB26cSkj04EAACyK+rEkpKS4uLiP31hxYoVmTxQJwYy OhEAAMiuqBOLioruu+++iYdEwagTkzU6EQAAyC6vO0366EQAACC7dGLSRycC AADZpROTPjoRAADILp2Y9NGJAABAdunEpI9OBAAAsksnJn10IgAAkF06Memj EwEAgOzSiUkfnQgAAGSXTkz66EQAACC7dGLSRycCAADZpROTPjoRAADILp2Y 9NGJAABAdunEpI9OBAAAsksnJn10IgAAkF1RJz733HNjx46ddsjMmTNXrFiR yQN1YiCjEwEAgOzSiUkfnQgAAGSX150mfXQiAACQXTox6aMTAQCA7NKJSR+d CAAAZJdOTProRAAAILt0YtJHJwIAANmlE5M+OhEAAMgunZj00YkAAEB26cSk j04EAACySycmfXQiAACQXTox6aMTAQCA7NKJSR+dCAAAZJdOTProRAAAILt0 YtJHJwIAANmlE5M+OhEAAMiuqBOLiopGjhz58CGPPfbYzJkzM3mgTgxkdCIA AJBdUSeWlJRMmTLlg0PWrFlTVlaWyQN1YiCjEwEAgOzyutOkj04EAACySycm fXQiAACQXTox6aMTAQCA7NKJSR+dCAAAxLZt2zZ9+vRRo0YNGzZszJgx7733 Xvpby8rKZs2aVfRlzz///IIFC6qcRycmfXQiAABQUVGxePHiXr16NUpTWFh4 ++2379ixI16zZMmSs88+u9GXnXjiiYMHD65yNp2Y9NGJAADAmjVrrrnmmuOP P/473/nOSy+9tHTp0ocffvjUU09t27bt6NGj4zVvv/12QUFB+/bthw4dOuwL I0aMmDp1apWz6cSkj04EAIAGrqKiImrAwsLC7t27L1++PD64a9eup59+unHj xr179y4vL9+zZ09xcXHz5s379+9f6wl1YtJHJwIAQAO3c+fOGTNmdOzY8fvf /3768Tlz5uTn55977rkbN26M0u/+++9v06bNqFGjaj2hTkz66EQAAKC6KB6f euqpJk2a9OnTJ5VKrVu3bsCAAe3bt7/zzjvvuOOOq6666rbbbps8efK+ffuq P1YnJn10IgAAUEVFRcX8+fO7det20kkn3XPPPdGR5cuXV/kpN5H8/Px+/fp9 8sknVR6uE5M+OhEAAKgiqsKLLrqoadOmvXv3jjNw7ty57dq1a9my5WWXXfbq q68uXrz4wQcfbNu2bWFh4dChQ6OuTH+4Tkz66EQAAKDSgQMH5s+f/+1vfzsv L+/8889ftmxZfPyzzz6bOnXq5MmTy8rK4iM7d+6cOHFi1JI9evRYtWpV+kl0 YtJHJwIAALHy8vLXXnutU6dOzZo1u/DCC99///2a1y9cuLBz584dO3Z85ZVX 0o/rxKSPTgQAACK7d++eMmXK17/+9RYtWlx55ZWlpaXpb02lUtu2bduyZUv6 waVLl/bo0eMb3/jGjBkz0o9HnThx4sSbD+fhhx+uYQ86MZDZ9vbPih666jt/ 1+XaK3ub+p1+V1w45Cc3rV+/PotPdgAAyMTevXunTZt28sknt2rVauDAgVES pr91x44d48ePz8vLu/jii8vLy+ODFRUVs2bNat26dfpvXYz5fmLS5/M5d/1h zPcfH3Fpve/E/Pn1OyaP/cns2bOP5gkOAABHYPHixWeffXYciXv27Km+IPoy NXrrmWeeWVRUFB9Zu3Zt//798/Pzr7vuulQqlb5YJyZ9dGI4oxMBAKgXGzdu HDNmTKNGjZo0aVJQUHDSl/Xu3Xv9+vWbNm0aPnx4tCY6cu211w4ZMqR79+6N GzeO/nf+/PlVTqgTkz46MZzRiQAA1IulS5cOGjSo0Vfo2bPnmjVromVRKt57 771RJ8bHW7Rocemll86bN6/6CXVi0kcnhjM6EQCA8O3Zs+fDDz9cuHBhlR90 k04nJn10YjijEwEAyA06MemjE8MZnQgAQG7QiUkfnRjO6EQAAHKDTkz66MRw RicCAJAbdGLSRyeGMzoRAIDcoBOTPjoxnNGJAADkBp2Y9NGJ4YxOBAAgN+jE pI9ODGd0IgAAuUEnJn10YjijEwEAyA06MemjE8MZnQgAQG6IOrGkpGTKlCkf fGHDhg2ZPFAnBjI6MZzRiQAA5IaoE4uKikaOHPnwIWPHjs3we4s6MZDRieGM TgQAIDd43WnSRyeGMzoRAIDcoBOTPjoxnNGJAADkBp2Y9NGJ4YxOBAAgN+jE pI9ODGd0IgAAuUEnJn10YjijEwEAyA06MemjE8MZnQgAQG7QiUkfnRjO6EQA AHKDTkz66MRwRicCAJAbdGLSRyeGMzoRAIDcoBOTPjoxnNGJAADkBp2Y9NGJ 4YxOBAAgN+jEpI9ODGd0IgAAuUEnJn10YjijEwEAyA06MemjE8MZnQgAQG6I OrGkpKS4uPhPX1ixYkUmD9SJgYxODGd0IgAAuSHqxKKiovvuu2/iIVEw6sRk jU4MZ3QiAAC5wetOkz46MZzRiQAA5AadmPTRieGMTgQAIDfoxKSPTgxndCIA ALlBJyZ9dGI4oxMBAMgNOjHpoxPDGZ0IAEBu0IlJH50YzuhEAAByg05M+ujE cEYnAgCQG3Ri0kcnhjM6EQCA3KATkz46MZzRiQAA5AadmPTRieGMTgQAIDfo xKSPTgxndCIAALlBJyZ9dGI4oxMBAMgNOjHpoxPDGZ0IAEBu0IlJH50YzuhE AAByQ9SJzz333GOPPTbxkEmTJmXYjDoxkNGJ4YxOBAAgN8SdOG7cuD8dMm/e vA0bNmTyQJ0YyOjEcEYnAgCQG7zuNOmjE8MZnQgAQG7QiUkfnRjO6EQAAHKD Tkz66MRwRicCAJAbdGLSRyeGMzoRAIDcoBOTPjoxnNGJAADkBp2Y9NGJ4YxO BAAgN+jEpI9ODGd0IgAAuUEnJn10YjijEwEAyA06MemjE8MZnQgAQG7QiUkf nRjO6EQAAHKDTkz66MRwRicCAJAbdGLSRyeGMzoRAIDcoBOTPjoxnNGJAADk Bp2Y9NGJ4YxOBAAgN0SdWFRUNPwLI0eOLC4uzuSBOjGQ0YnhjE4EACA3RJ1Y UlIybdq0LYeUlZVl+ECdGMjoxHBGJwIAkBu87jTpoxPDGZ0IAEBu0IlJH50Y zuhEAAByg05M+ujEcEYnAgCQG3Ri0kcnhjM6EQCA+rVt27bp06ePGjVq2LBh Y8aMee+996os2LVrV/T16siRI2+//faxY8euXr36sOfRiUkfnRjO6EQAAOpL RUXF4sWLe/Xq1ShNYWFh1IM7duyI15SWlg4ePDgvL69yQYcOHQ77Cy90YtJH J4YzOhEAgPqyZs2aa6655vjjj//Od77z0ksvLV269OGHHz711FPbtm07evTo aEFZWdnYsWOjSOzUqdOkSZOir1oHDhwY/bFnz54rV66scjadmPTRieGMTgQA oF5UVFS8/fbbhYWF3bt3X758eXxw165dTz/9dOPGjXv37l1eXr5ixYpevXqd ddZZRUVF8YKPPvro5ptvbteu3UMPPVTlhDox6aMTwxmdCABAvdi5c+eMGTM6 duz4/e9/P/34nDlz8vPzzz333I0bN0ZfprZs2fK8887bvHlz/NYoHqdOnZqX l9enT59UKpX+QJ2Y9NGJ4YxOBAAgHFE8PvXUU02aNIkycMeOHc8880yzZs36 9u2bviYqwdatW59zzjkbNmxIP64Tkz46MZzRiQAABKKiomL+/PndunU76aST 7rnnnu3btz/++OMnnHDCTTfdlL5swYIFHTp06N69+7Jly9KP68Skj04MZ3Qi AACBWL58+UUXXdS0adPevXt/8sknW7duffTRRwsKCm699db0ZYsWLerSpUvX rl2jqEw/rhOTPjoxnNGJAADUuwMHDkTR9+1vfzsvL+/888+Pv1EYd2KrVq0G DRqUvjjqxM6dO3fr1m3x4sXpx3Vi0kcnhjM6EQCA+lVeXv7aa6916tSpWbNm F1544fvvvx8f3759+xNPPJGfnz9gwID09fPmzTvjjDN69OixatWq9ONRJ5aU lESpuKWasrKyGjagEwMZnRjO6EQAAOrR7t27p0yZ8vWvf71FixZXXnllaWlp 5ZuifnzhhRfy8vIuueSSioqKyuNvvfVWFI/nn3/+559/nn6qqBOLioqGH874 8eNr2INODGR0YjijEwEAqC979+6dNm3aySef3KpVq4EDB27btq3Kgrlz50Zv 7dGjx4cffhgf2bFjx5NPPtm8efN+/fpVWex1p0kfnRjO6EQAAOrL4sWLzz77 7DgS9+zZU33B6tWrr7766tatW99111379++vqKhYtGjRueeee9ppp40bN67K Yp2Y9NGJ4YxOBACgXmzcuHHMmDGNGjVq0qRJQUHBSV/Wu3fv9evXl5eXT58+ vbCwMFrQp0+fW265pWPHjscdd9wVV1xR5UWnB3Vi8kcnhjM6EQCAerF06dJB gwY1+go9e/Zcs2ZNtGzPnj0zZszo3Llz48aNo+NRMN54441r166tfkKdmPTR ieGMTgQAIHz79+8vLS2N0vLTTz/9qjU6MemjE8MZnQgAQG7QiUkfnRjO6EQA AHKDTkz66MRwRicCAJAbdGLSRyeGMzoRAIDcoBOTPjoxnNGJAADkBp2Y9NGJ 4YxOBAAgN+jEpI9ODGd0IgAAuUEnJn10YjijEwEAyA06MemjE8MZnQgAQG7Q iUkfnRjO6EQAAHJD1IklJSVTpkz54AsbNmzI5IE6MZDRieGMTgQAIDdEnVhU VDRy5MiHDxk7dmyG31vUiYGMTgxndCIAUKsFCxb88pe/TKVS9b0RqInXnSZ9 dGI4oxMBgFr9x3/8R6NGjXbt2lXfG4Ga6MSkj04MZ3QiAFArnUgi6MSkj04M Z3QiAFArnUgi6MSkj04MZ3QiAFArnUgi6MSkj04MZ3QiAFArnUgi6MSkj04M Z3QiAFArnUgi6MSkj04MZ3QiAFArnUgi6MSkj04MZ3QiAFArnUgi6MSkj04M Z3QiAFArnUgi6MSkj04MZ3QiAFCrmjtx4cKFb775Zh1vCarTiUkfnRjO6EQA oFY1d+Ltt9/ev3//Ot7SX+v+++/v2bPnXXfdVVFRUd97ObYazntanU5M+ujE cEYnAgC1qrkTL7/88vz8/B07dhy7DZSXl//+979/5plnVq9eXf2tM2fOjN5U w/c0lyxZ0ugLr7zyyrHbZ71rOO/pYenEpI9ODGd0IgBQq5o7MWq06K0TJ048 dhv485//HLfPU089Vf2t55xzTvSmf/qnf/qqh7///vuV9fTSSy8du33Wu4bz nh6WTkz66MRwRicCALWquRN37tx5wgknXHjhhcduA0fZiZH//M//jBY88MAD Of9qzIbznlYXdeJzzz03duzYaYfMnDlzxYoVmTxQJwYyOjGc0YkAQK1q/Xmn 119/fZMmTT7++ONjtIGj70QaAp2Y9NGJ4YxOBABqVWsnvvrqq9GCBx544Bht oM46saKi4pNPPnn33XeXL19eXl6e+faih2TyNzQ///zzJUuWzJ07NyqazHcV byl6bOYPydC+ffui93ThwoXRP2T95HXP606TPjoxnNGJAECtau3EVCp1yimn dO7c+Rht4K/txD/+8Y8nHk5hYeFX/Ss2b948aNCgVq1aVf79vmbNmv3t3/7t //pf/2v37t3pK6Owis4TvbPRex2l8X//7/89Xt+kSZNu3botWLCg+sm3bt0a XcPu3bs3bty48vzRFSsqKqq++Kqrroq2Gr1p//79//Zv/9alS5fKh5x77rmr Vq1KX3wE7+nBQzn829/+NnoXmjZtGp/5uOOO69GjR3FxcQ2PCp9OTProxHBG JwIAtaq1EyP/8i//Eq05bCUdvb+2E1944YVGX+Gw59+5c2dUSfGCtm3bnnfe eR07dqxsqK9//evbt2+vXPz222/Hx+N/b6Rly5Z5eXnxPzdv3nzmzJnpJ4/+ +LWvfa1yA1HBpdfoL3/5yyqb6d27d3T8Zz/72YUXXhivyc/Pr1x/6qmn7t27 94jf08inn35aufP45P/tv/23yj/+8Ic/rPFW1GL16tW///3vj+YMR0MnJn10 YjijEwGAWmXSiYsXL47WDBky5FhsoLIThw0bNq+azp07V+nEPXv2lH7Zvffe W0M9Pfvss/Fbx4wZc+DAgfjgvn37Jk6ceMYZZ0SPTV9c2YmRv//7v3/vvfdS qVRUmo899lhci9F+Kk8S+b//9/9GyXnmmWeOGzduzZo18cE5c+ZEZ44WFxQU ROGWfv64E2PXX3/9unXr4itwxRVXxAeffPLJI35PKyoqvve978ULLr744uXL l8dbXbt27Y9+9KPo4OOPP57pXTmcKHsbN24cvadHc5IjphOTPjoxnNGJAECt MunESJcuXU4++eT9+/dnfQOVnViDmv9+YhSANdTT3XffHb/1s88+q/KmKKOq /ODQyk6MojUqxPQ33XPPPfGbpk+fnn783Xffrf63Hf/4xz/GiydPnpx+PO7E qLZ+9atfpR+P9tayZcvoTbfeeusRv6dPPPFE/Nabbrqp+lsz/KkvNYsuS7T5 8ePHH/2p/lo6MemjE8MZnQgA1CrDTnzwwQerJ1JWVHZis2bNmlcT/6W/o+nE +OfwRM4+++wXXngh/YWd1VV2YvUX2X7yySfxm/793/+9hjNEdblmzZrXX3/9 sIvjTrz44ourPzB+veill15aw8lrfk8vv/zyRodeW1tWVlbDSY7SkCFDopuS /n3PuqETkz46MZzRiQBArTLsxA0bNjRp0qRfv35Z38DR/7zTmuspcu+991b+ kJkTTzzxxhtvnDp16mF/hGkNnRiJ/yrij3/84yrHd+7c+eyzzw4YMKBLly7H H398+ndC77zzzvSVcSdedtll1U8evY/Rm7773e8e8Xt66qmnRm+6+uqrazhD Vtx2223R9Tzs/Tp2dGLSRyeGMzoRAKhVhp0YueCCC5o3b75t27bsbqAOOjGy aNGif/7nf07/oTEtW7YcOnRold8LWXMn/s3f/E30pug86Qeff/75wsLC+FEt WrQ477zz+vfvf88998Rlmnkn9u3b92g6ce/evfG/8e67767hDDXYvn37zzMT vXd5eXnRv27ChAlH9u86Ajox6aMTwxmdCADUKvNOjDruq2ruaNRNJ8Z27979 4osvDhw4sF27dvFDWrduvXz58soFNXTigQMHmjVr1ujQTyutPDh+/Pi4zqI8 nDp1avpljL+7V2edGIl/tOn1119fwxlqEIXYBRmLt3HHHXcc2b/ryLanExM9 OjGc0YkAQK0y78QJEyZEKx988MHsbqAuO7HSvn37HnnkkfhHmF5zzTWVx2vo xPfeey9+0+9+97vKgz179oyO/MM//EP1H2VT95140UUXRW8644wzav47mEfv mWeeadKkyaBBg47pv6UKnZj00YnhjE4EAGqVeSdeeOGFUR1UeaHm0TvWnbh6 9eqVK1ce9k1///d/Hz0qar3KI5Wd+MYbb1RZ/I//+I/R8aZNm0YnjI9Ufocx uoZVFkeZecIJJ9RxJ/7iF7+I3zpixIjqb/3okBpOnqGioqKorwcOHFjlR8Ue azox6aMTwxmdCADUKsNOLC0tjSLxH/7hH7K+gWPaiTt37uzUqVMUd4MGDVq/ fn36m15++eUWLVo0+vKvoqjsxJYtWz7yyCO7d++ODm7btu26666Ljw8dOjT9 JPHvSfzbv/3bTZs2xUc2b978r//6r/F3Kuu4E/fu3RvtJF5w/fXXf/LJJ/Hx LVu2PProo1G3Rhezyi/7+Gs9++yz0bv24x//uI4j8aBOTP7oxHBGJwIAtcqw E+NlzzzzTNY3cEw7ccOGDem/2v7EE0/8u7/7u0suuaRjx47xkVNPPTX9u2yV nRiLAvNv/uZvokCO/xhVWJUf4zN69Oj4Tccdd1zXrl3POuus+I/Rozp37lzH nRhZtmxZ+/btK/d/yimnxCUbi06+devWGs5fs8mTJ0eR+MMf/rDuI/GgTkz+ 6MRwRicCALXKsBO7devWqlWr+Ptr2fXpp5/W0Inf/va3j6YTY2+99VZ0hsqf XRM7/vjjb7zxxsoXkcYqO/EPf/jDxRdfXPnbNJo3b/6Tn/xkz549Vc4cFdO9 995bUFBQedozzzzz7rvvjq7nsGHDGlX76aPROaODV155ZfVNZqUTDx76Fmr0 L41/hUcsei86dOjw9NNP1/CoTLzyyiuDBw8+cODAUZ7nyOjEpI9ODGd0IgBQ q0w6cfny5dGam266qc52dYx89tlnc+bMiWJw5cqV1aPvYFonLlq0KPrj1q1b X3vttYULF1b/MTXpojRbsGDBzJkz//znPx+rrf/1os1EXwfOmzevrKysvveS BVEnFhUVjRw58uFDHnvsseiCZ/JAnRjI6MRwRicCALXKpBN/9rOfRWuihqqz XdWXmn9/IvUo6sSSkpIpU6Z8cMiaNWsy7F+dGMjoxHBGJwIAtaq1EysqKk4/ /fT/8T/+R13uqr7oxGB53WnSRyeGMzoRAKhVrZ345ptvRgvuu+++utxVfdGJ wdKJSR+dGM7oRACgVrV24k033dS4ceOs/Oq98OnEYOnEpI9ODGd0IgBQq5o7 ce/evYWFhRdddFEd76q+bNmy5fnnn588efK+ffvqey98iU5M+ujEcEYnAgC1 qrkTp0yZEr31d7/7XR3vCqrQiUkfnRjO6EQAoFY1d+I///M/FxQU1PrbFeFY 04lJH50YzuhEAKBWNXfioEGDfvjDH9bxlqA6nZj00YnhjE4EAGpVcye+//77 fqILIdCJSR+dGM7oRACgVrX+vFMIgU5M+ujEcEYnAgC10okkgk5M+ujEcEYn AgC10okkgk5M+ujEcEYnAgC10okkgk5M+ujEcEYnAgC10okkgk5M+ujEcEYn AgC10okkgk5M+ujEcEYnAgC10okkQtSJEydOvPkLQ4cOLS4uzuSBOjGQ0Ynh jE4EAGqlE0kE309M+ujEcEYnAgC10okkgk5M+ujEcEYnAgC1mjp16sUXX7xv 37763gjURCcmfXRiOKMTAQDIDTox6aMTwxmdCABAbtCJSR+dGM7oRAAAQlBR UfH+++/ffffdb7zxRvrxsrKyWbNmFX3Z888/v2DBgipn0IlJH50YzuhEAABC sGrVqssvv/zUU0/91a9+lX58yZIlZ599dqMvO/HEEwcPHlzlDDox6aMTwxmd CABA/UqlUgsWLPje974XBWD1Tnz77bcLCgrat28/dOjQYV8YMWLE1KlTq5xH JyZ9dGI4oxMBAKhHW7ZseeSRR9q0aRN/o7BKJ+7Zs6e4uLh58+b9+/ev9VQ6 MemjE8MZnQgAQD16++23TzjhhMLCwr59+1577bVVOjFKv/vvvz+qyFGjRtV6 Kp2Y9NGJ4YxOBACgHi1dunTAgAFvvvnmxx9/PGTIkCqduG7duuit7du3v/PO O++4446rrrrqtttumzx58mF/m6dOTProxHBGJwIAEILS0tLqnbh8+fJevXpV +SE2+fn5/fr1++STT6qcQScmfXRiOKMTAQAIwWE7ce7cue3atWvZsuVll132 6quvLl68+MEHH2zbtm1hYeHQoUMrKirSz6ATkz46MZzRiQAAhOCwnfjZZ59N nTp18uTJZWVl8ZGdO3dOnDixadOmPXr0WLVqVfoZdGLSRyeGMzoRAIAQHLYT D2vhwoWdO3fu2LHjK6+8kn486sSSkpIoFbdUU5mZh6UTAxmdGM7oRAAAQnDY TkylUtu2bYtCL33l0qVLe/To8Y1vfGPGjBnpx6NOLCoqGn4448ePr+FfrRMD GZ0YzuhEAABCUL0Td+zYEfVdXl7exRdfXF5eHh+sqKiYNWtW69atu3fvvnz5 8vQzeN1p0kcnhjM6EQCAEBz2+4nRl6mtWrU688wzi4qK4iNr167t379/fn7+ ddddl0ql0s+gE5M+OjGc0YkAAITgsJ24adOm4cOHN2rU6KSTTrr22mujBd27 d2/cuHH0v/Pnz69yBp2Y9NGJ4YxOBAAgBBs3bhw6dGj79u2r/BybKBXvvffe qBPjX57YokWLSy+9dN68edXPoBOTPjoxnNGJAACEb8+ePR9++OHChQtLS0u/ ao1OTProxHBGJwIAkBt0YtJHJ4YzOhEAgNygE5M+OjGc0YkAAOQGnZj00Ynh jE4EACA36MSkj04MZ3QiAAC5QScmfXRiOKMTAQDIDTox6aMTwxmdCABAbtCJ SR+dGM7oRAAAcoNOTProxHBGJwIAkBt0YtJHJ4YzOhEAgNwQdWJJSUlxcfGf vrBixYpMHqgTAxmdGM7oRAAAckPUiUVFRffdd9/EQ6Jg1InJGp0YzuhEAABy g9edJn10YjijEwEAyA06MemjE8MZnQgAQG7QiUkfnRjO6EQAAHKDTkz66MRw RicCAJAbdGLSRyeGMzoRAIDcoBOTPjoxnNGJAADkBp2Y9NGJ4YxOBAAgN+jE pI9ODGd0IgAAuUEnJn10YjijEwEAyA06MemjE8MZnQgAQG7QiUkfnRjO6EQA AHKDTkz66MRwRicCAJAbdGLSRyeGMzoRAIDcoBOTPjoxnNGJAADkhqgTn3vu uccee2ziIZMmTcqwGXViIKMTwxmdCABAbog7cdy4cX86ZN68eRs2bMjkgTox kNGJ4YxOBAAgN3jdadJHJ4YzOhEAgNygE5M+OjGc0YkAAOQGnZj00YnhjE4E ACA36MSkj04MZ3QiAAC5QScmfXRiOKMTAQDIDTox6aMTwxmdCABAbtCJSR+d GM7oRAAAcoNOTProxHBGJwIAkBt0YtJHJ4YzOhEAgNygE5M+OjGc0YkAAOQG nZj00YnhjE4EACA36MSkj04MZ3QiAAC5QScmfXRiOKMTAQDIDTox6aMTwxmd CABAbog6saioaPgXRo4cWVxcnMkDdWIgoxPDGZ0IAEBuiDqxpKRk2rRpWw4p KyvL8IE6MZDRieGMTgQAIDd43WnSRyeGMzoRAIDcoBOTPjoxnNGJAADkBp2Y 9NGJ4YxOBID/v737gYn6vv84Dj11CmIZ8Q9bsbEmpEiMynAaHb9fu9VQo/6q aVLdtK30F+O/KaLVObXlh7N2dVOnP9H6p/7KhrderIC1DlrrRNRKhwiiuCgg KmKj1ioHIi1Q+H3iRXI9kLt6X+/en7vnI68s6/UL475fv8c9K7MAfAOdqPvo RDmjEwEAAOAb6ETdRyfKGZ0IAAAA30An6j46Uc7oRAAAAPgGOlH30YlyRicC AADAN9CJuo9OlDM6EQAAAL6BTtR9dKKc0YkAAADwDXSi7qMT5YxOBAAAgG+g E3UfnShndCIAAAB8A52o++hEOaMTAQAA4BvoRN1HJ8oZnQgAAADfQCfqPjpR zuhEAAAA+AbViWlpaTPvS0pKslgsrnwgnShkdKKc0YkAAADwDfx+ou6jE+WM TgQAAIBvoBN1H50oZ3QiAAAAfAOdqPvoRDmjEwEAAOAb6ETdRyfKGZ0IAAAA 30An6j46Uc7oRAAAAEjQ0tJy9uzZFStWHDp0yOFv1dfXq/erycnJiYmJqamp FRUVHX4GOlH30YlyRicCAABAgrKyshdeeOGJJ57YtGmT/ePV1dXz5s0zmUwB 90VGRnb4L7ygE3UfnShndCIAAAC8q7m5ubCwcOzYsaoBHTrRarWmpqaqSBw0 aFB6erp61zp79mz1l8OHDz9//rzD56ETdR+dKGd0IgAAALzo5s2b69at69On j+33Ch06sbS0NC4ubuDAgWaz2fbI5cuXZ86cGR4evmbNGodPRSfqPjpRzuhE AAAAeNGxY8d69uwZGho6adKkadOmOXSiepsaHBw8atSoGzdu2B5pbGzMzMw0 mUzx8fHNzc32n4pO1H10opzRiQAAAPCikpKShISEw4cPX7lyZcGCBfadePfu 3V27dnXt2lUlpP2HqBIMCwsbMWJEVVWV/eN0ou6jE+WMTgQAAIAE1dXVDp1Y W1u7ZcuWnj17zpgxw/7IwsLCyMjImJiY06dP2z9OJ+o+OlHO6EQAAABI0L4T b9++vX79+pCQkDlz5tgfWVRUNHjw4CFDhpw4ccL+cTpR99GJckYnAgAAQIIH dWKvXr3mzp1rf6TqxOjo6KFDhxYXF9s/TifqPjpRzuhEAAAASNDhz51u3bo1 KCgoISHB/siCgoIBAwbExsaWlZXZP646MSsrKyMj41w7Dv9PRgd0opDRiXJG JwIAAECC9p3Y2Ni4d+9ek8k0bty4lpaWtiOPHDmi4nH06NG3bt2y/wyqE81m c3Jy8tp2LBZLJ//TdKKQ0YlyRicCAABAgvadqOTn5/ft2zc2Nra8vNz2SF1d 3Y4dO7p37z5lyhSHz8DPneo+OlHO6EQAAABI0GEnVlRUTJ48OSwsbOnSpU1N TS0tLUVFRSNHjuzfv/+2bdscPgOdqPvoRDmjEwEAACBBh53Y2Ni4f//+0NDQ kJCQ+Pj4WbNmRUVFdevWbeLEiQ4/dNpKJ+o/OlHO6EQAAABIcPXq1aSkpIiI CPtOVBoaGrKzs6OjowMDAwMCAlQwTp8+vbKysv1noBN1H50oZ3QiAAAA5Gtq aqquri4pKbl+/fqDjqETdR+dKGd0IgAAAHwDnaj76EQ5oxMBAADgG+hE3Ucn yhmdCAAAAN9AJ+o+OlHO6EQAAAD4BjpR99GJckYnAgAAwDfQibqPTpQzOhEA AAC+gU7UfXSinNGJAAAA8A10ou6jE+WMTgQAAIBvoBN1H50oZ3QiAAAAfAOd qPvoRDmjEwEAAOAbVCfu3r07NTV13z05OTmlpaWufCCdKGR0opzRiQAAAPAN dKLuoxPljE4EAACAb+DnTnUfnShndCIAAAB8A52o++hEOaMTAQAA4BvoRN1H J8oZnQgAAADfQCfqPjpRzuhEAAAA+AY6UffRiXJGJwIAAMA30Im6j06UMzoR AAAAvoFO1H10opzRiQAAAPANdKLuoxPljE4EAACAb6ATdR+dKGd0IgAAAHwD naj76EQ5oxMBAADgG+hE3UcnyhmdCAAAAN9AJ+o+OlHO6EQAAAD4BjpR99GJ ckYnAgAAwDfQibqPTpQzOhEAAAC+QXWi2WxOTk5ee8/GjRtzcnJc+UA6Ucjo RDn78uDrG5a/OCNh2qr/+T3z7lYm/37r5v+tqal51C+hAAAAPkl1YlZWVkZG xrl7Lly4YLVaXflAOlHI6EQ5u/RJ0vrFY9NWTTqTMZd5d4fee23TH2aWlJQ8 6pdQAAAAn8TPneo+OlHOVCduXjb+8HvTvf6VsMufLvrbXxLpRAAAgIdDJ+o+ OlHO6EQ5oxMBAADcQSfqPjpRzuhEOaMTAQAA3EEn6j46Uc7oRDmjEwEAANxB J+o+OlHO6EQ5oxMBAADcQSfqPjpRzuhEOaMTAQAA3EEn6j46Uc7oRDmjEwEA ANxBJ+o+OlHO6EQ5oxMBAADcQSfqPjpRzuhEOaMTAQAA3EEn6j46Uc7oRDmj EwEAANxBJ+o+OlHO6EQ5oxMBAADcQSfqPjpRzuhEOaMTAQAA3EEn6j46Uc7o RDmjEwEAANxBJ+o+OlHO6EQ5oxMBAADcoTrRbDYvuy85OdlisbjygXSikNGJ ckYnyhmdCAAA4A7ViVlZWfv27bt5j9VqdfED6UQhoxPljE6UMzoRAADAHfzc qe6jE+WMTpQzOhEAAMAddKLuoxPljE6UMzoRAADAHXSi7qMT5YxOlDM6EQAA wB10ou6jE+WMTpQzOhEAAMAddKLuoxPljE6UMzoRAADAHXSi7qMT5YxOlDM6 EQAAwB10ou6jE+WMTpQzOhEAAMAddKLuoxPljE6UMzoRAADAHXSi7qMT5YxO lDM6EQAAwB10ou6jE+WMTpQzOhEAAMAddKLuoxPljE6UMzoRAADAHXSi7qMT 5YxOlDM6EQAAwB10ou6jE+WMTpQzOhEAAMAdqhOzsrJUKt68z2q1uvKBdKKQ 0YlyRifKmerEnX+ac/Dgwa/gbTU1NY/6GxkAADCc+iZuNpuX3bdq1aqcnBxX PpBOFDI6Uc7oRDkr35+48JX/mP7ifyYmjGXe3dyECatS3qAWAQDQy1f83Knm oxPljE6Us/KPE/+8KL7YMsvrXwk7ty9p3aqFdCIAAHqhE3UfnShndKKc0Yly RicCAKAjOlH30YlyRifKGZ0oZ3QiAAA6ohN1H50oZ3SinNGJckYnAgCgIzpR 99GJckYnyhmdKGd0IgAAQlit1tzcXPP3ffjhh4WFhe0PphN1H50oZ3SinNGJ ckYnAgAgxKlTp4YNGxbwfY8//vi8efPaH0wn6j46Uc7oRDmjE+WMTgQAQIhj x46FhIREREQkJSUtvG/58uWZmZntD6YTdR+dKGd0opzRiXJGJwIAIEFDQ4PF YunevfvUqVNdOZ5O1H10opzRiXJGJ8oZnQgAgASq+956660+ffqkpKS4eDyd qPXoRDmjE+WMTpQzOhEAAAkuXryYkJAQERGxZMmSxYsXv/TSS/Pnz9+zZ8+3 337b4fF0ou6jE+WMTpQzOlHO6EQAACQ4c+ZMXFycwx9iExQUNGXKlC+//LL9 8XSi7qMT5YxOlDM6Uc7oRAAAJMjPzw8PDw8ODp4wYcKBAweKi4vfeeedfv36 hYaGJiUltbS0OBxPJ+o+OlHO6EQ5oxPljE4EAECCr7/+OjMzc8+ePVar1fbI nTt30tLSunTpEhsbW1ZW5nA8naj76EQ5oxPljE6UMzoRAACxTp48GR0dHRUV 9emnnzr8LdWJWVlZFovl83ZKS0s7+Zx0opDRiXJGJ8oZnShndCIAABI0Nzer b8c3b960f7CkpCQ2Nvbpp5/Ozs52OF51otlsXrVqVVo7OTk5nfwP0YlCRifK GZ0oZ3SinNGJAAB4XV1d3fbt200m0/PPP9/Y2Gh7sKWlJTc3NywsLCYm5syZ Mw4fws+d6j46Uc7oRDmjE+WMTgQAQIK8vLxevXo99dRTZrPZ9khlZeXUqVOD goJefvnl5uZmh+PpRN1HJ8oZnShndKKc0YkAAEhw7dq1ZcuWBQQE9O7de9q0 aQsWLIiJiQkMDFT/eeLEifbH04m6j06UMzpRzuhEOaMTAQAQQqXiypUrVSfa /uWJPXr0GD9+fEFBQYcH04m6j06UMzpRzuhEOaMTAQAQpaGhoby8/OTJk9XV 1Z0cRifqPjpRzuhEOaMT5YxOBABAR3Si7qMT5YxOlDM6Uc7oRAAAdEQn6j46 Uc7oRDmjE+WMTgQAQEd0ou6jE+WMTpQzOlHO6EQAAHREJ+o+OlHO6EQ5oxPl jE4EAEBHdKLuoxPljE6UMzpRzuhEAAB0RCfqPjpRzuhEOaMT5YxOBABAR3Si 7qMT5YxOlDM6Uc7oRAAAdEQn6j46Uc7oRDmjE+WMTgQAQEd0ou6jE+WMTpQz OlHO6EQAAHSkOnH37t0bN25Muyc9Pd3FZqQThYxOlDM6Uc7oRDmjEwEA0JGt E7dt2/b5PQUFBVVVVa58IJ0oZHSinNGJckYnyhmdCACAjvi5U91HJ8oZnShn dKKc0YkAAOiITtR9dKKc0YlyRifKGZ0IAICO6ETdRyfKGZ0oZ3SinNGJAADo iE7UfXSinNGJckYnyhmdCACAjuhE3UcnyhmdKGd0opzRiQAA6IhO1H10opzR iXJGJ8oZnQgAgI7oRN1HJ8oZnShndKKc0YkAAOiITtR9dKKc0YlyRifKGZ0I AICO6ETdRyfKGZ0oZ3SinNGJAADoiE7UfXSinNGJckYnyhmdCACAjuhE3Ucn yhmdKGd0opzRiQAA6IhO1H10opzRiXJGJ8oZnQgAgI7oRN1HJ8oZnShndKKc 0YkAAOiITtR9dKKc0YlyRifKGZ0IAICOVCeazebk5OS192zcuDEnJ8eVD6QT hYxOlDM6Uc7oRDmjEwEA0JHqxKysrIyMjHP3XLhwwWq1uvKBdKKQ0YlyRifK GZ0oZ3QiAAA64udOdR+dKGd0opzRiXJGJwIAoCM6UffRiXJGJ8oZnShndCIA ADqiE3UfnShndKKc0YlyRicCAKAjOlH30YlyRifKGZ0oZ3QiAAA6ohN1H50o Z3SinNGJckYnAgCgIzpR99GJckYnyhmdKGd0IgAAOqITdR+dKGd0opzRiXJG JwIAoCM6UffRiXJGJ8oZnShndCIAADqiE3UfnShndKKc0YlyRicCAKAjOlH3 0YlyRifKGZ0oZ3QiAAA6ohN1H50oZ3SinNGJckYnAgCgIzpR99GJckYnyhmd KGd0IgAAOqITdR+dKGd0opzRiXJGJwIAoCM6UffRiXJGJ8oZnShndCIAADpS nZiWljbzvqSkJIvF4soH0olCRifKGZ0oZ3SinNGJAADoiN9P1H10opzRiXJG J8oZnQgAgI7oRN1HJ8oZnShndKKc0YkAAOiITtR9dKKc0YlyRifKGZ0IAICO 6ETdRyfKGZ0oZ3SinNGJAADoiE7UfXSinNGJckYnyhmdCACAjuhE3Ucnyhmd KGd0opzRiQAA6IhO1H10opzRiXJGJ8oZnQgAgI7oRN1HJ8oZnShndKKc0YkA AOiITtR9dKKc0YlyRifKGZ0IAICO6ETdRyfKGZ0oZ3SinNGJAADoiE7UfXSi nNGJckYnyhmdCACAjuhE3UcnyhmdKGd0opzRiQAA6IhO1H10opzRiXJGJ8oZ nQgAgI7oRN1HJ8oZnShndKKc0YkAAOhIdWJWVlZGRsa5+6qqqlz5QDpRyOhE OaMT5YxOlLN/701MXvzf+fn5/wZwX3l5Of/wBIBwqhPNZnNycvLae1JTU138 vUU6UcjoRDmjE+WMTpSz4t2zJz8/NPm341Yt+C/m3S149bmxzwz1+pfBVs6f sPL1qRm7P3jU7/EAwB383KnuoxPljE6UMzpRzko+nLP0tbiv837n9a/Ez1f/ xYpD701fOedZr38lzHp8We5f5//t/e2P4n0dABiFTtR9dKKc0YlyRifKGZ0o ZHSinNGJALRAJ+o+OlHO6EQ5oxPljE4UMjpRzuhEAFqgE3UfnShndKKc0Yly RicKGZ0oZ3QiAC3QibqPTpQzOlHO6EQ5oxOFjE6UMzoRgBfV19fn5eUlJycn JiampqZWVFQ86Eg6UffRiXJGJ8oZnShndKKQ0YlyRicC8Jbq6up58+aZTKaA +yIjIy0WS4cH04m6j06UMzpRzuhEOaMThYxOlDM6EYBXWK3W1NRUFYmDBg1K T0/Py8ubPXu2+svhw4efP3++/fEP3Ynqs61Nmef1F1tW+8WbRz9YYtmU5PWv hF3PW/HR9kXHPlji9a+EVR9abtm08PRHS73+lbDynGWb/zj/1rE3vP6V+Pka Trx5MmPp9j8lev0rYeob97Hdbz7oH+DDkx76bTCgo9LS0ri4uIEDB5rNZtsj ly9fnjlzZnh4+Jo1a9ofTyfqPjpRzuhEOaMT5YxOFDI6Uc7oRDnoRPiVvLy8 4ODgUaNG3bhxw/ZIY2NjZmamyWSKj49vbm52OJ5O1H10opzRiXJGJ8oZnShk dKKc0Yly0InwH3fv3t21a1fXrl0nTZpk/7j69R8WFjZixIiqqiqHDxHSieoN 9oG/Lr50YJlRr8AnM35n4Nv1i58tNzDEDH+yxnaieoOtYseoN3Xq86gLYeDb dWOvhbFP1vBOFP4LT/JdZmwnqjfY/97/++ydr8t8ssZeWcOfrLGdKPwlxdhr YeyTNbwT/eolxfAna2wnXrt2Tb2Xs1qthnw29c7w4MGD7d8xPpzGxsaysrKc nBxDPlvrI3iydCL8RG1t7ZYtW3r27Dljxgz7xwsLCyMjI2NiYk6fPu3wIUI6 Ub2p2/PuIgO/tx746+sGvs9R75qMfbIGvoM1vBONfVNnbDoZ/j7Hr56setf0 f+sWcJc9xAy/y/zqyXKX8WRdmb+9pBjbiZWVlZs3bzYqnVSIqa+ttLTUkM9W X1+v3mSKfbJ0IvzH7du3169fHxISMmfOHPvHi4qKBg8ePGTIkBMnTjh8CJ3o A99ueFPHk3VlvKl76NGJ7oy7jCfryvztJYVOfGh0IvBwbJ3Yq1evuXPn2j+u OjE6Onro0KHFxcUOH0In+sC3G97U8WRdGW/qHnp0ojvjLuPJujJ/e0mhEx8a nQg8nNra2q1btwYFBSUkJNg/XlBQMGDAgNjY2LKyMocPUa8GO3bsSE1N3fcD paWlLXt9jvoWZsg+SF24YdW89A1Jhny2Pe8u3Px24h/f/K1RX576Vmjsk/1z yrzNb8+X+WTVVfjD8rlGXQuerK8+We4ynqyL4y7jyXrly5N+l72z6O233/6h b70exGw2r1q1Kj093ZDPtnv37rVr1z7EO8MOZWVlbdmyxSefbF5enlG5CnhA Y2Pj3r17TSbTuHHjWlpa2h4/cuSIisfRo0ffunXL4UMeuhMBAAAA/0QnQjv5 +fl9+/aNjY0tLy+3PVJXV6dKsHv37lOmTGl/PL/hDgAAAAC+raKiYvLkyWFh YUuXLm1qamppaSkqKho5cmT//v23bdvW/ng6EQAAAAB8W2Nj4/79+0NDQ0NC QuLj42fNmhUVFdWtW7eJEye2/6HTVjoRAAAAAPxAQ0NDdnZ2dHR0YGBgQECA Csbp06dXVlZ2eDCdCAAAAAB+oqmpqbq6uqSk5Pr1650cRicCAAAAAOzRiQAA AAAAe3QiAAAAAMAenQgAAAAAsPdDO7G+vj4vLy85OTkxMTE1NbWiouKRfnn+ w2q15ubmmr/vww8/LCwsbDvG6cl3/wC/1dLScvbs2RUrVhw6dMjhb7l+0rhA 7nvQhXDlBmljyHn252tRU1Ozf//+lJSUhQsXbtiw4V//+pfDAdwUHtP5tfDw feHn1+L27dtZWVnLly9ftGjRzp07r1696nAA94XHdH4tuC8Ao/ygTqyurp43 b57JZAq4LzIy0mKxPOov0h+cOnVq2LBhAd/3+OOPqxNuO8DpyXf/AH9WVlb2 wgsvPPHEE5s2bbJ/3PWTxgUyxIMuhNMbpI0h59lvr4Xq9OLi4ri4OPvzHBoa qt7/1NXV2Y7hpvAMV66FJ+8Lf74WyunTp5999tm25x4YGDho0KDMzMy2A7gv PMbpteC+AIxSU1Nz+PBhVzrRarWmpqaqe0Hdj+np6Xl5ebNnz1Z/OXz48PPn z3vgS/Vtx44dCwkJiYiISEpKWnjf8uXLbS99Tk+++wf4rebm5sLCwrFjx6qX d4c8cf2kcYHc18mFaHV2g7Qx5Dz787W4cOHCb37zmx/96EfPPPPMP/7xj5KS krVr16rL0a9fv9WrV7dyU3iQ02vR6sH7ws+vxaVLl1577TV1LX75y1/+85// /OKLL2xp8POf//zcuXOt3Bce5PRatHJfAN5QWloaFxc3cOBAs9lse+Ty5csz Z84MDw9fs2aNd7823TU0NFgslu7du0+dOrXDA5yefPcP8E83b95ct25dnz59 bP8Y0CFPXD9pXCA3dX4hnN4gbQw5z/58LdT7q9DQ0JiYmDNnztgeqa+vf//9 9wMDA8eMGdPY2MhN4TFOr4Un7ws/vxbHjx9Xr05Dhgxp+8FFVQETJkzo37// 1q1bW/lm4UFOrwX3BeAVeXl5wcHBo0aNunHjhu0R9X0qMzPTZDLFx8c3Nzd7 98vT2ldfffXWW2+pl76UlJQOD3B68t0/wANPUyD1Tqxnz57qzdikSZOmTZvm kCeunzQukJs6vxBOb5A2hpxnv70W3377bXZ2dlRU1K9//Wv7x48ePRoUFDRy 5MirV69yU3iGK9fCk/eFP1+L77777sKFC6tXr16/fr3677YHL168+Oqrr/7k Jz9Zt25dK98sPMWVa8F9AXje3bt3d+3a1bVrV/Uuzv7x48ePh4WFjRgxoqqq yltfmw9Qr3IJCQkRERFLlixZvHjxSy+9NH/+/D179qi3Cq0unPzz58+7eYDf Xr6SkhJ15g8fPnzlypUFCxbY54nrv+a5QO7r5EK0OrtB2rh/IdR55rXOwZ07 d957773HHntMve2pq6vjpvAi+2uh3oJ67L7gWjhQZ/jjjz8ODw9XIf/JJ5/w zcKLHK5Fqwe/X3AtgDa1tbVbtmzp2bPnjBkz7B8vLCyMjIyMiYk5ffq0t742 H3DmzBmHP6xACQoKmjJlypdffun05H/++eduHsDlq66udsgT13/Nc4EM1P5C tDq7QdoOc/9CqPPMa529lpaWEydODB06tHfv3m+88QY3hRc5XItWD94XXIs2 165d27Bhw5gxY3r06NGnT58333xTBTv3hVd0eC1auS8Ab7h9+/b69etDQkLm zJlj/3hRUdHgwYOHDBmivn9562vzAfn5+eHh4cHBwRMmTDhw4EBxcfE777zT r1+/0NDQpKSkW7dudX7yP/vsMzcP4PK1zxPXf807PZIL5LoOO7HzG0S9f7Yd 5v6FUOeZ1zp76h3Xc88916VLF/VmTL3F4qbwIodr0erB+4Jr0ebUqVPq/b8t PdSpTklJqaur477wig6vRSv3BeANthuqV69ec+fOtX9c3Q7R0dFDhw5Vd6K3 vjYf8PXXX2dmZu7Zs8dqtdoeuXPnTlpamnpLEBsbW1BQ0PnJz83NdfMALt+D OtGVk+b0SC6Q6zrsxM5vkLKyMtuD7l8IdZ55rbP57rvv1JucX/ziFyaTafTo 0bZ/MM5N4RUdXotWD94XXIs233zzTVVV1cWLF999990nn3xSpYd6veK+8IoO r0Ur9wXgDbW1tVu3bg0KCkpISLB/XCXMgAED7G89GOXkyZPqpSYqKkq93HV+ 8tWRbh7A5evw505dPGlOj+QCua7DTuxQ2w3y6aef2h5x/0Ko88xrXeu9P4rh 4MGDgwYN6tq1669+9auzZ8/aHuem8LwHXYsHeRT3Bdeivbt37/79739X6TF8 +PCSkhLuCy+yvxaVlZUdHsN9ATxS6lvV3r17TSbTuHHj2n7XXjly5Ii6R0aP Hn3r1i0vfnm6a25urqmpuXnzpv2D6luPep15+umnP/roo85P/vXr1908gMvX Pk9c/zXv9EgukOs67MTOb5Ds7GzbI+5fCHWeea1Tb7oyMjJ++tOf9ujR48UX X1RXpO1vcVN4WCfXotWD9wXXQl2IixcvOjSIyoEnn3xy8ODBhw4d4r7wmM6v xdGjR7kvAK/Iz8/v27evutHKy8ttj9TV1e3YsaN79+5Tpkzx7temNXUat2/f rl5qnn/+efXCZXtQvebk5uaGhYXZ/uVZTk+++wf4uQf93+JcPGlcIKO0vxCu 3CBtH27Iefbna/HNN9/s27dPPf1evXrNnj1bvd1yOICbwmM6vxYevi/8+Vrc uXNn586dXbp0GT9+fFNTk+1Bdarz8vJCQkLUOTl37hz3hWc4vRaFhYXcF4BX VFRUTJ48Wd1oS5cuVbenuu+KiopGjhzZv3//bdu2efur05t6iVPvBJ566qm2 f1VrZWXl1KlTg4KCXn755ebmZqcn3/0D/FyHnej6SeMCGaXDC+H0Bmk70pDz 7M/Xori4eNiwYbYwaWhoaH8AN4XHOL0Wnrwv/PxaHD169Mc//vHAgQMtFovt EXWqX3nlla5du06cONGV79FtuBZucnotuC8Ar2hsbNy/f39oaGhISEh8fPys WbOioqK6deumbkx+b91N165dW7ZsWUBAQO/evadNm6beJ8fExAQGBqr/tP15 WU5PvvsH+LkO88T1k8YFMkqHF8LpDdLGkPPst9eipqZmw4YN6jw/9thj6rn3 /r4xY8ZcunSJm8IzXLkWnrwv/PlaKDdu3EhJSVGnuk+fPq+++qo61T/72c/U Xw4ZMuT48eOtfLPwIKfXgvsC8JaGhobs7Ozo6Gh1x6l7UN0X06dPf9D/axg/ iHplW7lypXpZs/0hzz169Bg/fnxBQUHbAU5PvvsH+LOrV68mJSVFREQ4/PEp rp80LpAhHnQhnN4gbQw5z/55La5cuTJ37tyABxg+fPiFCxdauSk8wsVr4cn7 wm+vhY3Kk9WrV6s2afv38U2aNMn+D7TkvvAYp9eC+wLwoqampurq6pKSkuvX r3v7a/E16gWnvLz85MmTDn9eQRunJ9/9A9Ce6yeNC/RIOb1B2hhynrkWneCm kMOT94WfX4u7d++eP3++qKjo9u3bHR7AfeExTq8F9wUAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAACPT/zHGQmA== "], {{0, 1036.}, {604., 0}}, {0, 255}, ColorFunction->RGBColor, ImageResolution->144.], BoxForm`ImageTag["Byte", ColorSpace -> "RGB", Interleaving -> True], Selectable->False], DefaultBaseStyle->"ImageGraphics", ImageSizeRaw->{604., 1036.}, PlotRange->{{0, 604.}, {0, 1036.}}]], "Output", TaggingRules->{}, CellChangeTimes->{{3.834229988015402*^9, 3.834230010245013*^9}, { 3.834230046093093*^9, 3.8342301144800797`*^9}, 3.8343110549283*^9, 3.8343137252545023`*^9, 3.834405408095542*^9}, CellLabel->"Out[1]=", CellID->119065486] }, Open ]], Cell[CellGroupData[{ Cell[BoxData[ InterpretationBox[Cell["\t", "ExampleDelimiter"], $Line = 0; Null]], "ExampleDelimiter", TaggingRules->{}, CellID->1807415723], Cell["\<\ Show the distribution of wages based on treatment (without regard to \ covariates):\ \>", "Text", TaggingRules->{}, CellChangeTimes->{{3.834230123005165*^9, 3.8342301737408237`*^9}}, CellID->383614075], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"KeySort", "@", RowBox[{ RowBox[{ InterpretationBox[ TagBox[ DynamicModuleBox[{Typeset`open = False}, FrameBox[ PaneSelectorBox[{False->GridBox[{ { PaneBox[GridBox[{ { StyleBox[ StyleBox[ AdjustmentBox["\<\"[\[FilledSmallSquare]]\"\>", BoxBaselineShift->-0.25, BoxMargins->{{0, 0}, {-1, -1}}], "ResourceFunctionIcon", FontColor->RGBColor[ 0.8745098039215686, 0.2784313725490196, 0.03137254901960784]], ShowStringCharacters->False, FontFamily->"Source Sans Pro Black", FontSize->0.6538461538461539 Inherited, FontWeight->"Heavy", PrivateFontOptions->{"OperatorSubstitution"->False}], StyleBox[ RowBox[{ StyleBox["MapReduceOperator", "ResourceFunctionLabel"], " "}], ShowAutoStyles->False, ShowStringCharacters->False, FontSize->Rational[12, 13] Inherited, FontColor->GrayLevel[0.1]]} }, GridBoxSpacings->{"Columns" -> {{0.25}}}], Alignment->Left, BaseStyle->{LineSpacing -> {0, 0}, LineBreakWithin -> False}, BaselinePosition->Baseline, FrameMargins->{{3, 0}, {0, 0}}], ItemBox[ PaneBox[ TogglerBox[Dynamic[Typeset`open], {True-> DynamicBox[FEPrivate`FrontEndResource[ "FEBitmaps", "IconizeCloser"], ImageSizeCache->{11., {2., 9.}}], False-> DynamicBox[FEPrivate`FrontEndResource[ "FEBitmaps", "IconizeOpener"], ImageSizeCache->{11., {2., 9.}}]}, Appearance->None, BaselinePosition->Baseline, ContentPadding->False, FrameMargins->0], Alignment->Left, BaselinePosition->Baseline, FrameMargins->{{1, 1}, {0, 0}}], Frame->{{ RGBColor[ 0.8313725490196079, 0.8470588235294118, 0.8509803921568627, 0.5], False}, {False, False}}]} }, BaselinePosition->{1, 1}, GridBoxAlignment->{"Columns" -> {{Left}}, "Rows" -> {{Baseline}}}, GridBoxItemSize->{ "Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}, GridBoxSpacings->{"Columns" -> {{0}}, "Rows" -> {{0}}}], True-> GridBox[{ {GridBox[{ { PaneBox[GridBox[{ { StyleBox[ StyleBox[ AdjustmentBox["\<\"[\[FilledSmallSquare]]\"\>", BoxBaselineShift->-0.25, BoxMargins->{{0, 0}, {-1, -1}}], "ResourceFunctionIcon", FontColor->RGBColor[ 0.8745098039215686, 0.2784313725490196, 0.03137254901960784]], ShowStringCharacters->False, FontFamily->"Source Sans Pro Black", FontSize->0.6538461538461539 Inherited, FontWeight->"Heavy", PrivateFontOptions->{"OperatorSubstitution"->False}], StyleBox[ RowBox[{ StyleBox["MapReduceOperator", "ResourceFunctionLabel"], " "}], ShowAutoStyles->False, ShowStringCharacters->False, FontSize->Rational[12, 13] Inherited, FontColor->GrayLevel[0.1]]} }, GridBoxSpacings->{"Columns" -> {{0.25}}}], Alignment->Left, BaseStyle->{LineSpacing -> {0, 0}, LineBreakWithin -> False}, BaselinePosition->Baseline, FrameMargins->{{3, 0}, {0, 0}}], ItemBox[ PaneBox[ TogglerBox[Dynamic[Typeset`open], {True-> DynamicBox[FEPrivate`FrontEndResource[ "FEBitmaps", "IconizeCloser"]], False-> DynamicBox[FEPrivate`FrontEndResource[ "FEBitmaps", "IconizeOpener"]]}, Appearance->None, BaselinePosition->Baseline, ContentPadding->False, FrameMargins->0], Alignment->Left, BaselinePosition->Baseline, FrameMargins->{{1, 1}, {0, 0}}], Frame->{{ RGBColor[ 0.8313725490196079, 0.8470588235294118, 0.8509803921568627, 0.5], False}, {False, False}}]} }, BaselinePosition->{1, 1}, GridBoxAlignment->{"Columns" -> {{Left}}, "Rows" -> {{Baseline}}}, GridBoxItemSize->{ "Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}, GridBoxSpacings->{"Columns" -> {{0}}, "Rows" -> {{0}}}]}, { StyleBox[ PaneBox[GridBox[{ { RowBox[{ TagBox["\<\"Version (latest): \"\>", "IconizedLabel"], " ", TagBox["\<\"1.0.0\"\>", "IconizedItem"]}]}, { TagBox[ TemplateBox[{ "\"Documentation \[RightGuillemet]\"", "https://resources.wolframcloud.com/FunctionRepository/\ resources/MapReduceOperator"}, "HyperlinkURL"], "IconizedItem"]} }, DefaultBaseStyle->"Column", GridBoxAlignment->{"Columns" -> {{Left}}}, GridBoxItemSize->{ "Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}], Alignment->Left, BaselinePosition->Baseline, FrameMargins->{{5, 4}, {0, 4}}], "DialogStyle", FontFamily->"Roboto", FontSize->11]} }, BaselinePosition->{1, 1}, GridBoxAlignment->{"Columns" -> {{Left}}, "Rows" -> {{Baseline}}}, GridBoxDividers->{"Columns" -> {{None}}, "Rows" -> {False, { GrayLevel[0.8]}, False}}, GridBoxItemSize->{ "Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}]}, Dynamic[ Typeset`open], BaselinePosition->Baseline, ImageSize->Automatic], Background->RGBColor[ 0.9686274509803922, 0.9764705882352941, 0.984313725490196], BaselinePosition->Baseline, DefaultBaseStyle->{}, FrameMargins->{{0, 0}, {1, 0}}, FrameStyle->RGBColor[ 0.8313725490196079, 0.8470588235294118, 0.8509803921568627], RoundingRadius->4]], {"FunctionResourceBox", RGBColor[0.8745098039215686, 0.2784313725490196, 0.03137254901960784], "MapReduceOperator"}, TagBoxNote->"FunctionResourceBox"], ResourceFunction[ ResourceObject[ Association[ "Name" -> "MapReduceOperator", "ShortName" -> "MapReduceOperator", "UUID" -> "856f4937-9a4c-44a9-88ae-cfc2efd4698f", "ResourceType" -> "Function", "Version" -> "1.0.0", "Description" -> "Like an operator form of GroupBy, but where one also specifies a \ reducer function to be applied", "RepositoryLocation" -> URL["https://www.wolframcloud.com/obj/resourcesystem/api/1.0"], "SymbolName" -> "FunctionRepository`$ad7fe533436b4f8294edfa758a34ac26`\ MapReduceOperator", "FunctionLocation" -> CloudObject[ "https://www.wolframcloud.com/obj/6d981522-1eb3-4b54-84f6-\ 55667fb2e236"]], ResourceSystemBase -> Automatic]], Selectable->False], "[", RowBox[{ RowBox[{ RowBox[{"(", RowBox[{ RowBox[{"If", "[", RowBox[{ RowBox[{"#treated", "==", "1"}], ",", "\"\\"", ",", "\"\\""}], "]"}], "&"}], ")"}], "->", RowBox[{"(", RowBox[{"#re78", "&"}], ")"}]}], ",", RowBox[{ RowBox[{"Histogram", "[", RowBox[{"#", ",", RowBox[{"PlotRange", "->", RowBox[{"{", RowBox[{ RowBox[{"{", RowBox[{"0", ",", "35000"}], "}"}], ",", "All"}], "}"}]}], ",", RowBox[{"ImageSize", "->", "500"}]}], "]"}], "&"}]}], "]"}], "[", RowBox[{"Normal", "@", "jobs"}], "]"}]}]], "Input", TaggingRules->{}, CellChangeTimes->{{3.834229932431963*^9, 3.834230113947672*^9}, { 3.834230176206521*^9, 3.834230223779228*^9}, {3.834313763713846*^9, 3.834313795874537*^9}}, CellLabel->"In[1]:=", CellID->655850231], Cell[BoxData[ GraphicsBox[ TagBox[RasterBox[CompressedData[" 1:eJzs3Q2U1XWd+PEZUCwSUlt8IpRycw20lpMySG4pmUbWSvhUrq7GPxWMFqFS Z3cTHzKIps3/H7TTWu74VOvqSTfNksSHlAQRiMAEU8mHDHAmLTN5uAz/7/o7 3MPeOw937tw785H7ep3v8Qzf+d3ffOb36zK84zLzrknTJp7br66u7otvSf+Z +NmLjpkx47ONJ+2RfnHKP31xynn/dM7nxv/Tl84575wZYyb1T5sDd62re3GX urr/eXsbAAAAAAAAAAAAAADUqh+9oa+nAAAAgEKKFQAAgJgUKwAAADEpVgAA AGJSrAAAAMSkWAEAAIhJsQIAABCTYgUAACAmxQoAAEBMihUAAICYFCsAAAAx KVYAAABiUqwAAADEpFgBAACISbECAAAQk2IFAAAgppSrTU1NzdupVwAAAILI inXhdqtXr+7riQAAAOB/eFUwAAAAMSlWAAAAYlKsAAAAxKRYAQAAiEmxAgAA EJNiBQAAICbFCgAAQEyKFQAAgJgUKwAAADEpVgAAAGIqu1hbA6v4VQIAAKD3 lVesqQonTzrtsumnBFyNn//Uf37/hmpcKwAAAHpTecX6m9/85v995bTcsksC rjX/PXXet75WjWsFAABAb1KsAAAAxKRYAQAAiEmxAgAAEJNiBQAAICbFCgAA QEyKFQAAgJgUKwAAADEpVgAAAGJSrAAAAMSkWAEAAIhJsQIAABCTYgUAACAm xQoAAEBMihUAAICYFCsAAAAxKVYAAABiSrna1NTUvF2J9apYAQAAqLasWBdu t3r16lIepVgBAACoNq8KBgAAICbFCgAAQEyKFQAAgJgUKwAAADEpVgAAAGJS rAAAAMSkWAEAAIhJsQIAABCTYgUAACAmxQoAAEBMihUAAICYFCsAAAAxKVYA AABiUqwAAADEpFgBAACISbECAAAQk2IFAAAgJsUKAABATIoVAACAmBQrAAAA MSlWAAAAYlKsAAAAxKRYAQAAiEmxAgAAEJNiBQAAICbFCgAAQEyKFQAAgJgU KwAAADEpVgAAAGJKudrU1NS8XYn1qlgBAACotqxYF263evXqUh6lWAEAAKg2 rwoGAAAgJsUKAABATIoVAACAmBQrAAAAMSlWAAAAYlKsAAAAxKRYAQAAiEmx AgAAEJNiBQAAICbFCgAAQEyKFQAAgJgUKwAAADEpVgAAAGJSrAAAAMSkWAEA AIhJsQIAABCTYgUAACAmxQoAAEBMihUAAIDu2rhx49SpU2+55ZaC/UceeeTm Ivfee++Oxzz++ONXXnnllClTZs6cuWLFik4+imIFAACguyZPnlxXVzdt2rSC /aOPPrquyFFHHZU/4Kqrrurfv3/+Xf369WtqaurooyhWAAAASvfKK6+cffbZ WW8WF+uee+5ZX1+f9qfvYN68edl7Fy5cmBJ1wIABc+fOXbly5Zw5c1K9puOX Ll3a7sdSrAAAAJTozjvvHDp0aP5vSAuKde3atWnzoIMO6ujhEyZMSAfMnj07 vzNr1qy0M2nSpHaPV6wAAACUaI899kiBOW7cuMbGxuJivf3229Pmqaee2u5j t27dOnjw4HTACy+8kN9ct25dfX39wIEDt2zZUvwQxQoAAECJjjvuuNtuu62t rW3u3LnFxTpz5sy0OX369EsvvXTixIlnnXXWvHnzXn/99ey9Tz75ZHrv3nvv XXDOfffdN+2vWbOm+MMpVgAAALqr3WI98cQTi7/t0ogRI5555pn03kWLFqVf jho1quBUaSftP/zww8UfRbECAADQXe0W64EHHpg2Dz300B/+8IdPPfXUrbfe esABB6SdY445pq2tbcGCBenthoaGglONHTs27c+fP7/4oyhWAAAAuqvdYr37 7ruvueaa1tbW/M7KlSt33XXXdOSyZcuyYj3yyCMLTjVmzJi0f//99xd/FMUK AABAd7VbrO1qaGhIR95www2PPvpoemPkyJEFB4wYMSJL2uLHKlYAAAC6q91i ffXVV1988cWCI4899th05HXXXbdhw4b0xuDBg9va2vLvTW8PGjQo7a9fv774 o6RcbWpqam5PJ7MpVgAAgFpWXKxLly5NO7vvvvumTZvymxs3btxnn33S/sKF C9Mvhw0blt5evnx5/oBly5alneHDh7f7UbJiXdieTmZTrAAAALWsuFhzudxe e+2VNmfNmpXfvPjii9POIYcckv241RkzZqRfnnDCCZs3b06/TP8dP3582rno oova/SheFQwAAEB3tfuq4Ouvvz77iTYTJky45JJLjjnmmPT2Lrvskv8+wOvX rx8yZEjaHD16dKrUI444Ir09dOjQdl8SvE2xAgAA0H1XX311is0LLrigYP/G G2/cf//98z+M9eCDD77nnnt2PGDt2rVjxoypr6/PDmhoaFi1alVHH0WxAgAA UEFtbW3PPvvs4sWLUzl2dExra+uSJUuee+65zk+lWAEAAIhJsQIAABCTYgUA ACAmxQoAAEBMihUAAICYFCsAAAAxKVYAAABiUqwAAADEpFgBAACISbECAAAQ k2IFAAAgJsUKAABATIoVAACAmBQrAAAAMSlWAAAAYlKsAAAAxKRYAQAAiEmx AgAAEJNiBQAAICbFCgAAQEyKFQAAgJgUKwAAADEpVgAAAGJSrAAAAMSkWAEA AIhJsQIAABBTytWmpqbm7UqsV8UKAABAtWXFunC71atXl/IoxQoAAEC1eVUw AAAAMSlWAAAAYlKsAAAAxKRYAQAAiEmxAgAAEJNiBQAAICbFCgAAQEyKFQAA gJgUKwAAADEpVgAAAGJSrAAAAMSkWAEAAIhJsQIAABCTYgUAACAmxQoAAEBM ihUAAICYFCsAAAAxKVYAAABiUqwAAADEpFgBAACISbECAAAQk2IFAAAgJsUK AABATIoVAACAmBQrAAAAMSlWAAAAYlKsAAAAxKRYAQAAiEmxAgAAEFPK1aam pubtSqxXxQoAAEC1ZcW6cLvVq1eX8ijFCgAAQLV5VTAAAAAxKVYAAABiUqwA AADEpFgBAACISbECAAAQk2IFAAAgJsUKAABATIoVAACAmBQrAAAAMSlWAAAA YlKsAAAAxKRYAQAAiEmxAgAAEJNiBQAAICbFCgAAQEyKFQAAgJgUKwAAADEp VgAAAGJSrAAAAMSkWAEAAIhJsQIAABCTYgUAACAmxQoAAEBMihUAAICYFCsA AAAxKVYAAABiUqwAAADEpFgBAACISbECAAAQU8rVpqam5u1KrFfFCgAAQLVl xbpwu9WrV5fyKMUKAABAtXlVMAAAADEpVgAAAGJSrAAAAMSkWAEAAIhJsQIA ABCTYgUAACAmxQoAAEBMihUAAICYFCsAAAAxKVYAAABiUqwAAADEpFgBAACI SbECAAAQk2IFAAAgJsUKAABATIoVAACAmBQrAAAAMSlWAAAAYlKsAAAAxKRY AQAAiEmxAgAAEJNiBQAAICbFCgAAQEyKFQAAgJgUKwAAADEpVgAAAGJSrAAA AMSkWAEAAIhJsQIAABBTytXGxsam7Zqbm0t5lGIFAACg2lKxpkpdvV1LS0sp j1KsAAAAVJtXBQMAABCTYgUAACAmxQoAAEBMihUAAICYFCsAAAAxKVYAAABi UqwAAADEpFgBAACISbECAAAQk2IFAAAgJsUKAABATIoVAACAmBQrAAAAMSlW AAAAYlKsAAAAxKRYAQAAiEmxAgAAEJNiBQAAICbFCgAAQHdt3Lhx6tSpt9xy S/G7Hn/88SuvvHLKlCkzZ85csWJFGQfkKVYAAAC6a/LkyXV1ddOmTSvYv+qq q/r371+3Xb9+/Zqamrp1wI4UKwAAAKV75ZVXzj777Kw3C4p14cKFqUAHDBgw d+7clStXzpkzJ8VpfX390qVLSzyggGIFAACgRHfeeefQoUPzf0NaUKwTJkxI m7Nnz87vzJo1K+1MmjSpxAMKKFYAAABKtMcee6TAHDduXGNjY0Gxbt26dfDg wWnzhRdeyG+uW7euvr5+4MCBW7Zs6fKA4g+nWAEAACjRcccdd9ttt7W1tc2d O7egWJ988sm0s/feexc8ZN999037a9as6fKA4g+nWAEAAOiu4mJdtGhR2hk1 alTBkWkn7T/88MNdHlD8URQrAAAA3VVcrAsWLEg7DQ0NBUeOHTs27c+fP7/L A4o/imIFAACguzoq1iOPPLLgyDFjxqT9+++/v8sDij+KYgUAAKC7iov10Ucf TTsjR44sOHLEiBFpf9myZV0eUPxRFCsAAADdVVysGzZsSDuDBw9ua2vLb6a3 Bw0alPbXr1/f5QHFHyXlamNjY1N7OplNsQIAANSy4mJNhg0bljaXL1+e31m2 bFnaGT58eIkHFEjF2tzcvLo9ncymWAEAAGpZu8U6Y8aMtHnCCSds3rw5/TL9 d/z48WnnoosuKvGAAl4VDAAAQHe1W6zr168fMmRI2h89enSK0COOOCK9PXTo 0Pwrfrs8oIBiBQAAoLuuvvrqFJsXXHBBwf7atWvHjBlTX19f94aGhoZVq1Z1 64AdKVYAAAAqq7W1dcmSJc8991zZB2QUKwAAADEpVgAAAGJSrAAAAMSkWAEA AIhJsQIAABCTYgUAACAmxQoAAEBMihUAAICYFCsAAET2H//xH3fddVdfTwF9 Q7ECAEBkI0aMOO200/p6CugbihUAACJTrNQyxQoAAJEpVmqZYgUAgMgUK7VM sQIAQGSKlVqmWAEAIDLFSi1TrAAAEJlipZYpVgAAiEyxUssUKwAARKZYqWWK FQAA+taLL774gx/8oKP3KlZqmWIFAIC+1dTUNHbs2I7eq1ipZYoVAAD61vvf //799tuvo/cqVgpcfvnl6X8zEydO7OtBeoNiBQCAPrRixYq6uroLLrigowMU KwXOPffc9L+Z9D+Mvh6kNyhWAADoQ1/60pdSfSxdurSjA0IV6+LFi7/2ta/d d999b7qTV1UvT65Yu6RYAQCg57Zu3br//vt3nh6hivUrX/lKaqUzzjjjTXfy qurlyRVrlxQrAAD03Pz581N6fO1rnf3xNVSxTp48uXppVtWTV1UvT65Yu6RY AQCg584888z6+vpnn322k2PKLtYXX3xx0aJFf/jDH7o88plnnnnkkUdeeeWV Lo88+eSTq5dmPTl5S0vLkiVLFi9evH79+o6OaWtrSyGTjvnTn/7U5QlzudzT Tz+9cOHCJ554YvPmzZ0fXMbk3Rpm06ZNjz32WJon++V5551XU8Xa2NjYtF1z c3Mpj1KsAADQQ3/+85/f9ra3HX300Z0fVmKxnnLKKW9/+9tvvvnmLVu2zJw5 89BDD63brqGh4cknnyx+SGtr62c/+9n0qPyRBx100PXXX19w2I9//OP3vve9 w4cP32effXbZZZd02K677vr2/+0DH/hA6Z94z08+ZsyYtJ9yMr39wAMPfOhD H8p/CvX19VdccUXB8SneP/3pTw8ePDh/TPpM//M//7PdkR566KHTTz99zz33 zJ8zDTZx4sR169ZV5LJ0a5jf/va3xx9//G677ZYdPGTIkCuvvHLq1Kk1Vayp Uldv19LSUsqjFCsAAPTQDTfckLrje9/7XueHlVisxx57bDrbxRdfPG7cuKxu Bg4cmG+uoUOHbty4ccfjFy9enFIrX2R/9Vd/lT/4E5/4RMre/JE33XRTXVfe 8573lHcRyjt52kn7qWXuuuuufM3lpQu748HpsD322CN71/7773/YYYe99a1v zX45efLkgjOPHz8+f55+/fql49N/s1/ut99+f/zjH3s4ebeGue+++/IHpzEG DRq048krUqxPPfXUD37wg56fp3q8KhgAAPrERz/60be85S07RlC7ulWsmTPP PHPt2rVp8/e///2JJ56YbV577bX5g19//fW/+Zu/SZsDBgyYN2/eX/7yl7T5 7LPP5nttx39am977wnbve9/70nsnTpz4wv9W8PePpSvv5FmxnnrqqWn+lNvn n3/+L3/5y5Tkra2td9xxR/pv/sjnn38+i770+T744IP5Tz/7Fs3Jvffeu+OZ zzjjjP79+6f/PvTQQ3/+85/Tzquvvjpt2rTs4AsvvLAnk3drmNdee+3AAw9M m2mer3/969n/Tp544on8PapIsc6ZM6e+vv473/lOz09VJYoVAAB63+9+97t+ /fql5uryyG4Va6qPuXPn7rif8u1tb3tbeteUKVPym1dccUW7fx25devW7BW2 u+22W7v/uvbwww+vq9q/Yy395FmxJrvvvvtPf/rTTo785Cc/mQ57xzveUfzp nHDCCeldBa/K3rBhw5o1a4rPc9RRR6WDDz300J5M3q1hLr300uxzvPrqqwsO TmlcqWJNpk+fnv5n8+///u8VOVvFKVYAAOh9c+bMSdFx5513dnlkt4r1+OOP L37X6NGj07tSE+V3UhmlnZEjR7a1tRUc/POf/zwLpe9///vFp4pWrJ2/onXL li1vectb0mGXXXZZ8XtvueWWrB87/1ivvPLK0qVLTz/99HRwOlvxFStx8u4O 83d/93dp5+CDD87lcgUHV/x7BU+bNi1F645/Cx+HYgUAgN532GGHDRkyZMd/ LtqRbhXrJz7xieJ3ZX+1t+Pf3+21115p57zzzis+OPXRrrvuWvfGP4ktfm+o Ym33k93RihUrsrAdO3bsmUWybE+Kv5nPL3/5yyuuuGLcuHH77rtv3f/22muv lTd5d4fJvvXT5z73ueJTVeOn23zhC19I0frd7363guesCMUKAAC9LAVRKo7P f/7zpRzc82KdMGHCjsX66quvZnF0ySWXtHuqYcOGpfeefPLJxe8KVaxdXsA7 7rijrgS///3v8w95+eWXsxfoZt797nd//OMfTx8o/8+Eyy7Wbg3zpz/9qZN7 VHqxpvN8pTT/+q//2r9//xStXX4rsF6mWAEAoJctWLCgD4u1ra0t+9Eq7Q6w devW7LvvfvGLXyx+b5Zm//AP/1DK5N1V+slLLNYlS5Zk3TdnzpzHOpY+5ez4 P//5zwcffHDdG/+Md+bMmU888UT+VNdee22Xxdr55N0aJn8Xpk+fXnyq0ov1 pZdeOqZk2Xhf+tKXujxtb1KsAADQy1KPDB06tNdeFVxQrMkHP/jBtNPuTwtN 0ZSVS8E3ZcpkL14dP358l/OUofSTl1isr7/+evaTUtt9hXOxu+66K/vcH3jg gYJ3dV6spUze3WEOOeSQdPBHPvKR4ndV41XBN910U79+/c4///wKnrMiFCsA APRQW1vbI4888uCDD27evLnEh6RsSdGREqnLI6tRrI2NjVl/3XHHHQUHZz88 pX///unP/MWn+sd//Mf03ne+852ltHZ3lX7yEos1yb718Vvf+tZVq1Z1efDl l19e98ZP/CnY37Rp09lnn91JsZY4ebeGSTc9+4jLly/fcb+lpWXUqFGVLdab b7453fHJkye3+32l+pZiBQCAnti6deuYMWOyuCj9Lx+feOKJdPynP/3pLo+s RrH+6U9/OuCAA+re+OkwqVayV6K+/PLLn/nMZ7JPpKO/B/zOd76Tf+1ovs42 btz48MMPdzlhl0o/eenFunr16oEDB6aD99tvv5tuuilfZJs3b/7e977X0NCw fv36/MHNzc3ZAN/+9reznVwud99996VbkP93pu0Wa4mTd2uYX/3qV/369UsH Dxs2bNGiRdkwDzzwwLve9a7sY1WqWL///e+nXD3nnHMC5uo2xQoAAD2T/3Ew mR2jo3OpUFK/pHjs/LBqFGuSQuztb397NnPq1r/+67/O+ig56qijUm21+1HS fvZ9mZI999zzve9972GHHZZV2G9/+9suh+xc6ScvvVi3vfEXiNlJkkGDBh1+ +OEjR47M/pVocvnll+eP3LBhw/7775/tDx8+/NBDD81+lG19ff3HPvaxToq1 9MlLH2bbGz8pNf+/q3TaAQMG5Fu1UsV62223pVydNGlSzFzdplgBAKBnXnrp pXxxJCkBSnzgt7/97XT89ddf3/lhJRbr8ccfn842ceLE4ne1W6zJCy+88KlP fSr7WTaZd7zjHVdddVX+OxG16/nnnx83btyOkZ6CLpXXY4891uWQXSrx5Nm/ 8SyxWJNnn332pJNOyn4caibl+fve974bbrih4Ked/upXv0rBnj/srW9960c+ 8pFf/OIXf/zjH+veeLF0Ry1f+mUpfZjku9/9bvZjbjLpE7/99tt//OMfp7f/ 9m//tsRPvxP33HPP1KlTO7/jfUuxAgBAD1177bUf+MAHsqa45pprSnzUyy+/ nFL3ox/9aOeHlVisZdu0adOyZcseeuihVFKlPyp1+oIFCx588MH02D/84Q+V HalKJ089uGbNmpR7y5cv/8tf/tLJkWvXrp0/f/6jjz5a+j9MzpQ+eenDJE8/ /fTPf/7z3/3ud90aZuegWAEAoOc2bNjQv3//VKz33HNP6Y869dRT+/Xr9+KL L3ZyTLWLFSJTrAAA0HPZ994ZMGDAunXrSn/U3XffnR71zW9+s5NjFCu1TLEC AEAPPf300wceeGC3/mVlJpfL7bfffqNGjerkGMVKLVOsAADQE1dfffU73vGO lKvvf//7W1pauvvwL3/5y+mxjz/+eEcHKFZqmWIFAICytbW1HXjggbvtttsV V1zR3e/Sk1m1alUq1sbGxo4OUKzUMsUKAAA98eMf//jXv/51T85w+OGHDx8+ vKP3KlZqmWIFAIC+NW/evNGjR3f0XsVKLVOsAADQt1paWu66666O3qtYqWWK FQAAIlOs1DLFCgAAkSlWapliBQCAyBQrtUyxAgBAZIqVWqZYAQAgMsVKLVOs AAAQmWKllilWAACITLFSyxQrAABEplipZYoVAAAiU6zUspSrjY2NTds1NzeX 8ijFCgAAveO8886bPXt2X08BfSMVa6rU1du1tLSU8ijFCgAAQLV5VTAAAAAx KVYAAABiUqwAAADEpFgBAACISbECAAAQk2IFAAAgJsUKAABATIoVAACAmBQr AAAAMSlWAAAAYlKsAAAAxKRYAQAAiEmxAgAAEJNiBQAAICbFCgAAQEyKFQAA gJgUKwAAADEpVgAAAGJSrAAAAMSkWAEAAIhJsQIAABCTYgUAACAmxQoAAEBM ihUAAICYFCsAAAAxKVYAAABiUqwAAADEpFgBAACISbECAAAQU8rVxsbGpu2a m5tLeZRiBQAAoNpSsaZKXb1dS0tLKY9SrAAAAFSbVwUDAAAQk2IFAAAgJsUK AABATIoVAACAmBQrAAAAMSlWAAAAYlKsAAAAxKRYAQAAiEmxAgAAEJNiBQAA ICbFCgAAQEyKFQAAgJgUKwAAADEpVgAAAGJSrAAAAMSkWAEAAIhJsQIAABCT YgUAACAmxQoAAEBMihUAAICYFCsAAAAxKVYAAABiUqwAAADEpFgBAACISbEC AAAQk2IFAAAgJsUKAABATIoVAACAmBQrAAAAMaVcbWxsbNquubm5lEcpVgAA AKotFWuq1NXbtbS0lPIoxQoAAEC1eVUwAAAAMSlWAAAAYlKsAAAAxKRYAQAA iEmxAgAAEJNiBQAAICbFCgAAQEyKFQAAgJgUKwAAADEpVgAAAGJSrAAAAMSk WAEAAIhJsQIAABCTYgUAACAmxQoAAEBMihUAAICYFCsAAAAxKVYAAABiUqwA AABU0COPPHJzkXvvvXfHYx5//PErr7xyypQpM2fOXLFiRUenUqwAAABU0NFH H11X5KijjsofcNVVV/Xv3z//rn79+jU1NbV7KsUKAABABe2555719fXTpk2b voN58+Zl7124cGFK1AEDBsydO3flypVz5sxJ9ZqOX7p0afGpFCsAAACVsnbt 2rq6uoMOOqijAyZMmJAOmD17dn5n1qxZaWfSpEnFBytWAAAAKuX2229P+Xnq qae2+96tW7cOHjw4HfDCCy/kN9etW1dfXz9w4MAtW7YUHK9YAQAAqJSZM2em IJ0+ffqll146ceLEs846a968ea+//nr23ieffDK9d++99y541L777pv216xZ U7CvWAEAAKiUE088sfjbLo0YMeKZZ55J7120aFH65ahRowoelXbS/sMPP1yw r1gBAAColAMPPDC156GHHvrDH/7wqaeeuvXWWw844IC0c8wxx7S1tS1YsCC9 3dDQUPCosWPHpv358+cX7CvWXvbVyy+5/J+/EHNd971r+/ryAAAAb2533333 Nddc09ramt9ZuXLlrrvumoJ02bJlWbEeeeSRBY8aM2ZM2r///vsL9hVrb0rX 7bJpf7/hgS8HXM/fMz1Fa19fIQAAYCfU0NCQgvSGG2549NFH0xsjR44sOGDE iBFZ0hbsK9beFPm6pWhVrAAAQA+9+uqrL774YsHmsccem4L0uuuu27BhQ3pj 8ODBbW1t+femtwcNGpT2169fX/DAlKuNjY1N7elkhsjlpVgVKwAA0CeWLl2a wnP33XfftGlTfnPjxo377LNP2l+4cGH65bBhw9Lby5cvzx+wbNmytDN8+PDi E6ZibW5uXt2eTsaIXF6KVbECAAB9IpfL7bXXXik/Z82ald+8+OKL084hhxyS /bjVGTNmpF+ecMIJmzdvTr9M/x0/fnzaueiii4pP6FXBvSnydVOsAABAz11/ /fXZT7SZMGHCJZdccswxx6S3d9lll/z3AV6/fv2QIUPS5ujRo1OlHnHEEent oUOHFr8keJti7V2Rr5tiBQAAKuLGG2/cf//98z+M9eCDD77nnnt2PGDt2rVj xoypr6/PDmhoaFi1alW7p1KsvSnydVOsAABApbS1tT377LOLFy9OEdTRMa2t rUuWLHnuuec6OY9i7U2Rr5tiBQAAolGsvSnydVOsAABANIq1N0W+booVAACI RrH2psjXTbECAADRKNbeFPm6KVYAACAaxdqbIl83xQoAAESjWHtT5OumWAEA gGgUa2+KfN0UKwAAEI1i7U2Rr5tiBQAAolGsvSnydVOsAABANIq1N0W+bqlY /3nGOb+JqrW1ta/vHgAA0NsUa2+KfN1SsX7sQyPTeDHXP194QV/fPQAAoLcp 1t4U+bo9f8/0Kace3udjtLu8YhkAAGrTTlmsTbMu6dNXsHbopz/96Zwvn9jn l6jdpVgBAIBodr5iXXHr5I99+H19/irWdtfMqeNnTv5wn1+idpdiBQAAotn5 inXxTZ+74vPH9PkYb7rZFCsAABCNYjVbthQrAAAQjWI1W7YUKwAAEI1iNVu2 FCsAABCNYjVbthQrAAAQjWI1W7YUKwAAEI1iNVu2FCsAABCNYjVbthQrAAAQ jWI1W7YUKwAAEI1iNVu2FCsAABCNYjVbthQrAAAQTcrVxsbGpu2am5tLeZRi 3flmU6wAAEA0qVhTpa7erqWlpZRHKdadbzbFCgAARONVwWbLlmIFAACiUaxm y5ZiBQAAolGsZsuWYgUAAKJRrGbLlmIFAACiUaxmy5ZiBQAAolGsZsuWYgUA AKJRrGbLlmIFAACiUaxmy5ZiBQAAolGsZsuWYgUAAKJRrGbLlmIFAACiUaxm y5ZiBQAAolGsZsuWYgUAAKJRrGbLlmIFAACiUaxmy5ZiLVtra+viqNJTta8v DwAAlE+xmi1birVsZ//DSTd9/TMx14zP/2NfXx4AACifYjVbthRr2aadc1Kf X6KOVpqtry8PAACUT7GaLVuKtWyKFQAAqkSxmi1birVsihUAAKpEsZotW4q1 bIoVAACqRLGaLVuKtWyKFQAAqkSxmi1birVsihUAAKpEsZotW4q1bIoVAACq RLGaLVuKtWyKFQAAqkSxmi1birVsihUAAKpEsZotW4q1bIoVAACqRLGaLVuK tWyKFQAAqkSxmi1birVsihUAAKpEsZotW4q1bIoVAACqRLGaLVuKtWyKFQAA qiTlauMOmpqaSnmUYt35ZlOsZVOsAABQJalYm5ubW7Yr8VGKdeebTbGWTbEC AECVeFWw2bKlWMumWAEAoEoUq9myFbxYTz/puHnf+lrMdfLHG/r8EnW0FCsA AG9qitVs2YpcrGm2T39s5Jr/nhpzffLDB/f5JepoKVYAAN7UFKvZshW8WMPO ltaEY/6mz2foaClWAADe1BSr2bIVuQojz5ZTrAAAUDWK1WzZilyFkWfLKVYA AKgaxWq2bEWuwsiz5RQrAABUjWI1W7YiV2Hk2XKKFQAAqkaxmi1bkasw8mw5 xQoAAFWjWM2WrchVGHm2nGIFAICqUaxmy1bkKow8W06xAgBA1ShWs2UrchVG ni2nWAEAoGoUq9myFbkKI8+WU6wAAFA1itVs2YpchZFnyylWAACoGsVqtmxF rsLIs+UUKwAAVI1iNVu2Ildh5NlyihUAAKpGsZotW5GrMPJsOcUKAABVo1jN lq3IVRh5tpxiBQCAqlGsZstW5CqMPFtOsQIAQNUoVrNlK3IVRp4tp1gBAKBq FKvZshW5CiPPllOsAABQNYrVbNmKXIWRZ8spVgAAqBrFarZsRa7CyLPlFCsA AFSNYjVbtiJXYeTZcooVAACqRrGaLVuRqzDybDnFCgAAVaNYzZatyFUYebac YgUAgKpRrGbLVuQqjDxbTrECAEDVKFazZStyFUaeLadYAQCgahSr2bIVuQoj z5ZTrAAAUDUpVxt30NTUVMqjFOvON1vkKow8W06xAgBA1aRibW5ubtmuxEcp 1p1vtshVGHm2nGIFAICq8apgs2UrchVGni2nWAEAoGoUq9myFbkKI8+WU6wA AFA1itVs2YpchZFnyylWAACoGsVqtmxFrsLIs+UUKwAAVI1iNVu2Ildh5Nly ihUAAKpGsZotW5GrMPJsOcUKAABVo1jNlq3IVRh5tpxiBQCAqlGsZstW5CqM PFtOsQIAQNUoVrNlK3IVRp4tp1gBAKBqFKvZshW5CiPPllOsAABQNYrVbNmK XIWRZ8spVgAAqBrFarZsRa7CyLPlFCsAAFSNYjVbtiJXYeTZcooVAACqRrGa LVuRqzDybDnFCgAAVaNYzZatyFUYebacYgUAgKpRrGbLVuQqjDxbTrECAEDV KFazZStyFUaeLadYAQCgahSr2bIVuQojz5ZTrAAAUDWK1WzZilyFkWfLKVYA AKgaxWq2bEWuwsiz5RQrAABUjWI1W7YiV2Hk2XKKFQAAqkaxmi1bkasw8mw5 xQoAAFWjWM2WrchVGHm2nGIFAICqUaxmy1bkKow8Wy52sR7dcHCK1pjrq5df Uo3f0wAA2JkoVrNlK3IVRp4tF7tYI8/m738BAOiSYjVbtiJXYeTZcrGrMPJs ihUAgC4pVrNlK3IVRp4tF7sKI88W+RXLMy6YWo3fbwEA6C7FarZsRa7CyLPl Yleh2cpbl00/pbW1tRq/5QIA0C2K1WzZilyFkWfLxS4vs5W3FCsAQBApVxt3 0NTUVMqjFOvON1vkKow8Wy52eZmtvKVYAQCCSMXa3Nzcsl2Jj1KsO99skasw 8my52OVltvKWYgUACMKrgs2WrchVGHm2XOzyMlt5S7ECAAShWM2WrchVGHm2 XOzyMlt5a8rpH2qadcm8b30t4Lrue9dW42sBAEBMitVs2YpchZFny8UuL7OV t04ff+gjN/6fNf89NeBq/Pyn/P0vAFA7FKvZshW5CiPPlotdXmYrb02a8Lcb Hvhyn4/R7vKKZQCgpihWs2UrchVGni0Xu7zMVt5SrAAAQShWs2UrchVGni0X u7zMVt5SrAAAQShWs2UrchVGni0Xu7zMVt5SrAAAQShWs2UrchVGni0Xu7zM Vt5SrAAAQShWs2UrchVGni0Xu7zMVt5SrAAAQShWs2UrchVGni0Xu7zMVt5S rAAAQShWs2UrchVGni0Xu7zMVt5SrAAAQShWs2UrchVGni0Xu7zMVt5SrAAA QShWs2UrchVGni0Xu7zMVt5SrAAAQShWs2UrchVGni0Xu7zMVt6KXKyNUz75 05/+dHFI6UtDNb5OAQC1TLGaLVuRqzDybLnY5WW28lbkYp147KHfvfyUm77+ mYBrxuTT/P0vAFBZitVs2YpchZFny8UuL7OVtyIXa+TZvGIZAKg4xWq2bEWu wsiz5WKXl9nKW5GrMPJsjVM+mb02OKZqfA0FAKpNsZotW5GrMPJsudjlZbby VuQqjDzb6R9//zcuOil9dQi4rrjwLNEKAG9GitVs2YpchZFny8UuL7OVtyJX odnKW+lrlmIFgDgef/zxK6+8csqUKTNnzlyxYkUnRypWs2UrchVGni0Xu7zM Vt6KXF5mK28pVgCI46qrrurfv3/ddv369WtqauroYMVqtmxFrsLIs+Vil5fZ yluRy8ts5S3FCgBBLFy4MCXqgAED5s6du3Llyjlz5qR6ra+vX7p0abvHl12s 37rks33+J5B2V+QqrPhsD/+gYn84jFyFkWdL64yJQf/3lotdhZWdrYLPhVzs 8oo82z999uNhZ6udYl29enVLS0tfT1Hr3IUI0i1IN6KvpwDaMWHChLq6utmz Z+d3Zs2alXYmTZrU7vHlFWv6HeAbl07t8z+BtLtqp1jXP/gvjV+cUqmzRa7C yLOlde655/b5DB2t2inWyt6FyFUYebb0O1L6fanPx2h31U6xNjU1+VN6n2tu bl64cGFfT1HryvsjLlBtW7duHTx4cOrTF154Ib+5bt26+vr6gQMHbtmypfgh ivXNO5tiDbIUa4TZFGuEpVgjUKwRKNYIFCvE9OSTT6Zc3XvvvQv2991337S/ Zs2a4ocEKdYK/iEnVeHlU4+vhdkqXqyTPz025nWLPFuu0q1U2dlO/MhhNTJb Ze/C2Z8aXcEqrOx1izxbZYu1srP930vOrGCxVvYFn5U9W2WLNfJnGvlslS3W yJ9p5LMpVohp0aJFqUxHjRpVsJ920v7DDz9c/JAIxfrruy52tjJWZYvV2cpe lW0lZ4twtsqWl7OVt9LvlpU9WwU7rrJVWDtnq2zHOVuEs1W2CiOfDaiUBQsW pDJtaGgo2B87dmzanz9/fvFDFOub92yRO652zpaLXV7OVt6K3HG1c7aKF+sX zv30tHNOqsiafO5nK3u2sI0ZvFinfu60St2F88/7P7VzNsUK9KGsWI888siC /TFjxqT9+++/v/ghivXNe7bIHVc7Z8vFLi9nK29F7rjaOVvFizX9Dhz2bGEb M3ixVvCbhF/3zWm1czbFCvShRx99NJXpyJEjC/ZHjBiR9pctW1b8EMX65j1b 5I6rnbPlYpeXs5W3Indc7ZxNsZZHsZa3gjemYu3zswGVsmHDhlSmgwcPbmtr y2+mtwcNGpT2169fX/yQ9MWosfvSHw7Tn0wqtZzN2d68Zws+nrM5m7O9Oc5W ObV1tsj3NPLZKnsXdsaz9eIf3qEWDRs2LMXp8uXL8zvLli1LO8OHD2/3+B/9 6EfNzc0tAABARb9lMVBsxowZqU9POOGEzZs3p1+m/44fPz7tXHTRRe0e7yUT AAAA9I7169cPGTIkJero0aNTpR5xxBHp7aFDh7b7kuBtihUAAIBetHbt2jFj xtTX19e9oaGhYdWqVR0drFgBAADoZa2trUuWLHnuuec6P0yxAgAAEJNiBQAA ICbFCgAAQEyKFQAAgJi6W6yPP/74lVdeOWXKlJkzZ65YsaJ6g9WORx555OYi 9957747HdHnZe35ADdq4cePUqVNvueWW4neVfrncmh7q6C6U8rzIcxfK9vOf /3z27Nnnn3/+V7/61Z/85CfFB3gu9ILO70IvPxdKPGbnc99991166aXpLqR7 8fTTT7d7TIlXpiIXuTbvwrauboQvDVCbulWsV111Vf/+/eu269evX1NTU1XH qwVHH310XZGjjjoqf0CXl73nB9SmyZMnp6sxbdq0gv3SL5db03Md3YUunxd5 7kJ5Nm3aNGHChIIrPG7cuFdeeSV/jOdCtZVyF3rzuVDiMTuZXC53yimn7Hh5 d91113nz5hUcVuKVqchFrsG7sK20G+FLA9Sm0ot14cKF6dk6YMCAuXPnrly5 cs6cOemJXF9fv3Tp0moPuXPbc88902VMf2KfvoP8b9FdXvaeH1CD0p8Gzz77 7OxrUEErlX653Joe6uQubOvqeZHnLpTtwgsvTFf+ne985y233PKb3/zmjjvu OOigg9LOmWeemR3gudALurwL23rxuVDiMTufyy67LLsL6Y9DTz311Le+9a30 Wafr8Nhjj+WPKfHKVOQi1+Zd2FbajfClAWrT6tWrW1paSjky+/+BZ8+end+Z NWtW2pk0aVLVptv5rV27Nl3D9EeUjg7o8rL3/IBac+eddw4dOjT//5oWtFLp l8ut6YnO70KXz4s8d6E8bW1tQ4YMSZ9m+lNZfnPVqlVpZ7fddtu0adM2z4Xq K+Uu9OZzocRjdjLpLqRESp/jz372s/zmOeeck3YuvPDC/E6JV6YiF7kG78K2 0m6ELw1A57Zu3Tp48OD0VH3hhRfym+vWrauvrx84cOCWLVv6cLY3tdtvvz1d 1VNPPbXd93Z52Xt+QPU+tbD22GOPujded9fY2FjQSqVfLremhzq5C9u6el7k uQtl+93vfjdixIh3vetdBfuDBg1KV+Ppp5/2XOgFXd6Fbb34XCjlJD3/lAN6 /fXX/+3f/m3KlCnp089vfuUrX0nX4bzzzst+WeKVqchFrs27sK20G+FLA9C5 J598Mj1z995774L9fffdN+2vWbOmT6baCcycOTNdwOnTp1966aUTJ04866yz 5s2bl37fzt7b5WXv+QFV+rwiO+6442677ba2tra5c+cWtFLpl8ut6aFO7sK2 rp4Xee5CZf3yl79Mn3X25zHPhb6y413Y1ovPhVJOUsnPM7AUKcOHD0+f8vXX X5/tlHhlKnKR3YW84hvhSwPQuUWLFqUn6ahRowr2007af/jhh/tkqp3AiSee WFdkxIgRzzzzzLYSLnvPD6jepxZfcSuVfrncmkppt1g7f17kuQsVtHnz5g99 6EPps/7Upz61zXOhjxTchW29+Fwo5SSV/FRDuvHGG0877bS3ve1t6fM9+eST 83+VVuKVqchFdhe2dXwjfGkAOrdgwYL0JG1oaCjYHzt2bNqfP39+n0y1Ezjw wAPTBTz00EN/+MMfPvXUU7feeusBBxyQdo455pi2trYuL3vPD6ji5xZecSuV frncmkppt1g7f17kD3MXKiWXy33mM59Jn/Kee+6Z/dnPc6H3Fd+Fbb34XCjl JBX+hONJVzXfQWecccbLL7+c7Zd4ZSpykd2FbR3fCF8agM5lT94jjzyyYH/M mDFp//777++LoXYGd9999zXXXNPa2prfWbly5a677pqu6rJly7q87D0/oAqf 05tGR8VayuVyayql3WLt/HmR33QXKuK11177+7//+/T5Dho06KGHHso2PRd6 Wbt3YVsvPhdKOUkFPs/Yfvvb37700ks/+9nPDjnkkPQpH3300dl+iVemIhfZ XdjW8Y3wpQHo3KOPPpqepCNHjizYHzFiRMFvFPRcQ0NDuqo33HBDl5e95wdU 8dMIr7iVSr9cbk2ltFus7co/L/I77kLPrV+/fvTo0emTHTJkyKJFi/L7ngu9 qaO70JFqPBdKOUk5n9ub029+85usg1IQbSv5ylTkIrsLOyq4Ee3ypQHI27Bh Q3qSDh48eMfXXaS3s29pmL7a9uFsb2qvvvrqiy++WLB57LHHpqt63XXXdXnZ e35AtT/ByIpbqfTL5dZUSrvF2vnzIr/jLvRQ+tPgu9/97vSZvuc970lv7/gu z4Ve08ld2NaLz4VSTlLBzzqOrVu3Pvfcc6tWrSrYz/5274477thW8pWpyEWu zbuwrbQb4UsD0KVhw4al5+ny5cvzO8uWLUs7w4cP78Op3tSWLl2aLuDuu++e /dy9zMaNG/fZZ5+67T+hr8vL3vMDala7rVT65XJrKqL4LpTyvMhzF8r2/PPP Z5/7Bz/4wXZ/KrfnQi/o/C708nOhxGN2MitWrKh7498Ob968Ob+5ZcuWtJP2 H3vssWynxCtTkYtcg3dhWwk3wpcGoBQzZsxIT9UTTjgh+80k/Xf8+PFp56KL Lurr0d6scrncXnvtla7hrFmz8psXX3xx2jnkkEOyb47X5WXv+QE1q91iLf1y uTUVUXwXSnle5LkLZfvwhz9c98Y/1HrttdfaPcBzoRd0fhd6+blQ4jE7ma1b t+69997pc/zGN76R3/yXf/mXujd+uEn+Ipd4ZSpykWvwLmwr4Ub40gCUYv36 9UOGDEnP1tGjR6cn7BFHHJHeHjp0qFdH9MT1119f94YJEyZccskl2ffH22WX XfLfia7Ly97zA2pWu8Va+uVyayqi3bvQ5fMiz10ozwMPPJBd4YEDB/5VkV// +tfbPBeqr5S70JvPhRKP2fn813/9V/o06+vrTzrppHSRx40bl/3yrrvuyh9T 4pWpyEWuzbuwrYQb4UsDUIq1a9eOGTMm/e6R/Y7R0NBQ/C8O6K4bb7xx//33 r9vu4IMPvueee3Y8oMvL3vMDatPVV1+drsYFF1xQsF/65XJreq6ju9Dl8yLP XSjDN7/5zbqO/epXv8oO81yoqhLvQm8+F0o8Zudzyy23pBjJX+QRI0Y88MAD BceUeGUqcpFr8y5sK+FG+NIAlKi1tXXJkiXPPfdcXw+y82hra3v22WcXL15c /G038rq87D0/gB2Vfrncmiop5XmR5y5Uj+dCn+vl50KJx+xk0kVOhbJw4cIN GzZ0cliJV6YiF7kG78K2Em6ELw0AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADuZl1566Uc/+tEvfvGL vh4EAAAA/hfFCgAAQEyKFQAAgJgUKwAAADEpVgAAAGJSrAAAAMSkWAEAAIhJ sQIAABCTYgUAACAmxQoAAEBMihUAAICYFCsAAAAxKVYAAABiUqwAAADEpFgB AACISbECAAAQk2IFAAAgJsUKAABATIoVAACAmBQrAAAAMSlWAAAAYlKsAAAA xKRYAQAAiCkV6+23356itWW7P/7xj309FAAAAPxPsd58882N211xxRU/+clP +nooAAAA8KpgAAAAglKsAAAAxKRYAQAAiKnsYt2yZUtrVL55FAAAwE6gvGJN ubrk0Uf+6ZxTL5t+SrQ184KTv/bP5y64954qXTEAAID/z979gGdZ14sfByaI wDiTY0QqRnQoIAL5QZJE+Tcy6oh5SkorOYX8CwFTI5XIJI9iymW/iPijRcpq HZWhB7HUlKGGwtzkjykwVGBWcBAcSMB4GL/7132x62nAnodxb3xhr9f1vbrk 3ve5+ey+ebbn3cagYdStWP/+97+/9PyTj/z0P1Mlk0JbO5bc/GLBd+c9NLee rhgAAAANQ7ECAAAQJsUKAABAmBQrAAAAYVKsAAAAhEmxAgAAECbFCgAAQJgU KwAAAGFSrAAAAIRJsQIAABAmxQoAAECYFCsAAABhUqwAAACESbECAAAQJsUK AABAmBQrAAAAYVKsAAAAhEmxAgAAEKaoWAsLC6No3XJARUVFxkcpVgAAAOpb VKz5+fk3HTB58uQnnngi46MUKwAAAPXNdwUDAAAQJsUKAABAmBQrAAAAYVKs AAAAhEmxAgAAECbFCgAAQJgUKwAAAGFSrAAAAIRJsQIAABAmxQoAAECYFCsA AABhUqwAAACESbECAAAQJsUKAADAkaqqqnr11VdvueWWZ555psabdu7cWVRU NGnSpLFjx06bNq2srOxIN1RTrAAAABypNWvWXHrppWecccbPfvaz9OPl5eVj xozJyclpckCXLl0KCgqy35BOsQIAAJC9VCpVXFx8ySWXRLFZo1grKiqmTZsW 1Wi3bt0efPDBoqKikSNHRr/s27fv6tWrs9lQg2IFAAAgS1u2bLnnnnve9773 xV8erVGsq1atGjBgQOfOnfPz8+Mj69evHz58eIcOHaZMmZLNhhoUKwAAAFl6 /vnn27Rpk5eXd9lll1111VU1irWoqKh169bnnnvu5s2b4yOVlZXz5s3LyckZ OHBgKpXKuKHGb6dYAQAAyNLy5cuHDh26aNGijRs3jhs3Lr1Yo06cO3du8+bN o5hNf0jUm+3atTvnnHNWr15d+4YNGzbU+O0UKwAAAEeqvLy8RrFu3759+vTp bdq0GTZsWPrO4uLiLl269O7d+4UXXqh9w4oVK2r8LooVAACAI3VwsW7btm3q 1Km5ubmjRo1K31lSUtKjR4+ePXs+9dRTtW9YtmxZjd9FsQIAAHCkDlesbdu2 HT16dPrOKEi7d+/eq1evZ599tvYNpaWlNX4XxQoAAMCROuR3Bc+YMaNVq1ZD hw5N37l06dJOnTr16dPn5Zdfrn3DmjVravwuUbEWFhZG0brlIBUVFYebTbEC AAA0ZgcXa2Vl5fz583NycgYNGlRVVVW9c/HixVGl9u/ff9OmTbVv2Lp1a43f JSrW/Pz8mw5l1qxZh5tNsQIAADRmBxdrZMmSJe3bt+/Tp8/atWvjIzt27Jg9 e3bLli2HDBmSzYYafFcwAAAAR+qQxVpWVnbFFVe0a9duwoQJe/furaqqKikp 6devX8eOHWfOnJnNhhoUKwAAAEfqkMVaWVm5YMGCvLy83NzcgQMHjhgxomvX ri1atBg8eHD8Hb8ZN9SgWAEAADhSb7/99vjx488888z0Yo3s2rVr4cKF3bt3 b9q0aZMmTaIyvfrqq994443sN6RTrAAAACRr79695eXly5cv37RpU902xBQr AAAAYVKsAAAAhEmxAgAAECbFCgAAQJgUKwAAAGFSrAAAAIRJsQIAABAmxQoA AECYFCsAAABhUqwAAACESbECAAAQJsUKAABAmBQrAAAAYVKsAAAAhEmxAgAA ECbFCgAAQJgUKwAAAGFSrAAAAIRJsQIAABCmqFgLCwujaN1yQEVFRcZHKVYA AADqW1Ss+fn5Nx0wefLkJ554IuOjFCsAAAD1zXcFAwAAECbFCgAAQJgUKwAA AGFSrAAAAIRJsQIAABAmxQoAAECYFCsAAABhUqwAAACESbECAAAQJsUKAABA mBQrAAAAYVKsAAAAhEmxAgAAECbFCgAAQJgUKwAAAGFSrAAAAIRJsQIAABAm xQoAAECYFCsAAABhUqwAAACESbECAAAQJsUKAABAmBQrAAAAYYqKtbCwMIrW LQdUVFRkfJRiBQAAoL5FxZqfn3/TAZMnT37iiScyPkqxAgAAUN98VzAAAABh UqwAAACESbECAAAQJsUKAABAmBQrAAAAYVKsAAAAhEmxAgAAECbFCgAAQJgU KwAAAGFSrAAAAIRJsQIAABAmxQoAAECYFCsAAABhUqwAAACESbECAAAQJsUK AABAmBQrAAAAYVKsAAAAhEmxAgAAECbFCgAAQJgUKwAAAGFSrAAAAIRJsQIA ABCmqFgLCwujaN1yQEVFRcZHKVYAAADqW1Ss+fn5Nx0wefLkJ554IuOjFCsA AAD1zXcFAwAAECbFCgAAQJgUKwAAAGFSrAAAAIRJsQIAABAmxQoAAECYFCsA AABhUqwAAACESbECAAAQJsUKAABAmBQrAAAAYVKsAAAAhEmxAgAAECbFCgAA QLK2b9/+1FNP3Xrrrddff/3MmTNff/31Ght27txZVFQ0adKksWPHTps2rays 7JDnUawAAAAkKMrPIUOGNGvWrMkBH/zgB6dPn169oby8fMyYMTk5OdUbunTp UlBQcPCpFCsAAABJiRpz8uTJUa726tXr4YcfLi4uvummm0455ZTu3bsvWrQo 2lBRUTFt2rQoV7t16/bggw8WFRWNHDky+mXfvn1Xr1598NkUKwAAAIl47bXX Lrnkkk6dOs2ZMyc+sn79+mHDhrVv3z4q2eiXq1atGjBgQOfOnfPz86s3DB8+ vEOHDlOmTKlxNsUKAABAUlauXNm/f/+PfOQjjz76aHxk06ZNP/jBD/Ly8saP Hx/9sqioqHXr1ueee+7mzZvjDZWVlfPmzcvJyRk4cGAqlUo/m2IFAAAgKevX r7/mmmvatGlz9dVX79ixIyrQF1544WMf+9hZZ501c+bMqCXnzp3bvHnzyy67 LP1RUZO2a9funHPO2bBhQ/pxxQoAAEBS9u3bt3Tp0n79+kVZ2qlTp09+8pNR ip566qnjxo2LAnb79u3Tp0+PenbYsGHpjyouLu7SpUvv3r1XrFiRflyxAgAA kJSoWKPq/NKXvtQkzemnn37PPffs3r1727ZtU6dOzc3NHTVqVPqjSkpKevTo 0bNnz2XLlqUfV6wAAAAkZdWqVZ/+9KdbtWp16aWXRvm5YcOG2bNnf+hDHzrt tNMmTpwYF2vbtm1Hjx6d/qioWLt3796rV6/S0tL044oVAACAROzateu3v/1t s2bNBgwYUF5eHh/cvXv3Qw89dMopp3ziE59YuXLljBkzop4dOnRo+gOXLl3a qVOnPn36rFmzJv14VKyFhYVRtG45SEVFxeHGUKwAAADUsHXr1p/85CcHB2lp aWmvXr26d+/+1FNPzZ8/PycnZ9CgQVVVVdUbFi9eHD2qf//+0RnSHxgVa35+ /k2HMmvWrMONoVgBAACo4b333rvvvvtOOumkL3zhC/v27as+vmzZsg9/+MNR tL788stLlixp3759nz591q5dG791x44ds2fPbtmy5ZAhQ2qc0HcFAwAAkIiq qqpFixa1bdu2U6dOv/71r+ODmzdvnjRpUrNmzS6++OKdO3eWlZVdccUV7dq1 mzBhwt69e6OHlJSU9OvXr2PHjjNnzqxxQsUKAABAUjZt2hT1aZMmTfLy8r70 pS9997vfveCCC6Jc7dKly/z586MNlZWVCxYsiN6am5s7cODAESNGdO3atUWL FoMHD67xLcH7FSsAAACJ2rJlyz333NOhQ4f4n7aJavQzn/nMH//4x+oNu3bt WrhwYffu3Zs2bRptiNL16quvfuONNw4+lWIFAAAgcXv27HnrrbdWrFgRVech N+zdu7e8vHz58uWbNm063EkUKwAAAGFSrAAAAIRJsQIAABAmxQoAAECYFCsA AABhUqwAAACESbECAAAQJsUKAABAmBQrAAAAYVKsAAAAhEmxAgAAECbFCgAA QJgUKwAAAGFSrAAAAIRJsQIAABAmxQoAAECYFCsAAABhUqwAAACESbECAAAQ pqhYCwsLo2jdckBFRUXGRylWAAAA6ltUrPn5+TcdMHny5CeeeCLjoxQrAAAA 9c13BQMAABAmxQoAAECYFCsAAABhUqwAAACESbECAAAQJsUKAABAmBQrAAAA YVKsAAAAhEmxAgAAECbFCgAAQJgUKwAAAGFSrAAAAIRJsQIAABAmxQoAAECY FCsAAABhUqwAAACESbECAAAQJsUKAABAmBQrAAAAYVKsAAAAhEmxAgAAECbF CgAAQJgUKwAAAGGKirWwsDCK1i0HVFRUZHyUYgUAAKC+RcWan59/0wGTJ09+ 4oknMj5KsQIAAFDffFcwAAAAYVKsAAAAhEmxAgAAECbFCgAAQJgUKwAAAGFS rAAAAIRJsQIAABAmxQoAAECYFCsAAABhUqwAAACESbECAAAQJsUKAABAmBQr AAAAYVKsAAAAhEmxAgAAECbFCgAAQJgUKwAAAGFSrAAAAIRJsQIAABAmxQoA AECYFCsAAABhUqwAAACESbECAAAQJsUKAABAmKJinTNnzvADxo8fX1BQkPFR ihUAAID65musAAAAhEmxAgAAECbFCgAAQJgUKwAAAGFSrAAAAIRJsQIAABAm xQoAAECYFCsAAABhUqwAAACESbECAAAQJsUKAABAmBQrAAAAYVKsAAAAhEmx AgAAkKw9e/asWLHizjvvHDdu3I9//ONnn31237596Rt27txZVFQ0adKksWPH Tps2rays7JDnUawAAAAkaMuWLXfccUerVq2aHJCbm3vNNddElRpvKC8vHzNm TE5OTvWGLl26FBQUHHwqxQoAAEBSolrMz89v0aJFx44d77nnnpUrV06fPj36 79NPPz36ZbShoqJi2rRpUa5269btwQcfLCoqGjlyZPTLvn37rl69usbZFCsA AABJee211wYOHHjGGWfce++98ZHt27fPnDnzpJNOuvTSS/ft27dq1aoBAwZ0 7tw5Ctt4w/r164cPH96hQ4cpU6bUOJtiBQAAICmLFi1q1apV1KRbt26tPvjO O+/8/ve/X7FiRfTfRUVFrVu3Pvfcczdv3hy/tbKyct68eTk5OVHqplKp9LMp VgAAABKxc+fOBx54oE2bNt/+9rejYi0sLJwyZcr999//6quvxhuilpw7d27z 5s0vu+yy9AdGTdquXbtzzjlnw4YN6ccVKwAAAIl45513okTNy8u74IIL+vTp U/2Dldq3b3/bbbft3r17+/bt06dPj5J22LBh6Q8sLi7u0qVL796946/DVlOs AAAAJGLz5s0//OEP40o9/fTTb7zxxl/+8pff+ta3WrZs2bFjxxkzZmzbtm3q 1Km5ubmjRo1Kf2BJSUmPHj169uy5bNmy9OOKFQAAgERs2rRp4sSJUa526tTp gQceiA++8847d911V9OmTc8///zy8vKoWNu2bTt69Oj0B0bF2r179169epWW lqYfV6wAAAAkYsuWLbfffntUrOedd16UjdXHX3rppTPPPPP//J//8+KLL86Y MaNVq1ZDhw5Nf+DSpUujyO3Tp8+aNWvSjytWAAAAErFjx47Zs2c3bdr0ggsu SP+pvyUlJR//h6Kiovnz5+fk5AwaNKiqqqp6w+LFi6OM7d+/f/pPGN7/j2Kd M2fO8EO5++67DzeGYgUAAKCGKEKffvrpli1b9unT5/XXX68+GAVpu3bt+vbt u27duiVLlrRv3z7asHbt2nhD3LnRo4YMGVLjhL7GCgAAQFJee+21z372s23b th0xYsSePXuiIxs2bBg1alQUpFdeeWVUr2VlZVdccUUUsBMmTNi7d290pKSk pF+/fh07dpw5c2aNsylWAAAAkrJ79+7CwsK8vLw2bdpcdNFFN9xww3nnndek SZOPf/zjzz//fLShsrJywYIF0Ybc3NyBAwdGYdu1a9cWLVoMHjy4xrcE71es AAAAJCoKxkcfffTss89u2rRp1Konn3zy+eef/+yzz1Zv2LVr18KFC7t37x5v iNL16quvfuONNw4+lWIFAIBEFBcX33XXXek/bQYasz179kQRGj0v3nzzzUM+ L/bu3VteXr58+fJNmzYd7iSKFQAAEvGTn/ykSZMmO3fuPNaDwIlDsQIAQCIU KyROsQIAQCIUKyROsQIAQCIUKyROsQIAQCIUKyROsQIAQCIUKyROsQIAQCIU KyROsQIAQCIUKyROsQIAQCIUKyROsQIAQCJqL9aXX3550aJFDTwSHO8UKwAA JKL2Yh07duyVV17ZwCNxQrrtttt69ep1+eWXH+tBGoJiBQCARNRerJdeemmr Vq127NjRwFNx4hk+fHj0J6179+7HepCGoFgBACARtRfr3Llzo7fOmTOngac6 jrz00kv/9V//9cwzzxzrQY5YA0+uWDNSrAAAUEPtxfree++1adPmwgsvbOCp jiM/+MEPogv49a9//VgPcsQaeHLFmpFiBQCAGjL+rOBvfOMbzZo127hxY0NO dRwZOXLkcVqsDTy5Ys1IsQIAQA0Zi/XJJ5+MNtxxxx0NOdVx5Mtf/nKdu2/L li3Lli176aWXNm3adLg9VVVVa9eujfZs37494wlTqdS6deteeOGF1157rbKy svbNdZj8iIbZs2dPcXFxNE/8yxEjRijW2ilWAACoIWOxRhH0gQ98oJGERpYe f/zxbt26derU6f3vf/9JJ50UXcDmzZv/yz/r06dPjUd98pOfjI5HORn996JF iz7zmc80OaBp06aTJ0+usf8vf/nLV7/61bZt21bv+fCHP1xQUHDIkZ577rkr r7zy1FNPrT5nNNjll1/+t7/97egnP9Jh3nrrrc997nMnn3xyvPl973vf7bff PmbMGMVaO8UKAAA1ZCzWyPXXXx/tKS4ubrCpAhf/QKradenSpcajoiPR8Shk FixYUF1z1R544IH0zdG2vLy8+E2nn376xz/+8VNOOSX+5ciRI2uc+fOf/3z1 eZo1axbtj/43/uUHPvCBioqKo5z8iIZ55plnqjdHY+Tm5qafPJFiLSsr++1v f3v056k/ihUAABKRTbGWlpZGe8aNG9dgUwUuKovyA3r27BldnMsvv7z8n9X4 4ub+A8V6xRVXtGjR4qSTTho9evQrr7yye/fud955Z/78+dH/Vu/cuHFjHH0f /ehHi4qK4oO7du264YYb4u57+umn08/89a9/PScnJ/rf55577r333ouO7Nix I7pf8ebvfe97RzP5EQ0T/UH64Ac/GB2M5pkyZUocy6+99lp1UydSrHfddVfT pk1nzpx59KeqJ4oVAAASkU2xRnr06NG+ffu9e/c2zFTHkb59+zbJ7m+DxsUa adOmze9///tadv77v/97tO1f//Vf169fX+NNX/jCF6I3nX/++ekHN2/evHr1 6oPPM2DAgGhzdO+OZvIjGubWW2+N38ef//znNTZHaZxUsUauu+66KFpnzZqV yNkSFxXrnDlzhh8wfvz4w30HdTrFCgAANWRZrHfeeWe0bcGCBQ0z1XGkDsVa +3e07t27t2XLltG2H/3oRwe/9Xe/+13cj7X/Xu++++7LL7985ZVXRpujs1VV VdVt8iMd5tOf/nR05CMf+UgqlaqxOfGfFTxu3LgoWmfPnp3UCRPka6wAAJCI LIt1w4YNzZo1GzJkSMNMdRw50mL94he/WPu25cuXx2Hbv3//bxzk/PPPj9+6 ZcuWGg985ZVXJk+efOGFF3bo0KHGX0095P3NZvIjHSb+0U/Dhg07+FT18a/b XHvttVG03nfffQmeMxGKFQAAEpFlsUYuuOCCli1bvvvuuw0w1XHkSIv1O9/5 Tu3b5s+f3yQLf/3rX6sfsm3btvgbdGOdO3ceNGhQ9BtdfPHFR1msRzTM9u3b 419OmjTp4FNlX6zReX6QnYkTJ+bk5ETRev/992c8bUNSrAAAkIjsi/W+++6L dgb49axjK+6+q666KuPOLIt12bJlcffdddddxYe3b9++eP977733kY98JNp/ 8skn//CHP3zttdeqTzV79uyMxVr75Ec0TPS/8c9Avu666w4+VfbFGuXeBVmL x7vhhhsynrYhKVYAAEhE9sV6//33RzvvvPPOBpjqOBJ/Z+znP//5jDuzLNZd u3bF/1Lq97///WwGWLBgQVxtixYtqvGm2os1m8mPdJiuXbtGmy+66KKD31Qf 3xU8d+7cZs2ajR49OsFzJkKxAgBAIrIv1gsvvDCqg40bNzbAVMeRb37zm9EF PPPMMzP+IOUsizXymc98Jtp5yimnrFq1KuPm2267LdrcokWLGsf37NkzdOjQ Woo1y8mPaJghQ4bEv2NpaWn68S1btvTu3TvZYs3Pz8/JyRk5cuQhf67UsaVY AQAgEVkWa3l5eZSrn/3sZxtmquPIzJkzq78xtTr9du/e/fzzz9fYmX2xvv76 661atYo2f+ADH5g7d251kVVWVt5///39+vXbtGlT9eY5c+bEA/ziF7+Ij6RS qWeeeSZqw+q/Z3rI+5vl5Ec0zIoVK6I/J9Hmjh07vvjii/EwixYt+tCHPhT/ XkkV629+85soV6+55poAc3W/YgUAgIRkWazxtihYGmaq40iUeFGdxTl26qmn duvW7eMf/3iceG+99Vb6zuyLdf8/voAYnySSm5vbt2/fj33sY/HfEo3cdttt 1Ts3b958+umnx8c7derUo0eP1q1bR//dtGnTSy65pJZizX7y7IfZ/49/KbW6 lKPTtmjRorpVkyrWhx9+OMrVb33rW2Hm6n7FCgAACcmyWHv16tW2bdvoFXXD THV82bhx44UXXtgkTVSLUdYVFxenb4v/jmeWxRpZv379f/zHf8T/HGqsWbNm PXv2fOCBB2r8a6crVqwYMGBA9bZTTjnloosuinKpoqIi+mUUd1GcHs3kRzTM /n/8kK74n7mJRe94YWHh448/Hv332WefneW7X4s//OEPY8aMqf7ZUwFSrAAA kIhsinXlypVNDvOPbFItipQ//vGPRUVFJSUlW7duTeq0UQ+uXr06yr3S0tLa /x+DN99888knn1y6dGllZeUR/RbZT579MJF169YtXrz47bffPqJhTgyKFQAA EpFNsX7/+9+P9hz8FzOBQ1KsAACQiIzFWlVVddZZZ/3bv/1bQ04FxzXFCgAA ichYrIsWLYo2TJ48uSGnguOaYgUAgERkLNZhw4Y1bdp0/fr1DTkVHNcUKwAA JKL2Yt29e3deXt5FF13UwFPBcU2xAgBAImov1kceeSR66wMPPNDAU8FxTbEC AEAiai/WL3/5y7m5uRn/tVYgnWIFAIBE1F6so0eP/ta3vtXAI8HxTrECAEAi ai/WV199tbi4uIFHguOdYgUAgERk/FnBwJFSrAAAkAjFColTrAAAkAjFColT rAAAkAjFColTrAAAkAjFColTrAAAkAjFComLinXOnDnDDxg/fnxBQUHGRylW AACoQbFC4nyNFQAAEqFYIXGKFQAAEqFYIXGKFQAAEjFv3rzPfe5ze/bsOdaD wIlDsQIAABAmxQoAAECYFCsAAABhUqwAAACESbECAAAQJsUKAABAmBQrAAAA YVKsAAAAhEmxAgAAECbFCgAAQJgUKwAAAGFSrAAAAIRJsQIAABAmxQoAAECY FCsAAABhUqwAAACESbECAAAQJsUKAABAmBQrAAAAYVKsAAAAhEmxAgAAEKao WOfMmTP8gPHjxxcUFGR8lGIFAACgvvkaKwAAAGFSrAAAAIRJsQIAABAmxQoA AECYFCsAAABhUqwAAACESbECAAAQJsUKAABAmBQrAAAAYVKsAAAAhEmxAgAA ECbFCgAAQJgUKwAAAGFSrAAAANSTd9555ze/+c1PfvKTsrKy9OM7d+4sKiqa NGnS2LFjp02bVuOt1RQrAAAA9WHXrl0PPfRQmzZtunfv/swzz1QfLy8vHzNm TE5OTpMDunTpUlBQcPAZFCsAAAD1obi4uGfPnlGQphdrRUXFtGnTolzt1q3b gw8+WFRUNHLkyOiXffv2Xb16dY0zKFYAAAASt379+muuuSb+Emp6sa5atWrA gAGdO3fOz8+v3jl8+PAOHTpMmTKlxkkUKwAAAMnasWPHrFmzcnNzzz777KhP 04u1qKiodevW55577ubNm+MjlZWV8+bNy8nJGThwYCqVSj+PYgUAACBB+/bt W7x48Qc/+MFPfOITjzzyyEUXXVRdrFFLzp07t3nz5pdddln6Q6Imbdeu3Tnn nLNhw4b044oVAACABK1Zs2bw4MFRsf7qV7/685//nF6s27dvnz59eps2bYYN G5b+kOLi4i5duvTu3XvFihXpxxUrAAAASXnnnXfuuOOOtm3bjhgxIpVK1SjW bdu2TZ06NTc3d9SoUemPKikp6dGjR8+ePZctW5Z+XLECAACQiMrKygULFpx2 2mmf+cxn1q9fHx05ZLFGPTt69Oj0B0bFGu3p1atXaWlp+nHFCgAAQCJWrVp1 3nnnRcU6ZsyYJ/9h9uzZZ5999llnnTVlypQlS5a8/fbbM2bMaNWq1dChQ9Mf uHTp0k6dOvXp02fNmjXpxxUrAAAAiXjxxRfPOOOMJofRs2fPqD3nz5+fk5Mz aNCgqqqq6gcuXrw4ytj+/ftv3bo1/YRRsc6ZM2f4odx9992HG0OxAgAAUMNb b701adKka9NcddVVZ5555qmnnvqlL33pzjvv3LBhw5IlS9q3b9+nT5+1a9fG j9qxY8fs2bNbtmw5ZMiQGif0NVYAAADqSY2/xxopKyu74oor2rVrN2HChL17 91ZVVZWUlPTr169jx44zZ86s8XDFCgAAQD05uFjjn86Ul5eXm5s7cODAESNG dO3atUWLFoMHD67xLcH7FSsAAAD1JirWz372s1GxPvvss9UHd+3atXDhwuhg 06ZNmzRpEqXr1Vdf/cYbbxz8cMUKAABAw9u7d295efny5cs3bdp0uD2KFQAA gDApVgAAAMKkWAEAAAiTYgUAACBMihUAAIAwKVYAAADCpFgBAAAIk2IFAAAg TIoVAACAMClWAAAAwqRYAQAACJNiBQAAIEyKFQAAgDApVgAAAMKkWAEAAAiT YgUAACBMihUAAIAwKVYAAADCpFgBAAAIk2IFAAAgTFGxzpkzZ/gB48ePLygo yPgoxQoAAEB98zVWAAAAwqRYAQAACJNiBQAAIEyKFQAAgDCdeMVa8cJN8+79 2uWD+o+75j9CW9d++/KhX/viVV/5/DGf5JDre2OH/q7gt/X0Jw0AAOBInXjF uu357z/x86se+PFlx3yS42u2XcsmvjLv+l/P/mk9/UkDAAA4UorVbPFSrAAA QGgUq9nipVgBAIDQKFazxUuxAgAAoVGsZouXYgUAAEKjWM0WL8UKAACERrGa LV6KFQAACI1iNVu8FCsAABAaxWq2eClWAAAgNIrVbPFSrAAAQGgUq9nipVgB AIDQKFazxUuxAgAAoVGsZouXYgUAAEKjWM0WL8UKAACERrGaLV6KFQAACI1i NVu8FCsAABAaxWq2eClWAAAgNIrVbPFSrAAAQGgUq9nipVgBAIDQRMU6Z86c 4QeMHz++oKAg46MU64k3m2IFAABC42usZouXYgUAAEKjWM0WL8UKAACERrGa LV6KFQAACI1iNVu8FCsAABAaxWq2eClWAAAgNIrVbPFSrAAAQGgUq9nipVgB AIDQKFazxUuxAgAAoVGsZouXYgUAAEKjWM0Wr6hYF//q21/6fL9x1/xHgOt7 Y4f+ruC39fQsAAAAwqRYzRavnS/d8sKcb029YeAxn+Tg5eu/AADQOClWs8VL sQIAAKFRrGaLl2IFAABCo1jNFi/FCgAAhEaxmi1egRdryD8V6sYx33zg17+s p2coAAA0ZorVbPEKuVhDni1ary+4/mc/mVRPz1AAAGjMFKvZ4hVyFYY8W0qx AgBAvVGsZotXyFUY8mwpxQoAAPVGsZotXiFXYcizpRQrAADUG8VqtniFXIUh z5ZSrAAAUG8Uq9niFXIVhjxbSrECAEC9Uaxmi1fIVRjybCnFCgAA9Uaxmi1e IVdhyLOlFCsAANQbxWq2eIVchSHPllKsAABQb6JinTNnzvADxo8fX1BQkPFR ivXEmy3kKgx5tpRiBQCAeuNrrGaLV8hVGPJsKcUKAAD1RrGaLV4hV2HIs6UU KwAA1BvFarZ4hVyFIc+WUqwAAFBvFKvZ4hVyFYY8W0qxAgBAvVGsZotXyFUY 8mwpxQoAAPVGsZotXiFXYcizpRQrAADUG8VqtniFXIUhz5ZSrAAAUG8Uq9ni FXIVhjxbSrECAEC9Uaxmi1fIVRjybCnFCgAA9Uaxmi1eIVdhyLNFq/S/R3/l 3z/1ownXBLgm3zzmgV//sp4+egAAQH1TrGaLV8hVGPJs0Vr+0Mhbhn36mI9x yOXrvwAAHNcUq9niFXIVhjxbSrECAEC9Uaxmi1fIVRjybCnFCgAA9Uaxmi1e IVdhyLOlFCsAABzk3XffXbBgwa233nrdddfde++9L730Uo0NO3fuLCoqmjRp 0tixY6dNm1ZWVnbI8yhWs8Ur5CoMebaUYgUAgDRVVVWlpaUDBgxokiYvLy8q 0x07dsR7ysvLx4wZk5OTU72hS5cuBQUFB59NsZotXiFXYcizpRQrAACkWbdu 3de+9rWTTz75vPPOe/zxx5cvX3733XefccYZ73//+2+//fZoQ0VFxbRp06Jc 7dat24MPPlhUVDRy5Mjol3379l29enWNsylWs8Ur5CoMebaUYgUAgDTPP/98 Xl5e7969V65cGR/ZuXPnr371q6ZNm1588cWVlZWrVq0aMGBA586d8/Pz4w3r 168fPnx4hw4dpkyZUuNsitVs8Qq5CkOeLaVYAQDggD179ixcuLBr165f/epX 048/99xzrVq16tev39tvv11UVNS6detzzz138+bN8VujjJ03b15OTs7AgQNT qVT6AxWr2eIVchWGPFtKsQIAQK3ee++9++67r1mzZlGQ7tixY+7cuc2bN7/s ssvS90RN2q5du3POOWfDhg3pxxWr2eIVchWGPFtKsQIAwOFVVVUtW7asV69e p5122sSJE7dv3z59+vQ2bdoMGzYsfVtxcXGXLl169+69YsWK9OOK1WzxCrkK Q54tpVgBAODwVq5cedFFF5100kkXX3zxX/7yl23btk2dOjU3N3fUqFHp20pK Snr06NGzZ88ob9OPK1azxSvkKgx5tpRiBQCAQ9m3b1+Un5/61KdycnL69+8f f/E0Lta2bduOHj06fXNUrN27d+/Vq1dpaWn6ccVqtniFXIUhz5ZSrAAAcJDK ysqnn366W7duzZs3v/DCC1999dX4+Pbt22fMmNGqVauhQ4em71+6dGmnTp36 9OmzZs2a9OOK1WzxCrkKQ54tpVgBAOCfRcH4yCOPnH766aeccsrll19eXl5e /aaoZOfPn5+TkzNo0KCqqqrq44sXL44ytn///lu3bk0/VVSs+fn5Nx3KrFmz ahlAsZ5gs4VchSHPllKsAACQZvfu3Y899lj79u3btm07cuTId999t8aGJUuW RG/t06fP2rVr4yM7duyYPXt2y5YthwwZUmNzVKyFhYXRCbccpKKi4nAzKNYT b7aQqzDk2VKKFQAA0pSWlp599tlxru7atevgDWVlZVdccUW7du0mTJiwd+/e qqqqkpKSfv36dezYcebMmTU2+65gs8Ur5CoMebaUYgUAgAPefffde++9t0mT Js2aNcvNzT3tn1188cVvvfVWZWXlggUL8vLyog0DBw4cMWJE165dW7RoMXjw 4BrfErxfsZrtwAq5CkOeLaVYAQDggI0bN44ePbrJYfTt23fdunXRtl27di1c uLB79+5NmzaNjkfpevXVV7/xxhsHn1Cxmi1eIVdhyLOlFCsAANTJ3r17y8vL ly9fvmnTpsPtUaxmi1fIVRjybCnFCgAA9Uaxmi1eIVdhyLOlFCsAANQbxWq2 eIVchSHPllKsAABQbxSr2eIVchWGPFtKsQIAQL1RrGaLV8hVGPJsKcUKAAD1 RrGaLV4hV2HIs6UUKwAA1BvFarZ4hVyFIc+WUqwAAFBvFKvZ4hVyFYY8W0qx AgBAvVGsZotXyFUY8mwpxQoAAPVGsZotXiFXYcizpRQrAADUG8VqtniFXIUh z5ZSrAAAUG8Uq9niFXIVhjxbSrECAEC9Uaxmi1fIVRjybCnFCgAA9Uaxmi1e IVdhyLOlFCsAANQbxWq2eIVchSHPllKsAABQbxSr2eIVchWGPFtKsQIAQL1R rGaLV8hVGPJsKcUKAAD1RrGaLV4hV2HIs6UUKwAA1BvFarZ4hVyFIc+WUqwA AFBvFKvZ4hVyFYY8W0qxAgBAvYmKNT8//6YDJk2aVFBQkPFRivXEmy3kKgx5 tpRiBQCAehMVa2Fh4WOPPbblHyoqKrJ5lGI98WYLuQpDni2lWAEAoN74rmCz xSvkKgx5tpRiBQCAeqNYzRavkKsw5NlSihUAAOqNYjVbvEKuwpBnSylWAACo N4rVbPEKuQpDni0VdrGW/PfoIYMvuO3mawNcd9x64/zCh+vpIxsAACcGxWq2 eIVchSHPlgq7WEt/N3L8Nz65edGNAa5lvxvj678AANROsZotXiFXYcizpcIu 1pBn8x3LAABkpFjNFq+QqzDk2VJhV2HIsylWAAAyUqxmi1fIVRjybKmwqzDk 2RQrAAAZKVazxSvkKgx5tlTYVRjybIoVAICMFKvZ4hVyFYY8WyrsKgx5NsUK AEBGitVs8Qq5CkOeLRV2FYY8m2IFACAjxWq2eIVchSHPlgq7CkOeTbECAJCR YjVbvEKuwpBnS4VdhSHPplgBAMhIsZotXiFXYcizpcKuwpBnU6wAAGSkWM0W r5CrMOTZUmFXYcizKVYAADJSrGaLV8hVGPJsqbCrMOTZFCsAABkpVrPFK+Qq DHm2VNhVGPJsihUAgIwUq9niFXIVhjxbKuwqDHk2xQoAQEaK1WzxCrkKQ54t FXYVhjybYgUAICPFarZ4hVyFIc+WCrsKQ55NsQIAkJFiNVu8Qq7CkGdLhV2F Ic+mWAEAyEixmi1eIVdhyLOlwq7CkGdTrAAAZKRYzRavkKsw5NlSYVdhyLMp VgAAMoqKNT8//6YDJk2aVFBQkPFRivXEmy3kKgx5tlTYVRjybIoVAICMomIt LCx87LHHtvxDRUVFNo9SrCfebCFXYcizpcKuwpBnU6wAAGTku4LNFq+QqzDk 2VJhV2HIsylWAAAyUqxmi1fIVRjybKmwqzDk2RQrAAAZKVazxSvkKgx5tlTY VRjybIoVAICMFKvZ4hVyFYY8WyrsKgx5NsUKAEBGitVs8Qq5CkOeLRV2FYY8 m2IFACAjxWq2eIVchSHPlgq7CkOeTbECAJCRYjVbvEKuwpBnS4VdhSHPplgB AMhIsZotXiFXYcizpcKuwpBnU6wAAGSkWM0Wr5CrMOTZUmFXYcizlfz36CGD L7jt5msDXJMnXvfAr39ZTx91AQDInmI1W7xCrsKQZ0uFXYUhz1b6u5Hjv/HJ zYtuDHCVPjTm/p9OrKioqKcPvAAAZEmxmi1eIVdhyLOlwq5Cs9VtlS28XrEC AIRAsZotXiFXYcizpcIuL7PVbSlWAIBAKFazxSvkKgx5tlTY5WW2ui3FCgAQ CMVqtniFXIUhz5YKu7zMVrf1auG1I6/67MQbRhzznwHlp0IBAI2cYjVbvEKu wpBnS4VdXmar21r5yOibvz1g3eNjj/nPgPJToQCARk6xmi1eIVdhyLOlwi4v s9VtvVr4nR+PuWDbcxOO+SQHL9+xDAA0KorVbPEKuQpDni0VdnmZrW5LsQIA BEKxmi1eIVdhyLOlwi4vs9VtKVYAgEAoVrPFK+QqDHm2VNjlZba6LcUKABAI xWq2eIVchSHPlgq7vMxWt6VYAQACoVjNFq+QqzDk2VJhl5fZ6rYUKwBAIBSr 2eIVchWGPFsq7PIyW92WYgUACIRiNVu8Qq7CkGdLhV1eZqvbUqwAAIGIijU/ P/+mAyZNmlRQUJDxUYr1xJst5CoMebZU2OVltrotxQoAEIioWAsLCx977LEt /5DlqyDFeuLNFnIVhjxbKuzyMlvdlmIFAAiE7wo2W7xCrsKQZ0uFXV5mq9tS rAAAgVCsZotXyFUY8mypsMvLbHVbihUAIBCK1WzxCrkKQ54tFXZ5ma1uS7EC AARCsZotXiFXYcizpcIuL7PVbYVcrK8WXjvyqs9OvGHEbTdfG9qaPPG6B379 y3r6bAUANE6K1WzxCrkKQ54tFXZ5ma1uK+RiXfnI6Ju/PWDd42M3L7oxtFX6 0Bhf/wUAkqVYzRavkKsw5NlSYZeX2eq2Qi7WkGfzHcsAQOIUq9niFXIVhjxb KuzyMlvdVshVGPJsihUASJxiNVu8Qq7CkGdLhV1eZqvbCrkKQ55NsQIAiVOs ZotXyFUY8mypsMvLbHVbIVdhyLMpVgAgcYrVbPEKuQpDni0VdnmZrW4r5CoM eTbFCgAkTrGaLV4hV2HIs6XCLi+z1W2FXIUhz6ZYAYDEKVazxSvkKgx5tlTY 5WW2uq2QqzDk2RQrAJA4xWq2eIVchSHPlgq7vMxWtxVyFYY8m2IFALK0c+fO oqKiSZMmjR07dtq0aWVlZYfbqVjNFq+QqzDk2VJhl5fZ6rZCrsKQZwu5WLdv 3/74gkcn3jj6tpuvDW7dMu5Xs3/uuh3pmjThOz+9544wrxsAtSsvLx8zZkxO Tk6TA7p06VJQUHDIzYrVbPEKuQpDni0VdnmZrW4r5CoMebaQi3XjhrdmTb35 lYdGbl50Y2hrVeG1c6d9P/rcfawv0iGEfN1ef/TaebO+9+c///lYXyQAjkz0 UmHatGlRrnbr1u3BBx8sKioaOXJk9Mu+ffuuXr364P11LtYXn3/6of876pi/ QDp4hVyFic+29fmJKx5N5oVryFUY8mzRWlU47sfXhfjnLRV2FSY7W4LPhVTY VRjybGVPTJj5fyeHWaxvb1yfP+PWDb8ff8yv0sFr/ZM3JFisO3fuXLduXVJ3 IeTr9vbTNwRbrMneBeqmsrLyr3/964YNG471IEBNq1atGjBgQOfOnfPz8+Mj 69evHz58eIcOHaZMmXLw/roVa/Sh+IXnnin4WYifwhpPse5a9oOXH5kw666x iZwt5CoMebZo/XnB9+++dcwxH+OQq/EUa7J3IeQqDHm2tU/c9POfTgnzVXrI 5ZVssW7cuPGXv/xlUq/SQ75uIRfr3/72t4KCgugl2bEepFGr20tcoAEUFRW1 bt363HPP3bx5c3yksrJy3rx5OTk5AwcOTKVSNfYr1uN3NsUayFKsIcymWENY irVuS7HWbSlWaqdYIUx///vf586d27x588suuyz9ePRsbdeu3TnnnHPwp7BA ijWKr63PT0zkVP+/Cqd//dc//soJP1vyxfrrb99z4xcCvG4hz5ZKupWSne2V h0bfMvyCxjBb0sU65rZrByZVhclet5Bni4r15htG/ejmMQn9aJ2xt95yXVI/ ped7Y4feeM2gpMor2eu27onrJ437yvfGDUvkPb3lxlFjv3PNrTcnc+lCvm5v //F7j8y6OaliraysTPD/bEm2WJOdLfCzlby87HvjRyTyp3fijaPGf+fbk26+ IZGz/fD737lx/Ij02ZJ6r6Gx2b59+/Tp09u0aTNs2LD048XFxV26dOndu/eK FStqPCSEYt3+4g+e++2NznakK9lidbY6r2RbydlCONv//1rhHdcm9dK6UZ3t p5O/U/bE9Yn8XJ0V86//1d3XJni2X94z7q0nb0rkPX3zqZuTPdusKWNKHrku qfc02bMl+J6WP3Nzwc+uS+qvnEdn++2sHydVhck2ZuM5W7Jfx4zONu+/f/3Y zDGJ/OktW3jDw78Yn9TZVs0bPeOWLzz+86uqjyTyLkMjtG3btqlTp+bm5o4a NSr9eElJSY8ePXr27Lls2bIaD1Gsx+/ZQu64xnO2VNjl5Wx1W4FXYSM5W9Rx D947LsGzhVysjeRsivXEO1vixfo/jzz4/G9vTORPyKaiWx6d9d2kzrbhD9f9 8rbBSx74dvWRRN5laITiYm3btu3o0aPTj0fF2r179169epWWltZ4iGI9fs8W csc1nrOlwi4vZ6vbCrnjGs/ZFOuJdzbFeuKdTbECR2r79u0zZsxo1arV0KFD 048vXbq0U6dOffr0WbNmTY2HKNbj92whd1zjOVsq7PJytrqtkDuu8ZxNsZ54 Z1OsJ97ZFCtwpCorK+fPn5+TkzNo0KCqqqrq44sXL44ytn///lu3bq3xkI0b N95xxx03HaEbbrjhO6NG3HT9qETWDeNHOpuzHadnC3w8Z3M2Z3O2E/ls3/nO kb6Aqe2FjbOFcLYw/rzd/N1v/+i6r6SvG0cO/tplF9w4YnD1kYZ9jQ8nlCVL lrRv375Pnz5r166Nj+zYsWP27NktW7YcMmTIwfv/93//t7Cw8LHHHtsCAACN 3jtZaNgX+HBCKSsru+KKK9q1azdhwoS9e/dWVVWVlJT069evY8eOM2fOPHi/ f6wKAACAhlFZWblgwYK8vLzc3NyBAweOGDGia9euLVq0GDx48MHfErxfsQIA ANCAdu3atXDhwu7duzdt2rRJkyZRul599dVvvPHGITcrVgAAABrY3r17y8vL ly9fvmnTplq2KVYAAADCpFgBAAAIk2IFAAAgTIoVAACAMB1pse7cubOoqGjS pEljx46dNm1aWVlZvY7XGFRUVDz77LP5/+yhhx4qLi6u3pPxsh/9hkaoqqrq 1VdfveWWW5555pkab8r+crk1R+lwdyGb50U1d6HO3n333QULFtx6663XXXfd vffe+9JLL9XY4LnQAGq/Cw38XMhyz4ln27ZthYWFN99883e/+93777//7bff PnhPllcmkYvcOO/C/kw3wqcGaJyOqFjLy8vHjBmTk5PT5IAuXboUFBTU95An tldeeeXss89u8s/+5V/+JbrU8YaMl/3oNzROa9asufTSS88444yf/exn6cez v1xuzdE73F3I+Lyo5i7UTVVVVWlp6YABA9KvcF5eXvTCbMeOHfEez4X6ls1d aMjnQpZ7TjwrVqw4//zzq9/lpk2bduvWbd68eel7srwyiVzkxnkX9mdxI3xq gMYp+2KtqKiYNm1a9MyNPno8+OCDRUVFI0eOjH7Zt2/f1atXN8CoJ6rnn38+ Nzf3zDPPHD9+/HUH3HzzzfGH6IyX/eg3NEKpVKq4uPiSSy6JPgHVaKXsL5db c5RquQv7Mz0vqrkLdbZu3bqvfe1rJ5988nnnnff4448vX7787rvvjm7E+9// /ttvv32/50KDyHgX9jfgcyHLPSeet9566z//8z+ju3DBBRf88Y9/fPHFF+NO +cQnPvH666/He7K8Molc5MZ5F/ZndyN8aoDGaefOndFnzOiJmXHnqlWrBgwY 0Llz5/z8/PjI+vXrhw8f3qFDhylTptTzmCesXbt2FRQUtGzZ8sorrzzkhoyX /eg3NDZbtmy555573ve+98X/l2mNVsr+crk1R6P2u5DxeVHNXaiz6IVfXl5e 7969V65cGR+JPh386le/atq06cUXX1xZWem50AAy3oWGfC5kuefE86c//Sn6 WNSzZ8/q7yyNkuSLX/xix44dZ8yYER/J8sokcpEb513Yn8WN8KkByKioqKh1 69bnnnvu5s2b4yPRJ9N58+bl5OQMHDgwlUod2/GOU//7v//74x//OPoQfeut tx5yQ8bLfvQbGuDdDEr0ErFNmzbRq8TLLrvsqquuqtFK2V8ut+Zo1H4XMj4v qrkLdbNnz56FCxd27dr1q1/9avrx5557rlWrVv369Xv77bc9F+pbNnehIZ8L 2Zwk4UsQgH379q1bt+7222+fOnVq9N/xwTfffPOb3/zmBz7wgXvuuSc+kuWV SeQiN8K7sD+7G+FTA1C7v//973Pnzm3evHn08jL9+J/+9Kd27dqdc845GzZs OFazHdeij8ZDhw4988wzb7zxxhtuuOErX/nKtdde+/DDD0evZPZncdlXr159 lBsa4Y1bvnx5dM0XLVq0cePGcePGpbdS9n/O3ZqjVMtd2J/peVHNXUjWe++9 d9999zVr1ix6PbZjxw7PhWMi/S5Er4ob7LkQXWSf6GPRtf2f//mfDh06dO3a 9fe///3+rD81ZLMtkTtVX+95YA6+ET41ALXbvn379OnT27RpM2zYsPTjxcXF Xbp06d2794oVK47VbMe1lStX1viZG5FWrVoNGTLkL3/5S8bL/sILLxzlhsZ8 48rLy2u0UvZ/zt2apBx8F/Znel5Ub3MXElRVVbVs2bJevXqddtppEydO9Fw4 Jmrchf0N+FyILrJP9H/729/uvffeiy+++JRTTnnf+973gx/8IP5SWpZXJptt idyp+nr/g3G4G+FTA1C7bdu2TZ06NTc3d9SoUenHS0pKevTo0bNnz+iT7LGa 7bi2ZMmSDh06tG7d+otf/OKTTz5ZWlp65513vv/978/Lyxs/fvzWrVtrv+xP PfXUUW5ozDfu4FbK/s95xp1uTZYOWay1Py+iV/XxNnchQdFLwYsuuuikk06K XiVGr/08F46JGndhfwM+F6KL7BP9K6+8EsVI3EHRRb711lvjn9ic5ZXJZlsi d6q+3v9gHO5G+NQA1C5++rdt23b06NHpx6Mnb/fu3Xv16hV93DhWsx3X3nnn nXnz5j388MPVP/zqvffemzNnTvSKpU+fPkuXLq39sj/77LNHuaEx37jDFWs2 lyvjTrcmS4cs1tqfF2vWrIkPuguJ2LdvX/Tq61Of+lROTk7//v3jrx14LjSw Q96F/Q34XIgusk/0u3fv3rBhw5tvvvmLX/zirLPOijoo+ui0P+unQzbbErlT 9fX+B+NwN8KnBqB227dvnzFjRqtWrYYOHZp+PEqqTp06pX+g4Oi9/PLL0YfE rl27Rh+Wa7/s0c6j3NCYb9whvys4y8uVcadbk6VDFushVT8v/vCHP8RH3IWj V1lZ+fTTT3fr1q158+YXXnjhq6++Gh/3XGhIh7sLh1Mfz4XoIvtEX+3vf//7 b37zm6iD+vbt+8Ybb2R5ZbLZlsidqrf3Ozg1bsQh9/jUAFSLPp/Onz8/Jydn 0KBB1d93EVm8eHH0jO7fv//WrVuP4XjHr1Qq9e67727ZsiX94PLly6OPhx/9 6EcfffTR2i/7pk2bjnJDY75xB7dS9n/OM+50a7J0yGKt/XmxcOHC+Ii7cJSi V4OPPPLI6aeffsopp1x++eXRvah+k+dCg6nlLuxvwOdCdJEb7Sf66Ba8+eab NYIoapOzzjqrR48ezz33XJZXJpttidyperwWx1TGG+FTA5DRkiVL2rdvH31Y WLt2bXxkx44ds2fPbtmy5ZAhQ47tbMep6ALOmjUr+pD4uc99LvoAGx+MPjY+ ++yz7dq1i/+FvoyX/eg3NFqH+xuUWV4utyYRB9+FbJ4X1Q93F+ps9+7djz32 WPS+t23bduTIkdHrwBobPBcaQO13oYGfC1nuOcG89957999//0knnfSFL3xh 79698cHoIhcVFeXm5kaX4vXXX9+f9ZVJ5CI3wruwP4sbUVxc7FMDkFFZWdkV V1wRfViYMGFC9MEk+ihRUlLSr1+/jh07zpw581hPd7yKPhRHL1Q+9KEPVf8T 1W+88caVV17ZqlWrr3/966lUKuNlP/oNjdYhizX7y+XWJOKQdyHj86J6p7tQ Z6WlpWeffXYcSrt27Tp4g+dCA8h4FxryuZDlnhPPc889d+qpp3bu3LmgoCA+ El3kb3zjG82bNx88eHB8kbO8Molc5MZ5F/ZncSN8agAyqqysXLBgQV5eXm5u 7sCBA0eMGPH/2rt/llTDMA7A5iC469YHkAZB8SO4tbQHuju5utXg7qfxIzgI Bo0SQaCCuuRUg4PnAUHOkeR98N8Jva4hCO6hfj/u5+2FeioUCplMJhwjfjti b9PptNVqpVKpXC73+PgYfnQvlUo3Nzfh4/omusTYDx+4Wj++K8XHpZqj+LGF xL3Y0MJ+FotFp9MJCafT6fCN5/5VrVY/Pj7swqnFtHDOXYicuTzz+fzp6SmE nM/na7VaCLlcLodPi8Vir9dbz0Qmc5SQr7OFVUQRHg1AjO/v7263e3d3F86H cGKELa7X67v+Fp5I4QR+fn4Ox+/6IvdsNnt/f9/v9zcDibEfPnCdJpNJs9m8 vb3duvMnPi7VHG5XC4l7saGFPYxGo0ajkdqhUqm8v7+v7MKJRbZwzl2InLk8 4V2p3W6HF6XNv/h8eHjYug82MpmjhHydLawiivBoACItl8vxePz6+jqbzf73 13I5wsH49vY2GAy2rt3YSIz98AH+Fh+Xak4ncS82tHA6duE3OOcuRM5cnq+v r+Fw+PLy8vn5uWsmMpmjhHydLawiivBoAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAB+rT9fxLiZ "], {{0, 645.}, {627., 0}}, {0, 255}, ColorFunction->RGBColor, ImageResolution->144.], BoxForm`ImageTag["Byte", ColorSpace -> "RGB", Interleaving -> True], Selectable->False], DefaultBaseStyle->"ImageGraphics", ImageSizeRaw->{627., 645.}, PlotRange->{{0, 627.}, {0, 645.}}]], "Output", TaggingRules->{}, CellChangeTimes->{{3.834229988015402*^9, 3.834230010245013*^9}, { 3.834230046093093*^9, 3.8342301144800797`*^9}, {3.834230183297825*^9, 3.8342301896950912`*^9}, 3.834230224360376*^9, 3.834311056168963*^9, { 3.8343137602632523`*^9, 3.834313796627092*^9}, 3.834405409418787*^9}, CellLabel->"Out[1]=", CellID->1957083920] }, Open ]] }, Open ]] }, Closed]], Cell[CellGroupData[{ Cell[TextData[{ "Analysis", "\[NonBreakingSpace]", Cell["(5)", "ExampleCount"], "\[NonBreakingSpace]" }], "Subsection", TaggingRules->{}, CellID->866856397], Cell["\<\ Perform a linear regression on the data to see what effect job training may \ have had:\ \>", "Text", TaggingRules->{}, CellChangeTimes->{{3.834229421861315*^9, 3.834229438286172*^9}, { 3.834229486110046*^9, 3.8342294863783607`*^9}}, CellID->528714309], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"jobslmf", "=", RowBox[{ RowBox[{"Query", "[", RowBox[{ RowBox[{ RowBox[{"LinearModelFit", "[", RowBox[{"#", ",", RowBox[{"{", RowBox[{ "1", ",", "black", ",", "hispanic", ",", "nodegree", ",", "age", ",", "education", ",", "married", ",", "re74", ",", "re75", ",", "u74", ",", "u75", ",", "treatment"}], "}"}], ",", RowBox[{"{", RowBox[{ "black", ",", "hispanic", ",", "nodegree", ",", "age", ",", "education", ",", "married", ",", "re74", ",", "re75", ",", "u74", ",", "u75", ",", "treatment"}], "}"}], ",", RowBox[{"NominalVariables", "->", RowBox[{"{", RowBox[{ "black", ",", "hispanic", ",", "nodegree", ",", "married", ",", "u74", ",", "u75", ",", "treatment"}], "}"}]}]}], "]"}], "&"}], ",", RowBox[{ RowBox[{"KeyTake", "[", RowBox[{"{", RowBox[{ "\"\\"", ",", "\"\\"", ",", "\"\\"", ",", "\"\\"", ",", "\"\\"", ",", "\"\\"", ",", "\"\\"", ",", "\"\\"", ",", "\"\\"", ",", "\"\\"", ",", "\"\\"", ",", "\"\\""}], "}"}], "]"}], "/*", "Values"}]}], "]"}], "[", "jobs", "]"}]}]], "Input", TaggingRules->{}, CellChangeTimes->{{3.83422944763634*^9, 3.834229462979413*^9}, { 3.8342294958485622`*^9, 3.83422951417314*^9}, {3.8342295876775627`*^9, 3.834229627811808*^9}, {3.8342296708144827`*^9, 3.8342297052071543`*^9}, { 3.834229735415289*^9, 3.834229779775478*^9}, {3.834313816396366*^9, 3.834313909922303*^9}, {3.8343139785637608`*^9, 3.834313979201833*^9}}, CellLabel->"In[1]:=", CellID->1421261372], Cell[BoxData[ TagBox[ RowBox[{"FittedModel", "[", TagBox[ PanelBox[ TagBox[ RowBox[{"3162.458272052068`", "\[VeryThinSpace]", "+", RowBox[{"10.241884703122095`", " ", "age"}], "+", RowBox[{"\[LeftSkeleton]", "13", "\[RightSkeleton]"}], "+", RowBox[{"1005.8378954491972`", " ", RowBox[{"DiscreteIndicator", "[", RowBox[{"u75", ",", "0", ",", RowBox[{"{", RowBox[{"0", ",", "1"}], "}"}]}], "]"}]}]}], Short[#, 2]& ], FrameMargins->5], Editable -> False], "]"}], InterpretTemplate[ FittedModel[{ "Linear", {3162.458272052068, 1419.1896090444263`, -299.28824685492333`, 299.61071304751727`, 10.241884703122095`, 200.5935621716987, -48.543634232139986`, 0.12741237337440678`, 0.06471958734865164, -1529.929357277792, 1005.8378954491972`, -823.6546406991051}, {{{$CellContext`black, {0, 1}}, {$CellContext`hispanic, {0, 1}}, {$CellContext`nodegree, {0, 1}}, $CellContext`age, $CellContext`education, {$CellContext`married, \ {0, 1}}, $CellContext`re74, $CellContext`re75, {$CellContext`u74, {0, 1}}, {$CellContext`u75, {0, 1}}, {$CellContext`treatment, {0, 1}}}, { 1, $CellContext`black[0], $CellContext`hispanic[0], $CellContext`nodegree[0], $CellContext`age, $CellContext`education, $CellContext`married[0], $CellContext`re74, $CellContext`re75, $CellContext`u74[0], $CellContext`u75[0], $CellContext`treatment[0]}}, {0, 0}}, CompressedData[" 1:eJztxkENACEQBMEFJyjBAxJIeOMVJUgAzsP9apJOTemzjRwR6bbe+bYrSZIk SZIkSZJ/egDleVk8 "], {{0, 0, 0, 33, 12, 1, 0, 0, 1, 1, 1, 12418.0703125}, { 0, 0, 0, 20, 12, 1, 8644.15625, 8644.15625, 0, 0, 1, 11656.505859375}, { 1, 0, 0, 39, 12, 1, 19785.3203125, 6608.13720703125, 0, 0, 0, 499.257202148438}, { 0, 0, 1, 49, 8, 1, 9714.5966796875, 7285.94775390625, 0, 0, 1, 16717.12109375}, { 0, 0, 1, 26, 8, 1, 37211.7578125, 36941.265625, 0, 0, 0, 30247.5}, { 1, 0, 1, 38, 10, 1, 14759.0634765625, 14701.947265625, 0, 0, 0, 4393.52294921875}, { 0, 0, 0, 28, 12, 0, 0, 803.343017578125, 1, 0, 0, 16477.01953125}, { 1, 0, 0, 27, 12, 1, 0, 752.390075683594, 1, 0, 0, 0}, { 1, 0, 0, 33, 12, 1, 20279.94921875, 10941.349609375, 0, 0, 1, 15952.599609375}, { 1, 0, 1, 44, 9, 1, 12260.7802734375, 10857.240234375, 0, 0, 0, 12359.3095703125}, { 0, 0, 1, 28, 10, 1, 5440.818359375, 6346.19287109375, 0, 0, 0, 6793.0771484375}, { 0, 0, 0, 31, 12, 0, 0, 494.643005371094, 1, 0, 0, 0}, { 0, 0, 0, 24, 12, 1, 28262.130859375, 24294.74609375, 0, 0, 0, 665.737426757812}, { 0, 0, 0, 29, 12, 0, 12116.611328125, 12205.142578125, 0, 0, 0, 16328.9638671875}, { 0, 0, 1, 20, 11, 1, 7254.419921875, 8159.5068359375, 0, 0, 0, 6336.78271484375}, { 0, 0, 1, 22, 10, 1, 25720.919921875, 23031.98046875, 0, 0, 0, 5448.80078125}, { 1, 0, 1, 35, 9, 1, 13602.4296875, 13830.6396484375, 0, 0, 1, 12803.9697265625}, { 1, 0, 1, 42, 9, 1, 0, 3058.53100585938, 1, 0, 1, 1294.40905761719}, { 0, 0, 0, 21, 13, 0, 9978.7509765625, 8616.8525390625, 0, 0, 1, 99.5700149536133}, { 0, 0, 0, 23, 12, 0, 5204.259765625, 3903.19482421875, 0, 0, 1, 796.559692382812}, { 0, 0, 0, 18, 12, 0, 735.205078125, 735.205078125, 0, 0, 0, 2502.52587890625}, { 0, 0, 0, 22, 12, 0, 6759.994140625, 8455.50390625, 0, 0, 1, 12590.7099609375}, { 0, 0, 0, 20, 12, 0, 4139.75341796875, 3104.81518554688, 0, 0, 0, 12573.9658203125}, { 1, 0, 0, 22, 12, 1, 9420.671875, 9352.19921875, 0, 0, 0, 8072.181640625}, { 1, 0, 1, 35, 11, 1, 7096.7158203125, 8720.9052734375, 0, 0, 0, 0}, { 0, 0, 0, 27, 13, 1, 9381.56640625, 853.722473144531, 0, 0, 1, 0}, { 1, 0, 0, 41, 12, 0, 10707.1611328125, 9818.466796875, 0, 0, 0, 0}, { 1, 0, 1, 30, 11, 1, 0, 9311.9384765625, 1, 0, 0, 3982.80102539062}, { 0, 1, 0, 22, 12, 1, 492.230499267578, 7055.7021484375, 0, 0, 1, 10092.830078125}, { 1, 0, 0, 27, 12, 1, 10408.515625, 7806.38671875, 0, 0, 1, 2777.58520507812}, { 1, 0, 0, 42, 14, 1, 0, 0, 1, 1, 1, 13167.51953125}, { 1, 0, 1, 27, 10, 1, 13542.26953125, 11280.7109375, 0, 0, 0, 0}, { 0, 0, 0, 41, 14, 0, 0, 0, 1, 1, 1, 5149.5009765625}, { 1, 0, 1, 50, 8, 1, 7506.73388671875, 5630.05029296875, 0, 0, 0, 8997.193359375}, { 1, 0, 1, 26, 10, 1, 9773.953125, 8141.703125, 0, 0, 1, 4635.66015625}, { 1, 0, 1, 25, 10, 1, 12428.1220703125, 11696.3720703125, 0, 0, 0, 11127.2802734375}, { 1, 0, 1, 35, 8, 1, 13732.0703125, 17976.150390625, 0, 0, 1, 3786.6279296875}, { 1, 0, 1, 25, 9, 1, 24731.619140625, 16946.630859375, 0, 0, 0, 7343.9638671875}, { 1, 0, 1, 33, 11, 1, 14660.7099609375, 25142.240234375, 0, 0, 1, 4181.94189453125}, {1, 0, 0, 28, 12, 1, 0, 0, 1, 1, 0, 0}, { 1, 0, 1, 27, 11, 1, 20249.224609375, 20035.255859375, 0, 0, 0, 9074.763671875}, {1, 0, 1, 46, 8, 1, 0, 0, 1, 1, 1, 3094.15600585938}, { 1, 0, 0, 30, 14, 1, 0, 0, 1, 1, 0, 7344.67822265625}, { 0, 0, 1, 19, 10, 0, 0, 5324.10888671875, 1, 0, 1, 13829.6201171875}, { 1, 0, 0, 34, 13, 1, 0, 0, 1, 1, 0, 5193.2470703125}, { 0, 0, 1, 26, 11, 0, 0, 2226.26611328125, 1, 0, 1, 13385.8603515625}, { 1, 0, 1, 30, 11, 1, 24286.5078125, 24105.38671875, 0, 0, 0, 7263.02490234375}, { 1, 0, 0, 36, 12, 0, 0, 142.39729309082, 1, 0, 0, 0}, { 1, 0, 1, 45, 5, 1, 0, 0, 1, 1, 1, 8546.71484375}, { 1, 0, 1, 23, 10, 1, 0, 936.438598632812, 1, 0, 1, 11233.259765625}, { 1, 0, 0, 39, 12, 1, 0, 0, 1, 1, 0, 3795.79907226562}, { 1, 0, 1, 23, 9, 1, 19047.07421875, 19289.8515625, 0, 0, 0, 7600.1044921875}, { 1, 0, 1, 27, 11, 1, 0, 4491.8837890625, 1, 0, 0, 0}, { 0, 0, 0, 38, 12, 0, 0, 0, 1, 1, 1, 4941.84912109375}, { 0, 0, 1, 24, 11, 0, 2669.72509765625, 1468.38305664062, 0, 0, 0, 10361.6904296875}, { 1, 0, 1, 26, 11, 1, 0, 1392.85302734375, 1, 0, 1, 1460.35998535156}, { 1, 0, 0, 29, 14, 0, 0, 679.673400878906, 1, 0, 1, 17814.98046875}, { 1, 0, 1, 27, 11, 1, 2336.11401367188, 2142.21655273438, 0, 0, 1, 2186.18920898438}, { 1, 0, 1, 55, 3, 0, 0, 0, 1, 1, 0, 5843.7958984375}, { 0, 0, 1, 20, 11, 0, 1413.10668945312, 1177.11791992188, 0, 0, 0, 8743.0703125}, {1, 0, 1, 54, 11, 0, 0, 0, 1, 1, 0, 7812.52197265625}, { 0, 1, 0, 36, 12, 0, 13661.177734375, 11491.9521484375, 0, 0, 0, 4978.50048828125}, {1, 0, 1, 23, 10, 1, 0, 0, 1, 1, 0, 0}, { 0, 0, 1, 28, 11, 0, 2787.76123046875, 3200.91186523438, 0, 0, 0, 7645.0546875}, {0, 1, 0, 32, 12, 1, 0, 0, 1, 1, 0, 6735.31982421875}, { 1, 0, 0, 32, 12, 0, 13283.171875, 12419.1845703125, 0, 0, 0, 10673.697265625}, { 0, 0, 1, 18, 9, 0, 0, 559.596008300781, 1, 0, 0, 7300.498046875}, { 0, 0, 1, 26, 11, 0, 22893.87109375, 22959.76171875, 0, 0, 1, 2746.61401367188}, { 1, 0, 1, 28, 11, 1, 6702.451171875, 6952.57958984375, 0, 0, 1, 7318.18603515625}, { 1, 0, 1, 28, 11, 1, 824.388916015625, 10033.91015625, 0, 0, 0, 0}, { 1, 0, 0, 26, 12, 0, 8811.7373046875, 6608.80322265625, 0, 0, 1, 5811.76513671875}, { 1, 0, 0, 31, 13, 0, 10255.5361328125, 10393.61328125, 0, 0, 0, 7404.76220703125}, { 1, 0, 1, 28, 9, 1, 10222.41015625, 9210.447265625, 0, 0, 0, 1239.84399414062}, { 1, 0, 0, 24, 12, 0, 11972.125, 12982.3408203125, 0, 0, 1, 0}, { 1, 0, 0, 46, 13, 0, 0, 0, 1, 1, 1, 647.20458984375}, { 1, 0, 1, 18, 10, 1, 785.059020996094, 1958.09631347656, 0, 0, 1, 10989.927734375}, { 1, 0, 1, 26, 11, 1, 0, 4184.73193359375, 1, 0, 0, 0}, { 1, 0, 1, 26, 11, 1, 0, 2754.64599609375, 1, 0, 1, 26372.279296875}, { 1, 0, 0, 22, 12, 0, 4380.01708984375, 2003.68005371094, 0, 0, 0, 439.688110351562}, { 1, 0, 1, 41, 9, 1, 7864.9072265625, 7212.1201171875, 0, 0, 1, 3154.71997070312}, { 0, 0, 1, 22, 10, 0, 27864.359375, 10598.669921875, 0, 0, 0, 7094.919921875}, { 0, 0, 1, 19, 10, 0, 0, 0, 1, 1, 0, 3194.01000976562}, { 1, 0, 1, 25, 11, 1, 2336.11401367188, 3266.40576171875, 0, 0, 0, 479.267700195312}, { 1, 0, 0, 29, 13, 0, 718.249206542969, 2265.5791015625, 0, 0, 0, 2725.32202148438}, { 0, 0, 1, 21, 9, 0, 2988.412109375, 1577.1669921875, 0, 0, 0, 1740.19897460938}, { 1, 0, 1, 24, 11, 1, 824.388610839844, 1666.11303710938, 0, 0, 1, 4032.7080078125}, { 1, 0, 0, 29, 12, 0, 9748.38671875, 4878.93701171875, 0, 0, 1, 10976.509765625}, { 1, 0, 0, 29, 12, 0, 0, 1359.34655761719, 1, 0, 0, 132.759994506836}, { 0, 0, 1, 19, 8, 0, 4101.92919921875, 3416.90698242188, 0, 0, 1, 9251.3564453125}, {1, 0, 1, 25, 9, 1, 0, 0, 1, 1, 0, 4715.37109375}, { 1, 0, 1, 31, 9, 0, 0, 1698.60705566406, 1, 0, 1, 10363.26953125}, {1, 0, 0, 28, 13, 0, 0, 0, 1, 1, 0, 0}, { 1, 0, 1, 21, 11, 1, 3523.63696289062, 4167.998046875, 0, 0, 1, 11300.23828125}, { 1, 0, 1, 21, 10, 1, 1946.76098632812, 1785.17980957031, 0, 0, 0, 6831.10546875}, {0, 0, 0, 27, 12, 0, 0, 0, 1, 1, 0, 14792.900390625}, { 1, 0, 0, 27, 12, 0, 1971.31030273438, 4310.455078125, 0, 0, 1, 0}, { 0, 0, 0, 26, 12, 0, 0, 0, 1, 1, 0, 12383.6796875}, { 0, 0, 0, 31, 13, 0, 5996.01904296875, 5498.349609375, 0, 0, 1, 2329.86645507812}, { 1, 0, 0, 24, 15, 0, 12813.505859375, 13008.4970703125, 0, 0, 1, 14683.62890625}, { 0, 0, 1, 23, 8, 1, 0, 1713.15002441406, 1, 0, 1, 4232.30908203125}, { 1, 0, 0, 28, 12, 1, 10585.1298828125, 5551.4560546875, 0, 0, 0, 12780.01953125}, { 0, 0, 0, 31, 12, 0, 0, 2611.21801757812, 1, 0, 1, 2484.54907226562}, { 1, 0, 0, 26, 12, 0, 1051.25085449219, 1088.90698242188, 0, 0, 1, 11460.5634765625}, { 1, 0, 0, 25, 13, 0, 12362.9296875, 3090.73193359375, 0, 0, 1, 0}, { 1, 0, 0, 26, 14, 1, 2025.56628417969, 2025.56628417969, 0, 0, 0, 0}, {0, 1, 1, 33, 9, 1, 0, 0, 1, 1, 0, 0}, { 0, 1, 0, 24, 12, 1, 7864.89697265625, 7212.1103515625, 0, 0, 1, 6485.50830078125}, { 1, 0, 0, 21, 14, 0, 6463.24365234375, 5926.79443359375, 0, 0, 0, 29408.037109375}, { 1, 0, 0, 26, 12, 1, 8408.76171875, 5794.8310546875, 0, 0, 1, 1424.94396972656}, { 1, 0, 0, 24, 12, 0, 0, 159.884704589844, 1, 0, 0, 0}, { 1, 0, 0, 24, 12, 0, 4739.03076171875, 8100.533203125, 0, 0, 0, 11235.10546875}, {0, 0, 0, 25, 12, 0, 0, 0, 1, 1, 1, 5587.5029296875}, { 1, 0, 0, 42, 14, 0, 0, 0, 1, 1, 1, 20505.9296875}, { 1, 0, 0, 18, 12, 0, 562.657043457031, 562.657043457031, 0, 0, 1, 0}, {1, 0, 0, 25, 12, 1, 0, 0, 1, 1, 0, 0}, { 1, 0, 0, 21, 12, 0, 6473.68310546875, 3332.40893554688, 0, 0, 1, 9371.037109375}, { 1, 0, 0, 20, 12, 0, 7182.4921875, 6004.72802734375, 0, 0, 0, 4779.72021484375}, {1, 0, 0, 20, 12, 0, 0, 0, 1, 1, 1, 0}, { 1, 0, 0, 20, 12, 0, 557.698791503906, 1371.47302246094, 0, 0, 0, 20893.109375}, { 0, 1, 1, 19, 11, 1, 308.21923828125, 308.21923828125, 0, 0, 0, 0}, { 1, 0, 0, 23, 12, 1, 0, 0, 1, 1, 1, 5911.55078125}, { 1, 0, 0, 26, 12, 1, 0, 0, 1, 1, 0, 6191.94287109375}, { 1, 0, 1, 31, 11, 1, 0, 0, 1, 1, 1, 14509.9296875}, { 0, 0, 0, 27, 13, 0, 5214.30615234375, 474.501800537109, 0, 0, 0, 4812.576171875}, {1, 0, 1, 41, 4, 1, 0, 0, 1, 1, 1, 7284.98583984375}, { 0, 0, 0, 27, 12, 0, 6044.951171875, 5035.4443359375, 0, 0, 0, 0}, { 0, 0, 0, 26, 13, 0, 6476.73291015625, 5395.11865234375, 0, 0, 0, 9766.2001953125}, { 1, 0, 1, 39, 11, 0, 0, 83.6896362304688, 1, 0, 0, 0}, { 1, 0, 1, 30, 11, 1, 0, 0, 1, 1, 1, 590.781799316406}, { 1, 0, 0, 28, 12, 1, 31634.794921875, 29009.107421875, 0, 0, 0, 10067.4345703125}, { 0, 0, 0, 21, 12, 0, 0, 0, 1, 1, 1, 8048.60302734375}, { 0, 1, 0, 23, 12, 0, 9385.740234375, 1117.43896484375, 0, 0, 1, 559.443176269531}, { 1, 0, 0, 27, 12, 1, 3670.8720703125, 334.049285888672, 0, 0, 1, 0}, { 0, 0, 0, 24, 12, 0, 0, 0, 1, 1, 0, 11294.6298828125}, { 1, 0, 0, 25, 14, 1, 35040.0703125, 11536.5703125, 0, 0, 1, 36646.94921875}, { 0, 0, 0, 21, 12, 0, 7254.419921875, 5440.81494140625, 0, 0, 0, 11544.0087890625}, { 0, 0, 0, 21, 12, 0, 8059.57958984375, 7390.63427734375, 0, 0, 1, 3557.93432617188}, { 0, 0, 0, 21, 12, 0, 3670.8720703125, 334.049407958984, 0, 0, 1, 12558.01953125}, {1, 0, 1, 38, 11, 0, 0, 0, 1, 1, 1, 0}, { 1, 0, 0, 40, 12, 0, 0, 0, 1, 1, 1, 10804.3203125}, { 1, 0, 1, 41, 9, 1, 6307.50390625, 5783.9814453125, 0, 0, 1, 0}, { 1, 0, 1, 28, 10, 1, 8293.3447265625, 6449.47998046875, 0, 0, 0, 16988.1796875}, {1, 0, 0, 42, 12, 0, 0, 0, 1, 1, 1, 2456.15307617188}, { 1, 0, 1, 27, 8, 0, 6150.48291015625, 7218.27734375, 0, 0, 1, 7976.931640625}, { 1, 0, 0, 23, 12, 1, 15979.0126953125, 15079.947265625, 0, 0, 1, 10283.48828125}, {1, 0, 1, 48, 4, 0, 0, 0, 1, 1, 1, 6551.591796875}, { 0, 1, 1, 29, 10, 0, 0, 8853.673828125, 1, 0, 1, 5112.01416015625}, {1, 0, 1, 27, 7, 1, 0, 0, 1, 1, 0, 0}, {1, 0, 1, 44, 11, 0, 0, 0, 1, 1, 1, 0}, {1, 0, 1, 26, 8, 1, 3854.58520507812, 4084.25805664062, 0, 0, 0, 0}, {1, 0, 1, 37, 11, 1, 0, 0, 1, 1, 1, 9930.0458984375}, { 1, 0, 1, 31, 9, 0, 10717.0302734375, 5517.8408203125, 0, 0, 1, 9558.5009765625}, { 1, 0, 1, 36, 11, 0, 5519.66943359375, 7311.5615234375, 0, 0, 0, 0}, { 1, 0, 1, 22, 7, 0, 13503.76953125, 13503.76953125, 0, 0, 0, 11607.0849609375}, {0, 1, 1, 50, 10, 0, 0, 0, 1, 1, 0, 0}, { 1, 0, 1, 29, 11, 1, 0, 0, 1, 1, 1, 9642.9990234375}, { 0, 0, 1, 38, 9, 0, 0, 0, 1, 1, 1, 6408.9501953125}, { 1, 0, 1, 28, 11, 0, 17491.44921875, 13371.25, 0, 0, 0, 0}, { 0, 1, 1, 36, 11, 0, 5443.73388671875, 3063.8779296875, 0, 0, 0, 1324.5419921875}, { 1, 0, 1, 22, 11, 1, 0, 0, 1, 1, 0, 1698.30395507812}, { 1, 0, 1, 33, 11, 0, 0, 7867.916015625, 1, 0, 1, 6281.43310546875}, { 1, 0, 1, 31, 4, 0, 8517.5888671875, 4023.2109375, 0, 0, 1, 7382.548828125}, {1, 0, 1, 37, 9, 0, 0, 0, 1, 1, 1, 1067.50598144531}, { 1, 0, 1, 29, 11, 1, 0, 0, 1, 1, 0, 10225.8798828125}, { 1, 0, 0, 34, 12, 0, 4906.619140625, 4087.2138671875, 0, 0, 0, 5670.8193359375}, { 1, 0, 1, 45, 9, 0, 0, 0, 1, 1, 0, 4844.80322265625}, { 1, 0, 1, 31, 11, 0, 19555.31640625, 18291.412109375, 0, 0, 0, 0}, { 1, 0, 1, 31, 11, 0, 8419.75, 7505.21337890625, 0, 0, 0, 308.335235595703}, { 0, 1, 1, 19, 9, 0, 3083.6689453125, 3276.150390625, 0, 0, 1, 14271.2353515625}, { 1, 0, 0, 33, 12, 0, 0, 0, 1, 1, 0, 5970.2568359375}, { 1, 0, 1, 17, 10, 0, 1291.46801757812, 5793.85205078125, 0, 0, 1, 5522.7880859375}, { 1, 0, 1, 25, 11, 1, 0, 0, 1, 1, 1, 1574.42395019531}, { 1, 0, 1, 22, 11, 0, 1167.01538085938, 875.261535644531, 0, 0, 0, 9139.65234375}, { 1, 0, 1, 22, 11, 0, 8176.39208984375, 7497.75146484375, 0, 0, 1, 668.74658203125}, { 0, 1, 1, 19, 8, 0, 1087.80322265625, 1087.80322265625, 0, 0, 0, 2987.10083007812}, { 1, 0, 1, 22, 11, 0, 12300.9228515625, 12170.943359375, 0, 0, 1, 8408.392578125}, { 1, 0, 1, 25, 10, 1, 0, 0, 1, 1, 0, 3701.81201171875}, { 1, 0, 1, 25, 10, 1, 13519.9697265625, 9319.4443359375, 0, 0, 0, 0}, { 1, 0, 1, 43, 10, 0, 2502.86791992188, 4128.44287109375, 0, 0, 0, 7565.27294921875}, { 1, 0, 1, 34, 11, 1, 0, 0, 1, 1, 0, 6040.3349609375}, { 1, 0, 1, 46, 8, 0, 3165.65795898438, 2594.72290039062, 0, 0, 1, 0}, { 1, 0, 1, 21, 11, 1, 9659.4150390625, 7244.56103515625, 0, 0, 0, 8976.826171875}, { 1, 0, 1, 22, 9, 0, 0, 506.407592773438, 1, 0, 0, 604.19873046875}, {1, 0, 1, 34, 10, 1, 0, 0, 1, 1, 0, 0}, { 1, 0, 1, 29, 10, 0, 0, 4398.9501953125, 1, 0, 1, 0}, { 1, 0, 1, 28, 11, 0, 1929.02905273438, 6871.85595703125, 0, 0, 1, 0}, { 1, 0, 1, 28, 11, 0, 0, 1284.07897949219, 1, 0, 1, 60307.9296875}, { 1, 0, 1, 28, 10, 0, 0, 2836.50610351562, 1, 0, 1, 3196.57104492188}, { 1, 0, 1, 45, 11, 0, 0, 0, 1, 1, 0, 11796.4697265625}, { 1, 0, 1, 27, 11, 0, 0, 0, 1, 1, 0, 11197.33984375}, { 1, 0, 1, 27, 11, 0, 3311.802734375, 2483.85205078125, 0, 0, 1, 0}, { 1, 0, 0, 26, 12, 0, 9247.1015625, 8479.5927734375, 0, 0, 0, 10820.552734375}, { 1, 0, 1, 27, 10, 0, 3504.16821289062, 3213.322265625, 0, 0, 1, 2094.11889648438}, { 1, 0, 1, 29, 4, 0, 0, 0, 1, 1, 1, 762.914611816406}, { 1, 0, 1, 25, 11, 0, 0, 0, 1, 1, 1, 9897.048828125}, { 1, 0, 1, 18, 7, 0, 0, 0, 1, 1, 0, 4132.5771484375}, { 1, 0, 0, 23, 12, 0, 9342.1875, 7782.0419921875, 0, 0, 1, 14416.8359375}, { 1, 0, 0, 23, 12, 0, 5506.30810546875, 501.074096679688, 0, 0, 1, 671.331787109375}, { 1, 0, 0, 34, 12, 0, 0, 0, 1, 1, 0, 2113.72192382812}, { 1, 0, 1, 24, 11, 0, 9344.451171875, 9180.919921875, 0, 0, 1, 0}, { 1, 0, 1, 24, 11, 0, 0, 1327.99304199219, 1, 0, 0, 9495.90234375}, { 1, 0, 0, 30, 12, 0, 0, 0, 1, 1, 0, 13613.349609375}, { 1, 0, 1, 26, 8, 0, 1126.2900390625, 5562.59814453125, 0, 0, 0, 3523.57788085938}, { 1, 0, 0, 21, 12, 0, 473.114196777344, 807.951110839844, 0, 0, 1, 4350.9072265625}, { 1, 0, 1, 23, 11, 0, 0, 0, 1, 1, 0, 4350.9072265625}, { 1, 0, 1, 23, 11, 0, 7617.36181640625, 5716.4072265625, 0, 0, 0, 10274.83984375}, { 1, 0, 1, 25, 9, 0, 1419.34362792969, 1536.5029296875, 0, 0, 1, 1709.34240722656}, { 1, 0, 1, 24, 10, 0, 4250.40185546875, 2421.94702148438, 0, 0, 1, 1660.50805664062}, { 1, 0, 1, 31, 11, 1, 0, 0, 1, 1, 1, 8087.48681640625}, { 1, 0, 1, 18, 11, 0, 0, 0, 1, 1, 1, 4814.626953125}, { 1, 0, 1, 18, 11, 0, 0, 0, 1, 1, 0, 11303.1396484375}, { 1, 0, 1, 18, 11, 0, 788.523315429688, 2041.36303710938, 0, 0, 0, 796.559020996094}, { 1, 0, 1, 18, 11, 0, 8784.2373046875, 8784.2373046875, 0, 0, 0, 1824.37658691406}, { 1, 0, 1, 17, 10, 0, 0, 0, 1, 1, 0, 2657.705078125}, { 1, 0, 1, 25, 11, 1, 0, 0, 1, 1, 0, 44.7554588317871}, { 1, 0, 1, 19, 11, 0, 3504.01391601562, 3285.67993164062, 0, 0, 0, 11195.9296875}, { 1, 0, 1, 19, 11, 0, 6337.4921875, 4503.06396484375, 0, 0, 0, 0}, { 1, 0, 1, 19, 11, 0, 1868.02136230469, 1868.02136230469, 0, 0, 0, 0}, { 1, 0, 1, 19, 11, 0, 5217.3173828125, 5160.2587890625, 0, 0, 0, 0}, { 1, 0, 1, 19, 11, 0, 2305.02587890625, 2615.27587890625, 0, 0, 1, 4146.60302734375}, { 1, 0, 1, 19, 11, 0, 9856.537109375, 10224.375, 0, 0, 1, 11293.8095703125}, { 1, 0, 1, 19, 11, 0, 946.227722167969, 1615.90100097656, 0, 0, 0, 8823.0595703125}, { 1, 0, 1, 19, 11, 0, 393.859802246094, 393.859802246094, 0, 0, 0, 2481.51831054688}, { 1, 0, 1, 21, 11, 0, 2956.96557617188, 2217.72436523438, 0, 0, 1, 4082.36987304688}, { 1, 0, 1, 20, 11, 0, 7964.08642578125, 8012.08544921875, 0, 0, 0, 18155.83203125}, { 1, 0, 1, 20, 11, 0, 2569.724609375, 2531.31103515625, 0, 0, 0, 0}, { 1, 0, 1, 20, 11, 0, 7967.20947265625, 7967.20947265625, 0, 0, 0, 0}, { 1, 0, 1, 20, 11, 0, 1650.4609375, 1650.4609375, 0, 0, 0, 278.875061035156}, { 1, 0, 1, 20, 11, 0, 1168.05676269531, 1633.20239257812, 0, 0, 0, 1310.7265625}, { 1, 0, 1, 20, 11, 0, 3637.498046875, 1220.83605957031, 0, 0, 1, 1085.43994140625}, { 1, 0, 1, 24, 9, 0, 2621.84252929688, 4118.6806640625, 0, 0, 0, 597.419982910156}, { 0, 0, 1, 30, 9, 0, 3426.29956054688, 3141.91674804688, 0, 0, 0, 1320.92248535156}, {1, 0, 1, 31, 10, 1, 0, 0, 1, 1, 0, 0}, { 1, 0, 1, 18, 10, 0, 678.130187988281, 2456.994140625, 0, 0, 0, 13674.74609375}, { 1, 0, 1, 18, 10, 0, 0, 273.552490234375, 1, 0, 0, 5514.365234375}, { 1, 0, 1, 18, 10, 0, 2143.41088867188, 1784.27404785156, 0, 0, 1, 11141.3896484375}, { 0, 1, 1, 28, 11, 1, 3472.94799804688, 0, 0, 1, 0, 6771.6220703125}, {1, 0, 1, 17, 9, 0, 0, 0, 1, 1, 1, 0}, { 1, 0, 1, 17, 9, 0, 375.104705810547, 375.104705810547, 0, 0, 1, 3621.70703125}, { 1, 0, 1, 17, 9, 0, 1716.50903320312, 1253.43896484375, 0, 0, 1, 5445.2001953125}, {1, 0, 1, 25, 10, 1, 0, 0, 1, 1, 0, 0}, {1, 0, 1, 44, 11, 0, 0, 0, 1, 1, 0, 0}, { 1, 0, 1, 22, 10, 0, 0, 2174.955078125, 1, 0, 0, 5344.02099609375}, { 1, 0, 1, 19, 10, 0, 2959.07666015625, 3213.11279296875, 0, 0, 1, 2975.03149414062}, { 1, 0, 1, 19, 10, 0, 4121.94921875, 6056.75390625, 0, 0, 1, 0}, { 1, 0, 1, 21, 10, 0, 6661.06298828125, 1162.36206054688, 0, 0, 0, 39483.53125}, { 1, 0, 0, 29, 12, 0, 22859.439453125, 2080.208984375, 0, 0, 0, 16969.94921875}, { 1, 0, 0, 29, 12, 0, 10881.9404296875, 1817.28405761719, 0, 0, 1, 0}, { 1, 0, 1, 20, 10, 0, 825.230163574219, 825.230163574219, 0, 0, 0, 0}, { 1, 0, 1, 20, 10, 0, 11073.0888671875, 11073.0888671875, 0, 0, 1, 0}, { 1, 0, 1, 20, 10, 0, 3114.81713867188, 2856.28735351562, 0, 0, 0, 18347.255859375}, { 1, 0, 1, 20, 10, 0, 3666.63623046875, 2749.97705078125, 0, 0, 0, 6867.74072265625}, { 1, 0, 1, 20, 10, 0, 0, 0, 1, 1, 0, 8598.5224609375}, {1, 0, 1, 18, 9, 0, 0, 0, 1, 1, 0, 0}, { 1, 0, 1, 18, 9, 0, 2138.09399414062, 2138.09399414062, 0, 0, 0, 5767.70458984375}, {1, 0, 1, 17, 8, 0, 0, 0, 1, 1, 1, 0}, {1, 0, 1, 17, 8, 0, 0, 0, 1, 1, 1, 0}, { 1, 0, 1, 26, 10, 1, 6140.3671875, 558.773376464844, 0, 0, 0, 0}, { 1, 0, 1, 26, 10, 1, 2027.9990234375, 0, 0, 1, 1, 0}, { 1, 0, 1, 22, 9, 0, 2192.876953125, 3836.98608398438, 0, 0, 1, 3462.56396484375}, { 1, 0, 1, 19, 9, 0, 8409.626953125, 1778.08898925781, 0, 0, 0, 7171}, { 1, 0, 1, 21, 9, 0, 31886.4296875, 12357.2197265625, 0, 0, 0, 0}, { 1, 0, 1, 20, 9, 0, 7688.107421875, 9164.44921875, 0, 0, 0, 8924.3955078125}, { 1, 0, 1, 20, 9, 0, 9718.20703125, 9691.033203125, 0, 0, 0, 8562.603515625}, { 0, 0, 1, 31, 4, 0, 11680.564453125, 10711.0771484375, 0, 0, 0, 0}, { 1, 0, 1, 44, 9, 0, 0, 0, 1, 1, 0, 9722.0029296875}, { 0, 1, 1, 18, 7, 1, 3059.47412109375, 2672.2021484375, 0, 0, 0, 6330.8876953125}, { 1, 0, 1, 19, 8, 0, 2636.35302734375, 2937.26391601562, 0, 0, 1, 7535.94189453125}, { 1, 0, 1, 21, 8, 0, 989.267822265625, 3695.89697265625, 0, 0, 1, 4279.61279296875}, { 1, 0, 1, 19, 10, 1, 0, 0, 1, 1, 0, 12797.669921875}, { 0, 1, 1, 17, 10, 0, 1203.60900878906, 1239.62805175781, 0, 0, 0, 5088.98583984375}, {1, 0, 1, 19, 9, 1, 0, 0, 1, 1, 0, 16658.25}, { 0, 1, 1, 19, 6, 0, 0, 0, 1, 1, 0, 5071.80078125}, {1, 0, 1, 29, 11, 1, 0, 0, 1, 1, 0, 0}, {0, 1, 1, 17, 9, 0, 0, 0, 1, 1, 0, 5114.81396484375}, { 1, 0, 1, 25, 8, 1, 330.949493408203, 515.827453613281, 0, 0, 0, 14281.306640625}, { 1, 0, 1, 29, 10, 1, 4687.8017578125, 4923.26318359375, 0, 0, 1, 130.679122924805}, { 1, 0, 1, 24, 10, 1, 11703.2001953125, 4078.15209960938, 0, 0, 1, 0}, { 0, 0, 1, 28, 8, 0, 0, 0, 1, 1, 0, 8190.4208984375}, { 1, 0, 1, 33, 11, 0, 10523.830078125, 2899.82006835938, 0, 0, 0, 18859.890625}, { 0, 0, 1, 21, 11, 0, 1261.63720703125, 946.227905273438, 0, 0, 1, 11412.2353515625}, { 0, 0, 1, 26, 11, 0, 3807.53588867188, 4676.45703125, 0, 0, 1, 1973.34533691406}, { 1, 0, 1, 28, 11, 1, 8593.1630859375, 5393.89501953125, 0, 0, 0, 4974.5859375}, { 1, 0, 1, 23, 10, 1, 10746.078125, 9854.1533203125, 0, 0, 0, 0}, { 1, 0, 0, 31, 12, 0, 0, 5613.9091796875, 1, 0, 0, 0}, { 1, 0, 0, 31, 12, 0, 0, 0, 1, 1, 0, 7284.39404296875}, { 0, 0, 1, 25, 11, 0, 0, 0, 1, 1, 1, 18783.349609375}, { 1, 0, 0, 25, 12, 0, 3297.2490234375, 2746.6083984375, 0, 0, 1, 2090.67846679688}, { 1, 0, 0, 25, 12, 0, 0, 0, 1, 1, 1, 11965.8095703125}, { 0, 1, 1, 24, 10, 0, 3000.83764648438, 3000.83764648438, 0, 0, 0, 5350.0380859375}, { 0, 1, 1, 18, 11, 0, 5491.53515625, 5491.53515625, 0, 0, 1, 2454.5}, { 1, 0, 1, 27, 11, 1, 1122.62060546875, 1122.62060546875, 0, 0, 0, 215.834808349609}, { 0, 1, 1, 17, 10, 0, 2102.50244140625, 2202.79638671875, 0, 0, 1, 4387.05712890625}, { 0, 1, 1, 17, 10, 0, 1442.68103027344, 1734.55895996094, 0, 0, 0, 6354.19384765625}, { 0, 1, 1, 17, 10, 0, 4905.119140625, 1168.90197753906, 0, 0, 0, 11306.26953125}, {1, 0, 1, 43, 9, 0, 0, 0, 1, 1, 1, 0}, { 0, 0, 1, 25, 10, 0, 5139.44775390625, 4712.8740234375, 0, 0, 1, 6103.74609375}, { 0, 0, 1, 25, 10, 0, 8877.2109375, 8140.40234375, 0, 0, 0, 3412.95483398438}, { 1, 0, 0, 21, 14, 0, 0, 0, 1, 1, 0, 5775.06201171875}, { 0, 1, 1, 21, 11, 0, 6852.5986328125, 6658.5625, 0, 0, 1, 9381.294921875}, { 0, 1, 1, 21, 11, 0, 2992.53393554688, 8920.470703125, 0, 0, 0, 20857.83984375}, {0, 0, 1, 17, 11, 0, 0, 0, 1, 1, 0, 0}, { 0, 1, 1, 18, 10, 0, 12717.955078125, 12625.5107421875, 0, 0, 1, 0}, { 0, 1, 1, 18, 10, 0, 0, 0, 1, 1, 0, 1859.1669921875}, { 1, 0, 1, 27, 10, 1, 0, 0, 1, 1, 1, 18739.9296875}, { 1, 0, 1, 22, 10, 1, 1468.34802246094, 5588.6640625, 0, 0, 1, 13228.2802734375}, { 1, 0, 0, 30, 13, 0, 6073.89453125, 5569.76123046875, 0, 0, 0, 0}, { 0, 0, 1, 23, 11, 0, 21492.203125, 19995.642578125, 0, 0, 0, 12896.216796875}, {0, 1, 1, 19, 10, 0, 0, 0, 1, 1, 0, 0}, { 0, 1, 1, 19, 10, 0, 1261.63818359375, 2275.36767578125, 0, 0, 0, 4335.1591796875}, { 0, 0, 1, 18, 11, 0, 3678.23095703125, 919.557922363281, 0, 0, 1, 4321.705078125}, { 0, 1, 0, 27, 12, 0, 27913.662109375, 24276.974609375, 0, 0, 0, 15762.876953125}, { 0, 0, 1, 17, 10, 0, 2417.982421875, 2465.61328125, 0, 0, 0, 4211.66162109375}, { 1, 0, 0, 26, 12, 0, 0, 0, 1, 1, 1, 10747.349609375}, {1, 0, 0, 26, 12, 0, 0, 0, 1, 1, 0, 0}, { 0, 0, 1, 22, 11, 0, 0, 0, 1, 1, 1, 1048.43200683594}, { 0, 0, 1, 19, 11, 0, 823.25439453125, 823.25439453125, 0, 0, 0, 2649.59887695312}, {0, 0, 1, 19, 11, 0, 0, 0, 1, 1, 0, 0}, { 1, 0, 1, 27, 9, 1, 0, 934.445373535156, 1, 0, 1, 1773.42297363281}, { 1, 0, 0, 30, 12, 0, 0, 0, 1, 1, 1, 24909.44921875}, { 0, 0, 1, 20, 11, 0, 1545.50732421875, 1159.13049316406, 0, 0, 0, 0}, { 0, 0, 1, 25, 8, 0, 24415.234375, 23096.65234375, 0, 0, 1, 6421.5302734375}, { 0, 1, 1, 19, 9, 0, 2686.53002929688, 2750.8408203125, 0, 0, 0, 15581.4365234375}, { 1, 0, 0, 29, 14, 0, 3357.18530273438, 3357.18530273438, 0, 0, 0, 7226.04931640625}, { 1, 0, 1, 26, 10, 1, 12143.2412109375, 9107.4306640625, 0, 0, 0, 12659.576171875}, { 0, 0, 1, 17, 9, 0, 1618.14978027344, 1618.14978027344, 0, 0, 1, 6011.02099609375}, {1, 0, 1, 25, 11, 1, 0, 0, 1, 1, 1, 0}, { 0, 0, 1, 24, 8, 0, 0, 0, 1, 1, 0, 10569.26953125}, { 1, 0, 0, 29, 13, 0, 0, 0, 1, 1, 1, 7479.65576171875}, { 1, 0, 0, 25, 12, 0, 6939.01220703125, 5204.25927734375, 0, 0, 1, 0}, { 0, 0, 1, 17, 8, 0, 0, 0, 1, 1, 0, 10210.990234375}, { 1, 0, 0, 28, 15, 0, 0, 0, 1, 1, 1, 9598.541015625}, { 0, 0, 1, 19, 9, 0, 0, 0, 1, 1, 1, 13188.830078125}, { 1, 0, 0, 25, 12, 0, 14426.7900390625, 2409.27392578125, 0, 0, 1, 0}, {1, 0, 1, 25, 10, 1, 0, 0, 1, 1, 0, 0}, { 0, 0, 1, 18, 8, 0, 1168.05639648438, 1345.90942382812, 0, 0, 0, 1725.59240722656}, { 1, 0, 0, 29, 12, 0, 0, 0, 1, 1, 0, 1890.93701171875}, { 1, 0, 0, 28, 14, 0, 7850.5849609375, 6539.53759765625, 0, 0, 0, 3123.1162109375}, { 0, 1, 1, 18, 6, 0, 6385.3740234375, 6142.6806640625, 0, 0, 0, 3825.3076171875}, {1, 0, 1, 42, 10, 0, 0, 0, 1, 1, 0, 6930.3359375}, {0, 0, 1, 23, 7, 0, 0, 0, 1, 1, 1, 0}, { 0, 1, 1, 18, 8, 1, 0, 0, 1, 1, 1, 2787.9599609375}, { 1, 0, 1, 21, 11, 1, 7652.11962890625, 7652.11962890625, 0, 0, 0, 8396.654296875}, { 1, 0, 1, 23, 10, 1, 9462.2880859375, 10495.083984375, 0, 0, 1, 3385.81079101562}, {1, 0, 0, 24, 12, 0, 0, 0, 1, 1, 0, 0}, { 1, 0, 0, 27, 13, 0, 9593.6181640625, 8797.34765625, 0, 0, 0, 1653.48120117188}, {1, 0, 0, 27, 13, 0, 0, 0, 1, 1, 1, 0}, { 1, 0, 0, 27, 13, 0, 0, 0, 1, 1, 1, 14581.8603515625}, { 1, 0, 0, 27, 13, 0, 0, 0, 1, 1, 0, 7609.51806640625}, { 1, 0, 0, 27, 13, 0, 0, 0, 1, 1, 1, 34099.28125}, { 1, 0, 0, 27, 12, 0, 31166.80859375, 32984.25, 0, 0, 0, 10833.341796875}, { 1, 0, 0, 27, 12, 0, 2143.4130859375, 357.949890136719, 0, 0, 1, 22163.25}, { 1, 0, 0, 27, 12, 0, 3311.8017578125, 2483.85131835938, 0, 0, 0, 3552.26806640625}, {1, 0, 1, 38, 10, 0, 0, 0, 1, 1, 0, 0}, { 1, 0, 0, 22, 16, 0, 0, 0, 1, 1, 1, 2164.02197265625}, { 1, 0, 0, 26, 12, 0, 7322.03662109375, 7322.03662109375, 0, 0, 0, 4677.04931640625}, { 1, 0, 0, 22, 12, 0, 5605.85205078125, 936.177307128906, 0, 0, 1, 0}, {1, 0, 0, 22, 12, 0, 0, 0, 1, 1, 0, 0}, { 1, 0, 0, 25, 13, 0, 10240.357421875, 10240.357421875, 0, 0, 0, 10020.71484375}, { 1, 0, 0, 25, 13, 0, 3751.04760742188, 3751.04760742188, 0, 0, 0, 1672.77600097656}, { 1, 0, 0, 25, 12, 0, 0, 0, 1, 1, 1, 3191.7529296875}, {1, 0, 0, 25, 12, 0, 0, 0, 1, 1, 1, 0}, { 1, 0, 0, 25, 12, 0, 0, 0, 1, 1, 0, 3418.09692382812}, {1, 0, 0, 25, 12, 0, 0, 0, 1, 1, 0, 0}, { 1, 0, 0, 25, 12, 0, 0, 0, 1, 1, 1, 2348.97290039062}, { 1, 0, 0, 21, 13, 0, 0, 0, 1, 1, 1, 17094.640625}, { 0, 1, 1, 25, 11, 1, 0, 0, 1, 1, 0, 4485.6162109375}, { 1, 0, 0, 24, 12, 0, 20580.48046875, 18154.0546875, 0, 0, 1, 0}, { 1, 0, 0, 24, 12, 0, 13765.75, 2842.76391601562, 0, 0, 1, 6167.68115234375}, { 1, 0, 0, 21, 13, 0, 27052.154296875, 24806.826171875, 0, 0, 1, 8803.666015625}, { 1, 0, 0, 20, 13, 0, 9106.6767578125, 8965.2431640625, 0, 0, 0, 9199.5625}, { 1, 0, 0, 20, 12, 0, 989.267822265625, 165.207702636719, 0, 0, 1, 0}, { 1, 0, 0, 23, 12, 0, 6614.12158203125, 7841.97216796875, 0, 0, 1, 7947.2568359375}, {1, 0, 0, 23, 12, 0, 0, 0, 1, 1, 1, 0}, { 1, 0, 0, 23, 12, 0, 0, 0, 1, 1, 1, 4843.17578125}, { 1, 0, 0, 23, 12, 0, 6269.3408203125, 3039.9599609375, 0, 0, 1, 8484.2392578125}, {1, 0, 0, 20, 12, 0, 0, 0, 1, 1, 0, 0}, { 1, 0, 0, 18, 12, 0, 0, 0, 1, 1, 1, 2321.10693359375}, { 1, 0, 0, 18, 12, 0, 0, 1405.51196289062, 1, 0, 0, 0}, { 1, 0, 0, 18, 12, 0, 700.833740234375, 642.664489746094, 0, 0, 0, 14444.7099609375}, { 1, 0, 0, 22, 12, 0, 2590.6962890625, 2158.05004882812, 0, 0, 0, 0}, { 1, 0, 0, 22, 12, 0, 0, 0, 1, 1, 1, 18678.080078125}, { 1, 0, 0, 22, 12, 0, 709.671447753906, 532.253601074219, 0, 0, 0, 1333.44506835938}, { 1, 0, 0, 19, 12, 0, 3042.10375976562, 2534.07250976562, 0, 0, 1, 4551.978515625}, {1, 0, 0, 21, 12, 0, 0, 0, 1, 1, 1, 1254.58203125}, { 1, 0, 0, 21, 12, 0, 0, 0, 1, 1, 1, 9983.7841796875}, { 1, 0, 0, 21, 12, 0, 449.047943115234, 449.047943115234, 0, 0, 0, 0}, { 1, 0, 0, 20, 12, 0, 6576.2861328125, 4932.21484375, 0, 0, 0, 6086.96630859375}, { 1, 0, 0, 20, 12, 0, 1265.39416503906, 1160.36645507812, 0, 0, 1, 702.183898925781}, { 1, 0, 0, 20, 12, 0, 8113.89501953125, 6085.42138671875, 0, 0, 1, 9750.66015625}, { 1, 0, 0, 20, 12, 0, 0, 591.815124511719, 1, 0, 0, 4159.9189453125}, { 1, 0, 0, 20, 12, 0, 9227.55859375, 9227.55859375, 0, 0, 1, 0}, { 1, 0, 0, 20, 12, 0, 0, 377.568603515625, 1, 0, 1, 1652.63696289062}, { 1, 0, 0, 19, 12, 0, 8417, 2814.19506835938, 0, 0, 0, 1720.90698242188}, { 0, 1, 1, 19, 11, 1, 5424.48486328125, 5463.80322265625, 0, 0, 1, 6788.462890625}, {1, 0, 0, 19, 12, 0, 0, 0, 1, 1, 0, 0}, { 1, 0, 1, 40, 11, 0, 0, 0, 1, 1, 1, 23005.599609375}, { 0, 1, 1, 18, 10, 1, 2308.07373046875, 1922.62548828125, 0, 0, 0, 4768.3544921875}, { 0, 1, 0, 27, 12, 0, 2920.14233398438, 2677.7705078125, 0, 0, 0, 17395.16796875}, { 0, 1, 1, 20, 6, 1, 10802.982421875, 10802.982421875, 0, 0, 1, 4488.49462890625}, { 1, 0, 1, 36, 7, 0, 1752.08532714844, 1606.66223144531, 0, 0, 0, 20780.6484375}, {1, 0, 1, 24, 7, 0, 0, 0, 1, 1, 0, 1455.68994140625}, { 1, 0, 1, 21, 8, 0, 879.26611328125, 2237.20849609375, 0, 0, 0, 1764.10766601562}, {1, 0, 1, 35, 11, 0, 0, 0, 1, 1, 0, 0}, {1, 0, 1, 39, 9, 0, 0, 0, 1, 1, 0, 0}, { 0, 1, 0, 23, 12, 0, 5721.69580078125, 8960.68359375, 0, 0, 0, 7078.17822265625}, { 0, 1, 0, 18, 12, 0, 584.027526855469, 535.553283691406, 0, 0, 1, 1115.85961914062}, { 1, 0, 1, 29, 9, 0, 9268.943359375, 9160.693359375, 0, 0, 0, 0}, { 1, 0, 1, 36, 10, 0, 0, 0, 1, 1, 0, 14690.349609375}, { 1, 0, 1, 33, 8, 0, 0, 0, 1, 1, 1, 289.789886474609}, { 1, 0, 1, 32, 11, 0, 0, 0, 1, 1, 0, 11120.5302734375}, {1, 0, 1, 39, 6, 0, 0, 0, 1, 1, 0, 0}, { 1, 0, 1, 38, 10, 0, 0, 0, 1, 1, 0, 12429.91015625}, {1, 0, 1, 38, 8, 0, 0, 0, 1, 1, 0, 0}, { 1, 0, 1, 37, 10, 0, 8606.7431640625, 8606.7431640625, 0, 0, 0, 0}, { 1, 0, 1, 20, 10, 0, 690.192016601562, 690.192016601562, 0, 0, 0, 10864.8759765625}, {1, 0, 1, 36, 10, 0, 0, 0, 1, 1, 0, 0}, { 1, 0, 1, 27, 11, 0, 0, 0, 1, 1, 1, 7506.14599609375}, { 1, 0, 1, 35, 10, 0, 0, 0, 1, 1, 1, 4666.23583984375}, { 1, 0, 1, 35, 10, 0, 9902.7626953125, 9902.7626953125, 0, 0, 0, 9453.8271484375}, { 1, 0, 1, 35, 10, 0, 0, 0, 1, 1, 0, 445.830902099609}, { 1, 0, 1, 25, 10, 0, 0, 0, 1, 1, 0, 20942.240234375}, { 1, 0, 1, 24, 11, 0, 0, 0, 1, 1, 0, 14626.3896484375}, {1, 0, 1, 30, 11, 0, 0, 0, 1, 1, 1, 0}, { 0, 1, 1, 20, 9, 0, 0, 0, 1, 1, 0, 8329.8232421875}, { 1, 0, 1, 23, 11, 0, 37849.0703125, 29897.189453125, 0, 0, 0, 23483.451171875}, { 1, 0, 1, 34, 11, 0, 0, 0, 1, 1, 0, 2920.19897460938}, { 1, 0, 1, 34, 11, 0, 8912.484375, 8912.484375, 0, 0, 0, 5611.8662109375}, { 1, 0, 1, 23, 10, 0, 17131.466796875, 15709.5556640625, 0, 0, 1, 5665.06640625}, { 1, 0, 1, 25, 8, 0, 37431.66015625, 37431.66015625, 0, 0, 1, 2346.828125}, {1, 0, 1, 34, 10, 0, 0, 0, 1, 1, 0, 7952.5400390625}, { 1, 0, 1, 30, 8, 0, 0, 1706.65698242188, 1, 0, 0, 0}, {0, 1, 1, 21, 7, 0, 0, 0, 1, 1, 0, 0}, { 1, 0, 1, 34, 9, 0, 5244.193359375, 4368.4130859375, 0, 0, 0, 14051.15625}, { 1, 0, 1, 20, 9, 0, 6083.994140625, 0, 0, 1, 1, 8881.6650390625}, {1, 0, 1, 29, 11, 0, 0, 0, 1, 1, 0, 0}, {1, 0, 1, 33, 11, 0, 0, 0, 1, 1, 0, 0}, {1, 0, 1, 34, 7, 0, 1143.359375, 857.51953125, 0, 0, 1, 0}, { 1, 0, 1, 29, 8, 0, 0, 0, 1, 1, 1, 1923.93798828125}, { 1, 0, 1, 28, 10, 0, 10979.7138671875, 10068.3974609375, 0, 0, 0, 0}, { 0, 1, 1, 29, 9, 0, 9594.3076171875, 16341.16015625, 0, 0, 0, 16900.30078125}, {1, 0, 1, 32, 11, 0, 0, 0, 1, 1, 1, 8472.158203125}, { 1, 0, 1, 27, 11, 0, 17075.033203125, 14223.501953125, 0, 0, 1, 7661.97265625}, { 1, 0, 1, 32, 10, 0, 2786.95874023438, 2321.53662109375, 0, 0, 1, 179.021728515625}, {1, 0, 1, 32, 10, 0, 0, 0, 1, 1, 0, 0}, { 1, 0, 1, 26, 11, 0, 4699.962890625, 1174.99096679688, 0, 0, 0, 6672.02099609375}, { 1, 0, 1, 27, 11, 0, 3065.19409179688, 766.298583984375, 0, 0, 0, 0}, { 1, 0, 1, 27, 10, 0, 1001.14599609375, 3550.07495117188, 0, 0, 1, 0}, { 0, 1, 1, 34, 11, 0, 5061.57861328125, 4641.46728515625, 0, 0, 1, 2877.68212890625}, { 1, 0, 1, 26, 10, 0, 0, 0, 1, 1, 0, 3931.23803710938}, { 1, 0, 1, 32, 8, 0, 0, 0, 1, 1, 0, 7152.1318359375}, { 1, 0, 1, 31, 11, 0, 17711.880859375, 1726.44494628906, 0, 0, 0, 0}, { 1, 0, 1, 31, 11, 0, 7662.1689453125, 6382.5869140625, 0, 0, 0, 0}, { 0, 1, 1, 21, 11, 0, 21589.083984375, 17983.70703125, 0, 0, 1, 15191.9111328125}, { 1, 0, 1, 25, 11, 0, 0, 0, 1, 1, 1, 485.229797363281}, {1, 0, 1, 31, 10, 0, 0, 0, 1, 1, 0, 0}, { 1, 0, 1, 31, 10, 0, 0, 0, 1, 1, 0, 17014.58984375}, { 1, 0, 1, 31, 10, 0, 0, 520.4462890625, 1, 0, 0, 14527.8798828125}, {1, 0, 1, 17, 10, 0, 0, 0, 1, 1, 1, 0}, { 1, 0, 1, 30, 11, 0, 1913.03369140625, 1913.03369140625, 0, 0, 1, 0}, { 1, 0, 1, 19, 11, 0, 0, 0, 1, 1, 1, 7458.10498046875}, { 1, 0, 1, 31, 8, 0, 5495.41455078125, 4577.68017578125, 0, 0, 0, 6153.82958984375}, { 1, 0, 1, 20, 11, 0, 0, 0, 1, 1, 1, 3972.5400390625}, { 0, 1, 1, 20, 8, 0, 1350.30126953125, 1124.80102539062, 0, 0, 0, 11731.197265625}, { 1, 0, 1, 23, 10, 0, 0, 0, 1, 1, 1, 7693.39990234375}, { 1, 0, 1, 24, 9, 0, 2788.498046875, 0, 0, 1, 0, 16461.5703125}, { 1, 0, 1, 18, 10, 0, 0, 798.907897949219, 1, 0, 1, 9737.154296875}, { 1, 0, 1, 18, 10, 0, 1557.40856933594, 1428.14367675781, 0, 0, 0, 11602.935546875}, { 1, 0, 1, 30, 10, 0, 3140.23608398438, 2615.81665039062, 0, 0, 1, 0}, { 1, 0, 1, 22, 10, 0, 16486.171875, 13732.98046875, 0, 0, 0, 10770.962890625}, { 1, 0, 1, 25, 11, 0, 2622.09716796875, 2455.0673828125, 0, 0, 0, 8764.1474609375}, { 1, 0, 1, 20, 10, 0, 6123.45654296875, 5100.8388671875, 0, 0, 1, 13626.0439453125}, { 1, 0, 1, 20, 10, 0, 0, 0, 1, 1, 0, 2015.50305175781}, {1, 0, 1, 25, 11, 0, 0, 0, 1, 1, 0, 0}, {1, 0, 1, 25, 11, 0, 0, 0, 1, 1, 1, 0}, { 1, 0, 1, 20, 9, 0, 3311.802734375, 2483.85205078125, 0, 0, 0, 0}, { 1, 0, 1, 29, 11, 0, 3550.888671875, 3256.16479492188, 0, 0, 0, 3290.29321289062}, {1, 0, 1, 24, 11, 0, 0, 0, 1, 1, 0, 0}, { 1, 0, 1, 20, 7, 0, 1884.1416015625, 1569.48999023438, 0, 0, 1, 5518.0830078125}, { 1, 0, 1, 29, 9, 0, 0, 1203.82397460938, 1, 0, 0, 9378.6533203125}, { 1, 0, 1, 24, 9, 0, 9154.7001953125, 2288.67504882812, 0, 0, 1, 4849.55908203125}, { 1, 0, 1, 28, 11, 0, 2431.94897460938, 863.4794921875, 0, 0, 0, 0}, { 1, 0, 1, 28, 11, 0, 0, 0, 1, 1, 0, 5767.1328125}, {1, 0, 1, 29, 8, 0, 0, 0, 1, 1, 0, 0}, { 0, 1, 1, 27, 9, 0, 2129.015625, 1596.76184082031, 0, 0, 0, 6723.4140625}, { 1, 0, 1, 28, 10, 0, 1471.29296875, 367.823394775391, 0, 0, 0, 4858.89501953125}, { 1, 0, 1, 28, 10, 0, 6567.3291015625, 6567.3291015625, 0, 0, 0, 5435.78662109375}, { 1, 0, 1, 28, 10, 0, 5377.65576171875, 4479.5869140625, 0, 0, 0, 128.671997070312}, { 1, 0, 1, 23, 11, 0, 6382.30908203125, 580.790100097656, 0, 0, 0, 0}, {0, 1, 1, 33, 5, 0, 0, 0, 1, 1, 0, 0}, {1, 0, 1, 23, 10, 0, 0, 0, 1, 1, 0, 0}, {1, 0, 1, 27, 11, 0, 2206.93994140625, 2666.27392578125, 0, 0, 1, 0}, {1, 0, 1, 27, 11, 0, 0, 0, 1, 1, 1, 0}, { 1, 0, 1, 27, 11, 0, 0, 0, 1, 1, 1, 549.298400878906}, { 1, 0, 1, 28, 9, 0, 0, 0, 1, 1, 1, 10694.2900390625}, {1, 0, 1, 28, 9, 0, 0, 0, 1, 1, 0, 0}, { 1, 0, 1, 27, 10, 0, 0, 0, 1, 1, 0, 1135.46997070312}, { 1, 0, 1, 27, 10, 0, 0, 0, 1, 1, 0, 1184.88195800781}, { 1, 0, 1, 28, 8, 0, 1577.04797363281, 1182.78601074219, 0, 0, 1, 7779.26318359375}, { 1, 0, 1, 28, 8, 0, 11870.0908203125, 10610.080078125, 0, 0, 0, 4623.18798828125}, {1, 0, 1, 28, 8, 0, 0, 0, 1, 1, 1, 0}, { 1, 0, 1, 26, 11, 0, 0, 0, 1, 1, 1, 17230.9609375}, { 1, 0, 1, 26, 11, 0, 0, 0, 1, 1, 0, 7176.18701171875}, { 0, 1, 1, 31, 9, 0, 0, 0, 1, 1, 1, 26817.599609375}, { 1, 0, 1, 27, 9, 0, 2838.68774414062, 2129.01586914062, 0, 0, 0, 0}, { 1, 0, 1, 27, 9, 0, 3059.47290039062, 2294.6044921875, 0, 0, 1, 2018.55590820312}, {1, 0, 1, 27, 9, 0, 0, 0, 1, 1, 1, 0}, { 1, 0, 1, 26, 10, 0, 0, 0, 1, 1, 1, 9265.7880859375}, { 1, 0, 1, 26, 10, 0, 25929.6796875, 6788.9580078125, 0, 0, 1, 672.877319335938}, { 1, 0, 1, 26, 10, 0, 0, 0, 1, 1, 0, 2169.02709960938}, {1, 0, 1, 27, 8, 0, 0, 0, 1, 1, 1, 0}, { 1, 0, 1, 27, 8, 0, 0, 0, 1, 1, 0, 3783.65991210938}, { 0, 1, 1, 20, 11, 0, 0, 0, 1, 1, 0, 6378.72021484375}, { 1, 0, 1, 25, 11, 0, 0, 0, 1, 1, 0, 1264.23205566406}, { 1, 0, 1, 25, 11, 0, 3649.7685546875, 3649.7685546875, 0, 0, 0, 0}, { 1, 0, 1, 25, 11, 0, 15209.990234375, 3072.72607421875, 0, 0, 0, 284.658386230469}, { 1, 0, 1, 25, 11, 0, 5281.2451171875, 0, 0, 1, 0, 0}, { 1, 0, 1, 25, 11, 0, 4672.2236328125, 4284.42919921875, 0, 0, 0, 4116.7021484375}, {1, 0, 1, 25, 11, 0, 0, 0, 1, 1, 0, 4196.375}, { 0, 1, 1, 17, 9, 0, 731.981567382812, 671.22705078125, 0, 0, 0, 4209.57958984375}, { 1, 0, 1, 26, 9, 0, 7584.57373046875, 6955.05419921875, 0, 0, 0, 11856.875}, { 1, 0, 1, 20, 11, 0, 1931.24499511719, 3248.93701171875, 0, 0, 0, 6027.638671875}, {1, 0, 1, 25, 10, 0, 0, 0, 1, 1, 0, 0}, { 1, 0, 1, 25, 10, 0, 0, 0, 1, 1, 0, 7367.0400390625}, { 1, 0, 1, 25, 10, 0, 0, 0, 1, 1, 0, 289.789886474609}, {1, 0, 1, 25, 10, 0, 0, 0, 1, 1, 1, 0}, { 1, 0, 1, 24, 11, 0, 0, 0, 1, 1, 1, 1991.40002441406}, { 1, 0, 1, 24, 11, 0, 13627.26953125, 13370.5751953125, 0, 0, 0, 19533.8828125}, {1, 0, 1, 24, 11, 0, 0, 0, 1, 1, 1, 995.7001953125}, { 1, 0, 1, 17, 11, 0, 0, 0, 1, 1, 0, 8551.533203125}, { 1, 0, 1, 17, 11, 0, 4080.72998046875, 3796.02905273438, 0, 0, 0, 0}, { 1, 0, 1, 17, 11, 0, 989.267822265625, 165.207702636719, 0, 0, 0, 4251.126953125}, { 1, 0, 1, 17, 11, 0, 1261.50122070312, 1156.79663085938, 0, 0, 0, 6365.03125}, { 1, 0, 1, 23, 11, 0, 11946.119140625, 8959.5888671875, 0, 0, 1, 1659.49975585938}, {1, 0, 1, 23, 11, 0, 0, 0, 1, 1, 1, 0}, {1, 0, 1, 23, 11, 0, 0, 0, 1, 1, 0, 0}, { 1, 0, 1, 23, 11, 0, 2355.17846679688, 1961.86364746094, 0, 0, 1, 3689.1259765625}, {1, 0, 1, 23, 11, 0, 0, 0, 1, 1, 0, 5088.759765625}, { 1, 0, 1, 23, 11, 0, 0, 1896.02294921875, 1, 0, 0, 5573.546875}, {1, 0, 1, 23, 11, 0, 0, 0, 1, 1, 0, 0}, {1, 0, 1, 24, 10, 0, 0, 0, 1, 1, 0, 0}, { 1, 0, 1, 24, 10, 0, 1570.11804199219, 1307.90832519531, 0, 0, 0, 0}, {1, 0, 1, 24, 10, 0, 0, 0, 1, 1, 1, 0}, {1, 0, 1, 24, 10, 0, 0, 0, 1, 1, 0, 0}, {1, 0, 1, 24, 10, 0, 0, 0, 1, 1, 1, 0}, { 1, 0, 1, 24, 10, 0, 0, 0, 1, 1, 0, 7618.63916015625}, { 1, 0, 1, 18, 11, 0, 1064.69897460938, 645.272277832031, 0, 0, 0, 5497.5908203125}, { 1, 0, 1, 18, 11, 0, 1170.3271484375, 1170.3271484375, 0, 0, 0, 604.228698730469}, {1, 0, 1, 18, 11, 0, 0, 0, 1, 1, 0, 0}, { 1, 0, 1, 18, 11, 0, 1135.47473144531, 851.606079101562, 0, 0, 1, 0}, { 1, 0, 1, 18, 11, 0, 858.254272460938, 214.563598632812, 0, 0, 1, 929.883911132812}, {1, 0, 1, 18, 11, 0, 0, 0, 1, 1, 1, 0}, { 1, 0, 1, 18, 11, 0, 2499.64111328125, 2292.1708984375, 0, 0, 0, 4994.2265625}, {0, 1, 1, 18, 8, 0, 0, 0, 1, 1, 0, 10798.5595703125}, {1, 0, 1, 17, 10, 0, 0, 0, 1, 1, 0, 0}, {1, 0, 1, 17, 10, 0, 0, 0, 1, 1, 1, 0}, {1, 0, 1, 17, 10, 0, 0, 0, 1, 1, 0, 275.566101074219}, { 1, 0, 1, 17, 10, 0, 0, 0, 1, 1, 0, 1143.38696289062}, { 1, 0, 1, 17, 10, 0, 188.414260864258, 156.949081420898, 0, 0, 1, 10641.240234375}, {1, 0, 1, 17, 10, 0, 0, 0, 1, 1, 1, 0}, { 1, 0, 1, 17, 10, 0, 0, 0, 1, 1, 0, 2189.42602539062}, { 1, 0, 1, 17, 10, 0, 0, 0, 1, 1, 1, 16218.0400390625}, { 1, 0, 1, 17, 10, 0, 2072.5556640625, 1726.43884277344, 0, 0, 0, 11061.615234375}, { 1, 0, 1, 17, 10, 0, 1201.140625, 1000.55010986328, 0, 0, 1, 1433.53125}, { 1, 0, 1, 17, 10, 0, 934.445373535156, 856.886413574219, 0, 0, 1, 3251.099609375}, { 1, 0, 1, 17, 10, 0, 0, 664.568969726562, 1, 0, 0, 0}, { 1, 0, 1, 22, 11, 0, 7914.130859375, 1321.66003417969, 0, 0, 0, 2639.2900390625}, { 1, 0, 1, 22, 11, 0, 0, 0, 1, 1, 1, 6456.69677734375}, { 1, 0, 1, 22, 11, 0, 8810.0673828125, 6974.48388671875, 0, 0, 0, 3708.71899414062}, { 1, 0, 1, 22, 11, 0, 8125.35107421875, 8774.791015625, 0, 0, 1, 18295.81640625}, { 1, 0, 1, 22, 11, 0, 661.8984375, 606.960876464844, 0, 0, 1, 317.895172119141}, {1, 0, 1, 22, 11, 0, 0, 0, 1, 1, 0, 0}, { 1, 0, 1, 22, 11, 0, 3250.14624023438, 2707.37182617188, 0, 0, 0, 100.699821472168}, {1, 0, 1, 19, 11, 0, 0, 0, 1, 1, 0, 0}, {1, 0, 1, 19, 11, 0, 0, 0, 1, 1, 0, 0}, { 1, 0, 1, 19, 11, 0, 721.340576171875, 2445.58911132812, 0, 0, 0, 9772.283203125}, {1, 0, 1, 19, 11, 0, 0, 0, 1, 1, 0, 0}, { 1, 0, 1, 19, 11, 0, 2102.50244140625, 1927.99475097656, 0, 0, 1, 11965.8125}, {1, 0, 1, 19, 11, 0, 1626.623046875, 0, 0, 1, 0, 0}, { 1, 0, 1, 19, 11, 0, 11827.8388671875, 8870.87890625, 0, 0, 0, 7396.29248046875}, { 1, 0, 1, 19, 11, 0, 705.196350097656, 705.196350097656, 0, 0, 0, 0}, { 1, 0, 1, 19, 11, 0, 709.671447753906, 532.253601074219, 0, 0, 1, 0}, { 1, 0, 1, 19, 11, 0, 210.250274658203, 192.799499511719, 0, 0, 1, 661.1923828125}, { 1, 0, 1, 19, 11, 0, 6971.326171875, 6409.02685546875, 0, 0, 1, 11648.69921875}, { 1, 0, 1, 21, 11, 0, 2878.11303710938, 2158.5849609375, 0, 0, 1, 0}, { 1, 0, 1, 21, 11, 0, 6374.677734375, 5310.1064453125, 0, 0, 1, 7906.3408203125}, { 1, 0, 1, 21, 11, 0, 2483.85180664062, 1862.88879394531, 0, 0, 0, 11630.6640625}, {1, 0, 1, 21, 11, 0, 0, 0, 1, 1, 0, 1553.291015625}, { 1, 0, 1, 20, 11, 0, 7008.33837890625, 6426.646484375, 0, 0, 1, 16451.462890625}, { 1, 0, 1, 20, 11, 0, 16318.6201171875, 1484.99401855469, 0, 0, 1, 6943.341796875}, { 1, 0, 1, 20, 11, 0, 8009.1640625, 7666.875, 0, 0, 0, 7659.2177734375}, { 1, 0, 1, 20, 11, 0, 759.23583984375, 696.21923828125, 0, 0, 1, 0}, { 1, 0, 1, 20, 11, 0, 3784.50219726562, 3470.388671875, 0, 0, 0, 7896.70703125}, {1, 0, 1, 23, 10, 0, 0, 0, 1, 1, 0, 0}, {1, 0, 1, 23, 10, 0, 0, 0, 1, 1, 0, 0}, {1, 0, 1, 23, 10, 0, 0, 0, 1, 1, 0, 0}, { 1, 0, 1, 23, 10, 0, 2956.96557617188, 2217.72436523438, 0, 0, 1, 0}, { 1, 0, 1, 25, 8, 0, 0, 0, 1, 1, 0, 3515.92895507812}, {1, 0, 1, 25, 8, 0, 0, 0, 1, 1, 1, 0}, { 1, 0, 1, 19, 10, 0, 0, 604.153869628906, 1, 0, 0, 0}, {1, 0, 1, 20, 8, 0, 0, 0, 1, 1, 0, 0}, { 1, 0, 1, 24, 9, 0, 1275.35620117188, 1275.35620117188, 0, 0, 0, 8644.427734375}, { 1, 0, 1, 24, 9, 0, 9662.669921875, 9662.669921875, 0, 0, 0, 0}, { 1, 0, 1, 18, 10, 0, 0, 0, 1, 1, 0, 11048.080078125}, { 1, 0, 1, 18, 10, 0, 0, 0, 1, 1, 0, 781.224304199219}, { 1, 0, 1, 18, 10, 0, 0, 0, 1, 1, 1, 11163.169921875}, { 1, 0, 1, 18, 10, 0, 763.12939453125, 699.789611816406, 0, 0, 1, 480.527008056641}, { 1, 0, 1, 18, 10, 0, 960.426818847656, 240.106704711914, 0, 0, 0, 2891.66796875}, { 1, 0, 1, 18, 10, 0, 1495.11157226562, 1371.01733398438, 0, 0, 1, 12064.4130859375}, { 1, 0, 1, 18, 10, 0, 1791.01989746094, 1642.365234375, 0, 0, 0, 0}, { 1, 0, 1, 18, 10, 0, 1563.2490234375, 0, 0, 1, 0, 9602.439453125}, { 1, 0, 1, 18, 10, 0, 2491.85400390625, 2285.03002929688, 0, 0, 0, 3268.93823242188}, { 1, 0, 1, 18, 10, 0, 0, 0, 1, 1, 0, 202.284698486328}, { 1, 0, 1, 18, 11, 0, 4985.125, 4152.609375, 0, 0, 0, 4164.76708984375}, { 1, 0, 1, 17, 9, 0, 0, 2595.26708984375, 1, 0, 0, 0}, { 1, 0, 1, 17, 9, 0, 827.950256347656, 620.962707519531, 0, 0, 0, 18085.734375}, {1, 0, 1, 17, 9, 0, 0, 0, 1, 1, 0, 3590.70190429688}, { 1, 0, 1, 17, 9, 0, 1608.58862304688, 1206.44140625, 0, 0, 0, 0}, { 1, 0, 1, 17, 9, 0, 2001.90026855469, 1667.5830078125, 0, 0, 1, 5594.7294921875}, {1, 0, 1, 17, 9, 0, 0, 0, 1, 1, 0, 0}, { 1, 0, 1, 17, 9, 0, 0, 0, 1, 1, 1, 1953.26794433594}, { 1, 0, 1, 17, 9, 0, 1466.65246582031, 1341.65112304688, 0, 0, 1, 1374.06579589844}, { 1, 0, 1, 17, 11, 0, 9266.560546875, 8497.435546875, 0, 0, 1, 9017.00390625}, { 1, 0, 1, 22, 10, 0, 1168.05639648438, 1071.10766601562, 0, 0, 1, 7315.4521484375}, { 1, 0, 1, 22, 10, 0, 2119.65893554688, 1765.67602539062, 0, 0, 0, 0}, {1, 0, 1, 22, 10, 0, 0, 0, 1, 1, 0, 0}, { 1, 0, 1, 22, 10, 0, 785.059020996094, 653.954162597656, 0, 0, 1, 11134.978515625}, {1, 0, 1, 22, 10, 0, 0, 0, 1, 1, 0, 0}, { 1, 0, 1, 26, 6, 0, 0, 0, 1, 1, 0, 4813.0498046875}, { 1, 0, 1, 19, 10, 0, 1499.00476074219, 1374.58740234375, 0, 0, 1, 13278.974609375}, { 1, 0, 1, 19, 10, 0, 1051.25085449219, 963.9970703125, 0, 0, 0, 7047.080078125}, { 1, 0, 1, 19, 10, 0, 0, 385.274108886719, 1, 0, 1, 8124.71484375}, { 1, 0, 1, 19, 10, 0, 0, 0, 1, 1, 0, 5286.39599609375}, { 1, 0, 1, 19, 10, 0, 1557.40856933594, 1428.14367675781, 0, 0, 0, 4687.9375}, {1, 0, 1, 19, 10, 0, 0, 0, 1, 1, 1, 3228.5029296875}, { 1, 0, 1, 19, 10, 0, 3878.19287109375, 3473.8076171875, 0, 0, 1, 5861.603515625}, {1, 0, 1, 19, 10, 0, 0, 0, 1, 1, 0, 4309.8779296875}, { 1, 0, 1, 18, 11, 0, 0, 0, 1, 1, 0, 4657.27294921875}, { 1, 0, 1, 18, 10, 0, 1716.50903320312, 2682.125, 0, 0, 0, 0}, { 1, 0, 1, 21, 10, 0, 1230.09729003906, 922.572937011719, 0, 0, 0, 0}, { 1, 0, 1, 20, 10, 0, 3745.48901367188, 3677.58837890625, 0, 0, 0, 557.749877929688}, { 1, 0, 1, 20, 10, 0, 5005.73095703125, 2777.35498046875, 0, 0, 1, 5615.18896484375}, { 1, 0, 1, 23, 9, 0, 2365.5732421875, 1774.18005371094, 0, 0, 1, 11464.7138671875}, { 0, 1, 1, 23, 8, 0, 0, 0, 1, 1, 1, 3881.28393554688}, { 1, 0, 1, 18, 9, 0, 2198.16430664062, 2006.62866210938, 0, 0, 0, 6546.00927734375}, { 1, 0, 1, 18, 9, 0, 0, 0, 1, 1, 0, 12486.169921875}, { 1, 0, 1, 18, 9, 0, 1401.66809082031, 1285.32958984375, 0, 0, 1, 15369.4775390625}, { 1, 0, 1, 18, 9, 0, 0, 0, 1, 1, 0, 10877.349609375}, { 1, 0, 1, 18, 9, 0, 0, 0, 1, 1, 0, 2389.67895507812}, {1, 0, 1, 18, 9, 0, 0, 0, 1, 1, 0, 0}, { 1, 0, 1, 18, 9, 0, 1605.4482421875, 1605.4482421875, 0, 0, 0, 0}, { 1, 0, 1, 18, 9, 0, 1577.04797363281, 1182.78601074219, 0, 0, 1, 5100.46875}, { 1, 0, 1, 18, 9, 0, 588.794311523438, 490.465637207031, 0, 0, 0, 3480.84741210938}, { 1, 0, 1, 18, 9, 0, 1541.8349609375, 1413.86267089844, 0, 0, 0, 3475.52001953125}, { 1, 0, 1, 18, 9, 0, 0, 0, 1, 1, 0, 9227.0517578125}, { 1, 0, 1, 18, 9, 0, 0, 3287.375, 1, 0, 1, 5010.341796875}, { 1, 0, 1, 17, 8, 0, 1962.33557128906, 2036.79040527344, 0, 0, 0, 0}, { 1, 0, 1, 17, 8, 0, 0, 0, 1, 1, 0, 7010.44384765625}, { 1, 0, 1, 17, 8, 0, 0, 0, 1, 1, 0, 8993.865234375}, { 1, 0, 1, 17, 8, 0, 1025.08032226562, 768.810180664062, 0, 0, 0, 3020.94775390625}, { 1, 0, 1, 17, 8, 0, 0, 0, 1, 1, 1, 8061.48486328125}, { 1, 0, 1, 17, 8, 0, 1284.86254882812, 1178.21899414062, 0, 0, 0, 14380.1728515625}, { 1, 0, 1, 22, 9, 0, 0, 0, 1, 1, 0, 12898.3798828125}, {1, 0, 1, 22, 9, 0, 0, 0, 1, 1, 0, 0}, { 1, 0, 1, 22, 9, 0, 0, 0, 1, 1, 1, 4056.49389648438}, {1, 0, 1, 26, 5, 0, 0, 0, 1, 1, 0, 0}, { 1, 0, 1, 19, 9, 0, 0, 798.907897949219, 1, 0, 1, 17685.1796875}, { 1, 0, 1, 19, 9, 0, 1083.38122558594, 902.45654296875, 0, 0, 0, 1914.41357421875}, {1, 0, 1, 19, 9, 0, 0, 0, 1, 1, 1, 8173.908203125}, { 1, 0, 1, 19, 9, 0, 0, 0, 1, 1, 0, 5712.64306640625}, { 1, 0, 1, 19, 9, 0, 2881.20678710938, 2642.06665039062, 0, 0, 0, 2652.62451171875}, { 1, 0, 1, 19, 9, 0, 1537.92932128906, 1537.92932128906, 0, 0, 0, 4188.7353515625}, { 1, 0, 1, 18, 9, 0, 0, 0, 1, 1, 1, 4482.84521484375}, {1, 0, 1, 18, 9, 0, 0, 0, 1, 1, 0, 0}, {1, 0, 1, 18, 9, 0, 0, 0, 1, 1, 0, 10740.080078125}, { 1, 0, 1, 21, 9, 0, 0, 0, 1, 1, 1, 0}, { 1, 0, 1, 21, 9, 0, 591.499084472656, 0, 0, 1, 0, 3343.22412109375}, { 1, 0, 1, 21, 9, 0, 6416.47021484375, 5749.3310546875, 0, 0, 1, 743.666625976562}, { 1, 0, 1, 20, 9, 0, 720.300842285156, 660.515869140625, 0, 0, 1, 12229.6259765625}, { 1, 0, 1, 20, 9, 0, 0, 0, 1, 1, 0, 5193.0888671875}, { 1, 0, 1, 20, 9, 0, 0, 0, 1, 1, 0, 2035.91394042969}, { 1, 0, 1, 23, 8, 0, 0, 0, 1, 1, 0, 3746.70092773438}, { 1, 0, 1, 23, 8, 0, 7724.283203125, 3403.05590820312, 0, 0, 0, 0}, { 1, 0, 1, 24, 7, 0, 0, 0, 1, 1, 0, 2193.52807617188}, { 1, 0, 1, 18, 8, 0, 1308.2236328125, 1199.64099121094, 0, 0, 1, 0}, {1, 0, 1, 18, 8, 0, 0, 0, 1, 1, 1, 0}, { 1, 0, 1, 18, 8, 0, 0, 0, 1, 1, 0, 1568.15002441406}, { 1, 0, 1, 17, 7, 0, 0, 0, 1, 1, 1, 3023.87890625}, { 1, 0, 1, 22, 8, 0, 0, 0, 1, 1, 0, 1390.50903320312}, { 1, 0, 1, 22, 8, 0, 6891.69384765625, 6150.5205078125, 0, 0, 0, 0}, { 1, 0, 1, 19, 8, 0, 0, 2657.05688476562, 1, 0, 1, 9970.6806640625}, { 1, 0, 1, 21, 8, 0, 39570.6796875, 6608.30419921875, 0, 0, 0, 3083.5810546875}, { 1, 0, 1, 20, 8, 0, 0, 0, 1, 1, 0, 3644.65502929688}, { 1, 0, 1, 25, 5, 0, 0, 0, 1, 1, 1, 12187.41015625}, { 1, 0, 1, 25, 5, 0, 0, 0, 1, 1, 1, 6181.8798828125}, { 1, 0, 1, 21, 7, 0, 33799.94921875, 0, 0, 1, 0, 11011.5703125}, {1, 0, 1, 18, 6, 0, 0, 0, 1, 1, 1, 0}, { 0, 1, 1, 27, 10, 0, 459.435577392578, 421.302429199219, 0, 0, 1, 3075.86181640625}, { 0, 1, 1, 27, 10, 0, 0, 0, 1, 1, 1, 11142.8701171875}, { 0, 1, 1, 26, 11, 0, 1789.935546875, 1491.01623535156, 0, 0, 0, 0}, { 1, 0, 1, 20, 6, 0, 6006.87890625, 2850.61010742188, 0, 0, 0, 0}, { 0, 1, 1, 20, 9, 0, 12260.7802734375, 5875.048828125, 0, 0, 1, 1358.64294433594}, { 0, 1, 1, 20, 7, 0, 0, 0, 1, 1, 0, 4374.0400390625}, { 0, 1, 1, 25, 9, 0, 0, 0, 1, 1, 0, 15791.1298828125}, { 0, 1, 1, 17, 10, 0, 0, 0, 1, 1, 0, 8469.275390625}, { 0, 1, 1, 21, 11, 0, 1395.39001464844, 1395.39001464844, 0, 0, 1, 0}, { 0, 1, 1, 18, 10, 0, 0, 0, 1, 1, 0, 6527.9189453125}, { 0, 1, 1, 18, 10, 0, 2589.0419921875, 2589.0419921875, 0, 0, 1, 0}, { 0, 1, 1, 17, 9, 0, 445.17041015625, 74.3434524536133, 0, 0, 1, 6210.669921875}, { 0, 1, 1, 19, 10, 0, 1197.46154785156, 1197.46154785156, 0, 0, 0, 11223.7197265625}, { 0, 1, 1, 25, 7, 0, 1413.10668945312, 1177.11791992188, 0, 0, 0, 9722.3486328125}, { 0, 1, 1, 18, 9, 0, 1646.51013183594, 1646.51013183594, 0, 0, 0, 1065.67224121094}, { 0, 1, 1, 18, 9, 0, 1868.89111328125, 1713.77319335938, 0, 0, 1, 8836.5654296875}, { 0, 1, 1, 18, 9, 0, 2927.927734375, 2684.90991210938, 0, 0, 0, 2978.66943359375}, { 0, 1, 1, 22, 9, 0, 0, 0, 1, 1, 1, 3595.89404296875}, { 0, 1, 1, 19, 9, 0, 6511.1240234375, 6511.1240234375, 0, 0, 1, 7913.3857421875}, { 0, 1, 1, 19, 9, 0, 0, 0, 1, 1, 0, 3811.68310546875}, { 0, 1, 1, 20, 9, 0, 8740.939453125, 8015.44189453125, 0, 0, 1, 5487.9697265625}, { 0, 1, 1, 18, 8, 0, 3311.802734375, 2483.85205078125, 0, 0, 0, 11135.5341796875}, {0, 1, 1, 22, 8, 0, 0, 0, 1, 1, 0, 9920.9453125}, { 0, 1, 1, 19, 8, 0, 2483.85180664062, 1862.88879394531, 0, 0, 1, 8774.623046875}, { 0, 1, 1, 24, 4, 0, 0, 0, 1, 1, 0, 3880.8330078125}}, CompressedData[" 1:eJzlXQmcFMXVH9DIoRESQOQQhlGJCoioqKBICR6QEA2IoJKfjIJAOFTk/OSw 5RJQ5L4FhpVLQFC5T3vXRVQg4ELwloGAn4HApxgVFNhv6/W83enqetPVXd1A kvf77dSvprf+9aaq+tWrV69e1Xji6VZPFo9EInkFH5dEkL5tnDE1GLPy17CI L3LBj9wsxS9/wGTpqbOciE/UZzSV4RutW+3m+eiVu9YR+G4pIjVLlY+m48db nMzh+cTxPhsV+aeeN5Ths/61DvC8sajUAUX+xfqQsH2uSsePjty9nefZydXb FfGp9I5UeWx/W/3G5sqj0uv1wD+mtwv4RVQwzJPVhr6ohy8f/+apHTB+zKWP bWFqeFT+3lT5WDp+ssPxrYA/+2YKX7W/sf1t/Wt8xFbxfLzeh2t94mPaOFXe 2b8XFvDf/PDg9Hp94N8m4APFR07/nOfN5es+1uS/kYx/dv9iaH82Ye9Wn/hI KN9qpvNvdGsDcsF8Yd87mvi3pMrb+jfZsNqngH9m0F5FfCRRvt2VKh9Lx4/c Ov5Dnk+2vGAbgU/gOfL3SPE5XVyAX+bQQuF7VfmJaf1U+evS+YzGB73L89Ee e1TnFyq9NVXeNn6MJq1X8rzZYfAyTfwbZfiReSdHsPTU//jH8WPn/0zn9Txv mEN120ec3+H7pFnrTVbQv/G5dy1S5J+qT+TfojadcoD/Snfn+MRPUeH4t72/ bM3aDTyf7FUyWxGfSnH+wvFp1fp+qxx2Jj9/ztY3RhP4BL+O+evuVHlb+ycm /SOX583b67yryD9V352p8rb2QYqeqJsjfO+1Hmf/FrRL9TffhXnFXDF2A8E/ xS/V/vbx06Q1tE+kw+DNmvgo32qziC9yqw/5t8/vsV4gnyPtSr7H5HwppoXj B/kX69flv3kK16bfJje02sTzyUvOrFLkn8JH/dnWPpGfL7PG/cod1Pyryn8D GX6izL3v83yi7ieq7U/gF8ofW/tESuVB/yZKvfJRQPzHbPz/b81PeD6+6OVd mvyj/myTD8lHs2BdER875hOf/GMqri9USbU+fL9s/Mfvfw/0//iucbuZHE8V /75UeXv/qpNb+6B89ip/VH/PTSlcHP+258kq21cK9XrtXxz/tvlRgVT5R/mA /Wt7zkZ2nCvU6/X9ks6PRmINrFvMh0errl9EfEybpMo7118/FOgPq7c/TvCv +v7i+rS8gKNKbu2D+rNdPnM6lZ/feEjTMcL3XvUfqf1HgVTfX+Q/lo5v5k7P 43mj7948JsfTkj9I7MBLbwnfe53nxfbxO79TvwfX77b3i/X6fB47XNC/I7+b wuTlvc7v0vYx/vLuZOF7r/KnUaq8U//hemiHRsN94iM5+5evixqyLL4+Sj61 MSHU6xHf+EOqfEkH/5lJtT9E+8a3jXm/9ig/hV1W8B707ziB4F/19yD/Uvns g3/xeZMUrm38m1OWWfaNkz019bcM8i0zqdaH+o+tfczhW+fD+Mkr9roi/9Tv 8Gv/V52/RHzr+ynffcDzial3ve+Rf7E+tJ/EmPicr69zB77AMvPnS38w+7UD u5v5bTfK/ibWR+XF/gUyel0P9pn4pGv8rn8z41d9F+zyxr+m+bUPYIrtY+vf +Ff1wW6S2EHuv7jxjynan232vURuCVi/G39tkeuTfyRsnxhL+9K4+SdoF9bg tKr9h+JftG9b35d9AuzOiVrtPiDwKX7F/8P1ha19PJBbffh+Fck3vu/Sf+RI xvXPJ6bNFur1Oj4zzu/xDQ+/KXwfqP5g/n74fALfLY+p1L4av7DqW2D/XHv/ HCYvp8h/oX0mls4n+2Yt2K3MarV19x/F/QXrecVbrP2Xh1aovl8Ufsb1YwZS 7V+0bzj1K87/zmGq8yNVb6NU+aL3q2D8x8/0BLu8cc2KuUxeTrU+3L+I2XB2 P2ztiyyZN80n/0iifm69v1UK5D/X33qVnEHgU3hU+9j3F+qVBvmZ/L9LV4SB D8T1nycaUfq/1/GJ9hl4njz6K2v/Ytdbi33yjyS1vymQan04PyJ+0XMun3uM xv6lyru1P86PXucXVf5x/BeNzwK+q9/74hKeN/LvQ/nvt/0Rv2j+4uuXi7vN 5vNX9bUHZxL4qqm4vxBU+9D4/L1ttAPmXaP3XtX2oVKp/uaDfyqV6m/JC7uv Af4feY6y/4v1UPWifLs+Hcc0L7LWF427qOpX1HNc3zntt/z9mp4b1PtlW38l xi219u96L1fdH3Frf7n98OlO8zT5d44fLpcjPcbB/JK4YDzBvyo+yk+7/Ol3 Nawbzb5XU/vLXvkvsp9z/psumMMwlZdzGzcpKtwfiTHpc5JU53mp/0zikKUf sqrK+iFVL8pPm30sfmdjWNfFt9VcQ+Crvm/i+AEyHu8OfgnmnqzVmvzL11+c ShXU88CpDun1+uBfip8sMfRtnjdGzvPr/yOOf137LcW/uH8dNL7Tv4XbZe6a /ALDVF5OFV9sH698q45/e/8ua74O9MOPSvrVDzGV+s9EBl8K7218fW/d90uO r07e8bndfGqPF6B9DvaYzOTlFOVnoX5uX78PadqLpaf+20f07/JKvuSbB1y3 8YTzu9S+4aMeMRX9lyz5f/9jK9nJ/Pz2JRYNYmo4BP+F9o2yIfNvX59+/yq8 V2bPJdT6VDXF/rXbJze0sObHauU0/VsK9zed+yMXFvxd+c/WTF5edfxk9O9S ILfxL9Vvk28f/Irnk/e2/oLJy6mmUvnsg3/qd0j9P82vWufwvJHdR1e/lfqn GZvfWMq4neaDZG9FfCql9Z/MpFofzr82/xNWvOp+yDcfSe2vqfIv7V/0r07s YrrrIyl+fNIA0NviWV/5ta9m5p/3L5efHyr3L5Wn5YMauf0eqX9pAPgpktu3 Iy+8BucW2C9T/er/SNLzEdGlN8L6IvlcPdXzQSI+pqg/h9Q+hfLBZp9MDLjI Otdx34266zup/pN4of5Onmcne+3wiY+E56dwfg9KPiM1SuFK9xfY3t/j/h3F n2r7VxVw3Ei1vdD/Kuj3F8l5vozbfWpdsQzsn8O+WS7U61U/aZIqH7T+gCS1 byeHHgP7GPt4jF/7IVKTVHlb+8eXjAVcc+xY6v1SrQ/313D8WPL/QHvwP8FU AZ8aF+hfHdb+Gr5fQa8vkHD+ijH5c1V8Ki/3n/mivOVXnbhCt/2byPCTG5rC +DGy/29h+vcKuNT48dr+qu+v6L9te24c3oDnIyj+VN9fm/yPb8uyzh11f4qS P4r4hfInJuNfgcIa/6r6A/o/2/XD2dfButTocUxXPovnT4OWD6L9AZ6zPZdb /vOffUGdX9DCjyR7wvhJ3NJykyI+9bvRvhRLxzHaVLT2Ff44jfIfoPgV/s9x vjjo9q+Xwi2S/3x/c1+dSTwf/Wyurv7s9K/wRl7lm7W/WeaOCWAfrvjoS8xe Tld+WvKn47fW+s5sTo0fEc9t/Nj0c+OW/HEsLQ2a/2jfi6z9r/5bqPPLFB7V v7r+q57wo6c6W/63a2/KYXI8VfmG66+i8c/tzhvHvMbz8Z5Ran9clX/p+VAF UsUXzwdZ+6d/2rAI9PNn75nH7OW89q/Tv4JXturLHJ5PjG0RlH4bY+Jzvr82 fMHzzP7/fts/rPGJ87t8/fj99dT5C9W8U//k8u2Kq8B/I/r5x+uFevXx8QmM o7KTAuLf2T58fF5w7QI9fMPv+le1vcTzF2cHf+Y+0BvYys6vCfV61B8I/5wx ZXJ4PrltLHV+3yv/Rf3L+7VNHPyvIvMr+9V/kPD90rX/UHlRPwkaH/evbet3 D7hu/SvdX4hc3jiH583SnXTPp0vxk/cPWcW4fK7b8nkmL6eKL12/KJBqfdLz ZWYDlsPzZp9jlH8+haeED8Tl54l3KPmp2r+4vxZW+4j2Pev5tdNhXyo6fb3f /UdMnfsXPO7P7vGDYH9qZuNRTF6O4lfM4/gMSz5L8RN/zt7E86xLa2p9pIov +vda+lv7Ota5xwuKU/7bFB41Pm3zb7LTDPBPTh48kqWJH5T/AJVH+Rm2fA5r /Djx+fivO2+k5T9/SPN8h5z/eLVrQK5hqoHvtA9ElMjr+A9rfST6/1j6SYkC /YSnf17i93wHjc8/B2fD/lq87NS3mL2cPj5/byvcMIthGgL/rOXjEJeBNblm JbOXCwb/18WzAH/MU7rrUym+eTezziXecII6n+gfn5/fyRs/Bs6vzWg8ncD3 3z5cPmTP6s8w1cMX1++WfeNYpYUwfp59kDp/ROGJeef57oKP6GVVwT6WrNBC 1X/VE36kVzkYN2zOe9T6SAs/Ub868I1p4Px/8oE1bjANEp/LtYZsAsRP2Dks eHz+md91KcjPh8tNJPApPEo/iRXi8PG5fIDVv8suU7W/UfWj/mzHn567GPxL //aS3/hySKj/hGVfEs/PWv7PpetZcSVO19Md/0789KcfX96TZebbOz5v97zN CZCfl++nzk9R9YnkPJ/O8Q/VWELw7YZL6T8xJn/uRt7x+bpoac9+DFNv/Lrj c7lQKg/OHRnZk3Xf37Dt/2Hb9+Tnl5H+cRrjq1C4qvO7DZ/9rhrs65sNtO1j 8vPR31ey5Gb2Isr/XxVfPP9oze9lf7Ue1i/PXus3fgumjVLlbfaN6BetrLgY 3RgV30YLP36CbQH9J/bkqwS+iEflcX4sah+uH378wSiGqT/+aXzO/0sJiPuM aaD8c/mZ9ZC1fzTxTwuYvVwg/Jv3zF/K8+xfA+Yze7lA8BXI6/wVO2v4vN1v WAnzI6RyPK/zl92+p07nGl8en/OxU2tgfqzblzr/pdo+cvz85XOY/f+o36U6 v8RsfGbXmAvy58TJNwj+VfGl/idsV3cTxk+791XPp1P40vgSxswr9/F89NWY avwlCh/fXzt+lZKwb2E0+FrXfiXHr/xLDs+zYUdzFPGp+qT+aazZLji3kBx1 RDe+UFD+V1Q5cX8KyDz+NegnRuckZZ/xqv/Y5IO5cto8GJ8vHqfmFwqPGp92 /5mrDr/C82zbvqWa+KJ+pUqq7e+0r8K5+r9OgPYZ+hiln3tt/xjzwr06/4h/ Ocv8f2744vdIqL/p2ofd2j/GMv+fG774PRKuj4r85/n51nrLe4N/2uxqhlCv 1/7F9rGNT7bjATg/Hm96u9/4OUji/hFQYvrn78H89ccvVP1vqee4fveqPyj+ Hrl/Mjs8Jhf4f22hqn5L1ee0r/JaX149Eey33E6sxi/13Bk/kOvnv33H0hv6 3P22Ij6Vl8b3S056H/xCood+oxtfXR7fsvRpmBcT5Rrrxg+k45MUkPnNUeSf Kq+Fn4FUf09Y/ieYSuM/GM/ngN3BuLUPtf7SwvfBP1XOuX99MdjFrP0Lnspx vOo/9vYf1csaN5j6f7+k8ZmNAaPBvwJTDf6l+7OsWA9YN5pT66vu76jjc/mz bvQUsJ/fUdlr/Csl/o1q164A+/DMpprxgQ3x/iDd8Um9vzb+E8+Ws+732ddF VT57wo/MbGOtizpv97s/hak0Po8CqfavdH5k03Mtv9VxxVX9V9Xx+bj8zeMg F9gd3f3ev4OE4zPo+NJI0v0X9vNeWFfHsxdR62uqHiV8BVKVd9L7ZTyQG77z /jLevx1TceEX79GN/4b7g/b4YIfWgd08mZPtNz4Aktw/7den9/B89B8jVO8H ofotqPionvCNMathXco2vqcbv1Q6/7K+G5fC+qjvxpcJfOp3iN9L47fEuzaB +LTxO1f5vR8QSe4f9dPfsyDfsKnf/VlM/cZXFPGCxlf9PdLzRx7IDV/uf1Vq vbXvgqn//pXje+db/B4Jx2eMiQjcTjDuJs37TXz7D1N4Ynmpf4hRouY00N+G V1XV39z0H7t9YOfH1v04176iGj/fTb7FCnH4/FLmNNg9jWWD/O5PYdooVb5I v+Lrlscagv8JpP7wkaT7F8nZa+BcX3TNB0HpP7F0/uPTm01nmHrjl+rfsOID 4PoxaPuS2L9Bx8/HVLq+jl5QBe51iKfuEfXAL9W/YbUP2pds8XU9kKp8jmni U/XJ2//JOtb91y/nZTF5OTd8pKD8Z9z0q6L+vQzOv1j+ga+VUI2vSNWL4z+s +GA4fmzrU3PyYtgfTIx6yev97CJh+9j2L8z231nxo5ad8Rs/E8l5vljOh0iq 9eH6wuv5MqoekcTx45Xc2kd6vjhR7eAmlpZK+FRtf6n9OXnVgByeN0c/T+3P uvGPadjx8aTxM6M7NwL/xtLrde+XkeJ7oH9X/POtfezxIVc8DfEtIyWrJJm8 nFf+7fb/3ZsTsD91Y/u+BL4W/8aMfa+DfruCPL+sWh/GdwrafoUprk9v8Igv 4lH48vutSs2w4jOnUkk5r/zb9ZPPDq3i9pPq3e5S3X/0hK9AqvxL4/ObdSLW /QWpVFLOPz5ft1x+/xsM02Dww7LPnK/4YbfP+dL+qJ/oyn+qXFjnHzPqJ2a1 SmB/Noau93s/eGb9J3ngQ8u/q75f/66M7Z8s1e0znk+ubKB7Pzjar+zxt9tY dpPonCG6+pU0fj74p/H4CfMXdNTkXxqfIVp9L6yLkifmhhL/wQOdl/is7SBY 3xlH3qH8B1Tx/d6PoDp+nPdfqOGLeN7xuX3gkh9V70fzhG8s+Xo4+G//qcIw Ji+npf8YHRuBf+mcSl/onh/xq/+IeOr4XG9rX2cEnB8pkeXXPxBTtI8V4XO5 XD93Icjnq7frxhcKKn7++YPP46rnlh3IMJWX05L/rPmblt3q1rtV749Qx+fj fuKWiWDf61eD2n8R8TzxHz30lnW+u9sWv+fL3OUnH//DBw9hmcv7m3+3/iqH paWh8M+p0nQ8v+mGR+E731/+aTxjwvq6wynV/SnqvZHejxMdeAP4PSR2tNX1 r5Dz706q9WH8/6D2l0Vy3l9cQObVJbN43jBKU/cXq45Tp/2E92uzXaA3G6OP qJ6/oPjH8Yn2bYv/QZXg3CCmCnxS+Sap8kX2Z76ubhufCfPLgsq68QdwfRF0 /DQk6fmRSI3nIG6s2fU5Vf8WAt8Q438GzH/h/T4xTXyqHvn64u1HIO6V0dbw ur8s4jv1Qy73b2NDwH7VYyOl/4g4VL5RqnwsHcfsXzmH582HD+qeL2uSKh+S f13h+YWwzldmjN+ugKs6PsM6HxSWfVjED+t8SpMUrv18ffud1vmLVCrBU+Xf eT76VIFeOGr8cIapHr6UfwVSxfcbf5XCo+SzV/618BOPt4VzL5hGfLe/b/5V 21/ufx4cPh0/Mxh88X66oPVD6fliDVwxL/UfZrEBB3g+2uLGL4V6vb6/Iesn BP6dXax5F1P//Iv7+/A8+eX6XTwf3brsrwQ+hUeNf5v8j/5w7wGWlmrgh/3+ 4vh3zl9wv/Zi6v5xt35AQv05LP0c2yeWjm/UG2idr/lLWd34ivL4AGfeRbsG VY76ntI/w9KvQtbfCtvf3r/PfGmdW7tm+2ihXq/jH9snLP1WGn8+/nlPWPcm b1ysOv9SfCH/MRv+7zbl8Lwx6fBORXyKf7/9S+Ep6VfRnKUgP5m5dZtQr9f+ Rf7t5zcPbwG7lfnbK7MCx48okWp9zvPXBR/Gj+3ehvhXP7bTPJ8oaX9uP+lw 3Iq/0eH4iwHh29unVvOx4P9Qc+MSZi9H4VHtKp3fo5O2gF070a+G7vl00b89 6P71u75WxZfePxU/8YePYH9q68kZzF7Oa//K78+qdWwTz7Mpx9Yq4rvN73b+ 76nwN8A/Otav/wBS2PcviPE5w+rfc4M/oSnaxyhcN3y/8b0pPEr/tMvP2MFZ LC31wK+Y93s+SxUf29+u/3ftB/si0WX7g9I/w16f2uMj3VxpMuwP3jxRNf6A WB+SdP3lgdzwnfGB+Wf++7jv5Ybjhk+fj+Z26KY/jVSsRx3fiq84LRVfcYpQ r9f2x/fLrl/1H2nFb8x6Wje+ujw+6kuVQD80BuWp3n9K4Uvlv1lvoHX+LnZ9 UPG97faB3kdgfWfu/adufKSw40/6nR/DxqfwqPaJ2fDd77dS5b9Rqryd/62b loB+2/oZ3fsdwrZPYvvY19fjn5kF64sfhuve/4LtE2Pic47/h524fqH49iWf E4/szwb99pH9uvcLOO+n4/LtUH4W5Pf+QtkfVOuj77/LTF7Hp+76gionnh+3 /q9klhU/9pLvqfHjtX3s8q326SmsREE/1z5NnR/RwmetF4J/FKZB4ycXFgP9 jU15Xld/k9rnow8UrLv4/nWrmkMU8Yn+LbTvlWcZ/89BYetvqvhS+xLb3wjk Ant5j65+EvL+YOj4OD5j5xm+1/49W/GFgsZ3xp/kcrPevGngX3fVAL/+kzR+ wUfi4roQn4GVXqcbH9hv/EwKT8xL7Z/nAb7q/Ijr95hHfLEeKu+cf/n4iddZ APMvn4cz8+sPH+Mz1z+qq1/J9Qd1Uu3fsOwDUnyjW6tPeZ79MEz3fuSw7c/Y /mG9v37xVd8vv/Yr1fY5J+tfY9RQiBuOqQRPC988s3IHz5u9iunePyXHv3Mu xoWhymnhswYM9nWSPTaq3s/lCV+BVMenM34R93v7bN8IyH900u/5IySUD0X4 BR/JCYlNPJ/8da0NmvjO94vrJT/XBr9zc9/juvaNsP27/lvxKTwx79e+pIVv ZM0Cu22i+RW69tuw+Q8q/qonfLPP/uWg/1+6yG98gIz4Cud//ePzfZ1iqyeC /bBuOV3/BLn/YZmHYP0S3/a87v3pfu/vDhvff/twu3O7Tllgf+69kDofROGd e/7THhoVE7NYZtxzzb/0fvNQ8a39tWmp/bVJQr1+5WfY88vZax81fArvbPPv jD/M5ea2rHEQf7jOdbr6ofx+gQeaW3IZ06Dx3ck/Ph/3X28aD+dHPn1V1//T ic/jUpXMG82O5efnl8zrGjh+xBP5wmfNXoR9TaPtN17jY4vkjN/ojW9V/SGs 9yts/6LzlX//+Hxf7a4xndl3+fn7HpvdQajX6/j/b21/Ck8J36haLwH6W7a2 f6ac/3G5cL/bnE53jyXw/fOPccO5/1LFrbrymW5/Lv/H5OL8KJZXrU8aH96o cXizdT/XAl39Kqj4857wEyXrw7ooOeBOXfuPFD/5yCaIv8GevFJ3/SVvnxuW D4P4PMt/GhIKvjv5bx8+bn7fEeJPxjfX1L1fOOz4/+cE31g3egTInzpVdP2v zgn/cL8V5//n6rrrazn/g7ti3Ao3HF/48SOVwH5iPPOg7vlHJz6X/xNKjWCY hoHvHp+KwnPH5/NK8WZdwT42uVKXwPELPowHbwe5nLyoFOVfpIovvX8q2WCg Fb+l1pfU/q9/fI77xtdWfPIqm1Xj/6vj8/Ud98vk6StVZhH4qvVJ+VcgVXzp /mOiTRz615hfWbd/pfjxB/btBP3k5E1Tw8CPXNfcutcteXwTga/VPpG/r7PO lWVnU/Ef/PPPx02x8ZbdfGtV6nyZan1h+6f95+FzvedYJet+zGcf1PUPkd+/ 406q9fnFp/Ao+e9sfz6P3b5/CJOX9/p+hdU+Tv9qbp/sftTa3+FpZr694xd8 mA/ty2FpqQZ+2PcPnq/4FJ4afqlPXwT9reVXuvLZic9xu17wCrPSpwh8/+3D 9x+f/fNUGJ+/6U7dL+Offy7fyp+y7p3t+7pufDl5++d3R795Nz594bNV/V6D 9WmZzzTjy5218W/XH6L/Y52bevU2Xf8fp39O2kNz5L3oH07x7R2fj/sZ+0bB /s6KzkPD5D8D+cfn79WjMWvc536qu38kvd8tOf2+OZB/6FLK/knhnfv2UcOn 8NzxuX776uEpIN9m/EjZJ73i2/1P7quUw/PmmmlU/DdVfOf5zfT7v57aOC5w fC7XWo5NAP7KKar3g6vjR5TIP35x2N8fCfNj1tPU/i+Fd7b5R//DsOIrOvVn Lh8u7A7rXuOR53Tndzl+pIc1Ln+u/gqB759/fML3mZ8o359541cdPzNptX+A 58f98k/hKeGbT7RYxvPGrCtV4894wvfAtz/9J3h8u/5cKs+ad9m/VOM/U/ho fyvC53bzDrdNBPvt5D3U/dqq+M7z73x8/twE7D+szgZd/3np+frohjGgf5qr 3qb2dyg8MY/2k1g6jnH1AMt/b+1aav4S8ahxgfhh2Tdw/MQK8blcaF17Lugn Qx+dI9Trtf2d+N749sf/0q9hXjFaVtDVr84+//8J+NsqWOsLTIMcP8Ger3Ti 83n90U5DwD+t18LBgfPPP9tWBP7jvSrqzr/ndvyYy/D8CFXeLe+8P5rPL21f nw3tc+1uXfkT1v3U5w6fz++tLhwH43PARSOZvdzZah8KT43/XuUmwfou2UXX /oDroxiTP3fj+98Vn8IT87i+Cyv+A31/ejDxr5z4XC6MOGqt7xbepKt/hnX/ uy6+f/75vPiXIdb58dg8Xf3fic/l85oSsH6EVA/f7/xF4Z1f86M+Pq6/vOJT eO743G51ZsAQpobji/94/7LgV2G27rOa2ct5lQ/O+GCc/y5DwG/JiM7Ttb+F HZ//bMX/D0s/keIbzWpvBvlQdcpioV6v8iHs+5vk5yMOPTMJ9M9xm4NaX4S9 fg9b//Ta/hQepf+Exb8UP/HWEYjrwYZfpBrfmMLH+dHJP59/r8ih9h9V89L7 xcyHWv2d55N/3KUb3yxs/xP0z/Gqf1J4Zxtfej9F/IyRZGo4qu+v1/0Xt3qQ nPGv+Px4YvVAxu0ce1s/J9Sr+jtofGXWlfDl8Z02/wT3A84p24Lyn/Q6/u3t v7/vGpD/t91D3T/uVo+IH0vHSXY4bvlvX/K9qv7jhq87P1Ll8P2KhYQf1Pkv qpzUvzo+dsdkhmlmHDf8oPxnPOEbFRpa8Y1TaQYc1faP2XDW5w1gxQomhV8m t9LEd+6v8ff2k1XW+TWeynG8js+i8c/tSj3KW/4V/Tvq4jvXd3zclF4/nWEa NH7BhzHuJit+b8Wtqv4znvAjn+9bAPbzj07q3g8blP3KbfzY8M3qT1vrx1Sa AccXvgJpyX/W/7dwbifa84F3hHr99q9df/MeH9utf8OK/+bUn9XOj1D1iITr x7Is8/+J9G3j/wcJv+eQ "], Function[Null, Internal`LocalizedBlock[{$CellContext`age, $CellContext`black, \ $CellContext`education, $CellContext`hispanic, $CellContext`married, \ $CellContext`nodegree, $CellContext`re74, $CellContext`re75, \ $CellContext`treatment, $CellContext`u74, $CellContext`u75, $CellContext`black[0], $CellContext`black[1], $CellContext`hispanic[0], $CellContext`hispanic[1], $CellContext`married[0], $CellContext`married[1], $CellContext`nodegree[0], $CellContext`nodegree[1], $CellContext`treatment[0], $CellContext`treatment[1], $CellContext`u74[0], $CellContext`u74[1], $CellContext`u75[0], $CellContext`u75[1]}, #], {HoldAll}]]& ], Editable->False, SelectWithContents->True, Selectable->True]], "Output", TaggingRules->{}, CellChangeTimes->{ 3.834229628331485*^9, {3.834229674906176*^9, 3.834229681470845*^9}, { 3.8342297178579607`*^9, 3.8342297364005136`*^9}, {3.834229774887061*^9, 3.834229780813037*^9}, 3.83431105675102*^9, {3.834313808753068*^9, 3.8343138193982897`*^9}, 3.8343139186949997`*^9, 3.834313979864463*^9, 3.8344054107605352`*^9}, CellLabel->"Out[1]=", CellID->2006433985] }, Open ]], Cell[TextData[{ "Examine the parameters and the adjusted ", Cell[BoxData[ FormBox[ SuperscriptBox["R", "2"], TraditionalForm]]], ". It appears that this form of the model does not have much predictive \ value. Not receiving job training lowered wages by $824, but one can not be \ certain that the result is statistically significant:" }], "Text", TaggingRules->{}, CellChangeTimes->{{3.834314023590033*^9, 3.834314091006207*^9}}, CellID->409335614], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"jobslmf", "[", RowBox[{"{", RowBox[{"\"\\"", ",", "\"\\""}], "}"}], "]"}]], "Input", TaggingRules->{}, CellChangeTimes->{{3.8343139714081507`*^9, 3.834313995976913*^9}, { 3.8343140349846153`*^9, 3.834314035858481*^9}}, NumberMarks->False, CellLabel->"In[2]:=", CellID->215245991], Cell[BoxData[ RowBox[{"{", RowBox[{ StyleBox[ TagBox[GridBox[{ {"\<\"\"\>", "\<\"Estimate\"\>", "\<\"Standard Error\"\>", "\<\"t\ \[Hyphen]Statistic\"\>", "\<\"P\[Hyphen]Value\"\>"}, {"1", "3162.458272052068`", "2376.9567674939885`", "1.3304652046264305`", "0.18379220108650274`"}, { RowBox[{"black", "[", "0", "]"}], "1419.1896090444263`", "801.946594528375`", "1.7696809472444384`", "0.07720947298350146`"}, { RowBox[{"hispanic", "[", "0", "]"}], RowBox[{"-", "299.28824685492333`"}], "1050.4602108871784`", RowBox[{"-", "0.2849115499597609`"}], "0.775794991342216`"}, { RowBox[{"nodegree", "[", "0", "]"}], "299.61071304751727`", "747.9791145768136`", "0.40056026593339966`", "0.688864404650342`"}, {"age", "10.241884703122095`", "37.06638561454467`", "0.2763119342044299`", "0.7823889012019125`"}, {"education", "200.5935621716987`", "180.52113230795635`", "1.1111915796622647`", "0.2668620397732666`"}, { RowBox[{"married", "[", "0", "]"}], RowBox[{"-", "48.543634232139986`"}], "652.2011684314178`", RowBox[{"-", "0.07443046192157289`"}], "0.9406888357666755`"}, {"re74", "0.12741237337440678`", "0.07526341904638421`", "1.692885800150583`", "0.0909158924061717`"}, {"re75", "0.06471958734865164`", "0.09144997354042231`", "0.7077048231188889`", "0.4793608209247451`"}, { RowBox[{"u74", "[", "0", "]"}], RowBox[{"-", "1529.929357277792`"}], "937.8173052324786`", RowBox[{"-", "1.6313724951988733`"}], "0.10325519360781432`"}, { RowBox[{"u75", "[", "0", "]"}], "1005.8378954491972`", "917.0245559134402`", "1.096849467075929`", "0.27307907297200884`"}, { RowBox[{"treatment", "[", "0", "]"}], RowBox[{"-", "823.6546406991051`"}], "468.46208603346383`", RowBox[{"-", "1.758209821574053`"}], "0.07914264484392669`"} }, AutoDelete->False, GridBoxAlignment->{"Columns" -> {{Left}}, "Rows" -> {{Automatic}}}, GridBoxDividers->{ "ColumnsIndexed" -> {2 -> GrayLevel[0.7]}, "RowsIndexed" -> {2 -> GrayLevel[0.7]}}, GridBoxItemSize->{"Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}, GridBoxSpacings->{ "ColumnsIndexed" -> {2 -> 1}, "RowsIndexed" -> {2 -> 0.75}}], "Grid"], "DialogStyle", StripOnInput->False], ",", "0.03352851894649713`"}], "}"}]], "Output", TaggingRules->{}, CellChangeTimes->{3.834313998901071*^9, 3.834405411914435*^9}, CellLabel->"Out[2]=", CellID->949571729] }, Open ]], Cell["\<\ Perform a logistic regression to determine what factors affected whether one \ received \"treatment\" (i.e. was enrolled in the job training program):\ \>", "Text", TaggingRules->{}, CellChangeTimes->{{3.8343141223593283`*^9, 3.834314154197076*^9}}, CellID->1543900077], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"jobslomf", "=", RowBox[{ RowBox[{"Query", "[", RowBox[{ RowBox[{ RowBox[{"LogitModelFit", "[", RowBox[{"#", ",", RowBox[{"{", RowBox[{ "1", ",", "black", ",", "hispanic", ",", "nodegree", ",", "age", ",", "education", ",", "married", ",", "re74", ",", "re75", ",", "u74", ",", "u75"}], "}"}], ",", RowBox[{"{", RowBox[{ "black", ",", "hispanic", ",", "nodegree", ",", "age", ",", "education", ",", "married", ",", "re74", ",", "re75", ",", "u74", ",", "u75"}], "}"}], ",", RowBox[{"NominalVariables", "->", RowBox[{"{", RowBox[{ "black", ",", "hispanic", ",", "nodegree", ",", "married", ",", "u74", ",", "u75"}], "}"}]}]}], "]"}], "&"}], ",", RowBox[{ RowBox[{"KeyTake", "[", RowBox[{"{", RowBox[{ "\"\\"", ",", "\"\\"", ",", "\"\\"", ",", "\"\\"", ",", "\"\\"", ",", "\"\\"", ",", "\"\\"", ",", "\"\\"", ",", "\"\\"", ",", "\"\\"", ",", "\"\\""}], "}"}], "]"}], "/*", "Values"}]}], "]"}], "[", "jobs", "]"}]}]], "Input", TaggingRules->{}, CellChangeTimes->{{3.834314179940029*^9, 3.834314201384122*^9}, { 3.834314315149762*^9, 3.834314316734371*^9}}, CellLabel->"In[3]:=", CellID->291264735], Cell[BoxData[ TagBox[ RowBox[{"FittedModel", "[", TagBox[ PanelBox[ TagBox[ FractionBox["1", RowBox[{"1", "+", SuperscriptBox["\[ExponentialE]", RowBox[{"\[LeftSkeleton]", "1", "\[RightSkeleton]"}]]}]], Short[#, 2]& ], FrameMargins->5], Editable -> False], "]"}], InterpretTemplate[ FittedModel[{ "GeneralizedLinear", {-0.4302561345403661, 0.07737696261527197, 0.25361132012869864`, 0.5612508654902697, 0.001146620718823997, -0.0337248113089838, -0.09605468873917286, \ -0.000021071867643249587`, 7.102645421724936*^-6, -0.10056661684404344`, 0.34970828258495734`}, {{{$CellContext`black, {0, 1}}, {$CellContext`hispanic, {0, 1}}, {$CellContext`nodegree, {0, 1}}, $CellContext`age, $CellContext`education, {$CellContext`married, \ {0, 1}}, $CellContext`re74, $CellContext`re75, {$CellContext`u74, {0, 1}}, {$CellContext`u75, {0, 1}}}, {1, $CellContext`black[0], $CellContext`hispanic[0], $CellContext`nodegree[0], $CellContext`age, $CellContext`education, $CellContext`married[0], $CellContext`re74, $CellContext`re75, $CellContext`u74[0], $CellContext`u75[0]}}, { "Binomial", Log[#/(1 - #)]& , 1/(1 + Exp[-#])& , {0, 0}, (If[1 - # == 0, 0, Log[1 - #]] (1 - #2) - If[1 - #2 == 0, 0, Log[1 - #2]] (1 - #2) - (-If[# == 0, 0, Log[#]] + If[#2 == 0, 0, Log[#2]]) #2) #3& }}, {CompressedData[" 1:eJztxTERwCAABLCnTlCCByT0jhmvKEECCxbYkiX1n32UJOvLtZtt27Zt27Zt 2y8/sZlZOg== "], (-(-1 + #)) #& , CompressedData[" 1:eJyFV3k41t3WfowVdUyREoqSSChTpXdVGigaZKqEJlKJ8FIaDFEhQ4gGlQxF IQ0yJCUqw/Pbu0JlyFwJkZAxzuoPz3Ouc673+/5a1/4Ne6+91r3Wfa/Zu51N 9/GwWKx3vCzWji9Gwd7fKazKzMk8c5uCVAA7VxLXM21e8lyMo5DwKNunYCED 8uwPtQ5RBOJCtr5qFKYwZcLoYnYPAebzjbU8Pymwbi4uTRgkEC1x8srWyxSi nTKIihAFwyb6trSLggDp9TzVS0Hb9sH3CFMK6XGtF668ZMD9LrPCuIFA0kTz sBMDFLZkHw8IHKIgv+PpXjlKQTL6700C1yjEbSyUlX9D4Tpr3cfdrygMLDU/ 4dNI4Wys1JqHIhSUqGezxRcKF7d/GSGjFIrk5/te6aAQn6w1ltJMYbtxZc4M vFdmftindyMU1le/toj1JdBYuGxaOZ6rtJ3J4DlGgSdoYUd6NYFQ68JMyxQC U+esNFmxmMLaz64zeFYxwFrTc+EeIfCO/LR5NIZ+yoWahqYzILKQdT+6lABh abbqov+nnMvk+hooHOqXizuP38WfqOhyKkL/2KY8GX8xUO9zs0nOnsJiiXOT m7ZQ6LiyTamulkLuEV31F+j/eRep0UATAve6IK+gmkJq88r2Htwnbcwhs0SW wibNgB89bApgm7pK6y6FgzqHd0UZUriz1+qX8XIKyrfs+k10KSytKIvUcGFA tHDBzjrM72fr9Ve/qBLQzuuee1SDgo329raJuM/HXaWKvLh/sZp28Fb0e7fl pOWqSxnIOu1s/niUwK4aMxPtRAqdUrtN3qB/soMmz5rw+9jp6q3JiIfrbqmf x3A9J6QtPRPjHjnDMSrUn8JxDc37Xz9ifPk784beUQhclPW7GvHy+qLugKox BVGj4rVmGWUQrzBLWppFQFhAVsRiE4Wag+pucTUUViuHVjvuxXuEJ7Ds91EI ipLZ1ofnWE1T23QE13Uyro2fvChcuqB31fgQgQwH6C7Lp2DQtejqn7yfTtNq 5Mf8XZcVEQ51ovDSp5a3Af+/fYHq6iOeqmXOdlbimq1pZKzY8Gc/R9OD+F+q t6gW4PPddR7XHvRgnraukJfoI3Bwq7ysRDkFaT37rjHEW7jb7YSCEgqOY04b qDwDJ9+1zYj9TWGZwG6fP362m1ZeU0OczuDbcbrEHfGV1bTDE/dXXGt3rBTf S2kmjAVjfnN9m/YlVFCwfNh3vAqfm9mW1g4OU9iRayLujLa6lHfZsk4KVz0E +NOxLow3ltdb7ibA195g3o7fr1YvOknQnn/8sT32NwPZOqsCprdj3MukDCxd KdzNarJcjfEvih0sFmylwMs3ZJbqR0FlqWCewzADJnVvzs+8h3G6UFAbivt8 U9QXV0xBP+7eqTPMpdA/4JjwFJ8Hf1IafWKKfeBMuLNnE4ULGVA1iPdptDAj CoijYzUzv10yxP2WEJOWPtwnq/3hH5wvSpwe2of9gX9swpqgXxR+e+5bXyCF cd3JHI3/g6f2itLrghTcykuPrJlGweRkTuzNWgLnzQI3hVoyUD6o2vYd+0AX PTD0UgjPd/uWIbScQFRu7PqORAL7dMN/zo0lMOu9GMRPZ8PXFUo+moMM+Hoe jC7YR4DVOnTadoANxnlSWnOnMTCqEJ186RsDzW7TrzumU5D7fJPHej+FCSnv m6TKGc7/fs22Vv6IZ3/5E1v1PjPwxm7V4rQONty3ufpiohaB+S/tnLq/MqD0 O2eZO+IkM6JQb7syBYcTE+Q3/mBgssunlPxyAhEPWM93qRB4b+1+V8uKQPXK lvvnXjDglLbQYJYGAUkrz+Eb9gSeRorBKTkKHwRD1XX4CQiGJp9yskQ8CxWQ 09EE/H597/LKwH7aHqXQuYpArPvkWIEHFGQ8IpSW8VP4+fZtXtgpvM/Gw0mT kilMWrxi2aTtDDicLUmtWMBAuMwzQYFk9KNOOt8W4z7FylD11ycCrw7+olFf CVwP3784bx4D7TUi/i/RX80TBou3Y97fbqxvP4U4LDN09n6L9xSWirMTnob4 41tcqYn1dyb0iZ4Z1vWpo7arA09TMPrXUYVtWF/nNcSE4xQYEPRNrWzB/RY/ uSMxVZvCARehpULZhINX6aDi2BJJrp24N2aKwmcCqu2r21gyBKRtNgsH7WQ4 ca1Sisl6c5VAWKZHt+BqAoGzK83uvCDwu7rV5NHfBPpSGs7F5BHoTDMPKBQg 8Ax+BbniPZcqtt5430jg5MwFyj+SCLyJ3KI6U5uAikzf+YxHBFYOlP12wLzO /decRfIlBJZv8fjb+iOBSrpBJcOHwHB97/dMFeyvcs/Uk45QiEoINXyoT2DJ Y/vHuTKIk9kNBo4hFPi6Ja8LtREo21C3vTe9FBYcqPa7lcjAibNv1jBLKcx6 O1M0GvM8nv9xnM9mr/s25Tri8Jqo5R7008XxgeEw8pFRefCRT8gHH87cjk83 Qv6a7zKnBnlm5y99ySHk2T7pBM1wBQItz3faueL3Z9Z98L/zmoDDtrbJ3XsZ mD5F6BjvbQZ4t2n9TJ5JofWNiIt9K8OxC25c25g9jHEa0PUdFmbg/oclU18j P2ia7u3mNSYwuY+9yNm1FMSvhiTrPCPwcmdXLdubgFtV7o/gm8jDAuXP+psY iKkuGY3BPkl76xXqVmE/8DkUNMEN+1yX6W57RQI2wg26x98xsFlBqH6rHYHb H4aE9SZw60126vCP9SJsiHKQnJZfSOEyT3BhL/JBaEaF0nGsj8KVu+7cDybg c6MmxiYedcmRxxF3MJ4sxZMGy+VRX5hM3hb0gMCI0ODs2mICMdGX4iOAQuXT TZcyEZ+fr7UuPVLGQKz2HgE/7N+tzkXvrH5QGAnzSvC5x4BN4ZMIvSkMKHeK J3/bSeGLZO+S+bkMZBSkVh6hDOgluwhPV2PA/ss3xccNDDyOEMnynkvh5lmf 5HVViD9JiyoZ7IN95lPPRXxhw7ZSIlWUw8DAoOCSnmwGXj9VE2YdZkP4ktyD Y0/KoKqt9LI54i1A4oVoYySFltZb/hTrrC7yymVYw8AKlzMt/fllYNoRkq7U jP/P3jA9qBt1V4Wa8WAkAY+KxbVnTCgs750jVPCDa6PMhiICixgoWb9LfTbm IT5erezSUwaEV451WXdy69XnzL7yBOTHNWatR5ZhXEI6c0d+Y30ZTap8eQLx lfLMRk46hoBewya546e4fW2LjK8nnz+BRcJxVdotFLKShwvt8PuW5uQZDh5Y R9djJecibxjEqIsIA+ZDLI4Zrufi/WtSXBrPLezzX5K9urtRfyg+DxDDPvOI mK89sI6AnOPeWRpXGEgIiu8QyCHQkOyTabsacZr/delWrOOTAWcF4vuxb93U X96F+jJEZ+Svt/g/fVrGmtn8z3airvW7pKMUNmwwXfsdeXm3pnqHG+LgROLn psRwBqSKaxxcLyFvHh3s9UFdeHzmiM8ZzMfm7rH+aXiOkrmb5jk854fiidat vVz8/H/WeWRZbRreX9G47XjNITYYWZdphVVScG+68yH8K/LslICrgwdRxxjn 8X3/c4+Z3iumoy644Fp4cR2esyjthpF/F9eO9ByTlv0PXSD6rlZwG+rbDrnZ kVnI96XHJy/if491eaFFaloz1/8Ecy+HRtQvna3FCbV47thEF8M7nVy7xScn 8upbChLftPt3YT4dMp1WOFRRWFAf/OYPX+gda0qz86CQoTOi7YtriZb5Er88 sR/a+PuOoN/idc6Jzz9hnRiV0VL0J3XnynmXTRh4udxb4S7y8M8HMU+rB1G/ X1+o1BNI4Pk9A16HsxT0hVlD+3UJtH129jpwgIKqX4232EoGyOPX+5M+MDBp 06m/azAOBe3Zrfx4rtBjVq/bFQJDX3R3VgUzcNcoxbdUmMDGZOaO7xIGVI3L 388LJ5y8fvwlEhQvRWCViPi1jX4EKoo15cKnUM7/4/xoUNcx2zWQgeSl7R1L bAlnrXNAY7nWEQZefG2LWjiXgTz5Oe6uWgwwxTa9P6exYXSdvqj+oxJwPlk3 LwT1fbJw276rS3CucX+/9ulBPH9Pu6W1Xxm8e6LUMeksA990ks5ZoN7La7i8 Ki+fDe4rqyY6Yl3HqH6c/i28DHTClN2/aaI/sTkNE/QRlwbd59zPI69ul0gS 4iOw333gyth8AlejIftVEsO5962XgZIWqG+2ekaqqOB8VsLERej4MZCiXeF0 FuPflHpHMx11ksmT1ubjyGPPTlTKvcf9N/s81UhyY2DNpSj52EkELj30nsrS YMOsIV79CNQZw7unHIuOKeXoAwvJa85l3lwbHqT5V0ks5fD0HttEv5EK5GHx RznW0hiPN8GL1qDeS7y14HvnDAZyHg8c+LyZgP2AmOumQwyY+lUYHjBhQ67a z4s/wylo7ekJWzhAoDk3o1SyFfXbyFCX1X4G59J1TvJPCKhfU5lwMYzLb+N+ jVtz/rUxh3hQr1zc4OSYSjh5U9PJM45E3k7pZ2sFYH/664em7JUVBLK9Drf8 K5NAVc+3M7bqDCfO7dbmFl4Yj3WnBMU3sijss1wXsiuOQHGljtv7DOSj0+LL D6EOUqiY4RXkznDus0+79OIpnBPHcTVu7dWcxffkc62c5Cw1SQ+ule05OS1p O4W2BJebaWcwf/19wh9xLsq5rPzFUIXh2GGz0T53XTbw1bsUJWJcmxS/fuoR w/mh301BLI+bz3jPhxZTJco465CS7n2Jo1yrpVFrftKxjBM3s2jrEY1bBB5u tila/5gN9vOEHXSml4Gi1M3eZUFcfXhdwTlrSI6AlUf/rzeom3xq3Lwc3xJO nfyTHc9DvvqU+tAb3HWALDtaW4KBGine313YF9I1+T5OQv3FlM7qmFSGuiE8 dcZtxPm4nhy378PNiqtzCWctL1z3XOkkV3fOiP1g+uIw8p5n/N7NmL/x9T9Z 74hpqZEMgYGmT/CJEo4ejV87gG2AgLuW/7zg/3huMPWK/iPEgYv93PDifWwO /v/JigTqOcuJ0398r5825frPb9iflkaItIwg7zr5mFvyoc5rUT39K5SCh4p0 mFYj6mnx5dGisxA3GtcO78R5SORBstYA6lB9lnocbSCc9+0y7EtOCdw6HLeB eqI7RL9znxsrl8vEY13VbNSe+vRdGXgpKi+fFMfArpFlPLtqCAjIfG20R30T tXdsiTPqcsnsM7ANdffPVxMGu24TsKuIn8Dagnq51DWr+j7OMYZvdHxkGZyn elX012B+Lqa0qJizwczuh0DdQsR16eV7G3Df6r7FZF40tw/8t53b3KD/Cvle WfVi75IhhmOX6gbczsE+Mfo7IEy2iwFLPu/7cjhX7JpgJjgH8dPOW3mH1475 HzvgkZSydyKF4vn8p4VHCeQvrPI9inNpWhZvhCn2y45tg+J5ymyw6r8C3i3c fcwfGhytdiZww0SVRmdRGBYpmSOgSTm6fpN0f9yc2RTCLT6D3wzu83ErYLbh wkYlrOsQ0ycPeAj0rDMTu4jz7nyNMBs97PtHvP66WuOIdVEfKHYNeWl8Dd7u LHFPAsL6amN8qM99JOzSVHDOe135+/XKMOR/ZWULr93Y/9UXBFO8x/j6HJ+J e88r7nocr4f8d24UxDpI3GOQ9QD747M1MfaLEc/VTbzMIpynxGR3rT+G/sec l+7KcWNDra1F0ZPplDM/sGpZ5yfP567/205VfwWH8Z4L4gT0jRUp7C9Qyk/U RT1jeC/GQYn7X/bT9sg5eRTu/tBc5G/0v/PIl1i5qjkW3OfX9z1/orIV555l C6z2ZzD/Y+187lsWpBP4/uD2/D33UA+uMzwYr47xubvEZWcKw7ELS08r/cD4 FhfO3a/4f9yjUHyenWg6A6tvZ+/PfsSGv4rWmv4oInCUf1VviB6F3+zJ+7Pu MhxbNZa/tayPgbYmv9T5ONeO6xlFS9vGGWYU1I/J+Ce2MRy79Xmc9y1l5DGr Pa4RPdiHbRxX7+1EvfFEuus1zs2ngqcoD/kWc/A9z7me9R15bNxOTdo0LBb5 EhI9bl8wcCDgvZR/dM0I1mub2t2b78sgrEFii1QIA8uVNxfNx/lnUUulkft+ Noh1VY4J5bHhnYlHz2YlNjgXOC4TzMHvX4t4iVxgOHOJWZ5S3KI0BsqzqnKs cK48eKBz4OF71FX19xpHI3DOzD8wuRZ5UXQgSOUd4tkq8PThzzifJNvxez6S ZUN+1L1pH09jHfvumCwmyYbZCRuaRfUYeNZT9zpEHPtFhHtGtwsb6gO6Myeo EdAIs3b1x/7+b1Fgb6g= "]}, {{0, 0, 0, 33, 12, 1, 0, 0, 1, 1, 1}, { 0, 0, 0, 20, 12, 1, 8644.15625, 8644.15625, 0, 0, 1}, { 1, 0, 0, 39, 12, 1, 19785.3203125, 6608.13720703125, 0, 0, 0}, { 0, 0, 1, 49, 8, 1, 9714.5966796875, 7285.94775390625, 0, 0, 1}, { 0, 0, 1, 26, 8, 1, 37211.7578125, 36941.265625, 0, 0, 0}, { 1, 0, 1, 38, 10, 1, 14759.0634765625, 14701.947265625, 0, 0, 0}, { 0, 0, 0, 28, 12, 0, 0, 803.343017578125, 1, 0, 0}, { 1, 0, 0, 27, 12, 1, 0, 752.390075683594, 1, 0, 0}, { 1, 0, 0, 33, 12, 1, 20279.94921875, 10941.349609375, 0, 0, 1}, { 1, 0, 1, 44, 9, 1, 12260.7802734375, 10857.240234375, 0, 0, 0}, { 0, 0, 1, 28, 10, 1, 5440.818359375, 6346.19287109375, 0, 0, 0}, { 0, 0, 0, 31, 12, 0, 0, 494.643005371094, 1, 0, 0}, { 0, 0, 0, 24, 12, 1, 28262.130859375, 24294.74609375, 0, 0, 0}, { 0, 0, 0, 29, 12, 0, 12116.611328125, 12205.142578125, 0, 0, 0}, { 0, 0, 1, 20, 11, 1, 7254.419921875, 8159.5068359375, 0, 0, 0}, { 0, 0, 1, 22, 10, 1, 25720.919921875, 23031.98046875, 0, 0, 0}, { 1, 0, 1, 35, 9, 1, 13602.4296875, 13830.6396484375, 0, 0, 1}, { 1, 0, 1, 42, 9, 1, 0, 3058.53100585938, 1, 0, 1}, { 0, 0, 0, 21, 13, 0, 9978.7509765625, 8616.8525390625, 0, 0, 1}, { 0, 0, 0, 23, 12, 0, 5204.259765625, 3903.19482421875, 0, 0, 1}, { 0, 0, 0, 18, 12, 0, 735.205078125, 735.205078125, 0, 0, 0}, { 0, 0, 0, 22, 12, 0, 6759.994140625, 8455.50390625, 0, 0, 1}, { 0, 0, 0, 20, 12, 0, 4139.75341796875, 3104.81518554688, 0, 0, 0}, { 1, 0, 0, 22, 12, 1, 9420.671875, 9352.19921875, 0, 0, 0}, { 1, 0, 1, 35, 11, 1, 7096.7158203125, 8720.9052734375, 0, 0, 0}, { 0, 0, 0, 27, 13, 1, 9381.56640625, 853.722473144531, 0, 0, 1}, { 1, 0, 0, 41, 12, 0, 10707.1611328125, 9818.466796875, 0, 0, 0}, { 1, 0, 1, 30, 11, 1, 0, 9311.9384765625, 1, 0, 0}, { 0, 1, 0, 22, 12, 1, 492.230499267578, 7055.7021484375, 0, 0, 1}, { 1, 0, 0, 27, 12, 1, 10408.515625, 7806.38671875, 0, 0, 1}, {1, 0, 0, 42, 14, 1, 0, 0, 1, 1, 1}, { 1, 0, 1, 27, 10, 1, 13542.26953125, 11280.7109375, 0, 0, 0}, {0, 0, 0, 41, 14, 0, 0, 0, 1, 1, 1}, { 1, 0, 1, 50, 8, 1, 7506.73388671875, 5630.05029296875, 0, 0, 0}, { 1, 0, 1, 26, 10, 1, 9773.953125, 8141.703125, 0, 0, 1}, { 1, 0, 1, 25, 10, 1, 12428.1220703125, 11696.3720703125, 0, 0, 0}, { 1, 0, 1, 35, 8, 1, 13732.0703125, 17976.150390625, 0, 0, 1}, { 1, 0, 1, 25, 9, 1, 24731.619140625, 16946.630859375, 0, 0, 0}, { 1, 0, 1, 33, 11, 1, 14660.7099609375, 25142.240234375, 0, 0, 1}, {1, 0, 0, 28, 12, 1, 0, 0, 1, 1, 0}, { 1, 0, 1, 27, 11, 1, 20249.224609375, 20035.255859375, 0, 0, 0}, {1, 0, 1, 46, 8, 1, 0, 0, 1, 1, 1}, {1, 0, 0, 30, 14, 1, 0, 0, 1, 1, 0}, { 0, 0, 1, 19, 10, 0, 0, 5324.10888671875, 1, 0, 1}, {1, 0, 0, 34, 13, 1, 0, 0, 1, 1, 0}, {0, 0, 1, 26, 11, 0, 0, 2226.26611328125, 1, 0, 1}, { 1, 0, 1, 30, 11, 1, 24286.5078125, 24105.38671875, 0, 0, 0}, { 1, 0, 0, 36, 12, 0, 0, 142.39729309082, 1, 0, 0}, {1, 0, 1, 45, 5, 1, 0, 0, 1, 1, 1}, {1, 0, 1, 23, 10, 1, 0, 936.438598632812, 1, 0, 1}, {1, 0, 0, 39, 12, 1, 0, 0, 1, 1, 0}, { 1, 0, 1, 23, 9, 1, 19047.07421875, 19289.8515625, 0, 0, 0}, { 1, 0, 1, 27, 11, 1, 0, 4491.8837890625, 1, 0, 0}, {0, 0, 0, 38, 12, 0, 0, 0, 1, 1, 1}, { 0, 0, 1, 24, 11, 0, 2669.72509765625, 1468.38305664062, 0, 0, 0}, { 1, 0, 1, 26, 11, 1, 0, 1392.85302734375, 1, 0, 1}, { 1, 0, 0, 29, 14, 0, 0, 679.673400878906, 1, 0, 1}, { 1, 0, 1, 27, 11, 1, 2336.11401367188, 2142.21655273438, 0, 0, 1}, {1, 0, 1, 55, 3, 0, 0, 0, 1, 1, 0}, { 0, 0, 1, 20, 11, 0, 1413.10668945312, 1177.11791992188, 0, 0, 0}, {1, 0, 1, 54, 11, 0, 0, 0, 1, 1, 0}, { 0, 1, 0, 36, 12, 0, 13661.177734375, 11491.9521484375, 0, 0, 0}, {1, 0, 1, 23, 10, 1, 0, 0, 1, 1, 0}, { 0, 0, 1, 28, 11, 0, 2787.76123046875, 3200.91186523438, 0, 0, 0}, {0, 1, 0, 32, 12, 1, 0, 0, 1, 1, 0}, { 1, 0, 0, 32, 12, 0, 13283.171875, 12419.1845703125, 0, 0, 0}, { 0, 0, 1, 18, 9, 0, 0, 559.596008300781, 1, 0, 0}, { 0, 0, 1, 26, 11, 0, 22893.87109375, 22959.76171875, 0, 0, 1}, { 1, 0, 1, 28, 11, 1, 6702.451171875, 6952.57958984375, 0, 0, 1}, { 1, 0, 1, 28, 11, 1, 824.388916015625, 10033.91015625, 0, 0, 0}, { 1, 0, 0, 26, 12, 0, 8811.7373046875, 6608.80322265625, 0, 0, 1}, { 1, 0, 0, 31, 13, 0, 10255.5361328125, 10393.61328125, 0, 0, 0}, { 1, 0, 1, 28, 9, 1, 10222.41015625, 9210.447265625, 0, 0, 0}, { 1, 0, 0, 24, 12, 0, 11972.125, 12982.3408203125, 0, 0, 1}, {1, 0, 0, 46, 13, 0, 0, 0, 1, 1, 1}, { 1, 0, 1, 18, 10, 1, 785.059020996094, 1958.09631347656, 0, 0, 1}, { 1, 0, 1, 26, 11, 1, 0, 4184.73193359375, 1, 0, 0}, { 1, 0, 1, 26, 11, 1, 0, 2754.64599609375, 1, 0, 1}, { 1, 0, 0, 22, 12, 0, 4380.01708984375, 2003.68005371094, 0, 0, 0}, { 1, 0, 1, 41, 9, 1, 7864.9072265625, 7212.1201171875, 0, 0, 1}, { 0, 0, 1, 22, 10, 0, 27864.359375, 10598.669921875, 0, 0, 0}, {0, 0, 1, 19, 10, 0, 0, 0, 1, 1, 0}, { 1, 0, 1, 25, 11, 1, 2336.11401367188, 3266.40576171875, 0, 0, 0}, { 1, 0, 0, 29, 13, 0, 718.249206542969, 2265.5791015625, 0, 0, 0}, { 0, 0, 1, 21, 9, 0, 2988.412109375, 1577.1669921875, 0, 0, 0}, { 1, 0, 1, 24, 11, 1, 824.388610839844, 1666.11303710938, 0, 0, 1}, { 1, 0, 0, 29, 12, 0, 9748.38671875, 4878.93701171875, 0, 0, 1}, { 1, 0, 0, 29, 12, 0, 0, 1359.34655761719, 1, 0, 0}, { 0, 0, 1, 19, 8, 0, 4101.92919921875, 3416.90698242188, 0, 0, 1}, {1, 0, 1, 25, 9, 1, 0, 0, 1, 1, 0}, { 1, 0, 1, 31, 9, 0, 0, 1698.60705566406, 1, 0, 1}, {1, 0, 0, 28, 13, 0, 0, 0, 1, 1, 0}, { 1, 0, 1, 21, 11, 1, 3523.63696289062, 4167.998046875, 0, 0, 1}, { 1, 0, 1, 21, 10, 1, 1946.76098632812, 1785.17980957031, 0, 0, 0}, {0, 0, 0, 27, 12, 0, 0, 0, 1, 1, 0}, { 1, 0, 0, 27, 12, 0, 1971.31030273438, 4310.455078125, 0, 0, 1}, {0, 0, 0, 26, 12, 0, 0, 0, 1, 1, 0}, { 0, 0, 0, 31, 13, 0, 5996.01904296875, 5498.349609375, 0, 0, 1}, { 1, 0, 0, 24, 15, 0, 12813.505859375, 13008.4970703125, 0, 0, 1}, { 0, 0, 1, 23, 8, 1, 0, 1713.15002441406, 1, 0, 1}, { 1, 0, 0, 28, 12, 1, 10585.1298828125, 5551.4560546875, 0, 0, 0}, { 0, 0, 0, 31, 12, 0, 0, 2611.21801757812, 1, 0, 1}, { 1, 0, 0, 26, 12, 0, 1051.25085449219, 1088.90698242188, 0, 0, 1}, { 1, 0, 0, 25, 13, 0, 12362.9296875, 3090.73193359375, 0, 0, 1}, { 1, 0, 0, 26, 14, 1, 2025.56628417969, 2025.56628417969, 0, 0, 0}, {0, 1, 1, 33, 9, 1, 0, 0, 1, 1, 0}, { 0, 1, 0, 24, 12, 1, 7864.89697265625, 7212.1103515625, 0, 0, 1}, { 1, 0, 0, 21, 14, 0, 6463.24365234375, 5926.79443359375, 0, 0, 0}, { 1, 0, 0, 26, 12, 1, 8408.76171875, 5794.8310546875, 0, 0, 1}, { 1, 0, 0, 24, 12, 0, 0, 159.884704589844, 1, 0, 0}, { 1, 0, 0, 24, 12, 0, 4739.03076171875, 8100.533203125, 0, 0, 0}, {0, 0, 0, 25, 12, 0, 0, 0, 1, 1, 1}, {1, 0, 0, 42, 14, 0, 0, 0, 1, 1, 1}, { 1, 0, 0, 18, 12, 0, 562.657043457031, 562.657043457031, 0, 0, 1}, {1, 0, 0, 25, 12, 1, 0, 0, 1, 1, 0}, { 1, 0, 0, 21, 12, 0, 6473.68310546875, 3332.40893554688, 0, 0, 1}, { 1, 0, 0, 20, 12, 0, 7182.4921875, 6004.72802734375, 0, 0, 0}, {1, 0, 0, 20, 12, 0, 0, 0, 1, 1, 1}, { 1, 0, 0, 20, 12, 0, 557.698791503906, 1371.47302246094, 0, 0, 0}, { 0, 1, 1, 19, 11, 1, 308.21923828125, 308.21923828125, 0, 0, 0}, {1, 0, 0, 23, 12, 1, 0, 0, 1, 1, 1}, {1, 0, 0, 26, 12, 1, 0, 0, 1, 1, 0}, {1, 0, 1, 31, 11, 1, 0, 0, 1, 1, 1}, { 0, 0, 0, 27, 13, 0, 5214.30615234375, 474.501800537109, 0, 0, 0}, {1, 0, 1, 41, 4, 1, 0, 0, 1, 1, 1}, { 0, 0, 0, 27, 12, 0, 6044.951171875, 5035.4443359375, 0, 0, 0}, { 0, 0, 0, 26, 13, 0, 6476.73291015625, 5395.11865234375, 0, 0, 0}, { 1, 0, 1, 39, 11, 0, 0, 83.6896362304688, 1, 0, 0}, {1, 0, 1, 30, 11, 1, 0, 0, 1, 1, 1}, { 1, 0, 0, 28, 12, 1, 31634.794921875, 29009.107421875, 0, 0, 0}, {0, 0, 0, 21, 12, 0, 0, 0, 1, 1, 1}, { 0, 1, 0, 23, 12, 0, 9385.740234375, 1117.43896484375, 0, 0, 1}, { 1, 0, 0, 27, 12, 1, 3670.8720703125, 334.049285888672, 0, 0, 1}, {0, 0, 0, 24, 12, 0, 0, 0, 1, 1, 0}, { 1, 0, 0, 25, 14, 1, 35040.0703125, 11536.5703125, 0, 0, 1}, { 0, 0, 0, 21, 12, 0, 7254.419921875, 5440.81494140625, 0, 0, 0}, { 0, 0, 0, 21, 12, 0, 8059.57958984375, 7390.63427734375, 0, 0, 1}, { 0, 0, 0, 21, 12, 0, 3670.8720703125, 334.049407958984, 0, 0, 1}, {1, 0, 1, 38, 11, 0, 0, 0, 1, 1, 1}, {1, 0, 0, 40, 12, 0, 0, 0, 1, 1, 1}, { 1, 0, 1, 41, 9, 1, 6307.50390625, 5783.9814453125, 0, 0, 1}, { 1, 0, 1, 28, 10, 1, 8293.3447265625, 6449.47998046875, 0, 0, 0}, {1, 0, 0, 42, 12, 0, 0, 0, 1, 1, 1}, { 1, 0, 1, 27, 8, 0, 6150.48291015625, 7218.27734375, 0, 0, 1}, { 1, 0, 0, 23, 12, 1, 15979.0126953125, 15079.947265625, 0, 0, 1}, {1, 0, 1, 48, 4, 0, 0, 0, 1, 1, 1}, { 0, 1, 1, 29, 10, 0, 0, 8853.673828125, 1, 0, 1}, {1, 0, 1, 27, 7, 1, 0, 0, 1, 1, 0}, {1, 0, 1, 44, 11, 0, 0, 0, 1, 1, 1}, { 1, 0, 1, 26, 8, 1, 3854.58520507812, 4084.25805664062, 0, 0, 0}, {1, 0, 1, 37, 11, 1, 0, 0, 1, 1, 1}, { 1, 0, 1, 31, 9, 0, 10717.0302734375, 5517.8408203125, 0, 0, 1}, { 1, 0, 1, 36, 11, 0, 5519.66943359375, 7311.5615234375, 0, 0, 0}, { 1, 0, 1, 22, 7, 0, 13503.76953125, 13503.76953125, 0, 0, 0}, {0, 1, 1, 50, 10, 0, 0, 0, 1, 1, 0}, {1, 0, 1, 29, 11, 1, 0, 0, 1, 1, 1}, {0, 0, 1, 38, 9, 0, 0, 0, 1, 1, 1}, { 1, 0, 1, 28, 11, 0, 17491.44921875, 13371.25, 0, 0, 0}, { 0, 1, 1, 36, 11, 0, 5443.73388671875, 3063.8779296875, 0, 0, 0}, {1, 0, 1, 22, 11, 1, 0, 0, 1, 1, 0}, { 1, 0, 1, 33, 11, 0, 0, 7867.916015625, 1, 0, 1}, { 1, 0, 1, 31, 4, 0, 8517.5888671875, 4023.2109375, 0, 0, 1}, {1, 0, 1, 37, 9, 0, 0, 0, 1, 1, 1}, {1, 0, 1, 29, 11, 1, 0, 0, 1, 1, 0}, { 1, 0, 0, 34, 12, 0, 4906.619140625, 4087.2138671875, 0, 0, 0}, {1, 0, 1, 45, 9, 0, 0, 0, 1, 1, 0}, { 1, 0, 1, 31, 11, 0, 19555.31640625, 18291.412109375, 0, 0, 0}, { 1, 0, 1, 31, 11, 0, 8419.75, 7505.21337890625, 0, 0, 0}, { 0, 1, 1, 19, 9, 0, 3083.6689453125, 3276.150390625, 0, 0, 1}, {1, 0, 0, 33, 12, 0, 0, 0, 1, 1, 0}, { 1, 0, 1, 17, 10, 0, 1291.46801757812, 5793.85205078125, 0, 0, 1}, {1, 0, 1, 25, 11, 1, 0, 0, 1, 1, 1}, { 1, 0, 1, 22, 11, 0, 1167.01538085938, 875.261535644531, 0, 0, 0}, { 1, 0, 1, 22, 11, 0, 8176.39208984375, 7497.75146484375, 0, 0, 1}, { 0, 1, 1, 19, 8, 0, 1087.80322265625, 1087.80322265625, 0, 0, 0}, { 1, 0, 1, 22, 11, 0, 12300.9228515625, 12170.943359375, 0, 0, 1}, {1, 0, 1, 25, 10, 1, 0, 0, 1, 1, 0}, { 1, 0, 1, 25, 10, 1, 13519.9697265625, 9319.4443359375, 0, 0, 0}, { 1, 0, 1, 43, 10, 0, 2502.86791992188, 4128.44287109375, 0, 0, 0}, {1, 0, 1, 34, 11, 1, 0, 0, 1, 1, 0}, { 1, 0, 1, 46, 8, 0, 3165.65795898438, 2594.72290039062, 0, 0, 1}, { 1, 0, 1, 21, 11, 1, 9659.4150390625, 7244.56103515625, 0, 0, 0}, { 1, 0, 1, 22, 9, 0, 0, 506.407592773438, 1, 0, 0}, {1, 0, 1, 34, 10, 1, 0, 0, 1, 1, 0}, {1, 0, 1, 29, 10, 0, 0, 4398.9501953125, 1, 0, 1}, { 1, 0, 1, 28, 11, 0, 1929.02905273438, 6871.85595703125, 0, 0, 1}, { 1, 0, 1, 28, 11, 0, 0, 1284.07897949219, 1, 0, 1}, { 1, 0, 1, 28, 10, 0, 0, 2836.50610351562, 1, 0, 1}, {1, 0, 1, 45, 11, 0, 0, 0, 1, 1, 0}, {1, 0, 1, 27, 11, 0, 0, 0, 1, 1, 0}, { 1, 0, 1, 27, 11, 0, 3311.802734375, 2483.85205078125, 0, 0, 1}, { 1, 0, 0, 26, 12, 0, 9247.1015625, 8479.5927734375, 0, 0, 0}, { 1, 0, 1, 27, 10, 0, 3504.16821289062, 3213.322265625, 0, 0, 1}, {1, 0, 1, 29, 4, 0, 0, 0, 1, 1, 1}, {1, 0, 1, 25, 11, 0, 0, 0, 1, 1, 1}, {1, 0, 1, 18, 7, 0, 0, 0, 1, 1, 0}, { 1, 0, 0, 23, 12, 0, 9342.1875, 7782.0419921875, 0, 0, 1}, { 1, 0, 0, 23, 12, 0, 5506.30810546875, 501.074096679688, 0, 0, 1}, {1, 0, 0, 34, 12, 0, 0, 0, 1, 1, 0}, { 1, 0, 1, 24, 11, 0, 9344.451171875, 9180.919921875, 0, 0, 1}, { 1, 0, 1, 24, 11, 0, 0, 1327.99304199219, 1, 0, 0}, {1, 0, 0, 30, 12, 0, 0, 0, 1, 1, 0}, { 1, 0, 1, 26, 8, 0, 1126.2900390625, 5562.59814453125, 0, 0, 0}, { 1, 0, 0, 21, 12, 0, 473.114196777344, 807.951110839844, 0, 0, 1}, {1, 0, 1, 23, 11, 0, 0, 0, 1, 1, 0}, { 1, 0, 1, 23, 11, 0, 7617.36181640625, 5716.4072265625, 0, 0, 0}, { 1, 0, 1, 25, 9, 0, 1419.34362792969, 1536.5029296875, 0, 0, 1}, { 1, 0, 1, 24, 10, 0, 4250.40185546875, 2421.94702148438, 0, 0, 1}, {1, 0, 1, 31, 11, 1, 0, 0, 1, 1, 1}, {1, 0, 1, 18, 11, 0, 0, 0, 1, 1, 1}, {1, 0, 1, 18, 11, 0, 0, 0, 1, 1, 0}, { 1, 0, 1, 18, 11, 0, 788.523315429688, 2041.36303710938, 0, 0, 0}, { 1, 0, 1, 18, 11, 0, 8784.2373046875, 8784.2373046875, 0, 0, 0}, {1, 0, 1, 17, 10, 0, 0, 0, 1, 1, 0}, {1, 0, 1, 25, 11, 1, 0, 0, 1, 1, 0}, { 1, 0, 1, 19, 11, 0, 3504.01391601562, 3285.67993164062, 0, 0, 0}, { 1, 0, 1, 19, 11, 0, 6337.4921875, 4503.06396484375, 0, 0, 0}, { 1, 0, 1, 19, 11, 0, 1868.02136230469, 1868.02136230469, 0, 0, 0}, { 1, 0, 1, 19, 11, 0, 5217.3173828125, 5160.2587890625, 0, 0, 0}, { 1, 0, 1, 19, 11, 0, 2305.02587890625, 2615.27587890625, 0, 0, 1}, { 1, 0, 1, 19, 11, 0, 9856.537109375, 10224.375, 0, 0, 1}, { 1, 0, 1, 19, 11, 0, 946.227722167969, 1615.90100097656, 0, 0, 0}, { 1, 0, 1, 19, 11, 0, 393.859802246094, 393.859802246094, 0, 0, 0}, { 1, 0, 1, 21, 11, 0, 2956.96557617188, 2217.72436523438, 0, 0, 1}, { 1, 0, 1, 20, 11, 0, 7964.08642578125, 8012.08544921875, 0, 0, 0}, { 1, 0, 1, 20, 11, 0, 2569.724609375, 2531.31103515625, 0, 0, 0}, { 1, 0, 1, 20, 11, 0, 7967.20947265625, 7967.20947265625, 0, 0, 0}, { 1, 0, 1, 20, 11, 0, 1650.4609375, 1650.4609375, 0, 0, 0}, { 1, 0, 1, 20, 11, 0, 1168.05676269531, 1633.20239257812, 0, 0, 0}, { 1, 0, 1, 20, 11, 0, 3637.498046875, 1220.83605957031, 0, 0, 1}, { 1, 0, 1, 24, 9, 0, 2621.84252929688, 4118.6806640625, 0, 0, 0}, { 0, 0, 1, 30, 9, 0, 3426.29956054688, 3141.91674804688, 0, 0, 0}, {1, 0, 1, 31, 10, 1, 0, 0, 1, 1, 0}, { 1, 0, 1, 18, 10, 0, 678.130187988281, 2456.994140625, 0, 0, 0}, { 1, 0, 1, 18, 10, 0, 0, 273.552490234375, 1, 0, 0}, { 1, 0, 1, 18, 10, 0, 2143.41088867188, 1784.27404785156, 0, 0, 1}, { 0, 1, 1, 28, 11, 1, 3472.94799804688, 0, 0, 1, 0}, {1, 0, 1, 17, 9, 0, 0, 0, 1, 1, 1}, { 1, 0, 1, 17, 9, 0, 375.104705810547, 375.104705810547, 0, 0, 1}, { 1, 0, 1, 17, 9, 0, 1716.50903320312, 1253.43896484375, 0, 0, 1}, {1, 0, 1, 25, 10, 1, 0, 0, 1, 1, 0}, {1, 0, 1, 44, 11, 0, 0, 0, 1, 1, 0}, { 1, 0, 1, 22, 10, 0, 0, 2174.955078125, 1, 0, 0}, { 1, 0, 1, 19, 10, 0, 2959.07666015625, 3213.11279296875, 0, 0, 1}, { 1, 0, 1, 19, 10, 0, 4121.94921875, 6056.75390625, 0, 0, 1}, { 1, 0, 1, 21, 10, 0, 6661.06298828125, 1162.36206054688, 0, 0, 0}, { 1, 0, 0, 29, 12, 0, 22859.439453125, 2080.208984375, 0, 0, 0}, { 1, 0, 0, 29, 12, 0, 10881.9404296875, 1817.28405761719, 0, 0, 1}, { 1, 0, 1, 20, 10, 0, 825.230163574219, 825.230163574219, 0, 0, 0}, { 1, 0, 1, 20, 10, 0, 11073.0888671875, 11073.0888671875, 0, 0, 1}, { 1, 0, 1, 20, 10, 0, 3114.81713867188, 2856.28735351562, 0, 0, 0}, { 1, 0, 1, 20, 10, 0, 3666.63623046875, 2749.97705078125, 0, 0, 0}, {1, 0, 1, 20, 10, 0, 0, 0, 1, 1, 0}, {1, 0, 1, 18, 9, 0, 0, 0, 1, 1, 0}, { 1, 0, 1, 18, 9, 0, 2138.09399414062, 2138.09399414062, 0, 0, 0}, {1, 0, 1, 17, 8, 0, 0, 0, 1, 1, 1}, {1, 0, 1, 17, 8, 0, 0, 0, 1, 1, 1}, { 1, 0, 1, 26, 10, 1, 6140.3671875, 558.773376464844, 0, 0, 0}, { 1, 0, 1, 26, 10, 1, 2027.9990234375, 0, 0, 1, 1}, { 1, 0, 1, 22, 9, 0, 2192.876953125, 3836.98608398438, 0, 0, 1}, { 1, 0, 1, 19, 9, 0, 8409.626953125, 1778.08898925781, 0, 0, 0}, { 1, 0, 1, 21, 9, 0, 31886.4296875, 12357.2197265625, 0, 0, 0}, { 1, 0, 1, 20, 9, 0, 7688.107421875, 9164.44921875, 0, 0, 0}, { 1, 0, 1, 20, 9, 0, 9718.20703125, 9691.033203125, 0, 0, 0}, { 0, 0, 1, 31, 4, 0, 11680.564453125, 10711.0771484375, 0, 0, 0}, {1, 0, 1, 44, 9, 0, 0, 0, 1, 1, 0}, { 0, 1, 1, 18, 7, 1, 3059.47412109375, 2672.2021484375, 0, 0, 0}, { 1, 0, 1, 19, 8, 0, 2636.35302734375, 2937.26391601562, 0, 0, 1}, { 1, 0, 1, 21, 8, 0, 989.267822265625, 3695.89697265625, 0, 0, 1}, {1, 0, 1, 19, 10, 1, 0, 0, 1, 1, 0}, { 0, 1, 1, 17, 10, 0, 1203.60900878906, 1239.62805175781, 0, 0, 0}, {1, 0, 1, 19, 9, 1, 0, 0, 1, 1, 0}, {0, 1, 1, 19, 6, 0, 0, 0, 1, 1, 0}, {1, 0, 1, 29, 11, 1, 0, 0, 1, 1, 0}, {0, 1, 1, 17, 9, 0, 0, 0, 1, 1, 0}, { 1, 0, 1, 25, 8, 1, 330.949493408203, 515.827453613281, 0, 0, 0}, { 1, 0, 1, 29, 10, 1, 4687.8017578125, 4923.26318359375, 0, 0, 1}, { 1, 0, 1, 24, 10, 1, 11703.2001953125, 4078.15209960938, 0, 0, 1}, {0, 0, 1, 28, 8, 0, 0, 0, 1, 1, 0}, { 1, 0, 1, 33, 11, 0, 10523.830078125, 2899.82006835938, 0, 0, 0}, { 0, 0, 1, 21, 11, 0, 1261.63720703125, 946.227905273438, 0, 0, 1}, { 0, 0, 1, 26, 11, 0, 3807.53588867188, 4676.45703125, 0, 0, 1}, { 1, 0, 1, 28, 11, 1, 8593.1630859375, 5393.89501953125, 0, 0, 0}, { 1, 0, 1, 23, 10, 1, 10746.078125, 9854.1533203125, 0, 0, 0}, { 1, 0, 0, 31, 12, 0, 0, 5613.9091796875, 1, 0, 0}, {1, 0, 0, 31, 12, 0, 0, 0, 1, 1, 0}, {0, 0, 1, 25, 11, 0, 0, 0, 1, 1, 1}, { 1, 0, 0, 25, 12, 0, 3297.2490234375, 2746.6083984375, 0, 0, 1}, {1, 0, 0, 25, 12, 0, 0, 0, 1, 1, 1}, { 0, 1, 1, 24, 10, 0, 3000.83764648438, 3000.83764648438, 0, 0, 0}, { 0, 1, 1, 18, 11, 0, 5491.53515625, 5491.53515625, 0, 0, 1}, { 1, 0, 1, 27, 11, 1, 1122.62060546875, 1122.62060546875, 0, 0, 0}, { 0, 1, 1, 17, 10, 0, 2102.50244140625, 2202.79638671875, 0, 0, 1}, { 0, 1, 1, 17, 10, 0, 1442.68103027344, 1734.55895996094, 0, 0, 0}, { 0, 1, 1, 17, 10, 0, 4905.119140625, 1168.90197753906, 0, 0, 0}, {1, 0, 1, 43, 9, 0, 0, 0, 1, 1, 1}, { 0, 0, 1, 25, 10, 0, 5139.44775390625, 4712.8740234375, 0, 0, 1}, { 0, 0, 1, 25, 10, 0, 8877.2109375, 8140.40234375, 0, 0, 0}, {1, 0, 0, 21, 14, 0, 0, 0, 1, 1, 0}, { 0, 1, 1, 21, 11, 0, 6852.5986328125, 6658.5625, 0, 0, 1}, { 0, 1, 1, 21, 11, 0, 2992.53393554688, 8920.470703125, 0, 0, 0}, {0, 0, 1, 17, 11, 0, 0, 0, 1, 1, 0}, { 0, 1, 1, 18, 10, 0, 12717.955078125, 12625.5107421875, 0, 0, 1}, {0, 1, 1, 18, 10, 0, 0, 0, 1, 1, 0}, {1, 0, 1, 27, 10, 1, 0, 0, 1, 1, 1}, { 1, 0, 1, 22, 10, 1, 1468.34802246094, 5588.6640625, 0, 0, 1}, { 1, 0, 0, 30, 13, 0, 6073.89453125, 5569.76123046875, 0, 0, 0}, { 0, 0, 1, 23, 11, 0, 21492.203125, 19995.642578125, 0, 0, 0}, {0, 1, 1, 19, 10, 0, 0, 0, 1, 1, 0}, { 0, 1, 1, 19, 10, 0, 1261.63818359375, 2275.36767578125, 0, 0, 0}, { 0, 0, 1, 18, 11, 0, 3678.23095703125, 919.557922363281, 0, 0, 1}, { 0, 1, 0, 27, 12, 0, 27913.662109375, 24276.974609375, 0, 0, 0}, { 0, 0, 1, 17, 10, 0, 2417.982421875, 2465.61328125, 0, 0, 0}, {1, 0, 0, 26, 12, 0, 0, 0, 1, 1, 1}, {1, 0, 0, 26, 12, 0, 0, 0, 1, 1, 0}, {0, 0, 1, 22, 11, 0, 0, 0, 1, 1, 1}, { 0, 0, 1, 19, 11, 0, 823.25439453125, 823.25439453125, 0, 0, 0}, {0, 0, 1, 19, 11, 0, 0, 0, 1, 1, 0}, { 1, 0, 1, 27, 9, 1, 0, 934.445373535156, 1, 0, 1}, {1, 0, 0, 30, 12, 0, 0, 0, 1, 1, 1}, { 0, 0, 1, 20, 11, 0, 1545.50732421875, 1159.13049316406, 0, 0, 0}, { 0, 0, 1, 25, 8, 0, 24415.234375, 23096.65234375, 0, 0, 1}, { 0, 1, 1, 19, 9, 0, 2686.53002929688, 2750.8408203125, 0, 0, 0}, { 1, 0, 0, 29, 14, 0, 3357.18530273438, 3357.18530273438, 0, 0, 0}, { 1, 0, 1, 26, 10, 1, 12143.2412109375, 9107.4306640625, 0, 0, 0}, { 0, 0, 1, 17, 9, 0, 1618.14978027344, 1618.14978027344, 0, 0, 1}, {1, 0, 1, 25, 11, 1, 0, 0, 1, 1, 1}, {0, 0, 1, 24, 8, 0, 0, 0, 1, 1, 0}, {1, 0, 0, 29, 13, 0, 0, 0, 1, 1, 1}, { 1, 0, 0, 25, 12, 0, 6939.01220703125, 5204.25927734375, 0, 0, 1}, {0, 0, 1, 17, 8, 0, 0, 0, 1, 1, 0}, {1, 0, 0, 28, 15, 0, 0, 0, 1, 1, 1}, {0, 0, 1, 19, 9, 0, 0, 0, 1, 1, 1}, { 1, 0, 0, 25, 12, 0, 14426.7900390625, 2409.27392578125, 0, 0, 1}, {1, 0, 1, 25, 10, 1, 0, 0, 1, 1, 0}, { 0, 0, 1, 18, 8, 0, 1168.05639648438, 1345.90942382812, 0, 0, 0}, {1, 0, 0, 29, 12, 0, 0, 0, 1, 1, 0}, { 1, 0, 0, 28, 14, 0, 7850.5849609375, 6539.53759765625, 0, 0, 0}, { 0, 1, 1, 18, 6, 0, 6385.3740234375, 6142.6806640625, 0, 0, 0}, {1, 0, 1, 42, 10, 0, 0, 0, 1, 1, 0}, {0, 0, 1, 23, 7, 0, 0, 0, 1, 1, 1}, {0, 1, 1, 18, 8, 1, 0, 0, 1, 1, 1}, { 1, 0, 1, 21, 11, 1, 7652.11962890625, 7652.11962890625, 0, 0, 0}, { 1, 0, 1, 23, 10, 1, 9462.2880859375, 10495.083984375, 0, 0, 1}, {1, 0, 0, 24, 12, 0, 0, 0, 1, 1, 0}, { 1, 0, 0, 27, 13, 0, 9593.6181640625, 8797.34765625, 0, 0, 0}, {1, 0, 0, 27, 13, 0, 0, 0, 1, 1, 1}, {1, 0, 0, 27, 13, 0, 0, 0, 1, 1, 1}, {1, 0, 0, 27, 13, 0, 0, 0, 1, 1, 0}, {1, 0, 0, 27, 13, 0, 0, 0, 1, 1, 1}, { 1, 0, 0, 27, 12, 0, 31166.80859375, 32984.25, 0, 0, 0}, { 1, 0, 0, 27, 12, 0, 2143.4130859375, 357.949890136719, 0, 0, 1}, { 1, 0, 0, 27, 12, 0, 3311.8017578125, 2483.85131835938, 0, 0, 0}, {1, 0, 1, 38, 10, 0, 0, 0, 1, 1, 0}, {1, 0, 0, 22, 16, 0, 0, 0, 1, 1, 1}, { 1, 0, 0, 26, 12, 0, 7322.03662109375, 7322.03662109375, 0, 0, 0}, { 1, 0, 0, 22, 12, 0, 5605.85205078125, 936.177307128906, 0, 0, 1}, {1, 0, 0, 22, 12, 0, 0, 0, 1, 1, 0}, { 1, 0, 0, 25, 13, 0, 10240.357421875, 10240.357421875, 0, 0, 0}, { 1, 0, 0, 25, 13, 0, 3751.04760742188, 3751.04760742188, 0, 0, 0}, {1, 0, 0, 25, 12, 0, 0, 0, 1, 1, 1}, {1, 0, 0, 25, 12, 0, 0, 0, 1, 1, 1}, {1, 0, 0, 25, 12, 0, 0, 0, 1, 1, 0}, {1, 0, 0, 25, 12, 0, 0, 0, 1, 1, 0}, {1, 0, 0, 25, 12, 0, 0, 0, 1, 1, 1}, {1, 0, 0, 21, 13, 0, 0, 0, 1, 1, 1}, {0, 1, 1, 25, 11, 1, 0, 0, 1, 1, 0}, { 1, 0, 0, 24, 12, 0, 20580.48046875, 18154.0546875, 0, 0, 1}, { 1, 0, 0, 24, 12, 0, 13765.75, 2842.76391601562, 0, 0, 1}, { 1, 0, 0, 21, 13, 0, 27052.154296875, 24806.826171875, 0, 0, 1}, { 1, 0, 0, 20, 13, 0, 9106.6767578125, 8965.2431640625, 0, 0, 0}, { 1, 0, 0, 20, 12, 0, 989.267822265625, 165.207702636719, 0, 0, 1}, { 1, 0, 0, 23, 12, 0, 6614.12158203125, 7841.97216796875, 0, 0, 1}, {1, 0, 0, 23, 12, 0, 0, 0, 1, 1, 1}, {1, 0, 0, 23, 12, 0, 0, 0, 1, 1, 1}, { 1, 0, 0, 23, 12, 0, 6269.3408203125, 3039.9599609375, 0, 0, 1}, {1, 0, 0, 20, 12, 0, 0, 0, 1, 1, 0}, {1, 0, 0, 18, 12, 0, 0, 0, 1, 1, 1}, { 1, 0, 0, 18, 12, 0, 0, 1405.51196289062, 1, 0, 0}, { 1, 0, 0, 18, 12, 0, 700.833740234375, 642.664489746094, 0, 0, 0}, { 1, 0, 0, 22, 12, 0, 2590.6962890625, 2158.05004882812, 0, 0, 0}, {1, 0, 0, 22, 12, 0, 0, 0, 1, 1, 1}, { 1, 0, 0, 22, 12, 0, 709.671447753906, 532.253601074219, 0, 0, 0}, { 1, 0, 0, 19, 12, 0, 3042.10375976562, 2534.07250976562, 0, 0, 1}, {1, 0, 0, 21, 12, 0, 0, 0, 1, 1, 1}, {1, 0, 0, 21, 12, 0, 0, 0, 1, 1, 1}, { 1, 0, 0, 21, 12, 0, 449.047943115234, 449.047943115234, 0, 0, 0}, { 1, 0, 0, 20, 12, 0, 6576.2861328125, 4932.21484375, 0, 0, 0}, { 1, 0, 0, 20, 12, 0, 1265.39416503906, 1160.36645507812, 0, 0, 1}, { 1, 0, 0, 20, 12, 0, 8113.89501953125, 6085.42138671875, 0, 0, 1}, { 1, 0, 0, 20, 12, 0, 0, 591.815124511719, 1, 0, 0}, { 1, 0, 0, 20, 12, 0, 9227.55859375, 9227.55859375, 0, 0, 1}, { 1, 0, 0, 20, 12, 0, 0, 377.568603515625, 1, 0, 1}, { 1, 0, 0, 19, 12, 0, 8417, 2814.19506835938, 0, 0, 0}, { 0, 1, 1, 19, 11, 1, 5424.48486328125, 5463.80322265625, 0, 0, 1}, {1, 0, 0, 19, 12, 0, 0, 0, 1, 1, 0}, {1, 0, 1, 40, 11, 0, 0, 0, 1, 1, 1}, { 0, 1, 1, 18, 10, 1, 2308.07373046875, 1922.62548828125, 0, 0, 0}, { 0, 1, 0, 27, 12, 0, 2920.14233398438, 2677.7705078125, 0, 0, 0}, { 0, 1, 1, 20, 6, 1, 10802.982421875, 10802.982421875, 0, 0, 1}, { 1, 0, 1, 36, 7, 0, 1752.08532714844, 1606.66223144531, 0, 0, 0}, {1, 0, 1, 24, 7, 0, 0, 0, 1, 1, 0}, { 1, 0, 1, 21, 8, 0, 879.26611328125, 2237.20849609375, 0, 0, 0}, {1, 0, 1, 35, 11, 0, 0, 0, 1, 1, 0}, {1, 0, 1, 39, 9, 0, 0, 0, 1, 1, 0}, { 0, 1, 0, 23, 12, 0, 5721.69580078125, 8960.68359375, 0, 0, 0}, { 0, 1, 0, 18, 12, 0, 584.027526855469, 535.553283691406, 0, 0, 1}, { 1, 0, 1, 29, 9, 0, 9268.943359375, 9160.693359375, 0, 0, 0}, {1, 0, 1, 36, 10, 0, 0, 0, 1, 1, 0}, {1, 0, 1, 33, 8, 0, 0, 0, 1, 1, 1}, {1, 0, 1, 32, 11, 0, 0, 0, 1, 1, 0}, {1, 0, 1, 39, 6, 0, 0, 0, 1, 1, 0}, {1, 0, 1, 38, 10, 0, 0, 0, 1, 1, 0}, {1, 0, 1, 38, 8, 0, 0, 0, 1, 1, 0}, { 1, 0, 1, 37, 10, 0, 8606.7431640625, 8606.7431640625, 0, 0, 0}, { 1, 0, 1, 20, 10, 0, 690.192016601562, 690.192016601562, 0, 0, 0}, {1, 0, 1, 36, 10, 0, 0, 0, 1, 1, 0}, {1, 0, 1, 27, 11, 0, 0, 0, 1, 1, 1}, {1, 0, 1, 35, 10, 0, 0, 0, 1, 1, 1}, { 1, 0, 1, 35, 10, 0, 9902.7626953125, 9902.7626953125, 0, 0, 0}, {1, 0, 1, 35, 10, 0, 0, 0, 1, 1, 0}, {1, 0, 1, 25, 10, 0, 0, 0, 1, 1, 0}, {1, 0, 1, 24, 11, 0, 0, 0, 1, 1, 0}, {1, 0, 1, 30, 11, 0, 0, 0, 1, 1, 1}, {0, 1, 1, 20, 9, 0, 0, 0, 1, 1, 0}, { 1, 0, 1, 23, 11, 0, 37849.0703125, 29897.189453125, 0, 0, 0}, {1, 0, 1, 34, 11, 0, 0, 0, 1, 1, 0}, { 1, 0, 1, 34, 11, 0, 8912.484375, 8912.484375, 0, 0, 0}, { 1, 0, 1, 23, 10, 0, 17131.466796875, 15709.5556640625, 0, 0, 1}, { 1, 0, 1, 25, 8, 0, 37431.66015625, 37431.66015625, 0, 0, 1}, {1, 0, 1, 34, 10, 0, 0, 0, 1, 1, 0}, { 1, 0, 1, 30, 8, 0, 0, 1706.65698242188, 1, 0, 0}, {0, 1, 1, 21, 7, 0, 0, 0, 1, 1, 0}, { 1, 0, 1, 34, 9, 0, 5244.193359375, 4368.4130859375, 0, 0, 0}, { 1, 0, 1, 20, 9, 0, 6083.994140625, 0, 0, 1, 1}, {1, 0, 1, 29, 11, 0, 0, 0, 1, 1, 0}, {1, 0, 1, 33, 11, 0, 0, 0, 1, 1, 0}, { 1, 0, 1, 34, 7, 0, 1143.359375, 857.51953125, 0, 0, 1}, {1, 0, 1, 29, 8, 0, 0, 0, 1, 1, 1}, { 1, 0, 1, 28, 10, 0, 10979.7138671875, 10068.3974609375, 0, 0, 0}, { 0, 1, 1, 29, 9, 0, 9594.3076171875, 16341.16015625, 0, 0, 0}, {1, 0, 1, 32, 11, 0, 0, 0, 1, 1, 1}, { 1, 0, 1, 27, 11, 0, 17075.033203125, 14223.501953125, 0, 0, 1}, { 1, 0, 1, 32, 10, 0, 2786.95874023438, 2321.53662109375, 0, 0, 1}, {1, 0, 1, 32, 10, 0, 0, 0, 1, 1, 0}, { 1, 0, 1, 26, 11, 0, 4699.962890625, 1174.99096679688, 0, 0, 0}, { 1, 0, 1, 27, 11, 0, 3065.19409179688, 766.298583984375, 0, 0, 0}, { 1, 0, 1, 27, 10, 0, 1001.14599609375, 3550.07495117188, 0, 0, 1}, { 0, 1, 1, 34, 11, 0, 5061.57861328125, 4641.46728515625, 0, 0, 1}, {1, 0, 1, 26, 10, 0, 0, 0, 1, 1, 0}, {1, 0, 1, 32, 8, 0, 0, 0, 1, 1, 0}, { 1, 0, 1, 31, 11, 0, 17711.880859375, 1726.44494628906, 0, 0, 0}, { 1, 0, 1, 31, 11, 0, 7662.1689453125, 6382.5869140625, 0, 0, 0}, { 0, 1, 1, 21, 11, 0, 21589.083984375, 17983.70703125, 0, 0, 1}, {1, 0, 1, 25, 11, 0, 0, 0, 1, 1, 1}, {1, 0, 1, 31, 10, 0, 0, 0, 1, 1, 0}, {1, 0, 1, 31, 10, 0, 0, 0, 1, 1, 0}, { 1, 0, 1, 31, 10, 0, 0, 520.4462890625, 1, 0, 0}, {1, 0, 1, 17, 10, 0, 0, 0, 1, 1, 1}, { 1, 0, 1, 30, 11, 0, 1913.03369140625, 1913.03369140625, 0, 0, 1}, {1, 0, 1, 19, 11, 0, 0, 0, 1, 1, 1}, { 1, 0, 1, 31, 8, 0, 5495.41455078125, 4577.68017578125, 0, 0, 0}, {1, 0, 1, 20, 11, 0, 0, 0, 1, 1, 1}, { 0, 1, 1, 20, 8, 0, 1350.30126953125, 1124.80102539062, 0, 0, 0}, {1, 0, 1, 23, 10, 0, 0, 0, 1, 1, 1}, { 1, 0, 1, 24, 9, 0, 2788.498046875, 0, 0, 1, 0}, { 1, 0, 1, 18, 10, 0, 0, 798.907897949219, 1, 0, 1}, { 1, 0, 1, 18, 10, 0, 1557.40856933594, 1428.14367675781, 0, 0, 0}, { 1, 0, 1, 30, 10, 0, 3140.23608398438, 2615.81665039062, 0, 0, 1}, { 1, 0, 1, 22, 10, 0, 16486.171875, 13732.98046875, 0, 0, 0}, { 1, 0, 1, 25, 11, 0, 2622.09716796875, 2455.0673828125, 0, 0, 0}, { 1, 0, 1, 20, 10, 0, 6123.45654296875, 5100.8388671875, 0, 0, 1}, {1, 0, 1, 20, 10, 0, 0, 0, 1, 1, 0}, {1, 0, 1, 25, 11, 0, 0, 0, 1, 1, 0}, {1, 0, 1, 25, 11, 0, 0, 0, 1, 1, 1}, { 1, 0, 1, 20, 9, 0, 3311.802734375, 2483.85205078125, 0, 0, 0}, { 1, 0, 1, 29, 11, 0, 3550.888671875, 3256.16479492188, 0, 0, 0}, {1, 0, 1, 24, 11, 0, 0, 0, 1, 1, 0}, { 1, 0, 1, 20, 7, 0, 1884.1416015625, 1569.48999023438, 0, 0, 1}, { 1, 0, 1, 29, 9, 0, 0, 1203.82397460938, 1, 0, 0}, { 1, 0, 1, 24, 9, 0, 9154.7001953125, 2288.67504882812, 0, 0, 1}, { 1, 0, 1, 28, 11, 0, 2431.94897460938, 863.4794921875, 0, 0, 0}, {1, 0, 1, 28, 11, 0, 0, 0, 1, 1, 0}, {1, 0, 1, 29, 8, 0, 0, 0, 1, 1, 0}, { 0, 1, 1, 27, 9, 0, 2129.015625, 1596.76184082031, 0, 0, 0}, { 1, 0, 1, 28, 10, 0, 1471.29296875, 367.823394775391, 0, 0, 0}, { 1, 0, 1, 28, 10, 0, 6567.3291015625, 6567.3291015625, 0, 0, 0}, { 1, 0, 1, 28, 10, 0, 5377.65576171875, 4479.5869140625, 0, 0, 0}, { 1, 0, 1, 23, 11, 0, 6382.30908203125, 580.790100097656, 0, 0, 0}, {0, 1, 1, 33, 5, 0, 0, 0, 1, 1, 0}, {1, 0, 1, 23, 10, 0, 0, 0, 1, 1, 0}, { 1, 0, 1, 27, 11, 0, 2206.93994140625, 2666.27392578125, 0, 0, 1}, {1, 0, 1, 27, 11, 0, 0, 0, 1, 1, 1}, {1, 0, 1, 27, 11, 0, 0, 0, 1, 1, 1}, {1, 0, 1, 28, 9, 0, 0, 0, 1, 1, 1}, {1, 0, 1, 28, 9, 0, 0, 0, 1, 1, 0}, {1, 0, 1, 27, 10, 0, 0, 0, 1, 1, 0}, {1, 0, 1, 27, 10, 0, 0, 0, 1, 1, 0}, { 1, 0, 1, 28, 8, 0, 1577.04797363281, 1182.78601074219, 0, 0, 1}, { 1, 0, 1, 28, 8, 0, 11870.0908203125, 10610.080078125, 0, 0, 0}, {1, 0, 1, 28, 8, 0, 0, 0, 1, 1, 1}, {1, 0, 1, 26, 11, 0, 0, 0, 1, 1, 1}, {1, 0, 1, 26, 11, 0, 0, 0, 1, 1, 0}, {0, 1, 1, 31, 9, 0, 0, 0, 1, 1, 1}, { 1, 0, 1, 27, 9, 0, 2838.68774414062, 2129.01586914062, 0, 0, 0}, { 1, 0, 1, 27, 9, 0, 3059.47290039062, 2294.6044921875, 0, 0, 1}, {1, 0, 1, 27, 9, 0, 0, 0, 1, 1, 1}, {1, 0, 1, 26, 10, 0, 0, 0, 1, 1, 1}, { 1, 0, 1, 26, 10, 0, 25929.6796875, 6788.9580078125, 0, 0, 1}, {1, 0, 1, 26, 10, 0, 0, 0, 1, 1, 0}, {1, 0, 1, 27, 8, 0, 0, 0, 1, 1, 1}, {1, 0, 1, 27, 8, 0, 0, 0, 1, 1, 0}, {0, 1, 1, 20, 11, 0, 0, 0, 1, 1, 0}, {1, 0, 1, 25, 11, 0, 0, 0, 1, 1, 0}, { 1, 0, 1, 25, 11, 0, 3649.7685546875, 3649.7685546875, 0, 0, 0}, { 1, 0, 1, 25, 11, 0, 15209.990234375, 3072.72607421875, 0, 0, 0}, { 1, 0, 1, 25, 11, 0, 5281.2451171875, 0, 0, 1, 0}, { 1, 0, 1, 25, 11, 0, 4672.2236328125, 4284.42919921875, 0, 0, 0}, {1, 0, 1, 25, 11, 0, 0, 0, 1, 1, 0}, { 0, 1, 1, 17, 9, 0, 731.981567382812, 671.22705078125, 0, 0, 0}, { 1, 0, 1, 26, 9, 0, 7584.57373046875, 6955.05419921875, 0, 0, 0}, { 1, 0, 1, 20, 11, 0, 1931.24499511719, 3248.93701171875, 0, 0, 0}, {1, 0, 1, 25, 10, 0, 0, 0, 1, 1, 0}, {1, 0, 1, 25, 10, 0, 0, 0, 1, 1, 0}, {1, 0, 1, 25, 10, 0, 0, 0, 1, 1, 0}, {1, 0, 1, 25, 10, 0, 0, 0, 1, 1, 1}, {1, 0, 1, 24, 11, 0, 0, 0, 1, 1, 1}, { 1, 0, 1, 24, 11, 0, 13627.26953125, 13370.5751953125, 0, 0, 0}, {1, 0, 1, 24, 11, 0, 0, 0, 1, 1, 1}, {1, 0, 1, 17, 11, 0, 0, 0, 1, 1, 0}, { 1, 0, 1, 17, 11, 0, 4080.72998046875, 3796.02905273438, 0, 0, 0}, { 1, 0, 1, 17, 11, 0, 989.267822265625, 165.207702636719, 0, 0, 0}, { 1, 0, 1, 17, 11, 0, 1261.50122070312, 1156.79663085938, 0, 0, 0}, { 1, 0, 1, 23, 11, 0, 11946.119140625, 8959.5888671875, 0, 0, 1}, {1, 0, 1, 23, 11, 0, 0, 0, 1, 1, 1}, {1, 0, 1, 23, 11, 0, 0, 0, 1, 1, 0}, { 1, 0, 1, 23, 11, 0, 2355.17846679688, 1961.86364746094, 0, 0, 1}, {1, 0, 1, 23, 11, 0, 0, 0, 1, 1, 0}, { 1, 0, 1, 23, 11, 0, 0, 1896.02294921875, 1, 0, 0}, {1, 0, 1, 23, 11, 0, 0, 0, 1, 1, 0}, {1, 0, 1, 24, 10, 0, 0, 0, 1, 1, 0}, { 1, 0, 1, 24, 10, 0, 1570.11804199219, 1307.90832519531, 0, 0, 0}, {1, 0, 1, 24, 10, 0, 0, 0, 1, 1, 1}, {1, 0, 1, 24, 10, 0, 0, 0, 1, 1, 0}, {1, 0, 1, 24, 10, 0, 0, 0, 1, 1, 1}, {1, 0, 1, 24, 10, 0, 0, 0, 1, 1, 0}, { 1, 0, 1, 18, 11, 0, 1064.69897460938, 645.272277832031, 0, 0, 0}, { 1, 0, 1, 18, 11, 0, 1170.3271484375, 1170.3271484375, 0, 0, 0}, {1, 0, 1, 18, 11, 0, 0, 0, 1, 1, 0}, { 1, 0, 1, 18, 11, 0, 1135.47473144531, 851.606079101562, 0, 0, 1}, { 1, 0, 1, 18, 11, 0, 858.254272460938, 214.563598632812, 0, 0, 1}, {1, 0, 1, 18, 11, 0, 0, 0, 1, 1, 1}, { 1, 0, 1, 18, 11, 0, 2499.64111328125, 2292.1708984375, 0, 0, 0}, {0, 1, 1, 18, 8, 0, 0, 0, 1, 1, 0}, {1, 0, 1, 17, 10, 0, 0, 0, 1, 1, 0}, {1, 0, 1, 17, 10, 0, 0, 0, 1, 1, 1}, {1, 0, 1, 17, 10, 0, 0, 0, 1, 1, 0}, {1, 0, 1, 17, 10, 0, 0, 0, 1, 1, 0}, { 1, 0, 1, 17, 10, 0, 188.414260864258, 156.949081420898, 0, 0, 1}, {1, 0, 1, 17, 10, 0, 0, 0, 1, 1, 1}, {1, 0, 1, 17, 10, 0, 0, 0, 1, 1, 0}, {1, 0, 1, 17, 10, 0, 0, 0, 1, 1, 1}, { 1, 0, 1, 17, 10, 0, 2072.5556640625, 1726.43884277344, 0, 0, 0}, { 1, 0, 1, 17, 10, 0, 1201.140625, 1000.55010986328, 0, 0, 1}, { 1, 0, 1, 17, 10, 0, 934.445373535156, 856.886413574219, 0, 0, 1}, { 1, 0, 1, 17, 10, 0, 0, 664.568969726562, 1, 0, 0}, { 1, 0, 1, 22, 11, 0, 7914.130859375, 1321.66003417969, 0, 0, 0}, {1, 0, 1, 22, 11, 0, 0, 0, 1, 1, 1}, { 1, 0, 1, 22, 11, 0, 8810.0673828125, 6974.48388671875, 0, 0, 0}, { 1, 0, 1, 22, 11, 0, 8125.35107421875, 8774.791015625, 0, 0, 1}, { 1, 0, 1, 22, 11, 0, 661.8984375, 606.960876464844, 0, 0, 1}, {1, 0, 1, 22, 11, 0, 0, 0, 1, 1, 0}, { 1, 0, 1, 22, 11, 0, 3250.14624023438, 2707.37182617188, 0, 0, 0}, {1, 0, 1, 19, 11, 0, 0, 0, 1, 1, 0}, {1, 0, 1, 19, 11, 0, 0, 0, 1, 1, 0}, { 1, 0, 1, 19, 11, 0, 721.340576171875, 2445.58911132812, 0, 0, 0}, {1, 0, 1, 19, 11, 0, 0, 0, 1, 1, 0}, { 1, 0, 1, 19, 11, 0, 2102.50244140625, 1927.99475097656, 0, 0, 1}, { 1, 0, 1, 19, 11, 0, 1626.623046875, 0, 0, 1, 0}, { 1, 0, 1, 19, 11, 0, 11827.8388671875, 8870.87890625, 0, 0, 0}, { 1, 0, 1, 19, 11, 0, 705.196350097656, 705.196350097656, 0, 0, 0}, { 1, 0, 1, 19, 11, 0, 709.671447753906, 532.253601074219, 0, 0, 1}, { 1, 0, 1, 19, 11, 0, 210.250274658203, 192.799499511719, 0, 0, 1}, { 1, 0, 1, 19, 11, 0, 6971.326171875, 6409.02685546875, 0, 0, 1}, { 1, 0, 1, 21, 11, 0, 2878.11303710938, 2158.5849609375, 0, 0, 1}, { 1, 0, 1, 21, 11, 0, 6374.677734375, 5310.1064453125, 0, 0, 1}, { 1, 0, 1, 21, 11, 0, 2483.85180664062, 1862.88879394531, 0, 0, 0}, {1, 0, 1, 21, 11, 0, 0, 0, 1, 1, 0}, { 1, 0, 1, 20, 11, 0, 7008.33837890625, 6426.646484375, 0, 0, 1}, { 1, 0, 1, 20, 11, 0, 16318.6201171875, 1484.99401855469, 0, 0, 1}, { 1, 0, 1, 20, 11, 0, 8009.1640625, 7666.875, 0, 0, 0}, { 1, 0, 1, 20, 11, 0, 759.23583984375, 696.21923828125, 0, 0, 1}, { 1, 0, 1, 20, 11, 0, 3784.50219726562, 3470.388671875, 0, 0, 0}, {1, 0, 1, 23, 10, 0, 0, 0, 1, 1, 0}, {1, 0, 1, 23, 10, 0, 0, 0, 1, 1, 0}, {1, 0, 1, 23, 10, 0, 0, 0, 1, 1, 0}, { 1, 0, 1, 23, 10, 0, 2956.96557617188, 2217.72436523438, 0, 0, 1}, {1, 0, 1, 25, 8, 0, 0, 0, 1, 1, 0}, {1, 0, 1, 25, 8, 0, 0, 0, 1, 1, 1}, { 1, 0, 1, 19, 10, 0, 0, 604.153869628906, 1, 0, 0}, {1, 0, 1, 20, 8, 0, 0, 0, 1, 1, 0}, { 1, 0, 1, 24, 9, 0, 1275.35620117188, 1275.35620117188, 0, 0, 0}, { 1, 0, 1, 24, 9, 0, 9662.669921875, 9662.669921875, 0, 0, 0}, {1, 0, 1, 18, 10, 0, 0, 0, 1, 1, 0}, {1, 0, 1, 18, 10, 0, 0, 0, 1, 1, 0}, {1, 0, 1, 18, 10, 0, 0, 0, 1, 1, 1}, { 1, 0, 1, 18, 10, 0, 763.12939453125, 699.789611816406, 0, 0, 1}, { 1, 0, 1, 18, 10, 0, 960.426818847656, 240.106704711914, 0, 0, 0}, { 1, 0, 1, 18, 10, 0, 1495.11157226562, 1371.01733398438, 0, 0, 1}, { 1, 0, 1, 18, 10, 0, 1791.01989746094, 1642.365234375, 0, 0, 0}, { 1, 0, 1, 18, 10, 0, 1563.2490234375, 0, 0, 1, 0}, { 1, 0, 1, 18, 10, 0, 2491.85400390625, 2285.03002929688, 0, 0, 0}, {1, 0, 1, 18, 10, 0, 0, 0, 1, 1, 0}, { 1, 0, 1, 18, 11, 0, 4985.125, 4152.609375, 0, 0, 0}, { 1, 0, 1, 17, 9, 0, 0, 2595.26708984375, 1, 0, 0}, { 1, 0, 1, 17, 9, 0, 827.950256347656, 620.962707519531, 0, 0, 0}, {1, 0, 1, 17, 9, 0, 0, 0, 1, 1, 0}, { 1, 0, 1, 17, 9, 0, 1608.58862304688, 1206.44140625, 0, 0, 0}, { 1, 0, 1, 17, 9, 0, 2001.90026855469, 1667.5830078125, 0, 0, 1}, {1, 0, 1, 17, 9, 0, 0, 0, 1, 1, 0}, {1, 0, 1, 17, 9, 0, 0, 0, 1, 1, 1}, { 1, 0, 1, 17, 9, 0, 1466.65246582031, 1341.65112304688, 0, 0, 1}, { 1, 0, 1, 17, 11, 0, 9266.560546875, 8497.435546875, 0, 0, 1}, { 1, 0, 1, 22, 10, 0, 1168.05639648438, 1071.10766601562, 0, 0, 1}, { 1, 0, 1, 22, 10, 0, 2119.65893554688, 1765.67602539062, 0, 0, 0}, {1, 0, 1, 22, 10, 0, 0, 0, 1, 1, 0}, { 1, 0, 1, 22, 10, 0, 785.059020996094, 653.954162597656, 0, 0, 1}, {1, 0, 1, 22, 10, 0, 0, 0, 1, 1, 0}, {1, 0, 1, 26, 6, 0, 0, 0, 1, 1, 0}, { 1, 0, 1, 19, 10, 0, 1499.00476074219, 1374.58740234375, 0, 0, 1}, { 1, 0, 1, 19, 10, 0, 1051.25085449219, 963.9970703125, 0, 0, 0}, { 1, 0, 1, 19, 10, 0, 0, 385.274108886719, 1, 0, 1}, {1, 0, 1, 19, 10, 0, 0, 0, 1, 1, 0}, { 1, 0, 1, 19, 10, 0, 1557.40856933594, 1428.14367675781, 0, 0, 0}, {1, 0, 1, 19, 10, 0, 0, 0, 1, 1, 1}, { 1, 0, 1, 19, 10, 0, 3878.19287109375, 3473.8076171875, 0, 0, 1}, {1, 0, 1, 19, 10, 0, 0, 0, 1, 1, 0}, {1, 0, 1, 18, 11, 0, 0, 0, 1, 1, 0}, { 1, 0, 1, 18, 10, 0, 1716.50903320312, 2682.125, 0, 0, 0}, { 1, 0, 1, 21, 10, 0, 1230.09729003906, 922.572937011719, 0, 0, 0}, { 1, 0, 1, 20, 10, 0, 3745.48901367188, 3677.58837890625, 0, 0, 0}, { 1, 0, 1, 20, 10, 0, 5005.73095703125, 2777.35498046875, 0, 0, 1}, { 1, 0, 1, 23, 9, 0, 2365.5732421875, 1774.18005371094, 0, 0, 1}, {0, 1, 1, 23, 8, 0, 0, 0, 1, 1, 1}, { 1, 0, 1, 18, 9, 0, 2198.16430664062, 2006.62866210938, 0, 0, 0}, {1, 0, 1, 18, 9, 0, 0, 0, 1, 1, 0}, { 1, 0, 1, 18, 9, 0, 1401.66809082031, 1285.32958984375, 0, 0, 1}, {1, 0, 1, 18, 9, 0, 0, 0, 1, 1, 0}, {1, 0, 1, 18, 9, 0, 0, 0, 1, 1, 0}, {1, 0, 1, 18, 9, 0, 0, 0, 1, 1, 0}, { 1, 0, 1, 18, 9, 0, 1605.4482421875, 1605.4482421875, 0, 0, 0}, { 1, 0, 1, 18, 9, 0, 1577.04797363281, 1182.78601074219, 0, 0, 1}, { 1, 0, 1, 18, 9, 0, 588.794311523438, 490.465637207031, 0, 0, 0}, { 1, 0, 1, 18, 9, 0, 1541.8349609375, 1413.86267089844, 0, 0, 0}, {1, 0, 1, 18, 9, 0, 0, 0, 1, 1, 0}, {1, 0, 1, 18, 9, 0, 0, 3287.375, 1, 0, 1}, { 1, 0, 1, 17, 8, 0, 1962.33557128906, 2036.79040527344, 0, 0, 0}, {1, 0, 1, 17, 8, 0, 0, 0, 1, 1, 0}, {1, 0, 1, 17, 8, 0, 0, 0, 1, 1, 0}, { 1, 0, 1, 17, 8, 0, 1025.08032226562, 768.810180664062, 0, 0, 0}, {1, 0, 1, 17, 8, 0, 0, 0, 1, 1, 1}, { 1, 0, 1, 17, 8, 0, 1284.86254882812, 1178.21899414062, 0, 0, 0}, {1, 0, 1, 22, 9, 0, 0, 0, 1, 1, 0}, {1, 0, 1, 22, 9, 0, 0, 0, 1, 1, 0}, {1, 0, 1, 22, 9, 0, 0, 0, 1, 1, 1}, {1, 0, 1, 26, 5, 0, 0, 0, 1, 1, 0}, { 1, 0, 1, 19, 9, 0, 0, 798.907897949219, 1, 0, 1}, { 1, 0, 1, 19, 9, 0, 1083.38122558594, 902.45654296875, 0, 0, 0}, {1, 0, 1, 19, 9, 0, 0, 0, 1, 1, 1}, {1, 0, 1, 19, 9, 0, 0, 0, 1, 1, 0}, { 1, 0, 1, 19, 9, 0, 2881.20678710938, 2642.06665039062, 0, 0, 0}, { 1, 0, 1, 19, 9, 0, 1537.92932128906, 1537.92932128906, 0, 0, 0}, {1, 0, 1, 18, 9, 0, 0, 0, 1, 1, 1}, {1, 0, 1, 18, 9, 0, 0, 0, 1, 1, 0}, {1, 0, 1, 18, 9, 0, 0, 0, 1, 1, 0}, {1, 0, 1, 21, 9, 0, 0, 0, 1, 1, 1}, { 1, 0, 1, 21, 9, 0, 591.499084472656, 0, 0, 1, 0}, { 1, 0, 1, 21, 9, 0, 6416.47021484375, 5749.3310546875, 0, 0, 1}, { 1, 0, 1, 20, 9, 0, 720.300842285156, 660.515869140625, 0, 0, 1}, {1, 0, 1, 20, 9, 0, 0, 0, 1, 1, 0}, {1, 0, 1, 20, 9, 0, 0, 0, 1, 1, 0}, {1, 0, 1, 23, 8, 0, 0, 0, 1, 1, 0}, { 1, 0, 1, 23, 8, 0, 7724.283203125, 3403.05590820312, 0, 0, 0}, {1, 0, 1, 24, 7, 0, 0, 0, 1, 1, 0}, { 1, 0, 1, 18, 8, 0, 1308.2236328125, 1199.64099121094, 0, 0, 1}, {1, 0, 1, 18, 8, 0, 0, 0, 1, 1, 1}, {1, 0, 1, 18, 8, 0, 0, 0, 1, 1, 0}, {1, 0, 1, 17, 7, 0, 0, 0, 1, 1, 1}, {1, 0, 1, 22, 8, 0, 0, 0, 1, 1, 0}, { 1, 0, 1, 22, 8, 0, 6891.69384765625, 6150.5205078125, 0, 0, 0}, { 1, 0, 1, 19, 8, 0, 0, 2657.05688476562, 1, 0, 1}, { 1, 0, 1, 21, 8, 0, 39570.6796875, 6608.30419921875, 0, 0, 0}, {1, 0, 1, 20, 8, 0, 0, 0, 1, 1, 0}, {1, 0, 1, 25, 5, 0, 0, 0, 1, 1, 1}, {1, 0, 1, 25, 5, 0, 0, 0, 1, 1, 1}, { 1, 0, 1, 21, 7, 0, 33799.94921875, 0, 0, 1, 0}, {1, 0, 1, 18, 6, 0, 0, 0, 1, 1, 1}, { 0, 1, 1, 27, 10, 0, 459.435577392578, 421.302429199219, 0, 0, 1}, {0, 1, 1, 27, 10, 0, 0, 0, 1, 1, 1}, { 0, 1, 1, 26, 11, 0, 1789.935546875, 1491.01623535156, 0, 0, 0}, { 1, 0, 1, 20, 6, 0, 6006.87890625, 2850.61010742188, 0, 0, 0}, { 0, 1, 1, 20, 9, 0, 12260.7802734375, 5875.048828125, 0, 0, 1}, {0, 1, 1, 20, 7, 0, 0, 0, 1, 1, 0}, {0, 1, 1, 25, 9, 0, 0, 0, 1, 1, 0}, {0, 1, 1, 17, 10, 0, 0, 0, 1, 1, 0}, { 0, 1, 1, 21, 11, 0, 1395.39001464844, 1395.39001464844, 0, 0, 1}, {0, 1, 1, 18, 10, 0, 0, 0, 1, 1, 0}, { 0, 1, 1, 18, 10, 0, 2589.0419921875, 2589.0419921875, 0, 0, 1}, { 0, 1, 1, 17, 9, 0, 445.17041015625, 74.3434524536133, 0, 0, 1}, { 0, 1, 1, 19, 10, 0, 1197.46154785156, 1197.46154785156, 0, 0, 0}, { 0, 1, 1, 25, 7, 0, 1413.10668945312, 1177.11791992188, 0, 0, 0}, { 0, 1, 1, 18, 9, 0, 1646.51013183594, 1646.51013183594, 0, 0, 0}, { 0, 1, 1, 18, 9, 0, 1868.89111328125, 1713.77319335938, 0, 0, 1}, { 0, 1, 1, 18, 9, 0, 2927.927734375, 2684.90991210938, 0, 0, 0}, {0, 1, 1, 22, 9, 0, 0, 0, 1, 1, 1}, { 0, 1, 1, 19, 9, 0, 6511.1240234375, 6511.1240234375, 0, 0, 1}, {0, 1, 1, 19, 9, 0, 0, 0, 1, 1, 0}, { 0, 1, 1, 20, 9, 0, 8740.939453125, 8015.44189453125, 0, 0, 1}, { 0, 1, 1, 18, 8, 0, 3311.802734375, 2483.85205078125, 0, 0, 0}, {0, 1, 1, 22, 8, 0, 0, 0, 1, 1, 0}, { 0, 1, 1, 19, 8, 0, 2483.85180664062, 1862.88879394531, 0, 0, 1}, {0, 1, 1, 24, 4, 0, 0, 0, 1, 1, 0}}, CompressedData[" 1:eJztXQmYFMX1H9DIoRESQOQQhlGJCoioqKBICR6QEA2IoJJPRkEgHCpy/uWw XQ4XFLmPBYFh5RIQVO7T3nURFQi4ELzdhoB/A4EoRgUFNluv5u1Od1dNdVV3 oya+72Pqq52pXz+q63jv1Xuv6jzyeLtHS0cikfyij/MjSF82T1sahLD6FSSi RBLcyPVc3MoHTJJaitsLnmO05OEa7dvtofXopbvXS3AFzzFaJdtFU3HjbU7m 0nri+IBNeriRpjxcMrjeAVo3Fpc7IMFFctaxHy5LxY1m7tlB6+Tkmh2K/GJ5 S7Id9q/tucaW6mNSn+eBXyxvduCWUNFwtWqNeFYPlz9+zVM7YTyYyx7aSrjt pP17Z7JdLBXX6nJ8G+DOuV6GK3oO9q/tvRnvkdW0Hm/07jpFXCybJ9u539u5 Rfy2Pjw89XkKuDc5cIHimVkf07q5Yv37mvw24/FL7l4C/Usm7dumiIuE607d VH6NXh1g/prPFLyhiXtDsp3tvVlNa30IuGeG7ZPgIjnXnduS7WKpuJEbJ75L 61bbc7br4Ubu4OJSKtobrAqHFjn+7rU/GifbXZXKVzQ+7E1aj/bZK1vXReWN yXa28WC0aL+K1s0uw5dr4l7Lw43MPzmapJbquDge7Pye6b6B1g1zhG4/OPdN +Ltl1nuVFL23+LzbFktwkWT8MurQLRf4rXZ7riJukorHr22+kbXrNtK61a9s jgRXVOJ+geOMPe3tdrnkTGHh3G2vjFXkF/eL25PtbP2bmPKPPFo3b27wpiIu 0q3JdrZ+QIqeaJjr+LtXfPd7K/r/1371TVjPzZXjNyrym34/btEe+iHSZfgW TVxcd+qTiBLJ+hf5te+bsX6wTkY6lX1Lwq+gLB4PyK/zubr8tk7i2eQ+a2O7 zbRuXXBmtYRfES7Kk7Z+iHx/ERu3q3bK9jcRbhMebqLCnW/TeqLhB7L+FeAW rw+2foiUy4f3lij3wns++Y3Z+P3/uh/Qenzx87s1+UV50jaPrQezQZ6Ojx/3 gSIulk652it5nRc2fuN3vwXyb3z3hD2K/CLdlWxnf2/6/GKJ66Tu+iDi/7ok Ho5f2/dWjR2rHM/z+t5w/Nr2oQD4xXmM7832PcnsOk+RXyTuPmQk1oKcbt4/ Viavi57XItnOrV98U7Qfr9nxsB6/BupZlR3tZSTrB5Qn7eskpVOFhc0zWo5z /N2r/MC1P3ggr/zGUnHNvKx8WjcG7ssnfD611gckcuC51xx/97p/OvtBdd8U 8Y/6pm1ekH4fzyeHi95b5lfTCL+9132T2w/GX96c6vi713nRLNnOLT9QOa1L s1GKuEju90bl/6Ykm+oB1mObEo7neZ1vf0i2K+viNz3JnuPUu79sTt9Xn8rT yEVF43hw10l6/EaQX+46qcCv8+8tkni28WtOW8707pN9NeWdNOuOGr9OXJQf bP1gjtq2AMZDfqmXJfyK+PZr/xXx78Rlf5/21Tu0nph+29se+XU+B/X5GHF+ T/XCvKHPkPR8Ke3H5qBOYNcxv+ylad9xvTcgo9/VYCeIT7lCVX9Lj1vzTbDP Gv+eoarHYon9YHtv8c8agx6f2Cm1r4tw0T5psxsl8sqAvmn8tU2eIi4S9kOM pPzRuP47+P+TJqdl9gcRv067J/t7xUfALpmo1+kdTVyUq2394IFk/YDzomTd oXb1wZmZhMpnj8yYo8gvUtp9M77x/lcdfw8E1/z9qAWKuFhy7XLxc2u+Bvaz dXfPJfx2sn0T7QSxVL7IF+vATmLWqq97PuS0K7Pvq97A7Ov3rZTNCxFuWn0o DcneG+rdbrmE8rtrpGwfcuI65aiSeVE0fuNn+oJ91rhi5TzCbyd7DtqrY7b2 e+5n9u+l82co8ovklFPZfKtRtP5Seadf2ZmKuM5+sNuVG5WH9cz614Urg8QF ovLDI81k8q9snKGdAL63jv6K2at3v7ZEkV8krn3HA8lwcR9C3JLv6TrZZyy+ N2d7Wf/iPhT0uo7jt2ScFfFZ+85nl9K6UXgXrr+q/Yu4JfsFldfP7zWH7he1 1x2cJcEVlU67st9+EOPSedZsJ+xrRv99sn4QlVx5R4FfUcmVd6xze68Ffh94 StX+65RLrk5tb5rnMbm6eQ+ZXCJ6DuotbnsfnRdZeX7nhU2/SExYxs5b+q+Q 9YOsf/n2qMe7zdfk1z0e6PoY6TMB1vXEORMl/IpwcT2zrw+DLgc9yBx4ue65 HvJbYk+l/LZcOJdgmR5XMN6L7eAxwv1eSDK+uf4EiUNMjiI1pXKU6Dm4ntns MPFbm4O+Et9ed60mrnM8ABkP94bzXXNv9hpNXL5+QalcEf49p7qkPs8vrlVm xOu0bmTOV/V/cI7foNd153lhULjuc39qH7ht6jMES347r/M4aHsJjl/7e1ve ej3IUe+VVZWjsOT6E0SGXwjzLL6hv+684OPKSR2X2lGn93kG+uFgn6mE306y nhXLqXZ9M6NlP5JaqveD02/FK2mtOz5wkXDf5OrdHnBFpdNfg62/dz+0ipws LOxcZvEwkr69gN9ivbtiSPza9ayvX4T5YPZdKtOzRCW+N7uda2Mbtg/VqqR5 7l987uS2g59b9O/Sf7Yn/Pay8ZDWbyUNycYvV+6zXj/4Ga1bd7b/hPDbyUru OqnAr4hvrv+Z+Vn7XFo3cgboyn1cPxtjyyvLCLUXvGP1l+CKSrH8kJ687m+2 83lSuuZ+qLfO1PSz4b839MtM7Ca6egAXNz5lCMg58ezPVO1y6fml742uZ+9K 3xuSsy6ex+lJ9t64fm0+cJPEt3tGnnkJ/JTJD9NV5V8krh90dNm1IFdbTzXS PWdAeTLgfiiexzY7V2LIecxf+65rdfUWrvyQeKbxLlonJ/vtVMRFwngG3Df9 rpNIzZJ4XLsy2fd7PG+R4Yn6t6ajvYxkuOhfEtR8Q3LHdVC7Q71LloP9bOQX KxzP87rPt3Dw65Vk/HLtntaIY2CHIe+P0/VHdPLL1t+l4wHPHD9edV4g4bkI jge2/h7oDOfzWKbBFb1n9MsM+lwE50VQcjUS7hcxwv9ehiuq8/0JPqnM/DET l+j2bwserrWxJYwHI+dfi1L/ngZPNB4Cnhcuf0/b98bhjegH7Wzvdb7Z1t/4 9mwWF9D7Mb/rQ4zHbxoKe/w661ii/6RdjppzFehXRp9juv3gjM8Kqh+c+jF8 T/ZezPxpP/pE11+Zixux+sJ4SNzQdrMEV/T/Q7tGLLW90aEqsyf/cYbmeawr ri6o/m2UxCtZf+m5U0GDKbQe/WierjzpPpcOhl/nusPOnSrcMgnsiFUffE6T Xzdu0Ue865dMbzFb+x0PNjnVuKFwAkkpg+I3OvA8dn4xeKssXk+Ey4078EBa uNFT3Zl/37rrcgmfT9m6g/pFyfildslN416i9XjfqO55LDeOygPJcJ1+/Ow8 608bF4Oc+uQd84m9nVd+3efS9CGrP82l9cT4Nn7lvhhxfk/PRUYtfJrYf6/a v0GPM9w3+frQ11fL/KtFuG75jK47l1wG593Rj9/f4HiePi5+A+Oi4hSf/Lr7 gY6zc65cqIdrhKW/Of2rw8WdVQD7MFnV/SXH8zzuxwI/hXEVcmnd2j5eNZ+C k9+S90bfV4c4+JdEFlRXlR+QcF7o2h9Edec+HxQunhfa9E0fuFhy7cqRi5vn 0rpZvptunCUX17o7YzWh62TDtk8TfjsZLlde90Cy/uXGdZhNSC6tmwOOqfrp psUFouvZiTdU1zMs8Vwk6H5w2o3Y91dmwflCNGuDrl+Q215N8z7smTgMzhlm NR9D+O1k/OI4C3q+cXETf87ZTOukR3u/ekCsuD0dB50bsLihc0r79fe07W9W t5ng52gdPJKtiev3PFZUx/UsrHUyfFw6fhvOz2T+tIc0/bb5/MZrXQHrDZYa uG49NuKJVPc3ryTDdfo/sH2+TNE+T8s/L9Xd59249HN4DpyLxCtOf43Y2+nj 0nlW5ZrZBMsA+SVtH4b4YNLiilXE3s4f7q9LZwPuuMd09Swurnk7YfE915zQ zVfhxqX+9vkTx0G8yMzmWYHh0nmcM3swwVIP16lvMr37WLVFMB6evFcWHyDC dcctFn1EL6oJdhirShvdfApc3Ei/SjAOyNy3VPWAtLiJxrWBTywD4/eDd9g4 wDIIXLreNCWTIJ5318jgcOlnYc9lsJ7dX2myJi7u87Hi9nScrRjC3tvyi1T9 zJFQnrTjZuUtAb+2vz2n6v+LhPJD0HYNZzwZ858s34jFM59upDt+3bip375/ cV/Cb6+OS/s1f0sC1rOL93uNZ3CSO86S4h6qs1TAp9c6yg8xwv9eROq4VP5f 1ncQwdLeTlXeKcGl87dcPsQFGDlTdedbWPbfsOxG/Hg9pH+cxrh8Z3uv+6YN l/yuFpyXmk207TD8OMCvq7F1LGexqv8vkjN+iO2bFX+1AeT1J69UjffHslmy nU3vjn7SjsVf9yKqeQ/S4sZPkK0gP8QefVERFwn3oZJ+oHLU+++MIVgGhUv5 fS4BeSexDASXrmfZ97HzgMl/Wkjs7Xzxa96xYBmtk38PWUDs7XzheiCv+0Us dFzar9esgn0ISns71fXXbjfyz29YuPw8ZQ+dWgv7UMOBqvEX6XELV8wl9t+p 7sf8c6ecOvNgfThx8hVNfrnn82R3bxPGQ6e3ZXKJCJcb12zMurSA1qMvxmT5 NUS4ON/suDXKgp3aaPK5rr2Ej1v9h1xaJyOP5kpwRc/h+tmQVrvBT9kac0Q3 r4Rf/xLR753nDEDm8c9hnze6W37tBLZ5bK6aMR/G2bPHddd1HGd2f4LLDr9A 62R7wTKf/PrNk+Mkt10O4kH/Ogn6YcRDunKqcx4HxS/iXkzS/06EK6qjvOPX H9FJYektqAeU+NPSuK9GK/qDn82cWobjeV7fG/aDbZyRnfdAPGS85c2q+RSQ nOcBQImsj9+C/eKPn8j82kTPQX0z4P2Y7+dIDo/LA35fWiRbH0T8uu1y9GnP r5kM9j5qT9TDdedjonLqb99g+/CA21+X4CKJ+tee33PK23B+Hj30G10/aH7e r/KnYf9JVGqum49JHOdeROYXR5FfUXst3DQk4z/o83ksufHHxtO5oBcbNw5Q 1S/S4irwK/q9+7zwfLC/MHs1LdPzK5Mf7P07ph8bB1iqj19unkhjyFg4l8ZS g1/uORkp1Qf0IHN6Y695feS4dH1YP3Ya2FNvqa5r7+Pya9S6ciXYEWe11Mxj aDjz8Qc932z8Jp6sxPLmF/SQrZNKuJFZHZj8332H6jkDltw8DR5I9t64+xDJ ymN+chNKy/zlvOPS8fWbh2H+klt6q+a3R8JxFlReSySufZ18vw/0wXjOYq95 Fz3hKvArqnPzugeA676vg763rsn8sUv26vrZ4DmOPY/LofVgR7Vyc1TjWJH4 fja/Pr2X1qP/GC3L4y16H37zwCnhGuPWgH5FNr2lm6+Nu7+RgZuWgR4wcNPz HnGd/w9uvH+8ZwvIsxe/dbXqPTZIfD+Q7/6eDfWmLYP195RT2Lgi/rnxAQHg 8v1Lym1gdnUs1d8bH9c7v6I6jrMYcbak+uyE6zTzjvv2RxQ9h3uObpSpOwPk nVE1ZfKOTH6w67G73md56K98QZZPV7buxIrb03W9wmmwmxnLh+nqAc2S7Urk EiqnP9QUzuehVMNF4tqrrTlrIV4muvYdv/JDLJXfeFarLIKlHr9+/c9Ez0F9 KCi7hvO9BZV3EUuuXhg9pwbka44n76XitPP63oLuB7Rr2PICKuDK1smYJq7o Ofz+fbQBu7/w+fxswm/ndfwGnX8d5ZKS93YR+K8zP6aXysjyUomeg+M36H0e x4NNzzKnLoFznMSY57zek+kk7Aebvdrs/BXLB7L8jK4/gTuujv98J8meg3K1 blyHrB+Cuh8HiRtXl6h1cDNJKTn8yXC59knrsiG5tG6OfVr1nAzLsPIQcfOJ RXdtAn6NZVfr5nXn4obF7/8wrj2P1srHIe9XpGwNi/DbaeEae7Yk4Jzh2s4D A8WdWfAyyH0rleP1kJz38wbVv6hnXRMwLv/eh3IzWZ7IZMlp55Vf+z7/0aHV VJ+v3es22fmQEq4H8iqX2OaF2SDC8hUnSw1+3bhUTr/47lcIlv5ww+qHX3DD weXeo6uAK/p90PFDafd5s1Y1sE8aIzao5kVJLz9YB95lfiuNVf1W0vavVa7X R7RurWqie48j2kvs+T07MD0+OjdDVy7h5tMFPxsaz7tgYVdNXG6ccLT2PpD/ rRPzAo0/9kA/KVzScRjoLcaRN1TzUqV9bwHwy7+3Okxcqsde8K3uPXhcXGPp 56PA3/NPVUYSfjutfd7o2gz82uZW+0TXHzws+cGNS+Wczg1Ggz94mWxVPyYs 0Q5TgkvXx8Z5i2CdvHyH7jmD33y6Pz4uzceaV3EowZLfTmsek9avMjvJjbfr 3rvjxqXjdvLWyWA3GlRHN06Cy2/00GssbrHX1uDzutPxO2p4BknfXgnX2Par XJJSBsovpWpZGO+kyq97vtFP4wkT9MIup2TnDKLxzs1DHx16DZwbJ3Z21D2X 5vMrJxEuEub/DdtPAci8vGw2rRtGedl9dSK+3fo8fV+tdoMcaYw9IvOvFvGL 4wztnozfYdUgDgdLBT6RWiTbldgnqT7YMT4L1vWF1XXPcVCuDiqfDRLXHzxS 5ynIg2f2fEozDttw5j8LiN/ivPkxTVwRPl+ufv0ByFtidDS8nus5cd1yFF13 byIZYC/ps8mvH3Qstb05uHourZv3H9SN62iRbBewX1Cxv3LQcUlp873q4xaP s6D9+IO2Izpxg/Yzb5HEs8eFdt7F/KuTZUR9nLnjAOk97mMmjiJY6uFy+fVA MtyQ8ssZzjyGoeImHu4IfutYRpT7Nyx+Bf6p/nHF+cT84TrvWQlKjuLG1Sng iupcf0QSG3KA1qNtrv3U8Tyv8y2kfV6Ae2sPtq9hqc6v89wUvrc+3bCb1qPb lv9VE5frHxX95s4DJKXUwA1rvuH4de8XcC/iEtn9kCI+UJ4MWk7Ffoil4hqN hjK/+L9U1M1LxY9jPfMm6tte+RPJZ0HvFyHJO8X9a39vT3zK4kSu2DHW8TxV OTVouY+bnzb+cV/Q26xrl8j2NxEfXLk6/rvNubRuTDm8S4Ir4jes98aVS6K5 y2A9I+a27T75tcc7Hd4KdhLzt5dmB4Yb8UQyXHd8YdGH8W2n1yF/ybedNON8 OP1L9fkux1l8d5fjz/rEtfdDvdbj4fy47qalxN7O6/jl7pvRKVvB3pkYVEc3 ztLp9xrUewtLL+TezxA/8Yf34Jxh20nV+9zT4pJ6xzbTOpl2bJ0EV7Zv2vm9 o8rfAPfoeN17KsLKr+zMU/bzxp3UEu0wzvYy3LDyhnL1CyN2cDZJKTl8ynDD ipPA/rXLvz0Hgf07uny/X/ksLD3Lngfj+mpT4Rzn+smq90MicfULBX5FdXce Q/pZ+DaeW3jFcdbFcYDUTtnyu0wBvjouy0s1I5mXaprjeV5xcV7Y5ZLBmSzf VfbjuvlW+HngnqsGcpQxLF92f5YIl7v+mo2GsviW2NV+84ba9dj+R9h9t/v+ qZsHI6z8XGHtQ2Hh8u0w+vc+IDXj8rtt81KQ+9o/oZsXMCw7F/aDXS+c+MRs kKu/GaWbfx37Iebil+L+YRfK6872Wutk4oH9OSD3PbBfN6+w+54Vuu4cKsyG +r4fdPVj8f0t6clr/+rK1aLfO+Mh2e/KZrN8eBd8rTse+PfN1D89jZQpen/1 T6v6g6fFJe0XgR8IlkHhWotKgbxDpj2tK+9w7bTRe4r0Cnpe2K5uhgRX8N6K 7UaVSdrfuejHkne4dg2yvxnMX/L8Xt19/uzeE+QfF8dZ7GeCy7dr/HRx3fm5 6DrWaP4M8Au6bIiu3Yib9ytxfkOIEybl1+vmMQwpnxjffvYTwBX9nnv/sQKu qO7e3+h4iDdYCPsb3ef4/OnhYp7Ixkd15RL+fiwnr+8tLDutPc9Ir3Yf0jr5 ZqTuPXhh8cu9J/4ngCv6vV97iah+VvU3Y8wIyEOKZUR9PPD17jOrdtK62a+U 7v0MfNxb52H+AFE7LVzShIC93uqzSfceUr/vTfR7d74K6rfzUcFoqL93UjU+ AAnncQlu0Yc1KbGZ1q1f19uoieueF3R//74++KWaBQ/r6t1h+a38gssoLLsG F9fIng12vkTrS3TtfWHx6ze/nBKuOWD/CpB/L1ysmyedi+sj/k2MS+31pdZM BntUw0q68jrfP6rCfSCvx7c/7dff6KzctxgKLrVLduqWDfbJ/otkfvw/Pr8p XxpVE7MJv/2PxS/33slQcNm5yIzkucgUYm+nup6Fz+//Jq47PyJdx7ZnT4D8 iA2u0pWj+HmF72nN1kcsg8KVkzouHbefb54I/uAfvqhrX+ffQ1o2fyw5VlhY WDa/Z2C4EU+khUtaPQvnTUbHL3TzK7vzXQXDb1j+D7/ginDpecht47qTrwoL Cx6a08XxPFU56ufbDyHiGjUbJUDeydH2E+PzOyEP7i2Z2+328YHhYh5S6q9R dZvuOinuX7r+jsvDfcjZXobLzSNr1Dm8hd1TsVBXLvGbn1YJN1G2Mcj/1pBb de0PXFzrgc0Q300evVRXv+D3wzUrRkKehhXfZQSKKyd1XDoOft8V8nPFt9TV vVcuLL+2s4prrB87GtaHBjV0/UvOKr9w7wPl9/vaunqh4L7xnhgn7ZU/T7jx I9VAnzeeuFd3vrlx6fo7qdxogmWQuPp5RsS4dD0v3aon2GGmVusRGG7Rh3Hv zbA+WueV0/Wr4N7PYDUZyuL9632qe/7Gv9folc9ZntMaW3TviXfjUr2F+ofR 8oUas4Pk1wPJcPn3jXeIw3szFlTXfW9c3Pg9Bbtgnz953fQgcSNXtWb3lVjH NweK+/f1LJ4jJ0c3/ph/P3qpicyOuq2mLK5DhBuWn83PH5fKDceqsXuYnrxX 9xydn9/eP79h4YrvAaH7xs37Mwi/vdfxGzS/br9MaufqfZTZ7WnJ51Mdt+jD vK8gl6SUGrhh3Y/z34Fb7sNnQd5p+5nuOunGpXg9z3mBsPKxwHDp+dCTf54O 4+w3vVXzuotx6bpT+RS7l2zgy7p5ffj9W9gb/WhF7bRwyepBL4GeVeEj3XUy 7HFm34+j/8fiGF68Sdf/Ie39pmbmnehH6myvjkvH7cyCMWC3X9l9RBj8piF1 XDofHoyxcZv3oe55APfeEivrLnZ/930X+rWfleBGPNFPB5fKfS8engbrzsxv de1c/PP5u6rl0rq5doZu/h13vFPq/RePbZoQGC5db9qOTwDuqmm696Pz47Pk pI5bGs5NM2Efyn5c9/wtLH7RPyrovFRueZLO43N7g95mPPCU7r7Jx430YePr +9ovBIaL39DzvUcqDyb89vq46UmrHwKIhzx7/EZov7ZZTuvG7Et19diw+A37 PNYuT5bLZ/sa+bduHArad0pwqR21y02Twd43da/sXkQRrjt+k46z71uA/YE0 2KjrT8uNC41uHAfymbn6dV27PerzsdT2xuVDmL/RunW69/4ibtB6LI6HEn7p /G1ffx7s8yMenCvh1ztumPwu+xzWc6NtFV255Ozx+3PE3V6FydVYBtG/wcQl uXHpfvlgtwzws+m3aHhguPSzY1XgN96vqu7+9uOMB3M5+oM728tw3fcB0nW9 48tzoB+u3KO7PgR9z+DZx6X7ZrtzJ8A4G3JeJrG3++n1A+W3X6UpoLdYPXTn G+oBsYD5/bnhot4SdPyx+D5Lf/lL+PfSjj7K9JZF1+nKZ0Hfv3n2cen+85cM Fg8Zm68r/7px6Tq5tgy7p5iWerj/HfKDf1zUL8LHpXaSM0MyiBqOJ37jgyvC ebTZfsAaYm/ndTy487hQfntkgJ+GEZ2va98JK09v2Pl/g943ubhGq/pbYB7X nKZ7z3ZY9x3w/aAPPTEF5LMJW/zK1WHpm2HJO0H3L8oPQfPLxU28dgTixcmo 82R5F0W4uA+5+aX72yW5uudD3Hs1zPva/Z3WrT/u9utnE5afQtDyWVi43DzT 8TMG3kfrFUc033Tt66Lfu/OX0H3oxJqhcN/tvvZPOZ4nGxdi3GD45eft2PId 3GMzt2Ibv/5c9v7dP3AtrL833SG7H1LEL3fftLocZ/6eF3wtkx9kuEHfN47z wsZvALh+4yREv+f6ZcbH75xKsOTjyHD9+hMo4RpVmrK8i8lSg1/++fGG/CGk VNGi/MPUdpq47nMROs8+WM3iRWiph4vjrGT8UntGn8rsXHpwV11ct95Cx0H5 DVkEy6Bwiz6MCdexfINVt8n8CZRwIx8XLAR76nsndceDX3uJbDzYcM3ajzN9 KFlq8OvXTqC0/pLBv2X36Pa95w1Nfvnyr/c8nLL3FnT+Hbc8qeYPLsJFfaii Z37/A3zX8Go= "], Function[Null, Internal`LocalizedBlock[{$CellContext`age, $CellContext`black, \ $CellContext`education, $CellContext`hispanic, $CellContext`married, \ $CellContext`nodegree, $CellContext`re74, $CellContext`re75, \ $CellContext`u74, $CellContext`u75, $CellContext`black[0], $CellContext`black[1], $CellContext`hispanic[0], $CellContext`hispanic[1], $CellContext`married[0], $CellContext`married[1], $CellContext`nodegree[0], $CellContext`nodegree[1], $CellContext`u74[0], $CellContext`u74[1], $CellContext`u75[0], $CellContext`u75[1]}, #], {HoldAll}]]& ], Editable->False, SelectWithContents->True, Selectable->True]], "Output", TaggingRules->{}, CellChangeTimes->{3.834314202984508*^9, 3.834314317364313*^9, 3.834405413197603*^9}, CellLabel->"Out[3]=", CellID->1966970613] }, Open ]], Cell[TextData[{ "The only statistically significant factor in determining treatment is the \ absence of a high school degree. The very low Cragg Uhler Pseudo ", Cell[BoxData[ FormBox[ SuperscriptBox["R", "2"], TraditionalForm]]], " suggests that the model does not have much explanatory power:" }], "Text", TaggingRules->{}, CellChangeTimes->{{3.83431423363311*^9, 3.8343142577686453`*^9}, { 3.834314330885186*^9, 3.834314353362102*^9}}, CellID->49759380], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"jobslomf", "[", RowBox[{"{", RowBox[{ "\"\\"", ",", "\"\\""}], "}"}], "]"}]], "Input", TaggingRules->{}, CellChangeTimes->{{3.834314267558758*^9, 3.8343142814494743`*^9}}, NumberMarks->False, CellLabel->"In[4]:=", CellID->1585553464], Cell[BoxData[ RowBox[{"{", RowBox[{ StyleBox[ TagBox[GridBox[{ {"\<\"\"\>", "\<\"Estimate\"\>", "\<\"Standard Error\"\>", "\<\"z\ \[Hyphen]Statistic\"\>", "\<\"P\[Hyphen]Value\"\>"}, {"1", RowBox[{"-", "0.4302561345403661`"}], "0.7873551141184653`", RowBox[{"-", "0.5464575346311015`"}], "0.5847514704780463`"}, { RowBox[{"black", "[", "0", "]"}], "0.07737696261527197`", "0.2645061328917776`", "0.29253371847877213`", "0.769878583164105`"}, { RowBox[{"hispanic", "[", "0", "]"}], "0.25361132012869864`", "0.35056587356752666`", "0.7234341367795675`", "0.4694132048994301`"}, { RowBox[{"nodegree", "[", "0", "]"}], "0.5612508654902697`", "0.24680150505101506`", "2.2740982287537364`", "0.022960081178368417`"}, {"age", "0.001146620718823997`", "0.012360130224837677`", "0.09276768917206576`", "0.9260881205919707`"}, {"education", RowBox[{"-", "0.0337248113089838`"}], "0.060176586044564814`", RowBox[{"-", "0.5604307842257502`"}], "0.5751856388544596`"}, { RowBox[{"married", "[", "0", "]"}], RowBox[{"-", "0.09605468873917286`"}], "0.21635720036497869`", RowBox[{"-", "0.4439634483027866`"}], "0.6570690106519358`"}, {"re74", RowBox[{"-", "0.000021071867643249587`"}], "0.000026171603767202237`", RowBox[{"-", "0.805142391375207`"}], "0.42073751570399004`"}, {"re75", "7.102645421724936`*^-6", "0.00003131180866603167`", "0.2268359997175818`", "0.8205512695255739`"}, { RowBox[{"u74", "[", "0", "]"}], RowBox[{"-", "0.10056661684404344`"}], "0.3110840505238964`", RowBox[{"-", "0.32327795872105713`"}], "0.7464847449670091`"}, { RowBox[{"u75", "[", "0", "]"}], "0.34970828258495734`", "0.3040669752709315`", "1.1501028096634245`", "0.2501015293005458`"} }, AutoDelete->False, GridBoxAlignment->{"Columns" -> {{Left}}, "Rows" -> {{Automatic}}}, GridBoxDividers->{ "ColumnsIndexed" -> {2 -> GrayLevel[0.7]}, "RowsIndexed" -> {2 -> GrayLevel[0.7]}}, GridBoxItemSize->{"Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}, GridBoxSpacings->{ "ColumnsIndexed" -> {2 -> 1}, "RowsIndexed" -> {2 -> 0.75}}], "Grid"], "DialogStyle", StripOnInput->False], ",", "0.020144511704001045`"}], "}"}]], "Output", TaggingRules->{}, CellChangeTimes->{3.8343142151210423`*^9, 3.834314282518649*^9, 3.8343143228442173`*^9, 3.8344054145110188`*^9}, CellLabel->"Out[4]=", CellID->511830773] }, Open ]], Cell["See if a probit model performs any better; it does not:", "Text", TaggingRules->{}, CellChangeTimes->{{3.8343334080925617`*^9, 3.834333416433343*^9}, { 3.8343334673868732`*^9, 3.834333470005677*^9}}, CellID->2104111132], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{ RowBox[{"(", RowBox[{"jobspromf", "=", RowBox[{ RowBox[{"Query", "[", RowBox[{ RowBox[{ RowBox[{"ProbitModelFit", "[", RowBox[{"#", ",", RowBox[{"{", RowBox[{ "1", ",", "black", ",", "hispanic", ",", "nodegree", ",", "age", ",", "education", ",", "married", ",", "re74", ",", "re75", ",", "u74", ",", "u75"}], "}"}], ",", RowBox[{"{", RowBox[{ "black", ",", "hispanic", ",", "nodegree", ",", "age", ",", "education", ",", "married", ",", "re74", ",", "re75", ",", "u74", ",", "u75"}], "}"}], ",", RowBox[{"NominalVariables", "->", RowBox[{"{", RowBox[{ "black", ",", "hispanic", ",", "nodegree", ",", "married", ",", "u74", ",", "u75"}], "}"}]}]}], "]"}], "&"}], ",", RowBox[{ RowBox[{"KeyTake", "[", RowBox[{"{", RowBox[{ "\"\\"", ",", "\"\\"", ",", "\"\\"", ",", "\"\\"", ",", "\"\\"", ",", "\"\\"", ",", "\"\\"", ",", "\"\\"", ",", "\"\\"", ",", "\"\\"", ",", "\"\\""}], "}"}], "]"}], "/*", "Values"}]}], "]"}], "[", "jobs", "]"}]}], ")"}], "[", RowBox[{"{", RowBox[{ "\"\\"", ",", "\"\\""}], "}"}], "]"}]], "Input", TaggingRules->{}, CellChangeTimes->{{3.834333429405746*^9, 3.834333450191724*^9}}, CellLabel->"In[5]:=", CellID->1839014768], Cell[BoxData[ RowBox[{"{", RowBox[{ StyleBox[ TagBox[GridBox[{ {"\<\"\"\>", "\<\"Estimate\"\>", "\<\"Standard Error\"\>", "\<\"z\ \[Hyphen]Statistic\"\>", "\<\"P\[Hyphen]Value\"\>"}, {"1", RowBox[{"-", "0.2721396303969444`"}], "0.48823126856072974`", RowBox[{"-", "0.5573990195244811`"}], "0.5772548352348954`"}, { RowBox[{"black", "[", "0", "]"}], "0.04806578731687601`", "0.16479391992910156`", "0.29167209164971075`", "0.7705373494549569`"}, { RowBox[{"hispanic", "[", "0", "]"}], "0.15655137015625972`", "0.2172945041045783`", "0.7204571086662899`", "0.4712435989190481`"}, { RowBox[{"nodegree", "[", "0", "]"}], "0.349318524477409`", "0.1534985205315288`", "2.275712647052247`", "0.02286321390660317`"}, {"age", "0.0007100669272793597`", "0.007665817166324052`", "0.09262768885210111`", "0.9261993457862973`"}, {"education", RowBox[{"-", "0.02065963261130693`"}], "0.03732531223634716`", RowBox[{"-", "0.5535019367149112`"}], "0.5799197547825545`"}, { RowBox[{"married", "[", "0", "]"}], RowBox[{"-", "0.058752221440460856`"}], "0.13446952598532783`", RowBox[{"-", "0.4369184840204717`"}], "0.6621704663684241`"}, {"re74", RowBox[{"-", "0.000013352279904944983`"}], "0.000015987217629770648`", RowBox[{"-", "0.8351847215791316`"}], "0.40361371068731233`"}, {"re75", "4.818611565335127`*^-6", "0.000019235788699353814`", "0.2505024171687329`", "0.8021988356494913`"}, { RowBox[{"u74", "[", "0", "]"}], RowBox[{"-", "0.06285712475719897`"}], "0.19339104987842848`", RowBox[{"-", "0.3250260278162453`"}], "0.7451613728930525`"}, { RowBox[{"u75", "[", "0", "]"}], "0.2182973999073426`", "0.1889900580763448`", "1.1550734579866562`", "0.2480603462606005`"} }, AutoDelete->False, GridBoxAlignment->{"Columns" -> {{Left}}, "Rows" -> {{Automatic}}}, GridBoxDividers->{ "ColumnsIndexed" -> {2 -> GrayLevel[0.7]}, "RowsIndexed" -> {2 -> GrayLevel[0.7]}}, GridBoxItemSize->{"Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}, GridBoxSpacings->{ "ColumnsIndexed" -> {2 -> 1}, "RowsIndexed" -> {2 -> 0.75}}], "Grid"], "DialogStyle", StripOnInput->False], ",", "0.020224549594158054`"}], "}"}]], "Output", TaggingRules->{}, CellChangeTimes->{3.834333453465629*^9, 3.834405416218113*^9}, CellLabel->"Out[5]=", CellID->1683685188] }, Open ]], Cell[CellGroupData[{ Cell[BoxData[ InterpretationBox[Cell["\t", "ExampleDelimiter"], $Line = 0; Null]], "ExampleDelimiter", TaggingRules->{}, CellID->1773330632], Cell["Split the data into training and test set:", "Text", TaggingRules->{}, CellChangeTimes->{{3.834314407845928*^9, 3.834314422894093*^9}, 3.834336083202218*^9}, CellID->1846917249], Cell[BoxData[ RowBox[{ RowBox[{ RowBox[{"{", RowBox[{"training", ",", "test"}], "}"}], "=", RowBox[{"TakeDrop", "[", RowBox[{ RowBox[{"RandomSample", "[", "jobs", "]"}], ",", "500"}], "]"}]}], ";"}]], "Input", TaggingRules->{}, CellChangeTimes->{{3.83431442518018*^9, 3.8343144527709713`*^9}}, CellLabel->"In[1]:=", CellID->391616158], Cell["Run a classifier on the training set:", "Text", TaggingRules->{}, CellChangeTimes->{{3.834314456709733*^9, 3.834314466797327*^9}}, CellID->586386716], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"cl", "=", RowBox[{ RowBox[{"Query", "[", RowBox[{ RowBox[{ RowBox[{"Classify", "[", RowBox[{"#", ",", RowBox[{"TrainingProgressReporting", "\[Rule]", "None"}]}], "]"}], "&"}], ",", RowBox[{ RowBox[{"KeyTake", "[", RowBox[{"{", RowBox[{ "\"\\"", ",", "\"\\"", ",", "\"\\"", ",", "\"\\"", ",", "\"\\"", ",", "\"\\"", ",", "\"\\"", ",", "\"\\"", ",", "\"\\"", ",", "\"\\"", ",", "\"\\""}], "}"}], "]"}], "/*", "Values", "/*", RowBox[{"(", RowBox[{ RowBox[{ RowBox[{"Most", "@", "#"}], "->", RowBox[{"Last", "@", "#"}]}], "&"}], ")"}]}]}], "]"}], "[", "training", "]"}]}]], "Input", TaggingRules->{}, CellChangeTimes->{{3.834314471786839*^9, 3.834314559736719*^9}, { 3.834314611729989*^9, 3.834314622817795*^9}}, CellLabel->"In[2]:=", CellID->1739416228], Cell[BoxData[ InterpretationBox[ RowBox[{ TagBox["ClassifierFunction", "SummaryHead"], "[", DynamicModuleBox[{Typeset`open$$ = False, Typeset`embedState$$ = "Ready"}, TemplateBox[{ PaneSelectorBox[{False -> GridBox[{{ PaneBox[ ButtonBox[ DynamicBox[ FEPrivate`FrontEndResource["FEBitmaps", "SummaryBoxOpener"]], ButtonFunction :> (Typeset`open$$ = True), Appearance -> None, BaseStyle -> {}, Evaluator -> Automatic, Method -> "Preemptive"], Alignment -> {Center, Center}, ImageSize -> Dynamic[{ Automatic, 3.5 (CurrentValue["FontCapHeight"]/AbsoluteCurrentValue[ Magnification])}]], GraphicsBox[{{ PointSize[0.13], GrayLevel[0.45], PointBox[{{0.9821769431797024, -0.440194219686987}, { 1.1339776261519132`, 0.8056918676854272}, {0.5279892326667741, 0.6574306661126254}, {0.022147046479890797`, 1.4937877187998898`}}], GrayLevel[0.7], PointBox[{{-0.9815166384819979, 0.15045697525228735`}, {-0.5923526886966953, \ -0.33441771553094035`}, {-0.005656646679640442, -1.462421365651345}, \ {-1.0734370436522753`, -1.3729645043477454`}}]}, { GrayLevel[0.55], AbsoluteThickness[1.5], LineBox[{{-1., 1.5}, {1, -1.6}}]}}, { Axes -> {False, False}, AxesLabel -> {None, None}, AxesOrigin -> {0, 0}, BaseStyle -> {FontFamily -> "Arial", AbsoluteThickness[1.5]}, DisplayFunction -> Identity, Frame -> {{True, True}, {True, True}}, FrameLabel -> {{None, None}, {None, None}}, FrameStyle -> Directive[ Thickness[Tiny], GrayLevel[0.7]], FrameTicks -> {{None, None}, {None, None}}, GridLines -> {None, None}, LabelStyle -> {FontFamily -> "Arial"}, Method -> {"ScalingFunctions" -> None}, PlotRange -> {{-1., 1}, {-1.3, 1.1}}, PlotRangeClipping -> True, PlotRangePadding -> {{0.7, 0.7}, {0.7, 0.7}}, Ticks -> {None, None}}, Axes -> False, AspectRatio -> 1, ImageSize -> Dynamic[{ Automatic, 3.5 (CurrentValue["FontCapHeight"]/AbsoluteCurrentValue[ Magnification])}], Frame -> True, FrameTicks -> None, FrameStyle -> Directive[ Opacity[0.5], Thickness[Tiny], RGBColor[0.368417, 0.506779, 0.709798]], Background -> GrayLevel[0.94]], GridBox[{{ RowBox[{ TagBox["\"Input type: \"", "SummaryItemAnnotation"], "\[InvisibleSpace]", TagBox[ TagBox[ TooltipBox[ TemplateBox[{"\"Mixed\"", StyleBox[ TemplateBox[{"\" (number: \"", "10", "\")\""}, "RowDefault"], GrayLevel[0.5], StripOnInput -> False]}, "RowDefault"], TagBox[ RowBox[{"{", RowBox[{ "\"Boolean\"", ",", "\"Boolean\"", ",", "\"Boolean\"", ",", "\"Numerical\"", ",", "\"Numerical\"", ",", "\"Boolean\"", ",", "\"Numerical\"", ",", "\"Numerical\"", ",", "\"Boolean\"", ",", "\"Boolean\""}], "}"}], Short[#, 10]& ]], Annotation[#, Short[{"Boolean", "Boolean", "Boolean", "Numerical", "Numerical", "Boolean", "Numerical", "Numerical", "Boolean", "Boolean"}, 10], "Tooltip"]& ], "SummaryItem"]}]}, { RowBox[{ TagBox["\"Classes: \"", "SummaryItemAnnotation"], "\[InvisibleSpace]", TagBox[ TemplateBox[{",", "\",\"", "0", "1"}, "RowWithSeparators"], "SummaryItem"]}]}}, GridBoxAlignment -> { "Columns" -> {{Left}}, "Rows" -> {{Automatic}}}, AutoDelete -> False, GridBoxItemSize -> { "Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}, GridBoxSpacings -> {"Columns" -> {{2}}, "Rows" -> {{Automatic}}}, BaseStyle -> { ShowStringCharacters -> False, NumberMarks -> False, PrintPrecision -> 3, ShowSyntaxStyles -> False}]}}, GridBoxAlignment -> {"Columns" -> {{Left}}, "Rows" -> {{Top}}}, AutoDelete -> False, GridBoxItemSize -> { "Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}, BaselinePosition -> {1, 1}], True -> GridBox[{{ PaneBox[ ButtonBox[ DynamicBox[ FEPrivate`FrontEndResource["FEBitmaps", "SummaryBoxCloser"]], ButtonFunction :> (Typeset`open$$ = False), Appearance -> None, BaseStyle -> {}, Evaluator -> Automatic, Method -> "Preemptive"], Alignment -> {Center, Center}, ImageSize -> Dynamic[{ Automatic, 3.5 (CurrentValue["FontCapHeight"]/AbsoluteCurrentValue[ Magnification])}]], GraphicsBox[{{ PointSize[0.13], GrayLevel[0.45], PointBox[{{0.9821769431797024, -0.440194219686987}, { 1.1339776261519132`, 0.8056918676854272}, {0.5279892326667741, 0.6574306661126254}, {0.022147046479890797`, 1.4937877187998898`}}], GrayLevel[0.7], PointBox[{{-0.9815166384819979, 0.15045697525228735`}, {-0.5923526886966953, \ -0.33441771553094035`}, {-0.005656646679640442, -1.462421365651345}, \ {-1.0734370436522753`, -1.3729645043477454`}}]}, { GrayLevel[0.55], AbsoluteThickness[1.5], LineBox[{{-1., 1.5}, {1, -1.6}}]}}, { Axes -> {False, False}, AxesLabel -> {None, None}, AxesOrigin -> {0, 0}, BaseStyle -> {FontFamily -> "Arial", AbsoluteThickness[1.5]}, DisplayFunction -> Identity, Frame -> {{True, True}, {True, True}}, FrameLabel -> {{None, None}, {None, None}}, FrameStyle -> Directive[ Thickness[Tiny], GrayLevel[0.7]], FrameTicks -> {{None, None}, {None, None}}, GridLines -> {None, None}, LabelStyle -> {FontFamily -> "Arial"}, Method -> {"ScalingFunctions" -> None}, PlotRange -> {{-1., 1}, {-1.3, 1.1}}, PlotRangeClipping -> True, PlotRangePadding -> {{0.7, 0.7}, {0.7, 0.7}}, Ticks -> {None, None}}, Axes -> False, AspectRatio -> 1, ImageSize -> Dynamic[{ Automatic, 3.5 (CurrentValue["FontCapHeight"]/AbsoluteCurrentValue[ Magnification])}], Frame -> True, FrameTicks -> None, FrameStyle -> Directive[ Opacity[0.5], Thickness[Tiny], RGBColor[0.368417, 0.506779, 0.709798]], Background -> GrayLevel[0.94]], GridBox[{{ RowBox[{ TagBox["\"Input type: \"", "SummaryItemAnnotation"], "\[InvisibleSpace]", TagBox[ TagBox[ TooltipBox[ TemplateBox[{"\"Mixed\"", StyleBox[ TemplateBox[{"\" (number: \"", "10", "\")\""}, "RowDefault"], GrayLevel[0.5], StripOnInput -> False]}, "RowDefault"], TagBox[ RowBox[{"{", RowBox[{ "\"Boolean\"", ",", "\"Boolean\"", ",", "\"Boolean\"", ",", "\"Numerical\"", ",", "\"Numerical\"", ",", "\"Boolean\"", ",", "\"Numerical\"", ",", "\"Numerical\"", ",", "\"Boolean\"", ",", "\"Boolean\""}], "}"}], Short[#, 10]& ]], Annotation[#, Short[{"Boolean", "Boolean", "Boolean", "Numerical", "Numerical", "Boolean", "Numerical", "Numerical", "Boolean", "Boolean"}, 10], "Tooltip"]& ], "SummaryItem"]}]}, { RowBox[{ TagBox["\"Classes: \"", "SummaryItemAnnotation"], "\[InvisibleSpace]", TagBox[ TemplateBox[{",", "\",\"", "0", "1"}, "RowWithSeparators"], "SummaryItem"]}]}, { RowBox[{ TagBox["\"Method: \"", "SummaryItemAnnotation"], "\[InvisibleSpace]", TagBox["\"GradientBoostedTrees\"", "SummaryItem"]}]}, { RowBox[{ TagBox[ "\"Number of training examples: \"", "SummaryItemAnnotation"], "\[InvisibleSpace]", TagBox["500", "SummaryItem"]}]}}, GridBoxAlignment -> { "Columns" -> {{Left}}, "Rows" -> {{Automatic}}}, AutoDelete -> False, GridBoxItemSize -> { "Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}, GridBoxSpacings -> {"Columns" -> {{2}}, "Rows" -> {{Automatic}}}, BaseStyle -> { ShowStringCharacters -> False, NumberMarks -> False, PrintPrecision -> 3, ShowSyntaxStyles -> False}]}}, GridBoxAlignment -> {"Columns" -> {{Left}}, "Rows" -> {{Top}}}, AutoDelete -> False, GridBoxItemSize -> { "Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}, BaselinePosition -> {1, 1}]}, Dynamic[Typeset`open$$], ImageSize -> Automatic]}, "SummaryPanel"], DynamicModuleValues:>{}], "]"}], ClassifierFunction[ Association[ "ExampleNumber" -> 500, "ClassNumber" -> 2, "Input" -> Association["Preprocessor" -> MachineLearning`MLProcessor["ToMLDataset", Association[ "Input" -> Association[ "f1" -> Association["Type" -> "Boolean"], "f2" -> Association["Type" -> "Boolean"], "f3" -> Association["Type" -> "Boolean"], "f4" -> Association["Type" -> "Numerical"], "f5" -> Association["Type" -> "Numerical"], "f6" -> Association["Type" -> "Boolean"], "f7" -> Association["Type" -> "Numerical"], "f8" -> Association["Type" -> "Numerical"], "f9" -> Association["Type" -> "Boolean"], "f10" -> Association["Type" -> "Boolean"]], "Output" -> Association[ "f1" -> Association["Type" -> "Boolean", "Weight" -> 1], "f2" -> Association["Type" -> "Boolean", "Weight" -> 1], "f3" -> Association["Type" -> "Boolean", "Weight" -> 1], "f4" -> Association["Type" -> "Numerical", "Weight" -> 1], "f5" -> Association["Type" -> "Numerical", "Weight" -> 1], "f6" -> Association["Type" -> "Boolean", "Weight" -> 1], "f7" -> Association["Type" -> "Numerical", "Weight" -> 1], "f8" -> Association["Type" -> "Numerical", "Weight" -> 1], "f9" -> Association["Type" -> "Boolean", "Weight" -> 1], "f10" -> Association["Type" -> "Boolean", "Weight" -> 1]], "Preprocessor" -> MachineLearning`MLProcessor["Sequence", Association["Processors" -> { MachineLearning`MLProcessor["Transpose", Association["FeatureNumber" -> 10]], MachineLearning`MLProcessor["WrapMLDataset", Association[ "FeatureTypes" -> { "Boolean", "Boolean", "Boolean", "Numerical", "Numerical", "Boolean", "Numerical", "Numerical", "Boolean", "Boolean"}, "FeatureKeys" -> { "f1", "f2", "f3", "f4", "f5", "f6", "f7", "f8", "f9", "f10"}, "FeatureWeights" -> Automatic, "ExampleWeights" -> Automatic, "RawExample" -> Missing["KeyAbsent", "RawExample"], "StructurePreserving" -> False]]}]], "ScalarFeature" -> False, "Invertibility" -> "Perfect", "StructurePreserving" -> False, "Missing" -> "Allowed"]], "Processor" -> MachineLearning`MLProcessor["Sequence", Association[ "Input" -> Association[ "f1" -> Association["Type" -> "Boolean", "Weight" -> 1], "f2" -> Association["Type" -> "Boolean", "Weight" -> 1], "f3" -> Association["Type" -> "Boolean", "Weight" -> 1], "f4" -> Association["Type" -> "Numerical", "Weight" -> 1], "f5" -> Association["Type" -> "Numerical", "Weight" -> 1], "f6" -> Association["Type" -> "Boolean", "Weight" -> 1], "f7" -> Association["Type" -> "Numerical", "Weight" -> 1], "f8" -> Association["Type" -> "Numerical", "Weight" -> 1], "f9" -> Association["Type" -> "Boolean", "Weight" -> 1], "f10" -> Association["Type" -> "Boolean", "Weight" -> 1]], "Output" -> Association[ "((f4f5f7f8)(f1f2f3f6f9f10))" -> Association["Type" -> "NumericalVector", "Weight" -> 10.]], "Processors" -> { MachineLearning`MLProcessor["SynthesizeMissingValues", Association[ "Invertibility" -> "Perfect", "Missing" -> "Imputed", "StructurePreserving" -> True, "Input" -> Association[ "f1" -> Association["Type" -> "Boolean", "Weight" -> 1], "f2" -> Association["Type" -> "Boolean", "Weight" -> 1], "f3" -> Association["Type" -> "Boolean", "Weight" -> 1], "f4" -> Association["Type" -> "Numerical", "Weight" -> 1], "f5" -> Association["Type" -> "Numerical", "Weight" -> 1], "f6" -> Association["Type" -> "Boolean", "Weight" -> 1], "f7" -> Association["Type" -> "Numerical", "Weight" -> 1], "f8" -> Association["Type" -> "Numerical", "Weight" -> 1], "f9" -> Association["Type" -> "Boolean", "Weight" -> 1], "f10" -> Association["Type" -> "Boolean", "Weight" -> 1]], "Distribution" -> LearnedDistribution[ Association[ "ExampleNumber" -> 500, "Preprocessor" -> MachineLearning`MLProcessor["ToMLDataset", Association[ "Input" -> Association[ "f1" -> Association["Type" -> "Boolean"], "f2" -> Association["Type" -> "Boolean"], "f3" -> Association["Type" -> "Boolean"], "f4" -> Association["Type" -> "Numerical"], "f5" -> Association["Type" -> "Numerical"], "f6" -> Association["Type" -> "Boolean"], "f7" -> Association["Type" -> "Numerical"], "f8" -> Association["Type" -> "Numerical"], "f9" -> Association["Type" -> "Boolean"], "f10" -> Association["Type" -> "Boolean"]], "Output" -> Association[ "f1" -> Association["Type" -> "Boolean", "Weight" -> 1], "f2" -> Association["Type" -> "Boolean", "Weight" -> 1], "f3" -> Association["Type" -> "Boolean", "Weight" -> 1], "f4" -> Association["Type" -> "Numerical", "Weight" -> 1], "f5" -> Association[ "Type" -> "Numerical", "Weight" -> 1], "f6" -> Association["Type" -> "Boolean", "Weight" -> 1], "f7" -> Association["Type" -> "Numerical", "Weight" -> 1], "f8" -> Association["Type" -> "Numerical", "Weight" -> 1], "f9" -> Association["Type" -> "Boolean", "Weight" -> 1], "f10" -> Association["Type" -> "Boolean", "Weight" -> 1]], "Preprocessor" -> MachineLearning`MLProcessor["Identity"], "ScalarFeature" -> False, "Invertibility" -> "Perfect", "StructurePreserving" -> False, "Missing" -> "Allowed"]], "Processor" -> MachineLearning`MLProcessor["Sequence", Association[ "Input" -> Association[ "f4" -> Association["Type" -> "Numerical", "Weight" -> 1], "f5" -> Association[ "Type" -> "Numerical", "Weight" -> 1], "f7" -> Association["Type" -> "Numerical", "Weight" -> 1], "f8" -> Association["Type" -> "Numerical", "Weight" -> 1], "f1" -> Association["Type" -> "Boolean", "Weight" -> 1], "f2" -> Association["Type" -> "Boolean", "Weight" -> 1], "f3" -> Association["Type" -> "Boolean", "Weight" -> 1], "f6" -> Association["Type" -> "Boolean", "Weight" -> 1], "f9" -> Association["Type" -> "Boolean", "Weight" -> 1], "f10" -> Association["Type" -> "Boolean", "Weight" -> 1]], "Output" -> Association[ "((f4f5f7f8)(f1f2f3f6f9f10))" -> Association[ "Weight" -> {1., 1., 1., 1., 1., 1., 1., 1., 1., 1.}, "Type" -> "NumericalVector"]], "Processors" -> { MachineLearning`MLProcessor["Threads", Association[ "Input" -> Association[ "f4" -> Association["Type" -> "Numerical", "Weight" -> 1], "f5" -> Association[ "Type" -> "Numerical", "Weight" -> 1], "f7" -> Association["Type" -> "Numerical", "Weight" -> 1], "f8" -> Association["Type" -> "Numerical", "Weight" -> 1], "f1" -> Association["Type" -> "Boolean", "Weight" -> 1], "f2" -> Association["Type" -> "Boolean", "Weight" -> 1], "f3" -> Association["Type" -> "Boolean", "Weight" -> 1], "f6" -> Association["Type" -> "Boolean", "Weight" -> 1], "f9" -> Association["Type" -> "Boolean", "Weight" -> 1], "f10" -> Association["Type" -> "Boolean", "Weight" -> 1]], "Output" -> Association[ "(f4f5f7f8)" -> Association["Type" -> "NumericalVector", "Weight" -> 4], "(f1f2f3f6f9f10)" -> Association["Type" -> "BooleanVector", "Weight" -> 6]], "Processors" -> { MachineLearning`MLProcessor["ToVector", Association[ "Invertibility" -> "Perfect", "Missing" -> "Allowed", "StructurePreserving" -> True, "Input" -> Association[ "f4" -> Association["Type" -> "Numerical", "Weight" -> 1], "f5" -> Association[ "Type" -> "Numerical", "Weight" -> 1], "f7" -> Association["Type" -> "Numerical", "Weight" -> 1], "f8" -> Association["Type" -> "Numerical", "Weight" -> 1]], "Output" -> Association[ "(f4f5f7f8)" -> Association["Type" -> "NumericalVector", "Weight" -> 4]], "Version" -> {12.3, 0}, "ID" -> 3316698883640303724]], MachineLearning`MLProcessor["ToVector", Association[ "Invertibility" -> "Perfect", "Missing" -> "Allowed", "StructurePreserving" -> True, "Input" -> Association[ "f1" -> Association["Type" -> "Boolean", "Weight" -> 1], "f2" -> Association["Type" -> "Boolean", "Weight" -> 1], "f3" -> Association["Type" -> "Boolean", "Weight" -> 1], "f6" -> Association["Type" -> "Boolean", "Weight" -> 1], "f9" -> Association["Type" -> "Boolean", "Weight" -> 1], "f10" -> Association["Type" -> "Boolean", "Weight" -> 1]], "Output" -> Association[ "(f1f2f3f6f9f10)" -> Association["Type" -> "BooleanVector", "Weight" -> 6]], "Version" -> {12.3, 0}, "ID" -> 1543560422633817687]]}, "Invertibility" -> "Perfect", "StructurePreserving" -> True, "Missing" -> "Allowed"]], MachineLearning`MLProcessor["ConformBooleanVector", Association[ "Invertibility" -> "Perfect", "Missing" -> "Allowed", "StructurePreserving" -> True, "Input" -> Association[ "(f1f2f3f6f9f10)" -> Association["Type" -> "BooleanVector", "Weight" -> 6]], "Version" -> {12.3, 0}, "ID" -> 4011134228092904529, "Output" -> Association[ "(f1f2f3f6f9f10)" -> Association["Type" -> "BooleanVector", "Weight" -> 6]]]], MachineLearning`MLProcessor[ "BooleanVectorToNominalVector", Association[ "Invertibility" -> "Perfect", "Missing" -> "Allowed", "StructurePreserving" -> True, "Input" -> Association[ "(f1f2f3f6f9f10)" -> Association["Type" -> "BooleanVector", "Weight" -> 6]], "Version" -> {12.3, 0}, "ID" -> 8177418994704305658, "Output" -> Association[ "(f1f2f3f6f9f10)" -> Association["Type" -> "NominalVector", "Weight" -> 6]]]], MachineLearning`MLProcessor[ "IntegerEncodeNominalVector", Association[ "Invertibility" -> "Perfect", "Missing" -> "Allowed", "StructurePreserving" -> True, "Input" -> Association[ "(f1f2f3f6f9f10)" -> Association["Type" -> "NominalVector", "Weight" -> 6]], "Index" -> { Association[0 -> 1, 1 -> 2], Association[0 -> 1, 1 -> 2], Association[0 -> 1, 1 -> 2], Association[0 -> 1, 1 -> 2], Association[0 -> 1, 1 -> 2], Association[0 -> 1, 1 -> 2]}, "MissingCode" -> Indeterminate, "Version" -> {12.3, 0}, "ID" -> 1022096664884957757, "Output" -> Association[ "(f1f2f3f6f9f10)" -> Association["Type" -> "NominalVector", "Weight" -> 6]]]], MachineLearning`MLProcessor["NumericalizeNominalVector", Association[ "Invertibility" -> "Approximate", "Missing" -> "Allowed", "StructurePreserving" -> True, "Input" -> Association[ "(f1f2f3f6f9f10)" -> Association[ "Type" -> "NominalVector", "Weight" -> 6, "SetSize" -> {2, 2, 2, 2, 2, 2}]], "Boundaries" -> {{-0.5, 0., 0.5}, {-0.5, 0., 0.5}, {-0.5, 0., 0.5}, {-0.5, 0., 0.5}, {-0.5, 0., 0.5}, {-0.5, 0., 0.5}}, "Version" -> {12.3, 0}, "ID" -> 2653124012696996535, "Output" -> Association[ "(f1f2f3f6f9f10)" -> Association[ "Type" -> "NumericalVector", "Weight" -> 6]]]], MachineLearning`MLProcessor["MergeVectors", Association[ "Invertibility" -> "Perfect", "Missing" -> "Allowed", "StructurePreserving" -> True, "Input" -> Association[ "(f4f5f7f8)" -> Association["Type" -> "NumericalVector", "Weight" -> 4], "(f1f2f3f6f9f10)" -> Association["Type" -> "NumericalVector", "Weight" -> 6]], "Spans" -> { Span[1, 4], Span[5, 10]}, "Wrappers" -> {Identity, Identity}, "Output" -> Association[ "((f4f5f7f8)(f1f2f3f6f9f10))" -> Association[ "Weight" -> {1., 1., 1., 1., 1., 1., 1., 1., 1., 1.}, "Type" -> "NumericalVector"]], "Version" -> {12.3, 0}, "ID" -> 7449476533421130286]]}, "Invertibility" -> "Approximate", "StructurePreserving" -> True, "Missing" -> "Allowed"]], "PerformanceGoal" -> "DirectTraining", "BatchProcessing" -> Automatic, "Model" -> Association["RotationMatrix" -> CompressedData[" 1:eJwBMQPO/CFib1JlAgAAAAoAAAAKAAAADwiwSrnOAz+8d8INzP8lP/BAjGMY /u8/lLdUIbpSkT+JK863wWGHv5a9NRCXf3O/Kny6D+JfaT9TxR4m7xFwP0w4 6tunyyu/5x9rsAibSz9m7YYzOUL9PshKmZjp3NU+oo3Ug07kkD+EYMBEMN3v v2/p/mF6W3q/bKXSQrQVgr81knQbo0iYv3M4VzcdcLU/eVV+ZHwDjz/U8Kx6 ykiRv7kEC0CzYOk/7jj5Uyp+47+KX7L3VecSP7dgKQogqPU+6orRodFQ8j4+ qYEEghTZvqY6fLQOUtK+mEklCKzU1D5wv9CPO1XSvuxcF4GTtry+xb1gVSp+ 4z+YwGg4s2DpP5wA3I61eyS/LeOWuxjq8j7hi7suHRvyPv5DHlVEz98+biph JgN58T6YhthuUAWyviHEvhmModE+ToS8pyBTn76XvnQM5y6/vguaWtVlreK+ uOJBm0CbcT9gXhkBvp90v4RKxe3GwJE/VUnYqXQl6j9g76FMR7TCP1MUK26W UKC/7zA2MvYrsL9n3EW6SrHhv5y6+JwDQ5k+VHomI2Rv3D7yYqXcJDNhv+6U ayrLMY4/aX34oiGLq7937d/2SFzhv6kuvw1RILq/wSeAcRSPtb/lT1ZYlmiQ P2DYGzQVe+q/8kshT6cuw752PQ8iyt3ZPveYGrowtHG/RYED/7u9tj82/dII hL6ov8Aq6+/mb6s/EH+w29cQ1L9NAeDIVmPtP3qbveMxIck/f3wCg+LVtL/6 iEtoZ3LSPulXa4u3Sek+4GtVC40dez+4yjdZrxR+v3EqxR3assE/KLin1SnP xT9ZxBE6u2PtvyJcRhxNvNS/e0CtwCQEqj9O6ElkKgSbP3tadYo+U/O+FiZ+ PLsx0z6J5kxYMZV8P2Tbo9zmBF0/1QsWyLzo5j+46KSzTpyzvy6ZFWZjv3+/ rHMmtLQOxz9lA4F8LG3lv3vPDVNiSZu/kIKenqhQ8b7EioU9MiDgvo9KfGpI ZH8/QcYOnpknX79qSeBhE8TlP2rLvCq/34y/QffUnfuOxD/er/58wD+wv8Mm XVmkw+Y/v4jZDUi3ob/x4JgC "], "Precisions" -> {1.*^-6, 1.*^-6, 0.022856810057507688`, 0.38689895865155344`, 10.57935559665159, 14.205388502322698`, 20.592782324022085`, 25.50148838835205, 32.1042961457214, 32.30264445259112}, "NoisePrecision" -> None, "Processor" -> MachineLearning`MLProcessor["Center", Association[ "Invertibility" -> "Perfect", "Missing" -> "Allowed", "StructurePreserving" -> True, "Input" -> Association[ "((f4f5f7f8)(f1f2f3f6f9f10))" -> Association[ "Weight" -> {1., 1., 1., 1., 1., 1., 1., 1., 1., 1.}, "Type" -> "NumericalVector"]], "Mean" -> {24.632, 10.392, 3517.7927107849123`, 2981.388433151245, 0.1426727988002656, -0.19668399454225596`, 0.1388625841404933, -0.16287633582074032`, \ -0.019775698305439616`, -0.040446925895776316`}, "Output" -> Association[ "((f4f5f7f8)(f1f2f3f6f9f10))" -> Association[ "Type" -> "NumericalVector", "Weight" -> 10.]], "Version" -> {12.3, 0}, "ID" -> 234699001300146701]], "PostProcessor" -> MachineLearning`MLProcessor["FirstValues", Association[ "Info" -> Association[ "Type" -> "NumericalVector", "Weight" -> 10.], "Key" -> "((f4f5f7f8)(f1f2f3f6f9f10))", "Invertibility" -> "Perfect", "StructurePreserving" -> False, "Missing" -> "Allowed"]], "Method" -> "Multinormal", "Options" -> Association[ "CovarianceType" -> Association[ "Value" -> "Full", "Options" -> Association[]], "IntrinsicDimension" -> Association["Value" -> 10, "Options" -> Association[]]]], "TrainingInformation" -> Association["Configurations" -> Dataset[ Association[ Association[ "Value" -> "Multinormal", "Options" -> Association[ "CovarianceType" -> Association["Value" -> "Full"], "IntrinsicDimension" -> Association["Value" -> "Heuristic"]], "NaiveImputer" -> MachineLearning`MLProcessor["ImputeMissing", Association[ "Invertibility" -> "Perfect", "Missing" -> "Imputed", "StructurePreserving" -> True, "Input" -> Association[ "((f4f5f7f8)(f1f2f3f6f9f10))" -> Association[ "Weight" -> {1., 1., 1., 1., 1., 1., 1., 1., 1., 1.}, "Type" -> "NumericalVector"]], "Mean" -> {24.632, 10.392, 3517.7927107849123`, 2981.388433151245, 0.1426727988002656, -0.19668399454225596`, 0.1388625841404933, -0.16287633582074035`, \ -0.019775698305439616`, -0.04044692589577634}, "StandardDeviation" -> {6.62786360752844, 1.6181273126673357`, 6041.363324324119, 4822.270799347922, 0.24308065642958543`, 0.2096172697624777, 0.24766674808869532`, 0.22972111865332548`, 0.2919209597626786, 0.28094908774794775`}, "Method" -> "NaiveSampler", "VectorLength" -> 10, "Output" -> Association[ "((f4f5f7f8)(f1f2f3f6f9f10))" -> Association[ "Type" -> "NumericalVector", "Weight" -> 10.]], "Type" -> "NumericalVector", "Version" -> {12.3, 0}, "ID" -> 3712465554580130660]], "EMIterations" -> 1] -> Association[]], TypeSystem`Assoc[ TypeSystem`Struct[{ "Value", "Options", "NaiveImputer", "EMIterations"}, { TypeSystem`Atom[String], TypeSystem`Assoc[ TypeSystem`Atom[String], TypeSystem`Struct[{"Value"}, { TypeSystem`Atom[String]}], 2], TypeSystem`AnyType, TypeSystem`Atom[Integer]}], TypeSystem`Assoc[ TypeSystem`UnknownType, TypeSystem`UnknownType, TypeSystem`AnyLength], 1], Association[]], "BestModelInformation" -> Dataset[ Association[ "Configuration" -> { "Multinormal", "CovarianceType" -> "Full", "IntrinsicDimension" -> "Heuristic"}, "ModelUtility" -> Missing[]], TypeSystem`Struct[{"Configuration", "ModelUtility"}, { TypeSystem`Tuple[{ TypeSystem`Atom[String], TypeSystem`AnyType, TypeSystem`AnyType}], TypeSystem`UnknownType}], Association[]]], "NaiveImputer" -> MachineLearning`MLProcessor["ImputeMissing", Association[ "Invertibility" -> "Perfect", "Missing" -> "Imputed", "StructurePreserving" -> True, "Input" -> Association[ "((f4f5f7f8)(f1f2f3f6f9f10))" -> Association[ "Weight" -> {1., 1., 1., 1., 1., 1., 1., 1., 1., 1.}, "Type" -> "NumericalVector"]], "Mean" -> {24.632, 10.392, 3517.7927107849123`, 2981.388433151245, 0.1426727988002656, -0.19668399454225596`, 0.1388625841404933, -0.16287633582074035`, \ -0.019775698305439616`, -0.04044692589577634}, "StandardDeviation" -> {6.62786360752844, 1.6181273126673357`, 6041.363324324119, 4822.270799347922, 0.24308065642958543`, 0.2096172697624777, 0.24766674808869532`, 0.22972111865332548`, 0.2919209597626786, 0.28094908774794775`}, "Method" -> "NaiveSampler", "VectorLength" -> 10, "Output" -> Association[ "((f4f5f7f8)(f1f2f3f6f9f10))" -> Association[ "Type" -> "NumericalVector", "Weight" -> 10.]], "Type" -> "NumericalVector", "Version" -> {12.3, 0}, "ID" -> 3712465554580130660]], "InputDimension" -> 0, "OutputDimension" -> 10, "Log" -> Association["Example" -> MachineLearning`MLDataset[ Association[ "f1" -> Association[ "Type" -> "Boolean", "Weight" -> 1, "Values" -> {1}, "ID" -> 2088960393445371139], "f2" -> Association[ "Type" -> "Boolean", "Weight" -> 1, "Values" -> {0}, "ID" -> 2808916298067735746], "f3" -> Association[ "Type" -> "Boolean", "Weight" -> 1, "Values" -> {1}, "ID" -> 6848592649459883519], "f4" -> Association[ "Type" -> "Numerical", "Weight" -> 1, "Values" -> {17}, "ID" -> 7818302300378383162], "f5" -> Association[ "Type" -> "Numerical", "Weight" -> 1, "Values" -> {7}, "ID" -> 5732213614699672942], "f6" -> Association[ "Type" -> "Boolean", "Weight" -> 1, "Values" -> {0}, "ID" -> 4541606426648771723], "f7" -> Association[ "Type" -> "Numerical", "Weight" -> 1, "Values" -> {0}, "ID" -> 4888551857821753089], "f8" -> Association[ "Type" -> "Numerical", "Weight" -> 1, "Values" -> {0}, "ID" -> 6749501619406737415], "f9" -> Association[ "Type" -> "Boolean", "Weight" -> 1, "Values" -> {1}, "ID" -> 7726663053373864391], "f10" -> Association[ "Type" -> "Boolean", "Weight" -> 1, "Values" -> {1}, "ID" -> 5972767125695252816]], Association[ "ExampleNumber" -> 1, "ExampleWeights" -> 1, "LogDensityRatios" -> 0, "RawExample" -> False]], "TrainingTime" -> 0.323246, "MaxTrainingMemory" -> 5108640, "DataMemory" -> 135168, "FunctionMemory" -> 96240, "LanguageVersion" -> {12.3, 0}, "Date" -> DateObject[{2021, 7, 4, 10, 37, 1.104464`6.795726553498322}, "Instant", "Gregorian", -6.], "ProcessorCount" -> 2, "ProcessorType" -> "x86-64", "OperatingSystem" -> "MacOSX", "SystemWordLength" -> 64, "Evaluations" -> {}], "LogPDFDistribution" -> MachineLearning`TailedQuantileDistribution[ Association[ "Quantiles" -> {-3.0506499598055687`, -3.0506499598055687`, \ -3.0159000559703624`, -2.7316348774864374`, -2.720202948801694, \ -2.6967682702519453`, -2.5830906800566384`, -2.5802559617783047`, \ -2.4921898471238846`, -2.4308948302827416`, -2.3194090170986685`}, "LeftBoundary" -> -3.0159000559703624`, "LeftScale" -> 0.017374951917603187`, "LeftTailNorm" -> 0.2]], "Entropy" -> Around[26.620996448656246`, 0.7440592951458566], "EntropySampleSize" -> 10]], "Output" -> Association[ "f1" -> Association["Type" -> "Boolean", "Weight" -> 1], "f2" -> Association["Type" -> "Boolean", "Weight" -> 1], "f3" -> Association["Type" -> "Boolean", "Weight" -> 1], "f4" -> Association["Type" -> "Numerical", "Weight" -> 1], "f5" -> Association["Type" -> "Numerical", "Weight" -> 1], "f6" -> Association["Type" -> "Boolean", "Weight" -> 1], "f7" -> Association["Type" -> "Numerical", "Weight" -> 1], "f8" -> Association["Type" -> "Numerical", "Weight" -> 1], "f9" -> Association["Type" -> "Boolean", "Weight" -> 1], "f10" -> Association["Type" -> "Boolean", "Weight" -> 1]], "EvaluationStrategy" -> "ModeFinding", "Version" -> {12.3, 0}, "ID" -> 3492945905634660037]], MachineLearning`MLProcessor["Threads", Association[ "Input" -> Association[ "f4" -> Association["Type" -> "Numerical", "Weight" -> 1], "f5" -> Association["Type" -> "Numerical", "Weight" -> 1], "f7" -> Association["Type" -> "Numerical", "Weight" -> 1], "f8" -> Association["Type" -> "Numerical", "Weight" -> 1], "f1" -> Association["Type" -> "Boolean", "Weight" -> 1], "f2" -> Association["Type" -> "Boolean", "Weight" -> 1], "f3" -> Association["Type" -> "Boolean", "Weight" -> 1], "f6" -> Association["Type" -> "Boolean", "Weight" -> 1], "f9" -> Association["Type" -> "Boolean", "Weight" -> 1], "f10" -> Association["Type" -> "Boolean", "Weight" -> 1]], "Output" -> Association[ "(f4f5f7f8)" -> Association["Type" -> "NumericalVector", "Weight" -> 4], "(f1f2f3f6f9f10)" -> Association["Type" -> "BooleanVector", "Weight" -> 6]], "Processors" -> { MachineLearning`MLProcessor["ToVector", Association[ "Invertibility" -> "Perfect", "Missing" -> "Allowed", "StructurePreserving" -> True, "Input" -> Association[ "f4" -> Association["Type" -> "Numerical", "Weight" -> 1], "f5" -> Association["Type" -> "Numerical", "Weight" -> 1], "f7" -> Association["Type" -> "Numerical", "Weight" -> 1], "f8" -> Association["Type" -> "Numerical", "Weight" -> 1]], "Output" -> Association[ "(f4f5f7f8)" -> Association["Type" -> "NumericalVector", "Weight" -> 4]], "Version" -> {12.3, 0}, "ID" -> 7154538275436769813]], MachineLearning`MLProcessor["ToVector", Association[ "Invertibility" -> "Perfect", "Missing" -> "Allowed", "StructurePreserving" -> True, "Input" -> Association[ "f1" -> Association["Type" -> "Boolean", "Weight" -> 1], "f2" -> Association["Type" -> "Boolean", "Weight" -> 1], "f3" -> Association["Type" -> "Boolean", "Weight" -> 1], "f6" -> Association["Type" -> "Boolean", "Weight" -> 1], "f9" -> Association["Type" -> "Boolean", "Weight" -> 1], "f10" -> Association["Type" -> "Boolean", "Weight" -> 1]], "Output" -> Association[ "(f1f2f3f6f9f10)" -> Association["Type" -> "BooleanVector", "Weight" -> 6]], "Version" -> {12.3, 0}, "ID" -> 7398228362387862015]]}, "Invertibility" -> "Perfect", "StructurePreserving" -> True, "Missing" -> "Allowed"]], MachineLearning`MLProcessor["ConformBooleanVector", Association[ "Invertibility" -> "Perfect", "Missing" -> "Allowed", "StructurePreserving" -> True, "Input" -> Association[ "(f1f2f3f6f9f10)" -> Association["Type" -> "BooleanVector", "Weight" -> 6]], "Version" -> {12.3, 0}, "ID" -> 3751650204265563348, "Output" -> Association[ "(f1f2f3f6f9f10)" -> Association["Type" -> "BooleanVector", "Weight" -> 6]]]], MachineLearning`MLProcessor["BooleanVectorToNumericalVector", Association[ "Invertibility" -> "Perfect", "Missing" -> "Allowed", "StructurePreserving" -> True, "Input" -> Association[ "(f1f2f3f6f9f10)" -> Association["Type" -> "BooleanVector", "Weight" -> 6]], "Version" -> {12.3, 0}, "ID" -> 2685019987694741464, "Output" -> Association[ "(f1f2f3f6f9f10)" -> Association["Type" -> "NumericalVector", "Weight" -> 6]]]], MachineLearning`MLProcessor["MergeVectors", Association[ "Invertibility" -> "Perfect", "Missing" -> "Allowed", "StructurePreserving" -> True, "Input" -> Association[ "(f4f5f7f8)" -> Association["Type" -> "NumericalVector", "Weight" -> 4], "(f1f2f3f6f9f10)" -> Association["Type" -> "NumericalVector", "Weight" -> 6]], "Spans" -> { Span[1, 4], Span[5, 10]}, "Wrappers" -> {Identity, Identity}, "Output" -> Association[ "((f4f5f7f8)(f1f2f3f6f9f10))" -> Association[ "Weight" -> {1., 1., 1., 1., 1., 1., 1., 1., 1., 1.}, "Type" -> "NumericalVector"]], "Version" -> {12.3, 0}, "ID" -> 8406531381003353507]], MachineLearning`MLProcessor["Standardize", Association[ "Invertibility" -> "Perfect", "Missing" -> "Allowed", "StructurePreserving" -> True, "Input" -> Association[ "((f4f5f7f8)(f1f2f3f6f9f10))" -> Association[ "Weight" -> {1., 1., 1., 1., 1., 1., 1., 1., 1., 1.}, "Type" -> "NumericalVector"]], "Mean" -> {24.632, 10.392, 3517.7927107849123`, 2981.388433151245, 0.804, 0.106, 0.764, 0.16, 0.47200000000000003`, 0.402}, "StandardDeviation" -> {6.62786360752844, 1.6181273126673357`, 6041.363324324119, 4822.270799347922, 0.39696851260521904`, 0.3078376195334157, 0.42462218500686, 0.3666060555964672, 0.49921538437832624`, 0.49030194778320024`}, "Output" -> Association[ "((f4f5f7f8)(f1f2f3f6f9f10))" -> Association["Type" -> "NumericalVector", "Weight" -> 10.]], "Version" -> {12.3, 0}, "ID" -> 6350676892446284557]]}, "Invertibility" -> "Perfect", "StructurePreserving" -> True, "Missing" -> "Imputed"]]], "Output" -> Association["Preprocessor" -> MachineLearning`MLProcessor["ToMLDataset", Association[ "Input" -> Association["f1" -> Association["Type" -> "Nominal"]], "Output" -> Association["f1" -> Association["Type" -> "Nominal", "Weight" -> 1]], "Preprocessor" -> MachineLearning`MLProcessor["Sequence", Association["Processors" -> { MachineLearning`MLProcessor["List"], MachineLearning`MLProcessor["WrapMLDataset", Association[ "FeatureTypes" -> {"Nominal"}, "FeatureKeys" -> {"f1"}, "FeatureWeights" -> Automatic, "ExampleWeights" -> Automatic, "RawExample" -> Missing["KeyAbsent", "RawExample"], "StructurePreserving" -> False]]}]], "ScalarFeature" -> True, "Invertibility" -> "Perfect", "StructurePreserving" -> False, "Missing" -> "Allowed"]], "Processor" -> MachineLearning`MLProcessor["Sequence", Association[ "Input" -> Association[ "f1" -> Association["Type" -> "Nominal", "Weight" -> 1]], "Output" -> Association["f1" -> Association["Type" -> "Nominal", "Weight" -> 1]], "Processors" -> { MachineLearning`MLProcessor["ToVector", Association[ "Invertibility" -> "Perfect", "Missing" -> "Allowed", "StructurePreserving" -> True, "Input" -> Association[ "f1" -> Association["Type" -> "Nominal", "Weight" -> 1]], "Output" -> Association[ "f1" -> Association["Type" -> "NominalVector", "Weight" -> 1]], "Version" -> {12.3, 0}, "ID" -> 66260684687212962]], MachineLearning`MLProcessor["IntegerEncodeNominalVector", Association[ "Invertibility" -> "Perfect", "Missing" -> "Allowed", "StructurePreserving" -> True, "Input" -> Association[ "f1" -> Association["Type" -> "NominalVector", "Weight" -> 1]], "Index" -> { Association[0 -> 1, 1 -> 2]}, "MissingCode" -> 0, "Version" -> {12.3, 0}, "ID" -> 7619159391572131345, "Output" -> Association[ "f1" -> Association[ "Type" -> "NominalVector", "Weight" -> 1]]]], MachineLearning`MLProcessor["FromVector", Association[ "Invertibility" -> "Perfect", "Missing" -> "Allowed", "StructurePreserving" -> True, "Input" -> Association[ "f1" -> Association[ "Type" -> "NominalVector", "Weight" -> 1, "SetSize" -> {2}]], "Output" -> Association[ "f1" -> Association["Type" -> "Nominal", "Weight" -> 1]], "Version" -> {12.3, 0}, "ID" -> 827783155253348485]], MachineLearning`MLProcessor["FirstValues", Association[ "Info" -> Association[ "Type" -> "Nominal", "Weight" -> 1, "SetSize" -> 2], "Key" -> "f1", "Invertibility" -> "Perfect", "StructurePreserving" -> False, "Missing" -> "Allowed"]]}, "Invertibility" -> "Perfect", "StructurePreserving" -> False, "Missing" -> "Allowed"]], "ProbabilityPostprocessor" -> Identity, "Name" -> "class", "Marginal" -> Association[0 -> 0.5896414342629482, 1 -> 0.41035856573705176`]], "LabelSplitter" -> MachineLearning`MLProcessor["FeatureLabelSplit", Association[ "Processor" -> MachineLearning`MLProcessor["ListSplit"], "PreferLabeled" -> True, "KeepLabelsFormat" -> False]], "RecalibrationFunction" -> None, "ImputationStrategy" -> Automatic, "Prior" -> Automatic, "Utility" -> SparseArray[ Automatic, {2, 3}, 0., {1, {{0, 1, 2}, {{2}, {3}}}, {1., 1.}}], "Threshold" -> 0, "TieBreaker" -> RandomChoice, "PerformanceGoal" -> Automatic, "BatchProcessing" -> Automatic, "Model" -> Association["Trees" -> { MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{2, 4, 1, 2, 7, 8, 5, 7, 10, 7, 1}], "NumericalThresholds" -> {0.9946039617061616, -0.20965487509965894`, 0.20368486642837527`, 0.9892682433128358, 0.5462826192378999, 2.288970589637757, 0.49391707777976995`, 0.5480149388313295, -0.8163892924785613, 0.5590475499629975, 0.3569940775632859}, "LeafValues" -> RawArray[ "Real32",{-0.28246772289276123`, -0.20274174213409424`, \ -0.2972317934036255, -0.4507780969142914, -0.45395806431770325`, \ -0.4817826449871063, -0.3811538517475128, -0.3972248136997223, \ -0.34661218523979187`, -0.23817551136016846`, -0.46050503849983215`, \ -0.3037935793399811}], "Children" -> RawArray["Integer16",{{4, 2}, {11, 3}, {-3, -4}, {5, -5}, {-1, 6}, { 7, 9}, {8, 10}, {-6, -9}, {-7, -10}, {-8, -11}, {-2, -12}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{2, 9, 1, 5, 4, 4, 2, 9, 6, 8, 1, 7, 2, 7}], "NumericalThresholds" -> {0.9946039617061616, -0.9484192430973052, 0.3569940775632859, -2.0331373214721675`, 1.2015681862831118`, -0.4418590813875198, 0.377995178103447, -0.9472792744636535, -0.3380902707576751, \ -0.43944269418716425`, -0.2538562566041946, 0.5592621862888337, 0.36961247026920324`, -1.8007637262344358`}, "LeafValues" -> RawArray["Real32",{-0.11379241198301315`, -0.04178677126765251, 0.09265776723623276, 0.004428837914019823, 0.06392040103673935, -0.1130148395895958, -0.016950644552707672`, \ -0.003985628020018339, 0.0959237813949585, -0.12138312309980392`, -0.03290383145213127, \ -0.14212816953659058`, -0.008572163060307503, -0.007090551778674126, 0.17898595333099365`}], "Children" -> RawArray["Integer16",{{4, 2}, {-2, 3}, {14, -4}, {-1, 5}, {6, 13}, { 9, 7}, {8, 11}, {-7, -9}, {10, 12}, {-5, -11}, {-8, -12}, {-10, -13}, {-6, -14}, {-3, -15}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{2, 4, 1, 8, 6, 2, 5, 5, 7, 3, 1, 3, 3}], "NumericalThresholds" -> {0.9946039617061616, -0.20965487509965894`, 0.20368486642837527`, 2.290973067283631, -0.3431983441114425, 0.9898003041744233, 0.49221146106719976`, 0.4913096725940705, 0.5593489706516267, -0.5844773352146148, -0.5408473312854766, \ -0.581521451473236, -0.5747654438018798}, "LeafValues" -> RawArray["Real32",{0.02937297336757183, 0.055015455931425095`, 0.05232948437333107, -0.07843836396932602, 0.11794556677341461`, -0.06093272939324379, -0.09440325200557709, \ -0.08992607146501541, 0.15295028686523438`, -0.007890762761235237, 0.01669510267674923, -0.0313449464738369, -0.1090421974658966, 0.14428676664829254`}], "Children" -> RawArray["Integer16",{{4, 2}, {13, 3}, {-3, -4}, {6, 5}, {-5, -6}, { 7, -7}, {8, 9}, {11, -9}, {10, 12}, {-8, -11}, {-1, -12}, {-10, -13}, {-2, -14}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{5, 4, 2, 5, 8, 4, 6, 9, 10, 1, 5}], "NumericalThresholds" -> {-2.0331373214721675`, 1.2015681862831118`, 0.9946039617061616, -2.02499508857727, -0.4436304867267608, \ -0.4418590813875198, -0.3415212929248809, -0.9437983036041259, \ -0.8212886154651641, 0.3569940775632859, 0.4905501604080201}, "LeafValues" -> RawArray["Real32",{-0.10982800275087357`, 0.08634704351425171, -0.08523072302341461, 0.06304624676704407, 0.0010636488441377878`, -0.01729017123579979, 0.030929503962397575`, -0.12647300958633423`, -0.1242932677268982, 0.057089295238256454`, 0.017277151346206665`, 0.13246257603168488`}], "Children" -> RawArray["Integer16",{{-1, 2}, {3, 8}, {4, 10}, {-2, 5}, {7, 6}, {-6, -7}, {-5, -8}, {9, -9}, {-3, -10}, { 11, -11}, {-4, -12}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{10, 10, 10, 3, 9, 9, 4, 10, 6}], "NumericalThresholds" -> {1.227887034416199, 1.223118603229523, 1.2210953235626223`, -0.5833496749401091, 1.0642827153205874`, 1.0566862821578982`, -0.6107302606105803, 1.215612292289734, -0.34527122974395746`}, "LeafValues" -> RawArray["Real32",{-0.005485105328261852, 0.09937714040279388, -0.14057794213294983`, 0.12027261406183243`, 0.05224660411477089, 0.07033649832010269, -0.11312967538833618`, 0.09890403598546982, 0.10729849338531494`, -0.06241648644208908}], "Children" -> RawArray["Integer16",{{2, -2}, {3, 4}, {5, -4}, {-3, 9}, {6, -6}, { 8, 7}, {-7, -8}, {-1, -9}, {-5, -10}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{5, 4, 2, 5, 8, 9, 6, 9, 3, 3, 9, 10}], "NumericalThresholds" -> {-2.0331373214721675`, 1.2015681862831118`, 0.9935066699981691, -2.02499508857727, 2.2892187833786015`, -0.9472792744636535, -0.34522888064384455`, \ -0.9431619644165038, 1.7604972720146181`, -0.5811982750892638, -0.9384241998195647, \ -0.8202808499336242}, "LeafValues" -> RawArray["Real32",{-0.10107319802045822`, 0.07676833868026733, -0.07263188064098358, \ -0.013729282654821873`, -0.06049637869000435, 0.12217910587787628`, -0.0010744675528258085`, \ -0.006031245458871126, -0.1301155388355255, 0.04173324629664421, 0.09070824831724167, 0.1320883184671402, -0.011849921196699142`}], "Children" -> RawArray["Integer16",{{-1, 2}, {3, 8}, {4, 10}, {-2, 5}, {6, 7}, {-5, -7}, {-6, -8}, {9, -9}, {-3, -10}, {-4, 11}, { 12, -12}, {-11, -13}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{5, 10, 2, 7, 10, 8, 6, 6}], "NumericalThresholds" -> {-2.0331373214721675`, \ -0.8277533352375029, 0.9988001286983491, -1.802540838718414, -0.8240376114845275, \ -0.43759447336196894`, -0.3380902707576751, -0.3434351980686187}, "LeafValues" -> RawArray[ "Real32",{-0.09400851279497147, -0.07442595809698105, \ -0.09416601061820984, 0.1167166531085968, -0.05235907435417175, 0.01218756940215826, 0.10398072749376297`, -0.038623373955488205`, 0.017473552376031876`}], "Children" -> RawArray["Integer16",{{-1, 2}, {-2, 3}, {4, 8}, {-3, 5}, {6, 7}, {-5, -7}, {-6, -8}, {-4, -9}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{10, 10, 4, 5, 7, 3, 5, 1, 1, 2, 1}], "NumericalThresholds" -> {1.227887034416199, 1.2253705859184267`, 1.213660538196564, 0.4939893335103989, 0.5590475499629975, -0.5844773352146148, 0.4905501604080201, -0.40114213526248926`, 0.3569940775632859, -0.8505318760871886, 0.35338997840881353`}, "LeafValues" -> RawArray["Real32",{0.02683987468481064, 0.08470366150140762, -0.07503930479288101, \ -0.0020881013479083776`, -0.07839035987854004, -0.0044061848893761635`, 0.025851484388113022`, 0.002719931537285447, 0.12856487929821014`, -0.043117132037878036`, \ -0.11631377041339874`, -0.10330282896757126`}], "Children" -> RawArray["Integer16",{{2, -2}, {3, -3}, {4, 11}, {7, 5}, {6, 10}, {-5, -7}, {9, 8}, {-8, -9}, {-1, -10}, {-6, -11}, {-4, -12}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{1, 5, 10, 10, 2, 9, 7, 6, 9}], "NumericalThresholds" -> { 2.168297648429871, -2.0331373214721675`, -0.82693213224411, \ -0.8248376846313475, -0.8531274795532225, -0.9451540112495421, 0.564066231250763, -0.3439845591783523, -0.9452153146266936}, "LeafValues" -> RawArray["Real32",{-0.08857879787683487, 0.06900189071893692, -0.10140085965394974`, 0.042071662843227386`, -0.16277280449867249`, 0.0154299046844244, -0.008282490074634552, -0.10262588411569595`, \ -0.017811521887779236`, 0.13405157625675201`}], "Children" -> RawArray["Integer16",{{2, -2}, {-1, 3}, {8, 4}, {9, 5}, {6, 7}, {-5, -7}, {-6, -8}, {-3, -9}, {-4, -10}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{4, 3, 9, 3, 4, 8, 3, 4, 9, 10}], "NumericalThresholds" -> {1.213660538196564, 0.8227382600307466, -0.9498173296451567, 0.5635337233543397, 0.4667876064777375, -0.42982073128223414`, -0.5740084946155547, \ -0.23320889472961423`, -0.9437983036041259, -0.8212886154651641}, "LeafValues" -> RawArray["Real32",{-0.16289514303207397`, -0.07371203601360321, 0.08415218442678452, -0.011185657232999802`, \ -0.12108992785215378`, 0.12777866423130035`, 0.042899683117866516`, 0.038330189883708954`, -0.044795140624046326`, \ -0.10550433397293091`, 0.060172807425260544`}], "Children" -> RawArray["Integer16",{{2, 9}, {3, -3}, {6, 4}, {5, -5}, {7, -6}, { 8, -7}, {-4, -8}, {-1, -9}, {10, -10}, {-2, -11}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{10, 10, 10, 9, 6, 9, 1, 6, 9}], "NumericalThresholds" -> {1.227887034416199, 1.223118603229523, 1.2210953235626223`, 1.061862349510193, -0.3402366489171981, 1.0642827153205874`, 0.3569940775632859, -0.3503588438034057, 1.058703660964966}, "LeafValues" -> RawArray["Real32",{0.013907281681895256`, 0.07482340931892395, -0.02766731195151806, 0.10506171733140945`, 0.033709630370140076`, -0.15546397864818573`, 0.06057272478938103, 0.07300766557455063, -0.05542740225791931, \ -0.08789139240980148}], "Children" -> RawArray["Integer16",{{2, -2}, {3, 4}, {6, -4}, {5, -5}, {-3, -6}, { 7, -7}, {9, 8}, {-8, -9}, {-1, -10}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{1, 5, 5, 7, 2, 2, 5, 1, 1, 8, 5, 5, 3, 4, 10}], "NumericalThresholds" -> { 2.168297648429871, -2.0331373214721675`, 0.4939893335103989, 0.5590475499629975, -0.8610092699527739, 0.3734677135944367, 0.4905501604080201, -0.5446600914001464, -0.6940572261810302, \ -0.4361160993576049, 0.4921253472566605, 0.4931399524211884, -0.5794537663459777, -0.4512042254209518, \ -0.8114331066608428}, "LeafValues" -> RawArray["Real32",{-0.08324574679136276, 0.0632319524884224, 0.012032954022288322`, -0.10615351796150208`, \ -0.008923418819904327, 0.06237564608454704, -0.015577285550534725`, 0.06181494891643524, 0.010214737616479397`, 0.1445033699274063, -0.06077848747372627, -0.03733331710100174, 0.0805339366197586, -0.15323764085769653`, -0.067890465259552, 0.13604076206684113`}], "Children" -> RawArray["Integer16",{{2, -2}, {-1, 3}, {7, 4}, {5, 13}, {-4, 6}, {-6, -7}, {8, 11}, {9, 10}, {-3, -10}, {-9, -11}, {15, 12}, {-12, -13}, {-5, 14}, {-14, -15}, {-8, -16}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{4, 4, 3, 9, 9, 2, 9, 1, 6, 1, 2}], "NumericalThresholds" -> {-0.6204328536987304, -0.6182262003421782, \ -0.583036035299301, 1.0553134083747866`, 1.06148898601532, 0.38292969763278967`, 1.0592303872108462`, -0.09474795684218405, -0.3503588438034057, \ -0.2519650608301162, -1.0000000180025095`*^-35}, "LeafValues" -> RawArray["Real32",{-0.0807669535279274, -0.10860709846019745`, 0.002660777885466814, 0.17371921241283417`, 0.016456980258226395`, 0.11840459704399109`, -0.026238197460770607`, \ -0.10964296013116837`, 0.10308130085468292`, -0.04552840441465378, 0.09459187090396881, 0.029247865080833435`}], "Children" -> RawArray["Integer16",{{3, 2}, {-2, 5}, {11, 4}, {-4, -5}, {7, 6}, {-6, -7}, {8, -8}, {10, 9}, {-9, -10}, {-3, -11}, {-1, -12}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{5, 10, 2, 7, 4, 7, 6}], "NumericalThresholds" -> {-2.0331373214721675`, -0.8277533352375029, 0.9988001286983491, -1.802540838718414, 1.8391690254211428`, 0.5460203289985658, -0.3434351980686187}, "LeafValues" -> RawArray[ "Real32",{-0.07818107306957245, -0.061218757182359695`, \ -0.08611754328012466, 0.09558787941932678, 0.058075498789548874`, -0.06765440851449966, 0.0013667166931554675`, 0.017629828304052353`}], "Children" -> RawArray["Integer16",{{-1, 2}, {-2, 3}, {4, 7}, {-3, 5}, { 6, -6}, {-5, -7}, {-4, -8}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{1, 1, 4, 4, 6, 8, 9, 2, 3, 9}], "NumericalThresholds" -> { 2.168297648429871, -1.149351358413696, -0.6212775111198424, \ -0.5498932898044585, -0.3365022242069244, -0.4408219158649444, 1.0567567944526675`, -0.8629825115203856, -0.583036035299301, 1.0568774342536928`}, "LeafValues" -> RawArray["Real32",{0.0556267574429512, 0.05606580525636673, -0.02751605212688446, 0.10038811713457108`, -0.06839734315872192, \ -0.14235612750053406`, -0.05089084059000015, 0.10756261646747589`, 0.0044924188405275345`, 0.13783368468284607`, 0.008490861393511295}], "Children" -> RawArray["Integer16",{{2, -2}, {-1, 3}, {9, 4}, {5, 7}, { 6, -6}, {-4, -7}, {8, -8}, {-5, -9}, {-3, 10}, {-10, -11}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{10, 10, 5, 6, 4, 6, 6, 5, 7}], "NumericalThresholds" -> {1.227887034416199, 1.2253705859184267`, 0.49431209266185766`, -0.35246877372264857`, 1.7197774648666384`, -0.35075858235359186`, -0.3524067103862762, 0.49594092369079595`, 0.5576067864894868}, "LeafValues" -> RawArray["Real32",{-0.09979532659053802, 0.06579889357089996, -0.06416003406047821, 0.05917096138000488, 0.1026785746216774, -0.09006994217634201, 0.01888679340481758, -0.09864611178636551, 0.005464034155011177, -0.06879612803459167}], "Children" -> RawArray["Integer16",{{2, -2}, {3, -3}, {4, 7}, {-1, 5}, { 6, -6}, {-5, -7}, {-4, 8}, {-8, 9}, {-9, -10}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{5, 10, 10, 10, 9, 9, 3, 1}], "NumericalThresholds" -> {-2.0331373214721675`, \ -0.8277533352375029, -0.8240376114845275, -0.8262295722961425, \ -0.9529974162578582, -0.9498173296451567, 0.5635337233543397, -0.23872810602188108`}, "LeafValues" -> RawArray[ "Real32",{-0.07511106878519058, -0.05867835506796837, \ -0.009042284451425076, 0.07876475155353546, 0.10301697999238968`, -0.02216830849647522, 0.010442028753459454`, -0.05527728423476219, \ -0.15162508189678192`}], "Children" -> RawArray["Integer16",{{-1, 2}, {-2, 3}, {4, 5}, {-3, -5}, {-4, 6}, { 8, 7}, {-7, -8}, {-6, -9}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{8, 6, 10, 4, 3, 9, 3, 3, 9, 8, 2}], "NumericalThresholds" -> { 2.290973067283631, -0.3415212929248809, -0.81562402844429, 1.213660538196564, 0.8227382600307466, -0.9498173296451567, 0.517758071422577, -0.5740084946155547, -0.9515863060951232, \ -0.43954972922801966`, -0.24474039673805234`}, "LeafValues" -> RawArray["Real32",{-0.1274843066930771, 0.026812827214598656`, -0.05680535361170769, 0.14041057229042053`, 0.015049098990857601`, 0.08528543263673782, -0.011166729964315891`, -0.0933583527803421, 0.0381106436252594, -0.09266754239797592, 0.016585085541009903`, -0.0647759661078453}], "Children" -> RawArray["Integer16",{{4, 2}, {3, -3}, {-2, -4}, {5, 9}, {6, -6}, { 10, 7}, {8, -8}, {-7, -9}, {-5, -10}, {-1, 11}, {-11, -12}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{2, 7, 9, 7, 7, 3, 9, 10, 9, 9, 3, 3, 5, 10, 4, 8, 9, 8, 2, 4, 3}], "NumericalThresholds" -> {0.9946039617061616, 0.5530049800872804, -0.9439638853073119, 0.5506651699543, 0.55691796541214, -0.581521451473236, -0.9476423561573027, \ -0.820649355649948, 1.0517516732215884`, -0.942828208208084, -0.583538919687271, 0.2773242741823197, 0.496099278330803, 1.223118603229523, -0.20965487509965894`, -0.4320432096719741, 1.0593642592430117`, -0.4360803067684173, 0.36654223501682287`, -0.16288959980010984`, -0.5747654438018798}, "LeafValues" -> RawArray["Real32",{0.16332441568374634`, 0.031107990071177483`, -0.04152211919426918, \ -0.15138177573680878`, -0.07357568293809891, 0.09053770452737808, -0.04838695749640465, 0.14068977534770966`, 0.021346531808376312`, -0.1524510234594345, 0.02967524528503418, -0.053779613226652145`, \ -0.11570779234170914`, 0.08679532259702682, -0.02224348485469818, -0.058576665818691254`, 0.051775768399238586`, -0.03541095182299614, 0.01441084872931242, -0.0627681314945221, 0.10224144160747528`, 0.11196355521678925`}], "Children" -> RawArray["Integer16",{{2, 15}, {3, 5}, {4, 11}, {8, -5}, {7, 6}, { 14, 9}, {-3, 18}, {-1, -9}, {10, 17}, {-7, -11}, {19, 12}, { 13, -13}, {-12, -14}, {-6, -15}, {21, 16}, {-16, -17}, {-10, -18}, {-8, 20}, {-4, -20}, {-19, -21}, {-2, -22}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{10, 10, 10, 3, 9, 9, 6, 2, 6}], "NumericalThresholds" -> {1.227887034416199, 1.223118603229523, 1.2210953235626223`, -0.5833496749401091, 1.060893535614014, 1.0642827153205874`, -0.3467642813920974, -0.8622403144836425, \ -0.350375548005104}, "LeafValues" -> RawArray["Real32",{0.0025560916401445866`, 0.06033267080783844, -0.11831891536712646`, 0.08858782798051834, -0.048395268619060516`, 0.047261640429496765`, 0.047752171754837036`, -0.0547817088663578, 0.019702300429344177`, -0.07058724761009216}], "Children" -> RawArray["Integer16",{{2, -2}, {3, 4}, {6, -4}, {-3, 5}, {-5, -6}, { 7, -7}, {9, 8}, {-8, -9}, {-1, -10}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{5, 10, 2, 7, 10, 8, 5, 6}], "NumericalThresholds" -> {-2.0331373214721675`, \ -0.8277533352375029, 0.9988001286983491, -1.802540838718414, -0.8240376114845275, \ -0.43759447336196894`, -2.027588605880737, -0.3434351980686187}, "LeafValues" -> RawArray[ "Real32",{-0.07021109014749527, -0.05219881236553192, \ -0.08047624677419662, 0.08106954395771027, -0.04515235871076584, 0.06950714439153671, 0.08009843528270721, -0.00540145905688405, 0.012792401947081089`}], "Children" -> RawArray["Integer16",{{-1, 2}, {-2, 3}, {4, 8}, {-3, 5}, {6, 7}, {-5, -7}, {-6, -8}, {-4, -9}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{8, 7, 1, 6, 1, 1, 10, 6, 3, 8, 8, 7, 4, 6}], "NumericalThresholds" -> {-0.4436304867267608, 0.5576067864894868, -1.149351358413696, -0.3434351980686187, \ -0.6931055188179015, -0.5408473312854766, 1.2108204960823061`, -0.34547176957130427`, -0.22744652628898618`, \ -0.4371733665466308, 2.290973067283631, 0.5595479011535646, -0.015494070015847681`, -0.3507079184055328}, "LeafValues" -> RawArray["Real32",{0.05375656858086586, 0.08827731758356094, -0.10347200930118561`, 0.006133949384093285, -0.04044635593891144, 0.08408129960298538, 0.10743118822574615`, -0.0326664038002491, 0.016094837337732315`, -0.11794087290763855`, \ -0.029403990134596825`, 0.03728644177317619, -0.14798787236213684`, 0.11035103350877762`, 0.021340716630220413`}], "Children" -> RawArray["Integer16",{{2, 3}, {4, -3}, {-2, 5}, {-1, -5}, {7, 6}, {-6, 10}, {8, 12}, {9, 13}, {-4, -10}, {14, 11}, {-11, -12}, {-8, -13}, {-9, -14}, {-7, -15}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{4, 4, 6, 1, 9, 6, 6, 6, 1, 5, 2}], "NumericalThresholds" -> {-0.6204328536987304, -0.6182262003421782, \ -0.35060438513755793`, -0.23952846974134442`, 1.0568774342536928`, -0.35290552675724024`, -0.35075858235359186`, \ -0.3470014631748199, -0.704359084367752, 0.4964315593242646, 0.3714872002601624}, "LeafValues" -> RawArray[ "Real32",{-0.06398831307888031, -0.09942092001438141, \ -0.05828031897544861, -0.017169289290905, 0.1538705974817276, 0.018255243077874184`, -0.00190968147944659, \ -0.015946563333272934`, 0.01934155449271202, 0.12747734785079956`, -0.028009828180074692`, \ -0.08644550293684006}], "Children" -> RawArray["Integer16",{{3, 2}, {-2, 6}, {-1, 4}, {-4, 5}, {-5, -6}, {-3, 7}, {9, 8}, {11, 10}, {-7, -10}, {-9, -11}, {-8, -12}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{3, 9, 9, 4, 7, 6, 5, 3, 1, 6, 8, 9, 5}], "NumericalThresholds" -> {-0.5910580456256865, 1.061119616031647, 1.0551514625549319`, -0.6168223917484282, 0.552327185869217, -0.34161131083965296`, 0.49449042975902563`, -0.5825454890727996, -0.09474795684218405, \ -0.3503588438034057, -0.4397531151771545, 1.0642827153205874`, 0.4947826117277146}, "LeafValues" -> RawArray["Real32",{-0.06261944025754929, 0.019714010879397392`, -0.06886085122823715, \ -0.15030814707279205`, -0.033200908452272415`, 0.1385807991027832, -0.004543809685856104, 0.028511883690953255`, 0.11811849474906921`, 0.07474195212125778, -0.040277183055877686`, 0.030362525954842567`, 0.03679408133029938, -0.0457494743168354}], "Children" -> RawArray["Integer16",{{-1, 2}, {3, 5}, {4, 6}, {8, 9}, {12, 7}, { 13, -7}, {-6, -8}, {-2, -9}, {11, 10}, {-10, -11}, {-5, -12}, {-3, -13}, {-4, -14}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{5, 10, 1, 6, 6, 4, 10}], "NumericalThresholds" -> {-2.0331373214721675`, 1.227887034416199, -1.149351358413696, -0.35290552675724024`, \ -0.35140699148178095`, -0.47695702314376826`, -0.8200730383396148}, "LeafValues" -> RawArray["Real32",{-0.06691570580005646, 0.06034402549266815, 0.06166359409689903, -0.05390859395265579, 0.10163062065839767`, -0.025040339678525925`, 0.009936594404280186, 0.03313332796096802}], "Children" -> RawArray["Integer16",{{-1, 2}, {3, -3}, {-2, 4}, {-4, 5}, {7, 6}, {-6, -7}, {-5, -8}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{9, 8, 7, 8, 7, 7, 2, 5, 7, 7, 8, 9, 2, 1, 8, 5, 2, 5, 3}], "NumericalThresholds" -> {-0.9493410289287566, -0.4412997514009475, 0.5507496595382692, -0.4341561943292617, 0.5530049800872804, 0.5567439198493959, -0.8527612388134002, 0.4939893335103989, 0.564066231250763, 0.5607570111751558, -0.4351986348628997, -0.9469074606895446, 0.9946039617061616, 0.3569940775632859, -0.4302958250045776, 0.496099278330803, -1.4696708321571348`, 0.4896071404218674, -0.3827435821294784}, "LeafValues" -> RawArray["Real32",{-0.12663456797599792`, -0.028760425746440887`, 0.060283806174993515`, 0.04779020696878433, -0.08669585734605789, 0.021495867520570755`, 0.0048383986577391624`, 0.08580773323774338, -0.09190991520881653, -0.06439715623855591, 0.09540100395679474, 0.07173727452754974, -0.05926065891981125, 0.09767965972423553, -0.020006608217954636`, -0.04784100130200386, 0.00828627124428749, -0.11795599013566971`, 0.11759018152952194`, 0.055790528655052185`}], "Children" -> RawArray["Integer16",{{2, 5}, {-1, 3}, {-3, 4}, {-4, -5}, {7, 6}, { 18, 8}, {17, 12}, {9, 11}, {10, -10}, {-7, -11}, {-9, 15}, {-8, 13}, {16, 14}, {-14, -15}, {-12, -16}, {-13, -17}, {-2, -18}, {-6, 19}, {-19, -20}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{4, 4, 4, 3, 9, 1, 9, 1, 6, 1}], "NumericalThresholds" -> {-0.6204328536987304, -0.6182262003421782, \ -0.6229110956192015, -0.5824227929115294, 1.06148898601532, 0.2078627794981003, 1.0584094524383547`, -0.09474795684218405, -0.3503588438034057, \ -0.3918608278036117}, "LeafValues" -> RawArray["Real32",{-0.05764861777424812, -0.09339263290166855, 0.0018554902635514736`, 0.07973083108663559, 0.046828147023916245`, -0.00751421507447958, 0.11345936357975006`, -0.08129241317510605, 0.06932307034730911, -0.035023283213377, 0.07400686293840408}], "Children" -> RawArray["Integer16",{{3, 2}, {-2, 5}, {4, -4}, {-1, -5}, {7, 6}, {-6, -7}, {8, -8}, {10, 9}, {-9, -10}, {-3, -11}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{3, 9, 9, 4, 6, 4, 5, 3, 7, 3, 3, 6, 9}], "NumericalThresholds" -> {-0.5910580456256865, 1.061119616031647, 1.0551514625549319`, -0.6168223917484282, -0.3386283069849014, \ -0.6123723983764647, 0.49630032479763037`, -0.5743220448493956, 0.5533436536788942, -0.4942383170127868, -0.5825454890727996, \ -0.3433855324983596, 1.0643647313117983`}, "LeafValues" -> RawArray["Real32",{-0.058326512575149536`, 0.02304753102362156, 0.05857298895716667, -0.1223280057311058, -0.11592449247837067`, \ -0.02629830129444599, 0.016058150678873062`, 0.12099988013505936`, 0.08536171168088913, 0.0027576014399528503`, -0.004213320557028055, 0.10734029859304428`, -0.03656696528196335, \ -0.0028991259168833494`}], "Children" -> RawArray["Integer16",{{-1, 2}, {3, 5}, {4, 6}, {11, 8}, {7, -6}, { 12, -7}, {13, -8}, {9, 10}, {-5, -10}, {-9, -11}, {-2, -12}, {-4, -13}, {-3, -14}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{10, 10, 5, 6, 4, 5, 8, 7, 7, 2}], "NumericalThresholds" -> {1.227887034416199, 1.2253705859184267`, 0.49431209266185766`, -0.35246877372264857`, 1.7197774648666384`, 0.4931399524211884, 2.291669845581055, 0.5462826192378999, -1.7991518974304197`, 0.3754096776247025}, "LeafValues" -> RawArray["Real32",{-0.08755452930927277, 0.05326111614704132, -0.057408735156059265`, -0.05863082781434059, 0.015731271356344223`, -0.07548416405916214, 0.09825050830841064, 0.04899125173687935, -0.01992187276482582, 0.11018669605255127`, -0.1020333394408226}], "Children" -> RawArray["Integer16",{{2, -2}, {3, -3}, {4, 7}, {-1, 5}, { 6, -6}, {-5, -7}, {8, -8}, {9, 10}, {-4, -10}, {-9, -11}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{5, 10, 10, 3, 9, 9, 3, 1, 9}], "NumericalThresholds" -> {-2.0331373214721675`, \ -0.8263826668262481, -0.8248376846313475, -0.3692857772111892, \ -0.9529974162578582, -0.9498173296451567, 0.5635337233543397, -0.23872810602188108`, -0.9472792744636535}, "LeafValues" -> RawArray["Real32",{-0.06381769478321075, -0.1248110830783844, 0.0947592630982399, 0.0790131464600563, -0.05192947015166283, -0.027403321117162704`, 0.010088642127811909`, -0.05118744075298309, \ -0.13957245647907257`, 0.061669524759054184`}], "Children" -> RawArray["Integer16",{{-1, 2}, {4, 3}, {-3, 5}, {-2, 9}, {-4, 6}, { 8, 7}, {-7, -8}, {-6, -9}, {-5, -10}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{1, 7, 7, 7, 6, 6, 10}], "NumericalThresholds" -> {1.7190739512443545`, 0.564066231250763, 0.5630619823932649, 0.5619760751724244, -0.35290552675724024`, -0.35140699148178095`, 1.2146451473236086`}, "LeafValues" -> RawArray[ "Real32",{-0.05337623134255409, -0.0013315515825524926`, \ -0.06776665151119232, 0.08616762608289719, -0.09815308451652527, 0.07769645005464554, 0.0007419019239023328, 0.0740438923239708}], "Children" -> RawArray["Integer16",{{2, 7}, {3, -3}, {4, -4}, {5, -5}, {-1, 6}, {-6, -7}, {-2, -8}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{3, 9, 9, 4, 10, 6, 10, 2, 5, 2, 10, 4}], "NumericalThresholds" -> {-0.5910580456256865, 1.061119616031647, 1.0551514625549319`, -0.6168223917484282, 1.2127711176872256`, -0.3386283069849014, 1.224208474159241, -0.8629825115203856, 0.49630032479763037`, -0.23314782977104184`, 1.2170100808143618`, -0.6172350645065307}, "LeafValues" -> RawArray["Real32",{-0.05517899990081787, 0.008790743537247181, -0.0048638298176229, 0.02453920990228653, -0.04632601514458656, -0.13300298154354095`, \ -0.02380310744047165, -0.07420092821121216, 0.0073662204667925835`, 0.10995166003704071`, 0.09362394362688065, -0.043389685451984406`, 0.05065007135272026}], "Children" -> RawArray["Integer16",{{-1, 2}, {3, 6}, {4, 5}, {10, 7}, {-4, 11}, { 9, -7}, {8, -8}, {-5, -9}, { 12, -10}, {-2, -11}, {-6, -12}, {-3, -13}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{9, 8, 7, 8, 9, 6, 10, 3, 4, 8, 6, 4, 8, 2, 2, 2}], "NumericalThresholds" -> {-0.9493410289287566, \ -0.4412997514009475, 0.5507496595382692, -0.4341561943292617, -0.9415125250816344, \ -0.33491583168506617`, -0.8127984404563903, 0.046939844265580184`, -0.4512042254209518, -0.435415968298912, \ -0.3365022242069244, -0.6204328536987304, -0.44185335934162134`, 0.3835651427507401, -0.23845230042934415`, 0.3801019340753556}, "LeafValues" -> RawArray["Real32",{-0.11908897757530212`, -0.002318295184522867, 0.05496500805020332, 0.04571598023176193, -0.078372061252594, 0.05702004209160805, 0.1193239763379097, 0.11898119747638702`, -0.09588539600372314, -0.01622927002608776, 0.1130552813410759, 0.013009332120418549`, -0.1181936040520668, -0.008801891468465328, 0.04079211875796318, -0.10317884385585785`, \ -0.014912600629031658`}], "Children" -> RawArray["Integer16",{{2, 5}, {-1, 3}, {-3, 4}, {-4, -5}, {6, 8}, { 7, -7}, {14, -8}, {9, 16}, {11, 10}, {-10, -11}, {13, 12}, {-12, -13}, {-6, -14}, {15, -15}, {-2, -16}, {-9, -17}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{3, 3, 7, 7, 7, 7, 6, 4, 8, 10, 2}], "NumericalThresholds" -> {-0.5850712656974791, -0.5818327069282531, 0.5532377958297731, 0.5645610094070436, 0.5630619823932649, 0.5604738891124726, -0.35282145440578455`, -0.2615253329277038, \ -0.43317085504531855`, 1.2164499163627627`, -0.24575550854206082`}, "LeafValues" -> RawArray["Real32",{-0.030517732724547386`, 0.10816465318202972`, -0.07566802203655243, 0.08515068888664246, -0.11106475442647934`, 0.10668563842773438`, -0.11595644056797028`, 0.010290698148310184`, -0.00700045470148325, -0.1423792541027069, \ -0.02443888410925865, 0.0349414199590683}], "Children" -> RawArray["Integer16",{{3, 2}, {11, 4}, {9, 10}, {5, -5}, {6, -6}, { 7, 8}, {-3, -8}, {-7, -9}, {-1, -10}, {-4, -11}, {-2, -12}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{5, 10, 10, 6, 10, 9, 9, 2}], "NumericalThresholds" -> {-2.0331373214721675`, -0.82693213224411, \ -0.8240376114845275, -0.34540908038616175`, -0.8231001496315001, \ -0.9529974162578582, -0.9505229890346526, 1.0000000180025095`*^-35}, "LeafValues" -> RawArray["Real32",{-0.05868002399802208, 0.017692046239972115`, -0.0011709540849551558`, \ -0.06609052419662476, 0.09658203274011612, 0.09280272573232651, -0.08341778814792633, 0.0037753325887024403`, -0.06485161930322647}], "Children" -> RawArray["Integer16",{{-1, 2}, {8, 3}, {4, 5}, {-3, -5}, {-4, 6}, {-6, 7}, {-7, -8}, {-2, -9}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{10, 10, 5, 6, 4, 5, 5, 10, 10, 7, 8}], "NumericalThresholds" -> {1.227887034416199, 1.2253705859184267`, 0.49431209266185766`, -0.35246877372264857`, 1.7197774648666384`, 0.4931399524211884, 0.5008907616138459, -0.8170699179172515, 1.2163570523262026`, 0.5598454773426057, -0.4315378367900848}, "LeafValues" -> RawArray["Real32",{-0.0729067400097847, 0.047844428569078445`, -0.051866695284843445`, 0.004268902353942394, 0.014018497429788113`, -0.06788143515586853, 0.08681480586528778, -0.056268855929374695`, 0.025632517412304878`, -0.09585871547460556, -0.07947301864624023, 0.09509548544883728}], "Children" -> RawArray["Integer16",{{2, -2}, {3, -3}, {4, 7}, {-1, 5}, { 6, -6}, {-5, -7}, {9, 8}, {-8, 11}, { 10, -10}, {-4, -11}, {-9, -12}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{8, 7, 7, 1, 2, 7, 7, 4, 9, 2}], "NumericalThresholds" -> {-0.4436304867267608, 0.5576067864894868, 0.5516092479228974, -1.149351358413696, 1.6101221442222597`, 0.5535474121570588, 0.5557118952274324, -0.6212775111198424, -0.9523951709270476, \ -0.8519331216812133}, "LeafValues" -> RawArray["Real32",{-0.03493637219071388, 0.07091165333986282, -0.09346189349889755, 0.05930103361606598, 0.07899037003517151, 0.052276361733675, 0.08645115047693253, 0.08471483737230301, -0.013024895451962948`, \ -0.07375771552324295, -0.012805797159671783`}], "Children" -> RawArray["Integer16",{{2, 4}, {3, -3}, {-1, -4}, {-2, 5}, {6, -6}, { 9, 7}, {-7, 8}, {-8, -9}, {-5, 10}, {-10, -11}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{9, 8, 9, 9, 10, 6, 4, 4, 6, 6, 6, 9, 4, 4, 10, 3}], "NumericalThresholds" -> {-0.9493410289287566, \ -0.4412997514009475, -0.9529974162578582, -0.942076414823532, \ -0.8127984404563903, -0.33588908612728113`, 0.078564390540123, -0.307495892047882, -0.3365022242069244, \ -0.3416405469179153, -0.34412729740142817`, 1.0547537803649905`, -0.37125894427299494`, 0.09189115837216379, -0.8177161812782286, 0.46568275988101965`}, "LeafValues" -> RawArray["Real32",{-0.11393443495035172`, 0.05479630082845688, 0.05742434784770012, 0.03225291520357132, -0.010349826887249947`, 0.12551657855510712`, 0.08823178708553314, -0.1131189838051796, 0.09046449512243271, -0.12806068360805511`, 0.05036503076553345, -0.0013892954448238015`, \ -0.017575928941369057`, -0.11015000939369202`, -0.00557201262563467, \ -0.08277042955160141, -0.029636505991220474`}], "Children" -> RawArray["Integer16",{{2, 4}, {-1, 3}, {-3, 15}, {5, 7}, {6, -6}, { 13, -7}, {8, 11}, {9, -9}, {10, 12}, {-5, -11}, {-8, -12}, {-10, -13}, {-2, 14}, {-14, -15}, { 16, -16}, {-4, -17}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{5, 10, 10, 3, 4, 9, 9, 1, 4}], "NumericalThresholds" -> {-2.0331373214721675`, \ -0.8262295722961425, -0.8248376846313475, -0.3911129087209701, 0.05109641700983048, -0.9529974162578582, -0.9498173296451567, \ -0.23872810602188108`, 1.040290415287018}, "LeafValues" -> RawArray["Real32",{-0.05646447464823723, -0.1227642074227333, 0.10349228978157043`, 0.061034392565488815`, 0.09599524736404419, -0.05575861409306526, -0.013486920855939388`, 0.006212935782968998, -0.12992677092552185`, \ -0.07040613889694214}], "Children" -> RawArray["Integer16",{{-1, 2}, {4, 3}, {-3, 6}, {-2, 5}, {-5, -6}, {-4, 7}, {8, 9}, {-7, -9}, {-8, -10}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{1, 3, 3, 1, 9, 8, 3, 5, 3, 5, 4}], "NumericalThresholds" -> {2.3958830833435063`, 0.8227382600307466, 0.5819390416145326, -0.4055047929286956, -0.9510375559329985, \ -0.4317405372858047, -0.5740084946155547, 0.4964315593242646, -0.5761171877384185, 0.49004910886287695`, 1.3016189932823183`}, "LeafValues" -> RawArray["Real32",{-0.10579629242420197`, 0.04078599810600281, 0.10666055977344513`, -0.09238333255052567, -0.06365589052438736, 0.00019358255667611957`, 0.014084097929298878`, 0.047225359827280045`, -0.029680320993065834`, \ -0.09041345119476318, 0.0645611435174942, -0.0052194977179169655`}], "Children" -> RawArray["Integer16",{{2, -2}, {3, 4}, {5, -4}, {-3, 10}, {6, 7}, {-1, -7}, {9, 8}, {-8, -9}, {-6, -10}, {-5, 11}, {-11, -12}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{3, 3, 7, 7, 7, 7, 8, 10, 7, 10}], "NumericalThresholds" -> {-0.5850712656974791, -0.5818327069282531, 0.5532377958297731, 0.5645610094070436, 0.5630619823932649, 0.5619760751724244, -0.43317085504531855`, 1.2164499163627627`, 0.5570109188556672, 1.2144270539283755`}, "LeafValues" -> RawArray["Real32",{-0.02696494199335575, 0.1061871200799942, 0.010205598548054695`, 0.07430695742368698, -0.10507312417030334`, 0.09223105758428574, -0.11197467148303986`, -0.1366673856973648, \ -0.023925846442580223`, -0.025170885026454926`, 0.033838775008916855`}], "Children" -> RawArray["Integer16",{{3, 2}, {10, 4}, {7, 8}, {5, -5}, {6, -6}, { 9, -7}, {-1, -8}, {-4, -9}, {-3, -10}, {-2, -11}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{10, 10, 5, 9, 6, 5, 7, 6, 1}], "NumericalThresholds" -> {1.227887034416199, 1.2253705859184267`, 0.49431209266185766`, 1.0642827153205874`, -0.3524067103862762, 0.49594092369079595`, 0.5576067864894868, -0.35246877372264857`, 0.3569940775632859}, "LeafValues" -> RawArray["Real32",{-0.06709199398756027, 0.04400274530053139, -0.045593973249197006`, 0.04761075600981712, 0.08126123249530792, -0.08455123752355576, 0.003804353065788746, -0.053605660796165466`, 0.019861215725541115`, -0.022006303071975708`}], "Children" -> RawArray["Integer16",{{2, -2}, {3, -3}, {4, 5}, {8, -5}, {-4, 6}, {-6, 7}, {-7, -8}, {-1, 9}, {-9, -10}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{5, 10, 10, 3, 4, 2, 9, 7, 5, 5, 10}], "NumericalThresholds" -> {-2.0331373214721675`, \ -0.8262295722961425, -0.8248376846313475, -0.3911129087209701, 0.05109641700983048, -0.8531274795532225, -0.9451540112495421, 0.5530049800872804, 0.4849177300930024, 0.48638170957565313`, -0.819242089986801}, "LeafValues" -> RawArray["Real32",{-0.054849773645401, -0.11576145887374878`, 0.09432215243577957, -0.13206550478935242`, 0.0840216651558876, -0.05243261903524399, 0.11428283154964447`, -0.0808028057217598, 0.03716102987527847, -0.08793489634990692, 0.005879785865545273, 0.017459608614444733`}], "Children" -> RawArray["Integer16",{{-1, 2}, {4, 3}, {-3, 6}, {-2, 5}, {-5, -6}, { 7, 9}, {-4, 8}, {-8, -9}, {11, 10}, {-10, -11}, {-7, -12}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{1, 7, 2, 3, 7, 2, 10, 6, 3, 10, 6, 1}], "NumericalThresholds" -> {1.7190739512443545`, 0.564066231250763, -0.8575963079929351, -0.5778716504573821, 0.5541078746318818, -0.244769349694252, 1.2233945131301882`, -0.3421326130628585, -0.3168442100286483, 1.2265378236770632`, -0.35290552675724024`, 2.619623422622681}, "LeafValues" -> RawArray["Real32",{0.023836569860577583`, 0.06954967975616455, -0.057994939386844635`, 0.01820066012442112, -0.11557790637016296`, -0.07501950114965439, \ -0.09510066360235214, -0.06429354846477509, 0.10355555266141891`, 0.025599783286452293`, 0.06051535904407501, -0.0009630155400373042, \ -0.002784088021144271}], "Children" -> RawArray["Integer16",{{2, 12}, {3, -3}, {4, 6}, {-1, 5}, {-5, 9}, { 7, 10}, {8, -8}, {-4, -9}, {-6, -10}, { 11, -11}, {-7, -12}, {-2, -13}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{3, 3, 7, 8, 7, 7, 7, 10, 3, 5}], "NumericalThresholds" -> {-0.5850712656974791, -0.5818327069282531, 0.5535474121570588, -0.43317085504531855`, 0.5645610094070436, 0.5630619823932649, 0.5619760751724244, 1.218697011470795, -0.5789092481136321, 0.496099278330803}, "LeafValues" -> RawArray["Real32",{-0.01959371380507946, 0.030786026269197464`, -0.04512399435043335, 0.06704197078943253, -0.13331320881843567`, -0.09816192835569382, 0.08432132750749588, -0.10697996616363525`, -0.02689381316304207, 0.005463432986289263, 0.09840323776006699}], "Children" -> RawArray["Integer16",{{3, 2}, {10, 5}, {4, 8}, {-1, -5}, {6, -6}, { 7, -7}, {9, -8}, {-4, -9}, {-3, -10}, {-2, -11}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{7, 9, 9, 8, 8, 6, 6, 4, 9}], "NumericalThresholds" -> {-1.8040050268173216`, 1.0635292530059817`, 1.064914107322693, 2.293729186058045, -0.4265634715557098, -0.3409142047166824, \ -0.34161131083965296`, -0.6229110956192015, -0.9498173296451567}, "LeafValues" -> RawArray["Real32",{-0.03383150324225426, -0.10816341638565063`, 0.10426793992519379`, -0.00040015942067839205`, 0.043002985417842865`, -0.0033177072182297707`, \ -0.11530647426843643`, -0.0457066185772419, -0.005314735695719719, 0.026326939463615417`}], "Children" -> RawArray["Integer16",{{-1, 2}, {4, 3}, {-3, -4}, {5, -5}, {7, 6}, {-6, -7}, {8, 9}, {-2, -9}, {-8, -10}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{5, 10, 10, 3, 4, 9, 9, 4}], "NumericalThresholds" -> {-2.0331373214721675`, \ -0.8262295722961425, -0.8248376846313475, -0.3911129087209701, 0.05109641700983048, -0.9529974162578582, -0.9505229890346526, 1.040290415287018}, "LeafValues" -> RawArray["Real32",{-0.053691256791353226`, -0.11071203649044037`, 0.08622249960899353, 0.060913074761629105`, 0.07415039837360382, -0.04816310107707977, -0.058652184903621674`, 0.00466707069426775, -0.06700655817985535}], "Children" -> RawArray["Integer16",{{-1, 2}, {4, 3}, {-3, 6}, {-2, 5}, {-5, -6}, {-4, 7}, {-7, 8}, {-8, -9}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{3, 3, 1, 9, 7, 7, 4, 8, 5, 10, 6}], "NumericalThresholds" -> {0.8227382600307466, 0.5819390416145326, -0.4055047929286956, -0.9510375559329985, \ -1.7995272278785703`, -1.7944434285163877`, -0.307495892047882, \ -0.4317405372858047, 0.49004910886287695`, -0.8135946691036223, -0.34547176957130427`}, "LeafValues" -> RawArray["Real32",{-0.08792906999588013, 0.10178151726722717`, -0.07823143154382706, -0.05678991600871086, \ -0.09162372350692749, 0.12799254059791565`, -0.007620026357471943, 0.03367585688829422, 0.014404350891709328`, 0.05528849735856056, -0.005630811210721731, \ -0.014291821047663689`}], "Children" -> RawArray["Integer16",{{2, 3}, {4, -3}, {-2, 9}, {8, 5}, {10, 6}, {-6, 7}, {-7, -8}, {-1, -9}, {-4, 11}, {-5, -11}, {-10, -12}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{1, 7, 3, 9, 7, 2, 7, 7, 1, 4, 4, 10}], "NumericalThresholds" -> {1.7190739512443545`, 0.564066231250763, 0.7496035099029542, -0.9510375559329985, 0.5543988049030305, 0.38223026692867285`, -1.7995272278785703`, -1.7944434285163877`, 0.050164995715022094`, -0.08696661517024039, 1.2379853725433352`, 1.2146451473236086`}, "LeafValues" -> RawArray[ "Real32",{-0.10126804560422897`, -0.006557280197739601, \ -0.054089587181806564`, -0.09124533832073212, 0.008189487271010876, 0.09068042784929276, 0.08853966742753983, 0.09599640220403671, 0.0010454666335135698`, -0.0924818143248558, \ -0.012345118448138237`, 0.013081430457532406`, 0.06668461859226227}], "Children" -> RawArray["Integer16",{{2, 12}, {3, -3}, {4, 5}, {10, 7}, { 6, -6}, {-4, 11}, {9, 8}, {-8, -9}, {-5, -10}, {-1, -11}, {-7, -12}, {-2, -13}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{3, 2, 2, 7, 9, 1, 7, 9, 4}], "NumericalThresholds" -> {-0.5910580456256865, 0.9946039617061616, 0.9892682433128358, 0.5460203289985658, -0.9484192430973052, 0.3569940775632859, 0.5482727885246278, -0.9398628771305083, -0.6212775111198424}, "LeafValues" -> RawArray["Real32",{-0.04634443297982216, 0.06550665944814682, -0.049840379506349564`, -0.06753822416067123, 0.03116266056895256, 0.08457233756780624, -0.00621438305824995, 0.053626649081707, -0.12060374021530151`, \ -0.0036542806774377823`}], "Children" -> RawArray["Integer16",{{-1, 2}, {3, 5}, {4, -4}, {-2, 7}, {-3, 6}, {-6, -7}, {8, 9}, {-5, -9}, {-8, -10}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]]}, "ClassNumber" -> 2, "IterationsNumber" -> 50, "Processor" -> MachineLearning`MLProcessor["Sequence", Association[ "Input" -> Association[ "((f4f5f7f8)(f1f2f3f6f9f10))" -> Association["Type" -> "NumericalVector", "Weight" -> 10.]], "Output" -> Association[ "((f4f5f7f8)(f1f2f3f6f9f10))" -> Association["Type" -> "NumericalVector", "Weight" -> 10.]], "Processors" -> { MachineLearning`MLProcessor["DensifyNumericalVector", Association[ "Invertibility" -> "Perfect", "Missing" -> "Allowed", "StructurePreserving" -> True, "Input" -> Association[ "((f4f5f7f8)(f1f2f3f6f9f10))" -> Association["Type" -> "NumericalVector", "Weight" -> 10.]], "Version" -> {12.3, 0}, "ID" -> 53022847167159310, "Output" -> Association[ "((f4f5f7f8)(f1f2f3f6f9f10))" -> Association["Type" -> "NumericalVector", "Weight" -> 10.]]]], MachineLearning`MLProcessor["FirstValues", Association[ "Info" -> Association[ "Type" -> "NumericalVector", "Weight" -> 10.], "Key" -> "((f4f5f7f8)(f1f2f3f6f9f10))", "Invertibility" -> "Perfect", "StructurePreserving" -> False, "Missing" -> "Allowed"]]}, "Invertibility" -> "Perfect", "StructurePreserving" -> False, "Missing" -> "Allowed"]], "Calibrator" -> None, "Method" -> "GradientBoostedTrees", "PostProcessor" -> MachineLearning`MLProcessor["Identity"], "Options" -> Association[ "BoostingMethod" -> Association["Value" -> "Gradient", "Options" -> Association[]], MaxTrainingRounds -> Association["Value" -> 50, "Options" -> Association[]], "LeavesNumber" -> Association["Value" -> 25, "Options" -> Association[]], "LearningRate" -> Association["Value" -> 0.1, "Options" -> Association[]], ValidationSet -> Association["Value" -> Automatic, "Options" -> Association[]], "MaxBinNumber" -> Association["Value" -> 255, "Options" -> Association[]], "ThreadNumber" -> Association["Value" -> 2, "Options" -> Association[]], "MaxDepth" -> Association["Value" -> 6, "Options" -> Association[]], "LeafSize" -> Association["Value" -> 15, "Options" -> Association[]], "FeatureFraction" -> Association["Value" -> 1, "Options" -> Association[]], "BaggingFraction" -> Association["Value" -> 1, "Options" -> Association[]], "BaggingFrequency" -> Association["Value" -> 0, "Options" -> Association[]], "MinGainToSplit" -> Association["Value" -> 0, "Options" -> Association[]], "L1Regularization" -> Association["Value" -> 0, "Options" -> Association[]], "L2Regularization" -> Association["Value" -> 0, "Options" -> Association[]], "LossFunction" -> Association["Value" -> Automatic, "Options" -> Association[]]]], "TrainingInformation" -> Association[ "PanelCell" -> None, "TrainingFunction" -> Classify, "EMIterations" -> Missing["KeyAbsent", "EMIterations"], "ProcessorEntropyShift" -> 0, "PreprocessingTime" -> 2.893001`6.912893577263006, "LossName" -> "MeanCrossEntropy", "BestModelInformation" -> Dataset[ Association[ "MeanCrossEntropy" -> Around[0.6846873873289868, 0.013212609824147065`], "Accuracy" -> Around[0.5928701750112667, 0.030936108312668027`], "EvaluationTime" -> 0.000016172379514909317`, "TestSize" -> 490, "ModelMemory" -> 20360, "ModelUtility" -> -0.31822651676835234`, "TrainingSize" -> 10, "TrainingTime" -> 0.012589254117941668`, "TrainingMemory" -> 64464, "ExperimentCount" -> 1, "MeanCrossEntropyHistory" -> { Around[0.6846873873289868, 0.009342726003826386]}, "AccuracyHistory" -> { Around[0.5928701750112667, 0.021875131971409084`]}, "Configuration" -> { "GradientBoostedTrees", "BoostingMethod" -> "Gradient", MaxTrainingRounds -> 50, "LeavesNumber" -> 25, "LearningRate" -> 0.1, ValidationSet -> Automatic, "MaxBinNumber" -> 255, "ThreadNumber" -> 2, "MaxDepth" -> 6, "LeafSize" -> 15, "FeatureFraction" -> 1, "BaggingFraction" -> 1, "BaggingFrequency" -> 0, "MinGainToSplit" -> 0, "L1Regularization" -> 0, "L2Regularization" -> 0, "LossFunction" -> Automatic}, "FinalTrainingSize" -> 500], TypeSystem`Struct[{ "MeanCrossEntropy", "Accuracy", "EvaluationTime", "TestSize", "ModelMemory", "ModelUtility", "TrainingSize", "TrainingTime", "TrainingMemory", "ExperimentCount", "MeanCrossEntropyHistory", "AccuracyHistory", "Configuration", "FinalTrainingSize"}, { TypeSystem`AnyType, TypeSystem`AnyType, TypeSystem`Atom[Real], TypeSystem`Atom[Integer], TypeSystem`Atom[Integer], TypeSystem`Atom[Real], TypeSystem`Atom[Integer], TypeSystem`Atom[Real], TypeSystem`Atom[Integer], TypeSystem`Atom[Integer], TypeSystem`Vector[TypeSystem`AnyType, 1], TypeSystem`Vector[TypeSystem`AnyType, 1], TypeSystem`Vector[TypeSystem`AnyType, 17], TypeSystem`Atom[Integer]}], Association[]], "Configurations" -> Dataset[ Association[ Association[ "Value" -> "LogisticRegression", "Options" -> Association[ "L1Regularization" -> Association["Value" -> 0], "L2Regularization" -> Association["Value" -> 100000.], "OptimizationMethod" -> Association["Value" -> Automatic], MaxIterations -> Association["Value" -> 30]]] -> Association["Experiments" -> { Association[ "MeanCrossEntropy" -> Around[0.692658352681846, 0.004807567271947119], "Accuracy" -> Around[0.49313137503386084`, 0.040556135389053546`], "EvaluationTime" -> 7.5376662634957014`*^-6, "TestSize" -> 300, "ModelMemory" -> 9088, "ModelUtility" -> -0.3273348687536135, "TrainingSize" -> 10, "TrainingTime" -> 0.012589254117941668`, "TrainingMemory" -> 319680, "ExperimentCount" -> 1, "MeanCrossEntropyHistory" -> { Around[0.692658352681846, 0.0033994634190043184`]}, "AccuracyHistory" -> { Around[0.49313137503386084`, 0.02867751835231948]}], Association[ "MeanCrossEntropy" -> Around[0.6926615997897231, 0.004807582248922156], "Accuracy" -> Around[0.5169188505588942, 0.0405848590715794], "EvaluationTime" -> 3.98107170553497*^-6, "TestSize" -> 300, "ModelMemory" -> 9088, "ModelUtility" -> -0.32733955089853417`, "TrainingSize" -> 70, "TrainingTime" -> 0.007943282347242814, "TrainingMemory" -> 83816, "ExperimentCount" -> 1, "MeanCrossEntropyHistory" -> { Around[0.6926615997897231, 0.0033994740093249284`]}, "AccuracyHistory" -> { Around[0.5169188505588942, 0.028697829063014164`]}], Association[ "MeanCrossEntropy" -> Around[0.6927173496473608, 0.008327306769775044], "Accuracy" -> Around[0.5099816319152365, 0.07051967931980226], "EvaluationTime" -> 3.981071705534969*^-6, "TestSize" -> 100, "ModelMemory" -> 9088, "ModelUtility" -> -0.3284342073733497, "TrainingSize" -> 400, "TrainingTime" -> 0.012589254117941668`, "TrainingMemory" -> 195080, "ExperimentCount" -> 1, "MeanCrossEntropyHistory" -> { Around[0.6927173496473608, 0.005888295085928578]}, "AccuracyHistory" -> { Around[0.5099816319152365, 0.049864943454132914`]}]}, "PredictedPerformances" -> Association[ "EvaluationTime" -> 3.981071705534969*^-6, "MeanCrossEntropy" -> Around[0.6927173496473608, 0.008327306769775044], "ModelMemory" -> 9088, "TrainingMemory" -> 195080, "TrainingTime" -> 0.028325821765368756`], "Index" -> 1], Association[ "Value" -> "DecisionTree", "Options" -> Association[ "DistributionSmoothing" -> Association["Value" -> 1], "FeatureFraction" -> Association["Value" -> 1]]] -> Association["Experiments" -> { Association[ "MeanCrossEntropy" -> Around[0.942831674581134, 0.04835211689547175], "Accuracy" -> Around[0.5000862266844561, 0.031851914616923875`], "EvaluationTime" -> 0.000010841181751658396`, "TestSize" -> 490, "ModelMemory" -> 6312, "ModelUtility" -> -0.6445027913484569, "TrainingSize" -> 10, "TrainingTime" -> 0.05011872336272722, "TrainingMemory" -> 1070680, "ExperimentCount" -> 1, "MeanCrossEntropyHistory" -> { Around[0.942831674581134, 0.03419010974151271]}, "AccuracyHistory" -> { Around[0.5000862266844561, 0.022522704819401784`]}], Association[ "MeanCrossEntropy" -> Around[1.1485483250765534`, 0.07846125121501961], "Accuracy" -> Around[0.5093107677892749, 0.03397364428572245], "EvaluationTime" -> 7.175133578414785*^-6, "TestSize" -> 430, "ModelMemory" -> 6632, "ModelUtility" -> -0.8452348021178129, "TrainingSize" -> 70, "TrainingTime" -> 0.01995262314968879, "TrainingMemory" -> 105072, "ExperimentCount" -> 1, "MeanCrossEntropyHistory" -> { Around[1.1485483250765534`, 0.055480482794521606`]}, "AccuracyHistory" -> { Around[0.5093107677892749, 0.024022994256053944`]}], Association[ "MeanCrossEntropy" -> Around[1.2849160253881657`, 0.178216250672188], "Accuracy" -> Around[0.5099816319152365, 0.07051967931980226], "EvaluationTime" -> 6.30957344480193*^-6, "TestSize" -> 100, "ModelMemory" -> 8424, "ModelUtility" -> -0.9712213425300288, "TrainingSize" -> 400, "TrainingTime" -> 0.03981071705534971, "TrainingMemory" -> 257672, "ExperimentCount" -> 1, "MeanCrossEntropyHistory" -> { Around[1.2849160253881657`, 0.12601791936794574`]}, "AccuracyHistory" -> { Around[0.5099816319152365, 0.049864943454132914`]}]}, "PredictedPerformances" -> Association[ "EvaluationTime" -> 6.30957344480193*^-6, "MeanCrossEntropy" -> Around[1.2849160253881657`, 0.178216250672188], "ModelMemory" -> 8424, "TrainingMemory" -> 257672, "TrainingTime" -> 0.09988211968191436], "Index" -> 2], Association[ "Value" -> "NaiveBayes", "Options" -> Association["SmoothingParameter" -> Association["Value" -> 0.2]]] -> Association["Experiments" -> { Association[ "MeanCrossEntropy" -> Around[1.4391639780645753`, 0.2557633505078575], "Accuracy" -> Around[0.5297836121132563, 0.07040848867954916], "EvaluationTime" -> 0.00007943282347242814, "TestSize" -> 100, "ModelMemory" -> 21800, "ModelUtility" -> -1.0921562453082798`, "TrainingSize" -> 10, "TrainingTime" -> 0.015848931924611134`, "TrainingMemory" -> 69856, "ExperimentCount" -> 1, "MeanCrossEntropyHistory" -> { Around[1.4391639780645753`, 0.18085199952309783`]}, "AccuracyHistory" -> { Around[0.5297836121132563, 0.04978631979840547]}], Association[ "MeanCrossEntropy" -> Around[1.1754011605585502`, 0.1457666196833766], "Accuracy" -> Around[0.5644499255905765, 0.04945634219336507], "EvaluationTime" -> 0.0000501187233627272, "TestSize" -> 200, "ModelMemory" -> 26344, "ModelUtility" -> -0.8792779082263507, "TrainingSize" -> 70, "TrainingTime" -> 0.015848931924611134`, "TrainingMemory" -> 140376, "ExperimentCount" -> 1, "MeanCrossEntropyHistory" -> { Around[1.1754011605585502`, 0.10307256524875605`]}, "AccuracyHistory" -> { Around[0.5644499255905765, 0.03497091493761081]}], Association[ "MeanCrossEntropy" -> Around[0.9799484917940694, 0.1278976021105866], "Accuracy" -> Around[0.5594865824102861, 0.07003276772286877], "EvaluationTime" -> 0.0000501187233627272, "TestSize" -> 100, "ModelMemory" -> 40232, "ModelUtility" -> -0.6986825376687522, "TrainingSize" -> 400, "TrainingTime" -> 0.01995262314968879, "TrainingMemory" -> 524744, "ExperimentCount" -> 1, "MeanCrossEntropyHistory" -> { Around[0.9799484917940694, 0.09043726174989467]}, "AccuracyHistory" -> { Around[0.5594865824102861, 0.04952064496210288]}]}, "PredictedPerformances" -> Association[ "EvaluationTime" -> 0.0000501187233627272, "MeanCrossEntropy" -> Around[0.9799484917940694, 0.1278976021105866], "ModelMemory" -> 40232, "TrainingMemory" -> 524744, "TrainingTime" -> 0.04078971086172212], "Index" -> 3], Association[ "Value" -> "NearestNeighbors", "Options" -> Association[ "NeighborsNumber" -> Association["Value" -> Automatic], "DistributionSmoothing" -> Association["Value" -> 0.5], "NearestMethod" -> Association["Value" -> Automatic]]] -> Association["Experiments" -> { Association[ "MeanCrossEntropy" -> Around[1.0702232244946768`, 0.05842998303813494], "Accuracy" -> Around[0.4749503684152869, 0.03502617088708018], "EvaluationTime" -> 0.000016059935279213177`, "TestSize" -> 400, "ModelMemory" -> 7112, "ModelUtility" -> -0.7718931368387377, "TrainingSize" -> 10, "TrainingTime" -> 0.005011872336272719, "TrainingMemory" -> 85976, "ExperimentCount" -> 1, "MeanCrossEntropyHistory" -> { Around[1.0702232244946768`, 0.04131623723088016]}, "AccuracyHistory" -> { Around[0.4749503684152869, 0.024767242953253228`]}], Association[ "MeanCrossEntropy" -> Around[0.7716086151601412, 0.04833625796593123], "Accuracy" -> Around[0.5000806418162266, 0.07053373477499103], "EvaluationTime" -> 0.00001995262314968879, "TestSize" -> 100, "ModelMemory" -> 12360, "ModelUtility" -> -0.44633950490518237`, "TrainingSize" -> 70, "TrainingTime" -> 0.00630957344480193, "TrainingMemory" -> 171992, "ExperimentCount" -> 1, "MeanCrossEntropyHistory" -> { Around[0.7716086151601412, 0.03417889578489225]}, "AccuracyHistory" -> { Around[0.5000806418162266, 0.04987488216180956]}], Association[ "MeanCrossEntropy" -> Around[0.7068064105821534, 0.04354767765861151], "Accuracy" -> Around[0.579288562608306, 0.08632411063465446], "EvaluationTime" -> 0.00003345215066430626, "TestSize" -> 200, "ModelMemory" -> 40840., "ModelUtility" -> -0.35841840938655556`, "TrainingSize" -> 400, "TrainingTime" -> 0.05011872336272722, "TrainingMemory" -> 2.747581333333333*^6, "ExperimentCount" -> 2, "MeanCrossEntropyHistory" -> { Around[0.748490443429975, 0.02649850187870261], Around[0.665122377734332, 0.014327554395641188`]}, "AccuracyHistory" -> { Around[0.5000806418162266, 0.04987488216180956], Around[0.6584964834003855, 0.04730272977511044]}]}, "PredictedPerformances" -> Association[ "EvaluationTime" -> 0.00003345215066430626, "MeanCrossEntropy" -> Around[0.7068064105821534, 0.04354767765861151], "ModelMemory" -> 40840., "TrainingMemory" -> 2.747581333333333*^6, "TrainingTime" -> 0.06766027653968175], "Index" -> 4], Association[ "Value" -> "RandomForest", "Options" -> Association[ "FeatureFraction" -> Association["Value" -> Automatic], "LeafSize" -> Association["Value" -> Automatic], "TreeNumber" -> Association["Value" -> Automatic], "DistributionSmoothing" -> Association["Value" -> 0.5], "Implementation" -> Association["Value" -> Automatic]]] -> Association["Experiments" -> { Association[ "MeanCrossEntropy" -> Around[0.7237702667982351, 0.019754921442549082`], "Accuracy" -> Around[0.5000806418162266, 0.049874882161809575`], "EvaluationTime" -> 0.00005660722890537325, "TestSize" -> 200, "ModelMemory" -> 97192, "ModelUtility" -> -0.37533918537170363`, "TrainingSize" -> 10, "TrainingTime" -> 0.03162277660168379, "TrainingMemory" -> 179760, "ExperimentCount" -> 1, "MeanCrossEntropyHistory" -> { Around[0.7237702667982351, 0.013968838913833988`]}, "AccuracyHistory" -> { Around[0.5000806418162266, 0.03526686738749552]}], Association[ "MeanCrossEntropy" -> Around[0.7084618631408025, 0.016567678104440885`], "Accuracy" -> Around[0.5000806418162266, 0.07053373477499103], "EvaluationTime" -> 0.000039810717055349695`, "TestSize" -> 100, "ModelMemory" -> 97464, "ModelUtility" -> -0.3531835326954468, "TrainingSize" -> 70, "TrainingTime" -> 0.012589254117941668`, "TrainingMemory" -> 178464, "ExperimentCount" -> 1, "MeanCrossEntropyHistory" -> { Around[0.7084618631408025, 0.011715117536166036`]}, "AccuracyHistory" -> { Around[0.5000806418162266, 0.04987488216180956]}], Association[ "MeanCrossEntropy" -> Around[0.6979134347917816, 0.007343609297358526], "Accuracy" -> Around[0.40602123587563227`, 0.07292384435571113], "EvaluationTime" -> 0.000037788092188698256`, "TestSize" -> 200, "ModelMemory" -> 119522.66666666666`, "ModelUtility" -> -0.3356210001080919, "TrainingSize" -> 400, "TrainingTime" -> 0.023842674300353785`, "TrainingMemory" -> 370608., "ExperimentCount" -> 2, "MeanCrossEntropyHistory" -> { Around[0.7031743337390263, 0.010398199246676895`], Around[0.6926525358445369, 0.005888036014965507]}, "AccuracyHistory" -> { Around[0.4703776715191968, 0.049787277212341226`], Around[0.34166480023206774`, 0.0473081038263069]}]}, "PredictedPerformances" -> Association[ "EvaluationTime" -> 0.000037788092188698256`, "MeanCrossEntropy" -> Around[0.6979134347917816, 0.007343609297358526], "ModelMemory" -> 119522.66666666666`, "TrainingMemory" -> 370608., "TrainingTime" -> 0.06142611947712602], "Index" -> 5], Association[ "Value" -> "LogisticRegression", "Options" -> Association[ "L1Regularization" -> Association["Value" -> 0], "L2Regularization" -> Association["Value" -> 0.1], "OptimizationMethod" -> Association["Value" -> Automatic], MaxIterations -> Association["Value" -> 30]]] -> Association["Experiments" -> { Association[ "MeanCrossEntropy" -> Around[2.442620624111142, 0.19421412099550087`], "Accuracy" -> Around[0.486136941187794, 0.031724963644630885`], "EvaluationTime" -> 4.2726855425331836`*^-6, "TestSize" -> 490, "ModelMemory" -> 9088, "ModelUtility" -> -1.6020145636110579`, "TrainingSize" -> 10, "TrainingTime" -> 0.01995262314968879, "TrainingMemory" -> 81944, "ExperimentCount" -> 1, "MeanCrossEntropyHistory" -> { Around[2.442620624111142, 0.1373301219581033]}, "AccuracyHistory" -> { Around[0.486136941187794, 0.022432936926015185`]}], Association[ "MeanCrossEntropy" -> Around[0.8226111381199295, 0.039013521127060746`], "Accuracy" -> Around[0.5451362919040729, 0.033698259018898134`], "EvaluationTime" -> 7.41485465533054*^-6, "TestSize" -> 430, "ModelMemory" -> 9088, "ModelUtility" -> -0.5073349832734241, "TrainingSize" -> 70, "TrainingTime" -> 0.01995262314968879, "TrainingMemory" -> 94192, "ExperimentCount" -> 1, "MeanCrossEntropyHistory" -> { Around[0.8226111381199295, 0.02758672534690929]}, "AccuracyHistory" -> { Around[0.5451362919040729, 0.023828267466443603`]}], Association[ "MeanCrossEntropy" -> Around[0.7627395724597211, 0.039437781269565104`], "Accuracy" -> Around[0.4901796517172166, 0.07052012994032744], "EvaluationTime" -> 3.981071705534969*^-6, "TestSize" -> 100, "ModelMemory" -> 9088, "ModelUtility" -> -0.4326154343117441, "TrainingSize" -> 400, "TrainingTime" -> 0.03162277660168379, "TrainingMemory" -> 195080, "ExperimentCount" -> 1, "MeanCrossEntropyHistory" -> { Around[0.7627395724597211, 0.027886722570661295`]}, "AccuracyHistory" -> { Around[0.4901796517172166, 0.049865262090962016`]}]}, "PredictedPerformances" -> Association[ "EvaluationTime" -> 3.981071705534969*^-6, "MeanCrossEntropy" -> Around[0.7627395724597211, 0.039437781269565104`], "ModelMemory" -> 9088, "TrainingMemory" -> 195080, "TrainingTime" -> 0.05948109390179353], "Index" -> 6], Association[ "Value" -> "LogisticRegression", "Options" -> Association[ "L1Regularization" -> Association["Value" -> 0], "L2Regularization" -> Association["Value" -> 0.0001], "OptimizationMethod" -> Association["Value" -> Automatic], MaxIterations -> Association["Value" -> 30]]] -> Association["Experiments" -> { Association[ "MeanCrossEntropy" -> Around[6.624973268464014, 0.539803278840754], "Accuracy" -> Around[0.4776833958729391, 0.031767576608218905`], "EvaluationTime" -> 3.333330228030076*^-6, "TestSize" -> 490, "ModelMemory" -> 9088, "ModelUtility" -> -2.6001770722066433`, "TrainingSize" -> 10, "TrainingTime" -> 0.03162277660168379, "TrainingMemory" -> 82240, "ExperimentCount" -> 1, "MeanCrossEntropyHistory" -> { Around[6.624973268464014, 0.3816985589750299]}, "AccuracyHistory" -> { Around[0.4776833958729391, 0.022463068841534727`]}], Association[ "MeanCrossEntropy" -> Around[0.8263586315620132, 0.03974592245525732], "Accuracy" -> Around[0.5428716958220009, 0.03369677505956971], "EvaluationTime" -> 4.980247958505863*^-6, "TestSize" -> 430, "ModelMemory" -> 9088, "ModelUtility" -> -0.5120132173355927, "TrainingSize" -> 70, "TrainingTime" -> 0.025118864315095794`, "TrainingMemory" -> 94200, "ExperimentCount" -> 1, "MeanCrossEntropyHistory" -> { Around[0.8263586315620132, 0.028104611292627122`]}, "AccuracyHistory" -> { Around[0.5428716958220009, 0.023827218148739466`]}], Association[ "MeanCrossEntropy" -> Around[0.7628330966242052, 0.03946987160694067], "Accuracy" -> Around[0.4901796517172166, 0.07052012994032744], "EvaluationTime" -> 6.30957344480193*^-6, "TestSize" -> 100, "ModelMemory" -> 9088, "ModelUtility" -> -0.4327451177117626, "TrainingSize" -> 400, "TrainingTime" -> 0.03981071705534971, "TrainingMemory" -> 195016, "ExperimentCount" -> 1, "MeanCrossEntropyHistory" -> { Around[0.7628330966242052, 0.027909413865830117`]}, "AccuracyHistory" -> { Around[0.4901796517172166, 0.049865262090962016`]}]}, "PredictedPerformances" -> Association[ "EvaluationTime" -> 6.30957344480193*^-6, "MeanCrossEntropy" -> Around[0.7628330966242052, 0.03946987160694067], "ModelMemory" -> 9088, "TrainingMemory" -> 195016, "TrainingTime" -> 0.08138617292087094], "Index" -> 7], Association[ "Value" -> "LogisticRegression", "Options" -> Association[ "L1Regularization" -> Association["Value" -> 0], "L2Regularization" -> Association["Value" -> 100.], "OptimizationMethod" -> Association["Value" -> Automatic], MaxIterations -> Association["Value" -> 30]]] -> Association["Experiments" -> { Association[ "MeanCrossEntropy" -> Around[0.6991063679364197, 0.004359531597980095], "Accuracy" -> Around[0.48991652608577385`, 0.03184506128528466], "EvaluationTime" -> 3.068493267105747*^-6, "TestSize" -> 490, "ModelMemory" -> 9088, "ModelUtility" -> -0.3364600949479881, "TrainingSize" -> 10, "TrainingTime" -> 0.01, "TrainingMemory" -> 75216, "ExperimentCount" -> 1, "MeanCrossEntropyHistory" -> { Around[0.6991063679364197, 0.003082654355728751]}, "AccuracyHistory" -> { Around[0.48991652608577385`, 0.022517858782125973`]}], Association[ "MeanCrossEntropy" -> Around[0.6971950262400385, 0.008320865392307081], "Accuracy" -> Around[0.5526717388465917, 0.033535075755444614`], "EvaluationTime" -> 5.650107332190072*^-6, "TestSize" -> 430, "ModelMemory" -> 9088, "ModelUtility" -> -0.33486009009858264`, "TrainingSize" -> 70, "TrainingTime" -> 0.015848931924611134`, "TrainingMemory" -> 92824, "ExperimentCount" -> 1, "MeanCrossEntropyHistory" -> { Around[0.6971950262400385, 0.005883740344240799]}, "AccuracyHistory" -> { Around[0.5526717388465917, 0.023712879474279466`]}], Association[ "MeanCrossEntropy" -> Around[0.7260712833897033, 0.02452566724932744], "Accuracy" -> Around[0.48027866161820676`, 0.07047884879766003], "EvaluationTime" -> 3.981071705534969*^-6, "TestSize" -> 100, "ModelMemory" -> 9088, "ModelUtility" -> -0.3797919957999203, "TrainingSize" -> 400, "TrainingTime" -> 0.03162277660168379, "TrainingMemory" -> 195256, "ExperimentCount" -> 1, "MeanCrossEntropyHistory" -> { Around[0.7260712833897033, 0.017342265625124254`]}, "AccuracyHistory" -> { Around[0.48027866161820676`, 0.049836071915046756`]}]}, "PredictedPerformances" -> Association[ "EvaluationTime" -> 3.981071705534969*^-6, "MeanCrossEntropy" -> Around[0.7260712833897033, 0.02452566724932744], "ModelMemory" -> 9088, "TrainingMemory" -> 195256, "TrainingTime" -> 0.049528470752104745`], "Index" -> 8], Association[ "Value" -> "LogisticRegression", "Options" -> Association[ "L1Regularization" -> Association["Value" -> 0], "L2Regularization" -> Association["Value" -> 0.01], "OptimizationMethod" -> Association["Value" -> Automatic], MaxIterations -> Association["Value" -> 30]]] -> Association["Experiments" -> { Association[ "MeanCrossEntropy" -> Around[3.7813268364390744`, 0.3059862274871794], "Accuracy" -> Around[0.4818462966194605, 0.03176696928093725], "EvaluationTime" -> 2.8581257914449814`*^-6, "TestSize" -> 490, "ModelMemory" -> 9088, "ModelUtility" -> -2.0392955396034864`, "TrainingSize" -> 10, "TrainingTime" -> 0.025118864315095794`, "TrainingMemory" -> 81880, "ExperimentCount" -> 1, "MeanCrossEntropyHistory" -> { Around[3.7813268364390744`, 0.2163649364058741]}, "AccuracyHistory" -> { Around[0.4818462966194605, 0.02246263939629547]}], Association[ "MeanCrossEntropy" -> Around[0.8259696588128372, 0.03966962634619469], "Accuracy" -> Around[0.5428716958220009, 0.03369677505956971], "EvaluationTime" -> 6.795201275521555*^-6, "TestSize" -> 430, "ModelMemory" -> 9088, "ModelUtility" -> -0.511528589949634, "TrainingSize" -> 70, "TrainingTime" -> 0.01995262314968879, "TrainingMemory" -> 94432, "ExperimentCount" -> 1, "MeanCrossEntropyHistory" -> { Around[0.8259696588128372, 0.02805066179653079]}, "AccuracyHistory" -> { Around[0.5428716958220009, 0.023827218148739466`]}], Association[ "MeanCrossEntropy" -> Around[0.7628238127290095, 0.03946668713206145], "Accuracy" -> Around[0.4901796517172166, 0.07052012994032744], "EvaluationTime" -> 5.01187233627272*^-6, "TestSize" -> 100, "ModelMemory" -> 9088, "ModelUtility" -> -0.432732244342486, "TrainingSize" -> 400, "TrainingTime" -> 0.03981071705534971, "TrainingMemory" -> 195016, "ExperimentCount" -> 1, "MeanCrossEntropyHistory" -> { Around[0.7628238127290095, 0.027907162102048504`]}, "AccuracyHistory" -> { Around[0.4901796517172166, 0.049865262090962016`]}]}, "PredictedPerformances" -> Association[ "EvaluationTime" -> 5.01187233627272*^-6, "MeanCrossEntropy" -> Around[0.7628238127290095, 0.03946668713206145], "ModelMemory" -> 9088, "TrainingMemory" -> 195016, "TrainingTime" -> 0.07488226063428294], "Index" -> 9], Association[ "Value" -> "LogisticRegression", "Options" -> Association[ "L1Regularization" -> Association["Value" -> 0], "L2Regularization" -> Association["Value" -> 10000.], "OptimizationMethod" -> Association["Value" -> Automatic], MaxIterations -> Association["Value" -> 30]]] -> Association["Experiments" -> { Association[ "MeanCrossEntropy" -> Around[0.692636851399596, 0.004114718388112021], "Accuracy" -> Around[0.5549592110026919, 0.03351508511326898], "EvaluationTime" -> 4.438735926118199*^-6, "TestSize" -> 430, "ModelMemory" -> 9088, "ModelUtility" -> -0.32710406267457526`, "TrainingSize" -> 70, "TrainingTime" -> 0.01, "TrainingMemory" -> 85184, "ExperimentCount" -> 1, "MeanCrossEntropyHistory" -> { Around[0.692636851399596, 0.00290954527490699]}, "AccuracyHistory" -> { Around[0.5549592110026919, 0.0236987439556368]}], Association[ "MeanCrossEntropy" -> Around[0.6925907119148926, 0.004232190376259261], "Accuracy" -> Around[0.5693875725092961, 0.06877433075410107], "EvaluationTime" -> 4.324671915780886*^-6, "TestSize" -> 200, "ModelMemory" -> 9088., "ModelUtility" -> -0.32707140680024516`, "TrainingSize" -> 400, "TrainingTime" -> 0.013675813386831489`, "TrainingMemory" -> 195325.3333333333, "ExperimentCount" -> 2, "MeanCrossEntropyHistory" -> { Around[0.6932967891988984, 0.00590805136702492], Around[0.6918846346308869, 0.005894645829956957]}, "AccuracyHistory" -> { Around[0.5099816319152365, 0.049864943454132914`], Around[0.6287935131033556, 0.04819186002346042]}]}, "PredictedPerformances" -> Association[ "EvaluationTime" -> 4.324671915780886*^-6, "MeanCrossEntropy" -> Around[0.6925907119148926, 0.004232190376259261], "ModelMemory" -> 9088., "TrainingMemory" -> 195325.3333333333, "TrainingTime" -> 0.02709476673353936], "Index" -> 10], Association[ "Value" -> "LogisticRegression", "Options" -> Association[ "L1Regularization" -> Association["Value" -> 0], "L2Regularization" -> Association["Value" -> 10.], "OptimizationMethod" -> Association["Value" -> Automatic], MaxIterations -> Association["Value" -> 30]]] -> Association["Experiments" -> { Association[ "MeanCrossEntropy" -> Around[0.7402021633169515, 0.023141193786312728`], "Accuracy" -> Around[0.5446010022984445, 0.03368391878557934], "EvaluationTime" -> 7.41485465533054*^-6, "TestSize" -> 430, "ModelMemory" -> 9088, "ModelUtility" -> -0.3985673457509543, "TrainingSize" -> 70, "TrainingTime" -> 0.01995262314968879, "TrainingMemory" -> 94424, "ExperimentCount" -> 1, "MeanCrossEntropyHistory" -> { Around[0.7402021633169515, 0.016363295051053727`]}, "AccuracyHistory" -> { Around[0.5446010022984445, 0.023818127390220087`]}], Association[ "MeanCrossEntropy" -> Around[0.7548990500210783, 0.036667002588448586`], "Accuracy" -> Around[0.5000806418162266, 0.07053373477499103], "EvaluationTime" -> 5.01187233627272*^-6, "TestSize" -> 100, "ModelMemory" -> 9088, "ModelUtility" -> -0.4216623614745957, "TrainingSize" -> 400, "TrainingTime" -> 0.03162277660168379, "TrainingMemory" -> 195080, "ExperimentCount" -> 1, "MeanCrossEntropyHistory" -> { Around[0.7548990500210783, 0.025927486176076684`]}, "AccuracyHistory" -> { Around[0.5000806418162266, 0.04987488216180956]}]}, "PredictedPerformances" -> Association[ "EvaluationTime" -> 5.01187233627272*^-6, "MeanCrossEntropy" -> Around[0.7548990500210783, 0.036667002588448586`], "ModelMemory" -> 9088, "TrainingMemory" -> 195080, "TrainingTime" -> 0.05948109390179353], "Index" -> 11], Association[ "Value" -> "LogisticRegression", "Options" -> Association[ "L1Regularization" -> Association["Value" -> 0], "L2Regularization" -> Association["Value" -> 0.001], "OptimizationMethod" -> Association["Value" -> Automatic], MaxIterations -> Association["Value" -> 30]]] -> Association["Experiments" -> { Association[ "MeanCrossEntropy" -> Around[0.826323101437631, 0.039738949201182906`], "Accuracy" -> Around[0.5428716958220009, 0.03369677505956971], "EvaluationTime" -> 6.795201275521555*^-6, "TestSize" -> 430, "ModelMemory" -> 9088, "ModelUtility" -> -0.5119689601952557, "TrainingSize" -> 70, "TrainingTime" -> 0.01995262314968879, "TrainingMemory" -> 94320, "ExperimentCount" -> 1, "MeanCrossEntropyHistory" -> { Around[0.826323101437631, 0.028099680457384167`]}, "AccuracyHistory" -> { Around[0.5428716958220009, 0.023827218148739466`]}], Association[ "MeanCrossEntropy" -> Around[0.7628322531169399, 0.03946958199626084], "Accuracy" -> Around[0.4901796517172166, 0.07052012994032744], "EvaluationTime" -> 6.30957344480193*^-6, "TestSize" -> 100, "ModelMemory" -> 9088, "ModelUtility" -> -0.43274394812792105`, "TrainingSize" -> 400, "TrainingTime" -> 0.03162277660168379, "TrainingMemory" -> 195080, "ExperimentCount" -> 1, "MeanCrossEntropyHistory" -> { Around[0.7628322531169399, 0.02790920908015451]}, "AccuracyHistory" -> { Around[0.4901796517172166, 0.049865262090962016`]}]}, "PredictedPerformances" -> Association[ "EvaluationTime" -> 6.30957344480193*^-6, "MeanCrossEntropy" -> Around[0.7628322531169399, 0.03946958199626084], "ModelMemory" -> 9088, "TrainingMemory" -> 195080, "TrainingTime" -> 0.05948109390179353], "Index" -> 12], Association[ "Value" -> "LogisticRegression", "Options" -> Association[ "L1Regularization" -> Association["Value" -> 0], "L2Regularization" -> Association["Value" -> 1.], "OptimizationMethod" -> Association["Value" -> Automatic], MaxIterations -> Association["Value" -> 30]]] -> Association["Experiments" -> { Association[ "MeanCrossEntropy" -> Around[0.7995476547154556, 0.034654482737103476`], "Accuracy" -> Around[0.5422370860080747, 0.03376493949964979], "EvaluationTime" -> 4.678457003033954*^-6, "TestSize" -> 430, "ModelMemory" -> 9088, "ModelUtility" -> -0.47808810114658995`, "TrainingSize" -> 70, "TrainingTime" -> 0.025118864315095794`, "TrainingMemory" -> 94320, "ExperimentCount" -> 1, "MeanCrossEntropyHistory" -> { Around[0.7995476547154556, 0.024504419741918013`]}, "AccuracyHistory" -> { Around[0.5422370860080747, 0.023875417686555874`]}], Association[ "MeanCrossEntropy" -> Around[0.7619127232448369, 0.0391531313602023], "Accuracy" -> Around[0.4901796517172166, 0.07052012994032744], "EvaluationTime" -> 3.981071705534969*^-6, "TestSize" -> 100, "ModelMemory" -> 9088, "ModelUtility" -> -0.43146794504702246`, "TrainingSize" -> 400, "TrainingTime" -> 0.03162277660168379, "TrainingMemory" -> 195080, "ExperimentCount" -> 1, "MeanCrossEntropyHistory" -> { Around[0.7619127232448369, 0.027685444689486718`]}, "AccuracyHistory" -> { Around[0.4901796517172166, 0.049865262090962016`]}]}, "PredictedPerformances" -> Association[ "EvaluationTime" -> 3.981071705534969*^-6, "MeanCrossEntropy" -> Around[0.7619127232448369, 0.0391531313602023], "ModelMemory" -> 9088, "TrainingMemory" -> 195080, "TrainingTime" -> 0.06464733506720054], "Index" -> 13], Association[ "Value" -> "GradientBoostedTrees", "Options" -> Association[ "BoostingMethod" -> Association["Value" -> "Gradient"], MaxTrainingRounds -> Association["Value" -> 50], "LeavesNumber" -> Association["Value" -> 25], "LearningRate" -> Association["Value" -> 0.1], ValidationSet -> Association["Value" -> Automatic], "MaxBinNumber" -> Association["Value" -> 255], "ThreadNumber" -> Association["Value" -> 2], "MaxDepth" -> Association["Value" -> 6], "LeafSize" -> Association["Value" -> 15], "FeatureFraction" -> Association["Value" -> 1], "BaggingFraction" -> Association["Value" -> 1], "BaggingFrequency" -> Association["Value" -> 0], "MinGainToSplit" -> Association["Value" -> 0], "L1Regularization" -> Association["Value" -> 0], "L2Regularization" -> Association["Value" -> 0], "LossFunction" -> Association["Value" -> Automatic]]] -> Association["Experiments" -> { Association[ "MeanCrossEntropy" -> Around[0.8027146262123053, 0.048956317945488874`], "Accuracy" -> Around[0.5200506149380347, 0.04060441549342177], "EvaluationTime" -> 0.00009031990838464046, "TestSize" -> 300, "ModelMemory" -> 117280, "ModelUtility" -> -0.48554664278522996`, "TrainingSize" -> 70, "TrainingTime" -> 0.03981071705534971, "TrainingMemory" -> 315952, "ExperimentCount" -> 1, "MeanCrossEntropyHistory" -> { Around[0.8027146262123053, 0.03461734440117985]}, "AccuracyHistory" -> { Around[0.5200506149380347, 0.028711657541514644`]}], Association[ "MeanCrossEntropy" -> Around[0.8570503183021748, 0.08703659727704363], "Accuracy" -> Around[0.4901796517172166, 0.07052012994032744], "EvaluationTime" -> 0.000039810717055349695`, "TestSize" -> 100, "ModelMemory" -> 127256, "ModelUtility" -> -0.5590284420843604, "TrainingSize" -> 400, "TrainingTime" -> 0.05011872336272722, "TrainingMemory" -> 492392, "ExperimentCount" -> 1, "MeanCrossEntropyHistory" -> { Around[0.8570503183021748, 0.061544168146000146`]}, "AccuracyHistory" -> { Around[0.4901796517172166, 0.049865262090962016`]}], Association[ "MeanCrossEntropy" -> Around[0.6846873873289868, 0.013212609824147065`], "Accuracy" -> Around[0.5928701750112667, 0.030936108312668027`], "EvaluationTime" -> 0.000016172379514909317`, "TestSize" -> 490, "ModelMemory" -> 20360, "ModelUtility" -> -0.31822651676835234`, "TrainingSize" -> 10, "TrainingTime" -> 0.012589254117941668`, "TrainingMemory" -> 64464, "ExperimentCount" -> 1, "MeanCrossEntropyHistory" -> { Around[0.6846873873289868, 0.009342726003826386]}, "AccuracyHistory" -> { Around[0.5928701750112667, 0.021875131971409084`]}]}, "PredictedPerformances" -> Association[ "EvaluationTime" -> 0.000016172379514909317`, "MeanCrossEntropy" -> Around[0.6846873873289868, 0.013212609824147065`], "ModelMemory" -> 20360, "TrainingMemory" -> 64464, "TrainingTime" -> 0.10275698764505806`], "Index" -> 14]], TypeSystem`Assoc[ TypeSystem`Struct[{"Value", "Options"}, { TypeSystem`Atom[ TypeSystem`Enumeration[ "DecisionTree", "GradientBoostedTrees", "LogisticRegression", "NaiveBayes", "NearestNeighbors", "RandomForest"]], TypeSystem`Assoc[TypeSystem`AnyType, TypeSystem`Struct[{"Value"}, {TypeSystem`AnyType}], TypeSystem`AnyLength]}], TypeSystem`Struct[{"Experiments", "PredictedPerformances", "Index"}, { TypeSystem`Vector[ TypeSystem`Struct[{ "MeanCrossEntropy", "Accuracy", "EvaluationTime", "TestSize", "ModelMemory", "ModelUtility", "TrainingSize", "TrainingTime", "TrainingMemory", "ExperimentCount", "MeanCrossEntropyHistory", "AccuracyHistory"}, {TypeSystem`AnyType, TypeSystem`AnyType, TypeSystem`Atom[Real], TypeSystem`Atom[Integer], TypeSystem`Atom[Real], TypeSystem`Atom[Real], TypeSystem`Atom[Integer], TypeSystem`Atom[Real], TypeSystem`Atom[Real], TypeSystem`Atom[Integer], TypeSystem`Vector[TypeSystem`AnyType, TypeSystem`AnyLength], TypeSystem`Vector[TypeSystem`AnyType, TypeSystem`AnyLength]}], TypeSystem`AnyLength], TypeSystem`Struct[{ "EvaluationTime", "MeanCrossEntropy", "ModelMemory", "TrainingMemory", "TrainingTime"}, { TypeSystem`Atom[Real], TypeSystem`AnyType, TypeSystem`Atom[Real], TypeSystem`Atom[Real], TypeSystem`Atom[Real]}], TypeSystem`Atom[Integer]}], 14], Association[]], "MaxTrainingSize" -> 500, "PreprocessorEvaluationTime" -> 6.2265625*^-6, "PreprocessorMemory" -> 153144, "InputDimension" -> 10, "OutputDimension" -> 1, "BaselineLogProbability" -> -0.676988786523252, "VariableBudget" -> True, "CheckpointingInfo" -> Association["Checkpointing" -> False], "UserStop" -> False, "NaturalStop" -> True, "AbortStop" -> False, "RoundPartitioning" -> Dataset[{ Association[ "TrainingSizes" -> 10, "TimeBudgets" -> 0.28351409800000005`, "ElapsedTimes" -> 0.30385, "ExperimentCounts" -> 10], Association[ "TrainingSizes" -> 70, "TimeBudgets" -> 0.40502014000000003`, "ElapsedTimes" -> 0.4427789999999999, "ExperimentCounts" -> 14], Association[ "TrainingSizes" -> 400, "TimeBudgets" -> 0.5786002, "ElapsedTimes" -> 0.6156550000000001, "ExperimentCounts" -> 17]}, TypeSystem`Vector[ TypeSystem`Struct[{ "TrainingSizes", "TimeBudgets", "ElapsedTimes", "ExperimentCounts"}, { TypeSystem`Atom[Integer], TypeSystem`Atom[Real], TypeSystem`Atom[Real], TypeSystem`Atom[Integer]}], 3], Association[]]], "AnomalyDetector" -> None, "Log" -> Association["Example" -> MachineLearning`MLDataset[ Association[ "f1" -> Association[ "Type" -> "Boolean", "Weight" -> 1, "Values" -> {1}, "ID" -> 3632508870957651678], "f2" -> Association[ "Type" -> "Boolean", "Weight" -> 1, "Values" -> {0}, "ID" -> 387810808159763064], "f3" -> Association[ "Type" -> "Boolean", "Weight" -> 1, "Values" -> {1}, "ID" -> 7761553465653086836], "f4" -> Association[ "Type" -> "Numerical", "Weight" -> 1, "Values" -> {17}, "ID" -> 5101722074622675686], "f5" -> Association[ "Type" -> "Numerical", "Weight" -> 1, "Values" -> {7}, "ID" -> 28875055331300735], "f6" -> Association[ "Type" -> "Boolean", "Weight" -> 1, "Values" -> {0}, "ID" -> 8362908829548180133], "f7" -> Association[ "Type" -> "Numerical", "Weight" -> 1, "Values" -> {0}, "ID" -> 8672745575801952756], "f8" -> Association[ "Type" -> "Numerical", "Weight" -> 1, "Values" -> {0}, "ID" -> 8897874437666715294], "f9" -> Association[ "Type" -> "Boolean", "Weight" -> 1, "Values" -> {1}, "ID" -> 4060294726305383761], "f10" -> Association[ "Type" -> "Boolean", "Weight" -> 1, "Values" -> {1}, "ID" -> 2296921329445426742]], Association[ "ExampleNumber" -> 1, "ExampleWeights" -> 1, "LogDensityRatios" -> 0, "RawExample" -> False]], "TrainingTime" -> 2.526113, "MaxTrainingMemory" -> 11305720, "DataMemory" -> 170640, "FunctionMemory" -> 498880, "LanguageVersion" -> {12.3, 0}, "Date" -> DateObject[{2021, 7, 4, 10, 37, 3.292274`7.270070960609794}, "Instant", "Gregorian", -6.], "ProcessorCount" -> 2, "ProcessorType" -> "x86-64", "OperatingSystem" -> "MacOSX", "SystemWordLength" -> 64, "Evaluations" -> {}]]], Editable->False, SelectWithContents->True, Selectable->False]], "Output", TaggingRules->{}, CellChangeTimes->{3.8343145434580383`*^9, 3.834314580355092*^9, 3.834314625333243*^9, 3.834405423367136*^9}, CellLabel->"Out[2]=", CellID->1275084553] }, Open ]], Cell["\<\ Create a classifier measurements object using the classifier just built and \ the test data:\ \>", "Text", TaggingRules->{}, CellChangeTimes->{{3.834314679163089*^9, 3.8343146936681747`*^9}}, CellID->1174495922], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"cmo", "=", RowBox[{ RowBox[{"Query", "[", RowBox[{ RowBox[{ RowBox[{"ClassifierMeasurements", "[", RowBox[{"cl", ",", "#"}], "]"}], "&"}], ",", RowBox[{ RowBox[{"KeyTake", "[", RowBox[{"{", RowBox[{ "\"\\"", ",", "\"\\"", ",", "\"\\"", ",", "\"\\"", ",", "\"\\"", ",", "\"\\"", ",", "\"\\"", ",", "\"\\"", ",", "\"\\"", ",", "\"\\"", ",", "\"\\""}], "}"}], "]"}], "/*", "Values", "/*", RowBox[{"(", RowBox[{ RowBox[{ RowBox[{"Most", "@", "#"}], "->", RowBox[{"Last", "@", "#"}]}], "&"}], ")"}]}]}], "]"}], "[", "test", "]"}]}]], "Input", TaggingRules->{}, CellChangeTimes->{{3.834314471786839*^9, 3.834314559736719*^9}, { 3.834314597376586*^9, 3.8343146429652576`*^9}}, CellLabel->"In[3]:=", CellID->1175297382], Cell[BoxData[ InterpretationBox[ TagBox[ StyleBox[ FrameBox[GridBox[{ { ItemBox[ FrameBox[ StyleBox["\<\"Classifier Measurements\"\>", "SuggestionsBarText", StripOnInput->False, FontSize->12], FrameMargins->{{10, 5}, {-4, 2}}, FrameStyle->None, StripOnInput->False], Alignment->{Left, Bottom}, Background->RGBColor[0.96, 0.96, 0.96], Frame->{{False, False}, {True, False}}, FrameStyle->Opacity[0.1], ItemSize->{Automatic, 1}, StripOnInput->False]}, { ItemBox[ TagBox[ FrameBox[ TagBox[GridBox[{ { TemplateBox[{ TemplateBox[{5}, "Spacer1"], StyleBox[ InterpretationBox[ Cell["Classifier method"], TextCell["Classifier method"]], GrayLevel[0.4], StripOnInput -> False]}, "RowDefault"], InterpretationBox[Cell["GradientBoostedTrees"], TextCell["GradientBoostedTrees"]]}, { TemplateBox[{ TemplateBox[{5}, "Spacer1"], StyleBox[ InterpretationBox[ Cell["Number of test examples"], TextCell["Number of test examples"]], GrayLevel[0.4], StripOnInput -> False]}, "RowDefault"], "222"}, { TemplateBox[{ TemplateBox[{5}, "Spacer1"], StyleBox[ InterpretationBox[ Cell["Accuracy"], TextCell["Accuracy"]], GrayLevel[0.4], StripOnInput -> False]}, "RowDefault"], TemplateBox[{ RowBox[{"(", InterpretationBox[ TemplateBox[{"52.3", "3.4"}, "Around", SyntaxForm -> PlusMinus], Around[ 52.2522522522522478994`3., 3.3599500102617767539`3.]], ")"}], "\"%\"", "percent", "\"Percent\""}, "QuantityPostfix"]}, { TemplateBox[{ TemplateBox[{5}, "Spacer1"], StyleBox[ TagBox[ TooltipBox[ InterpretationBox[ Cell["Accuracy baseline"], TextCell["Accuracy baseline"]], "\"Accuracy if predicting the commonest class\"", LabelStyle -> "TextStyling"], Annotation[#, "Accuracy if predicting the commonest class", "Tooltip"]& ], GrayLevel[0.4], StripOnInput -> False]}, "RowDefault"], TemplateBox[{ RowBox[{"(", InterpretationBox[ TemplateBox[{"58.6", "3.3"}, "Around", SyntaxForm -> PlusMinus], Around[ 58.5585585585585590707`3., 3.3137251088211012728`3.]], ")"}], "\"%\"", "percent", "\"Percent\""}, "QuantityPostfix"]}, { TemplateBox[{ TemplateBox[{5}, "Spacer1"], StyleBox[ InterpretationBox[ Cell["Geometric mean of probabilities"], TextCell["Geometric mean of probabilities"]], GrayLevel[0.4], StripOnInput -> False]}, "RowDefault"], TagBox[ TooltipBox[ TemplateBox[{"0.4831456404814380434`3.", StyleBox[ TemplateBox[{ "\" \[PlusMinus] \"", "0.0086924394359217116`2."}, "RowDefault"], {10, Opacity[0.5]}, StripOnInput -> False]}, "RowDefault"], TemplateBox[{ "\"95% confidence interval: [\"", "0.4657607616095946201`3.", "\", \"", "0.5005305193532814112`3.", "\"]\""}, "RowDefault"], TooltipStyle->{}], Annotation[#, Row[{"95% confidence interval: [", 0.4657607616095946201`3., ", ", 0.5005305193532814112`3., "]"}], "Tooltip"]& ]}, { TemplateBox[{ TemplateBox[{5}, "Spacer1"], StyleBox[ InterpretationBox[ Cell["Mean cross entropy"], TextCell["Mean cross entropy"]], GrayLevel[0.4], StripOnInput -> False]}, "RowDefault"], TagBox[ TooltipBox[ TemplateBox[{"0.7274371376936658029`3.", StyleBox[ TemplateBox[{ "\" \[PlusMinus] \"", "0.0179903735758927263`2."}, "RowDefault"], {10, Opacity[0.5]}, StripOnInput -> False]}, "RowDefault"], TemplateBox[{ "\"95% confidence interval: [\"", "0.6914563905418803502`3.", "\", \"", "0.7634178848454512556`3.", "\"]\""}, "RowDefault"], TooltipStyle->{}], Annotation[#, Row[{"95% confidence interval: [", 0.6914563905418803502`3., ", ", 0.7634178848454512556`3., "]"}], "Tooltip"]& ]}, { TemplateBox[{ TemplateBox[{5}, "Spacer1"], StyleBox[ InterpretationBox[ Cell["Single evaluation time"], TextCell["Single evaluation time"]], GrayLevel[0.4], StripOnInput -> False]}, "RowDefault"], TemplateBox[{"13.9`", RowBox[{ "\"ms\"", "\[InvisibleSpace]", "\"/\"", "\[InvisibleSpace]", "\"example\""}], "milliseconds per example", FractionBox["\"Milliseconds\"", "\"Examples\""]}, "Quantity"]}, { TemplateBox[{ TemplateBox[{5}, "Spacer1"], StyleBox[ InterpretationBox[ Cell["Batch evaluation speed"], TextCell["Batch evaluation speed"]], GrayLevel[0.4], StripOnInput -> False]}, "RowDefault"], TemplateBox[{"2.73`", RowBox[{ "\"examples\"", "\[InvisibleSpace]", "\"/\"", "\[InvisibleSpace]", "\"ms\""}], "examples per millisecond", FractionBox["\"Examples\"", "\"Milliseconds\""]}, "Quantity"]}, { ItemBox[ With[{DefinitionNotebookClient`Utilities`PackagePrivate`\ compressed$ = Compress[{{{0.8738685013009065, 0.6184170247140988, 0.12413780417930659`}, {0.8959867816754365, 0.7151430366181464, 0.3143556371104974}}, {{1., 0.42, 0.}, {0.9121599485674269, 0.5072580420332117, 0.00437331039070316}}}]}, With[{DefinitionNotebookClient`Utilities`PackagePrivate`\ compressed$ = Compress[ CompressedData[ DefinitionNotebookClient`Utilities`PackagePrivate`\ compressed$]]}, GraphicsBox[ RasterBox[ CompressedData[ DefinitionNotebookClient`Utilities`PackagePrivate`\ compressed$], {{0, 0}, {2, 2}}, {0, 1}], Epilog -> {{ TagBox[ TooltipBox[ InsetBox[ GraphicsBox[{ Opacity[1.], InsetBox[ FormBox["71", TraditionalForm], {0.5, 0.5}, BaseStyle -> 10], Opacity[0.], RectangleBox[{0, 0}, {1, 1}]}], {0.5, 1.5}, Automatic, { 1., 1.}], TagBox[ GridBox[{{ TemplateBox[{ "71", "\" examples of \"", "0", "\" correctly classified\""}, "RowDefault"]}, { TemplateBox[{"\"Precision = \"", "0.6016949152542372`"}, "RowDefault"]}, { TemplateBox[{"\"Recall = \"", "0.5461538461538461`"}, "RowDefault"]}, { TemplateBox[{"\"FScore = \"", "0.5725806451612901`"}, "RowDefault"]}}, GridBoxAlignment -> {"Columns" -> {{Left}}}, DefaultBaseStyle -> "Column", GridBoxItemSize -> { "Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}], "Column"]], Annotation[#, Column[{ Row[{71, " examples of ", 0, " correctly classified"}], Row[{"Precision = ", 0.6016949152542372}], Row[{"Recall = ", 0.5461538461538461}], Row[{"FScore = ", 0.5725806451612901}]}], "Tooltip"]& ], TagBox[ TooltipBox[ InsetBox[ GraphicsBox[{ Opacity[1.], InsetBox[ FormBox["47", TraditionalForm], {0.5, 0.5}, BaseStyle -> 10], Opacity[0.], RectangleBox[{0, 0}, {1, 1}]}], {0.5, 0.5}, Automatic, { 1., 1.}], TagBox[ GridBox[{{ TemplateBox[{ "47", "\" examples of \"", "1", "\" misclassified as \"", "0"}, "RowDefault"]}, { TemplateBox[{ "\"Column fraction = \"", "0.3983050847457627`"}, "RowDefault"]}, { TemplateBox[{ "\"Row fraction = \"", "0.5108695652173914`"}, "RowDefault"]}}, GridBoxAlignment -> {"Columns" -> {{Left}}}, DefaultBaseStyle -> "Column", GridBoxItemSize -> { "Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}], "Column"]], Annotation[#, Column[{ Row[{47, " examples of ", 1, " misclassified as ", 0}], Row[{"Column fraction = ", 0.3983050847457627}], Row[{"Row fraction = ", 0.5108695652173914}]}], "Tooltip"]& ]}, { TagBox[ TooltipBox[ InsetBox[ GraphicsBox[{ Opacity[1.], InsetBox[ FormBox["59", TraditionalForm], {0.5, 0.5}, BaseStyle -> 10], Opacity[0.], RectangleBox[{0, 0}, {1, 1}]}], {1.5, 1.5}, Automatic, { 1., 1.}], TagBox[ GridBox[{{ TemplateBox[{ "59", "\" examples of \"", "0", "\" misclassified as \"", "1"}, "RowDefault"]}, { TemplateBox[{ "\"Column fraction = \"", "0.5673076923076923`"}, "RowDefault"]}, { TemplateBox[{ "\"Row fraction = \"", "0.45384615384615384`"}, "RowDefault"]}}, GridBoxAlignment -> {"Columns" -> {{Left}}}, DefaultBaseStyle -> "Column", GridBoxItemSize -> { "Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}], "Column"]], Annotation[#, Column[{ Row[{59, " examples of ", 0, " misclassified as ", 1}], Row[{"Column fraction = ", 0.5673076923076923}], Row[{"Row fraction = ", 0.45384615384615384`}]}], "Tooltip"]& ], TagBox[ TooltipBox[ InsetBox[ GraphicsBox[{ Opacity[1.], InsetBox[ FormBox["45", TraditionalForm], {0.5, 0.5}, BaseStyle -> 10], Opacity[0.], RectangleBox[{0, 0}, {1, 1}]}], {1.5, 0.5}, Automatic, { 1., 1.}], TagBox[ GridBox[{{ TemplateBox[{ "45", "\" examples of \"", "1", "\" correctly classified\""}, "RowDefault"]}, { TemplateBox[{"\"Precision = \"", "0.4326923076923077`"}, "RowDefault"]}, { TemplateBox[{"\"Recall = \"", "0.4891304347826087`"}, "RowDefault"]}, { TemplateBox[{"\"FScore = \"", "0.4591836734693878`"}, "RowDefault"]}}, GridBoxAlignment -> {"Columns" -> {{Left}}}, DefaultBaseStyle -> "Column", GridBoxItemSize -> { "Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}], "Column"]], Annotation[#, Column[{ Row[{45, " examples of ", 1, " correctly classified"}], Row[{"Precision = ", 0.4326923076923077}], Row[{"Recall = ", 0.4891304347826087}], Row[{"FScore = ", 0.4591836734693878}]}], "Tooltip"]& ]}, TagBox[ TooltipBox[ InsetBox[ GraphicsBox[{ Opacity[0.], RectangleBox[{0, 0}, {1, 1}]}], {2.5, 1.5}, Automatic, { 10., 1.}], TagBox[ GridBox[{{ TemplateBox[{ "130", "\" example\"", "\"s\"", "\" of class \"", "0"}, "RowDefault"]}, { TemplateBox[{"\"Recall = \"", "0.5461538461538461`"}, "RowDefault"]}}, GridBoxAlignment -> {"Columns" -> {{Left}}}, DefaultBaseStyle -> "Column", GridBoxItemSize -> { "Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}], "Column"]], Annotation[#, Column[{ Row[{130, " example", "s", " of class ", 0}], Row[{"Recall = ", 0.5461538461538461}]}], "Tooltip"]& ], TagBox[ TooltipBox[ InsetBox[ GraphicsBox[{ Opacity[0.], RectangleBox[{0, 0}, {1, 1}]}], {2.5, 0.5}, Automatic, { 10., 1.}], TagBox[ GridBox[{{ TemplateBox[{ "92", "\" example\"", "\"s\"", "\" of class \"", "1"}, "RowDefault"]}, { TemplateBox[{"\"Recall = \"", "0.4891304347826087`"}, "RowDefault"]}}, GridBoxAlignment -> {"Columns" -> {{Left}}}, DefaultBaseStyle -> "Column", GridBoxItemSize -> { "Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}], "Column"]], Annotation[#, Column[{ Row[{92, " example", "s", " of class ", 1}], Row[{"Recall = ", 0.4891304347826087}]}], "Tooltip"]& ], TagBox[ TooltipBox[ InsetBox[ GraphicsBox[{ Opacity[0.], RectangleBox[{0, 0}, {1, 1}]}], {0.5, -0.5}, Automatic, { 1., 10.}], TagBox[ GridBox[{{ TemplateBox[{ "118", "\" example\"", "\"s\"", "\" classified as \"", "0"}, "RowDefault"]}, { TemplateBox[{"\"Precision = \"", "0.6016949152542372`"}, "RowDefault"]}}, GridBoxAlignment -> {"Columns" -> {{Left}}}, DefaultBaseStyle -> "Column", GridBoxItemSize -> { "Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}], "Column"]], Annotation[#, Column[{ Row[{118, " example", "s", " classified as ", 0}], Row[{"Precision = ", 0.6016949152542372}]}], "Tooltip"]& ], TagBox[ TooltipBox[ InsetBox[ GraphicsBox[{ Opacity[0.], RectangleBox[{0, 0}, {1, 1}]}], {1.5, -0.5}, Automatic, { 1., 10.}], TagBox[ GridBox[{{ TemplateBox[{ "104", "\" example\"", "\"s\"", "\" classified as \"", "1"}, "RowDefault"]}, { TemplateBox[{"\"Precision = \"", "0.4326923076923077`"}, "RowDefault"]}}, GridBoxAlignment -> {"Columns" -> {{Left}}}, DefaultBaseStyle -> "Column", GridBoxItemSize -> { "Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}], "Column"]], Annotation[#, Column[{ Row[{104, " example", "s", " classified as ", 1}], Row[{"Precision = ", 0.4326923076923077}]}], "Tooltip"]& ]}, Frame -> True, FrameLabel -> { FormBox["\"predicted class\"", TraditionalForm], FormBox["\"actual class\"", TraditionalForm]}, FrameTicks -> {{{{1.5, FormBox[ RotationBox["0", BoxRotation -> 0.], TraditionalForm]}, { 0.5, FormBox[ RotationBox["1", BoxRotation -> 0.], TraditionalForm]}}, {{1.5, FormBox["130", TraditionalForm]}, {0.5, FormBox["92", TraditionalForm]}}}, {{{0.5, FormBox[ RotationBox["118", BoxRotation -> 1.5707963267948966`], TraditionalForm]}, {1.5, FormBox[ RotationBox["104", BoxRotation -> 1.5707963267948966`], TraditionalForm]}}, {{0.5, FormBox[ RotationBox["0", BoxRotation -> 1.5707963267948966`], TraditionalForm]}, {1.5, FormBox[ RotationBox["1", BoxRotation -> 1.5707963267948966`], TraditionalForm]}}}}, FrameTicksStyle -> 13, GridLinesStyle -> Directive[ GrayLevel[0.5, 0.4]], ImageSize -> 181.2, Method -> { "AxisPadding" -> Scaled[0.02], "DefaultBoundaryStyle" -> Automatic, "DefaultGraphicsInteraction" -> { "Version" -> 1.2, "TrackMousePosition" -> {True, False}, "Effects" -> { "Highlight" -> {"ratio" -> 2}, "HighlightPoint" -> {"ratio" -> 2}, "Droplines" -> { "freeformCursorMode" -> True, "placement" -> {"x" -> "All", "y" -> "None"}}}}, "DefaultPlotStyle" -> Automatic, "DomainPadding" -> Scaled[0.02], "RangePadding" -> Scaled[0.05]}, PlotRangePadding -> None]]], Alignment->Center, StripOnInput->False], "\[SpanFromLeft]"} }, AutoDelete->False, ColumnsEqual->False, GridBoxAlignment->{ "Columns" -> {Right, {Left}}, "Rows" -> {{Baseline}}}, GridBoxDividers->{"Columns" -> {False, { Opacity[0.15]}, False}}, GridBoxItemSize->{ "Columns" -> {Automatic, {Automatic}}, "Rows" -> {{1.}}}, GridBoxSpacings->{"Columns" -> { Offset[0.27999999999999997`], Offset[1.1199999999999999`], { Offset[1.75]}, Offset[0.27999999999999997`]}, "Rows" -> { Offset[0.2], { Offset[0.8]}, Offset[0.2]}}], "Grid"], FrameMargins->{{10, 10}, {10, 5}}, FrameStyle->None, StripOnInput->False], Deploy, DefaultBaseStyle->"Deploy"], Alignment->Center, BaseStyle->{ FontWeight -> "Light", FontSize -> 11, FontFamily -> ".AppleSystemUIFont", NumberMarks -> False, Deployed -> False}, StripOnInput->False]} }, DefaultBaseStyle->"Column", GridBoxAlignment->{"Columns" -> {{Left}}, "Rows" -> {{Baseline}}}, GridBoxDividers->{"Columns" -> {{False}}, "Rows" -> {{False}}}, GridBoxItemSize->{"Columns" -> {{Automatic}}, "Rows" -> {{1.}}}, GridBoxSpacings->{"Columns" -> { Offset[0.27999999999999997`], { Offset[0.5599999999999999]}, Offset[0.27999999999999997`]}, "Rows" -> { Offset[0.2], Offset[0.8], { Offset[0.4]}, Offset[0.2]}}], Background->GrayLevel[1], FrameMargins->{{0, 0}, {0, 0}}, FrameStyle->GrayLevel[0.85], RoundingRadius->5, StripOnInput->False], StripOnInput->False, LineBreakWithin->False], Deploy, DefaultBaseStyle->"Deploy"], ClassifierMeasurementsObject[ Association["Model" -> ClassifierFunction[ Association[ "ExampleNumber" -> 500, "ClassNumber" -> 2, "Input" -> Association[ "Preprocessor" -> MachineLearning`MLProcessor["ToMLDataset", Association[ "Input" -> Association[ "f1" -> Association["Type" -> "Boolean"], "f2" -> Association["Type" -> "Boolean"], "f3" -> Association["Type" -> "Boolean"], "f4" -> Association["Type" -> "Numerical"], "f5" -> Association["Type" -> "Numerical"], "f6" -> Association["Type" -> "Boolean"], "f7" -> Association["Type" -> "Numerical"], "f8" -> Association["Type" -> "Numerical"], "f9" -> Association["Type" -> "Boolean"], "f10" -> Association["Type" -> "Boolean"]], "Output" -> Association[ "f1" -> Association["Type" -> "Boolean", "Weight" -> 1], "f2" -> Association["Type" -> "Boolean", "Weight" -> 1], "f3" -> Association["Type" -> "Boolean", "Weight" -> 1], "f4" -> Association["Type" -> "Numerical", "Weight" -> 1], "f5" -> Association["Type" -> "Numerical", "Weight" -> 1], "f6" -> Association["Type" -> "Boolean", "Weight" -> 1], "f7" -> Association["Type" -> "Numerical", "Weight" -> 1], "f8" -> Association["Type" -> "Numerical", "Weight" -> 1], "f9" -> Association["Type" -> "Boolean", "Weight" -> 1], "f10" -> Association["Type" -> "Boolean", "Weight" -> 1]], "Preprocessor" -> MachineLearning`MLProcessor["Sequence", Association["Processors" -> { MachineLearning`MLProcessor["Transpose", Association["FeatureNumber" -> 10]], MachineLearning`MLProcessor["WrapMLDataset", Association[ "FeatureTypes" -> { "Boolean", "Boolean", "Boolean", "Numerical", "Numerical", "Boolean", "Numerical", "Numerical", "Boolean", "Boolean"}, "FeatureKeys" -> { "f1", "f2", "f3", "f4", "f5", "f6", "f7", "f8", "f9", "f10"}, "FeatureWeights" -> Automatic, "ExampleWeights" -> Automatic, "RawExample" -> Missing["KeyAbsent", "RawExample"], "StructurePreserving" -> False]]}]], "ScalarFeature" -> False, "Invertibility" -> "Perfect", "StructurePreserving" -> False, "Missing" -> "Allowed"]], "Processor" -> MachineLearning`MLProcessor["Sequence", Association[ "Input" -> Association[ "f1" -> Association["Type" -> "Boolean", "Weight" -> 1], "f2" -> Association["Type" -> "Boolean", "Weight" -> 1], "f3" -> Association["Type" -> "Boolean", "Weight" -> 1], "f4" -> Association["Type" -> "Numerical", "Weight" -> 1], "f5" -> Association["Type" -> "Numerical", "Weight" -> 1], "f6" -> Association["Type" -> "Boolean", "Weight" -> 1], "f7" -> Association["Type" -> "Numerical", "Weight" -> 1], "f8" -> Association["Type" -> "Numerical", "Weight" -> 1], "f9" -> Association["Type" -> "Boolean", "Weight" -> 1], "f10" -> Association["Type" -> "Boolean", "Weight" -> 1]], "Output" -> Association[ "((f4f5f7f8)(f1f2f3f6f9f10))" -> Association["Type" -> "NumericalVector", "Weight" -> 10.]], "Processors" -> { MachineLearning`MLProcessor["SynthesizeMissingValues", Association[ "Invertibility" -> "Perfect", "Missing" -> "Imputed", "StructurePreserving" -> True, "Input" -> Association[ "f1" -> Association["Type" -> "Boolean", "Weight" -> 1], "f2" -> Association["Type" -> "Boolean", "Weight" -> 1], "f3" -> Association["Type" -> "Boolean", "Weight" -> 1], "f4" -> Association["Type" -> "Numerical", "Weight" -> 1], "f5" -> Association["Type" -> "Numerical", "Weight" -> 1], "f6" -> Association["Type" -> "Boolean", "Weight" -> 1], "f7" -> Association["Type" -> "Numerical", "Weight" -> 1], "f8" -> Association["Type" -> "Numerical", "Weight" -> 1], "f9" -> Association["Type" -> "Boolean", "Weight" -> 1], "f10" -> Association["Type" -> "Boolean", "Weight" -> 1]], "Distribution" -> LearnedDistribution[ Association[ "ExampleNumber" -> 500, "Preprocessor" -> MachineLearning`MLProcessor["ToMLDataset", Association[ "Input" -> Association[ "f1" -> Association["Type" -> "Boolean"], "f2" -> Association["Type" -> "Boolean"], "f3" -> Association["Type" -> "Boolean"], "f4" -> Association["Type" -> "Numerical"], "f5" -> Association["Type" -> "Numerical"], "f6" -> Association["Type" -> "Boolean"], "f7" -> Association["Type" -> "Numerical"], "f8" -> Association["Type" -> "Numerical"], "f9" -> Association["Type" -> "Boolean"], "f10" -> Association["Type" -> "Boolean"]], "Output" -> Association[ "f1" -> Association["Type" -> "Boolean", "Weight" -> 1], "f2" -> Association["Type" -> "Boolean", "Weight" -> 1], "f3" -> Association["Type" -> "Boolean", "Weight" -> 1], "f4" -> Association["Type" -> "Numerical", "Weight" -> 1], "f5" -> Association[ "Type" -> "Numerical", "Weight" -> 1], "f6" -> Association["Type" -> "Boolean", "Weight" -> 1], "f7" -> Association["Type" -> "Numerical", "Weight" -> 1], "f8" -> Association["Type" -> "Numerical", "Weight" -> 1], "f9" -> Association["Type" -> "Boolean", "Weight" -> 1], "f10" -> Association["Type" -> "Boolean", "Weight" -> 1]], "Preprocessor" -> MachineLearning`MLProcessor["Identity"], "ScalarFeature" -> False, "Invertibility" -> "Perfect", "StructurePreserving" -> False, "Missing" -> "Allowed"]], "Processor" -> MachineLearning`MLProcessor["Sequence", Association[ "Input" -> Association[ "f4" -> Association["Type" -> "Numerical", "Weight" -> 1], "f5" -> Association[ "Type" -> "Numerical", "Weight" -> 1], "f7" -> Association["Type" -> "Numerical", "Weight" -> 1], "f8" -> Association["Type" -> "Numerical", "Weight" -> 1], "f1" -> Association["Type" -> "Boolean", "Weight" -> 1], "f2" -> Association["Type" -> "Boolean", "Weight" -> 1], "f3" -> Association["Type" -> "Boolean", "Weight" -> 1], "f6" -> Association["Type" -> "Boolean", "Weight" -> 1], "f9" -> Association["Type" -> "Boolean", "Weight" -> 1], "f10" -> Association["Type" -> "Boolean", "Weight" -> 1]], "Output" -> Association[ "((f4f5f7f8)(f1f2f3f6f9f10))" -> Association[ "Weight" -> {1., 1., 1., 1., 1., 1., 1., 1., 1., 1.}, "Type" -> "NumericalVector"]], "Processors" -> { MachineLearning`MLProcessor["Threads", Association[ "Input" -> Association[ "f4" -> Association["Type" -> "Numerical", "Weight" -> 1], "f5" -> Association[ "Type" -> "Numerical", "Weight" -> 1], "f7" -> Association["Type" -> "Numerical", "Weight" -> 1], "f8" -> Association["Type" -> "Numerical", "Weight" -> 1], "f1" -> Association["Type" -> "Boolean", "Weight" -> 1], "f2" -> Association["Type" -> "Boolean", "Weight" -> 1], "f3" -> Association["Type" -> "Boolean", "Weight" -> 1], "f6" -> Association["Type" -> "Boolean", "Weight" -> 1], "f9" -> Association["Type" -> "Boolean", "Weight" -> 1], "f10" -> Association["Type" -> "Boolean", "Weight" -> 1]], "Output" -> Association[ "(f4f5f7f8)" -> Association["Type" -> "NumericalVector", "Weight" -> 4], "(f1f2f3f6f9f10)" -> Association["Type" -> "BooleanVector", "Weight" -> 6]], "Processors" -> { MachineLearning`MLProcessor["ToVector", Association[ "Invertibility" -> "Perfect", "Missing" -> "Allowed", "StructurePreserving" -> True, "Input" -> Association[ "f4" -> Association["Type" -> "Numerical", "Weight" -> 1], "f5" -> Association[ "Type" -> "Numerical", "Weight" -> 1], "f7" -> Association["Type" -> "Numerical", "Weight" -> 1], "f8" -> Association["Type" -> "Numerical", "Weight" -> 1]], "Output" -> Association[ "(f4f5f7f8)" -> Association["Type" -> "NumericalVector", "Weight" -> 4]], "Version" -> {12.3, 0}, "ID" -> 3316698883640303724]], MachineLearning`MLProcessor["ToVector", Association[ "Invertibility" -> "Perfect", "Missing" -> "Allowed", "StructurePreserving" -> True, "Input" -> Association[ "f1" -> Association["Type" -> "Boolean", "Weight" -> 1], "f2" -> Association["Type" -> "Boolean", "Weight" -> 1], "f3" -> Association["Type" -> "Boolean", "Weight" -> 1], "f6" -> Association["Type" -> "Boolean", "Weight" -> 1], "f9" -> Association["Type" -> "Boolean", "Weight" -> 1], "f10" -> Association["Type" -> "Boolean", "Weight" -> 1]], "Output" -> Association[ "(f1f2f3f6f9f10)" -> Association["Type" -> "BooleanVector", "Weight" -> 6]], "Version" -> {12.3, 0}, "ID" -> 1543560422633817687]]}, "Invertibility" -> "Perfect", "StructurePreserving" -> True, "Missing" -> "Allowed"]], MachineLearning`MLProcessor["ConformBooleanVector", Association[ "Invertibility" -> "Perfect", "Missing" -> "Allowed", "StructurePreserving" -> True, "Input" -> Association[ "(f1f2f3f6f9f10)" -> Association["Type" -> "BooleanVector", "Weight" -> 6]], "Version" -> {12.3, 0}, "ID" -> 4011134228092904529, "Output" -> Association[ "(f1f2f3f6f9f10)" -> Association["Type" -> "BooleanVector", "Weight" -> 6]]]], MachineLearning`MLProcessor[ "BooleanVectorToNominalVector", Association[ "Invertibility" -> "Perfect", "Missing" -> "Allowed", "StructurePreserving" -> True, "Input" -> Association[ "(f1f2f3f6f9f10)" -> Association["Type" -> "BooleanVector", "Weight" -> 6]], "Version" -> {12.3, 0}, "ID" -> 8177418994704305658, "Output" -> Association[ "(f1f2f3f6f9f10)" -> Association["Type" -> "NominalVector", "Weight" -> 6]]]], MachineLearning`MLProcessor[ "IntegerEncodeNominalVector", Association[ "Invertibility" -> "Perfect", "Missing" -> "Allowed", "StructurePreserving" -> True, "Input" -> Association[ "(f1f2f3f6f9f10)" -> Association["Type" -> "NominalVector", "Weight" -> 6]], "Index" -> { Association[0 -> 1, 1 -> 2], Association[0 -> 1, 1 -> 2], Association[0 -> 1, 1 -> 2], Association[0 -> 1, 1 -> 2], Association[0 -> 1, 1 -> 2], Association[0 -> 1, 1 -> 2]}, "MissingCode" -> Indeterminate, "Version" -> {12.3, 0}, "ID" -> 1022096664884957757, "Output" -> Association[ "(f1f2f3f6f9f10)" -> Association["Type" -> "NominalVector", "Weight" -> 6]]]], MachineLearning`MLProcessor["NumericalizeNominalVector", Association[ "Invertibility" -> "Approximate", "Missing" -> "Allowed", "StructurePreserving" -> True, "Input" -> Association[ "(f1f2f3f6f9f10)" -> Association[ "Type" -> "NominalVector", "Weight" -> 6, "SetSize" -> {2, 2, 2, 2, 2, 2}]], "Boundaries" -> {{-0.5, 0., 0.5}, {-0.5, 0., 0.5}, {-0.5, 0., 0.5}, {-0.5, 0., 0.5}, {-0.5, 0., 0.5}, {-0.5, 0., 0.5}}, "Version" -> {12.3, 0}, "ID" -> 2653124012696996535, "Output" -> Association[ "(f1f2f3f6f9f10)" -> Association[ "Type" -> "NumericalVector", "Weight" -> 6]]]], MachineLearning`MLProcessor["MergeVectors", Association[ "Invertibility" -> "Perfect", "Missing" -> "Allowed", "StructurePreserving" -> True, "Input" -> Association[ "(f4f5f7f8)" -> Association["Type" -> "NumericalVector", "Weight" -> 4], "(f1f2f3f6f9f10)" -> Association["Type" -> "NumericalVector", "Weight" -> 6]], "Spans" -> { Span[1, 4], Span[5, 10]}, "Wrappers" -> {Identity, Identity}, "Output" -> Association[ "((f4f5f7f8)(f1f2f3f6f9f10))" -> Association[ "Weight" -> {1., 1., 1., 1., 1., 1., 1., 1., 1., 1.}, "Type" -> "NumericalVector"]], "Version" -> {12.3, 0}, "ID" -> 7449476533421130286]]}, "Invertibility" -> "Approximate", "StructurePreserving" -> True, "Missing" -> "Allowed"]], "PerformanceGoal" -> "DirectTraining", "BatchProcessing" -> Automatic, "Model" -> Association["RotationMatrix" -> CompressedData[" 1:eJwBMQPO/CFib1JlAgAAAAoAAAAKAAAADwiwSrnOAz+8d8INzP8lP/BAjGMY /u8/lLdUIbpSkT+JK863wWGHv5a9NRCXf3O/Kny6D+JfaT9TxR4m7xFwP0w4 6tunyyu/5x9rsAibSz9m7YYzOUL9PshKmZjp3NU+oo3Ug07kkD+EYMBEMN3v v2/p/mF6W3q/bKXSQrQVgr81knQbo0iYv3M4VzcdcLU/eVV+ZHwDjz/U8Kx6 ykiRv7kEC0CzYOk/7jj5Uyp+47+KX7L3VecSP7dgKQogqPU+6orRodFQ8j4+ qYEEghTZvqY6fLQOUtK+mEklCKzU1D5wv9CPO1XSvuxcF4GTtry+xb1gVSp+ 4z+YwGg4s2DpP5wA3I61eyS/LeOWuxjq8j7hi7suHRvyPv5DHlVEz98+biph JgN58T6YhthuUAWyviHEvhmModE+ToS8pyBTn76XvnQM5y6/vguaWtVlreK+ uOJBm0CbcT9gXhkBvp90v4RKxe3GwJE/VUnYqXQl6j9g76FMR7TCP1MUK26W UKC/7zA2MvYrsL9n3EW6SrHhv5y6+JwDQ5k+VHomI2Rv3D7yYqXcJDNhv+6U ayrLMY4/aX34oiGLq7937d/2SFzhv6kuvw1RILq/wSeAcRSPtb/lT1ZYlmiQ P2DYGzQVe+q/8kshT6cuw752PQ8iyt3ZPveYGrowtHG/RYED/7u9tj82/dII hL6ov8Aq6+/mb6s/EH+w29cQ1L9NAeDIVmPtP3qbveMxIck/f3wCg+LVtL/6 iEtoZ3LSPulXa4u3Sek+4GtVC40dez+4yjdZrxR+v3EqxR3assE/KLin1SnP xT9ZxBE6u2PtvyJcRhxNvNS/e0CtwCQEqj9O6ElkKgSbP3tadYo+U/O+FiZ+ PLsx0z6J5kxYMZV8P2Tbo9zmBF0/1QsWyLzo5j+46KSzTpyzvy6ZFWZjv3+/ rHMmtLQOxz9lA4F8LG3lv3vPDVNiSZu/kIKenqhQ8b7EioU9MiDgvo9KfGpI ZH8/QcYOnpknX79qSeBhE8TlP2rLvCq/34y/QffUnfuOxD/er/58wD+wv8Mm XVmkw+Y/v4jZDUi3ob/x4JgC "], "Precisions" -> {1.*^-6, 1.*^-6, 0.022856810057507688`, 0.38689895865155344`, 10.57935559665159, 14.205388502322698`, 20.592782324022085`, 25.50148838835205, 32.1042961457214, 32.30264445259112}, "NoisePrecision" -> None, "Processor" -> MachineLearning`MLProcessor["Center", Association[ "Invertibility" -> "Perfect", "Missing" -> "Allowed", "StructurePreserving" -> True, "Input" -> Association[ "((f4f5f7f8)(f1f2f3f6f9f10))" -> Association[ "Weight" -> {1., 1., 1., 1., 1., 1., 1., 1., 1., 1.}, "Type" -> "NumericalVector"]], "Mean" -> {24.632, 10.392, 3517.7927107849123`, 2981.388433151245, 0.1426727988002656, -0.19668399454225596`, 0.1388625841404933, -0.16287633582074032`, \ -0.019775698305439616`, -0.040446925895776316`}, "Output" -> Association[ "((f4f5f7f8)(f1f2f3f6f9f10))" -> Association[ "Type" -> "NumericalVector", "Weight" -> 10.]], "Version" -> {12.3, 0}, "ID" -> 234699001300146701]], "PostProcessor" -> MachineLearning`MLProcessor["FirstValues", Association[ "Info" -> Association[ "Type" -> "NumericalVector", "Weight" -> 10.], "Key" -> "((f4f5f7f8)(f1f2f3f6f9f10))", "Invertibility" -> "Perfect", "StructurePreserving" -> False, "Missing" -> "Allowed"]], "Method" -> "Multinormal", "Options" -> Association[ "CovarianceType" -> Association[ "Value" -> "Full", "Options" -> Association[]], "IntrinsicDimension" -> Association["Value" -> 10, "Options" -> Association[]]]], "TrainingInformation" -> Association["Configurations" -> Dataset[ Association[ Association[ "Value" -> "Multinormal", "Options" -> Association[ "CovarianceType" -> Association["Value" -> "Full"], "IntrinsicDimension" -> Association["Value" -> "Heuristic"]], "NaiveImputer" -> MachineLearning`MLProcessor["ImputeMissing", Association[ "Invertibility" -> "Perfect", "Missing" -> "Imputed", "StructurePreserving" -> True, "Input" -> Association[ "((f4f5f7f8)(f1f2f3f6f9f10))" -> Association["Weight" -> {1., 1., 1., 1., 1., 1., 1., 1., 1., 1.}, "Type" -> "NumericalVector"]], "Mean" -> {24.632, 10.392, 3517.7927107849123`, 2981.388433151245, 0.1426727988002656, -0.19668399454225596`, 0.1388625841404933, -0.16287633582074035`, \ -0.019775698305439616`, -0.04044692589577634}, "StandardDeviation" -> {6.62786360752844, 1.6181273126673357`, 6041.363324324119, 4822.270799347922, 0.24308065642958543`, 0.2096172697624777, 0.24766674808869532`, 0.22972111865332548`, 0.2919209597626786, 0.28094908774794775`}, "Method" -> "NaiveSampler", "VectorLength" -> 10, "Output" -> Association[ "((f4f5f7f8)(f1f2f3f6f9f10))" -> Association["Type" -> "NumericalVector", "Weight" -> 10.]], "Type" -> "NumericalVector", "Version" -> {12.3, 0}, "ID" -> 3712465554580130660]], "EMIterations" -> 1] -> Association[]], TypeSystem`Assoc[ TypeSystem`Struct[{ "Value", "Options", "NaiveImputer", "EMIterations"}, { TypeSystem`Atom[String], TypeSystem`Assoc[ TypeSystem`Atom[String], TypeSystem`Struct[{"Value"}, { TypeSystem`Atom[String]}], 2], TypeSystem`AnyType, TypeSystem`Atom[Integer]}], TypeSystem`Assoc[ TypeSystem`UnknownType, TypeSystem`UnknownType, TypeSystem`AnyLength], 1], Association[]], "BestModelInformation" -> Dataset[ Association[ "Configuration" -> { "Multinormal", "CovarianceType" -> "Full", "IntrinsicDimension" -> "Heuristic"}, "ModelUtility" -> Missing[]], TypeSystem`Struct[{"Configuration", "ModelUtility"}, { TypeSystem`Tuple[{ TypeSystem`Atom[String], TypeSystem`AnyType, TypeSystem`AnyType}], TypeSystem`UnknownType}], Association[]]], "NaiveImputer" -> MachineLearning`MLProcessor["ImputeMissing", Association[ "Invertibility" -> "Perfect", "Missing" -> "Imputed", "StructurePreserving" -> True, "Input" -> Association[ "((f4f5f7f8)(f1f2f3f6f9f10))" -> Association[ "Weight" -> {1., 1., 1., 1., 1., 1., 1., 1., 1., 1.}, "Type" -> "NumericalVector"]], "Mean" -> {24.632, 10.392, 3517.7927107849123`, 2981.388433151245, 0.1426727988002656, -0.19668399454225596`, 0.1388625841404933, -0.16287633582074035`, \ -0.019775698305439616`, -0.04044692589577634}, "StandardDeviation" -> {6.62786360752844, 1.6181273126673357`, 6041.363324324119, 4822.270799347922, 0.24308065642958543`, 0.2096172697624777, 0.24766674808869532`, 0.22972111865332548`, 0.2919209597626786, 0.28094908774794775`}, "Method" -> "NaiveSampler", "VectorLength" -> 10, "Output" -> Association[ "((f4f5f7f8)(f1f2f3f6f9f10))" -> Association[ "Type" -> "NumericalVector", "Weight" -> 10.]], "Type" -> "NumericalVector", "Version" -> {12.3, 0}, "ID" -> 3712465554580130660]], "InputDimension" -> 0, "OutputDimension" -> 10, "Log" -> Association["Example" -> MachineLearning`MLDataset[ Association[ "f1" -> Association[ "Type" -> "Boolean", "Weight" -> 1, "Values" -> {1}, "ID" -> 2088960393445371139], "f2" -> Association[ "Type" -> "Boolean", "Weight" -> 1, "Values" -> {0}, "ID" -> 2808916298067735746], "f3" -> Association[ "Type" -> "Boolean", "Weight" -> 1, "Values" -> {1}, "ID" -> 6848592649459883519], "f4" -> Association[ "Type" -> "Numerical", "Weight" -> 1, "Values" -> {17}, "ID" -> 7818302300378383162], "f5" -> Association[ "Type" -> "Numerical", "Weight" -> 1, "Values" -> {7}, "ID" -> 5732213614699672942], "f6" -> Association[ "Type" -> "Boolean", "Weight" -> 1, "Values" -> {0}, "ID" -> 4541606426648771723], "f7" -> Association[ "Type" -> "Numerical", "Weight" -> 1, "Values" -> {0}, "ID" -> 4888551857821753089], "f8" -> Association[ "Type" -> "Numerical", "Weight" -> 1, "Values" -> {0}, "ID" -> 6749501619406737415], "f9" -> Association[ "Type" -> "Boolean", "Weight" -> 1, "Values" -> {1}, "ID" -> 7726663053373864391], "f10" -> Association[ "Type" -> "Boolean", "Weight" -> 1, "Values" -> {1}, "ID" -> 5972767125695252816]], Association[ "ExampleNumber" -> 1, "ExampleWeights" -> 1, "LogDensityRatios" -> 0, "RawExample" -> False]], "TrainingTime" -> 0.323246, "MaxTrainingMemory" -> 5108640, "DataMemory" -> 135168, "FunctionMemory" -> 96240, "LanguageVersion" -> {12.3, 0}, "Date" -> DateObject[{ 2021, 7, 4, 10, 37, 1.104464`6.795726553498322}, "Instant", "Gregorian", -6.], "ProcessorCount" -> 2, "ProcessorType" -> "x86-64", "OperatingSystem" -> "MacOSX", "SystemWordLength" -> 64, "Evaluations" -> {}], "LogPDFDistribution" -> MachineLearning`TailedQuantileDistribution[ Association[ "Quantiles" -> {-3.0506499598055687`, \ -3.0506499598055687`, -3.0159000559703624`, -2.7316348774864374`, \ -2.720202948801694, -2.6967682702519453`, -2.5830906800566384`, \ -2.5802559617783047`, -2.4921898471238846`, -2.4308948302827416`, \ -2.3194090170986685`}, "LeftBoundary" -> -3.0159000559703624`, "LeftScale" -> 0.017374951917603187`, "LeftTailNorm" -> 0.2]], "Entropy" -> Around[26.620996448656246`, 0.7440592951458566], "EntropySampleSize" -> 10]], "Output" -> Association[ "f1" -> Association["Type" -> "Boolean", "Weight" -> 1], "f2" -> Association["Type" -> "Boolean", "Weight" -> 1], "f3" -> Association["Type" -> "Boolean", "Weight" -> 1], "f4" -> Association["Type" -> "Numerical", "Weight" -> 1], "f5" -> Association["Type" -> "Numerical", "Weight" -> 1], "f6" -> Association["Type" -> "Boolean", "Weight" -> 1], "f7" -> Association["Type" -> "Numerical", "Weight" -> 1], "f8" -> Association["Type" -> "Numerical", "Weight" -> 1], "f9" -> Association["Type" -> "Boolean", "Weight" -> 1], "f10" -> Association["Type" -> "Boolean", "Weight" -> 1]], "EvaluationStrategy" -> "ModeFinding", "Version" -> {12.3, 0}, "ID" -> 3492945905634660037]], MachineLearning`MLProcessor["Threads", Association[ "Input" -> Association[ "f4" -> Association["Type" -> "Numerical", "Weight" -> 1], "f5" -> Association["Type" -> "Numerical", "Weight" -> 1], "f7" -> Association["Type" -> "Numerical", "Weight" -> 1], "f8" -> Association["Type" -> "Numerical", "Weight" -> 1], "f1" -> Association["Type" -> "Boolean", "Weight" -> 1], "f2" -> Association["Type" -> "Boolean", "Weight" -> 1], "f3" -> Association["Type" -> "Boolean", "Weight" -> 1], "f6" -> Association["Type" -> "Boolean", "Weight" -> 1], "f9" -> Association["Type" -> "Boolean", "Weight" -> 1], "f10" -> Association["Type" -> "Boolean", "Weight" -> 1]], "Output" -> Association[ "(f4f5f7f8)" -> Association["Type" -> "NumericalVector", "Weight" -> 4], "(f1f2f3f6f9f10)" -> Association["Type" -> "BooleanVector", "Weight" -> 6]], "Processors" -> { MachineLearning`MLProcessor["ToVector", Association[ "Invertibility" -> "Perfect", "Missing" -> "Allowed", "StructurePreserving" -> True, "Input" -> Association[ "f4" -> Association["Type" -> "Numerical", "Weight" -> 1], "f5" -> Association[ "Type" -> "Numerical", "Weight" -> 1], "f7" -> Association["Type" -> "Numerical", "Weight" -> 1], "f8" -> Association["Type" -> "Numerical", "Weight" -> 1]], "Output" -> Association[ "(f4f5f7f8)" -> Association["Type" -> "NumericalVector", "Weight" -> 4]], "Version" -> {12.3, 0}, "ID" -> 7154538275436769813]], MachineLearning`MLProcessor["ToVector", Association[ "Invertibility" -> "Perfect", "Missing" -> "Allowed", "StructurePreserving" -> True, "Input" -> Association[ "f1" -> Association["Type" -> "Boolean", "Weight" -> 1], "f2" -> Association["Type" -> "Boolean", "Weight" -> 1], "f3" -> Association["Type" -> "Boolean", "Weight" -> 1], "f6" -> Association["Type" -> "Boolean", "Weight" -> 1], "f9" -> Association["Type" -> "Boolean", "Weight" -> 1], "f10" -> Association["Type" -> "Boolean", "Weight" -> 1]], "Output" -> Association[ "(f1f2f3f6f9f10)" -> Association["Type" -> "BooleanVector", "Weight" -> 6]], "Version" -> {12.3, 0}, "ID" -> 7398228362387862015]]}, "Invertibility" -> "Perfect", "StructurePreserving" -> True, "Missing" -> "Allowed"]], MachineLearning`MLProcessor["ConformBooleanVector", Association[ "Invertibility" -> "Perfect", "Missing" -> "Allowed", "StructurePreserving" -> True, "Input" -> Association[ "(f1f2f3f6f9f10)" -> Association["Type" -> "BooleanVector", "Weight" -> 6]], "Version" -> {12.3, 0}, "ID" -> 3751650204265563348, "Output" -> Association[ "(f1f2f3f6f9f10)" -> Association["Type" -> "BooleanVector", "Weight" -> 6]]]], MachineLearning`MLProcessor["BooleanVectorToNumericalVector", Association[ "Invertibility" -> "Perfect", "Missing" -> "Allowed", "StructurePreserving" -> True, "Input" -> Association[ "(f1f2f3f6f9f10)" -> Association["Type" -> "BooleanVector", "Weight" -> 6]], "Version" -> {12.3, 0}, "ID" -> 2685019987694741464, "Output" -> Association[ "(f1f2f3f6f9f10)" -> Association["Type" -> "NumericalVector", "Weight" -> 6]]]], MachineLearning`MLProcessor["MergeVectors", Association[ "Invertibility" -> "Perfect", "Missing" -> "Allowed", "StructurePreserving" -> True, "Input" -> Association[ "(f4f5f7f8)" -> Association["Type" -> "NumericalVector", "Weight" -> 4], "(f1f2f3f6f9f10)" -> Association["Type" -> "NumericalVector", "Weight" -> 6]], "Spans" -> { Span[1, 4], Span[5, 10]}, "Wrappers" -> {Identity, Identity}, "Output" -> Association[ "((f4f5f7f8)(f1f2f3f6f9f10))" -> Association[ "Weight" -> {1., 1., 1., 1., 1., 1., 1., 1., 1., 1.}, "Type" -> "NumericalVector"]], "Version" -> {12.3, 0}, "ID" -> 8406531381003353507]], MachineLearning`MLProcessor["Standardize", Association[ "Invertibility" -> "Perfect", "Missing" -> "Allowed", "StructurePreserving" -> True, "Input" -> Association[ "((f4f5f7f8)(f1f2f3f6f9f10))" -> Association[ "Weight" -> {1., 1., 1., 1., 1., 1., 1., 1., 1., 1.}, "Type" -> "NumericalVector"]], "Mean" -> {24.632, 10.392, 3517.7927107849123`, 2981.388433151245, 0.804, 0.106, 0.764, 0.16, 0.47200000000000003`, 0.402}, "StandardDeviation" -> {6.62786360752844, 1.6181273126673357`, 6041.363324324119, 4822.270799347922, 0.39696851260521904`, 0.3078376195334157, 0.42462218500686, 0.3666060555964672, 0.49921538437832624`, 0.49030194778320024`}, "Output" -> Association[ "((f4f5f7f8)(f1f2f3f6f9f10))" -> Association["Type" -> "NumericalVector", "Weight" -> 10.]], "Version" -> {12.3, 0}, "ID" -> 6350676892446284557]]}, "Invertibility" -> "Perfect", "StructurePreserving" -> True, "Missing" -> "Imputed"]]], "Output" -> Association[ "Preprocessor" -> MachineLearning`MLProcessor["ToMLDataset", Association[ "Input" -> Association["f1" -> Association["Type" -> "Nominal"]], "Output" -> Association[ "f1" -> Association["Type" -> "Nominal", "Weight" -> 1]], "Preprocessor" -> MachineLearning`MLProcessor["Sequence", Association["Processors" -> { MachineLearning`MLProcessor["List"], MachineLearning`MLProcessor["WrapMLDataset", Association[ "FeatureTypes" -> {"Nominal"}, "FeatureKeys" -> {"f1"}, "FeatureWeights" -> Automatic, "ExampleWeights" -> Automatic, "RawExample" -> Missing["KeyAbsent", "RawExample"], "StructurePreserving" -> False]]}]], "ScalarFeature" -> True, "Invertibility" -> "Perfect", "StructurePreserving" -> False, "Missing" -> "Allowed"]], "Processor" -> MachineLearning`MLProcessor["Sequence", Association[ "Input" -> Association[ "f1" -> Association["Type" -> "Nominal", "Weight" -> 1]], "Output" -> Association[ "f1" -> Association["Type" -> "Nominal", "Weight" -> 1]], "Processors" -> { MachineLearning`MLProcessor["ToVector", Association[ "Invertibility" -> "Perfect", "Missing" -> "Allowed", "StructurePreserving" -> True, "Input" -> Association[ "f1" -> Association["Type" -> "Nominal", "Weight" -> 1]], "Output" -> Association[ "f1" -> Association[ "Type" -> "NominalVector", "Weight" -> 1]], "Version" -> {12.3, 0}, "ID" -> 66260684687212962]], MachineLearning`MLProcessor["IntegerEncodeNominalVector", Association[ "Invertibility" -> "Perfect", "Missing" -> "Allowed", "StructurePreserving" -> True, "Input" -> Association[ "f1" -> Association[ "Type" -> "NominalVector", "Weight" -> 1]], "Index" -> { Association[0 -> 1, 1 -> 2]}, "MissingCode" -> 0, "Version" -> {12.3, 0}, "ID" -> 7619159391572131345, "Output" -> Association[ "f1" -> Association[ "Type" -> "NominalVector", "Weight" -> 1]]]], MachineLearning`MLProcessor["FromVector", Association[ "Invertibility" -> "Perfect", "Missing" -> "Allowed", "StructurePreserving" -> True, "Input" -> Association[ "f1" -> Association[ "Type" -> "NominalVector", "Weight" -> 1, "SetSize" -> {2}]], "Output" -> Association[ "f1" -> Association["Type" -> "Nominal", "Weight" -> 1]], "Version" -> {12.3, 0}, "ID" -> 827783155253348485]], MachineLearning`MLProcessor["FirstValues", Association[ "Info" -> Association[ "Type" -> "Nominal", "Weight" -> 1, "SetSize" -> 2], "Key" -> "f1", "Invertibility" -> "Perfect", "StructurePreserving" -> False, "Missing" -> "Allowed"]]}, "Invertibility" -> "Perfect", "StructurePreserving" -> False, "Missing" -> "Allowed"]], "ProbabilityPostprocessor" -> Identity, "Name" -> "class", "Marginal" -> Association[0 -> 0.5896414342629482, 1 -> 0.41035856573705176`]], "LabelSplitter" -> MachineLearning`MLProcessor["FeatureLabelSplit", Association[ "Processor" -> MachineLearning`MLProcessor["ListSplit"], "PreferLabeled" -> True, "KeepLabelsFormat" -> False]], "RecalibrationFunction" -> None, "ImputationStrategy" -> Automatic, "Prior" -> Automatic, "Utility" -> SparseArray[ Automatic, {2, 3}, 0., {1, {{0, 1, 2}, {{2}, {3}}}, {1., 1.