{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Convolutional Neural Networks\n", "In this notebook we will work with convolutional neural networks for classification\n", "\n", "<img src=\"cnn.png\">" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercise 1 (Load the data set)\n", "Import the digits data set (MNIST) and visualize 10 instances of each digit. Prepare the data set so that each digit is a tensor of size $28 \\times 28 \\times 1$. The last component represent the number of channels. If the pictures would have colors then there would be 3 channels." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using TensorFlow backend.\n" ] }, { "data": { "image/png": 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wJQBpveRisZw84BHb8os5NJTkRlIzyu9lqQz9b2xGLR4/k0wSHYHzAMuTJNj5UF8JTLal73CLrXnF0XLhsenzgIzqz9nJ/WE8eLfUhCr3vkwqPPlwSyomdobksM96m7xgbWYTB++QazC1rAQb+/hb+Pk2OZeO8mXHLDLmf3s7ccYqj8FgHzm6UmsvGnIynBPWzXLNdXtjGF8NFzfspiYymUK8SdQIGUj0FKkdlb43wd0mXhVzeDEuVsqapLE8KZBhP8pLKvofWZya6tdwJvKsqymV2DemyPXw1L89KfGxJCCkFpHXR9/xE4kOkvs4jkgXH0XOHPgiCoB3BtVlz2XpjHC2h0xsQ65Snw0g7lmxeesjjmwufz4+I0lLR7oVz9PvMAKBB7PX3ruS9js7AhA5Kv26M4jdgSlQnhWOzhQAQYNlQmuqGEF8v/IA3HWHZNMPKTmZCr5yPTomWBb7Isb0bXEs57JmersCY0+vFUVRFEVRDIohlahzj4j68mqpUc4aJht/lpVBBW5c+k+zWZxS4U875PdGY6w6UQ63kEOOJVOnuNWHKgEQkZBzoLnRMAXY1YqW0nR4yISZtA6ShpjH7anHy5OK8lqcrJJm1ZgBZJXmA31E6dj7oL0Gyq5ArMnJGA2zXVfxyWV9kqGyZd/3UzVRsW7rIcGQYV+5N2g5JVlWslZsTH/lIwAWDqybbdyL4VLp2AcTSTYJfD9ikZXjuJOtuGPZswAU+Ufu3TI/i0ph2n+Ikztk1VjKLOfTiE2kr0WxaasZuEvOUeh7hwCYFSkNubY9Oo7YCAnmrfqGXAOeCra+krN3x/BPvzFZtvVf8ATRQ+U6c5SfSP3gQpZEHoCH/xwEQNV+O7HUFRX54XeW2sckM3rDnQBEb/fMczR8qrj810z1A+zNs51fc+bco/KeWd1jlH2LXK+J1jS+mCXHU36fsV3NDky+vhRpKveZD9lLwrTb2UG+aSPXq41DbrPterAly/tgbYofjQPkGbFg2TdA5hCIDJYlFSfe/jBtHXQJgA2pch6LfOGeom2qRCmKoiiKouQDQypR6bJYJczHn9XJokZEfiGF0PITTO4TLPE0O0fVtG/ZSI+99wAQ+4z7ihleD3GviM2Z44IcVHhPvnpDLIlPYCCnH5IYmpXvfOLcXmOWrGzLL5fjC1i8nvAyspKYtVQqzg4Nz1DaHKuSfx+T39H04GBKfWHv85SY0YfNU5j+kr5Nn90vvQtfeiycCktlFW9Oyv2qjX9C1J+dbSe62MJrE9VTEhdqvDuQiIaHcx23/IQED5/8sTzh2+S8+P/kSP9OI4YNWcY7ruDDLzajYYCsDr+5VK7gDPcAjvOd2EUCYRs+JNfz2hfHsLO1KHU9Kt0FwPlbPWBgDpyulV2hqDI0Q+2s/L0oGaPLZlSsf/IZSYOPtseiJt3TkKVTsyb7xC5+1lAFN/PKn++Kym8la3mR28Y9T/n3vUOBcnD+h4r8UVuC/3NKCbHa1SmjqyaW4xLsPvzpJxk1Sa6z2vbT8+WFCEasEEUtZoZ4IXyPn6fkLCm50TriNwB6LRcl+MrnkKsw+v9UURRFURTFkBhSicrMaUshIP+ZLj7Bwex6T2JxdnaUlcePiWEcGS/ZHIXPGq9YnrVlPUY0mJ9t+51buwFQaIPxY6EccVA7P6zNzo6fZNnXcdf9xIyUOBHHysM3ojx1FkqW4bDw7QCct4qS03jOUMrEyrhfa30LwOr/fcJD3dsDcOoTOb+BpzPaUZh/90xshiO1O/KFvI2vFi+ZRLR1kUH5oPLLeYslKMP1ZYUGtzjp/P7V5dKnLYYbz7b1JI7rt9Qn8jX5hXSCTbJ0nlJpEQDtOz1L8DzPZ3elhVmcsXpttnYBIIh9znIGnYpJpqEPPtSeIspahfmiyDjab/Uf853zdzjGxLzuXaoNQPz4xviZRE28Mj6x/K8XDK/ym6MqAxDfV3qP7qw93qlADT8h53NOXF223iotwmoXEWXZ+G8OwX/pBl6p3Cjb9iufFxc7NmJxBWnzkmaT6zIoIXvhYldi+EnU8391BSCGjdf1OceD4cRzSexoIJOnNlskhTyk7V4KY7zJk4O3Z0ympl/W2/j5oy0I6268HmNXYvKVS2rXx3UA2NlhvLOuTIdPZWZRadoe0u0vn7Q7xHVX8/1/GF5SzvH0CxUBmPl/Ugcrau4aZ82hVnfKg/vyQ+eZV28KAOU/yQhAX3RZxk2O8Uyq9fVy/IEoT5vgESouMPpr6uo4mjXv6SoJHzXrJgA4J1AAY8/IMyh4gXvcCnnBEZxrtWV37aXZHI3dk6GGVIQevHsnACXM4q77/mwjZtwr5WYqn3J9DZ6CxifQfr5q7neGSjj+J/UmPANAxN+en/BeiyPtygCwvYejNIyJ6jMHAhA9UqrRB/UoBHZX8pK9MgmugPclclyN9CCfbOex8gxZ3LmrjqS68xRFURRFUfKBMZUo+yLJBx/G3DoLgPHE5Omjjl5Pcx79EIAYP39uWScdnMt22l7AhrqGev4+2QLKV0+/hZJnjS+bHxwmEuzODpJKfSQ9ha7vDQOg0nxx4Z25vTK2noUBmF1TxpUwB1DjG7t7YLIUqgzelbEidHTrDp3l+Apd+ouyVarL/gwDhjp6vm0r0OPKCYfL8lzXehRdIH/PejFvff6ODpUeegsGf2DfEpD7YMUQmBpIYkrcYH+mNP8cgBaB2XsbptjErbzmjLhcsB51j4HXoOIPNpAqIk63+N339OdkXUluiPRz9MTzZ1OzaUBGqQ5Hsc2VoxsTFm9cFT83zKGhABx4Ws7hxqgxODSEhusfBaDSVCk+abEaW1tL7NSYWc85yjLIc6PZSwOImvuvbKokBSmf659RZT7tQE5lc72fwt+sgdGetUGVKEVRFEVRlHxgTCXKHi5hxUrLIFEenp0hsTNVplvxOyar/eMtJSi32EOHGFRBijfeEyxxNQsvSz+kR7e0pfin3jELPzhbVkmOgMfMlPn9lFfEHkzskzX9OdAE9/X7A4BygyWmq1foD5lGyEqqxteDiXpZ4i4s6XnzZpecIMqcLcufzD01v6BIvk/UtrDnxfe+ImosndZ3l527cleifMuU5nAXidX6dpCsJDMXFHUUHvVL8u54oathNsm67WyMqB+lf/SkNdfGt3JF9vQuC8DrD0nRv86FcmvpA68cb8CKMdIDpujn7in2l1fMKVaO2OMTHdfdL1Mn5djH0cG+dEkldxTbjHZzAdiCwBxejJOfS/uWjfUyio3W/tQePD9CFG+jK1AODreBGD+J7ep9oBUARWauxmZXxvd3krjQ2ICj+Nj7rAacvjn1kovdmsB1xksXNMacRGUi0CQm7rhzEgB/3hZIfIpkJPQOS8g2/pkjtwHw0yoJ/Ix+xvg3vSMI/uO6XwJSG+q8VR5eDX+U6s+x+73DFfnHpVgAGgdIAGMxcwCvFM86KWy/8wEOrBbJOXK2NHeN2rYRWx4nT57m7rdXAFnrWO18RdwFXGqc6+e6NVvN/JKLAbCStc9Vr4S72T1dqtSHzzXWy7cgsdjsL2yDPtMdVbvP15fA3Yfe/Il+RebmOn7oUZkwrZ7QAIBiM9ZR1GrM8+f720a6/9/zAEQ+LcHHn1dalm1cnb8ex7Rd3O0lNsk9GV0APUs9haVKOf6sNy3Lti8vRFDhTeOHR+SITboKAFjtGWmmgABO9roFgM1PS7D5tlQr1f6QmkmV3/HSY70G5yM9/yDxvAWKoiiKoiheiCGVqFK/S/r7i0815f3SWVd1LQJTuTUwIcu2f1J86L6iLwAxvUXaizZwCYMrSS4mMvqtgZftW8wsTZQVsaMScE5VaI3Iqtbi+mjc43YAztdJxfekqC4xk8TV5nvsBJWSDwLec1zXYscdn+ZxpKxbHJX4+6yVoNaoPvGEXzamguEKEht6vsq8A98yomyfmRbC05VFZexe+Hiu4wcevpW/J4rSXXy2qJHFLnrHuQv7Up6Lp0X0pj31s42peJOkwZsaSv24vc9llHOYel7c6Yu7NAXiPGHWDWMOT3F+H3dWQlpqrDjGDxHjsox7avizVHZT/zhPUW5FIn4Dc+9H6g5UiVIURVEURckHhlSiLHF7AIjvWonqgyT4b/uDY7ONi13SH4CqExKJ+cezwWWKYDktadKlPhEffKlM+7wj4una/Da4OQBf9JcA883Np11tOF9eiADgaFoRpv0tn42aIkGskfYebDeLInctHIHlnib17gakDpFr9ZWoJQDcFXQ5x7HHLUkAtFg4FIDYV3dS7Jys8P8r580bOf6qlJrY0mCmc9uEL6WAb/nt3hsj5BsXDC3l+7/qSrKDDya2pMoTtvMCKRoaM2/rTX99mv7axIwL0r+ye2HxdCTWkHhG/4OH3GKDISdRDtL3JhA1JAGADkMaZtsfg7i6vD2XKXTTMQAGHRIX2KSIFZ40R7kGjpYylddJk+j6g5/h86c+BqCmv7gObt/yEOd/FzdRxW/l5k7ft59oD2eSeJKUZSWw1DXGYz3hfh/ian2fbfv4c1UAGLNCmgebLCZiR0iT8ujj9iwuN9mo5A9bU+mWULLQaee26ssl3CP65wsyxv1mFRiRk/ZQw1+qky/rORKAVw+3Y93Pkt0dNVwmiMa401zPR59KC6Puz0vmZZn/Sb2v0+dqw5p/Xf73jbEsVBRFURRF8TIMrUT9V0jfJxW3D0m2dI7BnorxsCZKcHS591bxyntZm2UWYi+FkArtN4sb80Yp/dEq2n0kadiRZK+F5k5inl5H+6dzv88yNzpV5cm7iO9lb34eOw+AeZdKEv2xuPZsXtC8/VqkHztO5Vck8aHPK/bmeFygIt7rorwRys2Uch0P3S8N6b+NksbfLV/rTrGHwwCwnDvvsr+vSpSiKIqiKEo+UCVKURRFuWkot8xe0kBiyPnw3W4U3XBzp/r/l3H0VU3tLJXaq41+CpCyMx1in5BBLoyN0kmUoiiKctMQMlsSADrMlmSkougE6r+AYzIV3Uu+dqAhoIHliqIoiqIohsRks3lzsqeiKIqiKIpnUCVKURRFURQlH+gkSlEURVEUJR/oJEpRFEVRFCUf6CRKURRFURQlH+gkSlEURVEUJR/oJEpRFEVRFCUf6CRKURRFURQlH+gkSlEURVEUJR+4te3LnT5dvbay5y/W7015GXezH+PNfnygx2h09BiFm/34QI/R6OgxqhKlKIqiKIqSL3QSpSiKoiiKkg90EqUoiqIoipIPdBKlKIqiKIqSD9waWK4oiqJ4Hz61YwE4/a6F3+rMBKBLpz4A2NZv8ZhdiuJpVIlSFEVRFEXJBze9EpXYqTG/jZsAgJ/JDECL/n0Jmr/Ok2blmbjp9QHYd/dnfHgmEoBlDzYAwLI9zmN2KXmgSW0A9nUIAWB45+/4MK4NABe3hGcZWuXNf7AmJ7vXPoMR/ldRAHxMNk42O+dhaxSAlHYNAZg+8SMA1iZHUGvpQACqHzwIQLpnTFMUQ3DTTqKODWkGwP2PrSDNZsmyr/Ubf/FdnZYAVHxnAwC2tFT3GngNzDWqArCg9XgA0mx+DCi6C4DZte8CoPB2z9hWEBwfLOfHdvtZAHpFraVvkawHNOSwTDgOtw/GcvKkew28QQ6/1Iwl/T8AoIJvIef2HvW/k2/qZx1/68anCJmz1l3mGYq4z2RRsL7CGACarhxAJJs8adJ/nvM9mwCw6N3RADT8+VkAqg2JI+aCPDN18mRwmtRm3zNS4iiy1CkAfopdDEDrbR05vL5sluFRY/ZgOX7CvTbeBKg7T1EURVEUJR/cdEqUQ4Gq0WUHAC+G/5NtzIvh//BiH9ne5fuegAFdY4ePATA4rhsAv9SY40lrCgTf8uUASJzuy/rqYwHYkZYGwAt7O/PziWoATI76FoBJ5VfK1xUVWVg9/MpfZ2gqfr6XI32DAKiQh7tsyuiPeML3OQAKf7vGlaYZiriJjVh/l7iKLlqlqHHoiiBPmvSfx1SvBvPeGQXAk3s7AxBjf15arJZcP6cYi7gn/Ylr8WmWbWn2uuE/V58L1bOO39Y9nZ6fDgGg3Hur3GHiTYEqUYqiKIqiKPnAq5Uoc6mSAFxsVpln358FQOPAPwEo5uPvHDfpnKTn+plkFfVEWLxPh2NTAAAgAElEQVQ7zcwXlnPnAdh/KFo21PCgMQVEvUUHAOgStoGYBYMAqP6WbLMdPewc17fh0wBMnD0RgCfD9vLR6HsBqDLUO1Sa9KPHeGKKHOOypyU2qoxvIRZeDgagQ0hilvHV/IM5eqdEmRT+1o2GephW9XZQ2H6v9t/fFoDin672pEkFjrl6DNaQAADie0iSwayOY537H9vYG4CILlvdb1wmfELEttITDzDqZAsAUjvbI59UgfI6JraamW3bGyfrArD6VOVs+6bHfM0tHeUaPP6ea20rKHzLlCbhMUm4Gv3EZwC0Ccp4tlb/UpIgIl903TPFKydRZ3o3BaDEo/sBWBo91pl5l2bzzzb+8ynycHbobk8M/dj1Rt4gjgnibdUM5mbMB5celCDV4SUkSL7J348Q01+yI3MKTnXUnWkz/3kA4rtM4P37vgZg8tBIF1tbcJR/VyTx6d0livyV4rvYnVJadobszTY+9pNLAFjdY16BktSxEQDFh+4j5SG5F9OPHst1/In+4nZ/v9RHfHmhIgBnX64AgA+nXWmqy7nUtTEAxzpKssqiW8cT4xcIgBVHH9YMJ8Dg6ssBmEcJ9xmZAzs/FP/OzHIf82ibRwGwnNrjSZPcisnX/jqsVTX7vl37sCYmZtvuLUy/EAHAPx0rAeC7/4Bzn28lue+2LwvnxTJLAXh4yFAAys8+QPrBQ260NG+k3CNZo9VG/MP80hIsb7U/OTM/P7f2/ASAelVct1BRd56iKIqiKEo+8Dol6vigZqx/aewVW81OJSpHTFl/vOpYo1BYpPV2xdZn23WivhxQkX9jAAMGxV+BxU++fnFBAsvNc/IWJF7le3vdpC5QwveCfLa4fNZyynvUirljbwfAOsjEq8V35jrOGujnLpMKnJ7vLQKgd+hB7qgv7tjARbkrUb0GLAGgbkAAfd7qBECxld7rxkv4VmqCdYjewnulJl6xN5CEdFEx7lopLt6Qf4IoN2kzANbLl91mZ074BIuL+fM7pwDw+N7OWOKMpUCd7dWUop/f+PXheH6c6BRD6R4JABTySwEgxCzK4eSI7G6wql8PoMow770+D6dKDbb0/Qed23xLlwJg1wB5LosbTDw5G56Xd+xtdz5EWDs3GnoNHMlJd4/8FYABRXexLkXe573WSAX9EotE9U3tfoZVt3wFZKi9CwIjCrwenypRiqIoiqIo+cBrlKjjgySGYsZzH5FmEyXmlFVWDiuTKlLFT4qE1fbPGgB5ypqK30WJQ0gLlc9dWXzTiFh27wPg1R8eAqBz9/HOfdsetvt5zz8DQITBlaii8yXGac4PopwVvZC3FZ05OSNiqnmAeLr395F4BUe8kTcQPkWOd/Wyqoz8QUo6DCuWfaV/6U1RJAq1dZ9tBcXR1CIAWNlPepAp13HWlvUA6FhIVrpptiDSA3Mfb1R8y0mhwvhREse049bpAGxJTeN/JyRe4+fxzQEovukiPpdF7YjakVFyxSixb/FviopWyfdnABJfL4uZ3FVETxA+d+t1/798K0oc0JH7IjgfLZ9u3VhiYn6IyHieDj9ZB4Bvlsn5eun2y7xXamOW37Wq2ygeGdY8P6YbgntDpXjt8o4DACi87gDVFx8HYH6pJdnGO96RKYtLArvdY2QeqDxPioY6Ck93ju9AWqujAFS5okCvaUcNts6Vd3+7QjJ+TsO78FmZvezRjWD4SZQjiNzhwnNMoABeONgBgLPNz3BsiEw2Vl8RNN52ygtEfCYvXEcNKW+iyvP2bLTunrXjRsi3u2KLZFGOPRfJoCISiJ0YmVZQZrmNEwPlujtXM52FRefZt2YXgc+skaDzQmQPOjcq8Z9IEPW8cLk/J56LocgaybS8MmnAXCSMU8/LtVDWV7LVhhxpRqnP5IVlw3vY/pZMohx1eKJ+7gtAtef2YjkrVfjDkcmzDTDysq1NC3ErDt4nNaHMy//2pDk5Yr148bo/0+5Hean2C1uA2ST3m8UmkymzyYdb/30AgLCn5JlSZb88a5cObcZ7z210jgNotnJAtpe0N1HPX47j1wlXupqzMudScQBGTJcXTrnxxlqsnkoNyfJzwk+VKcfRa34uPi0MAL9j5wv8XlR3nqIoiqIoSj4wtBJ1bEgzvn92pP0nCRY7ZU3NUKCGlrfvO+P8zDunJJ187pzbAKjw3gbnCrfcDKli/sLDt/FBmZUutb2g8TOZndVm/yvYUsQFcskS6GFLrh9Tw1rc//lvADwaKuposI8/V1u3VJor17FR3DzXwlw1ipntZWWbaJPV/Nz/u4uggzk3946fUJmtt0jw8rKkwrKtYYobLL0xzKGhAOx6U0oAvNtuFqPeFoW8+R9Shyb2+38BsHg4SPx6Sb6vER+VHQdApweesG89nm1cYidRHIOOJWNavdld5t0QVpvcaycsibx7ojUA6z6W90Oxv88QslNc6un2GlgJ9nP6+yMfABJs/81FCciu+uq5m7ZX4MjTcl3/1TEWyxFx45ZLMZYC5cDH/jb3sT9HA87anPXNLLWjAIh/TILjm9eKZ8VlqRH501NS98wUX/BqoipRiqIoiqIo+cDQStTL/WZR3pw17bvN18OIfMkRmJyhQJVdfg6Af+ZJJdaIBJlJZxZvHLEK59KKuMZgF5JmsziLif1XcKReF/c9mbHtkheUpwBO1yrEQ4UlpivYJzhPn9k1VMZF93KZWQWCrblUPe722SIaBMgqPvYnSXKImZ9dhUoYISv8DS0+xPHIeXHq4wCUw5gr3szsfFd6Ou6631EstjslZ4vy5Ij389Y783D3VMafk+Pz2SrKjJWM+NGZz3wIQA0/iZM6a02ixaRhAES8bexz9/WIewBYsqM51k3bAQhD4p4yx8Wc7SXX58pHpV9guE8wcy6LAvV557sBsO7NvTSJ0TA1qAlAafNacnvFp9ksNPnwWQDKfyWB45bj+91i340woIyUNnC8C1v1XYvPU/KWH1FqapaxzV8fzMkpMi8wuTCezZCTKEcGT6T/emdNp/blRIaNJOfMLsdNkpeHmY/J6h21ov7j2KpLdfI+YX86t1VYmj0s0FE75HyT8hxrLOJq1LcSiGrb4JlWGsWmraZZeam4vrKPuKSLm0Ou9hHKlDrncrvyi8nPn6MDGwAZNWTExSz/7wfqykt24ftNiXpD3D0+paXqfod28uIyY6LuKpk8VfCiBqd7O0nwuMWe1GKeHY71srEzYvPKphaTaPWGNJ0NT5Rnq09gIK89/SUAj78t+0rOleymY12r8vv/yfXcca9UtQ6dZcxWTA67cnsnONx3vzwixxNuX+wsTizEtIfaA2Dbus21RhYQJj9/Lna6BYBhb8u5q+Gf8Xrfly61kXpufQyAsHdDKPOn3INGTnq4kr+TRCSJ9JXz8k7ptU7XnuM8H0mXEIHwLe5xras7T1EURVEUJR8YSomyNZN6HU9Mng9ATT9bgdZ0MhcViTbUN9ErakX913C474iWXmqHW4ZmG3PnB38A8MXjjegZK9XcawdJNdp7gy85K0N3iHwKgPKdXWryVanwpqz07tstK/bkIhlrFpv9zpszVJoTV/Er