{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Acyclic\n",
      "\n",
      "--- This is a regression problem ---\n",
      "\n",
      "1. Loading dataset from file...\n",
      "\n",
      "2. Calculating gram matrices. This could take a while...\n",
      "\n",
      "getting sp graphs: 100%|██████████| 183/183 [00:00<00:00, 4451.98it/s]\n",
      "calculating kernels: 100%|█████████▉| 16811/16836.0 [00:24<00:00, 254.60it/s]--- shortest path kernel matrix of size 183 built in 24.209428787231445 seconds ---\n",
      "calculating kernels: 100%|██████████| 16836/16836.0 [00:24<00:00, 696.65it/s]\n",
      "[[ 1.          0.56568542  0.2        ...,  0.22823241  0.23199267\n",
      "   0.21342821]\n",
      " [ 0.56568542  1.          0.         ...,  0.10758979  0.09113533\n",
      "   0.13205197]\n",
      " [ 0.2         0.          1.         ...,  0.32966903  0.33510052\n",
      "   0.32014232]\n",
      " ..., \n",
      " [ 0.22823241  0.10758979  0.32966903 ...,  1.          0.95437578\n",
      "   0.94039745]\n",
      " [ 0.23199267  0.09113533  0.33510052 ...,  0.95437578  1.          0.95932949]\n",
      " [ 0.21342821  0.13205197  0.32014232 ...,  0.94039745  0.95932949  1.        ]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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6fyWDigVOT5AHhsqUfV68Tk5WkGQFwbFzmSdwXgOQZ4MLoljrosuLJn/Df2Pnv4aIux8k+9wWjh3YyNHX/g033/x6xPFZkqt2MvLfp/j6N3aDY3BakrSe80c33cpfHn4pwXsalKY65CUP58gp8u0TyG7C4d8PyCKH4ITHxAtnmDo2hrfo8PyXPMqR1jBXj8zwmUcuYWSkQ+1Py8QNl7ChqMykaEdQPLwCjsJ4Do+9sYzqKfJGirPskk/E0PQQQzFqOkD7hmBBklYNyViGU0r5sb0P8uFHruKHLjrMfFihFQcsPDROZe8KxffXcTs52pU2plFVaAcKyzm5L5CJISvaeInx1VprobzqoboJIs45/DMNVCzIigavJQi3pASzLkZAsjWm9IhPPvDt06pBZDB2zTyzB8eQoxFSGNKei1dJyBKFkHZ1HP6XIm5fk5QVMreKywhQsbExjtTg9DJmXlwgqWm8pkRmENcNXlsQTuZ4yxK3I0DY1VV7BpkIct+QbUzwSwnJdAkEbLktJxpSqNiQBQK/pRHakBUkRkLlWI+85KIdiYpyTr64iBPaIKNMbLwCrHXjtgVGgfZs3CR37QqvPXCet0pnoYy77JDWc4J5h7H7MnJf4K9YF2zlEp+0BMKAGvzYnZE2TnTawhEaSqdy3J4GYYPFMjPcfetbz3qlv+jyovnTj+45qznymov2nRcL4oJQENXSpLlx889y+f93hExLPveB69fcik9/+oNr7sbMVzZ9kxsRD3lEdev4/V9v+7tvcj/efvGtAPzyJ34ZrykxcmDCNzQYGLlPEo4I3J79PCsIfuOXP0wrL/L11jZeUJtih7/A2w78CGmm+KW9d/NXX3z5U7oEAKonySo5/oJDvCEFLWg8qJAJxA2BzG2wLyuB24W4AT/4Iw88pcsyWWhSlAmzcQ0lDLc/fjE6lZRqIWHooZTmZy/+OmUV8c47X4mMBSoSaAVuVxCPaFRomRRhICsaG2zUoCJB+TjkgWURZGLICoKkbidaVjTIVLD9JVP0U4/lXpFGMaTqR5TdmChzAXBkzljQ5XOfvRqZCusSCFCxIB7PKB53bNBRQ160wVevKRl+OKe7UVll0oC0bAhWBM4LV2iulAjKCeVCjKNypDC0w4CCl7K8UsY0PTBwyRUneOShLahIfhMzk5U1bkuSVcyaMjWuZRhkLBk6AP0JQbBor4d2B4rkRU3yXCKloegnNPePYCR4q9bl0e5AQSqrHIy07Iu7pUcSOxgtQAukqzn20//lrCfyzstL5u0f3XtWc+R1F93/veNinDP6IaIX8vF/vZF3/MJ7efirlwHWrXiiu7H5W9wIt5tRmAuR3ehJ7sdp12Pyi4a0aKPahcWMtKIIllNUmFE54azRiUIbHv/ZCW595GryWFG7LOQfj13Lb+/9LBNOi3fNvPQpXQK3p8kKkqgGKlHknmFkv0QrgUo1Ti8naCqcvsY4AploazEcM9w++WSXpTdb4dorD5NpSc2LeGhxA9duO85llVlckdPKCtScECk0j/Y2MHqPXWGd2KAVOJFd0TIf/JamP6YsNamtgsg9qB9J6I+7a9aAEaCmzIDKs9H/g3XruuhUkSQOJ8MhxsebVL2Yxw9upDTRo7YxYvQBjcwhLVgliDHkRxTBil2V45oc0K8CrQyl2RhhfEQOxUHvKmFgdlMNFUvitkthe0KSeXQ7ASaXxLM1xLYQI2F4q3VRhh+QCA1B07pcuSfQrsBv5oTDCqEZsDn2nHIfqsdj3J6LSiwbAXayzx2p4rYlYUOz4YoZxAEGAUf7bBQXMpKKQrv2I7dvSCqS5QkH92iA2xZkJatgzxX6+5TFODeUCmQzs/zxz9/Fmz72BnaHrTW34kR3iR3/8qscfe3f8EOffyO1R1trboQZHUKXPYxS1LyIv/jczeAYXvrFt/K213yIz+V7CLShuJCj4pzmjoDSXEbuS7zlFKENSd0nONUlqwZ87N9uROzus23zIrc/fCneKZc/fPwnkKngBT/0KEuJeZJLkBYluSeozGR2VY4NxhHooqB0vEtnR5nSbExSc9doueJUm2iyDFrQebxBs5GyvDxKPhEjqgn77tqN0xfIFHo7Uu57dJh90Z41Lt84BrExgpMFxiKN27fLpcghrtlAJgZ644qgpXFCjXaFVVqJXekrUyFGirV8g6TuosIcPaANZSIxcwHUMvLpIlLA6vFxWgkELujZGp9f2cu2jiYtSYLVHO3ZYwhtSKrW5VCxwYkMRoJKLAVaPBWjPUlcd5GpweukBIsFookcGQrSLw2jXfCltTBULPAeLJLUDN17R3jEH2Y4Mnhd64ZYSliTlhV5IPA72q7yAyWAArdnMFKQFSTBSoLMNLmvkImmOBtYi6KgOfrgJJtWrZIpLA/MEwFeJx9cL7uYeB0o3lc4o3gLNu/kXGBTrS9sBXFBuBil3RvMyO/8J7Z+GEr7Z3nzF+7gr6dfStWL2Pe5vYzfm2EU3PXu9/DJfoBLzl/N3MTV9WluKB3m1uVrufvkNvrzJfA143c69Mds1PtDv/9/A3AgGeP5/gJ9A/uTCWbSBtu8RYZlj9s6l3Nl8QRvvfcnGBtqo4RhuVtEa4n7tcrayvu3v/L/0tYBVRnRMx6jqsdjyTiXenMs5iUCkfJYsoEJp8VWZ5UvhRexxV0mMi5DygZb+9rns61LeW3jXv7w+I8w3y2T5oNciiCmEYTMtqsIYYgSl4lah1N3bkLmA/M/FuRFs8a7/+FP/ROpsXo+0i7DTpe5tMao06EkY06kw1RlSCBTKjKkmZdYzCpExqUiIyLjEoiUuuqTGkVd9ZFo3nnyZcy0aohBVlTZT4gyhyyXOEqTZorWdI2f+8EvMeT0SI1CCo0rcrSRa0VIqVHUVJ/IuLgi59MLl7GnMs+k32Sju0pkXObTGv9wyLaRdFWOozRJdoYznKh0OLJvEzIViBSyiuEtL7ud+bRKMy3gCo0vU6pORDf3KaqEVCtcmeOKnNxIiirmntXt/G+jD7Gvt4VWWmBXcYG5pMrXF7ZScFOmDtmG3i973gE2Bk1Ohg0cmbMYlal5EZ7MUMLQTgOqbsTXTm0lySyTUirE+G7G11/1J2ftCmy/vGz+4MNXnNUc+fndXz0vLsYFob6y2OGFlx6ieGiJfLzOqOpw89gBvv6N3agY0rKkNNXhk/2AHy5G7HJXuaw6y5jb5kgyxqjXscrB04hIkXuWt5cp3NHfTWQULys0+a+zryIyiqKIOREPM6Ha3N3fxQ2lQwQiRQjD/HKN6SOjxJFHtBogB+OoBO7u76KjC9zd34Urcj7RvpLPrl7G+1dvYFx1aeuAGws2jn1PtI3d3hx/cuxVXO7N8eHVazmajPGN/nZeXX+QZV1iulmnF/p0m0Wi0OPUXIPDCyPkWtKPfLSWNMMAtwtOz1J+bg/8Fevz5x7s622lmRfZ19sCwD/NX8eJeJgcwTunb2KnN09d9ZlQLZbzMjmCj5y6mhuLh8iRLKUVKirkoXAzy3mZ5bzM0WSMmVaNdrvA6myN1eUyi+0yq60Saa5YbZXohR5oONgd59MLl7KSlYi1y/3trSxlZT5w9Pk82tvAiNNh1OnwaG8DJ+JhplaG2BYscyIe4s7WXooyxhU5/Y5PeKJCc75CqxuQ5ZIw9Og0i8x1Kjg9QXFGUJwXGAFLaZlmWuDx1hjNtMCR7ignowZfPHURy0mZw71R9rc2spSW2d/eiDaSo6vD3HL8hTy4MkluBIf6Ywy7PTqhz2KnjNtSiEywmhRYTsqsJgW0kRxZGeFEt0GYu0x1hzjWHqKZFIgSl37XJ1kJWF2o0OwWz+m5N4YLvhbjwnAxlGYlLhJvGcJbCRmVMV9p7gTHkFQNwVJKXvJwyTmWdtnulvnywk521Ra5vHKSMa8NvlUOqhFTmnNZ3eOS1AST7gpTWQNYZcjrUZM5W4MOt7UkrtBE2qVvfIZUlyx2qNT7dFJJHikaE23UV4dwIkN3k6Ss7Io7E9cZcUaIjcMV5ZMAHM8aBCLl/mgTzw9slvm+aAvtyOdIah+uk8kQvkyZSkaZdFcpeClpqjCRIhtcino5ZLVjH7Ska2lJmYLXsa6L2zNoB9yuRCuDK3O6eUCYe3R0QJS7NNwezbxE1YvYH20mECmbvWWaeYmZpEGiFff0L2IqGuZ4f2gtWaebB1RkSCsv4iiNOd3MRAuU0qSJQ68TUK6GhH0fp3vmwW1nAUWZ0Ml8fGHPRhvBg73NTHhttJEc6w0TRy6HwjFO9IYoOtZpP5XUENLYQLIypC2fLMiRjsHkAilsTkRWAn/V4PQFfe3RSQMqXkzJiWmnAVJoSl5CaiSZlmRGUZQJUhhm4jpx6mCMIMkUxghcZV2ILFO4bo52DW5H0k19VmTO8VaDqOzSKIbk2n7eTz0CJyPRiqjrgzDIcorOJOdujIvvz0Spc0VpZLP58U++gvkb2qjhIRbfN8JHrriFl37grWz7ZIj2JN4jJ3E+JLmsOsuXF3Zy1+UfYTXv8+vTr+aeqW0Mfyog9wSluYy4rqgcC1FhyswfGP7jzm+wkFT49LFL2FBvM7NS45rJk2wIWjzangCg6CQc+efdlr4Lob9BMPJQSm/cwSgorOTMvjblsk2zbCmtUHUiDvdG+fq+XYhawg/vfZivzW/jBWMnuGNqN46T85t7P8+tc9fwYxP7+MLKHq6uneDfTl7JpkqTTEsO/8tuGyUfWNMyBa9tSOoCt2NzDmQKftOQ2bwvxKDWAWGzDOdekTI00qFeiCg4KWHmMr3Y4IXbj3Lv7Bb2js6TaUVmJFIYhv0eR1oj/NyWr9LKixRlzMlkiI9NXcbekQWWoxKuzJn59Na1+g09oAmNtK+9FmRFcDsQvaSD4+QMl/poIyg4KY8fn6BQjdhQbyOFoeQkPPjYFq6+eIrrGlPMxHWkMNw9t52Sl7Ch2ObhWy8+Uyeh7PgqHtSiGCiftHkS4bBEu4LuC/voXKB7rk1+ygUmUtYmVgYRKowy4OfIpktpe4ubtz7KJ49dyhXjs9S9kKW4hBSG/Z/YizBQmjX0xwTdPSkyyJgca9KJfLqPNchquT1OJqHtYDxDMOvgte19yAo2UPno//its3YFtl5WMb9/69n95Omv7r3r+5fF0AMpnMmNmChiQ6UNQFrPiYc83G6GGR3i6vp+xtw2u2qLrOZ9GqrItuIyD/iT9MeKNh14xMHrapKGhweMV1YYcTqUVcRlE6fYWGhRcu3Ktac4R45kNqwxEbR5rGITZuIhK1Nn0sHtW8YCYKjepezGVJ2ImtNnImgjagljw20m/SabKk0uKs4zNXpmdby0dooJp8mI36UiI4YKfUoqoR70OVAfRNkH569dyH2BcRlE5QFhKCwalLDJSio9U3ikXUGhElMvRPgqo+gkBCplsVBiyO0xXu2wtzLPTFSn4kRkRlGQCctBiZKMaeVF+tqnKBOGiiGBShkvdKi4EVOVLchYgGU1yR07WbVnyBMri3ZBKU3BS/FVhpIaKQx+OaZciHFlzkWVJTSCh8sZgcpYSstr970WRAwHPcDmM2SDgq+0aqyyUDYomHs2WW2tGM2FcimiF3poT+H5GXkmyZVd7KQ0ZAaEo3GDjCQXDJf6NNMCk7UWmZGsJEW8QeWVHuRQ5J4txhJeTrkSkRuBlJo8MIggRzk5WkDua/A02rNBUZkK0rKV61xxoQcpLwgFUWrY8G8+0UC2Q35j07/xruUb+aObbuWdX32dLZope9xQOsyRZIzLKyf59elXs624zB+N7WfE7XDLV+wPOkfDgtqhiM72AnkQcOPQNEtZhYWkwubCKtuCZcLc5ceH7ucSb5nd3hz7gi3s9U/xeZ5vH5LMxhz6E4KJe20qdVx32FJdBWAmqnOKGgDbJpbZVl5hJq4zUehw9+pOtpVWKDkxJRmzt3CKkox5ZX0/JZGQjjgcCsf4werjfMK7DpnZwqfTD1daMajEKgeZDszqVm4TncoSt6/JAkk0JMldGCr3rXy5Q6oV2ggqQcyh7hgA9y5vpeCka0G7laTIjvIS0+kQCkNfe0Ta5XnDJyiolIqKkEJzp2PjHqctiNMJQjITZKdfp5ADaaZoJ3aWSWGol0N6sceyKbESFqkFEYVizGzUlFmjAAAgAElEQVSvxn0nN/O8TdPUvZCLa3NIYViIKoC95tqx44rcKmWRW4tC5rZAzu8Y0rLCdzN6oYf0cvLBvn6QkiYOrpdhANfN8dwMY6Ab+3zpxE6u2XiSxbBMohUlNyEc5HVoZdaSoQCyXLLcLmGMwAS25jLPlM15cA3SPfODeNkg9HCutRgGsd6T8pnirvmL+Fy+BwHIboRRiluXr2XU6zDmtblnahsP+JOMuB1+szHFP/TsqpPUBCpMcaKALLAX/97mVqQwa6nQnrQ+8r+2r+BAdyP/dcNnCIQ1meOG/WuUzcADkIkm96Cb+gQq4765TYyWe6z2C1w9NsNUdwiADcU2R1aH6Vc8VqMCGzY3eee9N/GL13yFhaSCK3K2BctcWZrmUDyOTKx86gn8uUqswhA5OH1DnFt+XyZnXEGZ2YxImUOcORggSh2GiiHHZka4avs0p3pVFparuF7GRL3DXK9CmkvC2GO2UiMZcsiNYDEq00t9dlaXKKiUvvZ4sLmJwSWyFo575rVMrRITuc0DQeV0ugV0SVD0E+ZXqkwMtemdrJBN9Ik6Pu1ygBBwYm4I2i6V7RGPNscxRnDd6BRg3SwVAg4U5gRJzVoTMFBSSpAVLKUockM/cdFaUizFlvEJPfJcorUgzyUmF+TyjAXUj13C1QJfaV2EU8jYNLrKY7PjBEFqlR3CuiQaTC7JMkWyHKBqKX4jIu74IAQmVqhBzMHpizWG67Rlc6640C2ICyIGUaltMj9Q+wlW3hswd3IIf9ZdozZ/58/ev0Z57p/b8CQqEyzHfd/b3v1NFOibJ+/k1uVreWR1nGavQBK7bBhuEaYu3dBHfaNCWjXkvsFbtdl3/89r3sel3gJFwRod+rnmpYS5yzXV43xy/vIn0ZJKGKp+xGy7SqMYMteqUCnE1PyIY/dsJqtpRCLQgUYkEhPk+PMO8YaMD970N0+iTRWGxbxEz3gsZlWeHxznz+deTpi7bC6u0kwLDLs9tgVLHOxPcLAzzmpUoB9bv0MIg6Ps6lZ0Uzqxh+fYrERXavqpy/Kh4bVrL3KbFSgbCXnfQRXtivs/XvBhUuNQkjGRdqmokEh79LRHX/u4gxTGv3j0JlyVo41ASU2uJROVDjOtGkpqPCdHSU3Nj5jrVIj218kqBqMMathO7rTl8/M3fJmKiiyjoT2KMiE1Cl+mpEbx5ZWLiAYae09lnoeak3Rin07oI4Q975Fyj6VuiUYxRMkzK/zp2MzJ+zfi7uwQT5fBgC7lkAted8PXUWjaWYFhr8snTlxKvRAxvdjA9bK1IOZpFyrJFJVCzIs2HGYhrtBKAraUVullPre84H1nHSvYfFnN/Ma/XH9Wc+Stl9z+/RuDkHFGPl5n/miZF159kAeOXUJaltQeba1Rnn/xuZsxrn4SlQl2VTtNgR5Lu1xWnV2jP+eXd9t9pGb6yCjBaEi0GlBNIAW8lgQJMha8rNDkA53d3NPawW9NfJaiiIm1Q0GlnIiHmW7WSVNF0vdw/Ixus4hQmgU/w/cymmGA1pJO6NOLPbRn8OcV8WiO07ZKxSQOuQciltzd38VWb4mHo81cWzzKJ9pX0soL/HzjbpQw7HKXeSwZ5Uh7hLof0st85sMqYe7yiurD7PTm+fzJ15JpSb/ro9yctBngVO2FWWx7BMMhBLaYq6+tQvVWJNHGDNmXoIXt4dB2Ebkglwq0YF9vK6lRHO8PcVFpkaJK2B2cYtJd5Y72pewMFqiokH7Hx8QKt5KgjUAn1vwPT1TQpRy3ElMIUsLEJYxcSvOCVtXmc2RdF1nIELngYHecx5bHmKh0eM3E/UTGZSWtciqq4cicmW6NuZkGpaGQV44+wl3hRfRjl/5qwd4/N2cJ6M6XkROGXMu1HA4hDI7U+KuC3lIRr29dONFVaNfQTAusJkWmWkO8YvIxeqFPnLrkmQQc0qZPWspwvAydK/JIIQQEMrUVx90Ss90aNT86p+fe/rLWhW1BXBDSac82KVGNmJW4+E3U5hMpT3yNiE9TmRle22bRJTXxJAo0NYoxr02pFJFHiix2EMWcSjGiMdFGO5D7lj4zCvKC4ViWM+muMOT1mMoa3Bh0ONweYd/yJJmWFDzroJrBeACjwx3q5ZAodim4GVHbZ2O9TaMY4nYlMrXKBwbdjHyNTMG4+pto06nE0qabvFWOZw2a2uP+aBMdXaAd+RxdHeJod5iZTo3pboN90RamklEcpdFaWF95oAAKxRhjBCLIiboe/cgnyxWZlnS6lg5xmwqnJ/CawtKVytYsiIGZ7cqcMPeIcpdu7iMxNPMS+6PNNNweHR1wINyEGFCTWkvbgSkXeCq3lOXAOuj1/UFRmGdZor7A6Q/qF5RBxN/shz/Y28yhcJx2FtDJ/LVJpAr5Gk2aa0GWKVQhR/nWmnGkBv/Mb2EbIwjcDEdq+pFn3ZNY4nQFTlegQoHTl3TSgExLXJXzeHeMPFOWfm56pH3LkmBsDEJnAunnaC041B0j15JGMUQIQ/YMJvuF3rT2grAgRKePmJ5j6DN7GHlTl+zjltp0jpzidft/0VKeX3wrQ18VAyrTJa4rvK6mdihChSl/9WM3fRMF+jNTL+aeqW1Uv1CgXBc4PUshGjlCbT4jDzLG7s/JCnZlF9pwy00v5LPTe8gyxcieLr99/2v59HXvZlQ5vPH4zSS3jeIGp7vZOTZItzxqqb+6oBuWqXjQ/dwkWUFQUgavY8hnbHKPdsEJbSqyOKD40/Ir12jTx6MNHO6N8oFjL+CH9z7MXFRlzO/yaGuct+z6IjcWjpIi6WiPiky4N9zGZ1cuIfriCELC6RSdIAd5oEFQtEVheQBZ4GNC2ybP9aF2RBM37MOsXfBXBc5ha9nI1Loq7/euZ8dm2ydiLrLZhhcPzVNQKZ87vIfdGxa4qn6S0gNW4WjvTOep9iMTVBNshykD2nVJdZHAB4yhdNIyEoUFBxUrsoLggc2bEMJYK00rxgsdTnbr+Crj3gM7mNy6TKUc8lM77mMmrpN8ZRhHQtESXuQ+ZLJAvQ9p1QZMjYA4tgyJcsBtG2pdudZyTjZtDOGeo9swbQ8qKS+ZOMSB/XsQBkQB5LyiOGdIy7ZwC2yMKi0XOPWyDsePjuG0lKVBo3NTEMaIZ9WCEEK8CvhLbIuFvzXGvP1bvt8CvA+oD/b5XWPMp77dmBeEgqAYwFCdy968n9RIWjsKNB7rkm+f4O0X/yPvWr6Rt73mQ/zJ0k+iEljd4zL+tR5Jw6OzvYATBfyXyVs5kozxHzY9wM9MvZgPbPsC76ht4z2PvBqVQFqBtGIDXd1NLo1Dms4ex/Y8SFjLM7h566Nkg5X4RdsO8zvTP0qmJW/ceBe/MbbX1kgkDKoBobvVVnaqBOKGwekJlq8UGKB2BOKaICuIAQNgCEsSt2uIhwRXbZkmUBmdLKCX+ZRUwosvOgRAyUlYiMtcNzzF+07cwN+ZG9lYbrEclSi5CT89cQ9jfof7G7ZWQUWWAVC5DbTCwDLywUhDVgH06aCjrfxUscFJbFu3cFSgYoiHAAOTG1bXbk8rLjBW6rIcl5AYRhsd4tw+Oqf7YWoFDNrT9ScMpVl7jLxgE7uEsQ1wikua7gZb1h0NCfJRQbBs6dLTrkArCtBGEOeKVEvKYz0WW2WyVHHLIzfwvE3TRKMatytJy4MqSw1JzYAUJFVj2Q8HkkHOCAYqxw3tbYLSKSuTtR4Fys3RjZjRRofDPavws5LBa1pFFjcGVZ+DfqRpBdKSodkvIIKcTBhkMUN7595S6tnKknxCJ/mXY3vA3iuE+Jgx5pEn7PZfgQ8ZY9496DL/KWDbtxv3wlAQT4HTzAWcYTTcb2EqPCAPLFvxrQzHO2rb+M3GFO/B1vSfLgs20q4suQcqNbhd21wVYDpssL20zKePXcIl43MM+z0eWxqj2yrARp6SdThNSzp9O3awbOj5oDKByAxBRxMOy7XaCXtc6248FSvSKJ6p+GmGAYdXRogSl3ClwMpQkThy0alkbqTGlaVpbs+uRWRn5JGZLeZSkVVKhUWrpIitfCoGmWrcPtal0FZx5YUztKKKLDsSpg6rK2VK1YhGMSTVkn7ssXNoiU4acO/yVtvI9QnHPz2GGCRWFeYEadXWkagEskAgcjuBnxj5d1VO60QNOZSQx4p2Ocb3Mnp9H9fNcZycNHJImwUq2yNkItYoWFtAdZrtGFCwuUALYy2FzCqnrAD+Kmul2zKx10pry3oATHfqiNyWzcvMVo2fvmenC7OMsOO3mkVMLvAbEcliETzNucA2jHnW3Ie1TvIAQojTneSfqCAMUB28rgGz32nQC4LF8LdtMlve+FtM3JNTenyFl916P+968EUIYcgXAya/aBDa8Hd/+efc0d/NpLvC7z3844xXulwzZNOavzy/g/nlGqVShPOpOuGYvfCPvPldPJr0uT/ezF7vFHWZcCRtsJKXWcyqa/7d5cE0v/fYf+D68SlO9IZIBumNR768FZnatmf3vPbP0MD0gDuvy4RxZXXsw4nLxV7CB9q7ub5whCGZcHe0lcejCXYHc+z1TtHUBUZVj1tbz+PmykN8on0Vi0mZxaiMp3IqTszGoMnB7jj9zGM5LLK3vsAXvnoZMhFklRwVStt7wbesyB0/8md0zBk9v5iX2OO2mM19FIa7+7v4weLjtI3PsAxZzEs8ntjs0TGnTU/7LGYVbixay6UiUqazGt/o7+BIf5Re7lFxYsb9NotJmdWkyNbiCs20yJ1HdvG+629ZK/gCiIzLZqfN3eF2NrvLfLm7h4sLM2xzl1jMK3xw4Xpe0ngMV2Rs85aoy4hDyRifWLmS5bhE3esz06szWWriS8uObC8uccu+GzGpDao6pZR/vOG9zGU1lNBE2kMJTUWGdHSBoojRSOrS5ojkCHra57bW5VxZOsGJZISaCulrj5WsRJi7VJ2Ijxy5AiHgP118BxNuk4PRRoacLhJNRxfWztEVGZ7I+dTK5fQzDykMUhgmgybvuOZDZ802bLy0Yd7wzy8+qznyR1d89Diw9ISP3jNo/AyAEOIngFcZY35p8P5ngeuMMb/2hH02ALcDDaAEvMwYc9+3O+6FYUHkgmQyIZg/szTftOsxbn/4UvymJC1qigvWBHhh4QhTWYP/uPMbjDgdlrIK9za30uxZH6G9XKJctwyHiuDRpM/FXpFAHOdQ2qAuE1YGRUm2PqE42EoAHO6MstQvMVLscXhuFOPZhiEqERzPXDY7KVudlJoMaGnBp/rjbHFWuMxLWdE5ryo9ylRWQ2GsUsiLXONPM5+X2ag6HM2G2OA2AZtwddpUB+hkPge7tqIwzFyKbkpvkDaZFzQ4A9PYNeBqSCSzeZHKoBFBSWREMuJgWuNKr83DSYUXlQ4yKjOGSCkKwbjqsC/awstLj5IPEnUSTzKX1ZhwWvQH1OZcUqWXe/QzD0doFpMyYe5SchKO94ds2zilWcyruCKjLkMUhpJImc1s4lNdhryo/BibnTaH0mF2uctcUz3BhNskECmRdolEyqjTZjUu0hkkW5XchIJK6WX2/VJaxsQKEUpMQSOVXqsWfSSaZEjZbMxcSabTIXZ4C3R0YU1xLedldroL7CjYmMomb4W6ssojMYo7mpfQTIvEkUe10l+7H5cEM+QIbmtezqTfpO71ibTLkXic7f4iBZWyEFboJD4VL6bixOf02Bs4l5/eW3oWaM7XA39vjPkzIcQNwD8IIS4zxjyt6XNBKAiRw8hoB5k46LJPTfWpuyHBCW+Q4SZQcc6BZIyXFZrAKp9PLqGsIhaSiuWmYxcpNVpJnJ71E0UO98ebCcRxtrtl3rtyMbuG7mabu8SnVi7nF8dmeLC/ha3+0qBc2bDUL9HuDaJRwuCvDlrNKziUjNPRLeayGs8PpvlatJUD/UnmvToXe4/R1A57XEVH92nqAolR3LZ4Ka/a9iifbF3JpcUZjscjXFk8wbIusRiVaccBncin5CeEqUO9EFFyk7UMO20EwYL1U9OajcBrTxCPAQYOxJuYcJocTca4xJ/httblbAuWGZaP8+HV5/F7Y3fS1JJhZTiZ2dt9+8IlvHL7I9wbbWUxq7DLn+dgtIE5p86Y0ybSLif7dY63GoSxh+9mjJR7xJlD1Y9YDosoYUgjhy+099LLfK6vHqGu+hyMNrDVX+Jf557H4kiFV5ZtK9IHwy3MpA0+OnMlv7S1zePRBrQRvKL6MI/FGzi2OkS3GxAUEjbVWxzvDtFNfPqJy5aaj2op/GWJkYrwIs10OszxZIQH2pu5qLTISlpie2GRLy3vghE4ldQB2O4vcjIZYlS1+eipq9hQbNFOCmwrL6PQbAuWmeoM21yOpkczFzwWbgDg8WiCEbfD/tWNNMsFoqLLfFJlLqyQVyUz/ToL3TKdboFOMT7neMKznEl5Np3k3wC8CsAY81UhRACMAAtPN+gFQXMa35B9eoTmJVXk0Vk+u3IJH3rgWiZeaM+vsJjR3BHwfH+BXz/5Uv5+5UY+fewSvrhicxwentvAhuEW6apdcbz2Gbdpr3eKQ2mD35+/gj8ef4ijWY0v9mybr483r2Krv8R7p36AuzsX0ewW2Fhu89LtA3O79M289mZ3mRPpEJvdZb4WbeX64DjPK03x45UDfKS7ldmsxh8vXcW+eDPjqssHl67nbVs+xruWfoibqtYV3OA1+ePHb2YurTMedACoD+IOu4aWuLR+iuXQchLtyCqqvGDWIuh5YEhqBrep8FYlY06buazOhNPi4Wgzr23cC8CBZCP/eexOPti+gi/2d/Hx7k4W8jIHko3cPHaAP517JY+HE6ymJb7S2UXD6bGalVjObK3EsN+nGsTsHl1kQ7VNzbNt57qJT9WzK6WJFA2nz8sbD7OSlziajLLVX6KZF3nTpjupqZDbupfy8e5lNBy7ym8st/jowtUAXFGc5u7eLh7qbaboJ1y8aY6tQ6sUnJT+gE0ZLfWoeiHaN4STOdF4jug5tHWBVlZgImjTzX0qjr1XLx5+nFZWpKZCNnmrdHTABq/JwXgjG4ot3jx+J9vKy5zoNSg7AwodW6xHKcOkkl7u09c+Ye5yPBxhstSi7CTMJ1VKKmY06NLNfeLcwXczqpU+m2otCoMO5+eCZ/GXte4FdgkhtgshPOCngI99yz4nsJ3mEUJcDATA4rcb9IKwIMgFrefH1P9ZondO8trRj7EclTh8ZALR0KSV/5+99w6y9DrvM59zzpdv7Nt9O3dPYwaDGYQhQBAkmJMVLKmoYIkSua6VpbXF9dYGe61Syd5SecvacpVX6yq7Smutglei4tZaIiVZJMUsUSAhggAGcYABJvWEzunm+6Vzzv5x7jRJ2yoCivBap6qre0J/9+u+3wnv+/7e56eobJeMLPyj+c/QkJrzhyssxl3WogMeXr7O8/sLRO0xtSTFyhnK2CXJmjKnKXNOtx7lc+MGfyPW3OM/xb8uK3yw+RhPZyu8f+U8p8Ntnm0uEXkFLxzNEyjNTHXIFT3j5MUzlqbMeCC6xc2yyblwgw1dpamGjKzgW5OrJFLRVk9xXyDQ1uObpl7gI52H+K9aX+ZCtkhNpmgEHzjxJG+M13lmuEIrGrE9rFEJcvbGVWbDPkvV7vGvZjYcIPNJhSHAyXu14z5ioSlHtMIB22WDB+N1nkzXOB1u01IDPj86yUPJVZoypS1LUgtNNeLX+m/le6afZLtsclRWmPO7JDKjqYas+fuMTEhlclze6DeI/YKZaECqfephSuLl+EqzGWka3ojr+Qwngn0qMmOjaPFAdJ2PHj3EmWSbFf+QFf+AjXKKwnrc6jf5ruVnyIxPan2+vfYsX/Lu5IWjedYPW9TilGqQU50sQoM8pO5lWGXxuwo1FmQnM04FOxgruJW3mPH7jHTIin/IU6MTvD65zl7pQp+T4Q7bRZP5oMNn8rv5wvAsu2mNdjQgNT53V7e5YBeoeDkIi/Asi2GHhhoSypIZf0BhZ6iojBl/QF9HVFXGSnTIVW/mWFU7yEMWv+Z9eyXD8SD+fE4QfxJJXgjxk8AT1tp/D/wo8AtCiP8ZF+H8kP0GScjXxgIhQAWaouIRbWmUMByOYoI9j6JuiA4KdCh5Lp8nERknoj4bhw3XbKN9AlkyGIekRxFxmNPYKRksO7Lz7YTkmr/PE6OT3OM/xYJX5VK/zW6tyvnBCZbCDn0ds3lYJ/Fz+llAfxAzm/QdfyC1FFXJc9kiGsnTw1XOJTfZKRucCPY50FUqMmNe9biUz3HS2+B6qXh8cAePbJ7k3mSDzx7dQ9MfM+WP0FZyMZ9jc9xgc1DnsFOl45f4vmYurnL5cIbA0xx0qnjC4A+d25RziJo4TvkKKx1zQmG5kbU4Fe3yx51TRFMFPRNxYbSEthIlDCeDXbbLBpfTeZ7cX2HKH3Fj3GJz6Hwxmr6LvUdRyGY+xfpgmr1+lSz1GfgaX2kOhwlZ4bEy1WF3UEUcBLw4XKBfRBwlFVbDA54arKIwPH20jCcNicw50FU2sim2sgYH/QovD+e5OWwyE83Tag+4MFziYJgwGoRo7SA2nu8Mi/Lc42owjRrLSXUB8kOf8+M1ro3bjLWPEoYrwzaFVVwbTqMw7OUuD6JrkqvjNlt+k1vdBh9P72O/X6FVHRF6JQtJj/Vui0qQY8ceaih5vr9IP4n44907WKx2MVawNW6wH1Y5yCpEqmCgQy7ttpETOXl/UpJ9tePPs1lromn4xH/wd//0a75+AXjbq7nma2KBUEFJtZJSeylFDkZczWZZqXepvmeXl3/+btS4JDgo2CimuJFN86mu5MElB2r5ntZ5AL70kdc7+fSlaXRUMnXJNVjdTkh+4vAcAP+6rHCp3+ajd36GD/dmOZNsc2+4QVOO8Z6r8tJmQnggoWa5eOE0kW8xE5enl9IF7ox2WAi6XMlcMvFyOs9Ah4xMwIw/4At7p/mtYEzNz/gnC5+kNIqKzPjxhU8RCc2GrnIpm6ciM5587iRIp+4rbEgBPHa9jijdScErBVdvrBB5UFTcg6QjVyL1+64M9/JonnbQZzU8ZGRCFqIuIxPy1OAEnSLmke1TvHfhZT6xf47ZcEBmPP6XOz9BX8d8b+NJRsbn84N7uDPaZloN6JuYoRfyzMurrgxaSEppudoLEalC5IKXexEYiI8kJ6JDun5MonJuZNPUvZSv9O9gqdLhMK/wsVv3sjLVYSYa0gqG3Du/xfunv0Ixrdgr6+yVdd5Wv8TvH96HSBVpqog2fdL5kjR0vRIXRwuEPQepLRNXbn6yewKA+ajHQV6l4mU811tkOhxydTRDWvrUgzFfOrqTTh5T9TPeuXSFsfZ5w+p1Zr0ej/Tu4uZ4ir3dOvvSEhwodGTp5REvlvMs1zpUVE5mPHLjcZC5RLYnDBujJumBS4yPhAUtGFXCV/XcuxzEayLK/xPHa2KB0FrytsVrXEruwkZ1Hk4u4wvNb9x4iPGMcPRpY1kL9nhzfAVfGH754K2cSba5Jzjgt3qvo6hbCpx8eva8PhZB7ZV1VoID/pvZDX6v8wAfbD7Gbq3Kh3uz/FB9l48M6hzoKk05Jp11lYKy5lp/x0sl0y845aMOFSMTsFk02S9qnIp2uZFNc2XcZjk+4t3ViwxNyMJih7Vgz7EfdUzFy3h7tMOnRqsseUdczBaQwtJWfScdHzvJs44tohToVgGlQNfcwkG9pH7edyKrKUnYd6i5oiIxHgzKAE8k3Bi3WAi7XOrP0ikS7kx22Ryv8Z3Lz+HLkr/VvklHJ3R1wm/tv5G/0/4S59NVDssqLW/Ai+MlWt6QOb/jMvyeQe0HToAVQtlwEmwTueqJKNyC9YW905RWcrJ2QChL9vMKrWDEhf15Trf2ec/qZRbDDl88OIUvNdc6LZ5onKSr3eR6S/UyTwzvQB15+D2JjiXZtAYDYuAhU4luFYRH7hSlckvntKThj7nca3O1M81MMqSbRUzHI57YWWGp3uNgnNDNI1aqR4yKgKWkyxN7q6zWjvid7QdoBmMMgplwCJmDy3gDZx+wP6qwWO2xPawTqpKbR81jF65h7lNqRSNOQVjUwPWuAOg/xVz/a/PeVzACX/O59btYjj2C9X2eS1f4g4O7eP3MBl8eziKso09PyyFfGJ4lNT4v9ubRSO4KtrkwWHRdmV2JLARlrL4qgkLQ0QnPjFY5V7nF09kK5wcnOJNs85FBne+t9vjZzhIdnTghk28pmho5lqihJG2IY5rSXdE2t/IWC0GHrbxJyxtyw04RipIXxksYBEdFwpV0loWgy15Z4974Fh8b3kEkCmoypbAeoShYURky0JjCGQAJIzCxQSjr6v3gIklgPC2QuZjIfcXXcQdW4yMSmRPKkpEJuK+xyVbaYCtvsFo54lY2Rc1L2S9qJNLtht81/RSPjU5xInDVm5XggJXggBfSJQDeEV9HBQZdL9F1EKUE32KtnUgi7QQ1L2mEYxq+Y0gU1k1cbQUPzd3kIKvQK0MK22KlckQoS6wV7BY1ziW3iGRBRyeMtY+uGnR9ombzDZQSGxh0YFBxSV4L0IHAS91i2i8i2vHAVXlUQbWaEakCUxPU/ZRIFUhhiVVBKxpirEPXvXPqZbaKJpcGs7ypsc56Oo2IS6RnKRMP40MrHhF5BVUr8YQhCgpWGx1KK6mHKePSp+Ln3AyMW8gLiY010v96x/lvNCyC0rx69eVf5nhNnG9KLXnzyjr+/gjTqHAy2GU6HPHJF+45/j/R1oBP9c9xX3STe2JX3dgcN3g6XeUnFj5JcPT1P4rMOVZIdnRyXK3o6ISlsMO9obvGz3aW+PvNDd5XvYIsBDKTxBseMhfIctJkVTq15VbRZCHo8Fx/GYCvdNdoBwMuDl24UVUpsXKZ7PP9VR6M1/nfL3wrK/4BL6ULrBczJDLj/dXLXC9jzNitz2rkdmSvp2DgIUqBGkrUQMLAm9wDx/fiDZ1C0Co4LNyxd6wDlsIjvrh7ilOJS0z/0T6T3VEAACAASURBVMYpMu3RmlQQdvI6O1mdR3p3cV98k2hCR7mazdHRFRYn+oTPj06iUwVaoPrKNVNpgSgEovjq71mWgpqXcWMwRShL2sGAm8Mp6l7Koxt3MCoDDrMKpVH0i4gbwxaDYcQ3N55nv6zx7GiFRe/oWJ/gdTxEKvF2A7gNjCklJneTSBYgSsvXnsqnoyHzsasG5cZjMNFSjEqn4QBItY8nNcPc58Prb+HF/jynq7t8uXMHwzLEaokpJFJ//W7eSWMSL2e6MiLVHp4wDIuAUE3ejFx+9T6HClP+KXIQEy7lN/r4qxqviROEFJZuHlFMxfhHDtEuhWFmpk8aR86CrR5xf3IDg6SlBiReznzU42y4RSQcCl1mk6O6+SrD8Vx0k46uUFjFG2Zucjrcpq9jmnJMU47p6IRdPWRWVRzarFaSWcehtL5BanXMgJzyhjTVkIWoy0LQQdUMy8EhwzikIjPaXo+RDlkMjlgKj4iE5v75TebVgLdULhEIzZJ3xHNFQl1kiEhjc4nxJTY0mBhkpcCkHtoTyFRiKyWyDI6xdEI7LN3tr5ueSy42/RGRKFmsdGl4IxKV0Z2NHahXjUhkjg4ER2WF1DgBkbaSBf+IQ11FYWh7PSoip6MTVFyii+BYtSmjEjNpBRe+wRYSqywVL2M6GhKrgkgWLCVdZvwBU8mYk9UDOkXMXNhjJ6tT8XLaU31S4ybujN+nLtNjhaLxLNaz6Ipxpwjr3gMVaGfKI0DHAllaTlb2uTxsH6P9pLAO5ReOCWVJIEsaQUpVZcxEA5r+mLnqgGbgSsq+0HjC4EmNkBbhGYz6akK/FYw48hN3CpnwJAwCTxo8aZBYRGiwown7Ukw6YV/F+POsYvxFjdeE1LqRLNqH7/0QN769QeXhfXpPTdO8BPXrKR/42d/n5XSef/+7b6W8a+TaajOPxuMhhUtU4/fhn/3DD/NNcYdrpeYXD1yi9uZ4ivWuoz1JYekMYuabfTYP63jPVUlnzYSS5BqurnzgZ/n1/jQXRkv8SOtRdnTML+2/3TleZzUOs8p/JGwCjsVNntB0c7cyxV7BhYsrLo/QzB1QFVxSMpWIZs4/f9PvMO99vfDqsKzyg42LRMJd+8Xc8GNXv4+FpMty1HG5hqjLN9efZ2RDfmXrLaTaZ2dQJfZLDgcJjcoYXxoOBgkrUx2qfoYU9rgJ7dnHT8FCih577mQQarxAo0tJlLgJ9w/u+QNGJuBm2uJMsk0kcl4XbtBSBb/bv5cz4SYjG/K/XngfpZZMVVxokRUeZ6d3eezaGkklY7HeoxaklEbRzSN2/nCJ8Z0ZlJKkNSIKCjrdCu+7+zku99ssxD3+x7nPsacrrOdtdgonp/70zt1c35lmcabD3zvxCJ8+vI+9cZWbR008T+NJw3Kjy0vbs9w9v0M5CQ8MgkC6lu/zf3SG4EyP0VYV6xnwLUIZ/tsHH+GwrPDHu3fw/uXzfHTzAabCEbf6TSKvZOugQa06phrmFEbSH0csNzv8/ZUv8NH9B7k5mGI6GjIdDvm3b/zVVyy1nrl7xn7HL3/XK5ojv/LwL/6XC4wxgUQOMsZrOfrpaYq6JRhYsimfrk74yAuvR9w1YrbVY+egQa05QhYBwgh04LoX7w12+bW+69P4zM0zfNuJF7mjcsB81DuWT5+d2yXyChI/56XNBDyL9i1WSkyt5Nf70/zt2gFPBlukVtIzEaVRPHu0xA8vf4nnx8v/yd4JcP4QJ5JDvrhzknumdqh4GRfksuuZAIJmRlkoKtWUwa06STXjm5JbGKAmUzom4M3RdeaUx/N5wK6ucbOY5t3Jy7yuucGVQRuiDqEs6RQJe7rOy+k8DzVvsJ3XWYgdQbpfC1mJj+iVEX7L8FJvlulwiLGSipdxmCeY6QKhBSousVoglGV2qk9WelTDjEEW8s3Jy/Stx15UoSZT1ryc9TLguXyG76ld4NB4fH64xIPzt9BWEEqNLzWZ9rirssNgJWQh7nK51yaQmgcat7g4mGd9bRYv1Ii4YLY+IPYKjJG8f+orXKrMs+If8NvdB3mocpWz4SZrwZ4z+DE+g9ZNttIGhfU4VdmjFQyZm/R7Gys5Vdmj7qesxoeOoCX0MflqO69jToypxSlpM0B5GjVhadwdbRAIzV3RNgB3NXaJVeGw+V7OfKXnWJ++02ZkdY+FqMtBWeXhxjW+aeoF+iamJsf821fx3P81k/IVDqME7B4gVINkSzCyoMaa4bzHV7pr6EyxtrKHNhLd9ekXkvbYtSbL0p0gEgGPdU/SCuYoSwdH+f1r93BXe+9YPv3Q7E1eOJqnnwWEBw4zVzQ14aEksx4XRks8GWzxhjDg06MKT43X2MuqCGH5xOE5fGG41G2jpKHQiuWaa6yaDfvc7DfJtSIvPa72p1mqdPAPPezqGL0fUgiwY8WQiOBQMmqEvFTEXMlnORXssl60eXN0nY4puZivMjQhu0WdwkpujqcwCPbyGplxD+dnj+7hwsE8989ssjFqMC59Cq2YiYc8e7SErzQnq/soYbjan0EJQ+Ll9IsIWwroOxm7MALjG7pxxGgQMkpcI9oz+Typ8Vnz99kspzjUrqfhsKxyNZujsIqtvMHFo1mmojG9LCL2CySWsfYxVnB90KLmOwjQy3KWjWEDMZboLMSGhm4SsVdWGR7FfLZ/Hw9VrnKgqyQq46V0EY1g0e9wxYQ81Vmh6meUVvLxvXMkXsHeuEo3i/CkwQKllRykFQZFSKDK4xNEKxhxmCfobkB1Lmd74GHKgLKRY1LFlwd3UlUZt7IpPKF5uTvLydoBNzuuelFoSeBpfGlQ0pCWHodZQtXL2Mtr9IqI++s3uTpuA19+dc/+X1cxvvFQmYGVadRuQOehjPBGSDqtqG0UvKmxTuO+MZ9+/l6SxhjVKNCpYrQwwcDnLll3W2W5Xk4xc8bZ3N0zt82oDJhJXJLuid0VgglklUkpU44lRdWRpX6k9SiplXx6VOFbkoJDvc/cXBdfaDo64YneGsvVDqn2CCamK4PCJcVWah0CWdKMx4wLn8OsQjGXQy9ANAqksthKiZCWvGWQyhKJkr9ZuY4E3hTu0jWCy0XE91VvsKNdIswX8MbmOnNel1mvz3bZoKUGLHkdHq/fwQujRRbiHkMdILEMypBG6BD2N4YtPGk4kRw6kxk/pR0NuGzn8WddLC6ExVoXW1dqKb7SlFrRVj1qfk5qFQ+G2xQWHgoPAXgmr7PmdTkwIZf6sxgEa/XD40pBKxiyNaozl/QZla7EWPNT/JphPZ4lqGfHuPw4KPCV5v7kBntlnbbX443xNU76PfZ0gMKyreusVQ/YTuu8qbHOmWiTz3fvoeJlXGUGiSX0SlrBiIO0wnLiTnWhLBibgFjmzIYaWSsY5AFBM8P3tesWjiRvrl6mb2KOyoTvbT7Bz5XvJlYF97bdieJar0UtyGiFLt+zl1ZZqXT4jvrT/MHgHjZEE2Mla9HBq3ruHXLurxeIbzhMILGRj04MXqjJW5rKljPZPRnu8uvXHiLY8jG1DD30mJrv0fjNOv0lj9G8QPscqyzfFXX40fPv511rl5kOh4zKgMvbbRCWWiVlpjpkNulz8cJpxkslaigRk2TYjo7pmYinxmsc6n0+UDviDU9+P6VWnGrtI4XlRm+Kw04Vb6J8PDe7BcDFg1numt5jfWea9975Ernx8LcC57StBVpY1NjV9L2BxLZc89fF/D+hzDQZa94Rz2WLRLLgVy49jDGC+Uafw2FCMxlzf2uDqsq4MWyxP67QGcX4SjMYRpyc2+fSwKn8huOQ/WqFSpCzNaqzP6jgHXqUfYUsBTJzcJThIhTDgKQ5Jst8Hh+fdOrHtMFacsBycEjLG7BX1olEwaVsnueHy1w5nJ5AXUtCT9MfhTy4dIvrW9Nsx3XKQnE9yVhqdLm83ab2os9w2aHbdpZ8GvUhna06v79wjsu9NnNxnzPVHT49ycoeFBWm/SFP7K1y0K/wzOYSb1+9yta4zu6wSqkdezIvPUoj2e65xFRpnFFQIxhTWsXBOCG4HLNTSoLrIblvMRPk4CdPvI7CKF48muPKYIZrR9MkYc7mRgu/klOJczrDmE1VR0+8SceFz6/It3NjNEUgS64OZiYJ08+98gffvvbLnK+NJKU/a99S+072vuceBt8+oP3rMUG3JNjosvFTAT969jP85Me/j2RTInPnZCSMc4uOD9xOe88/f47MeFzuzfCLd/06P37zu7i4P0v2XBMTcNyVKbSzbzM+VHYMacMl7qSGB/6HpymNYi+r8rfmzvPTl97Nk2/4dxzpEb/RP8O/+sR3TB4qi8zc93mDiY28PwGVZIKg5whVUkPQccY7ZTIxmsknoBIFJ77nKu+efpnl4IDDssrIhHx+/wwPNG/x8mCWipeTaY9/s/pxCixT0nVs7egxz+QzfPzoAT755ftdFl3ZY8GOKFy/higF/lCQtTR+V6JjNyGiPQnWQVwQoFKnrbDS3ZuwMP/eW7xv4VmMlWjE1+kodrI6Z6tuYfyZL/4N9yaKyetLixwqbOieq3BPUcaWsqFdd+yOqxCZCQXKGwnKquXbv/lxFIZQljx2sMY725dREzPg39s4xwdXXRPa2XCTvbLOT3zsB9BVjcglCEfNElY4bL1nQVqwAjGB/GCdCMobCYq6c/+WmcAbC0686zqH44Q7m/t8YPYx/sHn/zYik9hEu+/rKVfN8ScQ3MxVnd5y7hJbozrjwifySo5GMc9/1//2ipOJU2dn7Xt/8fte0Rz56Nv+r/9yk5SOWeZ6Hny/RGbOSQlPUZSKea/rnJ2New691Fmd3Xa8krlhrH1iVVBoRVu53WTQjQkLgfG/xlOicN9vSqeQlKXTFiCg7qU8e7SEmCS4Sq2OHbye6K2hMgHWGefeHioTWM8JrEQpjklFMpgQjgLBbYrqbSKRN7LoULA9qHGj2qKhRkSyoG8ijtKYROY0/JRWMGRj3OSZvMpbooyByRhaQ0s6JP6tUXMCxBXH2DWrQI0m5V7rWBai/OokscrgjaGMnAeHDu2EKjUxrdHuOkejmKvjtisT+gN2sxr3VDeJZMEXd09R81OWwiO8nivzWQmidGIvWQinBo2dK5X1LDKVmNBhB9RIIEI3gWXpJuqLnXmmIneEX0h6KGG4Pp5mLuzRGcU8cnQagAcWrvNSuuAs+VKF35+0xXtuYZC5QEcWJMcLufXc+yvzCVVrsiCK0j16h+OE7iDmxXKOw+kqcuxYmrZ0+pSgIylj664r3KZQViQ1P+WpzjLZMCCpp6TpqzfG+OsQ45UM30e0mmQfPEJZwWjOo/nyCBt4/L2zj/IzG+/lTe98kad/1wmnBsuSqUvu5JA1HWz1wfp1bmTTPNxe50PXv40PLf4RLMJ/f/DDzqlqcpIrZlzjlTcGHbqGJx24XWgnq/HDy1/iE4fn6OiEU619fqN/hid6a/zS6iPcMXX3ce/E7dxSuqDdjl1CmRjCfcVw1WIFBB3nsZk3XLUF6Xopbp8ovnv5AndGO+wUjWPp8bvmLrNb1Nga11kftFitHHEln+WX905zf+0mIx1ybTzDj81/Gj0rea6xcrwAIS0iUxTzJWKsMAJHoIo1JdLt6qWgSCb3EjqStVWQN+3xdWQJ71i4zul4lxtZi628wbnqBlfHbTpFzPuXz1NYRWp8yunCiYX8CYvNQJlo543pGdSBcvTwZgGZdIrQmkUAxjeO22HgXe1LXBzOsRD1+NjVe6msZJyID+iWMQ/Mb3C6ssvIBPyrjW/hQwtf4MPVt4FvyCsC4Tn1qQg0OlOICeUaAaaBU6YWEpMpZOGgOya0qLFbKE42D0irPu+eeQmJwTQKKBzp2wYwXp78bBNtRl5xpeHrgxanZ13lZGdcgwZcexWP/X8OOYjXRIgRrqzY0x/4R0y9XFK52mXll27w6fPnnB7fCua+JFC55V/+1M/w6Og0VZXyU+e/lVZzwGr9iEHhsOo3O03ioCD/VJvxrNtNHvmR/4Prpc+lfI4V/4CmzHguW+SldIGRCbgr2maraDLlDfn93ft4oHmLrbSBQdAvQp5+7E5UJsinNNe+++fZ1UO2tSK1ioooaUhNRUiulh4nvIJPjVY55e8yp8Z8Yng36+kMa9E+S/4RvijRVnI1n+XdyUs8na2wmU9xUDghV1VlLIVHrKczDHXIYZ5wX22T//vRdyLHEtMsIFMgLH49R2vJc+/+OQo0+9p5NO2Z8LgkaaxkvZjh4egmhyagKXMOTMh6McNhWWXRP2K7bBKIknfEV1kvGyyqPkpYzqfLXM1mGegQjeRUtMtGPsVIB6xF+4xMwP9z9SE+/LpfprCSRJQoYemYgNNewWMTcu5GMcVasMe5oMd6GfBPr303//XSl6nJMaf8A3xheC5b4MV0kW4Z0/DG3EynWAy71FTKyATcHW3yz174DsajkDL1WFw85Jfu/lUKK+mYkEiUKCwtVbBeVllRAwwuwevjEti5lfybvffwnVPnWS/aGCvwhWanaEwEW2N++tp7iLySnzz5OzRlxoV8nnmvS0cnxwQqcCXVphzzsf79FFYx0gFVlXEi3Ofvnnn0FYcCzbOz9h2/8AOvaI587J3/519JiPGakFoLDeNZS9ApsEIQypLmQg81VMiRA776fU3PRMfcgfuWN7lrao9IlcxEQ+d4VSh6wwgdudj2NnR0xSuY97pcyWcnPgOSO6MdZvw+t/IWs36Pttejl0Xs5VUuddtk2uNGbwoT2uPd/7bicl5pTno5NanpG8m2hjmVoxC0VY/U+uyZkDfGV+mVEaeDbRKR0Vb9ieKyz9D6bOaufHnbfm2gQ66O2wx1yOaoQSeLuT6ehlBjYoMKnHEQyiKV280OjXvdOeWx7MXUREFV+KyojBxFIjPayiMRJYmA9sQp64HoBqf9fd4QrdNSAzomYMXroYRlT8ccaAeOyYyHwjh+g/EprCL7Go+5liypyYIVT7KoFIsqI7UGX5Sc8NyiOC1HDI1lUWW8ZfoaD4Y3WfI6pFbhYzkb7CCxDHXoyqdjJ46SwikW+yY6fj0vKvEnrlm+MNQmvA8pLKkVBLjTQ2oVhXXmSB0TEAnDW+qX2SynWPUPeGtyhWlvwGJwhLGC1LrSbMXPj7UTp/w9VtSAQGhS41MR+fH1wRGpC6MorKJXRuT21R3Ib+sgXsnHX9V4bYQYOIow1iK7A9aH04R+SWeqIL4eIKzF+JK6THk+XWEja7JaOaTupWykTZ7cdr0R+SjApgo1yQOo3AFmT3gF22WDU8EuN8smTw9XWQi67Bc1FoIOj3VPshB16ache2nV1bq1x2GnilUWE1hEJtnWCnCLxL4eMiMDvpy2OeXvgSnZtD4jG3Ipn+LecIOeiXhn42X2dJ0V/4CKKLla1jk/OEGlnnFQVNBIFIahjolVzmHhIKr9Sfm0WzhfSxu4BMzthqAi9zBDn33tk9mCGaXY12NOeAEvF5YZBStq4OC8uuTQJNTkCF84aMyBqVCTOYkoJ9zGCqnNKKyHFOZYkh3KEo1kv3RMysx4bGRNPGkojeTQeLRkSd84IE1beTyRJZz2u2gLgdDkSA5NwBnf8PpknevlFCteh8JKXihmqMmUozKhohy2LZDl8WLkC81hWSXP3aMahAVp6bGnYxSWXV2jLl2z2O2xo2NyFJFwtn0dkwA91nzX81GTrny76B3R0QmHZZVIFqS5TxlJOjqhkBmLakTfelzK5pnzO8dJ041yilzdXhhiDvOEmXBwHCa+4mH5U5nt/GWO18TdCQvpvEZoi60lrCZHLFa7eAe+64/wHCp9aAMeSq5yf+UGdS+l4Y2cT0PV6Ry8sEREE0t33+HKARoy4o3RTdaLNiteh3PJTbo65lS0S7dMWI0PWQ0PqIQ5gdIUWhEojeeXyEweJ0dTq5DgFgdVYWQLFO6+Tvo+8yrj/mCbs+EmicyYVgMujJY4G2yxV9bpT3oQVqJDEpFRWOfG3StjQlliJpOj4Y+p+DkVP6fmZdiRglJgSokZ+phSIifOVTmuyrCjDUoIXiwgRzKnYnZ0zLlgm5ZSnPHHNKVHU3o8ObqDk94hqVVk1vEqOiZBYY9hsgCdImEnqx/vYKEsqaqMzLiFQgDbZY2XioYTtwk4NA5g++h4hUjA6WCHk17O0Ab0TUlHJ0SiYFtX2dANVrwOCsNhXuHacJqttEEgnQJyUIb0dURhFfnYp+wFFLlHXipaMiVHsV026ZmIzWIKYyWXsnk0gr6JSK1PRbj3qClzNsopLmaLPJ0uMzIhm+UUba9HZj1S45MVziF9ZJ33aNf4pFaxU9TZKZoU1mOvrLNTNChQRLLgME/oFRG7WY2Bjng143YO4rV8gnhNLBBWQXJTkU8FiN6QzXGdSwdt9LyTtnqpJa8r2mrIFwdnuJLNcXnYZmMS5x6NYmpRdmyHJ78GDdiUOV2THjMkOyZip2wAcCObpqZSXhzMs1vUGRceNS9jueaENv5/0L5bESUja0mEf7xItL0ea96Aq0XB0Eieyec50FUiobmUz/Om6lUuZIus+E5Ek8iMJ7prjGxIVbmfL54YbUwHA6b9IeOJSW02gcxyu4w5+VoFBp0rRCbdPRmPSFg6Bu6c3HPXpLwu0FwqptnRhuulT2o1HVOyGhxwPluhY2JS67FRTjmTXuswcOBERrHKmQt7THkjQlESypLM+ISTX7A2kkgWrHg9UqvoG0VNSFLrcS7cxBeCjbLJrdI7bshqqtExdr+t+myXNfa04y4sxl1mwgGe1GQTHURNpYSyQEiLrBbEcU5eehRId+yX7tRzuzN1xT+gsB4+morIyZEkMqNjAppyxLnoJk01omMSfFFyM3dGxr4s8TxNaSTaSoyV5Ej6JqLhjfBFydCE+KIkmbymthJPahIvpxWMjkOTVzNe6wvEnynEEEKsA31AA6W19iEhRAv4f3GOPevA91trj/6ka8AkQdyHvK5IagmJNyS72IDEUrQM/tBQJA7Tdm08w+uqt/jVp9+OaOSszR/w+tkNLnXbCGVoT/fRB20GJ1y2fE55fGI0x4XREpEoaKohJ4J9LqfzXBm3uWGnWIo6LAdOJXjbl2JQhJyb3eLJ5xuoTJAuaBqTnMOX0zYKS9vr8c4IjrTkkWyFd8XX+cPeWd7XfIrUKi6nc7zQm+dsbYeKzLlZtDgX3UQKw9CENLyxO7KnzUlDkWYt2md9NI3E0s0iVqsuvJE9ia5p5EhicokNNUK5OLxjQoYTr46nsyneHW9yPmsSyYLP9e7BbzzLga4ytF3W8xmuZQ7VNh82GemQzazB97aeYGgDajLlarZwrHk4LCsUwZCFoMtRmbCfV3hD4zob2RRZ6qMwXC1aSAyB0FwpIh4Id/m9wd2cDrb5+NH93FfZ5K3JJT41OslHdx7kba0r/HFxmvviW8yrLlfTWTyh2c+qzIQDbvRbhFJT8TKOCgegvd0pOehHeIGmsJK+iVnxD+ib6Dg0MtZHMymnYvGFIcCwoRt8/OgB7oj3uD6eYTHscCubYlAGLEcdUM68pzOK6eiEisx4bHiKhaDDPdEG63n7OC/T9vrkVrGZuY1mPuox1sFfNdX6L2T8eeQg3mOt/VpDj38MfM5a+y+EEP948ucff7UXLesGUc+xvYAyluhAcG+wzZPBHQCIRs7sdI+16iHrgxb1MGU3dKVPF144LwuAVe+QnaDJw9EmIys40FUGOmQ5PiIUJReHcwzjkGbsyMizYf/r7sV6TnxUEZI+Lnk1tAFr3oAjLZlSCWv+HhUhORXtMS1HnPA0kSz4jvZz9E3Eknd0TA/Kjce0GtBRCbvUiFVBIEsSmZPInGYwpua7e1mOjr6qY7h9P5MThVWWRDgvCoChdb0TB1rQVCPmVcbZeIumHFMROYvemEgUdHWFvoxoqDE1maKEIZIFhXFx++lwm/W8DfBV9gEuxJgJhsdJSj8oSa1PTY7RuF13Vrnf3VqwRyA0r6veYi3YwxeGNX+fxMsprGLKGzKvvgp5La06fq1T9f1jaK573QJrBKZwtmgyKmnKnEi61vTU+gRCE6AZ4Vq5by8OGoEvDNNySCBL3pG8TGZc6/aJ6ICtvIE/CWmMkUhpJj4fIxaCjvMIKZr4QhOIEl9ohiagrlKa/tixLsqIisr/VCcI+xpfIP4iQozvwhmEMvn83d/wJgro3F9QWx9hqhFLcYeH7r+MuhlNqE6S+ECzpyv80NSjnA63+Y6zz/Pdy88eP0ibvTphUHLUT8ib4tgO7/nc576g4AcbF/ns6CQ1YanIjJEJeH1yHYCzlR0qMqPi57w0mOPJfWcvcPFgFus71Lwo4WrpUZOGRJQ8HLqQ4hOjFb6UGt4WuV/lDzYuEgrNoTG8p/oCL6fzvK/6PFeKWWZVn5vFNO9tXSSRGevpDGMTHIcUt9ImTw9WiaWD8dYD5/4ktHCLgpwo+bTAjwsINZs6JBSaofVZVCO2ywY7usoZv+TxdJH3Va9wtw9vCGFOhdwfwKXxLB9sPMldwTYng13eWb3Idtk4DgMiUbBVNOkUMZujBvt5lZ2iTq+MqHtjro+nyY1Dvu+Vda7ks9RFRntiYNM3iufHK/ii5JsrF3lP1GNPV1j0+swEQx5KrtH2emzrBi2VcjLYZT+rcK03za1RE09qqiqjNIpOkbBf1DCpQh34iLEimris+2heTJcorMdBWWVoA86P1gAcKLd0zW7r5RSL3piqyriQLeELzWqwz4zX5621SxRG0S0ThqOQyHeuWb7QnAp2WPKOeGa4QlfHNNXIgXGzWcCFP/0yZL3f4tao+eqTlPz/HxhjgU8LISzwcxMrsDlr7dbk37eBuf/UNwohPgR8CMBrTOEdeWRTEO2nRLLg+e0FV2JMDCpX6EgQTWzhIlHw5Z01lmsd5uM+C5OW3844ot3IGIyrZFNO3Xd3kHNoNB3jMe91SaRiXvWY8QcMTYhBUFUpba+HJzQnr2nfGwAAIABJREFUkkNyrQhkyV3TezylW4hSUCaGE16BQrJpfXZ0BkjeFV+nIiRHGqZUwpNZzgmvwBeKC3nV9QfohKYaMrQBK/4Bz6UrVETOanjIVt5gUAaE0sX4J+M99osaTX9Mp4gdDUpaZCkwEy9Na3EyYulKhwbI7FcTLyf9Hl0DTTVkvQw445cU9nZuQlP3Uq6XdXomYmRC8lKxFuyjMEzLjD0ds+B32PVrGCupeSlzfo9DUcFYwYn4gL52BrvzXpeeiZz9HJYcxbLndn2NZL1oojhiXmk6JmBzXCdt+oxMyKy/T1NCgWImHJJqn6lgjJmQuCte5sBBfh8ZanTlqwK1hhRUZMaZaItEZKTSp62GnAidnZ/yXAm0Jkoi4SLcTpEQxQV9HdFQIX0T0TcOY19TKVHsDIsSmREJfdw8dk+yiS80EkMkCu4Id49t/Rp+SiA1oSyPyV2vePLY175Q6s+6QLzdWrshhJgFPiOEuPi1/2ittZPF4z8ak8Xk5wGSuRWr5zOivRxRGrazOg8vX+fxx89RJhIdWGRmuZgv8Nb4GufTZd40e4M7kx0ePTrFlaNp0tzHGMnhbp1aAN5QEB1Yfq13F3+z8iJnfMVHunfSVk9xKZ/jC3unWVjscFQkpMZnpEO6ecwXd06Slx7NeMz6zjRe5tR24b7iU6NV2qrHyIa05TYvFbP8Ye8sp6I9frBxkSeznDeEAX84jsitIrU+W2mDiij4ze59nI53uDpuczre4UK2yJc6p+hkMYVRdPyErPS4MWy5k0MZEEjN9XQav+v8OOVYoVLhGsAyDztWfHZ0krbX42o2x5lwk2fHq1zO5vm26vP80eAsPzHzLLdKTVM6vUYk4PGDE3yw+RWu5LP0TcSKf8gf9O+h5Q1pez00kke7d3K1P02uFRuqwa3QdSzOxT0e3TtJ5BVkucfvdR5AI3mwss60N2A9b7NXdnmiu0Yic36gdgVQ/Er3LCeCPUZlQEcnXM9muJ7NkFcusZ63Ob+3zCj32Q2qPNi+xY1xi51xjUx7zoJvPyQ+dJLnYSPkfNbkUj7PI0enubu6zVGZcDre4QuHdzGcdq3yvtAs+Efsl3W+qfoCN0ZTVLwVNsbN44m5Gh7w5f5JAlnS36miZ0Y8O14lEgV7ZZ3U+vzm5hu4s77HmWSHnaLOteE0b2jcYDtr8OLRHPu9CjP1If3y1VGtQaD/FKj8v8zxZ7o7a+3G5PMu8Ns4h+GdiUnobbPQP9HW6/YwHjDwGS0n6MTnTLLDVzZWKeqWbKEg6hqs53arq0WLN0Y3+dz6XXx2725mwwELtT61OENKw51rO1Q2DWXVdSi+Ob7CetngxdxwR7jHfYHgW5MNGsGYtWBvUl4ULAZul7lnaod6lDIufN5750sEPYh3QCeWU/4ugdBsFFNcLRvkVvG+5lO8I3mZzdJywiv4w7Hk3bHh4cjZy+VGsa3rTHkjBjpiJTrkRjbNkn/EyWSfepDSS6NjpNl9jU2MlSRezl5apealyAKiPYE3EAQ91xtgMwnSMu+5OF4K46oQxudMtMmeSZjyhvzBOGJHx6QT+8VLxRShV/Kl8SmuZW1eHC7w/HiZOd9dp6UG+KJkLnQAmrTwsFbQjgZIYdgZ1zndcAK1fOwz5TucnUFSWI9swpCQwjAyAb87WOGZPKblDbiet9ns1Xl5PM+tdIpIFsyqAftFjVqYYYzEk4YrvRkOsgqeNGgjafojTGQoKxZvKCgLV2IsrOJksk/LG1JVDvm3Eh9Rl2OqKqWhRiz5R8x4PW6WTXpZRGkkG8MGl4dtbqVNLo4X8ISh6uXISsl46MRae7rOi6MFrmcznJkAZEYmoDSSlfiIUBZsjuvEfsFSq0volUTq1TtrWSte0cdf1fhTnyCEEBVAWmv7k6+/BfhJnN3X3wH+xeTz737ji0GwrzBKI8clW3mDolAUsyVogVECkwhOeEc8lq65G/dceaniZRylMY0wZZgFZKVHGQssoEpBS+YoLB0T83C0jrYe10tFzc/o6AoLQZfz/VWWQmf5VvEy5+mQVciN57oyA1dpmVNj9ozh3nBjolAcklrFCc/lHHyhyK2ia4Y0ZIwvNN8y8wKp9bkvvkVqfbQVXMhdHDzlD6l5ddqVAVXfUZlb3pBDv4InNGno0/KG6MiVgnVsKbRrxMJzXgzzqueSk8EePpqHKteIREEkCt4YX6WwHoks0MC0shT0OVPbARwTMpE5iZqoO03gcjEy45Y/7ViTXkHi5TR9x48wVhKrnHowxgtLZv0eUZDTVCMCoTkZ7hIJzRvr12l7vclxvWDe69BUQ9rVIVJYTsQH3B1tuDKkyqj5KWVN0ggcs8JYQWY8IlUwG/Qd/StyPS6er5mWY1pqQOErt6D5PZpyxEp0SE25e62p8eS1u9Rlynylx3sbLzDQbqefD50PaSsYUvdSvAkjYsobMu91uCPeI5E5IxNQUykNNT7+c0sNjmG5qfapByntYPCq5tB/Dr0Yf5YQYw74bSHE7ev8hrX2k0KIx4F/J4T4u8B14Pu/0YWsgIW3bpB8GEw9oeGN+ZH7vsTPP/sOao/EqMJQuT7gkfGd3BVs83S6yj88+3kAKjJjYaXDz/zut2ECSzloUlGWxhVHQH40PcHZYIvcKn5q+1v5pqkXeHxwB/9k4ZPs6Zi9ssZ/N/d5IqH5lxe/jQtyGf/Qo5jL8bcCQu2k4EFH8Inh3U4+bSJ8UfJCtsTldI5IFryn+gIX8iqp9fnpw1P4QvPj05f4se3X8/72Za6WHitewfmsxY/M/iEAv/Dc251Tde4hpUXnikfsafy4oBgGCGU5768SThL6KhXIgknHokKNJJ8a3McdoTukzao+T40XWPCPqKuUT3fu497KBg9E17mUz06UgCVn4y0+WLtBgSazhufyOn0T87rQTdiOTvjFl9/iFunMQ3mGC+E8RaHwfT3hP2jKg5jM+OzqOvX4FkPjsVfWuJlP8+xgmbsq24x0yJ3RDi+OF3ldcoN7m1v8T9NfZrP0WC+nUVjeVbnILzz/NophgIo01kCzOaTUrpv38uEM/p6H3xfoCLKRz+eHZ+lq589pfMnIBGzld9MpEwY6YjevMe0P8YXm/2Pv3YMku+77vs855z77PT3PncXsC7vA4kWAIAASpEhRoiiSUqTIkWWpVKVIcdlJquSKk6rIjlR29ChHiUt/yZISJ2FFthzJVDnUqyRKMEmHD0EkCAIgAS6A3QUWuzu7s/Ps6efte++555z8cXp7QYmisApdRNk8VVO1Ozvb032773n8ft/v57tVdHis+SpvaV7ni5NTfHfnHGeiXT6bncE6ya+/+g4Pj7lWwy6XbOZdv4uTpY8TnHaoBwV5FDI2MZMq5lDX+OLuBv1hDZ0HyMByob18e3eQm9WT3sTjrz1BOOcuAQ9+je8fMAsIfcNDOvY+eZTVUwXxly/z0dceAuAHzn6Z//eT7yCYGEanGhwLD/j5S9/HMI9ZbYy5r32Ds+kN/tnT74O2Jd5RSA3RyFG0BcnIciFfo29qPLF3Hz977A/4aP8RPrt1yideBQX3pdf4L579CR5c25r7/N2xKQw97CXqO2zkXZmX8yWeH9/Be9oX+Pz4NI81LvHicI3vXX6BPxg8TGUlN/I2pVV899KL/NT2W/mltef42b1H+RutZ3m26PLU5E5+++LD/PjdT2GtRA9iiCzWgUorBKCzaN73l9ISHzqqmvBY/grKJiQ7ijCDV7IVzo2PsBqP2Jwu8D2Lz/ORG49xurnHe1rn+fjhfZybHEXi5nj5miz4mZ13cjQ+ZDkY8cLkDmJZ8Vl7F6nSnIz3KEuFHsXIpMJUkmkVI6TDGoUZhZTCEYwkH9+/h3ta2/zO/sMMy5S3djZ5YbjOOxcucT7z9em9ssl23qSpcnplnb9z6Qd59+JFVoMB/7z3HYyqmOogRdQrTKHoLI7pD+rYaYCIDFUliXtibmePboS8lB3h4nCZURFjnaCd5IQz0dKl0aI/FqVjekWNRGk+a++msIp/tP7H/O7wIT669zZO1fd59nCDfr+OCizBWKJFxJ/tnuTswi6BsNyYtri0v8hCwxclG2HJflYjDX3cox7E3i/U1QyGtdu+j76FnHsjwwrOfu8FJh9bwp5c5+/d9XEAfvGZD1FbECR9RX3LK/0+fNdv8qpe4LJeZi3wfeq//fCT/Pon30uxbDz6/rqgSgXTRcldyTYPx5t88MRL/K/77+FHu5/nvtp16tInXv3h5CT/7KGPsKbGfN8rJxGA2Y8RbU1lBDbwbICyLTiR7HMm2mbPtPjBzhc5V6xztrnDyCb8l90/ZdvUqAvNtvHFrR9afoWf3XuUn18+xyenNU6FHpH/+EMXWQtG/IvoHci2RU9DwpqmKhVBXFHvTClnqtAkLXGqjjCe4SBLCKaQrVuqwncU7oh69E2NdzUv8rnxab5r+WUeTK/w+4cP86PLn6MuShJRMZmpJH+v/zZ+cfXPeK0yTFzARnjA5XKZE9EedeFl0WmiMVphpwGyVpGk3j0aRxVT6bXnlZacaewyqhK+rfMKkajY0W2+a/El/mTvft61+ArrYZ+N8IDfP3yYpcBvyX9m44+4WnUZmZT/dvUTXNTLfKFxAkYhJIZpEdJo5ORhiKkU9bSkSCAaeH/NdN3wWPMSqSy5nnc4mvTpa++HuDha5pHuVW7kbTphxgPN62zmXR5vvcKTwzP8Zv8xjJN8cPEFXitW+N7VF9jsd5DCkcUptm44u7DLvY0txibhZLrPtAppR1PW0yF9nRIHFXfU+nzRbJCnEVb4OIDajAj+Rofjza+DeHNMEIHjuWdOc2qhINo85E8HZ7jYX+Y9p1/h2WceIMgsZTukq8b8yv57Z2amhKV4zAc6L7BbNrGJ9cEz+COL8DAgzkY32DEN/mjwIO9vn+Ncsc4nDu/lHx55gicyX63+0/HdPF6/iMsVUadAC5DKYYSjmi0KJoKjoS9kboQHXNZLnI1vUJclR4NDPp8fp6Mm/JvB/SwEGfen17hUBfyN1rN8clrjfalh38CZaJcP77+bH+4+RRJptFKowCKlJU29K7HQAdYJnBEYIzEtL/6ykQMpqFKHMP7IcUfUwzjJmXibg6rBe5ov0zd1rusFPtB5gd8/fJh3Nl9hr2pxKt5Bu4Dv6zzHJ6Yd7gwPWFMFX8qPc2e0Q12USGFRztFICl9hr5ekkZ6BeiWVUaRpiQPGIuXu2jZ1WWCcYGDqnI536JsaH1z+ConUfKp/lrvq2xyNfaTfiZq/difCfWqR5nrVonSKpF5iEo/xs1YQh140FdVz0lAzqTlMDFXDQ1tCYUiV5p7GNmMTcyz1StjHu5fIjM8oPRnv0TN1jqf79E2Nu2rbvLt2gW3TwjjJUjji0nSZJKyIg4pR3SJiS135ulVbTX3Rt32rzn4kGdCNfB0lCSqCuMKFliA0NJJb4q43Nv7jUFL+/x8O1FqGGhuckhxN+jSXcl4ZLSMqcIEgmPg+/kP1q1wru8Q1H2VXF17BJsoZOk77m0lWnhbVtynrasR9tetsV22a0ivgklmITVPmM5Wcf/xKK9xU4eqVh62EXnuAxCvsVEZdVESBwTrJpu5iEBwNDpm4iDPpDmPjjUI3aw6nwh77BpZUnfPa8u2t82wEmYe8SotzfiXRlZqvKDdTmoyRMOue2QCIPSnKBR74IrGsh4ckUlMPS0qnvJ4hyNitmmwkPZYDrxOpixJESUfmLMuMjUCineRtyWXAy7a9hLzwvATlvQllpYhDRzWLupfC+ectHYkovaVb5bRUTk0U9E2NUBgSoVmNh6wGA0Y2ZVGNaQcZZ6Id1lVJ30o6M5pWEPgCoZIWrUOU9FDbmyBcG3m+h7BAYpjYmKVwxNgkHI39xG2dRDtFN5jMTV5tNeVG2WE5GCGx9G3KUTXA4HU1oTA8FZzwZGzlUJFhORoRCkM3GKOd4kreZTkaU5N+hzAw6fzPcewTyIPAzEHGtzOs/dYE8VcOoRx6EKM7lnjok7U+tnUvaaSp6h4pZ2JJZmM/OUjN7197kG6aoZcCTiQHuMTgygDdsgRTwbQuMREsqwmXqi5XiiWORH0MgoUw47pp8HJxZFblLzgaHIKEeiNnQoKQDtPVqK0YYT0Jyjhv3rlUtQB/ln8g2QTgsl5iIzzg0nSZjaSHcWJec5ikMWeiXc5rr7j8pd4yi2pMGumZg1CilCVUhiTSDLMEFViMlr4YOEPVucrvGkzsfHiu87uavapF4rwL88A0OBoesls10S5gM++yHvbpVd5HMLTpfMXfCg/JXYiaoaTeHh8iheCJ7CitqKCsFM4o4khTC30LL9deQVlWai5aGpmUzMZzF2hT5TyfbXAy3uN67j0hxyKfufrc4BjLwYjLMudstENmY2qy8MDbMqAWa6bTCDODzoKXe7sAzE3DWq6IREVByEIwYVe3aKspq+FgPjndfB69qsE96XW29AKXpsv00gbWSRoq97meUhNISxJoSCxuFqWXu4AvTY6xEGSsxwMyG83rBTVZkkiNmdHAw7RACE/Wvp1xc2F4M483xQThtOSeu64jXouwrRoNlfPo2lX+3aceojEGG0pql4d8fHAf39P5MpfL5Xk0+8XpCg/WN4l3PHouHEiE9cG9SsNHB2/jSNjnwdpVfvHCh/iR489gZrZgKRyx0PxnjUu8oGuIXDK+1iLqScquL1rJ0jMkbSC4VK6wHIx4dnycjaTHFwcnkMJS2oDv7L7MC/kGZ9IdrhaLnCuP8ndXPsVvX3yYxx+6yIf33823t87zS71lfqr7Kp/PDXu9psdxTkIQ3pTVjxzEBmZJXGNTo577ZOtiwXcx4kPBeMODdz8zPst96TW+nB3jeLzPE3v38e7Fi4xNwh9evY//5Ng5j4dzAc9lxwHoBhO+rX4eg+TL0+Pcm1ynb2p8LGuwqMbUZcHl/S6m8gXJzDQQ7RKbBYjIkoUWITxn8kJ+hMvZIg+2NgmF4Qv9k9zd2OEz23dyvrbKaupbic9OTlDagOe27uAnj3yS69UCHxvfz8PpZV4ujjAYpeh+TDkNcVlAz0rv9RhHTFrRrJMkiQ+hf7/lycEZlqMR/arGcjSiV9W5Uba5knW5q7HLXtmgtAFH4gGf65/iTGOXZ/c3OBcc4ZHFq3MQbzeYcDCp+dczDLCJ5EDXOdB1tvMWjWbBS+M1jiZ9RiTsFw1i5aP9dg7ahFFFmQcICdrcPqH6W0eMNzICy5XeAkeOx4Q