}}], "Threshold" -> 0, "TieBreaker" -> RandomChoice, "PerformanceGoal" -> Automatic, "BatchProcessing" -> Automatic, "Model" -> Association["Trees" -> { MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{2, 4, 1, 2, 7, 8, 5, 7, 10, 7, 1}], "NumericalThresholds" -> { 0.9946039617061616, -0.20965487509965894`, 0.20368486642837527`, 0.9892682433128358, 0.5462826192378999, 2.288970589637757, 0.49391707777976995`, 0.5480149388313295, -0.8163892924785613, 0.5590475499629975, 0.3569940775632859}, "LeafValues" -> RawArray[ "Real32",{-0.28246772289276123`, -0.20274174213409424`, \ -0.2972317934036255, -0.4507780969142914, -0.45395806431770325`, \ -0.4817826449871063, -0.3811538517475128, -0.3972248136997223, \ -0.34661218523979187`, -0.23817551136016846`, -0.46050503849983215`, \ -0.3037935793399811}], "Children" -> RawArray["Integer16",{{4, 2}, {11, 3}, {-3, -4}, {5, -5}, {-1, 6}, {7, 9}, {8, 10}, {-6, -9}, {-7, -10}, {-8, -11}, {-2, -12}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{2, 9, 1, 5, 4, 4, 2, 9, 6, 8, 1, 7, 2, 7}], "NumericalThresholds" -> { 0.9946039617061616, -0.9484192430973052, 0.3569940775632859, -2.0331373214721675`, 1.2015681862831118`, -0.4418590813875198, 0.377995178103447, -0.9472792744636535, -0.3380902707576751, \ -0.43944269418716425`, -0.2538562566041946, 0.5592621862888337, 0.36961247026920324`, -1.8007637262344358`}, "LeafValues" -> RawArray["Real32",{-0.11379241198301315`, -0.04178677126765251, 0.09265776723623276, 0.004428837914019823, 0.06392040103673935, -0.1130148395895958, \ -0.016950644552707672`, -0.003985628020018339, 0.0959237813949585, -0.12138312309980392`, \ -0.03290383145213127, -0.14212816953659058`, -0.008572163060307503, \ -0.007090551778674126, 0.17898595333099365`}], "Children" -> RawArray["Integer16",{{4, 2}, {-2, 3}, {14, -4}, {-1, 5}, {6, 13}, {9, 7}, {8, 11}, {-7, -9}, {10, 12}, {-5, -11}, {-8, -12}, {-10, -13}, {-6, -14}, {-3, -15}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{2, 4, 1, 8, 6, 2, 5, 5, 7, 3, 1, 3, 3}], "NumericalThresholds" -> { 0.9946039617061616, -0.20965487509965894`, 0.20368486642837527`, 2.290973067283631, -0.3431983441114425, 0.9898003041744233, 0.49221146106719976`, 0.4913096725940705, 0.5593489706516267, -0.5844773352146148, -0.5408473312854766, \ -0.581521451473236, -0.5747654438018798}, "LeafValues" -> RawArray["Real32",{0.02937297336757183, 0.055015455931425095`, 0.05232948437333107, -0.07843836396932602, 0.11794556677341461`, -0.06093272939324379, \ -0.09440325200557709, -0.08992607146501541, 0.15295028686523438`, -0.007890762761235237, 0.01669510267674923, -0.0313449464738369, -0.1090421974658966, 0.14428676664829254`}], "Children" -> RawArray["Integer16",{{4, 2}, {13, 3}, {-3, -4}, {6, 5}, {-5, -6}, {7, -7}, {8, 9}, {11, -9}, {10, 12}, {-8, -11}, {-1, -12}, {-10, -13}, {-2, -14}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{5, 4, 2, 5, 8, 4, 6, 9, 10, 1, 5}], "NumericalThresholds" -> {-2.0331373214721675`, 1.2015681862831118`, 0.9946039617061616, -2.02499508857727, -0.4436304867267608, \ -0.4418590813875198, -0.3415212929248809, -0.9437983036041259, \ -0.8212886154651641, 0.3569940775632859, 0.4905501604080201}, "LeafValues" -> RawArray["Real32",{-0.10982800275087357`, 0.08634704351425171, -0.08523072302341461, 0.06304624676704407, 0.0010636488441377878`, -0.01729017123579979, 0.030929503962397575`, -0.12647300958633423`, \ -0.1242932677268982, 0.057089295238256454`, 0.017277151346206665`, 0.13246257603168488`}], "Children" -> RawArray["Integer16",{{-1, 2}, {3, 8}, {4, 10}, {-2, 5}, {7, 6}, {-6, -7}, {-5, -8}, {9, -9}, {-3, -10}, { 11, -11}, {-4, -12}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{10, 10, 10, 3, 9, 9, 4, 10, 6}], "NumericalThresholds" -> {1.227887034416199, 1.223118603229523, 1.2210953235626223`, -0.5833496749401091, 1.0642827153205874`, 1.0566862821578982`, -0.6107302606105803, 1.215612292289734, -0.34527122974395746`}, "LeafValues" -> RawArray["Real32",{-0.005485105328261852, 0.09937714040279388, -0.14057794213294983`, 0.12027261406183243`, 0.05224660411477089, 0.07033649832010269, -0.11312967538833618`, 0.09890403598546982, 0.10729849338531494`, -0.06241648644208908}], "Children" -> RawArray["Integer16",{{2, -2}, {3, 4}, {5, -4}, {-3, 9}, { 6, -6}, {8, 7}, {-7, -8}, {-1, -9}, {-5, -10}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{5, 4, 2, 5, 8, 9, 6, 9, 3, 3, 9, 10}], "NumericalThresholds" -> {-2.0331373214721675`, 1.2015681862831118`, 0.9935066699981691, -2.02499508857727, 2.2892187833786015`, -0.9472792744636535, \ -0.34522888064384455`, -0.9431619644165038, 1.7604972720146181`, -0.5811982750892638, -0.9384241998195647, \ -0.8202808499336242}, "LeafValues" -> RawArray["Real32",{-0.10107319802045822`, 0.07676833868026733, -0.07263188064098358, \ -0.013729282654821873`, -0.06049637869000435, 0.12217910587787628`, -0.0010744675528258085`, \ -0.006031245458871126, -0.1301155388355255, 0.04173324629664421, 0.09070824831724167, 0.1320883184671402, -0.011849921196699142`}], "Children" -> RawArray["Integer16",{{-1, 2}, {3, 8}, {4, 10}, {-2, 5}, {6, 7}, {-5, -7}, {-6, -8}, {9, -9}, {-3, -10}, {-4, 11}, { 12, -12}, {-11, -13}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{5, 10, 2, 7, 10, 8, 6, 6}], "NumericalThresholds" -> {-2.0331373214721675`, \ -0.8277533352375029, 0.9988001286983491, -1.802540838718414, -0.8240376114845275, \ -0.43759447336196894`, -0.3380902707576751, -0.3434351980686187}, "LeafValues" -> RawArray[ "Real32",{-0.09400851279497147, -0.07442595809698105, \ -0.09416601061820984, 0.1167166531085968, -0.05235907435417175, 0.01218756940215826, 0.10398072749376297`, -0.038623373955488205`, 0.017473552376031876`}], "Children" -> RawArray["Integer16",{{-1, 2}, {-2, 3}, {4, 8}, {-3, 5}, {6, 7}, {-5, -7}, {-6, -8}, {-4, -9}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{10, 10, 4, 5, 7, 3, 5, 1, 1, 2, 1}], "NumericalThresholds" -> {1.227887034416199, 1.2253705859184267`, 1.213660538196564, 0.4939893335103989, 0.5590475499629975, -0.5844773352146148, 0.4905501604080201, -0.40114213526248926`, 0.3569940775632859, -0.8505318760871886, 0.35338997840881353`}, "LeafValues" -> RawArray["Real32",{0.02683987468481064, 0.08470366150140762, -0.07503930479288101, \ -0.0020881013479083776`, -0.07839035987854004, -0.0044061848893761635`, 0.025851484388113022`, 0.002719931537285447, 0.12856487929821014`, -0.043117132037878036`, \ -0.11631377041339874`, -0.10330282896757126`}], "Children" -> RawArray["Integer16",{{2, -2}, {3, -3}, {4, 11}, {7, 5}, {6, 10}, {-5, -7}, {9, 8}, {-8, -9}, {-1, -10}, {-6, -11}, {-4, -12}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{1, 5, 10, 10, 2, 9, 7, 6, 9}], "NumericalThresholds" -> { 2.168297648429871, -2.0331373214721675`, -0.82693213224411, \ -0.8248376846313475, -0.8531274795532225, -0.9451540112495421, 0.564066231250763, -0.3439845591783523, -0.9452153146266936}, "LeafValues" -> RawArray["Real32",{-0.08857879787683487, 0.06900189071893692, -0.10140085965394974`, 0.042071662843227386`, -0.16277280449867249`, 0.0154299046844244, -0.008282490074634552, \ -0.10262588411569595`, -0.017811521887779236`, 0.13405157625675201`}], "Children" -> RawArray["Integer16",{{2, -2}, {-1, 3}, {8, 4}, {9, 5}, {6, 7}, {-5, -7}, {-6, -8}, {-3, -9}, {-4, -10}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{4, 3, 9, 3, 4, 8, 3, 4, 9, 10}], "NumericalThresholds" -> {1.213660538196564, 0.8227382600307466, -0.9498173296451567, 0.5635337233543397, 0.4667876064777375, -0.42982073128223414`, -0.5740084946155547, \ -0.23320889472961423`, -0.9437983036041259, -0.8212886154651641}, "LeafValues" -> RawArray["Real32",{-0.16289514303207397`, -0.07371203601360321, 0.08415218442678452, -0.011185657232999802`, \ -0.12108992785215378`, 0.12777866423130035`, 0.042899683117866516`, 0.038330189883708954`, -0.044795140624046326`, \ -0.10550433397293091`, 0.060172807425260544`}], "Children" -> RawArray["Integer16",{{2, 9}, {3, -3}, {6, 4}, {5, -5}, { 7, -6}, {8, -7}, {-4, -8}, {-1, -9}, {10, -10}, {-2, -11}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{10, 10, 10, 9, 6, 9, 1, 6, 9}], "NumericalThresholds" -> {1.227887034416199, 1.223118603229523, 1.2210953235626223`, 1.061862349510193, -0.3402366489171981, 1.0642827153205874`, 0.3569940775632859, -0.3503588438034057, 1.058703660964966}, "LeafValues" -> RawArray["Real32",{0.013907281681895256`, 0.07482340931892395, -0.02766731195151806, 0.10506171733140945`, 0.033709630370140076`, -0.15546397864818573`, 0.06057272478938103, 0.07300766557455063, -0.05542740225791931, \ -0.08789139240980148}], "Children" -> RawArray["Integer16",{{2, -2}, {3, 4}, {6, -4}, { 5, -5}, {-3, -6}, {7, -7}, {9, 8}, {-8, -9}, {-1, -10}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{1, 5, 5, 7, 2, 2, 5, 1, 1, 8, 5, 5, 3, 4, 10}], "NumericalThresholds" -> { 2.168297648429871, -2.0331373214721675`, 0.4939893335103989, 0.5590475499629975, -0.8610092699527739, 0.3734677135944367, 0.4905501604080201, -0.5446600914001464, -0.6940572261810302, \ -0.4361160993576049, 0.4921253472566605, 0.4931399524211884, -0.5794537663459777, -0.4512042254209518, \ -0.8114331066608428}, "LeafValues" -> RawArray["Real32",{-0.08324574679136276, 0.0632319524884224, 0.012032954022288322`, -0.10615351796150208`, \ -0.008923418819904327, 0.06237564608454704, -0.015577285550534725`, 0.06181494891643524, 0.010214737616479397`, 0.1445033699274063, -0.06077848747372627, -0.03733331710100174, 0.0805339366197586, -0.15323764085769653`, -0.067890465259552, 0.13604076206684113`}], "Children" -> RawArray["Integer16",{{2, -2}, {-1, 3}, {7, 4}, {5, 13}, {-4, 6}, {-6, -7}, {8, 11}, {9, 10}, {-3, -10}, {-9, -11}, {15, 12}, {-12, -13}, {-5, 14}, {-14, -15}, {-8, -16}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{4, 4, 3, 9, 9, 2, 9, 1, 6, 1, 2}], "NumericalThresholds" -> {-0.6204328536987304, \ -0.6182262003421782, -0.583036035299301, 1.0553134083747866`, 1.06148898601532, 0.38292969763278967`, 1.0592303872108462`, -0.09474795684218405, -0.3503588438034057, \ -0.2519650608301162, -1.0000000180025095`*^-35}, "LeafValues" -> RawArray["Real32",{-0.0807669535279274, -0.10860709846019745`, 0.002660777885466814, 0.17371921241283417`, 0.016456980258226395`, 0.11840459704399109`, -0.026238197460770607`, \ -0.10964296013116837`, 0.10308130085468292`, -0.04552840441465378, 0.09459187090396881, 0.029247865080833435`}], "Children" -> RawArray["Integer16",{{3, 2}, {-2, 5}, {11, 4}, {-4, -5}, {7, 6}, {-6, -7}, {8, -8}, {10, 9}, {-9, -10}, {-3, -11}, {-1, -12}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{5, 10, 2, 7, 4, 7, 6}], "NumericalThresholds" -> {-2.0331373214721675`, \ -0.8277533352375029, 0.9988001286983491, -1.802540838718414, 1.8391690254211428`, 0.5460203289985658, -0.3434351980686187}, "LeafValues" -> RawArray[ "Real32",{-0.07818107306957245, -0.061218757182359695`, \ -0.08611754328012466, 0.09558787941932678, 0.058075498789548874`, -0.06765440851449966, 0.0013667166931554675`, 0.017629828304052353`}], "Children" -> RawArray["Integer16",{{-1, 2}, {-2, 3}, {4, 7}, {-3, 5}, { 6, -6}, {-5, -7}, {-4, -8}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{1, 1, 4, 4, 6, 8, 9, 2, 3, 9}], "NumericalThresholds" -> { 2.168297648429871, -1.149351358413696, -0.6212775111198424, \ -0.5498932898044585, -0.3365022242069244, -0.4408219158649444, 1.0567567944526675`, -0.8629825115203856, -0.583036035299301, 1.0568774342536928`}, "LeafValues" -> RawArray["Real32",{0.0556267574429512, 0.05606580525636673, -0.02751605212688446, 0.10038811713457108`, -0.06839734315872192, \ -0.14235612750053406`, -0.05089084059000015, 0.10756261646747589`, 0.0044924188405275345`, 0.13783368468284607`, 0.008490861393511295}], "Children" -> RawArray["Integer16",{{2, -2}, {-1, 3}, {9, 4}, {5, 7}, { 6, -6}, {-4, -7}, {8, -8}, {-5, -9}, {-3, 10}, {-10, -11}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{10, 10, 5, 6, 4, 6, 6, 5, 7}], "NumericalThresholds" -> {1.227887034416199, 1.2253705859184267`, 0.49431209266185766`, -0.35246877372264857`, 1.7197774648666384`, -0.35075858235359186`, -0.3524067103862762, 0.49594092369079595`, 0.5576067864894868}, "LeafValues" -> RawArray["Real32",{-0.09979532659053802, 0.06579889357089996, -0.06416003406047821, 0.05917096138000488, 0.1026785746216774, -0.09006994217634201, 0.01888679340481758, -0.09864611178636551, 0.005464034155011177, -0.06879612803459167}], "Children" -> RawArray["Integer16",{{2, -2}, {3, -3}, {4, 7}, {-1, 5}, { 6, -6}, {-5, -7}, {-4, 8}, {-8, 9}, {-9, -10}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{5, 10, 10, 10, 9, 9, 3, 1}], "NumericalThresholds" -> {-2.0331373214721675`, \ -0.8277533352375029, -0.8240376114845275, -0.8262295722961425, \ -0.9529974162578582, -0.9498173296451567, 0.5635337233543397, -0.23872810602188108`}, "LeafValues" -> RawArray[ "Real32",{-0.07511106878519058, -0.05867835506796837, \ -0.009042284451425076, 0.07876475155353546, 0.10301697999238968`, -0.02216830849647522, 0.010442028753459454`, -0.05527728423476219, \ -0.15162508189678192`}], "Children" -> RawArray["Integer16",{{-1, 2}, {-2, 3}, {4, 5}, {-3, -5}, {-4, 6}, {8, 7}, {-7, -8}, {-6, -9}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{8, 6, 10, 4, 3, 9, 3, 3, 9, 8, 2}], "NumericalThresholds" -> { 2.290973067283631, -0.3415212929248809, -0.81562402844429, 1.213660538196564, 0.8227382600307466, -0.9498173296451567, 0.517758071422577, -0.5740084946155547, -0.9515863060951232, \ -0.43954972922801966`, -0.24474039673805234`}, "LeafValues" -> RawArray["Real32",{-0.1274843066930771, 0.026812827214598656`, -0.05680535361170769, 0.14041057229042053`, 0.015049098990857601`, 0.08528543263673782, -0.011166729964315891`, \ -0.0933583527803421, 0.0381106436252594, -0.09266754239797592, 0.016585085541009903`, -0.0647759661078453}], "Children" -> RawArray["Integer16",{{4, 2}, {3, -3}, {-2, -4}, {5, 9}, { 6, -6}, {10, 7}, {8, -8}, {-7, -9}, {-5, -10}, {-1, 11}, {-11, -12}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{2, 7, 9, 7, 7, 3, 9, 10, 9, 9, 3, 3, 5, 10, 4, 8, 9, 8, 2, 4, 3}], "NumericalThresholds" -> {0.9946039617061616, 0.5530049800872804, -0.9439638853073119, 0.5506651699543, 0.55691796541214, -0.581521451473236, -0.9476423561573027, \ -0.820649355649948, 1.0517516732215884`, -0.942828208208084, -0.583538919687271, 0.2773242741823197, 0.496099278330803, 1.223118603229523, -0.20965487509965894`, -0.4320432096719741, 1.0593642592430117`, -0.4360803067684173, 0.36654223501682287`, -0.16288959980010984`, \ -0.5747654438018798}, "LeafValues" -> RawArray["Real32",{0.16332441568374634`, 0.031107990071177483`, -0.04152211919426918, \ -0.15138177573680878`, -0.07357568293809891, 0.09053770452737808, -0.04838695749640465, 0.14068977534770966`, 0.021346531808376312`, -0.1524510234594345, 0.02967524528503418, -0.053779613226652145`, \ -0.11570779234170914`, 0.08679532259702682, -0.02224348485469818, \ -0.058576665818691254`, 0.051775768399238586`, -0.03541095182299614, 0.01441084872931242, -0.0627681314945221, 0.10224144160747528`, 0.11196355521678925`}], "Children" -> RawArray["Integer16",{{2, 15}, {3, 5}, {4, 11}, {8, -5}, {7, 6}, {14, 9}, {-3, 18}, {-1, -9}, {10, 17}, {-7, -11}, {19, 12}, {13, -13}, {-12, -14}, {-6, -15}, {21, 16}, {-16, -17}, {-10, -18}, {-8, 20}, {-4, -20}, {-19, -21}, {-2, -22}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{10, 10, 10, 3, 9, 9, 6, 2, 6}], "NumericalThresholds" -> {1.227887034416199, 1.223118603229523, 1.2210953235626223`, -0.5833496749401091, 1.060893535614014, 1.0642827153205874`, -0.3467642813920974, -0.8622403144836425, \ -0.350375548005104}, "LeafValues" -> RawArray["Real32",{0.0025560916401445866`, 0.06033267080783844, -0.11831891536712646`, 0.08858782798051834, -0.048395268619060516`, 0.047261640429496765`, 0.047752171754837036`, -0.0547817088663578, 0.019702300429344177`, -0.07058724761009216}], "Children" -> RawArray["Integer16",{{2, -2}, {3, 4}, {6, -4}, {-3, 5}, {-5, -6}, {7, -7}, {9, 8}, {-8, -9}, {-1, -10}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{5, 10, 2, 7, 10, 8, 5, 6}], "NumericalThresholds" -> {-2.0331373214721675`, \ -0.8277533352375029, 0.9988001286983491, -1.802540838718414, -0.8240376114845275, \ -0.43759447336196894`, -2.027588605880737, -0.3434351980686187}, "LeafValues" -> RawArray[ "Real32",{-0.07021109014749527, -0.05219881236553192, \ -0.08047624677419662, 0.08106954395771027, -0.04515235871076584, 0.06950714439153671, 0.08009843528270721, -0.00540145905688405, 0.012792401947081089`}], "Children" -> RawArray["Integer16",{{-1, 2}, {-2, 3}, {4, 8}, {-3, 5}, {6, 7}, {-5, -7}, {-6, -8}, {-4, -9}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{8, 7, 1, 6, 1, 1, 10, 6, 3, 8, 8, 7, 4, 6}], "NumericalThresholds" -> {-0.4436304867267608, 0.5576067864894868, -1.149351358413696, -0.3434351980686187, \ -0.6931055188179015, -0.5408473312854766, 1.2108204960823061`, -0.34547176957130427`, \ -0.22744652628898618`, -0.4371733665466308, 2.290973067283631, 0.5595479011535646, -0.015494070015847681`, \ -0.3507079184055328}, "LeafValues" -> RawArray["Real32",{0.05375656858086586, 0.08827731758356094, -0.10347200930118561`, 0.006133949384093285, -0.04044635593891144, 0.08408129960298538, 0.10743118822574615`, -0.0326664038002491, 0.016094837337732315`, -0.11794087290763855`, \ -0.029403990134596825`, 0.03728644177317619, -0.14798787236213684`, 0.11035103350877762`, 0.021340716630220413`}], "Children" -> RawArray["Integer16",{{2, 3}, {4, -3}, {-2, 5}, {-1, -5}, {7, 6}, {-6, 10}, {8, 12}, {9, 13}, {-4, -10}, {14, 11}, {-11, -12}, {-8, -13}, {-9, -14}, {-7, -15}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{4, 4, 6, 1, 9, 6, 6, 6, 1, 5, 2}], "NumericalThresholds" -> {-0.6204328536987304, \ -0.6182262003421782, -0.35060438513755793`, -0.23952846974134442`, 1.0568774342536928`, -0.35290552675724024`, \ -0.35075858235359186`, -0.3470014631748199, -0.704359084367752, 0.4964315593242646, 0.3714872002601624}, "LeafValues" -> RawArray[ "Real32",{-0.06398831307888031, -0.09942092001438141, \ -0.05828031897544861, -0.017169289290905, 0.1538705974817276, 0.018255243077874184`, -0.00190968147944659, \ -0.015946563333272934`, 0.01934155449271202, 0.12747734785079956`, -0.028009828180074692`, \ -0.08644550293684006}], "Children" -> RawArray["Integer16",{{3, 2}, {-2, 6}, {-1, 4}, {-4, 5}, {-5, -6}, {-3, 7}, {9, 8}, {11, 10}, {-7, -10}, {-9, -11}, {-8, -12}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{3, 9, 9, 4, 7, 6, 5, 3, 1, 6, 8, 9, 5}], "NumericalThresholds" -> {-0.5910580456256865, 1.061119616031647, 1.0551514625549319`, -0.6168223917484282, 0.552327185869217, -0.34161131083965296`, 0.49449042975902563`, -0.5825454890727996, \ -0.09474795684218405, -0.3503588438034057, -0.4397531151771545, 1.0642827153205874`, 0.4947826117277146}, "LeafValues" -> RawArray["Real32",{-0.06261944025754929, 0.019714010879397392`, -0.06886085122823715, \ -0.15030814707279205`, -0.033200908452272415`, 0.1385807991027832, -0.004543809685856104, 0.028511883690953255`, 0.11811849474906921`, 0.07474195212125778, -0.040277183055877686`, 0.030362525954842567`, 0.03679408133029938, -0.0457494743168354}], "Children" -> RawArray["Integer16",{{-1, 2}, {3, 5}, {4, 6}, {8, 9}, {12, 7}, { 13, -7}, {-6, -8}, {-2, -9}, {11, 10}, {-10, -11}, {-5, -12}, {-3, -13}, {-4, -14}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{5, 10, 1, 6, 6, 4, 10}], "NumericalThresholds" -> {-2.0331373214721675`, 1.227887034416199, -1.149351358413696, -0.35290552675724024`, \ -0.35140699148178095`, -0.47695702314376826`, -0.8200730383396148}, "LeafValues" -> RawArray["Real32",{-0.06691570580005646, 0.06034402549266815, 0.06166359409689903, -0.05390859395265579, 0.10163062065839767`, -0.025040339678525925`, 0.009936594404280186, 0.03313332796096802}], "Children" -> RawArray["Integer16",{{-1, 2}, {3, -3}, {-2, 4}, {-4, 5}, {7, 6}, {-6, -7}, {-5, -8}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{9, 8, 7, 8, 7, 7, 2, 5, 7, 7, 8, 9, 2, 1, 8, 5, 2, 5, 3}], "NumericalThresholds" -> {-0.9493410289287566, \ -0.4412997514009475, 0.5507496595382692, -0.4341561943292617, 0.5530049800872804, 0.5567439198493959, -0.8527612388134002, 0.4939893335103989, 0.564066231250763, 0.5607570111751558, -0.4351986348628997, -0.9469074606895446, 0.9946039617061616, 0.3569940775632859, -0.4302958250045776, 0.496099278330803, -1.4696708321571348`, 0.4896071404218674, -0.3827435821294784}, "LeafValues" -> RawArray["Real32",{-0.12663456797599792`, -0.028760425746440887`, 0.060283806174993515`, 0.04779020696878433, -0.08669585734605789, 0.021495867520570755`, 0.0048383986577391624`, 0.08580773323774338, -0.09190991520881653, \ -0.06439715623855591, 0.09540100395679474, 0.07173727452754974, -0.05926065891981125, 0.09767965972423553, -0.020006608217954636`, \ -0.04784100130200386, 0.00828627124428749, -0.11795599013566971`, 0.11759018152952194`, 0.055790528655052185`}], "Children" -> RawArray["Integer16",{{2, 5}, {-1, 3}, {-3, 4}, {-4, -5}, {7, 6}, {18, 8}, {17, 12}, {9, 11}, {10, -10}, {-7, -11}, {-9, 15}, {-8, 13}, {16, 14}, {-14, -15}, {-12, -16}, {-13, -17}, {-2, -18}, {-6, 19}, {-19, -20}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{4, 4, 4, 3, 9, 1, 9, 1, 6, 1}], "NumericalThresholds" -> {-0.6204328536987304, \ -0.6182262003421782, -0.6229110956192015, -0.5824227929115294, 1.06148898601532, 0.2078627794981003, 1.0584094524383547`, -0.09474795684218405, -0.3503588438034057, \ -0.3918608278036117}, "LeafValues" -> RawArray["Real32",{-0.05764861777424812, -0.09339263290166855, 0.0018554902635514736`, 0.07973083108663559, 0.046828147023916245`, -0.00751421507447958, 0.11345936357975006`, -0.08129241317510605, 0.06932307034730911, -0.035023283213377, 0.07400686293840408}], "Children" -> RawArray["Integer16",{{3, 2}, {-2, 5}, {4, -4}, {-1, -5}, {7, 6}, {-6, -7}, {8, -8}, {10, 9}, {-9, -10}, {-3, -11}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{3, 9, 9, 4, 6, 4, 5, 3, 7, 3, 3, 6, 9}], "NumericalThresholds" -> {-0.5910580456256865, 1.061119616031647, 1.0551514625549319`, -0.6168223917484282, -0.3386283069849014, \ -0.6123723983764647, 0.49630032479763037`, -0.5743220448493956, 0.5533436536788942, -0.4942383170127868, -0.5825454890727996, \ -0.3433855324983596, 1.0643647313117983`}, "LeafValues" -> RawArray["Real32",{-0.058326512575149536`, 0.02304753102362156, 0.05857298895716667, -0.1223280057311058, \ -0.11592449247837067`, -0.02629830129444599, 0.016058150678873062`, 0.12099988013505936`, 0.08536171168088913, 0.0027576014399528503`, -0.004213320557028055, 0.10734029859304428`, -0.03656696528196335, \ -0.0028991259168833494`}], "Children" -> RawArray["Integer16",{{-1, 2}, {3, 5}, {4, 6}, {11, 8}, { 7, -6}, {12, -7}, {13, -8}, {9, 10}, {-5, -10}, {-9, -11}, {-2, -12}, {-4, -13}, {-3, -14}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{10, 10, 5, 6, 4, 5, 8, 7, 7, 2}], "NumericalThresholds" -> {1.227887034416199, 1.2253705859184267`, 0.49431209266185766`, -0.35246877372264857`, 1.7197774648666384`, 0.4931399524211884, 2.291669845581055, 0.5462826192378999, -1.7991518974304197`, 0.3754096776247025}, "LeafValues" -> RawArray["Real32",{-0.08755452930927277, 0.05326111614704132, -0.057408735156059265`, \ -0.05863082781434059, 0.015731271356344223`, -0.07548416405916214, 0.09825050830841064, 0.04899125173687935, -0.01992187276482582, 0.11018669605255127`, -0.1020333394408226}], "Children" -> RawArray["Integer16",{{2, -2}, {3, -3}, {4, 7}, {-1, 5}, { 6, -6}, {-5, -7}, {8, -8}, {9, 10}, {-4, -10}, {-9, -11}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{5, 10, 10, 3, 9, 9, 3, 1, 9}], "NumericalThresholds" -> {-2.0331373214721675`, \ -0.8263826668262481, -0.8248376846313475, -0.3692857772111892, \ -0.9529974162578582, -0.9498173296451567, 0.5635337233543397, -0.23872810602188108`, -0.9472792744636535}, "LeafValues" -> RawArray["Real32",{-0.06381769478321075, -0.1248110830783844, 0.0947592630982399, 0.0790131464600563, -0.05192947015166283, \ -0.027403321117162704`, 0.010088642127811909`, -0.05118744075298309, \ -0.13957245647907257`, 0.061669524759054184`}], "Children" -> RawArray["Integer16",{{-1, 2}, {4, 3}, {-3, 5}, {-2, 9}, {-4, 6}, {8, 7}, {-7, -8}, {-6, -9}, {-5, -10}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{1, 7, 7, 7, 6, 6, 10}], "NumericalThresholds" -> {1.7190739512443545`, 0.564066231250763, 0.5630619823932649, 0.5619760751724244, -0.35290552675724024`, \ -0.35140699148178095`, 1.2146451473236086`}, "LeafValues" -> RawArray[ "Real32",{-0.05337623134255409, -0.0013315515825524926`, \ -0.06776665151119232, 0.08616762608289719, -0.09815308451652527, 0.07769645005464554, 0.0007419019239023328, 0.0740438923239708}], "Children" -> RawArray["Integer16",{{2, 7}, {3, -3}, {4, -4}, {5, -5}, {-1, 6}, {-6, -7}, {-2, -8}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{3, 9, 9, 4, 10, 6, 10, 2, 5, 2, 10, 4}], "NumericalThresholds" -> {-0.5910580456256865, 1.061119616031647, 1.0551514625549319`, -0.6168223917484282, 1.2127711176872256`, -0.3386283069849014, 1.224208474159241, -0.8629825115203856, 0.49630032479763037`, -0.23314782977104184`, 1.2170100808143618`, -0.6172350645065307}, "LeafValues" -> RawArray["Real32",{-0.05517899990081787, 0.008790743537247181, -0.0048638298176229, 0.02453920990228653, -0.04632601514458656, \ -0.13300298154354095`, -0.02380310744047165, -0.07420092821121216, 0.0073662204667925835`, 0.10995166003704071`, 0.09362394362688065, -0.043389685451984406`, 0.05065007135272026}], "Children" -> RawArray["Integer16",{{-1, 2}, {3, 6}, {4, 5}, {10, 7}, {-4, 11}, {9, -7}, {8, -8}, {-5, -9}, { 12, -10}, {-2, -11}, {-6, -12}, {-3, -13}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{9, 8, 7, 8, 9, 6, 10, 3, 4, 8, 6, 4, 8, 2, 2, 2}], "NumericalThresholds" -> {-0.9493410289287566, \ -0.4412997514009475, 0.5507496595382692, -0.4341561943292617, -0.9415125250816344, \ -0.33491583168506617`, -0.8127984404563903, 0.046939844265580184`, -0.4512042254209518, -0.435415968298912, \ -0.3365022242069244, -0.6204328536987304, -0.44185335934162134`, 0.3835651427507401, -0.23845230042934415`, 0.3801019340753556}, "LeafValues" -> RawArray["Real32",{-0.11908897757530212`, -0.002318295184522867, 0.05496500805020332, 0.04571598023176193, -0.078372061252594, 0.05702004209160805, 0.1193239763379097, 0.11898119747638702`, -0.09588539600372314, \ -0.01622927002608776, 0.1130552813410759, 0.013009332120418549`, -0.1181936040520668, \ -0.008801891468465328, 0.04079211875796318, -0.10317884385585785`, \ -0.014912600629031658`}], "Children" -> RawArray["Integer16",{{2, 5}, {-1, 3}, {-3, 4}, {-4, -5}, {6, 8}, {7, -7}, {14, -8}, {9, 16}, {11, 10}, {-10, -11}, {13, 12}, {-12, -13}, {-6, -14}, {15, -15}, {-2, -16}, {-9, -17}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{3, 3, 7, 7, 7, 7, 6, 4, 8, 10, 2}], "NumericalThresholds" -> {-0.5850712656974791, \ -0.5818327069282531, 0.5532377958297731, 0.5645610094070436, 0.5630619823932649, 0.5604738891124726, -0.35282145440578455`, -0.2615253329277038, \ -0.43317085504531855`, 1.2164499163627627`, -0.24575550854206082`}, "LeafValues" -> RawArray["Real32",{-0.030517732724547386`, 0.10816465318202972`, -0.07566802203655243, 0.08515068888664246, -0.11106475442647934`, 0.10668563842773438`, -0.11595644056797028`, 0.010290698148310184`, -0.00700045470148325, \ -0.1423792541027069, -0.02443888410925865, 0.0349414199590683}], "Children" -> RawArray["Integer16",{{3, 2}, {11, 4}, {9, 10}, {5, -5}, { 6, -6}, {7, 8}, {-3, -8}, {-7, -9}, {-1, -10}, {-4, -11}, {-2, -12}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{5, 10, 10, 6, 10, 9, 9, 2}], "NumericalThresholds" -> {-2.0331373214721675`, \ -0.82693213224411, -0.8240376114845275, -0.34540908038616175`, \ -0.8231001496315001, -0.9529974162578582, -0.9505229890346526, 1.0000000180025095`*^-35}, "LeafValues" -> RawArray["Real32",{-0.05868002399802208, 0.017692046239972115`, -0.0011709540849551558`, \ -0.06609052419662476, 0.09658203274011612, 0.09280272573232651, -0.08341778814792633, 0.0037753325887024403`, -0.06485161930322647}], "Children" -> RawArray["Integer16",{{-1, 2}, {8, 3}, {4, 5}, {-3, -5}, {-4, 6}, {-6, 7}, {-7, -8}, {-2, -9}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{10, 10, 5, 6, 4, 5, 5, 10, 10, 7, 8}], "NumericalThresholds" -> {1.227887034416199, 1.2253705859184267`, 0.49431209266185766`, -0.35246877372264857`, 1.7197774648666384`, 0.4931399524211884, 0.5008907616138459, -0.8170699179172515, 1.2163570523262026`, 0.5598454773426057, -0.4315378367900848}, "LeafValues" -> RawArray["Real32",{-0.0729067400097847, 0.047844428569078445`, -0.051866695284843445`, 0.004268902353942394, 0.014018497429788113`, -0.06788143515586853, 0.08681480586528778, -0.056268855929374695`, 0.025632517412304878`, -0.09585871547460556, \ -0.07947301864624023, 0.09509548544883728}], "Children" -> RawArray["Integer16",{{2, -2}, {3, -3}, {4, 7}, {-1, 5}, { 6, -6}, {-5, -7}, {9, 8}, {-8, 11}, { 10, -10}, {-4, -11}, {-9, -12}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{8, 7, 7, 1, 2, 7, 7, 4, 9, 2}], "NumericalThresholds" -> {-0.4436304867267608, 0.5576067864894868, 0.5516092479228974, -1.149351358413696, 1.6101221442222597`, 0.5535474121570588, 0.5557118952274324, -0.6212775111198424, -0.9523951709270476, \ -0.8519331216812133}, "LeafValues" -> RawArray["Real32",{-0.03493637219071388, 0.07091165333986282, -0.09346189349889755, 0.05930103361606598, 0.07899037003517151, 0.052276361733675, 0.08645115047693253, 0.08471483737230301, -0.013024895451962948`, \ -0.07375771552324295, -0.012805797159671783`}], "Children" -> RawArray["Integer16",{{2, 4}, {3, -3}, {-1, -4}, {-2, 5}, { 6, -6}, {9, 7}, {-7, 8}, {-8, -9}, {-5, 10}, {-10, -11}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{9, 8, 9, 9, 10, 6, 4, 4, 6, 6, 6, 9, 4, 4, 10, 3}], "NumericalThresholds" -> {-0.9493410289287566, \ -0.4412997514009475, -0.9529974162578582, -0.942076414823532, \ -0.8127984404563903, -0.33588908612728113`, 0.078564390540123, -0.307495892047882, -0.3365022242069244, \ -0.3416405469179153, -0.34412729740142817`, 1.0547537803649905`, -0.37125894427299494`, 0.09189115837216379, -0.8177161812782286, 0.46568275988101965`}, "LeafValues" -> RawArray["Real32",{-0.11393443495035172`, 0.05479630082845688, 0.05742434784770012, 0.03225291520357132, -0.010349826887249947`, 0.12551657855510712`, 0.08823178708553314, -0.1131189838051796, 0.09046449512243271, -0.12806068360805511`, 0.05036503076553345, -0.0013892954448238015`, \ -0.017575928941369057`, -0.11015000939369202`, -0.00557201262563467, \ -0.08277042955160141, -0.029636505991220474`}], "Children" -> RawArray["Integer16",{{2, 4}, {-1, 3}, {-3, 15}, {5, 7}, { 6, -6}, {13, -7}, {8, 11}, {9, -9}, {10, 12}, {-5, -11}, {-8, -12}, {-10, -13}, {-2, 14}, {-14, -15}, { 16, -16}, {-4, -17}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{5, 10, 10, 3, 4, 9, 9, 1, 4}], "NumericalThresholds" -> {-2.0331373214721675`, \ -0.8262295722961425, -0.8248376846313475, -0.3911129087209701, 0.05109641700983048, -0.9529974162578582, -0.9498173296451567, \ -0.23872810602188108`, 1.040290415287018}, "LeafValues" -> RawArray["Real32",{-0.05646447464823723, -0.1227642074227333, 0.10349228978157043`, 0.061034392565488815`, 0.09599524736404419, -0.05575861409306526, \ -0.013486920855939388`, 0.006212935782968998, -0.12992677092552185`, \ -0.07040613889694214}], "Children" -> RawArray["Integer16",{{-1, 2}, {4, 3}, {-3, 6}, {-2, 5}, {-5, -6}, {-4, 7}, {8, 9}, {-7, -9}, {-8, -10}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{1, 3, 3, 1, 9, 8, 3, 5, 3, 5, 4}], "NumericalThresholds" -> {2.3958830833435063`, 0.8227382600307466, 0.5819390416145326, -0.4055047929286956, -0.9510375559329985, \ -0.4317405372858047, -0.5740084946155547, 0.4964315593242646, -0.5761171877384185, 0.49004910886287695`, 1.3016189932823183`}, "LeafValues" -> RawArray["Real32",{-0.10579629242420197`, 0.04078599810600281, 0.10666055977344513`, -0.09238333255052567, \ -0.06365589052438736, 0.00019358255667611957`, 0.014084097929298878`, 0.047225359827280045`, -0.029680320993065834`, \ -0.09041345119476318, 0.0645611435174942, -0.0052194977179169655`}], "Children" -> RawArray["Integer16",{{2, -2}, {3, 4}, {5, -4}, {-3, 10}, {6, 7}, {-1, -7}, {9, 8}, {-8, -9}, {-6, -10}, {-5, 11}, {-11, -12}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{3, 3, 7, 7, 7, 7, 8, 10, 7, 10}], "NumericalThresholds" -> {-0.5850712656974791, \ -0.5818327069282531, 0.5532377958297731, 0.5645610094070436, 0.5630619823932649, 0.5619760751724244, -0.43317085504531855`, 1.2164499163627627`, 0.5570109188556672, 1.2144270539283755`}, "LeafValues" -> RawArray["Real32",{-0.02696494199335575, 0.1061871200799942, 0.010205598548054695`, 0.07430695742368698, -0.10507312417030334`, 0.09223105758428574, -0.11197467148303986`, \ -0.1366673856973648, -0.023925846442580223`, -0.025170885026454926`, 0.033838775008916855`}], "Children" -> RawArray["Integer16",{{3, 2}, {10, 4}, {7, 8}, {5, -5}, { 6, -6}, {9, -7}, {-1, -8}, {-4, -9}, {-3, -10}, {-2, -11}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{10, 10, 5, 9, 6, 5, 7, 6, 1}], "NumericalThresholds" -> {1.227887034416199, 1.2253705859184267`, 0.49431209266185766`, 1.0642827153205874`, -0.3524067103862762, 0.49594092369079595`, 0.5576067864894868, -0.35246877372264857`, 0.3569940775632859}, "LeafValues" -> RawArray["Real32",{-0.06709199398756027, 0.04400274530053139, -0.045593973249197006`, 0.04761075600981712, 0.08126123249530792, -0.08455123752355576, 0.003804353065788746, -0.053605660796165466`, 0.019861215725541115`, -0.022006303071975708`}], "Children" -> RawArray["Integer16",{{2, -2}, {3, -3}, {4, 5}, {8, -5}, {-4, 6}, {-6, 7}, {-7, -8}, {-1, 9}, {-9, -10}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{5, 10, 10, 3, 4, 2, 9, 7, 5, 5, 10}], "NumericalThresholds" -> {-2.0331373214721675`, \ -0.8262295722961425, -0.8248376846313475, -0.3911129087209701, 0.05109641700983048, -0.8531274795532225, -0.9451540112495421, 0.5530049800872804, 0.4849177300930024, 0.48638170957565313`, -0.819242089986801}, "LeafValues" -> RawArray["Real32",{-0.054849773645401, -0.11576145887374878`, 0.09432215243577957, -0.13206550478935242`, 0.0840216651558876, -0.05243261903524399, 0.11428283154964447`, -0.0808028057217598, 0.03716102987527847, -0.08793489634990692, 0.005879785865545273, 0.017459608614444733`}], "Children" -> RawArray["Integer16",{{-1, 2}, {4, 3}, {-3, 6}, {-2, 5}, {-5, -6}, {7, 9}, {-4, 8}, {-8, -9}, {11, 10}, {-10, -11}, {-7, -12}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{1, 7, 2, 3, 7, 2, 10, 6, 3, 10, 6, 1}], "NumericalThresholds" -> {1.7190739512443545`, 0.564066231250763, -0.8575963079929351, -0.5778716504573821, 0.5541078746318818, -0.244769349694252, 1.2233945131301882`, -0.3421326130628585, -0.3168442100286483, 1.2265378236770632`, -0.35290552675724024`, 2.619623422622681}, "LeafValues" -> RawArray["Real32",{0.023836569860577583`, 0.06954967975616455, -0.057994939386844635`, 0.01820066012442112, -0.11557790637016296`, \ -0.07501950114965439, -0.09510066360235214, -0.06429354846477509, 0.10355555266141891`, 0.025599783286452293`, 0.06051535904407501, -0.0009630155400373042, \ -0.002784088021144271}], "Children" -> RawArray["Integer16",{{2, 12}, {3, -3}, {4, 6}, {-1, 5}, {-5, 9}, {7, 10}, {8, -8}, {-4, -9}, {-6, -10}, { 11, -11}, {-7, -12}, {-2, -13}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{3, 3, 7, 8, 7, 7, 7, 10, 3, 5}], "NumericalThresholds" -> {-0.5850712656974791, \ -0.5818327069282531, 0.5535474121570588, -0.43317085504531855`, 0.5645610094070436, 0.5630619823932649, 0.5619760751724244, 1.218697011470795, -0.5789092481136321, 0.496099278330803}, "LeafValues" -> RawArray["Real32",{-0.01959371380507946, 0.030786026269197464`, -0.04512399435043335, 0.06704197078943253, -0.13331320881843567`, \ -0.09816192835569382, 0.08432132750749588, -0.10697996616363525`, \ -0.02689381316304207, 0.005463432986289263, 0.09840323776006699}], "Children" -> RawArray["Integer16",{{3, 2}, {10, 5}, {4, 8}, {-1, -5}, { 6, -6}, {7, -7}, {9, -8}, {-4, -9}, {-3, -10}, {-2, -11}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{7, 9, 9, 8, 8, 6, 6, 4, 9}], "NumericalThresholds" -> {-1.8040050268173216`, 1.0635292530059817`, 1.064914107322693, 2.293729186058045, -0.4265634715557098, -0.3409142047166824, \ -0.34161131083965296`, -0.6229110956192015, -0.9498173296451567}, "LeafValues" -> RawArray["Real32",{-0.03383150324225426, -0.10816341638565063`, 0.10426793992519379`, -0.00040015942067839205`, 0.043002985417842865`, -0.0033177072182297707`, \ -0.11530647426843643`, -0.0457066185772419, -0.005314735695719719, 0.026326939463615417`}], "Children" -> RawArray["Integer16",{{-1, 2}, {4, 3}, {-3, -4}, {5, -5}, {7, 6}, {-6, -7}, {8, 9}, {-2, -9}, {-8, -10}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{5, 10, 10, 3, 4, 9, 9, 4}], "NumericalThresholds" -> {-2.0331373214721675`, \ -0.8262295722961425, -0.8248376846313475, -0.3911129087209701, 0.05109641700983048, -0.9529974162578582, -0.9505229890346526, 1.040290415287018}, "LeafValues" -> RawArray["Real32",{-0.053691256791353226`, -0.11071203649044037`, 0.08622249960899353, 0.060913074761629105`, 0.07415039837360382, -0.04816310107707977, \ -0.058652184903621674`, 0.00466707069426775, -0.06700655817985535}], "Children" -> RawArray["Integer16",{{-1, 2}, {4, 3}, {-3, 6}, {-2, 5}, {-5, -6}, {-4, 7}, {-7, 8}, {-8, -9}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{3, 3, 1, 9, 7, 7, 4, 8, 5, 10, 6}], "NumericalThresholds" -> {0.8227382600307466, 0.5819390416145326, -0.4055047929286956, -0.9510375559329985, \ -1.7995272278785703`, -1.7944434285163877`, -0.307495892047882, \ -0.4317405372858047, 0.49004910886287695`, -0.8135946691036223, \ -0.34547176957130427`}, "LeafValues" -> RawArray["Real32",{-0.08792906999588013, 0.10178151726722717`, -0.07823143154382706, \ -0.05678991600871086, -0.09162372350692749, 0.12799254059791565`, -0.007620026357471943, 0.03367585688829422, 0.014404350891709328`, 0.05528849735856056, -0.005630811210721731, \ -0.014291821047663689`}], "Children" -> RawArray["Integer16",{{2, 3}, {4, -3}, {-2, 9}, {8, 5}, {10, 6}, {-6, 7}, {-7, -8}, {-1, -9}, {-4, 11}, {-5, -11}, {-10, -12}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{1, 7, 3, 9, 7, 2, 7, 7, 1, 4, 4, 10}], "NumericalThresholds" -> {1.7190739512443545`, 0.564066231250763, 0.7496035099029542, -0.9510375559329985, 0.5543988049030305, 0.38223026692867285`, -1.7995272278785703`, \ -1.7944434285163877`, 0.050164995715022094`, -0.08696661517024039, 1.2379853725433352`, 1.2146451473236086`}, "LeafValues" -> RawArray[ "Real32",{-0.10126804560422897`, -0.006557280197739601, \ -0.054089587181806564`, -0.09124533832073212, 0.008189487271010876, 0.09068042784929276, 0.08853966742753983, 0.09599640220403671, 0.0010454666335135698`, -0.0924818143248558, \ -0.012345118448138237`, 0.013081430457532406`, 0.06668461859226227}], "Children" -> RawArray["Integer16",{{2, 12}, {3, -3}, {4, 5}, {10, 7}, { 6, -6}, {-4, 11}, {9, 8}, {-8, -9}, {-5, -10}, {-1, -11}, {-7, -12}, {-2, -13}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]], MachineLearning`DecisionTree[ Association[ "FeatureIndices" -> RawArray["Integer16",{3, 2, 2, 7, 9, 1, 7, 9, 4}], "NumericalThresholds" -> {-0.5910580456256865, 0.9946039617061616, 0.9892682433128358, 0.5460203289985658, -0.9484192430973052, 0.3569940775632859, 0.5482727885246278, -0.9398628771305083, -0.6212775111198424}, "LeafValues" -> RawArray["Real32",{-0.04634443297982216, 0.06550665944814682, -0.049840379506349564`, \ -0.06753822416067123, 0.03116266056895256, 0.08457233756780624, -0.00621438305824995, 0.053626649081707, -0.12060374021530151`, \ -0.0036542806774377823`}], "Children" -> RawArray["Integer16",{{-1, 2}, {3, 5}, {4, -4}, {-2, 7}, {-3, 6}, {-6, -7}, {8, 9}, {-5, -9}, {-8, -10}}], "NominalSplits" -> {}, "RootIndex" -> 1, "NominalDimension" -> 0, "NominalNodeNumber" -> 0]]}, "ClassNumber" -> 2, "IterationsNumber" -> 50, "Processor" -> MachineLearning`MLProcessor["Sequence", Association[ "Input" -> Association[ "((f4f5f7f8)(f1f2f3f6f9f10))" -> Association["Type" -> "NumericalVector", "Weight" -> 10.]], "Output" -> Association[ "((f4f5f7f8)(f1f2f3f6f9f10))" -> Association["Type" -> "NumericalVector", "Weight" -> 10.]], "Processors" -> { MachineLearning`MLProcessor["DensifyNumericalVector", Association[ "Invertibility" -> "Perfect", "Missing" -> "Allowed", "StructurePreserving" -> True, "Input" -> Association[ "((f4f5f7f8)(f1f2f3f6f9f10))" -> Association["Type" -> "NumericalVector", "Weight" -> 10.]], "Version" -> {12.3, 0}, "ID" -> 53022847167159310, "Output" -> Association[ "((f4f5f7f8)(f1f2f3f6f9f10))" -> Association[ "Type" -> "NumericalVector", "Weight" -> 10.]]]], MachineLearning`MLProcessor["FirstValues", Association[ "Info" -> Association[ "Type" -> "NumericalVector", "Weight" -> 10.], "Key" -> "((f4f5f7f8)(f1f2f3f6f9f10))", "Invertibility" -> "Perfect", "StructurePreserving" -> False, "Missing" -> "Allowed"]]}, "Invertibility" -> "Perfect", "StructurePreserving" -> False, "Missing" -> "Allowed"]], "Calibrator" -> None, "Method" -> "GradientBoostedTrees", "PostProcessor" -> MachineLearning`MLProcessor["Identity"], "Options" -> Association[ "BoostingMethod" -> Association["Value" -> "Gradient", "Options" -> Association[]], MaxTrainingRounds -> Association["Value" -> 50, "Options" -> Association[]], "LeavesNumber" -> Association["Value" -> 25, "Options" -> Association[]], "LearningRate" -> Association["Value" -> 0.1, "Options" -> Association[]], ValidationSet -> Association["Value" -> Automatic, "Options" -> Association[]], "MaxBinNumber" -> Association["Value" -> 255, "Options" -> Association[]], "ThreadNumber" -> Association["Value" -> 2, "Options" -> Association[]], "MaxDepth" -> Association["Value" -> 6, "Options" -> Association[]], "LeafSize" -> Association["Value" -> 15, "Options" -> Association[]], "FeatureFraction" -> Association["Value" -> 1, "Options" -> Association[]], "BaggingFraction" -> Association["Value" -> 1, "Options" -> Association[]], "BaggingFrequency" -> Association["Value" -> 0, "Options" -> Association[]], "MinGainToSplit" -> Association["Value" -> 0, "Options" -> Association[]], "L1Regularization" -> Association["Value" -> 0, "Options" -> Association[]], "L2Regularization" -> Association["Value" -> 0, "Options" -> Association[]], "LossFunction" -> Association["Value" -> Automatic, "Options" -> Association[]]]], "TrainingInformation" -> Association[ "PanelCell" -> None, "TrainingFunction" -> Classify, "EMIterations" -> Missing["KeyAbsent", "EMIterations"], "ProcessorEntropyShift" -> 0, "PreprocessingTime" -> 2.893001`6.912893577263006, "LossName" -> "MeanCrossEntropy", "BestModelInformation" -> Dataset[ Association[ "MeanCrossEntropy" -> Around[0.6846873873289868, 0.013212609824147065`], "Accuracy" -> Around[0.5928701750112667, 0.030936108312668027`], "EvaluationTime" -> 0.000016172379514909317`, "TestSize" -> 490, "ModelMemory" -> 20360, "ModelUtility" -> -0.31822651676835234`, "TrainingSize" -> 10, "TrainingTime" -> 0.012589254117941668`, "TrainingMemory" -> 64464, "ExperimentCount" -> 1, "MeanCrossEntropyHistory" -> { Around[0.6846873873289868, 0.009342726003826386]}, "AccuracyHistory" -> { Around[0.5928701750112667, 0.021875131971409084`]}, "Configuration" -> { "GradientBoostedTrees", "BoostingMethod" -> "Gradient", MaxTrainingRounds -> 50, "LeavesNumber" -> 25, "LearningRate" -> 0.1, ValidationSet -> Automatic, "MaxBinNumber" -> 255, "ThreadNumber" -> 2, "MaxDepth" -> 6, "LeafSize" -> 15, "FeatureFraction" -> 1, "BaggingFraction" -> 1, "BaggingFrequency" -> 0, "MinGainToSplit" -> 0, "L1Regularization" -> 0, "L2Regularization" -> 0, "LossFunction" -> Automatic}, "FinalTrainingSize" -> 500], TypeSystem`Struct[{ "MeanCrossEntropy", "Accuracy", "EvaluationTime", "TestSize", "ModelMemory", "ModelUtility", "TrainingSize", "TrainingTime", "TrainingMemory", "ExperimentCount", "MeanCrossEntropyHistory", "AccuracyHistory", "Configuration", "FinalTrainingSize"}, { TypeSystem`AnyType, TypeSystem`AnyType, TypeSystem`Atom[Real], TypeSystem`Atom[Integer], TypeSystem`Atom[Integer], TypeSystem`Atom[Real], TypeSystem`Atom[Integer], TypeSystem`Atom[Real], TypeSystem`Atom[Integer], TypeSystem`Atom[Integer], TypeSystem`Vector[TypeSystem`AnyType, 1], TypeSystem`Vector[TypeSystem`AnyType, 1], TypeSystem`Vector[TypeSystem`AnyType, 17], TypeSystem`Atom[Integer]}], Association[]], "Configurations" -> Dataset[ Association[ Association[ "Value" -> "LogisticRegression", "Options" -> Association[ "L1Regularization" -> Association["Value" -> 0], "L2Regularization" -> Association["Value" -> 100000.], "OptimizationMethod" -> Association["Value" -> Automatic], MaxIterations -> Association["Value" -> 30]]] -> Association["Experiments" -> { Association[ "MeanCrossEntropy" -> Around[0.692658352681846, 0.004807567271947119], "Accuracy" -> Around[0.49313137503386084`, 0.040556135389053546`], "EvaluationTime" -> 7.5376662634957014`*^-6, "TestSize" -> 300, "ModelMemory" -> 9088, "ModelUtility" -> -0.3273348687536135, "TrainingSize" -> 10, "TrainingTime" -> 0.012589254117941668`, "TrainingMemory" -> 319680, "ExperimentCount" -> 1, "MeanCrossEntropyHistory" -> { Around[0.692658352681846, 0.0033994634190043184`]}, "AccuracyHistory" -> { Around[0.49313137503386084`, 0.02867751835231948]}], Association[ "MeanCrossEntropy" -> Around[0.6926615997897231, 0.004807582248922156], "Accuracy" -> Around[0.5169188505588942, 0.0405848590715794], "EvaluationTime" -> 3.98107170553497*^-6, "TestSize" -> 300, "ModelMemory" -> 9088, "ModelUtility" -> -0.32733955089853417`, "TrainingSize" -> 70, "TrainingTime" -> 0.007943282347242814, "TrainingMemory" -> 83816, "ExperimentCount" -> 1, "MeanCrossEntropyHistory" -> { Around[0.6926615997897231, 0.0033994740093249284`]}, "AccuracyHistory" -> { Around[0.5169188505588942, 0.028697829063014164`]}], Association[ "MeanCrossEntropy" -> Around[0.6927173496473608, 0.008327306769775044], "Accuracy" -> Around[0.5099816319152365, 0.07051967931980226], "EvaluationTime" -> 3.981071705534969*^-6, "TestSize" -> 100, "ModelMemory" -> 9088, "ModelUtility" -> -0.3284342073733497, "TrainingSize" -> 400, "TrainingTime" -> 0.012589254117941668`, "TrainingMemory" -> 195080, "ExperimentCount" -> 1, "MeanCrossEntropyHistory" -> { Around[0.6927173496473608, 0.005888295085928578]}, "AccuracyHistory" -> { Around[0.5099816319152365, 0.049864943454132914`]}]}, "PredictedPerformances" -> Association[ "EvaluationTime" -> 3.981071705534969*^-6, "MeanCrossEntropy" -> Around[0.6927173496473608, 0.008327306769775044], "ModelMemory" -> 9088, "TrainingMemory" -> 195080, "TrainingTime" -> 0.028325821765368756`], "Index" -> 1], Association[ "Value" -> "DecisionTree", "Options" -> Association[ "DistributionSmoothing" -> Association["Value" -> 1], "FeatureFraction" -> Association["Value" -> 1]]] -> Association["Experiments" -> { Association[ "MeanCrossEntropy" -> Around[0.942831674581134, 0.04835211689547175], "Accuracy" -> Around[0.5000862266844561, 0.031851914616923875`], "EvaluationTime" -> 0.000010841181751658396`, "TestSize" -> 490, "ModelMemory" -> 6312, "ModelUtility" -> -0.6445027913484569, "TrainingSize" -> 10, "TrainingTime" -> 0.05011872336272722, "TrainingMemory" -> 1070680, "ExperimentCount" -> 1, "MeanCrossEntropyHistory" -> { Around[0.942831674581134, 0.03419010974151271]}, "AccuracyHistory" -> { Around[0.5000862266844561, 0.022522704819401784`]}], Association[ "MeanCrossEntropy" -> Around[1.1485483250765534`, 0.07846125121501961], "Accuracy" -> Around[0.5093107677892749, 0.03397364428572245], "EvaluationTime" -> 7.175133578414785*^-6, "TestSize" -> 430, "ModelMemory" -> 6632, "ModelUtility" -> -0.8452348021178129, "TrainingSize" -> 70, "TrainingTime" -> 0.01995262314968879, "TrainingMemory" -> 105072, "ExperimentCount" -> 1, "MeanCrossEntropyHistory" -> { Around[1.1485483250765534`, 0.055480482794521606`]}, "AccuracyHistory" -> { Around[0.5093107677892749, 0.024022994256053944`]}], Association[ "MeanCrossEntropy" -> Around[1.2849160253881657`, 0.178216250672188], "Accuracy" -> Around[0.5099816319152365, 0.07051967931980226], "EvaluationTime" -> 6.30957344480193*^-6, "TestSize" -> 100, "ModelMemory" -> 8424, "ModelUtility" -> -0.9712213425300288, "TrainingSize" -> 400, "TrainingTime" -> 0.03981071705534971, "TrainingMemory" -> 257672, "ExperimentCount" -> 1, "MeanCrossEntropyHistory" -> { Around[1.2849160253881657`, 0.12601791936794574`]}, "AccuracyHistory" -> { Around[0.5099816319152365, 0.049864943454132914`]}]}, "PredictedPerformances" -> Association[ "EvaluationTime" -> 6.30957344480193*^-6, "MeanCrossEntropy" -> Around[1.2849160253881657`, 0.178216250672188], "ModelMemory" -> 8424, "TrainingMemory" -> 257672, "TrainingTime" -> 0.09988211968191436], "Index" -> 2], Association[ "Value" -> "NaiveBayes", "Options" -> Association[ "SmoothingParameter" -> Association["Value" -> 0.2]]] -> Association["Experiments" -> { Association[ "MeanCrossEntropy" -> Around[1.4391639780645753`, 0.2557633505078575], "Accuracy" -> Around[0.5297836121132563, 0.07040848867954916], "EvaluationTime" -> 0.00007943282347242814, "TestSize" -> 100, "ModelMemory" -> 21800, "ModelUtility" -> -1.0921562453082798`, "TrainingSize" -> 10, "TrainingTime" -> 0.015848931924611134`, "TrainingMemory" -> 69856, "ExperimentCount" -> 1, "MeanCrossEntropyHistory" -> { Around[1.4391639780645753`, 0.18085199952309783`]}, "AccuracyHistory" -> { Around[0.5297836121132563, 0.04978631979840547]}], Association[ "MeanCrossEntropy" -> Around[1.1754011605585502`, 0.1457666196833766], "Accuracy" -> Around[0.5644499255905765, 0.04945634219336507], "EvaluationTime" -> 0.0000501187233627272, "TestSize" -> 200, "ModelMemory" -> 26344, "ModelUtility" -> -0.8792779082263507, "TrainingSize" -> 70, "TrainingTime" -> 0.015848931924611134`, "TrainingMemory" -> 140376, "ExperimentCount" -> 1, "MeanCrossEntropyHistory" -> { Around[1.1754011605585502`, 0.10307256524875605`]}, "AccuracyHistory" -> { Around[0.5644499255905765, 0.03497091493761081]}], Association[ "MeanCrossEntropy" -> Around[0.9799484917940694, 0.1278976021105866], "Accuracy" -> Around[0.5594865824102861, 0.07003276772286877], "EvaluationTime" -> 0.0000501187233627272, "TestSize" -> 100, "ModelMemory" -> 40232, "ModelUtility" -> -0.6986825376687522, "TrainingSize" -> 400, "TrainingTime" -> 0.01995262314968879, "TrainingMemory" -> 524744, "ExperimentCount" -> 1, "MeanCrossEntropyHistory" -> { Around[0.9799484917940694, 0.09043726174989467]}, "AccuracyHistory" -> { Around[0.5594865824102861, 0.04952064496210288]}]}, "PredictedPerformances" -> Association[ "EvaluationTime" -> 0.0000501187233627272, "MeanCrossEntropy" -> Around[0.9799484917940694, 0.1278976021105866], "ModelMemory" -> 40232, "TrainingMemory" -> 524744, "TrainingTime" -> 0.04078971086172212], "Index" -> 3], Association[ "Value" -> "NearestNeighbors", "Options" -> Association[ "NeighborsNumber" -> Association["Value" -> Automatic], "DistributionSmoothing" -> Association["Value" -> 0.5], "NearestMethod" -> Association["Value" -> Automatic]]] -> Association["Experiments" -> { Association[ "MeanCrossEntropy" -> Around[1.0702232244946768`, 0.05842998303813494], "Accuracy" -> Around[0.4749503684152869, 0.03502617088708018], "EvaluationTime" -> 0.000016059935279213177`, "TestSize" -> 400, "ModelMemory" -> 7112, "ModelUtility" -> -0.7718931368387377, "TrainingSize" -> 10, "TrainingTime" -> 0.005011872336272719, "TrainingMemory" -> 85976, "ExperimentCount" -> 1, "MeanCrossEntropyHistory" -> { Around[1.0702232244946768`, 0.04131623723088016]}, "AccuracyHistory" -> { Around[0.4749503684152869, 0.024767242953253228`]}], Association[ "MeanCrossEntropy" -> Around[0.7716086151601412, 0.04833625796593123], "Accuracy" -> Around[0.5000806418162266, 0.07053373477499103], "EvaluationTime" -> 0.00001995262314968879, "TestSize" -> 100, "ModelMemory" -> 12360, "ModelUtility" -> -0.44633950490518237`, "TrainingSize" -> 70, "TrainingTime" -> 0.00630957344480193, "TrainingMemory" -> 171992, "ExperimentCount" -> 1, "MeanCrossEntropyHistory" -> { Around[0.7716086151601412, 0.03417889578489225]}, "AccuracyHistory" -> { Around[0.5000806418162266, 0.04987488216180956]}], Association[ "MeanCrossEntropy" -> Around[0.7068064105821534, 0.04354767765861151], "Accuracy" -> Around[0.579288562608306, 0.08632411063465446], "EvaluationTime" -> 0.00003345215066430626, "TestSize" -> 200, "ModelMemory" -> 40840., "ModelUtility" -> -0.35841840938655556`, "TrainingSize" -> 400, "TrainingTime" -> 0.05011872336272722, "TrainingMemory" -> 2.747581333333333*^6, "ExperimentCount" -> 2, "MeanCrossEntropyHistory" -> { Around[0.748490443429975, 0.02649850187870261], Around[0.665122377734332, 0.014327554395641188`]}, "AccuracyHistory" -> { Around[0.5000806418162266, 0.04987488216180956], Around[0.6584964834003855, 0.04730272977511044]}]}, "PredictedPerformances" -> Association[ "EvaluationTime" -> 0.00003345215066430626, "MeanCrossEntropy" -> Around[0.7068064105821534, 0.04354767765861151], "ModelMemory" -> 40840., "TrainingMemory" -> 2.747581333333333*^6, "TrainingTime" -> 0.06766027653968175], "Index" -> 4], Association[ "Value" -> "RandomForest", "Options" -> Association[ "FeatureFraction" -> Association["Value" -> Automatic], "LeafSize" -> Association["Value" -> Automatic], "TreeNumber" -> Association["Value" -> Automatic], "DistributionSmoothing" -> Association["Value" -> 0.5], "Implementation" -> Association["Value" -> Automatic]]] -> Association["Experiments" -> { Association[ "MeanCrossEntropy" -> Around[0.7237702667982351, 0.019754921442549082`], "Accuracy" -> Around[0.5000806418162266, 0.049874882161809575`], "EvaluationTime" -> 0.00005660722890537325, "TestSize" -> 200, "ModelMemory" -> 97192, "ModelUtility" -> -0.37533918537170363`, "TrainingSize" -> 10, "TrainingTime" -> 0.03162277660168379, "TrainingMemory" -> 179760, "ExperimentCount" -> 1, "MeanCrossEntropyHistory" -> { Around[0.7237702667982351, 0.013968838913833988`]}, "AccuracyHistory" -> { Around[0.5000806418162266, 0.03526686738749552]}], Association[ "MeanCrossEntropy" -> Around[0.7084618631408025, 0.016567678104440885`], "Accuracy" -> Around[0.5000806418162266, 0.07053373477499103], "EvaluationTime" -> 0.000039810717055349695`, "TestSize" -> 100, "ModelMemory" -> 97464, "ModelUtility" -> -0.3531835326954468, "TrainingSize" -> 70, "TrainingTime" -> 0.012589254117941668`, "TrainingMemory" -> 178464, "ExperimentCount" -> 1, "MeanCrossEntropyHistory" -> { Around[0.7084618631408025, 0.011715117536166036`]}, "AccuracyHistory" -> { Around[0.5000806418162266, 0.04987488216180956]}], Association[ "MeanCrossEntropy" -> Around[0.6979134347917816, 0.007343609297358526], "Accuracy" -> Around[0.40602123587563227`, 0.07292384435571113], "EvaluationTime" -> 0.000037788092188698256`, "TestSize" -> 200, "ModelMemory" -> 119522.66666666666`, "ModelUtility" -> -0.3356210001080919, "TrainingSize" -> 400, "TrainingTime" -> 0.023842674300353785`, "TrainingMemory" -> 370608., "ExperimentCount" -> 2, "MeanCrossEntropyHistory" -> { Around[0.7031743337390263, 0.010398199246676895`], Around[0.6926525358445369, 0.005888036014965507]}, "AccuracyHistory" -> { Around[0.4703776715191968, 0.049787277212341226`], Around[0.34166480023206774`, 0.0473081038263069]}]}, "PredictedPerformances" -> Association[ "EvaluationTime" -> 0.000037788092188698256`, "MeanCrossEntropy" -> Around[0.6979134347917816, 0.007343609297358526], "ModelMemory" -> 119522.66666666666`, "TrainingMemory" -> 370608., "TrainingTime" -> 0.06142611947712602], "Index" -> 5], Association[ "Value" -> "LogisticRegression", "Options" -> Association[ "L1Regularization" -> Association["Value" -> 0], "L2Regularization" -> Association["Value" -> 0.1], "OptimizationMethod" -> Association["Value" -> Automatic], MaxIterations -> Association["Value" -> 30]]] -> Association["Experiments" -> { Association[ "MeanCrossEntropy" -> Around[2.442620624111142, 0.19421412099550087`], "Accuracy" -> Around[0.486136941187794, 0.031724963644630885`], "EvaluationTime" -> 4.2726855425331836`*^-6, "TestSize" -> 490, "ModelMemory" -> 9088, "ModelUtility" -> -1.6020145636110579`, "TrainingSize" -> 10, "TrainingTime" -> 0.01995262314968879, "TrainingMemory" -> 81944, "ExperimentCount" -> 1, "MeanCrossEntropyHistory" -> { Around[2.442620624111142, 0.1373301219581033]}, "AccuracyHistory" -> { Around[0.486136941187794, 0.022432936926015185`]}], Association[ "MeanCrossEntropy" -> Around[0.8226111381199295, 0.039013521127060746`], "Accuracy" -> Around[0.5451362919040729, 0.033698259018898134`], "EvaluationTime" -> 7.41485465533054*^-6, "TestSize" -> 430, "ModelMemory" -> 9088, "ModelUtility" -> -0.5073349832734241, "TrainingSize" -> 70, "TrainingTime" -> 0.01995262314968879, "TrainingMemory" -> 94192, "ExperimentCount" -> 1, "MeanCrossEntropyHistory" -> { Around[0.8226111381199295, 0.02758672534690929]}, "AccuracyHistory" -> { Around[0.5451362919040729, 0.023828267466443603`]}], Association[ "MeanCrossEntropy" -> Around[0.7627395724597211, 0.039437781269565104`], "Accuracy" -> Around[0.4901796517172166, 0.07052012994032744], "EvaluationTime" -> 3.981071705534969*^-6, "TestSize" -> 100, "ModelMemory" -> 9088, "ModelUtility" -> -0.4326154343117441, "TrainingSize" -> 400, "TrainingTime" -> 0.03162277660168379, "TrainingMemory" -> 195080, "ExperimentCount" -> 1, "MeanCrossEntropyHistory" -> { Around[0.7627395724597211, 0.027886722570661295`]}, "AccuracyHistory" -> { Around[0.4901796517172166, 0.049865262090962016`]}]}, "PredictedPerformances" -> Association[ "EvaluationTime" -> 3.981071705534969*^-6, "MeanCrossEntropy" -> Around[0.7627395724597211, 0.039437781269565104`], "ModelMemory" -> 9088, "TrainingMemory" -> 195080, "TrainingTime" -> 0.05948109390179353], "Index" -> 6], Association[ "Value" -> "LogisticRegression", "Options" -> Association[ "L1Regularization" -> Association["Value" -> 0], "L2Regularization" -> Association["Value" -> 0.0001], "OptimizationMethod" -> Association["Value" -> Automatic], MaxIterations -> Association["Value" -> 30]]] -> Association["Experiments" -> { Association[ "MeanCrossEntropy" -> Around[6.624973268464014, 0.539803278840754], "Accuracy" -> Around[0.4776833958729391, 0.031767576608218905`], "EvaluationTime" -> 3.333330228030076*^-6, "TestSize" -> 490, "ModelMemory" -> 9088, "ModelUtility" -> -2.6001770722066433`, "TrainingSize" -> 10, "TrainingTime" -> 0.03162277660168379, "TrainingMemory" -> 82240, "ExperimentCount" -> 1, "MeanCrossEntropyHistory" -> { Around[6.624973268464014, 0.3816985589750299]}, "AccuracyHistory" -> { Around[0.4776833958729391, 0.022463068841534727`]}], Association[ "MeanCrossEntropy" -> Around[0.8263586315620132, 0.03974592245525732], "Accuracy" -> Around[0.5428716958220009, 0.03369677505956971], "EvaluationTime" -> 4.980247958505863*^-6, "TestSize" -> 430, "ModelMemory" -> 9088, "ModelUtility" -> -0.5120132173355927, "TrainingSize" -> 70, "TrainingTime" -> 0.025118864315095794`, "TrainingMemory" -> 94200, "ExperimentCount" -> 1, "MeanCrossEntropyHistory" -> { Around[0.8263586315620132, 0.028104611292627122`]}, "AccuracyHistory" -> { Around[0.5428716958220009, 0.023827218148739466`]}], Association[ "MeanCrossEntropy" -> Around[0.7628330966242052, 0.03946987160694067], "Accuracy" -> Around[0.4901796517172166, 0.07052012994032744], "EvaluationTime" -> 6.30957344480193*^-6, "TestSize" -> 100, "ModelMemory" -> 9088, "ModelUtility" -> -0.4327451177117626, "TrainingSize" -> 400, "TrainingTime" -> 0.03981071705534971, "TrainingMemory" -> 195016, "ExperimentCount" -> 1, "MeanCrossEntropyHistory" -> { Around[0.7628330966242052, 0.027909413865830117`]}, "AccuracyHistory" -> { Around[0.4901796517172166, 0.049865262090962016`]}]}, "PredictedPerformances" -> Association[ "EvaluationTime" -> 6.30957344480193*^-6, "MeanCrossEntropy" -> Around[0.7628330966242052, 0.03946987160694067], "ModelMemory" -> 9088, "TrainingMemory" -> 195016, "TrainingTime" -> 0.08138617292087094], "Index" -> 7], Association[ "Value" -> "LogisticRegression", "Options" -> Association[ "L1Regularization" -> Association["Value" -> 0], "L2Regularization" -> Association["Value" -> 100.], "OptimizationMethod" -> Association["Value" -> Automatic], MaxIterations -> Association["Value" -> 30]]] -> Association["Experiments" -> { Association[ "MeanCrossEntropy" -> Around[0.6991063679364197, 0.004359531597980095], "Accuracy" -> Around[0.48991652608577385`, 0.03184506128528466], "EvaluationTime" -> 3.068493267105747*^-6, "TestSize" -> 490, "ModelMemory" -> 9088, "ModelUtility" -> -0.3364600949479881, "TrainingSize" -> 10, "TrainingTime" -> 0.01, "TrainingMemory" -> 75216, "ExperimentCount" -> 1, "MeanCrossEntropyHistory" -> { Around[0.6991063679364197, 0.003082654355728751]}, "AccuracyHistory" -> { Around[0.48991652608577385`, 0.022517858782125973`]}], Association[ "MeanCrossEntropy" -> Around[0.6971950262400385, 0.008320865392307081], "Accuracy" -> Around[0.5526717388465917, 0.033535075755444614`], "EvaluationTime" -> 5.650107332190072*^-6, "TestSize" -> 430, "ModelMemory" -> 9088, "ModelUtility" -> -0.33486009009858264`, "TrainingSize" -> 70, "TrainingTime" -> 0.015848931924611134`, "TrainingMemory" -> 92824, "ExperimentCount" -> 1, "MeanCrossEntropyHistory" -> { Around[0.6971950262400385, 0.005883740344240799]}, "AccuracyHistory" -> { Around[0.5526717388465917, 0.023712879474279466`]}], Association[ "MeanCrossEntropy" -> Around[0.7260712833897033, 0.02452566724932744], "Accuracy" -> Around[0.48027866161820676`, 0.07047884879766003], "EvaluationTime" -> 3.981071705534969*^-6, "TestSize" -> 100, "ModelMemory" -> 9088, "ModelUtility" -> -0.3797919957999203, "TrainingSize" -> 400, "TrainingTime" -> 0.03162277660168379, "TrainingMemory" -> 195256, "ExperimentCount" -> 1, "MeanCrossEntropyHistory" -> { Around[0.7260712833897033, 0.017342265625124254`]}, "AccuracyHistory" -> { Around[0.48027866161820676`, 0.049836071915046756`]}]}, "PredictedPerformances" -> Association[ "EvaluationTime" -> 3.981071705534969*^-6, "MeanCrossEntropy" -> Around[0.7260712833897033, 0.02452566724932744], "ModelMemory" -> 9088, "TrainingMemory" -> 195256, "TrainingTime" -> 0.049528470752104745`], "Index" -> 8], Association[ "Value" -> "LogisticRegression", "Options" -> Association[ "L1Regularization" -> Association["Value" -> 0], "L2Regularization" -> Association["Value" -> 0.01], "OptimizationMethod" -> Association["Value" -> Automatic], MaxIterations -> Association["Value" -> 30]]] -> Association["Experiments" -> { Association[ "MeanCrossEntropy" -> Around[3.7813268364390744`, 0.3059862274871794], "Accuracy" -> Around[0.4818462966194605, 0.03176696928093725], "EvaluationTime" -> 2.8581257914449814`*^-6, "TestSize" -> 490, "ModelMemory" -> 9088, "ModelUtility" -> -2.0392955396034864`, "TrainingSize" -> 10, "TrainingTime" -> 0.025118864315095794`, "TrainingMemory" -> 81880, "ExperimentCount" -> 1, "MeanCrossEntropyHistory" -> { Around[3.7813268364390744`, 0.2163649364058741]}, "AccuracyHistory" -> { Around[0.4818462966194605, 0.02246263939629547]}], Association[ "MeanCrossEntropy" -> Around[0.8259696588128372, 0.03966962634619469], "Accuracy" -> Around[0.5428716958220009, 0.03369677505956971], "EvaluationTime" -> 6.795201275521555*^-6, "TestSize" -> 430, "ModelMemory" -> 9088, "ModelUtility" -> -0.511528589949634, "TrainingSize" -> 70, "TrainingTime" -> 0.01995262314968879, "TrainingMemory" -> 94432, "ExperimentCount" -> 1, "MeanCrossEntropyHistory" -> { Around[0.8259696588128372, 0.02805066179653079]}, "AccuracyHistory" -> { Around[0.5428716958220009, 0.023827218148739466`]}], Association[ "MeanCrossEntropy" -> Around[0.7628238127290095, 0.03946668713206145], "Accuracy" -> Around[0.4901796517172166, 0.07052012994032744], "EvaluationTime" -> 5.01187233627272*^-6, "TestSize" -> 100, "ModelMemory" -> 9088, "ModelUtility" -> -0.432732244342486, "TrainingSize" -> 400, "TrainingTime" -> 0.03981071705534971, "TrainingMemory" -> 195016, "ExperimentCount" -> 1, "MeanCrossEntropyHistory" -> { Around[0.7628238127290095, 0.027907162102048504`]}, "AccuracyHistory" -> { Around[0.4901796517172166, 0.049865262090962016`]}]}, "PredictedPerformances" -> Association[ "EvaluationTime" -> 5.01187233627272*^-6, "MeanCrossEntropy" -> Around[0.7628238127290095, 0.03946668713206145], "ModelMemory" -> 9088, "TrainingMemory" -> 195016, "TrainingTime" -> 0.07488226063428294], "Index" -> 9], Association[ "Value" -> "LogisticRegression", "Options" -> Association[ "L1Regularization" -> Association["Value" -> 0], "L2Regularization" -> Association["Value" -> 10000.], "OptimizationMethod" -> Association["Value" -> Automatic], MaxIterations -> Association["Value" -> 30]]] -> Association["Experiments" -> { Association[ "MeanCrossEntropy" -> Around[0.692636851399596, 0.004114718388112021], "Accuracy" -> Around[0.5549592110026919, 0.03351508511326898], "EvaluationTime" -> 4.438735926118199*^-6, "TestSize" -> 430, "ModelMemory" -> 9088, "ModelUtility" -> -0.32710406267457526`, "TrainingSize" -> 70, "TrainingTime" -> 0.01, "TrainingMemory" -> 85184, "ExperimentCount" -> 1, "MeanCrossEntropyHistory" -> { Around[0.692636851399596, 0.00290954527490699]}, "AccuracyHistory" -> { Around[0.5549592110026919, 0.0236987439556368]}], Association[ "MeanCrossEntropy" -> Around[0.6925907119148926, 0.004232190376259261], "Accuracy" -> Around[0.5693875725092961, 0.06877433075410107], "EvaluationTime" -> 4.324671915780886*^-6, "TestSize" -> 200, "ModelMemory" -> 9088., "ModelUtility" -> -0.32707140680024516`, "TrainingSize" -> 400, "TrainingTime" -> 0.013675813386831489`, "TrainingMemory" -> 195325.3333333333, "ExperimentCount" -> 2, "MeanCrossEntropyHistory" -> { Around[0.6932967891988984, 0.00590805136702492], Around[0.6918846346308869, 0.005894645829956957]}, "AccuracyHistory" -> { Around[0.5099816319152365, 0.049864943454132914`], Around[0.6287935131033556, 0.04819186002346042]}]}, "PredictedPerformances" -> Association[ "EvaluationTime" -> 4.324671915780886*^-6, "MeanCrossEntropy" -> Around[0.6925907119148926, 0.004232190376259261], "ModelMemory" -> 9088., "TrainingMemory" -> 195325.3333333333, "TrainingTime" -> 0.02709476673353936], "Index" -> 10], Association[ "Value" -> "LogisticRegression", "Options" -> Association[ "L1Regularization" -> Association["Value" -> 0], "L2Regularization" -> Association["Value" -> 10.], "OptimizationMethod" -> Association["Value" -> Automatic], MaxIterations -> Association["Value" -> 30]]] -> Association["Experiments" -> { Association[ "MeanCrossEntropy" -> Around[0.7402021633169515, 0.023141193786312728`], "Accuracy" -> Around[0.5446010022984445, 0.03368391878557934], "EvaluationTime" -> 7.41485465533054*^-6, "TestSize" -> 430, "ModelMemory" -> 9088, "ModelUtility" -> -0.3985673457509543, "TrainingSize" -> 70, "TrainingTime" -> 0.01995262314968879, "TrainingMemory" -> 94424, "ExperimentCount" -> 1, "MeanCrossEntropyHistory" -> { Around[0.7402021633169515, 0.016363295051053727`]}, "AccuracyHistory" -> { Around[0.5446010022984445, 0.023818127390220087`]}], Association[ "MeanCrossEntropy" -> Around[0.7548990500210783, 0.036667002588448586`], "Accuracy" -> Around[0.5000806418162266, 0.07053373477499103], "EvaluationTime" -> 5.01187233627272*^-6, "TestSize" -> 100, "ModelMemory" -> 9088, "ModelUtility" -> -0.4216623614745957, "TrainingSize" -> 400, "TrainingTime" -> 0.03162277660168379, "TrainingMemory" -> 195080, "ExperimentCount" -> 1, "MeanCrossEntropyHistory" -> { Around[0.7548990500210783, 0.025927486176076684`]}, "AccuracyHistory" -> { Around[0.5000806418162266, 0.04987488216180956]}]}, "PredictedPerformances" -> Association[ "EvaluationTime" -> 5.01187233627272*^-6, "MeanCrossEntropy" -> Around[0.7548990500210783, 0.036667002588448586`], "ModelMemory" -> 9088, "TrainingMemory" -> 195080, "TrainingTime" -> 0.05948109390179353], "Index" -> 11], Association[ "Value" -> "LogisticRegression", "Options" -> Association[ "L1Regularization" -> Association["Value" -> 0], "L2Regularization" -> Association["Value" -> 0.001], "OptimizationMethod" -> Association["Value" -> Automatic], MaxIterations -> Association["Value" -> 30]]] -> Association["Experiments" -> { Association[ "MeanCrossEntropy" -> Around[0.826323101437631, 0.039738949201182906`], "Accuracy" -> Around[0.5428716958220009, 0.03369677505956971], "EvaluationTime" -> 6.795201275521555*^-6, "TestSize" -> 430, "ModelMemory" -> 9088, "ModelUtility" -> -0.5119689601952557, "TrainingSize" -> 70, "TrainingTime" -> 0.01995262314968879, "TrainingMemory" -> 94320, "ExperimentCount" -> 1, "MeanCrossEntropyHistory" -> { Around[0.826323101437631, 0.028099680457384167`]}, "AccuracyHistory" -> { Around[0.5428716958220009, 0.023827218148739466`]}], Association[ "MeanCrossEntropy" -> Around[0.7628322531169399, 0.03946958199626084], "Accuracy" -> Around[0.4901796517172166, 0.07052012994032744], "EvaluationTime" -> 6.30957344480193*^-6, "TestSize" -> 100, "ModelMemory" -> 9088, "ModelUtility" -> -0.43274394812792105`, "TrainingSize" -> 400, "TrainingTime" -> 0.03162277660168379, "TrainingMemory" -> 195080, "ExperimentCount" -> 1, "MeanCrossEntropyHistory" -> { Around[0.7628322531169399, 0.02790920908015451]}, "AccuracyHistory" -> { Around[0.4901796517172166, 0.049865262090962016`]}]}, "PredictedPerformances" -> Association[ "EvaluationTime" -> 6.30957344480193*^-6, "MeanCrossEntropy" -> Around[0.7628322531169399, 0.03946958199626084], "ModelMemory" -> 9088, "TrainingMemory" -> 195080, "TrainingTime" -> 0.05948109390179353], "Index" -> 12], Association[ "Value" -> "LogisticRegression", "Options" -> Association[ "L1Regularization" -> Association["Value" -> 0], "L2Regularization" -> Association["Value" -> 1.], "OptimizationMethod" -> Association["Value" -> Automatic], MaxIterations -> Association["Value" -> 30]]] -> Association["Experiments" -> { Association[ "MeanCrossEntropy" -> Around[0.7995476547154556, 0.034654482737103476`], "Accuracy" -> Around[0.5422370860080747, 0.03376493949964979], "EvaluationTime" -> 4.678457003033954*^-6, "TestSize" -> 430, "ModelMemory" -> 9088, "ModelUtility" -> -0.47808810114658995`, "TrainingSize" -> 70, "TrainingTime" -> 0.025118864315095794`, "TrainingMemory" -> 94320, "ExperimentCount" -> 1, "MeanCrossEntropyHistory" -> { Around[0.7995476547154556, 0.024504419741918013`]}, "AccuracyHistory" -> { Around[0.5422370860080747, 0.023875417686555874`]}], Association[ "MeanCrossEntropy" -> Around[0.7619127232448369, 0.0391531313602023], "Accuracy" -> Around[0.4901796517172166, 0.07052012994032744], "EvaluationTime" -> 3.981071705534969*^-6, "TestSize" -> 100, "ModelMemory" -> 9088, "ModelUtility" -> -0.43146794504702246`, "TrainingSize" -> 400, "TrainingTime" -> 0.03162277660168379, "TrainingMemory" -> 195080, "ExperimentCount" -> 1, "MeanCrossEntropyHistory" -> { Around[0.7619127232448369, 0.027685444689486718`]}, "AccuracyHistory" -> { Around[0.4901796517172166, 0.049865262090962016`]}]}, "PredictedPerformances" -> Association[ "EvaluationTime" -> 3.981071705534969*^-6, "MeanCrossEntropy" -> Around[0.7619127232448369, 0.0391531313602023], "ModelMemory" -> 9088, "TrainingMemory" -> 195080, "TrainingTime" -> 0.06464733506720054], "Index" -> 13], Association[ "Value" -> "GradientBoostedTrees", "Options" -> Association[ "BoostingMethod" -> Association["Value" -> "Gradient"], MaxTrainingRounds -> Association["Value" -> 50], "LeavesNumber" -> Association["Value" -> 25], "LearningRate" -> Association["Value" -> 0.1], ValidationSet -> Association["Value" -> Automatic], "MaxBinNumber" -> Association["Value" -> 255], "ThreadNumber" -> Association["Value" -> 2], "MaxDepth" -> Association["Value" -> 6], "LeafSize" -> Association["Value" -> 15], "FeatureFraction" -> Association["Value" -> 1], "BaggingFraction" -> Association["Value" -> 1], "BaggingFrequency" -> Association["Value" -> 0], "MinGainToSplit" -> Association["Value" -> 0], "L1Regularization" -> Association["Value" -> 0], "L2Regularization" -> Association["Value" -> 0], "LossFunction" -> Association["Value" -> Automatic]]] -> Association["Experiments" -> { Association[ "MeanCrossEntropy" -> Around[0.8027146262123053, 0.048956317945488874`], "Accuracy" -> Around[0.5200506149380347, 0.04060441549342177], "EvaluationTime" -> 0.00009031990838464046, "TestSize" -> 300, "ModelMemory" -> 117280, "ModelUtility" -> -0.48554664278522996`, "TrainingSize" -> 70, "TrainingTime" -> 0.03981071705534971, "TrainingMemory" -> 315952, "ExperimentCount" -> 1, "MeanCrossEntropyHistory" -> { Around[0.8027146262123053, 0.03461734440117985]}, "AccuracyHistory" -> { Around[0.5200506149380347, 0.028711657541514644`]}], Association[ "MeanCrossEntropy" -> Around[0.8570503183021748, 0.08703659727704363], "Accuracy" -> Around[0.4901796517172166, 0.07052012994032744], "EvaluationTime" -> 0.000039810717055349695`, "TestSize" -> 100, "ModelMemory" -> 127256, "ModelUtility" -> -0.5590284420843604, "TrainingSize" -> 400, "TrainingTime" -> 0.05011872336272722, "TrainingMemory" -> 492392, "ExperimentCount" -> 1, "MeanCrossEntropyHistory" -> { Around[0.8570503183021748, 0.061544168146000146`]}, "AccuracyHistory" -> { Around[0.4901796517172166, 0.049865262090962016`]}], Association[ "MeanCrossEntropy" -> Around[0.6846873873289868, 0.013212609824147065`], "Accuracy" -> Around[0.5928701750112667, 0.030936108312668027`], "EvaluationTime" -> 0.000016172379514909317`, "TestSize" -> 490, "ModelMemory" -> 20360, "ModelUtility" -> -0.31822651676835234`, "TrainingSize" -> 10, "TrainingTime" -> 0.012589254117941668`, "TrainingMemory" -> 64464, "ExperimentCount" -> 1, "MeanCrossEntropyHistory" -> { Around[0.6846873873289868, 0.009342726003826386]}, "AccuracyHistory" -> { Around[0.5928701750112667, 0.021875131971409084`]}]}, "PredictedPerformances" -> Association[ "EvaluationTime" -> 0.000016172379514909317`, "MeanCrossEntropy" -> Around[0.6846873873289868, 0.013212609824147065`], "ModelMemory" -> 20360, "TrainingMemory" -> 64464, "TrainingTime" -> 0.10275698764505806`], "Index" -> 14]], TypeSystem`Assoc[ TypeSystem`Struct[{"Value", "Options"}, { TypeSystem`Atom[ TypeSystem`Enumeration[ "DecisionTree", "GradientBoostedTrees", "LogisticRegression", "NaiveBayes", "NearestNeighbors", "RandomForest"]], TypeSystem`Assoc[TypeSystem`AnyType, TypeSystem`Struct[{"Value"}, {TypeSystem`AnyType}], TypeSystem`AnyLength]}], TypeSystem`Struct[{ "Experiments", "PredictedPerformances", "Index"}, { TypeSystem`Vector[ TypeSystem`Struct[{ "MeanCrossEntropy", "Accuracy", "EvaluationTime", "TestSize", "ModelMemory", "ModelUtility", "TrainingSize", "TrainingTime", "TrainingMemory", "ExperimentCount", "MeanCrossEntropyHistory", "AccuracyHistory"}, { TypeSystem`AnyType, TypeSystem`AnyType, TypeSystem`Atom[Real], TypeSystem`Atom[Integer], TypeSystem`Atom[Real], TypeSystem`Atom[Real], TypeSystem`Atom[Integer], TypeSystem`Atom[Real], TypeSystem`Atom[Real], TypeSystem`Atom[Integer], TypeSystem`Vector[TypeSystem`AnyType, TypeSystem`AnyLength], TypeSystem`Vector[ TypeSystem`AnyType, TypeSystem`AnyLength]}], TypeSystem`AnyLength], TypeSystem`Struct[{ "EvaluationTime", "MeanCrossEntropy", "ModelMemory", "TrainingMemory", "TrainingTime"}, { TypeSystem`Atom[Real], TypeSystem`AnyType, TypeSystem`Atom[Real], TypeSystem`Atom[Real], TypeSystem`Atom[Real]}], TypeSystem`Atom[Integer]}], 14], Association[]], "MaxTrainingSize" -> 500, "PreprocessorEvaluationTime" -> 6.2265625*^-6, "PreprocessorMemory" -> 153144, "InputDimension" -> 10, "OutputDimension" -> 1, "BaselineLogProbability" -> -0.676988786523252, "VariableBudget" -> True, "CheckpointingInfo" -> Association["Checkpointing" -> False], "UserStop" -> False, "NaturalStop" -> True, "AbortStop" -> False, "RoundPartitioning" -> Dataset[{ Association[ "TrainingSizes" -> 10, "TimeBudgets" -> 0.28351409800000005`, "ElapsedTimes" -> 0.30385, "ExperimentCounts" -> 10], Association[ "TrainingSizes" -> 70, "TimeBudgets" -> 0.40502014000000003`, "ElapsedTimes" -> 0.4427789999999999, "ExperimentCounts" -> 14], Association[ "TrainingSizes" -> 400, "TimeBudgets" -> 0.5786002, "ElapsedTimes" -> 0.6156550000000001, "ExperimentCounts" -> 17]}, TypeSystem`Vector[ TypeSystem`Struct[{ "TrainingSizes", "TimeBudgets", "ElapsedTimes", "ExperimentCounts"}, { TypeSystem`Atom[Integer], TypeSystem`Atom[Real], TypeSystem`Atom[Real], TypeSystem`Atom[Integer]}], 3], Association[]]], "AnomalyDetector" -> None, "Log" -> Association["Example" -> MachineLearning`MLDataset[ Association[ "f1" -> Association[ "Type" -> "Boolean", "Weight" -> 1, "Values" -> {1}, "ID" -> 3632508870957651678], "f2" -> Association[ "Type" -> "Boolean", "Weight" -> 1, "Values" -> {0}, "ID" -> 387810808159763064], "f3" -> Association[ "Type" -> "Boolean", "Weight" -> 1, "Values" -> {1}, "ID" -> 7761553465653086836], "f4" -> Association[ "Type" -> "Numerical", "Weight" -> 1, "Values" -> {17}, "ID" -> 5101722074622675686], "f5" -> Association[ "Type" -> "Numerical", "Weight" -> 1, "Values" -> {7}, "ID" -> 28875055331300735], "f6" -> Association[ "Type" -> "Boolean", "Weight" -> 1, "Values" -> {0}, "ID" -> 8362908829548180133], "f7" -> Association[ "Type" -> "Numerical", "Weight" -> 1, "Values" -> {0}, "ID" -> 8672745575801952756], "f8" -> Association[ "Type" -> "Numerical", "Weight" -> 1, "Values" -> {0}, "ID" -> 8897874437666715294], "f9" -> Association[ "Type" -> "Boolean", "Weight" -> 1, "Values" -> {1}, "ID" -> 4060294726305383761], "f10" -> Association[ "Type" -> "Boolean", "Weight" -> 1, "Values" -> {1}, "ID" -> 2296921329445426742]], Association[ "ExampleNumber" -> 1, "ExampleWeights" -> 1, "LogDensityRatios" -> 0, "RawExample" -> False]], "TrainingTime" -> 2.526113, "MaxTrainingMemory" -> 11305720, "DataMemory" -> 170640, "FunctionMemory" -> 498880, "LanguageVersion" -> {12.3, 0}, "Date" -> DateObject[{2021, 7, 4, 10, 37, 3.292274`7.270070960609794}, "Instant", "Gregorian", -6.], "ProcessorCount" -> 2, "ProcessorType" -> "x86-64", "OperatingSystem" -> "MacOSX", "SystemWordLength" -> 64, "Evaluations" -> {}]]], "TestSet" -> Association[ "Input" -> {{0, 0, 1, 23, 8, 1, 0, 1713.15002441406, 1, 0}, { 1, 0, 1, 20, 9, 0, 9718.20703125, 9691.033203125, 0, 0}, {1, 0, 1, 24, 10, 0, 0, 0, 1, 1}, {1, 0, 0, 29, 13, 0, 0, 0, 1, 1}, {0, 1, 1, 18, 10, 0, 0, 0, 1, 1}, { 1, 0, 0, 41, 12, 0, 10707.1611328125, 9818.466796875, 0, 0}, {0, 0, 1, 22, 11, 0, 0, 0, 1, 1}, { 0, 1, 1, 19, 9, 0, 2686.53002929688, 2750.8408203125, 0, 0}, { 1, 0, 1, 28, 10, 0, 5377.65576171875, 4479.5869140625, 0, 0}, { 1, 0, 0, 25, 12, 0, 6939.01220703125, 5204.25927734375, 0, 0}, {1, 0, 1, 38, 10, 0, 0, 0, 1, 1}, {1, 0, 1, 23, 10, 0, 0, 0, 1, 1}, {1, 0, 1, 24, 7, 0, 0, 0, 1, 1}, { 1, 0, 1, 17, 9, 0, 1466.65246582031, 1341.65112304688, 0, 0}, {1, 0, 1, 34, 10, 0, 0, 0, 1, 1}, {1, 0, 1, 18, 11, 0, 0, 0, 1, 1}, { 0, 0, 1, 20, 11, 0, 1413.10668945312, 1177.11791992188, 0, 0}, { 1, 0, 1, 28, 8, 0, 1577.04797363281, 1182.78601074219, 0, 0}, { 1, 0, 1, 17, 8, 0, 1962.33557128906, 2036.79040527344, 0, 0}, { 0, 1, 0, 23, 12, 0, 9385.740234375, 1117.43896484375, 0, 0}, { 0, 0, 0, 22, 12, 0, 6759.994140625, 8455.50390625, 0, 0}, { 0, 1, 1, 19, 8, 0, 1087.80322265625, 1087.80322265625, 0, 0}, { 1, 0, 0, 27, 12, 1, 10408.515625, 7806.38671875, 0, 0}, {1, 0, 1, 28, 8, 0, 0, 0, 1, 1}, { 1, 0, 1, 25, 10, 1, 12428.1220703125, 11696.3720703125, 0, 0}, { 0, 0, 1, 18, 9, 0, 0, 559.596008300781, 1, 0}, { 1, 0, 0, 24, 12, 0, 4739.03076171875, 8100.533203125, 0, 0}, {0, 0, 1, 24, 8, 0, 0, 0, 1, 1}, { 1, 0, 1, 19, 10, 0, 3878.19287109375, 3473.8076171875, 0, 0}, {1, 0, 1, 35, 11, 0, 0, 0, 1, 1}, { 1, 0, 1, 19, 10, 0, 4121.94921875, 6056.75390625, 0, 0}, {1, 0, 1, 36, 10, 0, 0, 0, 1, 1}, { 1, 0, 1, 28, 10, 1, 8293.3447265625, 6449.47998046875, 0, 0}, {1, 0, 1, 18, 9, 0, 0, 0, 1, 1}, {1, 0, 1, 17, 10, 0, 0, 0, 1, 1}, { 1, 0, 1, 24, 11, 0, 13627.26953125, 13370.5751953125, 0, 0}, { 0, 1, 0, 27, 12, 0, 2920.14233398438, 2677.7705078125, 0, 0}, { 1, 0, 0, 19, 12, 0, 3042.10375976562, 2534.07250976562, 0, 0}, { 1, 0, 1, 24, 9, 0, 2788.498046875, 0, 0, 1}, { 0, 1, 1, 17, 10, 0, 1203.60900878906, 1239.62805175781, 0, 0}, { 1, 0, 1, 22, 11, 0, 661.8984375, 606.960876464844, 0, 0}, {1, 0, 1, 27, 11, 0, 0, 0, 1, 1}, { 1, 0, 1, 22, 11, 0, 1167.01538085938, 875.261535644531, 0, 0}, {1, 0, 0, 39, 12, 1, 0, 0, 1, 1}, { 0, 1, 1, 19, 9, 0, 3083.6689453125, 3276.150390625, 0, 0}, {1, 0, 1, 26, 6, 0, 0, 0, 1, 1}, {1, 0, 1, 19, 10, 0, 0, 0, 1, 1}, { 1, 0, 0, 29, 12, 0, 22859.439453125, 2080.208984375, 0, 0}, {1, 0, 1, 55, 3, 0, 0, 0, 1, 1}, {0, 0, 1, 28, 8, 0, 0, 0, 1, 1}, {0, 1, 1, 24, 4, 0, 0, 0, 1, 1}, {1, 0, 1, 18, 10, 0, 0, 0, 1, 1}, { 0, 1, 1, 26, 11, 0, 1789.935546875, 1491.01623535156, 0, 0}, { 0, 1, 1, 18, 7, 1, 3059.47412109375, 2672.2021484375, 0, 0}, { 1, 0, 1, 23, 10, 0, 17131.466796875, 15709.5556640625, 0, 0}, { 1, 0, 1, 18, 8, 0, 1308.2236328125, 1199.64099121094, 0, 0}, {1, 0, 1, 27, 8, 0, 0, 0, 1, 1}, { 1, 0, 1, 20, 11, 0, 1931.24499511719, 3248.93701171875, 0, 0}, { 1, 0, 1, 21, 7, 0, 33799.94921875, 0, 0, 1}, {0, 1, 1, 19, 6, 0, 0, 0, 1, 1}, { 1, 0, 1, 20, 11, 0, 759.23583984375, 696.21923828125, 0, 0}, {1, 0, 1, 21, 11, 0, 0, 0, 1, 1}, { 1, 0, 1, 18, 10, 0, 1563.2490234375, 0, 0, 1}, {1, 0, 0, 34, 12, 0, 0, 0, 1, 1}, { 1, 0, 1, 27, 10, 1, 13542.26953125, 11280.7109375, 0, 0}, {1, 0, 1, 43, 9, 0, 0, 0, 1, 1}, { 1, 0, 1, 18, 11, 0, 858.254272460938, 214.563598632812, 0, 0}, {1, 0, 1, 38, 8, 0, 0, 0, 1, 1}, {1, 0, 1, 22, 11, 1, 0, 0, 1, 1}, { 1, 0, 1, 17, 11, 0, 1261.50122070312, 1156.79663085938, 0, 0}, { 1, 0, 1, 24, 10, 0, 4250.40185546875, 2421.94702148438, 0, 0}, {1, 0, 1, 18, 10, 0, 0, 0, 1, 1}, {1, 0, 1, 25, 5, 0, 0, 0, 1, 1}, {0, 1, 1, 31, 9, 0, 0, 0, 1, 1}, {1, 0, 1, 27, 10, 1, 0, 0, 1, 1}, { 1, 0, 1, 22, 11, 0, 8810.0673828125, 6974.48388671875, 0, 0}, { 1, 0, 1, 20, 10, 0, 3666.63623046875, 2749.97705078125, 0, 0}, {1, 0, 1, 24, 10, 0, 0, 0, 1, 1}, {1, 0, 1, 20, 10, 0, 0, 0, 1, 1}, { 0, 0, 0, 21, 12, 0, 7254.419921875, 5440.81494140625, 0, 0}, { 1, 0, 1, 44, 9, 1, 12260.7802734375, 10857.240234375, 0, 0}, {1, 0, 1, 19, 11, 0, 0, 0, 1, 1}, { 1, 0, 0, 27, 12, 1, 3670.8720703125, 334.049285888672, 0, 0}, { 1, 0, 1, 26, 10, 1, 12143.2412109375, 9107.4306640625, 0, 0}, {1, 0, 1, 18, 7, 0, 0, 0, 1, 1}, {1, 0, 1, 17, 10, 0, 0, 0, 1, 1}, { 1, 0, 0, 26, 12, 1, 8408.76171875, 5794.8310546875, 0, 0}, { 1, 0, 1, 25, 10, 1, 13519.9697265625, 9319.4443359375, 0, 0}, { 1, 0, 1, 18, 9, 0, 1605.4482421875, 1605.4482421875, 0, 0}, { 1, 0, 1, 21, 9, 0, 591.499084472656, 0, 0, 1}, { 1, 0, 1, 26, 11, 0, 4699.962890625, 1174.99096679688, 0, 0}, {1, 0, 0, 20, 12, 0, 0, 0, 1, 1}, {1, 0, 1, 39, 9, 0, 0, 0, 1, 1}, { 1, 0, 1, 17, 9, 0, 2001.90026855469, 1667.5830078125, 0, 0}, { 1, 0, 1, 23, 9, 0, 2365.5732421875, 1774.18005371094, 0, 0}, { 0, 0, 1, 24, 11, 0, 2669.72509765625, 1468.38305664062, 0, 0}, { 1, 0, 0, 26, 14, 1, 2025.56628417969, 2025.56628417969, 0, 0}, { 1, 0, 1, 22, 11, 0, 3250.14624023438, 2707.37182617188, 0, 0}, {1, 0, 1, 24, 7, 0, 0, 0, 1, 1}, { 1, 0, 1, 18, 10, 0, 678.130187988281, 2456.994140625, 0, 0}, { 0, 1, 1, 21, 11, 0, 21589.083984375, 17983.70703125, 0, 0}, {1, 0, 0, 23, 12, 0, 0, 0, 1, 1}, { 1, 0, 0, 20, 12, 0, 9227.55859375, 9227.55859375, 0, 0}, { 1, 0, 1, 18, 9, 0, 1401.66809082031, 1285.32958984375, 0, 0}, { 1, 0, 0, 20, 12, 0, 989.267822265625, 165.207702636719, 0, 0}, {1, 0, 1, 17, 11, 0, 0, 0, 1, 1}, { 0, 0, 0, 31, 12, 0, 0, 2611.21801757812, 1, 0}, { 1, 0, 0, 30, 13, 0, 6073.89453125, 5569.76123046875, 0, 0}, {1, 0, 0, 21, 12, 0, 0, 0, 1, 1}, { 1, 0, 0, 24, 15, 0, 12813.505859375, 13008.4970703125, 0, 0}, {0, 1, 1, 22, 8, 0, 0, 0, 1, 1}, { 0, 1, 1, 18, 10, 0, 12717.955078125, 12625.5107421875, 0, 0}, {1, 0, 1, 17, 10, 0, 0, 0, 1, 1}, {1, 0, 1, 30, 11, 0, 0, 0, 1, 1}, {1, 0, 1, 22, 9, 0, 0, 0, 1, 1}, { 0, 1, 0, 18, 12, 0, 584.027526855469, 535.553283691406, 0, 0}, { 1, 0, 1, 31, 9, 0, 10717.0302734375, 5517.8408203125, 0, 0}, {1, 0, 1, 25, 11, 1, 0, 0, 1, 1}, {1, 0, 1, 25, 10, 1, 0, 0, 1, 1}, { 1, 0, 1, 28, 11, 0, 2431.94897460938, 863.4794921875, 0, 0}, { 1, 0, 1, 20, 11, 0, 7967.20947265625, 7967.20947265625, 0, 0}, { 1, 0, 0, 21, 12, 0, 449.047943115234, 449.047943115234, 0, 0}, { 1, 0, 1, 20, 11, 0, 2569.724609375, 2531.31103515625, 0, 0}, { 1, 0, 1, 23, 11, 0, 11946.119140625, 8959.5888671875, 0, 0}, {0, 1, 1, 21, 7, 0, 0, 0, 1, 1}, {0, 0, 1, 38, 9, 0, 0, 0, 1, 1}, { 1, 0, 1, 21, 11, 0, 2956.96557617188, 2217.72436523438, 0, 0}, { 1, 0, 1, 36, 7, 0, 1752.08532714844, 1606.66223144531, 0, 0}, { 1, 0, 1, 22, 10, 0, 1168.05639648438, 1071.10766601562, 0, 0}, { 1, 0, 1, 25, 11, 0, 4672.2236328125, 4284.42919921875, 0, 0}, { 1, 0, 1, 29, 11, 0, 3550.888671875, 3256.16479492188, 0, 0}, { 1, 0, 1, 17, 9, 0, 827.950256347656, 620.962707519531, 0, 0}, { 1, 0, 1, 19, 9, 0, 1537.92932128906, 1537.92932128906, 0, 0}, { 1, 0, 1, 23, 11, 0, 0, 1896.02294921875, 1, 0}, { 1, 0, 1, 19, 11, 0, 2305.02587890625, 2615.27587890625, 0, 0}, { 1, 0, 0, 34, 12, 0, 4906.619140625, 4087.2138671875, 0, 0}, {1, 0, 1, 18, 6, 0, 0, 0, 1, 1}, { 0, 0, 1, 18, 8, 0, 1168.05639648438, 1345.90942382812, 0, 0}, { 1, 0, 0, 27, 12, 0, 1971.31030273438, 4310.455078125, 0, 0}, { 0, 1, 0, 24, 12, 1, 7864.89697265625, 7212.1103515625, 0, 0}, { 1, 0, 1, 18, 10, 0, 1495.11157226562, 1371.01733398438, 0, 0}, { 1, 0, 1, 21, 11, 0, 2483.85180664062, 1862.88879394531, 0, 0}, { 0, 0, 1, 21, 11, 0, 1261.63720703125, 946.227905273438, 0, 0}, { 0, 1, 1, 18, 8, 0, 3311.802734375, 2483.85205078125, 0, 0}, { 0, 0, 1, 49, 8, 1, 9714.5966796875, 7285.94775390625, 0, 0}, { 1, 0, 0, 24, 12, 0, 11972.125, 12982.3408203125, 0, 0}, {0, 1, 1, 18, 8, 1, 0, 0, 1, 1}, {1, 0, 1, 18, 9, 0, 0, 3287.375, 1, 0}, {1, 0, 1, 17, 10, 0, 0, 0, 1, 1}, {0, 0, 0, 38, 12, 0, 0, 0, 1, 1}, { 1, 0, 0, 25, 12, 0, 14426.7900390625, 2409.27392578125, 0, 0}, {1, 0, 1, 24, 10, 0, 0, 0, 1, 1}, { 1, 0, 1, 29, 10, 1, 4687.8017578125, 4923.26318359375, 0, 0}, {1, 0, 1, 18, 9, 0, 0, 0, 1, 1}, { 1, 0, 1, 23, 11, 0, 37849.0703125, 29897.189453125, 0, 0}, { 1, 0, 1, 23, 9, 1, 19047.07421875, 19289.8515625, 0, 0}, {1, 0, 0, 22, 16, 0, 0, 0, 1, 1}, { 1, 0, 1, 28, 11, 0, 1929.02905273438, 6871.85595703125, 0, 0}, { 1, 0, 1, 35, 8, 1, 13732.0703125, 17976.150390625, 0, 0}, { 1, 0, 1, 22, 11, 0, 8176.39208984375, 7497.75146484375, 0, 0}, { 1, 0, 0, 24, 12, 0, 13765.75, 2842.76391601562, 0, 0}, {1, 0, 1, 29, 4, 0, 0, 0, 1, 1}, { 1, 0, 1, 20, 9, 0, 3311.802734375, 2483.85205078125, 0, 0}, { 1, 0, 1, 24, 10, 1, 11703.2001953125, 4078.15209960938, 0, 0}, {1, 0, 0, 28, 12, 1, 0, 0, 1, 1}, { 1, 0, 1, 19, 11, 0, 1868.02136230469, 1868.02136230469, 0, 0}, {1, 0, 0, 34, 13, 1, 0, 0, 1, 1}, { 1, 0, 1, 35, 10, 0, 9902.7626953125, 9902.7626953125, 0, 0}, {1, 0, 1, 28, 9, 0, 0, 0, 1, 1}, { 1, 0, 1, 17, 10, 0, 2072.5556640625, 1726.43884277344, 0, 0}, { 0, 0, 0, 18, 12, 0, 735.205078125, 735.205078125, 0, 0}, {1, 0, 1, 25, 5, 0, 0, 0, 1, 1}, {0, 0, 0, 31, 12, 0, 0, 494.643005371094, 1, 0}, { 0, 0, 1, 25, 8, 0, 24415.234375, 23096.65234375, 0, 0}, { 0, 0, 1, 19, 8, 0, 4101.92919921875, 3416.90698242188, 0, 0}, { 1, 0, 1, 20, 11, 0, 1168.05676269531, 1633.20239257812, 0, 0}, { 1, 0, 1, 18, 11, 0, 788.523315429688, 2041.36303710938, 0, 0}, { 1, 0, 1, 19, 11, 0, 721.340576171875, 2445.58911132812, 0, 0}, {1, 0, 1, 25, 9, 1, 0, 0, 1, 1}, {1, 0, 1, 23, 10, 0, 0, 0, 1, 1}, { 1, 0, 1, 18, 10, 0, 1791.01989746094, 1642.365234375, 0, 0}, { 1, 0, 1, 35, 9, 1, 13602.4296875, 13830.6396484375, 0, 0}, { 1, 0, 1, 27, 11, 0, 3065.19409179688, 766.298583984375, 0, 0}, {1, 0, 1, 28, 11, 0, 0, 0, 1, 1}, {1, 0, 0, 26, 12, 0, 0, 0, 1, 1}, { 1, 0, 1, 20, 9, 0, 6083.994140625, 0, 0, 1}, {1, 0, 1, 18, 8, 0, 0, 0, 1, 1}, { 1, 0, 1, 21, 9, 0, 6416.47021484375, 5749.3310546875, 0, 0}, { 0, 1, 1, 19, 11, 1, 5424.48486328125, 5463.80322265625, 0, 0}, { 1, 0, 1, 22, 11, 0, 12300.9228515625, 12170.943359375, 0, 0}, { 0, 1, 1, 19, 11, 1, 308.21923828125, 308.21923828125, 0, 0}, {1, 0, 1, 23, 11, 0, 0, 0, 1, 1}, { 0, 0, 1, 26, 11, 0, 3807.53588867188, 4676.45703125, 0, 0}, { 1, 0, 0, 23, 12, 0, 9342.1875, 7782.0419921875, 0, 0}, { 1, 0, 1, 17, 9, 0, 1608.58862304688, 1206.44140625, 0, 0}, { 1, 0, 1, 21, 10, 1, 1946.76098632812, 1785.17980957031, 0, 0}, { 1, 0, 1, 18, 10, 0, 0, 798.907897949219, 1, 0}, { 1, 0, 1, 34, 7, 0, 1143.359375, 857.51953125, 0, 0}, { 1, 0, 1, 22, 10, 0, 0, 2174.955078125, 1, 0}, {0, 1, 1, 25, 11, 1, 0, 0, 1, 1}, { 1, 0, 1, 21, 10, 0, 1230.09729003906, 922.572937011719, 0, 0}, { 0, 0, 1, 17, 10, 0, 2417.982421875, 2465.61328125, 0, 0}, {1, 0, 1, 19, 9, 0, 0, 0, 1, 1}, {1, 0, 1, 25, 8, 0, 0, 0, 1, 1}, { 0, 0, 1, 31, 4, 0, 11680.564453125, 10711.0771484375, 0, 0}, { 1, 0, 1, 27, 11, 0, 2206.93994140625, 2666.27392578125, 0, 0}, { 1, 0, 1, 25, 8, 0, 37431.66015625, 37431.66015625, 0, 0}, {1, 0, 1, 23, 11, 0, 0, 0, 1, 1}, { 1, 0, 1, 21, 11, 1, 9659.4150390625, 7244.56103515625, 0, 0}, {1, 0, 1, 46, 8, 1, 0, 0, 1, 1}, {1, 0, 1, 25, 8, 0, 0, 0, 1, 1}, {1, 0, 1, 32, 8, 0, 0, 0, 1, 1}, { 1, 0, 1, 20, 10, 0, 690.192016601562, 690.192016601562, 0, 0}, {1, 0, 0, 40, 12, 0, 0, 0, 1, 1}, { 0, 0, 1, 26, 8, 1, 37211.7578125, 36941.265625, 0, 0}, {1, 0, 1, 17, 10, 0, 0, 0, 1, 1}, {1, 0, 1, 25, 11, 0, 0, 0, 1, 1}, { 1, 0, 1, 31, 11, 0, 17711.880859375, 1726.44494628906, 0, 0}, {1, 0, 1, 34, 11, 1, 0, 0, 1, 1}, { 1, 0, 1, 28, 11, 1, 824.388916015625, 10033.91015625, 0, 0}, {1, 0, 1, 17, 9, 0, 0, 0, 1, 1}, { 1, 0, 1, 37, 10, 0, 8606.7431640625, 8606.7431640625, 0, 0}}, "Output" -> {1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0}], "Predictions" -> {1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1}, "LogProbabilities" -> CompressedData[" 1:eJyFlnk4VdsfxnVzlUKTSleUIboNkriUNI9I4hibkCGlkjSnMuRWEnUlihLl SpKLWyr1VpI06shwOPPkDM5oyNxv33/tP35/7Ofz7GevZ6/vWu963+8yCTrg EfKLhoYGk3j+45Sh6PYtTp1w7+HtPrC4Ab014c2tQUIM1GjraPQJUOCarnsz ko37rlqOZ61kYG79w+f0NQFBb5q8Voj+qMURwdU0vNzWzeQWquD9vPCZ14xW NBZ7RFJHqMBqiomQbWpBz+G/DwhCVbBY+qlD5CSH8zHfisTTLGxwsJVwzORI jTz2uvw2CzYHa3ii1RyCy0dnD0phHeGam/iCA6sIvk2St5RUj9f9ecnKJjbB NCeN6+0wgVvAxMB2zETxqrhBNnb+rjy/24iJEn3R9itbFViS4GspecSA8aqB kWefKJA1NvCVlYcSUVYUZdYtOry1vMJDV/DhpeW14JujCD1x1NMH89oJ2lRT vrJhuW16UunENphvS/tVy12AMDPdfmqSkODaU6+/CVA9TVffaLCN4D7zucf4 CPTWNg/JESLAe4RP6QsBVgYNfb7CZMJkYVi8+ws5xl137sx6y8J5l8+KKaPk UDseGi+fowDzaPdUM18miuEwdblFOx6h5GGPBQfl2ceNop+1YF9YUMkYGxVy OHvKHLtUiE08mSBdRCOtVxZOGRIr1Hj6t0mGcUgT6XvqRWm2RUwbwUOsxHY+ dqUXJb42YyM4vfORc64Mu6YKos1+5yNo6r8vnLxFoFF642cGsQluaTFcI0On mrnmwSk+OtTeb6b2tMFsiTb1XbAYpkvujKOt4oHzLWdZUGg7uN9yzD/0siE+ ba8V86ULef0PBwykVJyW7nI9WS/HYKOPuUUVk1Tfh1De2WkZKhjfNEqokdCQ krfH7c0CDi7nXe1nTmtH7NzesWvHKaFZfJceY88gnYfhehfslN4wAYcgLyuA IoVmp27BaPAIbp/ZWCeCw69xnGCNJmyKDda//HsHkpLDHUOUzdi1a+jvAwZq 0v+qrA0iOGOV0K6yHueznIFevT+/3hPy0KMXNr8wV4Ti0GnrK79zUBT6z+AW eykiNMcelv7Fx15N6fzlzDbSefg0XhprnqtAtonFwfm1DES82sLePF6BhrN5 wvhjTGy5b+1kZt0CypwSy/TDKrhH/YjPV8iQy19WWcdlwfd9pZn+BjZ83uuE Lt4rQ9NAjOmUcwoU9n50eCJloEOhW3RTtxEDJz2PRT3twIJz57uia1vgYvKu qm6OCnMnpbAKGHzMnsTSdU5uQ82J0JNCu3a8P6EXwjMkfOkwsepyPQeLHAq9 hQ5S6IaIwvpz+NAJqXD/+LUNJwYM2opparSryvriLzXhbFJF0cxoNYbmqwtP rGkm7d9wvRw8tc8erWvEZ4rF+pC1HciI1Ch5PsDFzci6hPhnYkTQLDr5GiqU hOw1dTFpRRGjPNnyrQJbny9mDeUwSPkwfL6jqSGRxmUcHE41upkVLMWOLO2Y PyI5/zFrwispaXxMledT0KQ4U+WyumA7B0/0DpSnObXhX73rc+9aCqDzWfFJ vp+JRCFo8wwUJL9QzLcPccUcgl7FYw2lkKwsjdtvKCTon266Ugj7VIUlc4UE i1O/Opy+wkWs8yw9rygpwV/LfxZw4Od+Y0blEzke5LxNiREwsW13sjyvogFT /HdFJ8k6EECvzH/F4GI7naKUssX4tHRq+LmZRK4dlzvWBDNBfXDhzlqGHHk+ kzInPWFixZjEWKfDciTo0/NTHFjQ2DhtTpxHN36YNRz3XkzFjYM+pTX+Cizl LRP5GzNJ+ryLNZfbxysxcVRQ5+iFdNi7T6Of0SJ0c1eFpThxcEPnIldk1wyL ePVX7VNqnLynObG4gY9T9+iDvdfaIKorckz60AZJnU5f5T0+WsJnWXv5iUEL j6G7ufJI++cWomMRmM/DppAiz28yETbqpU3fRuiSPt3ggn4JA+apX7kh3U3Q 5h/6rfaWGmbvzTtrJkgJVm5pVnJI/j0l3lRw0U+IGPGsVLuRQtJ8w/Onc82D 4LlNHHSvmfRRz1YKwZKoKbF3JOAvkSjWr+MiQ/tqIGOZGNe1S7nrg3gkPwx/ 50ZvCr5/hcjD6Gs9ea1sxP3I0HI7JyD4iv6uRQhFdOz+sJUyKKMz0xW72Ohf 4MfhV8swsGBE4SkdNvb5aQbN0RfjgF+EQiORB2kGI7v4JwvRnx5X+NFkGLFb dF18nI5ffks3iA1S4ju/VFVsqEB+7b+p9/cw8bJ08Z2jIh7Bp3aXc0Q4Xx5q 7FiihhG1cN6XxiY4rLuTUuQjx5FL1BEt21lYaNWp+d5ZAhurSu/yC1zsOXr3 rsxDhr1HNVscfdnY63FmcmWMCOEeOyfnavFR3nel9vWYblAllkE/M6ik+u34 9X7hzwQEXfTf5QlJ/qFqUVY+WstDvVZ0qk+gmDQ+y8y0a8cdFj5PuHAmzVSO vsbnZa1KORQP1xcM3WKS1ktP3Fj78aAaUYbdgzUbmjE7QMuKekSCOQHCVLcw Lm5V2XX62ghwu8pu/VybNpS9clbPe9qI+SXLxmh5dsAq87Hm48jvcMmfMKca nXBrP/62N64LNv8cDdtAqSedn/l2zmbZhvVwSRY2RDzowqAtY/n2zTwM2TZ4 3fUSk/LpTWRdT8wLBc5yIuObCxik/vuj+tWJk0IOeqoNqq7NIPrHqJMNei1s gkllqr/aMVG68OP1bSpsLPqzURLUAi2fMlqPRIaxxd1edBELR85mGsffrYNx wND6n9d+gFWWn8t5JgSn7DJ75F0BqOtcZj3yF+HbutUVTrP4pH40PJ8WRmUF 8DZ0ovp9rNc6ywZsjVMvoO1jwWpl5qqxLnKS3tQxTa2+S8UEfzwMCOZhyiob r4R+FnRMOPQUugxaH+4p5+5gY/QH3dWjnIlzX2BKdc1UA3n+lJ0azTB+1DaD Yi0luHNeJ41DyqO+gXW8LzuIfjsgTNdeLcaNDKPbhu6fMalsr//VwF6wppYt 54uFBD03NxwRoOjEmmd2A3J4dzXH6F9h/t9378rM6pD1QlAqA9sWTRaS9Bve f3QS3o4Lr6/HSBPtGss5XTCJupWd5ismKFwjJ/JteL4Mz8uMsEM/P4+W4EaY Z0HrCy7JH19MBXmP/yDuB2svJrRuZEJrndRpbrcSxgYxdQP2rVBt4nNDq+iI mRTi2b5Sid7JGdSNgUL0TV4xOrmffJ9faVJs4zRbiuUm6RQNNgdGtZ+aNlhJ YFy75ohZDhct0774vt0sR8WibRn0MBaWbos7MyFfgWMVaYWJbxmwvfpw38gP CoSb97+adoMB2RPX0rKFgv9o20r4KfIN/0WDcSdeax9S5YY3gD7dVHe57nfY /2ntmvlLFxZacjx2clSoyqKUhPjTYCPzfP+Wqsa78tu/NmQ0kfwwXP8xY209 XnZ+RmBsaNrVib2kvNJZNT7nBIWK+PGXaH1O3aT1JzE2uxXaqjH7SMpkxrNm SCB1GBMrghQ3I96M5JPmG+4Pv3+Sj1xSKFAcJK2fvp+4fyacPvPAvRWT73SE JUuUiB7wKX0Y/A1GP7O7VvR0k/JieD05Wdyi+S8lBJ1bVltzSfcfj1rLvFJC xy21KTAjdB3MmRRNey9Af05rlypNiBns6U6Jq6QwYU+/v+sTB/8DxJ4I5g== "], "CountMatrix" -> {{71, 59, 0}, {47, 45, 0}}, "IndicesMatrix" -> {{{5, 6, 11, 12, 13, 15, 16, 25, 28, 30, 32, 34, 36, 37, 44, 46, 47, 48, 49, 50, 51, 52, 54, 57, 60, 62, 64, 65, 68, 69, 72, 78, 79, 80, 81, 82, 85, 86, 88, 93, 99, 106, 111, 113, 119, 121, 125, 128, 136, 138, 152, 154, 155, 156, 165, 168, 169, 173, 180, 184, 185, 187, 192, 200, 204, 205, 212, 215, 216, 219, 220}, {2, 8, 9, 17, 19, 22, 26, 27, 33, 39, 40, 43, 53, 58, 59, 63, 70, 76, 77, 84, 89, 90, 91, 96, 97, 98, 100, 108, 120, 122, 123, 130, 131, 132, 133, 134, 142, 144, 163, 166, 167, 170, 171, 176, 177, 178, 179, 181, 183, 191, 195, 196, 199, 201, 202, 209, 213, 218, 222}, {}}, {{3, 4, 7, 10, 18, 24, 35, 42, 55, 56, 66, 73, 74, 75, 87, 92, 102, 103, 107, 109, 114, 115, 118, 126, 137, 139, 140, 145, 146, 147, 149, 150, 151, 159, 162, 172, 174, 182, 190, 198, 203, 207, 208, 211, 214, 217, 221}, {1, 14, 20, 21, 23, 29, 31, 38, 41, 45, 61, 67, 71, 83, 94, 95, 101, 104, 105, 110, 112, 116, 117, 124, 127, 129, 135, 141, 143, 148, 153, 157, 158, 160, 161, 164, 175, 186, 188, 189, 193, 194, 197, 206, 210}, {}}}, "ExtendedClasses" -> {0, 1}, "Weights" -> SparseArray[Automatic, {222}, 1., {1, {{0, 0}, {}}, {}}], "BatchEvaluationTime" -> 0.0003667117117117117, "SingleEvaluationTime" -> 0.013962, "Version" -> {12.3, 0}]]]], "Output", TaggingRules->{}, CellChangeTimes->{3.8343146449372597`*^9, 3.834405425287222*^9}, CellLabel->"Out[3]=", CellID->1064024291] }, Open ]], Cell["\<\ Assess classifier performance. The machine learning classifier does not \ perform particularly well:\ \>", "Text", TaggingRules->{}, CellChangeTimes->{{3.8343146966363153`*^9, 3.834314734033906*^9}, { 3.834333367310051*^9, 3.834333367741436*^9}}, CellID->1555758181], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"cmo", "[", RowBox[{"{", RowBox[{"\"\\"", ",", "\"\\"", ",", RowBox[{"\"\\"", "->", "1"}]}], "}"}], "]"}]], "Input",\ TaggingRules->{}, CellChangeTimes->{{3.834314653316152*^9, 3.834314672958064*^9}, { 3.834314737032626*^9, 3.834314744690543*^9}}, CellLabel->"In[4]:=", CellID->1342459492], Cell[BoxData[ RowBox[{"{", RowBox[{ "0.5225225225225225`", ",", "0.03462422054479808`", ",", "0.5025919732441471`"}], "}"}]], "Output", TaggingRules->{}, CellChangeTimes->{{3.8343146668402348`*^9, 3.8343146734689827`*^9}, 3.834405426666012*^9}, CellLabel->"Out[4]=", CellID->481888444] }, Open ]], Cell["\<\ A look at the probabilities of being treated shows the classifier is \ extremely uncertain in its results; the range of probabilities is extremely \ small:\ \>", "Text", TaggingRules->{}, CellChangeTimes->{{3.834314868031734*^9, 3.834314897138853*^9}}, CellID->251741330], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"Histogram", "[", RowBox[{"cmo", "[", "\"\\"", "]"}], "]"}]], "Input", TaggingRules->{}, CellChangeTimes->{{3.8343148351594257`*^9, 3.834314863817822*^9}, { 3.834314903601514*^9, 3.8343149066682863`*^9}}, CellLabel->"In[5]:=", CellID->1338088870], Cell[BoxData[ GraphicsBox[ TagBox[RasterBox[CompressedData[" 1:eJztnQtwVNedp9WASAqDxmLCo3jblCki2ClTG5DAxCBCnGGZGBlsHMe4cIix LcIGQxJA4zWSx8bSQtsmQSaprSwRUJ4E4wFsU3h5GTZGxVPCWMAAAmQIJgij sTOE4SGp2f9yS12avgc9Wqdv33PP99WvqO6j07d+3G7p/6lbat0zY87kZ9ul pKT87Ovyz+QfLcieN+9HeVPuliuP/fRnuc/9dOYzE37685nPzZyXNaO9LO4O paSUy/7/f/kWAAAAgDm8f5tktwAAAIBggmkAAABA4sA0AAAAIHFgGgAAAJA4 MA0AAABIHJgGAAAAJA5MAwAAABIHpgEAAACJA9MAAACAxCGaEQ6H32+gtLQ0 2Y0AAAAgOGAaAAAA4Oajjz4qKCiYNWtWUVHR6dOnYz66Z8+et11s377dfRxe PQEAAIDG1NXVPfbYYymNSE1NLS4ubrxn7NixKS5Gjx7tPhqmAQAAAI15+eWX RRv69OkjhnDq1Kk333yzffv27dq1O3jwYHRPenp6KBSaM2fO3EbE2IgDpgEA AABRIpGIOIaYxrZt26KLM2fOlJX58+c7V6uqquTqwIEDW3JATAMAAACiXLt2 7Y033sjNza2vr48uvvTSS6IWzz33nHN1w4YNcnXq1KktOSCmAQAAAE1w/vz5 AQMGiFqsWrXKWcnPz5erc+fOLSgomDx58vTp04uLi0VRlDfHNAAAAEDJmjVr Hn/88bvuuku84tFHH62trXXWJ02a5P5x0IyMjDNnzrgPgmkAAACAkuzs7KhI TJs27csvv3TW+/fvLytDhw5dv379qVOn1q1b169fP1mR/ZFIJOYgmAYAAAAo +eyzz7744ott27YNHjxYRGLs2LHO+ubNm1esWFFTUxPdWVFRkZqaKnvKy8tj DoJpAAAAQNNUVlY6IiFGcac9mZmZsmH16tUx66IZJSUlx1UkuDX4msuXL5/w PVIy2ecJACBo1NfXnzt37siRIzHrztMaGzdulMtXrly5cOFCzIbx48fLhpUr V8asi2nk5eWFVSTufwH+5/lnnlryi0d8HimZ7PMEABA0Dh8+LMKQnp5+8+bN 6GJtba2syPrBgwfLysrkQufOnW/cuBHdcP369R49esi6+8+a8OoJKJk57R/q yhf5PFIy2ecJACBo1NfXd+/eXZxh6dKl0cUXX3xRVmRdlKOurq5r165ytbCw MLph4cKFsjJ48ODo76dEwTRACaYBAGAt77zzjmhDKBSaMmXKokWLxo0b51zd tGmTs2HVqlXOL6Tk5OTIBudXVDp06LB161b30TANUIJpAADYzNq1a3v37t34 vTJ27drVeMOaNWt69eoV3TBo0KAtW7YoD4VpgBJMAwDAciKRSFVVVWlp6aVL l+604ezZs/v27ausrGziOJgGKME0AABAC5gGKME0AABAC5gGKME0AABAC5gG KME0AABAC5gGKME0AABAC5gGKME0AABAC867kUfhTcjBAdMAAAAtOH9h7XID ya4DfgHTAAAALfDqCSjBNAAAQAuYBijBNAAAQAuYBijBNAAAQAuYBijBNAAA QAuYBijBNAAAQAuYBijBNAAAQAuYBijBNAAAQAuYBijBNAAAQAuYBijBNAAA QAuiGeFwuKQBrAMcMA2AOLh8+fLK3/7G5/n1iuJknyewC8c0Shs4fvx4shuB L8A0AOJAhvhb/zjx49/9yM955ad/L1/tk32qwCJ49QSUYBoAcbDyt7+RUZ70 T42m879feQzTAC/BNEAJpgEQB5gGgBtMA5RgGgBxgGkAuME0QAmmARAHmAaA G0wDlGAaAHGAaQC4wTRACaYBEAeYBoAbTAOUYBoAcYBpALjBNEAJpgEQB5gG gBtMA5RgGgBxgGkAuBHNKCkpOd6IZDcCX4BpAMQBpgHgRkwjLy8v3ADPb4AD pgEQB5gGgBtePQElmAZAHGAaAG4wDVCCaQDEAaYB4AbTACWYBkAcYBoAbjAN UIJpAMQBpgHgBtMAJZgGQBxgGgBuMA1QgmkAxAGmAeAG0wAlmAZAHGAaAG4w DVCCaQDEAaYB4AbTACWYBkAcYBoAbjANUIJpAMQBpgHgRjTj2UaEw+FkNwJf gGmA33hn7e+fmvr3cqf7Od/O/C+YBgSGjz76qKCgYNasWUVFRadPn3ZvOHr0 6OLFi3Nzc/Pz8w8fPnyn4/CcBijBNMBvLH1t0bENP0n6o67p5D8/BtOAAFBX V/fYY4+lNCI1NbW4uLjxnmXLlrVv3z66oV27dnd6sgLTACWYBvgNTENXMA1o lpdfflnkoU+fPmIIp06devPNN0UqxCUOHjzobJCHkFzt2LHj8uXLKyoqlixZ IhtCoVBZWZn7aJgGKME0wG9gGrqCaUDTRCIRcQwxjW3btkUXZ86cKSvz5893 rubk5MjVoqKi6IbCwkJZmTFjhvuAmAYowTTAb2AauoJpQNNcu3btjTfeyM3N ra+vjy6+9NJLIhLPPfecXJb1tLQ0uXr+/PnohosXL4ZCoU6dOtXW1sYcENMA JZgG+A1MQ1cwDWgtYhQDBgwQtVi1apVcPXnypFzu3r17zLaePXvK+okTJ2LW MQ1QgmmA38A0dAXTgJazZs2axx9//K677hKFePTRR53nK/bu3StXhw0bFrNZ VmR99+7dMeuYBijBNMBvYBq6gmlAy8nOzo7+dsm0adO+/PJLWdyxY4dczczM jNk8atQoWd+6dWvMOqYBSjAN8BuYhq5gGtByPvvssy+++GLbtm2DBw8Wixg7 duytBtMYOXJkzOasrCxZ37lzZ8w6pgFKMA3wG5iGrmAaEAeVlZWpqakiEhUV Ffv375cLQ4YMidmTkZEh6+Xl5THrmAYowTTAb2AauoJpQNPU19efO3fuyJEj MevO0xobN268dOmSXEhLS4tEItGPyuUuXbrIenV1dcwNRTPy8vLCKhL+nwEf g2mA38A0dAXTgKY5fPiwCEN6evrNmzeji7W1tbIi686bd/Xt21cuHzp0KLqh vLxcVgYMGOA+oJhGSUnJcRUe/HfAt2Aa4DcwDV3BNKBp6uvru3fvLtqwdOnS 6OKLL77o/Gar8+sn8+bNk6sTJ050bET+nTBhgqwsWLDAfUBePQElmAb4DUxD VzANaJZ33nlHtCEUCk2ZMmXRokXjxo1zrm7atMnZUF1d3a1bN1kcMWKE2MXw 4cPlcu/evd0vndzCNOAOYBrgNzANXcE0oCWsXbtWzCH6K64ZGRm7du1qvKGq qiorK0v0w9mQmZnp/tEOB0wDlGAa4DcwDV3BNKCFRCIR0Ql5tFy6dOlOe2pq ag4cOHDu3LkmjoNpgBJMA/wGpqErmAZ4DKYBSjAN8BuYhq5gGuAxmAYowTTA b2AauoJpgMdgGqAE0wC/gWnoCqYBHoNpgBJMA/wGpqErmAZ4DKYBSjAN8BuY hq5gGuAxznuEljYi2Y3AF2AatlHqe3KfmYZpaAmmAR7j/N2TkgZ4+IEDpmEV r77y8is//XsZQH7OlPHfxDS0BNMAj+HVE1CCaViFES9MzP7BCP+XxDQA3GAa oATTsApMQ1cwDQA3mAYowTSsAtPQFUwDwA2mAUowDavANHQF0wBwg2mAEkzD KjANXcE0ANxgGqAE07AKTENXMA0AN5gGKME0rALT0BVMA8ANpgFKMA2rwDR0 BdMAcINpgBJMwyowDV3BNADcOO9GfrkRyW4EvgDTsApMQ1cwDQA3zruRRxHr SHYj8AWYhlVgGrqCaQC44dUTUIJpWAWmoSuYBoAbTAOUYBpWgWnoCqYB4AbT ACWYhlVgGrqCaQC4wTRACaZhFZiGrmAaAG4wDVCCaVgFpqErmAaAG0wDlGAa VoFp6AqmAeAG0wAlmIZVYBq6gmkAuME0QAmmYRWYhq5gGgBuMA1QgmlYBaah K5gGgBtMA5RgGlaBaegKpgHgxnk38nADvBs5OGAaVoFp6AqmAeDG+Qtrxxvg L6yBA6ZhFZiGrmAaAG549QSUYBpWgWnoCqYB4AbTACWYhlVgGrqCaQC4wTRA CaZhFZiGrmAaAG4wDVCCaVgFpqErmAaAG0wDlGAaVoFp6AqmAeAG0wAlmIZV YBq6gmkAuME0QAmmYRWYhq5gGgBuMA1QgmlYBaahK5gGgBtMA5RgGlaBaegK pgHgRjQjHA6/3wAPP3DANKwC09AVTAPADaYBSjANq8A0dAXTgCDxxz/+saio aNasWa+++uqHH34Y89E9e/a87WL79u3u4/DqCSjBNKwC09AVTAOCwY0bN3Jy clL+M+PGjfvqq6+ie8aOHZviYvTo0e6jYRqgBNOwCkxDVzANCAbz588XbejT p8/atWsrKys3btw4cOBAWXnqqaeie9LT00Oh0Jw5c+Y2ori42H00TAOUYBpW gWnoCqYBASASiXTr1k28ovHj5MiRI7Lyta997caNG3K1qqpKrop+tOSAmAYo wTSsAtPQFUwDAsDnn3+ekZFxzz33xKx36dJF7OL06dNyecOGDXJ56tSpLTkg pgFKMA2rwDR0BdOAoPLJJ5+IWnTq1Km2tlau5ufny9W5c+cWFBRMnjx5+vTp xcXF165dU94W0wAlmIZVYBq6gmlAILl58+aDDz4oavHII484K5MmTXL/OGhG RsaZM2fcN8c0QAmmYRWYhq5gGhA86urqnnjiCRGJ9PT0qEj0799fVoYOHbp+ /fpTp06tW7euX79+spKdnR2JRGKOgGmAEkzDKjANXcE0IGBcvXr14YcfFoXo 0qXLxx9/HF3fvHnzihUrampqoisVFRWpqamys7y8POYgmAYowTSsAtPQFUwD gkR1dfWIESNEHrp167Z3795m92dmZsrm1atXx6yLZpSUlFxWkZjiYAaYhlVg GrqCaUBgqKysvPfee8Uc7rvvPrkc89ErV65cuHAhZnH8+PGyf+XKlTHrYhp5 dyCB/wHwPZiGVWAauoJpQDD405/+1LdvX9GGBx54wP3MQ1lZmXyoc+fOzntr OFy/fr1Hjx4x78LhwKsnoATTsApMQ1cwDQgGY8aMEWcYOXLk1atX3R+tq6vr 2rWrbCgsLIwuLly4UFYGDx7s/BpsYzANUIJpWAWmoSuYBgSAXbt2Ob+12qlT p2+4OHbsmOxZtWqVsycnJ2fRokXZ2dlyuUOHDlu3bnUfENMAJZiGVWAauoJp QAB4/fXX3e+VEeXTTz91tq1Zs6ZXr17R9UGDBm3ZskV5QEwDlGAaVoFp6Aqm AVYRiUTOnj27b98+94+MNgbTACWYhlVgGrqCaQC4wTRACaZhFZiGrmAaAG4w DVCCaVgFpqErmAaAG0wDlGAaVoFp6AqmAeAG0wAlmIZVYBq6gmkAuME0QAmm YRWYhq5gGgBuYt6NPBwOJ7sR+AIjTGNM1pAFc2f6PP/jHxck+85sHkxDVzAN ADcxf2Et2XXALxhhGg/+1/7VH/3c51mQO8n/n1mYhq5gGgBuePUElBhhGg+N HJj0Ds0G09AVTENXMA3wGEwDlGAauoJp6AqmoSuYBngMpgFKMA1dwTR0BdPQ FUwDPAbTACWYhq5gGrqCaegKpgEeg2mAEkxDVzANXcE0dAXTAI/BNEAJpqEr mIauYBq6gmmAx2AaoATT0BVMQ1cwDV3BNMBjMA1QgmnoCqahK5iGrmAa4DGY BijBNHQF09AVTENXMA3wGNGMcDj8fgM8/MAB09CV53849ne/+937/ib3mWn+ H+KYhq5gGuAx72MaoALT0JVHvvPNPyx5dOOyH/g5U8Z/0/9DHNPQFUwDPMYR jGS3AN+BaejKExOGVn/086TXaDpGDHEjSmIaAG4wDVCCaegKpmFVSUwDwA2m AUowDV3BNKwqiWkAuME0QAmmoSuYhlUlMQ0AN5gGKME0dAXTsKokpgHgBtMA JZiGrmAaVpXENADcYBqgBNPQFUzDqpKYBoAbTAOUYBq6gmlYVRLTAHCDaYAS TENXMA2rSmIaAG5EM0pKSo43ItmNwBdgGrqCaVhVEtMAcCOmkZeXF26A5zfA AdPQFUzDqpKYBoAbXj0BJZiGrmAaVpXENADcYBqgBNPQFUzDqpKYBoAbTAOU YBq6gmlYVRLTAHCDaYASTENXMA2rSmIaAG4wDVCCaegKpmFVSUwDwA2mAUow DV3BNKwqiWkAuME0QAmmoSuYhlUlMQ0AN5gGKME0dAXTsKokpgHgBtMAJZiG rmAaVpXENADcYBqgBNPQFUzDqpKYBoAb593Io4TD4WQ3Al+AaegKpmFVSUwD gsQf//jHoqKiWbNmvfrqqx9++KF7w9GjRxcvXpybm5ufn3/48OE7Hcf5C2uX G0hkZTAJTENXMA2rSmIaEAxu3LiRk5OT8p8ZN27cV199Fd2zbNmy9u3bRz/a rl27Oz1ZwasnoATT0BVMw6qSmAYEg/nz54s89OnTZ+3atZWVlRs3bhw4cKCs PPXUU84GeQiJWnTs2HH58uUVFRVLliwR6wiFQmVlZe6jYRqgBNPQFUzDqpKY BgSASCTSrVs38YrGj5MjR47Iyte+9rUbN27IVecZj6KiouiGwsJCWZkxY4b7 gJgGKME0dAXTsKokpgEB4PPPP8/IyLjnnnti1rt06SIucfr06fr6+rS0NLl8 /vz56EcvXrwYCoU6depUW1sbc0NMA5RgGrqCaVhVEtOAoPLJJ5+IWjgicfLk SbncvXv3mD09e/aU9RMnTsSsYxqgBNPQFUzDqpKYBgSSmzdvPvjgg2IRjzzy iFzdu3evXB42bFjMNlmR9d27d8esYxqgBNPQFUzDqpKYBgSPurq6J554QhQi PT39zJkzsrJjxw65mpmZGbNz1KhRsr5169aYdUwDlGAauoJpWFUS04CAcfXq 1Ycfflj8oUuXLh9//LGz6JjGyJEjYzZnZWXJ+s6dO2PWMQ1QgmnoCqZhVUlM A4JEdXX1iBEjRB66deu2d+/e6Pr+/ftlcciQITH7MzIyZL28vDxmHdMAJZiG rmAaVpXENCAwVFZW3nvvvWIO9913n1xu/KFLly7JelpaWiQSiS7KZeeXU8RP Yg4lmhEOh0tUePE/Ab+CaegKpmFVSUwDgsGf/vSnvn37ijY88MADyvcPdz56 6NCh6Ep5ebmsDBgwwL3ZMY1SFQn8P4DvwTR0BdOwqiSmAcFgzJgxzk9iXL16 Vblh3rx5smHixIk3b968dfuXUyZMmCArCxYscG/m1RNQgmnoCqZhVUlMAwLA rl27nD9l0qlTp2+4OHbs2K3bP8LhvI/oiBEjxC6GDx8ul3v37u1+6eQWpgF3 ANPQFUzDqpKYBgSA119/PeXOfPrpp862qqqqrKysUCjkrGdmZh45ckR5QEwD lGAauoJpWFUS0wDbqKmpOXDgwLlz55rYg2mAEkxDVzANq0piGgBuMA1Qgmno CqZhVUlMA8ANpgFKMA1dwTSsKolpALjBNEAJpqErmIZVJTENADeYBijBNHQF 07CqJKYB4AbTACWYhq5gGlaVxDQA3IhmlJSU8NagEAOmoSuYhlUlMQ0AN2Ia eXl50b91wsMPHDANXcE0rCqJaQC44dUTUIJp6AqmYVVJTAPADaYBSjANXcE0 rCqJaQC4wTRACaahK5iGVSUxDQA3mAYowTR0BdOwqiSmAeAG0wAlmIauYBpW lcQ0ANxgGqAE09AVTMOqkpgGgBtMA5RgGrqCaVhVEtMAcINpgBJMQ1cwDatK YhoAbjANUIJp6AqmYVVJTAPADaYBSjANXcE0rCqJaQC4Ec14thHhcDjZjcAX YBq6gmlYVRLTAHDDcxqgBNPQFUzDqpKYBoAbTAOUYBq6gmlYVRLTAHCDaYAS TENXMA2rSmIaAG4wDVCCaegKpmFVSUwDwA2mAUowDV3BNKwqiWkAuME0QAmm oSuYhlUlMQ0AN5gGKME0dAXTsKokpgHgBtMAJZiGrmAaVpXENADcYBqgBNPQ FUzDqpKYBoAbTAOUYBq6gmlYVRLTAHAjmpGXlxduoKSkJNmNwBdgGrqCaVhV EtMAcCOmIXZxvIHLly8nuxH4AkxDVzANq0piGgBuePUElGAauoJpWFUS0wBw g2mAEkxDVzANq0piGgBuMA1QgmnoCqZhVUlMA8ANpgFKMA1dwTSsKolpALjB NEAJpqErmIZVJTENADeYBijBNHQF07CqJKYB4AbTACWYhq5gGlaVxDQA3GAa oATT0BVMw6qSmAaAG0wDlGAauoJpWFUS0wBw47xHaGkjkt0IfAGmoSuYhlUl TTGNX//616W+J9lfBUEbzt89KWmAOxccMA1dwTSsKmmEaSz48YMvPjdefMPP kYY83+4Trl+/Pnv27LVr18as79mz520X27dvdx+BV09ACaahK5iGVSWNMA0j Sm5c9gNmk094/vnnU1JS5syZE7M+duzYFBejR492HwHTACWYhq5gGlaVNGKI G1ES0/ADX3311dNPP+0ohNs00tPTQ6GQrM9tRHFxsfs4mAYowTR0BdOwqqQR Q9yIkphG0vnggw969+4dfbIixjSqqqpkceDAgS05FKYBSjANXcE0rCppxBA3 oiSmkXTuvvtucYlx48bl5eW5TWPDhg2yOHXq1JYcCtMAJZiGrmAaVpU0Yogb URLTSDoPPfTQu+++G4lEli9f7jaN/Px8WZw7d25BQcHkyZOnT59eXFx87do1 5aEwDVCCaegKpmFVSSOGuBElMQ3/oDSNSZMmuX8cNCMj48yZM+4jYBqgBNPQ FUzDqpJGDHEjSmIa/kFpGv3795fFoUOHrl+//tSpU+vWrevXr5+sZGdnRyKR mCNgGqAE09AVTMOqkkYMcSNKYhr+QWkamzdvXrFiRU1NTXSloqIiNTVVdpaX l8ccAdMAJZiGrmAaVpU0YogbURLT8A9K01CSmZkpO1evXh2z7rwb+WUViakM ZoBp6AqmYVVJI4a4ESUxDf+gNI0rV65cuHAhZuf48eNl58qVK2PWnXcjV5LY 6uBvMA1dwTSsKmnEEDeiJKbhH9ymUVZWJiudO3e+ceNGdPH69es9evSQdfef NeHVE1CCaegKpmFVSSOGuBElMQ3/4DaNurq6rl27ymJhYWF0ceHChbIyePDg 2tramCNgGqAE09AVTMOqkkYMcSNKYhr+QfnqyapVq5zfbM3JyVm0aFF2drZc 7tChw9atW91HwDRACaahK5iGVSWNGOJGlMQ0/MNbb70lFvHCCy/ErK9Zs6ZX r17RN9MYNGjQli1blEfANEAJpqErmIZVJY0Y4kaUxDSMIBKJnD17dt++fZWV lU1swzRACaahK5iGVSWNGOJGlMQ0ggSmAUowDV3BNKwqacQQN6IkphEkMA1Q gmnoCqZhVUkjhrgRJTGNIIFpgBJMQ1cwDatKGjHEjSiJaQQJTAOUYBq6gmlY VdKIIW5ESUwjSGAaoATT0BVMw6qSRgxxI0piGkHCeTfycAMlJSXJbgS+ANPQ FUzDqpJGDHEjSmIaQcL5C2vHG+APq4EDpqErmIZVJY0Y4kaUxDSCBK+egBJM Q1cwDatKGjHEjSiJaQQJTAOUYBq6gmlYVdKIIW5ESUwjSGAaoATT0BVMw6qS RgxxI0piGkEC0wAlmIauYBpWlTRiiBtREtMIEpgGKME0dAXTsKqkEUPciJKY RpDANEAJpqErmIZVJY0Y4kaUxDSCBKYBSjANXcE0rCppxBA3oiSmESQwDVCC aegKpmFVSSOGuBElMY0ggWmAEkxDVzANq0oaMcSNKIlpBAm5K8Ph8PsNlJaW JrsR+AJMQ1cwDatKGjHEjSiJaQQJTAOUYBq6gmlYVdKIIW5ESUwjSPDqCSjB NHQF07CqpBFD3IiSmEaQwDRACaahK5iGVSWNGOJGlMQ0ggSmAUowDV3BNKwq acQQN6IkphEkMA1QgmnoCqZhVUkjhrgRJTGNIIFpgBJMQ1cwDatKGjHEjSiJ aQQJTAOUYBq6gmlYVdKIIW5ESUwjSGAaoATT0BVMw6qSRgxxI0piGkEC0wAl mIauYBpWlTRiiBtREtMIEpgGKME0dAXTsKqkEUPciJKYRpCQu7KkpORyI5Ld CHwBpqErmIZVJY0Y4kaUxDSChNyVeY0Q60h2I/AFmIauYBpWlTRiiBtREtMI Erx6AkowDV3BNKwqacQQN6IkphEkMA1QgmnoCqZhVUkjhrgRJTGNIIFpgBJM Q1cwDatKGjHEjSiJaQQJTAOUYBq6gmlYVdKIIW5ESUwjSGAaoATT0BVMw6qS RgxxI0piGkEC0wAlmIauYBpWlTRiiBtREtMIEpgGKME0dAXTsKqkEUPciJKY RpDANEAJpqErmIZVJY0Y4kaUxDSCBKYBSjANXcE0rCppxBA3oiSmESQwDVCC aegKpmFVSSOGuBElMY0gEfNu5OFwONmNgs+yN5YumDvT5xk74r6kf6lpNpiG rhgxxI0oacQQN6IkpuEfrl+/Pnv27LVr17o/dPTo0cWLF+fm5ubn5x8+fPhO R4j5C2uJLAv/HznJzzzxXRk9Po8RQ9yIkpiGVSWNGOJGlMQ0/MPzzz+fkpIy Z86cmPVly5a1b98+pYF27drd6ckKXj3xGDGNBbmTkv5Z3GyMGOJGlMQ0rCpp xBA3oiSm4Qe++uqrp59+2hGJGNMoLS0VtejYsePy5csrKiqWLFki1hEKhcrK ytzHwTQ8BtOwrSSmYVVJI4a4ESUxjaTzwQcf9O7dO/qURYxp5OTkyGJRUVF0 pbCwUFZmzJjhPhSm4TGYhm0lMQ2rShoxxI0oiWkknbvvvlvMYdy4cXl5eTGm UV9fn5aWJovnz5+PLl68eDEUCnXq1Km2tjbmUJiGx2AatpXENKwqacQQN6Ik ppF0HnrooXfffTcSiSxfvjzGNE6ePCkr3bt3j7lJz549Zf3EiRMx65iGx2Aa tpXENKwqacQQN6IkpuEf3Kaxd+9eWRk2bFjMTlmR9d27d8esYxoeg2nYVhLT sKqkEUPciJKYhn9wm8aOHTtkJTMzM2bnqFGjZH3r1q0x65iGx2AatpXENKwq acQQN6IkpuEf7mQaI0eOjNmZlZUl6zt37oxZxzQ8BtOwrSSmYVVJI4a4ESUx Df/gNo39+/fLypAhQ2J2ZmRkyHp5eXnMOqbhMZiGbSUxDatKGjHEjSiJafgH t2lcunRJVtLS0iKRSHRRLnfp0kXWq6urY44gd2U4HH5fhUf/B8vANGwriWlY VdKIIW5ESUzDP7hNQ+jbt68sHjp0KLpSXl4uKwMGDHAfAdPwGEzDtpKYhlUl jRjiRpTENPyD0jTmzZsnixMnTrx586ZclX8nTJggKwsWLHAfAanwGEzDtpKY hlUljRjiRpTENPyD0jSqq6u7desm6yNGjBC7GD58uFzu3bu3+6WTW5iG52Aa tpXENKwqacQQN6IkpuEf3nrrLbGIF154IWa9qqoqKysrFAo5b1eemZl55MgR 5REwDY/BNGwriWlYVdKIIW5ESUzDFGpqag4cOHDu3Lkm9mAaHoNp2FYS07Cq pBFD3IiSmEaQwDQ8BtOwrSSmYVVJI4a4ESUxjSCBaXgMpmFbSUzDqpJGDHEj SmIaQQLT8BhMw7aSmIZVJY0Y4kaUxDSCBKbhMZiGbSUxDatKGjHEjSiJaQQJ TMNjMA3bSmIaVpU0YogbURLTCBJyV5aUlBxvRLIbtQmZ4yf8TWlp6ZwffS/p n8XNxoghbkRJTMOqkkYMcSNKYhpBQu7KvLy8cANG37OiGVNzxi/5xSN+zis/ /W9PfX9Y0j+Lm40RQ9yIkpiGVSWNGOJGlMQ0gkSQXj05ceKEjPKkf4I0nbP/ Z+7Tk+5Peo1mY8QQN6IkpmFVSSOGuBElMY0ggWl4HEzDtpKYhlUljRjiRpTE NIIEpuFxMA3bSmIaVpU0YogbURLTCBKYhsfBNGwriWlYVdKIIW5ESUwjSGAa HgfTsK0kpmFVSSOGuBElMY0ggWl4HEzDtpKYhlUljRjiRpTENIIEpuFxMA3b SmIaVpU0YogbURLTCBKYhsfBNGwriWlYVdKIIW5ESUwjSGAaHgfTsK0kpmFV SSOGuBElMY0ggWl4HEzDtpKYhlUljRjiRpTENIKE3JXPNiIcDie7UfxgGhpj xBA3oiSmYVVJI4a4ESUxjSDBcxoeB9OwrSSmYVVJI4a4ESUxjSCBaXgcTMO2 kpiGVSWNGOJGlMQ0ggSm4XEwDdtKYhpWlTRiiBtREtMIEpiGx8E0bCuJaVhV 0oghbkRJTCNIYBoeB9OwrSSmYVVJI4a4ESUxjSCBaXgcTMO2kpiGVSWNGOJG lMQ0ggSm4XEwDdtKYhpWlTRiiBtREtMIEpiGx8E0bCuJaVhV0oghbkRJTCNI YBoeB9OwrSSmYVVJI4a4ESUxjSCBaXgcTMO2kpiGVSWNGOJGlMQ0goTcleFw uKQBo+9ZTENjjBjiRpTENKwqacQQN6IkphEkHNMobeD48ePJbhQ/mIbGGDHE jSiJaVhV0oghbkRJTCNI8OqJx8E0bCuJaVhV0oghbkRJTCNIYBoeB9OwrSSm YVVJI4a4ESUxjSCBaXgcTMO2kpiGVSWNGOJGlMQ0ggSm4XEwDdtKYhpWlTRi iBtREtMIEpiGx8E0bCuJaVhV0oghbkRJTCNIYBoeB9OwrSSmYVVJI4a4ESUx jSCBaXgcTMO2kpiGVSWNGOJGlMQ0ggSm4XEwDdtKYhpWlTRiiBtREtMIEpiG x8E0bCuJaVhV0oghbkRJTCNIyF1ZUlJS2ohkN4ofTENjjBjiRpTENKwqacQQ N6IkphEk5K7My8uL/t0TTCPRwTRsK4lpWFXSiCFuRElMw+fs2bPnbRfbt29X bubVE4+DadhWEtOwqqQRQ9yIkpiGzxk7dmyKi9GjRys3YxoeB9OwrSSmYVVJ I4a4ESUxDZ+Tnp4eCoXmzJkztxHFxcXKzZiGx8E0bCuJaVhV0oghbkRJTMPP VFVVpaSkDBw4sIX7MQ2Pg2nYVhLTsKqkEUPciJKYhp/ZsGGDmMbUqVNbuB/T 8DiYhm0lMQ2rShoxxI0oiWn4mfz8fDGNuXPnFhQUTJ48efr06cXFxdeuXbvT fkzD42AatpXENKwqacQQN6IkpuFnJk2a5P5x0IyMjDNnzij3YxoeB9OwrSSm YVVJI4a4ESUxDT/Tv39/UYuhQ4euX7/+1KlT69at69evn6xkZ2dHIhH3fkzD 42AatpXENKwqacQQN6IkpuFnNm/evGLFipqamuhKRUVFamqqyEZ5ebl7P6bh cTAN20piGlaVNGKIG1ES0zCOzMxMMY3Vq1e7P4RpeBxMw7aSmIZVJY0Y4kaU xDT8zJUrVy5cuBCzOH78eDGNlStXuvfLXfnsHfCkr04wDY0xYogbURLTsKqk EUPciJKrXs15aOy3Zk77Bz9nas54o/+ER3yUlZWJUXTu3PnGjRvRxevXr/fo 0UPWlSeE5zQ8DqZhW0lMw6qSRgxxI0r+asGEjct+kPQaTUdO48rf/ibZo89r 6urqunbtKlJRWFgYXVy4cKGsDB48uLa21n0TTMPjYBq2lcQ0rCppxBA3oiSm 4WdWrVrl/GZrTk7OokWLsrOz5XKHDh22bt2q3I9peBxMw7aSmIZVJY0Y4kaU xDR8zpo1a3r16hV9M41BgwZt2bLlTpsxDY+DadhWEtOwqqQRQ9yIkpiG/4lE ImfPnt23b19lZWXTOzENj4Np2FYS07CqpBFD3IiSmEaQwDQ8DqZhW0lMw6qS RgxxI0piGkEC0/A4mIZtJTENq0oaMcSNKIlpBAlMw+NgGraVxDSsKmnEEDei JKYRJDANj4Np2FYS07CqpBFD3IiSmEaQwDQ8DqZhW0lMw6qSRgxxI0piGkFC NCMvLy/cQElJyZ12/npF8dLXFvk5L/zkxwt//GDSH3tNB9OwrSSmYVVJI4a4 ESUxjSAhpiF2cbyBy5cvK7edOHFiztPj5dPcz3nvlz94cea3k/7YazqYhm0l MQ2rShoxxI0oiWkEiRa+emLECxN71vwY09AVI4a4ESUxDatKGjHEjSiJaQQJ TMPjYBq2lcQ0rCppxBA3oiSmESQwDY+DadhWEtOwqqQRQ9yIkphGkMA0PA6m YVtJTMOqkkYMcSNKYhpBAtPwOJiGbSUxDatKGjHEjSiJaQQJTMPjYBq2lcQ0 rCppxBA3oiSmESQwDY+DadhWEtOwqqQRQ9yIkphGkMA0PA6mYVtJTMOqkkYM cSNKYhpBAtPwOJiGbSUxDatKGjHEjSiJaQQJ5z1CSxuh3IZp6AqmYVtJTMOq kkYMcSNKYhpBwvm7JyUNYBqJDqZhW0lMw6qSRgxxI0piGkGCV088DqZhW0lM w6qSRgxxI0piGkEC0/A4mIZtJTENq0oaMcSNKIlpBAlMw+NgGraVxDSsKmnE EDeiJKYRJDANj4Np2FYS07CqpBFD3IiSmEaQwDQ8DqZhW0lMw6qSRgxxI0pi GkEC0/A4mIZtJTENq0oaMcSNKIlpBAlMw+NgGraVxDSsKmnEEDeiJKYRJDAN j4Np2FYS07CqpBFD3IiSmEaQwDQ8DqZhW0lMw6qSRgxxI0piGkHCeTfyy41Q bsM0dAXTsK0kpmFVSSOGuBElTTGNpf9z8WXf47FXuHHejTyKWIdyG6ahK5iG bSUxDatKGjHEjShphGlsWv7Dyd/71oLcSX7OU1OyP9qxzWO1iIFXTzwOpmFb SUzDqpJGDHEjShphGu++PvWtf5yY9BpNxw8v8WAaHgfTsK0kpmFVSSOGuBEl MQ1dwTT0BtPQGCOGuBElMQ2rShoxxI0oiWnoCqahN5iGxhgxxI0oiWlYVdKI IW5ESUxDVzANvcE0NMaIIW5ESUzDqpJGDHEjSmIauoJp6A2moTFGDHEjSmIa VpU0YogbURLT0BVMQ28wDY0xYogbURLTsKqkEUPciJKYhq5gGnqDaWiMEUPc iJKYhlUljRjiRpTENHQF09AbTENjjBjiRpTENKwqacQQN6IkpqErmIbeYBoa Y8QQN6IkpmFVSSOGuBElMQ1d8Ylp5OXlhRvg3cgTHUzDtpKYhlUljRjiRpTE NHTFJ6YhdnG8gTv9KRZMQ1cwDdtKYhpWlTRiiBtREtPQlcSZxtGjRxcvXpyb m5ufn3/48OGmTYNXT7wMpmFbSUzDqpJGDHEjSmIaupIg01i2bFn79u1TGmjX rl04HL7TZkzD42AatpXENKwqacQQN6IkpqEriTCN0tJSUYuOHTsuX768oqJi yZIlYh2hUKisrKwtpnH8+PGlBbOTfsaajhGmUf1/X8z7WW7SazQbI4b4s88+ m/QOzcYI05DP7mObFia9RtMxwjRWvj5n9+9/kfQaTccI03jvf82TJL1G0zHC NOQBeaefwIybnJyclJSUoqKi6EphYaGszJgxQ7kf0/A4mIbGYBq6gmnoCqah K5iGrmg3jfr6+rS0NPGK8+fPRxcvXrwYCoU6depUW1uLaSQ9mIbGYBq6gmno CqahK5iGrmg3jZMnT4pmdO/ePWa9Z8+esn7ixAn3TTwzDZmw8jmY0CO0xDTa 7kttPw9tNw0P/hfNmkbSz2SdDtOQR5TcIwk9QrOm4UGHltwXbTQNGQqJPkKz puFBh5bcF200DQ/+F82ahvwX2vi/aPsR2m4abe8gp7HpDs2aRrNHaHuHlpwH vaaxd+9eMYphw4bFrMuKrO/evdt9E89MQ05Xoo/QrGm0fcoH4wh1LZjRzZpG 26e8H44gZ7KNM7rZIzRrGh50aDZtNw0PjtCsabS9Q9s9wYgjNGsabZ/ywTiC nMamv71t1jSaPULbO7TkCHpNY8eOHWIUmZmZMeujRo2S9a1bt7pvgmm0KsE4 Qh2m0RBMwwmm4cQIT2j7ETCNFgbTUOKYxsiRI2PWs7KyZH3nzp3um2AarUow jlCHaTQE03CCaTgxwhPafgRMo4XBNJTs379fjGLIkCEx6xkZGbJeXl7uvolo Rjgcfr85pKd8NXPuuPgi5yrRR1jx6swXZk5ObgcjjiCRGd30hqk549t4hLZ3 8OAIciblfCb0CLnTH1655PnkdgjGETw4k+IqksAf4ZVfPLn0xemm/y88OEJw hk5eXrNTvoVPOwiXLl0So0hLS4tEItFFudylSxdZr66ujts0AAAAINi08GmN vn37ilQcOnQoulJeXi4rAwYMUO5v1cEBAADAcubNmydeMXHixJs3b8pV+XfC hAmysmDBAuV+TAMAAABaTnV1dbdu3UQtRowYIXYxfPhwudy7d2/lSye3MA0A AABoJVVVVVlZWaFQyPkLa5mZmUeOHLnTZkwDAAAA4qCmpubAgQPnzp1rehum AQAAAIkD0wAAAIDEgWkAAABA4jh+m2S3AAAAAIAWcfTo0cWLF+fm5ubn5x8+ fLglN/noo48KCgpmzZpVVFR0+vTpRDc0hTjOZJTNmzf//Oc/b+2tgkprz+Se PXvedrF9+3YPqvqcOB6Tf/7zn3/5y1/Onj174cKF77zzTn19faJLmkILT+Zf /vKXNXem2Z9FtIHWPiwPHDggs+YnP/mJ/Hvw4EEPGoJeli1b1r59+5QG2rVr Fw6Hm9hfV1f32GOPpTQiNTW1uLjYs8K+pbVnsjFVVVV/8zd/I7dau3ZtQksa QRxncuzYsSkuRo8e7U1h3xLHmdy4caPzFs1RRowY8e///u/eFPYzLT+ZR44c cT8ao6xbt87j5n6jtQ/Ln/3sZ7Kn8X75psyzttB2SktL5V7r2LHj8uXLKyoq lixZIg+AUChUVlZ2p5u8/PLLcl/36dPn/fffP3Xq1Jtvvik3kYNY7plxnMko Im/f/va3nU8iTCO+M5meni575syZM7cRlgtwHGfyxIkTX//61+VxKF/JKysr t23bNmjQILn6/PPPe9nch7TqZFZXV8918a1vfUvOpFjcyZMnve/vH1r7sFy/ fr2ct06dOv32t7+VibNy5cq77roLYTOLnJwcucuKioqiK4WFhbIyY8YM5f5I JCKOIRvkS1B0cebMmbIyf/78hNf1Ma09k4157bXXorqOacRxJquqqmTDwIED PSloDHGcyaeeeko2zJ49O7py4MABWfnbv/1by19Dacsn+K3bD9GuXbvKPP2X f/mXhHU0g9aeyenTp8tH5Tvc6MrixYtl5Yc//GHCu4IO5EtHWlqa3GXnz5+P Ll68eFE+HUQga2tr3Te5du3aG2+8kZub2/jLzksvvSQHee6557wo7UviOJNR Dh48mJqaKt85Tpo0CdOI70xu2LBBbjJ16lSvahpAHGeyrq5OvuOW7zdj3p95 06ZNu3fvTnhjH9OWT3CHcePGtVxLAkwcZ/L73/++7P/Nb34TXfnnf/5nWcnO zvaiMbSZkydPyv3VvXv3mPWePXvK+okTJ1pyEHnADBgwQPavWrUqAR3NIO4z efXq1cGDB3fo0GHfvn1PPPEEphHfmczPz5ePzp07t6CgYPLkyfJNUHFxsVhx 4vv6lzjO5L/+67/Kh+6///5IJLJjx47XXntt2bJle/bs8aSvr2njl0r5pJZt 3/jGNy5fvpywjmYQx5l0nvKVh+WXX34pV69cuTJy5EhZWbhwoReNoc3s3btX 7q9hw4bFrMuKrDf7XcyaNWsef/xx5yWzRx99tCViH1TiPpO5ubmyQQalXMY0 bsV7Jp2ng2LIyMg4c+ZM4iv7lDjO5ObNm51vFb/zne80PpNPPvnkf/zHf3jS 2qe05UtlXV3dvffeK9t+9atfJbKjGcRxJuXbMeeProqqjR8/vkePHnL5u9/9 riMe4H/k25aU239lJmZ91KhRsr5169amby5fkaJfi6ZNm2bz/R7fmdy0aVMo FBoxYoQjaZjGrXjPZP/+/eWjQ4cOXb9+/alTp9atW9evXz9naMq354lv7Ufi OJPOt96CPCyfeeaZP/zhDwUFBZ06deKnsNrypVJOo+yR79kttzWHOM7kX//6 19mzZ8d8HzFr1izOpyk4d/rIkSNj1rOysmR9586dTd/8s88+++KLL7Zt2zZ4 8GDZP3bs2EQV9T1xnMnq6mqRc/kyHn0bOkzjVryPSflmfMWKFTU1NdGVioqK 1NRUuUl5eXni2vqZOM7k22+/7XwZf+WVV6KLH374Ycrt32T/y1/+ktDCfqYt Xyqdp/ob/wCkzcRxJh955BH50De/+U356L/927/Jv/I9hazIuheNoc3s379f 7q8hQ4bErGdkZLTqS3RlZaXzVV2+vOvuaAZxnMkpU6bIhyZPnry1gTFjxshK Xl6eXJZPKE+K+w5dj0lBvm+Sm6xevVprQWOI40y+9957KbffrEC+i2y8ft99 98n63r17E1jX38T9sDx37lwoFJJT2vgHIG2mtWfyzJkzcgI7dux46tSp6OLZ s2edV+2PHTuW8MbQZi5duiR3VlpaWuNnmOWy8749MT9/7lBfXy+fO0eOHIlZ d57W2LhxY2Ib+5U4zuTAgQPdP1oQpdmXroJKHGfy1u0fErtw4ULM4vjx4+Um K1euTGBdHxPHmZSv8/KhDh06xPzM1QMPPCDrmzZtSnhpvxLfw/LW7beoSuG3 JBrR2jO5ZcsWpZk8+OCDKbylhjn07dtX7q9Dhw5FV5yvNgMGDFDuP3z4sHw0 PT395s2b0UX5uiQrsm7zm3e19ky+8sor//0/43zn+L3vfU8ut/AXfwJJa89k WVmZfLRz5843btyILl6/ft35ybHS0tKEN/YrrT2TctKcn8po/Iksn93Omfz0 008T3tjHtPZkOkyePFn2FBYWJr6gMbTqTH7yySfyobvvvjvm7Vz+7u/+TtZ3 7NiR8Lqgg3nz5sn9NXHiRMcc5F/np3wXLFjgbPjyyy937969b98+56rc3d27 d5cNS5cujR7kxRdfTLn9i0s2//pJa8+kG35Ow6G1Z7Kurq5r164xX88XLlwo K4MHD+Yx2arH5LRp02RDVlZW9HeE5TM95fa7oln7s7UO8X2C9+rVS/Z88MEH SWjsV1p1JuWjzmf3P/3TP0WP4PyQrVgxb5JvCtXV1d26dUu5/acN5I4ePny4 XO7du3f0WSz5HHGexIje5J133km5/dPpU6ZMWbRokfOONHLV5idXb8V1JmPA NBziOJOrVq1yXnXKycmRx6TzW1EdOnSw9kUohzjO5Llz55yb3H///fn5+c5P E8ln93vvvZek/4RfiONk/vWvf3Ueljb/trWb1p7J3//+985p/M53viPf1U6d OtX5GygrVqxI3n8CWk1VVZV8CyNfTJx7MzMzs/GPYYg/yKJYZeObyDSUB0b0 hwoyMjJ27drleXHfEceZbMyTTz4pG0TkPCnra+I4k2vWrHG+f3QYNGjQli1b PC/uO+I4k8ePHx8zZkz0Jn379uUx6dDak+m8SX7nzp0tfzrITWvPpIiu8/6Q DjJ9RD+SURzaSk1NzYEDB1r+54zlc0ceLaWlpZcuXUpoMeNo7ZmEOxHHY/Ls 2bP79u2rrKxMaDHjiOMx+fnnn8uZPHbsmM0vPynhE1wXrT2Tf/7zn2W/PDIT 2goAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAg2Pw/GLNN 0Q== "], {{0, 226.}, {359., 0}}, {0, 255}, ColorFunction->RGBColor, ImageResolution->144.], BoxForm`ImageTag["Byte", ColorSpace -> "RGB", Interleaving -> True], Selectable->False], DefaultBaseStyle->"ImageGraphics", ImageSizeRaw->{359., 226.}, PlotRange->{{0, 359.}, {0, 226.}}]], "Output", TaggingRules->{}, CellChangeTimes->{{3.8343148415259123`*^9, 3.834314864176794*^9}, 3.834314907074937*^9, 3.834405427974238*^9}, CellLabel->"Out[5]=", CellID->938245471] }, Open ]] }, Open ]], Cell[CellGroupData[{ Cell[BoxData[ InterpretationBox[Cell["\t", "ExampleDelimiter"], $Line = 0; Null]], "ExampleDelimiter", TaggingRules->{}, CellID->595150754], Cell["\<\ Use the \"MatchIt Lalonde\" data and compare the mean values of various \ covariates among the treated and untreated groups:\ \>", "Text", TaggingRules->{}, CellChangeTimes->{{3.834317953954956*^9, 3.834317991339834*^9}, { 3.834318139729558*^9, 3.834318139787497*^9}, 3.8343183976199512`*^9, { 3.83431861745956*^9, 3.834318636043727*^9}}, CellID->53006316], Cell[BoxData[ RowBox[{ RowBox[{"lalonde", "=", RowBox[{"ResourceData", "[", RowBox[{ TagBox["\"\\"", #& , BoxID -> "ResourceTag-Job Training Efficacy-Input", AutoDelete->True], ",", "\"\\""}], "]"}]}], ";"}]], "Input", TaggingRules->{}, CellChangeTimes->{{3.834318004164736*^9, 3.834318012896844*^9}, { 3.834318052642558*^9, 3.834318059732793*^9}}, CellLabel->"In[1]:=", CellID->557350363], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{ RowBox[{ InterpretationBox[ TagBox[ DynamicModuleBox[{Typeset`open = False}, FrameBox[ PaneSelectorBox[{False->GridBox[{ { PaneBox[GridBox[{ { StyleBox[ StyleBox[ AdjustmentBox["\<\"[\[FilledSmallSquare]]\"\>", BoxBaselineShift->-0.25, BoxMargins->{{0, 0}, {-1, -1}}], "ResourceFunctionIcon", FontColor->RGBColor[ 0.8745098039215686, 0.2784313725490196, 0.03137254901960784]], ShowStringCharacters->False, FontFamily->"Source Sans Pro Black", FontSize->0.6538461538461539 Inherited, FontWeight->"Heavy", PrivateFontOptions->{"OperatorSubstitution"->False}], StyleBox[ RowBox[{ StyleBox["MapReduceOperator", "ResourceFunctionLabel"], " "}], ShowAutoStyles->False, ShowStringCharacters->False, FontSize->Rational[12, 13] Inherited, FontColor->GrayLevel[0.1]]} }, GridBoxSpacings->{"Columns" -> {{0.25}}}], Alignment->Left, BaseStyle->{LineSpacing -> {0, 0}, LineBreakWithin -> False}, BaselinePosition->Baseline, FrameMargins->{{3, 0}, {0, 0}}], ItemBox[ PaneBox[ TogglerBox[Dynamic[Typeset`open], {True-> DynamicBox[FEPrivate`FrontEndResource[ "FEBitmaps", "IconizeCloser"], ImageSizeCache->{11., {2., 9.}}], False-> DynamicBox[FEPrivate`FrontEndResource[ "FEBitmaps", "IconizeOpener"], ImageSizeCache->{11., {2., 9.}}]}, Appearance->None, BaselinePosition->Baseline, ContentPadding->False, FrameMargins->0], Alignment->Left, BaselinePosition->Baseline, FrameMargins->{{1, 1}, {0, 0}}], Frame->{{ RGBColor[ 0.8313725490196079, 0.8470588235294118, 0.8509803921568627, 0.5], False}, {False, False}}]} }, BaselinePosition->{1, 1}, GridBoxAlignment->{"Columns" -> {{Left}}, "Rows" -> {{Baseline}}}, GridBoxItemSize->{ "Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}, GridBoxSpacings->{"Columns" -> {{0}}, "Rows" -> {{0}}}], True-> GridBox[{ {GridBox[{ { PaneBox[GridBox[{ { StyleBox[ StyleBox[ AdjustmentBox["\<\"[\[FilledSmallSquare]]\"\>", BoxBaselineShift->-0.25, BoxMargins->{{0, 0}, {-1, -1}}], "ResourceFunctionIcon", FontColor->RGBColor[ 0.8745098039215686, 0.2784313725490196, 0.03137254901960784]], ShowStringCharacters->False, FontFamily->"Source Sans Pro Black", FontSize->0.6538461538461539 Inherited, FontWeight->"Heavy", PrivateFontOptions->{"OperatorSubstitution"->False}], StyleBox[ RowBox[{ StyleBox["MapReduceOperator", "ResourceFunctionLabel"], " "}], ShowAutoStyles->False, ShowStringCharacters->False, FontSize->Rational[12, 13] Inherited, FontColor->GrayLevel[0.1]]} }, GridBoxSpacings->{"Columns" -> {{0.25}}}], Alignment->Left, BaseStyle->{LineSpacing -> {0, 0}, LineBreakWithin -> False}, BaselinePosition->Baseline, FrameMargins->{{3, 0}, {0, 0}}], ItemBox[ PaneBox[ TogglerBox[Dynamic[Typeset`open], {True-> DynamicBox[FEPrivate`FrontEndResource[ "FEBitmaps", "IconizeCloser"]], False-> DynamicBox[FEPrivate`FrontEndResource[ "FEBitmaps", "IconizeOpener"]]}, Appearance->None, BaselinePosition->Baseline, ContentPadding->False, FrameMargins->0], Alignment->Left, BaselinePosition->Baseline, FrameMargins->{{1, 1}, {0, 0}}], Frame->{{ RGBColor[ 0.8313725490196079, 0.8470588235294118, 0.8509803921568627, 0.5], False}, {False, False}}]} }, BaselinePosition->{1, 1}, GridBoxAlignment->{"Columns" -> {{Left}}, "Rows" -> {{Baseline}}}, GridBoxItemSize->{ "Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}, GridBoxSpacings->{"Columns" -> {{0}}, "Rows" -> {{0}}}]}, { StyleBox[ PaneBox[GridBox[{ { RowBox[{ TagBox["\<\"Version (latest): \"\>", "IconizedLabel"], " ", TagBox["\<\"1.0.0\"\>", "IconizedItem"]}]}, { TagBox[ TemplateBox[{ "\"Documentation \[RightGuillemet]\"", "https://resources.wolframcloud.com/FunctionRepository/\ resources/MapReduceOperator"}, "HyperlinkURL"], "IconizedItem"]} }, DefaultBaseStyle->"Column", GridBoxAlignment->{"Columns" -> {{Left}}}, GridBoxItemSize->{ "Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}], Alignment->Left, BaselinePosition->Baseline, FrameMargins->{{5, 4}, {0, 4}}], "DialogStyle", FontFamily->"Roboto", FontSize->11]} }, BaselinePosition->{1, 1}, GridBoxAlignment->{"Columns" -> {{Left}}, "Rows" -> {{Baseline}}}, GridBoxDividers->{"Columns" -> {{None}}, "Rows" -> {False, { GrayLevel[0.8]}, False}}, GridBoxItemSize->{ "Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}]}, Dynamic[ Typeset`open], BaselinePosition->Baseline, ImageSize->Automatic], Background->RGBColor[ 0.9686274509803922, 0.9764705882352941, 0.984313725490196], BaselinePosition->Baseline, DefaultBaseStyle->{}, FrameMargins->{{0, 0}, {1, 0}}, FrameStyle->RGBColor[ 0.8313725490196079, 0.8470588235294118, 0.8509803921568627], RoundingRadius->4]], {"FunctionResourceBox", RGBColor[0.8745098039215686, 0.2784313725490196, 0.03137254901960784], "MapReduceOperator"}, TagBoxNote->"FunctionResourceBox"], ResourceFunction[ ResourceObject[ Association[ "Name" -> "MapReduceOperator", "ShortName" -> "MapReduceOperator", "UUID" -> "856f4937-9a4c-44a9-88ae-cfc2efd4698f", "ResourceType" -> "Function", "Version" -> "1.0.0", "Description" -> "Like an operator form of GroupBy, but where one also specifies a \ reducer function to be applied", "RepositoryLocation" -> URL["https://www.wolframcloud.com/obj/resourcesystem/api/1.0"], "SymbolName" -> "FunctionRepository`$ad7fe533436b4f8294edfa758a34ac26`\ MapReduceOperator", "FunctionLocation" -> CloudObject[ "https://www.wolframcloud.com/obj/6d981522-1eb3-4b54-84f6-\ 55667fb2e236"]], ResourceSystemBase -> Automatic]], Selectable->False], "[", RowBox[{ RowBox[{ RowBox[{"(", RowBox[{ RowBox[{"If", "[", RowBox[{ RowBox[{"#treat", "==", "1"}], ",", "\"\\"", ",", "\"\\""}], "]"}], "&"}], ")"}], "->", RowBox[{"KeyDrop", "[", RowBox[{"{", RowBox[{"\"\\"", ",", "\"\\"", ",", "\"\\""}], "}"}], "]"}]}], ",", RowBox[{"Merge", "[", RowBox[{"Mean", "/*", "N"}], "]"}]}], "]"}], "[", "lalonde", "]"}]], "Input", TaggingRules->{}, CellChangeTimes->{{3.834318086442609*^9, 3.834318111572878*^9}, { 3.834318144579329*^9, 3.834318368734473*^9}}, CellLabel->"In[2]:=", CellID->1070301680], Cell[BoxData[ GraphicsBox[ TagBox[RasterBox[CompressedData[" 1:eJztnQeUFMX69odgAhEQUDGhKCiKSBREJKgIFwxIUFFRAUHlKvgXQRAJKkkQ BSQpV5KSBEQEye5Fcs5RwsLCAktagldAwfneb95jnbarp6d6pmf23fX5nQNn p7q6u7qeequeTtW3NmtTv2X2QCDQ9nL6r37Td2u8/XbTjg3y0Y9Grdu+/mrr Fq/8q/U7LV5t8XalZjkocTX9S6E//v/fQQAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAgBv4LAAAAAAAAAAAAAAAAAAADTgMQAu1BJtBFJtBFJtBFJtBF JtBFJnDpQAftQSbQRSbQRSbQRSbQRSbQRSZw6UAH7UEm0EUm0EUm0EUm0EUm 0EUmcOlAB+1BJtBFJtBFJtBFJtBFJtBFJnDpQAftQSbQRSbQRSbQRSbQRSbQ RSZw6UAH7UEm0EUm0EUm0EUm0EUm0EUmcOlAB+1BJtBFJtBFJtBFJtBFJtBF JnDpQAftQSbQRSbQRSbQRSbQRSbQRSZw6UAH7UEm0EUm0EUm0EUm0EUm0EUm cOlAB+1BJtBFJtBFJtBFJtBFJtBFJnDpQAftQSbQRSbQRSbQRSbQRSbQRSZw 6UAH7UEm0EUm0EUm0EUm0EUm0EUmcOlAB+1BJtBFJtBFJtBFJtBFJtBFJnDp QAftQSbQRSbQRSbQRSbQRSbQRSZw6UAH7UEm0EUm0EUm0EUm0EUm0EUmcOlA B+1BJtBFJtBFJtBFJtBFJtBFJnDpQAftQSbQRSbQRSbQRSbQRSbQRSZw6UAH 7UEmidFl0aJF06dPnzNnTrx3lGVAvJiQ+HYlU5eUlJTpIbZv357RZckYZOoC oItM4NKBDtqDTBKjy4MPPhgIBAoVKhTvHWUZslK8bNiw4ZVXXnnttde2bdvm 75YT365k6jJr1qxAiM8++yyjy5IxyNQFQBeZwKUDHbQHmcClyyQrxUvdunXZ Q7788sv+bhkunYFLT4wuycnJiyNx5MiRcKtv3bqV8+zbty/eRRWCTF2WL18e Ltvx48fjXVoJwKUDHbQHmcClyyQrxUuTJk3YQ7Zo0cLfLcOlM3DpidHl448/ DkTi888/d1x39+7dV199NecZPnx4vIsqBJm6XHbZZeGyjRkzJt6llQBcOtBB e5AJXLpMslK87Nmz56OPPurZs6fvlxDh0hm4dDlucNSoUY7rPvHEEyoPXLq/ eNLlyJEjUciXxYBLBzpoDzKBS5cJ4sUEuHQGLj0xuqSmpm52YsmSJZdffjnV f8WKFU+ePKmvOHLkSKsVhEv3F0+67Nixg1X48MMP9VUOHz4c79JKAC4d6Hht Dxs3bpw5c+Yvv/zinm3//v3z5s2bO3fu3r17I25z27ZtNJzt2bPHvBhZHq+6 GNbhoUOHSJR169adOnWKflavXt1HN0XbXL9+/ezZs1NSUnzZoECi6D+PHz9O sbBz50590bJlyxYtWnT06FH3LRgGHUOhRxIkJydHsdQGFSwpKWnp0qUnTpxw zxnXdmVCdOPaypUr6QCp8O7ZKKwouLZs2WKyTaoEqoqDBw/S3/RHRJduErnk ZNauXTtjxgwqMNdwRMIJba6peU4XotAl9pIrmjdvTpV/xRVXkCj60l27dvGz LuXLl4dLj0i8dVmxYgWrMHHiRE8Fy0rApQMdk/ZA48Lo0aMffPDBvHnzqssO 11133dSpU/XMCxYsoBNklS1btmz5LNSrV8+62Z49e9J2VOYbbrhh5MiRPh9h 5sQwTs3rkAxhhQoVsmfPztlIiy5dutSuXdvmpg4fPkxbo6VvvvmmbQvvv/8+ i0ijm23R7t27GzZsmCdPHlWMYsWKff31196PWzomupQqVYpqafDgwVRR9evX v/LKK7lOSpYsOXbsWMpAw9k777xz0003cXqOHDk6depkc1/mQce7Gzp06I4d Ox577LFLL72UchYsWDDiUmonLKg+ZeKGDRvIaV9yySW8X1qL1nX09obtKt54 0oX8xosvvnjNNdeoPqpp06bHjh3TV/nkk08ooFT9k6lr06aNY8709PR27doV KFBAbbNSpUpffvllOJduGLlHjhyhtpE7d26VjbyNtUf9+eefrUcXrhmc9qKp ec6ImOviS8mtzJ8/n1Sg/L1793bMQBuhpbTZadOmwaXrJFgXOgXg7fyTDSpc OtCJ2B4OHDhA7iLgBMXaDz/8YM1MgabGa/Jp1gGOqVmzJudMS0ujv1U63/9i OnToEL/jzSyYxKl5HY4aNcq61IbVTaWkpHBi8+bNbbtr27YtL7Jd1KUxTr1+ ZeOrr76KrRrEYaLLLbfcQsdOA5by4Yqrrrpq1apVjz76qF5X/fr1U1vwFHS8 u/bt25ctW1Zlq1WrVsSlX3zxBf+cMWOGdYMTJkxQJ1w5c+akkwj++/rrr9+6 das1p3m7ijfmujzxxBO33XabXtrWrVtbM1NwPfLII47Hdffdd9vmrkxNTa1S pUq4eghoLt0wcsnJP/zww5xOJ2u0X/31up9++sl6dOGagbmm5jlNMNcl9pLb KF26NGWj/x3vPlDXxNt5//33161bx3/DpVtJsC7jx4/nLRjetMqSwKUDHZP2 UK1aNYqdBg0aTJw4cefOnWTSlGF74IEHVLYTJ07ceOONlEjBS3adE2l44pzt 2rVbvXq1Gt0o9jm9fv36nLhx40Z+nJVCnv6Oy9FmHkx0MazDXbt2qcu5L7/8 8tKlS3fs2DFmzJgiRYpwYiwufd++fQULFuT0Ro0a/fzzz+RYpk+fXqpUKerb Iz7LkekwH92YevXqkalesWKF9SU1gupnxIgRa9as6dixI6fQWtaNGAadbXf3 3HPPgAEDFixYsHjx4ohLHV16cnJy/vz5KbFAgQLUSI4dO0YiDho0iJ1kkyZN VE5P7SreeNWldu3a33zzDdmz3r17s8GgEyjrs6+qtosXL04K7t+/PykpiZ/k If71r39Zt9ymTRtOv+6662izu3fvXrRo0SuvvKJ2Z3PphpFLYnG2p59+mstG mdnk0HkxVTj1qGoiOxehzTU1zxknXaIuuQ11YfbLL7/Ul6pnXcqVK5eenk5h yJnh0q0kWJchQ4bwIvL/FH0vvPAChQmdTNEpbezHm1mASwc6Ju2BxjJ1X1Wh Biz1EDKNGpzy4YcfWnPytMxkS1TK2rVr+fZZnTp1rDkPHTrEnafvU8NlOiLq Yl6HasK9t956y5azRIkSgdhcutr4G2+8Yc18/PhxMjZejjhz4Gl069Spk0ok l6WeXalVq5b1WWi2Z4T1ZrFh0Fl3V7lyZf20yGWpo0unwZFSsmfPTiOyNTMF dSD0cI56wN5Tu4o3nnSh0d96He+ZZ57hdOrBOIXcL9/Hv/XWW61VTa1aSTB+ /HhOXL9+PWemuLNdYx86dChntrp088jlRzIo0Xr6sGTJElsBbEenC22uqXlO QzzpEkvJbTz11FPsIR0vFPCQRJaShKafcOmOJFiXnj17Bpy44YYbbE09CwOX DnSibg/du3fnIKJRg1PUoD9r1ixrzm7dugVC14jS09M5hWOZ2Lx5s22zr7/+ OqVXqlQpiiJlJSLqYl6HfMGzYMGC+lty+lwcnlw6WR1+YpY2zq/LZXnMR7eK FSva0tUTFLYHmzt37mwLpXDoQad2ly1bNrYcjoVxXKq7dBI0V65clPL888/b MquGoZ638dSu4k0sugwePJgPbdKkSZzywQcfcMqgQYNsmZOSknjR008/zSlK FNvVidNh5ngxj1wuMFWmLRs/9/Lee+/pR6cLba6pJ/UNMdcllpLb2LFjBw03 tJS6LH0pWXFeVz0XDZfuSIJ1US79/vvvb9OmTevWrUuVKsUpdFa7fPlyb0eY OYFLBzqe2sOKFSsGDBhAsVm6dGl1s/u7777jpTNnzuQUfkVOQeEWCF0RUinP PvtsIPSk5XwNPkm/9tprfTq+zEpEXQzrcOvWrSzKSy+9pG8kRpe+ceNGTmnW rFmsB5xJMB/dKleubEt/7rnnuLrU6SozcOBATqcI0rfmHnSnw5vPiEt1l75h wwZOoZjV2xUvosKc9t6u4k0supA552NRr1FwcBEHDhzQt8PP9d1zzz38U91T IBNiy+no0s17P37W/e6777Zuk06HeZt9+vTRj04X2lxT85zmxHL2FHV5+vbt y0spdmyLdu7cyY9qUN2q+ylw6Y4kUheGAnDatGnq58mTJ9u1a8er6GGbJYFL BzqG7YHCh5+H1Jk8eTLnOXr0KF/kqVatmlpx//79PJdCjRo1VGK4TSly5Mjh +5FmLiLqYliHZOf4p36h73TMLn3ChAmc0qtXr1gPOJMQixt88cUXubpsLl09 F2Fz6SZB57K7iEt1l65e4HKhc+fOp723q3gTiy7ff/89H4ty6ffeey/9JDvn uB02z5deeinrWKFChUBo6hU9p6NLN+/91DsL1ruT6jr/3LlzTY7OXFPznObE okvU5alfvz4tolNafY70OnXq8Irz5s375S/mzJnDiWQj6WdqaqqnY8yMSNMl HJSTTodpLbIWtm4zSwKXDnRM2oO6FUUDU8OGDUeMGLF69WplLayGoUOHDpz4 6KOPfvPNNyNHjrzjjjsCobtm1oHmvvvu47irHAY1Fcw/loi6GNah6lE//vhj fSNVq1alRXQapVI8ufSvv/6aUz755JNYDziTkDCXbh50Prp0illOKVasWLh2 xR8B9Nqu4o2/Lp2DyzrpnBV+sTdnzpw8QTTfl8+XL5+eU70r179/f5Vo3vvt 2LHj2muvDYTebKUT4W+//bZ169Y8iVaNGjVsU2SEOzpzTc1zmhOLLlGXp3Dh wgGnJ4VWrlzp5iz/ItxtqayEKF3cefPNN3l3jk/0ZTHg0oFOxPagbgfT4GL9 9IaaCthqGA4dOsT3E63kzp3bdv+Lz6nz5s1r+IWOfyARdTGsQ/WuWZs2bfSl LtfS9ScZdJeuRr1WrVqZHlgmJzEu3VPQ+ejSaRzkFDpHcD9Gr+0q3vjr0hs0 aMApjh884u3ceeed/JNfRST0zyM6Xkv31Pt17dpVt5F0CPrbeeGOzlxT85zm xKJLdOXZtGlTuJapNuhOmTJlzHeXSRGlizvqoZeIr+1kAeDSgU7E9tCiRQuO kd27d1vTHQ0Dz5ZQpUqV999/n0a6Jk2a9OrVS39cs1OnTryu7aYtUETUxbAO 9+/fz9lKly6tL9Xd1PHjx/mTE+Q9bJnJivOmlEunzDyLHfXn9LfpsWVmEuPS PQWdjy79xIkTPF1J9erV3Y/Ra7uKN/66dPU+r/6ZIfWFRDLbnKLiQv9moqNL N+/96CyY5KAQ69atW9OmTevVq/f222+H+zJjuKMz19Q8pzmx6BJdedRjeI5z MKakpOzVUE9T9+/fn346voyQxZCmiwv8dQna4z9hiIFLBzoR2wPPBkbOzXr1 5tixY40bN7YZhrS0tCuuuCJgmSohHAsXLmQrWLJkyX9C6EVBRF3M65CnxSNs n638+eefeeY3m5vi9wiuvfZa6xefR48erb5bYZ2JUX2cpUePHrb9rlq1KuJh ZjoS49LNg85ldxGXOs7EqK4MR3y2wWu7iiv+uvTFixdzcNExWqdrPnXqlKof 9aaheu6rUqVKVmXp75deekl36eaRy8+lly1bNvLxuwptrql5TkNi0SW68qhJ 5h0/je0I3h51JJG6LFq06O6771ZfeVD8+OOPvJbj1YCsB1w60InYHv7v//6P w6RZs2bJyck0rMyZM6dMmTKBv1CGITU1lZ+ZrFOnzubNm8njnQrhuNlXX32V V7/33nupAPw6yY4dOzp06EDdwj/hPRF3TOLUsA6Vi8iVKxcNQ/v379++fXu/ fv34lEp3U2rCwObNm5NLXLZsGW1QfVI28HeXTgOc+kJ0mzZtSHfaL3W5Tz31 FFmRrHevJDEu3TzoXHYXcamjS9+yZQtPs5YzZ87OnTvzbOF0gjBt2rRq1aqN GDFC5fTaruKKvy79tCW4yCSTrybJ1q5dqz5NVaVKFZWTujgyGJxeq1YtOjk9 evQotfyHHnpI6WX7qpFh5PLHkujseNKkSdS7unSnLkd32oum5jkNidENRlGe 9957j+uWVDMsJFy6I4nUhd9fu+yyy959993Zs2cfOXLkwIEDZOzV502nTJkS 1YFmMuDSgU7E9kAmjb/BEQhd3OOrQIHQY5m6YbB+9lpBo0zBggUbNWr07bff qpwUg5UqVVJ5Lr/8cvXNl0BW/LK8V0zi1LAOaWR3/CQ9wXPK2dwUCarnvPLK K9966y3+2+rST4fm11KX2QOhT12ov1u2bOlvtWQ4iXHpnoLOX5d+OjShtPWb 9QUKFFCa3n777Sqb13YVV3x36RRc5cuXdzy6IkWKkGO3boEqkE2LjZtvvpn/ sLl0w8idN2+eNZoUtK9SpUqRO9q1a1fEo2MMNfWU04QY3WAU5aETW15q/qF5 uHRHEqnLN998o87uA38fRALeH2XPvMClAx2T9kARxE9BMDQE9+7dm06cOZSs hmHcuHH6mGLF+pWQkydPdu/enW+OK6hnoBHN9tmXfyCGcWpYh5Stffv21k6V OtKJEyeSHCyobbPqs+ncYd53332LFi0i68gpNpd+OvSQA09ep7j++uuHDRsW ez1Iw0SXYsWKBf4+9SjDLj1nzpy2icjIlXGlWed4MQ+6cLuLuHTkyJG8cf2W B/mWBx980DpWXnLJJQ0aNFi5cqU1m9d2FT9i0UU9PW67OHDixIn33ntPzVEf CM23QyI6ztS3YsUKdUU9EJqYsXHjxvv377/hhhvo5+DBg235TSKX/nCftvGO O+5QXxNzbwanjTX1lDMisegSXXn45VzC/Mvymzdv5lVGjx5tuEpmR6AuW7du bdKkibp4ztx6663hXsTIksClAx3D9kCRlZSUNGPGDJcLFDTW83j9+eefL1++ fP78+fPmzZszZ86QIUPUV0LKlSunr0jhOW3aNLIo1i+k/8PxGqcmdUiuY+nS pVOmTNm+fXvEDZIVIUF/+OEHchqGZThw4MDs2bOpGCbbz6Qksv80CboEFGD6 9OmrVq2yvqRgw1O7ihPx0+XUqVPkRujolixZEvElGoo+0osCx/x1G5fI5cm9 6fx39erVdHTzQkyaNOmDDz7guxWBv5+vmWCoqaecLvioiy/lAYxYXY4ePUqB RuMOxZE+70SWBy4d6PjYHho2bEijxpNPPqkvOnnyJJ0UBxJ7EzxTgziVCXSR SdbTRT2G4fihxmHDhvFS27M00sh6umQNoItM4NKBjo/toUiRIjRqNG3aVF90 8OBB/jxHnTp1fNlXlgdxKhPoIpOsp4vy4bbZOJnu3bvz0kWLFiW+bOZkPV2y BtBFJnDpQMfH9vDkk0/yo5tDhgzZu3evSl+8eHHFihV5TBk/frwv+8ryIE5l Al1kkvV0UROzV69efdmyZepdhrS0tF69evFrI2XKlBH+Ybisp0vWALrIBC4d 6PjYHpKSkqyvfhQrVuyhhx7iN1CYPn36+LKjfwKIU5lAF5lkSV3UJ1ADoUmW qlSpct9991111VWcUrx4cet3aWWSJXXJAkAXmcClAx1/28OSJUueeuopNYG2 Gl+ef/751atX+7WXfwKIU5lAF5lkSV3S09O7d+9etGhRa3eaLVu2EiVKDBo0 KFNMhJUldckCQBeZwKUDnXi0h7S0tLVr186aNSspKQnTtkQH4lQm0EUmWViX U6dO7dq1a+HChTNmzKB+NVOYc0UW1iVTA11kApcOdNAeZAJdZAJdZAJdZAJd ZAJdZAKXDnTQHmQCXWQCXWQCXWQCXWQCXWQClw500B5kAl1kAl1kAl1kAl1k Al1kApcOdNAeZAJdZAJdZAJdZAJdZAJdZAKXDnTQHmQCXWQCXWQCXWQCXWQC XWQClw500B5kAl1kAl1kAl1kAl1kAl1kApcOdNAeZAJdZAJdZAJdZAJdZAJd ZAKXDnTQHmQCXWQCXWQCXWQCXWQCXWQClw500B5kAl1kAl1kAl1kAl1kAl1k 8l8AAAAAAAAAAAAAAAAAABiT0df1gRS4PQSBMKCLTKCLTKCLTKCLTKCLTODS gQ6iVSbQRSbQRSbQRSbQRSbQRSZw6UAH0SoT6CIT6CIT6CIT6CIT6CITuHSg g2iVCXSRCXSRCXSRCXSRCXSRCVw60EG0ygS6yAS6yAS6yAS6yAS6yAQuHegg WmUCXWQCXWQCXWQCXWQCXWQClw50EK0ygS4ygS4ygS4ygS4ygS4ygUsHOohW mUAXmUAXmUAXmUAXmUAXmcClAx1Eq0ygi0ygi0ygi0ygi0ygi0zg0oEOolUm 0EUm0EUm0EUm0EUm0EUmcOlAB9EqE+giE+giE+giE+giE+giE7h0oINolQl0 kQl0kQl0kQl0kQl0kQlcOtBBtMoEusgEusgEusgEusgEusgELh3oIFplAl1k Al1kAl1kAl1kAl1kApcOdBCtMoEuMoEuMoEuMoEuMoEuMoFLNyclJWV6iO3b t2d0WeILolUm0EUm0EUm0EUm0EUm0EUmcOnmzJo1KxDis88+y+iyxBdEq0yg i0ygi0ygi0ygi0ygi0zMXfoPP/xQx4yffvopMWYywcCl6xw6dGjChAldu3Yd OXLkqlWrHPNs2rRpXRh+//13w4ZKGxk1alSXLl2++OKLJUuW/Pnnny6ZL1y4 sGHDhvXr17tnI1JTU6dOnfrBBx/Qxrdt2+aY/48//li+fHnfEDNmzDh69Khh mX3Hay968OBBqmT63yVPWlra5MmTu3XrRvVw/PhxT+U5d+7c0qVLP/300x49 ekycODE5OdlkrZSUFFb/xIkTtkWemsqZM2fmzp3bq1evgQMHrly58vz5854K 7yPmulDLpDChGhswYAAd1MWLF73uy7BtG8aL1zr0d+9bt26lfoPa3nfffefe SqMjrrpQPxCurSrOnj1rW8s83OLR4xnunVrFzJkzqbujuCZpqJN32WYU+Du+ MOaHZtLgYxyzTFaPrv3EFX/HF091aD6UxD4cexryTBxCvDF36UOGDAmYMWjQ oMSYSZ3u3btXrVq1Y8eO8dg4XLqVFStW3HXXXTbpa9euvXfvXlvOyy+/PFxT mTRpUsQmSmH4zDPP2FasUqXK9u3b9cyU2L9//9tuu42zLViwINxmf/vttxde eMG22dKlS+/cuVPloc7kzTffzJ07tzVP3rx5adyMWOx4YNiL0khEbZWOLmfO nFTg6tWrh8vZtm1b66Fly5atZ8+eJiWhmunQocOll15qXT1HjhzNmjU7deqU y4rUQxYsWJDzf/PNN7al5k1lzpw5119/vTXDDTfc4D6Oxw9DXaiTp0Jay1ys WDE6ZzHci2HbNo8XT3Xo796pk2natKkt27///W8agg1rw4S46kJ+PlxbVQwf Pty6imG4xanHM9w7eaTChQtbc+bKlatPnz7uteEJf8eXoPGhmTf4GMcsk9Wj aD/xxt/xxbAOzYcSX4Zj8yHPxCEkBnOXPm/evGZ/J1++fHyYtnQ6V02Il3Tg iSeeoCLR//HYOFy6YuzYsSoGr7322vvvv/+qq67inyVKlLBeoDh79qxLL0Qj gnv7vHjx4sMPP8yZ8+fP36RJk0ceeYR/0khKcWTNTKO8bfs//fST42bp9L9s 2bKcJ3v27HfeeScFO/+kVk01QHmOHTtG/Y/KU758+TvuuENtecyYMYYh5iMm vSgdGneeigcffNAxJ/V4nIH6vUqVKl1xxRX888MPP4y4C1V7FP40mFIbULur V6+eywWHBg0aqJw2l27eVEhW2i8lUt9OZ+UVKlTgQ6ZDmDFjhnvh44GJLlu3 blW1RO3t9ttv579vueWWffv2RdyFYds2jxdPdejv3ql5VKtWjdOLFClSq1at 6667jn8+9NBD5vfXIhJXXUxclrXRGoZbnHo8w70nJSVRX8eLKleu/NJLLynH 7qNj9HF8MT808wYf45hluLrX9pMAfBxfDCvBfCjxZTg2H/JMHELCMHfpOlxL 1HX4bxOjpUqVKgG49Jhxj9bjx49zn0kj2qJFi/jucHp6+uOPP87107t3b5U5 NTWVE/v06bNX43//+597+/z+++959fbt2587d44Tv/76a07s16+fNTPFYL4Q uXLl4gzhxqzmzZtzhkaNGvEJOx0FRToF77BhwzhPp06dOE/Hjh3VbbXp06fz xgsUKPDrr7+6F953THpRqvB8f8EDrmMvumHDBj66cuXKnTlzJhiStXjx4oHQ dYz9+/e77IJayD333EM5X3/9deo8g6Hao4LdeuutvM2lS5c6rjhhwgRrX21z 6YZNhZoB9TmUrVChQmvWrOHEVatWsdMrWrTohQsX3KvId0x0eeqpp3gkGjdu HKcMGjSIj7dly5YRd2HYtg3jxWsd+rv3ESNGcMqrr77KF8/p/9dee40TaWnE 2jAkrrpQFOitlFi/fj2P/g888IB6csY83OLR45nvvXTp0oGQMd62bRunnDhx ghxRIGROonhAyxEfxxfDQ/PU4GMcswxX99R+EoOP44t5JRgOJbEPx56GPBOH kDAS6dKpHmbPnp2cnGxLpzqnU3iSgzoE9y0cOnSIeqHFixfTKo4Z+B5ZRJdO XRBZ7j179kQs87p16+bOnUsnVvQ3/cHC/cNdOkFi1a1b98iRI9ZEijLuXWvX rq0SN2/ezJVGARVF++zQoUMgdOZruxVOPQOlN27c2HEtNag5jlnUS1xyySW0 9Omnn7Zd9T158qT6m/ptClXdM6juYvny5VEcUSwY3pFU8H1wx170jTfe0Hsn 1Y9FvJy+b98+shO2xPHjx/PqAwYM0Fc5fPgwP+tSsWJFzmZz6YZNhQZuzjZk yBBr+g8//OC42QQQURfquPgCFPlSazpbxDx58pif8bm3bcN4iboOfdk7Xyu+ 6aablAtlaPSkdBo3E+MGg77qoiC/EQg9JWK9M24ebvHo8Qz3Tt6J8lBKr169 rKvTNjnnjh07PFREeHwcXwwPzVODj3HMinF1x/aTGHwcX8wrwXAoiX04No9B Q4eQMOLq0kuVKkUnXEOHDqXofuyxx/jRIxqpVQaqourVq3OFBEK3oiibbuO3 b9/esmVL2hHfseKqpgqkPlblufvuuwsXLswZqONVp3tjx45VeeicqGfPnuru aiD0WNrIkSP1ktOZe7t27egEjbPRZitVqvTll1/yT7j0cPA9qSJFiqiUhQsX cqWtXLkyig1yl0VC2NL5odYqVao4ruU+ZvE2iS1btkRRpAULFvDq1HKiWD0W /OpFf//99/z58wecHimkOAqEbvdHUbwlS5ZwzXTt2lVfys6HYnzevHn6yBg0 biqDBw/mbGT7HQtfrVq1KAofCxF16du3L5fZ9tjwpEmTOH3UqFGG+zJp2xHj Jeo69GXv11xzDf187bXXbNm+/fZb3nh0nY9OInVhli1bxhcY+/fvrxI9hZvv PZ753qkx8EY+//xzazbyUZw+f/78CMdvhl/ji/mheWrwMY5Zsazu2H4Sho8u PcY6dB9KrBgOx1HEYNQOwXfi6tLpwAOhO3fqCR+iVq1avHTChAl58uThRPLV fBZPXH/99Vu3blUboZMp25sFisqVK6tsysDbUCY8LS2tZs2aKt36akOHDh2s xU5NTeUnZ8IBlx6OMmXKUP1Qs1cp06ZN40ozefhWR13rsJWnZMmSlEgjl+Na 7l6iRIkStOiRRx6JojxByxGZvEbkL371onQizIdgu4FOvPvuu7wo4o1dHToF 5nXVwwMKSuFFH3300c6dO/lvm0s3bCodO3YMhE6c9aff+akJOvX2WvIYiajL Sy+9FAg9M2B7kuTMmTN8Lbddu3aG+3Jv24bxEnUdxr738+fPcx59/FUu0a9H oBOpC8O3A+h/a8V6CjffezxPe+eR+qGHHrLezpg8eXIsfbiOX+OL+aF5avAx jlmxrO7YfhKGjy49xjp0GUrC7ch9OPYUBTE6BN9JgEtn7rnnHvLbdOKzePFi WkSVxqc2BQoUGDNmzLFjx44ePTpo0CA2z02aNFEbmT17diBk3T/++GM6QTt0 6NDcuXPVY0s//vgjZ6POjfRiq08d2rS/oJ6fM9DJAq9Sv379bdu2UcrGjRv5 NiJ1yPS32mObNm0453XXXUdGYvfu3YsWLXrllVfUscClO5Kens7XAZo3b64S 6SyJK40Eeu+992iU6dy5M4We7TWocJw9e5bvclI7SUpK4kR1B1al2HD3Evwk GxWGfx48eHDFihXmd7Lefvtt3rj7w9vxwK9edNmyZXwIU6ZMsS0aOnQoL9q1 a5f5jsjkjB07ls+mb7rpJpu4FIN8W+q+++6jnDt27OBd2Fy6YVNRJdTrn04B AqGXfXx8A9GEiLrUqlWL+0B9Eb8t9cILLxjuy71tG8ZL1HXoy975kK29BEMG np/Iff/99w1rw51E6hK0XD+kWrKmewo333s8T3vv0aMHp9D5y+nQK3IUsGTa KaVq1armVeGOX+OL+aF5avAxjllRrx6u/SQMH1161JXgPpToGA7HnqIgRofg O4lx6ZUrV7Y9Sc5T3FBokG+3pn/44YeB0AMtO3fuVInjx49PS0uzZlOPiL/+ +uvWdH54Rn8ufe3atax7nTp1rOnk+a+++mpKb9GiBaesX7+eN0LpbOYVSk24 dEfU7ePRo0erxE8//TTgBAUgnUOZbJb6LnXPpV69enROx5bv2WefDbeKy5h1 5MgRXjRs2DDaFF+hYmiwpn25F4ZClefyovNEk8L7i1+96HfffceHrM/bpp46 oLPpiNs/cOAAndI2bNiwSJEivBbti85qbdlItUBoLgWeSi6cSzdsKvPnz+d0 28VY6hPUO/t6GeJKRF1KlSoVCDNlGb9KY37dxt0nB83iJeo69GXvfKcyb968 5LtU4rlz5+rWrcsrNmnSxLA23EmkLsTTTz8dCL2faHve3mu4+dvjedo7OVUK Z04sXLhwnz59+DW6yy+/fN26deZV4Y5f44v5oXlq8DGOWVGvHq79JAwfXbrX SjAcSmyYD8fmTSVGhxAPEuDSs2XLtnr1amv6qVOn+Gzl+eeft62SkpLCFUKn YO5752kzqWO3JoZz6Wz+ic2bN9sW8TNIlSpV4p/du3fnnLSKLSfmeHFhz549 rGmJEiWs7z2paKWga9++/TvvvMN3LYnLLrts06ZNEbdMAwcNT7ZgL1eunMsj GS5j1qpVq1TE8R80IKopWKmtuk/lp2aiSPwrikH/elF1vrlx40bbInXVTr/m oLNy5UpbD0zxZctDFcVL1ZOWEV26e1M5f/48T9dAwd6zZ0/qyenM+qOPPqI8 qiSUYl5LsRNRF74wW79+fX0R386jscBwXxF9skm8RF2HvuxdXWcrW7YsjXqp qamTJ0+uWrWqyk/W1LA23EmkLnQUPPqoS3AKr+Hmb4/nde9r1qxRb4opTHoD c/waX8wPzVODj3HMim51l/aTMOLh0g0rwWQo0TEfjs2bSowOIR4kwKVXrFjR lq7eq23duvV8DV40YMAA21pHjx6dMGFCp06dqA/n+XOIChUqWPOEc+nc6eXN m1ffHV/Vp+6aczZp0oS3TF7CthG49HBcvHhRTepLtWRbOm7cuHnz5lkzv//+ +5w54l3UM2fO8PBNkfLGG2+oN3+zZ8/epUuXcGu5jFnqsc9AaDYJOrOmPv/P P/+kMOeLKkWLFg13KYMy8+sPdE6XqZ8b/Oqrr7gG1q5da1vED5gFzCYoSE5O btSoEW1ffRSG6od0UZWj7lVVr15dJYZz6UHjpkJ59E9m5M+fX3kbrx9RjZGI utCgw/2SvojaUiDkwQz35e6TzeMlujr0Ze/UEqyvCClo1/wY5JtvvmlYG+4k UpfPP/+cj0J3F57Czfcez9Pe6XSJx9DatWvXqlVLve1FXaVfE7wEvfdj4cYX T4fmqcHHMmZFt7pL+0kYPrr0oMdKiDiU6Hgajs2bSiwOIU4kwKVb3/Fk1DQ7 LnTu3Fnl3759++uvv84duA3qRa1bDufSeRpYF3LkyME5yfYHQnfn9cOBSw8H nW1xzRg+xkkBy4pQt+k+u3Xjxo0DoaeP+FXx8+fPU29WqFAh3l2PHj0c1zJ5 SrNEiRK2L1+rOZ0cv0b3yy+/8ESCuXLlotNMk8P0Hb960R9//JGPVJ+0YezY sS6VEA7qxObMmaOiTH0Mjr8yFghNe3vwL9SsaCQlz3HqsuVwTYXkqF+//o03 3hgIzfnQokUL6iV4FCB7Y15yX4ioC/cqjhN08CW+unXrGu7L3Sd7ipco6tCv vZOsAwYMKFOmDPW0l1566UMPPTR48GAa+/jBY/0Nr+hIpC78tVCqN30aSU/h 5nuPZ7538of8zuzLL7/MsUYpKoQLFy7MM1rHjl/ji9d+LJZOw3zMim51l/aT MPx16TomdRhuKLHhdTg2bypRO4T4kSEuXd0Ep3Urh2HUqFGceffu3erzx5S/ a9euVOH79u3jF0gNXfp9990XCN1qCbe7mjVrck5+UpE/L2VDnXP179/fa11l LjxFq7q3RVoYvl8TtHyo1/Gz18zGjRs5D50WWdP37t3LT6/REK/PrBV0HbMo 7niRPvMqnWXzIv298iNHjqhGmODvwVnxqxdVR/rtt9/aFg0cOJAXRfFuLNUt P0LAEyZs2bIlYABFn/tm3ZvK2bNn1d8vvvhiwMtTCn4RURe2OnfddZe+iO81 6K9ShsOlbUcdL+Z16PveaeBWn5Lcs2cPb2Hy5Mlhj98LidSFrwHWqFFDX2Qe bvHo8cz3zt8FLlCggG2qdhpzOdvbb79tVBeR8Gt8ibofi67TMBmzol7dpf0k jHi79KBxHdqGEhtRDMfmTSU6hxBXMsSlr169mg+2Z8+eEffCHz7LkSPH0KFD remeXDqdQQdCT7ycOnXKfXfqDSY1OYwC19J1Jk2axJe/KKY8+Tp188vlEeJh w4ZxHn02p9GjR/Mix1dRXMYsOlXn+57vvPOObRGV33GI/PXXX/n2dyBDHxoM +teLHjx4kA+nc+fOtkU8l1G2bNmimyZFPTB27Nixbdu2BQygAHffpklTCYYm B7j55psDXq5/+kVEXVq2bBkIXUGyfSVHNblOnToZ7sulbUcdL4qIdRjXvQ8f Ppyz+XWpKmG6qHne2rdvry81D7d49Hjme+ena5o1a6Zvn0fbMmXKuNWCMX6N L7H3Y546DcOOKIrV3dtPwkiASzevQ+tQYk2Pbjg2bypROIR4kyEu/cSJE2yn q1ev7r4L1XrVHCwKR5fOE7moKdkV6lbF3Llz3ffYqlUrzknnaLZFcOk2aLxg HfPkyeN1YK1Tp04g9I0bl16UpwWjEzTrpQ9GveLh+Lle9/vyPCdtyZIlbQ+z qScxrK+H/PbbbzVq1OD05557LgNvRwZ97UX5hr5tDjqqEB6pI17fDvccIF+Y ItLS0oKhydOOa6hbiqQd/eTvL7tg0lSCli/R/Oc//3HfoO9E1EXdPbS9hTdk yBDVLxnuy6VtRx0vioh1GNe983QK1LHbruVGTcJ0Uc+yhptDzzDc4tTjmeyd fvKrlI7Ohy9eUf6wVeAFH8eXGPsxT52GYUcUxeoR209iSIBL1yvBcChhYhmO zZuKJ4eQADLEpZ+2XLJWT7Y4snTpUs7WvHlzm4288sorA5pL57skhQoVsm1n 4cKF/KIB1TwZA5c9qu6OztfIY6h0+ps/gRGASw9BDZXPiejEM1zmtWvXlipV Sp/Ci/KzHNaXsygAx4wZM27cODVCqfk29c+K9evXjxdRC9H36z5mqaVTp061 pvPX/YiUlBROoZKot5Yef/xxv8xD1PjYi/bq1YuPi3oelUgVwolfffWVStR1 WbNmDfVsek91+PBh/qyk9eOzOo5vj3pqKufOnbNdiqETf35WjXad4MnSgwa6 UB3my5ePilezZk01slA5+UYhlVkl6rVtw6Vte4qX6OrQr71v2LDBdoBkkzjP l19+6bjrKEiYLl988QUXfs6cOY47Mgy3OPV4hnunkToQehzX9mDJr7/+Wrhw YVr06KOPOh6dV/waX4Je+jHDBh/jmOVpdUXE9pMY/BpfzCvB01BiPhw7Rqt5 UzF3CIkho1z6li1beGKlnDlzdu7cmY6aEo8dO0anz9WqVRsxYgRno9MolpXO ppOSkk6dOkVDfNeuXfkll4Dm0itWrMjpH3/8MQmdHIIXvfrqq7zo3nvvpYM9 efIkJdLWOnToQCVUhpx2wV+MDYSuydMp/NGjR6nz5C87MHDps2bNUnNYUQXS z++//36qBf76Bs85TN1sly5d6ESJQoaql3ok/mwHKWt9YV99nkBdzDlz5gw/ rZc7d24KHHVi+91333HjufHGG9Xb1qmpqUv+gloUb6pnz56cYn1x/sKFC9y9 UKOiYtNATCmkKd9aVdOy0Zb5oycEFZh6kunTp0/9OwcPHvQtFA0w6UWXL1+u 6oFrjw5WpagL11RyDiLqDFesWEF1S31X3rx5KYXOf6nm1QZ1XXiKKpKvVatW M2fOpG1Sh0kFo8jinG3btnUpoaNLN28qVNRGjRpR4anXPXToEMlEJb/zzjt5 m0OHDvVer7Fioou6Sde6dWvqfMghNGvWjFO6deumsum1HTRu2+bx4qkOfd87 tTdKKVu2LBlOajn79++nGuB+/qabblKPqcdOvHVRfPDBB7yUXIfjXgzDLU49 nuHe1RPgderUUY+XHDlyRL1AOnjw4EhVboRf44v5oZk3+BjHLE+rKyK2n8Tg 1/hiXgnmQ4mn4dgxWs2HPEOHkDAyyqUTw4cPt86MVKBAAa4E4vbbb1fZKLhU Hv60KDcAfpvG5tIHDRqkMtOZOKlPMc6LDhw4oB5n4i2wQAydSamNUAPgLtEG P8AW+Me7dIoXfUorG3QqTTmnTJlirUmST03tFdC+vs23mYgHHnhAJdI4zldU AqH7rbRIfSqL0pctW6ZyfvLJJy7loTC07ovCkz8UEgi9kKU+bFGoUCEa+zgP jdHux0jQ6OlHFJpi0ovyPaZwUC2pnGSSVUApXWhw/PHHH60b1HWhMlgnXKKw VWfNgdBNKHej5ejSzZvKvn37+DKLyqn+fuutt6KYfiF2THQhB6iuIVgrvGbN mtYLPo5RYN62DePFUx36vnfqkx13TR2svy4l3roo1LzNLt9DNwy3OPV4Jnsn 36IMOe2L+vDy5curqHzyySf9mn7Wr/HF/NDMG3yMY5an1RUm7ScB+DW+mFeC +VDiaTgOF62GMRg0cwgJIxaXzneLSpYsGS4DPwhUo0aNcBmoT37wwQeVOQ+E PjrQoEGDlStXqjwpKSn87UKGeo9HH3103bp13NXbXPqJEyfeeOMNq3CPPfaY Wkodcvfu3fnlfQX1geS6jx07Zt0OnWqpK+osU+PGjffv388nj4MHD/ZaV5mL iL2otZdzpEKFCpyZ5GvWrBmfRCvoRFWfjvvjjz/mperzN8y2bdsef/xx2/br 1q27detWazb3MSt37ty23e3evfv++++3tj1qKtaZl9S7DC7o0R1XYu9F+/bt a808btw4njKaKVq0qH5EjrqkpaW1atXKFkqkMsWXy7dXmL1793J+2+v25k2F ZCKxlJkJhK4xZuAjnYZ3iqn/sRabrMgzzzxje4LCsbY9tW3DeDGvw3jsfcSI EWpi5EBooKS1jh49GrEOPRFvXRQ8jR7hPsmVSbgF49bjmez9jz/+GDRokJr4 kSlYsODAgQN9fJDMx/HF/NDMG3yMY5b56grD9hNvfBxfzCvBcCjxNBy7RKth DAYNHELCiMWl+wXJlJSURPKtWrWKbLZjns2bN8+ePXvu3LnUk0fcYHJy8rRp 0+jEisy246Qu1N1RhpkzZ6rnYcJtZ8aMGbRT90fZsx6Go5s5/Ezg/PnzSWiX U9EtW7aE+zQblYqaB7UB+v+06wzbnqBN/fzzz3Sw1o+Vi8V3XRjqjkgXl8s4 4XShgXvDhg0URxQjO3bs8OW5fcOmEgxNJU0BTrtO/MUNG550oYF45cqVixcv DvdpDJcoMMcwXuJUh4Z7P3DgwJw5c6g2fHzKxYpAXYIG4cbEqccz2fvFixdp 7KNs8+bN27Nnj++vzGdUPxb00uBjHLPM+zE5ZNS4H4zDUOIerYYxGJThECS4 dCCNOPWiIEagi0ygi0ygi0ygi0ygi0zg0oEOolUm0EUm0EUm0EUm0EUm0EUm cOlAB9EqE+giE+giE+giE+giE+giE7h0oINolQl0kQl0kQl0kQl0kQl0kQlc OtBBtMoEusgEusgEusgEusgEusgELh3oIFplAl1kAl1kAl1kAl1kAl1kApcO dBCtMoEuMoEuMoEuMoEuMoEuMoFLBzqIVplAF5lAF5lAF5lAF5lAF5nApQMd RKtMoItMoItMoItMoItMoItM4NKBDqJVJtBFJtBFJtBFJtBFJtBFJnDpQAfR KhPoIhPoIhPoIhPoIhPoIhO4dKCDaJUJdJEJdJEJdJEJdJEJdJEJXDrQQbTK BLrIBLrIBLrIBLrIBLrIBC4d6CBaZQJdZAJdZAJdZAJdZAJdZAKXDnQQrTKB LjKBLjKBLjKBLjKBLjL5LwAAAAAAAAAAAAAAAAAAjMno5yyAFLg9ZPR9HmAH usgEusgEusgEusgEusgELh3oIFplAl1kAl1kAl1kAl1kAl1kApcOdBCtMoEu MoEuMoEuMoEuMoEuMoFLBzqIVplAF5lAF5lAF5lAF5lAF5nApQMdRKtMoItM oItMoItMoItMoItM4NKBDqJVJtBFJtBFJtBFJtBFJtBFJnDpQAfRKhPoIhPo IhPoIhPoIhPoIhO4dKCDaJUJdJEJdJEJdJEJdJEJdJEJXDrQQbTKBLrIBLrI BLrIBLrIBLrIBC4d6CBaZQJdZAJdZAJdZAJdZAJdZAKXDnQQrTKBLjKBLjKB LjKBLjKBLjKBSwc6iFaZQBeZQBeZQBeZQBeZQBeZwKUDHUSrTKCLTKCLTKCL TKCLTKCLTODSgQ6iVSbQRSbQRSbQRSbQRSbQRSZw6UAH0SoT6CIT6CIT6CIT 6CIT6CITuHR3du7cOT3Evn37MrosiQPRKhPoIhPoIhPoIhPoIhPoIhNfXHp6 erqPFlEUX3zxRSDEjBkzMrosiQPRKhPoIhPoIhPoIhPoIhPoIpOoXXpaWlrf vn2fffbZ4sWLZ8uWrWjRos8999zQoUNPnjwZJ+uYIcClu3Po0KEJEyZ07dp1 5MiRq1atcsm5adOmUaNGdenShap0yZIlf/75p6eGSu1t8uTJ3bp1mzp16vHj x8NlO3fu3NKlSz/99NMePXpMnDgxOTk5wduMK1570YMHD65bt47+d8ljWAmO /PHHH8uXL+8bggLk6NGj4XIa1iE1knVh+P33311KkpKSwtlOnDjh6RB8wVyX CxcuUJhQPQwYMIBKe/HiRcNdRFEztK8NGzasX7/eJdbMFQwah7CnbQbNWml0 SNPlzJkzc+fO7dWr18CBA1euXHn+/Hn3jaemplJUfvDBB1Tt27Zts1Y41Wq4 /SrOnj1r3VrEGNy4caP7Bv3q+kx0cTlACnbHVVyqy4ZJaHgVy4bXKGAySz9m Pu4rTA7NXEHznLEUKUbT4hfRuXRqtCVKlAg4UbVq1V27dsXPQCqox6gagvqW +O0FLj0cK1asuOuuu2zq165de+/evbac1EE988wztpxVqlTZvn27YStt27at dV06K+zZs6ctD42JHTp0uPTSS605c+TI0axZs1OnTiVmm/HGsBel8WXWrFkv vPBCzpw5qcDVq1cPl9OkEhyhmnnzzTdz585tXT1v3rwUL3pO8zq8/PLLHXsV YtKkSeEKQycaBQsW5GzffPONSfn9xVAXGk1uuOEG60EVK1YsnOWw4almKLL6 9+9/2223cYYFCxboGzRXMGgcwp62ad5Ko0aULnPmzLn++uutGWin4RzOb7/9 RjVj22Dp0qV37tzJGehsItx+FcOHD+fMhjGYP39+9w1SgU3qJCImuvTu3Ttc Me6++26v1aUwCY2gR7FseIoCK5miHzMf961EPDRzBc1zuuNepNhNi49E4dLp HEp1TUWKFHn++efpfPPVV18tXrw4J9apUyeuHpKhE1XeHf0Rv73ApTsyduxY 1Qauvfba+++//6qrruKfdPpmvexw8eLFhx9+mBfRKNCkSZNHHnmEf9JQSBEX sYlSj8f5qd+rVKnSFVdcwT8//PBDlefgwYNly5bldLKa1I1QqVRw1atXz3YW HI9tJgCTXpSKzbZH8eCDDzrmNKkER44dO0aeijNnz569fPnyd9xxh9rdmDFj rIUxr8OzZ88GwjNx4sRw5WnQoIHKJnZ027p1qzr2O++88/bbb+e/b7nlln37 9rmv66lm/v3vf9sy/PTTT7YNmisYNA5hT9s0b6WxIEcXkoDaPyWSVa5atWqF ChX48CnoaGSxbdYaNVSTVCryePwzX7581D8HzVw6F8A8BiO6dOrbPSrgjIku 7777rmExTKqLMQmNoEexbHiKAhvy+zHzcd+G+6GZK2ieMyIuRYrdtPiLV5d+ 6NCh6667jgv80ksvUZtUi9LT099+++1rrrlmy5YtcfGOf2fmzJlcDLh033GP 1uPHj3Ns0oi2aNEivjtM6j/++ONcV71791aZv//+e05s3779uXPnOPHrr7/m xH79+rm3zw0bNnDOcuXKnTlzhvfO54M5cuTYv38/Z6My33PPPZT4+uuvU5sM hgKNDuHWW2/l1ZcuXRrXbSYGk9EtNTU1319QPxYI438MK8GRTp068bodO3ZU d3KnT5+eK1cuSixQoMCvv/7KiZ7qkErOiX369Nmr8b///c+xMBMmTAhYkDm6 EU899VQgZJPGjRvHKYMGDeIyt2zZ0n1dTzVDJ1+sPssRcLIi5goGjUPY0zYN W2mMCNGFKo0Gd8pWqFChNWvWcOKqVat4JC1atOiFCxesm23evDlvtlGjRnyt m6KGDB6dTQ8bNozzUGTpeyTWr1/Pp9sPPPAA98zmMZiSkuK4zVatWnHO2bNn m1a9Kya6tGjRgvZ4/fXX6+WhavdaXYxJaHgVy4anKLAivx/zNO5biXho5gqa 53THvUgxmhbf8erS27Vrx0WlyHXMQC3TMf3EiRNLlixJSkpKS0szt4srV66k VejUQF9EFcslMXHpixcvpmxUBq+lgkt3hDr2unXrHjlyxJpIQwBHce3atVVi hw4dAqGrtX/88Yc1Mw3KlN64cWP39vnGG28ENN+oHKb1qu++ffsouGyrjx8/ nnMOGDAgrttMDIZ38BV8Y9fR/5hXgg6NU9RbjhgxwpauRiiKNZVoXoebN2/m RBrUDA/w8OHDfNeyYsWKYke3YOgxTr4c9+qrr1rT2SLmyZMn3NjNRFEzQcuw olsRTwoahrCnbVpxaaUxIkQXsjScbciQIdb0H374QW+05EIvueQSSnz66adt d+tOnjzpsheGrDitS57Q+gBALP1YcnIyO0yyzRH3bohJP9awYcNA6BqCe7bo qsslNDyJpRNdFGSWfsx83FdEPDRzBWMMDfMixWhafMeTS6dg5/sdV155JdWY oeXbtGnTI488ctlll3GFZM+evWzZsmSM9ZylSpWi89zBgweTc3jxxRevueYa XiVbtmxNmzZV1+0HDhxIp7TUhfJS+oNPkCmiOcPIkSPpZ/Hixelv6oIoM+f8 9NNPvZYKLt0TfLOvSJEiKoVHjQIFCthykqCB0INeLlv7/fff+Sas/szq3Xff HQjdlXYvD52CsXxdu3aN3zYThl8uPfZKcGTBggVcMxSA7jkd63DhwoWcSOfm hntkN3XppZfOmzdP8ujWt29fLp7tOdhJkyZx+qhRo1xWj6Jmgq5WJByOCsYS wuG2aSUDXXpidKERjbORPbAt4nCrVq2aSuHaJrZs2eKyTUeWLVvGNyb69+8f MbNhP/avf/2L8tx8882nvTxO4I5JP/bQQw8FQk/PumeLrrpcQsOTWOa4R0Fm 6cfCoY/7ioiHZq5gLKERRZGi7vF8x5NLnzVrFh9UmzZtDP0enbArO20lR44c PXr0sGUmb0CLnnjiCfVyh5XWrVtzNnVaaqNYsWKcga01nXaNHj3amoEixWup 4NI9UaZMmcDf3+5RlyBsGyxZsiQlUrN32VpycjKvq99jUk8thnsQgunZsydn U7ez47HNhOGXS4+9EhyZNm0ar+vypifjWIdq9YjPAzO0Luf/6KOPdu7cyX/L HN1eeumlQOjJSdvt8jNnzvC13Hbt2rms7rVmmChcuqOCsYRwuG1ayUCXnhhd OnbsGAhda9LfZHnttdcCf38rk6dleOSRR1w2GI5y5coFQtefTV6ZMenHlPR0 pFGUJxwm/RgPJRFbV3TV5RIansQyxyUKMlE/Fg593GdMDs1cwVhCw1ORYuzx fMeTSx84cKA6KBOzt3v3buoAeZX27duvX7/+l19+odN8viBPgaBsM8Munald uzbtZd26db179ybzTClXXXUVnd5Sto0bN1Kbb9KkCeekDNNCqEdflLWmnvay yy5r1qzZ7NmzJ0+ezE+8eCoVXLo56enpfCWnefPmKvHs2bN8O4zOTJOSkjiR +kauVZXiyLJlyzjblClTbIuGDh3Ki3bt2uW4Lg27Y8eO5WkNbrrpJvXGRzy2 mTD8cumxVIILb7/9Nq/r8li7Sx2OHDmSV6dO8r333qPOsHPnztSpOtYzdQXU oijzfffdR9vcsWOH5NGtVq1aVLZ77rlHX8Sv8r3wwgsuq3uqGUUULt1RwVhC ONw2rWSgS0+MLiqm9BogqxAI3clV0zby4yW0Nf558ODBFStWmNzQVxf2SXf3 nOb9GF/Qvv322/19U96kHytSpAg7ogEDBrwegv5Yu3atLVt01eUSGp7EMidc FGSufswRx3E/aHxo5gpGHRpeixRjj+c7nlw6P8tKLFq0yMTsNW7cmPP36dPH mv7jjz9yeunSpU+dOqXSlUsn82xNV1PiLF26VCV27dqVE/Xn0pW1Jsv97bff xlIquHRz1O3j0aNHW9Np+FB3LurVqzdmzBiOlGeffdZ9g9999x2vpc+XRbLy osWLF1vTDxw40KZNm4YNG3Inz6M/nZfFdZsJwy+XHkUlRIR6S5677NZbb9WX mtThp59+GnCCjIR+KY8aUiA06wJPjSV8dCtVqlQgzGSDPK2Z+wUiTzWj8OrS XRSMOoTdWwWTgS49MbrMnz+f023PlsydO1dNrMSxcOTIEf45bNgwqmS+dsfQ qQSp4FKYp59+OhB651G972bDaz+2ZcsWzkaH6bLfKDDpx9TkIVZoQH/xxReV MYu6ulxCw1wsc1yiIHP1Y46EG/dNDs1cwVhCw1ORmKh7vHjgyaXXrVuXi71n z56Imallcqu++eab9dc2a9asyZtat26dSmSXXrFiRVtm9ajYpEmTVKKJS2/U qFGMpYJLN4SaBJ/qlihRwvbOxe+//05t29bflitXLuIzFeqyxsaNG22L1Imt 7WrwypUrrXuh4XLz5s3x3mbC8MulR1EJEeHbwY49XtCsDpXnoQLTefo777zD N1KJyy67bNOmTSqnenNcPX8rfHTjC7P169fXF/EbSTTouKxuXjNWvLp0FwWj DmH3VsFkoEtPjC7nz5/naUMuueSSnj17ksdbv379Rx99pN6KIiglGJpLhH/y rCyB0FtXauZt8qjhZgJMTU3lF+vUlUYdr/0YT+1CvXp6erpLtigwd+lXX311 06ZNu3Xr1qRJE2WSn3/+ec4TdXW5hIa5WOaEi4JM14/phBv3DQ/NXMGotfZa JCbqHi8eeHLp/BRfIPSyTMTMar6If//73/pSZbzHjx+vEtmlV65c2ZZZvcvz 1VdfqUQTl/7999/HWCq4dBMuXryoJhSdNWuWddGZM2eqVq3KMfXGG2+oaTyz Z8/epUsX982S3JxZv9E5e/ZsXmSbXSE5OZlOzWjQVJ8pofilHak7tvHYZsLw y6VHUQnuLFiwgGcYrlSpkmO1GNbhuHHj5s2bp35Su3r//fc5P7UiTjx06BCN 3YHQJVC1uvDRjRxRIPTGjb6IaixgMJeFSc3Y8OTSXRSMOoQjtgomA116wnSh PPr3j/Lnz6+cAH/2Vz0QSxQvXpwqkJwPVR21avaoRYsWdbxU/vnnn/NaLsbb az/G82A0bNjQvQaiwKQfO3DgAJ0EWU8Qdu7cefPNN3PJ+amDqKvLPTQMxTIk XBRkxn7MRrhx3/zQzBWMWmuvRQrGZlrigSeXzs9lEfpjJDpqoifbgyWMeryk c+fOKjGcS1fTV3p16bq19loquHQTWrduzbWkP8bJzxdRgPA0COfPn6cBpVCh Qpy/R48eLptVcsyfP9+2aOzYsbwo3PfgKBLnzJlTunRpzqa++xaPbSYMv1x6 LJWg88svv/CAnitXLjoLds/stQ5pIODMNG7yK37kqXjdpUuXHvwLNX8atS76 edq/+ShMiKhLhQoVAmEmB+ALd3Xr1vW6U71mbJi7dHcFowth81aRgS49kbpQ hdSvX//GG28MhGbDaNGixfbt29nSkxngPOqFkRIlSpCvsG5WzZngGJj8UCht h2exdsckBrdt28ZL+/bt6+34DYjuyYqg5VG97t27B2OoroihYSKWCS5RkBn7 MRvhxn3zQzNXMGqtvRYpGJtpiQeeXLqyuB9++GHEzOr+gnX+QwX1Ery0Y8eO KjEBLt1rqeDSI6Ju+5YrV872ItLGjRt50WeffWZN37t3Lz8bSWfB+oRXirVr 1/LqdFZoW6ReZHb//g6FM9/UVi/mx2ObCcMvlx57JSiOHDmiZmRy+TyoDU91 2LZtW94+DZTqWVl3qA8xLIkvRNSFx4i77rpLX8RXeGwvXhlirRl9qaFLd1cw uhD21Coy0KVniC5nz55Vf7/44osBy3M1FBe8oj7htopZxylZ+PJ4jRo1zAvp HoPDhw/n3f3888/m2zQkapdOYxOXih8Pjrq6zE9gXcSKiEsUZNJ+zEq4cd/T oZkrGLXWXosUo2mJB55c+po1a7j8VFr9oW4bq1ev5syO0zYq9ztmzBiVmACX 7rVUcOnuTJo0id/vpg5f93XDhg3j2tNnKlOTZLq8/kant5ync+fOtkWvvPJK IHTHNuLr9mouIP7uXjy2mTD8cum+VALx66+/8oMBAdcHYh0xr0P1CMH69evV JT53ypcv76kwMRJRl5YtWwZCF1dtX8mhkOECd+rUKYr9WmtGX2piRSIqGEUI e20VGejSM0QXxYULF/gRDnXF/s8//+RnLd555x1bZlUkm3kIWiZWbd++vady usQgP91Kfbv7d52iI2qXfv78eZ7wrV69esFoqysY1fRHuljuuEdBJu3HFC7j vqdDM1cwaq29FilG0xIPPLl0gqevD4RukzlmUN8JTU9P57me7rjjDuuELQy/ bEuQbVaJnlx6t27dONE2neNpV2vttVRw6S5QW+VXlvLkyeN4s6lHjx6B0Cz0 1isSjHoZxP3Dvnzf2TZVGgUsPypmvdQQ7ulKvgBCpKWlxW+bicEvlx70Ugnh +O2332rUqMH18Nxzz4W71R57HdapUycQ+ggFnzhQCB/XUPdDqTnRT/54dMKI qIu6hWd7J3fIkCGcPnfu3Cj2a6sZGxGtiImCXkPYsFVYyUCXniG6KNT7Vv/5 z39UIs95XrJkSVvgqLvz+lty6pHdcHMwRhGDZcuWpXSSxqX8URO1S1cvwNLo zylRVFcwKpfuKFY4TKIgM/ZjTMRx39OhmSsYndZeixS7afEdry596dKl/CpE 7ty59afTqc3TKN+hQwf++dhjj/FB2Sz9zz//zBshq0C1p9I9ufQBAwZwov7s iru19lQquPRwUIXw+Q6d4YbLTGMc157+wbV+/frxImpRnEI925gxY8aNG2eN jl69enE2ikSVOHXqVNUeOGXNmjXU8PQgPXz4MH/B1vpZtHhsMzH46NINKyEY Rhf6W7039Pjjj9tm9VGY1+HatWtLlSq1bt06/ZA5Kt3f4xP+1hXVIX+joWbN mmrIJgtXvnx5rgSVqNd21DXjbkUMFfQUwobbtJGBLj1hupw7d852Xf3EiRM8 DyTtxWrmlWoUjNb8/N1DIiUlxbZHNUjNmTNHP8bo+rHChQvTovvvv19fFDsR dSFn265dO1v7IW/G87cHLO+2R1FdwUihYS5WLH2jI8L7saDZuO9IuEMzV9A8 p6MuhkXy1OMlBq8unXj55Ze5qNQXvfbaa+PHj9+wYQOdabZu3Zrlo3TqwSjn li1beJYeOjEhV5CcnHzo0KFRo0apqVB//PFH65Y9uXSVSJ3h4sWL6TzI8AK4 p1JNnDiRE+nsybB+sgARo3XWrFlqZio6KaOfJMdUC/wO/pkzZ/iBSTqnoxBT p8DfffcdS3DjjTeq97LVdx+s9wcPHjzIHwGkAWXFihW0BXKVefPmpZQrr7yS ts/ZeHYmanitWrWaOXMmNQbqG+kQ7r33Xt5m27Zt47rNxGDSiy5fvnzJX3Dl kwtSKeoKhmElBJ10Icn4czCB0LfGKMpo0Jz6d2j7QS91yNNTU8/fpUuXhQsX Uu9KmSmQOSppC7a5g2zIH914XrtA6BvKJ0+epHG/WbNmnKIuDAadattTzaSm piqtO3fuzJvq2bMnp6gJQMwVNA9h820GjVtp1tCFKq1Ro0YUbjTc0FhDFUWx duedd/IGhw4dai3PhQsX+LQlT5481KnSaQKlfPbZZ/yAgeOkkR988AFvigy5 vjSKfox2yg+WkMmMUMVR4a6L+mRDlSpVKJz37t1L5dm0aZNqXQ888IB6Lde8 ugxDw5NYsfSNjgjvxwzHfUfCHZq5guY5Hb2EYZE8mZbEEIVLT09Pb9++PV8u 0MmXL9/kyZNVZjK3bN11qHOwbdmTS6diqHmZAqFH+mlHR44cOW1wAdy8VNRF cDr1WibvzGYN3KOVmqg+UZWNUqVKcWY661RVfd1111EHq75dRenLli1Tm+X7 WYFQJ2zdHUUQDxmB0FjDf1BfQSdT1macP39+tXcKW7agTKVKlc6fPx/vbSYA E9dBHttFl08++UTlNKmEoJMu6mEzF6hPC3qpwylTpnAfyFDBrD2M+4fag+JH t2DoyyYVK1ZUR6SOrmbNmtYLPnpte6oZ0tdFFGobnM1cwaBxCHvapnkrjQUh uuzbt4+vWquc6u+33npLn5+HbCF/QiUQGtfUPOGFChUiq6kfgpqOW3+SNhhV P3b48GFeGqePobvrQucRjz76qLU9WAtMNZmcnGzNb1hdhqHhSaxY+kZHJPdj nsZ9HZdDM2/whjnDeQnDIpmblsQQhUtnyABT56ZqKRCauKZOnTrr16+35Vy+ fDnfQFQULVpUn8mc4Gdla9SoYUunUzZe0erSiYULF6rPqAVC/Q9/FFV9uHnu 3Lnhym9eqhdeeIFP1ho0aGBYOZmdiNFq7bscqVChgsq/bdu2xx9/3Jahbt26 W7dutW72448/5kXqowOKcePG8czGSimbkyTS0tJatWrFMzMorrrqqu7duzt+ iSAe24w3sbt027xqJpWg66JmvnJBbce8DlNSUpo1a2b75uBtt91mMnO7OpvW Z61JACa6BEOG8LHHHlP9Pw15zzzzjO2erGMUmNeMuxXJnTs3Z/OkYNAshD1t 01MrjRo5uhw6dMi6i0Doily4x8iJ3bt333///TzuMLS6bQI6hfo2t22KLYXX fky9bef1dVRDIury559/jh49mp+NV5BXb9GiheNc5SbVZRgaQS9ixdg36kju x7yO+zbcD828wZvkdPEShkUyNC2JIWqXzqSnp5PdnTJlivUToo4cOHCAPPPM mTOpkqNyjs6cOHGCTm2okmnL1Gd6Xd2wVCQZ7WLVqlUxlDQzYTi6eYI2SxU4 e/ZsrkbHPFu2bAn3IcVgKDyTkpIcrxcpfv/99w0bNpCgJCudKUd8JjAe24wf 8dAlaFAJ7rqYYF6H/FDo/PnzqUiOVw4F4kkXslIrV65cvHhxuNum4Wo7w2vG JIRFIU2X8+fPr1ixgkLAUL7Tobel6BB8+fpnZuzH9u/fv3Dhwnnz5q1duzbi Ywb+VpehWLH3jXKI0/hijrmCEXP6oouQHi9Glw6yJBkercAR6CIT6CIT6CIT 6CIT6CITuHSgg2iVCXSRCXSRCXSRCXSRCXSRCVw60EG0ygS6yAS6yAS6yAS6 yAS6yAQuHeggWmUCXWQCXWQCXWQCXWQCXWQClw50EK0ygS4ygS4ygS4ygS4y gS4ygUsHOohWmUAXmUAXmUAXmUAXmUAXmcClAx1Eq0ygi0ygi0ygi0ygi0yg i0zg0oEOolUm0EUm0EUm0EUm0EUm0EUmcOlAB9EqE+giE+giE+giE+giE+gi E7h0oINolQl0kQl0kQl0kQl0kQl0kQlcOtBBtMoEusgEusgEusgEusgEusgE Lh3oIFplAl1kAl1kAl1kAl1kAl1kApcOdBCtMoEuMoEuMoEuMoEuMoEuMoFL BzqIVplAF5lAF5lAF5lAF5lAF5nApQMdRKtMoItMoItMoItMoItMoItM/gsA AAAAAAAAAAAAAAAAAAMy+oo+AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAEBG8v8Ad3Dz jw== "], {{0, 102.}, {497., 0}}, {0, 255}, ColorFunction->RGBColor, ImageResolution->144.], BoxForm`ImageTag["Byte", ColorSpace -> "RGB", Interleaving -> True], Selectable->False], DefaultBaseStyle->"ImageGraphics", ImageSizeRaw->{497., 102.}, PlotRange->{{0, 497.}, {0, 102.}}]], "Output", TaggingRules->{}, CellChangeTimes->{{3.834318312088228*^9, 3.834318369196093*^9}, 3.834405440679968*^9}, CellLabel->"Out[2]=", CellID->2127476028] }, Open ]], Cell["\<\ Generate an anomaly detector function of the covariates of the treated \ population:\ \>", "Text", TaggingRules->{}, CellChangeTimes->{{3.8343186564064837`*^9, 3.834318691958363*^9}, { 3.834318787145864*^9, 3.8343187936828737`*^9}}, CellID->788491045], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"adfTreated", "=", RowBox[{ RowBox[{"Query", "[", RowBox[{ RowBox[{ RowBox[{"Select", "[", RowBox[{ RowBox[{"#treat", "==", "1"}], "&"}], "]"}], "/*", "AnomalyDetection"}], ",", RowBox[{"KeyDrop", "[", RowBox[{"{", RowBox[{"\"\\"", ",", "\"\\""}], "}"}], "]"}]}], "]"}], "[", "lalonde", "]"}]}]], "Input", TaggingRules->{}, CellChangeTimes->{{3.834318686049137*^9, 3.834318735822226*^9}, { 3.834318797816992*^9, 3.834318824566594*^9}, {3.834318937320372*^9, 3.834318943850749*^9}, {3.834319464707427*^9, 3.834319465917754*^9}, { 3.834334787598551*^9, 3.8343347883399477`*^9}}, CellLabel->"In[3]:=", CellID->862773238], Cell[BoxData[ InterpretationBox[ RowBox[{ TagBox["AnomalyDetectorFunction", "SummaryHead"], "[", DynamicModuleBox[{Typeset`open$$ = False, Typeset`embedState$$ = "Ready"}, TemplateBox[{ PaneSelectorBox[{False -> GridBox[{{ PaneBox[ ButtonBox[ DynamicBox[ FEPrivate`FrontEndResource["FEBitmaps", "SummaryBoxOpener"]], ButtonFunction :> (Typeset`open$$ = True), Appearance -> None, BaseStyle -> {}, Evaluator -> Automatic, Method -> "Preemptive"], Alignment -> {Center, Center}, ImageSize -> Dynamic[{ Automatic, 3.5 (CurrentValue["FontCapHeight"]/AbsoluteCurrentValue[ Magnification])}]], GraphicsBox[{{ GraphicsComplexBox[CompressedData[" 1:eJx1nAdYU8n39++NAUIR0bWDixV7LwuizsXeK6hYsCtr113XsigoVuwoFlSs a1td0bXrOhcUwRU1FJUiGJASMEAIIQQI4UV+5ua53/2/efI8PMfkzsxnZs6Z c86c2Gr+qsmLJAzDZEkZ5tvf3rUvW56pfcn4FokeNW8rQbZaN7HmXUeQMzs/ q3kbqUm+vyK15q0T5IZzihvNKS4Q5A1R0/yipn0SZP7kt9c1YpK1jb49kCHI LqOC244KVgvy5NoB6AWZ1g6A4Uzy+2/DbSEV5K/n7PPP2csE+X9/7QRZzCsF XinwSoFXCrxS4JUCrxR4pcArBV4p8EqBVwq8UuCVAq8UeBngZYCXAV4GeBng ZYCXAV4GeBngZYCXAV4GeBngZYCXAV49FfPqqZhXT8W8eirm1VMxr56KefVU zKunYl49FfPqqZhXT8W8eirm1VMxr56KefVUzKsGXjXwqoFXDbxq4FUDrxp4 1cCrBl418KqBVw28auBVA68aeBXAqwBeBfAqgFcBvArgVQCvAngVwKsAXgXw KoBXAbwK4FUAL8OA/jKgvwzoLwP6y4D+MqC/DOgvA/rLgP4yoL8M6C8D+suA /jJiXjyPFATWl8D6ElhfAutLYH0JrC+B9SWwvgTWl8D6ElhfAutLYH2JmFdB QH+BVw28auBVA68aeNXAqwZeNfCqgVcNvGrgVQOvGnjVwKsGXj3w6oFXD7x6 4NUDrx549cCrB1498OqBVw+8euDVA68eePXAy3CgvxzoLwf6y4H+cqC/HOgv B/rLgf5yoL8c6C8H+suB/nKgv5yYl+HAvwJeKfBKgVcKvFLglQKvFHilwCsF XinwSoFXCrxS4DX9u+BfAa8MeGXAKwNeGfDKgFcGvDLglQGvDHhlwCsDXhnw yoDX9Ffwn4HXDnjtgNcOeO2A1w547YDXDnjtgNcOeO2A1w547YDXDnjt/vNX zOsAvA7A6wC8DsDrALwOwOsAvA7A6wC8DsDrALwOwOsAvA7A68BdDexf87b+ zmMJ/rQl/9s3XCsbziSLn7fkxc/bw/zZc+LnUcb+ZdCfDNrH8dtzK79Ndz+b 759b8e7fHqhjy5lk8ef1OPHn9WC8+LwMnsf+60H/GH9awXxi/w6cX+1+YL9/ LuFja18V1CTXLl++RpCVtf+QI8jct+3R9pUgi+fLQGs/HmUhyOL+DLRl7f7O IyZZ/LyWisejpeLxaKl4PFoqHo+WHrqlD7qljycm2Xfxt5dBkMX9KaE/JfSn hP6U0J8S2pNDe3JoT07nflPPRpacSRbPh5yIxysn0B8R8ymJ+HklPK8k4v2t hPa00J4W2tOSAbWAWkGG+SRiHi30pyVifgMRz6eBjK4dj4QzyeL2DDA+CSce n4QTj0/Ciccn4cTtS8B+Snjx/sR8hATyERLIR0h4GB/IBogfcb9jvsAA8Z+B ivlQxv2M8TbqE8bfWohHtRCPamF/oqyEeFRLxfONMuqLAsaH8TLqI8bPStB3 lOUwX0qQUR+VICtQvwnoN+xvOegP6jfDgH6DfWIYsA9EbE9RVkK8Igf9wvEp 4XO0HwrQbwXosxxkBRHrjxJkLcRPSoifcDxaiKeUEE+hfVPDeNSwPlqQDdA/ 9qeF/rQQv2khftNCfIb2EONJA8yHAewxyhLwr9BeYjwmAX9OAv6cBPw5Cfhz EmwP7BnmV9EeYX4S7QXm59A+YP4P9RnzYajPmP9Ce8Mw4v3AMGL9wHyUHOwb 2gvMl6F+Yn4I9VEB+x/zOXLIV6B+qeF5zN/g/sf8Be637/mAmaa8N/u/fP0g k59ZQWunN9GUJ9bQ2ukqNuVRs/63Pg2svo9fRxy+4bas8102ksHfnveUfG/P SG1q+bK/z28WSasdQMV3WUPCvy2/jTVnkkO/bY9mFt/lCrL92/J2YL/LLBdT O+CXxDT+p7X6l0JM/d3+1l6Qhph4or9Nh5/uu6yD8Whov1p/uuC7rKLi/jRU zKei4v6zqLj/JOg/C/pPomK+LCqef56I558n4vnnSe3yZ2q/y0kwnzysD0+m f+t+mCVv+n7tdhlruufJIsbqb688oT1YbxJfO6BM+n9/XwXrlwTrryLi/aaB 9jSkdnqiDN9lHfBXkLG1CpL4XTYSee0E3Pkus5y4PRW0rwb/Eu8X5LD+PNgj NRGvtwby6UoqjteyYH+oQP/UwKMBHg0Rt6cC+6qA/ZsE40+C/agiYlkD9lsD 8Y8KzjMl2AMVyGp4Xg3xk5qKeVUU1gv8PRWMTwk8qG8KOB/UoH8qWG9sPwn6 16A/Af4rA/GzAfxXBuJpA8wPA/G1AeaL4cXnixbiCy3YC1w/OcgKaF8OsgLi eTn0J0f7BP67HM4HnoI/CfdTcrRP6K+DPyYnsL/BP5JDfl1OSmoHZPz/fF8J 31fC91VgLzA+1YM/bQBZD+e1hIP158T2juXF9pzlxfaV5cX6w8L+kIDM8GJ7 b6Rie28EeyLhIX7hxfbFCPbFCPtBB/tfT8U8OtgfOtifeirm1YF90MF+1YM9 0VGx/dRBvGUAWU/F56WOitdfB/ZcCfktJdwHK6l4PVUgZwFfFowfz/8k6E8O /cmh/SSQeZjvJNA3PC942P88nFeYn1LA+ZUF51cWtJcF8ZIS/F8l+A9ZIKvg /NUR8f7UQf5KCbKaiM8DDRH7Yxrw1zGfpQd/qwL8owrwh4xgLxj0Vznx+cVC vovhxHwsJ14vFvJhBuBlODEvy4l5WcifGSDeZjixfrCceD1ZyK+xUP8j4cX+ HwsyA/k4lhf7Eyzk51hePN8s5OtYXmwvWMjfob3UU7F9rAAZz3M12DMNyJh/ U4E90kC8q4HPtaC/mB/LAnuI9gPzc3KMB2B9edAvBex3NexvFcS3Kvgc80tq 0E8VyErIryhhvCqQ1TB+jDfUYJ/0YI90kP/Rweda8I91kB9C+6IHf0QH9gVl LcYrUN/GwvmrB/tcAf5rBXxugHhABbIaznvcn1mwP7Mg/skCf1oJ+9EI+QaU JRz4L5x4vVjMZ0E8z0J9nwHi6QrwX5XgvyrhfFWBjPqlBH8B9UsJ/WfB9xXg XyTB+BhGbG94yPdgPkgO8XoS+Jc1My46f3iYfznEw0ngXythP2dBvpVhxPqG 41VAvJ8F8ZMS8lVKyAegvy0HWQHnP7avhvyBBuJXLeQTdMBnAH/EQMT+UgX4 L5ivlXDi/BDLiddXh/E4yJgPYeE+Sw75Tjnkj5LwfgV4k4BXD/ZTB/ZfwkE8 y0E+lBP3b8R8PfjTGsgHKyG/poL8sALybVno/8J685ivAH/tP/kqzHeAPqtA f1UQP/Gwf7NgPOi/VkA+k4V6DgPkU4xw/hvAnzdCvsKA9hjsuQHsvRH2H9bn yqG9JGhPDu2hfVOAP5UE8T3DiM8THu87GPH+4OF8VsP3FZAfzoLz2wDPV4D9 M4D9qwB7i+dLBayHBvxFDdp/8Gcwn6EC/0kF8Qn6Hzrwv4xwH6GG+VdBPMkw YnvNY/0A5G8UFPK7eL8M49GAjPWiBvCHKkDGekwJB/4/3HdpwR/SgayH/IEl +B9STtx/HU58/ljCfZklfN8Cvi+FeM+CC1wo7z04NMG0//npqy97rtn8398T RB5emdu6d7WQn/xnsO0HPtdIf5zl9Li7VZnAQ2NsZ69tYa4HMM3X29ZOqtY6 831/oxalpT83LxX8l03jIwd26VdG/W6GLz0jM9/PayNjbBwbm+ZXQZfErFbb jDHVGSto0pN9B+bW0QlyUKug8ndXzPXjDUZ2fN64iel58/1aykR/mdsf6fTU m73yT83+W+8d9jHn6jTX9zT1p+gfs6+azlvzfduN+Q/ZyNd3yaycDXSYzX/v 27i3dnHe7x6RwHWzRvfrbqp7N9dbX+v72qfhx8fkr3Xeinc/muuvGU+PY9vq GAXZdP7HDJe3br6JF/yvXpsP+PX9K5c8q1w6dV9L832caX7Px7N3Z3fOEPa3 9rcPjXKb5wr+ju0NY96+fA0xOs7xjNOY641Nz/vqRjs0DywW4qH6u5s85ncW E4tFBW0bbjPNh/l+eYedokvPenrygFnp7/eLxDRfwv6sDJp5PC7XXK8b2m18 T4tTZn0JLt0+iKaxwv3wOOsZiZb3WOF+eNWp9vPXFzJc51W3X412ZXhv3Yz7 Mz4x/KGcH7fGBF6ln9Jyhsx+zfAWm4odxiy04n2PuF8qGFazf4d0eDl3rZ7+ nXgvJ+l0Me3Q74/uVW5pZPHm9Ts7Li6mkwpj1+UNKaI3Y6/7rp2WQRNteg0e prLiX0mGj+hx5h7dsWfJs2uZNfs4WrvEoz6lgeumF0f8W0imFPw9fFTDv6lU 5v1+xAQ1mb/bzqepOp103ei4bd9jK27z4xejl3iVkbcdBh2yGKOgnRW/yvxV RqK5tWT+uGN1uH4hTve6FhrJgp6yiZ9rzsHkiFHkyQKWT17QyTXUtYQeaDLV v0MSyyf65l972yWXhj51dF72gOWl+vSl86/E0/i8/veC+tfEa3UPj1i3zoKP OBNC8r0NVCbzXSebKuUVUyI6Dy2oogset3H+uiyb6GdESBpoquh+zudKb6uv pHGe5zt9toEuP3A62kNSQje2OFW0r6mW3j+YeXfDCw096hTh08pFSy9dKd5R vEBJLTPzZhu6ldJuk1sp/R5+oWnKJieXdC+l6XlLfdxt39LovlGs41kt5Zkd 2xveiKKF7ea//HO1lu77faJ79tVYoj6x7MKFeA290LyxdERBBfEPG3zo2JZS +uffUZuXPzGQ8kePgjymaumKhz7t6rlb8z26X7PpJ1HSvKDiiDo18vR29l30 VbnU6tRoj8PTDTR82Nowts1XOsAi6GmTd7m03rXDE6zdcmjWx9c7z3V4Rav+ OHpTb8ylAcFf5uy/LuPP3tjvuuBQIl3TOEaSbl1Mb7K/vzhx+iPNNaR08Pes sZtMaflflXF0U1jmsLgRVTXnmuvsnSkxxLezbp3/DEofvBt9acHJbNJ0Ox20 e308WfFCNSDQQ0mGBXz68Pp+Lsn/ya7ZoFU5ZHjkkTGdllUR5d39HnmF2aTu BN/0x12tud4jrxy2259FWg0d5d/RzZqb1VDvcXDoV3JjwNm+E9LiqVfulN6+ SzRkcdGMjH3pkfT+Yj5gap8SEjK3sVE5Vk5+dRyYtuymlmh6NXucW5RDqrYW 6YZ0LSVnc8c5+/TXklc/XK34414JoX0C6qwaXEq+fLXxXTeohNgXTbk7L6GY /vVlkMuo7ArieiCwfEqEkrpXZCfM8KkiW5Y+X1CQZcEZT9yesu90JYmfdKQN WfOSpEx4nrpIynKDrYds62qXRLrumn2870yWC+43bP/CfkryYf/hul2estyW kcGz3V3yScsrD7wavGS5lDbXG2ztqSXXsiMsPD+w3MvB9rrQDC35/fLcGdPl LLc84Gzo3DmVpORnm64hm1huxrQs7UIbI/k4ySa0tJM5f1f/+Aj7Vo1ZIR93 vuukhI6bzPVx3ouONnoTYc633exuxyoSzPk118StK5+uZoXfA2bfensrKlhP mzv8sSssRUNm6eZ6v5lYIdRnFJU9ibzcUinUr/2Ve3Hrkq0aIX45Oaf+pctt igR/c6Tz3JX1LpZQ31vMY/7rZZO/QcePu+iVKleRC7aeQ91Pf6Ujl6VPnfdQ wkV2XRTzZlq+0N9rbUl8QGEudbzbZv+xgAwhvlcOtxq1Z+wrYhd3aqjD6yw6 aele72dNSkjkxfzssy2yqWpIwrbOjUz+ukKoB8lud3BZ7PZEGplesmRo4wo6 +dIEhyP9/iUPD2ddGvchjyQaq2O4rnE0Wtt3iNvTCjJ1g/uWrpsjqU97x+Xt HMz1X6Z46eVRv3mOx66RvWzfHFfWkve6aj+x4v17wd777Fh3+47nJ+L/9g1z LKRciH9i3zo67XRR0sGpd75MJolk7Z3UrDfdNdTDQ/p70505gj85rI3F7C1+ 2cRrfvW4ZyMkvJdxyMnYN1+F87N+s4+SOdn5pCA5xsZJ/kbIB5jOjxsnuv0w xUZJ6ty88yDolLk+q6Jekz2Bzc35t1RFtzsj4yspM7aZzXDrUuE8PODs7mRj pSUuw61cJeSykA8ztZ+/NHTkhs6lwnntstw3Iv+2ud5K4Xe/44/VpSR//O3y jyf+W3/lJHO/0bRXpcAz+uzGvLDOBpJyeuH080cLTfGR8HubQztazZCOMRKf wyNOXr5iWl9zvVVATID9pfaM4M95Xe05stTKXA/lOLPTgfqrKulPk17MSjlc KMRXJv88dWPUuehD5UK+beOqKzObhRcI8W+b+tnxo8KLadc7s0LCulQJ/ist Kzpn19kUv6iFeLikY8rcOH+VoA8DLC0HjZ2eI+TnppTs1jrt+UIvbJP6PG9Z LOSnTOOpmOxf+O7PTCF/ELaA2v8+K4umPuvjc22cKb5W0txBCY1SmxoE/82k j0+TbNIfz+YJ3WZtu/JYuRD/NU6Y2mnxbkvetB9N8UfTj+yyAe5pxPiJBByo 0UPT56kH63/eWF0q+GemeOlAdewvIyZnC/msxS1aHpm/4iPZsvveIN02c32U 5+Uxl1IG/rc+6tK86mUBg4qE58OP26w+Pr+InEpu0TS2X5UQn5h4Zrk2W8m8 KCUBYU2l744nCPkNUzww56dN4a6jGS6txZCS9lcZ/uruuk073mH4DN+onGj7 OnxIk4u+zzZV08eu6127zisnRpfndsFFavLqZJuQMyoZ9yhmxl7fyGKyvGGk qixWxt33tA99s1lDrixd+fu1Dwz3cuGP07mbDJfaeskADxXLhQY2MrZyY7gB lp3GLrv4lDzPfll3bwDDNzxztvT6jXhyKikt4aI1w/96iHG4M+Yzia+0l3Zv WU2Pp7juS3tcTH549magc0IF7bU/Kvh6aRlpUp95/O+WMtplXfbwB9szSX1m ypzio2rq71kxMsy2iPy0LLzL05b5VDtjzoIX5xmuk80ItluRmvoE3v3S8jYl dgH/7r8Tmk0beZCwNqUK0uHgwF7WHT/T6Em66K8DyshvDuclm+qmUv/Fb2Y6 X5By7ts8vnZ0zKTjt3Xh12+14lc6FrwranOOrP/cOfX19CKaM63B2bb/XiY6 i3Zvw1bpaGSnaSu2j04n/biP2oiQSlKvibLbpHXXaeSz7St6v7Ti2qZUy/b7 XCCl7/ZGNBsg5d+eTDG6P/1C4ioLRv2iSKNvxqzx7h6tIPmnGCe3+Hzq6TSi /rD3KnK4q9VZ/0W5dNbAdr06Xioki6c733X8IOMcNuqyZTeTSdNk/x4uV2Wc /EJJui4tkTR0ublvZxeG90gxXnh/rpjkhO/vNn68gj71dvTp3KiYGPr9EBSS paNDbF1ej7LWEx9vm2kr16RQl/wtLa5sryYHYzx3FS48R89PvDj95+sM16tP WJM+ds/psePt7xfNZzg2bsTTMdeyacw/4Z96nSwi3S8cXmodmyXEw6b8ysNz Lv2GXjXXJz04fX7xrd9q/MKrWZF+S/MFfcsM9Dk1fXcB2fw5cdAO7whB/21L Yt6PHakkL/8a6BOed5fs/qWs60S/EiHejeIs23Wt0hC59YZjgwe/p+M7h3St 20cjnA9XnG6Nmv1jPPU+1czRducX8ly/z7fFhVSaMco7+vGFbNLeeZah4HIS bT6tyx6L4ARyonfztdHjlOREeLXzIM8MEtm+nh1jqyStYqPDFucmE/eG0RmO kcVC/Nr3df/S9AQ13f7St/jsqQoa1o1b6Ha6iFYVS87MXGyuVzflv9ZbH36Y P76Yuk2YGvjzk1QaXTzaduKEGr/408mdDiWfaLfSUyejxiuF+HbzhLDGoyYr 6Xnd+lVvrn8kQUsjS1/MyxTyW93S5gftDs2g5Ic+YV7v1OTglIYlab8rhfsU v+Mjs4/EKGnZvuR2+e4fhfyFyV7EHPN6d/MAK9T7OPh1WpKXxgr1Pb8/G/Jw +SNWqOdpnnzeum1nlh8cNs6lQ6jpfkFLq9fM5w/yMn5Xk0NDDspK6JKmJYte OljzgXeHDwj3NNdP3ZyRdnqMIkdYv+Y9U1al1PjXm48s3uy429SenMZ9+ZhT 3lLGP/rz549ZyRF01xHX3lGdi4X83ZN8v+tOeWpqFVp/m2wIT2Ov/xvrts38 e5QpH92GVdSz4l7ZNy7/rR1Ph/ZoZf2yvhV3YnnfF3OKqDA/B2/8uarj7RQy dsT5izf6mM4XOdmZIelw/1iVIGdYtd7wVl5F8t1CTl549p5MX+sen55XRSZ0 uWh1NTWZtLhfFTm2TZXgP1SdT5rsrTSQsgeOjtet8oT9v6bpltzd8ebfp5zv MsdtQ89Swb7nf/ZmetoZifNUbrrr5CracNzrBgtGsPybni5HZi3T0cSw+3va R7N8u3GO0avdCmhgtCzeO47ldZKH4ZriD/TH/HpvX0w03weneaV9/TvUQOsw jQtWjpHyVoyvU/+qKuH8tv/96OTRs6po+50j/G8PyyYFfw4bGlRRRet3VwQF yq34Yauvzt2SrqP7hkbk2LnI+KeRybb13pUK+UC7kSXnOjprqVv9Dy6pR4po uWvPGcMTzb9vWnT6bFxTj1KaZbf8c37RV6rMiT25VlpKx89J9wlfqqDePYJb HG5TKuyHXtt6v3m5T1vjd3s363o8iSZsXdTofIn5908PXN1Xzz+qpYftbu+M mFZBvq6e0PtRXin9/ENlkL27+fdQ/V/b5nm1qRL84TtnBtfNTK6iOy0nXuj5 LoPmTfZaunhvFd04pv7jQ4ez6d3+iWffqfJp/5zB/Qeps+i7qOchmRoZr1rz eP61x2k0/5VmwrCQKmH/kd712sv1VbRB6cv2LT9/ovcG7xjR6kkVvdqlCd/N MZGetLSI/zXDQL9s2lRa6faMBi166sgyVbRwbh/LopQIauj2ig2tU0zPLerh XjEqiZbxMW+SB6hJ55DK9g3/DBHue1ePPFuvSXgWCTv9Ln3PmY90+uJQReai TGKha3Oo3DOBym7ue5654gtZOMHKkj46SO7nWezpk5FDPA+dr7M1sYpsyFue EJeTSTTpTFercSZ/VkkOz54WcsKlJn5Me7Ym5+AXcsunwT9/9bXmBi2OrT/5 poqkXJnewDn8Opk76fRviTIt2VZVVJ+N+EKcbt5k5/UtFfK1FyxfjR4YUkKG 2E+SFVfqSF7lX3+u3WquNwl9uDOAWVFB+GfvC/TPdHTX8fOz0p305LRLzIfu S7W0ILDzgjs7yknsnfJ9o9YU1ISda+foOXM9Ggke9yRiEstZPZjZt48hikh6 1y2zqMNy3i0m+UUdTydpHm23DN9ori9pPX9U56oYluuz2cPd82smGTH8Y3bA KZabEq+/4jVMTfyPPNpalmSuL2ndxX3iyhq5JIoZbJmuJkc7NCg+msxy//Zc k9o7u4y8HPbkN9sH5noSq6fJLw4uY7k7z0vsHfuVk0khd7X1wljOcZ5Xj3En jOSJtPtzixYs5+U3sPkZz2pTPpf3SFc7/3GH5UfbNHjdYxbDH2ZOJrT/xVwv YspX/51yb5XKnuFVqa5xS6vK6csua8e+Pcby7YPGBvTyLaPxR1dsOPGQ5Y9N uWhXeU5D7edr/ihMYXl7Q/Mi59OFtOPadb63E1l++uSGT+fcyKKX1rDO7S+z fM++/RL9GipouweTRzbczvKdlnv/6bvFku8dZWPj/0sF/ftXj4Atl8y/dzTl 14PbL38W/76Cpu3yb19So2eHmITtFrYltKPdhQk3RpUI8arpPmqT3W+nPq4t oW3qaveudzDXi4XRviNGrz1H5v4x7P29eyXC/cpb76RdJ1toaZ8e0V6VlSb9 MteHtPhlrlPqlUxa7tJga7fZKtP9E/1jd7Jn+phsen21W+LS/rn00vHWB9d7 mX/PaLqPb6BtnbBlipIO3bd/26OOBtJlx4KDTPQBsvGM3962ReZ6kWOOv+6M Y831Iqb7gPX/TLMfUa0gZb6hG4Y/KaJ37voOD5LnkT2dttnUa2Uaj4qcaHV2 6aCWpvNJKdwPNIxNOXpxsJIUzp63K9A3g3wZ+PzQIWulUC+i2n41qEHfXOJs vPoycG4l2TYpzu+iqoCEjyvo5bfEXB9iul8ZK9nzwqakSLiPPN10iFOJPJ+4 qRfYrHX4P+pHJgTNyQ1SE/2w6ODU7Kvkb/cu0XcszPUao3NtFNF7SsiS28RB G/CByCwtPk/L1pJKxYq2oevTSfCJoeGnGpWSjdeOpz1c9JUUrrKMzK+Rr8xb 5nnvdBGJPxbyc9RLLQnuX29V07bFJPleu6GqI1pyoLf7xjbDS4R42HQf1v/r zVcz3bRkVu4O13kH79M6hy/JLDLM/x/SLM2RnGZhDB9Q9HUd6acmZzs1TTp3 oVK477iVsGpHmpeBupedurHtbS7Jdd6YP3ZXIS3b29qw+E2WcN9nmt/W23v4 OUQU0ZHu3S59bqkS7jvHDHAoOe/8it7u8dyL1vgf8mllQ8Z32koCG54J//Fy Di1YuIJ5wMtN9/FCfUjo7Y+K3spsmqc+dO143S/0c9tTEcqN5nqQ8MDysmle 1eRkg54ey17H0ow1rlOHuzNcUL3Tm8PaJpCzbuMHBVswfMhbmfLzhlIyv2Le hii9np4ZLj1/wdmaN564a1AFF9GDTm87xtla8+8lE6u2/KMQ7ivtg3pV2Bfm 0i+zHXv/M6CUPJn3aNyu9RnUNkK3dV7DcvJ21dYrf+yMp4OqWnBWsZZ8z4KF IzN2xZAfy47t2d3Ogm+1rvy1MT2dxP/s3zqkrorOW+myOdYzhrj/PbH5x4gv 1N/q3KKZXqnEuLjB8M7pKuKZf+/E02tfyJZxkzfEz5ZxwembjXsq/iWOm2b+ ZD+iDh8R/JdbcnIu6eIUOOjTIJbfOerB1LzQIhL3+oL1jOA7RHM1tPn5obmk 8sCSpydaGqk+/bBb27klpNAp7s21peV06nXG8ckJHRk/8rVTd4Ult9w6pPP0 0HLSvs31GauIhDs/cM1TzpHh9ld3sV8SaMEtdp9iuDUjlS5beKNrynQ9HZPy YMlb2w/kVMTxRYOWy7i91dlniv/Rksxz873HxUo5n011CjouqCIxzeo4P7Kt JhzpEN7mlobG5awav+kOy7UuSkyT9Cyklt7tP/X/uw5XHfUi7NmkHPrn9tKJ VmGVdPvl5Y7eu68TP1nWX2/maumA7WnvZ+gzhfvWgF0Z66TXiuniBuWp45yK yfwLsQ+Tz74mR/t329S7YyF9/lvQ80lOBvL/ADpSCkY= "], {{{ EdgeForm[], GrayLevel[0.9], GraphicsGroupBox[{ PolygonBox[CompressedData[" 1:eJxNmHmwl2UVx9/397xoSoVeitiXmWS5LBcwA0VnGGbKFIi0GSYwzRhmYknN xhIuZqmVgJmUsikEiSwSQaEYIDuxyXpBMLlsKiEJomwiqdH34/n+Bv84v+c8 +3nO8j3n/bUadM+td5eyLGumn6T2TRH9K0X/Em0UbRK97vYVUV1RE1FjUR3R l0VfEhVu64s2i14WLfOepe5v8dhyn7fEc9tFK0WrRNtEK9zf6/2ct9X7mFsv ekn0D9EGt4st72Kfe4mogeW7VPQV9z8nauj+Qd+9Q3TIbY3oXdFR0U7RadF7 omOis6KTnj8oRR0SfTXXeWobiVpq/DXLhCwH/B7uqPU70MFlokaW43Lrkv4+ z/PWZF1WiN6yba4Q7ff8Ntuqwvbaoc5p0XbRHusIOY6I1oleFY0UjRGNFt0l gzWS7GN10Xy1D4t6iqo1N9br7syDH4U84m9Q20nUQXS9qIeovdvrRIN09w9F //WbKkXtRDM11lTUxndPEk20XItsS96G/lqI5uiuB0RV1m1LvxGZJns/cj3t fn/LPVz0BVEzUVPR593ir4c9/kXbeLVojeiM6H3RcdubsbX2hVVet9uyotN6 uus2tX3QiWQriYaIH5aFDOi4bx591jWTfgeovRndaW2N5nbkobPeHn9R/dGi 3nnokbGbrD/619rfsO8M/EptQ507xXav8fg0jXXUGTfLtlPEV4r/lvhnxLcV /5D4yeLbiH9Q/HTxnbhXfGOdcZeoi/pr1Z+q84ZqvkJjz4rmYSeNPyn+Byns A/+/LGyE3VvZdk+VYn6H1j2odrH2zfA5nLvd4x+m8BX2Vrgd5HP+pLkOWnNT Ee/e4Te2y4NfmMX4Qfs8fdoX1K5Qu9J2wfdmlcKXTqmtFW0RjSvivU8UEduH 7RvE81aNPyd+ivjB9ufBlg2eOc6YmcWbeNt9edwz037OXZwzPov557yGsVOW ob5odyl0fL/mOqZYg333i57R3rlZ8H/JAm8OWg9tUqzDB1p5D+t53z5iJo+2 1jJsMT/BejroN/a2j+Jz+HJz8QOzaFvYd5lvbr7W8o/323fbpsw3817igr0l v6N1CvkrzCMzbRvz5Ib2KXID72CcfR1SjON7XdV2wV9TjKOrv2ahrw5e09lz jHMWa5d7b5XHr8rjHHjWdvYd7b2GfVUeZw1ru5qvMt/JxNx8jc+WrJWi7uIv ZIGRLYw3HbPAywVZ5LQVfnuFbUqeOWRMOiF6J7uI9bTgHr6B3MgwQm3rPO4l x5CnyTlg4hxkySIG1hrLyIWbvPbvxrNVxjNwjbxQzqPrs4v5nnPLOXWDfRBf xMf+5jNWes8en3VvHr7Imm32f+Jpke/7p+iVFGv+kMIn8ceZlnui3wGug++P ZZH/91omdEfeI/+VczY5fKHfutpz5Mh9lm+796zynnL/gNdt9xkH7M/40zzr ElnIL1sdr5/Gvc+u9X01Ppd1z3vfm7Yj+Zn8+7bfjr3xdWqe6jwwGV3u+AyG zbY9XvKbdvoOfAmMai6a5Xn0etx3YMdReeAjdkbWF+0LVfbXZZZvse9437Id 8dn/9vnEAnKih/nWO3E0Mo/cscA+8Zptv9n62Gu51tveE/2eWX4zttxkHWx2 n/cd9RuX235bvP+I9YY/zLV+l3l+s/f9x37A3BK/7aTHj3rfUs89bzuVz6PF V9fn4btDXXeQ06gPZzpvUKdR7xDHxHO57qFPXUPt1jaLXPpJFnUme6g1L/NZ l7rP3CXudyuinngoRa1HzUcd3dN1ADVAhf2IuXKdXa676/isRr6HWhIcvy67 WF/0sOzUFsyB4fgtPlKOm4X2E3I/+NUyC/x4wT7UxO+t9F3t/N6m1gFzjX1/ XesIf23uPY19BvkBmQZaxsrP6tF7wFHGr/VcE8+1892cBd5ek8dYuc6j7qP2 +di6oXb4yDojn7R2TtntOqTCOfLRLDBoYx44S/wvtX8Qr52dF8oxwRhx0cnY j8+u1Z5JedTL1KjUgNX2Nfpg2SLNPZFHbTjW89TVy9Qfn0cNC96M9h5q8xFZ yMda6snh9jf8Ev8iLpGBeFyVBZZgV75XiOVyzYtuqG3wb3z+Meu1rnVH3dXQ NRg1IzVOffsBeYgctNL3cAd5HRsOtX+ANWB4OcbQHXE4x/3b81jLe8Cd2Z4D K2a5v934is/VuN4Dy+aUwt7kVnB4m2uPSo/jAw1dC5FXNipAeqmdq7EL2COP GOvllrr9da3pA45pzZoi6rAb1W5Mka+eTPHWNsZn6maw+mmNjygiFz+VooZi PXLX2p/wpVFF5OgJKepszpmU4qy2xmfs1tpYOlPtj/LYT9vIfvnLIjD3Nu29 OwUudaFuTYEznYuwGbbDRkUpai/qXmpg7ITeqT032re5f5RloH65yrXEj1PE TlURMVLfdR2yV1sPDxfBf19rh6eIr05F1HrcOyC7WOcRI/UdZ9R4t/hM+K55 fE9SJ03No1/GHVpqp0eLqLd/U0StgG3vEz/BcUbsUC99kEWuBfPPZ4H75LUP s8ht+DH+XGEs+MhjxCDnEAtlvaPzyc4DjO8pon9M999TxJpV4u/Iw6fx5wck 4+o8xnZZvqMpvj+wAevq+XuK7yr0hQyji7AfdvxdEXkcucl57/o9R/0Oxnc5 rvk/op7fypo3ssgv+EYD6+SUSNd+ioMf+91veY7a5JjPOuw9n3gttmf9WLXn vKaeW/pgCn5DXC60XGcsG7KctTxgzFDXgehvvXXKd2+NY3B/Ef857JKuuqWI 7/Nq1+WxB/2DRVOMR+h3tfVOfmQvOWKB2kfyyJXkBL4ryU1Xp/gOO5ci7sEB vsfJg6O9/kSK8+8glosYr03x/cq3LbUA366cyTdrlesD9M3aBT6H++dbrvN+ Sw+176X4lv2tzhiW4rvteyny7FT7/Lwi4v+kxs+IZos/nQKDwJYtKeKVcfS+ Jg8fw/+HpMDC/mqfTfH+mhTYxn8Kvf3uRc4756yTr6utUwRO9BU/Lo881N85 6uU8ck3dInR+eRGxMsl22V0Ef6v27iwif31b/J48/uPAzuBBD2MCtupYCntR Z/UzVo9JgXX8FwWetzfO/7yINT8rYqzSezt6DfxAzXUQX43/5HE3/0+18Ldq yWs7+Myri8CcrmqblOL/OmxDy38fLc039Pge+ytnUo9Uli7WIE2df4+niGvi e0sRuvtmCn2ssY36WqfoE72OM4+ux3vNN1LYApvwXyzn48PYea3xrp/mmmv8 Xr7LS7GurWWBx67YZJ1txFhzj9+ivS3Ff8e+3MJ7wV3G0Qt5YIRzQSPj2Qhj yQnjx8AUa6p1zjsp7sMXri8i99ygdrDzbvci4or4+nUROYxc9gvyS4qYOVAE fp8wXt1uX3+kiPmejq8D7u/R/E9S2K+v7dnNNpqYIp+Se8lP5Klf4ZvOceRt as6exo3vpsAZML5fCl2D3392fP5e/KspfBesOpviv60P1E4rAvenq/1jitx2 P3pIUWdQg+xLgRHgSnf76NfYV4QO0eVPbcslGruiiBx9pdobHZNbxb9tfyLO 5ju2qV9m+MwN4g+l8DF8EHztZYwFU8CWUylq/97W5wXbnvyL7/Ne/L/knDXE +YTvmQbGcuIWPKfmmuP6mxioMh4Os6/j2+OMJfB3uh5DNyOLqLEHpHgb696w 3fANaqFqxwZxwX88rOd/j8eLkGe66w5yA3mc96MHdNzOOQDf7mP8A3/GGP/A fzCpj8c3G5PBZu7i/yBqpDFFfD/wH+R6YyxYe43xHFwnD61wfUX+5NuDHPpY Efw011OcQ/79P1EtLYs= "]], PolygonBox[CompressedData[" 1:eJwtlVeIVlcUhe+Zc8iDikbyYsEGRo0V6wijIIIltrGAFQsiqBhnVOyO3Wgs UUnsjljGjowvaggj2GtUHKzgPCiISiwPNgTrt1g+LP617jr37nP23mf/DcYW DizIybJsEkhgVcyymiHLxoNCHtxAt4LvZ9EF9C5QBB/FswL4S/y96BJwHj0P 3QK+BP82/BpoCH/Iswr8f8AG+EGevcMbDf7O8TN5r9H78OqDS/ClIA8vgjz8 1uBL5ndOwO+DcvgvrL/O2qGgJPMzeXnojujm6OFai27P2oPoi+i+6An4p9Dl 6MHojegb6HvJe9BeKtAP0HOjv9UMDEMfR5fhr9MZ4f15Vh+vDboduhDUQtcE ffCmouvC64B+yTlT7nYQ8wXeCHRL9F3eL0K3Qcfgbw6AT4xe+xP6bPIetVfl +DLec7AVfgGcgb+PzrVqqFo+A5vgZ0EZfD4oD46p2MqBcjGSZ0/wOoDGmfec n5wz5U45zY2OoVhbwP/wKvjngmPkR8dQrI3gKbwS/pngHtqZfEadNReURMdU bK2pnNyUx+B9QCneJ1AKXwruwKtqT8FnHoQOqgl8JeiC/gF9Er4e9EDPQOcT eyp6CPoNOACfB0bCD4OuwTlRbgZGn01n0tnaJe9VNWmb3HPqPX3jLbxf9FkV U7FPR98l9fSR5Duju6M7thJvkXoK3lhnQFdHXw6+U7pbY1RvdGuwG340Ohe6 E7ob81k/B90IbNZ9ST5LE7Ad/Qf6o/pf8eDdo3tVOVKuHmhNcI6V656gafA7 k/B+TX73E/o3dC90C/Rn9OTomaDZoB5pi/4AFgTfsT3wZfjFwd8sRv+Lrpfj NTeja6BaqIaqpWqm2n0F4+DDwKzgnPyIPwsM4P1i4l3BuxU9a9QT6o3f8cfi j9OdQi8G1YJrOka5BrPhP6sn8Xok3yV98y/VOroWyulCzQdQG68B8dpHx1Ts Gvw+xjukmgTnsAK+PHmt9rAieQ/ai3LwCv4ouhc1MzQ7NkXXTnsqYv10vR/c ozPha5NrPxm9Hr4umWumaLbcjb4L2oP2siH6bMrZHNZ2TM6lcpCbPHM0e1SD adEzSbNJZ5zy/Uw6m/ZwNXrGadZppmu2d0anHM/MbuhVyb2hnlgNX5PcGy3R fybPQM1C9bB6eVt0b6rnlyTPVM1WzdyL0f8R+q/QDP8veuZr9itmJ3jv5G/r P6AA/xsMWNd7 "]]}]}, { EdgeForm[], GrayLevel[0.78], GraphicsGroupBox[{ PolygonBox[CompressedData[" 1:eJxNmXmQ18URxX+/mdFabiUqJV5hAYNaKRZZgZTiHY8kgJFDURAURbxAUSAa 1yOe3KArGIzKIjGCIAqCZTRVgMYzSUWTgKg5EBUh4VIgqLnex36U+WN2+tff OXu6X7+Z7XDJmHNHp0qlcrH+ZNWd9KezynTJT6vuW61U/qJ631KpXC1dX8nL VQZKv171NNW/VBmg8rx+D1b9oeqXVW9RuVKlq8buq74nqVykcer0e4jqhaoX qPSR/tsl2g9Q3+bSNVMZJ/0J0reV/nj66ffJHqdUQ+6m8k/Ju1VOlLyv+u+j UqP+26TbqlJrPWOOlFzj7+Mlt9S4N6s+V7oXVIap/Seqs/SjpV8heXY19jOC vUp/u+q3pV+lMkry31W3ln6K2p8n+QPJ70l/ieQFKqdIXqP6ZvYm+UJ9v60a bWpUFkt/tup3Vf9XZZDGOV31Eq3xqRS/K+pzvqr3pN/MnCpn6HdV+iGqz2B/ OfbWQn1+Jf1itXkix3oXqfR2v8XuuyHF2s6T/C+1O0v1H3K0fb8aa7w0x/gn e/yWPpd2qnfpe2fJB+ewP+eypxrnwVm8nULuKjmpTVXl3/r9pfseKf3nkj/z ON+SvFPycZIPsr6Tx7kqxRwPaF8nqH5cY70u3WspzmCEfW2a2h+h+lqVxkro 2lt/kPqMlnxfJebf6Xl7sHaVMyU3qF6gUqeyyz7UUfoDLNfa5/CrY1WPVN1K 3+6UfKbqNilip0UOeYbk/VXvZ7lVjt8zK/G9tdsPdJu5ks9K0Yb257rNo5WY p6XnulXzPlmNtR8o3Q6vs4Pk7ZK7V0K33Xr64Bd3EeOqP5W+nthxX+yMvT+1 zHn9pxqxuSzFuQ2VvFz2n6nfy1Rvcmw0k/xoCr88237WJYePHaX6aJWelYih K31WH6j9euJc8uAS9U059tPF7Tuo/qZKH7V7zLFDDL3hM2+oRNuj3H5Wirke UL0lRfywnp457PWK5ume44x/Lfl9xyYxusGxRgzQnzHZR88Sduil+mKVnvo2 QvXh1bDp/ZXwM2Rsy95Y8y2SX8vhh2tL4M3llcCc54yr4OtvVcZLfqcEjjYa S1sYnxhzk+TVKfDod17/btWnS3eayoRK+EhH4/b11ZAfkXyL9LX0SWFH1ja1 EnUHr/MRtf9ONTCVdh1s84kp8gHjoat1e8bu5LkmpcgXtOXsNhlb8BcwH595 Xd/H6veEEnHWzLF2Yw78O1D6b5Twpbaqh2msoSrzKp4zxbxd7B+MP19yL8kz 1f4nKhepzRzpjlPpjt+WGPsE48l+JewxOMcaN3sc/GST17PImMe5T8mBG+3V r8G+tzDHupirqRK42tvj4DPIPYhZYyq5Zq30a1Jg92iN9V1iXPU/NNZD5BXJ T6bIhcxBPgRHsR15ktxJnjvWOQ/seUV9x5A/GM85uLfkP+aw7Y8dE13swwON e9erzRs+iw8lL5PcX/LDkj/O4Xtv2cd7qPTTXG9KP458IH1TibOYr/pVr+FT 1RulW45d1H6b6hUp+MHxzt135IiPRscLazncOExsECODcuDfQGMd9SDL4DS5 Aby6uYR+Fv4tuTd4yH5TtOGsiXPi/S3pm3Lkn+HGAPwHrGBNrG1JDlt39X6/ wr8U+DlQ43Qm36nNgBRYzHrAVHCWM+mYQqb9eV7/XOeROuc4vte6/ViNlYgN 8rl0g90evzjdsYz/VqvBd2bAu6SfVmKMXs475+t3F9oThynGIqeMgcSpzXTn yaNsk8Fug9+CfawPLOlrXAATLvR6aNMthf/he3uxo8E4gx2IpXNS9J/q3N3F Z8R8dY6FTbY58cX+zlC52/aus82v9Zp/UML2nW1PMKmb1wB346yJqdNsK8bp WKJdJ+P0cSly1qgUHIJcNkPjH6ZvY9Wmvhr272FsPc3j3Khv/SU3qu3kFLkc fLukxBpnl7DNBT6vW0vIDxoThhivpqXI3zPs6+jx8zEl9j4pR3zuzXtwQeK+ wXF5ju05MkVeJ3f387p7wLNS+Cj2GV5iv7fnOJP+Phc4wqkpeNoQYylnenUJ /b05bNrVWEOs93T8TrBNyD1bjTe1KofkaHeRz7beNmxtvMVX7yNHcNaa58gU Z4k/XJmCL8K19qSIZ/RfpBgLf9mZIq7w7X5eE3HZxz4Gt57uc3y2BBZy7uxj lb6vTMHd2zqPbDIukkvJu6UE1oF55Fpy7vk5MBQcA886VuMMOTfaguP4G9gH Bv7GuYh44J6DjRh/gnG+recammOcz8yXyeV32q74HP3w5f4e/1XVc6qRZ59N wbcGOoe39pludm6FzzyVI59wJxjis8XHyCXklC3mSH1sN/Y13nt7vBp7AYs3 5+BRq5yLmJ+54AbwGPyQ+wxjjCuRM/DR133OY92G3E9fOPa7ktel4DdLVT+T wo4tc+APsbPGcUyObG7cB1c5N+Yl1x/hNWIvcgU8Gxsy3wjP9dccYy81R5lj fRPzpPh9Uo77E/eqP+Xgd9x1+uW493H/+1htP0rBBV9KwbX4tiEH5+IuuLAa +YPcscnn3+Dzb7QPkDuR+6jfmTnuy9ybW1hPjh2e4i5BTGHzV233jTn8lzso dy3uXNztBtjPLjcG1jiu8bVp9rc/54jLS9V+e4o9MBZ3OO6YcJo5KfgNnGVX jrz0nH1/bjXsTW6BCxJbw5036+EI9oHWxli4Hesjz5P32effcvgsbwMXmOcd b8yAAxLLl5lbwKNnp7Ap9txof7mmEnzlNmPLYq8XftW5xNouNt7Mc46ES8yv ho/AIx633+DjTdXwfcaqMc7Ue1/nlOD7+Cf+wVicCXg3Ogdefb9EroDbMcZX d7IUefaGHLm/v9osLaF/psR9arex7KocZ30WeJMDEweVuFeCd+ArZ8BZDNP3 a3LkrO+R83Pg/9mSV5S4r3Yr4dO19vMxPjfyF7E13bkGLgmn5A6+NcU9i72i X5Qi3zytsaZKXqK6fY5vtF/kduTQ+Y47bAv+H5rjLMmj4DB4TF/u//DWdjnu 2Kzj6RT3L2yLnjsjfe5zPIOx3B0mO89OzZFzry1xn+jkOwW+UOMzJH/em2K/ veyn5IspKcaCr7cqcc88oMTbBz70ZQ5/OcY+073EO0W935TQce4T9fsy5igx FvNj50Ul5l2Z446JP+zlcNTXSf/DEm8dx6qeB/9Umxel/5G56SzpHiuBG7yT scdJ3jt2vjuFrckv9AUDF5TQPwHvzsGFGkvkvDr7Km8iyORx3lW+cH7lXYVc C1c4pcSb2qmqJ+bgmtxFwH7eT8Cxu4254C0+Bn7iZ+yXWCVOyUvkJ2L/wRTf 2D9r4H2GdYCdrxk/eVe4PAUH4+0E28JntjnH4UuTS7SZVMK/8LPljsul9p+V xhnwAexZ5jXAi5mXeP4Kj72XK1JwDvgGY4/yGqaW+DZF9cM5/OGuEud/jOP/ Hv0egY+VeA+5wuPw3gSfPlj6X1jmLapNjnXAX+/nLkMMmqsj46c/z8FV4Kqz zSeHltjTIcbuucYvzoCcxv7Jj8+lwNmrzROa0td5rtb5lziGd8I5n3Gs0/do 3wPx162OR3j4TGMFONZofkA8gpfjjZnjS/CuG0q8U031+FvMFfaXfnUJP7ki x1yz7Cfsa6XXvN0yZ7cjRW5CxhdG2h9eth6fwQdW2Deo2f9A52XaDbPPrvZZ M+Y6/yaeJhoHlhiD4Eu8IfB+0eC1rPLaHi2Rt5ur3plj77zH4Ktg9FbnGfwB /+Cs4bv4HLyFNz5s8lCON4s7SnCZ5tavt92x+c+c++8s8Q4KXpITyXvrbJMH fM+8yTZvYf7HeW7wmc7Occ9sKDE+72hwmp/muL/d5nslfIvz3c8yvAseyvvw eHMJ3sThEzXWwyvIra19HyPftTI/+yRFnsZXwZc9jgv6NXNf5mnleXmDZ3x4 3rwcuX6P94N94Hsv5cDup5yL13svH3q/yNT8hpvhD4caH+Bgm1PEPTz3DfsD 7T6yrd60Hu7S3pg+2dyYb6PsJ1t8Rqs9LuPwtgqH4e660fvnrDa7DbwQvOdt ei/+YTcwEPlt4xL3gndS8LWFWsM9kp8scf9b73lZLxwUTg83/sjyctv8Gq9t nedlDN6CyRfYmT7wQL6/6zbMufb/xlzrNoy9xvZ52HeiZ+2P5HHyCHcWzrSF deT4E43/2xyP7Plg64nN7V7n73PkhudLxNp2x+wK9yWulzguOPNxxptGj7Mj fb3XHR4TPgWvAsPhVTuNG9yHtjjGyXO73Ya9EGN97KvkRLgfPvu5/faFEpz4 xRI5hfbEIzyLPEq/OveFnzHnLs+7Lsd79yr13eo8Qj6h7Zduv93r4Z4OXvG/ ir05kb3wfav3Aofh7YOYG2OMaeMY5H8R9V4LuN3O/ApsaO54git19B2oQwnO z/+g4N6HmLPB6dr7nMlXbYxj8Gc422G2ITbibg7P538A2AY8gbc1VeK+Sa6Y V4lYb2l8wH7JNse/9nHsf2Yuwtg11uOHzNnO87IffjPHtBzvJ9dp/f8D+z1E Zg== "]], PolygonBox[CompressedData[" 1:eJwtlnuQjmUYxr/vfZ5mrDJSqqmmP8ihRoUQiqRYdLDabbEOS06hza7TioqU nMkpRLOndLAOaROlmkkUWU2lmGwJZcpQSRLVTNPvmssf9zf3772f732f973v +7qfBkMKs8ckqVRqKxaxp/npz4Vy/AEhlfoK3gk/CWfBR+DjcD58CD4Kn4DH wD/Cf2FPpVOpJvBZ/P3EPyU+kmsn4O/hY3B34vvhGrgG7kl8K7wGngrnwS/j 19fz8Vdgl+P3xpoQb83/b2f9FPghuBTup2fDfeAyuD/8FrwYXgx/DNeDV3Ov EvhS/BzseuKt4PbEN8Lz4WtYMx8+CH8D9yW+Ee4ILyR2GdYBPyN6b1OJ18I/ jP3E+s5c+471h+Af4Az4a7gM7gpPYn0p/jpsFjyC+Gt6F6xR4ncsZn07+D9i A1g/DJ4BDybWEM6HB8At4JbYVPgjbB7+XGwDsezg3CkHysVxeFDaOVVue8IF xLfD++ApwXvthPWDP4RnE8vECqO/qb5tP3gavArOxx+ITYfzgr9tDjwZXhbs Z2OPw+fhSuUT/jw4Z8pdb9UY8fHYGOLXYYuCa1C1qGc8Q2xl8LPz9L7wUThX tQN/BmcGf6vN8F64L1xMvBwbjf+7aiztZ+TiHwuuNdWwallrtHYiVo2/FHsV vwjbjf+w3g+/HbYG/9vg2lDOlfuR+j7ECrFP8JcHv1su9gTxU3AFsVZYG/VD cO2rp9RbvYOfpXvoXme0PuV3+BP/AWwZ/jbsItafDO6tav5/AN4S3IvqKfXW v/AG4iexm+A3gmtXNa3afhYblPgZA4k9BzcjPpRrs/BnRvvas/b+DrwcXol9 AZ/GZqZcI+uJ7YOnp/3MD+CxcILfGsuCb8XO4Z/XN1CvYaP47xm4BX4Z60cn vrYguqZUW9rjamLN4dOsvQIugL/EpqVdUxX42cQbJO7pCXAfuCn+jao/+By2 Nm2NaoWfSbye9AhbGLwH7eVK+DG4JXwWvkoaB1fpGXAOFonVju5l5eTi6Bwr 19KgFcE1qlqVpr4ID4Xb4mfxnyHRPa/en8m1tfivR/uqUdXq+3q/xJpYHq0x 0hpdq8CfjQ3HP8X6W9Rv0VrQHn6F/24O3mtHrpUQW4IVST+w8cSel+gTL4AX 4U+QJuD3kmYG96x6Vxr7Arw4uvZ1j6X4pao//EewefDcaF85Uq5KiI9IvMc5 0XvUXuvDjxKbBI+DG2JLNG+CZ4M0U9rZRf0DV8HVun+wFnVRfvT+8N34l2D3 wPfDE1O+VhA9IzQrbpCG635YncSaW6ncBcdUI6qVGcHfKgO7C+6gnuP/beDB 8LDo2accDsf/h/h69r4Kfi+6J9WbmhFdtTfVSOI9aW9/Y+vSrtm26pXge0vj 26rWo2tDmp6vXouu3Q6aD8EzSrOqsTSF2MRgXzNDs0M5VW7VA+oFabq0XZr/ M7E/grVJPavelWZKOzUDugV/Y31r1fjhYA2Vlkpjj8ALo2ulOevL4T3wpsQ5 6gTXhXelPWN/0beP7vU7ibeDu8J1Wd9N/a1vG62FqsEieJdmIrHamv/w1dHv XgubDO+G1yfOiXLzbrQWSWPGwdtVk3APaZD6Jzo30qRt+NdK4/F/xW7WvA32 dcaoIy0MF84u8JboGatZK038Tf0UPZt0pnhbeootSPxOejfNXM1enSF2BNeQ akk10Dm6ZlW7OgPM0awOPmtoT9qbali1rBpVrXaP7hXN/JdYu1M5SPzMTcRq gt9VOVAuKoO/nb6RvpVmrGatNFvarTOQzkKL4CppQXQudGbQ2WFv8OzTzNPs ezN6rTRSWnkg+GyjM5zOcj2itVg9rl6/L1orpeHS8rHxgrYm3vsQzUydjdTz ql/NgJQ1p1j3is611jSInlmaXSewZvi9omtPM+TB6HvoXupB9eK90dotTZI2 3RY8e6T50v5G0dqoGd44uiZVm6o51V63aC0q0/eI1kBpoTRqD/4OrpUnrnHV uvakvanmDhK/I/jspB5Xr48Kvpf+o/9qRmtW651ziP0PVHhuAg== "]]}]}, { EdgeForm[], GrayLevel[0.65], GraphicsGroupBox[{ PolygonBox[CompressedData[" 1:eJxFV1lsVmUQvfd+U6uhEiqtaHDDKInbi4nGQqyUFrGFRMAihqCGyqJSQCig xUTaUsBQMQLGiBiIiuKSugAKIpvbQ4uBBDAICfviAz7w7EI8x3OID39n7nxz vzvfzJkzXwc1zR43q8iybHCeZQnyJ/xphj4nsmwv5EEsjuQa7A2QUzOtH4K9 HXod/K7Dcy1kO2w35LKPwPNA6F9A7sult2XS+S6fa7HnFNimw6cPno/jNwbP kyCvwVoFZCl+p+kL+xDIfrCXQM7GHuXQGTxt5bafd8wj4H8W+im/e9lvOPRl uc4yHvr40L5VSevU6dMF+Sh+T0Mf6jgux3CFfVbZZ3am71woZK/APjugd2Sy /YHf1bnioU7fvj5XHXzqC/m3+OzM57xM3xnq73J9p32eCNmH4DuVSd9j7g/C Vuk814TyNwxyO9YHQF8G2Rs6/1Ho5xwT92csZ6y3Fso/Y6iGbIS8BLmmUH6Z W8rf8/+xEa5lSaFn4oR1Lk3CQw++243nz4yjZP95rts4rN+N31Lof8F2NNe7 xMt/mHI+d+XKO3NOe7Pxtg/vroT+SFJNVtrn4ZDfV5ArIL81hq/E8x7oV0HW uXasUYvjWRU6H8/J89+O55PQb4MsM1ZrMskT1ucU0vvYdtL2waHcvhvKa39j e0PS+uSkOnQ7hoWFfFqNiwrXb0ESJhpDGP/bOB/uGt3o75xzHYk17ss9Owph hXls954dxmql99+E92dC/oznm6AfZu5DtWW9WNuZrgffPZCrPm2udbP7nPX9 xf01A/JX+zCeC8Y/sXoIv0WwTzMWyAesOffa5/cO+t1FSee4D/FsxG8JsQR5 KoSHs9wjV0z0JwYmuH9vCeHpZsgWvLfffVfp/RlDqTFHLK0nxnPVeWAozi6s 74e+mvmFXgt5b6FaH0nC7V2hHmXNmM9e/Hq8/1Mh/ydDXMkefxXvdRbqf/Za mb9LDmRtWWNy8CtJ35kU4rEy44o5PuI8V+eqF3FwxDrtR0N5+AZ7NDmeibly sNd5qDVmx5iv2A/kMfbQTvfR5Fy9R/2lJD6cEMLjKfvfGsJjU1JflHlP8sba QjHeH+K+XSFuYh90wv9Yrn5kbMQ9scY+Zb1Z93dC3FfiXDFP5d4/L8TXfJ6e 5DcKcjme+xkDJ0PPx2Gv9l70Zb+Q+xjTA4VywXdWh2bMStY0FP+BpDPz7C9C /wB6G+wb2CMhbtkE+3fOw5/Qt5qLiEdiZ7fPSF5Z45xMC53nwaT1Jvu8b44i VzWHZsNo+MwtFHOp95zoujAW1miy63zMa3vw7jqs/cYeCuGxMYmLuCfzSa5b 51l/mLiE/CephrM81wb5TlAJ2WDcLs6U16pCeZtfCOfM49SQfUpordb1WpHE E7wj8HudxiHnGefa454Dm4zhzbm4m7xS5Rn9WGjms0Ylrn2Xa01uYF+xx74m P0DfcrkP7EOOfbkQ/tYak23Yb13IvgX6Rs5A6G/B9noS73GeNBr38z3n6z2b Rjufw7HeFMLTM5C7Q2c87Jla7zl+ybxN/qbvfOeN9yHyYUNSvMsdM+f2ZueB uVnm/PR3Ldk3U5P6YUiIj5hHztVRhfJA/uZsOu6evzbEIwMgtybliJzRmsQR 10M/kZT/vSFMj3CvvxfC2o4kHC91PMR2tblodCGsECfMb19zwE7z9SFjaZRz uKAQ/9a5nlvdO+QY5pxz7e2Q35pQLhv8LvuC/E4svZH07ZZQr8y1/XRSP/K+ wNrSXmouHGkcMp8LjY1OcwPtxBp7oSYT9vr5/sm7CzHIuwPPw3ONTeLv896X eFziPPS4xxcZC522HzNnkavIc63mOs5j1m0z7J96dvDezfnFb3HePZSEh173 GvuMmBmftPfFpDoTO8TWRPMEeaN/aFZVQP6QNLNf8Pyvdk3LjUHu2+E7RHsm TBAbdXivKuTXkVRPxsy6NLh/yeesKzmFfLIk6QzPhvIxrNAz91/sGcq7SYf1 ezyLOZO35cIqsdEdipmxPx/a5znIT3xP2Og73np/e3uIe3dAvpZ0z/jcMVS6 Lgudf8a8x7lizr4M3XP4fxJn42rn8EzSPYT3kYvO+cchnpvhGo015mfA/lHo jLuT6tnpWnOPbT5XfdL+vHf86LrwrB8m3TXfhH5HiLvvhOxO4oXvof8LxqqO Ew== "]], PolygonBox[CompressedData[" 1:eJwllNlv1VUUhe+5Z0NjTImg1aogEFDBxBefBGOxBCIID0RkeKgaStugVSsU UDFRmQ2jlEEGKVEKFhTpxFQthfhUNWhQApIwhEIIf4MS+FbWw2rX2vvc89vT 2SOrG17/oFgoFOpBgFb+rOT/aoyH4B/lQqEkFQovgbnoX8AufJM58zN8O1iE Xgy+5+xR9JOcfQ+0we9iWwV/FfwI/xrbEs6+CKrha7E9hm8l+iC6HQxFvw86 4FtAA75KvtfC2SfQf+O7ADagO8NnmzhzDF6JbT06oWvh/4E16ArQCv8p/Nt6 8Cu6Hl0DLwOr0V3hu6aBzegy9O/4/gSfoCeBjfBOcA9eg38cZ9ehr6AXoV9D TwdfKTf0H/hqiL8OPT+c+0PY5sFXYXsE/im2ZvRZ0JEc87/4vgTl8CrVFN8U 9Bb4CVBEvw0mwUvB5/gug7XJdxyBPx6OvVH1Rfdm13oFOICvK/vby8Be9QY9 kPPjwaxwDVQL5aTcGsO5aUZmZfdUvW3GdgbfpWx+G9tY9MzwXQPAYnwLw7Oh Hm9CfxjuhWZmI/oG+v+iaz4S3g8C+wBsE+Dn4XuKnsFu9Hr0P8kzodl4E7xQ 9Ixp1haEe/sK+t1wjsq1lvg7+e03+gb+QWAP/J3w2Wr0jOweq9eq6QT1Cv9v qjW/L4X/EH4r6tHh8Ixp1hRjT7jGqvVyzp/G1wFOJc/QtewYFItswdk16PPJ M9mT3WP1WjOt2T4FTsO/4M794ZqoNo3cUQEvAb34t4KpnD0JepJn5obmD+yD L+U3Ozn7RnYuquHz6N3hWTqCvw/ehv84eo5mTt8O10pvuhd+E1tTckyKLWM7 mWzrz545zZ52hHZFFXgwuUdvaZeEY9EMa5avZr8lfVPfflbfgN8CY+AvY5uc nINyGYXtGnyh5lmxZseqGBTLUHBRbwm045sd3mWa8aXoFWBw8p11+B4AZ/SW wZ3sN6q3qjdeq/0nW/KZ78IzollRzVX7cnAuuYfq5a3sXqgH6kVT+C1op22F DwF9yTP1sOYpu/bKWblvC+emmn2GryXce8XwLbw6exfrzejtTAzvCu2USu2i 7N2jN6S3tCO8q7XTP87OUbkqBsUyL3vXXkc/jR4OLifnpNzUA/VCb+YZ+Ojw WfVAvVCNVWvtvEfRf4Geond8N77x4d2smo+DD9MMFtyjp8JvRG9F3xyBnpY9 27pDdz0X3i36TQW++7Fi1mc= "]]}]}, { EdgeForm[], GrayLevel[0.5], GraphicsGroupBox[{ PolygonBox[CompressedData[" 1:eJxNVD1rk1EUfnPvpfkBhejg52LV1S4WfUuXEnVJmkXoYJuQ4gdoFaRugpC6 +AP8iJPoooPZYoqtSyoOgmNmRXDwD8Q0qc/D8xQ6HM55z3vu+XjOc+/p+r2l uyHLsoeQCDmVsqxZyLJtfKxBvsM+At8C7BX8D7Cn4XsAuwF5hMOzkEX4/kIG kGeQHnzXoVcRU7aP8fTnkHXYOXL+gf88cvbhuw9fHXIl6n8R/p9RebrQHyAf YY+jcjCWZ7qO+X0QG1SXMioobgLfP9hLsPcK8tNmnp7jt2yzdgO5qtCXoS/4 LL85657PDgsERP6m8SFOrP8p6HvofmgTYJ6tZcq3EZTnrTHfwf9vSf6X0I9d +0YSfhVjyJ7n3ecyfG+8D844Dqq5H4Q7Z3+XFPM06lxurBk78o4yn1vxDnJj UoXvUtDOyQn2yX7XIHPwP4HvYtL5Oehd6K/O30Jsp6A+viTtjjvMYU9xhqT9 M4ax8/b/gv6MuBJ8m955y7gteHbmn0ni3fvo+t4Fcd73bB1zjz0QR+L5IgnH vjn0OqhGzfwmBmXX5zzEtmt8Vn1u1z2cSOL8cehO1I6OwT7r3ma8u4H3t+m8 R5NwYh5+kxfse+Td0WY/z5Nmahz6Vzd/Ju6p47t3MCdjeLaUVLPje1x1D+eS 8BzA/yOJQ9eiONI0l1rOVTMfyuZexXlok+Nd3/MNvwHkcytqT3eQ+1XSG9H2 u7Hs/BXf8VtJPJz4LpwxbsSPs43NQ8479B0c2c/4dtD+2GcxKD/r7CT9207q d/rQ+7PlnrkDYn8S+nYSr24m8Y88vBqli+Y/d9R2Le6tb25QMzdt9j7r3npR /RBj3qEp5+G7tm4MiHfJmPwHr+ar3w== "]], PolygonBox[CompressedData[" 1:eJwlkblKg1EQhW/+O+gbJI1roWBaFzBoRAVxaYyVYCNCEhdwqbRThOAruCSV YGOjpYqJnViojaC1IFj4BKLid5jiwDl3ljtzpnNpY249CSF0AQPjMYRCKoRH UIJ/gHn4DUnPJCyDEfgoqBCbBIvUXaGfiK2AzeA5q/AZ4gvUN6PL8D3QB98B J+Y1qtUf78Re0PfoNPoWXUYPJT6TZqujq+gf9AB8WPPyX463EvoAfZryP6/h v+ACXiP+Cm8hZxb9BtrgGfNd9ZaGt5v/XQQd8AI1W8Fn0myt5rVfYIxYFv0J r4Ae8x21axPIo9fMvVIP9crJ5MQ9K6L70d/U9vK2i66aezPBWzd8OnoveSAv GtFn045n6P3ou6vnoGaLPot2vkOfR++1TfzB/Ia6pW40Fd1Deamb6DbaSbtd gr/onsk7ed4g98h8Vnl+CD82760dtItqVCtP8vB/O7hJZQ== "]]}]}}, {{}, TagBox[ TooltipBox[{ Directive[ Opacity[0.5], CapForm["Butt"], Thickness[0.03], GrayLevel[0.3]], LineBox[CompressedData[" 1:eJwl0L9KQnEcxuFfKnQH1lBqi/3ZCwwqrKVysW5AUNJKqEsogvIW0lxzsaUt AqvVJRsCnY0uQ+k5NDx8vq9wOAeXypfHFzMhhCJX8RDWEyG06NO1l3WFLG0e uPb7hr7xztCe8MSHPdJVXWPbPWWXRTvFAs92UueouveouQ/1R3eiZ6nZBb7c r9qgE71XH6Nv0IymmefO7vHtPtK63uqpnnNG0Z7VAx3or74w5tPe15jm9YRN d44bd1W3tMK9u0kp8f/f/QHp9yfE "]]}, "0.09`"], Annotation[#, 0.09, "Tooltip"]& ], TagBox[ TooltipBox[{ Directive[ Opacity[0.5], CapForm["Butt"], Thickness[0.03], GrayLevel[0.3]], LineBox[CompressedData[" 1:eJwl0rdPVWEYwOFDuAMOLJRBmACZwD8AFukTE9UBSUDcCEiHhYmBBLgURSZN EJBiUFGEUBxoi2xumkhf3JhhkOcLwy/P+96Sc/Kdk9XcXtWWEEVRXAOJUZQe i6IsXajZns0b7nBX2/pl/8El3irfnKff5j2u8o9+mp/wv8p0bD/lV8XM3/iW 63zHIRaxWCWqtLdymKUs04o+alHL4R507ftadZkn2MlxrvN7uI6OwnW5pgrF 7R085Bd+VrnG7C+5z1Ue8BNHmcERZvKxaszJTFOqUvTPZw+YpPfhzOyznNec tuxXOjdfMpEb4by4Ge5FJzqz/2ULX6hJz8Mz0YyeqVENGvS711xgDh8pN/zH Pq9J8ytNqctexW5WsyCcHXtYw17WsZBP2cd69vNNOHdO8wMfxu7fmTsx7U1A "]]}, "0.05`"], Annotation[#, 0.05, "Tooltip"]& ], TagBox[ TooltipBox[{ Directive[ Opacity[0.5], CapForm["Butt"], Thickness[0.03], GrayLevel[0.3]], LineBox[CompressedData[" 1:eJwl08lvTWEYwOHTlC4Qw5/gHzC1RYLEhio6qKS0RUUsTL0XqakUVVNRQ001 tDWsiLBSxMZctIaWqqalJIIYKsaSWHi+WPzyvO/JvTf3fOfewfPiObGEKIra VJMYRVt6RdE2bdUXe6oyzEs5lXHu4V7t1kn7XKUo2z5NI5SsFI12vYGnuU4F 5lKe1V3z2PB+lnFUeL3mm8crwTyRTXzFi3qtB/Y0JjGTLXzHq3qvJ/Ys9mMO W/mB1/VRz+zT2Z8zuIIrVax99vt6Y77HVaxiGm9oofkmB+iT+RY/cxAHKs+8 hge4mgdZwvxw/zzEtTzMUhZwA6u5nke4kbO4iUdZxmMs52x+149wDnru2kwu UqM67U3cH85XbfZcLtAdddgb+E1fdc5+jXHuYBErGOMJ1amP+uqt683MYG9O 4LjwDDVG5fYLajefZ8QOdrGTZ8LZ66W9lX/Ds9IL+9NwLsozlzA/fCb/hN9F +O72Zp7Sbz3SZdce8wqXM5PLmMWR7FGybtsf8pd+6pK9kPUcrph5SrhfVnKX hqnIPjmcAXeG89FQLbGns5YV3K4hWmyfxOPczDnsTvz/v/oHtCx55Q== "]]}, "0.01`"], Annotation[#, 0.01, "Tooltip"]& ], {}, {}}}], {}}, {{}, {{{ Directive[ AbsoluteThickness[1.6], RGBColor[0, 0, NCache[ Rational[2, 3], 0.6666666666666666]], PointSize[0.08]], PointBox[{{4.905308194867242, 2.6308363915989275`}, { 0.20100883034436162`, 0.023821365695765692`}, { 1.0719666600928879`, -1.2506326268721977`}, { 3.0527398070698992`, 0.38721583002375826`}, {-1.8438997564108928`, \ -1.5026726898055591`}, {-2.913246104009823, -1.3391199692975575`}, { 0.3149621009629985, 1.9477027131642348`}, {-0.9556834229157016, \ -0.4484871006542206}, {3.2651237363484125`, 0.2704700013829126}}]}}}, {{}, {}}}, {{}, {{{ Directive[ AbsoluteThickness[1.6], RGBColor[ NCache[ Rational[2, 3], 0.6666666666666666], 0, 0], PointSize[0.08]], PointBox[{{-2.4, 2.7}, {-2.4, 2.7}}]}}}, {{}, {}}}}, { FrameStyle -> Directive[ Thickness[Tiny], GrayLevel[0.7]], Axes -> False, AspectRatio -> 1, ImageSize -> Dynamic[{ Automatic, 3.5 (CurrentValue["FontCapHeight"]/AbsoluteCurrentValue[ Magnification])}], Frame -> True, FrameTicks -> None, FrameStyle -> Directive[ Opacity[0.5], Thickness[Tiny], RGBColor[0.368417, 0.506779, 0.709798]], DisplayFunction -> Identity, DisplayFunction -> Identity, Ticks -> {Automatic, Automatic}, AxesOrigin -> {0., 0.