5F7jrpNj/Rqw7vkxQMaKL82WUYX+ndJr5WvPtbxyhwQh3xn2IwCtg6Qn4NqUQCp03eJGqwuGYcdEGX+n1AYAhv9vOu8kPQZAoe+MqcJcC1tTedYGmf7GdEXEdHKrWry4Xlx8Vew1zhxPyxKTVtOkqVRdb/2MqLyHZrne3oLm3KNNWddLXJWFTPYg8ktS82vcG10J/cc7zqtPTQmc3vV0mNPdnBOd7C5Yb6qvlxOLasg7fG5bUUgvRviy+vVxWca032B/9q/51y02qRKlKIqiKIqSDwylRJUcJYFtHUKyp9gWBIcfk9XV7DIfX2Ok8cipxEFosxOeMeY68Sks6eymiDIAnGgazumGsrbt3jhjxVfS/wgAg4r8SW48W0yUjKp1MwqsDflBOs6PWZyG/3Hxg5c3UCxD6NdyjFl0NZPE19wVKXFTex6cRP/KKwD4qnobwBg9EU/2k7iRVS9+zEWrlDHYniaxXf/3/FMEnpauAb++kwDA9Eo/O1WpK2MVGvinMmS3lBkZ01kKHVo373D5MVwPqXdL3FfgClFZrMnJbL9XeozFviDVnnc+OJ7YkRKE3D9B+gSyzrsUtsRyGWVDim27lGVf0Po9VN0i+3NK64+eKFsnzZHrtT31XWOkCzjfswkA37w10qlA/ZAod+bMbtIqwBtUqPTb5X/+zKciA94TfPVipEV231yeF/+fxAtRzJ7kkpmId+SruyoCqRKlKIqiKIqSDwylROXELZ9JGmbFG0iFNjWsBcCUwWOc22ZfkrYNpsSC7ejsKnIqcbCijqxCOjR5Atzk/80rDvVp58hqPN9CYmP6ha3Idfye9CQS7KUnkmyibgSZ/J37q/3RG4AKkyWrMnNriigyVo7ekmruExQEiALl4KKjqGi6cVaN1R8VpWjh5VK8M1laQZQZLfdiMGud404Plf5rQ8bexkdlcy5kazaZGLZFgtTKbt7uMpuvF9/ISgA0mBdPh9AJADxhT/8uNXYV6UelAGHsaHtG74NQwVfOX0pxOWcBbrS3oDHvkTY9jqvOcvpM7oMB8+7DLrao4HEoUL+9J31HA0zBfHVRskdndbkD8J5MPGvLegybLBl4bYISndtT7AVv222T+/TXmrPdb5ybOVkn2Jlp76li1IafRAVc/X6+JqaGtej6xS9ARv+g1SlmZva5DwCfhIJtRugqYn97ku23T85xX1xff2IMpkAHLZaXy+4qkzhrTQKg3U7pbxh/sBRlF8mlZ06WKz8k7jSWOKlTk7BD3JRPhB4CJOAzqv8BIKPWl7ez86Ma9u8yFgcfzZVK/JXi8tag2R1sXCrVjM98U5wyu3JfyCSVkvM9qMRvgNR2a/KmVPMuvjkj1Thid9YXthF4cZkkskT7XqLN5BcAiBib/Vh3vFje+f1De8T1E7xO+hwa6XjygqMFqaM33PVwup00Et+XvrAgTXIZ5x5tyjdvSRmDAFNGzbavH5E6Urat3uWK7TDhtyyTJ4D2OzuSNFaEgZN17K/1mu62zP34JmUkn/VKkMmwj71xvbvuSXXnKYqiKIqi5ANDKVE+JnHGZC6EueEFKezXfkzeghcdZQwufSOuoV9rznDum3VRAkS/7tkWnw3eoUA5CIgLgts9bUXemRsl6t93l4oyue+TAJh/FxdcNIeyjbf6+RM3qREA7UIk9XhNiqwap/friPms8TrL+5aTlV/qF2ZOzY0AoOQ1up47XEfL2n5k35JR2iDyO1HZjOSSrPDG1QvymUtIWvihzrL6i/IL4KuLkkBQ/NPsipoRFZsnvpPg8D8eHsmWp+3p0k9n7J9xQc7zY6HSJ3D+5aJcGC7n23zKeNdlXjDZXR8W2/VdbaaAAMJ7SwJQ29+l1EE0xvwfmKtKL7UHhi2jgq88S9bYWzU+884AwjcYTL6/BscHSRX5J8I+5pXjUkZk253yvjNdOklQijxXgx6v6hkD3UkjCdEp1vMg61JEVo2bIeUewre5V8lXJUpRFEVRFCUfGEqJ2jtaShBsGykqRoyfybmv1GpJQ7XafJxxGkXiZBVVZdBOp4oV6iu+4g/KLAXgqCWVNl9LobEqsz3bCuRGiHhrFbN6SGHDHoWPZtm3r+1U7qkjwYRGSRl3rHB3JJXD9y/5f+cU9+fowG1dWJTdsRJkfdYq5/2loSIHBC9fm8MnPc+RCXJN/lPtGyYPFLXiy8PSLiIk4ZKzFZEjHflMbACd+/0GZC+uWXlRH2L3GCfYOq/ED5XV/o42ErC7OsWP7zrcZt+7x0NWXR+OXpyt0ocRXEvUwIm1vnLurxV4EIB7d90vG14oiu8mSeTwUCzrDRP2t5SR+T3Zj/19ZQV/tUKMpgAJnd/3RVUGlpQEEdMjcp/mVAbBkzjaQPVfvAiAtkGJzjIGHw57GIDw+caJO7wWjl54rwySa9LPZCbFKq/unJIAfq033f5dAKtTxKsTfDTF9Ya6AYfy3e+rOQDcHXye2tMGA1BpimfOqaEmUSGz5WU5OF1k4k8+GuucSH1WQapSp9ks0OfnbJ/NiNAXh8Hj+yXwc8d3sUSOyd6M2BuZcUDk3O41vs+y3VNZCVfjswsShPtq8a3U/Fo66pYteh6AfdvKUjhBRNAnn1wMQN8ivzP0mNQk2mrP9Ar+3ZiTJwdhkyQDcXC5hnxSVuqW9J0gwf9zLoXy2eFbAZgUKVmhlTNNnByTzEnnpTp7tRfisFx2T6+ngsJcPYa3On0DgMUmF2Hvhf2IivMuN4mDSq9mPISH51j76PAVX72X9L0JAAya9hS/95eg69bIYrPilF2k1pTr8mRdSRgY/NRcAE6ln+HH++V/Yzm0150m55mLDWQS1dYefJ1kS+XjZ2SRGbQke4Nso5NaTM5Bk0DHdRfEkuVSz6yKvZesqX4N4nrL8yXYZ73zs71XSVZz1J/eFb6SG/Fj5NzeHSzvkkbrH6XS/zw7IVZ3nqIoiqIoSj4wlBLlIGi+rBZe2dadXQOklsfOrrn3BQL4I1lqCr20Q+rQlOgtsnzpk97dKygzKTNKyzcjPWtHXphTTc7be5/cx7pOEiju50injs0Y13ZLTwC+HnGPs+u62aCBqlcS8KOs+H54oCG/zmkIwLZBUmeoc6ELdK66xD4ye1+8bWlSC2th9XD7lvMutdUVPDj3dzoVknIUt6yxr3if9U4V6r9KxNuraGVXoByKVNGBGdXM96VLHb2286X/Y9UXN2FNNqYC5SDgtNRLOmoRJerhHY8QtGT91T5iaA61kpIhZcxBzm1zu0piyrBbugDQJ+IHOoQ4yr/Ic7b3/jZUfdZeGsZNtrqSS10b88etowE4bj+ggPlFPGiRoEqUoiiKoihKPjCkEuXAEr+XqGdl1dMgQeKkevdZQt8w6Sk26ZxIGp9PaUvR3bL6KLZYVhw3w8z7SopukiDC8WclhXVA0V2eNCdPRA9eS4/BzXPdH+oMPvaOIOSciOmzHp9gSaGuWigjNz6klpyvvxt869wWlyZxT8/1luvZW1S3nHh7QWe695SA8qAlodcYrRiViLdFrX/k7dzv02h7VwAjld/IDZ+VEv/T925RRwudPOPV74NKi0RR++shUaSaB6ZRzU++XxS7INv44xYpbhw/sRpFTnlPAH1umGvI+27iyIyOIw+8Iepp+AzPH5+hJ1GZKf2x3Og/flyEH2mUdd8NtITxJhwNaZfWlBfWUhpm2muMrLz/KtZEedBV+r/sN/XdZG+S6c2TJweRL66mw4tyDYbj+YeZomTGsiPe0yYUCKZVmwF451FptP7qzBk0Dcg+LewUL5nB58ZWAKDIHO++J52tw16UDO6qfmZu2/QIAOGfGefY1J2nKIqiKIqSD7xGiVIURVGU/yqmvzYB8HZkdmVbkPqBIRzNZb93cWCQVCXf1Ua6ljxz5DbCu0qZByO5lVWJUhRFURRFyQeqRCmKoiiKYhjS7mrAR49PASD2+wEAVJ1wEmui8RKQdBKlKIqiKIph8Pt5A6OjagAQZc8MNWqGpbrzFEVRFEVR8oHJZjNg4zVFURRFURSDo0qUoiiKoihKPtBJlKIoiqIoSj7QSZSiKIqiKEo+0EmUoiiKoihKPtBJlKIoiqIoSj7QSZSiKIqiKEo+0EmUoiiKoihKPtBJlKIoiqIoSj5wa9uXO326em1lz1+s35vyMu5mP8ab/fhAj9Ho6DEKN/vxgR6j0dFjVCVKURRFURQlX2gDYkVRFCVH4sc2BmBv508BWHg5mEmNZJvl7FmP2aUoRkGVKEVRFEVRlHygSpSiKIrixOTrS/zIBgCsuX80AB3iOwOw7e9KRNZIBsDnT1WiFEWVKEVRFEVRlHygSpTiUU4/2RQAS6CJc/VSAdh3z1QAeia0AmDb19UJ3yarX/Pyv91vpFIgmOrXoPjYwwCc7VkEgPS9CR60SMmJ4/0aEffgOACi5z8nXwesBSCKYx6zS3ENCSPkGfzroyO5Y8YwACq+ttqTJnkVN80kKu2O+gD89sVn8rPNkm1Mta8HAlBlmPdcIKaAAAAS76lD7f/bDEB8wxRPmpRvfAoXBiCtfjS+rx0HYEn0KACK+gQ6x6XZk2GnV/xVvnn5V767VBKAN/5uD0D0kGNYTp0GwJae7nLbzUWLAnDwiWr4ynyOc3Vl0udXKJU/m08E4PE9XQCIO1Yix9+TfiIIgMoLxGbfXze6zObrxRxeDABTWCi2U2cAsFy4UGC/f/99YSyoOAOAGv0HARD18hFsaakF9jeU/JN2l7jwpg79mOEn5XladdgWAKwes0q5EWxN68hX36xOJ9/zSVj/3ZllWylzEN07rgBg1Wv+7jHwJkDdeYqiKIqiKPngplGi/F8RmdmhQOWkRP3bfQwANYo/TcR8MwBBC9a5ycL8YS5RHIDl4yexMllO18jK9wGQvm+/x+zKK9Zb63K4ZTAA5W4/CMCPsVMyjQjM4VPZebDQCfnaYpps2AjVvhkAQORckYZMf20qAItzZse70QDsvm9cLiNEYVoQvVh+jL7670vvLNfnJ2djAZi8+C6iZkqgrnXrzlw/50p2vi5G73hgHHWmPgNAxeGrCuz3l9yYDn3k+23dxwJw/5e9sG3aXmB/w4iYq0ax43lRMu+rJ9foJ2XXM/hIQwB2NUjzmG2ZOdpHFO6a/ia6/3QrAJUTvUe1VwTfcmUBODQ+jF/qTwAylH6rXVPcnArD73sk22d/PVYVgCD2ucPUAsVcPQaAHc+HArDlrnEEmURRa7ixOwAlOuwq8L+rSpSiKIqiKEo+8GolylxDZs2dZ6+gVfCf9q1B1/xc3F2TqbNbYjIiFrjKuoLntkCJo3m7gsSu+HiBEnW4ZTCb+4/Ndf/Cy7JCT7b55Trm9qD9FDdnP687uo0HoO5pOZfl/7oRS6/OiNZzct23KTWd0UfuznX/2n2VAGhcOYFou6L2WnGJNXmuaLx87RlP8y39AQjbWhAW3xgLHxsJQM99zwNQdMaNKxKXS5tv+HcYnVNPNeVMA7lPM1Sn2TmOXXeiIgBh7HaPcbmQdH8jADY2FZW1yYZHqfySdyhQPiEhAJjKl3FuO3a7xCPaTNKt42KVq0d0mdJkXPT0kwBY9yS4Jc6yoEluL+exzMtyPa2rtADIObapjj9YwkSdevL+nwH4J9VK4V6JAHjT0R8d2gyAeYM/AGD6WQmUb/bhcySWk3PveFfc1/AxbOu3FOjf9+pJlNVfzO9e+AC5XSw3E2aTdwqHrbd0BWB5re8B+CPZn74/iF+n6hsir16t+vGI1x7i36dyn4i5gy8fvAuAsTXDKLr1fJZ9PheTrpplFoUc22ngXHgpAH5YIxPg+4IzArdPtxO3ZNiXBWV1/qnoK/fT9Nc/BGDwwYH5DoJ3BOU3e2pDtn27u4cR6TovrMswV40CcLrpxrWeCcC9wTkfjMN199cUCd4u/ulqj0+eHJyqIc/RAJN8PXc0lJKeNOg6OPVtOQDW1Pvmxn+Z3bt1x/ZOBD8mLtb0w0du/Pe6gfM9mjD7XUnSKWEOcG6vtuwp+cbe/e3pWyRw/Pd7qxP/f7JwnVtkGwAbUoJJP3bcTRbfGD6BMgHc/Xo9VveQBV+jBUMBiH1LXJFljq/C0voW+UA3++fOXSZ7oM8N2lLAv09RFEVRFOU/gVcrUSfeuLbo2HT0s1ysJyv8rbdPcrVJLsViE2kyLdi+cvSkMXmk8md7sH1qd29UfgwAn8RUoratAcjTqqDU+jR4Kvv2Q+lJAASecn2DcOvmHQCEbc6e7n096d9Hu0kg+X3By7JsP2tNImKaZ91dIfuz//0oP/u19upRTNtERbve1WpqncoAjCzz6Q1aaBzOfCRf99WdkmV7k01dSFuQtbxF8U9XA6JsFMd4brLIO2Xl/nuyKBNVP0vC9XdUwRDiL+Uxhp+UVP5zacHX/TsCfOTcfFBalNJl1efRvLXdtf6ldyhRa0ZOIs0mIQ8jTtUGYEP7ykQflLp6iZ2k3+GE5NsBKDHhHLOqyfswwCTn/em/e1CBgnV1uYqDz4rCtP2RsdSZJLWtot+SJJjM75QDd8tbcnWKPNss8XsL3BZVohRFURRFUfKB1ylRFx5uwh8jx9t/csRoZKygq86WtPfoZ0TpKM0qUl+TwDO/NjLOz2R2+oi9kRP1ZeUQ8aOHDckDWVQLe3HMa6lPjgKjuz6RFdVHt8/KcVz7SS8AUH5ywaXhuwKH/z5+Wiyrbhtp35o1UL7bI4Pw+92zhTfLfSzlPmqUHuQsQeBgXswCGjwiZQ/Kjrw+Jcr/kMSEzbpYju6FD2fZFzXrvNcVcoyb0pBxMRID1WSTFFcNHCuxUWE/rgeDxDrlBd/SpXi5gpTleGTJ0wBEr1/rSZOui+AuUhR2Q4ooULaU6y9EbPKT+L++K1sAMDniD47fJk8pI8Qn5oU0m8VZvmDWdom7q3IuQ3UJnifntKyvKFK/ffy1c1+7nQ8AUKGr8VUo3zKlAZj2lJQrar7pYSq8JwrileqpuXoMwzt953qbXP4XXEBONaAcOCZPmTHZcvicl+jVtjSRmuPSkonxk5dxUuWbs8Jz4gNygwcOEAk9LjZ39+usi6Wo9I28kI2aSXK5sxzP6W6S8bKr2TQck6dLNnnYNx8nwZAR6zd7fDLhyEiKeX8Pn98rmWO9QjMyQB965DcAVn8dCeQ96Da1vEwwrpxAeRunnpKsn333TnQGihcbIvssu9Z7yqwbYserlWhijwsIOux92ZMFUVHfp0gYAJMjljq3Vf7e03fj9RHzw9PsvE/EhS0tpG3WkN9uI/75ekBGpuLwd6c5P7M8qZB882Zx+5aD7jE2n5j8/LnvV5norUqUmnYlnrxIei4dDyJmHKRbIcm4jFok8SAxFPx9qu48RVEURVGUfOB1SlSLYdmVJoBlSUVy3K7xZKMAACAASURBVO4TGEhKuHetKjJjOS51hQbveYifYr2oqNV1cvS5Zqx6TtLpHYGOORH7rb1K+bwUfPb94xbb8kPaXQ34eYy4xByp45mx2kQKLXRQrk0j1aWxnDzJh1vaANCrecbKdVi4rALbR4ki45ODEuUTGMj+Ybdk2da8/WZXmepWmvfJKNHwwz91ASjWSs5t4UhR2wJ+9C5F6qU2Pzi/rzhbuj4UdAq44nqqPrOZdlGdAVhSTWrafVR2JRumS6iD2e56qRcgz5vlSYUY1aeH7FvhHU3dTzxZnyfCJDGjzQBxPQcdzd5x5MQAe92osh9zIF1UqurvSgiCK56yqkQpiqIoiqLkA69TomZvq8fwVtlnn+/971EACpNVqdr7f/XY0mWMW2xT8o71VlnJ77tf4rw2dfswVwXqlCWJB7b1AiBm2jn5vIf6y+WVfV1MOSpQDkLtvaz++kB6W73y/C3M+bUJAJHzXN8L8Fr4rS8s3zTPvu9IM4nrKr8CUtpJbNDRZnKs6SE2tj947ftt4jmJafA5ec7jsWDX4vwSKazpqDw++EhDAo7K8b45bDoAb8W1ByB4bxSWXcYPLHcE6Fbw20G/Q7cBYN134Lp+h7manEPLjviCNc7NHO7haHS59KrjjIwtJYWA7lLyJfYtUet33jeeBgGiK/rY9ZLVKfKMHf3Ew16jQDko320fk89XAiD4R1G3M4c2+1aWOM6JQ8UD4Gcy0+YHiTmNTnBdsoTXTaJ2tp5K2hVB4Xdt7UbRPyUo7kq57oUu89xjmBspVCzR0ybkC0fWna1ODN2m/ATAo6GOYGM/UmwSRH/RmvUstvp8GJX+JzKu0V+4DirOh/ui5cX6eiVxw9b3zz1w952Sf/NOd3mopXeXB1/s4v5Uf1tcLOn73Rv0WXakuAEaNZfFyYaGGWlKmwbaM/cGQkaGrOBnMpNmu7bAPaDIHgA+696OsqOMV4sn5R6ZHNZ+a5Nz8hQ7VVwIkV+dpOIuuR4HlpEy1/vulXpR7ejiblPzxeV6EQDcGZTEMz9LFmyl9NxrWPlGVgJg+4sl8AuTpIilTWUBcMbqz4PzBwMQNSTncAsjk3pFJMgPiaEEbpH7zeHaPN+zCemBuad0h0/1fP0vy0kJoi6+Tib9Pvdl3Id+Jnn2/HFJ6tT5rDBuKERuzItaQtWvZYJYJSXj/20uHg5A5e/kWVnfnigR+9uTxL4uCxpXuqjVnacoiqIoipIPvEaJ8vtdGkw6ZtQA0y/Iaiqk7d5cA8bMJmuWzwB0im9PxNvGri10NebcIqveQTn5WgzM7hEScLzj4XHZ9vU92Ip1C2oBUP7drOemkgGrPF+LgCXrsSyR71+v9jAAqaULc7mM1KQ53UHUxG23iTvIJ1PhMl973bPd935K71qtADje3H4NW90b9lviY3HdWb/KmwaYZsNZryYvpDS4lC+7XM2xpvJo/L3seqcCVfE1uQ5vtsDrortyr/dy+klJInj1RamLdVfQGer++SQAd/41EIBdLacx4E5pYruUUFeaeuM0kmeMJdiP5OLi2hr3SNZK+vcEn6XWurlZtlXw3ei8Rw+ky7074mhbVv0oKl64S43OG+m31wfg7VekxIEVK2vt7juz/Z5sX1jcYH8064tplXckfFzu0tj+3d9Ezk/Ksu9itya88Jao5I4+pDMvylyh6nOHsNhrE7oSVaIURVEURVHygeGVqKT7GwHQu8z3gBTMdBTNtNpy91Gf7iMrqLoBH5N2xbiT0ypRhKOuMNdlHPwzAmI9bcX1Yy4SRlotKc74Rofs1WMfSbgTgIuPhFJ+r/eqg1fDEXhr3oFznR5qLxjcaOAgAG7vvcbZuysz0yv8DkC1ERILUPkVY6tyX1woh8W+Nnv3z3sBMF8QFW1bt7G5fs5oOFSndl91ccY/5cS41jPdZZLLuBgh5yss07bTT8jz89fXpezI7IvS//Cefg9TeZEk9pjq1ZDBLWHSorsBqOxh1dhcJIwz91YD4Hy0HFfFFvu5tbjE4PUrKgV8i/oE5fwL7FjsqtPSS9UB+HDNnZRdKq/L0F3nAemnWQFjPLMsrW7hxclfANAySJSy5UmFGP2EqOC7u4si5SjIuXegiSrGMP2aBB+VGLzT1iQ6TvkVgCr+UvqnccAqzlhFZTObpHjo+7MkLrHCSfccoCpRiqIoiqIo+cDwStSpWmJihxBHv66rtyYw20v4J7UV/2iMX4YK1XT0swCU/sJLpuCZKHQwI26hsL2Pjbl6DACW7XEesSkvJAyoweb+2RWIXgl3AHChg/xsOZ3gRquMQ8lxci1u+9SfJ1e2BGBqxIrsAysbLyNzcyosPC9xbosnS5p8yQkZ95ajxYKllb34Zjf32lcQ5FauYP+botTcGyxlKCov7gNAjBe2fwlNyBrD5lumND+9PgqApmukXUalfqLcB55ah0+w9Kmr+dl252dK/O3ZPlq+5csB8L8/FtIwYPlVRmYoUIsTRbm4NzhrXN6trw6m2PSsiloMGSqxETOER0ybkqWQJsCoPj0yyhh0b5Rl/OM1V/NX6UrAFf1NDYij1Mudo4bRoPu/AEw7KfefeXY4w/8ncaX/OyTbKo2SWC93nSfDT6Kuxvt/iLsghoy6UbvGietoS+NPs40POWrEyz9v+GSKnDfb+yBZg3Kv7O1x7AGc7z02I9uu7nvvJqmnPMwsp6+dum+uHoMl1N438I2LALQunX3ieCipKHveFCk/YIl3vcxsaan8vqWO/JDDJMq0J9jNFgn+W6V3Xt1Vj9MkIgGAlXskhTpygs35gCuZT7fGqPqz+bS0TMDc/TB39MIr/un1uaD2v9mU0Q/Lg3txolyX1UZJk2VvCToPOiwTh7i0ZI63larOod/Ic8Vy5izN/pJg+vAFct05AnRNfv6U/U0cGO+UkolF7JcDiVooKfOeesLakqW22s7UMjQMkFT3qAX9APA/bSZq6iEZmJ5xhmyhIQCELJIg8laBUmKl5M/7DduP80oOfC/P2foBG53/+9dGPA5A0eW5X9eRASf4K7iaq80rUEqPWcUhewm64iYJkYibVphbA+XeGz1c3v1+l93byF3deYqiKIqiKPnAq5Wo2ImiSliB+DFS7XlXawmcy1yQ866t4kfIrSCnN1B0xmomvSAVWfuFiToQP0TS5aN6esysbFhbStfwHp8uBuCe4IvZxvyzP4KImnLpBWQqIrn7IzmHNnNW18B7bb+hU8iZa/7tBh8MovQSz7hqHcUIdw2QStBhcabrUjhMvr40rr4n2/Ykm6gEpdd6RuNwKBAVup7GURKzCgVXqO/u4PN8GhhQYL/vetg4fCIAg/tIYc2/pjTI8ZzFTZH9joKasIkmmyR4tdgQ2eINVcozY928A4CVSVHE3yEp8fWGSMmCMh+uonI3cZuYw4sBcPZhuTdffuMLp/srdqaMj3xptcddXI7rdNJbDzCmqGgDsdPtbp3Ll3N+5tvr/K6+LBXLWwVuz2mUITk+WPrD/dssozp35Z/E9RozI/s17BMiKpujcvm4fa0J2bvXHaa6hER7wlncXROoOltuwuhlnin0qkqUoiiKoihKPjC8EuWoTuAomJm5cObhO6Rz+uVh9ZwK1JWFNRus70np+2XV5Y0KVGZGrZE04rZtPgYg5imJC/L0KjAzfv8mADDi73YA9Gj5WbYxO1tPZVtzORtxH5V0bu8YInFMPvmc2yeW9Uxwq2/lirRYsA2AhcUkvuK+unfnKT7Gt1IFALa/VJrdlSZl2z/+rMQ8BP6QvV+kt+B3SoLiVyQFO9OvM7PnA0kGqdRDlFVbWqpb7HLEM31SVq67xcO28FZHadXzv5hFANwbnAxI3NfgI6JI7e5RkbBdrm8n4Q6mvt+Rdm+OBGDFEAkmX9W/mHN/YR+JNWoesAyAw5ZEqk8bBkDU256Ng8qJsK8y1Agj2VXQ+NwhypujsO15ayqlfsv+Orc1lTjLnbeL2rjwsrwzw/pbvfp9+NYoUYV/TQom9vVdgOfuRcNPouyJaM7aUJm/XzPk40zbsn7OMabs8JvvZnLUMLEmJXvYkuxYzkqQX/STUtujybf/z955BzZVtX/8k6R7AIWyN7SFsodsRBEFERQEQYaCiAPZoKgvP98Xcb2KKCAgIsMJiIKAC0WZyhBkySiUvYfs0QJtkt8fT9I2pIU2tMkN7/P5J+m9J+lzc+8995zvM05X1tSZ5daualCA4zWjmy5ng6f4meJOCD4rv0fM2E0+OdcnxwfzQsGdLttSqpQiYIOcH9vFdJemOVIW9k0cKTV2FnWUB1e5gPTAcYtJfod9KZf48d/NAQjFfwdRzsWi/9uvJ5YPJSC7aUj6tbu5yXQAHg6XjE3rOe8Mov7zbi8AfnlaAqQ/KLGONrVknTynu67/kSjKOopXBy90Jiv4l+vuRkR9upp7K8igqGRj8W8tip+ftn/TNXnUVlr+DAAVxtspt9q/1rG8GfuSogGwRPuPY6ZDOddq4wdSTZjFY8fFR8X1ejbezPLe7zpayIRh3L4WAITu3ecVO3Ob1BZSlf2OYBksN5g4lFJnfZtt7z9XjaIoiqIoioEwvBIVJMVhOWaV2WmZgBtXmnWupzdmdnsAyu7wbrqjN6jo+A1O95LgukLTjFfF2pYkbpuArwvxeEGpSv5FuV9z9B0/JombZ+zAroT9td9tf8XTDnXGsZ6cr2bGV1ZEQ23XbT/PnMZrp8QVt+dy4bTtFcNlpfUfoj90bHEvXbAvRQJ3H3/+ecLn/5n7BvuIoF/+4s1nnwDg9Y9Fjr8jOF1hvnR3JQBC53tHdXMGke90VENpRa20ffkdalN+t0/dfjirszt5gDpubSo6XJq3I6t+lvXvrE+v8LEl2WfuPrlWhxXaAkB8YCBL389sRQBxkVdeKusdVn5B0kP80ZVnDg+n2ftyrf6SJGEgZSdt87lLXZUoRVEURVEUDzC8ElX0A/F3tgt7EYCNA268/tbceBmhOtc08m0d3dzlk7skduSsTVayjv5bFAsjH2OBL1Zz4QcJZqzfVdaJu1TGzvbHJ7i0i1/2FJErXVXGAntEfQz+ZZ3PZxs3otRPZ6jXtCsA6+qmx3/9J1pmiUTf/DuS7deo/sNAAMrNE00t/JfbR4VyEvibKMP935N4tvbPLOOz5VJss/IyiZ0y8rlWFCNQcJwUCq3cWdbUTHxwUtq+4ccbAFAs+DxLH70DgJhtkgTgjwqUk4MDa/JdtDz/23TuDYDpnO8VUsMPopyUfFsGRfGF+vN3Vylb+ltyAQDe/nePtHaR+KZWhDcYliDBro+UlRvCfFmCt43+0HEGmzuXBCkCtH25rkub3Kw95G1sW3dQtIu45er1lE7tUrOktCrjze77O63t8r0xLp+NWCFtCiZcJW6Z/waP55QiE+VaWDUxiFhksGj061i5PakwWeoltVgndZZCThi/LwpYIpORuCXyd9s+GftTmYRtJRJwTXjxZ/o+/j3vnJaEHPNaqellBAFB3XmKoiiKoige4DdKlJOKw1bz8DDXxRRvZ/UpIwXbSl2oJYQ7thh34eH/NZyB9IUnrXa8pu87+Er6+/K4piYriuJbUo/JWnvBP8qrEdQNxZ1aIQd4ZqqEAZRK8W1Zg4yoEqUoiqIoiuIBfqdEKYqiKIryv8VrFepQCuMoUE5UiVIURVEURfEAHUQpiqIoiqJ4gMlu1zA6RVEURVGUnKJKlKIoiqIoigfoIEpRFEVRFMUDdBClKIqiKIriATqIUhRFURRF8QAdRCmKoiiKoniADqIURVEURVE8QAdRiqIoiqIoHqCDKEVRFEVRFA/w6tp595k7+W1lz19t35iy0+52P8bb/fhAj9Ho6DEKt/vxgR6j0dFjVCVKURRFURTFI3QQpSiKoiiK4gE6iFIURVEURfEAr8ZEKYqiKMbnYpeGAMQM2g7A6hVVASi8wU7k7DU+s0tRjIYqUYqiKIqiKB5w2ylRF7rJDGrFuxMBWJwcxsj/9AKg4O+HAUg9dNg3ximKovgBx1tfA2B5maUAmB9bBkCvZndz6pf8AFjPnfeJbYpiJG67QZSTFLsVgGYhF1k86gMA7t/2KAChrXxmVvYxSVblme9jAfi6+nT63dcTAGviHp+ZdTPO9GoEQOxTOwD4otxiLCYRPK12m1v75ls7AnB2cXFK/3AKgGtFIwAI2rRPPnf2bN4anQeYIyM59Ug1ANoNkQfRK9E7Mv0NAGq/15/gs65ZwFE7kzCt2py3hirKdZzv3pDFd492/BXqsm9amaW0i35E/tBBlDExWwDY+2V1Hq2yHoBN50oBsG13SQDKfmsifMNBAFKPn/CBkbcP6s5TFEVRFEXxgNtCibIULcLxKVEAvF1lWpbtjm0qBkAF9nnFrlvBEhkJwJuV5wFQJiCMQ+2KAlDiXWMqUWd6NeLrV98FoFSAzGBtgM2hCmbG4mpz5E01+KRXaQBKBp4B4K/LFQC4kBqS1n79v+sCEPzTuly13RNMd4jSdOAl91psEaFXWVV7gsu2lBuUm1v//Hi3bWPPxrG4p7in7eu33YKlxsASH8vzP3wLwDuPPwZwWyptu8c0dPl7z6Mf3bD9nf2eBSBs3p95ZlN2MNeMB+CTN99Pu3+vp87axyl5YLc3zVJyik3624pjrCx7XTwZ31f9EoB+gW0AGDjhNwJN0u6V9uLhsG1O8LaltwWqRCmKoiiKonjAbaFEXWpYjucrzQWgeegVIPNZ/+bu4wC4O2EQUZ+u9pp9nmC9cAGAz082AaBF2SVciTZ25fwrhU1ZzmCzQ698h1z+bhm6xa3N4g9EkRl17TECf1vv8f/KDQZ8JSpay9DLefL9g6MS6fCtKDX3LR0EQOwTvj3mjJjqVsWcJAHI1oRdN//Ah5c5Zw0HIOBcsnwuz6zzLk71SVSnTTn67O8TJwPQal6t3DYrR+wcEgZATGCw276u++4DoORrJuwp17xqV55gMmGpWA6Afd2LA9CszUYAuhb6k1FtJVYzW9e1AQgoVpQTbUS5v1pIlPFLla+RWP1TAM7a5NlxcEwcACN3FaP/XPFytJu1AoB5VQp70+Rcw95E7pujd8r1u3lAugfAGY/bfpcEQp+YXJ58M3O3RMdtMYi6EmWhdODpbLcf85+JDLX1A6DA58YeTO2YLhI7I5cQEmfsQM4/B44FLHn6P1qEJgHQ6NMJdN0lHZ2pqzyKvR0g2X/x4wAktnV31ySmXKPjZ88DMLjTAgB65z+Y4/9RxjEofe6OZQD82qgpptW+dYGdeVKSB74e8S6tP30RgLIjsn7YXG1dD4AFsR9QZ84QAGK23x61hpIebgDc3GV3PT0ONGPlmiou22LwzW+SOFnOz6xmWR/D+abO/jX7/ayRsMRVBGB/JwmJuLPdRj4sOTfTtsesSZguJnnNNk8wBctAN/E9GUB833YsVYMym8BKf1zEIpOXr95/D4CHRwxj55USAPQuIBPTBTWb+Y1L79Qz0gdZ2p1iQXUZNEVb0kNInDhDSb6J+QmAla8HMrBYHwCKv78qV2xRd56iKIqiKIoH+KUSlfiJBBcntvzYsSXdxRFourkS0iTEzMUyInkWyHXrcpciv/+T9n7FHVMBeKxCNwBS9+73hUlZUnPGIAY/+IPLtgnb7qbI564zpBN3BDCg0/cu23rm20WwKTDb/yvMFMSCOPmONhV7A2DyshJVaYq4pB6a2N1tnynFStkEmel8N1tcsqNebkWl0TLDrfu5zP4ezi/Xbo2gG1+394VL5ejvit1LWC7YfisEdZbf+aENz1B2xM1ncwceknstzBREhTlX8tQ2b+N0xTnJTGEqscKeSdD4BZ8pT9dTr+peAOpm8OI5S8RU/bkvAHH85XW7bhVbU1FpzryUxG+1PgUgn1mSVOZejiL216cBMAWIdpHYXJKSuiU8TuhhYycfHZstrruEeqLCTDoXT6cpDwBQfobUQbQViGBPF6nptbCrJPzkN8u9eLWgiegACRmptWgAAHGbjX+OT/ZvDMCkoZKIUzcYaq2RxIygxXKstiBp++HACdQPdg2BaRKSQlKJzMvMeIoqUYqiKIqiKB7gl0oUdhlNp9wgdb7xxq4AXF4dTURjUXOW1ZyRtr9Wa/H9nv9Uio+lHj6SJ6bmFhaTOW0WdaCz+LJLvr3fhxa5U+Gl1Xz3UiGXbWVwDw4v+z18N8K13Yf/epDUMNdZwzePjwEgPvDGCtWeTvK7xKzMscm3xImG+QAoMuHGaox1eyIAsT3S/fXre0p5hEvTZfr/brHM09tPWEXtevr1YQAUnOf7GL7/xkmZgp6LnslW+4hilwAw414Kwh9wxj0dbSb2xwxJV5DcyxMYR2HKDvZGNXm2+Jdu22v98RQAcU8ZX53ISFKHBgx+exYAd4ZKh1DIHErl5aKolZglMkX48h3EXhAV2HZXbflwc3k5klCUGAOXwbHdWZu19cQL03iDqODRDyZSGumHUjO0Le/IcRgwpoPsqyBB9DXe2855R5JHlTdOuX3OqMR22Qmkq6bVPu1PhTclRtSWJCq/OUSeB49V6sOSB94H0kvupNitBF7KXe3IbwZRlliRL4/dV4x5zcc4tkqnlpAC65LLAzApsRkAJZ+WCyP64kESCzuyXmqmf9/Usr8A0LGQ1MjA4IOojJWubdn3evkNpf7rPhB5eazI06Z8kZwcL3fNV9WnA+kB1wAP3ikdvbdDIm82eLIUEHnZXsrRcX2xI21fxZDlgHtGYkYOpibzyDsSuF1keu4EQd4Klhi5xwqbc2bLorrS4R+zgiU5BQBj55mmU3R1Pj4v6+qyazUkPYvO17WdbpVdzwTQLMQ92y5gS4QPrLl1kqLNjN9/DwCvJUkfEbSgABU+WysNHDWUbpQVarli7MG+5UoqVrvcQU9XlIHid9GVsZ66QdB/srjR9/SVAcT8MotoMHYwACX2+r5vyS6lQ11XrzClmrBduQrAuccl2Nzk+G3urbPFLVv8jjVPUubV3D1edecpiqIoiqJ4gOGVKNudIrW2miQz9wFRu0ixu84UHlncN012LubQI5wzDVPtqmx55APvGKvkKtbTUrmcs+dJ/l1cKoE13NutmCIp2oXxvavLiaVAfk58IenUa+rMuElrVxIddXh6vj6MItOMM0u07hYXx5ZroqyZwlIxh0mYu1NKz4wiFmnzxqlqsFnk+Ox8zpc4XXgZVaiKsyU12p/cdTfjkZobfG1CrhL98Wpw5BsVy+Zngkced/k7ZsweQ9cvs6/bQtUf+gOw7yE52Hrr9tFzsihLpcfKObVdSU/i2P1xOQA+uMPh6nxtECU+Nk7fkl02nZU1ACkmK1bM6zmaQ91F8d+fcgzIXN1vvqUTAOWePJjr51aVKEVRFEVRFA8wvBJ15G7xafYp4IwnyVkxR/O+w1RbIjPIrfe4F5M7OlJei7X32EQlj7EULsTGgc615dJ93EOOSrpr8YWS0muowMjChVhTZ5ZHH+02Top0FjOQCpWRl5Y8CkDig5No8k0XAAr9S+5L2987svxc1wLr2PN7awD2jJEyABFfG1PVyVi6oMcBibPMGFDu7zjXfWyR75tMA/6L3i0xonsjGzk+IC8xIza6KBy3Aw0LGjeIPCsq9Re1qeXnEtMbM24nWwd+CECHB6S6/NWeRUkYLKrxtw1ktY5/tX8CgOjNxlHtc0LwczJkefkr8T68XWwdMYGO6zE06/jSc0tFlwy/sDfXbTL0IMpSID9XK0l2krP+U6DJwvizsqji4ofEtxO398YZJCazPcvvKDVUvt9QD+BMsJjMLsHl/wsElJUFiU2fuZ+dFLuV5d9IvbASB4w32DBdSmL4iTsAeKtozjKcxvWXwf4bG5/Assx47pYqrx4AoEX5R1hdazYAlp9F1K6/sRNJVyUDqkkp6bCcSy9UDAhl7SJ5eJf92njnLCs+LyvLYvRYLYOplWuqUGKF9Cn+Glh+uoYscN4iNInMepVFVSQDE0fJK+dA62CXJEYclYSP0w/LhMbbKwXkFcNP1gHA5gwjMDD2VOkTnQt476kH1V+UDMT5/UYBUHFlenJA0wEDAQjf7J/XqxPrLulTtjeRDLx2ES3B0b8cfFKe6ekTbqi5WgaZZcZKJmZeJLSoO09RFEVRFMUDDK1EJYyKY9vdEwHXBYWnzrofgNLZSM20lS/Flrsnu3zH+LOxLOwjs0rz3pwtFuor/hdUKFOAXI5HBtcHoHkXCR58r3i6G+W8TaTbBnOfJ2aUcdWM1GPH2fqkTOMbNGzotn/x/8kaVhFm98Vem4RIGYA9XQKIW5ZnJnqMU3kIaxPAHc9KgGv4gxKc26bUtrR21ULFzeq8dhv/qx/lZokqZ/QSB84g8oxr4jkVKcqugEcd7Zo5gs1vI1ffjSgVEMq0MksBqDpIzn3sxADD19nLDOd6ev2ivgCg9ZYeAORP3e0zm26FEo7+sOc9chx/1Pg2bd+ZePHChHvfrDwhzaV85QrUrw7AwCfmu7TpdaAF5QeJqph69Wqe2aJKlKIoiqIoigcYWolqUi3zleGvRsvM1rmStT2Ho8wfBzYn4I/1N29oUKK3GD2CyzOcCtSGIeOzbDP0sAQmxww2/szftknWuyucidjZ7fN7Adg1NQ6AhLumubXZ0vYD2raRda2Cf1yXR1Z6jj01lSITHWqgCMasIiht/7yBjwHQ/iXHKusrjpCa4l7Y0Yg4laVWQ2qxe4yrkphRnXK+v3PF9ZXLjU3EEVE7E1JSMl0R4K1TMrv/9VhlAJZXn+PWZlsPOa/d72zJ5a6Sep566HCe2JsX7O8kJUicK0EETyroS3NuDbOFfTOrApBQ/VMAarzXn1qPbAVge18JOq9qlbipzIob+yOW+Fh2DRaVzVna4JJNxgPHXqlIwJG8f86rEqUoiqIoiuIBhlai1i2Oh16L3LbXqy9rkV0sHA24rntnqVoJgP0dZG22q4XSY4labpV07HP1uEdpFQAAIABJREFUgym5JG9s9gYR26W8v5ELwmWX/W9KCvU7nb/gjuA/HFtdS/XvTrnK860ly8KU7FQdL3jJwrzBWWSyxGxRbhIbXyMuMMilTbApELvJ2EtQ3IiL5W6POL7r453uXPEs5V+Uor7OOClnSYRW82rhDwT9IrFpX5xplGn26Kcb5L6MHy3rHt5X/GkA7n3/D4YV2u7Sdkb5RdTuJoppyXf8R4kKaSD9aKqjJw3fLUuK+GO/erJvAxKbidpUYe5zAMS+t4pTn4q6Nn5FWQBG9ZZls97e0dNvVNMbcaRlYbbfJZ4LmyPPtMEMKRETu32vV7LuDT2IevGReW7bhp+4gwtPyQDJetjd3Xe2ZhQAfz071m2f7dMiAJT86vaQMv2Vqw/UI6nfOQD6l/sJgDZh57l+8OSsA7V2Yh2iEvyzrsnNiNh9HoCLtiC3fZWXPkXcb38DZJqGblSc1ci73iPrer3vKCdiO302y8/4E2Hz/mRlM4eLz1n+wFFLyl8G91faiuv86UJjgBC3/YktpRL2jEZSZ+jzgQ8BYDFlfiXe21nWpkt4J7ctzTuqFZYK12+fkkVVrQmZh48YmYBSJQGY+cJoQO67yhPSJ9nOVR++HSq1o2ZOkedi9eGbObBOPuuPSQHOlUxWDnsfm6N2ZPxicanHvizPCm8Fvag7T1EURVEUxQMMrURN3HUXPerOdNn2brGNVBwuknmRhTIbPPnAVXY0nwpAoEmieFPs6ZXN436WEWrcV8YPRs4O9lB31cKfuFg6gDW1vnLbfjhVCp/es3AoAPEj9gMQdeL2U6FMtSUItNb0LQDUda90gO1SoF9WhzYXLQzAiMK/A1BpvgSzxl70f/cByLp6TRpuv3lDAxO+4x8Axp5swbgSK7Ns1z1S1JonPpX+NcXu7uw6Zk1myUxRtoqjKr83OdpO3HTxQWHMuCgeGvsBd5eq033bcvyLAGwZ8iHxveW+LDPSf5SogArlADC9LiVVgk2BjDgpqlRsD98UJlYlSlEURVEUxQMMrUSdPZ4v05nPluaOda2ap29Lua56n/Nza6+GUGb+7TVWPPCQxH2V3uxjQzzk2cELMt1+z6IhAJReKH/bL132lknZ5nLHBnQY+avb9u9eagFA6JF0m82nJT7GHiwp5PaIUHYNE8nprXpSCO/hcOMvMZFTDnYs6fJ3+bn+UZJj95iGaQrTvlHxgGvJgqSHGwCu6+o5WblGCqvG4B9qt3W3rBd34NFyDJktsYdjSnimInXd3oPi7/mXAmWJLsToUt8B0Hd/O8fWU74zyEMuxKTHqL32TWcAyl3JWrkv842oVOcHJZOSz58iLYX68ySpbHi0KPhfXyrC2qGyvJYF3yhRhh5EZVUn6kY4XUK/XJYsvdlDWhPyy9pctcubOKXZ8ecqMKBA7i+e6E2cdb3uDN0NuPuvEh9w1N+Rpbm4e0snAC4kuwe+Fvg8kohFUgMFu4ygnRlvecnVfGYGRLlflwM+dt/W/0hTAGpHHASgd/6D2fof1Vc+AUCRVTlbbNsoJNVMdvk7YImxa7I560BlrP/EREd18okZW7oX/PL3xYlT9+5nX6cyAFQe1A+ARR1GUyYg9EYfA2DYcRlUFng2xfBrj17PsS6VKGSWYzw0RRIfCvjhICojRf+6eV7h1XKS0R5m8p+QEOdKFsefq8/L0bKQ8gmrZGm/9dmjlFrq2wH87SXRKIqiKIqieAlDK1FnH43gqa9aATC17C83bJtWA2pZMQBKvi2j0yDca6D4E87A4pPX8qVtK3m3VGbldV9Y5Dmnu8kq6YUt2SvStaz6N1nvrJ/+1jkj/uutBoTPNU7w8oSSf9y8kYOElBRS7DKnKfeubLOv87+AetudtUm8R6qv93UocZCc9QcMgIsClU2ca+v5qwKVkdT9opDGDJHXAW88xO5houTXbyb1sC6kiBpcKuwcvy12BPKOPyCfP3LIq/bmBvkfOpr2Pt8B/0vecGJKlTpy523JRCRIaEBmepSpriSynH9B6n4FmiyEnvAPDSX1zhoAvDloOodTRYF6cKoEyJd+y/duZP/4FRVFURRFUQyGoZWo1EOHOfe0rC327y8kbuHtYulriDV6bzAA4cdsRP0hs6Hww74fmeYFc3bW4vUiEpNRNPQiAP/40iAPKPiJKCst8g+j+eMSp3Z/fgkQbBGas3imry8VoWSAFG8MNMnca9zo8QyfW/9GH7tl8u+7SrU/egGwsvEk2WZ2j9m6GTVXSwV2q1XmMRUHHsd64qRj75ZbN9RHXIkOwobEqC3aIrPfOIOrwU5VKaMi5dzWpOH29KDxDKqTvwSQe4L19BnKOwoWXt/H7AHK491ihnnFHkf8bOBRKXjrj5XK46bKGdr5SAAtv5X7bNK3rdP2X4uWo9rXToqnHksVJarCnOeJHWXsZ6Wpnqzf+M50uS9rBFmI+17K31T5UsoyGOEaNPQgCsC6XaLxt9aVv9tSN21fsQw1SYzwY+YlMa9f5fnPZYCw8Xvp1Ev5aU2WYmNXkeAoKL+lhSxSO6x2eqB51H1Sm2