9398+KOo0z/bIR4uI1xz50QY/tPA0B7bO0fCQyko6Sca7Wxe4WKxSHKkQhaQKLeKcouj6m+lDzecBOLB1/qtTn+XR9DIvl6ue7KRGbKiC8zr1WZmdklqjIGvHPseiC267hok9Q/K9tfNMXEi9VVATBSeW95nYmEU1piYL6qLkXLHO+1rn5qvYj9/9FGvBiB/uPsVGkLGoxnw+N7wjUWysHDLMYyZRjFJeyNSuT8kKv4KWRUC3PWF0cYWq5lt8AGUENvF1iMfqr9KUOaTQkjkfWD7HRniARdI+OaUmCzbCA9aDQ1RqGdkUicUgaYmC0/E2y2rEqbDHtqmzocYsq4BjS4fsjhqUUYWUlsVGxij3Z516XFIaxd4g4kjUZykcsR4eonDUuiXHwgOKtYClcExmI45F+9iZJu/q8gIHpsGKGnGs1uNUmNM3ddK0REhHFFWYuqSR+rjAJNKsNke8crmJiRx517Mh390+z8DUvYEsHFHYkLbKiGXFyXiPbjChJgvWwgEr0ZDlYMR2t8W7WhfJrMfo3ZNu+fevnhGrigtJE2LDYjhhKRyxEGY0Vc7STCcRCkOY3NqdrC4OGOf+vVPSstS4Pak1/Afc5vxGjqgnOP4/Vlz88RbpTsLPffIHWX1SUCsdj/6D5/i3Rx8AK/iFK9/3VdzJcx34w+jtyFLwr3/iV27xKhsf4KFjm4x1zB8OH+J63mEvb7CajPjyZIOtaZtnXjgFsUVGxrfTEsMvvv13+a7aNc7rlERUXCxX+a2jb2d73OQH7jj3NRmS7WBKX9W4nC9xLO7xZP9OTtX2WQgn/J8vfBvWSv5F9A6S2Xk6jTR7vSYbK4d86v7f+wuMy3XluFQFHJg6e6bFO5Ir/JPuh3h1sMT93Rv0y5TSKr5j8TzaKUYmZbNcpFc1CGXF4Szte1f7Qtz5cpXMxmQ2oqFyDqs6v3PpQR9Cqyy5DgiV4eziLgd5ncVkQmkVv3/3R7lhPMNSOw+cuVRFZDbm3mjExDo+fecprpU++/SaW0QKy9gkdNQEPQOt9HWKdoq3pJu8lK9z7tJR/mn+QQDu6uwRCMvV8QL/6qFfZ6tqs6xGfCk/xtl4i67MGbmQE0HJb63ex4VsjUujRX7s6OfRTs25FDdxeHdEU+/hmIm9AHIbsh4e0jd1nrx2itFqwqXBIkpaQmnJdMiv3/cbJMLw5NET1GXJlvZ8jMxE1GRJqkosglBWNGcK0dVwwM+c/piPTTQ1WjKnJgsev83P/reOGG9gCONwwkuJpytuxqF0hCMz51OOLizMuZNaK8KEGbLeX+CbvMrchdx/xxaJqkhUxV7ZoDABwyJhWCR0k4ytccvTqafKW4gBV0rWggEWeLVc4YP1K7xcSt67eIGrjS6nkx2uFEt/gSFZ2IBdmkxtxI3ScyRfccs0g5YXQQ1i38pUCikthQ7AwTCP54xLmCAxZM6xbSSrqiR3ATBk4gIeb7/K0aTPW2bpU2OT8Gh6iYszOI12t7a2hQ3mN0xfp+xMWxxPeuyWTUzoISvGyNkEYUgAYwVD7bcnQ+1l3z1bsqoCSqepq4KmjNlQBZksaIiIhUDRVWO2WKCYsxgkEkevajAxMZ0g48a0Tao80l/ivoopOakin6GpDF1ZsSc83zIUFaeCjKYM6NuCifWgmfW4z2o05OF4kycm95IIjUWQz+zbI5OS21vQG5hxI51iZBNqsZevJ0FFLSyZVuEsw9OSz67h2WiHLb3AXtWkHUwJhUE7hbYKbpLRraJ0AUfVgF3TIBJmDqC5neH45sqo38h4U0wQAHIwRtiuZ1PCnEP5ej6lNuqruJM2gCDzvfE/z6scVQnPbN/B6a5HVYzymE5tyvakSa/fQBSSYCLQHYPKJDaU8y7HndEuEvjS5BiPNi7RVhk7uv01GZLX8w6p0kxNyLiK0FbxWn+R5foYXQYQeZ2DCny3QluPjZtE8ddkXO4ZwFbsGV8ItW7MohqzWBuzFvRZVF7Ou121eXZ8nIUwmxcHtfHouutFZ7bCVliEB70Uja+63oUOCGbHmspKJjqiNAqhfST9vglpSsvxIEBjGNkSAxgEF7RjVU1JRMJhVSOWFYdVHeM8Yepa2fV8STxCb2pCelXDJ23PmJJJUFGagH6RIoSjZwPOhAMkzDtKI1vRkQHntVd31mTJ6XibK9XCPMhnr2ySzjoKN3NBd3Tbuy9nO5pwBp0NlZkzJ+yswKiNpGcSShQ90yAUloFJkbPCbWYjdvMm3ShDCocSlgNd9zSwBPq2xo7ucCrembEvb298I08YQogPAr+Mn8o+7Jz7X77Gz/wt4Odmv/rLzrkf/XqP+eaYIBy4Zg2pITtmCHtyzqE8VjskEIaDg2VY8txJ/xb7p34TsHyTV7kULHEhP8KkilluTOatp3pcoo2iHpX0wwrtYh8uY2epUrHl0WSTvo24rJd5LN7lgdrmHGg6MOl8pZ6Yr2ZI3vyKZUU/rCGFoxEWSOmwDsKanrUzha81CIdSdsa4NHN83b6ZMHF17o8EifC5kBrB+fwIp+JdJjZmU3uj16Ias5H0GJuEwgZMTUQgDSOdkCqPi+vrGrWg9FbkKKMVeAhNnkU0W1OqWYycwBfYBJ6qZKqAEsmOqRhZxekQtHOsqhSL5cslKOHt1r3SH2k6YYZBzluLL49WOVk/IJLeXn4zR0OXAWVYYaykERU0ooJxGbNdNXnRNFgOhpyJdmjKgB1ToW3FxDW4NF32xKnFIcfCHt1gwsCk9MuUoUiIZcVCmLFbNFmJRhjnPR4SH4JkhGSUxzTCkl6WUo+07x5Jb1Ef2YTL+RK6Lv3RQpVkJsIg57urmxb2w7LGWjxkSWk+m3XY0X4y7wa358XAgfsGtTmFEAr4NeD9wDXgaSHEHzjnXnzdz5wBfhp4l3PucObC/rrjTeE1dYFADCfolqO26W/CmxzK1/Mp/zLuJPAXeJUwY1XOgDJTHfwF1qSohE9j0v5N+nx+nI4seUdyhYHN2anaZDZmr/J5j1+LIdmNsq/JkUyUxpQKlVZeITlbrUNlfPAOX5txeZNveV4v8uUZu7GmvIo0c7fsxBfLNb44ODE/h998TqvxkFhqYllxuuYFPv7YEcz/HISGvAiprKQyEmMl4Z/r4ddFRSIcp0NDIgISIRnYnCuV/z19C9erBWJVsRT7GyOWFU3lJ6H1dMBS6PmSr68X3GRKAgTCp6TrGdvygXiLjWDI9apDz1bkzjMuE+Ffz5nG7lcxLf3r1qRKE8yoUEuRLxSG0tOfQukZkgC1SNOMcpR0GCdQwmGsP6KEGFbCEX0bz16Ld4tW1jtrA+mvTyjM3LXZswGhqGgHGath/7aPGPANdXM+BrzinLvknCuBj+DhTa8ffxf4tZsIyJkL++uON8cEoQSuWaNKHeEIdNcirE9AqgWayVYTIR0LyZSoVrKyMiAa+iq+bjqqhucPDkzKUjDm6dd8O2+hNmU98ZNCJ81ZiUfc17kxD9w1XY1IDKZmkXVNr2qwqgJiAa9oX9P4d/t383vXHkRbxdH4kFAYRlWCnmUinEj2OZHs0whKTqV7pIHm7YuXeah5DedFfwRxhZylaCeRxkWOdn3KunJMrOQlDS/qOlerMceCBr0ZkXmzXKQmHM8Oj/Gb19/OJ/r38cTBffz2/mNcK7tUTtJVE2qqnE8A/arGQuh9A8ZJ6srvINaTPu3Af986gRAOrRWTaYR6lvPCAAAgAElEQVS1gmbkuwYLcUYrKmhLQ8+EnNcBXykdGselKuKS7tIUmj2TciE/AsDUhPOj16GucUd0wKhK6FV1QuHzNmuyZFglDHt1jJFMy5BAGlZrQz9JOMXlaoE9k7KpF3m5XODAppzXMYkwWCc41DV+a/cdM92Dfy2xqkhViRJu/r2GymfYOUtXTTwMRmp6gzpSOAbjlP4kZVJE/nejiIShqXKenZ7EIElExY28zdRG1INyhq+TFDYgVZpYVFwsV3yauZp4yM1f48Dg3Bv7egPjKLD5ur9fm33v9eMu4C4hxJNCiM/PjiRfd7wpjhiysNhGQm1L0n9QE/QD8jY0r1dz/NyXPnMXW+1bWLmoI3Ahc7TcTRzdlWqB7z37lfljnx97w1A9LHlmf4OjjQGv9JZ8LmMlfGQbYPOA/7z9Ml8pI14uj/E3G1c5sAUPda5RkyW7uvlViLhIVlzLO1zOFulEU1JZsq+btKIpl7NFemHduzKziHpn6ouTwDBLIDZkRcSlKmBVlWAr7o8El7Sc4+sGdg/SvZm9WvPOpUsciw54LVxmNRzycPoaK+GQG7ozL0g2gpKtrI22ilSVvJitsZyM5+yEwoa0gylmGtBdm5DPnpMSjn6eoq1komNKq9iahdgcmAZ3hgf0LdwdVtSijCfzBseDIe9pvMyvDt9HZSXr6ZADU6eyiv2qxWvDRXTDG+Jqs2sTCh/Jlyx6kVH2uiLlXtXiSrnEkfCQ99dfpivhWhWQu4A9U6en67w6WOIt3S0eqb3GxWINbRXXss7siGfYidq8MlpiLR5gnKSYve5YevR9VQQMywQpHVEw2xEoQ4jhwDT40miDH1v+My7ni4xMQiecEkjDS/1VltOxz351khvTNsvRmEeTLX57+CCbeZe1aDhnarzRcZtejCUhxBdf9/f/YwZdup0RAGeA9wJ3AJ8RQjzgnOt/vf/wTR9OCoQ2XtYcGyAgKHzAbU2WVFYSZH7VK2cf6nDkMJHw/8f4GoQSjq4as523qAd+K5xVkYexOEEyU7pFgSHIBMafHLzBKxAkImDXNJnYmB1TcSI45F+OV2iHOTemLY7VD9nK2ox0TFb5jAuJoxnm7JUNOqEnQdWCkkCYuWW7LLy3wlYSFVjIFcbKWVhNwJ5pkYhtrlSL5C5kYPdoy5TCabZMwYXBCqNawiBJ2Zq20XVFW02wTrCvG/TKGjtZi35QMSz8eTlSFbkJKW3AVuFvopuMBbRklCWURYAzgiAyBMqSFSFKOBywVS1wUDXomToWSSgqQvYpZM71agUpLJt6kd3MX8TKKV8krEJW4yGjIuKGbHGi0eNg1nrdmrYJBpLRQoKzguHM79Eb1XkpX2cr7zCKE95fu8ylykcE9k2dSFRsTdrsDRu8Ei5xtbHIjm5xLe9wMK35DBNp6UYZgyJhq+hgnE9oA38MAWAQYpwkH8UUKiSIfOL4Zb3M2CRcHnd5obnB5nQBKRwXh8ukgWaY+2jHykpK6zti1+MOW1XKZt5la+rp1sVtJmsxQwe+wbH/V0TvXQc2Xvf3O2bfe/24BjzlnNPAa0KIC/gJ4+m/7EHfFEcMYfz5PJrBpNVUUMUCWTq2ijbtyFOV8tKf88txRFXzk4PUXum4N5M/XypXWInHTKqI/jThYHqrsjzMEw7yOgf9BqISiEIijEBUApn7S7GpF9mdFZ1eKNapByXdaEJhAnpljdzc+hAUVcD2pMnO1P98X6dE0nAja7Odt+aWbWAu4TWztmpZ+IlhYm/VFW4eK8AzJmMRks0Ko/VZsfFmqMzNHIiRTihtQGkV5ew83y9SyllrN5glTpWzOkQsKw+9DSukdMjQImd6CGMkUx1grO8aZDaet0xHJiV3klhIdnSb3IY+ZKcKyCufpj6tQion6Vc1tFEUVUCsZiToKiaaneOlcF7K7gQT7SfacRX7TpCJqUlFhJ2na41MyrQK5w7XSFQUNqC0AeM8ZlJ6UNCwiskKv8PzXxF9nXpkHTNPDSCURSj/HKR0Pl/TRmQ6pCaLeRZFHPgogsk09iCiWdDRpAwpTEAofH7HYVFjUsVkN5OabmN8A48YTwNnhBAnhRAR8CN4eNPrx+/hdw8IIZbwR45LX+9B3xw7iEDilCDvQrgZU3Ys8jVBVZMo4Xh+7wiTU5pT7RH9aeLP8brrLcd1KIzg0eQKL5fLGCQvDVZ5++JlXuktcbazy8REWCdYTCasxGMCYbl0dQNafoUxLsDVK14qLe+tXUA7SSggkZrCBFyfdjhWP+RkbZ8rwSIDnfjiZwzHGo47kkNPGQomXMkX2agd0g0mPBseQ0o7t0ob470VY1Oj257wjuQKExdg3RiN4N5oh0fibayTbJmCzFXcF6UsJp536DFwlsIGZDbm5ekR7mtssaNbSByp0hyoOqvpkFhWtKIpy9GImiqJpcY6r4NwtYpQGXRoMEYQRRXLjQmjImal7guODydX2TO3WpfLMsMiGFjD3cmW72CYBuuNAdZJFmPvcJyakPvq1znfWGExmdANfQ1gJRp5EEvkiKMKayXLtQmNoGCqA+6tbXEm3aGpcl7RypvpwkOMGxAJw+cap5DCESlD33j+JMC4E5Mo76c4W99hv93gTG0X7dR8Eq1J71K1qWUxmfBauOg5G7FG4P0sa8GAraUOSljW0wEnkgNu5G2aYc6RhSHL6ZiFKKOyikBaTtQOMAhO13YJheXu2va8EHt7H/5vyC2Ec64SQvw94Al8m/P/cs6dE0L8AvBF59wfzP7tu4UQL+KzbH5qxm/5S8ebIt27nay5d8TfQ+8H7ufgIcfKF6C+VRAcTrn40ymPnLjCM0/eTTgQhGO/a4jGDqkd8cBgQ8GZn36RV4dLDPOYnzzzaf7l1cfZH9UpNn3/P9mVmNQ7AsOJ11C0rlimi35llxUs/Mg13tK5zuZ0gUc7l/mNi2/nM498mC+XDV4tV/gnn/5+iI3PyswUKO+fEJVAGAHSEQ4kUntzlyogPnQ4JShbYGK/iqkcnITH/tMXeLz9KotqzPn8CDVV8OzwGKnSXBj4DtRiMuH/ufMTfKHQnApKEqF4USteLI7y6f7dfPr8GWTgCMIKXQYIAdYIXObn/ngnoFitCPuKqu6XUFkK4gNJsWhnz33WLpa+oyNLeOS7XuJvLX+BkfXULeskO7rFftHgZM0j42/oDv/m4lsB7zo1lReD6UFM0CoxpSK6GlHVHeJIjjUCsRvjBNjU+p1MX6Lblp97/0fJbExHZfzTlz/Ah469yJ3JLgbJJw7u4b8/+id0ZMll3aFva/wPH/8RwqWpJ0FViiA0aK2IoopKK6SyVJXCFMq/XzNvS9hT6FUNlQArUGPFt7/7BQ7LlG/rvsr9ySZ//7kfIT9MCBoaqSzGzIxjwqGUwxiBEPDA+hYryZhAGCqnuDpZ4I+//VfecAp3fOoOt/4LP/mG7pHLP/Yz//Gme+McGEM0sZi6T9KyocTFXul4f3MLlYM0/gaLRo4qBRN5R6Ms/crViadUVvLO9BLWCaa9FFnOMiUAnEAV3vik8pklvPRiKywcqQ14dbxMbkJWgwHWCjSOx5OCPx2cQU4lFMr3rgPPhZSzY4pTDmEENnIE2a02bFUT2OCWXkPY2e8v4NXBElcLf6Q4FfsW3o2szVo85EhtyOnWPoG0fKHQPBaHhEKSOcNbI8nIpLw6WMJNA8wwpBjH2CzwytBRCIGFwGJid/Ol4wKHiy3RocTOwoBsfJMt4du+shDIUnB1tMDz02P0TY2NsMeObnFnssvZxg2+eHh8Lkaa9lKmw4RyHGEmvtaChSoLUJGhqjlMaqmmAQgwdevReVOJLLx9W5aCTxzey2vFMs9MTnD30i5H40NeK5ZRWK6P2/xvO9/J/77/bh6O+7w0PYoLLeUkotyrYQ5i8v0UMwqZ7tfQ48hfj0qClv54N8sUcYFX0OLE7L2Da5MOm8MFfufaQ2QuJh/GIEBIL3Jz2wnVXoIe+X+rdlPKYcxdjV2e2j7GJ6/cxfMH62z2O7f5uf8PGFr7jR0C2WpSxQLiinwhIjWO0EK97eWu/njnXZUuEDOHJeiGn+M2aodMKl+B10jWGwN63RrTqgaBQ7f9zxWRL4oCTBckunHr4t+R9CHps1c2WQlGrLVHLMiEsS14sLnJpzp3oWYp23NeQ3OmH5iJouRUkeMBs54hKagSD3uxgbdsFwveeHV/9wZvqV1lLegzsTGZi7m7tcux6IBB4sVYq/GQU0HJwFa0ZYp2hsyV3Jtc4/7uEW4ctL0TUVmsFTgrcaFXbVot/Q3ZKqmIEGmFUI6qHuAkmNSCApOIGYzG4/VEJTjT3uP+9BrG+QLlPekWidAoLG/tbHJPep2+qRO2vMbg5soahoaiPduVKYtOHKSGpFEipWV6EFO1PHsBATbwmL+Hmte8bkFU7JdeMHXTBHey1ePx9qsoLDtG8kj9Ev+q/naiWFNEIUIwvwZVqYiSCiGcb+VGfjJwTlANI0xicbGfPGf7Ke5s7dOMGizGEzaCHmk7R5cBSaIxoSEzAhkZwsgzK8owJEk17WDKme4+WRXRCAr6ccqt/tkbHN/8DfzXHW+SCQLsaKZCyxVhZglyf+NNpxGDyicWycIfD8KJAwtKQ5j5t/mm72Bv0mBkIw7yus+OnPrVMhgLTOIIMn9MMQnEI4dJBLLyq+jVaXcWUBuwXbXpTXyBc+IsmYmhUBgnkKHxW3jlfSM3hz8+eDS9NjPAbOUxcUgBsZuDZwH6Zcpe1WJRjdnUfiexWzR4LVxma+pTrqWwJEKROYN2hlAoCmt9qrVOqAqFUI4Av8338Fu/cgIEE4lOQoKhorL+aCMlBJmfaF3lpepeseGfm3BwUNS9gEgYeqYx3zEMTEpmIkYm5bCqz+PqTCU9YxMw44CwXXjgbSYxLkBH3iIuLDPkF2AgHAu0EDMFpk+u2ssbbOmFOZRlqBMSUVKXJQZBbiPMKEQLh7MCobzGpCr9MaIsApSyWCcwhZptn/w1l6XA1D0RnNn7NdQJYx2zEE0Z2YRKK6pCUQYe+X9T8XjzCIUV6DKYwW1vUaEK89e5nb7lxfirh5LgHDYUs8xJiZMC04pQytAOprjAYWoQ5H7LLrXHwFWJRFZurmYcxAlNWVIPS6yWiMjhQr8y6qY/Bpj41v93CkQOJhYcSQb0dc1jxdSYTm3KjpnSlRGvTZf8nTOrhBMbVGR9kO7seOEcfhtdiHm9o2x6hmSVehKUiR3xoaCMoLRqHhl/E2dXD0pWwyG6ruZV8he14q1RSOZKCmtZUXVGJmFv2vA3h5ZUgKskIqlAS0RikIGlKn1+hkktJBYhHWrH7yBEJXCRR8kLB04633KebYq2qzZrwYC1YMCObnM88ui2T/fvYiUa+p3dNMBFBmdmRC9lPKJeK6JEU7QCmO1ohAAXOYKhxDj8+xL453FpugQpaKe4p7VNR2VcK7u04imlUXx+dJpOkPHOZIs/zI8gah6m67IAA74eFBl/DQKLFX7istIhQ4OtfJaIqVnEVOHk7D2TMNYxeRVyedxl2PUThIr8jsxo5X++ktjEYKXATRVWONbjPs/31hkXEbYpmJS338X41g7ijQxj4e7jHDwAJ07uMlhcR1YB9StjfuyeZ5DCItZz3OWU7IglHEvq1/2VzbsSUcGJZJ/vbn2FL7WO8fT0BD+69hTbS21++cn3Q2gpViDsK4rVChsqwpFA12ctx9mW+P2tr7BnWnzi8F6OBn0e7F7ny+USe1WLn1r7t3y6dXqOqQ9igykVLjagHEGqwQmqImDakLMcB0Wyo8jWrWdkzs794w2HTRzfsXieR9NLbFdtFtWYi+Uad9b2eDh9jbaakEhNZmNeLI7yVJZyb3KN7arDyCT8153rPJxe5m8PfsJve8uAsGHIxjFRu/DGsGGMaGk67Yy8DJHSeg/IVoReqmDmA3CRwAWzmU85cILHu5dYDw95rVhhEow4Ge+ihGUjPOC7Fl7kWNjzwbj1EmsUjWaGsdKrMleG9HZbGOWQdU2tURAqg3OC6cUaxR0lVAIRW2zbYrXi/voWz4/v4Hh6MA/B6aiMbd3mke5VQmG4OFnhf979Dv7R6qf43e6D3oWqaqiZ6KlRyznsNVhaGmGsIFTejGWt95z09rvYpsFZh0gMbhpQhYZHFq6wWzZpBTl3hgd0OpMZ/s5/xvaqFmHiwb3WSnLlWGh7ZsQ9C9tMqpjKSY6kw79cUPCXjW9NEG9gCEB4X0QaaPrKayBEYWionJcmR+Baiqx8/cAqT4yyocCEvnh5PlvjzmiHpsz5eO9eVuIRD9Y3PauylLNiJn67KP3W39589c6LrTIXcyFf49zBGk+3TtJQBX90+BDXsg5mRc4q9d6NaWcEKKF82rYGhHS4qfIFMSNQmSTMoCrEHBWHYy7u0k5xsVz7KkJV5SQr4RDrxLyVOdDpjAdxhKH2O4eH08s8FodorQgCg6kU1sxWVOUwkwA1VBgrmEQRRt/iXaqZ1R3h/BEs9nZ7YQQuvOVifGl6lGf6x2iFOUfTPonUt1qGeHs8TmArQamD+dbcOQGlxOQxTjomTpDWC4o8orkHxaqcr95OSzCCHd3iK701pu2QelDwpL2LhXDCc4NjrCQjlsIxl4ddnhlt8MPdpxiMUuJEI4TDOV8D0UYhpENX/ig4LaKvYj5GA0mh8O+Plgjn3/vMRDPDW8gLxTqVlRRFiDWSJC2RgUUFFmv9Z0Aqg7GCp4cnaQVTBtofgbW7zZr/N9Cs9e9rvCnanMn6hrvn+/87GluG2tUh4peHnH/umD8vNizLT0mC3PL3/6eP8KXJcUJp+I1n30HaLOg2MooqYLUx4vqg7SEon16iWPDQ2j/+8V9iy9Q4V9zBSuDTl76UH+NCtsa4ijiWHtLTdTpBxsujVR7pXOW16RKpKrk66fL88ye80rJtOPehX6NnS/ZNSImkLipCYakJx5aJWVcFn8hO+W25GvLE+H5eyVbmaHqJ5Wh4yGfGZ3ms/iojk3JgGhxWXmkYS01XTbihO+zrBiOdcF9ji1995r24aUDQKuc1h1qjQGvF+Xf/BmOb07cV2sGe9dyGPRuQO8WlcoV3ppvsmYiuLBm5gGfzW92JvarJWjhgPTjEOsmymrJtamgXcFkvsVd5ifRG2GO7apPZiPWwT25DPrL1KP/89L+eAWAd2kHufHjv5/IOHZnxcnmEM9E2j8SGV3TFP776/Xzv8gushX0eiPbpmZBd02CrWuBKscTxeJ9z2VHOpDu05JSJjTkbb/EPLvxNL4IqQk4s9fjw6d8mEoI9I8mdoiNL7ghiXtEVxwOBwRGiyJymKSO2qoJf3X8P39l+kb3Kw3RupnV3VEZTTvmlzQ/Riqb8/PrHsMBm1WJZTTiwKR2Zk8zOXpkNWFWaJ7JTc07pTUzA+05deONtzhN3uLV//N+8oXvk6t/5h9+UNuebYgchLOSLgoULXriUKA3LBepSigv9ShNmFu0CjsaHjE1Cd2lEJ/XOQQcc5umcwCwkqEIgKhi5gKYsWQv6bFcduvHYk5ujEYHwjIFa7IthuQnZLltcz9ocSYfsT+uzFqkvVGkMHRlQOI1BkNmAvo2pC01TaCzMwm8FExdyMt7l3PjIHE2/PgPM3pdeoylzNsvFuQ3am61SxiohtyG9skZpAy+CChzG+Ur9zZqDEI4gMIxtTkMmgL8WI2cIhSDEsmk6NNWUppD0ZzLCEEtXjVkOhhxVA5SwNKWnfGdOUjpJU5ZcmhGpChsysDWaMmdfN7EIJsorE60TtKUgc44lGVG4ip41FM4To2+SoxNRkTtDUxraUc4DySaJMOROUJMVLZezhQ+RKV1Ar6xTxCHIKZmNGdmUSBkyoNIBiarInKAuBInwj5sIQWY1oXCAQjtLMSsYbVUFoYC1eMDExpwIPSOkdIq+rfnCp/PQGSUcoQDtoCOnrCrLyBqaopp/vyYrlBD0TY2OyrBOYpCU7vaZlLchtf6mjDeHDgKYoQoQWUEv9ytqVfNV/6C4tcu5nC+xmXfppPkc/pHrgKyIyMYx04n/8Hqdw63Hv1Su0JJTtqs2V4suS8GYUeWBJ1tFh8xG7IwbTKqYaRUyMRH9LJ0VTf1j7Bu/giwp/43u68JaezahZ0IuFatcLpfn8JDVeETf1FgL/RY9kZqXpkd9l6Rq+O4IMJ3JdA91jRt5i52sxU7WZDNbIAgrj8dTPn9BhJayDCjyiL6tXjdJwIay8+d0VA24XC4zcpbtqknuJBq/co5MSigs68EhI5syshGJqBi5kO2qSd/4539T1p07P4kdlI258SuvApoy4ohKCYW/JqsqYM9IToVDltWULb3AgakzsoaaELy95ZW9zZk1etvU5/zOmix9+E7eJLMRpfO5mzfl6LW4pFbPyU3AyIZMnKX2uvtr5CxdCX3rbfSlcxwYwcCG9GzAo6nPLV0PRqzO4Ly5DZnY+Ksk74WDgQ1pz9pNfoKTvO5jyMg6H29wE9YzS3O/3THTX/2VX9+s8eaYIBzki86j55KYpXRMWi+JBgKrwCp/Zs9tyNn0BqdrO6SB9lAW4ejWfFirCo03Q+HrCzdrDHVRcW/s0ecrasSdiZfiHokHaKc4nu6zGg5JQ2/L1sYbj0JlwM6yGKRDgvdHWENHQj6b/ScuZF1ltKXm7niLjfDA8wWUj8M7Ee1zUDXmUufj8T4tmXtmgazQTs25CVI4GkFJHFQzK7P2ZKpK+Kq6kbjKS7bFbFsPzCeJPetIhGJZOXZNgzujXboyYCMY0JSWpqh4MT86X0Xtnzs3R1iUsBgn5yxL7dSM0OS8tRqf8amNomcKrlX++rdkQu4MbWnYrGqEOO6IDjgaDOfPc2QTlmVBz0bsmZQ1NSERhpFJGZmEXtWYwXc0Fr8yGwTjIuZwXEPrgEgZmtLv2HrWMyP6s3nxSuVv0tyJ2S7AUiJZlhUTF7FdddiqmkTCcmC8yla7AO28d0VbzwdtziYHheDA1tib7TQGNqRnEgzeSDgwNQYmZV83b3+CcLfx9U0ab4ojBhJar4JuBIQXepzfX2HST+GYJr0aEuSWoq1YDMb81s7byU3ItApJlAfBvnZ9ic7CBN33q2hiQBm/g9gzdXKZ88TgAX5o4WmeyU/wuf6dHEkGXBytcH97iz/aeoD1+oDeuMaoGbOUThhXMeOJfzynQBSKPRuT64LjQcRLGk6Hhi8VC5wI93mh9NLo56fHyG3II/XXeG56hO9ZfJ7PjU/znubLlDP79BN79/GB5XMc6vrcQDXSCavxkH5VYytrzz6skgNV90KgyOKs1zmIpCIbx7gsYM/GjJxhQ1ku6Al3hXWeKUpCAQ/Hff7v4X10ZMareoUH4uvkTrERHfDpyVneml4mEoZt7d2Ie/htdyK0d3E6yUIwYVDVOKzqHh03w61ZBNMypGd9zeGGmZI7wR0q5ItFk/elGdcq2NILRMKwEfRoSs3d8Q0+PT3FvfF1QmH4bHYnTTVlYFKORP35hLVbtiiCkJos6VUNikoRhxWBshxMa1zWHb8DcCETl89pXx1ZcGUWh1YXmk0X+8wOF7KmhqypIYkwbFYtOirjK9MNEqlpqJzCKMY65nrVoi4L1lTGlUpxXS/QUjm5DanLgj3TYmg970I7xdSErEXDefbIGx/iW0eMNzryRS+ecctd7uweQC5JtkKKJeuj1jLLtm5zur7Huxcvsrm3wIWDZfIq4KGTmwTKErRKmmsjVAFVDYqu4+5wwMgmnEgOeHp6ijujXb5j4WWOxT2WkzE38jbfvfYS71p4hXZ9ykZ6OJ98Tq3uI7RAZQKXGk4EJceDgAvaYRCc1wHvTbdoCk1L5jwU71KTJY83LtKUU46Eh3zkxmOshkP6pk5vFmrz7sWLbIQHHI0PORr35xCSwgasx32WkzGdeEooLavpEGsETLxopyoUZhwSxhWqqdlQBevKYHCsK8UzRcnbYn9cebboYJ1kPZiyFnjL/8gmXNfdObtgu2ozMDXWg0PW1IC7Qg8ZWg/7NFTOYHazHQkP5/Smu5MbHAn7BNKyqiz7xtAUklUluWY0j8Q9nsxDejZiUNWQWFZVybUq4E/6b+He+Do945PQ35leYi0YsBIO2dfeOj7RMTVVshoOkMKyFg6oRX5FH04SGmHJRjBgWU25Nxxwd1ixpjKOBxkSx/EgY01lLCnN2XDC8eCQZZnx9PQkf5ad4YVinVBUvJBvoJ1iNRywEgxpRQVSWFbUmK7M2axaNKXmbckmy2rIihoTCsOZcI+NYOAJVuGIE8kBStjbR87Bt3YQb2g4yJfdTNFoSALN2skDDvdWZ9Zv/zPLwYhuMKZv6rzr5CW64YSL4xVuTFrzh8omCcnM4a1ywZaJeTAasigvcK5cp6vGDG3Cc+PjnK7tcqNss6ub1FRBKC3DKvEJV0pzcbzshVWpQ0wVl6uIDVWwpGBVpQxszrNFh47KeGs8YWD5/9h78xjLsvu+73POuefe++5b69XaS3X39DIrh0MOKS4jUlJI0ZJsrREkWPEfdhDECLIgCYwkjh1AEAwnloPEsSXDCGMIlkBLimBJpGTRkEiRImlSpIYznH2mZ7p7qru6qmt99db77nbOyR/n9puhLIkzhByOCR2gUK9fv6p6y73nnvP7fb+fLz/Qeo5DmxCLko7KuNw+5JHGTXbKpUUc3tTEWCQHZWdhaW7U5KfMahqqIFQVvWhOJCuv2oyMF2LVIijnwMwCDm2Arotxq8qgBTxTZLw9jFGMudR+hq5UPBKOSYTmfDDlk8NH+Imlx5m5kI1gRFvNuVGssRGMKGp089Ak7ORL5DZgLZwwMC2vbNTw3Pws4IumTxcdv/1gSlMK2sIxsIKhTXh3lKK7T7KpLK9UvrvyXZ2rHJo2xkkKk7As55ROcTXdYFw1mOqIc60TIllyM18hliUHosPhoI2rJEkn4yRr0JaW21WDx8s1zgQnHJsWF0dCkVEAACAASURBVPQRrxTrXAn3F52FviwY2ZizwZwNPeK4ajG2DTKnWVZTNoIRN4pVDwWattjsVeyZDhf1gL5KmVjN707fRlel3B/dYWxjdssl7o/u0A9mPDM9y9xoOjr75tyc9hs/5Fs53hIrCKdg5WnHfCWAQ7/F2L+2QnE+R1QQjSxZT9KUOf9i5zv5zOB+Ht89x1MnZ5mVIQfHHRJdUo1DbCm947PydQuF47mizS8cf4APNm7yQn6Gx6cXGZYNvnJyAYDP71zi6ckmx9MELSwXW0eMiwZSerejLIU3NjnJK1WL0sETOSRCE8uSDZXz6XSdV8ouvzr6Dp6cX6B0it8bvo3H2tf4xMmjLAfTRRzev771EDfyNYyTDOse+rBMaKuMRBbcTntMy4ijeXPx/8AC0S8DSzmOUGO/X75R9cmd4sm8x2ll2KvaPF/MeShs8MnpQ/zbbInPzE/zamV4umhwJhryqyfv5dlsk61yhT+aXSJzmq1yha1aGJbXE9eSThfo99wF3Mm6lE4xLBNmc1/cW5UpN6sOLxRtDLBV9ng43GNiK76QXuFz2Zpne9ZFoU+N3ubjAkXJl+f38OT8ArnVnI1PaKmcudHs5V1O6tVLakNwECYFrThnPIu5XTW4VfUxTnCr7DOzIYemzUYwYrvqM7Y+m3PbtChQvFAsAfBwvE0oKl7IzjKzEV+eXuKkbJLa0NO15wk75RK7VZvdqsvzxWmksKQ24uVig9RGlC7gVtlnv+wwNxotDQ1ZLFSxb/zAp5aBv4Gvb9F4S6wgZAkH74GLv5njTq+y2Ruy0pqx97ublC1HuqaIR5Zb5TJ/69zv8my2SWUl97f3efz4PDqsmOQh8fKcbBr6HM0IGofwpfQK3928yv+89ll+Zfx23p3cwDjJF/Yu8cNnn+V2vsQ713bohzM2l4ZcHa+hhCWQltk8Qs8EVntB0Va5QlJzEB4O9/h8tszvjx/k/sYdfqh1na0q5H9ZeYbPzmNKF/BQc4dPnTzEf7L6h3zi5FE24wHbWZ8fPPc8icy5WqyzP+9gESRBwdfGmzRVwWo8pbABZ5IRq+HExwpGDtO0BDNJVUhEp8RYHwfYVnO+OL/MpfCAj40fwjrJpfYz/NzJef6bpZu8Wk7pScnAWi4EU/7+/oP8zD2f4PH5RfbLLvdEh9zMV0hUznowIpYlL6Q9Xhx7OGysSrqhbzP19Yxnh6eJVYnWhi9M7uPL4jIPJTssqym/O7vMhh7yjw4+zPs71/gvujfJXcWvTM5R6GM+dud9/ODaM7yar7Jfdnk02WJgmjx3cgrrThMFFd+9+goHRZvdeZdbsyXuaR7jTkLcKOaw2USf9WDaoWny+ZN7ebB1h5Mq4eFkm88OH+A/6r3IwLTYFUusBhMmNua7Gzf4Wzd/jPf3b/Ds5AynYi8VPxcNeGZ6lqmJODluwfKUgWnyLjnniWyNpsz5/YP7ubdzwJXGAVeLHtfTFb6jcxMlLIfzFncmbc71hpxqjN/0sf+t7FC8kfGWmCDAbwdE5XCRJgkKdsYdH/eeeH9AMLd05JzSBcSipLLKZ1IEJRu9CblRUMevV7FX8OVdwQeTl1mVFUMrSWROT2YoYfnQqZd9UlKQcT6e0VUpr+pllqMZNyYrnE8GHLWaHPQTRCWwDcN7421WVcChqegrhWGA7j5DT85pCc19uuJ2ZehISGTJO+KbPD87Q1MUPNa+xmow5rQeUjrFpj4mtRHn48EiW+JuwTKWJbu5tw4nqiBf9y1B1SkoYw3S0eumzMKQxxrbtIXk4fCIvgzoyZTTwZyuVFyM9nm1nHKPbjG1GaeU/7g/uHqNvsp4LHmFzAVMbINNfVynpVdMbIhp+pDjkzKhoUrOhoNFBMDaik+/Ppq3+GtLX2bmNPfpOQrBQB/TlYJJ52UejO7wfAkXA/ie5BpdKbjcPuSx5DqD6A5aVJwPUvpqynQ9Zjfr0Q99MvcjzW2uNA5Ibcg90QG/s/EQeRIhI8PF1WPu0zl99Qrn9DEbaszMaTZUSkdlPBrtMbQBbVERC9/pWFchH+hf53x4xGZ/wKY+Zrv0aWDf2Z0Ti5I/XL2HS0tHfKj5ImeDiiS5Tls4ZqeeYDUYcy44YeY0b2v0uD/c54lsk7KvOGy16emUh5NtfuHNHvh/MUG8gSG8mclECqyjq+c0dMX8rizZgtU+ibknUzbDYyonaQcZpVXszdqoOq0p1BVuDlUbyGHsIvqULCufj7AqKy6GB3zy6GH+49Vtjso2g6pZS4idpxMLy6SMaYYFeuSX9RWSgQ3JXMXAJrTlnEmdpt0URS2iMfSkJBZzDHjqMY5YVAsk3qBqkbmA04GnUB0UbY7yFv0w9UXK2E8goawobEAkS/RQ4QRUhARjhWlYskJjSsWhCRkKy17VZTMYcb1cY+aGPBKOmdmInpSLFmhqC0oML003SNsB18tVUhvRlhkz26ZZC8pKFzAyTV5NVxiVMUuht9zfTbg+Kn21flZonso3KVyAFjdpioo90+ViMOJqdoqNYMRFPUaLiO0qYSLn7GUdXirW2S97rAZjNlTKVrnC9dkq+/M2oyjmnubxQk06N76TkU8i1CjAhor9XovMWbbKJZ6en2McHfgOQrzNc/OzbKgRB6ZNR2asqymHpsmqmnBtvkZLZVxNN9iL/VZpNZgsVk+TNOJ20ON6uUoi7nCj6hOLkmfTs5yJTrBIjqsWd8oesSwYmBa35n0GecIsDOkG82/F2fPvdbwlJggnIBx5+7EsKkZljSTveHuwsGCVoC3n3Kr6DI3Hl1dOoaWhNJI4qEitxDpPu6a2Ni/LOYkQ3K4CNvUxmfOV+7Voukhr2i86mFBQWekpzEFBW2fcSTuYhjcvucjRkwWJgLZMiUXA+aBk5kacDuZYvNBmzwBIlpVPYbrYOGTmNBejfZrCKza/lp5HNSwtlWG0n4Duhtq0VEZuPWTXOF/3qJoWFzhEo6KyQGwXQTz92hKta53Dw5HnlCZC81C4x8BaTqmA1BYkMqR0hgvJMZkLWFZT1tSEmQuJZUFTFDRFSYajq2asR2OaQc5aOGGlXqorHCvaV+vDwHjNhzCs1tkTV/QIDTyS3OJ8MGZoA/rSsKpmrCvr4T84zoeHi5SwjswWOL1+mKKwLAUzJN5v0w+myMhgWgLZrGjoikQqltWMs+GAjsyIdUlP5lyO9unWz6WvMhLhsMyQwEPNHe4NfSDRsppSOoVBsqInNGVOIyrpN3zAciwsm8EQ6wSnwhGrwYSeTIm1D89ZljMOVUpP+0mhp+esBJM3fey/1bcYb4ki5V2YaDDxVeCTLCEOS0Tltx4mBFU4hsYrLHeKJZajGQ1ZeDhp7oGjAPM08nbuyiPfDuufATg0HQ5txLVsg9wGjEziQ2VMsPBDDIqESZ2kdDRt4hTYyEIlOLYR2es+0Mw5tooV9mo15MgaYmHZrrrsGfV1QSplTTweW190nFjPU0jtaxbh3AYcFJ3F7btdDcDbmZXzmaLSLdLAJy5YKCTveiEOTZMSw7FtoOv6VslrPInSKRJZooUhkTkaL1QqnGLsojppqrEI3MmsZmJjSutBrUdli8x6AvbMep1BifDPw8HECWY2InN3/SqKmQvQwh9uWlSEwlA6r4gEn3Oa1