}, FrameTicks -> {{Automatic, Automatic}, {Automatic, Automatic}}, GridLines -> {None, None}, AxesLabel -> {None, None}, FrameLabel -> {{None, None}, {None, None}}, DisplayFunction -> Identity, AspectRatio -> 1, AxesLabel -> {None, None}, DisplayFunction :> Identity, Frame -> True, FrameLabel -> {{None, None}, {None, None}}, FrameTicks -> {{Automatic, Automatic}, {Automatic, Automatic}}, GridLinesStyle -> Directive[ GrayLevel[0.5, 0.4]], Method -> { "DefaultBoundaryStyle" -> Automatic, "DefaultGraphicsInteraction" -> { "Version" -> 1.2, "TrackMousePosition" -> {True, False}, "Effects" -> { "Highlight" -> {"ratio" -> 2}, "HighlightPoint" -> {"ratio" -> 2}, "Droplines" -> { "freeformCursorMode" -> True, "placement" -> {"x" -> "All", "y" -> "None"}}}}, "GridLinesInFront" -> True}, PlotRange -> {{-3.4, 3.5}, {-3., 4}}, PlotRangeClipping -> True, PlotRangePadding -> {{ Scaled[0.02], Scaled[0.02]}, { Scaled[0.02], Scaled[0.02]}}, Ticks -> {Automatic, Automatic}}], GridBox[{{ RowBox[{ TagBox["\"Input type: \"", "SummaryItemAnnotation"], "\[InvisibleSpace]", TagBox[ TagBox[ TooltipBox[ TemplateBox[{"\"Mixed\"", StyleBox[ TemplateBox[{"\" (number: \"", "7", "\")\""}, "RowDefault"], GrayLevel[0.5], StripOnInput -> False]}, "RowDefault"], TagBox[ RowBox[{"{", RowBox[{ "\"Numerical\"", ",", "\"Numerical\"", ",", "\"Nominal\"", ",", "\"Nominal\"", ",", "\"Nominal\"", ",", "\"Numerical\"", ",", "\"Numerical\""}], "}"}], Short[#, 10]& ]], Annotation[#, Short[{"Numerical", "Numerical", "Nominal", "Nominal", "Nominal", "Numerical", "Numerical"}, 10], "Tooltip"]& ], "SummaryItem"]}]}, { RowBox[{ TagBox["\"False positive rate: \"", "SummaryItemAnnotation"], "\[InvisibleSpace]", TagBox["0.001`", "SummaryItem"]}]}}, GridBoxAlignment -> { "Columns" -> {{Left}}, "Rows" -> {{Automatic}}}, AutoDelete -> False, GridBoxItemSize -> { "Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}, GridBoxSpacings -> {"Columns" -> {{2}}, "Rows" -> {{Automatic}}}, BaseStyle -> { ShowStringCharacters -> False, NumberMarks -> False, PrintPrecision -> 3, ShowSyntaxStyles -> False}]}}, GridBoxAlignment -> {"Columns" -> {{Left}}, "Rows" -> {{Top}}}, AutoDelete -> False, GridBoxItemSize -> { "Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}, BaselinePosition -> {1, 1}], True -> GridBox[{{ PaneBox[ ButtonBox[ DynamicBox[ FEPrivate`FrontEndResource["FEBitmaps", "SummaryBoxCloser"]], ButtonFunction :> (Typeset`open$$ = False), Appearance -> None, BaseStyle -> {}, Evaluator -> Automatic, Method -> "Preemptive"], Alignment -> {Center, Center}, ImageSize -> Dynamic[{ Automatic, 3.5 (CurrentValue["FontCapHeight"]/AbsoluteCurrentValue[ Magnification])}]], GraphicsBox[{{ GraphicsComplexBox[CompressedData[" 1:eJx1nAdYU8n39++NAUIR0bWDixV7LwuizsXeK6hYsCtr113XsigoVuwoFlSs a1td0bXrOhcUwRU1FJUiGJASMEAIIQQI4UV+5ua53/2/efI8PMfkzsxnZs6Z c86c2Gr+qsmLJAzDZEkZ5tvf3rUvW56pfcn4FokeNW8rQbZaN7HmXUeQMzs/ q3kbqUm+vyK15q0T5IZzihvNKS4Q5A1R0/yipn0SZP7kt9c1YpK1jb49kCHI LqOC244KVgvy5NoB6AWZ1g6A4Uzy+2/DbSEV5K/n7PPP2csE+X9/7QRZzCsF XinwSoFXCrxS4JUCrxR4pcArBV4p8EqBVwq8UuCVAq8UeBngZYCXAV4GeBng ZYCXAV4GeBngZYCXAV4GeBngZYCXAV49FfPqqZhXT8W8eirm1VMxr56KefVU zKunYl49FfPqqZhXT8W8eirm1VMxr56KefVUzKsGXjXwqoFXDbxq4FUDrxp4 1cCrBl418KqBVw28auBVA68aeBXAqwBeBfAqgFcBvArgVQCvAngVwKsAXgXw KoBXAbwK4FUAL8OA/jKgvwzoLwP6y4D+MqC/DOgvA/rLgP4yoL8M6C8D+suA /jJiXjyPFATWl8D6ElhfAutLYH0JrC+B9SWwvgTWl8D6ElhfAutLYH2JmFdB QH+BVw28auBVA68aeNXAqwZeNfCqgVcNvGrgVQOvGnjVwKsGXj3w6oFXD7x6 4NUDrx549cCrB1498OqBVw+8euDVA68eePXAy3CgvxzoLwf6y4H+cqC/HOgv B/rLgf5yoL8c6C8H+suB/nKgv5yYl+HAvwJeKfBKgVcKvFLglQKvFHilwCsF XinwSoFXCrxS4DX9u+BfAa8MeGXAKwNeGfDKgFcGvDLglQGvDHhlwCsDXhnw yoDX9Ffwn4HXDnjtgNcOeO2A1w547YDXDnjtgNcOeO2A1w547YDXDnjt/vNX zOsAvA7A6wC8DsDrALwOwOsAvA7A6wC8DsDrALwOwOsAvA7A68BdDexf87b+ zmMJ/rQl/9s3XCsbziSLn7fkxc/bw/zZc+LnUcb+ZdCfDNrH8dtzK79Ndz+b 759b8e7fHqhjy5lk8ef1OPHn9WC8+LwMnsf+60H/GH9awXxi/w6cX+1+YL9/ LuFja18V1CTXLl++RpCVtf+QI8jct+3R9pUgi+fLQGs/HmUhyOL+DLRl7f7O IyZZ/LyWisejpeLxaKl4PFoqHo+WHrqlD7qljycm2Xfxt5dBkMX9KaE/JfSn hP6U0J8S2pNDe3JoT07nflPPRpacSRbPh5yIxysn0B8R8ymJ+HklPK8k4v2t hPa00J4W2tOSAbWAWkGG+SRiHi30pyVifgMRz6eBjK4dj4QzyeL2DDA+CSce n4QTj0/Ciccn4cTtS8B+Snjx/sR8hATyERLIR0h4GB/IBogfcb9jvsAA8Z+B ivlQxv2M8TbqE8bfWohHtRCPamF/oqyEeFRLxfONMuqLAsaH8TLqI8bPStB3 lOUwX0qQUR+VICtQvwnoN+xvOegP6jfDgH6DfWIYsA9EbE9RVkK8Igf9wvEp 4XO0HwrQbwXosxxkBRHrjxJkLcRPSoifcDxaiKeUEE+hfVPDeNSwPlqQDdA/ 9qeF/rQQv2khftNCfIb2EONJA8yHAewxyhLwr9BeYjwmAX9OAv6cBPw5Cfhz EmwP7BnmV9EeYX4S7QXm59A+YP4P9RnzYajPmP9Ce8Mw4v3AMGL9wHyUHOwb 2gvMl6F+Yn4I9VEB+x/zOXLIV6B+qeF5zN/g/sf8Be637/mAmaa8N/u/fP0g k59ZQWunN9GUJ9bQ2ukqNuVRs/63Pg2svo9fRxy+4bas8102ksHfnveUfG/P SG1q+bK/z28WSasdQMV3WUPCvy2/jTVnkkO/bY9mFt/lCrL92/J2YL/LLBdT O+CXxDT+p7X6l0JM/d3+1l6Qhph4or9Nh5/uu6yD8Whov1p/uuC7rKLi/jRU zKei4v6zqLj/JOg/C/pPomK+LCqef56I558n4vnnSe3yZ2q/y0kwnzysD0+m f+t+mCVv+n7tdhlruufJIsbqb688oT1YbxJfO6BM+n9/XwXrlwTrryLi/aaB 9jSkdnqiDN9lHfBXkLG1CpL4XTYSee0E3Pkus5y4PRW0rwb/Eu8X5LD+PNgj NRGvtwby6UoqjteyYH+oQP/UwKMBHg0Rt6cC+6qA/ZsE40+C/agiYlkD9lsD 8Y8KzjMl2AMVyGp4Xg3xk5qKeVUU1gv8PRWMTwk8qG8KOB/UoH8qWG9sPwn6 16A/Af4rA/GzAfxXBuJpA8wPA/G1AeaL4cXnixbiCy3YC1w/OcgKaF8OsgLi eTn0J0f7BP67HM4HnoI/CfdTcrRP6K+DPyYnsL/BP5JDfl1OSmoHZPz/fF8J 31fC91VgLzA+1YM/bQBZD+e1hIP158T2juXF9pzlxfaV5cX6w8L+kIDM8GJ7 b6Rie28EeyLhIX7hxfbFCPbFCPtBB/tfT8U8OtgfOtifeirm1YF90MF+1YM9 0VGx/dRBvGUAWU/F56WOitdfB/ZcCfktJdwHK6l4PVUgZwFfFowfz/8k6E8O /cmh/SSQeZjvJNA3PC942P88nFeYn1LA+ZUF51cWtJcF8ZIS/F8l+A9ZIKvg /NUR8f7UQf5KCbKaiM8DDRH7Yxrw1zGfpQd/qwL8owrwh4xgLxj0Vznx+cVC vovhxHwsJ14vFvJhBuBlODEvy4l5WcifGSDeZjixfrCceD1ZyK+xUP8j4cX+ HwsyA/k4lhf7Eyzk51hePN8s5OtYXmwvWMjfob3UU7F9rAAZz3M12DMNyJh/ U4E90kC8q4HPtaC/mB/LAnuI9gPzc3KMB2B9edAvBex3NexvFcS3Kvgc80tq 0E8VyErIryhhvCqQ1TB+jDfUYJ/0YI90kP/Rweda8I91kB9C+6IHf0QH9gVl LcYrUN/GwvmrB/tcAf5rBXxugHhABbIaznvcn1mwP7Mg/skCf1oJ+9EI+QaU JRz4L5x4vVjMZ0E8z0J9nwHi6QrwX5XgvyrhfFWBjPqlBH8B9UsJ/WfB9xXg XyTB+BhGbG94yPdgPkgO8XoS+Jc1My46f3iYfznEw0ngXythP2dBvpVhxPqG 41VAvJ8F8ZMS8lVKyAegvy0HWQHnP7avhvyBBuJXLeQTdMBnAH/EQMT+UgX4 L5ivlXDi/BDLiddXh/E4yJgPYeE+Sw75Tjnkj5LwfgV4k4BXD/ZTB/ZfwkE8 y0E+lBP3b8R8PfjTGsgHKyG/poL8sALybVno/8J685ivAH/tP/kqzHeAPqtA f1UQP/Gwf7NgPOi/VkA+k4V6DgPkU4xw/hvAnzdCvsKA9hjsuQHsvRH2H9bn yqG9JGhPDu2hfVOAP5UE8T3DiM8THu87GPH+4OF8VsP3FZAfzoLz2wDPV4D9 M4D9qwB7i+dLBayHBvxFDdp/8Gcwn6EC/0kF8Qn6Hzrwv4xwH6GG+VdBPMkw YnvNY/0A5G8UFPK7eL8M49GAjPWiBvCHKkDGekwJB/4/3HdpwR/SgayH/IEl +B9STtx/HU58/ljCfZklfN8Cvi+FeM+CC1wo7z04NMG0//npqy97rtn8398T RB5emdu6d7WQn/xnsO0HPtdIf5zl9Li7VZnAQ2NsZ69tYa4HMM3X29ZOqtY6 831/oxalpT83LxX8l03jIwd26VdG/W6GLz0jM9/PayNjbBwbm+ZXQZfErFbb jDHVGSto0pN9B+bW0QlyUKug8ndXzPXjDUZ2fN64iel58/1aykR/mdsf6fTU m73yT83+W+8d9jHn6jTX9zT1p+gfs6+azlvzfduN+Q/ZyNd3yaycDXSYzX/v 27i3dnHe7x6RwHWzRvfrbqp7N9dbX+v72qfhx8fkr3Xeinc/muuvGU+PY9vq GAXZdP7HDJe3br6JF/yvXpsP+PX9K5c8q1w6dV9L832caX7Px7N3Z3fOEPa3 9rcPjXKb5wr+ju0NY96+fA0xOs7xjNOY641Nz/vqRjs0DywW4qH6u5s85ncW E4tFBW0bbjPNh/l+eYedokvPenrygFnp7/eLxDRfwv6sDJp5PC7XXK8b2m18 T4tTZn0JLt0+iKaxwv3wOOsZiZb3WOF+eNWp9vPXFzJc51W3X412ZXhv3Yz7 Mz4x/KGcH7fGBF6ln9Jyhsx+zfAWm4odxiy04n2PuF8qGFazf4d0eDl3rZ7+ nXgvJ+l0Me3Q74/uVW5pZPHm9Ts7Li6mkwpj1+UNKaI3Y6/7rp2WQRNteg0e prLiX0mGj+hx5h7dsWfJs2uZNfs4WrvEoz6lgeumF0f8W0imFPw9fFTDv6lU 5v1+xAQ1mb/bzqepOp103ei4bd9jK27z4xejl3iVkbcdBh2yGKOgnRW/yvxV RqK5tWT+uGN1uH4hTve6FhrJgp6yiZ9rzsHkiFHkyQKWT17QyTXUtYQeaDLV v0MSyyf65l972yWXhj51dF72gOWl+vSl86/E0/i8/veC+tfEa3UPj1i3zoKP OBNC8r0NVCbzXSebKuUVUyI6Dy2oogset3H+uiyb6GdESBpoquh+zudKb6uv pHGe5zt9toEuP3A62kNSQje2OFW0r6mW3j+YeXfDCw096hTh08pFSy9dKd5R vEBJLTPzZhu6ldJuk1sp/R5+oWnKJieXdC+l6XlLfdxt39LovlGs41kt5Zkd 2xveiKKF7ea//HO1lu77faJ79tVYoj6x7MKFeA290LyxdERBBfEPG3zo2JZS +uffUZuXPzGQ8kePgjymaumKhz7t6rlb8z26X7PpJ1HSvKDiiDo18vR29l30 VbnU6tRoj8PTDTR82Nowts1XOsAi6GmTd7m03rXDE6zdcmjWx9c7z3V4Rav+ OHpTb8ylAcFf5uy/LuPP3tjvuuBQIl3TOEaSbl1Mb7K/vzhx+iPNNaR08Pes sZtMaflflXF0U1jmsLgRVTXnmuvsnSkxxLezbp3/DEofvBt9acHJbNJ0Ox20 e308WfFCNSDQQ0mGBXz68Pp+Lsn/ya7ZoFU5ZHjkkTGdllUR5d39HnmF2aTu BN/0x12tud4jrxy2259FWg0d5d/RzZqb1VDvcXDoV3JjwNm+E9LiqVfulN6+ SzRkcdGMjH3pkfT+Yj5gap8SEjK3sVE5Vk5+dRyYtuymlmh6NXucW5RDqrYW 6YZ0LSVnc8c5+/TXklc/XK34414JoX0C6qwaXEq+fLXxXTeohNgXTbk7L6GY /vVlkMuo7ArieiCwfEqEkrpXZCfM8KkiW5Y+X1CQZcEZT9yesu90JYmfdKQN WfOSpEx4nrpIynKDrYds62qXRLrumn2870yWC+43bP/CfkryYf/hul2estyW kcGz3V3yScsrD7wavGS5lDbXG2ztqSXXsiMsPD+w3MvB9rrQDC35/fLcGdPl LLc84Gzo3DmVpORnm64hm1huxrQs7UIbI/k4ySa0tJM5f1f/+Aj7Vo1ZIR93 vuukhI6bzPVx3ouONnoTYc633exuxyoSzPk118StK5+uZoXfA2bfensrKlhP mzv8sSssRUNm6eZ6v5lYIdRnFJU9ibzcUinUr/2Ve3Hrkq0aIX45Oaf+pctt igR/c6Tz3JX1LpZQ31vMY/7rZZO/QcePu+iVKleRC7aeQ91Pf6Ujl6VPnfdQ wkV2XRTzZlq+0N9rbUl8QGEudbzbZv+xgAwhvlcOtxq1Z+wrYhd3aqjD6yw6 aele72dNSkjkxfzssy2yqWpIwrbOjUz+ukKoB8lud3BZ7PZEGplesmRo4wo6 +dIEhyP9/iUPD2ddGvchjyQaq2O4rnE0Wtt3iNvTCjJ1g/uWrpsjqU97x+Xt HMz1X6Z46eVRv3mOx66RvWzfHFfWkve6aj+x4v17wd777Fh3+47nJ+L/9g1z LKRciH9i3zo67XRR0sGpd75MJolk7Z3UrDfdNdTDQ/p70505gj85rI3F7C1+ 2cRrfvW4ZyMkvJdxyMnYN1+F87N+s4+SOdn5pCA5xsZJ/kbIB5jOjxsnuv0w xUZJ6ty88yDolLk+q6Jekz2Bzc35t1RFtzsj4yspM7aZzXDrUuE8PODs7mRj pSUuw61cJeSykA8ztZ+/NHTkhs6lwnntstw3Iv+2ud5K4Xe/44/VpSR//O3y jyf+W3/lJHO/0bRXpcAz+uzGvLDOBpJyeuH080cLTfGR8HubQztazZCOMRKf wyNOXr5iWl9zvVVATID9pfaM4M95Xe05stTKXA/lOLPTgfqrKulPk17MSjlc KMRXJv88dWPUuehD5UK+beOqKzObhRcI8W+b+tnxo8KLadc7s0LCulQJ/ist Kzpn19kUv6iFeLikY8rcOH+VoA8DLC0HjZ2eI+TnppTs1jrt+UIvbJP6PG9Z LOSnTOOpmOxf+O7PTCF/ELaA2v8+K4umPuvjc22cKb5W0txBCY1SmxoE/82k j0+TbNIfz+YJ3WZtu/JYuRD/NU6Y2mnxbkvetB9N8UfTj+yyAe5pxPiJBByo 0UPT56kH63/eWF0q+GemeOlAdewvIyZnC/msxS1aHpm/4iPZsvveIN02c32U 5+Uxl1IG/rc+6tK86mUBg4qE58OP26w+Pr+InEpu0TS2X5UQn5h4Zrk2W8m8 KCUBYU2l744nCPkNUzww56dN4a6jGS6txZCS9lcZ/uruuk073mH4DN+onGj7 OnxIk4u+zzZV08eu6127zisnRpfndsFFavLqZJuQMyoZ9yhmxl7fyGKyvGGk qixWxt33tA99s1lDrixd+fu1Dwz3cuGP07mbDJfaeskADxXLhQY2MrZyY7gB lp3GLrv4lDzPfll3bwDDNzxztvT6jXhyKikt4aI1w/96iHG4M+Yzia+0l3Zv WU2Pp7juS3tcTH549magc0IF7bU/Kvh6aRlpUp95/O+WMtplXfbwB9szSX1m ypzio2rq71kxMsy2iPy0LLzL05b5VDtjzoIX5xmuk80ItluRmvoE3v3S8jYl dgH/7r8Tmk0beZCwNqUK0uHgwF7WHT/T6Em66K8DyshvDuclm+qmUv/Fb2Y6 X5By7ts8vnZ0zKTjt3Xh12+14lc6FrwranOOrP/cOfX19CKaM63B2bb/XiY6 i3Zvw1bpaGSnaSu2j04n/biP2oiQSlKvibLbpHXXaeSz7St6v7Ti2qZUy/b7 XCCl7/ZGNBsg5d+eTDG6P/1C4ioLRv2iSKNvxqzx7h6tIPmnGCe3+Hzq6TSi /rD3KnK4q9VZ/0W5dNbAdr06Xioki6c733X8IOMcNuqyZTeTSdNk/x4uV2Wc /EJJui4tkTR0ublvZxeG90gxXnh/rpjkhO/vNn68gj71dvTp3KiYGPr9EBSS paNDbF1ej7LWEx9vm2kr16RQl/wtLa5sryYHYzx3FS48R89PvDj95+sM16tP WJM+ds/psePt7xfNZzg2bsTTMdeyacw/4Z96nSwi3S8cXmodmyXEw6b8ysNz Lv2GXjXXJz04fX7xrd9q/MKrWZF+S/MFfcsM9Dk1fXcB2fw5cdAO7whB/21L Yt6PHakkL/8a6BOed5fs/qWs60S/EiHejeIs23Wt0hC59YZjgwe/p+M7h3St 20cjnA9XnG6Nmv1jPPU+1czRducX8ly/z7fFhVSaMco7+vGFbNLeeZah4HIS bT6tyx6L4ARyonfztdHjlOREeLXzIM8MEtm+nh1jqyStYqPDFucmE/eG0RmO kcVC/Nr3df/S9AQ13f7St/jsqQoa1o1b6Ha6iFYVS87MXGyuVzflv9ZbH36Y P76Yuk2YGvjzk1QaXTzaduKEGr/408mdDiWfaLfSUyejxiuF+HbzhLDGoyYr 6Xnd+lVvrn8kQUsjS1/MyxTyW93S5gftDs2g5Ic+YV7v1OTglIYlab8rhfsU v+Mjs4/EKGnZvuR2+e4fhfyFyV7EHPN6d/MAK9T7OPh1WpKXxgr1Pb8/G/Jw +SNWqOdpnnzeum1nlh8cNs6lQ6jpfkFLq9fM5w/yMn5Xk0NDDspK6JKmJYte OljzgXeHDwj3NNdP3ZyRdnqMIkdYv+Y9U1al1PjXm48s3uy429SenMZ9+ZhT 3lLGP/rz549ZyRF01xHX3lGdi4X83ZN8v+tOeWpqFVp/m2wIT2Ov/xvrts38 e5QpH92GVdSz4l7ZNy7/rR1Ph/ZoZf2yvhV3YnnfF3OKqDA/B2/8uarj7RQy dsT5izf6mM4XOdmZIelw/1iVIGdYtd7wVl5F8t1CTl549p5MX+sen55XRSZ0 uWh1NTWZtLhfFTm2TZXgP1SdT5rsrTSQsgeOjtet8oT9v6bpltzd8ebfp5zv MsdtQ89Swb7nf/ZmetoZifNUbrrr5CracNzrBgtGsPybni5HZi3T0cSw+3va R7N8u3GO0avdCmhgtCzeO47ldZKH4ZriD/TH/HpvX0w03weneaV9/TvUQOsw jQtWjpHyVoyvU/+qKuH8tv/96OTRs6po+50j/G8PyyYFfw4bGlRRRet3VwQF yq34Yauvzt2SrqP7hkbk2LnI+KeRybb13pUK+UC7kSXnOjprqVv9Dy6pR4po uWvPGcMTzb9vWnT6bFxTj1KaZbf8c37RV6rMiT25VlpKx89J9wlfqqDePYJb HG5TKuyHXtt6v3m5T1vjd3s363o8iSZsXdTofIn5908PXN1Xzz+qpYftbu+M mFZBvq6e0PtRXin9/ENlkL27+fdQ/V/b5nm1qRL84TtnBtfNTK6iOy0nXuj5 LoPmTfZaunhvFd04pv7jQ4ez6d3+iWffqfJp/5zB/Qeps+i7qOchmRoZr1rz eP61x2k0/5VmwrCQKmH/kd712sv1VbRB6cv2LT9/ovcG7xjR6kkVvdqlCd/N MZGetLSI/zXDQL9s2lRa6faMBi166sgyVbRwbh/LopQIauj2ig2tU0zPLerh XjEqiZbxMW+SB6hJ55DK9g3/DBHue1ePPFuvSXgWCTv9Ln3PmY90+uJQReai TGKha3Oo3DOBym7ue5654gtZOMHKkj46SO7nWezpk5FDPA+dr7M1sYpsyFue EJeTSTTpTFercSZ/VkkOz54WcsKlJn5Me7Ym5+AXcsunwT9/9bXmBi2OrT/5 poqkXJnewDn8Opk76fRviTIt2VZVVJ+N+EKcbt5k5/UtFfK1FyxfjR4YUkKG 2E+SFVfqSF7lX3+u3WquNwl9uDOAWVFB+GfvC/TPdHTX8fOz0p305LRLzIfu S7W0ILDzgjs7yknsnfJ9o9YU1ISda+foOXM9Ggke9yRiEstZPZjZt48hikh6 1y2zqMNy3i0m+UUdTydpHm23DN9ori9pPX9U56oYluuz2cPd82smGTH8Y3bA KZabEq+/4jVMTfyPPNpalmSuL2ndxX3iyhq5JIoZbJmuJkc7NCg+msxy//Zc k9o7u4y8HPbkN9sH5noSq6fJLw4uY7k7z0vsHfuVk0khd7X1wljOcZ5Xj3En jOSJtPtzixYs5+U3sPkZz2pTPpf3SFc7/3GH5UfbNHjdYxbDH2ZOJrT/xVwv YspX/51yb5XKnuFVqa5xS6vK6csua8e+Pcby7YPGBvTyLaPxR1dsOPGQ5Y9N uWhXeU5D7edr/ihMYXl7Q/Mi59OFtOPadb63E1l++uSGT+fcyKKX1rDO7S+z fM++/RL9GipouweTRzbczvKdlnv/6bvFku8dZWPj/0sF/ftXj4Atl8y/dzTl 14PbL38W/76Cpu3yb19So2eHmITtFrYltKPdhQk3RpUI8arpPmqT3W+nPq4t oW3qaveudzDXi4XRviNGrz1H5v4x7P29eyXC/cpb76RdJ1toaZ8e0V6VlSb9 MteHtPhlrlPqlUxa7tJga7fZKtP9E/1jd7Jn+phsen21W+LS/rn00vHWB9d7 mX/PaLqPb6BtnbBlipIO3bd/26OOBtJlx4KDTPQBsvGM3962ReZ6kWOOv+6M Y831Iqb7gPX/TLMfUa0gZb6hG4Y/KaJ37voOD5LnkT2dttnUa2Uaj4qcaHV2 6aCWpvNJKdwPNIxNOXpxsJIUzp63K9A3g3wZ+PzQIWulUC+i2n41qEHfXOJs vPoycG4l2TYpzu+iqoCEjyvo5bfEXB9iul8ZK9nzwqakSLiPPN10iFOJPJ+4 qRfYrHX4P+pHJgTNyQ1SE/2w6ODU7Kvkb/cu0XcszPUao3NtFNF7SsiS28RB G/CByCwtPk/L1pJKxYq2oevTSfCJoeGnGpWSjdeOpz1c9JUUrrKMzK+Rr8xb 5nnvdBGJPxbyc9RLLQnuX29V07bFJPleu6GqI1pyoLf7xjbDS4R42HQf1v/r zVcz3bRkVu4O13kH79M6hy/JLDLM/x/SLM2RnGZhDB9Q9HUd6acmZzs1TTp3 oVK477iVsGpHmpeBupedurHtbS7Jdd6YP3ZXIS3b29qw+E2WcN9nmt/W23v4 OUQU0ZHu3S59bqkS7jvHDHAoOe/8it7u8dyL1vgf8mllQ8Z32koCG54J//Fy Di1YuIJ5wMtN9/FCfUjo7Y+K3spsmqc+dO143S/0c9tTEcqN5nqQ8MDysmle 1eRkg54ey17H0ow1rlOHuzNcUL3Tm8PaJpCzbuMHBVswfMhbmfLzhlIyv2Le hii9np4ZLj1/wdmaN564a1AFF9GDTm87xtla8+8lE6u2/KMQ7ivtg3pV2Bfm 0i+zHXv/M6CUPJn3aNyu9RnUNkK3dV7DcvJ21dYrf+yMp4OqWnBWsZZ8z4KF IzN2xZAfy47t2d3Ogm+1rvy1MT2dxP/s3zqkrorOW+myOdYzhrj/PbH5x4gv 1N/q3KKZXqnEuLjB8M7pKuKZf+/E02tfyJZxkzfEz5ZxwembjXsq/iWOm2b+ ZD+iDh8R/JdbcnIu6eIUOOjTIJbfOerB1LzQIhL3+oL1jOA7RHM1tPn5obmk 8sCSpydaGqk+/bBb27klpNAp7s21peV06nXG8ckJHRk/8rVTd4Ult9w6pPP0 0HLSvs31GauIhDs/cM1TzpHh9ld3sV8SaMEtdp9iuDUjlS5beKNrynQ9HZPy YMlb2w/kVMTxRYOWy7i91dlniv/Rksxz873HxUo5n011CjouqCIxzeo4P7Kt JhzpEN7mlobG5awav+kOy7UuSkyT9Cyklt7tP/X/uw5XHfUi7NmkHPrn9tKJ VmGVdPvl5Y7eu68TP1nWX2/maumA7WnvZ+gzhfvWgF0Z66TXiuniBuWp45yK yfwLsQ+Tz74mR/t329S7YyF9/lvQ80lOBvL/ADpSCkY= "], {{{ EdgeForm[], GrayLevel[0.9], GraphicsGroupBox[{ PolygonBox[CompressedData[" 1:eJxNmHmwl2UVx9/397xoSoVeitiXmWS5LBcwA0VnGGbKFIi0GSYwzRhmYknN xhIuZqmVgJmUsikEiSwSQaEYIDuxyXpBMLlsKiEJomwiqdH34/n+Bv84v+c8 +3nO8j3n/bUadM+td5eyLGumn6T2TRH9K0X/Em0UbRK97vYVUV1RE1FjUR3R l0VfEhVu64s2i14WLfOepe5v8dhyn7fEc9tFK0WrRNtEK9zf6/2ct9X7mFsv ekn0D9EGt4st72Kfe4mogeW7VPQV9z8nauj+Qd+9Q3TIbY3oXdFR0U7RadF7 omOis6KTnj8oRR0SfTXXeWobiVpq/DXLhCwH/B7uqPU70MFlokaW43Lrkv4+ z/PWZF1WiN6yba4Q7ff8Ntuqwvbaoc5p0XbRHusIOY6I1oleFY0UjRGNFt0l gzWS7GN10Xy1D4t6iqo1N9br7syDH4U84m9Q20nUQXS9qIeovdvrRIN09w9F //WbKkXtRDM11lTUxndPEk20XItsS96G/lqI5uiuB0RV1m1LvxGZJns/cj3t fn/LPVz0BVEzUVPR593ir4c9/kXbeLVojeiM6H3RcdubsbX2hVVet9uyotN6 uus2tX3QiWQriYaIH5aFDOi4bx591jWTfgeovRndaW2N5nbkobPeHn9R/dGi 3nnokbGbrD/619rfsO8M/EptQ507xXav8fg0jXXUGTfLtlPEV4r/lvhnxLcV /5D4yeLbiH9Q/HTxnbhXfGOdcZeoi/pr1Z+q84ZqvkJjz4rmYSeNPyn+Byns A/+/LGyE3VvZdk+VYn6H1j2odrH2zfA5nLvd4x+m8BX2Vrgd5HP+pLkOWnNT Ee/e4Te2y4NfmMX4Qfs8fdoX1K5Qu9J2wfdmlcKXTqmtFW0RjSvivU8UEduH 7RvE81aNPyd+ivjB9ufBlg2eOc6YmcWbeNt9edwz037OXZwzPov557yGsVOW ob5odyl0fL/mOqZYg333i57R3rlZ8H/JAm8OWg9tUqzDB1p5D+t53z5iJo+2 1jJsMT/BejroN/a2j+Jz+HJz8QOzaFvYd5lvbr7W8o/323fbpsw3817igr0l v6N1CvkrzCMzbRvz5Ib2KXID72CcfR1SjON7XdV2wV9TjKOrv2ahrw5e09lz jHMWa5d7b5XHr8rjHHjWdvYd7b2GfVUeZw1ru5qvMt/JxNx8jc+WrJWi7uIv ZIGRLYw3HbPAywVZ5LQVfnuFbUqeOWRMOiF6J7uI9bTgHr6B3MgwQm3rPO4l x5CnyTlg4hxkySIG1hrLyIWbvPbvxrNVxjNwjbxQzqPrs4v5nnPLOXWDfRBf xMf+5jNWes8en3VvHr7Imm32f+Jpke/7p+iVFGv+kMIn8ceZlnui3wGug++P ZZH/91omdEfeI/+VczY5fKHfutpz5Mh9lm+796zynnL/gNdt9xkH7M/40zzr ElnIL1sdr5/Gvc+u9X01Ppd1z3vfm7Yj+Zn8+7bfjr3xdWqe6jwwGV3u+AyG zbY9XvKbdvoOfAmMai6a5Xn0etx3YMdReeAjdkbWF+0LVfbXZZZvse9437Id 8dn/9vnEAnKih/nWO3E0Mo/cscA+8Zptv9n62Gu51tveE/2eWX4zttxkHWx2 n/cd9RuX235bvP+I9YY/zLV+l3l+s/f9x37A3BK/7aTHj3rfUs89bzuVz6PF V9fn4btDXXeQ06gPZzpvUKdR7xDHxHO57qFPXUPt1jaLXPpJFnUme6g1L/NZ l7rP3CXudyuinngoRa1HzUcd3dN1ADVAhf2IuXKdXa676/isRr6HWhIcvy67 WF/0sOzUFsyB4fgtPlKOm4X2E3I/+NUyC/x4wT7UxO+t9F3t/N6m1gFzjX1/ XesIf23uPY19BvkBmQZaxsrP6tF7wFHGr/VcE8+1892cBd5ek8dYuc6j7qP2 +di6oXb4yDojn7R2TtntOqTCOfLRLDBoYx44S/wvtX8Qr52dF8oxwRhx0cnY j8+u1Z5JedTL1KjUgNX2Nfpg2SLNPZFHbTjW89TVy9Qfn0cNC96M9h5q8xFZ yMda6snh9jf8Ev8iLpGBeFyVBZZgV75XiOVyzYtuqG3wb3z+Meu1rnVH3dXQ NRg1IzVOffsBeYgctNL3cAd5HRsOtX+ANWB4OcbQHXE4x/3b81jLe8Cd2Z4D K2a5v934is/VuN4Dy+aUwt7kVnB4m2uPSo/jAw1dC5FXNipAeqmdq7EL2COP GOvllrr9da3pA45pzZoi6rAb1W5Mka+eTPHWNsZn6maw+mmNjygiFz+VooZi PXLX2p/wpVFF5OgJKepszpmU4qy2xmfs1tpYOlPtj/LYT9vIfvnLIjD3Nu29 OwUudaFuTYEznYuwGbbDRkUpai/qXmpg7ITeqT032re5f5RloH65yrXEj1PE TlURMVLfdR2yV1sPDxfBf19rh6eIr05F1HrcOyC7WOcRI/UdZ9R4t/hM+K55 fE9SJ03No1/GHVpqp0eLqLd/U0StgG3vEz/BcUbsUC99kEWuBfPPZ4H75LUP s8ht+DH+XGEs+MhjxCDnEAtlvaPzyc4DjO8pon9M999TxJpV4u/Iw6fx5wck 4+o8xnZZvqMpvj+wAevq+XuK7yr0hQyji7AfdvxdEXkcucl57/o9R/0Oxnc5 rvk/op7fypo3ssgv+EYD6+SUSNd+ioMf+91veY7a5JjPOuw9n3gttmf9WLXn vKaeW/pgCn5DXC60XGcsG7KctTxgzFDXgehvvXXKd2+NY3B/Ef857JKuuqWI 7/Nq1+WxB/2DRVOMR+h3tfVOfmQvOWKB2kfyyJXkBL4ryU1Xp/gOO5ci7sEB vsfJg6O9/kSK8+8glosYr03x/cq3LbUA366cyTdrlesD9M3aBT6H++dbrvN+ Sw+176X4lv2tzhiW4rvteyny7FT7/Lwi4v+kxs+IZos/nQKDwJYtKeKVcfS+ Jg8fw/+HpMDC/mqfTfH+mhTYxn8Kvf3uRc4756yTr6utUwRO9BU/Lo881N85 6uU8ck3dInR+eRGxMsl22V0Ef6v27iwif31b/J48/uPAzuBBD2MCtupYCntR Z/UzVo9JgXX8FwWetzfO/7yINT8rYqzSezt6DfxAzXUQX43/5HE3/0+18Ldq yWs7+Myri8CcrmqblOL/OmxDy38fLc039Pge+ytnUo9Uli7WIE2df4+niGvi e0sRuvtmCn2ssY36WqfoE72OM4+ux3vNN1LYApvwXyzn48PYea3xrp/mmmv8 Xr7LS7GurWWBx67YZJ1txFhzj9+ivS3Ff8e+3MJ7wV3G0Qt5YIRzQSPj2Qhj yQnjx8AUa6p1zjsp7sMXri8i99ygdrDzbvci4or4+nUROYxc9gvyS4qYOVAE fp8wXt1uX3+kiPmejq8D7u/R/E9S2K+v7dnNNpqYIp+Se8lP5Klf4ZvOceRt as6exo3vpsAZML5fCl2D3392fP5e/KspfBesOpviv60P1E4rAvenq/1jitx2 P3pIUWdQg+xLgRHgSnf76NfYV4QO0eVPbcslGruiiBx9pdobHZNbxb9tfyLO 5ju2qV9m+MwN4g+l8DF8EHztZYwFU8CWUylq/97W5wXbnvyL7/Ne/L/knDXE +YTvmQbGcuIWPKfmmuP6mxioMh4Os6/j2+OMJfB3uh5DNyOLqLEHpHgb696w 3fANaqFqxwZxwX88rOd/j8eLkGe66w5yA3mc96MHdNzOOQDf7mP8A3/GGP/A fzCpj8c3G5PBZu7i/yBqpDFFfD/wH+R6YyxYe43xHFwnD61wfUX+5NuDHPpY Efw011OcQ/79P1EtLYs= "]], PolygonBox[CompressedData[" 1:eJwtlVeIVlcUhe+Zc8iDikbyYsEGRo0V6wijIIIltrGAFQsiqBhnVOyO3Wgs UUnsjljGjowvaggj2GtUHKzgPCiISiwPNgTrt1g+LP617jr37nP23mf/DcYW DizIybJsEkhgVcyymiHLxoNCHtxAt4LvZ9EF9C5QBB/FswL4S/y96BJwHj0P 3QK+BP82/BpoCH/Iswr8f8AG+EGevcMbDf7O8TN5r9H78OqDS/ClIA8vgjz8 1uBL5ndOwO+DcvgvrL/O2qGgJPMzeXnojujm6OFai27P2oPoi+i+6An4p9Dl 6MHojegb6HvJe9BeKtAP0HOjv9UMDEMfR5fhr9MZ4f15Vh+vDboduhDUQtcE ffCmouvC64B+yTlT7nYQ8wXeCHRL9F3eL0K3Qcfgbw6AT4xe+xP6bPIetVfl +DLec7AVfgGcgb+PzrVqqFo+A5vgZ0EZfD4oD46p2MqBcjGSZ0/wOoDGmfec n5wz5U45zY2OoVhbwP/wKvjngmPkR8dQrI3gKbwS/pngHtqZfEadNReURMdU bK2pnNyUx+B9QCneJ1AKXwruwKtqT8FnHoQOqgl8JeiC/gF9Er4e9EDPQOcT eyp6CPoNOACfB0bCD4OuwTlRbgZGn01n0tnaJe9VNWmb3HPqPX3jLbxf9FkV U7FPR98l9fSR5Duju6M7thJvkXoK3lhnQFdHXw6+U7pbY1RvdGuwG340Ohe6 E7ob81k/B90IbNZ9ST5LE7Ad/Qf6o/pf8eDdo3tVOVKuHmhNcI6V656gafA7 k/B+TX73E/o3dC90C/Rn9OTomaDZoB5pi/4AFgTfsT3wZfjFwd8sRv+Lrpfj NTeja6BaqIaqpWqm2n0F4+DDwKzgnPyIPwsM4P1i4l3BuxU9a9QT6o3f8cfi j9OdQi8G1YJrOka5BrPhP6sn8Xok3yV98y/VOroWyulCzQdQG68B8dpHx1Ts Gvw+xjukmgTnsAK+PHmt9rAieQ/ai3LwCv4ouhc1MzQ7NkXXTnsqYv10vR/c ozPha5NrPxm9Hr4umWumaLbcjb4L2oP2siH6bMrZHNZ2TM6lcpCbPHM0e1SD adEzSbNJZ5zy/Uw6m/ZwNXrGadZppmu2d0anHM/MbuhVyb2hnlgNX5PcGy3R fybPQM1C9bB6eVt0b6rnlyTPVM1WzdyL0f8R+q/QDP8veuZr9itmJ3jv5G/r P6AA/xsMWNd7 "]]}]}, { EdgeForm[], GrayLevel[0.78], GraphicsGroupBox[{ PolygonBox[CompressedData[" 1:eJxNmXmQ18URxX+/mdFabiUqJV5hAYNaKRZZgZTiHY8kgJFDURAURbxAUSAa 1yOe3KArGIzKIjGCIAqCZTRVgMYzSUWTgKg5EBUh4VIgqLnex36U+WN2+tff OXu6X7+Z7XDJmHNHp0qlcrH+ZNWd9KezynTJT6vuW61U/qJ631KpXC1dX8nL VQZKv171NNW/VBmg8rx+D1b9oeqXVW9RuVKlq8buq74nqVykcer0e4jqhaoX qPSR/tsl2g9Q3+bSNVMZJ/0J0reV/nj66ffJHqdUQ+6m8k/Ju1VOlLyv+u+j UqP+26TbqlJrPWOOlFzj7+Mlt9S4N6s+V7oXVIap/Seqs/SjpV8heXY19jOC vUp/u+q3pV+lMkry31W3ln6K2p8n+QPJ70l/ieQFKqdIXqP6ZvYm+UJ9v60a bWpUFkt/tup3Vf9XZZDGOV31Eq3xqRS/K+pzvqr3pN/MnCpn6HdV+iGqz2B/ OfbWQn1+Jf1itXkix3oXqfR2v8XuuyHF2s6T/C+1O0v1H3K0fb8aa7w0x/gn e/yWPpd2qnfpe2fJB+ewP+eypxrnwVm8nULuKjmpTVXl3/r9pfseKf3nkj/z ON+SvFPycZIPsr6Tx7kqxRwPaF8nqH5cY70u3WspzmCEfW2a2h+h+lqVxkro 2lt/kPqMlnxfJebf6Xl7sHaVMyU3qF6gUqeyyz7UUfoDLNfa5/CrY1WPVN1K 3+6UfKbqNilip0UOeYbk/VXvZ7lVjt8zK/G9tdsPdJu5ks9K0Yb257rNo5WY p6XnulXzPlmNtR8o3Q6vs4Pk7ZK7V0K33Xr64Bd3EeOqP5W+nthxX+yMvT+1 zHn9pxqxuSzFuQ2VvFz2n6nfy1Rvcmw0k/xoCr88237WJYePHaX6aJWelYih K31WH6j9euJc8uAS9U059tPF7Tuo/qZKH7V7zLFDDL3hM2+oRNuj3H5Wirke UL0lRfywnp457PWK5ume44x/Lfl9xyYxusGxRgzQnzHZR88Sduil+mKVnvo2 QvXh1bDp/ZXwM2Rsy95Y8y2SX8vhh2tL4M3llcCc54yr4OtvVcZLfqcEjjYa S1sYnxhzk+TVKfDod17/btWnS3eayoRK+EhH4/b11ZAfkXyL9LX0SWFH1ja1 EnUHr/MRtf9ONTCVdh1s84kp8gHjoat1e8bu5LkmpcgXtOXsNhlb8BcwH595 Xd/H6veEEnHWzLF2Yw78O1D6b5Twpbaqh2msoSrzKp4zxbxd7B+MP19yL8kz 1f4nKhepzRzpjlPpjt+WGPsE48l+JewxOMcaN3sc/GST17PImMe5T8mBG+3V r8G+tzDHupirqRK42tvj4DPIPYhZYyq5Zq30a1Jg92iN9V1iXPU/NNZD5BXJ T6bIhcxBPgRHsR15ktxJnjvWOQ/seUV9x5A/GM85uLfkP+aw7Y8dE13swwON e9erzRs+iw8lL5PcX/LDkj/O4Xtv2cd7qPTTXG9KP458IH1TibOYr/pVr+FT 1RulW45d1H6b6hUp+MHxzt135IiPRscLazncOExsECODcuDfQGMd9SDL4DS5 Aby6uYR+Fv4tuTd4yH5TtOGsiXPi/S3pm3Lkn+HGAPwHrGBNrG1JDlt39X6/ wr8U+DlQ43Qm36nNgBRYzHrAVHCWM+mYQqb9eV7/XOeROuc4vte6/ViNlYgN 8rl0g90evzjdsYz/VqvBd2bAu6SfVmKMXs475+t3F9oThynGIqeMgcSpzXTn yaNsk8Fug9+CfawPLOlrXAATLvR6aNMthf/he3uxo8E4gx2IpXNS9J/q3N3F Z8R8dY6FTbY58cX+zlC52/aus82v9Zp/UML2nW1PMKmb1wB346yJqdNsK8bp WKJdJ+P0cSly1qgUHIJcNkPjH6ZvY9Wmvhr272FsPc3j3Khv/SU3qu3kFLkc fLukxBpnl7DNBT6vW0vIDxoThhivpqXI3zPs6+jx8zEl9j4pR3zuzXtwQeK+ wXF5ju05MkVeJ3f387p7wLNS+Cj2GV5iv7fnOJP+Phc4wqkpeNoQYylnenUJ /b05bNrVWEOs93T8TrBNyD1bjTe1KofkaHeRz7beNmxtvMVX7yNHcNaa58gU Z4k/XJmCL8K19qSIZ/RfpBgLf9mZIq7w7X5eE3HZxz4Gt57uc3y2BBZy7uxj lb6vTMHd2zqPbDIukkvJu6UE1oF55Fpy7vk5MBQcA886VuMMOTfaguP4G9gH Bv7GuYh44J6DjRh/gnG+recammOcz8yXyeV32q74HP3w5f4e/1XVc6qRZ59N wbcGOoe39pludm6FzzyVI59wJxjis8XHyCXklC3mSH1sN/Y13nt7vBp7AYs3 5+BRq5yLmJ+54AbwGPyQ+wxjjCuRM/DR133OY92G3E9fOPa7ktel4DdLVT+T wo4tc+APsbPGcUyObG7cB1c5N+Yl1x/hNWIvcgU8Gxsy3wjP9dccYy81R5lj fRPzpPh9Uo77E/eqP+Xgd9x1+uW493H/+1htP0rBBV9KwbX4tiEH5+IuuLAa +YPcscnn3+Dzb7QPkDuR+6jfmTnuy9ybW1hPjh2e4i5BTGHzV233jTn8lzso dy3uXNztBtjPLjcG1jiu8bVp9rc/54jLS9V+e4o9MBZ3OO6YcJo5KfgNnGVX jrz0nH1/bjXsTW6BCxJbw5036+EI9oHWxli4Hesjz5P32effcvgsbwMXmOcd b8yAAxLLl5lbwKNnp7Ap9txof7mmEnzlNmPLYq8XftW5xNouNt7Mc46ES8yv ho/AIx633+DjTdXwfcaqMc7Ue1/nlOD7+Cf+wVicCXg3Ogdefb9EroDbMcZX d7IUefaGHLm/v9osLaF/psR9arex7KocZ30WeJMDEweVuFeCd+ArZ8BZDNP3 a3LkrO+R83Pg/9mSV5S4r3Yr4dO19vMxPjfyF7E13bkGLgmn5A6+NcU9i72i X5Qi3zytsaZKXqK6fY5vtF/kduTQ+Y47bAv+H5rjLMmj4DB4TF/u//DWdjnu 2Kzj6RT3L2yLnjsjfe5zPIOx3B0mO89OzZFzry1xn+jkOwW+UOMzJH/em2K/ veyn5IspKcaCr7cqcc88oMTbBz70ZQ5/OcY+073EO0W935TQce4T9fsy5igx FvNj50Ul5l2Z446JP+zlcNTXSf/DEm8dx6qeB/9Umxel/5G56SzpHiuBG7yT scdJ3jt2vjuFrckv9AUDF5TQPwHvzsGFGkvkvDr7Km8iyORx3lW+cH7lXYVc C1c4pcSb2qmqJ+bgmtxFwH7eT8Cxu4254C0+Bn7iZ+yXWCVOyUvkJ2L/wRTf 2D9r4H2GdYCdrxk/eVe4PAUH4+0E28JntjnH4UuTS7SZVMK/8LPljsul9p+V xhnwAexZ5jXAi5mXeP4Kj72XK1JwDvgGY4/yGqaW+DZF9cM5/OGuEud/jOP/ Hv0egY+VeA+5wuPw3gSfPlj6X1jmLapNjnXAX+/nLkMMmqsj46c/z8FV4Kqz zSeHltjTIcbuucYvzoCcxv7Jj8+lwNmrzROa0td5rtb5lziGd8I5n3Gs0/do 3wPx162OR3j4TGMFONZofkA8gpfjjZnjS/CuG0q8U031+FvMFfaXfnUJP7ki x1yz7Cfsa6XXvN0yZ7cjRW5CxhdG2h9eth6fwQdW2Deo2f9A52XaDbPPrvZZ M+Y6/yaeJhoHlhiD4Eu8IfB+0eC1rPLaHi2Rt5ur3plj77zH4Ktg9FbnGfwB /+Cs4bv4HLyFNz5s8lCON4s7SnCZ5tavt92x+c+c++8s8Q4KXpITyXvrbJMH fM+8yTZvYf7HeW7wmc7Occ9sKDE+72hwmp/muL/d5nslfIvz3c8yvAseyvvw eHMJ3sThEzXWwyvIra19HyPftTI/+yRFnsZXwZc9jgv6NXNf5mnleXmDZ3x4 3rwcuX6P94N94Hsv5cDup5yL13svH3q/yNT8hpvhD4caH+Bgm1PEPTz3DfsD 7T6yrd60Hu7S3pg+2dyYb6PsJ1t8Rqs9LuPwtgqH4e660fvnrDa7DbwQvOdt ei/+YTcwEPlt4xL3gndS8LWFWsM9kp8scf9b73lZLxwUTg83/sjyctv8Gq9t nedlDN6CyRfYmT7wQL6/6zbMufb/xlzrNoy9xvZ52HeiZ+2P5HHyCHcWzrSF deT4E43/2xyP7Plg64nN7V7n73PkhudLxNp2x+wK9yWulzguOPNxxptGj7Mj fb3XHR4TPgWvAsPhVTuNG9yHtjjGyXO73Ya9EGN97KvkRLgfPvu5/faFEpz4 xRI5hfbEIzyLPEq/OveFnzHnLs+7Lsd79yr13eo8Qj6h7Zduv93r4Z4OXvG/ ir05kb3wfav3Aofh7YOYG2OMaeMY5H8R9V4LuN3O/ApsaO54git19B2oQwnO z/+g4N6HmLPB6dr7nMlXbYxj8Gc422G2ITbibg7P538A2AY8gbc1VeK+Sa6Y V4lYb2l8wH7JNse/9nHsf2Yuwtg11uOHzNnO87IffjPHtBzvJ9dp/f8D+z1E Zg== "]], PolygonBox[CompressedData[" 1:eJwtlnuQjmUYxr/vfZ5mrDJSqqmmP8ihRoUQiqRYdLDabbEOS06hza7TioqU nMkpRLOndLAOaROlmkkUWU2lmGwJZcpQSRLVTNPvmssf9zf3772f732f973v +7qfBkMKs8ckqVRqKxaxp/npz4Vy/AEhlfoK3gk/CWfBR+DjcD58CD4Kn4DH wD/Cf2FPpVOpJvBZ/P3EPyU+kmsn4O/hY3B34vvhGrgG7kl8K7wGngrnwS/j 19fz8Vdgl+P3xpoQb83/b2f9FPghuBTup2fDfeAyuD/8FrwYXgx/DNeDV3Ov EvhS/BzseuKt4PbEN8Lz4WtYMx8+CH8D9yW+Ee4ILyR2GdYBPyN6b1OJ18I/ jP3E+s5c+471h+Af4Az4a7gM7gpPYn0p/jpsFjyC+Gt6F6xR4ncsZn07+D9i A1g/DJ4BDybWEM6HB8At4JbYVPgjbB7+XGwDsezg3CkHysVxeFDaOVVue8IF xLfD++ApwXvthPWDP4RnE8vECqO/qb5tP3gavArOxx+ITYfzgr9tDjwZXhbs Z2OPw+fhSuUT/jw4Z8pdb9UY8fHYGOLXYYuCa1C1qGc8Q2xl8LPz9L7wUThX tQN/BmcGf6vN8F64L1xMvBwbjf+7aiztZ+TiHwuuNdWwallrtHYiVo2/FHsV vwjbjf+w3g+/HbYG/9vg2lDOlfuR+j7ECrFP8JcHv1su9gTxU3AFsVZYG/VD cO2rp9RbvYOfpXvoXme0PuV3+BP/AWwZ/jbsItafDO6tav5/AN4S3IvqKfXW v/AG4iexm+A3gmtXNa3afhYblPgZA4k9BzcjPpRrs/BnRvvas/b+DrwcXol9 AZ/GZqZcI+uJ7YOnp/3MD+CxcILfGsuCb8XO4Z/XN1CvYaP47xm4BX4Z60cn vrYguqZUW9rjamLN4dOsvQIugL/EpqVdUxX42cQbJO7pCXAfuCn+jao/+By2 Nm2NaoWfSbye9AhbGLwH7eVK+DG4JXwWvkoaB1fpGXAOFonVju5l5eTi6Bwr 19KgFcE1qlqVpr4ID4Xb4mfxnyHRPa/en8m1tfivR/uqUdXq+3q/xJpYHq0x 0hpdq8CfjQ3HP8X6W9Rv0VrQHn6F/24O3mtHrpUQW4IVST+w8cSel+gTL4AX 4U+QJuD3kmYG96x6Vxr7Arw4uvZ1j6X4pao//EewefDcaF85Uq5KiI9IvMc5 0XvUXuvDjxKbBI+DG2JLNG+CZ4M0U9rZRf0DV8HVun+wFnVRfvT+8N34l2D3 wPfDE1O+VhA9IzQrbpCG635YncSaW6ncBcdUI6qVGcHfKgO7C+6gnuP/beDB 8LDo2accDsf/h/h69r4Kfi+6J9WbmhFdtTfVSOI9aW9/Y+vSrtm26pXge0vj 26rWo2tDmp6vXouu3Q6aD8EzSrOqsTSF2MRgXzNDs0M5VW7VA+oFabq0XZr/ M7E/grVJPavelWZKOzUDugV/Y31r1fjhYA2Vlkpjj8ALo2ulOevL4T3wpsQ5 6gTXhXelPWN/0beP7vU7ibeDu8J1Wd9N/a1vG62FqsEieJdmIrHamv/w1dHv XgubDO+G1yfOiXLzbrQWSWPGwdtVk3APaZD6Jzo30qRt+NdK4/F/xW7WvA32 dcaoIy0MF84u8JboGatZK038Tf0UPZt0pnhbeootSPxOejfNXM1enSF2BNeQ akk10Dm6ZlW7OgPM0awOPmtoT9qbali1rBpVrXaP7hXN/JdYu1M5SPzMTcRq gt9VOVAuKoO/nb6RvpVmrGatNFvarTOQzkKL4CppQXQudGbQ2WFv8OzTzNPs ezN6rTRSWnkg+GyjM5zOcj2itVg9rl6/L1orpeHS8rHxgrYm3vsQzUydjdTz ql/NgJQ1p1j3is611jSInlmaXSewZvi9omtPM+TB6HvoXupB9eK90dotTZI2 3RY8e6T50v5G0dqoGd44uiZVm6o51V63aC0q0/eI1kBpoTRqD/4OrpUnrnHV uvakvanmDhK/I/jspB5Xr48Kvpf+o/9qRmtW651ziP0PVHhuAg== "]]}]}, { EdgeForm[], GrayLevel[0.65], GraphicsGroupBox[{ PolygonBox[CompressedData[" 1:eJxFV1lsVmUQvfd+U6uhEiqtaHDDKInbi4nGQqyUFrGFRMAihqCGyqJSQCig xUTaUsBQMQLGiBiIiuKSugAKIpvbQ4uBBDAICfviAz7w7EI8x3OID39n7nxz vzvfzJkzXwc1zR43q8iybHCeZQnyJ/xphj4nsmwv5EEsjuQa7A2QUzOtH4K9 HXod/K7Dcy1kO2w35LKPwPNA6F9A7sult2XS+S6fa7HnFNimw6cPno/jNwbP kyCvwVoFZCl+p+kL+xDIfrCXQM7GHuXQGTxt5bafd8wj4H8W+im/e9lvOPRl uc4yHvr40L5VSevU6dMF+Sh+T0Mf6jgux3CFfVbZZ3am71woZK/APjugd2Sy /YHf1bnioU7fvj5XHXzqC/m3+OzM57xM3xnq73J9p32eCNmH4DuVSd9j7g/C Vuk814TyNwxyO9YHQF8G2Rs6/1Ho5xwT92csZ6y3Fso/Y6iGbIS8BLmmUH6Z W8rf8/+xEa5lSaFn4oR1Lk3CQw++243nz4yjZP95rts4rN+N31Lof8F2NNe7 xMt/mHI+d+XKO3NOe7Pxtg/vroT+SFJNVtrn4ZDfV5ArIL81hq/E8x7oV0HW uXasUYvjWRU6H8/J89+O55PQb4MsM1ZrMskT1ucU0vvYdtL2waHcvhvKa39j e0PS+uSkOnQ7hoWFfFqNiwrXb0ESJhpDGP/bOB/uGt3o75xzHYk17ss9Owph hXls954dxmql99+E92dC/oznm6AfZu5DtWW9WNuZrgffPZCrPm2udbP7nPX9 xf01A/JX+zCeC8Y/sXoIv0WwTzMWyAesOffa5/cO+t1FSee4D/FsxG8JsQR5 KoSHs9wjV0z0JwYmuH9vCeHpZsgWvLfffVfp/RlDqTFHLK0nxnPVeWAozi6s 74e+mvmFXgt5b6FaH0nC7V2hHmXNmM9e/Hq8/1Mh/ydDXMkefxXvdRbqf/Za mb9LDmRtWWNy8CtJ35kU4rEy44o5PuI8V+eqF3FwxDrtR0N5+AZ7NDmeibly sNd5qDVmx5iv2A/kMfbQTvfR5Fy9R/2lJD6cEMLjKfvfGsJjU1JflHlP8sba QjHeH+K+XSFuYh90wv9Yrn5kbMQ9scY+Zb1Z93dC3FfiXDFP5d4/L8TXfJ6e 5DcKcjme+xkDJ0PPx2Gv9l70Zb+Q+xjTA4VywXdWh2bMStY0FP+BpDPz7C9C /wB6G+wb2CMhbtkE+3fOw5/Qt5qLiEdiZ7fPSF5Z45xMC53nwaT1Jvu8b44i VzWHZsNo+MwtFHOp95zoujAW1miy63zMa3vw7jqs/cYeCuGxMYmLuCfzSa5b 51l/mLiE/CephrM81wb5TlAJ2WDcLs6U16pCeZtfCOfM49SQfUpordb1WpHE E7wj8HudxiHnGefa454Dm4zhzbm4m7xS5Rn9WGjms0Ylrn2Xa01uYF+xx74m P0DfcrkP7EOOfbkQ/tYak23Yb13IvgX6Rs5A6G/B9noS73GeNBr38z3n6z2b Rjufw7HeFMLTM5C7Q2c87Jla7zl+ybxN/qbvfOeN9yHyYUNSvMsdM+f2ZueB uVnm/PR3Ldk3U5P6YUiIj5hHztVRhfJA/uZsOu6evzbEIwMgtybliJzRmsQR 10M/kZT/vSFMj3CvvxfC2o4kHC91PMR2tblodCGsECfMb19zwE7z9SFjaZRz uKAQ/9a5nlvdO+QY5pxz7e2Q35pQLhv8LvuC/E4svZH07ZZQr8y1/XRSP/K+ wNrSXmouHGkcMp8LjY1OcwPtxBp7oSYT9vr5/sm7CzHIuwPPw3ONTeLv896X eFziPPS4xxcZC522HzNnkavIc63mOs5j1m0z7J96dvDezfnFb3HePZSEh173 GvuMmBmftPfFpDoTO8TWRPMEeaN/aFZVQP6QNLNf8Pyvdk3LjUHu2+E7RHsm TBAbdXivKuTXkVRPxsy6NLh/yeesKzmFfLIk6QzPhvIxrNAz91/sGcq7SYf1 ezyLOZO35cIqsdEdipmxPx/a5znIT3xP2Og73np/e3uIe3dAvpZ0z/jcMVS6 Lgudf8a8x7lizr4M3XP4fxJn42rn8EzSPYT3kYvO+cchnpvhGo015mfA/lHo jLuT6tnpWnOPbT5XfdL+vHf86LrwrB8m3TXfhH5HiLvvhOxO4oXvof8LxqqO Ew== "]], PolygonBox[CompressedData[" 1:eJwllNlv1VUUhe+5Z0NjTImg1aogEFDBxBefBGOxBCIID0RkeKgaStugVSsU UDFRmQ2jlEEGKVEKFhTpxFQthfhUNWhQApIwhEIIf4MS+FbWw2rX2vvc89vT 2SOrG17/oFgoFOpBgFb+rOT/aoyH4B/lQqEkFQovgbnoX8AufJM58zN8O1iE Xgy+5+xR9JOcfQ+0we9iWwV/FfwI/xrbEs6+CKrha7E9hm8l+iC6HQxFvw86 4FtAA75KvtfC2SfQf+O7ADagO8NnmzhzDF6JbT06oWvh/4E16ArQCv8p/Nt6 8Cu6Hl0DLwOr0V3hu6aBzegy9O/4/gSfoCeBjfBOcA9eg38cZ9ehr6AXoV9D TwdfKTf0H/hqiL8OPT+c+0PY5sFXYXsE/im2ZvRZ0JEc87/4vgTl8CrVFN8U 9Bb4CVBEvw0mwUvB5/gug7XJdxyBPx6OvVH1Rfdm13oFOICvK/vby8Be9QY9 kPPjwaxwDVQL5aTcGsO5aUZmZfdUvW3GdgbfpWx+G9tY9MzwXQPAYnwLw7Oh Hm9CfxjuhWZmI/oG+v+iaz4S3g8C+wBsE+Dn4XuKnsFu9Hr0P8kzodl4E7xQ 9Ixp1haEe/sK+t1wjsq1lvg7+e03+gb+QWAP/J3w2Wr0jOweq9eq6QT1Cv9v qjW/L4X/EH4r6tHh8Ixp1hRjT7jGqvVyzp/G1wFOJc/QtewYFItswdk16PPJ M9mT3WP1WjOt2T4FTsO/4M794ZqoNo3cUQEvAb34t4KpnD0JepJn5obmD+yD L+U3Ozn7RnYuquHz6N3hWTqCvw/ehv84eo5mTt8O10pvuhd+E1tTckyKLWM7 mWzrz545zZ52hHZFFXgwuUdvaZeEY9EMa5avZr8lfVPfflbfgN8CY+AvY5uc nINyGYXtGnyh5lmxZseqGBTLUHBRbwm045sd3mWa8aXoFWBw8p11+B4AZ/SW wZ3sN6q3qjdeq/0nW/KZ78IzollRzVX7cnAuuYfq5a3sXqgH6kVT+C1op22F DwF9yTP1sOYpu/bKWblvC+emmn2GryXce8XwLbw6exfrzejtTAzvCu2USu2i 7N2jN6S3tCO8q7XTP87OUbkqBsUyL3vXXkc/jR4OLifnpNzUA/VCb+YZ+Ojw WfVAvVCNVWvtvEfRf4Geond8N77x4d2smo+DD9MMFtyjp8JvRG9F3xyBnpY9 27pDdz0X3i36TQW++7Fi1mc= "]]}]}, { EdgeForm[], GrayLevel[0.5], GraphicsGroupBox[{ PolygonBox[CompressedData[" 1:eJxNVD1rk1EUfnPvpfkBhejg52LV1S4WfUuXEnVJmkXoYJuQ4gdoFaRugpC6 +AP8iJPoooPZYoqtSyoOgmNmRXDwD8Q0qc/D8xQ6HM55z3vu+XjOc+/p+r2l uyHLsoeQCDmVsqxZyLJtfKxBvsM+At8C7BX8D7Cn4XsAuwF5hMOzkEX4/kIG kGeQHnzXoVcRU7aP8fTnkHXYOXL+gf88cvbhuw9fHXIl6n8R/p9RebrQHyAf YY+jcjCWZ7qO+X0QG1SXMioobgLfP9hLsPcK8tNmnp7jt2yzdgO5qtCXoS/4 LL85657PDgsERP6m8SFOrP8p6HvofmgTYJ6tZcq3EZTnrTHfwf9vSf6X0I9d +0YSfhVjyJ7n3ecyfG+8D844Dqq5H4Q7Z3+XFPM06lxurBk78o4yn1vxDnJj UoXvUtDOyQn2yX7XIHPwP4HvYtL5Oehd6K/O30Jsp6A+viTtjjvMYU9xhqT9 M4ax8/b/gv6MuBJ8m955y7gteHbmn0ni3fvo+t4Fcd73bB1zjz0QR+L5IgnH vjn0OqhGzfwmBmXX5zzEtmt8Vn1u1z2cSOL8cehO1I6OwT7r3ma8u4H3t+m8 R5NwYh5+kxfse+Td0WY/z5Nmahz6Vzd/Ju6p47t3MCdjeLaUVLPje1x1D+eS 8BzA/yOJQ9eiONI0l1rOVTMfyuZexXlok+Nd3/MNvwHkcytqT3eQ+1XSG9H2 u7Hs/BXf8VtJPJz4LpwxbsSPs43NQ8479B0c2c/4dtD+2GcxKD/r7CT9207q d/rQ+7PlnrkDYn8S+nYSr24m8Y88vBqli+Y/d9R2Le6tb25QMzdt9j7r3npR /RBj3qEp5+G7tm4MiHfJmPwHr+ar3w== "]], PolygonBox[CompressedData[" 1:eJwlkblKg1EQhW/+O+gbJI1roWBaFzBoRAVxaYyVYCNCEhdwqbRThOAruCSV YGOjpYqJnViojaC1IFj4BKLid5jiwDl3ljtzpnNpY249CSF0AQPjMYRCKoRH UIJ/gHn4DUnPJCyDEfgoqBCbBIvUXaGfiK2AzeA5q/AZ4gvUN6PL8D3QB98B J+Y1qtUf78Re0PfoNPoWXUYPJT6TZqujq+gf9AB8WPPyX463EvoAfZryP6/h v+ACXiP+Cm8hZxb9BtrgGfNd9ZaGt5v/XQQd8AI1W8Fn0myt5rVfYIxYFv0J r4Ae8x21axPIo9fMvVIP9crJ5MQ9K6L70d/U9vK2i66aezPBWzd8OnoveSAv GtFn045n6P3ou6vnoGaLPot2vkOfR++1TfzB/Ia6pW40Fd1Deamb6DbaSbtd gr/onsk7ed4g98h8Vnl+CD82760dtItqVCtP8vB/O7hJZQ== "]]}]}}, {{}, TagBox[ TooltipBox[{ Directive[ Opacity[0.5], CapForm["Butt"], Thickness[0.03], GrayLevel[0.3]], LineBox[CompressedData[" 1:eJwl0L9KQnEcxuFfKnQH1lBqi/3ZCwwqrKVysW5AUNJKqEsogvIW0lxzsaUt AqvVJRsCnY0uQ+k5NDx8vq9wOAeXypfHFzMhhCJX8RDWEyG06NO1l3WFLG0e uPb7hr7xztCe8MSHPdJVXWPbPWWXRTvFAs92UueouveouQ/1R3eiZ6nZBb7c r9qgE71XH6Nv0IymmefO7vHtPtK63uqpnnNG0Z7VAx3or74w5tPe15jm9YRN d44bd1W3tMK9u0kp8f/f/QHp9yfE "]]}, "0.09`"], Annotation[#, 0.09, "Tooltip"]& ], TagBox[ TooltipBox[{ Directive[ Opacity[0.5], CapForm["Butt"], Thickness[0.03], GrayLevel[0.3]], LineBox[CompressedData[" 1:eJwl0rdPVWEYwOFDuAMOLJRBmACZwD8AFukTE9UBSUDcCEiHhYmBBLgURSZN EJBiUFGEUBxoi2xumkhf3JhhkOcLwy/P+96Sc/Kdk9XcXtWWEEVRXAOJUZQe i6IsXajZns0b7nBX2/pl/8El3irfnKff5j2u8o9+mp/wv8p0bD/lV8XM3/iW 63zHIRaxWCWqtLdymKUs04o+alHL4R507ftadZkn2MlxrvN7uI6OwnW5pgrF 7R085Bd+VrnG7C+5z1Ue8BNHmcERZvKxaszJTFOqUvTPZw+YpPfhzOyznNec tuxXOjdfMpEb4by4Ge5FJzqz/2ULX6hJz8Mz0YyeqVENGvS711xgDh8pN/zH Pq9J8ytNqctexW5WsyCcHXtYw17WsZBP2cd69vNNOHdO8wMfxu7fmTsx7U1A "]]}, "0.05`"], Annotation[#, 0.05, "Tooltip"]& ], TagBox[ TooltipBox[{ Directive[ Opacity[0.5], CapForm["Butt"], Thickness[0.03], GrayLevel[0.3]], LineBox[CompressedData[" 1:eJwl08lvTWEYwOHTlC4Qw5/gHzC1RYLEhio6qKS0RUUsTL0XqakUVVNRQ001 tDWsiLBSxMZctIaWqqalJIIYKsaSWHi+WPzyvO/JvTf3fOfewfPiObGEKIra VJMYRVt6RdE2bdUXe6oyzEs5lXHu4V7t1kn7XKUo2z5NI5SsFI12vYGnuU4F 5lKe1V3z2PB+lnFUeL3mm8crwTyRTXzFi3qtB/Y0JjGTLXzHq3qvJ/Ys9mMO W/mB1/VRz+zT2Z8zuIIrVax99vt6Y77HVaxiGm9oofkmB+iT+RY/cxAHKs+8 hge4mgdZwvxw/zzEtTzMUhZwA6u5nke4kbO4iUdZxmMs52x+149wDnru2kwu UqM67U3cH85XbfZcLtAdddgb+E1fdc5+jXHuYBErGOMJ1amP+uqt683MYG9O 4LjwDDVG5fYLajefZ8QOdrGTZ8LZ66W9lX/Ds9IL+9NwLsozlzA/fCb/hN9F +O72Zp7Sbz3SZdce8wqXM5PLmMWR7FGybtsf8pd+6pK9kPUcrph5SrhfVnKX hqnIPjmcAXeG89FQLbGns5YV3K4hWmyfxOPczDnsTvz/v/oHtCx55Q== "]]}, "0.01`"], Annotation[#, 0.01, "Tooltip"]& ], {}, {}}}], {}}, {{}, {{{ Directive[ AbsoluteThickness[1.6], RGBColor[0, 0, NCache[ Rational[2, 3], 0.6666666666666666]], PointSize[0.08]], PointBox[{{4.905308194867242, 2.6308363915989275`}, { 0.20100883034436162`, 0.023821365695765692`}, { 1.0719666600928879`, -1.2506326268721977`}, { 3.0527398070698992`, 0.38721583002375826`}, {-1.8438997564108928`, \ -1.5026726898055591`}, {-2.913246104009823, -1.3391199692975575`}, { 0.3149621009629985, 1.9477027131642348`}, {-0.9556834229157016, \ -0.4484871006542206}, {3.2651237363484125`, 0.2704700013829126}}]}}}, {{}, {}}}, {{}, {{{ Directive[ AbsoluteThickness[1.6], RGBColor[ NCache[ Rational[2, 3], 0.6666666666666666], 0, 0], PointSize[0.08]], PointBox[{{-2.4, 2.7}, {-2.4, 2.7}}]}}}, {{}, {}}}}, { FrameStyle -> Directive[ Thickness[Tiny], GrayLevel[0.7]], Axes -> False, AspectRatio -> 1, ImageSize -> Dynamic[{ Automatic, 3.5 (CurrentValue["FontCapHeight"]/AbsoluteCurrentValue[ Magnification])}], Frame -> True, FrameTicks -> None, FrameStyle -> Directive[ Opacity[0.5], Thickness[Tiny], RGBColor[0.368417, 0.506779, 0.709798]], DisplayFunction -> Identity, DisplayFunction -> Identity, Ticks -> {Automatic, Automatic}, AxesOrigin -> {0., 0.}, FrameTicks -> {{Automatic, Automatic}, {Automatic, Automatic}}, GridLines -> {None, None}, AxesLabel -> {None, None}, FrameLabel -> {{None, None}, {None, None}}, DisplayFunction -> Identity, AspectRatio -> 1, AxesLabel -> {None, None}, DisplayFunction :> Identity, Frame -> True, FrameLabel -> {{None, None}, {None, None}}, FrameTicks -> {{Automatic, Automatic}, {Automatic, Automatic}}, GridLinesStyle -> Directive[ GrayLevel[0.5, 0.4]], Method -> { "DefaultBoundaryStyle" -> Automatic, "DefaultGraphicsInteraction" -> { "Version" -> 1.2, "TrackMousePosition" -> {True, False}, "Effects" -> { "Highlight" -> {"ratio" -> 2}, "HighlightPoint" -> {"ratio" -> 2}, "Droplines" -> { "freeformCursorMode" -> True, "placement" -> {"x" -> "All", "y" -> "None"}}}}, "GridLinesInFront" -> True}, PlotRange -> {{-3.4, 3.5}, {-3., 4}}, PlotRangeClipping -> True, PlotRangePadding -> {{ Scaled[0.02], Scaled[0.02]}, { Scaled[0.02], Scaled[0.02]}}, Ticks -> {Automatic, Automatic}}], GridBox[{{ RowBox[{ TagBox["\"Input type: \"", "SummaryItemAnnotation"], "\[InvisibleSpace]", TagBox[ TagBox[ TooltipBox[ TemplateBox[{"\"Mixed\"", StyleBox[ TemplateBox[{"\" (number: \"", "7", "\")\""}, "RowDefault"], GrayLevel[0.5], StripOnInput -> False]}, "RowDefault"], TagBox[ RowBox[{"{", RowBox[{ "\"Numerical\"", ",", "\"Numerical\"", ",", "\"Nominal\"", ",", "\"Nominal\"", ",", "\"Nominal\"", ",", "\"Numerical\"", ",", "\"Numerical\""}], "}"}], Short[#, 10]& ]], Annotation[#, Short[{"Numerical", "Numerical", "Nominal", "Nominal", "Nominal", "Numerical", "Numerical"}, 10], "Tooltip"]& ], "SummaryItem"]}]}, { RowBox[{ TagBox["\"False positive rate: \"", "SummaryItemAnnotation"], "\[InvisibleSpace]", TagBox["0.001`", "SummaryItem"]}]}, { RowBox[{ TagBox["\"Distribution method: \"", "SummaryItemAnnotation"], "\[InvisibleSpace]", TagBox["\"Multinormal\"", "SummaryItem"]}]}, { RowBox[{ TagBox[ "\"Number of training examples: \"", "SummaryItemAnnotation"], "\[InvisibleSpace]", TagBox["185", "SummaryItem"]}]}}, GridBoxAlignment -> { "Columns" -> {{Left}}, "Rows" -> {{Automatic}}}, AutoDelete -> False, GridBoxItemSize -> { "Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}, GridBoxSpacings -> {"Columns" -> {{2}}, "Rows" -> {{Automatic}}}, BaseStyle -> { ShowStringCharacters -> False, NumberMarks -> False, PrintPrecision -> 3, ShowSyntaxStyles -> False}]}}, GridBoxAlignment -> {"Columns" -> {{Left}}, "Rows" -> {{Top}}}, AutoDelete -> False, GridBoxItemSize -> { "Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}, BaselinePosition -> {1, 1}]}, Dynamic[Typeset`open$$], ImageSize -> Automatic]}, "SummaryPanel"], DynamicModuleValues:>{}], "]"}], AnomalyDetectorFunction[ Association["Preprocessor" -> MachineLearning`MLProcessor["ToMLDataset", Association[ "Input" -> Association[ "age" -> Association["Type" -> "Numerical"], "educ" -> Association["Type" -> "Numerical"], "race" -> Association["Type" -> "Nominal"], "married" -> Association["Type" -> "Nominal"], "nodegree" -> Association["Type" -> "Nominal"], "re74" -> Association["Type" -> "Numerical"], "re75" -> Association["Type" -> "Numerical"]], "Output" -> Association[ "f1" -> Association["Type" -> "Numerical", "Weight" -> 1], "f2" -> Association["Type" -> "Numerical", "Weight" -> 1], "f3" -> Association["Type" -> "Nominal", "Weight" -> 1], "f4" -> Association["Type" -> "Nominal", "Weight" -> 1], "f5" -> Association["Type" -> "Nominal", "Weight" -> 1], "f6" -> Association["Type" -> "Numerical", "Weight" -> 1], "f7" -> Association["Type" -> "Numerical", "Weight" -> 1]], "Preprocessor" -> MachineLearning`MLProcessor["Sequence", Association["Processors" -> { MachineLearning`MLProcessor["FromNamedFeatures", Association[ "FeatureNames" -> { "age", "educ", "race", "married", "nodegree", "re74", "re75"}]], MachineLearning`MLProcessor["Transpose", Association["FeatureNumber" -> 7]], MachineLearning`MLProcessor["WrapMLDataset", Association[ "FeatureTypes" -> { "Numerical", "Numerical", "Nominal", "Nominal", "Nominal", "Numerical", "Numerical"}, "FeatureKeys" -> {"f1", "f2", "f3", "f4", "f5", "f6", "f7"}, "FeatureWeights" -> Automatic, "ExampleWeights" -> Automatic, "RawExample" -> Missing["KeyAbsent", "RawExample"], "StructurePreserving" -> False]]}]], "ScalarFeature" -> False, "Invertibility" -> "Perfect", "StructurePreserving" -> False, "Missing" -> "Allowed"]], "Processor" -> MachineLearning`MLProcessor["Identity"], "Distribution" -> LearnedDistribution[ Association[ "ExampleNumber" -> 184, "Preprocessor" -> MachineLearning`MLProcessor["ToMLDataset", Association[ "Input" -> Association[ "f1" -> Association["Type" -> "Numerical"], "f2" -> Association["Type" -> "Numerical"], "f3" -> Association["Type" -> "Nominal"], "f4" -> Association["Type" -> "Nominal"], "f5" -> Association["Type" -> "Nominal"], "f6" -> Association["Type" -> "Numerical"], "f7" -> Association["Type" -> "Numerical"]], "Output" -> Association[ "f1" -> Association["Type" -> "Numerical", "Weight" -> 1], "f2" -> Association["Type" -> "Numerical", "Weight" -> 1], "f3" -> Association["Type" -> "Nominal", "Weight" -> 1], "f4" -> Association["Type" -> "Nominal", "Weight" -> 1], "f5" -> Association["Type" -> "Nominal", "Weight" -> 1], "f6" -> Association["Type" -> "Numerical", "Weight" -> 1], "f7" -> Association["Type" -> "Numerical", "Weight" -> 1]], "Preprocessor" -> MachineLearning`MLProcessor["Identity"], "ScalarFeature" -> False, "Invertibility" -> "Perfect", "StructurePreserving" -> False, "Missing" -> "Allowed"]], "Processor" -> MachineLearning`MLProcessor["Sequence", Association[ "Input" -> Association[ "f1" -> Association["Type" -> "Numerical", "Weight" -> 1], "f2" -> Association["Type" -> "Numerical", "Weight" -> 1], "f6" -> Association["Type" -> "Numerical", "Weight" -> 1], "f7" -> Association["Type" -> "Numerical", "Weight" -> 1], "f3" -> Association["Type" -> "Nominal", "Weight" -> 1], "f4" -> Association["Type" -> "Nominal", "Weight" -> 1], "f5" -> Association["Type" -> "Nominal", "Weight" -> 1]], "Output" -> Association[ "((f3f4f5)(f1f2f6f7))" -> Association[ "Weight" -> {1., 1., 1., 1., 1., 1., 1.}, "Type" -> "NumericalVector"]], "Processors" -> { MachineLearning`MLProcessor["Threads", Association[ "Input" -> Association[ "f1" -> Association["Type" -> "Numerical", "Weight" -> 1], "f2" -> Association["Type" -> "Numerical", "Weight" -> 1], "f6" -> Association["Type" -> "Numerical", "Weight" -> 1], "f7" -> Association["Type" -> "Numerical", "Weight" -> 1], "f3" -> Association["Type" -> "Nominal", "Weight" -> 1], "f4" -> Association["Type" -> "Nominal", "Weight" -> 1], "f5" -> Association["Type" -> "Nominal", "Weight" -> 1]], "Output" -> Association[ "(f1f2f6f7)" -> Association["Type" -> "NumericalVector", "Weight" -> 4], "(f3f4f5)" -> Association["Type" -> "NominalVector", "Weight" -> 3]], "Processors" -> { MachineLearning`MLProcessor["ToVector", Association[ "Invertibility" -> "Perfect", "Missing" -> "Allowed", "StructurePreserving" -> True, "Input" -> Association[ "f1" -> Association["Type" -> "Numerical", "Weight" -> 1], "f2" -> Association["Type" -> "Numerical", "Weight" -> 1], "f6" -> Association[ "Type" -> "Numerical", "Weight" -> 1], "f7" -> Association["Type" -> "Numerical", "Weight" -> 1]], "Output" -> Association[ "(f1f2f6f7)" -> Association["Type" -> "NumericalVector", "Weight" -> 4]], "Version" -> {12.3, 0}, "ID" -> 925386845398377649]], MachineLearning`MLProcessor["ToVector", Association[ "Invertibility" -> "Perfect", "Missing" -> "Allowed", "StructurePreserving" -> True, "Input" -> Association[ "f3" -> Association["Type" -> "Nominal", "Weight" -> 1], "f4" -> Association["Type" -> "Nominal", "Weight" -> 1], "f5" -> Association["Type" -> "Nominal", "Weight" -> 1]], "Output" -> Association[ "(f3f4f5)" -> Association["Type" -> "NominalVector", "Weight" -> 3]], "Version" -> {12.3, 0}, "ID" -> 8236867426451684890]]}, "Invertibility" -> "Perfect", "StructurePreserving" -> True, "Missing" -> "Allowed"]], MachineLearning`MLProcessor["IntegerEncodeNominalVector", Association[ "Invertibility" -> "Perfect", "Missing" -> "Allowed", "StructurePreserving" -> True, "Input" -> Association[ "(f3f4f5)" -> Association["Type" -> "NominalVector", "Weight" -> 3]], "Index" -> { Association["black" -> 1, "hispan" -> 2, "white" -> 3], Association[0 -> 1, 1 -> 2], Association[0 -> 1, 1 -> 2]}, "MissingCode" -> Indeterminate, "Version" -> {12.3, 0}, "ID" -> 1699083991186911779, "Output" -> Association[ "(f3f4f5)" -> Association["Type" -> "NominalVector", "Weight" -> 3]]]], MachineLearning`MLProcessor["Standardize", Association[ "Invertibility" -> "Perfect", "Missing" -> "Allowed", "StructurePreserving" -> True, "Input" -> Association[ "(f1f2f6f7)" -> Association["Type" -> "NumericalVector", "Weight" -> 4]], "Mean" -> {25.777173913043477`, 10.342391304347826`, 2027.284904347826, 1403.7390926630435`}, "StandardDeviation" -> {7.135285912751556, 2.0100688770186843`, 4797.534495393728, 2705.0954570939325`}, "Output" -> Association[ "(f1f2f6f7)" -> Association["Type" -> "NumericalVector", "Weight" -> 4]], "Version" -> {12.3, 0}, "ID" -> 4243571982754252895]], MachineLearning`MLProcessor["NumericalizeNominalVector", Association[ "Invertibility" -> "Approximate", "Missing" -> "Allowed", "StructurePreserving" -> True, "Input" -> Association[ "(f3f4f5)" -> Association[ "Type" -> "NominalVector", "Weight" -> 3, "SetSize" -> {3, 2, 2}]], "Boundaries" -> {{-0.5, -0.16666666666666669`, 0.16666666666666663`, 0.5}, {-0.5, 0., 0.5}, {-0.5, 0., 0.5}}, "Version" -> {12.3, 0}, "ID" -> 7190231150816541564, "Output" -> Association[ "(f3f4f5)" -> Association["Type" -> "NumericalVector", "Weight" -> 3]]]], MachineLearning`MLProcessor["MergeVectors", Association[ "Invertibility" -> "Perfect", "Missing" -> "Allowed", "StructurePreserving" -> True, "Input" -> Association[ "(f3f4f5)" -> Association["Type" -> "NumericalVector", "Weight" -> 3], "(f1f2f6f7)" -> Association["Type" -> "NumericalVector", "Weight" -> 4]], "Spans" -> { Span[1, 3], Span[4, 7]}, "Wrappers" -> {Identity, Identity}, "Output" -> Association[ "((f3f4f5)(f1f2f6f7))" -> Association[ "Weight" -> {1., 1., 1., 1., 1., 1., 1.}, "Type" -> "NumericalVector"]], "Version" -> {12.3, 0}, "ID" -> 4096278183524584137]]}, "Invertibility" -> "Approximate", "StructurePreserving" -> True, "Missing" -> "Allowed"]], "PerformanceGoal" -> Automatic, "BatchProcessing" -> Automatic, "Model" -> Association["RotationMatrix" -> CompressedData[" 1:eJwBmQFm/iFib1JlAgAAAAcAAAAHAAAACchQhSB/kz+O5KlBFV5vP2dGZ8rc EJW/nfTl6KXerD88VP2hqmDtv4b8NI13Cc4/6N2eqpYM1D8Q9BLzSrCjP2C5 29npCq6/sfuQx6kimj8E3EDValWrP8rcBHwqGLc/T83zeKHE7D/Oy1FjeMra v1xfAMMJ1YG/tshkdKqDrD80cGg5ORvFP30QU9nzgXa/MNiWUvHe1z+aFGkw hZ3WPztaAzZh4eo/t8KR7FLBjj+4HlXWf9nuv6Oy2jSpls8/ocitjaEks79s ujqdFaWIv2T1K5V/tqe/DSXHNzl2pD/h5c7Zdcq0P7+af424+8+/VS7KJh6P 7b9kFv2MfOLLP4m4GNx5G7g/Qb4jfJ69qD9VrillkarBP9vo6UskVeY/hOuz XDSmoj8QGjUpt8G8v1fSxQbnk+a/7sTV4tO4i79BB8F8jYObP67+zGdZXpE/ 3OfdYe+45j8/uGyf7lSRPzlumQOpI8s/vKbskPZM5T8KXdJNDL6bP0QwPNnB 47S/Rg/7lILhgb+trtT3 "], "Precisions" -> {0.6042281540130963, 0.9999427122638173, 1.1210798690011472`, 2.819917740400251, 18.3529394922037, 20.694778310703228`, 26.118690765830053`}, "NoisePrecision" -> None, "Processor" -> MachineLearning`MLProcessor["Center", Association[ "Invertibility" -> "Perfect", "Missing" -> "Allowed", "StructurePreserving" -> True, "Input" -> Association[ "((f3f4f5)(f1f2f6f7))" -> Association["Type" -> "NumericalVector", "Weight" -> 7.]], "Mean" -> {-0.23587434866878, -0.19185160808885485`, 0.10906182258024429`, -0.039020521443480706`, \ -0.018905891598501697`, -0.01840732636652058, 0.007567905422906979}, "Output" -> Association[ "((f3f4f5)(f1f2f6f7))" -> Association["Type" -> "NumericalVector", "Weight" -> 7.]], "Version" -> {12.3, 0}, "ID" -> 4617481818399337741]], "PostProcessor" -> MachineLearning`MLProcessor["FirstValues", Association[ "Info" -> Association["Type" -> "NumericalVector", "Weight" -> 7.], "Key" -> "((f3f4f5)(f1f2f6f7))", "Invertibility" -> "Perfect", "StructurePreserving" -> False, "Missing" -> "Allowed"]], "Method" -> "Multinormal", "Options" -> Association[ "CovarianceType" -> Association["Value" -> "Full", "Options" -> Association[]], "IntrinsicDimension" -> Association["Value" -> 7, "Options" -> Association[]]]], "TrainingInformation" -> Association[ "PanelCell" -> None, "TrainingFunction" -> LearnDistribution, "EMIterations" -> 1, "ProcessorEntropyShift" -> 40.568849066939606`, "PreprocessingTime" -> 0.093251`5.421198491265202, "LossName" -> "MeanCrossEntropy", "BestModelInformation" -> Dataset[ Association[ "MeanCrossEntropy" -> Around[6.191166821972038, 0.18721202156651492`], "EvaluationTime" -> 7.3748202911599445`*^-6, "SamplingTime" -> 0.00010499999999999998`, "TestSize" -> 74, "ModelMemory" -> 7944., "ModelUtility" -> -62.28609771662228, "TrainingSize" -> 147, "TrainingTime" -> 0.02511886431509579, "TrainingMemory" -> 157381.3333333333, "ExperimentCount" -> 2, "MeanCrossEntropyHistory" -> { Around[6.213213030234166, 0.27257207606973766`], Around[6.16912061370991, 0.25421769405641]}, "Configuration" -> { "Multinormal", "CovarianceType" -> "Full", "IntrinsicDimension" -> 7}], TypeSystem`Struct[{ "MeanCrossEntropy", "EvaluationTime", "SamplingTime", "TestSize", "ModelMemory", "ModelUtility", "TrainingSize", "TrainingTime", "TrainingMemory", "ExperimentCount", "MeanCrossEntropyHistory", "Configuration"}, {TypeSystem`AnyType, TypeSystem`Atom[Real], TypeSystem`Atom[Real], TypeSystem`Atom[Integer], TypeSystem`Atom[Real], TypeSystem`Atom[Real], TypeSystem`Atom[Integer], TypeSystem`Atom[Real], TypeSystem`Atom[Real], TypeSystem`Atom[Integer], TypeSystem`Vector[TypeSystem`AnyType, 2], TypeSystem`Tuple[{ TypeSystem`Atom[String], TypeSystem`AnyType, TypeSystem`AnyType}]}], Association[]], "Configurations" -> Dataset[ Association[ Association[ "Value" -> "Multinormal", "Options" -> Association[ "CovarianceType" -> Association["Value" -> "Full"], "IntrinsicDimension" -> Association["Value" -> 7]]] -> Association["Experiments" -> { Association[ "MeanCrossEntropy" -> Around[5416.941634274719, 2207.4696380172054`], "EvaluationTime" -> 2.0341792655852223`*^-6, "SamplingTime" -> 0.000048, "TestSize" -> 176, "ModelMemory" -> 7944, "ModelUtility" -> -58584.355624173026`, "TrainingSize" -> 8, "TrainingTime" -> 0.01995262314968879, "TrainingMemory" -> 139600, "ExperimentCount" -> 1, "MeanCrossEntropyHistory" -> { Around[5416.941634274719, 1560.9167503053793`]}], Association[ "MeanCrossEntropy" -> Around[6.573586411593294, 0.2784277251652573], "EvaluationTime" -> 0.00001012813641174343, "SamplingTime" -> 0.000076, "TestSize" -> 144, "ModelMemory" -> 7944, "ModelUtility" -> -66.29272499378565, "TrainingSize" -> 40, "TrainingTime" -> 0.015848931924611134`, "TrainingMemory" -> 143552, "ExperimentCount" -> 1, "MeanCrossEntropyHistory" -> { Around[6.573586411593294, 0.19687813253469777`]}], Association[ "MeanCrossEntropy" -> Around[6.191166821972038, 0.18721202156651492`], "EvaluationTime" -> 7.3748202911599445`*^-6, "SamplingTime" -> 0.00010499999999999998`, "TestSize" -> 74, "ModelMemory" -> 7944., "ModelUtility" -> -62.28609771662228, "TrainingSize" -> 147, "TrainingTime" -> 0.02511886431509579, "TrainingMemory" -> 157381.3333333333, "ExperimentCount" -> 2, "MeanCrossEntropyHistory" -> { Around[6.213213030234166, 0.27257207606973766`], Around[6.16912061370991, 0.25421769405641]}]}, "PredictedPerformances" -> Association[ "EvaluationTime" -> 7.3748202911599445`*^-6, "MeanCrossEntropy" -> Around[6.191166821972038, 0.18721202156651492`], "ModelMemory" -> 7944., "TrainingMemory" -> 157381.3333333333, "TrainingTime" -> 0.05139392270055698], "Index" -> 1]], TypeSystem`Assoc[ TypeSystem`Struct[{"Value", "Options"}, { TypeSystem`Atom[String], TypeSystem`Assoc[ TypeSystem`Atom[String], TypeSystem`Struct[{"Value"}, {TypeSystem`AnyType}], 2]}], TypeSystem`Struct[{ "Experiments", "PredictedPerformances", "Index"}, { TypeSystem`Vector[ TypeSystem`Struct[{ "MeanCrossEntropy", "EvaluationTime", "SamplingTime", "TestSize", "ModelMemory", "ModelUtility", "TrainingSize", "TrainingTime", "TrainingMemory", "ExperimentCount", "MeanCrossEntropyHistory"}, {TypeSystem`AnyType, TypeSystem`Atom[Real], TypeSystem`Atom[Real], TypeSystem`Atom[Integer], TypeSystem`Atom[Real], TypeSystem`Atom[Real], TypeSystem`Atom[Integer], TypeSystem`Atom[Real], TypeSystem`Atom[Real], TypeSystem`Atom[Integer], TypeSystem`Vector[ TypeSystem`AnyType, TypeSystem`AnyLength]}], 3], TypeSystem`Struct[{ "EvaluationTime", "MeanCrossEntropy", "ModelMemory", "TrainingMemory", "TrainingTime"}, { TypeSystem`Atom[Real], TypeSystem`AnyType, TypeSystem`Atom[Real], TypeSystem`Atom[Real], TypeSystem`Atom[Real]}], TypeSystem`Atom[Integer]}], 1], Association[]], "MaxTrainingSize" -> 184, "PreprocessorEvaluationTime" -> 3.984375*^-6, "PreprocessorMemory" -> 37904, "BaselineLogProbability" -> -0.8181154816743093, "VariableBudget" -> True, "CheckpointingInfo" -> Association["Checkpointing" -> False], "UserStop" -> False, "NaturalStop" -> True, "AbortStop" -> False, "RoundPartitioning" -> Dataset[{ Association[ "TrainingSizes" -> 8, "TimeBudgets" -> 0.0024874658587069587`, "ElapsedTimes" -> 0.024074, "ExperimentCounts" -> 1], Association[ "TrainingSizes" -> 40, "TimeBudgets" -> 0.012437329293534796`, "ElapsedTimes" -> 0.022178, "ExperimentCounts" -> 1], Association[ "TrainingSizes" -> 147, "TimeBudgets" -> 0.06218664646767395, "ElapsedTimes" -> 0.058914999999999995`, "ExperimentCounts" -> 2]}, TypeSystem`Vector[ TypeSystem`Struct[{ "TrainingSizes", "TimeBudgets", "ElapsedTimes", "ExperimentCounts"}, { TypeSystem`Atom[Integer], TypeSystem`Atom[Real], TypeSystem`Atom[Real], TypeSystem`Atom[Integer]}], 3], Association[]]], "NaiveImputer" -> MachineLearning`MLProcessor["ImputeMissing", Association[ "Invertibility" -> "Perfect", "Missing" -> "Imputed", "StructurePreserving" -> True, "Input" -> Association[ "((f3f4f5)(f1f2f6f7))" -> Association["Type" -> "NumericalVector", "Weight" -> 7.]], "Mean" -> {-0.24420833211554677`, -0.1807260366170029, 0.10006185722935712`, 0.001049416422125361, -0.0005823239490933973, 0.00004206241281333637, 0.00019102017255430244`}, "StandardDeviation" -> {0.23252070237163464`, 0.23977021154605038`, 0.2674015604714661, 1.0000393198930289`, 0.9995970864594235, 0.9998995152657396, 1.0005570200138585`}, "Method" -> "NaiveSampler", "VectorLength" -> 7, "Output" -> Association[ "((f3f4f5)(f1f2f6f7))" -> Association["Type" -> "NumericalVector", "Weight" -> 7.]], "Type" -> "NumericalVector", "Version" -> {12.3, 0}, "ID" -> 1142690375611020183]], "InputDimension" -> 0, "OutputDimension" -> 7, "Log" -> Association["Example" -> MachineLearning`MLDataset[ Association[ "f1" -> Association[ "Type" -> "Numerical", "Weight" -> 1, "Values" -> {43}, "ID" -> 7034351111016118673], "f2" -> Association[ "Type" -> "Numerical", "Weight" -> 1, "Values" -> {9}, "ID" -> 6017782406600041298], "f3" -> Association[ "Type" -> "Nominal", "Weight" -> 1, "Values" -> {"black"}, "ID" -> 719579589149147236], "f4" -> Association[ "Type" -> "Nominal", "Weight" -> 1, "Values" -> {0}, "ID" -> 2558277092035415739], "f5" -> Association[ "Type" -> "Nominal", "Weight" -> 1, "Values" -> {1}, "ID" -> 3909078940328374320], "f6" -> Association[ "Type" -> "Numerical", "Weight" -> 1, "Values" -> {0}, "ID" -> 647132074108547313], "f7" -> Association[ "Type" -> "Numerical", "Weight" -> 1, "Values" -> {0}, "ID" -> 3207616046394426832]], Association[ "ExampleNumber" -> 1, "ExampleWeights" -> 1, "LogDensityRatios" -> 0, "RawExample" -> False, "LogDensityRatio" -> 0]], "TrainingTime" -> 0.308458, "MaxTrainingMemory" -> 669720, "DataMemory" -> 42048, "FunctionMemory" -> 97040, "LanguageVersion" -> {12.3, 0}, "Date" -> DateObject[{2021, 7, 4, 10, 37, 27.158934`8.18648770149086}, "Instant", "Gregorian", -6.], "ProcessorCount" -> 2, "ProcessorType" -> "x86-64", "OperatingSystem" -> "MacOSX", "SystemWordLength" -> 64, "Evaluations" -> {}], "LogPDFDistribution" -> MachineLearning`TailedQuantileDistribution[ Association["Quantiles" -> CompressedData[" 1:eJwNzX1QzHkAx/F1pWi/3+/vt1Tbw1pqYhtLyTWx0tq5ZHf0ILvR06CuFldR qMjdkOiBErWSpxYNxRhDeehJ+ijdnWjUiCZ56Dp5mgx6kCPb9cf7j9dfb6df E7X6CQKBQD/e25c/f7gnmI7EWx2rvc464lVxML9ivz0iRvWmqI92uHzYer1x +1Q4ybN+lwRNwbYD7vMCOkWI++NMwuQ1IqRJY271eIqg9ppYmGkpgi5Haezv 5tHcGuAxfJfHSpeW8D3nePin7M0IMfAIK90q897FI/fUL8eViTz8/FMHWTSP m7cLjK1aHqnOR1cu9eHxpVO+YLucR3elR51UwuPlQ1GjjvD4+72DsXiYQ7Fy +W5NF4edy8XxojoOcYFt9XnlHAav/hXllMuh55pNnEUKh839EVmmSA5a1wsV pmUc2ibK8p1lHDSkwNRgy2EOS4uvNuPwHaUXBd8ZTHL7K16DDDlR5VX1rxhe JGytqW1jUERuaqptYrDWhwz9qGQ4W5bk2X6e4fQ6U7TZcYZz2Z2du/MZvjrM TSAZDJPKekvykhmoQZptiGEQ933z7g1kCF+cYeu7lCFwYK2bzIPBLTdky7IZ DEdk5o3/8gwxsxfp2RiFT4NZid0QhXbfmHfka4oUV8WTQ88pXFfE6n60U8yY /3VgYwuF2u3Cf6pqipNpySdyL1OMJow8+Kl03OIn5tOMFMqeb9nW+RT7Mh3v 6PdQBFxnz21TKQq5j+83J1GIVN3qqlgKGjPzkUUYRWieV22vZvwfqsFhH4qI L54Hk+ZRWFV0vDF3pfitSrlDYE8R6b8muIGjqPSz/2eWJcV6SbPtpVGCwG71 sdDPBKPOi/eH9xFcXCKR+nYRtBQppo20EERr+iY/qx53ObpiywjGstqdduYR qO5cz0zPJGgSbzDY7CK4MVYvUSUSPHVRj4StJQh+Z0h/u5ogq6Mox0JDcE23 ytCwkKBC4Hc/2Z3AUqf4NMuFIL+GxIscCIYXnIg9QwgGquRlf04gkKRunE2H hNC5B6nKe4SYaVVQXPRYCD93u6DGGiHcbAr7FUeEaKpLP70lQwipo29rc6gQ aq04vqTfCv8DId5y2g== "], "LeftBoundary" -> -6.7825434861242835`, "LeftScale" -> 1.015036234662487, "LeftTailNorm" -> 0.011]], "Entropy" -> Around[27.266408804490016`, 0.1315765745740166], "EntropySampleSize" -> 1000]], "AnomalyNumber" -> 1, "PerformanceGoal" -> Automatic, "AcceptanceThreshold" -> 0.001, "ExampleNumber" -> 185, "Log" -> Association[ "TrainingTime" -> 5.079322, "MaxTrainingMemory" -> 7322608, "DataMemory" -> 211696, "FunctionMemory" -> 112272, "LanguageVersion" -> {12.3, 0}, "Date" -> DateObject[{2021, 7, 4, 10, 37, 27.159407`8.18649526509778}, "Instant", "Gregorian", -6.], "ProcessorCount" -> 2, "ProcessorType" -> "x86-64", "OperatingSystem" -> "MacOSX", "SystemWordLength" -> 64, "Evaluations" -> {}]]], Editable->False, SelectWithContents->True, Selectable->False]], "Output", TaggingRules->{}, CellChangeTimes->{ 3.8343187420539227`*^9, {3.8343188108782988`*^9, 3.8343188343576527`*^9}, 3.8343189525023527`*^9, 3.834319482401178*^9, 3.834334816300014*^9, 3.834405447394335*^9}, CellLabel->"Out[3]=", CellID->50245876] }, Open ]], Cell[TextData[{ "Use the anomaly detector function on the untreated (control) population but \ set the ", Cell[BoxData[ TagBox[ ButtonBox[ StyleBox["AcceptanceThreshold", "SymbolsRefLink", ShowStringCharacters->True, FontFamily->"Source Sans Pro"], BaseStyle->Dynamic[ FEPrivate`If[ CurrentValue["MouseOver"], { "Link", FontColor -> RGBColor[0.854902, 0.396078, 0.145098]}, { "Link"}]], ButtonData->"paclet:ref/AcceptanceThreshold", ContentPadding->False], MouseAppearanceTag["LinkHand"]]], "InlineFormula", FontFamily->"Source Sans Pro"], " to be extremely high so that most members are treated as anomalous, which \ will bring the number of persons in the remaining untreated population down \ to about the number of persons in the treated population:" }], "Text", TaggingRules->{}, CellChangeTimes->{{3.834318843978812*^9, 3.834318854658311*^9}, { 3.8343189174470243`*^9, 3.834318919157166*^9}, {3.834319075085404*^9, 3.834319124102366*^9}, {3.8343192732217693`*^9, 3.834319274540484*^9}, { 3.8343195928853188`*^9, 3.834319593368908*^9}, 3.8344056052814493`*^9}, CellID->38380953], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"nonanomalousControl", "=", RowBox[{ RowBox[{ RowBox[{"Query", "[", RowBox[{"Select", "[", RowBox[{ RowBox[{ RowBox[{"#treat", "==", "0"}], " ", "&&", " ", RowBox[{"Not", "[", RowBox[{"adfTreated", "[", RowBox[{ RowBox[{"KeyDrop", "[", RowBox[{"#", ",", RowBox[{"{", RowBox[{"\"\\"", ",", "\"\\""}], "}"}]}], "]"}], ",", RowBox[{"AcceptanceThreshold", "->", "0.175"}]}], "]"}], "]"}]}], "&"}], "]"}], "]"}], "[", "lalonde", "]"}], "//", RowBox[{ InterpretationBox[ TagBox[ DynamicModuleBox[{Typeset`open = False}, FrameBox[ PaneSelectorBox[{False->GridBox[{ { PaneBox[GridBox[{ { StyleBox[ StyleBox[ AdjustmentBox["\<\"[\[FilledSmallSquare]]\"\>", BoxBaselineShift->-0.25, BoxMargins->{{0, 0}, {-1, -1}}], "ResourceFunctionIcon", FontColor->RGBColor[ 0.8745098039215686, 0.2784313725490196, 0.03137254901960784]], ShowStringCharacters->False, FontFamily->"Source Sans Pro Black", FontSize->0.6538461538461539 Inherited, FontWeight->"Heavy", PrivateFontOptions->{"OperatorSubstitution"->False}], StyleBox[ RowBox[{ StyleBox["FormatDataset", "ResourceFunctionLabel"], " "}], ShowAutoStyles->False, ShowStringCharacters->False, FontSize->Rational[12, 13] Inherited, FontColor->GrayLevel[0.1]]} }, GridBoxSpacings->{"Columns" -> {{0.25}}}], Alignment->Left, BaseStyle->{LineSpacing -> {0, 0}, LineBreakWithin -> False}, BaselinePosition->Baseline, FrameMargins->{{3, 0}, {0, 0}}], ItemBox[ PaneBox[ TogglerBox[Dynamic[Typeset`open], {True-> DynamicBox[FEPrivate`FrontEndResource[ "FEBitmaps", "IconizeCloser"], ImageSizeCache->{11., {2., 9.}}], False-> DynamicBox[FEPrivate`FrontEndResource[ "FEBitmaps", "IconizeOpener"], ImageSizeCache->{11., {2., 9.}}]}, Appearance->None, BaselinePosition->Baseline, ContentPadding->False, FrameMargins->0], Alignment->Left, BaselinePosition->Baseline, FrameMargins->{{1, 1}, {0, 0}}], Frame->{{ RGBColor[ 0.8313725490196079, 0.8470588235294118, 0.8509803921568627, 0.5], False}, {False, False}}]} }, BaselinePosition->{1, 1}, GridBoxAlignment->{"Columns" -> {{Left}}, "Rows" -> {{Baseline}}}, GridBoxItemSize->{ "Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}, GridBoxSpacings->{"Columns" -> {{0}}, "Rows" -> {{0}}}], True-> GridBox[{ {GridBox[{ { PaneBox[GridBox[{ { StyleBox[ StyleBox[ AdjustmentBox["\<\"[\[FilledSmallSquare]]\"\>", BoxBaselineShift->-0.25, BoxMargins->{{0, 0}, {-1, -1}}], "ResourceFunctionIcon", FontColor->RGBColor[ 0.8745098039215686, 0.2784313725490196, 0.03137254901960784]], ShowStringCharacters->False, FontFamily->"Source Sans Pro Black", FontSize->0.6538461538461539 Inherited, FontWeight->"Heavy", PrivateFontOptions->{"OperatorSubstitution"->False}], StyleBox[ RowBox[{ StyleBox["FormatDataset", "ResourceFunctionLabel"], " "}], ShowAutoStyles->False, ShowStringCharacters->False, FontSize->Rational[12, 13] Inherited, FontColor->GrayLevel[0.1]]} }, GridBoxSpacings->{"Columns" -> {{0.25}}}], Alignment->Left, BaseStyle->{LineSpacing -> {0, 0}, LineBreakWithin -> False}, BaselinePosition->Baseline, FrameMargins->{{3, 0}, {0, 0}}], ItemBox[ PaneBox[ TogglerBox[Dynamic[Typeset`open], {True-> DynamicBox[FEPrivate`FrontEndResource[ "FEBitmaps", "IconizeCloser"]], False-> DynamicBox[FEPrivate`FrontEndResource[ "FEBitmaps", "IconizeOpener"]]}, Appearance->None, BaselinePosition->Baseline, ContentPadding->False, FrameMargins->0], Alignment->Left, BaselinePosition->Baseline, FrameMargins->{{1, 1}, {0, 0}}], Frame->{{ RGBColor[ 0.8313725490196079, 0.8470588235294118, 0.8509803921568627, 0.5], False}, {False, False}}]} }, BaselinePosition->{1, 1}, GridBoxAlignment->{"Columns" -> {{Left}}, "Rows" -> {{Baseline}}}, GridBoxItemSize->{ "Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}, GridBoxSpacings->{"Columns" -> {{0}}, "Rows" -> {{0}}}]}, { StyleBox[ PaneBox[GridBox[{ { RowBox[{ TagBox["\<\"Version (latest): \"\>", "IconizedLabel"], " ", TagBox["\<\"1.0.0\"\>", "IconizedItem"]}]}, { TagBox[ TemplateBox[{ "\"Documentation \[RightGuillemet]\"", "https://resources.wolframcloud.com/FunctionRepository/\ resources/FormatDataset"}, "HyperlinkURL"], "IconizedItem"]} }, DefaultBaseStyle->"Column", GridBoxAlignment->{"Columns" -> {{Left}}}, GridBoxItemSize->{ "Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}], Alignment->Left, BaselinePosition->Baseline, FrameMargins->{{5, 4}, {0, 4}}], "DialogStyle", FontFamily->"Roboto", FontSize->11]} }, BaselinePosition->{1, 1}, GridBoxAlignment->{"Columns" -> {{Left}}, "Rows" -> {{Baseline}}}, GridBoxDividers->{"Columns" -> {{None}}, "Rows" -> {False, { GrayLevel[0.8]}, False}}, GridBoxItemSize->{ "Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}]}, Dynamic[ Typeset`open], BaselinePosition->Baseline, ImageSize->Automatic], Background->RGBColor[ 0.9686274509803922, 0.9764705882352941, 0.984313725490196], BaselinePosition->Baseline, DefaultBaseStyle->{}, FrameMargins->{{0, 0}, {1, 0}}, FrameStyle->RGBColor[ 0.8313725490196079, 0.8470588235294118, 0.8509803921568627], RoundingRadius->4]], {"FunctionResourceBox", RGBColor[0.8745098039215686, 0.2784313725490196, 0.03137254901960784], "FormatDataset"}, TagBoxNote->"FunctionResourceBox"], ResourceFunction[ ResourceObject[ Association[ "Name" -> "FormatDataset", "ShortName" -> "FormatDataset", "UUID" -> "76670bca-1587-4e7e-9e89-5b698a30759d", "ResourceType" -> "Function", "Version" -> "1.0.0", "Description" -> "Format a dataset using a given set of option values", "RepositoryLocation" -> URL["https://www.wolframcloud.com/obj/resourcesystem/api/1.0"], "SymbolName" -> "FunctionRepository`$66a3086203b4405b88cdb0de8a5c3128`FormatDataset", "FunctionLocation" -> CloudObject[ "https://www.wolframcloud.com/obj/70389ad6-7dbc-48c8-b898-\ 72c65c00f14e"]], ResourceSystemBase -> Automatic]], Selectable->False], "[", RowBox[{"MaxItems", "->", "5"}], "]"}]}]}]], "Input", TaggingRules->{}, CellChangeTimes->{{3.834318922294248*^9, 3.834318924322604*^9}, { 3.834318983779044*^9, 3.8343190682231903`*^9}, {3.834319142148355*^9, 3.834319182885583*^9}, {3.834319216887053*^9, 3.8343192688012743`*^9}, { 3.8343193515498037`*^9, 3.834319352617749*^9}, {3.834319458055284*^9, 3.834319458188381*^9}, {3.83431951536856*^9, 3.834319586664919*^9}, { 3.8343199414893436`*^9, 3.834319958552806*^9}, {3.834319990047235*^9, 3.834319996173711*^9}, {3.834333500959714*^9, 3.8343335171432133`*^9}, { 3.8343335546752367`*^9, 3.834333559274624*^9}, {3.834334790980578*^9, 3.834334791755097*^9}}, CellLabel->"In[5]:=", CellID->1093852509], Cell[BoxData[ GraphicsBox[ TagBox[RasterBox[CompressedData[" 1:eJzsvXec1NT3xz0U6U1QRKSKIEUQFlFQFEERBPGLCCgo+t1VEKSpCKJUERfs rFItFAWkI01Y2gNLX76UpSPCLgvb6CwqRWCf85vzeJ+YzCQ3mUxyZua8/+DF 3txJ7v2cm5NzkpubyjF923XL7fF4+hWAf9pFv9f0nXei33++BPzRoU+/Hm/0 6fr6033e7fpG13caxuSBwom5PJ6GeT2e//t/DsMwDMMwDMMwDMMwDMMwDMMw DMMwDMMwDMMwDMMwDMMwDMMwDMMEjZte7Nrb/8MwDMMwDMMwDCOBfmZx/Pjx 1NRUzlMYhmEYhmEYhnESnbTi6tWrO7xcu3bNxjwlO4KJZAW47263gjGGLWUX rKS7sP40YbvQhO1CE8M85fjx4//zYtcjFR4JkawA993tVjDGsKXsgpV0F9af JmwXmrBdaKKfp4iHKQj8yXlK4ESyAtx3t1vBGMOWsgtW0l1Yf5qwXWjCdqGJ fp6CD1MyvMB/4E/OUwInkhXgvrvdCsYYtpRdsJLuwvrThO1CE7YLTXTyFHyY snv37hs3bly/fh3+Y8sjFR4JkawA993tVjDGsKXsgpV0F9afJmwXmrBdaKKT p4iHKfinXY9UeCREsgLcd7dbwRjDlrILVtJdWH+asF1ownahib88RfkwBUvs eqTCIyGSFeC+u90Kxhi2lF2wku7C+tOE7UITtgtN/OUp+DAlMzNTWWjLIxUe CZGsAPfd7VYwxrCl7IKVdBfWnyZsF5qwXWjiM0/RPkxB4M/AH6nwSIhkBbjv breCMYYtZRespLuw/jRhu9CE7UITn3lKSkqK9mEKAoUBPlLhkRDJCnDf3W4F Ywxbyi5YSXdh/WnCdqEJ24Um2jzF38MUJPBHKjwSIlkB7rvbrWCMYUvZBSvp Lqw/TdguNGG70ESbp+g8TEHwkQpU4zzFGpGsAPfd7VYwxrCl7IKVdBfWnyZs F5qwXWiiylOuXLmi8zAFCfCRCo+ESFaA++52Kxhj2FJ2wUq6C+tPE7YLTdgu NFHlKYYPU5BAHqnwSIhkBbjvbreCMYYtZRespLuw/jRhu9CE7UITZZ6CD1Mg ATl69GiyLlABqkFl+AnnKWaJZAW47263gjGGLWUXrKS7sP40YbvQhO1CE2We kpWV9T+TwE84TzFLJCvAfXe7FYwxbCm7YCXdhfWnCduFJmwXmqjmfTkAj4RI VoD77nYrGGPYUnbBSroL608TtgtN2C404TzFeSJZAWf6vmHDhiVLlsTHxwf7 QKaIZLuHFmwpGWTOMppKpqamLvFy6NAht9sSXGjqz7BdaMJ2oQnnKc4TyQo4 0/dHH33U4/HcfvvtwT6QKSLZ7qFFOFkqKSnp9ddf7969+8GDB+3ds8xZRlPJ 5cuXe7x89dVXbrcluNDUn2G70ITtQhPOU5wnkhXgPMXtVjDGhJOlWrdujTH5 f//7X3v3zHkKfZzRPzk5eaMRp06d8vfzAwcOYJ3jx48Hu6lEoGmXrVu3+qt2 9uzZYLeWAjTtgoB1JkyY8N5778XFxa1cufLixYvBbicdKOcpYM3HvOzZsyfI MjjaGJrXbmfgPMXtVjDGhJOlunTpgjF5165d7d0z5yn0cUb/Tz75xGPEN998 4/O3R48eLVmyJNb57rvvgt1UItC0S/78+f1V+/HHH4PdWgrQtAsEn+3atVNV aNiw4f/+979gN5UIlPMUyB/RIvCfYOvgZGNoXrudgfMUt1vBGBNOljp27NhH H30UGxtr+81qzlPoQyfumjp1qs/fPvvss6IO5yn2Ysoup06dsmC+MIOgXS5c uNCkSRMsLFGixIsvvvj444/jn1WqVMnKygp2aylAOU/59ddf0RwU8hQbG0Pz 2u0MnKe43QrGGLaUDJyn0McZ/dPS0vb5YtOmTQUKFACdH3roIQi3tD+cMmWK MjbjPMVeTNnl8OHDaIURI0Zof5KZmRns1lKAoF1+/vlntEvfvn1Pnz6Nhd9+ +y0Wfvzxx8FuLQUo5ynTp0+XTA1OnDixYsWK5ORk7aaDBw/CVenYsWOGh9uz Zw8kI7/99luAjTHE7Lmg3zABiLBq1aqVK1empKQY7lNeFnsx23fJdmZkZEDH d+3ahZM28YaDXXkK7HP37t0wwFJTUwPZjwUf6G9gQ3/XrFmzceNG4bj8Idl4 2M/atWs3b9587tw5Uy0MSyxY6uzZs3D2HTlyRLtpy5YtGzZsMLSU5GmO6Hg8 w60q5E1v4Syzdt1PTEyEJsHh9KuBWwDnsH//fpl9QrOh8enp6fB/+I9hniLj eSCW2Llz59KlS6HBktPF/ZlG3gqmTlUbfY4FF/Haa6+ByAULFgTxtVt///13 nPH1wAMPcJ5iSLDtsm3bNrTC7NmzTTUsnCBol7fffhsKCxUqpNpPo0aNoLx9 +/amWhui0MxTvv7667vvvrto0aJ44sB/SnipX78+VqhTpw78OWHChMOHDz/z zDP58uWDarfddpvYA1w1YmNjy5QpI+7V3HXXXVOmTFEdCKpNmzbt0UcfLV68 uKgJv1q4cKF8Y6xprl9HpmGCdevWQQIuquXKlauEgrZt25qVJXhI+gH5dkIQ 2KBBg9y5c2M16O/QoUNbtmypiqAyMzNhb7C1d+/eqj0MHjwYhYLrpmrT0aNH wQ8I0wNVq1b96aefzPf7/5Dpu/7APnToULdu3aANYGJsT548eTp27OgzopNs fFJSEgSct9xyC9aBI8JxJUPccEXeUuPGjYNh065duyJFiqCA991334wZM6AC XFbefffd8uXLC0sNGjRIFc3Kn+b6A0NnK5w1OLy1CwjLm17yLAtQSbjiv/LK K6VLlxZ+LDo6+syZM9qffP755+AQhGIQ7vbt29dnzfPnz/fv379UqVJinw0b NhR3I7V5iqTnOXXqFFizcOHCohpEF0qvu379emXvdC5V8lawcKoG7nOsHRdY vXo1uqnRo0f7rAA7ga2w20WLFuGeOU9R4rBdINjG/RB8AOoYBO2CyQu4OFX9 l19+2eN9S8VE90IWmnkKXAI8voBACytUqlQJ/hwwYEBUVJTY2qJFC9yalZXV vHlzUY6P0pCBAweKo5w8eRKCCp8HggGzePFiycZY01yngmTDEHAvIn6AJikv 3whIYUqWoCJjffl2Tp06VblVhTKCSk1NxUI461WH69evH25S3cqGq6d4wVPF Dz/8EKS+6wzsuLg4dIlaHn74YdV+JBs/a9YskcjkzZsXYmn8f9myZQ8cOGCh j+GBvKXgAiQyEUGxYsW2b9/+1FNPacX/4osvxB5Mneb6Hk9n66RJk/DPpUuX Kncob3r5sywQJZ999tkqVapo99+nTx9lZXAOTz75pM+W1KpVS7X2clpaWuPG jf213KPJUyQ9D+QyTzzxBJZDggnH1b6AvGbNGhnDyVvB2qkaoM+xfFygbt26 UA3+9fmkCbwQ7mfw4MG7du3C/3OeosRhu4j5RZIPKMMSgnaBHeIeli1bpiyv UaMGFEK2YqmjIQbNPGXPnj0QaImVaiDBXORFzLnCoYLUrl0bQrh169Zt3LgR t8IQwk3t2rXDKxfsEGdTw8hRLtiFLyg9//zzs2fPPnLkCESqImp95JFHJBtj TXP9OjINy/besy1XrpzH+5QHEhYshIsv1uzfv////vc/ce2WlyV4yPRdsp2/ //67uIn93//+d/PmzYcPH/7xxx8rVqyIhYHkKcePH7/tttuwvEOHDuvXr4eY Z8mSJXXq1AHvZDiHx3LfdQY23u8Cd/fJJ58kJCTgJJzKlStrnZhk45OTk2+9 9VaoU6pUKdDtzJkzsGns2LEYnsGAt9DH8MCspdq2bQtpxbZt25QvBQMg+OTJ k3fs2PH+++9jCfxKuRPJ0zzbyOPpbPWZp8ib3tRZFriSLVu2nD59OgSu4Gbx Eg9Jn3JuvNCnWrVqoPmJEyfWrl0rXix9+umnlXvu27cvlpcpUwZ2e/To0Q0b Nrz++uvicKo8RdLzgLxYrWPHjtg2qIxhRsmSJUEi8LpiWVEd08hbwfKpGqDP sXxccXP+22+/1W4VM77q169//vx5OEGwMucpShy2y/jx43ETBMZwlkEADKcD pJMR8qY2QtAu4Ekw8YFTRvhwuJprvXoYg3b5n1WClKcgw4YNQ1toMwIxVB5+ +GFV0Lhz50687dyqVStlOcR16BuV63PCBVE8oBeIq55ySr9OY8wio4Bkw+Ca iCUjRoxQ1sSPJkCYJEpMyRI8DPsu306ROb711luqmnirIZA8Rey8V69eyspn z56F0MhMj/9/TPlA7cDO9t7yUl01xEz7Hj16mG08PjjOnTs3uFllNRhLHu88 JZ9vW0QCpiw1aNAgUQhRq5jB1aJFC+V8PAx3AeUEAHn/oz8wdLb6zFPkTW/q LNNiSkmIi5T3El944QUsBy+HJXDFwdkUkJ4rxYGBLUSDcwQLd+/ejZXBb6ie s0yYMAErK/MUec+DE5agUJlAbdq0SdUAVe+0ppG3guVTNUCfY/m4zz33nMcb rfm8pYOXJwjecGFVzlN84rBdYmNjPb646667VEM6jCFol2xvFiNuFsG5Ay4d PdLzzz9vsn+hishT0s3jep6SK1cu7QrSOCSAffv2qTZBLOeRmNE3cuRI3ANc emQaYxZ5BQwbJoKQ5cuXK2sOHz7c470HeP78eSwJXBZbMOy7fDvxzL3tttu0 b2doVyIyladAsIQzz2Hn+OKtLcj7QJ8D2x/YVHBf+Kdk46FaoUKFoNpLL72k 2iS0Uk09ihzkLfXQQw+pysWsJNXrEkOGDNF6FZ/49D/6A0NnqzZPMWV6U2eZ lkCUHDduHDZm7ty5WPLhhx9iydixY1WV165di5s6duyIJUJG1T2cbD/rfcl7 HmwwdF9VDWd/ffDBB9reaU0jb4VATtVAfI7l4x4+fBguPbAVXKt2KyQj+Fsx D5/zFJ84bBeRpzRq1Khv3759+vSpU6cOlkD+TmHNVQcgaJds730YSEk8/6Zu 3boRsghbdojnKdpLG/Diiy96vNOGV2vAbPeOO+7Q/mrbtm1xcXEwwMD6InVd sGCBTGPMYipP0W+YWC0ZX90VgJPx/PvdK2uy2I5h3yXbeeDAAez4q6++qt1J gHnKnj17sCQmJibQDisIJGZTcvr06VmzZg0aNKht27bVqlXDpjZo0AC3SjY+ KSkJq8FQ0UqNm2Dgme1jeCBvKe2bQZ07d0b1xC0C5Ouvv8ZyOGe1ezP0P/oD Q2erNk+RN73Zs0xLIEpCeoJHF29UoXMATp48qd0PzoCtXbs2/imeBEEYoKrp M0+R95D4zkutWrWU+4QLIu7z008/1fZOaxp5KwRyqgbicywf97PPPsOtMKpV m44cOYITY0BD8eyM8xSfOGkXBE60RYsWiT8vXLjQv39//In29AxLCNoFHAuI D1vhutCtWzflMiPvvfee5Z6GFiGdp/g8d3CesA558uRR1odz099P5s2bJ9MY s0gqINMwCFnxJl6TJk3ED0+cOIGDuWnTppZlCRKGfZdsJ4Rw+Kf2Zml2wHmK eHNt1KhRgXZYQSAxG3Lo0KEePXrghV6FWH1OsvHirUkdhgwZYq2noU4glnrl lVdQPVWeIuYaqfIUSf+jPzB0tmrzFHnTmz3LtASi5C+//IJHF3nK/fffD3/C +Pe5H0wf8uXLh8pD5u7xLsOlrekzT5H3kOJtI+VTbPGsZ+XKlTK9k7dCIKdq IPpbPi5+OxvCKu03U1q1aoU/XLVq1W//EB8fj4UQsMGfaWlp+g0OA6jZxR9Q ExJ/+BWEGSqHFpYQtEv79u09XqeHDTtz5gycJmIBw6FDh1rtaygRfnnKgw8+ iKfVw34Q619lK551wtUNxsPkyZOhUyKicDFPkW/YwIEDsfCpp56aPn36lClT 7r33Xo833VZeRk3JEjwM+y7ZTuETPvnkE+1OHnvsMdgEyZooMZWn/PTTT1jy +eefB9phBQHmKUePHhULIlWtWhUG5LJly44fP46v0os8RbLx4ntAsCt/UkfI N4i1OJanyJ/mNuYp8qY3e5ZpsTdPQeegXAJUCS5KkDdvXvzQAM5aKVGihLam eGt1zJgxolDeQx4+fPiOO+7weN/xHzVq1Jw5c/r06YOLLjZt2lS1XI+/3slb IZBTNRD9LR/3zjvv9PiaF5eYmOiRQP9pcnhAyi769O7dGw8nPxU5dKFmly1b tuA+VXcd9+3bh+tMFihQQPs9hfAj/PIUTE6LFy9u+OEtMa8ArlDK73mJ1fXd ylNMNSwjI0N7g71w4cKqJ4zysgQVw75LtlO8tdq3b1/tVp3nKdoZLNo8RVxP 33zzTdmOSRBgnoJfQ8uTJw/EscpyVZ4i2Xg4ebEahMom+xH+OJOnmDrNbcxT 5E1v9izTYm+eIiZp+/xgEO6nevXq+Ce+rA1oZ3H7fJ5iykOKy4ES6IL2PVl/ vZO3QiCnaiD6Wzvu3r17/Y0ZsUN96tWrJ3+4EIWUXfQRU78MX6wLA6jZZcyY MbhVu1j0xIkTcVMkrHJAOU/Bl8EB1eIJ2brXZfG5E9Xzdy1du3bFmkePHlWW +4wTdBpjFkMFTDUMV8Vp3Ljx4MGD4TrepUsXSL21U7LlZQkqhn2XbOeJEyew Wt26dbVbtRHU2bNn8SNK4n1zAcTzuCuRp0BlXBMVhhn8X7ZvRgTiA5OTk7GR 2mXZVHmKZOPPnTuHqyE9/vjj5rsS5jiTp5g6zW3MU+RNb/Ys02JvniLWItB+ eFF8TRvSDSwR57X2+9o+8xR5D5mYmAgCwlkGF4Xo6Oi2bdu+8847/r7i7a93 8lYI5FQNRH9rxxXzTn2uSJyampqiQczeh6gM/vT58lGYQc0uOuB3oOCINl4K yULNLkOHDvV4FxAT65wLIBAVZ4384UIUkacQXJdYLFP/5ZdfqjbpXJcTEhIw Ir3vvvv0zyxcXhIqK2+CnTlzplOnTto4QacxZjFUQL5hWVlZBQsW9CiWxPGH vCxBxbDv8u3EZVEB1ce7169fj6v2qSIofGfnjjvuwGkhyLRp08SXmJTrEovP vX388ceq427fvt2wmz4JxAeKBahV89Zgh/jatchT5BsvbjhH7PwufziTp5jy PzbmKdlmTG/2LFNhb56yceNGdA7QKuUC3RcvXhQ9Eu9iiwmQDRs2VNoC/v/q q6/iJmWeIu958P2UqKgo/X7p9y7bjBUsn6oBPsO1cFxxoVQNGB34PXqfOGmX DRs21KpVS3yPSbBs2TL8lc87FeEHNbsIHzh+/HjVJri446ZVq1ZJHit0EXmK hYwj2HmKsBHEYHAGwcVIzJDUv2q/8cYb+MP7778fjoVvJx0+fHjgwIHwE3HN evvtt7FaTExMcnIyXJvi4+Pr1avn+QdlnKDTGGua61SQb1haWhrOi27VqtW+ ffsgAr/oJRBZgoqM9SXbKeKQQoUKwQXuxIkThw4d+uKLLzBx82giKLFgLIT6 EBlu2bIFdojqIco8BS6deG/E430gC9rCccGZP/fccxDMWHsmFYgPhKgMg6ii RYuuXbsWTAyCDBs2DNcz9Pw7T5Fs/P79+3EdRdjJkCFD8IMUECcvWrSoSZMm kydPttDH8MCZPMWU/7E3T5E3vdmzTIW9eUq2wjlAmgCZBYi8c+dO8XnNxo0b i5pwjkDoheUtWrSADP306dMw+Js1ayYUVn3nUdLz4Ocj8+TJM3fuXPDAOi5X p3fZZqxg+VQNMO6ycNwPPvgANQTr6B9XwHmKT5y0C77Tmj9//vfee2/FihWn Tp06efIkhNDi0+rz58+31NEQg5pd0tPT8e0V2O23334r/MyMGTPQA5ctW9ba V6dDC8p5ClwXKlSoIK4pYJd8+fLh8y/9qzacYg0bNhQ/LFCggPj4mvKqB5Eq ftjL472riXGgxzvDWRsn6DTGmuY6FUw1TNw8VwLX0Ntuu61Dhw5z5swxK0tQ kbG+ZDvhnMVH0lpwhVJVBAWiaWsWKVLkrbfewv8r85Rs72qB4lGLx/vsVfy/ W7duQeq7zsAGayrtK8TBL4Mr8xT5xkNggJ/KRUqVKiVq3nPPPRb6GB44k6eY Os3tzVOypU1v9ixTYXueAs4BX9TSAicC5CzKPUCXMWxQITy5Kk+R9DyrVq1S nlACOFadOnUgPlG+2apvOPkT0NqpGqDPsXBcSLpxq3ZGvT84T/GJk3aZPn26 uPPg+ff1wmP+lZbQhZpdsr3eRtx1LF269EMPPSTcF5SvXr3abB9DEcp5Srb3 WTyGYeL02bBhA5RXrVrV8+91d1VcuHBh5MiRODNBAAMMLkzK76/B6SnWo/Z4 r7yjR4+GCji0lHGCTmOsaa5fR75hM2fO9Oii/CaapCzBQ9L6ku2EagMGDFC6 BXAFs2fPhi6jaKrdgoYiegclH3zwQbCgWFJDladkeye34FKogrJly06cODF4 fdcZ2KmpqW3bthUtgRAXAshdu3bhgm+qPEW+8RAkPProo8oLE3i/559/PjEx 0Vo3w4BALIV5St68eVWLTEKUi/Iq1/uSP831PZ7O1ilTpuDOtQ8BJU1v9ixT EoiS4i0S1S2Uc+fOffDBB+IrM3gugOw+17Pdtm2beKri8d5f6tSp04kTJ+66 6y74c9y4car6Mp4H/qO/iPG9996b/s8nVg0vVfInoIVTNUCfY+G4uBwBoJyY p8++ffvwJ9OmTZP8SahD0C4HDhzo0qWLeICCVK5c2d+LV2EJQbtke9/Qf/rp p1VOBp8Ry3ctpCGep2R7r0oQSc6ZMweu7/gczRRw9i1atAh+m5yc7LMCDI+1 a9cuXbpU5v5PgI1BJBWQaRjEHhg/fPPNN1u3boXkGrLv+Pj48ePHi2+iaSPY bAlZgoRZ68u0E4yyefPm+fPnHzp0yHCHEMyAaIsXL4ZYRbINJ0+eXLFiBTRD Zv86mO27T+CaDo2BLkg+7ZVsPA62JUuWgOtTvr8TmdhiKUlM+Z/gNcDQ9KbO MkHwlLx48SLEA9CeTZs2Gb5wB94DFIazRv7VPB3Pgx8Buf/+++EKCL1b5WXu 3LkffvghPmPyaO5xGSJ/Apo6VW3Un12EjZC1C1xW4ISC6yOcL9rVeMIesnbJ 9oYu69atW7BgAfwbCd8YUkI/Twk/bFQAvwH0n//8R7vpwoULuBKU/sQMh4lk 60dy30MLtpRdhJ+SYpKSz49Hi8VCVTPK3CL89A8P2C40YbvQhPMU57FRAZyH Fh0drd2Unp6OHyNr1aqVLceyhUi2fiT3PbRgS9lF+CkpMhHVatLIyJEjcau1 KcG2E376hwdsF5qwXWjCeYrz2KjAf/7zH493evb48eNTUlJE+caNGx966CG8 YpL6DFAkWz+S+x5asKXsIvyUFB9qefzxx7ds2SLeQsrKyho1ahS+/lavXj13 P6crCD/9wwO2C03YLjThPMV5bFRg7dq1yhffqlat2qxZM3zPC/n0009tOZBd RLL1I7nvoQVbyi7CUsnnn39eONgiRYo0btz4wQcfLFasGJZUq1bt2LFjbrfx /yMs9Q8D2C40YbvQhPMU57FXgU2bNj333HNi5Tpx9XzppZcsf+EleESy9SO5 76EFW8ouwlLJ8+fPjxw58u6771a63Fy5ctWoUWPs2LHOLJwoSVjqHwawXWjC dqEJ5ynOEwwFsrKydu7cuXz58rVr1zq8hJcpItn6kdz30IItZRdhrOTFixd/ //33hISEpUuXgu8llZ4Iwlj/kIbtQhO2C004T3GeSFaA++52Kxhj2FJ2wUq6 C+tPE7YLTdguNOE8xXkiWQHuu9utYIxhS9kFK+kurD9N2C40YbvQhPMU54lk BbjvbreCMYYtZRespLuw/jRhu9CE7UITzlOcJ5IV4L673QrGGLaUXbCS7sL6 04TtQhO2C004T3GeSFaA++52Kxhj2FJ2wUq6C+tPE7YLTdguNOE8xXkiWQHu u9utYIxhS9kFK+kurD9N2C40YbvQhPMU54lkBbjvbreCMYYtZRespLuw/jRh u9CE7UITzlOcJ5IV4L673QrGGLaUXbCS7sL604TtQhO2C004T3GeSFaA++52 Kxhj2FJ2wUq6C+tPE7YLTdguNHErT2EYhmEYhmEYhtGH8xSGYRiGYRiGYajh fJ5i4YdhQyQrwH13uxWMMWwpu2Al3YX1pwnbhSZsF5pwnuI8kawA993tVjDG sKXsgpV0F9afJmwXmrBdaMJ5ivNEsgLcd7dbwRjDlrILVtJdWH+asF1ownah CecpzhPJCnDf3W4FYwxbyi5YSXdh/WnCdqEJ24UmnKc4TyQrwH13uxWMMWwp u2Al3YX1pwnbhSZsF5pwnuI8kawA993tVjDGsKXsgpV0F9afJmwXmrBdaMJ5 ivNEsgLcd7dbwRjDlrILVtJdWH+asF1ownahCecpzhPJCnDf3W4FYwxbyi5Y SXdh/WnCdqEJ24UmnKc4TyQrwH13uxWMMWwpu2Al3YX1pwnbhSZsF5pwnuI8 kawA993tVjDGsKXsgpV0F9afJmwXmrBdaMJ5ivNEsgLcd7dbwRjDlrILVtJd WH+asF1ownahCecpzhPJCnDf3W4FYwxbyi5YSXdh/WnCdqEJ24UmnKc4TyQr wH13uxWMMWwpu2Al3YX1pwnbhSZsF5pwnuI8kawA993tVjDGsKXsgpV0F9af JmwXmrBdaMJ5ivNEsgLcd7dbwRjDlrILVtJdWH+asF1ownahCecpzhPJCnDf 9ets3rx5zZo1O3bskNmhqcqWycjIWOPlwoULQT0QHSJ5lNoLK+kurD9N2C40 YbvQhPMU54lkBbjv+nXuvvtuj8fz2GOPyezQVGXL/Pjjjx4vkBYF9UB0iORR ai+spLuw/jRhu9CE7UIT+nnK9evXt2/f/uWXX8bFxe3atevGjRsWDkoKeQUy MjJmzZo1bNiwKVOmgAj+qv39999bt279zMvSpUtPnz5tW1vtxqz109PTwejw r78KYdZ3zlMoYPsojVjklQw/P08Bef0PHDgAV5nhw4cvWLBAO5LBr+4y4vLl y8qfyHvmS5curVy5ctSoUV9//XViYuLVq1dlGhzUJgUbQ7vs2bNHv2vJycnK +leuXAH/DKfPxx9/PHv2bNXWHMfl2rt3r7+jXLt2zVpNB3AxNjO0oApwmElJ Sbt3775586ZMg+UPZGGoBBviecrBgwfvuusuj4KqVaumpqZaOC4dZBTYtm1b zZo1Pf+mZcuWKSkpympwIvfu3btw4cLKasWLF580aVIQOxAAktaHK9fy5ctf fvnlvHnzQo8ef/xxbZ2w7DvnKRSwcZRGOJJKhqWfp4CM/tnZ2dHR0aprTc+e PSHEEnUgefQY8d1332FlU545Pj6+bNmyypowEnQCPwea5ACGdrn11lv1uwYq YU3o2sCBA/Ply6fcmidPnpiYmIsXL4odOixXgQIF/B1l7ty51mo6gCuxmaQF BYcOHRozZkyVKlWw5rp16yR7F4yh4gyU85QDBw7ccccdKEv16tXvuece/H+l SpWOHz9u4dBEMFRgxowZ4uQFBRo1alSsWDH8s0aNGuJ205kzZyA0wvLcuXM/ 8MAD9957rxhIEF460RmTyFg/PT0dAz/Bo48+qqoTrn3nPIUCdo1SRkbJcPXz FDDU/+bNm02aNEHBK1as2KJFizJlyuCfzZo1Eze0ZUKX2bNn55j0zGvWrMmV KxeUQ+wEfqxBgwZ4ThUsWHDp0qX6XQtSk5wh8DwFIoEcrxeKiorCElAS4mdx KgFt27YVN9udlOvy5cuGRzFb0xmcj83kLYj07NlTpRKcRDJdC8ZQcQzKecpz zz2Hks6cORNLxo4diyp169bNwqGJoK/A2bNnceTD9XrDhg04/+H8+fNt2rTB vo8ePRprDho0CEvef/998TxxyZIlhQoVgsJSpUr98ccfwe+NOWSsn5aWVuIf 4Bz3+IoAw7XvnKdQwK5RysgoGa5+ngKG+k+ePBmlfuONN/ABCvzbvXt3LISt WC07OzvFF7t374acAmo+8sgjeKmS98xXrlypWrUqFN5+++1iMZDt27djogTO 7fr16zotD0aTHMPQLqmpqT579+abb2JfVqxYkeMVoXbt2vBnjx49IDaGEugy 7Lly5coqp+2kXOAbcQ+ffvqp9oh//vmnhZrO4HxsJm9BpHfv3njRwV15pPOU YAwVxyCbp2RkZOCtFfCfynK8qBUtWpRgICqJoQIwYFq3bn3q1CllIQwtPEda tmyJJeDGX3vtNXEpEYhzZOvWrbY23Abks1QEH25qI8Bw7TumHk2aNME/4VK+ ZcuWQ4cO+ZyDapinwDkCOuzatQv2o39c2P/vv/+ekJAAXle1ifMUQ/yNUsZQ yTD28xQw1P+JJ54AncuXL69yEfXr14fyatWq6QckEPNANQiZjhw5giXynhki PSwZP368subixYuxfPr06XK9tK1JjmHWwyDJyckYnXbt2lUUHj9+/JdfflHV /Pnnn7FrcXFx+vsMhlz79u3DmhCZ21XTGVyJzaxZ8KeffjKVp1g+kEA7VByD bJ7y2WefoXqq2Xdz587F8qlTp1o4OgWs+SgAnyRWrFhRvxoohhJNmTLFwlGC SrAjwFDvO6YeTz755MGDB8HpiUfMJUqU6N27t+q9Qn95ysmTJ3v16nXvvffi fX4AQsGXXnrJZ8iXlZXVqVMn8fA6V65c8MN58+aJCv7ylLfffvtWL3CqmtKB Ppyn2IWhkmHs5ylgqH/p0qVB5O7du6vK58yZg/rr/HzLli3oYcaMGWPYEq1n HjduHJZkZmaqKteqVUt5u0aeAJvkGNZigKeffhpaW6FChezsbP2amzZtwq4N GzZMp1qQ5EpISMCaiYmJdtV0BjqxmaEFLeQp1g6EmBoqtkM2T3n11VcxPFM9 /L106RLef+vfv7+Fo1PA8rlQr1496Dj4cP1qixYtwoHn/GtohgQ7Agz1vmPq Afh8uxCcoXIxHJ95yqRJk/Lnz6/9rbYmsGrVqttuu81nZTEJx2eeIqaLQDKl Pz0jFOE8xS4MlQxjP08Bff3BmfgLUSB3wE06L8ziMxf4V2bFIa1nfv/99z3e GyPan+PEM/GquDwBNskxLMQA4jETNNuwcmxsrMqN+yRIcomahu+Xydd0Bjqx maEF7cpTgjFUbIdsntKiRQuQpXbt2tpN+PrPyy+/bOHoFLB2Lpw/fx7z2dde e02/5jvvvIMD78SJExabGDSCHQGGet9FnuLx3uSE1CAtLW3OnDkim/j+++9V lVXZB96hKleuXFxc3I4dO/7444+NGzeKtUGUDTh37hzeTQU6d+4Mp3N2djY4 PXC5DRo0EPNAtHnK1q1bMRWqWbOmzwVJQh3OU+zCUMkw9vMUMNQfRdZeUyCF wbnogwcP9vlDcScc4iWZlmg984QJE/z56o8++sjjfQHZ1Mq0gTfJMSzEAM2a NfN434zQDxQh358xYwau6VS+fPm//vrLX83gyTVlyhSsCbnVBx98EB0dPWTI EAiDtY2Rr+kMFGIzSQsGnqcEb6jYDtk8pU6dOh4/S33ionBPPvmkhaNTwNq5 ICZITJs2TafahQsXcJnHypUrW29i0AhqBBgGfcfUo3jx4qrlbvbs2YML41St WlVcp/zN+1q0aJHK50CqgoOnb9++ohCcKhaC81RWhthA+el5VZ6Snp6OIkPq dPToUcm+hxacp9iFoZJh7OcpYKh/48aN0eEoX0y7cuVK69at8azv0qWLzx92 7NjR430F3vDdtxw/nnn16tV4CNXTnJUrV2KKBJjyMIE3yTHMepj9+/ejIF9+ +aXPCidPngTf3r59+4oVK2JNcEf66gVPLmikxxcQDKseBsnXdAYXYzOzFrSc pwR7qAQDsnkK3udp166ddhOoCpvuu+8+C0engIVz4dixY/gCXY0aNZTL2msR S7VYewkx2AQ1AgyDvuu8Gv/UU09h7zIyMgwraylSpIjHu/wg/gnJDpaULl36 0qVLOj9U5ilXr15t1KgR/P+WW25Zv369zHFDEc5T7ELyfn5Y+nkKGOovbmhH RUUlJCSkpaXNmzcPXIqIGIXHUALVwAPA1g8++ECmGT49MzgTXO8LdhUbGwuR 0u7duz/66CPltFUokeypLU1yDLMeBpf5ghhAu84JkpiYqIrz9+3bp7PDoMol sg84hQcMGPDuu+/itCgAjLt3714LNZ3BxdjMrAUt5ynBHirBgGyeAuqBMs8+ +6x2U8OGDT3emXIWjk4Bs+fCjRs3nnzySRxUy5cv16m5bt06vOsOErkyjdCQ 4EWA4dF3ndRjxIgROAbEUiH6ecqVK1cWL14Mv+rQoQO4UPwtiINbwbtiifYV WhUiT9m0aZN4BCMWYAxLOE+xC0Mlw9jPU8BQf3CVzZs392h48cUX8RMevXv3 1v7qm2++wWr6EQ6i45lXrVqlfREPjgtHx/+fPXtWsqd2NckZzHoYnPfbvn17 fxWSk5PBz4MLEt9LhQ4OHTrUX++CLdfMmTPBuOJPiGEGDx6MR1RdsORrOoCL sZlZC1rOU4I6VIIE2TylQYMGoEzjxo21m/AmTOvWrS0cnQJmz4U+ffrgONGf qv3bb7+hN4PsPikpKdBWBocgRYBh03ed1GPSpEk4DObMmaNfGR/slixZUht+ PPjgg1hHvJX51Vdf6TdJ5CmdO3cW+2nVqpVMl0MUzlPswlDJMPbzFJAZyRBr xcXF1atXr2DBgvny5WvWrNm4ceOuXLmCU+6/+OIL7U9eeOEFj3fVaMPPKBh6 ZqjQrl27cuXKebzLJXXt2vXQoUMYqcL+pTtqZ5McwJSHOXjwIHpdmZUVIdqM j4+vW7cu/sTfF+SdlwsOhK2CzFR/6RX5mrZDITaTtGDg76fYPlSCB9k85dln n/V4X9TVbsIAzPCVJbKYOhfEg9H69evrvOh06tQp8a60819xlScYEWA49V0n TxGTYPELX/4qZ2VlYYDn8X7dOzY2Fg567tw5lEjkKfPmzcM6Y8eO1W+SyFOQ woUL439+/vlnyY6HHJyn2IWhkmHs5ylgaiRDHCKWExTPW5VLlAvwTmzTpk31 d2jKM1++fFn8/5VXXvGYnPIXjCYFD1N2+e6777DB8lNtMzIycEalvzXTXJGr X79+uCtIRe2qaS90YjNDC9q13pddQyWokM1TunXrhgm16qMPJ06cQOsMGjTI wtEpIH8uzJ07F29qwUDSWSACJMI5Eh5X5xDKYHsEGGZ918lTxJeIU1JSdCo/ 9NBDHu8HU1QfnlDlKeLFzLfeeku/Sco8pU6dOpmZmZUqVfJ4X2yB9Ef/tyEK 5yl2YahkGPt5CpgdyQIRG2/fvl21KTk5GTcNGDBAZw+WPfP169crVKjgMfMo LdhNsh1TdsG1uyESMPXN0y5dumBP8ePjStySS0zoMnztSL6mvZCKzXQsmGNf nqJ/IMmhEmzI5inTp09HfebPn68sHz9+PJavXLnSwtEpIKnAokWL8PWlokWL ai8WAkjkIdVFTTp37uziszkZ7I0Aw6/v/vKUa9eu4Uz+ggUL6qz3dfr0aVSj Z8+eqj2o8hTYIX6fAnaiv/inyFPuvPPO1NTUHMUi8DExMcbdDkE4T7ELQyXD 2M9TwHKect9993m8CxNpXw0WU0Z1FikNxDOLT3wq12DXJ9hNsh1TdomKioI2 g5PxudXfawX4TArIyspSbXJLrlatWsHe8uXLZ7jctHxNe3ElNrNgwRxLeUqQ hooDkM1TwMQlSpQAfZo3by7sC4P2gQce8HgnshIPSnWQUWDp0qW4rnWBAgV0 Kl++fFm8xtWmTRv95SYoYGMEGJZ9x9Sjdu3aqgUA4+LisKedOnVSVVbmKUlJ SVitR48eyp8nJiaCR1XmKTn/fN3Y42sK+oEDB8T/RZ4CLksUguBYuHbtWsOO hxycp9iFoZJh7OcpIDOSwWko51wBkCDg2f3tt99q64sX5eLj433uUN4zg5dT 3TA/d+4cLlUNplfGqDBOwBHNnDlT1VTbm+QMpjzMnXfeCc1u1KiRdtOOHTvK lCmjWsQ+x/uZTvw2ls8vpNsol9YuO3fuBAvu2rVLVRP6iy+Si5Ux5Gs6hvOx mTUL5hjlKVq7BGmoOAPZPCVHMdGlT58+Fy5cAA8WExODJcOHD7dwaCIYKrB8 +XKxNuPAgQPhz19++WWhAgwOwcnjV9KAYsWKwQhcsmTJwn+Tnp7uUK/kkLH+ 1q1bN/0Dzo2EOFCU4IcFw7Xv4juPVatWXbRoUXZ2NvRixIgR6LdhVIhJXzn/ fCIWxBGxHHgnfBgNmmzbtu3mzZtpaWmxsbF480eVpxw+fBj9rcf7VBf2fP36 dbh2dOzYEXayceNGrObze/THjh3DbxxAO31GDiGNXaOUkVEyXP08BQz1By9R qFChqKgoOLshlDpx4gRojt6mfPny4nUVJR9++CFaByIf7VZ5zwzeqUOHDnnz 5h01alRGRgb8cMOGDdWrV8ffTpgwQblb8YE8n5Nn7GqSY8hHQeDb8+TJg7Gu dmvt2rU93vWa4CT69ddfwe2AEWHP999/P/a3X79+2l/ZKJfWLvjZI4jhhw4d mpCQAFcHaBWEu7ArbKpYF0u+pmM4H5uZsiBczcUlZsiQIVgBru9YolyPS2uX YAwVx6Ccp8A1CyfbI+g8Pd47byEdGukrACNcu1Sjijp16kBNuKDoVwMWLFjg XMckkLE+ftfDH59//nlO+PYd8xSfAwCuVqp33vv27YubSpYsKdZFUa7KhTO7 PN7ZYpUrV/b8O0/J8S45KOrgIcT/e/XqhXV85ik5/3wz2uP2TO9gYNcoZWSU DFc/TwFD/SHW8nn6V6hQwV9kIj4Dcfz4ce1Wec8MP8cbudqjv/XWW6qFnvCe DPDII48Er0mOIR8FZWZmYgujo6N97geXj0Zy586t9OcNGzb0mWnaKJfWLvPn z8fviQizijMa6N+/vziQfE3HcD42M2VBuKzo7BAuSaKm1i7BGCqOQTlPyfFe wp555hlx1xcGyQsvvBDqFy/Dc0HpsX3SoEEDqDlo0CDDc2HZsmXOdUyCwCNA XJsxXPuOtxMnT54MGYTSq1SqVEk7wwoSB/FJWbEuyvnz5zt06CB+mD9//lat Wh05cmTYsGEeTZ6S4z2Lo6KilBeIcuXKKb+rO2vWLCxXne8wUKtVq+bxfqbN XSdmO3aNUkbS24eln6eAjP7gbcSXFNBjtGnT5vTp0/7q4zqlgM81jkx55oyM DKXd0fn4nAn/ySefYIUxY8YEtUnOIB8FiUWJ/b3InJWV9eabb6pWoS9WrNjI kSP//PNPnz+xUS6fdklNTY2JicHHIoIqVaosWbJEdSz5ms7gSmwmb0H9PKVw 4cKipk+72D5UHIN4noKARImJiRs3blRN2g9RLCgQNnDf5evfuHEjKSkpPj5e ZzkRqJOQkLB8+XJVUJeSkgLl8qfMxYsXof6qVatOnjwp38JwJZJHqb2YUjLM /DwF5PWHEx9cDejv87ZqUIEjbtu2beXKlWlpaTrV9u/f7/wHyoOE7R7m2rVr cLH49ddfQcbDhw87+QKOP7vgy0erV69eu3atvmXlawYbFz2/7Rb0ZxcXh4pl QiJPCTMiWQHuu9utYIxhS9kFK+kurD9N2C40YbvQhPMU54lkBbjvbreCMYYt ZRespLuw/jRhu9CE7UITzlOcJ5IV4L673QrGGLaUXbCS7sL604TtQhO2C004 T3GeSFaA++52Kxhj2FJ2wUq6C+tPE7YLTdguNOE8xXkiWQHuu9utYIxhS9kF K+kurD9N2C40YbvQhPMU54lkBbjvbreCMYYtZRespLuw/jRhu9CE7UITzlOc J5IV4L673QrGGLaUXbCS7sL604TtQhO2C004T3GeSFaA++52Kxhj2FJ2wUq6 C+tPE7YLTdguNOE8xXkiWQHuu9utYIxhS9kFK+kurD9N2C40YbvQhPMU54lk BbjvbreCMYYtZRespLuw/jRhu9CE7UITzlOcJ5IV4L673QrGGLaUXbCS7sL6 04TtQhO2C004T3GeSFaA++52Kxhj2FJ2wUq6C+tPE7YLTdguNOE8xXkiWQHu u9utYIxhS9kFK+kurD9N2C40YbvQhPMU54lkBbjvbreCMYYtZRespLuw/jRh u9CE7UITzlOcJ5IV4L673QrGGLaUXbCS7sL604TtQhO2C03cylMYhmEYhmEY hmH04TyFYRiGYRiGYRhq8LwvJ4lkBbjvbreCMYYtZRespLuw/jRhu9CE7UIT zlOcJ5IV4L673QrGGLaUXbCS7sL604TtQhO2C004T3GeSFaA++52Kxhj2FJ2 wUq6C+tPE7YLTdguNOE8xXkiWQHuu9utYIxhS9kFK+kurD9N2C40YbvQhPMU 54lkBbjvbreCMYYtZRespLuw/jRhu9CE7UITzlOcJ5IV4L673QrGGLaUXbCS 7sL604TtQhO2C004T3GeSFaA++52Kxhj2FJ2wUq6C+tPE7YLTdguNOE8xXki WQHuu9utYIxhS9kFK+kurD9N2C40YbvQhPMU54lkBbjvbreCMYYtZRespLuw /jRhu9CE7UITzlOcJ5IV4L673QrGGLaUXbCS7sL604TtQhO2C004T3GeSFaA ++52Kxhj2FJ2wUq6C+tPE7YLTdguNOE8xXkiWQHuu9utYIxhS9kFK+kurD9N 2C40YbvQhPMU54lkBbjvbreCMYYtZRespLuw/jRhu9CE7UITzlOcJ5IV4L67 3QrGGLaUXbCS7sL604TtQhO2C004T3GeSFaA++52Kxhj2FJ2wUq6C+tPE7YL TdguNOE8xXkiWQHuu36dzZs3r1mzZseOHY60iPFNJI9Se2El3YX1pwnbhSZs F5pwnuI8kawA912/zt133+3xeB577DGZHf7www8vvPDCzJkzbWgcoyCSR6m9 sJLuwvrThO1CE7YLTejnKdevX9++ffuXX34ZFxe3a9euGzduWDgoKeQVOHHi xJw5c4YNGzZhwoQ1a9b8/fffPqtduXJl8+bNINHHH388e/bs5ORkG1trL/J9 P3DgwJQpU4YPH75gwYL09PQgt8sJ7M1Tdu/e7fGSJ08eGCf2NJHxYtZHwfgE 1xQeo9ReWEl3CZL+cFFOSkoCF3Tz5k3JPcv487179+7yw7Vr11SVL126tHLl ylGjRn399deJiYlXr16VbImW1NRUPMq5c+cs78QUhnbZs2ePPykQ7VU+Kytr 3rx5oPDChQvPnj2rs3PQeerUqUOHDp00adKmTZtURjx9+rT+oYHLly/LdFP/ QAIbTRkg8udLRkbGrFmzIDaDUQ0Bqs86psazQEd/GKg+f5KWlgZG//DDD0Ht gwcPGp6V8gPexsEQCMTzFND8rrvu8iioWrWqP2OFCjIK7Nix47777vP8m3vu uWfJkiXKajDaBw4cmC9fPmU1CFxjYmIuXrwYxD5YRabv2dnZ0dHRqr737NnT X5oWKtibp8B1CgwNlW+55RZwU/Y0kfEi6aPg8rp8+fKXX345b968YIjHH388 +E0LMVhJd7Fd/0OHDo0ZM6ZKlSroltetW2e4c3l/XqBAAY8f5s6dq6wZHx9f tmxZZQWIE/yFi/pAeH/bbbfhTqZPn25hDxYwtMutt97qTwrRX2X9fv36Kbfm ypUrNjZWu1sIO1944QXVrho3bgxmFXXi4uL0Dw189913+h2UORBioykDR+Z8 2bZtW82aNVVda9myZUpKiqqm/HhWMnr0aH+/qlWrlqryX3/9BaetqlrdunWP HDnib/+mBrwtgyFwKOcpBw4cuOOOO1CK6tWrQ5SO/69UqdLx48ctHJoIhgp8 8803EHxiZ0uUKFGvXr3cuXPjn5CSbN26Faulp6dHRUVhOfglOHeEXEDbtm3l b3Y5hmHfoc1NmjTBLlSsWLFFixZlypTBP5s1a6ZzF4I+9uYpOV4P//7778uE CowpZCwFZx8GdYJHH33UkdaFEqyku9irPyQXqhBlzZo1+juX9+eXL1/WiYVm z54tasJB4XqHV0NwlQ0aNMD2FyxYcOnSpbLS/MPzzz8vjhJCeUqNGjVE5d69 e2Nh4cKFGzZsCDrgnyNGjFDu88aNG0888QRugv136dLlySefxD+rVq0KES9W kwlNlebQInmgHLtNGTiGdpkxY4bIPiDcatSoUbFixYRFlE+C5Mezivfee0/G 6Dn/jgAhRIQguXjx4vgnxI3Z2dk+929qwAc+GGyBcp7y3HPPebwRuJiBP3bs WFSmW7duFg5NBEMFpk6dCn0sV67cqlWrcJ5bRkZG//79se9PPfUUVoNxWLt2 bSjp0aPHmTNncrz+AfZcuXJlrLl58+bg98Ychn2fPHkyNv6NN97AG27wb/fu 3bEQtjrU0CBge57CBAkZS6WlpZX4B7yNwNG1FlbSXezVH+JhrFaoUCF0yIZ5 irw/h2Zg4aeffpqi4c8//8RqV65cgVgXqt1+++1ivZHt27dj+gP+8/r163La /B+zZs1SRlx08pTU1FStCMCbb76JTV2xYgXWTEpKwpL69etfunQJSs6ePVut WjWPZkrwL7/8gjUHDBgAMmLhTz/9hIVffPEFlkBc4fPQu3fvxgzokUce0Z9+ L3kge01pC/p2AWExK7nnnns2bNiAIpw/f75NmzbYtdGjR4vKkuNZS9euXeFX ZcuW1f5KNXHitddew0N06NAB589Ak3788UdIVydOnOhz52YHfOCDwRbI5ikQ mWNmDf5NWY7JS9GiRf/44w8LR6eAjAKQtmPqIbh58ybky9D3kiVLisLjx4+D T1D99ueff8ZBCLmwTU22DcO+432Y8uXLC/+GgBOGcnC/ofuCknye0qRJE1Gy f//+bdu2mR3t4F7A82/ZsgWvXPrAtQy87oULFwxrHjt2bN26dfKvD0AzEhIS kpOTCT7a00H+XgqC02A4utbCSrpLkPQXMadhniLvz/ft24f7VM1tVgGeCquN Hz9eWb548WKzuUZmZiZOgHnooYeo5Sk+AUeKGSKEsqKwV69e2pREJC/KRyoD Bw70eJ+5qGbcgbmhvFOnTvpH79GjB1SDBujMKTJ1IBtNaReGdtm8eXPr1q1P nTqlLIRQDfOXli1bikLJ8aylffv2mHXqV4NkAWfddOzYUXV59Xcpt3HAyw8G WyCbp3z22WeopGpay9y5c7F86tSpFo5OAWs+CmjevDl6JP37DJs2bUKJhg0b Zq2FwcOw76VLl4aWd+/eXVU+Z84c7JQ16Sggn6c89dRT4Gq6det25513Yq9z 584NCbvqBcN69erdeuut4DaVheA3nn32WXHDJFeuXLVq1VLOhp01axb86p57 7oGLSGxsrHj6hlMHYfComgQ+EMRv2rRpiRIlxG7Lli0bHx+vqol7htgD/r9w 4cJGjRqJ+STlypXT7pksHF3bBSvpLq7nKfL+PCEhAUsSExN1djhu3DisBkGX ahM4OtVNHn3wnme+fPlWrVoVEnnK008/DY2sUKGCmNJz7do1nCGmfaUI1ahU qZIowdiyVKlSqpr49lDjxo11Dr1lyxZ81jZmzBjDdkoeyEZT2oXl2Az093hn NooSyfGspVmzZvCrVq1a6VdDkYH9+/dL7tmuAW9qMNgC2Tzl1Vdf9Xhn2ali 8kuXLmHw079/fwtHp4C1c+HixYu33367x3sPSr8mBJ84CAmuWKvfd4jD/WVY 4MpwkwMvbQUJ+TwF3J14U1XJu+++q62snCR29uzZ8uXLi/piegYwadIkrPPj jz9iyf333689RP78+SHFEDuEUeezmsebOq1evVrZHrHngQMH4qxjJQULFjx9 +nSAGjoDR9d2wUq6i7t5iil/vmjRIizRf/n0/fff93hvqmgf0eJ0MtUL5v6A iyMe7qOPPjpy5Aj+n3KeIp4ygFCiMDk5GQvFZCqBeM1BTDESe1AdGlfsgSRC 5+j4/Av+lXkyLnkgu0xpI5bzlHr16nn+/Z675Hj2tyt9cwA1atSAak8++aTk bm0c8KYGgy2QzVNatGgBUtSuXVu7Cd8Wf/nlly0cnQIWzoWUlBQUBBg7dqy/ apDTzZgxA5f/gnhV+cIaEQz7jsZ97bXXVOVwycP5kIMHDw5i+4KJfJ6CPPPM M/PnzweXMmbMGMzNixcvrpzXqs1TRIr61ltvnTx5EtzInj17/vOf/9StW1cs HiiyCY/3btvUqVNTU1N37tz5yiuvYGGFChWUz+ufeOIJuJS8+OKLS5YsycjI SE9P/+CDD7Cm6n6Xcs+33nrr6NGjd+3alZiYKJ4yjxw5MmAVnYCja7tgJd3F 9ecp8v58ypQpuE+IcsHDQJw2ZMgQCK5UV7EJEyZgNe1i7BCAebz3TwyXW4Es qVSpUlD5wQcfhIvm4cOH6ecpeJv9nnvuUQaHW7ZswZbDlUJVXwj1+++/Ywlc AnB6EvR97dq1WAgWxGqiRIt4NAB2l2mq5IFsMaW9WMtTzp8/j88XlONccjxr qVixIuYpcXFxPbzAf+ACraqGNyFhz/gnXJe3bdumM+PLrgFvdjDYAtk8pU6d Oh4/CyTionDyiSQ15M8FiCFjYmLAQeEKtDAyv/76a20OCxFp375927dvjyMc LzRHjx61v+kBY9j3xo0bY0AO574ovHLlSuvWrbFrXbp0CXorg4OpPAV8mtLQ Yu3BpKQkVWVlnoJTWyFRVTlDpbcX2US7du1Ubq1Dhw5a9wWJkvZMF+u3KM0k 9gzRjnL9SdgDPl557rnn9LtPBI6u7YKVdBfX8xR5f/7ll196fFG+fHnlE4TV q1djueoZzcqVK8U6V4bXvrZt23q8T3jRTdHPU/bv348tBJWU5QsWLMBy7aqP Ymbdxo0bRSEEmUWLFsVyEAE8NoavL774os7RO3bs6PG+7a56yUgHmQPZYkp7 sZaniJcUpk2bJgolx7MWsYCYEriAvvLKK+J6ferUKSyfOHEiaKv8hkXt2rVB fNU+bRzwFgZD4JDNU/A+DIRS2k34NhaYxsLRKSB/LrRq1Uo5VocPH+7zkzqJ iYmqE2Hfvn02N9omDPsu7kJERUXB6ZaWljZv3jwIxUXv4IxzqrE2I5+nPPLI I6pysWzOsmXLVJWVeQqkq1hNZ3l2kU1ob6Bt3rwZN2nnk6v4/PPPsebu3bu1 e161apWqfoUKFaC8QYMG+rslAkfXdsFKuovreYq8PxdxHRx9wIAB7777Lk6A 8Xgno+7duxerXb16FReJuuWWW2JjYyGOBRf00UcfQR2xT6VT0gKxGVYTs+vp 5ym4zFehQoWU6V6O4pHEnj17VD8Rzy+Uj1quXbsGmYLn39SvX19n+SkwGb6v LW7dyyBzoMBNaTsW8pRjx47ho40aNWoo5yFIjmctmKeULFkyOjoaQj5I5EXW 9tJLL2Gd7du3Ywmu+OrxLi1VuHBh/D8kNcpVnW0c8NYGQ+CQzVNwmv2zzz6r 3dSwYUOPxHoIZJE/FyAafPrpp2F4iy85Vq9e/cCBA6pqycnJHTp0gNNBfBMT BurQoUMJLrJk2HdoMy4XoAKcHr4w2Lt3b6caazPyeYp2XWJIT1AH5TtH2sqQ aIhX12HkLFmyRLvkgk6ecuPGDfy5ct0SASS/kyZN+u9//wunnrhXJpbH1N8z ZCgeiVeriMDRtV2wku7iep5iyp+Dc1Pe4gB3NHjwYKyv9HJQR/sFPdibCIx1 vsaekZEBEaDHO1VDXB/p5ym4TFP79u1V5T/88AO2XDsvCDwzbhLrTV26dAkz RPDevXr1Eh+yyZ07N0QL/g79zTffYDX5m5/yBwrElMHArF1giIqpBcuXL1dt lRzPKk6ePAk5jjIhPXLkCN7oE9dW8QYQXlXXrVsHKRKMZxjAmNRAbIDPO+wd 8BYGgy2QzVMwsPG5BgXm4KpljkIICzl7ZmYmnOA4eQZGoL8pjjAO4+Pj69at i2NJvDpNB5m+w+kcFxcH2RmccZCgNWvWbNy4cXDS4RRQ7QuDoUIgecrKlSvR pvp5So53VWrlwlyQuo4ePVr5iFYnmwBwhTHVd2/hoPjqnJZff/1VZs8PP/yw h/OUyIOVdBfX85ScwPw5/BYvZxDNKm+5/Pbbb+3atStXrpzHu+pI165dDx06 hEEgxMY6OxRrIW7evDn9H8QCuRCGwZ/+PpBnI6bscvDgQWzeZ599ptok7l+p ljTJ8X7aADeJb7t36tTJ471RjytQXb16FfqLi/MAH3/8sc+j42flQVX5LwKY OpBlUwYDs+dLnz59sFOSr0v7G8+GiAl++I6neC+pRo0akIkoaw4aNEhpd3sH vIXBYAtk8xSUt2bNmtpNmB5qX80LFSzkKciwYcNwdOmveQXjFmfNOb9chiGm +g7ngliJ99ixY9j3efPmBatxQcaZPAXIysqCoYKeH4G849y5c7hVP08pUqSI x7u+uigRz6/z588PFyDIg+C6iZ8i5TwF4ejaH6yku1DIUwTW/Hm/fv2wpvKV N4FyIjSuBKIzIVy85aEPOCv5TlnDlF3gco8NW79+vWrTzp07cdOcOXNUm77+ +mvchG+p79mzB//86quvlNVSUlLwtVbIIrXrAwM4SaNp06aSrbV8IFOmDBKm 7CKujPXr15dfs0h/PPsDUgn8Fb7gAzEe/qn98rUYEhAq2D7gzQ4GuyCbp3Tr 1s3jzTpVX7iDkw61hbTRwtEpYDlPOXr0KPa9V69e+jW7dOmCNVUfi3Qdy30X vlrcHQo5HMtTkOvXry9cuBAXnQBeffVVLNfJJsTbeaLysmXL8Cleo0aNlB+3 EoEK5yk5HF37h5V0F1J5ihJ5fy6myui/qgDuDufG6Ey0EA8m9HnggQesdUoe U3bBbzTkzp1b+7Xf9PR0bPOQIUNUm15//XWPdwY4LqIyceJErKldJnfatGm4 Sft+t1j3eMCAAZKttXYgJTKmDBLydpk7dy4+ELzjjju065XpIDmeVUCCj4sp 4ftcN2/exPlyqk8V5CgiZMgT7R3wFgaDXZDNU8S7P6oF98aPH4/lELlZODoF DBXw916JuAfVo0cP/ZpijdmsrKzAGmszlvMUXNGicuXKqk/chhAO5ylIdnY2 zpMUD9d0sgnxVmZsbCyW9OzZEy92qoHEeYoSjq79wUq6C9k8Rd6f42Iy+fLl 01+iVnwA+vvvv9epdv78+bMaxCwaiLHhz4sXL1rpkhlM2SUqKsrjXUTR51Z0 76ovOEBggG+FiFvlH3/8scf7kWjtUjzipWzovmqTeA9CfhFaawdSImnKYCBp F8iz8HXyokWLmr1xKjmeVYi1koYPH44lOBMbziNVECjmdOGr9DYOeAuDwS7I 5il//fUXTrNv3ry5mAsHloXUz+Odx+jwBDkbMVSgY8eO7dq1004aFPO+pkyZ An/u2LEDfJFyYQckMzMTvwKs/DoqEWSsn5SUpHJx4K+w499++20QGxdkHMhT Dh06pP06LX4qt1ixYvinyCZmzJihrHbhwoVKlSrhJUbsBL9gmzt3buUk2KtX r4pEmPOUHI6u/cNKuguFPEXGn+/cubNOnTq7du3Sth+f5yqXzbly5YrqXvS5 c+fwQwZwyVOGfxBFgFMCn+lznUwB8ffo8Z3BRo0a+dw6atQobDwEqKJw4cKF WPjDDz9gibiCYPCg5IsvvsBNmzdvVm2aNGkSboqPj/d5dK3Cpg4kb0pnkLEL RFy4rlGBAgX8VTY1npV07ty5f//+quQdMhH8eo5HsSqCOAeV32UGoqOjsTw1 NdVfF/wNeP3zxXAwBA+yeUrOP2vxAX369IEgCgZwTEyMKqkMRfQVmD9/Pvax evXqkO1CxAij9PTp04MHD8YHf8WLFz958iTUxCXpYNiDUBAuQlIMwxv2LD4g 3q9fP+d6JYeh9bdt21aoUKGoqChwZdCdEydOgK3x1C5fvryY3hyKBDtPwUcn 4EKHDh2KT6L//PNP8RVacHRYTWQTkH307NkTXBaounHjxlq1amF5t27dxCEG DhyIhd27dz9z5gxcOOBqiPcKIjxP2bp166Z/wFm7EOOJEgduyYYErKS72Kh/ WlqaKBwyZAie6bGxsVjib/0fSX+O01Mh8APflZCQAGESHBfiIlyjFeqLxZTg atihQ4e8efNCfJ6RkQGBLngkuFZieyZMmKA8+jvvvIPl+suoUs5Tbty4gdf9 Nm3a+KyQnp6OizSWLl0a1AZ9QBAIEqCkSJEily5dwmrwHzRu4cKFIcQVN+EX LFiAy+qWK1dO+0WMDz/8EJXZsWOHz6NrFZY/kClTOoOhXWAcimWT4eIIf/7y yy8LFeC1T348KxGfvGncuDEMxZSUFLD+3r17xTe+H3nkEfH2PfwH7yoULVoU 2gA1oeSrr77C2Wg+v+gh8Dfg9c8Xw8EQPCjnKZCbiC9Zo3HxP82bN9e/PUIc fQXAdeOiCgKxKDGKIOZ2wk5waUcRdoo1aYGGDRsSjOoNrS8CY4/3xr74f4UK FZw/O+wl2HlKYmIiPkdD4P9i3fVbbrkFLmFYTfnVeC2Q/CqneO3Zs0e45dxe 8P8iqYnYPAUXHPDH559/7khLqcNKuouN+otPJvkEduJz55L+fP78+RjEipri cg/0799f1Dx+/LjSyyn3+dZbb6nWUBKrFGq/SKWEcp6SmZmJbYuOjvZXB5ot dBC6gd9Wfmwrx7tqvYglypQpA5rgA3SPN8bYsmWLds/du3fHCtqXTRCfCkse yJQpnUHfLpBJaVdRVlGnTp0cM+NZCeQyTz31lHJvyogOtEpOTlbWh7QOv57p 8a5OIC73t99+e1pamk43/Q14/fPFcDAED8p5So43VXnmmWfEmIdBAjF8SCcp OXIKzJs3Dz8To6RJkyYi2kQgpHzzzTdxATQB5OwjR47U+XKTi8j0ffLkyeJD MB6vv23Tps3p06cdaWAQkek73k2CTFxVvn79elRDmadoK8P58vbbb4sVIJG6 desqv1YssonvvvuuadOmohqcZTExMdp1S8DligXwPd4AY8yYMeK1PmWeMmvW LKyzadMm1U7wQJGTp2hXEI1MWEl3sVF//TylcOHC/vYv6c9TU1PB/6g+xl2l ShUx0UWQkZGhjAo83lv0PufMf/LJJ1hBfOHOJykpKVhNu2pWkJCPgsSr0Pov L8N1AT85h9x9992qJEXsDcRX2a5169baj7Ih4papv/Ws/CkseSB5UzqDYZ6i TKZ8Ir5lLD+eldy8eXPatGn4RpIAspWuXbv6/JTM0aNHGzVqJO4fAqCnaqVi Lf4GvP75YjgYggfxPAUBWRITEzdu3Kh9LhmKyCuQnp4OvV66dOnmzZt1Pnh0 7dq1pKQkiBhXrlwJmTLlN83l+37y5Mn4+HiwO8GnQtawMPItA+qt9nLixAnV e3YiT8HkBQIGGDlbtmzRObngBIQEee3atc7fSHEFJy0V3rCS7kJHf0l/ji8s gOMCb6N/Txj2A04JLnn61fbv36/z7W+3CJJdIGqV8dLZ2dnbt29fsWIF/Bv4 x2J0FJY8kKQpHcB2u8iPZxVw4U5ISFi1atXOnTsN417Qdv369dBy5dchrUH5 fCGep4QZkawA993tVhh8P4XJIWOpMICVdBfWnyZsF5qwXWjCeYrzRLIC3He3 W8F5ijFELBUGsJLuwvrThO1CE7YLTThPcZ5IVoD77nYrOE8xhoilwgBW0l1Y f5qwXWjCdqEJ5ynOE8kKcN/dbgXnKcYQsVQYwEq6C+tPE7YLTdguNOE8xXki WQHuu9ut+L+lw5p58bfGC0PEUmEAK+kurD9N2C40YbvQhPMU54lkBbjvbreC MYYtZRespLuw/jRhu9CE7UITzlOcJ5IV4L673QrGGLaUXbCS7sL604TtQhO2 C004T3GeSFaA++52Kxhj2FJ2wUq6C+tPE7YLTdguNOE8xXkiWQHuu9utYIxh S9kFK+kurD9N2C40YbvQhPMU54lkBbjvbreCMYYtZRespLuw/jRhu9CE7UIT zlOcJ5IV4L673QrGGLaUXbCS7sL604TtQhO2C004T3GeSFaA++52Kxhj2FJ2 wUq6C+tPE7YLTdguNOE8xXkiWQHuu9utYIxhS9kFK+kurD9N2C40YbvQhPMU 54lkBbjvbreCMYYtZRespLuw/jRhu9CE7UITzlOcJ5IV4L673QrGGLaUXbCS 7sL604TtQhO2C03cylMYhmEYhmEYhmH04TyFYRiGYRiGYRhq8LwvJ4lkBbjv breCMYYtZRespLuw/jRhu9CE7UITzlOcJ5IV4L673QrGGLaUXbCS7sL604Tt QhO2C004T3GeSFaA++52Kxhj2FJ2wUq6C+tPE7YLTdguNOE8xXkiWQHuu9ut YIxhS9kFK+kurD9N2C40YbvQhPMU54lkBbjvbreCMYYtZRespLuw/jRhu9CE 7UITzlOcJ5IV4L673QrGGLaUXbCS7sL604TtQhO2C004T3GeSFaA++52Kxhj 2FJ2wUq6C+tPE7YLTdguNOE8xXkiWQHuu9utYIxhS9kFK+kurD9N2C40YbvQ hPMU54lkBbjvbreCMYYtZRespLuw/jRhu9CE7UITzlOcJ5IV4L673QrGGLaU XbCS7sL604TtQhO2C004T3GeSFaA++52Kxhj2FJ2wUq6C+tPE7YLTdguNOE8 xXkiWQHuu9utYIxhS9kFK+kurD9N2C40YbvQhPMU54lkBbjvbreCMYYtZRes pLuw/jRhu9CE7UITzlOcJ5IV4L673QrGGLaUXbCS7sL604TtQhO2C004T3Ge SFaA++52Kxhj2FJ2wUq6C+tPE7YLTdguNOE8xXkiWQHuu36dzZs3r1mzZseO HTI7NFXZMhkZGWu8XLhwIagHokMkj1J7YSXdhfWnCduFJmwXmnCe4jyRrAD3 Xb/O3Xff7fF4HnvsMZkdmqpsmR9//NHjBdKioB6IDpE8Su2FlXQX1p8mbBea sF1oQj9PuX79+vbt27/88su4uLhdu3bduHHDwkFJIa9ARkbGrFmzhg0bNmXK FBBBp2ZWVta8efOGDx++cOHCs2fP2tPQIGDW+unp6WB0+Ndfhb///nvr1q2f eVm6dOnp06dtaGVw4DwlVLB9lEYs8kqGn5+ngLz+Bw4cgKsMXEEWLFjgbyTv 3bt3lx+uXbsW7Jr+uHLlCrgmGDkff/zx7Nmzk5OTZX6VmpqKRzl37pxMfXsx tMuePXv8yYJouykTAzhpFyQtLQ3a8+GHH06dOvXgwYM3b94MvGbwYH9FE+J5 CgzXu+66y6OgatWq4GEsHJcOMgps27atZs2ann/TsmXLlJQUbeV+/fopq+XK lSs2NjYoTQ8YSetfunRp+fLlL7/8ct68eaFHjz/+uLYOeMvevXsXLlxY2ffi xYtPmjTJ/nbbAecpoYKNozTCkVQyLP08BWT0z87Ojo6OVl1revbs+ffff6tq FihQwOOHuXPnBrumFrgEDBw4MF++fMpf5cmTJyYm5uLFizo/hJD+tttuw/rT p0/XP0owMLTLrbfe6k8WBM4XZX3JGMAZuyB//fUX+EbVb+vWrXvkyBHLNYMN +yuaUM5TDhw4cMcdd+AwqF69+j333IP/r1Sp0vHjxy0cmgiGCsyYMUN4CVCg UaNGxYoVwz9r1Khx9epVZWWI1XETROwNGzYsWLAg/jlixIjgdsMSMtZPT0/H wE/w6KOPquqcOXMGwkLcmjt37gceeODee+8V9SG0DlYHAoDzlFDBrlHKyCgZ rn6eAob637x5s0mTJih4xYoVW7RoUaZMGfyzWbNmyjvnly9fVgesCmbPnh3U mlrgBIyKisKaEJbXrFlTjCKgbdu2Ojfkn3/+eVEzRPMUiAREZckYwBm7IErr wAUazuvixYvjnyVKlIDU2EJNB2B/RRPKecpzzz2HLmjmzJlYMnbsWBwS3bp1 s3BoIugrcPbsWcxKYPxv2LABnyeeP3++TZs22PfRo0eLyklJSVhYv379S5cu 4c+rVavm8d5WOnHiRPB7Yw4Z66elpZX4B3BcHl8R4KBBg7Dj77//vpjrtWTJ kkKFCkFhqVKl/vjjj2C0PxA4TwkV7BqljIyS4ernKWCo/+TJk1HqN954Ax+g wL/du3fHQtgqasKAx8JPP/00RcOff/4Z1JpaIIKtXbs2/LZHjx5nzpyBErhW QmcrV66s769mzZqljLpp5impqalaQYA333wTm71ixQqsKR8DOGMX5LXXXsM9 dOjQAZ9tgXXgUgKZ1MSJE63VdAD2VzQhm6dkZGTg7Urwn8pyHCRFixYlGIhK YqgAONjWrVufOnVKWQiuGPOXli1bisJevXpp3ZFwXAQfqchnqUiVKlV8RoDX r18H/6a8jCIif9m6dWuATbUd+TylSZMm+OeVK1e2bNly6NAhn/cGDfMUOEdA h127dsF+9I8L+//9998TEhIgI1Zt4jzFEH+jlDFUMoz9PAUM9X/iiSdA5/Ll y6tcBAS9UA7hrph4v2/fPvQDS5Ys0T9oMGr65Pjx47/88ouq8Oeff8Z9xsXF aX+SmZmJM74eeughynmKT5KTk/FGXNeuXUWhfAzgmF0gl7nlllvg5x07dlRd uVSLRsrXdAb2VzQhm6d89tlneKasW7dOWT537lwsnzp1qoWjU8CajwJwplPF ihXxz2vXruHTYe3E+Fq1anm8DyIDa6n9BDsChNGCw2PKlCkWmhdU5POUJ598 8uDBg5CQiul/JUqU6N27t+oFRn95ysmTJ+Hide+99+J9fgBc60svveTThWZl ZXXq1ElMLMyVKxf8cN68eaKCvzzl7bffvtULnKqmdKAP5yl2YahkGPt5Chjq X7p0aRC5e/fuqvI5c+ag/uLnCQkJWJKYmKh/0GDUlGfTpk24z2HDhmm3YjyZ L1++VatWhVye8vTTT0ODK1SoIGZDmYoBHLNLjx498Of79++3q6YzsL+iCdk8 5dVXX8Xw7Pr168ryS5cuYT7bv39/C0engOU8pV69etBx8D/4Z3JyMp4aX3zx harme++9h5tkHtE6SbAjwEWLFmHHJd/1cxL5PAXw+RojXIyUbyf5zFMmTZqU P39+7W+1NQG4WIv3SVWIh9o+8xQxXQSSKdUZGgZwnmIXhkqGsZ+ngL7+4Ez8 hfSZmZm46bvvvsMS4VoNJ+EHo6Y8sbGxKg8mgBLc9NFHHx05cgT/Hyp5yuLF i7HBIJooNBUDOGaXGjVqeLx322ys6Qzsr2hCNk9p0aIFGL127draTfgS08sv v2zh6BSwlqecP38eb4+/9tprWLJlyxZ0JvPnz1dVnjBhAm76/fffA2+wjQQ7 AnznnXew4yH6bo7IUzzem5yQGqSlpc2ZM0dkE99//72qsir7wFth5cqVi4uL 27Fjxx9//LFx40aU0aO4OwqcO3cO76YCnTt3htM5Ozt7zZo1kA43aNBAzAPR 5ilbt27FVKhmzZr66+qEKJyn2IWhkmHs5ylgqD+KLK4pAkhh8HXswYMHY8mU KVPQD0DA/MEHH0RHRw8ZMgQi/7/++kv122DUlAFCxxkzZuDyX+XLl1ftBDKv UqVKwaYHH3wQah4+fDi08pRmzZp5vG+tKudHmYoBHLMLTk6DH+Kf6enp27Zt 8zmPS76mM7C/ognZPKVOnToeP0t94oK9dHJws1jLU8QDx2nTpmHJggULsET1 CDJH8dQeYtTAG2wjQY0Awb+VLVsW6leuXNli+4KJfJ5SvHjxpUuXKsv37NmT K1cuj3f9Q3Gd8jfva9GiRaoLCgwDHA99+/YVheIFRkjulJWvXbumvFKo8hS4 lKDIkDodPXpUsu+hBecpdmGoZBj7eQoY6t+4cWN0OMoX065cudK6dWs867t0 6YKFX375pccXkBEo7/AHqaYOJ0+eBLfWvn37ihUr4s/hTNS6prZt28ImSL4O HToEf4ZWnrJ//35sLSimLDcVAzhjl1OnTmHliRMnwrXjvvvuEz+H8D4hIcFC Tcdgf0UTsnkKJqft2rXTbgIvBJtgVFs4OgUs5CnHjh3DOw81atQQy9qLGyYQ xKrqr1mzBjdpb7O4S1AjQLFMjSuXHkPk8xSfr8Y/9dRT2LuMjAzDylqKFCni 8S7XiX9CsoMlpUuXxlVi/KHMU65evdqoUSP4/y233LJ+/XqZ44YinKfYheT9 /LD08xQw1F/cOY+KioLIMC0tbd68eeBSRMQoPIaIXcEuAwYMePfdd3EeMpA/ f/69e/eKfQajpg6JiYmqWHrfvn2qOnBFwK1jxozBktDKU3CZL4gBVOucmIoB nLHL9u3bRa6B/ylatKj4zFmuXLnELTj5mo7B/oomZPMU8DZg9GeffVa7qWHD hh7vKnwWjk4Bsz7qxo0bkKTjybt8+XJR/sMPP2Dhzp07VT9ZsWIFbrK2Xkfw CF4EuG7dOnziAMPDlU/ZGhJgnjJixAi0qVjKTD9PuXLlyuLFi+FXHTp0wGnA KA5uhcwXS7Sv0KoQecqmTZvEIxjl4tjhB+cpdmGoZBj7eQoY6g+usnnz5h4N L774Ir6g3bt3b1F55syZq1atEn/ChWnw4MFYX+WFglHTH8nJyeDi4OwTn96D C8HQoUPFVSAjI6NkyZIe721wURhaeQrO+23fvr2q3GwM4IBdxHs0Hu96cXBd /vvvv0F20BlnEsJlCycVy9d0DPZXNCGbpzRo0ACM3rhxY+2mqlWrwqbWrVtb ODoFzPqoPn364Lmsmvq4bNkyLF+9erXqJzNmzMBN27dvD7zBNhKkCPC3335D T16oUKGkpKSAmhg0AsxTJk2ahDadM2eOfmWcCIGXZhUPPvgg1hHXiK+++kq/ SSJP6dy5s9hPq1atZLoconCeYheGSoaxn6eAzEiGEDQuLq5evXoQHObLl69Z s2bjxo2D+BBfh9S+oK36bd26dT3edT/019MIRk0VEOLGx8fjbwFwmFgOUSWW bN68Of0fNmzYgIXffPMN/Enwe4KCgwcPYlO1KysGHgPYbhfxykyNGjXEo39E fDUAmyRf0zHYX9GEbJ6CvqVmzZraTRiAaV/9CxVM+SjxBBbydNVLBzt37lQF roKvv/4aN1F7nTwYEeCpU6fEe+Iyn8p1iwDzFPGCkvjCl8/KWVlZ6DA93q/l xsbGwkHPnTuHEok8Zd68eVhn7Nix+k0SeQoinsv//PPPkh0POThPsQtDJcPY z1PA1EiGQFQsJyietyqXKPdJv379sCa+9+FwTS0Q8eLknLvuuitH8WaHPg8/ /LDZAwWCKbt899132EjtVFtbYgB77QL6Yx3t181Ea3EpNvmajsH+iiZk85Ru 3bp5vJm76qMPcNLhAIaM28LRKSDvo+bOnYs3tcDxar1Neno6SjFkyBDVptdf f93jffyt+uKG69geAcLwwOetHsWyITQJME8RXyJOSUnRqYzfL8ubN69qIXdV niIu32+99ZZ+k5R5Sp06dTIzMytVquTxvtgC6Y/+b0MUzlPswlDJMPbzFDA7 kgUiNja8oS2mA+3evdv5mj7p0qUL/vzMmTPiYYQ+DzzwgIUDWcaUXXAtXIgE tB/AsiUGsNcuN2/exEX13333XdUmcVLjQ3z5mo7B/oomZPMU8eKb6k3w8ePH Y/nKlSstHJ0CkgosWrQIP9VatGhRfxcLvHOuWiUPTv8yZcp4HL9HJIO9EeBf f/3VtGlTHA+dO3cWn06mSSB5ClxrcGZswYIFddb7On36NKrRs2dP1R5UeQrs ENd7h53oX8hEnnLnnXempqbmKFbXj4mJMe52CMJ5il0YKhnGfp4ClvMUXHyp cuXKYtkWf7Rq1crj/XKiYTxse01/7yG+8sorOHiysrJyvEv6n9UgJh1NnDgR /nR4fXVTdomKioJ2gpPxuTXwGMB2u9SvX9/jfaNcZSAx1068IC9f0xnYX9GE bJ4CIWiJEiXA7s2bNxfxJ5wdDzzwgMf7TXbiQakOMgrA6YnrwEPmrlN51KhR eHbAeS0KFy5ciIU//PCDTU22DRsjwMuXL4vlBdq0aWN4PXUd+TwFLjqq9wfj 4uKwp506dVJVVuYpSUlJWK1Hjx7KnycmJkK2q8xTcv75urHH1xT0AwcOiP+L PGXx4sWiEATHwrVr1xp2POTgPMUuDJUMYz9PAZmRDE4DfKmy5Pvvv8ez+9tv v8WSnTt31qlTZ9euXdr94+ol4vXhYNTM8Y4TcEQzZ84UTd2xYweE4to4NjMz Ez8LBYNHp9ch9B79nXfeCe1s1KiRz62SMYBjdgF++uknPDo0Q7mH6OhoLMf7 XaZqOgP7K5qQzVNyFBNd+vTpc+HChXPnzsXExGDJ8OHDLRyaCIYKLF++XHxS fODAgfDnL7/8slCBCA7T09Pxrjh45m3btt28eROcVfHixaGkSJEi+uvNuoKM 9bdu3brpH3AJF4gDRQne+IIwHr+4BBQrVgyuVkuWLFn4b0AcJ7okjXye4vF+ J2XRokXZ2dnQixEjRuAFAkaFmPSV88/NKBBH+EbwojhREDTB8ZCWlhYbG4sP 5lR5ClypMRcGBgwYAHu+fv06XKQ6duwIOxGr7vv8Hv2xY8dwSRZopyrICQPs GqWMjJLh6ucpYKg/eIlChQpFRUXB2f3333+fOHECNEdvU758efG6Cn4bokCB AkOHDk1ISIBTHkb4pEmTwM94vJOLxEKUwaiZo/iAr5jci4vZQjUYP7/++iv8 FtoPnb3//vuxZr9+/XQ6Hip5Cvj2PHnyeLz34nxWkIwBHLNLjveDm3jrpmjR ohC6QBeg5KuvvsJrk3JRX/mazsD+iiaU8xQYAzjZHkHniZlsSIdG+gpABI6T NnWoU6eOqA9uFv2YUiIIaJctW+ZEZ0wiY338roc/Pv/8c6gDDkFfImDBggVO dEka+TzF5wAAK6veee/bty9uKlmypFiARbkqF16/PN7ZYpUrV/b8O08Bvvnm G1EHDyH+36tXL6zjM08BPvroIywn/lqQBewapYyMkuHq5ylgqP/AgQN9nv4V KlTYsWOHqDZ//nz8gJeoKcwE9O/fP6g1c/65JwM88sgjomu4cjICka3SlTVs 2FAkWT4JlTwlMzMT2xkdHe2vjkwM4JhdEMiVSpUqhZsKesH/33777WlpadZq OgD7K5pQzlNyvEPimWeeEXd9IX574YUXQn0wGOYpyuuFTxo0aKD8ycyZM/Hl BQRiXZpJSo4dESCuzSjWLdSBmggyfa9evbrHu/4JZBDKq3ClSpW0M6wgcRCf YBYLsJw/f75Dhw7ih3C1atWq1ZEjR4YNG+bR5Ck53rM4KipKeSUqV67ctGnT RIVZs2Zhuep8h4FarVo1j/ebj8ePH7emCU3sGqWMpLcPSz9PARn9wduIL4+g x2jTps3p06dV1VJTU2NiYvCmuqBKlSraT3QFo+Ynn3yCW8WHGnO8axu++eab qgXYYW8jR478888/9XudkpKC9bUrZTmAfBQk1gEYMGCATjWZGMAxuyBHjx5t 1KgRPhlB4BxXrT9stmawYX9FE+J5CvLXX38lJiZu3LjR4Y/+BAkLCsgAJzuE ssQjxiD1PSQw2/cbN24kJSXFx8frLCwJdRISEpYvX65yknAVhnL5U+bixYtQ f9WqVSdPnpRvYbgSyaPUXkwpGWZ+ngLy+sOJD64G9Nd/DAGm2b179+rVq+Fa o3+72/aa+/fv9/kl9GvXroGf/PXXX1euXHn48GH6LyrmuBoDOGYXJDs7e/36 9dDZ8+fP6zdevmbwYH9Fk5DIU8KMSFaA++52Kxhj2FJ2wUq6C+tPE7YLTdgu NOE8xXkiWQHuu9utYIxhS9kFK+kurD9N2C40YbvQhPMU54lkBbjvbreCMYYt ZRespLuw/jRhu9CE7UITzlOcJ5IV4L673QrGGLaUXbCS7sL604TtQhO2C004 T3GeSFaA++52Kxhj2FJ2wUq6C+tPE7YLTdguNOE8xXkiWQHuu9utYIxhS9kF K+kurD9N2C40YbvQhPMU54lkBbjvbreCMYYtZRespLuw/jRhu9CE7UITzlOc J5IV4L673QrGGLaUXbCS7sL604TtQhO2C004T3GeSFaA++52Kxhj2FJ2wUq6 C+tPE7YLTdguNOE8xXkiWQHuu9utYIxhS9kFK+kurD9N2C40YbvQhPMU54lk BbjvbreCMYYtZRespLuw/jRhu9CE7UITzlOcJ5IV4L673QrGGLaUXbCS7sL6 04TtQhO2C004T3GeSFaA++52Kxhj2FJ2wUq6C+tPE7YLTdguNOE8xXkiWQHu u9utYIxhS9kFK+kurD9N2C40YbvQhPMU54lkBbjvbreCMYYtZRespLuw/jRh u9CE7UITt/IUhmEYhmEYhmEYfThPYRiGYRiGYRiGGjzvy0kiWQHuu9utYIxh S9kFK+kurD9N2C40YbvQhPMU54lkBbjvbreCMYYtZRespLuw/jRhu9CE7UKT /7e9Mw+vosj6/wVFNtnFAUXFBQ2oCKKjiDOKCiq4oCM4Oqgv+IoLLjMqjI6y CBpR9uUVEFlE9h3DDuFhDRAmQEA2IQQSskBYE7awmN/53fNYT9t9b3d1377d ldzv5488ud11u6rOqa4639tV1dAp3hPLFkDd/S4FsAaecgtY0l9gfzWBX9QE flET6BTviWULoO5+lwJYA0+5BSzpL7C/msAvagK/qAl0ivfEsgVQd79LAayB p9wClvQX2F9N4Bc1gV/UBDrFe2LZAqi736UA1sBTbgFL+gvsrybwi5rAL2oC neI9sWwB1N3vUgBr4Cm3gCX9BfZXE/hFTeAXNYFO8Z5YtgDq7ncpgDXwlFvA kv4C+6sJ/KIm8IuaQKd4TyxbAHX3uxTAGnjKLWBJf4H91QR+URP4RU2gU7wn li2AuvtdCmANPOUWsKS/wP5qAr+oCfyiJtAp3hPLFkDd/S4FsAaecgtY0l9g fzWBX9QEflET6BTviWULoO5+lwJYA0+5BSzpL7C/msAvagK/qAl0ivfEsgVQ d79LAayBp9wClvQX2F9N4Bc1gV/UBDrFe2LZAqi736UA1sBTbgFL+gvsrybw i5rAL2oCneI9sWwB1N3vUgBr4Cm3gCX9BfZXE/hFTeAXNYFO8Z5YtgDqbp4m KSkpMTExJSVF5oK2EjsmJycnMciJEyeimpE6xHIrdRdY0l9gfzWBX9QEflET 6BTviWULoO7maW666aZAIPDXv/5V5oK2Ejtm/PjxgSAki6KakTrEcit1F1jS X2B/NYFf1AR+URP1dcrFixc3btw4YMCAwYMHb968+dKlSw4yVQp5C2RmZk6b Nq1Hjx7Dhw9PTEy8cOGCSeKsrKzZs2d/8cUX48aN27lz52+//eZOcV3Frvez s7PJ6fQ3XAKyyfr16/sGmTdvXl5enguljA7QKcUF11tpzCJvyZLXz6tAlFoy OSs1NXXLli3yo4wD/2ZkZGwOcuzYsZAJDh06NGPGjJ49e9LAd/ToUcmSENu2 bdschvPnz8tfxzG2/FJYWJicnDxs2LD4+PilS5eePHnSmEZ+HLQ7YjrwNXPu 3DkaMsjjX3311dSpU9PT03UJKOtwXhCcPXvWVqYRYumXrVu3mhdYV03Xre24 6UZobcubMaoorlMo3r722msDGurVq0cWc5CvOshYICUl5Y477gj8kVtuuSUh IcGY+MyZM+3bt9clbtSo0Z49e6JSgQiQ9H5BQcHChQupUpdffjnV5eGHHzam obvyvffeq1ixorbWVapUGTlypPvldgPolOKCi600xpG0ZIns51XA9Za8a9eu QYMG3XzzzeymFStWyBTDgX9Jg1x11VWceMKECcYEH330kfaCpUqVojBepjBE uXLlAmGYPn265EUiQT4KWr58ubADQz7q06ePiGPlx0G7I6YzX3NGn3zyyRVX XKHN6LLLLuvYsaNWZJFiDecFwahRoyQzdQVLv1SrVs28wNTOOWWUrO246UZi bcubMdqorFN27Njxpz/9iY0TFxdHUTr/X7du3QMHDjjIWhEsLTB06NAyZcpw ZatWrdq4cePSpUvzR7r3SZ5rE2dnZ9999918lpKRoeheEN/Nz8+PbmVsIuN9 qhEPl4K//OUvujRHjhyhwVTU+p577rnttttEegqto1WBCIBOKS641UqBjCVL aj+vAu625M6dO+sCm8TERMsyOPPv3/72N5GLMTSiCJBPURx4//33ly9fnj/2 6tXLsjxnz54NhGfq1KmWV4gcySho2LBhwjXVq1e/9dZbSY7xx969exfZGQft jpjOfF30x4CEStugQQPhfaJNmzZCYclEzt64QxC5Tqlfv35R1KwdSdONxNrm N6MHqKxTnnvuuUCwqU+aNImP0G3LturUqZODrBXB0gLjxo2jOtapU2fp0qX8 fDwnJ6dLly5c95YtW2oTv/7663y8bdu2/GMFfYVuBOrAR4wYEc16OEHG+1lZ WVV/hwWacdz87LPPuNaffvqpeJaakJBQoUIFOlijRo1Tp05Fo/yRAJ1SXHCr lQIZS5bUfl4F3G3JpA44GXez4aIpHQ78O2XKFG34pAuNUlNT+XiTJk0KCgro yNGjRymGDwR/tM/MzLSsL3/922+/3W/g9OnTljWKHBm/7Nu3j0VKlSpVFi5c yOE9qYAWLVpQ8M/Tb+THQbsjpjNfE/n5+XfeeSelf/vttylcLwoGJFTZG2+8 UTeOUEqj/YktW7aw8GzWrJnH8z8t/ZKRkRGyzO+88w7XbtGiRUVRs3YkTdex tc1vRm9QVqdQZM436Ztvvqk9zp1epUqVFAxEJZGxwMSJE/keF1A3FRcXFwj+ riIOUjPjJy/t2rXTTWhUc3cmeZXK8GNQ47h58eJFEmhjxozRHRf9g+6pkwrI 65SHHnqIP547d27dunW7du0KOVvVUqfQPUJ22Lx5M13HPF+6/t69e1etWnX8 +HHdKegUS8K1UmBpyRLcz6tAlFryTz/9JBm7OvBvbm4uTzK57777QoZG7777 rlGSCPFi+Ujll19+4ZQhJ1F7g4xf3njjDa4mVU17nIJJMU1Cfhx0PGLK+1pw 4MCBOXPm6A5OnjyZrzN48GDzr5PAoWQUsXs/cd3u/cKkp6ezviCX8ZEoWTsa Tdfc2pY3ozcoq1P69u3LZtHN05s+fTofHzdunIPcVcDZvUC0aNGCOy66C/gI tzFi+/btbhYxakQ7AqTWwgYZO3asg+JFFXmd8thjj+3cufOJJ54Qk1GrVq36 3nvv6RbKhdMpBw8epKH8tttuE9MFKVT4xz/+ETLkO3To0EsvvVS5cmVOWapU KfrijBkzRIJwOuVf//pXtSB0q9qyg/pAp7iFpSVLcD+vAr7rFAf+ZQlzxRVX LF261BgaUR/Ic2+Mi2huv/32QHA6mXmRVq1axZdNTk42Txk9LP1CEozXd7Rt 29bB9eXHQcuUDnRKSNauXcvX6dGjh0mydevW8bA1aNCgSLJzhrPY7Mknn6QC X3/99ZbT7CO0tutN19La5jejZyirU1577TUOz0RMzhQUFPDvM126dHGQuwo4 uxdOnjxZs2ZNqvitt94qDtavX5/DWjfLF02iHQHOnTuX7yZvlkPaQl6nECGX y9HQXFhYqEus0ykjR44sW7as8bvGlAR1PrpFmgIxSSOkThkzZgwfJDGlu0NL ANApbmFpyRLcz6uA7zrFrn+p2+Er9+7de8+ePcbQKD09nQ/2799fl9e///1v PmU+AUaMET6ufrL0y9SpU7mQa9ascXB9+XHQMqVbOiU+Pl43soSkSZMmgeCM Pl82LHUQm/38889cL7KkZeIIre160zW3tuXN6BnK6pTHH3+cbHLnnXcaT/Gy rPbt2zvIXQUc3Av79+9ngxDDhg0Tx/lp43/+8x/+mJ2dvWHDBjVnfDHRjgA/ /PBDtpLlLGXvsaVTiLfeeoukQVZW1rRp04Sa+OGHH3SJdeqDf3KpU6fO4MGD U1JSTp06RSOd2EVEW4Bjx45dffXVfPzll1+m2zk/P5+6x8aNG997771iqphR p6xfv56lUIMGDUJuklncgU5xC0tLluB+XgV81ym2/Jubm1ujRg06+Oc//5l0 ze7du42h0bp16/jgzJkzdRccPnw4n9q7d69JkcaOHcvJKMKkobNDhw7dunWj kOzMmTPmdXERS7988803geDTbe6HL1y4sHHjRqqX5HoN+XHQMmXkOoVcOXHi RH48dN1115nYWTwvoEyd5RUhDmKzRx55JBDci1VGWEVobXebrrm1ZW5Gz1BW pzRs2DAQZoNEio4Cxeohgg75e2HcuHEdO3akG+Gyyy4LBOcQDhkyRNwOhw8f 5pYzYsQICia1+xjTuECNMIp1cEpUI0ASaNdccw2lv/HGGx2WL5rI65QqVarM mzdPe3zr1q2800u9evVEAwg372vu3Lm6joukCjeMDz74QBwUOzBQ56lNfP78 ea3U1ekU0sJsZJJOaWlpknUvXkCnuIWlJUtwP68CvusUW/5t06YNHSlfvvyu XbvoY8jQaNasWXzQuHHrtGnT+JT5M4gBAwYEQkEhtMxP4q5g6Rdel12rVi0a 5du1ayfm5ZYrV466a/NFW/LjoExKxzrl4MGDNNy88MILN9xwA1+B2pX5kEE1 pWQ1a9a0XFAZJezeL9u3b+eqUaOyTBy5td1tuubWlrkZPUNZncI/tjz//PPG U9Ta6RSF5Q5yVwH5e6FVq1ba1tizZ0/ti3g2btwoVAn/U6lSJbFfN4W1ulhX BaIaAb711ls+3kqWyOuUkEvjW7ZsybXLycmxTGzkyiuvDAS3heSPJHb4yNVX X8175oRDq1MKCwubNm1K/5cpU2blypUy+RZHoFPcwtKSJbifVwHfdYq8f6nT 5muKqfIhQyPx0GTr1q26C1Jh+JTxUYsWEexRAbp27frxxx83btyYj5QtW3bb tm3mNXIFS7+0bt06EPxlUmyTRR212JT4vvvuM3mwIj8OyqR0rFOSk5N1sfQv v/xikj4rK4s3BRLzQ7zH7v3CcpLcZNx/xkjk1nax6ZpbW/Jm9AxldQq1arLJ M888Yzx1//33B4Jz6hzkrgLy90K/fv2efPJJaorilUlxcXE7duzgs2JiZCC4 aGXFihUXLlyg+JMaEm80R3GsX79LhCN6ESBVn7txah6+TG21JEKd0qtXL/a1 2CrEXKeQ66mF0Lfatm3L65jYOHx23759fIQ6T/MiCZ2ydu1a8QimT58+5t8q 1kCnuIWlJUtwP68CvusUSf/m5ORUr149EHzyInrvkKHR6NGj+eCmTZt0F1y0 aBGfstwNadKkSUuXLhUfKeb//PPP+bvR3uadsfSLeAUJ0a1bN4oqi4JWeuyx x/jg999/H/KL8uOgZErHOiU9PZ2GHmpL4hWflF337t3D5TV06FBOZi5noord +4XnY7/wwguWKd2ytltN18Ta8jejZyirU+69916yyYMPPmg8Va9ePTrVunVr B7mrgN17oSg4V5BucG7nFJ3yrB4xU5eiUPEbOyO2v9u4caOLJY+cKI2bv/76 K/cYFSpU0O3iqA4R6pSRI0eyT6dNm2aemB+4c1ej489//jOnESJ34MCB5kUS OuXll18W12nVqpVMlYsp0CluYWnJEtzPq4DvOkXSvyRk+IJJSUnZv7N69Wo+ SDEVfeTNlObPn88Hly1bprvgxIkTHY96FO81atQoEJxY5cHGIJZ+EctRdbv4 Hj16tFKlSuHuC/lxUD5l5OtTKNZdvHgxm5cI90L2F198MRCcE+LxO1O02Lpf du7cyTWy3PEyqtZ21nRNrC1/M3qGsjqFbdWgQQPjKQ7AXn/9dQe5q4ADncL0 6NGDm8qoUaOKgrKXPxq36d60aROfMt9bw3uiMW4ePnxYrBP3+PW1tohQp4gd PvlNUuESHzp0iAOAQPDpW3x8PGV67NgxNpHQKTNmzOA02m0ZQiJ0CiMmFk6e PFmy4sUO6BS3sLRkCe7nVcB3nSLjXzHJ35wHHnigSDO0iZ9rBEOGDOFTzjZR +eijj/jrPCE/qlj65c033wwEt7o1nmKTarf9ZOTHQVsjplv7fVG4wpMAr732 2pAJ+LFL8+bNI8klQmzdLxSGsWXMp0B7YG0HTTectW3djJ6hrE7p1KlTIKgQ dUvGqAtiQ3322WcOclcBxzolLS2N6/7uu+8WBX+m4N1rP/74Y11KYSXLX8s9 xvVxk5oHzx8I+DqvVYYIdYp44+3+/ftNEvP7mC6//HLdiwl0OkV0R//85z/N i6TVKQ0bNszNza1bt24gOF+a34lc8oBOcQtLS5bgfl4FfNcpMv4Vv0ubc889 9xQF9/Hgj926ddPl9b//+7+B4OQi3XumJBHzZ7Zs2eLg67aw9MvXX3/NdTEu HuTJt1WqVNEelB8H7Y6YbukU4pVXXuFL6d5hXaTZbrpr164R5hIJtu4X3nO7 dOnSJtsaeGNtu03XxNq2bkbPUFaniIU8ujVx3333HR9fsmSJg9xVwNIC4aYv ijUFb7/9Nh/h7a/vuOMO3VfEQzrVltK7O26eOXOmefPmXNOXX37Zx+fFMkSi U2jk5Zne5cuXN9nvKy8vj63RuXNn3RV0OoUuyO8voIuYD+tCp9SuXTsjI6NI s4t7x44dratdDIFOcQtLS5bgfl4FfNcpkv49fvz4UQNiYvOIESPoo9gCnZ8X 6/Y6pl6xVq1agQh+6eVda6644gpnMscWln5JSEjguhtftPHoo4/qAkX5cdDB iOkgcg4XwLz66qt8qUOHDulOiXnIfu1IzNi6X3gNEd0y4RJ4Y+0i+03X3Nq2 bkZvUFankIurVq1KZmnRooXwL3mBbk86eMMNNygelJpgaYF27do9//zzxhmA Yt6XeJmpaNWzZ8/WpuzQoQMf58BSHVwcN8+ePSsWFT799NMXLlxwrZTRQV6n 0BCs2wBh8ODBXNOXXnpJl1irU1JTU3VKlklOTuZZzUKnFP3+Ft1AqDemib0a ijQ6hTo3cZAMzgeXL19uWfFiB3SKW1hasgT38yrgu06JxL/hlu7yswZi9erV 4iCNgHxw9OjR2typ+5o0aZLYJ3PTpk0NGzbcvHmzLi+yEi//9GbfBku/kFlu vfVWKg8Fw1oT7d27l7dpEr8RyY+DzkZMc18bLZySkkKC0fgDaW5uLr+ui5xu vI5Yfbl48WKZUkUJW/dL7dq1qcBNmzYNedZ1a9tquka/aHFgbayjD4eY6PL+ +++fOHHi2LFjdG/ykZ49ezrIWhHMLTBz5kyuY1xcHEnX7du3//bbb3l5eZ9/ /jm/RaVKlSoHDx7kxBcvXuSRhaLQOXPmUIdGRwYOHFi6dOlAmN0g/UXG++vX r1/7OzyLkuoojrCQpzBerDSsXLky9YoJCQmz/0h2drYXVZJGXqcEgu9JmTt3 LmlVqkWvXr24IypbtqyY9FX0+9M0Mo4YyKh3YteTTTZs2EAtJysrKz4+noc2 nU6hnkfsI9e1a1e6MjUe6gxJJtNFxDsIQr6Pft++fbynHJUzZE9YrHGrlQIZ S5bUfl4FXGzJ1JOIg926dWMHUd/CR0z2aHLs33ChEXWJ/CyY4l7u5Uiw0LBI R6688krtRCnxWj0x5YZf2lKuXLnu3buvWrWK+i6qIIVt/IIS6mYXLlxobi5X kPGLeKnfs88+yxOlqNflFdMUCfB2Z/LjoK0RU97XRgvzWxLIkuT3BQsWkHkp RKfK3nXXXZzyo48+Mlb2iy++4LMkc1ywr1Pko1MaczkeIw1iPBsNa9tquka/ aHFgbeiUcFCfxpPtGbF5eIsWLYp1aGRugcLCQt6KQSCCSTaC7oU+1EXza0MD wUlBHD0Ggq/v4c0MlULG+/xej3D069eP0tAAZ5KGmTVrlhdVkkZep/CyIx3U K+rWvH/wwQd8qnr16mKjD+2uXDyac8Pgffi1OqUouDmhSMNZiP95DVRRGJ1C 9O7dm48rvizIAW61UiBjyZLaz6uAiy2Z/jFJRhcJd33H/jUJjeiI6KzEBcuW LTt//nxtMv4lh2jWrBkfmTlzZoUKFURh6CLi60SXLl3MbeUWMn6h8L59+/ai jrwInRGLtuTHQVsjpryvjRamelWrVk0kLl26tHaIuf/++ynCMVZWvFvkwIED EVvXOfLRaW5uLhe4Q4cOxrPRsLatpmv0ixYH1oZOMYG6uKeeekoE6hS/UQxf 3AcvGQvMmDFDLL8SPPTQQxs2bDAmTktLa9q0Kf+QzpDRdDsVK0Lk4ybvASg2 XjZBN2b5jkzd4+LiAsEN3EhBaHv7unXrGmdYkXAQr/oVG30cP368bdu24os0 drdq1WrPnj08aVCnU4qCd/Hdd9+t7fHq1Knz448/igRTpkzh47r7/dy5czwz oUyZMv4OLq7jVisFkr19ieznVcDFlmweTVWsWNEkC2f+3b9/P6c3bu1VFHyR BC/ZY2666SZjh//NN9/wWfG6OiIjI6Njx47iDe/MzTffbPnWFReRj4L69Omj 3WGe/h8/frw4Kz8O2hox5X0d0sKHDh165513dBvjk8G//PLL06dPh6ym+G2W 37ngF/J+EevNQy78j5K15ZtuSL8IHFjb/GaMNorrFIaMmZycvGbNGtXeWugM eQtkZ2dTrefNm0cR6dGjR80T5+fnr1y5kq4s82pUv3Dg/RKD3bpfunQpNTV1 8eLFJttsUppVq1YtXLhQN+hTr0LH5W+ZkydPUvqlS5eKKYWxTCy3UnexZckS 1s+rgFItORr+TUtLW758ucnvJNu3bw/5nm4qw5YtW5YtW0Zf937igS2//Pbb b7t3705MTKS6KLhcK5yFz58/T+PXggULlixZQuVXfwFpkWL3Szgkm244vxRH ioVOKWHEsgVQd79LAayBp9wClvQX2F9N4Bc1gV/UBDrFe2LZAqi736UA1sBT bgFL+gvsrybwi5rAL2oCneI9sWwB1N3vUgBr4Cm3gCX9BfZXE/hFTeAXNYFO 8Z5YtgDq7ncpgDXwlFvAkv4C+6sJ/KIm8IuaQKd4TyxbAHX3uxTAGnjKLWBJ f4H91QR+URP4RU2gU7wnli2AuvtdCmANPOUWsKS/wP5qAr+oCfyiJtAp3hPL FkDd/S4FsAaecgtY0l9gfzWBX9QEflET6BTviWULoO5+lwJYA0+5BSzpL7C/ msAvagK/qAl0ivfEsgVQd79LAayBp9wClvQX2F9N4Bc1gV/UBDrFe2LZAqi7 36UA1sBTbgFL+gvsrybwi5rAL2oCneI9sWwB1N3vUgBr4Cm3gCX9BfZXE/hF TeAXNYFO8Z5YtgDq7ncpgDXwlFvAkv4C+6sJ/KIm8IuaQKd4TyxbAHX3uxTA GnjKLWBJf4H91QR+URP4RU2gU7wnli2AuvtdCmANPOUWsKS/wP5qAr+oCfyi JtAp3hPLFkDd/S4FsAaecgtY0l9gfzWBX9QEflETv3QKAAAAAAAAAJgDnQIA AAAAAABQDcz78pJYtgDq7ncpgDXwlFvAkv4C+6sJ/KIm8IuaQKd4TyxbAHX3 uxTAGnjKLWBJf4H91QR+URP4RU2gU7wnli2AuvtdCmANPOUWsKS/wP5qAr+o CfyiJtAp3hPLFkDd/S4FsAaecgtY0l9gfzWBX9QEflET6BTviWULoO5+lwJY A0+5BSzpL7C/msAvagK/qAl0ivfEsgVQd79LAayBp9wClvQX2F9N4Bc1gV/U BDrFe2LZAqi736UA1sBTbgFL+gvsrybwi5rAL2oCneI9sWwB1N3vUgBr4Cm3 gCX9BfZXE/hFTeAXNYFO8Z5YtgDq7ncpgDXwlFvAkv4C+6sJ/KIm8IuaQKd4 TyxbAHX3uxTAGnjKLWBJf4H91QR+URP4RU2gU7wnli2AuvtdCmANPOUWsKS/ wP5qAr+oCfyiJtAp3hPLFkDd/S4FsAaecgtY0l9gfzWBX9QEflET6BTviWUL oO5+lwJYA0+5BSzpL7C/msAvagK/qAl0ivfEsgVQd79LAayBp9wClvQX2F9N 4Bc1gV/UBDrFe2LZAqi736UA1sBTbgFL+gvsrybwi5rAL2oCneI9sWwB1N08 TVJSUmJiYkpKiiclAqGJ5VbqLrCkv8D+agK/qAn8oibQKd4TyxZA3c3T3HTT TYFA4K9//avMBUePHv3iiy9OmjTJhcIBDbHcSt0FlvQX2F9N4Bc1gV/URH2d cvHixY0bNw4YMGDw4MGbN2++dOmSg0yVQt4COTk5U6ZM6dGjx9ixY8kI5omz srJmz579xRdfjBs3bufOnb/99psLZXUb+boXFBQsWLCgb9++X3311axZs8gU US5a1HFXp2zZsiUQ5LLLLsvMzHSniCCIfCsteb2Tu9jt7bOzs8mM9DdqJYot 5O1Pfci0adNorBk+fHhiYuKFCxdMElOzT01NpS7IcpSRHJW2bdtGCbp37z5y 5Mi1a9c6GLxkinTu3LmkpCS6W2lMmTp1anp6ut1c3ELGL3l5eZvDkJGRYUxv bkOTqwnOnj2r/Qq1gfXr1/cNMm/ePLqCrTra/boKAUyU+iv5+8Xu9e02acdG jrAxRIjQKc5wnKNkYrLktddeG9BQr169kDdpMULGAhs2bGjQoEHgjzzxxBP7 9+83Jj5z5kz79u11iRs1arRnz56oVCACJL1Pd1zt2rW11alQocK3334b/QJG EXd1CvVIpFAocZkyZajzcaeIIIhkKy2RvZO7SFqyoKBg4cKF1IldfvnlZMaH H344+kWLCWTsn5KScscdd+iGj1tuuSUhIcGYeNeuXYMGDbr55ps52YoVK8Jd VnJUooDnxRdf1CV78MEHKSPJOsoU6fz585988skVV1yhzYX6z44dO548eVIy IxeR8UufPn0CYbj99tu1KWVsOHjw4HBXE4waNYoTk7nee++9ihUras9WqVKF FJBM7ex+XZ0AxvX+Sv5+sXt9u03asZEjbAyu4P1zLvkcd+zY8ac//YnNEhcX Rz0n/1+3bt0DBw5EuZhRxNICEydOLFeuHFeWLNC0adPKlSvzx/r16xcWFmoT k9a+++67+Wzp0qXJUNSE+GPVqlXz8/OjWxmbyHh/+fLlVBGuwgMPPPDaa68J zSI60uKIuzqFWLx48aeffirZ9QF5ZDxVUnsnd5GxJPVgPBwL/vKXv3hSupKP pf2HDh1apkwZMV40btxY9L0UAq1fv16buHPnzro4JzExMeRlJUelS5cuPfro o3y8WrVqr7zyymOPPcYfSfJTZGVZQZkiaQtTqlSpBg0aiDuXaNOmjfc/3cvc F//+978DYaAwQCSTtKGMTpk6dSqlPHLkCAXGwnf33HPPbbfdJtKMHz/evNh2 v65UAONufyV/v9i9vt0m7djIETYGt1BZpzz33HPsBTEDf9iwYWyfTp06RbGI UcbcAkePHmVVQpHP6tWreSbJ8ePHn376aa57nz59tOlff/11Pt62bVvW0fQV aj+kf0eMGBHlqthGxvuk8QNBgbZz504+cuzYMbpB+J4qvlNrXNcpIErIeKqk 9k7uImPJrKysqr/DQTJ0iltY2n/cuHFk8Dp16ixdupS71pycnC5dunBLbtmy pTbxe++9x26qUKGCedwlOSrNmTOHk3Xt2vXcuXN88KeffuKD/fv3t6ygTJEo DLvzzjvp1Ntvv01xFxeGzHLjjTfyV5KSkiwzcheZ++KNN96gsl1zzTX7DWgf oEvakIxgvA6xZcuW8uXLU8pmzZpxA/jss8/4u59++qmY3pOQkMAWrlGjxqlT p0yKbffrSgUw7vZX8veL3evbbdKOjRxhY3ALZXUK9ZYsKt98803tcQ4PKlWq 5I19ooGlBaiNtW7d+vDhw9qD1BpZvzzxxBPiIPUz/GtYu3btdAr6xIkTrpba HSzrfvr0aZ7O9PXXX2uP0w3O98vu3bujW8SoIa9THnroIXFk+/btGzZssNva qR9LSUlZt25dQUGBZeLMzExSxDINZt++fStWrJBfPkDFWLVqVXp6upqrpcJh 6akS3Du5i93xhSdIQKe4hYz9J06cyKGOgO7WuLg4ckT16tVDfkWEwSHjLvlR 6ZNPPqFkFCzplsNQA6DjL730knnJ5Yt04MABiud1BydPnsxfGTx4sHxGriDj lxdeeIHK1qRJE/NkEdqQAt1AcFq1mP9z8eJFCmvHjBmjSylCVt1TNh22vq5a ABOl/sq8cTq7vnyTjsTIETYGt1BWp/Tt25ftoJvWMn36dD4+bty4aBUxyji2 OT+Au+GGG8QR7mQIimbdKl5Usax7bm4u12jo0KHa43RX8vFly5ZFt4hRQ16n tGzZkjqQTp06iQlvpUuXppBYN+WvcePG1apVI0mrPUjDzTPPPBP4nVKlSt1+ ++1014gEU6ZMoW/dcsstNK7Fx8eLX2D48fHatWt1RaKebdq0ac2bN69ataq4 7DXXXLN48WJdSr7yrbfeSv/Pnj27adOm4vl1nTp1jFdWFktPleDeyV2gU/zF 8VjTokWLQHC6OwUqxrPmcZf8qMQpa9SooTveoUOHQHCFhXyBHYSC1CPxV3r0 6CGfkSvI+OWRRx6hsrVq1co8WSQ2XLduHf9iP2jQIMsyU1/H5ho7dqxlYsmv qxbAFCOdEpKQTToaRo6wMdhFWZ3y2muvBYLzfHT9ZEFBAQc/Xbp0iVYRo4xj m1NcGvjjGrr69evTkccee8y1wkUZmbrzRErqpbVTvGbMmMH3RfGd/C+vU0iK ipV3Wj7++GNjYu0ksaNHj1533XUivXjcTIhVb+PHj+cjd911lzGLsmXLksQQ Fzx58mTIZIGgdNJpRnHlTz75hFSPLn358uU93iTEMZaeKsG9k7tAp/iLs7GG 7vqaNWuSI/g3ByPmcZf8qPTzzz/zdXSF5HX9FGnLl9lBKBgfH89f8X5rdxm/ 8HBvaYRIbNikSZNA8JGNzPPuuXPnckbaX73kCfl11QKY4q5TQjbpaBg5wsZg F2V1yuOPP05GuPPOO42neMVQ+/bt3S+cJziz+fHjx/mnj9dff10c5ED0P//5 D3/Mzs7esGGDmjO+GJm6f/XVV3wLUDTIK7woGuQfl4r1wg15ncI89dRTM2fO 3LNnz6BBgzj6rVKlyunTp3WJtTYR3dQ///nPgwcP0uizdevWZ599tlGjRmLP SaEmAsFF3+PGjcvIyNi0adOrr77KB6+//nrtFIJHH32URMff//73hISEnJwc amPU3jildn6a7srVqlXr06fP5s2bk5OT77vvPj745ZdfRmxFL7D0VAnundwF OsVfHIw1+/fv5+ZNDBs2LGQa87hLflSiToknM9eoUWP58uV8UEzxFUdksBUK 0oAyceJE3ivpuuuuk1mw7y4yfrnhhhtYaAwePPjtIPQPddS6ZI5tuGrVKk5D ppMp84cffsjpnW2DH/LrqgUwxVenmDTpaBg5wsZgF2V1SsOGDQNhNmTjDXvV 0eB2cWZzMdXkxx9/5COHDx/mIyNGjKAQUbu3JEVQ1Au5XG43kKn7+fPneWou Ubt27W+//ZZXgZUrV47iXk+KGRVs6ZRu3bppf+MSOwqmpqbqEmt1CtuNOivd yEsmFf8LNfH888/rOqu2bdvyqQkTJoiDJJSMO5CLLWVIPhuvTL2rdktMugI/ XnnuuefMq68Ilp4qwb2Tu0Cn+Iu8/ceNG9exY8dHHnmElwdSbDNkyJBwP7Ob xF12RyU6UqlSJU7Qpk0b+grF2/T/3//+d1s1lQkFDx48+MEHH1AnyRKAW1pa WpqtjFxBxi9ik08t1JG++uqrun7bmQ3btWtHaWrWrClW35tAOV5zzTWU/sYb b5Son9TXFQxgip1OsWzS0TByhI3BAVq/UKd0ySYOlsdKtgT+WZJCKeMpXiBG BrebtSI40Cn79u1jUVy/fn3xW/fGjRtFe+N/qLMS21xThzZv3jz3Sx8ZknVP SUkRu2UKZs6cGf0CRhF5ndKsWTPd8TFjxrAR5s+fr0us1SnUZXEyk6exQk0Y f2pLSkriU2+99ZZ5Ofv168cpt2zZYrzy0qVLdemvv/56On7vvfeaX1YRLD1V gnsnd4FO8Rd5+7dq1Urb2fbs2VP31j8tJnGX3VHp/PnzFE7ruvomTZpoHxzL IBMKJicna3O57rrrfvnlF1u5uIW8TqlevXqHDh3IHa+88gpvzEX84x//0KZ0 YMOsrCweYcXP7ObQiMCX1f6EJU/IrysYwBQ7nWLZpKNh5AgbgwO0fjl06JDd 9zzSVyLJ0QSeZv/MM88YT91///0BiX0wlMXuvUB6UPx8vXDhQnFcTEwNBCcS r1ixgiQMKUdqPNyhURwr81OJl8jUfcaMGdyFPvHEE48//rhY6UB1LL6bfRVF ti8xyRM2gnbeqTExCQ2xdP3JJ59MSEgwLoM10SnU0vjr2j3lBNQBjhw58n/+ 53/o1hM/3y1atEjmyqRQAuGnu6uGpadKcO/kLtAp/iJv/379+lGP0bhxY/Hm uLi4uB07doRMbBJ32RqVCgoKqPvi2Ondd9+tVasWf7F06dLdu3e3VVOZUDA9 Pb1t27bUusTrWWlwoYzUfH/KwYMHBwwYoH1gvWfPHv7BR9vHOrPh0KFDOZmM UiMn8ihMnZsDW4X7uoIBTLHTKZZN2nUjR9gYnKH1S2FhYUpKCqkPqvsBUygB JaPEug2I7OZoAgc2IXerqFevHp3SbXNUjLB7L7z//vvczHST3tetW8fH69ev n5OToz0ldo0jNe1Kmd3Csu7UbXKoTPEwx9h0RGxgVbt2bd0WmsWISHTKkiVL ZHRKUXBnQu3GXNR99enTR9sRmagJgncY073vmDLlFZdGFixYIHPlBx54IFCC dEoJ7p3cBTrFXxw8u8/NzaU4h0MR6mFCrt0wibtsjUovvfRSIPjIIDk5uSgY gVD8zEv4ia+++kq+2LZCQYqvFi9ezC/qCmj2GPEMB35hZs2axWUWa/2c2ZDf X0/SxvJ9ZL/++utVV10VCE4F1M46lsTk6woGMMVOpwjCNWl3jRxhY3CMzi+k QUiA6KpjhBJQMmc7L0m2BA5NGzRoYDxFt2Tgj8vJixe27oUBAwZwW6JAUTdk kBf4lHF3602bNhnDWhWwrPvf/va3QHBJoG43+B49enCNPvzww+gWMWp4o1OK gg9GyVx16tQRaoJ0x7Fjx/isuU658sor6dR9990njogWWLZsWRoTSQft3LmT Xw8XszqlBPdO7gKd4i+O42HR344aNcp41iTukh+Vtm7dyh8HDhyoTbZ//36e bF++fHkSTZIFdhAKUlF5Aue1114r+RW3cOyX/Px8riavPXFsQ/75vXnz5ubZ HT58WOw8yW+rt4X51xUMYIqvTmGMTdpFI0fYGCJB5xd+pLJly5aQu6YzdIoS OHuYYswxHJ06dQoEl07r3piWmZnJhiIx6CB3FZC/F6ZPn857fFHbM+6rQAqa 7BMwbFdbpLGSrvvyHcu682Prjh07Gk/xmz4aN24crcJFGc90CkP36ezZs3lZ dyC4eRofN1ETYs2dSDx//nz+ZbVp06baF4+Kjjc2dUoJ7p3cBTrFXxzHw2lp adyS3333XeNZk7hLflQaMWIEfzT+4Pnjjz/yqblz50oW2Fko+Morr/C3PH5M 79gvFHTxRgdt2rQpcmrD9PR0PtW1a1eTvKhz41msAellLLa+rmAAU9x1SpGh Sbtl5AgbQ4QY/WL5SCWShykhcwzJhAkT2Ca61dPfffcdH6fIzVkBfEfSAtS9 8DKNSpUqhXswx7Nx7rjjDt1EwdWrV7OVVFtKb153qkXZsmXD3QitW7emUyRk oli+aOKxTmHy8/N5JpL4gcVETQwfPpxPxcfH85HOnTsHglNedSvRYlynlODe yV2gU/zF0v7hZpjv27ePW/Lbb79tPGsed0mOSrz/PEXdxgX7YvEvxeGm9ZMt Urhqis3YHay0jQTHOkWsm+7Zs2eRUxuKNQsmOxKfOXOmefPmnOzll1+2nB7m 7OuqBTDFSKfIN+nIjRxhY4gco1/4kcrmzZtDPlKhg3TK8cOUkDmGhCzD0+xb tGghzHL+/Pl77rknEHwRnve2cgsZC1DL4fWMpIVNEov2r303X9Hv76IlMjIy 3Ciya1jWnQPa+vXr6ya5kZznpRMtW7aMbhGjhgc6ZdeuXcZ3zj788MOUrHLl yvxRqImJEydqk504caJu3bo86omLPPfcc4HgkkztDxd074vOMDZ1SgnundwF OsVfLO3frl27559/nl9TpUXM+wr5vmnzuEtyVBJ9mjGL/v3786mkpCQ+Qncc dS/U+4XbhcykSBSu1KpVyxiM5ebmXn311XzDhrxm9LD0C0WDXbp00U1+pjiT 3yNGJCQkFNm0oWDkyJF8avHixSFzJyOLrXuefvppXTG0hPSL/NdVC2CKi06x 1aTljRyhN6NHSL/wI5Xs7GxjejoYycOUcDmG5J133mHjvP/++xREHTt2rGPH jnyEf0woplhaYOHChfxYIRB8tTd9nDNnzmwNIg4k2cgtuVKlSpSGoiM6MnDg QJ4tFnLfVH+xrLtYDdGqVSsx1e3w4cNiKf3//d//eVHQKBBtncKPTkjedu/e nU13+vRpSs9fpAGOkwk1QY2kc+fOu3fvJt2xZs2a22+/nY936tRJZEHNjw++ 9dZbR44coVB89erVHI0zsalTikpu7+QuMpZcv3792t/hafPUp4kjJ0+e9KSk JRNz+8+cOZNbbFxc3IgRI7Zv306RcF5e3ueff86Ti6pUqXLw4EFOnJWVJZzS rVs3/mJ8fDwf0W4bJTkqFRQUsLsrVqxIoZT4sXfWrFm8CX+dOnXEBiDivXLa R+2SReIdWUuVKkX3LPVX1KIo1iKz3HXXXfyVjz76yDWLy2Hul2nTpnHBHnzw wQkTJuzfv59suG3bNvH+zWbNmvHPyLZsKPjiiy/4OhTuGnOn9CKjypUrUzBM mmj2HxHBodEvtr6uWgDjYn8lf784uL6tJi1v5Ai9GT1C+iXcI5XIH6aEyzEk NPqLN1mzU/ifFi1amGztrj7mFqCGwfMJTWjYsKFIT3Ejv9QpEFw0J7ZYr1mz Jt0pXtTHDpbep55WSBIKuammFBVzl0s8++yz3u8h6RbR1inJycn8WwpD/4vG UKZMmQ0bNnAy7VvjjVAHqJ0CsXXrViGZSwfh/4WoiVmdUlJ7J3eRsSRv3RCO fv36eVLSkom5/Wkc532fBGJTYm7S2qUN4pVJISEnaq8sOSolJSWJHGvVqkXh Nz/S5ZKsW7dOpBT7DWrfLSVZJLJAtWrVxHHqxMTm7YHg9qqRxDPOMPcLhZ0t W7bU1kVbYOrY09PTRWJ5GwrE+y9C/uDcs2dPE6sypIM4sdEvtr5epFgA42J/ Zet+sXt9u01a0siRezNKhPNLyEcq/DAlwidxtp6sUTDw1FNPiduQAnjqV4t7 GGCpU/i3LBN078tLS0tr2rSpiCEJMprlpm2+IOP9CxcuDBs2TOysyFx11VVD hgzRvle92CFT97i4uEAw1tUdX7lyJdtBq1OMiel++de//qUzXaNGjVasWCHS CDUxatQoMek0EBzUOnbsaNyGdObMmWJPfuL6668fNGiQWM6p1SlTpkzhNGvX rtVdhDMqSTqlqIT2Tu4S+bjft29fT0paMpGx/4wZM8QKWcFDDz0kftlgzOOu ihUr6i4rOSrt3Lnz6aef1l2tdevWule3fPPNN3yKOh8HRTp06NA777zDe/EJ Kleu/OWXX9p9oaQryPxe9+OPP959993aAlMs+sYbbxw9elSXWNKGAiFOQ246 LbarNUG8btjoF1tfZ9QJYFzsr+zeL3avb7dJyxjZFW9Gg3B+MT5SEQ9TIowV bekUhu6m5OTkNWvWqPbWQmc4sIAM+fn5FM3SlbVvhlIN+bpfunQpPT19+fLl S5cu3bdvXwmY8B8lv4fk4MGDy4JkZmbqnkAJncLiJS8vj7TGunXrTG4uugEp aCFfRDLhsxhhy1MlrHdyFy/bPDAib//s7Gxqw/PmzUtKSjJGwo6RHJUo2caN GxctWkR/jYtlmO3bt2/bti2SwlDokpqaSt3dkiVLdu/e7ctMe0beL9SBr1q1 igbBTZs2mfcwMjaMBpH7hVEhgCl2/ZXdJm1pZLe86S4mfsnIyNA+UnHlYYp5 jjFCLFsAdfe7FBbvTwFFyniqBABL+gvsrybwi5rAL2pi4hdSauKRilsPU8xz jBFi2QKou9+lgE6xRhFPlQBgSX+B/dUEflET+EVNzP0iHqlkZWW58jDFMsdY IJYtgLr7XQroFGsU8VQJAJb0F9hfTeAXNYFf1MTcL/xI5b9BXHmYYpljLBDL FkDd/S4FdIo1iniqBABL+gvsrybwi5rAL2pi6Rd+pOLWwxSZHEs8sWwB1N3v Uvz/rcMeCRJuNxigiKdKALCkv8D+agK/qAn8oiaWfuFHKps2bXJrS1i0hFi2 AOrudymANfCUW8CS/gL7qwn8oibwi5rI+CUjI0O8GdybHEs2sWwB1N3vUgBr 4Cm3gCX9BfZXE/hFTeAXNfHeL2gJsWwB1N3vUgBr4Cm3gCX9BfZXE/hFTeAX NYFO8Z5YtgDq7ncpgDXwlFvAkv4C+6sJ/KIm8IuaQKd4TyxbAHX3uxTAGnjK LWBJf4H91QR+URP4RU2gU7wnli2AuvtdCmANPOUWsKS/wP5qAr+oCfyiJtAp 3hPLFkDd/S4FsAaecgtY0l9gfzWBX9QEflET6BTviWULoO5+lwJYA0+5BSzp L7C/msAvagK/qAl0ivfEsgVQd79LAayBp9wClvQX2F9N4Bc1gV/UBDrFe2LZ Aqi736UA1sBTbgFL+gvsrybwi5rAL2ril04BAAAAAAAAAHOgUwAAAAAAAACq 4b1OKQQAAAAAAACAMECnAAAAAAAAAFQDOgUAAAAAAACgGtApAAAAAAAAANWA TgEAAAAAAACoBnQKAAAAAAAAQDWgUwAAAAAAAACqAZ0CAAAAAAAAUA3oFAAA AAAAAIBqQKcAAAAAAAAAVAM6BQAAAAAAAKAa0CkAAAAAAAAA1YBOAQAAAAAA AKgGdAoAAAAAAABANaBTgArs379/0qRJI0aMmDhxYn5+vt/FAQAAAAAAPqO+ Tjl16lR2GFauXJmWlhby1OHDh6NntJSUlOPHj0fv+j6SmZmZkZEhmfjEiRP7 /gjJDWf5Dho0qFevXgMGDBg2bNi5c+dCpsnLy8vKyjp27JjuOOma3bt3k1N+ /fVXai3GL54+fTorSLgrS7J3715bft+5cydlHUmO/uYLAAAAAOAj6usUUiLf 2GfixIlRshiFxHz9s2fPRikL7ykoKEhMTBwxYgSJhYEDB0p+67///W+vP0KW cZA7KQj67rhx40LqCFJDmzdvJoNzFqNHj9aeTU1N/fbbb0UBSOlQVC/Opqen U7369evHZ3ft2uWgeMzWrVspI3m/czsZM2bMmTNnHGfqY74AAAAAAP6ivk7J yMgYZWDkyJEUvLEkGTx4sDFBQkJCNMx1+PDhQYMGcb6LFy+ORha+cPz4capR fHy8LZ2yZs0aSv/zzz+v+53k5GQHuW/atImus2zZspBnly9frpVC5FxxKi8v j8tMfPnll/xP//79RYiuk1E7d+50UDzm2LFjw4YNk/S7aCcrV650nKO/+QIA AAAA+Iv6OsXIqVOnfvjhBwrGxo8f37dvX/pn8+bNbhnEr3z37t2bkpJy7ty5 HTt2zJ8/PzU1lQ7Sx+3bty9ZsmTRokVbtmwRv6hTSf773/9qnw7Q/xs3bjxx 4gR/JN1BH9PS0uj/I0eOUNRKUS79tZzT9fXXX8vrlKVLl1Lwz7lYEq4umZmZ pHToOlOmTKEyZ2dn6764atUq0qR9+vQx6hQWOMS8efPogrNnz+aPoprfBund u3fkOqUwuIiG/U7lN0km2sn06dMjnGnmb74AAAAAAD5SHHXKrFmzKBj7/vvv CwoKKFyn//v16ye/qkLNfOniFEiTQuGIeuHChZTLTz/9pHuUcPToUUp88uRJ ir0HDBggvk7/UwKK8/kj/UMfKcI/ePAgSQ/tRcwfedjSKQkJCXTB3Nxcy5Qm dWGRItiwYUPIK5AQM+oU0ol8kB+fUbvijzqxM3r0aFd0SmFwqpul37XtJMLs fM8XAAAAAMAvip1OWbNmDQVjFEvn5OTwkQULFtCR7777Lqpr26OdL+sUYsmS JSQuKIYnqUIfx4wZQznm5eVNmTKFPk6dOpXTc+zNATmv7yCmT5/OZ2fOnMmP FVhKJCYmnj59mq4zd+5c4zp0LbZ0CmVHFycBQkYYO3YsuTXkMnbCpC5HjhxZ sWIFPxOhiotHQjpC6hSyvJjuNWPGjL59+9I/w4cP133XRZ1SaOV3YztxC7/y BQAAAADwheKlU3799VdeG6INOM+cOTN+/Piorm33IF/WKUKGkKygCDw+Pl7I CjrSv39/SsNbmfGqjfXr19P/a9eu5QC+X79+POGH4lUK2ul/ngqVlJQkWQxb OmXy5MmsC4QQILVinHFkWReevkVqxSSvkDqlUPNIRWAM1N3VKSZ+D9lO3MKv fAEAAAAAfKEY6RSTNcK21hqrmS/rFLHWIzMzkz5SXKpNM2fOHDq4ffv2wuBO VkLXUOA6dOjQ1atX8zOU3Nxc8Wxl7969/MThhx9+2Lhxo+V2tUadsn///sWh oIzy8/PF5lqHDh3ifbd27Nihu6ZlXRzrlJMnT+qmkxHff/89Hdd+112dUhjG 7x6sYfcrXwAAAAAA7ykuOsVyjbDkWmNl82WdQuqDP3JYPm3aNG2aRYsW9fp9 gQkVhnQBZX3mzJk+ffr8/PPPBw4c6BVck0IJemnWoZCgmDx5Mq8lp4DWuFBd i1GnkJT4v1Ds3r1b912eY2aMli3r4liniAUpVEfSIEOGDOGP/JhJ4LpOKTT4 3bM17H7lCwAAAADgMcVFp8isEY7GmnrP8tXplMOHD9PHESNGaNNMmjSJDopH GBT500d+iUlqaiorl/Hjx/Ol8vLytN89cuTIjBkz6PiECRNMimFr3pcO3qbY KDcs6+JYp4wcObJX8KUtHKLv2bOH0+jenhMNnVL4R797uYbdr3wBAAAAALyk WOgU+TXC7q5t9zJfnU4pDK4xoSPisUVWVlbv3r1JR+Tn5/MR3tRr2LBh9JeX n0+dOpUSDB48eOjQoZwmMzNTFOnkyZOUUpwKibxOIZvMnz9fLJw/c+YMvyYy pBYwr4u5Tlm/fv3ixYvFex4HDBjAE8/o1NixY/ngypUrSQ2JrcNmzpxJZ3Nz czkluYaPk7Kjj8YnQY5hv/fv3z96a9jJwtkGWJ6QVOHcyea6BLzwBwAAAACg +KK+TrG1RtjFte0e52vUKVu3bqUj8fHxFFovWbKEV3+QdBIJjhw5wuG3eFSx YcMGPsL79JIKoFCWvrh06dLk5GReykEZGXOnswlBeHoY/3/gwAGTAvMWykOG DKFYfdWqVSxSRo8eHbL65nUx1ykkrHqFgk6lpKQYj1MV9uzZUxhqiT2TmJho 6gobCL9Hbw17WlraN/bRPVECAAAAACh2qK9TsrOzR44cKb9GmNcaUxYRTtf3 ON+5c+f20rygkKFIm/faJfr06ZOUlKS7+HfffUenFi1axB95hhWxbds2cQWx aqNXcA/hkE98eAqZDhIXJgWmEH3ZsmWsOMTTCpNNj03qsmXLFmc6pTAozXjr MIZc8Msvv4gco61TCn/3e/TWsFOTGBWK77//noxPzg15loUqAAAAAEDxRX2d Uhh8S6Ct4F/MjIoQv/LVQgXIDRKJ7CJtcvDgwWi8X+bs2bM5OTl0cZm6u1KX kJfNy8vLzMw0fzVM9Dh06JAva9j9yhcAAAAAwAOKhU4BAAAAAAAAxBTQKQAA AAAAAADVgE4BAAAAAAAAqAZ0CgAAAAAAAEA1oFMAAAAAAAAAqgGdAgAAAAAA AFAN6BQAAAAAAACAakCnAAAAAAAAAFQDOgUAAAAAAACgGtApAAAAAAAAANWA TgEAAAAAAACohl86BQAAAAAAAADMgU4BAAAAAAAAqIaXOgUAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAANX4f2chfXU= "], {{0, 204.}, {540., 0}}, {0, 255}, ColorFunction->RGBColor, ImageResolution->144.], BoxForm`ImageTag["Byte", ColorSpace -> "RGB", Interleaving -> True], Selectable->False], DefaultBaseStyle->"ImageGraphics", ImageSizeRaw->{540., 204.}, PlotRange->{{0, 540.}, {0, 204.}}]], "Output", TaggingRules->{}, CellChangeTimes->{{3.8343190142242823`*^9, 3.834319068882141*^9}, 3.834319195856484*^9, {3.834319230942265*^9, 3.8343192584775877`*^9}, 3.8343192893774757`*^9, 3.834319365047743*^9, {3.8343195035716877`*^9, 3.83431960052633*^9}, 3.834319976593903*^9, 3.834320011439209*^9, 3.834333530494079*^9, 3.8343335739434032`*^9, 3.8343348438197203`*^9, 3.834405462515171*^9, 3.834405624344139*^9}, CellLabel->"Out[5]=", CellID->891061898] }, Open ]], Cell["\<\ Join the treated persons with the non-anomalous members of the control group:\ \ \>", "Text", TaggingRules->{}, CellChangeTimes->{{3.834319302353422*^9, 3.834319314282258*^9}, { 3.834336109888043*^9, 3.834336124378241*^9}}, CellID->65353880], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"matched", "=", RowBox[{ RowBox[{"Join", "[", RowBox[{ RowBox[{ RowBox[{"Query", "[", RowBox[{"Select", "[", RowBox[{ RowBox[{"#treat", "==", "1"}], "&"}], "]"}], "]"}], "[", "lalonde", "]"}], ",", "nonanomalousControl"}], "]"}], "//", RowBox[{ InterpretationBox[ TagBox[ DynamicModuleBox[{Typeset`open = False}, FrameBox[ PaneSelectorBox[{False->GridBox[{ { PaneBox[GridBox[{ { StyleBox[ StyleBox[ AdjustmentBox["\<\"[\[FilledSmallSquare]]\"\>", BoxBaselineShift->-0.25, BoxMargins->{{0, 0}, {-1, -1}}], "ResourceFunctionIcon", FontColor->RGBColor[ 0.8745098039215686, 0.2784313725490196, 0.03137254901960784]], ShowStringCharacters->False, FontFamily->"Source Sans Pro Black", FontSize->0.6538461538461539 Inherited, FontWeight->"Heavy", PrivateFontOptions->{"OperatorSubstitution"->False}], StyleBox[ RowBox[{ StyleBox["FormatDataset", "ResourceFunctionLabel"], " "}], ShowAutoStyles->False, ShowStringCharacters->False, FontSize->Rational[12, 13] Inherited, FontColor->GrayLevel[0.1]]} }, GridBoxSpacings->{"Columns" -> {{0.25}}}], Alignment->Left, BaseStyle->{LineSpacing -> {0, 0}, LineBreakWithin -> False}, BaselinePosition->Baseline, FrameMargins->{{3, 0}, {0, 0}}], ItemBox[ PaneBox[ TogglerBox[Dynamic[Typeset`open], {True-> DynamicBox[FEPrivate`FrontEndResource[ "FEBitmaps", "IconizeCloser"], ImageSizeCache->{11., {2., 9.}}], False-> DynamicBox[FEPrivate`FrontEndResource[ "FEBitmaps", "IconizeOpener"], ImageSizeCache->{11., {2., 9.}}]}, Appearance->None, BaselinePosition->Baseline, ContentPadding->False, FrameMargins->0], Alignment->Left, BaselinePosition->Baseline, FrameMargins->{{1, 1}, {0, 0}}], Frame->{{ RGBColor[ 0.8313725490196079, 0.8470588235294118, 0.8509803921568627, 0.5], False}, {False, False}}]} }, BaselinePosition->{1, 1}, GridBoxAlignment->{"Columns" -> {{Left}}, "Rows" -> {{Baseline}}}, GridBoxItemSize->{ "Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}, GridBoxSpacings->{"Columns" -> {{0}}, "Rows" -> {{0}}}], True-> GridBox[{ {GridBox[{ { PaneBox[GridBox[{ { StyleBox[ StyleBox[ AdjustmentBox["\<\"[\[FilledSmallSquare]]\"\>", BoxBaselineShift->-0.25, BoxMargins->{{0, 0}, {-1, -1}}], "ResourceFunctionIcon", FontColor->RGBColor[ 0.8745098039215686, 0.2784313725490196, 0.03137254901960784]], ShowStringCharacters->False, FontFamily->"Source Sans Pro Black", FontSize->0.6538461538461539 Inherited, FontWeight->"Heavy", PrivateFontOptions->{"OperatorSubstitution"->False}], StyleBox[ RowBox[{ StyleBox["FormatDataset", "ResourceFunctionLabel"], " "}], ShowAutoStyles->False, ShowStringCharacters->False, FontSize->Rational[12, 13] Inherited, FontColor->GrayLevel[0.1]]} }, GridBoxSpacings->{"Columns" -> {{0.25}}}], Alignment->Left, BaseStyle->{LineSpacing -> {0, 0}, LineBreakWithin -> False}, BaselinePosition->Baseline, FrameMargins->{{3, 0}, {0, 0}}], ItemBox[ PaneBox[ TogglerBox[Dynamic[Typeset`open], {True-> DynamicBox[FEPrivate`FrontEndResource[ "FEBitmaps", "IconizeCloser"]], False-> DynamicBox[FEPrivate`FrontEndResource[ "FEBitmaps", "IconizeOpener"]]}, Appearance->None, BaselinePosition->Baseline, ContentPadding->False, FrameMargins->0], Alignment->Left, BaselinePosition->Baseline, FrameMargins->{{1, 1}, {0, 0}}], Frame->{{ RGBColor[ 0.8313725490196079, 0.8470588235294118, 0.8509803921568627, 0.5], False}, {False, False}}]} }, BaselinePosition->{1, 1}, GridBoxAlignment->{"Columns" -> {{Left}}, "Rows" -> {{Baseline}}}, GridBoxItemSize->{ "Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}, GridBoxSpacings->{"Columns" -> {{0}}, "Rows" -> {{0}}}]}, { StyleBox[ PaneBox[GridBox[{ { RowBox[{ TagBox["\<\"Version (latest): \"\>", "IconizedLabel"], " ", TagBox["\<\"1.0.0\"\>", "IconizedItem"]}]}, { TagBox[ TemplateBox[{ "\"Documentation \[RightGuillemet]\"", "https://resources.wolframcloud.com/FunctionRepository/\ resources/FormatDataset"}, "HyperlinkURL"], "IconizedItem"]} }, DefaultBaseStyle->"Column", GridBoxAlignment->{"Columns" -> {{Left}}}, GridBoxItemSize->{ "Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}], Alignment->Left, BaselinePosition->Baseline, FrameMargins->{{5, 4}, {0, 4}}], "DialogStyle", FontFamily->"Roboto", FontSize->11]} }, BaselinePosition->{1, 1}, GridBoxAlignment->{"Columns" -> {{Left}}, "Rows" -> {{Baseline}}}, GridBoxDividers->{"Columns" -> {{None}}, "Rows" -> {False, { GrayLevel[0.8]}, False}}, GridBoxItemSize->{ "Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}]}, Dynamic[ Typeset`open], BaselinePosition->Baseline, ImageSize->Automatic], Background->RGBColor[ 0.9686274509803922, 0.9764705882352941, 0.984313725490196], BaselinePosition->Baseline, DefaultBaseStyle->{}, FrameMargins->{{0, 0}, {1, 0}}, FrameStyle->RGBColor[ 0.8313725490196079, 0.8470588235294118, 0.8509803921568627], RoundingRadius->4]], {"FunctionResourceBox", RGBColor[0.8745098039215686, 0.2784313725490196, 0.03137254901960784], "FormatDataset"}, TagBoxNote->"FunctionResourceBox"], ResourceFunction[ ResourceObject[ Association[ "Name" -> "FormatDataset", "ShortName" -> "FormatDataset", "UUID" -> "76670bca-1587-4e7e-9e89-5b698a30759d", "ResourceType" -> "Function", "Version" -> "1.0.0", "Description" -> "Format a dataset using a given set of option values", "RepositoryLocation" -> URL["https://www.wolframcloud.com/obj/resourcesystem/api/1.0"], "SymbolName" -> "FunctionRepository`$66a3086203b4405b88cdb0de8a5c3128`FormatDataset", "FunctionLocation" -> CloudObject[ "https://www.wolframcloud.com/obj/70389ad6-7dbc-48c8-b898-\ 72c65c00f14e"]], ResourceSystemBase -> Automatic]], Selectable->False], "[", RowBox[{"MaxItems", "->", "5"}], "]"}]}]}]], "Input", TaggingRules->{}, CellChangeTimes->{{3.8343193179298162`*^9, 3.83431934433959*^9}, { 3.834319374915636*^9, 3.834319398899755*^9}, {3.834319439246731*^9, 3.834319446927463*^9}, {3.8343196124541283`*^9, 3.83431964902608*^9}, { 3.8343196976905193`*^9, 3.834319697873932*^9}, {3.83431976099284*^9, 3.83431981221021*^9}, {3.8343200244851837`*^9, 3.83432003380547*^9}, { 3.834333542507495*^9, 3.8343335497003098`*^9}}, CellLabel->"In[6]:=", CellID->833444543], Cell[BoxData[ GraphicsBox[ TagBox[RasterBox[CompressedData[" 1:eJzsvXd8FNX6x7/0XkXgSpciXYoIKAqidPECCoqCXlARpCqCSBc1oCDSEbkC ShEhgDQpIfww9HAhdIK0kJCEEGpAmiC/57vPz/MbZzczs7uzM2czn/cfvMjM 2ZnnPJ+Zc85nyply3ft36JHZ5XINzEn/dOj28XMfftjtk5cL0h8d+w3s9V6/ d99p1e+jd99798MG3bPQwm8zuVwNsrpc//f/BwAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAEjb/cmLW1/wcAAAAAAAAADKDtLM6ePRsfHw+fAgAA AAAAALASDVtx586dvW7u3r1rok9JczBOzgDqbncUQB8oZRbIpL0g/3ICXeQE usiJrk85e/bs/9yYdUsFR4KTM4C62x0F0AdKmQUyaS/Iv5xAFzmBLnKi7VPE zRSG/oRPCRwnZwB1tzsKoA+UMgtk0l6QfzmBLnICXeRE26fwzZRkN/Qf+hM+ JXCcnAHU3e4ogD5QyiyQSXtB/uUEusgJdJETDZ/CN1P2799///79e/fu0X9M uaWCI8HJGUDd7Y4C6AOlzAKZtBfkX06gi5xAFznR8CniZgr/adYtFRwJTs4A 6m53FEAfKGUWyKS9IP9yAl3kBLrISXo+RXkzhZeYdUsFR4KTM4C62x0F0AdK mQUyaS/Iv5xAFzmBLnKSnk/hmynnz59XLjTllgqOBCdnAHW3OwqgD5QyC2TS XpB/OYEucgJd5MSrT/G8mcLQn4HfUsGR4OQMoO52RwH0gVJmgUzaC/IvJ9BF TqCLnHj1KXFxcZ43UxhaGOAtFRwJTs4A6m53FEAfKGUWyKS9IP9yAl3kBLrI iadPSe9mChP4LRUcCU7OAOpudxRAHyhlFsikvSD/cgJd5AS6yImnT9G4mcLw LRUqBp/iH07OAOpudxRAHyhlFsikvSD/cgJd5AS6yInKp9y+fVvjZgoT4C0V HAlOzgDqbncUQB8oZRbIpL0g/3ICXeQEusiJyqfo3kxhArmlgiPByRlA3e2O AugDpcwCmbQX5F9OoIucQBc5UfoUvplCBuTUqVNnNKECVIwK00/gU3zFyRlA 3e2OAugDpcwCmbQX5F9OoIucQBc5UfqUlJSU//kI/QQ+xVecnAHU3e4ogD5Q yiyQSXtB/uUEusgJdJET1XNfFoAjwckZQN3tjgLoA6XMApm0F+RfTqCLnEAX OYFPsR4nZ8Caum/dunX16tUbNmwI9o58wsm6hxZQyghGzjI5MxkfH7/aTWxs rN2xBBc58w+gi5xAFzmBT7EeJ2fAmro/88wzLpfr4YcfDvaOfMLJuocWGUmp AwcOvPPOOz179jx27Ji5WzZylsmZyXXr1rncfPPNN3bHElzkzD+ALnICXeQE PsV6nJwB+BS7owD6ZCSl2rRpw2Py//znP+ZuGT5FfqzJ/5kzZ7bpceHChfR+ fvToUS5z9uzZYIcqCXLqsmvXrvSKXbp0KdjRyoCcujCkzsyZMz/++OPJkydv 3Ljx2rVrwY5THmT2KaTms24OHjwY5DRYGoycfbc1wKfYHQXQJyMp1bVrVx6T v/vuu+ZuGT5FfqzJ/5dffunSY+rUqV5/e+rUqcKFC3OZ2bNnBztUSZBTlxw5 cqRX7Mcffwx2tDIgpy40+OzQoYOqQIMGDf73v/8FO1RJkNmnkH9kReg/wc6D lcHI2XdbA3yK3VEAfTKSUqdPn/7ss8/CwsJMv1gNnyI/8oy75s2b5/W3L730 kigDn2IuPuly4cIFP+TLYEioy9WrVxs3bswLCxYs+NprrzVp0oT/LF++fEpK SrCjlQGZfcqvv/7KcsjgU0wMRs6+2xrgU+yOAugDpYwAnyI/1uQ/MTHxsDe2 b9+eM2dOynP9+vVpuOX5w7lz5yrHZvAp5uKTLsePH2cVxowZ4/mT8+fPBzta GZBQl59++ol16d+/f2pqKi/87rvveOEXX3wR7GhlQGafsmDBAoPWICEhYf36 9WfOnPFcdezYMeqVTp8+rbu7gwcPkhn5/fffAwxGF1/PBe3ABJSEiIiIjRs3 xsXF6W7TeFrMxde6G4wzOTmZKh4TE8MPbfIFB7N8Cm1z//79dIDFx8cHsh0/ 2sD0Dmyqb2Rk5LZt20TDlR4Gg6ftbN68eceOHZcvX/YpwgyJH0pdunSJzr4T J054rtq5c+fWrVt1lTJ4mjMaLZ7uWhXGpffjLPOv34+OjqaQaHfaxahZoMbh yJEjRrZJYVPwSUlJ9H/6j65PMdLy0Fhi3759a9asoYANPi6enjTGVfDpVDWx zfGjiXj77bcpybly5aLke649efIkP/H1xBNPwKfoEmxddu/ezSr8/PPPPgWW kZBQlw8++IAW5s6dW7Wdhg0b0vJXXnnFp2hDFDl9ypQpUx599NF8+fLxiUP/ Keimbt26XKBmzZr058yZM48fP/7iiy9mz56dihUpUkRsgXqNsLCw4sWLi2s1 JUqUmDt3rmpHVOyHH3545plnChQoIErSr1asWGE8GP9yrl3GSGCCLVu2kAEX xTJlylRQQbt27XxNS/Aw2A4Yj5MGgfXq1cucOTMXo/qOHDmyZcuWqhHU+fPn aWu0tm/fvqotDB8+nBNF/aZq1alTp6gdENITFStWnD9/vu/1/j+M1F37wI6N je3RowfFQBJzPFmyZOnUqZPXEZ3B4A8cOEADzmzZsnEZ2iPt1+AQN6NiXKnp 06fTYdOhQ4e8efNyAqtXr75w4UIqQN3KRx99VKpUKaHUsGHDVKNZ46e59oGh sZbOGj68PScQNi69wbMswExSj//mm28WLVpUtGPdunW7ePGi508mTJhADYLI GA13+/fv77XklStXBg0a9NBDD4ltNmjQQFyN9PQpBlueCxcukJp58uQRxWh0 oWx1f/vtN2XtNLoq4yr4caoG3ub4t19i06ZN3EyNGzfOawHaCK2lza5cuZK3 DJ+ixGJdaLDN25HwBqhlSKgLmxdq4lTlu3Tp4nK/peJD9UIWOX0KdQEub9BA iwuULVuW/hw8eHCdOnXE2hYtWvDalJSUZs2aieV8K40ZMmSI2Mu5c+doUOF1 R3TArFq1ymAw/uVco4DBwBhqXsT4gUJSdt8MpcKntAQVI+obj3PevHnKtSqU I6j4+HheSGe9ancDBw7kVapL2dR7ihc8VXz//fdBqrvGgT158mRuEj156qmn VNsxGPzixYuFkcmaNSuNpfn/jzzyyNGjR/2oY8bAuFLUAQknIsifP/+ePXua N2/umfyvv/5abMGn01y7xdNYO2vWLP5zzZo1yg0al974WRZIJl966aXy5ct7 br9fv37KwtQ4vPDCC14jqVatmmru5cTExEaNGqUXucvDpxhsecjLPP/887yc DCbt1/MF5MjISCPCGVfBv1M1wDbH7/0StWrVomL0r9c7TdQK8XaGDx8eExPD /4dPUWKxLuL5IoM3KDMkEupCG+QtrF27Vrm8SpUqtJDcil8VDTHk9CkHDx6k gZaYqYYM5ko34pkrPlSYGjVq0BBuy5Yt27Zt47V0CPGqDh06cM9FG+SnqenI UU7YxS8ovfzyyz///POJEydopCpGrU8//bTBYPzLuXYZI4Glua/ZlixZ0uW+ y0OGhRdS58slBw0a9L///U/03cbTEjyM1N1gnCdPnhQXsf/zn//s2LHj+PHj P/74Y5kyZXhhID7l7NmzRYoU4eUdO3b87bffaMyzevXqmjVrUuuk+wyP33XX OLD5ehc1d19++WVUVBQ/hFOuXDnPRsxg8GfOnClUqBCVeeihhyhvFy9epFXT pk3j4Rkd8H7UMWPgq1Lt2rUjW7F7927lS8EEJXzOnDl79+795JNPeAn9SrkR g6d5ml6Lp7HWq08xLr1PZ1ngmWzZsuWCBQto4ErNLHfxZPqUz8aL/FSqVIly npCQsHnzZvFiaatWrZRb7t+/Py8vXrw4bfbUqVNbt2595513xO5UPsVgy0Pp 5WKdOnXi2KgwDzMKFy5MKaJWV0wrqiGNcRX8PlUDbHP83q+4OP/dd995rhVP fNWtW/fKlSt0gnBh+BQlFusyY8YMXkUDYzrLaABMpwPZSYe8qc1IqAu1JGx8 6JQRbTj15p6tegaGdfmfvwTJpzCjRo1iLTwdgThUnnrqKdWgcd++fXzZuXXr 1srlNK7jtlE5Pyd1iOIGvUD0espH+jWC8RUjGTAYGPWJvGTMmDHKkvzRBBom iSU+pSV46NbdeJzCOQ4YMEBVki81BOJTxMb79OmjLHzp0iUaGvlS4/8fn9pA zwM7zX3JS9VriCfte/Xq5WvwfOM4c+bM1Mwqi9Gx5HI/p+T1bQsn4JNSw4YN Ewtp1Cqe4GrRooXyeTwe7hLKBwCMtz/aB4bGWq8+xbj0Pp1lnviUSRoXKa8l vvrqq7ycWjleQj0OP01B9lyZHDqwRdLoHOGF+/fv58LUbqjus8ycOZMLK32K 8ZaHH1iihUoDtX37dlUAqtp5SmNcBb9P1QDbHL/32759e5d7tOb1kg53TzR4 44lV4VO8YrEuYWFhLm+UKFFCdUhnYCTUJc3tYsTFIjp3qEnnFunll1/2sX6h ivApSb5ju0/JlCmT5wzSfEgQhw8fVq2isZzLwBN9n3/+OW+Buh4jwfiK8Qzo BiYGIevWrVOWHD16tMt9DfDKlSu8JPC0mIJu3Y3HyWdukSJFPN/O8JyJyCef QoMlfvKcNs4v3pqC8TbQ64GdHhwqNV/8p8HgqVju3Lmp2BtvvKFaJXKlevTI ORhXqn79+qrl4qkk1esSI0aM8GxVvOK1/dE+MDTWevoUn6T36SzzJJBMTp8+ nYNZunQpL/n00095ybRp01SFN2/ezKs6derES0QaVddw0tKZ78t4y8MBU/VV xfjpr6FDh3rWzlMa4yoEcqoG0ub4vd/jx49T10NrqWn1XEtmhH8rnsOHT/GK xboIn9KwYcP+/fv369evZs2avIT8uwxzrlqAhLqkua/DkCVx/ZNatWo5ZBK2 tBD3KZ5dG/Haa6+53I8Nb/KA3W6xYsU8f7V79+7JkyfTAUbqC+u6fPlyI8H4 ik8+RTswMVsyv7oroEbG9c93r/xLi+no1t1gnEePHuWKv/XWW54bCdCnHDx4 kJd079490AorCGTMpiQ1NXXx4sXDhg1r165dpUqVONR69erxWoPBHzhwgIvR oeKZal5FB56vdcwYGFfK882g119/nbMnLhEwU6ZM4eV0znpuTbf90T4wNNZ6 +hTj0vt6lnkSSCbJnvDexRtV3DgQ586d89wOPwFbo0YN/lPcCaJhgKqkV59i vIXkd16qVaum3CZ1iLzNr776yrN2ntIYVyGQUzWQNsfv/Y4fP57X0lGtWnXi xAl+MIZyKO6dwad4xUpdGDrRVq5cKf68evXqoEGD+Ceep2eGREJdqGGh5NNa 6hd69OihnGbk448/9rumoUVI+xSv5w4/J6xBlixZlOXp3EzvJ+Hh4UaC8RWD GTASGA1Z+SJe48aNxQ8TEhL4YH7uuef8TkuQ0K27wThpCMd/el4sTQvYp4g3 18aOHRtohRUEMmZjYmNje/XqxR29CjH7nMHgxVuTGowYMcK/moY6gSj15ptv cvZUPkU8a6TyKQbbH+0DQ2Otp08xLr2vZ5kngWTyl19+4b0Ln/L444/Tn3T8 e90O24fs2bNz5sm5u9zTcHmW9OpTjLeQ4m0j5V1sca9n48aNRmpnXIVATtVA 8u/3fvnb2TSs8vxmSuvWrfmHERERv//Nhg0beCEN2OjPxMRE7YAzALLpkh5U kow//YqGGaoGLUMioS6vvPKKy93ocWAXL16k00RMYDhy5Eh/6xpKZDyf8uST T/Jp9VQ6iPmv0hT3Oql3o+Nhzpw5VCkxorDRpxgPbMiQIbywefPmCxYsmDt3 7mOPPeZy221lN+pTWoKHbt0NxinahC+//NJzI88++yytIrMmlvjkU+bPn89L JkyYEGiFFQToU06dOiUmRKpYsSIdkGvXrj179iy/Si98isHgxfeAaFPppdoh 3yD2xDKfYvw0N9GnGJfe17PME3N9CjcOyilAlfCkBFmzZuUPDfBTKwULFvQs Kd5anTRpklhovIU8fvx4sWLFXO53/MeOHbtkyZJ+/frxpIvPPfecarqe9Gpn XIVATtVA8u/3fv/1r3+5vD0XFx0d7TKA9t3kjIFUumjTt29f3p3xR5FDF9l0 2blzJ29TddXx8OHDPM9kzpw5Pb+nkPEQPiUU36P3PFTS/janBQoU0P3wlniu gHoo5fe8xOz6dvkUnwJLTk72vMCeJ08e1R1G42kJKrp1NxineGu1f//+nms1 7qd4PsHi6VNEf/r+++8brZgBAvQp/DW0LFmy0DhWuVzlUwwGTycvF6Ohso/1 yPhY41N8Os1N9CnGpff1LPPEXJ8iHtL2+sEg3k7lypX5T35Zm/B8itvr/RSf WkjRHSihKni+J5te7YyrEMipGkj+/dvvoUOH0jtmxAa1qV27tvHdhShS6aKN ePRL98W6DIBsukyaNInXek4W/e233/IqJ8xyIHyKH44j2D6FXwYnVJMnpGn2 y+JzJ6r77568++67XPLUqVPK5V7HCRrB+IpuBnwKjGfFadSo0fDhw6kf79q1 K1lvz0eyjaclqOjW3WCcCQkJXKxWrVqeaz1HUJcuXeKPKIn3zQU0nudNCZ9C hXlOVDrM6P9G66ZHIG3gmTNnOEjPadlUPsVg8JcvX+bZkJo0aeJ7VTI41vgU n05zE32Kcel9Pcs8MdeniLkIPD+8KL6mTXaDl4jz2vP72l59ivEWMjo6mhJI Zxl1Ct26dWvXrt2HH36Y3le806udcRUCOVUDyb9/+xXPnXqdkTg+Pj7OA/H0 Po3K6E+vLx9lMGTTRQP+DhTt0cSuUFpk02XkyJEu9wRiYp5zAQ1ExVljfHch isw+RUxTP3HiRNUqjX45KiqKR6TVq1fXPrN4ekkqrLwIdvHixc6dO3uOEzSC 8RXdDBgPLCUlJVeuXC7FlDjpYTwtQUW37sbj5GlRCdXHu3/77TeetU81guJ3 dooVK8aPhTA//PCD+BKTcl5i8bm3L774QrXfPXv26FbTK4G0gWICatVza7RB fu1a+BTjwYsLzo59vis9rPEpPrU/JvqUNF+k9/UsU2GuT9m2bRs3DhSVcoLu a9euiRqJd7HFA5ANGjRQakH/f+utt3iV0qcYb3n4/ZQ6depo10u7dmm+qOD3 qRrgPVw/9is6StUBowHeo/eKlbps3bq1WrVq4ntMgrVr1/KvvF6pyHjIpoto A2fMmKFaRZ07r4qIiDC4r9BFZp8iNKIxGJ1B1BmJJyS1e+333nuPf/j444/T vvjtpOPHjw8ZMoR+IvqsDz74gIt17979zJkz1Ddt2LChdu3arr9RjhM0gvEv 5xoFjAeWmJjIz0W3bt368OHDNAK/5iaQtAQVI+objFOMQ3Lnzk0dXEJCQmxs 7Ndff83GzeUxghITxtJQn0aGO3fupA1y9hilT6Guk6+NuNw3ZCm3tF9qzNu3 b0+DGf/uSQXSBtKojAdR+fLl27x5M0lMCRk1ahTPZ+j6p08xGPyRI0d4HkXa yIgRI/iDFDROXrlyZePGjefMmeNHHTMG1vgUn9ofc32Kcel9PctUmOtT0hSN A9kEchaU5H379onPazZq1EiUpHOEhl68vEWLFuTQU1NT6eBv2rSpyLDqO48G Wx7+fGSWLFmWLl1KLbBGk6tRuzRfVPD7VA1w3OXHfocOHco5JHW09yuAT/GK lbrwO605cuT4+OOP169ff+HChXPnztEQWnxafdmyZX5VNMSQTZekpCR+e4U2 +91334l2ZuHChdwCP/LII/59dTq0kNmnUL9QunRp0aeQLtmzZ+f7X9q9Np1i DRo0ED/MmTOn+PiastejkSp/2MvlvqrJ40CX+wlnz3GCRjD+5VyjgE+BiYvn SqgPLVKkSMeOHZcsWeJrWoKKEfUNxknnLN+S9oRnKFWNoChpniXz5s07YMAA /r/Sp6S5ZwsUt1pc7nuv4v89evQIUt01DmxSU6mvSA5/GVzpU4wHTwMD/lQu 89BDD4mSFSpU8KOOGQNrfIpPp7m5PiXNsPS+nmUqTPcp1Djwi1qe0IlAnkW5 BaoyDxtUiJZc5VMMtjwRERHKE0pA+6pZsyaNT5RvtmoLZ/wE9O9UDbDN8WO/ ZLp5recT9ekBn+IVK3VZsGCBuPLg+md/4fL9lZbQRTZd0tytjbjqWLRo0fr1 64vmi5Zv2rTJ1zqGIjL7lDT3vXgehonTZ+vWrbS8YsWKrn/Ou6vi6tWrn3/+ OT+ZIKADjDom5ffX6PQU81G73D3vuHHjqAAfWspxgkYw/uVcu4zxwBYtWuTS RPlNNINpCR4G1TcYJxUbPHiwslmgpuDnn3+mKnPSVJulHIrRO2XyySefJAXF lBoqn5LmfriFp0IVPPLII99++23w6q5xYMfHx7dr105EQkNcGkDGxMTwhG8q n2I8eBokPPPMM8qOiVq/l19+OTo62r9qZgACUYp9StasWVWTTNIol9OrnO/L +Gmu3eJprJ07dy5v3PMmoEHpfT3LlASSSfEWieoSyuXLl4cOHSq+MsPnAqXd 63y2u3fvFndVXO7rS507d05ISChRogT9OX36dFV5Iy0P/Ud7EuPHHnss6e9P rOp2VcZPQD9O1QDbHD/2y9MREMoH87Q5fPgw/+SHH34w+JNQR0Jdjh492rVr V3EDhSlXrlx6L15lSCTUJc39hn6rVq1UjQzfIzZetZBGcp+S5u6VaCS5ZMkS 6t/5PppP0Nm3cuVK+u2ZM2e8FqDDY/PmzWvWrDFy/SfAYBiDGTASGI09ePww derUXbt2kbkm971hw4YZM2aIb6J5jmDTDKQlSPiqvpE4SZQdO3YsW7YsNjZW d4M0mKGkrVq1isYqBmM4d+7c+vXrKQwj29fA17p7hfp0CoaqYPBur8Hg+WBb vXo1NX3K93eciSlKGcSn9id4AehK79NZJgheJq9du0bjAYpn+/btui/cUetB GaazxvireRotD38E5PHHH6cekGoX4Wbp0qWffvop32NyeVzj0sX4CejTqWpi /tFEmIi0ulC3QicU9Y90vnjOxpPhkVaXNPfQZcuWLcuXL6d/nfCNISXy+5SM h4kZ4G8A/fvf//ZcdfXqVZ4JSvvBDItxsvpOrntoAaXMIuNlUjyk5PXj0WKy UNUTZXaR8fKfMYAucgJd5AQ+xXpMzAA/h9atWzfPVUlJSfwxstatW5uyL1Nw svpOrntoAaXMIuNlUjgR1WzSzOeff85r/Xsk2HQyXv4zBtBFTqCLnMCnWI+J Gfj3v//tcj+ePWPGjLi4OLF827Zt9evX5x5Tqs8AOVl9J9c9tIBSZpHxMik+ 1NKkSZOdO3eKt5BSUlLGjh3Lr7/Vrl3b3s/pCjJe/jMG0EVOoIucwKdYj4kZ 2Lx5s/LFt4oVKzZt2pTf82K++uorU3ZkFk5W38l1Dy2glFlkyEy+/PLLooHN mzdvo0aNnnzyyfz58/OSSpUqnT592u4Y/z8yZP4zANBFTqCLnMCnWI+5Gdi+ fXv79u3FzHWi93zjjTf8/sJL8HCy+k6ue2gBpcwiQ2byypUrn3/++aOPPqps cjNlylSlSpVp06ZZM3GiQTJk/jMA0EVOoIucwKdYTzAykJKSsm/fvnXr1m3e vNniKbx8wsnqO7nuoQWUMosMnMlr166dPHkyKipqzZo11PZKZU8EGTj/IQ10 kRPoIifwKdbj5Ayg7nZHAfSBUmaBTNoL8i8n0EVOoIucwKdYj5MzgLrbHQXQ B0qZBTJpL8i/nEAXOYEucgKfYj1OzgDqbncUQB8oZRbIpL0g/3ICXeQEusgJ fIr1ODkDqLvdUQB9oJRZIJP2gvzLCXSRE+giJ/Ap1uPkDKDudkcB9IFSZoFM 2gvyLyfQRU6gi5zAp1iPkzOAutsdBdAHSpkFMmkvyL+cQBc5gS5yAp9iPU7O AOpudxRAHyhlFsikvSD/cgJd5AS6yAl8ivU4OQOou91RAH2glFkgk/aC/MsJ dJET6CIn8CnW4+QMoO52RwH0gVJmgUzaC/IvJ9BFTqCLnNjlUwAAAAAAAABA G/gUAAAAAAAAgGxY71P8+GGGwckZQN3tjgLoA6XMApm0F+RfTqCLnEAXOYFP sR4nZwB1tzsKoA+UMgtk0l6QfzmBLnICXeQEPsV6nJwB1N3uKIA+UMoskEl7 Qf7lBLrICXSRE/gU63FyBlB3u6MA+kAps0Am7QX5lxPoIifQRU7gU6zHyRlA 3e2OAugDpcwCmbQX5F9OoIucQBc5gU+xHidnAHW3OwqgD5QyC2TSXpB/OYEu cgJd5AQ+xXqcnAHU3e4ogD5QyiyQSXtB/uUEusgJdJET+BTrcXIGUHe7owD6 QCmzQCbtBfmXE+giJ9BFTuBTrMfJGUDd7Y4C6AOlzAKZtBfkX06gi5xAFzmB T7EeJ2cAdbc7CqAPlDILZNJekH85gS5yAl3kBD7FepycAdTd7iiAPlDKLJBJ e0H+5QS6yAl0kRP4FOtxcgZQd7ujAPpAKbNAJu0F+ZcT6CIn0EVO4FOsx8kZ QN3tjgLoA6XMApm0F+RfTqCLnEAXOYFPsR4nZwB1tzsKoA+UMgtk0l6QfzmB LnICXeQEPsV6nJwB1N3uKIA+UMoskEl7Qf7lBLrICXSRE/gU63FyBlB37TI7 duyIjIzcu3evkQ36VNhvkpOTI91cvXo1qDuSBycfpeaCTNoL8i8n0EVOoIuc wKdYj5MzgLprl3n00UddLtezzz5rZIM+FfabH3/80eWGbFFQdyQPTj5KzQWZ tBfkX06gi5xAFzkJFZ+SlJQUExND//qxR9kwnoGEhIQlS5aMGjVq5syZkZGR f/75p3JtampqjB63bt0KSh38xVf1Dep+7969AwcO7N+//6+//goovmACnxIq BOkodSDIpL0Yzz81oXv27Jk4ceLkyZNJgvv37wc5NEcDXeTEuC5Hjx6dO3fu 6NGjly9frtFeXb9+fePGjWPHjp0yZUp0dPSdO3fSK3n79m3qYUnoL7744uef fz5z5ozG3lNSUsLDw2nvK1asuHTpkpGAff35oUOH0htV3r1719c9BojkPoVU XrduXZcuXbJmzUojpSZNmvixR9kwkoG9e/dWr17d9U8qVKiwevVqUYYaLpce s2fPDm5lfMSg+sZ1j42NnTRpUvny5bm+W7ZsMTNcU4FPCRVMP0odCzJpLwbz f+zYsRIlSig7jooVK8bHxwc/QIcCXeTEiC5paWndunVTDbR69+6tuoxMbNiw 4ZFHHlEWIzXJdaqK0bB/yJAh2bNnV5bMkiVL9+7dr1275hnAwIEDlSUzZcoU FhZmvI4Gf54zZ870RpVLly41vjtTkNmnkEvlbkvwzDPP+LFH2dDNwNSpU7Nl y8ZVLliwYO3atTNnzsx/0sG8a9cuLmbEp5Axt6JKhjGivnHdqXFQ1TcyMtL8 oE0CPiVUMPcodTLIpL0Yyf/Ro0eLFSvGma9cuXKFChX4/2XLlj179qwlYToO 6CInurr89ddfjRs3ZiHKlCnTokWL4sWL859NmzZV3migoQhZAB6zUR9dr149 buVy5cq1Zs0aUYxavzp16vAWqHzVqlWF6ES7du1Uj4j07duXV+XJk6dBgwa0 Nf5zzJgxRipo8Oe3bt2SalQps09JTEws+Dc8UM8Y/ZduBubNm0eVLVmyZERE BN/nTU5OHjRoEB8kzZs352Lk6+O8sX//fj78nn76adluExtR37judNJxsdy5 c8OnBAn4FK9k1NbJXJBJezGS//bt2/MYadGiRbxk2rRpfMr36NEj6CE6Eugi J7q6zJkzhyV47733+AYK/duzZ09eSGu52O3btytWrEhLHn74YTHRzZ49e9jU UMd97949XkijuBo1atDCXr16Xbx4kZbQmI1iKFeunGe3e+DAAV5Yt27d69ev 05JLly5VqlTJ5b7/kpCQoF074z+nNplLfvXVV54jzD/++MO3tAaMzD5FCT/Y kzH6LyMZWLhwIR+0ArLVlStXpiQULlxY+7d0wFMxGrqfOHEiwFBNx1f1Deo+ f/78jORTGjduzH9Sc7dz587Y2Fiv793o+pQbN27s2rUrJiaGtqO9X9r+yZMn o6Kirly5oloFn6JLRmqdzAWZtBfd/CcnJ/NlXhp3KZfzIDlfvnzUhgQ3REcC XeREV5fnn3+e8l+qVClVl0ojf1pOY36+Mrx161buNGfMmKEstmrVKl6+YMEC sfDs2bO//PKLakc//fQTl5w8ebJY2KdPH09PIdyH7i0V4z8/fPgwL1S+aGAj 8CnW418GiGbNmvFhJsy4JzSs5WuSkyZN8j/EoAGfol2GrccLL7xw7Nixli1b imdECxYs2LdvX9X7a+n5lHPnzlGL9Nhjj4nHBanLe+ONN7x2bSkpKZ07d86f Pz+XzJQpE/0wPDxcFEjPp3zwwQeF3IwfP96nPMgPRtdmgUzai27+6eTls1v1 Zt/SpUt5+bx584IboiOBLnKiq0vRokUp+T179lQtX7JkCevCP58+fTr/ef78 eVXJatWqKS9Fpsf27dt5C6NGjeIl1PtTb+vy9voeb7Ns2bIaG/Tp51FRUbz3 6Oho7TitAT7FevzLwLVr1x5++GH27BrF2NfTv3LOfAWfol2GrQfh9S02amGU E4Z49SmzZs3KkSOH52+9OpqIiIgiRYp4LSweNvDqU8TtbzJTGq45RMHo2iyQ SXvRzf9bb73lcl8GUZ3F169f5+v5gwYNCm6IjgS6yIm2LtT5qryDgPwIr+KZ iz755BOX+6Kf5zCMHxIrUaKEdiRhYWGqjvjMmTO85Ouvv1YV/vjjj3mVxhNZ Pv185cqVvESSN6HgU6zHjwzExcW1aNGCj5xp06alV0y4YBq3BxplcIBP0S4j fIrLfdGGrEFiYuKSJUuEm/jvf/+rKqxyH3wMlCxZcvLkyXv37r1x48a2bdvE fGjKAC5fvsxXh4jXX3+dTue0tDRKYO3atevVqyfua3v6lF27drEVqlq1qtcJ SUIdjK7NApm0F938c7dSo0YNz1X8Pm+XLl2CFZyDgS5yoqsLJ//tt99WLScL wy8FDx8+nP6cOXMmd5qe74x89tlntDxz5szpze5LznThwoU8/VepUqVu3rzJ y3fu3MnbXLZsmeonYncnT55ML3Kffj537lxesmrVqqFDh3br1m3EiBHkmEQw FgOfYj3GMzBv3rzu3bs3bdo0S5YsLvcrJ1OmTNG4UdKpUyeX+9Ut3fcR7AI+ RbsMW48CBQoopwQhDh48yJOHVKxYURwA6T33tXLlSlV7QlaF89O/f3+xkBpb Xvjhhx8qC1P7qfz0vMqnJCUl8VyLZJ1OnTplsO6hBUbXZoFM2otu/mvWrOlK ZyLoqlWrutzPoAYrOAcDXeREV5dGjRpxB618kZOGW23atOFesmvXrrRk06ZN /KfqzsvGjRvFFFuq3vPcuXPUO7/yyitlypThAtQMKsssX76cl3t+fEE8dUYd fXqR+/TziRMnurxBvolGFxr5CRLwKdZjPAOtW7dWHiSjR4/W+G5jYmIiz2ZM /te0WM0GPkW7jMar8c2bN+c6Jicn6xb2JG/evC73PIf8J5kdXlK0aFGe+iM9 lD7lzp07DRs2pP/Tkfbbb78Z2W8ogtG1WSCT9mLw+nCHDh08V5EKtKp69erB Cs7BQBc50dVF3GioU6dOVFQUDbrCw8OpCxaDNO5hqaPk+b6oowwLCyO7sX// /s8++0z5SDYtUW45Ojpa5QgOHz6sLCDuehw8eFAVFQ17eJXnvRL/fi58Ch1s gwcP/uijj2rXrs1LqAqHDh3SyaPZwKdYj/EMTJgwoVWrVnSEiG8AVa5c+ejR o14LT506lcuoDm+pgE/RLqNhPcaMGcN1FB/Q0fYpt2/fXrVqFf2qY8eOVapU 4d82aNCA154+fZqXeL4SqEL4lO3bt4tbMOPGjdP+VUiD0bVZIJP2opt/Gg5R wl966SXPVdRWuNyvOgYrOAcDXeREV5e//vqLpzNS8dprr/Fb6n379uWSERER ni+ZUhkqyf9XfQj+zJkz1FNT0ye+7JkpU6aRI0eKxye+//57Xr5v3z5VVOvX r+dVGtNz+frzRYsWURXEn/fv3x8+fDgXC/anEDyBT7EePzJw/vx5OmL5yR8a nXp9SvDVV191uWcslO2bKUrgU7TLaFiPWbNmcR2XLFmiXZjvIBcuXNizOX3y ySe5jJgg8ZtvvtEOSfiU119/XWyndevWRqocomB0bRbIpL3o5r9evXqU8EaN Gnmu4gvCbdq0CVZwDga6yImR9orGV5MnT65du3auXLmyZ8/etGnT6dOn3759 m2fXVL6l/vvvv3fo0KFkyZIu90ch33333djYWB7t0zgtve2TMdmwYUOtWrW4 q6V+n5evXbuWl2zatEn1k4ULF/Iqz4/dCwL8OVecoyL/ZfHkOfAp1uNfBohR o0bx4cRzSqhgG/7cc88FGl8wgU/RLqPhU8RMlevXr9conJKSwh2Zy333LSws jHZ6+fJlzqTwKeHh4VxGY1oGRvgUJk+ePPyfn376yWDFQw6Mrs0CmbQX3fy/ 9NJLLveEGJ6r+EKH5yvDIHCgi5z41F7RuF1MvymeT1BO6S9QPq7/5ptvugw8 tpecnMzP/omZwfbt26e6UCmYMmUKr9L41GOAP2cGDhzIJclwaZc0F/gU6/Hb p5w6dYoPkj59+qhWiUnnBg8ebEKIQQM+RbuMhk95//33uY5xcXEahevXr+9y fzBFNcG+yqccOXKEtzZgwADtkJQ+pWbNmufPny9btqzL/WIL2R/t34YoGF2b BTJpL7r579GjB18gVX1ciUYsfMoPGzYsuCE6EugiJ36PzWbPnm3klsS9e/dK ly7tMnY7rGvXrrxN/uR3UlIS/zlixAhVyXfeecflfk4svTnEAv85Ix79Ur1c E2zgU6xHNwPpzeglPHuvXr1Uq8RjPNLOSMzAp2iXSc+nUAPCTyznypVLY76v 1NRUzkPv3r1VW1D5FNogz8NPG9FunYRP+de//hUfH/9AMbl69+7d9asdgmB0 bRbIpL3o5n/BggV8LqvewJ0xYwYv37hxY3BDdCTQRU789inVq1cnUcqVK/fn n39qFBOf6VR+XyC98R7feSFSUlJ4CT8poZqtmn5evHhxWv7UU09pBxngzx/8 PbNT9uzZdR2NucCnWI9uBjp16tShQ4e0tDTVcvHc19y5c1WrxMsLGzZsMDVY k4FP0S7D1oNaEtXM0pMnT+YKdu7cWVVY6VMOHDjg1clGR0fny5dP6VOIVq1a cWHPDz8p52oQPoW8sFjYtm1bXrh582bdioccGF2bBTJpL7r5v3nzZsGCBSnn zZo1Ey820iDkiSeecLkfqpf5bcfQBbrIiZH2ijpZ1bSrZDq4N/zuu+/EQurB VTcdLl++zNNNk3xinL93716yCarPEDxwv5LMXzejwmLh2LFjeUdbt24VC1es WMELv//+e7GQjh/quBctWqQM1eDP9+3bR3HGxMR4JodfkbZ+DgeZfcquXbu2 /w2/fEG9mFgSuh+Y087AsmXL+LCpXLnyt99+e+TIETK8qampw4cP56+oFChQ 4Ny5c6pfffrpp/wrOuyDG31gGFHfoO6JiYli4YgRI7j6YWFhvETCSc+M+xSX +zspK1euJK+alJQ0ZswYbh9y5MghHvoiqLng5Ig+i1onfpsvf/78u3fvpiOH skQ54QmrVT7l+PHjYh65wYMH05bv3btHbRTZZNqImErd6/foT58+zfPAU5wa c2WHKCYepQ4HmbQXI/kXD5T269fv6tWrNJrq3r07Lxk9erQlYToO6CInurpQ r5o7d+46depQb/jnn38mJCSQFtw7lypVSryuQj1vx44ds2bNStYgOTmZPAtZ AxrRsXwzZ84UG6xRo4bL/cwVyf3rr79Sc0ebpRgef/xxLjxw4EBRmAYD/BQE WRju32mzNCCkJXnz5lV+X+DDDz/knyu/UmHw5/yBnpw5c44cOTIqKor6d4pq 1qxZNKjgUNetW2dGsn1AZp/C33dIjwkTJvixdxnQzgAd6jxzl0AMJvkg8fqd nZ49e3KBs2fPBjH0gDGivkHd6T8axWgjQa+Mjxj3KZ7zGRLkUlXvvPfv359X FS5cWMy/oZyVixsll/tpsXLlyrn+6VMeuOeyFmV4F+L/4h0orz7lwd/f1XXJ /b0e/zDxKHU4yKS9GMk/jYH5pTaGB10u95X8jHcJQhKgi5zo6jJkyBCv3WXp 0qWVl4hpGMZ3QzxLDhgwQDlZFu2OJzRmMmfOrOyRGzRoILwPs2DBArE1cUjk yJFj7dq1ymJ8DZN4+umnff35smXLyIspgxcliUGDBvmV2oAIXZ8yfvx4P/Yu A0YyEB4eztOkK2ncuDG5YK/lhbXxOmWxPAQ+bhG6a/uUPHnyBL0yPmKk7nzJ Zc6cOeQglM1X2bJlPZ+wIuMgvl0r5t+4cuVKx44dxQ+pCWrduvWJEyf4oUGV T3ngPovr1KmjbIhKliz5ww8/iAKLFy/m5arz/fbt25UqVXK5P2UluTv2FROP UoeDTNqLwd6WhsQvvviiuCCWM2dO6lAwGA4e0EVOjOhCvbP4xAn3sG3btk1N TVUVS05OVmrHHavX14dTUlLef/991XcE8ufP//nnn//xxx+e5RctWsQvqzKP PvqoyqQQX375Ja+dNGmSHz+Pj4/v3r0730ARlC9fXuP7LEFFZp+SUTGegaSk pG3btq1Zs4ZGpKqvAoUoTlbf17rfv3//wIEDGzZs0JgtkMpERUWtW7dO1XnF xcXRcjp4VO+5pMe1a9eofEREhOcjhQ7EyUepuSCT9uJT/m/evBkdHW280QB+ A13kxLgu1FFS10y6qO53qKC1u3fv3rhxY2JiovYG7969S939r7/+SoWPHz+u /T7+A/fsr5s3b9a4QnjkyBGND8fr/vzB36/YbNq0iUrqxh9U4FOsx8kZQN3t jgLoA6XMApm0F+RfTqCLnEAXOYFPsR4nZwB1tzsKoA+UMgtk0l6QfzmBLnIC XeQEPsV6nJwB1N3uKIA+UMoskEl7Qf7lBLrICXSRE/gU63FyBlB3u6MA+kAp s0Am7QX5lxPoIifQRU7gU6zHyRlA3e2OAugDpcwCmbQX5F9OoIucQBc5gU+x HidnAHW3OwqgD5QyC2TSXpB/OYEucgJd5AQ+xXqcnAHU3e4ogD5QyiyQSXtB /uUEusgJdJET+BTrcXIGUHe7owD6QCmzQCbtBfmXE+giJ9BFTuBTrMfJGUDd 7Y4C6AOlzAKZtBfkX06gi5xAFzmBT7EeJ2cAdbc7CqAPlDILZNJekH85gS5y Al3kBD7FepycAdTd7iiAPlDKLJBJe0H+5QS6yAl0kRP4FOtxcgZQd7ujAPpA KbNAJu0F+ZcT6CIn0EVO4FOsx8kZQN3tjgLoA6XMApm0F+RfTqCLnEAXOYFP sR4nZwB1tzsKoA+UMgtk0l6QfzmBLnICXeQEPsV6nJwB1N3uKIA+UMoskEl7 Qf7lBLrICXSRE7t8CgAAAAAAAABoA58CAAAAAAAAkA0892UlTs4A6m53FEAf KGUWyKS9IP9yAl3kBLrICXyK9Tg5A6i73VEAfaCUWSCT9oL8ywl0kRPoIifw Kdbj5Ayg7nZHAfSBUmaBTNoL8i8n0EVOoIucwKdYj5MzgLrbHQXQB0qZBTJp L8i/nEAXOYEucgKfYj1OzgDqbncUQB8oZRbIpL0g/3ICXeQEusgJfIr1ODkD qLvdUQB9oJRZIJP2gvzLCXSRE+giJ/Ap1uPkDKDudkcB9IFSZoFM2gvyLyfQ RU6gi5zAp1iPkzOAutsdBdAHSpkFMmkvyL+cQBc5gS5yAp9iPU7OAOpudxRA HyhlFsikvSD/cgJd5AS6yAl8ivU4OQOou91RAH2glFkgk/aC/MsJdJET6CIn 8CnW4+QMoO52RwH0gVJmgUzaC/IvJ9BFTqCLnMCnWI+TM4C62x0F0AdKmQUy aS/Iv5xAFzmBLnICn2I9Ts4A6m53FEAfKGUWyKS9IP9yAl3kBLrICXyK9Tg5 A6i73VEAfaCUWSCT9oL8ywl0kRPoIifwKdbj5Ayg7nZHAfSBUmaBTNoL8i8n 0EVOoIucwKdYj5MzgLprl9mxY0dkZOTevXstiQh4x8lHqbkgk/aC/MsJdJET 6CIn8CnW4+QMoO7aZR599FGXy/Xss88a2eD333//6quvLlq0yITggAInH6Xm gkzaC/IvJ9BFTqCLnISKT0lKSoqJiaF//dijbBjPQHJy8uLFi0eNGjV37tw9 e/akV+zPP//ctWvXeDdr1qxJTU01LVazMV73o0ePUq1Hjx69fPly5+hu3Kfs 37/f5SZLliwJCQnmhAjcGD9K7927RyfmxIkTJ0+eTA3U/fv3gxxaiOFra5+R 2nkZwJEsJ9BFTozrQn3ukiVLaGw2c+bMyMhIGoN5ljl06FBMOty9e1dV+Pr1 6xs3bhw7duyUKVOio6Pv3Lnjdb8+bTM9aCPz5s0bOXLkrFmztm/f/tdff3kt ZjAkC5Dcp1Ci1q1b16VLl6xZs9KQrEmTJn7sUTaMZGD37t1Vq1Z1/ZOWLVvG xcUpi9GR2bdv3zx58iiLFShQgA6/IFYgAIzUPS0trVu3bqq69+7d22tTEEKY 61POnDlDDoUKZ8uWLTEx0ZwQgRuDbdSxY8dKlCihPEorVqwYHx8f/ABDBoOZ zJDtvAzgSJYT6CInRnTZu3dv9erVVeOTChUqrF69WlUyZ86crnRYunSpsuSG DRseeeQRZQHS3eulaePb9Epqauqrr76q+mGjRo1iY2NVJY2HZAEy+5SkpCTu tgTPPPOMH3uUDd0MLFy4UByNxYoVa9iwYf78+fnPKlWqCFd78eJF6tB5eebM mZ944onHHntM5OrHH3+0ojI+olt3svaNGzfmKpQpU6ZFixbFixfnP5s2bWr8 ioGEmOtTHrhbkk8++WTLli0mBAcUGFHq6NGjdG7ykVm5cmXqp/j/ZcuWPXv2 rCVhhgBGMplR23kZwJEsJ9BFTnR1mTp1arZs2ViIggUL1q5dm4Ze/Gf27Nl3 7dolSt66dSs9Q0H8/PPPomRkZGSmTJl4C9T116tXj9vDXLlyrVmzRrl349v0 yv37959//nkuXKhQoa5du77wwgv8J/nfmzdv+hGSNcjsUxITEwv+DR8MGaP/ 0s7ApUuX2JVQu7R161a+z3vlypW2bdvyETVu3DguOWzYMF5Cg1XxrBeZ+ty5 c9PChx566MaNG8GvjW/oqj9nzhyu1Hvvvcc3UOjfnj178kJaa1GgQcB0nwKC hBGl2rdvT0pRYy7eD5o2bRofpT169Ah6iCGCkUxm1HZeBnAkywl0kRNdXebN m0f5L1myZEREBI/NkpOTBw0axLo0b95clKRmjRd+9dVXcR788ccfXOz27dvk EajYww8/LObP2bNnD1+epfHAvXv3fN1mevzyyy/888GDB9N+eeH8+fN54ddf f+1HSNYgs09RUr58+QzTf+lmYMeOHW3atLlw4YJy4cWLF9m/tGzZkpfQ0fL2 2297Dt2Ff1G6e0nQrTv7/VKlSonziKlbty4tr1SpUug+oGvcpzRu3FgsOXLk yO7du321nGlpadTC7Ny58/r167qFExISyBFfvXpVt+Tp06e3bNli/PUBCiMq KurMmTPpPQErJ7pKUd/E15fITSuX89AiX758El4isAVfW/uM1M7LAI5kOYEu cmKkvVq4cCENxpRLqHerXLky6VK4cGGx8PDhwzwM83weTAn1vFxsxowZyuWr Vq3i5QsWLPB1m+kxZMgQ+m2ePHlUj9BTe0vLO3fu7EdI1gCfYj3+ZYDgp7zK lCmjXYxGknw4zZ0714+9BBXduhctWpQi79mzp2r5kiVLuFL+pU4GjPuU5s2b k2vo0aPHv/71L6515syZqcNSvchWu3btQoUKkaVVLjxx4sRLL73k+ptMmTJV q1ZN+eTq4sWL6VcVKlSgxiosLKxcuXKiZNWqVbdv364KiRphSv5zzz1XsGBB sdlHHnlkw4YNqpK8ZfKS9P8VK1Y0bNhQPM9TsmRJzy1Li65S48eP53qpHrqj PPPyefPmBTfEEAE+xV5wJMsJdJETv8dmzZo1c7nntBH3GqKiolip6OhojR9O nz6di50/f161ijpu1UVLg9tMj169erncT9qolvPrwI0aNfIjJGuAT7Eev88F GpdSEuhQ0S62cuVKPsyMvFdlMdp1p3E4Rz5q1CjVKjpleNXs2bODGmHwMO5T yIryAa/io48+8iysfEjs0qVLpUqVEuX5CUBGTK3w448/8pLHH3/ccxc5cuQg iyE2eO3aNa/FXG7rtGnTJmU8YstDhgzhp1uV5MqVS+aZ6JToKvXWW2+53M8n q+6AX79+na3ZoEGDghtiiACfYi84kuUEusiJf2Mz6iUffvhhl/t5D7FQDMO0 Xyb65JNPXO6LhJ6PHPDj7iVKlPB1m+khboio6sjTApBb8SMka4BPsR7/MnDl yhV+ePvtt9/WLvnhhx/y0SjhdLW6dec3Bz3rSBaGBrq0avjw4UGML5gY9ynM iy++uGzZshMnTkyaNIn7pgIFCiifQfX0KWFhYfzbAQMGnDt3jtqZgwcP/vvf /65Vq9atW7e4jHATLvcrmfPmzYuPj9+3b9+bb77JC0uXLq28L/z8889Tk/Xa a6+tXr06OTk5KSlp6NChXFJ1XUW55UKFCo0bNy4mJiY6Orp+/fq88PPPPw84 i1agq1SLFi2oOjVq1PBcxQdwly5dghVcSAGfYi84kuUEusiJH2OzuLg4FouY Nm2aWD537lxeSO6AekxyASNGjFi0aJHydXVi5syZ6Y3WPvvsM74eKKYPMrjN 9KAxAL878NBDD23evJkXRkZG8jbFEp9Csgb4FOvxLwPiRvAPP/ygUezq1as8 m1y5cuX8DzFo6Na9UaNGPCAnXyYW3r59u02bNlz9rl27Bj3K4OCTT6H2R3k1 g3olXn7gwAFVYaVPeeWVV1zuOTpUDZeyVRFuokOHDqp3Ujp27MirlA+gklHy PNPFPCFKmcSW6WxVznNIW+DbK+3bt9euviToKlWzZk1XOtPn8nTilJ9gBRdS wKfYC45kOYEucmK8vZo3b1737t2bNm3KXwfInTv3lClTlF32xIkTXd4oVarU ypUrRbFNmzbxctUzJBs3buQLs8SpU6d82qYGUVFR+fLl41+1a9eOumzyLPT/ 1157zb+QrAE+xXr8yMDp06f5GZ4qVapof0ZETI1l/btORtCtu7hiUKdOHTqn EhMTw8PDaSguTkk6uawK1mSM+5Snn35atVxMg7Z27VpVYaVP6d+/PxfTeORP uAlx/USwY8cOXuX5fpCKCRMmcMn9+/d7bjkiIkJVvnTp0rS8Xr162puVBIN3 /cjoea7idxKrV68erOBCCvgUe8GRLCfQRU6Mt1etW7dWOoXRo0eLJxYY4SlI r8GDB3/00Uf83L7L/XD1oUOHuNidO3d4cq1s2bKFhYXR+J+61M8++4zKiI2L TtbgNjW4e/cuWRKVzalbt67yOQ2fQrIG+BTr8TUD9+/fF5ev161bp1Fyy5Yt fOG6QYMGcs6wpFt3CptfSVNBJ1ehQoXoP3379rUqWJMx7lM85yUme8J5EHNU ei1MRkO8ut6qVavVq1d7TiGo4VPoSOOfiznllBw+fHjWrFn/+c9/qFkT12TW r19vZMvkUFz/fHxXZnSV4peAXnrpJc9VdOpxyx+s4EIK+BR7wZEsJ9BFToy3 VxMmTKAelmxC9uzZuderXLny0aNHlWWos1ZesqPudfjw4VxY2WtTGc+vN9Jo RxiKS5cu+bpNr1y/fp0v+VL33adPH/FlusyZM48cOVJZ0qeQLAA+xXp8zUC/ fv342NB+JPX3338vUqSIy30LUvl0kFQYqTudepMnT6YWIFeuXNQING3adPr0 6bdv3+bXc8Qs3yFHID5l48aNRnwK8dNPPykn5ipRosS4ceOUkzxruAmCZxhT zdVAO+V5oT359ddfjWz5qaeeykg+hW2XmCBFCV+JUk3C5ljgU+wFR7KcQBc5 8WN0ev78eRrk8/Vh6pG1XxWhsU2tWrWoJLkA5SVEGrx16NChZMmSLvcsOu++ +25sbCwbEPIU2gGkt01POnfu7HJPnszThd25c2fq1Kk8AwDxxRdfKAsHEpLp wKdYj08ZEHf6aKCocQpcuHBBzBCl+1lSG/Gp7nQCipl4T58+zbULDw8PVnBB xhqfQqSkpIwaNYpbGIZ8x+XLl3mttk/Jmzcvrapfv75YIo7AHDlyUENHPujY sWP8uSvH+hSe+blq1aqeq6gXcBmY7MIhwKfYC45kOYEucuLf6JSgDpf7Pt35 SAcOHMglla9wCpQPj/HMNkYe8NPeJnPw4EEu88033yiXx8XFkQ1xuSfk9JyI 2O+QzAU+xXqMZ2Dp0qV8E6FYsWIak3fduHGD7wUTQ4cONS3QIOB3O0CnP1dw z549ZgdlEZb5FObevXsrVqzgly6Jt956i5druAlyu6rCa9eu5StFDRs2VH54 VHzE1pk+pUePHnz9SvW1NTpJOQPDhg0LboghAnyKveBIlhPoIid+j09OnTrF uvTp00e7pHhMS/sVD+q++aVOIzfOjGzz22+/5TKecxr/8MMPvEr7ZXyfQjIX +BTrMZgBOmayZcvmct9l0xic37x587nnnuPD7PXXX5f8c+1+twM8xXe5cuW0 pxGQGYt9CpOWlsbPCYg5zzXchJiQMCwsjJf07t3b5Z5KPSUlRVnS4T5lwYIF XNNly5Ypl8+YMYOXk17BDTFEgE+xFxzJcgJd5ERXl/Re+xXPe/Tq1Ut7F/wC fvbs2bWn9hUf9Pzvf/+rF7WhbX7xxRcu95coVe/7EzS85H2RlzErJHOBT7Ee IxlYs2YNv5+VM2dOjcJ0yIlX7Nu2bSv/GN5I3Q8cOKA6lei84Dp+9913QQwu yFjgU2JjY48cOaL6bZMmTahY/vz5+U/hJhYuXKgsdvXq1bJly3JTJjbSvn17 l/s9u+TkZFHyzp074mMrzvQpN2/e5JeAmjVrJq4MUB/xxBNPuNxP80p+ucAy 4FPsBUeynEAXOdHVpVOnTh06dEhLS1MtF899zZ07l/7ct29fzZo1Y2JiPLfP zycop0G4ffu26j7I5cuXeWJqElpYD5+2SccPdcc0WhBDKTGE4AiVfP3117xq x44dPoVkGTL7lF27dm3/mxIlSrjc32UQS65du+bH3mVANwPr1q0TU8ANGTKE /vzll19WKOBxIB1L4gNDNAola7N69eoV/yQpKcmiWhlDt+67d+/OnTt3nTp1 6JQh25WQkDB69Gg+DUuVKiVeVwlFgu1T+NYJ2duRI0fyU4J//PEHlecfNm3a lIsJN0Huo3fv3sePH6esbtu2rVq1ary8R48eYhd0+PHCnj17Xrx4kRqorVu3 cl/pZJ9CvP/++1zZfv36kcWjZrx79+68hI5YS8IMAYxkMqO28zKAI1lOoIuc aOuybNkyzn/lypW//fbbI0eO/PXXX6mpqcOHD+evqBQoUODcuXMP/v7GTc6c OakvjoqKIrNA7disWbP4M4s0nhETt9IWOnbsmDVr1rFjxyYnJ9OgjnpY2j7v aObMmWLvxrf5QPGxb/EiwPXr17l1zZMnz/z588WNoeXLl/M3L0qWLMnz7RgP yTJk9in8Sm96TJgwwY+9y4B2Buio8JwRTgUZWypJjZV2MYIOQusqZgBd9cXA 2OW+sC/+X7p06b1791oVZlAItk+Jjo4uWrSoyBj9X3yVKVu2bGQAuZjyq/Ge 1KhRQ/mI18GDB4VlzuyG/y9MjWN9Co0c6tevL/LGVtrlvv7peWPdsRjJZEZt 52UAR7KcQBc50dblzp07r776qrJ1EpMSs0Di/Q5yNDz4Z2gkI+QjBg0aJLZ5 9uxZZa+tHPMMGDBAOX+X8W0SYn5O5bfYduzYIQIuXrw4reInKLgiO3fu9DUk ywhdnzJ+/Hg/9i4Duj5FeWB4hb+XN2zYMO1irn9+FlAGjKg/Z84cNv4MjZPb tm2bmppqSYBBxEjd+aoF9USq5b/99htnQ+lTPAtT1/bBBx+ImQaZWrVqbdmy RZQRbmL27NnizSaXu6Xq3r2755xy1DyKidZdbsM4adIkarH5KFX6lMWLF3OZ 7du3qzbCO8pIPuWBO9svvviiaPlz5sxJvRiGEEoC9ymh287LAI5kOYEucmJE l/DwcDFtkaBx48biSiATHx9P/Snf7BCUL19+9erVqg0mJycrVXa5b23Mnz/f c9fGt/nll1/yWuqslcuPHTtGoylV8G3atFF9+cV4SNYgs0/JqDg5A8brfu7c uQ0bNkRHR4f0s15KrNSdsrfJTUJCgurVP+FT2LyQASSvsXPnTuU3VlSQeaFG ePPmzZ5ThWRIfFKKkkNH6bZt2zQS6Fic3NbJAI5kOYEucmJcl6SkJFJkzZo1 O3bs0PjoIb/oQR0x9Z6JiYkaG6RxDnWyGzdu1C5mfJtHjhxJ7wv1aWlpe/bs Wb9+Pf3r+a6NHyEFG/gU63FyBlB3u6PQ+X4KeCCNUhkAZNJekH85gS5yAl3k BD7FepycAdTd7ijgU/SRRKkMADJpL8i/nEAXOYEucgKfYj1OzgDqbncU8Cn6 SKJUBgCZtBfkX06gi5xAFzmBT7EeJ2cAdbc7CvgUfSRRKgOATNoL8i8n0EVO oIucwKdYj5MzgLrbHcX/TR3W1I1qig8gkESpDAAyaS/Iv5xAFzmBLnICn2I9 Ts4A6m53FEAfKGUWyKS9IP9yAl3kBLrICXyK9Tg5A6i73VEAfaCUWSCT9oL8 ywl0kRPoIifwKdbj5Ayg7nZHAfSBUmaBTNoL8i8n0EVOoIucwKdYj5MzgLrb HQXQB0qZBTJpL8i/nEAXOYEucgKfYj1OzgDqbncUQB8oZRbIpL0g/3ICXeQE usgJfIr1ODkDqLvdUQB9oJRZIJP2gvzLCXSRE+giJ/Ap1uPkDKDudkcB9IFS ZoFM2gvyLyfQRU6gi5zAp1iPkzOAutsdBdAHSpkFMmkvyL+cQBc5gS5yAp9i PU7OAOpudxRAHyhlFsikvSD/cgJd5AS6yAl8ivU4OQOou91RAH2glFkgk/aC /MsJdJET6CIndvkUAAAAAAAAANAGPgUAAAAAAAAgG3juy0qcnAHU3e4ogD5Q yiyQSXtB/uUEusgJdJET+BTrcXIGUHe7owD6QCmzQCbtBfmXE+giJ9BFTuBT rMfJGUDd7Y4C6AOlzAKZtBfkX06gi5xAFzmBT7EeJ2cAdbc7CqAPlDILZNJe kH85gS5yAl3kBD7FepycAdTd7iiAPlDKLJBJe0H+5QS6yAl0kRP4FOtxcgZQ d7ujAPpAKbNAJu0F+ZcT6CIn0EVO4FOsx8kZQN3tjgLoA6XMApm0F+RfTqCL nEAXOYFPsR4nZwB1tzsKoA+UMgtk0l6QfzmBLnICXeQEPsV6nJwB1N3uKIA+ UMoskEl7Qf7lBLrICXSRE/gU63FyBlB3u6MA+kAps0Am7QX5lxPoIifQRU7g U6zHyRlA3e2OAugDpcwCmbQX5F9OoIucQBc5gU+xHidnAHW3OwqgD5QyC2TS XpB/OYEucgJd5AQ+xXqcnAHU3e4ogD5QyiyQSXtB/uUEusgJdJET+BTrcXIG UHe7owD6QCmzQCbtBfmXE+giJ9BFTuBTrMfJGUDd7Y4C6AOlzAKZtBfkX06g i5xAFzmBT7EeJ2cAddcus2PHjsjIyL179xrZoE+F/SY5OTnSzdWrV4O6I3lw 8lFqLsikvSD/cgJd5AS6yAl8ivU4OQOou3aZRx991OVyPfvss0Y26FNhv/nx xx9dbsgWBXVH8uDko9RckEl7Qf7lBLrICXSRk1DxKUlJSTExMfSvH3uUDeMZ SEhIWLJkyahRo2bOnBkZGfnnn3+mVzIlJSU8PHz06NErVqy4dOmSabGaja/q 6+pOOdm1a9d4N2vWrElNTTUhyuAAnxIqGD9K7927t2fPnokTJ06ePJkO1Pv3 7wc5tBADmbQX5F9OoIucGNclOTl58eLFNDabO3cuCWTkJ/Hx8TFuLl++rFv4 6NGjtGUa0S1fvlx73Gtw7Hf9+vWNGzeOHTt2ypQp0dHRd+7cMRIzc+jQoZh0 uHv3rvHt+I3kPoVyu27dui5dumTNmpVGSk2aNPFjj7JhJAN79+6tXr26659U qFBh9erVnoUHDhyoLJYpU6awsLCghB4wBtU3ojudIH379s2TJ4+y7gUKFJg1 a5b5cZsBfEqoYPAoPXbsWIkSJZSHX8WKFakzCn6AIQMyaS/Iv5xAFzkxosvu 3burVq2qGpu1bNkyLi5O41fkJooUKcKFFyxYoFEyLS2tW7duqu337t3b62Vq g2O/DRs2PPLII8qSdFwZtFdEzpw5XemwdOlSgxsJBJl9CrlIHqYKnnnmGT/2 KBu6GZg6dWq2bNm4ygULFqxdu3bmzJn5z+zZs+/atUtZmMbqvIpG7A0aNMiV Kxf/OWbMmOBWwy+MqG9E94sXL5J54bWUnCeeeOKxxx4T5WloHawKBAB8Sqhg RKmjR48WK1aMM1O5cuUKFSrw/8uWLXv27FlLwgwBkEl7Qf7lBLrIia4uCxcu FIN2Uqdhw4b58+fnP6tUqaJxk+Lll18W4xMNn/LXX381btyYi5UpU6ZFixbF ixfnP5s2baq6eWFw7BcZGUn+hUePNFqoV68ej6+o/Jo1a3RzcuvWLVf6/Pzz z7pbCByZfUpiYmLBv+GBukN8yrx586iyJUuWjIiI4Pu8ycnJgwYN4gOjefPm ouSBAwd4Yd26da9fv05LLl26VKlSJVqSJUuWhISEIFfFZ4yob0T3YcOGccU/ +eQT8azX6tWrc+fOTQsfeuihGzduBCP+QIBPCRWMKNW+fXuX+/rVokWLeMm0 adM4UT169Ah6iCECMmkvyL+cQBc50daFBlfsSsgzbt26lcdmV65cadu2Lesy btw4rz9cvHixcmyv4VPmzJnDZd577z2+gUL/9uzZkxfSWlHS4Njv9u3bFStW pIUPP/ywmHJnz549bH9oCHHv3j3tnNB4jHf01VdfxXnwxx9/aP/cFGT2KUrK ly/vHJ/ywG3bL168qFxCRrty5cqUhMKFC4uFffr08bQk4gCW8JaKr+qnpzud XG+//bbytGWEf1HddZIB4z6lcePG/Cc1Mjt37oyNjSX10yus4VPIrFEeYmJi aDva+6Xtnzx5Mioqilpd1Sr4FE+Sk5P5khT1JsrlPLTIly+fhDbZFpBJe0H+ 5QS6yImuLtQJtmnT5sKFC8qFNFRj/9KyZUvPn5w/f56f+Kpfv76uT3n++eep QKlSpVRdNpkRWk42RLygZHDsR36Kl8yYMUO5wVWrVukGwxw+fJhLen3pwBrg U6zHvwwQzZo14yOTLfDdu3cLFSrk8vb6RrVq1VzuG8SBR2suZvmU9NiyZQuf U3PnzvUjvKBi3Ke88MILx44do0ZP3GIuWLBg3759Vbd90/Mp586do0bsscce E48LUpf3xhtveO3aUlJSOnfuLG5eZ8qUiX4YHh4uCqTnUz744INCbsaPH+9T HuRHVymqMueEjjfl8qVLl/LyefPmBTfEEAGZtBfkX06gi5z4PTbjp9DLlCnj uYqtZfbs2SMiInStQdGiRalAz549VcuXLFnCv+XwjI/9pk+fzj8ku+S1pLgo mh5RUVG8hejoaO2SwQM+xXr8y8C1a9cefvhh9tS85MyZM3z8fP3116rCH3/8 Ma+y5q6ccYLtU1auXMkVt+b1Lp8w7lMIr2+uUaOkfALWq0+ZNWtWjhw5PH/r 1dFQyyle7lMhHjbw6lPE7WkyU7o3jkMOXaXeeustl9s8qup+/fp1vgo6aNCg 4IYYIiCT9oL8ywl0kRO/fUrt2rVJFBr5q5ZTN8od5WeffXbixAltn0KdOxcY NWqUahW5DF41e/bsB76M/T755BOX+/Kj5yMZ/DhZiRIltKsmxlQ2vhUFn2I9 fmQgLi6uRYsWfLRMmzaNF+7cuZOXLFu2TFV+5syZvOrkyZOmxGwWwfYpH374 IVc8RN/NET7F5b6oQtYgMTFxyZIlwk3897//VRVWuQ+++lGyZMnJkyfv3bv3 xo0b27Zt4zS6/r4aw1y+fJmv3hCvv/46nc5paWmRkZHU5NarV0/cd/b0Kbt2 7WIrVLVqVbLPZuRGLnSV4pOxRo0anqv41dcuXboEK7iQApm0F+RfTqCLnPg3 Or1y5Qo/uvD2228rl5O5eOihh2j5k08+SX7z+PHj2j7lwd/iqrbzwG1h+DX5 4cOHP/Bl7Cf+9BwRkXVyuach0p5beO7cubyFVatWDR06tFu3biNGjCD/dfPm Tb3EmAZ8ivUYz8C8efO6d+/etGnTLFmyUPVz5849ZcoU4YuXL1/Ox4/q1vAD xV1CGqOaG3yABNWnXL16lSffK1eunJ/xBRPjPqVAgQKqiTgOHjzIU3ZUrFhR HADpPfe1cuVKVRtChwEfD/379xcLqTHkhWTulIWp1VJ+el7lU5KSkjjJZJ1O nTplsO6hha5SNWvWdKUzXTZPWfnCCy8EK7iQApm0F+RfTqCLnPg3OhUP6f3w ww/K5e3atXO5p9WKjY2lP434lEaNGvEAQPmi6O3bt9u0acO/7dq16wNfxn6b Nm3iP1X3aDZu3CjmB9PuxydOnOjyRqlSpWikYTBFAQKfYj3GM9C6dWvlgTF6 9Ohbt26JtcIp0yBW9cPIyMj07La9BNWniGkxdF8NswXjPsXrq/HNmzfn2iUn J+sW9iRv3rxUmFpO/pPMDi8pWrQozxaSHkqfcufOnYYNG9L/s2XL9ttvvxnZ byiiqxRf9erQoYPnKjpWaVX16tWDFVxIgUzaC/IvJ9BFTvwYnZ4+fZpnGa1S pYryEyc0COF+c9KkSbzEiE8RNy/q1KkTFRWVmJgYHh5OXbwYBHIPbnzsR102 z/dFXXZYWBhZkv3793/22WfKh8NpiUYFhU+hA2/w4MEfffQRP+RG0EYOHTrk U7r8Az7FeoxnYMKECa1ataKjInv27HxgVK5c+ejRo7z2+++/54X79u1T/XD9 +vW8ysYpGrwSPJ+yZcsWvuPQoEEDr7Nj2U6APmXMmDGsqZjKTNun3L59e9Wq VfSrjh07UhPKv6Xk8FpqXXmJ5yt7KoRP2b59u7gFk94EjBkDXaVKlSpFSXjp pZc8V1GGXe65IoMVXEiBTNoL8i8n0EVOfB2f3L9//4UXXuA+cd26dWJ5cnJy 4cKFXe47YmI0YsSnUGGeLknFa6+9xi/O9+3b94GPY7+IiAjP111pa7RN/r/G V+yZRYsW0UaUtR4+fDj/NtifRWDgU6zHjwycP39+5MiRPA6n0Sk/1bN27Vo+ VDZt2qQqv3DhQl5l/JOj1hAkn/L777/zGxy5c+c+cOBAQCEGjQB9yqxZs1jT JUuWaBc+d+5c//79uZ1U8eSTT3IZMS3hN998ox2S8Cmvv/662E7r1q2NVDlE 0VWqXr16lIRGjRp5ruKLV23atAlWcCEFMmkvyL+cQBc58XV80q9fP+4QVa8L kcHk5Tt27Ej6GzFF8NSpU+nPtLQ0r9skFzB58uTatWvnypUre/bsTZs2nT59 +u3bt/kVGH5x3texHw2QOnToULJkSZd7UrJ33303NjaWvUa+fPmM11cZZK1a tVzuCX8smEgHPsV6/MsAMWrUKD4Cec4HstKqgatgypQpvEq218mD4VMuXLgg 3hO35uuo/hGgTxEPwa5fv16jcEpKCndkLvfdt7CwMNrp5cuXOUXCp4SHh3MZ MS1DegifwuTJk4f/89NPPxmseMihqxR3Q1WrVvVcxfbQ80VIZ4JM2gvyLyfQ RU58Gp+IB6Lq1q2rfCH0yJEjnlcIPXnqqae0t09eQEzvKZ5/4E8G+D32U744 8Oabb7oCeIBw4MCBvCN++yaowKdYj98+5dSpU3xg9OnT54H7jWb+c8SIEaqS 77zzjss9GZ32TA7WY7pPuXHjBt8HJ4YOHWpCiEEjQJ/y/vvvczXj4uI0CvPH pLJmzaqaYF/lU0RbOmDAAO2QlD6lZs2a58+fL1u2rMv9YgvZH+3fhii6SvXo 0cPlvpSk+iQNdQ2cqGHDhgU3xBABmbQX5F9OoIucGB+fLF26lG9wFCtWTOUI jh07ZsSnPPHEE8YDmz17Nv+K75IEPva7d+9e6dKlXQHcmBOPfmm/3mIK8CnW o5uB9F6vEJ66V69evISvnKtmL6SfFy9e3GXAsFuPuT7l5s2bzz33HOfk9ddf F59qlZNAfAq1OfzEcq5cuTTm+0pNTeVs9O7dW7UFlU+hDfI8/LQR7QZN+JR/ /etf8fHxDxQTqnfv3l2/2iGIrlLiHUnVPBUzZszg5Rs3bgxuiCECMmkvyL+c QBc5MTg+oR4wW7ZsLvdDU14frb9y5colD8Rkwt9++y396dOU/tWrV3e5JzIV r+oHOPYTHwxVfunAJ3iWp+zZs1twMRw+xXp0M9CpU6cOHTp4Pr4onvsSH1sf O3YsL9m6dasotmLFCl74/fffmxx6wJjoU27duiVeYWvbtq1yqg05Me5TqPER ny9hJk+ezDXt3LmzqrDSpxw4cEDlZJno6GhqUZU+hWjVqhUX9vxWlJir4YHC p6xatUospITzws2bN+tWPOTQVYoMcsGCBan6zZo1E+6YmusnnnjC5X4AWHLL bBnIpL0g/3ICXeTESB+9Zs0antcoZ86cPg1mjLxH/8DdiSufziLISvAPv/vu O7HQ+NiPxhKqWx6XL1/mia/pQFK6DDrqqLtftGiRCGDfvn1UMiYmRhUkVZxf l7ZmPgeZfcquXbu2/02JEiUoJzRqFUtC9wNz2hlYtmwZH2mVK1cm333kyBHy yKmpqcOHD+evqBQoUODcuXNcOCkpia+KFy1adPfu3VSSDloqQEvy5s2rPd+s LRhR34judOqJD1/mz5+fmo7Vq1ev+CeUHCuqZBjjPsXl/k7KypUryatSLcaM GcNtQo4cOcRDXwQ1EZwc0WdRO8M3oyknfDwkJiaGhYXxxR+VT6FmU8wjN3jw YNryvXv3qF0im0wbEV/e8fo9+tOnT/Ps6xSnqlHNABhRSjyG169fv6tXr1LL 3717d14yevRoS8IMAZBJe0H+5QS6yImuLuvWrRMz+g4ZMoT+/OWXX5SjDo0L d0Z8CvXauXPnrlOnDvW2f/75Z0JCAmnNvX+pUqXE6yoPDI/9aHnHjh2pJPma 5ORkGjhRMRpbciQzZ85U7l18Jls8Qs8f6yFHNnLkyKioKOrraQA2a9YsGmC4 3E+XKWc5Cx4y+xT+vkN6TJgwwY+9y4B2BuhQfPXVV5U1FYNJPjBU39ahY579 C6/l/9CptHbt2qDXxHeMqG9Edzp5Ncowy5cvt6JKhjHuUzxnESRIZdU77/37 9+dVhQsXFnNuKGfl4nbM5X5arFy5cq5/+hRi6tSpogzvQvyf34F6kI5PefD3 12xd0r8W5AdGlKKRA78KpDr1mjVrlvGMm98gk/aC/MsJdJETbV1okO+1a1ZS s2bN9H5uxKeQ9/HaHZcuXXrv3r2qwkbGfmfPniUj43WbAwYMUE3VxVc+iaef fpqXLFu2jL8OI34udkQMGjRIM52mEbo+Zfz48X7sXQaMZCA8PFy8Hi5o3Lgx GWfPwosWLeKXFxga68ppUh6Y4VNY92HDhmmUYWRLgpG684WOOXPmkIPg+dKZ smXLel6oIeNQpkwZLiDm3Lhy5UrHjh3FD6nVat269YkTJ/ihQZVPeeA+i+vU qaNsfEqWLKn8ru7ixYt5uep8p0a7UqVKLvcHpKgx9C8ncmKwjaKBxIsvvigu I1AX9uqrr2IIoQSZtBfkX06gi5zo+hTlON8r9erVS+/ncXFxXMZzki4l1Pvz YySiB2/btm1qaqrXwkbGfsnJycqjiLv4+fPne27tyy+/5ALi25REfHx89+7d +QaKoHz58lZ+m09mn5JRMZ6BpKSkbdu2rVmzhkakut/iOXXqFA1lJR8xOll9 X+t+//79AwcObNiwQWNyaSoTFRW1bt06VedFTSItp4NH9Z5Lely7do3KR0RE iEcKnYxPSt28eTM6Otp4qh0FMmkvyL+cQBc5kWd8Qh0xdf2ku/JZr/QwMvaj 7ezevXvjxo2JiYkaxY4cOeL1E/P8ksumTZtoR9pbCAbwKdbj5Ayg7nZHAfSB UmaBTNoL8i8n0EVOoIucwKdYj5MzgLrbHQXQB0qZBTJpL8i/nEAXOYEucgKf Yj1OzgDqbncUQB8oZRbIpL0g/3ICXeQEusgJfIr1ODkDqLvdUQB9oJRZIJP2 gvzLCXSRE+giJ/Ap1uPkDKDudkcB9IFSZoFM2gvyLyfQRU6gi5zAp1iPkzOA utsdBdAHSpkFMmkvyL+cQBc5gS5yAp9iPU7OAOpudxRAHyhlFsikvSD/cgJd 5AS6yAl8ivU4OQOou91RAH2glFkgk/aC/MsJdJET6CIn8CnW4+QMoO52RwH0 gVJmgUzaC/IvJ9BFTqCLnMCnWI+TM4C62x0F0AdKmQUyaS/Iv5xAFzmBLnIC n2I9Ts4A6m53FEAfKGUWyKS9IP9yAl3kBLrICXyK9Tg5A6i73VEAfaCUWSCT 9oL8ywl0kRPoIifwKdbj5Ayg7nZHAfSBUmaBTNoL8i8n0EVOoIucwKdYj5Mz gLrbHQXQB0qZBTJpL8i/nEAXOYEucgKfYj1OzgDqbncUQB8oZRbIpL0g/3IC XeQEusiJXT4FAAAAAAAAALSBTwEAAAAAAADIBp77shInZwB1tzsKoA+UMgtk 0l6QfzmBLnICXeQEPsV6nJwB1N3uKIA+UMoskEl7Qf7lBLrICXSRE/gU63Fy BlB3u6MA+kAps0Am7QX5lxPoIifQRU7gU6zHyRlA3e2OAugDpcwCmbQX5F9O oIucQBc5gU+xHidnAHW3OwqgD5QyC2TSXpB/OYEucgJd5AQ+xXqcnAHU3e4o gD5QyiyQSXtB/uUEusgJdJET+BTrcXIGUHe7owD6QCmzQCbtBfmXE+giJ9BF TuBTrMfJGUDd7Y4C6AOlzAKZtBfkX06gi5xAFzmBT7EeJ2cAdbc7CqAPlDIL ZNJekH85gS5yAl3kBD7FepycAdTd7iiAPlDKLJBJe0H+5QS6yAl0kRP4FOtx cgZQd7ujAPpAKbNAJu0F+ZcT6CIn0EVO4FOsx8kZQN3tjgLoA6XMApm0F+Rf TqCLnEAXOYFPsR4nZwB1tzsKoA+UMgtk0l6QfzmBLnICXeQEPsV6nJwB1N3u KIA+UMoskEl7Qf7lBLrICXSRE/gU63FyBlB3u6MA+kAps0Am7QX5lxPoIifQ RU7gU6zHyRlA3bXL7NixIzIycu/evUY26FNhv0lOTo50c/Xq1aDuSB6cfJSa CzJpL8i/nEAXOYEucgKfYj1OzgDqrl3m0Ucfdblczz77rJEN+lTYb3788UeX G7JFQd2RPDj5KDUXZNJekH85gS5yAl3kJFR8SlJSUkxMDP3rxx5lw3gGkpOT Fy9ePGrUqLlz5+7Zs0e1NjU1NUaPW7dumV+BAPBVfYO637t378CBA/v37//r r78Cii+YwKeECsaPUjrw6MScOHHi5MmT6UC9f/9+kEMLMYJ0vgOD4EiWE+gi J7q6HDx4UHvEdebMGVH40KFD6RW7e/eu58ZTUlLCw8NHjx69YsWKS5cuaYea mJhIxT799NN58+YdO3bM15GP7pDJ1+CDiuQ+5fr16+vWrevSpUvWrFlppNSk SRM/9igbRjKwe/fuqlWruv5Jy5Yt4+LiRBlquFx6zJ49O7iV8RGD6hvXPTY2 dtKkSeXLl+f6btmyxcxwTQU+JVQweJRS71CiRAnl6VaxYsX4+PjgBxgymH6+ A5/AkSwn0EVOdHUpVKiQ9oiL9BKFc+bMmV6xpUuXqrY8cOBAZYFMmTKFhYV5 jeHmzZvUVKo2WKtWrRMnThipo8Ehk0/BBxuZfUpSUhJ3W4JnnnnGjz3Khm4G Fi5cKA6SYsWKNWzYMH/+/PxnlSpV7ty5w8WM+JSff/7ZiioZxoj6xnXv3bu3 qr6RkZHmB20S8CmhghGljh49SucmZ6Zy5coVKlTg/5ctW/bs2bOWhBkCmHu+ A1/BkSwn0EVOAvcpNELjkrdu3TI+MOvbty8vz5MnT4MGDXLlysV/jhkzRhUA tZZ16tThtZkzZ6YDo0CBAvxnwYIF09LStCtocMjkU/AWILNPSUxMLPg3pEiG 6b+0M3Dp0iV2JdQubd26le/zXrlypW3btnyQjBs3jkvSMRnnjf379/Nx/vTT T8t2m9iI+sZ1p7Obi+XOnVvjpJME+JRQwYhS7du3d7mvei1atIiXTJs2jRPV o0ePoIcYIph7vgNfwZEsJ9BFTnR1iY+P9zroev/991ma9evXc0lq1njJV199 5Vn+jz/+ENs8cOAAl6xbt+7169cfuAeBlSpVoiVZsmRJSEhQBvD2229z4Y4d O167do2W0BiP+mgyON9++61uBQ0OmYwHbw0y+xQlfJcqY/RfuhmgAWGbNm0u XLigXHjx4kX2Ly1bttTefq9evagYHYcG7wNaia/qG9R9/vz5GcmnNG7cmP+8 ffv2zp07Y2NjvT5EqutTbty4sWvXrpiYGNqO9n5p+ydPnoyKiiJHrFoFn+JJ cnIy3wJ47733lMt5aJEvXz7KfHBDDBGCdL4Dg+BIlhPoIif+jU7PnDnDw/53 331XLDx8+DD3m6tXr9b+eZ8+fTwtiTAvylsq5BGyZctGCzt16qQaEvg6G6f2 kMl48NYAn2I9/mWAaNKkCSWhTJkyGmVoWMvXJCdNmuRfeEEFPkW7DFuPF154 4dixY2RIxeN/BQsW7Nu3r+r9tfR8yrlz56jpe+yxx/hIIKjLe+ONN7x2bSkp KZ07dxYPFmbKlIl+GB4eLgqk51M++OCDQm7Gjx/vUx7kR1cpqjLnRPVw79Kl S3n5vHnzghtiiACfYi84kuUEusiJf2OzVq1akSKlS5dWPnYVFRXFSkVHR2v8 lvp0fpbM86W8atWqudyP+YklfAmaOHLkiK9BqtAeMhkM3jLgU6zHb59Su3Zt SgIdvRpl6tatyzcQ5Zz5Cj5FuwxbD8LrW2zUlIm3kx6k41NmzZqVI0cOz996 dTQRERFFihTxWlg8bODVp8yZM4cXkpm6d+9eIGmREF2l3nrrLZfbPKrqfv36 db4KOmjQoOCGGCLAp9gLjmQ5gS5y4sfYbNWqVdwVrly5Urmc/uTl2i8TnTlz 5v9t797Do6jOP4AvP6yIyFVFUSh4o0CByiUqauulgBrEWipQqNYSWwtKwaJS 6g0NEpRwCyAQgQACAcJFgkBIQngkBMnFkBAMCUog5EoIJiRAuJPf++z7cJ7p 7GZmdrM7czb7/fyRJztzdufMeWdnzrszc4aLzZw5UzXrP//5D88S11l17drV Zv8Z06UaOqXdZTJYedMgTzGfey1QWVnJP4+/9tprdZURWTBthPWqotcgT9Eu I/IUMnr0aEoNiouLo6KiRDaxZMkSVWFV9sHbQPv27cPCwtLT08+ePZuUlCQG 91BWoKKiom3btjx95MiR9HWurq6mBqR0OCAgQFwq5pinJCcncyrUrVs3vkS2 gdGN1DPPPEOr36NHD8dZfOvryy+/7K3K+RTkKdbCliwnxEVObvTNnn76aZv9 bmLVL8PLli3j4yYlMu+9996oUaM+/PDDyMjImpoaZbF9+/ZxsY0bN6o+eeHC hTzryJEjPIWvLqNP45clJSUpKSnuPX9Zu8tksPKmQZ5iPvdaQJwIXrFiRV1l hg0bRgVuv/123fsRrII8RbsMpx4tW7bcunWrcnpWVlajRo1s9nEpxf6wruu+ oqOjVfsTSlW4fcaPHy8mijvyJkyYoCx86dIl5a5PlafQvvGuu+6il5Q65eXl GVx336IbqZ49e9rqGD6XhxP3yE9eDQDyFGthS5YT4iInV/dX2dnZfHCcNWuW ahZNsTnToUMH5ZmXTZs28XTH8YGjoqJ4Fh2+6eXJkyf55aJFi+ig3L17d/GZ lM8mJia6tKbaXSaDlTcN8hTzudECR48e5VS6a9euly9fdlqmuLiY77ES6baE kKdol9G4NX7gwIG8jqWlpbqFHd1yyy1U+MUXX+SXlOzwlLZt2/IYI3VR5ikX L17s168f/U9b2u7du40s1xfpRop/0hwyZIjjLNpWaRYdRLxVOZ+CPMVa2JLl hLjIydX9FQ/zRX0zx/FnRFef4jVx4sR33nmHr9snTZo0OXjwIBcTJ02ysrJU n0CdGZ7Fp1rS0tJEVsL/NG/evFmzZvx/o0aNVL9tajOYp2hX3jTIU8znagtc vXq1f//+vJHExMTUVWzevHlc5ocffvBALb0DeYp2GY3UIzg4mNcxOTlZt3Ct faywLVu20LuGDh3K17WSRx55hOdS5stTRo8erV0lkafs3btXnIIRg2M3SLqR 6tChAzXCCy+84DiLWthmv0HMW5XzKchTrIUtWU6Ii5xc3V/x9dgvvfSS07mR kZHx8fHiJXXkPvjgAz6AiqP20qVLecr+/ftVb9+xYwfP4kG3xI0wpHPnzt9+ ++3ly5evXbu2atUqfg4F9QeMX0ij22UyUnnTIE8xn6stMG7cON48tC9JHT58 OKfYsj0zRQl5inYZjdQjPDyc1zEqKkq7cFFR0fjx49u0aWNz8NBDD3EZsdOb PXu2dpVEnjJy5EjxOYGBgUZW2UfpRiogIIAa4fHHH3ec9cADD9CsQYMGeaty PgV5irWwJcsJcZGTS/urnJwcPhoaH/GS+mYPPvigzT5ODo+QsG3bNv6QnTt3 qgqvXr2aZ6WlpdUq7mTp2rWruKaCvf/++8qSRrjRZXKsvGmQp5jPpRYQJ+D6 9OmjfRPT3XffTcWeeuopD1TRa5CnaJfRyFPEDUriSVJOC5eVlfGBzGZ/inFI SAgttKKigltS5CkbNmzgMvPnz9eukshTmDjRvGbNGoMr7nN0I/XCCy/Y7MMI OM7i9FBjsAu/gjzFWtiS5YS4yMml/dXixYv5UOjSJdBvv/02vys3N5de7t+/ X/XzozB37lyexc9VodyEX0ZERKhKig8Ro3Tqcq/LpKq8aZCnmM94C6xfv57H +LrjjjtUjyVVEaPbTZw40TO19A7kKdplNPIU8cTb/Px8jcIPP/ywzf7AFNUA +6o8RdwA+NZbb2lXSZmn9OzZ88SJE506dbLZb2yh9Ef7vT5KN1Kvv/46/6yk eiQNfUm5od5//33vVtFHIE+xFrZkOSEucnJpf8VjR1MPzaVnboqrpzIzM2vt 49Lwyw8//FBV8u9//7vNfuMJPzft2rVr/LSCd955R1VSbBW6V0cI7nWZVJU3 DfIU8xlsgejoaL4vvnnz5rqn88RlPNKOSMyQp2iXqStPoT0VX7HctGlTjfG+ ysvLuR3efPNN1Seo8hT6QB6Hnz5E9fhIFZGntGvXrqCgoFYxuHpQUJD+avsg 3UitWrWKW0A1mOSCBQt4elxcnHer6COQp1gLW7KcEBc5ubS/6t27NwWCdlku LSIwMJDedeONN4rDLl//oBqDmo7yd955J01/9NFHxUR+Ol737t1VYyDv2bOH twrjt9K712VyrLw5kKeYz0gL0PZGGwP/omKkucTNC7GxsR6ppJcgT9Euw6kH 7bJUN8SFhYXxCo4YMUJVWJmnHDhwgIuNGTNG+fbU1FTKdpV5Su31p+janD1h 6tChQ+J/kadQLiwmDh48mCfu2rVLd8V9jm6kampqWrVqRas/YMAAcTsY7br7 9u1LEzt27CjzPWJmQp5iLWzJckJc5OTS/qpdu3YUi379+jnO2r9/f8+ePTMy Mhw/n58voBwGYdq0aXwwpXRDTPz666954tKlS8VE0c+hucqPHTVqFE/nHxJr 7dsPHbgjIyPPnz/vtPIaXSaXKm8OmfOU5OTkvdfxzRd0FBNTfPcBc7otEBMT Ix4pPmnSJHq5efPmrxUcO4effPIJl09PT/di1evNSPQNxr24uFhM/PDDD3n1 Q0JCeIqEg54Zz1Ns9uekREdHV1dXl5SUBAcH8/6Btgpx0Vft9V9XqHHEMYv2 TnyhYIsWLVJSUq5du0atRG3CJ+ZUecrhw4c5F7bZLxekT75y5Qrto4YNG0Yf wmO219bxPPqjR4/yGCNUz7r2hL7LSKTEZXjjxo07ffp0RUVFUFAQT/n4449N qaYP8OD3HdyALVlOiIucjPdO6ZjbuHFjisXgwYMd5/Izbm666aaPPvooMTGR DpG0HwsPD6fjss1+KZdy4FY6xPO1DW3btuWjNiUsLVu2pCm33HKL8qkBdIDm X3KaN29OfUKqA02ZPXs2H/SVo1hPmDCBNxXlUyoMdplcqrw5ZM5T+PkOdZkx Y4YbS5eBdgtcuHCBr0LUQNmu6l2jR4/mWcePH/du7evHSPQNxp3+0ShGH+L1 lXGR8TzF6QZAe0XVPe/jx4/nWW3atBHjbyhH5eK9n81+tdg999xj+988pdY+ lrUow4sQ/48dO5bLOM1TyJQpU3i6zM/rcY+RSFHPgW8FYpxI2uy/fza8xM1t Hvy+gxuwJcsJcZGT8d7piRMnOByjRo1ynLtx40Z+4J04sIrwkXfffVdVftWq VeLgK0o2adJk27ZtqpKUwtx6663isM6/FtrsT/emNEQU498wyWOPPSYmGuwy uVp5E/hunmJ8LDjZ6OYpyu6iUwEBAap38aDERHtMMMvVv98i4q79pWvWrJnX V8ZFRta9S5cuNvuAHpRBtG7dWqxOp06dHE+iUeLQsWNHLiDG36isrBw6dKh4 I+3rAgMDf/rpp8mTJ9sc8pRa+7e4d+/eyh1R+/btV6xYIQqsXbuWp6u+77Sh du7c2WZ/5qPk2bGrDO6jqCPx/PPPi3NSlF3S1xBdCCUPft/BDdiS5YS4yMl4 71QMSlzXyEUFBQVBQUF8DkK47777+GEojiIjI/kWVHbvvfc6JiksLy+vX79+ fA6F0UaiGqn4888/51lz5swRE413mVytvLfJnKc0VP7cAlh34+WvXr164MCB 2NhYjaHeqExiYmJMTIzq4JWfn0/Tk5KSDD74qaqqisrHx8cXFRUZr2FD5VKk ampqUlNTjTe1X/Hn77sMsCXLCXGRk8f3VxSyzMzMnTt37tq1S3m+oy6Ug1BJ I7/7VVdX7969m2pbWVnptEB2dnY9HxzvauW9B3mK+fy5BbDuVtcC9CFSnoKW tBbaX06Ii5wQFzkhTzGfP7cA1t3qWoA+RMpT0JLWQvvLCXGRE+IiJ+Qp5vPn FsC6W10L0IdIeQpa0lpofzkhLnJCXOSEPMV8/twCWHerawH6EClPQUtaC+0v J8RFToiLnJCnmM+fWwDrbnUtQB8i5SloSWuh/eWEuMgJcZET8hTz+XMLYN2t rgXoQ6Q8BS1pLbS/nBAXOSEuckKeYj5/bgGsu9W1AH2IlKegJa2F9pcT4iIn xEVOyFPM588tgHW3uhagD5HyFLSktdD+ckJc5IS4yAl5ivn8uQWw7lbXAvQh Up6ClrQW2l9OiIucEBc5IU8xnz+3ANbd6lqAPkTKU9CS1kL7ywlxkRPiIifk Kebz5xbAultdC9CHSHkKWtJaaH85IS5yQlzkhDzFfP7cAlh3q2sB+hApT0FL WgvtLyfERU6Ii5yQp5jPn1sA6251LUAfIuUpaElrof3lhLjICXGRE/IU8/lz C2Ddra4F6EOkPAUtaS20v5wQFzkhLnJCnmI+f24BrLvVtQB9iJSnoCWthfaX E+IiJ8RFTlblKQAAAAAAANqQpwAAAAAAgGxw3ZeZ/LkFsO5W1wL0IVKegpa0 FtpfToiLnBAXOSFPMZ8/twDW3epagD5EylPQktZC+8sJcZET4iIn5Cnm8+cW wLpbXQvQh0h5ClrSWmh/OSEuckJc5IQ8xXz+3AJYd6trAfoQKU9BS1oL7S8n xEVOiIuckKeYz59bAOtudS1AHyLlKWhJa6H95YS4yAlxkRPyFPP5cwtg3a2u BehDpDwFLWkttL+cEBc5IS5yQp5iPn9uAay71bUAfYiUp6AlrYX2lxPiIifE RU7IU8znzy2Adbe6FqAPkfIUtKS10P5yQlzkhLjICXmK+fy5BbDuVtcC9CFS noKWtBbaX06Ii5wQFzkhTzGfP7cA1t3qWoA+RMpT0JLWQvvLCXGRE+IiJ+Qp 5vPnFsC6W10L0IdIeQpa0lpofzkhLnJCXOSEPMV8/twCWHerawH6EClPQUta C+0vJ8RFToiLnJCnmM+fWwDrbnUtQB8i5SloSWuh/eWEuMgJcZET8hTz+XML YN2trgXoQ6Q8BS1pLbS/nBAXOSEuckKeYj5/bgGsu9W1AH2IlKegJa2F9pcT 4iInxEVOyFPM588tgHXXLvPdd98lJCSkp6cb+UCXCruttLQ0we706dNeXZA8 /Hkr9Sy0pLXQ/nJCXOSEuMgJeYr5/LkFsO7aZe69916bzfa73/3OyAe6VNht X331lc2O0iKvLkge/ryVehZa0lpofzkhLnJCXOTkK3lKSUlJRkYG/XVjibIx 3gKFhYVRUVGTJ09euHBhQkLC5cuXnRa7cOEC9SFnzZo1derUdevWHTt2zIO1 9Szj637mzJnt27eHhobSSm3atKm0tNTLVfM65Cm+wvhWeuXKlbS0NPrqhYWF 0Q7q6tWrXq6aj3F1b9+Q9vMywJYsJ8RFTsbjQh2StWvXUt9s2bJlFCCNkgcP Hly+fPlHH30UHh6+d+/ea9euOS1GHZ64uLhp06bNnTs3NTX14sWLRqpRXl6e oef8+fP1X5C1RJ7iHreXaLAwNWlMTMzLL798ww03UE/pySefdGOJsjHSAunp 6d27d7f9r/vvv/+bb75RFrt06dKkSZNuvPFGZbHGjRsHBQVVVVV5cR3cZTD6 lG21a9dOuVI333zz9OnTvV9BL0Ke4isMbqU5OTl33323cit94IEHCgoKvF9B n2GwJRvkfl4G2JLlhLjIyUhcUlJSunXrpuqbPfvss/n5+aqSlEQMHz5cVfLx xx/Pzc1VlYyNjb3rrruUxSju2ukPo+zVpmfx4sX1X5C1zD/PZXyJJSUlfNgS fvvb33q5dmbQbYF58+b94he/4FVu1apVr169/u///o9fUkqSnJzMxah9evfu zdMbNWpE35077rhDtNWLL75YV+ZuISPR37Vrl1jfRx999NVXXxU5i/Ib53OQ p/gKI5E6dOiQ+Lp16dLl/vvv5/87dep0/PhxU6rpA4y0ZEPdz8sAW7KcEBc5 6cZl9erVN910EweCotOvX78WLVrwy65duyrPTVy9evX3v/89z2rduvUrr7zS v39/fknJZk1NjSiZkJBA/Tfu3dHRPCAggPeHTZs23bp1q3aFjeQp69atq/+C rCVznlJcXNzqOu64Nozjl24LLF++nFa2ffv28fHxfJ63tLT03Xff5a1u4MCB XKy6urpHjx40ZcyYMadOnaq1fzXok++55x5pO5ZGov/ggw/yTiAnJ4enVFRU 9O3bl7M23z3xjTzFVxiJ1B//+Ef+fSAyMpKnzJ8/nxvq9ddf93oVfYSRlmyo +3kZYEuWE+IiJ+24/Pzzz5yVUM64Z88e7opUVlYOHjyY4/LZZ5+Jwps3b+aJ EydOvHDhAk9cuXIlT5w5cyZPoVmUttCU22+/XQyJk5aWduedd9JEOsRfuXJF o8LUCcx3JjMzk7IP+oTHHnuM61nPBVlL5jxF6b777mswxy8jLUBpO6cewrVr 17p06UKN0KZNGzHx+PHj9HVQvXfNmjX8XaBc20NV9hjddT937lzjxo2p8tOm TVNOT0hI4JU6fPiwd6voNcbzlCeeeIJf0r5l3759ubm5Tk+N6eYpZ8+eTU5O zsjIEPvJutDnHzlyJDExkfa6qlnIUxyVlpbyL1H//Oc/ldO5a9G8eXNqee9W 0Ue4urdvSPt5GWBLlhPiIifduNBBcNCgQSdPnlROpK4a5y/PPvusmDhp0iSa 0qxZM9VtxbRzo+kjRozgl5Tv8OF1wYIFymJbtmzh6atWrXJjRcaMGWOzXy3/ 008/eXVB5kCeYj6323zAgAE2++0n2pnv3r17ecObPHmyezX0Ht11P3HiBFd+ 3rx5yumUkfH0nTt3ereKXmM8T+nfv39OTg7t9MQp5latWv3rX/+6dOmSY2HH PKWoqGjs2LG/+tWvxOVzdMj7y1/+4vTQVlZWRvtMcfK6UaNG9MYNGzaIAnXl Kf/+979b24WGhrrUDvLTjRStMrfJt99+q5y+fv16nr58+XLvVtFHIE+xFrZk OSEucnK7b/bkk09SUDp27CimcKZw6623qkqOGjXKZr9LhV9+8cUXHFDq+ahK /vrXv1b+aGncvn37+NA/Z84cMdEbCzIN8hTzudcCVVVVt99+OzVC586dtUuG hITwBinOF8vDyLrzTTdPP/208hIv6jnzSvnupbnG8xQiMhQl2hkqr4B1mqeE h4c3adLE8b1OM5r4+PjbbrvNaWGx8TjNUyIiIngiJVMyny92j26kXn31VZs9 eVSt+5kzZ/hX0Hfffde7VfQRyFOshS1ZToiLnNzuD/fq1YuCQh1+MUWcp1B9 II+PRNkKv/zvf/9rs/886HjJxOjRo232+9xdrUyfPn3ojfRX+ZneWJBpkKeY z40WyM/Pf+aZZ3iznz9/fl3FaJ+2evVqHv6rQ4cOynu1JGFk3adOncprSvvq 6urqWvt6UdritKftQ1zKUwjtPSg1KC4ujoqKEtnEkiVLVIVVbZKYmGiz39wU FhaWnp5+9uzZpKQk/vqo9pkVFRVt27bl6SNHjvz++++ptRMSEmiXGxAQIC4V c8xTkpOTORXq1q2bnMPK1ZNupPjL2KNHD8dZfOvryy+/7K3K+RTkKdbCliwn xEVO7vVOKysr+fzFa6+9JiaeP3+er1K49dZbd+3axRPF5etiysKFC3lKYWGh 6mOnTJlC0+mTVddRaOMOAFm5cqVyuscXZCbkKeYz3gLLly8PCgqiLjrfsnHz zTfPnTvXMR0uKioaP378Sy+91LFjR94UqaHy8vI8X/V6M7Lu9GWhdeEVadeu 3fTp0+nrb7OfYsjIyDClml5hPE9p2bKlavyNrKwsHqnjgQceEBtAXdd9RUdH q1JUSlW4PWk7ERO5VcmECROUhan9lY+eV+UpJSUlPLAhpU5ybmP1pxupnj17 2uoYPpeHrOzfv7+3KudTkKdYC1uynBAXObnXOxUX6a1YsUI5nVKG5s2b86wX X3yRjqSUs9D/f/7zn0WZnTt3cgHVVfpxcXF8Izxx6Tg7bNgwm/1medVNqR5f kJmUcaH+z1UXuTHyLfIU4y0QGBhoU/j4449Vj+xhqampymIdOnT44YcfPFxp DzG47unp6WJkZmHjxo3er6AXGc9TnJ42GjhwILeDeOSlS+N93XLLLby35Jf0 zeUpbdu2PXPmjMYblXnKxYsX+/XrR/9TdHbv3m1kub5IN1L8k+aQIUMcZ/Ft kt27d/dW5XwK8hRrYUuWE+IiJzd6p0ePHr355ptt9nGJVbfMX7p0iVISVTem T58+586dE2XokMrDcNEhNSQkhDKFzMzMKVOmKC/epikGK1NcXMwdp/fee081 y7MLMpkyLmVlZa4+55HeUp8lGteQjl/GW2DGjBnPPfdcr169xJMcu3TpcujQ IVWxY8eODR06lBpHPBOqUaNGH330kY8+P2XDhg38XXv22WefeeYZPo9gs9+Y 47uDfdXWO08JDg7mdhAP0NHOUy5cuLBlyxZ6F20btAvl9z7yyCM8l/auPGX0 6NHaVRJ5yt69e8UpGOUAjA2PbqQ6dOhAjfDCCy84zqIW5oORtyrnU5CnWAtb spwQFzm5ur+6evWqeCpKTEyMctaZM2fo0GyzD842duxYHv7XZr+8ivpmypLx 8fGOt6O2bt1a5Dg///yzwfrMmzeP3+L0l2oPLshkyrhQwpWenk7ZB/V7j2ui AlSMCivv6nVjicY1pOOXGy1w4sQJ2ra5x06907puPKHEJDY2lp8/QsLDwz1Q XY/SXXf6fvF9gn/729/4FkKaQrtrXqN27dqphmv2IfXMUyia3AhRUVHahfk6 wDZt2tgcPPTQQ1xG3OU3e/Zs7SqJPGXkyJHicwIDA42sso/SjVRAQIBNMWaL Ev9mNWjQIG9VzqcgT7EWtmQ5IS5ycnV/NW7cOD4gOt4uNGLECJv9KRKpqam1 9t41JRE8FBKZOnWqsvCPP/44ZMiQ9u3b2+yDhv3jH//Izc394IMPOM0xXp/h w4fzW+p6zJynFmQyVVwoB6EERFxYUhcqQMXcG3kJeYp7LUAmT57MG7n2Y9kp OnzWWMIBHHTX/U9/+pPNfuuZ6hSqWHfVzRQ+pJ55irgIdseOHRqFy8rK+EBm s599CwkJoYVWVFTwN0jkKWL8NI1hGZjIU1izZs34nzVr1hhccZ+jGylOnLt1 6+Y4i9ND5Q2V/gx5irWwJcsJcZGTS/urWbNm8aGwT58+qp+Os7KynP4MmJ+f zzcRN23a1HF84Fr73ffi/7/+9a82Fy/w4ytqnnrqKd2S9VyQyVRx4VMqmZmZ GmON0iwq4N7JFMclGtSQjl9u5yl5eXm88Y8dO1a75CuvvMIlZTv7oLvufHo0 KCjIcdY999xDs3r16uWtynlZPfOUN954g2NK+zqNwg8//LDN/sAU1QD7qjwl OzubP+2tt97SrpIyT+nZsyftXTt16mSz39hC6Y/2e32UbqRef/11m31gB9Uj aQoLC7mh3n//fe9W0UcgT7EWtmQ5IS5yMr6/Wr9+PY/xdccddziOoLVo0SIO k+OP+StWrOBZ0dHRGp9P3exf/vKXNldOnB07dow/eeLEiQbf4t6CzOcYF91T KvU5meJ0iUY0pOOXbgvUdV+JuKdgzJgx2iU5QSZu3EDkVdrrTqvDd3U53gVG 6HtEsyiR8WL9vKk+ecqlS5f4iuWmTZtqjPdVXl7OcX/zzTdVn6DKU+gD+fo6 +hDt0QhFntKuXbuCgoJa+3hiPMVpOtkA6EZq1apV3AKqsR0WLFjA0+Pi4rxb RR+BPMVa2JLlhLjIyeD+io6AfAtt8+bN09LSHAvwsxUaN27sOPARlecIUi6j sQjxQE/lkwi0iWu5VSMSa3NjQeZzjAufUsnIyHB6SoUm0iy3T6Y4XaIRDen4 pdsCw4YNGzJkCD86RElc+7Rs2bJa+6BY1GlXDWBba7+ZhZ+LoXw6qiR01/3R Rx+12YfOUJ1IPXv2LPWTadbAgQO9W0WvMZ6n9OjRQzWoYFhYGId+xIgRqsLK POXAgQOqTJalpqbyAIkiTyHPPfccF545c6aqGsqxGkSeQrtBMXHw4ME8UYwD 35DoRoo2zlatWtHqDxgwQFwJTOle3759+XtX1+XB/gZ5irWwJcsJcZGTkf0V 9bh4XKObbrqprsKURSq7akp0tOVZ4nlkdKxXDbRVUVHBA1NToJW/ItJWQYfj yMhIp+O+ijtYY2NjndbK+IJk4zQufEqlpKTEsTxNrM/JlLqW6FRycvLe6/i6 OzqKiSm++4A57RbYuHEjb2xdunShjDs7O/vatWvl5eUffPABP0WlZcuWRUVF VJJ6szb70F5vvPHG9u3bqUEuX75Mn/yb3/yGP+Htt982b62M0Y2+uOYzMDBQ nE49efKkuJX+iy++MKOiXmA8T7HZn5MSHR1NuSp944KDg3kIhSZNmoiLvmqv P3aWvhTimEX7MT4Z3aJFi5SUFNpyiouLQ0JCxCDPyjzl8OHDYhy5iRMn0idf uXJl//79lCbThyQlJXExp8+jP3r0KA+6TvV0us/0aUYiJS7DGzdu3OnTp2mH HxQUxFM+/vhjU6rpA4y0ZEPdz8sAW7KcEBc56cYlJiZGDOQ7adIkerl58+av FfiHuzNnzvCurFmzZitXrhSXQGzatIkHMW7fvj3/FEmzhg4desMNN0ybNq20 tJQm7tmzh/p+vIiFCxcqlz5hwgSe7vSCk08++YTnpqenO851aUGycRqXuk6p 1P9kSl1LdIqf71CXGTNmuF0Ha2m3ALUtD9ogiM4kZyXiskb6kNatW4tZ1Lfk K3nYI488Up8weYlu9OnbJFISWnFK9vv27ctfbfKHP/xBwsGWDTKepzgOHmiz n0RW3fM+fvx4ntWmTRvxVVWOyiW2B8op+O4eZZ5Sax/GULnNcCLMxD1QTvOU 2usPsa1rn+nTjESKeg58K5D4YvI/AwYMaHiJm9uMtGRD3c/LAFuynBAXOWnH hfr2Tg/NStRj4cJ0uBQ9tzvvvPOxxx7j+zpt9o7Nvn37uNjx48f56hfHQ/Bb b72l6oHzL5OEPs2xeqNHj+a5Ts8juLQg2dQVF6enVPhkCl+j7vElOtI+foWG htanGhYy0gIbNmzgYdKVnnjiiZSUFGWxsrKyN954QzUCbYsWLT799FPls4Tk YWTdL1++TB1yMYIfu+222+bOnSvzqUldRtadf9+IiIigDEKZhNIuzvEKK9oT 8uAhJDc3lydWVlYOHTpUvLFJkyaBgYE//fQTXzSoylMIfaN79+4tDoI2+089 yufqrl27lqdTSeUbaafduXNnm/25UfU5wSohg/so6kg8//zz4mBEh7Dhw4ej C6FU/zzFd/fzMsCWLCfERU66eYqye+9UQECAKJ+TkyMukBYGDRqkegReaWmp Msp8CHZ6j8nnn3/OBebMmeM4V/y+XddzK4wvSDZ1xcXxlIo4mVLPvqLxPKWh Mt4ClBgmJSVt3bqVeqQaj+ChiBw4cGD79u1xcXGHDx9WjegrFePrfvXq1WPH jlHnPD4+/ujRow3gclxXt3xaZQprbGys43AiyjKJiYkxMTGqg1d+fj5Np41H dZ9LXaqqqqg8NTVfUujnXIoUHRRSU1ONN7Vfwd7eWtiS5YS4yMkb+6vq6uq0 tLQdO3bQX8ebjgXqcqekpFAXrri4WOPTsrOzDx48WJ/6GFyQVDTiUlBQoDyl 4pGTKdpL9BP+3AJYd6trAfoQKU9BS1oL7S8nxEVOiIucNOJy6dIlcUrFUydT tJfoJ/y5BbDuVtcC9CFSnoKWtBbaX06Ii5wQFzlpx0WcUikuLvbIyRTdJfoD f24BrLvVtQB9iJSnoCWthfaXE+IiJ8RFTtpx4VMq39t55GSK7hL9gT+3ANbd 6lqAPkTKU9CS1kL7ywlxkRPiIifduPApFU+dTDGyxAbPn1sA6251LUAfIuUp aElrof3lhLjICXGRk25c+JTK/v37PTUkLLYEf24BrLvVtQB9iJSnoCWthfaX E+IiJ8RFTkbiUlBQoDEyqjeW2LD5cwtg3a2uBehDpDwFLWkttL+cEBc5IS5y Mj8u2BL8uQWw7lbXAvQhUp6ClrQW2l9OiIucEBc5IU8xnz+3ANbd6lqAPkTK U9CS1kL7ywlxkRPiIifkKebz5xbAultdC9CHSHkKWtJaaH85IS5yQlzkhDzF fP7cAlh3q2sB+hApT0FLWgvtLyfERU6Ii5yQp5jPn1sA6251LUAfIuUpaElr of3lhLjICXGRE/IU8/lzC2Ddra4F6EOkPAUtaS20v5wQFzkhLnJCnmI+f24B rLvVtQB9iJSnoCWthfaXE+IiJ8RFTshTzOfPLYB1t7oWoA+R8hS0pLXQ/nJC XOSEuMjJqjwFAAAAAABAG/IUAAAAAACQjfl5ykUAAAAAAIA6IE8BAAAAAADZ IE8BAAAAAADZIE8BAAAAAADZIE8BAAAAAADZIE8BAAAAAADZIE8BAAAAAADZ IE8BAAAAAADZIE8BAAAAAADZIE8BAAAAAADZIE8BAAAAAADZIE8BAAAAAADZ IE8BAAAAAADZIE8BAAAAAADZIE8BGeTn50dGRi5atGj16tXV1dVWVwcAAAAA LCZ/nnL27NmSOuzevTsvL8/prJMnT3qv0dLT0ysrK733+RYqLCwsKCgwWPj0 6dNH/xelG+4td86cOcHBwbNmzZo/f/6FCxdUc2kboA+nZs/Nza2oqHB8O03M zs7OyspyGnftucYdOXLEpbjn5OScO3euPku0drkAAAAAFpI/T6FM5HPXrV69 2kstdvjwYf788+fPe2kR5jtz5kxCQsKiRYsoWZg9e7bBd33//ffB/4taxo2l FxcX03uXL1/umKFQIycmJoaEhIhFTJ06NS4uTllgx44dyjosWbLkxIkTRua6 hNKc6dOnG487bycRERE1NTVuLM7y5QIAAABYS/48paCgYLGD8PBw6rxxShIW FuZY4JtvvvFGc508eXLOnDm83NjYWG8swhKVlZW0RpwOGM9TkpKSqPyWLVv2 XZeamurG0vfv30+fs3PnTsdZ1OsOdobewgVo6Y5zxQamPdclFRUV8+fPNxh3 sZ3s3r3bjWXJsFwAAAAAa8mfpzg6e/bskiVLqDP21VdfhYaG0j8ZGRmeahCr lnvkyJH09PQLFy4cOnRo27ZtBw4coIn0Mjs7Oy4ubseOHZmZmeIXdarJ999/ n5ubK95O/6elpZ0+fZpfUt5BL/Py8uj/U6dOUa+Vern0V/earmnTphnPU+Lj 46nbz0vRVde6FBYWcjaxdu1aqnNJSYnqjVRzqhI1dX5+fkREBOcaGzZsoFlU eMqUKfSSqk0pEiU1a9asWb9+PZ+X0Z7rBqoAx53qr1FMbCf1WZYMywUAAACw kC/mKZs2baLO2JdffnnmzBnqrtP/M2bMMH5XhZzLpQ+nHjVlKNwPj4mJoaWs XLlSeSJg8eLFP//8MxWuqqqiHvisWbPE2+l/KkD9fH5J/9DLxMTEoqIi6qUr P0T7lIdLeco333xDH2jkMiqNdVGd8khJSVG9l7rc1APn/xMSErjY5s2bL9pT GH7p9PSB9lz3GIm7cjvx9eUCAAAAWMXn8pSkpCTqjFFfurS0lKds376dpixY sMCr97Z7e7mcp5C4uDhKLqgPT6kKvYyIiKAllpeXr127ll6uW7eOyy9dupRe 8tkHvr+DrF+/nudu3LiRXlKfllMJ6t6fO3eOPic6OtrpfeiCS3kKLY4+nBIQ aoRly5ZRWEVCoaKxLqdOnfr222/p5datW2nFxSkhperqaspfaF2mTp3Ka3r4 8GGazp9DMjIyaJXXrFkTHx8vVlB7rtu04+64nXiKVcsFAAAAsIRv5Sk//vgj 3xuSk5MjJtbU1Hz11VdevbfdhOVyniLSEEorPv3005CQENGvpikzZ86kMjxo 1a5du+j/5ORk+n/v3r18hmLGjBl8wQ/1V0NDQ+n/r7/+mmZ99913BqvhUp5C PX/68IULF3LSRChbcbziSHdd+P4UylbqWhD1vZXnXPiiOEKLDnYwffp0yn10 57pNI+5OtxNPsWq5AAAAAJbwoTxF4x5hl+41lnO5nKeIez0KCwvpJfVLlWU2 b95ME7Ozs+n/Y8eOibyGOq7z5s3bs2cPn0M5ceKEOLdy5MgRyhGC7eNcpaWl 6Q5X65in5OfnxzpDC6qurqbP52JlZWWUAtCCDh06pPpM3XXRzVPow5VXr1E6 xudT5s6dy1M+++wzagqqOb/kURS059aH07ibcA+7VcsFAAAAMJ+v5Cm69wgb vNdY2uVynkLZB7/Mzc2ll1FRUcoyPMQu32BClaG8gBZdU1ND/fAtW7YcP348 2H5PChUIVtyHQgnFmjVr+I5y6tA63qiu5JinUCrxhTOcKSjxNWaOvWXdddHN U9jp06fj4uI414iIiKAplPvwSx5SgHM3snTpUt259aSKu2n3sFu1XAAAAACT +UqeYuQeYW/cU2/aclV5ysmTJ+nlokWLlGUiIyNpojiFQT1/eskPMTlw4ABn LtQ5548qLy9XvvfUqVMbNmyg6atWrdKohkvXfanwMMWO6YbuumjnKVlZWXzH PaGVEmdVqqurOTMSJ3HE5WGLFy++eD1vqmtu/SnjbuY97FYtFwAAAMBMPpGn GL9H2LP3tpu5XFWectF+j0nw9RvGL9pvlp8yZQrlEdQ/5yk8qNf8+fPpL99+ vm7dOioQFhY2b948LlNYWCiqVFVVRSXFLKeM5ynUJtu2bRM3ztfU1PBjIp3e IqG9Lhp5ytGjR6lkaGhoXFzc3r17xY0wNOWi/b4MfvnFF1/k5uZy4kaio6N1 53oEx33mzJneu4edWrjEAacnlKrw0qnNVQX4xh8AAAAA3yV/nuLSPcIevLfd 5OU65ilZWVk0JSQkJDY2lnrpfPcHpU6iwKlTp7jjLU5VpKSkKG/BoCyAurL0 xvj4+NTUVB4ZmBbkuHSa+40dXx7G/x8/flyjwjyE8ty5c6mvnpiYyEkK5RFO V197XTTyFHFORGXXrl1cgBpcNWvq1Kni2jbtufUn4u69e9jz8vI+dx2tuDcq AwAAAGAa+fMU6lWGh4cbv0eY7zWmRdTzcn2TlxsdHR1svwteOTEjIyM0NJQ7 2J999tl3332n+vAFCxbQrB07dvBLvsKKHDx4UHyCuJ082D6GsNMzPuJcgxIl FxoVpi76zp07OeNg9CEao/5qrEtmZmZdeQqVoSRozpw5YikzZ85UJmvnzp2j XIbHCuC8iRJMg3M9guPuvXvYaZNY7MyXX35JjU9r5HRu/ccKAAAAALCW/HnK RftTAl3q/Isro+rJquUqUQVO2NUn7aLcpKioyBvPlzl//nxpaSl9uJF1r8+6 VFVVFRYWlpeXO30v5SPFxcWqW3IMzq2/srIyS+5ht2q5AAAAACbwiTwFAAAA AAD8CvIUAAAAAACQDfIUAAAAAACQDfIUAAAAAACQDfIUAAAAAACQDfIUAAAA AACQDfIUAAAAAACQDfIUAAAAAACQDfIUAAAAAACQDfIUAAAAAACQDfIUAAAA AACQjVV5CgAAAAAAgDbkKQAAAAAAIBsz8xQAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAADZ/D8Imnzn "], {{0, 204.}, {540., 0}}, {0, 255}, ColorFunction->RGBColor, ImageResolution->144.], BoxForm`ImageTag["Byte", ColorSpace -> "RGB", Interleaving -> True], Selectable->False], DefaultBaseStyle->"ImageGraphics", ImageSizeRaw->{540., 204.}, PlotRange->{{0, 540.}, {0, 204.}}]], "Output", TaggingRules->{}, CellChangeTimes->{{3.834319385372796*^9, 3.8343193994862022`*^9}, 3.834319651187138*^9, 3.8343196983840313`*^9, 3.8343198126503*^9, 3.834320035985876*^9, 3.834333575928726*^9, 3.834334846338291*^9, 3.83440563511432*^9}, CellLabel->"Out[6]=", CellID->1028883790] }, Open ]], Cell["\<\ And now compare the mean values of their covariates, which are now \ considerably more similar to each other:\ \>", "Text", TaggingRules->{}, CellChangeTimes->{{3.834319714432394*^9, 3.834319723949896*^9}, { 3.8343198402004232`*^9, 3.834319858605132*^9}}, CellID->1960079553], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{ RowBox[{ InterpretationBox[ TagBox[ DynamicModuleBox[{Typeset`open = False}, FrameBox[ PaneSelectorBox[{False->GridBox[{ { PaneBox[GridBox[{ { StyleBox[ StyleBox[ AdjustmentBox["\<\"[\[FilledSmallSquare]]\"\>", BoxBaselineShift->-0.25, BoxMargins->{{0, 0}, {-1, -1}}], "ResourceFunctionIcon", FontColor->RGBColor[ 0.8745098039215686, 0.2784313725490196, 0.03137254901960784]], ShowStringCharacters->False, FontFamily->"Source Sans Pro Black", FontSize->0.6538461538461539 Inherited, FontWeight->"Heavy", PrivateFontOptions->{"OperatorSubstitution"->False}], StyleBox[ RowBox[{ StyleBox["MapReduceOperator", "ResourceFunctionLabel"], " "}], ShowAutoStyles->False, ShowStringCharacters->False, FontSize->Rational[12, 13] Inherited, FontColor->GrayLevel[0.1]]} }, GridBoxSpacings->{"Columns" -> {{0.25}}}], Alignment->Left, BaseStyle->{LineSpacing -> {0, 0}, LineBreakWithin -> False}, BaselinePosition->Baseline, FrameMargins->{{3, 0}, {0, 0}}], ItemBox[ PaneBox[ TogglerBox[Dynamic[Typeset`open], {True-> DynamicBox[FEPrivate`FrontEndResource[ "FEBitmaps", "IconizeCloser"], ImageSizeCache->{11., {2., 9.}}], False-> DynamicBox[FEPrivate`FrontEndResource[ "FEBitmaps", "IconizeOpener"], ImageSizeCache->{11., {2., 9.