ZxNfdFT6t82R+Ait9c5EphCQwN3y41Q57o1IgSefybWJZtoNwyed/kjRcA2Nprwg0/E/+VBOwW/Dt9CZcyM2QyYE+VK9cfO/DMONzKzsZrkvUT/6IkQhj92DIuNpy2zTFIOsHtPWD6X6VLqXVsuloCAOsu/03Yse7cDcDglwfw0TvSqQ7uvd+tXbMtDwMQNlyqmseuN07YQ2ZYChem6TSZaNcIciwwfKAF8WPEZZm674DPbLsedecpiqIoiqJ4gOGVKEWwbttJgkOE81cFKjMCFkv6e4nFGTaOlpeMqqOTCg5Xgp30IglOFbLEaO8GuJZ7RWxp+4q7nRmpmImSYc+k3e3Cp6fuBNLX7VIUo7D/dVlYuU+BScQsewKAipmUrvA3ImevYdjshlnuD0Xqghm937HES7mJzvOW0zhUbK7060B57bMN25XdPrMtK1SJUhRFURRF8QBVohRFyTXi+qxlj6+NUJQsSCmYXqW76LxMFqxUfIIpUIp/HnhDXsfvupuPv+wAQOxsUfKNWl9dB1GKoijK/wSx/SSgulW/WkRg7ODq/yXsKdcAKNVxm48tyTnqzlMURVEURfEAk91u9FAzRVEURVEU46FKlKIoiqIoigfoIEpRFEVRFMUDdBClKIqiKIriATqIUhRFURRF8QAdRCmKoiiKoniADqIURVEURVE8QAdRiqIoiqIoHqCDKEVRFEVRFA/w6rIv95k7+W1lz19t35iy0+52P8bb/fhAj9Ho6DEKt/vxgR6j0dFjVCVKURRFURTFI3QQpSiKoiiK4gE6iFIURVEURfEAHUQpiqIoiqJ4gA6iFEVRFEVRPMCr2XmKcj3mGpUBOFU3ym1f+IlUAIJ/WudVmxTlfxqzhd3v1QNgz6MfARD3+XMAlH95tc/MUhQjclsMomxNa3H0zjAA/u4/Ict2FpMIbwnXkhjc+VnZuHZLntvnDfa+3QiAHY9PBCDmuz7EPbfWlya5kThZOuaw6KS0bfeXSwDgveIb3NovTrYAMCL0KcLn/ukFCxXlfxdzNZnQHH7dzI760o+kOBLTy9c75CuzFMXQqDtPURRFURTFA/xSiTLVrgrA3k75AHjjkZl0DD8LgI2sa3rZ7FYAYgKDaTV9JQA/DL0HgMBFf+WZvXmNpVBBpnSaDKQf/4a2Y2m25wUASoxe5XWbTHdUAyCpZBhnel4GILGBuAYCTZZMP7P2aorL3y1C5bX62PfpYBoKQPgcVaQUJS+4/N5VADZUm5O27ZJNth3+rQwApTjsfcMUj7BUimFH32iXbWMf+ByAh8LTvQFVJvUFoOx7m9g7vCYAv/Z4F4Bx/zRje5MgAGxXruS5zTnFUiWO4tOPAvBx6RVA+jPQjIkPz5UH4Of7qwOQeij3r19VohRFURRFUTzA75QoS3Qh4qdKHM33xTIGHGer+nwaA6J2ATC5WSsAyi3KFfO8iilQZgi7n69Ek5BfXfb9fiWaEn9c8rpN9ia1AGj78VIABkQdyLBXFKjOe1vw95I4t88W2OlQER2ncvk74wEoYgmnyf+JArVpjtvHvI6lQH4ATOHhABzuVI4L1a959F2Vx8g5sm3dkTvGGZGGNQA4/nIKa+vJTPi+fv0ACJ1vrLg9b2EOCcEeX9Flm33jNp/YYqoryv6z5X5229douqjZZf/rfTXbI0wmDr3SKMvddkffYrLD1UI2AHZ0kvivGpMHpO0rN/cUANbtiXlobO5idvRHR58WNWngs9/SI9+RTNumZHDYbO4j/Sx9AH53bBU3wCtF/qB7SGvZZCQlqr4oSxdeu8y80vKssTk0IRs2RyMzzxTYDcCvX8YDkHpX7pti+EGUpWgRAA58WBiAGXWmUzUo+2aftV1hyzVx+zUL8exBZ1SS75cBy7ae7sH0b73ag/xr1njbJN74cgoA9YMDATiWeolZF+Qh+t2LLQAI33iIsseyzvKxxMcCkOJwvwabAvPM3uxysl9jAC40TKZ3TXmgDCu0/Za/9/PGJQH49v56pB7w/+DdgHJlsOWTJA/b3zIwnPb1hwAUt4RRbcogAMrMz/qhfOqZRkR/7P9ZYM6H2rUGlTnaNFi21TwPQNfY9bxU6HOX9m1L1vWugcj56jZTBk+dI0667S/6V6q3TcoRAaVLAXC4g7gbB/b5lsfyjcuyvdntQUvau7+fHZ+2b2nPCAD6/vIEALFfJMOav3PT9FzFEluB1vPXA9CnwIq07ftSZeDT6je570IOysQ7pVISCXdNy/L7+h5uBsDOt6oSes44E50TA6Qf/miInKtSAcmMPCnbvvuqKQBlZ8jE/cydpVkxWgbIW3bJdRLH8Vy3Sd15iqIoiqIoHmB4JerQ4zEAbGrokBxzaPLsC1X4+JM2AGwYMv4mrf0DS9VKANzzxh9u+7ruFfdk1HfbMsy1vEeXH/q7/B1+0EKJd0V1CEbcrzeb254aLa8R5pDcNs9jNv6fqCkpdmuaQjbvcuG0/a9tbQvA5VOiwkQkZq6eXYoXNTTxfkkEcMrt7z7ZgbIj/FeJcrqWo2ZeoHPh3wCYGCsu21IBMqvvsPs+yo8Xdcp6g+9KDcuZa95IpNxbl30dxW09qJnECPQrsAKzw0edWeLL4/vvc7w74xUbM2ItFJmpAtX/iMzqI7bIzN2oelTSdPmt11bJWn3yhOah4mZPaC8q/4L7onlpaWcA4l8SF5/13Plc/Z+eYImtAEDr+evpU2AvANPOiyo3/ot2lF0gbsm47ZI4ZQ6T/mn3VPdwCoAT1mQAto4Td1m+Bd73ZtwIpwK1Jllc4TPea03B6aJaly19EIAD3csC0ODhv2n2t5yzKq8eA/LmOlYlSlEURVEUxQMMrUQFVChHw06bb9qu0ty+ROyXGUlIi38AWFnrKwCmTW4DQXlnoy+4d7YoOs7geIDdKZKKfGy8KHcRF30zg4gd4HkJgotdGgLwSdUxji2hafvW/Kc+ACH4xj8/7XwxAM5Yw/lqfEsAoienx+2U5OZBwZZKMRxsHJzpvoiDWZfmMDLmEFELz34rMQc/lv2aQUclsDfpYYnZs9qlkOqZd8oRfNr/q88nt69PcpT0N3We2wTA5FJyLTiPFWDuZanC3y7xQcwmOb82R2RzQkIpKswVPS5g8XrvGJ4JzjIxGel/pCmHO4vKmrr/oLdNyhGPlcq6v6nytQSKB5/OmVYw5ckJ3BHsqpU+HH6Gdm1FjW6+VL438ivfqzQ7/yNJLgsK7GXlFVG/F3S5E4BSf69yU3wvtJX41O13Tcz0+zr8ZxgAUTONF5O4d1Qj6gXL/fXY6uYAVJyebufFOiUAuFpQ7rWPS6+g0p7eAOQ7vCfP7DL0IKr+vESGR7tWFE+xW/n7mnRg3edLhk+lEduwXbwIQMAsedg9WKIHAMU2r8UcJZ1ZsxYi7a2o8XXeG5+HDIqSjIOM7ro2PwwBIPZr39/YnmAKDOJsR6knVTUo1GVf74NNCV+zD7ixGygv+Tq+WNr7aHLWwTgrQdf6YjsLimx02ddi6yMAFJm/02fH5inmyEh2vlUFgF01JwEw4p+a7OtUFIBTT7jWAwvbdSZbxxhyxlgDSmdyS+/f5d5qFbbGLdnB6hgctdvVBntXsd9+WWrxWC8cdTvuOI7mocXZJyjuQtr7WRflvB1+tAip+w9k2t5StAi7xsjDynZCBtCVppzxWRbb171kQvPeC9Ibbmz0Sdq+PvdJxvLSznVzZN9rb9RJq0W44IdPc8nSvOd4qiNr+Ii7e9YSJ+6vUx2T3PY5abm9A9HzJVnGiH1Rp/tW3rAOZOgCmWDHD5W+2oadwMTQLNvnFurOUxRFURRF8QBDKlHXWt0BQNcC4wDX4OK/r1kYUUFSgWOQmWFGRSb1mCOF8ViGVMbCokSVjPB+4GZukjhVfheLSVwIzgrsXfe2otJLW2Wbb0zzGGdA8s4Pa7KvyRSXfa+clODGYz2LYf1nt9dtuxUs+fJx6mGZzY4ZIdJ5/WD3WVTIq+JOsZ7e5z3jbhGnC+/kzOLsqiMK1LQL4s77Y3hDwk0yE/6p1ygAfkwSJYfzF7P1/QU+970rwRQgXWPi2Lp8dP90AJqHSrr4eZuVmRcloHfUBknkKLpA3LT5l+/FesJdCTAaAeUk+Hh0jfTCa2/O6QRAuX3uv39KS+l76r6zlgVFFrrs+7Ftfqbc2QSA1OMn8sTeLHGUHSjTXX7/6q8O5OkHJaB/YJQkMUzu1ZK4TySQ+lYUsz+vivoYejLlJi29R8m5YtO2pql0jJAg8o9mSZ8S1s2E9dRpeT9NguC3VnD3wgw6KucutMtFQwTLZ8X6Z2tinifPvkG1lgAwbmZzinwn/ZH5Cbnvfoj5BoAfk/JTYZooqnmZGKFKlKIoiqIoigcYUoniBQkOLx/gnuLefX6/NAUquxx6QNYP2lBh1q3b5iN2f1GbqY3F32+1i970zmlROpK7hWC7fMpntt0KB1+UGe6+Nh+67Zu9SNKsK+z0vTKRUxJGVSbxQfciqNdTcoykJR9LKpm2bdfm0gBUmnoW67adeWPgLXCmU20A1tZJD04dv+NuAIJKBXDgIYmtKRMg6dT/OiwJA5kpNLa7anOmktznxX5zpCHv3Z8ndueEKy3lGHc+/GGG8gRCk89eoPxICQavmOIa42bEWJLMSOwr11uL0CSOOdLai/3pbn1AMTmXvCTK/sjrYvoA2oSdZ0qob8uR2K9KYk35f61m6Rfiqdj9qSigLe7axKFPSufo+3Y+F+a27dn1jwNQZonvEgGux1nxv2/IIJa/J/fjr1XnAtBixiMEvS3p/qXC3AuFJqSIorZhrCSA5D9t8HjatVtotkViSJdUnw1An7v2YrtL7szrC6muvVSB1MOZV2zPTYw5iMqESnNlkcRKI3xT/8hXOGX0jxt/kqHiunTqs2bJ4smlDvnJkgwZCCguwX+dOi9P29b/SAMAVk2rA0DFqZLJZaww4+xRuvw/2Wr3cell7hulDBhN4rpQsJ3covZU41TqiZol56V6ux5saSRVtzfV/1J21ndv/2RRWUqi74zH0rYNqS21pHrmm0SoSVy6TZLkHs9vgEFUyAkJwF2cHMZ9ocku+8Z1mc7ww08CUPgj/xvgA5jKXE57P/uCLBMS8r175uvBSYUA2FD5i7RtTvfPLztkKY0d90zNMzs9wemy2+9yLebAjdewBv+9y3V9qd4Hm1O+rwwkjThQzv/jNuoVlqzBj4dKzazF1ebAl5m3T0hJ4Zn/GyyfnWHwwVMGIu6XSWeT3gMBuPbgOdqWlczoksFnAXgm/35AKpiXJO+fjerOUxRFURRF8QBDKVH7/iv1ZRKqON0EJtaISkuRtQ5J/WL2glMzUmK0jEZrNHgCgK2NP0vfafDiyAefEAXi7pAUnMZ+eK48AGW/EfeHEWdGN8JcM577Z8o5cS5QnGK38stiUaAqOGb3/qhAOQkdEcm9hZ/Lcv/lopL+H/mopLpfnlGCi2Xk/P7+9LuA1Drrv0pcmgeayHzHnuL79R+dqljZHntp1FnKjJyqI/pwcPEk1/sLSHEsPP1+/dlu39Xn4P2s2i1B2nE7pEq0Ec67fb3Mbse1f5gJEyTYdkbMtwDcF2pixbMrAVj/kX/NQwNKiRvvk/qfpm1bcFiUqHD2urW/o7hrFf0VV4LY30NcYxEtHenj9+SBoT6k66c/pwVpO3WG7dOrUugf46qOtosXKTre0aeeF5Xmj/+6hxNsuyb3bp9/D/YrBep6Ck1znItpsN5xjkYe2Q+4rovoDfyrB1AURVEURTEIhlKinFPQjAW1ev0psQflv7z1UbPNlsn6VUaY9mbCwVdlZeq/m40FwEZ64cKF7USxse72n7T4jOx6rECaAtX7oCgta7+tQYVR/hfblSVr/iazUNurbeoBUP0pKUlxopfEhgUlrCbK0abL7xLbUP/9v5hQUtZHbFu3V9r3GgVbUhJRn8qMMOpT2bb7/YZp+2MXPyWvPTZc/9EMnCMW2e+8FS92aWiIatAAtq07sN0t79s8IjEkS8d9SJlgSR1fT+EsPmlQgiQlPmO5jeR5EjyeUYlKvUeCs/9TXOJrVlyRQo6v93+S4ASJibvjk2N5b68XOT5I+ty24e9icyxz8cFZKZRbaIpxVSgnlgJyji63v5Blm0dW9QGgYi48T43C6d7iwTI7+pH1V0UbKvmOd54nqkQpiqIoiqJ4gLGUKAWQNdae7fQTAIEmUaCu2lOouVRibGJ2u6cZ+wPnesiMYcmj73LJJpfe70ukoGb520mFyoJr99ej0ghRoHaOqAaQNqvPiGWpzKjmLWjKyKflXL84YwYAoypW94apOcZZvPGvTu/z8gnJ3rqxApU1+XdcMGQGrj1D/OTYrRIIVJYtWbT2Hyr1kKKUpyfL3wGlShL1uqjcpQIk7umen2QNsriFa9n/htzHM0qOBmDWxbLYk1wzGP2JgNJSKLbXM9LnRpqD+C05EpAlYwTfLGuTXSz58nHoGSl5s7HB+LTtzjIGSTZRIAODjJPlmyvUr863IySG1OZYa3XovyROMzKHpZA8xTCDKFvTWrzxyMxc/15zWBh7h0vg5O+NRzu2hvDgzocAqPCOcSp9m2vJOmRtZv6elqbppPoPA4nr45vFd3OL51+R81smIILFyTI4LD/c+DJ5bnFt8GkKB0nw9OGNsrDrjbq0omtTuNRbMiuaOXyDo/LSwFvgeEsJWM5nDmHBz+LSK5/DNQad2DZtzzW7skPih5ILnz8hIC04NzMK99+f9j71YHhem5UnONfz+/yCnK8e+Y7wQZkfAGjX+XkAgi5YmV/uI5fPma+I02LX+AasbS/96E+XpQbRzK6tsJ+4+QLcRmX7SHGpzy+wAJBnwYCFPQGI3e75gureZMcb8ezsON5lW/MtnYj4PxlYRIyVSvLxRaVMw2VuDy5UDKe4RY5xxEmp7RY527uuSnXnKYqiKIqieIBhlCjzH5t4ZU43AB7uefNKz9ll7/CabO3l/L70UN+kFAkcDPWgZEJecbaarHl0vQoFUPZ7g0bA54AV5yVIs3PEnzQKFvl/z7viGqg47PZXpOIK/MOIwrL2U6fZJQC4+L6oIM4VyAFOPSO/SeoD54gwB3vZypxhiZJw+Eo9xSX0a3IosZMPA3m7XlVusrudqC5f3FOMGRsfAKQ/cmKJlRIMY8p97tgSSplF/nJ0rjirxr+zuSUAPe78hPxm6Rffe1tKy1yxB7p9bkfniRn+kvb//aozAGU2+qcr/ugLEkie2EoUHGfoxNeXChL7RZLP7MoJe0aL6jv/obGAnLfq0/oDUOGDRKynnMkC0T6wLg+pL2ENY96cmFbS4NfxEkZQ0EMF3FNUiVIURVEURfEAwyhRuc3B/8gs4/ee78J1yeb7Uq9gm1LE+Zd3DbsBVwq6j2l77r8XgPD1B/1mZp8ViYNlmQi++ZMwsyiB998twce/vtGIAo5l4gp8ITMJZ5mHUkuTMS93Daa3FC3CngEVs/xf5V4xnrK1bHslcCzz8k2MBLEeGy+K3Lp3S6S1axUmPv1gU7oicMdoKXtQzAvLGOSEvUNEXdxWTpSKmuP7U/KAsWy8GU02i6KysubXPD57OgBTzktByQpBJ2kZJqqU1S7rqcXOe47YRf4RK5MVMSPlups2twy980t8Xt000TPlhp+tMkOUjopv/gUYtkrMDQkoXYr7u0kf4VQyUhwHMmlAJ4LW/uUr07JFcjtRsBc8MgaAuMAgWm7vAECFcdKRWk+fSQuabxwlcYZ/nMm6z/Qn9j4SAUC9YBPrHCUNCk73TZ9v+EHUvEYitf++PQaAOX1bEbz7hFu78w3kYnnsdQmSvC9cQnCjzKGcciyweSBVAtCGPT+U8HnG6wRfHfC527bETx11So4bb1CQUwK2yYA1bkUPEpvJsU4o6TgPT/7J+qtSjfvjAXcB8FNJccN+1bUwK87HuXxX4aBj/FRkEQD7UiRYO8meXktr6CuN8ugoPKdy/+3ET5Usp4S7pgGkBUU+FH42Q8v0wVP8cmkfM17cfUZ6YNkb1+S9brIodott0oGXHr/ZEEkaOaHgC9IJ13j9cTrHyGB9eLRk3X18vhzPLrkfgNCDcl4qT9ntd6sEXI81YRcA33VszDtDxYU54k4JrO4emV7/qfHGrgAkrRJ3ULlP9lLxH8ealgZazzGnHO5Qhm+LznPZdt+2jgCErU409Pm1FMjP1A9k8FQ+QASCRcnhhHY6B4D13Pm0tkcnyGBjQJSc7/G/y6Q8juyt7Wk0nIPCt9tLtrINO70+kwlmGR9NMNWdpyiKoiiK4gGGUqLy7ZHXFVfE1dMs5BpxgfI+ziE5954xJdPPmrm+GrnM8PelXqH9xy8CUPpNGamGYSwVyt5ISjCUDnAGF1uI/0pqXVScevtUlnXOkKJ+Cife8jhYm2gmAAAgAElEQVQArSokADC2+F/UDZZzPbmUU3WTMX73yNN0j3RV4ppteZj4XbUAKPu2aB/O9c6Mii0pidgnJQC7fVQbABIHyzqIqQXdZ/VR6wOoOFnOv91uJA1K2N3HwsqLDoVwnFTutl3e7zuDPMS6XWoAleoIa0IkueOhynJ9mvYfIe6cay0vI6sUOcWasIu4p+X97GpS+8o85ze6RoraHza5AAAFv5e+03+1J1cG9vnWbZttooR4WC/s97I1OSPx3/GUD1gCwDGHl+Wt4f2IOOf6rDg2tDG/1ZEaSouTCwJQebKo9v6mFjs5P0WeEU7lPm7hs8SN9G34gCpRiqIoiqIoHmAoJcq5MvMbXdoCsKiK+2whp7SfMixNgTIqx5pI4b4KAc55ngXzNUd5ZAMqELdKgc9XU8AR/rWzmsR81a9bL0ffEf3DTkJPS4yVP/1CtitX5PWYFL2r8NJxX5rjEZYYUc8W3/UB9/wyBIC4H90rr/sjzvODlwt+GgHbVlFJZx5twNIwWX8t5Mf1vjQp17mwUAKre+Rbj1NDuHPzowCcrymPw8gCjYj6zLgxqNawdB2p9y6JWTtTxcwZRyJO09abAfiqxGgiHOUrRoyUNWgLbDLucd2M070bsbq6xMlOPCfnscqrx3yujhpqEOUkeLhI6ru/uUpMYM7q5NT+swcA0VMkk6bMb38Z/iFbYrQM8n5/VoI3Swaco8wvV31pktdwdtxRW3P2udvJpeJv7Bgobo9Ht/aiypsyCPR1R6bkHvZ7jnDU10bkEStqfA2kZ+QBLK85CwBzTRlUtXzyWe8b5iE/VZ4vbypntjeYuIVyLJVmOZIBvGNWruJcWLnn0J/Sanl9sLgVALGHfR+ao+48RVEURVEUDzCkEmVfJ+nFg8s1zvFnS+IaXOxPI++Jselp/BY8W7xVUfKKgJJSy2pm2w8BGPZCX1IP+H4mqCi5QdUV4vKKMXiJg/j/7IOH3Lf/fU2s7rxgIAAVv7lC3Gop2WG3GfmIbszVOlLe6JkCv9HroCQ/VJ50BjCGR0KVKEVRFEVRFA8wpBKlKIrxuDhd4hOf/ESK25X+1tgJG4qSHZpu7A5A+a4SkG0EdeNGWP/5h7Yl62a5P4bbpywOQMASSW54qGQ9wLnWrXHWvNVBlKIo2SK0lWRDljbQUkmKkhMyG3wUJNEHlii3C+rOUxRFURRF8QCTESshK4qiKIqiGB1VohRFURRFUTxAB1GKoiiKoigeoIMoRVEURVEUD9BBlKIoiqIoigfoIEpRFEVRFMUDdBClKIqiKIriATqIUhRFURRF8QAdRCmKoiiKoniADqIURVEURVE8wKtr591n7uS35dF/tX1jyk672/0Yb/fjAz1Go6PHKNzuxwd6jEZHj1GVKEVRFEVRFI/QQZSiKIqiKIoH6CBKURRFURTFA7waE6XkDqe+jwNgeKWfAZgwoDNBv/zlS5NyhDk8nNOP1ADg/AOXAXiyymq+2lsXgMJvBwMQkLAfAOu58943UlEURVFugipRiqIoiqIoHqBKlB/SpPg+ADpGXADg8rj5zKxZEQD71as+s+tm7H+jEQCmuEtsbvKB2/7BBbcDYJ4jY/s3Tola9ceQhgQsWe8lKxVFcWKpFANA5wUrAHgi30nWXk0B4JXHngbAtHKTb4xTFAPgl4Ooa/fXA+Bk7UAAhj8xmy4R/wDQvF8fAELnr/WNcXmIs0N7q9iXji0hAPTId4pZQfGAsQdRz7b/BYABUbuwZaP98GjpnH+ZvI9JNWsBYEtKyivzlDxiz+iGAOzu9hHvn6kAwBcf3g9AkQ9X+cwuJWssVSRkoP3cPwDoHnkMgBQ71A6SSU63aT8BMKtyCR9YqGTFuR4yWX30xV8YFLXbZZ/FJOfOarcRO+85ACq9tBUA2+XLmGtVAeBIiwIAlBi3Fntqqlfs9lfUnacoiqIoiuIBfqNEBRQvBsCxyfn5sNpHANQNTt/vVDY+GDMegF9er+b2Hd++ey8A0b/uI/XY8bwzNo9IjY4AIMIc4rL97q3tCb58