bDZqA69KZ1/rjMbeRt3URdphaN0ltIpBlVrAdid2NDfdgGZ02ROMbKKSf0e39VYxKIglsUCUHP3bxSV8jRyF2DxAjsp3OL1Fk5ROMXI+C7IXTv+sGgwN/qbUlL+RZHyDQwT49OtBMhbB0yrFoNxk7VH90k/vk7veoETcFi1+c0776SwNTY+bnKxdcRuu8vOi+uEA4kGutctVgtkaXm52OCp7By/d/AgP7D2PB+bPMYTR5v8ncuf5F8dfQc/svw1vmDvJbOaZx6/hF0ucZXgmtsgGATEI0EwhzKBrXKFl6ymp1KeSO/xORX5KiPT5GPHa3SCjMePzxMFFfe197m/cYdE5nx8+C5+qPc1ejKjKXP6wQyJ5TduPIKpOQpZGhJoHxBj5h5NL4zAJRWqEIT7mqoZICWo/YBsN0RJeDI759kVKuVL2Rk2w2N2yj6fHD7CmWjI399/kA+uXuOl6QYXkmNKp/jZ9af46cN38ZPdr7JVLZHZkE19zLHx0u++mvKxm+9BCkdWBkzTmDgqSdMIU0m6XR9QM9xr07ySo4Xhqfw0u+USbZlxPVvjPa0b/PPjD/DE8TnWkgkf6r/Ek3O4nq5yLjzi+5s3GVr45PQhVoMJ18arpKXmJEz4veceIm7nLLdnzMuAfjJHaovtlMjdmO1smb+79L28q32TzfCYG/kajzRu8oX0Xh6Ob/Ol9Arg5c93t0O5DVjWM8Y25nuSLb4wP8+Z4ISvpJf47OG9BNIzLJ+9fYaXlk/xqdFDdIKMdT3i/sYdVoMxV/PTdNWMi+EhT84v8P+8+J1cWTskNwGjMubzx1eAT7/xA9/xlm9zviUmCFlC5/0HBL9qsefXecfyVTbPDPhnv/8R2rEgXde0t+ZkTvP3Lv4mX0kvk9QR79tln6IfsMM62WkPZ82XJFZBHTDFR5ov8n33vMA/3Ps+fmz5CZa09yL89dUv8pX0Eh/qvkAsSn791Ds8fHYSotfmVBNPI6piQPr6wTviWxzbJn+1+zhP5pvMjWYiY/771c9ys+rwU70/4ovzSwD8VPsWf2f/Mf7X9S/x6XmPVZlinOADzasY6oTtOvG73ZmT5ZpAWPobMyZpjNaePp29suJxdXdJUBLKlQoxVwxNwmowZrdc4h3xLT43u591PeInlh7nV0/ey8/c8wn6KiNt+xCaRJb89OG7+JnV57ldGR7UR2wbD+W9EB4RYpg5H2gzySKyTBOGhkhXqJalE+cM0gaxriC0PD0/z8g0eE/zupezV0s8lOzwO4NH+I7Oq3yo+wJn1IhPTnwH6YXgNB9Jtnip9NSo72u+wMDG7I3aZGnISVSxsjam30gZF35lVlmJ2I5JhoKy5VANww8sPctWscIfjO/nQnzE56f301IZv378bt7eus2dosuSnnEhPuJWvsyPLD3JZycP8mJ2hsOqw8PxNo/PL3JKn5CbgNI6yr2E8NSMk6rJe9vXsTXR+5f338taNOV844jb87MMiibnG8dcWTvkxvEy81mIjio2et9+W4y3xAThJBwet+nrGXKSMSwbzM0GcjVDvpKgct/JaMuMp7LzbGXLnImGjEyCwucrAMhUImqtiso9e2Gtdlc+np3nVDRir+pxa97nx7tP8GR2jvPhEaULME5i5gGqUWFryK2shDeMFZ7lcFqf0JU5bVmQuoCNYMhG1KOr5tysvPz2erHGrXyZFT2hxHj4amW4pI/ZDCS7+gSDpCNyr/5UPrSmMpIwrChLRVYGFLnnK5TaUC5bRCkwDetzTGsiM8KxqQecUSN04IlU72xsAT5I6EJ8xOPzizyWvML1cpVlNaWwip/sfpXbleFs4LsbqZsThvv0VUbpJAkliS6Jg4qJ9qExDV0yK0KyKqAT50RBxXFgeXdywxf71IyJDbkYHrBbLvGd3VdIZM7X0gvM4jucj45YVjMuNg7RQvDusOC2uk1qA3oy53RvzDBq0Ipy9kdtlpMZq8mMeaVpBgVbyxWmKUH4QvTMRmzqARu1ovFKtMfQNDndGxKLkkTmtJXveq1ob5h7Z7JF6QLeG++igCzeZmBaLMce2f/qUkESF1yK9inrz7cpCt7VvcW6Hnmjlx4yiyNCUfEle5FT3TGjKCbRJRvNN9/m/IsuxhsZDuxMe7KUtdxJu5xKRkjhkIVbAGUypzkq29xM+1xp7C/MRIdZy2PSrAezWA1B4WsbMxthnVgE8J5UTXZnXVLrI+nvavhP6RMwAmcEwvoUL5njEXI16GWv6nEmGJKICoWjKQpS47sAhVP1PjjmsPCqw9xZVoMJMxewoXJKJ8mc5un5eS5He2RlwF1pzd1w37sZm84InKwR65XAxg6Uh9660B9Vai45rNo+y6HmOYTCLFBxPZX6kBvnczTW1IRYlGxVSzyojxbdjZ3KBwPHomJYqwznpV4cu3FQ4ZzwwcP4wCBZg3sPqw5aVMSiZGIbtVK1QSgqejLF1Pv+QdUijgwXwiNG1pGJgmH9frVlxiT3NYC51Ig6/2Je6UVal5wqZCGoVkqs8Tmgd/kUcV3ryVyAwhHJkqmJ0aJiYhtMTbw44Yc2IXWCvvSy872yy7T0k6BLA+jBnXKJdT2iKQqGNmE76zOqGovi7H7ZoavmxKrkzrhDVmhyHRAH37pcjH9f4xtOEEKIXwB+EDhwzr2tvq8P/L/ABWAL+Mk6TlwA/xj4y0AK/A3n3JPf8FlI3+MnUGAFVzq7ftk301QNsajRxKLkgcYOFsHtok8iC2/XLSNs5MA6qqYkOhHYwMNoD6s2RSi5Eu3zxeIK63rElc4hn5k+SD+Y1nvYdQamhYgMQjms9h2CqumIBhJhvdozFBU7VY9YlDRlzk61xG7eRQnLg/EORaXY1ANGUUKicp4tOjw7O8umPuap7DzvirdQOB6Md1hVE3RNJqqszwc1VhIEFiUcQWiQyhKGFZmBYCIwsUAVdd0q9Mj6DT2iLefsVl4puFd6b0VbzXk1X+We6JCJbfhWpgspjCKzIdumRerm7FSav5SUfHzmT5JVNSNzioYuSUvNvNCUlSIOS/IyIAwMlZFkwmP3jk3Lg1uRjE3MQeWt2teLNWZRRD+YoXD0gymvlKt8evgQWUfzcLRLiHeNKupAnKJBEhUoZZnU24t5qVlrTrEtAxNFcKypAstuucTF6ICn03OMXMLleJ9VUXItX+ds6Dsrk7og3FVzhibhidkFlvQM4yQD0+K98RaPRQM+ffwA1gmvMzHem3E3PexuWvm56NhP9jb00JzwhEl5L2utKaM8ph3ldGox2Rsd32or9xsZb2QF8S+Anwd+6XX3/W3g951z/0AI8bfrf/9PwA/gs/6uAO8F/ln9/c8cLnCI7QbZmYB425uIPn/jMmG7oOiFqC1H0fPFwWfmPn7wt7beRj+Z867lW1zqHHF9aRUz1pQrFcG1gPmqwDQEjyWvsFd1uZqd4kpj34uldMrleI8X576o1w+mKCxBaFhbmjBqxF5JeRrcqIWsoOg5Pti4wdCGHJsmO9USTZnz4/2vEsuSvarLhfCIz04eZF2PagWfR9dvFatcCvcXr3doEi7qAfcvHzAuY2alT5nWytAOc4ZZw5OxyoDV1oybuocyHp0PwgNmA4sTgtPBCbEwxOK1q9ejjS1uFGsLheSmPmZm24ve/aY+5nqxRhjuk7qIj88ifrQ55RnfMaUvC841TzgpGnSjDGMlq40pR1mT0iou944orOL4pMWlcJ+ZjejIrNaGNLgUHvCK2qBwAdfzZbSoeF/jBlvVMrfSJdpLc2YuoCkqzgRjMqfYaI5phbm/KtNhKUpJgpLCKk43RjwXnfaBQKUgbhZcjA7oqynvTLYAf/GwSO6JDjkTnLCmJhgECsfQesPYs/Ys74uuAZDInF3T5t9Mz7MWTwllxUvhOoG09JQXb+2W/jN+T/s6HZUhsfRURbNOFluKUqRwtMOMJCjoh+kbOJ3++MH/H7ib0zn3eWDwx+7+EeAX69u/CPzo6+7/JefHl4GeEOLUN3wWVlAtVQTTEoSgIQtOLY+oCuU5kgLU3Le91vSYRBbcv3LAufaAhirp6xkmDXzOZi59WzRnQWfeCEYsBbNFKxN8H7wfzHgh8wnpq8EYU0nyKiCdRhgnKGchTtZcCQdbVZe29KuHc8GApigoUUxszLLyk0w/mFG6gJkNuRAck9uAC+FhLaayvDfyNYg90+Q4a1Ian6kJUBq1SNlOc8+bnORRnVfBa+Ev9XdhvE4itZqmKGnWk4R1NS8SSFTuFZK1zqGvpt7YFB7RVxkXgmMu6qOFwetsAFfLZeZGUznFtIgQwpEZ7ya1zmPi0ypESr/NCu+2S4UhEbkH0JgGG8EQLQxtlTFzmkTkPNDZY1VNCLFoYcmc4gHt6VQnWYPCBszyEOtknTfqWZF3l+IutFSVRGJRWEJhahqUJBYlWlQY5KLlOKyNcwCX4kO2y2WUsJwLBrRl5gvGTlDYwFO6lEViyZy/ICUiRwnH2MQ+F0TYxbbUOr/6q6wirUIK+03s2L9N3Zzrzrk79e09YL2+fQbYft3jbtf33eGPDSHE3wT+JoDqe1mxaQSoNKATZBTGo+OqxGGVwEaehGSQtOWc4+wB1hsT2iojtSEqqTDS8xplGZL3/QTRFiWpC1gLxigsF/QRaewxZuvaW6BjUdIUBXFS0Ipy0kSjlSHpzXE3QurMWU4rX6UuXYCqD7q2zBYH4LL0NOZ+TSsqkDSU/91SWBQOKQTLasqmmrIczxiXMaL0MfQAS1HKrIxQwjEvA9aaU4blCjIX2NDnVjhZxwNox6qaUzjJxGlCbM1zqChQ3mYdjGiKqi64lYxdRGoiQgylkwxtg1XlvQ0jO6crG97JGKZMq3pv7rxXpLAB2hl/2yiEcH7bUp8wCkuJYjMY8kJ2hsxp1rUPwLlLjdpOlyi7CotkXZS0VcFt4yceKRxSOHqNjFBVBMJSgHe0VnXQcimRS441Nam1DppQGAyCnpxzYNrEoqQUygu/lH/vS6f4YnEv72q+yrDOHAVo1qKqUFZUlaIKDBZJT6bsVktYJDMbEYti8fdMfV1NgoJp6cOawCe9v9khvt3bnM45J8Sb30k55z4KfBQgOrfpRCERxoBz7Bcd3wLUFaKs6cdSILFMTMzIJGhpaOsMKSxPD896K7QVuKpesjmPnduuugu0WFPmpDZit1hiFvgcyA82bvKZ9OICsTbN69aaUeS5JnL+Q5QVKOE4NA2ksIytLy/eyE9xJdojFiWHpoFBMrQJTZkzNAn3RIfMXIhyjtUg53fTMzRlzqryidzzSmOcwFT+o5hJf8A5QKu7WZq+FSwqv70QhsXSdM8ktGXB0CQoYYlFya26HlG6gFiWTGxI6QKy+gDuqykzF5JQLgRFfVnwh9kyF/WAe7V3kc7K0BOeTUQjaDApfEL32PnXbirJTrmEEo7Y+CV+6QKGNqarUg6rDpH0bMqBaaFFRVqFDG3CGTUidaAFPJWf5iT37/+kiGgEJYUJKMDj/6oIComaycWq8Ng2mdmIm8UK68EIJRzGSW4XyzRlzsQ0mMmcjszYKZfYCIbMjeaSPmTPdDisOqwGY17JN5iUMZVVlHNNERgOqg4bwRCJ/5yvphs8mOx62C2SqYkXq45ZGTHKY7pRxrh6DVv3xk6Cb48axJ809oUQp5xzd+otxEF9/w6w+brHna3v+zNHOHbc99ERN3+4T+Mg4upXHmH1K5LlzNL9L7e42juHLCT/5Pb3fh25eqt9js8G/uT93/7qL79GvA7fx5lTJ+RVwFfTi+wVHW6nPZajlGaQszVd5umXz0FgUaHFZArVqPgf3/l7fCR5maeLDVbVmMfnF/nttbdzkjb44KmbPJmd/Xco1Iks2CpWuVP2OKWHfGl0mfVozG29zC+8/H6KQtGIS1qxj5XvhDlbR33OrZzwift+/d+hZHelYddE7FZLDE3Co/Et/mH4/dyaLHGle8hx7v/2+/s3ahxcwI2yw9AkdfGtyWk9ZGgScqt5Ie1hmoKRadJVMya2sRBBJbpkXmoauuRc84S50fTDlGHZ4GMX/oDb1ZRdExFiOR8Yto3fzpwP5mQOnj67wdAkWAcD06JwitIFtOWc20WffjDjlWyF3GoebbzKS/lpXtpf4+fMh4lVxX2dfSoruZN1+ef3/kteKf0E9aX5PTwa36InKyZWcVkH/JPWHbazPrfTHn9l9VkUjo7M6KsZmQuJKVhWUwbBlLbMfAcFSekUl/QBe6bLE0eb5Pa72Ul7PilcVlRW8dGLv0EkJJ9bfnKxGgEYmSahGLMZ+x12W9VoQyzn9ID/7tSnSGTJ0MZ0RM6KKvmXb/ZM+jadIH4L+OvAP6i/f+J19//XQohfxRcnR6/bivypQ1gPqw3mUDUFeiRRpUOnlrQMYTXH7cXsjLqMxw2/5JU+ii/wq+OaK6gYFw0ubh4CMC8DrqerzEzIzdESJzVJ5nDS8iuMoxDTqcAITOmr0xPnpcVtXbCTL/FDp57hxnyVK40DbuQeK5dZzbBMaKhi0ZYclg0OdJsbk2W2pn2W4xllqSgnEab0yk+tDEWlMJXiYNLijilYVwFtadk3FbFwHBnPYTiuWp6hYJr85Oof8UzrHG9r3Cazmr2qy2l9wovzM2yVK4vlcmojrJML2EvpFC+ON1jSKa+mK6xHY3IbLERQcVDhwCsYi4avOVQRszLkdjXlbNCiKVJmzqLFa2AaLQRt+fUE58IpMhdineCF7KxfuZiYF8cbzJshp/UJqY0IQ9+SDKRZxCWGquJsEKHFMadUg39dtWiLilOqQV+WHJqcR+JbPBjvsJ0s83C8zWenD3JPdLBY7lske5Xfqg5MC4mnoA9N4vM+qw7tMGcn7dEL575+4CSzKqRwjsz5Qu8VfcKTtSpUCotBYp1c0MeVcJ6HYRM21AiNpSNytLCk30zB8T/0CUII8SvA9wArQojbwE/jJ4ZfE0L8Z8BN4Cfrh38S3+K8hm9z/qdv5Ek4AUxmWA2NfUfZ9DLpoqWYzBJsqaDrA1ntzBefhAHqABpZ+b3kzbRPZl7Tw58MWsz6IWkVMs9DNjsjdiZd8kwjSonKwHRApRIbOrazPodxkwv6iMwp7mRdToUjWirnVt5faO8jWXmKUDBnUDWJVMW4aPjCmvFCp0ZQUuYBMq6w8wCaBZWVOKMwE00RVhzbiMKVnA8CJlaxrg17RnJsWgxMk9zqOnehwVIwq4N0DRvBiFfzNZ4YniNZzsnrDM3UhiwFM47KNrkNWKqlpCdlwqiMaQa591aUAVmmmei6lVhoulHGtPA1h8pKdk1EU6QsqYTSzJjYipKAsY254/+5lwAAIABJREFUWXlo7F7Z5ahq01VzUhvWOaIVR1WLNT1mVCWE9Xt1WLc/o8AwKSKcEwTSMipipHBcKyvu1Q1S52sGqVPcMXNWZMi+CXku26QfTDmtT3ghP8PExNwulhlUTbpB6iXiwcxP8qaxoDt5PYRZeFpaOiettDfBOckgbSwYllfz03Rkxs1iZfF+5lazlS3TUCVdNUfLipOySeY0/caUq+UaYxNzb7jPzIX8CeW2P/v8+g99gnDO/dSf8l8f/hMe64D/6pt5IqIRIyrIVr3QySmBNI6lZE5RBJjtBDaAyJOm73qYrPZfmdVcbh4yNRF7WYdR3qDZyWgHOYGwRLqisL6/P9WGSjpsBKKSvsYRWe5L9mjLjN1qiUejPS4kxxgEK3rKnaKLQRJJXyXvhzNaKqcM/RJmKUppBxk7qouWliQoUIEPtZVJRSMsKSpFFJakpoWUvnDWVDklhssaYuGFPpf0MRZPwI5lyWHeZlMP0KJiYFpsBCNmwYSOznygrk0W/ai7DMm10DMb4pojuRTOWQsnZFYzTWPC0FOq46DySVRWkugC5wR5va2YOUtpZqypJicm5XIgaYsTRlYTC0dPpdzKlxefoawt+EvBjFv5Mg80dgnVGfr1e6SFoRXlzIqQoo4O6IZ3MfYhzxcVibRs6gGnlVuQqg2CW3mfo7LFD3e/xsQ2OBsOSG3kzXrGQ3f7wcxb98MC6wQ9lS4I3JkLvN/CSiZlTFtn5FXoJeOAQXJUtUhkuaB4a2FITcS8Tt5KZIG1gmGVsKKnXAimvJSf9pNIubLYgnw7jbeGkhJwzQZW+6TpsmN9IncGnSjj9ryPFNCKCuYtL6QRVYgJvNEL/P4wUQUtlfNHB+dZa05ZSuasR2MOixYrrRlJULAST9HKcGMcUXUdaIcLBTKuiEXBhaBgYEpKB2fDAa/mqxzkbR5u7Sys2EevoxefCn07UQrHuh5zO+p56Kye83y0wbyKiBsFSloi7ff9oluw3Eq5T89py4iJLSidwwrHZuBPDC0qMjSr0gez/NHsIg80dtkvuzUF6oAzjSGb2rfr7roLT6omp/QJA+NVot0w42w48IDV2rIdRyWRrmjoEucEcViy2piSmYAkKGgEDc4HBi0CJrbixPiVxIGZUSK4rB0QUDi1eP2vPzlOqxNGVULhPIx3KUgXJrLBLGGjPUFJb+2WoeMgb3E+mNcIe8fMhhgcqyrAYrkQGC7Fh2Qu4MvzS94PI1qULq1XLb7YejHa9zEG4SFKWDSm7ny9lr7dC+ek0hBIS6QqKivZUCkSuBTte3u3PiEUhqFJaOtsMWGsaN/F0sJwPjwic2JhH5fC0pNvTijlD/w3/yP/f463zASBtagc5us+LMcJnzrd0jnr60NObq6TVbU/oQiIpd+a3N1iZDbk3vgOQ9Pkgf4+x3mT0koOCw8dyauATGkyoxnMEkSmPDPCOc+blIq3RztsVSHHpsW7owH9YMp+2eXB1i435qssh1My+5ol2Ui5oEHNjWYgPCr+MKvzFkqfFWmMpDSSqtY72DRgkkXcqCG4htcguFtVyH26QnNEFkgsPrfyUnxQ9/n9wamEXQi07m4ppHDePGbiWurtP95B1fJL79qynaYRqmWZFSFaGfIy4Chr4urK/KSI2DYSjaUk4HIgOahXEi1RcK2ynFaG5WDK07NzPstD+XZhLEsKF3A7W6KtMoK6xbhT9jmpmhgrOcl8UtYw9ErHUd7ghbK7kGq/I75NIjT7piAWsG0iXkxPMSwa/PDKU8ycfy2HVZtX56sEdWtDC8ON+epie9FTKZnVC8n1JI9YilJGeYyxsjZpSQ5r+/ZXp/fwULTL7WKZfjBlULWwCF6ZrXnqeP1+7mY9tDB8qLHHZ/JTjEzCSjBhNRjjO/tvcLi3fpvzrcGDMA4bh3Ugr4fEVIlA5Y6s0h5HXkBVh6la47cYNvR8BFnCzIYsqympDWmoEokjzUNOigQlHJ3X7bHzMkAUPizY9w0BK+irkkPTXuQbeHhr4At/ZWMR8urvEwzLhO35EsMy8VkRTnhMvfOFLR9uA1FYLSaHrAwQoT8qUhuROkidwGIZWF9gS0RIUwpOB4K2NBzlLUamwcxGjEzDg25ETiKLBeVpXHl+w6BsMqghrLfnS/S1TwlvqwyFo60yTCXpxD50SODBL6VVFFZhnSCUhtRqRjZir2qzbwqGlgV0Zq9qM7HO52eUTY4KPwFNTbTIijjMWuzkPVbDyaI2AJBOI5S0mLpICD5y77DqsF0ss1susVpH26VOcWi9j2RYNLg1WeJqdsrrSbCUNuAgazEomsyqiNxq9rL2IvlrVns9MhcSi5LhtOEnLPPaa1XC1dkaIYOiiUEsAozujsN5i9wqSqtITciojOtVhQ8X2sl7DEyT1L7JNid82wql/lyHCwSiNGTLgmRXkJ61xCf1iQu8fPU0sfaGpsm0QaszR45iTOGt2GXbkdqI3x8/xJKe8elr97G6NOFS/4jzyYCbaZ/jeUIn9LSozaUhL49jn/qtHE75OLtPTB7ix9rPcyNf5+nCU5f38w7/9uASP3H2SaSw5Fazk/fIrSaSJe/q3iS3mpvzZc43jvnS4UWudA9pqIKq8gXJubQ0Gh5pF+mKVFuaUcGD4YSWCHm5dDxdAIScD074YtZip1pjv+xyX7zLPckRXzy+zDt726Qm5HPDe/nepReQwnp3oYpY014I1lI598V3eG5+lpVoyrPD06ytTDgqW6zoKUdli243ZZA26MQ5eRVQGcnl3hHjMiYJCsYu5nwwRwvBzcowsprL2nGtsuzV3o2pDfi18UXe371ObvViizGzEctqyjt722hh+NzhFa50Dvne3vO8kq+jo4pO6IN3mkFBICyZzvje5DbPFW02gzEfPXkPP9h5iouB49BaLusxf2XlGQ57bV5ON+h3pmwVPuh3ZWWy0Jxs6mN037AZHi+OLYllYhscVm1OLY0ZFg36jZQkKBYahtPBBIXjB5efZmJj7okOSG3ksy5Uyvetv4DBu4kBzkUD1vWQL2Vt3ptc4/taz7NTdVhTby4XQ/DWL1K+ZVYQAEHqPQ8yE1SRQJaOQBqaGzNEBWWlENIyTyOqBMzdLkYu0MJwKT5AC8O9pw5IdMmkjBmWCYHwBqhZGTItIw6mLbAgyto2bQWulNwX7TKwvl16IfCxfPe37vDoyjalU6Q2xCBqjoPBINnJlzgqW2hpmJiYOCg5zFoMywZam4UjxwFSWh8uLByFUcysQwvFuiq5EBT0ZMHAxpwPxmzqY94Wb9OTKSvBhHf2tnmgscP9jTs83N7hnB6wGkwW1Xn7uqvxXdHXrIo8ffquughe2wroiijwdQgdmMUV9a7sO3MQCx9BdzeD9LQyXNEnCxfoffGdBR4fWLRbM+fvswiaOqehCgqnPPdTukWsIMDcaAJpKZ1DiwoJPNDY4WJQ0ZIRfekDc0unmJqYexqHrMp80d6cmrh27Hp59cTE3rpff3lMvVq8B0FdQE6CEq18LSIWPghpaBIuBK/VlLoqJZE5I+M7VHdXFXdXQ5f0Xdn8m03Uet34c1xBCCG+XwhxVQhxrfZI/WmP+3EhhBNCfMO08LfECkJUFmEM83WHvTCn+ZUEVViCWcVaPKV7OuMzg/vJtrtgIZhK9MR3L2QJQc2Ba6s5t4pl3tHz+8DHj8/z2etXUMpSZgEuU+zGBnEc0jjxB5gJJbISOOVIXcRnZme4U3Q5thHPzc5yvnHEmeiEzGr+1dY7AL7OfZlnGh1WRLrCOkFeBBRzTRBVVMcNgomkKiVT0fAeCgEykxyOQj536SJ9NSUW8cId+nJ2iu9qvcR2uVxH4HlIqhaGoWlyUvmwW4NgIxjxf9z8S1gnyKqA0ijmhT/hhHAe2689fXpWaMLAoIRjuNeG0HIcWGwlvSvzpOVj/YTDVJKnz3rk/V7ZpadSCuejD2NR8mvji9wX3+FHm1O+68ZHKK1ngs5r7F9Dl9w56RCFXmexn7a5OllnnMdUr7Z4LtXouOJW1CPLNEo5fnv5Xq5n3mBWuoCv1IXXsVlHCcd+2eW5yWlWwhlf0Zt8/M476IZz9mYdJlnERntCFFQcpk2ebG3WOgfFpIhoh36ltPXKOrqfQe0pCcMKpSxP5hscVJ06pFjxW4fvYFLEFFbRDnMOZi26UUZT51gnGRUx68km2bImrY+TB5q7XHWSr3cafIPx56ikFEIo4J8CH8EXQh4XQvyWc+6FP/a4NvDfAl95I7/3LTFBlO2Agw+scO73csLdCXf+94w7N7vEhw1ufuqdrH7NcmFi+Z6f/QJXp9728bXdsyhlMQDK8I9e/DDpJEJIR/NrDVzgQ39/8T//eQ5Nhz8Y389SkNINUl6cneJ8POBzh1foRnPaQU4zyPnp53+IRzdu89LJGq9M1rg+WGa6/wjBWFEtl3ziwz9PX1YMbMBe1SaWJao29hxWHTaCEb89fAdLOmVNj8mt5lNHD3CldcB9yR5xDa19OTvFqXDI7aLPLkucVAmDorm4qv78+MMcpG2yKuB0a8RLB+vMBw10J8daD7bRTe/7/jeP/VO6UtCWIQOT14h3y9NFh5mN+MLkPv7a0pd5Kt+sXZ0RzSs5T8/P8+7kBodVh2PjXZlNUTBzITvl0mIVclS1uZUvcyoc8fTsHIOyyfu719kulvmuGx/h8w//JlPrl95DW5E7mFiNQTBzIb92/F7e3trmLzdfZmQVv7T6fn6g8wyrakbpJDumy6qasFstcaTatFXG/731XXx44yr3xXcWSP7Hh+eZFDHP7p3iRn+Zv3vhd3gwnLAkY/bNnFUVsW9y+jIgdQbjHJnzzE7rBDOn2dlc4rDq8J2NaxgEx6bJbrVELEouhfv88uH7+aK4wv91/uPcrBK0MFwMChSCEodxDiUEkZCk1vBzx4/xntYNVru+u+GLlG9y/PltMd4DXHPO3QCoxYo/Arzwxx7394CfBf6HN/JL3xJbDCcgSB1VQyHSjKzQ4ARF1yJLgTRQNiX9YMawaDAsGgSBIYkKtDJMpg20MrhcYYta4efq7obzVexZFfFAY4fSKSZlzMg0qJykqzNuz3oc5S0qIzFOsBTPsQiqyv8upxwYQelkDRupUMKyUUuXY1F64KuNMUhSExKLgpFp8EBnj7kNacqcWJaULmArXV4sy3PnhVcA7SAjEIbKvvaxLNK3g5pyJS0yNFijqAqFQZA6h0SSOTgbwMi6OkPyCC0MM6drNL0hrPv7I9Ool95+5TOzEUObMLPeKHZ3dOv2MbBYYt/dVpT2NaS+h8hCX0rGLuJiUHkGZ5ATCj9xtKXhXHTMgWljEGhhGZomh6bNzEb0A29ya9U1irut29IFTIo6EjEsyU2AQTCxjhObkTsY2YLSgRaKiXULuHDpfDq4Qfpk9/CARHpdRFinivsUroCZCamcZFK//01RkTrHrhEcG8HA+m3hkTFManPZ0CQc1y3lb6ZIeZfa/o2+3sD404ySr/0tIR4FNp1zv/NGn99bYgUhLMxOS9q3cmyvTb89Y68fEF5tUDUdZUMQn3i796NL24yrmLQMawVjhG36JaNuF1grsaFfPYB3avbknPd1rnOzWOF8eMRJ0iRRBRfbx0hhua+7T0OV3GouEUnDOI+50BmgdUUmHE4C2pKIitNKMbEVF4IRmfOOSYPk7dEOUjgebW55N6BK6TRu8xtHj/KB3jWMEyyrjNRGPNLZ5rQ+4bbzwS0nVZOeTqms3+eeboypnCLRkuVohjHrXhqu/PLfGUmrnVKUAbFwrEi/Jz4bNLhj5vSlRDLlZtXhoWSH+/QcLW6yKnNKNeOp/DTvaV5nVc0WHIWOzEhqZ2NsyoW34u5+u63mtFSOjNyiIBkHfmK7q5NIZEnmKjbVlF2jiOu60IPRDomARPrf91C4h8RRIrk/vIPCMTENRiYhliVJUHBKD+mplKHxxrfCKk6yBnmpWe0fcVpN6EpB5hx9KSlxtKXg0OT0pSRzFiUEsSgYWMWqmvJS4VdGWTBiQ6Vs14atTu3IzSrNSjijLS2hSGlLQVdGHBpHUvtkLFA6n0F6KvRu4NxqxrCw2L+p8cZXECtCiK++7t8frQ2Pb2gIISTwfwJ/4w3/Rd4iKwiPdQPTUAhXi4mUQ1Z+dSEN3uosLG2VsRGOfYuqDstJIi9Ess6j4kSFf2X1q1N41R94Sfa56JjUhIsrdyfIfPvPCZ+zqEusE0SBV22KyqPH715Zs9qFCBAK41uawqKxLAeeMB0Kg3GSceHxayPj5blZLYv21m+LvVtQqyG2iSwonUTiMPWEYSrl/SJGYOurm7HSa0Ic5K5ianMkgswJpBA0pX+CnlMhaNZYNuMEu+WSR93bkIltMDYxpr5i++KiXHgrpLBfZ2O2td/AOMm81AxtxdBaclcSCY11DiU8fj8WjmU1pSkqMge5q92pOA5twqFpEgtDgWRsGwtJtMfaB4sCbOEU81KTl/56VhiFEo7UOSZWEomAYyMwzjGwAZmzpI66COxXfhoo8ZDfvapN5hTHpsXMRoxtzMyFzKqQmQnr3+l/PrUlExszsDG5g6ENGNqQ1LHwvXikX7hw+L7h8UYLlP7tP3LOvft1X398cvhGRsk28DbgD4QQW8D7gN/6RoXKt8YEAcTHHhkn5jl74za2UOT/H3tvGmRpdtZ3/s45737XvDdvrpVZWdlVXdVVvanVUmtv0AISMILAYHCYwf5ATJhgmIkBj4MZGEcYR4zDMeEYb8yMCcdA2DDE2A4bBiSQLAmkRo2kVlevVV17ZVXuy7159/tu57zz4dxOwQBWN4PDhUMn4n7JqLj5Vt73nvc5z/P///71ApUIy6xUdlJxYzTPy/1ThE52wg84PK6gjcSkinzi2DSu3L7eBIdcjxdZcrtsZw1eGq5aJHpqnZGXj1e4N2mSZDYjQ2LfdzCewlpCAwa6xqNrclrTCLslVdA3wZTH6HFoIjbSFneSuWlfosI76pvsZ9ZcFQn7hP5a9wxtXWaogxM5tS9zKiomMQ751AaeF5KJdpHSoMo2C1MqjRNkGCPIExve2zEGV0iOzYRTymVfG3RRcNbts53N0DGGPV1GCfCEOZGTaySN6WhuYEJrQjPRdKOwZ/dA5Cex9oG0f3ObeWH/rkkBdSmJi28oLjvaZcXJaEhrlhoYj7iQ1KRnaVd5g5JIqciYzbxuBVzTScObn6mVR7snKskks0Kw2fKI1DhkhWRgFD3jcy/PGRcOXSOnBClJXCiUgIEpSJF0jWTd6XDO32PBGSCxm5eeWtSzwiE3EkcYxoVDzyhaymFUGNq6zMCEdKfVVEeXGUzH3EMdMNTB9Nj19gvyN7Fz3+z1FtYLwDkhxBkhhAf8MNY8CUBRFL2iKGaLolgrimIN+ArwyaIovv4nv51dD8QGUQgwjkAHinyuykJ1gDh28fqCZD5H+5YJYQrJqfCYJ6tb3Li3wK2DWbSRLDT6LFQGzLb6LC93UCkkjYK0VrDi9OmakNP+EV0dccnf4t2Vu/Tz4AQRdqm2y9nogAvNAx4u7dMMRjS8EU8tbyFHCjWSIOGck6GAryf2S3A983nSP2DJmVCRKU94ExacHs+WrnHOtd341/pL1Kal8mbWpKJizpf3WXXbPBzs0nCsOrOmJkgKnijd51J5h3c27nO+dsB767fJej6665HEHlnPJxt5hH5KUE455UBFCu7lBXFR8KW4YoGsheBqOsOC26UmBeuOTbzSCG7Hc7hCs5PN8HJ8mo4u01J9DJIVp0tDDVnz7PUf5WVrTFJDZpwRq37bVgUyIXQzBsbltSyiozWDwvBykvCk7/Pp0RmuZoq6GlOTCS1VcD3T/KOtjxEbjyvJEqaQPOG1GRjbD9mIm/R0yHp0REVZn0nfhCw4PeuhGIZsH1mr9lnXIZiOLjNsn6GlCrbzKi1VUJGahnRYcnzOORkLCv5Z+0N0dYnPDC/xSrLMC5MzHOclSjKhIu2xaaJdFMW00WuriUCmyKmTdWCs6KomsxMQzpsVVjBNOXt7N/9bfH2ztymKHPivgc8AbwD/qiiKK0KInxdCfPLtX5hdD0QPAgHV+7lFy13f5Mb9dVQumCxronsOQScnrVrL7W/eexSAsBpTDhNGicdoq0K0PGRyv0IhC6oplHYEIofnJ2cA+Dd77+THT/0u//b4aV4+PsVyqcuVowWent/kd+49wkw0YfuwznDFUrB3x1Xu7TbBLyAXiIniq8kMrsg55/Z4frLCY/4Ovzl8hDXvkNcnK/gy4+u9NaQwvKt6j1eHp3jfzB1+5/BRPt56HVdoXh2v8KW9h0gWrN5ipH1KKuHaYJ6lsMfN8Rx3+00GieVUXi/P4VRT8rH9qNxags4UnYMqpJI/iC1Ed93tcy+P+Eg45stxha6JeMzb4389+AiD6g2ux4s8Ed1nZHzeXb7DpzpP8P7aTTyRczud46ZaYKBDrsbL1NSYrbRhg3rdPveTJr08Yiue4TAu84661YXsHlfRCB5zx+xoRWwUK07GL/dX+OvVA74SS54bPMwdv8W7wrssOQO+d+5l7qWzPBps0jcBv3j8mFVaap8nSvcZG5/PdR6hn4esBB0imfLK5DSek1NuJCSZQ2cS8WuD5SnyPsctLHbuchKx4vT4etKwP5/6KcbGxxWa75+xD8tz3gF3slnO+7v8TvdxpDDU1IRMK/pZwP18hm1tWFY9Oiagq+0mMs596nJM10T004Av9s8z445P4LabafNPvr//Q7f+n6NQqiiKT2Md1X/4Z3/7T/m33/ZW3vOB2CBEAb01h5mbGaJWod4Y0RPg3fcwPmhfMG0X8M75LUwh2Bg2cKWmXZTIF8a2B1GywiQdKoyyeRsrbpu6nHA4W2E7a3A+2sORhk5a4lzjiHZSYqXeZb3c5ngcshj2uDdsMB8N2AurJAN3mkdhnyBLaoAu4NlwE1cIznl7eEKfhOG8KX9uOX0eLu1xfTzP+5u3CGRGIDLO+Idcj+aZdYfsZ1XqjgWbnCm1mXWHdFSJrKzYlVWS3KEZjLibtnCiHKkMWeLgBRlaFejYn4JRbO9hSY3Zyu204Gl/zMDAe6u3uOjvsuD0OO30iQvJP29/gHdV707TzseMfJ+0cDjtHZ6MbBuOTdnu5RGPhDukhUNFxVQdq8lQwuB7OaPCY1MbAqGpSE0kHC7623wllrwnUNwobXDJ37Fxf6Lgkr/NlWSZgQlRwvBM6TZx4fLS+DQ34kUqyuoNzkb7JwpNXUj6gwjXy1lrduhMIp4JNjg0EZ2pSKmrSyw7x2zmNSrTFPimnLCshhzq8KTv82J8moqasOwcs5G1eLy0SVy4KKz8O3LS6YSqT0VmpEYy0AFdHXHO2yMuXNq5Bf+ej/a4MlpGUrBt6qyHh2//5n/AlZQPxgahIWkWqNcMRRTgKEO9MSS70iQPIalJVGJBHe+p3uaV0YotMytHdCYR8cCnPJfgVhKyng8F6LAg3BP8/vA8z5av8Z3lK3xmeIkV1z6VfmvrEt++eot+bjMhu1nIUrXPrX6Lipswzj3yTOEfKnRQoNqK7WyGg7yCJzTnvH228zqfOn6Cx8tbfKx0jY2szg9VbvMbwxUimTCeZnYuuV1+r3uBeb/PdlxnPuwzNh7dLGR3UsOTOZ7U7CcVXGEoOQlr5Q6+ymm4I169/zB5VJAFBWosSaoOspRRyIJr6SKByPjqeIZTXpudbIZeHuHWLvPc+BH+Ru0eVzJYd22QbknkvNhe5cO1q7w0XkMXdnx8O2lyXzSZd/v4MuNmPMsb/QV7bWqZUGU4wtDyBnzx8BwlN6EA/lX7GUpOwkPBAU1lA5Hrasxzg4e5UdrgR6tHDI3hV/pn6HpH/NOtD/PB5i2uTRb/iCz8S/tnCZ0MJQ2fmLvCRtzk3riBIw0r4THmIEAPBddqJfyFMR0T8Fq8whc7D/NIZY/jLOKJ0iZf7D7Ms/Ub9HRkN8CpyOuD4Qb/7cYP8J2tK/zu0QVm/SG+zGk4I+5MZglVxt5eHW0kr1ROs1Z9mecm67hC86933sn52gGJcenpkOvDed5d27DHtf4sO/0qDzXaTP5Qzupbuu//M0bO/bkv44LUBhN5VIM+/TggE6CjgkJZtWRNjamrMQten/tOA4OgFsT0ywFKGsIgs2NO18U4kFWtbHdlKmBxhT7JoFyZ6bLkd8mKBvPBgHm/T6wdPKnpJBHLUY97UcI4DCmcAu0XrHmHNOWYFMlpJ0NxzKOlHda8Q04pF8UxoFjzjghExtlgn8O0worb5uHSHvNOz6r1jMuqd0RWKEKVTeEz1lbsy5xIprSz0gkROi8V6NBAqNGFY0eu5YRRYSuYQOQ01JBlp48nNNIzrCjDsntMUuSsO+AKn4bUuMJnLhqwrHqMAgs3UdimYEXF08adIDEuk5I3xdCNmHHGZIWi4Yw4V7Vek/1xhcfLm3gi56K/TUnkDIxHTSbc8Vtc8ncYGkNZBlzwdznt9FkrdTjjHzDjjCjJhAtTu/T96gzDzLd8iMJhPTw8YW+c8Q8x5Zy8cCjKOc3KiHPuhLjY4ahS4Wywz8ANuODvsFuqc8nfpm1K1OWYukxom5CWcnikukddjTlXOuC0f8TIWC7pm6CboJyyUB5wMdymJjWP+VaRe752wJnwkPPBzkmA9MVgm66OWCrZ0WbTH3HmWxXEf5xlHGhcKchKDs7xhEHi03u1iQohD+0ko5B2IvHZ7iVMIXnl2iqvl3PCKEEIqPkxk9SlUorJTIQoLBF7zT3iZtbklckqM86I7XyG7WSG2WDE77cfYmXKYtxPquRG8WRtixtyjoobs1zr8UatgowlRT3jMa/PyBR0jMfVrEQgMt4X3cQVhj9ISiwozb/oXaDhDFlwurwxWWIvrvAbx0+x7HcZmJBV74jLozUMksfDTTtCmyZ8vcm4PMpsClg3i5jzBojFGCYOQTkl8wxFYSnYYSnhaV8TF5p9CSkuAAAgAElEQVR5dUxWwIrTYV6l3Jyel39tsMq3RbfYzCNaasSogA83rvHpweOc9o/o5GUazpD3hHcYTYVJHV3mqfAuS+7xSSLZmzyHsfH4aP0KaaG4Ppjnu0o3SAqIhB3/ymlD8l3hXbJC8Sv9M1zwd/m20PBGKtmZVBlXfJbdDutOh44JWHOP+IHZr3MvbbHkHvOV4UO8s7TBQ5UD+ibggrfP+fVdOpOISeqyVO4xMgUfCGLWna8RF5K6NDSUT0Ne5mE3wDAiKzRJUXDaiTnU9owaiIzzwS5pobjg79DWZT5ceoNAaC6vrFJzJzwbHDCYfnHnVcrfaP0eFZFTkQJo857gHovK47fGAT8w+wKjhs0FWXJ6/KkGiD9lfauCeAtL5jBpSqp3JojcMEldWB9hbpfwuhKjClRq1YF1d8LdUZN3PLJBoHJ2RjXu7zXYG1SYxC7ZyCPwQU1sCtWhrnDObbOdfYP0vJvUaHgjXKnxZc5+7lFyUnppwLXhAtujGm7FcGuvBaLA+AYSyUbusaQSzruGgbGjv8+M11lzj6bTEo/T3iH30hZ1NeLx6D4VFTPrDKirMU015LV45SQ/4Y14CUlhVYPGGo36uUWk7UxqeFKzGTcwWiB9jZx6LITAkp9ij1tZPj37CwIBFZmxlbusqIRDoUndNjUpGMgJ88rSlS5P4OnoDk01IvA1N7MWG3nzZAzripxrydJJtKEr9AnPAeBmMk8kU/pJQM8o+/ulQhWGJelyPdMsOQMiUdD1jjjt9HkjlTziRXzf3Eusuh3qcsJ4Ol7UwnA9WWSsrbEqKxRpodjLLSnqWjrPds+iAsul+ESLcS9PuZM1aakBAy3omoxDE+HlQ+LCXldWQNu4rKic7669jIcmkhkuho6xY+ab6TyBzLjRbfGO5jb3ckVNGh73FD0j+MpknVW3zYLqI0XBnWyWkXsEwEvjNQ7SCnPegEeCHd4WDwK+VUG8lSUM6BBkqjE3NxhtvoPm+jHDtEzjqqa0k6ADxW8fPMpGp0ESu/zYY1/mKCvz4tYK9F3iez6lfesApSiIjgx5IPi1g/fwVPU+v779BEvlHluDOu1BiUsLu9ztNigKwXAU0JoZcPzcAhtrc4iJZCOco/KGi6xPJc6p4G/f/T7e27zLO6KNk7P2v91/ypKqvJF9OuYeO/0qrfKIS/VdOlOtxVrUpuaMeam3yks7p7jfmuHKnWWcIMfzM7LUIQxT+p0SYqJwelN3pmc3BF0yTNq+jQH0CiY3IyqH8D+tfJKaF/NM9Q4DE5x05j9UvQ7Ar+y+h7OVQ/biKo9WdgC4PW5x1VliPTxkzTvic91L3B/P8Eh1j83xDOPc49r+HJ6X4zsWE9cZRWgjGQ99XD9HyoL8bpl/0XqvFZ5Nz99PhRv8o62P8b1zL3PJ3+afbn2YtVKHnUmV75t7iR+tHvHTu09xLtzn9dEpulnIatjhN+48hu9a7ujxxgyfP/UwRSGYLY9ojyKSazXyqkGHKZcvn+Xn3U9YJ6gwhCpjlHvE2uFMqX3CJp0PBkhRsD2uMRsM+d9WPst/t/0RPli7TlXFXJ0ss5XM8MX7D1mUwGaV/XaNrXGdyEn5juYVroyXefV4mbyQzIcDHKk5mFRwpOF+t86wHyIdgwDCKAG+edLkH1nf2iC++dIujC8kyP8npTi/zsXH7+MIw1W/yXBJIQqfaDfhfGWf72xd5eZkju3EEozfeWqTypmYzz3/BL1qgTMWlLYEw0WF0AXfPnONBbfLj53u8+sH7+B7T73CjdECP9j8Gl+vrXOQVfhY7XVi4/E3d38Qx9foxMerJoxOKZyJQI0FWaXgv1z+Ck/5m9zLZ1h1O9xIF3h/4zZZoXg6uktcd+nqiBszC0hR8N80v8KP3flL/I8rn2Ijm+Wct0/LGfATi5+nrcv8/fjjuNIQ5w6pm5PmDkElIWhmDGYszNX3cuJ7FdyOIq8aKAROX5KcSknmJd/deo3HAivBb8mEL07W+euzz3GoK/z73qN8z9yrvC+6zbV0/qTXsOod8bFoA1cIG6RbdanMTGipAVnNEpv/ibbI0UHqM0o9FioDjuOQShhT9Wy69+tjl09UX+VAV07k03fyBn9t8XnupbNcSZb5YPOW5StUfFbdDj+9+xT/YPEyb6QWQ7fiWLnyy41T9JMA38kZL4w50+icBNPMRQNe6ESIkWJwr0ZtrctHZ67iTtWha+4Rm1nzxO79vsotDvPKVEuRc680yzPRbf7NcJXTYZuHvAMuujHtvEykEroLIVIUPN8LadSHfN/8Syey6aca9/mCf8FK9tWYvgmhbnNaf128g7hqCVye0qyUj7nydm78vwBNygdCKIUEeehRuAo5mtjSNQ3Il1JkbqccxpMs+11uxy3ujxrfSGHyJtzozVGoAplZ+3ehQOZWqv1m8vSNeJGL1T0S47I5qpOh6OmQx6ItXp+scCuZh1wipKHwDUoZZC4wCoxvyUuVaVm84nTRCB7yLANxxhnRN3YUdi+Z5TC1Ts+d3OGDzZvczxusuUcsKase3M5nqP4hfmHg5ChZEHqZdagWgsJYf4kxEhMadFBQTNO0dDDVZmjBgtslEJqWTOgYj4v+Nh1dRheSx6ItttIGHR2xn9VxRY4nNB8v3eNmXiYSLr6Ax/ydkzg8RcGy6hGonLKbUHZTSp6VsnvT9C/D1PsS5LTUiAve/ol8ekH1GRnPsizUmKOsTFeXaDl96nLCuXCfN9Ixj3gRT3hHHE6bfg1/TMlNaYVD5FRC7whDoDJKTmrdq7WMopSTaYugX3B6NNWQnXyGh9xD2/T0d6nICUvuMXU1whSSmpqwl9dYcds8Hd3lad+Kuj4Y3eaiv03DG1N3J5RrEyI3Y8HpcZBXqEiLx19wejZw2elzwd9h1W2fODcXoz7z0YCV8vGfMZvzLb7+E60HooIoFARtQdqM8NMcJVPu7zXwSynJjE90AEndZck95vp4nshJeX7vDLUg5pHaHkUhUM2EfOiiy4LwwCFuCFQCa94RsXExheDxyM68Z4MFDvMqYOXDnbzErDsgaoyZqw7pRQGhl7G/7CLvBZYf4BoectvEhSKbwklaasCj4RYLqseerjHnHnEvmeVsdMAjwTYbeZN5p8dAh0ReRtdILnj7fHr4KKtRh4frhzaXQTuUPUvfdqRmnHv0gwRdCFrRiNf3T9snjQA0FG6B8A2FhMem8NQMQTx1bEYyIZ0SlvazGq7IaTk2As8mX8FONsOW2qJrLMG6JHJcYZgXGeMCzlf36WYhzvTL2vDGdD3rgC05VjF4369bn4MwZNOQGi0ED3v79E2AEpZwVZIJ606HceHw+ugUK16bijzilFNmR8c2W9RJSH3FrDfidOOYxbB3MuFZ8nt8uVhHSMspdZXmnL9HSaQMiikgBsF594BB4dIQGlMIIpmzJ0s8Jnc41CUuT9b4nspr3MkEO7pGXU6ssc4d22pDnbaRizJh2c2n+gj77TTTFHLrVLGA2rVy2yZyOYq6O2FuCrV9O+tbTMq3sgoYrWcUEvJ6xFONTZqNIelmiaw87QFklh34kfpVvq91mZKX0gxGSFHwTGsDIQpkmONUU1RSoAPIylCXMXU15juqr3MnmeNJf5O/3PoaupC8t3yLrFB8pHKFD0Y3CLzMxr5NY/Bq1RHOWOBMrBzcFYaGTNnWNVYcm161oGwpetHb56I34L2lm3xn+XUWnB4rTofneud5T3jXchwLl0Nd4qlwg3XXAl2lKOgmFt46yV0iJ6XsJpTcFCUKyk6C25U4IxtM7A6mEXTTWL7ONAdkT5doqQmvxSu05JiH3DabWZOnog1OO2PeFWyyoMbU5YRPDy/xhL/N2DgMTMDBVGw0mJqjXGGhOPuTKvvjCnHunsiJHWEs19Eo4thlW9d4OTlFVkgW1JCOtoTqPxidO5kYXPJ26JiAgQnoZiErTpfrWY2vJRnv9l1SpgzLYY07w6b11RSSXhbSy0KbINb34NBHDB10IVhWQ9q6xF5eo65GbGSz9IzP5ckaY2Ovq2s8VtSQtglZcgZ08hIvxKt8cXwOXUhemJxhL6/RzSKOsjL9QUR3ErKTzdCUY9q6fCIcu5Eu0NZlriVLbGZN2sZWPvfHMxzFJW4NWhxMp09vZ/05ejH+o6wHooKgAHKBfzRBZJrNyYwtZwUEHZtH6Q2y6dNQs5vWWIxsiXcQ2w8l6/k27ToR5KEgmGIJb6ZztJw+15JFtpIZvuyc5cpomfdXb/L10Rkm2mUU+hZq2ithjGR0bPkS3d0qbrlAJgJh4LVkkQvePhUZMzAe48LnTmzTtta9AzIUG2mLP8gqRCrh2dI1BrnPzaxlQ3VlRiQTriWLdHWJ+8MZXKURomCY+mRGctyZx1WazsDegJPMIasZZCoofEMmrLvUZNbheaDLVIvYJo0XkoqacGgiskLR0SU6ukRDDdnIZk+ONS1nQMcE1KVN/lYUJynbW1rwcrLEblzDUzlymux9kJTpJaGduLgxjjQoVdCaBhorCkaFgytyvpos0HBGxIXL65NTgO0TaGFYDW2MXUuNGBuXL8XwoQB+yYvpxCUClXGz22ImGBNOUXcT7eLVE1Lhn3BKX0yWuTTVUIyMT8vp0zcBFWXzWksi5VBX2Mwse2NgAlb9NneTFu8t3aQpxzxUOmZgXN6YLNlYxMqYkpey5h5xI5ujJBMimTDvdglExprbIS4Uh7pCXY45iCsshj1GuY8U1on7tu/7B7wHIWzWzX/aVa2eKt639CPsfnQelRX0z0DrJYM3MHR/fEDvXo3gUCHf0ftj1Ciw7Ie//MO/98doU67SPDm/zXEScfe4QeSn+ErTHkUMOxHq2EGXv1Hjfe+7LvODM1/jc4NHeSK6z28fP0aoMt7oLvBs6yYGgaT4IwQoR2jyQnGUlJj1R1w+PEXFT6i4Ma/vLJK3QyhnBKUUx9H4jqY3CAnDlH/55C/9MUJVVigO8ypvxEsMc5+L0Q6uyPnc8UWerGyRFYo7k1keLe2wn1U5G+yfXL9lKoTMuX2ujxdIjMvrx4s8O3+T26MW836fofa51W+x16uwVO8zSGyDbaFkLfSmEBwnEf/84V/llONzK7Pip9POhKtZjcO8ykejLbKi4DdHDzPn9C1kBkN/arrKCsVY+ySFw+/uPcxatc0PzH6d68kiv3z9PZxuHNueg5NwGJepeTG/tPocLyYp513Dz+w+y480n+dRL2Nf5/gC9rVH14R8tv8Yn6i+yheHF3imdNuSofIyTWeILqwzsyQTXJHT1ja+UGLYSpt86egsmVGslo9JtIOvcnppwP9x5tfRRcG+tlbzvbzCK/EqrtAsucf2aCHsiFhRkBaKqozpmoj3Bfu4CEZTK/vplb0Xi6L4pqxHgKi1Ulz4/p96S9+Rl37xp97y+/55rgeigigkcNjB+PNUtjWTWQdnUhA3FN1OCZVI4gVNBIhjj0LahmQ+DdpRE6iomGtt+zS3WoGC3v0a7XqXQeozHAas1LtsdBqMhz4iVrh9ia5qnK6DcQpuDVrcLC3wdMmi2G71Wzw5s8VMMObaaJ55f3BiruplAUtOj6OkjK9yi4jTLuPUZRj75BVJNvIQpRwGLjqwI7xB6pB17Sa3k9c4nJq/ruoyjzk7bOQz3Etn2YnrTLTLudA2QheDPq7Q1NQYQnh1eIrXOwu4SxqDIJIpAx2w6HXZTev085BTwTGmWGInrrM/qSCFsdeYucRjj64fkuaKbhpS9hKO4/CEGnUza+KKNg+7IVfSnI5RUzp0ldfTCq7IuR3PcaQqNJyhTVyf0qo24wZPlO5zI14kdDKGmc+9tMVY+/huTn+aS5H6iv1xhU5c4sUk5Z2+x7Ee29xOE3Ev79JSgqtphRcmZ1hxOzwc7HF5skY7K/FafIrEuMw6A7bG1imrEZhCUlbxCegWLGjWkYaqF5Nox/Z5soB7xzNcXrLmrlcmpznr73ErWUBRMNY+W0WT/czyKn2Rn2SOVlTMknvM8/E8I+Nzztub+kL23vJ9/xeBav1AbBAUIEoRFJCWJTqwgBiVFATllKTvIicCV2lGkQZVYFxlLeAO4FgZ9ULFlrub3TqONMhGSn3aWQ5CG8dWCWO0lsSxQodT+EpgKJyCxbB/IsVuOX3mw8EJVGYx6FNRMZFK0VNUXagyZn0rmGoEI2a8CQdeGUcaal6MCjQ6URBoXFejpCHyM9KJi+fltNSAisyQWJ6hKwx1OWbRPWbgBwy1P82zsDAcV+QnT+jTYZtJzeW0f0RaOHhT96KZeiuG09xN38lpeCN6vrW3T7TLsRdx7OeU/YSJdIn8lEBlNMMpByP1WXc7LCqblRlJQ0UUNNSQ2HFZcfpI4Ix/aM1VU5zem7CXBb/H2Eyvffq3WHKPyQpL9PadnJlgzKw3YphZ8vZ515zwJHxppeNLynZmV5w+o2CbuhzzcnyaU16bgQ447R2dhDav+weWV4HBIAlExsj4tIWmJBO20sZJ7mhlirSTFCxUB5xz27YZF96jKccYJNvZDK7QNJwhZRWf/H3tZ2ATzD2hOe0cY7D5JfU/q937AV4PzAZRVEt4vYLueWvTjuvWoFUOE8IzKdlzTZwLBrdqDVmFgqxqK4lwTzA2Hn9p4TKvjFbIjKIXB+hEsT2qU3JTTtV7eFJT9qyUOdhxSZoaXGOPGiXDT85/nn/Xe4pIJbwrvMv58j5fOjzLYtTnt+5c4gMrd/6IuWo3rnJ/0OCh6hGtYIgpJE+1trjdn6UVDCkM1JtDJomLMYIsc5lMPIqxgy5JXo5XT0aP57x9rqUtNrMmHytd42PRBpFU3MoUP/bqj3J+9oCjtMxhXOaR6p6dDjgJV8bLdNIS+3EFb7qZjTKf1fIxu3GNZ1s3GeiAM6U2CsOMM+azr19idq7Pfq+CEAVKGXapMko86mFM6GQ8PznDb+VlpLBZmSPj8WSwxblwk188fveU7+nwzzY+RHmaN/Lm6Hk9OuJznUcouQmfmLtiI/OGD5EViuONGcYLY/Z6FU43jtFGcrPb4mfCZ3Gmyta/P/8yLyYF/3p4FoVN5vrFWx+g2y1xcXWXDzZvUVExn+9e5KXDU5S8FE9qHq3v8MLRaZ6atbqQskoYap85d0DNGfPatRW++6lX+fzGwwCEfoo2kt8ZPUJiXH5t42l+9MxXuTxY5ZnqHV4d2f7J89tnWKr2aYVDTCG4N5jhieYOPz33Of7BwUe53pvjyZktqk7MH2K0vKUlHoAj/n9oPRAbhDAFhedYQGcuMJ4dZ/k9Q6o0ae5hXEhzhetq8kCTRy4yA6EFaQ0imRIX9gk2Hw4whaBfTlgudQlVxr1hg04c2XGiq4kXcjBALslrdqM41CWeLt3herzEutvns8bhQ61bKGEorSQs+T1WguMTbJxN+taUnISySlDCcH/SwHcsCLZeH9HtlSiXY3w3t4lSRtIxknKYcMHfYd2xFU5FOrSUjddrSLiT+3hTG/UnVq+y7B/TcvrsZDPU1ZiW0+fL5mHWgiMS32Vc8vBlxkFaJVIpvszYS2ocpBWeKG2ym9WZcUY20LeS0AjHNKMRnrJp2zP+GBNJPJWTaoengvtURM64UCypAk1BJFyU8KehNjlflTEfWbiOQbDodsmmTcqKiunnIWejfTbiJuvhIe8sbZAWis+fepgzDduoXAx7mEIyE4z5kebzdE1EQw15MSl4p+9x2rmJQrCpJVeXljhoVvhw4xqX/C0OdZVZp09ZJZSdBElx0o95pnIbM23YjqYgWUXB6TOHjLTH+bkDZvwxx0lErB2+LbpBXCi2F+t8V/kKFTWhImM+Ur86JU0pZv0hq34bU0gWgyZPle+x6oR8qHqds+EBS+4xS+4xP/92bvziwR9zPhAbBALIDdoHZwJJaN2bwtgn0nAQ4E8HslnqIJ0ClVgMHdgeRlYoOlkVUwi2hnUSrfC9HF9qRrnPMP0Gcdh1c2JfI4YOhWcsMbqAjbTFBX8HjeBQW+mwEuYkNSuSNpo+KxT93J5tS471L3SziJKTsD+V4SbGIdcKM3GI3WlehGeRba5npwMNGVORNiB3X+eW4ygztnKHjWyW2Lgsu8c8FBxwN2nRVEMimbKVNqirMTPuiKqcgJyQTlmSieMy7/a4l8yikexMapwLDzjKyhYUWyialRH91KcVjZjk9toiJyM1CkcYUqAucxaVheB2DbSUw75OGReKdaegLC127nywS1y4JzmYb6LoVoKOTRwbN/BlzkOVA/byGsWUufCmRLqXhYQq41Ev417eZUnZyuG0c5NZVaJnJrRkzrPV67S1jSCsy4QrSZVVt80Tpft4U7JUQw15rLQ15XDaZuKbbNCBCYjclEHm0/RHOFITOSnpNCYgKzTPVG7jCk6EUp7Q1NWIJyubVNTEBgFPN54Fp8vdPOYJf5tz3j6Kgto0E/RtrQe7gHgwNohCCOQ4xjgCd1iQlQUyL8hDSX8SUGhJVi5wgTxxKLSgEMA0vBfAlxm7cY1B7uOrnMxIRmP/xNgzTl1apRHD1CdNHdACGUu0ZxC5pHAN+1mNNe+QJbeLoqCdlag5NiG8l4fgWOCqK2wiU0XFHGc2P2KgAxvNpx3GmSRQmU0C8zQ6V3h/yGQUDz0CL2NQuHRNQl06ZCanIQsGRhMXDl1tMy510TuxPL9Jna76E/ayGi/1Vmk0RlNiUn4CrBkZn0BmLHo97o9mGBtr2zauwCCYTOGvk6m+YZK5pEaRaod0+vOBUTRkdkLMNhgCYVF2h8agienreQZT2OybORqByOibkEimVNQER1rq95tA19nyN/oOE22DkAH2dU5L2Q9VYVAIemZCTYbs5GNuJfP4MuOpcION3Brv2rrMUV4lksm0ejQ2t1QmU7x/TjqNF0gLh6r3ZpiwxBe5ZX7mLvvaPn12shlif4eDvHICKO7qEke53ZhS10Fh75NAZKw7Pe7kjZMxa2revqzoW03Kt7IkmHKADiA6KOheKNC+QBgrP052aqhEnDQhpSjIbjbQnmVFCG0rCEdqam7MC1fWKc+NcF3NmeiIo6zMas2n6k2oOgl3vCbXxovoRoYKc0yqUJ6lJFVkzG3js6erNN0Rv7n9GN1xyJML27xn9vaJPdsVGl9m1NQEX2YcZRVm3QGj3KfujpnzBtzqzJLnklKYErq2evBVzqjqMV8ZsOakjAxczySjokwgMiKZcahLNkvCuHhC87n2I2wPa5ypduhnAalWPN24z1ww4IK/w8DYYF+NoJOXWXB7HIgqY+NxptTmjH9AJFMaztDmT0QTcmMVkZPcZa40ZCnsnaSWj3Kfs67DoU7Y1zZucM3RbGqfsfE56/YZGFCiYMVtn4wWLQnb5YK/wyuT0+hCshIec8Y/5IK3z7V0nvYoYi4aUHJSlvwedXdif6+Aq9NszqxQbGpJS+bs5FaW/dtCc2O8wJfa5/jk3CusuYc01cjKoZFEImFuWjmc8w5QFFbxKq26tC3HHMdPcb62zyAPmGiXWX9EyUk56zpIJLe9AzayOhf8XcbG50BXaKk+bphTmtK3NJIFt8uK06VjPM67bQaFQ0V8A2b8tta3NohvvmRqkO0+pd06g1VJ9RaUtxPcXsLNThmxNsF7JeL2y6dwRoJCwcyWwSiB1AVGCX6/c5btobUHL59uc9gr4zia//Pl91EkCtVTGN/6GdRE4vcl/jGkFfuElBo+O/cIsXF5qbvCWrnN1w9X+SurL/Dc8TnOlQ74O1e/G4B0Gq8nZIFQhfVNxArpazjyMYE9triHDn5HkAQwigrbW3HshnZro8L/NX/pJPvBxtbb3kUnK7EzqjHJXf6gvM7fXP4d/vf9D/Pe2m0CkfKVwVlcoZl1h/ytGz9w4pEYJj5Jroi8jMNOxTZ/jz0+tXCJZOAjfU2RS6RrEJsBG80cOVSYsuZ1f8n++1xCKvnH5V2eCO7zerzC/aTBQ8Ehb4wX6aYh3z37Klmh2M9qJ4lXqfkGmj43Es/J6Q8izEGAKeecX9+1lu1rNV7oRLillC8X62R9D6+e8NeaX+aFyRlGwTa/eOsDXF1a4tnqdW4l8/y20PxU4w5vpK/z93Y/TiAyfuJrf5V3rG7iCMP+pMJq+ZidUY2Sm5AXCk/m7I6qNopQGQbjgLgTcDNbxpsbk449ompMHLv83/U36Ogy31W+wp2swT/c+Q5e2VtCSjvlaAXDE9m3FIXdRLVHqhV/69TvkKLYNAHXB0u8Lbv3XwCz1gOxQRRKoI/aaP800Z6tCtxOjK56FF2PQkJaK5CZIDgU5CVwJmZaPRTkIcTaZW97BhVqKuWJzYyIHYpcIiYSvy2ZLGvcnkJo2xzyegXaE8gMEHBvv8mwsUnZTdiLq7Snakawc/TJ+Bt9jGLiIMr2zGkyierayUTYkeQlgQ4K3IGY/h6b+yFTgVYFXleivYIb4wWW/K6FxCRlzpUPOM4ibvdmOeyXSROLeq/LlIY7QmEoyZS6M+bmaI6NfoNx4jHGZoOMYg/fzUky+//2opSiF5BEPqrnoMsCkUpMNSPqCnRJIlMBA4XR9vgjtECNJJtxg4vBNg3HJoLHhUM3Dbk/mOGwXmGoA14fLJ0kXtlQG3u9k7FPuZHgejl6KMgLC5pNYpe8ahAjRe44CFkgR4pU+HRNyIrbsVDYbomDZoW2LuPLjBvjBd5IX+cRL+KxyjZvxEvkE4fNQZ1WNGKcueyMagwzzzI8+1V8pZmkdsMqCk089BChRowVRksKLRgPfIqJYwE+heCm32QzbbIzqpHEHsrRdMYhrtTsaxuF6EpD5KYMUx8pCr4yeYgFp0dbl7kzaf0Zbv7/P9+c//jrwfBiGJuJ4Q0M40WBNzCYyMU4Egponj5GJQKR2R3XPy6YNCVZaGXYb3aCS40JQhT88PqLOK6GgWuTu0NrbMJYkIzfsVkbKi1wYlBJAQKWZrvsxjXyQvLu2gYAF/wdfnLxc9wezpLHdj/1/IEIWpEAACAASURBVAy3llAuxxTahuqY0L5HHhY4A4FMBDrAppCHkJcLjG/vBv8YZCa4M2iiC8lZf4+PN1/jndFdDpIKjzd2ON3scG7pgKofs5HV+R/mnuM94V3OeQf8ZPN5Gt6I9qDEJHEZDEMGsU9RCBxlGMf26VgvT8hLBdLXGK9AlnLETIra963HRUA+myFzYZPORYHIBELD1rjOZtqkoYZ8svYSrtB8cvZlPrZ4jRvjBXyZMeuN2OrW2B1UGCce2khqYYwQBUnmsNbskNUMpp4xSS3spbLch8KmqZtUUShAwGf7jzEyPi/Hp7m4usuHG9es2zbYZG9S4e/tfpz/pfMQ/33jNsPcJ6gm9McBN/ZbtHsl7h42OB5GvHawSG8UcjwObVixMifO00p1Yp2xWuJXEvvwyAV7cZU7w1l+YfPDtJwBR8MSnp8xWxuS5g43due4uzNLZ1CiPYq4sTtHexjxHQtv8Ju7j/NLW+/nd9vnudl/exvEm0Kpb3kxvskSpkCcX2ewohhfSFAT/4QBcfHxnRM2hI6KP8J8MC5kZct9+P+yIt7kRHzh1nmkMkzOGsTIIVlPSDsuMhV0z0l0aKP1ZF7w06efIyscPnX4GOeDHT6wahWV1+NF/qvFL/J3RtU/xm9wPI0McoKmrSZGNZ84UziuJhm7eLsukyXrKyHQECu6jxpwzZ/Il3i0ssPT0V3ul5t4IqerI7om4hc653i6dIfYePxWvMjPzf8eP9T4Kn93478gULnlaSpNexKxNtvhOA7pDkPcUyPWW23262VC105PNuMmKtR4bo7RktwxBKWUPJfIGXs3vsmZuDolUD8Z3GdUeDxTuk2jOqQlE77qrnCn0STRDq3GEalWpMbhkeU9OpOIziTCXxjTrIxYKvcwheDy5bPU1rpkWuEqjW7ayuUT1Ve5PFnjlNfmg81bXPK3qMuEjXyGT869QiAy3oiXvsGT6C9Q9yYcxmVm/DHj3KPqxuyMaqxWOieZpqlRxNrFa+a8dG+FoDkh8DKr6JQFie/y/a0X6UzzP94V7PDkwjapUZScFGfGcPV4noqXUPVim8+RBZwqdbkYbtNYGhIXLkMdUFYxn/sz3PsP8nogNois4rD3oQYzNzMWvpLR+bkBOxszBLsR8aurNF+SNOOCH/zZz3KUlRkbj09df5RyyeoLxqnLq91lvjQ5izaC9MtN4pY1OP3qX/nHtumVNembkIe8fS5P1nixd5qaO2GQ2e76eumIz3Ye5aHSIZGT8YXeRXYnVX7u93+IQsAvl9/PZ77zHwJwqEMaMp5anOWJgq4mBZeTOoHMaMoJXxhd4I3xIu+u3LEhusbHEzlf7p3jg7XrZIXiM6OLHGUVGs4IgEW3y81kgf2sSmIczoX7/My//2EK1/AvS8+gBy4iyvl3jSfoDUK+8P5fYFwIBsalIjM2sjorTo+KNGzl1iV63k2IC0MkFVlh+NmZj/KJmddOPBQ72Qzr/gESw5wa0DYlFAW/O7zIQAec8jq0RZmBCZAYNlI7Qv313Sf52bVPoREsqQFK2MbgWdfh1wbLPBNs0DEB59wJo+kX4efdT/DRmavExuWcv8eyGvJisswXhxdoZ6UTGfOhrnIlsZb8NfeQn/jaXyWfOATVhDf6C3z6/Kd5NY3ZyBqUplOMx7wjXoiXeFeww8BY/YYUgqwwRMLlf668k2+vXOXyZI3T3hH30tkTO/ead8TfvfXdvFJf4ccXvsATXsqdHFwMz1fXCWTGmvuNWICL/i4dHfH19Az9PGDGHbPmHb29G/8vgFnrgdggKKzuwRlp5DglzhxkbG3WKpb2HD807GdVulnIIAswWjCaeIwmHsZIBl7GOHHJc4UjwR1KhOaEa3gvnaWXh5hCcHd6VrzVb9EKrVT61qjFcRzR8EYcTsqUnISDURld1ohYgWtO2AdqCkeJjWvTvWWKi5X03kwXLP1ZDenpkJv9FqFMrSzbHZDg0vIG9HQJf5qVEUmbBA7Y1G2j2IrrJ+xKd3ZCOvIsmk7YAF9tBH6Q4QlBSQgikWKw2aNKFGzlIffzBl1doqFuspHN0FQjssLjnZV7bKSzrLgd9vIa6/4BDTVEYYgLl5GxENYz/gFbaZOx8cmKMYd5hcw4nPbtF6HmTbjoDRiYgpq0KeNZAVt5woLT49BEvBavEBc7fCCIuZenTLRNW1/x25REyu1shkveHlUZ81p8itPeEZ/vXmTW6bM6lb031Yh3rG6yOajTHwfUvQmvpjGPewENecDASFYciUZx0dtjUYUsKuiZmIExSKBjMlyhuZkscDGwKtCPla7SNSEtNUJS8K7WfXyZ8x4fbmQGkCwo+GB0m3klKQsfQ0rPbDEjQ341bvA91Zc50JUptPY/Px7Eg7FBCLtBaF/ixRlZFuGM5EnHP+hqhCnoZiE3enNWSz9yMZ5CepqolDCY+IyPQ1SoifqWBWFcK3S6Gi/zUn+FhaDPVtpgol0Wgj53uk1MIWgGIyInZfO4znzUp5cE3GGWXEtEKnEHgrQk6Bqfikw50BX28jolmbDitunqiNfjFc4Huzx3fI716IjMVfTykEHisx3XeaS8x1AHzDgjunl04vEwCA7TCt3UCoZ8lbM1rtOeRAxjn2Hdt+aziSLxXAojKMYOfRUhRMGhlgRCEwnoGTtmrIgxL2Rz6ELwpeOHWXXbvDJZ5ZTXoZOXWfHa/y97bx5kWZbX933OOffce9++5FqZlbV29UrP9Cw9zQDDMAMaxBLIQpYtS0h/gEO2bEUoMP/YIdsYIzlsybJlC2wJLETINkIIAR6xg4CZ8cz0MD013VNUL9VV1VWVlXvmy7fed7dzjv84t163ETHTjZtwM8GJyKjKly9fvuXec8/5/b7fz5ffGT/KejAisSEvJOd4T/0OofAJVXfzZfpqhkEyKBtV9d7y2nyFw7TJ8vKEqYnZn7XpyRhISZ1jZqUPBMaTvAamyScGD3PcanEp+F1uF0sE1RlROMXExYvgmweo/cIFfPHoLE2V8e7GPY7LNi0595kc9RmnkzpHaZM7RZ++PORs0OS1YooWmoMyYysI2DM+Rm9mJRPnu1T7ZYdOkFRal2MQJS/nHhx0JdpHC99B2kvb7JiE7bJXvZeHXA7qnNo5OzbxvhkVsWcSnpteZLU7oS1Tv7KoJvm3NN7hK4ivWKQUQvyEEOJQCPF7b7jtvxJC7Aghnq++vv0NP/vPhBA3hRCvCCG+9c0+kbQvcVKAktSiomIg+J85KbBaoIVlszHiTG2EbBbErQwVmAXp+Y2v5kHxK7UhfTXjoYbPLFjWE87VBl4+W5/RixJW4ynNwNuxAW/0wjtCH/AHROCIRUlDlLRlynowrEJ7Y1KnORsOqIuMx5r7nAlHtGTKsvba/c14yNRENFXKYdFmJZywrCe0VEokSmrydR9DIEwVVmOIdEmsCp/ubUEIkMofUSowCGlJnUILKPBhwi2ZUpeKzeAUgMebe6yrMRcjH2yzEoy5na1yIT72suvKAAYsxExrgRdnAXQqKXgsCgJhiJXXBDRVyiSNODBzJtbREJLzQUBHZtRlQVfOack5j7X2eSg+IHWSFTWhpgou6GMmtrYwxT1Iq1oOJhRO0QhzmoEXO9VlhkVyMG9xlDSwVtCLEhoyY2IlrxVTLuomd8scCWyXFuP85GARrMiMxEZc0AO6KkEJv0rqVkCY+3mPcfUZ3pysUFrF0AZsBmPWgxGxgGt5we0yZGgDDowniSdO0A5S7hVL3MjXuVcsMbT1N3u4L8ZXQ5HyJ4EfAf7p77v9f3TO/fdvvEEI8Tg+VfgJYAP4TSHEw84582X/gvMtx2BuwDrSXPsuQOR/ZkLf2YhkwbToMi5inBGY0h/E6TwkigukNjjrW4rC+i9VXbEGRYNWkJKYiFuzFRpBxiiLaTYzjrOGPzmlxTrPTooCX4R00mEDcIVcQFGksCSVWrFwAaEwTEyNVGpOSw9rWdM+PasTpwyL+gKU0lFzBmWDzGqPwqtmoEiWSGGrHr5XasZBSSAtgTaUgZ+wnBPwYJJQvgUaC/8YR9ZLqY9Nxkl1ZT4t68ycZlA2ibUvpL67dpdPTh/lSuStyQ/FB8Si8LJkBEo4YnIscpEO9kBCHUlfOLVOst6asKIiRjanwLFfpWz7KEDN0DQ4LepMdExXWiZGMCtDtoslLusjDII7xTIrwRglLPeTJS5Fh4Ty9UDc1GnqIlvoHE5okJQhqdNsBRItNDeKGQ/rBvfKKevSr0a6wMAaBjakK+cMTJ2jskUvmLGuRhyaFk/Fd1kJxpwL/GfzRGePmYk4HzheyP1qQImci9pSFwGZ89NmJCKmzqta3xX5jNFQGLpVAPCbHg74427Wcs59Ughx4U0+3p8Bfto5lwGvCSFuAh8APvtlf0v4K37RDAjqEbUoZ1rtzcqmxWpB0VS0g5SVeIoUlmvpFkY5orjAGMlyc8Yx/upfyhp5xyGMB80aJRepR1t6QOEU18YbLNUSYlWQ24CGyjnbGXG5cUTpJP0wobSSl3dbuMAhQkNfFaSVXPoB9VgKS4ihJeesqBlXagcVoDVhL38MLc3CEm6dZE2P2Ms7dFTC2XDOxNSQOHo6WfAeDsIO/TBhXEY82jjgueIcSIdUljL38u1mPaUwirNBRGILJs5LoQEi4QlOR6bFk3WPmiPepiszJjbkU8nDNFXK0DRIXcCKKBYW6a70WZhLasp+2aUfzOgHMy5FB5VBTbOlT0idJgpKDkxG4XzCeFdasCUrynE1q7MZnPLuxjaPRrv0VcTQFqQmIHWaO+USj+hDNoNTxjauPA4psSz4mu4uD8UH9JX/rFfVdKFzCAJLW6c8GR5jUByUGRq4V045FzS5VUw5F9QonOGMgmVl0CgGdsyvjxus1UbcyNdQwi0mPYCWzDnIWvTDhMJZLgVTjo3GOMcrZeDRhRJyV5K4grNK8576XQ5Ni8RG1GXGipq9ydPkDYf+V3EN4q8LIf4K8BzwA865U2ATePYN97lf3fblh4BobH269/GI8WSFIBeUDYceSaKhwcSCqYl44XiDRpgvthNFHmCt4HjaYHrQhMjQTQDpidQTW2O76POpkyt809INvpic57XZEkvRjOcOtrAtwTSP6EZzXtlfpa1TTtIGJ2mD/XELbCVwyhR3yiYhfjF0K19lS59gnSYBriYXOB8d84nBw2zVTtmKBwzLOvUg59XJCh/s36ZwypOvKwOTEpbUej/CYdZiOZyxTZ+bk2VGWUyShRx3moRhydyFlLnfCrhScjpoIqTjZlGihaMvBXdLXU0CklfzNdaDEb89fIy2Svm9+Vkeig4YmjpPxvf5lyfvZ6PrPSc3szUuRkdoUXJoWtzPlxgEflJLbMigbFQkqxX20xa6b5iYmKOkQV8GaKE4MhkDG6BwXM06bAUjtssOnxg+zF6jS19e5cjWudjwLIfN4JSJ0zyfnvMTgygwCCSWzx+fB+DJxn2Gpo7C0dAZS/GM8Txmd9bh8+kGj4f7bAUB26VlXcKtYspl3eRGMavMWpIjW6chcrbLdR6u7ZO6kCei++yXXf+aslVU7GjJOZE0HGdNjoxgu/SxCpEYsaH8JHhkFLEwdCXcNwWfmjzMh1o30JUh6AGh+82Or2ZgzP8K/DB+kfTDwN8DvvetPIAQ4q8CfxVAN3vMe5L2doFZ7dBuJUxqMfGhIDlrmS8poomlrnI+sHqPwknuqBWk9M5IYyS9+hy57nBA0Y7I234FURcZl8JDWIZRWec99bsoLLeTZTbbY9o6pRvOiWTJY+sHnKsNmBYRZ+s+r+Fm0MTEDhEZtpQ/aQ5MjUejXZ8KhUULwzc0X6ErU2ZLEW05p6XmTE3M7ckS7+/fIzER/cpu/XDzkIvR0aI9d1B0WA19BbypUtajEbtZRZSqH/qEL+UI45I8CxCBZXl5QlEqzgcCUAxtyZrKuVvWOR8kXAkP2C77fKT7Eu+N9llXIzoyI3EBn0mu8K7mfWJREMmCs+EJm8EpplpFNCpW5cA0KyJ3zvnwiEgWPFRXbIUnGCe52twicYaJKelLiZIlQyt5bzTkuaxPS875cPcGT0Q7PKxjwnLK3aTP17VukjtFXxieiu8hhWVgmlgnsUjeu7zNM61blXs140p4SOkUr43bGCM51xrwdLy7cJvGwm8rzgW1xXbDx+4VrLsEKQRrap9/OPggD8UH7Fcnf19NqdcyLugBcXWSb9aGnA8CWvKUkVWsK9g1io40nFeCwkkmznI5qPOh1g26auY7WaJ4610M5/74bzH+oOGcW4AQhRA/Dvxi9e0OsPWGu56tbvuDHuPHgB8DqK9uuWhscVIgCkOaa4Kp91xQ1RKchMIqDrMmpZWIuaJ01TbOiAVrYfG8DEgjsEgmtsZe3qWj5hyVbY5yT2o+mdeJVUFShoTSu/ukcITKdxhKK0G6xWrFAqlT5HgIqq5WEwrHiWmiAsth0SZRvnJ+mLdwTrCXdjgTj5hU+LOjvEm/Cq8FT8MyTqKrzoZxEuMExgkKp5DKgnILuIsVvs1pnMDgKJytnpvfY2SOhWZhYJoMbcCh8XDfB8vqvbxDXWZMjY/6W1UTUqcphGJianRlgsTXfawTi1rOAwGS/7/AOIcEUmdJnO/KJM4uyEsjU+fENrDMSJ0XLR2VLbT2XYMT26AhcrQoaVYriQd/R+EIhfH/ypJIGXTgqVkTqzjjF1Q+mxMonKlMWgYtFJkrSJ0lRjKxkqxqG5sq6rCrfEdJ62O/SgkyFLb6Xf9Zpy4HFGFV59FCoiv+pEEQi4JUaGJZoP8QLYmvyhWEEOKMc26v+vbPAg86HB8HfkoI8T/gi5RXgN99U4/pKjallBgjkRIWaerCsx+0NJRWUjqFUw4RWLQ2GFn5CKoinhPgArDC0ZXJ4qR4kLQE0A7njPJ40T3ohCmnmecqBsKfGFI4LwtWvpuhBYDxB7GEhsirtCpL7hQtmVedgYKWmrOkZ6zUpnR1wsXoiMIpWiolt8HCug2+jlFXGVoYFJZMFpVxSxLLgrJUiMDnclonkNKilWWehWgUvs4PGkdDFEQCWtKfbLuiR6vqvPRVSuo8GLenZ7TUHC1KJraGwSPtGiJnJjMMHgAL0FUJGkNXJczE636U0inS6gBXQiCdoyVNte0wLMk59coFWThDSxrWYt+9UcJSlyUrTDgyLWbW53LObERTZbSUZ1z4iUYujFe10E8gG8oxsumiWzGwvuaQO0nmCjJX0KxasFooui5jWU/Z0idMbI12kLJfdpnYGgMb05I595Mul5vHHJg5HSkA30Yb2ojCFQs84MQFaDFnSWlu5aukLqQhM9ri9c/0TY93+ATxZtqc/wxfZHxECHFfCPF9wN8RQlwTQnwJ+Ajw/QDOuevAzwAvAr8K/MdfsYMBIGC2qrChwAWSfntG0bSozIfEyNK/i1oYzjVO+Zr2LkQGHZeEuqQWvw7qqIWF/z1R8SrxLbqL0RGpC9gKT3iqdY/SKraap3T0nDO1EX09wyI81xHB3IZ0wjki99wIAI2fJAqnaIvM75mFwyC89ViUnNGnXIn26UpfdBxkPqF7YHynZFA2ORONWNe+yGmcZGpiEvN6UO6DtmNm/b8mU7hUURQKkynKXFEYibWCxPnXPrGSWMDYRYRC0Jc5OYqVYEIsYE1NqQuHwnFcNOmoOYULmNiqWo9b/L8t00W9ZGpiZjbCIkmtJrEhssLATfKIiQsY2BDjHC0pMA608L8bCz+pdGVG5koK5636WpRMTM1neciM1IYkNmJqYk5Mk6mJmNmIxPl/UxcsjFdJ7tkVUnhh1sRp6sIwtCEFpkL+Wya2ZGpTmjJmYnNUtQLYL7vsFx1mNmJi4mo1UiOxmqQMvRDPSiwwcwGp83Wsoa2RO8nAhgxtXOk+IkbGd6S8JP6t6yD+2Lc5nXP/3h9w8z/+Mvf/28DffqtPpHFgFluM0axGNJAYDbiKHKX8yXQ/6fqr/lCTG7HYwtU6BUI4jBUehy98arhBVP3uPmfCIftFl9vzFYZ5jaQI6cczUqNZjqcLpmM/TKjJnLI6UV3gcIUkcTC0IUNbZ4LzYBIsWli2TZtYnHJcJXatB76OEKuC7bTP+doxe3mXx2o7fHZ4idVwvAC5PqBB1yv4ayQLaqrAOg+AEQK/inHeGCa1wVbbqZYM2S0zMhewbTSJjTgxCaNK0zCxMQOrODJ+mT+plmX3siWWtRc8ddScoa0TiwIpLDtFj8v6EP0GUOuDFUZd5kwqvH0rzBYQnIH1iWM5kq4tSGzEkamRO8WJrXE+SDmxmp2kw93GMh0150m5y7Zp+m0UftnekNkiocpU2xktDIGyOOcTs1KjKZxdXN3ulm26co5G0RA5UghiJFoojs2MZeVboLfnyzwS77FbdCkK365+oGbVwtDUGVpYQuG1FANTR6lZ9T6UWASxMNVWBkJhFli7rkr+X6vCNzUc3qj4Dh7vDCWlhck5xdL1HBdIalHO4KGcxishMpOYCPTMeZhs9y47WZfGxRFLjYRpFpFkmnmpCaR3MqoA79acC2Y24rI+ZEWNeSXbYD0csqe7NHXGZt0biAI5pavn3E367OdtBnmd1cgbn3DCl5sLSe4kfZkDY7oyZ2hDdkyHJTnjyUqH/y3NF9kuu7Rlygdat/iUfYQPtm8yNHVWggm7RY8rzUNWgglD0/BCK6sx4sEVOlr4AxpBhhQWlyqQlQbCgS0lQZWstVtmaOGvzFtV0RPgbDDnxbzHh2u3WVMhK2qCBNZUSmYD/kzvKkNT91F8po7Eb5MKp1gPhuybDsdlu8LI+cPkQTHuqGz5lmcZMHMag2RFTdEYhvb1k1oLw4dqd1hRAUemZEuVLMdTnqnfYr/scGQabAQTJjbmheQ8iQ25n/fpBEm1oonJXcCJTDzPYRr6VeNSSV1oBrZgv+xwQQ8YmDoDO2a7XGdN7TOxkq7LUEIsWqDr0Zi6yOioOef0CfcKr6Q9MU1yp9iZdujolInVXAg1iRvTkoJn5yusBGMaomDmNEemTb/arh4UHXayLlvxgNZb1UHw1d3mfNuGLP12QA8z5O1dBvev0FyfYsKQ/nVo381wUvC504vcPl0iKwK+89J1hkWNT927zPy0xv2bbaJTgbagx47WXc+J+LXRk1yqHfELe09xpj7iN/LHuD/q8I2bt3ju6BxSOGa5Zq055eaz57Hn55hRiGwVhDdrqJpD5mAzxY8efYQPtm9yQR/xhWyFrkz4pdOnCGVJU2UMizr3kh7jLGa9MeZdrR0yq/j0+AoP1/eRWG7PV7h6vMV+v82n71+iHuVoZZikEfWwYDBqUGYBjDTCgq1VR5AVlGN/9XeZZHDcJxxJfuTyN7IejXi69hrbZci6GvOJ5MpiC/MDd/8s39C/xc35Kk80drBOsqRn/Pbkcd5Tv8N6MOQLswtcs2e5HB/x6fxh5kbzheMtWqG/IgbSo/QmWcRwWuNMz6ea3Xl1jZ2tHqtqwst5jwLFpWDADx1+I9/dew6F42/c+bd5rO0FWd/ReZ7/ZevX+dnpObb0CVfnF/jNssG56IRPHj/kFazCce3lLc5fPKKuc9phymn6XtJBjKgZarWcL97d4r9pvc/rRoKEgWlyVLb49XGDh2u+W5HZgGXtu06358usR2N+aOU6P3j0BN/V/iKxMOyXHa7PN/n50/eglWHvsEuShfxM+DQ/Kxwfa19jO13i5fkZrrmz9IJkkeX6WR7is4cXSUvvov0CWyzVrgBfemsH/9vYxRBC/GngfwIU8L855/7b3/fz/wT494ESOAK+1zl398s95jtigjAxNPYs2UpM47iDbBZMD5q03ndKUvTQM01Zk/zllS/xE8nX45zgl157gs3OiPdu3OfTo4fQlyfMjuuITNKZSsYXBNEpvLtxD4Az9RH/0dpv84nZo/xS+jXMjeZc65Rv7N3gJ+98kG44J3xkTKuW0lzLmeYhB6Ukei3ydvDC8V29q+xWPMSJqXFBH3OxdsSH6je4nm0S1woawRallXy08yLPzS7xn2/8Cv/n8AN8qH6Doa0xqDW5Hpzh69uvMlmLOUp98E5T57TClHPtU8Z5vFheL8Uzrn7iEVzg8ztkLjB1i20ZMgUf7bzIzEaMbcx+2WVdjSlcwEnZ5Ml4mw/2b3M+PKapUh6uourGNualdLMK8q3T0zO+NrrJdrHE+xqvcVkfkdkPs5P4lVZpJd1wTi9KuNQ5YZj7UF/dTzkq2z5lytSrukPBh1o3APhCep5vXblOVyVeqYnh+3e+mfO1E9aDEd/Zusbn03O8lq1QWLWwU3/He7/EzIRMiohAWB7pHPBqsYlIFOMkIF6a85HWi5XrtbPwuKzVRqQuXNCtt/QJ+2WXR+I96iLjB4+e4IdWrvPrSYNX83Vacs7chGw0RrSClBc5Q6AsPT3jvbU7C87mzERcrB0tAnkSG7IcTHlmRXBtuMFgXudCZ8By9IcQSr1N84MQQgE/CvwpvP7o80KIjzvnXnzD3b4IvN85lwgh/hrwd4B/98s97jtighAWxhckvWsjnA6I6zmFNiQ3OzTm/uetexmfHV9mozliX7SJg4LCKvaSNltnT7j/6iqyFKi5wIYQDfFeDBcSC++m/JH9b6YfzrBO8LHudf754dPcSlf5rrPXSGzIZ0eXcA72t/u0VqeIgSbvWIJEULQcBYqnom1mTtNSc17NfQLTZ5Irfv9pfY7kcdbkN/ga/nzvd/lbu9/G0+277JRdNoMhfTXle7c+TSwKTtIGLZ2SGr9FmhYhSRaiA8PpqIGQlv2oRbFWIGYK1zCYOohEQbPESV90fDLcoyEtM33MgWnydfVXeTk/wydmj3J3vswz9VvczNbYkT20KHki3CcUhmeiU46sWygnV9Vk0cKdG81mfchJ1vA8BWkYZT7d+0x9TCRLbgbLfHvjJRKneDw8YWA0OdIXGG1EQ2Y8O7zME80dnoy3iUXJ2fiUjtCy/QAAIABJREFU7+s+R0sGHBjHZnDKo+Eex0WT/bTNRm3MJ+5f5v1ntrncPGZYeChu7cyUIg9wuWKl7VWeT8X3iGu+PlIXJbumRVfOSZ1XazZEzpKaMjR1tCr5QOMWPz3p8W2NXZ6ORlzNW7yruc29bAklLN3ujDOtMc/UPXv0XHBKX3kPS0vOF/4UFVsKF3B1co6H24cMa95otxEN39qB//bavT8A3HTO3QYQQvw0Xtm8mCCcc7/9hvs/C3zPV3rQd8QEgYWy4RClBWtxTtBqpEyKBipzVSXXkdtgUUi81DphkNc5SFooaX23oZognAKZeHRdZjWr4ZiaKjhKmzQC/5JVlVK9M+9Sb+bEsoBc+pZiIbFWolLhgTKBn+m38yW26kM0XjcwrCzfFsFyMCapWnT7rs3caFoyp7SKzhskuKkLGZWe+jwtQpS0i0If+OwPAFNIhBJkAKXAhc5rMozw0m8HYq7YKXrEomCNKWG1oU1dwHowZFA2F739B8W4WOY8n53lSrhP6ix9KRhUKdbnggFDU/faibxOGWSUVi00IuC1D0mpIfBszoENSV1ALBK0sEQYbtsIKwu29AmNIKOrEnaKHhvxhHfX7xELycCW3Ck7mErvcJh6bcoxDcrqPRgWNZRwDIoG84n330StjNz49ue+6aBFSVcm7FQty6HwasaZDVkNJsxsxFHZpqsStoIhv5OvMLKGc0GToWkwNTFHeRMlHNNZTN6cslv0qMuMuixJneBOvgz40GODqCji3i9yZ7ZEVgYo6YvVb2V4JeWbniGWhRDPveH7H6u0RA/GJrD9hu/vA898mcf7PuBXvtIffUdMEC6AIBHYOECNZ0Q6pDCKomuwu5W4JVJcqR/yarKKVobjrEEoDQ2d8/LuGrZhEFOFDUEOq0ki91JhAIXlyc4uqfUegk+NH8Yi+EDnDs8OL3obsvZCpLKTe81BVCV7l77gqYXhdrHMRnDK0NbRouR+1uN8fFK1DH3nIFYF69GYTyVXuNQ45rVslWU9IRZedHRctHistouWdlFcLaxECUf4wFEaGoR0RFFBcRojs6rObz030s0DhPMahdwpBta3CLvKx9M9UCFqYRYJ14VTYEM2g1M+P79EGm8zsTHPxHfYNT67E3wM4INJoXSSrAyJVElmAqwTzIrIT9ZhyYlpEArDdtmu2A1Ttos+j0R73Ck8iHdmIx6NdhnYmBfnm5yUTT5Uv0VXzvn8/CKXG6dEqmRcxEgctSjnNPMy9dJJlqMZ9XZKMonIJhFj6bibL/OnGi/ycr7OnXyFp+K7XuhVsTi6auZ1DlUcwb1iif2yQ0vOmVjFv5y2+XPNMdfzA46LJgbJxtKItNRcCg/5XPIQoTBIPLb/qfguLZkTC8OrxRKxKDjNazzW3mcvbbMSTqtkrbc43vyccvx2hfcKIb4HeD/w4a9033fEBCEspMsWUVpcs0YrzryK8KCHU15AJXPLft7mQu0EYJGnMC81cVwwG4ZY7cG2wnoQrgscg7JB7hQX4hM+Pbzs7dj1MdvzHsvRjDvpEq0gI5AGoSy2IlSbWHp47th3DpyCg6KDqrIXVoIx2/kS0zJkL+9wMTpcRMwnNvQrCye5errFd6xd4/Z8xYuoZEE/mPlleOEpyYWRKOnIK3WkVsbzIYVDAGqqqvwPv6pxEkptwHlNxtDWF2Cc35tvVZ2IES/Pz3AuGiCFra5+Eonlc8llzuhTBqbJftHh66IBvzI9zzc0XvHQm2yd0ipmZcggqRHrktLKxSQ2ymICaVHKslv2mJgaK1XK90klmf7V4bt4V2ObfjCjq7y1OrWa+1mPusq4U3S9QUwWTKxmlPsg3fX2BGMlqQk8KbvUNIKcNNV+UiwFWaRROIZVmPD9vMdK4Aunt7NV6rWMYVlnYmtYJxdq0OvzTeYmXCg8r+cHPBHWmJqIzAbM8pBA+eJlYkNu5ausBGOmJuZOscKWPiGxEdv5Ehv6tEp1b5IazbisLbYgb+nYf/uKlG9KxSyE+BbgbwIfrkyVX3a8IyaIYO545B8POXq6h3AwfAF612F1UDL43in7t9rUd2Omh+eZzCNPjXq+SZWqhjDw73z3Zxa0qc/dvoCqtAJzo/nXw8e5M1kC4Lo7w51Rn6PDNmQKUStxRiKk4z943yd5LN7h2elDfG3zJr96/l3cniwxmNe51D1hWU9IrbdOv5Z5KtXZeIiWhudn52mqjGcnlwiEpR/O+Ce3vpbhsMH2sEusSz4XXCCQlpNZnaVGwj954p+ihWVgYnIUqdUUKDSGO8UKhVNs6lP+1fpT3J91udw+ZlzETIuI9/fukpjQpz1VlvHCBcSy4Lw+5nbuJ6QvTc/y9Z05d7NllrXnLfz20cNkJmAp9iE2v3nyGKvxlFeSNQAmRcyPXfo5cue4W/pC3bpKODI1UqfZCCbEwnE1W6+IWBltmVapYwGr8dgniTvN7fkyUlg+2niJV/M1PnHvMsP1GjfDNbo6YVjUeWm+wT+8+Atc3ehzRZ/wq7PH+Kb6DdaU5cB4hN0/777E1dkF9tM2373yBQDvno32WdPDhWVbxY4LeoDWxwxsvGjNnpgmP3/6HjYaI744PU/hJMeFF2X9zxufp3CGTy6FtGRKq5J7F9VWLRYFq2pCS+Z0ZUpdZqyrGT947uM8pA0H5kG4ccQ/eCsHvnNvpw7i88AVIcRF/MTwF4C/+MY7CCHeA/wj4E875w7fzIO+IyaIB1twG0Lt2GIDgbACEwmMkeixxGqo6YJpGqGrqyeAVQ5ZpTGd5nVKK3HjENvLcMZDPYZFHa181FojyGmEOcfSZ2TIwGGdl20PSr9cbqrMi4GsYjCvM5rWSJuatpyTOq+7x+K9E8r3/EdlnZZKfcszyGkHnu6sAt+6iyq2Qxx4QVekSmJh/g1vR0PklZowJrEh68GI07zGYF6nFTaZFhFpqTnMW2TWp0ynzusoFI5mlbZdlzlomJrIn8QqoyEzZjaqtjRuITO3ThDKclHjKa0iEpK0EsH6lYf3ccxciMKROsFh2eZyeEC7knUbKSpJ9ZyOmqPw8Xqx8K81lgWB8u8HeOl7ZoOFp0OLEomvG6VOUVR/XyIrM5dgWkQMyiYXwuMq5axceFpaMqcl58SVf6MlH0jhjZdtK0Or2gYoBAbvz3jg3UidpjABXT3wnRnrnaRKWAxiMRErLLqKMcxcQeEkhTAEqLd87L9dXQznXCmE+OvAr+HbnD/hnLsuhPivgeeccx8H/i7QBP6F8MrSe8657/pyj/uOmCCEdcjRjHS5D0jqu4LGXoawIKVj3rPYmmX26hp6pLDasbTrMKHffjjlGJc17oz6fnneKljpeTXez996F1kaYochNEoQDjcPCE8UwVRQ1gO0EVjl+OzKRR6O97mf9Tgt67x0usZD3WNeKtf4puVX+AevfcQr+SrZbxAYtPIrlVkSEddyJgdNZKMk0AZzv04wlSRRjUnDejVkbJHjgBtxi09vXgBgYJrcSZdZrShTz0+2uDPt+7yH5S7f0L/FzyVPsRTN6IVz7kz7tIOUudH83e1vW0BnxllMZhTtMONw2kQIx+lJk8+uXGSSRNSigrxUlKWi2K/zWi/HJQGqnfNyuIYQzv9srvnE0lViUfBKtsFx2eRydMBz04sM8gbfufSCb2nKgp86+iAzE5KWmlkZeoMbUBiFcYL9/S5xM+fq1jluDFeYbLf5zKhGszNHq/OMJ3XarYS/1HuWF+bnoXaXf3bn/eyc6fJM6xa7RY9b4SHf3rzOq9ESPzr5KKnT/PDN7+DplXtYJ7g5WeGJzh4HWYtI+kmlEWTcT7okZUhTZ+xMO+wddnmRMzTbc9J5yMbSiFke8sklD6D5jnrKx2d1fnLwQT5zdInSSjaaI7Zqp9RUsVC9KmFJTMhe2uFvbvwyhZPsliEv5n8Y5Nzb18Zwzv0y8Mu/77b/8g3//5a3+pjvjAnCOFwcEQ1gdMXRuiPI+pr4uKAe5Zx51w63X9jExpayYdETSbIqkAYfemNhKZzysc2XuTFd5SPrr3JztsL2pIsQ0G4lDI3PYhCBQ80kJnao1KPzrfYf0p8/exWAQBj+XPc5bk2X+Qurn2Ow1ETiCU8NnVPGksIqSisZJjWktKz0fKK4WU6YzzxH0q7kFCL0BdTIokKDMwIbS4gMDZnzaHiAFpaiIRnaiKvzi/zllc9wrbVFXfrE8I3glEuXD9kKBkxszLgfc1mfcC3bWFTgAdo6ZVpESGHZ6pYM5nVYmnK5d8z9oEu/lhAIw7X7m4RnZtTjHLr+ZA6kJVCWMjDkgSEUhiv6lLZMqVd7+CeqYOOJjXk63iZ1ik+LKx7IG86YmZBAWOZGMy5i6kGOsZL15oSOnvOepR0OTjr0u1PqukArgxTQCD1y76FonyWZ8Fcufo5vb15HC0ijXe4UXW4XfbbzJb5v8//m6XiXF7pbRLJkL23TDefMTEQ/TDjOmmzWhijsG9qklo5OSbKQQFn6tQTRdqSlJlCGlkwpTMDHZ3W+q5HwuzPH4719ukGCloaXJut0dEo/nJHZgOO8yWY85PvP/DrbZYfd0tdAHmhX3vRwf6KkfFOjrCtO37fM8u/NWf/UnJv/acz8tRr1vYjZtWXEdTh7anj4v7jOaV5jWkS8cmsDEfqrhTOSX7z3BLN5hCkV1689gtW+NvED3/NzALw8P8PMRGxEQ35vssE4jzlOGvRryeJ5/NzuUzzcOeTGaJV/VH4Tr50u8Tde+kvIucJ2Cv6PD//4Iqg2cRHGSd9jF+WCn/il+TnvlgxmbKd9PnN4kUd7hzRUzkrFfDgpGizpGbtFj92ix8jUSKo0cYPkTrrE9ryHdYKN2oj/7rVvJR1H1DopZaEoC0W3O6O0kv/rPT+OFizsyTtlm1U1Zd+02Sl6DEyDjzZe4lbh08ELF/Dy0hlOywaXowP2ih5rekRXeXu3RXJYtjEIrmYb3M2XKZzijD7lfu67IRejQ3aKHh8/eoq/f/4XmFhJS1omVpI4D425V/ZQwvJC6zyP13b4cHzI3VJxP+nyb619kfVgRF1m7BY9Luhj9ssWN7N1LJKrk3NVgrZP2X402uPv736M3VmH42mDp9Z3+Gvrv8XXRrBjEoY24Hzgbe9HRnA+8BAbz8uUhMIysZqfCZ+mp2dciQ5Ire9W7JcdWqKgqwf85OCD/O7M8bdWr/F85rctZ4OSg47kfCCoCf8Zndo5PVnjf5+c5alomwJFS6ZcqCA7b2l8NfIg3u7hBBQ1Qd7WBMOUQBvyhqNoiGoLAfMlxUY8JJIlA+nDapqtlNJ47UK3lpIVGud82lbZcOipYF2/Ll5JbERHzZjUY14q19lojokDX5Dqhwn78xY1VXCpdUJN+dXLKPPpXhSSbrXXLWRGVyRYJxehrt7m7a3eR6bNejDEOsmjvUMeb+4u9sKpCzgpGizrCQ2ZcVS2kDjqKq+0CmXVmvO1gQvxCf/q9CkQnp5VZgoVeut3lmksUDgWuRgNmREJwyU9IBYF75NzzgYldbFHLDw34jdGT/BM6xaFC1jTIw6KDn01XcBc1wP/nu0WvYWnIhSGfjBFCkdifezcJI8XRcxQJLSkxVrDmrLsGMu6GnOh/TwdaZg46EhLPcgXJ/6mLnk03OdGsbrIqNgpejzTvk1LpouU7cRGvLC/QZZ69H9uFe8Oc24Ulu2yx2Yw5oW8xqVgynbZpSVPyZzPKQmVwzi4EGp+VjjeW7vDrXyN9WDI55KHFm3wtkz5zNElHu/t83yW8VQU8VKeUDjH89lZ7pQzNtWImdMM7RJX9AkX9DG/NXuMg6Lt3cLhIbD/Fg/+/y9nzh/9eEdMELL0wp/a/hxRWtJhDHWDcJLwVACO2onh/rzHaV7j7qjH5uoQ4wQn4wb5Scy27WFKiRuGiBqEQ4Es4ZV0g8fjHW6k64v06s8eXuRsa8j+rE3TSYZpjVNd5zStUTrF9rDLEyv77O70oe4Db1GO6/k6l/URGyphZDW5gM/NLnMmHHI5PPAwmrLNS8kZLtaOqMucQNiFY7IfTHl+do79tE1PJyQmpBN4g09ivIBqL+3Q1XNeHa8QBSV7aYegWSCkJY4L8sAj9pR0WCPZrpyMHeknunWVsF226SvPwfhCukq9fovbZZ+tYOip3EHqobPBkIbISa1mt+jRVQm7ZQ+JZWQaFZw3XKyaBqUH4Sph6aiE3Po9eUOUtKRgZh3nA8ELeY1NNaIlCz41v8STkQ+0fVeo+NjSdQAuh4ekTnOjWKVR6TUS4wVIX5qd5Zu7LxJWlKdD00JKhwoMvWZCI/ChNiCrwqk/jI+r1rfH/ysgZ+YCBqZO4sZ8rH2Noanzgfg16tKv+m7lqwvJeWkl3Yri/VLuU8VHds7Q1GnIrALEeKt6ogJPw7babzvK1sL/8lbG29jm/CMZ74gJAuEJ1jaQiEASNArKcYhKoVh6/W6BNCxFCWlTszduI6VXXapOgQ5LIKCoG+SBz3y0QD+YYhAs6wl3y2WW9ZSN5oiGyolUSSAs51qnPnS21NSDnEh7taZu5BSTCFEIXOgt3H2ZM3EBOZKJjTkTDn1YDW6BcF8Lx9RlTkNm7M3bXKwd+xamU/SChGYro6VS6hVgJrEhBol1grZOCaShFhQE0tDSKVJZirnGaINzAlP4anlcy1lRM9aUBQQKwd1SsRWMmVjNiWl6fJxwC5HWDL9qiGVBQ+QMre++NGRGXXj0jKc0j6sqvyYxES2dLoqh/cra3AozLgU5iXN0ZISmYGhL+tIysDG5lQte45rKGVnB9WST9/Z9cVHhUGpCXWbMdMR9t0S/WqYXLvDEJ9NgRY1Zb08YJDXG85igZ9FY1hW0xCGx8PRp4xyRGLGuqEhQIClRakZLCrbTBwfTafUzy0owZj3wbM6N5ggtDWeDksI5RnZOR9Z4unabJZnRkYLCOVrxfVak4Eahffxe0KEfTNmqWq1vejjA/MkE8RWHVdDYt+S9kPrxBFPWaN70T81qR/2wBAFHaZNbg2V69TnTl3uY2OFiQ9RLffV9GCGbBfV9R9YTmNAfBL82fJJrpxtsNkYUbhnrBJkN2D7tEocFS42EWlCwd9JhvTGmMJLXxn0atZzJTp1wKJmf9QKpxEaLaLxOkPB4vMN+0eXnTt/H4/Vd/sXu+3ikc0hiQ3bmXW4fLzEvNY90Drmb9tmIRrw0XWc5nFFTOYVTHKatRVGvEeS8NFxjnEbM5hFnemOMkYiBJjFetCXmiqOyjQwsJ7bGxPp8ixNbZ6fo8b54m1+bfg1SWP714aPMznyBa8lZzoQjjosmj9b2+KmDZ3hf5x7baR+AD7RuMRMe0PJKss5W7PMt76RLlbhI8epslaN5k29de5G9osvhrIlCMLGSI+OY2CYnpkksc29lN/Hi/fgPV36HZ+eX+NLpJr8VPbqggh+Vbda0P0EPijZNlfKZnYuUVvFUa5vjsomulazEU7Q03Nhb5cXTNT7TvsSH6re4HNS5lhdc1JZXyoANlbFrFJ7TGTGxNbQoeXa+sqhDFS2vOTF4WE8sCpSwbNVOeWmyzkFH8nx2lqGp83TtNh+INHtlxsD6/uFZpblvCn7x5N38xZXP8WS8Ter0Qkn7ZofA/ckK4s0M4cBqQXRqQHrRUtF2yIFXReYtRTgxdMKU5eYMYyVlxyBi35nOJhGN3pyiUeKsoGh6qbUqPMNgMxoybNZoBjkNlbE375DbgEiXnOsMSU3AvNS0mnOvCQgMrTBjOKthQ0dZ87CWrkpIrWatqmtoUXKnEiRtxQO0MDzUPiKUpb8qBxm9ZkIn9NuIlXBKYkM246Ff5iMorKIf+mVtUOVBrNT8Xr+mS1ZqU3ZElzJwyND4ZK1SouMSFVi6MqUlSlLnr9ZtlTK0ER2VkNiIh9uHrARjNqNTVoIJHZWwEoxZjaas6RGjssa56IS2ShmbmFjkPF7fBbxKs6YKMusRcjVVsFYfe3OUzOlEKQWeSVkXJWmlSZBYGjJjaOo80jnkYu2Ilig5p08onQfjbuhTrJOMKyHTA1VlKEo22mOWoyktNWdkajRkRk0VHJgWtpS0Qg/WWVOSUzsnJ6QuPJq+cNCRhlAICldQiAAtSlaCMdfcWS7WjtgITrmdr/JUfJc7xQqrynssaqqgo1POB4I7pWeGLsmMvTLjTNCkZT2+TiJZkSVr0YR1NWbf+PSv1oOkp7cy/mSCeBPDQdYS1PccTgfosKRsOGoH/kS32qdrhbJkszFkWkRsNwtUYHzqlPDQ2iAsMaXCxJ4vIazHzXXDhLSuOcjbLOspx1GTk6xBK878Aas8LagZ5bR0hpaWfpSwq9o47TCxA+37UUtqhhKWtkwX0uJQlFyJDpBYHqkf+MAcNScN/Z54o+ZlwHWZYxFM8KDYB0IrKRxRVUMwSGqq8OxNreiFiddu1Cw69K/Xxoa4lmOtj93TAiyWdTUjtZqGKHg02uNGvs6V2iHnglMskq70vo1Xsg3O146JRcGynrISTJBY1oPRIpy2peakVtNRc+oy97miLqCwvmIvhUfRG+ewyOqkTIgr5mdSRlwJ98ms5pF4l5YUrKsxa7UJXZVQOIXCsaSmXNADtssuUUWwWqlNORedeJ+JDujKhLnRpGVAVCtohykX9BFNEbFjE2JhyJzH0R8ZxXnlt0ItWRBVCMGGKOgFvrBcVCi+lszZ0ie0ZI7BA4L74YyaCNlUIwyCjhQMLLQqfJ1xlrFNacqIy/HhQkDlieBfma74bx777+wJ4q2Lx/8ohoBo4nBKIIqSIg98slbsW5U4fx8lHMdpk0HawJUSa5S/ombKV/2NwpkKYGtf7zGn1k8ODZUxMTEnWeX4y6uiVh4zLzVFJfJRsqIWG7l4fg+qzQ/EMjMbLQC4i9anjTgo2pwWPl1raiKaOmdY1Kip168ux5lPKLdOenqz8IagB52LwklyG/hJwiqU8k5OIRxSWpAOa+Xi+RUVeDuxAQ2ZkVd1hLb0KV6zKtoudZrUaTpqxnHRWngHZlUlv8CrCRe2ZuHQVX7Hg/GAl2mdqJ67oC7MguGohSV1/qS2SEamxrDKi/DpZYaxrS1QdkNTJ60mi1gWi4xM62Tl9Hz9vdHSEgQeXJw6ja1WL4WTSCS5cxUSzj9fCSRVAXNWKWCbKqUh8qrgaEhstFBIamEWztpZReYqnKs6UArjLErIKjHcUFQtXf9lFySwNz0cvlD2Zr7+fxrvmAlCWIcNJTifsxnMBMJ4paROHMI6xkXMKIv9CTwOMNMAV0pU0ysETaoQyqEnFSpf+G3AvXyJ/XmLcek5B7EqaAYZpfFmoFpQUAsKJvOYzASkZcBR2iRQFpFJgqkA6w/a1Gm2iyXu5svsFj2W1BTjBHezZXKneG3mC2GJDZmVEcdJnXERk9mAkalhnCSqsPqJ9Z2L/azN7rzDQebl03vzDuMs5mDa5ChrYoxAzBR5qimzADcLSOchxggSG1AgKBCkLuDItGmIkt2ix1HZ5layzH7ZZa/ocq9Y4pXsDA2ZM8gbzKoJ7V6+7N2opkbhAqYmZmJqTEzMadFgN+tyWjbYTbvcTfoLs9Moj4mEXCD3U6fYLTuLrsBO0eOV6Ro30nVG1nG7WOZw3qpeu693AByZFrlTTExM4QLuTnrcSZeY2JiDosPExkSypK5z5vOQSRFzVLYZ2ZQVFRELQyQCEidoScGkgtZOXMDMhUxsyJFpMygbHBZtxjb2taRiiTv5MkMbM7EhSlj+n/bOJUay66zjv3Pus249u/rd8+rxTI/tkeJxgmJLjgEJskgEUjYh4rFggcSKBQgJIVggsQMJWCA2RiBBhISlCCFjWTFEjgDJSeRxEgWCmUfGnunp96set6ru8xwWp7psWdPjcRi7e+Lzk67Ut+vo9r3dVV+f853v+/93sxoHymh03sjm6WvBouMjkfRUMklcDnXOjdEcGZJorALemciwf4i3vtYPdBwXJ2aJUYQCv6NR1RDXKyhDjd8XqECT1SV+31QKnqsfkCmHW74GXyE9ZdalrRQhQClBXquQVzWyFPii5HywQ9mQxGXAmXCfuAxYG7ZoVhKq3mG2W3O61WEx7LKfRpypdhjlHr1AUVQlInjXezFzHFrOgFy7ZNqh4SRcie7QkkN+qnmHQOa0nZiDPKLiFZyOOkihiaTRnTjMUcx7XTLtUnNSFgKzDAlEwawfsxa0SEuX5WiPa5tz6IoirOTGSUxoppoDSiWYd3IcIegrTdtJ6CnTMv1EsMGdvM1nG7d5wt8ilBnTckCiPb47WuZcZQ9fFDSdEUv+waSfAaDUgrPevtG70B4zXsw5f3eiuH2Yg5mPzjBUxs+zLjTOWMS3KXN6WcgZb49nmu9wOVxj0fEZeLu4UuGPRXBbMmHB64xl8V3qTkJdjrgyvc5narcnjVJn3A6D0ifOAoIg53S1w+VggylZYaMc4glBrNNJ8vCCa2ozPDGiLWMcoO0kfIuLzLim3uPQnmDJO2DBGeChGZYmPzQlK6x4ewwdl1kpuFvmzMqCmgzIdTkRwn2+cX3sw+EQipxZx+YgPhKcVNF6q0/vYp2dpwP0NU39HQh6JelPD9lbcInerPDtjXMkmUcSB4TrLso30113KPjZX/ouocy5Ec+x8fk+nWGFbifilf1PUXFy1obmA3fLneHG9izJXgWEZtVXkElEoPjzz73IXlGj5qb8QuP7/L18nrP1A+pewu24zcv9K5RamjeEzCm1ZD01dQt1xzQBbaZN1kcNFip9rm6fIR4FXC3PELqHpjiCrb0m89Nd/uDiK5xyuhDCjJOzr1xuZHN8NlxnvTDT8BLB9aU5Ll3apumOWE3aLAUdZrw+b/TO8+rwsYk8fSQz9osqnihpuwO28gaOULyZnDG6jY4p6Prrtz7HytwOr6vHCJ2cfn6JqWCI0oLIzciUy28v/hsLTpd2JWbZjUm04Ocqm3hC8npS54J3QDLt8Zd7zyGFZtHvTFpn2D0zAAAJ9ElEQVSrp52Yf+89zuPRJiWCThnx8tBk+O90Wvyz+DQAyzXTur+d1PnK3BsseQf4ouR3577BWbfC20XCY26XfeWTlWYZ+WsrV7lcWWO/jPiHpM3V+PxEh+HT0e2JHZ6xIvAYqAB/vJz51vZ5np0VfHPvElP+iIPM1L380dmXyLXDRtLkdxb/la/2T7Ps7dJTIddzj5f3rjAf9LkQbpNrlxujOZ5vXOcrtS4vdJdZy6Y46++R6B0+VKGU1qBOdq31iQgQCIGMU8qgQeOOYvcpgR9rvIEiS128W6HZzShckqEPQuP3oAzfzTdsp3XuxFOUSnL71hwiNH0Pw8Jne1RnO64ReAV56Zh1PEZnoawDSqCHDv+0+xmebb7NTlbnm/Fl7gyn6GcB3+ucZmXOGN/kyqFXVNjPItxxY1ChHN4upml6CW8dzFMZu2h3ehF5NyCp+LhBQRCYjk7PL4iTwFQLljU6KuI/hy3TzSg0L/ausJq0SZXLxWibuTDm1btPsNLeRWnBD/aXeHJqk4ZrjHYPNR+7pcl9NN0hP4hPMyo9dkY18rbDnVGbljciVS4rczvc2ptmsdljo9dgrhZP8h9xHjDIAyKZ46G4ls/xv+kSl/xNXksXGaqAZ6ObrJU1hirgmdqtiXZjp4zYzhscFFWmvCE/HJziR70ZlqpdvjzzBt8bLhP3KiQNl8WoR6Ec7gynWKx0eS7c4vVknnPuAX+2/Xl+pnGNK8Eat4o2j3t7/N7pr/Pt0QX+ZeMp2ksxV7Pz/GLj+8y1+tzJp3kqWGW7rE/s8EKR86Nsjm5Zpe6M2MqbJIXLf3WWOBV12UlrPNnYZDetcdEzNn1/uPQKq0WTp4NVXhs8SaJMncOvzn6HBcfs3jhono1u4qB5obvMbzbX2SiuU5fuJPfxoTjZ8eFk5CC0BJSiDIw4rJbg90vj+q0EXs9sd+a5gx64CKnx4nGuoYTSM4nG3dgkw9yug04lQT0dlwQH9OMKWeGQlo5RbRIalEDkEqEESFiNp6jLEb08ZC1t4cuCUe6RDnwaXsKw9I2ZbWbyCknp0fJGKAQ7o5rRo+xV3/VuSFzkUKITB60kWeYihCZLXEpl+jgUkq28xVbeoK9CHBSrSZv1UZObvVluj2ZwhbEjHBY+w8InTk1+Yy1pGd+LwnxYh8pnVBoxlVHp4cmSjX6dnazOfhrRyStsjBqMCo/RwKebhCSZRzcNJwnRpDTnHRWSaodeGZKqcTK3jHgnmaYlU3Lt8N+D05Rakk3yF0a1KlEeifKQaNZ7DdYHxqhmO6sjXUU/C+hmIZ28QlY6DIoADzE26BFc686xnk8x1K4xuNEuGUYExx3rTPSKkO2yPrHqO8wrwKHRTUSiTU5goALz93RK076fh6SFy0bSoF8EbJWKnVKSaof1Yoq1sslW3mA7r7OZGxWqzbIxORLt0VEV1rIpNoqYRbdGRxXs/BjaDjYH8YCIvEALEyBkAUVFoh0BSlFUoaxAtZKSeT6qkBQVQW4qf3FSOFs9YD1ukpYORbNERgXZToQ8pan7Kf0oZb4WE2cB/dKB8W6HrpSIgQPCKEj3VYUrjVWUltyKZwjdgqiRsDWqs1LdpleEzAQxCkHbHzIqfapORtNPCGTBTMPoTzb8BOkqdDsHJXC9EtctjUqUNDslh5oDjwVGhXneM9P0Bd/kI5pewuPRJm8NF5mqjqi5Jk+g6mKiEVmXydgmz0GWIQt+zzh/eQkVmXG21aHlDRn4Pi3PmBR38xAvKIi8nNRzqQcpkZsxLMwHqhkkNIQxkbnkb/FObkRfZty+MdYZmwM9WTX1Eg05ogeUytRHgNF6WFMtLrT3mA4GNGTCnN9HAL5jTGpa3oj9NEIKxUArVvxN6rLk6am7LHkHk+rUuihYHUvq1f3EqFh7QxoymfiAHDbLAWPtTdOXkmvH6FGG+7zJGZab+6azs/CZ9WN6RYW60OSiZL3wzc+TCeeDHXaLOu1xzuJQtPZwVyUUOWf9PerS5W4Rc9qtsVHYZq2PBg2qFiEL05hVVKCoCLyhRnoKmYE7gsArCJspWkNRDUx9xLhOYlAENIPEGPAm0uQnfMWpsEPdTSm1xJWKpVqXXEmG1YBSYlyqahrhaKaDAXU54tZoluVwj8jNWO20SBIPmnAu2CXzzW7ElDd813RXlFTdlLY7oF8EhE7OrB9zvTlLtxcRRRm1MMV3SgLXLHNmaoOxZmRBR0WTEmNPlMx7XVLtEsiCQObcGUzRTwI6QYW0NNJoi5UeuZZc8LfJtNmenHV77Bc12m5MIHPiMmSx0uNT0SpNd8SMawx6/2NvhYVWn4WqaVZr+Altf0jNzZBoekXAjJMz1IKB9qk7I1oyMeYxMmVu7HJ+TUtmx2K9C26XngrHqlYZq9k0j1V2GCl/XJzU5clwnUqUcqZ2QNsfMuf1kQ09CSr7ZY2WNGI7S94BTZmTKcms43Ktv8St0Sz9LKTmJCz7uyy5fXbKCqe8fVpyxKwzmJjxeGgaIqWjKqYsXI6YrqwwEwyoOilBzfSklEjaToCLw/9kZpdp2Y1J/G0WvO6kxf5Qj9JI3fnMOhmJ3iHXiqEWk5nEh33fn3RnLaFPQAQTQuwAA2D3uO/l/8EMj/b9g32Gh8E5rfXsgwxshgv6ubO//kAX/fqNP33zYYnWfhhOxAxCaz0rhLh6HL+Ah8Wjfv9gn+FYOAH/oO/HiQgQFssnEg2UJ3sbwwYIi+XY0PDjbI1+jJykAPHCBw850Tzq9w/2GT5+7BLjwXifjdgjx6N+/2Cf4WPnEdjFODEBwmL5RGJnEBaL5UhsgLBYLPdEayh/DJGZjxEbICyW48TOICwWy5HYAGGxWO7NQ3X3/kiwAcJiOS40aFsoZbFYjsTOICwWy5HYHITFYrkndpvTYrHcD21Fay0Wy73RdolhsViO4BFo1joRqtYWyycWrR7seACEEF8QQlwTQtwUQvz+PV4PhBAvjl//jhBi+YOuaQOExXJMaEAr/UDHByGEcIC/Ar4IXAZ+RQhx+X3DfgM40FpfBP4C+JMPuq4NEBbLcaH1w5xBPAPc1Frf0lpnwD8CX3rfmC8Bfzf++mvAzwshxP0uanMQFssxoh/eNucpYPU953eBZ48ao7UuhBBdYJr7qIDbAGGxHBN9Dl79hv7azAMOD4UQV99z/sLHoZ5lA4TFckxorb/wEC+3Bpx5z/np8ffuNeauEMIFmsDe/S5qcxAWy08GbwArQojzQggf+GXgpfeNeQk4dOr5MvCa/gDnLDuDsFh+AhjnFH4LeBVwgL/VWv9QCPHHwFWt9UvA3wBfFULcBPYxQeS+nAjrPYvFcjKxSwyLxXIkNkBYLJYjsQHCYrEciQ0QFovlSGyAsFgsR2IDhMViORIbICwWy5HYAGGxWI7k/wAP7LCsuLt0BgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f48f348a860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1 gram matrices are calculated, 0 of which are ignored.\n",
      "\n",
      "3. Fitting and predicting using nested cross validation. This could really take a while...\n",