}}]}, Appearance->None, BaselinePosition->Baseline, ContentPadding->False, FrameMargins->0], Alignment->Left, BaselinePosition->Baseline, FrameMargins->{{1, 1}, {0, 0}}], Frame->{{ RGBColor[ 0.8313725490196079, 0.8470588235294118, 0.8509803921568627, 0.5], False}, {False, False}}]} }, BaselinePosition->{1, 1}, GridBoxAlignment->{"Columns" -> {{Left}}, "Rows" -> {{Baseline}}}, GridBoxItemSize->{ "Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}, GridBoxSpacings->{"Columns" -> {{0}}, "Rows" -> {{0}}}], True-> GridBox[{ {GridBox[{ { PaneBox[GridBox[{ { StyleBox[ StyleBox[ AdjustmentBox["\<\"[\[FilledSmallSquare]]\"\>", BoxBaselineShift->-0.25, BoxMargins->{{0, 0}, {-1, -1}}], "ResourceFunctionIcon", FontColor->RGBColor[ 0.8745098039215686, 0.2784313725490196, 0.03137254901960784]], ShowStringCharacters->False, FontFamily->"Source Sans Pro Black", FontSize->0.6538461538461539 Inherited, FontWeight->"Heavy", PrivateFontOptions->{"OperatorSubstitution"->False}], StyleBox[ RowBox[{ StyleBox["MapReduceOperator", "ResourceFunctionLabel"], " "}], ShowAutoStyles->False, ShowStringCharacters->False, FontSize->Rational[12, 13] Inherited, FontColor->GrayLevel[0.1]]} }, GridBoxSpacings->{"Columns" -> {{0.25}}}], Alignment->Left, BaseStyle->{LineSpacing -> {0, 0}, LineBreakWithin -> False}, BaselinePosition->Baseline, FrameMargins->{{3, 0}, {0, 0}}], ItemBox[ PaneBox[ TogglerBox[Dynamic[Typeset`open], {True-> DynamicBox[FEPrivate`FrontEndResource[ "FEBitmaps", "IconizeCloser"]], False-> DynamicBox[FEPrivate`FrontEndResource[ "FEBitmaps", "IconizeOpener"]]}, Appearance->None, BaselinePosition->Baseline, ContentPadding->False, FrameMargins->0], Alignment->Left, BaselinePosition->Baseline, FrameMargins->{{1, 1}, {0, 0}}], Frame->{{ RGBColor[ 0.8313725490196079, 0.8470588235294118, 0.8509803921568627, 0.5], False}, {False, False}}]} }, BaselinePosition->{1, 1}, GridBoxAlignment->{"Columns" -> {{Left}}, "Rows" -> {{Baseline}}}, GridBoxItemSize->{ "Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}, GridBoxSpacings->{"Columns" -> {{0}}, "Rows" -> {{0}}}]}, { StyleBox[ PaneBox[GridBox[{ { RowBox[{ TagBox["\<\"Version (latest): \"\>", "IconizedLabel"], " ", TagBox["\<\"1.0.0\"\>", "IconizedItem"]}]}, { TagBox[ TemplateBox[{ "\"Documentation \[RightGuillemet]\"", "https://resources.wolframcloud.com/FunctionRepository/\ resources/MapReduceOperator"}, "HyperlinkURL"], "IconizedItem"]} }, DefaultBaseStyle->"Column", GridBoxAlignment->{"Columns" -> {{Left}}}, GridBoxItemSize->{ "Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}], Alignment->Left, BaselinePosition->Baseline, FrameMargins->{{5, 4}, {0, 4}}], "DialogStyle", FontFamily->"Roboto", FontSize->11]} }, BaselinePosition->{1, 1}, GridBoxAlignment->{"Columns" -> {{Left}}, "Rows" -> {{Baseline}}}, GridBoxDividers->{"Columns" -> {{None}}, "Rows" -> {False, { GrayLevel[0.8]}, False}}, GridBoxItemSize->{ "Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}]}, Dynamic[ Typeset`open], BaselinePosition->Baseline, ImageSize->Automatic], Background->RGBColor[ 0.9686274509803922, 0.9764705882352941, 0.984313725490196], BaselinePosition->Baseline, DefaultBaseStyle->{}, FrameMargins->{{0, 0}, {1, 0}}, FrameStyle->RGBColor[ 0.8313725490196079, 0.8470588235294118, 0.8509803921568627], RoundingRadius->4]], {"FunctionResourceBox", RGBColor[0.8745098039215686, 0.2784313725490196, 0.03137254901960784], "MapReduceOperator"}, TagBoxNote->"FunctionResourceBox"], ResourceFunction[ ResourceObject[ Association[ "Name" -> "MapReduceOperator", "ShortName" -> "MapReduceOperator", "UUID" -> "856f4937-9a4c-44a9-88ae-cfc2efd4698f", "ResourceType" -> "Function", "Version" -> "1.0.0", "Description" -> "Like an operator form of GroupBy, but where one also specifies a \ reducer function to be applied", "RepositoryLocation" -> URL["https://www.wolframcloud.com/obj/resourcesystem/api/1.0"], "SymbolName" -> "FunctionRepository`$ad7fe533436b4f8294edfa758a34ac26`\ MapReduceOperator", "FunctionLocation" -> CloudObject[ "https://www.wolframcloud.com/obj/6d981522-1eb3-4b54-84f6-\ 55667fb2e236"]], ResourceSystemBase -> Automatic]], Selectable->False], "[", RowBox[{ RowBox[{ RowBox[{"(", RowBox[{ RowBox[{"If", "[", RowBox[{ RowBox[{"#treat", "==", "1"}], ",", "\"\\"", ",", "\"\\""}], "]"}], "&"}], ")"}], "->", RowBox[{"KeyDrop", "[", RowBox[{"{", RowBox[{"\"\\"", ",", "\"\\"", ",", "\"\\""}], "}"}], "]"}]}], ",", RowBox[{"Merge", "[", RowBox[{"Mean", "/*", "N"}], "]"}]}], "]"}], "[", "matched", "]"}]], "Input", TaggingRules->{}, CellChangeTimes->{{3.834318086442609*^9, 3.834318111572878*^9}, { 3.834318144579329*^9, 3.834318368734473*^9}, {3.834319735768073*^9, 3.834319736844186*^9}}, CellLabel->"In[7]:=", CellID->881938515], Cell[BoxData[ GraphicsBox[ TagBox[RasterBox[CompressedData[" 1:eJztnQnYVVP7/08KKalUZOwVRSQNUhIVUr8ypIQQKoleyg+lpAFNylBpwquJ BipJafb8aJ7nUcPTPM95VcTzv//nvqxr2+ucfe59zj6n+3l8P9dV13PWXnvv tdd33fe6195rr31N45Z1nz8nFAq9lpP+q9vojWqvvtqobb189KN+i9debNai 6XP/0+L1ps2avlqxcXZKXEz/ttMf///vDAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAgAf4PAAAAAAAAAAAAAAAAAAACjgMQBu1BJ9BFJ9BFJ9BFJ9BF J9BFJ4jSgQ3ag06gi06gi06gi06gi06gi04QpQMbtAedQBedQBedQBedQBed QBedIEoHNmgPOoEuOoEuOoEuOoEuOoEuOkGUDmzQHnQCXXQCXXQCXXQCXXQC XXSCKB3YoD3oBLroBLroBLroBLroBLroBFE6sEF70Al00Ql00Ql00Ql00Ql0 0QmidGCD9qAT6KIT6KIT6KIT6KIT6KITROnABu1BJ9BFJ9BFJ9BFJ9BFJ9BF J4jSgQ3ag06gi06gi06gi06gi06gi04QpQMbtAedQBedQBedQBedQBedQBed IEoHNmgPOoEuOoEuOoEuOoEuOoEuOkGUDmzQHnQCXXQCXXQCXXQCXXQCXXSC KB3YoD3oBLroBLroBLroBLroBLroBFE6sEF70Al00Ql00Ql00Ql00Ql00Qmi dGCD9qAT6KIT6KIT6KIT6KIT6KITROnABu1BJ9BFJ9BFJ9BFJ9BFJ9BFJ4jS gQ3ag05So8usWbMmTJgwderUZJ8oywB7kZD6dqVTl+3bt08Is379+rNdlrOD Tl0AdNEJonRgg/agk9Tocuedd4ZCoUKFCiX7RFmGrGQvK1aseO6551544YV1 69YFe+TUtyudukyePDkU5qOPPjrbZTk76NQFQBedIEoHNmgPOkGUrpOsZC+1 a9fmGPLZZ58N9siI0hlE6anRJT09fXYs9u/fH233tWvXcp5t27Ylu6hK0KnL /Pnzo2U7dOhQskurAUTpwAbtQSeI0nWSleylYcOGHEM2bdo02CMjSmcQpadG l/feey8Ui48//jjivps3b7744os5z2effZbsoipBpy7nn39+tGzDhg1Ldmk1 gCgd2KA96ARRuk6ykr1s2bLl3Xff7dq1a+C3EBGlM4jS9USDQ4YMibjvgw8+ aPIgSg8WX7rs378/DvmyGIjSgQ3ag04QpesE9iIBUTqDKD01uuzatWt1JObM mZMzZ06q/woVKhw9etTecfDgwc5QEFF6sPjSZcOGDazCO++8Y++yd+/eZJdW A4jSgY3f9rBy5cpJkyb9/PPP3tl27Ngxffr0adOmbd26NeYx161bR93Zli1b 5MXI8vjVRViHe/bsIVGWLVt27Ngx+lm1atUAoyk65vLly6dMmbJ9+/ZADqiQ OPznoUOHyBY2btxob5o3b96sWbMOHDjgfQSh0TFkeiRBenp6HFtdUMHS0tLm zp17+PBh75xJbVcS4uvXFi5cSBdIhffORmZFxrVmzRrJMakSqCp2795Nf9Mf MaN0ieVSJLN06dKJEydSgbmGYxJNaLmm8pwexKFL4iU3NGnShCr/ggsuIFHs rZs2beK5Lrfeeiui9JgkW5cFCxawCl999ZWvgmUlEKUDG0l7oH5h6NChd955 Z968ec1th8KFC48bN87O/OOPP9IA2WTLli1bPgd16tRxHrZr1650HJP5iiuu GDx4cMBXmDkR2qm8DikgLF++/DnnnMPZSIsOHTrUrFnTFU3t3buXjkZbX375 ZdcR3nrrLRaRejfXps2bNz/yyCN58uQxxShWrNgXX3zh/7q1I9GlVKlSVEv9 +vWjiqpbt+6FF17IdVKyZMnhw4dTBurOXn/99auuuorTs2fP3q5dO1f0JTc6 Pt2AAQM2bNhw//33n3feeZSzYMGCMbdSO2FB7SUTV6xYQZH2ueeey+elvWjf iLG9sF0lG1+6ULzx9NNPX3LJJcZHNWrU6ODBg/Yu77//PhmUqX8K6lq2bBkx 55EjR1q1alWgQAFzzIoVK3766afRonSh5e7fv5/aRu7cuU02im2cHvWnn35y Xl20ZnDcj6bynDGR6xJIyZ3MmDGDVKD83bt3j5iBDkJb6bDjx49HlG6TYl1o CMDH+ScHqIjSgU3M9rBz506KLkKRIFv77rvvnJnJ0Ex/TXGas4Njqlevzjn3 7dtHf5t0fv7FtGnTJnnXm1mQ2Km8DocMGeLc6sIZTW3fvp0TmzRp4jrda6+9 xptcN3WpjzOvX7n4/PPPE6sGdUh0+de//kXXTh2WicMNF1100aJFi+677z67 rj744ANzBF9Gx6dr3bp12bJlTbYaNWrE3PrJJ5/wz4kTJzoPOGrUKDPgypEj Bw0i+O/LL7987dq1zpzydpVs5Lo8+OCD1157rV3aFi1aODOTcd17770Rr+um m25yrV25a9euypUrR6uHkBWlCy2XIvl77rmH02mwRue1X6/74YcfnFcXrRnI NZXnlCDXJfGSuyhdujRlo/8jPn0g18THeeutt5YtW8Z/I0p3kmJdRo4cyUcQ PrTKkiBKBzaS9lClShWynXr16n311VcbN26kIM0EbHfccYfJdvjw4SuvvJIS yXgpXOdE6p44Z6tWrRYvXmx6N7J9Tq9bty4nrly5kqezksnT30m52syDRBdh HW7atMnczn322Wfnzp27YcOGYcOGFSlShBMTidK3bdtWsGBBTq9fv/5PP/1E EcuECRNKlSpFvj3mXI5Mh7x3Y+rUqUNB9YIFC5wvqRFUP4MGDVqyZEnbtm05 hfZyHkRodK7T3Xzzzb179/7xxx9nz54dc2vEKD09PT1//vyUWKBAAWokBw8e JBH79u3LkWTDhg1NTl/tKtn41aVmzZpffvklhWfdu3fnAIMGUM65r6a2ixcv Tgru2LEjLS2NZ/IQ//M//+M8csuWLTm9cOHCdNjNmzfPmjXrueeeM6dzRelC yyWxONujjz7KZaPMHOTQuJgqnDyqWcjOQ2i5pvKcSdIl7pK7MDdmP/30U3ur metSrly5I0eOkBlyZkTpTlKsS//+/XkTxf9kfU899RSZCQ2maEib+PVmFhCl AxtJe6C+zDxXNZgOy0xCpl6DU9555x1nTl6WmcISk7J06VJ+fFarVi1nzj17 9rDzDHxpuExHTF3kdWgW3HvllVdcOUuUKBFKLEo3B3/ppZecmQ8dOkSBjZ8r zhz46t3atWtnEinKMnNXatSo4ZwLzeEZ4XxYLDQ65+kqVapkD4s8tkaM0qlz pJRzzjmHemRnZjLqUHhyjplg76tdJRtfulDv77yP99hjj3E6eTBOoeiXn+Nf c801zqqmVm0kGDlyJCcuX76cM5Pdue6xDxgwgDM7o3S55fKUDEp0Dh/mzJnj KoDr6myh5ZrKcwrxpUsiJXfx8MMPcwwZ8UYBd0kUUpLQ9BNRekRSrEvXrl1D kbjiiitcTT0Lgygd2MTdHjp37sxGRL0Gp5hOf/Lkyc6cnTp1CoXvER05coRT 2JaJ1atXuw774osvUnrFihXjKFJWIqYu8jrkG54FCxa035Kz1+LwFaVTqMMz Zung/Lpclkfeu1WoUMGVbmZQuCY2t2/f3mVK0bCNzpwuW7ZsHHJELEzErXaU ToLmypWLUp588klXZtMwzHwbX+0q2SSiS79+/fjSRo8ezSlvv/02p/Tt29eV OS0tjTc9+uijnGJEcd2dOB5ljRe55XKBqTJd2Xjey5tvvmlfnS20XFNf6guR 65JIyV1s2LCBuhvaSi7L3kqhOO9r5kUjSo9IinUxUfrtt9/esmXLFi1alCpV ilNoVDt//nx/V5g5QZQObHy1hwULFvTu3Ztss3Tp0uZh9zfffMNbJ02axCn8 ipyBzC0UviNkUh5//PFQeKblDAsepF966aUBXV9mJaYuwjpcu3Yti/LMM8/Y B0kwSl+5ciWnNG7cONELziTIe7dKlSq50p944gmuLjNcZfr06cPpZEH20byN 7nj04DPmVjtKX7FiBaeQzdrtijdRYY77b1fJJhFdKDjnazGvUbBxETt37rSP w/P6br75Zv5pnilQEOLKGTFKl3s/nut+0003OY9Jw2E+Zo8ePeyrs4WWayrP KSeR0VPc5enZsydvJdtxbdq4cSNP1aC6Nc9TEKVHJJW6MGSA48ePNz+PHj3a qlUr3sU22ywJonRgI2wPZD48H9JmzJgxnOfAgQN8k6dKlSpmxx07dvBaCtWq VTOJ0Q5lyJ49e+BXmrmIqYuwDimc45/2jb7jCUfpo0aN4pRu3bolesGZhESi waeffpqryxWlm3kRrihdYnQep4u51Y7SzQtcHrRv3/64/3aVbBLR5dtvv+Vr MVH6LbfcQj8pnIt4HA6ezzvvPNaxfPnyofDSK3bOiFG63PuZdxacTyfNff5p 06ZJrk6uqTynnER0ibs8devWpU00pLXXSK9VqxbvOH369J//YurUqZxIYST9 3LVrl69rzIxo0yUalJOGw7QXhRYut5klQZQObCTtwTyKoo7pkUceGTRo0OLF i01o4QwY2rRpw4n33Xffl19+OXjw4Ouvvz4Ufmrm7Ghuu+02trtKUTBLwfxj iamLsA6NR33vvffsg9x11120iYZRJsVXlP7FF19wyvvvv5/oBWcSUhaly40u wCidbJZTihUrFq1d8UcA/barZBNslM7G5Vx0zgm/2JsjRw5eIJqfy+fLl8/O ad6V69Wrl0mUe78NGzZceumlofCbrTQQ/vrrr1u0aMGLaFWrVs21REa0q5Nr Ks8pJxFd4i7PZZddFoo0U2jhwoVekeVfRHsslZVQpYs3L7/8Mp8u4oy+LAai dGATsz2Yx8HUuTg/vWGWAnYGDHv27OHniU5y587tev7FY+q8efMKv9DxDySm LsI6NO+atWzZ0t7qcS/dnslgR+mm12vevLn0wjI5qYnSfRldgFE69YOcQmME 72v0266STbBRer169Tgl4geP+Dg33HAD/+RXEQn784gR76X78n4dO3a0w0i6 BPvtvGhXJ9dUnlNOIrrEV55Vq1ZFa5nmgN6UKVNGfrpMiipdvDGTXmK+tpMF QJQObGK2h6ZNm7KNbN682ZkeMWDg1RIqV6781ltvUU/XsGHDbt262dM127Vr x/u6HtoCQ0xdhHW4Y8cOzla6dGl7qx1NHTp0iD85QbGHKzOF4nwoE6VTZl7F jvw5/S29tsxMaqJ0X0YXYJR++PBhXq6katWq3tfot10lm2CjdPM+r/2ZIfOF RAq2OcXYhf3NxIhRutz70SiY5CAT69SpU6NGjerUqfPqq69G+zJjtKuTayrP KScRXeIrj5mGF3ENxu3bt2+1MLOpe/XqRT8jvoyQxdCmiwf8dQk64z+hi0GU DmxitgdeDYwiN+fdm4MHDzZo0MAVMOzbt++CCy4IOZZKiMbMmTM5FCxZsuQ/ wfTiIKYu8jrkZfEI12crf/rpJ175zRVN8XsEl156qfOLz0OHDjXfrXCuxGg+ ztKlSxfXeRctWhTzMjMdqYnS5UbncbqYWyOuxGjuDMec2+C3XSWVYKP02bNn s3HRNTqXaz527JipH/OmoZn3VbFiRaey9PczzzxjR+lyy+V56WXLlo19/Z5C yzWV5xSSiC7xlccsMh/x09gRwdujEUmlLrNmzbrpppvMVx4M33//Pe8V8W5A 1gNROrCJ2R7+93//l82kcePG6enp1K1MnTq1TJkyob8wAcOuXbt4zmStWrVW r15NMd6xMBEP26xZM979lltuoQLw6yQbNmxo06YNuYV/wnsi3kjsVFiHJorI lSsXdUM7duxYv379Bx98wEMqO5oyCwY2adKEosR58+bRAc0nZUN/j9KpgzNf iG7ZsiXpTucll/vwww9TKJL1npWkJkqXG53H6WJujRilr1mzhpdZy5EjR/v2 7Xm1cBogjB8/vkqVKoMGDTI5/barpBJslH7cYVwUJFNcTZItXbrUfJqqcuXK Jie5OAowOL1GjRo0OD1w4AC1/Lvvvtvo5fqqkdBy+WNJNDoePXo0eVcPd+px dcf9aCrPKSTBaDCO8rz55ptct6SasJCI0iOSSl34/bXzzz//jTfemDJlyv79 +3fu3EmBvfm86dixY+O60EwGonRgE7M9UJDG3+AIhW/u8V2gUHhaph0wOD97 baBepmDBgvXr1//6669NTrLBihUrmjw5c+Y033wJZcUvy/tFYqfCOqSePeIn 6QleU84VTZGgds4LL7zwlVde4b+dUfrx8Ppa5jZ7KPypC/P3888/H2y1nHVS E6X7Mrpgo/Tj4QWlnd+sL1CggNH0uuuuM9n8tqukEniUTsZ16623Rry6IkWK UMTuPAJVIActLq6++mr+wxWlCy13+vTpTmsy0LlKlSpF0dGmTZtiXh0j1NRX TgkJRoNxlIcGtrxV/qF5ROkRSaUuX375pRndh/7eiYT8T2XPvCBKBzaS9kAW xLMgGOqCu3fvTgNnNiVnwDBixAi7T3Hi/ErI0aNHO3fuzA/HDeQZqEdzffbl H4jQToV1SNlat27tdKrkSL/66iuSgwV1HdZ8Np0d5m233TZr1iwKHTnFFaUf D09y4MXrDJdffvnAgQMTrwdtSHQpVqxY6O9LjzIcpefIkcO1EBlFZVxpzjVe 5EYX7XQxtw4ePJgPbj/yoLjlzjvvdPaV5557br169RYuXOjM5rddJY9EdDGz x103Bw4fPvzmm2+aNepD4fV2SMSIK/UtWLDA3FEPhRdmbNCgwY4dO6644gr6 2a9fP1d+ieXSH97LNl5//fXma2LezeC4WFNfOWOSiC7xlYdfziXkX5ZfvXo1 7zJ06FDhLpkdhbqsXbu2YcOG5uY5c80110R7ESNLgigd2AjbA1lWWlraxIkT PW5QUF/P/fXHH388f/78GTNmTJ8+ferUqf379zdfCSlXrpy9I5nn+PHjKURx fiH9H45fO5XUIUUdc+fOHTt27Pr162MekEIREvS7776jSENYhp07d06ZMoWK ITl+JiWV/lNidCkowIQJExYtWuR8ScGFr3aVJJKny7FjxygaoaubM2dOzJdo yPpILzIc+es2HpbLi3vT+Hfx4sV0ddPDjB49+u233+anFaG/j9ckCDX1ldOD AHUJpDyAUavLgQMHyNCo3yE7stedyPIgSgc2AbaHRx55hHqNhx56yN509OhR GhSHUvsQPFMDO9UJdNFJ1tPFTMOI+KHGgQMH8lbXXBptZD1dsgbQRSeI0oFN gO2hSJEi1Gs0atTI3rR7927+PEetWrUCOVeWB3aqE+iik6yni4nDXatxMp07 d+ats2bNSn3Z5GQ9XbIG0EUniNKBTYDt4aGHHuKpm/3799+6datJnz17doUK FbhPGTlyZCDnyvLATnUCXXSS9XQxC7NXrVp13rx55l2Gffv2devWjV8bKVOm jPIPw2U9XbIG0EUniNKBTYDtIS0tzfnqR7Fixe6++25+A4Xp0aNHICf6JwA7 1Ql00UmW1MV8AjUUXmSpcuXKt91220UXXcQpxYsXd36XVidZUpcsAHTRCaJ0 YBNse5gzZ87DDz9sFtA2/cuTTz65ePHioM7yTwB2qhPoopMsqcuRI0c6d+5c tGhRpzvNli1biRIl+vbtmykWwsqSumQBoItOEKUDm2S0h3379i1dunTy5Mlp aWlYtiU+YKc6gS46ycK6HDt2bNOmTTNnzpw4cSL51UwRnBuysC6ZGuiiE0Tp wAbtQSfQRSfQRSfQRSfQRSfQRSeI0oEN2oNOoItOoItOoItOoItOoItOEKUD G7QHnUAXnUAXnUAXnUAXnUAXnSBKBzZoDzqBLjqBLjqBLjqBLjqBLjpBlA5s 0B50Al10Al10Al10Al10Al10gigd2KA96AS66AS66AS66AS66AS66ARROrBB e9AJdNEJdNEJdNEJdNEJdNEJonRgg/agE+iiE+iiE+iiE+iiE+iiE0TpwAbt QSfQRSfQRSfQRSfQRSfQRSf/BwAAAAAAAAAAAAAAAAAAMWf7vj7QAreHDKAM 6KIT6KIT6KIT6KIT6KITROnABtaqE+iiE+iiE+iiE+iiE+iiE0TpwAbWqhPo ohPoohPoohPoohPoohNE6cAG1qoT6KIT6KIT6KIT6KIT6KITROnABtaqE+ii E+iiE+iiE+iiE+iiE0TpwAbWqhPoohPoohPoohPoohPoohNE6cAG1qoT6KIT 6KIT6KIT6KIT6KITROnABtaqE+iiE+iiE+iiE+iiE+iiE0TpwAbWqhPoohPo ohPoohPoohPoohNE6cAG1qoT6KIT6KIT6KIT6KIT6KITROnABtaqE+iiE+ii E+iiE+iiE+iiE0TpwAbWqhPoohPoohPoohPoohPoohNE6cAG1qoT6KIT6KIT 6KIT6KIT6KITROnABtaqE+iiE+iiE+iiE+iiE+iiE0TpwAbWqhPoohPoohPo ohPoohPoohNE6XK2b98+Icz69evPdlmSC6xVJ9BFJ9BFJ9BFJ9BFJ9BFJ4jS 5UyePDkU5qOPPjrbZUkusFadQBedQBedQBedQBedQBedyKP07777rpaMH374 ITXBZIpBlG6zZ8+eUaNGdezYcfDgwYsWLYqYZ9WqVcui8NtvvwkbKh1kyJAh HTp0+OSTT+bMmfPnn396ZD5z5syKFSuWL1/unY3YtWvXuHHj3n77bTr4unXr Iub//fff58+f3zPMxIkTDxw4ICxz4Pj1ort376ZKpv898uzbt2/MmDGdOnWi ejh06JCv8pw6dWru3Lkffvhhly5dvvrqq/T0dMle27dvZ/UPHz7s2uSrqZw4 cWLatGndunXr06fPwoULT58+7avwASLXhVommQnVWO/evemi/vjjD7/nErZt ob34rcNgz7527VryG9T2vvnmG+9WGh9J1YX8QLS2ajh58qRrL7m5JcPjCc9O rWLSpEnk7siuSRpy8h7HjINg+xdGfmmSBp9gnyXZPb72k1SC7V981aG8K0m8 O/bV5UkihGQjj9L79+8fktG3b9/UBJM2nTt3vuuuu9q2bZuMgyNKd7JgwYIb b7zRJX3NmjW3bt3qypkzZ85oTWX06NExmyiZ4WOPPebasXLlyuvXr7czU2Kv Xr2uvfZazvbjjz9GO+yvv/761FNPuQ5bunTpjRs3mjzkTF5++eXcuXM78+TN m5f6zZjFTgZCL0o9EbVVurocOXJQgatWrRot52uvvea8tGzZsnXt2lVSEqqZ Nm3anHfeec7ds2fP3rhx42PHjnnsSB6yYMGCnP/LL790bZU3lalTp15++eXO DFdccYV3P548hLqQk6dCOstcrFgxGrMIzyJs23J78VWHwZ6dnEyjRo1c2f79 739TFyysDQlJ1YXi+Wht1fDZZ585dxGaW5I8nvDsFCNddtllzpy5cuXq0aOH d234Itj+JUN8afIGn2CfJdk9jvaTbILtX4R1KO9KAumO5V2eJEJIDfIoffr0 6Y3/Tr58+fgyXek0Vk1JLBmBBx98kIpE/yfj4IjSDcOHDzc2eOmll95+++0X XXQR/yxRooTzBsXJkyc9vBD1CN7t848//rjnnns4c/78+Rs2bHjvvffyT+pJ yY6cmamXdx3/hx9+iHhYGv6XLVuW85xzzjk33HADGTv/pFZNNUB5Dh48SP7H 5Ln11luvv/56c+Rhw4YJTSxAJF6ULo2dp+HOO++MmJM8Hmcgv1exYsULLriA f77zzjsxT2Fqj8yfOlNqA+Z0derU8bjhUK9ePZPTFaXLmwrJSuelRPLtNCov X748XzJdwsSJE70Lnwwkuqxdu9bUErW36667jv/+17/+tW3btpinELZtub34 qsNgz07No0qVKpxepEiRGjVqFC5cmH/efffd8udrMUmqLpIoy9loheaWJI8n PHtaWhr5Ot5UqVKlZ555xkTsAUaMAfYv8kuTN/gE+yzh7n7bTwoIsH8RVoK8 KwmkO5Z3eZIIIWXIo3QbriVyHcGHifFSuXLlEKL0hPG21kOHDrHPpB5t1qxZ /HT4yJEjDzzwANdP9+7dTeZdu3ZxYo8ePbZa/Pe///Vun99++y3v3rp161On TnHiF198wYkffPCBMzPZYL4wuXLl4gzR+qwmTZpwhvr16/OAna6CLJ2Md+DA gZynXbt2nKdt27bmsdqECRP44AUKFPjll1+8Cx84Ei9KFZ7vL7jDjehFV6xY wVdXrly5EydOZIRlLV68eCh8H2PHjh0ep6AWcvPNN1POF198kZxnRrj2qGDX XHMNH3Pu3LkRdxw1apTTV7uidGFToWZAPoeyFSpUaMmSJZy4aNEijvSKFi16 5swZ7yoKHIkuDz/8MPdEI0aM4JS+ffvy9T7//PMxTyFs20J78VuHwZ590KBB nNKsWTO+eU7/v/DCC5xIW2PWhpCk6kJWYLdSYvny5dz733HHHWbmjNzckuHx 5GcvXbp0KBwYr1u3jlMOHz5MEVEoHJzEMUErIgH2L8JL89XgE+yzhLv7aj+p IcD+RV4Jwq4k8e7YV5cniRBSRiqjdKqHKVOmpKenu9KpzmkIT3KQQ/A+wp49 e8gLzZ49m3aJmIGfkcWM0skFUci9ZcuWmGVetmzZtGnTaGBFf9MfLNw/PEon SKzatWvv37/fmUhWxt61Zs2aJnH16tVcaWRQcbTPNm3ahMIjX9ejcPIMlN6g QYOIe5lOLWKfRV7i3HPPpa2PPvqo667v0aNHzd/kt8lU7ZjBuIv58+fHcUWJ IHwiaeDn4BG96EsvvWR7J+PHYt5O37ZtG4UTrsSRI0fy7r1797Z32bt3L891 qVChAmdzRenCpkIdN2fr37+/M/27776LeNgUEFMXclx8A4riUmc6h4h58uSR j/i827bQXuKuw0DOzveKr7rqKhOFMtR7Ujr1m6mJBjMC1cVA8UYoPEvE+WRc bm7J8HjCs1PsRHkopVu3bs7d6Zicc8OGDT4qIjoB9i/CS/PV4BPssxLcPWL7 SQ0B9i/yShB2JYl3x3IbFEYIKSOpUXqpUqVowDVgwACy7vvvv5+nHlFPbTJQ FVWtWpUrJBR+FEXZ7DB+/fr1zz//PJ2In1hxVVMFko81eW666abLLruMM5Dj NcO94cOHmzw0Juratat5uhoKT0sbPHiwXXIaubdq1YoGaJyNDluxYsVPP/2U fyJKjwY/kypSpIhJmTlzJlfawoUL4zgguywSwpXOk1orV64ccS/vPouPSaxZ syaOIv3444+8O7WcOHZPhKC86G+//ZY/f/5QpCmFZEeh8OP+OIo3Z84crpmO HTvaWznyIRufPn263TNmiJtKv379OBuF/RELX6VKlTgKnwgxdenZsyeX2TVt ePTo0Zw+ZMgQ4bkkbTumvcRdh4Gc/ZJLLqGfL7zwgivb119/zQePz/nYpFIX Zt68eXyDsVevXibRl7kF7vHkZ6fGwAf5+OOPndkojuL0GTNmxLh+GUH1L/JL 89XgE+yzEtk9YvtJGQFG6QnWoXdX4kTYHcdhg3FHCIGT1CidLjwUfnJnZvgQ NWrU4K2jRo3KkycPJ1JczaN44vLLL1+7dq05CA2mXG8WGCpVqmSymQDehQnC 9+3bV716dZPufLWhTZs2zmLv2rWLZ85EA1F6NMqUKUP1Q83epIwfP54rTTL5 1sbc63CVp2TJkpRIPVfEvbxjiRIlStCme++9N47yZDiuSPIaUbAE5UVpIMyX 4HqATrzxxhu8KeaDXRsaAvO+ZvKAgVJ407vvvrtx40b+2xWlC5tK27ZtQ+GB sz37nWdN0NDbb8kTJKYuzzzzTCg8Z8A1k+TEiRN8L7dVq1bCc3m3baG9xF2H iZ/99OnTnMfuf02UGNQU6FTqwvDjAPrfWbG+zC1wj+fr7NxT33333c7HGWPG jEnEh9sE1b/IL81Xg0+wz0pk94jtJ2UEGKUnWIceXUm0E3l3x76sIMEIIXBS EKUzN998M8XbNPCZPXs2baJK46FNgQIFhg0bdvDgwQMHDvTt25eD54YNG5qD TJkyJRQO3d977z0aoO3Zs2fatGlm2tL333/P2ci5kV4c6pNDG/8X5Pk5Aw0W eJe6deuuW7eOUlauXMmPEckh09/mjC1btuSchQsXpkBi8+bNs2bNeu6558y1 IEqPyJEjR/g+QJMmTUwijZK40kigN998k3qZ9u3bk+m5XoOKxsmTJ/kpJ7WT tLQ0TjRPYE2KC+9YgmeyUWH45+7duxcsWCB/kvXqq6/ywb0nbyeDoLzovHnz +BLGjh3r2jRgwADetGnTJvmJKMgZPnw4j6avuuoql7hkg/xY6rbbbqOcGzZs 4FO4onRhUzEltOufhgCh8Ms+Ab6BKCGmLjVq1GAfaG/it6Weeuop4bm827bQ XuKuw0DOzpfs9BIMBfA8I/ett94S1oY3qdQlw3H/kGrJme7L3AL3eL7O3qVL F06h8cvx8CtyZLAUtFPKXXfdJa8Kb4LqX+SX5qvBJ9hnxb17tPaTMgKM0uOu BO+uxEbYHfuyggQjhMBJTZReqVIl10xyXuKGTIPidmf6O++8EwpPaNm4caNJ HDly5L59+5zZzBTxF1980ZnOk2fseelLly5l3WvVquVMp5j/4osvpvSmTZty yvLly/kglM7BvMGoiSg9Iubx8dChQ03ihx9+GIoEGSCNoSSHJd9lnrnUqVOH xnQc8j3++OPRdvHos/bv38+bBg4cSIfiO1QMddZ0Lu/CkKnyWl40TpQUPliC 8qLffPMNX7K9bpuZdUCj6ZjH37lzJw1pH3nkkSJFivBedC4a1bqykWqh8FoK vJRctChd2FRmzJjB6a6bseQTzDv7dhmSSkxdSpUqFYqyZBm/SiO/b+MdJ2fI 7CXuOgzk7PykMm/evBR3mcRTp07Vrl2bd2zYsKGwNrxJpS7Eo48+Ggq/n+ia b+/X3IL1eL7OTpEqmTMnXnbZZT169ODX6HLmzLls2TJ5VXgTVP8ivzRfDT7B Pivu3aO1n5QRYJTutxKEXYkLeXcsbyoJRgjJIAVRerZs2RYvXuxMP3bsGI9W nnzySdcu27dv5wqhIZj32XnZTHLszsRoUToH/8Tq1atdm3gOUsWKFfln586d OSft4sqJNV482LJlC2taokQJ53tPxlrJ6Fq3bv3666/zU0vi/PPPX7VqVcwj U8dB3ZPL2MuVK+cxJcOjz1q0aJGxOP6DOkSzBCu1Ve+l/MxKFKl/RTEjOC9q xpsrV650bTJ37ex7DjYLFy50eWCyL1ceqijeamZaxozSvZvK6dOnebkGMvau XbuSJ6eR9bvvvkt5TEkoRV5LiRNTF74xW7duXXsTP86jvkB4rphxssRe4q7D QM5u7rOVLVuWer1du3aNGTPmrrvuMvkpNBXWhjep1IWugnsfcwvO4NfcgvV4 fs++ZMkS86aYQeIN5ATVv8gvzVeDT7DPim93j/aTMpIRpQsrQdKV2Mi7Y3lT STBCSAYpiNIrVKjgSjfv1bZo0WKGBW/q3bu3a68DBw6MGjWqXbt25MN5/Ryi fPnyzjzRonR2ennz5rVPx3f1yV1zzoYNG/KRKZZwHQRRejT++OMPs6gv1ZJr 64gRI6ZPn+7M/NZbb3HmmE9RT5w4wd03WcpLL71k3vw955xzOnToEG0vjz7L TPsMhVeToJE1+fw///yTzJxvqhQtWjTarQzKzK8/0JguU88b/Pzzz7kGli5d 6trEE8xCsgUK0tPT69evT8c3H4Wh+iFdTOWYZ1VVq1Y1idGi9AxxU6E89icz 8ufPb2Ibvx9RTZCYulCnw37J3kRtKRSOwYTn8o6T5fYSXx0GcnZqCc5XhAx0 ap4G+fLLLwtrw5tU6vLxxx/zVdjRhS9zC9zj+To7DZe4D61Zs2aNGjXM217k KoNa4CXDvx+L1r/4ujRfDT6RPiu+3T3aT8oIMErP8FkJMbsSG1/dsbypJBIh JIkUROnOdzwZs8yOB+3btzf5169f/+KLL7IDd0Fe1HnkaFE6LwPrQfbs2Tkn hf2h8NN5+3IQpUeDRltcM8JpnGSwrAi5Te/VrRs0aBAKzz7iV8VPnz5N3qxQ oUJ8ui5dukTcSzJLs0SJEq4vX5s1nSJ+je7nn3/mhQRz5cpFw0zJZQZOUF70 +++/5yu1F20YPny4RyVEg5zY1KlTjZWZj8HxV8ZC4WVvd/+FWRWNpOQ1Tj2O HK2pkBx169a98sorQ+E1H5o2bUpegnsBCm/kJQ+EmLqwV4m4QAff4qtdu7bw XN5xsi97iaMOgzo7ydq7d+8yZcqQpz3vvPPuvvvufv36Ud/HE4/tN7ziI5W6 8NdCqd7sZSR9mVvgHk9+dooP+Z3ZZ599lm2NUowJX3bZZbyideIE1b/49WOJ OA15nxXf7h7tJ2UEG6XbSOowWlfiwm93LG8qcUcIyeOsROnmITjtWykKQ4YM 4cybN282nz+m/B07dqQK37ZtG79AKozSb7vttlD4UUu001WvXp1z8kxF/ryU CzPm6tWrl9+6ylz4slbzbIu0EL5fk+H4UG/Ez14zK1eu5Dw0LHKmb926lWev URdvr6yV4dlnkd3xJnvlVRpl8yb7vfL9+/ebRpji78E5CcqLmiv9+uuvXZv6 9OnDm+J4N5bqlqcQ8IIJa9asCQkg6/M+rHdTOXnypPn76aefDvmZpRAUMXXh UOfGG2+0N/GzBvtVymh4tO247UVeh4GfnTpu8ynJLVu28BHGjBkT9fr9kEpd +B5gtWrV7E1yc0uGx5Ofnb8LXKBAAddS7dTncrZXX31VVBexCKp/iduPxec0 JH1W3Lt7tJ+UkewoPUNch66uxEUc3bG8qcQXISSVsxKlL168mC+2a9euMc/C Hz7Lnj37gAEDnOm+onQaQYfCM16OHTvmfTrzBpNZHMaAe+k2o0eP5ttfZFO+ 4jrz8MtjCvHAgQM5j72a09ChQ3lTxFdRPPosGqrzc8/XX3/dtYnKH7GL/OWX X/jxd+isThrMCM6L7t69my+nffv2rk28llG2bNniWybFTBg7ePDgunXrQgLI wL2PKWkqGeHFAa6++uqQn/ufQRFTl+effz4UvoPk+kqOaXLt2rUTnsujbcdt L4aYdZjUs3/22WecLahbVSnTxazz1rp1a3ur3NyS4fHkZ+fZNY0bN7aPz71t mTJlvGpBTFD9S+J+zJfTEDqiOHb3bj8pIwVRurwOnV2JMz2+7ljeVOKIEJLN WYnSDx8+zOF01apVvU9hWq9Zg8UQMUrnhVzMkuwG86hi2rRp3mds3rw556Qx mmsTonQX1F+wjnny5PHbsdaqVSsU/saNhxflZcFogOa89cGYVzwifq7X+7k8 r0lbsmRJ12Q2MxPD+XrIr7/+Wq1aNU5/4oknzuLjyIxAvSg/0HetQUcVwj11 zPvb0eYB8o0pYt++fRnhxdMOWZhHiqQd/eTvL3sgaSoZji/R/Oc///E+YODE 1MU8PXS9hde/f3/jl4Tn8mjbcduLIWYdJvXsvJwCOXbXvdy4SZkuZi5rtDX0 hOaWJI8nOTv95FcpI0Y+fPOK8ketAj8E2L8k6Md8OQ2hI4pj95jtJzWkIEq3 K0HYlTCJdMfypuIrQkgBZyVKP+64ZW1mtkRk7ty5nK1JkyauMPLCCy8MWVE6 PyUpVKiQ6zgzZ87kFw2o5ikw8DijcXc0XqMYw6TT3/wJjBCi9DDUUHlMRAPP aJmXLl1aqlQpewkvys9yOF/OIgMcNmzYiBEjTA9l1tu0Pyv2wQcf8CZqIfZ5 vfsss3XcuHHOdP66H7F9+3ZOoZKYt5YeeOCBoIKHuAnQi3br1o2vizyPSaQK 4cTPP//cJNq6LFmyhDyb7an27t3Ln5V0fnzWJuLbo76ayqlTp1y3Ymjgz3PV 6NQpXiw9Q6AL1WG+fPmoeNWrVzc9C5WTHxRSmU2iXdsuPNq2L3uJrw6DOvuK FStcF0hhEuf59NNPI546DlKmyyeffMKFnzp1asQTCc0tSR5PeHbqqUPh6biu iSW//PLLZZddRpvuu+++iFfnl6D6lww/fkzY4BPss3ztbojZflJDUP2LvBJ8 dSXy7jiitcqbijxCSA1nK0pfs2YNL6yUI0eO9u3b01VT4sGDB2n4XKVKlUGD BnE2GkaxrDSaTktLO3bsGHXxHTt25JdcQlaUXqFCBU5/7733SOj0MLypWbNm vOmWW26hiz169Cgl0tHatGlDJTQBOZ2CvxgbCt+TpyH8gQMHyHnylx0YROmT J082a1hRBdLPb7/9dpwD/voGrzlMbrZDhw40UCKToeolj8Sf7SBlnS/sm88T mJs5J06c4Nl6uXPnJsMxA9tvvvmGG8+VV15p3rbetWvXnL+gFsWH6tq1K6c4 X5w/c+YMuxdqVFRs6ogphTTlR6tmWTY6Mn/0hKACkyeZMGHCuL+ze/fuwExR gMSLzp8/39QD1x5drEkxN66p5GxE5AwXLFhAdUu+K2/evJRC41+qeXNAWxde oorka968+aRJk+iY5DCpYGRZnPO1117zKGHEKF3eVKio9evXp8KT192zZw/J RCW/4YYb+JgDBgzwX6+JItHFPKRr0aIFOR+KEBo3bswpnTp1Mtns2s4Qt225 vfiqw8DPTu2NUsqWLUsBJ7WcHTt2UA2wn7/qqqvMNPXESbYuhrfffpu3UtQR 8SxCc0uSxxOe3cwAr1Wrlplesn//fvMCab9+/WJVuYig+hf5pckbfIJ9lq/d DTHbT2oIqn+RV4K8K/HVHUe0VnmXJ4wQUsbZitKJzz77zLkyUoECBbgSiOuu u85kI+MyefjTotwA+G0aV5Tet29fk5lG4qQ+2Thv2rlzp5nOxEdggRgaSZmD UANgl+iCJ7CF/vFROtmLvaSVCxpKU86xY8c6a5LkM0t7hayvb/NjJuKOO+4w idSP8x2VUPh5K20yn8qi9Hnz5pmc77//vkd5yAyd5yLz5A+FhMIvZJkPWxQq VIj6Ps5DfbT3NRLUewZhhVIkXpSfMUWDasnkpCDZGJTRhTrH77//3nlAWxcq g3PBJTJbM2oOhR9CeQdaEaN0eVPZtm0b32YxOc3fr7zyShzLLySORBeKAM09 BGeFV69e3XnDJ6IVyNu20F581WHgZyefHPHU5GCDjVKSrYvBrNvs8T10obkl yeNJzk5xiwnI6Vzkw2+99VZjlQ899FBQy88G1b/IL03e4BPss3ztbpC0nxQQ VP8irwR5V+KrO45mrUIbzJBFCCkjkSidnxaVLFkyWgaeCFStWrVoGcgn33nn nSY4D4U/OlCvXr2FCxeaPNu3b+dvFzLkPe67775ly5axq3dF6YcPH37ppZec wt1///1mKznkzp0788v7BvKBFHUfPHjQeRwaapk76ixTgwYNduzYwYPHfv36 +a2rzEVML+r0chEpX748Zyb5GjduzINoAw1U7eW433vvPd5qPn/DrFu37oEH HnAdv3bt2mvXrnVm8+6zcufO7Trd5s2bb7/9dmfbo6biXHnJvMvggW3dSSVx L9qzZ09n5hEjRvCS0UzRokXtK4qoy759+5o3b+4yJVKZ7Mvj2yvM1q1bOb/r dXt5UyGZSCwTzITC9xjP4pRO4ZNi8j/OYlMo8thjj7lmUESsbV9tW2gv8jpM xtkHDRpkFkYOhTtK2uvAgQMx69AXydbFwMvoEd6LXEnMLSNpHk9y9t9//71v 375m4UemYMGCffr0CXAiWYD9i/zS5A0+wT5LvrtB2H6STYD9i7wShF2Jr+7Y w1qFNpghiBBSRiJRelCQTGlpaSTfokWLKMyOmGf16tVTpkyZNm0aefKYB0xP Tx8/fjwNrCjYjrioC7k7yjBp0iQzHybacSZOnEgn9Z7KnvUQ9m5yeE7gjBkz SGiPoeiaNWuifZqNSkXNg9oA/X/cc4VtX9ChfvrpJ7pY58fK1RK4Lgy5I9LF 4zZONF2o416xYgXZEdnIhg0bApm3L2wqGeGlpMnA6dSpv7nhwpcu1BEvXLhw 9uzZ0T6N4WEFcoT2kqQ6FJ59586dU6dOpdoIcJaLE4W6ZAjMjUmSx5Oc/Y8/ /qC+j7JNnz59y5Ytgb8yf7b8WIafBp9gnyX3Y3o4W/1+RhK6Em9rFdpgho4I QUOUDrSRJC8KEgS66AS66AS66AS66AS66ARROrCBteoEuugEuugEuugEuugE uugEUTqwgbXqBLroBLroBLroBLroBLroBFE6sIG16gS66AS66AS66AS66AS6 6ARROrCBteoEuugEuugEuugEuugEuugEUTqwgbXqBLroBLroBLroBLroBLro BFE6sIG16gS66AS66AS66AS66AS66ARROrCBteoEuugEuugEuugEuugEuugE UTqwgbXqBLroBLroBLroBLroBLroBFE6sIG16gS66AS66AS66AS66AS66ARR OrCBteoEuugEuugEuugEuugEuugEUTqwgbXqBLroBLroBLroBLroBLroBFE6 sIG16gS66AS66AS66AS66AS66ARROrCBteoEuugEuugEuugEuugEuugEUTqw gbXqBLroBLroBLroBLroBLro5P8AAAAAAAAAAAAAAAAAACDmbM+zAFrg9nC2 n/MAN9BFJ9BFJ9BFJ9BFJ9BFJ4jSgQ2sVSfQRSfQRSfQRSfQRSfQRSeI0oEN rFUn0EUn0EUn0EUn0EUn0EUniNKBDaxVJ9BFJ9BFJ9BFJ9BFJ9BFJ4jSgQ2s VSfQRSfQRSfQRSfQRSfQRSeI0oENrFUn0EUn0EUn0EUn0EUn0EUniNKBDaxV J9BFJ9BFJ9BFJ9BFJ9BFJ4jSgQ2sVSfQRSfQRSfQRSfQRSfQRSeI0oENrFUn 0EUn0EUn0EUn0EUn0EUniNKBDaxVJ9BFJ9BFJ9BFJ9BFJ9BFJ4jSgQ2sVSfQ RSfQRSfQRSfQRSfQRSeI0oENrFUn0EUn0EUn0EUn0EUn0EUniNKBDaxVJ9BF J9BFJ9BFJ9BFJ9BFJ4jSgQ2sVSfQRSfQRSfQRSfQRSfQRSeI0oENrFUn0EUn 0EUn0EUn0EUn0EUniNK92bhx44Qw27ZtO9tlSR2wVp1AF51AF51AF51AF51A F50EEqUfOXIkwBBRFZ988kkozMSJE892WVIHrFUn0EUn0EUn0EUn0EUn0EUn cUfp+/bt69mz5+OPP168ePFs2bIVLVr0iSeeGDBgwNGjR5MUOp4VEKV7s2fP nlGjRnXs2HHw4MGLFi3yyEkNZsyYMZ06dRo3btyhQ4ckBz9w4MCyWJw8eTLa 7tu3b+c8hw8fdm1atWpVtAP+9ttvrswnTpyYNm1at27d+vTps3DhwtOnT0sK nwz8etHdu3fTFdH/Hnni0MXFrl27aN+33357yJAh69at+/PPP6PlPHPmzIoV K5YvX+6RJyOsDh2qQ4cOZH1z5szxyCzPmVTkulANkJl8+OGHvXv3Jmn++OMP v+eKWYe+2rYTD3sxSLT2ZS+0lfL07du3a9eu06dPP3bsmEdmv6RSF6EVeJub 3OPF4RtPnTo1d+5cusYuXbp89dVX6enpdvE8DkvNw2+dRCMoPxZHJfz+++/z 58/vGYb6dDqCpAAS03AhqW1DpvNjTMz+xVclZMg6iPgUjLtIGogvSie/WqJE iVAk7rrrrk2bNiUvgDRQ9d4VZuXKlck7C6L0aCxYsODGG290qV+zZs2tW7fa mV977TVnNhrWUaccs3FSjxmxjTn57LPPIu5LvWHBggU5z5dffunamjNnzmgH HD16tDPn1KlTL7/8cmeGK664wns8kjyEXpTCpMmTJz/11FM5cuSgAletWjVa zvh0Mfz66690FlcFli5deuPGja6c69ev79Wr17XXXst5fvzxx4gHJJf72GOP uQ5YuXJl2j3unClAqAsFb9R4nAUuVqyYPPgR1qG8bTvxtpcMsda+7CUtLc2c lKEW271796CilNToIreCmOYm93i+fCONztq0aXPeeec5t2bPnr1x48auYRFV frSj3XTTTcIKiUlQfsxvJbz88su5c+d2bs2bNy918d7FiGkaLuS1nZE5/Zik f/FVCRky5xa3gvEVSQ9xROmjRo0yHUGRIkWefPLJbt26NWvWrHjx4pxYq1at pMaQDI2n+HT0R/LOgig9IsOHDzdt4NJLL7399tsvuugi/knDN9fdM7Is3kT2 VbFixQsuuIB/vvPOO96NU+KEaTgccd969eqZPC7XevLkSeEBf/jhB+pJKZFM m8aD5cuXZ79El0DtwbvwyUDiRXfv3s2FNNx5550Rc8atizlR2bJleZdzzjnn hhtuIIfJP/Ply0etyOT897//7apkqlj7gH/88cc999zDGfLnz9+wYcN7772X f1LURLFQHDlTg0SXtWvXkqVwIamurrvuOv77X//617Zt22KeQliH8rbtwsNe MsRa+7KXvn37moZ68cUX8zNZ/vnuu+/GrBAJKdBFbgUSc5N7PHlOZwmphm+8 8UZzvUSdOnWcY6I33ngj2tHIsfus/qgE5cfklXDw4EEKJjmFZLr11luvv/56 k2fYsGEeJfE2DbvY8trOjH5MoouvSsiQObdEFIyjSKrwG6Xv2bOncOHCfGnP PPMMVZ3ZdOTIkVdfffWSSy5Zs2ZNcoLHvzFp0iQuBqL0wPG21kOHDnFMTj3a rFmz+Okwqf/AAw9wXXXv3t1kXrFiBSeWK1eOxuC8Ow/oaBi7Y8cOj8ZJJdka ieXLl3Mfd8cdd0R8Nk0DSafJu1zrrl27OL1Hjx72wf/73/9ytlOnTpGrpGyF ChVasmQJJy5atIjbf9GiRc+cOSO0sqCQeFG6unx/Qd4sohfNSEwXpkmTJnyE +vXr870I0oK8JQUhAwcOdOak+ITLkytXrmhOmPj22295a+vWranyOfGLL77g xA8++CCOnKlBosvDDz/MHcSIESM4hcJULvDzzz8f8xTCOhS2bRfe9pIh09qX vWzZsoX7eoppJ0+ezF0k9aTVq1enDlQ+r8CbFOgitAKhuck9nq+cN998M6W8 +OKL1F9zCalarrnmGi7S3LlzTTmbNm1KKZdffrl9ZGpaPqrek6D8mLwS2rVr xxfbtm1bM0diwoQJbE0FChT45ZdfIhYjpmnYRZLXdmb0Y0Jd5JWQIXNucSsY X5FU4TdKb9WqFV9U8+bNI2agCoyYTo53zpw5aWlp+/btk4eLCxcupF1oaGBv Invhkkii9NmzZ1M2KoPfUiFKjwg16dq1a+/fv9+ZSI2fo/eaNWuaxJdeeilk BX6mzxLetnVBhkb7knnaz5SJvXv38gPKChUqRHStq1ev5nSycY+z0ACEs/Xv 39+Z/t133wk9duAIn0ga+AFixCg9QV2oKzz33HMp56OPPuq6C3H06NFoe5kO KGKE2aZNm1D4TuPvv//uTKfyU3qDBg3iyJkaYupCHoyD0mbNmjnTOUTMkyeP dxfjxLsOhW3bSUx7EWrty144GqTmR03OmZm6zuOO+88Jkmxd5FaQoLl5e7yY Obdt20YBoSvnyJEj+ey9e/c2iY888kgoPJTwPkuCBOjHImJXAo0QaTw1aNAg V04T+1F4YB8npmlERF7bmc6PufDQRV4JTjycW3wKJl4kDfiK0ukyeZ7DhRde SA5KGPKtWrXq3nvvPf/887k2aPxVtmxZCoztnKVKlaLxVL9+/ciVPf3005dc cgnvki1btkaNGpn79n369ClatCi5UN5Kf/BAjHwLZxg8eDD9LF68OP1NlU+Z OeeHH37ot1SI0n3Bz6SKFCnCP3/77bf8+fOHIk1du+mmm0Lhx8p+TzFv3jwe wvfq1StiBu5hzzvvvOnTp0d0rTNnzuR0GgN6nIjaIWcjXx2x8FWqVPFb+AQJ yosmrgv3g8SaNWvk5fGOMPmYBQoUcKWT7YfCczXjyJkaYurSs2dPvnDXfMvR o0dz+pAhQ4Tn8q5DYdt2EtNehFrL7YXcO88OrV+/vrCQ8ZFsXYQ1k6C5xfR4 ceQk5syZw4Xv2LGjSbz77rtD4TmrMXdPhKRG6b4qgXTnSqCYwd4a0zTkRKzt TOfHXPgdPUWsBCfezi0i3gomXiQN+IrSJ0+ezFfUsmVLYbxHQxUTTjvJnj17 ly5dXJnJWdGmBx980LxE4KRFixaczYyeXBQrVowzcGh97rnnDh061JmBBPVb KkTpvihTpkzI8Z5Reno615798M5MgIz2FD4aNBYLhe/2RJxINmLECD7su+++ u3HjRv7b5VrHjx/P6d7zTtu2bRsKjxDtE73wwguh8GtxvkqeOEF50cR14ZfH aZwrL0xGLCdsbrq6rrFkyZKUSD1XHDlTQ0xdnnnmmVB4orJrltSJEyf4Xm6r Vq2E5/KuQ2HbNkjsRai13F6++uorPtHs2bMlhYybZOsirJkEzc3b48WXk+ja tSuf2kz1yfjLgSfbgpIaDfqqBGMv9ovVEtOQE7G2M50fc+E3So9YCU7iiNI9 FAykSBrwFaX36dPHtFVJsLd582ZygLxL69atly9f/vPPP9MIl2/Ikz83YTPD UTpTs2ZNOsuyZcu6d+9OwTOlXHTRRXv37qVsK1euJGkaNmzIOSnD+DBm6osJ rcnTnn/++Y0bN54yZcqYMWN4xouvUiFKl3PkyBG+idGkSRNOmTdvHtfe2LFj XZkHDBjAmzZt2iQ/hblVSOZsb6XmUaBAAdp62223Ube7YcOGiK6VBt2cTk7y zTffJGfYvn17MlLX2zqmhPYkbfLbofDzF++l7QInKC+auC48G5Bqj3/u3r17 wYIFHnNdGG8nfPLkSZ4xRSKmpaVxIuXkXUyKr5ypIaYuNWrUoILdfPPN9iZ+ iempp54Snsu7DoVtmxHai1Brub2899577Gl5Lu7vv/++aNEiam9xrH/oTbJ1 EdZMIubm7fHiy0laDx8+nB9nXHXVVc62UaRIkVA4Puzdu/eLYeiPpUuXeh/Q L8mLBuWVwLz66qsRG63QNCR41Ham82Mu5Lp4VIKTOKL0aAoGVSQN+IrSeXId MWvWLEmw16BBA87fo0cPZ/r333/P6aVLlz527JhJN1E6Bc/OdLNU0dy5c01i x44dOdGel25Ca+oIvv7660RKhShdjnl8PHToUE755ptvOMVeWIl04U2+7qc9 +uijofDraeZdGyd16tQJhReU4GWsornWDz/8MBQJMlUa65lsM2bM4HTXs7Bp 06aZ9RloxCcvfOIE5UUT1GX//v2cZ+DAgcOGDeM7PwwFPNRRRtsxphOmfc1D LlKTDs595eOPPx53zhQQU5dSpUqFoixZxsuZyp9KeNehsG0zEnuRay23l+bN m9PfhQsXpoOTRZvloXLmzEl9rnyKfkySqou8ZhIxN2+P5yvnzp07W7Zs+cgj j3Aozp7B5cGMFk6oG3366adjjsHlJC8alFdXRvjdAV419JprrnFtEnYlHkhq OyOz+TEXMXURVoLBb5TuoWBQRdKAryi9du3afF1btmyJmZkqkJ3z1Vdfbb+2 Wb16dT7UsmXLTCJH6RUqVHBlNjMeR48ebRIlUXr9+vUTLBWidCHUJPjOUokS Jcy7MOZO0cqVK135zR0D+/5SNHbt2sXvapmbV07M28RmOmLMKJ3Mk8aDr7/+ Oj/nJc4///xVq1ZxttOnT/OaFXTSrl27kiEvX7783XffNe8yEJQir6LECcqL JqjLokWLOA+/NR8KvxtilrGlPj3aMpUxnfBvv/1G3VPo75QrV86eDyDPmQJi 6sI3ZuvWrWtv4jfFKMYTnksYpXu37Qyxvci1ltsL9yPkLswCC5dccolZhpH8 f1A31ZOqi7xm4jY3b4/nN+fChQudxkIDt9WrV7vycJR+8cUXN2rUqFOnTg0b NjQjrCeffNK7DHKSFKXLq4vhiVghq4OQdyUeSGo7I7P5MRcxdRFWgsFvlB5N QQ/8FkkDvqJ0nsUXCr+aFDOzeYH93//+t73VBN4jR440iRylV6pUyZXZvMvz +eefm0RJlP7tt98mWCpE6RKoVzULvU6ePNmkk16caD8znTJlCm+SL0bx8ccf 8y62We3Zs4d6llD4tpiZjujhWkeMGDF9+nRn+d966y3OfNddd5l0ymN/IyZ/ /vzGr8b3sc64CcqLJqiLmU5JFC9e/Mcff6RxGVU71TP36UWLFo14L8vbCZ84 cYIqPxSOdl566SWz4us555zToUOH+HKmhpi6UF8QCr9xY2+qWLFiyM+qGjE7 MknbltuLL62F9mIWLibat2/PS/xRkYwP+fTTT4W14U1SdZHXTNzm5uHx4siZ np5ev3598gbmE040lCB7cc7f3rlzJw30jhw5YlI2btx49dVXc/6g5mAkKUqX V1dG+K1DHhiS0M4a8NuVRENS25nOj7mIqYukEpz4itKjKeiN3yJpwFeUztML CXsaiY1Z4sY1sYQx00vIS5vEaFG6WVbUb5Ruh9Z+S4UoXUKLFi24llzTOE19 zpgxw7XL8OHDeZP8O5488Ykcmn2rjfpZPtrcuXN3/4VZGo68N/087rnCGx2z dOnSofBjd+erZD///HPdunWvvPLKUHjtmqZNm65fv57DHiqJsORBEZQXTVAX M8+2RIkS1Kk5N5k3uyPu7u2EeSoadZG8Psnp06dJuEKFCvEuXbp0iSNnaoip S/ny5UNRFm3g+8+1a9cWniuOqZt225bbi1+tJfbCs8FD1upnFMPz0395bXiT VF3kNRO3uXl4vLhzEhSQTJ06lZsEEfPTjWbGTufOnWMeXEKSonR5JVAr5SUW c+XK5VoLNPGuxIVHbWc6P+ZCPhNJ2OTkzs1DQSF+reAs4itKNyHuO++8EzOz eWzkXP/QQPXDW9u2bWsSUxCl+y0VovSYmIfs5cqVc72CsXTpUt5EwzrXXuZN ZPlLHzz4rVatmit9zZo1IQHUrryPb77fHfHrzCdPnjR/P/300yE/sxSCIigv mqAuFJNwHnv1WnPkiK/MezjhlStX8qaPPvrImb5161aeQHjBBRfwEn/ynCkj pi7c9d944432Jr5xZ164jkkcUXrG39u2L3uJW2sPe2nWrFkoPOfQ3osrqnjx 4vJL8yCpushrJm5zi+bxEsnpLD9P+Im5VhX1CFzIoCZLJykaFFbC/v37zSJy rq/xBtWV2Ni1nRn9mAu/a7zEbHJC5+ahoF/kVnAW8RWlL1myhGuGWpE9qdvF 4sWLOXPEZRtN9Dts2DCTmIIo3W+pEKV7M3r0aF7XhZq63dHs3r2ba699+/au Tc8991wo/LBJuEyKWc2sdevWrk3r1q2TuNZbb73V+xRmYoD3bPMzZ87wI+Cg 7vjJCcqLJqjLn3/+yRMbXn/9ddcmagMRux7GwwkPHDiQN9lLCJr1VPn9R3nO lBFTl+effz4UvpXtejXSVFe7du2E54ovSne2bV/2ErfWBtteunXrxm2Mv8Lp hD/lmTdvXvmleZBUXeQ1E5+5eXi8uHO6MOuk8dcYo3H69GleZq1OnTq+jh+N ZESDwkogoXkuUyjS9PWgupKIuGo7M/oxF36j9IxYTU7i3LwVjAOhFZxFfEXp BH+2JhR+QBAxg/lO6JEjR3iVm+uvv965YAvD71ATFDabRF9ReqdOnTjRtZzj cc/Q2m+pEKV7QD6E39bJkydPtAkS/ODYtdYZdXA8B09+U8LMAo24xBbJesjC PJImf0g/+fvdHtSqVSsU/oaF98DBvCXxn//8R1j4oAjQiyaoCy9KXLJkSdd0 PvNoOOILpB5OuEuXLqHwBwuc92AZ85oef3JdnjNlxNTFPMJzvSTYv39/Tp82 bZrwXPFF6a627cte4tPaYNvLhAkTOMVe4viee+4JxRsF2SRbF3nNxGFu3h7P V85oc275GQexb98+j+ObF+6oz/UuiZBkRIOS6vr111+rVavG2Z544omIE2MS 70qEtZ0Z/ZgLD13ia3IxnZtEwWgkaAVnEb9R+ty5c3nGfu7cue3Z6VS35Hba tGnDP++//36+fFdI/9NPP/FByHeRUZh0X1F67969OdGeu+IdWvsqFaL0aFCF 8HgnZ86cHpn5vlkovHqnSRw3bpwR1CSSAQ4bNmzEiBG21yKMEFOnThW27Yiv /CxdurRUqVLLli1zZaZLYPWd74udOnXKdV/98OHDvHpbkSJFUrxYekagXjRB XYw7pV2ch+Wv5hHbt2+3T+rhhCkc4k32J+Q++OAD3kTOx1fOlBFTF6pD/kZD 9erVTc9C7YfCUW5LJtHbCjI869BX27aJ9oqcXGuhvdDFFi9enBLLli3r7Gc3 bdrEo/7GjRt7lFNOsnWR14zc3Axyj+edc8mSJdQp2yOpvXv38te9zaeiKexp 1aqV62v1FNvwB0lDft709yYZUXrM6iLhzOvJDzzwgOsyvYlmGnarkNd2ZvRj LqLpIq8EFzE/qCFUMBFdFOI3SieeffZZrijy/C+88MLIkSNXrFgxevToFi1a cNhG6dRfUM41a9bw6nw0YCQ3lZ6evmfPniFDhphFWb///nvnkX1F6SaRup7Z s2fT8FZ4A9xXqcxn8mhUK6yfLEBMa508ebJZYI0GZfST5BjnwKwGsHv3bv6K H9nCggULyOdTP5U3b15KufDCC52PvM3nCSI+xnr77bd5K5mbsG1HdK28DDKN LDp06DBz5kyyYmo51GBYfWq6Zo0aKmr9+vWp8NRIqIVQBEIlv+GGG/iYAwYM EBYjQCRedP78+XP+gidqki81KeYuUIK6nDlzhl10njx5SHoKZijlo48+4ulP zqXtdu3aZc7evn17PlTXrl05xazGQGfk0tLwn3y1ue/xzTffsLVeeeWVvGKG PGfKkOjCi4SHwt9QPnr0KIWvFItyivMWZcTaFtahvG1HJFooItTal72Yry89 9NBD/KB5y5Yt/CYXueWgPqOTbF3kViA3N4Pc43nn5IUiSX260kmTJlGToPCG quWWW27hvV577bUMx8rtlStXpgawdetWupxVq1aZV33vuOMO1xda4yZAPyas BGqN5kLIHCgwoBHHuL9DGkUrTDTTsFuFsLYzMq0fk+gir4QMsXPzpWAiuigk jij9yJEjrVu3NsvbusiXL9+YMWNMZgpuOXS3oWpxHdlXlE7FMCtEhcKvWtCJ 9u/ff1xwA1xeKnJWnE59h+Sd2ayBt7WSvdjrrbkoVaqUyU+ejac1spnwHxTk 02jIeVh+fBwKdwf2Sc3KqMJPn2dEca1jx45lH2hkdbZk5wfB6UQ8yjY5zd+v vPJKUB2WLyRelDp9D13ef/99kzNBXSjM4A9whMLWZ9ZVLlSoEK+tx9AZPcpD pTU5586da6yycOHCdDrzmTNKnzdvXhw5U4NEF4oAK1SoYC7cVHj16tWdt2cj 1rawDuVtOyIey81JtPZlL9RFPvXUU6Yq+AUuRj5FPybJ1iVDbAUZYnMzyD2e d06qgfz585sLpBEEjxeYihUrnj59mrJR3HLfffc525UzGymbnp7uXQw5wfox xrsSzPxYDyg8jlaYaKZhtwphbTOZ0Y9JdPFVCULn5kvBBHXRRhxROkMBMDk3 45RC4QWFatWqtXz5cldOGnzxA0RD0aJF7ZXMCZ68V61aNVf65MmTeUdnlE7M nDnTfECKa54/impu1EybNi1a+eWlot6E743Uq1dPWDmZnZhRurMLjkj58uWd u4wYMYKXJjZVbfdN/N3wkONbEk7M92flX/I1IyzX0grbt29v3Lix6yt71157 rf08d8+ePffff79zQHfllVcKvz2dDBL3oj179nRmTlCXzZs333777WwdDFWX a1U6byecO3duZ+Z169Y98MADrjy1a9deu3at69TynClA+KSYAkJnc6KhLrVq 18yWiLUtr0N527aJZi+MRGu/9tK9e3deSoWhv4cNGxaznHKSrQsjqRlGYm4G uceLmXPfvn3Nmzd3VnUofEOyc+fOzq/n/Pnnn0OHDnWuZh8Kx+pNmzYN9sMQ gfuxjFiVYNbG9MBDi2imEbFVCGubyXR+TKiLvBKEzs2Xgonrooq4o3TmyJEj FO6OHTvW+QnRiOzcuZNi5kmTJpFPiytyjMzhw4dpyEm2Q0emHsrv7sJSkSnR KRYtWpRASTMTwt7NL1TJaWlpHreG1qxZ4/w8YlLhObQzZsygIrnuermgUfaC BQuonXhnSwE6dTkefqeDCub8GEoi0AHJ1qZMmcIWF0jOpOJLF4oiFi5cOHv2 7GiPsxO3Annb9otEa1/2QpHhhg0bfvjhB7rqoD45akilLnIriGluSeK3335b sWIFdXYkDdW5x7TeHTt2zJw5c/r06UuXLk3GpIsk+bGzQrRWIa/tjMzpxyT4 qoRgCUQXJSQYpYMsSVbyolkJ6KIT6KIT6KIT6KIT6KITROnABtaqE+iiE+ii E+iiE+iiE+iiE0TpwAbWqhPoohPoohPoohPoohPoohNE6cAG1qoT6KIT6KIT 6KIT6KIT6KITROnABtaqE+iiE+iiE+iiE+iiE+iiE0TpwAbWqhPoohPoohPo ohPoohPoohNE6cAG1qoT6KIT6KIT6KIT6KIT6KITROnABtaqE+iiE+iiE+ii E+iiE+iiE0TpwAbWqhPoohPoohPoohPoohPoohNE6cAG1qoT6KIT6KIT6KIT 6KIT6KITROnABtaqE+iiE+iiE+iiE+iiE+iiE0TpwAbWqhPoohPoohPoohPo ohPoohNE6cAG1qoT6KIT6KIT6KIT6KIT6KITROnABtaqE+iiE+iiE+iiE+ii E+iiE0TpwAbWqhPoohPoohPoohPoohPoopP/AwAAAAAAAAAAAAAAAACAgLN9 Rx8AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAA4Gzy/wCMPwsM "], {{0, 102.}, {497., 0}}, {0, 255}, ColorFunction->RGBColor, ImageResolution->144.], BoxForm`ImageTag["Byte", ColorSpace -> "RGB", Interleaving -> True], Selectable->False], DefaultBaseStyle->"ImageGraphics", ImageSizeRaw->{497., 102.}, PlotRange->{{0, 497.}, {0, 102.}}]], "Output", TaggingRules->{}, CellChangeTimes->{3.834319737293241*^9, 3.834319829329213*^9, 3.8343200432658997`*^9, 3.8343348485779963`*^9, 3.834405641286523*^9}, CellLabel->"Out[7]=", CellID->561941367] }, Open ]], Cell["\<\ Compare the mean earnings in 1978 among the treated and untreated in the \ matched groups; the treated group has income about $800 higher even though \ their demographics are, on average, about the same:\ \>", "Text", TaggingRules->{}, CellChangeTimes->{{3.834319863251855*^9, 3.834319877251977*^9}, { 3.834320083608941*^9, 3.8343201057491207`*^9}}, CellID->1939299289], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{ RowBox[{ InterpretationBox[ TagBox[ DynamicModuleBox[{Typeset`open = False}, FrameBox[ PaneSelectorBox[{False->GridBox[{ { PaneBox[GridBox[{ { StyleBox[ StyleBox[ AdjustmentBox["\<\"[\[FilledSmallSquare]]\"\>", BoxBaselineShift->-0.25, BoxMargins->{{0, 0}, {-1, -1}}], "ResourceFunctionIcon", FontColor->RGBColor[ 0.8745098039215686, 0.2784313725490196, 0.03137254901960784]], ShowStringCharacters->False, FontFamily->"Source Sans Pro Black", FontSize->0.6538461538461539 Inherited, FontWeight->"Heavy", PrivateFontOptions->{"OperatorSubstitution"->False}], StyleBox[ RowBox[{ StyleBox["MapReduceOperator", "ResourceFunctionLabel"], " "}], ShowAutoStyles->False, ShowStringCharacters->False, FontSize->Rational[12, 13] Inherited, FontColor->GrayLevel[0.1]]} }, GridBoxSpacings->{"Columns" -> {{0.25}}}], Alignment->Left, BaseStyle->{LineSpacing -> {0, 0}, LineBreakWithin -> False}, BaselinePosition->Baseline, FrameMargins->{{3, 0}, {0, 0}}], ItemBox[ PaneBox[ TogglerBox[Dynamic[Typeset`open], {True-> DynamicBox[FEPrivate`FrontEndResource[ "FEBitmaps", "IconizeCloser"], ImageSizeCache->{11., {2., 9.}}], False-> DynamicBox[FEPrivate`FrontEndResource[ "FEBitmaps", "IconizeOpener"], ImageSizeCache->{11., {2., 9.}}]}, Appearance->None, BaselinePosition->Baseline, ContentPadding->False, FrameMargins->0], Alignment->Left, BaselinePosition->Baseline, FrameMargins->{{1, 1}, {0, 0}}], Frame->{{ RGBColor[ 0.8313725490196079, 0.8470588235294118, 0.8509803921568627, 0.5], False}, {False, False}}]} }, BaselinePosition->{1, 1}, GridBoxAlignment->{"Columns" -> {{Left}}, "Rows" -> {{Baseline}}}, GridBoxItemSize->{ "Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}, GridBoxSpacings->{"Columns" -> {{0}}, "Rows" -> {{0}}}], True-> GridBox[{ {GridBox[{ { PaneBox[GridBox[{ { StyleBox[ StyleBox[ AdjustmentBox["\<\"[\[FilledSmallSquare]]\"\>", BoxBaselineShift->-0.25, BoxMargins->{{0, 0}, {-1, -1}}], "ResourceFunctionIcon", FontColor->RGBColor[ 0.8745098039215686, 0.2784313725490196, 0.03137254901960784]], ShowStringCharacters->False, FontFamily->"Source Sans Pro Black", FontSize->0.6538461538461539 Inherited, FontWeight->"Heavy", PrivateFontOptions->{"OperatorSubstitution"->False}], StyleBox[ RowBox[{ StyleBox["MapReduceOperator", "ResourceFunctionLabel"], " "}], ShowAutoStyles->False, ShowStringCharacters->False, FontSize->Rational[12, 13] Inherited, FontColor->GrayLevel[0.1]]} }, GridBoxSpacings->{"Columns" -> {{0.25}}}], Alignment->Left, BaseStyle->{LineSpacing -> {0, 0}, LineBreakWithin -> False}, BaselinePosition->Baseline, FrameMargins->{{3, 0}, {0, 0}}], ItemBox[ PaneBox[ TogglerBox[Dynamic[Typeset`open], {True-> DynamicBox[FEPrivate`FrontEndResource[ "FEBitmaps", "IconizeCloser"]], False-> DynamicBox[FEPrivate`FrontEndResource[ "FEBitmaps", "IconizeOpener"]]}, Appearance->None, BaselinePosition->Baseline, ContentPadding->False, FrameMargins->0], Alignment->Left, BaselinePosition->Baseline, FrameMargins->{{1, 1}, {0, 0}}], Frame->{{ RGBColor[ 0.8313725490196079, 0.8470588235294118, 0.8509803921568627, 0.5], False}, {False, False}}]} }, BaselinePosition->{1, 1}, GridBoxAlignment->{"Columns" -> {{Left}}, "Rows" -> {{Baseline}}}, GridBoxItemSize->{ "Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}, GridBoxSpacings->{"Columns" -> {{0}}, "Rows" -> {{0}}}]}, { StyleBox[ PaneBox[GridBox[{ { RowBox[{ TagBox["\<\"Version (latest): \"\>", "IconizedLabel"], " ", TagBox["\<\"1.0.0\"\>", "IconizedItem"]}]}, { TagBox[ TemplateBox[{ "\"Documentation \[RightGuillemet]\"", "https://resources.wolframcloud.com/FunctionRepository/\ resources/MapReduceOperator"}, "HyperlinkURL"], "IconizedItem"]} }, DefaultBaseStyle->"Column", GridBoxAlignment->{"Columns" -> {{Left}}}, GridBoxItemSize->{ "Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}], Alignment->Left, BaselinePosition->Baseline, FrameMargins->{{5, 4}, {0, 4}}], "DialogStyle", FontFamily->"Roboto", FontSize->11]} }, BaselinePosition->{1, 1}, GridBoxAlignment->{"Columns" -> {{Left}}, "Rows" -> {{Baseline}}}, GridBoxDividers->{"Columns" -> {{None}}, "Rows" -> {False, { GrayLevel[0.8]}, False}}, GridBoxItemSize->{ "Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}]}, Dynamic[ Typeset`open], BaselinePosition->Baseline, ImageSize->Automatic], Background->RGBColor[ 0.9686274509803922, 0.9764705882352941, 0.984313725490196], BaselinePosition->Baseline, DefaultBaseStyle->{}, FrameMargins->{{0, 0}, {1, 0}}, FrameStyle->RGBColor[ 0.8313725490196079, 0.8470588235294118, 0.8509803921568627], RoundingRadius->4]], {"FunctionResourceBox", RGBColor[0.8745098039215686, 0.2784313725490196, 0.03137254901960784], "MapReduceOperator"}, TagBoxNote->"FunctionResourceBox"], ResourceFunction[ ResourceObject[ Association[ "Name" -> "MapReduceOperator", "ShortName" -> "MapReduceOperator", "UUID" -> "856f4937-9a4c-44a9-88ae-cfc2efd4698f", "ResourceType" -> "Function", "Version" -> "1.0.0", "Description" -> "Like an operator form of GroupBy, but where one also specifies a \ reducer function to be applied", "RepositoryLocation" -> URL["https://www.wolframcloud.com/obj/resourcesystem/api/1.0"], "SymbolName" -> "FunctionRepository`$ad7fe533436b4f8294edfa758a34ac26`\ MapReduceOperator", "FunctionLocation" -> CloudObject[ "https://www.wolframcloud.com/obj/6d981522-1eb3-4b54-84f6-\ 55667fb2e236"]], ResourceSystemBase -> Automatic]], Selectable->False], "[", RowBox[{ RowBox[{ RowBox[{"(", RowBox[{ RowBox[{"If", "[", RowBox[{ RowBox[{"#treat", "==", "1"}], ",", "\"\\"", ",", "\"\\""}], "]"}], "&"}], ")"}], "->", RowBox[{"(", RowBox[{"#re78", "&"}], ")"}]}], ",", "Mean"}], "]"}], "[", "matched", "]"}]], "Input", TaggingRules->{}, CellChangeTimes->{{3.834319880013258*^9, 3.834319911987419*^9}, { 3.834320062431499*^9, 3.834320078041237*^9}}, CellLabel->"In[8]:=", CellID->61502188], Cell[BoxData[ GraphicsBox[ TagBox[RasterBox[CompressedData[" 1:eJztnQlUVdX3x5+KimM5pC7nORVHHFeO5VTKUhMxzaGiP39Tl5JY5oiWE7bM 0DA1NZzCcApFUUAJBSdUBFEhJ2TKCXFKnMD+39/bq/O7/3ffu9z3gMe1uz9r 2XrvnH332e+c8z1nn3svqwbunkP/t7jBYJjqiP8M/eSrt728Ppnh+jq+uE2e On7cZI//eW/yFx7jPLy6uJdA4Wr8a1vMYPjP578ZhmEYhmEYhmEYRgW/MwzD MAxjgYeMbqARL+q8jDGF9ahPWI/ahPWoT1iP2oT1qE9Yj9qE9ahPWI/ahPWo T1iP2oT1qE9Yj9qE9ahPWI/ahPWoT1iP2oT1qE9Yj9qE9ahPWI/ahPWoT1iP 2oT1qE9Yj9qE9ahPWI/ahPWoT1iP2oT1qEBUVFRwcHBoaGhRB1LwsB61CetR ge7duxsMhjfeeKOoAyl4WI/ahPWoAOuRePbsWUxMjJ+f36JFi8LDwx88eCC3 efr06bFjx5YtW7Zw4cLAwMDk5GQ1nlNTU88aycrKktc+evQoLCxs8eLFK1as QAAIQ2XAUnJycuLj4+Pi4l6+fJn/kAoba/WIfu5h5Ny5c4U8ZYo+GNYjiIiI qFq1qkGCg4ODj4+PmN7Pnz+fPn16qVKlpDYlSpRwd3c3q1zBrVu3hOctW7aY 1OKYULNmTanPWrVqnTp1Sv3cTkpK8vX1bdSoEV0eGRmZ5yXKIdkBa/V44sQJ ihYfCnvOFHkwrEfsiVAfdXLlypWbNm1arFgx+jp//nwY/Pnnn87OzlSCqhYt WlSvXl0oaMiQIQq7kqurq7A0mfyHDh2ihiBzrLcdO3akMMqUKbN37141E3vi xImG/w985nmVQkj2wVo9hoSEaEePhR2MzvV47do1UsFrr722f/9+UhYE2Ldv X+iO0jm4atWqFWzGjx+fmZmJktzcXHhu0KABDQ2SWLPOf/31V6lYpJMfqW+T Jk2o58+cOUOF2Blr1KiBwoYNGyIFzXNiT5o06XUjZcuWValHhZDshrV6RJAq JZCWlnbgwAGklPKqxMREjC+GO8/mkIhCdJcuXcpPMHfu3EHShYmBKaTc3I0b N3BgwdkBiRa+9urVS8969PDwMBgzT5y/pOVQHDyIrykpKUFBQSbXbt26lYZm +fLlcs83b96ktLBz587yyR8VFUWFP/74o/SqPXv22KCUzZs3q9Gjckh2Q70e cabG0lShQgWKFh9o/Wnfvj0ZtG7dGl9XrVr1xx9/uLi40IECv1F4wCRftGgR rXIETgT+/v4mDcFs48aN2JuwLAtLXPXbb7+pD4bARIKmSpYsSWYICYGZXSKO Hz+OpKh48eJkCVfe3t7vvvuubvWI5ZRG0M3NzYZ5dfToUerJuXPnymvff/99 Go7w8HD55F+5ciUVQiMmFzo5OaG8Z8+e6iNRqUflkOyGej3OmjXLYA6kFmRQ v359fJ02bZo4UID+/ftTLU7KyHNEuaOjo/g8ffp00Up6enrLli3NNoQDBVZI lcEApB9CsMi7sM7T55o1a168eFH60zZs2CCNxwR96jEwMJB+fnR0tA3zCgsv XR4QEGBShRKqwgn08uXL8sk/Y8YMGm752fOzzz4zGJdx9ZGo0WOeIdkN9XpE 6rh79+4xY8ZQtD4+PruNiFyR9EjgTIFEJTIyEqNJtdApVQ0dOhT5KjmkAxrE Ir1BitUPhThZY0qgc5CsTp06la7t2rWrymCwCVaqVAlVVapU2bRpE442yFr9 /PxId7hQNHflypXy5cuTn48//hhpLfZ3XFKvXj0963HJkiUkCpzm8PXFixc4 waGvkKwqzygc7n755RfaW+vUqZOdnS2txZaHEUFVp06dYImulk9+pFhUiD3a xDn0gnKkMc+fP1c5w/PUo5qQ7Ia150ekHxSt/Mgm9PjWW29h8kurYmNjaYAG DBggLcd5rXLlyijHUUUU4vh2+PBhE+d0lAOpqalqghk9ejQNHNYEafk333xj MJ6JoHQqEaL+/PPPTWJr3ry5bvU4YcIEg/GYcPv27eHDh1esWJF6CQual5fX X3/9ZWKPxMbT03PYsGFiHcNie/XqVROzIUOGGIy3SZOSkvDV7OQ/ePAgFZrk ujja40Kqknu2RJ56VBOS3ShwPWJFPX36tEkVqQCcP3/epGr8+PEo79Kli3K7 CxYsIA84mOQZDE6gdFdt1KhRJn4gZ7pEpL60OeKcCwGaGOv5/urAgQPx29GN 4k5ptWrVxMOOzp07m2yUMTExBgnYGTHWJj7F/TdfX18qMTv5nz17RvdXcfBH 3gvpxcXFYWcsXbq08I8SlTNcWY8qQ7IbBa5HjJT8qhEjRhiMt80PyqCNrHr1 6vKrTp48iaQXmmrbtq1IKXft2pVnMPHx8VQ+efJkeYtUBc+wxEGSvn700Ufy APSsR+lNgDlz5mRkZKAQS1afPn2o8KeffpLa44Dg5uaGHsPhjgwgXm9vb3EG FLkQUh1RaGnyh4eHy0/0OIDQRAJ3795VOcMV9GhVSPahwPWIZFV+FQRlUAQJ pNR+/fr1li7ZsWNHnsGIm+0KYI7BEuqmr9jB5WHrWY/9+/ennjF5YAEh0F0y bKBmL8TEDg0NFcO3Zs0aKh80aBCV4IT+5z+IRxs//PADvj6UPEa5dOnS0KFD a9eujVrkwDjRIJ+cPXu2wXg7Xf0MV9CjtSHZAfvoESdlVCHfeMsCffv2Fcbi 1hyOnDiP/Pzzz0iAxRlfjR5FEoK0x1KLGzZseChR7pIlS+Rh9+jRw2DM09T0 zKtFnnocN24cfnvdunXlVTSNmzZtqnA5th56UYfuhV64cCHPFZImj9zVkydP xOexY8fCrGXLliqn99+W9ZifkAoP++gRC53BmK/Sc3YFtm/fTv4hYekLA8iO 1OsR+qVySFu5OfGYzNPTU16r5/1x8eLFBmPO+ejRI5OqTz/9lEZTeWqJG2WZ mZmJiYlqJn+HDh0UHObk5GB9MFjems1iSY8FElKBY60e582bR3Ga3Ld8qKhH 8bgwLCxM2T+9E2Iw3kCTlpvVo6VgsrKy6B0AnAuUm0tLSyMPyK/ktXrWY3Bw MPUMVkiTqt69e0snqqU3VGkvA7du3cLXe/fu3ZVx/Phxslm9ejW+Kr9/Ltbq devWqZ/hCvlq/kMqcKzVI04TFO2yZctMqhT0eOTIEbo1h0wDv1HBv4uLi8G4 LIvnEQAL7MiRI+V6VAiGbg8CyksVoIcaQPr+Dzh8+DAd9vWpx9zcXGSk+PnO zs7SW6lXrlyhtc7d3R1fz5w5U6NGDfk73jdv3kSebzAe/RRasXTz5OnTpyZ3 ULHGtm7dmhxKHz5mZ2dv2rQpICBAmtZKUfl+Tp4h2Qdr9RgUFETRtm/fPjo6 GquHeLqhoEdA5xHQpk0btHX//n0U4rdPnz4dl2ClIrMpU6aQGYY7OTkZ4g0N DW3Xrp3IH6R6VAgGpwN65OHg4DBnzhx6agld7969u2fPnjiTCidivGC/du1a 7JhJSUnfffedeNSlTz0Cf39/6oHBgwfTu+I4QdCNmhIlSsTGxqKEXibH+jlh woSQkBAMwYsXL+AZo0zXTp06VaEJs5MfG66bmxsGDjkzzqHQZlRUVLNmzchy 1apVUg9eXl5UPnPmTFGYkZFx9B8w+mSAwwuVyB/E5BmS3bBWjxAO5fAEJm2p UqVu3779MC89pqend+nSRVzo6OgofT11/fr1ZIZsQfwlXTEj9FkMh1SPCsEA iEt6z7xKlSri9dTGjRsLJ5hC/fr1M5iDbu7pVo9QFj2NorGQ/iEVDiBi/tB7 UAR6WPx9lsH4WFn5j4jNTv6UlBTaWwnxoqPB+M6GyR93YCmmqq5du4rCpUuX mh1Qonz58taGZDes1eNDY/Ip3sGgIcDyhXJ6hvv2229buhB74oIFCygJFEDF 33//PZZfYYZ+kA4HROHj4wMDUpNUjwrBEMimcAYUMjQYHzG7urrGxMSYBDZt 2jSpeCHYwMBAPz8/CkBlz7xCqNEjgc6XDhk+Iz+UGuB4iM3RZFgrVqyIsX78 +LGy8+vXr5P9tm3bpOXYFsWfJIhpgExG7oHe6zNIHuj/nZcey5UrZ0NI9sEG PT403jDBRoaAkaJIX2BTycWLF5E34lqzf2rx0PjyeUREBE4lSDvzHwx5Cw4O PnXqlMKfXKHq2LFjO3fuRL6q/re8oqjX49/GBBK7Bs5fGA5L76/iTBcfH48h CAsLgzH21vxPTuytJ0+ehEN6G8ESiCohISH/zWkB2/TIvOpYpUfGbrAe9Qnr UZuwHvUJ61GbsB71CetRm7Ae9QnrUZuwHvUJ61GbsB71CetRm7Ae9QnrUZuw HvUJ61GbsB71CetRm7Ae9QnrUZuwHvUJ61GbsB71CetRm7Ae9QnrUZuwHvUJ 61GbsB71CetRm7Ae9QnrUZv8zjAMwzCMIkWdQzH2g0a8qLMzxhTWoz5hPWoT 1qM+YT1qE9ajPmE9ahPWoz5hPWoT1qM+YT1qE9ajPmE9ahPWoz5hPWoT1qM+ YT1qE9ajPmE9ahPWoz5hPWoT1qM+YT1qE9ajPmE9ahPWo1kuX74cbCQlJaWo YykUWI/ahPVoljVr1tD/tHrv3r1FHUuhwHrUJqxHs7AewZ07d85aIDU1VWqZ kJBgyfL58+dmnefk5MTHx8fFxb18+dKsgQ0+i7ahAuGV02NycnIPI+fOnSu8 VliPwMfHx2ABJycnqaWjo6Mly+3bt5u4TUpK8vX1bdSoERlERkaabd0qn2ax W0MFyCunxxMnTlBf4UPhtcJ6BF999ZWlidq8eXNh9uTJE0tmIDAwUOpz4sSJ JgaHDh2SN22VT7PYraGC5ZXTY0hICPUV6zE/qNGjh4cHeqBmzZrXZWRkZAgz fKa++vbbb+WWjx8/lvqcNGnS60bKli2rIBOrfJrFbg0VLPnRI35LeHh4TEzM /fv3LdlkZWUdPXo0IiLi1q1b6t0eOHAAWf2DBw/ktVu2bFGvx+joaJghBpPy a9eu7d+//8KFC5YuZD2CYcOGoQfat2+vbHb+/Hnqq+DgYPUTb/PmzQoysc1n 0TZUINimR39//8aNG4ttvVy5ch988AFOdlIbCKpPnz6lS5cmm+LFizs7O0OY cldYxOANnwMCAjp16lSiRAm6BCtzWFiYsFyxYkXDhg0rVKhAtfhACyAmjNRV 06ZN8Xn58uUwJstly5YJJ0uXLq1Vq5aIvHLlyp6enpmZmSZRsR7BO++8gx4Y MGCAstmRI0eor7Ayq594yjKxzWfRNlQgWKvHu3fvDhkyRJZo/wccnIXZ1q1b hXCkQGsLFy6UOhQzf8qUKXJ7nLWFzGfNmmW23SZNmkhdlSxZcuPGjVIDHORR iw0a64NZD05OTomJiWaj0rMe27Vrhx745JNPlM12795NfZWSkqJ+4inLxDaf RdtQgWCtHr29vSl+7Czr1q27cuXKuXPnZs6c6eDggEySbK5evYp9isymTZsW Fxd36dIlX19fupFVrFgxEgghZj7AVV9//TWSTATToUMHKpwzZw5ZoiH03pgx Y6jcx8dntxGRuApXCAb7sru7O/LeHTt2UL46depUqsUGumfPnrS0NGzWvXr1 osL33ntP+jNZj6BevXqkRyQb443gQ2xsrIkZ0hLqK/QqZgLsMWRIdbKzsxWc K8vENp9F21CBYJUez549S/lnpUqVkHhLq3AiE59HjhxJvxFnZKnNvn37qLxt 27bibChmfoMGDU6fPi1ti8pdXFykTubOnUvl8vOjcAXJb9u2TVoFz9g3qZXU 1FRRju1eSBJ7utyVnvVYsWJFgwz07dixY+/fvy/McByQm4E6depgtbTkXFkm tvks2oYKBKv0OH/+fIr2m2++sWSDkSpTpgxs6tatK7+R0rdvX/IAuVGJmPn4 +SbGtWvXRjlOndJCNXp0c3MzqcK2S1V+fn4mVdglqWr48OFyV6xHJELYNebN m4fMhEYWjBo1SpiJKd29e3ekQ1988QUlugCrd0JCglnnKmVilc+ibahAsEqP YuNDmmrJJj4+nmwmTpwor125ciXVis1IYeZDiSinWz0CNXoMCgoyqRoxYgRV paeny6Mi4bdq1UruSs96RF9hut67d0+UXL58Gcss9QzWMVGOBC88PFx8zc3N nT17Npn16NHDrHNlmdjms2gbKhCs0iPyTASJZVPBBkKj32KSrBIiZRWnQoWZ 37lzZ9v0KHfVpk0bgzHNNhtzt27dUFuqVCnMvTxd/TtQo0ez7Nq1i3pmwYIF CmaY1TRbHB0dc3Jy5AZ5ysQGn2axW0MFglV67Nixo8GYwCjYiOeD0qcMgtDQ UKqdMWMGldhHj506dUJ51apVzcbcs2dPg/EukEiwWY+WwLXUM0g5lC3FDbSk pCR5rQ0yydOnWezWUIFglR7Fk460tDRLNqdPnyYbT09Pea2Y55s2bTIpKVQ9 urq6UtWNGzfkUdWvXx9VzZo1U+Pq34HNenz27Bk9IMZkULYUWV9cXJy81jaZ KPs0i90aKhCs0qN4oVF+V0SAlA+JH2zefPNN+Qs2QtHiVqq1epw3bx7ZSx+a 5OkK6TFV+fv7m1SdPHmSqoYOHarG1b8Dm/UYExNDPYOBULYcMGCAwXgKMPtX ErbJRNmnWezWUIFglR7xi+in1a5d2+QPdZOTk8UjDxcXFzLDrJbaHD58uFix YgbjE3w1JzWzely+fDnZy/NhBVfR0dHUdPPmzaVv7mHFGDhwIF21du1aNa7+ HeSpxw8//PDLL7988eKFtPDly5f00o7hnxfMYmNjW7duffbsWfm8og639Lqd gkys8pmdnY1cKyAg4MmTJ4XakH2wSo8Aw0S/rkGDBnv27Ll9+zaUuGrVqipV qvTu3ZtsLly4QC/xIrFZvHgxDJAlbtiwQTzP2rdvn3BorR6DgoLIHn0FlUFQ arZaMG7cOKp1dnY+cuQIFgQMx6BBg6iwW7duUuPAwEAqX716tZpueeVQ1uO2 bdtEt2zZsuX69eu5ubkJCQn9+/en8q5du9KNjhYtWhiM9z28vb3RqxAFRgQD QWONWb1//37hNiMj4+g/iIxl0aJFVHL+/Hkys8qnl5cX+Zk5c2ahNmQfrNXj 1atXnZycDP9QvHhx8bl8+fLibxIxjSlrlYOTstShtXqEjsQtd1CmTBk0hGVB 2RVIT08X7/yYUK9ePWhTaowZSFVYUhQetr66KOsR07Jfv37SLnJwcBCfq1Wr hjWWLHfu3Cn+gIK6i3YWAjus1O3SpUvN9r+YPzb4xLJM5VgiCrUh+2CtHkFm ZiYWpXLlykkHa/DgwUlJSVKzEydOmMz/hg0byp8MiheWpK+OE927dzfI9Aiw lNGrXATWhKioKGVXRFZWFlZRDIe4FloeO3YsllO58ejRo2m1cXV1VdkzrxB5 5qtITTdu3EiPgKUD7eHhcffuXallamqqu7u7ycs8jRo1kv/FhLJMMKNs8Llk yRKq9fX1LdSG7IMNeiSwhGJPwQoTGRl5584dS2bYlaCOkJAQbKz5nkT/Bco6 fvw40ip4lr7/pgZEfubMGUSO1AVTS8EyMTERTZw6dSp/wWoR9fdz0tLSsACG h4djuJ8+fWrJDFVxcXEHDx6MiIiQ/nVkflDpE+ejfL5IUxjB24bNemReaWy+ v8oUKqxHfcJ61CasR33CetQmrEd9wnrUJqxHfcJ61CasR33CetQmrEd9wnrU JqxHfcJ61CasR33CetQmrEd9wnrUJqxHfcJ61CasR33CetQmrEd9wnrUJqxH fcJ61CasR33CetQmrEd9wnrUJqxHfcJ61Ca/MwzDMAxjgaLepRmGYRiGYRiG YRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiG YRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRjm38D/ AdxBI/E= "], {{0, 76.}, {152., 0}}, {0, 255}, ColorFunction->RGBColor, ImageResolution->144.], BoxForm`ImageTag["Byte", ColorSpace -> "RGB", Interleaving -> True], Selectable->False], DefaultBaseStyle->"ImageGraphics", ImageSizeRaw->{152., 76.}, PlotRange->{{0, 152.}, {0, 76.}}]], "Output", TaggingRules->{}, CellChangeTimes->{ 3.834319912367325*^9, {3.8343200521146507`*^9, 3.834320078495665*^9}, 3.8343348503133087`*^9, 3.834405645065879*^9}, CellLabel->"Out[8]=", CellID->10847177] }, Open ]], Cell["\<\ The increase in median income is much smaller, suggesting that the increased \ income among the treated may come for a few high earners:\ \>", "Text", TaggingRules->{}, CellChangeTimes->{{3.83432401417966*^9, 3.834324035219223*^9}, { 3.834335188184976*^9, 3.834335191863256*^9}, {3.834405659116235*^9, 3.834405663289638*^9}}, CellID->814058795], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{ RowBox[{ InterpretationBox[ TagBox[ DynamicModuleBox[{Typeset`open = False}, FrameBox[ PaneSelectorBox[{False->GridBox[{ { PaneBox[GridBox[{ { StyleBox[ StyleBox[ AdjustmentBox["\<\"[\[FilledSmallSquare]]\"\>", BoxBaselineShift->-0.25, BoxMargins->{{0, 0}, {-1, -1}}], "ResourceFunctionIcon", FontColor->RGBColor[ 0.8745098039215686, 0.2784313725490196, 0.03137254901960784]], ShowStringCharacters->False, FontFamily->"Source Sans Pro Black", FontSize->0.6538461538461539 Inherited, FontWeight->"Heavy", PrivateFontOptions->{"OperatorSubstitution"->False}], StyleBox[ RowBox[{ StyleBox["MapReduceOperator", "ResourceFunctionLabel"], " "}], ShowAutoStyles->False, ShowStringCharacters->False, FontSize->Rational[12, 13] Inherited, FontColor->GrayLevel[0.1]]} }, GridBoxSpacings->{"Columns" -> {{0.25}}}], Alignment->Left, BaseStyle->{LineSpacing -> {0, 0}, LineBreakWithin -> False}, BaselinePosition->Baseline, FrameMargins->{{3, 0}, {0, 0}}], ItemBox[ PaneBox[ TogglerBox[Dynamic[Typeset`open], {True-> DynamicBox[FEPrivate`FrontEndResource[ "FEBitmaps", "IconizeCloser"], ImageSizeCache->{11., {2., 9.}}], False-> DynamicBox[FEPrivate`FrontEndResource[ "FEBitmaps", "IconizeOpener"], ImageSizeCache->{11., {2., 9.}}]}, Appearance->None, BaselinePosition->Baseline, ContentPadding->False, FrameMargins->0], Alignment->Left, BaselinePosition->Baseline, FrameMargins->{{1, 1}, {0, 0}}], Frame->{{ RGBColor[ 0.8313725490196079, 0.8470588235294118, 0.8509803921568627, 0.5], False}, {False, False}}]} }, BaselinePosition->{1, 1}, GridBoxAlignment->{"Columns" -> {{Left}}, "Rows" -> {{Baseline}}}, GridBoxItemSize->{ "Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}, GridBoxSpacings->{"Columns" -> {{0}}, "Rows" -> {{0}}}], True-> GridBox[{ {GridBox[{ { PaneBox[GridBox[{ { StyleBox[ StyleBox[ AdjustmentBox["\<\"[\[FilledSmallSquare]]\"\>", BoxBaselineShift->-0.25, BoxMargins->{{0, 0}, {-1, -1}}], "ResourceFunctionIcon", FontColor->RGBColor[ 0.8745098039215686, 0.2784313725490196, 0.03137254901960784]], ShowStringCharacters->False, FontFamily->"Source Sans Pro Black", FontSize->0.6538461538461539 Inherited, FontWeight->"Heavy", PrivateFontOptions->{"OperatorSubstitution"->False}], StyleBox[ RowBox[{ StyleBox["MapReduceOperator", "ResourceFunctionLabel"], " "}], ShowAutoStyles->False, ShowStringCharacters->False, FontSize->Rational[12, 13] Inherited, FontColor->GrayLevel[0.1]]} }, GridBoxSpacings->{"Columns" -> {{0.25}}}], Alignment->Left, BaseStyle->{LineSpacing -> {0, 0}, LineBreakWithin -> False}, BaselinePosition->Baseline, FrameMargins->{{3, 0}, {0, 0}}], ItemBox[ PaneBox[ TogglerBox[Dynamic[Typeset`open], {True-> DynamicBox[FEPrivate`FrontEndResource[ "FEBitmaps", "IconizeCloser"]], False-> DynamicBox[FEPrivate`FrontEndResource[ "FEBitmaps", "IconizeOpener"]]}, Appearance->None, BaselinePosition->Baseline, ContentPadding->False, FrameMargins->0], Alignment->Left, BaselinePosition->Baseline, FrameMargins->{{1, 1}, {0, 0}}], Frame->{{ RGBColor[ 0.8313725490196079, 0.8470588235294118, 0.8509803921568627, 0.5], False}, {False, False}}]} }, BaselinePosition->{1, 1}, GridBoxAlignment->{"Columns" -> {{Left}}, "Rows" -> {{Baseline}}}, GridBoxItemSize->{ "Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}, GridBoxSpacings->{"Columns" -> {{0}}, "Rows" -> {{0}}}]}, { StyleBox[ PaneBox[GridBox[{ { RowBox[{ TagBox["\<\"Version (latest): \"\>", "IconizedLabel"], " ", TagBox["\<\"1.0.0\"\>", "IconizedItem"]}]}, { TagBox[ TemplateBox[{ "\"Documentation \[RightGuillemet]\"", "https://resources.wolframcloud.com/FunctionRepository/\ resources/MapReduceOperator"}, "HyperlinkURL"], "IconizedItem"]} }, DefaultBaseStyle->"Column", GridBoxAlignment->{"Columns" -> {{Left}}}, GridBoxItemSize->{ "Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}], Alignment->Left, BaselinePosition->Baseline, FrameMargins->{{5, 4}, {0, 4}}], "DialogStyle", FontFamily->"Roboto", FontSize->11]} }, BaselinePosition->{1, 1}, GridBoxAlignment->{"Columns" -> {{Left}}, "Rows" -> {{Baseline}}}, GridBoxDividers->{"Columns" -> {{None}}, "Rows" -> {False, { GrayLevel[0.8]}, False}}, GridBoxItemSize->{ "Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}]}, Dynamic[ Typeset`open], BaselinePosition->Baseline, ImageSize->Automatic], Background->RGBColor[ 0.9686274509803922, 0.9764705882352941, 0.984313725490196], BaselinePosition->Baseline, DefaultBaseStyle->{}, FrameMargins->{{0, 0}, {1, 0}}, FrameStyle->RGBColor[ 0.8313725490196079, 0.8470588235294118, 0.8509803921568627], RoundingRadius->4]], {"FunctionResourceBox", RGBColor[0.8745098039215686, 0.2784313725490196, 0.03137254901960784], "MapReduceOperator"}, TagBoxNote->"FunctionResourceBox"], ResourceFunction[ ResourceObject[ Association[ "Name" -> "MapReduceOperator", "ShortName" -> "MapReduceOperator", "UUID" -> "856f4937-9a4c-44a9-88ae-cfc2efd4698f", "ResourceType" -> "Function", "Version" -> "1.0.0", "Description" -> "Like an operator form of GroupBy, but where one also specifies a \ reducer function to be applied", "RepositoryLocation" -> URL["https://www.wolframcloud.com/obj/resourcesystem/api/1.0"], "SymbolName" -> "FunctionRepository`$ad7fe533436b4f8294edfa758a34ac26`\ MapReduceOperator", "FunctionLocation" -> CloudObject[ "https://www.wolframcloud.com/obj/6d981522-1eb3-4b54-84f6-\ 55667fb2e236"]], ResourceSystemBase -> Automatic]], Selectable->False], "[", RowBox[{ RowBox[{ RowBox[{"(", RowBox[{ RowBox[{"If", "[", RowBox[{ RowBox[{"#treat", "==", "1"}], ",", "\"\\"", ",", "\"\\""}], "]"}], "&"}], ")"}], "->", RowBox[{"(", RowBox[{"#re78", "&"}], ")"}]}], ",", "Median"}], "]"}], "[", "matched", "]"}]], "Input", TaggingRules->{}, CellChangeTimes->{{3.834319880013258*^9, 3.834319911987419*^9}, { 3.834320062431499*^9, 3.834320078041237*^9}, {3.834324007096959*^9, 3.83432401055797*^9}}, CellLabel->"In[9]:=", CellID->722160800], Cell[BoxData[ GraphicsBox[ TagBox[RasterBox[CompressedData[" 1:eJztnQl0Tdf3x59ZzDPLEBJSRIwxrRqiNbWG1hIURUn//oTWWCpUqIqhfzNt qqaIoEnQmEKCFDFHEGOMQWKIeZYg/L//t9fv/G/vfe/mvgxy4+7PWu1675x9 z9k593zP2fvc+1oHj+Fd/zunyWQanR//6jrgx09GjRrg5V4MX7oPG+05aNjA //p82A8DBw0c1dQjFwr/wD/1cphM//f5HcMwDMMwDMMwDMNo4B+GYRiGYazw hDEMdMezOi5j5LAejQnrUZ+wHo0J61GfsB6NCetRn7AejQnrUZ+wHo0J61Gf sB6NCetRn7AejQnrUZ+wHo0J61GfsB6NCetRn7AejQnrUZ+wHo0J61GfsB6N CetRn7AejQnrUZ+wHo0J61GfsB5ViIyM3Lx5c1hYWFY7kvGwHvUJ61GFFi1a mEym0qVLZ7UjGQ/rUZ+wHlVgPUp59erVcTNnz55V1r5+/frQoUP/Y2bLli13 79611k5SUtKBAwfmzJnj4+MTGBgYFxdnzVJ7m+nsiHjz5k1MTMyJEyfevn1r U0cZiK16xB/V0szJkyczecpkvTOsRyne3t4mM9WqVZOWQ6fff/99wYIFTRKK Fi26ePFiWQuwHDduXN68eaWWuXLl8vDwePz4cdratIj2jojY2Nh58+ZVrVqV LHfv3m3TyGQgtuoR6xX5jA+ZPWey3BnWo+DYsWO5c+dW6vHevXutWrWi8pw5 czZs2LB69epCAv7+/sLy5s2bDRo0oPIcOXI4OzuXLVtWWHbp0kXsStrbtIj2 joihQ4ea/s2uXbu0j0zGYqseQ0ND9aPHzHaG9UgkJyfXqVNHTFepHidMmECF Xl5eIp7cvHlzgQIFUFiyZMlnz55RITqtXbs2Cj09PaE4lKSkpMAHBwcHagGx pa1tWkR7RwQ24mJmqP3spceAgACNEoiPj9++fTtCSmXVuXPntm3bduXKlVS7 QyAK0V24cCE9zuCeRkRE4C48ePBAvbtbt26Fh4cjRUJUg6+0SrMeJ06cSHtN 48aNZXpEzvXtt98uX75cdonQFG6NKLx27VpISIjMcu3atWQ5f/78NLRpEY0d yVi1alU20uOCBQscHR0LFy5MPuMDrSqurq5kgCUUX319fc+fP9+pUyeK3kuV KiVawCSfNm1auXLlxEpboUKFFStWyDqC2cqVK7E3IV8Qlrjq77//1u4MgfQc msqTJw+ZwSU4ZnGJOHjwYKNGjRAdkSWaQrr02WefsR6jo6MpUh1gxqTIHy2C FIxGEvdX3XL//v1kOWnSpIxqM20dZS89itVJhpOTExlUqVIFX8eOHSuid9C+ fXuqTUxMbNu2rSjPnz+/+IzUW/SSkJDg4uJisSOsz5s2bdLoDPjrr7+EYDGj kM7T5/Lly589e1b6p/n5+Un9kWFkPSJSpdgPKRjCjK+//tqkTY8bN26k0QsO Dla3xBJNlmvWrMmoNtPWUfbSI0JHDEjfvn3J5xkzZmw0I2JF0iOBm4ioAAva vn37qBY6paquXbsiXqUGKUGDWKQHpG5ubih0d3cPDAy8ePEigtXRo0fTtc2a NdPoDDbB4sWLm8zphr+/P/IITKdFixaR7nCh6O7SpUuFChWidvr374+wFvs7 LqlcuTLrUax7WAnxtVevXhr1OGrUKLoQaYs1G8Slq1evpjiqUqVKL168SH+b 6ekoe+mRwF5PPitTNqHHjz/+GJNfWnXs2DEajQ4dOkjLka+VKFEC5QMHDhSF SN/27Nkja1wcuF2/fl2LM3369DGZT+ewJkjLp0yZYjIffUPpVCJEPWLECJlv NWvWNLIeo6KiRKRKJRr1+OjRIwQhsHRwcFDWIgQaPnx4t27dxIqHZfny5cvp adMitnb0QeoRUeXRo0dlVaQCcPr0aVmVp6cnyps2bare79SpU6kFZAGpOoMM lM7KEF/J2oGcxYJPJbQ5Is+FAGXGRj5fTUpKqlWrFv58e3t78cxOox4HDx5M gxwQEKCsPXLkiEkCNizMilQnqnqbFrG1ow9Sj02aNFFe1bNnT5P5ee5OBbSR IT1RXnX48GEEvdBUvXr1REi5YcOGVJ2JiYmh8mHDhil7pCq0DEskkvT1m2++ UTpgZD16eXnR6iqdnFr0iIAEV9Eaa/FFF6QS3bt3x9hWqFCBBh/23t7eKm/F pNqmRWzt6IPUI4JV5VUQlEkVBJBS+2XLllm7ZN26dak6I062VZg4cSIsoW76 ih1c6bZh9YidhY6/EOzdlNClSxcU4kbTV+WFSPYRacAG8QlWRfW5B12EhYWJ G23t3Rub2kxPR8bRIz23ypcv38dWaNu2rTAW52BIOTEfli9fjgDY19dXux7F c0knJydrPfr5+T2RKHfmzJlKt1u2bImqMmXKaBmZ7IW6HjHsqS5oIDw8XHrV nTt3xCtngYGBGmcg0gR6fwa7mLI2bW2moaN3RtJj165dTeZ4lZ6zqxAcHEzt Q8LSFwb+/PNP7XqEfqkc0lbvTjyTQuKvrDXs/kj5Raps2bJFXPLs2TMEk1Q+ fvx4myahOFKj12kypE2bOiKyox4nT55MPsvOLZ+o6lEcm2NFVW9/4MCBZHn5 8mVpuUU9WnPmwYMH9A5Aq1at1LuLj4+nFhDMKGsNq8ekpKT7lujcubPJfMJJ X1+9ekX2L168+OSTT2gke/funZKSYrFZa4lbv3796NrExERRqLHN9HckyI56 nD9/Pvk8Z84cWZWKHvfu3Uv5uIuLC+6jSvudOnUymfNu8TwCYDWjkwSZHlWc 6dixI1VRXKoCPdQA0vd/wJ49e+hZjAH1aA2L5zkvX75s06YNjSEE+/r1a4vX RkdHlytXTrqlErdv30ZGgGsrV65sa5vvzLL19/dfs2YNLklDR1Kyox5DQkLI Z1dX13379iH+FE83VPQIBg0aRBfWrVsXfT169AiF58+fHzduHC55+PAhmY0c OZLMPDw84uLiIF5k4vXr1zf9B6keVZw5c+YMPfLInTv3xIkT6akldL1x40Y3 NzfkpKIRcRdgv2TJEuyYsbGxs2fPtrOzo3LWo0CpR+yk7du3p4EqUqQIVLB5 8+a//w2d/NB7PlhphwwZEhoaipsFlcEHzAe6fPTo0ba2+U7ykoAIaLV3BG7c uLH/P9BruiZzmkMlWh7EZCy26hHCsbe3F+rApM2bNy+S7iep6TEhIUHkAibz +3LS11OXLVtGZgcPHhQ/W8thhj7XqFFDqUcVZwDEJX0LrmTJkuL1VMwo0Qju V7t27UyWqFixIutRilKPImVQYcOGDTTT6I0pAvdC/IDLZH6QkZycbGubAEsx lTRr1kxMaY0dgVmzZqn0UqhQobQKK43Yqscn5uBTvPBAf29kZCTKnZyc8BUx v7ULsSdOnTqVgkABVDx37lzsXMIsICCA4goCopgxYwYMSE1SPao4QyB0QQ4o ZAiQV7q7ux85ckTm2NixY6XixZQLDAxctGgROaBxZLIRadNj//79MSDOzs6i xNqLxFK2bt1KxsjasGfJJgB2QMyK58+fp63NmTNnUsm8efNECxo7epeaHgsW LGjrEKWTNOjxifnABBtZUFAQ4gHpC2waOXv2LOJGXGvxpxZPzC+fR0REIFBB 2Jl+Z6g1xDxRUVEqP7lC1YEDB9avX494Vfvfkk1Jmx4zhFevXsXExOBmhYeH I2FRyQ01gkly6tSp99DReyBtemSyO1moR0YF1qMxYT3qE9ajMWE96hPWozFh PeoT1qMxYT3qE9ajMWE96hPWozFhPeoT1qMxYT3qE9ajMWE96hPWozFhPeoT 1qMxYT3qE9ajMWE96hPWozFhPeoT1qMxYT3qE9ajMWE96hPWozFhPeoT1qMx YT3qk38YhmEYhlElq2Mo5v1BdzyrozNGDuvRmLAe9Qnr0ZiwHvUJ69GYsB71 CevRmLAe9Qnr0ZiwHvUJ69GYsB71CevRmLAe9Qnr0ZiwHvUJ69GYsB71CevR mLAe9Qnr0ZiwHvUJ69GYsB71CevRIhcvXtxs5tq1a1ntS6bAetQnrEeLLF68 mP6P1Vu2bMlqXzIF1qM+YT1ahPUoiI+PDwoKmjRpkq+v765du6z9X7+fPn0a Hh4+ffr0BQsWHDlyJDk5WaXNU6dO+fn5eXt7Y5z379//9u1bi2ZJSUkHDhyY M2eOj49PYGBgXFycFodtdQnOHLfCq1evbOox/WQ7PeKmtDRz8uTJzOuF9Qii o6NdXFxM/6ZatWoI42WWYWFh5cuXl5pVqFAhKipK2ebdu3e/+uorWZvNmzeP jY2VmkEI48aNy5s3r9QsV65cHh4ejx8/1jKxtbuUP39+kxWCg4O19JWBZDs9 Hjp0iMYKHzKvF9bjwoUL8+TJQ4NQrFix+vXr58yZk75CJhh8YYlNM0eOHFSO dbJRo0a5c+fGVzs7O4yetM2UlJTWrVtTI8WLF+/bt2+bNm3oq5OT04sXL8js 5s2bDRo0oHK07OzsXLZsWaGRLl26WNtP0+DSy5cvrYkRYFO2SU3pJ9vpMTQ0 lPWYflLVI+JJ/PkVK1bcsWMHdISSW7dujRkzhoalXbt2ZIaQElJCSenSpbGf UiG2oXLlyqHQ0dHxzZs3os2QkBC6fOzYsbiQCletWkWFs2fPphK4V7t2bZR4 enreu3fvnVnI8NbBwYEsEcSqeG6TSzdu3KA2f/3116sKnj9/boOWMoL06BF/ C24WIvNHjx5Zs3nw4AEShIiIiMTERO3Nbt++HVE9IhNlbUBAgHY97tu3D2bw QVZ+5cqVbdu2nTlzxtqFrEewevVqkoMAG1ONGjUwLCVKlKCSyMhIGqjff/9d arlp0yYqx/0ShQhBUVKwYEFZEtqiRQuU9+rVS5Rcu3YN4pX5s3btWmpz/vz5 Km7b5NLp06epUBmEZwlp0+OKFSuQR4htHSOMpACZndQGgkI0ki9fPrJBtIMg BMJUNoVwCK3h85o1axo3bow0gS5B/I98XFgiK8fiVrhwYarFh2JmXF1dpU19 9NFH+IxbBmOynDNnjmhk1qxZyCOE55hXw4cPx6yTecV6tEbbtm1N5lSOdpnf fvuNBur27dsyy1q1aqHczc1NlGC/Q0nJkiVllgMGDDCZs0j1rrGwU1+TJk1S MbPJpb1795IxthX13t8Pturx/v37COBNlqhataoww1ImhCMF99HHx0faoJj5 I0eOVNoj1xYynzBhgsV+EZxIm0LWs3LlSqnB7t27UYsNWmQrMnCbzp07Z9Er 1qMURCwIAjEsWPSoxMvLy2TO8pQ53eDBg03mUxRRInYoWdd0agRVqvc+bdo0 uhzrtoqZTS5t3LiR2sSOrN77+8FWPXp7e5P/2FmWLl166dKlkydPjh8/Hvky wgCyuXz5MvYpMkOmcOLEiQsXLsybN48OsjBQJBBCzHyT+dzg559/RpAJZxo2 bEiFEydOJEt0hNHr27cvlc+YMWOjGRG4iqbgDPZlDw8PxL3r1q2jeHX06NFU i7mEiREfH4/NulWrVlT4+eefS/9M1qMS5FPt27enYVm0aBEV+vr6UgnGU2b/ yy+/mMxxkXhq8PLlyyJFipjMWyQGnwp37dpFLYgSJdiLETzTcWulSpXEyY9F bHIJMRUZY0pgGmNNwHyD3tW7yDxs0uPx48cp/ixevDgCb2kVMjLxGYkA/Y3I kaU2W7dupfJ69eqJ3FDMfGTrR48elfZF5Z06dZI2gliFypX5o2gKkg8KCpJW oWU6LUQv169fF+XY7oUksacrm2I9+vn5YWX79NNPKY8oUKAAEgex9ezcuZMG ShZDItGws7OjKqzPohzxoQicEGj5+/tDm/jcs2dPZdcJCQnIJrp161a5cmW6 BJmmtDWL2OQSchmTJaB6LPVaxidjsUmPtLyAKVOmWLN59OgR/dX29vbKgxTK PgDkRiVi5uPPlxlXrFgR5cg6pYVa9Ni9e3dZFbZdsbDLqrAsU1WPHj2UTbEe O3ToIJ2okydPxjYnapOTk+kwE8sd4knMc4RDmCfi3ACgRNhjY4L0ZJPf1dXV 4kkmcjqZRrALpOqwTS4JPULpiOV++OGH+vXrUwnsT506pWWIMhCb9Cg2PoSp 1mxiYmLIZujQocpakWuLzUhl5tNDKDrqEWjRY0hIiKxKzAEsuUqvSPi1a9dW NsV6nDVrFoJ5zFLxdL5GjRpnz54VBjt27FA+UkcEJcYcQQhZPn36tGXLlibz Wdx3331HDyBM5gASeZCy67i4OCytUIo4gkPkA8tUnz9qdwkgOoW9+JqSkvLT Tz+RGbzVMkQZiE16RJwJJ5ECqNiIQ2lZsEqIkFVkhSozv0mTJmnTo7KpunXr 0h2x6HPz5s1N5mfHDx8+TLWpD4M05I+3b9+GFug5u6OjozTDunDhQteuXWlZ Q2w5cODA2NhYmtWQnjCj9bxEiRJ0mImNbOHChXRABHx8fKx1DQGGhYXR9AO4 O6l6q9Eli0CS1BdELX1Y+R6wSY+NGjWi8VSxEc8HpU8ZBBhVqvXy8qKS96PH xo0bo7xUqVIWfXZzczOZT4FEgM16tIYY/yVLlihrpaFsv379YObi4kJfT548 SRfOnTtXesnVq1cpPUSao3xCIeXWrVv0oo70gDRVVFxSQZz+yV7ky2xs0qN4 0hEfH2/N5ujRo2SDTFxZK+Y5EnlZSabq0d3dnapwT5VeValSxWQOw7Q09WGQ Zj0iHaORQcCpYoZtxd7eHmYdO3akkj/++IMuVD5ZEM+nUj1CEafrshcVtKB0 SQURskqT3/eATXr88ccfyUnlqYgAIR8lGtWrV1e+YCMULY5SbdXj5MmTyV76 0CTVphAeU9WKFStkVYcPH6YqhDdamvowSFWP1nK0K1eu0Mh4enqqXB4cHExm S5cupRKEoybzA2jphkVERUWRMTSr3jttcCAxMVGld40uqUCnWJjJ7/knHjbp UTwqQlgu+6EuUm/xyKNTp05khlkttdmzZw9lH05OTloyNYt6nD9/Ptkr42GV pvbt20dd16xZU/rmHlYMrJZ0FQIwLU19GKSqxx49etACJSsX8QlWNipJSkqS bSII++vUqWMyJ25iPoeHh8suFMyePZuq6MXU6OjocuXKyV78fmdOYMuUKUPN ikKksYi11qxZI5W5RpeOHTuGwuPHjytFQbPF1dVVZYgyA5v0CHr37k1D5+Dg sGnTpjt37kCJvr6+JUuWbN26NdmcOXOmQIECtBhOnz4dBogS/fz86Fkw2Lp1 q2jQVj2Kd5IxVlAZBKVlqwWDBg2i2gYNGuzduxcLAm7HF198QYXNmzeXGgcG BooVW8uwZDvU9bh+/Xr68xHDYwRwQ7Fh3b17F1EcPYUsWrRoQkLCO/NG1r17 d6TeuNG4yxBCZGQkveMKMDFEm0+fPqVj0oIFC65atUrsgBs2bKDZgkWeXjKn l8mhiCFDhoSGhuIWv379Gt7SoRxAcieaHTVqFBWOHz+eSrS75OzsbDIf2nh7 e2NKQNHoC7OIJioc2LZtW0aKTQO26hHpA70ESIjf4IBChQqJ3yTiJsp+vCbA YEobtFWP0BFlAYSdnR06wrKg3hTA/BHv/MjAmgltSo2vXr1KVZh+Kg9bsy/q ekxOTpb9SlF6NzFRRaKHMIn2LDFc4vOIESNkh5PY/kQ72AGbNWtGmTu1f/Dg QTEnixcvLp1j9GspomnTptJfFmNZpnK0ZqtLWHZoKRCWtC0SY8aMySCR2YCt egRIpbEoYZUTnmO4vvzyy9jYWKnZoUOHZPPf0dFR+WRQvLAkfXWcoNf+ZXoE WMrECxt0v7AAqjdFIGjBKop1QzrNkJLcuHFDadynTx9abdzd3TWOTDZCy3nO unXrMPlN/8bNzQ0Zt9QMexAyFKlgsdNhB7TY5rlz5zp37ixrEymD9IEmQE6B zbFEiRJSM2xbU6dOlb05MHPmTKqdN29eGly6fv26h4eHiNyIqlWrZtXPPdKg RwI7O/YUrDC7d+9GJGPNDLsS1IGoAxtruifR/wNlYTkNCgpCy9L337QAz5Gh wPP9+/ffv39fxRKTB11ERUWlz1k9ov189ebNm8gLEHJgd5M+RpeBPQs6xb3G 4pZqm3AAo7p9+3YaW2tmSPRiYmJwi9Hs+fPnrf2nQhBOW3yRRrtLlG/u3Lkz IiJCi/+ZR5r1yGRr0vy8g8lUWI/GhPWoT1iPxoT1qE9Yj8aE9ahPWI/GhPWo T1iPxoT1qE9Yj8aE9ahPWI/GhPWoT1iPxoT1qE9Yj8aE9ahPWI/GhPWoT1iP xoT1qE9Yj8aE9ahPWI/GhPWoT1iPxoT1qE9Yj8aE9ahPWI/GhPWoT/5hGIZh GMYKWb1LMwzDMAzDMAzDMAzDMAzDMAzDMAzDMAzDMAzDMAzDMAzDMAzDMAzD MAzDMAzDMAzDMAzDMAzDMAzDMAzDMAzDMAzDMAzDMAzDMAzDMAzDMAzDMAzD MAzDMAzDMAzDMMyHwP8Cxg2VvA== "], {{0, 76.}, {152., 0}}, {0, 255}, ColorFunction->RGBColor, ImageResolution->144.], BoxForm`ImageTag["Byte", ColorSpace -> "RGB", Interleaving -> True], Selectable->False], DefaultBaseStyle->"ImageGraphics", ImageSizeRaw->{152., 76.}, PlotRange->{{0, 152.}, {0, 76.}}]], "Output", TaggingRules->{}, CellChangeTimes->{3.834324010955233*^9, 3.8343348518555937`*^9, 3.834405647911044*^9}, CellLabel->"Out[9]=", CellID->1619290134] }, Open ]], Cell["\<\ Compare the mean and median income in 1978 by race among the matched \ individuals:\ \>", "Text", TaggingRules->{}, CellChangeTimes->{{3.834336368484049*^9, 3.834336385125092*^9}, { 3.834336421605472*^9, 3.834336422420623*^9}, {3.8344056832927933`*^9, 3.834405685609919*^9}}, CellID->1463347969], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{ RowBox[{ InterpretationBox[ TagBox[ DynamicModuleBox[{Typeset`open = False}, FrameBox[ PaneSelectorBox[{False->GridBox[{ { PaneBox[GridBox[{ { StyleBox[ StyleBox[ AdjustmentBox["\<\"[\[FilledSmallSquare]]\"\>", BoxBaselineShift->-0.25, BoxMargins->{{0, 0}, {-1, -1}}], "ResourceFunctionIcon", FontColor->RGBColor[ 0.8745098039215686, 0.2784313725490196, 0.03137254901960784]], ShowStringCharacters->False, FontFamily->"Source Sans Pro Black", FontSize->0.6538461538461539 Inherited, FontWeight->"Heavy", PrivateFontOptions->{"OperatorSubstitution"->False}], StyleBox[ RowBox[{ StyleBox["MapReduceOperator", "ResourceFunctionLabel"], " "}], ShowAutoStyles->False, ShowStringCharacters->False, FontSize->Rational[12, 13] Inherited, FontColor->GrayLevel[0.1]]} }, GridBoxSpacings->{"Columns" -> {{0.25}}}], Alignment->Left, BaseStyle->{LineSpacing -> {0, 0}, LineBreakWithin -> False}, BaselinePosition->Baseline, FrameMargins->{{3, 0}, {0, 0}}], ItemBox[ PaneBox[ TogglerBox[Dynamic[Typeset`open], {True-> DynamicBox[FEPrivate`FrontEndResource[ "FEBitmaps", "IconizeCloser"], ImageSizeCache->{11., {2., 9.}}], False-> DynamicBox[FEPrivate`FrontEndResource[ "FEBitmaps", "IconizeOpener"], ImageSizeCache->{11., {2., 9.}}]}, Appearance->None, BaselinePosition->Baseline, ContentPadding->False, FrameMargins->0], Alignment->Left, BaselinePosition->Baseline, FrameMargins->{{1, 1}, {0, 0}}], Frame->{{ RGBColor[ 0.8313725490196079, 0.8470588235294118, 0.8509803921568627, 0.5], False}, {False, False}}]} }, BaselinePosition->{1, 1}, GridBoxAlignment->{"Columns" -> {{Left}}, "Rows" -> {{Baseline}}}, GridBoxItemSize->{ "Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}, GridBoxSpacings->{"Columns" -> {{0}}, "Rows" -> {{0}}}], True-> GridBox[{ {GridBox[{ { PaneBox[GridBox[{ { StyleBox[ StyleBox[ AdjustmentBox["\<\"[\[FilledSmallSquare]]\"\>", BoxBaselineShift->-0.25, BoxMargins->{{0, 0}, {-1, -1}}], "ResourceFunctionIcon", FontColor->RGBColor[ 0.8745098039215686, 0.2784313725490196, 0.03137254901960784]], ShowStringCharacters->False, FontFamily->"Source Sans Pro Black", FontSize->0.6538461538461539 Inherited, FontWeight->"Heavy", PrivateFontOptions->{"OperatorSubstitution"->False}], StyleBox[ RowBox[{ StyleBox["MapReduceOperator", "ResourceFunctionLabel"], " "}], ShowAutoStyles->False, ShowStringCharacters->False, FontSize->Rational[12, 13] Inherited, FontColor->GrayLevel[0.1]]} }, GridBoxSpacings->{"Columns" -> {{0.25}}}], Alignment->Left, BaseStyle->{LineSpacing -> {0, 0}, LineBreakWithin -> False}, BaselinePosition->Baseline, FrameMargins->{{3, 0}, {0, 0}}], ItemBox[ PaneBox[ TogglerBox[Dynamic[Typeset`open], {True-> DynamicBox[FEPrivate`FrontEndResource[ "FEBitmaps", "IconizeCloser"]], False-> DynamicBox[FEPrivate`FrontEndResource[ "FEBitmaps", "IconizeOpener"]]}, Appearance->None, BaselinePosition->Baseline, ContentPadding->False, FrameMargins->0], Alignment->Left, BaselinePosition->Baseline, FrameMargins->{{1, 1}, {0, 0}}], Frame->{{ RGBColor[ 0.8313725490196079, 0.8470588235294118, 0.8509803921568627, 0.5], False}, {False, False}}]} }, BaselinePosition->{1, 1}, GridBoxAlignment->{"Columns" -> {{Left}}, "Rows" -> {{Baseline}}}, GridBoxItemSize->{ "Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}, GridBoxSpacings->{"Columns" -> {{0}}, "Rows" -> {{0}}}]}, { StyleBox[ PaneBox[GridBox[{ { RowBox[{ TagBox["\<\"Version (latest): \"\>", "IconizedLabel"], " ", TagBox["\<\"1.0.0\"\>", "IconizedItem"]}]}, { TagBox[ TemplateBox[{ "\"Documentation \[RightGuillemet]\"", "https://resources.wolframcloud.com/FunctionRepository/\ resources/MapReduceOperator"}, "HyperlinkURL"], "IconizedItem"]} }, DefaultBaseStyle->"Column", GridBoxAlignment->{"Columns" -> {{Left}}}, GridBoxItemSize->{ "Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}], Alignment->Left, BaselinePosition->Baseline, FrameMargins->{{5, 4}, {0, 4}}], "DialogStyle", FontFamily->"Roboto", FontSize->11]} }, BaselinePosition->{1, 1}, GridBoxAlignment->{"Columns" -> {{Left}}, "Rows" -> {{Baseline}}}, GridBoxDividers->{"Columns" -> {{None}}, "Rows" -> {False, { GrayLevel[0.8]}, False}}, GridBoxItemSize->{ "Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}]}, Dynamic[ Typeset`open], BaselinePosition->Baseline, ImageSize->Automatic], Background->RGBColor[ 0.9686274509803922, 0.9764705882352941, 0.984313725490196], BaselinePosition->Baseline, DefaultBaseStyle->{}, FrameMargins->{{0, 0}, {1, 0}}, FrameStyle->RGBColor[ 0.8313725490196079, 0.8470588235294118, 0.8509803921568627], RoundingRadius->4]], {"FunctionResourceBox", RGBColor[0.8745098039215686, 0.2784313725490196, 0.03137254901960784], "MapReduceOperator"}, TagBoxNote->"FunctionResourceBox"], ResourceFunction[ ResourceObject[ Association[ "Name" -> "MapReduceOperator", "ShortName" -> "MapReduceOperator", "UUID" -> "856f4937-9a4c-44a9-88ae-cfc2efd4698f", "ResourceType" -> "Function", "Version" -> "1.0.0", "Description" -> "Like an operator form of GroupBy, but where one also specifies a \ reducer function to be applied", "RepositoryLocation" -> URL["https://www.wolframcloud.com/obj/resourcesystem/api/1.0"], "SymbolName" -> "FunctionRepository`$ad7fe533436b4f8294edfa758a34ac26`\ MapReduceOperator", "FunctionLocation" -> CloudObject[ "https://www.wolframcloud.com/obj/6d981522-1eb3-4b54-84f6-\ 55667fb2e236"]], ResourceSystemBase -> Automatic]], Selectable->False], "[", RowBox[{ RowBox[{ RowBox[{"(", RowBox[{"#race", "&"}], ")"}], "->", RowBox[{"(", RowBox[{"#re78", "&"}], ")"}]}], ",", RowBox[{ RowBox[{"Association", "[", RowBox[{ RowBox[{"\"\\"", "->", RowBox[{"Mean", "[", "#", "]"}]}], ",", RowBox[{"\"\\"", "->", RowBox[{"Median", "[", "#", "]"}]}]}], "]"}], "&"}]}], "]"}], "[", "matched", "]"}]], "Input", TaggingRules->{}, CellChangeTimes->{{3.834319880013258*^9, 3.834319911987419*^9}, { 3.834320062431499*^9, 3.834320078041237*^9}, {3.834324007096959*^9, 3.83432401055797*^9}, {3.834336407146615*^9, 3.8343364381156063`*^9}}, CellLabel->"In[10]:=", CellID->1499898732], Cell[BoxData[ GraphicsBox[ TagBox[RasterBox[CompressedData[" 1:eJztnAl0FUX2/xsIiLKDIgMRRHZBZJcfOwyLQlQMBBWFYTEiLqDsiGwDBnAU WcTIICCIyCZb2BIgg2GTBAJhS1hDEkLYF2GQgMr/+3/3WKen+71+9ZYkleZ+ zoHzUn2rXnXdvt+61V39KvYZGPx2Xk3TBhfEf8G9h7ceNKj3yC7F8UfIgMH9 +w0IfeuFAUNC+4UOatwnHwoD8mjaCvz7/5/vMwzDMAzDMAzDMAzDMAzzwPAf hmEYhmEY5gHmV8YWsDfVhP2iJqyB9oO9qSbsFzVhDbQf7E01Yb+oCWug/WBv qgn7RU1YA+0He1NN2C9qwhpoP9ibasJ+URPWQPvB3lQT9ouasAbaD/ammrBf 1IQ10H6wN9WE/aImrIH2g72pJuwXNWENtB/sTTVhv6gJa6D9YG+qCftFTVgD 7Qd7U03YL2rCGmg/2Jtqwn5RE9ZA+8HeVBP2i5qwBtoP9qaa5CK/pKamRjhI SkrK6b5kOayB9oO9qSa5yC8bN27UHHz55ZeiMCEh4a233nrnnXcSExNzsG9+ hzXQfrA31SQX+cWpBnbq1IkKe/XqlYN98zusgfaDvakmucgvTjWwR48eVBga GpqDffM7rIH2g72pJrnIL0418PTp0xMmTAgLC0tJScnBvvkd1kD7wd5Uk1zk F6caaFdYA+2HF968cuXK5s2bT5w4YT60e/fu7du3X7p0ybqFxMREBA5SBbff dfDgwQ0bNhw/flyyb7GxsdHR0RkZGZL2yqK4X/bv3x8VFXXu3Dl8xgcvNBA+ 2rp1644dO9z2ikhPT9+0adOhQ4du3Lgh/y1+hzXQfsh4s3bt2sWLF581a9bJ kyeDg4MLFy5M13ytWrV++OEHGFy9enXIkCFPPPEElefLl2/UqFHmaxUlWByV KVNG+4ty5crNnz/fbLZgwYLmzZsXK1ZMWKLWqlWrXHUsLS2tZ8+epUuXJuM8 efL07t378uXLvo1NTqKgX8C1a9eGDh1aqlQpMc6NGzf+97//bdZAVC/uIDIy Ut9CUlLS22+/XaVKFdQVverWrZth2qLqlStXxufFixc3atQIZmRftmxZqK4n Y+lPWAPth4w3n3zySVx7QUFBIpoERYsWjYuLa9++vWbiiy++0Ddy4cKFdu3a iaMFCxYUn0eMGCHMzp49ixA2t0YRt3btWnPHXnrppUqVKpntBwwY4L9xym5U 88uvjkysWbNmTl1D6DVw9uzZVLhu3TpROH369AIFCjit26RJE/13ieofffSR 2RidTE5O9n5wfYA10H7IxxrRuXNnCNGePXugPPrLEjnJvHnz9u3bN3LkSCpB LX0jw4YNo3JkLLRnDOtcJHsoCQgIwGdh2bJlSxR26dJl6dKlWNlhITx48GCq 27RpU1cde/755xctWoQ12uTJkylngA6cP3/ebyOVvSjol4EDB5IlMkYM9alT p7C+fuutt+Q1EItZzZHITZkyJSYmBrkfMrqKFSuS5fr1683VARLC8ePHY9WM AWnQoAEVjh492rcB9hLWQPvhUaxhJSUKIS9irdqhQwf9WoYiCIjJOj4+nhKA jh076ltGrZIlS2r/u4MCOvbzzz8b+tCqVStqMzU11dwxBLJ+iffqq69S+a5d u2QHQjFU88uBAwfy58+PEpQbtj2Hh4dLaiD48ccfkXnqS8TtxP79+5urQyH3 7t0rynFtUDmyX+vBySJYA+2HfKw999xzhvK2bdvSBWm484Y5msp37txJJf/8 5z+p5PDhw4ZGcOWjvHHjxtZ9mDhxoqFNi47NmjWLjJcvX27drLKo5hcx/qhi sHT6XNiVBjqlUKFCsOzUqZO5+po1awzGgYGBKK9Xr57bZrMC1kD7IR9rhjs2 oHv37nShXrt2TV8+Y8YMKt+wYQOVvPbaa/gT+ckWE2+++SYOPf744+bvxcpu +vTpb7zxRp06dcQN/5UrV7rtGKSPjOfOnSs5Dqqhml/Enudjx44Zvs4LDbx0 6dKSJUuQvmIJX7VqVbJs2LChTHWoH8rpcUn2wxpoP3yJtZ49ezqNNbE4ErEG EdMsyZcvn74FaJerKitWrHDbsdWrVz/IGpgVfoFA4c+HH37Y3A2PNDApKQkZ ZokSJczfVb9+fbfVAfJejTWQ8R/ZE2uNGjXCnw899FATF7Rr105UDwsLo+oF ChTo2rXrvHnz9u7dK9pkDRRkp19q166tOR5PmLtBTzrAtGnTRKFTETt16pR4 gl+lSpWxY8euX78+JSWFHouwBjI5QvbEWnBwsOZYc7nd4CqWsQhP/WZdsQmN NVCQnX4Rv4FgftQunwfSU13kluiJvgXWQCYHyZ5YGzVqFJW43d0aGhpKlsgZ 9OWsgWay0y/vvvsuWS5dutRwSFIDk5OTqcT8KwqsgUwOkj2xFhMTQ+8F1KpV 68qVKxbfFRQUpDm2Q+vf+bp8+fLrr7/OGmggO/3y/fffU93GjRvrm8Xnf/zj HzIauGvXLirp27ev4UzpgRdrIJMjZE+sgX79+lHhs88+i2+8fv06Co8dOzZi xAi0LFoQ7wX06dMHmQMCMzIysm7dutpfsAYKstMvWCzXrFmTLDt06BAXF3fp 0iVkj23atBGusdbACxcukN4WKVIkOjoaDeJbxo4dGxAQQJasgUyOkG2xdvbs WaQQIl4KFiyofx1YiNXu3bvF61R5HNDn6tWrswYayE6/AMjRI488opkoX768 jAaCkJAQUUu8/4tvrFChAmsgk1PIeLNKlSq46lq3bm0op1jDPE7JgwCBY441 ALOJEyfSCwgCBDJiR7+bd9GiReLXD0BgYODkyZNhkDdvXoMGuuqYuENlbw3M Zr/86tixKbJBzbFV5vXXX09LSytXrhz+nDVrlrCcP38+2ejvNKampnbu3FlU x2TXvn37/fv3I+c0aKDT6gS97cIayPiLHPHm0aNH16xZg0h09eY71k1YLiEH OHLkSDb3TRHU9AuBo3AN1Mn6FqIrDh8+vGnTJlSX/NUspWANtB/sTTVhv6gJ a6D9YG+qCftFTVgD7Qd7U03YL2rCGmg/2Jtqwn5RE9ZA+8HeVBP2i5qwBtoP 9qaasF/UhDXQfrA31YT9oiasgfaDvakm7Bc1YQ20H+xNNWG/qAlroP1gb6oJ +0VNWAPtB3tTTdgvasIaaD/Ym2rCflET1kD7wd5UE/aLmrAG2g/2ppqwX9SE NdB+sDfVhP2iJqyB9oO9qSbsFzVhDbQf7E01Yb+oyX8YhmEYhmGYB56czkkZ /0DevM8oBvtFTVgD7QfHmpqwX9SENdB+cKypCftFTVgD7QfHmpqwX9SENdB+ cKypCftFTVgD7QfHmpqwX9SENdB+cKypCftFTVgD7QfHmpqwX9SENdB+cKyp CftFTVgD7QfHmpqwX9SENdB+cKypCftFTVgD7QfHmpqwX9SENdB+cKypCftF TTzVwM2bN0dERMTExPjd2GtOnDgR4SAtLS1Lvyi3wLGmJuwXNfFUA5988klN 05o0aeJ3Y6+ZPXu25gCSm6VflFvgWFMT9ouasAbaD5lYu3Tp0n4XpKamurUR /Pbbb66+Au2QzdWrV2W+V6ZNwb1793755Zd/OVi3bh2atbZPT09ftWrV+PHj v/vuu8TExD///NPtV/gdeQ3EimbZsmVjx44NDw/funUrTtbC+Pfff09ISDhw 4IDFSd28eTMqKmrSpEkzZsyIjY3NzMyU6YZTD8og0yVCHb+wBtoJmVibPHmy 5oKaNWuSzfTp013ZCObMmeO0/QsXLjz66KNks2jRIlHuS5vE3bt3P/jgg0KF CumrFCtWDJeBU/vbt2+/+eabhq+oU6fOiRMnrIfI78j4Zd++fbVq1TL0tnLl yhEREWbjpKSkadOmVapUicy2bdvmtM3IyMiyZcvqGyxXrlxcXJx1T1x50BrJ Lt1Xzy+sgXZCJtaGDx/uSoJq1KhBNjJ6tXTpUqftd+nSRdh4qoGu2gSXL19u 1aoVmeXNm7dBgwbVqlUTFRcuXGiwP3fuXL169YR99erVoZb0Z/HixTFWMjHi L9z6ZebMmfnz5xfdq1u3LvpMfxYoUAB5r974vffeM4wbMkZzmyjMkycPtdCi RYuGDRsGBATgz4cffhj5s0VnXHnQAsku3VfSL6yBdkJGA0NDQzFiSA/OmMDy hGzQlPkowBoHEYTqTZs2/eOPP8yNL1myRB8I+gjyuk1i1KhR1ObIkSPF+hc5 0iOPPILCUqVK3bp1S2/ft29fsg8JCblx4wZK0DikEmnkN998IxUh/sOtX7Ae RFcDAwNxJdMgZGRkDB06lE6hffv2emMkw8Ud0Lk7FZw7d+5UqVIFhx577DFk mFSIDLBMmTIofOqpp7BoddoTCw9aINMlQkG/eKqBuFDpT1yHW7Zs2bt3L07E lbGFBsLFGKUdO3agHevvRfsIk02bNqWmphoOsQYakNHArl27YsTq16/vxTXT v39/1MV17nTZcv78eVpDPffcc/IRZN2mADGL8Jk3b56hXGijPlmCtFJa1a1b N8ONpuvXr7vtkt+R8csPP/yAXFdfgp4jTcJZlCxZ0mmV77//3pXgbN++nQ59 /fXX+vK1a9dauMY7D0p26b6qfvFUA7Eege61bdv2oYceopNFKtuvX78rV66Y jc0amJSU9Pbbb2OGoiwd5MuXDwMCSTR/46lTpxCwRYoUEbMSKmKQhYErDURm TrPShAkTZOXDFsjEWps2bTBiHTt29PSC2b17Ny3Qpk2b5tTglVde0RwrL7hD MoLctumWbdu20XfNnz9fFJKugiNHjnjXrH/x+rlwu3btKEacpm0WgjNr1iw6 BFkzHKpZsybKW7ZsaW7QCw/Kd+m+qn7xVAOBUD89zZo1wyxmMDZo4PTp0zG8 5rpO1XLNmjWY/pwaz507l2ycaqDwPoT62rVrXqpJ7kQm1urWrYvB6d27t6cX DFJHzZFAOn2Et3jxYhp2zDvI6CQjyLpNGXCd0HctX75cFNaoUYMuAO/a9Dve aSBWQFjJ4kSqVq3q1MBCcEaOHIlyZBrmgX3nnXc0x8MRQ7l3HpTv0n1V/eKF BoI+ffpAdo4dO7ZgwQKhVF999ZXB2KBsWM9qjjtRU6ZMiYmJQe4XFRVVsWJF qr5+/XphmZKSIp5MhYSE/Pzzz+np6REREbVr165Xr55YPps1ECNPMlutWrWz Z8/6qim5DZlYq1ChAmkgpqT+DvAhPj7euhb8RUONi9x8FMlGqVKlcLRRo0bI WHBhyESQdZuSDBo0iBpJS0sThXRX6uOPP6Y/z507t2fPnhxZbRFeaCCWjR06 dBCR5dTGQnDCw8PNw0JA4jTH84i7d++KQu886FGX7qvqF081sGjRosuWLdOX YzlDZ12pUiVxb9DVWvjHH3+8cOGCvgQySNURjKKwR48eVPj+++/rjbHi1r8S YtDA48eP0y1fyHJCQoLMSdkMmViDBzUTSBh69uxpcTV269ZNc9xgv3Pnjvlo 586dNccDx6SkJPwpGUHWbcqADtPeD8ykovDixYv07d98883ChQv1G06eeeYZ CK933+UL8hr43XffIcFo06YN1r+a4zbpjBkzXCXJFoKzZcsWOjR27Fh9OcKN nkCBU6dOiXLvPOhRl5T1i1+eC9MtJoAU2q2xGdrx1alTJ/oTQkolSAUxU1hU 1GsgVuKYwvA5ICBg48aNMt9rP+Q1ENMEUsFx48ZhuhFB8cYbbzitgiScbmWL GVwPwoSqi3t6MhFk3aYktKwzfFFcXJyILPpQpEgRsasQam+9MyQrkNfAjh07 6ucmOMhi37iF4GRmZtJzYQxyWFgY5O7AgQPIAPU3slBCxt550NMuKesXv2ig eDaHE3dr/KvjmfKSJUtQC7NP1apVqW7Dhg3p6MGDB6kEE6J1l4QGYnYTqeP4 8eNlTseWyMTa2bNnp06deu3aNVGCmat8+fI0etHR0eYqM2fOpKOHDx82HMrI yKCbIa1atRLpikwEWbQpybZt2+jJWuPGjfWZknj0qTnupMHs3r17MEBnSO2f euoprzNP75DXwM8///yFF16oW7euuHNevXr1o0ePOjW2XngiMShYsKD2v5Qo UeK1116jz1hV3ffBg552SVm/+EUDxfbXBQsWWBsj08aaF47QTNSvX59sxA6l SZMmWXdJaGBISIhop3379jKnY0u8fv64cuVKGr2JEyeaj7766quaY9Y27997 6aWXqOKuXbvO/YXYmAGho0zeozZlOH78ON0xxmoxISFBf0jcnKlRowYCXH9I TNZu35XwL1745fz582PGjCGRhzjcvn3bbGOtgfcdoxQcHBwYGAibChUqhIaG IgA/+eQTGnmy8dqDTrHokrJ+8YsG0l1WgFCyMEZCLl6lQaI+duzY9evXp6Sk 0GMRoYFiGDEnWndJaCAh9mfOmzdP5ozsh9caiLo0dEgSzEfLlSuHQ61btzaU HzlyxDyXmcFlIN+mDBcvXhQXkvnVEsSXuAwMh+Lj4+nQ4sWLvfher/HaL4gR 6rDTtwjdaqBAv6Du2bMnqtSqVeu+bx50ikWXlPWLXzTwrbfeolPAusbCuEGD Bppjs1N4eLi+3KCBsbGx1Nq7775r3SW9BtasWfPkyZO0pkOGAGmVOSmb4XWs ZWZm0k34zp07Gw4lJyfTCA8bNsxwKDExUSaC4Hf5Nt1y69YtLH6putN7iVhe 0RpwyJAhhkNpaWlU8csvv/T0e33Ba78gbaAOv//+++aj8hoo+P333ylGOnXq dN8HD7rCokvK+sV3Dbxy5QrN6ThBi+fC4rJHQm5owaCBaJDiEY0Ytl4bEBr4 +OOPHz169FfHc2cq6dGjh8xJ2QyvY03MO+PGjTMcErdxnO5guXbt2hUTYtXz zTff4E96JUq+TQuwJETqSHW7d+/uah1N2w6R6hieqIolXjbffnfrF1dPfk+f Pk0d7t+/v/moFxq4fPlyqvLtt99SiXcedIV1l9T0i6ca+PTTTxvebpsyZQr1 v2vXrgZjvQbu2rWLzPr27WsI28KFC+s1END2ePDpp58auhEXFyc+Cw1csmSJ KHzhhRfEeMqcl51wG2vQjaFDhxp+kQkXpHiyb/6VEjHIkZGRkpeW2zvqbtuE 1i1cuBArI/0iDp/btm1LFV988UWL35USkbhq1Sp9ee/evalc/EpY9uDWL926 dQsODv7VdNtNrIX1b8EIrAXnzp074skvcfXq1dq1a2uOe4P6zYFmXHnQqV/k u6SmXzzVQM2xDxDpVnp6+vHjx8XNzAIFCoiFMKhTp47m2LV1/fp1Krlw4QLd 4C1SpEh0dDRmFowzXEy/ZWHQwH379omf0Rg4cCBaxmyFyeKVV15BI1FRUWTm 9D2RgwcPUsqNfl68eNFnXclNWMfasmXLaLiaNWuGa/vMmTPIow4dOiT24jZt 2tT8Ttb48ePpqHj13i1uNdBtm2Lns1jtIqJFP4sWLYoJDnK96n85d+4cGeMs 6IYhLrbVq1fjNFGCdRa9lwe1kTwRf2Htl59++onOq3r16si7jhw5glkJmcYn n3xCC6JixYqdPXuWjBF3O/9i9OjRVDEsLIxKxEN2tBASEoLgmjRpUkZGBkYP 4UNvH4Pw8HDrDrvyoNkv8l26r6pfPNVApy/K4RQMzy/Ei4ElSpQQL6zpn96S czXHCpreXNBrIPjXv/4lbOgrxOe3336bbFy9L0xPvsDgwYO9F5RciHWsYd5p 37693nFiAgKlS5dOTk421xJ78FJSUiQvLbca6LZNWjRpDlmmEizSzReegZUr V4oWEPL04oPm2Por9kA+9thj4udxsg1rv2RmZtJTcoH+lVJM+mvWrBHGCDSL EcCSiswwsHCoKNeH0ocffujqR2MErjxo9ot8lwgF/SKvgbTlctasWVCn4sWL i3MsX768edUJUXriiSfIYO/evVSIRJe2owtHIyT3798/YsQIzaSB4Oeff372 2Wf141m2bFlMlMIACwQqh6W+IubQypUra44Yx6zqsZTkWmTuOy1YsED8gBuB UQoNDaXdYmZEeDrdnuEUZJhUBZmnd22KGyxi165YcViwfv16fSOnTp36v//7 P/3sGRQUZNiVkT3I3KddsWKFeNYjaNmy5Z49e/Rm1oJTqFAhYYkzxfnq5TQw MFDyBqwrD5r94lGXCNX8Iq+BerDC3bVrF1YfiYmJFjabNm1Cnm9YkCI3RjnW s25/NYvAKgD2mAqTkpI87eeDhvwzkbS0tJiYGExV8fHx2bwxVRJMXlin+97O r47JFMOi3xaezcj7Bcv5HTt2IKlAfLmalTwCSSZUFOHmryzLfn7xTgMZNfH6 uTCTpbBf1IQ10H5wrKkJ+0VNWAPtB8eamrBf1IQ10H5wrKkJ+0VNWAPtB8ea mrBf1IQ10H5wrKkJ+0VNWAPtB8eamrBf1IQ10H5wrKkJ+0VNWAPtB8eamrBf 1IQ10H5wrKkJ+0VNWAPtB8eamrBf1IQ10H5wrKkJ+0VNWAPtB8eamrBf1IQ1 0H5wrKkJ+0VNWAPtB8eamrBf1IQ10H5wrKkJ+0VNWAPtB8eamrBf1OQ/DMMw DMMwzANPTq/hGP9A3szpFQZjhP2iJqyB9oNjTU3YL2rCGmg/ONbUhP2iJqyB 9oNjTU3YL2rCGmg/ONbUhP2iJqyB9oNjTU3YL2rCGmg/ONbUhP2iJqyB9oNj TU3YL2rCGmg/ONbUhP2iJqyB9oNjTU3YL2rCGmg/ONbUhP2iJqyB9oNjTU3Y L2rCGmg/ONbUhP2iJp5q4ObNmyMiImJiYrI6kBmv4VhTE/aLmniqgU8++aSm aU2aNJEx/uqrr4KDg+fOnetbTDOewbGmJuwXNck6Ddy5c6fmIG/evImJiT5H NiOLR7GWmZkZGxuL2SosLAxJ/o0bN8w2N2/ejIqKmjRp0owZM2CMKtZt/v77 7wkJCQcOHPjzzz9d2dy7d++XX375l4N169ZdunRJssPEnTt3du3aNXXq1E8/ /XTp0qXJyckeVc8RZPyCcdjvgtTUVKdVLEbbojXBb7/9pq/iy8AeOnTI1bfc vXvXYOzjBeBHsk4DMSBQPxgHBAQcO3bM58hmZJHXwOjo6EcffVTTAWdNnjxZ H02RkZFly5bV25QrVy4uLs5pg0lJSdOmTatUqRJZbtu2zWyDcPjggw8KFSqk b7NYsWKzZ8+W6TOqjxgxokCBAvrq+fLl69Onj1MBVwcZv2DwNRfUrFnTYOx2 tKdPn+6qNcGcOXPI2PeBLViwoKtvWb58uTDz8QLwO1m6Fl61atWgQYM2bNjg W0wzniGpgcj9oHh0BZYsWbJq1ap58uShPydMmEA2W7dupUKERosWLRo2bEhV Hn74Yczdhgbfe+89w5WP6gaby5cvt2rVio5iimzQoEG1atWE/cKFC637fO7c uXr16pExOvb0008//vjjonrnzp0tMs8cR8Yvw4cPdyUjNWrU0FvKjLaMBiLZ u++PgUU+6fZb7vt8AWQFWaqBTI4gE2unT58mNcP8u3HjRrrCEQjt2rXD9X/1 6tX7jmVRlSpVYPPYY4/t27ePKiIDLFOmDAqfeuoprML0bWJyL+7gkUcecRWV o0aNokMjR44Uy5+IiAiqUqpUqVu3bll0G2f3zDPPwLJ///6IJpT88ccfONmK FStSs1jHyV/82YyMX0JDQ3EWSLzPmEhPT9dbyow2hsvcDsDCGbMYqjRt2hQD eN8fA4vukeVnn31m/sb//ve/ZObjBZAVeKeBGDpREhsbiyVVRkaGR3GKEYuJ idmyZQvizq1xYmIiVmRpaWluLQ8ePIi08/jx4/Ld2LRpE5btyPYlq6iPfKxh pZOQkKAvx5WPFujz9u3b6XL9+uuv9TZr166l8kWLFjlt/Pvvv3cVlZDNvn37 zps3z1AuQuOXX36x7nlKSsrq1asNhT/++CNVR+ZjXT0HkfFL165dcRb169eX b9ZitF0BoYM9ZOfEiROi0MeBPXz4MFlC0CzMfL8A/I53GtimTRsoUq9evUTC jPy5d+/emEH0xrVr18Y81aFDB33h/v37O3bsqM+Tq1evjhxYGMyfPx+1kGYg Gxk7dmyFChWEJdLmqKgoQ5cgXwsWLGjevDlSGmGJXAUrcYMltVy5cmV8Xrx4 caNGjSACZI+Z19xyLsVtrMF3dNsnJCTEwmzWrFk0OOfPnzccqlmzJspbtmzp tKIXUblt2zaqAh9JVtEjHsDhgvGievYgo4GILJwFAkS+WU9He/fu3XSjftq0 aW6N5QcWKQ1ZIimS6YYBHy8AX/BOA5944gmRJOsZMGCA2Vi/cEZWXK5cOWFP CbmYaMhm9uzZVFKrVi3zVyB4IV+iwbNnzzo10xyyjIxF3x/R8kcffWS2L1iw YHJystfKow5uY23p0qV0yjt27LAww2qFhtF8L+idd97RHA9HnFb0QgPXrFlD VfQ3z+UJCwuj6rg2vKiePchoYN26dXEWSCfkm/V0tJFkao5UU+beqfzACg8i n5Tqt4vq3l0AvuCdBhLPP/88VkPI6yZPnkwJVdGiRZEzGIz1GojZhOq+++67 SUlJSOEwK2HWe+aZZy5evEg2QqlA+fLlw8PDjx49inXZ66+/ToWBgYFIEUWb yEZQ2KVLF4Q2cnsshAcPHkyW+jW7oWUkhOPHj4cI4MQbNGhAhaNHj/ZJfdTA baxNmTKFxO3OnTv3HbsU4uLiTp48SbeGBBh5GhbkjYYWJkyYoDnuaZv3PNz3 SgMHDRrk6ruswdrqhx9+oLQWU/Pt27c9qp6dyGggrXqggUgJ+jvAh/j4eIsq Ho22yNZQy9rS04FF/kYtI/H4+OOPcQqIJiinpEe8vgB8x2sNHDZsmP4e2quv vkrlu3btMhjrNfDll19GSf78+S9cuKBv9sqVK+KzUKoXX3zRcA+wc+fOdGjO nDmiECL8888/G/opnj2lpqaaW0YSu3fvXn0LVB4UFCQzCIrjNtYwAWmO2wWY d7p164aZi04fmTAuRXFTesuWLVRuWAdFRUWJBP7UqVPm9j3VwOvXr9P2G/hF 8rpF/j9w4MCuXbuKWyXNmzd32hl1kNFA4Qs9mK169uyJUXJaxaPRhrs1x0Mu mv7MeD2wU6dONfec9BM5nnVdLy4AP+KdBj733HOGcnHvCKmswVivgXQzVnM8 BHf1FUKp1q1bZzi0efNmOtSnTx/rfk6cOJEsd+7caW4ZTjHYI7dEeb169ayb zRW4jbVOnTppjlvi4oZG6dKlxcYYOJcSwszMTHoujDkLayIEwoEDB5ABPvTQ Q+IKR4m5fU81kFbWmuuHLGZiY2MNgXb48GHJujmFvAaWLFkSedS4ceN69Ogh pps33njDaRX50U5PT4crYYk8zZWN1wMrNBCaiQRpyJAhtK4HuGAOHTpkUdeL C8CPeKeB5r0xkD46C/2bcWZjiJh4DNGuXTusXq9du2ZoykIDMV9Q9bZt25r7 tmfPHiwccKnUqVOncOHC1MjKlStlWqbNUfS4JLfjNtbETjDNsfynTRcZGRkY VSr897//TZbwl3nja4kSJV577TX6jATe3L5HGrht2zaS38aNG8vv7ktOTg4J CUG4idvLaGTMmDG5fX8g0jCICYJClJw4caJ8+fJ0jtHR0eYq8qM9c+ZMsrSQ NV8GFitfXDDiT8ykn3zyCTXSokULV7W8uwD8iL80cPXq1TIaCObNm6d/gPu3 v/1t/Pjxly5dEgYWSgXoSXT16tX1hfhS6J7mjBUrVsi0jOTnwdHADh060DgY NjxA0IoUKYJyJIqi8Pjx48HBwZQnY30UGhqalJRE1zaMnbYvH5VonF5UQVJq 2KUjCaImMjJSeD+n3jWQQUYDnYJ5nM4OqxvzUfnRphtW8Jrhxq9T/DKw+CJq ATOpYTcp4fsF4DvZr4EAq6oRI0bo38CCpqWkpNBRaw2kV2waNGggSsSjqwIF CnTt2hUau3fvXnE/nzXQTL9+/TTH8ybzoZdeegmHqlataj6kf7G0Z8+emuPB vdP2JaPy4sWL4j0v8R6BdyCJpcnR1aNqFfBaA+FTGiWk3+aj8hpIqV3r1q3l v9r3gRUPKDF1Gg758QLwhRzRQAIJP5Jn8aZM9+7dqdxCqU6fPm0wFmvwRo0a 4aiwxGqONdAVkyZN0hxrnJs3bxoO9e3bV3O8PGJRHRM6rc706aIemai8desW 1j5kZnF7Sp4ePXpQa/Sag4J4rYGZmZl0C6hz587mo5IaiEUumQ0bNsyjb/dx YMVy2HDr2O8XgNfkoAYS6enpNBdgUUwlFkr15Zdf0qGxY8dSCb3voDkeUOot WQMtnB4REUHjYN6L9fe//11zpNkW1cW88+233zo1cBuVt2/fRjYipjOZpZnA 1S0jSk3BhQsX5FvLTrzWQPGcYty4ceajkhoo3u5xtSsmiwaWXojAGk2/jcqX C8DvZLMGYpUKhxrqNmvWTHPcpqA/hVIhxPRmaWlplH7kzZtXNBIUFEQpzYkT J4QlJiyxmZA10AwuOax2NcdzcP3ld/LkSXpu2KdPHyq5c+eOYfq+evVq7dq1 Nce9QaebA++7i0qsqcXDlxdffPHevXuu+olIWbhwIRYLYhm+b9++MmXKmH+u 4fz586VLl6ZeWZx4zuLWL1CDoUOHGgYE0kQvj2guXkOT1EBx8UdGRpqPejSw Zr/Ex8fjqti/f7+hOs6Xnnfo3/6TvwCyh+zUQEr5EGXDhw+nXxTECMOeKrZo 0YLMhLMwekjz4B1oWlRUVPXq1am8V69e4ivEGx8IW2T7V65cgYvFQ3nWQFeI Ha0vv/wyrXFOnz5Nt6+x7KJNuYi+kJCQgIAArJ0zMjKgh9u3bxdeCA8P1zcI 5+78i9GjR5NNWFgYlYgHkWhEPJEpWrQoHIG4XvW/nDt3jozFvlmxVqL3+nFh vPvuuxs2bLhx4wYiCCf77LPPkuXgwYP9Gh/+xNovy5Yto1NASrBo0aIzZ85g ejp06JAYrqZNm4rHCpKjrWf8+PFkJn7+Qo9HA2v2y9NPP605HnyMGTMmJiYG KocWEG601QfNbty4kSw9ugCyh+zUQHyF/tfq8Fnsu0CgRUdHk5n+bQ4zGG39 snf37t3iF8/yOKDPIlRZA52CK/zNN98U46b/oaRRo0aRTUpKCuUAhNjUBD78 8EPDY77PP//cwmuFCxcmM6zmLMyIlStXkjG91aU5wl9criVKlBCWWBGIn//S HJsr3P6+aw5i7ReIRvv27fXjoD81OEL/c6aSo61H7MFz+i6bRwNr9stPP/0k fr5Gc1wqIhIBkltR16MLIHvwVANp0yzW8oZy6Dz1X6+BZmOsZ997771SpUrp TxlzkP43BoVSzZw5s3nz5sIMCWSPHj0ML5gATJr6UA0MDJw8eTJyG3oxXK+B Ivkx/zwCfdGDo4EEBqpkyZJi6PDZ8ANuSP+CgoL0v6uJ4XV6Q8k6KgsVKkRm 4udBLFi/fj0Z0zt92v++3Y8LALmKvtuaI6OYOHGi+IEmNXHrFyTeCxYs0O/e 1BxKiNWQYR+m5GjrEW9yuXp5TX5gnfolNTUVazHDey5Y9xnW7x5dANmDpxro L5KSktY6wKLY8LtVQgNJGDH9Qce2bNmi30NoAO5DGons7siRI1nedeXx6N47 4u7YsWNbt27F0Lm6NY0cYM+ePZg4DD9hlw2gV05fMbh7925CQgKuEPQK/c/x e0oyyPsFqQJWlJs3b46Pj3f1UlsWITmwrvxCN5ARrYjH7L9avCOnNNAC6/2B jFu8fv7IZCnsFzVhDbQfHGtqwn5RE9ZA+8GxpibsFzVhDbQfHGtqwn5REwU1 cOPGjS0cxMXF5XRfciUca2rCflETBTWQ8RGONTVhv6gJa6D94FhTE/aLmrAG 2g+ONTVhv6gJa6D94FhTE/aLmrAG2g+ONTVhv6gJa6D94FhTE/aLmrAG2g+O NTVhv6gJa6D94FhTE/aLmrAG2g+ONTVhv6gJa6D94FhTE/aLmrAG2g+ONTVh v6gJa6D94FhTE/aLmvyHYRiGYRiGeeDJ6TUc4x/Imzm9wmCMsF/UhDXQfnCs qQn7RU1YA+0Hx5qasF/UhDXQfnCsqQn7RU1YA+0Hx5qasF/UhDXQfnCsqQn7 RU1YA+0Hx5qasF/UhDXQfnCsqQn7RU1YA+0Hx5qasF/UhDXQfnCsqQn7RU1Y A+0Hx5qasF/UhDXQfnCsqQn7RU1YA+0Hx5qasF/UJDs1cPPmzRERETExMVn9 RQ84HGtqwn5Rk+zUwCeffFLTtCZNmsgYf/XVV8HBwXPnzs3qXtkPjjU1Yb+o iZoauHPnTs1B3rx5ExMTs7pjNkMm1i5durTfBampqWb7Q4cOfffdd2PGjJk9 eza88+eff7pqOSssfal+7969X3755V8O1q1bhxP36Fv8iBcaePfuXXLK0aNH zUdv3rwZFRU1adKkGTNmxMbGZmZmGgwsvCz47bffhD2G1JUZeuK2t15U//33 3xMSEg4cOOCp9/2ImhqIwYT6wTggIODYsWOiPDk5uYWDgwcPZmFHczkysTZ5 8mTNBTVr1tRbIo5effVVg02zZs2SkpIMbWaFpVMkqyPuPvjgg0KFCunNihUr Bs2U+Ra/44UGQuGp25UrVzYcioyMLFu2rP7UypUrFxcXp7eZPn26Ky8L5syZ I+wLFizoymz58uVue+tRdThr2rRplSpVIoNt27Z5NDJ+RE0NBKtWrRo0aNCG DRv0hZjQacTwIWv6aAdkYm348OGuLtcaNWoIsz/++OPvf/87lZcoUaJHjx5t 27alP6tUqXL79u0stXSKZPXLly+3atWKyjGfNmjQoFq1auIcFy5c6Gmk+I6n GhgfH48cgDps0MCtW7fmyZMH5QUKFEBK0LBhQ7J8+OGHkesKMxkNXLp0KRkj IZQxc4VH1d977z2DAc5IfmT8i7Ia6BRIIo0Ya6AFMrEWGhqKYUQiccZEenq6 MFu9ejUN+LBhw+7cuUOF33//PRV+8cUXWWrpFMnqo0aNopKRI0eK9W9ERMQj jzyCwlKlSt26dcv6i/yORxqIhW3t2rWFROg1EGcNtUfhY489tm/fPipEBlim TBkUPvXUU1hgUiEuBrN/AdaeUEsYN23aFHMKGcPv9F2fffaZucp///tf6w57 VB35eXEH5I4HTQMx7KIkNjY2Ojo6IyNDsoVFixbJaCCueTS7a9euq1ev+tTj 3IlMrHXt2hXDWL9+fWuzESNGwAzLyXv37unLmzdvjvLXX389Sy196RJ0oG/f vvPmzTNUF9qIS8j6i/yORxo4evRodBLJXqNGjQwauH37djqFr7/+Wl9l7dq1 VI4wsW68f//+MIP+nDhxQhQePnyYqmOmkD0lHd5VF5NXrtDAb7/9lqR7xYoV +vKVK1dSObymL4eylSxZEuVYdlEJaWCbNm3S0tJ69er1+OOP0+nD0b1798bi RV8dkyDqdujQgf6cMWMGJrgiRYpQFXygL0UU62slJCRgBZQ/f34yw0ohKCgo OTnZOzHJpcjEGryA8enYsaO1GQULsiZDOfylOW7BZamlj11yyrZt2+jamD9/ vrWl35HXQGR3tLbt7cCggbNmzaJTOH/+vKFizZo1Ud6yZUuLxnfv3k0326dN m6Yvj4mJoWaRmUieke/Vc5cGIq2i3r7//vv6crG0b9iwob581apVVL506VIq IQ184oknKlasqJkYMGCAvrph4SymbwNYFIgqS5YsESKJSyhfvnz0GSu+o0eP ei0puQ6ZWKtbty6FmLWZSC0MDdaqVctQPSssfeySU9asWUPVZW7y+xdJDcQq +JlnnkEPkSRgRfPGG28YNBCre82ROZifpb7zzjua4+GIRftIGzTHEsBQXYxM SkqKB2flW/XcpYGAMjc4SF/49NNP01lgckGCJ8o/+ugj0qJz585RCcka8fzz zyNj379//+TJk0msihYtinlNVDdo4MGDBzHIPXr0oOqotcaBWBQj2StRooTm yBAWLlyIrBLXz1dffUXPqlDRR2HJRcjEWoUKFUgxpk+f3t8BPsTHxxvMfvvt N/iFRjU6OpoKcbmSF0RJFlk6xcfqgwYNIktcq9aWfkdSA8V0D7XHn1jdGzQw PDzc1SlMmDCBItHVXhSRrUF8DIeQGIvv/fjjj3FtYGW3ePFit0+pfKme6zSw e/fu1OEzZ85QyYkTJzTHkvPRRx/Fhx9++EEYN2jQACWNGzcWJUIDhw0bduPG DVEu9jkg1TQYGx6gjB07lizN9wPffPNN8j4WO/ryf/7znyiHzKKr7uXDFsjE GsmIAaQWPXv2vH79ut4SUSOy686dO2N+gfjg82uvvWZoMyssneJ1dZwa7SfB SkTmi/yLjF/i4uLEKphKzBq4ZcsWOneEg75uVFQUPekAp06dctp+t27dNMfD FPE4STB16lTzJaE5Fm5INtyenXfVc50Gzp07lzqMFI5K5syZgz+fe+65Ll26 4EPfvn2pPD09nbI7zAiiOskajA3NivsbWJ4YjCU1EIpKD5iwcDA0npqaSlUw Pbk9QXsgr4ElS5ZErI0bNw55sggfjKHeEhkFtMVwYWMlZX5QmBWWTvG6Oi0V NYmnBlmBW79Al+iGXvny5XFJU6FZA7FYpufC+fPnDwsLg9wdOHAAGeBDDz0k RgMl5vYRlXSrHFFpPipErHnz5shShgwZQjdMAFo+dOiQ9dl5Vz3XaSDWm7Qr KTQ0lEroZsXgwYO/+OILzTG9UvmKFSvo1DA3iequ9sZA+shY/2acRxqYkJBA 5QMGDNhigg5hreeJkORiZDTw7NmzuGivXbsmSpAnI/RorMSK8ubNmy1atNAc D6Hef/992n2hOfLtMWPG6BvMCkuneF0dCwS6erE2yZG3Etz6Rdzo0wuCWQPB 5s2bzRuSS5QoIaaGK1eumNufOXMmHT18+LDTDmDpipbFn3/88ccnn3xCVTDm bk/Qi+q5TgMBaXu1atXoT1pZIMUSu5fpDY6BAwfSVarfneJKA8WOL6818Mcf f9TcYXhsbWPknz8aWLlyJY3VxIkTqYQCEOkiPexDBoI4wkqKzD799FNRNyss neJd9ePHj9PtGqwXMGN6MTi+Y+0XnA4tnbp27XpOBxb7KEQ40J/CHmcUHBwc GBiIoxUqVEBakpSURJqDuHP6FXTTCUfFnkC3wLJOnTqoBckV2w7lcVs9N2og Ulzq88mTJ/fu3as5Et2LFy8idafrkNKtevXqaY6tF/q6WaeBYt8g1ghNXPDd d995pCS5F681EHVpGOnGGqYz+vPLL7/Um505c4YeqWD5TNszssLSKd5Vx/Up 3sly+75D1mHtF9qx6RYsrAwV9S/89uzZEza1atVy+hXlypXD0datW3vUbazy 6Ksl32T0qHpu1MBNmzZRn9F5Wv8iy6VDISEh+POVV17JyMigGe2zzz7T1806 DSQ1BmFhYdJSYVu81kDkVOQ45B7485tvvqFRNe92WLBgAR2i291ZYekUL6rf unULi1865PQ+WLZh7RfzHU6n6F+FM4BEi+5mdOrUyXw0OTmZWhg2bJhH3Rbr Waf3GH2snhs1EGtbeiT3wQcfvPzyy/gwZswYOvT1119rjh0L69evp/OCNOnr +q6B48aNI0vDw1/0im72tmrVSl4r7IrXGojlGA0vxhl/Yl2pOR6p6zMNIi4u jiwhSllk6RRPq9++fRtpD5V3795dfg2YFVj75c6dO1ec8eKLL2qOO+30p8Xv t4j76t9++635qNhXad4VYw1Wc5pj74fMT8d4Wj03aiAICgrSHDeWafGLzlN5 YmIinQ49fy9btqyhou8aKN4Bnzp1qqERzH106MFZ87rCrQZCDYYOHWp41+zP P/+kl0e0v153wrKL/jS/UkFLAM2xnSmLLO87FGzhwoWLFy8WiudRddQSP6cA JTGcb/bj3dzk9JkIBNOQViENoPeLK1So4FRtZs+eTUMRGRlpPhofH4/q+/fv N/eZHiTpX6s0+8Wj6npyqQZOmzZN+wvkhNeuXROH6JE9Yd6m4rsGCksM6Y4d O27cuCFSzSNHjtD2mICAgNGjR6empqLw8uXLWBm1bNly3rx5kmdnA6xjbdmy ZTSGzZo1W7Ro0ZkzZ5AdHTp0qEOHDlTetGlTun198+ZNuoNUqFAhXKviWerK lStpqAMDA2mbWVZY3tftZxZrWPnq+F+cUdGiRbGEhLCv+l/0jxiyAX9pIM46 JCQE1/mkSZMyMjJwptu3b69evTqdbHh4uNN2xo8fTwbiZxb00JsOBQsWxLIu JiYG4obggmzSHiro2MaNG4Wx2S8eVU9PT9/5F/RatOa4i0Ulrp5ZZx1eaKC4 L62Znnr069dPHJozZ46hou8aCL0V+zc0xw1w5NgXL16ko/hG/YYBrMrpvUi6 hCTPzgZYxxouzvbt22s6xA80gdKlSycnJwtj5FQYYTpUpkwZyKPY6I7y3bt3 Z6klvdVFsuxpdXHbxAIopx9CSBp/aWBKSgrcJM5CvBMKPvzwQ1dPb8XeSKfv sv3000/iJ1yoTcrfCKwa9MZmv3hU/fPPP7dwSuHChT0dIh/xQgMBPEIdnjJl ir5cv0fl5MmThlqUJbZu3dpQjjmCqug10JUxZhl6AkhA5TAJiqOY45o3by6k T3PsI+3SpUtsbKz82eV23MYaEokFCxbQs3sBlDA0NNS8rywxMZFuSenp1KmT +ZeN/W6Jq4sOGd7ul6nu6gVzPevXr5eLEv/gnQb26tULXUWipS9E+hcUFCTm As2RAFvf6BNvY7l6eQ1Lpz59+hheIKpUqZL5d2Cc+kW+urUGIsP3YHT8gXca mLNcvXoV0z3WdBs2bKA1r4ELFy5ER0dj8OPi4h7An8+Sj7W0tDTMKZs3b46P jze/PKUHzWIwN23ahP/xOdssjxw54uoVA/kvUgSvn1W5IjMzc8+ePVFRUfqf fPQRutO4ZcsWRJBFs678IlldKXKjBjLW+D3WGL/AflET1kD7wbGmJuwXNWEN tB8ca2rCflET1kD7wbGmJuwXNWENtB8ca2rCflET1kD7wbGmJuwXNWENtB8c a2rCflET1kD7wbGmJuwXNWENtB8ca2rCflET1kD7wbGmJuwXNWENtB8ca2rC flET1kD7wbGmJuwXNWENtB8ca2rCflET1kD7wbGmJuwXNWENtB8ca2rCflET 1kD7wbGmJuwXNWENtB8ca2rCflGT/zAMwzAMwzAPMDmdjTIMwzAMwzAMwzAM wzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAM wzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAM wzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzBMbuL/AfeDCgk= "], {{0, 130.}, {214., 0}}, {0, 255}, ColorFunction->RGBColor, ImageResolution->144.], BoxForm`ImageTag["Byte", ColorSpace -> "RGB", Interleaving -> True], Selectable->False], DefaultBaseStyle->"ImageGraphics", ImageSizeRaw->{214., 130.}, PlotRange->{{0, 214.}, {0, 130.}}]], "Output", TaggingRules->{}, CellChangeTimes->{3.8343364093808813`*^9, 3.834336439563876*^9, 3.834405667225971*^9}, CellLabel->"Out[10]=", CellID->2006834304] }, Open ]] }, Open ]], Cell[CellGroupData[{ Cell[BoxData[ InterpretationBox[Cell["\t", "ExampleDelimiter"], $Line = 0; Null]], "ExampleDelimiter", TaggingRules->{}, CellID->379258169], Cell["\<\ The preceding example established what was \"normal\" by looking at the \ treatment data and then eliminated rows of the control data that looked \ anomalous by that standard. This use of the treatment data as the baseline is \ standard in the literature, but perhaps somewhat arbitrary. An alternative \ approach would be to take only treatment data that was \"normal\" by looking \ at the control data and taking only control data that was \"normal\" by \ looking at the treatment data. Such a method could use symmetric \"acceptance \ thresholds.\" Compute an anomaly detection function on the control data:\ \>", "Text", TaggingRules->{}, CellChangeTimes->{{3.834334574675744*^9, 3.834334771699716*^9}, { 3.83433500556947*^9, 3.8343350095187283`*^9}, {3.834405702327878*^9, 3.834405726999403*^9}}, CellID->939931350], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"adfControl", "=", RowBox[{ RowBox[{"Query", "[", RowBox[{ RowBox[{ RowBox[{"Select", "[", RowBox[{ RowBox[{"#treat", "==", "0"}], "&"}], "]"}], "/*", "AnomalyDetection"}], ",", RowBox[{"KeyDrop", "[", RowBox[{"{", RowBox[{"\"\\"", ",", "\"\\""}], "}"}], "]"}]}], "]"}], "[", "lalonde", "]"}]}]], "Input", TaggingRules->{}, CellChangeTimes->{{3.834318686049137*^9, 3.834318735822226*^9}, { 3.834318797816992*^9, 3.834318824566594*^9}, {3.834318937320372*^9, 3.834318943850749*^9}, {3.834319464707427*^9, 3.834319465917754*^9}, { 3.834334787598551*^9, 3.8343347883399477`*^9}, {3.834334895614303*^9, 3.8343348993910847`*^9}}, CellLabel->"In[11]:=", CellID->1207480353], Cell[BoxData[ InterpretationBox[ RowBox[{ TagBox["AnomalyDetectorFunction", "SummaryHead"], "[", DynamicModuleBox[{Typeset`open$$ = False, Typeset`embedState$$ = "Ready"}, TemplateBox[{ PaneSelectorBox[{False -> GridBox[{{ PaneBox[ ButtonBox[ DynamicBox[ FEPrivate`FrontEndResource["FEBitmaps", "SummaryBoxOpener"]], ButtonFunction :> (Typeset`open$$ = True), Appearance -> None, BaseStyle -> {}, Evaluator -> Automatic, Method -> "Preemptive"], Alignment -> {Center, Center}, ImageSize -> Dynamic[{ Automatic, 3.5 (CurrentValue["FontCapHeight"]/AbsoluteCurrentValue[ Magnification])}]], GraphicsBox[{{ GraphicsComplexBox[CompressedData[" 1:eJx1nAdYU8n39++NAUIR0bWDixV7LwuizsXeK6hYsCtr113XsigoVuwoFlSs a1td0bXrOhcUwRU1FJUiGJASMEAIIQQI4UV+5ua53/2/efI8PMfkzsxnZs6Z c86c2Gr+qsmLJAzDZEkZ5tvf3rUvW56pfcn4FokeNW8rQbZaN7HmXUeQMzs/ q3kbqUm+vyK15q0T5IZzihvNKS4Q5A1R0/yipn0SZP7kt9c1YpK1jb49kCHI LqOC244KVgvy5NoB6AWZ1g6A4Uzy+2/DbSEV5K/n7PPP2csE+X9/7QRZzCsF XinwSoFXCrxS4JUCrxR4pcArBV4p8EqBVwq8UuCVAq8UeBngZYCXAV4GeBng ZYCXAV4GeBngZYCXAV4GeBngZYCXAV49FfPqqZhXT8W8eirm1VMxr56KefVU zKunYl49FfPqqZhXT8W8eirm1VMxr56KefVUzKsGXjXwqoFXDbxq4FUDrxp4 1cCrBl418KqBVw28auBVA68aeBXAqwBeBfAqgFcBvArgVQCvAngVwKsAXgXw KoBXAbwK4FUAL8OA/jKgvwzoLwP6y4D+MqC/DOgvA/rLgP4yoL8M6C8D+suA /jJiXjyPFATWl8D6ElhfAutLYH0JrC+B9SWwvgTWl8D6ElhfAutLYH2JmFdB QH+BVw28auBVA68aeNXAqwZeNfCqgVcNvGrgVQOvGnjVwKsGXj3w6oFXD7x6 4NUDrx549cCrB1498OqBVw+8euDVA68eePXAy3CgvxzoLwf6y4H+cqC/HOgv B/rLgf5yoL8c6C8H+suB/nKgv5yYl+HAvwJeKfBKgVcKvFLglQKvFHilwCsF XinwSoFXCrxS4DX9u+BfAa8MeGXAKwNeGfDKgFcGvDLglQGvDHhlwCsDXhnw yoDX9Ffwn4HXDnjtgNcOeO2A1w547YDXDnjtgNcOeO2A1w547YDXDnjt/vNX zOsAvA7A6wC8DsDrALwOwOsAvA7A6wC8DsDrALwOwOsAvA7A68BdDexf87b+ zmMJ/rQl/9s3XCsbziSLn7fkxc/bw/zZc+LnUcb+ZdCfDNrH8dtzK79Ndz+b 759b8e7fHqhjy5lk8ef1OPHn9WC8+LwMnsf+60H/GH9awXxi/w6cX+1+YL9/ LuFja18V1CTXLl++RpCVtf+QI8jct+3R9pUgi+fLQGs/HmUhyOL+DLRl7f7O IyZZ/LyWisejpeLxaKl4PFoqHo+WHrqlD7qljycm2Xfxt5dBkMX9KaE/JfSn hP6U0J8S2pNDe3JoT07nflPPRpacSRbPh5yIxysn0B8R8ymJ+HklPK8k4v2t hPa00J4W2tOSAbWAWkGG+SRiHi30pyVifgMRz6eBjK4dj4QzyeL2DDA+CSce n4QTj0/Ciccn4cTtS8B+Snjx/sR8hATyERLIR0h4GB/IBogfcb9jvsAA8Z+B ivlQxv2M8TbqE8bfWohHtRCPamF/oqyEeFRLxfONMuqLAsaH8TLqI8bPStB3 lOUwX0qQUR+VICtQvwnoN+xvOegP6jfDgH6DfWIYsA9EbE9RVkK8Igf9wvEp 4XO0HwrQbwXosxxkBRHrjxJkLcRPSoifcDxaiKeUEE+hfVPDeNSwPlqQDdA/ 9qeF/rQQv2khftNCfIb2EONJA8yHAewxyhLwr9BeYjwmAX9OAv6cBPw5Cfhz EmwP7BnmV9EeYX4S7QXm59A+YP4P9RnzYajPmP9Ce8Mw4v3AMGL9wHyUHOwb 2gvMl6F+Yn4I9VEB+x/zOXLIV6B+qeF5zN/g/sf8Be637/mAmaa8N/u/fP0g k59ZQWunN9GUJ9bQ2ukqNuVRs/63Pg2svo9fRxy+4bas8102ksHfnveUfG/P SG1q+bK/z28WSasdQMV3WUPCvy2/jTVnkkO/bY9mFt/lCrL92/J2YL/LLBdT O+CXxDT+p7X6l0JM/d3+1l6Qhph4or9Nh5/uu6yD8Whov1p/uuC7rKLi/jRU zKei4v6zqLj/JOg/C/pPomK+LCqef56I558n4vnnSe3yZ2q/y0kwnzysD0+m f+t+mCVv+n7tdhlruufJIsbqb688oT1YbxJfO6BM+n9/XwXrlwTrryLi/aaB 9jSkdnqiDN9lHfBXkLG1CpL4XTYSee0E3Pkus5y4PRW0rwb/Eu8X5LD+PNgj NRGvtwby6UoqjteyYH+oQP/UwKMBHg0Rt6cC+6qA/ZsE40+C/agiYlkD9lsD 8Y8KzjMl2AMVyGp4Xg3xk5qKeVUU1gv8PRWMTwk8qG8KOB/UoH8qWG9sPwn6 16A/Af4rA/GzAfxXBuJpA8wPA/G1AeaL4cXnixbiCy3YC1w/OcgKaF8OsgLi eTn0J0f7BP67HM4HnoI/CfdTcrRP6K+DPyYnsL/BP5JDfl1OSmoHZPz/fF8J 31fC91VgLzA+1YM/bQBZD+e1hIP158T2juXF9pzlxfaV5cX6w8L+kIDM8GJ7 b6Rie28EeyLhIX7hxfbFCPbFCPtBB/tfT8U8OtgfOtifeirm1YF90MF+1YM9 0VGx/dRBvGUAWU/F56WOitdfB/ZcCfktJdwHK6l4PVUgZwFfFowfz/8k6E8O /cmh/SSQeZjvJNA3PC942P88nFeYn1LA+ZUF51cWtJcF8ZIS/F8l+A9ZIKvg /NUR8f7UQf5KCbKaiM8DDRH7Yxrw1zGfpQd/qwL8owrwh4xgLxj0Vznx+cVC vovhxHwsJ14vFvJhBuBlODEvy4l5WcifGSDeZjixfrCceD1ZyK+xUP8j4cX+ HwsyA/k4lhf7Eyzk51hePN8s5OtYXmwvWMjfob3UU7F9rAAZz3M12DMNyJh/ U4E90kC8q4HPtaC/mB/LAnuI9gPzc3KMB2B9edAvBex3NexvFcS3Kvgc80tq 0E8VyErIryhhvCqQ1TB+jDfUYJ/0YI90kP/Rweda8I91kB9C+6IHf0QH9gVl LcYrUN/GwvmrB/tcAf5rBXxugHhABbIaznvcn1mwP7Mg/skCf1oJ+9EI+QaU JRz4L5x4vVjMZ0E8z0J9nwHi6QrwX5XgvyrhfFWBjPqlBH8B9UsJ/WfB9xXg XyTB+BhGbG94yPdgPkgO8XoS+Jc1My46f3iYfznEw0ngXythP2dBvpVhxPqG 41VAvJ8F8ZMS8lVKyAegvy0HWQHnP7avhvyBBuJXLeQTdMBnAH/EQMT+UgX4 L5ivlXDi/BDLiddXh/E4yJgPYeE+Sw75Tjnkj5LwfgV4k4BXD/ZTB/ZfwkE8 y0E+lBP3b8R8PfjTGsgHKyG/poL8sALybVno/8J685ivAH/tP/kqzHeAPqtA f1UQP/Gwf7NgPOi/VkA+k4V6DgPkU4xw/hvAnzdCvsKA9hjsuQHsvRH2H9bn yqG9JGhPDu2hfVOAP5UE8T3DiM8THu87GPH+4OF8VsP3FZAfzoLz2wDPV4D9 M4D9qwB7i+dLBayHBvxFDdp/8Gcwn6EC/0kF8Qn6Hzrwv4xwH6GG+VdBPMkw YnvNY/0A5G8UFPK7eL8M49GAjPWiBvCHKkDGekwJB/4/3HdpwR/SgayH/IEl +B9STtx/HU58/ljCfZklfN8Cvi+FeM+CC1wo7z04NMG0//npqy97rtn8398T RB5emdu6d7WQn/xnsO0HPtdIf5zl9Li7VZnAQ2NsZ69tYa4HMM3X29ZOqtY6 831/oxalpT83LxX8l03jIwd26VdG/W6GLz0jM9/PayNjbBwbm+ZXQZfErFbb jDHVGSto0pN9B+bW0QlyUKug8ndXzPXjDUZ2fN64iel58/1aykR/mdsf6fTU m73yT83+W+8d9jHn6jTX9zT1p+gfs6+azlvzfduN+Q/ZyNd3yaycDXSYzX/v 27i3dnHe7x6RwHWzRvfrbqp7N9dbX+v72qfhx8fkr3Xeinc/muuvGU+PY9vq GAXZdP7HDJe3br6JF/yvXpsP+PX9K5c8q1w6dV9L832caX7Px7N3Z3fOEPa3 9rcPjXKb5wr+ju0NY96+fA0xOs7xjNOY641Nz/vqRjs0DywW4qH6u5s85ncW E4tFBW0bbjPNh/l+eYedokvPenrygFnp7/eLxDRfwv6sDJp5PC7XXK8b2m18 T4tTZn0JLt0+iKaxwv3wOOsZiZb3WOF+eNWp9vPXFzJc51W3X412ZXhv3Yz7 Mz4x/KGcH7fGBF6ln9Jyhsx+zfAWm4odxiy04n2PuF8qGFazf4d0eDl3rZ7+ nXgvJ+l0Me3Q74/uVW5pZPHm9Ts7Li6mkwpj1+UNKaI3Y6/7rp2WQRNteg0e prLiX0mGj+hx5h7dsWfJs2uZNfs4WrvEoz6lgeumF0f8W0imFPw9fFTDv6lU 5v1+xAQ1mb/bzqepOp103ei4bd9jK27z4xejl3iVkbcdBh2yGKOgnRW/yvxV RqK5tWT+uGN1uH4hTve6FhrJgp6yiZ9rzsHkiFHkyQKWT17QyTXUtYQeaDLV v0MSyyf65l972yWXhj51dF72gOWl+vSl86/E0/i8/veC+tfEa3UPj1i3zoKP OBNC8r0NVCbzXSebKuUVUyI6Dy2oogset3H+uiyb6GdESBpoquh+zudKb6uv pHGe5zt9toEuP3A62kNSQje2OFW0r6mW3j+YeXfDCw096hTh08pFSy9dKd5R vEBJLTPzZhu6ldJuk1sp/R5+oWnKJieXdC+l6XlLfdxt39LovlGs41kt5Zkd 2xveiKKF7ea//HO1lu77faJ79tVYoj6x7MKFeA290LyxdERBBfEPG3zo2JZS +uffUZuXPzGQ8kePgjymaumKhz7t6rlb8z26X7PpJ1HSvKDiiDo18vR29l30 VbnU6tRoj8PTDTR82Nowts1XOsAi6GmTd7m03rXDE6zdcmjWx9c7z3V4Rav+ OHpTb8ylAcFf5uy/LuPP3tjvuuBQIl3TOEaSbl1Mb7K/vzhx+iPNNaR08Pes sZtMaflflXF0U1jmsLgRVTXnmuvsnSkxxLezbp3/DEofvBt9acHJbNJ0Ox20 e308WfFCNSDQQ0mGBXz68Pp+Lsn/ya7ZoFU5ZHjkkTGdllUR5d39HnmF2aTu BN/0x12tud4jrxy2259FWg0d5d/RzZqb1VDvcXDoV3JjwNm+E9LiqVfulN6+ SzRkcdGMjH3pkfT+Yj5gap8SEjK3sVE5Vk5+dRyYtuymlmh6NXucW5RDqrYW 6YZ0LSVnc8c5+/TXklc/XK34414JoX0C6qwaXEq+fLXxXTeohNgXTbk7L6GY /vVlkMuo7ArieiCwfEqEkrpXZCfM8KkiW5Y+X1CQZcEZT9yesu90JYmfdKQN WfOSpEx4nrpIynKDrYds62qXRLrumn2870yWC+43bP/CfkryYf/hul2estyW kcGz3V3yScsrD7wavGS5lDbXG2ztqSXXsiMsPD+w3MvB9rrQDC35/fLcGdPl LLc84Gzo3DmVpORnm64hm1huxrQs7UIbI/k4ySa0tJM5f1f/+Aj7Vo1ZIR93 vuukhI6bzPVx3ouONnoTYc633exuxyoSzPk118StK5+uZoXfA2bfensrKlhP mzv8sSssRUNm6eZ6v5lYIdRnFJU9ibzcUinUr/2Ve3Hrkq0aIX45Oaf+pctt igR/c6Tz3JX1LpZQ31vMY/7rZZO/QcePu+iVKleRC7aeQ91Pf6Ujl6VPnfdQ wkV2XRTzZlq+0N9rbUl8QGEudbzbZv+xgAwhvlcOtxq1Z+wrYhd3aqjD6yw6 aele72dNSkjkxfzssy2yqWpIwrbOjUz+ukKoB8lud3BZ7PZEGplesmRo4wo6 +dIEhyP9/iUPD2ddGvchjyQaq2O4rnE0Wtt3iNvTCjJ1g/uWrpsjqU97x+Xt HMz1X6Z46eVRv3mOx66RvWzfHFfWkve6aj+x4v17wd777Fh3+47nJ+L/9g1z LKRciH9i3zo67XRR0sGpd75MJolk7Z3UrDfdNdTDQ/p70505gj85rI3F7C1+ 2cRrfvW4ZyMkvJdxyMnYN1+F87N+s4+SOdn5pCA5xsZJ/kbIB5jOjxsnuv0w xUZJ6ty88yDolLk+q6Jekz2Bzc35t1RFtzsj4yspM7aZzXDrUuE8PODs7mRj pSUuw61cJeSykA8ztZ+/NHTkhs6lwnntstw3Iv+2ud5K4Xe/44/VpSR//O3y jyf+W3/lJHO/0bRXpcAz+uzGvLDOBpJyeuH080cLTfGR8HubQztazZCOMRKf wyNOXr5iWl9zvVVATID9pfaM4M95Xe05stTKXA/lOLPTgfqrKulPk17MSjlc KMRXJv88dWPUuehD5UK+beOqKzObhRcI8W+b+tnxo8KLadc7s0LCulQJ/ist Kzpn19kUv6iFeLikY8rcOH+VoA8DLC0HjZ2eI+TnppTs1jrt+UIvbJP6PG9Z LOSnTOOpmOxf+O7PTCF/ELaA2v8+K4umPuvjc22cKb5W0txBCY1SmxoE/82k j0+TbNIfz+YJ3WZtu/JYuRD/NU6Y2mnxbkvetB9N8UfTj+yyAe5pxPiJBByo 0UPT56kH63/eWF0q+GemeOlAdewvIyZnC/msxS1aHpm/4iPZsvveIN02c32U 5+Uxl1IG/rc+6tK86mUBg4qE58OP26w+Pr+InEpu0TS2X5UQn5h4Zrk2W8m8 KCUBYU2l744nCPkNUzww56dN4a6jGS6txZCS9lcZ/uruuk073mH4DN+onGj7 OnxIk4u+zzZV08eu6127zisnRpfndsFFavLqZJuQMyoZ9yhmxl7fyGKyvGGk qixWxt33tA99s1lDrixd+fu1Dwz3cuGP07mbDJfaeskADxXLhQY2MrZyY7gB lp3GLrv4lDzPfll3bwDDNzxztvT6jXhyKikt4aI1w/96iHG4M+Yzia+0l3Zv WU2Pp7juS3tcTH549magc0IF7bU/Kvh6aRlpUp95/O+WMtplXfbwB9szSX1m ypzio2rq71kxMsy2iPy0LLzL05b5VDtjzoIX5xmuk80ItluRmvoE3v3S8jYl dgH/7r8Tmk0beZCwNqUK0uHgwF7WHT/T6Em66K8DyshvDuclm+qmUv/Fb2Y6 X5By7ts8vnZ0zKTjt3Xh12+14lc6FrwranOOrP/cOfX19CKaM63B2bb/XiY6 i3Zvw1bpaGSnaSu2j04n/biP2oiQSlKvibLbpHXXaeSz7St6v7Ti2qZUy/b7 XCCl7/ZGNBsg5d+eTDG6P/1C4ioLRv2iSKNvxqzx7h6tIPmnGCe3+Hzq6TSi /rD3KnK4q9VZ/0W5dNbAdr06Xioki6c733X8IOMcNuqyZTeTSdNk/x4uV2Wc /EJJui4tkTR0ublvZxeG90gxXnh/rpjkhO/vNn68gj71dvTp3KiYGPr9EBSS paNDbF1ej7LWEx9vm2kr16RQl/wtLa5sryYHYzx3FS48R89PvDj95+sM16tP WJM+ds/psePt7xfNZzg2bsTTMdeyacw/4Z96nSwi3S8cXmodmyXEw6b8ysNz Lv2GXjXXJz04fX7xrd9q/MKrWZF+S/MFfcsM9Dk1fXcB2fw5cdAO7whB/21L Yt6PHakkL/8a6BOed5fs/qWs60S/EiHejeIs23Wt0hC59YZjgwe/p+M7h3St 20cjnA9XnG6Nmv1jPPU+1czRducX8ly/z7fFhVSaMco7+vGFbNLeeZah4HIS bT6tyx6L4ARyonfztdHjlOREeLXzIM8MEtm+nh1jqyStYqPDFucmE/eG0RmO kcVC/Nr3df/S9AQ13f7St/jsqQoa1o1b6Ha6iFYVS87MXGyuVzflv9ZbH36Y P76Yuk2YGvjzk1QaXTzaduKEGr/408mdDiWfaLfSUyejxiuF+HbzhLDGoyYr 6Xnd+lVvrn8kQUsjS1/MyxTyW93S5gftDs2g5Ic+YV7v1OTglIYlab8rhfsU v+Mjs4/EKGnZvuR2+e4fhfyFyV7EHPN6d/MAK9T7OPh1WpKXxgr1Pb8/G/Jw +SNWqOdpnnzeum1nlh8cNs6lQ6jpfkFLq9fM5w/yMn5Xk0NDDspK6JKmJYte OljzgXeHDwj3NNdP3ZyRdnqMIkdYv+Y9U1al1PjXm48s3uy429SenMZ9+ZhT 3lLGP/rz549ZyRF01xHX3lGdi4X83ZN8v+tOeWpqFVp/m2wIT2Ov/xvrts38 e5QpH92GVdSz4l7ZNy7/rR1Ph/ZoZf2yvhV3YnnfF3OKqDA/B2/8uarj7RQy dsT5izf6mM4XOdmZIelw/1iVIGdYtd7wVl5F8t1CTl549p5MX+sen55XRSZ0 uWh1NTWZtLhfFTm2TZXgP1SdT5rsrTSQsgeOjtet8oT9v6bpltzd8ebfp5zv MsdtQ89Swb7nf/ZmetoZifNUbrrr5CracNzrBgtGsPybni5HZi3T0cSw+3va R7N8u3GO0avdCmhgtCzeO47ldZKH4ZriD/TH/HpvX0w03weneaV9/TvUQOsw jQtWjpHyVoyvU/+qKuH8tv/96OTRs6po+50j/G8PyyYFfw4bGlRRRet3VwQF yq34Yauvzt2SrqP7hkbk2LnI+KeRybb13pUK+UC7kSXnOjprqVv9Dy6pR4po uWvPGcMTzb9vWnT6bFxTj1KaZbf8c37RV6rMiT25VlpKx89J9wlfqqDePYJb HG5TKuyHXtt6v3m5T1vjd3s363o8iSZsXdTofIn5908PXN1Xzz+qpYftbu+M mFZBvq6e0PtRXin9/ENlkL27+fdQ/V/b5nm1qRL84TtnBtfNTK6iOy0nXuj5 LoPmTfZaunhvFd04pv7jQ4ez6d3+iWffqfJp/5zB/Qeps+i7qOchmRoZr1rz eP61x2k0/5VmwrCQKmH/kd712sv1VbRB6cv2LT9/ovcG7xjR6kkVvdqlCd/N MZGetLSI/zXDQL9s2lRa6faMBi166sgyVbRwbh/LopQIauj2ig2tU0zPLerh XjEqiZbxMW+SB6hJ55DK9g3/DBHue1ePPFuvSXgWCTv9Ln3PmY90+uJQReai TGKha3Oo3DOBym7ue5654gtZOMHKkj46SO7nWezpk5FDPA+dr7M1sYpsyFue EJeTSTTpTFercSZ/VkkOz54WcsKlJn5Me7Ym5+AXcsunwT9/9bXmBi2OrT/5 poqkXJnewDn8Opk76fRviTIt2VZVVJ+N+EKcbt5k5/UtFfK1FyxfjR4YUkKG 2E+SFVfqSF7lX3+u3WquNwl9uDOAWVFB+GfvC/TPdHTX8fOz0p305LRLzIfu S7W0ILDzgjs7yknsnfJ9o9YU1ISda+foOXM9Ggke9yRiEstZPZjZt48hikh6 1y2zqMNy3i0m+UUdTydpHm23DN9ori9pPX9U56oYluuz2cPd82smGTH8Y3bA KZabEq+/4jVMTfyPPNpalmSuL2ndxX3iyhq5JIoZbJmuJkc7NCg+msxy//Zc k9o7u4y8HPbkN9sH5noSq6fJLw4uY7k7z0vsHfuVk0khd7X1wljOcZ5Xj3En jOSJtPtzixYs5+U3sPkZz2pTPpf3SFc7/3GH5UfbNHjdYxbDH2ZOJrT/xVwv YspX/51yb5XKnuFVqa5xS6vK6csua8e+Pcby7YPGBvTyLaPxR1dsOPGQ5Y9N uWhXeU5D7edr/ihMYXl7Q/Mi59OFtOPadb63E1l++uSGT+fcyKKX1rDO7S+z fM++/RL9GipouweTRzbczvKdlnv/6bvFku8dZWPj/0sF/ftXj4Atl8y/dzTl 14PbL38W/76Cpu3yb19So2eHmITtFrYltKPdhQk3RpUI8arpPmqT3W+nPq4t oW3qaveudzDXi4XRviNGrz1H5v4x7P29eyXC/cpb76RdJ1toaZ8e0V6VlSb9 MteHtPhlrlPqlUxa7tJga7fZKtP9E/1jd7Jn+phsen21W+LS/rn00vHWB9d7 mX/PaLqPb6BtnbBlipIO3bd/26OOBtJlx4KDTPQBsvGM3962ReZ6kWOOv+6M Y831Iqb7gPX/TLMfUa0gZb6hG4Y/KaJ37voOD5LnkT2dttnUa2Uaj4qcaHV2 6aCWpvNJKdwPNIxNOXpxsJIUzp63K9A3g3wZ+PzQIWulUC+i2n41qEHfXOJs vPoycG4l2TYpzu+iqoCEjyvo5bfEXB9iul8ZK9nzwqakSLiPPN10iFOJPJ+4 qRfYrHX4P+pHJgTNyQ1SE/2w6ODU7Kvkb/cu0XcszPUao3NtFNF7SsiS28RB G/CByCwtPk/L1pJKxYq2oevTSfCJoeGnGpWSjdeOpz1c9JUUrrKMzK+Rr8xb 5nnvdBGJPxbyc9RLLQnuX29V07bFJPleu6GqI1pyoLf7xjbDS4R42HQf1v/r zVcz3bRkVu4O13kH79M6hy/JLDLM/x/SLM2RnGZhDB9Q9HUd6acmZzs1TTp3 oVK477iVsGpHmpeBupedurHtbS7Jdd6YP3ZXIS3b29qw+E2WcN9nmt/W23v4 OUQU0ZHu3S59bqkS7jvHDHAoOe/8it7u8dyL1vgf8mllQ8Z32koCG54J//Fy Di1YuIJ5wMtN9/FCfUjo7Y+K3spsmqc+dO143S/0c9tTEcqN5nqQ8MDysmle 1eRkg54ey17H0ow1rlOHuzNcUL3Tm8PaJpCzbuMHBVswfMhbmfLzhlIyv2Le hii9np4ZLj1/wdmaN564a1AFF9GDTm87xtla8+8lE6u2/KMQ7ivtg3pV2Bfm 0i+zHXv/M6CUPJn3aNyu9RnUNkK3dV7DcvJ21dYrf+yMp4OqWnBWsZZ8z4KF IzN2xZAfy47t2d3Ogm+1rvy1MT2dxP/s3zqkrorOW+myOdYzhrj/PbH5x4gv 1N/q3KKZXqnEuLjB8M7pKuKZf+/E02tfyJZxkzfEz5ZxwembjXsq/iWOm2b+ ZD+iDh8R/JdbcnIu6eIUOOjTIJbfOerB1LzQIhL3+oL1jOA7RHM1tPn5obmk 8sCSpydaGqk+/bBb27klpNAp7s21peV06nXG8ckJHRk/8rVTd4Ult9w6pPP0 0HLSvs31GauIhDs/cM1TzpHh9ld3sV8SaMEtdp9iuDUjlS5beKNrynQ9HZPy YMlb2w/kVMTxRYOWy7i91dlniv/Rksxz873HxUo5n011CjouqCIxzeo4P7Kt JhzpEN7mlobG5awav+kOy7UuSkyT9Cyklt7tP/X/uw5XHfUi7NmkHPrn9tKJ VmGVdPvl5Y7eu68TP1nWX2/maumA7WnvZ+gzhfvWgF0Z66TXiuniBuWp45yK yfwLsQ+Tz74mR/t329S7YyF9/lvQ80lOBvL/ADpSCkY= "], {{{ EdgeForm[], GrayLevel[0.9], GraphicsGroupBox[{ PolygonBox[CompressedData[" 1:eJxNmHmwl2UVx9/397xoSoVeitiXmWS5LBcwA0VnGGbKFIi0GSYwzRhmYknN xhIuZqmVgJmUsikEiSwSQaEYIDuxyXpBMLlsKiEJomwiqdH34/n+Bv84v+c8 +3nO8j3n/bUadM+td5eyLGumn6T2TRH9K0X/Em0UbRK97vYVUV1RE1FjUR3R l0VfEhVu64s2i14WLfOepe5v8dhyn7fEc9tFK0WrRNtEK9zf6/2ct9X7mFsv ekn0D9EGt4st72Kfe4mogeW7VPQV9z8nauj+Qd+9Q3TIbY3oXdFR0U7RadF7 omOis6KTnj8oRR0SfTXXeWobiVpq/DXLhCwH/B7uqPU70MFlokaW43Lrkv4+ z/PWZF1WiN6yba4Q7ff8Ntuqwvbaoc5p0XbRHusIOY6I1oleFY0UjRGNFt0l gzWS7GN10Xy1D4t6iqo1N9br7syDH4U84m9Q20nUQXS9qIeovdvrRIN09w9F //WbKkXtRDM11lTUxndPEk20XItsS96G/lqI5uiuB0RV1m1LvxGZJns/cj3t fn/LPVz0BVEzUVPR593ir4c9/kXbeLVojeiM6H3RcdubsbX2hVVet9uyotN6 uus2tX3QiWQriYaIH5aFDOi4bx591jWTfgeovRndaW2N5nbkobPeHn9R/dGi 3nnokbGbrD/619rfsO8M/EptQ507xXav8fg0jXXUGTfLtlPEV4r/lvhnxLcV /5D4yeLbiH9Q/HTxnbhXfGOdcZeoi/pr1Z+q84ZqvkJjz4rmYSeNPyn+Byns A/+/LGyE3VvZdk+VYn6H1j2odrH2zfA5nLvd4x+m8BX2Vrgd5HP+pLkOWnNT Ee/e4Te2y4NfmMX4Qfs8fdoX1K5Qu9J2wfdmlcKXTqmtFW0RjSvivU8UEduH 7RvE81aNPyd+ivjB9ufBlg2eOc6YmcWbeNt9edwz037OXZwzPov557yGsVOW ob5odyl0fL/mOqZYg333i57R3rlZ8H/JAm8OWg9tUqzDB1p5D+t53z5iJo+2 1jJsMT/BejroN/a2j+Jz+HJz8QOzaFvYd5lvbr7W8o/323fbpsw3817igr0l v6N1CvkrzCMzbRvz5Ib2KXID72CcfR1SjON7XdV2wV9TjKOrv2ahrw5e09lz jHMWa5d7b5XHr8rjHHjWdvYd7b2GfVUeZw1ru5qvMt/JxNx8jc+WrJWi7uIv ZIGRLYw3HbPAywVZ5LQVfnuFbUqeOWRMOiF6J7uI9bTgHr6B3MgwQm3rPO4l x5CnyTlg4hxkySIG1hrLyIWbvPbvxrNVxjNwjbxQzqPrs4v5nnPLOXWDfRBf xMf+5jNWes8en3VvHr7Imm32f+Jpke/7p+iVFGv+kMIn8ceZlnui3wGug++P ZZH/91omdEfeI/+VczY5fKHfutpz5Mh9lm+796zynnL/gNdt9xkH7M/40zzr ElnIL1sdr5/Gvc+u9X01Ppd1z3vfm7Yj+Zn8+7bfjr3xdWqe6jwwGV3u+AyG zbY9XvKbdvoOfAmMai6a5Xn0etx3YMdReeAjdkbWF+0LVfbXZZZvse9437Id 8dn/9vnEAnKih/nWO3E0Mo/cscA+8Zptv9n62Gu51tveE/2eWX4zttxkHWx2 n/cd9RuX235bvP+I9YY/zLV+l3l+s/f9x37A3BK/7aTHj3rfUs89bzuVz6PF V9fn4btDXXeQ06gPZzpvUKdR7xDHxHO57qFPXUPt1jaLXPpJFnUme6g1L/NZ l7rP3CXudyuinngoRa1HzUcd3dN1ADVAhf2IuXKdXa676/isRr6HWhIcvy67 WF/0sOzUFsyB4fgtPlKOm4X2E3I/+NUyC/x4wT7UxO+t9F3t/N6m1gFzjX1/ XesIf23uPY19BvkBmQZaxsrP6tF7wFHGr/VcE8+1892cBd5ek8dYuc6j7qP2 +di6oXb4yDojn7R2TtntOqTCOfLRLDBoYx44S/wvtX8Qr52dF8oxwRhx0cnY j8+u1Z5JedTL1KjUgNX2Nfpg2SLNPZFHbTjW89TVy9Qfn0cNC96M9h5q8xFZ yMda6snh9jf8Ev8iLpGBeFyVBZZgV75XiOVyzYtuqG3wb3z+Meu1rnVH3dXQ NRg1IzVOffsBeYgctNL3cAd5HRsOtX+ANWB4OcbQHXE4x/3b81jLe8Cd2Z4D K2a5v934is/VuN4Dy+aUwt7kVnB4m2uPSo/jAw1dC5FXNipAeqmdq7EL2COP GOvllrr9da3pA45pzZoi6rAb1W5Mka+eTPHWNsZn6maw+mmNjygiFz+VooZi PXLX2p/wpVFF5OgJKepszpmU4qy2xmfs1tpYOlPtj/LYT9vIfvnLIjD3Nu29 OwUudaFuTYEznYuwGbbDRkUpai/qXmpg7ITeqT032re5f5RloH65yrXEj1PE TlURMVLfdR2yV1sPDxfBf19rh6eIr05F1HrcOyC7WOcRI/UdZ9R4t/hM+K55 fE9SJ03No1/GHVpqp0eLqLd/U0StgG3vEz/BcUbsUC99kEWuBfPPZ4H75LUP s8ht+DH+XGEs+MhjxCDnEAtlvaPzyc4DjO8pon9M999TxJpV4u/Iw6fx5wck 4+o8xnZZvqMpvj+wAevq+XuK7yr0hQyji7AfdvxdEXkcucl57/o9R/0Oxnc5 rvk/op7fypo3ssgv+EYD6+SUSNd+ioMf+91veY7a5JjPOuw9n3gttmf9WLXn vKaeW/pgCn5DXC60XGcsG7KctTxgzFDXgehvvXXKd2+NY3B/Ef857JKuuqWI 7/Nq1+WxB/2DRVOMR+h3tfVOfmQvOWKB2kfyyJXkBL4ryU1Xp/gOO5ci7sEB vsfJg6O9/kSK8+8glosYr03x/cq3LbUA366cyTdrlesD9M3aBT6H++dbrvN+ Sw+176X4lv2tzhiW4rvteyny7FT7/Lwi4v+kxs+IZos/nQKDwJYtKeKVcfS+ Jg8fw/+HpMDC/mqfTfH+mhTYxn8Kvf3uRc4756yTr6utUwRO9BU/Lo881N85 6uU8ck3dInR+eRGxMsl22V0Ef6v27iwif31b/J48/uPAzuBBD2MCtupYCntR Z/UzVo9JgXX8FwWetzfO/7yINT8rYqzSezt6DfxAzXUQX43/5HE3/0+18Ldq yWs7+Myri8CcrmqblOL/OmxDy38fLc039Pge+ytnUo9Uli7WIE2df4+niGvi e0sRuvtmCn2ssY36WqfoE72OM4+ux3vNN1LYApvwXyzn48PYea3xrp/mmmv8 Xr7LS7GurWWBx67YZJ1txFhzj9+ivS3Ff8e+3MJ7wV3G0Qt5YIRzQSPj2Qhj yQnjx8AUa6p1zjsp7sMXri8i99ygdrDzbvci4or4+nUROYxc9gvyS4qYOVAE fp8wXt1uX3+kiPmejq8D7u/R/E9S2K+v7dnNNpqYIp+Se8lP5Klf4ZvOceRt as6exo3vpsAZML5fCl2D3392fP5e/KspfBesOpviv60P1E4rAvenq/1jitx2 P3pIUWdQg+xLgRHgSnf76NfYV4QO0eVPbcslGruiiBx9pdobHZNbxb9tfyLO 5ju2qV9m+MwN4g+l8DF8EHztZYwFU8CWUylq/97W5wXbnvyL7/Ne/L/knDXE +YTvmQbGcuIWPKfmmuP6mxioMh4Os6/j2+OMJfB3uh5DNyOLqLEHpHgb696w 3fANaqFqxwZxwX88rOd/j8eLkGe66w5yA3mc96MHdNzOOQDf7mP8A3/GGP/A fzCpj8c3G5PBZu7i/yBqpDFFfD/wH+R6YyxYe43xHFwnD61wfUX+5NuDHPpY Efw011OcQ/79P1EtLYs= "]], PolygonBox[CompressedData[" 1:eJwtlVeIVlcUhe+Zc8iDikbyYsEGRo0V6wijIIIltrGAFQsiqBhnVOyO3Wgs UUnsjljGjowvaggj2GtUHKzgPCiISiwPNgTrt1g+LP617jr37nP23mf/DcYW DizIybJsEkhgVcyymiHLxoNCHtxAt4LvZ9EF9C5QBB/FswL4S/y96BJwHj0P 3QK+BP82/BpoCH/Iswr8f8AG+EGevcMbDf7O8TN5r9H78OqDS/ClIA8vgjz8 1uBL5ndOwO+DcvgvrL/O2qGgJPMzeXnojujm6OFai27P2oPoi+i+6An4p9Dl 6MHojegb6HvJe9BeKtAP0HOjv9UMDEMfR5fhr9MZ4f15Vh+vDboduhDUQtcE ffCmouvC64B+yTlT7nYQ8wXeCHRL9F3eL0K3Qcfgbw6AT4xe+xP6bPIetVfl +DLec7AVfgGcgb+PzrVqqFo+A5vgZ0EZfD4oD46p2MqBcjGSZ0/wOoDGmfec n5wz5U45zY2OoVhbwP/wKvjngmPkR8dQrI3gKbwS/pngHtqZfEadNReURMdU bK2pnNyUx+B9QCneJ1AKXwruwKtqT8FnHoQOqgl8JeiC/gF9Er4e9EDPQOcT eyp6CPoNOACfB0bCD4OuwTlRbgZGn01n0tnaJe9VNWmb3HPqPX3jLbxf9FkV U7FPR98l9fSR5Duju6M7thJvkXoK3lhnQFdHXw6+U7pbY1RvdGuwG340Ohe6 E7ob81k/B90IbNZ9ST5LE7Ad/Qf6o/pf8eDdo3tVOVKuHmhNcI6V656gafA7 k/B+TX73E/o3dC90C/Rn9OTomaDZoB5pi/4AFgTfsT3wZfjFwd8sRv+Lrpfj NTeja6BaqIaqpWqm2n0F4+DDwKzgnPyIPwsM4P1i4l3BuxU9a9QT6o3f8cfi j9OdQi8G1YJrOka5BrPhP6sn8Xok3yV98y/VOroWyulCzQdQG68B8dpHx1Ts Gvw+xjukmgTnsAK+PHmt9rAieQ/ai3LwCv4ouhc1MzQ7NkXXTnsqYv10vR/c ozPha5NrPxm9Hr4umWumaLbcjb4L2oP2siH6bMrZHNZ2TM6lcpCbPHM0e1SD adEzSbNJZ5zy/Uw6m/ZwNXrGadZppmu2d0anHM/MbuhVyb2hnlgNX5PcGy3R fybPQM1C9bB6eVt0b6rnlyTPVM1WzdyL0f8R+q/QDP8veuZr9itmJ3jv5G/r P6AA/xsMWNd7 "]]}]}, { EdgeForm[], GrayLevel[0.78], GraphicsGroupBox[{ PolygonBox[CompressedData[" 1:eJxNmXmQ18URxX+/mdFabiUqJV5hAYNaKRZZgZTiHY8kgJFDURAURbxAUSAa 1yOe3KArGIzKIjGCIAqCZTRVgMYzSUWTgKg5EBUh4VIgqLnex36U+WN2+tff OXu6X7+Z7XDJmHNHp0qlcrH+ZNWd9KezynTJT6vuW61U/qJ631KpXC1dX8nL VQZKv171NNW/VBmg8rx+D1b9oeqXVW9RuVKlq8buq74nqVykcer0e4jqhaoX qPSR/tsl2g9Q3+bSNVMZJ/0J0reV/nj66ffJHqdUQ+6m8k/Ju1VOlLyv+u+j UqP+26TbqlJrPWOOlFzj7+Mlt9S4N6s+V7oXVIap/Seqs/SjpV8heXY19jOC vUp/u+q3pV+lMkry31W3ln6K2p8n+QPJ70l/ieQFKqdIXqP6ZvYm+UJ9v60a bWpUFkt/tup3Vf9XZZDGOV31Eq3xqRS/K+pzvqr3pN/MnCpn6HdV+iGqz2B/ OfbWQn1+Jf1itXkix3oXqfR2v8XuuyHF2s6T/C+1O0v1H3K0fb8aa7w0x/gn e/yWPpd2qnfpe2fJB+ewP+eypxrnwVm8nULuKjmpTVXl3/r9pfseKf3nkj/z ON+SvFPycZIPsr6Tx7kqxRwPaF8nqH5cY70u3WspzmCEfW2a2h+h+lqVxkro 2lt/kPqMlnxfJebf6Xl7sHaVMyU3qF6gUqeyyz7UUfoDLNfa5/CrY1WPVN1K 3+6UfKbqNilip0UOeYbk/VXvZ7lVjt8zK/G9tdsPdJu5ks9K0Yb257rNo5WY p6XnulXzPlmNtR8o3Q6vs4Pk7ZK7V0K33Xr64Bd3EeOqP5W+nthxX+yMvT+1 zHn9pxqxuSzFuQ2VvFz2n6nfy1Rvcmw0k/xoCr88237WJYePHaX6aJWelYih K31WH6j9euJc8uAS9U059tPF7Tuo/qZKH7V7zLFDDL3hM2+oRNuj3H5Wirke UL0lRfywnp457PWK5ume44x/Lfl9xyYxusGxRgzQnzHZR88Sduil+mKVnvo2 QvXh1bDp/ZXwM2Rsy95Y8y2SX8vhh2tL4M3llcCc54yr4OtvVcZLfqcEjjYa S1sYnxhzk+TVKfDod17/btWnS3eayoRK+EhH4/b11ZAfkXyL9LX0SWFH1ja1 EnUHr/MRtf9ONTCVdh1s84kp8gHjoat1e8bu5LkmpcgXtOXsNhlb8BcwH595 Xd/H6veEEnHWzLF2Yw78O1D6b5Twpbaqh2msoSrzKp4zxbxd7B+MP19yL8kz 1f4nKhepzRzpjlPpjt+WGPsE48l+JewxOMcaN3sc/GST17PImMe5T8mBG+3V r8G+tzDHupirqRK42tvj4DPIPYhZYyq5Zq30a1Jg92iN9V1iXPU/NNZD5BXJ T6bIhcxBPgRHsR15ktxJnjvWOQ/seUV9x5A/GM85uLfkP+aw7Y8dE13swwON e9erzRs+iw8lL5PcX/LDkj/O4Xtv2cd7qPTTXG9KP458IH1TibOYr/pVr+FT 1RulW45d1H6b6hUp+MHxzt135IiPRscLazncOExsECODcuDfQGMd9SDL4DS5 Aby6uYR+Fv4tuTd4yH5TtOGsiXPi/S3pm3Lkn+HGAPwHrGBNrG1JDlt39X6/ wr8U+DlQ43Qm36nNgBRYzHrAVHCWM+mYQqb9eV7/XOeROuc4vte6/ViNlYgN 8rl0g90evzjdsYz/VqvBd2bAu6SfVmKMXs475+t3F9oThynGIqeMgcSpzXTn yaNsk8Fug9+CfawPLOlrXAATLvR6aNMthf/he3uxo8E4gx2IpXNS9J/q3N3F Z8R8dY6FTbY58cX+zlC52/aus82v9Zp/UML2nW1PMKmb1wB346yJqdNsK8bp WKJdJ+P0cSly1qgUHIJcNkPjH6ZvY9Wmvhr272FsPc3j3Khv/SU3qu3kFLkc fLukxBpnl7DNBT6vW0vIDxoThhivpqXI3zPs6+jx8zEl9j4pR3zuzXtwQeK+ wXF5ju05MkVeJ3f387p7wLNS+Cj2GV5iv7fnOJP+Phc4wqkpeNoQYylnenUJ /b05bNrVWEOs93T8TrBNyD1bjTe1KofkaHeRz7beNmxtvMVX7yNHcNaa58gU Z4k/XJmCL8K19qSIZ/RfpBgLf9mZIq7w7X5eE3HZxz4Gt57uc3y2BBZy7uxj lb6vTMHd2zqPbDIukkvJu6UE1oF55Fpy7vk5MBQcA886VuMMOTfaguP4G9gH Bv7GuYh44J6DjRh/gnG+recammOcz8yXyeV32q74HP3w5f4e/1XVc6qRZ59N wbcGOoe39pludm6FzzyVI59wJxjis8XHyCXklC3mSH1sN/Y13nt7vBp7AYs3 5+BRq5yLmJ+54AbwGPyQ+wxjjCuRM/DR133OY92G3E9fOPa7ktel4DdLVT+T wo4tc+APsbPGcUyObG7cB1c5N+Yl1x/hNWIvcgU8Gxsy3wjP9dccYy81R5lj fRPzpPh9Uo77E/eqP+Xgd9x1+uW493H/+1htP0rBBV9KwbX4tiEH5+IuuLAa +YPcscnn3+Dzb7QPkDuR+6jfmTnuy9ybW1hPjh2e4i5BTGHzV233jTn8lzso dy3uXNztBtjPLjcG1jiu8bVp9rc/54jLS9V+e4o9MBZ3OO6YcJo5KfgNnGVX jrz0nH1/bjXsTW6BCxJbw5036+EI9oHWxli4Hesjz5P32effcvgsbwMXmOcd b8yAAxLLl5lbwKNnp7Ap9txof7mmEnzlNmPLYq8XftW5xNouNt7Mc46ES8yv ho/AIx633+DjTdXwfcaqMc7Ue1/nlOD7+Cf+wVicCXg3Ogdefb9EroDbMcZX d7IUefaGHLm/v9osLaF/psR9arex7KocZ30WeJMDEweVuFeCd+ArZ8BZDNP3 a3LkrO+R83Pg/9mSV5S4r3Yr4dO19vMxPjfyF7E13bkGLgmn5A6+NcU9i72i X5Qi3zytsaZKXqK6fY5vtF/kduTQ+Y47bAv+H5rjLMmj4DB4TF/u//DWdjnu 2Kzj6RT3L2yLnjsjfe5zPIOx3B0mO89OzZFzry1xn+jkOwW+UOMzJH/em2K/ veyn5IspKcaCr7cqcc88oMTbBz70ZQ5/OcY+073EO0W935TQce4T9fsy5igx FvNj50Ul5l2Z446JP+zlcNTXSf/DEm8dx6qeB/9Umxel/5G56SzpHiuBG7yT scdJ3jt2vjuFrckv9AUDF5TQPwHvzsGFGkvkvDr7Km8iyORx3lW+cH7lXYVc C1c4pcSb2qmqJ+bgmtxFwH7eT8Cxu4254C0+Bn7iZ+yXWCVOyUvkJ2L/wRTf 2D9r4H2GdYCdrxk/eVe4PAUH4+0E28JntjnH4UuTS7SZVMK/8LPljsul9p+V xhnwAexZ5jXAi5mXeP4Kj72XK1JwDvgGY4/yGqaW+DZF9cM5/OGuEud/jOP/ Hv0egY+VeA+5wuPw3gSfPlj6X1jmLapNjnXAX+/nLkMMmqsj46c/z8FV4Kqz zSeHltjTIcbuucYvzoCcxv7Jj8+lwNmrzROa0td5rtb5lziGd8I5n3Gs0/do 3wPx162OR3j4TGMFONZofkA8gpfjjZnjS/CuG0q8U031+FvMFfaXfnUJP7ki x1yz7Cfsa6XXvN0yZ7cjRW5CxhdG2h9eth6fwQdW2Deo2f9A52XaDbPPrvZZ M+Y6/yaeJhoHlhiD4Eu8IfB+0eC1rPLaHi2Rt5ur3plj77zH4Ktg9FbnGfwB /+Cs4bv4HLyFNz5s8lCON4s7SnCZ5tavt92x+c+c++8s8Q4KXpITyXvrbJMH fM+8yTZvYf7HeW7wmc7Occ9sKDE+72hwmp/muL/d5nslfIvz3c8yvAseyvvw eHMJ3sThEzXWwyvIra19HyPftTI/+yRFnsZXwZc9jgv6NXNf5mnleXmDZ3x4 3rwcuX6P94N94Hsv5cDup5yL13svH3q/yNT8hpvhD4caH+Bgm1PEPTz3DfsD 7T6yrd60Hu7S3pg+2dyYb6PsJ1t8Rqs9LuPwtgqH4e660fvnrDa7DbwQvOdt ei/+YTcwEPlt4xL3gndS8LWFWsM9kp8scf9b73lZLxwUTg83/sjyctv8Gq9t nedlDN6CyRfYmT7wQL6/6zbMufb/xlzrNoy9xvZ52HeiZ+2P5HHyCHcWzrSF deT4E43/2xyP7Plg64nN7V7n73PkhudLxNp2x+wK9yWulzguOPNxxptGj7Mj fb3XHR4TPgWvAsPhVTuNG9yHtjjGyXO73Ya9EGN97KvkRLgfPvu5/faFEpz4 xRI5hfbEIzyLPEq/OveFnzHnLs+7Lsd79yr13eo8Qj6h7Zduv93r4Z4OXvG/ ir05kb3wfav3Aofh7YOYG2OMaeMY5H8R9V4LuN3O/ApsaO54git19B2oQwnO z/+g4N6HmLPB6dr7nMlXbYxj8Gc422G2ITbibg7P538A2AY8gbc1VeK+Sa6Y V4lYb2l8wH7JNse/9nHsf2Yuwtg11uOHzNnO87IffjPHtBzvJ9dp/f8D+z1E Zg== "]], PolygonBox[CompressedData[" 1:eJwtlnuQjmUYxr/vfZ5mrDJSqqmmP8ihRoUQiqRYdLDabbEOS06hza7TioqU nMkpRLOndLAOaROlmkkUWU2lmGwJZcpQSRLVTNPvmssf9zf3772f732f973v +7qfBkMKs8ckqVRqKxaxp/npz4Vy/AEhlfoK3gk/CWfBR+DjcD58CD4Kn4DH wD/Cf2FPpVOpJvBZ/P3EPyU+kmsn4O/hY3B34vvhGrgG7kl8K7wGngrnwS/j 19fz8Vdgl+P3xpoQb83/b2f9FPghuBTup2fDfeAyuD/8FrwYXgx/DNeDV3Ov EvhS/BzseuKt4PbEN8Lz4WtYMx8+CH8D9yW+Ee4ILyR2GdYBPyN6b1OJ18I/ jP3E+s5c+471h+Af4Az4a7gM7gpPYn0p/jpsFjyC+Gt6F6xR4ncsZn07+D9i A1g/DJ4BDybWEM6HB8At4JbYVPgjbB7+XGwDsezg3CkHysVxeFDaOVVue8IF xLfD++ApwXvthPWDP4RnE8vECqO/qb5tP3gavArOxx+ITYfzgr9tDjwZXhbs Z2OPw+fhSuUT/jw4Z8pdb9UY8fHYGOLXYYuCa1C1qGc8Q2xl8LPz9L7wUThX tQN/BmcGf6vN8F64L1xMvBwbjf+7aiztZ+TiHwuuNdWwallrtHYiVo2/FHsV vwjbjf+w3g+/HbYG/9vg2lDOlfuR+j7ECrFP8JcHv1su9gTxU3AFsVZYG/VD cO2rp9RbvYOfpXvoXme0PuV3+BP/AWwZ/jbsItafDO6tav5/AN4S3IvqKfXW v/AG4iexm+A3gmtXNa3afhYblPgZA4k9BzcjPpRrs/BnRvvas/b+DrwcXol9 AZ/GZqZcI+uJ7YOnp/3MD+CxcILfGsuCb8XO4Z/XN1CvYaP47xm4BX4Z60cn vrYguqZUW9rjamLN4dOsvQIugL/EpqVdUxX42cQbJO7pCXAfuCn+jao/+By2 Nm2NaoWfSbye9AhbGLwH7eVK+DG4JXwWvkoaB1fpGXAOFonVju5l5eTi6Bwr 19KgFcE1qlqVpr4ID4Xb4mfxnyHRPa/en8m1tfivR/uqUdXq+3q/xJpYHq0x 0hpdq8CfjQ3HP8X6W9Rv0VrQHn6F/24O3mtHrpUQW4IVST+w8cSel+gTL4AX 4U+QJuD3kmYG96x6Vxr7Arw4uvZ1j6X4pao//EewefDcaF85Uq5KiI9IvMc5 0XvUXuvDjxKbBI+DG2JLNG+CZ4M0U9rZRf0DV8HVun+wFnVRfvT+8N34l2D3 wPfDE1O+VhA9IzQrbpCG635YncSaW6ncBcdUI6qVGcHfKgO7C+6gnuP/beDB 8LDo2accDsf/h/h69r4Kfi+6J9WbmhFdtTfVSOI9aW9/Y+vSrtm26pXge0vj 26rWo2tDmp6vXouu3Q6aD8EzSrOqsTSF2MRgXzNDs0M5VW7VA+oFabq0XZr/ M7E/grVJPavelWZKOzUDugV/Y31r1fjhYA2Vlkpjj8ALo2ulOevL4T3wpsQ5 6gTXhXelPWN/0beP7vU7ibeDu8J1Wd9N/a1vG62FqsEieJdmIrHamv/w1dHv XgubDO+G1yfOiXLzbrQWSWPGwdtVk3APaZD6Jzo30qRt+NdK4/F/xW7WvA32 dcaoIy0MF84u8JboGatZK038Tf0UPZt0pnhbeootSPxOejfNXM1enSF2BNeQ akk10Dm6ZlW7OgPM0awOPmtoT9qbali1rBpVrXaP7hXN/JdYu1M5SPzMTcRq gt9VOVAuKoO/nb6RvpVmrGatNFvarTOQzkKL4CppQXQudGbQ2WFv8OzTzNPs ezN6rTRSWnkg+GyjM5zOcj2itVg9rl6/L1orpeHS8rHxgrYm3vsQzUydjdTz ql/NgJQ1p1j3is611jSInlmaXSewZvi9omtPM+TB6HvoXupB9eK90dotTZI2 3RY8e6T50v5G0dqoGd44uiZVm6o51V63aC0q0/eI1kBpoTRqD/4OrpUnrnHV uvakvanmDhK/I/jspB5Xr48Kvpf+o/9qRmtW651ziP0PVHhuAg== "]]}]}, { EdgeForm[], GrayLevel[0.65], GraphicsGroupBox[{ PolygonBox[CompressedData[" 1:eJxFV1lsVmUQvfd+U6uhEiqtaHDDKInbi4nGQqyUFrGFRMAihqCGyqJSQCig xUTaUsBQMQLGiBiIiuKSugAKIpvbQ4uBBDAICfviAz7w7EI8x3OID39n7nxz vzvfzJkzXwc1zR43q8iybHCeZQnyJ/xphj4nsmwv5EEsjuQa7A2QUzOtH4K9 HXod/K7Dcy1kO2w35LKPwPNA6F9A7sult2XS+S6fa7HnFNimw6cPno/jNwbP kyCvwVoFZCl+p+kL+xDIfrCXQM7GHuXQGTxt5bafd8wj4H8W+im/e9lvOPRl uc4yHvr40L5VSevU6dMF+Sh+T0Mf6jgux3CFfVbZZ3am71woZK/APjugd2Sy /YHf1bnioU7fvj5XHXzqC/m3+OzM57xM3xnq73J9p32eCNmH4DuVSd9j7g/C Vuk814TyNwxyO9YHQF8G2Rs6/1Ho5xwT92csZ6y3Fso/Y6iGbIS8BLmmUH6Z W8rf8/+xEa5lSaFn4oR1Lk3CQw++243nz4yjZP95rts4rN+N31Lof8F2NNe7 xMt/mHI+d+XKO3NOe7Pxtg/vroT+SFJNVtrn4ZDfV5ArIL81hq/E8x7oV0HW uXasUYvjWRU6H8/J89+O55PQb4MsM1ZrMskT1ucU0vvYdtL2waHcvhvKa39j e0PS+uSkOnQ7hoWFfFqNiwrXb0ESJhpDGP/bOB/uGt3o75xzHYk17ss9Owph hXls954dxmql99+E92dC/oznm6AfZu5DtWW9WNuZrgffPZCrPm2udbP7nPX9 xf01A/JX+zCeC8Y/sXoIv0WwTzMWyAesOffa5/cO+t1FSee4D/FsxG8JsQR5 KoSHs9wjV0z0JwYmuH9vCeHpZsgWvLfffVfp/RlDqTFHLK0nxnPVeWAozi6s 74e+mvmFXgt5b6FaH0nC7V2hHmXNmM9e/Hq8/1Mh/ydDXMkefxXvdRbqf/Za mb9LDmRtWWNy8CtJ35kU4rEy44o5PuI8V+eqF3FwxDrtR0N5+AZ7NDmeibly sNd5qDVmx5iv2A/kMfbQTvfR5Fy9R/2lJD6cEMLjKfvfGsJjU1JflHlP8sba QjHeH+K+XSFuYh90wv9Yrn5kbMQ9scY+Zb1Z93dC3FfiXDFP5d4/L8TXfJ6e 5DcKcjme+xkDJ0PPx2Gv9l70Zb+Q+xjTA4VywXdWh2bMStY0FP+BpDPz7C9C /wB6G+wb2CMhbtkE+3fOw5/Qt5qLiEdiZ7fPSF5Z45xMC53nwaT1Jvu8b44i VzWHZsNo+MwtFHOp95zoujAW1miy63zMa3vw7jqs/cYeCuGxMYmLuCfzSa5b 51l/mLiE/CephrM81wb5TlAJ2WDcLs6U16pCeZtfCOfM49SQfUpordb1WpHE E7wj8HudxiHnGefa454Dm4zhzbm4m7xS5Rn9WGjms0Ylrn2Xa01uYF+xx74m P0DfcrkP7EOOfbkQ/tYak23Yb13IvgX6Rs5A6G/B9noS73GeNBr38z3n6z2b Rjufw7HeFMLTM5C7Q2c87Jla7zl+ybxN/qbvfOeN9yHyYUNSvMsdM+f2ZueB uVnm/PR3Ldk3U5P6YUiIj5hHztVRhfJA/uZsOu6evzbEIwMgtybliJzRmsQR 10M/kZT/vSFMj3CvvxfC2o4kHC91PMR2tblodCGsECfMb19zwE7z9SFjaZRz uKAQ/9a5nlvdO+QY5pxz7e2Q35pQLhv8LvuC/E4svZH07ZZQr8y1/XRSP/K+ wNrSXmouHGkcMp8LjY1OcwPtxBp7oSYT9vr5/sm7CzHIuwPPw3ONTeLv896X eFziPPS4xxcZC522HzNnkavIc63mOs5j1m0z7J96dvDezfnFb3HePZSEh173 GvuMmBmftPfFpDoTO8TWRPMEeaN/aFZVQP6QNLNf8Pyvdk3LjUHu2+E7RHsm TBAbdXivKuTXkVRPxsy6NLh/yeesKzmFfLIk6QzPhvIxrNAz91/sGcq7SYf1 ezyLOZO35cIqsdEdipmxPx/a5znIT3xP2Og73np/e3uIe3dAvpZ0z/jcMVS6 Lgudf8a8x7lizr4M3XP4fxJn42rn8EzSPYT3kYvO+cchnpvhGo015mfA/lHo jLuT6tnpWnOPbT5XfdL+vHf86LrwrB8m3TXfhH5HiLvvhOxO4oXvof8LxqqO Ew== "]], PolygonBox[CompressedData[" 1:eJwllNlv1VUUhe+5Z0NjTImg1aogEFDBxBefBGOxBCIID0RkeKgaStugVSsU UDFRmQ2jlEEGKVEKFhTpxFQthfhUNWhQApIwhEIIf4MS+FbWw2rX2vvc89vT 2SOrG17/oFgoFOpBgFb+rOT/aoyH4B/lQqEkFQovgbnoX8AufJM58zN8O1iE Xgy+5+xR9JOcfQ+0we9iWwV/FfwI/xrbEs6+CKrha7E9hm8l+iC6HQxFvw86 4FtAA75KvtfC2SfQf+O7ADagO8NnmzhzDF6JbT06oWvh/4E16ArQCv8p/Nt6 8Cu6Hl0DLwOr0V3hu6aBzegy9O/4/gSfoCeBjfBOcA9eg38cZ9ehr6AXoV9D TwdfKTf0H/hqiL8OPT+c+0PY5sFXYXsE/im2ZvRZ0JEc87/4vgTl8CrVFN8U 9Bb4CVBEvw0mwUvB5/gug7XJdxyBPx6OvVH1Rfdm13oFOICvK/vby8Be9QY9 kPPjwaxwDVQL5aTcGsO5aUZmZfdUvW3GdgbfpWx+G9tY9MzwXQPAYnwLw7Oh Hm9CfxjuhWZmI/oG+v+iaz4S3g8C+wBsE+Dn4XuKnsFu9Hr0P8kzodl4E7xQ 9Ixp1haEe/sK+t1wjsq1lvg7+e03+gb+QWAP/J3w2Wr0jOweq9eq6QT1Cv9v qjW/L4X/EH4r6tHh8Ixp1hRjT7jGqvVyzp/G1wFOJc/QtewYFItswdk16PPJ M9mT3WP1WjOt2T4FTsO/4M794ZqoNo3cUQEvAb34t4KpnD0JepJn5obmD+yD L+U3Ozn7RnYuquHz6N3hWTqCvw/ehv84eo5mTt8O10pvuhd+E1tTckyKLWM7 mWzrz545zZ52hHZFFXgwuUdvaZeEY9EMa5avZr8lfVPfflbfgN8CY+AvY5uc nINyGYXtGnyh5lmxZseqGBTLUHBRbwm045sd3mWa8aXoFWBw8p11+B4AZ/SW wZ3sN6q3qjdeq/0nW/KZ78IzollRzVX7cnAuuYfq5a3sXqgH6kVT+C1op22F DwF9yTP1sOYpu/bKWblvC+emmn2GryXce8XwLbw6exfrzejtTAzvCu2USu2i 7N2jN6S3tCO8q7XTP87OUbkqBsUyL3vXXkc/jR4OLifnpNzUA/VCb+YZ+Ojw WfVAvVCNVWvtvEfRf4Geond8N77x4d2smo+DD9MMFtyjp8JvRG9F3xyBnpY9 27pDdz0X3i36TQW++7Fi1mc= "]]}]}, { EdgeForm[], GrayLevel[0.5], GraphicsGroupBox[{ PolygonBox[CompressedData[" 1:eJxNVD1rk1EUfnPvpfkBhejg52LV1S4WfUuXEnVJmkXoYJuQ4gdoFaRugpC6 +AP8iJPoooPZYoqtSyoOgmNmRXDwD8Q0qc/D8xQ6HM55z3vu+XjOc+/p+r2l uyHLsoeQCDmVsqxZyLJtfKxBvsM+At8C7BX8D7Cn4XsAuwF5hMOzkEX4/kIG kGeQHnzXoVcRU7aP8fTnkHXYOXL+gf88cvbhuw9fHXIl6n8R/p9RebrQHyAf YY+jcjCWZ7qO+X0QG1SXMioobgLfP9hLsPcK8tNmnp7jt2yzdgO5qtCXoS/4 LL85657PDgsERP6m8SFOrP8p6HvofmgTYJ6tZcq3EZTnrTHfwf9vSf6X0I9d +0YSfhVjyJ7n3ecyfG+8D844Dqq5H4Q7Z3+XFPM06lxurBk78o4yn1vxDnJj UoXvUtDOyQn2yX7XIHPwP4HvYtL5Oehd6K/O30Jsp6A+viTtjjvMYU9xhqT9 M4ax8/b/gv6MuBJ8m955y7gteHbmn0ni3fvo+t4Fcd73bB1zjz0QR+L5IgnH vjn0OqhGzfwmBmXX5zzEtmt8Vn1u1z2cSOL8cehO1I6OwT7r3ma8u4H3t+m8 R5NwYh5+kxfse+Td0WY/z5Nmahz6Vzd/Ju6p47t3MCdjeLaUVLPje1x1D+eS 8BzA/yOJQ9eiONI0l1rOVTMfyuZexXlok+Nd3/MNvwHkcytqT3eQ+1XSG9H2 u7Hs/BXf8VtJPJz4LpwxbsSPs43NQ8479B0c2c/4dtD+2GcxKD/r7CT9207q d/rQ+7PlnrkDYn8S+nYSr24m8Y88vBqli+Y/d9R2Le6tb25QMzdt9j7r3npR /RBj3qEp5+G7tm4MiHfJmPwHr+ar3w== "]], PolygonBox[CompressedData[" 1:eJwlkblKg1EQhW/+O+gbJI1roWBaFzBoRAVxaYyVYCNCEhdwqbRThOAruCSV YGOjpYqJnViojaC1IFj4BKLid5jiwDl3ljtzpnNpY249CSF0AQPjMYRCKoRH UIJ/gHn4DUnPJCyDEfgoqBCbBIvUXaGfiK2AzeA5q/AZ4gvUN6PL8D3QB98B J+Y1qtUf78Re0PfoNPoWXUYPJT6TZqujq+gf9AB8WPPyX463EvoAfZryP6/h v+ACXiP+Cm8hZxb9BtrgGfNd9ZaGt5v/XQQd8AI1W8Fn0myt5rVfYIxYFv0J r4Ae8x21axPIo9fMvVIP9crJ5MQ9K6L70d/U9vK2i66aezPBWzd8OnoveSAv GtFn045n6P3ou6vnoGaLPot2vkOfR++1TfzB/Ia6pW40Fd1Deamb6DbaSbtd gr/onsk7ed4g98h8Vnl+CD82760dtItqVCtP8vB/O7hJZQ== "]]}]}}, {{}, TagBox[ TooltipBox[{ Directive[ Opacity[0.5], CapForm["Butt"], Thickness[0.03], GrayLevel[0.3]], LineBox[CompressedData[" 1:eJwl0L9KQnEcxuFfKnQH1lBqi/3ZCwwqrKVysW5AUNJKqEsogvIW0lxzsaUt AqvVJRsCnY0uQ+k5NDx8vq9wOAeXypfHFzMhhCJX8RDWEyG06NO1l3WFLG0e uPb7hr7xztCe8MSHPdJVXWPbPWWXRTvFAs92UueouveouQ/1R3eiZ6nZBb7c r9qgE71XH6Nv0IymmefO7vHtPtK63uqpnnNG0Z7VAx3or74w5tPe15jm9YRN d44bd1W3tMK9u0kp8f/f/QHp9yfE "]]}, "0.09`"], Annotation[#, 0.09, "Tooltip"]& ], TagBox[ TooltipBox[{ Directive[ Opacity[0.5], CapForm["Butt"], Thickness[0.03], GrayLevel[0.3]], LineBox[CompressedData[" 1:eJwl0rdPVWEYwOFDuAMOLJRBmACZwD8AFukTE9UBSUDcCEiHhYmBBLgURSZN EJBiUFGEUBxoi2xumkhf3JhhkOcLwy/P+96Sc/Kdk9XcXtWWEEVRXAOJUZQe i6IsXajZns0b7nBX2/pl/8El3irfnKff5j2u8o9+mp/wv8p0bD/lV8XM3/iW 63zHIRaxWCWqtLdymKUs04o+alHL4R507ftadZkn2MlxrvN7uI6OwnW5pgrF 7R085Bd+VrnG7C+5z1Ue8BNHmcERZvKxaszJTFOqUvTPZw+YpPfhzOyznNec tuxXOjdfMpEb4by4Ge5FJzqz/2ULX6hJz8Mz0YyeqVENGvS711xgDh8pN/zH Pq9J8ytNqctexW5WsyCcHXtYw17WsZBP2cd69vNNOHdO8wMfxu7fmTsx7U1A "]]}, "0.05`"], Annotation[#, 0.05, "Tooltip"]& ], TagBox[ TooltipBox[{ Directive[ Opacity[0.5], CapForm["Butt"], Thickness[0.03], GrayLevel[0.3]], LineBox[CompressedData[" 1:eJwl08lvTWEYwOHTlC4Qw5/gHzC1RYLEhio6qKS0RUUsTL0XqakUVVNRQ001 tDWsiLBSxMZctIaWqqalJIIYKsaSWHi+WPzyvO/JvTf3fOfewfPiObGEKIra VJMYRVt6RdE2bdUXe6oyzEs5lXHu4V7t1kn7XKUo2z5NI5SsFI12vYGnuU4F 5lKe1V3z2PB+lnFUeL3mm8crwTyRTXzFi3qtB/Y0JjGTLXzHq3qvJ/Ys9mMO W/mB1/VRz+zT2Z8zuIIrVax99vt6Y77HVaxiGm9oofkmB+iT+RY/cxAHKs+8 hge4mgdZwvxw/zzEtTzMUhZwA6u5nke4kbO4iUdZxmMs52x+149wDnru2kwu UqM67U3cH85XbfZcLtAdddgb+E1fdc5+jXHuYBErGOMJ1amP+uqt683MYG9O 4LjwDDVG5fYLajefZ8QOdrGTZ8LZ66W9lX/Ds9IL+9NwLsozlzA/fCb/hN9F +O72Zp7Sbz3SZdce8wqXM5PLmMWR7FGybtsf8pd+6pK9kPUcrph5SrhfVnKX hqnIPjmcAXeG89FQLbGns5YV3K4hWmyfxOPczDnsTvz/v/oHtCx55Q== "]]}, "0.01`"], Annotation[#, 0.01, "Tooltip"]& ], {}, {}}}], {}}, {{}, {{{ Directive[ AbsoluteThickness[1.6], RGBColor[0, 0, NCache[ Rational[2, 3], 0.6666666666666666]], PointSize[0.08]], PointBox[{{4.905308194867242, 2.6308363915989275`}, { 0.20100883034436162`, 0.023821365695765692`}, { 1.0719666600928879`, -1.2506326268721977`}, { 3.0527398070698992`, 0.38721583002375826`}, {-1.8438997564108928`, \ -1.5026726898055591`}, {-2.913246104009823, -1.3391199692975575`}, { 0.3149621009629985, 1.9477027131642348`}, {-0.9556834229157016, \ -0.4484871006542206}, {3.2651237363484125`, 0.2704700013829126}}]}}}, {{}, {}}}, {{}, {{{ Directive[ AbsoluteThickness[1.6], RGBColor[ NCache[ Rational[2, 3], 0.6666666666666666], 0, 0], PointSize[0.08]], PointBox[{{-2.4, 2.7}, {-2.4, 2.7}}]}}}, {{}, {}}}}, { FrameStyle -> Directive[ Thickness[Tiny], GrayLevel[0.7]], Axes -> False, AspectRatio -> 1, ImageSize -> Dynamic[{ Automatic, 3.5 (CurrentValue["FontCapHeight"]/AbsoluteCurrentValue[ Magnification])}], Frame -> True, FrameTicks -> None, FrameStyle -> Directive[ Opacity[0.5], Thickness[Tiny], RGBColor[0.368417, 0.506779, 0.709798]], DisplayFunction -> Identity, DisplayFunction -> Identity, Ticks -> {Automatic, Automatic}, AxesOrigin -> {0., 0.}, FrameTicks -> {{Automatic, Automatic}, {Automatic, Automatic}}, GridLines -> {None, None}, AxesLabel -> {None, None}, FrameLabel -> {{None, None}, {None, None}}, DisplayFunction -> Identity, AspectRatio -> 1, AxesLabel -> {None, None}, DisplayFunction :> Identity, Frame -> True, FrameLabel -> {{None, None}, {None, None}}, FrameTicks -> {{Automatic, Automatic}, {Automatic, Automatic}}, GridLinesStyle -> Directive[ GrayLevel[0.5, 0.4]], Method -> { "DefaultBoundaryStyle" -> Automatic, "DefaultGraphicsInteraction" -> { "Version" -> 1.2, "TrackMousePosition" -> {True, False}, "Effects" -> { "Highlight" -> {"ratio" -> 2}, "HighlightPoint" -> {"ratio" -> 2}, "Droplines" -> { "freeformCursorMode" -> True, "placement" -> {"x" -> "All", "y" -> "None"}}}}, "GridLinesInFront" -> True}, PlotRange -> {{-3.4, 3.5}, {-3., 4}}, PlotRangeClipping -> True, PlotRangePadding -> {{ Scaled[0.02], Scaled[0.02]}, { Scaled[0.02], Scaled[0.02]}}, Ticks -> {Automatic, Automatic}}], GridBox[{{ RowBox[{ TagBox["\"Input type: \"", "SummaryItemAnnotation"], "\[InvisibleSpace]", TagBox[ TagBox[ TooltipBox[ TemplateBox[{"\"Mixed\"", StyleBox[ TemplateBox[{"\" (number: \"", "7", "\")\""}, "RowDefault"], GrayLevel[0.5], StripOnInput -> False]}, "RowDefault"], TagBox[ RowBox[{"{", RowBox[{ "\"Numerical\"", ",", "\"Numerical\"", ",", "\"Nominal\"", ",", "\"Nominal\"", ",", "\"Nominal\"", ",", "\"Numerical\"", ",", "\"Numerical\""}], "}"}], Short[#, 10]& ]], Annotation[#, Short[{"Numerical", "Numerical", "Nominal", "Nominal", "Nominal", "Numerical", "Numerical"}, 10], "Tooltip"]& ], "SummaryItem"]}]}, { RowBox[{ TagBox["\"False positive rate: \"", "SummaryItemAnnotation"], "\[InvisibleSpace]", TagBox["0.001`", "SummaryItem"]}]}}, GridBoxAlignment -> { "Columns" -> {{Left}}, "Rows" -> {{Automatic}}}, AutoDelete -> False, GridBoxItemSize -> { "Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}, GridBoxSpacings -> {"Columns" -> {{2}}, "Rows" -> {{Automatic}}}, BaseStyle -> { ShowStringCharacters -> False, NumberMarks -> False, PrintPrecision -> 3, ShowSyntaxStyles -> False}]}}, GridBoxAlignment -> {"Columns" -> {{Left}}, "Rows" -> {{Top}}}, AutoDelete -> False, GridBoxItemSize -> { "Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}, BaselinePosition -> {1, 1}], True -> GridBox[{{ PaneBox[ ButtonBox[ DynamicBox[ FEPrivate`FrontEndResource["FEBitmaps", "SummaryBoxCloser"]], ButtonFunction :> (Typeset`open$$ = False), Appearance -> None, BaseStyle -> {}, Evaluator -> Automatic, Method -> "Preemptive"], Alignment -> {Center, Center}, ImageSize -> Dynamic[{ Automatic, 3.5 (CurrentValue["FontCapHeight"]/AbsoluteCurrentValue[ Magnification])}]], GraphicsBox[{{ GraphicsComplexBox[CompressedData[" 1:eJx1nAdYU8n39++NAUIR0bWDixV7LwuizsXeK6hYsCtr113XsigoVuwoFlSs a1td0bXrOhcUwRU1FJUiGJASMEAIIQQI4UV+5ua53/2/efI8PMfkzsxnZs6Z c86c2Gr+qsmLJAzDZEkZ5tvf3rUvW56pfcn4FokeNW8rQbZaN7HmXUeQMzs/ q3kbqUm+vyK15q0T5IZzihvNKS4Q5A1R0/yipn0SZP7kt9c1YpK1jb49kCHI LqOC244KVgvy5NoB6AWZ1g6A4Uzy+2/DbSEV5K/n7PPP2csE+X9/7QRZzCsF XinwSoFXCrxS4JUCrxR4pcArBV4p8EqBVwq8UuCVAq8UeBngZYCXAV4GeBng ZYCXAV4GeBngZYCXAV4GeBngZYCXAV49FfPqqZhXT8W8eirm1VMxr56KefVU zKunYl49FfPqqZhXT8W8eirm1VMxr56KefVUzKsGXjXwqoFXDbxq4FUDrxp4 1cCrBl418KqBVw28auBVA68aeBXAqwBeBfAqgFcBvArgVQCvAngVwKsAXgXw KoBXAbwK4FUAL8OA/jKgvwzoLwP6y4D+MqC/DOgvA/rLgP4yoL8M6C8D+suA /jJiXjyPFATWl8D6ElhfAutLYH0JrC+B9SWwvgTWl8D6ElhfAutLYH2JmFdB QH+BVw28auBVA68aeNXAqwZeNfCqgVcNvGrgVQOvGnjVwKsGXj3w6oFXD7x6 4NUDrx549cCrB1498OqBVw+8euDVA68eePXAy3CgvxzoLwf6y4H+cqC/HOgv B/rLgf5yoL8c6C8H+suB/nKgv5yYl+HAvwJeKfBKgVcKvFLglQKvFHilwCsF XinwSoFXCrxS4DX9u+BfAa8MeGXAKwNeGfDKgFcGvDLglQGvDHhlwCsDXhnw yoDX9Ffwn4HXDnjtgNcOeO2A1w547YDXDnjtgNcOeO2A1w547YDXDnjt/vNX zOsAvA7A6wC8DsDrALwOwOsAvA7A6wC8DsDrALwOwOsAvA7A68BdDexf87b+ zmMJ/rQl/9s3XCsbziSLn7fkxc/bw/zZc+LnUcb+ZdCfDNrH8dtzK79Ndz+b 759b8e7fHqhjy5lk8ef1OPHn9WC8+LwMnsf+60H/GH9awXxi/w6cX+1+YL9/ LuFja18V1CTXLl++RpCVtf+QI8jct+3R9pUgi+fLQGs/HmUhyOL+DLRl7f7O IyZZ/LyWisejpeLxaKl4PFoqHo+WHrqlD7qljycm2Xfxt5dBkMX9KaE/JfSn hP6U0J8S2pNDe3JoT07nflPPRpacSRbPh5yIxysn0B8R8ymJ+HklPK8k4v2t hPa00J4W2tOSAbWAWkGG+SRiHi30pyVifgMRz6eBjK4dj4QzyeL2DDA+CSce n4QTj0/Ciccn4cTtS8B+Snjx/sR8hATyERLIR0h4GB/IBogfcb9jvsAA8Z+B ivlQxv2M8TbqE8bfWohHtRCPamF/oqyEeFRLxfONMuqLAsaH8TLqI8bPStB3 lOUwX0qQUR+VICtQvwnoN+xvOegP6jfDgH6DfWIYsA9EbE9RVkK8Igf9wvEp 4XO0HwrQbwXosxxkBRHrjxJkLcRPSoifcDxaiKeUEE+hfVPDeNSwPlqQDdA/ 9qeF/rQQv2khftNCfIb2EONJA8yHAewxyhLwr9BeYjwmAX9OAv6cBPw5Cfhz EmwP7BnmV9EeYX4S7QXm59A+YP4P9RnzYajPmP9Ce8Mw4v3AMGL9wHyUHOwb 2gvMl6F+Yn4I9VEB+x/zOXLIV6B+qeF5zN/g/sf8Be637/mAmaa8N/u/fP0g k59ZQWunN9GUJ9bQ2ukqNuVRs/63Pg2svo9fRxy+4bas8102ksHfnveUfG/P SG1q+bK/z28WSasdQMV3WUPCvy2/jTVnkkO/bY9mFt/lCrL92/J2YL/LLBdT O+CXxDT+p7X6l0JM/d3+1l6Qhph4or9Nh5/uu6yD8Whov1p/uuC7rKLi/jRU zKei4v6zqLj/JOg/C/pPomK+LCqef56I558n4vnnSe3yZ2q/y0kwnzysD0+m f+t+mCVv+n7tdhlruufJIsbqb688oT1YbxJfO6BM+n9/XwXrlwTrryLi/aaB 9jSkdnqiDN9lHfBXkLG1CpL4XTYSee0E3Pkus5y4PRW0rwb/Eu8X5LD+PNgj NRGvtwby6UoqjteyYH+oQP/UwKMBHg0Rt6cC+6qA/ZsE40+C/agiYlkD9lsD 8Y8KzjMl2AMVyGp4Xg3xk5qKeVUU1gv8PRWMTwk8qG8KOB/UoH8qWG9sPwn6 16A/Af4rA/GzAfxXBuJpA8wPA/G1AeaL4cXnixbiCy3YC1w/OcgKaF8OsgLi eTn0J0f7BP67HM4HnoI/CfdTcrRP6K+DPyYnsL/BP5JDfl1OSmoHZPz/fF8J 31fC91VgLzA+1YM/bQBZD+e1hIP158T2juXF9pzlxfaV5cX6w8L+kIDM8GJ7 b6Rie28EeyLhIX7hxfbFCPbFCPtBB/tfT8U8OtgfOtifeirm1YF90MF+1YM9 0VGx/dRBvGUAWU/F56WOitdfB/ZcCfktJdwHK6l4PVUgZwFfFowfz/8k6E8O /cmh/SSQeZjvJNA3PC942P88nFeYn1LA+ZUF51cWtJcF8ZIS/F8l+A9ZIKvg /NUR8f7UQf5KCbKaiM8DDRH7Yxrw1zGfpQd/qwL8owrwh4xgLxj0Vznx+cVC vovhxHwsJ14vFvJhBuBlODEvy4l5WcifGSDeZjixfrCceD1ZyK+xUP8j4cX+ HwsyA/k4lhf7Eyzk51hePN8s5OtYXmwvWMjfob3UU7F9rAAZz3M12DMNyJh/ U4E90kC8q4HPtaC/mB/LAnuI9gPzc3KMB2B9edAvBex3NexvFcS3Kvgc80tq 0E8VyErIryhhvCqQ1TB+jDfUYJ/0YI90kP/Rweda8I91kB9C+6IHf0QH9gVl LcYrUN/GwvmrB/tcAf5rBXxugHhABbIaznvcn1mwP7Mg/skCf1oJ+9EI+QaU JRz4L5x4vVjMZ0E8z0J9nwHi6QrwX5XgvyrhfFWBjPqlBH8B9UsJ/WfB9xXg XyTB+BhGbG94yPdgPkgO8XoS+Jc1My46f3iYfznEw0ngXythP2dBvpVhxPqG 41VAvJ8F8ZMS8lVKyAegvy0HWQHnP7avhvyBBuJXLeQTdMBnAH/EQMT+UgX4 L5ivlXDi/BDLiddXh/E4yJgPYeE+Sw75Tjnkj5LwfgV4k4BXD/ZTB/ZfwkE8 y0E+lBP3b8R8PfjTGsgHKyG/poL8sALybVno/8J685ivAH/tP/kqzHeAPqtA f1UQP/Gwf7NgPOi/VkA+k4V6DgPkU4xw/hvAnzdCvsKA9hjsuQHsvRH2H9bn yqG9JGhPDu2hfVOAP5UE8T3DiM8THu87GPH+4OF8VsP3FZAfzoLz2wDPV4D9 M4D9qwB7i+dLBayHBvxFDdp/8Gcwn6EC/0kF8Qn6Hzrwv4xwH6GG+VdBPMkw YnvNY/0A5G8UFPK7eL8M49GAjPWiBvCHKkDGekwJB/4/3HdpwR/SgayH/IEl +B9STtx/HU58/ljCfZklfN8Cvi+FeM+CC1wo7z04NMG0//npqy97rtn8398T RB5emdu6d7WQn/xnsO0HPtdIf5zl9Li7VZnAQ2NsZ69tYa4HMM3X29ZOqtY6 831/oxalpT83LxX8l03jIwd26VdG/W6GLz0jM9/PayNjbBwbm+ZXQZfErFbb jDHVGSto0pN9B+bW0QlyUKug8ndXzPXjDUZ2fN64iel58/1aykR/mdsf6fTU m73yT83+W+8d9jHn6jTX9zT1p+gfs6+azlvzfduN+Q/ZyNd3yaycDXSYzX/v 27i3dnHe7x6RwHWzRvfrbqp7N9dbX+v72qfhx8fkr3Xeinc/muuvGU+PY9vq GAXZdP7HDJe3br6JF/yvXpsP+PX9K5c8q1w6dV9L832caX7Px7N3Z3fOEPa3 9rcPjXKb5wr+ju0NY96+fA0xOs7xjNOY641Nz/vqRjs0DywW4qH6u5s85ncW E4tFBW0bbjPNh/l+eYedokvPenrygFnp7/eLxDRfwv6sDJp5PC7XXK8b2m18 T4tTZn0JLt0+iKaxwv3wOOsZiZb3WOF+eNWp9vPXFzJc51W3X412ZXhv3Yz7 Mz4x/KGcH7fGBF6ln9Jyhsx+zfAWm4odxiy04n2PuF8qGFazf4d0eDl3rZ7+ nXgvJ+l0Me3Q74/uVW5pZPHm9Ts7Li6mkwpj1+UNKaI3Y6/7rp2WQRNteg0e prLiX0mGj+hx5h7dsWfJs2uZNfs4WrvEoz6lgeumF0f8W0imFPw9fFTDv6lU 5v1+xAQ1mb/bzqepOp103ei4bd9jK27z4xejl3iVkbcdBh2yGKOgnRW/yvxV RqK5tWT+uGN1uH4hTve6FhrJgp6yiZ9rzsHkiFHkyQKWT17QyTXUtYQeaDLV v0MSyyf65l972yWXhj51dF72gOWl+vSl86/E0/i8/veC+tfEa3UPj1i3zoKP OBNC8r0NVCbzXSebKuUVUyI6Dy2oogset3H+uiyb6GdESBpoquh+zudKb6uv pHGe5zt9toEuP3A62kNSQje2OFW0r6mW3j+YeXfDCw096hTh08pFSy9dKd5R vEBJLTPzZhu6ldJuk1sp/R5+oWnKJieXdC+l6XlLfdxt39LovlGs41kt5Zkd 2xveiKKF7ea//HO1lu77faJ79tVYoj6x7MKFeA290LyxdERBBfEPG3zo2JZS +uffUZuXPzGQ8kePgjymaumKhz7t6rlb8z26X7PpJ1HSvKDiiDo18vR29l30 VbnU6tRoj8PTDTR82Nowts1XOsAi6GmTd7m03rXDE6zdcmjWx9c7z3V4Rav+ OHpTb8ylAcFf5uy/LuPP3tjvuuBQIl3TOEaSbl1Mb7K/vzhx+iPNNaR08Pes sZtMaflflXF0U1jmsLgRVTXnmuvsnSkxxLezbp3/DEofvBt9acHJbNJ0Ox20 e308WfFCNSDQQ0mGBXz68Pp+Lsn/ya7ZoFU5ZHjkkTGdllUR5d39HnmF2aTu BN/0x12tud4jrxy2259FWg0d5d/RzZqb1VDvcXDoV3JjwNm+E9LiqVfulN6+ SzRkcdGMjH3pkfT+Yj5gap8SEjK3sVE5Vk5+dRyYtuymlmh6NXucW5RDqrYW 6YZ0LSVnc8c5+/TXklc/XK34414JoX0C6qwaXEq+fLXxXTeohNgXTbk7L6GY /vVlkMuo7ArieiCwfEqEkrpXZCfM8KkiW5Y+X1CQZcEZT9yesu90JYmfdKQN WfOSpEx4nrpIynKDrYds62qXRLrumn2870yWC+43bP/CfkryYf/hul2estyW kcGz3V3yScsrD7wavGS5lDbXG2ztqSXXsiMsPD+w3MvB9rrQDC35/fLcGdPl LLc84Gzo3DmVpORnm64hm1huxrQs7UIbI/k4ySa0tJM5f1f/+Aj7Vo1ZIR93 vuukhI6bzPVx3ouONnoTYc633exuxyoSzPk118StK5+uZoXfA2bfensrKlhP mzv8sSssRUNm6eZ6v5lYIdRnFJU9ibzcUinUr/2Ve3Hrkq0aIX45Oaf+pctt igR/c6Tz3JX1LpZQ31vMY/7rZZO/QcePu+iVKleRC7aeQ91Pf6Ujl6VPnfdQ wkV2XRTzZlq+0N9rbUl8QGEudbzbZv+xgAwhvlcOtxq1Z+wrYhd3aqjD6yw6 aele72dNSkjkxfzssy2yqWpIwrbOjUz+ukKoB8lud3BZ7PZEGplesmRo4wo6 +dIEhyP9/iUPD2ddGvchjyQaq2O4rnE0Wtt3iNvTCjJ1g/uWrpsjqU97x+Xt HMz1X6Z46eVRv3mOx66RvWzfHFfWkve6aj+x4v17wd777Fh3+47nJ+L/9g1z LKRciH9i3zo67XRR0sGpd75MJolk7Z3UrDfdNdTDQ/p70505gj85rI3F7C1+ 2cRrfvW4ZyMkvJdxyMnYN1+F87N+s4+SOdn5pCA5xsZJ/kbIB5jOjxsnuv0w xUZJ6ty88yDolLk+q6Jekz2Bzc35t1RFtzsj4yspM7aZzXDrUuE8PODs7mRj pSUuw61cJeSykA8ztZ+/NHTkhs6lwnntstw3Iv+2ud5K4Xe/44/VpSR//O3y jyf+W3/lJHO/0bRXpcAz+uzGvLDOBpJyeuH080cLTfGR8HubQztazZCOMRKf wyNOXr5iWl9zvVVATID9pfaM4M95Xe05stTKXA/lOLPTgfqrKulPk17MSjlc KMRXJv88dWPUuehD5UK+beOqKzObhRcI8W+b+tnxo8KLadc7s0LCulQJ/ist Kzpn19kUv6iFeLikY8rcOH+VoA8DLC0HjZ2eI+TnppTs1jrt+UIvbJP6PG9Z LOSnTOOpmOxf+O7PTCF/ELaA2v8+K4umPuvjc22cKb5W0txBCY1SmxoE/82k j0+TbNIfz+YJ3WZtu/JYuRD/NU6Y2mnxbkvetB9N8UfTj+yyAe5pxPiJBByo 0UPT56kH63/eWF0q+GemeOlAdewvIyZnC/msxS1aHpm/4iPZsvveIN02c32U 5+Uxl1IG/rc+6tK86mUBg4qE58OP26w+Pr+InEpu0TS2X5UQn5h4Zrk2W8m8 KCUBYU2l744nCPkNUzww56dN4a6jGS6txZCS9lcZ/uruuk073mH4DN+onGj7 OnxIk4u+zzZV08eu6127zisnRpfndsFFavLqZJuQMyoZ9yhmxl7fyGKyvGGk qixWxt33tA99s1lDrixd+fu1Dwz3cuGP07mbDJfaeskADxXLhQY2MrZyY7gB lp3GLrv4lDzPfll3bwDDNzxztvT6jXhyKikt4aI1w/96iHG4M+Yzia+0l3Zv WU2Pp7juS3tcTH549magc0IF7bU/Kvh6aRlpUp95/O+WMtplXfbwB9szSX1m ypzio2rq71kxMsy2iPy0LLzL05b5VDtjzoIX5xmuk80ItluRmvoE3v3S8jYl dgH/7r8Tmk0beZCwNqUK0uHgwF7WHT/T6Em66K8DyshvDuclm+qmUv/Fb2Y6 X5By7ts8vnZ0zKTjt3Xh12+14lc6FrwranOOrP/cOfX19CKaM63B2bb/XiY6 i3Zvw1bpaGSnaSu2j04n/biP2oiQSlKvibLbpHXXaeSz7St6v7Ti2qZUy/b7 XCCl7/ZGNBsg5d+eTDG6P/1C4ioLRv2iSKNvxqzx7h6tIPmnGCe3+Hzq6TSi /rD3KnK4q9VZ/0W5dNbAdr06Xioki6c733X8IOMcNuqyZTeTSdNk/x4uV2Wc /EJJui4tkTR0ublvZxeG90gxXnh/rpjkhO/vNn68gj71dvTp3KiYGPr9EBSS paNDbF1ej7LWEx9vm2kr16RQl/wtLa5sryYHYzx3FS48R89PvDj95+sM16tP WJM+ds/psePt7xfNZzg2bsTTMdeyacw/4Z96nSwi3S8cXmodmyXEw6b8ysNz Lv2GXjXXJz04fX7xrd9q/MKrWZF+S/MFfcsM9Dk1fXcB2fw5cdAO7whB/21L Yt6PHakkL/8a6BOed5fs/qWs60S/EiHejeIs23Wt0hC59YZjgwe/p+M7h3St 20cjnA9XnG6Nmv1jPPU+1czRducX8ly/z7fFhVSaMco7+vGFbNLeeZah4HIS bT6tyx6L4ARyonfztdHjlOREeLXzIM8MEtm+nh1jqyStYqPDFucmE/eG0RmO kcVC/Nr3df/S9AQ13f7St/jsqQoa1o1b6Ha6iFYVS87MXGyuVzflv9ZbH36Y P76Yuk2YGvjzk1QaXTzaduKEGr/408mdDiWfaLfSUyejxiuF+HbzhLDGoyYr 6Xnd+lVvrn8kQUsjS1/MyxTyW93S5gftDs2g5Ic+YV7v1OTglIYlab8rhfsU v+Mjs4/EKGnZvuR2+e4fhfyFyV7EHPN6d/MAK9T7OPh1WpKXxgr1Pb8/G/Jw +SNWqOdpnnzeum1nlh8cNs6lQ6jpfkFLq9fM5w/yMn5Xk0NDDspK6JKmJYte OljzgXeHDwj3NNdP3ZyRdnqMIkdYv+Y9U1al1PjXm48s3uy429SenMZ9+ZhT 3lLGP/rz549ZyRF01xHX3lGdi4X83ZN8v+tOeWpqFVp/m2wIT2Ov/xvrts38 e5QpH92GVdSz4l7ZNy7/rR1Ph/ZoZf2yvhV3YnnfF3OKqDA/B2/8uarj7RQy dsT5izf6mM4XOdmZIelw/1iVIGdYtd7wVl5F8t1CTl549p5MX+sen55XRSZ0 uWh1NTWZtLhfFTm2TZXgP1SdT5rsrTSQsgeOjtet8oT9v6bpltzd8ebfp5zv MsdtQ89Swb7nf/ZmetoZifNUbrrr5CracNzrBgtGsPybni5HZi3T0cSw+3va R7N8u3GO0avdCmhgtCzeO47ldZKH4ZriD/TH/HpvX0w03weneaV9/TvUQOsw jQtWjpHyVoyvU/+qKuH8tv/96OTRs6po+50j/G8PyyYFfw4bGlRRRet3VwQF yq34Yauvzt2SrqP7hkbk2LnI+KeRybb13pUK+UC7kSXnOjprqVv9Dy6pR4po uWvPGcMTzb9vWnT6bFxTj1KaZbf8c37RV6rMiT25VlpKx89J9wlfqqDePYJb HG5TKuyHXtt6v3m5T1vjd3s363o8iSZsXdTofIn5908PXN1Xzz+qpYftbu+M mFZBvq6e0PtRXin9/ENlkL27+fdQ/V/b5nm1qRL84TtnBtfNTK6iOy0nXuj5 LoPmTfZaunhvFd04pv7jQ4ez6d3+iWffqfJp/5zB/Qeps+i7qOchmRoZr1rz eP61x2k0/5VmwrCQKmH/kd712sv1VbRB6cv2LT9/ovcG7xjR6kkVvdqlCd/N MZGetLSI/zXDQL9s2lRa6faMBi166sgyVbRwbh/LopQIauj2ig2tU0zPLerh XjEqiZbxMW+SB6hJ55DK9g3/DBHue1ePPFuvSXgWCTv9Ln3PmY90+uJQReai TGKha3Oo3DOBym7ue5654gtZOMHKkj46SO7nWezpk5FDPA+dr7M1sYpsyFue EJeTSTTpTFercSZ/VkkOz54WcsKlJn5Me7Ym5+AXcsunwT9/9bXmBi2OrT/5 poqkXJnewDn8Opk76fRviTIt2VZVVJ+N+EKcbt5k5/UtFfK1FyxfjR4YUkKG 2E+SFVfqSF7lX3+u3WquNwl9uDOAWVFB+GfvC/TPdHTX8fOz0p305LRLzIfu S7W0ILDzgjs7yknsnfJ9o9YU1ISda+foOXM9Ggke9yRiEstZPZjZt48hikh6 1y2zqMNy3i0m+UUdTydpHm23DN9ori9pPX9U56oYluuz2cPd82smGTH8Y3bA KZabEq+/4jVMTfyPPNpalmSuL2ndxX3iyhq5JIoZbJmuJkc7NCg+msxy//Zc k9o7u4y8HPbkN9sH5noSq6fJLw4uY7k7z0vsHfuVk0khd7X1wljOcZ5Xj3En jOSJtPtzixYs5+U3sPkZz2pTPpf3SFc7/3GH5UfbNHjdYxbDH2ZOJrT/xVwv YspX/51yb5XKnuFVqa5xS6vK6csua8e+Pcby7YPGBvTyLaPxR1dsOPGQ5Y9N uWhXeU5D7edr/ihMYXl7Q/Mi59OFtOPadb63E1l++uSGT+fcyKKX1rDO7S+z fM++/RL9GipouweTRzbczvKdlnv/6bvFku8dZWPj/0sF/ftXj4Atl8y/dzTl 14PbL38W/76Cpu3yb19So2eHmITtFrYltKPdhQk3RpUI8arpPmqT3W+nPq4t oW3qaveudzDXi4XRviNGrz1H5v4x7P29eyXC/cpb76RdJ1toaZ8e0V6VlSb9 MteHtPhlrlPqlUxa7tJga7fZKtP9E/1jd7Jn+phsen21W+LS/rn00vHWB9d7 mX/PaLqPb6BtnbBlipIO3bd/26OOBtJlx4KDTPQBsvGM3962ReZ6kWOOv+6M Y831Iqb7gPX/TLMfUa0gZb6hG4Y/KaJ37voOD5LnkT2dttnUa2Uaj4qcaHV2 6aCWpvNJKdwPNIxNOXpxsJIUzp63K9A3g3wZ+PzQIWulUC+i2n41qEHfXOJs vPoycG4l2TYpzu+iqoCEjyvo5bfEXB9iul8ZK9nzwqakSLiPPN10iFOJPJ+4 qRfYrHX4P+pHJgTNyQ1SE/2w6ODU7Kvkb/cu0XcszPUao3NtFNF7SsiS28RB G/CByCwtPk/L1pJKxYq2oevTSfCJoeGnGpWSjdeOpz1c9JUUrrKMzK+Rr8xb 5nnvdBGJPxbyc9RLLQnuX29V07bFJPleu6GqI1pyoLf7xjbDS4R42HQf1v/r zVcz3bRkVu4O13kH79M6hy/JLDLM/x/SLM2RnGZhDB9Q9HUd6acmZzs1TTp3 oVK477iVsGpHmpeBupedurHtbS7Jdd6YP3ZXIS3b29qw+E2WcN9nmt/W23v4 OUQU0ZHu3S59bqkS7jvHDHAoOe/8it7u8dyL1vgf8mllQ8Z32koCG54J//Fy Di1YuIJ5wMtN9/FCfUjo7Y+K3spsmqc+dO143S/0c9tTEcqN5nqQ8MDysmle 1eRkg54ey17H0ow1rlOHuzNcUL3Tm8PaJpCzbuMHBVswfMhbmfLzhlIyv2Le hii9np4ZLj1/wdmaN564a1AFF9GDTm87xtla8+8lE6u2/KMQ7ivtg3pV2Bfm 0i+zHXv/M6CUPJn3aNyu9RnUNkK3dV7DcvJ21dYrf+yMp4OqWnBWsZZ8z4KF IzN2xZAfy47t2d3Ogm+1rvy1MT2dxP/s3zqkrorOW+myOdYzhrj/PbH5x4gv 1N/q3KKZXqnEuLjB8M7pKuKZf+/E02tfyJZxkzfEz5ZxwembjXsq/iWOm2b+ ZD+iDh8R/JdbcnIu6eIUOOjTIJbfOerB1LzQIhL3+oL1jOA7RHM1tPn5obmk 8sCSpydaGqk+/bBb27klpNAp7s21peV06nXG8ckJHRk/8rVTd4Ult9w6pPP0 0HLSvs31GauIhDs/cM1TzpHh9ld3sV8SaMEtdp9iuDUjlS5beKNrynQ9HZPy YMlb2w/kVMTxRYOWy7i91dlniv/Rksxz873HxUo5n011CjouqCIxzeo4P7Kt JhzpEN7mlobG5awav+kOy7UuSkyT9Cyklt7tP/X/uw5XHfUi7NmkHPrn9tKJ VmGVdPvl5Y7eu68TP1nWX2/maumA7WnvZ+gzhfvWgF0Z66TXiuniBuWp45yK yfwLsQ+Tz74mR/t329S7YyF9/lvQ80lOBvL/ADpSCkY= "], {{{ EdgeForm[], GrayLevel[0.9], GraphicsGroupBox[{ PolygonBox[CompressedData[" 1:eJxNmHmwl2UVx9/397xoSoVeitiXmWS5LBcwA0VnGGbKFIi0GSYwzRhmYknN xhIuZqmVgJmUsikEiSwSQaEYIDuxyXpBMLlsKiEJomwiqdH34/n+Bv84v+c8 +3nO8j3n/bUadM+td5eyLGumn6T2TRH9K0X/Em0UbRK97vYVUV1RE1FjUR3R l0VfEhVu64s2i14WLfOepe5v8dhyn7fEc9tFK0WrRNtEK9zf6/2ct9X7mFsv ekn0D9EGt4st72Kfe4mogeW7VPQV9z8nauj+Qd+9Q3TIbY3oXdFR0U7RadF7 omOis6KTnj8oRR0SfTXXeWobiVpq/DXLhCwH/B7uqPU70MFlokaW43Lrkv4+ z/PWZF1WiN6yba4Q7ff8Ntuqwvbaoc5p0XbRHusIOY6I1oleFY0UjRGNFt0l gzWS7GN10Xy1D4t6iqo1N9br7syDH4U84m9Q20nUQXS9qIeovdvrRIN09w9F //WbKkXtRDM11lTUxndPEk20XItsS96G/lqI5uiuB0RV1m1LvxGZJns/cj3t fn/LPVz0BVEzUVPR593ir4c9/kXbeLVojeiM6H3RcdubsbX2hVVet9uyotN6 uus2tX3QiWQriYaIH5aFDOi4bx591jWTfgeovRndaW2N5nbkobPeHn9R/dGi 3nnokbGbrD/619rfsO8M/EptQ507xXav8fg0jXXUGTfLtlPEV4r/lvhnxLcV /5D4yeLbiH9Q/HTxnbhXfGOdcZeoi/pr1Z+q84ZqvkJjz4rmYSeNPyn+Byns A/+/LGyE3VvZdk+VYn6H1j2odrH2zfA5nLvd4x+m8BX2Vrgd5HP+pLkOWnNT Ee/e4Te2y4NfmMX4Qfs8fdoX1K5Qu9J2wfdmlcKXTqmtFW0RjSvivU8UEduH 7RvE81aNPyd+ivjB9ufBlg2eOc6YmcWbeNt9edwz037OXZwzPov557yGsVOW ob5odyl0fL/mOqZYg333i57R3rlZ8H/JAm8OWg9tUqzDB1p5D+t53z5iJo+2 1jJsMT/BejroN/a2j+Jz+HJz8QOzaFvYd5lvbr7W8o/323fbpsw3817igr0l v6N1CvkrzCMzbRvz5Ib2KXID72CcfR1SjON7XdV2wV9TjKOrv2ahrw5e09lz jHMWa5d7b5XHr8rjHHjWdvYd7b2GfVUeZw1ru5qvMt/JxNx8jc+WrJWi7uIv ZIGRLYw3HbPAywVZ5LQVfnuFbUqeOWRMOiF6J7uI9bTgHr6B3MgwQm3rPO4l x5CnyTlg4hxkySIG1hrLyIWbvPbvxrNVxjNwjbxQzqPrs4v5nnPLOXWDfRBf xMf+5jNWes8en3VvHr7Imm32f+Jpke/7p+iVFGv+kMIn8ceZlnui3wGug++P ZZH/91omdEfeI/+VczY5fKHfutpz5Mh9lm+796zynnL/gNdt9xkH7M/40zzr ElnIL1sdr5/Gvc+u9X01Ppd1z3vfm7Yj+Zn8+7bfjr3xdWqe6jwwGV3u+AyG zbY9XvKbdvoOfAmMai6a5Xn0etx3YMdReeAjdkbWF+0LVfbXZZZvse9437Id 8dn/9vnEAnKih/nWO3E0Mo/cscA+8Zptv9n62Gu51tveE/2eWX4zttxkHWx2 n/cd9RuX235bvP+I9YY/zLV+l3l+s/f9x37A3BK/7aTHj3rfUs89bzuVz6PF V9fn4btDXXeQ06gPZzpvUKdR7xDHxHO57qFPXUPt1jaLXPpJFnUme6g1L/NZ l7rP3CXudyuinngoRa1HzUcd3dN1ADVAhf2IuXKdXa676/isRr6HWhIcvy67 WF/0sOzUFsyB4fgtPlKOm4X2E3I/+NUyC/x4wT7UxO+t9F3t/N6m1gFzjX1/ XesIf23uPY19BvkBmQZaxsrP6tF7wFHGr/VcE8+1892cBd5ek8dYuc6j7qP2 +di6oXb4yDojn7R2TtntOqTCOfLRLDBoYx44S/wvtX8Qr52dF8oxwRhx0cnY j8+u1Z5JedTL1KjUgNX2Nfpg2SLNPZFHbTjW89TVy9Qfn0cNC96M9h5q8xFZ yMda6snh9jf8Ev8iLpGBeFyVBZZgV75XiOVyzYtuqG3wb3z+Meu1rnVH3dXQ NRg1IzVOffsBeYgctNL3cAd5HRsOtX+ANWB4OcbQHXE4x/3b81jLe8Cd2Z4D K2a5v934is/VuN4Dy+aUwt7kVnB4m2uPSo/jAw1dC5FXNipAeqmdq7EL2COP GOvllrr9da3pA45pzZoi6rAb1W5Mka+eTPHWNsZn6maw+mmNjygiFz+VooZi PXLX2p/wpVFF5OgJKepszpmU4qy2xmfs1tpYOlPtj/LYT9vIfvnLIjD3Nu29 OwUudaFuTYEznYuwGbbDRkUpai/qXmpg7ITeqT032re5f5RloH65yrXEj1PE TlURMVLfdR2yV1sPDxfBf19rh6eIr05F1HrcOyC7WOcRI/UdZ9R4t/hM+K55 fE9SJ03No1/GHVpqp0eLqLd/U0StgG3vEz/BcUbsUC99kEWuBfPPZ4H75LUP s8ht+DH+XGEs+MhjxCDnEAtlvaPzyc4DjO8pon9M999TxJpV4u/Iw6fx5wck 4+o8xnZZvqMpvj+wAevq+XuK7yr0hQyji7AfdvxdEXkcucl57/o9R/0Oxnc5 rvk/op7fypo3ssgv+EYD6+SUSNd+ioMf+91veY7a5JjPOuw9n3gttmf9WLXn vKaeW/pgCn5DXC60XGcsG7KctTxgzFDXgehvvXXKd2+NY3B/Ef857JKuuqWI 7/Nq1+WxB/2DRVOMR+h3tfVOfmQvOWKB2kfyyJXkBL4ryU1Xp/gOO5ci7sEB vsfJg6O9/kSK8+8glosYr03x/cq3LbUA366cyTdrlesD9M3aBT6H++dbrvN+ Sw+176X4lv2tzhiW4rvteyny7FT7/Lwi4v+kxs+IZos/nQKDwJYtKeKVcfS+ Jg8fw/+HpMDC/mqfTfH+mhTYxn8Kvf3uRc4756yTr6utUwRO9BU/Lo881N85 6uU8ck3dInR+eRGxMsl22V0Ef6v27iwif31b/J48/uPAzuBBD2MCtupYCntR Z/UzVo9JgXX8FwWetzfO/7yINT8rYqzSezt6DfxAzXUQX43/5HE3/0+18Ldq yWs7+Myri8CcrmqblOL/OmxDy38fLc039Pge+ytnUo9Uli7WIE2df4+niGvi e0sRuvtmCn2ssY36WqfoE72OM4+ux3vNN1LYApvwXyzn48PYea3xrp/mmmv8 Xr7LS7GurWWBx67YZJ1txFhzj9+ivS3Ff8e+3MJ7wV3G0Qt5YIRzQSPj2Qhj yQnjx8AUa6p1zjsp7sMXri8i99ygdrDzbvci4or4+nUROYxc9gvyS4qYOVAE fp8wXt1uX3+kiPmejq8D7u/R/E9S2K+v7dnNNpqYIp+Se8lP5Klf4ZvOceRt as6exo3vpsAZML5fCl2D3392fP5e/KspfBesOpviv60P1E4rAvenq/1jitx2 P3pIUWdQg+xLgRHgSnf76NfYV4QO0eVPbcslGruiiBx9pdobHZNbxb9tfyLO 5ju2qV9m+MwN4g+l8DF8EHztZYwFU8CWUylq/97W5wXbnvyL7/Ne/L/knDXE +YTvmQbGcuIWPKfmmuP6mxioMh4Os6/j2+OMJfB3uh5DNyOLqLEHpHgb696w 3fANaqFqxwZxwX88rOd/j8eLkGe66w5yA3mc96MHdNzOOQDf7mP8A3/GGP/A fzCpj8c3G5PBZu7i/yBqpDFFfD/wH+R6YyxYe43xHFwnD61wfUX+5NuDHPpY Efw011OcQ/79P1EtLYs= "]], PolygonBox[CompressedData[" 1:eJwtlVeIVlcUhe+Zc8iDikbyYsEGRo0V6wijIIIltrGAFQsiqBhnVOyO3Wgs UUnsjljGjowvaggj2GtUHKzgPCiISiwPNgTrt1g+LP617jr37nP23mf/DcYW DizIybJsEkhgVcyymiHLxoNCHtxAt4LvZ9EF9C5QBB/FswL4S/y96BJwHj0P 3QK+BP82/BpoCH/Iswr8f8AG+EGevcMbDf7O8TN5r9H78OqDS/ClIA8vgjz8 1uBL5ndOwO+DcvgvrL/O2qGgJPMzeXnojujm6OFai27P2oPoi+i+6An4p9Dl 6MHojegb6HvJe9BeKtAP0HOjv9UMDEMfR5fhr9MZ4f15Vh+vDboduhDUQtcE ffCmouvC64B+yTlT7nYQ8wXeCHRL9F3eL0K3Qcfgbw6AT4xe+xP6bPIetVfl +DLec7AVfgGcgb+PzrVqqFo+A5vgZ0EZfD4oD46p2MqBcjGSZ0/wOoDGmfec n5wz5U45zY2OoVhbwP/wKvjngmPkR8dQrI3gKbwS/pngHtqZfEadNReURMdU bK2pnNyUx+B9QCneJ1AKXwruwKtqT8FnHoQOqgl8JeiC/gF9Er4e9EDPQOcT eyp6CPoNOACfB0bCD4OuwTlRbgZGn01n0tnaJe9VNWmb3HPqPX3jLbxf9FkV U7FPR98l9fSR5Duju6M7thJvkXoK3lhnQFdHXw6+U7pbY1RvdGuwG340Ohe6 E7ob81k/B90IbNZ9ST5LE7Ad/Qf6o/pf8eDdo3tVOVKuHmhNcI6V656gafA7 k/B+TX73E/o3dC90C/Rn9OTomaDZoB5pi/4AFgTfsT3wZfjFwd8sRv+Lrpfj NTeja6BaqIaqpWqm2n0F4+DDwKzgnPyIPwsM4P1i4l3BuxU9a9QT6o3f8cfi j9OdQi8G1YJrOka5BrPhP6sn8Xok3yV98y/VOroWyulCzQdQG68B8dpHx1Ts Gvw+xjukmgTnsAK+PHmt9rAieQ/ai3LwCv4ouhc1MzQ7NkXXTnsqYv10vR/c ozPha5NrPxm9Hr4umWumaLbcjb4L2oP2siH6bMrZHNZ2TM6lcpCbPHM0e1SD adEzSbNJZ5zy/Uw6m/ZwNXrGadZppmu2d0anHM/MbuhVyb2hnlgNX5PcGy3R fybPQM1C9bB6eVt0b6rnlyTPVM1WzdyL0f8R+q/QDP8veuZr9itmJ3jv5G/r P6AA/xsMWNd7 "]]}]}, { EdgeForm[], GrayLevel[0.78], GraphicsGroupBox[{ PolygonBox[CompressedData[" 1:eJxNmXmQ18URxX+/mdFabiUqJV5hAYNaKRZZgZTiHY8kgJFDURAURbxAUSAa 1yOe3KArGIzKIjGCIAqCZTRVgMYzSUWTgKg5EBUh4VIgqLnex36U+WN2+tff OXu6X7+Z7XDJmHNHp0qlcrH+ZNWd9KezynTJT6vuW61U/qJ631KpXC1dX8nL VQZKv171NNW/VBmg8rx+D1b9oeqXVW9RuVKlq8buq74nqVykcer0e4jqhaoX qPSR/tsl2g9Q3+bSNVMZJ/0J0reV/nj66ffJHqdUQ+6m8k/Ju1VOlLyv+u+j UqP+26TbqlJrPWOOlFzj7+Mlt9S4N6s+V7oXVIap/Seqs/SjpV8heXY19jOC vUp/u+q3pV+lMkry31W3ln6K2p8n+QPJ70l/ieQFKqdIXqP6ZvYm+UJ9v60a bWpUFkt/tup3Vf9XZZDGOV31Eq3xqRS/K+pzvqr3pN/MnCpn6HdV+iGqz2B/ OfbWQn1+Jf1itXkix3oXqfR2v8XuuyHF2s6T/C+1O0v1H3K0fb8aa7w0x/gn e/yWPpd2qnfpe2fJB+ewP+eypxrnwVm8nULuKjmpTVXl3/r9pfseKf3nkj/z ON+SvFPycZIPsr6Tx7kqxRwPaF8nqH5cY70u3WspzmCEfW2a2h+h+lqVxkro 2lt/kPqMlnxfJebf6Xl7sHaVMyU3qF6gUqeyyz7UUfoDLNfa5/CrY1WPVN1K 3+6UfKbqNilip0UOeYbk/VXvZ7lVjt8zK/G9tdsPdJu5ks9K0Yb257rNo5WY p6XnulXzPlmNtR8o3Q6vs4Pk7ZK7V0K33Xr64Bd3EeOqP5W+nthxX+yMvT+1 zHn9pxqxuSzFuQ2VvFz2n6nfy1Rvcmw0k/xoCr88237WJYePHaX6aJWelYih K31WH6j9euJc8uAS9U059tPF7Tuo/qZKH7V7zLFDDL3hM2+oRNuj3H5Wirke UL0lRfywnp457PWK5ume44x/Lfl9xyYxusGxRgzQnzHZR88Sduil+mKVnvo2 QvXh1bDp/ZXwM2Rsy95Y8y2SX8vhh2tL4M3llcCc54yr4OtvVcZLfqcEjjYa S1sYnxhzk+TVKfDod17/btWnS3eayoRK+EhH4/b11ZAfkXyL9LX0SWFH1ja1 EnUHr/MRtf9ONTCVdh1s84kp8gHjoat1e8bu5LkmpcgXtOXsNhlb8BcwH595 Xd/H6veEEnHWzLF2Yw78O1D6b5Twpbaqh2msoSrzKp4zxbxd7B+MP19yL8kz 1f4nKhepzRzpjlPpjt+WGPsE48l+JewxOMcaN3sc/GST17PImMe5T8mBG+3V r8G+tzDHupirqRK42tvj4DPIPYhZYyq5Zq30a1Jg92iN9V1iXPU/NNZD5BXJ T6bIhcxBPgRHsR15ktxJnjvWOQ/seUV9x5A/GM85uLfkP+aw7Y8dE13swwON e9erzRs+iw8lL5PcX/LDkj/O4Xtv2cd7qPTTXG9KP458IH1TibOYr/pVr+FT 1RulW45d1H6b6hUp+MHxzt135IiPRscLazncOExsECODcuDfQGMd9SDL4DS5 Aby6uYR+Fv4tuTd4yH5TtOGsiXPi/S3pm3Lkn+HGAPwHrGBNrG1JDlt39X6/ wr8U+DlQ43Qm36nNgBRYzHrAVHCWM+mYQqb9eV7/XOeROuc4vte6/ViNlYgN 8rl0g90evzjdsYz/VqvBd2bAu6SfVmKMXs475+t3F9oThynGIqeMgcSpzXTn yaNsk8Fug9+CfawPLOlrXAATLvR6aNMthf/he3uxo8E4gx2IpXNS9J/q3N3F Z8R8dY6FTbY58cX+zlC52/aus82v9Zp/UML2nW1PMKmb1wB346yJqdNsK8bp WKJdJ+P0cSly1qgUHIJcNkPjH6ZvY9Wmvhr272FsPc3j3Khv/SU3qu3kFLkc fLukxBpnl7DNBT6vW0vIDxoThhivpqXI3zPs6+jx8zEl9j4pR3zuzXtwQeK+ wXF5ju05MkVeJ3f387p7wLNS+Cj2GV5iv7fnOJP+Phc4wqkpeNoQYylnenUJ /b05bNrVWEOs93T8TrBNyD1bjTe1KofkaHeRz7beNmxtvMVX7yNHcNaa58gU Z4k/XJmCL8K19qSIZ/RfpBgLf9mZIq7w7X5eE3HZxz4Gt57uc3y2BBZy7uxj lb6vTMHd2zqPbDIukkvJu6UE1oF55Fpy7vk5MBQcA886VuMMOTfaguP4G9gH Bv7GuYh44J6DjRh/gnG+recammOcz8yXyeV32q74HP3w5f4e/1XVc6qRZ59N wbcGOoe39pludm6FzzyVI59wJxjis8XHyCXklC3mSH1sN/Y13nt7vBp7AYs3 5+BRq5yLmJ+54AbwGPyQ+wxjjCuRM/DR133OY92G3E9fOPa7ktel4DdLVT+T wo4tc+APsbPGcUyObG7cB1c5N+Yl1x/hNWIvcgU8Gxsy3wjP9dccYy81R5lj fRPzpPh9Uo77E/eqP+Xgd9x1+uW493H/+1htP0rBBV9KwbX4tiEH5+IuuLAa +YPcscnn3+Dzb7QPkDuR+6jfmTnuy9ybW1hPjh2e4i5BTGHzV233jTn8lzso dy3uXNztBtjPLjcG1jiu8bVp9rc/54jLS9V+e4o9MBZ3OO6YcJo5KfgNnGVX jrz0nH1/bjXsTW6BCxJbw5036+EI9oHWxli4Hesjz5P32effcvgsbwMXmOcd b8yAAxLLl5lbwKNnp7Ap9txof7mmEnzlNmPLYq8XftW5xNouNt7Mc46ES8yv ho/AIx633+DjTdXwfcaqMc7Ue1/nlOD7+Cf+wVicCXg3Ogdefb9EroDbMcZX d7IUefaGHLm/v9osLaF/psR9arex7KocZ30WeJMDEweVuFeCd+ArZ8BZDNP3 a3LkrO+R83Pg/9mSV5S4r3Yr4dO19vMxPjfyF7E13bkGLgmn5A6+NcU9i72i X5Qi3zytsaZKXqK6fY5vtF/kduTQ+Y47bAv+H5rjLMmj4DB4TF/u//DWdjnu 2Kzj6RT3L2yLnjsjfe5zPIOx3B0mO89OzZFzry1xn+jkOwW+UOMzJH/em2K/ veyn5IspKcaCr7cqcc88oMTbBz70ZQ5/OcY+073EO0W935TQce4T9fsy5igx FvNj50Ul5l2Z446JP+zlcNTXSf/DEm8dx6qeB/9Umxel/5G56SzpHiuBG7yT scdJ3jt2vjuFrckv9AUDF5TQPwHvzsGFGkvkvDr7Km8iyORx3lW+cH7lXYVc C1c4pcSb2qmqJ+bgmtxFwH7eT8Cxu4254C0+Bn7iZ+yXWCVOyUvkJ2L/wRTf 2D9r4H2GdYCdrxk/eVe4PAUH4+0E28JntjnH4UuTS7SZVMK/8LPljsul9p+V xhnwAexZ5jXAi5mXeP4Kj72XK1JwDvgGY4/yGqaW+DZF9cM5/OGuEud/jOP/ Hv0egY+VeA+5wuPw3gSfPlj6X1jmLapNjnXAX+/nLkMMmqsj46c/z8FV4Kqz zSeHltjTIcbuucYvzoCcxv7Jj8+lwNmrzROa0td5rtb5lziGd8I5n3Gs0/do 3wPx162OR3j4TGMFONZofkA8gpfjjZnjS/CuG0q8U031+FvMFfaXfnUJP7ki x1yz7Cfsa6XXvN0yZ7cjRW5CxhdG2h9eth6fwQdW2Deo2f9A52XaDbPPrvZZ M+Y6/yaeJhoHlhiD4Eu8IfB+0eC1rPLaHi2Rt5ur3plj77zH4Ktg9FbnGfwB /+Cs4bv4HLyFNz5s8lCON4s7SnCZ5tavt92x+c+c++8s8Q4KXpITyXvrbJMH fM+8yTZvYf7HeW7wmc7Occ9sKDE+72hwmp/muL/d5nslfIvz3c8yvAseyvvw eHMJ3sThEzXWwyvIra19HyPftTI/+yRFnsZXwZc9jgv6NXNf5mnleXmDZ3x4 3rwcuX6P94N94Hsv5cDup5yL13svH3q/yNT8hpvhD4caH+Bgm1PEPTz3DfsD 7T6yrd60Hu7S3pg+2dyYb6PsJ1t8Rqs9LuPwtgqH4e660fvnrDa7DbwQvOdt ei/+YTcwEPlt4xL3gndS8LWFWsM9kp8scf9b73lZLxwUTg83/sjyctv8Gq9t nedlDN6CyRfYmT7wQL6/6zbMufb/xlzrNoy9xvZ52HeiZ+2P5HHyCHcWzrSF deT4E43/2xyP7Plg64nN7V7n73PkhudLxNp2x+wK9yWulzguOPNxxptGj7Mj fb3XHR4TPgWvAsPhVTuNG9yHtjjGyXO73Ya9EGN97KvkRLgfPvu5/faFEpz4 xRI5hfbEIzyLPEq/OveFnzHnLs+7Lsd79yr13eo8Qj6h7Zduv93r4Z4OXvG/ ir05kb3wfav3Aofh7YOYG2OMaeMY5H8R9V4LuN3O/ApsaO54git19B2oQwnO z/+g4N6HmLPB6dr7nMlXbYxj8Gc422G2ITbibg7P538A2AY8gbc1VeK+Sa6Y V4lYb2l8wH7JNse/9nHsf2Yuwtg11uOHzNnO87IffjPHtBzvJ9dp/f8D+z1E Zg== "]], PolygonBox[CompressedData[" 1:eJwtlnuQjmUYxr/vfZ5mrDJSqqmmP8ihRoUQiqRYdLDabbEOS06hza7TioqU nMkpRLOndLAOaROlmkkUWU2lmGwJZcpQSRLVTNPvmssf9zf3772f732f973v +7qfBkMKs8ckqVRqKxaxp/npz4Vy/AEhlfoK3gk/CWfBR+DjcD58CD4Kn4DH wD/Cf2FPpVOpJvBZ/P3EPyU+kmsn4O/hY3B34vvhGrgG7kl8K7wGngrnwS/j 19fz8Vdgl+P3xpoQb83/b2f9FPghuBTup2fDfeAyuD/8FrwYXgx/DNeDV3Ov EvhS/BzseuKt4PbEN8Lz4WtYMx8+CH8D9yW+Ee4ILyR2GdYBPyN6b1OJ18I/ jP3E+s5c+471h+Af4Az4a7gM7gpPYn0p/jpsFjyC+Gt6F6xR4ncsZn07+D9i A1g/DJ4BDybWEM6HB8At4JbYVPgjbB7+XGwDsezg3CkHysVxeFDaOVVue8IF xLfD++ApwXvthPWDP4RnE8vECqO/qb5tP3gavArOxx+ITYfzgr9tDjwZXhbs Z2OPw+fhSuUT/jw4Z8pdb9UY8fHYGOLXYYuCa1C1qGc8Q2xl8LPz9L7wUThX tQN/BmcGf6vN8F64L1xMvBwbjf+7aiztZ+TiHwuuNdWwallrtHYiVo2/FHsV vwjbjf+w3g+/HbYG/9vg2lDOlfuR+j7ECrFP8JcHv1su9gTxU3AFsVZYG/VD cO2rp9RbvYOfpXvoXme0PuV3+BP/AWwZ/jbsItafDO6tav5/AN4S3IvqKfXW v/AG4iexm+A3gmtXNa3afhYblPgZA4k9BzcjPpRrs/BnRvvas/b+DrwcXol9 AZ/GZqZcI+uJ7YOnp/3MD+CxcILfGsuCb8XO4Z/XN1CvYaP47xm4BX4Z60cn vrYguqZUW9rjamLN4dOsvQIugL/EpqVdUxX42cQbJO7pCXAfuCn+jao/+By2 Nm2NaoWfSbye9AhbGLwH7eVK+DG4JXwWvkoaB1fpGXAOFonVju5l5eTi6Bwr 19KgFcE1qlqVpr4ID4Xb4mfxnyHRPa/en8m1tfivR/uqUdXq+3q/xJpYHq0x 0hpdq8CfjQ3HP8X6W9Rv0VrQHn6F/24O3mtHrpUQW4IVST+w8cSel+gTL4AX 4U+QJuD3kmYG96x6Vxr7Arw4uvZ1j6X4pao//EewefDcaF85Uq5KiI9IvMc5 0XvUXuvDjxKbBI+DG2JLNG+CZ4M0U9rZRf0DV8HVun+wFnVRfvT+8N34l2D3 wPfDE1O+VhA9IzQrbpCG635YncSaW6ncBcdUI6qVGcHfKgO7C+6gnuP/beDB 8LDo2accDsf/h/h69r4Kfi+6J9WbmhFdtTfVSOI9aW9/Y+vSrtm26pXge0vj 26rWo2tDmp6vXouu3Q6aD8EzSrOqsTSF2MRgXzNDs0M5VW7VA+oFabq0XZr/ M7E/grVJPavelWZKOzUDugV/Y31r1fjhYA2Vlkpjj8ALo2ulOevL4T3wpsQ5 6gTXhXelPWN/0beP7vU7ibeDu8J1Wd9N/a1vG62FqsEieJdmIrHamv/w1dHv XgubDO+G1yfOiXLzbrQWSWPGwdtVk3APaZD6Jzo30qRt+NdK4/F/xW7WvA32 dcaoIy0MF84u8JboGatZK038Tf0UPZt0pnhbeootSPxOejfNXM1enSF2BNeQ akk10Dm6ZlW7OgPM0awOPmtoT9qbali1rBpVrXaP7hXN/JdYu1M5SPzMTcRq gt9VOVAuKoO/nb6RvpVmrGatNFvarTOQzkKL4CppQXQudGbQ2WFv8OzTzNPs ezN6rTRSWnkg+GyjM5zOcj2itVg9rl6/L1orpeHS8rHxgrYm3vsQzUydjdTz ql/NgJQ1p1j3is611jSInlmaXSewZvi9omtPM+TB6HvoXupB9eK90dotTZI2 3RY8e6T50v5G0dqoGd44uiZVm6o51V63aC0q0/eI1kBpoTRqD/4OrpUnrnHV uvakvanmDhK/I/jspB5Xr48Kvpf+o/9qRmtW651ziP0PVHhuAg== "]]}]}, { EdgeForm[], GrayLevel[0.65], GraphicsGroupBox[{ PolygonBox[CompressedData[" 1:eJxFV1lsVmUQvfd+U6uhEiqtaHDDKInbi4nGQqyUFrGFRMAihqCGyqJSQCig xUTaUsBQMQLGiBiIiuKSugAKIpvbQ4uBBDAICfviAz7w7EI8x3OID39n7nxz vzvfzJkzXwc1zR43q8iybHCeZQnyJ/xphj4nsmwv5EEsjuQa7A2QUzOtH4K9 HXod/K7Dcy1kO2w35LKPwPNA6F9A7sult2XS+S6fa7HnFNimw6cPno/jNwbP kyCvwVoFZCl+p+kL+xDIfrCXQM7GHuXQGTxt5bafd8wj4H8W+im/e9lvOPRl uc4yHvr40L5VSevU6dMF+Sh+T0Mf6jgux3CFfVbZZ3am71woZK/APjugd2Sy /YHf1bnioU7fvj5XHXzqC/m3+OzM57xM3xnq73J9p32eCNmH4DuVSd9j7g/C Vuk814TyNwxyO9YHQF8G2Rs6/1Ho5xwT92csZ6y3Fso/Y6iGbIS8BLmmUH6Z W8rf8/+xEa5lSaFn4oR1Lk3CQw++243nz4yjZP95rts4rN+N31Lof8F2NNe7 xMt/mHI+d+XKO3NOe7Pxtg/vroT+SFJNVtrn4ZDfV5ArIL81hq/E8x7oV0HW uXasUYvjWRU6H8/J89+O55PQb4MsM1ZrMskT1ucU0vvYdtL2waHcvhvKa39j e0PS+uSkOnQ7hoWFfFqNiwrXb0ESJhpDGP/bOB/uGt3o75xzHYk17ss9Owph hXls954dxmql99+E92dC/oznm6AfZu5DtWW9WNuZrgffPZCrPm2udbP7nPX9 xf01A/JX+zCeC8Y/sXoIv0WwTzMWyAesOffa5/cO+t1FSee4D/FsxG8JsQR5 KoSHs9wjV0z0JwYmuH9vCeHpZsgWvLfffVfp/RlDqTFHLK0nxnPVeWAozi6s 74e+mvmFXgt5b6FaH0nC7V2hHmXNmM9e/Hq8/1Mh/ydDXMkefxXvdRbqf/Za mb9LDmRtWWNy8CtJ35kU4rEy44o5PuI8V+eqF3FwxDrtR0N5+AZ7NDmeibly sNd5qDVmx5iv2A/kMfbQTvfR5Fy9R/2lJD6cEMLjKfvfGsJjU1JflHlP8sba QjHeH+K+XSFuYh90wv9Yrn5kbMQ9scY+Zb1Z93dC3FfiXDFP5d4/L8TXfJ6e 5DcKcjme+xkDJ0PPx2Gv9l70Zb+Q+xjTA4VywXdWh2bMStY0FP+BpDPz7C9C /wB6G+wb2CMhbtkE+3fOw5/Qt5qLiEdiZ7fPSF5Z45xMC53nwaT1Jvu8b44i VzWHZsNo+MwtFHOp95zoujAW1miy63zMa3vw7jqs/cYeCuGxMYmLuCfzSa5b 51l/mLiE/CephrM81wb5TlAJ2WDcLs6U16pCeZtfCOfM49SQfUpordb1WpHE E7wj8HudxiHnGefa454Dm4zhzbm4m7xS5Rn9WGjms0Ylrn2Xa01uYF+xx74m P0DfcrkP7EOOfbkQ/tYak23Yb13IvgX6Rs5A6G/B9noS73GeNBr38z3n6z2b Rjufw7HeFMLTM5C7Q2c87Jla7zl+ybxN/qbvfOeN9yHyYUNSvMsdM+f2ZueB uVnm/PR3Ldk3U5P6YUiIj5hHztVRhfJA/uZsOu6evzbEIwMgtybliJzRmsQR 10M/kZT/vSFMj3CvvxfC2o4kHC91PMR2tblodCGsECfMb19zwE7z9SFjaZRz uKAQ/9a5nlvdO+QY5pxz7e2Q35pQLhv8LvuC/E4svZH07ZZQr8y1/XRSP/K+ wNrSXmouHGkcMp8LjY1OcwPtxBp7oSYT9vr5/sm7CzHIuwPPw3ONTeLv896X eFziPPS4xxcZC522HzNnkavIc63mOs5j1m0z7J96dvDezfnFb3HePZSEh173 GvuMmBmftPfFpDoTO8TWRPMEeaN/aFZVQP6QNLNf8Pyvdk3LjUHu2+E7RHsm TBAbdXivKuTXkVRPxsy6NLh/yeesKzmFfLIk6QzPhvIxrNAz91/sGcq7SYf1 ezyLOZO35cIqsdEdipmxPx/a5znIT3xP2Og73np/e3uIe3dAvpZ0z/jcMVS6 Lgudf8a8x7lizr4M3XP4fxJn42rn8EzSPYT3kYvO+cchnpvhGo015mfA/lHo jLuT6tnpWnOPbT5XfdL+vHf86LrwrB8m3TXfhH5HiLvvhOxO4oXvof8LxqqO Ew== "]], PolygonBox[CompressedData[" 1:eJwllNlv1VUUhe+5Z0NjTImg1aogEFDBxBefBGOxBCIID0RkeKgaStugVSsU UDFRmQ2jlEEGKVEKFhTpxFQthfhUNWhQApIwhEIIf4MS+FbWw2rX2vvc89vT 2SOrG17/oFgoFOpBgFb+rOT/aoyH4B/lQqEkFQovgbnoX8AufJM58zN8O1iE Xgy+5+xR9JOcfQ+0we9iWwV/FfwI/xrbEs6+CKrha7E9hm8l+iC6HQxFvw86 4FtAA75KvtfC2SfQf+O7ADagO8NnmzhzDF6JbT06oWvh/4E16ArQCv8p/Nt6 8Cu6Hl0DLwOr0V3hu6aBzegy9O/4/gSfoCeBjfBOcA9eg38cZ9ehr6AXoV9D TwdfKTf0H/hqiL8OPT+c+0PY5sFXYXsE/im2ZvRZ0JEc87/4vgTl8CrVFN8U 9Bb4CVBEvw0mwUvB5/gug7XJdxyBPx6OvVH1Rfdm13oFOICvK/vby8Be9QY9 kPPjwaxwDVQL5aTcGsO5aUZmZfdUvW3GdgbfpWx+G9tY9MzwXQPAYnwLw7Oh Hm9CfxjuhWZmI/oG+v+iaz4S3g8C+wBsE+Dn4XuKnsFu9Hr0P8kzodl4E7xQ 9Ixp1haEe/sK+t1wjsq1lvg7+e03+gb+QWAP/J3w2Wr0jOweq9eq6QT1Cv9v qjW/L4X/EH4r6tHh8Ixp1hRjT7jGqvVyzp/G1wFOJc/QtewYFItswdk16PPJ M9mT3WP1WjOt2T4FTsO/4M794ZqoNo3cUQEvAb34t4KpnD0JepJn5obmD+yD L+U3Ozn7RnYuquHz6N3hWTqCvw/ehv84eo5mTt8O10pvuhd+E1tTckyKLWM7 mWzrz545zZ52hHZFFXgwuUdvaZeEY9EMa5avZr8lfVPfflbfgN8CY+AvY5uc nINyGYXtGnyh5lmxZseqGBTLUHBRbwm045sd3mWa8aXoFWBw8p11+B4AZ/SW wZ3sN6q3qjdeq/0nW/KZ78IzollRzVX7cnAuuYfq5a3sXqgH6kVT+C1op22F DwF9yTP1sOYpu/bKWblvC+emmn2GryXce8XwLbw6exfrzejtTAzvCu2USu2i 7N2jN6S3tCO8q7XTP87OUbkqBsUyL3vXXkc/jR4OLifnpNzUA/VCb+YZ+Ojw WfVAvVCNVWvtvEfRf4Geond8N77x4d2smo+DD9MMFtyjp8JvRG9F3xyBnpY9 27pDdz0X3i36TQW++7Fi1mc= "]]}]}, { EdgeForm[], GrayLevel[0.5], GraphicsGroupBox[{ PolygonBox[CompressedData[" 1:eJxNVD1rk1EUfnPvpfkBhejg52LV1S4WfUuXEnVJmkXoYJuQ4gdoFaRugpC6 +AP8iJPoooPZYoqtSyoOgmNmRXDwD8Q0qc/D8xQ6HM55z3vu+XjOc+/p+r2l uyHLsoeQCDmVsqxZyLJtfKxBvsM+At8C7BX8D7Cn4XsAuwF5hMOzkEX4/kIG kGeQHnzXoVcRU7aP8fTnkHXYOXL+gf88cvbhuw9fHXIl6n8R/p9RebrQHyAf YY+jcjCWZ7qO+X0QG1SXMioobgLfP9hLsPcK8tNmnp7jt2yzdgO5qtCXoS/4 LL85657PDgsERP6m8SFOrP8p6HvofmgTYJ6tZcq3EZTnrTHfwf9vSf6X0I9d +0YSfhVjyJ7n3ecyfG+8D844Dqq5H4Q7Z3+XFPM06lxurBk78o4yn1vxDnJj UoXvUtDOyQn2yX7XIHPwP4HvYtL5Oehd6K/O30Jsp6A+viTtjjvMYU9xhqT9 M4ax8/b/gv6MuBJ8m955y7gteHbmn0ni3fvo+t4Fcd73bB1zjz0QR+L5IgnH vjn0OqhGzfwmBmXX5zzEtmt8Vn1u1z2cSOL8cehO1I6OwT7r3ma8u4H3t+m8 R5NwYh5+kxfse+Td0WY/z5Nmahz6Vzd/Ju6p47t3MCdjeLaUVLPje1x1D+eS 8BzA/yOJQ9eiONI0l1rOVTMfyuZexXlok+Nd3/MNvwHkcytqT3eQ+1XSG9H2 u7Hs/BXf8VtJPJz4LpwxbsSPs43NQ8479B0c2c/4dtD+2GcxKD/r7CT9207q d/rQ+7PlnrkDYn8S+nYSr24m8Y88vBqli+Y/d9R2Le6tb25QMzdt9j7r3npR /RBj3qEp5+G7tm4MiHfJmPwHr+ar3w== "]], PolygonBox[CompressedData[" 1:eJwlkblKg1EQhW/+O+gbJI1roWBaFzBoRAVxaYyVYCNCEhdwqbRThOAruCSV YGOjpYqJnViojaC1IFj4BKLid5jiwDl3ljtzpnNpY249CSF0AQPjMYRCKoRH UIJ/gHn4DUnPJCyDEfgoqBCbBIvUXaGfiK2AzeA5q/AZ4gvUN6PL8D3QB98B J+Y1qtUf78Re0PfoNPoWXUYPJT6TZqujq+gf9AB8WPPyX463EvoAfZryP6/h v+ACXiP+Cm8hZxb9BtrgGfNd9ZaGt5v/XQQd8AI1W8Fn0myt5rVfYIxYFv0J r4Ae8x21axPIo9fMvVIP9crJ5MQ9K6L70d/U9vK2i66aezPBWzd8OnoveSAv GtFn045n6P3ou6vnoGaLPot2vkOfR++1TfzB/Ia6pW40Fd1Deamb6DbaSbtd gr/onsk7ed4g98h8Vnl+CD82760dtItqVCtP8vB/O7hJZQ== "]]}]}}, {{}, TagBox[ TooltipBox[{ Directive[ Opacity[0.5], CapForm["Butt"], Thickness[0.03], GrayLevel[0.3]], LineBox[CompressedData[" 1:eJwl0L9KQnEcxuFfKnQH1lBqi/3ZCwwqrKVysW5AUNJKqEsogvIW0lxzsaUt AqvVJRsCnY0uQ+k5NDx8vq9wOAeXypfHFzMhhCJX8RDWEyG06NO1l3WFLG0e uPb7hr7xztCe8MSHPdJVXWPbPWWXRTvFAs92UueouveouQ/1R3eiZ6nZBb7c r9qgE71XH6Nv0IymmefO7vHtPtK63uqpnnNG0Z7VAx3or74w5tPe15jm9YRN d44bd1W3tMK9u0kp8f/f/QHp9yfE "]]}, "0.09`"], Annotation[#, 0.09, "Tooltip"]& ], TagBox[ TooltipBox[{ Directive[ Opacity[0.5], CapForm["Butt"], Thickness[0.03], GrayLevel[0.3]], LineBox[CompressedData[" 1:eJwl0rdPVWEYwOFDuAMOLJRBmACZwD8AFukTE9UBSUDcCEiHhYmBBLgURSZN EJBiUFGEUBxoi2xumkhf3JhhkOcLwy/P+96Sc/Kdk9XcXtWWEEVRXAOJUZQe i6IsXajZns0b7nBX2/pl/8El3irfnKff5j2u8o9+mp/wv8p0bD/lV8XM3/iW 63zHIRaxWCWqtLdymKUs04o+alHL4R507ftadZkn2MlxrvN7uI6OwnW5pgrF 7R085Bd+VrnG7C+5z1Ue8BNHmcERZvKxaszJTFOqUvTPZw+YpPfhzOyznNec tuxXOjdfMpEb4by4Ge5FJzqz/2ULX6hJz8Mz0YyeqVENGvS711xgDh8pN/zH Pq9J8ytNqctexW5WsyCcHXtYw17WsZBP2cd69vNNOHdO8wMfxu7fmTsx7U1A "]]}, "0.05`"], Annotation[#, 0.05, "Tooltip"]& ], TagBox[ TooltipBox[{ Directive[ Opacity[0.5], CapForm["Butt"], Thickness[0.03], GrayLevel[0.3]], LineBox[CompressedData[" 1:eJwl08lvTWEYwOHTlC4Qw5/gHzC1RYLEhio6qKS0RUUsTL0XqakUVVNRQ001 tDWsiLBSxMZctIaWqqalJIIYKsaSWHi+WPzyvO/JvTf3fOfewfPiObGEKIra VJMYRVt6RdE2bdUXe6oyzEs5lXHu4V7t1kn7XKUo2z5NI5SsFI12vYGnuU4F 5lKe1V3z2PB+lnFUeL3mm8crwTyRTXzFi3qtB/Y0JjGTLXzHq3qvJ/Ys9mMO W/mB1/VRz+zT2Z8zuIIrVax99vt6Y77HVaxiGm9oofkmB+iT+RY/cxAHKs+8 hge4mgdZwvxw/zzEtTzMUhZwA6u5nke4kbO4iUdZxmMs52x+149wDnru2kwu UqM67U3cH85XbfZcLtAdddgb+E1fdc5+jXHuYBErGOMJ1amP+uqt683MYG9O 4LjwDDVG5fYLajefZ8QOdrGTZ8LZ66W9lX/Ds9IL+9NwLsozlzA/fCb/hN9F +O72Zp7Sbz3SZdce8wqXM5PLmMWR7FGybtsf8pd+6pK9kPUcrph5SrhfVnKX hqnIPjmcAXeG89FQLbGns5YV3K4hWmyfxOPczDnsTvz/v/oHtCx55Q== "]]}, "0.01`"], Annotation[#, 0.01, "Tooltip"]& ], {}, {}}}], {}}, {{}, {{{ Directive[ AbsoluteThickness[1.6], RGBColor[0, 0, NCache[ Rational[2, 3], 0.6666666666666666]], PointSize[0.08]], PointBox[{{4.905308194867242, 2.6308363915989275`}, { 0.20100883034436162`, 0.023821365695765692`}, { 1.0719666600928879`, -1.2506326268721977`}, { 3.0527398070698992`, 0.38721583002375826`}, {-1.8438997564108928`, \ -1.5026726898055591`}, {-2.913246104009823, -1.3391199692975575`}, { 0.3149621009629985, 1.9477027131642348`}, {-0.9556834229157016, \ -0.4484871006542206}, {3.2651237363484125`, 0.2704700013829126}}]}}}, {{}, {}}}, {{}, {{{ Directive[ AbsoluteThickness[1.6], RGBColor[ NCache[ Rational[2, 3], 0.6666666666666666], 0, 0], PointSize[0.08]], PointBox[{{-2.4, 2.7}, {-2.4, 2.7}}]}}}, {{}, {}}}}, { FrameStyle -> Directive[ Thickness[Tiny], GrayLevel[0.7]], Axes -> False, AspectRatio -> 1, ImageSize -> Dynamic[{ Automatic, 3.5 (CurrentValue["FontCapHeight"]/AbsoluteCurrentValue[ Magnification])}], Frame -> True, FrameTicks -> None, FrameStyle -> Directive[ Opacity[0.5], Thickness[Tiny], RGBColor[0.368417, 0.506779, 0.709798]], DisplayFunction -> Identity, DisplayFunction -> Identity, Ticks -> {Automatic, Automatic}, AxesOrigin -> {0., 0.}, FrameTicks -> {{Automatic, Automatic}, {Automatic, Automatic}}, GridLines -> {None, None}, AxesLabel -> {None, None}, FrameLabel -> {{None, None}, {None, None}}, DisplayFunction -> Identity, AspectRatio -> 1, AxesLabel -> {None, None}, DisplayFunction :> Identity, Frame -> True, FrameLabel -> {{None, None}, {None, None}}, FrameTicks -> {{Automatic, Automatic}, {Automatic, Automatic}}, GridLinesStyle -> Directive[ GrayLevel[0.5, 0.4]], Method -> { "DefaultBoundaryStyle" -> Automatic, "DefaultGraphicsInteraction" -> { "Version" -> 1.2, "TrackMousePosition" -> {True, False}, "Effects" -> { "Highlight" -> {"ratio" -> 2}, "HighlightPoint" -> {"ratio" -> 2}, "Droplines" -> { "freeformCursorMode" -> True, "placement" -> {"x" -> "All", "y" -> "None"}}}}, "GridLinesInFront" -> True}, PlotRange -> {{-3.4, 3.5}, {-3., 4}}, PlotRangeClipping -> True, PlotRangePadding -> {{ Scaled[0.02], Scaled[0.02]}, { Scaled[0.02], Scaled[0.02]}}, Ticks -> {Automatic, Automatic}}], GridBox[{{ RowBox[{ TagBox["\"Input type: \"", "SummaryItemAnnotation"], "\[InvisibleSpace]", TagBox[ TagBox[ TooltipBox[ TemplateBox[{"\"Mixed\"", StyleBox[ TemplateBox[{"\" (number: \"", "7", "\")\""}, "RowDefault"], GrayLevel[0.5], StripOnInput -> False]}, "RowDefault"], TagBox[ RowBox[{"{", RowBox[{ "\"Numerical\"", ",", "\"Numerical\"", ",", "\"Nominal\"", ",", "\"Nominal\"", ",", "\"Nominal\"", ",", "\"Numerical\"", ",", "\"Numerical\""}], "}"}], Short[#, 10]& ]], Annotation[#, Short[{"Numerical", "Numerical", "Nominal", "Nominal", "Nominal", "Numerical", "Numerical"}, 10], "Tooltip"]& ], "SummaryItem"]}]}, { RowBox[{ TagBox["\"False positive rate: \"", "SummaryItemAnnotation"], "\[InvisibleSpace]", TagBox["0.001`", "SummaryItem"]}]}, { RowBox[{ TagBox["\"Distribution method: \"", "SummaryItemAnnotation"], "\[InvisibleSpace]", TagBox["\"Multinormal\"", "SummaryItem"]}]}, { RowBox[{ TagBox[ "\"Number of training examples: \"", "SummaryItemAnnotation"], "\[InvisibleSpace]", TagBox["429", "SummaryItem"]}]}}, GridBoxAlignment -> { "Columns" -> {{Left}}, "Rows" -> {{Automatic}}}, AutoDelete -> False, GridBoxItemSize -> { "Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}, GridBoxSpacings -> {"Columns" -> {{2}}, "Rows" -> {{Automatic}}}, BaseStyle -> { ShowStringCharacters -> False, NumberMarks -> False, PrintPrecision -> 3, ShowSyntaxStyles -> False}]}}, GridBoxAlignment -> {"Columns" -> {{Left}}, "Rows" -> {{Top}}}, AutoDelete -> False, GridBoxItemSize -> { "Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}, BaselinePosition -> {1, 1}]}, Dynamic[Typeset`open$$], ImageSize -> Automatic]}, "SummaryPanel"], DynamicModuleValues:>{}], "]"}], AnomalyDetectorFunction[ Association["Preprocessor" -> MachineLearning`MLProcessor["ToMLDataset", Association[ "Input" -> Association[ "age" -> Association["Type" -> "Numerical"], "educ" -> Association["Type" -> "Numerical"], "race" -> Association["Type" -> "Nominal"], "married" -> Association["Type" -> "Nominal"], "nodegree" -> Association["Type" -> "Nominal"], "re74" -> Association["Type" -> "Numerical"], "re75" -> Association["Type" -> "Numerical"]], "Output" -> Association[ "f1" -> Association["Type" -> "Numerical", "Weight" -> 1], "f2" -> Association["Type" -> "Numerical", "Weight" -> 1], "f3" -> Association["Type" -> "Nominal", "Weight" -> 1], "f4" -> Association["Type" -> "Nominal", "Weight" -> 1], "f5" -> Association["Type" -> "Nominal", "Weight" -> 1], "f6" -> Association["Type" -> "Numerical", "Weight" -> 1], "f7" -> Association["Type" -> "Numerical", "Weight" -> 1]], "Preprocessor" -> MachineLearning`MLProcessor["Sequence", Association["Processors" -> { MachineLearning`MLProcessor["FromNamedFeatures", Association[ "FeatureNames" -> { "age", "educ", "race", "married", "nodegree", "re74", "re75"}]], MachineLearning`MLProcessor["Transpose", Association["FeatureNumber" -> 7]], MachineLearning`MLProcessor["WrapMLDataset", Association[ "FeatureTypes" -> { "Numerical", "Numerical", "Nominal", "Nominal", "Nominal", "Numerical", "Numerical"}, "FeatureKeys" -> {"f1", "f2", "f3", "f4", "f5", "f6", "f7"}, "FeatureWeights" -> Automatic, "ExampleWeights" -> Automatic, "RawExample" -> Missing["KeyAbsent", "RawExample"], "StructurePreserving" -> False]]}]], "ScalarFeature" -> False, "Invertibility" -> "Perfect", "StructurePreserving" -> False, "Missing" -> "Allowed"]], "Processor" -> MachineLearning`MLProcessor["Identity"], "Distribution" -> LearnedDistribution[ Association[ "ExampleNumber" -> 429, "Preprocessor" -> MachineLearning`MLProcessor["ToMLDataset", Association[ "Input" -> Association[ "f1" -> Association["Type" -> "Numerical"], "f2" -> Association["Type" -> "Numerical"], "f3" -> Association["Type" -> "Nominal"], "f4" -> Association["Type" -> "Nominal"], "f5" -> Association["Type" -> "Nominal"], "f6" -> Association["Type" -> "Numerical"], "f7" -> Association["Type" -> "Numerical"]], "Output" -> Association[ "f1" -> Association["Type" -> "Numerical", "Weight" -> 1], "f2" -> Association["Type" -> "Numerical", "Weight" -> 1], "f3" -> Association["Type" -> "Nominal", "Weight" -> 1], "f4" -> Association["Type" -> "Nominal", "Weight" -> 1], "f5" -> Association["Type" -> "Nominal", "Weight" -> 1], "f6" -> Association["Type" -> "Numerical", "Weight" -> 1], "f7" -> Association["Type" -> "Numerical", "Weight" -> 1]], "Preprocessor" -> MachineLearning`MLProcessor["Identity"], "ScalarFeature" -> False, "Invertibility" -> "Perfect", "StructurePreserving" -> False, "Missing" -> "Allowed"]], "Processor" -> MachineLearning`MLProcessor["Sequence", Association[ "Input" -> Association[ "f1" -> Association["Type" -> "Numerical", "Weight" -> 1], "f2" -> Association["Type" -> "Numerical", "Weight" -> 1], "f6" -> Association["Type" -> "Numerical", "Weight" -> 1], "f7" -> Association["Type" -> "Numerical", "Weight" -> 1], "f3" -> Association["Type" -> "Nominal", "Weight" -> 1], "f4" -> Association["Type" -> "Nominal", "Weight" -> 1], "f5" -> Association["Type" -> "Nominal", "Weight" -> 1]], "Output" -> Association[ "((f3f4f5)(f1f2f6f7))" -> Association[ "Weight" -> {1., 1., 1., 1., 1., 1., 1.}, "Type" -> "NumericalVector"]], "Processors" -> { MachineLearning`MLProcessor["Threads", Association[ "Input" -> Association[ "f1" -> Association["Type" -> "Numerical", "Weight" -> 1], "f2" -> Association["Type" -> "Numerical", "Weight" -> 1], "f6" -> Association["Type" -> "Numerical", "Weight" -> 1], "f7" -> Association["Type" -> "Numerical", "Weight" -> 1], "f3" -> Association["Type" -> "Nominal", "Weight" -> 1], "f4" -> Association["Type" -> "Nominal", "Weight" -> 1], "f5" -> Association["Type" -> "Nominal", "Weight" -> 1]], "Output" -> Association[ "(f1f2f6f7)" -> Association["Type" -> "NumericalVector", "Weight" -> 4], "(f3f4f5)" -> Association["Type" -> "NominalVector", "Weight" -> 3]], "Processors" -> { MachineLearning`MLProcessor["ToVector", Association[ "Invertibility" -> "Perfect", "Missing" -> "Allowed", "StructurePreserving" -> True, "Input" -> Association[ "f1" -> Association["Type" -> "Numerical", "Weight" -> 1], "f2" -> Association["Type" -> "Numerical", "Weight" -> 1], "f6" -> Association[ "Type" -> "Numerical", "Weight" -> 1], "f7" -> Association["Type" -> "Numerical", "Weight" -> 1]], "Output" -> Association[ "(f1f2f6f7)" -> Association["Type" -> "NumericalVector", "Weight" -> 4]], "Version" -> {12.3, 0}, "ID" -> 2006545953042025660]], MachineLearning`MLProcessor["ToVector", Association[ "Invertibility" -> "Perfect", "Missing" -> "Allowed", "StructurePreserving" -> True, "Input" -> Association[ "f3" -> Association["Type" -> "Nominal", "Weight" -> 1], "f4" -> Association["Type" -> "Nominal", "Weight" -> 1], "f5" -> Association["Type" -> "Nominal", "Weight" -> 1]], "Output" -> Association[ "(f3f4f5)" -> Association["Type" -> "NominalVector", "Weight" -> 3]], "Version" -> {12.3, 0}, "ID" -> 5548686460849302999]]}, "Invertibility" -> "Perfect", "StructurePreserving" -> True, "Missing" -> "Allowed"]], MachineLearning`MLProcessor["IntegerEncodeNominalVector", Association[ "Invertibility" -> "Perfect", "Missing" -> "Allowed", "StructurePreserving" -> True, "Input" -> Association[ "(f3f4f5)" -> Association["Type" -> "NominalVector", "Weight" -> 3]], "Index" -> { Association["black" -> 1, "hispan" -> 2, "white" -> 3], Association[0 -> 1, 1 -> 2], Association[0 -> 1, 1 -> 2]}, "MissingCode" -> Indeterminate, "Version" -> {12.3, 0}, "ID" -> 4504061958562262152, "Output" -> Association[ "(f3f4f5)" -> Association["Type" -> "NominalVector", "Weight" -> 3]]]], MachineLearning`MLProcessor["Standardize", Association[ "Invertibility" -> "Perfect", "Missing" -> "Allowed", "StructurePreserving" -> True, "Input" -> Association[ "(f1f2f6f7)" -> Association["Type" -> "NumericalVector", "Weight" -> 4]], "Mean" -> {28.03030303030303, 10.235431235431236`, 5619.2365063869465`, 2466.4844431235424`}, "StandardDeviation" -> {10.774073825992629`, 2.8519087156138965`, 6780.8338832122945`, 3288.157119400187}, "Output" -> Association[ "(f1f2f6f7)" -> Association["Type" -> "NumericalVector", "Weight" -> 4]], "Version" -> {12.3, 0}, "ID" -> 2774902631301394164]], MachineLearning`MLProcessor["NumericalizeNominalVector", Association[ "Invertibility" -> "Approximate", "Missing" -> "Allowed", "StructurePreserving" -> True, "Input" -> Association[ "(f3f4f5)" -> Association[ "Type" -> "NominalVector", "Weight" -> 3, "SetSize" -> {3, 2, 2}]], "Boundaries" -> {{-0.5, -0.16666666666666669`, 0.16666666666666663`, 0.5}, {-0.5, 0., 0.5}, {-0.5, 0., 0.5}}, "Version" -> {12.3, 0}, "ID" -> 8420052953686808815, "Output" -> Association[ "(f3f4f5)" -> Association["Type" -> "NumericalVector", "Weight" -> 3]]]], MachineLearning`MLProcessor["MergeVectors", Association[ "Invertibility" -> "Perfect", "Missing" -> "Allowed", "StructurePreserving" -> True, "Input" -> Association[ "(f3f4f5)" -> Association["Type" -> "NumericalVector", "Weight" -> 3], "(f1f2f6f7)" -> Association["Type" -> "NumericalVector", "Weight" -> 4]], "Spans" -> { Span[1, 3], Span[4, 7]}, "Wrappers" -> {Identity, Identity}, "Output" -> Association[ "((f3f4f5)(f1f2f6f7))" -> Association[ "Weight" -> {1., 1., 1., 1., 1., 1., 1.}, "Type" -> "NumericalVector"]], "Version" -> {12.3, 0}, "ID" -> 4527015743637340821]]}, "Invertibility" -> "Approximate", "StructurePreserving" -> True, "Missing" -> "Allowed"]], "PerformanceGoal" -> Automatic, "BatchProcessing" -> Automatic, "Model" -> Association["RotationMatrix" -> CompressedData[" 1:eJwBmQFm/iFib1JlAgAAAAcAAAAHAAAAGQrS4Q5zqL/RJna0F/mfP4vujcyR z52/vOMN5pZTmz8g75CAKfjqv825ze0xM94/wfh+OI/tz78OS+pZR027vzME bKG5K6m/J1oyQot1jz/ZCb5JRICJP4FSC5rQ0OC/C2YB8zov6b8powWb+zrT P2KtPztXXaA/7wEmbYLpw79lB47mgQG0P/5HuVXOJbE/2qYBh/Voqj+qAmKU /drXvzHQuTLeBe2/pMMpYq+k4b/JiLmhsRXXv7iQ1bDQBOW/78t73o7y1r/m pEHxccKsP/D2qljA2pc/MEgjB84SqL8PNcyN+RSnv5yhn4ajk+w/8rcYTCfl 0r+02Mxn0pjRv9gBl6Itroc/UHJD9lvSv7/MCWgTpgrDv6n1z0pko+W/rXZK gJ69yj8MTGQF0AS1P+5U5g+rQ+Y/hkC4rIDPtT9kbh4hRZifP113DGqKgoK/ nAZPALMo3r8/gOVm/IWhP7v6K74A4eU/r56dqpO24b+0uTcrEDOTPwncda6U O6c/PEHvXpuOlr86YdKG "], "Precisions" -> {0.5917915308859684, 0.9058835416603214, 1.3407581534992632`, 2.5448626460262136`, 12.58832604494137, 17.666208126558306`, 20.62321991261682}, "NoisePrecision" -> None, "Processor" -> MachineLearning`MLProcessor["Center", Association[ "Invertibility" -> "Perfect", "Missing" -> "Allowed", "StructurePreserving" -> True, "Input" -> Association[ "((f3f4f5)(f1f2f6f7))" -> Association["Type" -> "NumericalVector", "Weight" -> 7.]], "Mean" -> {0.14114824098989376`, -0.0040186646690022676`, 0.06211409619808966, 0.010011274703182286`, 0.016450562861479306`, -0.0027348578662433148`, \ -0.040732155554956406`}, "Output" -> Association[ "((f3f4f5)(f1f2f6f7))" -> Association["Type" -> "NumericalVector", "Weight" -> 7.]], "Version" -> {12.3, 0}, "ID" -> 3117917857777355243]], "PostProcessor" -> MachineLearning`MLProcessor["FirstValues", Association[ "Info" -> Association["Type" -> "NumericalVector", "Weight" -> 7.], "Key" -> "((f3f4f5)(f1f2f6f7))", "Invertibility" -> "Perfect", "StructurePreserving" -> False, "Missing" -> "Allowed"]], "Method" -> "Multinormal", "Options" -> Association[ "CovarianceType" -> Association["Value" -> "Full", "Options" -> Association[]], "IntrinsicDimension" -> Association["Value" -> 7, "Options" -> Association[]]]], "TrainingInformation" -> Association[ "PanelCell" -> None, "TrainingFunction" -> LearnDistribution, "EMIterations" -> 1, "ProcessorEntropyShift" -> 43.1750448039854, "PreprocessingTime" -> 0.126372`5.553195852311365, "LossName" -> "MeanCrossEntropy", "BestModelInformation" -> Dataset[ Association[ "MeanCrossEntropy" -> Around[6.749598737607615, 0.06722608097923166], "EvaluationTime" -> 2.5914713767543942`*^-6, "SamplingTime" -> 0.00005940000000000001, "TestSize" -> 344, "ModelMemory" -> 7944., "ModelUtility" -> -67.63044494504513, "TrainingSize" -> 343, "TrainingTime" -> 0.019556904880805, "TrainingMemory" -> 180529.6, "ExperimentCount" -> 4, "MeanCrossEntropyHistory" -> { Around[6.719711056906771, 0.12025159961521219`], Around[6.818226570736059, 0.12368858007785172`], Around[6.689078890351845, 0.11966095539675843`], Around[6.771378432435785, 0.12369849783395227`]}, "Configuration" -> { "Multinormal", "CovarianceType" -> "Full", "IntrinsicDimension" -> 7}], TypeSystem`Struct[{ "MeanCrossEntropy", "EvaluationTime", "SamplingTime", "TestSize", "ModelMemory", "ModelUtility", "TrainingSize", "TrainingTime", "TrainingMemory", "ExperimentCount", "MeanCrossEntropyHistory", "Configuration"}, {TypeSystem`AnyType, TypeSystem`Atom[Real], TypeSystem`Atom[Real], TypeSystem`Atom[Integer], TypeSystem`Atom[Real], TypeSystem`Atom[Real], TypeSystem`Atom[Integer], TypeSystem`Atom[Real], TypeSystem`Atom[Real], TypeSystem`Atom[Integer], TypeSystem`Vector[TypeSystem`AnyType, 4], TypeSystem`Tuple[{ TypeSystem`Atom[String], TypeSystem`AnyType, TypeSystem`AnyType}]}], Association[]], "Configurations" -> Dataset[ Association[ Association[ "Value" -> "Multinormal", "Options" -> Association[ "CovarianceType" -> Association["Value" -> "Full"], "IntrinsicDimension" -> Association["Value" -> 7]]] -> Association["Experiments" -> { Association[ "MeanCrossEntropy" -> Around[8.283933040933125, 0.18215866766760938`], "EvaluationTime" -> 2.3084003451955995`*^-6, "SamplingTime" -> 0.000044, "TestSize" -> 419, "ModelMemory" -> 7944, "ModelUtility" -> -83.20365313599392, "TrainingSize" -> 10, "TrainingTime" -> 0.025118864315095794`, "TrainingMemory" -> 139648, "ExperimentCount" -> 1, "MeanCrossEntropyHistory" -> { Around[8.283933040933125, 0.12880562915967328`]}], Association[ "MeanCrossEntropy" -> Around[6.8460810514318995`, 0.08467220585777806], "EvaluationTime" -> 1.9692590810007903`*^-6, "SamplingTime" -> 0.000042, "TestSize" -> 369, "ModelMemory" -> 7944, "ModelUtility" -> -68.63016031502286, "TrainingSize" -> 60, "TrainingTime" -> 0.025118864315095794`, "TrainingMemory" -> 146248, "ExperimentCount" -> 1, "MeanCrossEntropyHistory" -> { Around[6.8460810514318995`, 0.05987229094005817]}], Association[ "MeanCrossEntropy" -> Around[6.749598737607615, 0.06722608097923166], "EvaluationTime" -> 2.5914713767543942`*^-6, "SamplingTime" -> 0.00005940000000000001, "TestSize" -> 344, "ModelMemory" -> 7944., "ModelUtility" -> -67.63044494504513, "TrainingSize" -> 343, "TrainingTime" -> 0.019556904880805, "TrainingMemory" -> 180529.6, "ExperimentCount" -> 4, "MeanCrossEntropyHistory" -> { Around[6.719711056906771, 0.12025159961521219`], Around[6.818226570736059, 0.12368858007785172`], Around[6.689078890351845, 0.11966095539675843`], Around[6.771378432435785, 0.12369849783395227`]}]}, "PredictedPerformances" -> Association[ "EvaluationTime" -> 2.5914713767543942`*^-6, "MeanCrossEntropy" -> Around[6.749598737607615, 0.06722608097923166], "ModelMemory" -> 7944., "TrainingMemory" -> 180529.6, "TrainingTime" -> 0.04957924971995102], "Index" -> 1]], TypeSystem`Assoc[ TypeSystem`Struct[{"Value", "Options"}, { TypeSystem`Atom[String], TypeSystem`Assoc[ TypeSystem`Atom[String], TypeSystem`Struct[{"Value"}, {TypeSystem`AnyType}], 2]}], TypeSystem`Struct[{ "Experiments", "PredictedPerformances", "Index"}, { TypeSystem`Vector[ TypeSystem`Struct[{ "MeanCrossEntropy", "EvaluationTime", "SamplingTime", "TestSize", "ModelMemory", "ModelUtility", "TrainingSize", "TrainingTime", "TrainingMemory", "ExperimentCount", "MeanCrossEntropyHistory"}, {TypeSystem`AnyType, TypeSystem`Atom[Real], TypeSystem`Atom[Real], TypeSystem`Atom[Integer], TypeSystem`Atom[Real], TypeSystem`Atom[Real], TypeSystem`Atom[Integer], TypeSystem`Atom[Real], TypeSystem`Atom[Real], TypeSystem`Atom[Integer], TypeSystem`Vector[ TypeSystem`AnyType, TypeSystem`AnyLength]}], 3], TypeSystem`Struct[{ "EvaluationTime", "MeanCrossEntropy", "ModelMemory", "TrainingMemory", "TrainingTime"}, { TypeSystem`Atom[Real], TypeSystem`AnyType, TypeSystem`Atom[Real], TypeSystem`Atom[Real], TypeSystem`Atom[Real]}], TypeSystem`Atom[Integer]}], 1], Association[]], "MaxTrainingSize" -> 429, "PreprocessorEvaluationTime" -> 7.609375*^-6, "PreprocessorMemory" -> 37904, "BaselineLogProbability" -> -0.8805708744758884, "VariableBudget" -> True, "CheckpointingInfo" -> Association["Checkpointing" -> False], "UserStop" -> False, "NaturalStop" -> True, "AbortStop" -> False, "RoundPartitioning" -> Dataset[{ Association[ "TrainingSizes" -> 10, "TimeBudgets" -> 0.0038646372693775565`, "ElapsedTimes" -> 0.033538, "ExperimentCounts" -> 1], Association[ "TrainingSizes" -> 60, "TimeBudgets" -> 0.019323186346887775`, "ElapsedTimes" -> 0.028851, "ExperimentCounts" -> 1], Association[ "TrainingSizes" -> 343, "TimeBudgets" -> 0.09661593173443882, "ElapsedTimes" -> 0.09411699999999999, "ExperimentCounts" -> 4]}, TypeSystem`Vector[ TypeSystem`Struct[{ "TrainingSizes", "TimeBudgets", "ElapsedTimes", "ExperimentCounts"}, { TypeSystem`Atom[Integer], TypeSystem`Atom[Real], TypeSystem`Atom[Real], TypeSystem`Atom[Integer]}], 3], Association[]]], "NaiveImputer" -> MachineLearning`MLProcessor["ImputeMissing", Association[ "Invertibility" -> "Perfect", "Missing" -> "Imputed", "StructurePreserving" -> True, "Input" -> Association[ "((f3f4f5)(f1f2f6f7))" -> Association["Type" -> "NumericalVector", "Weight" -> 7.]], "Mean" -> {0.14570739761129517`, 0.005077969949886176, 0.06443318182978763, -0.0003444388712252952, \ -0.00018126394265160662`, -0.0005018846412455033, 0.00015300800171307548`}, "StandardDeviation" -> {0.2801413878839222, 0.2905605139068831, 0.28360203669113354`, 1.0001694857847918`, 1.0000688724301545`, 0.9999146946173109, 0.9998609303219517}, "Method" -> "NaiveSampler", "VectorLength" -> 7, "Output" -> Association[ "((f3f4f5)(f1f2f6f7))" -> Association["Type" -> "NumericalVector", "Weight" -> 7.]], "Type" -> "NumericalVector", "Version" -> {12.3, 0}, "ID" -> 7146101971802037265]], "InputDimension" -> 0, "OutputDimension" -> 7, "Log" -> Association["Example" -> MachineLearning`MLDataset[ Association[ "f1" -> Association[ "Type" -> "Numerical", "Weight" -> 1, "Values" -> {16}, "ID" -> 5650167692583624283], "f2" -> Association[ "Type" -> "Numerical", "Weight" -> 1, "Values" -> {9}, "ID" -> 785873475763836364], "f3" -> Association[ "Type" -> "Nominal", "Weight" -> 1, "Values" -> {"white"}, "ID" -> 8374129019214215437], "f4" -> Association[ "Type" -> "Nominal", "Weight" -> 1, "Values" -> {0}, "ID" -> 7710991810802879757], "f5" -> Association[ "Type" -> "Nominal", "Weight" -> 1, "Values" -> {1}, "ID" -> 7247521548741291585], "f6" -> Association[ "Type" -> "Numerical", "Weight" -> 1, "Values" -> {0}, "ID" -> 8457447359297193165], "f7" -> Association[ "Type" -> "Numerical", "Weight" -> 1, "Values" -> {0}, "ID" -> 980603385963850631]], Association[ "ExampleNumber" -> 1, "ExampleWeights" -> 1, "LogDensityRatios" -> 0, "RawExample" -> False, "LogDensityRatio" -> 0]], "TrainingTime" -> 0.41134, "MaxTrainingMemory" -> 589696, "DataMemory" -> 91200, "FunctionMemory" -> 97040, "LanguageVersion" -> {12.3, 0}, "Date" -> DateObject[{2021, 7, 4, 10, 42, 19.276054`8.037593118242489}, "Instant", "Gregorian", -6.], "ProcessorCount" -> 2, "ProcessorType" -> "x86-64", "OperatingSystem" -> "MacOSX", "SystemWordLength" -> 64, "Evaluations" -> {}], "LogPDFDistribution" -> MachineLearning`TailedQuantileDistribution[ Association["Quantiles" -> CompressedData[" 1:eJwNjn8w1GkAh1dGbOz7vt/Nyli7Da0f+RGLiMYydW5qOLouGmVwaxYVNt00 Jq49t1RbZuNOl8mVJndd06yRGc1KkY9imlPZUNlUaGrGbJJdJ1GG88fz5zPP 46VU71bZ8Xg81QqJtnlN4nUv0A/ptep8CWp+nAgIqPLEpcHswFqLGB6dsdGN DWJYOlpkjrliiJSdreHeYpR6Os8kjHogbp/i3LDWA0drCqQx2WsReio5NrNa iPYj57XtaUKU/hT1Mk0sROFS2FTOaw4hHbcDbt3i0Oigae3TcMiuWypvO8Dh i7vBWrmPQ7inynUijoOycDzorZzDppqjA8Sfg9nX6F8m5FAm352gXmDg/3qC 5k0xbLUnV3RvGRybour1/QxRTpPq9B6G4ZxJJ7dWhlebp6Yzmhmue+8Z2/kX Q/Nv1hLZeQa3Dkl7vJ5huaQ32XqCQdHZZfujhOG/H+g59SGGNV363DPZDFa9 3/HkDAb3YudFwfcMFYpMqe1bhqJTZp3+GwbN0+/4fbEMpK3+F0k4w6TCNFQQ zFAgi5tr92NYnzoYly5Z6T7zvvtExJDSrI04zDHs9XG2qNYwfNoe+XTSnqHv ePTpi0sUi6G1PrMzFDEZW/0fvqe49iTS3DVOV77n7ZdMFKb7Gw/mgqKu599c /U2Knz9uKXK9QfHc5c97If9QBJYaZoouUFS03FNMVVOESGfHR3UUlQ22G5eP UURnblZGHqCoD9LOx2dRaK4qC5/vorjcazcgTaCYHm304kdT/K4sdLHKKUTl liDfQAojr/uBwZvi8x5Hoc86imWhSOTHpwiNzzDL7SjG7NJ6Mr8Q3OEnvVyw Eswt5+8YfkdQGZWfcnWEYN2Rm1kXTAQnjaut03cJxtZ+GmkyEuQFBy4UGAhS 0yLKt1wiSFI1uairCVY7b3vhULXireplG7QE71+dNRwqI+APbM8TFxG4jhbP VikJZNJY97ZkgsfGpDdvthFM6HQuKb4E1xItqQFSgsGcimMyIcEjybS5mEew K777q+WDAPsrwup2vhCgO3FTQ8QDASb6+Re7mgSwzbnJT1cL4DQz9HflYQFM LUNXLO4C/A+Mu3s4 "], "LeftBoundary" -> -7.3149611417815095`, "LeftScale" -> 2.0277318072042134`, "LeftTailNorm" -> 0.011]], "Entropy" -> Around[29.874952439021715`, 0.1916113674958325], "EntropySampleSize" -> 1000]], "AnomalyNumber" -> 0, "PerformanceGoal" -> Automatic, "AcceptanceThreshold" -> 0.001, "ExampleNumber" -> 429, "Log" -> Association[ "TrainingTime" -> 5.887397, "MaxTrainingMemory" -> 2872328, "DataMemory" -> 490928, "FunctionMemory" -> 112272, "LanguageVersion" -> {12.3, 0}, "Date" -> DateObject[{2021, 7, 4, 10, 42, 19.276461`8.037602287960944}, "Instant", "Gregorian", -6.], "ProcessorCount" -> 2, "ProcessorType" -> "x86-64", "OperatingSystem" -> "MacOSX", "SystemWordLength" -> 64, "Evaluations" -> {}]]], Editable->False, SelectWithContents->True, Selectable->False]], "Output", TaggingRules->{}, CellChangeTimes->{3.834334911457458*^9, 3.8344057394880733`*^9}, CellLabel->"Out[11]=", CellID->1832921591] }, Open ]], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"nonanomalousTreated", "=", RowBox[{ RowBox[{ RowBox[{"Query", "[", RowBox[{"Select", "[", RowBox[{ RowBox[{ RowBox[{"#treat", "==", "1"}], " ", "&&", " ", RowBox[{"Not", "[", RowBox[{"adfControl", "[", RowBox[{ RowBox[{"KeyDrop", "[", RowBox[{"#", ",", RowBox[{"{", RowBox[{"\"\\"", ",", "\"\\""}], "}"}]}], "]"}], ",", RowBox[{"AcceptanceThreshold", "->", "0.1"}]}], "]"}], "]"}]}], "&"}], "]"}], "]"}], "[", "lalonde", "]"}], "//", RowBox[{ InterpretationBox[ TagBox[ DynamicModuleBox[{Typeset`open = False}, FrameBox[ PaneSelectorBox[{False->GridBox[{ { PaneBox[GridBox[{ { StyleBox[ StyleBox[ AdjustmentBox["\<\"[\[FilledSmallSquare]]\"\>", BoxBaselineShift->-0.25, BoxMargins->{{0, 0}, {-1, -1}}], "ResourceFunctionIcon", FontColor->RGBColor[ 0.8745098039215686, 0.2784313725490196, 0.03137254901960784]], ShowStringCharacters->False, FontFamily->"Source Sans Pro Black", FontSize->0.6538461538461539 Inherited, FontWeight->"Heavy", PrivateFontOptions->{"OperatorSubstitution"->False}], StyleBox[ RowBox[{ StyleBox["FormatDataset", "ResourceFunctionLabel"], " "}], ShowAutoStyles->False, ShowStringCharacters->False, FontSize->Rational[12, 13] Inherited, FontColor->GrayLevel[0.1]]} }, GridBoxSpacings->{"Columns" -> {{0.25}}}], Alignment->Left, BaseStyle->{LineSpacing -> {0, 0}, LineBreakWithin -> False}, BaselinePosition->Baseline, FrameMargins->{{3, 0}, {0, 0}}], ItemBox[ PaneBox[ TogglerBox[Dynamic[Typeset`open], {True-> DynamicBox[FEPrivate`FrontEndResource[ "FEBitmaps", "IconizeCloser"], ImageSizeCache->{11., {2., 9.}}], False-> DynamicBox[FEPrivate`FrontEndResource[ "FEBitmaps", "IconizeOpener"], ImageSizeCache->{11., {2., 9.}}]}, Appearance->None, BaselinePosition->Baseline, ContentPadding->False, FrameMargins->0], Alignment->Left, BaselinePosition->Baseline, FrameMargins->{{1, 1}, {0, 0}}], Frame->{{ RGBColor[ 0.8313725490196079, 0.8470588235294118, 0.8509803921568627, 0.5], False}, {False, False}}]} }, BaselinePosition->{1, 1}, GridBoxAlignment->{"Columns" -> {{Left}}, "Rows" -> {{Baseline}}}, GridBoxItemSize->{ "Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}, GridBoxSpacings->{"Columns" -> {{0}}, "Rows" -> {{0}}}], True-> GridBox[{ {GridBox[{ { PaneBox[GridBox[{ { StyleBox[ StyleBox[ AdjustmentBox["\<\"[\[FilledSmallSquare]]\"\>", BoxBaselineShift->-0.25, BoxMargins->{{0, 0}, {-1, -1}}], "ResourceFunctionIcon", FontColor->RGBColor[ 0.8745098039215686, 0.2784313725490196, 0.03137254901960784]], ShowStringCharacters->False, FontFamily->"Source Sans Pro Black", FontSize->0.6538461538461539 Inherited, FontWeight->"Heavy", PrivateFontOptions->{"OperatorSubstitution"->False}], StyleBox[ RowBox[{ StyleBox["FormatDataset", "ResourceFunctionLabel"], " "}], ShowAutoStyles->False, ShowStringCharacters->False, FontSize->Rational[12, 13] Inherited, FontColor->GrayLevel[0.1]]} }, GridBoxSpacings->{"Columns" -> {{0.25}}}], Alignment->Left, BaseStyle->{LineSpacing -> {0, 0}, LineBreakWithin -> False}, BaselinePosition->Baseline, FrameMargins->{{3, 0}, {0, 0}}], ItemBox[ PaneBox[ TogglerBox[Dynamic[Typeset`open], {True-> DynamicBox[FEPrivate`FrontEndResource[ "FEBitmaps", "IconizeCloser"]], False-> DynamicBox[FEPrivate`FrontEndResource[ "FEBitmaps", "IconizeOpener"]]}, Appearance->None, BaselinePosition->Baseline, ContentPadding->False, FrameMargins->0], Alignment->Left, BaselinePosition->Baseline, FrameMargins->{{1, 1}, {0, 0}}], Frame->{{ RGBColor[ 0.8313725490196079, 0.8470588235294118, 0.8509803921568627, 0.5], False}, {False, False}}]} }, BaselinePosition->{1, 1}, GridBoxAlignment->{"Columns" -> {{Left}}, "Rows" -> {{Baseline}}}, GridBoxItemSize->{ "Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}, GridBoxSpacings->{"Columns" -> {{0}}, "Rows" -> {{0}}}]}, { StyleBox[ PaneBox[GridBox[{ { RowBox[{ TagBox["\<\"Version (latest): \"\>", "IconizedLabel"], " ", TagBox["\<\"1.0.0\"\>", "IconizedItem"]}]}, { TagBox[ TemplateBox[{ "\"Documentation \[RightGuillemet]\"", "https://resources.wolframcloud.com/FunctionRepository/\ resources/FormatDataset"}, "HyperlinkURL"], "IconizedItem"]} }, DefaultBaseStyle->"Column", GridBoxAlignment->{"Columns" -> {{Left}}}, GridBoxItemSize->{ "Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}], Alignment->Left, BaselinePosition->Baseline, FrameMargins->{{5, 4}, {0, 4}}], "DialogStyle", FontFamily->"Roboto", FontSize->11]} }, BaselinePosition->{1, 1}, GridBoxAlignment->{"Columns" -> {{Left}}, "Rows" -> {{Baseline}}}, GridBoxDividers->{"Columns" -> {{None}}, "Rows" -> {False, { GrayLevel[0.8]}, False}}, GridBoxItemSize->{ "Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}]}, Dynamic[ Typeset`open], BaselinePosition->Baseline, ImageSize->Automatic], Background->RGBColor[ 0.9686274509803922, 0.9764705882352941, 0.984313725490196], BaselinePosition->Baseline, DefaultBaseStyle->{}, FrameMargins->{{0, 0}, {1, 0}}, FrameStyle->RGBColor[ 0.8313725490196079, 0.8470588235294118, 0.8509803921568627], RoundingRadius->4]], {"FunctionResourceBox", RGBColor[0.8745098039215686, 0.2784313725490196, 0.03137254901960784], "FormatDataset"}, TagBoxNote->"FunctionResourceBox"], ResourceFunction[ ResourceObject[ Association[ "Name" -> "FormatDataset", "ShortName" -> "FormatDataset", "UUID" -> "76670bca-1587-4e7e-9e89-5b698a30759d", "ResourceType" -> "Function", "Version" -> "1.0.0", "Description" -> "Format a dataset using a given set of option values", "RepositoryLocation" -> URL["https://www.wolframcloud.com/obj/resourcesystem/api/1.0"], "SymbolName" -> "FunctionRepository`$66a3086203b4405b88cdb0de8a5c3128`FormatDataset", "FunctionLocation" -> CloudObject[ "https://www.wolframcloud.com/obj/70389ad6-7dbc-48c8-b898-\ 72c65c00f14e"]], ResourceSystemBase -> Automatic]], Selectable->False], "[", RowBox[{"MaxItems", "->", "5"}], "]"}]}]}]], "Input", TaggingRules->{}, CellChangeTimes->{{3.834334928752337*^9, 3.834334965395059*^9}}, CellLabel->"In[12]:=", CellID->1998625720], Cell[BoxData[ GraphicsBox[ TagBox[RasterBox[CompressedData[" 1:eJzsvXd8FNX6x7/0XkXgSpciXYoIKAqidPECCoqCXlARpCqCSBc1oCDSEbkC ShEhgDQpIfww9HAhdIK0kJCEEGpAmiC/57vPz/MbZzczs7uzM2czn/cfvMjM 2ZnnPJ+Zc85nyply3ft36JHZ5XINzEn/dOj28XMfftjtk5cL0h8d+w3s9V6/ d99p1e+jd99798MG3bPQwm8zuVwNsrpc//f/BwAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAEjb/cmLW1/wcAAAAAAAAADKDtLM6ePRsfHw+fAgAA AAAAALASDVtx586dvW7u3r1rok9JczBOzgDqbncUQB8oZRbIpL0g/3ICXeQE usiJrk85e/bs/9yYdUsFR4KTM4C62x0F0AdKmQUyaS/Iv5xAFzmBLnKi7VPE zRSG/oRPCRwnZwB1tzsKoA+UMgtk0l6QfzmBLnICXeRE26fwzZRkN/Qf+hM+ JXCcnAHU3e4ogD5QyiyQSXtB/uUEusgJdJETDZ/CN1P2799///79e/fu0X9M uaWCI8HJGUDd7Y4C6AOlzAKZtBfkX06gi5xAFznR8CniZgr/adYtFRwJTs4A 6m53FEAfKGUWyKS9IP9yAl3kBLrISXo+RXkzhZeYdUsFR4KTM4C62x0F0AdK mQUyaS/Iv5xAFzmBLnKSnk/hmynnz59XLjTllgqOBCdnAHW3OwqgD5QyC2TS XpB/OYEucgJd5MSrT/G8mcLQn4HfUsGR4OQMoO52RwH0gVJmgUzaC/IvJ9BF TqCLnHj1KXFxcZ43UxhaGOAtFRwJTs4A6m53FEAfKGUWyKS9IP9yAl3kBLrI iadPSe9mChP4LRUcCU7OAOpudxRAHyhlFsikvSD/cgJd5AS6yImnT9G4mcLw LRUqBp/iH07OAOpudxRAHyhlFsikvSD/cgJd5AS6yInKp9y+fVvjZgoT4C0V HAlOzgDqbncUQB8oZRbIpL0g/3ICXeQEusiJyqfo3kxhArmlgiPByRlA3e2O AugDpcwCmbQX5F9OoIucQBc5UfoUvplCBuTUqVNnNKECVIwK00/gU3zFyRlA 3e2OAugDpcwCmbQX5F9OoIucQBc5UfqUlJSU//kI/QQ+xVecnAHU3e4ogD5Q yiyQSXtB/uUEusgJdJET1XNfFoAjwckZQN3tjgLoA6XMApm0F+RfTqCLnEAX OYFPsR4nZ8Caum/dunX16tUbNmwI9o58wsm6hxZQyghGzjI5MxkfH7/aTWxs rN2xBBc58w+gi5xAFzmBT7EeJ2fAmro/88wzLpfr4YcfDvaOfMLJuocWGUmp AwcOvPPOOz179jx27Ji5WzZylsmZyXXr1rncfPPNN3bHElzkzD+ALnICXeQE PsV6nJwB+BS7owD6ZCSl2rRpw2Py//znP+ZuGT5FfqzJ/5kzZ7bpceHChfR+ fvToUS5z9uzZYIcqCXLqsmvXrvSKXbp0KdjRyoCcujCkzsyZMz/++OPJkydv 3Ljx2rVrwY5THmT2KaTms24OHjwY5DRYGoycfbc1wKfYHQXQJyMp1bVrVx6T v/vuu+ZuGT5FfqzJ/5dffunSY+rUqV5/e+rUqcKFC3OZ2bNnBztUSZBTlxw5 cqRX7Mcffwx2tDIgpy40+OzQoYOqQIMGDf73v/8FO1RJkNmnkH9kReg/wc6D lcHI2XdbA3yK3VEAfTKSUqdPn/7ss8/CwsJMv1gNnyI/8oy75s2b5/W3L730 kigDn2IuPuly4cIFP+TLYEioy9WrVxs3bswLCxYs+NprrzVp0oT/LF++fEpK SrCjlQGZfcqvv/7KcsjgU0wMRs6+2xrgU+yOAugDpYwAnyI/1uQ/MTHxsDe2 b9+eM2dOynP9+vVpuOX5w7lz5yrHZvAp5uKTLsePH2cVxowZ4/mT8+fPBzta GZBQl59++ol16d+/f2pqKi/87rvveOEXX3wR7GhlQGafsmDBAoPWICEhYf36 9WfOnPFcdezYMeqVTp8+rbu7gwcPkhn5/fffAwxGF1/PBe3ABJSEiIiIjRs3 xsXF6W7TeFrMxde6G4wzOTmZKh4TE8MPbfIFB7N8Cm1z//79dIDFx8cHsh0/ 2sD0Dmyqb2Rk5LZt20TDlR4Gg6ftbN68eceOHZcvX/YpwgyJH0pdunSJzr4T J054rtq5c+fWrVt1lTJ4mjMaLZ7uWhXGpffjLPOv34+OjqaQaHfaxahZoMbh yJEjRrZJYVPwSUlJ9H/6j65PMdLy0Fhi3759a9asoYANPi6enjTGVfDpVDWx zfGjiXj77bcpybly5aLke649efIkP/H1xBNPwKfoEmxddu/ezSr8/PPPPgWW kZBQlw8++IAW5s6dW7Wdhg0b0vJXXnnFp2hDFDl9ypQpUx599NF8+fLxiUP/ Keimbt26XKBmzZr058yZM48fP/7iiy9mz56dihUpUkRsgXqNsLCw4sWLi2s1 JUqUmDt3rmpHVOyHH3545plnChQoIErSr1asWGE8GP9yrl3GSGCCLVu2kAEX xTJlylRQQbt27XxNS/Aw2A4Yj5MGgfXq1cucOTMXo/qOHDmyZcuWqhHU+fPn aWu0tm/fvqotDB8+nBNF/aZq1alTp6gdENITFStWnD9/vu/1/j+M1F37wI6N je3RowfFQBJzPFmyZOnUqZPXEZ3B4A8cOEADzmzZsnEZ2iPt1+AQN6NiXKnp 06fTYdOhQ4e8efNyAqtXr75w4UIqQN3KRx99VKpUKaHUsGHDVKNZ46e59oGh sZbOGj68PScQNi69wbMswExSj//mm28WLVpUtGPdunW7ePGi508mTJhADYLI GA13+/fv77XklStXBg0a9NBDD4ltNmjQQFyN9PQpBlueCxcukJp58uQRxWh0 oWx1f/vtN2XtNLoq4yr4caoG3ub4t19i06ZN3EyNGzfOawHaCK2lza5cuZK3 DJ+ixGJdaLDN25HwBqhlSKgLmxdq4lTlu3Tp4nK/peJD9UIWOX0KdQEub9BA iwuULVuW/hw8eHCdOnXE2hYtWvDalJSUZs2aieV8K40ZMmSI2Mu5c+doUOF1 R3TArFq1ymAw/uVco4DBwBhqXsT4gUJSdt8MpcKntAQVI+obj3PevHnKtSqU I6j4+HheSGe9ancDBw7kVapL2dR7ihc8VXz//fdBqrvGgT158mRuEj156qmn VNsxGPzixYuFkcmaNSuNpfn/jzzyyNGjR/2oY8bAuFLUAQknIsifP/+ePXua N2/umfyvv/5abMGn01y7xdNYO2vWLP5zzZo1yg0al974WRZIJl966aXy5ct7 br9fv37KwtQ4vPDCC14jqVatmmru5cTExEaNGqUXucvDpxhsecjLPP/887yc DCbt1/MF5MjISCPCGVfBv1M1wDbH7/0StWrVomL0r9c7TdQK8XaGDx8eExPD /4dPUWKxLuL5IoM3KDMkEupCG+QtrF27Vrm8SpUqtJDcil8VDTHk9CkHDx6k gZaYqYYM5ko34pkrPlSYGjVq0BBuy5Yt27Zt47V0CPGqDh06cM9FG+SnqenI UU7YxS8ovfzyyz///POJEydopCpGrU8//bTBYPzLuXYZI4Glua/ZlixZ0uW+ y0OGhRdS58slBw0a9L///U/03cbTEjyM1N1gnCdPnhQXsf/zn//s2LHj+PHj P/74Y5kyZXhhID7l7NmzRYoU4eUdO3b87bffaMyzevXqmjVrUuuk+wyP33XX OLD5ehc1d19++WVUVBQ/hFOuXDnPRsxg8GfOnClUqBCVeeihhyhvFy9epFXT pk3j4Rkd8H7UMWPgq1Lt2rUjW7F7927lS8EEJXzOnDl79+795JNPeAn9SrkR g6d5ml6Lp7HWq08xLr1PZ1ngmWzZsuWCBQto4ErNLHfxZPqUz8aL/FSqVIly npCQsHnzZvFiaatWrZRb7t+/Py8vXrw4bfbUqVNbt2595513xO5UPsVgy0Pp 5WKdOnXi2KgwDzMKFy5MKaJWV0wrqiGNcRX8PlUDbHP83q+4OP/dd995rhVP fNWtW/fKlSt0gnBh+BQlFusyY8YMXkUDYzrLaABMpwPZSYe8qc1IqAu1JGx8 6JQRbTj15p6tegaGdfmfvwTJpzCjRo1iLTwdgThUnnrqKdWgcd++fXzZuXXr 1srlNK7jtlE5Pyd1iOIGvUD0espH+jWC8RUjGTAYGPWJvGTMmDHKkvzRBBom iSU+pSV46NbdeJzCOQ4YMEBVki81BOJTxMb79OmjLHzp0iUaGvlS4/8fn9pA zwM7zX3JS9VriCfte/Xq5WvwfOM4c+bM1Mwqi9Gx5HI/p+T1bQsn4JNSw4YN Ewtp1Cqe4GrRooXyeTwe7hLKBwCMtz/aB4bGWq8+xbj0Pp1lnviUSRoXKa8l vvrqq7ycWjleQj0OP01B9lyZHDqwRdLoHOGF+/fv58LUbqjus8ycOZMLK32K 8ZaHH1iihUoDtX37dlUAqtp5SmNcBb9P1QDbHL/32759e5d7tOb1kg53TzR4 44lV4VO8YrEuYWFhLm+UKFFCdUhnYCTUJc3tYsTFIjp3qEnnFunll1/2sX6h ivApSb5ju0/JlCmT5wzSfEgQhw8fVq2isZzLwBN9n3/+OW+Buh4jwfiK8Qzo BiYGIevWrVOWHD16tMt9DfDKlSu8JPC0mIJu3Y3HyWdukSJFPN/O8JyJyCef QoMlfvKcNs4v3pqC8TbQ64GdHhwqNV/8p8HgqVju3Lmp2BtvvKFaJXKlevTI ORhXqn79+qrl4qkk1esSI0aM8GxVvOK1/dE+MDTWevoUn6T36SzzJJBMTp8+ nYNZunQpL/n00095ybRp01SFN2/ezKs6derES0QaVddw0tKZ78t4y8MBU/VV xfjpr6FDh3rWzlMa4yoEcqoG0ub4vd/jx49T10NrqWn1XEtmhH8rnsOHT/GK xboIn9KwYcP+/fv369evZs2avIT8uwxzrlqAhLqkua/DkCVx/ZNatWo5ZBK2 tBD3KZ5dG/Haa6+53I8Nb/KA3W6xYsU8f7V79+7JkyfTAUbqC+u6fPlyI8H4 ik8+RTswMVsyv7oroEbG9c93r/xLi+no1t1gnEePHuWKv/XWW54bCdCnHDx4 kJd079490AorCGTMpiQ1NXXx4sXDhg1r165dpUqVONR69erxWoPBHzhwgIvR oeKZal5FB56vdcwYGFfK882g119/nbMnLhEwU6ZM4eV0znpuTbf90T4wNNZ6 +hTj0vt6lnkSSCbJnvDexRtV3DgQ586d89wOPwFbo0YN/lPcCaJhgKqkV59i vIXkd16qVaum3CZ1iLzNr776yrN2ntIYVyGQUzWQNsfv/Y4fP57X0lGtWnXi xAl+MIZyKO6dwad4xUpdGDrRVq5cKf68evXqoEGD+Ceep2eGREJdqGGh5NNa 6hd69OihnGbk448/9rumoUVI+xSv5w4/J6xBlixZlOXp3EzvJ+Hh4UaC8RWD GTASGA1Z+SJe48aNxQ8TEhL4YH7uuef8TkuQ0K27wThpCMd/el4sTQvYp4g3 18aOHRtohRUEMmZjYmNje/XqxR29CjH7nMHgxVuTGowYMcK/moY6gSj15ptv cvZUPkU8a6TyKQbbH+0DQ2Otp08xLr2vZ5kngWTyl19+4b0Ln/L444/Tn3T8 e90O24fs2bNz5sm5u9zTcHmW9OpTjLeQ4m0j5V1sca9n48aNRmpnXIVATtVA 8u/3fvnb2TSs8vxmSuvWrfmHERERv//Nhg0beCEN2OjPxMRE7YAzALLpkh5U kow//YqGGaoGLUMioS6vvPKKy93ocWAXL16k00RMYDhy5Eh/6xpKZDyf8uST T/Jp9VQ6iPmv0hT3Oql3o+Nhzpw5VCkxorDRpxgPbMiQIbywefPmCxYsmDt3 7mOPPeZy221lN+pTWoKHbt0NxinahC+//NJzI88++yytIrMmlvjkU+bPn89L JkyYEGiFFQToU06dOiUmRKpYsSIdkGvXrj179iy/Si98isHgxfeAaFPppdoh 3yD2xDKfYvw0N9GnGJfe17PME3N9CjcOyilAlfCkBFmzZuUPDfBTKwULFvQs Kd5anTRpklhovIU8fvx4sWLFXO53/MeOHbtkyZJ+/frxpIvPPfecarqe9Gpn XIVATtVA8u/3fv/1r3+5vD0XFx0d7TKA9t3kjIFUumjTt29f3p3xR5FDF9l0 2blzJ29TddXx8OHDPM9kzpw5Pb+nkPHIeD6FzWmBAgV0P7wlniugHkr5PS8x u75dPsWnwJKTkz0vsOfJk0d1h9F4WoKKbt0NxineWu3fv7/nWo37KZ5PsHj6 FNGfvv/++0YrZoAAfQp/DS1Lliw0jlUuV/kUg8HTycvFaKjsYz0yPtb4FJ9O cxN9inHpfT3LPDHXp4iHtL1+MIi3U7lyZf6TX9YmPJ/i9no/xacWUnQHSqgK nu/Jplc74yoEcqoGkn//9nvo0KH0jhmxQW1q165tfHchilS6aCMe/dJ9sS4D IJsukyZN4rWek0V/++23vMoJsxzI7FP4ZXBCNXlCmma/LD53orr/7sm7777L JU+dOqVc7nWcoBGMr+hmwKfAeFacRo0aDR8+nPrxrl27kvX2fCTbeFqCim7d DcaZkJDAxWrVquW51nMEdenSJf6IknjfXEDjed6U8ClUmOdEpcOM/m+0bnoE 0gaeOXOGg/Sclk3lUwwGf/nyZZ4NqUmTJr5XJYNjjU/x6TQ30acYl97Xs8wT c32KmIvA88OL4mvaZDd4iTivPb+v7dWnGG8ho6OjKYF0llGn0K1bt3bt2n34 4YfpfcU7vdoZVyGQUzWQ/Pu3X/HcqdcZiePj4+M8EE/v06iM/vT68lEGQzZd NODvQNEeTewKpUU2XUaOHOlyTyAm5jkX0EBUnDXGdxeiCJ8i4bzEYpr6iRMn qlZp9MtRUVE8Iq1evbr2mcXTS1Jh5UWwixcvdu7c2XOcoBGMr+hmwHhgKSkp uXLlcimmxEkP42kJKrp1Nx4nT4tKqD7e/dtvv/GsfaoRFL+zU6xYMX4shPnh hx/El5iU8xKLz7198cUXqv3u2bNHt5peCaQNFBNQq55bow3ya9fCpxgPXlxw duzzXelhjU/xqf0x0aek+SK9r2eZCnN9yrZt27hxoKiUE3Rfu3ZN1Ei8iy0e gGzQoIFSC/r/W2+9xauUPsV4y8Pvp9SpU0e7Xtq1S/NFBb9P1QDv4fqxX9FR qg4YDfAevVes1GXr1q3VqlUT32MSrF27ln/l9UpFxkM2XUQbOGPGDNUq6tx5 VUREhMF9hS7Cp/jhOILtU4RGNAajM4g6I/GEpHav/d577/EPH3/8cdoXv510 /PjxIUOG0E9En/XBBx9wse7du585c4b6pg0bNtSuXdv1N8pxgkYw/uVco4Dx wBITE/m56NatWx8+fJhG4NfcBJKWoGJEfYNxinFI7ty5qYNLSEiIjY39+uuv 2bi5PEZQYsJYGurTyHDnzp20Qc4eo/Qp1HXytRGX+4Ys5Zb2S415+/btaTDj 3z2pQNpAGpXxICpfvnybN28miSkho0aN4vkMXf/0KQaDP3LkCM+jSBsZMWIE f5CCxskrV65s3LjxnDlz/KhjxsAan+JT+2OuTzEuva9nmQpzfUqaonEgm0DO gpK8b98+8XnNRo0aiZJ0jtDQi5e3aNGCHHpqaiod/E2bNhUZVn3n0WDLw5+P zJIly9KlS6kF1mhyNWqX5osKfp+qAY67/Njv0KFDOYekjvZ+BfApXrFSF36n NUeOHB9//PH69esvXLhw7tw5GkKLT6svW7bMr4qGGLLpkpSUxG+v0Ga/++47 0c4sXLiQW+BHHnnEv69OhxYy+xTqF0qXLi36FNIle/bsfP9Lu9emU6xBgwbi hzlz5hQfX1P2ejRS5Q97udxXNXkc6HI/4ew5TtAIxr+caxTwKTBx8VwJ9aFF ihTp2LHjkiVLfE1LUDGivsE46ZzlW9Ke8AylqhEUJc2zZN68eQcMGMD/V/qU NPdsgeJWi8t971X8v0ePHkGqu8aBTWoq9RXJ4S+DK32K8eBpYMCfymUeeugh UbJChQp+1DFjYI1P8ek0N9enpBmW3tezTIXpPoUaB35RyxM6EcizKLdAVeZh gwrRkqt8isGWJyIiQnlCCWhfNWvWpPGJ8s1WbeGMn4D+naoBtjl+7JdMN6/1 fKI+PeBTvGKlLgsWLBBXHlz/7C9cvr/SErrIpkuau7URVx2LFi1av3590XzR 8k2bNvlax1BEZp+S5r4Xz8Mwcfps3bqVllesWNH1z3l3VVy9evXzzz/nJxME dIBRx6T8/hqdnmI+ape75x03bhwV4ENLOU7QCMa/nGuXMR7YokWLXJoov4lm MC3Bw6D6BuOkYoMHD1Y2C9QU/Pzzz1RlTppqs5RDMXqnTD755JOkoJhSQ+VT 0twPt/BUqIJHHnnk22+/DV7dNQ7s+Pj4du3aiUhoiEsDyJiYGJ7wTeVTjAdP g4RnnnlG2TFR6/fyyy9HR0f7V80MQCBKsU/JmjWrapJJGuVyepXzfRk/zbVb PI21c+fO5Y173gQ0KL2vZ5mSQDIp3iJRXUK5fPny0KFDxVdm+FygtHudz3b3 7t3irorLfX2pc+fOCQkJJUqUoD+nT5+uKm+k5aH/aE9i/NhjjyX9/YlV3a7K +Anox6kaYJvjx355OgJC+WCeNocPH+af/PDDDwZ/EupIqMvRo0e7du0qbqAw 5cqVS+/FqwyJhLqkud/Qb9WqlaqR4XvExqsW0kjuU9LcvRKNJJcsWUL9O99H 8wk6+1auXEm/PXPmjNcCdHhs3rx5zZo1Rq7/BBgMYzADRgKjsQePH6ZOnbpr 1y4y1+S+N2zYMGPGDPFNNM8RbJqBtAQJX9U3EieJsmPHjmXLlsXGxupukAYz lLRVq1bRWMVgDOfOnVu/fj2FYWT7Gvhad69Qn07BUBUM3u01GDwfbKtXr6am T/n+jjMxRSmD+NT+BC8AXel9OssEwcvktWvXaDxA8Wzfvl33hTtqPSjDdNYY fzVPo+Xhj4A8/vjj1ANS7SLcLF269NNPP+V7TC6Pa1y6GD8BfTpVTcw/mggT kVYX6lbohKL+kc4Xz9l4MjzS6pLmHrps2bJl+fLl9K8TvjGkRH6fkvEwMQP8 DaB///vfnquuXr3KM0FpP5hhMU5W38l1Dy2glFlkvEyKh5S8fjxaTBaqeqLM LjJe/jMG0EVOoIucwKdYj4kZ4OfQunXr5rkqKSmJP0bWunVrU/ZlCk5W38l1 Dy2glFlkvEwKJ6KaTZr5/PPPea1/jwSbTsbLf8YAusgJdJET+BTrMTED//73 v13ux7NnzJgRFxcnlm/btq1+/frcY0r1GSAnq+/kuocWUMosMl4mxYdamjRp snPnTvEWUkpKytixY/n1t9q1a9v7OV1Bxst/xgC6yAl0kRP4FOsxMQObN29W vvhWsWLFpk2b8ntezFdffWXKjszCyeo7ue6hBZQyiwyZyZdfflk0sHnz5m3U qNGTTz6ZP39+XlKpUqXTp0/bHeP/R4bMfwYAusgJdJET+BTrMTcD27dvb9++ vZi5TvSeb7zxht9feAkeTlbfyXUPLaCUWWTITF65cuXzzz9/9NFHlU1upkyZ qlSpMm3aNGsmTjRIhsx/BgC6yAl0kRP4FOsJRgZSUlL27du3bt26zZs3WzyF l084WX0n1z20gFJmkYEzee3atZMnT0ZFRa1Zs4baXqnsiSAD5z+kgS5yAl3k BD7FepycAdTd7iiAPlDKLJBJe0H+5QS6yAl0kRP4FOtxcgZQd7ujAPpAKbNA Ju0F+ZcT6CIn0EVO4FOsx8kZQN3tjgLoA6XMApm0F+RfTqCLnEAXOYFPsR4n ZwB1tzsKoA+UMgtk0l6QfzmBLnICXeQEPsV6nJwB1N3uKIA+UMoskEl7Qf7l BLrICXSRE/gU63FyBlB3u6MA+kAps0Am7QX5lxPoIifQRU7gU6zHyRlA3e2O AugDpcwCmbQX5F9OoIucQBc5gU+xHidnAHW3OwqgD5QyC2TSXpB/OYEucgJd 5AQ+xXqcnAHU3e4ogD5QyiyQSXtB/uUEusgJdJETu3wKAAAAAAAAAGgDnwIA AAAAAACQDet9ih8/zDA4OQOou91RAH2glFkgk/aC/MsJdJET6CIn8CnW4+QM oO52RwH0gVJmgUzaC/IvJ9BFTqCLnMCnWI+TM4C62x0F0AdKmQUyaS/Iv5xA FzmBLnICn2I9Ts4A6m53FEAfKGUWyKS9IP9yAl3kBLrICXyK9Tg5A6i73VEA faCUWSCT9oL8ywl0kRPoIifwKdbj5Ayg7nZHAfSBUmaBTNoL8i8n0EVOoIuc wKdYj5MzgLrbHQXQB0qZBTJpL8i/nEAXOYEucgKfYj1OzgDqbncUQB8oZRbI pL0g/3ICXeQEusgJfIr1ODkDqLvdUQB9oJRZIJP2gvzLCXSRE+giJ/Ap1uPk DKDudkcB9IFSZoFM2gvyLyfQRU6gi5zAp1iPkzOAutsdBdAHSpkFMmkvyL+c QBc5gS5yAp9iPU7OAOpudxRAHyhlFsikvSD/cgJd5AS6yAl8ivU4OQOou91R AH2glFkgk/aC/MsJdJET6CIn8CnW4+QMoO52RwH0gVJmgUzaC/IvJ9BFTqCL nMCnWI+TM4C62x0F0AdKmQUyaS/Iv5xAFzmBLnICn2I9Ts4A6q5dZseOHZGR kXv37jWyQZ8K+01ycnKkm6tXrwZ1R/Lg5KPUXJBJe0H+5QS6yAl0kRP4FOtx cgZQd+0yjz76qMvlevbZZ41s0KfCfvPjjz+63JAtCuqO5MHJR6m5IJP2gvzL CXSRE+giJ6HiU5KSkmJiYuhfP/YoG8YzkJCQsGTJklGjRs2cOTMyMvLPP/9U rk1NTY3R49atW0Gpg7/4qr5B3e/du3fgwIH9+/f/9ddfAcUXTOBTQoUgHaUO BJm0F+P5pyZ0z549EydOnDx5Mklw//79IIfmaKCLnBjX5ejRo3Pnzh09evTy 5cs12qvr169v3Lhx7NixU6ZMiY6OvnPnTnolb9++TT0sCf3FF1/8/PPPZ86c 0dh7SkpKeHg47X3FihWXLl0yErCvPz906FB6o8q7d+/6uscAkdynkMrr1q3r 0qVL1qxZaaTUpEkTP/YoG0YysHfv3urVq7v+SYUKFVavXi3KUMPl0mP27NnB rYyPGFTfuO6xsbGTJk0qX74813fLli1mhmsq8CmhgulHqWNBJu3FYP6PHTtW okQJZcdRsWLF+Pj44AfoUKCLnBjRJS0trVu3bqqBVu/evVWXkYkNGzY88sgj ymKkJrlOVTEa9g8ZMiR79uzKklmyZOnevfu1a9c8Axg4cKCyZKZMmcLCwozX 0eDPc+bMmd6ocunSpcZ3Zwoy+xRyqdxtCZ555hk/9igbuhmYOnVqtmzZuMoF CxasXbt25syZ+U86mHft2sXFjPgUMuZWVMkwRtQ3rjs1Dqr6RkZGmh+0ScCn hArmHqVOBpm0FyP5P3r0aLFixTjzlStXrlChAv+/bNmyZ8+etSRMxwFd5ERX l7/++qtx48YsRJkyZVq0aFG8eHH+s2nTpsobDTQUIQvAYzbqo+vVq8etXK5c udasWSOKUetXp04d3gKVr1q1qhCdaNeuneoRkb59+/KqPHnyNGjQgLbGf44Z M8ZIBQ3+/NatW1KNKmX2KYmJiQX/hgfqGaP/0s3AvHnzqLIlS5aMiIjg+7zJ ycmDBg3ig6R58+ZcjHx9nDf279/Ph9/TTz8t221iI+ob151OOi6WO3du+JQg AZ/ilYzaOpkLMmkvRvLfvn17HiMtWrSIl0ybNo1P+R49egQ9REcCXeREV5c5 c+awBO+99x7fQKF/e/bsyQtpLRe7fft2xYoVacnDDz8sJrrZs2cPmxrquO/d u8cLaRRXo0YNWtirV6+LFy/SEhqzUQzlypXz7HYPHDjAC+vWrXv9+nVacunS pUqVKrnc918SEhK0a2f859Qmc8mvvvrKc4T5xx9/+JbWgJHZpyjhB3syRv9l JAMLFy7kg1ZAtrpy5cqUhMKFC2v/lg54KkZD9xMnTgQYqun4qr5B3efPn5+R fErjxo35T2rudu7cGRsb6/W9G12fcuPGjV27dsXExNB2tPdL2z958mRUVNSV K1dUq+BTdMlIrZO5IJP2opv/5ORkvsxL4y7lch4k58uXj9qQ4IboSKCLnOjq 8vzzz1P+S5UqpepSaeRPy2nMz1eGt27dyp3mjBkzlMVWrVrFyxcsWCAWnj17 9pdfflHt6KeffuKSkydPFgv79Onj6SmE+9C9pWL854cPH+aFyhcNbAQ+xXr8 ywDRrFkzPsyEGfeEhrV8TXLSpEn+hxg04FO0y7D1eOGFF44dO9ayZUvxjGjB ggX79u2ren8tPZ9y7tw5apEee+wx8bggdXlvvPGG164tJSWlc+fO+fPn55KZ MmWiH4aHh4sC6fmUDz74oJCb8ePH+5QH+cHo2iyQSXvRzT+dvHx2q97sW7p0 KS+fN29ecEN0JNBFTnR1KVq0KCW/Z8+equVLlixhXfjn06dP5z/Pnz+vKlmt WjXlpcj02L59O29h1KhRvIR6f+ptXd5e3+Ntli1bVmODPv08KiqK9x4dHa0d pzXAp1iPfxm4du3aww8/zJ5doxj7evpXzpmv4FO0y7D1ILy+xUYtjHLCEK8+ ZdasWTly5PD8rVdHExERUaRIEa+FxcMGXn2KuP1NZkrDNYcoGF2bBTJpL7r5 f+utt1zuyyCqs/j69et8PX/QoEHBDdGRQBc50daFOl+VdxCQH+FVPHPRJ598 4nJf9PMchvFDYiVKlNCOJCwsTNURnzlzhpd8/fXXqsIff/wxr9J4Isunn69c uZKXSPImFHyK9fiRgbi4uBYtWvCRM23atPSKCRdM4/ZAowwO8CnaZYRPcbkv 2pA1SExMXLJkiXAT//3vf1WFVe6Dj4GSJUtOnjx57969N27c2LZtm5gPTRnA 5cuX+eoQ8frrr9PpnJaWRgmsXbt2vXr1xH1tT5+ya9cutkJVq1b1OiFJqIPR tVkgk/aim3/uVmrUqOG5it/n7dKlS7CCczDQRU50deHkv/3226rlZGH4peDh w4fTnzNnzuRO0/Odkc8++4yWZ86cOb3ZfcmZLly4kKf/KlWq1M2bN3n5zp07 eZvLli1T/UTs7uTJk+lF7tPP586dy0tWrVo1dOjQbt26jRgxghyTCMZi4FOs x3gG5s2b171796ZNm2bJksXlfuVkypQpGjdKOnXq5HK/uqX7PoJdwKdol2Hr UaBAAeWUIMTBgwd58pCKFSuKAyC9575Wrlypak/IqnB++vfvLxZSY8sLP/zw Q2Vhaj+Vn55X+ZSkpCSea5Gs06lTpwzWPbTA6NoskEl70c1/zZo1XelMBF21 alWX+xnUYAXnYKCLnOjq0qhRI+6glS9y0nCrTZs23Et27dqVlmzatIn/VN15 2bhxo5hiS9V7njt3jnrnV155pUyZMlyAmkFlmeXLl/Nyz48viKfOqKNPL3Kf fj5x4kSXN8g30ehCIz9BAj7FeoxnoHXr1sqDZPTo0RrfbUxMTOTZjMn/mhar 2cCnaJfReDW+efPmXMfk5GTdwp7kzZvX5Z7nkP8ks8NLihYtylN/pIfSp9y5 c6dhw4b0fzrSfvvtNyP7DUUwujYLZNJeDF4f7tChg+cqUoFWVa9ePVjBORjo Iie6uogbDXXq1ImKiqJBV3h4OHXBYpDGPSx1lDzfF3WUYWFhZDf279//2Wef KR/JpiXKLUdHR6scweHDh5UFxF2PgwcPqqKiYQ+v8rxX4t/PhU+hg23w4MEf ffRR7dq1eQlV4dChQzp5NBv4FOsxnoEJEya0atWKjhDxDaDKlSsfPXrUa+Gp U6dyGdXhLRXwKdplNKzHmDFjuI7iAzraPuX27durVq2iX3Xs2LFKlSr82wYN GvDa06dP8xLPVwJVCJ+yfft2cQtm3Lhx2r8KaTC6Ngtk0l5080/DIUr4Sy+9 5LmK2gqX+1XHYAXnYKCLnOjq8tdff/F0Ripee+01fku9b9++XDIiIsLzJVMq QyX5/6oPwZ85c4Z6amr6xJc9M2XKNHLkSPH4xPfff8/L9+3bp4pq/fr1vEpj ei5ff75o0SKqgvjz/v37w4cP52LB/hSCJ/Ap1uNHBs6fP09HLD/5Q6NTr08J vvrqqy73jIWyfTNFCXyKdhkN6zFr1iyu45IlS7QL8x3kwoULezanTz75JJcR EyR+88032iEJn/L666+L7bRu3dpIlUMUjK7NApm0F93816tXjxLeqFEjz1V8 QbhNmzbBCs7BQBc5MdJe0fhq8uTJtWvXzpUrV/bs2Zs2bTp9+vTbt2/z7JrK t9R///33Dh06lCxZ0uX+KOS7774bGxvLo30ap6W3fTImGzZsqFWrFne11O/z 8rVr1/KSTZs2qX6ycOFCXuX5sXtBgD/ninNU5L8snjwHPsV6/MsAMWrUKD6c eE4JFWzDn3vuuUDjCybwKdplNHyKmKly/fr1GoVTUlK4I3O5776FhYXRTi9f vsyZFD4lPDycy2hMy8AIn8LkyZOH//PTTz8ZrHjIgdG1WSCT9qKb/5deesnl nhDDcxVf6PB8ZRgEDnSRE5/aKxq3i+k3xfMJyin9BcrH9d98802Xgcf2kpOT +dk/MTPYvn37VBcqBVOmTOFVGp96DPDnzMCBA7kkGS7tkuYCn2I9fvuUU6dO 8UHSp08f1Sox6dzgwYNNCDFowKdol9HwKe+//z7XMS4uTqNw/fr1Xe4Ppqgm 2Ff5lCNHjvDWBgwYoB2S0qfUrFnz/PnzZcuWdblfbCH7o/3bEAWja7NAJu1F N/89evTgC6SqjyvRiIVP+WHDhgU3REcCXeTE77HZ7NmzjdySuHfvXunSpV3G bod17dqVt8mf/E5KSuI/R4wYoSr5zjvvuNzPiaU3h1jgP2fEo1+ql2uCDXyK 9ehmIL0ZvYRn79Wrl2qVeIxH2hmJGfgU7TLp+RRqQPiJ5Vy5cmnM95Wamsp5 6N27t2oLKp9CG+R5+Gkj2q2T8Cn/+te/4uPjHygmV+/evbt+tUMQjK7NApm0 F938L1iwgM9l1Ru4M2bM4OUbN24MboiOBLrIid8+pXr16iRKuXLl/vzzT41i 4jOdyu8LpDfe4zsvREpKCi/hJyVUs1XTz4sXL07Ln3rqKe0gA/z5g79ndsqe PbuuozEX+BTr0c1Ap06dOnTokJaWplounvuaO3euapV4eWHDhg2mBmsy8Cna Zdh6UEuimll68uTJXMHOnTurCit9yoEDB7w62ejo6Hz58il9CtGqVSsu7Pnh J+VcDcKnkBcWC9u2bcsLN2/erFvxkAOja7NAJu1FN/83b94sWLAg5bxZs2bi xUYahDzxxBMu90P1Mr/tGLpAFzkx0l5RJ6uadpVMB/eG3333nVhIPbjqpsPl y5d5ummST4zz9+7dSzZB9RmCB+5XkvnrZlRYLBw7dizvaOvWrWLhihUreOH3 338vFtLxQx33okWLlKEa/Pm+ffsozpiYGM/k8CvS1s/hILNP2bVr1/a/4Zcv qBcTS0L3A3PaGVi2bBkfNpUrV/7222+PHDlChjc1NXX48OH8FZUCBQqcO3dO 9atPP/2Uf0WHfXCjDwwj6hvUPTExUSwcMWIEVz8sLIyXSDjpmXGf4nJ/J2Xl ypXkVZOSksaMGcPtQ44cOcRDXwQ1F5wc0WdR68Rv8+XPn3/37t105FCWKCc8 YbXKpxw/flzMIzd48GDa8r1796iNIptMGxFTqXv9Hv3p06d5HniKU2Ou7BDF xKPU4SCT9mIk/+KB0n79+l29epVGU927d+clo0ePtiRMxwFd5ERXF+pVc+fO XadOHeoN//zzz4SEBNKCe+dSpUqJ11Wo5+3YsWPWrFnJGiQnJ5NnIWtAIzqW b+bMmWKDNWrUcLmfuSK5f/31V2ruaLMUw+OPP86FBw4cKArTYICfgiALw/07 bZYGhLQkb968yu8LfPjhh/xz5VcqDP6cP9CTM2fOkSNHRkVFUf9OUc2aNYsG FRzqunXrzEi2D8jsU/j7DukxYcIEP/YuA9oZoEOdZ+4SiMEkHyRev7PTs2dP LnD27Nkghh4wRtQ3qDv9R6MYbSTolfER4z7Fcz5Dglyq6p33/v3786rChQuL +TeUs3Jxo+RyPy1Wrlw51z99ygP3XNaiDO9C/F+8A+XVpzz4+7u6Lrm/1+Mf Jh6lDgeZtBcj+acxML/UxvCgy+W+kp/xLkFIAnSRE11dhgwZ4rW7LF26tPIS MQ3D+G6IZ8kBAwYoJ8ui3fGExkzmzJmVPXKDBg2E92EWLFggtiYOiRw5cqxd u1ZZjK9hEk8//bSvP1+2bBl5MWXwoiQxaNAgv1IbEKHrU8aPH+/H3mXASAbC w8N5mnQljRs3JhfstbywNl6nLJaHwMctQndtn5InT56gV8ZHjNSdL7nMmTOH HISy+SpbtqznE1ZkHMS3a8X8G1euXOnYsaP4ITVBrVu3PnHiBD80qPIpD9xn cZ06dZQNUcmSJX/44QdRYPHixbxcdb7fvn27UqVKLvenrCR3x75i4lHqcJBJ ezHY29KQ+MUXXxQXxHLmzEkdCgbDwQO6yIkRXah3Fp844R62bdu2qampqmLJ yclK7bhj9fr6cEpKyvvvv6/6jkD+/Pk///zzP/74w7P8okWL+GVV5tFHH1WZ FOLLL7/ktZMmTfLj5/Hx8d27d+cbKILy5ctrfJ8lqMjsUzIqxjOQlJS0bdu2 NWvW0IhU9VWgEMXJ6vta9/v37x84cGDDhg0aswVSmaioqHXr1qk6r7i4OFpO B4/qPZf0uHbtGpWPiIjwfKTQgTj5KDUXZNJefMr/zZs3o6OjjTcawG+gi5wY 14U6SuqaSRfV/Q4VtHb37t0bN25MTEzU3uDdu3epu//111+p8PHjx7Xfx3/g nv118+bNGlcIjxw5ovHheN2fP/j7FZtNmzZRSd34gwp8ivU4OQOou91RAH2g lFkgk/aC/MsJdJET6CIn8CnW4+QMoO52RwH0gVJmgUzaC/IvJ9BFTqCLnMCn WI+TM4C62x0F0AdKmQUyaS/Iv5xAFzmBLnICn2I9Ts4A6m53FEAfKGUWyKS9 IP9yAl3kBLrICXyK9Tg5A6i73VEAfaCUWSCT9oL8ywl0kRPoIifwKdbj5Ayg 7nZHAfSBUmaBTNoL8i8n0EVOoIucwKdYj5MzgLrbHQXQB0qZBTJpL8i/nEAX OYEucgKfYj1OzgDqbncUQB8oZRbIpL0g/3ICXeQEusgJfIr1ODkDqLvdUQB9 oJRZIJP2gvzLCXSRE+giJ/Ap1uPkDKDudkcB9IFSZoFM2gvyLyfQRU6gi5zA p1iPkzOAutsdBdAHSpkFMmkvyL+cQBc5gS5yAp9iPU7OAOpudxRAHyhlFsik vSD/cgJd5AS6yAl8ivU4OQOou91RAH2glFkgk/aC/MsJdJET6CIn8CnW4+QM oO52RwH0gVJmgUzaC/IvJ9BFTqCLnMCnWI+TM4C62x0F0AdKmQUyaS/Iv5xA FzmBLnJil08BAAAAAAAAAG3gUwAAAAAAAACygee+rMTJGUDd7Y4C6AOlzAKZ tBfkX06gi5xAFzmBT7EeJ2cAdbc7CqAPlDILZNJekH85gS5yAl3kBD7Fepyc AdTd7iiAPlDKLJBJe0H+5QS6yAl0kRP4FOtxcgZQd7ujAPpAKbNAJu0F+ZcT 6CIn0EVO4FOsx8kZQN3tjgLoA6XMApm0F+RfTqCLnEAXOYFPsR4nZwB1tzsK oA+UMgtk0l6QfzmBLnICXeQEPsV6nJwB1N3uKIA+UMoskEl7Qf7lBLrICXSR E/gU63FyBlB3u6MA+kAps0Am7QX5lxPoIifQRU7gU6zHyRlA3e2OAugDpcwC mbQX5F9OoIucQBc5gU+xHidnAHW3OwqgD5QyC2TSXpB/OYEucgJd5AQ+xXqc nAHU3e4ogD5QyiyQSXtB/uUEusgJdJET+BTrcXIGUHe7owD6QCmzQCbtBfmX E+giJ9BFTuBTrMfJGUDd7Y4C6AOlzAKZtBfkX06gi5xAFzmBT7EeJ2cAdbc7 CqAPlDILZNJekH85gS5yAl3kBD7FepycAdTd7iiAPlDKLJBJe0H+5QS6yAl0 kRP4FOtxcgZQd+0yO3bsiIyM3Lt3ryURAe84+Sg1F2TSXpB/OYEucgJd5AQ+ xXqcnAHUXbvMo48+6nK5nn32WSMb/P7771999dVFixaZEBxQ4OSj1FyQSXtB /uUEusgJdJGTUPEpSUlJMTEx9K8fe5QN4xlITk5evHjxqFGj5s6du2fPnvSK /fnnn7t27RrvZs2aNampqabFajbG63706FGq9ejRo5cvX+4c3Y37lP3797vc ZMmSJSEhwZwQgRvjR+m9e/foxJw4ceLkyZOpgbp//36QQwsxfG3tM1I7LwM4 kuUEusiJcV2oz12yZAmNzWbOnBkZGUljMM8yhw4dikmHu3fvqgpfv35948aN Y8eOnTJlSnR09J07d7zu16dtpgdtZN68eSNHjpw1a9b27dv/+usvr8UMhmQB kvsUStS6deu6dOmSNWtWGpI1adLEjz3KhpEM7N69u2rVqq5/0rJly7i4OGUx OjL79u2bJ08eZbECBQrQ4RfECgSAkbqnpaV169ZNVffevXt7bQpCCHN9ypkz Z8ihUOFs2bIlJiaaEyJwY7CNOnbsWIkSJZRHacWKFePj44MfYMhgMJMZsp2X ARzJcgJd5MSILnv37q1evbpqfFKhQoXVq1erSubMmdOVDkuXLlWW3LBhwyOP PKIsQLp7vTRtfJteSU1NffXVV1U/bNSoUWxsrKqk8ZAsQGafkpSUxN2W4Jln nvFjj7Khm4GFCxeKo7FYsWINGzbMnz8//1mlShXhai9evEgdOi/PnDnzE088 8dhjj4lc/fjjj1ZUxkd0607WvnHjxlyFMmXKtGjRonjx4vxn06ZNjV8xkBBz fcoDd0vyySefbNmyxYTggAIjSh09epTOTT4yK1euTP0U/79s2bJnz561JMwQ wEgmM2o7LwM4kuUEusiJri5Tp07Nli0bC1GwYMHatWvT0Iv/zJ49+65du0TJ W7dupWcoiJ9//lmUjIyMzJQpE2+Buv569epxe5grV641a9Yo9258m165f//+ 888/z4ULFSrUtWvXF154gf8k/3vz5k0/QrIGmX1KYmJiwb/hgyFj9F/aGbh0 6RK7EmqXtm7dyvd5r1y50rZtWz6ixo0bxyWHDRvGS2iwKp71IlOfO3duWvjQ Qw/duHEj+LXxDV3158yZw5V67733+AYK/duzZ09eSGstCjQImO5TQJAwolT7 9u1JKWrMxftB06ZN46O0R48eQQ8xRDCSyYzazssAjmQ5gS5yoqvLvHnzKP8l S5aMiIjgsVlycvKgQYNYl+bNm4uS1Kzxwq+++irOgz/++IOL3b59mzwCFXv4 4YfF/Dl79uzhy7M0Hrh3756v20yPX375hX8+ePBg2i8vnD9/Pi/8+uuv/QjJ GmT2KUrKly+fYfov3Qzs2LGjTZs2Fy5cUC68ePEi+5eWLVvyEjpa3n77bc+h u/AvSncvCbp1Z79fqlQpcR4xdevWpeWVKlUK3Qd0jfuUxo0biyVHjhzZvXu3 r5YzLS2NWpidO3dev35dt3BCQgI54qtXr+qWPH369JYtW4y/PkBhREVFnTlz Jr0nYOVEVynqm/j6Erlp5XIeWuTLl0/CSwS24Gtrn5HaeRnAkSwn0EVOjLRX CxcupMGYcgn1bpUrVyZdChcuLBYePnyYh2Gez4MpoZ6Xi82YMUO5fNWqVbx8 wYIFvm4zPYYMGUK/zZMnj+oRempvaXnnzp39CMka4FOsx78MEPyUV5kyZbSL 0UiSD6e5c+f6sZegolv3okWLUuQ9e/ZULV+yZAlXyr/UyYBxn9K8eXNyDT16 9PjXv/7Ftc6cOTN1WKoX2WrXrl2oUCGytMqFJ06ceOmll1x/kylTpmrVqimf XF28eDH9qkKFCtRYhYWFlStXTpSsWrXq9u3bVSFRI0zJf+655woWLCg2+8gj j2zYsEFVkrdMXpL+v2LFioYNG4rneUqWLOm5ZWnRVWr8+PFcL9VDd5RnXj5v 3rzghhgiwKfYC45kOYEucuL32KxZs2Yu95w24l5DVFQUKxUdHa3xw+nTp3Ox 8+fPq1ZRx626aGlwm+nRq1cvl/tJG9Vyfh24UaNGfoRkDfAp1uP3uUDjUkoC HSraxVauXMmHmZH3qixGu+40DufIR40apVpFpwyvmj17dlAjDB7GfQpZUT7g VXz00UeehZUPiV26dKlUqVKiPD8ByIipFX788Ude8vjjj3vuIkeOHGQxxAav XbvmtZjLbZ02bdqkjEdseciQIfx0q5JcuXLJPBOdEl2l3nrrLZf7+WTVHfDr 16+zNRs0aFBwQwwR4FPsBUeynEAXOfFvbEa95MMPP+xyP+8hFophmPbLRJ98 8onLfZHQ85EDfty9RIkSvm4zPcQNEVUdeVoAcit+hGQN8CnW418Grly5wg9v v/3229olP/zwQz4aJZyuVrfu/OagZx3JwtBAl1YNHz48iPEFE+M+hXnxxReX LVt24sSJSZMmcd9UoEAB5TOonj4lLCyMfztgwIBz585RO3Pw4MF///vftWrV unXrFpcRbsLlfiVz3rx58fHx+/bte/PNN3lh6dKllfeFn3/+eWqyXnvttdWr VycnJyclJQ0dOpRLqq6rKLdcqFChcePGxcTEREdH169fnxd+/vnnAWfRCnSV atGiBVWnRo0anqv4AO7SpUuwggsp4FPsBUeynEAXOfFjbBYXF8diEdOmTRPL 586dywvJHVCPSS5gxIgRixYtUr6uTsycOTO90dpnn33G1wPF9EEGt5keNAbg dwceeuihzZs388LIyEjepljiU0jWAJ9iPf5lQNwI/uGHHzSKXb16lWeTK1eu nP8hBg3dujdq1IgH5OTLxMLbt2+3adOGq9+1a9egRxkcfPIp1P4or2ZQr8TL Dxw4oCqs9CmvvPKKyz1Hh6rhUrYqwk106NBB9U5Kx44deZXyAVQySp5nupgn RCmT2DKdrcp5DmkLfHulffv22tWXBF2latas6Upn+lyeTpzyE6zgQgr4FHvB kSwn0EVOjLdX8+bN6969e9OmTfnrALlz554yZYqyy544caLLG6VKlVq5cqUo tmnTJl6ueoZk48aNfGGWOHXqlE/b1CAqKipfvnz8q3bt2lGXTZ6F/v/aa6/5 F5I1wKdYjx8ZOH36ND/DU6VKFe3PiIipsax/18kIunUXVwzq1KlD51RiYmJ4 eDgNxcUpSSeXVcGajHGf8vTTT6uWi2nQ1q5dqyqs9Cn9+/fnYhqP/Ak3Ia6f CHbs2MGrPN8PUjFhwgQuuX//fs8tR0REqMqXLl2alterV097s5Jg8K4fGT3P VfxOYvXq1YMVXEgBn2IvOJLlBLrIifH2qnXr1kqnMHr0aPHEAiM8Bek1ePDg jz76iJ/bd7kfrj506BAXu3PnDk+ulS1btrCwMBr/U5f62WefURmxcdHJGtym Bnfv3iVLorI5devWVT6n4VNI1gCfYj2+ZuD+/fvi8vW6des0Sm7ZsoUvXDdo 0EDOGZZ0605h8ytpKujkKlSoEP2nb9++VgVrMsZ9iue8xGRPOA9ijkqvhclo iFfXW7VqtXr1as8pBDV8Ch1p/HMxp5ySw4cPz5o16z//+Q81a+KazPr1641s mRyK65+P78qMrlL8EtBLL73kuYpOPW75gxVcSAGfYi84kuUEusiJ8fZqwoQJ 1MOSTciePTv3epUrVz569KiyDHXWykt21L0OHz6cCyt7bSrj+fVGGu0IQ3Hp 0iVft+mV69ev8yVf6r779OkjvkyXOXPmkSNHKkv6FJIFwKdYj68Z6NevHx8b 2o+k/v7770WKFHG5b0Eqnw6SCiN1p1Nv8uTJ1ALkypWLGoGmTZtOnz799u3b /HqOmOU75AjEp2zcuNGITyF++ukn5cRcJUqUGDdunHKSZw03QfAMY6q5Gmin PC+0J7/++quRLT/11FMZyaew7RITpCjhK1GqSdgcC3yKveBIlhPoIid+jE7P nz9Pg3y+Pkw9svarIjS2qVWrFpUkF6C8hEiDtw4dOpQsWdLlnkXn3XffjY2N ZQNCnkI7gPS26Unnzp1d7smTebqwO3fuTJ06lWcAIL744gtl4UBCMh34FOvx KQPiTh8NFDVOgQsXLogZonQ/S2ojPtWdTkAxE+/p06e5duHh4cEKLshY41OI lJSUUaNGcQvDkO+4fPkyr9X2KXnz5qVV9evXF0vEEZgjRw5q6MgHHTt2jD93 5VifwjM/V61a1XMV9QIuA5NdOAT4FHvBkSwn0EVO/BudEtThct+nOx/pwIED uaTyFU6B8uExntnGyAN+2ttkDh48yGW++eYb5fK4uDiyIS73hJyeExH7HZK5 wKdYj/EMLF26lG8iFCtWTGPyrhs3bvC9YGLo0KGmBRoE/G4H6PTnCu7Zs8fs oCzCMp/C3Lt3b8WKFfzSJfHWW2/xcg03QW5XVXjt2rV8pahhw4bKD4+Kj9g6 06f06NGDr1+pvrZGJylnYNiwYcENMUSAT7EXHMlyAl3kxO/xyalTp1iXPn36 aJcUj2lpv+JB3Te/1GnkxpmRbX777bdcxnNO4x9++IFXab+M71NI5gKfYj0G M0DHTLZs2Vzuu2wag/ObN28+99xzfJi9/vrrkn+u3e92gKf4LleunPY0AjJj sU9h0tLS+DkBMee5hpsQExKGhYXxkt69e7vcU6mnpKQoSzrcpyxYsIBrumzZ MuXyGTNm8HLSK7ghhgjwKfaCI1lOoIuc6OqS3mu/4nmPXr16ae+CX8DPnj27 9tS+4oOe//3vf/WiNrTNL774wuX+EqXqfX+Chpe8L/IyZoVkLvAp1mMkA2vW rOH3s3LmzKlRmA458Yp927Zt5R/DG6n7gQMHVKcSnRdcx++++y6IwQUZC3xK bGzskSNHVL9t0qQJFcufPz//KdzEwoULlcWuXr1atmxZbsrERtq3b+9yv2eX nJwsSt65c0d8bMWZPuXmzZv8ElCzZs3ElQHqI5544gmX+2leyS8XWAZ8ir3g SJYT6CInurp06tSpQ4cOaWlpquXiua+5c+fSn/v27atZs2ZMTIzn9vn5BOU0 CLdv31bdB7l8+TJPTE1CC+vh0zbp+KHumEYLYiglhhAcoZKvv/6aV+3YscOn kCxDZp+ya9eu7X9TokQJl/u7DGLJtWvX/Ni7DOhmYN26dWIKuCFDhtCfv/zy ywoFPA6kY0l8YIhGoWRtVq9eveKfJCUlWVQrY+jWfffu3blz565Tpw6dMmS7 EhISRo8ezadhqVKlxOsqoUiwfQrfOiF7O3LkSH5K8I8//qDy/MOmTZtyMeEm yH307t37+PHjlNVt27ZVq1aNl/fo0UPsgg4/XtizZ8+LFy9SA7V161buK53s U4j333+fK9uvXz+yeNSMd+/enZfQEWtJmCGAkUxm1HZeBnAkywl0kRNtXZYt W8b5r1y58rfffnvkyJG//vorNTV1+PDh/BWVAgUKnDt37sHf37jJmTMn9cVR UVFkFqgdmzVrFn9mkcYzYuJW2kLHjh2zZs06duzY5ORkGtRRD0vb5x3NnDlT 7N34Nh8oPvYtXgS4fv06t6558uSZP3++uDG0fPly/uZFyZIleb4d4yFZhsw+ hV/pTY8JEyb4sXcZ0M4AHRWeM8KpIGNLJamx0i5G0EFoXcUMoKu+GBi73Bf2 xf9Lly69d+9eq8IMCsH2KdHR0UWLFhUZo/+LrzJly5aNDCAXU3413pMaNWoo H/E6ePCgsMyZ3fD/halxrE+hkUP9+vVF3thKu9zXPz1vrDsWI5nMqO28DOBI lhPoIifauty5c+fVV19Vtk5iUmIWSLzfQY6GB/8MjWSEfMSgQYPENs+ePavs tZVjngEDBijn7zK+TULMz6n8FtuOHTtEwMWLF6dV/AQFV2Tnzp2+hmQZoetT xo8f78feZUDXpygPDK/w9/KGDRumXcz1z88CyoAR9efMmcPGn6Fxctu2bVNT Uy0JMIgYqTtftaCeSLX8t99+42wofYpnYeraPvjgAzHTIFOrVq0tW7aIMsJN zJ49W7zZ5HK3VN27d/ecU46aRzHRusttGCdNmkQtNh+lSp+yePFiLrN9+3bV RnhHGcmnPHBn+8UXXxQtf86cOakXwxBCSeA+JXTbeRnAkSwn0EVOjOgSHh4u pi0SNG7cWFwJZOLj46k/5ZsdgvLly69evVq1weTkZKXKLvetjfnz53vu2vg2 v/zyS15LnbVy+bFjx2g0pQq+TZs2qi+/GA/JGmT2KRkVJ2fAeN3PnTu3YcOG 6OjokH7WS4mVulP2NrlJSEhQvfonfAqbFzKA5DV27typ/MaKCjIv1Ahv3rzZ c6qQDIlPSlFy6Cjdtm2bRgIdi5PbOhnAkSwn0EVOjOuSlJREiqxZs2bHjh0a Hz3kFz2oI6beMzExUWODNM6hTnbjxo3axYxv88iRI+l9oT4tLW3Pnj3r16+n fz3ftfEjpGADn2I9Ts4A6m53FDrfTwEPpFEqA4BM2gvyLyfQRU6gi5zAp1iP kzOAutsdBXyKPpIolQFAJu0F+ZcT6CIn0EVO4FOsx8kZQN3tjgI+RR9JlMoA IJP2gvzLCXSRE+giJ/Ap1uPkDKDudkcBn6KPJEplAJBJe0H+5QS6yAl0kRP4 FOtxcgZQd7uj+L+pw5q6UU3xAQSSKJUBQCbtBfmXE+giJ9BFTuBTrMfJGUDd 7Y4C6AOlzAKZtBfkX06gi5xAFzmBT7EeJ2cAdbc7CqAPlDILZNJekH85gS5y Al3kBD7FepycAdTd7iiAPlDKLJBJe0H+5QS6yAl0kRP4FOtxcgZQd7ujAPpA KbNAJu0F+ZcT6CIn0EVO4FOsx8kZQN3tjgLoA6XMApm0F+RfTqCLnEAXOYFP sR4nZwB1tzsKoA+UMgtk0l6QfzmBLnICXeQEPsV6nJwB1N3uKIA+UMoskEl7 Qf7lBLrICXSRE/gU63FyBlB3u6MA+kAps0Am7QX5lxPoIifQRU7gU6zHyRlA 3e2OAugDpcwCmbQX5F9OoIucQBc5gU+xHidnAHW3OwqgD5QyC2TSXpB/OYEu cgJd5MQunwIAAAAAAAAA2sCnAAAAAAAAAGQDz31ZiZMzgLrbHQXQB0qZBTJp L8i/nEAXOYEucgKfYj1OzgDqbncUQB8oZRbIpL0g/3ICXeQEusgJfIr1ODkD qLvdUQB9oJRZIJP2gvzLCXSRE+giJ/Ap1uPkDKDudkcB9IFSZoFM2gvyLyfQ RU6gi5zAp1iPkzOAutsdBdAHSpkFMmkvyL+cQBc5gS5yAp9iPU7OAOpudxRA HyhlFsikvSD/cgJd5AS6yAl8ivU4OQOou91RAH2glFkgk/aC/MsJdJET6CIn 8CnW4+QMoO52RwH0gVJmgUzaC/IvJ9BFTqCLnMCnWI+TM4C62x0F0AdKmQUy aS/Iv5xAFzmBLnICn2I9Ts4A6m53FEAfKGUWyKS9IP9yAl3kBLrICXyK9Tg5 A6i73VEAfaCUWSCT9oL8ywl0kRPoIifwKdbj5Ayg7nZHAfSBUmaBTNoL8i8n 0EVOoIucwKdYj5MzgLrbHQXQB0qZBTJpL8i/nEAXOYEucgKfYj1OzgDqbncU QB8oZRbIpL0g/3ICXeQEusgJfIr1ODkDqLvdUQB9oJRZIJP2gvzLCXSRE+gi J/Ap1uPkDKDu2mV27NgRGRm5d+9eIxv0qbDfJCcnR7q5evVqUHckD04+Ss0F mbQX5F9OoIucQBc5gU+xHidnAHXXLvPoo4+6XK5nn33WyAZ9Kuw3P/74o8sN 2aKg7kgenHyUmgsyaS/Iv5xAFzmBLnISKj4lKSkpJiaG/vVjj7JhPAMJCQlL liwZNWrUzJkzIyMj//zzz/RKpqSkhIeHjx49esWKFZcuXTItVrPxVX1d3Skn u3btGu9mzZo1qampJkQZHOBTQgXjR+m9e/f27NkzceLEyZMn04F6//79IIcW YiCT9oL8ywl0kRPjuiQnJy9evJjGZnPnziWBjPwkPj4+xs3ly5d1Cx89epS2 TCO65cuXa497DY79rl+/vnHjxrFjx06ZMiU6OvrOnTtGYmYOHToUkw537941 vh2/kdynUG7XrVvXpUuXrFmz0kipSZMmfuxRNoxkYO/evdWrV3f9kwoVKqxe vdqz8MCBA5XFMmXKFBYWFpTQA8ag+kZ0pxOkb9++efLkUda9QIECs2bNMj9u M4BPCRUMHqXHjh0rUaKE8vCrWLEidUbBDzBkQCbtBfmXE+giJ0Z02b17d9Wq VVVjs5YtW8bFxWn8itxEkSJFuPCCBQs0SqalpXXr1k21/d69e3u9TG1w7Ldh w4ZHHnlEWZKOK4P2isiZM6crHZYuXWpwI4Egs08hF8nDVMEzzzzjxx5lQzcD U6dOzZYtG1e5YMGCtWvXzpw5M/+ZPXv2Xbt2KQvTWJ1X0Yi9QYMGuXLl4j/H jBkT3Gr4hRH1jeh+8eJFMi+8lpLzxBNPPPbYY6I8Da2DVYEAgE8JFYwodfTo 0WLFinFmKleuXKFCBf5/2bJlz549a0mYIQAyaS/Iv5xAFznR1WXhwoVi0E7q NGzYMH/+/PxnlSpVNG5SvPzyy2J8ouFT/vrrr8aNG3OxMmXKtGjRonjx4vxn 06ZNVTcvDI79IiMjyb/w6JFGC/Xq1ePxFZVfs2aNbk5u3brlSp+ff/5ZdwuB I7NPSUxMLPg3PFB3iE+ZN28eVbZkyZIRERF8nzc5OXnQoEF8YDRv3lyUPHDg AC+sW7fu9evXacmlS5cqVapES7JkyZKQkBDkqviMEfWN6D5s2DCu+CeffCKe 9Vq9enXu3Llp4UMPPXTjxo1gxB8I8CmhghGl2rdv73Jfv1q0aBEvmTZtGieq R48eQQ8xREAm7QX5lxPoIifautDgil0JecatW7fy2OzKlStt27ZlXcaNG+f1 h4sXL1aO7TV8ypw5c7jMe++9xzdQ6N+ePXvyQlorShoc+92+fbtixYq08OGH HxZT7uzZs4ftDw0h7t27p50TGo/xjr766qs4D/744w/tn5uCzD5FSfny5Z3j Ux64bfvFixeVS8hoV65cmZJQuHBhsbBPnz6elkQcwBLeUvFV/fR0p5Pr7bff Vp62jPAvqrtOMmDcpzRu3Jj/pEZm586dsbGxpH56hTV8Cpk1ykNMTAxtR3u/ tP2TJ09GRUVRq6taBZ/iSXJyMl+Sot5EuZyHFvny5ZPQJtsCMmkvyL+cQBc5 0dWFOsE2bdpcuHBBuZCGauxfWrZs6fmT8+fP8xNf9evX1/Upzz//PBUoVaqU qssmM0LLyYaIF5QMjv3IT/GSGTNmKDe4atUq3WCYw4cPc0mvLx1YA3yK9fiX AaJZs2Z8ZLIFvnv3bqFChVzeXt+oVq2ay32DOPBozcUsn5IeW7Zs4XNq7ty5 foQXVIz7lBdeeOHYsWPU6IlbzAULFuzbt6/qtm96PuXcuXPUiD322GPicUHq 8t544w2vXVtKSkrnzp3FzetMmTLRD8PDw0WB9HzKBx98UMjN+PHjfcqD/Ogq RVXmnNDxply+dOlSXj5v3rzghhgiIJP2gvzLCXSRE7/HZvwUepkyZTxXsbXM nj17RESErjUoWrQoFejZs6dq+ZIlS/i3HJ7xsd/06dP5h2SXvJYUF0XTIyoq ircQHR2tXTJ4wKdYj38ZuHbt2sMPP8yempecOXOGj5+vv/5aVfjjjz/mVdbc lTNOsH3KypUrueLWvN7lE8Z9CuH1zTVqlJRPwHr1KbNmzcqRI4fnb706Gmo5 xct9KsTDBl59irg9TWZK98ZxyKGr1FtvveVym0dV3a9fv85XQQcNGhTcEEME ZNJekH85gS5y4rdPqV27NolCI3/VcupGuaP87LPPTpw4oe1TqHPnAqNGjVKt IpfBq2bPnv3Al7HfJ5984nJffvR8JIMfJytRooR21cSYysa3ouBTrMePDMTF xbVo0YKPlmnTpvHCnTt38pJly5apys+cOZNXnTx50pSYzSLYPuXDDz/kiofo uznCp7jcF1XIGiQmJi5ZskS4if/+97+qwir3wVc/SpYsOXny5L179964cWPb tm2cRtffV2OYy5cv89Ub4vXXX6fTOS0tLTIykprcevXqifvOnj5l165dbIWq Vq1K9tmM3MiFrlJ8MtaoUcNzFb/62qVLl2AFF1Igk/aC/MsJdJET/0anV65c 4UcX3n77beVyMhcPPfQQLX/yySfJbx4/flzbpzz4W1zVdh64LQy/Jj98+PAH voz9xJ+eIyKyTi73NETacwvPnTuXt7Bq1aqhQ4d269ZtxIgR5L9u3ryplxjT gE+xHuMZmDdvXvfu3Zs2bZolSxaqfu7cuadMmSJ88fLly/n4Ud0afqC4S0hj VHODD5Cg+pSrV6/y5HvlypXzM75gYtynFChQQDURx8GDB3nKjooVK4oDIL3n vlauXKlqQ+gw4OOhf//+YiE1hryQzJ2yMLVayk/Pq3xKUlISJ5ms06lTpwzW PbTQVapmzZqudKbL5ikrX3jhhWAFF1Igk/aC/MsJdJET/0an4iG9H374Qbm8 Xbt2Lve0WrGxsfSnEZ/SqFEjHgAoXxS9fft2mzZt+Lddu3Z94MvYb9OmTfyn 6h7Nxo0bxfxg2v34xIkTXd4oVaoUjTQMpihA4FOsx3gGWrdurTwwRo8efevW LbFWOGUaxKp+GBkZmZ7dtpeg+hQxLYbuq2G2YNyneH01vnnz5ly75ORk3cKe 5M2blwpTy8l/ktnhJUWLFuXZQtJD6VPu3LnTsGFD+n+2bNl+++03I/sNRXSV 4qteHTp08FxFxyqtql69erCCCymQSXtB/uUEusiJH6PT06dP8yyjVapUUX7i hAYh3G9OmjSJlxjxKeLmRZ06daKiohITE8PDw6mLF4NA7sGNj/2oy+b5vqjL DgsLI0uyf//+zz77TPlwOC3RqKDwKXTgDR48+KOPPuKH3AjayKFDh3xKl3/A p1iP8QxMmDChVatWdFRkz56dD4zKlSsfPXqU137//fe8cN++faofrl+/nlfZ OEWDV4LnU7Zs2cJ3HBo0aOB1dizbCdCnjBkzhjUVU5lp+5Tbt2+vWrWKftWx Y0dqQvm3lBxeS60rL/F8ZU+F8Cnbt28Xt2DSm4AxY6CrVKlSpSgJL730kucq yrDLPVdksIILKZBJe0H+5QS6yImv45P79++/8MIL3CeuW7dOLE9OTi5cuLDL fUdMjEaM+BQqzNMlqXjttdf4xfm+ffs+8HHsFxER4fm6K22Ntsn/1/iKPbNo 0SLaiLLWw4cP598G+7MIDHyK9fiRgfPnz48cOZLH4TQ65ad61q5dy4fKpk2b VOUXLlzIq4x/ctQaguRTfv/9d36DI3fu3AcOHAgoxKARoE+ZNWsWa7pkyRLt wufOnevfvz+3kyqefPJJLiOmJfzmm2+0QxI+5fXXXxfbad26tZEqhyi6StWr V4+S0KhRI89VfPGqTZs2wQoupEAm7QX5lxPoIie+jk/69evHHaLqdSEymLx8 x44dSX8jpgieOnUq/ZmWluZ1m+QCJk+eXLt27Vy5cmXPnr1p06bTp0+/ffs2 vwLDL877OvajAVKHDh1Klizpck9K9u6778bGxrLXyJcvn/H6KoOsVauWyz3h jwUT6cCnWI9/GSBGjRrFRyDP+UBWWjVwFUyZMoVXyfY6eTB8yoULF8R74tZ8 HdU/AvQp4iHY9evXaxROSUnhjszlvvsWFhZGO718+TKnSPiU8PBwLiOmZUgP 4VOYPHny8H9++ukngxUPOXSV4m6oatWqnqvYHnq+COlMkEl7Qf7lBLrIiU/j E/FAVN26dZUvhB45csTzCqEnTz31lPb2yQuI6T3F8w/8yQC/x37KFwfefPNN VwAPEA4cOJB3xG/fBBX4FOvx26ecOnWKD4w+ffo8cL/RzH+OGDFCVfKdd95x uSej057JwXpM9yk3btzg++DE0KFDTQgxaAToU95//32uZlxcnEZh/phU1qxZ VRPsq3yKaEsHDBigHZLSp9SsWfP8+fNly5Z1uV9sIfuj/dsQRVepHj16uNyX klSfpKGugRM1bNiw4IYYIiCT9oL8ywl0kRPj45OlS5fyDY5ixYqpHMGxY8eM +JQnnnjCeGCzZ8/mX/FdksDHfvfu3StdurQrgBtz4tEv7ddbTAE+xXp0M5De 6xXCU/fq1YuX8JVz1eyF9PPixYu7DBh26zHXp9y8efO5557jnLz++uviU61y EohPoTaHn1jOlSuXxnxfqampnI3evXurtqDyKbRBnoefNqLdoAmf8q9//Ss+ Pv6BYkL17t2761c7BNFVSrwjqZqnYsaMGbx848aNwQ0xREAm7QX5lxPoIicG xyfUA2bLls3lfmjK66P1V65cueSBmEz422+/pT99mtK/evXqLvdEpuJV/QDH fuKDocovHfgEz/KUPXt2Cy6Gw6dYj24GOnXq1KFDB8/HF8VzX+Jj62PHjuUl W7duFcVWrFjBC7///nuTQw8YE33KrVu3xCtsbdu2VU61ISfGfQo1PuLzJczk yZO5pp07d1YVVvqUAwcOqJwsEx0dTS2q0qcQrVq14sKe34oSczU8UPiUVatW iYWUcF64efNm3YqHHLpKkUEuWLAgVb9Zs2bCHVNz/cQTT7jcDwBLbpktA5m0 F+RfTqCLnBjpo9esWcPzGuXMmdOnwYyR9+gfuDtx5dNZBFkJ/uF3330nFhof +9FYQnXL4/LlyzzxNR1ISpdBRx1194sWLRIB7Nu3j0rGxMSogqSK8+vS1szn ILNP2bVr1/a/KVGiBOWERq1iSeh+YE47A8uWLeMjrXLlyuS7jxw5Qh45NTV1 +PDh/BWVAgUKnDt3jgsnJSXxVfGiRYvu3r2bStJBSwVoSd68ebXnm7UFI+ob 0Z1OPfHhy/z581PTsXr16hX/hJJjRZUMY9ynuNzfSVm5ciV5VarFmDFjuE3I kSOHeOiLoCaCkyP6LGpn+GY05YSPh8TExLCwML74o/Ip1GyKeeQGDx5MW753 7x61S2STaSPiyztev0d/+vRpnn2d4lQ1qhkAI0qJx/D69et39epVavm7d+/O S0aPHm1JmCEAMmkvyL+cQBc50dVl3bp1YkbfIUOG0J+//PKLctShceHOiE+h Xjt37tx16tSh3vbPP/9MSEggrbn3L1WqlHhd5YHhsR8t79ixI5UkX5OcnEwD JypGY0uOZObMmcq9i89ki0fo+WM95MhGjhwZFRVFfT0NwGbNmkUDDJf76TLl LGfBQ2afwt93SI8JEyb4sXcZ0M4AHYqvvvqqsqZiMMkHhurbOnTMs3/htfwf OpXWrl0b9Jr4jhH1jehOJ69GGWb58uVWVMkwxn2K5yyCBKmseue9f//+vKpw 4cJizg3lrFzcjrncT4uVK1fO9U+fQkydOlWU4V2I//M7UA/S8SkP/v6arUv6 14L8wIhSNHLgV4FUp16zZs0ynnHzG2TSXpB/OYEucqKtCw3yvXbNSmrWrJne z434FPI+Xrvj0qVL7927V1XYyNjv7NmzZGS8bnPAgAGqqbr4yifx9NNP85Jl y5bx12HEz8WOiEGDBmmm0zRC16eMHz/ej73LgJEMhIeHi9fDBY0bNybj7Fl4 0aJF/PICQ2NdOU3KAzN8Cus+bNgwjTKMbEkwUne+0DFnzhxyEDxfOlO2bFnP CzVkHMqUKcMFxJwbV65c6dixo/ghtVqtW7c+ceIEPzSo8ikP3GdxnTp1lI1P yZIlld/VXbx4MS9Xne/UaFeqVMnl/oAUNYb+5URODLZRNJB48cUXxWUE6sJe ffVVDCGUIJP2gvzLCXSRE12fohzne6VevXrp/TwuLo7LeE7SpYR6f36MRPTg bdu2TU1N9VrYyNgvOTlZeRRxFz9//nzPrX355ZdcQHybkoiPj+/evTvfQBGU L1/eym/zyexTMirGM5CUlLRt27Y1a9bQiFT3WzynTp2ioazkI0Ynq+9r3e/f v3/gwIENGzZoTC5NZaKiotatW6fqvKhJpOV08Kjec0mPa9euUfmIiAjxSKGT 8UmpmzdvRkdHG0+1o0Am7QX5lxPoIifyjE+oI6aun3RXPuuVHkbGfrSd3bt3 b9y4MTExUaPYkSNHvH5inl9y2bRpE+1IewvBAD7FepycAdTd7iiAPlDKLJBJ e0H+5QS6yAl0kRP4FOtxcgZQd7ujAPpAKbNAJu0F+ZcT6CIn0EVO4FOsx8kZ QN3tjgLoA6XMApm0F+RfTqCLnEAXOYFPsR4nZwB1tzsKoA+UMgtk0l6QfzmB LnICXeQEPsV6nJwB1N3uKIA+UMoskEl7Qf7lBLrICXSRE/gU63FyBlB3u6MA +kAps0Am7QX5lxPoIifQRU7gU6zHyRlA3e2OAugDpcwCmbQX5F9OoIucQBc5 gU+xHidnAHW3OwqgD5QyC2TSXpB/OYEucgJd5AQ+xXqcnAHU3e4ogD5QyiyQ SXtB/uUEusgJdJET+BTrcXIGUHe7owD6QCmzQCbtBfmXE+giJ9BFTuBTrMfJ GUDd7Y4C6AOlzAKZtBfkX06gi5xAFzmBT7EeJ2cAdbc7CqAPlDILZNJekH85 gS5yAl3kBD7FepycAdTd7iiAPlDKLJBJe0H+5QS6yAl0kRP4FOtxcgZQd7uj APpAKbNAJu0F+ZcT6CIn0EVO4FOsx8kZQN3tjgLoA6XMApm0F+RfTqCLnEAX ObHLpwAAAAAAAACANvApAAAAAAAAANnAc19W4uQMoO52RwH0gVJmgUzaC/Iv J9BFTqCLnMCnWI+TM4C62x0F0AdKmQUyaS/Iv5xAFzmBLnICn2I9Ts4A6m53 FEAfKGUWyKS9IP9yAl3kBLrICXyK9Tg5A6i73VEAfaCUWSCT9oL8ywl0kRPo IifwKdbj5Ayg7nZHAfSBUmaBTNoL8i8n0EVOoIucwKdYj5MzgLrbHQXQB0qZ BTJpL8i/nEAXOYEucgKfYj1OzgDqbncUQB8oZRbIpL0g/3ICXeQEusgJfIr1 ODkDqLvdUQB9oJRZIJP2gvzLCXSRE+giJ/Ap1uPkDKDudkcB9IFSZoFM2gvy LyfQRU6gi5zAp1iPkzOAutsdBdAHSpkFMmkvyL+cQBc5gS5yAp9iPU7OAOpu dxRAHyhlFsikvSD/cgJd5AS6yAl8ivU4OQOou91RAH2glFkgk/aC/MsJdJET 6CIn8CnW4+QMoO52RwH0gVJmgUzaC/IvJ9BFTqCLnMCnWI+TM4C62x0F0AdK mQUyaS/Iv5xAFzmBLnICn2I9Ts4A6m53FEAfKGUWyKS9IP9yAl3kBLrICXyK 9Tg5A6i7dpkdO3ZERkbu3bvXyAZ9Kuw3ycnJkW6uXr0a1B3Jg5OPUnNBJu0F +ZcT6CIn0EVO4FOsx8kZQN21yzz66KMul+vZZ581skGfCvvNjz/+6HJDtiio O5IHJx+l5oJM2gvyLyfQRU6gi5yEik9JSkqKiYmhf/3Yo2wYz0BycvLixYtH jRo1d+7cPXv2qNampqbG6HHr1i3zKxAAvqpvUPd79+4dOHBg//79f/31V0Dx BRP4lFDB+FFKBx6dmBMnTpw8eTIdqPfv3w9yaCFGkM53YBAcyXICXeREV5eD Bw9qj7jOnDkjCh86dCi9Ynfv3vXceEpKSnh4+OjRo1esWHHp0iXtUBMTE6nY p59+Om/evGPHjvk68tEdMvkafFCR3Kdcv3593bp1Xbp0yZo1K42UmjRp4sce ZcNIBnbv3l21alXXP2nZsmVcXJwoQw2XS4/Zs2cHtzI+YlB947rHxsZOmjSp fPnyXN8tW7aYGa6pwKeECgaPUuodSpQooTzdKlasGB8fH/wAQwbTz3fgEziS 5QS6yImuLoUKFdIecZFeonDOnDnTK7Z06VLVlgcOHKgskClTprCwMK8x3Lx5 k5pK1QZr1ap14sQJI3U0OGTyKfhgI7NPSUpK4m5L8Mwzz/ixR9nQzcDChQvF QVKsWLGGDRvmz5+f/6xSpcqdO3e4mBGf8vPPP1tRJcMYUd+47r1791bVNzIy 0vygTQI+JVQwotTRo0fp3OTMVK5cuUKFCvz/smXLnj171pIwQwBzz3fgKziS 5QS6yEngPoVGaFzy1q1bxgdmffv25eV58uRp0KBBrly5+M8xY8aoAqDWsk6d Orw2c+bMdGAUKFCA/yxYsGBaWpp2BQ0OmXwK3gJk9imJiYkF/4YUyTD9l3YG Ll26xK6E2qWtW7fyfd4rV660bduWD5Jx48ZxSTom47yxf/9+Ps6ffvpp2W4T G1HfuO50dnOx3Llza5x0kgCfEioYUap9+/Yu91WvRYsW8ZJp06Zxonr06BH0 EEMEc8934Cs4kuUEusiJri7x8fFeB13vv/8+S7N+/XouSc0aL/nqq688y//x xx9imwcOHOCSdevWvX79+gP3ILBSpUq0JEuWLAkJCcoA3n77bS7csWPHa9eu 0RIa41EfTQbn22+/1a2gwSGT8eCtQWafooTvUmWM/ks3AzQgbNOmzYULF5QL L168yP6lZcuW2tvv1asXFaPj0OB9QCvxVX2Dus+fPz8j+ZTGjRvzn7dv3965 c2dsbKzXh0h1fcqNGzd27doVExND29HeL23/5MmTUVFR5IhVq+BTPElOTuZb AO+9955yOQ8t8uXLR5kPboghQpDOd2AQHMlyAl3kxL/R6ZkzZ3jY/+6774qF hw8f5n5z9erV2j/v06ePpyUR5kV5S4U8QrZs2Whhp06dVEMCX2fj1B4yGQ/e GuBTrMe/DBBNmjShJJQpU0ajDA1r+ZrkpEmT/AsvqMCnaJdh6/HCCy8cO3aM DKl4/K9gwYJ9+/ZVvb+Wnk85d+4cNX2PPfYYHwkEdXlvvPGG164tJSWlc+fO 4sHCTJky0Q/Dw8NFgfR8ygcffFDIzfjx433Kg/zoKkVV5pyoHu5dunQpL583 b15wQwwR4FPsBUeynEAXOfFvbNaqVStSpHTp0srHrqKiolip6Ohojd9Sn87P knm+lFetWjWX+zE/sYQvQRNHjhzxNUgV2kMmg8FbBnyK9fjtU2rXrk1JoKNX o0zdunX5BqKcM1/Bp2iXYetBeH2LjZoy8XbSg3R8yqxZs3LkyOH5W6+OJiIi okiRIl4Li4cNvPqUOXPm8EIyU/fu3QskLRKiq9Rbb73lcptHVd2vX7/OV0EH DRoU3BBDBPgUe8GRLCfQRU78GJutWrWKu8KVK1cql9OfvFz7ZaIzZ85wsa// 3/buPD6K8v4D+PLDioicKopCwYsCBSpHVNTWo4AKYi0VKFRria0FpWBRKfVC gwaU+xCIch8BwiFBICQhvCQEyGEuMISbkJCEEExIAuEmv+9rvy+e13R2MzO7 mZ15Nvt5/8GLnX2y8zzPd3bm+e7MPDNliuqt//znP/yWuM6qffv2DufPmB7V 0C3tIZPBylsGeYr1vOuB0tJS/nn8jTfeqK6MyIJpI6xRFX0GeYp2GZGnkGHD hlFqkJ+fHxERIbKJ+fPnqwqrsg/eBlq2bDljxozU1NRz584lJCSIyT2UFSgp KWnevDkvHzJkCH2dy8vLqQMpHQ4KChKXirnmKYmJiZwKdejQgS+RrWV0I/Xc c89R8zt16uT6Ft/6+uqrr/qqcn4FeYq9sCXLCXGRkxdjs2effdbhvJtY9cvw okWL+LhJicwHH3wwdOjQjz/+ODw8vLKyUllsz549XGzdunWqT547dy6/deTI EV7CV5fRp/HLgoKCpKQk756/rD1kMlh5yyBPsZ53PSBOBC9ZsqS6MgMHDqQC d955p+79CHZBnqJdhlOPxo0bb9q0Sbl87969derUcTjnpRT7w+qu+4qMjFTt TyhV4f4ZNWqUWCjuyBs9erSy8OXLl5W7PlWeQvvGe+65h15S6nT06FGDbfcv upHq3Lmzo5rpc3k6cVN+8qoFkKfYC1uynBAXOXm6v8rKyuKD49SpU1Vv0RKH O61atVKeeVm/fj0vd50fOCIigt+iwze9PH36NL+cN28eHZQ7duwoPpPy2fj4 eI9aqj1kMlh5yyBPsZ4XPXDs2DFOpdu3b3/lyhW3ZfLz8/keK5FuSwh5inYZ jVvje/fuzW0sLCzULezqtttuo8Ivv/wyv6Rkh5c0b96c5xipjjJPuXTpUo8e Pej/tKXt2LHDyHr9kW6k+CfN/v37u75F2yq9RQcRX1XOryBPsRe2ZDkhLnLy dH/F03zR2Mx1/hkx1Kd4jRkz5r333uPr9km9evX27dvHxcRJk71796o+gQYz /BafaklJSRFZCf+nYcOGDRo04P/XqVNH9dumNoN5inblLYM8xXqe9sC1a9d6 9uzJG0lUVFR1xWbNmsVlfvrpJxNq6RvIU7TLaKQeISEh3MbExETdwlXOucI2 btxIfzVgwAC+rpU89thj/C5lvrxk2LBh2lUSecquXbvEKRgxOXatpBupVq1a USe89NJLrm9RDzucN4j5qnJ+BXmKvbAlywlxkZOn+yu+HvuVV15x+254eHhs bKx4SQO5jz76iA+g4qi9YMECXpKWlqb6861bt/JbPOmWuBGGtG3b9ocffrhy 5cr169eXL1/Oz6Gg8YDxC2l0h0xGKm8Z5CnW87QHRo4cyZuH9iWpgwYN4hRb tmemKCFP0S6jkXqEhYVxGyMiIrQLnzx5ctSoUc2aNXO4eOSRR7iM2OlNmzZN u0oiTxkyZIj4nD59+hhpsp/SjVRQUBB1wpNPPun61kMPPURv9e3b11eV8yvI U+yFLVlOiIucPNpfZWdn89HQ+IyXNDZ7+OGHHc55cniGhM2bN/OHbNu2TVV4 xYoV/FZKSkqV4k6W9u3bi2sq2IcffqgsaYQXQybXylsGeYr1POoBcQKuW7du 2jcx3XvvvVTsmWeeMaGKPoM8RbuMRp4iblAST5JyW7ioqIgPZA7nU4xDQ0Np pSUlJdyTIk9Zu3Ytl5k9e7Z2lUSewsSJ5pUrVxpsuN/RjdRLL73kcE4j4PoW p4cak10EFOQp9sKWLCfERU4e7a++/fZbPhR6dAn0u+++y3914MABepmWlqb6 +VGYOXMmv8XPVaHchF8uXLhQVVJ8iJilU5d3QyZV5S2DPMV6xntgzZo1PMfX XXfdpXosqYqY3W7MmDHm1NI3kKdol9HIU8QTb3NycjQKP/roow7nA1NUE+yr 8hRxA+A777yjXSVlntK5c+dTp061adPG4byxhdIf7b/1U7qRevPNN/lnJdUj aehLyh314Ycf+raKfgJ5ir2wJcsJcZGTR/srnjuaRmgePXNTXD2VkZFR5ZyX hl9+/PHHqpJ///vfHc4bT/i5adevX+enFbz33nuqkmKr0L06QvBuyKSqvGWQ p1jPYA9ERkbyffENGzbUPZ0nLuORdkZihjxFu0x1eQrtqfiK5fr162vM91Vc XMz98Pbbb6s+QZWn0AfyPPz0IarHR6qIPKVFixa5ublVisnVg4OD9Zvth3Qj tXz5cu4B1WSSc+bM4eUxMTG+raKfQJ5iL2zJckJc5OTR/qpr164UCNplebSK Pn360F/dfPPN4rDL1z+o5qCmo/zdd99Nyx9//HGxkJ+O17FjR9UcyDt37uSt wvit9N4NmVwrbw3kKdYz0gO0vdHGwL+oGOkucfNCdHS0KZX0EeQp2mU49aBd luqGuBkzZnADBw8erCqszFMyMzO52PDhw5V/npycTNmuMk+puvEUXYe7J0zt 379f/F/kKZQLi4X9+vXjhdu3b9dtuN/RjVRlZWWTJk2o+b169RK3g9Guu3v3 7rSwdevWMt8jZiXkKfbCliwnxEVOHu2vWrRoQbHo0aOH61tpaWmdO3dOT093 /Xx+voByGoQJEybwwZTSDbHwu+++44ULFiwQC8U4h95VfuzQoUN5Of+QWOXc fujAHR4efuHCBbeV1xgyeVR5a8icpyQmJu66gW++oKOYWOK/D5jT7YGoqCjx SPGxY8fSyw0bNnyn4Do4/Oyzz7h8amqqD6teY0aibzDu+fn5YuHHH3/MzQ8N DeUlEk56ZjxPcTifkxIZGVleXl5QUBASEsL7B9oqxEVfVTd+XaHOEccs2jvx hYKNGjVKSkq6fv069RL1CZ+YU+UpBw8e5FzY4bxckD756tWrtI8aOHAgfQjP 2V5VzfPojx07xnOMUD2r2xP6LyOREpfhjRw58uzZsyUlJcHBwbzk008/taSa fsDE7zt4AVuynBAXORkfndIxt27duhSLfv36ub7Lz7i55ZZbPvnkk/j4eDpE 0n4sLCyMjssO56Vcyolb6RDP1zY0b96cj9qUsDRu3JiW3HbbbcqnBtABmn/J adiwIY0JqQ60ZNq0aXzQV85iPXr0aN5UlE+pMDhk8qjy1pA5T+HnO1Rn8uTJ XqxdBto9cPHiRb4KUQNlu6q/GjZsGL914sQJ39a+ZoxE32Dc6T8axehDfN4Y DxnPU9xuALRXVN3zPmrUKH6rWbNmYv4N5axcvPdzOK8Wu++++xz/m6dUOeey FmV4FeL/I0aM4DJu8xQyfvx4Xi7z83q8YyRSNHLgW4EYJ5IO5++ftS9x85qJ 33fwArZkOSEucjI+Oj116hSHY+jQoa7vrlu3jh94Jw6sInzk/fffV5Vfvny5 OPiKkvXq1du8ebOqJKUwt99+uzis86+FDufTvSkNEcX4N0zyxBNPiIUGh0ye Vt4C/punGJ8LTja6eYpyuOhWUFCQ6q94UmKiPSeY7Wo+bhFx1/7SNWjQwOeN 8ZCRtrdr187hnNCDMoimTZuK5rRp08b1JBolDq1bt+YCYv6N0tLSAQMGiD+k fV2fPn0OHz48btw4h0ueUuX8Fnft2lW5I2rZsuWSJUtEgVWrVvFy1fedNtS2 bds6nM98lDw79pTBfRQNJF588UVxToqyS/oaYgihZOL3HbyALVlOiIucjI9O xaTE1c1clJubGxwczOcghAceeIAfhuIqPDycb0Fl999/v2uSwo4ePdqjRw8+ h8JoI1HNVPzll1/yW9OnTxcLjQ+ZPK28r8mcp9RWgdwDaLvx8teuXcvMzIyO jtaY6o3KxMfHR0VFqQ5eOTk5tDwhIcHgg5/KysqofGxs7MmTJ43XsLbyKFKV lZXJycnGuzqgBPL3XQbYkuWEuMjJ9P0VhSwjI2Pbtm3bt29Xnu+oDuUgVNLI 737l5eU7duyg2paWlrotkJWVVcMHx3taed9BnmK9QO4BtN3uWoA+RMos6El7 of/lhLjICXGRE/IU6wVyD6DtdtcC9CFSZkFP2gv9LyfERU6Ii5yQp1gvkHsA bbe7FqAPkTILetJe6H85IS5yQlzkhDzFeoHcA2i73bUAfYiUWdCT9kL/ywlx kRPiIifkKdYL5B5A2+2uBehDpMyCnrQX+l9OiIucEBc5IU+xXiD3ANpudy1A HyJlFvSkvdD/ckJc5IS4yAl5ivUCuQfQdrtrAfoQKbOgJ+2F/pcT4iInxEVO yFOsF8g9gLbbXQvQh0iZBT1pL/S/nBAXOSEuckKeYr1A7gG03e5agD5Eyizo SXuh/+WEuMgJcZET8hTrBXIPoO121wL0IVJmQU/aC/0vJ8RFToiLnJCnWC+Q ewBtt7sWoA+RMgt60l7ofzkhLnJCXOSEPMV6gdwDaLvdtQB9iJRZ0JP2Qv/L CXGRE+IiJ+Qp1gvkHkDb7a4F6EOkzIKetBf6X06Ii5wQFzkhT7FeIPcA2m53 LUAfImUW9KS90P9yQlzkhLjICXmK9QK5B9B2u2sB+hAps6An7YX+lxPiIifE RU525SkAAAAAAADakKcAAAAAAIBscN2XlQK5B9B2u2sB+hAps6An7YX+lxPi IifERU7IU6wXyD2AtttdC9CHSJkFPWkv9L+cEBc5IS5yQp5ivUDuAbTd7lqA PkTKLOhJe6H/5YS4yAlxkRPyFOsFcg+g7XbXAvQhUmZBT9oL/S8nxEVOiIuc kKdYL5B7AG23uxagD5EyC3rSXuh/OSEuckJc5IQ8xXqB3ANou921AH2IlFnQ k/ZC/8sJcZET4iIn5CnWC+QeQNvtrgXoQ6TMgp60F/pfToiLnBAXOSFPsV4g 9wDabnctQB8iZRb0pL3Q/3JCXOSEuMgJeYr1ArkH0Ha7awH6ECmzoCfthf6X E+IiJ8RFTshTrBfIPYC2210L0IdImQU9aS/0v5wQFzkhLnJCnmK9QO4BtN3u WoA+RMos6El7of/lhLjICXGRE/IU6wVyD6DtdtcC9CFSZkFP2gv9LyfERU6I i5yQp1gvkHsAbbe7FqAPkTILetJe6H85IS5yQlzkhDzFeoHcA2i73bUAfYiU WdCT9kL/ywlxkRPiIifkKdYL5B5A2+2uBehDpMyCnrQX+l9OiIucEBc5IU+x XiD3ANquXWb37t1xcXGpqalGPtCjwl4rLCyMczp79qxPVySPQN5KzYWetBf6 X06Ii5wQFzkhT7FeIPcA2q5d5v7773c4HL/73e+MfKBHhb22dOlShxOlRT5d kTwCeSs1F3rSXuh/OSEuckJc5OQveUpBQUF6ejr968UaZWO8B/Ly8iIiIsaN Gzd37ty4uLgrV664LXbx4kUaQ06dOvWLL75YvXr18ePHTaytuYy3vaKiYsuW LZMmTaJGrV+/vrCw0MdV8znkKf7C+FZ69erVlJQU+urNmDGDdlDXrl3zcdX8 jKd7+9q0n5cBtmQ5IS5yMh4XGpCsWrWKxmaLFi2iAGmU3Ldv3+LFiz/55JOw sLBdu3Zdv37dbTEa8MTExEyYMGHmzJnJycmXLl0yUo3i4uJ0PRcuXKj5iuwl 8hTveL1Gg4WpS6Oiol599dWbbrqJRkpPP/20F2uUjZEeSE1N7dixo+N/Pfjg g99//72y2OXLl8eOHXvzzTcri9WtWzc4OLisrMyHbfCWwehTttWiRQtlo269 9davvvrK9xX0IeQp/sLgVpqdnX3vvfcqt9KHHnooNzfX9xX0GwZ7slbu52WA LVlOiIucjMQlKSmpQ4cOqrHZ888/n5OToypJScSgQYNUJZ988skDBw6oSkZH R99zzz3KYhR37fSHUfbq0PPtt9/WfEX2sv48l/E1FhQU8GFL+O1vf+vj2llB twdmzZr1i1/8gpvcpEmTLl26/N///R+/pJQkMTGRi1H/dO3alZfXqVOHvjt3 3XWX6KuXX365uszdRkaiv337dtHexx9//PXXXxc5i/Ib53eQp/gLI5Hav3+/ +Lq1a9fuwQcf5P+3adPmxIkTllTTDxjpydq6n5cBtmQ5IS5y0o3LihUrbrnl Fg4ERadHjx6NGjXil+3bt1eem7h27drvf/97fqtp06avvfZaz549+SUlm5WV laJkXFwcjd94dEdH86CgIN4f1q9ff9OmTdoVNpKnrF69uuYrspfMeUp+fn6T G3jgWjuOX7o9sHjxYmpsy5YtY2Nj+TxvYWHh+++/z1td7969uVh5eXmnTp1o yfDhw8+cOVPl/GrQJ993333SDiyNRP/hhx/mnUB2djYvKSkp6d69O2dt/nvi G3mKvzASqT/+8Y/8+0B4eDgvmT17NnfUm2++6fMq+gkjPVlb9/MywJYsJ8RF Ttpx+fnnnzkroZxx586dPBQpLS3t168fx2XixImi8IYNG3jhmDFjLl68yAuX LVvGC6dMmcJL6C1KW2jJnXfeKabESUlJufvuu2khHeKvXr2qUWEaBOa4k5GR QdkHfcITTzzB9azhiuwlc56i9MADD9Sa45eRHqC0nVMP4fr16+3ataNOaNas mVh44sQJ+jqo/nblypX8XaBc26Qqm0a37efPn69bty5VfsKECcrlcXFx3KiD Bw/6too+YzxPeeqpp/gl7Vv27Nlz4MABt6fGdPOUc+fOJSYmpqeni/1kdejz jxw5Eh8fT3td1VvIU1wVFhbyL1H//Oc/lct5aNGwYUPqed9W0U94urevTft5 GWBLlhPiIifduNBBsG/fvqdPn1YupKEa5y/PP/+8WDh27Fha0qBBA9VtxbRz o+WDBw/ml5Tv8OF1zpw5ymIbN27k5cuXL/eiIcOHD3c4r5Y/fPiwT1dkDeQp 1vO6z3v16uVw3n6infnu2rWLN7xx48Z5V0Pf0W37qVOnuPKzZs1SLqeMjJdv 27bNt1X0GeN5Ss+ePbOzs2mnJ04xN2nS5F//+tfly5ddC7vmKSdPnhwxYsSv fvUrcfkcHfL+8pe/uD20FRUV0T5TnLyuU6cO/eHatWtFgerylH//+99NnSZN muRRP8hPN1LUZO6TH374Qbl8zZo1vHzx4sW+raKfQJ5iL2zJckJc5OT12Ozp p5+moLRu3Vos4Uzh9ttvV5UcOnSow3mXCr/8+uuvOaA08lGV/PWvf6380dK4 PXv28KF/+vTpYqEvVmQZ5CnW864HysrK7rzzTuqEtm3bapcMDQ3lDVKcL5aH kbbzTTfPPvus8hIvGjlzo/z30lzjeQoRGYoS7QyVV8C6zVPCwsLq1avn+rdu M5rY2Ng77rjDbWGx8bjNUxYuXMgLKZmS+Xyxd3Qj9frrrzucyaOq7RUVFfwr 6Pvvv+/bKvoJ5Cn2wpYsJ8RFTl6Ph7t06UJBoQG/WCLOU6g+kOdHomyFX/73 v/91OH8edL1kYtiwYQ7nfe6eVqZbt270h/Sv8jN9sSLLIE+xnhc9kJOT89xz z/FmP3v27OqK0T5txYoVPP1Xq1atlPdqScJI27/44gtuKe2ry8vLq5ztorTF 7Ujbj3iUpxDae1BqkJ+fHxERIbKJ+fPnqwqr+iQ+Pt7hvLlpxowZqamp586d S0hI4K+Pap9ZUlLSvHlzXj5kyJAff/yRejsuLo52uUFBQeJSMdc8JTExkVOh Dh06yDmtXA3pRoq/jJ06dXJ9i299ffXVV31VOb+CPMVe2JLlhLjIybvRaWlp KZ+/eOONN8TCCxcu8FUKt99++/bt23mhuHxdLJk7dy4vycvLU33s+PHjaTl9 suo6Cm08ACDLli1TLjd9RVZCnmI94z2wePHi4OBgGqLzLRu33nrrzJkzXdPh kydPjho16pVXXmndujVvitRRR48eNb/qNWak7fRlobZwQ1q0aPHVV1/R19/h PMWQnp5uSTV9wnie0rhxY9X8G3v37uWZOh566CGxAVR33VdkZKQqRaVUhfuT thOxkHuVjB49WlmY+l/56HlVnlJQUMATG1LqJOc2VnO6kercubOjmulzecrK nj17+qpyfgV5ir2wJcsJcZGTd6NTcZHekiVLlMspZWjYsCG/9fLLL9ORlHIW +v+f//xnUWbbtm1cQHWVfkxMDN8ITzw6zg4cONDhvFledVOq6SuykjIuNP65 5iEvZr5FnmK8B/r06eNQ+PTTT1WP7GHJycnKYq1atfrpp59MrrRJDLY9NTVV zMwsrFu3zvcV9CHjeYrb00a9e/fmfhCPvPRovq/bbruN95b8kr65vKR58+YV FRUaf6jMUy5dutSjRw/6P0Vnx44dRtbrj3QjxT9p9u/f3/Utvk2yY8eOvqqc X0GeYi9syXJCXOTkxej02LFjt956q8M5L7HqlvnLly9TSqIaxnTr1u38+fOi DB1SeRouOqSGhoZSppCRkTF+/Hjlxdu0xGBl8vPzeeD0wQcfqN4yd0UWU8al qKjI0+c80p/UZI3G1abjl/EemDx58gsvvNClSxfxJMd27drt379fVez48eMD BgygzhHPhKpTp84nn3zip89PWbt2LX/Xnn/++eeee47PIzicN+b472RfVTXO U0JCQrgfxAN0tPOUixcvbty4kf6Ktg3ahfLfPvbYY/wu7V15ybBhw7SrJPKU Xbt2iVMwygkYax/dSLVq1Yo64aWXXnJ9i3qYD0a+qpxfQZ5iL2zJckJc5OTp /uratWviqShRUVHKtyoqKujQ7HBOzjZixAie/tfhvLyKxmbKkrGxsa63ozZt 2lTkOD///LPB+syaNYv/xO0v1SauyGLKuFDClZqaStkHjXtPaKICVIwKK+/q 9WKNxtWm45cXPXDq1CnatnnETqPT6m48ocQkOjqanz9CwsLCTKiuqXTbTt8v vk/wb3/7G99CSEtod80tatGihWq6Zj9SwzyFosmdEBERoV2YrwNs1qyZw8Uj jzzCZcRdftOmTdOukshThgwZIj6nT58+Rprsp3QjFRQU5FDM2aLEv1n17dvX V5XzK8hT7IUtWU6Ii5w83V+NHDmSD4iutwsNHjzY4XyKRHJycpVzdE1JBE+F RL744gtl4UOHDvXv379ly5YO56Rh//jHPw4cOPDRRx9xmmO8PoMGDeI/qe4x c2atyGKquFAOQgmIuLCkOlSAink38xLyFO96gIwbN443cu3HslN0+KyxhBM4 6Lb9T3/6k8N565nqFKpou+pmCj9SwzxFXAS7detWjcJFRUV8IHM4z76FhobS SktKSvgbJPIUMX+axrQMTOQprEGDBvyflStXGmy439GNFCfOHTp0cH2L00Pl DZWBDHmKvbAlywlxkZNH+6upU6fyobBbt26qn4737t3r9mfAnJwcvom4fv36 rvMDVznvvhf//+tf/+rw8AI/vqLmmWee0S1ZwxVZTBUXPqWSkZGhMdcovUUF vDuZ4rpGg2rT8cvrPOXo0aO88Y8YMUK75GuvvcYlZTv7oNt2Pj0aHBzs+tZ9 991Hb3Xp0sVXlfOxGuYpb731FseU9nUahR999FGH84Epqgn2VXlKVlYWf9o7 77yjXSVlntK5c2fau7Zp08bhvLGF0h/tv/VTupF68803Hc6JHVSPpMnLy+OO +vDDD31bRT+BPMVe2JLlhLjIyfj+as2aNTzH11133eU6g9a8efM4TK4/5i9Z soTfioyM1Ph8Gmb/8pe/dHhy4uz48eP8yWPGjDH4J96tyHqucdE9pVKTkylu 12hEbTp+6fZAdfeViHsKhg8frl2SE2TixQ1EPqXddmoO39XlehcYoe8RvUWJ jA/r50s1yVMuX77MVyzXr19fY76v4uJijvvbb7+t+gRVnkIfyNfX0Ydoz0Yo 8pQWLVrk5uZWOecT4yVu08laQDdSy5cv5x5Qze0wZ84cXh4TE+PbKvoJ5Cn2 wpYsJ8RFTgb3V3QE5FtoGzZsmJKS4lqAn61Qt25d14mPqDxHkHIZjVWIB3oq n0SgTVzLrZqRWJsXK7Kea1z4lEp6errbUyq0kN7y+mSK2zUaUZuOX7o9MHDg wP79+/OjQ5TEtU+LFi2qck6KRYN21QS2Vc6bWfi5GMqno0pCt+2PP/64wzl1 hupE6rlz52icTG/17t3bt1X0GeN5SqdOnVSTCs6YMYNDP3jwYFVhZZ6SmZmp ymRZcnIyT5Ao8hTywgsvcOEpU6aoqqGcq0HkKbQbFAv79evHC8U88LWJbqRo 42zSpAk1v1evXuJKYEr3unfvzt+76i4PDjTIU+yFLVlOiIucjOyvaMTF8xrd csst1RWmLFI5VFOioy2/JZ5HRsd61URbJSUlPDE1BVr5KyJtFXQ4Dg8Pdzvv q7iDNTo62m2tjK9INm7jwqdUCgoKXMvTwpqcTKlujW4lJibuuoGvu6OjmFji vw+Y0+6BdevW8cbWrl07yrizsrKuX79eXFz80Ucf8VNUGjdufPLkSSpJo1mH c2qvt956a8uWLdQhV65coU/+zW9+w5/w7rvvWtcqY3SjL6757NOnjzidevr0 aXEr/ddff21FRX3AeJ7icD4nJTIyknJV+saFhITwFAr16tUTF31V3XjsLH0p xDGL9mN8MrpRo0ZJSUm05eTn54eGhopJnpV5ysGDB8U8cmPGjKFPvnr1alpa GqXJ9CEJCQlczO3z6I8dO8aTrlM93e4z/ZqRSInL8EaOHHn27Fna4QcHB/OS Tz/91JJq+gEjPVlb9/MywJYsJ8RFTrpxiYqKEhP5jh07ll5u2LDhOwX+4a6i ooJ3ZQ0aNFi2bJm4BGL9+vU8iXHLli35p0h6a8CAATfddNOECRMKCwtp4c6d O2nsx6uYO3eucu2jR4/m5W4vOPnss8/43dTUVNd3PVqRbNzGpbpTKjU/mVLd Gt3i5ztUZ/LkyV7XwV7aPUB9y5M2CGIwyVmJuKyRPqRp06biLRpb8pU87LHH HqtJmHxEN/r0bRIpCTWckv3u3bvzV5v84Q9/kHCyZYOM5ymukwc6nCeRVfe8 jxo1it9q1qyZ+KoqZ+US2wPlFHx3jzJPqXJOY6jcZjgRZuIeKLd5StWNh9hW t8/0a0YiRSMHvhVIfDH5P7169ap9iZvXjPRkbd3PywBbspwQFzlpx4XG9m4P zUo0YuHCdLgUI7e77777iSee4Ps6Hc6BzZ49e7jYiRMn+OoX10PwO++8oxqB 8y+ThD7NtXrDhg3jd92eR/BoRbKpLi5uT6nwyRS+Rt30NbrSPn5NmjSpJtWw kZEeWLt2LU+TrvTUU08lJSUpixUVFb311luqGWgbNWr0+eefK58lJA8jbb9y 5QoNyMUMfuyOO+6YOXOmzKcmdRlpO/++sXDhQsoglEko7eJcr7CiPSFPHkIO HDjAC0tLSwcMGCD+sF69en369Dl8+DBfNKjKUwh9o7t27SoOgg7nTz3K5+qu WrWKl1NJ5R/STrtt27YO53OjanKCVUIG91E0kHjxxRfFwYgOYYMGDcIQQqnm eYr/7udlgC1ZToiLnHTzFOXw3q2goCBRPjs7W1wgLfTt21f1CLzCwkJllPkQ 7PYeky+//JILTJ8+3fVd8ft2dc+tML4i2VQXF9dTKuJkSg3HisbzlNrKeA9Q YpiQkLBp0yYakWo8gocikpmZuWXLlpiYmIMHD6pm9JWK8bZfu3bt+PHjNDiP jY09duxYLbgc19Mtn5pMYY2OjnadTkRZJj4+PioqSnXwysnJoeW08ajuc6lO WVkZlaeu5ksKA5xHkaKDQnJysvGuDijY29sLW7KcEBc5+WJ/VV5enpKSsnXr VvrX9aZjgYbcSUlJNITLz8/X+LSsrKx9+/bVpD4GVyQVjbjk5uYqT6mYcjJF e40BIpB7AG23uxagD5EyC3rSXuh/OSEuckJc5KQRl8uXL4tTKmadTNFeY4AI 5B5A2+2uBehDpMyCnrQX+l9OiIucEBc5acdFnFLJz8835WSK7hoDQSD3ANpu dy1AHyJlFvSkvdD/ckJc5IS4yEk7LnxK5UcnU06m6K4xEARyD6DtdtcC9CFS ZkFP2gv9LyfERU6Ii5x048KnVMw6mWJkjbVeIPcA2m53LUAfImUW9KS90P9y QlzkhLjISTcufEolLS3NrClhsSUEcg+g7XbXAvQhUmZBT9oL/S8nxEVOiIuc jMQlNzdXY2ZUX6yxdgvkHkDb7a4F6EOkzIKetBf6X06Ii5wQFzlZHxdsCYHc A2i73bUAfYiUWdCT9kL/ywlxkRPiIifkKdYL5B5A2+2uBehDpMyCnrQX+l9O iIucEBc5IU+xXiD3ANpudy1AHyJlFvSkvdD/ckJc5IS4yAl5ivUCuQfQdrtr AfoQKbOgJ+2F/pcT4iInxEVOyFOsF8g9gLbbXQvQh0iZBT1pL/S/nBAXOSEu ckKeYr1A7gG03e5agD5EyizoSXuh/+WEuMgJcZET8hTrBXIPoO121wL0IVJm QU/aC/0vJ8RFToiLnJCnWC+QewBtt7sWoA+RMgt60l7ofzkhLnJCXORkV54C AAAAAACgDXkKAAAAAADIxvo85RIAAAAAAEA1kKcAAAAAAIBskKcAAAAAAIBs kKcAAAAAAIBskKcAAAAAAIBskKcAAAAAAIBskKcAAAAAAIBskKcAAAAAAIBs kKcAAAAAAIBskKcAAAAAAIBskKcAAAAAAIBskKcAAAAAAIBskKcAAAAAAIBs kKcAAAAAAIBskKeADHJycsLDw+fNm7dixYry8nK7qwMAAAAANpM/Tzl37lxB NXbs2HH06FG3b50+fdp3nZaamlpaWuq7z7dRXl5ebm6uwcJnz5499r8o3fBu vdOnTw8JCZk6ders2bMvXrzotkxxcXF+fn5JSYlqOeU1Bw8epKAcOnSIthbV u5WVlcePH09LS6OmXbhwwbvqsSNHjngU9+zs7PPnz9dkjfauFwAAAMBG8ucp lIl86bkVK1b4qMdoSMyfX8NBr1QqKiri4uLmzZtHycK0adMM/tWPP/4Y8r+o Z7xYO2Uf9LeLFy92m6FQNpSenk4dzqtYsGCB8t3MzMyvvvpKVIAyHRrVKz+Z MyAWFhb2888/e1FDsnfvXlqR8bjzdrJw4UJKlLxbo73rBQAAALCX/HlKbm7u ty5owEmDN05JZsyY4Vrg+++/90V3nT59msa9vN7o6GhfrMIWpaWl1KLQ0FCP 8pSEhAQqv3Hjxj03JCcne7H2tLQ0+pxt27a5fXf79u3KVIiCK94qLi7mOpPP P/+c/zNlyhQeop87d27y5MmqTIq2HC9qSEpKSmbPnm0w7mI72bFjh3ers329 AAAAAPaSP09xRePP+fPn02Bs6dKlkyZNov+kp6eb1SF2rffIkSOpqakXL17c v3//5s2bMzMzaSG9zMrKiomJ2bp1a0ZGhvhFnWry448/HjhwQPw5/T8lJeXs 2bP8kvIOenn06FH6/5kzZ2jUSqNc+lf3mq4JEyYYz1NiY2Np5M9r0VVdW/Ly 8ijToc9ZtWoV1bmgoED1h/Hx8ZSTTpw40TVP4QSHbNq0iT7wu+++45fczMTE RH65YsWK/Px8ylD45aFDhww2UCUnJ4fjTvXXKCa2kzVr1lR3DZtfrBcAAADA Rv6Yp6xfv54GY998801FRQUN1+n/kydPNn5XhZzrpQ+nITRlKDyWjoqKorUs W7ZMdSqBL1sqKysbP3781KlTxZ/T/6kAjfP5Jf2HXtII/+TJk5R6KD9E+5SH R3nK999/Tx946tQp3ZIabeEkRUhKSnL7CZSIueYplCfyQj59RtsVv+RkR1wq xpmUSGq2bNlisIGujMRduZ14vSJJ1gsAAABgF7/LUxISEmgwRmPpwsJCXkLD TloyZ84cn97b7uv1cp5CYmJiKLmgMTylKvRy4cKFtMbi4uJVq1bRy9WrV3P5 BQsWiAE5399B1qxZw++uW7eOTytwKhEXF3f+/Hn6nMjISNf70JU8ylNodfTh lIBQJyxatIjC6nobO9Noy5kzZ3744Qc+J0INF6eEVNzmKdTz4nKvtWvXTpo0 if4zd+5cfpf+w29xrejD+SWt3WAD3dKOu+t2Yha71gsAAABgC//KUw4dOsT3 hmRnZ4uFlZWVS5cu9em97Rasl/MUkYZQWkEj8NDQUJFW0JIpU6ZQGZ7KjO/a SExMpP/v2rWLB/CTJ0/mC35ovEqDdvo/Xwq1e/dug9XwKE9ZuXIl5wWcNBHK VlyvONJtC5/poGxFY11u85RLilMqghioU2hCnPet8EvKj7jAN998Y7CBbmnE 3e12Yha71gsAAABgCz/KUzTuEfboXmM518t5irjXIy8vj17SuFRZZsOGDbQw KyuL/n/8+HGR19DAddasWTt37uRzKKdOnRLnVo4cOcJnHObPn5+SkqI7Xa1r npKTkxPtDq2ovLxcTK5VVFTE827t379f9Zm6bfE6TykrK1NdTsZpCC2nd/km +vHjx3PqRDXkAgsXLtTuBF1u427BPex2rRcAAADAev6Sp+jeI2zwXmNp18t5 CmUf/JKH5REREcoyW7duDblxgwlVhvICWnVlZeXEiRM3btx44sSJEOc9KVQg RHEfCiUUK1eupOE6LaQBreuN6kqueQqlEl+7c/DgQdXf8jVmrqNl3bZ4naeI G1KojdnZ2TNnzuSXfJqJEhZ+yWkL94/y6riaUMXdsnvY7VovAAAAgMX8JU8x co+wL+6pt2y9qjzl9OnT9HLevHnKMuHh4bRQnMKgkT+95IeYZGZmcuaydOlS /qji4mLl3545c2bt2rW0fPny5RrV8Oi6LxWeptg13dBti9d5Ck/hRZ3PQ/TD hw9zGX56zurVq/kln+LZvXs3v4yJifGugSrKuFt5D7td6wUAAACwkl/kKcbv ETb33nYr16vKUy457zGhJeK0RX5+/vjx4ymPKC8v5yU8qdfs2bPpX779nEbm VGDGjBmzZs3iMnl5eaJKZWVlVFK85ZbxPIX6ZPPmzeLG+crKSn5MpNtbJLTb op2nJCYmRkdHi8m7pk6dyhee0VuLFi3ihTt27KBsSEwdtm7dukvOM0H8cu7c ubQKnhKN0NqNNNAIjvuUKVN8dw879XCBC05PKFXhtVOfqwrwjT8AAAAA/kv+ PMWje4RNvLfd4vW65il79+6lJaGhoTQmj4mJ4bs/KHUSBc6cOcMDb3GqIikp iZfwPL2UBdBQlv4wNjY2OTmZb+WgFbmund793okvD+P/nzhxQqPCPIXyzJkz aaweHx/PScqCBQvcNl+7Ldp5CiVWIe7QW6mpqa7LqQmHDx++5Lw0jmulJGYq MIWIu+/uYT969OiXnuMzSgAAAAD+S/48paCgICwszPg9wnyvMa2ihpfrW7ze yMjIkBsPKBTS09N5rl0yceLE3bt3qz58zpw59NbWrVv5JV9hRfbt2yc+Qdy1 EeKcQ9jtGR++hEyFkguNCtMQfdu2bZxxMPoQjUmPNdqSkZHhXZ5yyZma8dRh jELw008/ib9V3mj/+eefU47Gj6o3Ecfdd/ew0ybxrTvffPMNdT4F1+27nKgC AAAA+C/585RLzqcEejT4F1dG1ZBd61WiCpxyqknaRbnJyZMnffF8mQsXLhQW FtKHG2m7KW1x+7HFxcV5eXnVZUlUN6phdc92qbmioiJb7mG3a70AAAAAFvCL PAUAAAAAAAIK8hQAAAAAAJAN8hQAAAAAAJAN8hQAAAAAAJAN8hQAAAAAAJAN 8hQAAAAAAJAN8hQAAAAAAJAN8hQAAAAAAJAN8hQAAAAAAJAN8hQAAAAAAJAN 8hQAAAAAAJCNXXkKAAAAAACANuQpAAAAAAAgGyvzFAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAANn8PwE+f8Y= "], {{0, 204.}, {540., 0}}, {0, 255}, ColorFunction->RGBColor, ImageResolution->144.], BoxForm`ImageTag["Byte", ColorSpace -> "RGB", Interleaving -> True], Selectable->False], DefaultBaseStyle->"ImageGraphics", ImageSizeRaw->{540., 204.}, PlotRange->{{0, 540.}, {0, 204.}}]], "Output", TaggingRules->{}, CellChangeTimes->{{3.834334949712008*^9, 3.834334973023217*^9}, 3.834405753959289*^9}, CellLabel->"Out[12]=", CellID->2032861437] }, Open ]], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"nonanomalousControl2", "=", RowBox[{ RowBox[{ RowBox[{"Query", "[", RowBox[{"Select", "[", RowBox[{ RowBox[{ RowBox[{"#treat", "==", "0"}], " ", "&&", " ", RowBox[{"Not", "[", RowBox[{"adfTreated", "[", RowBox[{ RowBox[{"KeyDrop", "[", RowBox[{"#", ",", RowBox[{"{", RowBox[{"\"\\"", ",", "\"\\""}], "}"}]}], "]"}], ",", RowBox[{"AcceptanceThreshold", "->", "0.1"}]}], "]"}], "]"}]}], "&"}], "]"}], "]"}], "[", "lalonde", "]"}], "//", RowBox[{ InterpretationBox[ TagBox[ DynamicModuleBox[{Typeset`open = False}, FrameBox[ PaneSelectorBox[{False->GridBox[{ { PaneBox[GridBox[{ { StyleBox[ StyleBox[ AdjustmentBox["\<\"[\[FilledSmallSquare]]\"\>", BoxBaselineShift->-0.25, BoxMargins->{{0, 0}, {-1, -1}}], "ResourceFunctionIcon", FontColor->RGBColor[ 0.8745098039215686, 0.2784313725490196, 0.03137254901960784]], ShowStringCharacters->False, FontFamily->"Source Sans Pro Black", FontSize->0.6538461538461539 Inherited, FontWeight->"Heavy", PrivateFontOptions->{"OperatorSubstitution"->False}], StyleBox[ RowBox[{ StyleBox["FormatDataset", "ResourceFunctionLabel"], " "}], ShowAutoStyles->False, ShowStringCharacters->False, FontSize->Rational[12, 13] Inherited, FontColor->GrayLevel[0.1]]} }, GridBoxSpacings->{"Columns" -> {{0.25}}}], Alignment->Left, BaseStyle->{LineSpacing -> {0, 0}, LineBreakWithin -> False}, BaselinePosition->Baseline, FrameMargins->{{3, 0}, {0, 0}}], ItemBox[ PaneBox[ TogglerBox[Dynamic[Typeset`open], {True-> DynamicBox[FEPrivate`FrontEndResource[ "FEBitmaps", "IconizeCloser"], ImageSizeCache->{11., {2., 9.}}], False-> DynamicBox[FEPrivate`FrontEndResource[ "FEBitmaps", "IconizeOpener"], ImageSizeCache->{11., {2., 9.}}]}, Appearance->None, BaselinePosition->Baseline, ContentPadding->False, FrameMargins->0], Alignment->Left, BaselinePosition->Baseline, FrameMargins->{{1, 1}, {0, 0}}], Frame->{{ RGBColor[ 0.8313725490196079, 0.8470588235294118, 0.8509803921568627, 0.5], False}, {False, False}}]} }, BaselinePosition->{1, 1}, GridBoxAlignment->{"Columns" -> {{Left}}, "Rows" -> {{Baseline}}}, GridBoxItemSize->{ "Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}, GridBoxSpacings->{"Columns" -> {{0}}, "Rows" -> {{0}}}], True-> GridBox[{ {GridBox[{ { PaneBox[GridBox[{ { StyleBox[ StyleBox[ AdjustmentBox["\<\"[\[FilledSmallSquare]]\"\>", BoxBaselineShift->-0.25, BoxMargins->{{0, 0}, {-1, -1}}], "ResourceFunctionIcon", FontColor->RGBColor[ 0.8745098039215686, 0.2784313725490196, 0.03137254901960784]], ShowStringCharacters->False, FontFamily->"Source Sans Pro Black", FontSize->0.6538461538461539 Inherited, FontWeight->"Heavy", PrivateFontOptions->{"OperatorSubstitution"->False}], StyleBox[ RowBox[{ StyleBox["FormatDataset", "ResourceFunctionLabel"], " "}], ShowAutoStyles->False, ShowStringCharacters->False, FontSize->Rational[12, 13] Inherited, FontColor->GrayLevel[0.1]]} }, GridBoxSpacings->{"Columns" -> {{0.25}}}], Alignment->Left, BaseStyle->{LineSpacing -> {0, 0}, LineBreakWithin -> False}, BaselinePosition->Baseline, FrameMargins->{{3, 0}, {0, 0}}], ItemBox[ PaneBox[ TogglerBox[Dynamic[Typeset`open], {True-> DynamicBox[FEPrivate`FrontEndResource[ "FEBitmaps", "IconizeCloser"]], False-> DynamicBox[FEPrivate`FrontEndResource[ "FEBitmaps", "IconizeOpener"]]}, Appearance->None, BaselinePosition->Baseline, ContentPadding->False, FrameMargins->0], Alignment->Left, BaselinePosition->Baseline, FrameMargins->{{1, 1}, {0, 0}}], Frame->{{ RGBColor[ 0.8313725490196079, 0.8470588235294118, 0.8509803921568627, 0.5], False}, {False, False}}]} }, BaselinePosition->{1, 1}, GridBoxAlignment->{"Columns" -> {{Left}}, "Rows" -> {{Baseline}}}, GridBoxItemSize->{ "Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}, GridBoxSpacings->{"Columns" -> {{0}}, "Rows" -> {{0}}}]}, { StyleBox[ PaneBox[GridBox[{ { RowBox[{ TagBox["\<\"Version (latest): \"\>", "IconizedLabel"], " ", TagBox["\<\"1.0.0\"\>", "IconizedItem"]}]}, { TagBox[ TemplateBox[{ "\"Documentation \[RightGuillemet]\"", "https://resources.wolframcloud.com/FunctionRepository/\ resources/FormatDataset"}, "HyperlinkURL"], "IconizedItem"]} }, DefaultBaseStyle->"Column", GridBoxAlignment->{"Columns" -> {{Left}}}, GridBoxItemSize->{ "Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}], Alignment->Left, BaselinePosition->Baseline, FrameMargins->{{5, 4}, {0, 4}}], "DialogStyle", FontFamily->"Roboto", FontSize->11]} }, BaselinePosition->{1, 1}, GridBoxAlignment->{"Columns" -> {{Left}}, "Rows" -> {{Baseline}}}, GridBoxDividers->{"Columns" -> {{None}}, "Rows" -> {False, { GrayLevel[0.8]}, False}}, GridBoxItemSize->{ "Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}]}, Dynamic[ Typeset`open], BaselinePosition->Baseline, ImageSize->Automatic], Background->RGBColor[ 0.9686274509803922, 0.9764705882352941, 0.984313725490196], BaselinePosition->Baseline, DefaultBaseStyle->{}, FrameMargins->{{0, 0}, {1, 0}}, FrameStyle->RGBColor[ 0.8313725490196079, 0.8470588235294118, 0.8509803921568627], RoundingRadius->4]], {"FunctionResourceBox", RGBColor[0.8745098039215686, 0.2784313725490196, 0.03137254901960784], "FormatDataset"}, TagBoxNote->"FunctionResourceBox"], ResourceFunction[ ResourceObject[ Association[ "Name" -> "FormatDataset", "ShortName" -> "FormatDataset", "UUID" -> "76670bca-1587-4e7e-9e89-5b698a30759d", "ResourceType" -> "Function", "Version" -> "1.0.0", "Description" -> "Format a dataset using a given set of option values", "RepositoryLocation" -> URL["https://www.wolframcloud.com/obj/resourcesystem/api/1.0"], "SymbolName" -> "FunctionRepository`$66a3086203b4405b88cdb0de8a5c3128`FormatDataset", "FunctionLocation" -> CloudObject[ "https://www.wolframcloud.com/obj/70389ad6-7dbc-48c8-b898-\ 72c65c00f14e"]], ResourceSystemBase -> Automatic]], Selectable->False], "[", RowBox[{"MaxItems", "->", "5"}], "]"}]}]}]], "Input", TaggingRules->{}, CellChangeTimes->{{3.83433499510703*^9, 3.8343349977811747`*^9}}, CellLabel->"In[13]:=", CellID->1168053742], Cell[BoxData[ GraphicsBox[ TagBox[RasterBox[CompressedData[" 1:eJzsvXd8FcX3x7303hEBqSJKlypFlKJ0UYqA+BURVERpKkWkKxBQUAGponSU JoigEAI81ISELy30L0JCIAmhEywEgTznd8/jPOvuvbuze+fuTu497z94kdm5 uzOfszNzzu7MbPnegzr1yawoyuCc8E+nXh81+/DDXh93Lgh/dBk4+N13Br79 VpuBQ95+5+0PG/TOAolzMylKg6yK8n//TycIgiAIgiAIgiAIgiAIgiAIgiAI giAIgiAIgiAIgiAIgiAIgiCIgPHAg6iz/T8EQRAEQRAEQRAcGEcW58+fT0hI oDiFIAiCIAiCIAgnMQgr0tLSDni4e/euwDglNYQJZQWo7m6XgjCHLCUKUtJd SH85IbvICdlFTkzjlPPnz//Xg6hXKnQnhLICVHe3S0GYQ5YSBSnpLqS/nJBd 5ITsIifGcQp7mYLAnxSn+E8oK0B1d7sUhDlkKVGQku5C+ssJ2UVOyC5yYhyn 4MuUZA/wH/iT4hT/CWUFqO5ul4IwhywlClLSXUh/OSG7yAnZRU4M4hR8mXL4 8OH79+/fu3cP/iPklQrdCaGsANXd7VIQ5pClREFKugvpLydkFzkhu8iJQZzC Xqbgn6JeqdCdEMoKUN3dLgVhDllKFKSku5D+ckJ2kROyi5z4ilPUL1MwRdQr FboTQlkBqrvbpSDMIUuJgpR0F9JfTsguckJ2kRNfcQq+TLl06ZI6UcgrFboT QlkBqrvbpSDMIUuJgpR0F9JfTsguckJ2kROvcYr+ZQoCf/r/SoXuhFBWgOru dikIc8hSoiAl3YX0lxOyi5yQXeTEa5wSHx+vf5mCQKKfr1ToTghlBajubpeC MIcsJQpS0l1Ifzkhu8gJ2UVO9HGKr5cpiP+vVOhOCGUFqO5ul4IwhywlClLS XUh/OSG7yAnZRU70cYrByxQEX6lANopT7BHKClDd3S4FYQ5ZShSkpLuQ/nJC dpETsoucaOKUO3fuGLxMQfx8pUJ3QigrQHV3uxSEOWQpUZCS7kL6ywnZRU7I LnKiiVNMX6Yg/rxSoTshlBWgurtdCsIcspQoSEl3If3lhOwiJ2QXOVHHKfgy BQKQs2fPxhkCGSAbZIafUJxilVBWgOrudikIc8hSoiAl3YX0lxOyi5yQXeRE HaekpKT81yLwE4pTrBLKClDd3S4FYQ5ZShSkpLuQ/nJCdpETsoucaOZ9OQDd CaGsANXd7VIQ5pClREFKugvpLydkFzkhu8gJxSnOE8oKOFP33bt3b9iwITw8 PNAXskQo2z1jQZbigaeVyalkQkLCBg+nTp1yuyyBRU79CbKLnJBd5ITiFOcJ ZQWcqfszzzyjKMpDDz0U6AtZIpTtnrEIJksdOXLkrbfe6tu378mTJ8WemaeV yankpk2bFA9fffWV22UJLHLqT5Bd5ITsIicUpzhPKCtAcYrbpSDMCSZLtWvX Dn3yN954Q+yZKU6RH2f0j4uL22PG5cuXff38xIkTmOf8+fOBLqokyGmXffv2 +cp27dq1QJdWBuS0CwLWmTNnzkcffTR9+vQtW7bcunUr0OWUB5njFLDmsx5i Y2MDLIOjhZFz7HYGilPcLgVhTjBZqkePHuiTv/3222LPTHGK/Dij/2effaaY 8fXXX3v97dmzZwsXLox55s+fH+iiSoKcdsmRI4evbEuWLAl0aWVATruA89mp UydNhgYNGvz3v/8NdFElQeY4BeJHtAj8J9A6OFkYOcduZ6A4xe1SEOYEk6XO nTs3fvz4sLAw4Q+rKU6RH3n8rkWLFnn97YsvvsjyUJwiFkt2uXz5sg3zBRkS 2uXmzZtNmjTBxIIFC77yyitNmzbFPytUqJCSkhLo0sqAzHHKr7/+iuaQIU4R WBg5x25noDjF7VIQ5pCleKA4RX6c0T8xMfGYN/bu3ZszZ07QuX79+uBu6X+4 cOFCtW9GcYpYLNnl9OnTaIVPP/1U/5NLly4FurQyIKFdfvjhB7TLoEGDrly5 gonffPMNJk6cODHQpZUBmeOUZcuWcYYGFy5c2Lx5c1xcnP7QyZMnYVQ6d+6c 6eViY2MhGPnf//7nZ2FMsdoWjAvGABEiIiK2bNkSHx9vek5+WcRite6c5UxO ToaKHzp0CCdt4gMHUXEKnPPw4cNwgyUkJPhzHht9oK8bG+q7bdu2PXv2sI7L F5yFh/Ns3749MjLy+vXrlkoYlNiw1LVr16D1nTlzRn8oKipq9+7dppbibOaI QY9nelQDv+lttDJ7435MTAwUCS5nnA26Begcjh8/znNOKDYUPikpCf4P/zGN U3h6HvAlDh48uHHjRigw53RxX6bht4Klpiqwz7HRRbz55psgcq5cuUB8/dHf fvsNZ3zVrVuX4hRTAm2X6OhotMLKlSstFSyYkNAuH3zwASTmzp1bc56GDRtC +ssvv2yptBkUOeOUGTNmPProo/ny5cOGA/8p6KFOnTqYoUaNGvDnnDlzTp8+ /cILL2TPnh2yFS1alJ0BRo2wsLDixYuzZzWPPPLIwoULNReCbIsXL37mmWcK FCjAcsKv1q1bx18Ye5ob5+EpGGPHjh0QgLNsmTJlKqiiQ4cOVmUJHJz9AH85 wQmsV69e5syZMRvUd8yYMa1bt9Z4UJcuXYKzwdEBAwZozjBq1CgUCsZNzaGz Z89CP8BMD1SsWHHp0qXW6/1/8NTd+MY+depUnz59oAxgYixPlixZunbt6tWj 4yz8kSNHwOHMli0b5oErwnU5Xdxghd9Ss2bNgtumU6dOefPmRQGrVau2fPly yADDypAhQ0qXLs0sNXLkSI03y9/MjW8Mg6PQavD21m8gzG96zlbmp5Iw4r/+ +uvFihVj/VivXr2uXr2q/8nUqVOhQ2CKgbs7aNAgrzlv3LgxdOjQIkWKsHM2 aNCAPY3UxymcPc/ly5fBmnny5GHZwLtQ97o7d+5U185gqOK3go2m6n+fY++6 wNatW7Gbmjx5stcMcBI4Cqddv349npniFDUO2wWcbTyPhC9AHUNCu2DwAl2c Jv9rr72meFapWKhehkXOOAWGAMUb4GhhhnLlysGfw4YNq127NjvaqlUrPJqS ktKiRQuWjq/SkOHDh7OrXLx4EZwKrxeCG+bnn3/mLIw9zQ0ycBYMge6F+Q9Q JPXwjYAUlmQJKDzW5y/nokWL1Ec1qD2ohIQETIRWr7nc4MGD8ZDmUTaMnmyB p4bvvvsuQHU3uLGnT5+OXaKeRo0aac7DWfgVK1awQCZr1qzgS+P/S5YseeLE CRt1DA74LQUDEItEGPnz59+/f3/Lli314n/xxRfsDJaauXGPZ3B03rx5+OfG jRvVJ+Q3PX8r80fJF198sUKFCvrzDxw4UJ0ZOofnn3/ea0mqVq2q2Xs5MTGx cePGvkqu6OIUzp4HYpnnnnsO0yHAhOvqFyBv27aNx3D8VrDXVP3sc2xfF6hZ syZkg3+9vmmCXgjPM2rUqEOHDuH/KU5R47Bd2PwizheUQYmEdoET4hl++eUX dXrlypUhEaIVWxXNYMgZp8TGxoKjxXaqgQBzvQc25wpvFaR69ergwu3YsWPP nj14FG4hPNSpUyccueCEOJsa7hz1hl24QKlz584rV648c+YMeKrMa3366ac5 C2NPc+M8PAVL9TyzLVWqlOJ5ywMBCybC4Is5hw4d+t///peN3fyyBA6eunOW 87fffmMPsd94443IyMjTp08vWbKkbNmymOhPnHL+/PmiRYtiepcuXXbu3Ak+ z4YNG2rUqAG9k+kcHtt1N7ix8XkXdHefffbZrl27cBJO+fLl9Z0YZ+Hj4uIK FSoEeYoUKQK6Xb16FQ7NnDkT3TO44W3UMTiwaqkOHTpAWBEdHa1eFAyA4AsW LDhw4MDHH3+MKfAr9Uk4m3mqWY9ncNRrnMJvekutzH8lW7duvWzZMnBcoZvF IR6CPvXceKbP448/DppfuHBh+/btbGFpmzZt1GceNGgQphcvXhxOe/bs2d27 d7/11lvscpo4hbPnAXkxW9euXbFskBndjMKFC4NE0OuybUUNTMNvBdtN1c8+ x/Z12cP5b775Rn+UzfiqU6fOjRs3oIFgZopT1Dhsl9mzZ+MhcIyhlYEDDM0B wskQWamNSGgX6Ekw8IEmw/pwGM31vXoQg3b5r10CFKcgY8eORVvoIwJ2qzRq 1EjjNB48eBAfO7dt21adDn4d9o3q/TlhQGQv6Bls1FNP6TcojFV4FOAsGIyJ mPLpp5+qc+JHE8BNYimWZAkcpnXnLyeLHN9//31NTnzU4E+cwk7ev39/deZr 166Ba2Slxv8/lvpA/Y2d6nnkpRk12Ez7d99912rh8cVx5syZoZtVZ4N7SfHM U/K62iIUsGSpkSNHskTwWtkMrlatWqnn46G7C6gnAPD3P8Y3hsFRr3EKv+kt tTI9lpQEv0j9LLFbt26YDr0cpsCIg7MpIDxXiwM3NhMN2ggmHj58GDNDv6F5 zzJnzhzMrI5T+HsenLAEieoAau/evZoCaGqnNw2/FWw3VT/7HNvX7dixo+Lx 1rw+0sHhCZw33FiV4hSvOGyXsLAwxRuPPPKI5pYOYiS0S6onimEPi6DtQJeO PVLnzp0t1i+jwuKUJOu4HqdkypRJv4M03hLAsWPHNIfAl1M4ZvRNmDABzwBD D09hrMKvgGnBmBOyadMmdc5x48YpnmeAN27cwBT/ZRGCad35y4ktt2jRovrV GfqdiCzFKeAs4cxzODkuvBUCfx/o9cb2BRYVui/8k7PwkC137tyQ7T//+Y/m ENNKM/UodOC3VP369TXpbFaSZrnE6NGj9b2KV7z2P8Y3hsFRfZxiyfSWWpke f5ScNWsWFmb16tWY8sknn2DKzJkzNZm3b9+Oh7p27YopTEbNM5xUH/t98fc8 WGCoviYbzv4aMWKEvnZ60/BbwZ+m6k+fY/u6p0+fhqEHjkLXqj8KwQj+ls3D pzjFKw7bhcUpDRs2HDRo0MCBA2vUqIEpEL/LsOeqA0hol1TPcxgISZR/U7Nm zRDZhC01g8cp+qENeOWVVxTPtOGtOjDaffjhh/W/io6Onj59OtxgYH0Wuq5d u5anMFaxFKcYF4ztloxLdxnQySj/XntlTxbhmNads5wnTpzAivfs2VN/Ej/j lNjYWEzp3bu3vxVW4Y/PpubKlSsrVqwYOXJkhw4dHn/8cSxqvXr18Chn4Y8c OYLZ4FbRS42H4MazWsfggN9S+pVBr776KqrHHhEgM2bMwHRos/qzmfY/xjeG wVF9nMJvequtTI8/SkJ4gldnK6qwcwAuXryoPw/OgK1evTr+yd4EgRugyek1 TuHvIXHNS9WqVdXnhAERz/n555/ra6c3Db8V/Gmq/vQ5tq87ZcoUPAp3tebQ mTNncGIMaMjenVGc4hUn7YJAQ1u/fj378+bNm0OHDsWf6JtnUCKhXaBjAfHh KIwLffr0UW8z8tFHH9muacYiQ8cpXtsOzhM2IEuWLOr80DZ9/WTNmjU8hbEK pwI8BQOXFR/iNWnShP3wwoULeDM3a9bMtiwBwrTunOUEFw7/1D8sTfU7TmEr 1yZNmuRvhVX447Mhp06devfdd3Gg18B2n+MsPFs1acDo0aPt1TSj44+lXn/9 dVRPE6ewuUaaOIWz/zG+MQyO6uMUftNbbWV6/FHyp59+wquzOOXJJ5+EP+H+ 93oeDB+yZ8+OykPkrni24dLn9Bqn8PeQbLWR+i02e9ezZcsWntrxW8GfpuqP /ravi9/OBrdK/82Utm3b4g8jIiL+9w/h4eGYCA4b/JmYmGhc4CBANrv4AnJC 4A+/AjdD06EFJRLa5eWXX1Y8nR4W7OrVq9BM2AaGY8aMsVvXjETwxSlPPfUU NqtGPmD7X6Wq3nXC6Ab3w4IFC6BSzKNwMU7hL9jw4cMxsWXLlsuWLVu4cOET TzyheMJt9TBqSZbAYVp3znKyPuGzzz7Tn+TZZ5+FQxCssRRLccrSpUsxZerU qf5WWIWfccrZs2fZhkgVK1aEG/KXX345f/48LqVncQpn4dn3gOBUvqQOkW8Q 63EsTuFv5gLjFH7TW21lesTGKdg5qLcAVYObEmTNmhU/NICzVgoWLKjPyVat Tps2jSXy95CnT59++OGHFc8a/0mTJq1atWrgwIG46WKzZs002/X4qh2/Ffxp qv7ob/u6JUqUULzNi4uJiVE4MH6bHBxIZRdjBgwYgJfjn4qccZHNLlFRUXhO zVPHY8eO4T6TOXPm1H9PIfgIvjgFg9MCBQqYfniLzSuAEUr9PS+2u75bcYql giUnJ+sfsOfJk0fzhpFfloBiWnfOcrJVq4MGDdIfNXifop/Boo9T2Hj63nvv 8VaMAz/jFPwaWpYsWcCPVadr4hTOwkPjxWzgKlusR/DjTJxiqZkLjFP4TW+1 lekRG6ewSdpePxiE56lUqRL+iYu1Af0sbq/vUyz1kGw4UANV0K+T9VU7fiv4 01T90d/edY8ePerrnmEnNKZWrVr8l8ugSGUXY9jUL9OFdUGAbHaZNm0aHtVv Fj137lw8FAq7HLA4RcL9vnAxOKDZPCHVcFxmnzvRvH/X8/bbb2POs2fPqtO9 +gkGhbGKqQKWCoa74jRu3HjUqFEwjvfo0QNCb/2UbH5ZAopp3TnLeeHCBcxW s2ZN/VG9B3Xt2jX8iBJbb84Afx5PxeIUyIx7osJtBv/nrZsZ/vSBcXFxWEj9 tmyaOIWz8NevX8fdkJo2bWq9KkGOM3GKpWYuME7hN73VVqZHbJzC9iLQf3iR fU0bwg1MYe1a/31tr3EKfw8ZExMDAkIrg0GhV69eHTp0+PDDD319xdtX7fit 4E9T9Ud/e9dl80697kickJAQr4PN3gevDP70uvgoyJDNLgbgd6DgigKHQmmR zS5jxoxRPBuIsX3OGeCIslbDf7kMCotTbEQcgY5T2Db1X375peaQwbi8a9cu 9EirVatm3LJwe0nIrH4IdvXq1e7du+v9BIPCWMVUAf6CpaSk5MqVS1FtieML flkCimnd+cuJ26ICmo9379y5E3ft03hQuGbn4YcfxmkhyOLFi9mXmNT7ErPP vU2cOFFz3f3795tW0yv+9IFsA2rNvDU4IS67ZnEKf+HZA+eQnd/lC2fiFEv9 j8A4JdWK6a22Mg1i45Q9e/Zg5wClUm/QfevWLVYjthabTYBs0KCB2hbw/549 e+IhdZzC3/Pg+pTatWsb18u4dqlWrGC7qfr5DtfGddlAqblhDKB19F5x0i67 d++uWrUq+x4T45dffsFfeX1SEXzIZhfWB86ePVtzCAZ3PBQREcF5rYyLzHEK sxH4YNCCYDBiMySNR+133nkHf/jkk0/CtXB10unTp4cPHw4/YWPWBx98gNl6 9+4dFxcHY1N4eHitWrWUf1D7CQaFsae5QQb+giUmJuK86LZt2x47dgw88Fse /JEloPBYn7OczA/JnTs3DHAXLlw4derUF198gYGbovOg2Iax4OqDZxgVFQUn RPUQdZwCQyc+G1E8L2RBW7gudOYdO3YEZ8beOyl/+kDwytCJypcv3/bt28HE IMjYsWNxP0Pl33EKZ+GPHz+O+yjCSUaPHo0fpAA/ef369U2aNFmwYIGNOgYH zsQplvofsXEKv+mttjINYuOUVFXnAGECRBYg8sGDB9nnNRs3bsxyQhsB1wvT W7VqBRH6lStX4OZv3rw5U1jznUfOngc/H5klS5bVq1dDD2zQ5RrULtWKFWw3 VT/9LhvXHTFiBGoI1jG+LoPiFK84aRdc05ojR46PPvpo8+bNly9fvnjxIrjQ 7NPqP/74o62KZjBks0tSUhKuXoHTfvPNN6yfWb58OfbAJUuWtPfV6YyFzHEK jAtlypRhYwrYJXv27Pj+y3jUhibWoEED9sOcOXOyj6+pRz3wVPHDXornqSb6 gYpnhrPeTzAojD3NDTJYKhh7eK4GxtCiRYt26dJl1apVVmUJKDzW5ywntFl8 Ja0HdyjVeFAgmj5n3rx533//ffy/Ok5J9ewWyF61KJ53r+z/ffr0CVDdDW5s sKbavkwc/DK4Ok7hLzw4BvipXKRIkSIs52OPPWajjsGBM3GKpWYuNk5J5Ta9 1VamQXicAp0DLtTSAw0BYhb1GaDK6DZoYD25Jk7h7HkiIiLUDYoB16pRowb4 J+qVrcaG42+A9pqqn32OjetC0I1H9TPqfUFxilectMuyZcvYkwfl3+OFYn1J S8ZFNrukenob9tSxWLFi9evXZ90XpG/dutVqHTMiMscpqZ538eiGseaze/du SK9YsaLy7313Ndy8eXPChAk4M4EBNxgMTOrvr0HzZPtRK56Rd/LkyZABby21 n2BQGHuaG+fhL9j333+vGKL+JhqnLIGD0/qc5YRsw4YNU3cL0BWsXLkSqoyi aU4LGjLvHZR86qmnwIJsSw1NnJLqmdyCW6EySpYsOXfu3MDV3eDGTkhI6NCh AysJuLjgQB46dAg3fNPEKfyFByfhmWeeUQ9M0Pt17tw5JibGXjWDAH8shXFK 1qxZNZtMgpeL8qr3++Jv5sY9nsHRhQsX4sn1LwE5TW+1lanxR0m2ikTzCOX6 9esjRoxgX5nBtgCye93PNjo6mr1VUTzPl7p3737hwoVHHnkE/pw1a5YmP0/P A/8x3sT4iSeeSPrnE6umQxV/A7TRVP3sc2xcF7cjANQT84w5duwY/mTx4sWc P8noSGiXEydO9OjRg71AQcqXL+9r4VVQIqFdUj0r9Nu0aaPpZPAdMX/VMjSS xympnlEJPMlVq1bB+I7v0SwBrW/9+vXw27i4OK8Z4PbYvn37xo0beZ7/+FkY hFMBnoKB74H+w9dff71v3z4IriH6Dg8Pnz17Nvsmmt6DTeWQJUBYtT5POcEo kZGRP/7446lTp0xPCM4MiPbzzz+Dr8JZhosXL27evBmKwXN+A6zW3SswpkNh oAqcb3s5C48324YNG6DrU6/fCU2EWIoTS/1P4ApganpLrYwROCVv3boF/gCU Z+/evaYL7qD3AIWh1fAvzTPoefAjIE8++SSMgFC7CA+rV6/+5JNP8B2TonvG ZQp/A7TUVAXqT12EQKS1Cwwr0KBgfIT2ot+NJ+iR1i6pHtdlx44da9euhX9D 4RtDauSPU4IPgQrgN4Beeukl/aGbN2/iTlDGEzMcJpStH8p1z1iQpUQRfEqy SUpePx7NNgvVzChzi+DTPzggu8gJ2UVOKE5xHoEK4Dy0Xr166Q8lJSXhx8ja tm0r5FpCCGXrh3LdMxZkKVEEn5IsEtHsJo1MmDABj9qbEiyc4NM/OCC7yAnZ RU4oTnEegQq89NJLimd69uzZs+Pj41n6nj176tevjyOmVJ8BCmXrh3LdMxZk KVEEn5LsQy1NmzaNiopiq5BSUlImTZqEy99q1arl7ud0GcGnf3BAdpETsouc UJziPAIV2L59u3rhW8WKFZs3b47rvJDPP/9cyIVEEcrWD+W6ZyzIUqIISiU7 d+7MOti8efM2btz4qaeeyp8/P6Y8/vjj586dc7uM/x9BqX8QQHaRE7KLnFCc 4jxiFdi7d2/Hjh3ZznVs9PzPf/5j+wsvgSOUrR/Kdc9YkKVEEZRK3rhxY8KE CY8++qi6y82UKVPlypVnzpzpzMaJnASl/kEA2UVOyC5yQnGK8wRCgZSUlIMH D27atGn79u0Ob+FliVC2fijXPWNBlhJFECt569at3377bdeuXRs3boS+V6rw hBHE+mdoyC5yQnaRE4pTnCeUFaC6u10KwhyylChISXch/eWE7CInZBc5oTjF eUJZAaq726UgzCFLiYKUdBfSX07ILnJCdpETilOcJ5QVoLq7XQrCHLKUKEhJ dyH95YTsIidkFzmhOMV5QlkBqrvbpSDMIUuJgpR0F9JfTsguckJ2kROKU5wn lBWgurtdCsIcspQoSEl3If3lhOwiJ2QXOaE4xXlCWQGqu9ulIMwhS4mClHQX 0l9OyC5yQnaRE4pTnCeUFaC6u10KwhyylChISXch/eWE7CInZBc5oTjFeUJZ Aaq726UgzCFLiYKUdBfSX07ILnJCdpETilOcJ5QVoLq7XQrCHLKUKEhJdyH9 5YTsIidkFzlxK04hCIIgCIIgCIIwhuIUgiAIgiAIgiBkw/k4xcYPg4ZQVoDq 7nYpCHPIUqIgJd2F9JcTsouckF3khOIU5wllBajubpeCMIcsJQpS0l1Ifzkh u8gJ2UVOKE5xnlBWgOrudikIc8hSoiAl3YX0lxOyi5yQXeSE4hTnCWUFqO5u l4IwhywlClLSXUh/OSG7yAnZRU4oTnGeUFaA6u52KQhzyFKiICXdhfSXE7KL nJBd5ITiFOcJZQWo7m6XgjCHLCUKUtJdSH85IbvICdlFTihOcZ5QVoDq7nYp CHPIUqIgJd2F9JcTsouckF3khOIU5wllBajubpeCMIcsJQpS0l1Ifzkhu8gJ 2UVOKE5xnlBWgOrudikIc8hSoiAl3YX0lxOyi5yQXeSE4hTnCWUFqO5ul4Iw hywlClLSXUh/OSG7yAnZRU4oTnGeUFaA6u52KQhzyFKiICXdhfSXE7KLnJBd 5ITiFOcJZQWo7m6XgjCHLCUKUtJdSH85IbvICdlFTihOcZ5QVoDq7nYpCHPI UqIgJd2F9JcTsouckF3khOIU5wllBajubpeCMIcsJQpS0l1Ifzkhu8gJ2UVO KE5xnlBWgOrudikIc8hSoiAl3YX0lxOyi5yQXeSE4hTnCWUFqO7GeSIjI7dt 23bgwAFHSkR4J5TvUrGQku5C+ssJ2UVOyC5yQnGK84SyAlR34zyPPvqooijP Pvsszwm/++67bt26ff/99wIKR6gI5btULKSku5D+ckJ2kROyi5zIH6fcu3dv //79X3755fTp0w8dOnT//n0bF5UKfgWSk5NXrFgxduzYhQsXggiao1euXDlk xl9//SW+An5g1fpJSUlQC/jXIE9KSsqaNWvGjRu3bt26a9eu+VvEgCE2Tjl8 +LDiIUuWLBcuXBBTRMJDIO7S0ISUdBex+h89etTXQHP37l2vP4mNjTUeoeLi 4vS/gkH/yJEj0MU9ePDAuMD8OQ1ISEjAwly/ft32SSzBb5cTJ07A6A+j29q1 a4WMg7dv396yZcukSZNmzJgRExOTlpZmcE6w+KJFi8aMGTNv3ry9e/faFtlY 4b///nvfvn1TPGzcuBEcG3tX8R9R7cV/34zT7+WX7s6dO5GRkXDCiRMnrly5 0mu748St9iJtnHLy5MlHHnlEUVGxYkVQycZ15YFHgejo6CpVqij/pnXr1vHx 8SwP3MCKGfPnzw9sZSzCaX3oSzdt2vTaa69lzZoVatG0aVNfOQcPHqyub6ZM mcLCwkSWWBxi4xToZyBCgczZsmVLTEwUU0TCg/C7NGQhJd1FrP45c+b0NdCs Xr3a608KFSpkPELB+K7Of+rUqWnTplWoUAGP7tixw1eZ+XMaA+590aJF8STL li2zdxKr8NglNTW1V69eGrn69esHrqk+M+c4GB4eXrJkSY3++keg6R5Pu1u3 bpqrN27cGGS3WlkDhSG8HTBgQJ48edRXKVCgAIRFVq8iBFHtxU/fjMfv5ZcO cg4fPjx79uzqnOA89O7d+9atW7zS/IOL7UXOOOXEiRMPP/wwClKpUqXHHnsM /1+uXLnz58/buLQkmCqwfPlyNiKAAg0bNsyfPz/+WblyZfYAhKctQODsRJW4 4bF+UlISNn/GM8884zUntFPMAK21QYMGuXLlwj8//fRT8UX3G7FxSrpn0Pn4 449tD9CEL8TepaEMKekuAvX/66+/bAw0pnEKjGgsMzjhmqPbtm3zelr+nKZ0 7tyZnUSeOOXBgwdNmjTBUpUtW7ZVq1bFixfHP5s3b655e8U5DoJEEL9AOris MMTUq1cP7Q75N27cqM55//795557Dk8CFuzRo8fzzz+Pf4LD/Oeff1qqrC+F r169Ch4+pmfOnLlu3bpPPPEEy7lkyRJLVxGCqPbij2/G4/fySwelrV27NqaD 9atUqcJODnTo0MHqOzIX24uccUrHjh1RWzYDf+bMmahPnz59bFxaEowVuHbt GkYlcH/u3r0b3/fduHGjffv2WPfJkydjztTU1HhvHD58GHuqp59+WrZpcjzW T0xMLPgP0Aa99gPAkSNHUJA6dercvn073SPd448/rsg6FUp4nEIECIF3aYhD SrqLQP0hG/a3n3/+uX7Q+eOPP7yePCEhwesg9d577+HZNm/ezDKDv43FyJ07 Nx71FX3w5zRmxYoVatdRnjhlwYIFWKR33nkHX6DAv3379sVEOMpyco6Dd+7c gRADEh966CG2T8v+/fsx/IFx5969e+ycP/30E55z2LBh8ENMXLp0KSZ+8cUX /DU1UHjkyJGY+PHHH7MJSxs2bECbFilS5Pfff+e/kBBEtRd/fDMev5dfOihJ 9erVIfHdd9+F6CbdE4RCHcuXL49niIyM5NfH3fYiYZySnJyMQSu0U3U6GjFf vnzO38OiMFUA7px27dpdvnxZnQj3GMYvrVu3Nj4/3JCQDe7YM2fO+F9asfBH qQi+1vc6bvbv318fkrBOW8JXKvxxSpMmTVjK8ePHo6Ojrd7t0DvBYBQVFYUj lzEgIETEN2/eNM157ty5HTt28C8fgGLs2rUrLi7On9njziPwLg1xSEl3Eaj/ sWPHsGsFd8jPUkGHgA7V22+/7TUDc4lNow/+nHouXbqEM1jq168vW5yCrzNK ly7NwgQEghFIhzCEebmc4yD08Jgye/Zs9Ql//vlnfd2HDx+ueN7OaOaYwY0B 6d27d+esprHCEBm9+eab6pgLYU74vn37OC8kikD3V6a+Gaffa0m68+fPQ+Cp yfnDDz9gzunTp3MW3vX2ImGcMmXKFJRCM61l9erVmL5o0SIbV5cBq22BgW/6 ypYta5AHXFOM8adNm2aveAFFVD9w9+5dnFGgnxpatWpVxfOS1M+iCoc/TmnZ siVEDX369ClRogTe7WBT6Lg0ax5r1aoFIkBIq06EDvDFF19kTzwyZcoEgqhn j69YsQJ+9dhjj8EYFBYWxp6r4EvhvXv3aooEIcaqVauaNWtWsGBBdtqSJUuG h4drcuKZYQyF/69bt65hw4bs/XipUqX0Z5YW8q5FQUq6i0D9d+3ahW05JibG z1K1adMGzlOmTJnU1FSvGZyJU9D3y549e0REhGxxSrFixaA8ffv21aRDV4xF xZ/zj4OzZs3CH4K36TWn+uEYutNFihTR5MT1Mo0bN+arpU2FweXDzAsXLuS8 kCgC2l/x+GZ++r380sFwjDnHjh3LU/h0CdqLhHFKz549QQdwjdSvI9M9K5jQ +Rk6dKiNq8uA7TgF/FKoOPQqBnnweQv8K+cTbFH9QFxcHLYU/Tvojz76CA/5 morgFvxxCoSibH2omiFDhugzqyeJXbt2rXTp0iw/mxQBsOV1S5YswZQnn3xS f4kcOXJAiMFOeOvWLa/ZFE/otHXrVnV52JmHDx+OE6HV5MqVy8WNXCxB3rUo SEl3Eaj/+vXrsSH7uTiUPcCHE/rK40Cc8v333+MPx48ff+bMGVf8Ll9H09LS sDx6HxKiDDyEq7D5x8GPP/5Y8TyM0jsGOJ1MvaEBs5GmkNWqVYNEiFZ46mhb YXan+dqcIXAEtL/i8c389Hv5pQsLC8OcnN81kKG9SBintGrVCnSoXr26/hCu A3rttddsXF0G7MUpN27cwGD8zTff9JWHPfKC3tuvIgYMUf1AVFQU1vTHH3/U HJozZw4e+u233/wsrVj44xTkhRdegNpBnzBt2jTsowoUKKAOvvRxCut83n// /YsXL0J/GBsb+9JLL9WsWZPtgsiiCcXztG3RokUJCQkHDx58/fXXMbFMmTLq 1/3PPfccjG6vvPLKhg0bkpOTk5KSRowYgTnVj+A0Zy5UqNDkyZMPHToUExPD XhNPmDDBbxWdgLxrUZCS7iJQ/4ULF2IrBicWegBwVkePHg3ei9VV1c2bN1c8 qy8NvLVAxyng7RcpUgR+9dRTT4FDePr0aVf8LoMM6OTox3oIYXCBw6hRo9Kt jIPsT/3KTfA8Fc9zJ7Y8HwYLnGQOKm3fvh0TQWE8A0sxwB+FP/zwQ19FDTSB 6684fTM//V4e6cAcy5cvx+2/SpcuzdN+JWkvEsYpNWrUUHxskIgb9j7//PM2 ri4D9uIU9kJw8eLFvvJ07dpV8SyU00xqlQdR/cDatWtRDf1uV+zN+J49e/ws rVgsxSngA6jHceidMP3IkSOazOo45eWXX1Y8b2Y1nY96fxgWTXTq1EmzJqVL ly76/gcCJX1LZ9u/QPisPzNYTb19JZwBX6907NjRuPqSQN61KEhJdxGo/5df fql4A1wdgzcjGo4fP46/grMZZAt0nNKhQwfF84YXuykJ45TGjRsrngdT6g4W hvV27dphUXv06JFuZRzcunUr/ql5R7Nlyxa2P9jZs2dZOvjV+fLlw3SQC/p2 9FRfeeUVngraVhiGJNw5uXz58jwXEkvg+itO38wfv9dYuosXLw4aNAg8hLJl y6ItoNhqixsgSXuRME7B4BFcKf0hXMxVrVo1G1eXARtxyrlz53AOT+XKlb1u n57u2YYiW7ZskGfEiBECShkYRPUD7OlQbGys5hB77KN/xOQu/HHK008/rUln 27/88ssvmszqOAU6Isxm8NqXRRP6x2KRkZF4SD8vWsPUqVMx5+HDh/VnjoiI 0OQvU6YMpNerV8/4tJJA3rUoSEl3CUScAkeHDRs2ZMgQnIeseCaLHj16lOf8 uM0XjGVq91tPQOMUcK7wJ2yZgIRxCnt7Vbt2bQgZYHBfs2YNdPXKP4DrmG5l HExLS8P9vsBJCAsLAwcVuu7x48eD7dg51Z353bt3ISRR/k2dOnV4ZlP7ozDb 08wxW6gJUH/F75v54/caSxcTE6M2ZenSpY8dO2ZcGESe9iJhnILT7F988UX9 oQYNGmCTsXF1GbDaFu7fv88eX2/atMlXtq+//hrzcN5+riCqH/juu++wsgcP HtQc2rx5Mx7yf18asfDHKfp9iSE8wUqpZ5PqM0OgwZaut2nTBhTQTHNNN4xT 4E7Dn3vdUw7uq3nz5r3xxhvQ9NijNvW2ogZnhghF8WxTY1x9SSDvWhSkpLuI 1R86H/UjCOguRo0ahU2ecyt13C/o5ZdfNs4WuDglOTm5cOHCiueRNXthLWGc AmVr0aKFogNiB1w4P2DAgHSL4yDYTv+lTjgbi0fYV+xv376NMRH08/3792ef bsmcOfOYMWOMq+aPwjt27MA37+DjubLANkD9Fb9vZtvvNZUuLi6uS5cuUFT2 BUnID9Y01lmq9iJhnIKOjdedJfCxgGabowyE1bYwcOBAvCuMpybi12OhY5Ht mylqRPUDzG/XLOVO93wlEw95/cyui/gTp2zZsgUrZRynpHv2G1RvzAWd0uTJ k9Xvmg2iCQB3GNPs1QAXxTWAen799VeeMzdq1EihOCX0ICXdJdD6w1hTs2ZN +Ak4wPpHIhpOnjyJ/cOUKVOMcwYuTmF7IUZGRib9A9uzF/xJ+NPXLmQC4bEL aDt9+vRatWrlypUre/bszZs3nzVrFvTkuEwVF85bHQf/97//derUqVSpUopn t5a333771KlTGGyC58Cyde/eHVLAQcW93dLS0kCZhx56CE84ceJEg2LbVhjK hmFs7ty51dObnSRA7YXfN7Pn91qSDsKN8PBwbLaKaoMdr0jVXiSMU1CfKlWq 6A9hfGewnFxyLLUF9rYdHEXjFU8YJjdr1kxAEQOGqH7g4MGDKMuqVas0h2bM mIGHZPvUozNxCpCSkjJ27FgcjBCIO65fv45HjeOUvHnzKp4N0lkKuwNz5MgB 4xfEQeBvLFq0iOIUBnnXviAl3cUB/QcPHoytXr0kzSvz58/HnDt37jTOGaA4 ha2OMQY6K9NT+Yklu4Bzy3akP3fuHBZyzZo16X6Mg2xbFQB3UGETimJjY/GH X331lfon8fHxuLQB4ib95saIbYUvX77Mtrj09aF2BwhQe+H3zWz4vfakS05O xjlm6n3eNMjWXiSMU/r06aN4ntJovnAHjQ7FGTlypI2rywB/W1i9ejU+PIE7 ytjrZvsTDhs2TEwpA4OofgCieKzv6NGjNYfeeustxfNOU714XAYci1OQe/fu rVu3DhffAT179sR0g2gCejxN5l9++QXfJjds2FD94VHmHlCckk7etW9ISXdx QH829Uu9usEruOcqjGimX60NUJzCXugYU7duXdNT+YlVuzBYrIdvSfwfB2GY wMWD7EH93Llz8Zz6DagXL16Mh3ztnGBPYbgfcFKT4vbq2kC0F0u+mVW/1x/p evTogT/E79Trka29SBinsMU7mtXQs2fPxnTw3GxcXQY4FYCuANde5cuXz3QK E9vwXNodiRGB/QC+BtXs4PfgwQOcTOtAjG8Vh+MUJDU1FYVij00Mogm2KjMs LAxT+vXrp3gGu5SUFHVOilPUkHftC1LSXRzQv23btopnj0HT50K1a9dWPJsB mp4zcPO+bty4cU0H290XXHT489atWzyn8gfbcQp+waR8+fJsOx0/x0H2AcFv v/0WUyZOnKh4PnCvfueCgB/ChPJ1QqsK//nnn82aNcOjr776qruz1gPRXiz5 Zpb8Xk7pfK1AYV8i0AzuaqRqLxLGKWACnGbfokULpj/0hBC7KZ6plTKvwjCG R4GNGzfiBtcQWfPINW/ePLxz9F8JlwqB/cCkSZOwyrt372aJ69atw8TvvvvO /9KKxYE45dSpU8ePH9f8tmnTppAtf/78+CeLJpYvX67OdvPmzXLlyuEIxU6C n6DNnDlzcnIyy5mWlsa6OIpT0sm79g0p6S6i9D948GCNGjUOHTqkPz++b+XZ 1gbXvjVs2NA0p6g4BbwI6JSgz9S73GokXEcPHDlyRFNsCCWwnN988w1L5B8H 79y5o3nndf36ddwIFxwqFmaysUb/TfMvvvgCD0VGRmKKnwrDr9geQe3bt/e1 l6ljBKK/suSb8fu9nNIdOHAAIlbwJzXply5dKlasGJ5TfXWZ24uEcUr6P3sY AgMHDgQnCtpU7969MWXcuHE2Li0Jpgps2rSJ7RY4fPhw+POnn35ap0LvB37y ySeYH27LABbdb3isv2/fvr3/gBM7oTdgKSx4T0pKws2poLlFR0c/ePAAOuoC BQpASt68eW/fvh3wylgk0HEKvjqB8HbMmDE4S/CPP/5gn5Ft3rw5ZmPRBEQf /fr1gz4H4o49e/ZUrVoV0/v06cMuAbcfJvbt2/fq1avQYYLI2GeGeJzCeZeG OKSku4jSH6eP5syZE/qWXbt2gRsD6eCA4dcAIVQx2IgSAf8qS5Ys6FN5zZCY mMguOnr0aOxJwsLCMEW9URJ/TvbZO+MpMRLGKTCi5c6du3bt2hARgAsK/Tn4 PBgSli5dmi1XSeceByG9S5cukBPimuTkZIhZIFulSpWw4nPmzGEnhJ/gbZAn Tx4IA9nT+LVr1+LHEUqVKsU2ZvFHYTgJftZQ8TxGA196w4YN6/4N1M6asv4R iP7Kqm/G4/fyS1e9enVsoXBaGKyheHA7QR2ffPJJ/PngwYPZpSVvL3LGKWAj 9iVrlBr/A5GmcbgnOcYKwB2o3zxQQ40aNTS/Yltn6+eUSgWP9XEpty+mTp3K ckIzwbFPfXtAiKf+yIg8BDpOiYmJwSckCPyffcArW7ZsMIRhNvVX4/VAt6Z+ CxwbG8tC5swe8P8sqAnZOIX/Lg1lSEl3EaX/jz/+iD4qAr0u62+BoUOHmpbk 0qVLmLlXr15eM7BPMnkFCmkjJ9ulUP9FKjUSxinsARGqzf5fpkwZvbvLMw6C Y6AeHdTnfP/99zV7tUFwhNM5gOLFi4N6+Kpd8Uzwi4qKYjn9URi8bgM7IhAc ccgpjED0V1Z9Mx6/l186qA5uZI3ACM6+XKB4NjFWx7yStxc545R0j8leeOEF 1mTAge/WrVuGDlLSOeIUdR/iFf338nDjO8B4TzDX8b8f0OxpCX47bjmOgOsu Z5CSzld3fMAFPZImfefOnVhBdZyizwzt5YMPPmAbSCI1a9ZUf62YRRPz589n s1sVzwDUu3dv/f0DLgrbP1/xDJTTpk2Dzg3vUnWcsmLFCsyzd+9ezUnwQqET p5juvBoikJLuIlD/hIQE6B/wBQqjQoUKnJ+pYmtyfa0mNo4+8uTJYyPnZ599 honsE3VeiY+Px2z6XbMCBI9dFixYwD51oXjijvbt21+5csVrZp5xMDk5We1N KZ43I74WTYC94HIabdu1a3fixAl1Nn8UHjlypIEdEYdH80D0VzZ8M1O/15J0 KSkp7733Hm4XxoCGPGHCBM1XOyVvL9LGKQjYNyYmZs+ePerPQGRcbCgQNASo 7mfPnt2+fXsQvEsSxcWLF7d6uHDhgmYZHYtTMHiBgQ9ijaioKIPGBQ0wOjpa foVFEcotVCykpLsI1x/XOEDHAr1BYmKiwDMHiOPHjx89etTtUmjhtwv05OHh 4eD/qJ97+4JnHITzQGe+ZcsWHvOlpqbu379/8+bN8K+vz2TIqbA9pOqvxPq9 d+/ePXLkCIz1YPrTp0/7Ws8ipzUzRJwSZISyAlR3t0th8v0UIl0aSwUBpKS7 kP5yQnaRE7KLnFCc4jyhrADV3e1SUJxijiSWCgJISXch/eWE7CInZBc5oTjF eUJZAaq726WgOMUcSSwVBJCS7kL6ywnZRU7ILnJCcYrzhLICVHe3S0FxijmS WCoIICXdhfSXE7KLnJBd5ITiFOcJZQWo7m6X4v+2DmvuQbNzC8GQxFJBACnp LqS/nJBd5ITsIicUpzhPKCtAdXe7FIQ5ZClRkJLuQvrLCdlFTsguckJxivOE sgJUd7dLQZhDlhIFKekupL+ckF3khOwiJxSnOE8oK0B1d7sUhDlkKVGQku5C +ssJ2UVOyC5yQnGK84SyAlR3t0tBmEOWEgUp6S6kv5yQXeSE7CInFKc4Tygr QHV3uxSEOWQpUZCS7kL6ywnZRU7ILnJCcYrzhLICVHe3S0GYQ5YSBSnpLqS/ nJBd5ITsIicUpzhPKCtAdXe7FIQ5ZClRkJLuQvrLCdlFTsguckJxivOEsgJU d7dLQZhDlhIFKekupL+ckF3khOwiJxSnOE8oK0B1d7sUhDlkKVGQku5C+ssJ 2UVOyC5yQnGK84SyAlR3t0tBmEOWEgUp6S6kv5yQXeSE7CInbsUpBEEQBEEQ BEEQxlCcQhAEQRAEQRCEbNC8LycJZQWo7m6XgjCHLCUKUtJdSH85IbvICdlF TihOcZ5QVoDq7nYpCHPIUqIgJd2F9JcTsouckF3khOIU5wllBajubpeCMIcs JQpS0l1Ifzkhu8gJ2UVOKE5xnlBWgOrudikIc8hSoiAl3YX0lxOyi5yQXeSE 4hTnCWUFqO5ul4IwhywlClLSXUh/OSG7yAnZRU4oTnGeUFaA6u52KQhzyFKi ICXdhfSXE7KLnJBd5ITiFOcJZQWo7m6XgjCHLCUKUtJdSH85IbvICdlFTihO cZ5QVoDq7nYpCHPIUqIgJd2F9JcTsouckF3khOIU5wllBajubpeCMIcsJQpS 0l1Ifzkhu8gJ2UVOKE5xnlBWgOrudikIc8hSoiAl3YX0lxOyi5yQXeSE4hTn CWUFqO5ul4IwhywlClLSXUh/OSG7yAnZRU4oTnGeUFaA6u52KQhzyFKiICXd hfSXE7KLnJBd5ITiFOcJZQWo7m6XgjCHLCUKUtJdSH85IbvICdlFTihOcZ5Q VoDq7nYpCHPIUqIgJd2F9JcTsouckF3khOIU5wllBajubpeCMIcsJQpS0l1I fzkhu8gJ2UVOKE5xnlBWgOpunCcyMnLbtm0HDhzgOaGlzLZJTk7e5uHmzZsB vZA8hPJdKhZS0l1Ifzkhu8gJ2UVOKE5xnlBWgOpunOfRRx9VFOXZZ5/lOaGl zLZZsmSJ4gHCooBeSB5C+S4VCynpLqS/nJBd5ITsIifyxyn37t3bv3//l19+ OX369EOHDt2/f9/GRaWCX4ELFy6sWrVq7Nixc+bM2bZt299//22QOTExcd26 dZ988smiRYtOnjz54MEDMcUVCn/dT5w4sXDhwnHjxq1duzYpKSnA5XICilMy Clb7KLg/oWsKjrtULKSkuwjX/86dO9APwHA8ceLElStXxsXFcZ45OTl5xYoV MJZBrw4Dutc8R48ePeSDu3fvev2JkFEvISEBr3L9+nUbP7cBv11u377966+/ TpkyBQSHoRBkNP2JQXV4FL5y5YqvPIy//vrLUn1NFYaCgQXHjBkzb968vXv3 uuW98Nsl+PxSmZE8ToGe55FHHlFUVKxYEe55G9eVBx4FDhw4UK1aNeXfPPbY Yxs2bNBn/vPPP1977TVN5po1a545cyYgFfADnrqnpqb26tVLU51+/foZh2ny Q3FKRoGzjwIXYtOmTdD0smbNCvo0bdo08EXLYJCS7iJQf/Bjhw8fnj17dnW3 nCVLlt69e9+6dcvg5NHR0VWqVNH0561bt46Pj9fkzJkzp+KD1atXazKLGvVS UlKKFi2KP1+2bJml39qG0y4QCZYoUUJdwdy5c3/++ecGPzGuDo/C4Hj7ysOY P38+f2WNiwRhUbdu3TTnb9y48alTp/gvIQpOuwSlXyozMscpJ06cePjhh/E2 qFSpEnjp+P9y5cqdP3/exqUlwVSBr7/+Olu2bFjZggUL1qpVK3PmzPgnDBP7 9u1TZ05KSqpduzYehWwgVIECBdhvwecPbGUsYlr3Bw8eNGnSBMtftmzZVq1a FS9eHP9s3ry5r6dqGQKKUzIKPJaCdodOHeOZZ55xpHQZCVLSXUTprx5lMmXK BHEHG5qBDh06+HoAvnz5cuYbw08aNmyYP39+/LNy5cppaWks519//aX1hlWA x+6rPH6Oep07d2ZXkSpO2b59Oxv3GzVq1LNnTxazGIQJBtXhVJgnTtGYwxiD It2/f/+5557DQ4UKFerRo8fzzz+Pf4LnD6Eo/1WEwGOXYPVLZUbmOKVjx46K p1f8/vvvMWXmzJl4S/Tp08fGpSXBVIFFixZBHUuVKhUREYHvE5OTk4cOHYp1 b9mypTrzm2++ieldunTB51rwE/At8+TJM3fu3EDWww6mdV+wYAFW55133sEX KPBv3759MRGOOlTQAEBxSkaBx1KJiYkF/wHdCfKu9ZCS7iJKf/D8q1evDofe fffdq1evpntGGThz+fLlDTqHa9euYVQCvtzu3btxLLtx40b79u3xV5MnT1YX AxM///zzeB1//PGH+syiRr0VK1ao3W+p4pSaNWsqnuDu5MmTmHL9+vW6desq nljM60Qj4+pwKgy21h8FDh8+nCtXLvj5008/zT/NybhIP/30E6YPGzbszp07 mLh06VJM/OKLLzivIgoeuwSrXyoz0sYp4JnjQx7wV9XpeJPky5fv999/t3F1 GeBRYPny5TgcMB48eADBO9S9cOHCLBF6D3zz0rVrV80TLTl3ZzKtOz5dKV26 NOu1kDp16kD6448/nnEngvLHKU2aNME/QYSoqKhTp055fVxpGqdAG9m3b9+h Q4c0YuqB8//222+7du0CL0JziOIUUypUqEDetVdISXcRqP/58+fBq9Qk/vDD D9g5TJ8+3esJodNo167d5cuX1YkwtGH80rp1a5Z47NgxPJXXuc1qRI16ly5d wvlI9evXly1OgaghS5YsUKRJkyap07dt24ZFPX36tOYnptXhV9grEKIqnoln /DPrTIs0fPhwSIToUjOpG+5ASO/evbuNcvqDqV2C2C+VGWnjlClTpuCNvWPH DnX66tWrMX3RokU2ri4DVscORosWLRTPlOB79+5hCnYdwPHjx0UWMWCY1r1Y sWJQnb59+2rSV61ahTW1J50M8Mcpzz///MmTJ2EQZ1MmChYsOGDAAM20N19x ysWLF/v37//EE0+waQPQtf7nP//x2oWmpKTAcMAmY2TKlAl+uGbNGpbBV5zy wQcfFPIATdWSDvJD3rUoSEl3CbT+e/fuxc5h7NixlgrWtGlTxTOzl6Xs2rUL TxUTE2P8W1GjHvqW2bNnj4iIkC1OAQ8fi/T111+r0yFaxPStW7dqfmJaHX6F 9URFReFoMm3aNP5fmRYJTVmkSBHND3GBauPGja2W009M7RLEfqnMSBun9OzZ E90z5pMjt2/fxnh26NChNq4uA/bilFu3bj300EOK550CS6xcuTK6tSLLF0iM 656WluZr1GP9tqUVfFLBH6cAXhc8wuCuntHtNU6ZN29ejhw59L/1GtHA8MFW OGpgL7W9xilseh4EU5oWGgSQdy0KUtJdAq1/WFiYprvgpFatWvCrqlWrspT1 69fjqUwn+QsZ9aDAeLnx48efOXPGqxcdOHjsggtwmjdvrp5CsGbNGq8q8VSH X2E9OJ8B/uXfiYunSD///DOma9TATYQgWrFaTj8xtUsQ+6UyI22c0qpVKzB6 9erV9YdwEdNrr71m4+oyYCNOiY+PR0GAmTNnsvTcuXNDyogRI/DPpKSk6Oho OWd8IaZ1R+O++eabmnTwz3Fy7KhRowJYvkBiKU5RPC+VIDRITExctWoViya+ /fZbTWZN9IEPzUqVKjV9+vQDBw78/vvve/bsQfdDMxxcv34d314Br776KjTn 1NTUbdu2gQtRr149NlVMH6fs27cPQ6EqVaoYb/WTQSHvWhSkpLsETn/w05Yv X47bf5UuXdrSkucbN27gw3l1P79w4ULsZ8B3hRENfNTRo0eDr6s/s/+j3qVL l4oUKQIneeqpp6Aip0+fljBOmThxIpYKfGPcGQCKCmGLvs/nrA6/whrYi5il S5dyVpCzSH/99Re+yofM27dvx0Q2t42lOIapXYLYL5UZaeOUGjVqKD42SMRN DjPQSwQN/GPHokWLevfuDV0TTlWF/nnGjBnsgcbly5exOc+dOxecSfU+xtCO oG8JYB3sYlr3xo0bQ/kLFCigXigBPnO7du2waj169Ah4KQMDf5wC1d+4caM6 PTY2NlOmTIpnFxR2A/ia97V+/XrN0AOhCqo3aNAglsjWon744YfqzHfv3lUP +po4BbyCkiVLwp8QOp09e5az7hkL8q5FQUq6i3D9L168CH3Iyy+/XLZsWewW ILPVfoBNnlm8eDFL/PLLLxVvQBAEHRrLJmTU69ChA+TPlSsXbn4rZ5wC/TDo jAUrUaLE559/jj12zpw5Dx06pM7JWR1OhfV07doVsj300EOm6xytFindEwTl y5cPj8KvwKYY4Lzyyiuc1xKIqV2C2C+VGWnjFAxOO3XqpD+Ea6ygg7JxdRng Hzvatm2r7k/GjRun/r7S/v37Wf+M/4H2nidPHvw/uLUaX1cGTOvOnvnUrl0b erDExMQ1a9aAK85EgK7MqcIKhj9O8bo0vmXLlqgA+9SXpf2+8ubNq1YPgh1M KVas2O3btw1+qI5T0tLSGjZsCP/Pli3bzp07ea6bESHvWhSkpLsI1z8mJkbj 4h47dsxSkc6dO4cvRCpXrqxePc28aLj6sGHDhgwZgnPDgBw5chw9ehSz+T/q gauMmdlSCznjlHTPZ9TYFwoYP/74ozoPf3U4FdYAQzCWgb3AMsWSwhCOQUii qWOdOnU0O7w5g6ldgtgvlRlp4xToAMHoL774ov5QgwYN8E62cXUZ4B87pk6d 2qZNG+hM2Ne1KlWqdOLECTzK5nYqnkUrO3bsgG4f/E/oCnCKFPix/A9AnMG0 7lB+3C5AA3RlhQoVgv8MGDDAqcIKxs845dNPP0Up2Ad0jOMUMD3cIfCrLl26 4IxuANoOHgVvAVP0WxZoYHHK3r172SsY9YaiwQd516IgJd1FuP5xcXHQn0AG 9p07CA3GjBnDuWzh/v377AMZmzZt0hz9/vvvIyIi1JlHjRqFmVkv5+eol5yc XLhwYcXzSJyVWc44Zc2aNRggtG7dulWrVvg+HWvNNvuyWh0ehTV8/fXXmIEz ILVUpNu3b+NDSAg2+/fvz76VljlzZripeC4nFlO7BLFfKjPSxin16tVTfGz4 ULFiRTjUrl07G1eXAatjR7pntic0W+ypoB/GWT1RUVHYqMELZc/YkZEjR+Kh /fv3Cyy5//DUHTrP6dOnQ3QG4w4EaM2bN581axYMPTil2fk91UXhZ5wyb948 tOmqVauMM+PcDBwsNDz11FOYhw33X331lXGRWJzy6quvsvO0bduWp8oZFPKu RUFKukvg9AcXNDw8HL/xAUDvxHP+gQMHYn7OafwwFuAlcubMiSuX/Rz1wMPE PJGRkUn/sHv3bkwEnxz+dODjyKZ2gaAA12W/8cYbWHFIYYUvUaIEfrbA/+ro FdaAH4uHOILziwCWitS9e3fF86kF3IIsLS0NMuB+QcDEiRN5rigQU7sEsV8q M9LGKXi3V6lSRX8IHTD9UuuMgo04BRk7diy2X9zzCnpp/FP/9cODBw/iIavb sAQaS3WHjpFtb8We/6u3zM1Y+BmnsEndmzdvNsickpKCHabiefsWFhYGF71+ /Tp6ICxOYfvGqLdl8AqLUxA2xeKHH37grHiGg7xrUZCS7hJo/WEMwpkwjzzy iGlmNu+oTp06/OvuBw8ejL/ClQ7+jHrHjx9XOGjUqBFn2Wxjahf8hnuRIkU0 HxZhPsCHH34oqjoahTXgi7NmzZrx1MtSkWJjY/FPzbOy+Ph4XP2UK1euS5cu 8VxXFKZ2CWK/VGakjVP69OmjeGJ8zUcfLly4gPf2yJEjbVxdBmzHKWfPnsW6 9+/fP93zRAt3rx0yZIgmJ1PJ9Gm5w9iuO4RmWCPZ3hDx42ec8t5776EC0I0b ZMYvamXNmlWzkbsmTmEDyvvvv29cJHWcUqNGDRg4ypUrp3gWtkD4Y/zbDAp5 16IgJd3FAf179OiBnYPmw8QaVq9ejS/EIa6B4Yn//Gxi0uHDh9P9G/VOnjzJ 40XXrVuXv3j2MLULzoDq3bu3/lD58uXhUK1atURVR6Owmri4ODw0bNgwnnpZ KtLcuXPxT/0+yYsXL8ZDxgv8hWNqlyD2S2VG2jiFLcXSrBqbPXs2pm/ZssXG 1WXAVAFfc33ZO4V3330XU3BX82rVqml+wl6zyraU3nacgvu6QBeteb6UgfAn Trl79y7OjM2VK5fBfl9XrlxBu/fr109zBk2cAifEeQVwEs3nIzWwOKVEiRIJ CQnpqn34vQ6jQQB516IgJd1FoP6+RqXXX38de4OUlBRfp4UeA5da5MuXz+qD JtxMJnv27Kyb8mfUu3HjxjUdbC4ZeM7wpwN7rRvbBeqFG797XbqOW19CIJMu qDp6hRlsejD/jsT8RcKNl7NkyaLeHQhhuyVAfs7rCsG0vQSxXyoz0sYpf/75 Z8GCBcHuLVq0YBMjoR1BJK54vmPLOVtSQkwV6Nq1a6dOnfTTStk734ULF2IK 9B6Ysm7dOnVO/JwrgI6lPPBY/8iRI5qO69tvv8XqfPPNNwEsXIDhj1OqV6+u WQo6ffp0VKB79+6azOo4BaTTRLJITEwM7v3I4hSgTZs2mFm/5Ift1ZCuilNg zGKJ7du3x0Tnt7h3APKuRUFKuoso/Q8cOACOsd7/v3TpEn6DSf1leQ3wK9wH JmfOnL4Kc/DgwRo1ami228Xy46pM9fJk/lEPvAjovr7//nu9J6xGwnX0jRo1 UjxrcDQT5H7//fcSJUrAoZYtW/r6rdfqWFKYwRZFhoeHe72WPwqDS6/xZxgw JOEh9ceFHcDULkHsl8qMtHFKumqiy8CBA2/evHn9+vXevXtjyrhx42xcWhKM FYA4HetYqVKluXPnHj9+/MGDB1euXBk1ahR+RaVAgQIXL17EzPfu3cORBbzQ n376CdoIpHz11Vf4ht3r7nnuYmr96Ojo3Llz165dGzqov//++8KFC2Br7EhL ly6t/hp7hoM/TlE830lZv349xKpJSUmffvopKpAjRw426Sv9n+eKcAOwvhF6 UTR9/vz5QUm4cxITE8PCwtjmluo4BcYOto/csGHD4Mxw88BwBmEynGTPnj2Y zev36M+dO4e760A5jUeojAiPpfbt27f3H3AKNxiCpQTl5y9tQEq6iyj9cRNg 6IVgUP71118hETpnOPOTTz6JncPgwYO9nnzTpk34agAYPnw4/Anj1DoV+KAD vz0BgcyYMWN27doFXQpcApxk/AggXFe9ORj/qPfhhx/ipY331JUwTmFredq2 bcumyV2+fJmtUp81a5av33qtjiWFGZ988gmeCgJVr9fyR+Hbt2/j/ZYnTx6I PdnbsbVr1+LO1aVKlXJ4w1Ke9hKsfqnMyBynwD2Ak+0Rti8fRLIZ2jUyVgBc cdxhg8GcSRRBM2Nz9+7d+F0kxTMpCL1HxfNVJvBRA14Zi5haH8YyVlmMy5Ay Zcr46iozCvxxCk7A1gBqaNa8Dxo0CA8VLlyYbdWi3pULZ3bhjYGzmtVxSrpn z0mWRyM4roFK9xGnAOPHj+cZoTIiPJbCr8/4YurUqY6UVHZISXcRpT+cBLeF RyAiUPcbDRo08PoECZxMr12Zmho1aqR7ns6ha4pAR8SGe2Do0KGaM3OOevgk B3j66acNFJAwTgGnnYUk4ACASnXr1mUSvfTSSwYbQXutjlWFkb59+2IG/RIS xE+FYUxh7k3x4sXhJLj4EWsdFRVlcM5AwNNegtUvlRmZ45R0zy3xwgsvsDsZ Oj3w4TP6zcCjwJo1a3A7bjVNmjSJjo7WZz579mzDhg3xaRIComn2bJQEnrov WLCAbc6veF4itG/f/sqVK44UMIDw1L1SpUqKZysbiCDUjgH03voZVtDJs69C s61abty40aVLF7V6bdu2PXPmDE4a1MQp6Z5WXLt2bfWYVapUKfV3olesWIHp mvYOTsjjjz+ueL756GsUy6D4791NmTLFkZLKDinpLgL1T0lJee+99zS7nefP n3/ChAm+PskHXYT60YdX6tWrh5kTEhJ69+6Nj/cZFSpU2LBhg9eT84x6n332 GR5iHxz0Snx8PGZjW74HGh67/P333zNnzmSb9CJFixadMWOG8YpCX9WxqnD6 P5sSA772Z/Nf4ZMnT7JZxIx27dqp5x47Bqd3GpR+qcxIHqcg0EZiYmL27Nkj 21cL7cGvQFJSEtR648aN4JFeu3bNOHNqaurOnTvhzOCpCihlYOCv+8WLF8PD w8HuGXqulxqrd/79+/ePHDkCIhhsjwN5du3atWnTJk0nCeMCpPM3mVu3bkH+ iIgINqUwlLHRRxFeISXdRbj+4CFDp/Trr79u2bLl9OnTwnc1gf7q8OHDW7du 3b59O898ANNR7/jx476+tO4i/HaBHj4uLg7UgM753Llz/i9/sKqwKUIUBjvu 379/8+bN8K8D36/xhaX2EmR+qcxkiDglyAhlBajubpeCMIcsJQpS0l1Ifzkh u8gJ2UVOKE5xnlBWgOrudikIc8hSoiAl3YX0lxOyi5yQXeSE4hTnCWUFqO5u l4IwhywlClLSXUh/OSG7yAnZRU4oTnGeUFaA6u52KQhzyFKiICXdhfSXE7KL nJBd5ITiFOcJZQWo7m6XgjCHLCUKUtJdSH85IbvICdlFTihOcZ5QVoDq7nYp CHPIUqIgJd2F9JcTsouckF3khOIU5wllBajubpeCMIcsJQpS0l1Ifzkhu8gJ 2UVOKE5xnlBWgOrudikIc8hSoiAl3YX0lxOyi5yQXeSE4hTnCWUFqO5ul4Iw hywlClLSXUh/OSG7yAnZRU4oTnGeUFaA6u52KQhzyFKiICXdhfSXE7KLnJBd 5ITiFOcJZQWo7m6XgjCHLCUKUtJdSH85IbvICdlFTihOcZ5QVoDq7nYpCHPI UqIgJd2F9JcTsouckF3khOIU5wllBajubpeCMIcsJQpS0l1Ifzkhu8gJ2UVO KE5xnlBWgOrudikIc8hSoiAl3YX0lxOyi5yQXeSE4hTnCWUFqO5ul4Iwhywl ClLSXUh/OSG7yAnZRU7cilMIgiAIgiAIgiCMoTiFIAiCIAiCIAjZoHlfThLK ClDd3S4FYQ5ZShSkpLuQ/nJCdpETsoucUJziPKGsANXd7VIQ5pClREFKugvp LydkFzkhu8gJxSnOE8oKUN3dLgVhDllKFKSku5D+ckJ2kROyi5xQnOI8oawA 1d3tUhDmkKVEQUq6C+kvJ2QXOSG7yAnFKc4TygpQ3d0uBWEOWUoUpKS7kP5y QnaRE7KLnFCc4jyhrADV3e1SEOaQpURBSroL6S8nZBc5IbvICcUpzhPKClDd 3S4FYQ5ZShSkpLuQ/nJCdpETsoucUJziPKGsANXd7VIQ5pClREFKugvpLydk Fzkhu8gJxSnOE8oKUN3dLgVhDllKFKSku5D+ckJ2kROyi5xQnOI8oawA1d3t UhDmkKVEQUq6C+kvJ2QXOSG7yAnFKc4TygpQ3d0uBWEOWUoUpKS7kP5yQnaR E7KLnFCc4jyhrADV3e1SEOaQpURBSroL6S8nZBc5IbvICcUpzhPKClDd3S4F YQ5ZShSkpLuQ/nJCdpETsoucUJziPKGsANXd7VIQ5pClREFKugvpLydkFzkh u8gJxSnOE8oKUN3dLgVhDllKFKSku5D+ckJ2kROyi5xQnOI8oawA1d04T2Rk 5LZt2w4cOOBIiQjvhPJdKhZS0l1Ifzkhu8gJ2UVOKE5xnlBWgOpunOfRRx9V FOXZZ5/lOeF3333XrVu377//XkDhCBWhfJeKhZR0F9JfTsguckJ2kRP545R7 9+7t37//yy+/nD59+qFDh+7fv2/jolLBr0BycvKKFSvGjh27cOFCEMFXtr// /nvfvn1TPGzcuPHKlSvCyioaq9ZPSkoCo8O/vjLcuXMnMjISbo+JEyeuXLky Li7O/0IGCLFxyuHDhxUPWbJkuXDhgpgiEh7479Lg653EQkq6i6X+Ni0tLSYm ZubMmWFhYREREbdu3TLOn5CQcMjD9evXvWZISUlZs2bNuHHj1q1bd+3aNZ4y mJ7TK0ePHl20aNGYMWPmzZu3d+/eBw8eBOhCojC1S2xs7CFD9CMdvwicOe35 FZDNuOTAX3/9pf7J7du3t2zZMmnSpBkzZsAdCPchz4UCAX97OXHiBHhlcG+v XbvWwD9RY3DLgUV8aXX37l1NZv99Hs6GKY9jKXmccvLkyUceeURRUbFiRTC3 jevKA48C0dHRVapUUf5N69at4+Pj1dngHh4wYECePHnU2QoUKAD9TwAr4Aec 1oeOa9OmTa+99lrWrFmhRk2bNtXngboPHz48e/bs6rqD0967d2/TEdYVxMYp 0DtBZSFztmzZEhMTxRSR8MB5lwZl7yQWUtJd+Efb7du3Fy1aVG0C6HsnT57s y4kFV4flX7ZsmT7D4MGD1WfLlCkThD/GZTA9px7wnbp166YZKBs3bnzq1Cmx FxKLqV0KFSqkGALthWXmF4Ezpz9+xfTp041LDsyfP5/lDw8PL1mypKZqBk9l AwpPe0lNTe3Vq5emRv369QOv3uBXxrdczpw5fWm1evVqlk2Iz8PTMGVzLGWO UyBiffjhh1GiSpUqPfbYY/j/cuXKnT9/3salJcFUgeXLl7P7FhRo2LBh/vz5 8c/KlSuzpw1Xr14FBx7TM2fOXLdu3SeeeILdVEuWLHGiMhbhsX5SUhKGJ4xn nnlGn6d27dqsoUFMx24VoEOHDjyP1BxGbJyS7unhP/744x07dggoHKGCx1LB 2juJhZR0F87RdubMmazLLVy48OOPPw6dKv45fvx4rz/p3Lkz62/1fhc4OXgI XJ0GDRrkypUL//z0008NimF8Tj33799/7rnnMD849j169Hj++efxT4hz//zz T1EXEo7/cQp4ApiTXwTOnH76FTxxysqVKzHztm3b8E4D3xtGvXr16uF9CDfM xo0bbctrG1O7gF/RpEkTrEXZsmVbtWpVvHhx/LN58+b6dx8Mg1vur7/+4tFK iM/D0zAldCxljlM6duyIFmEz8KE7Ra369Olj49KSYKzAtWvXMCqB8Xr37t04 /+HGjRvt27fHuk+ePBlzjhw5ElPAWWWv5DZs2JA7d25ILFKkyO+//x742liD x/qJiYkF/wGaieItTklNTa1evTocevfdd6FZpXs6YThz+fLlUZPIyMgAVcE2 wuMUIkDwWCpYeyexkJLuwqP/uXPn0DksUKDApk2b0NsBp6hFixbgC3mdFrVi xQq1K6Xxu44cOYLpderUuX37drpnUIPYRzGcpGp8Tq/89NNPmHnYsGF37tzB xKVLl2LiF198IepCwjG1S0JCQrw33nvvPSz25s2bMSe/CJw5/fQrYGj2WvLD hw+jV/z000+jVwNlgPgIUh566CG2dcz+/fvR84eh8N69exZ19RdTuyxYsADF eeedd/AFCvzbt29fTISjXn9lfMuBw4Ppn3/+uV63P/74A7P57/NwNkwJHUtp 45Tk5GTsPOF+UKfjoJYvXz4JnXBOTBWA+61du3aXL19WJ8KdifFL69atMQVa 8ZtvvqlvGuw227dvn9CCC4A/SkUqVKigeItTgPPnz0PHq0n84YcfsO7Tp0/3 p5yBgD9OadKkCUs5fvx4dHS01bsd+jTo+aOiorA7MgY6KIiIb968aZoTXJod O3ZwTsfFYuzatSsuLk7C11sGmFoqiHsnsZCS7sLT57z99tvoqIAbo04HLwja rz7/pUuXcAZL/fr1vfpd/fv314ckzEfy+krF9JxeGT58uOJ5MqyZcgPjBaR3 795d1IWEY3UcRKAjRV8RTMYS+UXgzBkgvwK8a/gtlP/MmTOYAoMOnnD27Nnq nD///LNbpjG1C76QKl26NAv0EPD8IR18fv3COtNb7tixY5gOsYBx8fz0eTgb poSOpbRxypQpU1ATzbSW1atXY/qiRYtsXF0G7PVRAL6MK1u2rHE2UAwlWrhw oY2rBBSBcYpX9u7di3UfO3asjeIFFP44pWXLlhA19OnTp0SJElidzJkzgyOn WWBYq1atQoUKQUirToRR4MUXX1T+IVOmTFWrVlXPcV2xYgX86rHHHoPRKiws jD2NwVfJIKCmSBBirFq1qlmzZgULFmSnLVmyZHh4uCYnnhn6avj/unXrGjZs yCaTlCpVSn9maTG1VBD3TmIhJd3FVH/wWHC6e5cuXTjPiSEk/CoiIkLvd929 exfnLOkXFUJHpHim81k9py/Q9S1SpIgmHZcPNG7cWNSFhGPPB2jTpg0UuEyZ Mur4kV8EG3Kp8ceviIqKwqkR06ZNY4mzZs3CE4Inr8mPt4r6eZ0zmNqlWLFi ULC+fftq0mGIxLrof256y+3atQvTY2JibJSZ0+ex1zDVuOhYShun9OzZEwQB 10jz7u/27dvo/AwdOtTG1WXAdpwCfilUHG4q42zr16/H20ntnUpCoOMUcLyx 7hLu1ssfp0AoihXXMGTIEH1m9SSxa9eulS5dmuXHh28IWwG3ZMkSTHnyySf1 l8iRIweEGOyEt27d8ppN8YROW7duVZeHnXn48OFsfjsjV65cMu9Ep8bUUkHc O4mFlHQXU/1XrlyJzXPPnj08J4R+FfOPHz/+zJkz+H+13xUXF4eJ+mlXH330 ER5iU1k4z+kL9uBdU8dq1apBIrjfVgvvGDZ8AFZZGN+9ppuKYFUuDf74Ffi6 Af5Vv1j/+OOPFc/zMf3bdpxJpd4rwBmM7ZKWloYK6CMCCLXwkHqLgHS+W44J a285HqfPY6NhanDRsZQ2TmnVqhUIUr16df0hXD302muv2bi6DNiLU27cuIFP JN58803jnB9++CHeThJuVxu4OAX8nOXLl+OzQfDVDRZRugV/nIK88MILP/74 I3Ru06ZNYxPI1T2JPk5hXdb7779/8eJF6P9jY2NfeumlmjVrsq0gWTSheB6h LFq0KCEh4eDBg6+//jomlilTRj0x4LnnnoOh5JVXXtmwYUNycnJSUtKIESMw p+Z5l/rMhQoVmjx58qFDh2JiYtj77gkTJvitohOYWiqIeyexkJLuYqr/Z599 hr4izmOBhr9///7ffvvN677Q4IwVKVIE8j/11FPQ354+fVrvd0VFRWEi9F2a n8+ZMwcPwfktndMX0KfhXGg4w/bt2zFx27ZteAaW4v+FhGPDB2jevLniWbWq 8er5RbAklx7bfgV7X7B06VJ1Orsf9CcEr17xPAozWJkeCEztgp2S3geDEAZX 34waNYolct5yCxcuxHQIJGFshYBx9OjREHSY+jCWfB6rDVOPi46ltHFKjRo1 FB8b0uKGvc8//7yNq8uAvTiFTZBYvHixQbabN2/iLn/ly5e3X8SAITxOAW98 0KBBL7/8ctmyZVEfyHz27FkBZRWNpTgFeir1eATeGqar55Dr4xTQQfG8YtZ0 WerenkUTnTp10qxJ6dKli74jhUBJ39LZRjEQPuvPDFZTb3QJZ8DXKx07djSu viSYWiqIeyexkJLuYqo/rssuXrz45cuXu3btyjaWzJkzJ7glmsVBHTp0UDwv RrF1e/W71q5di4n6fQjZ3Bj1uxuecxoAPnC+fPnwJ3Aq6ILQM3zllVc0Of28 kFisjoPHjx/H0n755Zf6o/wi8OfU4I9fAfeV4lksr1nTsXXrViyJ5vXEli1b 2D5UDg/lpnZp3Lix4nlgqB74oF7t2rXDAvfo0YOlc95yYFPFGxB6aN6dIfZ8 HqsNU4O7jqW0cQrGreBK6Q/hsq9q1arZuLoM2IhTzp07h3N4KleubLxNN9t6 wpXu1xThcUpMTIymaR87dkxAQQMAf5zy9NNPa9LZNiO//PKLJrM6ToHuC7MZ vJll0YT+AVpkZCQe0s+/1TB16lTMefjwYf2ZIyIiNPnLlCkD6fXq1TM+rSRw PlULyt5JLKSku5jqj/4VDC5snVqxYsXYpM369euzFyswoGAiW2Lg1e9iz2Zj Y2M112KP7tkTXc5zGnD37l3wsTUOXp06dTQzWPy/kFisjoMYToKZ1O4xg1ME Szk12PYrEhMTs2XLBj8cMWKE5lBaWhru9wUZwsLCwNOG0WT8+PE5cuRgZVOP Lw5gahf27qN27doQ9EHt1qxZA0MwKzDEJpiT/5ZjcQr0eMOGDRsyZAjO8Fc8 07CPHj2qKYM9n8dSw9TjrmMpbZyC0+xffPFF/aEGDRpg47JxdRmw2kfBSMEe X2/atMkgJ0TKOMSARHLusCQ8TomLi+vSpQtkYN+JAwXGjBkjYfX54xT9vsQQ nmDt1HNQ9Zkh0GBL19u0abNhwwb91o4GcQrcafhztqecGugM582b98Ybb0DT Yw/l2PaYxmeGCEXxbIdiXH1JMLVUEPdOYiEl3cVUf/ZFBsXzDhe/GJucnMxG nG+++QZTChcurHjefLGu1avf9d1332HiwYMHNdeCvgIP4b5G/Of0xe3bt9FF hO6of//+7EsWmTNnhiGAZfP/QsKxOg7ihlEvv/yy/hCnCJZyavDHr/j666/x Kl596YiICP0nDgsVKsSCKYOvpQcCU7tA9Vu0aKHogALjKvUBAwakW7/lYFhX P9yDgXjUqFGYWe8M2PN5+BumHtcdS2njFHRsvO5BgTG4ZpujDITVPmrgwIF4 FxlP1f7f//6HvVnu3Lk1O0zKg/A4hQHNJzw8vGbNmqiVWx9ONcCfOGXLli1Y L+M4Jd2zS6F6Yy7oyiZPnqx+4W4QTQC4w5hmrwa4KC6E1PPrr7/ynLlRo0ZK EMUpQdw7iYWUdBdT/XF9kKLb1BT8Q3wWgfqzLQQjIyOT/oHtKwu+KPyJm1Cx JyqaTTbSPd8vxkP4tXH+c/qie/fuiufDlLhRUlpaGvzqoYcewjNMnDgRs/l/ IeFYGgdPnjyJRZ0yZYr+KKcIlnKq8dOv6Natm+KJjLyueMLzd+rUqVSpUopn A5m333771KlT6KXDr6xezk947AIVgcZSq1atXLlyZc+evXnz5rNmzYIRFpcP 4yp1/285uAo6MxDH+fqOjCWfh79hapDBsZQ2TkFDV6lSRX8IA1XT5eTSYqmP Yu8EwVE0WCd1+fJltkMU+4CphAQuTkGSk5NxJonzW4WY4kycAqSkpIwdOxZ7 fgTiDva9NuM4JW/evIpnvgdLYXdgjhw5YKSDOAjGzUWLFmFiaMYpQdw7iYWU dBdT/d955x3Fs3WG/hCaBtosWxxhDLRx+NXBgwfxz1WrVmlOOGPGDDx04cIF S+f0SmxsLOb56quv1Onx8fE4bx/cyEuXLvl/oUBgaRycP38+FnLnzp2aQ5wi WMqpxn+/Ap/5N2vWzDQn2+kFwE1dnJ/zackuEEqwLwWcO3cOVVqzZo2oW27w 4MGYU73YUw+nz8PZMDWHJHEspY1T+vTpo3hiSc1SPlASRRs5cqSNq8sAf1tY vXo1BulwHxrssQAS4RwJxdssUKkIdJwC9OjRA6XAb7bKg2NxCnLv3r1169bh YmSgZ8+emG4QTUCnpMn8yy+/4Avfhg0bqj88yr5iHJpxShD3TmIhJd3FVP9J kyYpnnkj+g/CQoSoeJYMs+f5xtStWzfd8yF7/HP06NGaE7711lt4rbt371o6 p1fmzp2LefS7uS5evBgPrV+/3v8LBQJL4yDu3Q2egP6bp5wiWMrJ8N+vYHvh Dhs2jP9XMHLhekbn36Va9U8YLJbcv3+/qFuOTf0yXaTD4/NwNkx1ujyOpbRx CluFpFnaM3v2bEwHz83G1WWAUwHoNHABWr58+by+j0P+/PPPZs2aoSavvvqq r7erkiAwTvE1T5Ltr5uSkmKvkAHC4TgFSU1Nxfkz7GGLQTTBltqFhYVhSr9+ /bAH04gZ4nFKEPdOYiEl3cVU/w0bNqDO+p038NPb6E3duHHjmg620yn4wPDn rVu38IfY4Wj2mobuGhdEsMfIls6pZ+LEiYrn49rq5/AIDJfsJP5fKBBYGgdx DREMhfpD/CLw50SE+BXsiy2aHYmNYd94/fbbb21c1B9sxyn4DZry5cvjNkdC brm2bdsqnt07Wfjgp8/D2TARqRxLaeMUUAmn2bdo0YJJBPaCblPxzGOU3CE3 gEeBjRs34rbYOXPmNMgMfQ5b8Ni+fXvjrcBkQFSccuDAAWhcoJIm/dKlS/jF WLhD/CupeByIU06dOnX8+HHNb5s2bQrZ8ufPj3+yaGL58uXqbDdv3ixXrhyO Zewk+C3dzJkzJycns5xpaWmsYwzNOCWIeyexkJLuYqo/yAutUvHsX6SW+rff fsOnZL179/b1W1/rgvEdDbB7926WuG7dOkz87rvvDMrj65xwn0D3Ar0fc7NZ l6j/OvYXX3yBhyIjI61eyBksjYO4ZrBhw4b6Q/wiWJKL36/Q20XNvHnz8CTh 4eFef37nzh3Ny4Lr16/jXuXQ9h3+eEo6n12OHDmiqSzEU1hN3HTCF15vuYMH D0J9Dx06pC8JzmRgG4lY8nm82oW/YcrmWEobp6T/sxcfMHDgQHCi4AaGPhNT xo0bZ+PSkmCqwKZNm9jWfMOHD4c/f/rpp3Uq0A+ENs5WQYIXCjfwhg0b1v2b pKQkh2rFB4/19+3bt/cfcHYrRCssBZ9CVK9eXfE854ebBFxlSISmBGdmH08f PHiwE/WxQqDjFHx1AuHtmDFjcJbgH3/8wb6H27x5c8zGogmIPvr16wedJ8Qd e/bsqVq1Kqb36dOHXQJuP0zs27fv1atXYeCALg59SCQ045T04O2dxEJKuguP /myr1ZdeegnnjZw7dw4X52bJkkW/OxDDl6sPgw5uGwjuU3R09IMHD6DTKFCg AKTkzZtXP8GM55zsG3Ns/gmcB0eHPHnyLF26lD1qXrt2Le7hX6pUKc0HO3gu 5Az8XhAEj2AFdBf1R/lF4M9pya/Q20XNJ598gkfBx9YfhTJ06dIFbhXwn5OT k+G6cJ9UqlQJfzJnzhwefcRiahe4n0EuCOohpgOXA8ZZ6KMwoChdujRbruIV r7ccTszOmTMnjNq7du2CAAGcGYjv8EtGcGa2xasln8erXTgbpoSOpcxxCoxZ 7EvWaCD8T4sWLbwG7xkFYwXgJtHv1KcBAnDICQ3EOBsAvZBzFeOAx/q4lNsX U6dOxfPgNoAIuNxsP17Fs3uecY/hCoGOU2JiYvC5CgL/Z1/LypYtG/RLmE39 1Xg90Bmq3x3HxsaykDmzB/w/C2pCNk4J1t5JLKSku/DoD94O+4wsiI9rchHj xUEGrj6koHetNij0JOrPP1k6J9tvUP1tKfAVcdaB4vlUJRzCN8KKZ7ZMVFSU vcI7AL8XdOnSJSxnr169vGbgF4EzpyW/wqtdGOyjG/pFMQAkqgcsdsMA77// vq9NrgKKqV3YgztNgcuUKeM1FlPj9Zb78ccfMU5k52TtBRg6dKi6bPw+jy+7 8DRMCR1LmeOUdM8Q9sILL7DGBQ58t27dMvrgZRqnqO9/r+D38mAQMb2dTMcF h/E/TmF7M4I7/d577+GmQAwI/ydMmGD61SpX4Kk7Pk0CD02TvnPnTqygOk7R Z4b28sEHH7CtJpGaNWuqP0HLoon58+ezCaiKZ6jq3bu3fk856EjZTvuKp0Oe Nm0adIl4l6rjlBUrVmCevXv3ak6CFwqmOCU9SHsnsZCS7sI/2k6ePFndl8L/ oaMw/kl8fDxm1u8glO7ZzBw/joM8+uij/297Zx5eRZH1/4sKKgziMjg6wyqi wAijCA4MjAvi6Ij4OA6IOjLzgq+MiqgzCC9uoICAIkiUHQFF2fdFIGF52EmC LAHCIntCNsKaAEnY8ju/ex7rabvv7e7bt7q7cu/380ee3O7qrqrvqa4+p7uq 2s7NKNw5P/30U94uvprH7N69u23btrp7RJs2bXbt2hVN4d3Gvl3EpGyTqej2 RbCTMiK/IpxdGF6UmAi3VGlOTo72wg8EX+tENJlFLnbsMmHCBPHhkkDQySdJ 8/PzLU8ersllZGTQnZdfoAjq1Klj/JqJfZ/HxC6WF6aCjqXicQpDjTw1NXXd unUmr3HLEA4UiBmk1/3ChQtpaWnkLSclJe3du9f3gZQmeGn3o0ePLg+SmZmp m3wn4hQOXqiDJfU2btxocnHRBZiSkrJy5cqQj8Vij4gsFWO9k1ygpL9EpD91 FNSFrlixIj09Xda0oAMHDsjqN6hUxm9zMwUFBZs2bVq6dCn99fhLKM5w415g XwS5cpnYxSYlJSV0f6E7OH9m1Efs24XusImJidRfyRq5wVN16JZN14u5DjZ9 HnO7SLwwPaBMxCkxRjwrgLr7XQqL76eAUmUsFQNASX+B/moCu6gJ7KImiFO8 J54VQN39LgXiFGsUsVQMACX9BfqrCeyiJrCLmiBO8Z54VgB197sUiFOsUcRS MQCU9Bforyawi5rALmqCOMV74lkB1N3vUiBOsUYRS8UAUNJfoL+awC5qAruo CeIU74lnBVB3v0vx/5cOaxXEckmcuEURS8UAUNJfoL+awC5qAruoCeIU74ln BVB3v0sBrIGlZAEl/QX6qwnsoiawi5ogTvGeeFYAdfe7FMAaWEoWUNJfoL+a wC5qAruoCeIU74lnBVB3v0sBrIGlZAEl/QX6qwnsoiawi5ogTvGeeFYAdfe7 FMAaWEoWUNJfoL+awC5qAruoCeIU74lnBVB3v0sBrIGlZAEl/QX6qwnsoiaw i5ogTvGeeFYAdfe7FMAaWEoWUNJfoL+awC5qAruoCeIU74lnBVB3v0sBrIGl ZAEl/QX6qwnsoiawi5ogTvGeeFYAdfe7FMAaWEoWUNJfoL+awC5qAruoCeIU 74lnBVB3v0sBrIGlZAEl/QX6qwnsoiawi5ogTvGeeFYAdfe7FMAaWEoWUNJf oL+awC5qAruoiV9xCgAAAAAAAACYgzgFAAAAAAAAoBoY9+Ul8awA6u53KYA1 sJQsoKS/QH81gV3UBHZRE8Qp3hPPCqDufpcCWANLyQJK+gv0VxPYRU1gFzVB nOI98awA6u53KYA1sJQsoKS/QH81gV3UBHZRE8Qp3hPPCqDufpcCWANLyQJK +gv0VxPYRU1gFzVBnOI98awA6u53KYA1sJQsoKS/QH81gV3UBHZRE8Qp3hPP CqDufpcCWANLyQJK+gv0VxPYRU1gFzVBnOI98awA6u53KYA1sJQsoKS/QH81 gV3UBHZRE8Qp3hPPCqDufpcCWANLyQJK+gv0VxPYRU1gFzVBnOI98awA6u53 KYA1sJQsoKS/QH81gV3UBHZRE8Qp3hPPCqDufpcCWANLyQJK+gv0VxPYRU1g FzVBnOI98awA6u53KYA1sJQsoKS/QH81gV3UBHZRE8Qp3hPPCqDufpcCWANL yQJK+gv0VxPYRU1gFzVBnOI98awA6u53KYA1sJQsoKS/QH81gV3UBHZRE8Qp 3hPPCqDufpcCWANLyQJK+gv0VxPYRU1gFzVBnOI98awA6u53KYA1sJQsoKS/ QH81gV3UBHZRE8Qp3hPPCqDu7p1/w4YNK1as2Lx5s3tZxAnx3ErlAiX9Bfqr CeyiJrCLmiBO8Z54VgB1d+/8d9xxRyAQePDBB+0kHj9+fIcOHaZMmeJeecou 8dxK5QIl/QX6qwnsoiawi5qoH6dcunRp06ZNQ4cOTUhI2Lp16+XLlx1kqhT2 FcjJyZk2bVqfPn0mTpxIIuj25ufnb7WiqKhIfgWiIFLrZ2dnUy3ob7gEFy9e TE5OHhxk0aJFpImEUrqDOnHKtm3bAkGuvvrqzMxM94pURpHeSuMW+0rGXj+v Avb137VrF91lPvroozlz5hhbsrN7zY4dO7755pvevXuPGTNm/fr1V65cCZe7 /ZTGA8OV58KFCzZP4j2Wdtm+fbu52ocOHQp3bEZGBqc5efKkcW9hYWFSUtLA gQO//PLL1NTUkpIS86JmZWXNnTv3448/JgPt3r3bvmmIvLy8WbNmUaOiM5w4 ccIkpeMGIBdXe35zu+iw2R8WFxdv2LCBkn3yySfTp083aRWl8kSOqCJSUDxO oevid7/7XUBD3bp1SSUH+aqDHQVSUlIaNGgQ+CVPPPHE4cOHRRpqwAErxo0b 525lIsSm9akvXbJkyUsvvXTNNddQLR5++GFjGroNdevWrVKlStr6VqlSha5B +eWWgTpxCvVmFKFQ4vLly9NtSGwnb6RVkIMHD7pXTvWR2ErjHJtKxmQ/rwJ2 9C8oKOjUqZPuxtG1a9eLFy+KNJHea6gn6dChgy5By5Yt9+zZo8vdfsqQXHfd deHKM3PmTLsyeY6lXW666SZztel6CXkghQa//vWvOc3333+v25uYmPjb3/5W dx7jI1Dm/Pnz1Lnp8r333nv37dtnp47du3fXHliuXLkBAwYYk0XZAOTiXs9v bhcddvpD8n969epVoUIFbTK6rXfu3PnMmTO6E0oUOaKKyELlOGXXrl2/+c1v WJB69erdeeed/H+tWrWOHDniIGtFsFRg8uTJovslBZo3b37DDTfwz/r164sH IHbuHRRie1El29ixfnZ2Nl/+gj//+c+6NMePH6fOgfdeddVVTZo0ufvuu0X6 SZMmuVWBKFAnTikN3rDefffdVatWaTfu2LGDBaR/3Clj2UBWKwV2lIzVfl4F LPW/cuXKQw89xILXrFnz8ccfv+222/hnq1atxCuJiO41ly9ffvTRR3kj+dsd O3Zs3bo1/yR3i7xfkbv9lCEpKioqQ/c+LdHHKeQJhDzw73//u0ijcyNXrFhB wQJtJ+eWbhNNmzblHuz6669ftGiR7jzUvzVu3JjPQ3dYujCrVKnCP2+88UaK bc0r2K1bN05cqVKlZs2aURb8s2/fvtpkUTYA6bjX85vYRYed/lBrHbJpgwYN xCHEM888o31XIldk+xWRiMpxyt/+9je2ghhFP3z4cNanS5cuDrJWBHMFTpw4 wVEJtc+1a9fy+75Tp061bduW6z5o0CBOSX3F4VBs27aNu4UWLVqoNnzCjvWz srJu/BnqIUP2A++//z6rQc62GOu1cOHCihUr0sZbbrnl7NmzbpQ/GpSKU0JC YQurijhFSisFdpSM1X5eBSz1nzBhAkv973//m1+g0N9XX32VN9JeThbRvWbe vHl8eM+ePYuLi3njd999xxuHDBkicrefMiR0DXLKzz77zFi2c+fOOZHMEyzt kpGREVLw119/nau8dOlS41HTpk3T+s9aN5LkJaeUNlatWlWstbJp0yYOS+ne cenSJe2pXn75ZT5J+/bt+fk82XfSpEkUd4wePdq8dmlpaXzs/fffX1hYWBr0 au66666AYaRxlA1AOi71/CZ2MWKnP6TrsWHDhrTltddeO378eGnQOlTy2rVr c8oNGzaIE0oUOaKKSETZOCUnJ4eDVuo/tdvZiJUrV1bQEbWJpQLUxtq0aXPs 2DHtRmqNHL888cQT5uenpkvJyGO3+X7WSyL11evUqROyH6BOlTpScRsViPgl OTk5yqJKx5s45aGHHhJb0tPTU1JS7F8ps2fPthOnUF9Hp6WbkXZkSCwhq5UC SyVjuJ9XAUv9+UFr9erVhQPDkIdJ28m3NH/SFfJe06tXr0DwQbquf6ALhLa/ 8MILDlKGZOfOndxfLVy40Dylaji7Fxw6dIgfxL3yyivGvbm5uTwg549//KPR jVy7di1vHDlypPaoBQsWGBNTQFS+fHna+Nxzz+lmMZw+fdqynG+88YYxJBHB i/aVSpQNQDpu9PzmdtFhvz88cuQIBSC6w6dOncpZJCQkiI2yRI6oInJRNk4Z PHgwS6EbmjJz5kze/s033zjIXQUc+6s80qlmzZomaTZu3Mgx/rBhw5wVz1Xc 9gDFG4GJEyc6KJ6rmNd98uTJNwVZvHixdvvSpUt5e//+/bXbqb+iToO29+7d m7dwnPKXv/yFbiVdunS5/fbbWQpqD9Tp6eZL3nfffXQshcP8c+zYsXfeeacY Xkj/cKYPPPCA9qgDBw60bt1ajIm99tprqf/k5zmxBOIUWVgqGcP9vApY6n/r rbeSyK+++qpu+4wZM1h/k8PD3Ws4eLnlllt06XkWTMuWLR2kDMmaNWu4kKmp qeYpVcOZD/DXv/6VKlujRo2Qw67YlaXOedmyZUY3csSIEbyRvE3dgb///e91 D7jYLkR6enqkhbxw4QIPWjPO2uCMatWqpcvIcQOQjhs9v7lddETZH65fv56T 9enTR2yUJXJEFZGLsnHKv/71r0BwJKTudWRhYSHHmz169HCQuwo4jlPIt6SK 08Vukoafg9Ffv1bMMMdtD3D+/Pl8BSk4idK87uJx03//+1/t9v/85z+8vVmz ZtrtiYmJvF08S+Q4hcJYFk3HO++8oz1cN0isb9++xkMCwfGx4pAFCxaIQKZ8 +fJijG61atVibMoz4hRZWCoZw/28CpjrX1JSYvRqGPJmeZfJSizh7jXiEb0u 63vuuYc2knfkIGVIRG9f5uYxOfABhFZUa+PeKVOm8N5+/frt27fP6Ea+++67 geBoIqNjwMP8tBPz69evT1tat24dUQmZQ4cOce7GAUX/93//x7vEkLwoG4B0 pPf8lnbREWV/OGDAAM5C+8UBKSJHWhG5KBunPP7446RDw4YNjbt4xtBLL73k IHcVcBannDp1ih9evfzyy+HSiOdL3333XVRFdA23PUBy8lkBBZfbtaw7vwG5 9957tRt5GGog+Bpd+86dX+ZSvMADgEt/Dj2Yp556avbs2dSfDBs2jPu3KlWq aAds6+KUgwcPLlu2rHPnznw4HbUsiBgAlp+ff/PNNweCw5spBiQPp7i4ePz4 8Tw63aRNlkUQp8jCUskY7udVwFJ/Ftl4/dIFzpf2Bx98EPJAk3tNUVERP9C4 5ZZbVq5cyRtXrFjB6cWWiFKGZOLEiZySPLH33nuPPK4PP/yQHCrv519HigMf oFWrVoHgrFVjoEFBJQlIex944AHyb/fu3Wt0I0eNGhXuzkieZyD42l0sm8Cj y0hS/pmdnZ2SkmJnxFdp8C0bZ0Q3IN0uUYb9+/fzligbgHTk9vx27KLDcX9I 5588eTIPdahevbr2EoheZAcVkYuycUqjRo0CYRZ84wV7nQX7KuAsThEvBL/9 9ttwaZ577jn2JHWDjdXBVQ+QOlJedLF27doOy+cmNp8tlytXTiw1n5OTEwgO r+LhGXPmzBGJeYyo9qWtiFPoZq29l4m1JdPS0nSJdZPuxdMY4/wUfkdMsZJu EctPP/2UtlMoREW1qYP6IE6RhaWSMdzPq4Cl/tSB8EOMU6dOiY10+2jTpg13 BR07dgx5oPm9hqKYypUr8xmeeeaZSZMmsZ/z/PPPO05pZOjQoYFQkJ8W8qWD OkTaw6Snp3PVqMrGvaRbILhsFy8zG9KNXL58OW/UvTtLSkoSi3EdOHCAthw7 dox/jh49mszBT90Z8p/JXuZFpZsUJ9aNXCrVDCZct26d2BhNA5CO3J7fjl10 RNofHj169K233mrXrl3NmjX55FQYtqOWKEV2UBG5KBuncPD47LPPGnfx9B+6 fBzkrgIO4pSDBw/yI4769euHm7yclZXFc9/EYxAFcdUDFMvUeHb5RIRl3cWr VfEkiipCP//0pz9RfxIILu7B2wsKCvgtyccffywO59CjRYsWutOKJX1++OEH XWKbcQpFPfydmv/5n//RnZzcGz6E7oN2RCgTIE6Rhc3n+THZz6uApf7ilUTj xo3JmaGbyKxZs6hbEK4puSjGoyzvNRcuXOAuS8v9999vXIPLfkojIk6hptKz Z8933nmHh0YHgs92VF60MNIehpf5Ih9AG04yfI8IaGYJhXQjS0pKeL0vMhz1 8+TKbtu2rV+/fiSUkJ22lAYXAeOf4lU+ubjiO2XlypUzLmKsRbw02b59u26X eIavfdUSTQOQjsSe36ZddETaH6ampmp1owh9586dxmOjEdlZReSibJxCgpMO Tz/9tHFXs2bNWGQHuatApNfC5cuXxXrXS5YsCZfsq6++4jQhG6oiuOcBrlq1 iheHp+ZRRufm5Ofn89C+rl278haKCwJBZ4BnQZIavH3x4sVs6/Xr14vDw61L TOEJJ9aOWY0oTqGbGm8nT2CjAd6l7Oc1HYA4RRaWSsZwP68ClvpTV/nYY48F DJBXw7Ohu3XrZjzK/F5TWFjIkQ75t2+88Yb4IAt1bmLRj0hThoP6tGXLlomf dK/84IMP+CTRrNDuNpH2MLzOUrt27XTbc3JyeDjuww8/LO564dxIEsr4WUyy svBg+T2+mM4QCC74RjfWixcv0snpbPzmhe4dJgM2xo8fz8du2bJFt2vp0qW8 S8ypjL4ByEVWzx+RXbRE2h8eOnSoffv2VADxXUjygkg6rQsUjciOKyIXZeOU pk2bBsKsRcCPBcRSRWWOSK+FN998k1uF+VBt/t4oNUXVvpmixSUP8KeffuKe vGLFitrRTUphp+5NmjShWjRo0IB/VqtWLRB8VSG+wMhfiu/Ro0cguCqX9uVa uDglKSmJj3Ucp4j5qiboliMr0yBOkYWlkjHcz6uAnZZM94uEhIT77ruPvNAK FSq0atVqxIgR5IjyM5OQn1cwv9e88MILtJfcG16Gq6SkhOKaqlWrckfxySef OEhpHyrSvffeS4eTT66bjKwOEfUwu3fvZkEGDx6s20UOLe/asGFD9s+IJYhJ TPqpXRyMbpTPPvss31Zq1qz5yiuv7NmzhyM7sianEY+e6tevrxvNK5b9D/cJ +1LNYzHjG/bJkyfrDnejAUSDrJ4/UrsIHPeHFEQkJiZyyw/88rFhNCI7rohc lI1TWB/hsGnh+K7sTt2N6FoQr7YpjjafHsgB9SOPPCKhiK7hhgd47NgxscJV mf4GcanmRpCbm8u3J7rbFhUVUS/Eb4S5/+HeTPfUxb04RXxXpV69eg+GQWXl IwVxiiwslYzhfl4FImrJ5OGL1csPHjzIl/ysWbOMKU3uNdu3b+cDv/jiC+32 w4cP8xB6ioZ4aVz7KSOle/fufGYeTq8gEdll3LhxXJ3Vq1drt4tJK+b86U9/ Mp6T7ini/3/+858BzYAinhQZ0HzlU7BlyxbjrSRcmhkzZuh2ffnll7yL5/K7 1wAcI6Xnj8YuUfaHZDv2E8TqbdGIHE1F5KJsnNKlS5dA0EnTfeeLWjiLQx6d g9xVwP61MHPmTH6oRW3PfAErsRhgz5495ZTSHaR7gNQ8+H1oQO2JOaX26i5W 0SH3gMd6tWrVine9+OKLgeCHt6jKPDmFun3tse7FKeKBXshZnLEH4hRZWCoZ w/28CkTakgXCNzY+OTe/14wePZr3GtcK/vbbb3kXT3K3nzJSxNAvnnChIBHZ hddXIU9Ad42IbtmcJk2amJz80qVLNWrUCGge1F+5coWHh+mWsi/VXJU6p1dL dnY2p/nwww91u/73f/83EByYxAuLudcAHCOl54/GLtH3hx07duSU/F2zaESW 0sCkoGycIibv6Fa3GzlyJG8n78tB7ipgUwFqPDxXsXLlyiavWRkxplTZFYkZ uR7g+fPnH3nkEa44ufEqD3grtVf3ixcv8hKC3bt3b9euXUDzWpZnvFatWpVO wlWmbkR7bPRxysCBAzmlrr1RqXjBwzhZfAlxiiwslYzhfl4FHMcpvMpT7dq1 jcu2mN9rqL8KBBcG1D6xZ8QEbfKdIkoZKU8++WQg+EE6sdCuakRkl8aNGwc0 kxO1nDp16oQBMXCL1KOfZ86cMTm5+IDg119/LTbyl3GoDehmeooBP+ZT6XmE km5xXToVz4wQj9/dawCOkdXzO7aL/f4w3CRcfjtG5OXllUYtcvQNTArKxink gt54440kxWOPPSb8T+p2eAB/zZo1FXdKTbCjAPUD7BlSZG1HrjFjxnDLSUxM lFJIl5DoAdJ1J5YXaNu2bbhl0NTBZt35q68tW7bkF7jJycm8XTxR+cc//hEI fl1Rd2D0cYpoRdQr6k7CKxMG1B5ZJwvEKbKwVDKG+3kVsNOS09LSdD4Muax8 sY8dO9aY3vxeI3qbiRMn6nYNGTKEd23YsCGilKXBdjJp0iTqwURRt2zZ0qhR o61btxqrzAuqqLwCQ0Q9DH9Xq3nz5jbTh5vmXFxcrHvBdPLkSV4Ily40bUxH ESifYe7cudr0vDo9IT7sa7RLqeZ5F8U1YiOdijeOHz+et0TUALzB1Z7fzvRz m/3h5s2bKegzRou5ubn8CQNKyVuivMocV0QuysYppT+vxUe8+eabp0+fpmtK fIfuo48+cpC1IlgqsGTJErFaYK9evejnvHnz5mowfprn448/5vTUgF0setTY sT555ut/hgdCU28gtnDwTl0ufxEpEJxOThfswoUL5/6S7OxsL6pkG5stX7yo 5appp4LWq1dP7DIuERx9nCJSPvDAA+QAXLlyRQzwPnLkCC9NWb58+f79+/Py mCUlJcuWLXv00UenTp0agRDKI6uVAjtKxmo/rwKW+qekpFSsWLFx48bkq1y8 eDEzM5M0Zz+/evXqYrqKFvN7TWFhIV8O1F2Quyue+s6ZM4eX1q9WrRqvFmU/ ZanmA75icC9/TuK6667r3bv3mjVryLOii45iKH4fTVUwWRvTd+x7QeSXXn31 1YHgszibJw/pRpK87du3v+aaayiIyMnJIWEpiBA3lFGjRmnPQDcd9sArV65M vgeVgbZ88cUXPApdu2qu0S6lwaFfPDKZfGZqYJQ15VWlShXa8qtf/Up8mDii BuANrvb8Nt17O/0hLxlNjZwSL168mDKli5dK/oc//IFTdu/enVNGeZVFUxGJ qBynkI34Y3YMd56BYKRpHu4pjrkC1GaMiwfqaNSoke4o8ekQ4yhEpbBjferK TOr++eefUxq6YM0lCvzyq4gqYLPlixmsAcNM+W7duoldxi4i+jiFbka1atUS WVA/RvGyuNbEupRM1apV+QYaCC5faVuGMoCsVgrsKBmr/bwKWOrfq1cvoby4 nIkaNWqEe+Rlea+hkIcHAxC33XZbixYtRK9C2zdu3OggJY9ECmi+DzV79mx2 tEThRcshevToEalWXmLfC8rNzeUaderUyebJQ7qRZCx+0m609dtvv21cGI0i C/4UYCA41Vr0/NTtZ2VliWRGuzCUtchC2IXuJtpveJVG0gC8wdWe36Z7b6c/ pELysuEMxY8cGDLNmjXTPmGI5iqLpiISUTlOKQ2a7KmnnhIikwPfoUOHsn7z soxTtH1ISJo2bao7iheKJMzXBPOd6PsBXptRrItlgq5L9B37LZ/cfq5CQkKC drt2fWDjGh38cIx6M9321atX8yHaOCVcYnJOateuLXKhpqhdBp86qEceeUTb PunafP7559PT0+3Uq6wgq5UCm20+Jvt5FbCj/4QJE8THFwJBZ7Jt27b5+fnh 0tu51+zevZtOorso2rRps2vXLmcpP/30U94lPjZHZGRkdO7cmV+gCOrUqSM+ z6Es9u8FYi6z/RVyDh8+zIfoVtzKycnRXmWB4LN0kwmtBw4caN68Ob9DYehw 3UrFIe3C0O2GvwbC3HHHHSHvyPabige42vOHs4sRO/1hXl7e66+/zouACeha 6N+/v/HrjdFcZdFURBaKxykM9Yepqanr1q3z+CWgSzhQIGZA3f0uhTUXL17c vn37okWLVq1aZfz8cWnwekxJSVmxYgX1cupPC3JAWbGU+kSkZIz18ypgX/+j R48mJiaS/iHHejmjoKBg06ZNS5cupb/mH1mwkzI9PT3kJ+Z52sXy5ctXrlyp fdSvMj72MGRf6r2TkpJsakXmWL16NZU25L2gNLxdGAp2yC6WwzzsNxVXUarn t9MfXrhwIS0tbfHixWTQvXv3mt+Oo7nK/KVMxCkxRjwrgLr7XQpgDSwlCyjp L9BfTWAXNYFd1ARxivfEswKou9+lANbAUrKAkv4C/dUEdlET2EVNEKd4Tzwr gLr7XQpgDSwlCyjpL9BfTWAXNYFd1ARxivfEswKou9+lANbAUrKAkv4C/dUE dlET2EVNEKd4TzwrgLr7XQpgDSwlCyjpL9BfTWAXNYFd1ARxivfEswKou9+l ANbAUrKAkv4C/dUEdlET2EVNEKd4TzwrgLr7XQpgDSwlCyjpL9BfTWAXNYFd 1ARxivfEswKou9+lANbAUrKAkv4C/dUEdlET2EVNEKd4TzwrgLr7XQpgDSwl CyjpL9BfTWAXNYFd1ARxivfEswKou9+lANbAUrKAkv4C/dUEdlET2EVNEKd4 TzwrgLr7XQpgDSwlCyjpL9BfTWAXNYFd1ARxivfEswKou9+lANbAUrKAkv4C /dUEdlET2EVNEKd4TzwrgLr7XQpgDSwlCyjpL9BfTWAXNYFd1ARxivfEswKo u9+lANbAUrKAkv4C/dUEdlET2EVNEKd4TzwrgLr7XQpgDSwlCyjpL9BfTWAX NYFd1MSvOAUAAAAAAAAAzEGcAgAAAAAAAFANjPvyknhWAHX3uxTAGlhKFlDS X6C/msAuagK7qAniFO+JZwVQd79LAayBpWQBJf0F+qsJ7KImsIuaIE7xnnhW AHX3uxTAGlhKFlDSX6C/msAuagK7qAniFO+JZwVQd79LAayBpWQBJf0F+qsJ 7KImsIuaIE7xnnhWAHX3uxTAGlhKFlDSX6C/msAuagK7qAniFO+JZwVQd79L AayBpWQBJf0F+qsJ7KImsIuaIE7xnnhWAHX3uxTAGlhKFlDSX6C/msAuagK7 qAniFO+JZwVQd79LAayBpWQBJf0F+qsJ7KImsIuaIE7xnnhWAHX3uxTAGlhK FlDSX6C/msAuagK7qAniFO+JZwVQd79LAayBpWQBJf0F+qsJ7KImsIuaIE7x nnhWAHX3uxTAGlhKFlDSX6C/msAuagK7qAniFO+JZwVQd79LAayBpWQBJf0F +qsJ7KImsIuaIE7xnnhWAHX3uxTAGlhKFlDSX6C/msAuagK7qAniFO+JZwVQ d79LAayBpWQBJf0F+qsJ7KImsIuaIE7xnnhWAHX3uxTAGlhKFlDSX6C/msAu agK7qAniFO+JZwVQd/M0GzZsWLFixebNm+2cMKLEjsnJyVkR5PTp065mpA7x 3ErlAiX9BfqrCeyiJrCLmiBO8Z54VgB1N09zxx13BAKBBx980M4JI0rsmEmT JgWCUFjkakbqEM+tVC5Q0l+gv5rALmoCu6iJ+nHKpUuXNm3aNHTo0ISEhK1b t16+fNlBpkphX4GcnJxp06b16dNn4sSJJEK4ZBcvXkxOTh4cZNGiRfn5+dLK KptIrZ+dnU1Gp7/hEsRY3RGnqID0Vhq32Fcy9vp5FbCv/65du+gu89FHH82Z M8fYkqlf3WpFUVGR9hD7PXNhYWFSUtLAgQO//PLL1NTUkpISOwV2tUhuY2mX 7du3m1ft0KFD2vTFxcXUP9Pl88knn0yfPl23t9RzuXbs2BEulwsXLjhL6QE+ +maWFtRBHWZaWtq2bduuXLlip8D2M3LQVNxGxCnOcJyjzcS7d+/+3e9+F9BQ t27djIwMB/mqgx0FUlJSGjRoEPglTzzxxOHDh7XJ6ELu1q1bpUqVtMmqVKky ZswYFysQBTatT3euJUuWvPTSS9dccw3V6OGHHzamicm6I05RAYmtNM6xqWRM 9vMqYEf/goKCTp066e41Xbt2JRdLpKHgMWDFuHHjOHFEPXNiYuJvf/tbbUpq CSaOnwdF8gBLu9x0003mVSOVOCVVrVevXhUqVNDuvfrqqzt37nzmzBlxQo/l uu6668LlMnPmTGcpPcAX38ymBQV79uwZNmxYnTp1OOWqVats1s6NpuIN3r/n iugJz29+8xuWpV69enfeeSf/X6tWrSNHjrhcTBexVGDy5Mni4iUFmjdvfsMN N/DP+vXri8dNx48fJ9eIt1911VVNmjS5++67RUMi99KLykSIHetnZ2ez4yf4 85//rEsTq3VHnKICslopsKNkrPbzKmCp/5UrVx566CEWvGbNmo8//vhtt93G P1u1aiUeaNtxXaZPn14aYc+8YsWKcuXK0Xbynagfa9q0KV9T119//aJFi8yr 5lKRvCH6OIU8gdJgL9S4cWPeQkqS/ywuJeKZZ54RD9u9lKuoqMgyl0hTeoP3 vpl9CzJdu3bVqUQXkZ2qudFUPEPlOOVvf/sbSzplyhTeMnz4cFapS5cuLhbR ZcwVOHHiBLd8ul+vXbuWxz+cOnWqbdu2XPdBgwZxyvfff5+3vPvuu+J94sKF CytWrEgbb7nllrNnz7pfm8iwY/2srKwbf4au8UAoDzBW6444RQVktVJgR8lY 7edVwFL/CRMmsNT//ve/+QUK/X311Vd5I+3lZAUFBYdDsW3bNoopKGWLFi34 VmW/Zy4uLq5bty5trFq1qlgMZNOmTRwoUed26dIlk5K7USTPsLRLRkZGyNq9 /vrrXJelS5eWBkVo2LAh/XzttdfIN6YtVGU6c+3atXWdtpdyUd/IZ/jss8+M OZ47d85BSm/w3jezb0GmW7dufNPhUwVsxyluNBXPUDZOycnJ4Ucr1H9qt/NN rXLlygo6ojaxVIAaTJs2bY4dO6bdSE2Lr5EnnniCt1A3/vLLL4tbiUBcI8nJ yVILLoFI2xu/3DR6gLFadw49HnroIf5Jt/KNGzfu2bMn5BhUyziFrhHSYevW rXQe83zp/Pv371+zZg31urpdiFMsCddKgaWSMdzPq4Cl/o8++ijpXL16dV0X cf/999P2u+66y9whIZ+HkpHLtG/fPt5iv2cmT4+3jBw5UptywYIFvP3777+3 V0tpRfIMZ37XoUOH2Dt95ZVXxMYjR47MmzdPl3Lq1KlctYSEBPNzuiHXzp07 OSV55rJSeoMvvpkzC3733XcRxSmOMxIYm4pnKBunDB48mNXTjb6bOXMmb//m m2/cKqLLONac3yTWrFnTPBkpxhJNnDjRQS6u4rYHWNbrzqFH69atd+/eTZ2e eMV84403duvWTTevMFyccvTo0TfeeOPuu+/m5/wEuYL/+Mc/Qrp8eXl5L7zw gnh5Xa5cOTpw1qxZIkG4OOU///nPTUHoUo1IB/VBnCILSyVjuJ9XAUv9b731 VhL51Vdf1W2fMWMG629y+MaNG7mHGTZsmGVJjD3ziBEjeEtubq4u8e9//3vt 4xr7RFkkz3DmA/z1r3+l0taoUaOgoMA85fr167lqffr0MUnmklxr1qzhlKmp qbJSeoM6vpmlBR3EKc4yYiJqKtJRNk7517/+xe6Z7uVvYWEhP3/r0aOHW0V0 Gcea33fffVRx6sPNk82fP58bnvfT0Cxx2wMs63Xn0IMIObuQOkPtYjgh45Qx Y8Zce+21xmONKYlly5b9+te/DplYDMIJGaeI4SIUTJkPzyiLIE6RhaWSMdzP q4C5/tSZhHNRKHbgXSYTZvmdC/21s+KQsWd+9913A8EHI8bDeeCZmCpunyiL 5BkOfADxmomKbZl4wIABum48JC7JJVJazi+zn9Ib1PHNLC0oK05xo6lIR9k4 5fHHHydZGjZsaNzF039eeukl+YXzBGeanzp1iuPZl19+2Tzlf//7X254mZmZ DovoGm57gGW97iJOCQQfclJokJWVNWPGDBFNfP3117rEuuiDn1BVq1YtISFh 8+bNZ8+eXbdunVgbRFuAkydP8tNU4sUXX/zxxx8LCgqo06Mut2nTpmIciDFO SU5O5lCoQYMGIRckKesgTpGFpZIx3M+rgKX+LLLxnkIhDI9F/+CDD0IeKJ6E k79kpyTGnnnUqFHh+up+/foFghOQI1qZNvoieYYDH6BVq1aB4MwIc0eR4v3J kyfzmk7Vq1c/f/58uJTuyTVx4kROSbHVe++916lTpw8//JDcYGNh7Kf0BhV8 M5sWjD5Oca+pSEfZOKVRo0aBMEt98qJwrVu3ll84T3CmuRgg8e2335okO336 NC/zWLt2bedFdA1XPcAYqDuHHlWqVNEtd7N9+3ZeGKdu3briPhVu3Nf8+fN1 fQ6FKtx43nrrLbGROlXeSJ2nNjH5BtpPz+vilOzsbBaZQqcDBw7YrHvZAnGK LCyVjOF+XgUs9W/ZsiV3ONqJacXFxW3atOGrvmPHjiEPfO655wLBKfCWc99K w/TMy5cv5yx0b3OSkpI4RCIi6mGiL5JnRNrDpKensyBDhw4NmeDo0aPUt7dr 165mzZqckrojc/Xck4sKGQgFOcO6l0H2U3qDj75ZpBZ0HKe43VTcQGsX8n8u R4iDd0A2WwI/53n22WeNu0hV2nXPPfdEmrUiOLgWDh48yBPo6tevr13W3ohY qsXZJES3cdUDjIG6m0yN/8tf/sK1y8nJsUxs5Fe/+lUguPwg/6Qrl7fceuut hYWFJgdq45SSkpLmzZvT/+XLl1+9erWdfMsiiFNkYfN5fkz28ypgqb94oN24 ceM1a9ZkZWXNmjWLuhThMYoeQwslox6A9r733nt2ihGyZ6bOhNf7olMNGDCA PKVt27b169dPO2yVttisqZQieUakPQwv80U+gHGdEyY1NVXn5+/cudPkhK7K JaIPuoR79uz5zjvv8LAogoy7Y8cOBym9wUffLFILOo5T3G4qbqC1S15eXqTf eaRDosnRBFKPlHn66aeNu5o1axYIjpSLNGtFiPRaoHiwdevW3KiWLFliknLV qlX81J0k8mUYoSXueYCxUXeT0KNv377cBsRSIeZxSnFx8YIFC+io9u3bUxfK x5I4vJd6V95inEKrQ8Qp69evF69gxAKMMQniFFlYKhnD/bwKWOpPXeVjjz0W MPD888/zJzy6detmPOqrr77iZOYeDmPSMy9btsw4EY/ypdz5/xMnTtisqawi eUOkPQyP+23Xrl24BIcOHaJ+nrog8b1UqmDv3r3D1c5tuaZMmULGFT/Jh/ng gw84R90Ny35KD/DRN4vUgo7jFFebikto7VJSUrJ582aKPqgiR0yhBJSMEmtn 9TrI0YSmTZuSMi1btjTu4ocwbdq0iTRrRYj0WnjzzTe5nZgP1f7pp5+4N6Po Pi0tLdpSuoNLHmDM1N0k9BgzZgw3gxkzZpgn5he7N998s9H9eOCBBziNmJX5 xRdfmBdJxCkvvviiOM+TTz5pp8plFMQpsrBUMob7eRWw05LJ10pISLjvvvuu v/76ChUqtGrVasSIEcXFxTzkfsiQIcZDOnToEAiuGm35GQXLnpkSPPvss9Wq VQsEl0t65ZVX9uzZw54qnd92RWUWyQMi6mF2797Nva6dlRXJ20xMTLz33nv5 kHBfkPdeLsqIS0WRqfnSK/ZTSkcF38ymBaOfnyK9qbiHzi4Ug1AAIgaWhIMS UDJnSzTYbAlPP/10IDhR17iLHTDLKUvKEtG1IF6M3n///SYTnY4dOybmSnv/ FVf7uOEBxlLdTeIUMQiWv/AVLnFeXh47eIHg170HDBhAmZ48eZIlEnHKrFmz OM3w4cPNiyTiFKZSpUr8z9SpU21WvMyBOEUWlkrGcD+vAhG1ZPJDxINH8b5V u0S5gJ/EPvLII+YnjKhnLioqEv//85//DEQ45M+NIrlHRHYZN24cF9j+UFvy 0HhEZbg103yRq3v37nwqCkVlpZSLOr6ZpQVlrfclq6m4is4u/Epl27ZtJmEs 7aIEzl6mGHMMR5cuXTig1n30ITMzk63z/vvvO8hdBexfCzNnzuSHWtSQTBaI IIl4jETA1zGEdpDuAcZY3U3iFPEl4sOHD5sk/uMf/xgIfjBF9+EJXZwiJma+ /fbb5kXSximNGjXKzc2tVatWIDixhcIf82PLKIhTZGGpZAz38yoQaUsWCN94 06ZNul2HDh3iXT179jQ5g+OemRyMGjVqBCJ5leZ2kaQTkV147W7yBCL65mnH jh25pvzxcS1+ySUGdFlOO7KfUi5K+WYmFiyVF6eYZ2SzqbiN0S6Wr1SieZkS MseQfP/996zP7NmztdtHjhzJ25OSkpwVwHdsKjB//nyevlS5cmXjzUJAgTyF uqzJiy++6OO7OTvI9QBjr+7h4pQLFy7wSP7rr7/eZL2v/Px8VqNr1666M+ji FDohf5+CTmK++KeIU26//faMjIxSzSLwnTt3tq52GQRxiiwslYzhfl4FHMcp 99xzTyC4MJFxarAYMmqySGk0PbP4xKd2DXZz3C6SdCKyS+PGjanM1MmE3Btu WgG/kyKMk4j9kuvJJ5+ks1WoUMFyuWn7KeXii2/mwIKljuIUl5qKBxjtwq9U tm7dGvKVCm2kXY5fpoTMMSRk4htvvJH0eeyxx4R9qdE2adIkEBzIqrhTaoId BRYtWsTrWl933XUmiYuKisQ0rrZt25ovN6ECEj3AmKw7hx4NGzbULQCYkJDA NX3hhRd0ibVxSlpaGid77bXXtIenpqZSj6qNU0p//rpxINQQ9F27don/RZxC XZbYSILzxpUrV1pWvMyBOEUWlkrGcD+vAnZaMnUa2jFXBAUIfHWPHTvWmF5M lEtMTAx5Qvs9M/VyugfmJ0+e5KWqyfRaH5XaCXVEU6ZM0RVVepG8IaIe5vbb b6diN2/e3LiLPLHbbrtNt4h9afAznfxtrJBfSJcol9EuW7ZsIQuSl6hLSfXl ieRiZQz7KT3De9/MmQVLreIUo11caireENIu/EolOzvbmJ42RvMyJVyOIRED Xd58883Tp09TD9a5c2fe8tFHHzkugO9YKrBkyRKxNmOvXr3o57x58+ZqYOeQ Onn+Shpxww03UAtcuHDh3F8S0og+Ysf6ycnJ63+Gx0aSHyi28IcFY7Xu4juP devWnT9/fkFBAdWib9++3G9TqxCDvkp//kQsiSN8Oeqd+GU0aZKSknLlypWs rKwBAwbwwx9dnLJ3717ubwPBt7p05kuXLtG947nnnqOTrFu3jpOF/B79wYMH +RsHVM6QnkOZRlYrBXaUjNV+XgUs9adeomLFio0bN6arm1ypzMxM0px7m+rV q4d8Gvnxxx+zdcjzMe613zNT79S+fftrrrlm4MCBOTk5dODatWvr1avHx44a NUp7WvGBvJCDZ2QVyTPse0HUt1999dXs6xr3NmzYMBBcr4kuosWLF1O3Q0ak M//hD3/g+nbv3t14lES5jHbhzx6RD9+7d+81a9bQ3YFKRe4unYqLKtbFsp/S M7z3zSKyIN3NxS3mww8/5AR0f+ct2vW4jHZxo6l4Rki7hHulEv3LlHA5hoTu WTzYnuHOMxB88lamXSNzBaiFG5dq1NGoUSNKSTcU82TEnDlzvKuYDexYn7/r EY7PP/+8NHbrznFKyAZAdyvdnPe33nqLd918883iUtWuysUjuwLB0WK1a9cO /DJOKQ0uOSjScBbi/zfeeIPThIxTSn/+ZnTA75HebiCrlQI7SsZqP68ClvqT rxXy8q9Ro0Y4z0R8BiLk40r7PTMdzg9yjbm//fbbOt+Dn8kQLVq0cK9InmHf C8rNzeUSdurUKeR5ePlo5qqrrtL2582aNQvpqkmUy2iX2bNn8/dEhFnFFU30 6NFDZGQ/pWd475tFZEG6rZickG5JIqXRLm40Fc8IZ5eQr1T4ZQqPUZeeY0jo FvbUU0+Jp77USDp06FDWb16W14K2xw5J06ZNKeX7779veS388MMP3lXMBtF7 gLw2Y6zWnR8nTpgwgSIIba9Sq1Yt4wgrChzEJ2XFuiinTp1q3769OPDaa699 8skn9+3b16dPn4AhTiHoim7cuLH2BlGtWjXtd3WnTZvG2yml9kBqqHfddVcg +Jk2fzsx6chqpcBmbx+T/bwK2NGfehvxJQXuMdq2bZufnx8uPa9TSoRc4yii njknJ0drd+58Qo6E//TTTznBsGHDXC2SN9j3gsSixOEmMufl5b3++uu6Vehv uOGG/v37nzt3LuQhEuUKaRdyETt37syvRQR16tRZuHChLi/7Kb3BF9/MvgXN 45RKlSqJlCHtIr2peEY4uxhfqYiXKVHObIooTmFIotTU1HXr1ukG7ZdRHCgQ M6Du9tNfvnw5LS0tMTHRZDkRSrNmzZolS5bonLrDhw/TdvuXzJkzZyj9smXL jh49ar+EsUo8t1K5RKRkjPXzKmBff7rwqash/aMZLOEMyjElJSUpKSkrK8sk WXp6uvcfKHcJ6T0MeWV0s1i8eDHJuHfvXi8n4ISzC08+Wr58+cqVK80taz+l 2/jY80u3YDi7+NhUHGNiFwp1ta9UpLxMMc8xTohnBVB3v0sBrIGlZAEl/QX6 qwnsoiawi5qY2IXCLvFKRdbLFPMc44R4VgB197sUwBpYShZQ0l+gv5rALmoC u6iJuV3EK5WsrCwpL1Msc4wH4lkB1N3vUgBrYClZQEl/gf5qAruoCeyiJuZ2 4VcqPwaR8jLFMsd4IJ4VQN39LgWwBpaSBZT0F+ivJrCLmsAuamJpF36lIutl ip0cY554VgB197sUwBpYShZQ0l+gv5rALmoCu6iJpV34lcqWLVukvEyxk2PM E88KoO5+lwJYA0vJAkr6C/RXE9hFTWAXNbFjl4yMDJOVUd3IMbaJZwVQd79L AayBpWQBJf0F+qsJ7KImsIuaeG8XtIR4VgB197sUwBpYShZQ0l+gv5rALmoC u6gJ4hTviWcFUHe/SwGsgaVkASX9BfqrCeyiJrCLmiBO8Z54VgB197sUwBpY ShZQ0l+gv5rALmoCu6gJ4hTviWcFUHe/SwGsgaVkASX9BfqrCeyiJrCLmiBO 8Z54VgB197sUwBpYShZQ0l+gv5rALmoCu6gJ4hTviWcFUHe/SwGsgaVkASX9 BfqrCeyiJrCLmiBO8Z54VgB197sUwBpYShZQ0l+gv5rALmoCu6gJ4hTviWcF UHe/SwGsgaVkASX9BfqrCeyiJrCLmvgVpwAAAAAAAACAOYhTAAAAAAAAAKrh fZxSAgAAAAAAAABhQJwCAAAAAAAAUA3EKQAAAAAAAADVQJwCAAAAAAAAUA3E KQAAAAAAAADVQJwCAAAAAAAAUA3EKQAAAAAAAADVQJwCAAAAAAAAUA3EKQAA AAAAAADVQJwCAAAAAAAAUA3EKQAAAAAAAADVQJwCAAAAAAAAUA3EKQAAAAAA AADVQJwCAAAAAAAAUA3EKUAFDh8+PGXKlNGjR0+ePLmgoMDv4gAAAAAAAJ9R P045e/ZsdhhWr1594MCBkLuOHTvmnmibN28+deqUe+f3kczMzIyMDJuJT58+ ffCXULjhLN9hw4b17dt36NChw4cPLy4u1u0tKiqiUm3ZsmXfvn3GKObkyZPp 6elpaWlkd+OZ8/Pzd+7cuX379ry8PGdlE+zfvz8iu+/evfvcuXNRZupjvgAA AAAAPqJ+nEKRyKeRM3nyZJcU27t3L5+fnGeXsvCewsLCFStWjB49moKFL774 wuZRP/74Y99fQso4yD0rK4uO/eabb4wRSkkwKhw4cKDIYvDgwTt27OBd5L1P mjRJW4Bp06aJQIbOlpiY2K9fP7F37ty5VFMHJSQo0vnss8/s253byYQJE86f P+8sR3/zBQAAAADwF/XjlIyMjHEGxowZQ84bhyQJCQnGBAsXLnRDrmPHjg0b NozzJR/YjSx8gRx+qtGAAQMiilPWrVtH6RcsWLDxZ1JTUx3kvmXLFjrP8uXL jbvonH0NUDlzc3PPnj3Lb2F0zJ8/n49NTk427qVQxUEJS4JvbYYPH27T7qKd rF692ll2vucLAAAAAOAv6scpRshB/frrr8kZmzRp0uDBg+mfrVu3yhLEr3z3 79+/efPm4uLiXbt2/fDDD2lpaSXBNwLp6elJSUlLly7dtm2beKJOJfnxxx/3 7NkjDqf/N23adPr0af5JcQf9PHDgAP1//Phx8lrJy6W/lmO6Bg4caD9OWbZs GXn+nIsl4eqSmZlJkQ6/CqEy68ZuUUVGjRo1ZMgQEmTv3r0jRozgcGPt2rW0 NyUlhf6fN2/ewYMHKczhXYMGDeL3Mvx6iKDoiQpJ8Sz/PHr0qM0K6jh8+DDb ncpvkky0k5kzZ4Z8Q1RW8gUAAAAA8JGyGKfMmTOHnLGxY8cWFhaSu07/f/75 5/ZnVaiZL52cXGiKUNiXXrJkCeXy3Xffad8FjBs37sSJE5T4zJkz/fr1Gzp0 qDic/qcE5OfzT/qHfq5Zs4Z8cu2gKXbaTYoRUZyycOFCOmFubq5lSpO6cJAi oNBDdyxVlkIt/p9CLU4m3peJoIMCH1HTkydPkqPev3//vsGhaOy0i9cr69at s1lBI3bsrm0njjNSJF8AAAAAAL8oc3EKOZnkjJEvnZOTw1sWL15MW0aOHOnq 3Ha38+U4hUhKSiLfm3x4ClXo54QJEyjH/Pz8adOm0c/p06dz+vHjx9NPfvvA 8zuImTNn8t7Zs2fTT/JpOZRYsWLFuXPn6Dzz588nH96kGBHFKZQdnZwCEBJh 4sSJZNazZ8+GTGlSF4pBVq1aRT8XLVpEFRevhELC1QkZa1C9eJcITAYPHsxb KEdRBm2M4wxzuxvbiSz8yhcAAAAAwBfKVpzy008/8dyQ3bt3i43nz5+fNGmS q3PbPciX4xQRhlBY0b9//wEDBoiwgrYMGTKE0vBSZitXrqT/k5OT6f/169fz G4rPP/+cXXTyV8lLp//nzp1LuzZs2GCzGBHFKVOnTqWTjxo1ioMmgqIV44gj y7rw/BSKVsyzo3CMZ9D069fP6I2LQI9iNN5CduEtVCkx6Iv4/vvvbVYwJCZ2 D9lOZOFXvgAAAAAAvlCG4hSTOcIRzTVWM192s8Vcj8zMTPpJfqk2zbx582hj eno6/X/o0CER15Dj+tVXX61du5bfoeTm5op3K/v37+fhT19//fWmTZssl6s1 ximHDx9ODAVlVFBQQOfnZHl5eZ999hlltGvXLt05LetiJ06hvCggCvdCRKw8 RoEMvz0hKJahwK2vARHIOCak3T2Yw+5XvgAAAAAA3lNW4hTLOcI25xormy/H KRR98M89e/bQzxkzZmjTLF26tO/PE0yoMBQXUNbnz58fNGjQggULjhw50jc4 J4US9NXMQ6GAYurUqbw8Lzm0IT8yIjDGKRRKjAjF3r17dcfyoCyjt2xZF8s4 hawwYcIEjjJGjhypG122c+dOsfjw5s2btbsouklJSSGvntci4zQrV640UcAm Ort7Nofdr3wBAAAAADymrMQpduYIuzGn3rN8dXHKsWPH6Ofo0aO1aaZMmUIb xSsM8vzpJ79KSEtL48hl0qRJfCrxWoE5fvz4rFmzLEc9RTTuSwcvU2wMNyzr Yh6nkCsuPpJCIuum7VMY9cknn/DeJUuW6I49c+aM8N6p4pyM11KLHq3dvZzD 7le+AAAAAABeUibiFPtzhOXObfcyX12cUhKcY0JbxGuLrKysfv36URwhvmPI i3oNHz6c/vL08+nTp/NcjK+++orTZGZmiiKR004pxa6Q2I9TSJMffvhBvNo4 f/48rwMccoqEeV1M4hRKIN6k9A3OxJ87d+6cIBSIUcTBo9qYefPm8a7169fT sSdOnBg2bNi3336bnJzMMRpB4kj8ACLbfciQIe7NYSeFsw1weEKhCudOmusS 8MQfAAAAAICyi/pxSkRzhCXObfc4X2Ocsn37dp5wkZiYmJSUxLM/tOtcHT9+ nH1v8aqCvyciZnCQk0+uLB24bNmy1NRUXhmYMjLmTnsXBuEBVPz/kSNHTArM Syh/+eWX5KuvWbOGg5Tx48eHrL55XUziFDqzcYIJk5GRoVtyWTBlyhQ6VrcS MkMlsbSFfYTd3ZvDfuDAgU8jh9qhG4UBAAAAAPAM9eOU7OzsMWPG2J8jzHON KYsoh+t7nO/8+fPZ99Zu3Lp1q1hcd9CgQRs2bNCdfOTIkbRr6dKl/JNHWBE7 duwQZ6BQQnjp5LqHfOPDQ8gicunJRV++fDlHHAydxGTRY5O6bNu2LVycwmPJ QpKVlUUOechdvLxAfn4+uetiVFhCQoJxTk30sN3dm8NOTWJcKMaOHUvik3FD 7o1y7WUAAAAAAN9RP04pCX4lMCLnX4yMihK/8tVCBcgNEk3YRbHJ0aNH3fi+ TFFRUU5ODp3cTt2l1CVSKJ6ikFN8KdIN8vLyfJnD7le+AAAAAAAeUCbiFAAA AAAAAEBcgTgFAAAAAAAAoBqIUwAAAAAAAACqgTgFAAAAAAAAoBqIUwAAAAAA AACqgTgFAAAAAAAAoBqIUwAAAAAAAACqgTgFAAAAAAAAoBqIUwAAAAAAAACq gTgFAAAAAAAAoBqIUwAAAAAAAACq4VecAgAAAAAAAADmIE4BAAAAAAAAqIaX cQoAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAKrx /wA98sI7 "], {{0, 204.}, {540., 0}}, {0, 255}, ColorFunction->RGBColor, ImageResolution->144.], BoxForm`ImageTag["Byte", ColorSpace -> "RGB", Interleaving -> True], Selectable->False], DefaultBaseStyle->"ImageGraphics", ImageSizeRaw->{540., 204.}, PlotRange->{{0, 540.}, {0, 204.}}]], "Output", TaggingRules->{}, CellChangeTimes->{3.8343350276546288`*^9, 3.834405776040113*^9}, CellLabel->"Out[13]=", CellID->616280784] }, Open ]], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"matched2", "=", RowBox[{ RowBox[{"Join", "[", RowBox[{"nonanomalousTreated", ",", "nonanomalousControl2"}], "]"}], "//", RowBox[{ InterpretationBox[ TagBox[ DynamicModuleBox[{Typeset`open = False}, FrameBox[ PaneSelectorBox[{False->GridBox[{ { PaneBox[GridBox[{ { StyleBox[ StyleBox[ AdjustmentBox["\<\"[\[FilledSmallSquare]]\"\>", BoxBaselineShift->-0.25, BoxMargins->{{0, 0}, {-1, -1}}], "ResourceFunctionIcon", FontColor->RGBColor[ 0.8745098039215686, 0.2784313725490196, 0.03137254901960784]], ShowStringCharacters->False, FontFamily->"Source Sans Pro Black", FontSize->0.6538461538461539 Inherited, FontWeight->"Heavy", PrivateFontOptions->{"OperatorSubstitution"->False}], StyleBox[ RowBox[{ StyleBox["FormatDataset", "ResourceFunctionLabel"], " "}], ShowAutoStyles->False, ShowStringCharacters->False, FontSize->Rational[12, 13] Inherited, FontColor->GrayLevel[0.1]]} }, GridBoxSpacings->{"Columns" -> {{0.25}}}], Alignment->Left, BaseStyle->{LineSpacing -> {0, 0}, LineBreakWithin -> False}, BaselinePosition->Baseline, FrameMargins->{{3, 0}, {0, 0}}], ItemBox[ PaneBox[ TogglerBox[Dynamic[Typeset`open], {True-> DynamicBox[FEPrivate`FrontEndResource[ "FEBitmaps", "IconizeCloser"], ImageSizeCache->{11., {2., 9.}}], False-> DynamicBox[FEPrivate`FrontEndResource[ "FEBitmaps", "IconizeOpener"], ImageSizeCache->{11., {2., 9.}}]}, Appearance->None, BaselinePosition->Baseline, ContentPadding->False, FrameMargins->0], Alignment->Left, BaselinePosition->Baseline, FrameMargins->{{1, 1}, {0, 0}}], Frame->{{ RGBColor[ 0.8313725490196079, 0.8470588235294118, 0.8509803921568627, 0.5], False}, {False, False}}]} }, BaselinePosition->{1, 1}, GridBoxAlignment->{"Columns" -> {{Left}}, "Rows" -> {{Baseline}}}, GridBoxItemSize->{ "Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}, GridBoxSpacings->{"Columns" -> {{0}}, "Rows" -> {{0}}}], True-> GridBox[{ {GridBox[{ { PaneBox[GridBox[{ { StyleBox[ StyleBox[ AdjustmentBox["\<\"[\[FilledSmallSquare]]\"\>", BoxBaselineShift->-0.25, BoxMargins->{{0, 0}, {-1, -1}}], "ResourceFunctionIcon", FontColor->RGBColor[ 0.8745098039215686, 0.2784313725490196, 0.03137254901960784]], ShowStringCharacters->False, FontFamily->"Source Sans Pro Black", FontSize->0.6538461538461539 Inherited, FontWeight->"Heavy", PrivateFontOptions->{"OperatorSubstitution"->False}], StyleBox[ RowBox[{ StyleBox["FormatDataset", "ResourceFunctionLabel"], " "}], ShowAutoStyles->False, ShowStringCharacters->False, FontSize->Rational[12, 13] Inherited, FontColor->GrayLevel[0.1]]} }, GridBoxSpacings->{"Columns" -> {{0.25}}}], Alignment->Left, BaseStyle->{LineSpacing -> {0, 0}, LineBreakWithin -> False}, BaselinePosition->Baseline, FrameMargins->{{3, 0}, {0, 0}}], ItemBox[ PaneBox[ TogglerBox[Dynamic[Typeset`open], {True-> DynamicBox[FEPrivate`FrontEndResource[ "FEBitmaps", "IconizeCloser"]], False-> DynamicBox[FEPrivate`FrontEndResource[ "FEBitmaps", "IconizeOpener"]]}, Appearance->None, BaselinePosition->Baseline, ContentPadding->False, FrameMargins->0], Alignment->Left, BaselinePosition->Baseline, FrameMargins->{{1, 1}, {0, 0}}], Frame->{{ RGBColor[ 0.8313725490196079, 0.8470588235294118, 0.8509803921568627, 0.5], False}, {False, False}}]} }, BaselinePosition->{1, 1}, GridBoxAlignment->{"Columns" -> {{Left}}, "Rows" -> {{Baseline}}}, GridBoxItemSize->{ "Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}, GridBoxSpacings->{"Columns" -> {{0}}, "Rows" -> {{0}}}]}, { StyleBox[ PaneBox[GridBox[{ { RowBox[{ TagBox["\<\"Version (latest): \"\>", "IconizedLabel"], " ", TagBox["\<\"1.0.0\"\>", "IconizedItem"]}]}, { TagBox[ TemplateBox[{ "\"Documentation \[RightGuillemet]\"", "https://resources.wolframcloud.com/FunctionRepository/\ resources/FormatDataset"}, "HyperlinkURL"], "IconizedItem"]} }, DefaultBaseStyle->"Column", GridBoxAlignment->{"Columns" -> {{Left}}}, GridBoxItemSize->{ "Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}], Alignment->Left, BaselinePosition->Baseline, FrameMargins->{{5, 4}, {0, 4}}], "DialogStyle", FontFamily->"Roboto", FontSize->11]} }, BaselinePosition->{1, 1}, GridBoxAlignment->{"Columns" -> {{Left}}, "Rows" -> {{Baseline}}}, GridBoxDividers->{"Columns" -> {{None}}, "Rows" -> {False, { GrayLevel[0.8]}, False}}, GridBoxItemSize->{ "Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}]}, Dynamic[ Typeset`open], BaselinePosition->Baseline, ImageSize->Automatic], Background->RGBColor[ 0.9686274509803922, 0.9764705882352941, 0.984313725490196], BaselinePosition->Baseline, DefaultBaseStyle->{}, FrameMargins->{{0, 0}, {1, 0}}, FrameStyle->RGBColor[ 0.8313725490196079, 0.8470588235294118, 0.8509803921568627], RoundingRadius->4]], {"FunctionResourceBox", RGBColor[0.8745098039215686, 0.2784313725490196, 0.03137254901960784], "FormatDataset"}, TagBoxNote->"FunctionResourceBox"], ResourceFunction[ ResourceObject[ Association[ "Name" -> "FormatDataset", "ShortName" -> "FormatDataset", "UUID" -> "76670bca-1587-4e7e-9e89-5b698a30759d", "ResourceType" -> "Function", "Version" -> "1.0.0", "Description" -> "Format a dataset using a given set of option values", "RepositoryLocation" -> URL["https://www.wolframcloud.com/obj/resourcesystem/api/1.0"], "SymbolName" -> "FunctionRepository`$66a3086203b4405b88cdb0de8a5c3128`FormatDataset", "FunctionLocation" -> CloudObject[ "https://www.wolframcloud.com/obj/70389ad6-7dbc-48c8-b898-\ 72c65c00f14e"]], ResourceSystemBase -> Automatic]], Selectable->False], "[", RowBox[{"MaxItems", "->", "5"}], "]"}]}]}]], "Input", TaggingRules->{}, CellChangeTimes->{{3.8343350411206627`*^9, 3.8343350712399063`*^9}}, CellLabel->"In[14]:=", CellID->2071915279], Cell[BoxData[ GraphicsBox[ TagBox[RasterBox[CompressedData[" 1:eJzsvXd8FNX6x7/0XkXgSpciXYoIKAqidPECCoqCXlARpCqCSBc1oCDSEbkC ShEhgDQpIfww9HAhdIK0kJCEEGpAmiC/57vPz/MbZzczs7uzM2czn/cfvMjM 2ZnnPJ+Zc85nyply3ft36JHZ5XINzEn/dOj28XMfftjtk5cL0h8d+w3s9V6/ d99p1e+jd99798MG3bPQwm8zuVwNsrpc//f/BwAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAEjb/cmLW1/wcAAAAAAAAADKDtLM6ePRsfHw+fAgAA AAAAALASDVtx586dvW7u3r1rok9JczBOzgDqbncUQB8oZRbIpL0g/3ICXeQE usiJrk85e/bs/9yYdUsFR4KTM4C62x0F0AdKmQUyaS/Iv5xAFzmBLnKi7VPE zRSG/oRPCRwnZwB1tzsKoA+UMgtk0l6QfzmBLnICXeRE26fwzZRkN/Qf+hM+ JXCcnAHU3e4ogD5QyiyQSXtB/uUEusgJdJETDZ/CN1P2799///79e/fu0X9M uaWCI8HJGUDd7Y4C6AOlzAKZtBfkX06gi5xAFznR8CniZgr/adYtFRwJTs4A 6m53FEAfKGUWyKS9IP9yAl3kBLrISXo+RXkzhZeYdUsFR4KTM4C62x0F0AdK mQUyaS/Iv5xAFzmBLnKSnk/hmynnz59XLjTllgqOBCdnAHW3OwqgD5QyC2TS XpB/OYEucgJd5MSrT/G8mcLQn4HfUsGR4OQMoO52RwH0gVJmgUzaC/IvJ9BF TqCLnHj1KXFxcZ43UxhaGOAtFRwJTs4A6m53FEAfKGUWyKS9IP9yAl3kBLrI iadPSe9mChP4LRUcCU7OAOpudxRAHyhlFsikvSD/cgJd5AS6yImnT9G4mcLw LRUqBp/iH07OAOpudxRAHyhlFsikvSD/cgJd5AS6yInKp9y+fVvjZgoT4C0V HAlOzgDqbncUQB8oZRbIpL0g/3ICXeQEusiJyqfo3kxhArmlgiPByRlA3e2O AugDpcwCmbQX5F9OoIucQBc5UfoUvplCBuTUqVNnNKECVIwK00/gU3zFyRlA 3e2OAugDpcwCmbQX5F9OoIucQBc5UfqUlJSU//kI/QQ+xVecnAHU3e4ogD5Q yiyQSXtB/uUEusgJdJET1XNfFoAjwckZQN3tjgLoA6XMApm0F+RfTqCLnEAX OYFPsR4nZ8Caum/dunX16tUbNmwI9o58wsm6hxZQyghGzjI5MxkfH7/aTWxs rN2xBBc58w+gi5xAFzmBT7EeJ2fAmro/88wzLpfr4YcfDvaOfMLJuocWGUmp AwcOvPPOOz179jx27Ji5WzZylsmZyXXr1rncfPPNN3bHElzkzD+ALnICXeQE PsV6nJwB+BS7owD6ZCSl2rRpw2Py//znP+ZuGT5FfqzJ/5kzZ7bpceHChfR+ fvToUS5z9uzZYIcqCXLqsmvXrvSKXbp0KdjRyoCcujCkzsyZMz/++OPJkydv 3Ljx2rVrwY5THmT2KaTms24OHjwY5DRYGoycfbc1wKfYHQXQJyMp1bVrVx6T v/vuu+ZuGT5FfqzJ/5dffunSY+rUqV5/e+rUqcKFC3OZ2bNnBztUSZBTlxw5 cqRX7Mcffwx2tDIgpy40+OzQoYOqQIMGDf73v/8FO1RJkNmnkH9kReg/wc6D lcHI2XdbA3yK3VEAfTKSUqdPn/7ss8/CwsJMv1gNnyI/8oy75s2b5/W3L730 kigDn2IuPuly4cIFP+TLYEioy9WrVxs3bswLCxYs+NprrzVp0oT/LF++fEpK SrCjlQGZfcqvv/7KcsjgU0wMRs6+2xrgU+yOAugDpYwAnyI/1uQ/MTHxsDe2 b9+eM2dOynP9+vVpuOX5w7lz5yrHZvAp5uKTLsePH2cVxowZ4/mT8+fPBzta GZBQl59++ol16d+/f2pqKi/87rvveOEXX3wR7GhlQGafsmDBAoPWICEhYf36 9WfOnPFcdezYMeqVTp8+rbu7gwcPkhn5/fffAwxGF1/PBe3ABJSEiIiIjRs3 xsXF6W7TeFrMxde6G4wzOTmZKh4TE8MPbfIFB7N8Cm1z//79dIDFx8cHsh0/ 2sD0Dmyqb2Rk5LZt20TDlR4Gg6ftbN68eceOHZcvX/YpwgyJH0pdunSJzr4T J054rtq5c+fWrVt1lTJ4mjMaLZ7uWhXGpffjLPOv34+OjqaQaHfaxahZoMbh yJEjRrZJYVPwSUlJ9H/6j65PMdLy0Fhi3759a9asoYANPi6enjTGVfDpVDWx zfGjiXj77bcpybly5aLke649efIkP/H1xBNPwKfoEmxddu/ezSr8/PPPPgWW kZBQlw8++IAW5s6dW7Wdhg0b0vJXXnnFp2hDFDl9ypQpUx599NF8+fLxiUP/ Keimbt26XKBmzZr058yZM48fP/7iiy9mz56dihUpUkRsgXqNsLCw4sWLi2s1 JUqUmDt3rmpHVOyHH3545plnChQoIErSr1asWGE8GP9yrl3GSGCCLVu2kAEX xTJlylRQQbt27XxNS/Aw2A4Yj5MGgfXq1cucOTMXo/qOHDmyZcuWqhHU+fPn aWu0tm/fvqotDB8+nBNF/aZq1alTp6gdENITFStWnD9/vu/1/j+M1F37wI6N je3RowfFQBJzPFmyZOnUqZPXEZ3B4A8cOEADzmzZsnEZ2iPt1+AQN6NiXKnp 06fTYdOhQ4e8efNyAqtXr75w4UIqQN3KRx99VKpUKaHUsGHDVKNZ46e59oGh sZbOGj68PScQNi69wbMswExSj//mm28WLVpUtGPdunW7ePGi508mTJhADYLI GA13+/fv77XklStXBg0a9NBDD4ltNmjQQFyN9PQpBlueCxcukJp58uQRxWh0 oWx1f/vtN2XtNLoq4yr4caoG3ub4t19i06ZN3EyNGzfOawHaCK2lza5cuZK3 DJ+ixGJdaLDN25HwBqhlSKgLmxdq4lTlu3Tp4nK/peJD9UIWOX0KdQEub9BA iwuULVuW/hw8eHCdOnXE2hYtWvDalJSUZs2aieV8K40ZMmSI2Mu5c+doUOF1 R3TArFq1ymAw/uVco4DBwBhqXsT4gUJSdt8MpcKntAQVI+obj3PevHnKtSqU I6j4+HheSGe9ancDBw7kVapL2dR7ihc8VXz//fdBqrvGgT158mRuEj156qmn VNsxGPzixYuFkcmaNSuNpfn/jzzyyNGjR/2oY8bAuFLUAQknIsifP/+ePXua N2/umfyvv/5abMGn01y7xdNYO2vWLP5zzZo1yg0al974WRZIJl966aXy5ct7 br9fv37KwtQ4vPDCC14jqVatmmru5cTExEaNGqUXucvDpxhsecjLPP/887yc DCbt1/MF5MjISCPCGVfBv1M1wDbH7/0StWrVomL0r9c7TdQK8XaGDx8eExPD /4dPUWKxLuL5IoM3KDMkEupCG+QtrF27Vrm8SpUqtJDcil8VDTHk9CkHDx6k gZaYqYYM5ko34pkrPlSYGjVq0BBuy5Yt27Zt47V0CPGqDh06cM9FG+SnqenI UU7YxS8ovfzyyz///POJEydopCpGrU8//bTBYPzLuXYZI4Glua/ZlixZ0uW+ y0OGhRdS58slBw0a9L///U/03cbTEjyM1N1gnCdPnhQXsf/zn//s2LHj+PHj P/74Y5kyZXhhID7l7NmzRYoU4eUdO3b87bffaMyzevXqmjVrUuuk+wyP33XX OLD5ehc1d19++WVUVBQ/hFOuXDnPRsxg8GfOnClUqBCVeeihhyhvFy9epFXT pk3j4Rkd8H7UMWPgq1Lt2rUjW7F7927lS8EEJXzOnDl79+795JNPeAn9SrkR g6d5ml6Lp7HWq08xLr1PZ1ngmWzZsuWCBQto4ErNLHfxZPqUz8aL/FSqVIly npCQsHnzZvFiaatWrZRb7t+/Py8vXrw4bfbUqVNbt2595513xO5UPsVgy0Pp 5WKdOnXi2KgwDzMKFy5MKaJWV0wrqiGNcRX8PlUDbHP83q+4OP/dd995rhVP fNWtW/fKlSt0gnBh+BQlFusyY8YMXkUDYzrLaABMpwPZSYe8qc1IqAu1JGx8 6JQRbTj15p6tegaGdfmfvwTJpzCjRo1iLTwdgThUnnrqKdWgcd++fXzZuXXr 1srlNK7jtlE5Pyd1iOIGvUD0espH+jWC8RUjGTAYGPWJvGTMmDHKkvzRBBom iSU+pSV46NbdeJzCOQ4YMEBVki81BOJTxMb79OmjLHzp0iUaGvlS4/8fn9pA zwM7zX3JS9VriCfte/Xq5WvwfOM4c+bM1Mwqi9Gx5HI/p+T1bQsn4JNSw4YN Ewtp1Cqe4GrRooXyeTwe7hLKBwCMtz/aB4bGWq8+xbj0Pp1lnviUSRoXKa8l vvrqq7ycWjleQj0OP01B9lyZHDqwRdLoHOGF+/fv58LUbqjus8ycOZMLK32K 8ZaHH1iihUoDtX37dlUAqtp5SmNcBb9P1QDbHL/32759e5d7tOb1kg53TzR4 44lV4VO8YrEuYWFhLm+UKFFCdUhnYCTUJc3tYsTFIjp3qEnnFunll1/2sX6h ivApSb5ju0/JlCmT5wzSfEgQhw8fVq2isZzLwBN9n3/+OW+Buh4jwfiK8Qzo BiYGIevWrVOWHD16tMt9DfDKlSu8JPC0mIJu3Y3HyWdukSJFPN/O8JyJyCef QoMlfvKcNs4v3pqC8TbQ64GdHhwqNV/8p8HgqVju3Lmp2BtvvKFaJXKlevTI ORhXqn79+qrl4qkk1esSI0aM8GxVvOK1/dE+MDTWevoUn6T36SzzJJBMTp8+ nYNZunQpL/n00095ybRp01SFN2/ezKs6derES0QaVddw0tKZ78t4y8MBU/VV xfjpr6FDh3rWzlMa4yoEcqoG0ub4vd/jx49T10NrqWn1XEtmhH8rnsOHT/GK xboIn9KwYcP+/fv369evZs2avIT8uwxzrlqAhLqkua/DkCVx/ZNatWo5ZBK2 tBD3KZ5dG/Haa6+53I8Nb/KA3W6xYsU8f7V79+7JkyfTAUbqC+u6fPlyI8H4 ik8+RTswMVsyv7oroEbG9c93r/xLi+no1t1gnEePHuWKv/XWW54bCdCnHDx4 kJd079490AorCGTMpiQ1NXXx4sXDhg1r165dpUqVONR69erxWoPBHzhwgIvR oeKZal5FB56vdcwYGFfK882g119/nbMnLhEwU6ZM4eV0znpuTbf90T4wNNZ6 +hTj0vt6lnkSSCbJnvDexRtV3DgQ586d89wOPwFbo0YN/lPcCaJhgKqkV59i vIXkd16qVaum3CZ1iLzNr776yrN2ntIYVyGQUzWQNsfv/Y4fP57X0lGtWnXi xAl+MIZyKO6dwad4xUpdGDrRVq5cKf68evXqoEGD+Ceep2eGREJdqGGh5NNa 6hd69OihnGbk448/9rumoUVI+xSv5w4/J6xBlixZlOXp3EzvJ+Hh4UaC8RWD GTASGA1Z+SJe48aNxQ8TEhL4YH7uuef8TkuQ0K27wThpCMd/el4sTQvYp4g3 18aOHRtohRUEMmZjYmNje/XqxR29CjH7nMHgxVuTGowYMcK/moY6gSj15ptv cvZUPkU8a6TyKQbbH+0DQ2Otp08xLr2vZ5kngWTyl19+4b0Ln/L444/Tn3T8 e90O24fs2bNz5sm5u9zTcHmW9OpTjLeQ4m0j5V1sca9n48aNRmpnXIVATtVA 8u/3fvnb2TSs8vxmSuvWrfmHERERv//Nhg0beCEN2OjPxMRE7YAzALLpkh5U kow//YqGGaoGLUMioS6vvPKKy93ocWAXL16k00RMYDhy5Eh/6xpKZDyf8uST T/Jp9VQ6iPmv0hT3Oql3o+Nhzpw5VCkxorDRpxgPbMiQIbywefPmCxYsmDt3 7mOPPeZy221lN+pTWoKHbt0NxinahC+//NJzI88++yytIrMmlvjkU+bPn89L JkyYEGiFFQToU06dOiUmRKpYsSIdkGvXrj179iy/Si98isHgxfeAaFPppdoh 3yD2xDKfYvw0N9GnGJfe17PME3N9CjcOyilAlfCkBFmzZuUPDfBTKwULFvQs Kd5anTRpklhovIU8fvx4sWLFXO53/MeOHbtkyZJ+/frxpIvPPfecarqe9Gpn XIVATtVA8u/3fv/1r3+5vD0XFx0d7TKA9t3kjIFUumjTt29f3p3xR5FDF9l0 2blzJ29TddXx8OHDPM9kzpw5Pb+nkPEQPiUU36P3PFTS/janBQoU0P3wlniu gHoo5fe8xOz6dvkUnwJLTk72vMCeJ08e1R1G42kJKrp1NxineGu1f//+nms1 7qd4PsHi6VNEf/r+++8brZgBAvQp/DW0LFmy0DhWuVzlUwwGTycvF6Ohso/1 yPhY41N8Os1N9CnGpff1LPPEXJ8iHtL2+sEg3k7lypX5T35Zm/B8itvr/RSf WkjRHSihKni+J5te7YyrEMipGkj+/dvvoUOH0jtmxAa1qV27tvHdhShS6aKN ePRL98W6DIBsukyaNInXek4W/e233/IqJ8xyIHyKH44j2D6FXwYnVJMnpGn2 y+JzJ6r77568++67XPLUqVPK5V7HCRrB+IpuBnwKjGfFadSo0fDhw6kf79q1 K1lvz0eyjaclqOjW3WCcCQkJXKxWrVqeaz1HUJcuXeKPKIn3zQU0nudNCZ9C hXlOVDrM6P9G66ZHIG3gmTNnOEjPadlUPsVg8JcvX+bZkJo0aeJ7VTI41vgU n05zE32Kcel9Pcs8MdeniLkIPD+8KL6mTXaDl4jz2vP72l59ivEWMjo6mhJI Zxl1Ct26dWvXrt2HH36Y3le806udcRUCOVUDyb9/+xXPnXqdkTg+Pj7OA/H0 Po3K6E+vLx9lMGTTRQP+DhTt0cSuUFpk02XkyJEu9wRiYp5zAQ1ExVljfHch isw+RUxTP3HiRNUqjX45KiqKR6TVq1fXPrN4ekkqrLwIdvHixc6dO3uOEzSC 8RXdDBgPLCUlJVeuXC7FlDjpYTwtQUW37sbj5GlRCdXHu3/77TeetU81guJ3 dooVK8aPhTA//PCD+BKTcl5i8bm3L774QrXfPXv26FbTK4G0gWICatVza7RB fu1a+BTjwYsLzo59vis9rPEpPrU/JvqUNF+k9/UsU2GuT9m2bRs3DhSVcoLu a9euiRqJd7HFA5ANGjRQakH/f+utt3iV0qcYb3n4/ZQ6depo10u7dmm+qOD3 qRrgPVw/9is6StUBowHeo/eKlbps3bq1WrVq4ntMgrVr1/KvvF6pyHjIpoto A2fMmKFaRZ07r4qIiDC4r9BFZp8iNKIxGJ1B1BmJJyS1e+333nuPf/j444/T vvjtpOPHjw8ZMoR+IvqsDz74gIt17979zJkz1Ddt2LChdu3arr9RjhM0gvEv 5xoFjAeWmJjIz0W3bt368OHDNAK/5iaQtAQVI+objFOMQ3Lnzk0dXEJCQmxs 7Ndff83GzeUxghITxtJQn0aGO3fupA1y9hilT6Guk6+NuNw3ZCm3tF9qzNu3 b0+DGf/uSQXSBtKojAdR+fLl27x5M0lMCRk1ahTPZ+j6p08xGPyRI0d4HkXa yIgRI/iDFDROXrlyZePGjefMmeNHHTMG1vgUn9ofc32Kcel9PctUmOtT0hSN A9kEchaU5H379onPazZq1EiUpHOEhl68vEWLFuTQU1NT6eBv2rSpyLDqO48G Wx7+fGSWLFmWLl1KLbBGk6tRuzRfVPD7VA1w3OXHfocOHco5JHW09yuAT/GK lbrwO605cuT4+OOP169ff+HChXPnztEQWnxafdmyZX5VNMSQTZekpCR+e4U2 +91334l2ZuHChdwCP/LII/59dTq0kNmnUL9QunRp0aeQLtmzZ+f7X9q9Np1i DRo0ED/MmTOn+PiastejkSp/2MvlvqrJ40CX+wlnz3GCRjD+5VyjgE+BiYvn SqgPLVKkSMeOHZcsWeJrWoKKEfUNxknnLN+S9oRnKFWNoChpniXz5s07YMAA /r/Sp6S5ZwsUt1pc7nuv4v89evQIUt01DmxSU6mvSA5/GVzpU4wHTwMD/lQu 89BDD4mSFSpU8KOOGQNrfIpPp7m5PiXNsPS+nmUqTPcp1Djwi1qe0IlAnkW5 BaoyDxtUiJZc5VMMtjwRERHKE0pA+6pZsyaNT5RvtmoLZ/wE9O9UDbDN8WO/ ZLp5recT9ekBn+IVK3VZsGCBuPLg+md/4fL9lZbQRTZd0tytjbjqWLRo0fr1 64vmi5Zv2rTJ1zqGIjL7lDT3vXgehonTZ+vWrbS8YsWKrn/Ou6vi6tWrn3/+ OT+ZIKADjDom5ffX6PQU81G73D3vuHHjqAAfWspxgkYw/uVcu4zxwBYtWuTS RPlNNINpCR4G1TcYJxUbPHiwslmgpuDnn3+mKnPSVJulHIrRO2XyySefJAXF lBoqn5LmfriFp0IVPPLII99++23w6q5xYMfHx7dr105EQkNcGkDGxMTwhG8q n2I8eBokPPPMM8qOiVq/l19+OTo62r9qZgACUYp9StasWVWTTNIol9OrnO/L +Gmu3eJprJ07dy5v3PMmoEHpfT3LlASSSfEWieoSyuXLl4cOHSq+MsPnAqXd 63y2u3fvFndVXO7rS507d05ISChRogT9OX36dFV5Iy0P/Ud7EuPHHnss6e9P rOp2VcZPQD9O1QDbHD/2y9MREMoH87Q5fPgw/+SHH34w+JNQR0Jdjh492rVr V3EDhSlXrlx6L15lSCTUJc39hn6rVq1UjQzfIzZetZBGcp+S5u6VaCS5ZMkS 6t/5PppP0Nm3cuVK+u2ZM2e8FqDDY/PmzWvWrDFy/SfAYBiDGTASGI09ePww derUXbt2kbkm971hw4YZM2aIb6J5jmDTDKQlSPiqvpE4SZQdO3YsW7YsNjZW d4M0mKGkrVq1isYqBmM4d+7c+vXrKQwj29fA17p7hfp0CoaqYPBur8Hg+WBb vXo1NX3K93eciSlKGcSn9id4AehK79NZJgheJq9du0bjAYpn+/btui/cUetB GaazxvireRotD38E5PHHH6cekGoX4Wbp0qWffvop32NyeVzj0sX4CejTqWpi /tFEmIi0ulC3QicU9Y90vnjOxpPhkVaXNPfQZcuWLcuXL6d/nfCNISXy+5SM h4kZ4G8A/fvf//ZcdfXqVZ4JSvvBDItxsvpOrntoAaXMIuNlUjyk5PXj0WKy UNUTZXaR8fKfMYAucgJd5AQ+xXpMzAA/h9atWzfPVUlJSfwxstatW5uyL1Nw svpOrntoAaXMIuNlUjgR1WzSzOeff85r/Xsk2HQyXv4zBtBFTqCLnMCnWI+J Gfj3v//tcj+ePWPGjLi4OLF827Zt9evX5x5Tqs8AOVl9J9c9tIBSZpHxMik+ 1NKkSZOdO3eKt5BSUlLGjh3Lr7/Vrl3b3s/pCjJe/jMG0EVOoIucwKdYj4kZ 2Lx5s/LFt4oVKzZt2pTf82K++uorU3ZkFk5W38l1Dy2glFlkyEy+/PLLooHN mzdvo0aNnnzyyfz58/OSSpUqnT592u4Y/z8yZP4zANBFTqCLnMCnWI+5Gdi+ fXv79u3FzHWi93zjjTf8/sJL8HCy+k6ue2gBpcwiQ2byypUrn3/++aOPPqps cjNlylSlSpVp06ZZM3GiQTJk/jMA0EVOoIucwKdYTzAykJKSsm/fvnXr1m3e vNniKbx8wsnqO7nuoQWUMosMnMlr166dPHkyKipqzZo11PZKZU8EGTj/IQ10 kRPoIifwKdbj5Ayg7nZHAfSBUmaBTNoL8i8n0EVOoIucwKdYj5MzgLrbHQXQ B0qZBTJpL8i/nEAXOYEucgKfYj1OzgDqbncUQB8oZRbIpL0g/3ICXeQEusgJ fIr1ODkDqLvdUQB9oJRZIJP2gvzLCXSRE+giJ/Ap1uPkDKDudkcB9IFSZoFM 2gvyLyfQRU6gi5zAp1iPkzOAutsdBdAHSpkFMmkvyL+cQBc5gS5yAp9iPU7O AOpudxRAHyhlFsikvSD/cgJd5AS6yAl8ivU4OQOou91RAH2glFkgk/aC/MsJ dJET6CIn8CnW4+QMoO52RwH0gVJmgUzaC/IvJ9BFTqCLnNjlUwAAAAAAAABA G/gUAAAAAAAAgGxY71P8+GGGwckZQN3tjgLoA6XMApm0F+RfTqCLnEAXOYFP sR4nZwB1tzsKoA+UMgtk0l6QfzmBLnICXeQEPsV6nJwB1N3uKIA+UMoskEl7 Qf7lBLrICXSRE/gU63FyBlB3u6MA+kAps0Am7QX5lxPoIifQRU7gU6zHyRlA 3e2OAugDpcwCmbQX5F9OoIucQBc5gU+xHidnAHW3OwqgD5QyC2TSXpB/OYEu cgJd5AQ+xXqcnAHU3e4ogD5QyiyQSXtB/uUEusgJdJET+BTrcXIGUHe7owD6 QCmzQCbtBfmXE+giJ9BFTuBTrMfJGUDd7Y4C6AOlzAKZtBfkX06gi5xAFzmB T7EeJ2cAdbc7CqAPlDILZNJekH85gS5yAl3kBD7FepycAdTd7iiAPlDKLJBJ e0H+5QS6yAl0kRP4FOtxcgZQd7ujAPpAKbNAJu0F+ZcT6CIn0EVO4FOsx8kZ QN3tjgLoA6XMApm0F+RfTqCLnEAXOYFPsR4nZwB1tzsKoA+UMgtk0l6QfzmB LnICXeQEPsV6nJwB1N3uKIA+UMoskEl7Qf7lBLrICXSRE/gU63FyBlB37TI7 duyIjIzcu3evkQ36VNhvkpOTI91cvXo1qDuSBycfpeaCTNoL8i8n0EVOoIuc wKdYj5MzgLprl3n00UddLtezzz5rZIM+FfabH3/80eWGbFFQdyQPTj5KzQWZ tBfkX06gi5xAFzkJFZ+SlJQUExND//qxR9kwnoGEhIQlS5aMGjVq5syZkZGR f/75p3JtampqjB63bt0KSh38xVf1Dep+7969AwcO7N+//6+//goovmACnxIq BOkodSDIpL0Yzz81oXv27Jk4ceLkyZNJgvv37wc5NEcDXeTEuC5Hjx6dO3fu 6NGjly9frtFeXb9+fePGjWPHjp0yZUp0dPSdO3fSK3n79m3qYUnoL7744uef fz5z5ozG3lNSUsLDw2nvK1asuHTpkpGAff35oUOH0htV3r1719c9BojkPoVU XrduXZcuXbJmzUojpSZNmvixR9kwkoG9e/dWr17d9U8qVKiwevVqUYYaLpce s2fPDm5lfMSg+sZ1j42NnTRpUvny5bm+W7ZsMTNcU4FPCRVMP0odCzJpLwbz f+zYsRIlSig7jooVK8bHxwc/QIcCXeTEiC5paWndunVTDbR69+6tuoxMbNiw 4ZFHHlEWIzXJdaqK0bB/yJAh2bNnV5bMkiVL9+7dr1275hnAwIEDlSUzZcoU FhZmvI4Gf54zZ870RpVLly41vjtTkNmnkEvlbkvwzDPP+LFH2dDNwNSpU7Nl y8ZVLliwYO3atTNnzsx/0sG8a9cuLmbEp5Axt6JKhjGivnHdqXFQ1TcyMtL8 oE0CPiVUMPcodTLIpL0Yyf/Ro0eLFSvGma9cuXKFChX4/2XLlj179qwlYToO 6CInurr89ddfjRs3ZiHKlCnTokWL4sWL859NmzZV3migoQhZAB6zUR9dr149 buVy5cq1Zs0aUYxavzp16vAWqHzVqlWF6ES7du1Uj4j07duXV+XJk6dBgwa0 Nf5zzJgxRipo8Oe3bt2SalQps09JTEws+Dc8UM8Y/ZduBubNm0eVLVmyZERE BN/nTU5OHjRoEB8kzZs352Lk6+O8sX//fj78nn76adluExtR37judNJxsdy5 c8OnBAn4FK9k1NbJXJBJezGS//bt2/MYadGiRbxk2rRpfMr36NEj6CE6Eugi J7q6zJkzhyV47733+AYK/duzZ09eSGu52O3btytWrEhLHn74YTHRzZ49e9jU UMd97949XkijuBo1atDCXr16Xbx4kZbQmI1iKFeunGe3e+DAAV5Yt27d69ev 05JLly5VqlTJ5b7/kpCQoF074z+nNplLfvXVV54jzD/++MO3tAaMzD5FCT/Y kzH6LyMZWLhwIR+0ArLVlStXpiQULlxY+7d0wFMxGrqfOHEiwFBNx1f1Deo+ f/78jORTGjduzH9Sc7dz587Y2Fiv793o+pQbN27s2rUrJiaGtqO9X9r+yZMn o6Kirly5oloFn6JLRmqdzAWZtBfd/CcnJ/NlXhp3KZfzIDlfvnzUhgQ3REcC XeREV5fnn3+e8l+qVClVl0ojf1pOY36+Mrx161buNGfMmKEstmrVKl6+YMEC sfDs2bO//PKLakc//fQTl5w8ebJY2KdPH09PIdyH7i0V4z8/fPgwL1S+aGAj 8CnW418GiGbNmvFhJsy4JzSs5WuSkyZN8j/EoAGfol2GrccLL7xw7Nixli1b imdECxYs2LdvX9X7a+n5lHPnzlGL9Nhjj4nHBanLe+ONN7x2bSkpKZ07d86f Pz+XzJQpE/0wPDxcFEjPp3zwwQeF3IwfP96nPMgPRtdmgUzai27+6eTls1v1 Zt/SpUt5+bx584IboiOBLnKiq0vRokUp+T179lQtX7JkCevCP58+fTr/ef78 eVXJatWqKS9Fpsf27dt5C6NGjeIl1PtTb+vy9voeb7Ns2bIaG/Tp51FRUbz3 6Oho7TitAT7FevzLwLVr1x5++GH27BrF2NfTv3LOfAWfol2GrQfh9S02amGU E4Z49SmzZs3KkSOH52+9OpqIiIgiRYp4LSweNvDqU8TtbzJTGq45RMHo2iyQ SXvRzf9bb73lcl8GUZ3F169f5+v5gwYNCm6IjgS6yIm2LtT5qryDgPwIr+KZ iz755BOX+6Kf5zCMHxIrUaKEdiRhYWGqjvjMmTO85Ouvv1YV/vjjj3mVxhNZ Pv185cqVvESSN6HgU6zHjwzExcW1aNGCj5xp06alV0y4YBq3BxplcIBP0S4j fIrLfdGGrEFiYuKSJUuEm/jvf/+rKqxyH3wMlCxZcvLkyXv37r1x48a2bdvE fGjKAC5fvsxXh4jXX3+dTue0tDRKYO3atevVqyfua3v6lF27drEVqlq1qtcJ SUIdjK7NApm0F938c7dSo0YNz1X8Pm+XLl2CFZyDgS5yoqsLJ//tt99WLScL wy8FDx8+nP6cOXMmd5qe74x89tlntDxz5szpze5LznThwoU8/VepUqVu3rzJ y3fu3MnbXLZsmeonYncnT55ML3Kffj537lxesmrVqqFDh3br1m3EiBHkmEQw FgOfYj3GMzBv3rzu3bs3bdo0S5YsLvcrJ1OmTNG4UdKpUyeX+9Ut3fcR7AI+ RbsMW48CBQoopwQhDh48yJOHVKxYURwA6T33tXLlSlV7QlaF89O/f3+xkBpb Xvjhhx8qC1P7qfz0vMqnJCUl8VyLZJ1OnTplsO6hBUbXZoFM2otu/mvWrOlK ZyLoqlWrutzPoAYrOAcDXeREV5dGjRpxB618kZOGW23atOFesmvXrrRk06ZN /KfqzsvGjRvFFFuq3vPcuXPUO7/yyitlypThAtQMKsssX76cl3t+fEE8dUYd fXqR+/TziRMnurxBvolGFxr5CRLwKdZjPAOtW7dWHiSjR4/W+G5jYmIiz2ZM /te0WM0GPkW7jMar8c2bN+c6Jicn6xb2JG/evC73PIf8J5kdXlK0aFGe+iM9 lD7lzp07DRs2pP/Tkfbbb78Z2W8ogtG1WSCT9mLw+nCHDh08V5EKtKp69erB Cs7BQBc50dVF3GioU6dOVFQUDbrCw8OpCxaDNO5hqaPk+b6oowwLCyO7sX// /s8++0z5SDYtUW45Ojpa5QgOHz6sLCDuehw8eFAVFQ17eJXnvRL/fi58Ch1s gwcP/uijj2rXrs1LqAqHDh3SyaPZwKdYj/EMTJgwoVWrVnSEiG8AVa5c+ejR o14LT506lcuoDm+pgE/RLqNhPcaMGcN1FB/Q0fYpt2/fXrVqFf2qY8eOVapU 4d82aNCA154+fZqXeL4SqEL4lO3bt4tbMOPGjdP+VUiD0bVZIJP2opt/Gg5R wl966SXPVdRWuNyvOgYrOAcDXeREV5e//vqLpzNS8dprr/Fb6n379uWSERER ni+ZUhkqyf9XfQj+zJkz1FNT0ye+7JkpU6aRI0eKxye+//57Xr5v3z5VVOvX r+dVGtNz+frzRYsWURXEn/fv3x8+fDgXC/anEDyBT7EePzJw/vx5OmL5yR8a nXp9SvDVV191uWcslO2bKUrgU7TLaFiPWbNmcR2XLFmiXZjvIBcuXNizOX3y ySe5jJgg8ZtvvtEOSfiU119/XWyndevWRqocomB0bRbIpL3o5r9evXqU8EaN Gnmu4gvCbdq0CVZwDga6yImR9orGV5MnT65du3auXLmyZ8/etGnT6dOn3759 m2fXVL6l/vvvv3fo0KFkyZIu90ch33333djYWB7t0zgtve2TMdmwYUOtWrW4 q6V+n5evXbuWl2zatEn1k4ULF/Iqz4/dCwL8OVecoyL/ZfHkOfAp1uNfBohR o0bx4cRzSqhgG/7cc88FGl8wgU/RLqPhU8RMlevXr9conJKSwh2Zy333LSws jHZ6+fJlzqTwKeHh4VxGY1oGRvgUJk+ePPyfn376yWDFQw6Mrs0CmbQX3fy/ 9NJLLveEGJ6r+EKH5yvDIHCgi5z41F7RuF1MvymeT1BO6S9QPq7/5ptvugw8 tpecnMzP/omZwfbt26e6UCmYMmUKr9L41GOAP2cGDhzIJclwaZc0F/gU6/Hb p5w6dYoPkj59+qhWiUnnBg8ebEKIQQM+RbuMhk95//33uY5xcXEahevXr+9y fzBFNcG+yqccOXKEtzZgwADtkJQ+pWbNmufPny9btqzL/WIL2R/t34YoGF2b BTJpL7r579GjB18gVX1ciUYsfMoPGzYsuCE6EugiJ36PzWbPnm3klsS9e/dK ly7tMnY7rGvXrrxN/uR3UlIS/zlixAhVyXfeecflfk4svTnEAv85Ix79Ur1c E2zgU6xHNwPpzeglPHuvXr1Uq8RjPNLOSMzAp2iXSc+nUAPCTyznypVLY76v 1NRUzkPv3r1VW1D5FNogz8NPG9FunYRP+de//hUfH/9AMbl69+7d9asdgmB0 bRbIpL3o5n/BggV8LqvewJ0xYwYv37hxY3BDdCTQRU789inVq1cnUcqVK/fn n39qFBOf6VR+XyC98R7feSFSUlJ4CT8poZqtmn5evHhxWv7UU09pBxngzx/8 PbNT9uzZdR2NucCnWI9uBjp16tShQ4e0tDTVcvHc19y5c1WrxMsLGzZsMDVY k4FP0S7D1oNaEtXM0pMnT+YKdu7cWVVY6VMOHDjg1clGR0fny5dP6VOIVq1a cWHPDz8p52oQPoW8sFjYtm1bXrh582bdioccGF2bBTJpL7r5v3nzZsGCBSnn zZo1Ey820iDkiSeecLkfqpf5bcfQBbrIiZH2ijpZ1bSrZDq4N/zuu+/EQurB VTcdLl++zNNNk3xinL93716yCarPEDxwv5LMXzejwmLh2LFjeUdbt24VC1es WMELv//+e7GQjh/quBctWqQM1eDP9+3bR3HGxMR4JodfkbZ+DgeZfcquXbu2 /w2/fEG9mFgSuh+Y087AsmXL+LCpXLnyt99+e+TIETK8qampw4cP56+oFChQ 4Ny5c6pfffrpp/wrOuyDG31gGFHfoO6JiYli4YgRI7j6YWFhvETCSc+M+xSX +zspK1euJK+alJQ0ZswYbh9y5MghHvoiqLng5Ig+i1onfpsvf/78u3fvpiOH skQ54QmrVT7l+PHjYh65wYMH05bv3btHbRTZZNqImErd6/foT58+zfPAU5wa c2WHKCYepQ4HmbQXI/kXD5T269fv6tWrNJrq3r07Lxk9erQlYToO6CInurpQ r5o7d+46depQb/jnn38mJCSQFtw7lypVSryuQj1vx44ds2bNStYgOTmZPAtZ AxrRsXwzZ84UG6xRo4bL/cwVyf3rr79Sc0ebpRgef/xxLjxw4EBRmAYD/BQE WRju32mzNCCkJXnz5lV+X+DDDz/knyu/UmHw5/yBnpw5c44cOTIqKor6d4pq 1qxZNKjgUNetW2dGsn1AZp/C33dIjwkTJvixdxnQzgAd6jxzl0AMJvkg8fqd nZ49e3KBs2fPBjH0gDGivkHd6T8axWgjQa+Mjxj3KZ7zGRLkUlXvvPfv359X FS5cWMy/oZyVixsll/tpsXLlyrn+6VMeuOeyFmV4F+L/4h0orz7lwd/f1XXJ /b0e/zDxKHU4yKS9GMk/jYH5pTaGB10u95X8jHcJQhKgi5zo6jJkyBCv3WXp 0qWVl4hpGMZ3QzxLDhgwQDlZFu2OJzRmMmfOrOyRGzRoILwPs2DBArE1cUjk yJFj7dq1ymJ8DZN4+umnff35smXLyIspgxcliUGDBvmV2oAIXZ8yfvx4P/Yu A0YyEB4eztOkK2ncuDG5YK/lhbXxOmWxPAQ+bhG6a/uUPHnyBL0yPmKk7nzJ Zc6cOeQglM1X2bJlPZ+wIuMgvl0r5t+4cuVKx44dxQ+pCWrduvWJEyf4oUGV T3ngPovr1KmjbIhKliz5ww8/iAKLFy/m5arz/fbt25UqVXK5P2UluTv2FROP UoeDTNqLwd6WhsQvvviiuCCWM2dO6lAwGA4e0EVOjOhCvbP4xAn3sG3btk1N TVUVS05OVmrHHavX14dTUlLef/991XcE8ufP//nnn//xxx+e5RctWsQvqzKP PvqoyqQQX375Ja+dNGmSHz+Pj4/v3r0730ARlC9fXuP7LEFFZp+SUTGegaSk pG3btq1Zs4ZGpKqvAoUoTlbf17rfv3//wIEDGzZs0JgtkMpERUWtW7dO1XnF xcXRcjp4VO+5pMe1a9eofEREhOcjhQ7EyUepuSCT9uJT/m/evBkdHW280QB+ A13kxLgu1FFS10y6qO53qKC1u3fv3rhxY2JiovYG7969S939r7/+SoWPHz+u /T7+A/fsr5s3b9a4QnjkyBGND8fr/vzB36/YbNq0iUrqxh9U4FOsx8kZQN3t jgLoA6XMApm0F+RfTqCLnEAXOYFPsR4nZwB1tzsKoA+UMgtk0l6QfzmBLnIC XeQEPsV6nJwB1N3uKIA+UMoskEl7Qf7lBLrICXSRE/gU63FyBlB3u6MA+kAp s0Am7QX5lxPoIifQRU7gU6zHyRlA3e2OAugDpcwCmbQX5F9OoIucQBc5gU+x HidnAHW3OwqgD5QyC2TSXpB/OYEucgJd5AQ+xXqcnAHU3e4ogD5QyiyQSXtB /uUEusgJdJET+BTrcXIGUHe7owD6QCmzQCbtBfmXE+giJ9BFTuBTrMfJGUDd 7Y4C6AOlzAKZtBfkX06gi5xAFzmBT7EeJ2cAdbc7CqAPlDILZNJekH85gS5y Al3kBD7FepycAdTd7iiAPlDKLJBJe0H+5QS6yAl0kRP4FOtxcgZQd7ujAPpA KbNAJu0F+ZcT6CIn0EVO4FOsx8kZQN3tjgLoA6XMApm0F+RfTqCLnEAXOYFP sR4nZwB1tzsKoA+UMgtk0l6QfzmBLnICXeQEPsV6nJwB1N3uKIA+UMoskEl7 Qf7lBLrICXSRE7t8CgAAAAAAAABoA58CAAAAAAAAkA0892UlTs4A6m53FEAf KGUWyKS9IP9yAl3kBLrICXyK9Tg5A6i73VEAfaCUWSCT9oL8ywl0kRPoIifw Kdbj5Ayg7nZHAfSBUmaBTNoL8i8n0EVOoIucwKdYj5MzgLrbHQXQB0qZBTJp L8i/nEAXOYEucgKfYj1OzgDqbncUQB8oZRbIpL0g/3ICXeQEusgJfIr1ODkD qLvdUQB9oJRZIJP2gvzLCXSRE+giJ/Ap1uPkDKDudkcB9IFSZoFM2gvyLyfQ RU6gi5zAp1iPkzOAutsdBdAHSpkFMmkvyL+cQBc5gS5yAp9iPU7OAOpudxRA HyhlFsikvSD/cgJd5AS6yAl8ivU4OQOou91RAH2glFkgk/aC/MsJdJET6CIn 8CnW4+QMoO52RwH0gVJmgUzaC/IvJ9BFTqCLnMCnWI+TM4C62x0F0AdKmQUy aS/Iv5xAFzmBLnICn2I9Ts4A6m53FEAfKGUWyKS9IP9yAl3kBLrICXyK9Tg5 A6i73VEAfaCUWSCT9oL8ywl0kRPoIifwKdbj5Ayg7nZHAfSBUmaBTNoL8i8n 0EVOoIucwKdYj5MzgLprl9mxY0dkZOTevXstiQh4x8lHqbkgk/aC/MsJdJET 6CIn8CnW4+QMoO7aZR599FGXy/Xss88a2eD333//6quvLlq0yITggAInH6Xm gkzaC/IvJ9BFTqCLnISKT0lKSoqJiaF//dijbBjPQHJy8uLFi0eNGjV37tw9 e/akV+zPP//ctWvXeDdr1qxJTU01LVazMV73o0ePUq1Hjx69fPly5+hu3Kfs 37/f5SZLliwJCQnmhAjcGD9K7927RyfmxIkTJ0+eTA3U/fv3gxxaiOFra5+R 2nkZwJEsJ9BFTozrQn3ukiVLaGw2c+bMyMhIGoN5ljl06FBMOty9e1dV+Pr1 6xs3bhw7duyUKVOio6Pv3Lnjdb8+bTM9aCPz5s0bOXLkrFmztm/f/tdff3kt ZjAkC5Dcp1Ci1q1b16VLl6xZs9KQrEmTJn7sUTaMZGD37t1Vq1Z1/ZOWLVvG xcUpi9GR2bdv3zx58iiLFShQgA6/IFYgAIzUPS0trVu3bqq69+7d22tTEEKY 61POnDlDDoUKZ8uWLTEx0ZwQgRuDbdSxY8dKlCihPEorVqwYHx8f/ABDBoOZ zJDtvAzgSJYT6CInRnTZu3dv9erVVeOTChUqrF69WlUyZ86crnRYunSpsuSG DRseeeQRZQHS3eulaePb9Epqauqrr76q+mGjRo1iY2NVJY2HZAEy+5SkpCTu tgTPPPOMH3uUDd0MLFy4UByNxYoVa9iwYf78+fnPKlWqCFd78eJF6tB5eebM mZ944onHHntM5OrHH3+0ojI+olt3svaNGzfmKpQpU6ZFixbFixfnP5s2bWr8 ioGEmOtTHrhbkk8++WTLli0mBAcUGFHq6NGjdG7ykVm5cmXqp/j/ZcuWPXv2 rCVhhgBGMplR23kZwJEsJ9BFTnR1mTp1arZs2ViIggUL1q5dm4Ze/Gf27Nl3 7dolSt66dSs9Q0H8/PPPomRkZGSmTJl4C9T116tXj9vDXLlyrVmzRrl349v0 yv37959//nkuXKhQoa5du77wwgv8J/nfmzdv+hGSNcjsUxITEwv+DR8MGaP/ 0s7ApUuX2JVQu7R161a+z3vlypW2bdvyETVu3DguOWzYMF5Cg1XxrBeZ+ty5 c9PChx566MaNG8GvjW/oqj9nzhyu1Hvvvcc3UOjfnj178kJaa1GgQcB0nwKC hBGl2rdvT0pRYy7eD5o2bRofpT169Ah6iCGCkUxm1HZeBnAkywl0kRNdXebN m0f5L1myZEREBI/NkpOTBw0axLo0b95clKRmjRd+9dVXcR788ccfXOz27dvk EajYww8/LObP2bNnD1+epfHAvXv3fN1mevzyyy/888GDB9N+eeH8+fN54ddf f+1HSNYgs09RUr58+QzTf+lmYMeOHW3atLlw4YJy4cWLF9m/tGzZkpfQ0fL2 2297Dt2Ff1G6e0nQrTv7/VKlSonziKlbty4tr1SpUug+oGvcpzRu3FgsOXLk yO7du321nGlpadTC7Ny58/r167qFExISyBFfvXpVt+Tp06e3bNli/PUBCiMq KurMmTPpPQErJ7pKUd/E15fITSuX89AiX758El4isAVfW/uM1M7LAI5kOYEu cmKkvVq4cCENxpRLqHerXLky6VK4cGGx8PDhwzwM83weTAn1vFxsxowZyuWr Vq3i5QsWLPB1m+kxZMgQ+m2ePHlUj9BTe0vLO3fu7EdI1gCfYj3+ZYDgp7zK lCmjXYxGknw4zZ0714+9BBXduhctWpQi79mzp2r5kiVLuFL+pU4GjPuU5s2b k2vo0aPHv/71L6515syZqcNSvchWu3btQoUKkaVVLjxx4sRLL73k+ptMmTJV q1ZN+eTq4sWL6VcVKlSgxiosLKxcuXKiZNWqVbdv364KiRphSv5zzz1XsGBB sdlHHnlkw4YNqpK8ZfKS9P8VK1Y0bNhQPM9TsmRJzy1Li65S48eP53qpHrqj PPPyefPmBTfEEAE+xV5wJMsJdJETv8dmzZo1c7nntBH3GqKiolip6OhojR9O nz6di50/f161ijpu1UVLg9tMj169erncT9qolvPrwI0aNfIjJGuAT7Eev88F GpdSEuhQ0S62cuVKPsyMvFdlMdp1p3E4Rz5q1CjVKjpleNXs2bODGmHwMO5T yIryAa/io48+8iysfEjs0qVLpUqVEuX5CUBGTK3w448/8pLHH3/ccxc5cuQg iyE2eO3aNa/FXG7rtGnTJmU8YstDhgzhp1uV5MqVS+aZ6JToKvXWW2+53M8n q+6AX79+na3ZoEGDghtiiACfYi84kuUEusiJf2Mz6iUffvhhl/t5D7FQDMO0 Xyb65JNPXO6LhJ6PHPDj7iVKlPB1m+khboio6sjTApBb8SMka4BPsR7/MnDl yhV+ePvtt9/WLvnhhx/y0SjhdLW6dec3Bz3rSBaGBrq0avjw4UGML5gY9ynM iy++uGzZshMnTkyaNIn7pgIFCiifQfX0KWFhYfzbAQMGnDt3jtqZgwcP/vvf /65Vq9atW7e4jHATLvcrmfPmzYuPj9+3b9+bb77JC0uXLq28L/z8889Tk/Xa a6+tXr06OTk5KSlp6NChXFJ1XUW55UKFCo0bNy4mJiY6Orp+/fq88PPPPw84 i1agq1SLFi2oOjVq1PBcxQdwly5dghVcSAGfYi84kuUEusiJH2OzuLg4FouY Nm2aWD537lxeSO6AekxyASNGjFi0aJHydXVi5syZ6Y3WPvvsM74eKKYPMrjN 9KAxAL878NBDD23evJkXRkZG8jbFEp9Csgb4FOvxLwPiRvAPP/ygUezq1as8 m1y5cuX8DzFo6Na9UaNGPCAnXyYW3r59u02bNlz9rl27Bj3K4OCTT6H2R3k1 g3olXn7gwAFVYaVPeeWVV1zuOTpUDZeyVRFuokOHDqp3Ujp27MirlA+gklHy PNPFPCFKmcSW6WxVznNIW+DbK+3bt9euviToKlWzZk1XOtPn8nTilJ9gBRdS wKfYC45kOYEucmK8vZo3b1737t2bNm3KXwfInTv3lClTlF32xIkTXd4oVarU ypUrRbFNmzbxctUzJBs3buQLs8SpU6d82qYGUVFR+fLl41+1a9eOumzyLPT/ 1157zb+QrAE+xXr8yMDp06f5GZ4qVapof0ZETI1l/btORtCtu7hiUKdOHTqn EhMTw8PDaSguTkk6uawK1mSM+5Snn35atVxMg7Z27VpVYaVP6d+/PxfTeORP uAlx/USwY8cOXuX5fpCKCRMmcMn9+/d7bjkiIkJVvnTp0rS8Xr162puVBIN3 /cjoea7idxKrV68erOBCCvgUe8GRLCfQRU6Mt1etW7dWOoXRo0eLJxYY4SlI r8GDB3/00Uf83L7L/XD1oUOHuNidO3d4cq1s2bKFhYXR+J+61M8++4zKiI2L TtbgNjW4e/cuWRKVzalbt67yOQ2fQrIG+BTr8TUD9+/fF5ev161bp1Fyy5Yt fOG6QYMGcs6wpFt3CptfSVNBJ1ehQoXoP3379rUqWJMx7lM85yUme8J5EHNU ei1MRkO8ut6qVavVq1d7TiGo4VPoSOOfiznllBw+fHjWrFn/+c9/qFkT12TW r19vZMvkUFz/fHxXZnSV4peAXnrpJc9VdOpxyx+s4EIK+BR7wZEsJ9BFToy3 VxMmTKAelmxC9uzZuderXLny0aNHlWWos1ZesqPudfjw4VxY2WtTGc+vN9Jo RxiKS5cu+bpNr1y/fp0v+VL33adPH/FlusyZM48cOVJZ0qeQLAA+xXp8zUC/ fv342NB+JPX3338vUqSIy30LUvl0kFQYqTudepMnT6YWIFeuXNQING3adPr0 6bdv3+bXc8Qs3yFHID5l48aNRnwK8dNPPykn5ipRosS4ceOUkzxruAmCZxhT zdVAO+V5oT359ddfjWz5qaeeykg+hW2XmCBFCV+JUk3C5ljgU+wFR7KcQBc5 8WN0ev78eRrk8/Vh6pG1XxWhsU2tWrWoJLkA5SVEGrx16NChZMmSLvcsOu++ +25sbCwbEPIU2gGkt01POnfu7HJPnszThd25c2fq1Kk8AwDxxRdfKAsHEpLp wKdYj08ZEHf6aKCocQpcuHBBzBCl+1lSG/Gp7nQCipl4T58+zbULDw8PVnBB xhqfQqSkpIwaNYpbGIZ8x+XLl3mttk/Jmzcvrapfv75YIo7AHDlyUENHPujY sWP8uSvH+hSe+blq1aqeq6gXcBmY7MIhwKfYC45kOYEucuLf6JSgDpf7Pt35 SAcOHMglla9wCpQPj/HMNkYe8NPeJnPw4EEu88033yiXx8XFkQ1xuSfk9JyI 2O+QzAU+xXqMZ2Dp0qV8E6FYsWIak3fduHGD7wUTQ4cONS3QIOB3O0CnP1dw z549ZgdlEZb5FObevXsrVqzgly6Jt956i5druAlyu6rCa9eu5StFDRs2VH54 VHzE1pk+pUePHnz9SvW1NTpJOQPDhg0LboghAnyKveBIlhPoIid+j09OnTrF uvTp00e7pHhMS/sVD+q++aVOIzfOjGzz22+/5TKecxr/8MMPvEr7ZXyfQjIX +BTrMZgBOmayZcvmct9l0xic37x587nnnuPD7PXXX5f8c+1+twM8xXe5cuW0 pxGQGYt9CpOWlsbPCYg5zzXchJiQMCwsjJf07t3b5Z5KPSUlRVnS4T5lwYIF XNNly5Ypl8+YMYOXk17BDTFEgE+xFxzJcgJd5ERXl/Re+xXPe/Tq1Ut7F/wC fvbs2bWn9hUf9Pzvf/+rF7WhbX7xxRcu95coVe/7EzS85H2RlzErJHOBT7Ee IxlYs2YNv5+VM2dOjcJ0yIlX7Nu2bSv/GN5I3Q8cOKA6lei84Dp+9913QQwu yFjgU2JjY48cOaL6bZMmTahY/vz5+U/hJhYuXKgsdvXq1bJly3JTJjbSvn17 l/s9u+TkZFHyzp074mMrzvQpN2/e5JeAmjVrJq4MUB/xxBNPuNxP80p+ucAy 4FPsBUeynEAXOdHVpVOnTh06dEhLS1MtF899zZ07l/7ct29fzZo1Y2JiPLfP zycop0G4ffu26j7I5cuXeWJqElpYD5+2SccPdcc0WhBDKTGE4AiVfP3117xq x44dPoVkGTL7lF27dm3/mxIlSrjc32UQS65du+bH3mVANwPr1q0TU8ANGTKE /vzll19WKOBxIB1L4gNDNAola7N69eoV/yQpKcmiWhlDt+67d+/OnTt3nTp1 6JQh25WQkDB69Gg+DUuVKiVeVwlFgu1T+NYJ2duRI0fyU4J//PEHlecfNm3a lIsJN0Huo3fv3sePH6esbtu2rVq1ary8R48eYhd0+PHCnj17Xrx4kRqorVu3 cl/pZJ9CvP/++1zZfv36kcWjZrx79+68hI5YS8IMAYxkMqO28zKAI1lOoIuc aOuybNkyzn/lypW//fbbI0eO/PXXX6mpqcOHD+evqBQoUODcuXMP/v7GTc6c OakvjoqKIrNA7disWbP4M4s0nhETt9IWOnbsmDVr1rFjxyYnJ9OgjnpY2j7v aObMmWLvxrf5QPGxb/EiwPXr17l1zZMnz/z588WNoeXLl/M3L0qWLMnz7RgP yTJk9in8Sm96TJgwwY+9y4B2Buio8JwRTgUZWypJjZV2MYIOQusqZgBd9cXA 2OW+sC/+X7p06b1791oVZlAItk+Jjo4uWrSoyBj9X3yVKVu2bGQAuZjyq/Ge 1KhRQ/mI18GDB4VlzuyG/y9MjWN9Co0c6tevL/LGVtrlvv7peWPdsRjJZEZt 52UAR7KcQBc50dblzp07r776qrJ1EpMSs0Di/Q5yNDz4Z2gkI+QjBg0aJLZ5 9uxZZa+tHPMMGDBAOX+X8W0SYn5O5bfYduzYIQIuXrw4reInKLgiO3fu9DUk ywhdnzJ+/Hg/9i4Duj5FeWB4hb+XN2zYMO1irn9+FlAGjKg/Z84cNv4MjZPb tm2bmppqSYBBxEjd+aoF9USq5b/99htnQ+lTPAtT1/bBBx+ImQaZWrVqbdmy RZQRbmL27NnizSaXu6Xq3r2755xy1DyKidZdbsM4adIkarH5KFX6lMWLF3OZ 7du3qzbCO8pIPuWBO9svvviiaPlz5sxJvRiGEEoC9ymh287LAI5kOYEucmJE l/DwcDFtkaBx48biSiATHx9P/Snf7BCUL19+9erVqg0mJycrVXa5b23Mnz/f c9fGt/nll1/yWuqslcuPHTtGoylV8G3atFF9+cV4SNYgs0/JqDg5A8brfu7c uQ0bNkRHR4f0s15KrNSdsrfJTUJCgurVP+FT2LyQASSvsXPnTuU3VlSQeaFG ePPmzZ5ThWRIfFKKkkNH6bZt2zQS6Fic3NbJAI5kOYEucmJcl6SkJFJkzZo1 O3bs0PjoIb/oQR0x9Z6JiYkaG6RxDnWyGzdu1C5mfJtHjhxJ7wv1aWlpe/bs Wb9+Pf3r+a6NHyEFG/gU63FyBlB3u6PQ+X4KeCCNUhkAZNJekH85gS5yAl3k BD7FepycAdTd7ijgU/SRRKkMADJpL8i/nEAXOYEucgKfYj1OzgDqbncU8Cn6 SKJUBgCZtBfkX06gi5xAFzmBT7EeJ2cAdbc7CvgUfSRRKgOATNoL8i8n0EVO oIucwKdYj5MzgLrbHcX/TR3W1I1qig8gkESpDAAyaS/Iv5xAFzmBLnICn2I9 Ts4A6m53FEAfKGUWyKS9IP9yAl3kBLrICXyK9Tg5A6i73VEAfaCUWSCT9oL8 ywl0kRPoIifwKdbj5Ayg7nZHAfSBUmaBTNoL8i8n0EVOoIucwKdYj5MzgLrb HQXQB0qZBTJpL8i/nEAXOYEucgKfYj1OzgDqbncUQB8oZRbIpL0g/3ICXeQE usgJfIr1ODkDqLvdUQB9oJRZIJP2gvzLCXSRE+giJ/Ap1uPkDKDudkcB9IFS ZoFM2gvyLyfQRU6gi5zAp1iPkzOAutsdBdAHSpkFMmkvyL+cQBc5gS5yAp9i PU7OAOpudxRAHyhlFsikvSD/cgJd5AS6yAl8ivU4OQOou91RAH2glFkgk/aC /MsJdJET6CIndvkUAAAAAAAAANAGPgUAAAAAAAAgG3juy0qcnAHU3e4ogD5Q yiyQSXtB/uUEusgJdJET+BTrcXIGUHe7owD6QCmzQCbtBfmXE+giJ9BFTuBT rMfJGUDd7Y4C6AOlzAKZtBfkX06gi5xAFzmBT7EeJ2cAdbc7CqAPlDILZNJe kH85gS5yAl3kBD7FepycAdTd7iiAPlDKLJBJe0H+5QS6yAl0kRP4FOtxcgZQ d7ujAPpAKbNAJu0F+ZcT6CIn0EVO4FOsx8kZQN3tjgLoA6XMApm0F+RfTqCL nEAXOYFPsR4nZwB1tzsKoA+UMgtk0l6QfzmBLnICXeQEPsV6nJwB1N3uKIA+ UMoskEl7Qf7lBLrICXSRE/gU63FyBlB3u6MA+kAps0Am7QX5lxPoIifQRU7g U6zHyRlA3e2OAugDpcwCmbQX5F9OoIucQBc5gU+xHidnAHW3OwqgD5QyC2TS XpB/OYEucgJd5AQ+xXqcnAHU3e4ogD5QyiyQSXtB/uUEusgJdJET+BTrcXIG UHe7owD6QCmzQCbtBfmXE+giJ9BFTuBTrMfJGUDd7Y4C6AOlzAKZtBfkX06g i5xAFzmBT7EeJ2cAddcus2PHjsjIyL179xrZoE+F/SY5OTnSzdWrV4O6I3lw 8lFqLsikvSD/cgJd5AS6yAl8ivU4OQOou3aZRx991OVyPfvss0Y26FNhv/nx xx9dbsgWBXVH8uDko9RckEl7Qf7lBLrICXSRk1DxKUlJSTExMfSvH3uUDeMZ SEhIWLJkyahRo2bOnBkZGfnnn3+mVzIlJSU8PHz06NErVqy4dOmSabGaja/q 6+pOOdm1a9d4N2vWrElNTTUhyuAAnxIqGD9K7927t2fPnokTJ06ePJkO1Pv3 7wc5tBADmbQX5F9OoIucGNclOTl58eLFNDabO3cuCWTkJ/Hx8TFuLl++rFv4 6NGjtGUa0S1fvlx73Gtw7Hf9+vWNGzeOHTt2ypQp0dHRd+7cMRIzc+jQoZh0 uHv3rvHt+I3kPoVyu27dui5dumTNmpVGSk2aNPFjj7JhJAN79+6tXr26659U qFBh9erVnoUHDhyoLJYpU6awsLCghB4wBtU3ojudIH379s2TJ4+y7gUKFJg1 a5b5cZsBfEqoYPAoPXbsWIkSJZSHX8WKFakzCn6AIQMyaS/Iv5xAFzkxosvu 3burVq2qGpu1bNkyLi5O41fkJooUKcKFFyxYoFEyLS2tW7duqu337t3b62Vq g2O/DRs2PPLII8qSdFwZtFdEzpw5XemwdOlSgxsJBJl9CrlIHqYKnnnmGT/2 KBu6GZg6dWq2bNm4ygULFqxdu3bmzJn5z+zZs+/atUtZmMbqvIpG7A0aNMiV Kxf/OWbMmOBWwy+MqG9E94sXL5J54bWUnCeeeOKxxx4T5WloHawKBAB8Sqhg RKmjR48WK1aMM1O5cuUKFSrw/8uWLXv27FlLwgwBkEl7Qf7lBLrIia4uCxcu FIN2Uqdhw4b58+fnP6tUqaJxk+Lll18W4xMNn/LXX381btyYi5UpU6ZFixbF ixfnP5s2baq6eWFw7BcZGUn+hUePNFqoV68ej6+o/Jo1a3RzcuvWLVf6/Pzz z7pbCByZfUpiYmLBv+GBukN8yrx586iyJUuWjIiI4Pu8ycnJgwYN4gOjefPm ouSBAwd4Yd26da9fv05LLl26VKlSJVqSJUuWhISEIFfFZ4yob0T3YcOGccU/ +eQT8azX6tWrc+fOTQsfeuihGzduBCP+QIBPCRWMKNW+fXuX+/rVokWLeMm0 adM4UT169Ah6iCECMmkvyL+cQBc50daFBlfsSsgzbt26lcdmV65cadu2Lesy btw4rz9cvHixcmyv4VPmzJnDZd577z2+gUL/9uzZkxfSWlHS4Njv9u3bFStW pIUPP/ywmHJnz549bH9oCHHv3j3tnNB4jHf01VdfxXnwxx9/aP/cFGT2KUrK ly/vHJ/ywG3bL168qFxCRrty5cqUhMKFC4uFffr08bQk4gCW8JaKr+qnpzud XG+//bbytGWEf1HddZIB4z6lcePG/Cc1Mjt37oyNjSX10yus4VPIrFEeYmJi aDva+6Xtnzx5Mioqilpd1Sr4FE+Sk5P5khT1JsrlPLTIly+fhDbZFpBJe0H+ 5QS6yImuLtQJtmnT5sKFC8qFNFRj/9KyZUvPn5w/f56f+Kpfv76uT3n++eep QKlSpVRdNpkRWk42RLygZHDsR36Kl8yYMUO5wVWrVukGwxw+fJhLen3pwBrg U6zHvwwQzZo14yOTLfDdu3cLFSrk8vb6RrVq1VzuG8SBR2suZvmU9NiyZQuf U3PnzvUjvKBi3Ke88MILx44do0ZP3GIuWLBg3759Vbd90/Mp586do0bsscce E48LUpf3xhtveO3aUlJSOnfuLG5eZ8qUiX4YHh4uCqTnUz744INCbsaPH+9T HuRHVymqMueEjjfl8qVLl/LyefPmBTfEEAGZtBfkX06gi5z4PTbjp9DLlCnj uYqtZfbs2SMiInStQdGiRalAz549VcuXLFnCv+XwjI/9pk+fzj8ku+S1pLgo mh5RUVG8hejoaO2SwQM+xXr8y8C1a9cefvhh9tS85MyZM3z8fP3116rCH3/8 Ma+y5q6ccYLtU1auXMkVt+b1Lp8w7lMIr2+uUaOkfALWq0+ZNWtWjhw5PH/r 1dFQyyle7lMhHjbw6lPE7WkyU7o3jkMOXaXeeustl9s8qup+/fp1vgo6aNCg 4IYYIiCT9oL8ywl0kRO/fUrt2rVJFBr5q5ZTN8od5WeffXbixAltn0KdOxcY NWqUahW5DF41e/bsB76M/T755BOX+/Kj5yMZ/DhZiRIltKsmxlQ2vhUFn2I9 fmQgLi6uRYsWfLRMmzaNF+7cuZOXLFu2TFV+5syZvOrkyZOmxGwWwfYpH374 IVc8RN/NET7F5b6oQtYgMTFxyZIlwk3897//VRVWuQ+++lGyZMnJkyfv3bv3 xo0b27Zt4zS6/r4aw1y+fJmv3hCvv/46nc5paWmRkZHU5NarV0/cd/b0Kbt2 7WIrVLVqVbLPZuRGLnSV4pOxRo0anqv41dcuXboEK7iQApm0F+RfTqCLnPg3 Or1y5Qo/uvD2228rl5O5eOihh2j5k08+SX7z+PHj2j7lwd/iqrbzwG1h+DX5 4cOHP/Bl7Cf+9BwRkXVyuach0p5beO7cubyFVatWDR06tFu3biNGjCD/dfPm Tb3EmAZ8ivUYz8C8efO6d+/etGnTLFmyUPVz5849ZcoU4YuXL1/Ox4/q1vAD xV1CGqOaG3yABNWnXL16lSffK1eunJ/xBRPjPqVAgQKqiTgOHjzIU3ZUrFhR HADpPfe1cuVKVRtChwEfD/379xcLqTHkhWTulIWp1VJ+el7lU5KSkjjJZJ1O nTplsO6hha5SNWvWdKUzXTZPWfnCCy8EK7iQApm0F+RfTqCLnPg3OhUP6f3w ww/K5e3atXO5p9WKjY2lP434lEaNGvEAQPmi6O3bt9u0acO/7dq16wNfxn6b Nm3iP1X3aDZu3CjmB9PuxydOnOjyRqlSpWikYTBFAQKfYj3GM9C6dWvlgTF6 9Ohbt26JtcIp0yBW9cPIyMj07La9BNWniGkxdF8NswXjPsXrq/HNmzfn2iUn J+sW9iRv3rxUmFpO/pPMDi8pWrQozxaSHkqfcufOnYYNG9L/s2XL9ttvvxnZ byiiqxRf9erQoYPnKjpWaVX16tWDFVxIgUzaC/IvJ9BFTvwYnZ4+fZpnGa1S pYryEyc0COF+c9KkSbzEiE8RNy/q1KkTFRWVmJgYHh5OXbwYBHIPbnzsR102 z/dFXXZYWBhZkv3793/22WfKh8NpiUYFhU+hA2/w4MEfffQRP+RG0EYOHTrk U7r8Az7FeoxnYMKECa1ataKjInv27HxgVK5c+ejRo7z2+++/54X79u1T/XD9 +vW8ysYpGrwSPJ+yZcsWvuPQoEEDr7Nj2U6APmXMmDGsqZjKTNun3L59e9Wq VfSrjh07UhPKv6Xk8FpqXXmJ5yt7KoRP2b59u7gFk94EjBkDXaVKlSpFSXjp pZc8V1GGXe65IoMVXEiBTNoL8i8n0EVOfB2f3L9//4UXXuA+cd26dWJ5cnJy 4cKFXe47YmI0YsSnUGGeLknFa6+9xi/O9+3b94GPY7+IiAjP111pa7RN/r/G V+yZRYsW0UaUtR4+fDj/NtifRWDgU6zHjwycP39+5MiRPA6n0Sk/1bN27Vo+ VDZt2qQqv3DhQl5l/JOj1hAkn/L777/zGxy5c+c+cOBAQCEGjQB9yqxZs1jT JUuWaBc+d+5c//79uZ1U8eSTT3IZMS3hN998ox2S8Cmvv/662E7r1q2NVDlE 0VWqXr16lIRGjRp5ruKLV23atAlWcCEFMmkvyL+cQBc58XV80q9fP+4QVa8L kcHk5Tt27Ej6GzFF8NSpU+nPtLQ0r9skFzB58uTatWvnypUre/bsTZs2nT59 +u3bt/kVGH5x3texHw2QOnToULJkSZd7UrJ33303NjaWvUa+fPmM11cZZK1a tVzuCX8smEgHPsV6/MsAMWrUKD4Cec4HstKqgatgypQpvEq218mD4VMuXLgg 3hO35uuo/hGgTxEPwa5fv16jcEpKCndkLvfdt7CwMNrp5cuXOUXCp4SHh3MZ MS1DegifwuTJk4f/89NPPxmseMihqxR3Q1WrVvVcxfbQ80VIZ4JM2gvyLyfQ RU58Gp+IB6Lq1q2rfCH0yJEjnlcIPXnqqae0t09eQEzvKZ5/4E8G+D32U744 8Oabb7oCeIBw4MCBvCN++yaowKdYj98+5dSpU3xg9OnT54H7jWb+c8SIEaqS 77zzjss9GZ32TA7WY7pPuXHjBt8HJ4YOHWpCiEEjQJ/y/vvvczXj4uI0CvPH pLJmzaqaYF/lU0RbOmDAAO2QlD6lZs2a58+fL1u2rMv9YgvZH+3fhii6SvXo 0cPlvpSk+iQNdQ2cqGHDhgU3xBABmbQX5F9OoIucGB+fLF26lG9wFCtWTOUI jh07ZsSnPPHEE8YDmz17Nv+K75IEPva7d+9e6dKlXQHcmBOPfmm/3mIK8CnW o5uB9F6vEJ66V69evISvnKtmL6SfFy9e3GXAsFuPuT7l5s2bzz33HOfk9ddf F59qlZNAfAq1OfzEcq5cuTTm+0pNTeVs9O7dW7UFlU+hDfI8/LQR7QZN+JR/ /etf8fHxDxQTqnfv3l2/2iGIrlLiHUnVPBUzZszg5Rs3bgxuiCECMmkvyL+c QBc5MTg+oR4wW7ZsLvdDU14frb9y5colD8Rkwt9++y396dOU/tWrV3e5JzIV r+oHOPYTHwxVfunAJ3iWp+zZs1twMRw+xXp0M9CpU6cOHTp4Pr4onvsSH1sf O3YsL9m6dasotmLFCl74/fffmxx6wJjoU27duiVeYWvbtq1yqg05Me5TqPER ny9hJk+ezDXt3LmzqrDSpxw4cEDlZJno6GhqUZU+hWjVqhUX9vxWlJir4YHC p6xatUospITzws2bN+tWPOTQVYoMcsGCBan6zZo1E+6YmusnnnjC5X4AWHLL bBnIpL0g/3ICXeTESB+9Zs0antcoZ86cPg1mjLxH/8DdiSufziLISvAPv/vu O7HQ+NiPxhKqWx6XL1/mia/pQFK6DDrqqLtftGiRCGDfvn1UMiYmRhUkVZxf l7ZmPgeZfcquXbu2/02JEiUoJzRqFUtC9wNz2hlYtmwZH2mVK1cm333kyBHy yKmpqcOHD+evqBQoUODcuXNcOCkpia+KFy1adPfu3VSSDloqQEvy5s2rPd+s LRhR34judOqJD1/mz5+fmo7Vq1ev+CeUHCuqZBjjPsXl/k7KypUryatSLcaM GcNtQo4cOcRDXwQ1EZwc0WdRO8M3oyknfDwkJiaGhYXxxR+VT6FmU8wjN3jw YNryvXv3qF0im0wbEV/e8fo9+tOnT/Ps6xSnqlHNABhRSjyG169fv6tXr1LL 3717d14yevRoS8IMAZBJe0H+5QS6yImuLuvWrRMz+g4ZMoT+/OWXX5SjDo0L d0Z8CvXauXPnrlOnDvW2f/75Z0JCAmnNvX+pUqXE6yoPDI/9aHnHjh2pJPma 5ORkGjhRMRpbciQzZ85U7l18Jls8Qs8f6yFHNnLkyKioKOrraQA2a9YsGmC4 3E+XKWc5Cx4y+xT+vkN6TJgwwY+9y4B2BuhQfPXVV5U1FYNJPjBU39ahY579 C6/l/9CptHbt2qDXxHeMqG9Edzp5Ncowy5cvt6JKhjHuUzxnESRIZdU77/37 9+dVhQsXFnNuKGfl4nbM5X5arFy5cq5/+hRi6tSpogzvQvyf34F6kI5PefD3 12xd0r8W5AdGlKKRA78KpDr1mjVrlvGMm98gk/aC/MsJdJETbV1okO+1a1ZS s2bN9H5uxKeQ9/HaHZcuXXrv3r2qwkbGfmfPniUj43WbAwYMUE3VxVc+iaef fpqXLFu2jL8OI34udkQMGjRIM52mEbo+Zfz48X7sXQaMZCA8PFy8Hi5o3Lgx GWfPwosWLeKXFxga68ppUh6Y4VNY92HDhmmUYWRLgpG684WOOXPmkIPg+dKZ smXLel6oIeNQpkwZLiDm3Lhy5UrHjh3FD6nVat269YkTJ/ihQZVPeeA+i+vU qaNsfEqWLKn8ru7ixYt5uep8p0a7UqVKLvcHpKgx9C8ncmKwjaKBxIsvvigu I1AX9uqrr2IIoQSZtBfkX06gi5zo+hTlON8r9erVS+/ncXFxXMZzki4l1Pvz YySiB2/btm1qaqrXwkbGfsnJycqjiLv4+fPne27tyy+/5ALi25REfHx89+7d +QaKoHz58lZ+m09mn5JRMZ6BpKSkbdu2rVmzhkakut/iOXXqFA1lJR8xOll9 X+t+//79AwcObNiwQWNyaSoTFRW1bt06VedFTSItp4NH9Z5Lely7do3KR0RE iEcKnYxPSt28eTM6Otp4qh0FMmkvyL+cQBc5kWd8Qh0xdf2ku/JZr/QwMvaj 7ezevXvjxo2JiYkaxY4cOeL1E/P8ksumTZtoR9pbCAbwKdbj5Ayg7nZHAfSB UmaBTNoL8i8n0EVOoIucwKdYj5MzgLrbHQXQB0qZBTJpL8i/nEAXOYEucgKf Yj1OzgDqbncUQB8oZRbIpL0g/3ICXeQEusgJfIr1ODkDqLvdUQB9oJRZIJP2 gvzLCXSRE+giJ/Ap1uPkDKDudkcB9IFSZoFM2gvyLyfQRU6gi5zAp1iPkzOA utsdBdAHSpkFMmkvyL+cQBc5gS5yAp9iPU7OAOpudxRAHyhlFsikvSD/cgJd 5AS6yAl8ivU4OQOou91RAH2glFkgk/aC/MsJdJET6CIn8CnW4+QMoO52RwH0 gVJmgUzaC/IvJ9BFTqCLnMCnWI+TM4C62x0F0AdKmQUyaS/Iv5xAFzmBLnIC n2I9Ts4A6m53FEAfKGUWyKS9IP9yAl3kBLrICXyK9Tg5A6i73VEAfaCUWSCT 9oL8ywl0kRPoIifwKdbj5Ayg7nZHAfSBUmaBTNoL8i8n0EVOoIucwKdYj5Mz gLrbHQXQB0qZBTJpL8i/nEAXOYEucgKfYj1OzgDqbncUQB8oZRbIpL0g/3IC XeQEusiJXT4FAAAAAAAAALSBTwEAAAAAAADIBp77shInZwB1tzsKoA+UMgtk 0l6QfzmBLnICXeQEPsV6nJwB1N3uKIA+UMoskEl7Qf7lBLrICXSRE/gU63Fy BlB3u6MA+kAps0Am7QX5lxPoIifQRU7gU6zHyRlA3e2OAugDpcwCmbQX5F9O oIucQBc5gU+xHidnAHW3OwqgD5QyC2TSXpB/OYEucgJd5AQ+xXqcnAHU3e4o gD5QyiyQSXtB/uUEusgJdJET+BTrcXIGUHe7owD6QCmzQCbtBfmXE+giJ9BF TuBTrMfJGUDd7Y4C6AOlzAKZtBfkX06gi5xAFzmBT7EeJ2cAdbc7CqAPlDIL ZNJekH85gS5yAl3kBD7FepycAdTd7iiAPlDKLJBJe0H+5QS6yAl0kRP4FOtx cgZQd7ujAPpAKbNAJu0F+ZcT6CIn0EVO4FOsx8kZQN3tjgLoA6XMApm0F+Rf TqCLnEAXOYFPsR4nZwB1tzsKoA+UMgtk0l6QfzmBLnICXeQEPsV6nJwB1N3u KIA+UMoskEl7Qf7lBLrICXSRE/gU63FyBlB3u6MA+kAps0Am7QX5lxPoIifQ RU7gU6zHyRlA3bXL7NixIzIycu/evUY26FNhv0lOTo50c/Xq1aDuSB6cfJSa CzJpL8i/nEAXOYEucgKfYj1OzgDqrl3m0Ucfdblczz77rJEN+lTYb3788UeX G7JFQd2RPDj5KDUXZNJekH85gS5yAl3kJFR8SlJSUkxMDP3rxx5lw3gGkpOT Fy9ePGrUqLlz5+7Zs0e1NjU1NUaPW7dumV+BAPBVfYO637t378CBA/v37//r r78Cii+YwKeECsaPUjrw6MScOHHi5MmT6UC9f/9+kEMLMYJ0vgOD4EiWE+gi J7q6HDx4UHvEdebMGVH40KFD6RW7e/eu58ZTUlLCw8NHjx69YsWKS5cuaYea mJhIxT799NN58+YdO3bM15GP7pDJ1+CDiuQ+5fr16+vWrevSpUvWrFlppNSk SRM/9igbRjKwe/fuqlWruv5Jy5Yt4+LiRBlquFx6zJ49O7iV8RGD6hvXPTY2 dtKkSeXLl+f6btmyxcxwTQU+JVQweJRS71CiRAnl6VaxYsX4+PjgBxgymH6+ A5/AkSwn0EVOdHUpVKiQ9oiL9BKFc+bMmV6xpUuXqrY8cOBAZYFMmTKFhYV5 jeHmzZvUVKo2WKtWrRMnThipo8Ehk0/BBxuZfUpSUhJ3W4JnnnnGjz3Khm4G Fi5cKA6SYsWKNWzYMH/+/PxnlSpV7ty5w8WM+JSff/7ZiioZxoj6xnXv3bu3 qr6RkZHmB20S8CmhghGljh49SucmZ6Zy5coVKlTg/5ctW/bs2bOWhBkCmHu+ A1/BkSwn0EVOAvcpNELjkrdu3TI+MOvbty8vz5MnT4MGDXLlysV/jhkzRhUA tZZ16tThtZkzZ6YDo0CBAvxnwYIF09LStCtocMjkU/AWILNPSUxMLPg3pEiG 6b+0M3Dp0iV2JdQubd26le/zXrlypW3btnyQjBs3jkvSMRnnjf379/Nx/vTT T8t2m9iI+sZ1p7Obi+XOnVvjpJME+JRQwYhS7du3d7mvei1atIiXTJs2jRPV o0ePoIcYIph7vgNfwZEsJ9BFTnR1iY+P9zroev/991ma9evXc0lq1njJV199 5Vn+jz/+ENs8cOAAl6xbt+7169cfuAeBlSpVoiVZsmRJSEhQBvD2229z4Y4d O167do2W0BiP+mgyON9++61uBQ0OmYwHbw0y+xQlfJcqY/RfuhmgAWGbNm0u XLigXHjx4kX2Ly1bttTefq9evagYHYcG7wNaia/qG9R9/vz5GcmnNG7cmP+8 ffv2zp07Y2NjvT5EqutTbty4sWvXrpiYGNqO9n5p+ydPnoyKiiJHrFoFn+JJ cnIy3wJ47733lMt5aJEvXz7KfHBDDBGCdL4Dg+BIlhPoIif+jU7PnDnDw/53 331XLDx8+DD3m6tXr9b+eZ8+fTwtiTAvylsq5BGyZctGCzt16qQaEvg6G6f2 kMl48NYAn2I9/mWAaNKkCSWhTJkyGmVoWMvXJCdNmuRfeEEFPkW7DFuPF154 4dixY2RIxeN/BQsW7Nu3r+r9tfR8yrlz56jpe+yxx/hIIKjLe+ONN7x2bSkp KZ07dxYPFmbKlIl+GB4eLgqk51M++OCDQm7Gjx/vUx7kR1cpqjLnRPVw79Kl S3n5vHnzghtiiACfYi84kuUEusiJf2OzVq1akSKlS5dWPnYVFRXFSkVHR2v8 lvp0fpbM86W8atWqudyP+YklfAmaOHLkiK9BqtAeMhkM3jLgU6zHb59Su3Zt SgIdvRpl6tatyzcQ5Zz5Cj5FuwxbD8LrW2zUlIm3kx6k41NmzZqVI0cOz996 dTQRERFFihTxWlg8bODVp8yZM4cXkpm6d+9eIGmREF2l3nrrLZfbPKrqfv36 db4KOmjQoOCGGCLAp9gLjmQ5gS5y4sfYbNWqVdwVrly5Urmc/uTl2i8TnTlz 5v9t787joyjvP4AvP6yIyKmiKBS8KFCgcqmorUcBNYi1VKBQrSW2FpSCRaXU C00gKPcNkfsKGM4gEJIQXhIC5DAHYAgogRBIQggkkEC4ye/72u/L5zWd3czM bmZnns1+3n/klZ15dueZ5zs783x3Zp7hYpMnT1bN+s9//sOzxHVWbdu2dTh/ xvSohm5pd5kMVt4yyFOs510LlJaW8s/jb731VlVlRBZMG2G1qugzyFO0y4g8 hQwZMoRSg/z8/MjISJFNLFiwQFVYlX3wNtC8efPp06enpaVduHAhMTFRDO6h rEBJSUnTpk15+qBBg+jrXFZWRg1I6XC3bt3EpWKueUpSUhKnQu3ateNLZGsY 3Ui98MILtPodOnRwncW3vr7++uu+qpxfQZ5iL2zJckJc5ORF3+z55593OO8m Vv0yvHjxYj5uUiLz0UcfDR48+NNPP42IiKioqFAW27t3Lxdbt26d6pPnzp3L s44cOcJT+Ooy+jR+WVBQkJyc7N3zl7W7TAYrbxnkKdbzrgXEieClS5dWVaZ/ //5U4O6779a9H8EuyFO0y3Dq0bBhw82bNyun79+/v1atWg7nuJRif1jVdV9R UVGq/QmlKtw+I0aMEBPFHXkjR45UFr569apy16fKU2jfeN9999FLSp1ycnIM rrt/0Y1Ux44dHVUMn8vDiZvyk1cNgDzFXtiS5YS4yMnT/VVWVhYfHKdMmaKa RVMc7rRo0UJ55mX9+vU83XV84MjISJ5Fh296efr0aX45b948Oii3b99efCbl swkJCR6tqXaXyWDlLYM8xXpetMDRo0c5lW7btu21a9fclsnPz+d7rES6LSHk KdplNG6N79WrF69jYWGhbmFXd9xxBxV+9dVX+SUlOzyladOmPMZIVZR5ypUr V7p3707/05a2c+dOI8v1R7qR4p80+/bt6zqLtlWaRQcRX1XOryBPsRe2ZDkh LnLydH/Fw3xR38x1/BnR1ad4jRo16oMPPuDr9kmdOnUOHDjAxcRJk/3796s+ gTozPItPtaSmpoqshP+pX79+vXr1+P9atWqpftvUZjBP0a68ZZCnWM/TFrhx 40aPHj14I4mOjq6q2MyZM7nMDz/8YEItfQN5inYZjdQjJCSE1zEpKUm3cKVz rLBNmzbRu/r168fXtZInnniC51Lmy1OGDBmiXSWRp+zevVucghGDY9dIupFq 0aIFNcIrr7ziOota2OG8QcxXlfMryFPshS1ZToiLnDzdX/H12K+99prbuRER EXFxceIldeQ++eQTPoCKo/bChQt5Snp6uurt27Zt41k86Ja4EYa0bt36u+++ u3bt2s2bN1esWMHPoaD+gPELaXS7TEYqbxnkKdbztAWGDx/Om4f2JakDBgzg FFu2Z6YoIU/RLqOReoSHh/M6RkZGahc+efLkiBEjmjRp4nDx2GOPcRmx05s6 dap2lUSeMmjQIPE5QUFBRlbZT+lGqlu3btQITz/9tOusRx55hGb17t3bV5Xz K8hT7IUtWU6Ii5w82l9lZ2fz0dD4iJfUN3v00UcdznFyeISELVu28Ids375d VXjlypU8KzU1tVJxJ0vbtm3FNRXs448/VpY0wosuk2vlLYM8xXoetYA4Adel Sxftm5juv/9+Kvbcc8+ZUEWfQZ6iXUYjTxE3KIknSbktXFRUxAcyh/MpxmFh YbTQkpISbkmRp6xdu5bLzJo1S7tKIk9h4kTzqlWrDK6439GN1CuvvOJwDiPg OovTQ43BLgIK8hR7YUuWE+IiJ4/2V/Pnz+dDoUeXQL///vv8rkOHDtHL9PR0 1c+PwowZM3gWP1eFchN+uWjRIlVJ8SFilE5d3nWZVJW3DPIU6xlvgTVr1vAY X/fcc4/qsaQqYnS7UaNGmVNL30Ceol1GI08RT7zNzc3VKPz44487nA9MUQ2w r8pTxA2A7733nnaVlHlKx44dT5061apVK4fzxhZKf7Tf66d0I/X222/zz0qq R9LQl5Qb6uOPP/ZtFf0E8hR7YUuWE+IiJ4/2Vzx2NPXQPHrmprh6KjMzs9I5 Lg2//PTTT1Ul//73vzucN57wc9Nu3rzJTyv44IMPVCXFVqF7dYTgXZdJVXnL IE+xnsEWiIqK4vvi69evr3s6T1zGI+2IxAx5inaZqvIU2lPxFct169bVGO+r uLiY2+Hdd99VfYIqT6EP5HH46UNUj49UEXlKs2bN8vLyKhWDqwcHB+uvth/S jdSKFSu4BVSDSc6ZM4enx8bG+raKfgJ5ir2wJcsJcZGTR/urzp07UyBol+XR IoKCguhdt956qzjs8vUPqjGo6Sh/77330vQnn3xSTOSn47Vv3141BvKuXbt4 qzB+K713XSbXylsDeYr1jLQAbW+0MfAvKkaaS9y8EBMTY0olfQR5inYZTj1o l6W6IW769Om8ggMHDlQVVuYp+/bt42JDhw5Vvj0lJYWyXWWeUvnzU3Qd7p4w dfDgQfG/yFMoFxYT+/TpwxN37Nihu+J+RzdSFRUVjRo1otXv2bOnuB2Mdt1d u3aliS1btpT5HjErIU+xF7ZkOSEucvJof9WsWTOKRffu3V1npaend+zYMSMj w/Xz+fkCymEQxo8fzwdTSjfExA0bNvDEhQsXiomin0NzlR87ePBgns4/JFY6 tx86cEdERFy6dMlt5TW6TB5V3hoy5ylJSUm7f8Y3X9BRTEzx3wfM6bZAdHS0 eKT46NGj6eXGjRs3KLh2Dr/44gsun5aW5sOqV5uR6BuMe35+vpj46aef8uqH hYXxFAkHPTOepzicz0mJiooqKysrKCgICQnh/QNtFeKir8qff12hxhHHLNo7 8YWCDRo0SE5OvnnzJrUStQmfmFPlKYcPH+Zc2OG8XJA++fr167SP6t+/P30I j9leWcXz6I8ePcpjjFA9q9oT+i8jkRKX4Q0fPvzcuXMlJSXBwcE85fPPP7ek mn7AxO87eAFbspwQFzkZ753SMbd27doUiz59+rjO5Wfc3HbbbZ999llCQgId Imk/Fh4eTsdlh/NSLuXArXSI52sbmjZtykdtSlgaNmxIU+644w7lUwPoAM2/ 5NSvX5/6hFQHmjJ16lQ+6CtHsR45ciRvKsqnVBjsMnlUeWvInKfw8x2qMmnS JC+WLgPtFrh8+TJfhaiBsl3Vu4YMGcKzjh8/7tvaV4+R6BuMO/2jUYw+xOcr 4yHjeYrbDYD2iqp73keMGMGzmjRpIsbfUI7KxXs/h/NqsQceeMDxv3lKpXMs a1GGFyH+HzZsGJdxm6eQ0NBQni7z83q8YyRS1HPgW4EYJ5IO5++fNS9x85qJ 33fwArZkOSEucjLeOz116hSHY/Dgwa5z161bxw+8EwdWET7y4YcfqsqvWLFC HHxFyTp16mzZskVVklKYO++8UxzW+ddCh/Pp3pSGiGL8GyZ56qmnxESDXSZP K28B/81TjI8FJxvdPEXZXXSrW7duqnfxoMREe0ww21W/3yLirv2lq1evns9X xkNG1r1NmzYO54AelEE0btxYrE6rVq1cT6JR4tCyZUsuIMbfKC0t7devn3gj 7euCgoJ++umnMWPGOFzylErnt7hz587KHVHz5s2XLl0qCqxevZqnq77vtKG2 bt3a4Xzmo+TZsacM7qOoI/Hyyy+Lc1KUXdLXEF0IJRO/7+AFbMlyQlzkZLx3 KgYlrmrkory8vODgYD4HITz00EP8MBRXERERfAsqe/DBB12TFJaTk9O9e3c+ h8JoI1GNVPzVV1/xrGnTpomJxrtMnlbe12TOU2qqQG4BrLvx8jdu3Ni3b19M TIzGUG9UJiEhITo6WnXwys3NpemJiYkGH/x0/vx5Kh8XF3fy5EnjNaypPIpU RUVFSkqK8aYOKIH8fZcBtmQ5IS5yMn1/RSHLzMzcvn37jh07lOc7qkI5CJU0 8rtfWVnZzp07qbalpaVuC2RlZVXzwfGeVt53kKdYL5BbAOtudy1AHyJlFrSk vdD+ckJc5IS4yAl5ivUCuQWw7nbXAvQhUmZBS9oL7S8nxEVOiIuckKdYL5Bb AOtudy1AHyJlFrSkvdD+ckJc5IS4yAl5ivUCuQWw7nbXAvQhUmZBS9oL7S8n xEVOiIuckKdYL5BbAOtudy1AHyJlFrSkvdD+ckJc5IS4yAl5ivUCuQWw7nbX AvQhUmZBS9oL7S8nxEVOiIuckKdYL5BbAOtudy1AHyJlFrSkvdD+ckJc5IS4 yAl5ivUCuQWw7nbXAvQhUmZBS9oL7S8nxEVOiIuckKdYL5BbAOtudy1AHyJl FrSkvdD+ckJc5IS4yAl5ivUCuQWw7nbXAvQhUmZBS9oL7S8nxEVOiIuckKdY L5BbAOtudy1AHyJlFrSkvdD+ckJc5IS4yAl5ivUCuQWw7nbXAvQhUmZBS9oL 7S8nxEVOiIuckKdYL5BbAOtudy1AHyJlFrSkvdD+ckJc5IS4yAl5ivUCuQWw 7nbXAvQhUmZBS9oL7S8nxEVOiIuckKdYL5BbAOtudy1AHyJlFrSkvdD+ckJc 5IS4yMmuPAUAAAAAAEAb8hQAAAAAAJANrvuyUiC3ANbd7lqAPkTKLGhJe6H9 5YS4yAlxkRPyFOsFcgtg3e2uBehDpMyClrQX2l9OiIucEBc5IU+xXiC3ANbd 7lqAPkTKLGhJe6H95YS4yAlxkRPyFOsFcgtg3e2uBehDpMyClrQX2l9OiIuc EBc5IU+xXiC3ANbd7lqAPkTKLGhJe6H95YS4yAlxkRPyFOsFcgtg3e2uBehD pMyClrQX2l9OiIucEBc5IU+xXiC3ANbd7lqAPkTKLGhJe6H95YS4yAlxkRPy FOsFcgtg3e2uBehDpMyClrQX2l9OiIucEBc5IU+xXiC3ANbd7lqAPkTKLGhJ e6H95YS4yAlxkRPyFOsFcgtg3e2uBehDpMyClrQX2l9OiIucEBc5IU+xXiC3 ANbd7lqAPkTKLGhJe6H95YS4yAlxkRPyFOsFcgtg3e2uBehDpMyClrQX2l9O iIucEBc5IU+xXiC3ANbd7lqAPkTKLGhJe6H95YS4yAlxkRPyFOsFcgtg3e2u BehDpMyClrQX2l9OiIucEBc5IU+xXiC3ANbd7lqAPkTKLGhJe6H95YS4yAlx kRPyFOsFcgtg3bXL7NmzJz4+Pi0tzcgHelTYa4WFhfFO586d8+mC5BHIW6m5 0JL2QvvLCXGRE+IiJ+Qp1gvkFsC6a5d58MEHHQ7H7373OyMf6FFhry1btszh RGmRTxckj0DeSs2FlrQX2l9OiIucEBc5+UueUlBQkJGRQX+9WKJsjLfAiRMn IiMjx4wZM3fu3Pj4+GvXrrktdvnyZepDTpkyZdy4cd98882xY8dMrK25jK97 eXn51q1bJ06cSCu1fv36wsJCH1fN55Cn+AvjW+n169dTU1Ppqzd9+nTaQd24 ccPHVfMznu7ta9J+XgbYkuWEuMjJeFyoQ7J69Wrqmy1evJgCpFHywIEDS5Ys +eyzz8LDw3fv3n3z5k23xajDExsbO378+BkzZqSkpFy5csVINYqLizP0XLp0 qfoLspfIU7zj9RINFqYmjY6Ofv3112+55RbqKT377LNeLFE2RlogLS2tffv2 jv/18MMPf/vtt8piV69eHT169K233qosVrt27eDg4PPnz/twHbxlMPqUbTVr 1ky5UrfffvuECRN8X0EfQp7iLwxupdnZ2ffff79yK33kkUfy8vJ8X0G/YbAl a+R+XgbYkuWEuMjJSFySk5PbtWun6pu9+OKLubm5qpKURAwYMEBV8umnnz50 6JCqZExMzH333acsRnHXTn8YZa8OPfPnz6/+guxl/Xku40ssKCjgw5bw29/+ 1se1s4JuC8ycOfMXv/gFr3KjRo06der0f//3f/ySUpKkpCQuRu3TuXNnnl6r Vi367txzzz2irV599dWqMncbGYn+jh07xPo++eSTb775pshZlN84v4M8xV8Y idTBgwfF161NmzYPP/ww/9+qVavjx49bUk0/YKQla+p+XgbYkuWEuMhJNy4r V6687bbbOBAUne7duzdo0IBftm3bVnlu4saNG7///e95VuPGjd94440ePXrw S0o2KyoqRMn4+Hjqv3Hvjo7m3bp14/1h3bp1N2/erF1hI3nKN998U/0F2Uvm PCU/P7/Rz7jjWjOOX7otsGTJElrZ5s2bx8XF8XnewsLCDz/8kLe6Xr16cbGy srIOHTrQlKFDh545c6bS+dWgT37ggQek7Vgaif6jjz7KO4Hs7GyeUlJS0rVr V87a/PfEN/IUf2EkUn/84x/594GIiAieMmvWLG6ot99+2+dV9BNGWrKm7udl gC1ZToiLnLTjcvbsWc5KKGfctWsXd0VKS0v79OnDcfnyyy9F4Y0bN/LEUaNG Xb58mScuX76cJ06ePJmn0CxKW2jK3XffLYbESU1Nvffee2kiHeKvX7+uUWHq BOa6k5mZSdkHfcJTTz3F9azmguwlc56i9NBDD9WY45eRFqC0nVMP4ebNm23a tKFGaNKkiZh4/Phx+jqo3rtq1Sr+LlCubVKVTaO77hcvXqxduzZVfvz48crp 8fHxvFKHDx/2bRV9xnie8swzz/BL2rfs3bv30KFDbk+N6eYpFy5cSEpKysjI EPvJqtDnHzlyJCEhgfa6qlnIU1wVFhbyL1H//Oc/ldO5a1G/fn1qed9W0U94 urevSft5GWBLlhPiIifduNBBsHfv3qdPn1ZOpK4a5y8vvviimDh69GiaUq9e PdVtxbRzo+kDBw7kl5Tv8OF1zpw5ymKbNm3i6StWrPBiRYYOHepwXi3/008/ +XRB1kCeYj2v27xnz54O5+0n2pnv7t27ecMbM2aMdzX0Hd11P3XqFFd+5syZ yumUkfH07du3+7aKPmM8T+nRo0d2djbt9MQp5kaNGv3rX/+6evWqa2HXPOXk yZPDhg371a9+JS6fo0PeX/7yF7eHtqKiItpnipPXtWrVojeuXbtWFKgqT/n3 v//d2GnixIketYP8dCNFq8xt8t133ymnr1mzhqcvWbLEt1X0E8hT7IUtWU6I i5y87ps9++yzFJSWLVuKKZwp3HnnnaqSgwcPdjjvUuGXs2fP5oBSz0dV8te/ /rXyR0vj9u7dy4f+adOmiYm+WJBlkKdYz7sWOH/+/N13302N0Lp1a+2SYWFh vEGK88XyMLLufNPN888/r7zEi3rOvFL+e2mu8TyFiAxFiXaGyitg3eYp4eHh derUcX2v24wmLi7urrvucltYbDxu85RFixbxREqmZD5f7B3dSL355psOZ/Ko Wvfy8nL+FfTDDz/0bRX9BPIUe2FLlhPiIiev+8OdOnWioFCHX0wR5ylUH8jj I1G2wi//+9//Opw/D7peMjFkyBCH8z53TyvTpUsXeiP9VX6mLxZkGeQp1vOi BXJzc1944QXe7GfNmlVVMdqnrVy5kof/atGihfJeLUkYWfdx48bxmtK+uqys rNK5XpS2uO1p+xGP8hRCew9KDfLz8yMjI0U2sWDBAlVhVZskJCQ4nDc3TZ8+ PS0t7cKFC4mJifz1Ue0zS0pKmjZtytMHDRr0/fffU2vHx8fTLrdbt27iUjHX PCUpKYlToXbt2sk5rFw16UaKv4wdOnRwncW3vr7++uu+qpxfQZ5iL2zJckJc 5ORd77S0tJTPX7z11lti4qVLl/gqhTvvvHPHjh08UVy+LqbMnTuXp5w4cUL1 saGhoTSdPll1HYU27gCQ5cuXK6ebviArIU+xnvEWWLJkSXBwMHXR+ZaN22+/ fcaMGa7p8MmTJ0eMGPHaa6+1bNmSN0VqqJycHPOrXm1G1p2+LLQuvCLNmjWb MGECff0dzlMMGRkZllTTJ4znKQ0bNlSNv7F//34eqeORRx4RG0BV131FRUWp UlRKVbg9aTsRE7lVyciRI5WFqf2Vj55X5SkFBQU8sCGlTnJuY9WnG6mOHTs6 qhg+l4es7NGjh68q51eQp9gLW7KcEBc5edc7FRfpLV26VDmdUob69evzrFdf fZWOpJSz0P9//vOfRZnt27dzAdVV+rGxsXwjPPHoONu/f3+H82Z51U2ppi/I Ssq4UP/nhoe8GPkWeYrxFggKCnIofP7556pH9rCUlBRlsRYtWvzwww8mV9ok Btc9LS1NjMwsrFu3zvcV9CHjeYrb00a9evXidhCPvPRovK877riD95b8kr65 PKVp06bl5eUab1TmKVeuXOnevTv9T9HZuXOnkeX6I91I8U+affv2dZ3Ft0m2 b9/eV5XzK8hT7IUtWU6Ii5y86J0ePXr09ttvdzjHJVbdMn/16lVKSVTdmC5d uly8eFGUoUMqD8NFh9SwsDDKFDIzM0NDQ5UXb9MUg5XJz8/njtNHH32kmmXu giymjEtRUZGnz3mkt1RnicbVpOOX8RaYNGnSSy+91KlTJ/EkxzZt2hw8eFBV 7NixY/369aPGEc+EqlWr1meffeanz09Zu3Ytf9defPHFF154gc8jOJw35vjv YF+V1c5TQkJCuB3EA3S085TLly9v2rSJ3kXbBu1C+b1PPPEEz6W9K08ZMmSI dpVEnrJ7925xCkY5AGPNoxupFi1aUCO88sorrrOohflg5KvK+RXkKfbCliwn xEVOnu6vbty4IZ6KEh0drZxVXl5Oh2aHc3C2YcOG8fC/DuflVdQ3U5aMi4tz vR21cePGIsc5e/aswfrMnDmT3+L2l2oTF2QxZVwo4UpLS6Psg/q9xzVRASpG hZV39XqxRONq0vHLixY4deoUbdvcY6feaVU3nlBiEhMTw88fIeHh4SZU11S6 607fL75P8G9/+xvfQkhTaHfNa9SsWTPVcM1+pJp5CkWTGyEyMlK7MF8H2KRJ E4eLxx57jMuIu/ymTp2qXSWRpwwaNEh8TlBQkJFV9lO6kerWrZtDMWaLEv9m 1bt3b19Vzq8gT7EXtmQ5IS5y8nR/NXz4cD4gut4uNHDgQIfzKRIpKSmVzt41 JRE8FBIZN26csvCPP/7Yt2/f5s2bO5yDhv3jH/84dOjQJ598wmmO8foMGDCA 31LVY+bMWpDFVHGhHIQSEHFhSVWoABXzbuQl5CnetQAZM2YMb+Taj2Wn6PBZ YwkHcNBd9z/96U8O561nqlOoYt1VN1P4kWrmKeIi2G3btmkULioq4gOZw3n2 LSwsjBZaUlLC3yCRp4jx0zSGZWAiT2H16tXjf1atWmVwxf2ObqQ4cW7Xrp3r LE4PlTdUBjLkKfbCliwnxEVOHu2vpkyZwofCLl26qH463r9/v9ufAXNzc/km 4rp167qOD1zpvPte/P/Xv/7V4eEFfnxFzXPPPadbspoLspgqLnxKJTMzU2Os UZpFBbw7meK6RINq0vHL6zwlJyeHN/5hw4Zpl3zjjTe4pGxnH3TXnU+PBgcH u8564IEHaFanTp18VTkfq2ae8s4773BMaV+nUfjxxx93OB+YohpgX5WnZGVl 8ae999572lVS5ikdO3akvWurVq0czhtbKP3Rfq+f0o3U22+/7XAO7KB6JM2J Eye4oT7++GPfVtFPIE+xF7ZkOSEucjK+v1qzZg2P8XXPPfe4jqA1b948DpPr j/lLly7lWVFRURqfT93sX/7ylw5PTpwdO3aMP3nUqFEG3+LdgqznGhfdUyrV OZnidolG1KTjl24LVHVfibinYOjQodolOUEmXtxA5FPa606rw3d1ud4FRuh7 RLMokfFh/XypOnnK1atX+YrlunXraoz3VVxczHF/9913VZ+gylPoA/n6OvoQ 7dEIRZ7SrFmzvLy8Sud4YjzFbTpZA+hGasWKFdwCqrEd5syZw9NjY2N9W0U/ gTzFXtiS5YS4yMng/oqOgHwLbf369VNTU10L8LMVateu7TrwEZXnCFIuo7EI 8UBP5ZMItIlruVUjEmvzYkHWc40Ln1LJyMhwe0qFJtIsr0+muF2iETXp+KXb Av379+/bty8/OkRJXPu0ePHiSuegWNRpVw1gW+m8mYWfi6F8OqokdNf9ySef dDiHzlCdSL1w4QL1k2lWr169fFtFnzGep3To0EE1qOD06dM59AMHDlQVVuYp +/btU2WyLCUlhQdIFHkKeemll7jw5MmTVdVQjtUg8hTaDYqJffr04YliHPia RDdStHE2atSIVr9nz57iSmBK97p27crfu6ouDw40yFPshS1ZToiLnIzsr6jH xeMa3XbbbVUVpixS2VVToqMtzxLPI6NjvWqgrZKSEh6YmgKt/BWRtgo6HEdE RLgd91XcwRoTE+O2VsYXJBu3ceFTKgUFBa7laWJ1TqZUtUS3kpKSdv+Mr7uj o5iY4r8PmNNugXXr1vHG1qZNG8q4s7Kybt68WVxc/Mknn/BTVBo2bHjy5Ekq Sb1Zh3Nor3feeWfr1q3UINeuXaNP/s1vfsOf8P7771u3VsboRl9c8xkUFCRO p54+fVrcSj979mwrKuoDxvMUh/M5KVFRUZSr0jcuJCSEh1CoU6eOuOir8ufH ztKXQhyzaD/GJ6MbNGiQnJxMW05+fn5YWJgY5FmZpxw+fFiMIzdq1Cj65OvX r6enp1OaTB+SmJjIxdw+j/7o0aM86DrV0+0+068ZiZS4DG/48OHnzp2jHX5w cDBP+fzzzy2pph8w0pI1dT8vA2zJckJc5KQbl+joaDGQ7+jRo+nlxo0bNyjw D3fl5eW8K6tXr97y5cvFJRDr16/nQYybN2/OP0XSrH79+t1yyy3jx48vLCyk ibt27aK+Hy9i7ty5yqWPHDmSp7u94OSLL77guWlpaa5zPVqQbNzGpapTKtU/ mVLVEt3i5ztUZdKkSV7XwV7aLUBty4M2CKIzyVmJuKyRPqRx48ZiFvUt+Uoe 9sQTT1QnTD6iG336NomUhFackv2uXbvyV5v84Q9/kHCwZYOM5ymugwc6nCeR Vfe8jxgxgmc1adJEfFWVo3KJ7YFyCr67R5mnVDqHMVRuM5wIM3EPlNs8pfLn h9hWtc/0a0YiRT0HvhVIfDH5n549e9a8xM1rRlqypu7nZYAtWU6Ii5y040J9 e7eHZiXqsXBhOlyKntu999771FNP8X2dDmfHZu/evVzs+PHjfPWL6yH4vffe U/XA+ZdJQp/mWr0hQ4bwXLfnETxakGyqiovbUyp8MoWvUTd9ia60j18TJ06s TjVsZKQF1q5dy8OkKz3zzDPJycnKYkVFRe+8845qBNoGDRqMHTtW+SwheRhZ 92vXrlGHXIzgx+66664ZM2bIfGpSl5F15983Fi1aRBmEMgmlXZzrFVa0J+TB Q8ihQ4d4Ymlpab9+/cQb69SpExQU9NNPP/FFg6o8hdA3unPnzuIg6HD+1KN8 ru7q1at5OpVUvpF22q1bt3Y4nxtVnROsEjK4j6KOxMsvvywORnQIGzBgALoQ StXPU/x3Py8DbMlyQlzkpJunKLv3bnXr1k2Uz87OFhdIC71791Y9Aq+wsFAZ ZT4Eu73H5KuvvuIC06ZNc50rft+u6rkVxhckm6ri4npKRZxMqWZf0XieUlMZ bwFKDBMTEzdv3kw9Uo1H8FBE9u3bt3Xr1tjY2MOHD6tG9JWK8XW/cePGsWPH qHMeFxd39OjRGnA5rqdbPq0yhTUmJsZ1OBFlmYSEhOjoaNXBKzc3l6bTxqO6 z6Uq58+fp/LU1HxJYYDzKFJ0UEhJSTHe1AEFe3t7YUuWE+IiJ1/sr8rKylJT U7dt20Z/XW86FqjLnZycTF24/Px8jU/Lyso6cOBAdepjcEFS0YhLXl6e8pSK KSdTtJcYIAK5BbDudtcC9CFSZkFL2gvtLyfERU6Ii5w04nL16lVxSsWskyna SwwQgdwCWHe7awH6ECmzoCXthfaXE+IiJ8RFTtpxEadU8vPzTTmZorvEQBDI LYB1t7sWoA+RMgta0l5ofzkhLnJCXOSkHRc+pfK9kyknU3SXGAgCuQWw7nbX AvQhUmZBS9oL7S8nxEVOiIucdOPCp1TMOpliZIk1XiC3ANbd7lqAPkTKLGhJ e6H95YS4yAlxkZNuXPiUSnp6ullDwmJLCOQWwLrbXQvQh0iZBS1pL7S/nBAX OSEucjISl7y8PI2RUX2xxJotkFsA6253LUAfImUWtKS90P5yQlzkhLjIyfq4 YEsI5BbAuttdC9CHSJkFLWkvtL+cEBc5IS5yQp5ivUBuAay73bUAfYiUWdCS 9kL7ywlxkRPiIifkKdYL5BbAuttdC9CHSJkFLWkvtL+cEBc5IS5yQp5ivUBu Aay73bUAfYiUWdCS9kL7ywlxkRPiIifkKdYL5BbAuttdC9CHSJkFLWkvtL+c EBc5IS5yQp5ivUBuAay73bUAfYiUWdCS9kL7ywlxkRPiIifkKdYL5BbAuttd C9CHSJkFLWkvtL+cEBc5IS5yQp5ivUBuAay73bUAfYiUWdCS9kL7ywlxkRPi Iie78hQAAAAAAABtyFMAAAAAAEA21ucpVwAAAAAAAKqAPAUAAAAAAGSDPAUA AAAAAGSDPAUAAAAAAGSDPAUAAAAAAGSDPAUAAAAAAGSDPAUAAAAAAGSDPAUA AAAAAGSDPAUAAAAAAGSDPAUAAAAAAGSDPAUAAAAAAGSDPAUAAAAAAGSDPAUA AAAAAGSDPAUAAAAAAGSDPAVkkJubGxERMW/evJUrV5aVldldHQAAAACwmfx5 yoULFwqqsHPnzpycHLezTp8+7btGS0tLKy0t9d3n2+jEiRN5eXkGC587d+7o /6J0w7vlTps2LSQkZMqUKbNmzbp8+bLbMtTm+U4UX+X0ioqKY8eOpaenU+Uv XbqkeldxcfEPP/ywf//+oqIi7+omHDlyxKO4Z2dnX7x4sZoLtXG5AAAAADaS P0+hTOQrz61cudJHLXb48GH+fNcusf8qLy+Pj4+fN28eJQtTp041+K7vv/8+ 5H9Ry3ixdEo96L1LliypKkO54kxGKIURCxLJFL2XcxwWHh5+9uxZnkWfFhMT ExoaKuZu2LCB1tSLGhLKdCZMmGA87rydLFq0iGru3RLtXS4AAACAveTPU6hH Ot8FdUep88YpyfTp010LfPvtt75ortOnT1OvmJdLfWBfLMIWpaWltEZhYWEe 5SmJiYlUftOmTXt/lpKS4sXS09PT6XO2b9+uUea7775TJkTHjx+/4jzXNmnS JFWuRNsGvyUpKSnEBaUqXtSQlJSUUKJkMO5iO9m5c6d3i7N9uQAAAAD2kj9P cUW90wULFlBnbNmyZRMnTqR/MjIyzGoQu5Z75MiRtLS0y5cvHzx4cMuWLfv2 7bviPCOQlZUVGxu7bdu2zMxM8Ys61eT7778/dOiQeDv9n5qaeu7cOX5JeQe9 zMnJof/PnDlDvVbq5dJf3Wu6xo8fbzxPiYuLo54/L0VXVety4sQJynToc1av Xk11Vl3Txaj7zTmUKk8RmcjKlSvz8/MpQ+GXP/74I83l00OEsieqJOWz/PLk yZMGV1AlNzeX40711ygmtpM1a9ZonCGSf7kAAAAANvLHPGX9+vXUGfv666/L y8upu07/T5o0yfhdFXIulz6cutCUoXBfOjo6mpayfPlyZed8/vz5fFHT+fPn Q0NDp0yZIt5O/1MB6ufzS/qHXiYkJFCfnFIP5Ydon/LwKE/59ttv6QNPnTql W1JjXThJEZKTk13fHhERwXNnz56tzFMoPeGXnCvxeRmydetW6qiPHTs2xHkp GnfaRVKTmJhocAVdGYm7cjvxekGSLBcAAADALn6Xp1Ankzpj1JcuLCzkKdQp pSlz5szx6b3tvl4u5ykkNjaWkgvqw1OqQi8XLVpESywuLl69ejW9/Oabb7j8 woUL6SWffeD7O8iaNWt47rp160KcN3FwKhEfH3/x4kX6nKioqJKSEo1qeJSn 0OLowykBoUZYvHgxhfXChQtuS2qsy5kzZ/iars2bN9OKi1NCQlZWFq8dZSuR kZHKPGXu3Ln8kpdLb+eX9Pn0cuLEifySlijqQKp5TaB23F23E7PYtVwAAAAA W/hXnvLjjz/yvSHZ2dliYkVFxbJly3x6b7sFy+U8RaQhlFaMHTs2LCxMpBU0 ZfLkyVSGhzLbsWMH/Z+UlET/7969m89QTJo0ic8dUH+Veun0/4YNG2jWnj17 DFbDozxl1apV9OGULHDSRChbcb3iSHdd+DwIZSuui6AEhG+THzduXFFREbWP Mk+hxqf/6cO5MOUjPPfrr7++ojjbQislLvoiK1asMLiCbmnE3e12Yha7lgsA AABgCz/KUzTuEfboXmM5l8t5irjX48SJE/SS+qXKMhs3bqSJWVlZ9P+xY8dE XkMd15kzZ+7atYvPoZw6dUqcWzly5Ahf/rRgwYLU1FTd4Wpd85Tc3NwYd2hB ZWVl9PlcjJKICRMm0IIOHjyo+kzdddHIU/gWGD6ZQo1DCQi/TElJoSXyTfSh oaGcHNEUnrto0SJ6WVhY6HqXPVm3bp12I+hyG3cL7mG3a7kAAAAA1vOXPEX3 HmGD9xpLu1zOUyj74JeHDh2il5GRkcoy27ZtC/n5BhOqDOUFtOiKioovv/xy 06ZNx48fD3Hek0IFQhT3oVBCsWrVKh6elzq0bm9UF1zzFEolZrtz+PBh1Xv5 GjPX3rLuumjkKeLWeFdbtmwRacv58+epMLdAiOL6N8qkkpOTqVfPY5Hx3B07 dmi0gEGquFt2D7tdywUAAACwmL/kKUbuEfbFPfWWLVeVp5w+fZpezps3T1mG bycXpzD4Zg1+iMm+ffs4c1m2bBl/FN+UIZw5c2bt2rW6Vz15dN2XCg9T7Jpu 6K6L13mKuAyMT+Ls2bOHX8bGxvLbKX8RvXdacZ7LY6lVnzLuVt7DbtdyAQAA AKzkF3mK8XuEzb233crlqvKUK857TGiKOG2Rn58fGhpKeURZWRlP4UG9+OmH fPs59dv5XoyZM2dymRMnTogqUaedSopZbhnPU6hNKFMQN85XVFTwOMBub5HQ XheNPKWoqChfQQwaRrlGSUmJuMV+7ty59CE86BmhkvTes2fPTps2benSpUlJ SZyjEWocEx+AyHGfPHmy7+5hpxYucMHpCaUqvHRqc1UBvvEHAAAAwH/Jn6d4 dI+wife2W7xc1zxl//79NCUsLCwmJiY2Npbv/lCOqXvmzBnue4tTFcnJyTyF h7SiLIC6svTGuLi4lJQU7uTTglyXTnO/deLLw/h/vle9KjyE8owZM6ivnpCQ wEnKwoUL3a6+9rpo5CkqqvvoL1++LB6SIoixCFQjITOqie5SjBNx99097Dk5 OV95jrZDX1QGAAAAwDLy5ykFBQXh4eHG7xHme41pEdW8XN/i5UZFRYU474JX TszIyBCD63755Zd79uxRfficOXNo1rZt2/glX2FFDhw4ID6BUgnRS6euu9sz PmK8X+Ndeuqib9++nTMORh+iMeixxrpkZmZ6l6dccZ4kEvnI2LFjKQsTp0uK i4upuz5u3DhxJsX1nprq47j77h522iTmu/P1119T41Nw3c6t5tjLAAAAALaT P0+54nxKoEedf3FlVDXZtVwlqsApp+qkXZSbnDx50hfPl7l06VJhYSF9uJF1 N2Vd3KKlUx3cPr2F0hZKOc+cOWPuEpWKiopsuYfdruUCAAAAWMAv8hQAAAAA AAgoyFMAAAAAAEA2yFMAAAAAAEA2yFMAAAAAAEA2yFMAAAAAAEA2yFMAAAAA AEA2yFMAAAAAAEA2yFMAAAAAAEA2yFMAAAAAAEA2yFMAAAAAAEA2yFMAAAAA AEA2duUpAAAAAAAA2pCnAAAAAACAbKzMUwAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAGTz/zCJeyI= "], {{0, 204.}, {540., 0}}, {0, 255}, ColorFunction->RGBColor, ImageResolution->144.], BoxForm`ImageTag["Byte", ColorSpace -> "RGB", Interleaving -> True], Selectable->False], DefaultBaseStyle->"ImageGraphics", ImageSizeRaw->{540., 204.}, PlotRange->{{0, 540.}, {0, 204.}}]], "Output", TaggingRules->{}, CellChangeTimes->{3.834335072802125*^9, 3.834405780068575*^9}, CellLabel->"Out[14]=", CellID->1289005572] }, Open ]], Cell["\<\ Use of this alternative matching method results in the average treatment \ effect being smaller than in the prior example:\ \>", "Text", TaggingRules->{}, CellChangeTimes->{{3.834319863251855*^9, 3.834319877251977*^9}, { 3.834320083608941*^9, 3.8343201057491207`*^9}, {3.8343350974851713`*^9, 3.8343351647270927`*^9}, 3.8344057947921753`*^9}, CellID->691414126], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{ RowBox[{ InterpretationBox[ TagBox[ DynamicModuleBox[{Typeset`open = False}, FrameBox[ PaneSelectorBox[{False->GridBox[{ { PaneBox[GridBox[{ { StyleBox[ StyleBox[ AdjustmentBox["\<\"[\[FilledSmallSquare]]\"\>", BoxBaselineShift->-0.25, BoxMargins->{{0, 0}, {-1, -1}}], "ResourceFunctionIcon", FontColor->RGBColor[ 0.8745098039215686, 0.2784313725490196, 0.03137254901960784]], ShowStringCharacters->False, FontFamily->"Source Sans Pro Black", FontSize->0.6538461538461539 Inherited, FontWeight->"Heavy", PrivateFontOptions->{"OperatorSubstitution"->False}], StyleBox[ RowBox[{ StyleBox["MapReduceOperator", "ResourceFunctionLabel"], " "}], ShowAutoStyles->False, ShowStringCharacters->False, FontSize->Rational[12, 13] Inherited, FontColor->GrayLevel[0.1]]} }, GridBoxSpacings->{"Columns" -> {{0.25}}}], Alignment->Left, BaseStyle->{LineSpacing -> {0, 0}, LineBreakWithin -> False}, BaselinePosition->Baseline, FrameMargins->{{3, 0}, {0, 0}}], ItemBox[ PaneBox[ TogglerBox[Dynamic[Typeset`open], {True-> DynamicBox[FEPrivate`FrontEndResource[ "FEBitmaps", "IconizeCloser"], ImageSizeCache->{11., {2., 9.}}], False-> DynamicBox[FEPrivate`FrontEndResource[ "FEBitmaps", "IconizeOpener"], ImageSizeCache->{11., {2., 9.}}]}, Appearance->None, BaselinePosition->Baseline, ContentPadding->False, FrameMargins->0], Alignment->Left, BaselinePosition->Baseline, FrameMargins->{{1, 1}, {0, 0}}], Frame->{{ RGBColor[ 0.8313725490196079, 0.8470588235294118, 0.8509803921568627, 0.5], False}, {False, False}}]} }, BaselinePosition->{1, 1}, GridBoxAlignment->{"Columns" -> {{Left}}, "Rows" -> {{Baseline}}}, GridBoxItemSize->{ "Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}, GridBoxSpacings->{"Columns" -> {{0}}, "Rows" -> {{0}}}], True-> GridBox[{ {GridBox[{ { PaneBox[GridBox[{ { StyleBox[ StyleBox[ AdjustmentBox["\<\"[\[FilledSmallSquare]]\"\>", BoxBaselineShift->-0.25, BoxMargins->{{0, 0}, {-1, -1}}], "ResourceFunctionIcon", FontColor->RGBColor[ 0.8745098039215686, 0.2784313725490196, 0.03137254901960784]], ShowStringCharacters->False, FontFamily->"Source Sans Pro Black", FontSize->0.6538461538461539 Inherited, FontWeight->"Heavy", PrivateFontOptions->{"OperatorSubstitution"->False}], StyleBox[ RowBox[{ StyleBox["MapReduceOperator", "ResourceFunctionLabel"], " "}], ShowAutoStyles->False, ShowStringCharacters->False, FontSize->Rational[12, 13] Inherited, FontColor->GrayLevel[0.1]]} }, GridBoxSpacings->{"Columns" -> {{0.25}}}], Alignment->Left, BaseStyle->{LineSpacing -> {0, 0}, LineBreakWithin -> False}, BaselinePosition->Baseline, FrameMargins->{{3, 0}, {0, 0}}], ItemBox[ PaneBox[ TogglerBox[Dynamic[Typeset`open], {True-> DynamicBox[FEPrivate`FrontEndResource[ "FEBitmaps", "IconizeCloser"]], False-> DynamicBox[FEPrivate`FrontEndResource[ "FEBitmaps", "IconizeOpener"]]}, Appearance->None, BaselinePosition->Baseline, ContentPadding->False, FrameMargins->0], Alignment->Left, BaselinePosition->Baseline, FrameMargins->{{1, 1}, {0, 0}}], Frame->{{ RGBColor[ 0.8313725490196079, 0.8470588235294118, 0.8509803921568627, 0.5], False}, {False, False}}]} }, BaselinePosition->{1, 1}, GridBoxAlignment->{"Columns" -> {{Left}}, "Rows" -> {{Baseline}}}, GridBoxItemSize->{ "Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}, GridBoxSpacings->{"Columns" -> {{0}}, "Rows" -> {{0}}}]}, { StyleBox[ PaneBox[GridBox[{ { RowBox[{ TagBox["\<\"Version (latest): \"\>", "IconizedLabel"], " ", TagBox["\<\"1.0.0\"\>", "IconizedItem"]}]}, { TagBox[ TemplateBox[{ "\"Documentation \[RightGuillemet]\"", "https://resources.wolframcloud.com/FunctionRepository/\ resources/MapReduceOperator"}, "HyperlinkURL"], "IconizedItem"]} }, DefaultBaseStyle->"Column", GridBoxAlignment->{"Columns" -> {{Left}}}, GridBoxItemSize->{ "Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}], Alignment->Left, BaselinePosition->Baseline, FrameMargins->{{5, 4}, {0, 4}}], "DialogStyle", FontFamily->"Roboto", FontSize->11]} }, BaselinePosition->{1, 1}, GridBoxAlignment->{"Columns" -> {{Left}}, "Rows" -> {{Baseline}}}, GridBoxDividers->{"Columns" -> {{None}}, "Rows" -> {False, { GrayLevel[0.8]}, False}}, GridBoxItemSize->{ "Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}]}, Dynamic[ Typeset`open], BaselinePosition->Baseline, ImageSize->Automatic], Background->RGBColor[ 0.9686274509803922, 0.9764705882352941, 0.984313725490196], BaselinePosition->Baseline, DefaultBaseStyle->{}, FrameMargins->{{0, 0}, {1, 0}}, FrameStyle->RGBColor[ 0.8313725490196079, 0.8470588235294118, 0.8509803921568627], RoundingRadius->4]], {"FunctionResourceBox", RGBColor[0.8745098039215686, 0.2784313725490196, 0.03137254901960784], "MapReduceOperator"}, TagBoxNote->"FunctionResourceBox"], ResourceFunction[ ResourceObject[ Association[ "Name" -> "MapReduceOperator", "ShortName" -> "MapReduceOperator", "UUID" -> "856f4937-9a4c-44a9-88ae-cfc2efd4698f", "ResourceType" -> "Function", "Version" -> "1.0.0", "Description" -> "Like an operator form of GroupBy, but where one also specifies a \ reducer function to be applied", "RepositoryLocation" -> URL["https://www.wolframcloud.com/obj/resourcesystem/api/1.0"], "SymbolName" -> "FunctionRepository`$ad7fe533436b4f8294edfa758a34ac26`\ MapReduceOperator", "FunctionLocation" -> CloudObject[ "https://www.wolframcloud.com/obj/6d981522-1eb3-4b54-84f6-\ 55667fb2e236"]], ResourceSystemBase -> Automatic]], Selectable->False], "[", RowBox[{ RowBox[{ RowBox[{"(", RowBox[{ RowBox[{"If", "[", RowBox[{ RowBox[{"#treat", "==", "1"}], ",", "\"\\"", ",", "\"\\""}], "]"}], "&"}], ")"}], "->", RowBox[{"(", RowBox[{"#re78", "&"}], ")"}]}], ",", "Mean"}], "]"}], "[", "matched2", "]"}]], "Input", TaggingRules->{}, CellChangeTimes->{{3.834319880013258*^9, 3.834319911987419*^9}, { 3.834320062431499*^9, 3.834320078041237*^9}, 3.834335108794467*^9}, CellLabel->"In[15]:=", CellID->563201765], Cell[BoxData[ GraphicsBox[ TagBox[RasterBox[CompressedData[" 1:eJztnQl0TVfbxy9insXQhRpCDE0UialFUVNL0CXmqUXzoS2K1rgQQ4m+NTZd qJKIlAqCRGL6KBEiCWqOKZRELCQxNC8hhu//3md1f+c9996Tc+Le6+h5fmvp unfv5zx73733f+/nOedkteawsT3/J7/JZJpQBP/pOXRSu/Hjh07xLYMvvcdM GDVijN/nH4/5xm+E3/gWwwqgcAX+NcpnMv3n80uGYRiGYRiGYRiGUcHvDMMw DMPY4CFjGGjGX3dcxshhPRoT1qM+YT0aE9ajPmE9GhPWoz5hPRoT1qM+YT0a E9ajPmE9GhPWoz5hPRoT1qM+YT0aE9ajPmE9GhPWoz5hPRoT1qM+YT0aE9aj PmE9GhPWoz5hPRoT1qM+YT0aE9ajPmE9KnDo0KHIyMjdu3e/7o7YH9ajPmE9 KtC6dWuTyVShQoXX3RH7w3rUJ6xHBViPxJMnTxISEgIDA+fNm7d3794HDx5Y 2uTk5Bw9evRfZnbs2HH37l0Fh7dv3968ebO/v//WrVszMjJsmWnyKYDZH7nx +PFjTZZOQ6ser1279oGZ06dPO3jJvP7OsB7B/v37y5cvb5Lg4uISEBDw4sUL Mnj69Ono0aOLFy8utSlduvTKlSutOpwwYYLUMl++fJC5zEarTylLly415caq Vas0WToNrXrEfkX9xAdHr5nX3hnWI85EqI8GuVy5cnXq1IF86OucOXNgkJ6e 3rZtWyrJnz9/kyZN6tatKxZzSEiIzCFURlXQWosWLYoWLUpfZ8+eLWy0+pSh RmUbN27UZOk0tOoxOjpaP3p0dGcMrserV6+SGHEw7dy5kw7EtLS0jh07vvPO O5mZmfg6bdo0moIpU6aIeDIyMrJYsWIodHV1zcrKEg5PnTpFxt7e3n/99RdK EKxC4ygpUKBASkoKmWnyaQl+3Z/WOHnyJMm/ZcuWz58/12TpNLTqMTQ0VKUE MLy7du1CSGlZlZSUhPnFdOfaHAJRiO7SpUuv0hnMKYKuI0eOYAkpN3fr1q09 e/Yga0CKhK+0SxtWj35+fqQU6EhajiUKD/T52bNnw4cPX7NmjexaoSlMjSj8 6quvZNJ7KRGpOCI1+VTPqFGjcC1EffnyZXtZ2h31ely2bJmbm1vJkiVpTPCh jBnsdWTw7rvv4uvy5csvXrzo4+NTqFAhmCH1EB6wyJEpvPXWWyIYqFKlSlBQ kKwhmK1duxZnE7ZlYYmrkPur7wyBuYamChYsSGboEjpmdYuIi4tr2rQpoiOy hKsZM2Z89NFHhtUjJEMz2Lt37zysqwMHDtBIYn6pBClh2bJlUYIZkRl7eHig vEaNGlp9qgfzS5O7ZMkSe1k6AvV6FLuTDHd3dzLAeOLrxIkTvby8RG3nzp2p 9vbt24hzRHmRIkXE58mTJ4tWUlNTPT09rTaEzCUiIkJlZ8Bvv/0mBIu4C9sy fa5cufL58+elPy04OFjaHxnG1CPyJvr5sbGxeVhX27dvp8s3bdpEJdgGqWTh woUy40mTJlHVv//9b00+1YON2mSOk8VtqFe3dATq9YjQEQMyePBgGpOAgIDt ZkSsSHokGjRogGQZGxpmk2qhU6rq2bMn4lVySAkaxCK9QdqmTRsU+vr6Ykkg YECwKu7IIZ5X2RnMPu3GSDdCQkLS09MRtQYGBpLucKFo7sqVKyVKlCA/n332 GcJanO+4pHr16kbW44IFC2gPzM7Ofml+9JCYmIixUplPjR8/nkZPhKY4d6hk y5YtMmPEVFQF/5p8qiQmJoYuXLdunb0sHYTW/HHmzJnUYcuUTejx/fffx+KX Vp04cYKCny5dukjLka+VK1cO5UhVRCHSt4MHD8qcixtuN27cUNOZQYMGmcx3 57AnSMuRpJjMKQyUTiVC1F9//bWsb/Xr1zesHr/44guTOU24c+dOnz59SpUq RaOEDQ26UL6jcv/+fQQhMK5Zs6YoDA8PJw+YEZl9WFhYrmexVZ8qQf9pHmlv sYulg7C7HrGjHjt2TFZFKgBnz56VVVHu3KJFC+V2586dSx4OHz6ca2eQgdK9 uIEDB8r8QM50iQh96XBEngsByoyNfH+1a9euJvM9Dax/GrGKFSuKhx3NmzdX OChHjhxJZqGhoaJQHIKIbWT2+/bts3V0KvtUw82bN+kGwtSpU+1l6TjsrkfM lOVV/fr1M5lvm/+vBXSQVapUyfKq+Ph4BL3QVKNGjURIiW02186IW3Zjxoyx bJGq4BmWSCTp66effmrZASPrUXoTYPr06VirKMSW1aFDByr8+eefrV6I449k iz1WmoKtXr2aLkSwJLtk165dVBUZGanJpxp+/PFHcRbYy9Jx2F2PCFYtr4Kg TIoggJTaY+5sXbJ58+ZcO7Nhwwbl5miNwVIEUTjBLbttZD127tyZRgYbl7Q8 IyOD7pLhALW8Csk+vcyDg1X2lCQqKoocYkuUXfXrr79SFVJUTT7V0LdvX5P5 Dnyuma96S8fhHD02a9YMVYULF37fBh07dhTG8+bNoyaQcvbq1WvNmjUIgEW0 o0aP4rmku7u7rRaDg4MfSpS7YMECy25/8MEHJnOcpmZk3ixy1eOIESPw26tV q2ZZ1b17d1TVqVNHVo5Ms1atWjSelq+14FikKmSLsqply5ZRleWNGmWfaqhS pQqubdeunR0tHYdz9NizZ0+TOV6l5+wKbNq0ifxDwtIXBhAdqdcj9EvlkLZy c8hGyXLs2LGWtUY+H+fPn28y3w2gF2mkDB8+nGZTWpiVlYVgkgbTav6VlpYm IhNZ1eeff05tPX36VJPPXBEPWSZOnGgvS4eiVY/+/v7Ubdl9y4eKehSPC/fs 2aPsn94JAcnJydJyq3q01ZnMzExKzNu2bavcHDZk8oDw2LLWyHpEKkcjY/mw r3379ihv0qSJKHn06BGOFbIfMGCArXgP4YrJ/CxMWoh8kF4RwcqRlqv0qUxE RAR5yPX5hXpLh6JVj+IV3EWLFsmqFPQYExND+binpycSEAX/Pj4+tFWK5xEg PT29f//+lnpU6AzdHgQUlypADzWA9P0fcPDgQXoWY0w9Yv3Tm6VeXl5SLVy5 coX2umHDhlHJ48ePxU2ebt265eTk2PJJZy44dOiQKMSwU+Hq1atFoXqfkG1I SMj69eut/mHUypUrycnu3bsVfqwmS4eiVY/btm2jbnt7e8fGxiL+FE83FPQI KB8BDRs2RFv3799H4cWLFydPnoxL7t27R2bjxo0jM0w3QgiIF+PTuHFj099I 9ajQmXPnztEjDxcXFwRI9NQSut6+fXubNm2Qkwon2A/JCexXrVqFE/PChQsL Fy4Uf3pgTD2CoKAgGoEePXpg6F6a3zCn+2wFChSg26TZ2dnizk+pUqV27NiB g3Xrf4NIlRziA72gjpQ8Pj4eJyOESW9FlihRQgTGmnyKlwSsBrSzZs2i2uPH jyv/WPWWDkWrHiEc5PhCHVi0hQoVQtL9MDc9pqamilzAZH6sLH09FXsjmcXF xdGbAybzKSkeeNWrV89SjwqdARCX9C04V1dX8Xpq7dq1hROouFOnTiZrVK1a 1ch6xKlET6NoLipVqiRGBgkI2YiUQYHw8HDhMzQ0VLy4KCa3cOHCUVFRwkaT T3q9zWR+d8vyJ4inltevX1f+seotHYpWPT40B5/iXTKT+R0Y7HIop+wAMb+t C3Emzp07l4JAAVS8ePFibL/CDFOG/VMqioCAABiQmqR6VOgMgb0OOaCQIUCs 5evrm5CQIOsYsnipeCHYjRs3BgYGUgdUjswbhBo9Ehh86ZThs/QvEG29SCxF qjWA2PLtt98WtW5ubjIDTT7pvT6Tjde/6REGQFir/DPVWzqUPOjxofmGCQ6y sLCw6Oho6QtsKjl//jziRlxr9U8tHppfPt+/fz8CFYSdr94Z8oaYJzExUeFP rlB15MiRLVu2IF5V/1veUNTr8aX5lgsyi3379mE67PVsLjk5GZNil8MIvTpz 5syr+9EDedMj86ajSY+M02A9GhPWoz5hPRoT1qM+YT0aE9ajPmE9GhPWoz5h PRoT1qM+YT0aE9ajPmE9GhPWoz5hPRoT1qM+YT0aE9ajPmE9GhPWoz5hPRoT 1qM+YT0aE9ajPmE9GhPWoz5hPRoT1qM+YT0aE9ajPmE9GhPWoz75nWEYhmEY RV53DMU4D5rx1x2dMXJYj8aE9ahPWI/GhPWoT1iPxoT1qE9Yj8aE9ahPWI/G hPWoT1iPxoT1qE9Yj8aE9ahPWI/GhPWoT1iPxoT1qE9Yj8aE9ahPWI/GhPWo T1iPxoT1qE9Yj1a5fPlypJnr16+/7r44BNajPmE9WmXlypX0f6/esWPH6+6L Q2A96hPWo1VYj+Du3bt/2ODGjRtWL7l58+bWrVtnzZoVHByclJT04sULW87V WObk5Bw9evRfZjAR6I+Wpe3shuzCG6fHa9eufWDm9OnTjmuF9QgCAgJMNvDw 8JAZP3r0aNCgQTKzRo0aIfLPg+XTp09Hjx5dvHhxqU3p0qUxL+rXttMasiNv nB6xj9Gg4YPjWmE9gkmTJtnSY/369aWWaWlpXl5eVJU/f/569ephSdPXMmXK oDlNlunp6W3bthU2TZo0qVu3rmg6JCREzcJ2WkP25Y3TY3R0NOvx1VGjRz8/ P4xA5cqV/7QAQaDUcvjw4TRcvXv3fvDgAUqeP3+O9YxzZ8WKFVotp02bRjZT pkwRoWNkZGSxYsVQ6OrqmpWVlevCdlpD9uVV9IhJ2bt3b0JCwv37923ZZGZm Hj58eP/+/bdv31bvdteuXWfOnMEwWtaGhoaq12NsbCzM0AdZ+dWrV3fu3Hnu 3DlbF7IeQa9evTAC3t7eymaQZ8GCBWHZp08fWXaGhZEHy2fPnkFNa9askTUk 5IM5tUuXXr0hu5M3PQYFBdWuXVuc7Nhz+vbti8xOagNBdejQoXDhwiIeQPwA YVq6QvwAb/i8fv36Zs2aFShQgC7Bzrxnzx5huWzZMjc3t5IlS1ItPpQxgwUj dVWnTh18Xrp0KYzJctGiRcLJDz/8UKVKFdHzcuXKjR07FnGLrFesR/Dhhx9i BLp06aJsNmrUKBor7G/2srTKgQMH6HJMtE4asjta9ZiRkfHJJ5+YrFGrVi1h tmHDBiEcKdDad999J3UoVv64ceMs7YsUKSJkLnYtGe7u7lJX2BjXrl0rNcDw ohYHNPYHqx48PDySkpKs9srIemzcuDFGYOjQocpmyCVhhrHNdbGpt7TK9u3b aVI2bdqkk4bsjlY9zpgxg7qKk+WXX365cuXK6dOnp06d6uLigkiSbJKTk3FO kdnEiRNPnjx56dKlJUuWQFwoyZcvHwmEECvfZE60Z82ahSATnUFyTYXTp08n SzSEgRo8eDCVBwQEbDcjAlfhCp3BuTxs2DDEvZs3b6Z4dcKECVSLAzQiIiIl JQWHtUjnP/74Y+nPZD2C6tWrkx4RbIwygw8nTpyQmVG2hTVAX9PS0uLj42WR qlZLq4wfP54mBXOnbOm0huyOJj3+8ccfFH+WLVv27Nmz0ipkZOJz//796ed8 //33UpuoqCgqb9SokcgNxcqvWbPmsWPHpG1RuY+Pj9TJzJkzqdwyfxSuIPmw sDBpFTxTQoFWbty4Icpx3AtJ4ky3dGVkPZYqVcoylsDYDhkyRKztO3fuUPmK FStCQkI8PT2FZYMGDWJiYoQ39ZZWQYvIX2gGlS2d1pAj0KTHOXPm0O+aPXu2 LRv8nKJFi8KmWrVqljdSOnbsSB4gNyoRKx8nncy4atWqKEfWKS1Uo8fevXvL qnDsUlVgYKCsCqckVSH3t3TFekQghCPS398fkQnNLBg4cCDZJCYmiqVOH5Cn iMd5EC8GUKulVUaOHEmWCMOUu+20hhyBJj2Kgw9hqi2bU6dOkc2XX35pWfvT Tz9RrTiMFFY+PT+iWz0CNXrctm2brKpfv35UlZqaatkrEj6mz9KVkfWIsVq0 aNG9e/dEyeXLl7HN0shgH0MJIv+/D5//JALIRHJycl68eIHFTOJ1c3PLzs7W ZGkJjKEj2LRo0ULhnR/CaQ05Ak16RJyJrmLbVLCB0GgoZMEqIUJWkRUqrPzm zZvnTY+Wrho2bGgyh9lW+9yqVSvUFipUCGsvV1f/DNTo0Srh4eE0MnPnzsXX uLg4+lq/fv1bt25JLcX9NxxYmixlXLp0qXz58qhFVojdPtceOq0hR6BJj02b NjWZAxgFG/F8UPqUQbB7926qnTJlCpU4R4/NmjVDOUbbap/btGljMt8FEgE2 69EWuJZGBiEHvmLB01fLp3gnTpygqvXr12uylIJksFatWlS7ceNGNT10WkOO QJMexZOOlJQUWzbHjh0jm7Fjx1rWinWORFtW4lA9+vr6UhUmy7JXNWrUQFW9 evXUuPpnkGc9PnnyhB4QYzHgK4I6um3+zTffyCyxSGgMFy9erMlSkJWVhbiR qsTN0lxxWkOOQJMexQuNlndFBAj5EPjBpm7dupYv2AhFi1upWvXo7+9P9tKH Jrm6QnhMVUFBQbKq+Ph4qurZs6caV/8M8qzHhIQEGhlMBJV4e3vjq6enpyzh OnTokBhDrZYvzW+Dt2vXjsoHDBjw/Plz9Z10WkN2R5Me9+3bR92uWrWq7A91 r127Jh55+Pj4kBlWtdTm4MGDlCy7u7urydSs6nHp0qVkbxkPK7iKjY2lppFT SN/cw47RtWtXumrVqlVqXP0zyFWPWJnffvttTk6OtBDLm17aAZGRkVS4bt06 Ktm6davUeOjQoVQu/jhLveXjx4/FyxvdunWTdUMK1IRYC/EnLhGFjmjIOWjS I8A0Uedr1qwZERGBqBtKXL58uaura/v27cnm3Llz9EAWgc38+fNhgCgxODhY PM+KiooSDrXqcdu2bWSPPRAqg6DUHLVgxIgRVOvl5RUTE4MNAdlE9+7dqbBV q1ZSY2QQVL5ixQo1w/LGoazHsLAwMSyhoaF//vknTo0zZ8507tyZylu2bPns 2TMyxgfKvEqWLInZgSVKEBDmz5/fZI46hFuVltnZ2aIhrBnMJrS/9b9JS0sj Y/HsXhpnOqIh56BVj8nJyR4eHqa/oR9IlChRQvxNIpYxRa2WTJgwQepQqx6h I3HLHRQtWhQNYVtQdgVSU1PFOz8yqlevDm1KjbECqQpbisLD1jcXZT1il+vU qZN0iFxcXMTnihUrYo+V2iMOxIYsZkQ8pqxQoYLsL0HUWIqURIHw8HAyptDU ZN4iHNqQc9CqR5Ceno5NSfonnJisHj16XLhwQWp29OhR2fp3c3OzfDKIhI5q pa+OE61btzZZ6BHgdKNXuQjsCRh8ZVdEZmYmdlHsG+JaaHnIkCGYIEvjQYMG 0W7j6+urcmTeIHKNVxGarl27VvwJoZhoPz+/jIwMS3ts1O+99550f0baInvc oNLS1ovKUhBikfGCBQuoZMmSJQ5tyDnkQY8EtlCcKVu2bDlw4MDdu3dtmeFU gjqio6MxOK+8iP4fKCsuLg5hFTxL339TA3p+/Phx9Pzw4cNYWgqWSUlJaCIx MfHVOqtH1N/PSUlJwQa4d+9eTLetx+iCh+a7BPAsfYvgFS1zBfkRYmknNOQE 8qxH5o0mz/dXGYfCejQmrEd9wno0JqxHfcJ6NCasR33CejQmrEd9wno0JqxH fcJ6NCasR33CejQmrEd9wno0JqxHfcJ6NCasR33CejQmrEd9wno0JqxHfcJ6 NCasR33CejQmrEd9wno0JqxHfcJ6NCasR33CejQmrEd98jvDMAzDMDZ43ac0 wzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAM wzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAM wzAMw/wT+D87pNVk "], {{0, 76.}, {152., 0}}, {0, 255}, ColorFunction->RGBColor, ImageResolution->144.], BoxForm`ImageTag["Byte", ColorSpace -> "RGB", Interleaving -> True], Selectable->False], DefaultBaseStyle->"ImageGraphics", ImageSizeRaw->{152., 76.}, PlotRange->{{0, 152.}, {0, 76.}}]], "Output", TaggingRules->{}, CellChangeTimes->{ 3.834319912367325*^9, {3.8343200521146507`*^9, 3.834320078495665*^9}, 3.8343348503133087`*^9, 3.834335109392777*^9, 3.834405785159857*^9}, CellLabel->"Out[15]=", CellID->226984003] }, Open ]], Cell["\<\ The increase in median income caused by the treatment appears more robust \ against changes in the matching methodology:\ \>", "Text", TaggingRules->{}, CellChangeTimes->{{3.83432401417966*^9, 3.834324035219223*^9}, 3.8343351714772253`*^9, {3.834335203103218*^9, 3.8343352566092377`*^9}, { 3.834405804793447*^9, 3.834405805254977*^9}}, CellID->958695327], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{ RowBox[{ InterpretationBox[ TagBox[ DynamicModuleBox[{Typeset`open = False}, FrameBox[ PaneSelectorBox[{False->GridBox[{ { PaneBox[GridBox[{ { StyleBox[ StyleBox[ AdjustmentBox["\<\"[\[FilledSmallSquare]]\"\>", BoxBaselineShift->-0.25, BoxMargins->{{0, 0}, {-1, -1}}], "ResourceFunctionIcon", FontColor->RGBColor[ 0.8745098039215686, 0.2784313725490196, 0.03137254901960784]], ShowStringCharacters->False, FontFamily->"Source Sans Pro Black", FontSize->0.6538461538461539 Inherited, FontWeight->"Heavy", PrivateFontOptions->{"OperatorSubstitution"->False}], StyleBox[ RowBox[{ StyleBox["MapReduceOperator", "ResourceFunctionLabel"], " "}], ShowAutoStyles->False, ShowStringCharacters->False, FontSize->Rational[12, 13] Inherited, FontColor->GrayLevel[0.1]]} }, GridBoxSpacings->{"Columns" -> {{0.25}}}], Alignment->Left, BaseStyle->{LineSpacing -> {0, 0}, LineBreakWithin -> False}, BaselinePosition->Baseline, FrameMargins->{{3, 0}, {0, 0}}], ItemBox[ PaneBox[ TogglerBox[Dynamic[Typeset`open], {True-> DynamicBox[FEPrivate`FrontEndResource[ "FEBitmaps", "IconizeCloser"], ImageSizeCache->{11., {2., 9.}}], False-> DynamicBox[FEPrivate`FrontEndResource[ "FEBitmaps", "IconizeOpener"], ImageSizeCache->{11., {2., 9.