yBcm5QmLk8MAaB56CYBWYef5ebmc4f3dxW1pTdzjG+MycGy+qH+XDuWj/LyczdZCdp0AIPXQ4Vy3K6/YNaEBABVnXwPA/PvGG7Y3h8l5XNZ5NABWe1jazHhLjy0AHP0wT0zNW8wWAooWBuBaRemXdncPStv9e5v3ASgVIPfrnpRLtJv0IgAl3za+8napc0Nmj5ZzVtQS6rJv0NEmjCuxEoBAk9Xrtik3wCQ1Ic+3lX4zo+J/zJoMQLftPaTN4mL8PUiu09pJQwCIeXUzO4fIs6VOhUQAkj7Lj/XUaa+Y76+oEqUoiqIoiuIBfqNEnWhbHoA/6064YbvqQRInVbXgDrd9Q/8r2+7u1ol8HcMB8QP7A6bAIE6+eCXTfRfnFCfYtt+7BuUyvyVH8u6gxwEIPi1xXRPelji3ubHfMabE7wBU6TsAgJjBvlei2pbdBsDIehuxdZA5n9kxL7Fhc3nv3Od8/8PlQgCsv1xOXp+tCWu3eM327BJQrCgAh7tU5Pu27wFw8QFRXV6Nb5ytGLzilrC8M9ALWAqL6nSkWywA9uZnWV/vyyzbJ6ZYAPjtQhEAdl+pTumFZwGyFQvoK8zVKgPQ7dUf3RQoZ5LHvnsCafltBwCeKvOHdw1Ubog5WFwzW5p8CsDCpEhee7snABFHRSkPX7hOXtmLdZCsxLK0y7sADG3cjp9LiydnwKMSL8Wpv71ie06xFMgPQMh3QXwb8ysAKXZXZXTllUCe/Olpl22VJ53Fuj0xV20x/CDqamsJIv/s/953bAnKunE2WVb9G6q+0h+A8v9afcvf5w0Sp1Zjb73pvjbjljCbnIMJE/MuRwMw8tPuAJR6axXBrHNpbx0sQY4Lv4miTZjUitrRaSIALZY+R+gC3yYPrK8tg6TaLw/gcoy4uLrWzZ5NJYPloTqyiLjERn5sZ10tSx5YeWsk1SoDwIZhE4Bgl30mkwm/XRArB+z4twTD7+w4Pss2CSmSsfbZ6casf0XqnQUvzHg9J+SZfblF+U/2A/BM/v1p21ZekUnp8pcbAxB8cR1hj8ugeGZwY0cr/wslMIeEYC5UEICE4aVd9gUVTmJH0y9ctn10riRT338IgEJTjfnMCPxFgsG3XZMB09h+3Si06Oa2OgfMM8ov4vH9D8rGNcYbPJkjIznfpioAM94Rd3OJgGCuOjqh5Y4wkBCz3IuNQq6S0MFVdGlcrjvRD+ayXbn7dYqiKIqiKP8bGFqJskQXYuIkqScUF5g9BeqBHTJbqJjvFEBaEOT1zOsqytawUQ8AYD179pZszSuOviizvTX3vAuE+9aYW+S7QS0A+DaiJRGr9wNQ6kTWgba2TVI3auJTnWg982OXfYfa2olbkCdm5piMwcLrszkv+fOelgA89YUkSSzYV50SbM9945RbYt9XNVjTxKmCS9DtedsVmk0eBkCh7eJCCD0hbk3Tyk1uiqrR2fuOpMR/Vew9x5bgNAXq1QG9ZUsGVS31+Amv2pcbWPLlAyDhbXFZPlh/Ix+UkBINVru7k9V6ncT6dP5DjG0pAduFpuahobfAhPJzALhoE0U7+OglN/exuYYcf8Vp+wgxuT/+1+4uB0AsZ/LMzpxiiZNkouOjA1hd16kGiyq+9Zqd3u8PBqDoeOmHLdESKpHwdnl2tJ7k8l3nzocTncv2qRKlKIqiKIriAYZWouzFCmdLgbpku0rd7yRNM7bfnwAccJREaNSmP9NfGQNAfGBg2mfSvtdizHHksaGiQP0xUGaH+c3hjPhH/MEjC2/zmV23QuBvUnU8EMhJcnTg5r18cFZmUAOjJDngpSY/sSBEYhlsVzIPuDcyi7+cBkCKXa6/4B/y+9IcJQt6VFlL1HUlRbZei6T0G8YvVZAdzvZsxNpu0sdEmNNj3notfxKAuJ/8S1XLiFPBONWoCLX7SeHe70t+lKGF3HtX7RJD9PWlUml7igVIDOZ9oclesDR3GHlUCth+VHo5AJU+201CXdc2VT6RoOq3i63jeg1l/NlYKvXdCRgjAcISFQVAna/F5n8X3sBmCT2l22oJGK8wwU7RVa73YkpVieMcd9fMtG3tdrYHoNLzR3L07MkOhh5EHX3DlK12d8wZSuyQNS7bnHWgCk09zsAjktF1tJkc7qB2P6QFTx7pXgmAYuO82yk6L5CkxjGcqSyDu6S6Uo17QeNJVA1yLqUgQX9VJvWl7HcOl+NC/xxEeYr13Hk+/7QVAAOHyCCqd/6DzLy/DeBf1en3jhLXSYpdBpSV5vQDIHaaMYNVb8SZzrUp8Ln/2Z0TvtxRj5eauN5vT817hoqsyeIT/sW1fCaXwRNA38PNiB+6C8jZZMcIWPLlI+EdmXDNuV+CimsFZf6YO2WVzOw2w18AoMAXq9O+4/DTUmdw49D0wORr/xg7y/TY/eLGS9kqZ+2dYqupM/cJAAbELwOgV1rSgJlTjtpRXfoNBSBsWQK2yxe9Ze4NsURFceQTEULmFV4EwF9XLQwfJCuSVPzevc83hzgmO/+RrO5WYekL1ye/XxKAkBO5/6wwpgyjKIqiKIpicAypRDnLGiyrO47r06oB4r+S2Xvcf2W2FHt+ww1TrZ1BkeUXyt/f1a+ZpkQte0FSJTvsHETQz96Trq2xIh0/Pvp7eud3rZ6emGIjbrnU96gwSqTmMlv/whRX3mv2GY3SU0UNmPWUzCica3n5E7vGN2CnI+V2xMnaAMS/K+nhRl2d6mjTrLuIKn23cuJ7UVSdiRlnnmzEpdKiINdt5f+B8qHLIqGJvL9ql9TpUov9TZ9xx+nqatv7d7d9v22LJ+7cX9426ZYwR0YCsGdKOXY3dbrs0q/dH5OkevySC1I2ZcGfdSixTDSEAl+7qqmnHq7qokABPHOoGZX/JWUqjHr2redEean2rXhednb8kE0NP7+ulRzzMWsyPZ6VEJiQhaLOGMGF5+RazfKsrTfZZdvQEf0o8L278m1vXBOASuPlGfFOsXl5b2AGVIlSFEVRFEXxAEMqUQwRn2ZGX/2+VAkefu1IG+Imy/7cWNPH+T9SBp8m6Odb/rrs46hOPaduBWbXauWyK+BsEuUTNgNkq5hhanj2Ysf8Gecs65xV4hICTcYrTJkVzjionR0mpFUs3/ygBMWnHj7iM7uyQ9N7s66iPrX0claul3nYFZvE9dUJXkmUOTTLzzhJfE8UgQj+zAUrvcMVR0Vk1yKa/smhh6QS/YLCX6dtW+8oPl/2GxNHh0liy52dN7h9dvtZiVVhjFRyN9Lvce1CMA8mtgXg+NdlAYg8ZCV8n/Qf9j0HAIi74h4bk9y+PgCTXh2H89FYZ50UAy7+ZgD2C8ZbUSAzYr5yJNp0dN8342JxAKa/3J7QhcaNJT1dJT2Zw1lEs9CGM24q4NEXGjOxryzAWSFASlDMuSTn/ZGI44w8KZH14SvzLsbPkIOo1sXdA6f77e4CgLnFIeDcLX3/ifll4GXXbQ+U2MZybt755za2pCRMqza7bMvqRJvOSdDfCsc90sxxnUW1Pgpj8sjAPMBSuDAnHo65abvIQ+LkythJ2xzZbCl2K6eqyuVben4eGJkLHBgpD6Lt3aW2yY9J+RkztBsAIYeN24EBJLeTB8qIEs4aSZkH1TYJdjoBnMu/ZO8eCj5vVKfI/waNO7svHj3l5F0AhL14hF/jvs/6wyXkZel46YDG3dua1P0Hc93GnGC7KH1j3NPr0vrPwhxN23+jq83pCizzomSB1QoKSBs8lewloRZGrSOYEUu8LEv0z3AJGD9hTeaU1bEMmiO4/o3vZWRVcb6xE0IC2/yT9n5tkrieD7cqBK2kT+3YcxkArfONo5hF+p5nWktNs70jJPP+kSbTmbVJQoPizq7PM1vVnacoiqIoiuIBhlSiAk0ybzCT7qYy5+IqXUXHryIuXhZY3NdeKmFbTEYKq8uc1CMys5pzRkbXzUqIQnPijxKUYb+vzLoxZgsn+jUA4GJ5+Y2bNd7GvNIfZP0Rx9j+rE0kt10p6epGtSCn4hFE9DajhmMLqbFSssLpwrPazZyu4rjlqsiMqtB2OYaQE8mGWoA4coNca/33PQLAvJifcuV7d6TI+bsSJb9D4I0aK3lGx4LugePO+kIZ6bRbVnTY+52oAY/3+oXBUaLYNA+V+/PLWec40SivLM07nIvYPrFWysl0DBe1afy5CpTsIW5264ULvjHOAx6aK2V6diSLy+6JHgM5U1nCVf78twTK12kk5+5iWBi2pCQfWJk9QidFgSOufFgh6ReHPe/ePw4/3oAdHSTZiPOiGrasIMf159VA4iZdy3NbVYlSFEVRFEXxAEMqUSl2CRq2ZVCfTn4rVUiLcDhX/ofJbnL8L1G9rHb/HU+GHc89lS63OdGvAX++PM5lmxlzttJp85vFt31HcMaIhvRL9vC9cg5jDRoTVWK22F85SVTPrnXX8lO/UQCUtEiMkfMaN2NKe9/oP/0BKOTDApyphxz3WXeZ5VXv2Z/3e0276ef6z+9F8Gm5l37oI8daJiA9nmpPiqxrZe51UjbMzi2L84YS3x9k9QvSH9UMkuMy16iM7e8dvjQrT9mXeoWndjwGQGRvmckXPywqx6I/72Tw7ESX9t0K/8kY4r1rZC5w8R4pytkxfCkAiSmirM1/8T6CLxgnWP5GONfC2zE4gmMp0l+sHXUHAJHL1lBsg6wZOLJPLQBmlJfClc0f6Ev4HOMmdYTv+If4udIPvt96BgCtw9Lj0iovlD41/oVdWM9JmZjTvUUOnVdcvByv/1PHK+q+/44cFEVRFEVRfIghlajMiO4go00m5sKXmS3Yg/0/O8i5+nj4SeMeS5/nFuTZd3dpIjOvjZES2+DM0DEKoQskAy/O8ROsx8wz9WQGdaFiOABnK8k8pmCj4yypLrJMy/4rpf00389xnCUYSr95hDFv3lxtyLgcyj1xgwDY3XJK2rY2YZKGPK+AZN8cxdikHjqcVlYjzBGr+a/5X7E5uaxLuw9+lNih2Pf2YD1x0rtG5jKD93YitNU+wL0IbHJR9+LHzy3uQRz+odw4udqmHgs/EIX8iFWOsm8fKT4Z7MWiy7fK8aYFAUhsNZ7K30gR6pjZ6fegM6brp8lNARjxisR/HWllI26ONy3NGdbd+4gdKNfgJGIcr+nEITF9VsBSSH6D1a85C6RKvzljXUOvXJd+M4hqX1xO/g81m2LbnHBL32WpVIHE1pNv3tBHmGpX5Vph11TxgMupBJyQWierJkudlrnDRFZPKmwhsrRUQLdHSod/+C0LIfMKABD1mffdQqeeEWm1d/4JXC949j3cjFULpMps0bUSaOxcnBjS0+uPNxI3iqn8ZbY2/cTlOwJNFkYWkTTtx3++D4CzTXL5IPIA+zqRlyMd93akY/uFrg0xj/b9oCk3KbzMsch3S9/acau8s0cWdm1d/RsAGgVbaRS816VNn+7Sgfdq2oKDo+T69Zc1HZ8/1tDl72Nzy1HkuuGt0230r3c+S9uWZBdXX8H1fvMYIelhSXJZOD49xKBXLxnsBy32n8GTvZH0n6Ofl+fYJdtVKr0uz4PMptQBxo0hv2VOtpf1b238AsCcS/J8rDzxsleqsN9evbaiKIqiKIqXMOQU4qu9UmV0aMH04E3nWneWWTYWdJOicLZNnq3Ndfi/7oc9e28dinFrCpcnmENCsNWQImmJz4hUvqrlWIoHRLi0O29L5kCqBFLXCApx2dd55CTWDpd1vQqYZXYYFxhO+RNPAxD1GT7DlslcYMnq6pTeKPZeiZYk90DgYheZER+/X/b1v+M3APpF7XT7lqqrezCptgQc9i2+BIA3qZXb5nuNfx68kvZb/TpeJLWCGLsg3s34p5Fx3cw5IeIBqXJd7TUJdC24zc4/deRefPp+uUadfdUnZRcT10bu5ziDJjxcT+PI3QB8/uA9ABRJXJW2zxwmyvaO/qKZtgy9nLZv/BlZ/zH6Y+Nfp5Z8EmD97ntS3TrUFESttRI8X2Jx3hVizCuiR0tx02Yh0t9XmjeE2NNZB4qnRMr1ejhVCnFazvvPig83o8OgJS5/j/roUQCKbVqVWfNcR5UoRVEURVEUDzCkElWijyzrcvcnnVjmiENw0jv/QawzZeXx939uA0DcK39nq3DYha6idEyq8WHatmZ/dwagVJ8zbkGUeUnA/7N33oFNVW0cfpp0Q1tGKaNAKbSlZSh7+QkqIgKKICKigiLKRkDEvbeAoOwpCiICKqCCOHEhyJAlq2zZexdKm+T7481NWlpKCW1zg+/zT9JzR8/NXef83lVBUjYcGhXM8lrTLlrqVqF+OSfjXBuFGbvvZgBWbojN1f+I/zj/E415wqb2ozneTsKJg/zk+P55K4hqgTKjDfUTX5rsVKzDNvGhKjk2mFftkuY/paSoWeEZnJp9hb1PS9LNzU1GMeaEOFAW+9D8M/tccQ0EbwBgl+Oo8IL7vITPkM/fxlYFoPQCCb/uGHaQh+tJYMASZ2oHR7r5ksL2+ktUmI03TaJd4SMAvDdM1Iki79TAFiqvhvSBUp80udo417azzkQB8MdDtZ0tWct0mYm0W2tT4S2xWtQNEkWm5abWlGnrmSXD21jDw4kOkXdkn73iMJ743MYcS9vU6yz+oy2XSWBLpSd971mZHf6xMQwq/iUAJ+3yvgs6VrApf0w5iErfL5lHw9sVou18KSY5J/4b13LDtNetg4Tq1YrtROR4iXYKzCaywnAm/Pid9wCI9Xebw4p2kosxPQ+KGV8J27qII/imWmOyLLt9UyvOjpTlhRaIQ70jNRWQh12C89PMhB6WAdBp+wXCnPmeMhJxUZvkgrr05bjxguyvz5MDASj0o1u6Dr/aznoBf2cgQOv7/gAkX9TwH1sAEO+Dg8GM+MdIceWPm1w+r5Svk759JwDvfiyTsdt7DeG5SAkeuNPayLmS+QZRFUfJ/fRbg0CXSejPms6RYQ65u9IcNt5/R4612CrfGOyf7HeaMdEysF2eKi9Y2+tRWPMo52BBc6hDVeaVlECGynN7ARB/Kqspz1okgk0jJNP8kCh5z/y4+PoC6mXBkNy9jOv7G87ajwUdSKXmPEVRFEVRFA8wpRJlYD97FlrKzOHmL9sDsOgi8x7A3/WmcaS2OMylOJW8CxkykBe3yiykqEUUqG9TwhjXUELwbUeP5U/nL0PFWaJ8DWlXicVHxYyzfZ7MGkoP+5NQJD+PeXOR50zoHJkZtT/Xn4WTs6ptV8I/Fxy82KoTAIU2mDfL7pVwcqIocUaahtrLHyS+n28rUAaOIDm2G4LMX48yryj7tjixznywCj2KbL/M2t7Hb8kaAAY/8ABpn4j0dHOI5PCyZDO3NkzrVb/qQ8IU31CgjAzWs64bwuJUcZF49TFxAfD/xfecya1FiwLQqLu77mHRtRnOVb3qABy9Xo71bBk/NjcdCUCVX3sAkPCSmF59/c60VpW0BoPumsdBm7z7kzuUcy7dUaB9USVKURRFURTFA0ytRAEuh/GILvLZrFYPdt0pzoHDm34KQKvQk0RaJTmlBVlmz0bD2ZEuzswvjulLqSMFE/54KWzrNwPwY7Uw4CAApZ2f1xKBC5fTOrpuHuwp+fKr+AD750rW75XVPwHcgQ2l2xR8eo18wy7z3EM2uWejrO7aec8dqgXApg/EIdsXgwGywxonwR4Vg3yspt7StQyPk2vySWeQg73uKS5ckFeD3x55roYclOdqwnvefW7mBv/SkmzxwSe+BWB3ejiv9XwEgICfVlxyO7PjFy4KU6Ow1a62Wc8NAWDxgApcFyQqfdVAOXcb09JI+qw/APEviZ+e/aw7RYUv02iGKKkPhe9ij9Pl0La1YBUoA9MPogwMZ/Og+QdImC9t42reBcDzt0dQ7y65SKaUl8g9u8Mdq9B2izinH50gpRpKfWr+B4Hi++x6VV5KxevLtbt/Swk21xXTZsK33QGo8sp+IGt5DV/GeJi1G/QkAL8Pc5tzv50mv0npGdfWPbipn0Ss3RZylmHHJLs3Nt+KTox+1/fPiX+pkmx+sgIAXxVZAEDC7F7Efe/7g/X0XVL67KP7WhIw82sAWks8FeXDDtJn700A/DNUnMcjft5CpSNy3L5uvgOwVEukyscyme5bbJWz1Z92Q58CoCTeuX7VnKcoiqIoiuIBPqNEZYdjlTjJlV0F+96WtubZZq2W2X6481NRCoLoXyWn1WudpNLnQ0e70uQJKRKa4CwSei0pUBcT5jzGljNrudpKe2m2mN9ErnDOR++GWaNulbZ033DAvpZITYxm832ifMZ9LxUbEp9be00oMQaOVeuZkFARgAmZloiDdWGnidy3dNDLc6hRUeaWMgKLZOiS+G1PEkZ695miSpSiKIqiKIoH+LQSpShmxv9nCaN+raIoMZVYldPqig9jJPi74+PaRPp4zUNfxFpSfNIqDdnAT+ekBmnicHGizk01C8W3eO6AJNBOenKL1xU3HUQpiqIoPs2W4ZK5+pni3/PGAw8B4Ld2jTe7pOQxkROW0HqCEeltGGhPeqs7LtScpyiKoiiK4gF+Doev5sRWFEVRFEXxHqpEKYqiKIqieIAOohRFURRFUTxAB1GKoiiKoigeoIMoRVEURVEUD9BBlKIoiqIoigfoIEpRFEVRFMUDdBClKIqiKIriATqIUhRFURRF8YACLfvSzNLeZzN7/mCf7Zeb9a71Y7zWjw/0GM2OHqNwrR8f6DGaHT1GVaIURVEURVE8QgsQK4qiKLmm5JJwABYvrQJA3ICl3uyOongVVaIURVEURVE8QJUoRVEUJVd8t2+163tn5+dB73RFUUyBKlGKoiiKoigeoEqUoijKFbDnuUaM6DoegJuC0wCIn9tTPnv/5bV+5SeGH1RGDjY85YWeKIq5uGYGUf7lygKQ8qEVgO+rfOlalvRpHwAqDVpS8B3LQ+w/lQPgh6SvAeKV8d8AACAASURBVKg6shcAZd/+02t9Ui7PuTb1ADhUy8oXnYcBkBAQmGW9AD+5dls2uRsA25btBdTDKyPt1toABPy40uN9HH2sIQAlF+4mffeePOlXfmEtEgHAphGVANjSdBR2JGL7jOOCrJQuUdCW0FDsKSkF38l8whg8TY35zdVWaWYPAOJQh3KzY1y7G99NAKDf/34AoHeRbVy/5CEAQn4IAyByvLnfj7aba9FsxO8APF18CwA3rL2biE6nZfnhw17pl5rzFEVRFEVRPOCaUKL2PNuIqFv2AjA/aTYAaRlSe63t+AEAdY/1B3xTubEEB1M0+CwAaQ4bAP06zwVg7sQkbEeOeq1vuaZedQC297dmWRT+awglf5GZhG3z1gLtVn6wf2Aj3ujxEQCJgYsBiPUPxu685ezYs2xjXLP9vxWl8bwjgP4LxH03YarMthwr1+dnt3Pk9H0NABjzttxPi8/F8XXNMtKv1NRc7eNwT1GgFj0vilzztp2IaJnXPc1b0qvGArC56URnizv3nt0hJ83PeTr9/K+JR2q2ChRA512NNaWBybFclwjAgf8Vo8VjfwAwL2oc4H7u2IFVDafIBnJL0uRMXyKmm+/c2pvUBGDoh2OpHhgAwBHbOQB+rj6TJrf1BSBiuipRiqIoiqIoPoNPTptSW9YFoNN7MmO/KXQw5f1DgMwK1MV823MwALcFP0WFt8SfI7czaG/jV7gQMaGZ1aYu4bsBmFe4DphUiUppW5/YpzYC8EFZccYtag3NuuJNsOwZcdJ9tqv4XPj/7LnPTV5jjRM1wrFnP/bz5y+53tl76gPw+eNDiPE3/J6y+j/lRJMQt0/NxntGAlDjeD8AynvpJznVsQGj3hoB4JoNVg/cxTd+0QDktqaD1Xm7pTlkRjylylTunfMoAGXabsi7DhcQbTfeD0BJpz+57ZTvO1untK3P1Jjx2S77LziTW6tWZvNjRQHY1H40AN1238TBO4MB7/ne5ITtploUflWsMc+VnwbA9RkeO+233gHA5p/Er8//HHTs9BMAA4v/A0CDJ1awcXpB9Tj37G0s7/bqgQGsTxMfxGdadgFg87OF+Pat9wDov0rabBuSC7R/PjeIOtemHm8MmwBAnUCnU2cuX1KlrXIy/u76PrUQ017Myz5i2rPZSbVnPl2Js3oDkHBgdXZbmIL3ho2inPPN+fyBWwH4dsV1vHDTVwC0LCSmu9L+hakXJC/ngOMySDFTsaXdd5cGoPxsO/Yduy65XvjaIwC0mvEkKx4Uk9W+dDmSuIAgNl6QwcPnJ+sA8MXcGwGo2WwjH1f4Mcv+PjklwQQx804CBf+bGI6pjZ9aSo1Auf7SEXNy0k/dib+w5or2V+xDcV798WkJBGlf+CgDk+S4ZxWvBoDt6LGr73ge4n9CTActN7UBYGHiPNeyk3PFnBn1mY88R3Igpa1MAH4fnXUAdWPv7gCEcm1EH/qXK8ueu8sD8HgPCUKyOm2ypfw3cnPIGQCX0X1CuV+4fqw4Ype7xzyDqAP9GwEw