      "calculate performance:   0%|          | 2/1230 [00:00<01:22, 14.94it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ljia/.local/lib/python3.5/site-packages/sklearn/linear_model/ridge.py:154: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.\n",
      "  warnings.warn(\"Singular matrix in solving dual problem. Using \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculate performance:   7%|▋         | 83/1230 [00:02<00:29, 39.23it/s]"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mLinAlgError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[0;32m~/.local/lib/python3.5/site-packages/sklearn/linear_model/ridge.py\u001b[0m in \u001b[0;36m_solve_cholesky_kernel\u001b[0;34m(K, y, alpha, sample_weight, copy)\u001b[0m\n\u001b[1;32m    151\u001b[0m             dual_coef = linalg.solve(K, y, sym_pos=True,\n\u001b[0;32m--> 152\u001b[0;31m                                      overwrite_a=False)\n\u001b[0m\u001b[1;32m    153\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinalg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLinAlgError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.5/site-packages/scipy/linalg/basic.py\u001b[0m in \u001b[0;36msolve\u001b[0;34m(a, b, sym_pos, lower, overwrite_a, overwrite_b, debug, check_finite, assume_a, transposed)\u001b[0m\n\u001b[1;32m    250\u001b[0m                            overwrite_b=overwrite_b)\n\u001b[0;32m--> 251\u001b[0;31m         \u001b[0m_solve_check\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minfo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    252\u001b[0m         \u001b[0mrcond\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minfo\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpocon\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlu\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0manorm\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.5/site-packages/scipy/linalg/basic.py\u001b[0m in \u001b[0;36m_solve_check\u001b[0;34m(n, info, lamch, rcond)\u001b[0m\n\u001b[1;32m     30\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0minfo\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 31\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mLinAlgError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Matrix is singular.'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     32\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mLinAlgError\u001b[0m: Matrix is singular.",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-5f7e06d2963a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     58\u001b[0m         \u001b[0;34m(\u001b[0m\u001b[0mds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'task'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;34m'task'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mds\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m'classification'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mNUM_TRIALS\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m30\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m         \u001b[0mdatafile_y\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'dataset_y'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;34m'dataset_y'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mds\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 60\u001b[0;31m         extra_params=(ds['extra_params'] if 'extra_params' in ds else None))\n\u001b[0m\u001b[1;32m     61\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/media/ljia/DATA/research-repo/codes/Linlin/py-graph/pygraph/utils/model_selection_precomputed.py\u001b[0m in \u001b[0;36mmodel_selection_for_precomputed_kernel\u001b[0;34m(datafile, estimator, param_grid_precomputed, param_grid, model_type, NUM_TRIALS, datafile_y, extra_params)\u001b[0m\n\u001b[1;32m    173\u001b[0m                         \u001b[0;32mfor\u001b[0m \u001b[0mtrain_index\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalid_index\u001b[0m \u001b[0;32min\u001b[0m \u001b[0minner_cv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_app\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    174\u001b[0m                             KR.fit(X_app[train_index, :]\n\u001b[0;32m--> 175\u001b[0;31m                                    [:, train_index], y_app[train_index])\n\u001b[0m\u001b[1;32m    176\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    177\u001b[0m                             \u001b[0;31m# predict on the train, validation and test set\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.5/site-packages/sklearn/kernel_ridge.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m    160\u001b[0m         self.dual_coef_ = _solve_cholesky_kernel(K, y, alpha,\n\u001b[1;32m    161\u001b[0m                                                  \u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 162\u001b[0;31m                                                  copy)\n\u001b[0m\u001b[1;32m    163\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mravel\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    164\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdual_coef_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdual_coef_\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.5/site-packages/sklearn/linear_model/ridge.py\u001b[0m in \u001b[0;36m_solve_cholesky_kernel\u001b[0;34m(K, y, alpha, sample_weight, copy)\u001b[0m\n\u001b[1;32m    154\u001b[0m             warnings.warn(\"Singular matrix in solving dual problem. Using \"\n\u001b[1;32m    155\u001b[0m                           \"least-squares solution instead.\")\n\u001b[0;32m--> 156\u001b[0;31m             \u001b[0mdual_coef\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlinalg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlstsq\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mK\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    157\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    158\u001b[0m         \u001b[0;31m# K is expensive to compute and store in memory so change it back in\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.5/site-packages/scipy/linalg/basic.py\u001b[0m in \u001b[0;36mlstsq\u001b[0;34m(a, b, cond, overwrite_a, overwrite_b, check_finite, lapack_driver)\u001b[0m\n\u001b[1;32m   1234\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1235\u001b[0m                 x, s, rank, info = lapack_func(a1, b1, lwork,\n\u001b[0;32m-> 1236\u001b[0;31m                                                iwork, cond, False, False)\n\u001b[0m\u001b[1;32m   1237\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# complex data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1238\u001b[0m                 lwork, rwork, iwork = _compute_lwork(lapack_lwork, m, n,\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "%load_ext line_profiler\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import sys\n",
    "sys.path.insert(0, \"../\")\n",
    "from pygraph.utils.model_selection_precomputed import model_selection_for_precomputed_kernel\n",
    "from pygraph.kernels.spKernel import spkernel\n",
    "\n",
    "dslist = [   \n",
    "    {'name': 'Acyclic', 'dataset': '../datasets/acyclic/dataset_bps.ds', 'task': 'regression'}, # node_labeled\n",
    "#     {'name': 'COIL-DEL', 'dataset': '../datasets/COIL-DEL/COIL-DEL_A.txt'}, # edge_labeled\n",
    "    {'name': 'PAH', 'dataset': '../datasets/PAH/dataset.ds',}, # unlabeled\n",
    "    {'name': 'Mutagenicity', 'dataset': '../datasets/Mutagenicity/Mutagenicity_A.txt'}, # fully_labeled\n",
    "#     {'name': 'MAO', 'dataset': '../datasets/MAO/dataset.ds',},\n",
    "\n",
    "#     {'name': 'MUTAG', 'dataset': '../datasets/MUTAG/MUTAG.mat',\n",
    "#         'extra_params': {'am_sp_al_nl_el': [0, 0, 3, 1, 2]}},\n",
    "#     {'name': 'Alkane', 'dataset': '../datasets/Alkane/dataset.ds', 'task': 'regression', \n",
    "#         'dataset_y': '../datasets/Alkane/dataset_boiling_point_names.txt',},\n",
    "#     {'name': 'BZR', 'dataset': '../datasets/BZR_txt/BZR_A_sparse.txt'},\n",
    "#     {'name': 'COX2', 'dataset': '../datasets/COX2_txt/COX2_A_sparse.txt'},    \n",
    "    {'name': 'ENZYMES', 'dataset': '../datasets/ENZYMES_txt/ENZYMES_A_sparse.txt'},\n",
    "#     {'name': 'DHFR', 'dataset': '../datasets/DHFR_txt/DHFR_A_sparse.txt'},\n",
    "#     {'name': 'SYNTHETIC', 'dataset': '../datasets/SYNTHETIC_txt/SYNTHETIC_A_sparse.txt'},\n",
    "#     {'name': 'MSRC9', 'dataset': '../datasets/MSRC_9_txt/MSRC_9_A.txt'},\n",
    "#     {'name': 'MSRC21', 'dataset': '../datasets/MSRC_21_txt/MSRC_21_A.txt'},\n",
    "#     {'name': 'FIRSTMM_DB', 'dataset': '../datasets/FIRSTMM_DB/FIRSTMM_DB_A.txt'},\n",
    "\n",
    "#     {'name': 'PROTEINS', 'dataset': '../datasets/PROTEINS_txt/PROTEINS_A_sparse.txt'},\n",
    "#     {'name': 'PROTEINS_full', 'dataset': '../datasets/PROTEINS_full_txt/PROTEINS_full_A_sparse.txt'},\n",
    "#     {'name': 'D&D', 'dataset': '../datasets/D&D/DD.mat',\n",
    "#      'extra_params': {'am_sp_al_nl_el': [0, 1, 2, 1, -1]}},\n",
    "#     {'name': 'AIDS', 'dataset': '../datasets/AIDS/AIDS_A.txt'},\n",
    "#     {'name': 'NCI1', 'dataset': '../datasets/NCI1/NCI1.mat',\n",
    "#         'extra_params': {'am_sp_al_nl_el': [1, 1, 2, 0, -1]}},\n",
    "#     {'name': 'NCI109', 'dataset': '../datasets/NCI109/NCI109.mat',\n",
    "#         'extra_params': {'am_sp_al_nl_el': [1, 1, 2, 0, -1]}},\n",
    "#     {'name': 'NCI-HIV', 'dataset': '../datasets/NCI-HIV/AIDO99SD.sdf',\n",
    "#         'dataset_y': '../datasets/NCI-HIV/aids_conc_may04.txt',},\n",
    "    \n",
    "#     # not working below\n",
    "#     {'name': 'PTC_FM', 'dataset': '../datasets/PTC/Train/FM.ds',},\n",
    "#     {'name': 'PTC_FR', 'dataset': '../datasets/PTC/Train/FR.ds',},\n",
    "#     {'name': 'PTC_MM', 'dataset': '../datasets/PTC/Train/MM.ds',},\n",
    "#     {'name': 'PTC_MR', 'dataset': '../datasets/PTC/Train/MR.ds',},\n",
    "]\n",
    "estimator = spkernel\n",
    "param_grid_precomputed = {}\n",
    "param_grid = [{'C': np.logspace(-10, 10, num = 41, base = 10)}, \n",
    "              {'alpha': np.logspace(-10, 10, num = 41, base = 10)}]\n",
    "\n",
    "for ds in dslist:\n",
    "    print()\n",
    "    print(ds['name'])\n",
    "    model_selection_for_precomputed_kernel(\n",
    "        ds['dataset'], estimator, param_grid_precomputed, \n",
    "        (param_grid[1] if ('task' in ds and ds['task'] == 'regression') else param_grid[0]), \n",
    "        (ds['task'] if 'task' in ds else 'classification'), NUM_TRIALS=30,\n",
    "        datafile_y=(ds['dataset_y'] if 'dataset_y' in ds else None),\n",
    "        extra_params=(ds['extra_params'] if 'extra_params' in ds else None))\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--- This is a regression problem ---\n",
      "\n",
      "1. Loading dataset from file...\n",
      "\n",
      "2. Calculating gram matrices. This could take a while...\n",
      "--- shortest path kernel matrix of size 183 built in 13.54222846031189 seconds ---\n",
      "\n",
      "gram matrix with parameters {} is: \n",
      "[[1.         0.23570226 1.         ... 0.07784989 0.07784989 0.07784989]\n",
      " [0.23570226 1.         0.23570226 ... 0.         0.         0.16514456]\n",
      " [1.         0.23570226 1.         ... 0.07784989 0.07784989 0.07784989]\n",
      " ...\n",
      " [0.07784989 0.         0.07784989 ... 1.         0.38181818 0.12727273]\n",
      " [0.07784989 0.         0.07784989 ... 0.38181818 1.         0.12727273]\n",
      " [0.07784989 0.16514456 0.07784989 ... 0.12727273 0.12727273 1.        ]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7faafef48390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "3. Fitting and predicting using nested cross validation. This could really take a while...\n",
      "calculate performance:   0%|          | 2/1230 [00:00<01:34, 12.98it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ljia/.local/lib/python3.5/site-packages/sklearn/linear_model/ridge.py:154: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.\n",
      "  warnings.warn(\"Singular matrix in solving dual problem. Using \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                                          \n",
      "4. Getting final performances...\n",
      "\n",
      "best_params_out:  [{}]\n",
      "best_params_in:  [{'alpha': 1.0}]\n",
      "\n",
      "best_val_perf:  38.310091123281964\n",
      "best_val_std:  0.921980968599538\n",
      "final_performance:  39.40204478386939\n",
      "final_confidence:  6.323960893696847\n",
      "train_performance:  28.772353915665963\n",
      "train_std:  0.6079886263011134\n",
      "\n",
      "time to calculate gram matrix with different hyperpapams: 13.54±nan\n",
      "time to calculate best gram matrix:  13.54222846031189 s\n",
      "\n",
      "params                 train_perf      valid_perf      test_perf         gram_matrix_time\n",
      "---------------------  --------------  --------------  --------------  ------------------\n",
      "{'alpha': '1.00e-01'}  38.29±21.43     90.94±85.41     95.89±109.61                 13.54\n",
      "{'alpha': '1.12e-01'}  41.08±38.73     87.86±80.28     123.11±281.23                13.54\n",
      "{'alpha': '1.26e-01'}  32.82±6.74      65.09±20.13     71.11±39.78                  13.54\n",
      "{'alpha': '1.41e-01'}  30.31±2.59      57.03±8.94      59.35±14.38                  13.54\n",
      "{'alpha': '1.58e-01'}  29.75±2.56      54.54±9.19      56.87±14.53                  13.54\n",
      "{'alpha': '1.78e-01'}  29.95±7.00      57.65±46.21     56.04±27.59                  13.54\n",
      "{'alpha': '2.00e-01'}  28.41±1.44      48.10±5.22      49.80±8.33                   13.54\n",
      "{'alpha': '2.24e-01'}  28.07±1.22      46.08±2.85      47.97±7.38                   13.54\n",
      "{'alpha': '2.51e-01'}  27.83±1.10      44.68±2.22      46.57±6.89                   13.54\n",
      "{'alpha': '2.82e-01'}  27.65±1.00      43.54±1.90      45.39±6.54                   13.54\n",
      "{'alpha': '3.16e-01'}  27.51±0.92      42.57±1.68      44.36±6.28                   13.54\n",
      "{'alpha': '3.55e-01'}  27.42±0.85      41.74±1.53      43.45±6.09                   13.54\n",
      "{'alpha': '3.98e-01'}  27.38±0.80      41.01±1.40      42.66±5.97                   13.54\n",
      "{'alpha': '4.47e-01'}  27.37±0.75      40.38±1.30      41.96±5.91                   13.54\n",
      "{'alpha': '5.01e-01'}  27.41±0.72      39.84±1.22      41.35±5.89                   13.54\n",
      "{'alpha': '5.62e-01'}  27.50±0.69      39.39±1.15      40.82±5.91                   13.54\n",
      "{'alpha': '6.31e-01'}  27.64±0.66      39.02±1.09      40.38±5.96                   13.54\n",
      "{'alpha': '7.08e-01'}  27.83±0.64      38.72±1.04      40.02±6.03                   13.54\n",
      "{'alpha': '7.94e-01'}  28.08±0.63      38.51±0.99      39.73±6.11                   13.54\n",
      "{'alpha': '8.91e-01'}  28.39±0.62      38.37±0.95      39.53±6.21                   13.54\n",
      "{'alpha': '1.00e+00'}  28.77±0.61      38.31±0.92      39.40±6.32                   13.54\n",
      "{'alpha': '1.12e+00'}  29.22±0.60      38.33±0.89      39.36±6.44                   13.54\n",
      "{'alpha': '1.26e+00'}  29.74±0.60      38.44±0.87      39.40±6.56                   13.54\n",
      "{'alpha': '1.41e+00'}  30.34±0.59      38.63±0.85      39.53±6.68                   13.54\n",
      "{'alpha': '1.58e+00'}  32.30±4.94      40.59±6.49      41.14±9.07                   13.54\n",
      "{'alpha': '1.78e+00'}  65.08±118.93    70.87±110.34    70.05±105.01                 13.54\n",
      "{'alpha': '2.00e+00'}  61.14±47.95     63.56±40.28     63.69±32.83                  13.54\n",
      "{'alpha': '2.24e+00'}  517.26±2507.14  514.17±2482.82  385.18±1762.10               13.54\n",
      "{'alpha': '2.51e+00'}  38.51±2.50      43.44±2.15      44.18±8.19                   13.54\n",
      "{'alpha': '2.82e+00'}  37.61±0.88      42.78±0.93      43.42±7.80                   13.54\n",
      "{'alpha': '3.16e+00'}  38.17±0.62      43.21±0.76      43.79±7.82                   13.54\n",
      "{'alpha': '3.55e+00'}  39.19±0.57      44.01±0.73      44.54±7.90                   13.54\n",
      "{'alpha': '3.98e+00'}  40.46±0.56      45.03±0.73      45.51±8.00                   13.54\n",
      "{'alpha': '4.47e+00'}  41.92±0.56      46.23±0.74      46.66±8.13                   13.54\n",
      "{'alpha': '5.01e+00'}  43.55±0.56      47.59±0.75      47.98±8.26                   13.54\n",
      "{'alpha': '5.62e+00'}  45.34±0.57      49.12±0.76      49.46±8.40                   13.54\n",
      "{'alpha': '6.31e+00'}  47.28±0.57      50.81±0.77      51.11±8.54                   13.54\n",
      "{'alpha': '7.08e+00'}  49.37±0.57      52.66±0.78      52.92±8.69                   13.54\n",
      "{'alpha': '7.94e+00'}  51.60±0.57      54.67±0.79      54.89±8.84                   13.54\n",
      "{'alpha': '8.91e+00'}  53.99±0.57      56.84±0.79      57.02±8.99                   13.54\n",
      "{'alpha': '1.00e+01'}  56.52±0.57      59.17±0.79      59.32±9.13                   13.54\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/numpy/core/_methods.py:135: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  keepdims=keepdims)\n",
      "/usr/local/lib/python3.5/dist-packages/numpy/core/_methods.py:127: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "calculate performance: 100%|██████████| 1230/1230 [01:20<00:00, 19.86it/s]"
     ]
    }
   ],
   "source": [
    "%load_ext line_profiler\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import sys\n",
    "sys.path.insert(0, \"../\")\n",
    "from pygraph.utils.model_selection_precomputed import model_selection_for_precomputed_kernel\n",
    "from pygraph.kernels.spKernel import spkernel\n",
    "\n",
    "datafile = '../../../../datasets/acyclic/Acyclic/dataset_bps.ds'\n",
    "estimator = spkernel\n",
    "param_grid_precomputed = {}\n",
    "param_grid = {'alpha': np.logspace(-1, 1, num = 41, base = 10)}\n",
    "\n",
    "model_selection_for_precomputed_kernel(datafile, estimator, param_grid_precomputed, param_grid, \n",
    "                                       'regression', NUM_TRIALS=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- This is a regression problem ---\n",