}}]}, Appearance->None, BaselinePosition->Baseline, ContentPadding->False, FrameMargins->0], Alignment->Left, BaselinePosition->Baseline, FrameMargins->{{1, 1}, {0, 0}}], Frame->{{ RGBColor[ 0.8313725490196079, 0.8470588235294118, 0.8509803921568627, 0.5], False}, {False, False}}]} }, BaselinePosition->{1, 1}, GridBoxAlignment->{"Columns" -> {{Left}}, "Rows" -> {{Baseline}}}, GridBoxItemSize->{ "Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}, GridBoxSpacings->{"Columns" -> {{0}}, "Rows" -> {{0}}}], True-> GridBox[{ {GridBox[{ { PaneBox[GridBox[{ { StyleBox[ StyleBox[ AdjustmentBox["\<\"[\[FilledSmallSquare]]\"\>", BoxBaselineShift->-0.25, BoxMargins->{{0, 0}, {-1, -1}}], "ResourceFunctionIcon", FontColor->RGBColor[ 0.8745098039215686, 0.2784313725490196, 0.03137254901960784]], ShowStringCharacters->False, FontFamily->"Source Sans Pro Black", FontSize->0.6538461538461539 Inherited, FontWeight->"Heavy", PrivateFontOptions->{"OperatorSubstitution"->False}], StyleBox[ RowBox[{ StyleBox["MapReduceOperator", "ResourceFunctionLabel"], " "}], ShowAutoStyles->False, ShowStringCharacters->False, FontSize->Rational[12, 13] Inherited, FontColor->GrayLevel[0.1]]} }, GridBoxSpacings->{"Columns" -> {{0.25}}}], Alignment->Left, BaseStyle->{LineSpacing -> {0, 0}, LineBreakWithin -> False}, BaselinePosition->Baseline, FrameMargins->{{3, 0}, {0, 0}}], ItemBox[ PaneBox[ TogglerBox[Dynamic[Typeset`open], {True-> DynamicBox[FEPrivate`FrontEndResource[ "FEBitmaps", "IconizeCloser"]], False-> DynamicBox[FEPrivate`FrontEndResource[ "FEBitmaps", "IconizeOpener"]]}, Appearance->None, BaselinePosition->Baseline, ContentPadding->False, FrameMargins->0], Alignment->Left, BaselinePosition->Baseline, FrameMargins->{{1, 1}, {0, 0}}], Frame->{{ RGBColor[ 0.8313725490196079, 0.8470588235294118, 0.8509803921568627, 0.5], False}, {False, False}}]} }, BaselinePosition->{1, 1}, GridBoxAlignment->{"Columns" -> {{Left}}, "Rows" -> {{Baseline}}}, GridBoxItemSize->{ "Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}, GridBoxSpacings->{"Columns" -> {{0}}, "Rows" -> {{0}}}]}, { StyleBox[ PaneBox[GridBox[{ { RowBox[{ TagBox["\<\"Version (latest): \"\>", "IconizedLabel"], " ", TagBox["\<\"1.0.0\"\>", "IconizedItem"]}]}, { TagBox[ TemplateBox[{ "\"Documentation \[RightGuillemet]\"", "https://resources.wolframcloud.com/FunctionRepository/\ resources/MapReduceOperator"}, "HyperlinkURL"], "IconizedItem"]} }, DefaultBaseStyle->"Column", GridBoxAlignment->{"Columns" -> {{Left}}}, GridBoxItemSize->{ "Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}], Alignment->Left, BaselinePosition->Baseline, FrameMargins->{{5, 4}, {0, 4}}], "DialogStyle", FontFamily->"Roboto", FontSize->11]} }, BaselinePosition->{1, 1}, GridBoxAlignment->{"Columns" -> {{Left}}, "Rows" -> {{Baseline}}}, GridBoxDividers->{"Columns" -> {{None}}, "Rows" -> {False, { GrayLevel[0.8]}, False}}, GridBoxItemSize->{ "Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}]}, Dynamic[ Typeset`open], BaselinePosition->Baseline, ImageSize->Automatic], Background->RGBColor[ 0.9686274509803922, 0.9764705882352941, 0.984313725490196], BaselinePosition->Baseline, DefaultBaseStyle->{}, FrameMargins->{{0, 0}, {1, 0}}, FrameStyle->RGBColor[ 0.8313725490196079, 0.8470588235294118, 0.8509803921568627], RoundingRadius->4]], {"FunctionResourceBox", RGBColor[0.8745098039215686, 0.2784313725490196, 0.03137254901960784], "MapReduceOperator"}, TagBoxNote->"FunctionResourceBox"], ResourceFunction[ ResourceObject[ Association[ "Name" -> "MapReduceOperator", "ShortName" -> "MapReduceOperator", "UUID" -> "856f4937-9a4c-44a9-88ae-cfc2efd4698f", "ResourceType" -> "Function", "Version" -> "1.0.0", "Description" -> "Like an operator form of GroupBy, but where one also specifies a \ reducer function to be applied", "RepositoryLocation" -> URL["https://www.wolframcloud.com/obj/resourcesystem/api/1.0"], "SymbolName" -> "FunctionRepository`$ad7fe533436b4f8294edfa758a34ac26`\ MapReduceOperator", "FunctionLocation" -> CloudObject[ "https://www.wolframcloud.com/obj/6d981522-1eb3-4b54-84f6-\ 55667fb2e236"]], ResourceSystemBase -> Automatic]], Selectable->False], "[", RowBox[{ RowBox[{ RowBox[{"(", RowBox[{ RowBox[{"If", "[", RowBox[{ RowBox[{"#treat", "==", "1"}], ",", "\"\\"", ",", "\"\\""}], "]"}], "&"}], ")"}], "->", RowBox[{"(", RowBox[{"#re78", "&"}], ")"}]}], ",", "Median"}], "]"}], "[", "matched2", "]"}]], "Input", TaggingRules->{}, CellChangeTimes->{{3.834319880013258*^9, 3.834319911987419*^9}, { 3.834320062431499*^9, 3.834320078041237*^9}, {3.834324007096959*^9, 3.83432401055797*^9}, 3.834335136561735*^9}, CellLabel->"In[16]:=", CellID->222557448], Cell[BoxData[ GraphicsBox[ TagBox[RasterBox[CompressedData[" 1:eJztnQV0Fde3xgcnePCFE9wlWIuEFmuRlkUIBQoU0scDKjgUDa4PCxYoFqRQ tLiEQJFAIMEhIWiACBZcQ5D3/Wevd9505t7J3AgZOvu3Vrvu3bPnnHPPnO+c vc+ZtMU9+7T579SSJA3IiH+16fbbF/37dxvqngNfPHoP6NWjd/f/+rr3wO49 uvev45kGxgX4p2oqSfrP5w8MwzAMwzAMwzAMY4C/GYZhGIaxw1PGMtATT+m4 jFHDerQmrEdzwnq0JqxHc8J6tCasR3PCerQmrEdzwnq0JqxHc8J6tCasR3PC erQmrEdzwnq0JqxHc8J6tCasR3PCerQmrEdzwnq0JqxHc8J6tCasR3PCerQm rEdzwnq0JqxHc8J61OHw4cPbtm3bs2dPSjck6WE9mhPWow7169eXJClPnjwp 3ZCkh/VoTliPOrAelbx58+a0TGhoqPZqXFzcsWPH/kdm+/bt9+/ft1nI+fPn T9sB5SfM0x7Pnj3z8/ObNGnS7Nmzg4KCYmNj9f2joqL++uuvMWPG+Pr6Xrx4 8f3790ZqSVoc1WN4eHgDmXPnziXzkEn5xrAelXh5eUkyJUuWVNqhjl9//TVz 5sySguzZsy9cuFBbSMaMGSU7rF+/PmGeNkGWUaBAAeVdBQsWDA4Otun88uXL Tp06qWqpWrXqlStXHOmhJMBRPWIOpNbiQ3KPmRRvDOtRcOrUqbRp02r1GBMT 07BhQ7KnTp26Ro0aZcqUEUN6xYoVykJevXplT2Jg7dq1CfC0yb59+1KlSgXP 9OnTY7quWbMmNd7JyQlrt8o5Ojq6evXq4ieULVsWkwl9zZEjB/rKeC8lHkf1 uHPnTvPoMbkbw3okEOlVrlxZyEGpx+HDh5Nx6NChIkbdtm1bpkyZYMyVK9fz 58+FMwJCcp46deoNDS9evEiAp5bXr1+XKlWKHtzJkyfJiJUxf/78MLq4uLx9 +1bp/+OPP1JdHh4eT548geXdu3eYSbDiL1iwwGAXJRWO6nHVqlUGJRAREbF7 926ElNpLCM537dp1/fr1eKtDIArRXb58OTGNwTjZv3//0aNHHz58qF/d7du3 kXEgQ8FzwVea+VmPI0eORD9gxalVq5ZKjxjbGM9Lly5V3SJ0ikcjjBcuXCAj BKtfo3FPLYcPH6Z758+fr7Rv3bqV7Bg2wgh1p0uXDsZ27dqpEsbHjx87WnXi Ma5HJMWYW7JmzUo/Ch9yyLi6upIDplB89fHxuXTpUsuWLREqwC137tyiBAzy iRMn0jRFIKRftmyZqiK4LV++HGuTCBsA7kKubbwxxNmzZ6Ep6nCKXtAwm1NE YGAgohqEKyJQQbr01VdfsR6xxFCw101G0uSPNjlw4AD1JJ6vMB46dIiMQUFB +rcb99Qyb948uvfOnTuqSxUqVIDdzc1NWHr16kXOISEhjlaUHBjXo5jxVCA2 IIdixYrh6+DBg0U0Dpo1a0ZX796926RJE2FXZutDhgwRtURGRlasWNFmRZif McUZbAz4888/hWAxotKkSUOfkeaHhoYqf5qvr6/O7oGV9YhItVKlSuiEfPny Icz4/vvvJWN63LJlC/Wecu9FGG/evGnw9ng9tSByptGi3SDt2bOnJK8CwlKu XDlYGjdu7GgtyYRxPSJ0RC917tyZOmry5MlbZESsSHok8BC9vb0xSQYEBNBV 6JQutWnTBvEqFUgJGsSi3CDF9AWju7s70vYrV64gWB0wYADdW7duXYONwSLo 7OwsySkMcoGYmBgMp7lz55LucKOo7urVq1myZKFyunbtirAW6ztuKVq0KOtR zHuYCfG1Q4cOBvXYv39/uhFpizBirRSlDRs2DKstIuHVq1e/fPlSdbtxTy2I 0LRVE+PGjZPkTRtxYkJ5Lqqgr9HR0cePH0+RSJVwNH8cNWoU/Vhtyib0+Pnn n2PwKy+dOnWKwtfmzZsr7cjXcubMCXv37t2FEenbwYMHVYWLTbxbt24ZaQxt X6PnMSco7WPHjoUdayWUThYh6r59+6raRpOnZfUYHBwsIlWyGNQjxjOdNRQv XlxpnzFjhs0IpHDhwphLE+apxd/fn5wxPJR2Pz8/JycnunTt2jVY7t27R18X LFiAGVgZmGFBQcysX1FykOR6RJxw4sQJ1SVSAUCerrpEAXydOnX06x0/fjyV cOTIkXgbgwyU5j3EV6pyIGcx8ZKFFkfkuRCgytnK+6uvX7+mbKtIkSK06/jB sB4pLJT+uXPyQaEydCzipYEDB1arVo0sGTJkOH/+fAI8tSDGpv3VdOnSTZw4 EdI7c+YMVkbcKOQGywd5whHqow9IcMRBKkay9nAkuUlyPdauXVt7V/v27SX5 jNhfAy1kSE+0dyFyQNALTVWtWlWElJs2bYq3MWfPniV77969tTXSJZQMTySS 9PWHH37QNsDKehRZ2L59+4TRiB4RkNDZH+ZYbQaHmHPv3r3i67t370aMGEGP oEGDBgnz1IIbtRsCyF9oHIIHDx58UOy4gtKlS6PlcXFxaDOmEVpJXVxcMC/p 15W0JLkeEaxq74KgJF0QQCr9lyxZYu+WDRs2xNuYNWvW6FcHkI/AE+qmr1jB tc22rB6DgoJo+6tt27bRClq3bg0jHjR91d6IZB+RBnwQn2BWNDICITR61lCQ 6mQwwZ7UkjZt2hQqVAj+RYsWRUIUFhZGisYiSD6BgYH09JGYIDpS3i4SZ3uv 9CQTH0ePdG6FgOFzOzRp0kQ4I8agKpByYjwsXboUAbBI0o3oUZxLIm6xV6Ov r+9ThXKnTJmibTbmYVzKmzevkZ75tNDXI7o93gkNICNT3oV0rESJEnQp3ldo lIj9OkgmqTwFr169Ep+7dOmCe5En0ldokErTnp+eOnWKLmGZNvw7koCPo0fM VJIcr9I5uw7r16+n8iFh5QsDv//+u3E9Qr9kh7T1q0M2Sp59+vTRXrXs+iji On2U6dXz588RoJJdbFcaRASilNYliacWLKnIhXFvixYtyILQlMJa5Kcq54iI CKpo5syZjlaUGBzV4+jRo6mdqn3Lp7p6FKs/ZlT98hFXkCfScKXdph7tNebh w4f0DkDDhg31qxPdjkBIe9WyekTS9MAWrVq1kuRdU/oqTg1evnz5xRdfUE92 7NgRgaVDg7B58+aSHA7F+4cbxj21iKl+8eLFwujq6irJK6Yq1RUv+XzkLR1H 9ejt7U3tnDFjhuqSjh4PHTpEOT5+OJ6jTvktW7aU5G0EcR4BYmJiaCdBpUed xmAOpEsUl+pAhxpA+f4POHjwIJ3FWFCP9rC5n4OAsHHjxtSHEGxcXJzNexEB Vq5c+fTp09oRSGMD0nDU84M8FaxYsQJRpTIuxXyiWkAxRdMruMgllVpeuXKl ePpKf3oTSZLP1/T7JGlxVI+bN2+mdqJPAgICEH+K0w0dPYIePXrQjVWqVEFd jx8/hvHSpUtDhgzBLY8ePSK3fv36kZunp2d4eDjEu2fPHrHXrdKjTmNCQkLo yCNt2rQjR46kU0voesuWLW5ubsgXRCHiicB/0aJFWDGRm0yfPl2cVbEeBVo9 YuQ3a9aMOipbtmxYTbZt2/bXP6Gdn/Lly0vyVoyXlxfmZ8gHz2vhwoW4i2bg Xbt2UZnGPT8oXjwQQTJWOg8PDzz3SZMmIUNEC7HYlS1bltx8fHyUvwhBLOW8 WbNmxXDCyg4LYlR6cxJ5luOSShSO6hHCoSCcwKBF8IBE/ml8eoyMjBT5BfW2 8vXUJUuWkFtgYCC9OUA9T/MhEP2p1KNOYwDEpdz0zpUrl3g9FSNKFIJn3bRp U8kWtDvHehRo9ShSBh02bdoEz40bN9IMSaRJk0Y8XDBo0CBRpnHPD/8XcEry u1tkuXnzZt68eZW3i899+/bVbsxCrRgbYggp5+GoqChHuyiROKrHp3LwKd4l k+R3YPCLYKdDWOQR9m7Emjh+/HgKAgVQMaYjrFzCbdWqVcr+hCgmT54MB1KT Uo86jSFOnjyJHFDIUJLPiN3d3YOCglQNGzx4sFK8GHJr166dO3cuNcBgz3xC JEyPXbt2RYdg/RIWey8SK9mxYwc5I0pB2EPLnADLk/aPOIx7Tpkyha7OmjVL GLEsir9oEKMIgZC933Xt2rXPPvtMOU5wu+oE5OOQAD0+lTdMsJCtW7du586d yhfYDBIaGoq4Effa/FOLp/LL5/v370fwg7Az8Y2h0vAog4ODdf7kCpeOHj2K yRnxqvHf8omSMD0mCZTc+fv746HoL0AGPTFIbL6xExsbe/z4cT8/P4PL3FN5 0wDdgrjLiH9ykDA9Mp86KahHRgfWozVhPZoT1qM1YT2aE9ajNWE9mhPWozVh PZoT1qM1YT2aE9ajNWE9mhPWozVhPZoT1qM1YT2aE9ajNWE9mhPWozVhPZoT 1qM1YT2aE9ajNWE9mhPWozVhPZoT1qM1YT2aE9ajNWE9mhPWozVhPZqTvxmG YRiG0SWlYyjm40FPPKWjM0YN69GasB7NCevRmrAezQnr0ZqwHs0J69GasB7N CevRmrAezQnr0ZqwHs0J69GasB7NCevRmrAezQnr0ZqwHs0J69GasB7NCevR mrAezQnr0ZqwHs0J69EmV65c2SZz8+bNlG5LssB6NCesR5ssXLiQ/r/V27dv T+m2JAusR3PCerQJ61EQERGxbt26UaNG+fj47Nu3Ly4uzqbbs2fP/Pz8Jk2a NHv27KCgoNjYWJ0yz58/7+vr6+XlhX4+cuTI+/fvbbq9fv366NGjM2bMmDBh wtq1a8PDw4002NEmoTGn7fDmzRuHakw8n5we8VAayJw7dy75amE9gpMnT1as WFH6JyVLlkQYr/Lcs2dPgQIFlG4FCxYMDg7Wlnn//v3vvvtOVWa9evXCwsKU bhDCkCFD0qdPr3RLkyaNp6fnkydPjAxs403KmDGjZIf169cbqSsJ+eT0eOzY MeorfEi+WliPc+bMSZcuHXVCjhw5qlWrljp1avoKmaDzhScWzVSpUpEd82TN mjXTpk2Lr05OTug9ZZnv3r1r1KgRFeLs7Ny5c+fGjRvT11KlSr18+ZLcoqOj q1evTnaUXL58+Xz58gmNtG7d2t56moAmvXr1yp4YARZlh9SUeD45Pe7cuZP1 mHji1SPiSfz8QoUK7d27FzqC5fbt24MGDaJuadq0KbkhpISUYMmTJw/WUzJi GcqfPz+MLi4ub9++FWVu3ryZbh88eDBuJOPKlSvJOH36dLKgeZUqVYKlV69e MTExH2Qho7XFixcnTwSxOi13qElRUVFU5tSpU29oePHihQNaSgoSo0f8Fjws ROaPHz+25/Pw4UMkCPv37797967xYnfv3o2oHpGJ9uqqVauM6zEgIABuaIPK fv369V27doWEhNi7kfUI/vjjD5KDAAtT2bJl0S05c+Yky+HDh6mj5s+fr/Tc unUr2fG8hBEhKCyZM2dWJaH169eHvUOHDsJy8+ZNiFfVnjVr1lCZ3t7eOs12 qEkXLlwgozYITxESpsdly5YhjxDLOnoYSQEyO6UPBIVoJEOGDOSDaAdBCISp LQrhEErD59WrV9eqVQtpAt2C+B/5uPBEVo7JLWvWrHQVH3LIuLq6KosqXbo0 PuORwZk8Z8yYIQqZNm0a8gjRcoyrPn36YNSpWsV6tEeTJk0kOZWjVWbevHnU UXfu3FF5VqhQAXY3NzdhwXoHS65cuVSe3bp1k+QsUr9qTOxU16hRo3TcHGrS oUOHyBnLin7tHwdH9fjgwQME8JItSpQoIdwwlQnhKMFznDBhgrJAMfL79eun 9UeuLWQ+fPhwm/UiOFEWhaxn+fLlSocDBw7gKhZoka2owGO6ePGizVaxHpUg YkEQiG7BpEeWoUOHSnKWp83pevbsKcm7KMIiVihV1bRrBFXq1z5x4kS6HfO2 jptDTdqyZQuViRVZv/aPg6N69PLyovZjZVm8ePHVq1fPnTs3bNgw5MsIA8jn 2rVrWKfIDZnCmTNnLl++PGvWLNrIQkeRQAgx8iV532DMmDEIMtGYGjVqkHHk yJHkiYrQe507dyb75MmTt8iIwFUUhcZgXfb09ETcu2HDBopXBwwYQFcxljAw IiIisFg3bNiQjF9//bXyZ7IetSCfatasGXXL3Llzyejj40MW9KfKf9y4cZIc F4lTg1evXmXLlk2Sl0h0Phn37dtHJQiLFqzFCJ5pu7Vw4cJi58cmDjUJMRU5 Y0hgGGNOwHiD3vWrSD4c0uPp06cp/nR2dkbgrbyEjEx8RiJAvxE5stJnx44d ZK9atarIDcXIR7Z+4sQJZV1kb9mypbIQxCpk1+aPoihIft26dcpLKJl2C1HL rVu3hB3LvZAk1nRtUaxHX19fzGxffvkl5RGZMmVC4iCWHn9/f+ooVQyJRMPJ yYkuYX4WdsSHInBCoLVixQpoE5/bt2+vrToyMhLZRNu2bYsWLUq3INNUlmYT h5qEXEayBVSPqd5I/yQtDumRphcwduxYez6PHz+mX12kSBHtRgplHwByI4sY +fj5KudChQrBjqxTaTSiRw8PD9UlLLtiYlddwrRMl9q1a6ctivXYvHlz5UAd PXo0ljlxNTY2ljYzMd0hnsQ4RziEcSL2DQAswh8LE6SnGvyurq42dzKR06k0 glUg3gY71CShRygdsdzAgQOrVatGFvifP3/eSBclIQ7pUSx8CFPt+Zw9e5Z8 fv75Z+1VkWuLxUhn5NMhFG31CIzocfPmzapLYgxgytW2ioRfqVIlbVGsx2nT piGYxygVp/Nly5YNDQ0VDnv37tUeqSOCEn2OIIQ8nz171qBBA0nei/vll1/o AEKSA0jkQdqqw8PDMbVCKWILDpEPPOM9fzTeJIDoFP7i67t370aMGEFuaK2R LkpCHNIj4kw0EimAjo/YlFYFq4QIWUVWqDPya9eunTA9aouqUqUKPRGbba5X r54knx0/evQo3qL+HSQgf7xz5w60QOfsLi4uygzr8uXLbdq0oWkNsWX37t3D wsJoVEN6wo3m85w5c9JmJhayOXPm0AYRmDBhgr2qIcA9e/bQ8AN4OvG21mCT bAJJUl0QtfKw8iPgkB5r1qxJ/anjI84HlacMAvQqXR06dChZPo4ea9WqBXvu 3LltttnNzU2Sd4FEgM16tIfo/0WLFmmvKkPZLl26wK1ixYr09dy5c3TjzJkz lbfcuHGD0kOkOdoTCiW3b9+mF3WUG6TxotMkHcTun+pFvuTGIT2Kk46IiAh7 PidOnCAfZOLaq2KcI5FXWZJVj+7u7nQJz1TbqmLFiklyGGakqH8HCdYj0jHq GQScOm5YVooUKQK3Fi1akGXBggV0o/ZkQZxPxbuFInbXVS8qGEHbJB1EyKpM fj8CDunxt99+o0Zqd0UECPko0ShTpoz2BRuhaLGV6qgeR48eTf7KQ5N4i0J4 TJeWLVumunT8+HG6hPDGSFH/DuLVo70c7fr169QzvXr10rl9/fr15LZ48WKy IByV5ANo5YJFBAcHkzM0q187LXDg7t27OrUbbJIOtIuFkfyR/8TDIT2KoyKE 5ao/1EXqLY48WrZsSW4Y1UqfgwcPUvZRqlQpI5maTT16e3uTvzYe1ikqICCA qi5XrpzyzT3MGJgt6S4EYEaK+ncQrx7btWtHE5TKLuITzGxkef36tWoRQdhf uXJlSU7cxHj28/NT3SiYPn06XaIXU0+ePJk/f37Vi98f5AQ2b968VKwwIo1F rLV69WqlzA026dSpUzCePn1aKwoaLa6urjpdlBw4pEfQsWNH6rrixYtv3br1 3r17UKKPj0+uXLkaNWpEPiEhIZkyZaLJcNKkSXBAlOjr60tnwWDHjh2iQEf1 KN5JRl9BZRCUkaUW9OjRg65Wr1790KFDmBDwOL755hsy1qtXT+m8du1aMWMb 6ZZPDn09bty4kX4+Ynj0AB4oFqz79+8jiqNTyOzZs0dGRn6QFzIPDw+k3njQ eMoQwuHDh+kdV4CBIcp89uwZbZNmzpx55cqVYgXctGkTjRZM8vSSOb1MDkX8 9NNPO3fuxCOOi4tDa2lTDiC5E8X279+fjMOGDSOL8SaVL19ekjdtvLy8MCSg aNSFUUQDFQ3YtWtXUorNAI7qEekDvQRIiL/BAVmyZBF/k4iHqPrjNQE6U1mg o3qEjigLIJycnFARpgX9ogDGj3jnRwXmTGhT6Xzjxg26hOGnc9j66aKvx9jY WNVfKSqfJgaqSPQQJtGaJbpLfO7bt69qcxLLnygHK2DdunUpc6fyAwMDxZh0 dnZWjjH6aymiTp06yr8sxrRMdpTmaJMw7dBUIDxpWSQGDRqURCJzAEf1CJBK Y1LCLCdaju769ttvw8LClG7Hjh1TjX8XFxftyaB4YUn56jhBr/2r9AgwlYkX Nuh5YQLUL4pA0IJZFPOGcpghJYmKitI6d+rUiWYbd3d3gz3zCWFkP2fDhg0Y /NI/cXNzQ8atdMMahAxFKVisdFgBbZZ58eLFVq1aqcpEyqA80ATIKbA45syZ U+mGZWv8+PGqNwemTJlCV2fNmpWAJt26dcvT01NEbkSJEiVS6s89EqBHAis7 1hTMMAcOHEAkY88NqxLUgagDC2uiB9H/A2VhOl23bh1KVr7/ZgS0HBkKWn7k yJEHDx7oeGLwoIrg4ODENdaMGN9fjY6ORl6AkAOrm/IYXQXWLOgUzxqTW7xl ogHo1d27d1Pf2nNDonf27Fk8YhR76dIle/+pEITTNl+kMd4kyjf9/f33799v pP3JR4L1yHzSJPi8g0lWWI/WhPVoTliP1oT1aE5Yj9aE9WhOWI/WhPVoTliP 1oT1aE5Yj9aE9WhOWI/WhPVoTliP1oT1aE5Yj9aE9WhOWI/WhPVoTliP1oT1 aE5Yj9aE9WhOWI/WhPVoTliP1oT1aE5Yj9aE9WhOWI/WhPVoTv5mGIZhGMYO Kb1KMwzDMAzDMAzDMAzDMAzDMAzDMAzDMAzDMAzDMAzDMAzDMAzDMAzDMAzD MAzDMAzDMAzDMAzDMAzDMAzDMAzDMAzDMAzDMAzDMAzDMAzDMAzDMAzDMAzD MAzDMAzDMMy/gf8Fyff2ng== "], {{0, 76.}, {152., 0}}, {0, 255}, ColorFunction->RGBColor, ImageResolution->144.], BoxForm`ImageTag["Byte", ColorSpace -> "RGB", Interleaving -> True], Selectable->False], DefaultBaseStyle->"ImageGraphics", ImageSizeRaw->{152., 76.}, PlotRange->{{0, 152.}, {0, 76.}}]], "Output", TaggingRules->{}, CellChangeTimes->{3.834324010955233*^9, 3.8343348518555937`*^9, 3.8343351369803667`*^9, 3.8344057990592537`*^9}, CellLabel->"Out[16]=", CellID->1997659424] }, Open ]], Cell["\<\ Make a distribution chart showing the difference in the distribution of \ incomes between those not receiving job training and those doing so. The \ chart suggests that the gains in earnings come for a few people in the \ treated group earning what were high amounts of money:\ \>", "Text", TaggingRules->{}, CellChangeTimes->{{3.8343239513789663`*^9, 3.834323959571967*^9}, { 3.834324038796276*^9, 3.8343240594185762`*^9}, {3.834333321477507*^9, 3.834333351540154*^9}}, CellID->1735582061], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"DistributionChart", "[", RowBox[{ RowBox[{"Normal", "[", RowBox[{ RowBox[{"Query", "[", RowBox[{ RowBox[{ RowBox[{"GroupBy", "[", RowBox[{ RowBox[{"If", "[", RowBox[{ RowBox[{"#treat", "==", "1"}], ",", "\"\\"", ",", "\"\\""}], "]"}], "&"}], "]"}], "/*", RowBox[{"KeyTake", "[", RowBox[{"{", RowBox[{"\"\\"", ",", "\"\\""}], "}"}], "]"}]}], ",", "All", ",", RowBox[{"#re78", "&"}]}], "]"}], "[", "matched2", "]"}], "]"}], ",", RowBox[{"ChartLegends", "->", RowBox[{"{", RowBox[{"\"\\"", ",", "\"\\""}], "}"}]}], ",", RowBox[{"ChartStyle", "->", RowBox[{"{", RowBox[{"Red", ",", "Blue"}], "}"}]}], ",", RowBox[{ "PlotLabel", "->", "\"\\""}]}], "]"}]], "Input", TaggingRules->{}, CellChangeTimes->{{3.834323822009194*^9, 3.8343239846820517`*^9}, 3.834335278755974*^9}, CellLabel->"In[17]:=", CellID->2097567861], Cell[BoxData[ GraphicsBox[ TagBox[RasterBox[CompressedData[" 1:eJzs3Qt4FNd9/3/Fbi5WaqepnDhxoiZ9+nNImjQlyZPmH+X6Kwm5dZtf07RJ L+4lOBJgjABhQAIMBIykIEA2MVdjBLaECcYYMEKAgNhGgG1sMMIgJCGBhACB ECAuBglJ/y970Ml4Zi+zM5J2dvf9evbhQWfPzs7OnJ3z2Zk5M3/5q8yfpd+W lJSU9QH552f/O/7/jhnzv9n//Gfyx7+MzBqWMfLXD/xo5NhfZ/x6zP/3q9ul 8PR7kpJu/ufm/7sBAAAAAAAAAAAAAAAAAAAAAAAAAAAAAEAvaWlp2WxRXl7+ yiuvHDx48OrVq9aXVFdXS52KigoHb9fV1RW2jnX6R44ckZLdu3c7eEebc+Lm Q/WDvXv3LlmyZM6cOYsWLdqzZ4+dl+zcuXPt2rXXr18P+GxDQ4NUWL9+vazo M2fOWCscPXq0JqS2tjZj/RMnTsjSkwn+4Q9/qK2tdfAZo87jbQAAgH5WWVmZ HtyDDz64YsWKK1euGF/y3HPPyVOPPPJIpO9VV1c3Y8aMY8eOha5mnf6zzz4r Jb/5zW8ifUf7c+L4Q/WDsrIy40pZt25d2JccPnw4IyNDKl+6dMn0lKS7J554 wjhBqbl06VJTngzRKhSJl6qm/JR46qmn1NtpBQUFAcOnl3m5DQAA0P90Shw3 btzkHvJ/yYe6x5cSY9hw1pk2NzerIBH1lBhwTrycEMaMGSPzNmrUqNWrV69d u1Yibuj6NTU1I0eOVOvOlBI7OjrkM6qnpk6dWlxc/Nhjj6mlkZ+f39nZqWua Up+V3qW5ePFiVSLBW+bw8ccfV39OmTKlvb2915dG3/FyGwAAoP/plHj48GFj +Y0bNyRszJw5Uz07e/Zs/VRLS0t1dfXx48cjeqOmpiY1qbAp0Tr93k2JAefE 2YfqB5Lr1Nxu3rw5bOWurq4dO3YMHz5cZzlTStywYYMql0WqM+Gbb76pMuHW rVt1zStXrly2kEWkas6dO1e9vKGhQU3w6aef1gfxX375ZVW4bdu2XlsQfc+z bQAAgKgIlhKV69evP/roo6rCwYMH3byR/ZRo1Q8p0bPeeecdNbd79+4NXVMC W15enmmPnyklTpo0SQqnTZsmvwKM5UVFRVIuz4aY/rVr19R+yOzsbEmMqlAH QtNpiuqNnnrqqQg+KgAA8JLQKVE0NjaqCosXL1YlO3fufOyxx55++mljNYko S5cunThx4tChQx966KHp06evW7dOD36R/+sAk5ubKy+vra196aWX5D87duyQ pJGVlfXggw8WFBQcP37cOn2dEuVVc+bMkekPHz5cos62bduMg1DkT3nh73// e9NHKC8vl/Lnnnsu2JwE+1CSJ5csWSLRaNiwYZmZmbNmzdq1a5dp2Msrr7wi L5S3OHny5KJFi+SDZGRkPPzwwxKQzp49G3b5h34LeWr27NlqbuXzyhutWbMm 2KTWrl2raspsyMexpsSOjg5ZO1JYWlpqeu0bb7yh6re0tASb/urVq62/F2SZ B4yjU6dOlcL58+eHXQLScuRjyhKTeRs5cqQsgT179pgWcmdnZ0VFhXx8Wbyy oGTtS1JdtmyZLHNjtWAtyuY6MrWBSNfs66+//tvf/nbUqFEye/Lxt2/ffuTI EZmCzKfxw4b4mgAA4ClhU6JQuxPHjBmj+m7r6VsStIznMWpSR/WA0leanpJY UlJSku4/6dF4ClxVVVWw8xKlV5WEYJqO5BA97EL6d5X9TPOvdpRJDx5sTgJ+ qBdffFFlKpN58+YZT7eTUJruP8dvxIgRppqyxC5evBhi4Yd9C0kmpqck9gSb mqTEuXPnVldXy//379+v6hvDmyRAVWgdLf7222+rp6Q9BJy4JCW18CXRGcvr 6urUC1UIV2Qiap1a46iJZL+AS8D4LhJu5XNZ66T7m4QxsAVrUTbXkakNRLRm 1VubTJgwQf2r6oT9mgAA4Cl2UuIzzzyj6qjjjNZApfbOTZs2raamRvq7c+fO rVq1Sr2krKxMKkhX/vrrr6sSiSgNDQ1STXesEj+WL18unXJ+fn7A6auUmO4f jbtmzZrm5uYTJ04sXLhQFW7atElVs5MSA86J9U337t2r6uTk5Ozbt+/8+fPy 0ebMmaMKZc71xFWWUJ9CJlJfXy+J63e/+50q3LBhQ7ClauctmpqaJPXpJSlz G2L/pPEgcsCUKBVU0lu/fr3ptfrA8WuvvRZw4hJc0/1j3mU+Ay5eUVhYKLFQ VqV6F8lXptHxJpIwVUTMzs5+8803L1y4cOzYsQULFqip6SvSlJeXq5IVK1ac OnVK1pcsluLiYlUobUNPMFiLsrmOAqZEO2tWt6iZM2fKSpT2v2vXrszMTFWo U2LYrwkAAJ5iJyVKqFB1Tp8+3W3pTDs6OlQqkKRhfJX0idJRrly5Uv1pPRtQ 9+kvvPCC8YUhUqKxZldXl8pUY8aMuXbtWre9lBhwTqxvOmXKFPlz9OjRxpTV 2dlZUFCgYkNra6sq1Fli48aNumZ7e7s6hU+llIBsvoU+L1FyVLBJWQVMiUIi ihQ+/PDDxvwmb6rKxR/+8Afr1CSTq71zem0aSQPQ45q1iRMnWvOkiUqe0kj0 wuz2r1YVFMeOHat2qM6YMUOtVtNhaDW0atasWbokWIuyuY6CpcSwa1YdXpe0 b9zJfPToUbXQVEq0+TUBAMA77KREfb2+xsbGbktnKn23uu7K+PHj9+7dq4// msZHhEiJpoN3wVKivIvpwJw+TqrOLeytlKiPzBrjgSJvpJ7SVwtUWULygEqq 2vLly9ODj7ix/xa9mxJ3796tymXGqqurZZ4lARYWFup099JLL1mntnTpUlM2 1iRtSl5Sr508efKiRYv0uPjs7OwQl0yUQKXGYq9atSrYElArSH6bvPXWWw0N DcY6kmzVzuRHH31UFwZrUTbXUcCUGPZVZ8+eDbboVAxWKdHm1wQAAO+wkxL1 sIhz5851B0pxeq+LkK5/zpw5mzdvbm5uNk4kWErMzMw0vV2wlCjxw1Tz8uXL aprq6GRvpcRDhw4FWybSp6uDpM8//7zxs48ZM8ZUU5VPmTLFujwjeoveTYnC dEltZfr06eo/1iPOkgNVnAs4YFmfjbB161a9r+/gwYMqEclkg91tR4+Kkmz5 xLvpw+7GmZEfCDLZbdu2SbOZO3euPp5rTYnWFmVzHQVMiWFfJXOl5qS+vt5U U+2E10ec7XxNAADwDjspUZ2LJf1aR0dHd6AUJ8FGIo2+krOSkZExf/58vfcv WEqcNm2a6e2CpcR58+ZZ500dxVN75HorJeqIdeLECes7Pvzww+mGUxP1GAdT tdAp0f5b9HpKlNgm4WTcuHGqgqy1VatWnTp1Sv1pHb2ya9cu9dTbb79teqqz s1MFyIULF5qe2r59u3pVTU1NwDk8cuSINayaqMEv0rpk7Tz00EOm1qVWvTUl WluUzXUUbPRK6FdJlFWzZM17O3bsSDekRDtfEwAAvCNsSmxvbx81alS6YVde sFtUXL9+fd++fcXFxTk5OboTlFepvUnBUuL06dNN0wmWEufOnWuqKX2rmqZx X6J1l6O6OYj9lHjgwAFVwXo/YvksKhetXbtWlThLifbfotdTotbW1nbmzBl1 xLOqqkrVtx4jVvses7KyrMdG9XFz07l2orW1VT2lD82b6LUgGfXNINR5sGq1 pvvP4ZRAtX79ejXY56mnngqYEq0tqk9Tol50R48eNdU07UtUQn9NAADwjrAp UV8QTw/DtJ6XePbsWdM1txsbGyWVGfexuE+J0quaaurLsKgdVurop3VXkpoT +ylRHwzduXOnaVLyWdRT27dvVyXOUqL9t+jdlChJZteuXdb9e88//3y6f8CI qVxWrjq2u2LFCuu7yMSDfQodIF999dWAc9jR0aEGd1jPzJSnJDxLo+rs7Dx3 7pyqNnv2bFkUxmrq8jgzZszQJVFJiZJX1Se1DvyRTJtuOC/RztcEAADvCJ0S JWyoK7yNGjVK5w1TZ6p3i5nuL6yv9KLGvJw8edK0yyXSlJhu2fP25JNPpvsv mqdG7KrLPkuqMQ41lU5cfQSdEq1zYnpT6dDVrZMlIZj28OjT8PT1nJ2lRPtv 0bspUe0YNI28bmtrGz16dLr/Lnum6ehELdky4BuNHz9enp0zZ46pfMuWLaYF ZaWGBo8bN84U//QPk7feeksfmNa3jVZaW1vVZQyNPwqikhKFOqtz4sSJ6pQM RQ8MVynR5tcEAADv0Clx/fr1+/327dv32muvbd68Wd/1I91w8bpuS2d6/fp1 3V/rq/lJOFFDXyVeqsymdy49//zzUk0KHaRE6YjVUUhJVqWlpaoX1lf/kyCh qq1cuVL116dOndJ3GNQp0Ton1jfV9ztesmSJzjBlZWVqXInx6ivOUqL9t+jd lKivi6j3hUqKVpfBGTZsmPXGK/qkRFmSoT/FqlWr9LhdWRE6mYc4kCotTb12 7ty5Fy5cUIXSIFVzysnJUfsSVZ358+frDCZtQA+3Me5hjlZK1AkwLy+vurpa Eqx8g/QV0VVKtPk1AQDAO3RKDGb48OE6USjWFKeHKkjSkE5wxowZ+nYVO3bs UHVu3LhhHIBQXl7uICUKCVEyfbUjTnXW+kIlkiLUuI90/x7F7OxsFSPV/i6d Eq1zYn1TmZS6bqFaAr/5zW/0oNpx48YZ05TjlGjzLXo3Jcpn1zcolMidm5ur zoGUpWq9IYvYuHGjqmzcS2YkQU5fCUfmXyaox8WMHj067D0KFy1apFuOLEN1 9+d0//5hvadX33hF1uPvfvc7eQu1WseOHasWnR76Ea2U2N1zeRwTNVBF2qGq Y+drAgCAd+jreBg9+OCDEiEkw2zatKmtrc30EnUOm6n33Llzp/SGxonk5OSY zkmTnKMHeBYXF69cuTL93aMPgk1f9cuSKKQfV6lGdbVPPvmkKQU1NjZKitDz IGFj3bp1L730Uvq7721nmpOAbyq5aO3atcYRqdKnFxUVmRaIihbW6yKqw9/W kySN7LyFZGAVivbt2xdiUiZvvfWWmqC6XY6RTHzZsmXGe9hJrAp2Yz71Kazn K5o+RWlpqfFTSOaUVRP67oSKpFZ5rc78isRC4ymjsoqXLFlinGFpaS+//LIe l60PRgdrUTbXkakNRLRmu7q6KioqJIGrm4zLq1555RU1P8bfO3a+JgAAxB/p KFtbW2tra48cORLscsqSKOQpqdbZ2ensXST2VPuFGL179uzZw4cP19XVma6H 7GBOJMYcP3780KFDDQ0NIabmRj+8hdWVK1dk+VRVVZ07d65XRteq0RnyKZqa miK9UrS8Vl4lq6y+vj7YapVymWGpo0458BT5bWJN4909e0r1TmzFztcEAAAA cUDtHly3bp2xUHKj2r9qvOs3AAAAEoe6V+BDDz20a9euK1eutLe3V1dXq2FT GRkZ1usoAgAAIBE0NzerSwlZ6auMAgAAIAG1tLSUlJTk5OQMGzYsIyNDQuOC BQuqqqqiPV8AAADwhM7OzmBXDQIAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAEZdXV3x8dZvv/325s2b9+7d24vTjK05 YVVCkUW3ZMmSOXPmLFq0aM+ePaowis2jdzn4IEeOHJEWtWvXrtDVqqurpVpF RYXTWQOAuFJXVzdjxoxjx47FwVsXFRWlp6cXFBT01gRja05YlVDKysrSDdat W9cd1ebRu5x9kN///veyKKZOnRq62nPPPSfVHnnkERczCABxorm5OSMjQ7aK /d939MVbeyda9P+csCqhjRkzRpbeqFGjVq9evXbtWolVUWwevcvxByElAkCk mpqa1N6G/u87+uKtvRMt+n9OWJVQOjo61OrYvHmzLoxi8+hdjj+IzZTY0tJS XV19/PhxF/MIAHGCaNFHSIkueWdVxpx33nlHrQ7jWZ2kRJspEQBC6+zsrKio eOyxx7KysoYNGzZ8+PDs7Oxly5adPHlS13nllVekwrZt206dOvXEE0+MGjVK ak6cOLGsrEyebW9vX7dunbxq6NCh48ePf+qppy5cuGB6F9nWLVmy5JFHHpEX ZmZmzpo1a9euXaZTstW7lJeXy1svWrRI5icjI+Phhx+WCZ49e9Y0wddff/23 v/2tzInMsGwJt2/ffuTIEXm5zHmwTyozmZeXpza5ubm5Urm2tlbKX3rpJfn/ jh07Xn75ZXnTBx98UDpr/eO6oaFB5lxmQz7dyJEjZc737NljmvOwyzDYW9t/ C/Haa6/l5+dLBZnDmTNnVlZW2okW9tevzSXvbE6k8chbSM9lKpc3lfLnnnsu ojkJuDzdr0c7iyuKq1IJ2/JDLwc738SIVtbmzZtlBuQtRowYIe81bdq0LVu2 yDbB+EJZLEuXLpUthiyWhx56aPr06bIYr169GvbDhl0d8llmz56tVoe8tdRc s2aNy3UUegEaudwwumxs9fX1Tz75pExZXisfZ+7cuW+//bZ+VqdEmdrixYvH jBkjsyHVZPotLS262s6dO2WyTz/9tOlD9d12GEAM6ejokG1LeiCyMdfbBLXB efTRR2WLYaq2evVqvR3TpA+6ceOGfpcXX3xRNlDWt5g3b56xN9GbNeluTDVl E3fx4kVds6SkxDq1CRMmqH+DfVjZdple8sYbb+ipTZ48WZ3/o1RVVclTsg0P OOfSy0S0DIO9tc23EMXFxaYKqutJDxktIlq/dpa84zmRbijd39OZylU6ko4m ojkJuDxdrkePr0rFTssPsRxsfhMjWlk5OTnWaUpyu379uqomwUaClvVNZSsR OijaWR3WLZIsQ5frKMQCNHGzYXTZ2CSPBfwgpaWlxnmTHwIyNVOd0aNHt7a2 qmrW8xL7ejsMIIbID0b11V6xYoX8HJaNdlNTk+7Fnn32WVVNbTeE/GjduHGj 1KmsrFRbA+V3v/tdtZ/erMlvTPXavXv3qhLpTfbt23f+/Pmampo5c+aoQtnO 6Jkxvotsu+SXsvw0limrwg0bNqhqMmVVMnPmTJnUuXPndu3aJRtDvY0K9mFl w6tfu3v37oaGBtVJ6W2dvO/y5ctlNvLz87v9QwvVdlh+4L/55psXLlw4duzY ggULVGV97Qg7yzDYW9t8C70M5SNLn9vS0vLSSy/pbXiIaOFg/YZY8m7mJKLg EXZOAi5Pl+vR46uy23bLD7Yc7H8TI11ZatVcvnxZIoQsOlWyZs0aVU2FpWnT psnbybKS2V61apWqo/a5BWNndUiJbHb01GR1nPVzs46CLUArNxtGN41Nvhfq g8h77d+/Xxa7lEirUDXVbk89b1JTvkqNjY1SvnTpUlWodwgHS4npfbYdBhBD ZsyYoboD0yEntcGZNWuW+lNvN+QHrK6zc+dOVWh8+bVr19RPV70xmTJlSrr/ 1+ulS5f0azs7O6VDVBsi/atWv4tsb3XN9vZ22YJJod5Qy49c1dMZ934cPXpU /fAPvXUKeJKP7hReeOEFY+V58+al+3+M6zns9l+4THUrY8eOVTNgcxkGfOuI 3kI+mt4/I6SjVxMMES0iXb+hl7ybOYk0eISdE+vydLke7S+uqKzKbtstP9hy sP9NjHRlrV271lhNJYoHH3xQtgYdHR0yZfnz5ZdfNtaR6CjLauXKlSE+r83V oc9LlOCn67hZR8EWoJWbDaObxqZmWJaw8YO0tbWNHDlSyiWEG+dN4qiuI6tb NQM9/RApse+2wwBixenTp9966y35iWoslC3JwoUL0/1HUlSJ2m7Ib1LZIOtq stVSG5NXXnnF+PJp06al9+yaaGlpsW5wlNraWtPL1bvIRka2qMaa8nNeyn/z m990+39cq1e99NJLpgmqLsBNSjQeTJFN3/Dhw/UmN+Ccq4nYXIbWt7b5FjJX asNrHMLZ7e/d1KIOES0iWr+hl7xwMycRBY+wc9IdMiU6W4/2F1dUVqX9lh9w OUT0TYxoZUn4uXLlirFaTU2NmqAsTPloKrqMHz9+7969Ohsbz0gJxubqsJkS 7beEgAswIMcbRvufzvpBZNGpnc/G3b/K7t27JY0b9yXKRzZ9ldTKlayo/gyW Evt0Owwgtly9evXgwYPbtm2Tzc7cuXP1UQNTipDtvPFVp06dUtUOHz5sLFe/ hdUW7NChQwHrdPu3deqgyfPPP298lzFjxphqqnK1WZP5VBOsr683VVu/fn3Y rVOIlCif2lizsbFR1Zw8efIT76YP0r322mv2l6H1rW2+he5zjaemK+roXtiT 2Wyu39BLvtvQ+zuYk4iCR9g56Q6eEl2uRzuLKyqr0n7LD7gcIvomRrSy9GLR JF2o99q6dWv3uw9MS2iRBSIhubm5OdgnNQm7OmymRPstIeACDMjxhtH+p7N+ kNbWVlViSqEB5826PZS1LOWTJk1SfwZLiX26HQYQK6SDkK2E6fRm+RWpDhKZ UoSxj+42bAyPHj1qLDduDPfv36/qnDhxwvruDz/8sHGzGezSDcZ3l824mqC1 l9mxY0fYrVOIlCi/9I01jxw5kh6OOlHc5jK0vrXNt9DL0DrKUm2QQ0SLiNZv 6CXfbVibDuYk0tEroeekO3hKdLwe7S+uqKxK+y0/4HKI6JsY0cqaN2+edYJq GaojtrJUJZmoPYrGpTp//vzQo1dsrg6bKdF+Swi4AANyvGG0/+lCxN0DBw6E nTfrV8lmSuzT7TCAWKG6g3T/2Uqy0ZauSp3W/tRTT6WHSxF2NoayHVN1jBdw ULq6utQBIH1Sk52tU1VVVcA37Xa9L3H69OkBa65aterNIE6fPm1/GVrf2uZb 6N5N/mP6OGoLHzabOVu/3ZZO0P2cSNswlS9evDi9V1Oi4/Vof3FFZVXab/kB l0NE38SIVtacOXNM1drb29WBdbUvUbl+/boszOLi4pycnPQe8hYh7lBsc3XY TIn2W0LABRiQ4w2j/U9n/SBSR5Xo21Xbn7fuXkqJLrfDAGLCuXPn1MZ89uzZ xvNqhLpEw4wZM9SfjjeG+mfvzp07Te8uP0LVU/rEbztbJ72F/MMf/mCqJlva sFsn+ymxo6NDLRzreVzylHS1Z8+e7ezstL8MrW9t8y3OnDmjXmg9wKROYg8W LVyu325LNnM8J+KZZ55JD7R/RiKHNXj0Ykq0uZC7I1lc/b8quyNp+QGXQ0Tf xIhWlvUb19DQoCb4xhtvSAiUz37w4EHTzKhJpQfaGaXYXx02U6L9ltAPKdFN Y5NFqs5LfPHFF03zU15eLk3IOHqlj1Kiy+0wgJig92yYfpO2traqrZDuJhxv DGWDpm6xKq817TRQPZHQl5C1mRBk6y1/Tpw4Ubbtus6JEyfsjK2T97LOc7BO QQ3iGzdunGkzvm3bNjWRt956y/4yDPjWdt5C/pw8ebKalHEZSkejdgEFixYu 1293oGzmbE7E6tWr0/3nehlHREpfo66k5yAlWpenm/UY0eLq/1Wp2Gz5AZdD RN/EiFZWuuXQpzowLZ/o6tWreh9mXV2dsY6+LI8kxoAf1v7qCJgS3ayjfkiJ LhubmlR2drZxEJBEXPUBFy1aFGzeunspJXa72w4DiAnSN6ntj/z609/006dP q69/uv8qB6rQzYGVDRs2qGpLlizRG+eysjJ1wry+IEOwd+m2bJ10v5OXl1dd XS3b1ddee01f1Tb01kmP9JRN5dmzZ40XvrB2Cvr6JPLrXt80obKyUm3GZeGo fYk2l2HAt7bzFt09p/oI6X/Vu0hlfc3eEPsS3axf65J3PCdCekNVZ+XKleqF 0n4effRRVeggJVqXp5v1GNHi6v9Vqdhs+cGWg/1vYkQrK90/fEOfabl161Y1 QXU7j+vXr+vYo6/ifunSpfz8fCkcNWqU6S4tmv3VETAlullH/bMv0U1jk3lW hU8++aQaiSwTkZVlXA59nRLdbIcBxAp98X/Zzv/ud7/Lzc1VvwTHjh2b3rM3 oNtdSpTNl7ogm5rgb37zGz2OT37UG+8VZT8hqGsymKjT4+X3dYjPKz+9jaeL qyuJhegU5Fe5qjls2DCZMdm0qj9lIvpT21yGAd/a5lt0dXXpq/JKuVRTu57U ie4hooWb9RtwyTueE2kGaohEun8nlawmNSfqniMOUqJ1ebpcjx5flYqdlh9s Odj/Jka0shSpMHnyZB0SZE1dvnxZVdu+fbteLJIVZ8yYoa8iLpk5xIe1uToC pkQ366h/zkt02djUBXOELEyZVbWjWOhb4/V1Sux2sR0GECvkR/2SJUuMN6KS b/fLL7+sN3TqgIjakhivVtft/+Wr6piOJakdI/q+Ht3+Tmft2rXGQY6yZSsq KmprazO+MOC7dPcc/zKeJSVdbUVFhbyRbD9Vf/fKK6+on9LGzV1A0pXoOSku LpYS9ULrBT26/Zvo0tJSvQVWZPNuPK3R5jIM+NY230JVW7Nmje4v5O0WLFgg JemBhg9EOm/2l7zjOen2n40mXaGeE5nCunXrXnrppXRDOopoTkzL0+V6tL+4 rG8d0bs4XoDd9lp+iOVg85vYbW9l6diwceNGlXLT/fFGJmgavLxz505ZksbF kpOT8+qrr4b+sDZXx7Vr11Sdffv2GV/ueB2FWIAmbjaMLhtbZ2fn+vXrdc5P 9+f5srIyvWcy2FfphRdeMLYWFRqNmbB/tsMAYohsr2SDdvjwYT3Ysy/I9vn4 8eOHDh1qaGgwXa81ItJ/6d0URmovgd7REYJsSM+cOdPa2qoOMIUlG8OmpiZZ PvX19cb7VhjZXIbB3trOW3T7O0Tpzqqqqoz3XAirL9avsznp9l+PV+ZE5sdN G9AiWpU2F7JnV6X7lq/Y/yaGXlnGnU4yY9XV1UePHjVdYVuTxSKftLa29siR I7LcbM5qt7vW63Id9QOXjU1WpaxEWZWSLe1cqLwX9VZrBIDepXZKrFu3zlgo 2yv1c9t6PwIgPnit5Qc7NIlE4LXWCACKOiHnoYce2rVr15UrV9rb26urq9Wp 9RkZGdbrdwHxwWstn5SYyLzWGgFAaW5uHj16dHogZWVl0Z47oK94reWTEhOZ 11ojAGgtLS0lJSU5OTnDhg2T362ysVqwYEFVVVW05wvoW55q+Tt27MjPz9eD apFoPNUaAcCqs7PTeE1XIEHQ8uEdtEYAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAYrQcAANEQ7QgAhCJNtKCgINrfEsSq7Ozs aM8CEGXyLSgqKor2XCAmqf432kEACEo11GjPBWJSS0uLbOKiPRdAlMm3oKqq KtpzgZhEFwyPo4nCMVIi0E1KhAt0wfA4migcIyUC3aREuEAXDI+jicIxUiLQ TUqEC3TB8Dhpn0VFRRU92NbBPkmJ0Z4FIPr4IiAi0mB0n8voFXjc+p4Begop EQCAviPhUPe5pER4HLu7AQCICrpgeBxNFACAqKALhsfRRAEAiAq6YHgcTRQA gKigC4bH0UQBAIgKumB4HE0UAICooAuGx9FEAQCICrpgeBxNFACAqKALhsep e6+09Ij27AAAEM9aDEiJ8Dh17xWN5goAQN+pqKjQfW56ejrdLryMHzIAAEQF XTA8jiYKAEBU0AXD42iiAABEBV0wPI4mCgBAVNAFw+NoogAARAVdMDyOJgoA QFTQBcPjaKIAAEQFXTA8jiYKAEBU0AXD42iiAOBGc3NztGcBsYouOKFcvHhx 27ZtK1eufP755/fu3dvV1WWqcPLkydLS0uLiYmkVjY2NISYVtqb7Coo8m25A cwWAiBQWFlZWVkZ7LhAz6HYT0/79+0eOHGlc9TNnznznnXd0hfLy8qFDh+pn MzIytmzZEnBSYWu6r6DxQwYAHCspKRk0aPCQIUOiPSOISXTBCeL06dMPPvig 5LHVq1efOXPm0KFDkydPlj+Li4tVhdraWolqw4cP3759e1NT0+bNm1WKO378 uGlSYWu6r2BEEwUAxyQfJid/+itf+Ra7E+EAXXCCeOqppySGrVy5UpccO3ZM SkaPHq2OO8+fP1/+LCsr0xU2bdokJcuXLzdNKmxN9xWMaKIA4Jg/Jd6XljaY lAgH6IITQWdn58iRIzMyMtra2ozlBw4cqK2tlf9IUMzMzJScdv78ef3sxYsX 5SUjRoy4ceOGLgxb030F08zTRAHAMZ/Pl5T0+YEDfeXl5dGeF8QeuuBEcOrU KUll06dPl4R2+PDh0tLSbdu2HT16VFdobm6WCllZWaYXjh07VspPnz5tv6b7 CqZymigAOEZKhBt0wYmgsrJSAtjs2bPnzJljHL3y5JNPtre3S4W6ujoVI00v lBIpr6mp0SVha7qvYCqniQKAY6REuEEXnAhef/11PZR4xYoV8ueGDRtGjBgh JWvWrJEKhw8flv/n5uaaXpifny/lhw4d0iVha7qvYCqniQKAY6REuEEXnAhe ffVVlRI3btyoCw8ePCglw4YNe+edd1Ryy8vLM71QSqS8qqpKl4St6b6CqZwm CgCOkRLhBl1wIti/f7/akXjt2jVj+aRJk6S8rq6uvr5e/jN16lTTC6dMmSLl DQ0NuiRsTfcVTOU0UQBwjJQIN+iCE4FELwlgQ4cONY0gVgd5Dxw40NbWJv/J zMw03Y1FXYXbODI6bE33FUwzL+0zOzu7wIJ2CwBhkRJhk/Sq1q5W+l9627jX 0dGhzkI8duyYLpTEqIYVnzhxQv6cMGGCaVeeypbSQkxTC1vTfQUjaZ9FRUVV Fi0tLY4XCAAkCFIibJJe1drVSv9LSkwES5cuVWcDqkHNYvPmzVIyceJEtU9v 9erV8ue8efPU/kb59/HHH9fDW4zC1nRfwYjd3QDgGCkRbtAFJ4hz585lZWWp S9DIGl+4cKE6U3H//v2qQltbm6qQm5sraW3mzJny/3HjxlkPAYet6b6CEU0U ABzzp8T/Q0qEM3TBieP06dMFBQWSDNV45/Hjx+/du9dYoaWlJS8vT1eQCNfU 1BRwUmFruq+g0UQBwDH2JcINuuBEc+HChfr6+pMnT1rvhadcvnz52LFjra2t YScVtqb7Ct00UQBwgfs4ww26YHgcTRQAHCMlwg26YHgcTRQAHCMlwg26YHgc TRQAHMvJyUlJ+T9pad8lJcIBumB4HE0UAByTlHjnnX/xrW99t7m5OdrzgthD FwyPU1eDL+pRUVER7TkCgJhBSkSk1MW0Fe69Ao9TKbGiB7dcAQD7SImIlKRE 3edy7xV4HLu7AcCxwsLCO+645+tf/3q0ZwQxiS4YHkcTBQDHSIlwgy4YHkcT BQDHSIlwgy4YHkcTBQDHSIlwgy4YHkcTBQDHSkpK/vRP7/nhD38Y7RlBTKIL hsfRRAHAsWXLlt11FykRDtEFw+NoogDgGCkRbtAFw+NoogDgGCkRbtAFw+Ok fapbrihVVVXRniMAiBmkRESqpaVF97kFBQWkRHiZtM/s7Gx9tyBSIgDYt3bt 2rvvvueXv/xltGcEMUPdckUhJcLj2N0NAI6REuEGXTA8jiYKAI6REuEGXTA8 jiYKAI6REuEGXTA8jiYKAI6REuEGXTA8jiYKAI6REuEGXTA8jiYKAI6REuEG XTA8jiYKAI6REuEGXTA8jiYKAI6REuEGXTA8Tt17papHS0tLtOcIAGIGKRGR kn5W97nS/5IS4WXq3isFPSoqKqI9RwAQM0iJiJS6MZ8i/S8pEV7G7m4AcIyU CDfoguFxNFEAcIyUCDfoguFxNFEAcIyUCDfoguFxNFEAcIyUCDfoguFxNFEA cIyUCDfoguFxNFEAcKyiouKee+75p3/6p2jPCGISXTA8jiYKAI6REuEGXTA8 jiYKAI6REuEGXTA8jiYKAI6REuEGXXDiOHr06KsWhw8fNtY5efJkaWlpcXGx tIrGxsYQUwtb030FRZ5NN6C5AoB9pEREim43MRUUFKRb/Pa3v9UVysvLhw4d qp/KyMjYsmVLwEmFrem+gsYPGQBwjJQIN+iCE8eoUaMkjK1ater3Bjt27FDP 1tbWyrPDhw/fvn17U1PT5s2bVYo7fvy4aTpha7qvYEQTBQDHSIlwgy44QbS0 tEgMmzhxYrAK8+fPlwplZWW6ZNOmTVKyfPnySGu6r2BEEwUAx0iJcIMuOEHs 27dPYtiiRYsCPtvV1ZWZmSkVzp8/rwsvXryYkZExYsSIGzdu2K/pvoJp3mii AOAYKRFu0AUnCHUy6u9///sNGzYsWLBg2bJlO3bsaG9vV882NzfLs1lZWaZX jR07VspPnz6tS8LWdF/BOuc0UQBwhpQIN+iCE8QTTzxhHboyZcqUs2fPyrN1 dXXy5/Tp002vkhIpr6mp0SVha7qvYCqniQKAY6REuEEXnCCys7Mlg02dOvXN N988c+bMG2+8MWHCBCmZPXt2V1fX4cOH5f+5ubmmV+Xn50v5oUOHdEnYmu4r mMppogDgGCkRbtAFJ4jKyso//OEPly9f1iUnTpwYNmyYGlmsklteXp7pVVIi 5VVVVbokbE33FUzlNFEAcIyUCDfoghNZbm6uBLPdu3fX19erPY2mClOmTJHy hoYGXRK2pvsKpnKaKAA4RkqEG3TBCeLatWsXLlwwFc6dO1eCmWxD2tra5D+Z mZldXV3GCiNHjpRyeVaXhK3pvoJpJqV9ZgdSVFTkeGkAQIIgJcKmgL1tOvde SQDHjx+XFf3QQw91dHToQvm/GlZcW1srf6rTFI278uT/UiKNxDS1sDXdVzCS 9imBsMXC8dIAgMRBSoRN1n5WsC8xEXR2do4ePVpi2KZNm3Th888/LyWPPPKI PCt/rl69Wv6cN2+eumKh/Pv4449LyZo1a0xTC1vTfQUjmigAOEZKhBt0wQli 9+7d6uo38+fPlzU+e/Zs+f/QoUP1mOK2trasrCw1+ljS2syZM+X/48aNsx4C DlvTfQUjmigAOEZKhBt0wYljz549Dz/8sL5Y4uTJk99++21jhZaWlry8vIyM DFVBIlxTU1PASYWt6b6CRhMFAMdIiXCDLjihdHV1nTt3rr6+vrm5OVidy5cv Hzt2rLW1NezUwtZ0X6GbJgoALpAS4QZdMDyOJgoAjpES4QZdMDyOJgoAjpES 4QZdMDyOJgoAjpES4QZdMDyOJgoAjpES4QZdMDxO2mdBQUFRD9niRXuOACBm kBIRqaqqKt3nZmdnkxLhZSolVvTgrisAYB8pEZGSlKj7XAmKpER4Gbu7AcAx UiLcoAuGx9FEAcAxUiLcoAuGx9FEAcAxUiLcoAuGx9FEAcAxUiLcoAuGx9FE AcAxUiLcoAuGx9FEAcAxUiLcoAuGx9FEAcAxUiLcoAuGx9FEAcAxUiLcoAuG x6mraq/vUVVVFe05AoCYQUpEpKSf1X2u6n+jPUdAUKREAHCMlIhIkRIRQ9jd DQCOkRLhBl0wPI4mCgCOkRLhBl0wPI4mCgCOkRLhBl0wPI4mCgCOkRLhBl0w PI4mCgCOkRLhBl0wPI4mCgCOkRLhBl0wPI4mCgCOkRLhBl0wPI4mCgCOkRLh Bl0wPE7aZ1FRUVWPlpaWaM8RAMQMUiIiJf2s7nOl/yUlwsukfWZnZxf0kC1e tOcIAGIGKRGRkjaj+1zpf0mJ8DJ2dwOAY6REuEEXDI+jiQKAY6REuEEXDI+j iQKAY6REuEEXDI+jiQKAY6REuEEXDI+jiQKAY6REuEEXDI+jiQKAY6REuEEX DI+jiQKAY6REuEEXDI+jiQKAY6REuEEXDI9T915p6RHt2QGAWEJKRKRaDEiJ iamysvK5555rbGw0lZ88ebK0tLS4uFhahfXZiGq6r6Coe69oNFcAsI+UiEhJ m9F9bnp6Ot1uopFfB5mZmbLq9+7daywvLy8fOnRoeo+MjIwtW7YEnELYmu4r aPyQAQDHSIlwgy440XR2ds6aNUtlM2NKrK2tlag2fPjw7du3NzU1bd68WaW4 48ePm6YQtqb7CkY0UQBwbO3atXfffc8vf/nLaM8IYhJdcKLZtGmT3oNnTInz 58+XkrKyMlPN5cuXm6YQtqb7CkY0UQBwjJQIN+iCE8qxY8eGDRs2efLkJ554 wpgSu7q61DHo8+fP68oXL17MyMgYMWLEjRs3dGHYmu4rmOaZJgoAjpES4QZd cOK4fv36I488MnTo0Pr6+iVLlhhTYnNzs/yZlZVlesnYsWOl/PTp07okbE33 FUzlNFEAcIyUCDfogh1bt25de3t7706zsLDwo35nz57t3SmL4uJiPVjJlBLr 6urkz+nTp5teIiVSXlNTo0vC1nRfwVROEwUAx0iJcKOvu+D1jvTd/PSK6urq QYMGJSUlXbx4sXenLEkpye/kyZO9O+UDBw5kZGTk5uZ2dnZ2W1Li4cOH5U95 1vSq/Px8KT906JAuCVvTfQVTeUw0CQDwJlIi3OjrLtjn830nLc1n+3Gzss/X d/PTKyZOnKiyXKykxLa2trFjx44YMeLUqVOqJGBKzMvLM71QSqS8qqpKl4St 6b6CqZyUCACOkRLhRj+kxPuSkrJtP6QyKbHXU+LChQslgC1YsOBQj4KCAimR rYf8/8qVK/X19fLn1KlTTS+cMmWKlDc0NOiSsDXdVzCVkxIBwDFSItzoh5To S0rqtv3wOUqJ77zzjp3A1t7e3tbWZnOa169fl/gU8Ck7KfHSpUvBnjp//rw6 7GvVRylRZjg9OAmKsljkP5mZmV1dXcYXjhw5UsqNCy1sTfcVTDNvuveKVlRU 1DtLBwDiFykRNgXsbdP7+N4rfZoSGxsbx4wZ88lPfvI973mPJKuUlJR//Md/ fPPNN03Vzp07Jwnks5/97Hvf+16p9hd/8RdSbc+ePcY6q1ev/trXvvajH/1I 8tuMGTPS0tLe//7333bbbZ/73OdGjRp19epVVa2mpkaq3XPPPSrLffnLX5Y/ t27dKk+VlJTI/2XKBw8elJfLaz/96U/Lay9cuKBeu3Hjxu9+97t33323vDA5 OfmrX/3q5MmTr127ZpyNPkqJ8tYr323SpEmy6h977DH5vxpWPGHCBNOuPPm/ lEgjMU0tbE33FYzWv/s+ztzQGQDsIyXCJms/2w/3ce67lPjyyy+rxGUiifH5 55/X1crLy//8z//cWu3222+fOHGirlZYWCiFd95557/8y79YK3/hC1+4fPmy VJMIan22uLi42z/4Qv5/1113fepTn9JPffjDH5YceOPGjZ/97GfWF4q//uu/ rqys1LPRd6NXTEznJXb7c7KUzJs3T12xUP59/PHHpWTNmjWm14at6b6CEUec AcAxUiLciNGU2Nraqnfo/eIXv9iyZcuOHTuGDx/+vve9T+2pa2pqkmqnTp36 6Ec/qqr9x3/8x+bNm1999dVZs2Z98IMfVIUlJSVqgiolKvfee+9jjz22adOm 3NzclJQUVbhw4UKpJlnxxRdf/PnPf64Kn332WflT7YtTKVGRtPnjH/9YpjNs 2DB56pFHHlHlMs+LFy9+44031q1b973vfU8Vfv7zn+/o6FCzEcWU2NbWlpWV pUYfS1qbOXOm/H/cuHHWQ8Bha7qvYERKBADHSIlwI0ZToo5kY8aMMZaPHj1a lRcUFMif//Vf/6X+nDZtmrHagQMHPvCBD0j5xz/+cXVuoU6Jd999t/Fo5ksv vaTKf/CDH+jCgOcl6ln68Ic/rDJqt//kxpqamttuu02FTz3EuNt/P+X7779f veS3v/2tKuy3lLh06VJTSuz2723Oy8vLyMhQ5ytKhNMfxCRsTfcVNFIiADhG SoQbMZoS//Vf/1Wi1Ic+9CHjXd66/acgfv3rX5dnV6xYIX9KMEvyn4hoOv1P ZGVlqTy2YcOGbkNK1IFNk9Qn5V/5yld0SeiUaEqkixcvVuULFiwwTfn06dNq r6aeeL+lxBAuX7587Nix1tZW9zXdV+gmJQKAC6REuBGjKfEzn/mMRKkf/vCH IeocPXpUJa7hw4dbn929e7dxr6NOiaWlpaaaX/rSl6T8i1/8oi4JnRJXr15t fLneYXjmzBnrbPzgBz9QR6jVn15IiV5DSgQAx0iJcCMWU+KlS5fUMdxf//rX Iapt3bo12O7Bbv8pi+rZ9PT0bkNKNI4lUb7xjW9I+d/8zd/oktApcd++fdaX JycnB5zJjIwMYywkJVqREgHAMVIi3IjFlNjU1KSi1IMPPhiiWkVFhao2a9Ys 67NnzpxRzw4ZMqTbkBKPHDliqhlpSjx27Jjx5d///vel8IMf/GDAmRw+fLh6 VWNjYzcpMRBSIgA4RkqEG7GYEru6uu644w6JUj/96U+tz9b5dXR0nDhxQiWu 0aNHW6vt3btXPauu0dd3KfGBBx6wVtb+4R/+QT17/fr1blJiIKREAHBs2bJl d911T+gTtIBgYjElii984QsSpT71qU+pC+4Zffazn03yX4dQwmRycnLSuwee aAUFBSqPLV68uLsvU6Iuty7nK1euqAv13HvvvaqElGilrgZf0KOioiLacwQA MYOUiEhJP6v7XOl/YzElPvjggypNLV++3Fj+yiuvqPKxY8d2G/bUPffcc8Zq Fy5c+MhHPiLlt912m7o6TUQpcerUqaqy8e4hwVLi66+/rm4NM3DgQH1dRGXm zJnqJXp8DSnRSt17paoHd10BAPtIiYiU9LO6z5X+NxZT4pkzZz70oQ9Jmrrj jjsWL158+fLlrq6uLVu2fPKTn5TC973vfQcOHJBqEvnUdbY/8IEPPPHEExcv Xrxx48auXbvU/kZ9uLk7wpQ4a9YsVXno0KGy9NQphcFSovjv//5v9dQ3v/nN gwcPyqyePXt20qRJKj1+4hOfqKurUzVJiVYccQYAx0iJcKMfjjh/Jymp0vbj O7bv0LdixQqVANUuwdtvvz2ph/HKhBL/9FPv8dPVjFfkjigl6tHTirpAYoiU ePr0aXWIXDHO6sc+9jHjO5ISrUiJAOAYKRFu9ENKvC8lxWf7cbOyvZQo9u/f /3d/93fqqjjKfffdp66SbfTaa6995StfMVa79957JdQZ6zz++OPqqdraWtPL v/3tb6vjxcbCSZMmqbu3JPnv/ddtONHReBhau3bt2oQJE9T+T51sf/SjHx0+ fNhY7dFHH1XPNjc321wIcY+UCACOFRYW3nHHPV//+tejPSOISX3dBWc7EtFb XLly5dVXXy0vL1dHfkNUk7hYVlZ26NAh65gXB2SCNTU1kgkjmtqxY8dkHnbt 2nX27Fn385AISIkA4BgpEW7QBcPjaKIA4BgpEW7QBcPjaKIA4JikxA99KPXv /u7rnMgEB+iC4XE0UQBwLCcn5847/+Jb3/ouKREO0AXD42iiAOAYKRFu0AXD 46R9FhQUrO9RVVUV7TkCgJihU2JlZWW05wWxQfpZ3eeq/jfacwQERUoEAMeG DBmSnHxfWtpgUiJsIiUihrC7GwAcIyXCDbpgeBxNFAAcIyXCDbpgeBxNFAAc IyXCDbpgeBxNFAAc8/l8SUmfHzjQV15eHu15QeyhC4bH0UQBwDFSItygC4bH 0UQBwDFSItygC4bH0UQBwDFSItygC4bH0UQBwDFSItygC4bHSfssKiqq6MFV tQHAPlIiItXS0qL7XK6qDY+T9pmdnV3Ug5QIAPaREhEpCYe6zyUlwuPY3Q0A jpES4QZdMDyOJgoAjpES4QZdMDyOJgoAjpES4QZdMDyOJgoAjpES4UZMd8Gn Tp3auXNntOeie926de3t7b07zW9961sf/ehHf/GLX/TuZGNRTDdRAIguUiLc 6OsueL0jdqZcWFh41113jR49uu9mPqzq6upBgwYlJSVdvHixd6f82c9+Vib7 ve99r3cnG4tIiQDgGCkRbvR1FyztMy3tO2lpPtuP78hLwk62o6MjyS+6KXHi xIlqNkiJfYeUCACODRkyJDn5vrS0wZWVldGeF8SefkiJSUn3JSVl237cR0rs JiUakBIBwDFSItzol5Qoj27bD19vpcRLly4Fe0pC3Y0bN+zMf1dX19mzZ69c uRLwWTspUd7IZoZsbW3V/yclaureKy09oj07ABBLSImIVItBLKbE8ePHf/Wr X1Xx7CMf+cjXvvY1NcqjpKRE/v+P//iPBw8eTEtLu+222z796U+PGjXqwoUL 6oWvvPKKTDw1NVVeeMcdd8hEpk2bFnDgyWuvvfZv//Zvf/u3fyvV1BvJpEaO HKknVVNTI+91zz33qGe//OUvy59bt27VU7h69Wp2dra8hZqCvOlPf/rTN954 w/pee/bs+clPfvLBD35QqskEf/7zn8vE+yglVlVVbdiwQRbUpk2bJP1aK5w8 ebK0tLS4uFhaRWNjY4hJha3pvoKi7r2isV8RAOwjJSJSFRUVus9NT0+PuZQo OTDp3e677z4pz8/Pl//fddddn/rUp/RTH/7wh69du9bV1ZWTkyO5MclCAl5d XZ1x+hMmTHjPe95jrSk+8YlPnDt3Tuq8+eab1mcl8KgpHDhw4HOf+5y1wnvf +95Zs2YZ32v//v3JycmmaikpKWoGejEldnZ2Llq0KN1g2LBhO3bsMNYpLy8f OnSorpCRkbFly5aAUwtb030FjSPOAOAYKRFuxOK+REloL7zwggpUUvnFF198 +eWXu3tSonLnnXf++Mc/vvfeeyULyVNPP/20Kv/IRz6ycOFCyWabN2/++c9/ rgq/853v6IlXV1erhPbxj3988eLFkvfeeuutFStWSBBVladPny7VLl++LO+r p/Dss8/Kn6dPn5an2tvb1Z5AIRW2bdu2b98+edM///M/V4U6m0ng/Mu//Esp kfgqIXb37t0yke9+97v6U/RiStywYYOEsfHjx8vHOXPmjAppks2OHTumKtTW 1sqfw4cP3759e1NTkywfleKOHz9umlTYmu4rGJESAcAxUiLciMWU2B3kvESd Ej/84Q9L9lCF169fl0QncVHK/+zP/qyhocE4HXm5esnq1atVyYgRI1TJnj17 jDUlTd1+++1S/v3vf18XBjwvce7cuaowKyvLOIX6+nqZMSn/4he/qM6KlKSk ai5dutT40X70ox/1ekqUfCgx7NChQ7pEkrOUrFmzRv05f/58+bOsrExX2LRp k5QsX77cNKmwNd1XMCIlAoBjpES4EZcpcdq0acbKzz33nCovKCgwTefSpUvq gO8//dM/qZJnn3127NixY8aMsb6p2u+XlpamSwKmxC984QtScvfdd1+7ds00 hcmTJ6v6b731lvw5cOBA+f9nPvOZzs5OYzV9OLu3UmJ7e/vWrVuLi4u7urp0 4bp16ySYPfPMM93+QTqZmZny5/nz53UF+VCSYyU2G0f6hK3pvoJp5kmJAOAY KRFuxGVK1DsGldzcXFWenZ29yuJjH/uYPCXRzvoukljq6uo2bdo0e/bsn/70 p+9///ul5te+9jVdwZoSJQKp4SoSKa3v9dBDD6n6Elwlub3vfe+T/w8fPtz6 1p/+9Kd7d1+iiSQ0dVbq7t275c/m5mb5v2nnp5DALOXqSLoStqb7CqZyUiIA OEZKhBtxmRL37dtnrPyrX/0qKRyJdno/myQoCZYDBw5UKc4kdEpsaGgI+15C pn/gwAH1/5kzZ1o/4Le+9a0+Sol79uxZvHix5FWJZAsXLlS7MSUMy5/qlEsj KZHympoaXRK2pvsKpnJSIgA4RkqEG3GZEvWIDEWPMfnMZz7z2eCuX78ulY8f P/6JT3zCFCC/+c1vTps2TQ0/CZ0SDx48qEqSk5NDvNecOXNee+01VfPRRx+1 fsCf/OQnfZQSZ8+erUcWL126VF0K8vDhw/KnZFdTZVmkprMZw9Z0X8FUTkoE AMdIiXAjEVLi+PHjVfmRI0fCTvkb3/iGqvztb3971apVtbW1+qTBu+66S8q/ +tWv6srWlHj16lU1RPrf/u3fQr/RO++88yd/8idS86GHHrI+q05Z7IuU2NLS cunSJQljjzzyiKQyda6mSm55eXmmylIi5VVVVbokbE33FUzlpEQAcIyUCDcS ISUuXbpUlS9ZssQ0nRs3bmRlZUmMVE9duHBBXVNRQpq8hbHmiRMn1ES+/OUv 68KAo1fUVbs/9alPmaYgtm/fPmzYsNzcXPVtVVHwS1/6kqmaTE1dZLtP773S 3NwsMyPBrKmpqb6+Xv4zdepUU50pU6ZIuXFgeNia7iuYykmJAOAYKRFuxGhK 7OrqUrvsfvWrX+nCYCmxpqbmve99r5Tfe++9ppvl6YEtP/zhD7v9V8NWf/79 3/+96e3+93//Vz31N3/zN7pcoo4qNGab//mf/1GFpiHV586d+/jHP66eUpfZ eeCBB9Sfa9euNdaUvKTKeyslyvy3trbqqwNpanfi/v3729ra5D+ZmZnGQdBi 5MiRUi7P6pKwNd1XMM2ktM/0QIqKihwvEABIEP5e+PMDB/rKy8ujPS/wNOlV A/a2sZgShdrbJsHv2WefVY0/WEoUWVlZ6qnPf/7zFRUVN27ckFz3+OOPq2HL Qt3449q1aypPimeeeeby5cuSZA4ePCi/xfRpiqmpqXqys2bNUoVDhw7Vt5k7 efLknXfeKYW33377pEmTWlpa2tvbZQ6///3vq8r6Wjr19fUf+chH1EmMK1as kHeXjCTBVV2YsRdTosyYrOhRo0YZrzPT2dkpJfpa1hMmTDDtypP/S0l2drZp amFruq9gxL5EAHCMlAg3+mVf4neSkiptP75jMyXqEwjF+973vo6OjhApUdLX 1772taQgjEc/H374YV3+gQ98QN8w5U//9E//6q/+SmU/fbWWrVu3Gqejr9O4 fPlyeW3A95KQaUxHEll1Uk3y34RF/n3Pe96jTlnsxX2JkpMlhhnvgvfCCy+o K9KoEy9Xr14tf86bN08lSflXUrTxstta2JruKxiREgHAmebm5kGDpBf++oAB vpKSkmjPDmJPP6TElJT7UlJ8th/32UyJtbW1+kZ4orq6uqCgQP3fem5btz+K 5OXl3X333cbMJlMwfXHa29snT55svLeypLh//ud/bmxsfP7551XJ4sWLdf1J kybpQPgf//EfuvzQoUPf/va3VdhT3vve9/76179W+xuNNm7caPwgModPP/30 z372M/n/D37wgwiWdUh79+5N998ueeHChevWrVMjneXPAwcOqAoSpFWSzM3N lbQ2c+ZM+f+4ceOsh4DD1nRfwYiUCADOkBLhUl93wdmO2J/+8ePHJS6azjYM 7dSpU9u2bXv55ZdPnDhhOjVOu3LlyltvvVVaWiphzzoIxVq5pqZGoqn1viHX r1+X6WzatKmysvLy5cvBptDZ2VlVVVVWVmYd4duLXn/9dUli+kyDKVOmmAZ9 t7S0SJCW6KgqSISznspos6b7ChopEQCcka4nLU1S4uDUVF9hYWG0Zwexhy44 0Ug8k1x96dKlYBUkzR47dqy1tTXspMLWdF+hmyYKAE6Vl5cPHCgpcUhKii8n Jyfas4PYQxcMj6OJAoAzPSkxOznZN2TIkGjPDmIPXTA8jiYKAM6UlJQMGCAp sZCUCGfoguFxNFEAcKawsDA1VVJiuf2rzAFGdMHwOJooADiTk5OTkiIp8UBS 0pBBg3zNzc3RniPEGLpgeJy0z+zs7IIeFRUV0Z4jxBKuJIxE5r89n6TE5qSk 7LQ0Hzfpgx3Sz+o+V/pfUiK8TNpnUVFRVY+WlpZozxFihnSRPh+XiUPiMtz+ 7GZK5EcT7JB+Vve50v+SEuFl7O6GM9IhDh448N7kZE7aR8IypMQSLqwNB+iC 4XE0UTgjKdE3cODn/WftR3tegCjoufHKEFIiHKMLhsfRROGMdIi+AQO+Lilx 0CBO2kcC6rlYYqE/JR7gwtpwgC4YHkcThTOFhYW+1NTBkhLT0jhpHwmIlAj3 6ILhcTRROENKRILruaR2iT8lNnNhbThAFwyPo4nCmZycHJ//UnGkRCQm/yW1 B/svqU1KhEN0wfA4miicUSlxiKTEgQO5AAgSkOHGK93+x2BSIiJFFwyPo4nC mZsXS0xOLpSUOGAAQzuRgPw3Xhniv/GKSoncfgURowuGx0n7LCgoKOrBvVdg EykRCc5w45VufWFtzr5AWOpi2gr3XoHHqZRY0YN7r8Amn/+CwstIiUhUpEQ4 IylR97ncewUex+5uOKNSYrmkxNTUwsLCaM8O0N9IiXCPLhgeRxOFM6REJDjD 7fnUo3DgQG7ljMjQBcPjaKJwhpSIBEdKhHt0wfA4miicISUiwVlSIrdyRsTo guFxNFE4Q0pEImtubh40SL4BQ0iJcIMuGB5HE4UzpEQkMlIiegVdMDyOJgpn SIlIZAFTYmqqj+8CIkIXDI+jicIB6SL/mBJTUnJycqI9R0C/qqysTEuTb0C2 ISWWkxIRKbpgeJy0T3XLFaWqqirac4QYcDMlDhqkbk5GSkQCIiXCsZaWFt3n FhQUkBLhZereK+t7kBJhBykRCS5QSjyQkuLju4CwpJ/VfS4pER7H7m44QEpE gisvLx84UFJiISkRbtAFw+NoonCgsrLSl5aW7b85mS85eciQIdGeI6BfkRLR K+iC4XE0UTggXaRv4MBCUiISFSkRvYIuGB5HE4UDpaWlKiV2+28+4fP5oj1H QL8qKbl5dcSkpBJDSmxOTvbxiwkRoQuGx9FE4YB0kb4BA1QHmZ2UNHjQoObm 5mjPFNB/SInoFXTB8DiaKBwoLCz0paaW96REX1paZWVltGcK6D/yFUhNVVcM 7TY8fOxXR0ToguFxNFE4MGTIEF9ycrO64YR0jQMGcGMyJJQQKZH96rCPLhge RxOFA+rGK7dO2ediOEg80uBTUnz+5m9MiUMGDSIlIgJ0wfA4de+Vqh4tLS3R niN4nR7gfOtkLIY5I/FIg09O9vmbvzElZqel+Tj7AqFJP6v7XOl/SYmJo7q6 uqysrLi4eOPGjQcPHrRWOHnyZGlpqVSQVtHY2BhiUmFruq+gyLPZ2dkFPSoq Kmx8UCS0m0NXDCnx1jDnQYP27t0b7VkD+ol/b7rv3RFRHoUDB/rkZ1S05w6e pm7Mp0j/S0pMBB0dHfPnz09/tzlz5ly9elXXkU3H0KFD9bMZGRlbtmwJOLWw Nd1X0NjdjUjdPCkxJcV4PpYawELniMQRJCXeHPjMObqwjy44QaxZs0bC2Pjx 419//fUzZ87s379/4sSJUvLUU0+pCrW1tRLVhg8fvn379qamps2bN6sUd/z4 cdOkwtZ0X8GIJopIGU9KVI9yTk1EIgl0E+dbX4XUVF9hYWG0ZxAxgy44EXR1 dWVlZUkMk3imCyWeSYlEtY6ODvlT7WksKyvTFTZt2iQly5cvN00tbE33FYxo ooiI6aRETk1EAgp04xVuvwIn6IITwfnz56dMmWLdMowcOVKy2dmzZyVGZmZm yv+lpn724sWLGRkZI0aMuHHjhi4MW9N9BdNM0kQREeOVErstB505bx+JQL4F lktqc2FtOEEXnLAaGxslqkkw6+zsbG5ulv9nZWWZ6owdO1bKT58+rUvC1nRf wVROE0VEbh5u7rlS4rvOx5KUmJrKsTYkgiAXS7w1louL4cA+uuDEdOPGjVmz ZkkqW7BggfxZV1cn/58+fbqpmpRIeU1NjS4JW9N9BVM5TRT2BTzczEFnJJog l8HhYjiIGF1wAurs7FyyZIlEslGjRp09e1ZKDh8+LH/m5uaaaubn50v5oUOH dEnYmu4rmMpporAv2OFmDjojoQQZ4MwwZ0SMLjjRXL9+/YknnpA8NnLkSL3j TiW3vLw8U2UpkfKqqipdEram+wqmcpoo7FOjmwPuQNEHnTl1H/Et+NAVBrAg YnTBCaWtrS03N1edFlhXV6fL6+vrpXDq1Kmm+lOmTJHyhoYG+zXdVzCVq3uv tFg4+PiIbyEON3PQGYmjpKQkZEpkAAsCs/azgpSYOJqbm9U1EidNmmQ6dVnS o5RnZmZ2dXUZy9UgaHnWfk33FUyzre69YiXR0c3SQPzJyckJcbiZg85IECGH rtwawCKiPZvwnIC9rfTLpMRE0NraOn78eFnd+fn5ly9ftlaYMGGCaVee/F9K pJFEWtN9BSN+yMCm0Ieb/3h5bQ46I66FHLrCABZEhi44QRQUFKizAa9fvx6w wurVq6XCvHnz1BUL5d/HH39cStasWRNpTfcVjGiisCPs4WYOOiNBhBy6wgAW RIYuOBEcOXJE3S55xIgRYyxOnTrV7T+UrO7PkpubK2lt5syZ8v9x48ZZDwGH rem+ghFNFHaEHt3MQWckiHBDVxjAgsjQBSeCrVu3pgd34sQJVa2lpSUvLy8j I0OVS4RramoKOMGwNd1X0GiisMPO4WZGOiPu9ZyUaL3rCgNY4ARdMEwuX758 7Nix1tZW9zXdV+imicIGm4ebOeiMuCc/f8INXVGPIZyaCDvoguFxNFGEpQ43 h957wkFnJAL/DSrt7FZnAAtsoQuGx9FEEdaQIUOkazwQUUocMICz9xFnJPUN GhR26AoDWBABumB4HE0UYamTEm1GRHnslX40JYVTExFn7A1dYQALIkAXDI+j iSI0dVJidiQpkVMTEZdsXE+bASyIDF0wPM50NXiaK0wiGrqiHz7//SeiPe9A b5LUl5JiMyUygAVBVVRUcO8VxArTfZyjPTvwHPtXSmQAC+Kb7aErDGBBKNzH GTGEJorQ1NAVm/2ifhRKShw4kLP3EU9s3HXF+GAAC8KjC4bH0UQRms+/AyXS lFjCMGfEl0iGrqgHA1gQHl0wPI4mitAiHeB8q4NkmDPii/zkGTAg7F1XjA8G sCA8umB4HE0UIVRWVvrS0iIa4Hyrg2SYM+JLJAOc9ePmb6zm5uZozzu8iy4Y HkcTRQjOBjiTEhF/pDFHMnRFPRjAgjDoguFxNFGEUFJScvP0wshTojyGSFAc NIgdKYgPEQ5dUQ8GsCAMumB4HE0UITi7DM6t3ShcDAfxIvKhK+rBABaEQRcM j6OJIoRI7+BsfKiL4Uj3Gu0PAbjl6KTEbgawICy6YHicuvdKQY+KiopozxE8 xNnFEm8dbONiOIgXjk5KvLVPnVMTYSL9rO5zueUZPE7de6WqB7dfgZGzy+Dc OtjGxXAQLxydlHjr1xKnJsJE+lnd50r/S0qEl7G7G8E4vgzOrYNtDHNGXHB6 UuKtX0spKb77778/2h8CHkUXDI+jiSIYx5fBMaZEn88X7c8BuJKTkyNJL/KT Ev/4PeCgM4KhC4bH0UQRjJsBzrdOyWKYM2Jcc3Ozi8PN6lE4YIBPvk3R/ijw IrpgeBxNFMHk5OSQEpHg8vPzXRxuvnXQOTmZg84IjC4YHkcTRTBuBjirB8Oc Eet8Pp9/dLOzy0H98QdTWpqPq0LBii4YHkcTRTBuBjjf2ofCMGfEMvmB49+R 6HgIl36Up6Rw4UQEQBcMj6OJIiCXQ1fUg2HOiGm9tCNRfRXYnYgA6ILhcTRR BKTu4OwyJXb33M2ZUxMRc3pvR+IfdydydiJM6ILhcdI+CwoK1veoqqqK9hzB E9wPcFYPBrAgFqmhzb20I/GPuxMldnKaLqSf1X2u6n+jPUdAUKREBOR+6Ip6 MIAFsSgnJ2fAAJdDm62PA+q2zhJBo/35EE2kRMQQdncjIPdDV271iwxgQayp rKxMS1PXSHT/O8n0KExN9fF1gEYXDI+jicKqV4au6MNsDGBBbJFfSC5uthLm C5Gc/ADDWKDRBcPjaKKw6q2TEtWDUxMRQ3qONffWoBXro5zjztDoguFxNFFY 9dZJierBqYmIFeXl5X12rNn44LgzbqELhsfRRGE1ePDgXjkpUT3UqYlZWVnR /lhAKGpcs/9Yc2+Naw72uHXcmZ9OoAuGx9FEYdKLJyXe6hE5NRGxICcnJzW1 T481v+vHU3Ky79///QHOxEhwdMHwOJooTNRJiRt7tUfk1ER4XElJif9Y85B+ iYjqUSKhlF9PCY4uGB5HE4VJ756UeKs79J+aKPkz2h8OCEB+vwwapE5H7Otj zcbHzetsDxjg43uRyOiC4XHSPouKiip6cFXtBKcON/f6/pQDHHSGVxlORyzp x4h4KygmJ/s4QTHRtLS06D6Xq2onoI6OjpUrV77++uvWp06ePFlaWlpcXCyt orGxMcREwtZ0X0GRZ7Ozs4t6kBITnDrc3Ls3nFAPDjrDm/r+0jehH7cujMNX I3FIONR9LikxAT3zzDPp6emrVq0ylZeXlw8dOjS9R0ZGxpYtWwJOIWxN9xU0 dnfDqC8ON6tHSVLSAxx0hsdE43TEAF8OTlBMWHTBCeXq1avy00AFM1NKrK2t lag2fPjw7du3NzU1bd68WaW448ePmyYStqb7CkY0UWjqcHMf7VJhpDO8Jkqn Iwb8cnCCYoKiC04cBw4cGDdunN59Z0qJ8+fPl8KysjJdsmnTJilZvny5aTph a7qvYEQThZaVldWLt1yxPtRBZ+5NBo+Q3yxROh0xQFBMTvZJZOXbkWjoghPH qFGjJInNmTNn7dq1ppTY1dWVmZkphefPn9eFFy9ezMjIGDFixI0bN+zXdF/B NNs0USiVlZU+0ZcdYbn/8trsToQX5OTkDBwYxdMRA3w/uHNfAqILThyFhYVv vPGG/Gf79u2mlCjfeimx3nti7NixUn769Gn7Nd1XMJXTRKFIp9lH41b+uLvE f/oXY1gQdZ451mx63DzuzJ37EgpdcAKypsS6ujopmT59uqmmlEh5TU2N/Zru K5jKaaLoNuxI7NOb13azOxHe0HOsue9Or3D4Q0pdGIfjzomDLjgBWVPi4cOH pSQ3N9dUMz8/X8oPHTpkv6b7CqZymii6e3Yk9sPpWXp3Iv0goqWwsNBjx5rf 9UNKHXeO9kJCP6ELTkDBUmJeXp6pppRIufEShWFruq9gKqeJ4ubQ5rS0frsU SHnPYGfOv0L/U7vNvXes2fhgvHMCoQtOQNaUWF9fLyVTp0411ZwyZYqUNzQ0 2K/pvoKpXN17pcXCwQdHLFI3nvClpPRnl5ntv2Ef51+h/0mr88y45mCPm8ed //3fH+D03Thj7WcFKTEBWVNiW1ublGRmZnZ1dRlrjhw5UsrlWfs13Vcwza26 94qVREeXywEx4eax5gED+vnYm7p2oi8tjRuToT+Vl5d74Bradh43r7PNz6g4 E7C3lX6ZlJhorClRTJgwwbQrT/4vJdJITC8PW9N9BSN+yCSymxGxH481Gx/6 uDM7TNBvvDpoxfpoTk5+QAIt3464RxecgAKmxNWrV0vhvHnz1BUL5d/HH39c StasWWN6edia7isY0UQT1s3TEf1XA4nW6VklPeOdOUER/UAavIcHrVgf5RJo GcYS9+iCE1DAlNjW1paVlaVGH0tamzlzpvx/3Lhx1kPAYWu6r2BEE01Me/fu vRkR+/d0RNOjuecERbpC9IOeHYmeHbRi/X78J7sT4x5dcALasWOHNSV2+89c zcvLy8jIULfwkwjX1NQUcApha7qvoNFEE5D0O4MHD5aIGPUz+G9dGIegiD4W azsS1YPdifGPLhgmly9fPnbsWGtrq/ua7it000QTjx7U7JH+UgdFLv2BvhM7 ZySavxzsToxvdMHwOJpoQvFaRNR94QPJyQRF9JGeoc19epfyPnow2DnO0QXD 42iiiUMi4pAhQ3ypqZ6KiDoocm0c9BFJWZK1Ym1H4q1vhroVC7sT4xVdMDyO JpogJCKq2/B5MCISFNGnem620td3Ke+jR/bAgT6+FPGKLhgeRxNNEOrq2R6/ mrC6iKJv0CD6RPQWaUv+cSuF0W7djh8HuLNzHKMLhsdJ+0w3oLnGpShePZug iOiSfCUNKnYugBPwkc0YlnhCt4sYwg+ZuCdxS0XEWOkmS7gtC3pJLI9bedd3 gjEs8YouGB5HE41vErSie4MV50ExNZWjbHCpsLDQP24l6lcGdflo5qBzvKIL hsfRROOYvu5NzI3t1Ldl4do4cEOav/9wc4yOWzE+bh50Li8vj/YSRS+jC4bH 0UTjmBqx4tlBzWGDohryzHFnONNzv5XYHbdifBxISeGgcxyiC4bH0UTjlT7W HLu7UW6OZElJ4UAbnJFMFYP3Wwn2aPYP6mIMS7yhC4bH0UTjlbo64sJo922u +kV13DktjQNtiNTNX0m+OBi3YnwUDhjg4xyMOEMXDI+jicalW2ckRrtXc/9Q uxM50IZISZqSTBUvh5vVgzEscYguGB5HE41L0kX6Bg6Mgw7y1tmJPg60ITJx NG7F+Lg5hqW0tDTaSxe9hi4YHkcTjUs379eckhJbV78J2i8mJQ3mtn2IRHyN WzE+ylNS2J0YV+iC4XHSPrOzswt6VFRURHuO0Avi43CzehzgoDMiJDnKP24l Pn4nGR/N3IclDkg/q/tc6X9JifAyaZ9FRUVVPVpaWqI9R3CrvLzcN3BgjF4A J2C/qG7FEu3litjQc+PmuPkGmB7sTox50s/qPlf6X1IivIzd3fFHpcR4Oth2 c6iqzxft5YrY0HNGYvztSFSPZq6wHU/oguFxNNH4c3PoSmpqfFwkTj3U9XA4 yoawcnJy/EOb43VHonocuP32wUOGDGlubo728oZbdMHwOJpo/Lk5dCU5OZ7G dpb479bHABaEJr8j/BeSj7+hzdZHNtdOjA90wfA4mmj8iaehK+rBABaE1dDQ 4L9puc//qyLqbbavH83+21f6li1bFu0FD1foguFxNNE4U1JSEmcnJXb3DGAZ PHgwB50RTGIcazY+yiUo3n///XwpYhpdMDyOJhpn1OHm+Dtxv9B/0JlDbAhI ImJamkTEIdFup/38KElN9XGCYkyjC4bH0UTjSUlJyeDPfS4ud6ao3Yn/+q// yp4TmMhvB39EjONxzSG+FjdPUCQoxi66YHgcTTRu7N271+c/dz9ee8qbY1hS U++///5oL2l4SAJHRPUgKMY2umB4nLTPgoKCoh7ceyVGSQcxePBgX0pKHJ+4 3+w/oMhxZ2gJHxH/+M349Ke5Nk7MUBfTVrj3CjxOpcSKHtx7JRbd3IvoH94Z l8eaTd3hA8nJEhTz8/OjvdQRZT3nIiZ4RNTfjCHsUYwVkhJ1n8u9V+Bx7O6O dZWVlWovYtxHRN0dqqBIh5iwZL0TEQN9M7LVYJbS0tJoryLYRRcMj6OJxrSb 170ZNEgiYpxd+iZsd6gOPUuHyKW2E41ERFnv/oveDCEiWr4ZJfIT6t///QG+ F7GCLhgeRxONUWp3is+/PyWOz0UM1R36Rz1LSKZDTByVlZU+ny81NQEvemP/ UeL/Wvj4XsQEumB4HE00FuldiAm+M+VWUGSnYmKQVexv9Ql16WyH3wz/14LT FGMAXTA8jiYaWyorK29eN3vgwMTchWh9NPsTg9qpWFhYSJ8Yr2Tl9tyjmYZv 65uRnPwAQdH76ILhcTTRWHHrEDO7EK3dITsV4xpjVVx8M24NfC4vL4/2akRg dMHwOJpoTLh5iFlICmJPSvAe0ZgV6Rbjg4qI/n3n/DZy+LWQ7wS/njyLLhge RxP1uFuHmBN1lIqDTvHm8OeUFA5AxwEVERmr4vqxjIHPnkUXDI+T9qluuaJU VVVFe45wi3SRjFJx8OAAdNzoiYiMVXH/uHWFHPaxe0FLS4vucwsKCkiJ6H8n T54sLS0tLi6W5tfY2BiiplTIzs7WdwsiJXrErV2I/qvCsQvRwUOy4jIulRPL JCL2XBQx6q0pPh63Dj2zgz3q1C1XFFIi+p/8Whw6dGh6j4yMjC1btgSrzO5u D1IXhVO7EJuj3bXE9GOZYacinWMMkY1Yz3AVvgG9+ChMTfVJ/I726sUf0QWj n9XW1kosHD58+Pbt25uamjZv3qwS4/HjxwPWp4l6zc2ImHi3U+m7h/FGLQTF WCEry39dxI3Rbj5x9mhOTr5f4rdsZKK9hnELXTD62fz58yUTlpWV6ZJNmzZJ yfLlywPWp4l6jdqLuCza3Uk8PW5dU3HAAPaixAQJ8/IlSEr6TrQbTlw+Fg4Y wG1ZPIQuGP2pq6srMzNTMuH58+d14cWLFzMyMkaMGHHjxg3rS2iinlJYWOgb OHBEtDuS+Hs095yjyF4U7+s53Myglb547E1J4aCzh9AFoz/Jb3CJiFlZWaby sWPHSvnp06etL6GJesrNlJiaWh7tjiQuH7+WoJiWRkr0PllH/pTIr6W+eGxM TfXJdibaKxm30AWjP9XV1UkanD59uqlcSqS8pqbG+hKaqKfcTIkDBuRHuyOJ y8d/SkocNIhTE71P1tGgQTd3/XL9pz54jPh//49rznsIXTD60+HDhyUN5ubm msrz8/Ol/NChQ9aX0EQ95eYZWYMGPZCcvCzafUmcPbKTkganpnKgLVaUlJT0 3G+FMc69+ChMSbl5MZxor178EV0w+pNKiXl5eaZyKZHygNdCpIl6za0raRMU e+lxa+iK9I4+hnbGDHXXFf/1Ev+ToNhLj3z/l4BvgbfQBaM/1dfXSxqcOnWq qXzKlClS3tDQYH2JuvdKlUVLS0u/zDL+//bu56WK7g/geOugVbsvuHbzPHB5 HmghBF+4X3xW9x8QWt3wRoQGN8TrpiAwA4mLgltvUUaIBM+iX1x05araGJRR lBlGFy5GF2lT6fdz5zjjOHN/jDPqOTPzfiEhx6NM9pk+n/nMnDMt7BaKvLU5 2sfeS1gymXw+T3KMFykU5V/N3lib8yDiqVCiRNROsqo/1Ur+pUrEsWk0GlIN Dg8Pb29vu8eHhoZkXL7q/xb17pVJH+JWL9VLadaKPT15a3do+ikHSoplez9t yYzs+xFT6iWV1u6hqlbkMcUQp8KsOg+4UNJOsqo/1Ur+JdviOI2OjnrahvK5 jEgotpxPu9tk1Wp1t1a0WiqlEydY/tw5I86pPbSpDxNEzoKBgfPy72mtZylz wRT4Y87aJTTHiWAyUjCO2fz8vNSE09PTandE+XNqakpGFhYWWs4nRM1Xq9Wa a5+lVsxkVGuxTLno+vAWh9lsPp8nLSaJ01S0n1Qs01fsdkI0G7DyG6OFaDhS MI5Zo9EoFotqpbNUhuPj4/L5yMhIy9vNO4RofEii3GstSrloveU5zeWitzjs 61PFITkxqdy1otVXzKc4/DucE+pSifowHkjBOH71en1iYqJQKAxapFzc2Nho N5kQjR1vuWh1F0tWekhDe6VdccgWcCnh1IrWpZKztiXlt6Gb54T9nAX1YZyQ gqHL1tbW2tra5uZm52mEaHw55eL5gQEplqRclMIpn8S7caoybC5I8d1WJhum kxP89m3odD63617Hn1NnBPvGxwspGIYjRBNAZczms4tCZU3X/ej4NllUCiyp LCg1sNUooTiEm3pqd39rMXnXSS3PjLx9wZTjQdz4IgXDcIRowqj7ce770c49 uVgkzppV2c6524bWPWX5G5EH0Y6ntZjcO9ErqqdunRY0D5OAFAzDEaJJ1bnB qD3b+YvD3bahWpxJ2xChqKskFfL2FVIC7kTv3Vl2njzkQdxkIAXDcIRo4kne lEJrX4PRmHLRuxRFji1HhwRROZdI+xvqsbsTXfMvS6GjnjCkYBhO4nPQhXBN Nv/9aC3lYst1ypLTyYA4XLG9E91creV57JDrpsQg7SJGuJBJp5bb6Rx1q4Ud DqGLuj6SeDN7TbT3zjJbPCUeKRiGI0RTruVm3XNHlPzUY/eZDMUhdJGo862J NqG1yJ3llCIFw3CEKBSn2aKWuuQP6Y25K/tfrEz6gwlabbeo5alFNjxMO1Iw DEeIwkM1W6LXinv1od08JP3BNK22Wzz0Vnq7+tD75KHuXwY0IAXDcIQoWtrd UcSuFQ+UOGvOVtj2k4e6/zZAJ+6nFo++VtyrD8Xs7CxXT2lGCobhCFF04KkV g9yQcx6+pz5EvLhvQ7t2oz/c+rCk6kPODiikYBiOEEVXkj2by1v6+s6fPNkh a+62EE+fVmszdR81EJJTK9p9xehLoZvPH1Ifwo8UDMMRoghir6nYplDcLRF7 e8+dO8fiZSSA1IpS0WUyzdZ4tLVcc2pxP1dP8CMFw3CEKIKT8i9ndVj8hWKz RMxkxsbGdB8jcGjUxZG9DjrEDejdW8zyE+TU4PlD+JGCYTiJz5IL4YrOJNP9 c/Zs7uTJqqdE7O2lREQiObWi1VQMXiiuqBZiPp+nuw635eVlJ+fy7hUYTuKz UqnUbboPBzFQrVZz2WzezoVV+1lE3ccFHCEpFM+c+a/dVOxaIlatJf65YrGo +8BhnLoLvUQYjhBFCM3FLD09ZSsdNjdF7OujW4LEq9VqAwPnAxSKuyXi7Oys 7kOG6UjBMBwhihDUA4pq8aeUi9xrRkpIoZjP561CsdTuRrOUiIL3LyMIUjAM R4ginGY70Xrpcy6TISEiPVRH0dokx/+M4orVWadERFCkYBiOEEU4zY1xenv/ lKSYzbJ4E6kiAW8vZvHsNF+SErFcLus+QMQGKRiGI0QRzosXL/6XzZ61toHT fSzAcZOrJCkI9z+gOHf6dHNFs+5DQ5yQgmE4QhShqVffsm4FKaReSHTq1D+u N7M090XkdMCBkIJhOEIUAEKoVqt//91vtxOrPT05lnHhoEjBMBwhCgAhqKcT rWUsKydOlKRiZNEKDooUDMNJfE5OTlZsy8vLuo8IAOJhbGzM2hWn+aZmHtBF QKurq07O5ZVnMJyqEpdtvH4FAAJ69eqVtYalv6eHpc0ISqpEJ+dKoUiVCJPR 7gaAcNbX17NZqRL/6u3Nzc3N6T4cxA8pGIYjRAEgtFwud+rUf/744wwPJSIE UjAMR4gCQGhSHPb397O6GeGQgmE4QhQAopASkW0SEQ4pGIYjRAEA0IIUDMMR ogAAaEEKhuEIUQAAtCAFw3CEKAAAWpCCYTi1q/a/ttXVVd1HBABAYkmedXKu yr+6jwjH6ufPn/fv33/+/Ln/S1++fHn06NG9e/ckKj5//tzhh3SdGX2CQpUI AMCxoUpMubt37w4ODj548MAzXq1WL1y4MGgrFArPnj1r+RO6zow+wUG7GwAA LUjBqfLjx49KpaIKM0+V+P79eynVLl68uLi4uLGx8fTpU1XFffr0yfNDus6M PsGNEAUAQAtScHqsrKyMjIw47TtPlTgzMyODT548cUYeP34sI7dv3/b8nK4z o09wI0QBANCCFJwely9flkrs1q1bDx8+9FSJ29vbw8PDMvjt2zdn8Pv374VC 4dKlS79+/Qo+M/oEz2ETogAAaEEKTo9yufzy5Uv5ZHFx0VMl1mo1GSkWi55v uXLliox//fo1+MzoEzzjhCgAAFqQglPIXyV++PBBRq5fv+6ZKSMy/u7du+Az o0/wjBOiAABoQQpOIX+V+ObNGxm5ceOGZ+bNmzdl/PXr18FnRp/gGSdEAQDQ ghScAG/fvl1tY2Njwz+/XZU4MTHhmSkjMu7eorDrzOgTPOOEKEKr1+uTk5O6 jwLQTM4CdppFOKTguPv9+/dgezMzM/5v8VeJHz9+lJFr1655Zl69elXG19fX g8+MPsEzLvFZqVT8BbAUAAF+PUg1qkRghyoRwch/mP5UK/mXKjHu7ty5c7uN paUl/3x/ldhoNGRkeHh4e3vbPXNoaEjG5avBZ0af4Dlaic9SqTTpQ9yiK6pE YIcqEcGoN614SP4l26aNv0oUo6OjnlaefC4jEiGeb+86M/oEN9rdCI0qEdih SkQEpOAUalklzs/Py+D09LTasVD+nJqakpGFhQXPt3edGX2Cm7rjvAwcnLo0 1n0UgGaqHaT7KBBL3LlLoZZVYqPRKBaLavWxVGvj4+Py+cjIiP8WcNeZ0Se4 1ev1CgAA0OEIyhAYbWlpyV8l7lj12MTERKFQUCtfpIRruUQ6yMzoEwAAAGCU ra2ttbW1zc3N6DOjTwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAGn2f35pr6s= "], {{0, 244.}, {433., 0}}, {0, 255}, ColorFunction->RGBColor, ImageResolution->144.], BoxForm`ImageTag["Byte", ColorSpace -> "RGB", Interleaving -> True], Selectable->False], DefaultBaseStyle->"ImageGraphics", ImageSizeRaw->{433., 244.}, PlotRange->{{0, 433.}, {0, 244.}}]], "Output", TaggingRules->{}, CellChangeTimes->{ 3.834323869887108*^9, {3.834323925761251*^9, 3.834323944297655*^9}, 3.8343239857755203`*^9, 3.834335279445315*^9, 3.8344058113305197`*^9}, CellLabel->"Out[17]=", CellID->26690687] }, Open ]] }, Open ]], Cell[CellGroupData[{ Cell[BoxData[ InterpretationBox[Cell["\t", "ExampleDelimiter"], $Line = 0; Null]], "ExampleDelimiter", TaggingRules->{}, CellID->1065155690], Cell[TextData[{ "The \"fundamental problem of causal inference\" (", ButtonBox["https://en.wikipedia.org/wiki/Rubin_causal_model", BaseStyle->"Hyperlink", ButtonData->{ URL["https://en.wikipedia.org/wiki/Rubin_causal_model"], None}, ButtonNote->"https://en.wikipedia.org/wiki/Rubin_causal_model"], ") is said to be that we can observe only the treated outcome or the \ untreated outcome on the individual, but not both. The problem is essentially \ one of missing data. But Wolfram Language can impute missing values, which, \ as described in ", ButtonBox["these lecture notes", BaseStyle->"Hyperlink", ButtonData->{ URL["http://faculty.washington.edu/yenchic/19A_stat535/Lec12_causal_\ missing.pdf"], None}, ButtonNote-> "http://faculty.washington.edu/yenchic/19A_stat535/Lec12_causal_missing.\ pdf"], " and ", ButtonBox["this journal article", BaseStyle->"Hyperlink", ButtonData->{ URL["https://academic.oup.com/ije/article/44/5/1731/2594566"], None}, ButtonNote->"https://academic.oup.com/ije/article/44/5/1731/2594566"], ", suggests a direct approach to causal inference. First, create a new \ dataset with missings:" }], "Text", TaggingRules->{}, CellChangeTimes->{{3.8343335846266317`*^9, 3.834333612929496*^9}, { 3.83433375125255*^9, 3.834333812229053*^9}, {3.83433402626615*^9, 3.8343340443045673`*^9}, {3.83440582173698*^9, 3.8344058455646887`*^9}, { 3.834405924653019*^9, 3.8344059446955*^9}, {3.8344060724690857`*^9, 3.834406090857482*^9}}, CellID->1583796361], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"lalondeCounterfactual", "=", RowBox[{ RowBox[{ RowBox[{"Query", "[", RowBox[{"RandomSample", ",", RowBox[{ RowBox[{"Association", "[", RowBox[{ RowBox[{"KeyDrop", "[", RowBox[{"#", ",", RowBox[{"{", RowBox[{"\"\\"", ",", "\"\\""}], "}"}]}], "]"}], ",", RowBox[{"\"\\"", "->", RowBox[{"If", "[", RowBox[{ RowBox[{"#treat", "==", "1"}], ",", "#re78", ",", RowBox[{"Missing", "[", "]"}]}], "]"}]}], ",", RowBox[{"\"\\"", "->", RowBox[{"If", "[", RowBox[{ RowBox[{"#treat", "==", "0"}], ",", "#re78", ",", RowBox[{"Missing", "[", "]"}]}], "]"}]}]}], "]"}], "&"}]}], "]"}], "[", "lalonde", "]"}], "//", RowBox[{ InterpretationBox[ TagBox[ DynamicModuleBox[{Typeset`open = False}, FrameBox[ PaneSelectorBox[{False->GridBox[{ { PaneBox[GridBox[{ { StyleBox[ StyleBox[ AdjustmentBox["\<\"[\[FilledSmallSquare]]\"\>", BoxBaselineShift->-0.25, BoxMargins->{{0, 0}, {-1, -1}}], "ResourceFunctionIcon", FontColor->RGBColor[ 0.8745098039215686, 0.2784313725490196, 0.03137254901960784]], ShowStringCharacters->False, FontFamily->"Source Sans Pro Black", FontSize->0.6538461538461539 Inherited, FontWeight->"Heavy", PrivateFontOptions->{"OperatorSubstitution"->False}], StyleBox[ RowBox[{ StyleBox["FormatDataset", "ResourceFunctionLabel"], " "}], ShowAutoStyles->False, ShowStringCharacters->False, FontSize->Rational[12, 13] Inherited, FontColor->GrayLevel[0.1]]} }, GridBoxSpacings->{"Columns" -> {{0.25}}}], Alignment->Left, BaseStyle->{LineSpacing -> {0, 0}, LineBreakWithin -> False}, BaselinePosition->Baseline, FrameMargins->{{3, 0}, {0, 0}}], ItemBox[ PaneBox[ TogglerBox[Dynamic[Typeset`open], {True-> DynamicBox[FEPrivate`FrontEndResource[ "FEBitmaps", "IconizeCloser"], ImageSizeCache->{11., {2., 9.}}], False-> DynamicBox[FEPrivate`FrontEndResource[ "FEBitmaps", "IconizeOpener"], ImageSizeCache->{11., {2., 9.}}]}, Appearance->None, BaselinePosition->Baseline, ContentPadding->False, FrameMargins->0], Alignment->Left, BaselinePosition->Baseline, FrameMargins->{{1, 1}, {0, 0}}], Frame->{{ RGBColor[ 0.8313725490196079, 0.8470588235294118, 0.8509803921568627, 0.5], False}, {False, False}}]} }, BaselinePosition->{1, 1}, GridBoxAlignment->{"Columns" -> {{Left}}, "Rows" -> {{Baseline}}}, GridBoxItemSize->{ "Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}, GridBoxSpacings->{"Columns" -> {{0}}, "Rows" -> {{0}}}], True-> GridBox[{ {GridBox[{ { PaneBox[GridBox[{ { StyleBox[ StyleBox[ AdjustmentBox["\<\"[\[FilledSmallSquare]]\"\>", BoxBaselineShift->-0.25, BoxMargins->{{0, 0}, {-1, -1}}], "ResourceFunctionIcon", FontColor->RGBColor[ 0.8745098039215686, 0.2784313725490196, 0.03137254901960784]], ShowStringCharacters->False, FontFamily->"Source Sans Pro Black", FontSize->0.6538461538461539 Inherited, FontWeight->"Heavy", PrivateFontOptions->{"OperatorSubstitution"->False}], StyleBox[ RowBox[{ StyleBox["FormatDataset", "ResourceFunctionLabel"], " "}], ShowAutoStyles->False, ShowStringCharacters->False, FontSize->Rational[12, 13] Inherited, FontColor->GrayLevel[0.1]]} }, GridBoxSpacings->{"Columns" -> {{0.25}}}], Alignment->Left, BaseStyle->{LineSpacing -> {0, 0}, LineBreakWithin -> False}, BaselinePosition->Baseline, FrameMargins->{{3, 0}, {0, 0}}], ItemBox[ PaneBox[ TogglerBox[Dynamic[Typeset`open], {True-> DynamicBox[FEPrivate`FrontEndResource[ "FEBitmaps", "IconizeCloser"]], False-> DynamicBox[FEPrivate`FrontEndResource[ "FEBitmaps", "IconizeOpener"]]}, Appearance->None, BaselinePosition->Baseline, ContentPadding->False, FrameMargins->0], Alignment->Left, BaselinePosition->Baseline, FrameMargins->{{1, 1}, {0, 0}}], Frame->{{ RGBColor[ 0.8313725490196079, 0.8470588235294118, 0.8509803921568627, 0.5], False}, {False, False}}]} }, BaselinePosition->{1, 1}, GridBoxAlignment->{"Columns" -> {{Left}}, "Rows" -> {{Baseline}}}, GridBoxItemSize->{ "Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}, GridBoxSpacings->{"Columns" -> {{0}}, "Rows" -> {{0}}}]}, { StyleBox[ PaneBox[GridBox[{ { RowBox[{ TagBox["\<\"Version (latest): \"\>", "IconizedLabel"], " ", TagBox["\<\"1.0.0\"\>", "IconizedItem"]}]}, { TagBox[ TemplateBox[{ "\"Documentation \[RightGuillemet]\"", "https://resources.wolframcloud.com/FunctionRepository/\ resources/FormatDataset"}, "HyperlinkURL"], "IconizedItem"]} }, DefaultBaseStyle->"Column", GridBoxAlignment->{"Columns" -> {{Left}}}, GridBoxItemSize->{ "Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}], Alignment->Left, BaselinePosition->Baseline, FrameMargins->{{5, 4}, {0, 4}}], "DialogStyle", FontFamily->"Roboto", FontSize->11]} }, BaselinePosition->{1, 1}, GridBoxAlignment->{"Columns" -> {{Left}}, "Rows" -> {{Baseline}}}, GridBoxDividers->{"Columns" -> {{None}}, "Rows" -> {False, { GrayLevel[0.8]}, False}}, GridBoxItemSize->{ "Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}]}, Dynamic[ Typeset`open], BaselinePosition->Baseline, ImageSize->Automatic], Background->RGBColor[ 0.9686274509803922, 0.9764705882352941, 0.984313725490196], BaselinePosition->Baseline, DefaultBaseStyle->{}, FrameMargins->{{0, 0}, {1, 0}}, FrameStyle->RGBColor[ 0.8313725490196079, 0.8470588235294118, 0.8509803921568627], RoundingRadius->4]], {"FunctionResourceBox", RGBColor[0.8745098039215686, 0.2784313725490196, 0.03137254901960784], "FormatDataset"}, TagBoxNote->"FunctionResourceBox"], ResourceFunction[ ResourceObject[ Association[ "Name" -> "FormatDataset", "ShortName" -> "FormatDataset", "UUID" -> "76670bca-1587-4e7e-9e89-5b698a30759d", "ResourceType" -> "Function", "Version" -> "1.0.0", "Description" -> "Format a dataset using a given set of option values", "RepositoryLocation" -> URL["https://www.wolframcloud.com/obj/resourcesystem/api/1.0"], "SymbolName" -> "FunctionRepository`$66a3086203b4405b88cdb0de8a5c3128`FormatDataset", "FunctionLocation" -> CloudObject[ "https://www.wolframcloud.com/obj/70389ad6-7dbc-48c8-b898-\ 72c65c00f14e"]], ResourceSystemBase -> Automatic]], Selectable->False], "[", RowBox[{"MaxItems", "->", "5"}], "]"}]}]}]], "Input", TaggingRules->{}, CellChangeTimes->{{3.834333635488084*^9, 3.834333717277853*^9}, { 3.834333747607382*^9, 3.8343337493282146`*^9}, {3.8343338172482567`*^9, 3.8343338630766373`*^9}}, CellLabel->"In[18]:=", CellID->1722456074], Cell[BoxData[ GraphicsBox[ TagBox[RasterBox[CompressedData[" 1:eJzsvXm8TdX/x3+MmQlRUYYQkszRqKKkFEpK+vSh0ohSSskQQikRGSpTIbNM XUN8Zb58DJdMyezeax6uyOz3/p33r/Xb7XPOPuucvc/ea+/9ev5xH/esvc4+ a71fa6+1XntYu1Tr9k3bZA4EAu/moD9NW33wQIcOrT58qgB9aNbu3ddfbffK y4+2e++VV1/pULt1FkoclikQ+DVrIPD//n8VAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAMBRrgSxam//BwAAAAAAAADAVRi7vL179+7btw+e EQAAAAAAAAD8iYHFO3/+/NogFy5csNAzZvgYP0fAz3V3F1DKKhBJZ0H81QS6 qAl08TbQF5ghqmfcu3fv/4JYdakRLdbPEfBz3d0FlLIKRNJZEH81gS5qAl28 DfQFZjD2jOIiI0Mf4RnN4+cI+Lnu7gJKWQUi6SyIv5pAFzWBLt4G+gIzGHtG vsiYHoT+oY/wjObxcwT8XHd3AaWsApF0FsRfTaCLmkAXbwN9gRkMPCNfZNyw YcPly5cvXbpE/1hyqREt1s8R8HPd3QWUsgpE0lkQfzWBLmoCXbwN9AVmMPCM 4iIjf7TqUiNarJ8j4Oe6uwsoZRWIpLMg/moCXdQEungb6AvMEMkzai8ycopV lxrRYv0cAT/X3V1AKatAJJ0F8VcT6KIm0MXbQF9ghkiekS8yHjx4UJtoyaVG tFg/R8DPdXcXUMoqEElnQfzVBLqoCXTxNtAXmCGsZwy9yMjQR/OXGtFi/RwB P9fdXUApq0AknQXxVxPooibQxdtAX2CGsJ5xz549oRcZGUo0eakRLdbPEfBz 3d0FlLIKRNJZEH81gS5qAl28DfQFZgj1jJEuMjLmLzWixfo5An6uu7uAUlaB SDoL4q8m0EVNoIu3gb7ADKGe0eAiI8OXGikbPGN8+DkCfq67u4BSVoFIOgvi rybQRU2gi7eBvsAMOs947tw5g4uMjMlLjWixfo6An+vuLqCUVSCSzoL4qwl0 URPo4m2gLzCDzjNGvcjImLnUiBbr5wj4ue7uAkpZBSLpLIi/mkAXNYEu3gb6 AjNoPSNfZCQzuHPnzt2GUAbKRpnpK/CMseLnCPi57u4CSlkFIuksiL+aQBc1 gS7eBvoCM2g946FDh/4XI/QVeMZY8XME/Fx3dwGlrAKRdBbEX02gi5pAF28D fYEZdPem2gBarJ8j4Oe6uwsoZRWIpLMg/moCXdQEungb6AvMAM9oP/ZEYOnS pbNmzZo3b16ifygmoL5bgFIyyBxlakZy3759s4Js27bN6bIkFjXjD6CLmkAX HWpOpeIG+tqGJwdZeEb7sScC9957byAQuO666xL9QzEB9d2Cl5RKSUl5+eWX X3vtta1bt1q7Z5mjTM1IJiUlBYJ89dVXTpclsagZfwBd1AS66FBzKhU30Nc2 PDnIwjPaDzyj06UA0fGSUo899hh33f/973+t3TM8o/rYE//du3cvi8bhw4cj fX3Lli2cZ+/evYkuqiKoqcuqVasiZTt27FiiS6sCavZXDhK2k+/du3fDf9Oo UaOXX365a9euCxcuDN1JaH4dy5cvz4i3G6FGO3To0A8++GDgwIHz588/deqU QXWgr22EHWT/+OOPUPWfffbZt99+e/DgwampqbqdhM2v5Z133uGcK1euNG45 mzZt0u08LS1typQpPXv2pHY7bty4HTt2RK0UPKP9wDM6XQoQHS8p9cILL3DX /corr1i7Z3hG9bEn/p999lkgGoMGDQr73Z07dxYsWJDzfPfdd4kuqiKoqcs1 11wTKdsPP/yQ6NKqgIwuZG3uC7Jx40ZbCuVkYcJ28s8884xBi6pRo8b//vc/ +fzEjBkzMmJvrlT3pk2b6jLUrl1b9+taHB+PevXqRWJ9+OGHDpZBkNDChB1k 169fbyBunjx5PvroI+1OjPMTd955J+csUKCAcc4bbrhBu+fRo0cXLVpUmyFn zpw9evQwrhQ8o/3AMzpdChAdLym1a9eunj179u7d2/KLOPCM6qOON6ExOux3 n3jiCZEHntFaYtLl8OHDccjnMWR0WbVqFceE/rGnVA4WxtgzduzY8f0gr7zy SsOGDWnWzemlS5fes2dPaP5WrVq1Dgc/8hZTcz158uT999/PieQXnn322bp1 6/LHW2655dChQ2Gr4/h4xN0d/XWwDIKEFsbYM9aoUYNbzttvv92iRYuKFSsK iT///PPQ/FWqVAnbcgYOHMg5o3rGcuXKid3Onj07U6ZMnE6ukwog/GOkc5sM PKP9wDM6XQoQHSglAzyj+tgT/9TU1N/DsXz58hw5cvC4THO80C+OGjVKO6zD M1pLTLps376dVejRo0foVw4ePJjo0qqAjC6//PILB0oFz5jowhh7Rt2NoAcO HHj00Ud5U7du3aLm1xFTc/3pp594n+3btz9y5Agnfvvtt5z46aefhv0Jx8ej e+65Rx3PmNDCGHvGDz74QJd/6tSp2bNnDwSv9504cSJqfh1btmwJ23hefvll 3sO0adNE5ttvv51btbgkvXfv3qpVq1Ji/vz5ww5VjFs848aNG6ln+OOPP4yz 7d+/f8GCBfPnz9ee4YnE1q1bSdNdu3bFWhiTxBoByXKmp6dTxamBcafEZ5ys 8oy0zw0bNsydO3ffvn1m9hOH+qQp/e7u3bt16VTfhQsXLlu2TPSWkZAsPO1n 0aJFK1asOH78eEwl9CRxKHXs2DE6+sLeEr9y5cqlS5dGVUryMGciNQyZrTrk pY/jKIuvx1u9ejUViX7OOBt1C9Q5bN68WWafVGwqfFpaGv1P/0T1jDI9Dw0u 69atmz17NhXYeDokiCSNvAoxHaoW9jlxdBEvvfQSzwEo+KFb//zzT74rtUaN GvCMUUm0LsnJyazCxIkTYyqYl5DRZezYsZI2zaAflp+AGY8LkoWxtpM38IBk GzNnzqxzIpKeMRJhm+s777xDibly5dLVqE6dOpT+9NNPh92VDZ7RePDiC2oy Ns388R51lihZGPm2GnWQNfaA7dq10zVmec8Ylk2bNvG17xdffFEkHjx4kJto 9+7dtZlnzZrFv7V27dpIO1TZM9LBNWbMmHvvvZdsrzgNe/3110+fPj008+LF i++8806RLVOmTAU0NG7cWLvb3r17035E5mLFio0aNUqmSJYgGQH5ctKEvGbN mtwGAsG7FLp27dqgQQNdR0fthPZGW9u2bavbw8cff8yBojmMbtPOnTup88mb N68oRtmyZX/88cfY6/3/IlP3ypUrU0mGDh26ffv2xx9/nE+8FC5cmLdu27at TZs2VAZxYT1LlizUIYftoCQLn5KSQuNCtmzZOA/9Iv2upN3wKvJKffPNN9Rs mjZtmidPHg5gpUqVxo0bRxmoS3/vvfduuukmoVTnzp11g6b8YW7cMAy20lHD zTt0vXR56SWPMpORpFHyP//5T5EiRUQ/1qpVq6NHj4Z+5YsvvqAOQUSMrEf7 9u3D5jxx4kTHjh0LFSok9lm7dm1xOjrUM0r2PIcPHyY1c+fOLbLR2KTtdX/7 7Tdt7SIJlxGLCnEcqub7nPh+l/j111+5m+rbt2/YDLQT2kq7nTFjBu8ZnlGL zbrQBJX3o+CNAbZhrMvXX39dunRpMaTSP3ywV69enTNElUyye5EZF6IWhklE J2/sAW+++ebAv28FNOMZIzVXNpLU8+vyt2zZMhB8qjHs3qIed7FOFOUHr9tu u+2GG27gumTNmlWMFDxbyLDueJeZJUYtTEYsk3D5QdbYAw4bNoy3ijuQTXrG +vXr03eLFy+uXV2H5ON99uvXT5t58+bNnD5z5sxIO1TWMx44cIBmnoFwkBy6 GlFXLw5zaifaqRRDceOchw4d4hgyfLmf6dSpUyxSxI9MBOTLSU1Lu1WHtqPb t28fJ1JXo/u5d999lzfpTuXRTEYszqBjxIgRCap7yZIlaf/vv/9+tWrVxM89 8sgjtGngwIHcjYRy11136fYjWfgJEyaIQYe6Dupb+P8bb7xxy5YtcdTRG8gr RZ22cIWCfPnyrVmz5uGHHw4N/pdffin2ENNhbtAwjLcOHz6cP86ePVu7Q3np 5Y8yM5F84oknbrnlltD9t2vXTpuZOod69eqFLQmNg7r3idBIwbffRELnGSV7 HhpMH3roIU6nSR39bujiIWL9QGPh5FWI71A12efE/btElSpVAsHnUMJOFKkX 4v3QTExMDOAZtdisi7jZT/LCvScx1qVz585huxGad3EGY8kkuxfJcSFqYTIS 1skbeECyD7yfOnXqyOSPSqTmSlXjfc6ZM0ebXqFCBUok5xh2b1GPu1gnivKD l3BwOoQLs+R4l5wlRi2M/CQ8pkHW2AN27dqVt86aNUsmvzGihVDPptt0xx13 UPp9992nvQ31xx9/5PwGHaCynpHgx3ufeuqpiRMn7tixg1qpaLF33323yHb8 +HEy0YHgWSYyj5xIGnFO8v7/+9//xDyKWiOnN23alBM3btzI96tTI7RnETCZ CEiW888//xQXd/773/+uWLFi+/btP/zwQ4kSJTjRjGfcu3dv4cKFOb1Zs2a/ /fYbHRrUkitXrkxHdNT7DOOuO/cbzO233049wOLFi5ctW5bxz3lg6iI+++yz JUuW8D0kpUqV4szanlOy8Lt377722mspT6FChShuR48epU2DBw/m/uGFF16I o47eIFalGjduTEN5cnKydkEPggI+cuTItWvXfvjhh5xC39LuRPIwzzBsGMZb w3pGeeljOsrMR7JBgwZjx46lkaJv3748LJIB1z5LJeJTrlw5ivn+/fsXLVok Vj949NFHtXtu3749p19//fW02507dy5dulQ84BAI8YySPQ+Fl7PRXIjLRpl5 blOwYEEKEfW6Yk14A2nkVYj7UDXZ58T9u+Ki1bfffhu6VdyVWr16dZpk0gHC meEZtdisy5AhQ3gTzbXoKKNZNx0OZO0jLSfiSYx1oR5gxowZYiVq6qNmBBG3 0hn30vITMJlxIWphEtfJG3jA3r17i/3I5DfGoLlSB8seinoSMbSJ2wt1J0gF CfKMjPHgRaMVqcPplSpVmvEPIoMlx7vkLDFqYeTbakyDrIEH3LNnD1+hJqj5 Rc0fFbKEgeByTKGtTpjTFi1a8CVIGoY4f+j1Fy3cfv4XLwn1jBQrcXeTQEyN xLNpdHRzim6RWH4pG01ZRcq6dev4/EPDhg21OalR8fBt+VL8YYkaAflyit7y 7bff1uXkc01mPKPY+VtvvaXNfOzYMZqmxlLj/5+Y5gnUdEOd6U8//aQbwcVN 46+//nqshee7ODJnzkxdkzYbtaVA8H4GmRfWeJKYlOrcubNIpC5X3E30yCOP aO8G4f6W0N5MInmYZ0RrGAZbw3pGeeljOspCiSmSNE5pu/fmzZtzOvVynEK9 Lt+ZQ4OgNjjUsEXQxEnFDRs2cGbqN3TXH4cOHRo6nMn3PHxTJSVqzezy5ct1 BdDVLlQaeRXiPlRN9jlx/26TJk0CwRlO2NNrPDzRhIeXIIBnDIvNuojZvo5i xYqFnqj3KjK6dOvWjSMT+gihgWQxTcDkxwWDwiSukw/1gDThp18hn8g2hH50 zZo1ofmvCUH3BgQdxt0I+SNhdalLoZGOI0leO9IOE+cZow5eDA9JYR8htOp4 l5wlGhRGvq3GOsiGekAauzdt2jRs2LCyZcvyJu2zqNp3bYQ2HoPlCFavXs3f om4tdCv96JNPPskZihYtSmHkQ4D2Kc7whEV4xrTYSbRnDEuvXr24mvwK1AzN hDApKUmbs3v37oHgCQGxABG3LuL333/X7ZYaUiDyHeDWEjUC8uXk7qJw4cKh T/OFLvYVU1dAxz4/qUQ75+d5LUF+npApUyaDdwzp4KJSn8kfJQtP2XLlykXZ nn/+ed0mESuD+7q9jbxS4vVAAnHnpO7xui5duugO3kiEHuYZ0RqGwdZQzxiT 9DEdZaGYieQ333zDhZk8eTKnfPLJJ5wyePBgXeZFixbxJpqfcIoIY+hLl8Iu 6Sbf83CBqfq6bHyHqu4NU5GkkVfBzKFqps+J+3e3b99OQw9tpa41dCsZQ/6u eEAJnjEsNusiPGOdOnXat2/frl27ypUrcwpNIFVYI9QGLPGMYfth8xOwsONC pMIktJMXHjDzPwT+jfZ1CRmG72ekBhmpvsbNNSM48yd7qNthlSpVDNb4TZBn lBm8mKie0cLjXYtulmhcGPm2Gusgq/WAYVvOHXfcofW/xu9n7Nq1a6T68oXO nDlzRlr7ccmSJdy6tIwdOzbSDhlXeMbk5OSBAwdSa6FjQZxUEcvGipWWtc+u ZvyzAJH2AeFnn302EHz65tcQ+CQG2e2YChYfUSMgWc4tW7ZwxbULIglMesaN GzdySuvWrc1WWIOZ+bOWI0eOTJgwoXPnzo0bNy5XrhwXtWbNmrxVsvApKSmc jZpKaKh5k3j3jd+QVyr0ToYWLVpw9MTpGubrr7/mdDpmQ/dmfJhnRGsYBltD PaO89LEeZaGYiSSNtvzr4glc7hyIAwcOhO6H79K//fbb+aM4eS5udBGEHc7k e0h+fOO2227T7pMGBd6nbr4USRp5Fcwcqmb6nLh/t1+/fryVWrVuE80H+CYr iqE4LQ/PGBY7dWHoQOO3qzMnT57s2LEjf8X4li3PYIlnDNsPxzcBizouRCpM Qjt5Aw9IJm7RokW6nYj833///Yh/Y7AAo3Fzpf6W2mQg+CL4Nm3aaBefMbiP MUGeUWbwYqJ6RquOd+NZonFh5NtqrIOsgQckBbt06aK7QiryN2rUaEQIq1ev Dg0jw9dDn3zyybBbf/zxRzaM9erVE0sTEGXKlDFYNDVDec9IMeFnZEKZMmUK 56GGwSe377//fvHF/fv38xH0wAMPiMRIuxJkyZJFsmBmiBoByXJSt8kfQ89v ZJj2jOLh2T59+pitsAYz82dm27Ztr7/+Ok+6dIjV0iQLL1Y8MIAO4fhq6nbM KPWf//yHo6fzjOJWDZ1nlDnMDX4u6tZQzygvfaxHWShmIvnzzz/zr4thlx9d p/Yfdj9s5bJnz86Rp/ExEDzTGJoz7HAm30OKp1O1d3eIa6Dz58+XqZ28CmYO VTPxj/t3mzZtGgjO5UJfdNWwYUP+4oIFC/74h3nz5nEizRLpo3aNO6+imi6R oJz8LjOaZug6NE9iiWcM2w/HOgGTHBciFSahnbzwgDSWJQXhW1iJUMOYEe/z jMbN9emnnw4ExwLW6+jRo9R7iKU7I12Bss0zhg5eTFTPaP54l5klGhdGvq3G OsgKD9iiRQtuOZ9++imniNV+tMT3PCO5M/5Wz549Q7cmJyfzHdTPP/8892mU IgYmssMGS0+r7BnFjSI0BaKjY+TIkfSLYtqp7TQ6derEiQ8//PDYsWNHjRp1 6623BoLnW7RTmlq1agWCPf9dERDLqyaUqBGQLKc4jj777LPQnfDTrGScRUpM XYFYQOmLL74wW2ENJj3jzp07xdpcZcuWpcFizpw5e/fu5QecRW8gWXjxXifa VaRQixWP/YZtnlH+MLfQM8pLH+tRFoq1npE7B+3y41p44YisWbPyi6v4zroC BQqE5hRLKwwYMEAkyveQ27dvp5ElEFzioE+fPpMmTWrXrh3fZvPAAw/o5kWR aievgplD1Uz84/7dG264IRDu3l3xjIkxxndZeAOldDGmbdu2/HPyj0u4l8R5 xpgmYPLjQqTCJLSTD/WAoq+uUaNGqDGMzzMaNNeVK1fyDnUnxn///XdexjxH jhyhr07LcKdnjOl4l5wlGhdGvq3GOsiG9YDiRYHahm2QPyqDBg3ib+ke2WN4 ocKCBQvqXnApnJRuGRAtwjOqtgaOuK5N2mnfpCneeKKNbXp6eugphdy5c+uu VvNJm/z588f3WlWriBoByXKKFSfat28futXgOmPoDRihXYGY27zxxhuyFZPA pGfkN19nyZKFxg5tuq43kCy8OBUT9hlhn2OPZ4zpMLfQM8pLH+tRFoq1nlE8 vRL2haS8n/Lly/NHXmiFCH28Jewp0Jh6SDFV00JVCF17JFLt5FUwc6iaiX98 v7tp06ZIbUbs0JiqVavK/5xLUUoXY8TtqVEfxPYAifOM8t1LTONCpMIktJMP 6wH5TY6Ebn4SKb8xxs2VbAhvDX0tgnjHX9iFm+Q9o8xEMcMWzxjT8S45SzQu jHxbjXWQDesBFy9ezIllypQ5duyYdifxeUZ+PihTpkxh5wl8E2bYxaV5oWDt 2qE6hGeMw/0l1DO+8sorHKidO3dq08N2GrxA0z333PPxxx/TnIpC0adPn9C7 i8XbfHS3TtlM1AhIlnP//v2crUqVKqFbQzs6aor8ShrdU8AEeStdV0CZ+eI1 HcW6NmwGM/OE3bt3cyFDl7fV9QaShT9+/Dj3GHXr1o29Kh7HHs8Y02FuoWeU lz7WoywUaz2jWEco9CmY5ORk3kTjHaeI43rixIm6zGGHM/kecvXq1RRAOsq6 d+/eqlWrxo0bd+jQIfRXjGsnr4KZQ9VM/OP7XXFvfNi3bNCUbE8I4qkcmgrS x7APq3oM1XQxgN8zS79o4VCoLDK68AKDhG4RywzDfli+e4lpXIhUmIR28mE9 4Nq1a/kZMZqT624vj8MzGjdXfldC5syZxSuNBMKAaC9vCaLqG9NEMSN2z8jr kYa9D9P88S4/SzQujHxbjXWQjeQBn3vuOU7X3U0an2fkZ1ioyqGbqAVyrcMu rEShCBjeOqWsZ+S13Knpas9aHz16VARWdBqHDh3KmTNnIGR1plCWLFnCx0Kl SpUc7PyjRkC+nOIW+unTp2vTf/vtN34AVtfR8emFokWLai9JjxkzRrwdVdsV iPeZfvrpp7rf1S4iHRNm5gnipSq6WyZoh/xovLY3kCy8OEfk23tQI2GPZ5Q/ zA1+LurWsO/akJc+1qNMh7WecdmyZdw5UKm0D8vTQCBqJNZRETdp165dW6sF /f/iiy+GDmfyPQ8/z1itWjXjehnXLiMWFeI+VE3e2xDH74qXV+oajAFYAycs duqydOnS2267LXSd+Tlz5vC3whoK7yGji4hk//79dZsMJJPvXmIaFwwKk7hO PpIHFA5Cd3EwDs9o3FzF0DBkyBDdJvF83IIFC0K/KKNvTBPFWD0jP9QQdtA0 f7zHNEs0KIx8W411kI3kAbdv385rw1I5w66bGpNn5HrVqlUr7Fa+FbZcuXK6 9XbS09P5iw8++GCkPSvrGd955x0OVOvWrXfv3k2qzZs3r2rVqoF/EJ1Gamoq P0fTsGHD33//nRr5qSBhd/vqq6/y18mGUzH4yWISq1OnTtRQ7XnCXSYCkuUU zZUaG0029u/fv23bti+//JJNdOixIF6CQAcUNcuVK1fSDrWL/Wq7AprG8Lkd 7gAptvS7NLA2adKEjqb4rtWamSdQ8+ajOG/evIsWLSKJKSDdunUTywVrewPJ wm/evJmPU9pJly5deFFiGptmzJhx//33jxw5Mo46egN7PKP8YW7wc1G3hvWM 8tLHepTpsNYzZmg6B7JsNLRRkNetW8dPKASCt1uInHSM0DSY0x955JE1a9Yc OXKEGj+NCCLCuhc8SfY8/BZjmkJMnjyZemCDLtegdhmxqBD3oWrSm8Txux99 9BHHkNQx/l0BPGNY7NSF10C45ppraG42d+7cw4cPHzhwgObt/Np0YurUqXFV 1GXI6CL6JRpzyWXTsS+e9DTupSW7l5jGBYPCJK6Tj+QB6SfYY9L0g7rl0Pzj xo0bH47QW0yNm2taWho/7UgF/vbbb0UxaP9c5htvvDHsKx1l9I1pohirZxTP 7n322WcHDx7cHcR4VxnSUsY0SzQujGRbjXWQNfCA4uKm9pUiIn/9+vXDtpzQ UwpUVBbr0UcfDY1khuZh4Ycffli8U3LXrl1iGRxq+WG/mKGwZ6RWytdPA8HT TdwMAsFHdUI7DXFRSQvNZwoXLtysWbNJkyaJnDQK1K5dW+TJkSOHeP94aNtO EDIRkCwnNVe+bSYUXnVf19FR0EJz5smT5+233+b/tV1BRnCpZ3FmKRC8EUL8 36ZNmwTV3aDfIDW1+org8D3Yut5AsvA0QNAexKZChQqJnGXKlImjjt7AHs8Y 02FurWfMkJY+1qNMh+WekToHfmQjFDoQtBMVgqrMQ62Om2++mf/ReUbJnmfB ggWhL5YKBCcwlStXpjFdu/yCsXDyB2B8h6rJPieO36WJLm8NnQdGAp4xLHbq MnbsWGEQAv8eLwKxPwLpXmR0oY5ddCCB4KKR1I3zTZLGkkl2LzGNCwaFyUhY J29w3fCLL77gTbTD0PyRaNeunW4/UbsR6oTFifEiRYqQ/RFxoPRff/017Ldk 9I1pohirZxw8eLDYJylF4pILM94VIyllTLNEg8LIm4WYBlkDz0iGlxtbQLP6 rvH7GRndQ4s0+HJ6y5Ytw0aSGq2wh9RUyPNWrVpV9H60yeAMsLKeMSPYh4s3 zgSCR27fvn2PHj3K7UTbaZDXNg6p9v3X5MF79erF54IE1FZJVt0ryBOEZAQk y0nZ3n//fe2hRIfPxIkT+VigoOl2SzEUxxFFslatWkuXLhVrcOk8Y0bw3gy+ NVpw4403Dhs2LHF1L1u2bODfL0kR7Nu3r3HjxqIkdIxTt0zHFC/3pOsN5AtP E7Z7771XO0mg4+ipp54yePeN5zGjFHvGrFmz6lYIp26Ww6tdN1X+MDdoGMZb R40axTsPvTguKX2sR5kWM5EUD0Toht3jx49/9NFH4m1lfCxQ2MO+oyE5OVmc CA0E51TPPffc/v37ixUrRh+/+eYbXX6Znof+MV6N/NZbb01LSzOunUD+AIzj UDXZ58Txu7x+AqG788eA33//nb8yZswYya+4HQV12bJlywsvvCAuLDKlSpWK 9KCuJ5GcnyxZsoQn4QxJQBOJDAnJJCc28uOCQWGYRHTyBp6ROme+Zk3MmzdP lz8SoZ5RphuhSfijjz6q2xVf7Yr0FUl95SeKcQxeb731lrbAjz/+uPGuBDJS xjRLNChMRixmQX6QNb7XVMyRxKsD4/CMYskggzNdVPEvvvhCvJmFoZp+/vnn xvfiquwZM4K+m+w2uXiDE7Y0D+TDfNCgQatWrfr1118XLFhAh+qQIUPE+69D 3URGcICYMWMGTV8N3kWSCGKKQIZcOakBrFixYurUqdu2bYu6Q5pYUtBmzpxJ TVqyDAcOHJg7dy4VQ2b/BsRa97DQ/IoKQ1UIe+tFKJKF58Y2a9Ys6m91SxD7 EEuUkkTmMLehAFGlj+koEyQukjRdoTGUyrN8+fKoD2hT70ERpqNG/lFug56H z1LecccdNApQ7RYEmTx58ieffCLOlIYuG26M/AEY06FqYfzRRViIsrrQsEIH FI2PdLyErqTneeR1oVCTiZg0aRJ1EXyvYExEndjENC5ELUxCO3lnoRnd4sWL p02bRn+jvtpVXt84JorykOikPpWZ3FasLzKQkTKmWWLUwkiahTgGWWchU7xp 0yYqM9Vu48aNMm+tVdwzysAvNn3yySfDBoTXSjK+ecxm7JyNq4af6+4uoJRV eC+S4kZKGmFDt4qV3nV3vTqF9+LvDaCLmkAXbwN9gRk84Bn5noRWrVqFbkpL S+NVgBo2bGjhL5rEz8esn+vuLqCUVXgvksIV6lbCZ3r16sVbtbeHOYj34u8N oIuaQBdvA32BGTzgGZ988km+aXnIkCF79uwR6cuWLRNrIoV9t6lT+PmY9XPd 3QWUsgrvRVK8CLJu3borV64UN7QcOnSoT58+/BRM1apVY73jKEF4L/7eALqo CXTxNtAXmMEDnnHRokXah9bLli374IMP8rO0zOeff27hz5nHz8esn+vuLqCU VXgykk899ZToYPPkyXPPPffUqlUrX758nFKuXLldu3Y5Xcb/D0/G3wNAFzWB Lt4G+gIzeMAzEsuXL2/SpIlYdljMZJ5//nnxph518PMx6+e6uwsoZRWejOSJ Eyd69epVunRpbZebKVOmChUqDB482J4FqCXxZPw9AHRRE+jibaAvMIM3PCNz 6NChdevWJSUlLVq0yOalUGPCz8esn+vuLqCUVXg4kqdOnfrzzz+XLFkye/Zs 6nuVsooCD8ff1UAXNYEu3gb6AjN4yTO6BT9HwM91dxdQyioQSWdB/NUEuqgJ dPE20BeYAZ7RfvwcAT/X3V1AKatAJJ0F8VcT6KIm0MXbQF9gBnhG+/FzBPxc d3cBpawCkXQWxF9NoIuaQBdvA32BGeAZ7cfPEfBz3d0FlLIKRNJZEH81gS5q Al28DfQFZoBntB8/R8DPdXcXUMoqEElnQfzVBLqoCXTxNtAXmAGe0X78HAE/ 191dQCmrQCSdBfFXE+iiJtDF20BfYAZ4RvvxcwT8XHd3AaWsApF0FsRfTaCL mkAXbwN9gRngGe3HzxHwc93dBZSyCkTSWRB/NYEuagJdvA30BWaAZ7QfP0fA z3V3F1DKKhBJZ0H81QS6qAl08TbQF5jBKc8IAAAAAAAAAMAtwDMCAAAAAAAA AIiE/Z4xji96Bj9HwM91dxdQyioQSWdB/NUEuqgJdPE20BeYAZ7RfvwcAT/X 3V1AKatAJJ0F8VcT6KIm0MXbQF9gBnhG+/FzBPxcd3cBpawCkXQWxF9NoIua QBdvA32BGeAZ7cfPEfBz3d0FlLIKRNJZEH81gS5qAl28DfQFZoBntB8/R8DP dXcXUMoqEElnQfzVBLqoCXTxNtAXmAGe0X78HAE/191dQCmrQCSdBfFXE+ii JtDF20BfYAZ4RvvxcwT8XHd3AaWsApF0FsRfTaCLmkAXbwN9gRngGe3HzxHw c93dBZSyCkTSWRB/NYEuagJdvA30BWaAZ7QfP0fAz3V3F1DKKhBJZ0H81QS6 qAl08TbQF5gBntF+/BwBP9fdXUApq0AknQXxVxPooibQxdtAX2AGeEb78XME /Fx3dwGlrAKRdBbEX02gi5pAF28DfYEZ4Bntx88R8HPd3QWUsgpE0lkQfzWB LmoCXbwN9AVmgGe0Hz9HwM91dxdQyioQSWdB/NUEuqgJdPE20BeYAZ7Rfvwc AT/X3V1AKatAJJ0F8VcT6KIm0MXbQF9gBnhG+/FzBPxcd3cBpawCkXQWxF9N oIuaQBdvA32BGeAZ7cfPEZCp+4oVKxYuXLh27VqZHcaUOW7S09MXBjl58mRC f0gd/NxKrQWRdBbEX02gi5pAF28DfYEZ4Bntx88RkKl76dKlA4HAfffdJ7PD mDLHzQ8//BAIQhY1oT+kDn5updaCSDoL4q8m0EVNoIu3gb7ADOp7xvT09AkT JnTr1m3UqFFr1qyR+cq+ffvWBzl+/HgcJUw0sUYgLS2N6kJ/DfIcOnRoypQp 3bt3nz59+rFjx8wWMWHAM7oF+VZ66dIlOjD79+8/cOBAaqiXL19OcNFcRiKO dyAPWrKaQBc1gS7eBp4RmEFlz5icnFyxYsXAv2nQoMGePXsMvkXuqXDhwpx5 7NixcZQw0UhG4PTp00lJSS1btsyaNSvVpW7dupFyvvvuu9oQZcqUqXfv3laW 2DrgGd2CZCvdunVrsWLFtM2vbNmy+/btS3wBXYPlxzuICbRkNYEuagJdvA08 IzCDsp5x3LhxOXLk4L6oaNGiderUyZcvH3+sUKHC+fPnI33xqaeeEp2Yez1j WloaTx0F9957b9icbdu25Qy5c+euXbt2zpw5+WOPHj2sL7pp4BndgoxSW7Zs oWOTI1O+fPkyZcrw/yVLlty7d68txXQB1h7vIFbQktUEuqgJdPE28IzADGp6 xmPHjrFDpL5o6dKlfM/DiRMnGjVqxF1T3759w35xwoQJ2nmXez1jampqgX/I nDlzpDlkSkoK17R69eqnT5++GgxduXLlKCVLliz79+9PRPnNAM/oFmSUatKk SSB4XXv8+PGcMnjwYA5UmzZtEl5El2Dh8Q7iAC1ZTaCLmkAXbwPPCMygpme8 GlwP87HHHjt8+LA28ejRo+wlGzRoEPqVgwcP8l2pd955p9s9o5Zbbrkl0hzy rbfeCrWHwkgqeKlR3jPef//9/PHcuXMrV67ctm3blStXImU28Ix//fXXqlWr 1q9fT/sx/l3a/59//rlkyZITJ07oNsEzhpKens6Xxl599VVtOk8n8ubNS5FP bBFdgoXHO4gDtGQ1gS5qAl28DTwjMIOynjESdevWpX6pRIkSoZu4y8qePfuC BQv84BkvXLhw7bXXBsI9+nTbbbfxjSImi2o58p6xXr16W7dubdCggbhFuUCB Am3btqVah2YO9YwHDhwgQ33rrbfyVRuChrnnn38+7HB26NCh5557Ttz8nClT JvrilClTRIZInvGdd965Nki/fv1iioP6RFWKqswxWbx4sTZ98uTJnD569OjE FtElwDM6C1qymkAXNYEu3gaeEZjBdZ6xatWq1CmRJ9Kljx8/nvurnj177tix ww+ecffu3VzNL7/8Urfpgw8+4E1nzpwxWVprkfeMhHCLWsgga59mDesZhw8f fs0114R+N6y7XLBggVg0SYe48SasZxw5ciQnkrG9dOmSmbAoSFSlXnzxxUDQ yOvqfvr0aT4L3bFjx8QW0SXAMzoLWrKaQBc1gS7eBp4RmMFdnvHEiRN82eil l17Sph88eLBQoUKUXqtWLerHtm/f7gfPuHLlSq7m1KlTdZuGDh3Km/7880+T pbWWmDwj8dprr5FNS01NnTRpknB233//vS6zzgkuWbKEEosXLz5w4MC1a9f+ 9ddfy5Yt4zAS2gIcP368SJEinN6iRQtq0hkZGQsXLqxatWrNmjXF7ayhnnHV qlVsSytWrHjq1CkrYqMWUZV65JFHqPq333576CZeHqFly5aJKpyrgGd0FrRk NYEuagJdvA08IzCDuzyjuClizJgx2vTGjRtTYs6cObdt20YffeIZp02bxtXU 3SJCkMPiTeSVTJbWWuQ9Y/78+WfPnq1N37hxY6ZMmQLBNb3Fs42R7k2dMWPG 2bNntSkUCo5J+/btReJLL73EiR06dNBmvnDhwsmTJ8VHnWdMS0u78cYb6SPZ 2J07d0rW3V1EVapy5cqBCK+E4Ffk1KtXL1GFcxXwjM6Clqwm0EVNoIu3gWcE ZnCRZ9y1a1euXLkCwXdtXLx4UaSTMeT5/IABAzjFJ55RXEwkM6XbtHDhwkiX IJ1F3jOGXdbm4Ycf5nqlp6dHzRxKnjx5KHPjxo35IxlPTilSpAivOhsJrWc8 f/58nTp16P9s2bL99ttvMr/rRqIqxaeUmzZtGrqJ2iptqlSpUqIK5yrgGZ0F LVlNoIuaQBdvA88IzOAWz3j58uV69erxvD0pKUmkk3coWLAgn/US15584hlH jBjB1Vy3bp1u09y5c3nTrFmzTJbWWkx6xh49enC9Vq1aFTXz1eCaqzNnzqRv NWvWrEKFCvzd2rVr89Zdu3ZxymuvvWZcJOEZly9fLi5NRnrhizeIqtRNN91E QXjiiSdCN1GEA8H3vySqcK4CntFZ0JLVBLqoCXTxNsb6XrhwYZ8JQtecBx7D LZ6xXbt2PFHX3SpPHZe4AJT2D0uXLuXEQYMG0ceMjIw4ypk4rJpDzpkzh6v5 66+/6jaNGzeON61Zs8Zkaa3FpGccPnw412vSpEnGmQ8cONC+fXs+n6CjVq1a nIfsJKd89dVXxkUSnrFFixZiPw0bNpSpskuJqlTNmjUpCPfcc0/oprJly9Km xx57LFGFcxXwjM6Clqwm0EVNoIu3Mdb3zJkz/2eCHTt22FgV4ACu8Iz9+/fn WXr16tW1D6lt3rw51BGEctddd8VRzsRh1Rxy3bp1OgMl+Prrr3mT9r2NKmDS M4oHWufOnWuQ+dChQzx4EeXLl+/duzf96PHjxzmSwjNOmTKF8wwePNi4SMIz Mrlz5+Z/fvrpJ8mKu46oSvHpmooVK4ZuYquuW6jKt8AzOgtasppAFzWBLt7G WN+LFy9u2LBhXbzo3qgOvIf6nnHy5Mm8VmrRokV1Dmjr1q0ynrFGjRpxlDNx WDWHTEtL4wp26dJFt+nll18OBN8zqHuboeOY9IxvvPEGV3nPnj0Gme+8885A 8IWMuhdF6TyjOOfw9ttvGxdJ6xkrV6588ODBkiVLBoIPQpIVNf6uS4mqVJs2 bQLB96HoXnlJBykHqnPnzoktokuAZ3QWtGQ1gS5qAl28TRxXbQAQKO4ZZ8yY kS1bNuqF8ubNG/Y2yxMnThwLQbyEYtiwYfRRtVchWDiH5KtpulWvr1y5cv31 1wfUu8B61ZxnJP/LT1LkzJnTYN3UI0eOsPpvvvmmbg86z0g75PdJ0U6MzbXw jDfccMO+ffuuBlsmp7Ru3Tp6tV1IVKXE2lO6dZaGDBnC6fPnz09sEV0CPKOz oCWrCXRRE+jibeAZgRlU9oyzZ8/Onj07n9GKqZH7ZA0cok+fPlzTpUuXisTp 06dz4ogRI8yX1lrkPSMZYfF6RGbgwIFcr+eee06XWesZU1JSONvrr7+u/frq 1avz5s2r9YzEo48+ypm//PJLXTG2bNki/heecebMmSKxUaNGnLho0aKoFXcd UZU6e/ZsgQIFqPr169e/fPkyJ5L1rlGjBiWWKFFCJPoceEZnQUtWE+iiJtDF 28AzAjMo6xmTkpL4nelEp06d6OPPP/88XYPBRN0DnnHVqlXL/6FYsWJUF5pJ ihRx5TQtLY2vlBUpUiQ5OfnKlStkHvPnz08pefLkMX5/hCPIe8ZA8D2MM2bM yMjIoGr26NGDX85IrULcmEpUr16dgyPGKRrR+GbmfPnycUxSU1N79+7NF6x1 npGaCp+XIN5//33a86VLl9atW/fMM8/QTsTbLXXvZ2R27dqVM2dOLufff/9t VYgUQUYpcatwu3btTp48efz48datW3NK9+7dbSmmC7DweAdxgJasJtBFTaCL t4FnBGZQ0zOeO3cuR44cAUMqV64c6ese8Iz83sBIfPHFFyIn1TFLliyczq6K jdWcOXMSW424kPeMYRsA1VS3Xk379u15U8GCBcnucaJ2dVP21IHgHa2lSpUK /NszEoMGDRJ5+CfE/2+99RbnCesZiZ49e3L6Rx99ZDIyqiGjFM0W+NFRXfOr X7++90x03Fh7vINYQUtWE+iiJtDF28AzAjMo6xm1U/ew1KxZM9LX9+zZw3lC FxRVAfNzyH79+mkzjx8/nh/0Y8hzqWkYr8rVvXz58lSLkSNHkpu79tprRb1K liwZenGZTFyJEiU4w7Zt2zjxxIkTzZo1E18kB92wYcMdO3Z069YtEOIZrwZb crVq1cTARxQvXnzMmDEiw4QJEzhd1+apoZYrV47Ss2XLtnfv3vhioiaSIwtN Hh5//HFxrZacfvPmzTFt0GL58Q5iAi1ZTaCLmkAXbwPPCMygpmf0NgmKwM6d O8lSKe5cYq375cuXU1JS5s2bZ/DSEMqzZMmSpKQk3YC1Z88eSl+2bJnuuchI nDp1ivIvWLDgwIED8iX0KjEpdfbs2dWrV8uH2legx3MWtGQ1gS5qAl28DcYj YAZ4RvvxcwT8XHd3AaWsApF0FsRfTaCLmkAXbwN9gRngGe3HzxHwc93dBZSy CkTSWRB/NYEuagJdvA30BWaAZ7QfP0fAz3V3F1DKKhBJZ0H81QS6qAl08TbQ F5gBntF+/BwBP9fdXUApq0AknQXxVxPooibQxdtAX2AGeEb78XME/Fx3dwGl rAKRdBbEX02gi5pAF28DfYEZ4Bntx88R8HPd3QWUsgpE0lkQfzWBLmoCXbwN 9AVmgGe0Hz9HwM91dxdQyioQSWdB/NUEuqgJdPE20BeYAZ7RfvwcAT/X3V1A KatAJJ0F8VcT6KIm0MXbQF9gBnhG+/FzBPxcd3cBpawCkXQWxF9NoIuaQBdv A32BGeAZ7cfPEfBz3d0FlLIKRNJZEH81gS5qAl28DfQFZoBntB8/R8DPdXcX UMoqEElnQfzVBLqoCXTxNtAXmAGe0X78HAE/191dQCmrQCSdBfFXE+iiJtDF 20BfYAZ4RvvxcwT8XHd3AaWsApF0FsRfTaCLmkAXbwN9gRngGe3HzxHwc93d BZSyCkTSWRB/NYEuagJdvA30BWaAZ7QfP0fAz3V3F1DKKhBJZ0H81QS6qAl0 8TbQF5jBKc8IAAAAAAAAAMAtwDMCAAAAAAAAAIgE7k21Ez9HwM91dxdQyioQ SWdB/NUEuqgJdPE20BeYAZ7RfvwcAT/X3V1AKatAJJ0F8VcT6KIm0MXbQF9g BnhG+/FzBPxcd3cBpawCkXQWxF9NoIuaQBdvA32BGeAZ7cfPEfBz3d0FlLIK RNJZEH81gS5qAl28DfQFZoBntB8/R8DPdXcXUMoqEElnQfzVBLqoCXTxNtAX mAGe0X78HAE/191dQCmrQCSdBfFXE+iiJtDF20BfYAZ4RvvxcwT8XHd3AaWs ApF0FsRfTaCLmkAXbwN9gRngGe3HzxHwc93dBZSyCkTSWRB/NYEuagJdvA30 BWaAZ7QfP0fAz3V3F1DKKhBJZ0H81QS6qAl08TbQF5gBntF+/BwBP9fdXUAp q0AknQXxVxPooibQxdtAX2AGeEb78XME/Fx3dwGlrAKRdBbEX02gi5pAF28D fYEZ4Bntx88R8HPd3QWUsgpE0lkQfzWBLmoCXbwN9AVmgGe0Hz9HwM91dxdQ yioQSWdB/NUEuqgJdPE20BeYAZ7RfvwcAT/X3V1AKatAJJ0F8VcT6KIm0MXb QF9gBnhG+/FzBPxcd3cBpawCkXQWxF9NoIuaQBdvA32BGeAZ7cfPEUh03Ves WLFw4cK1a9cm7id8gp9bqbUgks6C+KsJdFET6OJtoC8wAzyj/fg5Aomue+nS pQOBwH333SeTecSIEc2bNx8/fnziyuNe/NxKrQWRdBbEX02gi5pAF28DfYEZ 3OIZ09LS1q9fT3+Ns6Wmpk6fPv2TTz4ZPXr01q1br1y5EkcJE418BLZs2TJq 1Kju3btPmzYtat1dgTqeccOGDYEgWbJk2b9/f+KK5FLklbp06dKaNWv69+8/ cOBAOkgvX76c4KK5jFjbvGRfBySRj396evqECRO6detGvS416bB5Nm3atD4C Fy5c0GU+d+7cihUr6ND49NNPJ06cuHv37kg/ffr06fnz5/fp0+frr79evXr1 +fPnZasX5OLFi6tWreoXZPbs2UeOHDHOT8dsSkoK9YEODpFxjAUUYQ41jYy6 TTK6UFgi5RH8/fff2t2a1IWJOi2JqVElGngKb4ORHZhBcc9IPXZSUlLLli2z Zs1K0/u6detGynn27FnKFvg3VapU2bFjRxyFTCgyEcjIyGjVqpWuOm+++SbN DWwpY6JQxzPS/I3cImXOli0bjekineYVDwbZtWtX4sqpPpJK0RSoWLFi2lZa tmzZffv2Jb6ArkEykvJ9HYgJmfgnJydXrFhR1982aNBgz549upw5cuQIRGDy 5MkiG031O3XqlD17dm0G6nBat2596tQp3T7nzZt34403anPSMRXJtOqgH2rb tm3u3Lm1X8+fP//w4cPD5t+2bduAAQNuueUWzrl48WKZX0kEcYwFXbt25WKX KVNGt0lGF5r6Rsoj+O6778Q+zejCSE5LJBuVPcAzehuM7MAMKnvGtLQ0nj4J 7r333kg5q1WrxnkyZ85cvnx5GjT5Y4ECBch/xVHOxBE1AleuXLn//vu5/CVK lHjkkUeuv/56/khexv4Tjxaijme8GpwSfPjhh7pZ06ZNmzjU9E9iyugOZJTa smVL0aJFOVx00NFEjv8vWbLk3r17bSmmC5CJpHxfB2IlavzHjRsnJu3UnuvU qZMvXz7+WKFCBe2lpb///jvS3J6YOHEiZ9OOR5kyZSI3Kg4TonHjxtorTQsX LqQ8lE4GkzqumjVrckvImTPn7Nmzjat29OjRunXrioGvRo0at956q/ihH374 QZf/zTff1JWZfj2mYFpIrGPBunXrxDGi84ySush4RpHZjC6M5LREsvC2Ac/o bTCyAzOo7BlTU1ML/AN1uYHI86iXXnqJm3SzZs34LO7ly5dpxMydO/ewYcPi KGRCiRqBkSNHcnVeffVVvrBIf1977TVOpK02FTQBKOUZw0IWkuMMzxhVqSZN mgSCs2LxTOjgwYM5em3atEl4EV2CTCTl+zoQK8bxP3bsGDtEmhctXbqU7786 ceJEo0aNuCX37dtXZCaZOPHzzz/fE8KZM2c4G9mB22+/nbK9/vrrZOuuBscj KkOpUqX46ytWrOCc586dK1u2LKVcd911YuWuNWvW8ElC6s0uXbpkULXOnTvz Dj/88ENxP+qsWbNy5cpFiYUKFfrrr7+0+du2bcttjDME3OMZyblXrlxZOCmd Z5TXJXQrsWHDBnKC9PW7776bG4BJXRjJaYlk4W0DntHbYGQHZlDZM2rhe2nC zqOoX82WLRttfeaZZ3RPCpw8eTKOEiaaqBF46KGHqDo33XQTjVza9OrVq1N6 uXLl3HtjuT2e8f777xcpmzdvTk5O1s2dDJg6daqMZyRpaLcpKSluv1s4ElGV Sk9P5xPvr776qjadh5u8efPKx9zbxNrmDfo6EAdR408O7rHHHjt8+LA2kbwe e8kGDRqIxN9//507B/Jlxj+6d+/en3/+WZf4008/8dcHDhzIKeRSOWXIkCHa nDNnzuT0sWPHGvwKORcyJqFnEYWXXLVqVdgv/vjjj+7yjF26dOFJbK1atUI9 o7wuYSFrT98lHy1uGTWpy9VYpiUmC2858IzeBiM7MIMHPCN3+AS5gzjKYz9R I1CkSBGqzmuvvaZLnzRpEtfUvV26cd3HjRt3bZBffvlFmz537lxO79Wrlzad +q7ChQtTeteuXTmFPePDDz9M43KbNm1uuOEGjljmzJmpA9StYFC1alX6Lk0X +eO3335LUxFxWxr9wz9KsxTtt3bu3FmvXj3xpNI111xDfSlfTfASUVtpv379 OAK6m3snT57M6aNHj05sEV0CPKOzxD0H5ts+S5QoIVKWLFnCbXv16tVx7HD5 8uX89W7dunHKN998wykHDx7UZb7tttt0p7/kETdLjBo1KmwGd3nGtWvX8iS2 VZBQz2hGl5UrV/KV/QEDBohE87rIT0tMNirLgWf0NhjZgRk84BkrVKhAm2ga H0dhHME4AuRrdPMKAY1fvEn7nL67MK57SkoKV7BDhw7a9HfeeYfTa9eurU2f N2+e7gwte0aa5olFHrS899572q/rbmTt0aNH6FcCwfv5xVdmzpwpTGW2bNnE 8zXFixf32OPhUY/TF198MRB8MEd3m9bp06c5LB07dkxsEV0CPKOzxD0Hrlq1 KglBHkGkzJgxg4/3+B7q6d27N39d3PH14YcfBoKXz0LX0uSHEYoVKxbHD4ly RlpBxUWekQZEvtG3aNGiR44cef7550M9oxld+O4d+quVwLwu8tMSk43KcuAZ vQ1GdmAGD3hGfjTjo48+4o9paWnJyclq3pXKRI0AP3380ksv6dJp9OTHLj7+ +OMEli+RRK07XxmsUqWKNpHnDIHgwoNaZTt16sTejXozTmEbyDz++ONTp07d sWPHgAEDuK/Lnz+/9vEQnWfctWvXggULWrduzV+nby0IIm5SpRlLwYIFA8GH XGgyRnKcO3duxIgRLEqoXq4mqlKPPPII1ZqkCd3EDbhly5aJKpyrgGd0lvhG nBMnTvDlJ+1xPWrUKO4cZs6cSSNOq1atunTpQgbw7Nmzxnuj2de4ceP45oSb brpJ5B86dCjvMPR1Pz179gwEb5CIY9GzDh06RNot4yLPKO6zpZjTx+eeey7U M8ati7jGRwHRppvXRX5aEnfhEwQ8o7fByA7M4HbPePjwYe5vhw0b9sMPP1Sq VElYBmrzNCLEUcJEEzUC99xzDxscmreIRLInjz32GFfthRdeSHgpE4PkOa5M mTIdO3aMU9LT0wPBW0D5lt1p06aJzHfeeSelULhEivCMNOxqTxGLBc9TUlJ0 mXUL5ohrAaHPM/JtUeRbdcutf/bZZ5ROtpSKKhkH9YmqFC9JEfaVEPzaAhdd +k8o8IzOEt+II27QGjNmjEjs379/IBxkA2fMmBG6kwMHDrRv3/7pp58uUaIE 5yRZd+7cKTL8+uuvnK67q2T+/Pl8JorQ5peBjAm/IaJUqVKR8rjFM1JPK+5K 5ZSwnjFWXQTPPPNMIHgOULd0gEldYpqWxF34BAHP6G0wsgMzuN0z0pgiumL+ J2/evOJlVWQ9JJfFtpOoERAnHqtVq0bjS2pq6pQpU8jaiNGkcePGdhXWYqLW ffz48VzHqVOncsrYsWPp41133fXss88GgksRcnpGRgZPJz755BPxdbaBd999 t263YinaOXPm6DJLekZyoNyu/vvf/+p2Ttaev0IzDZkguALJq+FNmzYN3UTH KW2imVKiCucq4BmdJY4RZ9euXXydqEKFCtpFrsT0ntR5//3333vvPb5/NRA8 qRV6lmn16tU6F/D7779rM5w/f57X58yWLRv1PGRDNmzY0LNnT9qb+BalxFR4 scK2wTotrvCM5OP44cGbb75ZvNTS2DNK6sLQwMrL1IirgQKTusQ0LYmv8IkD ntHbYGQHZnC7ZxTrmAWCC4ouXryYhnia3tNwyecDyRfoTiE6TtQIUPnr168f CIFM07XXXkv/tG3b1q7CWkzUuh85coRvCXvzzTc5hTwaD+u8LgG1BE7/5Zdf OCzLly8XX4/0rg2yipxZPEkUKXMkz0jTBk6nMX1lCLwp0nu03UhUpWgCTFV+ 4oknQjfVrl07EHxEKFGFcxXwjM4Sa/wvX75cr149PqKTkpJ0W6kDWbBggTbz xx9/zJlDu53du3c3a9aMpBRvxya/0LVrV+0tELS30Fe6Uz/Pp8gIcceFDDQC 8lsF6RgMfRZP4ArPKB4q1BYyrGe8GqMuzKBBgziDzsgzZnSJdVoSR+ETh7Eu Fy5c2GcC7a1TwBEwsgMzuN0ziul6hQoVdHcGiucgdHcSOo5MBGjUGDhwYNWq VWmIyZ49+4MPPkiOiUYZ9lNffvmlLSW1Hpm616hRg+pYsWJF/li8ePFA8BIe mTgWdNeuXZTesWPHQHB1U+2FgEiecf78+SY9o1ipwADdsq6uJqpSNWvWDPz7 xmABn6IXC9L6HHhGZ4k1/u3atePDWfKxHeqrq1SpQvnJYkR6bR/5hXnz5nG2 QMjJpT/++KNp06bc0ZUoUeKVV17Ztm0bu4a8efPKl5z2U7hw4UDwtRHam/BD Ud8zrl69OkuWLFTCp59+Ok1D48aNA8F3i/NHg/1H1aV58+Yc4UjvropbF/PT EplGlSCMdTlz5sz/mUC8zQQ4BUZ2YAa3e0Z+2C0Q7k3369atC7UJKhBTBGjs EG+IIK/ENZoyZUqiCpdgZOouRtWDBw9u3bqVx82///6b5l181wTPuLhn050N S5xnFO9tLF++/H0RmDhxYuwhUZSoSlHktdZeC68U5LFFgeIGntFZYoq/uFGw evXq8ouQvPvuu/wt8hQG2Wi04h4s0qqb1MuJ///zn/8EYrkN7PDhw2Kx6Kgd kfqekaxi1HN0BHXsBj9hrAtf/H3ggQeiFjVWXSyZlkg2Kssx1uXixYsbNmxY Fy+6t6AC+8HIDszgds9IPoJvING9RoHYv38/d7lfffVVHOVMHPFFgPjuu+9k TlGqjEzdxVp2ZI35ftQHH3yQN7Vo0SIQfEvyX3/9xQ8zfv3119rvJs4zsnsl aFYZY6VdSVSl2rRpw3Ze94ZfcdyR909sEV0CPKOzyMd/8uTJfCMHObtIK46G RdxJGPXZwxdeeIFzGr/R9dKlSzfffHNA+pQ+HYN821gg3NN5oajvGcUtoMYY r1dgoMvu3bt50/vvvy9fZkldLJmWyDcqa4l7fgJcAUZ2YAa3e8ar/7xfqVKl SrrHN5YuXSozrNhP3H0yL79WqlQp7d2Y7kKm7lQ7fgfiu+++y2ebP/30U97E qwNdd911tBMWl6yc9rvmPWOfPn3CGnMqFS+V75NFw6IqxWsTBTSrFTFDhgzh dONLAP4BntFZJOM/Y8YMXhElb968sZ6Ua9iwIX2R+gfx/oVIzxLyVSri0KFD BjsUr8/+/vvvo/762bNnH3jgAc7fokWLSHdaalHfM547d+5YOBo1asSDIH80 fuFFqC4C8cih7i0bxsjrYn5aYlD4hALP6G0wsgMzeMAziuFv+vTp2nR+MwKh 2svWZSKQkpKivR+GoEGKq/Ptt98msHAJRlL9Jk2aBIJ31POtXKtWreJ0caaL 3+xcvHhx3RfNe8bhw4dzTuohdTvhR2kCErd+eYCoStFMtUCBAhSN+vXri2kq TW/4cdQSJUrIzF39ADyjs8jEnybwfEYoR44ckTKvW7eucuXK69evD90/Lzsj loZYu3bt9ddfH2oKDh48yC8MoqNDJJI50l1IOn78OC93T9m0foGOuB9++IF6 MO3QQP+LFXvIT0meTlTfM0YidA0ceV20iH5+3rx5YX/IpC6S05L4Cp9Q4Bm9 DUZ2YAaVPSM5heX/wI8e0GxKpIjFty9dusSzrLx58/7888/Uninlq6++4ruM wq4Y7CxRI5CcnJwrV65q1aqtWLGC5gBklLp3784jyE033SQeb3QjkuoPGzYs 8A/58uXTLgJQvnx5sSn0tRfmPaPIWatWLRrKr1y5Ih4n2bt3L6+Xni1btl69 evEScCTHggULHnrooZ9++imGQCiPjFJvvPEGx6pdu3YnT56kOVXr1q05hVqs LcV0ATKRlOzrQBxEjX9SUpJ4h0KnTp3oI40j0zUsWrTo6j/vJiNT2bVr1yVL lpBBIF3IevBNEdQ/i0VW+Q0LlEIHyC+//ELZqBunMtxxxx38K++++y7npO6l WbNmWbNm7dOnT3p6OvmUpUuXii5u6NCh2nJ26NCB08Xdp5SfX8DN/SS51Fmz Zk3/N2KhmNTUVNGiunTpwt/q3bs3p4RdOzShWOUZ5XXR8sknn3AEyOCHbjWp y1XpaUl8hU8o8IzeBiM7MIPKnjFPnjyByHzxxRciJ/XnhQoV4vScQfj/6667 jgbKOAqZUKJGgOYtopq8dhxz8803hx3gXISk+mK1n0DIKjdt27YVm0JfQGbe M9LIXrJkSfETZN5pPilOIIvF0kUDEwKVK1dOOgwuQEYpGk3uvPNOEQ0+rREI np/UXSX3MzKRlO/rQKwYx5/sQOgrFXRUrlz5anAVLH5po+iZRYMnOnbsqP1F fikSQzaBH75mateuLc777d27l688in2K/99++23dgpl8u2NA8/5Zmr8Zl5yY Nm0aZ6ZWZJCNWqClUY+OVZ5RXhct4i2WJEHoVpO6MDLTkvgKn1DgGb0NRnZg Bvd6xn79+mkz79y5s06dOnwSj3n88cd1y1wrgkwERo4cKV7pFQi+27dRo0ZH jhyxpYAJRF59smBc94EDB2rTte+8OHjwoO5bfCqYejZd+m+//cZf0XrGSJnJ mJcqVUo7jq9bt05s3b59+wMPPKCdRWTPnv3ZZ5/dvHmzTL3cgqRSNLjQgcb3 9QWCJ8ybN2+OYUWLec+o6+tATET1jNpjOSw1a9bkzPv27WvdujVfAxLccsst s2bN0u320KFDb7zxBi8zKKAv9urV68yZM9qcNEhpj6BA8Jb7sA/ZffbZZ5xh wIABnCKWmDZgzpw5nNnYM+bOnTveAMdJfN6EX9erW9RRXhcBv2iDiLQ6rhld BDLTkjgKn1DgGb0NRnZgBpU9YxxkZGSQO6D9q/zqWPkIHDhwYN68eatXr3b1 /aha3DIeXbx4cePGjbNnz168eHHYtkQzjeTk5IULF27ZssW9SxIZEJNSFA1q pcuWLdO+qBowbmnzXsXy+POTbr/++uuiRYuM72O5cOFCSkrKL7/8Mn/+/O3b txt0FNTDU39C2Yx3uHnzZt16zu7FQV3ksUQXmWlJIgofH+ivvA1GdmAGj3lG V+DnCPi57u4CSlkFIuksiL+aQBc1gS7eBvoCM8Az2o+fI+DnursLKGUViKSz IP5qAl3UBLp4G+gLzADPaD9+joCf6+4uoJRVIJLOgvirCXRRE+jibaAvMAM8 o/34OQJ+rru7gFJWgUg6C+KvJtBFTaCLt4G+wAzwjPbj5wj4ue7uAkpZBSLp LIi/mkAXNYEu3gb6AjPAM9qPnyPg57q7CyhlFYiksyD+agJd1AS6eBvoC8wA z2g/fo6An+vuLqCUVSCSzoL4qwl0URPo4m2gLzADPKP9+DkCfq67u4BSVoFI OgvirybQRU2gi7eBvsAM8Iz24+cI+Lnu7gJKWQUi6SyIv5pAFzWBLt4G+gIz wDPaj58j4Oe6uwsoZRWIpLMg/moCXdQEungb6AvMAM9oP36OgJ/r7i6glFUg ks6C+KsJdFET6OJtoC8wAzyj/fg5An6uu7uAUlaBSDoL4q8m0EVNoIu3gb7A DPCM9uPnCPi57u4CSlkFIuksiL+aQBc1gS7eBvoCM8Az2o+fI+DnursLKGUV iKSzIP5qAl3UBLp4G+gLzADPaD9+joCf6+4uoJRVIJLOgvirCXRRE+jibaAv MINTnhEAAAAAAAAAgFuAZwQAAAAAAAAAEAncm2onfo6An+vuLqCUVSCSzoL4 qwl0URPo4m2gLzADPKP9+DkCfq67u4BSVoFIOgvirybQRU2gi7eBvsAM8Iz2 4+cI+Lnu7gJKWQUi6SyIv5pAFzWBLt4G+gIzwDPaj58j4Oe6uwsoZRWIpLMg /moCXdQEungb6AvMAM9oP36OgJ/r7i6glFUgks6C+KsJdFET6OJtoC8wAzyj /fg5An6uu7uAUlaBSDoL4q8m0EVNoIu3gb7ADPCM9uPnCPi57u4CSlkFIuks iL+aQBc1gS7eBvoCM8Az2o+fI+DnursLKGUViKSzIP5qAl3UBLp4G+gLzADP aD9+joCf6+4uoJRVIJLOgvirCXRRE+jibaAvMAM8o/34OQJ+rru7gFJWgUg6 C+KvJtBFTaCLt4G+wAzwjPbj5wj4ue7uAkpZBSLpLIi/mkAXNYEu3gb6AjPA M9qPnyPg57q7CyhlFYiksyD+agJd1AS6eBvoC8wAz2g/fo6An+vuLqCUVSCS zoL4qwl0URPo4m2gLzADPKP9+DkCfq67u4BSVoFIOgvirybQRU2gi7eBvsAM 8Iz24+cI+Lnu7gJKWQUi6SyIv5pAFzWBLt4G+gIzwDPaj58jIFP3FStWLFy4 cO3atTI7jClz3KSnpy8McvLkyYT+kDr4uZVaCyLpLIi/mkAXNYEu3gb6AjPA M9qPnyMgU/fSpUsHAoH77rtPZocxZY6bH374IRCELGpCf0gd/NxKrQWRdBbE X02gi5pAF28DfYEZ1PeM6enpEyZM6Nat26hRo9asWRMp28WLF1etWtUvyOzZ s48cORJH2ewh1gikpaWtX7+e/hpnS01NnT59+ieffDJ69OitW7deuXLFVCkT AzyjW5BvpZcuXaIDs3///gMHDqSGevny5QQXzWUgks6C+KsJdFETeV0k52YM iZiSkrJhw4aoM5NYZzL79u1bH+T48eMyxWYOHTo0ZcqU7t27028dO3YsUjYX TSwlgWcEZlDZMyYnJ1esWDHwbxo0aLBnzx5ttgsXLrRt2zZ37tzabPnz5x8+ fHgcxbMByQicPn06KSmpZcuWWbNmpRrVrVs3Us6zZ89SNl2gqlSpsmPHDivL bQXwjG5BspXSmF6sWDFtwytbtiwN4okvoGtAJJ0F8VcT6KImMrpIzs2Ybdu2 DRgw4JZbbuFsixcvjrTbOGYyZP0KFy7MOceOHStZx3fffVf7E5kyZerdu7cu j+smlpLAMwIzKOsZx40blyNHDj5OixYtWqdOnXz58vHHChUqnD9/nrMdPXqU zBSnZ86cuUaNGrfeeqs4wGmqH0cJE41MBNLS0tgqCu69995IOatVqyYiUL58 eerW+GOBAgUyMjKsr4AJ4BndgoxSW7ZsoWOTI0MNr0yZMvx/yZIl9+7da0sx XQAi6SyIv5pAFzWJqovk3Ix58803dR5w4cKFYXcb30zmqaeeEnuW9IzkBDk/ +cHatWvnzJmTP/bo0UPkcePEUhJ4RmAGNT3jsWPHuBeiMWLp0qV8L8qJEyca NWrEx2zfvn05Z+fOnTnlww8/FLcNzJo1K1euXJRYqFChv/76K45CJhSZCKSm phb4B+qyDDzjSy+9xBFo1qzZqVOnKIXCRX0a9YfDhg2zvPAmgWd0CzJKNWnS JBA8STt+/HhOGTx4MAeqTZs2CS+iS0AknQXxVxPooibGusjPzRgyaDyN4SmZ gWeMYyYzYcIErRuV8YwpKSmcuXr16qdPn+YalStXjlKyZMmyf/9+zubGiaUk 8IzADGp6xqvB9TAfe+yxw4cPaxOPHj3K/VWDBg045dKlS9TVjBw5Uvd1cciv WrUqjkImlFiPWb6pI6xn3LNnT7Zs2WjrM888o7vtX80VPuU94/33388fz507 t3Llym3btoV9riGqZ6S+ndrA+vXraT/Gv0v7//PPP5csWUIjoG4TPGMo6enp fCn81Vdf1abzNC9v3rzuHVWtBZF0FsRfTaCLmkTVRXJupuPHH3808IxxzGQO HjzId6Xeeeed8p7xrbfe0tnDqxojKS41unFiKQk8IzCDsp4xEnzDQIkSJYyz LV68mA/tUaNGxf1bCcJCz/j6669zNTdv3mxZ+RKJvGesV6/e1q1baQASt8EU KFCgbdu2Fy5cCM0c6hkPHDhAo8Ott97KV2kJmn48//zzYacZhw4deu6558QN NpkyZaIvTpkyRWSI5Bnfeeeda4P069cvpjioT1SlqMocE93zKZMnT+b00aNH J7aILgGRdBbEX02gi5rEPUMznpsZe8Y4ZjJ86iB79uwLFiyQ9Iw0eaDBOhBu dYjbbrstELzn2XgPKk8sJYFnBGZwnWesWrUqHbB0gBtnmzFjBh/aNL7E/VsJ wkLPWKFCBbZXlhUuwch7RkK4RS3U22ufmAjrGYcPH37NNdeEfjesu6QRRzxE r0PcEBXWM44cOZITydheunTJTFgUJKpSL774YiBo5HV1P336NF8d6NixY2KL 6BIQSWdB/NUEuqhJ3DM047mZsWeMdSZDQzPvrWfPnjt27JD0jLt37+acX375 pW7TBx98wJvOnDljsAeVJ5aSwDMCM7jLM544cYIvG7300kvGOTt06MCHtvYO BEWw0DPy3fUfffQRf0xLS0tOTlbzrlQmJs9IvPbaa2TTUlNTJ02aJJzd999/ r8usc4JLliyhxOLFiw8cOHDt2rV//fXXsmXLxLpt2gIcP368SJEinN6iRQtq 0hkZGTSo0fBXs2ZNcTtrqGdctWoV29KKFSvywxceI6pSjzzyCFX/9ttvD93E y1a0bNkyUYVzFYiksyD+agJd1CS+GVrUuZmxZ4xpJnPw4MFChQpR/lq1al26 dGn79u2SnnHlypWcc+rUqbpNQ4cO5U1//vmnwR5UnlhKAs8IzOAuzyhuVhkz ZoxBNupqbrzxRspWqlSp+H4ooVjlGQ8fPszRGDZsGJmaSpUqCatF4yz5JisL bRHynjF//vyzZ8/Wpm/cuDFTpkyB4Frr4pGHSPemzpgx4+zZs9oUso0cnPbt 24tE8dw9jQXazBcuXNAOWDrPSCMaNzCysTt37pSsu7uIqlTlypUDEV4Bw8uw u+jyd0JBJJ0F8VcT6KIm8c3Qos7NDDxjrDOZxo0b06acOXNu27aNPsp7xmnT pnHO0Pd9TJo0iTfRPCHS1xWfWEoCzwjM4CLPuGvXLj4ZVaFChYsXLxrkfO21 1yT7EEewyjOuWbNG9Kv8T968ecXrhMhe6TyXCsh7xrDL2jz88MNcu/T09KiZ Q8mTJw9lphGHP5Lx5JQiRYrwEmqR0HrG8+fP16lTh/7Pli3bb7/9JvO7biSq Unyqv2nTpqGbqK3SJhr6E1U4V4FIOgvirybQRU3imKHJzM0MPGNMMxma1HH6 gAEDOEXeM4qLiRs3btRtolLxptBLkALFJ5aSwDMCM7jFM16+fLlevXp8wCYl JRnkXLx4MV+Nql27dtiVNh3HKs84c+ZMcTquXLlyVHHqrqnK1KHxK4fIT0Vd LNRmTHrGHj16cH3FqmXGnpGqT1GibzVr1oyfmOCGwVtppOMUGguMiyQ84/Ll y8WlSd2i4h4jqlI33XQTBeGJJ54I3UQRDgQXM09U4VwFIuksiL+aQBc1iXV+ Ijk3M/CM8jOZ9PT0ggULBoJXn8XsTt4zjhgxgnOuW7dOt2nu3Lm8adasWWG/ q/7EUhJjfS9cuLDPBKFrzgOP4RbP2K5dOz6ijR9h+OOPP/ipt1y5cqWkpMRR PBuwyjOKm/PJDYnrboxYEXrNmjXmC2whJj3j8OHDuV6TJk0yznzgwIH27dvz +KKjVq1anEcMVV999ZVxkYRnbNGihdhPw4YNZarsUqIqVbNmTQrCPffcE7qp bNmytOmxxx5LVOFcBSLpLIi/mkAXNYl1fiI5NzPwjPIzmSeeeII/rlixIu0f li5dyomDBg2ijxkZGZHKMGfOHM7566+/6jaNGzfOYMrkiomlJMb6njlz5v9M sGPHDhurAhzAFZ6xf//+fDhXr15d95CalsOHD4t1TiZOnBhH2ezBKs9IvStX NvQtQuvWreNNYuVPRTDpGcVDE3PnzjXIfOjQIZ5UEOXLl+/duzf96PHjxzmS wjNOmTKF8wwePNi4SMIzMuK2mZ9++kmy4q4jqlI8fFesWDF0E1v1qAtV+QRE 0lkQfzWBLmoS0/xEcm521dAzSs5kNm/eHJDgrrvuilQGsTdxzlnw9ddf86bQ xW3cMrGUxFjfixcvbtiwYV286N7aCbyH+p5x8uTJvB5X0aJFDdaq+uuvv/h+ lYBm9S01scozXrlyhd9G8d577+k2UaA4FFGvoNmMSc/4xhtvcL327NljkJlf 8ps1a1bdC7x0nlGMQW+//bZxkbSesXLlygcPHixZsmQg+CAkWVHj77qUqEq1 adMmEHwfiu6Vl6Ltde7cObFFdAmIpLMg/moCXdREfn4iOTdjDDyj5Exm69at Mp6xRo0akcqQlpbGebp06aLb9PLLLweCz07qXgDtoomlJHFctQFAoLhnnDFj RrZs2QLBZ6INbrM8e/bsAw88wMd1ixYtLl++HEfBbMMqz0hUr149EFwKQHeD vbhbQ7VlcMx4RurM+QmXnDlzGqybeuTIEa77m2++qduDzjPSDvk9X7QT3Uih Q3jGG264Yd++fVc172lq3bp19Gq7kKhKibUIdIsGDBkyhNPnz5+f2CK6BETS WRB/NYEuaiI5P5GcmwmM37UhOZM5ceLEsRDEra3Dhg2jj8avvuIbkHQvcKEf vf766wMh1yjdNbGUBJ4RmEFlz0i9RPbs2QPBM40GX/n777/FI9iNGjUyXlJV BSz0jKIfnj59uja9VatWnM4GRx3kPSP16roFfAYOHMiVeu6553SZtZ4xJSWF s73++uvar69evZpGN61nJB599FHOHPqS3y1btoj/hWecOXOmSKTGxomLFi2K WnHXEVUpGk8LFChA1a9fv74YTMl616hRgxJLlCjhjRHWPIiksyD+agJd1ERm jJacm2kx9oxmZjKR1sCh9kMD9/jx42mKKBL79OnDmcmNikT6UU4cMWKESHTd xFISeEZgBmU9Y1JSEr8znejUqRN9/Pnnn6dr4Ik62Qp+8y+RL18+6spmzZo1 /d+kpaXFUc7EIROBVatWLf+HYsWKUe3IOYoUcSbt0qVL7CjJDVF8aAyllK++ +orvGAm7SrmzyHvGQPA9jDNmzMjIyCAFe/TowauWUasQN6Ze/ef8JAVBzB9o pODqU3tITk6+cuVKampq7969+aSozjPScMNjH/H+++/TnimA69ate+aZZ2gn 4lVNuvczMrt27eJV3aic2lHJG8goJW4Vbteu3cmTJ48fP966dWtO6d69uy3F dAGIpLMg/moCXdQkqi6SczOCRl4xaenSpQt/hcZiTvn999/FPs3MZCJ5xg4d OnC69p5SmkvwzUVFihTh6QGZx/z581NKnjx5xCu33DixlASeEZhBTc9IByzf 325A5cqVKScNHMbZiGnTpsVRzsQhEwF+b2AkvvjiC5GTerxChQpxes4g/P91 111HPXZiaxI78p4xbAPIkiWLbr2a9u3b86aCBQvSKMOJ2tVNeYDg4JQqVSrw b89IDBo0SOThnxD/v/XWW5wnrGckevbsyeneeNJBi4xSNIvjR0cZNvWB4HUB 75nouEEknQXxVxPooibGusjPzQiaqBhko0mOds9xz2QieUY+n0zcfffd2nTK JkZ50aLIBc+ZM0fkcePEUhJ4RmAGZT2jduoelpo1a17VLMVsgLYrUAHznrFf v37azDt37qxTpw6fkWMef/xx3ZrViiBT9/LlyweCS6iRm7v22mtFpUqWLBl6 FyiZuBIlSnCGbdu2ceKJEyeaNWsmvkjDQcOGDXfs2NGtW7dAiGe8GmzJ1apV E8MHUbx48TFjxogMEyZM4HRdm6eGWq5cOUrPli3b3r1744uJmkiOLDSpo8Ym rtXSdKJ58+aYzmlBJJ0F8VcT6KImUT2j5NzsajTPmDt3bt3O45vJ7NmzhzPr VkP97LPPOH3AgAG6r4wfP57XRmBKly6tmyW6cWIpCTwjMIOantHbJCgCGRkZ v/32G+1Z5deqxlr3y5cvp6SkzJs3z2BZNsqzZMmSpKQk3USChhJKX7Zsme65 yEicOnWK8i9YsODAgQPyJfQqMSl19uzZ1atXy4faVyCSzoL4qwl0URPHZ2gW zmQ2b968adOmSFvJoi5atMhjJ3uj4ri+wNXAM9qPnyPg57q7CyhlFYiksyD+ agJd1AS6eBvoC8wAz2g/fo6An+vuLqCUVSCSzoL4qwl0URPo4m2gLzADPKP9 +DkCfq67u4BSVoFIOgvirybQRU2gi7eBvsAM8Iz24+cI+Lnu7gJKWQUi6SyI v5pAFzWBLt4G+gIzwDPaj58j4Oe6uwsoZRWIpLMg/moCXdQEungb6AvMAM9o P36OgJ/r7i6glFUgks6C+KsJdFET6OJtoC8wAzyj/fg5An6uu7uAUlaBSDoL 4q8m0EVNoIu3gb7ADPCM9uPnCPi57u4CSlkFIuksiL+aQBc1gS7eBvoCM8Az 2o+fI+DnursLKGUViKSzIP5qAl3UBLp4G+gLzADPaD9+joCf6+4uoJRVIJLO gvirCXRRE+jibaAvMAM8o/34OQJ+rru7gFJWgUg6C+KvJtBFTaCLt4G+wAzw jPbj5wj4ue7uAkpZBSLpLIi/mkAXNYEu3gb6AjPAM9qPnyPg57q7CyhlFYik syD+agJd1AS6eBvoC8wAz2g/fo6An+vuLqCUVSCSzoL4qwl0URPo4m2gLzAD PKP9+DkCfq67u4BSVoFIOgvirybQRU2gi7eBvsAMTnlGAAAAAAAAAABuAZ4R AAAAAAAAAEAkcG+qnfg5An6uu7uAUlaBSDoL4q8m0EVNoIu3gb7ADPCM9uPn CPi57u4CSlkFIuksiL+aQBc1gS7eBvoCM8Az2o+fI+DnursLKGUViKSzIP5q Al3UBLp4G+gLzADPaD9+joCf6+4uoJRVIJLOgvirCXRRE+jibaAvMAM8o/34 OQJ+rru7gFJWgUg6C+KvJtBFTaCLt4G+wAzwjPbj5wj4ue7uAkpZBSLpLIi/ mkAXNYEu3gb6AjPAM9qPnyPg57q7CyhlFYiksyD+agJd1AS6eBvoC8wAz2g/ fo6An+vuLqCUVSCSzoL4qwl0URPo4m2gLzADPKP9+DkCfq67u4BSVoFIOgvi rybQRU2gi7eBvsAM8Iz24+cI+Lnu7gJKWQUi6SyIv5pAFzWBLt4G+gIzwDPa j58j4Oe6uwsoZRWIpLMg/moCXdQEungb6AvMAM9oP36OgJ/r7i6glFUgks6C +KsJdFET6OJtoC8wAzyj/fg5An6uu7uAUlaBSDoL4q8m0EVNoIu3gb7ADPCM 9uPnCPi57u4CSlkFIuksiL+aQBc1gS7eBvoCM8Az2o+fI+DnursLKGUViKSz IP5qAl3UBLp4G+gLzADPaD9+joBM3VesWLFw4cK1a9faUiIQHj+3UmtBJJ0F 8VcT6KIm0MXbQF9gBnhG+/FzBGTqXrp06UAgcN9998nscMSIEc2bNx8/frwF hQMa/NxKrQWRdBbEX02gi5pAF28DfYEZ1PeM+/fvnzRpUrdu3YYOHbpw4cKL Fy+GzXb69On58+f36dPn66+/Xr169fnz5+Momz3EFAGqCFVn8ODBvXv3XrBg walTpxJZtIRjrWfcsGFDIEiWLFmonVhTRBAk1uM0LS1t/fr19DdhJXIriKSz WB7/c+fOrVixon///p9++unEiRN3794tuef09PQJEybQWDZq1Kg1a9both45 cmR9NP7++2/tV0yOes4OmvK6UDl/+eWXfv36UcCnTZtGYYz6lX379nHEjh8/ bj7npUuXUlJSaLi5cuWKTIEFUZtKHKInGngKbyOvr3F/xWzatClSu71w4YLx /umwot3S0TFw4EDKf/ny5ahFinq0UnlGjx7dtWvX4cOHL1++PKYD1kUmwkFU 9oxr166tVKlS4N+UKVNm1qxZupzz5s278cYbtdmKFSsWqZE7jnwEFi1aVLhw YW29smbN2rdv31hHLnWw1jPSEExukTJny5YtNTXVmiKCIJKtlLrZpKSkli1b UsskIerWrZv4orkMRNJZLIw/zYI6deqUPXt2bZ9MXVDr1q2Nz+YlJydXrFhR N5Y1aNBgz549Ig9NnALR+O6770R+k6Oe44OmpC5ktW644QZtOXPlyvX5558b fOXQoUNi3Bw7dqyZnNu2bRswYMAtt9zCeRYvXhy1wIxkU4lVdBuAZ/Q2MvrK 9FdMjhw5IrXbyZMnG/zE1q1bqcPR5i9btixZQoOvGB+tR44cad68ua4M99xz Dx3CxpVlHO8P3YKynnHQoEFkBFi7AgUKVK1aNXPmzPyR+uFVq1aJnAsXLsyU KROnk9GoWbMmD/o5c+acPXt2HCVMNJIRGDx4MFeEKFiwYLly5biaRM+ePRNf zIRgrWe8GjzSP/zwQ/mhHEgio1RaWppoosy9995rS+ncBCLpLFbFn/JUq1aN t1JXTHOqokWLivyNGzeOdCpv3LhxYmZFX6lTp06+fPn4Y4UKFcTZbBn7QAaK M5sc9VQYNGV0WbRokRj377rrrhdffFH4RwMn9dRTT4mIGXtG45xvvvmmLv4U N5mqyTeVmES3B3hGbxNVX8n+ivj777/ja7dbtmwRR0T58uXLlCnD/5csWXLv 3r2RvmVwtF6+fPmhhx7iTddee+0LL7xQr149/khW9OzZs8YxUaE/dAvKesbR o0eTZMWLF1+wYAFfs05PT+/YsSM3g4cffpiznTt3jpoEpVx33XVi1ZQ1a9Zc f/31lEju49KlS3EUMqHIRGDXrl3caPPnz5+UlMRDDI1E9evXpwEo6v02ymK5 ZwQJQkap1NTUAv/AUzs4nVAQSWexKv4ZGRm33347bXr99dePHj16NThRoT2X KlWKR6UVK1aE7vnYsWM846J50dKlS3ksO3HiRKNGjfhbffv2FfvfE44NGzbQ 1IVy3n333fx1k6OeIoOmjC5VqlThievWrVs5hca+GjVqBIJnksPezDZhwgTt xNXAM0bN2bZtW24SuXLl4jySnlG+qciLbhvwjN7GWF/5/upqsNvkxM8//zy0 DZ85cybSrzRp0iQQPJ0iVqIYPHgw76pNmzZhv2J8tP7888+c/v7771P/xok/ /vgjJ3755ZcGAVGkP3QLynrGq8HTHdzfCsg6lS9fPhC87sYp1Kq5VQwZMkSb c+bMmVGHDKeQicArr/w/7Z15fBRF2sdHdpHDZRGv9QCjq+viLZeg67sKCrjg ieKxKx5hQcRjXRAXL0COcCOnEJD7hnCGKwnwQoAAQcIZAgJJIHfCmXAkIuR9 PvO81qe3e6anZ7qnj+nf9498MtXVXVXPU11Vv+7qqk4e71SWPXv2SMPpFqZe JoyZCzPaNeOTTz4pQtLT07dv337u3Lmg0iJDUQuwdevWsrKygJFzcnKoLp05 cyZgTJLzGzZs0P65GWUjOTk5KyvLWTOKgx058PQtKB0lsKS1GGj/Y8eO0eBE Fjh37lzua0aNGuXzgiQQ2rZtW1xcLA2kro3HZs8++6x6fkh3eLwTMg8fPswh Ons9m3SaAf1CY07+9GDgwIHScBJunM9Dhw7JTiksLOTZa02bNlUvi/aYlZLB p0bNWBlqVREonW4a0IyRTUD/am+v9u/fz/VZ+b2YCgUFBfxC5P3335eGs5Cs VauWcqQX8G7t2bMnBV5zzTWyBU+oGafwN998UyU/NmkPnYKdNaNPWrZsyWKK tf+4cePYrVSpZDHvv/9+mfSwCQEtQPqFv4No3769WZkyCe2asVWrVqTgOnfu LCYjValShRoZ2YfJDRo0qFOnDjVx0kDqZ1944QXxSOqqq66iyiCdXT9v3jw6 6+6776YWJiYmRjz+5XlEW7ZskWWJ5N6CBQuaN29+7bXXisveeuutCQkJsph8 5XvuuYf+X7JkyWOPPSbmvNWtW1d5ZdsCpWMUsKS1hNv+dFPzDd67d++gMvbU U0/RWVFRUSpxtm7dyu89R44cKQJ19no26TQD+oWyx/kcM2aMNJzkGIevXbtW dgoPO6n3TEpKUh/vaY9ZGZJm9InGquLT6aYBzRjZhOxfZXuVnJzM9Tk1NVX7 dYYOHcpnyT4pohEah0+bNk12SsC7lZ+xXH/99bIT33vvPY/3q0aV/NikPXQK ztKMZ8+evfHGG8mJPCYnvvjiCx7qK1/idOnSxeP9jjW0tMJHQAvMnz+f6/Dm zZvNypRJaNeM1DSJlQekfPbZZ8rI0omsJ0+erFevnogvphURsbGxHGfGjBkc 8vDDDyuTqFatGsk9cUGqdT6jebwyVjZuEVfu2bOn+P5UUKNGjZKSEp02NAco HaOAJa0l3PaPiYnhuzvYHX8aNGhAZ9GwRCVOo0aNKA79lXZwOns9m3SaWvzC XwW2aNFCOj8zLi6ODS779Insz+H9+vU7fPiwz7FlsDEZozSjxqri0+mmAc0Y 2YTsX2V7tWzZMp93ojrvvPOOxzu3XDbns6ysjB+w9+jRQxqu5W4V7wRlReNV NEk5quTHJu2hU3CQZszOzm7dujVXjLFjx3Lg+PHjOUS51QJVMB7VB1zy12QC WmDw4MFch3li9qVLl3bs2HHkyBGTv2sIB9o1I/Pcc88tWrSIGoqRI0eKDzyl 8+SVmlH0y59++mlubi61A3v37n3xxRcfeeQRsWi5UHYe72fX06ZNO378eFpa 2ttvv82Bt99+u3SSw9NPP03ueOONN+Lj4wsKCvLz87/88kuOKXsGJb1ynTp1 Bg0atGvXrtTUVDGnon///rqtaAZQOkYBS1pL+OxPY57Zs2fznJB69eoFXGlB yunTp/ldUseOHf3FEY/xSbNIw3X2ejbpNLX4ZcCAAZxVGmfyRxlkc5KQHsUH 74WFhddffz2FP/rooxTn0KFD/pSg9pgC/ZpRe1Xx53TTgGaMbELzr8/2aurU qVxXSbLRiIik2TfffEMST70l5GH8gw8+qDzEC+O89dZbIkTj3UpDO546S5HX r1/PgWISuwjxiU3aQ6dgf81I4/no6GjqJvjThpo1a44ePVo8EFi7di27WzbZ IzExkT8hJ44ePRpCPsNHQAt07dqVsn3zzTcXFxe/9tprYtGq6tWrd+vWLdjP +mxFUJqR2h/pkx9qSThc+o2nUjO++uqrHu80BlnDJb3rhbJr166d7BvG9u3b KxslEq3K2i4W5qLmVHllGnlKF3mmK/Brx5dfflm9+DYBSscoYElrMdz+ubm5 //rXv6idiYqK4pudIgfby4gJWtOnT/cXhxp/j3dlBrGqA6Oz17NJp6nFL9Ro c3tO3HLLLUOGDKEhK3eFu3btksZ86aWXPN6JHNzqqihB7TEFIWvGEKqKP6eb BjRjZBOaf322VyNGjPD4ol69esuWLfN3qYceesjjZzMj3uCDRlYiRPvdmpyc XKtWLT5KZ9FIjMXmG2+8oV40m7SHTsH+mrFNmzbS2tinTx/pFrcVFRW85FHV qlVjYmLIs7t37+7Xr1+1atXEKRQSQj7DR0ALtG3b1uNVx+I7u5tuuklMdGza tKlzXzhq14x/+ctfZOFTpkxhC6xcuVIWWaoZqY/maCrbAwllp3wAlZKSwoe6 dOmins9hw4YpK5i4clJSkiz+7bffTuFNmjRRv6xNgNIxCljSWgy3f2pqqmyA tH///qCylJmZyXPm7733XtmiDYK8vDzeberLL7+UHdLZ69mk09Tol507d4pd twSLFi2SxqEBJIeLDwD9jS21x5QSsmYMtqqoON00oBkjmxD866+9EpqRWsvP P//8s88+4/mrHu8HPvv27fN5NX6Z2K5dO+UhXrLmgQce4J9B3a0///wzyUNZ Q9GoUSOV5VsZm7SHTsH+mpFG5n/729+oKortcevXr3/gwAERgQbnyn1F69Sp I+rPyZMnQ8hn+AhoAbG1k8f7ro13qy8oKBAvtiZOnGhSXo1Gu2ZU7rVBUpGL L/0YRBmZRJ9YdoZqTnx8vHKpZBXNSHqcT/e5niH1+LGxse+++y61ReKh1po1 a7RcmdSiR/Iprs2B0jEKWNJaDLd/VlZW+/btKYLYk/qqq67q1auXxq/PqIUR Lfnq1av9RRszZgzH8akydPZ6dug0tfglLi6ONRS1xq1btxYPTqkVFYumUs94 3XXXebxvLoQLfI4ttceUEbJmDLaqqDvdHNT9QiPz4zqQzskBlhBse6jeXtFg TPp4nCJ//fXXHNnfdmm83MQLL7ygPNSsWTOPV+hVBnm3lpWVUXIe77KrH330 EW+T4fFOK6XbLWAZ7dAeOgX7a0ZBYWEheZ97DVIK0pmHP/30U7t27erWrevx rp3SqVOngwcPctWlKhRCWmEloAXEZ5uyFbmp3rJOkS0T6iD0aMbExEQ2i7pm rPQuaS5d4JT660GDBkmn+qgoO4JXapWtTUGJ8tIESlatWqXlyo8//rgHmtF9 wJLWEj7700gmISGB9xD0SJbYUueTTz7h+NLPdpS8/vrr3H/5m1Wis9ezvNMM 6BfSTfz47t133+XnfhQiFsSmVpq34hIhKSkp+b8i1s8nFUY/+VtI7TFl6P+e UWNVCeh0E1D3y/nz5/9XB+ZvHQJkBNseamyvBFR1uZ6TCvO5syE/PPe5lim/ 7+PxbVB365tvvunx7sHHK7hWVFRQBF4wkxgwYEDAbFveHjoFB2lGpnfv3lwN Jk2apDwqnbbK65mI19z2IaAF3n//fY93GRblIb6PnKI7lJijGYmioiKqKtwC MKQBT506xUfVNePvfvc7j3cOsAgRczCqVatGrRNp0oyMjGnTpkEzCqB0/AFL Wku47V9QUMCzrbQsrydakkaNGqmvFMFvppo3bx7wmjp7Pas6zYB+eeWVVzze RS1k03fFGKBbt27p6ekeDVDbqz2mMidGrZsasKpod3r4UPcL+WL37t1poSLb 9Q+YT1Dtofb2Skr37t35LOmSDgIexN53333KQ/xisWPHjkHdrXv37uWf3333 nfRq2dnZ/B1xjRo1lPto+MMRIsJCHKcZjx49ytXjo48+Uon2yy+/8OdjNnwl F9ACAwcO9HgnsSg3o+cVAGrXrh3G/IUT0zQjQ9VgyZIl/GG1x7v4HoerKDvq 1GSRV65cyW+3H3vsMWmXJwYS0IyVUDr+gSWtxQT7d+jQge96fvPlj4ULF/La gyQclGv0ScnKyuILfv7559qzobPXM7nTDOgXnmAWHR2tPMRf+jdo0CAjI0PL 2LJx48baYyqTM0ozVqpWldCcbjg6R2jA5mj3r/b2SoaYnurzM8DOnTt7vG8h Zcs5UhJ81ldffRXU3TphwgT+qdzyY/r06XxIZU0ef9hZRFiIbTWjvwn/mZmZ XAc++OADldPF9qA//PBDCJkMKwEtEB8fz5lXruLy9NNP++vXHIHJmpEpLS3l OQ/i6a6KshMLL8fExHDIhx9+6PFK+KKiImlMaEYpUDr+gCWtxUD7++uVxB49 siZCCg1a+NO8WrVq7dixQz0PYruxoDZc0NnrmdxpqvuFTM1rUPhcDYaXiSNR WendBeCkgq1bt3JZaDxJP8+ePRtUTBkhaMYQqkpoTjccaMbIRqN/g2qvZPDC lVdffbXPLSrEyjaylay+//57DqeRXmUwdyvvyPOb3/xG+oqQoZyL+EEVodLe IsJCbKsZX3vttXbt2ik/LhDzUqZOncoh5eXlsqcZp06d4uV8o6KibLivSkAL XL58mZQF5b9hw4bS7xqOHDnCd7HPR6+OwATNePDgwfT0dNm5Tz31FEX7/e9/ zz+Fsps9e7Y02pkzZ+644w5uf8RFXn75ZY/3Y+qCggIRs6KiQvT+0IyVUDr+ gSWtxSj779y5k0TKihUrZOGFhYU33XQTdzf+rkln8Rpu1atX15KZ2NhYbkYS EhJ8RtDe6124cIEaJWozpQMqO3SaAf3CDea9994rmxR37tw5/uS8VatW/s7V srKN9pjqmlFp4dCqSkCnmwM0Y2Sjxb9a2qu0tDRqMWRb3vD1eV4WL2WjhO4X Xm6iZcuWYnxLbU7jxo351lD5mNfn3SpGhkIUCIYPH86HUlJSROr2bA+dgj01 46JFi9jR9evXnzBhAo3er1y5UlJS8vXXX/MujbVr187Nza30Ps1r3779b3/7 24EDB9KQnly/adMmOotPHz9+fAg5DDdaLCA2S33xxRd5EktmZiZ/WUwWoLvV jIyGgXBrRn6lSM1dr169eDbF+fPnKT6f2KJFC44mlB0pwQ8//JAaItKAmzdv vv/++zm8c+fOIomePXtyYJcuXcgX1IBQHeP2zeWacdu2bVt+hb/EofG2CPH5 xN6FwJLWYpT9H3zwQY93vkHXrl3plqfAS5cu0ZUffvhhvuW7d+/u8+KrV68W y7ZTY0I/ly5dukSCsq349ttvOT6pD+UFg+r1unXrxuHihZ1NOs2AfhHfUrVp 00ZMjSsuLhaLY4wbN87fufo1Y15enqgA33zzDceJiYnhEOm6pkoLh1ZV1J1u GtCMkU1A/2psr/iTHxKVNNZKTk4mCUb1PDY2lvcTp8qvsig0b0FOfPLJJ2fO nCGBFh0dzSF9+vRRyZvPu7WsrIwb7WuuuWbmzJniFf/ixYt5i5C6deuKJRBt 2x46BXtqRhrA8wJiArHRBtdGMTn52LFj/OCOYUXJfPrppz5XbbIcLRagLkZs YU/l5Q/nma+++sqUbIaFcGvG1NRUaX2g/8WurFWrVt2+fTtHE8rOJ9TjSycO 7d27VzShVbzw/0JgulYz8mJB/hg2bJgpObU7sKS1GGV/ukidOnVEILUDYk8f olmzZtRtKa9MIxDlKu4yHnroIdlZXbp04UPKL3Qqg+z1xGrPYsdbm3SaAf1C YzkhD2kAQFZq3LgxDwI93qepKpub6NeMYvtdn1CFETGVFg6tqqg73TSgGSMb df9qb68WLVokbkZuRsRWOESPHj1U8kA6sWnTpiKyOLFly5bK+aVS/N2tKSkp QiPcfPPNdCfyhDFuOrZu3Spi2rY9dAr21IxMXFwcb9ci5cknnxQjf6agoOC5 556Tisq6deta+0WAOtotMGjQIF5IiqH/SZKEOXfhRUvZ+QkPtR6y8I0bN7Id pJpRGZmao3//+99imWXmkUce2bBhg4gjlN2kSZOaN28uolEtio6OVq4PRs2j 2PHH413SduTIkdTvc/Mi1Yzz5s3jOFu2bJFdhBNyj2YcOnSoKTm1O7CktRho /6Kioq5du0rbZI93xnv//v397RxNYzDpIMQnTZo0kZ0lHpn6W6tQe683ePBg jiD2xQ7q9PCh8dnp2LFjZY35DTfcMHr0aPUJY9nZ2Rx5wYIF6kn4i6muGa+5 5hoR06eFQ6gqAZ1uDtCMkU1Azai9vTp+/DiNl/jFouCuu+6Kj48PmA0ap0mb IBKqVP/VBWOl6n2dkZHx/PPPy7Latm1b6WbulTZuD52CnTUjk5+fv3nz5hUr VqSkpKhsrEkDeNKSiYmJeXl5IeTKTIKywJUrVw4dOrRu3br09HQL92wyCjP7 o9zc3LVecnJyZE+khWZkIVlSUkK6b+vWrdI9HGVQP04VbP369dY+BDYNjByM Apa0FsPtT2plz5491GJQd0ONs2wnCDPR2OtR37Fv376QTw8T2v1CHV9WVha1 vUlJSZmZmTbsB/1Z2D5VRTtoryIbw/3LHwPSQIvu0GBbEhpWpaam0ghfZegV FKWlpTt27FizZg399bnXaqVd20OnYH/NGHm42QI2Kbv6/oyg0jaeigBgSWuB /e0J/GJP4JfIBv4FeoBmNB83W8AmZYdmDIhNPBUBwJLWAvvbE/jFnsAvkQ38 C/QAzWg+braATcoOzRgQm3gqAoAlrQX2tyfwiz2BXyIb+BfoAZrRfNxsAZuU HZoxIDbxVAQAS1oL7G9P4Bd7Ar9ENvAv0AM0o/m42QI2KfvGjRtbeJGtqQUE NvFUBABLWgvsb0/gF3sCv0Q28C/QAzSj+bjZAm4uu7OAp4wClrQW2N+ewC/2 BH6JbOBfoAdoRvNxswXcXHZnAU8ZBSxpLbC/PYFf7An8EtnAv0AP0Izm42YL uLnszgKeMgpY0lpgf3sCv9gT+CWygX+BHqAZzcfNFnBz2Z0FPGUUsKS1wP72 BH6xJ/BLZAP/Aj1AM5qPmy3g5rI7C3jKKGBJa4H97Qn8Yk/gl8gG/gV6gGY0 HzdbwM1ldxbwlFHAktYC+9sT+MWewC+RDfwL9ADNaD5utoCby+4s4CmjgCWt Bfa3J/CLPYFfIhv4F+gBmtF83GwBN5fdWcBTRgFLWgvsb0/gF3sCv0Q28C/Q AzSj+bjZAm4uu7OAp4wClrQW2N+ewC/2BH6JbOBfoAdoRvNxswXcXHZnAU8Z BSxpLbC/PYFf7An8EtnAv0APVmlGAAAAAAAAAABOAZoRAAAAAAAAAIA/MDfV TNxsATeX3VnAU0YBS1oL7G9P4Bd7Ar9ENvAv0AM0o/m42QJuLruzgKeMApa0 FtjfnsAv9gR+iWzgX6AHaEbzcbMF3Fx2ZwFPGQUsaS2wvz2BX+wJ/BLZwL9A D9CM5uNmC7i57M4CnjIKWNJaYH97Ar/YE/glsoF/gR6gGc3HzRZwc9mdBTxl FLCktcD+9gR+sSfwS2QD/wI9QDOaj5st4OayOwt4yihgSWuB/e0J/GJP4JfI Bv4FeoBmNB83W8DNZXcW8JRRwJLWAvvbE/jFnsAvkQ38C/QAzWg+braAm8vu LOApo4AlrQX2tyfwiz2BXyIb+BfoAZrRfNxsATeX3VnAU0YBS1oL7G9P4Bd7 Ar9ENvAv0AM0o/m42QJuLruzgKeMApa0FtjfnsAv9gR+iWzgX6AHaEbzcbMF 3Fx2ZwFPGQUsaS2wvz2BX+wJ/BLZwL9AD9CM5uNmC7i57M4CnjIKWNJaYH97 Ar/YE/glsoF/gR6gGc3HzRZwc9mdBTxlFLCktcD+9gR+sSfwS2QD/wI9QDOa j5st4OayOwt4yihgSWuB/e0J/GJP4JfIBv4FeoBmNB83W8DNZXcW8JRRwJLW AvvbE/jFnsAvkQ38C/QAzWg+brZAuMuekpKybt26nTt3hi8Jl+DmWmossKS1 wP72BH6xJ/BLZAP/Aj1AM5qPmy0Q7rL/8Y9/9Hg8f/3rX7VEnjx58uuvvz5n zpzw5ce5uLmWGgssaS2wvz2BX+wJ/BLZwL9AD/bXjAUFBfPmzevdu/fUqVN3 7NihErOoqCguLq5Pnz5Lliw5efJkCHkzB+0WOHDgAJWaSrR48eL8/Pww58sM 7KMZd+/e7fHym9/8JicnJ3xZcijBeorq565duyKjlhoLLGktWuxfUlKyyw/H jx+XRb506dK2bduGelmxYgWd6++y2mOWlZUlJiYOHDhw9OjRqampFRUVwRQx iISIffv2TZs2rVevXrGxsVu2bLly5UpQaRmF9vvil19+oa5/xIgRo0aNIo9c vnw5zFlzNdAUkY2x/RE1Jv5azp9//ln9ytTorVq1ipqsAQMG0BCXhvr+omls G8vLy1NSUqihoAvOnz8/KytLcyn/H6eICAsRmjE0Qk5RS8zt27ffd999nv/m 2Wefzc7OVkbu3r27NNpVV10VExMTQvZMQIsFSktL33vvPVnZP/zwQxobmJLH cGEfzUjtCalFily1atW8vDwRTsOtFl4yMzPDl0/7o9FT1J6vXr36rbfe+u1v f0vGfOqpp8KfNYcBS1qLFvsPGjTI44f7779fRKNR0Mcff3zNNddII9SuXZvE l+yC2mMSCQkJt956qzTmbbfdpv6ANLSEqHF7/fXXZQV84oknDh48qCUtY9F4 X2RkZJA1pBn+05/+pBTywCigGSMbY/uj6tWr+2s5Fy5cqHJ90nS33HKLNH7N mjWHDBkii6axbaRmsGfPnldffbU0Jg3woqOjz549G7CwjINEhIWY3z5oTHH2 7NmiNv7hD3947LHHfv/73/PPe++9V/aogTpNPkRdZ7NmzWrUqME/+/btG65i 6CCgBa5cufLkk09yEaKiolq3bn3zzTfzT9IyAZ/e2Bn7aMZKb3P0xRdfbNiw QRq4b98+NjX9E548OgMtnsrPz+cORfA///M/puTOScCS1qLF/v/5z3/8jXyo u+E4J06coIETB1apUqVx48Z//vOfRbQZM2aIq2mPSaxbt44GJxROAx5quJo0 acI1gXqxFStWqGc7qIQuX7789NNP86E6dep06NDhmWee4Z+kwi5cuKDdpIag xS8HDhyg3p8zWb9+/bvvvpv/v+OOO44dO2ZKNl0HNGNkY2B/dPHiRX/NJkGq 0N/1169fT+0VR3v88cffeecdoR8nTZokomlsGym3DRs25NMp/n333ScaDeKl l17SMpXCWSLCQuypGU+ePMkKkfqITZs28VyU06dPP//88+zHQYMGich79uzh wEaNGpWVlfHp99xzj8eu0w4DWmDKlClcovfff59fLNLfLl26cCAdNSmjYcBW mtEnJCHZztCMAT2Vl5d37a9wFwClowSWtBYt9u/UqRPZ/NZbb81WICYhfPXV V9wyfPHFF2LyZ3x8fM2aNSnw+uuvP3fuXLAxy8vLSa9R4I033ihW7tqxYwc/ JKTW7JdfflHJtvaEiKVLl3Lkzz//nNLlwJkzZ3Lg8OHDNVnTOLT45eWXX+Zx oPjqfOzYsZzhzp07hz2LrgSaMbIxsD+iaHwzDhkyRNlynj9/3t/1H3nkEY/3 ZVBGRgaHnDp1qnHjxhRIKfKAX3vbWFpa+uCDD1LgBx98cOLEiUrv8zEq4513 3snZS0lJUS+v40SEhdhTM1Z6F8Bs27ZtcXGxNJDqA2vJZ599VgR+9NFHSs+K OmDDpwQBLcBPg+vVqyd6dobqM4VTTXbuBx3maMYnn3xShKSnp2/fvl06dlJn 0aJFWjQjuYYuS9XM6bOF/RGsp+666y4oHZ/Aktaixf6vvvoqDxhU4tAQpWPH jspHdkK4bdu2LdiYmzZt4pDvv/9eGnP58uUcPmvWLEOyRPTs2ZOfosuaLKpp FP7mm2+qJBQOAvqloKCAXyu8//770nAWkrVq1dLeqgPtQDNGNgb2R/v37+d2 Jj4+XvsFSUvyZ0EDBw6Uhq9bt46vdujQocog28Zjx44tXbpUltDcuXM55qhR o9Sz5DgRYSG21Yz+4Kk4UVFR/PPnn3+uU6eOx9d06/vvv9/jncQSclphIqAF brrpJsp5ly5dZOELFizgOuzcJl297LNnz67jZdWqVdLwNWvWcHj//v2l4TRm uOGGGyi8V69eHMKasVWrVmfOnOncubOY8FClShUaeMimNDdo0IDObdu2Lf+c OHHi3XffLaZA0z+c6KOPPio96+jRo88884yYOV+tWjUaw/DTrUgCSscoYElr 0WL/Fi1akM3btGkTwvXFzISpU6cGG3PcuHEcUlhYKIvM/Zf08ZfOLH3wwQce 78tHWWT+cP6JJ54IISE9BPTL0KFDuRSyzwcWLlzI4dOmTQtvFl0JNGNkY2B/ lJyczHdiamqq9gtSW8dnjRkzRhpOuo/D165dW2lE27hlyxa+Qu/evVWiOVFE WIjjNCON8z2SdQmysrK4Viin1ohPVFRekVuCugVI1/ir5+Jek075dhbqZRcP drp16yYN//e//83hzZo1k4YnJCRwuHjMxZoxKiqKGzoZn332mfR02UTWvn37 Kk/xeL+jEacsX75ciMqqVauKOf9169aNsGUZoHSMApa0Fi32526F1FMI11+2 bBk3AuprPviM+cUXX3i8cy+VX9zwxwi33XabUVkSz+dl1njggQdCLrseAvrl nXfe8Xjnqskm6JaVlXHD26NHj/Bm0ZVAM0Y2BvZHop0J9uNi/vywRYsW0ilz cXFx0qvpbxtjYmL4gur7qTlRRFiIszTj6dOneXJ1x44dOWTr1q3s00WLFski jx8/ng8dOXJET4YNJ6AF+ANeUUYByUn+Mvfrr78OY/7CScCy85vBRx55RBrI k9U93skDZ86cEeE824q0G09Br/xVBjLPPfcc1YrDhw+PHDmSxxi1a9eW3vsy zZiZmZmUlBQdHc2n01lJXsQk1ZKSkuuuu87jnWBPgzFyR3l5+eTJk9kpSn85 Gigdo4AlrUWL/aOiolg3jRo16gMv9E9aWpqW63fr1o1bjICfvShjik5KeW6/ fv083gkSISx65jNLFy9e5Odd119//fr16zlQzAcTIaYR0C+tW7emjFHjrzzE XeRbb70Vrsy5GGjGyMbA/mjq1KnceixfvvzLL7+k9vObb74hgRZwQa0BAwbw ie+8805paWmld5o9T/YQ4zE9bSNdbfbs2TwZrF69eur5caKIsBBnaUYxWWX6 9OkcsnjxYg6RTV+plMzk3Lx5s54MG05ACzzxxBMscEgji0CSJ23btuUSdejQ Iey5DA8any1fddVVYnOcgoICj3cKKE/ZJY+LyE2bNvX896wqoRmp7ZI+nqLR BYfv2bNHFlm2YI54NqX8npEncZFulS31PHjwYAonWepvgyEnAqVjFLCktWix v5g8IIVaobffflv6kEoJHeWl4O+88071JHzGXLt2Laclm1WSmJgoFu47evSo +pW1Zyk5OblWrVp82ZdeemnGjBmkH+n/N954I6gkDCGgXx566CGPn0X+eROu Z555JlyZczHQjJGNgf3RiBEjlM0my7Rly5apXJO0Hn9CTtxyyy1Dhgzp2LEj /V+9evVdu3ZxnBDaxtzc3H/96190ZX4GyNkO2H46UURYiLT+0Bj7cpCEsB1w yC1SZmYmLwd37733iq/4xXOAvXv3yuKLJ6jKpwfWEtAC4ulNw4YNqZfPy8uL i4sjaSNuSeruzcqswQQs+5w5c2RemzVrlse7IDMNbDzepbE4vLS0lN8efvvt t+J0loF/+ctfZJcVS9GuXLlSFlmjZqSqzpugvfvuu7KLk7TnU3gefmQApWMU sKS1aNeM11133XvvvdenT58OHTqIYck//vEPlRPFctbqi9X4i1lRUcFrA1at WpVaHhre7N69u1+/ftWqVROtPYVoLmuALNFQjVtRKY0aNbJk5pXG+Tbt2rVT HuJ1ex544IFwZc7FQDNGNuHQjHT0888//+yzz3iSv8f7kF99FcGdO3dSoydr i6Rj9RDaxtTUVOnVSLru378/YAGdKCIsRFp/ioqKfgwSOkVPitohfSo2k1q9 erUInzx5Mgcq5xGtWbOGDwW1ppMJBLQAyZOWLVt6FFB3z9/qfvzxx2Zl1mAC lr2kpISnH3/44YccQhqNfn755Zf8TTS1YBy+atUqNsuWLVvE6f722iCpyJGl M9uD0ozUZHE4NYxbFfAhn/toOxQoHaOAJa1Fi/1zc3Np/COd13H48OHbb7+d 72t/8zY3bNjA24c1a9ZM/fGpSsykpCTlvtjUzgtxJ2ZcaEElobKyMn7wWKtW rY8++kjs+UvtrVhDzEwC+oWGfJS9F154QXmISucJtM4tCA11v/z888/HdSC9 xYAlGNsf0YCKWjDxkwbqX3/9NTcsKluexcXFsWB89tlnW7duzU2Wx7spAC+a ygTbNmZlZbVv356yetttt3EEujI1buqNsxNFhIVI6w/petL+pATJ8sdUoQgU jSLLFqIMNkXtfPLJJ+w72ScMQgsoX/HMnj2bD8lmElqOFgvQrTdq1KgGDRrU qFHj6quvbtGiBSmm8vJy1lPm76VlFFrKztv03Hffffyzbt267F8ScezQzMxM Cu/Ro4fHu7qpdOl4f5oxMTFRp2YUn3urIFvW1dFA6RgFLGktIb83EXOWfN7X P/300w033EBHa9asKZ3xHkJMitCuXTtu6KKiojp16nTw4EEeepG+055h9YTe fPNNj/ddKi9ySH33mDFjbrzxRi7jgAEDtCdkCAH90qRJE4+fBV35BYRY8hoY iLpfzp8//786OHz4sIlFAT4Id39EY1fefpHkns/tZffv388zxN59912OQCEv vPACN0S33HKLdBX60NpGEokJCQmcDU+gh/lOFBEWIqs/pAdJDAb8LIsiULRg 10rymaIWxBvwRo0ayb5mTUtL40MLFiyQnTV69Gg+ZLcdOYOyAN2AQpiTVuIS xcXFhStzYUZL2cXmYoWFhRkZGdz4XLx4kdoBnq3ELQCPKGRPocOnGcW+jfXr 1/+rH+bPnx+8SWwKlI5RwJLWErJmLC0t5Vte+blfcXGxWJlZ/a7XHrPSu0yN +P/tt9/2BDP9Uj2hvXv38qHvvvtOGp6dnc3f/tSoUUO5pn1YCegXHkaKh4dS eC2yCFt2zCao++XSpUu7d+9OCxXZjtvAfEzoj7p3786tDek75dFXXnnF412J S7ZRbO/evfks2bL5TAhtI+kUHjGqr7DqRBFhIbL6w68aqU3w+XyAoUMUIbSX jMoUA7Jw4UJ+uUbeVzouPz+fffrNN9/IDv3zn//0eN9Nh7DuXFgJeQwzadIk pz/00FJ2sekPSWOej9qiRQs+9Pe//51+vvbaa+fOneNHVXRTS88Nn2Zk9UqM GDEiyEI7Eigdo4AlrSXk9pY6ON57Wvb9ODU+PDfS450zr3IF7TFlUCfLM2M1 vkoLmNCECRP4qPJJ7/Tp0/mQ+rIVhhPQL507d+YHhlQ6aTgNAzjDX331VXiz 6EpCvl+AIzChPxLTU31+i82z4qOjo5WH7rzzTjrUoEEDlYsH1TZ26NCBc6Ky g7YTRYSFKOtPwFeNel4y+kxRBerFeNpzrVq1/AklnqYiW5H7ypUrXDMff/zx 0PIZPkJuk3kjLbqtZM9nHISWslPpeEmK7t278+JaYt4Urw5044030kX4Nicp Jz1Xv2YcOHCgT2FOueKlm12yWB+UjlHAktYScnsrVlTo06ePCLxw4ULz5s05 /O9//7t0fzEZ2mMqEdvW//DDDwEja0mIF7cnCSx9XM9QQ8fnkq7UnkP9BPQL r37mUSxA8f3333M4terhzaIrgWaMbEzoj9q0aUOn0HhJKbVoZM6L2Ph8tMVb A9DQXeXiPttGf18s8htJQn3pFceJCAtR1h9+1bhr1y6frxopkA6F/JLRZ4r+ WLFiBY/Sq1evrnKKGORv2rRJBC5ZsoQDJ0+eHFo+w4cWC+zZs0fWudMNwiWa OHFiGDMXZjR6/+WXX/Z4v2ThqQXbtm3jcPGE+R//+Af9rVu3ruxE/ZoxNjaW Y9LIRHaRl156iQ9F0hxUf0DpGAUsaS0B7U86q0ePHrIHcTRg4P3CPJIFEKhN FkuxPf/88yrP7rTHLC8vlz2NP3XqFG8zERUVJR10kTacMWMGtWDSrkFjQqIB nDp1quzQ8OHD+VBKSoq/TIaDgH6h8l577bWUsZYtWwohTAbhD97JOEHJcKAR aMbIxqj+KC0tjZopsTWG9Pq8po2/JapIgnm82x/IPjQ7d+4cb8/dqlUrDtHY NpIeIXFHekGWUGFhIW/QRpFFoM9W1HEiwkJ81h9+1Zifn6+MT4F6XjL6S1HJ 6tWrxZq6PXv2pJ9Lly5dIkGsZUdZ4mmKVD22b99OfT35vXbt2hTyu9/9Tuz2 bh8CWoBKUbNmzYYNG1InTmMAEkp9+vTh27BevXohq3U7oNH7YiaVx7vKjfTx Rf369cUh5bYX+jWjiPnoo49Se0jVSczJp2rP221UrVq1f//+vAQcuSMpKenp p5+eO3duEIawPVo8RVp+y6/wSmXUv4iQs2fPmpJTuwNLWou6/cUOXE888cSs WbOys7NJiezbt483lPd4N+7h9ocGMCKQGiUaopCWXPLfcKepPSY1L+3bt6f+ iwYtBQUFdCJ1XqKJGz9+vDSr3bp143DxiF57QtQPcr2iFmzmzJnisfzixYt5 B6u6devS1cLiAD9ouS+6du3Kpfvkk0/OnDlDI8bo6GgOkb78BQYCzRjZGNUf 8R6p1atX79WrV3JyMkkwCo+NjeVJYjRelW5wIEWsT9KmTRvxuVlxcbFYBmfc uHGVwbSNDz74IKdIzcWqVasoGzRspjI+/PDDHLl79+4isrIVrXSgiLAQn/XH 36tG/S8Z/aUog6qHcoldGQ899JCIT309f3jCNYf/Ickp3YzPPgS0AGlkUUxR LuL2228n45uVzbCgsT8Sq/14FKvcfPzxx+KQcgMy/ZqRKvkdd9whkqABFVUk 8UiKUhQbt3m8s2SFg+655x7NZnAAWjxFzanKHTps2DBTcmp3YElrUbc/DTBa tWoltTYPHhgaQmRlZXFMEikqPmJIggUV89ixY/wknJG29p9++qms/23UqBEf EvvPak+ISElJ4Xk7Hu/sL7qIaOgofOvWrcabXhUt9wXpxKZNm4qyiM69ZcuW ykm2wBCgGSMbo/qjRYsW8eMm0XaJ25Po0aOHv4uTIhPykFoeGsk3btxYXOrF F1/kJ1ra20YqDm9Cx1SpUkXahjdr1kwqWJStKOMsEWEh/uqPz1eN/JLx+PHj 4UhRCmlGaQ3xSZMmTaSnkBbg7ZwYkgO29bUWC0yZMkVsMcNV9/nnny8pKTEl g2FEe39EEozLPmrUKGm4dM8L5UJ//BiKRhSy8I0bN/IpUs3oLzIJc/4WWzRW 0o17Dh061Lx5c2n9pHbvjTfeSE9P11Iup6C/Zxk6dKgpObU7sKS1BLQ/DVGm T5/esGFDqc1p1NGpUyfp/l9iPWcVuNPRHrPSuz7Ac889J9Scx/vKb+bMmcp8 Dh48mCOMHDky2CwxGRkZ1I/IIrRt2/bAgQO6zRw0GvsCko1S+1SvXv3111+H YAwf0IyRjYH9EWmB6OhofrEouOuuuwLuZnjp0qWxY8eKjX6YG264YfTo0dLZ +NrbxqKioq5du/JyygLKWP/+/c+fPy+NqWxFBQ4SERbir/4oXzWKl4w6VxAK a4t09OjR9evX65k6awLaLZCbm5uQkJCamuro+ahSnNIfUZu2d+/eFStWbNiw wec2xBcuXNi+ffu6detouOXcJYlUcIqn7A8saS3a7Z+Tk5OcnJyUlJSWlmby RE1q4ak9SUxMzMvLU4mWnp4uW885BEpLS3fs2LFmzRr6S//rvFrIBHVfUHtL /eDmzZtN9osLQXsV2RjuX/7qcO3atTT2Vm++ZFy+fDkrK4vOoiY3MzPT3+fJ GtvGSu/Hznv27Fm1ahVFPnTokL+BmXor6ggRYSEq9ef48ePSV42GvGRUT9El uNkCbi67s4CnjAKWtBbY357AL/YEfols4F+gB5X6Q5pdvGo06iWjeoouwc0W cHPZnQU8ZRSwpLXA/vYEfrEn8EtkA/8CPajXH/GqMS8vz5CXjAFTdANutoCb y+4s4CmjgCWtBfa3J/CLPYFfIhv4F+hBvf7wq8YfvRjykjFgim7AzRZwc9md BTxlFLCktcD+9gR+sSfwS2QD/wI9BKw//KrRqJeMWlKMeNxsATeX3VnAU0YB S1oL7G9P4Bd7Ar9ENvAv0EPA+sOvGtPS0gx5yaglxYjHzRZwc9mdBTxlFLCk tcD+9gR+sSfwS2QD/wI9aKk/x48fz8nJMTPFyMbNFnBz2Z0FPGUUsKS1wP72 BH6xJ/BLZAP/Aj2YX39QY91sATeX3VnAU0YBS1oL7G9P4Bd7Ar9ENvAv0AM0 o/m42QJuLruzgKeMApa0FtjfnsAv9gR+iWzgX6AHaEbzcbMF3Fx2ZwFPGQUs aS2wvz2BX+wJ/BLZwL9AD9CM5uNmC7i57M4CnjIKWNJaYH97Ar/YE/glsoF/ gR6gGc3HzRZwc9mdBTxlFLCktcD+9gR+sSfwS2QD/wI9QDOaj5st4OayOwt4 yihgSWuB/e0J/GJP4JfIBv4FeoBmNB83W8DNZXcW8JRRwJLWAvvbE/jFnsAv kQ38C/QAzWg+braAm8vuLOApo4AlrQX2tyfwiz2BXyIb+BfowSrNCAAAAAAA AADAKUAzAgAAAAAAAADwh/masQIAAAAAAAAAgO2BZgQAAAAAAAAA4A9oRgAA AAAAAAAA/oBmBAAAAAAAAADgD2hGAAAAAAAAAAD+gGYEAAAAAAAAAOAPaEYA AAAAAAAAAP6AZgQAAAAAAAAA4A9oRgAAAAAAAAAA/oBmBAAAAAAAAADgD2hG AAAAAAAAAAD+gGYEAAAAAAAAAOAPaEYAAAAAAAAAAP6AZgQAAAAAAAAA4A9o RmAHsrOz58yZM2HChNmzZ5eWllqdHQAAAAAAAMD/Y3/NeO7cuXw/bNy48ejR oz4PFRcXh89oO3fuPH36dPiubyE5OTnHjx/XGPnMmTOZ/w1Jv9DSHTlyZN++ fUeMGDF27Njy8nJlhIsXL1LG0tLSKBWqErKj58+fz/Pi81wKzPsVynNoOSSO HDkSlN8zMjIoYyEnZ3m6AAAAAAAAVDhBM5IqHBw8s2fPDpPFDh06xNcnFROm JMynrKxs3bp1EyZMIOH23XffaTzrxx9/7PvfkGVCSJ2kHJ07bdo0n4qPILU4 evRokcqAAQM2b97MkbOysijnw4YN40MHDx5Uz+ecOXNCyCGxd+/eIUOGaPc7 15MpU6ZcuHAhtBStTRcAAAAAAADG/pqR9MIkBbGxsTSQZnk4atQoZYT4+Phw mKu4uHjkyJGcbkJCQjiSsITTp09TiWJiYoLSjCTcKP7y5cu3/kpqamoIqael pdF11q5d6/NoUVERiUSZOCWRyG8MZeEZGRmy08+ePSsUJRHyw4RTp06NHTtW o99FPdm4cWNoyVmeLgAAAAAAAIz9NaOSc+fO/fDDDzQwnjFjxtChQ+mfXbt2 GWUQq9I9cuTIzp07y8vLDxw4sHLlyj179lR4J1Wmp6cnJiauWbNm9+7d4k0T 5eTHH3+UvlOj/3fs2CEmXpIGpJ9Hjx6l/0+cOEEKghQH/Q0473TgwIHaNWNS UhKpME4lIP7KkpOTQ6qTrjNv3jzKc35+vuzEuLg4lnuzZs06fPjwtm3bSBYd O3aMjw7x0q9fP3+aMT4+Xioq9byAzs7OZr9T/lWiiXqycOFCf29OHZEuAAAA AAAAFc7UjIsXL6aB8cSJE8vKykg60f/Dhg3T/hWePdOli5OiIbXI0mb16tWU ysyZM6V6Z9KkSSdPnqzwvjsjlTRixAhxOv1PEUhz8U/6h34mJyfn5uaSDJRe RP1VYFCakeVYYWFhwJgqZWHBKNi+fbv0RNJBHE7CUPkZo2Dy5Mk+NSOpLQ7/ /vvv9WvGCu8014B+l9YTPWnZIV0AAAAAAAAcpxk3b95MA2PSNQUFBRyyatUq CiFRENZ1acKdLmtGIjExkYQe6SmSjfRzypQplGJJScm8efPo5/z58zk+qyR+ K8ffAxILFy7ko4sWLaKfpC9Y1q1bt+78+fN0nWXLlp06dUolG0FpRkqOLk5i kIwwdepUcqs/WadSlhMnTmzYsIF+rlixggouW6OGQrhoFHnjxo2k+JYuXbpv 3z7Z9X1qxvLychJQHM7TX/VrxopAflfWE6OwKl0AAAAAAOBynKUZf/rpJ/6W UCoNLly4MGPGjLCuS2NCuqwZhSQkide/f/+YmBgh8Shk+PDhFIeXhF2/fj39 v23bNvp/y5Yt/OZu2LBhPCmRtMPQoUPp/yVLltChlJQUjdkISjPOnTuXLj5+ /Hgh2Ug5KmdFBiwLCzpSjsokSB729YXs4z6fmpGMw4GklIWs1q8ZVfzus54Y hVXpAgAAAAAAl+MgzaiyvkdQ64TYM13WjOLbwJycHPpJGkEaZ+nSpRSYnp5e 4V0vVGhMEhFjxozZtGkTv1ssLCwU7xyPHDlCeo1+/vDDDzt27Ai4BYNSM2Zn Zyf4ghIqLS2l63O0oqKiIUOGUEIHDhyQXTNgWVQ0o3g/SEycOFFMcO3Xrx9P bWWUmpFX9eHA/fv3b9++nf+nmGRknRrfp99NWH/GqnQBAAAAAICbcYpmDLi+ h8Z1QmybLmtGUoL88+DBg/RzwYIF0jhr1qzp++sHiZQZ0miU9IULFwYNGrR8 +fJjx4719X7DSBH6Sr5bJHE3d+5cXiWGxIVykRkpSs1Ism6cLw4dOiQ7l+fB KpVLwLKoaEbSd6z1pkyZwkJPyEZpBpSakbLt8wUlc/bsWRUjaEHmd9PWn7Eq XQAAAAAA4Fqcohm1rO8RjvVwTEtXphmLi4vp54QJE6Rx5syZQ4Hi1R6pMPrJ mw/u2bOHVeSMGTP4UiUlJdJzT5w4wQuQzpo1SyUbQc1NlcFbbyilX8CyqGjG U6dOiZeMHMIfUcpeaJqvGSv+2+9mrj9jVboAAAAAAMCdOEIzal/fw9h1acxM V6YZK7zfJErfpuXl5fXr1480XWlpKYfw4qhjx46lv7x0zPz58ynCqFGjxowZ w3FycnJElkgoUUxxyCfaNSPZZOXKlWLRmwsXLpAq7Otrt4uAZVHRjMTUqVNZ 6FG12bVrl5hxevLkycLCQp4oKwJJR9NPSog0VJ4EISHpavTTqFdy7Pfhw4eH b/0ZsnC+ApaKJBs5dbK5LAJ/KAoAAAAAAIB+7K8Zg1rfw8B1aUxOV6kZ9+7d SyExMTEkghITE/lrQZKxIsKJEydYB4lXeOKrvfj4ePpJioxkBZ2YlJSUmprK szopIWXqdDTeC09h5f/FHog+4W1BRo8eTbopOTmZBePkyZN9Fl+9LOqaMTs7 W2y/KFiyZAkdIgnp8zXiunXrZBcxcA0cKcLv4Vt/5ujRo4ODx9hiAgAAAAAA N2N/zZifnx8bG6t9fQ9eJ4SS0PkuyeR0ly1b1te7go00kDTR0KFDWewMGjQo JSVFdnHednDNmjX8k2eBEmI3CroCyTohpkg2+nwTytNcZZDQU8kwyaW1a9ey +hPv+FQ28lApy+7du1U0Y4VXN0lLQXqWp2JarhkrfvV7+NafoSoxyRcTJ04k 45NZfB7lhwYAAAAAAADox/6ascK7I3xQQkzM3tSJVelKoQwUetEjgUkn5ubm hmP/yosXLxYUFNDFtZRdZ1lOnDhB0s/fFpAWUlRUZMn6M1alCwAAAAAAXIUj NCMAAAAAAAAAAEuAZgQAAAAAAAAA4A9oRgAAAAAAAAAA/oBmBAAAAAAAAADg D2hGAAAAAAAAAAD+gGYEAAAAAAAAAOAPaEYAAAAAAAAAAP6AZgQAAAAAAAAA 4A9oRgAAAAAAAAAA/oBmBAAAAAAAAADgD2hGAAAAAAAAAAD+sEozAgAAAAAA AABwCtCMAAAAAAAAAAD8YaZmBAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AACczv8Bvg9zVw== "], {{0, 204.}, {606., 0}}, {0, 255}, ColorFunction->RGBColor, ImageResolution->144.], BoxForm`ImageTag["Byte", ColorSpace -> "RGB", Interleaving -> True], Selectable->False], DefaultBaseStyle->"ImageGraphics", ImageSizeRaw->{606., 204.}, PlotRange->{{0, 606.}, {0, 204.}}]], "Output", TaggingRules->{}, CellChangeTimes->{3.834333828110593*^9, 3.8343338635110493`*^9, 3.8344061026670094`*^9}, CellLabel->"Out[18]=", CellID->1870542746] }, Open ]], Cell[TextData[{ "Then use ", Cell[BoxData[ TagBox[ ButtonBox[ StyleBox["SynthesizeMissingValues", "SymbolsRefLink", ShowStringCharacters->True, FontFamily->"Source Sans Pro"], BaseStyle->Dynamic[ FEPrivate`If[ CurrentValue["MouseOver"], { "Link", FontColor -> RGBColor[0.854902, 0.396078, 0.145098]}, { "Link"}]], ButtonData->"paclet:ref/SynthesizeMissingValues", ContentPadding->False], MouseAppearanceTag["LinkHand"]]], "InlineFormula", FontFamily->"Source Sans Pro"], " to \"solve\" the fundamental problem of causal inference:" }], "Text", TaggingRules->{}, CellChangeTimes->{{3.834333976170957*^9, 3.8343340078381443`*^9}, 3.834406107962635*^9}, CellID->759535067], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"lalondeSynthesized", "=", RowBox[{ RowBox[{ RowBox[{"Query", "[", "SynthesizeMissingValues", "]"}], "[", "lalondeCounterfactual", "]"}], "//", RowBox[{ InterpretationBox[ TagBox[ DynamicModuleBox[{Typeset`open = False}, FrameBox[ PaneSelectorBox[{False->GridBox[{ { PaneBox[GridBox[{ { StyleBox[ StyleBox[ AdjustmentBox["\<\"[\[FilledSmallSquare]]\"\>", BoxBaselineShift->-0.25, BoxMargins->{{0, 0}, {-1, -1}}], "ResourceFunctionIcon", FontColor->RGBColor[ 0.8745098039215686, 0.2784313725490196, 0.03137254901960784]], ShowStringCharacters->False, FontFamily->"Source Sans Pro Black", FontSize->0.6538461538461539 Inherited, FontWeight->"Heavy", PrivateFontOptions->{"OperatorSubstitution"->False}], StyleBox[ RowBox[{ StyleBox["FormatDataset", "ResourceFunctionLabel"], " "}], ShowAutoStyles->False, ShowStringCharacters->False, FontSize->Rational[12, 13] Inherited, FontColor->GrayLevel[0.1]]} }, GridBoxSpacings->{"Columns" -> {{0.25}}}], Alignment->Left, BaseStyle->{LineSpacing -> {0, 0}, LineBreakWithin -> False}, BaselinePosition->Baseline, FrameMargins->{{3, 0}, {0, 0}}], ItemBox[ PaneBox[ TogglerBox[Dynamic[Typeset`open], {True-> DynamicBox[FEPrivate`FrontEndResource[ "FEBitmaps", "IconizeCloser"], ImageSizeCache->{11., {2., 9.}}], False-> DynamicBox[FEPrivate`FrontEndResource[ "FEBitmaps", "IconizeOpener"], ImageSizeCache->{11., {2., 9.}}]}, Appearance->None, BaselinePosition->Baseline, ContentPadding->False, FrameMargins->0], Alignment->Left, BaselinePosition->Baseline, FrameMargins->{{1, 1}, {0, 0}}], Frame->{{ RGBColor[ 0.8313725490196079, 0.8470588235294118, 0.8509803921568627, 0.5], False}, {False, False}}]} }, BaselinePosition->{1, 1}, GridBoxAlignment->{"Columns" -> {{Left}}, "Rows" -> {{Baseline}}}, GridBoxItemSize->{ "Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}, GridBoxSpacings->{"Columns" -> {{0}}, "Rows" -> {{0}}}], True-> GridBox[{ {GridBox[{ { PaneBox[GridBox[{ { StyleBox[ StyleBox[ AdjustmentBox["\<\"[\[FilledSmallSquare]]\"\>", BoxBaselineShift->-0.25, BoxMargins->{{0, 0}, {-1, -1}}], "ResourceFunctionIcon", FontColor->RGBColor[ 0.8745098039215686, 0.2784313725490196, 0.03137254901960784]], ShowStringCharacters->False, FontFamily->"Source Sans Pro Black", FontSize->0.6538461538461539 Inherited, FontWeight->"Heavy", PrivateFontOptions->{"OperatorSubstitution"->False}], StyleBox[ RowBox[{ StyleBox["FormatDataset", "ResourceFunctionLabel"], " "}], ShowAutoStyles->False, ShowStringCharacters->False, FontSize->Rational[12, 13] Inherited, FontColor->GrayLevel[0.1]]} }, GridBoxSpacings->{"Columns" -> {{0.25}}}], Alignment->Left, BaseStyle->{LineSpacing -> {0, 0}, LineBreakWithin -> False}, BaselinePosition->Baseline, FrameMargins->{{3, 0}, {0, 0}}], ItemBox[ PaneBox[ TogglerBox[Dynamic[Typeset`open], {True-> DynamicBox[FEPrivate`FrontEndResource[ "FEBitmaps", "IconizeCloser"]], False-> DynamicBox[FEPrivate`FrontEndResource[ "FEBitmaps", "IconizeOpener"]]}, Appearance->None, BaselinePosition->Baseline, ContentPadding->False, FrameMargins->0], Alignment->Left, BaselinePosition->Baseline, FrameMargins->{{1, 1}, {0, 0}}], Frame->{{ RGBColor[ 0.8313725490196079, 0.8470588235294118, 0.8509803921568627, 0.5], False}, {False, False}}]} }, BaselinePosition->{1, 1}, GridBoxAlignment->{"Columns" -> {{Left}}, "Rows" -> {{Baseline}}}, GridBoxItemSize->{ "Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}, GridBoxSpacings->{"Columns" -> {{0}}, "Rows" -> {{0}}}]}, { StyleBox[ PaneBox[GridBox[{ { RowBox[{ TagBox["\<\"Version (latest): \"\>", "IconizedLabel"], " ", TagBox["\<\"1.0.0\"\>", "IconizedItem"]}]}, { TagBox[ TemplateBox[{ "\"Documentation \[RightGuillemet]\"", "https://resources.wolframcloud.com/FunctionRepository/\ resources/FormatDataset"}, "HyperlinkURL"], "IconizedItem"]} }, DefaultBaseStyle->"Column", GridBoxAlignment->{"Columns" -> {{Left}}}, GridBoxItemSize->{ "Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}], Alignment->Left, BaselinePosition->Baseline, FrameMargins->{{5, 4}, {0, 4}}], "DialogStyle", FontFamily->"Roboto", FontSize->11]} }, BaselinePosition->{1, 1}, GridBoxAlignment->{"Columns" -> {{Left}}, "Rows" -> {{Baseline}}}, GridBoxDividers->{"Columns" -> {{None}}, "Rows" -> {False, { GrayLevel[0.8]}, False}}, GridBoxItemSize->{ "Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}]}, Dynamic[ Typeset`open], BaselinePosition->Baseline, ImageSize->Automatic], Background->RGBColor[ 0.9686274509803922, 0.9764705882352941, 0.984313725490196], BaselinePosition->Baseline, DefaultBaseStyle->{}, FrameMargins->{{0, 0}, {1, 0}}, FrameStyle->RGBColor[ 0.8313725490196079, 0.8470588235294118, 0.8509803921568627], RoundingRadius->4]], {"FunctionResourceBox", RGBColor[0.8745098039215686, 0.2784313725490196, 0.03137254901960784], "FormatDataset"}, TagBoxNote->"FunctionResourceBox"], ResourceFunction[ ResourceObject[ Association[ "Name" -> "FormatDataset", "ShortName" -> "FormatDataset", "UUID" -> "76670bca-1587-4e7e-9e89-5b698a30759d", "ResourceType" -> "Function", "Version" -> "1.0.0", "Description" -> "Format a dataset using a given set of option values", "RepositoryLocation" -> URL["https://www.wolframcloud.com/obj/resourcesystem/api/1.0"], "SymbolName" -> "FunctionRepository`$66a3086203b4405b88cdb0de8a5c3128`FormatDataset", "FunctionLocation" -> CloudObject[ "https://www.wolframcloud.com/obj/70389ad6-7dbc-48c8-b898-\ 72c65c00f14e"]], ResourceSystemBase -> Automatic]], Selectable->False], "[", RowBox[{"MaxItems", "->", "10"}], "]"}]}]}]], "Input", TaggingRules->{}, CellChangeTimes->{{3.834333889225443*^9, 3.83433394302452*^9}, { 3.8343342132602167`*^9, 3.834334214594523*^9}, 3.834406116776125*^9}, CellLabel->"In[19]:=", CellID->937403932], Cell[BoxData[ GraphicsBox[ TagBox[RasterBox[CompressedData[" 1:eJzsvXec1cQX93+p0kGQooCACgIC0gVFKYI0UUQBQVBBRVGKgiCI9CaCyipS VJoU6bgUKQs8SFt2+VKWjgjsAttoC4vSBPZ3nnt+zBOTe5PJTW4ySc77j33t nczNnTmfycw5yWSmTNfebbpl9vl8fXPAnzZdPmvYp0+Xga8WgA9te/Xt/n6v 995t3uvT995/r0+drlkgcQHk3ZDV5/u//2cQBEEQBEEQBEEQBEEQBEEQBEEQ BEGYyl0/dpeCIAiCIAiCIAiCEJGEhITTp0/bXQqCIAiCIAiCIAhCOG7evLnb z61bt0w54f8hCIIgCIIgCIIgHIVKiJeQkPA/P2Y9arS7rgRBEARBEARBEIQ+ gsV37CEjAh/NihnTPYyXLeDlujsLUsosyJL2QvYXE9JFTEgXd0P6EkZQjxnx IWOyH/gHPlLMaBwvW8DLdXcWpJRZkCXthewvJqSLmJAu7ob0JYygEjPiQ8Z9 +/bduXPn9u3b8I8pjxqpxXrZAl6uu7MgpcyCLGkvZH8xIV3EhHRxN6QvYQSV mJE9ZMSPZj1qpBbrZQt4ue7OgpQyC7KkvZD9xYR0ERPSxd2QvoQRgsWM0oeM mGLWo0ZqsV62gJfr7ixIKbMgS9oL2V9MSBcxIV3cDelLGCFYzIgPGVNSUqSJ pjxqpBbrZQt4ue7OgpQyC7KkvZD9xYR0ERPSxd2QvoQRAsaMyoeMCHw0/qiR WqyXLeDlujsLUsosyJL2QvYXE9JFTEgXd0P6EkYIGDPGx8crHzIikGjwUSO1 WC9bwMt1dxaklFmQJe2F7C8mpIuYkC7uhvQljKCMGYM9ZESMP2qkFutlC3i5 7s6ClDILsqS9kP3FhHQRE9LF3ZC+hBGUMaPKQ0YEHzVCNooZQ8PLFvBy3Z0F KWUWZEl7IfuLCekiJqSLuyF9CSPIYsYbN26oPGREDD5qpBbrZQt4ue7OgpQy C7KkvZD9xYR0ERPSxd2QvoQRZDGj5kNGxMijRmqxXraAl+vuLEgpsyBL2gvZ X0xIFzEhXdwN6UsYQRoz4kNGCAZPnDhxShXIANkgM3yFYka9eNkCXq67syCl zIIsaS9kfzEhXcSEdHE3pC9hBGnMmJqa+j+dwFcoZtSLly3g5bo7C1LKLMiS 9kL2FxPSRUxIF3dD+hJGkM1NtQBqsV62gJfr7ixIKbMgS9oL2V9MSBcxIV3c DelLGIFiRuuxxgJbt25duXLlunXrwv1DuiD1nQIpxQPPVSamJU+fPr3Sz9Gj R+0uS3gR0/4E6SImpIsMMV2pkCF9LcOVgyzFjNZjjQWeffZZn89XuHDhcP+Q Lkh9p+AmpeLi4t59990PPvjgyJEj5p6Z5yoT05Jr1qzx+fn222/tLkt4EdP+ BOkiJqSLDDFdqZAhfS3DlYMsxYzWQzGj3aUgtHGTUi1btsSu++233zb3zBQz io819j916tQ2Lc6dOxfs64cPH8Y8CQkJ4S6qIIipy86dO4Nlu3jxYrhLKwJi 9lc2ErCTHzNmTIv/0qpVq3fffXfIkCEbN25UnkSZX8b27dvTQ+1GoNFOmTLl s88+i4iIWL9+/ZUrV1SqQ/paRsBB9s8//1Sq//rrr3/88ceTJk1KTEyUnSRg fimffPIJ5oyOjlZvOQcOHJCdPCkpacmSJSNHjoR2O2/evOPHj2tWimJG66GY 0e5SENq4SanOnTtj1/3ee++Ze2aKGcXHGvuPGzfOp8X3338f8LsnTpwoWLAg 5vnpp5/CXVRBEFOX++67L1i2X375JdylFQEeXSC0ec7P/v37LSmUnYUJ2Mm3 a9dOpUXVrFnzf//7H39+IDIyMl1/c4W6t2nTRpahTp06sl+XYvt4NGrUKBBr 4MCBNpaBEdbCBBxk9+7dqyJunjx5Pv/8c+lJ1PMDTz31FOYsUKCAes4HH3xQ euZZs2YVLVpUmiFnzpwjRoxQrxTFjNZDMaPdpSC0cZNSJ0+eHDly5JgxY0x/ iEMxo/iIE5vAGB3wuy+99BLLQzGjuejS5dy5cyHI5zJ4dNm5cyfaBP6xplQ2 FkY9ZuzXr19/P++9916LFi3A68b0Rx55JD4+Xpm/S5cuXQOBr7zpaq6XL1+u X78+JkK88Prrrzdo0AA/Pvroo6mpqQGrY/t4hN0d/LWxDIywFkY9ZqxZsya2 nI8//rhjx44VK1ZkEn/11VfK/FWrVg3YciIiIjCnZsxYrlw5dtpVq1ZlypQJ 0yHqhAKw+DHYvU2EYkbroZjR7lIQ2pBSPFDMKD7W2D8xMfFgILZv354jRw4c l8HHU35x5syZ0mGdYkZz0aXLsWPHUIURI0Yov5KSkhLu0ooAjy6///47GkqE mDHchVGPGWUTQc+ePdu8eXM8NHToUM38MnQ1119//RXP2bt37/Pnz2Pijz/+ iImjR48O+BO2j0f16tUTJ2YMa2HUY8bPPvtMln/p0qXZs2f3+Z/3paWlaeaX cfjw4YCN591338UzLFu2jGWuXLkytmr2SDohIaFatWqQmD9//oBDFeKUmHH/ /v3QM/z555/q2c6cORMVFbV+/XrpHZ5gHDlyBDQ9efKk3sIYRK8FOMuZnJwM FYcGhp0S3nEyK2aEc+7bt2/t2rWnT582cp4Q1AdN4XdPnTolS4f6bty4cdu2 bay3DAZn4eE8mzZt2rFjx6VLl3SV0JWEoNTFixfh6gs4JT46Onrr1q2aSnFe 5kiwhsFzVAa/9CFcZaH1eLGxsVAk+Dn1bNAtQOdw6NAhnnNCsaHwSUlJ8D/8 oxkz8vQ8MLjs2bNn1apVUGB1d4gRTBp+FXRdqib2OSF0Ee+88w76AGB85dG/ /voLZ6XWrFmTYkZNwq1LTEwMqrBw4UJdBXMTPLrMnTuXM0xT6Yf5HTD1cYGz MOZ28ioxIISNmTNnlkUinDFjMAI2108++QQSc+XKJatR3bp1If21114LeCoL Ykb1wQsfqPGEacavd00vkbMw/G1Vc5BVjwF79eola8z8MWNADhw4gM++33rr LZaYkpKCTXTYsGHSzCtXrsTf2r17d7ATihwzwsU1e/bsZ599FsJedhu2WLFi y5cvV2bevHnzU089xbJlypSpgITWrVtLTztmzBg4D8tcvHjxmTNn8hTJFDgt wF9OcMhr1aqFbcDnn6UwZMiQZs2ayTo6aCdwNjjas2dP2Rm++OILNBT4MLJD J06cgM4nb968rBhly5adM2eO/nr/X3jqXqVKFSjJlClTjh079uKLL+KNlwce eACPHj16tFu3blAG9mA9S5Ys0CEH7KA4Cx8XFwfjQrZs2TAP/CL8Lme44Vb4 lfrhhx+g2bRp0yZPnjxowEqVKs2bNw8yQJf+6aeflixZkik1aNAg2aDJf5mr NwyVo3DVYPNWrpfOLz3nVWbQkjBKvvnmm0WKFGH9WJcuXS5cuKD8yoQJE6BD YBaD0KN3794Bc6alpfXr169QoULsnHXq1GG3o5UxI2fPc+7cOVAzd+7cLBuM TdJe948//pDWLphw6XpUCOFSNd7nhPa7wIYNG7Cb+vLLLwNmgJPAUThtZGQk npliRikW6wIOKp5HwIkBlqGuy3fffffII4+wIRX+wYu9Ro0amEFTMs7uhWdc 0CwMEo5OXj0GfPjhh33/nQpoJGYM1lwxkISeX5a/U6dOPv9bjQHPpnnd6XUU +QevJ5544sEHH8S6ZM2alY0U6C2km3e983iJmoVJ1+OE8w+y6jHg1KlT8Sib gWwwZmzSpAl8t0SJEtLVdUA+POf48eOlmQ8dOoTpK1asCHZCYWPGs2fPgufp CwTIIasRdPXsMod2InWlELAb5kxNTUUbIvi4HxkwYIAeKUKHxwL85YSmJT0q Q9rRnT59GhOhq5H9XN++ffGQ7FYeeDJscQYZ06dPD1PdS5cuDefv379/9erV 2c81bdoUDkVERGA3ouTpp5+WnYez8AsWLGCDDnQd0Lfg/w899NDhw4dDqKM7 4FcKOm0WFTLy5cu3a9euF154QWn8r7/+mp1B12Wu0jDUj06bNg0/rlq1SnpC fun5rzIjlnzppZceffRR5fl79eolzQydQ+PGjQOWBMZB2X4iMFLg9JtgyGJG zp4HBtPnn38e08Gpg99VLh7C1g9UF45fhdAuVYN9Tsi/C1StWtXnfw8loKMI vRCeBzwx5hhQzCjFYl3YZD/OB/euRF2XQYMGBexGwO/CDOqScXYvnOOCZmHS w9bJq8SAED7geerWrcuTX5NgzRWqhudcvXq1NL1ChQqQCJFjwLNpXnd6HUX+ wYtFcDJYFGbK9c7pJWoWht8J1zXIqseAQ4YMwaMrV67kya8OayHQs8kOPfnk k5D+3HPPSaehzpkzB/OrdIDCxowAvt776quvLly48Pjx49BKWYt95plnWLZL ly5BEO3z32WC4BETQSPMCbH///73P+ZHQWvE9DZt2mDi/v37cb46NEJrFgHj sQBnOf/66y/2cOftt9/esWPHsWPHfvnll1KlSmGikZgxISHhgQcewPS2bdv+ 8ccfcGlAS65SpQpc0ZrzDEOuO/YbSOXKlaEH2Lx587Zt29Lv3QeGLmLcuHFb tmzBOSRlypTBzNKek7Pwp06duv/++yFPoUKFwG4XLlyAQ5MmTcL+oXPnziHU 0R3oVap169YwlMfExEgX9ADA4DNmzNi9e/fAgQMxBb4lPQnnZZ6u2jDUjwaM Gfml13WVGbdks2bN5s6dCyPFl19+icMiBODSd6mYfcqVKwc2P3PmzKZNm9jq B82bN5eeuXfv3pherFgxOO2JEye2bt3KXnDwKWJGzp4HzIvZwBfCskFm9G0K FiwIJoJel60JryINvwohX6oG+5yQf5c9tPrxxx+VR9ms1Bo1aoCTCRcIZqaY UYrFukyePBkPga8FVxl43XA5QGgfbDkRV6KuC/QAkZGRbCVq6KMi/bCpdOq9 NL8DxjMuaBYmfJ28Sgw4ZswYdh6e/OqoNFfoYDGGgp6EDW1seqHsBikjTDEj oj54wWgF6mB6pUqVIu/BMphyvXN6iZqF4W+rugZZlRgwPj4en1AD0Pw082sC IaHPvxyTstWx4LRjx474CBKGIcyvfP4iBdvP/0IlrDEj2IrNbmIw14i9mwZX N6bIFonFTdnAZWUpe/bswfsPLVq0kOaERoXDt+lL8QdE0wL85WS95ccffyzL ifeajMSM7OQ9evSQZr548SK4qXpq/P/Q5SdA01VGpr/++qtsBGeTxrt37663 8DiLI3PmzNA1SbNBW/L55zPwbFjjSnQpNWjQIJYIXS6bTdS0aVPpbBDsbwHp ZBLOyzxdq2GoHA0YM/JLr+sqU6LLkjBOSbv39u3bYzr0cpgCvS7OzIFBUGoc aNjMaOym4r59+zAz9Buy549TpkxRDmf8PQ9OqoREaTC7fft2WQFktVNKw69C yJeqwT4n5N995ZVXfH4PJ+DtNRyewOHBJQgoZgyIxbowb19G8eLFlTfq3QqP LkOHDkXLKF8hVJFMlwPGPy6oFCZ8nbwyBgSHH34F4kQMQ+BHd+3apcx/nwLZ Dggy1LsRiI9YqAtdCox0aEmItYOdMHwxo+bgheCQFPAVQrOud04vUaUw/G1V 7yCrjAFh7D5w4MDUqVPLli2Lh6Tvokr32lA2HpXlCGJjY/Fb0K0pj8KPvvzy y5ihaNGiYEa8BOCc7A5PQFjMmKSfcMeMARk1ahRWE7dATZc4hGvWrJHmHDZs mM9/Q4AtQIStCzh48KDstNCQfMFngJuLpgX4y4ndxQMPPKB8m0+52JeurgCu fXxTCU6O7/OaAr+fkClTJpU9hmRgUaHPxI+chYdsuXLlgmxvvPGG7BCzlcq8 bnfDrxTbHojBZk7KXq8bPHiw7OINhvIyT9dqGCpHlTGjLul1XWVKjFjyhx9+ wMIsXrwYU4YPH44pkyZNkmXetGkTHgL/BFOYGZWbLgVc0o2/58ECQ/Vl2XCG qmyHqWDS8Ktg5FI10ueE/LvHjh2DoQeOQteqPAqBIX6XvaBEMWNALNaFxYx1 69bt3bt3r169qlSpgingQIqwRqgFmBIzBuyHjTtgAceFYIUJayfPYsDM9/D9 F+l2Cemq+zNCgwxWX/Xmmu73/CE8lJ2watWqKmv8hilm5Bm8EM2Y0cTrXYrM S1QvDH9b1TvISmPAgC3nySeflMa/6vszDhkyJFh98UFnzpw5g639uGXLFmxd UubOnRvshIgjYsaYmJiIiAhoLXAtsJsqbNlYttKy9N3V9HsLEElfEH799dd9 /rdvNijAmxgQbusqWGhoWoCznIcPH8aKSxdEYhiMGffv348pXbt2NVphCUb8 Zynnz59fsGDBoEGDWrduXa5cOSxqrVq18Chn4ePi4jAbNBWlqfEQ2/vGa/Ar pZzJ0LFjR7Qeu12DfPfdd5gO16zybOqXebpWw1A5qowZ+aXXe5UpMWJJGG3x 19kbuNg5AGfPnlWeB2fpV65cGT+ym+dsogsj4HDG30Pi6xtPPPGE9JwwKOA5 Zf5SMGn4VTByqRrpc0L+3fHjx+NRaNWyQ+AP4CQrsCG7LU8xY0Cs1AWBCw13 V0cuX77cr18//Ir6lC3XYErMGLAfDs0B0xwXghUmrJ28SgwIQdymTZtkJ2H5 f/755+n/RWUBRvXmCv0ttEmffyP4bt26SRefUZnHGKaYkWfwQjRjRrOud3Uv Ub0w/G1V7yCrEgOCgoMHD5Y9IWX5W7VqNV1BbGys0owIPg99+eWXAx6dM2cO BoyNGzdmSxMAjz32mMqiqenCx4xgE3xHRsmSJUswDzQMvLldv3599sUzZ87g FdSwYUOWGOxUjCxZsnAWzAiaFuAsJ3Sb+FF5fyPdcMzIXp4dO3as0QpLMOI/ I0ePHu3evTs6XTLYammchWcrHqgAl3BoNXU6RpR688030XqymJFN1ZDFjDyX ucrPaR5Vxoz80uu9ypQYseRvv/2Gv86GXXx1Hdp/wPNgKJc9e3a0PIyPPv+d RmXOgMMZfw/J3k6Vzu5gz0DXr1/PUzt+FYxcqkbsH/LvtmnTxuf35ZQbXbVo 0QK/GBUV9ec91q1bh4ngJcJH6Rp3bkU0XYIBOXEvM3AzZB2aKzElZgzYD+t1 wDjHhWCFCWsnz2JAGMvW+MEprIAyYEwP9X1G9eb62muv+fxjAep14cIF6D3Y 0p3BnkBZFjMqBy9EM2Y0fr3zeInqheFvq3oHWRYDduzYEVvO6NGjMYWt9iMl tPcZITrDb40cOVJ5NCYmBmdQv/HGG9inQQobmCAcVll6WuSYkU0UARcIro4Z M2bALzK3U9ppDBgwABNfeOGFuXPnzpw58/HHH/f577dIXZratWv7/D3/00Fg y6uGFU0LcJaTXUfjxo1TngTfZoXAmaXo6grYAkoTJkwwWmEJBmPGEydOsLW5 ypYtC4PF6tWrExIS8AVn1htwFp7t6wSnCmZqtuKx17AsZuS/zE2MGfml13uV KTE3ZsTOQbr8uBRcOCJr1qy4cRXOrCtQoIAyJ1taYeLEiSyRv4c8duwYjCw+ /xIHY8eOXbRoUa9evXCaTcOGDWV+UbDa8atg5FI1Yv+Qf/fBBx/0BZq7y94x UUd9loU7EEoXdXr27Ik/x/+6hHMJX8yoywHjHxeCFSasnbwyBmR9dc2aNZWB YWgxo0pzjY6OxhPKbowfPHgQlzHPkSOHcuu0dGfGjLqud04vUb0w/G1V7yAb MAZkGwVKG7ZKfk2+//57/JbslT0EFyosWLCgbINLFknJlgGRImzMyJ5rg3bS nTTZjidS2yYnJytvKeTOnVv2tBpv2uTPnz+0bVXNQtMCnOVkK0707t1beVTl OaNyAoayK2C+zYcffshbMQ4Mxoy483WWLFlg7JCmy3oDzsKzWzEB3xH2ONbE jLoucxNjRn7p9V5lSsyNGdnbKwE3JMXzlC9fHj/iQiuA8vWWgLdAdfWQzFWT AlVQrj0SrHb8Khi5VI3YP7TfPXDgQLA2w06oTrVq1fh/zqEIpYs6bHqq5ovY LiB8MSN/96JrXAhWmLB28gFjQNzJEZD5J8Hyq6PeXCEMwaPKbRHYHn8BF27i jxl5HMV0S2JGXdc7p5eoXhj+tqp3kA0YA27evBkTH3vssYsXL0pPElrMiO8H ZcqUKaCfgJMwAy4ujQsFS9cOlSFszPjee++hoU6cOCFND9hp4AJN9erV++KL L8CnAlOMHTtWObuY7eYjmzplMZoW4CznmTNnMFvVqlWVR5UdHTRF3JJG9hYw ALGVrCuAzPjwGq5iWRs2ghE/4dSpU1hI5fK2st6As/CXLl3CHqNBgwb6q+Jy rIkZdV3mJsaM/NLrvcqUmBszsnWElG/BxMTE4CEY7zCFXdcLFy6UZQ44nPH3 kLGxsWBAuMqGDRvWpUuX1q1b9+nTR/kr6rXjV8HIpWrE/qH9LpsbH3CXDXDJ 4hWwt3LAFYSPAV9WdRmi6aIC7jMLv2jiUCgsPLrgAoOAbBHLdNV+mL970TUu BCtMWDv5gDHg7t278R0x8Mll08tDiBnVmyvulZA5c2a2pRGDBSDSx1sMTX11 OYrp+mNGXI804DxM49c7v5eoXhj+tqp3kA0WA3bo0AHTZbNJQ4sZ8R0WqLLy ELRArHXAhZXAFD7VqVPCxoy4ljs0Xeld6wsXLjDDsk4jNTU1Z86cPsXqTEq2 bNmC10KlSpVs7Pw1LcBfTjaFfvny5dL0P/74A1+AlXV0eHuhaNGi0kfSs2fP ZrujSrsCtp/p6NGjZb8rXURaF0b8BLapimzKBJwQX42X9gachWf3iDw7BzUY 1sSM/Je5ys9pHg241wa/9HqvMhnmxozbtm3DzgFKJX1ZHgYCViO2jgqbpF2n Th2pFvD/W2+9pRzO+HsefJ+xevXq6vVSr126HhVCvlQNzm0I4XfZ5pWyBqMC rYETECt12bp16xNPPKFcZ3716tX4rYABhfvg0YVZ8ptvvpEdUpGMv3vRNS6o FCZ8nXywGJBFELKHgyHEjOrNlQ0NkydPlh1i78dFRUUpv8ijry5HUW/MiC81 BBw0jV/vurxElcLwt1W9g2ywGPDYsWO4NiyUM+C6qbpiRqxX7dq1Ax7FqbDl ypWTrbeTnJyMX2zUqFGwMwsbM37yySdoqK5du546dQpUW7duXbVq1Xz3YJ1G YmIivkfTokWLgwcPQiO/4ifgad9//338OoThUAx8sxjEGjBgADRUa95w57EA ZzlZc4XGBs7GmTNnjh49+vXXX2MQrbwW2CYIcEFBs4yOjoYTShf7lXYF4Mbg vR3sAMG28LswsL7yyitwNYX2rNaInwDNG6/ivHnzbtq0CSQGgwwdOpQtFyzt DTgLf+jQIbxO4SSDBw/GRYlhbIqMjKxfv/6MGTNCqKM7sCZm5L/MVX5O82jA mJFfer1XmQxzY8Z0SecAIRsMbWDkPXv24BsKPv90C5YTrhFwgzG9adOmu3bt On/+PDR+GBGYhWUbPHH2PLiLMbgQixcvhh5YpctVqV26HhVCvlQNxiYh/O7n n3+ONgR11H+XQTFjQKzUBddAuO+++8A3W7t27blz586ePQt+O26bDixdujSk ijoMHl1YvwRjLkTZcO2zNz3Ve2nO7kXXuKBSmPB18sFiQPgJjDHB/YBuWZl/ 3rx58wOhnGKq3lzBA8e3HaHAP/74IysGnB/L/NBDDwXc0pFHX12Oot6Ykb27 N27cuJSUlFN+1E+Vzi2lLi9RvTCcbVXvIKsSA7KHm9ItRVj+Jk2aBGw5ylsK UFQUq3nz5kpLpkteFn7hhRfYnpInT55ky+BAyw/4xXSBY0Zopfj81Oe/3YTN wOd/VUfZabCHSlLAn3nggQfatm27aNEilhNGgTp16rA8OXLkYPuPK9t2mOCx AGc5obnitBkluOq+rKMDoylz5smT5+OPP8b/pV1Bun+pZ3ZnyeefCMH+79at W5jqrtJvgJpSfZlxcA62rDfgLDwMEHAGdqhQoUIs52OPPRZCHd2BNTGjrsvc 3JgxnVt6vVeZDNNjRugc8JUNJXAhSB0VAKqMQ62Mhx9+GP+RxYycPU9UVJRy Yymf34GpUqUKjOnS5RfUheO/AEO7VA32OSH8Lji6eFTpBwaDYsaAWKnL3Llz WYDg++944dP/CqRz4dEFOnbWgfj8i0ZCN46TJNUl4+xedI0LKoVJD1snr/Lc cMKECXgITqjMH4xevXrJzqPZjUAnzG6MFylSBMIfZgdI37BhQ8Bv8eiry1HU GzNOmjSJnROUAnEhClM/FcIppS4vUaUw/MGCrkFWJWaEgBcbm0+y+q76/oyI 7KVFGHwxvVOnTgEtCY2WhYfQVCDmrVatGuv94JDKHWAWM4ZG+GLGdH8fznac 8fmv3C+//PLChQvYTqSdBsTa6iaV7n8NMfioUaPwXhAD2irIKtuCPExwWoCz nJCtf//+0ksJLp+FCxfitQBGk50WbMiuI7Bk7dq1t27dytbgksWM6f65GTg1 mvHQQw9NnTo1fHUvW7as77+bpDBOnz7dunVrVhK4xqFbhmsKl3uS9Qb8hQeH 7dlnn5U6CXAdvfrqqyp737geI0phzJg1a1bZCuHQzaJ5peum8l/mKg1D/ejM mTPx5MqH45zS673KpBixJHshQjbsXrp06fPPP2e7leG1AGYPuEdDTEwMuxHq 8/tUHTp0OHPmTPHixeHjDz/8IMvP0/PAP+qrkT/++ONJSUnqtWPwX4AhXKoG +5wQfhfXTwBkM39UOHjwIH5l9uzZnF9xOgLqcvjw4c6dO7MHi0iZMmWCvajr Sjj9ky1btqATjoAE4Eikc0jG6djwjwsqhUHC0cmrxIzQOeMza2DdunWy/MFQ xow83Qg44c2bN5edCp92BfsKp778jmIIg1ePHj2kBX7xxRfVT8XgkVKXl6hS mHQ9wQL/IKs+15T5SGzrwBBiRrZkkMqdLqj4hAkT2M4sCNT0q6++Up+Ly2LG EKI/C/ZnhIsFwm2I4lVu2IIfiJf5999/v3Pnzg0bNkRFRcGlOnnyZLb/tTKa SPcPEJGRkeC+quxFEg50WSCdr5zQAHbs2LF06dKjR49qnhAcSzDaihUroElz luHs2bNr166FYvCcXwW9dQ8I+FdQGKhCwKkXSjgLj41t5cqV0N/KliD2IKYo xQnPZW5BATSl13WVMcJnSXBXYAyF8mzfvl3zBW3oPcDCcNXwv8qt0vPgXcon n3wSRgGoXZSfxYsXDx8+nN0pVS4brg7/BajrUjXR/tRFmIiwusCwAhcUjI9w vShX0nM9/LqAqSGIWLRoEXQROFdQF5qOja5xQbMwYe3k7QU8us2bNy9btgz+ am7tyq9vCI4iPyA6qA9lhmhL70YGPFLq8hI1C8MZLIQwyNoLBMUHDhyAMkPt 9u/fz7NrreAxIw+4senLL78c0CC4VpL65DGLsdIbFw0v191ZkFJm4T5LsomU MMIqj7KV3mWzXu3CffZ3B6SLmJAu7ob0JYzggpgR5yR06dJFeSgpKQlXAWrR ooWJv2gQL1+zXq67syClzMJ9lmRRoWwlfGTUqFF4VDo9zEbcZ393QLqICeni bkhfwgguiBlffvllnLQ8efLk+Ph4lr5t2za2JlLAvU3twsvXrJfr7ixIKbNw nyXZRpANGjSIjo5mE1pSU1PHjh2Lb8FUq1ZN74yjMOE++7sD0kVMSBd3Q/oS RnBBzLhp0ybpS+tly5Zt1KgRvkuLfPXVVyb+nHG8fM16ue7OgpQyC1da8tVX X2UdbJ48eerVq1e7du18+fJhSrly5U6ePGl3Gf9/XGl/F0C6iAnp4m5IX8II LogZge3bt7/yyits2WHmybzxxhtspx5x8PI16+W6OwtSyixcacm0tLRRo0Y9 8sgj0i43U6ZMFSpUmDRpkjULUHPiSvu7ANJFTEgXd0P6EkZwR8yIpKam7tmz Z82aNZs2bbJ4KVRdePma9XLdnQUpZRYutuSVK1f++uuvLVu2rFq1CvpeoUJF hovt72hIFzEhXdwN6UsYwU0xo1PwsgW8XHdnQUqZBVnSXsj+YkK6iAnp4m5I X8IIFDNaj5ct4OW6OwtSyizIkvZC9hcT0kVMSBd3Q/oSRqCY0Xq8bAEv191Z kFJmQZa0F7K/mJAuYkK6uBvSlzACxYzW42ULeLnuzoKUMguypL2Q/cWEdBET 0sXdkL6EEShmtB4vW8DLdXcWpJRZkCXthewvJqSLmJAu7ob0JYxAMaP1eNkC Xq67syClzIIsaS9kfzEhXcSEdHE3pC9hBIoZrcfLFvBy3Z0FKWUWZEl7IfuL CekiJqSLuyF9CSNQzGg9XraAl+vuLEgpsyBL2gvZX0xIFzEhXdwN6UsYgWJG 6/GyBbxcd2dBSpkFWdJeyP5iQrqICenibkhfwggUM1qPly3g5bo7C1LKLMiS 9kL2FxPSRUxIF3dD+hJGoJjRerxsAS/X3VmQUmZBlrQXsr+YkC5iQrq4G9KX MIJdMSNBEARBEARBEAThFChmJAiCIAiCIAiCIIJhfcwYwhddg5ct4OW6OwtS yizIkvZC9hcT0kVMSBd3Q/oSRqCY0Xq8bAEv191ZkFJmQZa0F7K/mJAuYkK6 uBvSlzACxYzW42ULeLnuzoKUMguypL2Q/cWEdBET0sXdkL6EEShmtB4vW8DL dXcWpJRZkCXthewvJqSLmJAu7ob0JYxAMaP1eNkCXq67syClzIIsaS9kfzEh XcSEdHE3pC9hBIoZrcfLFvBy3Z0FKWUWZEl7IfuLCekiJqSLuyF9CSNQzGg9 XraAl+vuLEgpsyBL2gvZX0xIFzEhXdwN6UsYgWJG6/GyBbxcd2dBSpkFWdJe yP5iQrqICenibkhfwggUM1qPly3g5bo7C1LKLMiS9kL2FxPSRUxIF3dD+hJG oJjRerxsAS/X3VmQUmZBlrQXsr+YkC5iQrq4G9KXMALFjNbjZQt4ue7OgpQy C7KkvZD9xYR0ERPSxd2QvoQRKGa0Hi9bwMt1dxaklFmQJe2F7C8mpIuYkC7u hvQljEAxo/V42QJerruzIKXMgixpL2R/MSFdxIR0cTekL2EEihmtx8sW4Kn7 jh07Nm7cuHv3bp4T6socMsnJyRv9XL58Oaw/JA5ebqXmQpa0F7K/mJAuYkK6 uBvSlzACxYzW42UL8NT9kUce8fl8zz33HM8JdWUOmV9++cXnB0LUsP6QOHi5 lZoLWdJeyP5iQrqICenibkhfwgjix4zJyckLFiwYOnTozJkzd+3axfOV06dP 7/Vz6dKlEEoYbvRaICkpCeoCf1XypKamLlmyZNiwYcuXL7948aLRIoYNihmd An8rvX37NlyY33zzTUREBDTUO3fuhLloDiMc1zvBD7VkMSFdxIR0cTf8+h4+ fBi8bvAqly1bpj4eHThwYNasWUOGDJk2bdr27dvv3r0bMNu///67c+fO8X5W rVp1/vz5YCe8ceMG+FrQtEaPHr1w4cJTp07xFJj/6/DTe7W4fv26rh/1CCLH jDExMRUrVvT9l2bNmsXHx6t8C6KnBx54ADPPnTs3hBKGG04LXL16dc2aNZ06 dcqaNSvUpUGDBsFy9u3bV2qiTJkyjRkzxswSmwfFjE6Bs5UeOXKkePHi0uZX tmzZ06dPh7+AjsH0653QBbVkMSFdxIR0cTc8+qanp3fp0kXme3/00UcQ9Mly QvDVvn17Wc569eodPXpUmu3WrVs9e/bMnTu3NFv+/PkhxpSdEHIOGDAge/bs 0pxZsmTp2rXrlStXNGvH+fWIiAifFj/99JPmz3kQYWPGefPm5ciRA7UrWrRo 3bp18+XLhx8rVKhw8+bNYF989dVXmejOjRmTkpLQdWQ8++yzAXPClYgZ4Hqs U6dOzpw58eOIESPML7phKGZ0CjxKHT58GK5NtEz58uUfe+wx/L906dIJCQmW FNMBmHu9E3qhliwmpIuYkC7uRlPfu3fv1q9fHwUtVapU06ZNixUrhh8bNWoE QRnLeefOneeffx4P3X///Z07d27cuDF+LFu27LVr1zDbhQsXGjRogOmZM2eu WbPm448/zkY6cK7YCWEcrF69OqZnypSpYsWKrJkBrVu3DvYEU+/XeWLGhQsX hmxkFyNmzHjx4kWMEKEv2rp1K855SEtLa9WqFar55ZdfBvziggULpKI7N2ZM TEwscA+4yoL5kHFxcVjTGjVqXL16NcNvunLlyvn8t1bOnDkTjvIbgWJGp8Cj 1CuvvIKd8/z58zFl0qRJaKhu3bqFvYgOwcTrnQgBasliQrqICenibjT1nTFj Bkr5/vvv44NF+PvBBx9gIhxlOX/77TdM7N+//40bNzBxzpw5mPj1119jyqBB gzBl4MCBbD7qypUrc+XKBYmFChX6+++/MTE9Pb1y5cqQ2L17d4g0M/xhKZS2 TJkyPA4Y/9chZ3wg9u3bh49dnnnmGZprHRAxY8YM/3qYLVu2PHfunDQRmgHG ks2aNVN+JSUlBWelPvXUU06PGaU8+uijwXzIHj16KMNDFkgK+KiRP2asX78+ foS+KDo6+ujRowFvMWnGjNAd7dy5c+/evaxPCwac/6+//tqyZUtaWprsEMWM SpKTk/HRGIws0nR0J/LmzcsGAo9j4vVOhAC1ZDEhXcSEdHE3mvrio8OSJUvK XKYaNWpAerly5VgwNWDAAJ9/hptszioMXpDeoUMH/Hj79u133nlHGmwiLJYE D40lJiQkQCgqy/nrr79izoiICPXaGfw6BJuQDYLZ48ePq+f0LMLGjMHAZ9yl SpVSHsIuK3v27FFRUV6IGW/dunX//ff7Ar369MQTT/j8E0UMFtV0+GPGxo0b HzlypFmzZmyKcoECBXr27CmdGpERPGY8e/YsBNSPP/44PrUBYJh74403Ag5n qamp0L+xyc+ZMmWCLy5ZsoRlCBYzfvLJJ/f7GT9+vC47iI+mUlBltMnmzZul 6YsXL8b0WbNmhbeIDoFiRnuhliwmpIuYkC7uRlPfIkWKgIgffPCBLH3RokWo L/s6RliFChWS5cR3IevVq6deEmg/eMKZM2eq59y+fTvmHDp0qHpOI1+Pjo5G d3HixIkh/IpHcFzMWK1aNdAUYiJZ+vz587FVjBw58vjx416IGU+dOoXVZHMA GJ999hke+ueffwyW1lz4Y0aARYtSIECWvs0aMGacNm3afffdp/xuwOgyKiqK LZokg028CRgzsikcENjevn3biFkERFOpt956y+cP5GV1v3r1Kt6F7tevX3iL 6BAoZrQXasliQrqICenibtT1BecqWHiVkpKCh9jiMCtWrJBFkUilSpUgESJH 9ZJERkbi1xcvXqyec8yYMTKXTBecX8cHqfBX/a1Jj+OsmDEtLQ3vA7zzzjvS dGjMhQoVgvTatWtDP3bs2DEvxIzR0dFYzaVLl8oOTZkyBQ/99ddfBktrLrpi Rp//ZheEaYmJiYsWLWKR3c8//yzLLIsEt2zZAoklSpSIiIjYvXv333//vW3b NjSjrH+7dOkS3lUDOnbsCE06PT1948aN1apVq1WrFpuboYwZd+7ciWFpxYoV eZbzchyaSjVt2hSqX7lyZeUhfPG8U6dO4Sqco6CY0V6oJYsJ6SImpIu70dQX RZT52Bn+cBLf9fviiy8w5fr16zg7C9zvTZs2YSK4T+gssZRg9OnTB3OqrLwB /vy8efNwHdSSJUuydXU44f86Oo3AnDlzdP2E13BWzMgmRcyePVua3rp1a0iE 9owL/HokZly2bBlWUzZFJEMyiwBiJYOlNRf+mDF//vyrVq2Spu/fvz9Tpkw+ /5Jc7EZQsLmpkZGRsv4BTIE26d27N0uEjhETofuSZr5169bly5fZR1nMmJSU 9NBDD8FHCGNPnDjBWXdnoalUlSpVfEG2hMAtcho3bhyuwjkKihnthVqymJAu YkK6uBtNfevVq4cOmHRhhxs3brRs2RK9oM6dO7N0CLXy5s2L6eCHg6eEj29e f/119WKAf4VOVJkyZZRHz549C37aa6+9VqpUKTw5DIj8vlYIX2/Xrh1kK1y4 sObCFx7HQTHjyZMncZ2lChUqSF+5hcAQWwWbhOyRmJE9TIRgSnaI3epRPoK0 F/6YMeCyNi+88ALWKzk5WTOzkjx58mDPhh8h8MSUIkWK4KqzwZDGjDdv3qxb ty78ny1btj/++IPnd50I593INm3aKA/hK/CVKlUKV+EcBcWM9kItWUxIFzEh XdyNpr4zZ85Eb6d69eoQEiYmJi5ZsgRcLN89mAeV4b+7DuGh77/UqFFD860o thBrQC89NjZWesKSJUsePHiQv456vw51BHcOcn7++ef8v+JNnBIz3rlzh+38 smbNGpYOsUPBggXxrhd79uSRmHH69OlYzT179sgOrV27Fg+tXLnSYGnNxWDM OGLECKwXW2hLPWa8cePGihUr4Ftt27atUKECfrdOnTp49OTJk5iifN1bBosZ t2/fzh5NBtvwxR1oKgX9MBjhpZdeUh4CC+PAEa7COQqKGe2FWrKYkC5iQrq4 G019wZFu0qSJTwHEhrjoYs+ePTHn1atXMZbMmzdvjx492DaOmTNnHjJkiMpP bN68GeeMQYMJ+PLgqVOnwGeDQbB48eJ4TsgP5+R801Dv17///nvMpisy9SZO iRl79eqFmsqmykPHhek7duxIusfWrVsxEVoCfExPTw+hnOHDLB9y9erVWM0N GzbIDs2bNw8P7dq1y2BpzcVgzDht2jSs16JFi9Qz4+QEvJ8go3bt2piHvcH9 7bffqheJxYwdO3Zk52nRogVPlR2KplK1atXyBVkbrWzZsnCoZcuW4Sqco6CY 0V6oJYsJ6SImpIu74RmP7ty5ExERUa1atZw5c2bPnr1Ro0Y//PDDjRs3cDkR tuhihw4d4CN4WbGxsRn+Fx7B5S5cuDA6SKNHjw548j///BPXpsiVK1dcXJx6 SSDKW7duXdWqVfGc4AHqqizn19u3b4+RL+3JqIkjYsZvvvkGFa9Ro4b0JbVD hw4pIwIlTz/9dAjlDB9m+ZB79uyRBVCM7777Dg+pvFxsCwZjRvZC69q1a1Uy p6am4uAFlC9ffsyYMfCjly5dQkuymHHJkiWYZ9KkSepFYjEjkjt3bvzn119/ 5ay449BUCm/XVKxYUXkIQ3XlS/TehGJGe6GWLCaki5iQLu5G13gEMRRbpp7N y8JtyPbv348fZbfc4+Pj8S1CiDdTUlJkJzx37hxbjXDhwoWcxUhOTsYZ0cWL F+f8iq6v4+PIhg0bhnByryF+zLh48WK8uQGiyyKgI0eO8MSMNWvWDKGc4cMs HzIpKQkrOHjwYNmhd9991+d/HC/bzdB2DMaMH374IVYZ+iWVzE899ZTPvyGj bKMoWczI7jl8/PHH6kWSxoxVqlSBnrB06dI+/4uQEIqqf9ehaCrVrVs3n38/ FNmWl3CRoqEGDRoU3iI6BIoZ7YVaspiQLmJCuribEJ7aID/99BPqi7PXpk6d ih8TEhJkOWfPno2HIiMjpenQYHD2sk//m4OdO3fGL164cCGEwqt8nW1a179/ /xDO7DUEjxmhyeGrqXnz5g04zTItLe2iArYJBbRq+CjaVggm+pD4NE226vXd u3dxYrloD1gzjMWMEP/imxQ5c+ZUWTf1/PnzqP5HH30kO4MsZoQT4n5ScBL1 4JrFjA8++ODp06czJFsLde3aVbvaDkRTKbb2lGydpcmTJ2P6+vXrw1tEh0Ax o71QSxYT0kVMSBd3E3LMiLsulilTBpegHD16NHzMkiXL9evXZTnBV2ceOEu8 du1aw4YNMb1jx47BZoEGe+XwzTffxO+mpqaqFDKEr7N3lGiXDR5EjhlXrVqF +6rkyJFDVyP3yBo4wNixY7GmW7duZYnLly/HxOnTpxsvrbnwx4wQCMsWPY6I iMB6dejQQZZZGjPGxcVhtu7du0u/Hhsbi4tCs5gRaN68OWZmU/QZhw8fZv+z mBG6F5bYqlUrTNTch8iJaCoFQ0CBAgWg+k2aNGH9P4TeNWvWhMRSpUrRqwEI xYz2Qi1ZTEgXMSFd3A3PeAROlCwS/Pnnn9Hb+fHHHzFl/fr1mDJz5kzZ18Gb wkNsP2s4G1vEEhwn6cYHUnbv3l2sWDHZJmsZ/h3YcR9taF0sEdohOGbz589n RdX1dQZbJWPdunXBDEIwhI0Z16xZg3umAwMGDICPv/3223IJKo66C2LGnTt3 br8HzrUGT5KlsCenSUlJ+KQMroiYmJi7d+9C8Jg/f35IyZMnj/r+EbbAHzP6 /PswRkZGpqenQzVHjBiBC21Bq2ATU4EaNWqgcdg4BT0JTmbOly8f2iQxMXHM mDH4wFoWM0JTwfsSPv/MBDjz7du39+zZ065dOzgJ291Stj8jcvLkSdziFsqp vNXmdHiUYlOFe/Xqdfny5UuXLnXt2hVThg0bZkkxHYCJ1zsRAtSSxYR0ERPS xd1o6gteU65cuapXrw7eDgR3Z86cAU3R+ypZsiR7vRHcSxyqcufOPWfOHPaA b9myZbgpXokSJfC2P/xt2rQpNg/wyiCmW7ly5fL/Aj4e5KxcubLP/1IVNLDf f/8dBj4oAJT2ySefxK/37duXlbNPnz6YyKa56vo6Y/jw4XgUQk4T7Ot2xIwZ oY3lyJHDp0qVKlWCfd0FMSPuGxiMCRMmsJxQxyxZsmA6Xtc+f2C1evXq8FYj JPhjxoANAGoqW6+md+/eeKhgwYIQ7mGidHVTjKl9/hmtZcqU8f03ZszwL7PM 8uBPsP979OiBeQLGjMDIkSMx3X3b+vAoBd4Cvjoqa35NmjRxXxAdMuZe74Re qCWLCekiJqSLu9HUd8CAAQHdoYcfflgWVYE7xG65FytW7JlnnsF1HgBIj46O xmwQcqqMbghEmlg23M4DyZw5s9Q3q1OnDotYM+49LwDgd1nV+L/OYDtFKl/M JJQIGzNK22pAatWqFezr8fHxmEe5oKgIGPchx48fL808f/58fNEPgZhLzIAx g6/u5cuXh1rMmDEDojlpDwDdkfLhMvRauEgXcPToUUxMS0tr27Yt+yJE0C1a tDh+/PjQoUN9ipgxw9+Sq1evzgY+n/8W2ezZs1mGBQsWYLqszUNDLVeuHKRn y5bNZR0O53wAcB5efPFFNnBApN++fXtyG6SYfr0TuqCWLCaki5iQLu6GR1/w vtjOhuhBtWrV6vz588qcR44cYS/pMFq2bCl9tWfQoEEqoxvCXNbU1NQPP/xQ tktavnz5Ro0a9c8//0h/ety4cXh04sSJLJH/6wzcaAOQbspABEPMmNHdhMkC J06cgJBK8MhFb93v3LkTFxe3bt06lU1DIM+WLVvWrFkjG7Di4+Mhfdu2bbL3 IoNx5coVyB8VFXX27Fn+EroVXUpBZxsbG8tvak9BPZ69UEsWE9JFTEgXd8Ov LzhC4HqBvgEfz0lJT0/ftWvX2rVr4a8p+6HfunULHL/ff/99/fr1x44dC/b+ 46FDhw4cOBDy14kQoJjRerxsAS/X3VmQUmZBlrQXsr+YkC5iQrq4G9KXMALF jNbjZQt4ue7OgpQyC7KkvZD9xYR0ERPSxd2QvoQRKGa0Hi9bwMt1dxaklFmQ Je2F7C8mpIuYkC7uhvQljEAxo/V42QJerruzIKXMgixpL2R/MSFdxIR0cTek L2EEihmtx8sW8HLdnQUpZRZkSXsh+4sJ6SImpIu7IX0JI1DMaD1etoCX6+4s SCmzIEvaC9lfTEgXMSFd3A3pSxiBYkbr8bIFvFx3Z0FKmQVZ0l7I/mJCuogJ 6eJuSF/CCBQzWo+XLeDlujsLUsosyJL2QvYXE9JFTEgXd0P6EkagmNF6vGwB L9fdWZBSZkGWtBeyv5iQLmJCurgb0pcwAsWM1uNlC3i57s6ClDILsqS9kP3F hHQRE9LF3ZC+hBEoZrQeL1vAy3V3FqSUWZAl7YXsLyaki5iQLu6G9CWMQDGj 9XjZAl6uu7MgpcyCLGkvZH8xIV3EhHRxN6QvYQSKGa3Hyxbwct2dBSllFmRJ eyH7iwnpIiaki7shfQkjUMxoPV62gJfr7ixIKbMgS9oL2V9MSBcxIV3cDelL GIFiRuvxsgW8XHdnQUqZBVnSXsj+YkK6iAnp4m5IX8IIFDNaj5ct4OW6OwtS yizIkvZC9hcT0kVMSBd3Q/oSRqCY0Xq8bAEv191ZkFJmQZa0F7K/mJAuYkK6 uBvSlzCCXTEjQRAEQRAEQRAE4RQoZiQIgiAIgiAIgiCCQXNTrcTLFvBy3Z0F KWUWZEl7IfuLCekiJqSLuyF9CSNQzGg9XraAl+vuLEgpsyBL2gvZX0xIFzEh XdwN6UsYgWJG6/GyBbxcd2dBSpkFWdJeyP5iQrqICenibkhfwggUM1qPly3g 5bo7C1LKLMiS9kL2FxPSRUxIF3dD+hJGoJjRerxsAS/X3VmQUmZBlrQXsr+Y kC5iQrq4G9KXMALFjNbjZQt4ue7OgpQyC7KkvZD9xYR0ERPSxd2QvoQRKGa0 Hi9bwMt1dxaklFmQJe2F7C8mpIuYkC7uhvQljEAxo/V42QJerruzIKXMgixp L2R/MSFdxIR0cTekL2EEihmtx8sW8HLdnQUpZRZkSXsh+4sJ6SImpIu7IX0J I1DMaD1etoCX6+4sSCmzIEvaC9lfTEgXMSFd3A3pSxiBYkbr8bIFvFx3Z0FK mQVZ0l7I/mJCuogJ6eJuSF/CCBQzWo+XLeDlujsLUsosyJL2QvYXE9JFTEgX d0P6EkagmNF6vGwBL9fdWZBSZkGWtBeyv5iQLmJCurgb0pcwAsWM1uNlC4S7 7jt27Ni4cePu3bvD9xMewcut1FzIkvZC9hcT0kVMSBd3Q/oSRqCY0Xq8bIFw 1/2RRx7x+XzPPfccT+bp06e3b99+/vz54SuPc/FyKzUXsqS9kP3FhHQRE9LF 3ZC+hBGcEjMmJSXt3bsX/qpnS0xMXL58+fDhw2fNmnXkyJG7d++GUMJww2+B w4cPz5w5c9iwYcuWLdOsuyMQJ2bct2+fz0+WLFnOnDkTviI5FH6lbt++vWvX rm+++SYiIgIu0jt37oS5aA5Db5vn7OsITvjtn5ycvGDBgqFDh0KvC006YJ4D Bw7sDcKtW7dkmW/cuLFjxw64NEaPHr1w4cJTp04F++mrV6+uX79+7Nix3333 XWxs7M2bN3mr5+fff//duXPneD+rVq06f/68en64ZuPi4qAPtHGIDGEsAAuj qWFklB3i0QXMEiwP4/r169LTGtQF0XRLdDWqcBPW/ur06dNYr0uXLrHEEHTh v7KUCGVt66GRnTCC4DEj9Nhr1qzp1KlT1qxZwb1v0KBBsJzXrl2DbL7/UrVq 1ePHj4dQyLDCY4H09PQuXbrIqvPRRx+Bb2BJGcOFODEjjDIQLULmbNmywZjO 0mH8auTn5MmT4Sun+HAqBS5Q8eLFpa20bNmy4BiEv4COgdOS/H0doQse+8fE xFSsWFHW3zZr1iw+Pl6WM0eOHL4gLF68mGUD53PAgAHZs2eXZoAOp2vXrleu XJGdc926dQ899JA0J1xTwYJWGfBDPXv2zJ07t/Tr+fPnnzZtWsD8R48enThx 4qOPPoo5N2/ezPMr4SCEsWDIkCFY7Mcee0x2iEcXcH2D5WH89NNP7JxGdEE4 3RLORmUN4euvUlNTH3jgAazX3LlzWbouXXRdWQERytrWQyM7YQSRY8akpCTs jhjPPvtssJzVq1fHPJkzZy5fvjwMmvixQIECEH+FUM7woWmBu3fv1q9fH8tf qlSppk2bFitWDD9CLOPoW2HixIwZfpdg4MCBMq/pwIEDaGr4JzxldAY8Sh0+ fLho0aJoLrjowJHD/0uXLp2QkGBJMR0AjyX5+zpCL5r2nzdvHnMjoT3XrVs3 X758+LFChQrSR0vXr19XcWsXLlyI2aTjUaZMmSAaZZcJ0Lp1a+mTpo0bN0Ie SAc3GDquWrVqYUvImTPnqlWr1Kt24cIF8NXZwFezZs3HH3+c/dAvv/wiy//R Rx/Jygy/rsuYJqJ3LNizZw+7RmQxI6cuPLEJy2xEF4TTLeEsvGWEr7969dVX WX69MSPaQdeVFRDRrG09NLITRhA5ZkxMTCxwD+hyVfqld955B5t027Zt8V7T nTt3YMTMnTv31KlTQyhkWNG0wIwZM7A677//Pj5YhL8ffPABJsJRiwoaBoSK GQMCISTamWJGTaVeeeUVHLvZO6GTJk1C63Xr1i3sRXQIPJbk7+sIvajb/+LF ixghgl+0detWnH+VlpbWqlUrbMlffvklywwyYeJXX30Vr+Cff/7BbBAOVK5c GbJ1794dwroM/3gEZShTpgx+fceOHZjzxo0bZcuWhZTChQuzlbt27dqFNwmh N7t9+7ZK1QYNGoQnHDhwIJuPunLlyly5ckFioUKF/v77b2n+nj17YhvDDD7n xIwQuVepUoX59rKYkV8X5VFg3759EAnC15955hlsAAZ1QTjdEs7CW0aY+qsF CxZIozNpzMivC/+VpVJyoaxtPTSyE0YQOWaUgnNpAvZLcKVny5YNjrZr1052 l+ny5cshlDDcaFrg+eefh+qULFkSRi5peo0aNSC9XLlyzp1Ybk3MWL9+fZZy 6NChmJgYme+kwtKlS3liRpAGThsXF+f02cLB0FQqOTkZbzW///770nQcbvLm zctvc3ejt82r9HVECGjaH/zMli1bnjt3TpoIHinGks2aNWOJBw8exM4B4jL1 H01ISPjtt99kib/++it+PSIiAlMgSsWUyZMnS3OuWLFC6VorgcgFAhPlXUQW S+7cuTPgF+fMmeOsmHHw4MHoxNauXVsZM/LrEhAIQOC7EEezKaMGdcnQ45YY LLzphKO/SklJwVmpTz31FKcBMwLpksF9ZQVDNGtbD43shBFcEDNixwJAdBBC eaxH0wJFihSB6nzwwQey9EWLFmFNwxp2hRX1us+bN+9+P7///rs0fe3atZg+ atQoaTr0XTASQfqQIUMwBWPGF154Acblbt26Pfjgg2ixzJkzQwcoW8GgWrVq 8F1wF/Hjjz/+CK4Im5YG/+CPgpci/daJEycaN27M3qe47777oC/Fe55uQrOV jh8/Hi0gm9y7ePFiTJ81a1Z4i+gQKGa0l5DvU+G0z1KlSrGULVu2YNuOjY0N 4YTbt2/Hrw8dOhRTfvjhB0wBp1qW+YknnpDd/uKHTZaYOXNmwAzOihl3796N TmwXP8qY0Ygu0dHR+KRs4sSJLNG4LvxuicFGZTrh6K8w3IBBMyoqijNmDKhL MJRXVjBEs7b10MhOGMEFMWOFChXgELjxIRTGFtQtAHFNsN4Pxi88JH1P31mo 1z0uLg4r2KdPH2n6J598gul16tSRpq9bt052zxBjRnDz2CIPUj799FPp12UT WUeMGKH8is8/n599ZcWKFSyozJYtG3uno0SJEi57PVzzOn3rrbd8/hdzZNO0 rl69imbp169feIvoEChmtJeQY8Zq1aqBEBAjsJTIyEi83kN7qWfMmDH4dTbj a+DAgT7/4zPle1j4MkLx4sVD+CFWzmBrejgoZoQBEacjFi1a9Pz582+88YZP ETMa0QVn78BfqQTGdeF3Sww2KtMxvb+C1o4VHDly5PHjx/F/zZgxoC7BUF5Z wRDN2tZDIzthBBfEjPhqxueff44fk5KSYmJixJyVimhaAN8+fuedd2TpMHri 9P4vvvgijOULJ5p1xyeDVatWlSaiz+DzL48mVXbAgAEYu0FvhikYBiIvvvji 0qVLYZCaOHEi9nX58+eXvrAgixlPnjwZFRXVtWtX/Dp8K8oPm6QKHkvBggV9 /pdcwBkDOW7cuDF9+nQURamXo9FUqmnTplBrkEZ5CBtwp06dwlU4R0Exo72E NuKkpaXhYw7pdT1z5kzsHFasWAEjTpcuXQYPHgxu6rVr19TPBt7XvHnzcHJC yZIlWf4pU6bgCZXb/YCD7fNPkAhh0bM+ffoEOy3ioJiRzbMFm8PHDh06KGPG kHVhT53AINJ047rwuyUhFz5MmNtfpaSkFCpUCDLUrl0broJjx47xxIzBdFES 7MoKhmjWth4a2QkjOD1mPHfuHPYAU6dO/eWXXypVqsRCBmjz0POEUMJwo2mB evXqYYADfgtLhPCkZcuWWLXOnTuHvZThgfMeV6ZMmS5evIgpycnJPv8UUJyy u2zZMpYZX44Ac7EUFjPCQCC9P8kWPI+Li5Nlli2Yw+5YKt9nxGlRELfKllsf N24cpENYCkXltIP4aCqFS1IEXGIdty1w0KP/sEIxo72ENuKwCVqzZ89mid98 840vEOCsRkZGKk9y9uzZ3r17v/baa6VKlcKcIOuJEydYhg0bNmC6bFbJ+vXr 8U4UIM3PAwQmuENEmTJlguVxSswIPS2blYopAWNGvbow2rVr5/PfA5QtHWBQ F11uSciFDxPm9letW7f2+RebPXr0KHzkjBmD6cLQvLKCIZq1rYdGdsIITo8Z YUxhXTH+kzdvXrZZFYQenMtiW4mmBditsOrVq8P4kpiYuGTJEghtWP8G/bBV hTUZzbqzeSxLly7FFBhc4OPTTz/9+uuv+/wLpmF6eno6uhPDhw9nX8cw8Jln npGdli1Fu3r1allmzpgRIlBsV2+//bbs5BDa41fA0+AxgiPgfBrepk0b5SG4 TuEQeErhKpyjoJjRXkIYcU6ePInPiSpUqCBd5Io5nKBO//79P/30U5y/6vPf 1FLeZYqNjZX5pQcPHpRmuHnzJq7PmS1bNuh5wOndt2/fyJEj4WzsW5Ciq/Bs hW0Vt9wRMSPEC/jy4MMPP8y23lOPGTl1QWBgxWVq2NNAhkFddLkloRU+fJjY X+HY7ZO8k8gTM6rowtC8soIhmrWth0Z2wghOjxnZOmY+/4KimzdvhiEe3Hvo kfB+IMQFwW5V2YWmBaD8TZo08SmAoOn++++Hf3r27GlVYU1Gs+7nz5/HKWEf ffQRpkCMhsMHrksALQHTf//9dzTL9u3b2deD7bUBoSJmlr7voCtmBLcB02GU iVaAh4Lto+1ENJWCYRqq/NJLLykP1alTx+d/FSVchXMUFDPai17737lzp3Hj xnhFr1mzRnYUOpCoqChp5i+++AIzK7udU6dOtW3bFqRku2NDvDBkyBDpFAg4 m3KTcejn8RYZwGZc8AAjIO4qCNegyotgjogZ2UuF0kIGjBkzdOqCfP/995gh YLhhRBe9bkkIhQ8fZvVXycnJ+CpHgwYNWFPkiRnVdUF4rqxgCGVt66GRnTCC 02NG5q5XqFBBNjOQvQchm0loOzwWgH4sIiKiWrVqMMRkz569UaNGEDHBKIPx 1Ndff21JSc2Hp+41a9aEOlasWBE/lihRwud/hAdBHAp68uRJSO/Xr5/Pv7qp 9EFAsJhx/fr1+N2QY0b27rwKsmVdHY2mUrVq1fL9d2IwA2/RswVpPQ7FjPai 1/69evXCy5nztR3oq6tWrQr5IcQItm0fuLLr1q3DbD7FzaU///yzTZs22NGV KlXqvffeO3r0KPqxefPm5S85nAd3NMiVK5d0Er4S8WPG2NjYLFmyQAlfe+21 JAk417F06dL4UeX8mrq0b98eLRxs76qQdTHulvA0qjBhVn8FQQfWdMeOHUw+ tokJBIbwMT09XXlCTV2kqF9ZnNhobeuhkZ0wgtNjRnzZzRdop/s9e/YowwQR 0GUB6M3YDhEQK2GNlixZEq7ChRmeurNRNSUl5ciRI9iTX79+HUYHnDWB4wL2 bLK7YeGLGdm+jeXLl38uCAsXLtRvEkHRVApdAhbaS8Hbyy5bFChkKGa0F132 Z1PXatSowb8sRt++ffFb+NJWMGC0wh4s2Kqb0Mux/998802fnmlg586dY4tF a3ZE4seMECoGuzUnBTp2lZ9Q1wUfUTVs2FCzqHp1McUt4WxUpmNKf3Xo0CEe +Z5++mnlCfl1YWheWZrYZW3roZGdMILTY0aII3ACiWwbBeDMmTPYCXz77bch lDN8hGYB4KeffsIaifbklB+eurM10yA0xvmojRo1wkMdO3b0+XdJ/vvvv/Fl xu+++0763fDFjBi9AuBV6qy0I9FUqlu3bj5/OC/b4ZdddxD7h7eIDoFiRnvh t//ixYtxIgf4n8FWHA0Im9um+e5h586dMaf6jq63b99++OGHfdy39OEaxGlj PtW3wBjix4xsCqg66usVqOhy6tQpPNS/f3/+MnPqYopbwt+ozMWU/ooNl+rU rFlTdrbQdMngvrKCYZe1rYdGdsIITo8ZM+7t41OpUiXZVHY2C0K0ZXBCjhlx +bUyZcpIZ2M6C566Q+1wD8S+ffvi3ebRo0fjIVwdqHDhwnASFBfGJul3jceM Y8eOxZyywBxKhQt6e2TRME2l2PoGbLUiZPLkyZiu/gjAO1DMaC+c9o+MjMSV N/Lmzav3plyLFi18/i3L2f4Lwd6rwqdUQGpqqsoJ2fbZP//8s+avX7t2rWHD hpi/Y8eOPDP6xI8Zb9y4cTEQrVq1wkEQP6pveKHUhcFeOdTczUEKvy7G3RKV wocVs/qrtLQ0pXxs1u7UqVPhI1vaiKGpi8ErKxh2Wdt6aGQnjOCCmJENf8uX L5em484IgGibrfNYIC4uTjofBoBBCqvz448/hrFwYYZT/VdeecXnn1GPE052 7tyJ6exOF+7sXKJECdkXjceM06ZNw5zQQ8pOgq/S+DimfrkATaXAUy1QoABY o0mTJsxNhQEXX0ctVaoUj+/qBShmtBce+4MDj3eEcuTIESzznj17qlSpsnfv XuX5cdkZtjTE7t27ixUrpgwKUlJScMMguDpYIgRHskcbly5dwuXuIZvUg4Ur 7pdffoEeTDo0wP9sxR6IpzhvJ4ofMwZDuQYOvy5SWD+/bt26gD9kUBdOtyS0 woeVsPZXmmvgqOui68pS6iKgta2HRnbCCCLHjBApbL8HTnGH3omlsDtUt2/f xl4rb968v/32G7RnSPn2229xllHAFYPtRdMCMTExuXLlql69+o4dO8AHgEBp 2LBh2KeVLFmSvd7oRDjVnzp1qu8e+fLlk76WXr58eXZIue2F8ZiR5axduzYM Lnfv3mUvOCQkJOB66dmyZRs1ahTunglyREVFPf/887/++qsOQwgPj1Iffvgh 2qpXr16XL18Gn6pr166YAi3WkmI6AB5LcvZ1RAho2n/NmjVsD4UBAwbARxhH lkvYtGlTxr29ySCoHDJkyJYtW8ARBV3AxcVJEdA/s0VWcYcFSIEL5Pfff4ds 0I1DGZ588kn8lb59+2JO6F7atm2bNWvWsWPHJicnQ5yydetW1sVNmTJFWs4+ ffpgOpt9CvlxA27sJ8GXXrly5fL/whaKSUxMZC1q8ODB+K0xY8ZgCudWBSZi VszIr4uU4cOHowUgDFEeNahLBrdbElrhw0pY+yvNmFFdF/4rKyOQLgJa23po ZCeMIHLMmCdPHl9wJkyYwHJCf16oUCFMz+kH/y9cuDAMlCEUMqxoWgD8FlZN XDsOefjhhwN2pA6CU3222o9PscpNz5492SHluGM8ZoSRvXTp0uwnIHgHf5Ld qGSLpbMGxgQqV64ctxkcAI9SMJo89dRTzBp4W8Pnvz8pe0ruZXgsyd/XEXpR tz+EA8otFWRUqVIlw78KFm7ayHpm1uCBfv36SX8RN0VCIEzAl6+ROnXqsPt+ CQkJ+HyEnZP9//HHH8uWcMTpjj7J/rPgv6mXHFi2bBlmhlakkg1aoKlW18as mJFfFylsF0uQQHnUoC4Ij1sSWuHDSlj7K82YUV0X/isrI5AuAlrbemhkJ4zg 3Jhx/Pjx0swnTpyoW7cu3sRDXnzxRdky14LAY4EZM2awjYd8/t1mW7Vqdf78 eUsKGEb41YcQDOseEREhTZfueZGSkiL7Ft4Khp5Nlv7HH3/gV6QxY7DMEJiX KVNGOrLs2bOHHYVRr2HDhlIvInv27K+//vqhQ4d46uUUOJWCwQUuNJzX5/Pf wm3fvj0NK1KM+2Cyvo7QhWbMKL2WA1KrVi3MfPr06a5du+JTCcajjz66cuVK 2WlTU1M//PBDXGaQAV8cNWrUP//8I80Jg5T0CvL5p9wHfJlr3LhxmIHtkM6W mFZh9erVmFk9ZsydO3eoBg6R0GJG3K5Xtqgjvy4M3NABCLY6rhFdGDxuSQiF Dyth7a/i4+Mxz6JFiwJm0NSF/8oKqIto1rYeGtkJI4gcM4ZAeno6RAdwfpw3 KCb8Fjh79uy6detiY2MdPR9VSljVN5F///13//79q1at2rx5c8C2BCNaTEzM xo0bDx8+7NwliVTQpRRYA1rptm3bpBtVE4hT2rxbMd3++Kbbhg0bNm3apD6P 5datW3Fxcb///vv69euPHTum0lFADw/9CWRTP+GhQ4dk6zk7Fxt14ccUXXjc knAUPjQc0V9xXlnBdBHH2tZDIzthBJfFjI7Ayxbwct2dBSllFmRJeyH7iwnp Iiaki7shfQkjUMxoPV62gJfr7ixIKbMgS9oL2V9MSBcxIV3cDelLGIFiRuvx sgW8XHdnQUqZBVnSXsj+YkK6iAnp4m5IX8IIFDNaj5ct4OW6OwtSyizIkvZC 9hcT0kVMSBd3Q/oSRqCY0Xq8bAEv191ZkFJmQZa0F7K/mJAuYkK6uBvSlzAC xYzW42ULeLnuzoKUMguypL2Q/cWEdBET0sXdkL6EEShmtB4vW8DLdXcWpJRZ kCXthewvJqSLmJAu7ob0JYxAMaP1eNkCXq67syClzIIsaS9kfzEhXcSEdHE3 pC9hBIoZrcfLFvBy3Z0FKWUWZEl7IfuLCekiJqSLuyF9CSNQzGg9XraAl+vu LEgpsyBL2gvZX0xIFzEhXdwN6UsYgWJG6/GyBbxcd2dBSpkFWdJeyP5iQrqI CenibkhfwggUM1qPly3g5bo7C1LKLMiS9kL2FxPSRUxIF3dD+hJGoJjRerxs AS/X3VmQUmZBlrQXsr+YkC5iQrq4G9KXMALFjNbjZQt4ue7OgpQyC7KkvZD9 xYR0ERPSxd2QvoQRKGa0Hi9bwMt1dxaklFmQJe2F7C8mpIuYkC7uhvQljEAx o/V42QJerruzIKXMgixpL2R/MSFdxIR0cTekL2EEihmtx8sW8HLdnQUpZRZk SXsh+4sJ6SImpIu7IX0JI9gVMxIEQRAEQRAEQRBOgWJGgiAIgiAIgiAIIhg0 N9VKvGwBL9fdWZBSZkGWtBeyv5iQLmJCurgb0pcwAsWM1uNlC3i57s6ClDIL sqS9kP3FhHQRE9LF3ZC+hBEoZrQeL1vAy3V3FqSUWZAl7YXsLyaki5iQLu6G 9CWMQDGj9XjZAl6uu7MgpcyCLGkvZH8xIV3EhHRxN6QvYQSKGa3Hyxbwct2d BSllFmRJeyH7iwnpIiaki7shfQkjUMxoPV62gJfr7ixIKbMgS9oL2V9MSBcx IV3cDelLGIFiRuvxsgW8XHdnQUqZBVnSXsj+YkK6iAnp4m5IX8IIFDNaj5ct 4OW6OwtSyizIkvZC9hcT0kVMSBd3Q/oSRqCY0Xq8bAEv191ZkFJmQZa0F7K/ mJAuYkK6uBvSlzACxYzW42ULeLnuzoKUMguypL2Q/cWEdBET0sXdkL6EEShm tB4vW8DLdXcWpJRZkCXthewvJqSLmJAu7ob0JYxAMaP1eNkCXq67syClzIIs aS9kfzEhXcSEdHE3pC9hBIoZrcfLFvBy3Z0FKWUWZEl7IfuLCekiJqSLuyF9 CSNQzGg9XrYAT9137NixcePG3bt385xQV+aQSU5O3ujn8uXLYf0hcfByKzUX sqS9kP3FhHQRE9LF3ZC+hBEoZrQeL1uAp+6PPPKIz+d77rnneE6oK3PI/PLL Lz4/EKKG9YfEwcut1FzIkvZC9hcT0kVMSBd3Q/oSRhA/ZkxOTl6wYMHQoUNn zpy5a9euYNn+/fffnTt3jvezatWq8+fPh1A2a9BrgaSkpL1798Jf9WyJiYnL ly8fPnz4rFmzjhw5cvfuXUOlDA8UMzoF/lZ6+/ZtuDC/+eabiIgIaKh37twJ c9EcBlnSXsj+YkK6iAm/Lpy+GQIixsXF7du3T9Mz0evJnD59eq+fS5cu8RQb SU1NXbJkybBhw+C3Ll68GCybgxxLTvj1PXPmzKJFi0DfKVOmbNy4EUwRLOeB AwdArCFDhkybNm379u3BJDNuTHXV4IR7tbh+/brytPyNkxA5ZoyJialYsaLv vzRr1iw+Pl6a7datWz179sydO7c0W/78+aH1hlA8C+C0wNWrV9esWdOpU6es WbNCjRo0aBAs57Vr1yCbzFBVq1Y9fvy4meU2A4oZnQJnK4UxvXjx4tKGV7Zs WRjEw19Ax0CWtBeyv5iQLmLCowunb4YcPXp04sSJjz76KGbbvHlzsNOG4MlA EPHAAw9gzrlz53LWsW/fvtKfyJQp05gxY2R5HOdYcsKj7+7duytVqiQT4rHH Hlu5cqUsJ4Rp7du3l+WsV68eiC7NZooxNVWLiIjwafHTTz9Jv8LfOAlE2Jhx 3rx5OXLkQB2LFi1at27dfPny4ccKFSrcvHkTs124cAGCKUzPnDlzzZo1H3/8 cdY8wNUPoYThhscCSUlJGCoynn322WA5q1evzixQvnx5uBLxY4ECBdLT082v gAEoZnQKPEodPnwYrk20DDQ8GFPw/9KlSyckJFhSTAdAlrQXsr+YkC5ioqkL p2+GfPTRRzKnfePGjQFPG5on8+qrr7Izc8aMELxgfghh6tSpkzNnTvw4YsQI lseJjiUnmvp+//332bJlY5avVq0aWAA/Zs+efefOnSznnTt3nn/+eTx0//33 d+7cuXHjxvixbNmy165dw2ymGJNHNZ6YceHChSw/f+MkGGLGjBcvXsReCMaI rVu34lyUtLS0Vq1aobJffvkl5hw0aBCmDBw4kD3pXrlyZa5cuSCxUKFCf//9 dwiFDCs8FkhMTCxwD7xgg8WM77zzDlqgbdu2V65cyfBfyHAZwpU1depU0wtv EIoZnQKPUq+88orPf7tv/vz5mDJp0iQ0VLdu3cJeRIdAlrQXsr+YkC5ioq4L v2+GgKuPbgy6ZCpueQiezIIFC6QOP0/MGBcXh5lr1Khx9epVrFG5cuUgJUuW LGfOnMFsTnQsOdG87mbNmgV1LFGiRFRUFOqbnJzcr18/NMgLL7zAcv7222+Y 2L9//xs3bmDinDlzMPHrr7/GFOPG5FQtPT09PhD79u3DGPOZZ56Rzmznb5wE Q8yYMcO/HmbLli3PnTsnTbxw4QL2V82aNcOU27dvQ1czY8YM2ddZK5XeFREE Tgsw8Ll5wJgRLge8I9SuXTvZTGwxV/jkjxnr16+PH6Evio6OPnr0aMCp5pox I3RH0Ab27t3L+rRgwPn/+uuvLVu2wAgoO0QxoxIYR/BR+Pvvvy9NRzcvb968 zh1VzYUsaS9kfzEhXcREUxdO30wGCyUCuuUheDIpKSk4K/Wpp57ijxl79Ogh CzQyJCEJe2jlRMeSEx4fbN68eSCoNAVEKV++PFS8YMGCLHHAgAE+/4M/2auO 4KxCeocOHfCjcWNyqhaM7t27QzYIDINNclZvnIQUYWPGYOAz7lKlSqln27x5 M7aBmTNnhvxbYcLEmBGvBeDQoUOmlS+c8MeMjRs3PnLkCAxAbBpMgQIFevbs eevWLWVmZcx49uxZ6Gcef/xxNq0C3I833ngjoJuRmpoK/RubYJMpUyb44pIl S1iGYDHjJ598cr+f8ePH67KD+GgqBVVGm8heAVi8eDGmz5o1K7xFdAhkSXsh +4sJ6SImIXto6r6ZulsegieDtw6yZ88eFRXFGTOC8wCDtS/Q6hBPPPGEzz/n Wf0MIjuWnISsb5MmTTBwgxgQU1C1QoUKyXJ26dLF53+rUf2EnMY0qFp0dDQ6 gRMnTgyWh2JGfhwXM1arVg2Uhaaini0yMhLbAIwvIf9WmDAxZqxQoQKGV6YV Lszwx4wAixalQL8hfWMiYMw4bdq0++67T/ndgNEljDjsJXoZbEJUwJhxxowZ mAiBLetFXYOmUm+99ZbPH8jL6n716lV8OtCvX7/wFtEhkCXthewvJqSLmITs oan7ZupuuV5PBoZmPNvIkSOPHz/OGTOeOnUKc7Jpk4zPPvsMD/3zzz8qZxDZ seQkNH2vXLlSuHBhqHi5cuVY4ooVK9AashPi+jkQOaqfk9OYBlWrUaOGzz+p VWVNVIoZ+XFWzJiWloZ3DN555x31nH369ME2IH2WLQgmxow4Dfvzzz/Hj0lJ STExMWLOSkV0xYzABx98AGFaYmLiokWLWGT3888/yzLLIsEtW7b4/BPyIyIi du/e/ffff2/bto0tjSUtwKVLl4oUKYLpHTt2hCadnp4O/QYMf7Vq1WLTWZUx 486dOzEsrVixIr584TI0lWratClUv3LlyspDuGxFp06dwlU4R0GWtBeyv5iQ LmISmoem6Zupu+W6PJmUlJRChQpB/tq1a9++ffvYsWOcMWN0dDTmXLp0qezQ lClT8NBff/2lcgaRHUtOQtA3Pj4eL0Zg0qRJLP369es4Owvk2LRpEyaCvpiT pQSD05hGVENXEIDmp/ITFDPy46yYkU1WmT17tko26GoeeughyFamTJnQfiis mBUznjt3Dq0xdepUCGqkayPDOAsXi5mFNgn+mDF//vyrVq2Spu/fvz9Tpkw+ /5Jc7JZRsLmpkZGRbNkuBMJGNE7v3r1ZInvvHrovaeZbt25JByxZzAgjGjYw CGNPnDjBWXdnoalUlSpVfEG2gMFl2B30+DuskCXthewvJqSLmITmoWn6Zipu uV5PpnXr1nAoZ86cuKEDf8y4bNkyzKncUmHRokV4CPyEYF8X3LHkhF/fWbNm de3atVGjRlmyZPH53wf87rvvZE/rQJ28efOi6UAXkA/D+ddff1395PzGNKJa u3bt4GjhwoXVl7OgmJEfB8WMJ0+exJtRFSpUUNldFPjggw84+xBbMCtm3LVr F+tX8R+4eNkOOBBeyWIuEeCPGQMua/PCCy9g7ZKTkzUzK8mTJw/2bPgRej9M KVKkCC7GFQxpzHjz5s26devC/9myZfvjjz94fteJaCqFt/rbtGmjPISvwMPQ H67COQqypL2Q/cWEdBGTEDw0Ht9MxS3X5cmAU4fp7PU0/piRPZbav3+/7BB7 OqZ8mMUQ3LHkhF/fFi1a+CQMGzbs+vXrsjy3bt2C8ND3X2rUqKE+xTdDjzFD Vi0xMREXVmLPr4NBMSM/TokZ79y5w3Z+WbNmjUrOzZs349OoOnXqqExgthGz YkY2mdznn2QOFYfuGqoM1yAuLAzxlOZioRZjMGYcMWIE1pcttKUeM0L1wUrw rbZt2+IbE9gw8CiMdJgC3Zd6kVjMuH37dvZoUraouMvQVKpkyZJghJdeekl5 CCyMA0e4CucoyJL2QvYXE9JFTPT6J5y+mYpbzu/JJCcnFyxY0Od/+sy8O/6Y cfr06Zhzz549skNr167FQ8pt6xHxHUtO+PWdMGFC8+bNq1Wrlj17djRO+fLl Dx8+zDJcvXoVXC+fP8bv0aNHsWLFMFvmzJmHDBmicmZdxgxZte+//x6PHjx4 UP0nKGbkxykxY69evVBT9VcY/vzzT3zrLVeuXHFxcSEUzwLMihnZNG+Ihthz N4QtYrxr1y7jBTYRgzHjtGnTsF6LFi1Sz3z27NnevXvj+CKjdu3amIcNVd9+ +616kVjM2LFjR3aeFi1a8FTZoWgqVatWLV+QtdHKli0Lh1q2bBmuwjkKsqS9 kP3FhHQRE73+CadvpuKW83syL730En7csWNH0j22bt2KiRAjwMf09PRgZVi9 ejXm3LBhg+zQvHnzVFwmRziWnITggaekpEAMiFEeeFzsrZ8OHTr4/LtvxMbG wsebN2+CBLhUDjB69OiAZ9NrzJBVa9++vc8fz0r3ZAwIxYz8OCJm/Oabb1DQ GjVqyF5Sk3Lu3Dm2zsnChQtDKJs1mBUzQu+KlVVufLNnzx48xFb+FASDMSN7 aWLt2rUqmVNTU9Gp8PnvjI0ZMwZ+9NKlS2hJFjMuWbIE80hf6w4IixkRNm3m 119/5ay449BUCofvihUrKg9hqK65UJVHIEvaC9lfTEgXMdHln3D6Zhmqbjmn J3Po0CEfB08//XSwMrCzsXvOjO+++w4PKddjcYpjyUloT22AoUOHohF++umn DP/6EvhRdss9Pj6+VKlSPv8LpxBsyk4SgjFDUw0oXrw4HGrYsKHmT1DMyI/4 MePixYtxPa6iRYuqLK/0999/43wVH8fsZXsxK2a8e/cu7kbx6aefyg6BoQJe zrZjMGb88MMPsV7QL6lkxk1+s2bNKtvASxYzsjHo448/Vi+SNGasUqUK9ISl S5f2+V+EhFBU/bsORVOpbt26+fz7oci2vGRtb9CgQeEtokMgS9oL2V9MSBcx 4fdPOH0zRMUt5/Rkjhw5oh4tIjVr1gxWhqSkJMwzePBg2aF3333X5393UrYB tIMcS05CjhlPnDiBdujRowd8nDp1Kn5MSEiQ5Zw9ezYeioyMlKaHZswQVMuQ 7NDRv39/zZ+gmJEfwWNGaHL4EmvevHlVplleu3atYcOGKHrHjh01n0Tbi1kx Y8a9rWcqVaokmxPOZmuItgyOkZgRugV8wyVnzpwq66aeP38e6/7RRx/JziCL GeGEuM8XnETZ50hhMeODDz54+vTpDMnWQl27dtWutgPRVIqtRSB7/Xzy5MmY vn79+vAW0SGQJe2F7C8mpIuYcPonnL4ZQ90t5/Rk0tLSLipgU1shioGP6ltf 4QQk2QYu8KP4Lp7sGaWzHEtONPUN9oIhW/+he/fu8HH06NHwf5YsWZQL47BF jUARlmjEmLpUQ9ibR+q7bCAUM/IjcswIvQS+e5sjRw6Vr0CLZa9gt2rVSn1J VREwMWZkTX358uXS9C5dumA6BjjiwB8zQv8gW8AnIiICK9WhQwdZZmnMGBcX J+3ZGLGxsbgoNIsZgebNm2Nm5Xax0ne9WcwIHRFLhMaGiZr7EDkRTaVgCChQ oABUv0mTJqz/h9C7Zs2akFiqVCl3jLDGIUvaC9lfTEgXMeEZozl9MynqbrkR TybYGjjQfmDgnj9/vjSoGTt2LGaGaJQlwo9i4vTp01mi4xxLTjT1bdeuXZs2 bZSvhbK5qTNnzoSP69evl36UAt4UHmL7WfMb06BqDLb2xbp161Qqi1DMyI+w MeOaNWtwz3RgwIAB8PG3335bLgEddQgr2Gaj+fLlg65s5cqVy/9LUlJSCOUM HzwW2Llz5/Z74KxsiBxZCruTdvv2bYwoIRoC+8AYCinffvstzhgJuEq5vfDH jD7/PoyRkZHQd4GCI0aMwFewoVWwiakZ9+5PghGY/wB9DlYf2kNMTMzdu3cT ExPHjBmDN0VlMSMMN2xNsP79+8OZwYB79uyBbhNOwjb9ke3PiJw8eRJXdYNy Km+1OR0epdhU4V69el2+fPnSpUtdu3bFlGHDhllSTAdAlrQXsr+YkC5ioqkL p2+W4d/sgDktgwcPxq/AWIwp0tUsjXgywWJGtmW8dBok+BI4uahIkSLoHkAY kj9/fkjJkycP23LLiY4lJ+r6Ll26FGtdvnz5qVOnHjp0CEx0/vz5L774Andp BFudPXs2w79oKrqmuXPnhpiLPZ1ctmwZbrxSokQJvO2vy5hGVJMyfPhwPM/u 3bsD1pS/cRJSxIwZoY3h/HYVqlSpAjlh4FDPBkAbDqGc4YPHArhvYDAmTJjA csK1g5uo+vyTNjGK8fm3MYWLIrw10Q9/zBiwAUCvJVuvpnfv3nioYMGCMMpg onR1U+xq0DhlypTx/TdmzPAvyMzy4E+w/3HefkaQmBEYOXIkprvjTQcpPEqB F4evjiIY1Pv8zwXcF0SHDFnSXsj+YkK6iIm6Lvy+WYZ/swaVbODkSM8csicT LGbE+8nAM888I02HbGyUZy0KouDVq1ezPE50LDlR1/fmzZu43CiD3VRHc0lf UQR3iB0tVqwY2BnXecBvRUdHYzZdxjSimhS2/6PydUtEV+MkGMLGjFLXPSC1 atXKkCzFrEKwRmUXxmPG8ePHSzOfOHGibt26eEcOefHFF2VrVgsCT93Lly/v 8y+hBtHc/fffzyoF3ZFyFij0WrhIF3D06FFMTEtLa9u2LfsidCwtWrQ4fvw4 Tq6QxYwZ/pZcvXp11hH5/LfIZs+ezTIsWLAA02VtHhpquXLlID1btmzBuiaH wjkfAJw6aGxs4AB3AkYccuekkCXthewvJqSLmGjGjJy+WYaWW547d27ZyUPz ZOLj4zGzbF3NcePGYfrEiRNlX5k/fz6ujYA88sgjMi/RiY4lJzzX3ZIlS9hi NYz69evHxMTIch45coS9pPP/sXfeYVYU2d9vUPIiSZQwMKjoEgQZMoqSw4Ko oAQVVIYfScR1QVgQSRIFRaKALlFRMigIDAM8gDAwMw+ZISwyQ5wBRkCCZJj3 PH1e6+ntvre77u1Ufft8/phnbnV1d9X5Vjrd1VWMli1bKj/tCcmYZlRTwjzf YMv5hlo4CURMnzGysckCV69e3bJlC1wZPCbLL24Voeb9/v37+/bti4uL01mW DeJs3bp17dq1qoEEdCUQvm3bNtV3kcG4cuUKxI+Pj8epFz4nJKWgWU5KSuI3 ta8gS7oL2V9MSBcxcX2EZuFIJiUl5cCBA8GOgou6adOmCHvYawi/vunp6VDj Vq9enZCQcPHiRZ2YIFlycvK6devgr87+mJyQaiJDPqPz+NkCfs67tyClrIIs 6S5kfzEhXcSEdIlsSF/CDOQzOo+fLeDnvHsLUsoqyJLuQvYXE9JFTEiXyIb0 JcxAPqPz+NkCfs67tyClrIIs6S5kfzEhXcSEdIlsSF/CDOQzOo+fLeDnvHsL UsoqyJLuQvYXE9JFTEiXyIb0JcxAPqPz+NkCfs67tyClrIIs6S5kfzEhXcSE dIlsSF/CDOQzOo+fLeDnvHsLUsoqyJLuQvYXE9JFTEiXyIb0JcxAPqPz+NkC fs67tyClrIIs6S5kfzEhXcSEdIlsSF/CDOQzOo+fLeDnvHsLUsoqyJLuQvYX E9JFTEiXyIb0JcxAPqPz+NkCfs67tyClrIIs6S5kfzEhXcSEdIlsSF/CDOQz Oo+fLeDnvHsLUsoqyJLuQvYXE9JFTEiXyIb0JcxAPqPz+NkCfs67tyClrIIs 6S5kfzEhXcSEdIlsSF/CDOQzOo+fLeDnvHsLUsoqyJLuQvYXE9JFTEiXyIb0 JcxAPqPz+NkCfs67tyClrIIs6S5kfzEhXcSEdIlsSF/CDOQzOo+fLeDnvHsL UsoqyJLuQvYXE9JFTEiXyIb0JcxAPqPz+NkCfs67tyClrIIs6S5kfzEhXcSE dIlsSF/CDOQzOo+fLeDnvHsLUsoqyJLuQvYXE9JFTEiXyIb0JcxAPqPz+NkC fs67tyClrIIs6S5kfzEhXcSEdIlsSF/CDG75jARBEARBEARBEIRXIJ+RIAiC IAiCIAiCCAbNTXUSP1vAz3n3FqSUVZAl3YXsLyaki5iQLpEN6UuYgXxG5/Gz Bfycd29BSlkFWdJdyP5iQrqICekS2ZC+hBnIZ3QeP1vAz3n3FqSUVZAl3YXs Lyaki5iQLpEN6UuYgXxG5/GzBfycd29BSlkFWdJdyP5iQrqICekS2ZC+hBnI Z3QeP1vAz3n3FqSUVZAl3YXsLyaki5iQLpEN6UuYgXxG5/GzBfycd29BSlkF WdJdyP5iQrqICekS2ZC+hBnIZ3QeP1vAz3n3FqSUVZAl3YXsLyaki5iQLpEN 6UuYgXxG5/GzBfycd29BSlkFWdJdyP5iQrqICekS2ZC+hBnIZ3QeP1vAz3n3 FqSUVZAl3YXsLyaki5iQLpEN6UuYgXxG5/GzBfycd29BSlkFWdJdyP5iQrqI CekS2ZC+hBnIZ3QeP1vAz3n3FqSUVZAl3YXsLyaki5iQLpEN6UuYgXxG5/Gz Bfycd29BSlkFWdJdyP5iQrqICekS2ZC+hBnIZ3QeP1vAz3n3FqSUVZAl3YXs Lyaki5iQLpEN6UuYgXxG5/GzBXjynpCQsHHjxl27djmSIiIwfi6l1kKWdBey v5iQLmJCukQ2pC9hBvIZncfPFuDJ+5NPPilJ0ksvvcRzwVmzZrVv3/6HH36w IHGEAj+XUmshS7oL2V9MSBcxIV0iG9KXMIP4PuPp06cXL148dOjQ6dOnb9y4 8e7duwGjXbt2bf369WPGjJk8eXJSUtLt27fDSJszhGQByAhkZ+rUqaNHj46P j79y5YqdSbMda33GvXv3SjIPPfQQlBNrkkjIhFpP09PT9+zZA39tS5FXIUu6 i+X2v3XrVkJCwoQJE0aNGrVo0aK0tDTOK2dkZCxcuBD6sjlz5iQnJ6uOZmZm 7jHi5s2bylNM9nrudpr8ukA616xZM378eDD48uXLwYyGp5w6dQotdunSJfMx 7927t2/fPuhuHjx4wJNghmFRCUN0uzHUZf/+/foJVmUz1GJ29uzZFStWDB8+ fO7cuYcPHw5oc4dLftgFQED4651+e4UcOHAgWDG4c+eO/vXBqnBZqB2TJk2C +Pfv3zdMkmFthfRAsRkyZMjMmTO3b98ekl4eciJcRGSfcdeuXc8++6z0v5Qt W3bVqlWqmHFxcSVKlFBGK1myZLBC7jr8Fti0adOjjz6qzNfDDz88duxY7zZc 1vqM0DeBtwiRc+TIAR2NNUkkZDhLKTSza9eu7dixI5RMEKJ+/fr2J81jkCXd xUL7wyhowIABOXPmVLbJ0ATFxsbqP81LTEysUKGCqi9r3rz5iRMnWBwYOElG fPvttyy+yV7P9U6TUxdwtYoXL65MZ968eceNG6dzyvnz51m/+f3335uJeeTI kYkTJz711FMYZ/PmzYYJRjiLSqiiO4ChLoUKFdJPMBQkFjmkYnbjxg2ogKqr ValS5dixY8poTpb8sAuAsPDUO572CsmdO3ewYrBkyRKdWxw+fBjMroz/9NNP g0uoc4p+bc3MzGzfvr0qDXXr1gUF9TOLuN4eegVhfcYpU6aAI4DaFSxYMCYm Jnv27PgT2uGdO3eymBs3bsyWLRuGg6NRo0YN7PTz5MmzevXqMFJoN5wWmDp1 KmYEKFy48DPPPIPZBEaMGGF/Mm3BWp8xS67pAwcOjICWXDR4lEpPT2dFFHnx xRcdSZ2XIEu6i1X2hzhVq1bFo9AUw5jq8ccfZ/Ffe+21YI/yFixYwEZWcEqd OnUeeeQR/Fm+fHn2NJvHfQAHCiOb7PVE6DR5dNm0aRPr959//vl3332X+Y86 ntTrr7/OLKbvM+rH7NWrl8r+YDeerPEXlZBEdwbzPiOUaowZUjFTGg1EL1eu XIECBfAnjACvXr0axjW1hHR62AVAZAz15WyvgJs3b4ZXbg8dOsRqBAhdtmxZ /L9MmTInT54MdpZObb1//36jRo3wEJTPTp06NW7cGH+CK3rjxg19m4jQHnoF YX3GuXPngmRRUVHx8fH4zjojI6Nfv35YDJo2bYrRbt26BUUCQooWLcpWTUlO Ti5WrBgEgvdx7969MBJpKzwWSE1NxUILzebatWuxi4FGtUmTJtABGc63ERbL fUbCJniUOnv2bMG/wKEdeTpayJLuYpX9YdRaqVIlONSzZ8/ff/89Sx6owJWf eOIJ7JUSEhK0V7548SKOuGBc9Ouvv2Jfdvny5VatWuFZY8eOZdc/EYi9e/fC 0AVivvDCC3i6yV5PkE6TR5cqVargwPXw4cMYAn1f9erV0Y8IOJlt4cKFyoGr js9oGLN3795YJPLmzRuSy8BfVPhFdwxDXU6dOhUwze+//z7mbt26dVmhF7Mu Xbrg6W3btsVXsZDx+fPn58uXb8aMGRjH4ZIfdgEQGX19+durLLnZxMBx48Zp y8Off/4Z7C6tW7eW5McpbCWKqVOn4qW6desW8BT92rpy5UoM79+/P6iMgd99 9x0GfvnllzoGEaQ99ArC+oxZ8uMObG8Z4DqVK1dOkt+7YQiUaiwVX3/9tTLm zz//bNhluAWPBbp27SrJU1n27dunDIcqzB64eRF+n7FevXosJCUlJTEx8fr1 6yHdCwwFLcCOHTuuXbtmGPn06dNQlv744w/DmODOb968mf9zM0jG1q1b09LS vDWjmL+eIjh7hzwdLWRJd7HQ/idPnoTBiSrwxx9/xL5m0qRJAS8IDkLLli0v XLigDISuDcdmzZs3108P+B2SPCGTzdAz2esJ0mka6gJjTvz0YMyYMcpwGLdj Oo8ePao65dy5czh7rVatWvp54Y+ZpRh88rsM4RUVhlZ0xwi1viDQwaFjBUMX DAmpmIGLgfPK2rVrp+oolZ2yWyU/jAIgLIb68rdXBw8eRLNovxfTISMjA1+I dO/eXRmOjmT+/Pm1Iz3D2jpgwAAIzJcvn2rBE2jGIfzNN9/USY8g7aFXENln DEiTJk3QmULff9q0aSgrFCpVzIoVK6pcD0EwtAD4L/gdRNu2bZ1KlEPw+4xN mzaFzqJbt25sMlL27NmhkVF9mBwTE1OoUCFo4pSB0M++8sor0l9ky5YNCoNy dv3ChQvhrLJly0ILM3r0aPb4F+cRbd++XZUk6MUWL17coEGDggULssuWKFEi Li5OFROv/Mwzz8D/K1asqFOnDpvzFhUVpb2ysJCnYxVkSXex2/5QqbGCDx06 NKSE1a9fH86Kjo7WibNjxw587zlx4kQWaLLXE6TTNNQFkofpnDJlijIc3DEM 37Bhg+oUHHZC7xkfH68/3uOPmWWdy8BZVAKK7hjhjdD+8Y9/QIJLly7NnmmH VMzQRwZSUlJ07uJWyfeVzxgMbXu1detWNEtSUhL/dcaPH49nqT4pghEahs+d O1d1imFtxfJTpEgR1YmdO3eW5K8addIjSHvoFbzlM165cqVo0aIgIo7JgYED B+JQX/sSp0ePHtL/fo4tCIYWWLRoEZbhbdu2OZUoh+D3GaFpYh+eK/n444+1 kZUTWS9evFiqVCkWn80qAWbOnIlx5s+fjyHPPfec9ha5cuUCd49dEEpdwGiS 7Maqxi3sygMGDGDfnzLy5MmTmZlp0obOQJ6OVZAl3cVu+48ePRprd6g7/sTE xMBZMCzRiVOtWjWIA3+VHZzJXk+QTpNHF/zArWHDhsr5mUuXLkWDqz59Avtj +IgRI44dOxZwbBlqTMQql4GzqAQU3THCGKGxNzI//fQTCwypmJUvXx5CGjdu rH8jt0o++YxZgdorkDtgTdTn3XffleS55ao5n9euXcMH7P369VOG89RWVgJV WcNVNMFz1EmPIO2hV/CQz3jixIlmzZphwZg6dSoGTp8+HUO0Wy1AAcNRveGS vw5jaIHPP/8cyzBOzL57925ycvJvv/3m8HcNdsDvMyIvv/zysmXLoKGYOHEi +8BTOU9e6zOyfvmjjz46c+YMtAP79+9/9dVXq1SpwhYtZ56dJH92PXfu3FOn Tu3evfudd97BwNKlSysnOTRq1Ajk6NChw6pVqzIyMtLT0z/55BOMqXoGpbxy oUKFxo4du2fPnqSkJDanYuTIkaat6ATk6VgFWdJd7LM/jHkWLFiAc0JKlSpl uNKCksuXL+O7pC5dugSLwx7jw5BVGW6y1xOk0+TRZdSoUZhUGGfiCyywObiQ kuaD93PnzhUpUgTCa9asCXGOHj0azBPkj8kw7zLwF5VgojtGGCM0VKRs2bLK UXdIxQwf7UKvij+hh01MTNR+KuJWySefMWB7NWfOHDQLuGygHbhmgwcPBhdP vyXEYXylSpW0h3BhnI4dO7IQztoKQzucOguRN23ahIFsEjsLCYgg7aFXEN9n hPF8bGwsNEr4aQO0LZMnT2ZN04YNG1Bu1WSP9evX4yfkwPHjx8NIp30YWgC/ JS9WrNiFCxfatWvHFq3KnTt3nz59Qv2sTyhC8hmh/VH2QWwVbuU3nlqf8Y03 3pDkaQyqhktZ65ln16ZNG1XH1LZtW22jBE6rtrSzhbmgOdVeGUaeykWe4Qr4 2rF169b62RcE8nSsgizpLpbb/8yZM//85z+hnYmOjsbKDpFD7WXYBK158+YF iwONvySvzMBWdUBM9nqCdJo8ukCjje05ULx48XHjxuFKKdAV7tmzRxnztdde k+SJHNjq6niC/DEZYbsMYRSVYKI7Rqj1JSUlBbM2YcIEZTh/MYNxDv6cMWMG dKDKHdbAswAnOoxrBiTs08lnDNhegeJSIEqVKqV846yicuXKUpDNjHCDD+Xr Zv7aCuUkf/78eBTOgoKEzmaHDh30syZIe+gVxPcZW7RooSyNw4YNU25xe/v2 bVzyKEeOHKNHjwZl9+7dO2LEiFy5crFTICSMdNqHoQVatmwpyd4x+87uscce YxMda9Wq5d0Xjvw+4wsvvKAKnz17Nlrgl19+UUVW+ozQR2M0ne2BmGenfQCV kJCAh3r06KGfzi+++EJbwNiV4+PjVfFLly4N4TVq1NC/rCCQp2MVZEl3sdz+ SUlJqgHSwYMHQ0pSamoqvlgpX768atEGxtmzZ3FVEPbyhWGy1xOk0+TUZdeu XWzXLcayZcuUcWAAieHsA8BgY0v+mErCdhlCLSo6ojtGqPUFH3FDeVY+O80K pZglJyfjT1xsVpIXQsmXLx/+DyMftt+BWyXf5z5jsPaK+YzQWvbv3//jjz/G +auS/IHPgQMHAl4NXya2adNGewiXrHn22WfxZ0i19c6dO+AeqhqKatWq6Szf igjSHnoF8X1GGJn/4x//gKLItsctV67coUOHWAQYnGv3FS1UqBArPxcvXgwj nfZhaAG2S5Ekv2vD3eozMjLYi61vvvnGobRaDb/PqN1rA1xFzL7yYxBtZHD6 2LIzUHJWrVqlXSpZx2cEfxxPD7ieIfT4M2fOfO+996AtYg+1cGlxwyuDtygp PsUVHPJ0rIIs6S6W2z8tLa1t27YQge1JDcPaIUOGcH59Bi0Ma8nXrl0bLNqU KVMwTkAvw2SvJ0KnyaPL0qVL0YeC1rhZs2bswSm0omzRVOgZCxcuLMlvLpgE AceW/DFVhO0yhFpU9EV3hlDrCy5o+cYbb2gPcRYz9jEaKrt582ZwTMBEoAi+ 6IFenr11daXk+9ln1G+vYDCmfDwOkT/99FOMHGy7NFxu4pVXXtEeql27tiQ7 elkh1tZr167B7ST5acMHH3yA22RI8rRSqG6GeRShPfQK4vuMjHPnzoH62GtA G6Kcefjf//63TZs2UVFRkrx2SteuXY8cOYJFF4pQGPeyFUMLsM82VStyQ7lF P0W1TKiHMOMzrl+/Hs2i7zNmyUuaKxc4hf567Nixyqk+Op4dgCu1qtamgJvi 0gRa1qxZw3Pl559/XiKf0X+QJd3FPvvDSCYuLg73EJQUS2zp8+GHH2J85Wc7 Wtq3b4/9V7BZJSZ7Pdc7TUNdwG/Cx3fvvfcePveDELYgNrTSuBUXC0lISEj/ C7Z+Pnhh8BO/heSPqcK8y8BZVAxFd4CQ6svhw4cxR+PHjw8YgaeY7dixAy9S vnx58BSUpw8aNAgPJScnh3RNHcI43c8+I2d7xYCii+UcvLCAOxviw/OAa5ni +z4c34ZUW998801J3oMPV3C9ffs2RMAFM4FRo0YZJtv19tAreMhnRIYOHYrF 4Ntvv9UeVU5bxfVM2GtucTC0QPfu3SV5GRbtIaxHXvE7tDjjMwLnz5+HooIt AAI+4KVLl/Covs/4t7/9TZLnALMQNgcjV65c0DqBTwp95dy5czGQfMYs8nSC Q5Z0F7vtD6NcnG3Fs7wea0mqVaumv1IEvplq0KCB4TVN9npudZqGurz++uuS vKiFavouGwP06dOHfUynD7S9/DG1KbHKZTAsKvyi20dI9QWGYWiZLVu26MfU KWZgFrzI7NmzVWft3r0bDwVcadaxku9bn5G/vVLSt29fPEu5pAMDB7EVKlTQ HsIXi126dAmptu7fvx9/fvXVV8qrnThxAr8jzpMnj3YfjWB4wolwEc/5jMeP H8fi8cEHH+hEu3fvHn4+JuArOUMLjBkzRpInsWg3o8cVAAoUKGBj+uzEMZ8R gWKwYsUK/LBakhffw3Adz459j88i//LLL/h2u06dOsqNblk/Qj5jFnk6wSFL uosD9u/UqRPWenzzFYwlS5bg2oPgOGjX6FOSlpaGF+zfvz9/Mkz2eg53moa6 4ASz2NhY7SH80j8mJoa959KnevXq/DG1t7PQZdApKuGJbjkh1RfcNwFKNf/S fNpi9uDBA5wZqNpIK0veqxptonIHDK8ZEoan+9Nn5G+vVLDpqQE/A+zWrZsk v4VUlRmm9aBBg0KqrTNmzMCf2i0/5s2bh4d01uQJhshOhIsI6zMGm/CfmpqK ZaBnz546p7PtQf/zn/+EkUhbMbTAqlWrMPHaVVwaNWoUrF/zBA77jMjVq1dx zgN7uqvj2bGFl0ePHo0hvXr1kmQX/vz588qY5DMqIU8nGGRJd7HQ/sF6JbZH j6qJUAKDFvw0L3/+/MqJdgFhX3iFtOGCyV7P4U5TXxcwNa5BEXA1GFwmDpzK LHkXgIsa2HRHGE/CzytXroQUU0UYLkMYRSU80S0npPqCay9AleG/fsBiht99 PPvssyq7sbmIbBkc/muaTJISH/qMIbVXKnDhypw5cwbcooKtbKNayerrr7/G cBjpZYVSW3FHnoceekj5ihBhyytB/JCykCW2E+EiwvqM7dq1a9OmjfbjAjYv Zc6cORhy69Yt1dOMS5cu4XK+0dHRAu6rYmiB+/fvg2cB6YcGWfldw2+//Ya1 OOCjV0/ggM945MiRlJQU1bn169eHaI888gj+ZJ7dggULlNH++OOPMmXKYPvD LtK6dWtJfpSq/NTi9u3brPcnnzGLPJ3gkCXdxSr779q1C5wU7dj13Llzjz32 GHY3wa4JZ+Eabrlz5+ZJzMyZM7EZiYuLCxiBv9e7ceMGNErQZioHVCJ0moa6 YINZvnx51aS469ev4yfnTZs2DXYuz8o2/DH1XQathcMrKoaiO0NI9QWFqFOn TsCj/MWMWXjFihXK+J07d8bwU6dOhXpNC0u+33xGnvZq9+7dYDfVljd4fZyX hUvZaAFdcLmJJk2asPEtWL569eqogs7HvAFrKxsZMqeA8eWXX+KhhIQEdncx 20OvIKbPuGzZMhS6XLlyM2bMgNH7gwcPMjMzP/30U9ylsUCBAmfOnMmSn+a1 bdv24YcfHjNmDAzpQfpff/0VzsLTp0+fHkYK7YbHAmyz1FdffRUnsaSmpuKX xWABqK1OJNQG7PYZ8ZUiNHdDhgzB2RR//vknxMcTGzZsiNGYZweeYK9evaAh Ah9w27ZtFStWxPBu3bqxWwwYMAADe/ToAVpAAwJlDNs3n/uMO3fu3P4X+CUO jLdZSMAn9j6ELOkuVtkfNwKA4dD7778PVR4C7969C1d+7rnnsMr37ds34MXX rl3Llm2HxgR+rly5coUCbVsxfPhwjA/eh/aCIfV6ffr0wXD2wk6QTtNQF/Yt VYsWLdjUuAsXLrDFMaZNmxbsXPM+49mzZ1kBGDx4MMYZPXo0hijXNdVaOLyi oi+6Y/D7jDC2x/FYq1attEdDKmb37t3DZzX58+eH2gFXhpCvvvoK50ayfRmc LPn8BcBbGOrL2V7hJz/gVMJYa+vWreCCQTmfOXMm7icOhV9nUWjcnwX48MMP //jjD3DQYmNjMWTYsGE6aQtYW69du4aNdr58+cC7Z6+qly9fjluEREVFsSUQ hW0PvYKYPiMM4HEBMQbbaANLI5ucfPLkSXxwh2ALhnz00UcBV21yHR4LQBfD trCH/OKH88igQYMcSaYt2O0zJiUlKcsD/M92Zc2RI0diYiJGY55dQKDHV04c 2r9/P2tCs8vg/8zB9K3PiIsFBeOLL75wJKWiQ5Z0F6vsDxcpVKgQC4R2gO3p A9SuXRu6Le2VYQSiXcVdReXKlVVn9ejRAw9pv9DJCrHXY6s9sx1vBek0DXWB sRxzD2EAAFaqXr06DgIl+WmqzuYm5n1Gtv1uQKDAsJhaC4dXVPRFdwx+n/Hc uXOY4M6dO2uPhlrMYKCOm7BL8qIlrOMuWrQobjcW6jVNlnz+AuAt9PXlb6+W LVvGKiMak22FA/Tr108nDeAn1qpVi0VmJzZp0kQ7v1RJsNqakJDAfIRixYqB 4jhhTJKbjh07drCYwraHXkFMnxFZunQpbteipF69emzkj2RkZLz88stKpzIq KsrdLwL04bfA2LFjcSEpBP4Hl8Tm1NkLT97xCQ+0HqrwLVu2oB2UPqM2MjRH //rXv9gyy0iVKlU2b97M4jDP7ttvv23QoAGLBqUoNjZWuz4YNI9sxx9JXtJ2 4sSJ0O9j86L0GRcuXIhxtm/frroI3sg/PmOw1df9BlnSXSy0//nz599//31l myzJM95HjhwZbOdoGIMpByEBqVGjhuos9sg02FqF/L3e559/jhHYvtghnW4f nM9Op06dqmrMH3300cmTJ+tPGDtx4gRGXrx4sf4tgsXUdxny5cvHYga0cBhF xVB0Z+Afn7CFSoIt2hNqMTt+/HidOnXYU1kATlftvuFYyecvAN7C0Gfkb69O nToF4yV8sch46qmnVq1aZZgMGKcphQBHFcq/vsOYpVuvoTS2atVKldSWLVsq N3PPErg99Aoi+4xIenr6tm3bVq9enZCQoLOxJgzgwZdcv349eyQlLCFZ4MGD B0ePHt24cWNKSoqLezZZRajqm+HMmTMbZE6fPq16Is18RnQkMzMzwe/bsWOH cg9HFdCPQwHbtGmTuw+BHcNJpSIbsqS7WG5/8Fb27dsHLQZ0N9A4q3aCcBLO Xg/6jgMHDoR9uk3w6wIdX1paGrS98fHxqampAvaDwSwsTlHhx/L6Emoxu3r1 6pYtWyANly9fNnlNMUu+u1iuL34MCAMtqKGh2hOGVUlJSTDC1xl6hQQUnuTk 5HXr1sHfgHutZlGpMIf4PmPk4WcLCJJ3/f0ZiSxhlIoAyJLuQvYXE9JFTEiX yIb0JcxAPqPz+NkCguSdfEZDBFEqAiBLugvZX0xIFzEhXSIb0pcwA/mMzuNn CwiSd/IZDRFEqQiALOkuZH8xIV3EhHSJbEhfwgzkMzqPny0gSN7JZzREEKUi ALKku5D9xYR0ERPSJbIhfQkzkM/oPH62gCB537JlS0MZ1ZpaBEMQpSIAsqS7 kP3FhHQRE9IlsiF9CTOQz+g8fraAn/PuLUgpqyBLugvZX0xIFzEhXSIb0pcw A/mMzuNnC/g5796ClLIKsqS7kP3FhHQRE9IlsiF9CTOQz+g8fraAn/PuLUgp qyBLugvZX0xIFzEhXSIb0pcwA/mMzuNnC/g5796ClLIKsqS7kP3FhHQRE9Il siF9CTOQz+g8fraAn/PuLUgpqyBLugvZX0xIFzEhXSIb0pcwA/mMzuNnC/g5 796ClLIKsqS7kP3FhHQRE9IlsiF9CTOQz+g8fraAn/PuLUgpqyBLugvZX0xI FzEhXSIb0pcwA/mMzuNnC/g5796ClLIKsqS7kP3FhHQRE9IlsiF9CTOQz+g8 fraAn/PuLUgpqyBLugvZX0xIFzEhXSIb0pcwA/mMzuNnC/g5796ClLIKsqS7 kP3FhHQRE9IlsiF9CTOQz+g8fraAn/PuLUgpqyBLugvZX0xIFzEhXSIb0pcw A/mMzuNnC/g5796ClLIKsqS7kP3FhHQRE9IlsiF9CTO45TMSBEEQBEEQBEEQ XoF8RoIgCIIgCIIgCCIYNDfVSfxsAT/n3VuQUlZBlnQXsr+YkC5iQrpENqQv YQbyGZ3Hzxbwc969BSllFWRJdyH7iwnpIiakS2RD+hJmIJ/RefxsAT/n3VuQ UlZBlnQXsr+YkC5iQrpENqQvYQbyGZ3Hzxbwc969BSllFWRJdyH7iwnpIiak S2RD+hJmIJ/RefxsAT/n3VuQUlZBlnQXsr+YkC5iQrpENqQvYQbyGZ3Hzxbw c969BSllFWRJdyH7iwnpIiakS2RD+hJmIJ/RefxsAT/n3VuQUlZBlnQXsr+Y kC5iQrpENqQvYQbyGZ3Hzxbwc969BSllFWRJdyH7iwnpIiakS2RD+hJmIJ/R efxsAT/n3VuQUlZBlnQXsr+YkC5iQrpENqQvYQbyGZ3Hzxbwc969BSllFWRJ dyH7iwnpIiakS2RD+hJmIJ/RefxsAT/n3VuQUlZBlnQXsr+YkC5iQrpENqQv YQbyGZ3Hzxbwc969BSllFWRJdyH7iwnpIiakS2RD+hJmIJ/RefxsAT/n3VuQ UlZBlnQXsr+YkC5iQrpENqQvYQbyGZ3HzxawO+8JCQkbN27ctWuXfbfwCX4u pdZClnQXsr+YkC5iQrpENqQvYQbyGZ3HzxawO+9PPvmkJEkvvfQST+RZs2a1 b9/+hx9+sC893sXPpdRayJLuQvYXE9JFTEiXyIb0Jcwgvs+YkZGxcOHCoUOH zpkzJzk5WSfm+fPnly5dOmzYsBUrVly8eDGMtDkDvwUOHToEuYYcLV++PD09 3eZ0OYE4PuPevXslmYceeuj06dP2JcmjhKoUlM89e/ZERim1FrKku/DYPzMz c08QTp06pYp89+7dnTt3jpdZvXo1nBvssvwxr127tn79+jFjxkyePDkpKen2 7duhZDGEGwEHDhyYO3fukCFDZs6cuX379gcPHoR0L6vgrxf37t2Drn/ChAmT Jk0CRe7fv29z0nwNvy5QaNesWQNFbtSoUTBEgaGa4SlQm7BaXbp0KVgckHvf vn3QQRuWzLNnz8Jgb/jw4VCeDx8+zF+SQ61uYd9IQKztj6AxCdZy3rlzR//K nOWHX6xbt24lJCRAQwEXXLRoUVpaGncu/z9ecSJcRGSfMTExsUKFCtL/0rx5 8xMnTmgj9+3bVxktW7Zso0ePDiN5DsBjgatXr3bu3FmV9169esHYwJE02oU4 PiO0J+AtQuQcOXJAj8DCYbjVUCY1NdW+dIoPp1LQnq9du7Zjx44PP/wwGLN+ /fr2J81jkCXdhcf+Y8eOlYJQsWJFFg1GQb17986XL58yQoECBcD5Ul2QPyYQ FxdXokQJZcySJUvqPyAN70bQuLVv316Vwbp16x45coTnXtbCWS9giA7WUCb4 6aef1jryhFVw6gJj8uLFiyt1yZs377hx43ROgQH5o48+ipG///57bQQohxMn TnzqqacwzubNm4Nd6saNG9BUqkpylSpVjh07ZpjykKqbmRuJibX9Ue7cuaUg LFmyROf6nOWHUyxoBgcMGJAzZ05lTBjgxcbGXrlyxTCziIecCBcR1mdcsGAB K42PP/54nTp1HnnkEfxZvnx51aMG6DTxEHSdtWvXzpMnD/787LPPwkih3Rha 4MGDB/Xq1cMsREdHN2vWrFixYvgTfBnDpzciI47PmCU3RwMHDlR1TAcOHEBT wz/2pNEb8CiVnp6OHQrjxRdfdCR1XoIs6S489v/3v/8dbOQD3Q3G+f3332Hg hIHZs2evXr363//+dxZt/vz57Gr8MYGNGzfC4ATCYcADDVeNGjWwJEAvtnr1 av1kh3Sj+/fvN2rUCA8VKlSoU6dOjRs3xp/ghcHYmN+klsCjy6FDh6D3x0SW K1eubNmy+H+ZMmVOnjzpSDJ9B48umzZtgvKGWjz//PPvvvsuG/9/++23wc56 /fXXWeHU+oy9evVSVT2oGgGvA61l1apVWbGHglGgQAH8WbBgwatXr+qkPKTq ZuZGwmJhf3Tz5s1ATeb/B7zCYNfnLD+cYillgvgVKlRgjQbw2muv8bwX9pYT 4SJi+owXL15EDxH6iF9//RXnoly+fLlVq1ao49ixY1nkffv2YWC1atWuXbuG pz/zzDOSqNMODS0we/ZszFH37t3xxSL87dGjBwbCUYcSagNC+YwBARcS7Uw+ o6FSZ8+eLfgX2AWQp6OFLOkuPPbv2rUr2LxEiRInNLBJCIMGDcKWYeDAgWzy 56pVq/LmzQuBRYoUuX79eqgxb926Bf4aBBYtWpSt3JWcnIwPCaE1u3fvnk6y +W8ErFy5EiP3798f7ouB3333HQZ++eWXXNa0Dh5dWrdujeNA9tX51KlTMcHd unWzPYm+hEeXKlWqSPLD/MOHD2PIpUuXqlevjs5UwMnDCxcuVDoUWp8RBu3Y AGLplYL7jF26dMEIbdu2xbdIcMf58+fDaH/GjBk6yQ61uoV9I5GxsD+CaGif cePGaVvOP//8M9j1ecoPv1jgvFeqVAkCe/bs+fvvv2fJMkEen3jiCUxeQkKC fn4950S4iJg+Y5a8AGbLli0vXLigDITygL5k8+bNWeAHH3ygVZaVAQGfEhha AJ8GlypVivXsCJRnCIeS7N0POpzxGevVq8dCUlJSEhMTlWMnfZYtW8bjM4I0 cFkoZl6fLRyMUJXCCUXk6WghS7oLj/3feOMNHDDoxIEhCowhtY/smOO2c+fO UGP++uuvGPL1118rY/7888/BhtbhJQkYMGCAJD9FVzVZUNIg/M0339S5kR0Y 6pKRkYGvFbp3764MR0cyf/78/K06wY+hLuAL4GcdY8aMUYaDi4el7ujRo6pT zp07h7NSa9WqZViw2XOMgD4jOCM5cuSAo+3atVO9P/rjjz/0sxZSdTNzI5Gx sD86ePAg2m3VqlX8F+QsPyGJdfLkyZUrV6pu9OOPP2LMSZMm6SfJc06Eiwjr MwYDp+JER0fjzzt37hQqVEgKNN26YsWKkjyJJex72YShBR577DFIeY8ePVTh ixcvxjJsq9tlK/p5X7BgQSGZNWvWKMPXrVuH4SNHjlSGw5gBeiIIHzJkCIag z9i0aVNo1bt168YmPGTPnh0GHqopzTExMXBuy5Yt8ec333xTtmxZNgUa/sGb 1qxZU3nW8ePHGzduzGbO58qVC8Yw+HQrkiBPxyrIku7CY/+GDRuCzVu0aBHG 9dnMhDlz5oQac9q0aRgCg2pVZOy/lI+/TCapZ8+ekvzyURUZP5yvW7duGDcy g6Eu48ePx1yoPh9YsmQJhs+dO9feJPoSQ12grKL9p0yZogyHcTuGb9iwQXUK uvnQacbHx5v0GbEYAykpKSHkSiak6mbmRiJjYX+0detWNFFSUhL/BTnLj/m2 cfv27XiFoUOH6kTzohPhIp7zGWGcLynWJUhLS8NSoZ1awz5R0XlF7gr6FgC/ Jlg5Z3VN55MBwdHPO3uw06dPH2X4v/71LwyvXbu2MjwuLg7D2WMu9Bmjo6PZ d/RKPv74Y+Xpqomsn332mfYUSf6Ohp3y888/M6cyR44cbM5/VFRUhC3LQJ6O VZAl3YXH/titgPcUxvV/+uknbAT013wIGHPgwIGSPPdS+8UNfoxQsmRJq5LE ns+rrPHss8+GnXczGOry7rvvSvJcNdWMwWvXrmHD269fP3uT6Et46gt+Ptaw YUPllKelS5diAVN9avrDDz9g+IgRI44dO4b/h+0zli9fHg41btw4tFzJhFTd zNxIZCzsj1g7E+rHxTzlx3zbOHr0aLyg/n5qXnQiXMRbPuPly5dxcnWXLl0w ZMeOHajpsmXLVJGnT5+Oh3777bfwbmcThhbAD3hZHhngTuKXuZ9++qmN6bMT w7zjm8EqVaooA3GyuiRPHlBOC8HZVuC74RT0rL/cQOTll1+GUgGd1MSJE3GM UaBAAWXdV/mMqamp8fHxsbGxeDqcFS/DJqlmZmYWLlxYkifYw2AM5Lh169as WbNQFK1enoY8HasgS7oLj/2jo6PRb5o0aVJPGfhn9+7dPNfv06cPthiGn71o Y7JOSnsuDLAleYJEGIueBUzSzZs38XlXkSJFNm3ahIFsPhgLcQxDXZo1awYJ g8Zfewi7yI4dO9qVOB/DU19GjRqFxQb8elwKBvx6fFmvWkzg3LlzUN4gvGbN mhDn6NGjJn1G/Nrxk08+wZ/p6emJiYmck0VDqm5mbiQyFvZHc+bMQXv+/PPP YChoPwcPHgwOmuGCWjzlx0zbCFdbsGABTgYrVaqUfnq86ES4iLd8RjZZZd68 eRiyfPlyDNEuy8xmcm7bti2829mEoQXq1q2LDg74yCwQ3JOWLVtijjp16mR7 Ku2B89lytmzZ2OY4GRkZkjwFFKfsguIsMn4coZxVxXxGaLuUj6fYctn79u1T RVb1cezZlPZ7RpzEBX6raqnnzz//HMLBLeXZoMorkKdjFWRJd+GxP5s8oARa oXfeeUd/lAhHcSn4J554Qv8WAWNu2LAB76WaVbJ+/Xq2cN/x48f1r8yfpK1b t+bPnx8v+9prr82fPx/H8x06dAjpFpZgqEvlypWlIIv84yZckfcOSAR46guM 1fETYKB48eLjxo3D5WJy5869Z88eZUwoZpK80CXu52LSZ7xw4QIemjFjBpRe fEWOVKpUCYq3frL5q5vJG4mMhf3RhAkTtM2mJLtpP/30k841ecpPGG3jmTNn /vnPf8KV8RkgJtuw/fSiE+EiHvIZU1NT8clP+fLl2Vf87DnA/v37VfHZE1Tt 0wN3MbQAe3pTtWpVaJ3Onj27dOlScG1YlYR22KnEWoxh3tk8FqYadC6SvCAz DGwkeWksDL969Sq+PRw+fDg7Hd3AF154QXVZthTtL7/8oorM6TOCB4qboL33 3nuqi4Nrj6dov+PwLuTpWAVZ0l34fcbChQt37tx52LBhnTp1YsOSt99+W+dE tpy1/mI1wWLevn0b1wbMkSMHtDwwvNm7d++IESNy5crFWnsI4c6rQZJgqIat qJJq1aq5MvOKc75NmzZttIdw3R4YyduVOB/D2V7t2rULl4hRohprYd8tyZN2 MMSkz5icnIyH2NSj/Pnzs81Js2XLpr89DX91M3kjkbHDZ4Sj/fv3//jjj3GS vyQ/5NdfRdCw/ITRNiYlJSmvBq7rwYMHDTPoRSfCRbziM96/f59tJrV27VoW PmvWLAzUziNat24dHgppTScHMLQAuCdNmjSRNEB3j9/q9u7d26nEWoxh3jMz M3H6ca9evTAEfDRJniKC30RDC4bha9asQbNs376dnR5srw1wFTGycmZ7SD4j NFkYDg3jDg14KOA+2h6FPB2rIEu6C4/9z5w5A+Mf5byOY8eOlS5dGut1sHmb mzdvxu3Dateurb8FmE7M+Ph47b7Y0M4z547NuOBB50bXrl3DB48w+v3ggw/Y nr/Q3rI1xJzEUBcY8kHyXnnlFe0hyJ1ktM4tER489WXp0qU44G/evHmzZs2w yEnyou5s0dSMjAz8lKN+/fqsKJr0Gdk3uXgvKO13796Fi8PV8CEP9Omq1eZV cFY38zcSFmv7IxhQgUnZTxiof/rpp2g3nS3PeMpPVuhtY1paWtu2bSGpJUuW xAhwZWjc9BtnLzoRLuIVn/HDDz9E7VSfMDBfQPuKZ8GCBXhINZPQdXgsAFVv 0qRJMTEx0EDlzJmzYcOG4DFBG4X+lPN7aVkFT95xm54KFSrgz6ioKNQXnDgU NDU1FcL79esnyaubKpeOD+Yzrl+/Hs8N22dkn3vroFrW1dOQp2MVZEl3CXtm C5uzFLBe//e//8XtA/Lmzauc8R5GTIjQpk0bbOiio6O7du165MgRHHqBf8ef YP0bvfnmm5L8LhUXObx9+/aUKVOKFi2KeRw1ahT/jSzBUJcaNWpIQRZ0xRcQ bMlrwkIMdTl48CDO8HnvvfdweSIIAdceC1Lx4sVxFXEWkpCQkP4XbAMFKHvw E79lU6HjM7LHs+XLl1d9CcL2lzEc7/FUN0tuJCZ290cwdsXtF8HdC7i9LGf5 QcJrG8FJjIuLw2RIRg/zvehEuIgnfEb2BrxatWqqr1l3796NhxYvXqw6a/Lk yXhItB05Q7IAVEC2QwT4SpijpUuX2pU4m+HJO2uTz507d/jwYWx8bt68Ce0A zlbCFgBHFKqn0Pb5jGzfxnLlyr0UhEWLFoVuEkEhT8cqyJLuErbPCANarPLa z/0uXLjAVmbWr/X8MbPkZWrY/++8844UyvRL/Rvt378fD3311VfK8BMnTuC3 P3ny5NGuaW8rhrrgMJI9PFSCL7AibNkxQTDU5fXXX5fklZRUG30OHToUy1if Pn1SUlIkDp5//nnt9XV8RlzcANDuScqGgvqLZCrRqW7W3kgoHOiP+vbtiybC j1hV8JQf7VlhtI0gIo4Y9VdY9aIT4SLi+4xLlizBl2ugvla49PR01HTw4MGq Q//3f/8nye+mw1h3zlbCHsN8++23mFnvPvTgyTvb9AdcY5yP2rBhQzz01ltv SfIeu9evX8dHVVCplefa5zOi9wpMmDAhxEx7EvJ0rIIs6S5ht7e3b9/GvadV 349D44NzIyXFsooB4Y+p4t69ezgzlvNVmuGNZsyYgUe1q+LPmzcPD+kvW2E5 hrp069ZNkh8YQu6U4TAMwAQPGjTI3iT6EkNdcFZzbGys9tATTzwBh2JiYlh3 qU/16tW1F9HxGR88eICTFVXbZmUpSoXqqQgP2upm041EwIH+iE1PDfgtNk/5 0bl4SG1jp06dMCU6O2h70YlwEcF9RujFcNpz/vz5gzlKOE1FtSI3VHksmQEf ZLlL2GMYXLwLqpXq+YyH4Mk75A6XpOjbty8ursXmTeHqQEWLFoWLYDWHvkl5 rnmfccyYMRhTVd4gVbh0s08W6yNPxyrIku4SdnvLVlQYNmwYC7xx40aDBg0w /K233lLuL6aCP6YWtm39f/7zH8PIPDfCxe3BBVY+rkfYch/gV/Kn0DyGurAV VFQLUHz99dcYDq26vUn0Jfq6wMgKFyEJ+GgCl3aHoVeWvDTcRQ1szicUNvh5 5coV7UX099qoVq2aJL9jUn2kxma9hrE6TcDqZseNRMCB/qhFixZwCoyXtK4W f/kJRkCxgn2xiG8kgfPnz+tc03NOhIuI7DNClcRReu7cuXVOYYN8qMsscMWK FRg4a9asMBJpKzwW2Ldvn6pzhwqCOfrmm29sTJzNcKrfunVrSf6SBacW7Ny5 E8PZI763334b/kZFRalONO8zzpw5E2PCyER1EVw2XOKYYxYBkKdjFWRJdzG0 P/hZ/fr1Uz2IgwED7hcmKRZAgDaZLcXWqlUrnWd3/DFv3bqlehp/6dIl3GYi OjpaOegC33D+/PnQgim7Bs4bsQZwzpw5qkNffvklHkpISAiWSDsw1AXyW7Bg QUhYkyZNmCMMBsEP3sE4IbnhBCeGusAQWpI/9FN9KHT9+nXcXrlp06bBzjW/ PyM7CmM8ZTjuhAWcOnUKQwLWF/7qxn8jb2FVf7R7926wm2prFbw+rmkTbIkq /vLDKdauXbvAudO68OfOncMN2iAyCwxYKjznRLiIsD7j2rVr2Zq6AwYMgJ8r V65coYCtZZeeno7TFKF4JCYmQl8PuhcoUABC/va3v7Hd3sXB0AKQi7x581at WhU6cRgDgKM0bNgwrIalSpVinzd6EU712UwqSV7lRvkldbly5dgh7bYX5n1G FrNmzZrQHkJxYnPyT548iatt58iRY+TIkbjKIsgRHx/fqFGjH3/8MQRDCA+P UuDLb/8LXKkM+hcWEvAZsg8hS7qLvv3ZDlx169aFceyJEyfAEzlw4ABuKC/J G/dg+wMDGBYIjRIMUcCXXPG/QGcUUkxoXtq2bQv9FwxaMjIy4ETovFgTN336 dGVS+/Tpg+HsET3/jaAfxHIFLRgMhtlj+eXLl+MOVlFRUQ6vA8lTL95//33M 3YcffvjHH3/AiDE2NhZDlC9/CQsx1IWtL9GiRQv2udCFCxfYMibTpk0Ldm4w n/Hs2bOsuRs8eDDGGT16NIYod0yAyoheTP78+WFMCLUVQr766iv8gkm5M4u2 voRU3fhv5C2s6o9wj9TcuXMPGTJk69at4IJB+MyZM3GSGIxXlRscKOEsP/xi 4X4ocEdoLtasWQPJgGEz5PG5557DyH379mWRtaUiy4NOhIuI6TNC8dAusaui cuXKLD60P/jhCZYc/AdcTuVmfOJgaAHwkVk2Wb6A0qVL79q1y6lk2gKnz8hW +5E0q9z07t2bHdI+qzTvM0K/UKZMGXYLGFBBQWKPpNhS20jRokWZQM888wy3 GTwAj1LQnOrU0C+++MKRlIoOWdJd9O0PA4ymTZsqrY2DBwSGEGlpaRgTnBQd jRBwwUKKefLkSXwSjihb+48++ki16iBOlpMU+8/y3whISEjAeTuSPPsLLsIa OgjfsWOH9abXhadegJ9Yq1YtlhfWuTdp0kQ7yZawBENdYETNhvdQcmAkVr16 dXzyALz66qs6WxsE8xmhidMpw9A8KiPDeL5IkSJ4KI8M/g/dMfieLJq2voRU 3fhv5C2s6o+WLVvGREdjsuoJ9OvXL9jFOcsPv1iQHdyEDgGnXtmG165dW/me RVsqEG85ES4irM+oLCEBqVGjhvIU8AVwOycE3AFhteaxwOzZs9kWM1h0W7Vq lZmZ6UgCbYR/XgS4YJj3SZMmKcOVe15oF/rDx1AwolCFb9myBU9R+ozBIoNj jt9is8ZKuXEP9HoNGjRQlk9o9zp06JCSksKTL69gvmcZP368IykVHbKku/CM gefNm1e1alWlzWHU0bVrV+X+X2w9Zx2w0+GPmSUv7vfyyy8zb06SX/l99913 2nR+/vnnGIHtkB7SjbLkhbygH1FFaNmy5aFDh0ybOWQ4+wJwG5X2yZ07d/v2 7clhtA8eXe7evTt16lS2UQvy6KOPTp48WX+1kBMnTmBk1RqV+j5jvnz5VNc5 fvx4nTp18JUfAoVEtSmGtr5khVLd+G/kLSzsj06dOhUbG4svFhlPPfWU4W6G nOWHX6zz58+///77uJwyAxI2cuTIP//8UxkzYKlAPOREuIiYPmPYQAXftGmT dmk4oeC3wJkzZ+Li4pKSkjw9H1WJrepbCLRp+/fvX7169ebNm5U7fTNu3LiR mJi4ceNGGG55d0kiHbyilPiQJd2F3/6nT5/eunVrfHz87t27HZ6oCS08tCfr 16/Xf3+RkpKiWs85DK5evZqcnLxu3Tr4G3CDPGcIqV5Aewv94LZt2zy6kbqH 4Nfl/v37aWlpMOKCKpOamur856VQerds2QKpDdhHZwWvL5zVjf9GHsLy/gi/ OtywYQOUhJBev3KWH36xwN/ct2/fmjVrIPLRo0eDDcz0W1FPOBEuEmE+oyfw swX8nHdvQUpZBVnSXcj+YkK6iAnpEtmQvoQZyGd0Hj9bwM959xaklFWQJd2F 7C8mpIuYkC6RDelLmIF8RufxswX8nHdvQUpZBVnSXcj+YkK6iAnpEtmQvoQZ yGd0Hj9bwM959xaklFWQJd2F7C8mpIuYkC6RDelLmIF8RufxswX8nHdvQUpZ BVnSXcj+YkK6iAnpEtmQvoQZyGd0Hj9bwM959xaklFWQJd2F7C8mpIuYkC6R DelLmIF8RufxswX8nHdvQUpZBVnSXcj+YkK6iAnpEtmQvoQZyGd0Hj9bwM95 9xaklFWQJd2F7C8mpIuYkC6RDelLmIF8RufxswX8nHdvQUpZBVnSXcj+YkK6 iAnpEtmQvoQZyGd0Hj9bwM959xaklFWQJd2F7C8mpIuYkC6RDelLmIF8Rufx swX8nHdvQUpZBVnSXcj+YkK6iAnpEtmQvoQZyGd0Hj9bwM959xaklFWQJd2F 7C8mpIuYkC6RDelLmIF8RufxswX8nHdvQUpZBVnSXcj+YkK6iAnpEtmQvoQZ yGd0Hj9bwM959xaklFWQJd2F7C8mpIuYkC6RDelLmIF8RufxswX8nHdvQUpZ BVnSXcj+YkK6iAnpEtmQvoQZyGd0Hj9bwM959xaklFWQJd2F7C8mpIuYkC6R DelLmIF8RufxswX8nHdvQUpZBVnSXcj+YkK6iAnpEtmQvoQZ3PIZCYIgCIIg CIIgCK9APiNBEARBEARBEAQRDJqb6iR+toCf8+4tSCmrIEu6C9lfTEgXMSFd IhvSlzAD+YzO42cL+Dnv3oKUsgqypLuQ/cWEdBET0iWyIX0JM5DP6Dx+toCf 8+4tSCmrIEu6C9lfTEgXMSFdIhvSlzAD+YzO42cL+Dnv3oKUsgqypLuQ/cWE dBET0iWyIX0JM5DP6Dx+toCf8+4tSCmrIEu6C9lfTEgXMSFdIhvSlzAD+YzO 42cL+Dnv3oKUsgqypLuQ/cWEdBET0iWyIX0JM5DP6Dx+toCf8+4tSCmrIEu6 C9lfTEgXMSFdIhvSlzAD+YzO42cL+Dnv3oKUsgqypLuQ/cWEdBET0iWyIX0J M5DP6Dx+toCf8+4tSCmrIEu6C9lfTEgXMSFdIhvSlzAD+YzO42cL+Dnv3oKU sgqypLuQ/cWEdBET0iWyIX0JM5DP6Dx+toCf8+4tSCmrIEu6C9lfTEgXMSFd IhvSlzAD+YzO42cL+Dnv3oKUsgqypLuQ/cWEdBET0iWyIX0JM5DP6Dx+toCf 8+4tSCmrIEu6C9lfTEgXMSFdIhvSlzAD+YzO42cL2J33hISEjRs37tq1y75b +AQ/l1JrIUu6C9lfTEgXMSFdIhvSlzAD+YzO42cL2J33J598UpKkl156iSfy rFmz2rdv/8MPP9iXHu/i51JqLWRJdyH7iwnpIiakS2RD+hJm8JDPeOfOnT0y hw4dYoGZmZl7jLh582YYt7MPfgucPn168eLFQ4cOnT59+saNG+/evWtz0mxH HJ9x7969ksxDDz0EdrYvSR4lVKXS09OhrsFf21LkVciS7sJvf+hZ5syZM2zY sOXLl+vb//z580uXLoWYK1asuHjxIs/FT506hf3RpUuXWKCF/deBAwfmzp07 ZMiQmTNnbt++/cGDBwGjXbt2bf369WPGjJk8eXJSUtLt27d5Lm4HPLro2Afs qYocatbu3bu3b98+6AiC2SqMmAE5e/YslJPhw4eDQIcPH9ZeBLQLlk0Y9oRx RzPY3UeDTGvWrBk/fvyoUaOgomVkZASLyVmkYVy0c+fO8TKrV6+GMsOTjID1 kR/+003eyHJC0hcqEVSlqVOnjh49Oj4+/sqVK8FihlpHdMxiSXXgLDyIYQ0l GB7yGUF9HOSXLVuWBU6aNEky4ttvvw3jdvbBY4Fdu3Y9++yzqoxAxletWuVI Gu1CHJ8xLS0NvEWInCNHDmgxWDj0OA1lUlNT7Uun+HAqBQOAtWvXduzY8eGH HwZj1q9f3/6keQyypLvw2P/q1audO3dWtbe9evUK+Jiub9++ymjZsmWDAZX+ 9cHHfPTRRzH+999/z8It6b+gyWrfvr3qrLp16x45ckQVMy4urkSJEspoJUuW TE5O1r++TfDoMnbs2GBmqVixojJmSFkDy0ycOPGpp57CmJs3bw6WAP6YAblx 4wbUaFXKq1SpcuzYMWW03LlzB8vmkiVLQrqjeWztoxctWlS8eHFlBvPmzTtu 3DhVNM4iDR5E79698+XLp4xWoEABcBP0kxGsPnLCf7rJG9kBv76bNm1iiUeg b4IqqXKpwqgj+mYxWR3428Ms7hpKMLziM+7evRuHUlLoPiM0U2Gk0z4MLTBl yhRwZDDxBQsWjImJyZ49O/7MmTPnzp07nUqp9YjjM2bJw4yBAweqmrgDBw6g qeEfe9LoDXiUSk9PZ7USefHFFx1JnZcgS7qLof1hCFSvXj00e3R0dLNmzYoV K4Y/GzZsqHqyDWNUPAQj1dq1a+fJkwd/fvbZZzq3eP3115myofqM+v3X/fv3 GzVqhDELFSrUqVOnxo0b48+nn34aRkQs5saNG8G9xU4EWsgaNWpgkYMsrF69 Wt+GdsBTL/79738HM0v58uVZtJCy1qtXL9Wl4PSAd+ePGRCo1FWrVsUToQcv V64cuDP4E7r1q1evYrSbN2+Grb4d2NdHgw/CRjLPP//8u+++y/xH5YMRziL9 +++/169fn5m3evXqf//735nd5s+fr5OSYPWRE/7TTd7IDjj1nTp1KuuSChcu /Mwzz2AVA0aMGMGihVdHdMxisjrwt4dZ3DWUUOIJn/H27duVK1dmxUbpM4Ks JwKxd+9e7M1feOEFKEVhpNM+DC0wd+5cSHlUVFR8fDwmPiMjo1+/fpj9pk2b OpRQGxDKZwwIuJBoZ/IZDZU6e/Zswb/AwQB5OlrIku5iaP/Zs2djle/evTu+ WIS/PXr0wEA4ymLu27cPA6tVq3bt2jUIuXjxIoymJN0p7gsXLlQOe5RjJPP9 18qVK/Gy/fv3v3XrFgZ+9913GPjll19iCByCIROEFC1alC0RlpycjN4xNJv3 7t0zMqTF8NSLrl27QvJKlCihNRGbHBJq1sDrx4qWN29e/VEuf8yAdOnSBc9q 27YtTuoDKcGXyZcv34wZM1g0yAhGGzdunDabf/75J/8dLcG+PrpKlSqQzccf f/zw4cMYcunSJfD1cIjOyjlnkR40aBCGDBw4kM1HXbVqFYpVpEiR69evB0yG Tn3kgf90kzeyCR59U1NT0WEEH2rt2rX4YhE8rCZNmlSoUEE5mzSMOqJvFpPV gbPwIJw1lFDiCZ9x8ODBkjwFqGbNmtL/+ozB6NmzpyRPexDwFTOPBRYsWPD7 778rQ6DalitXTpKf+diYOJtxxmesV68eC0lJSUlMTAzWfWhZtmwZNiP6PiM0 R3BZGENGwEemAQlVKZyaQp6OFrKkuxjaH59LlypVio0xEHAMIRxcQjaa/eCD DySNe8gcyYCvGs+dO4ezsGrVqsU/dOTvvwYMGCDJLz1VDRGUHwh/88038eev v/6Kd//666+V0X7++We3BrQ89eKNN96QZA9dJ07YWWMjScNRLn9MBoxvcbJQ u3btVHP5/vjjD+XPgwcP4sUF+fDEpj4aRvv4MciYMWOU4WBSzP7Ro0cxhLNI 37t3D8b8ykc6CPMlA87ICq8+hnG6yRvZB/+zGtALGjdlOLSEwd6+cdYRQ7OY rA6chScrlBpKKBHfZ9y1axc+8egsw+Mz7tixA5/VT5w4MYwU2k3YbXKTJk2w Ijv/TNgq9PMOnnIhmTVr1ijD161bh+EjR45UhoMnCO0PhA8ZMgRD0Gds2rQp 1Ppu3bqxqS9QHrp3765aFSEmJgbObdmyJf785ptvoGg98sgjeAr8gzetWbOm 8qzjx483btw4Z86cGC1XrlytW7dWOfgRAHk6VkGWdBdD+z/22GNg8B49eqjC Fy9ejHUcT79z5w60BlKgT00rVqwI4WXKlNFeHBoHSZ4zGR8fzzl0DKn/Qu+y SJEiqnDsK+vWrYs/p02bhneHMVvAxCufszkDT71o2LAhpK1FixY6ccLOmq0+ I+oCpKSk6MfcunUrxkxKSuK8uK3Y5DOCOpjNKVOmKMNPnjyJ4Rs2bMAQziId DDZTaM6cOdqjYdTH8E43eSP7MNT39OnTOLxp27Yt/2U564ihWUxWB/7Cw19D CSWC+4wwyK9UqZIkz2fIzMx8++23eXxGfD4Mf8Vc/ii8NvnKlStFixaV5Ofe NiTKIfTzzp7Y9+nTRxn+r3/9C8Nr166tDI+Li8Nw9kgKfcbo6Gj2RbaSjz/+ WHm6aiLrZ599pj0FKFeuHDvl559/Zk5ljhw52IT/qKgo7Tp+noY8HasgS7qL vv2hi8EqPHToUNUhNsrFj63S0tLwp2qCU5biszvV1KkffvgBw0eMGHHs2DHO oWNI/Rd7m6bKIy6hBiMl/Dlw4EBJnqujvSbOwi1ZsqThvayFp17ExMQocxGQ sLNmq89Yvnx5iN+4cWPDmD/99BNeHLwnzovbin1zgfDbsYYNGyqnWy9dulSV fc4iHQxmT+16KeHVxzBON3kjWzHUd9GiRZjgbdu28V+Wp47wmMVkdeAvPPw1 lFAiuM/IphlASYCfb775pmTkM7LHFFCGw0ieA4TRJp84caJZs2aYr6lTp9qT LicwzDu+GaxSpYoyEJ8bSPI7VuW0AZyHAL4bfluU9ZcbiLz88svLli2Dpmni xIlscr5yUKfyGVNTU+Pj42NjY/F0OCtehk1SzczMLFy4sCR/OAP9EYw2b926 NWvWLPzyqEuXLlZYSBTI07EKsqS7GNr/8ccfD1h/oYJj1f7000+z5Nd/2DJA q6KKOX36dDz022+/sUBwOYsUKQKBNWvWvHfv3tGjR3mGjqH2Xzdv3sSnWHCv TZs2YSCb78dCWAq1H13C+E2SZ2I4vK0DT72Ijo7Gkd6kSZN6ysA/u3fvVsYJ O2u2+oz4edcnn3yCP9PT0xMTEwPOeZszZw4b5EB8yOzgwYNhdK1ar8Mx7PMZ R40ahTl99913cYoj1At8laxcgoCzSAejT58+ActDePUxjNNN3shuDPX9/PPP JfkhDM7Vv3v3bnJyMrRs+h9WG9YRTrOYrA78hYe/hhJKRPYZoaCyWakYwuMz tmvXDkf1qo9TxIHfAnPnzgUXBhpV/BAACvnkyZPFfHnKiWHeoTfB9ortepaR kSHJU0BxCtny5ctZZJwSr5xvwHxGaGeUhmLLKSvn5wdcMGf06NEYU/s9I05v AC1US7hjGwtlVWerKc9Bno5VkCXdxdD+0IBI8gOly5cvs0DoPlq2bIlNQadO nSAEWh78qV1Mns1iVT6Zf+211yR56U5c451z6BhG/wVuZv78+fHicNP58+fj 2KxDhw4szoYNGzCC6nXq+vXr2dKvx48f57yjJfDUCzapQwn0Du+88w4b3YWd Nft8xgsXLmD8GTNmgBzKbbMqVaoEeikjT5gwQZtHSf7A9qeffuK5nbXY5zOC 544fqALFixcfN24cLkKSO3fuPXv2KGPyFOmAQKnALVeeeOIJ1aHw6mMYp5u8 kd0Y6vv+++9DaosVKwbFGJojVgdBJvDHgy0NYVhHOM1ivjrwFJ6QaiihRFif EXpM/BihdOnSbCNRQ5/x7Nmz+Fkre3ogIPxtcosWLZS1ZtiwYZz7OwuLYd7Z 7AX2MB+aFElemhuqPPzTs2dPDL969So+Uhg+fDg7Hd3AF154QXVZtjTiL7/8 oorM6TOCB4r7QL333nuqi8NQE09hX2REAOTpWAVZ0l0M7c+ebFetWhVGC9CJ LF26FJoF1vDCwCNL8T5r//79qiuw59iqVktSfJPIM3QMr/+CoTi2jUqqVaum nFNx+/ZtXFwUrg9NHPhQe/fuHTFiRK5cudgpEMJ/U/Pw+4yFCxfu3LkzdH/g vDM38O2338Y4YWfNPp8xOTmZjT/xHxjEsm0EwedV7gDCBslQ5fv37//xxx/j jFxJflLq/PLd9vmMWfLyFGwfMYb2xT1PkQ4IW+5YVcvCq49hnG7yRg5gqC8+ K8ubNy/43Zjyxx57jG20UatWrYAvHPXrCL9ZzFcHnsITUg0llAjrM7KPFJQl 0NBnnDJlCop+8ODBMNLmDPxt8hdffPGPf/wDqgxbcaVcuXKHDh2yOYE2Ypj3 zMxMXP+hV69eGAI+miQPonCtAxhRY/iaNWvQJtu3b2enB9trA1xFjAw+qX7k YD4jDEUwHBqxHRrwkOFWwh6CPB2rIEu6i6H9Hzx4gMuLqYCBBy5607t3b4g2 a9YsDFfNjcySF+nCQ/hhdUZGBk5ir1+/PpvtwDN0DKP/unbtGrq3MOb54IMP 2M6S0IqylcGQ+Ph47WbZkEE2vmJTO5yBp16cOXMGxpDK97/Hjh0rXbo0JpjN NAsva/b5jOyjKklef2Dz5s13796FkgDSo88LXY/yPTL0SpAF9hPG5J9++ime bmbfqPAw1OXChQvHjQi478zSpUvRYWzevHmzZs2YGwImYoumZoVSpFWAnfGa tWvXVs4yCrs+hnq6yRs5g6G+bMtCSZ6vhZvaQNbYRofffPON9iydOhKqWcxU B87CE2oNJRhi+oxJSUk4G/ONN95IV4Bvt8uUKYM/tSe2b98eS4toezIqCeM5 3rlz56DAY3sIhdmtLx3Mw5N33LCpQoUK+DMqKkqSX+GBE4d1PDU1FcJxw8pH HnlEuahyMJ9x/fr1eG7YPiP7NFsH1bKunoY8HasgS7oLj/2hv5g0aVJMTAwM GHLmzNmwYcNp06bBmAGfX+GiN+y5k3Y6wYIFC/AQzlp/5ZVX8GdCQgLrvNiW EOAYws+AS9aH0X/hc1QYkuEyg7dv34br42ppwKhRo5SR//vf/7Zp0wZb1Ojo 6K5dux45cgTHY3BTzjtaRdjvs9gkYWV7G0bW7PMZ2VPE8uXLqz5YYEs0qD5w UAEFAHczBF/Y4WXSDXUBj8+wKyxevLjqrIMHD+K8oPfeew9zBCGspkB8tvZ4 SEWaAQUAN3HImzevaoeIsOtjqKebvJEzGOrLls6AJlEZfvHiRZzzyZaaV6JT R8ybhb86cBYe8zXUt4jpM7JJ7/qAI6A6sWTJkhDeoEGDMBLmGGH3lUOHDsWM 4zp+XoQn76zOgqd8+PBhbChu3rz54MEDXK0CX+fVqFED/ofmSHmufT4j27ex XLlyLwVh0aJFoZtEUMjTsQqypLuEZH8YnLAdeVJTU7HKL126FH7u3r0bfy5e vFh11uTJk/HQ6dOnU1JSeDqv559/Xnv3UPuv/fv349W++uorZfiJEydw9Rhw gbU7UGTJK0Ww/9955x2I+eyzz3Le1CrC7gdheIm5Dvh1G3/W7PMZ8Rt8QLt7 