      "\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "--- shortest path kernel matrix of size 185 built in 13.3865065574646 seconds ---\n",
      "[[ 3.  1.  3. ...  1.  1.  1.]\n",
      " [ 1.  6.  1. ...  0.  0.  3.]\n",
      " [ 3.  1.  3. ...  1.  1.  1.]\n",
      " ...\n",
      " [ 1.  0.  1. ... 55. 21.  7.]\n",
      " [ 1.  0.  1. ... 21. 55.  7.]\n",
      " [ 1.  3.  1. ...  7.  7. 55.]]\n",
      "\n",
      " Starting calculate accuracy/rmse...\n",
      "calculate performance:  94%|█████████▎| 936/1000 [00:01<00:00, 757.54it/s]\n",
      " Mean performance on train set: 28.360361\n",
      "With standard deviation: 1.357183\n",
      "\n",
      " Mean performance on test set: 35.191954\n",
      "With standard deviation: 4.495767\n",
      "calculate performance: 100%|██████████| 1000/1000 [00:01<00:00, 771.22it/s]\n",
      "\n",
      "\n",
      "  rmse_test    std_test    rmse_train    std_train    k_time\n",
      "-----------  ----------  ------------  -----------  --------\n",
      "     35.192     4.49577       28.3604      1.35718   13.3865\n"
     ]
    }
   ],
   "source": [
    "%load_ext line_profiler\n",
    "\n",
    "import sys\n",
    "sys.path.insert(0, \"../\")\n",
    "from pygraph.utils.utils import kernel_train_test\n",
    "from pygraph.kernels.spKernel import spkernel\n",
    "\n",
    "datafile = '../../../../datasets/acyclic/Acyclic/dataset_bps.ds'\n",
    "kernel_file_path = 'kernelmatrices_path_acyclic/'\n",
    "\n",
    "kernel_para = dict(edge_weight = 'atom')\n",
    "\n",
    "kernel_train_test(datafile, kernel_file_path, spkernel, kernel_para, normalize = False)\n",
    "\n",
    "# %lprun -f spkernel \\\n",
    "#     kernel_train_test(datafile, kernel_file_path, spkernel, kernel_para, normalize = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# results\n",
    "\n",
    "# with y normalization\n",
    "  RMSE_test    std_test    RMSE_train    std_train    k_time\n",
    "-----------  ----------  ------------  -----------  --------\n",
    "    35.6337     5.23183       32.3805      3.92531   14.9301\n",
    "\n",
    "# without y normalization\n",
    "  RMSE_test    std_test    RMSE_train    std_train    k_time\n",
    "-----------  ----------  ------------  -----------  --------\n",
    "     35.192     4.49577       28.3604      1.35718   14.5768"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "- This script take as input a kernel matrix\n",
      "and returns the classification or regression performance\n",
      "- The kernel matrix can be calculated using any of the graph kernels approaches\n",
      "- The criteria used for prediction are SVM for classification and kernel Ridge regression for regression\n",
      "- For predition we divide the data in training, validation and test. For each split, we first train on the train data, \n",
      "then evaluate the performance on the validation. We choose the optimal parameters for the validation set and finally\n",
      "provide the corresponding performance on the test set. If more than one split is performed, the final results \n",
      "correspond to the average of the performances on the test sets. \n",
      "\n",
      "@references\n",
      "    https://github.com/eghisu/GraphKernels/blob/master/GraphKernelsCollection/python_scripts/compute_perf_gk.py\n",
      "\n",
      "\n",
      " Loading dataset from file...\n",
      "[ -23.7   14.    37.3  109.7   10.8   39.    42.    66.6  135.   148.5\n",
      "   40.    34.6   32.    63.    53.5   67.    64.4   84.7   95.5   92.\n",
      "   84.4  154.   156.   166.   183.    70.3   63.6   52.5   59.    59.5\n",
      "   55.2   88.    83.   104.5  102.    92.   107.4  123.2  112.5  118.5\n",
      "  101.5  173.7  165.5  181.    99.5   92.3   90.1   80.2   82.    91.2\n",
      "   91.5   81.2   93.    69.    86.3   82.   103.   103.5   96.   112.   104.\n",
      "  132.5  123.5  120.3  145.   144.2  142.8  132.   134.2  137.   139.\n",
      "  133.6  120.4  120.   137.   195.8  177.2  181.   185.9  175.7  186.   211.\n",
      "  125.   118.   117.1  107.   102.5  112.    97.4   91.5   87.6  106.5\n",
      "  101.    99.3   90.   137.   114.   126.   124.   140.5  157.5  146.   145.\n",
      "  141.   171.   166.   155.   145.   159.   138.   142.   159.   163.5\n",
      "  229.5  142.   125.   132.   130.5  125.   122.   121.   122.2  112.   106.\n",
      "  114.5  151.   128.5  109.5  126.   147.   158.   147.   165.   188.9\n",
      "  170.   178.   148.5  165.   177.   167.   195.   226.   215.   201.   205.\n",
      "  151.5  165.5  157.   139.   163.   153.5  139.   162.   173.   159.5\n",
      "  159.5  155.5  141.   126.   164.   163.   166.5  146.   165.   159.   195.\n",
      "  218.   250.   235.   186.5  156.5  162.   162.   170.2  173.2  186.8\n",
      "  173.   187.   174.   188.5  199.   228.   215.   216.   240. ]\n",
      "\n",
      " Loading the matrix from file...\n",
      "[[  3.   1.   3. ...,   1.   1.   1.]\n",
      " [  1.   6.   1. ...,   0.   0.   3.]\n",
      " [  3.   1.   3. ...,   1.   1.   1.]\n",
      " ..., \n",
      " [  1.   0.   1. ...,  55.  21.   7.]\n",
      " [  1.   0.   1. ...,  21.  55.   7.]\n",
      " [  1.   3.   1. ...,   7.   7.  55.]]\n",
      "\n",
      " --- This is a regression problem ---\n",
      "\n",
      " Starting split 10...\n",
      "\n",
      " Normalizing output y...\n",
      "The best performance is for trial 12 with parameter alpha = 100.000000\n",
      "The best performance on the validation set is: 40.422382\n",
      "The corresponding performance on test set is: 47.424532\n",
      "\n",
      " Starting split 11...\n",
      "\n",
      " Normalizing output y...\n",
      "The best performance is for trial 12 with parameter alpha = 100.000000\n",
      "The best performance on the validation set is: 33.084913\n",
      "The corresponding performance on test set is: 35.493699\n",
      "\n",
      " Starting split 12...\n",
      "\n",
      " Normalizing output y...\n",
      "The best performance is for trial 12 with parameter alpha = 100.000000\n",
      "The best performance on the validation set is: 31.306710\n",
      "The corresponding performance on test set is: 33.173366\n",
      "\n",
      " Starting split 13...\n",
      "\n",
      " Normalizing output y...\n",
      "The best performance is for trial 12 with parameter alpha = 100.000000\n",
      "The best performance on the validation set is: 43.500424\n",
      "The corresponding performance on test set is: 32.633129\n",
      "\n",
      " Starting split 14...\n",
      "\n",
      " Normalizing output y...\n",
      "The best performance is for trial 10 with parameter alpha = 1.000000\n",
      "The best performance on the validation set is: 53.561752\n",
      "The corresponding performance on test set is: 42.883548\n",
      "\n",
      " Starting split 15...\n",
      "\n",
      " Normalizing output y...\n",
      "The best performance is for trial 12 with parameter alpha = 100.000000\n",
      "The best performance on the validation set is: 40.444773\n",
      "The corresponding performance on test set is: 32.713040\n",
      "\n",
      " Starting split 16...\n",
      "\n",
      " Normalizing output y...\n",
      "The best performance is for trial 11 with parameter alpha = 10.000000\n",
      "The best performance on the validation set is: 37.046818\n",
      "The corresponding performance on test set is: 37.337851\n",
      "\n",
      " Starting split 17...\n",
      "\n",
      " Normalizing output y...\n",
      "The best performance is for trial 12 with parameter alpha = 100.000000\n",
      "The best performance on the validation set is: 39.907628\n",
      "The corresponding performance on test set is: 38.889064\n",
      "\n",
      " Starting split 18...\n",
      "\n",
      " Normalizing output y...\n",
      "The best performance is for trial 12 with parameter alpha = 100.000000\n",
      "The best performance on the validation set is: 29.879950\n",
      "The corresponding performance on test set is: 27.652558\n",
      "\n",
      " Starting split 19...\n",
      "\n",
      " Normalizing output y...\n",
      "The best performance is for trial 11 with parameter alpha = 10.000000\n",
      "The best performance on the validation set is: 44.911892\n",
      "The corresponding performance on test set is: 35.804454\n",
      "\n",
      " Mean performance on val set: 39.406724\n",
      "With standard deviation: 6.720820\n",
      "\n",
      " Mean performance on test set: 36.400524\n",
      "With standard deviation: 5.352940\n"
     ]
    }
   ],
   "source": [
    "# Author: Elisabetta Ghisu\n",
    "\n",
    "\"\"\"\n",
    "- This script take as input a kernel matrix\n",
    "and returns the classification or regression performance\n",
    "- The kernel matrix can be calculated using any of the graph kernels approaches\n",
    "- The criteria used for prediction are SVM for classification and kernel Ridge regression for regression\n",
    "- For predition we divide the data in training, validation and test. For each split, we first train on the train data, \n",
    "then evaluate the performance on the validation. We choose the optimal parameters for the validation set and finally\n",
    "provide the corresponding performance on the test set. If more than one split is performed, the final results \n",
    "correspond to the average of the performances on the test sets. \n",
    "\n",
    "@references\n",
    "    https://github.com/eghisu/GraphKernels/blob/master/GraphKernelsCollection/python_scripts/compute_perf_gk.py\n",
    "\"\"\"\n",
    "\n",
    "print(__doc__)\n",
    "\n",
    "import sys\n",
    "import pathlib\n",
    "sys.path.insert(0, \"../\")\n",
    "from tabulate import tabulate\n",
    "\n",
    "import random\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.kernel_ridge import KernelRidge # 0.17\n",
    "from sklearn.metrics import accuracy_score, mean_squared_error\n",
    "from sklearn import svm\n",
    "\n",
    "from pygraph.kernels.spkernel import spkernel\n",
    "from pygraph.utils.graphfiles import loadDataset\n",
    "\n",
    "print('\\n Loading dataset from file...')\n",
    "dataset, y = loadDataset(\"../../../../datasets/acyclic/Acyclic/dataset_bps.ds\")\n",
    "y = np.array(y)\n",
    "print(y)\n",
    "\n",
    "kernel_file_path = 'kernelmatrix.ds'\n",
    "path = pathlib.Path(kernel_file_path)\n",
    "if path.is_file():\n",
    "    print('\\n Loading the matrix from file...')\n",
    "    Kmatrix = np.loadtxt(kernel_file_path)\n",
    "    print(Kmatrix)\n",
    "else:\n",
    "    print('\\n Calculating kernel matrix, this could take a while...')\n",
    "    #@Q: is it appropriate to use bond type between atoms as the edge weight to calculate shortest path????????\n",
    "    Kmatrix, run_time = spkernel(dataset, edge_weight = 'bond_type')\n",
    "    print(Kmatrix)\n",
    "    print('Saving kernel matrix to file...')\n",
    "    np.savetxt(kernel_file_path, Kmatrix)\n",
    "\n",
    "# setup the parameters\n",
    "model_type = 'regression' # Regression or classification problem\n",
    "print('\\n --- This is a %s problem ---' % model_type)\n",
    "\n",
    "datasize = len(dataset)\n",
    "trials = 21 # Trials for hyperparameters random search\n",
    "splits = 10 # Number of splits of the data\n",
    "alpha_grid = np.logspace(-10, 10, num = trials, base = 10) # corresponds to (2*C)^-1 in other linear models such as LogisticRegression\n",
    "C_grid = np.logspace(-10, 10, num = trials, base = 10)\n",
    "random.seed(20) # Set the seed for uniform parameter distribution\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "-  Here starts the main program\n",
    "-  First we permute the data, then for each split we evaluate corresponding performances\n",
    "-  In the end, the performances are averaged over the test sets\n",
    "\"\"\"\n",
    "\n",
    "# Initialize the performance of the best parameter trial on validation with the corresponding performance on test\n",
    "val_split = []\n",
    "test_split = []\n",
    "\n",
    "# For each split of the data\n",
    "for j in range(10, 10 + splits):\n",
    "    print('\\n Starting split %d...' % j)\n",
    "\n",
    "    # Set the random set for data permutation\n",
    "    random_state = int(j)\n",
    "    np.random.seed(random_state)\n",
    "    idx_perm = np.random.permutation(datasize)\n",
    "#     print(idx_perm)\n",
    "    \n",
    "    # Permute the data\n",
    "    y_perm = y[idx_perm] # targets permutation\n",
    "#     print(y_perm)\n",
    "    Kmatrix_perm = Kmatrix[:, idx_perm] # inputs permutation\n",
    "#     print(Kmatrix_perm)\n",
    "    Kmatrix_perm = Kmatrix_perm[idx_perm, :] # inputs permutation\n",
    "    \n",
    "    # Set the training, validation and test\n",
    "    # Note: the percentage can be set up by the user\n",
    "    num_train_val = int((datasize * 90) / 100)         # 90% (of entire dataset) for training and validation\n",
    "    num_test = datasize - num_train_val              # 10% (of entire dataset) for test\n",
    "    num_train = int((num_train_val * 90) / 100) # 90% (of train + val) for training\n",
    "    num_val = num_train_val - num_train # 10% (of train + val) for validation\n",
    "    \n",
    "    # Split the kernel matrix\n",
    "    Kmatrix_train = Kmatrix_perm[0:num_train, 0:num_train]\n",
    "    Kmatrix_val = Kmatrix_perm[num_train:(num_train + num_val), 0:num_train]\n",
    "    Kmatrix_test = Kmatrix_perm[(num_train + num_val):datasize, 0:num_train]\n",
    "\n",
    "    # Split the targets\n",
    "    y_train = y_perm[0:num_train]\n",
    "\n",
    "    # Normalization step (for real valued targets only)\n",
    "    print('\\n Normalizing output y...')\n",
    "    if model_type == 'regression':\n",
    "        y_train_mean = np.mean(y_train)\n",
    "        y_train_std = np.std(y_train)\n",
    "        y_train = (y_train - y_train_mean) / float(y_train_std)\n",
    "#         print(y)\n",
    "        \n",
    "    y_val = y_perm[num_train:(num_train + num_val)]\n",
    "    y_test = y_perm[(num_train + num_val):datasize]\n",
    "    \n",
    "    # Record the performance for each parameter trial respectively on validation and test set\n",
    "    perf_all_val = []\n",
    "    perf_all_test = []\n",
    "    \n",
    "    # For each parameter trial\n",
    "    for i in range(trials):\n",
    "        # For regression use the Kernel Ridge method\n",
    "        if model_type == 'regression':\n",
    "#             print('\\n Starting experiment for trial %d and parameter alpha = %3f\\n ' % (i, alpha_grid[i]))\n",
    "\n",
    "            # Fit the kernel ridge model\n",
    "            KR = KernelRidge(kernel = 'precomputed', alpha = alpha_grid[i])\n",
    "#             KR = svm.SVR(kernel = 'precomputed', C = C_grid[i])\n",
    "            KR.fit(Kmatrix_train, y_train)\n",
    "\n",
    "            # predict on the validation and test set\n",
    "            y_pred = KR.predict(Kmatrix_val)\n",
    "            y_pred_test = KR.predict(Kmatrix_test)\n",
    "#             print(y_pred)\n",
    "\n",
    "            # adjust prediction: needed because the training targets have been normalizaed\n",
    "            y_pred = y_pred * float(y_train_std) + y_train_mean\n",
    "#             print(y_pred)\n",
    "            y_pred_test = y_pred_test * float(y_train_std) + y_train_mean\n",
    "#             print(y_pred_test)\n",
    "\n",
    "            # root mean squared error on validation\n",
    "            rmse = np.sqrt(mean_squared_error(y_val, y_pred))\n",
    "            perf_all_val.append(rmse)\n",
    "\n",
    "            # root mean squared error in test \n",
    "            rmse_test = np.sqrt(mean_squared_error(y_test, y_pred_test))\n",
    "            perf_all_test.append(rmse_test)\n",
    "\n",
    "#             print('The performance on the validation set is: %3f' % rmse)\n",
    "#             print('The performance on the test set is: %3f' % rmse_test)\n",
    "            \n",
    "    # --- FIND THE OPTIMAL PARAMETERS --- #\n",
    "    # For regression: minimise the mean squared error\n",
    "    if model_type == 'regression':\n",
    "\n",
    "        # get optimal parameter on validation (argmin mean squared error)\n",
    "        min_idx = np.argmin(perf_all_test)\n",
    "        alpha_opt = alpha_grid[min_idx]\n",
    "\n",
    "        # performance corresponding to optimal parameter on val\n",
    "        perf_val_opt = perf_all_val[min_idx]\n",
    "\n",
    "        # corresponding performance on test for the same parameter\n",
    "        perf_test_opt = perf_all_test[min_idx]\n",
    "\n",
    "        print('The best performance is for trial %d with parameter alpha = %3f' % (min_idx, alpha_opt))\n",
    "        print('The best performance on the validation set is: %3f' % perf_val_opt)\n",
    "        print('The corresponding performance on test set is: %3f' % perf_test_opt)\n",
    "\n",
    "    # append the best performance on validation\n",
    "    # at the current split\n",
    "    val_split.append(perf_val_opt)\n",
    "\n",
    "    # append the correponding performance on the test set\n",
    "    test_split.append(perf_test_opt)\n",
    "\n",
    "# average the results\n",
    "# mean of the validation performances over the splits\n",
    "val_mean = np.mean(np.asarray(val_split))\n",
    "# std deviation of validation over the splits\n",
    "val_std = np.std(np.asarray(val_split))\n",
    "\n",
    "# mean of the test performances over the splits\n",
    "test_mean = np.mean(np.asarray(test_split))\n",
    "# std deviation of the test oer the splits\n",
    "test_std = np.std(np.asarray(test_split))\n",
    "\n",
    "print('\\n Mean performance on val set: %3f' % val_mean)\n",
    "print('With standard deviation: %3f' % val_std)\n",
    "print('\\n Mean performance on test set: %3f' % test_mean)\n",
    "print('With standard deviation: %3f' % test_std)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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