ICtFyp4oIH379sWY+P2XY9jkM77++uuSvDKJauM8NqrBxdLDK9IXLlxgK6Wr emEz9TGk003eyDEM9e3evbskfxSmPYTeX8Cl+4PVEavMwlMd+AuPJTXUn4jp M2pnIwdENeWYrYXev3//MBLmGGH3lWx65AcffGB1ohyCJ+9s5UAYquF81IYN G+Kht956S5L3YL1+/To+tITRmvJc+3xG9F6BCRMmhJhpT0KejlWQJd0l7Pb2 22+/xSqPD5zT09Px5+DBg1Ux/+///k+Sv6S4c+cOayj0qV69uuoiYfRfM2bM wFO069LPmzcPD+kvHHHv3j2c6hnw3YGthK0LOPU4DQm/Mw2GYdbs8xkfPHiA c2VVuztlyZvf4aVUw1otbD6eaN+ZQnVYZYTWUDhLMDY2VntB/G4uJiYmK6wi DYOB2rVr4yHth8Bh18dQTzd5I8cw1HfMmDGS3Jqx5egZuGZRgQIFtGcFqyNW mYWnOvAXHktqqD8R02e8devWxUC0atVKklfEwp+qBbTZFGVhd9lADC0QbGVU 9tybrQPjOXjUv3v3Li590LdvX3zjzGYU4GoVRYsWhYugKaBFUp5r3mfEBlPS zEyAVOFXpT7Z0Ic8HasgS7pL2L4JLqYH3Q17M4JrrVSqVEkZDZprHA+zR+WX L1/Wdl5sNhQMbOAnW9iNEUb/hZsXgAOlXRuNLfIAt9O5wpIlSzDaf/7zH86b WkXYuiQlJWGahw0bphPNMGu27rWBm2xCEVL15mxKnuEiG7gCHnQ6Au6BEipg BFySKODiTrjoClSirNCL9I0bNxo0aIDhb731VsBJ3eHVxzBON3kjZzDUF1x+ TLB2g8tGjRoF8+906oglZuGpDiEVHvM11J+I6TMGQ38NnJkzZ6LWcXFx4V3f GQwt0K5duzZt2mgneLNZHOA62Zc8W+FUv3Xr1pK8iQZORt25cyeGs0dAb7/9 NvyNiopSnWjeZ2Sl6Ouvv1ZdBD+nlTSzXyIS8nSsgizpLjz237dvn2qYAY4G Vnblgg/sgRKMK1jgihUrMHDWrFk6tzBcCsOw/4Lh8fz586EFY0llzZq2R/jy yy/xUEJCAobcunVL9Yj+0qVLlStXluQPAB12TLI4dAEXoF+/fqqpjDDAwx39 pL9WHMoKN2tW+YxaXZSnQPFQRsYNm4BTp05lyRPhIJ2qnSayZOPg8gUwstVP m+XY4TMCzz//vCR/PqZajeH69eu4KXPTpk2zQizSYHC2MEurVq1URUWfYPUx oJr8p5uJ6QyG+oLf/cwzz0jyOtJKH/y3337DJYwCviwO9blKQLOEVB1Mtoec NZRQEUk+4/Dhw1HrXbt2hXd9Z9C3gPK7uRkzZqSkpEAvmZmZ+emnn+KEnAIF Cpw5c8bB9FoJp/psjoEkr3Kj/OoZzMIOabe9MO8zspg1a9aEtguMz+bPnzx5 EldjhpZz5MiRuJrf7du34+PjGzVq9OOPP4ZgCOHhUQp8+e1/gZ9igb/DQlx/ oCoIZEl3MbR/YmJi3rx5YYAEwwkYdp4+fXrYsGE4RClVqhT7vDFLnp6KU+If e+wxOAsaB3AeoUGGkL/97W/aqVxKDIeOhv0X26ycvayBO2JpgXYJhkDsgfny 5ctxx+qoqChc/Q8OtW3bFhIPbm9GRgYEQspZWzp9+nSdlNuEvi5sy8u6deuC xU6cOAHD1wMHDrAFOl544QXsF0LK2tmzZ1m1Gjx4MMYZPXo0hiiXq+WPqdUl S54Ziw9/8ufPv3LlSkg8hHz11Ve4qlKbNm0wWoUKFST5g/0hQ4Zs3boVRr9Q 2WfOnIkzbaAQrl271iJ782KTz8j2UGjRogVbVfXChQtsgZRp06ZlhVKk4S8r DGCu1atXr1q1asX/EnClRCRYfQyoJv/pZmI6A4++bPuhV199FdcmSk1NxVVo YBTKFo7mryNaApolpOpgpj3M4q6hhIpI8hnZ1jzaycxCoW8BGKLg6nkMttEG 1hpX9vm1Ck712SxcSbPKTe/evdkhbSNs3meEdqNMmTLsFtDU5MqViz3IYksx I0WLFkVHXgryYbh34VEKxslScL744gtHUio6ZEl3MbT/gAEDmKlZdZbkVSC0 7hu0ACwO2ywAmgjlxq8BMRw6GvZfOJlK+t/9Z8HPZR1EsWLF4BBrviB8x44d GA2uCX5uwGx+9NFHDq/MiejrAmPFpk2bKmsBeusI5CUtLQ1jhpQ1qEo6FQ2q YRgxA+qSJU9yw83EJXnxDdZxQK+B+xdkyY+IcTTLEs8KFdCvXz+zVg4dm3xG GMAz9xBKZuXKlatXr87yDr4JG+FzFulhw4bpCISAsxAsPcHqYzA1OU83E9MZ ePS9e/dux44dMdlQIHG6FzJo0CAWjb+OaAlolpCqg5n2EOGpoYQKb/mMuFUf 24VBBXO1BN+KgscCS5cuZZ91M+rVq5eYmOhIGu2CX32cHSFpFnxW7nmhXT8N Hy83adJEFb5lyxY8RekzBosMA0W2m630vw/WsuS2rkGDBsqRCbRFHTp0SElJ 4cmXVzDv6YwfP96RlIoOWdJdeOw/e/ZsfECNgA/YqlWrzMzMgJGhDSlVqhSL /OSTTxo6jFny2n0YX7vsKmLYf33++ecYge2LjRw+fBi/9FfSsmVL1U6+GRkZ L7/8svIJZFRUlIvf/hvqAk7EvHnzlLvFSbLn2LVrV9V+i/xZ0x/l5suXL4yY wXTJkpetq1OnDr65QCCdqrX9T506FRsbi29SGE899RSbeeswNvmMWbInMnXq VLbxAfLoo49OnjxZNX+Yp0iz9dV10KmYweqjjpo8p5uJ6Qz8+o4dOxb3VUTg //nz5ysj8NcRLcHMwl8dTLaHCE8NJZR4y2eMDPgtkJ6evm3bttWrVyckJDi8 4bJNeEV96N32798Plt+8ebNyR2kGjOvAf9+4cSM0RCF9RuEVvKKU+JAl3YXf /mfOnImLi0tKSlLORw0GDDY2bdrk8JyWlJQU1XrOjKtXryYnJ69btw7+6mx2 BlmDhmv9+vWuP0jn1+X06dNbt26Nj4/fvXu3zkbbLmZNR5csWZotW7ZAZgN2 JQh+krlhwwYoVO5KY3d7df/+/bS0NMgmCJqamqqzFSlnkbYcfTW9Tkj6Pnjw 4OjRozDOAZs4uek5Z3Uw3x6yyIY1lEDIZ3QeP1vAz3n3FqSUVZAl3YXsLyak i5iQLpEN6UuYgXxG5/GzBfycd29BSlkFWdJdyP5iQrqICekS2ZC+hBnIZ3Qe P1vAz3n3FqSUVZAl3YXsLyaki5iQLpEN6UuYgXxG5/GzBfycd29BSlkFWdJd yP5iQrqICekS2ZC+hBnIZ3QeP1vAz3n3FqSUVZAl3YXsLyaki5iQLpEN6UuY gXxG5/GzBfycd29BSlkFWdJdyP5iQrqICekS2ZC+hBnIZ3QeP1vAz3n3FqSU VZAl3YXsLyaki5iQLpEN6UuYgXxG5/GzBfycd29BSlkFWdJdyP5iQrqICekS 2ZC+hBnIZ3QeP1vAz3n3FqSUVZAl3YXsLyaki5iQLpEN6UuYgXxG5/GzBfyc d29BSlkFWdJdyP5iQrqICekS2ZC+hBnIZ3QeP1vAz3n3FqSUVZAl3YXsLyak i5iQLpEN6UuYgXxG5/GzBfycd29BSlkFWdJdyP5iQrqICekS2ZC+hBnIZ3Qe P1vAz3n3FqSUVZAl3YXsLyaki5iQLpEN6UuYgXxG5/GzBfycd29BSlkFWdJd yP5iQrqICekS2ZC+hBnIZ3QeP1vAz3n3FqSUVZAl3YXsLyaki5iQLpEN6UuY gXxG5/GzBfycd29BSlkFWdJdyP5iQrqICekS2ZC+hBnIZ3QeP1vAz3n3FqSU VZAl3YXsLyaki5iQLpEN6UuYwS2fkSAIgiAIgiAIgvAK5DMSBEEQBEEQBEEQ waC5qU7iZwv4Oe/egpSyCrKku5D9xYR0ERPSJbIhfQkzkM/oPH62gJ/z7i1I KasgS7oL2V9MSBcxIV0iG9KXMAP5jM7jZwv4Oe/egpSyCrKku5D9xYR0ERPS JbIhfQkzkM/oPH62gJ/z7i1IKasgS7oL2V9MSBcxIV0iG9KXMAP5jM7jZwv4 Oe/egpSyCrKku5D9xYR0ERPSJbIhfQkzkM/oPH62gJ/z7i1IKasgS7oL2V9M SBcxIV0iG9KXMAP5jM7jZwv4Oe/egpSyCrKku5D9xYR0ERPSJbIhfQkzkM/o PH62gJ/z7i1IKasgS7oL2V9MSBcxIV0iG9KXMAP5jM7jZwv4Oe/egpSyCrKk u5D9xYR0ERPSJbIhfQkzkM/oPH62gJ/z7i1IKasgS7oL2V9MSBcxIV0iG9KX MAP5jM7jZwv4Oe/egpSyCrKku5D9xYR0ERPSJbIhfQkzkM/oPH62gJ/z7i1I KasgS7oL2V9MSBcxIV0iG9KXMAP5jM7jZwv4Oe/egpSyCrKku5D9xYR0ERPS JbIhfQkzkM/oPH62AE/eExISNm7cuGvXLkdSRATGz6XUWsiS7kL2FxPSRUxI l8iG9CXMQD6j8/jZAjx5f/LJJyVJeumll3guOGvWrPbt2//www8WJI5Q4OdS ai1kSXch+4sJ6SImpEtkQ/oSZhDfZzx9+vTixYuHDh06ffr0jRs33r17N2C0 W7duJSQkTJgwYdSoUYsWLUpLSwsjbc4QqgXS09P37NkDf4NFAJvs3LlzvMzq 1aszMzMtSKU9WOsz7t27V5J56KGHoJxYk0RCxvJS6lv4LXnv3r3k5GRoxCZN mgTGvH//vs1J8wVkfzEhXcTEppYfRNy3bx902Q8ePOC8ckZGxsKFC2HsN2fO HCgA+pHPnj27YsWK4cOHz5079/DhwwHvcu3atfXr148ZM2by5MlJSUm3b9/m TAnioUGmDjz6whhyTxBOnTqljX/gwAEw+5AhQ2bOnLl9+3YeieE6eMFLly4F ixNGmQnpRpDsYNm8c+dO2HeMbET2GXft2vXss89K/0vZsmVXrVqljAbiDhgw IGfOnMpo4ETExsZeuXIljBTaDacFoH1bu3Ztx44dH374YchR/fr1tXEg7717 986XL58y7wUKFICaa326rcBanxEabRAaIufIkQO6DGuSSMhYWEp9DqclYZxT smRJZUV++umnA3bQREiQ/cWEdBETy1v+I0eOTJw48amnnkL5Nm/ebHjxxMTE ChUqqMZ+zZs3P3HihDbyjRs3IBmqyFWqVDl27JgyWlxcXIkSJZRxoFwZuqKI 5waZOvDoO3bsWCkIFStWVMYE77J9+/aqOHXr1gXRda5//vz5Rx99FCN///33 2ghhlJkwbpQ7d+5g2VyyZEl4d4x4hPUZp0yZAo4AylewYMGYmJjs2bPjT6i5 O3fuxGjp6elVq1bF8GzZskE78/jjjzPdX3vtNTMPKGyCxwKQL2yKGS+++KIq zu+//w4NNR4F41SvXv3vf/87iz9//ny7MmACa33GLLkjGDhwYNhNChEMq0op wWPJQ4cOsYarXLlyZcuWxf/LlClz8uRJR5IZsZD9xYR0ERNrW/5evXqpRuMb N27Uv/iCBQvYYB7Ur1OnziOPPII/y5cvr3o5qBwBwigICkmBAgXYuPHq1asY DW4K40McPcLQokaNGpj+PHnyrF692jCznhtk6sCj77///W8pCCABi3b//v1G jRpheKFChTp16tS4cWP8+fTTT4MvH+z6r7/+Orug1pULo8yEcaObN28GyyOw aNGi8O4Y8QjrM86dOxeEi4qKio+Px7koGRkZ/fr1Q0GbNm2K0aBNqFSpEoT0 7NkTfKgsuRjD9Z944gmMmZCQEEYibYXHAmfPni34F+gsa9vkQYMGYR7BaWLz UVetWpU3b14ILFKkyPXr1+1Ivxks9xkJm7CqlBI8lmzdujUOSNiXuVOnTsXa 3a1bN9uTGNGQ/cWEdBETa1v+3r17YzQclhiO/y9evIgeYtmyZX/99Vcc+12+ fLlVq1Z4+tixY5Xxu3TpguFt27bFV35wyvz58/PlyzdjxgyMc+vWLXBhIE7R okXZ2nrJycnFihWDQBhv3Lt3TydJXhxk6sCjb9euXSFfJUqUOKFBOaFr5cqV aIH+/fuDkTHwu+++w8Avv/wy4MUXLlyo9M60PmOoZSYY+jeCjGD4uHHjtNn8 888/w7ijHxDWZ8ySHzdhDWU8ePCgXLlyoHLhwoVZ4MmTJ6Hoqs798ccfsTxM mjQpjETaCr8FEHxBr22ToaGDBnP27NmqcOZLsrex4sDvM9arV4+FpKSkJCYm huoCQ1MPHcSOHTuuXbtmGPn06dPQQ/3xxx+GMVNTUzdv3sz/4R4kY+vWrWlp aZH3NFJJsFJKGFoyIyMDH3p3795dGY4D5vz58wv48MdDkP3FhHQRE5tafuZK GI7/wQVr2bLlhQsXlIEwFERfsnnz5iwQxvY4G61du3aq7lXZlUPPjrf++uuv lXF+/vnnYG6LCs8NMnXg0feNN96AfFWrVk0/2oABAyAauOeqZUagMED4m2++ qT3l3LlzOFm0Vq1ahsbnLzNh3OjgwYMYrvrYjdBHZJ8xIE2aNJHkmeT6j4a2 b9+O5WHo0KFh38sm7B6Ng0eDeZ8zZ04YybMVfp+xadOm0Ox369atePHimJ3s 2bPD4EE1NSUmJqZQoULQxSgDjx079sorr7DnS9myZatYsaJygvrChQvhrLJl y0JbN3r0aPbAEGeeQOFRJQn6o8WLFzdo0KBgwYLssiVKlIiLi1PFxCs/88wz 8P+KFSvq1KnD5vBERUVpryws5DNahaElx48fjyVENcUaSiyGz507194kRjRk fzEhXcTEdZ8xGPglTnR0NAvp2bMnXjMlJUXnxGnTpmE08CNUh2BgoHpAzY/I g0wdePRt2LAh5KtFixb60dD+RYoUUYV37txZkr9q1J6CD3xy5swZHx9vq89o eKOtW7dieFJSUqgX9zPe8hmvXLlStGhRUBnH5DqAI4DlQcBdGOwejf/000+Y dwE/4+X3GaFrYF9AK/n444+1kZUTWS9evFiqVCkWn01vANjSQPPnz8eQ5557 TnuLXLlygbvHLgilLmA0SXZjN2zYoEwPu/KAAQPwAwolefLkEXlVWyXkM1qF oSXfffddSf76RvUc7Nq1a/jAoV+/fvYmMaIh+4sJ6SImwvqMMTEx0v+uwVK+ fHkIady4sf6JAwcOlOQHwtqpPj169JDkxXDCSI/Ig0wdePRFU4Prpx+NvahV XRDXrtSeDobC+CNGjDh27Bj+b4fPyHMjNk6mL6NDwkM+44kTJ5o1a4YqT506 NVg06F8WLFiAK1yB76DzHa5b2D0a79OnD1pJwO0n+H1G5OWXX162bBnU+okT J+I4oUCBAsqp5lqfkbXkH3300ZkzZ6Cb2L9//6uvvlqlSpWbN29iHObZSfJy CnPnzj116tTu3bvfeecdDCxdurRyukWjRo2gx+nQocOqVasyMjLS09M/+eQT jKl6RKm8cqFChcaOHbtnz56kpCQ2QWLkyJGmregE5DNahaElsVmrVKmS9hAu ttCxY0e7EucDyP5iQrqIiZg+4+XLl/HDyS5durBAfCAMfTH+hH45MTFR+4HJ 9OnTg42IwKeQ5Ge/Ie2tIP4gUwcefaOjo9HpmzRpUk8Z+AcGSKpoMKDCCcNF ihTZtGkTBoK+aG0Wgpw7dw6iQXjNmjXBgEePHrXJZ+S80Zw5czAcPF8oQpDZ wYMHg7PpLTWdR3yfEcbzsbGxDRs2xF0VoJWYPHmy9nkReAf//Oc/33jjDSzt 2IgdP348jBTaja2jcWgwcUHpJ554Isz02UlIPiNUYaXQbD3tffv2qSIrfUac ig/tuaruKzsF5tm1adNG1cW0bdtW28KA06ot7WyJMOjOtFcG1ZTLTcMV8LVj 69at9bMvCOQzWoWhJStXriwFWaweF5w3fJBO6ED2FxPSRUzE9BnZROV58+Zh yIULFzBkxowZ0O0q92WrVKnS1q1b2bkbNmzAcNUk0vXr1+fJkwcP8YwVPTTI 1IFHX7ZQrRIYwLzzzjuq8RLYOX/+/BjhtddeAyHQX+vQoYPqmnBUkqda4bjI Pp+R80YTJkzQ5lGSHwL89NNP/LfzG+L7jC1atFAKOmzYMPa2SElSUpJK94MH D4aRPAewdTSOcy30a6KL8PuML7zwgip89uzZmLVffvlFFVnpM0KrjtF0puYy z071KCxL/gAfD4El9dP5xRdfYMy9e/dqrxwfH6+KX7p0aQivUaOG/mUFgXxG qzC0JL40adOmjfYQLiYAw6H/x96ZR1lNZH/8jcguIDC4IqCOCIgMCiiIMwru Io7yExEUFxhxARkVYXBhEaRFUXZZVFYF2RcBgW7gsDZ099BsNotsTUN3sy/d sjRb/+5591gnJu/l5b0kL5Xk+/mjT79KJam6t1K536RSZVfhfADsLyfwi5xI qBn37NnDrxRr1qwpxv+kpaXxAXlS00BwWiSxVjUJHLGIRkFBAc+bWrRo0YSE BFJ5dMvu27dv8eLFRcSovImHw0VBpg7GNWOFChVee+01Crnbtm0rxPWLL76o zHn+/HmShyrZVa9ePdW8oxSO8qbBgwdzik2a0fiJhGakptutW7cPPviAR+QG gl8nbdmyxeAZ/Yb8mpEi8yeeeIK8KRZUrVGjxtatW1XZ9u7d27JlS/K+WP+X Oo2ePXtKOFmlfdH48uXL+WVWw4YNJax4obm1NkgqsmeVnw9oM5PoE9POUMuZ N2+edrokHc146dIl3l05P5uA7hGjR49+9dVXqVcUj9cWLVpk5MikFgMGPsWV BGhGq4hoSf789umnn9Zuogs5YGD+OqAD7C8n8IucyKYZ6Y4shvQsXLhQpIuP 6fiuSsEPyUkKe0gasMCh2EAsAJGUlKRdwL18+fJC7xw7dixiSVwUZOpgxL8H DhwgSaUcQLVz505+6K2MbfLz8yn0CgTVeqdOnXjtkkBwrC+ZReybm5tL8jMQ HDMgbGWHZoz2RBRJKp/tU0v75JNPODPWeguH/JpRcPDgQWqHrImoNwg36pia yuLFi+vWrcuuF9OeyINNffJvv/3GcwuXKlVKOXpTKsxoxsTERCOasTA4CbZy glPq4fv37y9uH4W6yo7gmVqV39oXBrsXClECofjll1+MHPm+++6DZvQhES3J DxNCzjLHj8dV0wKDqID95QR+kRPZNGPnzp15R9Xnq2vXruX0mjVrklhQbhLL jaWlpYlECpBatGhRuXLlQHCGvddff3379u2sEUjyRFUk+YNMHWKOwGfNmsX1 FXMytG7dOhB8HclTjxYUFAwbNoynqST69evH2cQk9snJyTl/IBZAoV3oZ15e nvaM0baZmE8kINnIbi1RooT+0gy+xUWakenVqxc3gO+++04nG/UhPLIlthmx bMWOPvnw4cNiltGpU6eaLaJtxEczEocOHaKmwjcIhjTg8ePHeau+ZrzqqqsC wWV9RIoYxlC8eHHqJ0mTbtu2bfz48dCMAmjGcES0JN/patWqpd3ET02V0z6A aIH95QR+kROpNKO489arV0/1moBiPN6kXaI6PT1dGyoIlB838ax3sQ1yljnI 1CHmCJzUFluVv1XcvHkz/xw0aJAyW2ZmJn/vWbJkyYMHD2ZkZAQMQNGR9oxR tRkzJ1LSpUsXzqmcjwIIXKcZd+/ezQ7t1KmTfs62bdtyzqNHj8Z8OjuwvE/+ /fffeaxOQDGHmJzETTMyFy9enD17Nk+YQLzyyiucrqPsxJf1IvOCBQv47Xaj Ro2UCw2LDg2asRCaMTwRLdmhQ4dA8MGmao3y/fv3c1v6+OOP7S2ip4H95QR+ kRN5NOP06dN5rlSSZtopTy9fvszDTVXLbxUqWohKzqig8IDHW8b8wlraIFOH mCPwgoICnojymWeeoZ+jRo3iumvXqpgwYQJvmjt37rZt24xIufr162vPGFWb MXMiJWJ4qpFPXH2ItJox3BDxPXv2sEPfeust/Zxi3YRDhw7FUE77sLZPPnPm TJMmTbimbdq0uXTpkjWltIc4a0YmLy+PxzKJ54E6yk7My52QkMApHTt2DAS/ XFA1JGhGJdCM4YhoSfHZ/syZM5XpI0aM4HRq+fYW0dPA/nICv8iJJJqR5EbR okUDwYGjyiGmSvhrkdq1a6uCQDEcUUyDExLSpJzt+++/1y+M64JMHWLWjGIK oN69e9PPfv360f+kIrWTUorpiUhXFgbXSTmmQQwtpjz089SpU9ozRttmYj6R Ep51s1ixYlEtv+IfpNWMzz//fIsWLbRjj8XY1HHjxtHP9evXX3fdddqe4eDB g9dcc00gOHA9hkLaioV9Ml2t4vPw5s2bK5cUlJM4aMbt27dnZGSo9n3wwQcp W9myZfmnUHaTJk1SZjt58mS1atW4JxQHefbZZwPBz7qVH00UFBSI+wU0YyE0 Y3giWvLMmTP8+e0jjzwinvnQDat+/frcg0n+IEhyYH85gV/kRAbNSBEdz3lY okQJncKIY86ePVuZ/tprr3F6VlYWp5w7d0712uj48eO8mAs1JKU6oFZHN3GK MYQUcmOQqUNE/7Zp06Zr166qYJJUc9OmTdmq8+bNK1TEYxyKK/n66695U3Jy crizmJ8DR+sp4ydKT08n72/YsEGVmSzDg8owv1Y45NSMM2fOZC/XqFFj1KhR FL1Tiz1y5Mgnn3zCL8fLlSt34MABysnTLJOX3377bQrdT506RU2djv/3v/+d j9ClS5cYCmkrRiywbt26NX/Ak3RRzyxS+FEJdYO86jGrIerT6Fqe/WdycnLi USXD2K0Z+ZUi3W569uzJo1lOnz5N+XlH6vQ4m1B2pAQ7duxIvQppwNWrV99x xx2c3qFDB3GK7t27c+Kbb7559OhRur+sWrWK4xafa0YjrRQYsSR1X9xsOnfu fPLkSYpn2rVrxyn8UBfEDOwvJ/CLnFjY82dnZ4vEHj16sOMSEhI4JdxCFQsX LhSrYNDNl37OmTNHGdWIe+vFixdZsZYpU4byXLp0iVIGDRrEI1rFKi0UPbZs 2fLKK6/8/PPPc3NzKXCiOzjFlnyKkSNHKs/+/vvvc7r4zMeNQaYO+v6dNm0a V+r+++8nkZWZmUlW3bJliwg1GzduzJPD5Ofns+tLly5N4k68ip01axYvjFK5 cmXlxIMqwmlG421G6ynjJ+LvlUqUKEGB4sqVK0l1kltHjx7Ni4yQr5Uz9AIl cmpGCuBbtWoVUCAW2mCHijU36VDly5cXm6ivEOssBIJLTtChYiikrRixAE/D Eo6vvvqK8tBNUycPQ9dvPKpkGLs1Y2pqKj/6Y+h/sa5Q0aJFU1JSOJtQdiGh e4RyqMnmzZvFLeyKIPy/EJi+1YxGWikwYkmKh++9915hOn7UGQi+YdF/iAoi AvvLCfwiJxb2/GIJ45DQQbRHJpWhXRRDRZ06dUR+Un+8iHwgOOmKuN1XqlSJ 1Afn2bdvnzIq4PcOzLvvvquaHlPMji5WiHZjkKmDvn9JOj366KNKaysrS2bc u3evyJycnCwi8+uuu44sxsO0AsGIfe3atTrFCKcZjbcZraeMn2jmzJksbEWT EH0L0bVrV50D+hw5NSMzY8YMMbWL4IEHHhCRP0Ph/dtvv80TqQnKli372Wef qdYVlQTzffKAAQMKFRNK67BgwYJ4VMkwRurODwApKlClr1ixgiul1IzazBRm vPfee2LCZ6Zu3brLly8XeYSy++6778TXoIFgR9euXTvtMi7Uw4i1h4gqVaoM HjxYfBKu1IxTpkzhPGvWrFEdhE/kH83IrRQY7PGo3T711FPiFkyBU6tWrRAY mwf2lxP4RU4s7Pn14//SpUtrj0yaUanpQtKgQQPlLrt3727UqJF4lktQg1Gt vkE/la0oEHwL9sMPP2gL8MUXX3AGsSh8oQuDTB0i+vfy5csTJky4++67lZUl 5fj6669rV7Hctm1b8+bNVQ5q1qyZdgl1FZmZmZx52rRpynTjbSakp4yfKCsr i4I9frEouPXWW3nkLQiHzJqRycnJWb169fz585OTk3XWXT1//vymTZsoek9M TNyxY4fMX/ZFawEvEc+6HzhwYEmQ/fv3q75hF5qRheSRI0eo5axdu1ZnKAUJ yZSUlGXLlmlnCfMkfm6l1hKVJamZpaamUo+n0xRBVMD+cgK/yIlLe/68vLwV K1ZQyZUr0asoKCigmziFiOIVZEgyMjK2bNmiTXdRkKmDcf9S4LRy5cqkpKT0 9HT9646Mn5aWtmjRIvqrvwCitYTzlHH4Q1eKEim0028VgJFfM3oPP1tAkrrr r88ICqXxlAeAJZ0F9pcT+EVO4BdvA/8CM0Azxh8/W0CSukMzRkQST3kAWNJZ YH85gV/kBH7xNvAvMAM0Y/zxswUkqTs0Y0Qk8ZQHgCWdBfaXE/hFTuAXbwP/ AjNAM8YfP1tAkrpDM0ZEEk95AFjSWWB/OYFf5AR+8TbwLzADNGP88bMFJKn7 ihUrmgaJOLuXb5HEUx4AlnQW2F9O4Bc5gV+8DfwLzADNGH/8bAE/191dwFNW AUs6C+wvJ/CLnMAv3gb+BWaAZow/fraAn+vuLuApq4AlnQX2lxP4RU7gF28D /wIzQDPGHz9bwM91dxfwlFXAks4C+8sJ/CIn8Iu3gX+BGaAZ44+fLeDnursL eMoqYElngf3lBH6RE/jF28C/wAzQjPHHzxbwc93dBTxlFbCks8D+cgK/yAn8 4m3gX2AGaMb442cL+Lnu7gKesgpY0llgfzmBX+QEfvE28C8wAzRj/PGzBfxc d3cBT1kFLOkssL+cwC9yAr94G/gXmAGaMf742QJ+rru7gKesApZ0FthfTuAX OYFfvA38C8wAzRh//GwBP9fdXcBTVgFLOgvsLyfwi5zAL94G/gVmgGaMP362 gJ/r7i7gKauAJZ0F9pcT+EVO4BdvA/8CM0Azxh8/W8DPdXcX8JRVwJLOAvvL CfwiJ/CLt4F/gRmgGeOPny3g57q7C3jKKmBJZ4H95QR+kRP4xdvAv8AMTmlG AAAAAAAAAABuAZoRAAAAAAAAAEA4MDY1nvjZAn6uu7uAp6wClnQW2F9O4Bc5 gV+8DfwLzADNGH/8bAE/191dwFNWAUs6C+wvJ/CLnMAv3gb+BWaAZow/fraA n+vuLuApq4AlnQX2lxP4RU7gF28D/wIzQDPGHz9bwM91dxfwlFXAks4C+8sJ /CIn8Iu3gX+BGaAZ44+fLeDnursLeMoqYElngf3lBH6RE/jF28C/wAzQjPHH zxbwc93dBTxlFbCks8D+cgK/yAn84m3gX2AGaMb442cL+Lnu7gKesgpY0llg fzmBX+QEfvE28C8wAzRj/PGzBfxcd3cBT1kFLOkssL+cwC9yAr94G/gXmAGa Mf742QJ+rru7gKesApZ0FthfTuAXOYFfvA38C8wAzRh//GwBP9fdXcBTVgFL OgvsLyfwi5zAL94G/gVmgGaMP362gJ/r7i7gKauAJZ0F9pcT+EVO4BdvA/8C M0Azxh8/W8DPdXcX8JRVwJLOAvvLCfwiJ/CLt4F/gRmgGeOPny3g57q7C3jK KmBJZ4H95QR+kRP4xdvAv8AM0Izxx88WMFL35OTkpUuXrl+/3sgBo8ocM7m5 uUuDnDx50tYTyYOfW6m1wJLOAvvLCfwiJ/CLt4F/gRmgGeOPny1gpO633HJL IBD45z//aeSAUWWOmYkTJwaCkES19UTy4OdWai2wpLPA/nICv8gJ/OJt4F9g BrdoxpycnA0bNtBf/WzZ2dmzZ8/+9NNPx48fv23btsuXL8dQQrsxboGtW7eO Gzeud+/es2bNilh3VwDN6BaMt9KLFy+mpaUNHDhwyJAhdJFeunTJ5qK5jGh7 PIN9HTAIWrKcGPdLbm7ulClTevXqRXdDcpBq65EjRzZE4uzZs1Hl1GHLli3h 9j1//nxMlpALm/orurg2bdq0cePGGKIyMixbmCIiM8e/cOHCunXrBgSZP38+ tYdoS3Lo0KEZM2ZQSEZx5rFjx6LdXQaM+3f//v3Tpk2j627kyJFLly4l64XM lp+fn5iY+Pnnnw8dOjQ1NbWgoEDnmHT5UHDes2fP0aNHr1mzJpyzzp07R7EW dcX9+vWbOnXq3r17jRQ42iJ5/lq2A8k1I7l+4cKFL7300pVXXkkR+4MPPhgu 55kzZyhb4M/UrVt3586dMRTSVoxYIC8v77XXXlNVp2PHjuEuW7cAzegWDF6n 27Ztu/HGG5Wt9LbbbsvKyrK/gK7BoCWN93UgKtCS5cSIX1JSUmrVqqW6Dz7+ +OOZmZkiDwn8QCS+++67qHLqUKJEiXD7Tp8+3bRVnMfy/mr79u2DBw++9dZb 2UrLly+PtkgkMXjfv/3tb7EdnyTAO++8U7p0aaW/ypUrR8rFeDG6dOmi3P0v f/lLQkJCtHVxHCP+Xb9+fe3atVXNm4w/b948Vc7FixffcMMNymzUi2of7BQG n9i0atVKdcz777+f3KfMRp7q3r17sWLFlNmKFCnSrl27U6dOGamg8SJ5/lq2 A5k1Y05ODndHgn/84x/hct59992c54orrqhRowb1Bvzz6quvJv0VQzntI6IF Ll++/MADD3D5q1at+thjj1133XX8s2nTpq5+AALN6BaMeGrr1q3XXnstW4Yu Orqn8P/VqlXbt29fXIrpAoxY0nhfB6IFLVlOIvpl0qRJIqgj7zRq1Khs2bL8 s2bNmuLdgRElOHXq1KhyhuPs2bMx7+sWrO2vOnbsqLLS0qVLoypPenq6OJdW Mxo5/tGjR0nS8laKD+vXr3/77beL/HRzN1IMkpycn4Rnw4YNS5YsyT/79OkT VXUcJ6J/hw0bVrRoUa4dxc933XUXGY1/kpRbt26dyEmmJuHM6RSDNWjQgD1F xpk/f77ymJcuXXrooYf4IOXLl2/btu3DDz/MP2+77bYzZ85wNmUkT0euVauW 6JaJZ555JuJLauNF8sO1bAcya8bs7Oyr/4Abbbh+qX379uzoli1b8rMIaqLU FdDVPWrUqBgKaSsRLTB27FiuzhtvvMEvFunvm2++yYm0NU4FtQFoRrdgxFPP Pvss9+2TJ0/mlOHDh7OhOnToYHsRXYIRSxrv60C0oCXLib5fjh07xgqRZMKq Vat4nPCJEyeaN2/Ofunfvz/nzMvLywzFxo0bObBv3Lgx7248ZzjoOuWzf/nl l9rjnD592koDOYS1/RVJLc5WqlSpcJpOh4KCgjp16ohIXqsZjRz/448/5k0f fvihGI86b9483qVixYq///67fjE2bdrER6hXr15+fn5hsH1Wr149EHwFtn// fuM1cpyI/h0/fjzVq3LlyklJSXw55Obmdu3alS3w6KOPcrZz586R3KOUSpUq iUkI09LS+AUHBWYXL14Ux5wzZw7v3q1bN9qRE3/44QdO/PrrrzmFrtA777yT Ut566y1S+oXBSJ5Ke/PNNxsJwKIqkh+uZTuQWTMq4YEHIfsl8i8/FXn++edV TyHknOUyogX4gcxNN90kLi6G+itKp57KvR/aGNeMDzzwAP8kI6xdu3b79u0h HzFF1Ix0O1i3bt2GDRtUxtRCx9+1a9fKlSspMlFtgmbUQvcRfoL3xhtvKNM5 /C5TpkzEG7FPiLbH0+nrQAygJctJRL9QZ9usWbPDhw8rEymSZC35+OOP6x+f wk7KRrog4vcpxnP++uuvfCPQDtLzDDb1V0IgRKUZe/TowQ9z7rnnnpCa0cjx SSm0b99e+7BdaEnlu7OQdOrUSSsPhZB016tGI/6dNGkSSzYBRUc1atSgylao UIFTVq1axdUfMWKEMufPP//M6T/++KNI7N69eyD4ilb1dRU1G0pv3bq1SNm3 bx8JTFV5fvrpJz7mkCFDdIodVZH8cC3bgQc0I3f4REZGRgzliT8RLXDNNddQ dd58801V+rRp07imMRhQEoxrxocffnjbtm0UGIjhSVdfffU777yjGpobTjMe OHCA+vnbb79dDKugsPDFF18MGf4dOnSIei0x8InuULTjjBkzRIZwmvG9994r H2TAgAFR2UF+InqKqsw2UX0/Mn36dE4fP368vUV0CdCMzoKWLCexRQIEjzOs WrWqTp61a9dyzz948GD9oxnPSaxcuZKbRGpqalRldhHyaMb169fzw5zXgsSs GcNB1zvvMm7cOJ1sFHLQLT4Q6pvNO+64IxAcwW7wjDIQ83X3yCOPsHDmt3Xf fPMNW+/gwYOqnGwW8di/8I8ovWLFiqqc7Nb7779f/9Rr1qzhc/Xq1UsnW1RF 8sO1bAce0Iw1a9ZkiRFDYRxB3wIFBQXhrg66EHhTxO/0pcW4ZiRCfqFM/bZy FqyQmnH06NHFixfX7htSXSYlJf31r38NmVkMVAupGcUQYhK2yjEP3iCip155 5ZVAUMir6p6fn883+q5du9pbRJcAzegsaMlyEnPsetddd5FTKAjUycNjcuhv xA+gjOck5s6dy32+h79ylUQz0l2ehylee+21R44cefHFFy3XjMKb+hOe7N27 l7OJIZSC//73v7zJRUMZY7vuTp06ValSpUBwnBunfPjhh4HgA3bthcMfUt14 440iRbzpU52aZ9oh5ah/9oSEBFVIFpKoiuSHa9kOPKAZeVD6Rx99xD9zcnJS UlLkHJXKRLQAf/bbvn17VTr1ovzZxSeffGJj+ewkKs0YCL5sJZmWnZ09bdo0 oey+//57VWaVEuQnSJUrVx4yZMj69et///331atXi3nVlAU4fvw4v9Ul2rRp Q006Ly+PbjoUljRo0EAMZ9VqxnXr1rEsrVWrlsHpvNxFRE899thjVH26rWs3 cQN+6aWX7Cqcq4BmdBa0ZDmJLRI4ceIEvxbU3h8F4g0C6Qj9oxnPyYwbN47z UwxMIQfFuj169KA4Vkzi4QEk0Yxi4CiZmn62bt3acs34/vvv8y76HySuXbuW s82cOVO1aeTIkbxp165dBk/qODFcd5mZmdxJEsOHD+dEUXet9fr27RsITjck RoWdPXuWx3FVrFhx2bJlnEie4iOIFC0XL16cNGkST6N600036V9oURXJD9ey HbhdMx4+fJj9PmrUKArslfMDUwxAd4QYSmg3ES1w//33B4ITQSs/rCP90qxZ M65a27ZtbS+lPRjXjFR91TxXmzdv5hmxbrvtNvEcKdzY1Llz56qufZKNbL3/ /Oc/IlHMnkS3D2Vm6liUjx1UmjEnJ4cncyYZu3v3boN1dxcRPcVTE4ScYp2n x3fRq39bgWZ0FrRkOYktEhADiSdMmBAuz/PPPx8IzoMR8Rt24zmZgQMHBkJB 0SzdcaKriazIoBnT0tLEqFROsVwz0v2db+I333yzfs5Zs2bxkbWreIjPhSi6 MHJSGTDu3/Hjx7dr165p06ZFihQJBD/4HTp0qAi9lixZwnVXjYhLTEwUk8oq oyOKxsuUKcPpzzzzDMVUpB/p/xdeeEF76gMHDlCc9txzz1WtWpV3oQYWMdaK qkh+uJbtwO2akfoWoRD5H2qWYhUekhgq3SEDES0gHoDcfffddKFlZ2fPmDGD ZJFo1XTFxauwFmNcM4ac1ubRRx9lC+Tm5kbMrOWqq65SWo96P0655ppreDK0 cCg1Y0FBQaNGjej/okWLrlixwsh53YjBt+EtWrTQbuIP22vXrm1X4VwFNKOz oCXLSQyRwJ49e3hYUc2aNcMtVUy3S54TTww9CofxnAIRZ1LD6Nat2wcffMAD ZYnixYtv2bIlqurIieOakfQ7f31WpUoVMYbHcs0oJqJXzosSEvH2avPmzapN 4k2Z9hWktBj375NPPqkUU7179z579qzYSoEQT1JKF1FCQgJpsY0bN/bt21f5 WRCliPznz58neagSaPXq1Qs5rDc1NVWl43799deIBY6qSH64lu3A7ZpRDJMO BAdaL1++nG4lpAWoH+AHC6QpDD5CjBsRLUDl58+NVdAVx99iv/POO/EqrMWY 1Ix9+vRhU4iJzvQ1I7meWgjt1bJlS/7ulWjYsCFvpQiEU7TTDakQmnHNmjXi 1aSY7N2TRPQUdeNkhKefflq7iSzMtwO7CucqoBmdBS1ZTqK9Li5duiTWdFu4 cGG4bMOGDeM8EYNM4zmVTJ48OSkpSVmqTz75hI9j95JP8cFxzSi+SlPmtFYz UqDIY5boAo/4HeuYMWP4yOnp6apNixYt4k0umnvTuH+/+uqrJ554gpQUDw0N BNeu3bp1q8hAF4J20gmKUYU2PHbsGOfMz8/ntx5lypTp1KmTWHD8iiuu6Nmz p/bUe/fupZiNGtWNN97IOclflDOis4wXqdAH17IduF0ziqHmpAjEuydGjIdP S0uLoZz2YcQC1HqHDBlCVyspX7pgmzZt+s0335AC4k85tN9iuwWTmnH06NHs 02nTpuln5rENFSpUCGi45557OI944DBo0CD9IgnN2KZNG3GcJ5980kiVXUpE TzVo0CAQZsYzftbXrFkzuwrnKqAZnQUtWU6ivS46d+7MHa/+56WtWrXi0DTi ilTGc+pDu9etWzcQnLTNA5OhOasZU1NTeSTkc889l6PgmWeeCQRnKOWfMR+f +O2333huhFKlSm3atCliBRcsWMBHXrJkiWrTpEmT5AwydYghAj948CDpNVbZ FHEpv/ohY7Zo0aJy5cqB4FTGr7/++vbt21l50ZUlsrHkp3iMJyktKCgYNmwY T6pD9OvXL9ypSSQuXryYry+CIsCIpTVYpJB47Fq2A7drRtKJ3Ja0i++kp6fz Jv2pluJPVBagNiymCRXvxZTLQLgLk5pRfMyyaNEincyHDh3iYC8QfDKWkJBA Jz1+/Di3IqEZyYycR3zWHQ6hGRkx+Pmnn34yWHHXEdFTTz/9dCA4BZB2E0t1 nUkqfAU0o7OgJctJVNeFGEhWr149/Ukq+MVEkyZNIh7TeM6IdOnShYtH0an5 ozmLs5qRpGLAAImJibEd//Dhw2I2vKlTpxqpoIgkxZNqwdChQ3mT/iw6UhFb BE706tWLKxty3n7lsNWXX345oBjSv3nzZt5R9XA+MzOTP1csWbKkdnUMJRTn 8xcEyolPI6JTJB28dC3bgds14+XLl/lN9AcffKDaRFdxyIbqODFfs3Spco1c 9FBLhUnN+Pbbb7MFqLfRyXzvvfcGggsyqhZWU2nGjIwMPtq7776rXySlZqxT pw71b9WqVQsEP4QkKaq/r0uJ6KkOHToEgo/jVEteiuvu448/treILgGa0VnQ kuXE+HUxffp0HmBDcaN+cC6WRejWrZv+MY3nNIIY0qb8gMulOKsZtZ+8hUQ7 T4WR49MFzqPNA9F8xJqTk8O79OjRQ7Xp3//+dyA4bFK1bLTMxBx/7t69m+3Q qVMnnWwXL16sUqVKQDE8Y9SoUbyjdlWLCRMm8KaI0860bduWcx49ejTakmuL pIOXrmU7cLtmLPxjfaXatWurhjqvWrUqXPfiLDFfszwr7M033xzu83/5MaMZ qVvmL49KliypM2/qkSNH2O8dO3ZUHUGlGemAPDkbHUS/zxea8frrr8/KyipU LO7Trl27yNV2IRE99eOPP7IFVJ//jxgxgtO1j4L9CTSjs6Aly4nB64J6Wp6p pkyZMhEflorPDSKunWE8pxF4tpBixYq5SDuEw1nNeO7cuWOhaN68OQc//FNr 54jHP3PmTJMmTThPmzZtohqQzMOWVMvxUBDC3+Xdd999xg/lOBH9G+6bQTHO 7a233tLZffr06ZxNrInWr18/+lmkSBHliz9GTGJJulL/7PyikDh06JDO2Q0W SQcvXct24AHNKPqK2bNnK9Nfe+01TucgXx6MWGDTpk2q64taO1fn22+/tbFw NmNcM1L/rJq8aMiQIWyB1q1bqzIrNSOZLmTPlpqaylM9C81IPPHEE5xZ+4mo 8ltvoRl5rSiG72IB3dWF3EtET9Et+Oqrr6bqP/LII+L+S91s/fr1A8HvCEx+ JeQZoBmdBS1ZToxcF/Pnz+f5N0qUKGHkIhIfvC9evNhkTmoV1O1PnjxZ3IjT 09Pr1KmzYcMGbUX4Uy9vzJXkrGYMh8k5cMiJYgIlunHrPHXX+p34/PPPed9V q1aJRAo4OXHMmDEx1MgpIvr3+eefb9GiRV5enipdjE0dN24cp1CEpnoZd/z4 cV66iLpNobkSExNVOwoo7uJNvIrZ+vXrSYZr3/IcPHiQ19Gmw4rEkJ4yWCSf XMt2ILNmXLdu3Zo/4E8PqHcSKWIS5osXL3KvRYpgzpw5dH+nlEGDBvFolpAz qDtLRAukpKSUKlXq7rvvpuuIOrf9+/f37t2bW/JNN90kPm90I8Y1YyC4DuPc uXOp78rJyenTpw9boHjx4mJgauEfb5mpAYi4jnoSdn3ZsmXJkpcvX87Ozk5I SOCH1SrNuGPHDjEnWLdu3ejI1HioP6Fukw4iFl1Src/I7Nmzh+fmpXJqH6C5 HSOeEkOFO3fufPLkSeqc27VrxynUYuNSTBdgxJIG+zoQA2jJchLRLwsXLhST 5Hfv3p1+0v19tgLtw7pPP/2U81PwqX/2iDnFgu9iECMv1knqtWfPnitXrqQ+ ny5M0p68WDndnnRmc3URFvZXdOcViT169GB70r2YU6KarjakZjR4fNIRYkl6 chZJknnz5s3+M2JeHa3fC4PDU3lIEikXDipIPJYrV45SrrrqKv2FumRD378z Z87k6teoUWPUqFEZGRlU2SNHjnzyySc8NxHV+sCBA4XBF4ItW7Yks5Cgzs3N JSOTTWgv3n3kyJHimGQfbiSlS5cmaS/eJM6aNYuXzqlcuTK/IOAl8+hSog75 l19+oYZEATCV9u9//zsftkuXLuKwWk8ZL5JPrmU7kFkz8tp54fjqq69ETmoY vDxoIDhwUSzfWalSJepVYiikrUS0AN0fRTX5OmWqVKkS8VYoOcY1o3bCZLaG ar6a//znP7ypQoUKYp4r5eym3NVzw7j55psDf9aMhcEZ10UelcHFuP2QmpHo 27evqtfyDEY8RdE1fzrKsKgPBN/XeE9Ex4wRSxrv60C0oCXLib5fKN4LeQtQ UqdOHdVeYtE97ZdT0ebkp5FE48aNOYXCaQ5xxZ1CtBOia9euUVReYizsr+gf nWx0EOOlCqkZDR6/d+/eOtkY0i+cWet35scffxSxgfB78eLFFyxYYLwWMqDv 34KCAp5PWCAeqnPFxYeHdOHwuz9GGTi9++67qklHKXASx7nuuuvItjwjBB9/ 7dq1omy8nBxzxRVXKGOzhg0bKt+YaD1lvEg+uZbtwL2accCAAcrMu3fvbtSo Eb9gYp566inV6huSYMQCY8eOFQvTcNfUvHnzI0eOxKWANmKk7vxciCxAak7Z gVAno32wTH0RT70VUMxzdeLEiZYtWyqt9+STT+7cuZMHV6g0Y2GwJd99993K TqNy5coTJkwQGaZMmcLpqjZPgU316tUDwTVkI0Yp7sLgdUrBNl1o4nZAYR7d cRBmKzEfg6n6OhAVaMlyElEzKkO+kDRo0EC1lwh39edWNZLziy++4AyDBw8W iVlZWe3ateOXEYJbb73VRcvzRcTC/kpf05UuXdp4qV599dWAZnJjg8cXy67p IKRfSL8zkydP5hkVmFtuucV1grHQmH9nzJghJgsSPPDAAykpKcpsFGAr+8xA MHAK94Hwtm3bxOc8gmbNmik/AioMTnr/9ttvq1ZJoyvus88+O336tDJnSE8Z L5IfrmU7kFkzxkBeXt6KFSvo+KQabDqFeYxb4MCBA4sXL05NTXX1eFQl0Xr/ 0qVLmzZtIiPoTJdHeVauXLlw4UJVgJeZmUnpq1evVn0XGY5Tp05R/qSkJB56 4XOi8hTFXdRKjZvaV9ja44GIoCXLifzXRUZGxpYtW7Tp/M3UkiVLli1bJuFA JpPI7xe7Ced3Zvfu3eR39z4iNu7fnJwc6gnnz5+fnJx87NixcNkoOiUtmZiY aORaoBA9LS1t0aJF9Ff7yaTg/PnzFPj98ssvdNgdO3aE+/40nKeMF8nb17Id eEwzugI/W8DPdXcX8JRVwJLOAvvLCfwiJ/CLt4F/gRmgGeOPny3g57q7C3jK KmBJZ4H95QR+kRP4xdvAv8AM0Izxx88W8HPd3QU8ZRWwpLPA/nICv8gJ/OJt 4F9gBmjG+ONnC/i57u4CnrIKWNJZYH85gV/kBH7xNvAvMAM0Y/zxswX8XHd3 AU9ZBSzpLLC/nMAvcgK/eBv4F5gBmjH++NkCfq67u4CnrAKWdBbYX07gFzmB X7wN/AvMAM0Yf/xsAT/X3V3AU1YBSzoL7C8n8IucwC/eBv4FZoBmjD9+toCf 6+4u4CmrgCWdBfaXE/hFTuAXbwP/AjNAM8YfP1vAz3V3F/CUVcCSzgL7ywn8 Iifwi7eBf4EZoBnjj58t4Oe6uwt4yipgSWeB/eUEfpET+MXbwL/ADNCM8cfP FvBz3d0FPGUVsKSzwP5yAr/ICfzibeBfYAZoxvjjZwv4ue7uAp6yCljSWWB/ OYFf5AR+8TbwLzADNGP88bMF/Fx3dwFPWQUs6Sywv5zAL3ICv3gb+BeYAZox /vjZAn6uu7uAp6wClnQW2F9O4Bc5gV+8DfwLzADNGH/8bAE/191dwFNWAUs6 C+wvJ/CLnMAv3gb+BWaAZow/fraAn+vuLuApq4AlnQX2lxP4RU7gF28D/wIz QDPGHz9bwM91dxfwlFXAks4C+8sJ/CIn8Iu3gX+BGZzSjAAAAAAAAAAA3AI0 IwAAAAAAAACAcGBsajzxswX8XHd3AU9ZBSzpLLC/nMAvcgK/eBv4F5gBmjH+ +NkCfq67u4CnrAKWdBbYX07gFzmBX7wN/AvMAM0Yf/xsAT/X3V3AU1YBSzoL 7C8n8IucwC/eBv4FZoBmjD9+toCf6+4u4CmrgCWdBfaXE/hFTuAXbwP/AjNA M8YfP1vAz3V3F/CUVcCSzgL7ywn8Iifwi7eBf4EZoBnjj58t4Oe6uwt4yipg SWeB/eUEfpET+MXbwL/ADNCM8cfPFvBz3d0FPGUVsKSzwP5yAr/ICfzibeBf YAZoxvjjZwv4ue7uAp6yCljSWWB/OYFf5AR+8TbwLzADNGP88bMF/Fx3dwFP WQUs6Sywv5zAL3ICv3gb+BeYAZox/vjZAn6uu7uAp6wClnQW2F9O4Bc5gV+8 DfwLzADNGH/8bAE/191dwFNWAUs6C+wvJ/CLnMAv3gb+BWaAZow/fraAn+vu LuApq4AlnQX2lxP4RU7gF28D/wIzQDPGHz9bwM91dxfwlFXAks4C+8sJ/CIn 8Iu3gX+BGaAZ44+fLWCk7snJyUuXLl2/fn1cSgRC4+dWai2wpLPA/nICv8gJ /OJt4F9gBmjG+ONnCxip+y233BIIBP75z38aOeCYMWNatWo1efJkCwoHFPi5 lVoLLOkssL+cwC9yAr94G/gXmEF+zZibmztlypRevXqNGzcuLS1NtfXIkSMb InH27NkYymkf0VogJyeHakF/dfIcOnRoxowZvXv3nj179rFjx8wW0Tas1Ywb N24MBClSpMj+/futKSIIYryVXrx4kS7MgQMHDhkyhBrqpUuXbC6ay7DjegfG QUuWE/hFTmzqr8iJmzZtolv25cuXoypPVP1hVlYWR33Hjx/Xbr1w4cK6desG BJk/fz5Fj1GVJD8/PzEx8fPPPx86dGhqampBQUFUu0uChPHn+fPn2Wtbt24N mWHLli3jx4/v2bPn6NGj16xZo9+EYm5p3vCv3cisGVNSUmrVqhX4M48//nhm ZqbIQzeRQCS+++67GMppHwYtQA144cKFL7300pVXXkm1ePDBB8Pl7NKli7K+ f/nLXxISEqwssXVYqxn37t1LapEyFy1aNDs725oigiAGW+m2bdtuvPFGZfO7 7bbb6MZtfwFdg+XXO4gKtGQ5gV/kxPL+avv27YMHD7711lvZfcuXLzdSjBj6 Q1Iuf/3rX/ksP/74o3ITqZJ33nmndOnSyoZUrlw50iBGCkMsXrz4hhtuUO5O zVL7FkN+JIw/SQzyvn/7299Um0jXt2rVShXS33///dSotMeJraUxnvGv3Uir GSdNmlSiRAn23bXXXtuoUaOyZcvyz5o1a4onAEY049SpU2Mop30YsUBOTg5f qoJ//OMfIXNST8gZqD9s2LBhyZIl+WefPn2sL7pprNWMhcEr/cMPP4yqcwBG MOKprVu30rXJ7a1GjRrU4fP/1apV27dvX1yK6QKsvd5BtKAlywn8IifW9lcd O3ZUxWNLly6NWIbY+sP/+7//E/mVmvHo0aMkeTj9iiuuqF+//u233y5yTpw4 MeKRqcwkhShzsWLFKDJp0KABF4/Crfnz50fcXSpkiz/T09PFuVSa8dKlSw89 9BBvKl++fNu2bR9++GH+edttt505c0aZObaWxnjJv3Yjp2Y8duwYK0RqQqtW reKxKCdOnGjevDk3hv79+3POvLy8zFBs3LiRW2/jxo1lG8pixALZ2dlX/wH1 cuGu2U2bNrFB6tWrl5+fXxg0XfXq1QOyDte0XDMCmzDiqWeffTYQfK4ovicd Pnw4N8gOHTrYXkSXYOH1DmIALVlO4Bc5sba/Ik3B2UqVKmU8ko+hP5wyZYpS Lyg148cff8yJH374oRiPOm/ePC5SxYoVf//9d50jnzt3jhQK5axUqZKYmi8t Le26666jRApXLl68GLFG8iBV/FlQUFCnTh3hNZVmnDNnDqd369aNvMCJP/zw Ayd+/fXXysyxtbRCz/nXbuTUjIXByTObNWt2+PBhZeLRo0dZSz7++OP6u7/1 1luUjRrPzp07YyikrRi0gIBftYe8Zjt16qS9PMWFLOGrRuOa8YEHHhApGRkZ KSkp+h27lry8POoB1q5dy72ZPmTAVatWnTx5MmLOPXv2LF++3PjnZlSMlStX 7t27N9rR9c4S0VO5ubn8LO6NN95QpnOYV6ZMmWj95VUsvN5BDKAlywn8Iic2 9Vci1Df+9sf48Q8ePMijUu+9916tZqSYv3379mPHjlXtJbTkunXrdA5OgQFn GzFihDL9559/1p5LfqSKP3v06MEPhe655x6tZuzevXsg+AbzwoULynQqDKW3 bt065DGjbWke86/dSKsZw8FjDKpWraqTh2QCPxsZPHhwzCeyD6uu2fPnz5cv Xz4Qaqj5HXfcEQgO4DFZVMsxrhkfffRRUnAdOnS4/vrr+coln1LwoPow+a67 7iIjNGvWTJm4c+fOp59+Wjy8oh6JDDJ9+nSRYcqUKbQXdVDUFyUkJNx8880i Z61atdasWaMqEsm9adOmNWnS5OqrrxaHveGGGxYvXqzKyUeuXr06/T979uxG jRqJcReVK1fWHllaInpqwIABXC/VwGCyM6ePHz/e3iK6BGhGZ0FLlhP4RU7c qBn5MUKxYsWSkpKMx/nUrjjzuHHjdLJ98803nI2UqWoTB1rK59vyI0/8uX79 eo6OXgui1Yz86qdixYqqHTnz/fffH/Kw0bY0j/nXblynGUkjkBPJlTp56tWr Fwi+LpfzzY5V1+zevXu5qave0RP//e9/edPp06dNltZajGvGqlWrim+ZlXzw wQfazMqBrMeOHbvppptEfjFQgRAfvE+cOJFT/v73v2tPUbx4cZJ74oCnTp0K mS0QlLFLlixRlkccuXv37jxCXknJkiWjnavNKSJ66pVXXqEakYhWjdzIz8/n G0HXrl3tLaJLgGZ0FrRkOYFf5MR1mnHy5Ml85L59++7cuZP/N6IZ586dy5mV z5O1fPjhh4Hg82RtPPnmm28GgpOlGKyLDEgSfxYUFNx5552B4HQlFBS9+OKL AY1mFG/6VAWuXbt2IKg0Qx452pbmMf/ajbs044kTJ/gFYvv27cPlWblyJTcY ajmxncVurLpm165dyzWdOXOmatPIkSN5065du0yW1lqMa0bmqaeeotrRjWDw 4MEcJ5QrV07ZEWk1Y0JCAu/77rvvHjhwgPqBzZs3/+tf/6pbt65YdUUou0Dw adj48eOzsrLS09NffvllTqxSpYpyOMRDDz1EXcoLL7wwb9683NzcnJycjz76 iHOqnkEpj1y+fPn+/ftv2LAhNTVVjJn57LPPTFsxHkT01GOPPUbVoW5fu4mn rXjppZfsKpyrgGZ0FrRkOYFf5MRdmvHgwYMVK1akDPfcc8/Fixd37NhhXDO+ //77nFn/yzsRTWmzkUoNBB8dnz9/3niNnEWS+FMMDCZhSD9bt26t1YwUsPHH aOTiZcuWcSK1H95RpKiItqV5zL924y7NKAarTJgwIVye559/PhD8mlV8Mysb Vl2zs2bNYmtoZw2dNm0ab1q9erXJ0lpLVJqxR48eyic/FCFw+qZNm1SZlZrx ueeeCwSHqaim1VJe9ULZtWjRQvUNY8uWLbU3HRKt2tYupvA6ceKE9sjkNeV0 0HQEfu347LPP6ldfEiJ6ij9dDzkFNy+RQ/axq3CuAprRWdCS5QR+kRN3acZn nnkmEBzAw3db45qR7vu8tsLNN9+sn3PJkiV8zF69einTExMTxTShu3fvjqJK jiJD/JmWliZGpXJKSM1YGHwHVKZMGT4a+ZriK35E8MILL4QrcLQtzWP+tRsX acY9e/bwOMOaNWuqPokVZGdnFy1alPJ89NFHMZwiPlh1zYrHI5s3b1ZtEo9i tI+AnMW4ZmzcuLEqfezYsVypBQsWqDIrNeN//vMfzqYz4EQoO+2jquTkZN70 5ptv6pfzq6++4pwbN27UHjkpKUmVv0qVKpTeoEED/cNKQkRP8aN+Et3aTfyJ eu3ate0qnKuAZnQWtGQ5gV/kxEWakYQhH1PMXGFcM/KwQyM5CwoKeF5Nii0T EhJIPtAdv2/fvsWLFw/8gTIGkBzH489z587xd4IUEZ06dYoTw2nG8+fPkzwM /Jl69erpjHqNtqV5zL92IzRjbMR8xmj3unTpknits3DhwnDZhg0bxnl+/fXX GMoWH6y6ZseMGcOVTU9PV21atGgRb5o3b57J0lqLcc2oXWuDpCJXSsy7HjIz iT4x7cwTTzxBFtBOlayjGaml8e4h5+aldjV69OhXX32Vei3x+IusbeTIpBYp nafHkZ+InuKPRp9++mntpoYNG3LHblfhXAU0o7OgJcsJ/CInbtGMubm5FSpU CATfRIvxSAY14/Lly3nYDzUkI7NeJCUlieXCBeXLlxdy5tixY1FVykEcjz/F 94PKlhBSM+bn51NoFwhOktypUyde/CIQHCzas2fPcAWOoaV5yb92E/Nbvzif sXPnzuw7/U8YWrVqxQ1MtjUZlVh1zQoNpZqGhZg0aRJvSktLM1laazGjGRMT E41oRuKnn35STnB644039u/fXzlWWUfZETxTq2qeJTopz62k5ZdffjFy5Pvu uy/gIc3IEjjk3GX81E41ma1vgWZ0FrRkOYFf5MQtmlFMjZ6cnJzzB2LdhGHD htHPvLw87QF/++03XpijVKlSyu9c9KG9WrRoUbly5UBwgr7XX399+/btn3zy CQecUdXIWZyNP1NTU4sUKUKbnnvuuRwFPMa4WrVq/JMzs5CsUKEC7VUYfCFI bq1UqRIfvF+/fiELHFtL84x/7cYVmnHgwIHcBihoV32kpoLUAWVr0qSJqSLa jFXXbHp6Optl2rRpqk1Dhw7lTUaWVY0n8dGMxKFDh3r16sU9AEMa8Pjx47xV XzNeddVVgeBKTyJFtMDixYtTP0aadNu2bePHj/ezZuRbdq1atbSb+PGvzkRV vgKa0VnQkuUEfpETV2jGjIyMUI9v1dA9V3W0w4cPi/nYp06dGlVJGDGTHsGT 5rlrjLSz8SdPNxERCvY2b97M/w8aNEh5hMzMTNJ0geBHrNrVMQpNtDTG7f61 G/k14/Tp03mu1GuvvVZfAYm5f7t162a2lHZi1TWbk5PD9e3Ro4dq07///e9A 8O2/bNM9xU0zMhcvXpw9ezZPmEC88sornK6j7Oieosq8YMECHsfSqFEj2ipy iq7Jn5qxQ4cOVJ0SJUqoVtami5Qt8PHHH9tbRJcAzegsaMlyAr/IiSs047Zt 24xIj/r16yv3oobEo5oDVkx5QdEFz1Hgrvfdzsaf2o8TQzJ//vxRo0bx//v2 7VMdZMKECbxp7ty52gKb1IwCl/rXbiTXjNQkeE6bMmXKRBxmKRZzkXaVDcbC PpmH6KhmI798+TIP/NY+ZHOcOGtGJi8vjw0l1tnRUXbiy+6EhARO6dixI3eA hw4dUub0uWYU8w+ovnMfMWIEp5O/7C2iS4BmdBa0ZDmBX+TEFZqxMLjy2jEN YgEIUhz0U0yxQpw5c6ZJkya8tU2bNuY/X5o+fTof7fvvvzd5qHjibPx57tw5 rdeI5s2bB4Jz2PJPEpv9+vWjlCJFiihf/DGkBYSXtaewSjO61L92I7NmnD9/ frFixQLBJ41Gdhk9ejS7ePHixSYLaSsWXrOff/45V3nVqlUicfbs2Zw4ZswY 86W1ljhoxu3bt2dkZKj2ffDBBylb2bJl+adQdpMmTVJmO3nyZLVq1binEgd5 9tlnA8HPrnNzc0XOgoICsZijPzUj3YL5o9FHHnlE3H+pq69fv34g+EWAzN8U xxNoRmdBS5YT+EVO3KIZQxJuDhzSHWISRZIn4SbeLwy2OrqJU4yhlCqkdFQz Zx4/fpzXgqF2KNtoLn3kjD+1c+CIeG/cuHGqzF9//TVvSk5O1h5Kv6V53r92 I61mXLhwoZjqtnv37vRzzpw5sxVoY/JPP/2U869fv96WoluEEQusW7duzR/w R5p05YoU8fQsJyeHJ/m85pprUlJSLl++TBdvuXLlKOWqq67Kz8+3vTJRYrdm 5FeKxYoV69mzJ49kPn36NOXnHZs2bcrZhLIjJdixY0e60ZAGXL16NU8BTXTo 0EGcgpofJ7755ptHjx6lDoSMzHGLnzUj8fbbb3NlO3fuTHKbutl27dpxSu/e veNSTBdg4fUOYgAtWU7gFzmxsL/Kzs4WiT169GDHJSQkcIrO5PYx94chNSMp gscee4zTy5YtO3/+/Hnz5s3+M2Lelffff59zisGrFFa1bNmSAi2SSLm5uXQ0 CgBq1KjB2UaOHGnMrrIgZ/yp1Yy0O5+6dOnSJAPF9LazZs3iRfcqV64sJjY0 3tI871+7UbYfMt2lKDEyTbHOGcNBXtPOfKuiTp06qr3Egjva8c9SYcQCPA1L OL766iuRk/pGnocqEBw/yf+Q3FYuYigPdmvG1NRU6r6Eoeh/sSpr0aJFqVvj bELZheTOO+9UDkPdvHmzeHxxRRD+XwhM32pGiuLuvfdeYTfR/B555BHteBLf Yu31DqIFLVlO4Bc5sbC/EksYh4QOYvL4WkJqxt69e+scjSElwpnF7OhihWiK J5VBhQi3iHfffVe7kpfkyBl/hlxrIzk5mYcaEtdddx15hIeBEZS+du1akdN4 S/O8f+1G2X4oTo52fUbVF17RnjEcpBmVjguJdm10XmiD0J9b1XHMX7MDBgxQ ZiYNxUtZMSSj5BSMhcbqzk94KCpQpa9YsYIrqNSM2swUZrz33ntiQmambt26 y5cvF3mEsvvuu+/ENw6BYEfUrl07bfuZOXOmWBsoEFyLdvDgwQUFBdxKlZpx ypQpnGfNmjWqg/CJvKQZC4PWfuqpp0THXqJECboMEc4psfx6B1GBliwn8Iuc WNhf6UfypUuXNnl8LZmZmZxHOZnnxx9/rHM0RoRMX3zxBafQLV4cITc3V9kI A8GXXJLPmxEOOePPV199NRBqkuRt27bxp45KmjVrtnXrVmU24y3N8/61G2X7 oRh4/fr1pAT37t27TxfKQNkoM+1i5oz+xCYL7N69e9myZR54x2oVBw4cWBJk //79qhfiQjOykDxy5AjpvrVr1yrXcFRBQjIlJUV+C1tFVJ4i46Smpq5evVrH gL4FPZ6zoCXLCfwiJ+ivMjIytmzZok2ncJdigMTExOzs7PiXyircGH/m5eWl paUtWrSI/oZcczMqvO1fu1G1H/I4iUHlXB8hoQyULbbmgR7JzxaQpO766zOC Qmk85QFgSWeB/eUEfpET+MXbwL/ADKr2w68aN27cqDOIlzZRhtheMmrP6EP8 bAFJ6g7NGBFJPOUBYElngf3lBH6RE/jF28C/wAza9hPxVaOZl4whz+g3/GwB SeoOzRgRSTzlAWBJZ4H95QR+kRP4xdvAv8AM2vbDrxo3bNgQ8lUjJdKmmF8y hjyj3/CzBSSpOzRjRCTxlAeAJZ0F9pcT+EVO4BdvA/8CM4RsP/yqUSxYo4QS zbxkDHdGX+FnC0hS9xUrVjQNopp9Cwgk8ZQHgCWdBfaXE/hFTuAXbwP/AjOE bD/hXjWaf8kY7oy+ws8W8HPd3QU8ZRWwpLPA/nICv8gJ/OJt4F9ghnDtJ+Sr Rn7JmJWVZccZ/YOfLeDnursLeMoqYElngf3lBH6RE/jF28C/wAzh2o/2VaN4 yXj+/Hk7zugf/GwBP9fdXcBTVgFLOgvsLyfwi5zAL94G/gVm0Gk/WVlZyleN lrxk1D+jT/CzBfxcd3cBT1kFLOkssL+cwC9yAr94G/gXmEGn/Zw/f168arTq JaP+GX2Cny3g57q7C3jKKmBJZ4H95QR+kRP4xdvAv8AM+u1HvGrMzs625CVj xDP6AT9bwM91dxfwlFXAks4C+8sJ/CIn8Iu3gX+BGfTbD79q/F8QS14yRjyj H/CzBfxcd3cBT1kFLOkssL+cwC9yAr94G/gXmCFi++FXjVa9ZDRyRs/jZwv4 ue7uAp6yCljSWWB/OYFf5AR+8TbwLzBDxPbDrxrT09Mteclo5Iyex88W8HPd 3QU8ZRWwpLPA/nICv8gJ/OJt4F9gBiPtJysra//+/fE8o7fxswX8XHd3AU9Z BSzpLLC/nMAvcgK/eBv4F5gh/u0HLdbPFvBz3d0FPGUVsKSzwP5yAr/ICfzi beBfYAZoxvjjZwv4ue7uAp6yCljSWWB/OYFf5AR+8TbwLzCDU5oRAAAAAAAA AIBbgGYEAAAAAAAAABCO+GvGeJ5RNvxsAT/X3V3AU1YBSzoL7C8n8IucwC/e Bv4FZoBmjD9+toCf6+4u4CmrgCWdBfaXE/hFTuAXbwP/AjNAM8YfP1vAz3V3 F/CUVcCSzgL7ywn8Iifwi7eBf4EZoBnjj58t4Oe6uwt4yipgSWeB/eUEfpET +MXbwL/ADNCM8cfPFvBz3d0FPGUVsKSzwP5yAr/ICfzibeBfYAZoxvjjZwv4 ue7uAp6yCljSWWB/OYFf5AR+8TbwLzADNGP88bMF/Fx3dwFPWQUs6Sywv5zA L3ICv3gb+BeYAZox/vjZAn6uu7uAp6wClnQW2F9O4Bc5gV+8DfwLzADNGH/8 bAE/191dwFNWAUs6C+wvJ/CLnMAv3gb+BWaAZow/fraAn+vuLuApq4AlnQX2 lxP4RU7gF28D/wIzQDPGHz9bwM91dxfwlFXAks4C+8sJ/CIn8Iu3gX+BGaAZ 44+fLeDnursLeMoqYElngf3lBH6RE/jF28C/wAzQjPHHzxbwc93dBTxlFbCk s8D+cgK/yAn84m3gX2AGaMb442cL2F335OTkpUuXrl+/3r5T+AQ/t1JrgSWd BfaXE/hFTuAXbwP/AjNAM8YfP1vA7rrfcsstgUDgn//8p5HMY8aMadWq1eTJ k+0rj3vxcyu1FljSWWB/OYFf5AR+8TbwLzCDWzRjTk7Ohg0b6K9Oni1btowf P75nz56jR49es2bN5cuXYy+lnUS0wObNmzfosnfv3jiV1Wrk0YwbN24MBClS pMj+/fvtK5JLMe6pixcvpqWlDRw4cMiQIdQ4L126ZHPRXEa0bd5IXweME0Of c/78ee5pt27dqt2an5+fmJj4+eefDx06NDU1taCgQP9odIFs2rSJOhyDt6Qj R47o9//E2bNno8qpIjs7e/bs2Z9++indMbdt2+bIvdKIX3QqmJWVpcxJd/9w Ocmb2iPHHC3YeqJom4odxCcm1O/lojWyktzc3ClTpvTq1WvcuHF0YwqX7cKF C+vWrRsQZP78+dTSdI556NChGTNm9O7dm66aY8eOGamgqjryhKZR+Zc6N+ri hg8fnpCQkJSUdOrUKf38dFWym44fP67dGpXNQ2LT1STDdecWJNeMdHdeuHDh Sy+9dOWVV1J4/+CDD4bMRm2vVatWgT9z//33b9++3bJyW0dEC5QvXz6gy403 3hivwlqMPJqRdDepRcpctGhRiqBEOrWlpkH27NljXznlx6CnKOCk1qhsnLfd dpsqnPM5Bi1psK8D0RJDn0MxCTfmv/3tb6pNixcvvuGGG1S9cbjQlG5AgwcP vvXWWznn8uXLjZx9yJAh+v0/8d1330WVU3DmzBlqY6o8devW3blzZ1QmMo8R v/Tv3z9cve644w5lzhIlSoTLOX36dGVOk9GCTSeKranYga33aIO9nHEjK0lJ SalVq5Yq/+OPP56ZmanMRqrznXfeKV26tDJbuXLlSIaEPGyXLl2UOf/yl7+Q gDJYXwlDU+P+XbZs2V//+ldlyclrdEmGE1akrEX+H3/8UbkpWptrselqkue6 cwsya8acnBzuWAT/+Mc/tNkuXbr00EMPcQZSW23btn344Yf5J4WvdIu0uAKm Ma8Za9asGa/CWow8mrEwGP59+OGHql5iy5YtbGT6x54yugMjntq6deu1117L 5qpRowYF2Px/tWrV9u3bF5diugAjljTY14EYiLbPSU9PF75QacalS5dSxEjp xYoVo06mQYMGnLNkyZLz589XHadjx46qfpt2N1IAI0pw6tSpUeVkqJndfffd nH7FFVfQNUthG/+8+uqr8/LyjFvJPEb88t///jdcvZQ3wbNnzxq0gMlowaYT xdxU7MC+e7TBXs64kZVMmjRJKE26KzVq1Khs2bKiqYjBAEePHiWhyul0CdSv X//2228XB584caLqsKR0eBPpnYYNG9KVzj/79OkTsb5yhqYG/Tt8+HDhrAoV KlSvXp27PqJv374hd/m///s/YUmlZozW5lpsupqkuu7cgsyaMTs7++o/oJYW roeZM2cOu7tbt27nzp3jxB9++IETv/76awsLbwkRLZCVlZUZirfffpsrtWjR ongV1mKk0owhIQnJRoZmjOipZ599NhB87iq+CaUbDVuvQ4cOthfRJRixpMG+ DsRAVH0OxZZ16tQRIYRSM9LNheITSqxUqZKYZSstLe26666jROp5Ll68qDwU RZvs0FKlSkUVkJB2C9n/b9y4kUPWxo0b8whw4zmZ9u3bc0latmzJw8xoK8Vs FA+PGjXKoImswohfXn/9dSrtDTfcoK2jcnAI/c/1+vLLL7U5T58+LXKajBZs OlHMTcUO7LtHG+zljBtZcOzYMVaIdMGuWrWK2/yJEyeaN2/Oh+rfvz/n/Pjj jznlww8/FGMj582bx5avWLHi77//Lg67adMmzlyvXr38/Hw+EamngLFPWuQM TY34d8+ePSwYy5Urt3DhQn6xSJL/kUceqVWrVshxp1OmTFGKL6VmjMrmIbHp apLqunMLMmtGJfzuOGQP071790DwKdCFCxeU6ZSZ0lu3bh1zUW0iNgvs3buX GzbdRm0oVJyIj2Z84IEHREpGRkZKSkrETkkwc+ZMI5qROi46LN1TVK3OM0T0 VG5uLt9W3njjDWU6C8kyZcoYt7m3ibbN6/R1IAaisn+PHj34Mcg999yj0owU i3LPMGLECOUuP//8szZMUiJiG5MByVtvvUUHobtAxHGkIXNSvF20aFFKf/75 51Wjy06ePGmmYLFhxC/PPfccR+z62X799Ve2MAWi+jlNRgt2n8iqpmKG+MSE Or2ccSMrSU5Obtas2eHDh5WJR48eZS35+OOPc8rFixfbt28/duxY1e5C16xb t04kdurUSSsPhZCM+KpRztDU+LMaqjhVVplOYjzkaISDBw/yqNR7771X2xlG ZfOQ2H01yXDduQUPaEa+P1asWFGV/tprrwWCA55jK6d9xGaBJ554gqpTpUqV OI8gshb9uk+aNKl8kF9++UWZvmjRIk7/7LPPlOmkSqinovSePXtyCmvGRx99 lKKgDh06XH/99dwVXHHFFSRtVLNV3HXXXbQv3Wj457fffkshohjQQv/wSSl6 VO61e/fuhx9+uFixYpytePHipJLo3mTCKjISsZUOGDCALaAa3Dt9+nROHz9+ vL1FdAnQjM5i3P7r16/nxyCvBVFpxm+++YYbNgVIqh3vuOMO1aMqJZYEJGvX ruVXM4MHD44tJ98oiYyMjJiLYSFG/NK0aVMq8JNPPqmfbeXKlVy11NRU/Zwm owW7TyRD7Oq4ZjRuZCPwqMiqVavqZxPji8aNG8cp58+f56+EtB9d8vVerVo1 /WPKGZpG9C8JZA5vWrZsafCY/KCY9kpKStJqxnBobR4Ou68mGa47t+ABzSge 86oOW7t2bb77x1pSu4jBAqKOc+fOtadQcUK/7uIJ3vvvv69Mf++99zi9YcOG yvTFixdzunggyZqRbhDio2YlH3zwgXJ31UDWPn36aHcJBL/UE7uQI4SoLFq0 qBjwX7lyZY9N/BKxlb7yyiuB4GdQqiF5+fn5bJauXbvaW0SXAM3oLAbtX1BQ cOeddwaCH0MdOXLkxRdfDPxZM3744YeB4CtI7RQQb775ZiD81GSWBCT16tUL BN+4RZzZL1zOmjVrUvrDDz8ccxmsxYhf7rrrLiM3cbotsoUjfkZtMlqw+0Qy xK6Oa0bjRjYCNyHVjEk6JxVz7Ozdu5dTtINIxWe2IQfKCuQMTSP6d+rUqVzs 1atXGzng5MmTOX/fvn137tzJ/xvRjFqbh8Puq0mG684teEAznj17lsP4ihUr Llu2jBPJ9dwGRIo8xGABftxKAYzbpwKOWHd+M1i3bl1lIsdygeBgCeUwKh6x QNqNvzUo/EMGMk899dTMmTOpExs8eLAYnK/s5FWacc+ePUlJSe3atePdaa+k IGKQKkWSFSpUCAQ/aKJejoLMc+fOjRkzhj8dat++vRUWkoWInnrssceo1uQa 7SaeGOell16yq3CuAprRWQzaXwyUoviEfrZu3VqlGUeOHMkZtN8xUbAUCA5m CLkQgPmARLx5oUPFnJM/bfjoo4/4Z05OTkpKiiOjUhkjfqlatSrHhEOGDHkr CP2Tnp6uyjZu3DjhO6og5e/RoweFstppRkxGC3afSIbY1XHNaNzIETlx4gS/ c494d37//fdVV/fatWs5haIIVWbRFezatUvnmHKGphH9+8UXXwSCD8f4y8EL Fy6kpaVRTUOuonXw4EGqHeW/5557Ll68uGPHDuOaUWvzcNh9Nclw3bkFD2jG wuCNskyZMuz0Z555ZuLEidyMX3jhBbPFtYFoLZCRkcFVGzhwoG2FihMG315R fyVWQcrNzQ0Eh4Bec8019M+sWbNEZh48rxyZIDQj3WWU+lpML68cnx9ywpyE hATOqf2ekQdCkG5VTa3PfSzJUiqqQTvIT0RP8VQhISdL5wnP5Xmj4SzQjM5i xP50RYtRqZyi1YxLlizhnqFXr17KfRMTE8Vsirt379Ye3HxA8vzzzweCj6rE /A/R5jx8+DCXYdSoUXR/5OfzzJ133kk30NgKZgYjfhGDOpTQ3eHll19Wql26 M2qzETfddJN2ZI6ZaMHuE8kQuzquGaMysj7iA4oJEyboZKO2xAvo3HzzzSKR Ig3eV7v+wrRp03hTxDdxEoamEf3Lcy1ed9111GlQfyKuwRIlSpDKU01TQJUK BCeO5jUvjGvGkDbXwdarSYbrzi14QzOeP3+eWo6qh6lXr57+yAGniNYCfAmX KlXqxIkTthUqTkSsuxjnIB7uUedDP++77z528VtvvcXpeXl5HOZ9+umnYneW gY0bN1YdduzYsXzYBQsWqDIb1IykQHl1oVdffVV1cPIL70JRpREjuIKInuKX iS1atNBu4i/TKS61q3CuAprRWSLan+QVf6BUpUoVsWi1VjMWFBTwvKlFixal XoLk4caNG/v27Vu8eHFx06EU7fFNBiTZ2dk8d414RRhDThLFXAYxZoMCMLFc Gqkw7VohdmNcM1aoUIG0fO/evdu2bSvk+YsvviiyCZVBV023bt0++OADHpEY CD5sVPXkZqIFu08kQ+wqj2Y0YmQd9uzZw+/Wa9asqT9VHY8tD/xZ6YiXiZs3 b1blF2+4tK8gVUgYmkb0b7NmzQLBgJPUHBf4mmuuEQtt3HvvveKFI8dmAcWn 08Y1Y0ib62Dr1STDdecWPKAZ8/PzKewPBG+CnTp14pnPA8GRQmJqFKmI1gI8 IdVzzz1nW4niR8S6HzlyhAeTdOzYkVNIowWCURDPQUEtgdN/+eUXdvSaNWvE 7uHW2iCpyJnFqhDhMofTjBQicjrdwtZq4E3GF6iVn4ieuummm6jKTz/9tHZT w4YNAwZmO/QJ0IzOEtH+4kNFZcCg1YxEUlKSdrXx8uXLi2BGjI5QYjIgGTZs GO/+66+/xpxTfBBEVK9effny5RRFX758mQI2VmHUGUZ8iWktRq6LAwcOkIJQ PizduXMnSXuuiHJMGnXs5B3xk8LaTz75hLMpe3jz0YKtJ5Ihdo3ol8OHD++O RMTRhvq9nEEj60C7iCX8Fi5cqJOTrgUWRHTbUo5NGjNmDO+uHQu9aNEi3qQ/ s6ucoWlE/4olXAPB8Vq8qE1ubq6w57fffssp/KnOgw8+KOxmUDOGs3k47L6a ZLju3IIHNCPf3Kn18ixbBQUFdOusVKkSt4F+/fqZL7O1RGWBbdu2cUUGDBhg Z6HihJG6169fn+pbq1Yt/lm5cuVA8BUeiTg2xZ49eyi9a9eugeDspspHiOE0 Y2JiIu8bs2YU32vroJrW1dVE9FSDBg0CYaYs49cxYkJanwPN6Cz69qe7RpEi RQLBh3I5CnjMVbVq1finyP/bb7+1aNGCO6WqVau+/vrr27dv54CW4pmQpzAZ kLRq1YoPHvJ7IoM5xXOtmjVrqobQiw85VUPu7Sbm2EMMGtTvb8kIdevWDQTH 1Il5uuyIFiw8kQyxa0S/PP744xFvhddff73+WaLt5UIaWYfOnTtzSfQ/q6fL mZ/JlypVSrWuhHjOrB0+NGnSJCOXjJyhaUT/8kwFxJAhQ5Tpx44d49GhfGd/ +umnOVtycrLoNsWCRFRT+hlykn8dm4fD7qtJhuvOLbhdM27evJl9PWjQIGV6 ZmYmfz5fsmRJ7dTozhKVBb777juu4IoVK+wsVJwwUncRw5DjWDLTbeLs2bOX L1/m8ZD8Oo81i+o9l32aUazbWKNGjX+GYerUqdGbRFIieopvGULaK+HHjx6b FChmoBmdRd/+vAJgRKgDUe1IPZL4/+WXXw6EH4xtMiC58cYbad8mTZqYyclf hRPaVdLS09O1fWMciDn2oECUCxzxo7AuXbpwTv7Yyr5owaoTyRC7yqkZCzVG 1kGMbq1Xr57OzDmHDx8W86tr793iupg2bZpq09ChQ3mTzutUaUPTiP594403 AsGB+tpNfNOvXr26mGRDn/vuu091BH2bhyQOV5MM151bcLtmHDVqFPtaOy3z hAkTeJNs61NEZQGeE+aKK67wxgrpRuoupv6bMWMGj0dt2rQpb2rTpk0guCY1 WYM/ZqTeW7mvfZpRvPD1wExERojoqQ4dOgSCcl7VMuk2yoYi7W9vEV0CNKOz 6Ntf+41MSHQ+97t48SKPlgz3Yt1MQCIm/O/WrZuZnJcvX+ZRtar1hgoVF6wq JLObmGOPgoICfjX8zDPP6OcUAxr5O1P7ogWrTiRD7BrRL2lpafMiEbH8MfRy KiOHY/r06fx5y7XXXquj6ei2xd9QBMJ8KZyTk8Nbe/Toodr073//OxAczR5y nmRG2tA0on8///xzrp2Yjl7Qvn37QHD+eREO6VO/fn3l7hFtHpI4XE0yXHdu we2asV+/foHgVJbKp76M+OqfmlzMpbWDqCzAY8vFR3xux0jdL1y4wFMfdOnS hd8CiLEHPAt3pUqV6CDsXOq7lPua14zcYQY0w06oVLzQrU+mA43oKfH9u2oe gBEjRnC69tWMP4FmdBZ9+587d+5YKJo3bx4IzunHP3WCQ4pRucF///33ITOY CUjEd4gRV9mImJPXbaxdu7bqAyIxnCzO0+DEHHukpqZygXv37q2f88knnwwE lxpn99kXLVh1Ihli1/jEhDH0ciojh4S0A08DVaZMGZ2Bo2fOnGnSpAmbuk2b NuFGffNHFqr1pOjy4e/ptC/RlEgbmkb0L0l+Lp522cSHHnpIKMETJ05ou00x Bp6qRj/FlGKFhm2uJQ5XkwzXnVtwu2YUWoDUhGrT119/zZuSk5NjLawtRGUB Xq+wUaNGdpYofhis+7PPPhsIfivHg1HXrVvH6eKROK+4XblyZdWO5jXj6NGj OSdpH9VB+BOngOExFa4moqfoFnD11VeTNR555BHR/9PdnD9HrVq1qvGbgreB ZnSW2O44IefAIYGpesdx/PhxXnSGGny4UFY/IKHraOLEidQvaSOiQkV3tHjx Yv0CR8wpijF79mxlOi8hRGRlZemfwloi+oUCy65du6pmvKSInVcrDvwxA0l6 ejq5YMOGDdrj8zwbYjKuqKIFrV9sOpESGWJXZzWjcSNrmT9/Pj/XLVGihE4V yKFiOpfmzZvrTKkqHiCvWrVKJNLlw4ljxowRidrWIm1oGtG/dOOuXr06Fe/u u+9W3sR37drFerxdu3bh9g03B45xm1toSWhGO5BZM5JSWPMH/KUG9TMihZ9g 5Ofn86bSpUuT38UT1FmzZvE0yyQr4jwdXESMW4AuWB6EQ1eZzYWKEwbrLkYj BIKz3Ci/ea9Ro4bYpF32wrxmFDnvueceunNRixJfT+zbt49np6ee87PPPuPZ /AoKCpKSkh566KGffvopCkNIjxFP8SowROfOnU+ePEnxM91NOCXiKwD/YMSS Rvo6EBtWaUbqClq2bHnllVdSJJmbm0u3FYokRXc0cuRI5e7Z2dnCfT169OA8 CQkJnKKc11QsbB1ysNann37KW9evX69f4Ig5qRflQL1MmTJz5syhmwulDBo0 iAfyhVw0x1b0/SKWwLv//vsp/szMzKQCb9myRUzQ0bhxY74v8GqwJBN69uy5 cuVKCjXpeiEFzYNVSGiIaTOjiha0frHpRMabSnywLyY00ssZN7IKSher3nTv 3p1+UiOfrYBn2SWziyZExySZOW/evNl/Rsx5Rf/wJzDXXHNNSkoK+ZEu+XLl ylHKVVddpRy6qW0t0oamRvzLA7qIf/3rX0ePHi0MLlzCcxBRRKqdSFYQUjNG ZXOTljR+Ncl23bkFmTUjXZWB8Hz11VecLTk5mR8uBYLrkNKtpFq1avyT0teu XWtjZWLCuAUOHjzIFRErTbsdg3WnDko4WjXLzTvvvCM2aedzNq8ZKQ4R7ScQ nNqL7kTikZeYmp6pVKkSi/pA8MNww2ZwAUY8RTrx3nvvFdYQSzg98sgjIV+a +BMjljTY14EYsEoz7tu3j0JH4RRx4RPvvvuuai5HcpmOQ8ndIicPGQ2EWlW2 ULGKmfZbnhhyUsTLC2EHghNHiK6M+jGeUT+e6PuFlMKjjz6qNBpH7ww5Yu/e vZxz5syZHDoKv4iOiOjatavysMajBa1fbDqR8aYSH+yLCY30clEZWUCSQbsI joo6depQzt69e+tnI0iMiCPTHV9c6aIkFBIoF3ouDHMVyxmaGvHvhQsXXnrp JVFrHu7F6E9TEFIzRmVzk5Y0fjXJdt25BfdqRuXaE9u2bePPT5Q0a9Zs69at dlXDBMYtID40jjgBglswXnceHRHQTPisXPNCO1kWP/YnzaJKX7FiBe+i1Izh Mq9fv16sZhvQPFijXrFJkybKiJF6rRdeeCEjI8NIvdyCQU+RbHzqqadEf043 7latWkEwKjGvGb2xzo5TxHaP42VhVdMC5+bmKlt7IPiIO+T3g/oBSenSpUXO L774ghPFuthKePkMQmf6x6hy7t69u1GjRvxukaEaqVbfiA8R/XL58uUJEyYo V4sLBJXj66+/rloHMysrq127dvwqSnDrrbeGXD7PYLQQ0i92nMh4U4kPTmlG 0ctFZWSGNKPyjhySBg0aFCpmZddBpQcpZuDFiJlbbrlFlaEw/FUsYWhq3L/9 +/fnKdAZ+n/ixIn6u2RmZnJm5WSzUdncpCWNX02yXXduQWbNGC15eXlpaWmL Fi2ivyHXhZGE+NtcHtxS9wsXLmzevHn+/PnLly9XrigtoMAsJSVl6dKl1GXp DM53L1F5iqyRmpq6evVq2caBy4Bb2rxXsdz+BQUFdO0nJiZa9W4uIyNDNUuz 3dD9ccWKFWSWkJ1bfDDul/37969cuTIpKSk9PV2nh+GvTZcsWbJs2bKIrjES LYTzi+UnkgpJ+quojBwHdu/eTSXReYmvcxVL1Qai8u/ly5d37NhBcQ7VLm4T FLjFkv7ES5rRLfjZAn6uu7uAp6wClnQW2F9O4Bc5gV+8DfwLzADNGH/8bAE/ 191dwFNWAUs6C+wvJ/CLnMAv3gb+BWaAZow/fraAn+vuLuApq4AlnQX2lxP4 RU7gF28D/wIzQDPGHz9bwM91dxfwlFXAks4C+8sJ/CIn8Iu3gX+BGaAZ44+f LeDnursLeMoqYElngf3lBH6RE/jF28C/wAzQjPHHzxbwc93dBTxlFbCks8D+ cgK/yAn84m3gX2AGaMb442cL+Lnu7gKesgpY0llgfzmBX+QEfvE28C8wAzRj /PGzBfxcd3cBT1kFLOkssL+cwC9yAr94G/gXmAGaMf742QJ+rru7gKesApZ0 FthfTuAXOYFfvA38C8wAzRh//GwBP9fdXcBTVgFLOgvsLyfwi5zAL94G/gVm gGaMP362gJ/r7i7gKauAJZ0F9pcT+EVO4BdvA/8CM0Azxh8/W8DPdXcX8JRV wJLOAvvLCfwiJ/CLt4F/gRmgGeOPny3g57q7C3jKKmBJZ4H95QR+kRP4xdvA v8AM0Izxx88W8HPd3QU8ZRWwpLPA/nICv8gJ/OJt4F9gBmjG+ONnC/i57u4C nrIKWNJZYH85gV/kBH7xNvAvMAM0Y/zxswX8XHd3AU9ZBSzpLLC/nMAvcgK/ eBv4F5gBmjH++NkCfq67u4CnrAKWdBbYX07gFzmBX7wN/AvM4JRmBAAAAAAA AADgFqAZAQAAAAAAAACEI/6asQAAAAAAAAAAgPRAMwIAAAAAAAAACAc0IwAA AAAAAACAcEAzAgAAAAAAAAAIBzQjAAAAAAAAAIBwQDMCAAAAAAAAAAgHNCMA AAAAAAAAgHBAMwIAAAAAAAAACAc0IwAAAAAAAACAcEAzAgAAAAAAAAAIBzQj AAAAAAAAAIBwQDMCAAAAAAAAAAgHNCOQmczMzMmTJ48aNWrSpEl5eXlOFwcA AAAAAADfAc0IlOzfvz8rK8uSvc6dO7dv375169Zt3779999/j608gwcP7tOn z8CBA4cPH04H1GY4e/YsnTo9PX3Pnj3as5w+fTo7SMh9KTH7D06ePBlbCQEA AAAAAPA28mtGEgI5YVixYsXu3btDbjp8+LB9Rlu/fv2JEyfsO378yc/PX7p0 6ahRo0igDRo0yPxeJOWmTp3a5w+GDRt25MiRaEtFUo72HT9+fEjFR5BaHDp0 qDhLv379Vq9ezZn37t1LZfvqq694E+lW7e7/+9//xL6TJ0+OtniCXbt2RdUe tm3bRmI25tM5fl4AAAAAAOAr5NeMpAq/iJ5JkybZZLEdO3bw8UkW2XSK+EPS gyqVkJAQlWbU2YvkPMs9ct+iRYv4/2hLlZ6eTjsuWbIk5NZDhw6RSOzzZ0gk 8htDVTrJJdXup06dEoqSiLnBbN68+csvvzTeHrj9jB079syZM7Gd0dnzAgAA AAAAvyG/ZszKyvpOw+jRoylgZnk4ZMgQbYZ58+bZYa7Dhw8PHjyYz7t48WIL j7xr167169efO3du69atCxYs2LRpU0Fw8GRGRkZiYiLJro0bNwp18Pvvv//v f/9Tvjuj/9PS0sQAS1Jz9JP0Gv1/9OhRUnBUWvobcdzp559/blwzhtuLij10 6NDPPvtMlGfMmDGky3Jzc7X7hqvj/v37f/75Z9prypQpVJecnBzVjjNmzGC5 9+OPP+7cuXPdunXkmn379vHWL4P07ds3nGakFqIUlTFrxuPHjw8fPtxgexDt h3wR2+kcPy8AAAAAAPAb8mtGLaSYvv/+ewqAJ06cOGDAAPpnw4YNVhnEqfPO mjWLlAupRZYwCxcuzM/P/+GHH5S6hrTwsWPHCoLvyEgNDRw4UOxO/1MG0lb8 k/6hnytXrjxw4AAJOuVBUlNTdYphiWYkhUInIiuJlFWrVlFKSkqKakedOrJg FKj2JV9wOglDnY8lWatqNWNmZianjxgxwqRm5KNxeyDNq5NNtJ/p06eHG23r ivMCAAAAAABf4UbNSPKKAuBvv/2WFMf//vc/+v+rr76KYeYWqc7LmpFITEwk oUe6iWQj/Rw7dmxubu6RI0emTJlCP6dOncr5WQ3x2zf+7o8gUcBbZ86cST+p bPw2benSpadPn6bjzJ079/jx4zrFsEQz7tmzh06qfNX766+/cjFUO+rU8ejR o8uXL6ef8+fPJ4Oo5qihFK4yZV6xYgUpvjlz5mzZskV1/JCakXQTOZHTefir Sc1YEPw0MmJ7ULYfM+eS4bwAAAAAAMA/uE4zrl69mgJgUihioOMvv/xCKSNG jLB1Xhq7z8uaUUhCknifffZZQkKCkHiU8vXXX1Ment5n2bJl9P+6devo/zVr 1vAbOtIO/CKJyjlgwAD6f/bs2bQpOTnZYDEs0YysEJXfIe7evVulIo3UkQUd KUftGUke9gmFaqBmSM1IRuNEUtBCbpv/AFa/PWjbj1U4dV4AAAAAAOAT3KUZ f/vtN/6WUCkBzpw5M3HiRFvnpYnDeVkz8heIBcGv+VTDO4k5c+ZQYkZGRkFw XlChMakAw4YN4/GfWVlZBw8eFO8cd+3aRbqMfn7//fdpaWkRp83UasbMzMzF oVC+2FLttXXrVn5hKlJ27tzZJzjyVnnkiHXU0Yzi/SDx7bffigGuffv25aGt jFYz8rw9nEjaNiUlhf+nnGR8M37UaQ8h249VOHVeAAAAAADgE1ykGXXm8Yhq PhA5z8uakZQg/9y+fTv9nDZtmjIPT0DKHySeO3fuyy+/HDBgAEmG/v37//zz z/v27esT/IaRMvRRfLdI4u6nn37i2WCoItrJZJRoNSPJt29CsWPHjnB70Rn5 LZ5I2bx5M6WoDBixjjqakV9c8rhWFkpCNioLptWMVJ2QLyiZU6dO6RgnIiHb Qxzmn3HqvAAAAAAAwA+4RTNGnMfD4Hwg0p5XpRl5GplRo0Yp80yePJkSd+3a xT9JbdFPXmRw06ZNrCInTpzIh1Kth3j06FGeaPTHH3/UKYYlY1NPnDhBJxo9 erRISUxM7BP8eFC5V8Q66mhGUkniJSOnkIM4ZevWrSJbnDVjgaY9xG3+GafO CwAAAAAAPI9bNKOReTzsmA8nbudVacaC4DeJyrdm2dnZffv2JXWWl5fHKTw5 6vDhw+kvTxEzdepUyjBkyJBhw4Zxnv3794vP3EgQUU6xKSSWaEZi7NixdC46 e0FQv1CR+vXrp1Vk+nXU0YzEuHHjWOhRc9qwYYMYcXrs2LGDBw/yAFqRSPqa ftKJyI/ZCoSEpKPRT0vklbI9xHP+GafOCwAAAAAAvI0rNKPxeTysnZcmnufV akYez5mQkEBiJzEx8csvv6SfVCSR4ejRo6x3xKs68XUezzZDyosUBO2YlJSU mprKozfpRNqz09Z5QXgIK/8v1joMh85emzZt6hMcCku+njBhQp8/D1U1WEd9 zZiZmSmWXxTMnj2bNpGEDPkaUTtxq4Vz4Cjh9vD111/bN/8MKfEcDSwVyel8 9m3btqky8ORCAAAAAAAAGEd+zRjVPB4WzksT5/OSpOoTnMFGmUjaZ8CAASxq +vfvn5ycrHoRxssLLlq0iH/yaE9CrDpBRxg6dKgQTSQbQ6paHuaqggSdfpn1 9yLpJ5aGpJxkopAH0anjxo0bdTRjQfCrRmXtSLHyazXHNaNoD/bNP0N1/yJ6 rK0mAAAAAADwA/JrxpycnNGjRxufx4PnA6FTmBxn6NR5VdDRDgYxc1jSiQcO HLB1LZKQkHQiUSYG04bDZB2PHj1KZ/n9999jKqNdcHuwb/6ZrKys70Lx7bff fvnllySlQ25VLXcCAAAAAABAROTXjER+fn5UaiKiSJH8vMAb/H87d0wEMAwE QYw/1GdhBNvmx45E4OptbmZW/me2dgEAeNIVzQgAAMAKzQgAAEDRjAAAABTN CAAAQNGMAAAAFM0IAABA0YwAAAAUzQgAAEDRjAAAABTNCAAAQNGMAAAAFM0I AABA2WpGAAAAbqEZAQAAKF82IwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAPzNAVew7ic= "], {{0, 340.}, {606., 0}}, {0, 255}, ColorFunction->RGBColor, ImageResolution->144.], BoxForm`ImageTag["Byte", ColorSpace -> "RGB", Interleaving -> True], Selectable->False], DefaultBaseStyle->"ImageGraphics", ImageSizeRaw->{606., 340.}, PlotRange->{{0, 606.}, {0, 340.}}]], "Output", TaggingRules->{}, CellChangeTimes->{3.8343339179175043`*^9, 3.834333956880542*^9, 3.83433423978514*^9, 3.834406129404488*^9}, CellLabel->"Out[19]=", CellID->1175035032] }, Open ]], Cell["\<\ Now find the mean earnings when the population is \"treated\" with job \ training and when it is not. This method suggests that job training causes a \ loss of earnings rather than the generally accepted notion that it results in \ a gain. This finding suggests that direct use of missing value imputation \ must be explored further before it is used as an accepted algorithm for \ making causal inferences:\ \>", "Text", TaggingRules->{}, CellChangeTimes->{{3.834334053137478*^9, 3.834334075879696*^9}, { 3.83433410691905*^9, 3.8343341130624332`*^9}, {3.834334159199683*^9, 3.8343342022648373`*^9}, {3.83433425587293*^9, 3.8343342563114233`*^9}, { 3.834334406008535*^9, 3.8343344291839323`*^9}, {3.834335298124206*^9, 3.8343353349792128`*^9}}, CellID->427154033], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"Normal", "@", RowBox[{ RowBox[{"Query", "[", RowBox[{"Mean", ",", RowBox[{ RowBox[{"{", RowBox[{"#treatedRE78", ",", "#untreatedRE78"}], "}"}], "&"}]}], "]"}], "[", "lalondeSynthesized", "]"}]}]], "Input", TaggingRules->{}, CellChangeTimes->{{3.8343340786027603`*^9, 3.8343340986830997`*^9}}, CellLabel->"In[20]:=", CellID->1857981584], Cell[BoxData[ RowBox[{"{", RowBox[{"5786.72936743084`", ",", "6746.156451917843`"}], "}"}]], "Output", TaggingRules->{}, CellChangeTimes->{3.834334099334635*^9, 3.8343342466603727`*^9, 3.834406136896493*^9}, CellLabel->"Out[20]=", CellID->1915561976] }, Open ]], Cell["\<\ We can see if using RandomSampling as the EvaluationStrategy instead of the \ default ModeFinding helps:\ \>", "Text", TaggingRules->{}, CellChangeTimes->{{3.8344066746031227`*^9, 3.834406694113554*^9}, 3.83440746642745*^9}, CellID->1821480515], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"lalondeSynthesizedMultiple", "=", RowBox[{ RowBox[{"Join", "@@", RowBox[{"Table", "[", RowBox[{ RowBox[{ RowBox[{"Query", "[", RowBox[{ RowBox[{"SynthesizeMissingValues", "[", RowBox[{"#", ",", RowBox[{"TrainingProgressReporting", "->", "None"}], ",", RowBox[{"Method", "\[Rule]", RowBox[{"<|", RowBox[{ RowBox[{ "\"\\"", "\[Rule]", "\"\\""}], ",", " ", RowBox[{ "\"\\"", "\[Rule]", "\"\\""}]}], "|>"}]}]}], "]"}], "&"}], "]"}], "[", "lalondeCounterfactual", "]"}], ",", "20"}], "]"}]}], "//", RowBox[{ InterpretationBox[ TagBox[ DynamicModuleBox[{Typeset`open = False}, FrameBox[ PaneSelectorBox[{False->GridBox[{ { PaneBox[GridBox[{ { StyleBox[ StyleBox[ AdjustmentBox["\<\"[\[FilledSmallSquare]]\"\>", BoxBaselineShift->-0.25, BoxMargins->{{0, 0}, {-1, -1}}], "ResourceFunctionIcon", FontColor->RGBColor[ 0.8745098039215686, 0.2784313725490196, 0.03137254901960784]], ShowStringCharacters->False, FontFamily->"Source Sans Pro Black", FontSize->0.6538461538461539 Inherited, FontWeight->"Heavy", PrivateFontOptions->{"OperatorSubstitution"->False}], StyleBox[ RowBox[{ StyleBox["FormatDataset", "ResourceFunctionLabel"], " "}], ShowAutoStyles->False, ShowStringCharacters->False, FontSize->Rational[12, 13] Inherited, FontColor->GrayLevel[0.1]]} }, GridBoxSpacings->{"Columns" -> {{0.25}}}], Alignment->Left, BaseStyle->{LineSpacing -> {0, 0}, LineBreakWithin -> False}, BaselinePosition->Baseline, FrameMargins->{{3, 0}, {0, 0}}], ItemBox[ PaneBox[ TogglerBox[Dynamic[Typeset`open], {True-> DynamicBox[FEPrivate`FrontEndResource[ "FEBitmaps", "IconizeCloser"], ImageSizeCache->{11., {2., 9.}}], False-> DynamicBox[FEPrivate`FrontEndResource[ "FEBitmaps", "IconizeOpener"], ImageSizeCache->{11., {2., 9.}}]}, Appearance->None, BaselinePosition->Baseline, ContentPadding->False, FrameMargins->0], Alignment->Left, BaselinePosition->Baseline, FrameMargins->{{1, 1}, {0, 0}}], Frame->{{ RGBColor[ 0.8313725490196079, 0.8470588235294118, 0.8509803921568627, 0.5], False}, {False, False}}]} }, BaselinePosition->{1, 1}, GridBoxAlignment->{"Columns" -> {{Left}}, "Rows" -> {{Baseline}}}, GridBoxItemSize->{ "Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}, GridBoxSpacings->{"Columns" -> {{0}}, "Rows" -> {{0}}}], True-> GridBox[{ {GridBox[{ { PaneBox[GridBox[{ { StyleBox[ StyleBox[ AdjustmentBox["\<\"[\[FilledSmallSquare]]\"\>", BoxBaselineShift->-0.25, BoxMargins->{{0, 0}, {-1, -1}}], "ResourceFunctionIcon", FontColor->RGBColor[ 0.8745098039215686, 0.2784313725490196, 0.03137254901960784]], ShowStringCharacters->False, FontFamily->"Source Sans Pro Black", FontSize->0.6538461538461539 Inherited, FontWeight->"Heavy", PrivateFontOptions->{"OperatorSubstitution"->False}], StyleBox[ RowBox[{ StyleBox["FormatDataset", "ResourceFunctionLabel"], " "}], ShowAutoStyles->False, ShowStringCharacters->False, FontSize->Rational[12, 13] Inherited, FontColor->GrayLevel[0.1]]} }, GridBoxSpacings->{"Columns" -> {{0.25}}}], Alignment->Left, BaseStyle->{LineSpacing -> {0, 0}, LineBreakWithin -> False}, BaselinePosition->Baseline, FrameMargins->{{3, 0}, {0, 0}}], ItemBox[ PaneBox[ TogglerBox[Dynamic[Typeset`open], {True-> DynamicBox[FEPrivate`FrontEndResource[ "FEBitmaps", "IconizeCloser"]], False-> DynamicBox[FEPrivate`FrontEndResource[ "FEBitmaps", "IconizeOpener"]]}, Appearance->None, BaselinePosition->Baseline, ContentPadding->False, FrameMargins->0], Alignment->Left, BaselinePosition->Baseline, FrameMargins->{{1, 1}, {0, 0}}], Frame->{{ RGBColor[ 0.8313725490196079, 0.8470588235294118, 0.8509803921568627, 0.5], False}, {False, False}}]} }, BaselinePosition->{1, 1}, GridBoxAlignment->{"Columns" -> {{Left}}, "Rows" -> {{Baseline}}}, GridBoxItemSize->{ "Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}, GridBoxSpacings->{"Columns" -> {{0}}, "Rows" -> {{0}}}]}, { StyleBox[ PaneBox[GridBox[{ { RowBox[{ TagBox["\<\"Version (latest): \"\>", "IconizedLabel"], " ", TagBox["\<\"1.0.0\"\>", "IconizedItem"]}]}, { TagBox[ TemplateBox[{ "\"Documentation \[RightGuillemet]\"", "https://resources.wolframcloud.com/FunctionRepository/\ resources/FormatDataset"}, "HyperlinkURL"], "IconizedItem"]} }, DefaultBaseStyle->"Column", GridBoxAlignment->{"Columns" -> {{Left}}}, GridBoxItemSize->{ "Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}], Alignment->Left, BaselinePosition->Baseline, FrameMargins->{{5, 4}, {0, 4}}], "DialogStyle", FontFamily->"Roboto", FontSize->11]} }, BaselinePosition->{1, 1}, GridBoxAlignment->{"Columns" -> {{Left}}, "Rows" -> {{Baseline}}}, GridBoxDividers->{"Columns" -> {{None}}, "Rows" -> {False, { GrayLevel[0.8]}, False}}, GridBoxItemSize->{ "Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}]}, Dynamic[ Typeset`open], BaselinePosition->Baseline, ImageSize->Automatic], Background->RGBColor[ 0.9686274509803922, 0.9764705882352941, 0.984313725490196], BaselinePosition->Baseline, DefaultBaseStyle->{}, FrameMargins->{{0, 0}, {1, 0}}, FrameStyle->RGBColor[ 0.8313725490196079, 0.8470588235294118, 0.8509803921568627], RoundingRadius->4]], {"FunctionResourceBox", RGBColor[0.8745098039215686, 0.2784313725490196, 0.03137254901960784], "FormatDataset"}, TagBoxNote->"FunctionResourceBox"], ResourceFunction[ ResourceObject[ Association[ "Name" -> "FormatDataset", "ShortName" -> "FormatDataset", "UUID" -> "76670bca-1587-4e7e-9e89-5b698a30759d", "ResourceType" -> "Function", "Version" -> "1.0.0", "Description" -> "Format a dataset using a given set of option values", "RepositoryLocation" -> URL["https://www.wolframcloud.com/obj/resourcesystem/api/1.0"], "SymbolName" -> "FunctionRepository`$66a3086203b4405b88cdb0de8a5c3128`FormatDataset", "FunctionLocation" -> CloudObject[ "https://www.wolframcloud.com/obj/70389ad6-7dbc-48c8-b898-\ 72c65c00f14e"]], ResourceSystemBase -> Automatic]], Selectable->False], "[", RowBox[{"MaxItems", "->", "10"}], "]"}]}]}]], "Input", TaggingRules->{}, CellChangeTimes->{{3.834406446363668*^9, 3.834406513472933*^9}, { 3.834406623825059*^9, 3.8344066439511423`*^9}, {3.83440669736751*^9, 3.834406697748654*^9}, {3.8344067871120977`*^9, 3.834406794186804*^9}, { 3.834407292666483*^9, 3.8344073022065687`*^9}}, CellLabel->"In[33]:=", CellID->1433913193], Cell[BoxData[ GraphicsBox[ TagBox[RasterBox[CompressedData[" 1:eJzsvXe81EQb9r9IERAEpYmggAoCItIFOz4qiKKIYkHUB1SsFFFURKQKIhaa CBawANIED0U6L9IPPJQD0kQ65xw6elQUpPzud++f88ZkNzvZZDezk+v7Bx9O MsnO3NdkZq5kMqnQpkPztueEQqGX89M/zVu/1rBTp9Zd7i9Kf7Ro//Jzz7R/ +qk727/y9DNPd6rfJjdtnE5pl+UJhf7v/88CAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAASSM2G8Otv/AQAAAAAAAACgEfYecPfu3Xv27IGj BAAAAAAAAABgxcYAnjhxYnWYkydPeugocwJMkCMQ5LLrBHT0CkTSXxB/NYEu agJd9Ab6AjfEdJS7d+/+XxivHlOixgY5AkEuu05AR69AJP0F8VcT6KIm0EVv oC9wg72jFA8oGfoTjtI9QY5AkMuuE9DRKxBJf0H81QS6qAl00RvoC9xg7yj5 AWV2GPoP/QlH6Z4gRyDIZdcJ6OgViKS/IP5qAl3UBLroDfQFbrBxlPyAct26 dadPnz516hT9x5PHlKixQY5AkMuuE9DRKxBJf0H81QS6qAl00RvoC9xg4yjF A0r+06vHlKixQY5AkMuuE9DRKxBJf0H81QS6qAl00RvoC9wQzVEaH1DyFq8e U6LGBjkCQS67TkBHr0Ak/QXxVxPooibQRW+gL3BDNEfJDyj3799v3OjJY0rU 2CBHIMhl1wno6BWIpL8g/moCXdQEuugN9AVuiOgorQ8oGfrT/WNK1NggRyDI ZdcJ6OgViKS/IP5qAl3UBLroDfQFbojoKHft2mV9QMnQRpePKVFjgxyBIJdd J6CjVyCS/oL4qwl0URPoojfQF7jB6iijPaBk3D+mRI0NcgSCXHadgI5egUj6 C+KvJtBFTaCL3kBf4Aaro7R5QMnwY0pKBkcZH0GOQJDLrhPQ0SsQSX9B/NUE uqgJdNEb6AvcYHKUf/31l80DSsblY0rU2CBHIMhl1wno6BWIpL8g/moCXdQE uugN9AVuMDnKmA8oGTePKVFjgxyBIJddJ6CjVyCS/oL4qwl0URPoojfQF7jB 6Cj5ASVZxe3bt++0hRJQMkpMh8BROiXIEQhy2XUCOnoFIukviL+aQBc1gS56 A32BG4yO8sCBA/9zCB0CR+mUIEcgyGXXCejoFYikvyD+agJd1AS66A30BW4w zXpNAqixQY5AkMuuE9DRKxBJf0H81QS6qAl00RvoC9wAR5l8khOBxYsXT5s2 bfbs2Yn+IUdAfT2AjjLIXINqRnLPnj3TwmzZssXvvCQWNeMPoIuaQBcTag60 4gb6Jg0tO1k4yuSTnAjceOONoVCoRIkSif4hR0B9PdBJx4yMjKeeeurZZ5/d vHmzt2eWuQbVjOTMmTNDYT788EO/85JY1Iw/gC5qAl1MqDnQihvomzS07GTh KJMPHKXfuQBu0UnHu+66ixv2//73v96eGY5SfZIT/507dy6JxcGDB6MdvmnT Jk6ze/fuRGdVEdTUZcWKFdGSHTlyJNG5VQE12ysfidjI9+3bt8m/adq06VNP PfXWW2/Nnz/fehJrehNLly7NibcZoUr78ccfv/baa4MGDZozZ86vv/5qUxzo mzQidrI//fSTVf2HH364Y8eOQ4cOzczMNJ0kYnojL730Eqdcvny5fc3ZsGGD 6eRZWVmTJk3q3bs31dsxY8Zs27YtZqHgKJMPHKXfuQBu0UnHxx57jBv2p59+ 2tszw1GqT3Li379//1AshgwZEvHY7du3X3jhhZzm008/TXRWFUFNXc4999xo yb766qtE51YFZHQh43NTmPXr1yclU35mJmIj/+CDD9rUqDp16vzvf/+TT0+k paXlOK+uVPbmzZubEtSvX9/060Z874/69OlDYnXp0sXHPAgSmpmInezatWtt xC1UqNAbb7xhPIl9euLaa6/llEWLFrVPWbp0aeOZv/jii1KlShkTFChQoFev XvaFgqNMPnCUfucCuEUnHXfs2NG7d+++fft6/gAIjlJ91HEu1INHPPaee+4R aeAovcWRLgcPHoxDPs2Q0WXFihUcE/pPcnLlY2bsHWXnzp1fDfP00083adKE xuS8/bLLLtu1a5c1fevWrdtEgl+1c1Rdf/nll5tvvpk3kpt4+OGHb7nlFv7z 8ssvP3DgQMTi+N4fcXNH//qYB0FCM2PvKOvUqcM1p2PHji1btqxataqQ+N13 37Wmr1GjRsSaM2jQIE4Z01FWqlRJnHb69Om5cuXi7eRJKQPCXUa788nAUSYf OEq/cwHcAh1lgKNUn+TEPzMz88dILF26NH/+/Nxr0wjQeuCoUaOMnT4cpbc4 0mXr1q2sQq9evayH7N+/P9G5VQEZXb7//nsOlAqOMtGZsXeUpimm+/btu/PO O3lX9+7dY6Y34ai6fvPNN3zODh06HDp0iDd+8sknvPHtt9+O+BO+90c33HCD Oo4yoZmxd5SvvfaaKf23336bL1++UPhZ4bFjx2KmN7Fp06aIleepp57iM0ye PFkkvvrqq7lWi8fZu3fvrlmzJm0sUqRIxK6KSRVHuX79emoZfvrpJ/tke/fu nTt37pw5c4z3f6KxefNm0nTHjh1OM+MSpxGQzGd2djYVnCoYN0p8P8orR0nn XLdu3axZs/bs2ePmPHGoT5rS7+7cudO0nco7f/78JUuWiNYyGpKZp/MsWLBg 2bJlR48edZTDABKHjkeOHKFrM+JU/OXLly9evDimjpKNABOt2sjsNSFfMeK4 BuNrD1euXElZop+zT0aNBjUdGzdulDknZZsyn5WVRf+n/8R0lDLtEnU9a9as mT59OmXYfrAkiCaNvAqOLmQPW6Q4GpAnn3ySRwgUfOven3/+mee71qlTB44y JonWJT09nVUYP368o4zphIwuo0ePljRxNu2w/PDMvl+QzIy3jbyNQyRTec45 55h8iqSjjEbE6vrSSy/RxoIFC5pK1KBBA9r+wAMPRDxVEhylfefFD+NkTJz7 6z3mGFIyM/J1NWYna+8Q27dvb6rM8o4yIhs2bODn5k888YTYuH//fq6iPXr0 MCaeNm0a/9bq1aujnVBlR0kX15dffnnjjTeSKRY3aS+66KIpU6ZYEy9cuPDa a68VyXLlylXUQLNmzYyn7du3L51HJC5TpsyoUaNksuQJkhGQzycNyOvWrct1 IBSe4fDWW281btzY1NBRPaGz0d527dqZzvDmm29yoGgMY9q1fft2anwKFy4s slGxYsWvv/7aebn/LzJlr169OuXk448/3rp169133823ZYoXL857t2zZ0rZt W8qDeCifO3duapAjNlCSmc/IyKB+IW/evJyGfpF+V9JuBBN5HT/66COqVM2b Ny9UqBCHt1q1amPGjKEE1OC/8sorl1xyidCxa9eupi5VvhGwrzY2e+ma4spv XQFevmJIXoMuI0l96OOPP16yZEnRyrVu3frw4cPWQ9577z1qLkTEyJh06NAh Yspjx4517ty5WLFi4pz169cXt7KtjlKyXTp48CCped5554lk1HMZ2+QffvjB WLpowuU4USGOC9l9ixTf7xLz5s3jRuydd96JmIBOQnvptGlpaXxmOEojSdaF hq98HgUnFSQNe10GDx582WWXiQ6X/sMXe+3atTlBTMkkmxeZfiFmZphENPL2 DvHSSy8N/XuSoRtHGa26ss2klt+UvlWrVqHw25QRzxbzunM6jJTvvK666qrS pUtzWfLkySN6Ch4t5Hh3vcuMIWNmJsfJEF2+k7V3iMOHD+e9Ym6zS0d5++23 07Fly5Y1rvlD8vE5BwwYYEy8ceNG3j516tRoJ1TWUe7bt49GnqFIkBymElFT Ly5zqifGoRRDceOUBw4c4BgyPFWAef31151IET8yEZDPJ1Ut414TxoZuz549 vJGaGtPPvfzyy7zLdKOPRjJiUQgTn3/+eYLKXr58eTr/q6++WqtWLfFzjRo1 ol2DBg3iZsTKddddZzqPZObHjRsnOh1qOqht4f9ffPHFmzZtiqOMQUBeR2rS hWcUnH/++atWrbrjjjus0rz//vviDI4aAZtqY793xIgR/Of06dONJ5SvGPLX oJtI3nPPPZdffrn1/O3btzcmpqbjtttui5gT6iVNX0ihfoQn9kTD5Cgl2yXq av/zn//wdhry0e9alzQRax7aCyevQnwXsssWKe7fJWrUqBEKv/8ScRhJbRSf h8ZpYtgAR2kkybqIaYSSD/21xF6Xrl27RmxGaFTGCewlk2xeJPuFmJnJSVgj b+MQyVzweRo0aCCTPibRqisVjc85Y8YM4/YqVarQRvKVEc8W87pzOoyU77yE vzMhPJon17vkGDJmZuSH6I46WXuH+NZbb/HeadOmyaS3R9QQatlMu6655hra ftNNNxknuH799dec3qYB5Przv3hJnKMk+LXi+++/f/z48du2baNaKmrs9ddf L5IdPXqULHYofA+KrCVvJI04ZefOnSmfYhxFtZG3N2/enDeuX7+eZ8JTJUzO 0mQyEZDM588//ywe/fz3v/9dtmzZ1q1bv/rqq3LlyvFGN45y9+7dxYsX5+0t WrT44Ycf6NKgmly9enW6omPOUYy77NxuMFdffTW1AAsXLlyyZEnOP3eJqYno 37//okWLeP5JhQoVOLGx5ZTM/M6dOy+44AJKU6xYMYrb4cOHadfQoUO5fXjs scfiKGMQcKpjs2bNqKNPT083LjNCkBwjR45cvXp1ly5deAsdZTyJZCOQY1tt 7PdGdJTyFcPRNeg+ko0bNx49ejT1I++88w53mmTPje9wifhUqlSJYr53794F CxaINRnuvPNO45k7dOjA2y+66CI67fbt2xcvXixerAhZHKVku0Th5WQ0UuK8 UWIe+Vx44YUUImqTxSr3NtLIqxD3heyyRYr7d8UDr08++cS6V8x3rV27Ng1B 6QLhxHCURpKsy7Bhw3gXjcToKqMxOV0OZPyjLXKiJfa6UAuQlpYmVs+mNiot jJikZ99Kyw/PZPqFmJlJXCNv4xD79u0rziOT3h6b6koNLDssaklE1yYmLppu nwoS5CgZ+86LeitSh7dXq1Yt7R9EAk+ud8kxZMzMyNdVR52sjUPctWsXP90m qPrFTB8TMoyh8CJR1lonrGvLli358SV1Q5ze+uzGiHCUWc5JtKOkWImZUQIx NBLvxNHVzVtMC9vyZ+ZoyCq2rFmzhu9ONGnSxJiSKhV3355/PiAiMSMgn0/R Wnbs2NGUku9EuXGU4uQvvviiMfGRI0domOqkxP8PR+MEqrpW3/rNN9+YenAx Hf25555zmnmeAXLOOedQ02RMRnUpFJ4LIfMJngDiSMeuXbuKjdQgi3lKjRo1 Ms4z4daYME5TkWwEcmJVG5u9ER2lfMVwdA1acRRJ6sWMjf9DDz3E26kN5C3U JvOcH+oijcGhai+CJm5Irlu3jhNTq2J6dvnxxx9bOzv5domna9JGo9VdunSp KQOm0lmlkVch7gvZZYsU9+/ed999ofD4J+KtOe68aDjECyPAUUYkyboIL2Ci TJky1pv8uiKjS/fu3Tky1lcXbSRzNDyT7xdsMpO4Rt7qEMkO0K+Qi2STQj+6 atUqa/pzLZi+6WDCvhkh9ySMMDUp1NNxJMmJRzth4hxlzM6L4S4p4quLXl3v kmNIm8zI11WnnazVIVLfvWHDhuHDh1esWJF3Gd+BNX49xFp5bJZBWLlyJR9F zZp1L/3ovffeywlKlSpFYeRLgM4p7v9ERGVHGZE+ffpwMfmTrzmGAeHMmTON KXv06BEK3y4QyyJx7SJ+/PFH02mpIoWizy33lpgRkM8nNxfFixe3vkVoXYLM UVNA1z6/A0Un5/eIPUF+nJArVy6bryaZ4KxSm8l/SmaekhUsWJCSPfroo6Zd IlY2M8aDjLyO4nNIAjEn0/RaX7du3UyXdjSsjUBOrGpjs9fqKB1VDEfXoBU3 kfzoo484MxMnTuQtPXv25C1Dhw41JV6wYAHvotELbxFhtH5kKuIydPLtEmeY im9KxnNfTV/UiiaNvApuLmQ3LVLcv7t161bqmGgvNbzWvWQb+VjxYhQcZUSS rItwlA0aNOjQoUP79u2rV6/OW2h4qcK6pknAE0cZsR12PzyL2C9Ey0xCG3nh EM/5h9C/MX4AIsf2e5RUIaOV17665oR9AZlH0wlr1Khhsy5xghylTOfFxHSU Hl7vRkxjSPvMyNdVp52s0SFGrDnXXHON0R3bf4/yrbfeilZefkhaoECBaOtV Llq0iGuXkdGjR0c7IZMSjjI9PX3QoEFUW+haELdcxFK3YnVo4zuzOf8si2R8 Mfnhhx8Ohd/rmWeBb3GQGXeUsfiIGQHJfG7atIkLblymSeDSUa5fv563tGnT xm2BDbgZPxs5dOjQuHHjunbt2qxZs0qVKnFW69aty3slM5+RkcHJqKpYQ827 xNd8gBF5Ha1zJFq2bMmxFbd6mMGDB/N2uqKtZ7NvBHJiVRubvVZHKV8xnF6D VtxEkvpi/nXxXjA3HcS+ffus5+G3A66++mr+U9x4F1NoBBE7O/n2k18bueqq q4znpC6Dz2kaTUWTRl4FNxeymxYp7t8dMGAA76VabdpFowWevkUxFLf04Sgj kkxdGLrQ+FvzzC+//NK5c2c+xH4ymDZ44igjtsPxDc9i9gvRMpPQRt7GIZLF W7BggekkIv1nn332+b+xWTTSvrpSe0t1kvZSWNq2bWtcEsdmhmSCHKVM58XE dJReXe/2Y0j7zMjXVaedrI1DJAW7detmeroq0jdt2vRzCytXrrSGkeFnqffe e2/EvV9//TXbydtuu00siUBcccUVNgu95ijvKCkm/PaNlUmTJnEaqhh86/vm m28WB+7du5evoIYNG4qN0U4lyJ07t2TG3BAzApL5pGaT/7Te/chx7SjFS7v9 +vVzW2ADbsbPzJYtW5577jkedJkQa7hJZl6stGADXcLxlVRv3Oj4+OOPc2xN jlJMAjE5SplGwObnYu61Okr5iuH0GrTiJpLfffcd/7rolPmFero6Ip6HjV6+ fPk48tR7hsJ3Ka0pI3Z28u2neCvWOG9EPD+dM2eOTOnkVXBzIbuJf9y/27x5 81B4pGf9sFeTJk34wLlz5/70D7Nnz+aNNIakP43r8umKarpEg1Lyt9toEGJq 0LTEE0cZsR12OjyT7BeiZSahjbxwiNSXzQzDk2MJq53Mifc9Svvq+sADD4TC fQHrdfjwYWo9xHKj0Z5eJc1RWjsvJqajdH+9y4wh7TMjX1eddrLCIbZs2ZJr zttvv81bxBpERuJ7j5K8Gx/Vu3dv69709HSem/3oo49ym0ZbRMdEZtlmuWyV HaWYZEJDILo6Ro4cSb8ohp3GRuP111/njXfcccfo0aNHjRp15ZVXhsJ3Y4xD mnr16oXCLf91URBLwiaUmBGQzKe4jvr37289Cb9FS7ZabHHUFIhlnd577z23 BTbg0lFu375drBhWsWJF6ixmzJixe/dufrFatAaSmRdfqqJTRQu1WKUZGEma o5RvBDx0lPIVw+k1aMVbR8lNh3FBdSO8nEWePHn4Q108Z69o0aLWlGLBh4ED B4qN8u3n1q1bqd8JhRde6Nev34QJE9q3b88TeBo2bGgaNUUrnbwKbi5kN/GP +3dLly4dijQrWLzbYo/9/A09UEoXe9q1a8c/J/+aRuqSOEfpaHgm3y9Ey0xC G3mrQxRtdZ06day2MT5HaVNdly9fzic03VT/8ccfeen1/PnzWz8Vl5OajtLR 9S45hrTPjHxdddrJRnSI4sOIxoptkz4mQ4YM4aNMrwoyvHzihRdeaPqgp/BZ psVJjCjrKMUzcdLO+OVQ8Q0XY2yzs7OtNxzOO+8805NuvqVTpEiR+D4j6xUx IyCZT7HSRYcOHax7bZ5RWidvWJsCMbZ5/vnnZQsmgUtHyV/6zp07N/Udxu2m 1kAy8+JGTcR3k4ENyXGUjhoBDx2lfMVweg1a8dZRirdmIn6elc9TuXJl/pOX fyGsr9VEvH3qqP0UAzkjVATriijRSievgpsL2U384/vdDRs2RKsz4oT21KxZ U/7nUhSldLFHTHyN+QK4BiTOUco3L476hWiZSWgjH9Eh8pcrCdPoJVp6e+yr K5kU3mv90IP4pmHE5aTkHaXMMDInKY7S0fUuOYa0z4x8XXXayUZ0iAsXLuSN V1xxxZEjR4wnic9R8ptHuXLlijhO4OmdERfE5sWNjeudmlDWUT799NMcqO3b txu3R2w0eNmoG2644c0336QxFYWiX79+1nnL4vtEpmlXSSZmBCTzuXfvXk5W o0YN615rQ0dVkT+yY3r7mCDnZWoKKDE/+Kar2FSH3eBmnLBz507OpHVJXlNr IJn5o0ePcotxyy23OC9KoEmOo3TUCHjoKOUrhtNr0Iq3jlKsbmR9+yY9PZ13 UW/IW8RVP378eFPiiJ2dfPu5cuVKCiBdgz169GjdunWzZs06depk/RX70smr 4OZCdhP/+H5XzMmP+N0QGrDtsiDeBqKBIv0Z8SVZzVBNFxv4u7r0ix52lMoi owsvikiYFt7MsW2H5ZsXR/1CtMwktJGP6BBXr17N76bRiN00cT0OR2lfXfnr D+ecc474SJNA2BPjozFBTH0dDSNznDtKXkM14gxP99e7/BjSPjPyddVpJxvN IT7yyCO83TRPNT5HyW/HUJGtu6gGcqkjLvdEoQjZTrtS1lHy+vNUdY33tA8f PiwCKxqNAwcOFChQIGRZM8rKokWL+FqoVq2aj41/zAjI51NMzp8yZYpx+w8/ /MAv3poaOr75UKpUKePj7C+//FJ8DdbYFIjvt7799tum3zUufO0IN+ME8ZkY 03QLOiG/km9sDSQzL+4gYXarI5LjKOUbAZufi7k34tdD5CuG02vQhLeOcsmS Jdx0UK6Mr/BTNyFKJFZ3EZPD69evb9SC/v/EE09YOzv5donfo6xVq5Z9uexL l+NEhbgvZJezJuL4XfGxTlOFsQEr80QkmbosXrz4qquusq6cP2PGDD4qot3Q DxldRCQ/+OAD0y4byeSbF0f9gk1mEtfIR3OIwl+YHizG4Sjtq6voGoYNG2ba Jd7Lmzt3rvVAGX0dDSOdOkp+XSJip+n+enc0hrTJjHxdddrJRnOIW7du5fVs KZ8R13p15Ci5XPXq1Yu4lyfZVqpUybQKUHZ2Nh946623Rjuzso7ypZde4kC1 adNm586dpNrs2bNr1qwZ+gfRaGRmZvIbOk2aNPnxxx+pkv8aJuJpn3nmGT6c TDplg99oJrFef/11qqjJebNeJgKS+RTVlSobDTb27t27ZcuW999/ny229VoQ H26gC4qq5fLly+mExgWKjU0BDWP4zg83gBRb+l3qWO+77z66muJ7zutmnEDV m6/iwoULL1iwgCSmgHTv3l0scWxsDSQzv3HjRr5O6STdunXjhZSpb0pLS7v5 5ptHjhwZRxmDQHIcpXwjYPNzMfdGdJTyFcPpNWjCW0eZY2g6yNBRx0dBXrNm Db8ZEQpP5BAp6QqiQTJvb9So0apVqw4dOkSXBvUXIsKmD1pJtkv8TWcaYEyc OJHaZ5sG2aZ0OU5UiPtCdulc4vjdN954g2NI6tj/rgCOMiLJ1IVXZjj33HNp 5DZr1qyDBw/u27ePRvX8EXni22+/jaugKYaMLqJdoh6ZPDhd++INU/tWWrJ5 cdQv2GQmcY18NIdIP8EOlAYn1Cxb048ZM2ZsJKyTV+2rK43P+S1LyvAnn3wi skHn5zxffPHFET9hKaOvo2GkU0cp3hns37///v37d4axP1WOtJSOxpD2mZGs q047WRuHKB6MGj+SItLffvvtEWuO9YYDZZXFuvPOO62RzDG8pHzHHXeIb2ju 2LFDLM5DNT/igTkGRxkfiXOUVEv52WsofDOKq0Eo/BKQtdEQD6SM0HimePHi LVq0mDBhgkhJvUD9+vVFmvz584vvrVvrdoKQiYBkPqm68pQbK/ylAFNDR0Gz pixUqFDHjh35/8amICe8PLW47xQKT6IQ/2/btm2Cym7TbpCaRn1FcHh2t6k1 kMw8dRB0BrGrWLFiIuUVV1wRRxmDQHIcpaNGwFtHmSNdMZxegyY8d5TUdPCr IlboMjEOYwgqMnfEJi699FL+j8lRSrZLc+fOtX5IKxQe3lSvXp16fOOiEPbC yV+e8V3ILlukOH6XhsG81zpKjAYcZUSSqcvo0aOFfQj9uzcJOX/1MnWR0YUa dtGAhMILXVIzztMv7SWTbF4c9Qs2mclJWCNv88zxvffe4110Qmv6aLRv3950 npjNCDXC4qZ6yZIlyRyJOND2efPmRTxKRl9Hw0injnLo0KHinKQUiUsezf5U jKSUjsaQNpmRtxKOOlkbR0l2mCtbyLBisP33KBnTy5LU+fL2Vq1aRYwkVVph HqmqkCOuWbOmaP1ol839YeEo4/CGiV7rldpw8Q2dUPjKfeeddw4fPsz1xNho kBO3D6nxe9/k0Pv06cN3igRUV0lW0yfXE4RkBCTzScleffVV46VEl8/48eP5 WqCgmU5LMRTXEUWyXr16ixcvFiuDmRxlTnheB0+6Flx88cXDhw9PXNkrVqwY +vdnXwR79uxp1qyZyAld49Qs0zXFi1CZWgP5zNOA7cYbbzQOEug6uv/++22+ 5hNw3OjIjjJPnjymNc+pEebgG9d6lW8EbKqN/d5Ro0bxya2P3SUrhtNr0Iib SIoXMUyd8tGjR9944w3xdTa+UijsEb86kZ6eLm6ihsIjrkceeWTv3r1lypSh Pz/66CNTepl2if5jv776lVdemZWVZV86gfzlGceF7LJFiuN3eVUHwjSnyIYf f/yRD/nyyy8lD0l1FNRl06ZNjz32mHgoyVSoUCHaC8JaIjl6WbRoEQ/RGZKA hhk5EpJJDnvk+wWbzDCJaORtHCU1zvy8m5g9e7YpfTSsjlKmGaEh+p133mk6 FT8pi3aIpL7yw8g4Oq8XX3zRmOG7777b/lQCGSkdjSFtMpPjxErId7L2s1jF GEl8KjEORykWMrK5D0YFf++998S3Zhgq6bvvvms/y1dlR5kTduVkxsnj29zO pXEgX+ZDhgxZsWLFvHnz5s6dS5fqsGHDxPe+rV4jJ9xBpKWl0fDV5usqicBR BHLk8kkVYNmyZd9+++2WLVtinpAGlhS0qVOnUpWWzMO+fftmzZpF2ZA5vw1O yx4RGl9RZqgIEadtWJHMPFe2adOmUXtrWjYZmPBER0lkGoEkZCBmxXB0DQoS F0kazFAPS/lZunRpzNfGqW2hCNM1Jf+CuU27xHc4r7nmGuojqHRzw0ycOLFn z57iLqt1IXR75C9PRxeyh/FHA+IhyupCnQ5dUNR70vViXf1Pe+R1oVCTxZgw YQI1ETwL0RExhz2O+oWYmUloI+8vNN5buHDh5MmT6d+Yn7KV1zeOYaQ8JDqp T3kmL+b00wwyUjoaQ8bMjKSViKOT9ReyzBs2bKA8U+nWr18v85VexR2lDPwh 13vvvTdiQHgFJ/uJZ0kmmaNx1Qhy2XUCOnqFfpEUUzSp/7XuFWvXm+bT+oV+ 8dcD6KIm0EVvoC9wgwaOkucztG7d2rorKyuL1yZq0qSJh7/okiBfs0Euu05A R6/QL5LCM5rW9mf69OnDe40Tz3xEv/jrAXRRE+iiN9AXuEEDR3nvvffydOhh w4bt2rVLbF+yZIlYqSnit1z9IsjXbJDLrhPQ0Sv0i6T48OUtt9yyfPlyMVXm wIED/fr147dvatas6XQuU4LQL/56AF3UBLroDfQFbtDAUS5YsMD4snzFihVv vfVWfoeXeffddz38OfcE+ZoNctl1Ajp6hZaRvP/++0XzW6hQoRtuuKFevXrn n38+b6lUqdKOHTv8zuP/j5bx1wDooibQRW+gL3CDBo6SWLp06X333SeWShYj mUcffVR8e0gdgnzNBrnsOgEdvULLSB47dqxPnz6XXXaZsUHOlStXlSpVhg4d mpwltSXRMv4aAF3UBLroDfQFbtDDUTIHDhxYs2bNzJkzFyxYkOTlWx0R5Gs2 yGXXCejoFRpH8tdff/35558XLVo0ffp0apmVMpICjeOf0kAXNYEuegN9gRt0 cpSpQpAjEOSy6wR09ApE0l8QfzWBLmoCXfQG+gI3wFEmnyBHIMhl1wno6BWI pL8g/moCXdQEuugN9AVugKNMPkGOQJDLrhPQ0SsQSX9B/NUEuqgJdNEb6Avc AEeZfIIcgSCXXSego1cgkv6C+KsJdFET6KI30Be4AY4y+QQ5AkEuu05AR69A JP0F8VcT6KIm0EVvoC9wAxxl8glyBIJcdp2Ajl6BSPoL4q8m0EVNoIveQF/g BjjK5BPkCAS57DoBHb0CkfQXxF9NoIuaQBe9gb7ADXCUySfIEQhy2XUCOnoF IukviL+aQBc1gS56A32BG+Aok0+QIxDksusEdPQKRNJfEH81gS5qAl30BvoC N/jlKAEAAAAAAAAA6AEcJQAAAAAAAACA+Ei+o4zjQG0IcgSCXHadgI5egUj6 C+KvJtBFTaCL3kBf4AY4yuQT5AgEuew6AR29ApH0F8RfTaCLmkAXvYG+wA1w lMknyBEIctl1Ajp6BSLpL4i/mkAXNYEuegN9gRvgKJNPkCMQ5LLrBHT0CkTS XxB/NYEuagJd9Ab6AjfAUSafIEcgyGXXCejoFYikvyD+agJd1AS66A30BW6A o0w+QY5AkMuuE9DRKxBJf0H81QS6qAl00RvoC9wAR5l8ghyBIJddJ6CjVyCS /oL4qwl0URPoojfQF7gBjjL5BDkCQS67TkBHr0Ak/QXxVxPooibQRW+gL3AD HGXyCXIEglx2nYCOXoFI+gvirybQRU2gi95AX+AGOMrkE+QIBLnsOgEdvQKR 9BfEX02gi5pAF72BvsANcJTJJ8gRCHLZdQI6egUi6S+Iv5pAFzWBLnoDfYEb 4CiTT5AjEOSy6wR09ApE0l8QfzWBLmoCXfQG+gI3wFEmnyBHIMhl1wno6BWI pL8g/moCXdQEuugN9AVugKNMPkGOQJDLrhPQ0SsQSX9B/NUEuqgJdNEb6Avc AEeZfIIcgSCXXSego1cgkv6C+KsJdFET6KI30Be4AY4y+QQ5AjJlX7Zs2fz5 81evXi1zQkeJ4yY7O3t+mF9++SWhP5QqBLkOewsi6S+Iv5pAFzWBLnoDfYEb 4CiTT5AjIFP2yy67LBQK3XTTTTIndJQ4br766qtQGDKwCf2hVCHIddhbEEl/ QfzVBLqoCXTRG+gL3KC+o8zOzh43blz37t1HjRq1atUqmUP27NmzNszRo0fj yGGicRqBrKwsKgv9a5PmwIEDkyZN6tGjx5QpU44cOeI2iwkDjlIP5OvwqVOn 6LL94IMPBg0aRNX49OnTCc5aipGI1gDIg5qsJtBFTaCL3iTHUcbsxX777bc5 c+b069dv8ODBK1euPHHihM3ZHI1+MzMzKVnPnj2/+OKLzZs3nzlzRjLPf//9 94oVKwaEmT59+qFDhyQPDBQqO8r09PSqVauG/k3jxo137dplcxTVruLFi3Pi 0aNHx5HDRCMZAbqmZs6c2apVqzx58lBZbrnllmgpX375ZWOIcuXK1bdvXy9z 7B1wlHogWYepxS5TpoyxclasWHHPnj2Jz2DK4HlrAByBmqwm0EVNoIveJNRR SvZis2fPvvjii42Vh+pStMdJ8qPf48eP00+bDEWNGjW2bdtmn+2TJ0+2a9fu vPPOMx5YpEiRESNGOCp+EFDWUY4ZMyZ//vysXalSpRo0aHD++efzn1WqVLG5 ZXH//fcL0VPXUWZlZfFFJ7jxxhsjpqSqzgmowtevX79AgQL8Z69evbzPumvg KPVARsdNmzbRlctxq1y58hVXXMH/L1++/O7du5OSzRTA29YAOAU1WU2gi5pA F71JnKOU7MXmz59PrpD25suXj8Z1devW5aNoZDt9+nRTYvnRL/16rVq1eO85 55xD1ZIsIf9ZtGjRnJycaNk+fPgwOV9xYJ06da688kpRBBoZuo+MTqjpKI8c OcL+kdqixYsX83yJY8eONW3alHV85513Ih44btw4Y41NXUeZmZlZ9B+oGke7 +jIyMriktWvX/u23386GQ1epUiXakjt37r179yYi/26Ao9QDGR3vu+++UPie 4dixY3nL0KFDOYxt27ZNeBZTBA9bAxAHqMlqAl3UBLroTeIcpUwv9tdff1Ws WJF2lShRQiy3uGrVqosuuog20mDv1KlTIrGj0e+TTz7JiVu0aPHrr7/SFrIV NK4jKzp8+HCbbHft2pUP7NKli5jpOm3atIIFC9LGYsWK/f77765jow9qOsqz 4TU877rrroMHDxo3Hj58mJ1m48aNrYfs37+f57tee+21qe4ojVx++eXRxpAv vvii9fIRF5qCjynlHeXNN9/Mf1Ijs3z58i1btkSc8R7TUdL1vmLFirVr19J5 7H+Xzv/zzz8vWrTo2LFjpl1wlCZi6pidnc23Fp955hnjdh5sFC5cGO0w42Fr AOIANVlNoIuaQBe9SZyjNBKtF1u8eDEPtIYNG2bcPnXqVOuQXn70u2vXrrx5 89LGBx980DSMjLl6P3lYcqMjR440bRdOk4aXEiUOCso6ymjwA+hy5cpZd3GT lS9fvrlz5wbBUZ48efKCCy4IRZqOftVVV4XCk0xcZtVz5B3lbbfdtnnz5saN G4vJz0WLFm3Xrh2V2prY6ij37dtHDc6VV17Jd8MI6uYeffTRiN3ZgQMHHnnk ETGtOleuXHTgpEmTRIJojvKll166IMyAAQMcxSHViakjBYQjtnDhQuP2iRMn 8vYvvvgisVlMEeAo/QU1WU2gi5pAF73x11F+9NFHXEn2799v2sVjWvGgwdHo 97nnnuPTbty40asiUPXmc44aNcqrc2pAyjnKmjVrkohUZ0zbx44dy/r27t17 27ZtQXCUO3fu5GK+//77pl2vvfYa7/rjjz9c5tZb5B0lIbykEWpAjG/RRnSU I0aMOPfcc63HRvSec+fOFUs5mRCTdiI6ypEjR/JGsr3GyRhBIKaOTzzxRCh8 E8AUmd9++43vYHfu3DmxWUwR4Cj9BTVZTaCLmkAXvfHXUXbp0iUUvqVvnZD2 7LPPhsJL9PCfjka/VapUCYUfUnhYhLS0NP6ViRMnenjaVCe1HOWxY8f4kdOT Tz5p3L5///5ixYrR9nr16lE7tnXr1iA4yuXLl3Mxv/32W9Oujz/+mHf9/PPP LnPrLY4cJUHNCJm4zMzMCRMmCN/32WefmRKbfOKiRYtoY9myZQcNGrR69erf f/99yZIlHEbCmIGjR4+WLFmSt7ds2ZKqdE5Ozvz582vWrFm3bl0xUdbqKFes WMGmtWrVqjwtP1DE1LFRo0YUnKuvvtq6ixdtaNWqVaIyl1LAUfoLarKaQBc1 gS5646+jFANX6xogvXv3DoXXxuFZao5Gv/zO4xtvvMF/ZmVlpaenx5zvak+n Tp2iZTXIpJajFBMqvvzyS+P2Zs2ahcKLQW3ZsoX+DIijnDx5MhfTNL2EIP/F u8hJucytt8g7yiJFipiW9lq/fj0vAlaxYkVxCyvarNe0tLTjx48bt1AoOCYd OnQQG8X72tQ+GBNTq2VscEyOklokXt2aTO727dsly64TMXWsXr16KMry4PxJ IG9vGKYucJT+gpqsJtBFTaCL3vjrKOfNm8cDre7duxu3z5kzR6zjyiMu+dHv wYMH+c/hw4fTQK5atWrigcXVV1+9aNGiOPJPg0MeAVaoUCGOwzUmhRzljh07 +FZDlSpV/v77b7GdbCNXj4EDB/KWgDhKcSuGrJZp1/z586PdwPEXeUcZcbGd O+64g8uVnZ0dM7GVQoUKUeJmzZrxn2RLeUvJkiV5rbBoGB3liRMnGjRoQP/P mzfvDz/8IPO7+hFTR74d3bx5c+suqsm0ixr2RGUupYCj9BfUZDWBLmoCXfTG X0dJgyte65UGV3379iXzuG7dut69extfYqItZ52MfletWiX8I/+ncOHC4uOS uXLlsn6UJCY8BVdZi+EjqeIoT58+fdttt7GIM2fOFNvJWVx44YV8T0w8twqI o/z888+5mGvWrDHtmjVrFu+aNm2ay9x6i0tH2atXLy6XWF/L3lH+9ddfU6dO paNatGjBc+mJ+vXr894dO3bwFmof7LMkHOXSpUvFY81on7AJAjF1vOSSSyhE 99xzj3UXxT8UXvE7UZlLKeAo/QU1WU2gi5pAF72Jqe/Bgwe3xyLmRFCbXmzu 3LnWBTQuuOCChx9+mP9/5MiRs05Gv2KdWKJSpUoLFy78+++/ySyQO+DnnjSG jPkhACN0Bp4sR/U54gcIgkyqOMr27dtzlTBNwqeGi7cvW7Ys6x/EEsRDhgyh P22+XuoLXo0hZ8yYwcWcN2+eadeYMWN416pVq1zm1ltcOsoRI0ZwuSZMmGCf eN++fR06dOC7DSbq1avHaURT8+GHH9pnSTjKli1bivM0adJEpshaElPHunXr UohuuOEG6y6+CXnXXXclKnMpBRylv6Amqwl0URPoojcx9W3cuLF1TGWidOnS 9r9i34v99NNPzZs3L1u2bCj8WYenn356y5Ytb775Zij8eJHTyI9+xRuXVapU EXPbGPEFEPlxMuWNF/QoWLBgRkaG5FHBISUc5QcffMC6165d2/hy3MaNG2PW beK6666LI5+Jw6sx5Jo1a7iAwl4JBg8ezLtUe2vYpaMUL9LOmjXLJvGBAwe4 8yIqV67ct29f+tGjR49yJIWjnDRpEqcZOnSofZaEo2TElIlvvvlGsuCaEVNH vtVTtWpV6y62+abFtQILHKW/oCarCXRRE+iiNyo4SsGff/4p/v/444+HDFOm 5Ue/5CL5T+s3JcVJxKr+9hw8eFAs8Dh+/HiZQ4KG+o5y4sSJvL5rqVKlTP5o 8+bNMes2UadOnTjymTi8GkNmZWVxAbt162ba9dRTT4XCU8RNX2/0HZeO8vnn n+ci79q1yybxtddeGwp/gNL06SuToxR3JDp27GifJaOjrF69+v79+8uXLx8K v4BJRtX+WC2JqWPbtm1D4e+/mD4ASpcwh7Fr166JzWKKAEfpL6jJagJd1AS6 6E1MfVetWjUtFvPnz7f/Fae92KlTpy699NKQ4QG3/Oj3zJkzPI32lVdeMaUU dTLmLDWC6jNP2w4Zlo0FJhR3lGlpaXnz5g2FH3ZHfDB97NixIxbEY+7hw4fT n6p93MHDMSQ/iTOt1E1X0EUXXRRS7+HsWXeOktoHfkejQIECNmu9Hjp0iNV/ 4YUXTGcwOUo6IX8hi05ib72FoyxduvSePXvOGr5G1KZNm9jF1o6YOor1skxr Qw0bNoy3z5kzJ7FZTBHgKP0FNVlNoIuaQBe9cdofxYfTXmzixIlceYxfjpMf /dauXTsUfr5peu1RvB8Xc3Ge48ePN2zYkBO3bNny9OnTkjkPGio7SlI5X758 fL/LUSUPyMo8RL9+/bikdGmIjVOmTOGNn3/+ufvceou8o6SGwvS69KBBg7hc jzzyiCmx0VFmZGRwsueee854+MqVKwsXLmx0lMSdd97Jia3fyd20aZP4v3CU U6dOFRubNm3KGxcsWBCz4JoRU0dqgYsWLUrBuf3220XzS7a9Tp06ofDLEWiT GThKf0FNVhPooibQRW98d5Q06uPVXAVHjx7lT9JQ5THe+Zcf/X799de8kfYa z9y6dWvezo8JzoZrLw32xo4da5xwS/8X64LSqM/4pQlgQllHOXPmTLFi8Ouv v05/fvfdd1MM2AzjNXCUK1asWPoPZcqUobLQNSi2iKeuWVlZ/JStZMmS6enp Z86coYurSJEitKVQoUL2X8TwBXlHGQp/dzItLS0nJ4eK2atXL15fi2qFmPJ6 9p+7TxQc0U9Rm8DTpM8//3yOSWZmZt++fflht8lRUlXhuxbEq6++Smc+derU mjVrHnzwQTqJ+Jqn6XuUzI4dO3itMMqnsf0JAjI6iinK7du3/+WXX6hfaNOm DW/p0aNHUrKZAnjYGoA4QE1WE+iiJtBFbxLnKGV6MRqttWjRgsa05Bazs7PJ XdKAtnLlylx5Pv74Y+MJ5Ue/NKhjD1u4cGHyETRWpC0ffvghDxSNX7rp1KkT /5aY10p5aNSoEW+kIeX06dOnTZs25d9QThIRsVRETUdJIloXEDZRvXr1aIdr 4Cj5O4nReO+990RKKmPu3Ll5O3uuUNh2zZgxI7HFiAt5RxmxAlBJTavodOjQ gXddeOGF1ErwRuOKrNzmhMJzZStUqBD6t6MkhgwZItLwT4j/v/jii5wmoqMk evfubWp/AoKMjjSW4BdaTZXz9ttvD5oBt8Hb1gA4BTVZTaCLmkAXvUmco5Tp xXbv3k32MOJgrGPHjmKAJ5Af/ZLZLFasGCcoEIb/X6JEiczMTJGMn1AQ119/ PW/p0aOHTbaZyZMnJyJiqYiyjtJYlyJSt27daIfv2rWL01iXgVIB92PIAQMG GBOPHTuWXzBkyJGpaSfPypWdb0mNHDmSvN4FF1wgylW+fHnrg2myeOXKleME W7Zs4Y3Hjh1r0aKFOJBamCZNmmzbtq179+4hi6M8G67JtWrVEi0SUbZs2S+/ /FIkGDduHG831XmqqJUqVQqFP8hLjWF8MUlFJPsdGlrcfffd4ilw/vz5H3ro IQwqjHjeGgBHoCarCXRRE+iiN345StGLZWdnG2sOD8a+/vrraKeVH/1u3769 QYMG/FySoR8yfU+kf//+vGvgwIG8RXxhxAZlx9vJR01HqTcJigBdL2S4FPc1 Tst++vTpjIyM2bNn23wGhdIsWrRo5syZpg5r165dtH3JkiWSn6/99ddfKf3c uXP37dsnn8Ng4kjH48ePr1y5Ul6IQIH20F9Qk9UEuqgJdNEbRfqjEydOpKen z5kzx/gA0Qb50W9OTs4PP/xAZTx27FjEBBs3btywYYOz7IJ/gKNMPkGOQJDL rhPQ0SsQSX9B/NUEuqgJdNEb6AvcAEeZfIIcgSCXXSego1cgkv6C+KsJdFET 6KI30Be4AY4y+QQ5AkEuu05AR69AJP0F8VcT6KIm0EVvoC9wAxxl8glyBIJc dp2Ajl6BSPoL4q8m0EVNoIveQF/gBjjK5BPkCAS57DoBHb0CkfQXxF9NoIua QBe9gb7ADXCUySfIEQhy2XUCOnoFIukviL+aQBc1gS56A32BG+Aok0+QIxDk susEdPQKRNJfEH81gS5qAl30BvoCN8BRJp8gRyDIZdcJ6OgViKS/IP5qAl3U BLroDfQFboCjTD5BjkCQy64T0NErEEl/QfzVBLqoCXTRG+gL3ABHmXyCHIEg l10noKNXIJL+gvirCXRRE+iiN9AXuAGOMvkEOQJBLrtOQEevQCT9BfFXE+ii JtBFb6AvcAMcZfIJcgSCXHadgI5egUj6C+KvJtBFTaCL3kBf4AY4yuQT5AgE uew6AR29ApH0F8RfTaCLmkAXvYG+wA1wlMknyBEIctl1Ajp6BSLpL4i/mkAX NYEuegN9gRvgKJNPkCMQ5LLrBHT0CkTSXxB/NYEuagJd9Ab6Ajf45SgBAAAA AAAAAOgBHCUAAAAAAAAAgPjArNdkEuQIBLnsOgEdvQKR9BfEX02gi5pAF72B vsANcJTJJ8gRCHLZdQI6egUi6S+Iv5pAFzWBLnoDfYEb4CiTT5AjEOSy6wR0 9ApE0l8QfzWBLmoCXfQG+gI3wFEmnyBHIMhl1wno6BWIpL8g/moCXdQEuugN 9AVugKNMPkGOQJDLrhPQ0SsQSX9B/NUEuqgJdNEb6AvcAEeZfIIcgSCXXSeg o1cgkv6C+KsJdFET6KI30Be4AY4y+QQ5AkEuu05AR69AJP0F8VcT6KIm0EVv oC9wAxxl8glyBIJcdp2Ajl6BSPoL4q8m0EVNoIveQF/gBjjK5BPkCAS57DoB Hb0CkfQXxF9NoIuaQBe9gb7ADXCUySfIEQhy2XUCOnoFIukviL+aQBc1gS56 A32BG+Aok0+QIxDksusEdPQKRNJfEH81gS5qAl30BvoCN8BRJp8gRyDIZdcJ 6OgViKS/IP5qAl3UBLroDfQFboCjTD5BjkCQy64T0NErEEl/QfzVBLqoCXTR G+gL3ABHmXyCHIEgl10noKNXIJL+gvirCXRRE+iiN9AXuAGOMvkEOQJBLrtO QEevQCT9BfFXE+iiJtBFb6AvcAMcZfIJcgQSXfZly5bNnz9/9erVifsJcDbY ddhbEEl/QfzVBLqoCXTRG+gL3ABHmXyCHIFEl/2yyy4LhUI33XSTTOLPP//8 oYceGjt2bOLyoytBrsPegkj6C+KvJtBFTaCL3kBf4IZUcZRZWVlr166lf+2T ZWZmTpkypWfPnl988cXmzZvPnDkTRw4TjXwENm3aNGrUqB49ekyePDlm2VMC dRzlunXrQmFy5869d+/exGVJS+R1PHXq1KpVqz744INBgwbRJXz69OkEZy3F cHpFSLaEQBL5+GdnZ48bN6579+7UJlOVjphmw4YNa6Nw8uRJU+K//vpr2bJl dGm8/fbb48eP37lzZ7Sf/u233+bMmdOvX7/BgwevXLnyxIkTssUL8/fff69Y sWJAmOnTpx86dMg+PV2zGRkZ1EL62IHG0VNQhDnU1G+adsnoQmGJlkbw559/ Gk/rUhcm5qDFUaVKNMlxHDFbOcnIxxE6l2NI+cNVuMqsoGcHblDcUVK7MXPm zFatWuXJk4cG/7fccku0lMePH6dkoX9To0aNbdu2xZHJhCITgZycnNatW5uK 88ILL9DYICl5TBTqOEoav5GXpMR58+alXkBsp3HFrWF27NiRuHymOpI6Upda pkwZYx2uWLHinj17Ep/BlEEykvItIXCETPzT09OrVq1qao0bN268a9cuU8r8 +fOHojBx4kSRjEazr7/+er58+YwJqDlq06bNr7/+ajrn7NmzL774YmNKuqai WVoT9EPt2rU777zzjIcXKVJkxIgREdNv2bJl4MCBl19+OadcuHChzK8kgjh6 irfeeouzfcUVV5h2yehCA+NoaQSffvqpOKcbXRjJQYtkpUoOCe3BJVs5+cg7 Cp3LMaT84epcZVbQswM3qOwos7KyuGER3HjjjdFS1qpVi9Occ845lStXpk6T /yxatCi5szjymThiRuDMmTM333wz579cuXKNGjW66KKL+E9yOsm/Lekh6jjK s+GOqUuXLqb2fMOGDRxq+k9i8qgDMjpu2rSpVKlSHEy6JGmYx/8vX7787t27 k5LNFEAmkvItIXBKzPiPGTNGjEupPjdo0OD888/nP6tUqWJ8OPLnn39GG74S 48eP52TG3ipXrlzkVcVlQjRr1sz4zGL+/PmUhraT/aRmrW7dulwTChQoMH36 dPuiHT58mMbkolusU6fOlVdeKX7oq6++MqV/4YUXTHmmX3cUTA9x2lOsWbNG XCMmRympi4yjFInd6MJIDlokM580EteDS7Zy8pF3FDqXY0j5w5W6yqygZwdu UNlRZmZmFv0HukijtTDEk08+yVW6RYsWfI/39OnT1GOed955w4cPjyOTCSVm BEaOHMnFeeaZZ/ihJP377LPP8kbam6SMJgClHGVEyGBynOEobZDR8b777guF x8ziTdWhQ4dybNu2bZvwLKYIMpGUbwmBU+zjf+TIEfaPNGpavHgxz+w6duxY 06ZNuSa/8847IjHJxBvffffdXRb++OMPTkbDy6uvvpqSPffcc2T6zoZ7K8pD hQoV+PBly5Zxyr/++qtixYq0pUSJEmK1sVWrVvENRmrrTp06ZVO0rl278gm7 dOkiZrpOmzatYMGCtLFYsWK///67MX27du24jnECf8e6jnoK8vXVq1cXQ3ST o5TXxbqXWLduHbkVOvz666/nCuBSF0Zy0CKZ+aSRuB5cppVzFHlHoXM5hpQ/ XKmrzAp6duAGlR2lEZ4hEHEcRY1D3rx5ae+DDz5ompH+yy+/xJHDRBMzAv/5 z3+oOJdccgm1n8bttWvXpu2VKlVK3SnryXGUN998s9iycePG9PR009jJhm+/ /VbGUZI0dNqMjIxUn4ccHzF1zM7O5lvHzzzzjHE7d0aFCxeWV0RvnF4RNi0h iIOY8Sd/d9dddx08eNC4kZwgO83GjRuLjT/++CM3HeTa7H909+7d3333nWnj N998w4cPGjSIt5CH5S3Dhg0zppw6dSpvHz16tM2v0OiaBrrWO5DCaa5YsSLi gV9//bXvY11H10W3bt14iFuvXj2ro5TXJSJk/OlYGv+L6YsudTnrZNDiMvOe k+genInWyjmKvHzoXI4h4ztchavMCnp24AYNHCU3+AR5hzjyk3xiRqBkyZJU nGeffda0fcKECVzSJDTpCcK+7GPGjLkgzPfff2/cPmvWLN7ep08f43Zqu4oX L07b33rrLd7CjvKOO+6glrxt27alS5fmiJ1zzjnUAJre369ZsyYdS8NF/vOT Tz6hoYiY0kb/4R+lUYrxqO3bt992223iHahzzz2X2lJ+1hAcYtbhAQMGcHxM k4onTpzI27/44ovEZjFFgKP0l7hHyDyhtFy5cmLLokWLuG6vXLkyjhMuXbqU D+/evTtv+eijj3jL/v37TYmvuuoq060zecQ0jFGjRkVMoMJYV16X1atX8xC3 dRiro3Sjy/Lly/l52cCBA8VG97rID1pcVirP8ddROoq8fOhcjiHjO1yFq8wK enbgBg0cZZUqVWgXDfLjyIwv2EeAXI9pXCGgVpR3GdcHSC3sy56RkcEF7NSp k3H7Sy+9xNvr169v3D579mzeLm5CsqOkYZ547d3IK6+8YjzcNEW2V69e1kNC 4TcFxCFTp04VljNv3rzivY+yZcsG6rX0mFfxE088EQq/QmKaAPbbb79x0Dp3 7pzYLKYIcJT+EvcIuWbNmiQEjWPFlrS0NG4N4nuZqG/fvny4mEvWpUuXUPjR m3U1SH4JokyZMnH8kMhntHVdVBjrSupC3SVPIS5VqtShQ4ceffTRkMVRutGF 5wXRv0YJ3OsiP2hxWak8x19H6Sjy8qFzOYaM73AVrjIr6NmBGzRwlDwd/Y03 3uA/s7Ky0tPT1ZzvysSMAL/1/OSTT5q2U+/JL3S8+eabCcxfIolZdn6qWKNG DeNGHjOEwsshGpV9/fXX2dlRa8Zb2CQyd99997fffrtt27aBAwdyW1ekSBHj 2xMmR7ljx465c+e2adOGD6ej5oYR019pxHLhhReGwi9x0GCM5Pjrr78+//xz FsWql8bE1LFRo0YUExLOuourd6tWrRKVuZQCjtJf4uuPjh07xo+ujFf9qFGj uOmYOnUq9UetW7fu1q0b2cPjx4/bn43GZmPGjOFpD5dccolI//HHH/MJrZ83 6t27dyg89SKOhdo6deoU7bSMCmNdSV3EDF6KOf35yCOPWB1l3LqIh1wUEON2 97rID1riznyC8NdROoq8fOhcjiHjO1yFq8wKenbghlR3lAcPHuSrcvjw4V99 9VW1atWEoaA6Tz1CHDlMNDEjcMMNN7D9oXGL2Ejm5a677uKiPfbYYwnPZWKQ vAOWK1euI0eO8Jbs7OxQeHIpTwaePHmySHzttdfSFgqX2CIcJfUdxtuYYlnv jIwMU2LTMj7iSYH1PUqeUkWu1rRQef/+/Wk7mVbKqmQcUp2YOvJCGRGXf+cP MaTQpIKEAkfpL/H1R2Lq15dffik2fvDBB6FIkElMS0uznmTfvn0dOnR44IEH ypUrxylJ1u3bt4sE8+bN4+2m+Spz5szhu1iEMb0MNNDlLy9UqFAhWhoVxroy ulA7LOa78paIjtKpLoIHH3wwFL5/aFrQwKUujgYtcWc+QfjrKB1FXjJ0LseQ cR+uwlVmBT07cEOqO0rqU8TFy/8pXLiw+PwWGRPJpbyTScwIiHtrtWrVohYp MzNz0qRJZHxES9WsWbNkZdZjYpZ97NixXMZvv/2Wt4wePZr+vO666x5++OFQ eIFE3p6Tk8PDiZ49e4rD2SRef/31ptOK5XNnzJhhSizpKMmfcr3673//azo5 GX8+hPo7mSBogORz9ubNm1t30VVMu6jnTVTmUgo4Sn+Joz/asWMHP5WoUqWK cWEuMYIldV599dVXXnmFZ8aGwjfErHeoVq5caRro/vjjj8YEJ06c4JUt8+bN S+0SDZXXrVvXu3dvOps4irY4yrxYM9xm9RgVxroxdSGXx6/OXXrppeIjnvaO UlIXhrpdXm5FPHsSuNTF0aAlvswnDn8dpaPIS4bO5Rgy7sNVuMqsoGcHbkh1 RynW+AqFF0FduHAhdfE0+Kfuku9ZkWsw3WD0nZgRoPzffvvtIQtkqS644AL6 T7t27ZKVWY+JWfZDhw7xdLIXXniBt5CD426d38qnmsDbv//+ew7L0qVLxeHR vh5CRpITi3eUoiWO5iip8+Lt1DEtt8C7on03XD9i6kjDYwrIPffcY91Vv379 UPjVpERlLqWAo/QXp/E/ffr0bbfdxtf7zJkzTXupeZk7d64x8ZtvvsmJrY3S zp07W7RoQVKKb4XT+POtt94yTq6gs1m/0k69AN9eI8RcDhmof+Rv+dE1aH0T TaDCWDemLuKVOmMmIzrKsw51YYYMGcIJTDafcaOL00FLHJlPHDF1OXjw4PZY RJtuLbBp5RxFXiZ0LseQcR+uwlVmBT07cEOqO0oxmK9SpYppzqF4w8I0R9F3 ZCJATd+gQYNq1qxJjVK+fPluvfVW8lPULrHbev/995OSU++RKXudOnWojFWr VuU/y5YtGwo//iOLx4Lu2LGDtnfu3DkUXpHV+JggmqOcM2cOHxu3oxSv+dtg WopWY2LqWLdu3dC/JyQL+CazWGI34MBR+ovT+Ldv354vdsnXhaglr1GjBqWn YXC0zxTS+HP27NmcLGS5MfXTTz81b96cm8Fy5co9/fTTW7Zs4YFx4cKF5XNO 5ylevHgo/CEM4+R/KyqMde11WblyZe7cuSmHDzzwQJaBZs2ahcJfWuc/bc4f U5eHHnqIIxztW11x6+J+0CJTqRJEzOulcePGMTvK0qVL2/+KfSvn5oqwhs6l HHEfrsJVZgU9O3BDqjtKfsmOsH51a82aNbzLaCJUwFEEqAEU37wgJ8UlmjRp UqIyl2Bkyi7a4f3792/evJkb/z///JPGXTzjgkdc3LKZ7pUlzlGK71RWrlz5 piiMHz/eeUhSkpg6ki4hw20BI7y6UaAWMrIBjtJfHMVfzKOrXbu2/NIoL7/8 Mh9F416bZNSXcfsWbaVQagPF/x9//PGQkwlmBw8eFMtfx2ymVBjr2utCRjKm bSGo2bf5CXtd+MFxw4YNY2bVqS6eDFokK5XnqOAoBfFdEabQuZQj7sNVuMqs oGcHbkh1R0kug6dAmD4MQezdu5cv2A8//DCOfCaO+CJAfPrpp1wi1Z66yiNT drHCHhlnnul666238q6WLVuGwt8R/v333/klysGDBxuPTZyjZG9L0KjSYaE1 JKaObdu2DYVvBZi+dyyuyq5duyY2iykCHKW/yMd/4sSJPEWEfF/MaXtGxES7 mO88PvbYY5zS/vu2p06duvTSS0PSjwPoGuQJaaFIbwVaUWGsa6+LmOJoj/0b cDa67Ny5k3e9+uqr8nmW1MWTQYt8pfKWmNcLDU6mxSJmvXLayjm6IkyhcylH 3IercJVZQc8O3JDqjvLsP1+MqlatmunFkMWLF8t0K8knbkfJy4hVqFDBOM8z tZApO5WOv/n48ssv873ot99+m3fxmkUlSpSgk7C4ZPSMx7p3lP369eOUJttO ueLl/bGU2VkJHXk9pZBhhSVm2LBhvN3+8UFwgKP0F8n4p6Wl8TothQsXdnpD r0mTJnQgtR7iuwbR3mHk5yzEgQMHbE4oPib+2Wefxfz148ePN2zYkNO3bNky 2hxOIyqMde11+euvv45EomnTptxF8p/2n/Cw6iIQL8eZvhtij7wu7gctNplP KHGPXhzhtJVzdEVYQ+dSjvgOV+Eqs4KeHbhBA0cpLswpU6YYt/O3HgjVPj0v E4GMjAzjjA6CmkouzieffJLAzCUYSfXvu+++UHiuPk8DW7FiBW8X98H4S9Zl y5Y1HejeUY4YMYJTUgtpOgm/pBOSmDamPTF1pHFs0aJFKVa33367GMRSD84v yZYrV05mZBsE4Cj9RSb+NCDku0n58+ePlnjNmjXVq1dfu3at9fy8GI5YsGL1 6tUXXXSRdZC5f/9+/kASXR1iI1kn00Ooo0eP8gL+lMzoJuiK++qrr6h9M3Yc 9H+xjhC5LclbkSqMdeMbJ1hX5pHXxYjoBWbPnh3xh1zqIjloiS/zCcV3RykZ eUehkx9DulHThApXmRX07MANKjtK8hFL/4FfaqB2RmwRC4afOnWK25/ChQt/ 9913VJ9py4cffsgzlCKucuwvMSOQnp5esGDBWrVqLVu2jMYAZKN69OjBzeAl l1wiXqtMRSTVHz58eOgfzj//fOPiA5UrVxa7rB/ycO8oRcp69epRf3TmzBnx osru3bt5VfC8efP26dOHvxZKcsydO/c///nPN9984yAQKY6Mjs8//zxHsn37 9r/88gv1+23atOEtVJ+Tks0UQCaSki0hiIOY8Z85c6b4NsHrr79Of1IvM8XA ggULzv7zLTaynG+99daiRYtowEm6kDHh6RbUeouFYfkrA7SFLpDvv/+eklEj T3m45ppr+FdefvllTkmNT4sWLfLkydOvX7/s7GwaSy9evFg0gB9//LExn506 deLtYl4rpefPkXMrSh522rRpU/6NWL4mMzNT1Khu3brxUX379uUtEdc7TShe OUp5XYz07NmTI0D237rXpS5npQct8WU+oSTOUcq0cvKRdxQ6+TGkGzXPqneV WUHPDtygsqMsVKhQKDrvvfeeSEmtSrFixXh7gTD8/xIlStAlHEcmE0rMCNC4 RRSTV7RjLr300ogdXAohqb5YgyhkWXunXbt2Ypf1k2ruHSX1BeXLlxc/Qdae xpPihqRYElxUMCFQpUqVpMOQ8sjoSH3NtddeK2LFt0RC4XubpufvQUYmkvIt IXCKffxpyGr9VIGJ6tWrnw2v3MUfqRTttqjwROfOnY2/yB+BYmjYya+EM/Xr 1xf3DHfv3s1PLcU5xf87duxoWuSTp96FDF/jpdGdfc6JyZMnc2KqRTbJqAZ6 GvXYeOUo5XUxIr7aSRJY97rUhZEZtMSX+YSSOEcp08rJR95p6CTHkG7UPKve VWYFPTtwQ+o6ygEDBhgTb9++vUGDBnxTiLn77rtNizkrgkwERo4cKT5SFgp/ kLdp06aHDh1KSgYTiLz6ZNC47IMGDTJuN37FY//+/aaj+HYltWym7T/88AMf YnSU0RKTba9QoYKxM1qzZo3Yu3Xr1oYNGxr7snz58j388MMbN26UKZceSOpI XQ9dhjxjMBS+Y/zQQw+h0zHi3lGaWkLgiJiO0nilR6Ru3bqceM+ePW3atOGH IILLL7982rRpptMeOHDg+eef56URBXRgnz59/vjjD2NK6sKMV1AoPNU/4st9 /fv35wQDBw7kLWLRbBtmzJjBie3Huuedd168AY6T+JwLf7zYtBClvC4C/nQI EW1FXze6CGQGLXFkPqH45ShFKycfeaehk5HDpZqqXWVW0LMDN6jsKOMgJyeH vAOdn2ckqol8BPbt2zd79uyVK1em9ExXIwlV30P+/vvv9evXT58+feHChRHr Eo000tPT58+fv2nTptRdKCluHOlIsaI6vGTJEpvvRAeWVLkidMXz+PN7XvPm zVuwYIH9DJmTJ09mZGR8//33c+bM2bp1q00zQu0/tTaUzP6EGzduNK1Qnbr4 qIs8nugiM2hJRObjQ5H2SjLyZ52HLqYc7tVUGfTswA2aOcqUIMgRCHLZdQI6 egUi6S+Iv5pAFzWBLnoDfYEb4CiTT5AjEOSy6wR09ApE0l8QfzWBLmoCXfQG +gI3wFEmnyBHIMhl1wno6BWIpL8g/moCXdQEuugN9AVugKNMPkGOQJDLrhPQ 0SsQSX9B/NUEuqgJdNEb6AvcAEeZfIIcgSCXXSego1cgkv6C+KsJdFET6KI3 0Be4AY4y+QQ5AkEuu05AR69AJP0F8VcT6KIm0EVvoC9wAxxl8glyBIJcdp2A jl6BSPoL4q8m0EVNoIveQF/gBjjK5BPkCAS57DoBHb0CkfQXxF9NoIuaQBe9 gb7ADXCUySfIEQhy2XUCOnoFIukviL+aQBc1gS56A32BG+Aok0+QIxDksusE dPQKRNJfEH81gS5qAl30BvoCN8BRJp8gRyDIZdcJ6OgViKS/IP5qAl3UBLro DfQFboCjTD5BjkCQy64T0NErEEl/QfzVBLqoCXTRG+gL3ABHmXyCHIEgl10n oKNXIJL+gvirCXRRE+iiN9AXuAGOMvkEOQJBLrtOQEevQCT9BfFXE+iiJtBF b6AvcAMcZfIJcgSCXHadgI5egUj6C+KvJtBFTaCL3kBf4Aa/HCUAAAAAAAAA AD2AowQAAAAAAAAAEB+Y9ZpMghyBIJddJ6CjVyCS/oL4qwl0URPoojfQF7gB jjL5BDkCQS67TkBHr0Ak/QXxVxPooibQRW+gL3ADHGXyCXIEglx2nYCOXoFI +gvirybQRU2gi95AX+AGOMrkE+QIBLnsOgEdvQKR9BfEX02gi5pAF72BvsAN cJTJJ8gRCHLZdQI6egUi6S+Iv5pAFzWBLnoDfYEb4CiTT5AjEOSy6wR09ApE 0l8QfzWBLmoCXfQG+gI3wFEmnyBHIMhl1wno6BWIpL8g/moCXdQEuugN9AVu gKNMPkGOQJDLrhPQ0SsQSX9B/NUEuqgJdNEb6AvcAEeZfIIcgSCXXSego1cg kv6C+KsJdFET6KI30Be4AY4y+QQ5AkEuu05AR69AJP0F8VcT6KIm0EVvoC9w Axxl8glyBIJcdp2Ajl6BSPoL4q8m0EVNoIveQF/gBjjK5BPkCAS57DoBHb0C kfQXxF9NoIuaQBe9gb7ADXCUySfIEQhy2XUCOnoFIukviL+aQBc1gS56A32B G+Aok0+QIxDksusEdPQKRNJfEH81gS5qAl30BvoCN8BRJp8gRyDIZdcJ6OgV iKS/IP5qAl3UBLroDfQFboCjTD5BjoBM2ZctWzZ//vzVq1fLnNBR4rjJzs6e H+aXX35J6A+lCkGuw96CSPoL4q8m0EVNoIveQF/gBjjK5BPkCMiU/bLLLguF QjfddJPMCR0ljpuvvvoqFIYMbEJ/KFUIch32FkTSXxB/NYEuagJd9Ab6Ajeo 7yizs7PHjRvXvXv3UaNGrVq1Klqyv//+e8WKFQPCTJ8+/dChQ3HkLTk4jUBW VtbatWvpX/tkmZmZU6ZM6dmz5xdffLF58+YzZ864ymVigKPUA/k6fOrUKbps P/jgg0GDBlE1Pn36dIKzlmIgkv6C+KsJdFETeV0kR24MiZiRkbFu3bqY4xan 45w9e/asDXP06FGZbDMHDhyYNGlSjx496LeOHDkSLVkKDTslkdf3t99++/77 76ngb7/99uTJk0lu414KxdpY/Pnnn8ZD4gtmfPpKHq6fvolGZUeZnp5etWrV 0L9p3Ljxrl27jMlOnjzZrl278847z5isSJEiI0aMiCN7SUAyAnTBzpw5s1Wr Vnny5KES3XLLLdFSHj9+nJKZAlWjRo1t27Z5mW8vgKPUA8k6TD1+mTJljNWy YsWK1IYnPoMpAyLpL4i/mkAXNZHRRXLkxmzZsmXgwIGXX345J1u4cGG008Yx ziFjWLx4cU45evRoyTK+/PLLxp/IlStX3759TWlSbtgpieR1N378+NKlSxvL XrBgwXfffVckGDRoUCgWn376KSeOO5jx6StzuK76JhplHeWYMWPy58/POpYq VapBgwbnn38+/1mlSpUTJ05wssOHD5PV4u3nnHNOnTp1rrzySlEByAjEkcNE IxOBrKwsNpKCG2+8MVrKWrVqiQhUrlyZqj3/WbRo0ZycHO8L4AI4Sj2Q0XHT pk105XLcqFpeccUV/P/y5cvv3r07KdlMARBJf0H81QS6qElMXSRHbswLL7xg chnz58+PeNr4xjn333+/OLOk4yAfwenJTdSvX79AgQL8Z69evUSaVBx2SiJz 3S1YsIBKzYW97rrrnnjiCeEuhUmUcZRkS8+6C2Yc+socrrG+iUZNR3nkyBFu haiPWLx4Mc9jOXbsWNOmTVnTd955h1N27dqVt3Tp0kU8kp42bVrBggVpY7Fi xX7//fc4MplQZCKQmZlZ9B/44o3mKJ988kmOQIsWLX799VfaQuGiOk/t4fDh wz3PvEvgKPVARsf77rsvFL7BO3bsWN4ydOhQDmPbtm0TnsUUAZH0F8RfTaCL mtjrIj9yY8i+8SCHB2yh6I4yjnHOuHHjjP5FxnFkZGRw4tq1a//2229cokqV KtGW3Llz7927l5Ol4rBTEpnrrkaNGqHw7YLNmzfzlqNHj5LtCoXdPYtOHn9X JNatW8cm/frrr+eUcQczDn0lD9dY30SjpqM8G17D86677jp48KBx4+HDh7m9 aty4MW85deoUNTUjR440HS6qxIoVK+LIZEKRjICAJ4REdJR0hebNm5f2Pvjg g6YXCtRclVTeUd588838519//bV8+fItW7ZEfGMipqOka5/qwNq1a+k89r9L 5//5558XLVpEPaBpFxyliZg6Zmdn80P2Z555xridB4GFCxdGm8wgkv6C+KsJ dFGTmLpIjtxMfP311zaOMo5xzv79+3lC47XXXivvOF588UWTeTxrsJniMWUq DjslianvH3/8QfGhMvbr18+4nYTjsm/dutXm8Oeeey4UniIr5irHF8z49JU8 XGN9E42yjjIa/DC6XLly9skWLlzI0o8aNSru30oQHjpKvjyJjRs3epa/RCLv KG+77bbNmzdTBySm0BQtWrRdu3YnT560JrY6yn379lHvcOWVV4rpGTT8ePTR RyMOMw4cOPDII4+IyTm5cuWiAydNmiQSRHOUL7300gVhBgwY4CgOqU5MHSkg HDHTezETJ07k7V988UVis5giIJL+gvirCXRRk7jHb/YjN3tHGcc4h28s5MuX b+7cuZKOg4YW1JWHIq1ZcdVVV4XCs6ntz6DysFOSmPqSF+MyDhkyxLh99+7d vH3evHnRjl2+fDmPxwYOHBgzJ/bBjENf94droG+iSTlHWbNmTRKULnD7ZGlp aSw99S9x/1aC8NBRVqlShc2XZ5lLMPKOkhBe0gi19sZ3MSI6yhEjRpx77rnW Y60pCWpSxNvZJsRkqoiOcuTIkbyRbO+pU6fchCXliKnjE088EQrfBDBF5rff fuMnC507d05sFlMERNJfEH81gS5qEvf4zX7kZu8onY5zqOPms/Xu3Xvbtm2S lmHnzp2c8v333zfteu2113jXH3/8YXMGlYedksjoyy+03nrrrcZ1lSdNmsRl t3mFuXbt2qHwjGKZLxHYBDM+fd0froG+iSa1HOWxY8f4FseTTz5pn7JTp04s vXH2giJ46Ch5Xvcbb7zBf2ZlZaWnp6s535Vx5CiJZ599lkxcZmbmhAkThO/7 7LPPTIlNPnHRokW0sWzZsoMGDVq9evXvv/++ZMkSsZqcMQNHjx4tWbIkb2/Z siVV6ZycHOrUqPurW7eumChrdZQrVqxg01q1alV+rSNQxNSxUaNGFJyrr77a uosX02jVqlWiMpdSIJL+gvirCXRRk/jGbzFHbvaO0tE4Z//+/cWKFaP09erV O3Xq1NatWyUtw/Llyznlt99+a9r18ccf866ff/7Z5gwqDzslkdH37bff5mI+ 8cQTvCYSxZkMpnUkZoRHZQRpLZOTaMGMW1/3h2ugb6JJLUcpJrp8+eWXNsmo qbn44ospWYUKFeL7oYTilaM8ePAgR2P48OFkeapVqyaMGPWzdP16mWmPkHeU RYoUmT59unH7+vXrc+XKFQqvDy/ucUWb9ZqWlnb8+HHjFjKVHJwOHTqIjeJ9 f2orjIlPnjxp7LBMjpJ6NK5gZHK3b98uWXadiKlj9erVQ1E+ecMLy6fQg/WE gkj6C+KvJtBFTeIbv8Ucudk4SqfjnGbNmtGuAgUKbNmyhf6UtwyTJ0/mlNYv mEyYMIF30Sgi2uGKDzslkdGXRkcPPPAAB6R06dLvvvsuj6Py58+/du3aaEc9 +OCDlKZEiRIxV7Q4axvMuPV1ebge+iaaFHKUO3bs4FtVVapU+fvvv21SPvvs s47qWJLxylGuWrVKtKv8n8KFC4sP6JD5MjkyFZB3lBFvdt1xxx1cOvE5XUdr vRYqVIgSU5PCf5It5S0lS5bkhd2iYXSUJ06caNCgAf0/b968P/zwg8zv6kdM HfkxQfPmza27qCbTLhoYJCpzKQUi6S+Iv5pAFzWJY/wmM3KzcZSOxjk05OPt 4k09ecsgHkSuX7/etEssO2N9fClQfNgpiaS+q1ev5rWSjNgEJzMzk9OLB832 RAumG31dHq6HvokmVRzl6dOnb7vtNhZ05syZNikXLlzIT7Lq168vM1s7+Xjl KKdOnSqu5UqVKlHBqbmmIlOF5/WZyW3J3A5KJi4dZa9evbi8Yq0te0dJxaco 0VEtWrTgdzG4YvBe6ul4C7UV9lkSjnLp0qXisaZpIfRAEVPHSy65hEJ0zz33 WHdR/EPhlykSlbmUApH0F8RfTaCLmjgdvUiO3Gwcpfw4Jzs7+8ILLwyFn1yL sZ+8Zfj888855Zo1a0y7Zs2axbumTZsW8Vj1h52SyOg7adIktoeNGzdu1KgR F5zVibbQ65AhQzjNjz/+GDMP0YLpUl83h2ujb6JJFUfZvn171t3+5YiffvqJ 37YrWLBgRkZGHNlLAl45SjHtn7ySeGbHiFWOV61a5T7DHuLSUY4YMYLLNWHC BPvE+/bt69ChAzcgJurVq8dpRFf14Ycf2mdJOMqWLVuK8zRp0kSmyFoSU8e6 detSiG644QbrrooVK9Kuu+66K1GZSykQSX9B/NUEuqiJ09GL5MjNxlHKj3Pu uece/nPZsmVZ/7B48WLeSKaG/uT3/iIyY8YMTmldrXTMmDE2A6qUGHZKElNf soS88tV///tfXhSLtojIly5d+vDhw9ajHnrooVD44bJxMZ+I2ATTpb5xH66T vokmJRzlBx98wKLXrl3b9HKckYMHD4rVV8aPHx9H3pKDV46SWlcurPW7OWvW rOFdYrVSRXDpKMXrGLNmzbJJfODAAR5UEJUrV+7bty/96NGjRzmSwlGK1cmG Dh1qnyXhKBkx5eabb76RLLhmxNSRW++qVatad7HNj7m4VkBAJP0F8VcT6KIm jkYvkiO3s7aOUnKcs3HjxpAE1113XbQ8iLOJ+9WCwYMH8y7rkiypMuyUJKa+ 999/P5W0WLFipgnM3bt35yCYlqRgypQpQ7saNmxo/+s2wXSpb9yHa6ZvolHf UU6cOJFXCStVqpTNCku///47z3UJSU/V9guvHOWZM2f4+xqvvPKKaRcFikMR 8+lbknHpKJ9//nku165du2wS81dr8+TJY/okmclRikamY8eO9lkyOsrq1avv 37+/fPnyofALmGRU7Y/Vkpg6tm3bNhR+Vd/0AVBRM7t27ZrYLKYIiKS/IP5q Al3URH70IjlyY2wcpeQ4Z/PmzTKWoU6dOtHykJWVxWm6detm2vXUU0+Fwu9s mj6HnULDTkli6nvRRRdRYdu0aWPdVaFCBdpVs2ZN03bxWZZXX33V5sz2wXSp b3yH66dvolHcUaalpfGE7cKFC9tM4Dx+/HjDhg1Z95YtW8Z8sO4vXjnKs/98 36datWqmqd3iUb5qi/O4cZTUmPO7MwUKFLBZ6/XQoUNc9hdeeMF0BpOjpBPy /A06iamnMCEcZenSpffs2XPW8GWiiE2r9sTUUbwCb3pbf9iwYbx9zpw5ic1i ioBI+gvirybQRU0kRy+SIzeB/ddDJMc5x44dO2JBTJodPnw4/Wn/qS+e2mT6 JA39KNso0wOs1Bp2SmKvL4WCP5oW0V7dddddtItiZdouXi+y+W6ITDBd6uv0 cC31TTQqO0pqJfLlyxcK34e0OeTPP/8Ur343bdrUfhlYFfDQUYp2eMqUKcbt rVu35u1sf9RB3lFSq25aVmjQoEFcqEceecSU2OgoMzIyONlzzz1nPHzlypXU uxkdJXHnnXdyYutHjTdt2iT+LxwltY1iI1U23rhgwYKYBdeMmDpSa1y0aFEK zu233y6aYrLtderUoY3lypVD+8wgkv6C+KsJdFETmR5ccuRmxN5RuhnnRFt6 heoPdetjx46lAaTY2K9fP05MXlVspB/ljZ9//rnYmHLDTkli6ku2OhR+p9U0 jfn3338vXbo07brjjjtMh4jlL2bPnh3xnG6C6Uhf+cN11TfRKOsoZ86cyTdD iNdff53+/O6776YY4GE8mQ7+0jFx/vnnU1M2bdq0Kf8mKysrjnwmDpkIrFix Yuk/8BR08pVii7iRcurUKfab5JUoPtSH0pYPP/yQZ5tEXFndX+QdZSj83cm0 tLScnBxSsFevXrzWFtUKMeX17D93LykIYvxALQkXn+pDenr6mTNnMjMz+/bt Kxa7NjpKak+47wuFp2TQmSmAa9asefDBB+kk4uNTpu9RMjt27OC15iif9q2W fsjoKKYot2/f/pdffjl69GibNm14S48ePZKSzRQAkfQXxF9NoIuaxNRFcuR2 NvxFCTGk6datGx9CPTVvMS4K6macE80yiK/VGx+30UiDpy2VLFmSBw9kLYsU KUJbChUqJD4xlorDTkli6ivejW3SpImYzHzw4EGx7s1HH31kOqRnz568a/Xq 1dYTugymI30lD9dY30SjpqMkQXnmvA3Vq1enlNRx2CcjJk+eHEc+E4dMBPg7 idF47733REpq8YoVK8bbC4Th/5coUYJa7MSWxDnyjjJiBcidO7dpFZ0OHTrw rgsvvJBXHiOMK7JyB8HB4Xn+Rkd5NryutUjDPyH+/+KLL3KaiI6S6N27N28P 2hx7GR1pjMcvtDJijfHbb789aAbcBkTSXxB/NYEuamKvi/zIjaBhjE0yGgIZ zxz3OCea4+B70cT1119v3E7JxBhA1CjyyDNmzBBpUnHYKUnM645ctjCP+fLl IzXr1KnD3xsl7r33XuvHNcSXHHfv3m09octgOtVX5nCN9U00yjpK48A+InXr 1j1rWD7aBmNToALuHeWAAQOMibdv396gQQO+X8fcfffdpnW2FUGm7JUrVw6F F3Yjr3fBBReIQpUvX946v5QsXrly5TjBli1beOOxY8datGghDqTuoEmTJtu2 bePlyEyO8my4JteqVUt0H0TZsmW//PJLkWDcuHG83VTnqaJWqlSJtufNmzdi a6krkjMNaMhHVVE8BabBxkMPPYTBnhFE0l8QfzWBLmoS01FKjtzOxnKU5513 nunk8Y1zdu3axYlNK7j279+ft4uP3QvGjh3LKzYwl112mWkMmYrDTklkrru/ //576NCh5OWN5S1evPjgwYMjrkfBnw4hIq736zKYcegb83CN9U00ajpKvUlQ BHJycn744Qc6M/kpz0/uFU7Lfvr06YyMjNmzZ9ssFkdpFi1aNHPmTNNAgtoK 2r5kyRLT+5jR+PXXXyn93Llz9+3bJ5/DYOJIR+pHVq5cKS9EoEAk/QXxVxPo oia+j988HOds3Lhxw4YN0faSgV2wYEGgbhSfdaIvjbt27txJIaIh044dOxR8 bdleX5AI4CiTT5AjEOSy6wR09ApE0l8QfzWBLmoCXfQG+gI3wFEmnyBHIMhl 1wno6BWIpL8g/moCXdQEuugN9AVugKNMPkGOQJDLrhPQ0SsQSX9B/NUEuqgJ dNEb6AvcAEeZfIIcgSCXXSego1cgkv6C+KsJdFET6KI30Be4AY4y+QQ5AkEu u05AR69AJP0F8VcT6KIm0EVvoC9wAxxl8glyBIJcdp2Ajl6BSPoL4q8m0EVN oIveQF/gBjjK5BPkCAS57DoBHb0CkfQXxF9NoIuaQBe9gb7ADXCUySfIEQhy 2XUCOnoFIukviL+aQBc1gS56A32BG+Aok0+QIxDksusEdPQKRNJfEH81gS5q Al30BvoCN8BRJp8gRyDIZdcJ6OgViKS/IP5qAl3UBLroDfQFboCjTD5BjkCQ y64T0NErEEl/QfzVBLqoCXTRG+gL3ABHmXyCHIEgl10noKNXIJL+gvirCXRR E+iiN9AXuAGOMvkEOQJBLrtOQEevQCT9BfFXE+iiJtBFb6AvcAMcZfIJcgSC XHadgI5egUj6C+KvJtBFTaCL3kBf4AY4yuQT5AgEuew6AR29ApH0F8RfTaCL mkAXvYG+wA1+OUoAAAAAAAAAAHoARwkAAAAAAAAAID4w6zWZBDkCQS67TkBH r0Ak/QXxVxPooibQRW+gL3ADHGXyCXIEglx2nYCOXoFI+gvirybQRU2gi95A X+AGOMrkE+QIBLnsOgEdvQKR9BfEX02gi5pAF72BvsANcJTJJ8gRCHLZdQI6 egUi+f+x995xVhPf/39A6SICopSFBUU+dOkgoHTwDaKAAqIgurxpAuqbJkhV qihIU0DeVEXpRRBYFnggyALLg85SRHZpuwus1EVgafs733t+ziPv5N5kbpKb zE3O84997J1Mkplzpr2SyYyzkP3FhPwiJuQXd0P+JcxAitJ+vGwBL+fdTZAf rYIs6SxkfzEhv4gJ+cXdkH8JM5CitB8vW8DLeXcT5EerIEs6C9lfTMgvYkJ+ cTfkX8IMpCjtx8sW8HLe3QT50SrIks5C9hcT8ouYkF/cDfmXMAMpSvvxsgW8 nHc3QX60CrKks5D9xYT8IibkF3dD/iXMQIrSfrxsAS/n3U2QH62CLOksZH8x Ib+ICfnF3ZB/CTOQorQfL1vAy3l3E+RHqyBLOgvZX0zIL2JCfnE35F/CDKQo 7cfLFvBy3t0E+dEqyJLOQvYXE/KLmJBf3A35lzADKUr78bIFvJx3N0F+tAqy pLOQ/cWE/CIm5Bd3Q/4lzECK0n68bAEv591NkB+tgizpLGR/MSG/iAn5xd2Q fwkzkKK0Hy9bwMt5dxPkR6sgSzoL2V9MyC9iQn5xN+RfwgykKO3Hyxbwct7d BPnRKsiSzkL2FxPyi5iQX9wN+ZcwAylK+/GyBXjyHhsbu2XLln379tmSIsII Xi7D1kKWdBayv5iQX8SE/OJuyL+EGUhR2o+XLcCT9+eee06SpFdeeYXngnPm zGnfvv1PP/1kQeIIbrxchq2FLOksZH8xIb+ICfnF3ZB/CTOIryjPnz+/dOnS ESNGzJgxY8uWLffv3/cbLS0tbdOmTePGjZs6dWpcXFx6erqBtNlDUBaAjEB2 pk+fPnbs2JiYmBs3boQyaSHHWkV58OBBycdjjz0G5cSaJBIcBFuLk5OTDxw4 AH9DlqJwhSzpLJbb/+7du7GxsZMmTRozZsySJUsSExM5r5ySkrJ48WLo6ebN m7d3717F0dTU1AN63LlzR36KyT7R2S6V3y+QzvXr13/11Vdg8JUrV4IZdU85 d+4cWuzq1avmYz548ODQoUPQGT169IgnwQzdomLA6aGG3y/a5VnOpUuXli9f PnLkyFWrVl25ciVQtGALpGG/8CfJfVjr3yNHjgQqt/fu3dO+PrgPLgu1Y8qU KRD/4cOHuknSra2Qnvnz5w8fPnzWrFk7d+4MqmCEkcRwEJEV5b59+8qXLy/9 LyVLlly7dq0iZnR0dOHCheXRihQpot2IOQi/BbZu3fr000/L8/X444+PHz/e QAspCNYqSuiCQUtC5CxZsiQlJVmTRIIDzjIMjfCGDRs6duwI5RbcVL9+/dAn LcwgSzqLhfaHMdKgQYOyZs0qb7GhgYqKitJ+Erhnz56yZcsqerpXX331zJkz LA4MqyQ9Zs+ezeKb7BMd71I5/QJCrFChQvJ05syZc8KECRqngFhgveqPP/5o JuaJEycmT578/PPPY5xt27bpJhjhLCrBOt0GePzCU54Z/fr1k0fLlCnT2LFj 1dGCKpCG/RJUklyJtf7Nnj17oHK7bNkyjVscP34c/CuP/8ILL4Bg1DhFu7am pqa2b99ekYa6detCUdHOLOJ4exguCKsop02bBjIBfffUU09Vrlw5c+bM+BPa 4d27d7OYW7ZsgSqP4SBDqlevjp1+jhw51q1bZyCFoYbTAtOnT8eMAPny5StV qhRmExg1alTokxkSrFWUGb6aPnjw4GC7DMIkPH5MTk5mBRh5+eWXbUldOEGW dBar7A9xqlSpwoagMOJ69tlnWfxWrVoFegy4aNEiNu6CU1566aUnn3wSf5Yp U4Y9CecRFyCvMLLJPlGELpXHL1u3bmWjgtq1a3fu3JmpSw2d9eabbzKLaStK 7Zi9evVS2B/sxpM1/qISlNPtQdcvnOUZ6dOnDx7KlStXrVq1oIDhzy+++EIe LagCadgvQSXJrVjo3zt37hgrt8eOHWM1onTp0iVLlsT/ixcvfvbs2UBnadTW hw8fNmrUCA/lzZu3U6dOjRs3xp8gVG/fvq1tExHaw3BBWEU5f/58cFlERERM TAy+705JSRkwYAAWg6ZNm2K0u3fvQpGAkAIFCrC1XPbu3VuwYEEIBG3y4MED A4kMKTwWSEhIwEKbJ0+eDRs2YBcDPVGTJk2gA9KdqyMslitKwhF4/JiUlPTU P+DAj3SQGrKks1hl/5s3b1aoUAEO9ezZ86+//srwDWPgyiVKlMA+KzY2Vn3l K1eu4HgMRk07duzAnu7atWstW7bEs8aPH8+uf8YfBw8exEFvnTp18HSTfaIg XSqPXypVqoTD2uPHj2MI9IzVqlWTfE+h/U6TW7x4sXxYq6EodWOC9MAikTNn zqCUC39R4Xe6bWj7hb88A4cOHcLAqlWrpqWl4emlSpWS/vczlmALpGG/8CfJ xVjoX2g2MXDChAnqMvz3338Hukvr1q0l38MWtj7G9OnT8VLdunXze4p2bV29 ejWGDxw4EIoTBv7www8YOHHiRA2DCNIehgvCKsoM38MQbG8ZIKxKly4t+d7Z YQiUaiwV3333nTzmL7/8ottlOAWPBbp27YqNGDRx8nCowtDLhDBxIYZfUdar V4+FxMfH79mz59atW0HdCwwFLcCuXbuwa9AGOgsoS9evX9eNCWJ/27Zt/B+y QTK2b9+emJgYvnOV1fDXYgQnIJEOUkOWdBYL7X/27FkYuigCf/75Z+yJpkyZ 4veCIB9atGhx+fJleSB0fDhye/XVV7XTA6pE8k31PHXqFIaY7BMF6VJ1/QIj UvzkYdy4cfJwkA+YzpMnTypOuXjxIs6Lq1mzpnZe+GNmyIam/MrFWFFhqJ1u G7p+4S/PvXv3Vis1punYO0HDBdKAXziT5GIs9O/Ro0fRburv1DRISUnBlynd u3eXh6PMzJ07t3ocqFtbBw0aJPleOiuWYYFmHMI7dOigkR5B2sNwQWRF6Zcm TZpglccnA99++y26FQqVIma5cuUUwkQQdC0ArRl+YdG2bVu7EmUT/IqyadOm oO+6devGJjJlzpwZGhnFtJnKlSvnzZsXmjh5IPSzr7/+uvQPmTJlgsIgn7e/ ePFiOKtkyZLQwowdO5Y9HMY5SDt37lQkCcTg0qVLGzRo8NRTT7HLFi5cODo6 WhETr1yqVCn4f9WqVS+99BKbLxcREaG+cphCOsgqyJLOEmr7Q5XH6j9ixIig Ela/fn04KzIyUiPOrl278J3p5MmTWaDJPlGQLlXXL5A8TOe0adPk4SDWMHzz 5s2KU3BQCn1rTEyM9miQP2aGIeXiF86i4tfptmF4/KYoz/fu3YOOUvL3STIW s+LFi+NPwwUyWL/wJ8nFWOVfYPv27Wj/uLg4/ut89dVXeJbiUyYYv2H4/Pnz Fafo1lZ8ApM/f37FiR988IHk+5pSIz2CtIfhQngpyhs3bhQoUACciCN2YPDg wSgE1C+AevToIfm+nzV2r9Cha4ElS5ZgGf7999/tSpRN8CtKaJrYl/Vy+vfv r44snyJ75cqVokWLsvhs6gswa9YsjLNw4UIMefHFF9W3yJYtG4hBdkEodX6j ST6Rqxi3sCsPGjSIfffKyJEjR2pqqkkbigDpIKsgSzpLqO0/duxYrPvB7nBU uXJlOAsGLRpxqlatKvlm6Mm7P5N9oiBdKo9f8GvEhg0bymd+Ll++HA2u+OQK 7I/ho0aNOnXqlN+RZ7AxEasUJWdR8et02zA8flOU58TERMyses7hp59+iodw YqThAhmsX/iT5GKs8i+wZs0avzVRm86dO0u+WeuK2aRpaWn4cH7AgAHycJ7a yt4nKrKGK3+CrtRIjyDtYbgQRoryzJkzzZo1w4Ixffp0DJwxYwaGqKe4QwHD Mb/uMsU2o2uBL7/8EsswTvm+f//+3r17//zzT5u/mAgF/IoSee2111asWAEN xeTJk9mHpfJWXa0oWb/8ySefXLhwAdqBw4cPv/HGG5UqVWILrTPdJ/kePM6f P//cuXP79+9/7733MLBYsWLyCRKNGjUCd7z99ttr165NSUlJTk7+7LPPMKbi CZX8ynnz5h0/fvyBAwfi4uLYfIzRo0ebtqLzkA6yCrKks4TO/jAiWrRoEc42 KVq0qO76D3KuXbuG76G6dOkSKA57BQAjZ3m4yT5RkC6Vxy9jxozBpMIoFD8G AZuDwJRUn+FfvHgxf/78EF6jRg2Ic/LkSb8jz6BiMswrSv6iEsjptmFs/KYu z7t27cKMQP+uiMxKIIx5MkwUyGD9wp8kF2OVf4F58+ah0UDQwXgJhNuwYcNA AGq3hDjIr1ChgvoQLtfTsWNHFsJZW2Hgh5NyIfLWrVsxkE2PZyF+EaQ9DBfE V5Qw2o+KioJuAj+ayJkz59SpU9njgs2bN6O7FRNFNm3axBbpOn36tIF0hg5d C3z44YeQ7IIFC16+fLldu3ZsKa3s2bP37ds32M8JhSIoRQntj/y5ELQkGC7/ tlStKN966y3JNwVC0XDJaz3TfW3atFF8O9m2bVt1owSSVl3a2XJh0Jyqrwwj T/nC1HAFfGXZunVr7eyHBaSDrIIs6SyW2//ChQsff/wxtEKRkZHYFEDkYPsg NvVrwYIFgeJA1yD51otga00gJvtEQbpUHr9Ak46tPVCoUKEJEybAgBY7ygMH DshjtmrVSvJNEcE2WUMn8sdkGFaUBopKIKfbhjHFoS7PK1euxBD1Ou1Lly7F QzhHy3CBDNYv/ElyMVb5F5g0aZLkj6JFi65ZsybQpSpWrCgF2J4JtyyBcRcL 4a+t27dvz507Nx6Fs2CchlL07bff1s6aIO1huCC+omzevLm8NI4cOVK+pW96 ejouxJQlS5axY8eCZw8ePDhq1Khs2bKxUyDEQDpDh64FWrRoIfm0M/u+75ln nmFTKGvWrBm+Lyv5FWWdOnUU4XPnzkUL/Prrr4rIckUJfTRG09jwiOk+9eOp 2NhYPNSjRw/tdH799dfqAsauHBMTo4hfrFgxCK9evbr2ZcMC0kFWQZZ0Fsvt HxcXpxg+HT16NKgkJSQk4Fz9MmXKKJaSYCQlJeHuWp999pnikMk+UZAuldMv +/btY7uMMRTvmGB4ieHsw8NAI0/+mHIMK8pgi4qG023DgOLwW57Zq5/Dhw8r 4rOXR+hHwwUyWL/wJ8nFWOXfDJmihNZy4MCB/fv3x5mxku/DoiNHjvi9Gr6I bNOmjfoQLqRTvnx5/BlUbb137x6IR0VDUbVqVd1pzIK0h+GC+IoSxu3/+te/ oCiy7YBLly597NgxFgGG7up9VPPmzcvKz5UrVwykM3ToWoBtViX53tNBP5Lh WwKLvRT7/vvvbUqr1fArSvXuISAkMfvyz0zUkUESssVwoOSsXbtWvbyzhqIE tY6n+11lEXr8WbNmvf/++9AWsUdeGzdu5LkyaElJ9glwWEM6yCrIks5iuf0T ExPbtm0LEdgO3ZkyZRo+fDjnV2/Q/rB2fsOGDYGiTZs2DeP41SAm+0QRulQe vyxfvhwVFrTVzZo1Yw9doY1lC71Cv5kvXz7J99aDucDvyJM/pgLDijLYoqLt dHsItr4EKs9z5szBwP379ytOgf4UD7E1Qo0VyGD9ElSS3IpV/kVgqCZ/tA6R hw4dipEDbQ+Hi2C8/vrr6kO1atWSfDIwI8jampaWBreTfEvF9u7dGzf+kHwT VqG66eZRhPYwXBBfUTIuXrwI3sdeA3SEfE7jH3/80aZNm4iICMm3okvXrl1P nDiBRReKkIF7hRRdC7DPRRWriEO5RRWjWNo0jDCjKDdt2oRm0VaUGb5l2OWL skJ/PX78ePk0IQ3dB+Dqsoo1MeCmuCSCmvXr1/NcuXbt2hIpSuJ/IUs6S+js D+Oc6Oho3DNRki0Lps1HH32E8eWfC6lp37499m6B5quY7BMd71J1/QKqCh/9 vf/++/jMEELYEt/QhuPWYywkNjY2+R/YjgCg0eAnfoPJH1OB+e8oOYuKrtNt INj6Eqg8s+fD6iV5Fy1ahIf27t3LAg0UyGD9EmySXIlV/g0EFF0s56DR/O7k iA/e/a6/iu8KcfQbVG3t0KGD5NtzEFedTU9Phwi4yCcwZswY3WQ73h6GC2Gk KJERI0ZgMZg9e7b6qHxCLK6ywl6Ri4OuBbp37y75FodRH8J6FL6qxB5FCVy6 dAmKCrYACCjEq1ev4lFtRfnEE09IvtnFLITN38iWLRu0TqBYjx8/Pn/+fAwk RakL6aBAkCWdJdT2T0lJwXlcPEsCsnamatWq2utX4FutBg0a6F7TZJ/oVJeq 65c333xT8i21oZgYzEYIffv2jY+PlziAlpk/pjolVq31qltU+J0eOoKqLxrl ef/+/Xho6dKlirOmTp2Kh9RroWQEUyCD9YvhJLkJq/yrQb9+/fAs+UITDBzi li1bVn0IX0p26dIlqNp6+PBh/PnNN9/Ir3bmzBn8fjlHjhzqnUECERYSw0HC TlGePn0ai0fv3r01oj148AA/WxPwdZ6uBcaNGyf5JsCkpaUpDuHKA3ny5Alh +kKJbYoSgWKwatUq/KBb8i0JiOEauu/y5cuKyL/++iu+GX/ppZfkG/uyDosU pS6kgwJBlnQWG+zfqVMnbBPwrVkgli1bhuslgqzQHriybQ4GDhzInwyTfaLN XaquX3DqWlRUlPoQrj9QuXLl48eP84w8q1Wrxh9TfTurFGWGZlEx5nTL4a8v 2uU5OTkZszNs2DDFoX//+9+Sb/yjvYSmboEM1i/mk+QCrPKvBmziq9/PD7t1 6yb53mAqlqCEW+BZQ4YMCaq2zpw5E3+qNzFZsGABHtJYKSgQIksMBxFWUQb6 lCAhIQHLQM+ePTVOZ9uh/ve//zWQyJCia4G1a9di4tVryzRq1ChQvxYW2Kwo kZs3b+J8CfbsV0P3sc/zx44diyG9evXC3uTSpUvymKQo+eOTDgoEWdJZLLR/ oD6L7UmkaEDkwJAGPwnMnTu37sw6tr1aUFtImOwTbe5Stf0CpsaVMfyuUYNL 24HkzPDta3BFBdsnAkab8PPGjRtBxVRgQFEaKCrGnG45nPWFpzxjp6zYJwIs g88K/L4OlqNbIA34xWSSXICF/g0ELraZNWtWv/KcrbejWATpu+++w3AYB2YE U1txj6HHHntM/noRgZSz+EFlIUNsieEgwirKdu3atWnTRv3ZApvTMm/ePAy5 e/eu4lnH1atXcQniyMhIAZ8p6Vrg4cOHoDsg/VWqVJF/MfHnn39iLfb7YDYs sEFRnjhxIj4+XnFu/fr1IdqTTz6JP5nuW7RokTza9evXixcvju0Pu0jr1q0l 30fcKSkpLGZ6ejrr/UlR6kI6KBBkSWexyv779u2DYee6desU4RcvXnzmmWew Mwp0TTgL153Lnj07T2JmzZqFjUx0dLTfCPx94u3bt6HJghZVPtwSoUvV9Qs2 p2XKlFFMt7t16xZ+CN+0adNA5/Kst8MfU1u5qC1srKjoOt0eeOoLZ3nGuVjA jh07WOCqVaswcM6cOSzQWIEM1i9BJcmtWOXf/fv3g4MUm/jg9XHGFy6wowb8 gotgNGnShI1+wcXVqlVDd2t8ROy3trJxI5MMjIkTJ+Kh2NhYdncx28NwQUxF uWLFCnR06dKlZ86cCWP7R48epaamDh06FHelzJMnz4ULFzJ8j4/atm37+OOP Q1MAA35wPTQFcBaePmPGDAMpDDU8FmCbw77xxhs4ASYhIQG/aAYLqNciCxdC rSjxdSQ0d8OHD8eZGH///TfExxMbNmyI0ZjuA53Yq1cvaIhAIf7+++/lypXD 8G7durFbDBo0CAN79OgBvoAGBMoYtm+kKDXYvXv3zn/AL4BgNM5C/D7t9yBk SWexyv4VKlSQfDMZPvzwQ2gQIPD+/ftw5RdffBEbhH79+vm9+IYNG9hC9NDU wM/Vq1evkqFuST7//HOMD9pEfcGg+sS+fftiOHvZJ0iXqusX9g1X8+bN2aS7 y5cvsyU7vv3220DnmleUSUlJrAAMGzYM44wdOxZD5Guxqi1srKhoO902dP3C X56Tk5NxbSXQ0Xv27IGCByUNhnYQ8sQTT7BPfoIqkGb8wp8kF2OVf/FTI5Cc MBLbvn07CDQo57NmzcLd1aHwayxkjRuyAx999NH169dBvkVFRWHIyJEjNdLm t7aC17DRzpUr1w8//MCmB6xcuRI3PYmIiGDLNgrbHoYLYipKGN7jsmYMtnUI lkY27fns2bP4WA9BvYl88sknfteSchweC0AX07FjR5Zf/GAfGTJkiC3JDAmh VpRxcXHy8gD/s11os2TJAn0ERmO6zy/Q48snHR0+fJg1oZl94P9MfpKi9Asu cBSIr7/+2paUig5Z0lmssj9cJG/evCwQWgm2hxFQq1Yt6NTUV4bxiXpdegUV K1ZUnNWjRw88pP4yKCPIPpGtX832/xWkS9X1C4z0mHiE4QFYqVq1ajhElHxP YjW2azGvKNlmxH6BAsNiqi1srKhoO902tP0SbHkGq7ICxjZ/gd5WvuV0UAXS jF/4k+RirPLvihUrWGVErzFjAgMGDNBIA6jImjVrssjsxCZNmqhnrsoJVFtj Y2OZgihYsCB4HKeiSb6mY9euXSymsO1huCCmokSWL1+OG9DIqVevHtMFSEpK ymuvvSaXnBEREc5+a6ANvwXGjx+Py1sh8D8IlhCnLrTw5B2f/0DroQj/7bff 0A5yRamODM3Rf/7zH7Y0NFKpUqVt27axOEz3zZ49u0GDBiwalKKoqCj1qmXQ PLI9jCTfMryTJ0+Gfh+bF7miXLx4McbZuXOn4iJ4I1KUyFdffWVLSkWHLOks Ftr/0qVLH374obzFlnwz7UePHh1oH20YocmHKH6pXr264iz2uDXQ+or8feKX X36JEdgu4UGdHjo4n7tOnz5d0dQ//fTTU6dO1Z6KdubMGYysXtWTM6a2csmV KxeL6dfCBoqKrtPtQVdxBFueoTfHLQiR5557Tq3d+AukSb/wJ8mtWOjfc+fO wWgKX0oynn/+eZ49PWEUJ/c4yFgo/9pyMkOzXh8/frxly5aKpLZo0UK+tX2G wO1huCCyokSSk5N///33devWxcbGamwkCsN7UJqbNm1KSkoykCo7CcoCjx49 Onny5JYtW+Lj4x3chcoqgvW+GS5cuLDZx/nz5xXPq5miRJmZmpoKqnDXrl3y PSsVQD8OBWzr1q3OPiIWBDv96G7Iks5iuf1Byxw6dAjaE+iMoOlW7G1hJ5x9 IvQsR44cMXx6iOD3C3SLiYmJ0DLHxMQkJCQI2EsGsrA4RYWfELVXp0+f1u1b LS+QgfzCnyT3Ybl/8SNEGIaBMYN1HAy64uLiYPyvMTALips3b+7du3fjxo3w 1+/eshmitofhgviK0n142QKC5F17P0pCF0H86ALIks5C9hcT8ouYkF/cDfmX MAMpSvvxsgUEyTspSpMI4kcXQJZ0FrK/mJBfxIT84m7Iv4QZSFHaj5ctIEje SVGaRBA/ugCypLOQ/cWE/CIm5Bd3Q/4lzECK0n68bAFB8k6K0iSC+NEFkCWd hewvJuQXMSG/uBvyL2EGUpT242ULCJL33377raEPxUpfBCeC+NEFkCWdhewv JuQXMSG/uBvyL2EGUpT242ULeDnvboL8aBVkSWch+4sJ+UVMyC/uhvxLmIEU pf142QJezrubID9aBVnSWcj+YkJ+ERPyi7sh/xJmIEVpP162gJfz7ibIj1ZB lnQWsr+YkF/EhPzibsi/hBlIUdqPly3g5by7CfKjVZAlnYXsLybkFzEhv7gb 8i9hBlKU9uNlC3g5726C/GgVZElnIfuLCflFTMgv7ob8S5iBFKX9eNkCXs67 myA/WgVZ0lnI/mJCfhET8ou7If8SZiBFaT9etoCX8+4myI9WQZZ0FrK/mJBf xIT84m7Iv4QZSFHaj5ct4OW8uwnyo1WQJZ2F7C8m5BcxIb+4G/IvYQZSlPbj ZQt4Oe9ugvxoFWRJZyH7iwn5RUzIL+6G/EuYgRSl/XjZAl7Ou5sgP1oFWdJZ yP5iQn4RE/KLuyH/EmZwSlESBEEQBEEQBEEQ7oAUJUEQBEEQBEEQBGEMmvVq J162gJfz7ibIj1ZBlnQWsr+YkF/EhPzibsi/hBlIUdqPly3g5by7CfKjVZAl nYXsLybkFzEhv7gb8i9hBlKU9uNlC3g5726C/GgVZElnIfuLCflFTMgv7ob8 S5iBFKX9eNkCXs67myA/WgVZ0lnI/mJCfhET8ou7If8SZiBFaT9etoCX8+4m yI9WQZZ0FrK/mJBfxIT84m7Iv4QZSFHaj5ct4OW8uwnyo1WQJZ2F7C8m5Bcx Ib+4G/IvYQZSlPbjZQt4Oe9ugvxoFWRJZyH7iwn5RUzIL+6G/EuYgRSl/XjZ Al7Ou5sgP1oFWdJZyP5iQn4RE/KLuyH/EmYgRWk/XraAl/PuJsiPVkGWdBay v5iQX8SE/OJuyL+EGUhR2o+XLeDlvLsJ8qNVkCWdhewvJuQXMSG/uBvyL2EG UpT242ULeDnvboL8aBVkSWch+4sJ+UVMyC/uhvxLmIEUpf142QJezrubID9a BVnSWcj+YkJ+ERPyi7sh/xJmIEVpP162gJfz7ibIj1ZBlnQWsr+YkF/EhPzi bsi/hBlIUdqPly3g5by7CfKjVZAlnYXsLybkFzEhv7gb8i9hBlKU9uNlC3g5 726C/GgVZElnIfuLCflFTMgv7ob8S5iBFKX9eNkCoc57bGzsli1b9u3bF7pb EBneLsPWQpZ0FrK/mJBfxIT84m7Iv4QZSFHaj5ctEOq8P/fcc5IkvfLKKzyR 58yZ0759+59++il06XErXi7D1kKWdBayv5iQX8SE/OJuyL+EGcRXlCkpKYsX Lx4xYsS8efP27t2rEfPSpUvLly8fOXLkqlWrrly5YiBt9sBvgWPHjkGuIUcr V65MTk4OcbrsQBxFefDgQcnHY489dv78+dAlyZUE60covQcOHHBHGbYWsqSz 8Ng/NTX1QADOnTuniHz//v3du3d/5WPdunVwbqDL8sdMS0vbtGnTuHHjpk6d GhcXl56eHkwWg7gRcOTIkfnz5w8fPnzWrFk7d+589OhRUPeyCv568eDBAxgY TJo0acqUKeCRhw8fhjhpniak7RXUJqxWV69e1Y3M43cozIFq7r179zCORu1m 3LlzhzO/kKpDhw7B6MKpimMSa/3LY/9AQKO3fv16aLLGjBkDA2AQAoGicbaN d+/ejY2NhQIDF1yyZEliYiJ3Lv9/wkViOIjIinLPnj1ly5aV/pdXX331zJkz 6sj9+vWTR8uUKdPYsWMNJM8GeCxw8+bNDz74QJH3Xr16wdjAljSGCnEUJbQn oCUhcpYsWZKSklg49C8NfSQkJIQuneEOpx+htd+wYUPHjh0ff/xxMHX9+vVD n7QwgyzpLDz2Hz9+vBSAcuXKsWgwRurTp0+uXLnkEfLkyQPSTHFB/phAdHR0 4cKF5TGLFCmi/XDV2I2g6Wvfvr0ig3Xr1j1x4gTPvayFs14cP34crCFP8Asv vKCW+YRVhK69guH6008/jU788ccftSNz+j179uyBau6yZcswDgjSQHEYs2fP 1k0/VJPJkyc///zzeMq2bdt0TxEQa/3LY3+/gOIrVKiQPH7OnDknTJigiMbZ NkIzOGjQoKxZs8pjwvAvKirqxo0buplFwkhiOIiwinLRokWsND777LMvvfTS k08+iT/LlCmjeBABnSYegq6zVq1aOXLkwJ9ffPGFgRSGGl0LPHr0qF69epiF yMjIZs2aFSxYEH+C0tF9tiMy4ijKDF9zNHjwYEXLf+TIETQ1/BOaNLoBHj8m Jydjd8N4+eWXbUldOEGWdBYe+3/66aeBxkXQGWGcv/76C4ZVGJg5c+Zq1ar9 3//9H4u2cOFCdjX+mMCWLVtg6ALhMByCZq169epYEqCPW7dunXayg7rRw4cP GzVqhIfy5s3bqVOnxo0b408Yq9++fZvfpJbA45djx47B2AATWbp06ZIlS+L/ xYsXP3v2rC3J9Byha6/efPNNFl9bUXL6/c6dO4GqLQCaBaPxKEoWORC9evVS nAI1VzfLAmKhfzntr2br1q3QXmG02rVrd+7cmalLubTnbBshtVWqVMHTIX7Z smVZ4QFatWrF8zY5vCSGg4ipKK9cuYL6EdqKHTt24HyGa9eutWzZEv04fvx4 FvnQoUMYWLVq1bS0NDy9VKlSkqgTGnUtMHfuXMxR9+7d8aUk/O3RowcGwlGb EhoChFKUfgGBiXYmRakBjx+TkpKe+gfsIEgHqSFLOguP/bt27Qo2L1y48BkV bHrDkCFDsN0YPHgwm1a6du3anDlzQmD+/Plv3boVbMy7d++CmoPAAgUKsNXG 9u7diw8Yoa178OCBRrL5bwSsXr0aIw8cOBDui4E//PADBk6cOJHLmtbB45fW rVvjKJF9Cz99+nRMcLdu3UKeRE8SovZq8eLFcrmhrSg5/Q7JwMAJEyaoa+7f f/+N0W7evKk+Chw8eBCFQ506dXSnUoPiwPxi5ZJcrSg5/ctpfzWVKlWSfC+S jh8/jiFXr16tVq0aBMId0Rf8bSP4t0KFChDYs2fPv/76K8P39AzyWKJECUxe bGysdn7DTmI4iJiKMsO3aGeLFi0uX74sD4TygErz1VdfZYG9e/dWe5aVAQGf IehaAJ8VFy1alPXsCJRnCIeSHL6fitijKOvVq8dC4uPj9+zZIx87abNixQoe RQmugctCMQv3ecjGCNaPOBeIdJAasqSz8Nj/rbfewuGERhwYwHTp0kX9uI/J ut27dwcbc8eOHRjy3XffyWP+8ssvPANv/hsBgwYNknxP4BUNGpQ0CO/QoYPG jUKBrl9SUlLwlUT37t3l4Sg3cufOzd/mE/yEor26ePEiznetWbOmbsHm9/vR o0fxamvXruVPMAMEiOSbbHnq1Cn+s9hDGBcrSjka/jVmf1Ca+DnSuHHj5OFg T7zayZMnM4JsG8+ePbt69WrFjX7++WeMOWXKFO0khZ3EcBBhFWUgcBpPZGQk /rx3717evHklfxO5y5UrJ/kmQhi+V4jQtcAzzzwDKe/Ro4cifOnSpViGQyrK Qop23hctWpTXx/r16+XhGzduxPDRo0fLw6HvgJ4IwocPH44hqCibNm16/fr1 bt26sckSmTNnhg5IMVm6cuXKcG6LFi3w5/fff1+yZEk2uRr+wZvWqFFDftbp 06cbN27M5uRny5YN+jJ89uUdSAdZBVnSWXjs37BhQ7B58+bNDVyfzXmYN29e sDG//fZbDIEhtyIy9m7yR2cmk4Tj5/z58ysi4+f8devWNXAjM+j65auvvsJc KD5bWLZsGYbPnz8/tEn0JKFor1AMQpcaExOjqyj5/b59+3YMiYuL408wsmvX Lnz7Nnny5KBOJEXJMGZ/aOvwrGnTpsnDQRVi+ObNmzOsaBt37tyJVxgxYoRG tHCUGA4SdooSVIAkWw8hMTERS4V6Wg77+EXj9bojaFsAVE+gcs7qGs934mKi nXf22Kdv377y8P/85z8YXqtWLXl4dHQ0hrOHYKgoIyMj2Qfycvr37y8/XTFF 9osvvlCfIvm+1GCn/PLLL0xyZsmShX1NEBER4anlIEgHWQVZ0ll47I+dDmgr A9dfs2YNNhHaK1H4jTl48GDJN7tP/aUPfgRRpEgRq5LEnu0rrFG+fHnDeTeD rl86d+4s+WbBKab+pqWlYbM8YMCA0CbRk1jeXv30009Y8EaNGnXq1Cn8X0NR 8vudlXMDH9XifDD4G+ySraQoGYbtj589NmzYUD4Zb/ny5fKrmW8bx44dixfU 3j8uHCWGg4SXorx27Ro+OOrSpQuG7Nq1C326YsUKReQZM2bgoT///NPY7UKE rgXww2GWRwaITZzYP3To0BCmL5To5h3fKlaqVEkeiNPgJd/Eg+vXr7NwnKkF yg4nt2f8IxKR1157DUoFdFKTJ0/GviZPnjzyuq9QlAkJCTExMVFRUXg6nBXj g01/TU1NzZcvn+Sbug+DMXDH3bt358yZg05R+8vFkA6yCrKks/DYPzIyElXV lClTevqAf/bv389z/b59+2J7ovu5jTom68LU58LwW/JNvTCwUJvfJN25cwef leXPn3/r1q0YyGaasRDb0PVLs2bNIGHQNagPYQfasWPHUCXOw1jbXl28eBHK G0SoUaMGKMSTJ0/qKkp+v8+bNw+v9ssvv3z22WdQf4cNGwbyQXeZKfZyDeQh d0b/f0hRMgzbf8yYMXhi586db968meGbwI8TRdhozUzbCFdbtGgRTjMrWrSo dnrCUWI4SHgpSjbhYcGCBRiycuVKDFGv1czmiP7+++/GbhcidC1Qt25dlD+g oFkgiJcWLVpgjjp16hTyVIYGzifPmTJlYtv9pKSkSL7JpTgZGDzOIuNnF/IZ WUxRQtslf3gFvQyGHzp0SBFZsYwPe3Kl/o4SJ4CBqlUsT/3ll19COIjWQFsm uQ/SQVZBlnQWHvuzaQlyoI1677335A+41MBRXNy+RIkS2rfwG3Pz5s14L8V8 lU2bNrHFBk+fPq19Zf4kwUA6d+7ceNlWrVotXLgQR/tvv/12ULewBF2/VKxY UQqwbQFuOta4ceNQJc7DWNteQTGTfItz4g41PIqS3++TJk1SV1sUEWvWrNFI c7t27STfc2PFQhY8kKJkGLY/KEH8dB0oVKjQhAkTunTpAv9nz579wIEDGMdA 23jhwoWPP/4YroxPCDHZuu1nOEoMBwkjRZmQkIDraJUpU4atHsCeEhw+fFgR nz1fVT9bcBZdC7BnO1WqVIFePikpafny5SB8WJWEdtiuxFqMbt7ZHBjmNehc JN8i0jCwkXwLdmH4zZs38c3j559/zk5HkVinTh3FZdnyub/++qsiMqeiBH2K 27q9//77iouD8MdTcIa/FyAdZBVkSWfhV5T58uX74IMPRo4c2alTJzZoeffd dzVOZAt06+6v5zdmeno6rmeYJUsWaJdg8HPw4MFRo0Zly5aN9QUQwp1XnSTB QA7bWDlVq1Z1ZE4X50yeNm3aqA/hakLly5cPVeI8jIXtFfbskuxbRR5Fye93 pmggfODAgf3798fp65LvAXWglfdguAXVDeJ89tln/NlkkKJkGLM/sm/fPvSC HPlI3kDbGBcXJ78aCNujR4/qZjAcJYaDhIuifPjwIdsea8OGDSx8zpw5GKie g7Rx40Y8ZGylr9ChawEQL02aNJFUQHeP3wj36dPHrsRajG7eU1NTcWJzr169 MAQUHDbv+C02tGAYvn79ejTLzp072emBdg8BIYmR5XPmg1KU0GRhODSMu1Tg Ib/7hrsS0kFWQZZ0Fh77X7hwAUZH8hkjp06dKlasGNb6QDNCt23bhtul1apV S/trLI2YMTEx6l3CoRdg0o/N5eBB40ZpaWn40DJ37ty9e/dmOyBDa8zWPbMT Xb/AgBCS9/rrr6sPQe4kvbV5CWNY1V6lpKTgJyT169dnRZFHUQbld+juoQax nzCMHDp0KN4i0BZj06ZNwwg8ckMNKUo5Buyf4ftkEuXkq6++2qxZM2yyJN82 B7jQKxJs25iYmNi2bVtIapEiRTACXBkaN+3GORwlhoOEi6L86KOP0HeKjyOY UlC/Hlq0aBEeUsxRdBweC0DVmzJlSuXKlXPkyJE1a9aGDRuCnrp79y6qLft3 B7MKnrzjxkNly5bFnxEREehfkHjo0ISEBAgfMGCA5FuRVb7cfSBFuWnTJjzX sKJkn5lroFiK1sWQDrIKsqSzGJ4zw2ZD+a31f/zxB26IkDNnTvlMewMxIUKb Nm2wGYyMjOzateuJEydwYAbqjz/B2jfq0KGD5HsPiwszpqenw9C6QIECmMcx Y8bw38gSdP1SvXp1KcAitPjygi3iTViIVe0VSEIsWrGxscn/wLaEgLIHP/Eb OgUm/Q4jK9zuEMSI3+1c27dvjzXL2B5tpCi10bU/CHmce/b+++9jBAhhpaVQ oULydfWNtY0gIaOjozEZkt6LgHCUGA4SFoqSvT2vWrWq4iva/fv346GlS5cq zpo6dSoeEm0H0qAsABWQ7XkBSgpztHz58lAlLsTw5J1tl3bx4sXjx49j43Pn zh1oB3DGC7YA2LMonlWGTlGyfSpLly79SgCWLFkSvEnCEtJBVkGWdBbDihKG u9ggqD8zvHz5MltrWrtN4I+Z4Vs8h/3/3nvvScFM7NS+0eHDh/HQN998Iw8/ c+YMfnOUI0cO9Sr9IUXXLzjIZA8e5eDLL08tlWYblrRX8fHxEge1a9dWX9C8 3/v164fXx483FeALrAYNGvDnUQ4pSl207f/mm29KvvXBFBvjjhgxAs9SbASA GGgbU1JScDypvSpsOEoMBxFfUS5btgxfzIH31Y5LTk5Gnw4bNkxx6N///rfk e69tYDW8kGJ4DDN79mzMbPg+EuHJO1tpDYQzznRt2LAhHnrnnXfgZ7t27W7d uoUPsqBSy88NnaJEbQtMmjQpyEy7ENJBVkGWdBbDrXF6ejruxK34qh2aJpx9 J+l9isUfU8GDBw9wzi3nazjdG82cOROPqtf5X7BgAR7SXkzDcnT90q1bN8n3 sJHtaI/AIAETPGTIkNAm0ZNY0l6xzlSbatWqqS9o3u9s4qX6G2S2VcTAgQP5 8yiHFKUuGvYHcL59VFSU+lCJEiXgUOXKlTUuHlTb2KlTJ0yJxn7i4SgxHERw RQm9GE6ozp07dyAZhVMdFKtJP3r0CEum38dczmJ4DINbg0G1Ujy9CSN48g65 w6Uw+vXrh0t+sTlXuGZRgQIF4CJYzaFvkp9rXlGOGzcOYyrKG6QKl5umJQQz SAdZB1nSWQy3xmydh5EjR7LA27dvN2jQAMPfeecdjYlz/DHVsM3c//vf/+pG 5rkRLtcPAln+qB+BZhDPBdXJn0Lz6PqFreuiWBbju+++w3Bo80ObRE9iVXt1 7dq1KyrYigRQ2ODnjRs31Bc07/fmzZtDNOjN1UKA7cpqYN8QhBSlLhr2h3E7 Lq3j98EXbnYAA3uNi/ttGwN9KYlvM4FLly5pXDPsJIaDiKwo161bh2P47Nmz a5zCJMCOHTtY4KpVqzBwzpw5BhIZUngscOjQIUXnDhUEc/T999+HMHEhhtP7 rVu3lnzfSuC0hN27d2M4ew757rvvwt+IiAjFieYV5axZszAm9FCKi+BS5xLH /DTXQzrIKsiSzqJrf1BhAwYMUDzEg+EE7o8myZZlgBabLR/XsmVLjed+/DHv 3r2reJJ/9epV3EAhMjJSPiQD5bhw4UJo3+QdB+eNWPM4b948xaGJEyfiodjY 2ECJDAW6foH8PvXUU5CwJk2aMJkMBsHP8ME4xr6DI7QJaXvFszIPp9/3798P 1YRtNiFPP6704nfhJtb7R0dHB7q7upbJIUWJGLM/AAJN8m3ooPjA7datW7hZ edOmTTGEs23ct28fSD9QE4obXbx4ETekg8gs0K9/w05iOIiwinLDhg1sHeBB gwbBz9WrV6+SwVbYS05OxgmQUDz27NkDfT34PU+ePBDyxBNPpKWlGUhkSNG1 AOQiZ86cVapUgU4cxgAgo0aOHInVsGjRouyzynCE0/tsFpbkW3tH/gV36dKl 2SH1Rh7mFSWLWaNGDWgPoTix2f5nz57FDUSyZMkyevRoXPsR3BETE9OoUaOf f/45CEOEOTx+3L17985/wI9ToPdhIX6fP3sQsqSzaNuf7ThWt25dGOWeOXMG xqtHjhzBbdYl30ZF2DrB8IYFQpMFAxhQmqv+F+iqgooJjU/btm2hd4MhTUpK CpwIXRtrAGfMmCFPat++fTGcPd7nvxH0kliuoH2DITF7pL9y5UrcsSsiIsLA 3nxm4KkXH374Iebuo48+un79Oowno6KiMET+4piwkJC2VzyKMoPP77g3Zfbs 2YcPH759+3YQCHBfEIw4/QlGU/ItAxiff/45XgdkiN9bq2tZhm/DEZa7YcOG YYSxY8diiLE1Y53CKv8as3+GbNWU5s2bs8/cLl++zBbn+fbbbzOCaRsrVKiA d4Ris379ekgGDKohjy+++CJG7tevH4vs179hJzEcRExFCcVDvSywgooVK7L4 0P7gJy1YcvAfEKTyzQfFQdcCoKBZNlm+gGLFigVq6MIFTkXJ1iCSVGvv9OnT hx1S9zvmFSWMD4sXL85uAQMqKEjsgRXckW1FJ/nm3zIHlSpVitsMYQ+PH6Gx 1ai/X3/9tS0pFR2ypLNo2x+GH02bNpVbG4cWCAwwEhMTMSYMZTV8hIBACyrm 2bNn8Sk6Iu8LPvnkE8VKiVWrVsVDbDde/hsBsbGxOCNI8s0rg4uwZhDCd+3a Zb3pNeGpF6AmatasyfLCuv4mTZoEeoVEmCSk7RWnouTx+4oVK/BhCKs7LBow YMAAv1dmu7WqPyhG1LUMgBxp5BesoW0uobDKv8bsn+GTikw8QssD4/xq1aqx S73xxhv4vIu/bYTs4KZ7SObMmeVteK1ateTvaPz6NyPcJIaDCKso5SXEL9Wr V5efAkoBNypCQCwI62seC8ydO5dtmoNFt2XLlqmpqbYkMITwz6kAgYZ5nzJl ijxcvouHevlBfEgFPYsi/LfffsNT5IoyUGSQ7fgNOGus5FsRQa/XoEEDefmE du/tt9+Oj4/nyZc7MN/vfPXVV7akVHTIks6ia38YwCxYsKBKlSpym8OYpGvX rvL9ztgK1Rpgl8QfM8O3IOFrr73GtJ7ke13o9yOvL7/8EiOw/eKDulGGb70U 6GUUEVq0aHHs2DHTZg4azp4CxIXcPtmzZ2/fvj3JydAR0vbqzJkzGEe9rqYC Hr+fO3cuKioKX4oxnn/+eY3dA3HrEEAx5ZKhrmUZeooyV65c2nkRCgv9a8D+ yP3796dPn862LkKefvrpqVOnyuf587eNly5d+vDDD3EpYAYkbPTo0X///bc8 pl//ImEkMRxETEVpmNOnT2/dujXQ8yVB4LfAhQsXoqOj4+Liwnqmq5yQet9C oE07fPjwunXrtm3bJt/ZnAE9zp49e7Zs2QLDrfBdKMkw4eJH8SFLOgu//c+f P799+/aYmJj9+/fbPAUU2n9obTZt2pSUlKQRLT4+XrFCtQFu3ry5d+/ejRs3 wl+/GwLaQ1D1Alpj6CV///13m/3iQYRqr3j8jl/bbd68GUaG2tWHE0tqmbBY 7l/D9n/48GFiYiKcBU1uQkJCoM+iOdvGDN/HtocOHVq/fj1EPnnyZKBhm7Z/ w0JiOIjLFGVY4GULeDnvboL8aBVkSWch+4sJ+UVMyC/uhvxLmIEUpf142QJe zrubID9aBVnSWcj+YkJ+ERPyi7sh/xJmIEVpP162gJfz7ibIj1ZBlnQWsr+Y kF/EhPzibsi/hBlIUdqPly3g5by7CfKjVZAlnYXsLybkFzEhv7gb8i9hBlKU 9uNlC3g5726C/GgVZElnIfuLCflFTMgv7ob8S5iBFKX9eNkCXs67myA/WgVZ 0lnI/mJCfhET8ou7If8SZiBFaT9etoCX8+4myI9WQZZ0FrK/mJBfxIT84m7I v4QZSFHaj5ct4OW8uwnyo1WQJZ2F7C8m5BcxIb+4G/IvYQZSlPbjZQt4Oe9u gvxoFWRJZyH7iwn5RUzIL+6G/EuYgRSl/XjZAl7Ou5sgP1oFWdJZyP5iQn4R E/KLuyH/EmYgRWk/XraAl/PuJsiPVkGWdBayv5iQX8SE/OJuyL+EGUhR2o+X LeDlvLsJ8qNVkCWdhewvJuQXMSG/uBvyL2EGUpT242ULeDnvboL8aBVkSWch +4sJ+UVMyC/uhvxLmIEUpf142QJezrubID9aBVnSWcj+YkJ+ERPyi7sh/xJm IEVpP162gJfz7ibIj1ZBlnQWsr+YkF/EhPzibsi/hBmcUpQEQRAEQRAEQRCE OyBFSRAEQRAEQRAEQRiDZr3aiZct4OW8uwnyo1WQJZ2F7C8m5BcxIb+4G/Iv YQZSlPbjZQt4Oe9ugvxoFWRJZyH7iwn5RUzIL+6G/EuYgRSl/XjZAl7Ou5sg P1oFWdJZyP5iQn4RE/KLuyH/EmYgRWk/XraAl/PuJsiPVkGWdBayv5iQX8SE /OJuyL+EGUhR2o+XLeDlvLsJ8qNVkCWdhewvJuQXMSG/uBvyL2EGUpT242UL eDnvboL8aBVkSWch+4sJ+UVMyC/uhvxLmIEUpf142QJezrubID9aBVnSWcj+ YkJ+ERPyi7sh/xJmIEVpP162gJfz7ibIj1ZBlnQWsr+YkF/EhPzibsi/hBlI UdqPly3g5by7CfKjVZAlnYXsLybkFzEhv7gb8i9hBlKU9uNlC3g5726C/GgV ZElnIfuLCflFTMgv7ob8S5iBFKX9eNkCXs67myA/WgVZ0lnI/mJCfhET8ou7 If8SZiBFaT9etoCX8+4myI9WQZZ0FrK/mJBfxIT84m7Iv4QZSFHaj5ct4OW8 uwnyo1WQJZ2F7C8m5BcxIb+4G/IvYQZSlPbjZQt4Oe9ugvxoFWRJZyH7iwn5 RUzIL+6G/EuYgRSl/XjZAl7Ou5sgP1oFWdJZyP5iQn4RE/KLuyH/EmYgRWk/ XrZAqPMeGxu7ZcuWffv2he4WRIa3y7C1kCWdhewvJuQXMSG/uBvyL2EGUpT2 42ULhDrvzz33nCRJr7zyCk/kOXPmtG/f/qeffgpdetyKl8uwtZAlnYXsLybk FzEhv7gb8i9hhjBSlPfu3Tvg49ixYywwNTX1gB537twxcLvQwW+B8+fPL126 dMSIETNmzNiyZcv9+/dDnLSQI46iPHjwoOTjscceAzuHLkmuJFg/JicnQ02E vyFLUbhClnQWfvtDvzNv3ryRI0euXLlS2/6XLl1avnw5xFy1atWVK1d4Ln7u 3Dnsra5evcoCLezdjhw5Mn/+/OHDh8+aNWvnzp2PHj3yGy0tLW3Tpk3jxo2b OnVqXFxceno6z8VDAY9fNOwD9lREDjZrDx48OHToEHQTgWxlIKZfkpKSoJx8 /vnn4KDjx4+rLwK+C5RNGBQZuKMZLPeLHL+1QAGntTkLvC48SXITQfVHUImg Kk2fPn3s2LExMTE3btwIFDPYOqJhdkuqQ1DFQ7eGEowwUpTgfZQAJUuWZIFT pkyR9Jg9e7aB24UOHgvs27evfPnyioxAxteuXWtLGkOFOIoyMTERtCREzpIl C7QYLBx6w4Y+EhISQpfOcIfTjzCK27BhQ8eOHR9//HEwdf369UOftDCDLOks PPa/efPmBx98oGiNe/Xq5fcRX79+/eTRMmXKBMMt7euDAn366acx/o8//sjC LendoEFr37694qy6deueOHFCETM6Orpw4cLyaEWKFNm7d6/29UMEj1/Gjx8f yCzlypWTxwwqa2CZyZMnP//88xhz27ZtgRLAH9Mvt2/fhhqtSHmlSpVOnTol j5Y9e/ZA2Vy2bFlQdzSPtX6RE6gWMDitzV/gddFNkvvgH6Ft3bqVGQeBvglc rxBcBuqIttlNVoegigdnDSUY4aIo9+/fj0MpKXhFuWTJEgPpDB26Fpg2bRrI HEz8U089Vbly5cyZM+PPrFmz7t69266UWo84ijLDN8wYPHiwook7cuQImhr+ CU0a3QCPH5OTk1mdRV5++WVbUhdOkCWdRdf+MECqV68emj0yMrJZs2YFCxbE nw0bNlQ8Fe/Tpw8eypUrV61atXLkyIE/v/jiC41bvPnmm8yzwSpK7d7t4cOH jRo1wph58+bt1KlT48aN8ecLL7wA4yUWc8uWLSB+sYuB9rN69epY5CAL69at 07ZhKOCpF59++mkgs5QpU4ZFCyprvXr1UlwKTvd7d/6YfoFKXaVKFTwR+vfS pUvnyZMHf0Knf/PmTYx2584dw94PBRb6RUGgWoBwWpu/wPOgnSRXwjlCmz59 OuuS8uXLV6pUKaxiwKhRo1g0Y3VEw+wmq0NQxYOzhhJywkJRpqenV6xYkRUb uaIEt57xx8GDB7E3r1OnDpQiA+kMHboWmD9/PqQ8IiIiJiYGE5+SkjJgwADM ftOmTW1KaAgQSlH6BQQm2pkUpQY8fkxKSnrqH/CRCOkgNWRJZ9G1/9y5c7FB 6N69O76UhL89evTAQDjKYh46dAgDq1atmpaWBiFXrlyBsZakObV+8eLF8kGR fARlvndbvXo1XnbgwIF3797FwB9++AEDJ06ciCFwCAZUEFKgQAG2rNnevXtR O0Oj+uDBAz1DWgxPvejatSskr3DhwmoTsWknwWatT58+WNFy5sypPQbmj+mX Ll264Flt27bF6YLgyoULF+bKlWvmzJksGmQEo02YMEGdzb///pv/jpZglV8U aNQChNPanAWeB90kuRIe/yYkJKCcBIW1YcMGfCkJ+qtJkyZly5aVz1M1UEe0 zW6yOgRVPDhrKCEnLBTlsGHDJN/0oRo1akj/qygD0bNnT4gJxVjA19M8Fli0 aNFff/0lD4FqW7p0acn3RCiEiQsx9ijKevXqsZD4+Pg9e/bcunWL8worVqzg UZTQHMFlYQzpgo9bDRCsH3HSC+kgNWRJZ9G1Pz7TLlq0KBuBICAbIRwEI9N0 vXv3VotHJjP9vqa8ePEizu+qWbMm/8CVv3cbNGiQ5HthqmimoPxAeIcOHfDn jh078O7fffedPNovv/zi1HCap1689dZbkk+/a8QxnDU2ztQdA/PHZMDoF6ch tWvXTjFL8Pr16/KfR48exYsL8sGLVX6RE1Qt0LY2Z4G3Nklugv+JATR00LjJ w6ElDPTmjrOO6JrdZHXgLx78NZSQI76i3LdvHz4P+cAHj6LctWsXPsmfPHmy gRSGGsOqqkmTJliR7X9ibBXaeQcdndfH+vXr5eEbN27E8NGjR8vDQSdC+wPh w4cPxxBUlE2bNoVa361bt0KFCmH7A+Whe/fuitUYKleuDOe2aNECf37//fdQ tJ588kk8Bf7Bm9aoUUN+1unTpxs3bpw1a1aMli1bttatWyvkv+shHWQVZEln 0bX/M888Awbv0aOHInzp0qXYAuDp9+7dg7ZC8veJa7ly5SC8ePHi6otD0yH5 ZmPGxMRwDlyD6t1Qe+bPn18Rjj1p3bp18ee3336Ld4cRnd/Ey5/R2QNPvWjY sCGkrXnz5hpxDGctpIoS/QLEx8drx9y+fTvGjIuL47x4SLHKL3KCqgXa1uYs 8NYmyU3o+vf8+fM4+Gnbti3/ZTnriK7ZTVYH/uLBX0MJOYIrSpAAFSpUALc+ ++yzqamp7777Lo+ixKfH8FfMRZmMKcobN24UKFBA8j0VD0GibEI77+x5ft++ feXh//nPfzC8Vq1a8vDo6GjFAytUlJGRkexLcDn9+/eXn66YIvvFF1+oTwFK ly7NTvnll1+Y5MySJQv7lCAiIkJ7FTuXQTrIKsiSzqJtf+iAsIKPGDFCcQgU Ch7CtXESExPxp3pmHfusTDEp66effsLwUaNGnTp1inPgGlTvxt7EKfKIy77B OAp/Dh48WPLNAlJfE+f3FilSRPde1sJTLypXrizPhV8MZy2kirJMmTIQv3Hj xrox16xZgxc/e/Ys58VDilV+YQRbC7StzVngrU2Sm9D175IlS9Agv//+O/9l eeoIj9lNVgf+4sFfQwk5givKIUOGYAGAkgA/O3ToIOkpSvYQA8qwgeTZgAFF eebMmWbNmmG+pk+fHpp02YFu3vGtYqVKleSB+FRB8r2flU85wDkMoOzwq6WM f0Qi8tprr61YsQKapsmTJ7Np//JBnUJRJiQkxMTEREVF4elwVowPNv01NTU1 X758ku+TnGXLlsFo8+7du3PmzMFvmrp06WKFhcID0kFWQZZ0Fl37P/vss35r N1R/rPhDhw7N8L06xHYD2hxFzBkzZuChP//8kwWCIM2fPz8E1qhR48GDBydP nuQZuAbbu925cwefgMG9tm7dioEwqMOLsBCWQvXHnjC6k3xzPGzeqIKnXkRG RuI4cMqUKT19wD/79++XxzGctZAqSvys7LPPPsOfycnJe/bs8Tubbt68eWwI BPEhs8OGDYOxd7CLzFiFVX5BDNQCbWtzFngNjFVM16Dr3y+//FLyPaLBrwDu 37+/d+9eaNm0P+jWrSOcZjdZHfiLB38NJeSIrCihoLL5rhjCoyjbtWuHY37F Zy/iwG+B+fPng8Bp2LAh7nMBhXzq1KlivnjlRDfvnTt3xvaK7eOWkpIi+SaX 4vSzlStXssg42V4+V4EpSmhn5IZiS0DLZ/77XcZn7NixGFP9HSVOjQBfKJad xzYWyiokldMO4Q7pIKsgSzqLrv2hecGHUdeuXWOB0Lm0aNECG4pOnTpBCLRL +FO9PD6bHyt/qt+qVSvJt9worlrPOXA10LuBCM2dOzdeHG66cOFCHLm9/fbb LM7mzZsxguJV7KZNm9hytadPn+a8oyXw1As2XUQO9B3vvfceG/sZzlroFOXl y5cx/syZM8Ed8m3CKlSoAP6SR540aZI6j5Lvw941a9bw3M5arPILYqAW6Fqb p8BrYKxiugZd/3744YdgjYIFC0IxhuaI+Tp79ux9+/YNtGCFrtc4zW6+OvAU j6BqKCFHWEUJPSZ+5lCsWDG2caquokxKSsLPadmzBQHhH0M2b95cXmtGjhzJ uZ+1sOjmnc18YI/6oUmBn7Vr14YqD//07NkTw2/evIkPHD7//HN2OorEOnXq KC7LFmz89ddfFZE5FSXo01y5ckH4+++/r7g4DDXxFBi98BjBBZAOsgqypLPo 2p89Fa9SpQqMJaCLWb58OTQarFmGYUmG7F3Y4cOHFVdgz8AVbZok+xaSZ+Bq rHe7d+8etpxyqlatKp+tkZ6ejguiwvWhAQSFdfDgwVGjRmXLlo2dAiH8NzUP v3LJly/fBx98AJ0jSHsmEt99912MYzhroVOUe/fuZaNT/AeGuNi5SD7lJd/T hA2hocoPHDiwf//+OKdU8j1ltX9Bcqv8kmG0Fuham6fAB8JYktyErn/xSVrO nDlLlCiBlnnmmWfY1iE1a9b0+7JS22v8ZjdfHXiKR1A1lJAjrKJknz/IS6Cu opw2bRo6/ejRowbSZg/8Y8ivv/76X//6F1QZtg5M6dKljx07FuIEhhDdvKem puK6E7169cIQUHCSbxCFayzAiBrD169fjzbZuXMnOz3Q7iEgJDEyKFbtyIEU JQxFMBwasV0q8NCsWbOCtkh4QjrIKsiSzqJr/0ePHuGSaApgWIJL8fTp0wei zZkzB8PVs/s2btyIh/Bz75SUFJw8X79+fTaPgmfgaqB3S0tLQ/ELI6LevXuz nTShjWWrmSExMTHqrcMhg2z0xSaN2ANPvbhw4QKMMOXvjk+dOlWsWDFMMJvD ZixroVOU7GMuybcqwrZt2+7fvw8lAVyPygs6Jvk7aOizIAvsJ4zYhw4diqeb 2SfLGFb5xXAt0LY2f4FXYzhJbkLXv2yLRsk3Ewy3gwHTsY0dv//+e/VZGl4L 1uxmqgNn8Qi2hhIMMRVlXFwczvN86623kmXgm/HixYvjT/WJ7du3x9Ii2h6U coIdQ2b4JplDgccHQVCYnfqGwjw8ea9WrRpks2zZsvgzIiJC8r3+A4mHdTwh IQHCcYPOJ598Ur4QdCBFuWnTJjzXsKJkn4RroFiK1sWQDrIKsqSz8NgfepMp U6ZUrlwZhhNZs2Zt2LDht99+CyMKfPaFS/GwZ1bqiQqLFi3CQzhb/vXXX8ef sbGxrGtjm1yAbISffhfhN9C74TNYGLDh0ojp6elwfVzhDRgzZow88h9//NGm TRtsbyMjI7t27XrixAkcrcFNOe9oFQZ6SYRNP5a3xgayFjpFyZ5AlilTRvGh BFs4QvFhhQIoAJUqVZJ8Uw1tXvjdKr8YrgXa1g6qwCswnCQ3oetftqAHNIny 8CtXruBsUrZ4vhwNr5k3O3914Cwe5muoZxFTUeJ+RrqATFCcWKRIEQhv0KCB gYTZhuE2ecSIEZhxXF0wHOHJO6uzoKOPHz+ODcWdO3cePXqEq2Tgq8Dq1avD /9Acyc8NnaJk+1SWLl36lQAsWbIkeJOEJaSDrIIs6SxB2R+GLmwHooSEBGwQ li9fDj/379+PP5cuXao4a+rUqXjo/Pnz8fHxPF1b7dq11XcPtnc7fPgwXu2b b76Rh585cwbXTgGBrN5TI8O3fgX7/7333oOY5cuX57ypVRjuJWHwibn2+90c f9ZCpyhxZQBg7ty5ikOsFMn7Kb/069cPY+J3Z7ZhiV/M1AINaxsu8Bm+fasN J8lN6Pq3e/fuku9jNPUh1IZ+NyMI5DWrzM5THfiLhyU11JuIqSjV85z9opjM zNZvHzhwoIGE2YbhNplNvOzdu7fVibIJnryz9QxhqIYzXRs2bIiH3nnnHcm3 5+ytW7fwI0oYrcnPDZ2iRG0LTJo0KchMuxDSQVZBlnQWw63x7NmzsUHAh9XJ ycn4c9iwYYqY//73vyXfFxz37t1jzYg21apVU1zEQO82c+ZMPEW90v6CBQvw kPZyFg8ePMDJin7fO4QUw34ByY8TnPD71kDoZi10ivLRo0c4C1exm1WGb7M/ vJRi0KuGzfQT8PtWv8j9YrgWZGha20yBN5MkN6Hr33Hjxkm+1owtsM/o0qWL 5FvETH1WIK9ZZXae6sBfPCypod5ETEV59+7dK/5o2bIluLJEiRL4U7HoN5v8 LOy+IYiuBQKt5sqeirPVacIOHu/fv38fP+3v168fvq1msxFwlYwCBQrARdAU 0CLJzzWvKLHBlFSzGiBV+DUrbVGUQTrIOsiSzmJ4hIwLAEJnxGbd4wowFSpU kEeDxhy/1mGP2a9du6bu2tg8Kxj2wE+2GB3DQO8GzabkW5tavZ4bW3oCbqdx hWXLlmG0//73v5w3tQrDfomLi8M0jxw5UiOabtZCunsIbioKRUjR17PJfrpL f+CqfdAlCbiri18UfjFWCzI0rW2ywBtOkpvQ9e/atWvRIFCDFIcaNWoUSP1p eM0Ss/NUh6CKh/ka6k3EVJSB0F6ZZ9asWejr6OhoY9e3B10LtGvXrk2bNuqp 42zWKwir0CUvpHB6v3Xr1pJvWxCc5rp7924MZw+I3n33XfgbERGhONG8omSl 6LvvvlNcBD/jBbwzuzUQpIOsgizpLDz2P3TokGIQAjIEmwL5MhTsYRSMOljg qlWrMHDOnDkat9BdAES3d7t9+/bChQuhfWNJZY2eur+YOHEiHoqNjcWQu3fv Kh7vX716tWLFipLvw0ObZUsGh1/eeeedAQMGyD+iz/Dp94YNG2LWcB2kDKNZ s0pRqv0iPwWKhzwyblAFnDt3LsM3xQ7SeeDAAcU1wTK4qAKMe7XTZjkW+kWN yZV5girwfv1iLEluQte/Dx8+LFWqlORb+1r+Qfeff/6JK1FHRUWpzwr2qYtf swdVHUy2h5w1lFDgJkX5+eefo6/37dtn7Pr2oG0B+fd6M2fOjI+Ph9Y4NTV1 6NChOGkkT548Fy5csDG9VsLpfTY/QfKtvSP/2hrMwg6pN/IwryhZzBo1akDb BcZnM/PPnj2LK0hDyzl69Ghcyy49PT0mJqZRo0Y///xzEIYIc3j8uHv37p3/ gJ+AgRpiIa5/2MsJWdJZdO2/Z8+enDlzwvAJBhswTj5//vzIkSNxAFO0aFH2 WWWGb+IrTsV/5pln4CxoOkBaQnMNIU888YR6kpgc3YGrbu/Wt29fjMD2FoE7 YmmBVgsGSOxh+8qVK3H/7oiICFyxEA61bdsWEg+iOCUlBQIh5aylnTFjhkbK Q4S2X9gWn3Xr1gWLnTlzBga3R44cYcuG1KlTB3uNoLKWlJTEqtWwYcMwztix YzFEvsQuf0y1XzJ8c27x0VDu3LlXr14NiYeQb775Btd6atOmDUYrW7as5FtG YPjw4du3b4exMVT2WbNm4RweKIQbNmywyN68WOUXvwSqBZzW5i/wGQH8wp8k t8LTH7ENld54442//vorwzd9DtfGgTEqW+yav46o8Wv2oKqDmfYwg7uGEgrc pCh79OiBRUg9TVootC0AQxRc04/Btg7BWuPIvsZWwel9Nr9XUq2906dPH3ZI 3cibV5TQbhQvXpzdApqabNmyscdcbPlopECBAijzpQAfpLsVHj/CKFoKzNdf f21LSkWHLOksuvYfNGgQMzWr7JJvbQq1uIP2gcVhe7RBAyLfBtcvugNX3d4N p2lJ/7sbL6hg1n0ULFgQDrHGDcJ37dqF0eCaoIL9ZvOTTz6xeTVRRNsvMJJs 2rSpvBaglkcgL4mJiRgzqKxBVdKoaFANDcT065cM3/Q53Fpd8i0JwroV6FNw R4YM3+NlHOuyxLNCBQwYMMCslYPHKr/4JVAt4Lc2Z4HPCOwXziS5FZ7+6P79 +x07dkSzQIHEiWTIkCFDWDR+r6nxa/agqoOZ9hDhqaGEgvBSlLg1IdtXQgET YoJvrsFjgeXLl9eqVUtRB+vVq7dnzx5b0hgq+L2PMysk1SLV8l081Ou24cPn Jk2aKMJ/++03PEWuKANFhoEi271X+t/Hbhm+tq5BgwbykQm0Rbh+HU++3IF5 HfTVV1/ZklLRIUs6C4/9586diw+3EVCILVu2TE1N9RsZWpiiRYuyyM8995yu nMzwrTeI8dVLxSK6vduXX36JEdgu4cjx48dx/QE5LVq0UOxrnJKS8tprr8mf XkZERDi4IoGuXx49erRgwQL57niST7907dpVsb8kf9a0x8C5cuUyEDOQXzJ8 S+299NJL+NYDgXQqdis4d+5cVFQUvoVhPP/88xpzR0OKhX5RE6gW8Fs7g7vA a/iFJ0luhX+ENn78eNxHEoH/Fy5cKI8QlNcUBDI7f3Uw2R4iPDWUkBNeitId 8FsgOTn5999/X7duXWxsrM0bTIeIcPH+/fv3Dx8+DJbftm2bfKdmBozrQN1v 2bIFGiLFNyNeIFz8KD5kSWfht/+FCxeio6Pj4uLkM10DAUORrVu32jxbJj4+ XrFCNePmzZt79+7duHEj/NXY3A2yBs3apk2bHH8Iz++X8+fPb9++PSYmZv/+ /RrbjjuYNQ2/ZPhc89tvv0Fm/XY0CH4KunnzZihUzrrGcr+ECJ4Cr+0XbxJU f/To0aOTJ0/CKAgsaecW8JzVwXx7yCLr1lACIUVpP162gJfz7ibIj1ZBlnQW sr+YkF/EhPzibsi/hBlIUdqPly3g5by7CfKjVZAlnYXsLybkFzEhv7gb8i9h BlKU9uNlC3g5726C/GgVZElnIfuLCflFTMgv7ob8S5iBFKX9eNkCXs67myA/ WgVZ0lnI/mJCfhET8ou7If8SZiBFaT9etoCX8+4myI9WQZZ0FrK/mJBfxIT8 4m7Iv4QZSFHaj5ct4OW8uwnyo1WQJZ2F7C8m5BcxIb+4G/IvYQZSlPbjZQt4 Oe9ugvxoFWRJZyH7iwn5RUzIL+6G/EuYgRSl/XjZAl7Ou5sgP1oFWdJZyP5i Qn4RE/KLuyH/EmYgRWk/XraAl/PuJsiPVkGWdBayv5iQX8SE/OJuyL+EGUhR 2o+XLeDlvLsJ8qNVkCWdhewvJuQXMSG/uBvyL2EGUpT242ULeDnvboL8aBVk SWch+4sJ+UVMyC/uhvxLmIEUpf142QJezrubID9aBVnSWcj+YkJ+ERPyi7sh /xJmIEVpP162gJfz7ibIj1ZBlnQWsr+YkF/EhPzibsi/hBlIUdqPly3g5by7 CfKjVZAlnYXsLybkFzEhv7gb8i9hBlKU9uNlC3g5726C/GgVZElnIfuLCflF TMgv7ob8S5jBKUVJEARBEARBEARBuANSlARBEARBEARBEIQxaNarnXjZAl7O u5sgP1oFWdJZyP5iQn4RE/KLuyH/EmYgRWk/XraAl/PuJsiPVkGWdBayv5iQ X8SE/OJuyL+EGUhR2o+XLeDlvLsJ8qNVkCWdhewvJuQXMSG/uBvyL2EGUpT2 42ULeDnvboL8aBVkSWch+4sJ+UVMyC/uhvxLmIEUpf142QJezrubID9aBVnS Wcj+YkJ+ERPyi7sh/xJmIEVpP162gJfz7ibIj1ZBlnQWsr+YkF/EhPzibsi/ hBlIUdqPly3g5by7CfKjVZAlnYXsLybkFzEhv7gb8i9hBlKU9uNlC3g5726C /GgVZElnIfuLCflFTMgv7ob8S5iBFKX9eNkCXs67myA/WgVZ0lnI/mJCfhET 8ou7If8SZiBFaT9etoCX8+4myI9WQZZ0FrK/mJBfxIT84m7Iv4QZSFHaj5ct 4OW8uwnyo1WQJZ2F7C8m5BcxIb+4G/IvYQZSlPbjZQt4Oe9ugvxoFWRJZyH7 iwn5RUzIL+6G/EuYgRSl/XjZAl7Ou5sgP1oFWdJZyP5iQn4RE/KLuyH/EmYg RWk/XraAl/PuJsiPVkGWdBayv5iQX8SE/OJuyL+EGUhR2o+XLeDlvLsJ8qNV kCWdhewvJuQXMSG/uBvyL2EGUpT242UL8OQ9NjZ2y5Yt+/btsyVFhBG8XIat hSzpLGR/MSG/iAn5xd2QfwkzkKK0Hy9bgCfvzz33nCRJr7zyCs8F58yZ0759 +59++smCxBHceLkMWwtZ0lnI/mJCfhET8ou7If8SZhBfUZ4/f37p0qUjRoyY MWPGli1b7t+/7zfa3bt3Y2NjJ02aNGbMmCVLliQmJhpImz0Ea4Hk5OQDBw7A 30ARwCa7d+/+yse6detSU1MtSGVosFZRHjx4UPLx2GOPQTmxJokEB5aXYc/C b8kHDx7s3bsXmrgpU6aAMR8+fBjipHkCsr+YkF/EJEQtPzjx0KFD0KE/evSI 88ppaWnr16+HMQ8M+VauXJmSkmLh9c+dO3fAx9WrV7VjwnDrgB537tzhzJTj 8PhXI8tgN3X8I0eOzJ8/f/jw4bNmzdq5cyePC3jsb6DMBHUjSHagbN67d8/w Hd2NyIpy37595cuXl/6XkiVLrl27Vh4NnDto0KCsWbPKo4HEiIqKunHjhoEU hhpOC0CDuWHDho4dOz7++OOQo/r166vjQN779OmTK1cued7z5MkDNdf6dFuB tYoyMTERHA2Rs2TJkpSUZE0SCQ4sLMMeh9OSx48fL1KkiLyav/DCC367byIo yP5iQn4RE8tb/hMnTkyePPn5559H923bto0nGUuWLClUqJDc7zlz5pwwYYIl 17906dLTTz+N8X/88UftyFOmTJH0mD17Nk+mRIDHv+PHjw+U03LlysljgvZs 3769Ik7dunXBKRrX17W/sTIT7I2yZ88eKJvLli0zdkfXI6yinDZtGsgEdN9T Tz1VuXLlzJkz408Qj7t378ZoycnJVapUwfBMmTKVLVv22WefZX5v1aqVmccX IYLHApAvbIoZL7/8siLOX3/9BQ01HgXjVKtW7f/+7/9Y/IULF4YqAyawVlEC 0dHRgwcPNtykEMawqgwTPJY8duwYa9ZKly5dsmRJ/L948eJnz561JZmuhewv JuQXMbG25e/Vq5dirL5lyxbdNGzdupWNBmvXrt25c2emLhXazdj133zzTRbf EkUJ+lf3poLA499PP/00UE7LlCnDoj18+LBRo0YYnjdv3k6dOjVu3Bh/vvDC C7dv3w50fW37G/NpsDe6c+eOOxxqM8Iqyvnz54PjIiIiYmJicB5LSkrKgAED 0KFNmzbFaDdv3qxQoQKE9OzZExRWhq8Yw/VLlCiBMWNjYw0kMqTwWCApKemp f8DGU90mDxkyBPMIkorNdF27dm3OnDkhMH/+/Ldu3QpF+s1guaIkHMGqMkzw WLJ169aS74kZ+154+vTpWPe7desW8iS6GrK/mJBfxMTalr9Pnz4YDQctnOqg UqVKEPPZZ589fvw4hly9erVatWqS7+2DfNqzgesvXrxYrh10FSUMQc/44+DB gzly5IAr1KlTJ4xmYvP4t2vXrpCvwoULq3Mtnyq2evVqtOHAgQPv3r2LgT/8 8AMGTpw40e/Fde1vrMwEeyPICIZPmDBBnc2///7bwB29gLCKEli0aBGKRMaj R49Kly4NXs6XLx8LPHv2LBRdxbk///wzlocpU6YYSGRI4bcAgi/31W3ygwcP unTpMnfuXEU4U5rsTa448CvKevXqsZD4+Pg9e/YEK5Chqd+3b9+uXbvS0tJ0 I58/f37Hjh3Xr1/XjZmQkLBt2zb+TwIhGdu3b09MTBTwdblhrCrDhK4lU1JS 8IF/9+7d5eE4nM6dO7eAD47CCLK/mJBfxCRELT8TGrrqAAbz+KnLuHHj5OFw Il7h5MmThq9/8eJFnAZZs2ZNTkUZiJ49e0q+ubinTp0ydgVH4PHvW2+9BVmr WrWqdrRBgwZBtFy5cikWP4HCAOEdOnRQnxKU/fnLjIEbHT16FMMVH9kR2ois KP3SpEkTyfeZJOgpjWg7d+7E8jBixAjD9woRoR6Ng97BvM+bN89A8kIKv6Js 2rQp6Ltu3bqxCS2ZM2eGwUN6ero8cuXKlfPmzduiRQt5ILThr7/+Onv6lClT pnLlysmnvi9evBjOKlmyJLR1Y8eOZW+0ceI0FB5FkkAMLl26tEGDBk899RS7 bOHChaOjoxUx8cqlSpWC/1etWvXSSy+x+T8RERHqK4cppCitQteSX331FZYf xdRuKM8YPn/+/NAm0dWQ/cWE/CImjitK0AIYc9q0afLws2fPYvjmzZsNXx8f R2TNmjUmJsaMoty1axe+nJ08ebKB0x2Ex78NGzaErDVv3lw7Gmrq/PnzK8I/ +OADyfc1pfqUoOxvRlHq3mj79u0YHhcXF+zFvUx4KcobN24UKFAAvIwjdg1A JmB5EHBfiVCPxtesWYN5F/DzYX5FGRkZyb68ltO/f391ZPkU2StXrhQtWpTF Z1MjALZg0cKFCzHkxRdfVN8iW7ZsIAbZBaHU+Y0m+USuov9iVx40aBDoU0X8 HDlyiLwSLz+kKK1C15KdO3eWfLO5FM/Q0tLS8GHFgAEDQptEV0P2FxPyi5g4 rigBXDoDdI18Nuny5cvxCn4/oeW5PowVMc6oUaNOnTqF/xtTlFWrVpV8b/HC bmISj38rV64MuQNhqB3tl19+QRsqLojrbapPD9b+hhUlz43YKJq+yA6KMFKU Z86cadasGXp5+vTpgaJB/7Jo0SJc+hWUhcb3v04R6tF437590UoCbqjBryiR 1157bcWKFVDrJ0+ejOOEPHnyyCexqxUle5jwySefXLhwAZr0w4cPv/HGG5Uq VWKLeDPdJ/mWcZg/f/65c+f279//3nvvYWCxYsXkUzUaNWoE8vDtt99eu3Zt SkpKcnLyZ599hjHls3MVV86bN+/48eMPHDgQFxfHJleMHj3atBWdhxSlVeha Ehu9ChUqqA/hsiQdO3YMVeI8ANlfTMgvYiKCohwzZgxG7ty5882bNzN8oz58 cRZo+QXd61+8eDF//vwQoUaNGnC1kydPGlaU7PUW3DTYcx2Hx7+RkZEoCadM mdLTB/wDwydFNBhuPfnkk5LvNeXWrVsxkE1OZiGIAfsbU5ScN5o3bx6Ggy6G wR5kdtiwYSBFBRQUQiG+ooTRflRUFDQXOHk+Z86cU6dOVT/5Ae3w8ccfv/XW W1jasRE7ffq0gRSGmpCOxq9fv164cGGIX6JECYPpCyVBKUqownJHwwgBww8d OqSILO9HcJJ/1qxZFXVfvoUQ031t2rRRfDvZtm1bdQsDklZd2tnCZdeuXVNf GbwmXyIbroCvLFu3bq2d/bCAFKVV6FqyYsWKUoDl98uWLQuHoByGKnEegOwv JuQXMRFBUUJXjr08UKhQoQkTJnTp0gX+z549+4EDB4xdv1WrVpJvEhH22mYU Zbt27eDEAgUKsOVowgge/6JOVADDm/fee08xmgJxnTt3bowAFobREaq5t99+ W3FNA/Y3pig5bzRp0iR1HiXfW6o1a9bw385riK8omzdvLnfoyJEj/W4XGxcX p/D70aNHDSTPBkI6Gu/Ro4fhltAG+BVlnTp1FOFz587FrP3666+KyHJF+fHH H2M0jUm/TPcpHpQBsbGxeAgsqZ3Or7/+GmMePHhQfeWYmBhF/GLFikF49erV tS8bFpCitApdS+ILlzZt2qgP4RIH5cuXD1XiPADZX0zIL2IigqLM8G1WznaX Y6xYscLY9WGwhEfZZ4+GFWVSUhIm7LPPPgvqREHgV5T58uX74IMPYEDeqVMn XNUWePfdd+UxQfuDeFS4qWrVqoq1Uo3Z34Ci5L8RU5RQdAcOHNi/f3+c6yv5 voo6cuQI5x29hviKEsbt//rXv8CbOJFV8u08dezYMUW0xMTEtm3bgvfZfseZ MmUaPny4gPPYQzca37ZtG74Iq1WrloAZzzC3ewgISfSs/NtYdWSQhGwxHCg5 a9euVS/ipKEoHz58iKe/+uqr6rQdPXp01qxZ77//PrSK7OHbxo0bea4MWlLi +AQ4LCBFaRW6lsSPgl9//XX1IajmEseae4QGZH8xIb+IiQiKcvny5ajaoI9u 1qwZW68A+la/C71qXz8lJQXEkeR7381GTYYV5bRp0/BEYd9oaMPj3wsXLoDg kk/NOnXqFD4wl4980tLSYGAm+RZe7t27d8GCBTFC5syZYWTOzjVs/2AVZbA3 gnGm/L0AjAyHDh2KkWlvu0CIrygZFy9ehHKIrQfoiEDzmaGoREdH445Fkmwx FnEIUZv8xx9/4HrIOXPmlM8LFQozinLTpk08ijLDt3eMfFHWIkWKjB8/Xj4F RUP3Abi6bLly5eSBcFP83F7N+vXrea5cu3ZtUpQhS1G4omtJfBDhd2W8F154 AQ4pFjomgoLsLybkFzFxXFGCUsNHvu+//z4+K4YQtrQ79N2KLed0r8/OjY2N Tf6HHTt2YCAoRPiJX2vy0L59e9RQYbQHpRzD4/OVK1eixdhKER06dJB8rzJx udT09HQwJi6tCYwZMwajGbZ/sIrSvKPBp6gssmfPrr3ZhGcJI0WJjBgxAgvA 7NmzNaKlpKTgrBhQE4bvFSJC0SZfvnyZrYy6ZMkSs0kMGfYoSuDSpUtQVCIi IpjuA4V49epVPKqtKJ944gnJt1ERC2FTILJlywbtJCjW48ePz58/nxQlJ6Qo A6FrSewHy5Ytqz6ET1y7dOkSqsR5ALK/mJBfxMRxRfnmm29KvsVeFLscspFh 3759+a8fHx8vcQB9N192M3COXIMGDTjji4bh8TloMbQVfiN5+PBh/PnNN9/I o505cwaXOsmRI8fFixfN2D8oRWmVo/v164cx5atkEIywU5SnT59Gh/bu3Vs7 ZqdOnTCm32dWDmJ5m3zr1i2c5yMJP3vfNkWJPHjwYNWqVbhQg+RbGg7DNXQf aHNF5F9//RXfjL/00ktwlMVkDRopSl1IUQZC15LdunWTfA9FFTu2nz9/Hkva kCFDQptEV0P2FxPyi5g4rihx8mRUVJT6EO4rXblyZf7rHz9+nEdoVKtWTS+j /4/ExESMP3DgQJ74AmJ4fJ6eno6LZ7Zq1Qp+zpw5E02h3n1jwYIFeGjNmjVm 7B+UorTK0Wziq3z1DIIhrKIM9BlgQkICOrRnz57aMdlOEJcuXTKQztBhbZt8 +/btBg0aYE7feecdweda2KwokZs3b+I8KPbCWkP3zZgxAw+NHTsWQ3r16iX5 PstVFCRSlPzxSVEGQteSbDEBxboT3333HYZDvQhtEl0N2V9MyC9i4qyihMFe tmzZpABPzlu0aAGHQHIGdf1r165dUbFr1y6MD8oIft64cUMvo/8PtgNjOO4b ghhWlGxtzJEjR2b8s8MLaEz1Qpp79+5lts0wYf9gZ71a4mhcKTRr1qzyvQMI hrCKsl27dm3atFHPamZzG+bNm5fhW/ILGpB169Ypol28ePGZZ56BaJGRkQYS GVIsbJOhtrI9LFq2bKmYByIgNijKEydOxMfHK86tX78+RHvyySfxJ9N9ixYt kke7fv168eLFsSVkF2ndurXk+5w8JSWFxUxPT2ePLEhR6kKKMhC6lrx9+zZ+ FNykSRP2vAi6s2rVqmH7JvhDJMEh+4sJ+UVMHH9Hid1omTJlFCtp3Lp1CxdA aNq0qZnrI4EWbIGbQhcPIxC/Ow7MmjULz4qOjua5i4Do+vedd94ZMGCAYqgJ Sh/3AwXWrl2bIRut4UBdzsSJE/FQbGxsoLuYX5lH21PaN9q/f3/FihXVO9GA ZXC6Gq36FQgxFeWKFSvQy6VLl545cyaM7aHEpqamDh06FF+s58mT58KFCxCz QoUKku/90YcffggD+xs3bkBRh+u/+OKL/x977wElRbG+/48gWVBAFCWJgQso iAQF4V4VxYR41SuiXtErfkUUMSFcFEEkLChBkgSRqCA5SN4FfsQFdu8CCyxB wsIuu0tOK2FJ+3/PvMf6tz0z3T3T3dM13c/ncDg71dXdVe9bVV1PdwW+QseO HSNIpK0YscCGDRvW/QmPzKeWWYTwi5SLFy/yLs+slUhWU12e81eys7OjkSXD 2K0o+XNk4cKFu3fvnpmZSSHnzp2j+HwiNXocTeg+0ont27enVoUU4tq1a++9 914Ob9u2rbhFly5dOLBdu3bHjx+nTsuaNWu43wJFqYGRMgyMWJIaNy5UH330 0enTp0+ePNmmTRsO4RfCIGJgfzmBX+TEwpY/KytLBHbr1o0dFxcXxyGh1koV axo8++yz/IjP989VEeuu/PDDD2auz4RSNJ999hmHB/1I+s033/DRlJQUbRNJ i7Z/p0+fzhls0qQJWebAgQNXr17dtm2b6Ig2btyYl6zJzc1l15coUYKknxhJ OHv27OLFi1N4xYoVNfbrDGV/4z7V9pT2jXieVNGiRakbuXr1atKkVGhHjx7N 26aQ3Fi8eLGOHb2KnIqSuve8ZJZAbB3CDhV7jNKlSpcuLQ6RQBA7R/j8m2jQ pSJIpK0YsQAvDhOKAQMGUBx6aGrEYaj+RiNLhrFbUSYlJfG3aYb+FjslFSpU aOPGjRxN6L6g1KpVSznAdevWrTzShgsYwX8L+QlFGRQjZRgYsST1lh966CFh OrFafrNmzbRfwAJdYH85gV/kxMKWX2zoHBS6SNCLkzYR4pG6hbVr165fvz6L FOKf//ynEC+RXZ8JpWjEeu+B+2XnK3YDD5w8GCto+5eE1ZNPPqk0o7K/Td2t 9PR0ETkxMVH028uXL08W4wFg7Lj169drJCOU/Y37VNtT2jeaNWuWKFE+/4g1 0bYQnTp10rigx5FTUTIzZ84UC84IHnnkEaELGOr8f/DBB7y8m6BUqVK9e/dW 7aMqCebb5P79+1Ocrl27asRhFi5cGI0sGcZI3qtXr+7z9wpU4atWreJMKRVl YGTqZnz66adikWqmTp06K1euFHGE7hszZoyYherzN3Rt2rQJ3JiGWhixmxJR uXLlwYMHi6noSkU5depUjrNu3TrVRfhGUJTKMgwMtodUqp977jnxgC5atGir Vq3QbTYP7C8n8IucWNjya6uDEiVKhLr+5cuXhw8frnrE33zzzUOHDlXObov4 +vn+JUk52vTp05Xh3377LYdTByDwLPEdJNTedvKj618S7BMnTqxbt67SmKQr 33333RMnTqgi79y5s0WLFirLN2/ePHBDeRWh7G/cp9qe0r1RRkYGdQX5o6Tg rrvu4jG9IBQyK0omOzt77dq1CxYsSExMDCyxAmpJUlNTqW8fHx+/e/dumWcU hmsBNxHNvB86dGiZn8zMTNXyTUJRssw8duwYlZz169drDMOgZ8TGjRtXrFgR u68fLcTLZdhawrIkFcKkpCRqDzUKKggL2F9O4Bc5kaflv3r1anp6Oj2RExIS 9u/fH7Vps2lpadu2bYvOvaKPcf9St2r16tVk/E2bNmnXu7NnzyYnJy9ZsoT+ N76zp3nMe4rytWXLFupDUjHLysqyKmEuRn5F6T68bAFJ8q69HyXQRRI/ugBY 0llgfzmBX+QEfnE38C8wAxRl9PGyBSTJOxSlSSTxowuAJZ0F9pcT+EVO4Bd3 A/8CM0BRRh8vW0CSvENRmkQSP7oAWNJZYH85gV/kBH5xN/AvMAMUZfTxsgUk yTsUpUkk8aMLgCWdBfaXE/hFTuAXdwP/AjNAUUYfL1tAkryvWrWqqR/dNcdA UCTxowuAJZ0F9pcT+EVO4Bd3A/8CM0BRRh8vW8DLeXcT8KNVwJLOAvvLCfwi J/CLu4F/gRmgKKOPly3g5by7CfjRKmBJZ4H95QR+kRP4xd3Av8AMUJTRx8sW 8HLe3QT8aBWwpLPA/nICv8gJ/OJu4F9gBijK6ONlC3g5724CfrQKWNJZYH85 gV/kBH5xN/AvMAMUZfTxsgW8nHc3AT9aBSzpLLC/nMAvcgK/uBv4F5gBijL6 eNkCXs67m4AfrQKWdBbYX07gFzmBX9wN/AvMAEUZfbxsAS/n3U3Aj1YBSzoL 7C8n8IucwC/uBv4FZoCijD5etoCX8+4m4EergCWdBfaXE/hFTuAXdwP/AjNA UUYfL1vAy3l3E/CjVcCSzgL7ywn8Iifwi7uBf4EZoCijj5ct4OW8uwn40Spg SWeB/eUEfpET+MXdwL/ADE4pSgAAAAAAAAAA7gCKEgAAAAAAAABAZGDUazTx sgW8nHc3AT9aBSzpLLC/nMAvcgK/uBv4F5gBijL6eNkCXs67m4AfrQKWdBbY X07gFzmBX9wN/AvMAEUZfbxsAS/n3U3Aj1YBSzoL7C8n8IucwC/uBv4FZoCi jD5etoCX8+4m4EergCWdBfaXE/hFTuAXdwP/AjNAUUYfL1vAy3l3E/CjVcCS zgL7ywn8Iifwi7uBf4EZoCijj5ct4OW8uwn40SpgSWeB/eUEfpET+MXdwL/A DFCU0cfLFvBy3t0E/GgVsKSzwP5yAr/ICfzibuBfYAYoyujjZQt4Oe9uAn60 CljSWWB/OYFf5AR+cTfwLzADFGX08bIFvJx3NwE/WgUs6Sywv5zAL3ICv7gb +BeYAYoy+njZAl7Ou5uAH60ClnQW2F9O4Bc5gV/cDfwLzABFGX28bAEv591N wI9WAUs6C+wvJ/CLnMAv7gb+BWaAoow+XraAl/PuJuBHq4AlnQX2lxP4RU7g F3cD/wIzQFFGHy9bwMt5dxPwo1XAks4C+8sJ/CIn8Iu7gX+BGaAoo4+XLeDl vLsJ+NEqYElngf3lBH6RE/jF3cC/wAxQlNHHyxbwct7dBPxoFbCks8D+cgK/ yAn84m7gX2AGKMro42ULGMl7YmLi8uXLU1JSjFwwrMgRk5OTs9zP6dOnbb1R rODlMmwtsKSzwP5yAr/ICfzibuBfYAYoyujjZQsYyfudd97p8/n+8Y9/GLlg WJEjZtKkST4/JGBtvVGs4OUybC2wpLPA/nICv8gJ/OJu4F9ghlhRlNnZ2Zs3 b6b/taNlZWXNmTPnm2++mTBhws6dO69duxZBCu3GuAV27Ngxfvz4Hj16zJ49 WzfvMQEUpTswXoavXLmSnJw8aNCgIUOGUBW+evWqzUmLMcJtDw22hMAgKMly YtwvOTk5U6dO/frrr+lZSQ5SHT127NhmPS5cuBBWTA22bdsW6txLly5FZAm5 iI7iMN/fi8ybR44cmTlzJvW46LInTpwIN9lhnU7tSWpq6pYtW6Tqphr3b2Zm 5vTp06nejRw5cvny5ZcvXw4aLTc3Nz4+vm/fvkOHDk1KSsrLy9O4JlUfcmX3 7t1Hjx69bt26UJa5ePEi9cSoKe7Tp8+0adPS09ONJDjcJLm+LtuB5IqSXL94 8eI33njj+uuvp/78o48+Girm+fPnKZrvr9SpU2fPnj0RJNJWjFjg7Nmzb7/9 tio77du3D1VtYwUoSndgsBbTU75ChQrKMnzPPfdkZGTYn8CYwaAljbeEICxQ kuXEiF82btxYs2ZN1VPy6aefPnDggIhD8t+nx5gxY8KKqUHRokVDnTtjxgzT VnEeWxWlhf29CLzZsWNH5dHrrrsuLi7OeOKNn75r167BgwffddddHHPlypXG 72I3RvybkpJy3333qYx59913z58/XxVz6dKlt99+uzIataKBr33y/W8AWrVq pbpmkyZNyFbKaCTlunTpUrhwYWW0ggULtmnT5syZM0YyaDxJrq/LdiCzoszO zuaGRfD3v/89VMy6detynAIFClSvXv3GG2/knzfddBOpswjSaR+6Frh27doj jzzC6a9SpcpTTz1Vvnx5/tm0adOYfj0CRekOjPhxx44dt956K9uNqiQ9cfjv O+644+DBg1FJZgxgxJLGW0IQLijJcqLrl8mTJ4suH3mnUaNGpUqV4p81atQQ 3x2MKItp06aFFTMUFy5ciPjcWME+RWltfy9cb3bo0IEDS5Qo0bBhw2LFivHP nj17Gkm88dPbt2+vSsby5csjtZn16Pp32LBhhQoVEtZ+4IEHyAX8k4Tehg0b REzKF8lqDqceWoMGDdi/ZJwFCxYor3n16tXHH3+cL1K6dOnWrVs/8cQT/POe e+45f/48R1P6na5cs2ZN0SwTL7zwgu7XXuNJ8kJdtgOZFWVWVtZNf8KFNlQL 884777CjW7ZsyW8qqIiSCqDaPWrUqAgSaSu6Fhg3bhxn57333uOPkvR/u3bt OJCORimhNgBF6Q6M+PHFF1/kln/KlCkcMnz4cDZj27ZtbU9ijGDEksZbQhAu KMlyou2XEydOsH4kdb9mzRoegXzq1KkWLVqwX/r168cxSV8cCMaWLVu429+4 cWM+3XjMUFA95bt/9913gdc5d+6clQZyCPsUpbX9vbC8mZqayhesV69ebm5u vr+AVatWzef//pWZmamd8rBOJ+3JeSxevHgsKsoJEyZQmitWrJiQkMAGzMnJ 6dSpE+flySef5GgXL14kMUgh5cqVEwsnJicn88cR6rZduXJFXHPu3Ll8eufO nelEDvz55585cODAgRxCPq1VqxaFvP/++8ePH8/3+51SW7VqVSPds7CS5IW6 bAcyK0olPEIgaAtD/uV3Jq+88orqHYWcK3PqWoBf11SqVElULobaKwqnlip2 p/AYV5SPPPII/yQjrF+/fteuXUFfQOkqyj/++GPDhg2bN29WGTMQuv7evXtX r15NPRPVIShKFbp+pKcMv/177733lOHcOS9ZsiT5xd4kxgjhtocaLSGIAJRk OdH1CzXFzZs3P3r0qDKQ+pmsNJ9++mnt61OnlKJRr153XozxmNu3b+fHRODw P9dgn6JUYl9/L6g3P/zww0D1J3Si7mfKyE4Xiim2FGW+f3gACzoBOaJ69eqU lzJlynDImjVrOHcjRoxQxvztt984/JdffhGBXbp08fk/76pmdVEBoPDXXntN hBw8eJDkpyo9v/76K19zyJAhGskOK0leqMt24AJFyU0EkZaWFkF6oo+uBW65 5RbKTrt27VTh06dP55xGoUm3CeOK8oknnti5cyd1DMTQpptuuqlDhw6qQb+h FOWhQ4eonf/b3/4mhmRQt/Df//530O7fkSNHqNUSg6auu+46OnHmzJkiQihF +emnn5b2079//7DsEOvo+pEMwhZTTRKZMWMGh0+YMMHeJMYIUJTOgpIsJxEr l0cffdTnny2iEWf9+vX8XBg8eLD21YzHJFavXs1FIikpKaw0xxCOK0oz/b2g 3qQeBT3BfcGmbd57770+/+B2jWtGfHrsKsqgNGvWjGU1f+n74YcfOHeHDx9W xWSziE8G+X/6tGzZsqqYvJZIkyZNtG+9bt06vtfXX3+tES2sJHmhLtuBCxRl jRo1WIBEkBhH0LZAXl5eqNpBFYEP6a4PIC3GFSURdGY0tdvKtbmCKsrRo0cX KVIk8Nyg2jMhIeHmm28OGlkMcguqKMXgZJK9yvESXkDXj2+99ZbP/xJAZZnc 3Fz+4tOpUyd7kxgjQFE6C0qynETcs33ggQfIKdRF1IjDo33of92JV8ZjEvPm zeMngotn1zquKM3094J6Mz09nb0mRlcK/vvf//IhjVGOEZ/uJkV55syZcuXK +fwj6Djkiy++8PlfzgdWHJ7AVaFCBREivhKqbs3r/5Cu1L57XFycqsMWlLCS 5IW6bAcuUJQ8HP3LL7/kn9nZ2Rs3bpRzvCujawGebvzOO++owklJ8RSAr776 ysb02UlYitLn/1BLIi4rK2v69OlC9/3000+qyCqdyO+XKlasOGTIkJSUlD/+ +GPt2rViaTVlAk6ePMlfhInXX3+divTZs2ephaduSYMGDcRA2UBFuWHDBhat NWvWNLjImJvQ9eNTTz1FxqlVq1bgIS7eb7zxhl2JiymgKJ0FJVlOIusnnDp1 ij9CBT49BeLrA3Xpta9mPCYzfvx4jk89ZOqQUE+4W7du1MsVS4u4AMcVZcT9 vVDeXL9+PYfPmjVLdcrIkSP50N69e0NdNuLTXaMoDxw4wI0kMXz4cA4UeQ+c hdqrVy+ff0klMd7swoULPEKsbNmyK1as4EAyC19BhARy5cqVyZMn89KvlSpV 0q5oYSXJC3XZDmJdUR49epT9PmrUKOr2K9c0pj4AtSERpNBudC3QpEkTSv+N N96onNBH6qZ58+actdatW9ueSnswrigp+6rVt7Zu3crrdN1zzz3iLVOoUa/z 5s1T1X0SlWy9jz/+WASKOf6fffaZMjI1LMqHlEpR0lOMF6Amkbtv3z6DeXcT un6sXbu2L8Ty77zgfwwNKrAVKEpnQUmWk8j6CWKI8sSJE0PFeeWVV3z+1Tl0 Z9Ybj8kMGjTIFwzq69LzKLycyIqzitJMfy+UN2fPns1XCNzFQ8wzos5DqMtG fHqsK8oJEya0adOmadOmBQsW9Pmnpg4dOlR0zJYtW8a5U421i4+PFwvhKvtO 5LuSJUty+AsvvEDOJXVJf7/66quBtz506BD14l5++eUqVarwKVRUdHtiYSXJ C3XZDmJdUSYnJ4v2hP+gYlmiRAn+mwSISpXIgK4FxOuRunXrUkXLysqaOXMm iSZRqqnGRSuxFmNcUQZdbOfJJ59kC+Tk5OhGDuSGG25QWo9aPw655ZZbeIm2 UCgVZV5eXqNGjejvQoUKrVq1ysh93YfB7+wvvfRS4CGebk+dAbsSF1NAUToL SrKcRNBP2L9/P3/AqlGjRqiNm+lhyuu6iI9coTAeUyB6oVQwOnfu/Pnnn/MQ XKJIkSLbtm0LKzty4qyijLi/p+FN8elq69atqkPiM1ng90fzp8e6onz22WeV UqtHjx4XLlwQR6mbxAurktnj4uJIqW3ZsqVXr17K6UgUIuJfunSJxKNKvtWr Vy/ogOGkpCSVytu+fbtugsNKkhfqsh3EuqIUA7B9/iHcK1eupEcJKYVffvmF XzuQ4jD4gjFq6FqA0s/TnFVQjeM54B06dIhWYi3GpKLs2bMnm0Jse6StKMn1 VELorJYtW/L8C6Jhw4Z8lHogHBK4CJIKoSjXrVsnPmuKBeo9iK4fqZEnEz3/ /POBh8j+/LCwK3ExBRSls6Aky0m49eLq1atiD7vFixeHijZs2DCOo9sFNR5T yZQpUxISEpSp+uqrr/g6dm9xFR10/XL06NF9eujux2F5f0/Dm2PHjuVDmzZt Uh1asmQJH9JY8DPi02NdUQ4YMOCZZ54hncWDTn3+vXp37NghIlBFCFwKg3qw QjmeOHGCY+bm5vIXk5IlS3744Ydi+/UCBQp079498Nbp6enUo6PiUaFCBY55 3XXXUUzdyc7Gk5TvgbpsB7GuKMUgdtIL4rsV07VrVz6UnJwcQTrtw4gFqPQO GTKEaiu1k1RhmzZt+sMPP1BTyZNEAueAxwomFeXo0aPZp9OnT9eOzOMiypQp 4wvgwQcf5Dji8fT9999rJ0koytdff11c59lnnzWSZVei68cGDRr4QqzSxu8J mzdvblfiYgooSmdBSZaTcOvFRx99xM2y9rTWVq1accdVdwcu4zG1odPr1Knj 8y8054IF3HT98vTTTwc+c1Xcdttt2nexvL+n4c2FCxfyicuWLVMdmjx5sm4f MuLTY11RCg4fPkxqjmckUX9MOdvo999/f+mllypWrOjzL7/87rvv7tq1i3UZ +UJEe+2113z+nUd4YdW8vLxhw4bxUj9Enz59Qt2aJOTSpUu5fhHUP9RNrcEk BcVlddkOYl1RUqvCZWncuHGqQ5s2beJD2gtARZ+wLEBlWCxtKr6pKTe2iC1M KkoxTWbJkiUakY8cOcKdPZ//vVlcXBzd9OTJk1yKhKIkM3IcMZ08FEJRMmKY za+//mow4y5D14/PP/+8z79sUeAhlvkaS2d4CihKZ0FJlpOw6oUYolavXj3t pTP4o8Zjjz2me03jMXXp2LEjJ4/6ruav5izOKsqI+3sa3hQnitfUgqFDh/Ih jY+qEZ/uGkXJfP3115ydoDsRKAfEvvnmmz7FZIGtW7fyiaoX+wcOHOBpksWK FQvc70MJlQqem6BcrFUXjSRp4Ka6bAexriivXbvGX7E///xz1SGqxUELquNE XGepqnKOZPvqahyTivKDDz5gC1BroxH5oYce8vk3oFRtFadSlGlpaXy1Tz75 RDtJSkVZu3Ztat/uuOMOn38CJglV7XNdia4f27Zt6/O/ylNtACpqZdeuXe1N YowAReksKMlyYrxezJgxg4fuUK9Sezil2Oihc+fO2tc0HtMIYrCccuJYjKLr F+qczNdDV0NZ29/T9mZ2djYf7datm+rQ//3f//n8IypVu2BbcrrLFOW+ffs4 Ox9++KFGtCtXrlSuXNmnGNoxatQoPjFwn46JEyfyId3FcFq3bs0xjx8/Hm7K A5OkgZvqsh3EuqLM/3OPofvuu081iHrNmjXsetkW54m4zvLKZlWrVg217ID8 mFGU1CzznKZixYpprPV67Ngx9nv79u1VV1ApSrog7yhHF9F4ZOQrFOVtt92W kZGRr9iuqE2bNvrZdh26fvzll1/YPqpFCUaMGMHh8fHx9iYxRoCidBaUZDkx WC+oHeYVV0qWLKn7olVMc9DdDcR4TCPwGiaFCxfWfsrEBBH3XsLC2v6erjd5 RJNqhyC6Pk/oe/jhh7VTG9npMaooQ81VFCPo3n//fY3TZ8yYwdHEHnB9+vSh nwULFlR+NGTEQkykOrXvzh8ZiSNHjmjc3WCSNHBTXbYDFyhKUTHnzJmjDH/7 7bc5nCWAPBixQGpqqqp+UWnn7Pz44482Js5mjCtKap9VU+yHDBnCFnjttddU kZWKkkwXtGVLSkri5amFoiSeeeYZjhw4NVU5x1woSno2icAWLVpwoMZ+SW5F 14/nz5+/6aabyDjNmjUTU1eoEa5fv77PP3/B5Owk1wBF6SwoyXJipF6QduBV QYoWLWqkEolp+EuXLjUZk0oFPRSmTJkiHtObNm2qXbv25s2bAzPCU8zcsYKT 44oygv6erjf79u3LEUiWikC6PgeOHTtWBAb6PazTg2YkthTlK6+88tJLL509 e1YVLka9jh8/nkOo/6b6kHfy5EnejImaTaHI4uPjVScKqFfGh3jXtpSUFBLp gW8MDh8+zLuK02VFYFBPGUySR+qyHcisKDds2LDuT3gYPLUzIkRsK3/lyhVu f0gvzJ07l57vFPL999/zSJigq747i64FNm7cWLx48bp161I9unz5cmZmZo8e PbgkV6pUSUyrjEWMK0qff9/JefPmUduVnZ3ds2dPtkCRIkXEkNf8P99YUgEQ /TpqSdj1pUqVIkteu3YtKysrLi6OX2WrFOXu3bvFSmWdO3emK1PhofaEmk26 iNhGSrUfJbN//35eX47SGfh6zd0Y8aMYovzRRx+dPn2amu42bdpwCJXnqCQz BjBiSYMtIYgAlGQ50fXL4sWLxbL/Xbp0oZ/09J+jIPBF3zfffMPxqWuqfXfd mJ999hlHELtR8OakpG27d+++evVqeiJQxSQtw1u308NLYwXaGMI+RWlff0/X m9TB4NFKJEy4z0Da8MYbb6SQG264QbmzWKDfwzqduiIiR926deNLUeeEQ8Ja VdgmtP07a9YsTnP16tVHjRqVlpZGmT127NhXX33Fu1JSrg8dOpTv/5jYsmVL MgvJ7ZycHJJyZBM6i08fOXKkuCbZh91dokQJUtniK+Ts2bN5M6CKFSvyxwXe MoaqEjXIixYtoiJB3WNK7f3338+X7dixo7hsoKeMJ8kjddkOZFaUvFdgKAYM GCBiUsHg7VB9/iGRYrvScuXKURWOIJG2omsBej6KbHI9ZSpXrqz7KJQc44oy cJFntoZqFZ2PP/6YD5UpU0asvqVckZWbei4YVatW9f1VUeb71xUXcVQGFzMC gipKolevXqpWyyMY8SP1vXlCK8MvBHz+bz1eE+AaGLGk8ZYQhAtKspxo+4V6 g0EfEEpq166tOqtdu3Z8KHDGVrgx+U0m0bhxYw6hzjZ3gMVzRJQTolOnTmFk XmLsU5T29feM+P2XX34Rj37huCJFiixcuFAZLdDvYZ1OudDII1kgQttZh7Z/ 8/LyeNVcgXghzxkXEx7J1PzdUFQH8fcnn3yiWiiVulXiOuXLlyfb8joVfP31 69eLtPH2eUyBAgWUPbeGDRsqv7YEesp4kjxSl+0gdhVl//79lZH37dvXqFEj fk/FPPfcc6r1pSXBiAXGjRsnttrx+ZumFi1aHDt2LCoJtBEjeee3RmQB0nrK BoQamcDXztQW8YJgPsXqW6dOnWrZsqXSes8+++yePXt4YIZKUeb7S3LdunWV jUbFihUnTpwoIkydOpXDVWWeOjbVqlXz+ffM1e2luAmDtZi64lQNxcOCOoH0 PEInXIl5RalqCUFYoCTLia6iVHYIg9KgQQPVWaIzrL0erJGY3377LUcYPHiw CMzIyGjTpg1/yBDcddddGrsZxhxOKUoz/T2Dfp8yZQov1MDceeedKj2YH8Lv xk/XVpQlSpQwZCk7MeLfmTNn8la8Sh555JGNGzcqo5E7lG2mz9+tCjWVdefO nWIakaB58+bKyUf5/mX8P/jgA9WucFTjevfufe7cOWXMoJ4yniQv1GU7kFlR RsDZs2dXrVpF1ydNYdMtzGPcAocOHVq6dGlSUlJMj3RVEq73r169mpqaSkbQ WMSP4qxevXrx4sWqDt6BAwcofO3atUG3PA7kzJkzFD8hIYGHbQANwvIjPcep DBt3hKewtT0EuqAky4n89SItLW3btm2B4TxXa9myZStWrJBwiJRJpPKLHf09 0qrkOI33w6H8bvB0yTHu3+zsbGoJFyxYkJiYeOLEiVDRqO9KSjM+Pt5IXSCH JicnL1myhP4PnKopuHTpEnULFy1aRJfdvXt3qJUqQ3nKeJLcXZftwGWKMibw sgW8nHc3AT9aBSzpLLC/nMAvcgK/uBv4F5gBijL6eNkCXs67m4AfrQKWdBbY X07gFzmBX9wN/AvMAEUZfbxsAS/n3U3Aj1YBSzoL7C8n8IucwC/uBv4FZoCi jD5etoCX8+4m4EergCWdBfaXE/hFTuAXdwP/AjNAUUYfL1vAy3l3E/CjVcCS zgL7ywn8Iifwi7uBf4EZoCijj5ct4OW8uwn40SpgSWeB/eUEfpET+MXdwL/A DFCU0cfLFvBy3t0E/GgVsKSzwP5yAr/ICfzibuBfYAYoyujjZQt4Oe9uAn60 CljSWWB/OYFf5AR+cTfwLzADFGX08bIFvJx3NwE/WgUs6Sywv5zAL3ICv7gb +BeYAYoy+njZAl7Ou5uAH60ClnQW2F9O4Bc5gV/cDfwLzABFGX28bAEv591N wI9WAUs6C+wvJ/CLnMAv7gb+BWaAoow+XraAl/PuJuBHq4AlnQX2lxP4RU7g F3cD/wIzQFFGHy9bwMt5dxPwo1XAks4C+8sJ/CIn8Iu7gX+BGaAoo4+XLeDl vLsJ+NEqYElngf3lBH6RE/jF3cC/wAxQlNHHyxbwct7dBPxoFbCks8D+cgK/ yAn84m7gX2AGpxQlAAAAAAAAAAB3AEUJAAAAAAAAACAyMOo1mnjZAl7Ou5uA H60ClnQW2F9O4Bc5gV/cDfwLzABFGX28bAEv591NwI9WAUs6C+wvJ/CLnMAv 7gb+BWaAoow+XraAl/PuJuBHq4AlnQX2lxP4RU7gF3cD/wIzQFFGHy9bwMt5 dxPwo1XAks4C+8sJ/CIn8Iu7gX+BGaAoo4+XLeDlvLsJ+NEqYElngf3lBH6R E/jF3cC/wAxQlNHHyxbwct7dBPxoFbCks8D+cgK/yAn84m7gX2AGKMro42UL eDnvbgJ+tApY0llgfzmBX+QEfnE38C8wAxRl9PGyBbycdzcBP1oFLOkssL+c wC9yAr+4G/gXmAGKMvp42QJezrubgB+tApZ0FthfTuAXOYFf3A38C8wARRl9 vGwBL+fdTcCPVgFLOgvsLyfwi5zAL+4G/gVmgKKMPl62gJfz7ibgR6uAJZ0F 9pcT+EVO4Bd3A/8CM0BRRh8vW8DLeXcT8KNVwJLOAvvLCfwiJ/CLu4F/gRmg KKOPly3g5by7CfjRKmBJZ4H95QR+kRP4xd3Av8AMUJTRx8sW8HLe3QT8aBWw pLPA/nICv8gJ/OJu4F9gBijK6ONlC3g5724CfrQKWNJZYH85gV/kBH5xN/Av MAMUZfTxsgWM5D0xMXH58uUpKSlRSRGIBC+XYWuBJZ0F9pcT+EVO4Bd3A/8C M0BRRh8vW8BI3u+8806fz/ePf/zDyAXHjh3bqlWrKVOmWJA4YBgvl2FrgSWd BfaXE/hFTuAXdwP/AjPIryhzcnKmTp369ddfjx8/Pjk5WXX02LFjm/W4cOFC BOm0j3AtkJ2dTbmg/zXiHDlyZObMmT169JgzZ86JEyfMJtE2rFWUW7Zs8fkp WLBgZmamNUkEBjBehq9cuULVdtCgQUOGDKFifPXqVZuTFmPY0RoA46Akywn8 Iic2tVfkxNTUVHqgX7t2Laz0hNUeZmRkcJ/w5MmTgUcvX768YcOG/n4WLFhA fcuwUpKbmxsfH9+3b9+hQ4cmJSXl5eWFdbokGPfvjh07qE9Ofc7Zs2dr2P/i xYuJiYlUPfv06TNt2rT09PRQMY3b36SniG3btk2YMKF79+6jR49et25dqFJn PPGAkVlRbty4sWbNmr6/8vTTTx84cEDEoYeIT48xY8ZEkE77MGgBaqAWL178 xhtvXH/99ZSLRx99NFTMjh07KvN73XXXxcXFWZli67BWUVIFJy1JkQsVKpSV lWVNEoEBDJbhnTt3VqhQQVk477nnHnqs25/AmMHy1gCEBUqynMAvcmJ5e7Vr 167Bgwffdddd7L6VK1caSUYE7eGRI0duvvlmvssvv/yiPHTp0qUOHTqUKFFC WZBuvPFGkhtGEkMsXbr09ttvV55OxTLwC4j8GPHv2bNn3377bVU3u3379iT0 lNHIql26dClcuLAyGnXY2rRpc+bMGVVMg/Y37ymSn61atVIlvkmTJlQOI0s8 UCKtopw8eXLRokXZj7feemujRo1KlSrFP2vUqCHe/xhRlNOmTYsgnfZhxALZ 2dncVAr+/ve/B41J9YsjUC1r2LBhsWLF+GfPnj2tT7pprFWU+f6W/IsvvjD4 GAJWYcSPO3bsoJrLpbF69ep33303/33HHXccPHgwKsmMAaxtDUC4oCTLCfwi J9a2VyRDVL215cuX66YhsvbwX//6l4ivVJTHjx8nQcrhBQoUqF+//t/+9jcR c9KkSbpXpjRfd911FJkECPVbGjRowMmjztiCBQt0T5cKXf9eu3btkUceYeNU qVLlqaeeKl++PP9s2rQpCTGORj6qW7cuh5NxatasKaoq8cILL4jPgsbtb95T V69effzxxzl+6dKlW7du/cQTT/DPe+655/z58+EmHqiQU1GeOHGC9SM9I9as WcPjWE6dOtWiRQv2ab9+/Tjm2bNnDwRjy5YtrK0aN24s2zAYIxbIysq66U+o 7oRqM1NTU9kg9erVy83Nzfebrlq1aj5ZB4JariiBIxjx44svvsgNspjlOnz4 cC6ubdu2tT2JMYKFrQGIAJRkOYFf5MTa9qpDhw4crXjx4sYVZQTt4dSpU5UK VKkou3btyoFffPGFGD85f/58TlLZsmX/+OMPjStfvHiRxAjFLFeunFhOMDk5 mXUWdWauXLmimyN50PXvuHHj2Fzvvfcef5Sk/9u1a8eBdJSjUc+8Vq1aFPL+ +++TEsz3qzm6ctWqVTlmYmIixzRuf5OeIubOnctX6Ny5MzmOA3/++WcOHDhw YLiJByrkVJT5/gU/mzdvfvToUWUgOZeV5tNPP619OpUEikYlbc+ePREk0lYM WkDAA0KCtpkffvhhoHgUMlPCz5TGFeUjjzwiQtLS0jZu3KjbXKigZoFa+PXr 17PW1oYMuGbNmtOnT+vG3L9//8qVK41PZKNkrF69Oj093U3vtXT9mJOTw+9p 6bmjDOdOYMmSJcP1pluxsDUAEYCSLCfwi5zY1F6JXr0RRRnu9Q8fPszjXR96 6KFARUmK75133hFSSCD0y4YNGzQuTt0GjjZixAhl+G+//RZ4L/nR9S9/46tU qZJQZEy9evUovFq1auILzsGDB0nBqU7/9ddf2SxDhgzhEOP2N+kpokuXLj7/ cD7VAF0qPxT+2muviRCDiQcqpFWUoeCv3lWqVNGIQyKC31wNHjw44hvZh1Vt 8qVLl0qXLu0LNong3nvv9fkH/5hMquUYV5RPPvkk6bu2bdvedtttXIvJp9R5 UE14f+CBB8gIzZs3Vwbu2bPn+eefFy8kr7vuOjLIjBkzRISpU6fSWXfffTc1 LHFxceLVE49wWLdunSpJJAanT5/+2GOP3XTTTeKyt99++9KlS1Ux+crUrtLf c+bMadSokRifU7FixcArxyi6fuzfvz/nWjUgmbzA4RMmTLA3iTECFKWzoCTL CfwiJ7GoKPklQ+HChRMSEoyrPCpXHHn8+PEa0X744QeORrpVdYi7Ycp34/Kj 699bbrmFMtWuXTtVOHWQ2A7ap1MXiKN9/fXX2ikxaP+wYvKXprJly6rCeVpo kyZNtE83nnjPEnOKkhQEOZSqqkYcfltC/8v5VciqNjk9PZ2Lt/hYL/jvf//L h86dO2cytdZiXFFWqVJFzNZX8vnnnwdGVg6RPXHiRKVKlUR8MZyGENO3J02a xCH3339/4C2KFClCYlBc8MyZM0Gj+fwid9myZcr0iCt36dKF51YoKVasWATr kkmIrh/feustyi8JcNWYn9zcXJbYnTp1sjeJMQIUpbOgJMsJ/CInMacop0yZ wlfu1avXnj17+G8jinLevHkcWfkuOpAvvvjC538XHdjb5LGgFSpUMJgXGdD2 b15eHtskUFKRoOZD2ithxsXFcTTdHd8M2j+smOLDsSqP9913HwWSrtQ+3Xji PUtsKcpTp07xx8d33nknVJzVq1ez06mNiuwudmNVm7x+/XrO6axZs1SHRo4c yYf27t1rMrXWYlxRMs899xzljh4EgwcP5n7CjTfeqJTJgYpS1PpPPvnk0KFD 1M5v3br1n//8Z506dcQ+MkL3+fxfcidMmJCRkbFp06Y333yTAytXrqwcF/H4 44/TI+PVV1+dP39+Tk5Odnb2l19+yTFVbyCVVy5dunS/fv02b96clJQkxtv0 7t3btBWdR9ePTz31FGW2Vq1agYd4kvsbb7xhV+JiCihKZ0FJlhP4RU5iS1GS zClbtixFePDBB69cubJ7927jivKzzz7jyNrrUYi+VmA00rA+/2tnsV6N/Oj6 lytXYA+cxCYvXfLVV18FPZHsP3nyZF49tVKlSmIZnFAYtH9YMakHyPPmqFSs WLGCA6nI8ekixHziPUtsKUox0GXixImh4rzyyis+/yxp1TBvebCqTZ49ezZb I3ClUzECYe3atSZTay1hKcpu3bop3/tRD4HDU1NTVZGVivLll1/2+Ye4qGq9 slUXuu+ll15SzZ1s2bJl4EOHJG1gaRerhJ06dSrwyuQ15XrUdAX+ZPniiy9q Zz8m0PVj7dq1fSEWdectgch6diUupoCidBaUZDmBX+QkthTlCy+84PMPDeJn sXFFSb0C3g2katWq2jGXLVvG11R9touPjxcL7+/bty+MLDmKrn+bNGni87/Y V3Z7qLPdvHlzzmzr1q2V8Q8dOvTxxx9Tr6xKlSocgZylaxDj9jcek1m9enXJ kiU5JVQ8qMPG7xxeffXVwMiRJd7LCEUZGRHfMYIT9+/fzyMYa9SooZpXK8jK yipUqBDF+fLLLyO4RXSwqk0WL8e2bt2qOiTeugR+vnQW44qycePGqnCxyNjC hQtVkZWKkloAjqYxBELovsC3UomJiXwocKaAigEDBnDMLVu2BF45ISFBFb9y 5coU3qBBA+3LxgQG32SSYA88xLPg77vvPrsSF1NAUToLSrKcwC9yEkOKkmQj X1Osp2FcUYrFS3Vj5uXl8Vqv1POMi4sjuUH9gV69ehUpUsT3J8oeguTo+nf8 +PGcqbp165I6oy73zJkzqQMmMksyTRk/KSnJp6BSpUrbt2/XTYZx+xuPyVy6 dInEo++v1KtXL+gEscgS72XMfDGM5h2vXr0qPgktXrw4VLRhw4ZxHJn9blWb PHbsWM7spk2bVIeWLFnCh+bPn28ytdZiXFEG7h5CQpIzpRzEHhiZJKFYDOeZ Z54hCwQu362hKKmk8elB1xOmcjV69Oj//Oc/1ASJN11kbSNXJi3p8y+Gpp39 mEDXjzyV9fnnnw881LBhQ27D7UpcTAFF6SwoyXICv8hJrCjKnJycMmXK+Pxf scVIJ4OKcuXKlTygiAqSkbU4EhISxObpgtKlSwvlcuLEibAy5SC6/iWDNGvW zBcAZZYXiuzQoYMyfnp6esuWLclBFSpU4Jhk2+7du2sY1rj9w/VUbm4ui1/q vH344YdiJ80CBQpQkgLjR5B4jxMrivKjjz5ih2pPjmjVqhWXFtn2oFRiVZss FJZqcRhi8uTJfCg5Odlkaq3FjKKMj483oijz/Ys8KxdlpdagX79+ylHQGrqP 4NVlVas/0U15xadAFi1aZOTKDz/8sHcUJcvnoIun8Rtd1fK8ngWK0llQkuUE fpGTWFGUYrH3xMTE7D8RO30MGzaMfp49ezbwgr///jtvNVK8eHHl/Bpt6KyX XnqpYsWKPv+igu++++6uXbu++uor7o6GlSNnMeJf6l0PGTLkgQceKFasWOHC hZs2bfrDDz9Q/4oXOQlcKJIhFbZ06dI6deqwC8QyiSqM2z8CT7322msUv0yZ MklJSfn+78tUEsqVK8dJ6tOnT6gTDSYexISiHDRoEPuRuvTaU2L5TcJjjz1m Kok2Y1WbvGnTJjbL9OnTVYeGDh3Kh3SnKkeZ6ChK4siRI19//TW38AwpxJMn T/JRbUV5ww03+Px7V4kQUQKLFClCjRIp1p07d06YMAGKMhT8QK9Zs2bgIX51 rLG4lqeAonQWlGQ5gV/kJCYUZVpaWtB3vyroiay62tGjR8UK89OmTQsrJYxY /Y/ghf5ia/R1WP4laSl2c9u/fz/bbebMmRqn5OTk8Hj1oEvgGrd/BJ7aunUr x//++++V4QcOHOBpkiSQA7eAMZ54kB8LinLGjBn86oNcqa2PxG4anTt3NptK O7GqTc7Ozub8duvWTXXo//7v/3z+D/SyLTIWNUXJXLlyZc6cObxQA/HWW29x uIbuo5ZKFXnhwoU8sqJRo0Z0VMQUD0EoykDatm1LmS1atKhqn3Gqwmyfrl27 2pvEGAGK0llQkuUEfpGTmFCUO3fuNKIo69evrzyLChKPl/ZZsRAH9T145YTY +lYesSIYM2YMm053XFzr1q055vHjx5Xhxu0fmadGjRrFpxw8eFB1aOLEiXxo 3rx5kSUeMJIrSvIvr7RTsmRJ3YIq9pqRdt8QxsI2mYf3qFZQv3btGo8PD3wF 5zhRVpTM2bNn2VDizZKG7hPrHcXFxXFI+/btfX55fuTIEWVMKEqNCGJVBNXa UCNGjOBw8qa9SYwRoCidBSVZTuAXOYkJRZnv32nuRABiwzUSF/TzzJkzIv75 8+cfe+wxPvr666+bnzY1Y8YMvtpPP/1k8lLRJGJFwFs6Vq1aVSybGWqyodij TdmhMm7/iD3Vp08fOqVgwYLK78gMiQtRMCJIPBDIrCgXLFjA+78ULVrUyCmj R49mXy9dutRkIm3Fwja5b9++nOU1a9aIwDlz5nDg2LFjzafWWqKgKHft2pWW lqY699FHH6VopUqV4p9C902ePFkZ7fTp03fccQc3O+IiL774os8/dzsnJ0fE zMvLE20LFGUg1OzzVNZmzZqJNv/SpUv169f3+WeayDzTOZpAUToLSrKcwC9y EiuKMiihVuYhiSEWfmzRokWorQTy/aWOHvHUA1GqkosXL6pWcz158iTvbkPl ULZxYtoY8W9qaqpKlJFqZuv9+OOPHJKSklK+fHnqw6vOPXz48C233MKWEYHG 7W/GU6IDOX78eFXkgQMH8qHExMRwEw+USKsoFy9eLJZf7tKlC/2cO3fuHAWB PfZvvvmG41N5sCXpFmHEAhs2bFj3Jzw5lFpOESLerWVnZ/PCpFTON27ceO3a NZKWN954I4XccMMNubm5tmcmTOxWlPw5snDhwt27d+cx0ufOnaP4fGLTpk05 mtB9pBPbt29PDxpSiGvXrr333ns5vG3btuIWVPw4sF27dsePH6cHBBmZ+y1Q lBp88MEHbIqPPvqIpDo9ZNu0acMhPXr0iEoyYwALWwMQASjJcgK/yImF7VVW VpYI7NatGzsuLi6OQzSW64+4PQyqKEkPPvXUUxxeqlQp0hHz58+f81eoo8WR P/vsM44pBltSp6tly5bUDevbt29OTg5djboH1atX52gjR440ZldZ0PUv9TOL Fy9et25dEl8k6KiXRXWNpwVVqlRJTKusVauWzz+yiyopdZDIKRSZrnz//fez ZTp27MgxjdvfpKeoP8ylpUSJEj///LP4Cjl79mzel7BixYq8fqPxxAMVyvJD Fr4aJhEsomukRSK3Bq7GrKJ27dqqs8TGNIHDpKXCiAV4cZhQDBgwQMSktrFg wYIczvXa519ARrlpozzYrSiTkpL4JRJDf4tdhgsVKkSNIUcTui8o1J4ohzRs 3bpVvNwo4If/FvITijIo1Md76KGHhFVF4WzWrFngsBPPYm1rAMIFJVlO4Bc5 sbC9Ehs6B4UuYvL6gQRVlCSINK7GkOjgyGK9d7FfNvU2lV0O0RkjPvnkk8Cd yyRH17/iBbsqs5UrV1Z+yqGL8GYiDPWaxJ5uPv9mH0J7Gre/SU/l+7eW43GP RPny5ekQj0kjKHz9+vXhJh6oUJYf6kX/L0wiGEtsUFEqy2pQAneK561DCO31 YB3HfJvcv39/ZWRSWLw5F0MiS045mW8s7/x+j3oFqvBVq1ZxBpWKMjAydTM+ /fRTsSI0U6dOnZUrV4o4QveNGTNGjMnnVqVNmzaB5WfWrFli6yKfv/EcPHgw tSpcSpWKcurUqRxn3bp1qovwjbyjKPP9vnjuuedEG160aFGqpOjsKbG8NQBh gZIsJ/CLnFjYXmkryhIlSpi8fiAHDhzgOMrl8bt27apxNUZ0qL799lsOoQ6A uEJOTo6yEPr8X7skX80jFEb8O27cOLE/o8///aJFixbHjh1TRSN18MEHH/DC y4JSpUr17t373LlzIppx+5v3VL5/1SZKreqs5s2b79ixI4LEAxXK8kM95JSU FNKJ6enpBzWhCBSNIkcg1Q0+KVyMTRbYt2/fihUrXPB91ioOHTq0zE9mZqbq Y7pQlCwzqTEkVbh+/XrlnpUqSGZu3LhRfgtHh7D8SKZLSkpau3athnk9C9pD Z0FJlhP4RU7QXqWlpW3bti0wnDrD1EOIj4/PysqKfqqswrh/qX+1dOlSqnfa KuDSpUupqanUvyLL7N69W2Pmo+WE8lS+f3pUcnLykiVL6P+g25IyDiY+RlGV H+otk1RUrkASFIpA0SLrWqNF8rIFJMm79n6UQBdJ/OgCYElngf3lBH6RE/jF 3cC/wAyq8sOfKbds2aIx/JsOUYTIPlAG3tGDeNkCkuQditIkkvjRBcCSzgL7 ywn8Iifwi7uBf4EZAsuP7mdKMx8og97Ra3jZApLkHYrSJJL40QXAks4C+8sJ /CIn8Iu7gX+BGQLLD3+m3Lx5c9DPlBRIhyL+QBn0jl7DyxaQJO9QlCaRxI8u AJZ0FthfTuAXOYFf3A38C8wQtPzwZ0qxsYsSCjTzgTLUHT2Fly0gSd5XrVrV 1I9qgS9gEEn86AJgSWeB/eUEfpET+MXdwL/ADEHLT6jPlOY/UIa6o6fwsgW8 nHc3AT9aBSzpLLC/nMAvcgK/uBv4F5ghVPkJ+pmSP1BmZGTYcUfv4GULeDnv bgJ+tApY0llgfzmBX+QEfnE38C8wQ6jyE/iZUnygvHTpkh139A5etoCX8+4m 4EergCWdBfaXE/hFTuAXdwP/AjNolJ+MjAzlZ0pLPlBq39EjeNkCXs67m4Af rQKWdBbYX07gFzmBX9wN/AvMoFF+Ll26JD5TWvWBUvuOHsHLFvBy3t0E/GgV sKSzwP5yAr/ICfzibuBfYAbt8iM+U2ZlZVnygVL3jl7Ayxbwct7dBPxoFbCk s8D+cgK/yAn84m7gX2AG7fLDnyn/58eSD5S6d/QCXraAl/PuJuBHq4AlnQX2 lxP4RU7gF3cD/wIz6JYf/kxp1QdKI3d0PV62gJfz7ibgR6uAJZ0F9pcT+EVO 4Bd3A/8CM+iWH/5MuWnTJks+UBq5o+vxsgW8nHc3AT9aBSzpLLC/nMAvcgK/ uBv4F5jBSPnJyMjIzMyM5h3djZct4OW8uwn40SpgSWeB/eUEfpET+MXdwL/A DNEvP/8PAAAAAAAAAICLgKIEAAAAAAAAABAZ0VeU0byjbHjZAl7Ou5uAH60C lnQW2F9O4Bc5gV/cDfwLzABFGX28bAEv591NwI9WAUs6C+wvJ/CLnMAv7gb+ BWaAoow+XraAl/PuJuBHq4AlnQX2lxP4RU7gF3cD/wIzQFFGHy9bwMt5dxPw o1XAks4C+8sJ/CIn8Iu7gX+BGaAoo4+XLeDlvLsJ+NEqYElngf3lBH6RE/jF 3cC/wAxQlNHHyxbwct7dBPxoFbCks8D+cgK/yAn84m7gX2AGKMro42ULeDnv bgJ+tApY0llgfzmBX+QEfnE38C8wAxRl9PGyBbycdzcBP1oFLOkssL+cwC9y Ar+4G/gXmAGKMvp42QJezrubgB+tApZ0FthfTuAXOYFf3A38C8wARRl9vGwB L+fdTcCPVgFLOgvsLyfwi5zAL+4G/gVmgKKMPl62gJfz7ibgR6uAJZ0F9pcT +EVO4Bd3A/8CM0BRRh8vW8DLeXcT8KNVwJLOAvvLCfwiJ/CLu4F/gRmgKKOP ly3g5by7CfjRKmBJZ4H95QR+kRP4xd3Av8AMUJTRx8sW8HLe3QT8aBWwpLPA /nICv8gJ/OJu4F9gBijK6ONlC3g5724CfrQKWNJZYH85gV/kBH5xN/AvMAMU ZfTxsgXszntiYuLy5ctTUlLsuwXI93YZthZY0llgfzmBX+QEfnE38C8wAxRl 9PGyBezO+5133unz+f7xj38YiTx27NhWrVpNmTLFvvS4FS+XYWuBJZ0F9pcT +EVO4Bd3A/8CM8SKoszOzt68eTP9rxFn27ZtEyZM6N69++jRo9etW3ft2rXI U2knuhbYunXrZk3S09OjlFarkUdRbtmyxeenYMGCmZmZ9iXJlRj345UrV5KT kwcNGjRkyBAqulevXrU5aTFGuDXCSEsIjBNBi3Tp0iVuh3fs2BF4NDc3Nz4+ vm/fvkOHDk1KSsrLy9O+GlWQ1NRUao4MPrCOHTum/XQgLly4EFZMFVlZWXPm zPnmm2/oebpz505HnqRG/KKRwYyMDGVM6huEikneDLxyxH0JW28UblGxg6j1 GLVrGaNRUCMo/BcvXkxMTKRHVZ8+faZNmxZZL0u37oRbQqJMWP6lxo2auOHD h8fFxSUkJJw5c0Y7PtVKzunJkycDj16+fHnDhg39/SxYsIA8GG7ibapNMtS7 WEFyRUlP58WLF7/xxhvXX389df4fffTRoNGo7LVq1cr3V5o0abJr1y7L0m0d uhYoXbq0T5MKFSpEK7EWI4+ipOcFaUmKXKhQIXoKiHAqS0397N+/3750xjoG /UiPVCqryqJ7zz33qDp7HsegJQ22hCBcImiRqMfChfnuu+9WHVq6dOntt9+u aquTk5ODXoceT4MHD77rrrs45sqVK43cfciQIdpPB2LMmDFhxRScP3+eypgq Tp06dfbs2ROWicxjxC/9+vULla97771XGbNo0aKhYs6YMUMZ02RfwqYbRVZU 7CBqPUaNWpZvoKCGVfhJynXp0qVw4cLKo9Q9aNOmja5KMp4kxngJcQTj/l2x YsXNN9+sTD89m6hKhpJdR44cEfF/+eUX5SGyf4cOHUqUKKG82o033kjC0GCy bapN8tS7WEFmRZmdnc3dJ8Hf//73wGhXr159/PHHOQJpsdatWz/xxBP8k7qv VM0tzoBpzCvKGjVqRCuxFiOPosz3d/+++OILVSuxbds2NjL9YU8a3YARP+7Y sePWW29lY1avXp06Bvz3HXfccfDgwagkMwYwYkmDLSGIgHBbpE2bNglfqPq6 y5cvv+666yicuqbUBDVo0IBjFitWbMGCBarrtG/fXtWq0+lGEmCkqzxt2rSw YjJUzOrWrcvhBQoUoDpLnTr+edNNN509e9a4lcxjxC///e9/Q+VL+Yi8cOGC QQuY7EvYdKOIi4odRKfHqFHL8o0VVOOFX3k1qr81a9YUjy3ihRdeMPJlymDd MV5CnMKgf4cPHy4cVKZMmWrVqnHTR/Tq1SvoKf/6179ETpWK8vjx448++qgw Xf369f/2t7+JmJMmTdJNjE21Sap6FyvIrCizsrJu+hMqab4Q/ai5c+eyuzt3 7nzx4kUO/Pnnnzlw4MCBFibeEnQtkJGRcSAYH3zwAWdqyZIl0UqsxUilKINC ApONDEWpgRE/vvjii/yMFjNV6THEtm3btq3tSYwRjFjSYEsIIiCsFikvL692 7dqig6Hs69Kjh3ovFFiuXDmxMlhycnL58uUpkNqlK1euKC/VoUMHdmjx4sXD 6q5Q7zTo02HLli0kXek6jRs35rHlxmMy77zzDqekZcuW/GmGjlKPrkSJEqNG jTJoIqsw4pd3332XUnv77bcH5lE57IT+5nx99913gTHPnTsnYprsS9h0o4iL ih1EoceoUcsYIwU1rGpSq1YtCnn//fdJ3fDVKI9Vq1bluyQmJuqm2WDdMV5C nMKIf/fv389yklTz4sWLWXGTpm7WrBnp8aAjWqdOnaqUZkpF2bVrVw784osv xEjX+fPnc2kvW7bsH3/8oZ0em2qTVPUuVpBZUSrh785B+1FdunShQ1RzL1++ rAynyBT+2muvRZxUm4jMAunp6Vyw6TFqQ6KiRHQU5SOPPCJC0tLSNm7cqNso CWbNmmVEUVLDRZdNTU1VlTqPoOvHnJwcfui89957ynCWmSVLljTuEXcTbo3Q aAlBBIRl/27duvFLkgcffFDV112zZg23GyNGjFCe8ttvvwV2opSIno/J7gr1 h+ki9IzQHaEaNCZ1aAsVKkThr7zyiuqjzOnTp80kLDKM+OXll1+mBNerV087 2vbt29nC1E3VjmmyL2H3jawqKmaIQo9Ro5blmy6oQQv/wYMHSZWoYv76669s 7SFDhmhf03iSjJcQpzD+JqdgwYLU/1GGk4gOOpLh8OHDPN71oYceCmwMr1y5 Qnp83LhxqrOE0tywYYN2euyuTTLUu1jBBYqSm4iyZcuqwt9++22ffyh1ZOm0 j8gs8Mwzz1B2KleuHOXRR9ainffJkyeX9rNo0SJl+JIlSzi8d+/eynBSJdRS UXj37t05hBXlk08+SS1527Ztb7vtNm4KChQoQNJGtUrGAw88QOc2b96cf/74 44/08CpVqhSfQn/wTem5pjxr3759TzzxhJhzUaRIEVJJ/G7TO+iW4f79+7N9 VIOKZ8yYweETJkywN4kxAhSlsxi3f0pKCr8keduPqq/7ww8/cMGm7pPqxHvv vVf1mkuJJd2V9evX88frwYMHRxaTH6NEWlpaxMmwECN+adq0KSX42Wef1Y62 evVqzlpSUpJ2TJN9CbtvJEPP1u4eo3YtyzdXUI1XE2LdunV8o6+//lo7pvEk GS8hTqHr38zMTO78tGzZ0uA1+TUynZWQkBCoKEMhRouNHz9eO6bdtUmGehcr uEBRipfAqsved9993C5FmlK7iMACIo/z5s2zJ1FRQjvvqampnM3PPvtMGf7p p59yeMOGDZXhS5cu5XDxxo8VZZUqVcRkaiWff/658nTVENmePXsGnuLzzwEU p5AjhOQsVKiQmEpQsWJFTy04o1uG33rrLZ9/ColqsF9ubi4brVOnTvYmMUaA onQWg/bPy8vjoXG33nrrsWPH/v3vf6v6ul988YXP/2ElcNZVu3btfKGXU7Ok u1KvXj2f/2ud7pyvUDFr1KhB4U888UTEabAWI3554IEHjDzi6aHJFtadvm2y L2H3jWTo2draY9StZfnmCqrxakLExcWxtXX3FzOeJOMlxCl0/Ttt2jTOwtq1 a41ckKzH8Xv16rVnzx7+24iiFLbSXbDI7tokQ72LFVygKC9cuMCd/LJly65Y sYIDyfVcBkSIPERgAX4ZS01rrC9frJt3/qpYp04dZSA/ZXz+gRbKYSQ82oGU HekUDmGRyDz33HOzZs2iRmzw4MFi2L9yqoJKUe7fvz8hIaFNmzZ8Op2V4EcM f6VnXJkyZXz+qVLUytHj7+LFi2PHjuV5Ge+8844VFooNdP341FNPkU3IcYGH eN2DN954w67ExRRQlM5i0P5iCBb1Xujna6+9purrjhw5kiMEbkVEXSmff5hE 0N0BzHdXxIcPulTEMXlKxZdffsk/s7OzN27c6Mh4V8aIX6pUqcI9xiFDhrzv h/7YtGmTKtr48eOF7yiDFL9bt27U0Q1cacdkX8LuG8nQs7W1x6hby/JNFFTj 1eTKlSuTJ0/mL3GVKlXSXZHJeJKMlxCn0PXvt99+6/O/OuMZi5cvX05OTt67 d2/QfcEOHz5MJZziP/jgg2TV3bt3G1eUn332WagWVYXdtUmGehcruEBR5vvb ipIlS7LTX3jhhUmTJnExfvXVV80m1wbCtUBaWhpnbdCgQbYlKkoY/LZF7dWJ Eyc4JCcnx+cfXHrLLbfQH7NnzxaReVi+clSDUJTUUCvVt1jWWznyP+gyPuLN ZOA8Sh5EQapWtR0At7EkWimpBu0Q6+j6kZdWCLrJRc2aNX0yfQ1xFihKZzFi f6rvYiQehwT2dZctW8bthmqMXHx8PL9xIvbt2xd4cfPdlVdeecXnf80lVqUI N+bRo0c5DaNGjaKnJ7/bZ2rVqkWP18gSZgYjfhHDRZTQs+PNN99U9ufpuRkY jcVC4JgfM30Ju28kQ8/Wvh6jkVpmpqDqVpNDhw59/PHHL7/8Mr+p4GY2aJ1V ElaSwiohjqDrX14fsnz58pRxMqmog0WLFiUNqFoegQq2z7/YNe/iYVxRUv3l bZiqVq1qJNm21iYZ6l2s4A5FeenSJSo5qkpar149GdbOCiRcC3AVLl68+KlT p2xLVJTQzbsYIzFr1iwOocaHfj788MPs4vfff5/Dz549yw+gb775RpzOIrFx 48aqy44bN44vu3DhQlVkg4qS9Cnvl/Sf//xHdXHyC59CvUojRnABun7kD5Ev vfRS4CGeL09PXrsSF1NAUTqLrv2p/8kTIStXriw2pwvs6+bl5fFar4UKFaI2 hDqiW7Zs6dWrV5EiRcQjiUICr2+yu5KVlcWrgohPJBHEpM686APzH9Q9E9vD kUYL3P3EbowryjJlypAG6dGjR+vWrYV4//e//y2iiW481ZrOnTt//vnnPFzW 539RqWrnzfQl7L6RDD1bm3qMBmtZxAXVSDVJSkpS+oJU3vbt23VTHlaSwioh jqDr3+bNm/v83VGxFu4tt9witg556KGHxMdK7rn5FLNWjStKnilgJCZja22S od7FCi5QlLm5uSQKuCJ/+OGHvFq7zz/KSCzYIhXhWoCXyXr55ZdtS1H00M37 sWPHeO58+/btOYQUHD8IeO0LKgkcvmjRInb0unXrxOmhdg8hIcmRlXMiwlKU 1EXkcHoKrA+ADxnfkDfW0fUjPY7JIM8//3zgoYYNG/oMrNDoEaAonUXX/mKC pLI7EXQ8XkJCQuD25aVLlxZdHTHuQonJ7sqwYcP4dN3er0ZMMRGJqFat2sqV Ky9fvnzt2jXqzrFGo6ZS9wOotRipF4cOHaIuuvJF6549e0iScEaUo92o2Sfv iJ/U6f3qq684mrL9N9+XsPVGMvRsdf1y9OjRfXoEjmM0WMsiLqhGqkl6enrL li2paa1QoQJHpvSQO7SnGoWbJIMlxCl0/Su23fT5R4LxNj05OTliC8gff/yR Q3iK0KOPPioMaFBRkg1ZolJXwcg8L7trkwz1LlZwgaLkZodKLy+flZeXR61H uXLluAz06dPHfJqtJSwL7Ny5kzPSv39/OxMVJYzkvX79+pTfmjVr8s+KFSv6 /J//SOKxKfbv30/hnTp18vlXZFUuGR1KUcbHx/O5EStKMU9cA9VStC5G148N GjTwhVhmjT/liCV2PQ4UpbNo25+eKQULFvT5X+hlK+DRXHfccQf/FPF///33 l156iZusKlWqvPvuu7t27eIeI/V2gt7CZHelVatWfPGg85gMxhTvxGrUqKEa ui+mtqmG+ttNxD2T2bNnG2mNyQh16tTx+UfridXD7OhLWHgjGXq2un55+umn dR+Ut912m/IU47Us4oJqvJrk+8cjLV26lL3m03tRbL7uBC0hTqHrX14hwRew qcqJEyd43Ck/2Z9//nmOlpiYKBwqtlii0k4/g25bQE0of0MpXry4aneSUNhd m2Sod7FCrCvKrVu3sq+///57ZfiBAwd4MHyxYsUCl3N3lrAsMGbMGM7gqlWr 7ExUlDCSd9EOk+NYUFNLe+HCBWrneSwlt/CsWVRfwexTlGKfyurVq/8jBNOm TQvfJDGJrh/5gSJeCyjhV5eeWshIAyhKZ9G2P+94qAs1L6oTqb0Sf7/55pu+ 0MO8TXZX+HvKY489ZiYmz1UnAneF27RpU2DLGQUi7plQN5UTrDvzsWPHjhyT J3nZ15ew6kYy9GztUJTGa1nEBdV4NRHQvbi/EWqVZhHNfN1RlRAH0fXve++9 5/MPTg48xA/9atWqiaU/tHn44YdVVzh69KhYpd9gbyoKtUmGehcrxLqiHDVq FPs6cDXmiRMn8iFJpjwLwrIAr1RToEABd+wIbyTvYk22mTNn8kjXpk2b8qHX X3/d599HmKzBkyiHDh2qPNc+RSk+FrtgfSTz6Pqxbdu2Pv+rAFW5zczMZDN2 7drV3iTGCFCUzqJt/8C5OUHRmGZ45coVHocZ6qO8me5Keno6n9u5c2czMa9d u8bjdVX7K+UrKqyqw2Y3EfdM8vLy+IPXCy+8oB1TjDbk+a329SWsupEMPVtd vyQnJ8/XQ5V+47UssoJqvJqoaN26NZ+osd+0JXVHVUIcRNe/ffv29fnHA4sF 9gXvvPOOz7+ivugsaVO/fn3l6dRV4BkxPgOzwgVRqE0y1LtYIdYVZZ8+fXz+ 5TeV74QZMWOailzEqbWDsCzAo9bF5MFYx0jeL1++zEsudOzYkd9einELvPh2 uXLl6CLsXGq7lOeaV5TcYPoCRqpQqng5cSxSmm/Aj2JWvlhhiRkxYgSHB37W 8SZQlM6ibf+LFy+eCEaLFi18/nUI+WfQbUGYGTNmcIH/6aefgkYw010Rc7h0 N0TQjclb9d13332qiUtioFqUF+eJuGci1lfp0aOHdsxnn33W5994nd1nX1/C qhvJ0LO1o8cYVi2LoKDqFv5Qk/V4dAFx5MgRjfSbrzuqEuIguv6dP38+Zypw m8jHH39c6MRTp04FOlSMEKbiTT/FEkzE+fPnH3vsMT76+uuvGxmczEShNslQ 72KFWFeUQimQ1lAdGjhwIB9KTEyMNLG2EJYFeH/GRo0a2Zmi6GEw7y+++KLP PwuPh51s2LCBw8VLP977uGLFiqoTzSvK0aNHc0zSPqqL8LQOn+HxGC5G14/0 gLjpppvIVs2aNRNPB3pc8iTZKlWqGH9kuBsoSmeJ7HkUdGUe6hirPjGcPHmS t9GhAh+qr6jdXaF6NGnSJGq1AvtL+YrGaunSpdoJ1o0pkjFnzhxlOG+ZRGRk ZGjfwlp0/ULdzk6dOikn0ef7pQHv3UxQ1zffP/KQXLB58+bA6/PqH2KJsLD6 EoF+selGSmTo2Uazxxi0lkVQULULf0pKSvny5QNF3+HDh3nDMqq8IjBofTSY JOMlxEF0/UsP7mrVqlFq69atq3yI7927l1fTbdOmTahzQ63MQ8YUC/u0aNFC VamVBNo/CrVJhnoXK8isKElHrPsTHgZPvSkRwu83cnNz+VCJEiXI7+Id0ezZ s3nbWRIdUV6kThfjFqAKywN4qJbZnKgoYTDvYiSDz7/2jnK6evXq1cWhwI08 zCtKEfPBBx+kxp9KlJjacPDgQV4VnFrO3r178xqDeXl5CQkJjz/++K+//hqG IWIcI37kXW+Ijz766PTp09S7pmcNh+h+PvAORixppCUEkWGVoqSGomXLltdf f33fvn1zcnLoobNmzRrRWI0cOVJ5elZWlnBft27dOE5cXByHKJejFNt8Bx0G 9s033/BR6hVrJ1g3JrWx/LKiZMmSc+fOpUcPhXz//fe88nbQbYBsRdsv06dP 5+w0adKEeqcHDhygBG/btk0sG9K4cWN+avDut0WLFu3evfvq1aupI0r1hSQG D4OhnvzixYv5mmH1JQL9YtONjBeV6OC4ooygoGoXft71gxxED6xFixaR10jR UB7vv/9+Pqtjx44ictD6aDBJxkuIgxjxLw8VI/75z3/yeOD9+/fz4kLUXyXh HOrEoIqSirqotmQKkvbz58+f81fE6meB9repNslW72IFmRXlDTfc4AvNgAED OFpiYiIPR/T5912lR8kdd9zBPyl8/fr1NmYmIoxb4PDhw5wRsedvrGMw79RA CUer1t7p0KGDOBS4BrV5RUnPAlF+fP4Fx4oUKSJeiIklwZly5cqx5Pf5J6Qb NkPMY8SPpCIfeughYSuxZVWzZs2CfnDxJkYsabAlBBFglaI8ePAgf9FgRLNA fPLJJ6olHMllGg4ld4uYPKDOF2yP3XzFrm2Bc4giiEkSmLcF9/mXsxANHbVy vEdANNH2C3XFn3zySaXReFo9Q45IT0/nmLNmzeKOpfCLaIiITp06KS9rvC8R 6BebbmS8qEQHxxVlfvgFVbvwU3ZKly4tTEoyUFmWGjZsmJeXJyKHqo9GkhRW CXEKI/4lxf3GG29wsikLPJCM0V4eIaii7NGjh0YJZ0gecuSg9rejNslW72KF 2FWUyt00du7cyUPulTRv3nzHjh12ZcMExi0gJjiHO6NcWoznnUdW+AIWqVbu 4hG4hBd/FCDNogpftWoVn6JUlKEip6SkiN17fQGv3ahVfOyxx5Q9Rmq1Xn31 1bS0NCP5cgcG/Uii8rnnnhOtfdGiRVu1agU5qcS8onTHvkJOEdkTkDfJVS1l nJOToyztPv/r8aBTt7S7KyVKlBAxv/32Ww4Uu4Qr4T0RiPPnz2sn2GDMffv2 NWrUiL+tMJQj1Z4I0UHXL9euXZs4caJydzyfX1e+++67qn0/MzIy2rRpw1+C BHfddRcPi1VhsC8R1C923Mh4UYkO0ewxBq1lTFgFVbfwHzly5IMPPuBFyAXk x969e587d04ZU6M+GklSWCXEEYz7t1+/fkqL0d+TJk3SPuXAgQMcefr06SJQ rO2vwcKFCzlyKPtbXptkq3exgsyKMlzOnj2bnJy8ZMkS+j/oTjeSEH2by0Os 5P3y5ctbt25dsGDBypUrlTtoC+jZtHHjxuXLl1OTpTHs362E5UeyVVJS0tq1 a2Ubfy4DsVIj3Irl9s/Ly6OWIT4+3qrvemlpaap1p+2Gnp6rVq0iswRt+qKD cb9kZmauXr06ISFh06ZNGi0Mz3JdtmzZihUrdF1jpC8Ryi+W30gqpGqvrC2o ly5dSk1NXbRoEVXe3bt3h3qsa9dHI0kKq4REmbD8e+3aNTIU9YLIJlFbGEHD /jFXm9yHmxRlrOBlC3g5724CfrQKWNJZYH85gV/kBH5xN/AvMAMUZfTxsgW8 nHc3AT9aBSzpLLC/nMAvcgK/uBv4F5gBijL6eNkCXs67m4AfrQKWdBbYX07g FzmBX9wN/AvMAEUZfbxsAS/n3U3Aj1YBSzoL7C8n8IucwC/uBv4FZoCijD5e toCX8+4m4EergCWdBfaXE/hFTuAXdwP/AjNAUUYfL1vAy3l3E/CjVcCSzgL7 ywn8Iifwi7uBf4EZoCijj5ct4OW8uwn40SpgSWeB/eUEfpET+MXdwL/ADFCU 0cfLFvBy3t0E/GgVsKSzwP5yAr/ICfzibuBfYAYoyujjZQt4Oe9uAn60CljS WWB/OYFf5AR+cTfwLzADFGX08bIFvJx3NwE/WgUs6Sywv5zAL3ICv7gb+BeY AYoy+njZAl7Ou5uAH60ClnQW2F9O4Bc5gV/cDfwLzABFGX28bAEv591NwI9W AUs6C+wvJ/CLnMAv7gb+BWaAoow+XraAl/PuJuBHq4AlnQX2lxP4RU7gF3cD /wIzQFFGHy9bwMt5dxPwo1XAks4C+8sJ/CIn8Iu7gX+BGaAoo4+XLeDlvLsJ +NEqYElngf3lBH6RE/jF3cC/wAxOKUoAAAAAAAAAAO4AihIAAAAAAAAAQGRE X1HmAQAAAAAAAACIcaAoAQAAAAAAAABEBhQlAAAAAAAAAIDIgKIEAAAAAAAA ABAZUJQAAAAAAAAAACIDihIAAAAAAAAAQGRAUQIAAAAAAAAAiAwoSgAAAAAA AAAAkQFFCQAAAAAAAAAgMqAoAQAAAAAAAABEBhQlAAAAAAAAAIDIgKIEAAAA AAAAABAZUJQAAAAAAAAAACIDihIAAAAAAAAAQGRAUQKZOXDgwJQpU0aNGjV5 8uSzZ886nRwAAAAAAADAX5BfUf7xxx/ZIVi1atW+ffuCHjp69Kh9RktJSTl1 6pR913eQzMzMjIwMS866ePHiwYMHN2zYsGvXLnJiZOkZPHhwz549Bw0aNHz4 cLpg0DjHjh3Lyso6efKkKpxC0tLSUlNTqTyEuv6FCxco5Zs2bdqzZ0+gYqWQ 3bt3k7t///33wCycP38+PT2dzqXs03XCz9z/z969e8MqUTt37jx37pyZOzp7 XwAAAAAA4BrkV5SkGb8Nn8mTJ9tkMZIYfH2TIkIqcnNzly9fPmrUKJJv33// vfmzyDjTpk3r+SfDhg0j3Rduqkgn0rkTJkwIqiVPnz69efNmcgTfYuzYseIQ qaRJkyb1VDB16tRAwUhSsW/fviJO//79t23bJo6SFP3uu+/EUVK1pL+UaWO1 y4wePfrEiRPhZpDZunUr3ch4ieISOG7cOJK0kd3R2fsCAAAAAAA3Ib+izMjI GBMAdeCpM8zicciQIYER5s+fb4e5jh49SjqC77t06VI7buEIJMEoR3FxcWEp So2zVq1axWJw3759S5Ys4b/DTdWmTZvoxGXLlgU9umLFCqVmJKdz+B9//KHU eoJ58+YpT1+/fn1gHMrL4cOH8/zfPTlfRO/evfmPgQMHspiiWwwYMEB1LpXJ cDPInDx5cvjw4QZLlCiBZOHIbuf4fQEAAAAAgJuQX1EGQv35n376iTq3kyZN 6t+/P/2xefNmqwzi1H337t2bkpJy8eLFHTt2LFy4MDU1Nc8/cDQtLS0+Pp5E 2ZYtW8S3JErJ//73v127donT6e/k5OTTp0/zT9J69JPUHP19/PhxUgGkGuh/ 3RGtffv2Na4oQ51FyR46dCgJMZGesWPHkubKyckJPDdUHjMzM3/77Tf+vEh5 CRy5unr16u+++65fv34qRUls3LiRQubOnbt//34SpByBYiq/dZKJRo4cSSKR TL179+4ffviBo61ZsybvTzFLLFiwgJI0Z84c/skG3LBhA/+cPHlyVlYWaUn+ +fvvv4drOubAgQNcosgCGtFECZwxY0aoMcAxcV8AAAAAAOAaYlFRzp49mzq3 P/74Y25uLgkr+nvAgAERzP6T6r50cZIkpCVZmyxevJju8vPPP6s+w/HQyjNn zvTq1WvQoEHidPqbIpDy4p/0B/0kzXXo0CHlwE4iKSlJIxmWKMqjR4/SjUh3 ixCSaRRCQk91okYeWU4KAs9lSEoHKkqCMs5/kB4UFlDNtSQzktzmv/mjKsFf tzdv3qz8SSWWf7KwFUNtWbML+blo0aJwTScwUqKUJTDiG0lyXwAAAAAA4A5i TlGuXbuWOrekX8QHL+rGU8iIESNsXS3H7vuyoiTi4+NJDZGqIlFJP8eNG0d3 PHbs2NSpU+nntGnTOD5/9WOBw/MNiRkzZvDRWbNm8Qc1EkT0x/Lly8+dO0fX mTdvXuAKNkosUZT79+8XWozZvn07J0N1okYeSeutXLmSvxKSQcTnThWhFKWA 8ssRyFka39fYUAQ5Os//BVMMdp05c2b//v3pj5EjR3Jk+oMP8XI9lDz+Sek3 Yq5QaJeowBJoFU7dFwAAAAAAuIDYUpS///47z2HcuXOnCDx//vykSZNsXS0n CvdlRSkEIwlAUjRxcXFCAFLIwIEDKQ4vY8uzCDds2EB/r1u3jiXVgAEDWDRR /59EEP3NwzUTExMNJsMSRcn6UTn/cd++fSqNaSSP/O2PdKXGrXUVpZDqpLJD XYQkOc+a7NWrl9BN4jOlQBwip/f0z6/kn6SFOcKPP/6okVRdNEpU0BJoFU7d FwAAAAAAuIAYUpQaa4OEtcaInPdl4cOjKPP8swh7/nXgKDF37lwKTEtLo7/T 09OFAiUhMGzYMB5ZmpGRcfjwYfG9cu/evfyt7aeffkpOTtbd+iFQUR44cGBp MJSDJFVn7dixgz+2ipA9e/b09I/pVV5ZN4/mFeX//vc/PkqCMdRis2fPnhXf HIXmPXPmjGo4LgtGCqejvCwPyU/W70eOHOEI48aN00iqEYKWqCisiuPUfQEA AAAAQKwTK4pSd20Qg2uMSHtfVpSkE/knC6Xp06cr4/CiqTwRkhLz3Xff0a3P nz/fr1+/33777eDBgz39cycpQk/FfEmSfr/++ivJHwokgaCxOWNeMEVJ4u6H YOzevTvUWXTHnn9dW3Xr1q0UohImunk0qSi3b9/OuSZSUlKCnk7+JRnIcUaM GCE2nRQTJykvO3fuHDp0KP/kj8IkLfknC0y2fE/FqGMzqEpU1FbFceq+AAAA AAAgpokVRWlkbRA7VumJ2n1VipIXtxk1apQyzpQpUyhQ7IpIWox+8me41NRU 1piTJk3iS6k+yR0/fnzmzJkU/ssvv2gkw5JRr6dOner519004uPjKYQUovIs 3TyaUZQkhPv06cOHFi9eHPRcEk1i20pyH+8bwvDyrWLqJX9j7elf3JV+iq02 d+zYQT8TExP5p/KzrBmUJSqaq+I4dV8AAAAAABC7xISiNL42iLWr5UTzvipF meefC0kh4lNgVlZWr169SLudPXuWQ3hB1+HDh9P/vHANKR2KMGTIkGHDhnGc zMxMkaQzZ85QTHEoKJYoSoI//NHd8/zCjZJE+o6/6CnRzqO2otywYcPSpUvF squDBg3i4bh0iPS1WFenp38bkdl+1q1bJ06nW4ivkzxgdc6cORyNxPj48eM5 fNWqVaR8xcKzPBmT5Cr/HDlyJCWSF9olKP3hmi4UXKIGDhxo36o45JfsAFhI kqjku+/cuVMVgae4AgAAAAAAwMivKMNaG8TC1XKifN9ARckjRePi4kglxcfH f/fddz3/XImUOX78OAsZ8ZmP92Hs+ed8QBJNJA3oxISEhKSkJJ4YSDcKvDsd ne+Hh4ny3wcPHtROs8ZZpOl6+gfZkq8nTpyoGgRrMI/aipKkcc9g5PkVbtBD U6ZMEaevXr06aJye/pGuKSkpgeGUzT179uT5hxyTzVVHxapKliBKlH2r4uzb t+/b8OGvtAAAAAAAADDyK8rs7OzRo0cbXxuE1xihW5ic/BXl+5LgYi2jDNy8 eTPvW0H069cvMTFRdfERI0bQoSVLlvBPHkdKbNu2TVxBzAEkSFQG/YrKA2hV kNzTTrP2WSQMhbKjmCSRgl5EI49btmzpGZGi5LVYA1GKPkpe0Dg9//zUSPKc F55lyLnbt28XpyuX7unduzfp9FAZjBguUfatikOFbUwwfvzxR5L2VGyCHlUt 2AsAAAAAADyO/IqSyM3NDUumiXGhJnHqvkooAYf9mBHIpCIPHTpk636dQSGR RepM1yyW5NEOKD3Hjh3LzMwMtYknZY0MK9bzsZwjR444YhOn7gsAAAAAAGKO mFCUAAAAAAAAAAAkBIoSAAAAAAAAAEBkQFECAAAAAAAAAIgMKEoAAAAAAAAA AJEBRQkAAAAAAAAAIDKgKAEAAAAAAAAARAYUJQAAAAAAAACAyICiBAAAAAAA AAAQGVCUAAAAAAAAAAAiA4oSAAAAAAAAAEBkQFECAAAAAAAAAIgMpxQlAAAA AAAAAAB3AEUJAAAAAAAAACAyoqkoAQAAAAAAAAAAAAAAAAAAAAAAAAAAAADI xrVr106cOJEOAAASQ80UNVZOt5cAAAAAAEAN5CQAICY4efKk0+0lAAAAAABQ w12106dPnwMAACmhBopbKqfbSwAAAAAAoIb7aU53GAEAQAsoSgAAAAAAOYGi BADIDxQlAAAAAICcQFECAOQHihIAAAAAQE6gKAEA8gNFCQAAAAAgJ1CUAAD5 gaIEAAAAAJATKEoAgPxAUQIAAAAAyAkUJQBAfqAoAQAAAADkBIoSACA/UJQA AAAAAHICRQkA0CAlJSXBHGvXrj127JjJZEBRAgAAAADICRQlAECDmTNnTjHN okWLTCYDihIAAAAAQE6gKAEAGhw/fpyaCBaGKSkp6WFCp/C5JpMBRQkAAAAA ICdQlHaQffio00kAwEpYFf7xxx/hnkinQFECAAAAALgYCxVlRlbOohXrlf8S k7fuO5BpycVjiH4//FK/eduRk+Y4nRCz/Do34ZGWH02Ybna8okGe+89/m73+ mfnrHDt+Ytma5JE/z+363ZhRP89dmZhy6vQZ85f1LDyP0owqhKIEAAAAAHAx FirKxf9vPSmpwH+vfdiTevgRXHDqvGWkC06eOm1J8kyyc0/6SL880Y35QddB lOsv+o2OQqqMYDzlKibMWEQZGf3LPDtSFcijLT9q+M/3TV4kbfe+F975UlUC 3/ioV3pGlogTsUG8iXIeZWRXgKIEAAAAAHAxlivKf7X9asTE2fRv0I9T23ze r9E/36dAUgor1v0v3Au+/F53Ojcr54glyTPJwuXrKDG9h07SjZmRlTNr4f/7 /9o786cojjaO/1Mk5qKSvOY175tIjFIeqHmJphSUQxEPkENAFI0KEowaUdHw ekA4vA80AjEKJAIlSDgEEZUEAyyX/Jp6v7sPztvZ2Z1t2BU2m++npqjZnu6e Z3t2qf7sTHf7z4Ov+pE78Zczyv6BweDQGMQcHrez/Gp1TX1zyeVKfCCR8nH4 lrGxMck25Qb5e6KOozQfzSsoyc4vMtrWJTRKQgghhJAAxudGGb/9azVxaHgk PacA6W/Nj7p7r2lSFf5FjdLf+PsYpbzTsOiMkdFRI3HQNgSdRHp1TYOa7a94 KWcQd1b4QdgGNObW3UcspJJGSQghhBASwLxqowSjY2Mb0vNwKDJxr5p+8/bP WQcKQ1cnz/18S1x63pXv7xqHzp6/uTJ+11ufrkWpz9ZlYv9aZa1OQTNnzt9A 8Z8aW06XVyCA2WFxMI5DhedGRkbVbA8f9Rw8WbZq85dzlsXHpuYeL7r82/OJ m4zIuXLjrvmrkhAMjqI2ZLA4Y+mVKuS5XlU7qQBmJPILFbeTvsz/KHzz8tjt 2flFD9o61aOGUdbWNydkfYNs/1m/A+dSnyD1GIN+HiejhAkmZB1GzN+WXLVu KIM9h04j4NS9R53Sq2oa8C7qGh7oNEjKnqMhKxIXRqbuzCv8obZRPSrF03MK 6ptaEdu/P9u0KfOgWtaiMc14f30Bvm6o5Pf+QSMlY/8JpNy732qk4DoiZQoP CQjW4yjx2QgOjbaWSholIYQQQkgAMw1GCTq7n+LQe4vWGSlHTl2QMW7/XLpB eqTY/lt6TY4ePXPx4/Atr4dEIPHD5RuxX3a1SqegmewjZx0yuw9/g0Nj3pof JUXgBf/vrD7phR0Y1coO7AlSg6PDIyNQ1/cXr0fiOwuiEUxYVLpFO3x9sswe Utl1/QBmJHKJU9xKKp+9JK6jq8fIIEa5Nin7bcfRfzjqsT/YHJHSPzCoGYN+ HtUokQ6HQp7olP14F9YN5RQwPhVtnY9dZrBukAMnSiWwdxfGSoO8NjdC/b3C NjQsrYRQ7bfdP11rCKnHxjTj/fUFUFqkGOOU+wdtbzieM99/tNioBIaLlLaH U/yaexxH6VEqaZSEEEIIIQHM9BglEB9p77J39R8+6oEtokMuz8Ginw//wtGQ FYlqEfNTr5oFVaTfjq3ows3RsTGo0PGiyyILUvOLFy8WRtpPhM75s1/75CxL YzKQsmXnIaMe/UclXRqlRQAzEvmVW/aLNTssrrG5bdzxcLJMURu6OtnII4KG LSopu+fpr0iBIi1asw0pm3dM1K8Tg2achlHahoZWb9mDozGT0UmAyt9btC7I MW53fdpXJZcrzbdT3TXIxRs/yrPZVXfr0dqQxzPnbiDlzXlrmn55KHnEKKWV GprbDHvSaUwzPrm+xRdv4SWqkpcV1XVBL6fDkhRUi68MPFq/GZ2QcZTW/yis pZJGSQghhBASwEybUUYk7MVR9NvHHc/BPu3t6/t9wDiKlA+Xb0QG6TkLZqPU LKgi/Xb16UT0eNH5R2JNffO4Y+ZPcVL1flnX46dQibfnR41OfjoXl0ZpEYA7 XmnkUEIk4pIZKfCXBavsk9h0dk8s+CJGieZVG7y96/GskMh3FkSLOOjEoBmn GCV0Uj4qsam5k9JJoan14bKY7UHKRK9LotLUt2ndIKfLK9TEjP0nkHjk1MRI UsMone736TSmGZ9c38dPe+WupRzNzD1pv7GbnGN8I27duYd9vBGd1vMGC6mk URJCCCGEBDDTZpSfrcvEUafBXC3tXdcqa0suV54oviJP96k9cIuZeawLqki/ 3WlRRXlWsKK6DvvnrlVjP3lPvlPBsKh0pDe3ToyG89IoLQJwxyuNfJ5jiY1T 5RVlV6uMTR6PvHJrwozEKDftOOhUv8ye2uqwKp0YNOOEUb42NyIy0a6Ta7bu m4JOGtxv6ThQULoyfhcqFAdctflLo0KLBnFaPvW645bfhowD8lKM0rxopk5j mvHV9V20Zhscf2DQhn3o59KYDHmD5Ver7WfJL8K+Map3UuQVlMDu9TeZAQlb 6r5jaj00SkIIIYSQAGZ6jHJkdFSm2THW1Pju0q3Fa9PUe0myeTRKnYIq0m+H e6qJ6PEa/Xbpch8uPOdUUPr2Fypuy0svjdIiAHe8usiNe20uN2NyVzFKaIVT /eu25SJd5krSiUEzThmcKBu0yCfrkLZ1Ps7MPSleeezsJesGMc8029L+COnz VyWp2eBNah7NxjTjq+u75/AZvKyqaWjveoydr46XPO8fgGMmZNlXRA2P2zHr k0h13Ks+Mo/rFDYn6aZREkIIIYQEMNNjlD81tgQpox3vt3Sgk//2/KjUvUcv 3vixpr75QVun+KO1UWoWVPHYbz9dXuFSFVfEZyEdkctLPzRKbyIXWaiuaahr bHHaZMjk+EujNE+dKo+VNjjGDOrEoBmnGOXug6dkLtatu49YNM6kwGdSvdXo skHmLIs3/3zxQ21jkOOGqbx0aZTjeo1pxlfXV4LMPVZcWHoNO3WO9C827Z4d Ftc/aINOYl+nlcx09zzDm9LcKu/UyzcxODS69s+Pc9MoCSGEEEICmGkwyifP fp27IkF1BEiKuIORZ3hkRAZhmY1S7ZNrFlTx2G+vb2rF/vLY7WqGQdvQuwtj Z4VE2oYmhrCJhqRlH/fYDtNmlN5EHpWUjUSn1TGcEKMMjUhRh8X1DwzC6FH/ 0PCIZgyacRoz8/zS8Sg4NCbI9ESoR5ZEpUHucDqn9COnzqO2lD0TauyyQaJT 9gfZJ039SU08XHguSJk31Z1R6jSmGV9dX1yIdxZEQzNjUvb/Y/F6uVj5p+1T IsvEyMY40FcHTopvt0udHKdREkIIIYQENK/UKHt/e45EmR10YWSqsY7exJwk ygC9fd9MzHupiqHcC0P/30jRLKjisd8OJ5UJQtEJNzIkZH2DlPC4HUYKXAMp kFyP7TBtRulN5OIaoauT+573S8qjnmfzvtgKN2lqnZja1JjrNSe/yCgo9/vC 43bKS50YNONUVw+5XlWLo6+HRNyum9A02BzM7kBB6aibRQ+BzLCKOtVfIeCn 8oPGpZt3LBrk6JmLSPwofLNxm/J+S4csm1J1t96IwaVR6jSmGV9d33GHDr/h mK4nIeuwpDS3diKb3PZtfNDuLgYdZD1KwWUGa50cp1ESQgghhAQ0PjfK4NCY xWvTsMl0JbJ9sjLxSe9vRs66hgcytG1pTAb6orJEgiwvooqh3JF8c96a6OSc 6poG/YIqOkJ3916TDPNcsCo5Lj1PpvqZs2yjOk8L6pdTf/rFVuuZM6fNKL2J fGR0VO6szV4StynzYGxqrsiLOvWoGKX8IIArCJeUaXWRU1aB0Y9BJ49qlOOO aWEkPMkjq3uofmem7WG3RPjuwth123KzDhRCtcQK8YExxhK6bBCIqowPhQZG Ju5buXHXrE8inWzanVHqNKYZX11fIM+7Br2cjUeAHQc5hjS+ePHCIgyPWK9H 6VEnx2mUhBBCCCEBjQ+N8lplbdCfJ+hABxs9+UOF58xLe1y5VfMvZel29IRj HI8dql3l7ie9G9LzZoXYO/boM+sXVJHpTUr/3G9Pyz7u9Ijj9z/ei0zcK/bx /uL16L3fb+lwqurs+ZtytwsOa9EOBx1GeerlOhSaAUx/5P2DtvScgrmfT8zP OTss7tC35ZAjI8PJ764GOVZLPHb2krgMjA/nuvPzfaf6dWLwmAcWBnsyXsKD oGbIHBaVjv2Orh58nOYsi1d/mjDztLcvOjlHZFC24NAYNJrTPD8uG8Q2NJT5 1bfGLyFLotJwKdVSQ8MjSHe5tqPHxjTjw+src/I4rXCKeJCY5PVwVFmP0p0V igJb6OQ4jZIQQgghJKDxoVFOAdjBo55n5iXRVdCN73ve73SfRafgFBgeGens fmJRLcJ43j9gDGHzH7yJHDLubjVPFQh+v2OJiinHoJnHHRA0a0dTz9L4oB0u Bg91d4fOokGgpcYT2pNFszEnizft5hPcWeEHYRusddKi7KSgURJCCCGE+Ccz a5SEED9HxlG6s8LunmfYrGugURJCCCGEBDA0SkKIBdbjKHWgURJCCCGEBDA0 SkKIBdbjKHWgURJCCCGEBDA0SkKIR8QKbTargbQuQREaJSGEEEJIAEOjJIRY oI6jLCsruzFJUAQFi4uLvQyDRkkIIYQQ4p/QKAkhFqjjKKcMtNTLMGiUhBBC CCH+CY2SEGKBjKP0hr4+HyynQqMkhBBCCPFPaJSEEP+HRkkIIYQQ4p/QKAkh /g+NkhBCCCHEP6FREkL8HxolIYQQQoh/QqMkhPg/NEpCCCGEEP+ERkkI8X9o lIQQQggh/on004aHh2e6w0gIIa7BPygaJSGEEEKIfzI4OOjl0gCEEDIN2Gy2 mf5/SQghhBBCnPnjjz8olYQQPwf/pvDP6n9UKJa1 "], {{0, 346.}, {611., 0}}, {0, 255}, ColorFunction->RGBColor, ImageResolution->144.], BoxForm`ImageTag["Byte", ColorSpace -> "RGB", Interleaving -> True], Selectable->False], DefaultBaseStyle->"ImageGraphics", ImageSizeRaw->{611., 346.}, PlotRange->{{0, 611.}, {0, 346.}}]], "Output", TaggingRules->{}, CellChangeTimes->{{3.834406637217443*^9, 3.8344066474646597`*^9}, 3.834406722091017*^9, 3.834406814886526*^9, 3.834407321070889*^9}, CellLabel->"Out[33]=", CellID->539878406] }, Open ]], Cell["\<\ We fine that it does not solve the problem. So, missing value imputation, \ although theoretically promising as a vehicle for causal inference, has \ tricky and presently unresolved implementation issues:\ \>", "Text", TaggingRules->{}, CellChangeTimes->{{3.834407079923574*^9, 3.83440711845011*^9}}, CellID->1306756126], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"Normal", "@", RowBox[{ RowBox[{"Query", "[", RowBox[{"Mean", ",", RowBox[{ RowBox[{"{", RowBox[{"#treatedRE78", ",", "#untreatedRE78"}], "}"}], "&"}]}], "]"}], "[", "lalondeSynthesizedMultiple", "]"}]}]], "Input", TaggingRules->{}, CellChangeTimes->{{3.8344066651816807`*^9, 3.834406666059853*^9}}, CellLabel->"In[27]:=", CellID->457564611], Cell[BoxData[ RowBox[{"{", RowBox[{"6353.303188387523`", ",", "7018.022855624385`"}], "}"}]], "Output",\ TaggingRules->{}, CellChangeTimes->{3.834406666459518*^9, 3.834406731166086*^9, 3.834406821839305*^9}, CellLabel->"Out[27]=", CellID->853050358] }, Open ]] }, Open ]] }, Closed]] }, WindowSize->Automatic, WindowMargins->Automatic, WindowTitle->"Job Training Efficacy | Example Notebook", FrontEndVersion->"12.3 for Linux x86 (64-bit) (May 11, 2021)", StyleDefinitions->Notebook[{ Cell[ StyleData[ StyleDefinitions -> FrontEnd`FileName[{"Wolfram"}, "Reference.nb", CharacterEncoding -> "UTF-8"]]], Cell[ StyleData[All, "Working"], Editable -> True, DockedCells -> {}], Cell[ StyleData["Notebook"], Editable -> True, DockedCells -> {}, ScrollingOptions -> {"VerticalScrollRange" -> Automatic}], Cell[ StyleData[ "Section", StyleDefinitions -> StyleData["PrimaryExamplesSection"]], Editable -> True, ShowGroupOpener -> "Inline", WholeCellGroupOpener -> True], Cell[ StyleData["Subsection", StyleDefinitions -> StyleData["ExampleSection"]], Editable -> True, ShowGroupOpener -> "Inline", WholeCellGroupOpener -> True], Cell[ StyleData[ "Subsubsection", StyleDefinitions -> StyleData["ExampleSubsection"]], Editable -> True, ShowGroupOpener -> "Inline", WholeCellGroupOpener -> True], Cell[ StyleData[ "Subsubsubsection", StyleDefinitions -> StyleData["ExampleSubsubsection"]], Editable -> True, ShowGroupOpener -> "Inline", WholeCellGroupOpener -> True], Cell[ StyleData["Text", StyleDefinitions -> StyleData["ExampleText"]], Editable -> True]}, Visible -> False, FrontEndVersion -> "12.3 for Linux x86 (64-bit) (May 11, 2021)", StyleDefinitions -> "PrivateStylesheetFormatting.nb"], $CellContext`ClosingSaveDialog -> False ] (* End of Notebook Content *) (* Internal cache information *) (*CellTagsOutline CellTagsIndex->{ "DefaultContent"->{ Cell[634, 23, 197, 8, 70, "Subsection",ExpressionUUID->"09e1520e-b0ac-40fe-9101-afd0bb6d4396", CellTags->"DefaultContent", CellID->619504543]} } *) (*CellTagsIndex CellTagsIndex->{ {"DefaultContent", 2208120, 39066} } *) (*NotebookFileOutline Notebook[{ Cell[CellGroupData[{ Cell[634, 23, 197, 8, 70, "Subsection",ExpressionUUID->"09e1520e-b0ac-40fe-9101-afd0bb6d4396", CellTags->"DefaultContent", CellID->619504543], Cell[834, 33, 73, 2, 70, "Text",ExpressionUUID->"b5206688-a7cd-4b6e-b98f-fb8c528db8d1", CellID->687106258], Cell[CellGroupData[{ Cell[932, 39, 396, 11, 70, "Input",ExpressionUUID->"29116a9a-f2bd-4176-bb3c-f99b6fd33cb0", CellID->257298660], Cell[1331, 52, 160476, 2637, 70, "Output",ExpressionUUID->"71b6126d-41ed-4250-b98e-4ce7c1935054", CellID->403275520] }, Open ]] }, Open ]], Cell[CellGroupData[{ Cell[161856, 2695, 181, 7, 70, "Subsection",ExpressionUUID->"8be638b7-5e22-4942-82ab-7926b59ea6bd", CellID->979821957], Cell[162040, 2704, 314, 5, 70, "Text",ExpressionUUID->"9987aa93-cce4-43a4-8d14-d1e70a0e92ac", CellID->851344267], Cell[CellGroupData[{ Cell[162379, 2713, 411, 11, 70, "Input",ExpressionUUID->"b46adb93-e76f-4044-8c97-3c76c806706b", CellID->1035485242], Cell[162793, 2726, 134684, 2215, 70, "Output",ExpressionUUID->"3f6bff67-dc22-41ae-bb6a-61993082ea56", CellID->965288770] }, Open ]], Cell[297492, 4944, 162, 3, 70, "Text",ExpressionUUID->"287f3a50-6910-446e-9e9b-a5f24b41c7d7", CellID->1903528209], Cell[CellGroupData[{ Cell[297679, 4951, 454, 12, 70, "Input",ExpressionUUID->"ec950995-9c9a-4b15-84ce-929b840c5f49", CellID->601946357], Cell[298136, 4965, 100479, 1654, 70, "Output",ExpressionUUID->"434f0c30-d608-4c61-9944-19aa9cd4aacf", CellID->144090406] }, Open ]], Cell[398630, 6622, 163, 3, 70, "Text",ExpressionUUID->"31ffe6d5-fb41-46c7-a899-26ebdc342361", CellID->1251899111], Cell[CellGroupData[{ Cell[398818, 6629, 368, 10, 70, "Input",ExpressionUUID->"71eb726c-b4d3-4549-9bd6-a459a09dde87", CellID->1955029757], Cell[399189, 6641, 86836, 1430, 70, "Output",ExpressionUUID->"521e41b9-1d95-4097-8e30-3dfd8fa65ab3", CellID->1547913447] }, Open ]] }, Closed]], Cell[CellGroupData[{ Cell[486074, 8077, 167, 7, 70, "Subsection",ExpressionUUID->"35ac11ab-cc22-4b99-ac73-61f7a096cb8a", CellID->50804313], Cell[486244, 8086, 168, 3, 70, "Text",ExpressionUUID->"2b1e0870-0d71-4d91-a3a4-49f0b41399c3", CellID->772534288], Cell[CellGroupData[{ Cell[486437, 8093, 8991, 223, 70, "Input",ExpressionUUID->"1a329200-f2cb-4cc1-87a4-8a95f0da0fdc", CellID->1338959095], Cell[495431, 8318, 78131, 1287, 70, "Output",ExpressionUUID->"97585417-3b9a-4352-99cc-915463d2202d", CellID->119065486] }, Open ]], Cell[CellGroupData[{ Cell[573599, 9610, 145, 4, 70, "ExampleDelimiter",ExpressionUUID->"2c373a94-5971-43cd-9b8e-45feb72f545e", CellID->1807415723], Cell[573747, 9616, 214, 6, 70, "Text",ExpressionUUID->"79882bf5-e106-45f6-9b87-a6efde114c07", CellID->383614075], Cell[CellGroupData[{ Cell[573986, 9626, 9074, 222, 70, "Input",ExpressionUUID->"b65ec5aa-a42d-433c-a434-a4d268429dec", CellID->655850231], Cell[583063, 9850, 43167, 714, 70, "Output",ExpressionUUID->"a361b27d-4b36-478f-b4a3-cd2ac8cae63b", CellID->1957083920] }, Open ]] }, Open ]] }, Closed]], Cell[CellGroupData[{ Cell[626291, 10571, 162, 7, 70, "Subsection",ExpressionUUID->"aec07349-df87-442c-ab55-cc46a1971b19", CellID->866856397], Cell[626456, 10580, 267, 7, 70, "Text",ExpressionUUID->"1391ea80-3f01-422f-ad23-16b8ccee6be7", CellID->528714309], Cell[CellGroupData[{ Cell[626748, 10591, 1803, 40, 70, "Input",ExpressionUUID->"ec97e673-8cb6-4cb5-8c0b-f46e845bf967", CellID->1421261372], Cell[628554, 10633, 67731, 1194, 70, "Output",ExpressionUUID->"7cf2f457-ac62-4dff-ade9-db91336b7f7e", CellID->2006433985] }, Open ]], Cell[696300, 11830, 460, 11, 70, "Text",ExpressionUUID->"8c147d4c-d25e-405a-b720-45e26425e545", CellID->409335614], Cell[CellGroupData[{ Cell[696785, 11845, 356, 10, 70, "Input",ExpressionUUID->"0d1f448f-fa15-4c92-8279-17046807b7cf", CellID->215245991], Cell[697144, 11857, 2655, 56, 70, "Output",ExpressionUUID->"5041fb13-2db2-40aa-827f-a1cce43bf420", CellID->949571729] }, Open ]], Cell[699814, 11916, 282, 6, 70, "Text",ExpressionUUID->"57cfac68-58ac-47ed-9863-37fd52f1e39d", CellID->1543900077], Cell[CellGroupData[{ Cell[700121, 11926, 1473, 36, 70, "Input",ExpressionUUID->"e5dedc64-4245-4045-87b5-0a83f5484437", CellID->291264735], Cell[701597, 11964, 61509, 979, 70, "Output",ExpressionUUID->"612c3f96-04bf-4e29-8eb2-45e38e1feb15", CellID->1966970613] }, Open ]], Cell[763121, 12946, 468, 11, 70, "Text",ExpressionUUID->"d105770e-bcc9-4db9-afc2-ea9558c47cc7", CellID->49759380], Cell[CellGroupData[{ Cell[763614, 12961, 319, 10, 70, "Input",ExpressionUUID->"20a76bc1-cdf7-4274-a70a-b590cc4dfc88", CellID->1585553464], Cell[763936, 12973, 2649, 57, 70, "Output",ExpressionUUID->"7ee7134f-144a-4f75-8fe8-4d18b4a7aa5d", CellID->511830773] }, Open ]], Cell[766600, 13033, 230, 4, 70, "Text",ExpressionUUID->"26f36038-9a8a-4739-828f-87bd6c167bc3", CellID->2104111132], Cell[CellGroupData[{ Cell[766855, 13041, 1649, 42, 70, "Input",ExpressionUUID->"aa731ceb-5d80-4d80-ac46-3bddb07b5899", CellID->1839014768], Cell[768507, 13085, 2580, 54, 70, "Output",ExpressionUUID->"c1db89aa-da1c-4eb2-9994-0018a2e69072", CellID->1683685188] }, Open ]], Cell[CellGroupData[{ Cell[771124, 13144, 145, 4, 70, "ExampleDelimiter",ExpressionUUID->"1737ea3d-3a44-4b7c-85d4-2aa97f1bfa7b", CellID->1773330632], Cell[771272, 13150, 190, 4, 70, "Text",ExpressionUUID->"c4e854a7-660f-49a3-8f85-284447ec2b7e", CellID->1846917249], Cell[771465, 13156, 366, 12, 70, "Input",ExpressionUUID->"27eefdcb-1e08-4f6e-bc1a-2df338dda2c8", CellID->391616158], Cell[771834, 13170, 158, 3, 70, "Text",ExpressionUUID->"0e0c52ee-94fa-4333-b26b-fbda071ad9c4", CellID->586386716], Cell[CellGroupData[{ Cell[772017, 13177, 1040, 29, 70, "Input",ExpressionUUID->"9bbf2b16-5684-4fea-8dc5-b07caac22014", CellID->1739416228], Cell[773060, 13208, 153857, 2727, 70, "Output",ExpressionUUID->"6daa6b49-367e-4440-8c2d-fe1088d3afde", CellID->1275084553] }, Open ]], Cell[926932, 15938, 224, 6, 70, "Text",ExpressionUUID->"db9ef819-5a6b-409f-a326-953f24672bb8", CellID->1174495922], Cell[CellGroupData[{ Cell[927181, 15948, 985, 27, 70, "Input",ExpressionUUID->"8327ce99-4125-49f8-b981-822563ace412", CellID->1175297382], Cell[928169, 15977, 193555, 3440, 70, "Output",ExpressionUUID->"1315ddd6-09a4-4aab-82b4-5c5ccf4ef0cb", CellID->1064024291] }, Open ]], Cell[1121739, 19420, 281, 7, 70, "Text",ExpressionUUID->"b8ab4a61-b021-4140-b6c7-1c387a0671c3", CellID->1555758181], Cell[CellGroupData[{ Cell[1122045, 19431, 375, 10, 70, "Input",ExpressionUUID->"23e7eee3-c115-496c-b3d2-607ff3a1e768", CellID->1342459492], Cell[1122423, 19443, 303, 9, 70, "Output",ExpressionUUID->"9ffb6040-1da8-4e3a-8190-2347c775d522", CellID->481888444] }, Open ]], Cell[1122741, 19455, 284, 7, 70, "Text",ExpressionUUID->"5eadc8dc-9418-4a87-87e4-b332d248d3ac", CellID->251741330], Cell[CellGroupData[{ Cell[1123050, 19466, 294, 7, 70, "Input",ExpressionUUID->"e3eb0068-e65b-4a9e-a166-cd2867a86a28", CellID->1338088870], Cell[1123347, 19475, 20383, 341, 70, "Output",ExpressionUUID->"4378758f-9925-4a2f-b78c-192e4e2cd5b0", CellID->938245471] }, Open ]] }, Open ]], Cell[CellGroupData[{ Cell[1143779, 19822, 144, 4, 70, "ExampleDelimiter",ExpressionUUID->"107c4f7e-a096-49e4-8fa6-36f17b4836a7", CellID->595150754], Cell[1143926, 19828, 375, 8, 70, "Text",ExpressionUUID->"4db98399-9543-4ba7-8f20-65318f0d9151", CellID->53006316], Cell[1144304, 19838, 475, 14, 70, "Input",ExpressionUUID->"22c408f3-9e0b-469c-b152-ddd96462f599", CellID->557350363], Cell[CellGroupData[{ Cell[1144804, 19856, 8599, 213, 70, "Input",ExpressionUUID->"1527dfb0-905a-4659-b5d0-6c7eb60d8034", CellID->1070301680], Cell[1153406, 20071, 29815, 496, 70, "Output",ExpressionUUID->"730de099-a91b-4607-bd2e-273ffd438da7", CellID->2127476028] }, Open ]], Cell[1183236, 20570, 266, 7, 70, "Text",ExpressionUUID->"762f5bd4-f620-441e-ba4b-cf192483de04", CellID->788491045], Cell[CellGroupData[{ Cell[1183527, 20581, 736, 20, 70, "Input",ExpressionUUID->"6ed240d6-0b32-4fec-995a-5aa7f634ea7a", CellID->862773238], Cell[1184266, 20603, 101042, 1823, 70, "Output",ExpressionUUID->"b53b80dc-ab5f-4366-b502-6aa196ef9d27", CellID->50245876] }, Open ]], Cell[1285323, 22429, 1162, 27, 70, "Text",ExpressionUUID->"1d602186-e744-44e6-9cbb-192937ea7646", CellID->38380953], Cell[CellGroupData[{ Cell[1286510, 22460, 9383, 224, 70, "Input",ExpressionUUID->"0bd344a3-db77-45bf-a556-2b23f5011321", CellID->1093852509], Cell[1295896, 22686, 59891, 988, 70, "Output",ExpressionUUID->"36358adb-3c53-4c63-a871-f0264f0c073c", CellID->891061898] }, Open ]], Cell[1355802, 23677, 256, 7, 70, "Text",ExpressionUUID->"712c1a18-32fc-4c80-a7af-95280912c8c0", CellID->65353880], Cell[CellGroupData[{ Cell[1356083, 23688, 8869, 213, 70, "Input",ExpressionUUID->"8f217c19-1bf7-4c46-97a6-92908de64b75", CellID->833444543], Cell[1364955, 23903, 52672, 870, 70, "Output",ExpressionUUID->"edd82917-67f3-4403-a45c-5e5daa35674b", CellID->1028883790] }, Open ]], Cell[1417642, 24776, 290, 7, 70, "Text",ExpressionUUID->"17a8198d-6ad2-4f15-9457-9acfd87ac61d", CellID->1960079553], Cell[CellGroupData[{ Cell[1417957, 24787, 8647, 214, 70, "Input",ExpressionUUID->"1b63938f-da20-4204-b567-98eceabceb8a", CellID->881938515], Cell[1426607, 25003, 30083, 500, 70, "Output",ExpressionUUID->"c33a8111-d141-4fe8-84bf-14976573a7a3", CellID->561941367] }, Open ]], Cell[1456705, 25506, 384, 8, 70, "Text",ExpressionUUID->"4ab7e575-e472-491e-983f-2ba49922bfbc", CellID->1939299289], Cell[CellGroupData[{ Cell[1457114, 25518, 8444, 209, 70, "Input",ExpressionUUID->"dc888ae4-94e1-422c-ac09-d425f5f71ad8", CellID->61502188], Cell[1465561, 25729, 8637, 149, 70, "Output",ExpressionUUID->"1f7170ed-85f9-463c-b950-e627cecbce6f", CellID->10847177] }, Open ]], Cell[1474213, 25881, 362, 8, 70, "Text",ExpressionUUID->"cfff1954-c696-4b20-adb8-f6e21573edf3", CellID->814058795], Cell[CellGroupData[{ Cell[1474600, 25893, 8495, 210, 70, "Input",ExpressionUUID->"96998c77-74ff-4693-a8e5-366070aa68fa", CellID->722160800], Cell[1483098, 26105, 9095, 156, 70, "Output",ExpressionUUID->"220cd0a1-2e52-4994-818a-835a53fec4dd", CellID->1619290134] }, Open ]], Cell[1492208, 26264, 313, 8, 70, "Text",ExpressionUUID->"a94555cb-0d40-4f66-9b98-57bfe78ec61d", CellID->1463347969], Cell[CellGroupData[{ Cell[1492546, 26276, 8643, 213, 70, "Input",ExpressionUUID->"b9efc508-1496-4cbd-8397-a609084880c0", CellID->1499898732], Cell[1501192, 26491, 20173, 338, 70, "Output",ExpressionUUID->"a6f65fee-7c70-4206-8f55-917570c2e8ec", CellID->2006834304] }, Open ]] }, Open ]], Cell[CellGroupData[{ Cell[1521414, 26835, 144, 4, 70, "ExampleDelimiter",ExpressionUUID->"c7797567-d04c-4068-bbbe-f687beac9dea", CellID->379258169], Cell[1521561, 26841, 838, 14, 70, "Text",ExpressionUUID->"1af13c55-0c49-4e7a-a13d-ec872942c0d0", CellID->939931350], Cell[CellGroupData[{ Cell[1522424, 26859, 789, 21, 70, "Input",ExpressionUUID->"4af13a24-e8e8-4c7a-93b2-1c6b4e95e35d", CellID->1207480353], Cell[1523216, 26882, 101147, 1823, 70, "Output",ExpressionUUID->"e8689cef-c534-4b05-a37b-53f42b62ea14", CellID->1832921591] }, Open ]], Cell[CellGroupData[{ Cell[1624400, 28710, 8841, 216, 70, "Input",ExpressionUUID->"ee9c3aaf-971f-47a6-83ae-3410ce0d26bb", CellID->1998625720], Cell[1633244, 28928, 52606, 870, 70, "Output",ExpressionUUID->"19725dd2-b68c-4d89-8686-2198b35762df", CellID->2032861437] }, Open ]], Cell[CellGroupData[{ Cell[1685887, 29803, 8843, 216, 70, "Input",ExpressionUUID->"8c06427b-b8b9-43f3-bdca-642f67d9441a", CellID->1168053742], Cell[1694733, 30021, 60720, 1002, 70, "Output",ExpressionUUID->"7a4954c0-83c5-4a41-a7d8-36f81b16037d", CellID->616280784] }, Open ]], Cell[CellGroupData[{ Cell[1755490, 31028, 8359, 201, 70, "Input",ExpressionUUID->"f12dda68-9b97-4d84-8328-c4c36c51c300", CellID->2071915279], Cell[1763852, 31231, 52626, 869, 70, "Output",ExpressionUUID->"0853ad7f-0f5d-4eeb-8a81-ece16467006f", CellID->1289005572] }, Open ]], Cell[1816493, 32103, 381, 8, 70, "Text",ExpressionUUID->"809930cb-df2c-4b53-9572-c4572fa15640", CellID->691414126], Cell[CellGroupData[{ Cell[1816899, 32115, 8470, 209, 70, "Input",ExpressionUUID->"d37bf48f-6532-4290-aceb-a31da7510fdd", CellID->563201765], Cell[1825372, 32326, 8608, 148, 70, "Output",ExpressionUUID->"cb3ae73f-8050-4a72-b6dc-6dcb3b0fa3f8", CellID->226984003] }, Open ]], Cell[1833995, 32477, 374, 8, 70, "Text",ExpressionUUID->"5a028779-40b4-47b8-b9fd-9bb3e22b1440", CellID->958695327], Cell[CellGroupData[{ Cell[1834394, 32489, 8521, 210, 70, "Input",ExpressionUUID->"a0805387-c108-478a-a5bc-18e0e57da079", CellID->222557448], Cell[1842918, 32701, 9728, 166, 70, "Output",ExpressionUUID->"af22f18d-9b7f-44f8-b717-8cf534649dbb", CellID->1997659424] }, Open ]], Cell[1852661, 32870, 508, 10, 70, "Text",ExpressionUUID->"6870e6ae-38ab-4ef7-b308-68091024a5c7", CellID->1735582061], Cell[CellGroupData[{ Cell[1853194, 32884, 1170, 33, 70, "Input",ExpressionUUID->"32a74bff-0520-4ded-a5c4-16035bdc1cca", CellID->2097567861], Cell[1854367, 32919, 36865, 611, 70, "Output",ExpressionUUID->"2eb90c06-04af-4d46-bb80-06c824b653a4", CellID->26690687] }, Open ]] }, Open ]], Cell[CellGroupData[{ Cell[1891281, 33536, 145, 4, 70, "ExampleDelimiter",ExpressionUUID->"7e88ca3e-7b3f-47e7-b276-26d70886491f", CellID->1065155690], Cell[1891429, 33542, 1524, 34, 70, "Text",ExpressionUUID->"a741102a-4450-4fe0-8c52-b04883f4fb00", CellID->1583796361], Cell[CellGroupData[{ Cell[1892978, 33580, 9201, 224, 70, "Input",ExpressionUUID->"5edf0afb-e89c-40d9-9d51-e7fa04a4a22c", CellID->1722456074], Cell[1902182, 33806, 57523, 950, 70, "Output",ExpressionUUID->"60dfdd56-a442-4195-a0b0-0fb19fba8035", CellID->1870542746] }, Open ]], Cell[1959720, 34759, 742, 22, 70, "Text",ExpressionUUID->"b0034c71-9f77-43c5-a3f3-78342ab4aadc", CellID->759535067], Cell[CellGroupData[{ Cell[1960487, 34785, 8470, 204, 70, "Input",ExpressionUUID->"342c89c3-5bee-4d4e-8934-2d5417df0b92", CellID->937403932], Cell[1968960, 34991, 111047, 1827, 70, "Output",ExpressionUUID->"fd0eeda5-7cdd-4652-99ee-922ecdab9e84", CellID->1175035032] }, Open ]], Cell[2080022, 36821, 786, 14, 70, "Text",ExpressionUUID->"54a964e1-eb1c-4466-9677-ea6ce2f01c9e", CellID->427154033], Cell[CellGroupData[{ Cell[2080833, 36839, 398, 12, 70, "Input",ExpressionUUID->"5df51441-dd1f-460d-ad07-d538bd47d8cc", CellID->1857981584], Cell[2081234, 36853, 261, 7, 70, "Output",ExpressionUUID->"ffa4b5c8-8a7b-4063-a594-36f7832e3f41", CellID->1915561976] }, Open ]], Cell[2081510, 36863, 261, 7, 70, "Text",ExpressionUUID->"13a17517-161f-4f5d-b27b-b82157c41ebd", CellID->1821480515], Cell[CellGroupData[{ Cell[2081796, 36874, 9207, 222, 70, "Input",ExpressionUUID->"d94d6cf3-552e-4491-8e30-0b81264031b1", CellID->1433913193], Cell[2091006, 37098, 114326, 1881, 70, "Output",ExpressionUUID->"9f282a3b-946d-4cf7-9986-9093edf43cea", CellID->539878406] }, Open ]], Cell[2205347, 38982, 333, 7, 70, "Text",ExpressionUUID->"45499a39-9831-4dea-b6fd-9c866bf9fd87", CellID->1306756126], Cell[CellGroupData[{ Cell[2205705, 38993, 403, 12, 70, "Input",ExpressionUUID->"f11aa991-dac3-44e7-870d-ff4e24788e48", CellID->457564611], Cell[2206111, 39007, 261, 8, 70, "Output",ExpressionUUID->"1ab49e1b-1d50-48cf-893a-8e517b9bdf47", CellID->853050358] }, Open ]] }, Open ]] }, Closed]] } ] *) (* End of internal cache information *)