/vGR1AzK7CJw+4Z74L0oAAIXLgegfIasXounVQDg1iUyiHq15O80f3AAABGfmM+sB3DXwscBSNiwDAC/Qw2o5BRQtnQuDkDFZwq2T2rOUxRFURRF8QCfU6J2t7ZnUKA8p397mU3OebnEVe+rILBVLseQUlMytQWWFUfznMxL3ubF2LoZ/pJ+JrCMWZQC4IsilQFYsOFX11rJD0vIbfyqguljbigzWJSG9MusZ0veBkDCBBv/OzIQgKJbZavDNfyJ/lV+g3295Rpe/dgHGbbOPKf5/EwpZt/fFADHKu84lO+cJErY11GLXG01/nwEgPjOf+fJ/+gUdgCA4Z3vAaDUcHOpOrb1mwGwNHU27PVeX/ITw+yeEZcCNcd3FShLcDCOJKcZa7ionEnhe/my5JzM6znvv+yCPgDC5oXlYy8946meMwGoHQQtNzlTo7xbEoCgRWtxpF1aNU+LlWdwEYs8i07aHRTee/Xv1v8aqkQpiqIoiqJ4gM8oUduGSnh1cvPRQNYQeSNRYU5kXKdCoPiu0MA5vVy69qr7qFw59jOiprXc3JIFlRcAMLDZfAC+orjX+nW1pG/fSZmhOwF3sk2HxcEHU8RR1Z1s0z2PMa7PuF+6yt+bQim/yjuqjO3mWgBMqz3O2eLPugvi+B89JsArfTIj/i2dz5Ex3u3H1ZBdOoNrQYHCT1JRbHulJus6SVDE5dSmnCi29oRzW/NgdfYmwM/q8ulKNzJwWPwusZUkhf33SVHIY/3FYb7Rqo4UW5Q36rI3sMSe9cr/9ZlB1MaO8vIxciRdisstN9ZpHCx5d3o+Ki+zBHP60bnY0d/bPchbLMFy4257WXKA/BX3HiBRe+/9JImD4n3YidU/ugz/PlABgC96DwFyzhP1+uFazJklTuZxg8Vp0pF+OeNh/pB+S23GTpGXjuG0CdD1bbkIIxddfWbjN9bLOW5ff5qr7YM+MmB7e/h1V73/guKL6z4EoGsTyVVj+dVENuhckF0kXuddjX128HSuTT2OVJd7bGKXUQDUCVqW7brN/rkXgN17ZLIW95G8O175eDJ1gjK/RxK+607CmhX50uer4eXZ9wHQrsso5lWWvIHIJUnTfn0o9Hn25/HAIzX4u4G4EixJlUlRsTeC87ezHmLN4K3SsLpkKt/cXZJbdW/+vWvZsNqzABhdQp6jBRVFqeY8RVEURVEUDzC9EmWNr+j8ljVBzkaxLlDEcsGVJ8rgjEMW1p8/gMSxojrtvVVyfywd8L5rvTlNReHqd2dfgr/OfsZiBgICsipsqSk+ZlaxiLnqZMe6VO0rYbXflh8LwP50O9cv6whA5WdkmZlk8ytl/9gwVtQ2nMazpuC4fYM4UTNUAhtCtxyh7HYx3Xk7tcPuWwMzKVAArx2pTtSMvDsv5R76F4A3/qjGC5Gy32C/tDzYc/4T9013ku8Q1cxIm7Krh/wqlf4KNnWgx8Vk50y+eGkVGJ77fZT5zeF15erU/eLuMf6t96kckNW1Y8RxyeL90yH53JwcTdJTmwBIOLUDgFe3yzsmY6oAY7sqL+2/bFCJN6g0Qxzlu9zSlMkxP2RaFvX4dlJ2VAXclQ52vC0Kzif3uQNaXukhedoClppPaQMoO1JMjNXrd2Zdw6nS+JLkuOq7rxENd4nrwZIa4mT/zEPxAJQZqkqUoiiKoiiKaTG1EmW/sSbNx/6apd1QoLq/IVmcz5fwY1XfkZnWqT9fkoYl9FjmmjkXqtogy74SAsT5Lq2QBXNahC9N/GjfmLlbEyS8eOPTogTuaDHOtSzVqRje8cYgSk0QXxtfVqAMSoWdznH53qOSxDJum2SaT9++M7+7lGsmdHD7yKy8IAroD2/dSNjpvHMctJ+W3+dUuvuui7BIUlZr1cqutAJmJP6jC/zUVPz3mjqzy69vLI4od8V0AB+o/bh1uDwLv4sZl2XZtg5Z23KkA9yIlx3RnfKt3eHHlJMVABg/5i4AQo7YKbJWFBuHM5t1AnsxtH3/cpL4tXaQKFF24KRdQv2nj28OQMk95kq7YWDcJ8fuKkHLGe0AWJD0BQAzKi2g9eC2AOw4LH6Gs+qLFeaALZzqUyVVScVFzuodBdftK8JQdmMe3Erzho9mWha06xhpLZxpimrIR812omwfGhlUIBVJTDmIMkx4zcf+So8im4xW1/Ju78jgKXKy28G18S7J3u3nvBKSFkgJicu5md/2jzjmFf1jtynlWv9SkvPj7evcOU3WOl9s1jNygVzeld57+AUEEjxJSkXsiJObe1lqGvcveQyAhJckE3fk1qt3VgawFi8m/7dwIQAcZ856JQO2X9dAanTul6mtRMP9/FBNnB/X3TgZgO0/yiCye//+hMw1hzn5phA7Nud99Px2yT0TNvPqB1D+sTGkxmSOuIwO+sX1PSFABlTtPv+VWUmlrvr/5Rd+S9bw9gDJXt1sXOYBx/5mUUT5wCDqcgOlSjN7XHLZDQ3k2Zoxmm9fY5mMxs3JdpN8J3yGXJ+Pp/QlZJ7cR1G4Bz7ZPSONwVO9b7Znah9xPNE9eBppzsHTxdgOHyaoh5SmmjNfspS3LXyIrxLlhFgSjahE+bz78y7EPyvPXLMOni7Gfv481ouiB9OBUlNlInpX+1YAzIuX6O4b2/TKk+fW5VBznqIoiqIoigeYUona30xmoX2LbsFQoDLmeIpamjVfhzETMchJncm4r0XVZwPQ+MbehM8wYSriEJmdtwh1m4ee3NoegMACLrToCWfvqMnCODEPnbGLLPtibAMqIfX+rlRF86tbHYBzJUO4ECZzANuD7uLL3Sv+DkDXCMmCPfVUJNMTy3rcf09J376T8q/szNJe9T0JhR/fZiIAjYPlGH4aPZbqtfoAUHG4qK+248cLoKc5MyhmIQDdx3Yhafili1wfbSCz37R7Lq36dYj9myeLXd5M1yhkuyujvWkxzEcXzeMj2+6GUV7oTy4xzHiQtd6moT6V+c1B3JxLz+Bd1df25W3f8gJDhcoNftPk6fNc5DoAhhytAsDilnGmNd/lhG2rOMhPvfs2AEp89TmNg8UsabzzYheK2TWhn8lz+lwBhmvA4U+k9uapl+U9c7ABhM3M//+vSpSiKIqiKIoHmEqJOvqYhF9OGSTxtWmOrBlX0xw2NvWUGkYJlzbb50h2CTn9fMUw7GPsbet2fg/xk1D/5El1iF4oM6OMyeBOdJLzn1JSznvwTZINum35Na512oaLqpUUGJrj//3emf7h7Rn3Uh7zzCorDZQZ4JvfPgzAW89IGO6CpC9Y11UkjLumidKIl5Soxuvasqia+K81DRG/u62tx0Hr/P2//6aLk3bfLo9jxXczJ5uZ7HyhmpcRj9w4cqdO5KRm+Qp7nm3EN7GDAbATBIgCBZC+x4QWiSvgVGIRAIpbUlw+UGnO91vEqqzpVq4Vik8SH6/3+0q91i5Nf+GPIAngyU8Hc1WiFEVRFEVRPMA0StS5NvWY9JyEXxppBzLy0zlRHhoHn+avVqJU1R8naQySntmM7cTJbPdrLRmFvYyEQL762mRXe8ZknCDRfGaOcvNVKnfbwH0/3QLAZ7E/A7Cj5SRSW8jvf3K4u2p4UYuE2uZUB/GkXa6N7zMkGu2z/H75sjOUMn9IjGXILxJBVP6seVSojAT8KMfqv6UcAIt+KuwKl9/4RCQACT23Z79xPhPSfAf1vpao1WW1Psuz/Xbb3ZhFf1XL1Dau1WSX2tVujdQMLOHD9buudVLa1s9WzYob4Bs+NscfFrX7mx6DKeMvCtRfzrInvq5AnW1XH4BhQ0XRTgr8b2okU5fcAMDWO8fROlqii/MzhYxpBlFpoZZsB09GCoJCrxQG3LXuAPAX13JbQnlYti7b/e7sFseK7jI4M17OaY7MeaTA3GkCfBn7+fMceEucwSvelgDAxDsm0tSZEDvKeums66NPyABjyYlKbJiWBEDhfXKmMjqQxrImy7Y+k2sqXY5n54VICJEs3kXWef+2LHHPLgDuLHwrAFsHVsYekzUTd9gSOZFhu2XweqiWPxVHbMqyHoDj3HniUzK/bL/7X3WahkimZNsi3yk4HffCBm93oUBIaSsvZiOFQcYBVOddjQE42PBUwXfMQxK7S+buMv5BrsHT650fBsDiw+ZJ/+gyjBoq9S7/q4On7DheTwJUwvJxEKW/tqIoiqIoigd4f8qbA7f9cx8Rj4rUn75HTBsJuVSNjYSdj3ZYmGVZtZ96uOom+aICdeSMJJIs4+V+5Jag+csBcOZA472Rrene3RnCHn0uy/q2VFEMk57ZLX8fPEQJ8iYZp9nY8XAFALpEzMOY01haiEM9Y7zTJ3A7Ytqcn7HP5e73j5mXu3vKcYM4M99ZZKpH/fMmO95uyNY/nZ66HaSiwtY0+Z1SxkdTmN3e6tplMdSjjIky3Y7ibtyKU1Z1xtcUKGvJKCLniopqHPesM5FMflAiJSzLfFeBSru1NgDW5/e7FKj/rXoAgKJvhvDy9CkA1Av6b0ZOnYiT3yQsH/+HKlGKoiiKoigeYCol6mKH4r17i1FoT+4qSxsp/FM+lH18X2V2hqXSdke0jNrj+dtnFKgL0UWztBWfVMgLPck7bFt3UGnQjsuvVwB9uRoO9m0EwPJnRrqu3ZZNxJHRtsXtFH6uTT0ADtWygjNtx2sdpwPQttBK51oWXj0s6kzJRyS1gdmP/2rwWyyz/69P1ODGUuasHn8pNnYenSXJZqtvxMcyfpa5Haxd6lGGRJlXUivvxt7dvVcfz0OO3F6JOeXFXyjNIbrB0z93IGGZOcosXQ0Bz0rq0wWVF/DRqWgASjwtz5h9TQu5FCi700vU//x/U5HKT0w1iLo4f1PnOkuY+qFEUyQ8kvOD1hg8zU+a7dyXtD+y63bWLkgEoJyJ8gXllq0Pm+oUKUD6LTIY/2qQO8+Mcb39207MlEHHSlKswx4AJsVJNGmsf7DrYWaQ8a+vp9wIQKnDvnedesraE9E8Z5FfIXq+vBDMMHi0N6kJwNbOVp5o8AMAXx2QIq5Wv9XYnc+qM3Yx4xX5x7dE/Rt7S+bq30ePz3b5xbXzjOi7UHxnALVzppyvD+uOdrU1Wy9FepOe2mSK68xTjj0i78U58UMA2HjBwsx7JAravl5cVc53aOh63rx/TLKxF5/ou24RZ+5tQK/X5f3+12lx1/l5dl2i3/Xu89K37nxFURRFURSTYBqZI+CsnWTndN5IdfB08VU8fdsqAGZtEnOdzZH9uK9zuJhFFp+XWnMTDjQB4HSHEMr5YB0kxbwErRFT5K1/9gJg3Y3u/GMr+36QzRZZswRvT5M8WQO2S3by1HdLU+q7/951GviohXUBlQCwJW/zcm/AWrUyAFvukXO2vrnbVNujiJhobQ4/ppyS9BvvT28DQLlxvnXuDJNc8zk1sl2e2+zlZsQwn8+uLya8ygFuNxH7aKnxaDu1s8D7lZfc1X8RACWskuvqmN3GyaqSqfzwc3JO598wFOPZ8+t9tZxbXr52pVnZd4sdm9MdonKo1EYd8vgHVCkt9UgTPhJTdeGSZwq0X6pEKYqiKIqieIBplKiQect4tER/AM42l5HkyobuGf4DYfuB7OveCTLb6PWh2PLLvWnMDL1Tfyw/eOGQ+OIY2bh9JqHkNYbt6DEA4l6Qukzbf0wjLiAoy3rnHZKA8seUkgAE+KXz/PiHASiyXa7j0C9FEQjEt7Mle0r6jl3e7oILxw012NZSkof6pYsqnuJII8KpRNVZ/iAA6X8VpcJHokqV2+9bCtQ1j58fP40e6/xDkmlOPRXNrCTxVQzB953JsyMpIIBFw0Ze1BrIiOPiD2xb77sKlEHimFN82k9Ua3ttOa4tI0uS3F5ywZy7R6pfBPnJeV99wUbsR/J8Sc/HfplmEAXuAoKRK6tKw9e52+766f2IXC0PvQqrpaCrLzsNZqToCmdG7xYwa5kUVkw4u9yLPVIMbFvFrNe9f3/oKdfdD9VmAZC4oBeFN8u5KzPU/aIt44PBDf8V/BavpsLizG0PDLjB9b0UG13f8/OhrHjOtiH1sTsHSkZG8smvtiHMh82T2fHD85Kr64FREnBV1j/EtczIE3Xhp0iiPzKuWd8XE+xr3ZUQ/P6UKhUb61qp8npvABY+KE725f3FhPnAtH7E7Ml/R3o15ymKoiiKoniAqZQoA8cqqW/Utmy9XK1fMUM262tFgTIoMU6O7Y5xtUlAFSgzEjJ3GcyV761xqoV6rhSlwDAqVLxx50xX26CXewJQ5DPfDeu/FMFfi9rW4+v/ZVlWjGTnt+Rr7n2YBbuNCs/L+e3xfObfIqaAqlyoEqUoiqIoiuIBplSiFEVRFCW32AuLT1DbwofosvM2AIov3Apce9YJxVzoIEpRFEXxaQwXkNbRdbkWnKgV30HNeYqiKIqiKB7g53BoQUJFURRFUZQrRZUoRVEURVEUD9BBlKIoiqIoigfoIEpRFEVRFMUDdBClKIqiKIriATqIUhRFURRF8QAdRCmKoiiKoniADqIURVEURVE8QAdRiqIoiqIoHlCgZV+aWdr7bGbPH+yz/XKz3rV+jNf68YEeo9nRYxSu9eMDPUazo8eoSpSiKIqiKIpHaAFiRVEUJRN+QUEAJA+rAcD2tuMBSJzYi5iX//RavxTFbKgSpSiKoiiK4gGqRCmKoiiZOPxwLQAG3vwNAGkOGwB+PuvZoij5gypRiqIoiqIoHuDTSpS1chwAx4bD0hqfZ1oWO/8xABIeW17g/SpoLKGhnJ9XAoAfq8wB4OuUcMbGx3mzW/9prEWLcq6+/P4773a2nbZStvoBAGoX/xeAHz9pAECpD/4Cu63gO6rkCf6lSgJw+PaKHL31PADbbpkCwNyzhZlwx+0A2DZv9U4Hr5DI8UsAGFqzBQDd7hwLQECN417rk6KYEZ8bRFkrxxE3fRcAI8p8nmX5/JRgAHa0mghA7e49XQ+Ea5ULDZL4vsoEAOzONpvDvCKjNS6WtNJFrmof/qdTsa/ekEc9unos1ycBcPB1OQPTrvuIxABxzj1qPwfAWbuDsv4hABy3y4t28JMrAGiyuyeFPv+rQPuseIYlLAwqlQNgU7fCAPx+xzAASlpDXOulOU1fdYIOMCZS1vPbXIAdvQr8o8sAcGedVZnao94PyW51xURYixeTL5HFXG2nqhUHIHzdEQBsydvc64eHS9uccMbFfQZAtwf7AmD5PfP5V7Ji3jetoiiKoiiKifEZJSp5Yl3ArTABNFh9DwDFBrhl8iPdGwLQ6mWRn1e+PJbYOtemac8vIBCAs0+ezLLsQHpEQXfnkiRPrgOAn1Wm5p1qLuWFyLVXtc+fzoUyuMeDAAT8uPLqOpgHBI04BkAZpwJ45y998DsWAEBJp8BUZO1R0osVAsB69gIAd336KwD+3Q5CVmH1mmXn63Kfzu40nEfXdwKgaKst3uzSJTHC/U+2qwlAfN8NTC4/7aK13ArNSafK+PahGwH4cWoDSi32rbQAIZ/J9fle6aUADDlaBYCgLQdI91qvlJww1MMNr8tncvPx2F22CWHKyQoAfLSzIelfiAtI1O+HALglaq1bKU8Ui07x3/O92z6PKlGKoiiKoigeYGolylo5jt7zJcS2VehqAB7fV5e1L0oCuIhvRVnK6I5r+D/NHxTs3O68S72av1XaBn7ahZiXfN9PKvk9mRlvvn6Mq23qqWgAvu7cBPjHG90CYO/TjQD4pc8QIix5rxQ1DUkhfvIHAPSK+V+e7/9KudDD6VewUdSUePZnWccGGPUDjPnhkfQwAGYlTefhyNay3pGj+dlV72GxcuDLBADW1h0BwJLzoUS8Xcibvbos52+5DoDfhoy+5Dpzzor/yfh/m2B7T5zMg5zPp1L4lgqVdlsdepYRp/j9NvHnmzWxKQAl93r/WPzLlQWg5Xdr6BaxE4CkX7te8X42N/kQADuikFucd2fjdfdw8Jjcz1FfyTsj7LOlV9XngiC5XwwAm5qPcLZk1Ui6OH+vrtf/y46qopg+9MxAWdvPnmV9M2C7SdJt7LwjkJnt5NhqB4kVxuawk/hJbwAqPn35d7q1eDE2vRYPQELSHgDODynjulc9wZSDKCPqbsEit30jcVJPAGJeWkIQlz/gPovERPB69HFOrBDZctOjYuJr9ehYYkv7vomv+80/Z2l78+e7AIhf4V0n5eb3ykMnwhLsajPMHDNPJ3JTaDIAv6QkXHZf94dtprAlKFPbQds5Wk4dBEAFvD8gNgZPueVCczFxPlFMBsA3rXmYokd9I3LLU9JvrsHfdQ1zvBWAPhN7EP2H91/Ml2Lv042Y1H1klvbvz8nAr//nXQCInyRRl/5bd+DPvwXXwTzEUi0RgGfGfEzjYDHn1Vkuz8lSI81zjs7UEHOV3bHOZa7a2GSytGHH4hw8GMssWDJ9N5aNOSHvmW5FjPtOlv1cfaZ7vSay3Q2FHqf4ZO8/Zy7Ftuk1+bPxUOdfMsBouakNAT0Ds11/3qJZtPyjDwBhxeVYHy+6CTMZp/bPlWCdP+vIMzLILwDjuWHkLQP450EZWDVbIoOpkLnLsuzLWjIKgIQFx5hXakymZdX+14cK33reT/P8YoqiKIqiKD6EKZUow4QHbufxKzW/JQ2VfCYpFYsS861s2/wlMQMmT6ybxcQ3Ii7x6jpdgJx8UHILtQ1/z9kSzNa0VAASh4uToBkzDtX7UmTj+H5L+WBGDwBiO6655Ppn2tcHoNnQTRS+aLj/3N6WVHjRvDPDS2EtIg7/HYbL1GfVBbkFS3RPId3hu+mgDfOtve4pYvvLvZe+Zy8gObMAWo/83rV+9SWdAagwZaspr1WD8yXs1M4sgpLwfTeqvCjKU8U9cg2a+Rhyy6ZeYsK6OeQ8Hbc3B6Bsn1MApnImD/5alIZvvi7KN/UeAWDvzWGXXv+II0cV6RvqZmlLb1obgIVTJXXM+Ui/LOuYAUNhqVTqMEWdqn/LTW0AsDTdfcnrsv7K+wlbIk7kQwdIXUQLFp47KAp58YneebYav/upAadZXnOqs18SoDPuREW+vVfefeyT99yF62PZd6Mcd/lv5LrI7im6faSY2OeVcktO7ba2AqDi66u4GkOmKlGKoiiKoigeYColKrWFzAgMJ/IGq+8hoqVnfiJGyoOgbJLbJTy2nMTXxMfK8JP6bkUAm+ukefS/CppSj+0AINZfRuBHbOfo3v8JAEK2ZrUHe4M5S+Rc/lYhjjMrIgFIeFMSSzqASt22A2SaAVjjYgHY3bY0AD89PgTANcPKyO9rK5OQC984M+FfNpqis84C8GC4JLtr1fNxAIL3mOO8XSn2JhLcsKD3YABO260MKtQp0zppn0uiyR4Ru9wKVM+DANgOHy6ornpE/Kvr6dfkBgA+KLMYgPXNxjKstjibz3v/ZgCKTXE6HvugmpjaSu7VKc0nudr2j6gEQOE9WX0r/ctK8EpKkHyMmQAAIABJREFUNfFNCt5/BvuajfndzexZtg6A6Dy+fXa0kVfjxSkCzEZagpyL+PCNrr7u/0ESwUaz+5LblXrsOK0X/QHA/4LFV9UOrK3l3es35ckTAPx53SxqjuwHQMy0nQA4Us5hO575hW795TjlfpHv2fX8UB9RyP9oOMTZEszaC6LPpbU8DYD9/Pmr6rOpBlGDRmbOveLpACo3GObBBnXEXLi0xufc1EKcKK/GUz8/SZ4kUuuOeHnYGRmRe+9sm60znTeJ7+N++BZDnMgzXuT203IBG7mudj1fh/va/gLAvEgjoMA9eDIcCa/7tTsASYM2m96E4h8r0TJbHpOXzQOtfuWFSImYPGWXB96/d8lnyPWNiJ0sA8v0/QcKuqseYS0ZxZ7HxQE52hoKQNsdLV0TGL+aVQF4v5Jcr8lpDir0dpqbTT54MrCfPs2ue+SltHKRtNUOsvJ08fUAPP26fNYqLhmey03aiO2475RGsYaHU+o5GdDfECyTyLZb7qDIH1IVAmc5m9MN5Fo+/tAZHoyT5+MTxb4CJGfbe13vB8Dy67WR4Xp7OzFxpZm48gO4M4ovXFKf4e0kqdOwR8VVZdgP9+JYuT7b7Wp9t98VqWdQZWZf4vBuFOKCasYYIMg1eErfu++K9mFEb0Z8dpaPysnkLsIipsv9tnP0elkCkoqczRuTpbmvEEVRFEVRFJNiKiWqVajIakb9u4LApXbtg/N9ZQYZdBXhjvnFubvqMelmCeO1OUS92JMuOVz2j6tEOL4xs7+YXc+Lurb2saxh5AZTT0Uz+PO2AFR60Tccec+1qUe/wVKHqk2hE1mWhztNlFtbTHC1De1QGYCfq5s7b5LBxrfLs7WB9H/1BXE9PvN6NAGIktZ/tiiKRg3BpKm9iT3oe8EA6bvELPJaQynGe+6TEKZVng64a+X93V+u34Sk7lR5XvLP+IKieKJlFeZVkPxXk0+WB8DR0eEyS6ZPF6fenxLHXnIfTUNS6NVVHK/jf83P3hYQ9aqT5pDcdoaJLGb6LlM5119M4vgT/NpS1OAmISkApM38gpEd2mVar9VUMeH1KLLdZaisMlNU1PinVmRrEitI3jospvO3Sq5gZ+cKAJQf5TS7Oa0XF3P6PnE2P1xTrsHR7UT5vjnkPBkrCQDcNuUpYqbmbboOVaIURVEURVE8wFRKlMHryXcAEEHBJR9MnNST21qK47OZCq2n3SZKzcvDJrsS4Bl5r+8Y9xQAZT81TyK83LJtqMwelnZwO/wZHHcm5eywSfwsggeGUGGtbykYAadtfLBDsjw/tVH8SgrvtBA9I/trelfXOP7sJSkrJg4dAEClJ82ZJdlIZ7Cp+QesviBz146fiRNo7I9LOP6Q1MVrFvI3AE3X3y3LnjeX396VYjso/lyBzeDWV+XeW/doZgU1ufl4+lSTDPp72olvRvruPQXYy9zhX7ECAMPecmdhHzFdEvWW2/8nuz+vBsCaxI8zbddjdxN+XiV19ArvkNfHt30G53d3C5T9z6W7spdX/kISUsZn42BvJmzrN/Ps4EcBWPryKEAsO4snieP9q1GZfdUmnKzA150aAxC3Up4z3lahADa0kmfloG/qs7qP3FszHpK2FHtQttvcVViSjEZaQ7JdDuLnB1Bx1JY8t2KYchD1YoLkiRpBweVuKrQXRpQRh8nm1Ciw/3s5/n1YROSbgtMwBk9jTkgUW8xsKS1idtOWfwUxE1woVxwA+8tHWZk4HIBQPxk8/Zt+jt3pkqem+6fy4DLyQJk7PiZ7/H9aif9P8j2OHa72S52rsm8d4t6m7QH4+p5hAPR/slF+dvGKOfqoDI7GdJeMv/5YeWa7vHiNejZbRtXnt9bGwFjMC/5vSUkU7Dtd0XxHqskDL2q0700AACqNkyCAZssk31nt18T8806p5YyKFpPJ5O/kup/VvwUB36/wQi8vzfF6pQCoHeTOK1RhkgzwD3ZryNy68mJakiqm5Ye+6wZA0tObSDglg+ETneR6KOR3jRg06lUH4JtaY7E7zUAV5/hGxDZAyV9kkN/+gZYAzI5bwMtRhllSeOmQRGKurgmQvdO5NzFM4Fvuiqbe6AcAaF1BBoLZFa3v+u/NfLZHjqlS+BEAxpVz25S/OCOR4fYO8uTNj4CWa+TqVxRFURRFKVhMpUQ9vk9GlIYiNCKnla9xrFWdTsb/E2nWnsFBbtSXMtOosNX8Jq5THRvww5D3AaP2kYGkNkhOExNlz/5PuNI0mKEWnjc49omE0vOyd/txKSxtZaZ3Q5BbG1yYOE++ZBKNRYGyOhWKj6bKnfzk7tb0LS3BEV2n9cnfzuYzxow5aL58bvpblJ03Fl7nmjF3jZAaeitf38i+DZLPx8ji7i0sYZLZu0Jft9PCsVlidoyMlRxms54fQnl/ed48s6sZAAk95d7MqKSeLyby49dny5MwXCommMEk5Cm7WslvU9oawvwUqSwQtEXymZnZqdyF3+Wzqq/rUMn5bVv+9uUqSd+zlyinyL2ibAUAbk9okGW9oL+3EnRiJwDbnPnOmOBWol6d3hGA8gfzT/FWJUpRFEVRFMUDTKVEfb9AnKh5VJSo1BZ1TZv4Ml+xWNk4wD0rMqj6u1SMj/WBmnFHHxN/iZkvDCHIL6vD3+Lzoko9/0wvAArPvXLHTT9/uXxPtZPr5kzHk5R9SpQtW7K5Z1o5EWYRpce/bLTXlYuMRD4hOkOtFm4V6Ux56evY1qIwNQ1J5ffzcl4mHGgCgB33DLnbKslmHvPtmfzv8BVypFtD7u37IwANC21xtfcaL9do9LuXns0aytSK28vx1kL5nZ6LFF+OMWV/o0YnCSMv+7Z3z+fJVpIA9StnWoMd6ecJOin9LTRYfCzL+4dQ9TepSRf3khFW7vYlsVQT2fHRbvMBeGf6vZRb5Zu+bYDLF+qjTuLIbMfOm2/LdVpsj/mftSDVHna+Kf6lq+JmOFuzaiRpY+X5aGlaUD27eoxnoH82z8KMyuiJSgGZlk0/XZqKk3fKPvKrc6gSpSiKoiiK4hGmUqIqTnfOdiRSk0EjpzEirmAi9I7VMY/V21q5IsktxmdqW5bqR/SUQC/1KPecaV8fgEnPiR+U4VsBEoEHcPviPpSdKrOGwgvdCpRfkISwWopEZNqn4/QZ/MKk/tqBtmLT97/zCAFWmYf8Wt0dpv3CTKkCLtEnvoVRKf60XeY2ZlKhwF2PstRmd5oGxw0SyVru7pMAJKfB2w9IxBpLs0bTlOVYPvfyyjn2TQIAf9YcgcU5rzxjFx+fh7bfTYnVFy657cWk7z/AX/eLssH361ztYTdK5BRv50GHr4KDrVIz/f1LSjwpUXLMMystBGBJqpXY4aIw2rZsz7T+sUca0nngAkASNgJ86DtVbrJl+z3ybKkbJPff8lQLxT70DQXKYOujpVjfcJTzLzmf1Sf34cbmcg+OKfsbAAsS5wLQtE1P05UK85RjXcTqMfWJYc4Webe8O+Meyu/Nf4XUVIMo4yHdYLW7nt3rC+KA/K2jBzDq5mkux3bwbljrsdrFs7T1WvsApb4zV5h0dtgfEefjqoFZL61H+kiR5Ipfu2/eI93kBjhTHvwSxMSz7oaPMm13w+r7WFzjs1z9//UnSzu/7b+SbpuCyb0+8HYXrgw/P7a1ExNCjNO0Wmtyf2KW+pZpx985GLdkEOYnnLgeANuD/gTuvrL7Lj2i4Cou5AXnSmZ2B9+XVpQ9t4o7AbdKmo2UijKQXH37MEL9ZDI34WQFAMp8sd03HK8vwqix9k4byT5vd7rFd/m4L+XxjWv4pPP9ODT+Y1Y6x8fdR4rpOGb4n/xURCa19rK/ZNoudPdZnw4CyEjdXpIDKylABk9G5v2KH+0ukOtSzXmKoiiKoigeYColyqCYJGxm/vxgltaQ+lsNFog6ldeKlLWyjORbha5m4KfioBzjpRD7Ux0lhPO918a42h7aeSsAZXsc84nZ3pLrvwAgLZtpzsBhnwBwYHARV9vthSQxY+kcss1eSoVKc4iC0GO3/EbL98QQ26vg65X5BQSyebQoF5X7inzuSE3NaRP3tk4FZ8tH1akdKBm+E2bLTNLbFdUvx9GuDUjuIKbUfvtuBCDmZd+YwWfkwpwoAP6tcs5lfn6i2CYAJr57A5WflvVyk3ncmhRPu8nfZWr7N/0c6XNKOP8quCoM2ZEwREzq3CIfD4btZFi8KMCGstQtYiftel+qlmUgVT+WwIK4IfIb2Y6bv0Zgdmx+VxIxti4k9kgjrUHFyeaukwdwqJcohMuudzvD/++FxwEoNUXuQWvxYjzU5PdM2xnJNh0rzZdo0xOsxYtRMlCuv/02ubbHTJbcCKV3FcyzSJUoRVEURVEUDzClEmX4Ro1udQfMlxIwhiIVO/ExEh67+rQHqS1kRD5o5DQA5qcEE/OSd50Jn35NlJp6QW4Z55/DksSv9IGNXunTlRL3y8MA/O5MEhpm8Xcl2WwRaoRLZ6zGfWkFyuC4/Tw2R2Zpa8qJ2sweJ3G6UWNkxlGedV4pgXP2jppsbSVV7u+MlxpN9ieL5jjbs1wnARMRY8XhOLnCZIYekwSriUN3A+ZN8GckbAxtf4BNaaK4rX/xOgAC8b2UJJET5L6/128Qs54TZdRQpDY2mczLX0mUwm9viP9exF9uRepk/bKZ9tXljXl0Ds8cEHDX2KeInmgOhc7+j6RuuH7JQwCsafhxFh/E7JhxWuqXzejQjNh1zsSbdrMXnMqZTxpIWg67syjKi/+0BqDMng1e61Nu8KtdlVcHfJSprfaIfpSbIUp2TmWy5n8mCla0j/h8XY59DyTyXOQPAPTa0xyA0u8V7LGZchBlYNu81RWd12eiDHp2tJoI+2S54YCeNq8EkeMvPQAyTHYHbxJJ/bVBU2gVuhqQwRM4B2xektodDcUUVM7fcLi2upY1LLMTgD3lypqykOnFVLpffteHkSKsR7o15FhDcUode6MMEpuGpGTZbsC+Rsz/p1q2+6zy7B7SDxzM0h5lkgdB2C/JLDwnWboXVJbIpS9mhPPmcKn9FHJEHmsHGvkREC1Zob+tL4Ou8v6y3dtHq7CktUSJpe/5t+A67wFbXpZcQ5urjaHGsq4AlFnoe4Oni4kcv4R7HYMAmPeCDKZKWkPcxVtHyOfUU9GubTqHf5VlPwedZoVW70mR4vKf5n3RU49xDnxiXnMO0b/NfrWvzhYF4LWxDwJQepQ41zvSfGMydzn2Pt2IukHGoEMMMmXetua0iWk4XCfcNSE1XBoK77VjP38+03q7uiUyzznAeOuIvGPKT3KaYAuqs/lE+i0ShT15wPsY78s/FjiPsYDfC2rOUxRFURRF8QBTK1EZMUx4tbv35IbHZFZkmPiowWXqja3O0hI7/zEAkoaKU6Fts/ccPv2PimPnWYeRB8o9Tzh4LhwAx7lzBd2tPCFywhIiJ8j3D66/G4BhhYOyrBew5ygJu1Zmuw+zmrUMbMeP88FD9wGwYbw4cj5RdAttXhidZV2jnpzNIQpUxx1Sm+zYCzFYd/5dEN29ah65bREAx+3nKDnCt8L5L4dh2uv2h2Ts3ntbJCsGZXayvthcl5HnDtbhl5ESIGI4+Jpx1m9fK4rEHdG1c1yvlHNWf62EwxtUbLndldKg8dp7AQhfti6nTUxDSkk/lwnymF1U/oO3pHMuUkx1Qc0k3+LqGiNd6320XJYlHDV/mpzccLq8vCuvC7Ry3C4KXMnl3klNpEqUoiiKoiiKB/iMEmUQOX4Jm53JvFtWFp+o7Q+UoEidwzlsJaTNK+HaR4LTAdYMs0SjztsJW6izxe14fWBiLAARR8wd7p4b7GvEnyK7WuNmV5suh99iUTt/fERmfOPub8Y3bSSD7j6bOGIP3tmCQ3MkEVzp2aJ82o9KBm9rum+oUAAtwySNQ93v+5Gw6NqY2V6MbUMyAKU2buGuBaJUbOwvSXBfvmUuNofMP99YJM7ISe8fBcCx/xDFTvtWtuv/ElZnNYRmkRuxOJ9EB7dIqoNwfK/eZklnaphNzcdC88zLDtpSuWOo+OUlTb82fKEMTtx+1vX9gWSxAgQt8I5fps8NojJimOBiXsqtKc67OVoux+h4cSzOaASKMHmuIOUinCaBuGXQv3+jixbuIQoJDvDFh5kRlRfgJyYCa7AvHsUV4nC4JjkJveRzBmVcixNwRqoVfM8UD0itJUFG3Yr8yPJUGQgnjpWJjM1ZiBiTm/Vi3/+HKkUll9yme91vCyMHlBGBFzN7HyW3m9ekfDWkHXO7EaSMk/sxLECCjxxpuS/TlBeoOU9RFEVRFMUDfFqJUhSl4LBESJDDrJOS2b9QofM5ra4opsWCxVVwuMXnoib+cCQJgNQmXutWrrCdOkXcALFQ3DEga2CAkQPK110kcqLEMmc6irvgl/elwkflG3sBEP/4X5faLF9QJUpRFEVRFMUDVIlSFCVXpO+R0P6l10sG+lJcG4kXlf8OQQckaGdZqp+rMoTN6WBue8A3km0qUOwfOY/XL3mICxdkGJM4/gRQ8P5fOohSFEVR/hMYUZevVayVzdJL5/9SzIVjxT8AlLvH3eYt53k15ymKoiiKoniAn8NxreWiVRRFURRFyX9UiVIURVEURfEAHUQpiqIoiqJ4gA6iFEVRFEVRPEAHUYqiKIqiKB6ggyhFURRFURQP0EGUoiiKoiiKB+ggSlEURVEUxQN0EKUoiqIoiuIBBVr2pZmlvc9m9vzBPtsvN+td68d4rR8f6DGaHT1G4Vo/PtBjNDt6jKpEKYqiKIqieIQOohRFURRFUTxAB1GKoihKjny3bzXf7VvN1uENvN0VRTEVOohSFEVRFEXxgAJ1LFcURVF8h5S29Z3fVnu1H4piVlSJUhRFURRF8QBVohRFUZRMGArU76PHe7knSn6T3rQ2ABM+/IB73xoEQOT4Jd7skk/h04Oo2qvsALwetZp0bNmuM+lkRb7scxsAwckHAEjfs7dgOughZ9rXp9C+VAC2dgjKsrzQHisA4TvkmM+WshK18iwAfn+uKaBe5h5LWJh8sUl//UKC2fZEZQAaNf0HgA2jq1HsC2ff7XJe7efPF2xH84DDPRoCcP7W06xp+DEA1y95CICF9cbxwt6WAOx6KxGA4K+XeaGXec++QY0AmN57GO0+GwBAxWdy9yC2Fi8GQFq1GAACk/eTvv9APvRSyS37GmefGiduwNIC7olypfj5y2vdEhHuatvZU54358qmZ1n/m9s/AKCCfyilvt8HQNa1lEuh5jxFURRFURQP8Ekl6txd9QC4LnQ2AOnYSHNkr0Q9GrGdh6aOBqD+X10BqNDP3GpU4dl/ub7HL87F+oB/dBkAji6IAyCi5db86FrOWKwcfUTOTWpRmcmerZrKsBtmAvDmZlFhQgLS2FBtdOZt3/mVlwdeD8Cni0XVoJDMh6K/8qfwwnUA2FNS8vUQPOXkAxL6/deLowCwY8fuXLaq4RTntyAml18EwPoRPwDQN+BxAEK/dJ9zX2LLSDH7zL7jfQCqBgRyd3NRoFY/c/ntLYUKYZsVCsDCxMkA1Brch1IfqBLlLVLa1ueGBhsytd3YuzsAofjmdfpfYtsbdQHY1CnjM/bHHLZwWzt2dYgGIHrwHmmwZ/9eVdyoEqUoiqIoiuIBPqdEnburHm3elFH1HYX2O1utudr2r/oy020T1wuriZWoS2GtLCrTjo5RAAQfkvaoMX9iK1McgCfivgJgMrEF3r89z9Rnbe9Rl1x+Z83Pctz+jShRm15tc5FfVzNov605ACkD5LgcK9dfRU/zFmtcLM2f+v2KtkkKlPlLoxdlZr/u12LYjh7L877lN8Nunw5AjUB5lKxPu8Df/WsCYGHVZbf3s1q5JWpz/nXQJNhvlN/kfIlAAE7EWfG/Qc736a1FAKg00Bz+RhmdyTvvagxA6BxVoMyMf4XybHpd3gFv1//U1X7KLn6lf18Qv9QSVvGdrRoQyFH7OQC6bb8bgC0LK/FVz8EAPP7xPQC+4ZtYrzoA2/rLOOCTBvKerxvkx5gT8r74eJhYQYpPznuHed8ZRDl/qF6DZ2cYPF0Z18/uD0Dlvzdcwg3d3GztUgKAjZ1koJLqEHNX9bjHiZ9x1mv9MljfZwy2fCozObvSdwDE9RCzQsJj+fN/PCGlciSfbxWTVJpdbuR526tnWc+xKoI1PUdmanstajkAdyY+ht9i3xtEXUyHj56g/K9/5n6DkGCaFNoEQKrTJB+xy3fdWq3Fi3HqpngA9t2VBsDr9edxS8gfAERZQ7Nsc7yWvMweGHhDAfUye0oucTsiG4Ongw1Peas7+c72dxpm+nt+x6EAxPoHM/10aQDenNvOtTz2K3ElMGPwztau0STfIuY7Y+AUP3cAUUvErSLiExmgH+ojrhIrnh1F83clEi9qlNyvZTnIPaeeAqDUSd/IC7Z9cEN+um8IAKWtIQAuRwo7FroVEbeWu16Wddo5BlHsw7wdSKk5T1EURVEUxQN8RolaMEfCxSWVQWbznT9WyD4iN9OyDfeKCtBgaz+iRl/BbNkk3Nb070x/B/nJ6Qvf7rtj4YEHxBH9bHoQE8td3os+KU7MsGZSEoPmL6fsfPm+ukhRAMqeyGpuTLu1NvQsyJ7lL7abalElUM7ZF2clsKHipF1XFB69tV8l6gbJDdrl31sBCJlr/rQPqS3Fedfe7wgAFj+RYJ+I/Z5Wodk58YoCZZilV22NocRvAQBE7BDlIDfmz/zAqIf3Xcw4V9uOwUnAteFI7l+qJDu6VgIg4bZtAAyu8CWx/isBMoSABLr+7hgmz5mOnUa49jOzjahT0xPLFkS3r4iwXe7vtb6XYJWE3u5zd66NPGcnDZQAkK9TilFy8UkAMhoPjPeiHfPhX64sG58Sx/fNd48BwMLfjDlRFYBhS5oBUH6evA8LbTvBS9/MAKBukNx/R2vYKZbH/fLdt6+iKIqiKIoX8RklKmGRpCdoWHEH48ovzLzQjywpDm5Z0wkAq8XOD9WnZ1r2+7PDuOm8JATMD0ez/CClbX1eKTXM+ZfYftddEJ+L4uvdSSlf+fgBAMpR8ErbZ6eL0r7wUQD228R/4ObPBhG6V5SGItuyahShP64FwH7+LE3u6CbbNhKlsfBuWWf5C+5Q3XdivwTgqZpdcawyj3O5ge3EyUsue2XCZAL85NjSnNO/m9e1B6DQYt/wQQB3Oo3nP/yQhIBCANwx434AYvfk7n7yr1AegI6tfnO1LV9YDYDyXrh2c8IvQBSKPQPrAJDYKplZsaJQFPaT8PB/0+V6X3OhFNNPS+DHa1/LuY2ffhLLSfFZTN8loeMJ9sMF1PucSWlbn20dxmVqu7F390s6kqe0rZ8lEWeZ3xymcjw/+aAoa0erSz8n3jOehsHfXLRW4BXvt27wvwBMub2t7GHhcs87mcdErnL7rn3TVCwuj903gCILNwLQ+BW5L786JQEOK5qXxXHAfM/P7PAvJ8rf9V/9y9yoeQCMPiHK4jd9biFwpfg9JZxaIRs4/ac39SxKzSDR1Bqv6wBA4gsb89yK4TODqMpvyUPIMjl7obHbrhYArJsrMnT5GaJvnpiUNeM3gJ8Z9cocKN5/J0UtIZnaurwnA8GoRX/iFxwMQIUd8lLzhrlrWosmTC4n5izLORkwVVyW80s142kI/kbMOLHO550lVCTYuHqPsfW2iYBElQAk9w8i/qG86nneYwkLI72GRFNu7SS3WUX/P0hzZHZ+PLGoFABhYYexnz7thZ7mHovzGjs0vjAAjYNhW9oZAGLmX1l2+QO3iSz/UuRcV1uFeScA75kS/AIC2TJFTAP2M2JqS3rvCPjLwHdNXwno2JmeQrM1XQDwnyYRUWHbnYEdy9a59lcJufbtmNM8AhD71EbX95wi8QyT38UDLgA6AM55TvMyNfK8jzlxorM4hx+pJbOS/s2+pW2YOIiXsGb/7L8c7x+rQv9iG7K0b0mLBMw1eDKwnEtjU5pUuUgMkOMe9877jBzQFIBXS4gzfNLvDwMQe2BtgffRU5Lfld99btQ8ai9/EIDSbeS6tfJ3lnfd3pslEnHL3aOwO41tJ53P2cKntud5/9ScpyiKoiiK4gE+o0S1cGbx7hKRNafM2BPxnHhEZoRlNospwDAc/Vx9uct0kpFiU3zDjGdQJTxrWoeQI+75ravOnBfrzaVv34ll+84825+RnbzSxw64LfOy9tf9zbpIOee2I0fz7H/mFZvfqMrGe0Ze1Jp1Zrzycalb1Xj/4xSZau5r8nAnMQX8VVNkB5sDHho4EIBCv+fOnGMpJErpTd3d67fbKiqy327v5qSxFAoh+ebJmdqqFX+I2J5ST6zq5N4AVBq7g6L7tzjX2IIvMzXGbU7Nzpk8JwWq0sweWZYZ6xdUjb1pr4vqFOOf0Tx3aQVqwglRhxcdTeDEa+WzXed4QhD9n8+qRD35hUjfsZjvPrVtSOaxZ8QyMe4dd/WAcWUlf93tm+4CIK6f3GNmCsy5HI0rirnOjt2lQOXE2fgLzvUdLsU/ZrpYpvIjeYoqUYqiKIqiKB7gM0rU5HGtAOj2TNaacN9WLQJkXyuuZaceLJiWdRZVe5WMUFfWNPc40nDATQgxRzZjs5AUso9/QswXamzUdfyy9QdcPEe5tU8f2r4uNfN6F82sqF7Xey175ogt34y+UX7+/gS1O5ipbfaZ4kQsvrIZ3rYXrwPgm1KiZh21n+PsS+KobjnqnRD/nDh/JpBDbRMAiHlJFAjfTQXqxlCMYLVLUYqbszTL8osVqM67GrsScMYh61eiR/a+UgXIilTxW5t5rD6/TZX0E45bjgOQtqIoxTaK9hK+TlJS2JK3EUD2zv3+5RtmaZt3NpL48aJImvX8h30m56NPl44ALKr2hWtZ3WJyn66MkfqkHDxUsJ27CiaUE7W08i/dqJRDGhCjjmdyCyP9gR8rU+UZnJ+1ck0/iDraVS7ov54Rs0fGKDzRrVcmAAAgAElEQVQjAq9oDpL6jrb+2RYnnv2dZAeuaEJpNiPbushAoVPYAax+ckFsvCBmruAjaV7rl7cZs60JRXebz5SSGi4P85/PJrHpgjzEP3y4NQChf61gepTkCer9UuZB1Kiyv1Dltb5AwZlCroS9sxNYXf0TAJcD69Q2t2Lbf2XnwFLxTKa/Pz1VFcuv5hg82c+dp+FqieL57jrJS7f1tokuU/JHT8hgb8T4u4n+WMwKtuPHC76jV0FKW3nRZBz0lPktq7/DxQWIc8pgXuY3hziXe4FOLzwJQNgeuSati/6mpBHdOSLr+rkxY/m3dw8wDtpkv69MfYByO8wVNXoxlusSARieMNXZ4n69vx4l0b8JfWsDENepQLt2Vdidmaw+bjiZd3+V8i3bF1R0La/YUpzFN8eNca5vuLlY6PKxPFPzM+LX3DKMoiiKoiiKSTG1EnWwbyNGDBiTpb3e0kcBiO0vs8Cc5NWNd4/K1rG84jPmVqAMurb7zvXd5pAR9vuHJGw14MeVXulTQWE4IacWsrpUOIOTqyIpanGGq9rN4yZZZJpcV99NCwekFpkf7lpbkeNleZ3wfgD83d/tfO4ofqGAepl7dr4hSvA/9UZhpP6/d7w4k5fdeGWzu39facSaGz5w/iWK3cgfbneZhbyNIzWVoq1EWWsyUBSO3we8h9V53A+Hiznn4UGj+Ki7qFKfdROneMvv5lDTLsfFOZ4672qcJaVBStv6TI0Zn6kttzX0slO18hPjfssLkj+UPGDfJI3EeDU2+1jqy1V4w9wqlLVoUQ6+Ke8Hoxj4Mwdrk2qX78NLyzn+6kZ5n3Z9cICrnp7ZefmQBLT0KP4nc+IWAGB/XK4zC+4iw0m/Si7JjU0kOGR+SgQVJ+efQ7mBKlGKoiiKoigeYGolqsyPh1nRXWyf9YPcPiQvVZdCZR+VvkMacnAaa/BWP35/dliW9v3OpJy5CZn0BoaD8j1hRt9D2enMirx8iiS0K5GNP5c1QTK52pK35X8n8xBr0aLs7yjnxM8hs4wufWTW0avI79gumuBu6DKa66uIYT/llCSBTHp2L+n7vRsmn1tKLT0HZLTfw8amMvtv8oDY8SOme3+m2Px2yQJswY/G6+4BoOw7l579W2pUYVerIgCcjxd/klW3SpLKUL+V/L+9Mw+Iqvz6+IcZEMQVEE1EBVkUtbLc93Y1szJzKcs00zTNXNJ227XcLdfKFtNKrbS0TN8s0zQTS0tRIXfcd1xQBGbeP87cYRccgbnj73z+cebOnfEZ7p17z/M953wfi0OBmnAqCoCabyaYst268nhRHrqMb4o1QAxk41+tCcD0e2Y5Van6s6W2aEg/sT8osWxDcQ/1isheAL57TEyO9fEyq1VGLRTkrURlNuw0k3N5fhhmvkkdpNnh59vFLiHQYiFm4UAAan0u9VFmPEczc/zeWqy/WZo1ZiRVByCuTQVIEw3mjZXi4j2ygpjBfvLWBIZuFuXG9o8574EGRvNX34b92TE4a8hS5o+SVJ69RZ44Tm3jmvrKlnsJ2Z/TqqKwUSVKURRFURTFBUytROHlhY+XzAG8HTPYDZesjH+nGwCBsfnnw+0WnOuVGUw/HVXIAy18zl8nY67m7e/c9sr+DgAET89bgdrbqRIAoaM9Q4k6/qTU3LTr/ztvVZR8vVH7lR//NP48y/N3b47htxtK5rG3ufBOEiXqH0cZ1I2ZvAKbDBU1Y9vc7O9yL4d2BgNQq4y0iR96pC42h69hg66yjMRbIR9Q0eqf7Z05zQ+nrb0NgOgT7l9Cw1pbLAzOxARQ6pucSorRgRc5WJTByRPbsuZ7qcczZvb7HpHrVOSyHG83DWJbkHWNxvyUo9wMOI0Ov9VTM+qmnDYJJqlvKwg7Rkq7/5ZHjTY+OU9v29yVqIHyfc2uQFmjJFPz2evjAVHkZ8S3BCDkWIYKs/9CQJb3Rfv4ke4vF52sVXLmxR67mYjuObfvd2SV4ht+DMATiXJtCelY9CoUmDyISnipFPMdDuVpjiCqx5q+RF6B2/ifz0/OYXGwZMBtVF75d+ENtAi4GJTz1P7vlNzEAsnZVm0rJzeuEuazGMqVo081A+D/XhgL4FgX8OqE0eeCtvEbN1/t0IoF27/bAXhynBSYr3txsvO1uNOVAbCQWPwDuwyrOowHIMWRRQ/zXpnLXv4sOi/pvGdXSt/79vYSHHtjdVoI1Hxagq7iLUPOnZ6LJPJ57vfORH+Tz85Acq1K3FvW2FEuoQG/+RXR6IqfyCHrnJYFRqAU0UqCpOZNtuYoOo+Y18+UthyXI7ljY/7rMR2AVLtcdyacFIuA8n0umdYLKjvbhsm6ctE+fk7rkYrv5z+R/CG5NN7H5WZh9kAxP+6pLgspG1YI8RNl/csyxRTQazpPURRFURTFBUytRFX50oclDWRW3rG0FPj1u2kVS+6RFn+/JevdNrai5plHF2V5fjQ9mbITyuS5v9clmTt5nzfD3D5/hgyaDxgKVOHQ6O9uVCCh0D6vODhXLefx2r2pCgARJlCiFm+Q9uKxHdZSOUeaDj4/K6ujf5Yoadmkb0Oc6eZoJFWXut+RkveycnqzzJwDUsxjlNqldBIAK+rGkRgZnuU1r/MXwFfSHomdxPh29sCJXF/CB4Ab/5TmhqpzxOKgYIlo95CbKWaGc3l2sqb9cnMkbzngSSCr07nZsQYFAvDNexNItYt6+O4JUS7+aC/r6qUl7nfP4K4Aa1mxT7njpjjntm7TxHok5Jf87RiGfteDiP8857jlxX/vN2aJowwkeqmcj9Hzivd7qRKlKIqiKIriAqZWovyWrGfjm9KuaShR/cvHUXX8SQA+PSLFGfbYzc737BojM+IH7vQMM82CcufUEVS5zAzDaFMN/CfPXUzDweHNaFtqrONZwZSouFSpwO75rqxUXiYxnUPNpE6u8lpROirtTnJ7fv9YPzn/zrVOJvyhvA+GpV5tAN7u+IU8x8KhdCk2jxhmnhlidD9Rex8Y05lztSsCcKi5/N0jZx2G09L6XuK4mNoFs9f5XksZUU4tmeZqUR8cAsy1/ljUyp4AbG/9MfyW9bXYFDs1faTWpKzFqHvyoW+itP5Xe0W+SfrFi8Ux1KvCf+GfMDXrtvzWvDMsDtask/M1ZJXdWYye3RrBzBjKTdJcqdcrZynhrIH6+kMpRK6UaG5Dzcx4VRBFbUbotwB8dS6Y0OlyH8yshia+IrWn80OlnrHrLmlOinxpoynqEV2mkVg2xD8wzWlpUO0792hCpg6ido1pyodBcrP19Srt3G4EVB0XSTW+N1bSnLfPrN0+3lid7QcNJkkRb8hKc/9YLrVpQMuSRseIXLgrbUhx34AKCe8aYQC81mcOQVeQxvswqSqLut8CQPDGjOA4fEnW/dwdQAGcqiejWNjkA/o+NhiAoG/Fx8QSWJ7ztSX99fjEhQDcV0o63f69ZGfo00MB8MN8aeq0XXvw27UHyPi75/f33t9PLnS+XhKZ9NvfkvQDh4pohK4T0UsmIDcMG8iSJ8cAGV2xDX29iEuVi/Om8xJEvjO3C9XGyGoB9pT47B9naowUXObOutzIvlaeJ3XdZccaFMixzySN/Pv1GS2vX35wJwCV3jf3/SA3tr4QnOX5RZsPlnISKO5+Vn53HTr8wTcVxWewpJd0Hm76U1KWESmeeTzPPCTp51XjZDZgwYtGo+W+XnGxe46jpvMURVEURVFcwNRKVI0Rf3C7TdYuqnCjqE//d30u5jle5LAxKNBrJiVpwFmifUSBMpyd/XYeM1UKxBVONhEV5v5Spwu0/840SW99/loHSm/0rJlTTAkLq0eJmjhkoPi23FDqb3qX2wdkdSoH6LJoEJFLPOs75sf12VYD+OtIKMEmVG7sKaLyho5aS79fngIguXKGUlomQc7X9DgZe1XWemwqxEjFtVlYL589C7ZWnpmxNxUfqBYz1zEsSGws9qfJse7T+xkq/ex5ChSAd+Xr2NzOWHNTmh56lD3A3Wt3AGTzaZPXo77pD0D08+JB56nnb+1nRNU3rp+NRj/jdCx3111elShFURRFURQXMLUSBVDjeamB8Q6Vtu/Gk3vzZ+NZV/QZfffKSutVVoq5mNmj8JdqLXXWeH06tw0AoXs8c9bkCofSZY3Au+eLChkx33OaBMok5PxJTQxZnelZ1nlLg8mSz4+evtnU7fGuEOZ/wt1DuHLWiQlo5rm8Z+nYijVY6oUOvyDF/sOCtvDmMTHhXfWqNH6U/Nl8dYcFJe3wEVq+I/WTG1+U9n4rUNm7dJb9bt7QlYpviBVH1N8OBcrmuWfzrjFNWVZNDFJ77ZNmgIpT1rr992n6IMogzbHIcNgzcMcHjwDw8w1zcux34wIp5g3/7pJzW4lDIk3b4zfn2N+stNz0MACho6+d4KncAnGJr9m+N441hvm1lcjSVaz+3F27tWy0yYsRZzwneDII/UycyKc+XpMBAVlTVy8ebsy3sQ1kv2XS7RC6VP4mNg/o7rpSfp7UHIDXR4mH0pn4QIIv9wZFuUqsQYEcniWda4vqyWS7z767Od5TmgJKxntu8OTEbqfiFLkvtJmSd1o2mHjTCwYF4URvCXxXdBvLuouSZk98QcpcrLh/5RFN5ymKoiiKoriAxyhRBmn7D1DubnnciZxuu7m14rpb7rtSPoiuQQDmcXQuLOwOr6eI7hud2/rQItMeScU8osIn/YR4mC2rW5ZlNMz2qo3obPYF11oKLzMBn4mSeM9n9QGIwPOURcUzMHygjn1WgUm15wHQK0HUfO879nEtFMr/r5J0uzQYVbaWZODu9gBYf3W/AmWgSpSiKIqiKIoLeJwSpSiKoigAFj+xgkmaL2aaX8V8zJM9BwHgbSK1QnEdo65r6ukI0rtb3TqW3NAgSlEURfFILi2RgvEWQbsAGNSsC9YDGjxdS0Q8LIthL6U8cMC9g8kFTecpiqIoiqK4gJfdfi00QSqKoiiKohQvqkQpiqIoiqK4gAZRiqIoiqIoLqBBlKIoiqIoigtoEKUoiqIoiuICGkQpiqIoiqK4gAZRiqIoiqIoLqBBlKIoiqIoigtoEKUoiqIoiuICxbrsy52Wzh7r7Pl/tgVeBdnvWv+O1/r3A/2OZke/o3Ctfz/Q72h29DuqEqUoiqIoiuISugCx4ha8GtQFYPi8LwG4vWQ635/3B2D6g/cBYPtnm3sGpyhKrnjXCKPDklgAqpY4AcDLEx+n4tS17hyWorgNVaIURVEURVFcQJUoDyG5Y2PCR4gys2NSbQDKzFvnziFdFS0+3gDALX6pAKTbob3/OQBSF/wAwOszHgGg8gSd5SqKO7D4izocPy0GgCENf6ZPucQs+4zZm1bs41IUs6BKlKIoiqIoiguoEmVyznVuDMCX48dT2VoSgOTxywDokPIMACUXrXfP4K6CXwc3B+DLJrcBELIqmR295HQc13I+ALHDJgNQK3oA0f087zsqiqezb3A9AP67c4pz2ynbBQAG7bsHgNJbDqNalFIcWIODAdj2ZjgADzaKZVQlyWr4eFkBWHPRBkCfWQOpNu4vAOwpKUU2pmsmiLpwXyMAzlaVr/R4f0kJPR2wl+XJPgA8N6kPAJXnbiP91Ck3jLLgWGOiABj6thReGwEUQKpdTpKSBy8U/8AKCe9f5OQO/SVjW/Tv8u8HjaSwPH3O9wDE3TOFVn0kYAz68I/iG6Si/A9ib16PYyMuAhDXcBog6XaAxcllefGzgQBUfctIs5v7Wvq/SnLHxhxsJd35zZtsBWB29VUARMzrR8gqOaj+C/90zwCvEGtwMN1WbwSga5kfndsnnaoFQIpN7vMB3ucB2PjUZK4vPQiA8BeK7r6h6TxFURRFURQXuCaUqLNdmzBq1AcANHcUKhuk2uHWkukArH/hfQBimvUm6gmR92zJycU40oJx5uEmfDRqIgDRPiVyvH7W5pgWrt9cnMMqPhzfa9qQLgB0+mAmJxpJwiDoQ/cMySiwBTj54I0AHG0t59rudh9d9r03rn8IAPvqAEKmFL287GmM3PU3AI18M/z4Gox9GoDrJmlTQXHhHV4dgNFzZ1DHcd1JtcsxiflC1KfoD49SNUGPiZlJ7iglIKunzsxzn51dZ0BXedxjRCsAjjQ9U+Rjuxq2vRnuVKA2poj+03/c01z3sVw/bBdFPbUGhQCw7+cg0v2K3uNTlShFURRFURQXuCaUqEXjxhNg8Svw/ttaz6LeYJnpho4yz6zqzMNNAJj+9uRcFaj/NXySMlTF0a2+BuATqrtlLEt3yHmSbrcBUrxlQ2Y5u1MzatN8HAsEVLZmKFd/N5wrDxrCV32lMHJOpzvlM7ZsL8phFzle3nIJOda7IQCBcRew/L6pwO8/OrAZUT5rALAh5/yGFCsB8amXe5vH4V0jDICUaoEA7L3blxKn5GQJHe3ea9DB4c0AGNZbfmN1fEow92xFAN7+ujMAES9LTUm6G8ZXFFj8/Tl79/UAHGgjNaY9GslxeD04zrlf1MqeANR4uODntLsoiAKVG0adFAehTUi9wh7WVWNvKsp/Qofp2BzbRiW2B6DitLXObQbpJ04CsHhnXWrOOC7binB8Hh1EeVcNBa5cTht7ojZhXx4AMFVXSVDfvQDUKZFxWHrvuxWACaFLKecIFO9YJQFgJBuLeYTuo6qPuCNb6rYBij/4+PeSSMUn0/3pP7sfAN6OTHDI2IyboHcVkZJ3PRHm3Hax6iUANredQrfSxwCYPk62lWpbpMMucvY/Kw0dfz0tnZSP7m7LufskUDAuZrnhXb0qAEMGzqecJeuE4aUdD+C7NLYohlvkeFcJ4eD9YQCUufcQAM+Er6BWCQlCavn4OveN+XxAsY8vO4lf16VSObkWBntnpHMmTJVUetj75plkuoq9eT0ulZWi4wOt5dpav2U834dNy7KfBQlqjRQmwNbWswC4h/rFMdSrwigiz0zEvH5ZnhvF5FcaaLmTve39c2w7/Il05wVwOM/3VZjjT1qgdOw9Fi/eZqM+61rowomm8xRFURRFUVzAo5WoHeNkxlvuClJ5AHsvBpK2e29RDOmqiPtPlDWiIPqnJwEIXC8zKJ9Xljn3sxy8su97LdDEMYE/cJcc88pbivf/Hx7WxPm4GnnPZNIOHJR9Xj+IxU+O08VbJG3w562lnA7tp5PFsqJUkYz26rGWLQtA+pm8i00tpUpRrc2eLNumVP+enhV7ypPLKFFn6oti17XMIeJTRWz/4Fhr+T+nVwL25PFO92OtU5MLk0SZTLogxzgpSWbLy1u9T5h3zpnzUwfED23533IuhH+dTvgK9604YBSRj6i7nNYldwEQaJVZe51ZQ6k+1TPa3nNj36uSnryzg6iZzwRPoZq3/N4MtclIxQPMSqoGwMJDNwGwpNZ3ztcabegOQEU8L+0eMa8fkUPyOMem5rE/5l0Fw8fLypqLcvwCN8t16XJl46WWb6FNrFyPu5Q+CkDVJ6bx9qjCTVmqEqUoiqIoiuICHqlEHevfFIBvG09wbMlZhP3NuQoAdCp9vLiGddXUGiqznXunPkqtBDFH8/KXGdS250pQ3zfPt16T7O6fkeM/mi4FSKUPZC8jNCcWf3+2vydrHO5ol1F/MO5kTQCqPy0GhWaqycvM4YfrABA8I6dJnTVI1MCkueX5JXpeltdunTac0G15K3VevnISBz2zx7ntqWfFSPV0pCghVRaasw7Hq778TUbM/5KWflmPnKFsLL9QgYe2dADgWGIAAMHrrATMFsf9aJt7nfeNOtK634oS/2iZw4AoZ3VmSY1W2Bux2G2eW0L+4+NjAAj1NgyKM4yKe+27BYA/V9QhYraoEySdBeBUO6nT4+2Mz6r0lmQCir5R/uox6p0M64KdXWfQcpVkNAxDzR0TDUU9o1DeqJvKU7VyM2FLxDwztVc6DXzl+r/rWQldIt+IIn3bf1n2t5QpA8DOD8PpX/43AGfxuc3uU+jj88ggasKzclPKrYNt2CE5SRL6i4vph6OT+ClmYfEN7iqwnZUfM5u2Zmx0+FhdtPsA11bHUl5YKwQB0DY64+8wJPFewHMWXY4ffQM72k3Lsf3GkvsAmDNFutmuGy9dUFfS0VYc5BY8WUpJ8jFpbnkAfrl+Hsk2OSdv/lmaHWI+jL9sJ8y+EVKguzFSCtE77+jAgTvk4h+6zNw37h3dJMXZ0i+NhFRJ593/+TAAKq+VoMp3aSwByEU9wA1jzI/dPSR19X3F753bZiRJai9ipgRWaWlmDe0LRo/BckyO3ZSRaKkxOR7IaHYI448c5+mxFvK3seDF6BMyAbLskqJ7c5+ZgtN5PFOqzli0fjfSubez64ziHtbVs+5fABaeD+S+UiKKbG4l3ny/Li7NjAO3APDvv2EA3N1ErqWLQmbl+KhFp2+msKeums5TFEVRFEVxAY9Roi61lZn7wV4pNPI1JHFH++KeOziTKgWets4SZdqPiev16QVN2f2izBrDvWWfSP+jJEZLcVl6ws5iGb9ScBImy4xwcaaZRPyXoixWvExRt5mwl01lncOU/M/kSAC6lP2XOx2ZhU2N5gDwwyelARi28DEiR4plheG8aya8fH15ctM/ALTzl1Rksi2V+t8PASB6gMyCLzdjT72jPgt6j3c8k0vPzPCvueeL4QD4LzTpuoheklZOL5cxg+21tQcAYa+YdMx5UK7FEQCsXhnz58/3iEpR7sCO/D/AYsUaUA6AxMflN3k+LJ1ycXItrjjN/b9PQ5GpnikBcbnz0hop7fIvN18CSGp2VmwLAKJPbCiSMRYlRnpuZ9cZGR5QU1fl2K/HXnEqN2saLzuftb2Vl/tUBsAnWgrL5978MfMi5bhZIuWctjmSd79eKM2wfx4EYFlDyV4tXdqQMAr3N6tKlKIoiqIoigt4jBL1wPjlAPQrvwtDgar7ey8AwrtvxZ6W+0riFWb+wdynxBDw5QqSWx0ckMCnD4ppY+goz1CiLF42fLzke5PTU83jsVYI4uj90QDMazbZsVVOz58u+FP5JzEu9ITaBIDoXn/xZsPHALDHiir6XYehHG4ix3Dkg/MBnOab7btPpVPjdgCkdpWZftrhI8U65tywBkhlz7mvytPOX5zal5yXmrW3J3YnanrBZ3VtJq4i0ifrJafnf92o+IX4VZixZcAaEED0z1KruKyyLNzYJ7ElF5ZJLVtQqLjVp+0/4J4BXgHela+jc1VRO8V5Xzj/q3yXcuRUogzz2OO3Sd3U6ZqwtZdRdPOzc7+7at4PgNeHUqdqT71UuIMvQhJelxq/nmWlHf6vSzZqTpHj6gkF5dlxKktdL7+f2dfKy86ud8sQ3jnr9eaZDk9zrrJcU32S5WgFrZbfov1iCqFHxH2+1wpZv3Rk5/nMfrlqoY7L9EHUrnekE69v+fccWyxEf9cfgJqDZOFBez6FkGsHSCqQef8WyRiLA5vdQqpdinirLfOcC1RBOdSlJhtemuJ4JqflCZtcyIZ/NpCqO9yfJrhSjODJwG/xesIWy+MvPpJ0wRtPyE1qdY9xfBO5FIDIV0WOj5lSnvS4+GIabVas5SWQO/WFdOKtrPsVSTY5794b1g2A4MUFC6BOPCG/4QfLjgWkO2/qKelStHa9QLrRUGFCDj0cw+Lr5LxMd9xRZ4SuhhGrAejV7RYAjjwj/k9mXhQ8rWow1Utk/R2NO1mTanPEJyrzVfTIIPFa6t5X/Ola+suJu/1SZX5IlhR0e/9zzv0/ivoCgAFRjwOQvjWh8L9AETG58ZdZns8+3gL7xrg89r42MLsnFGR02SW8IV2xNftsyzGJ9lu8nuyuiblFA6+EL870rHCDKE3nKYqiKIqiuICplShrdAQDOsjs3OKI987ZUqg9RtbLKWgrriXNjImCgnGxg6Qia/r8juF34r3iLzeOqHA510UsKT4aPonsp+OgfeK3U/VNz1Oh8sNwzA97Sf699dxwfntqLAA77pU25ObVuxDwvBTv2v4tXsfk+NdiANh6w/vObb6OYuS+476RDePgbLrMAyfPuw+AJu02c0eAWFNYHQm6ZiUlDVjJmmF0NnWjuJNHHjf3+o+V1p/l+ukDAQibn5FeTXxXvovRINDhtCzUa+Z0877hcH+p01m2NfbfyW+BN8uTQ3Jd3f3VDWxqMQmAZIf6/WiHPgDYNm0l/RbZv/3cj5yf03WLlFYEeJACZWstDuW1feT8THOopBsm30Q5k6s0rmJ2T6jMHOol6u7WLlLeccP5QYS97FpReN8NjwIwo/7cwhlcJlSJUhRFURRFcQFTK1E15u5nQHkp/N54SWa1g58fSpk95o+iC4tT0XKIKlhL5rOnYLlRFASvfYdJP5V7sb27sZYvx8n2Ms4Zb8ks4/oSGU6yrx67EYAzvQy7wrzXYLtWCB29li5/iXP3hJlSuLvmxvk0GSX1R4H3OpoKislJ2lZWVF5LpnmWv5cUDRvrUGWmd9/3c2wzGiFS7XLuLr9QisHr5ftEPmpuBcrAHruZqrIEm1NlstxQi77RawCcppteyeazpcjOpQM5V2ps6ZfG/E9FDV3+m6jCsc0n0HJjTwD8PpeauNKbMq653kni3RHnKB6vk4vpsSdwcJCM33A2f/FIAwDKzfHs+8uyg+Yy7jUD9gSp4wtslIx3jTAA0nbtKZTPViVKURRFURTFBUypRBmdQaW8k5zbxh1oC7i27EdKYNZF5yadiqb6Asn/m7mGoaBYypRh+0TpdvryNjEVe3hVX6J6mqt2yjtMTDRPTCvBmhuNJVFEgTplu8DfKdJq/Nvr0hnkn5D3SvLWCkGkHz9RdIN1Az7LxdjvkRliYLnm6fGsu+krANr7SIebPaV4ztioj6QWpsXKgVf83pNtpatya2sxS/33kox56n33USPOPLNk4zoz4i/ptHt9QG9K/BSb5/5eDsVlV+cAniq/G4DWmx8BoNT+XUU51EIh6rOz0Dnn9vdDHDWHD8m/kd8PJvSY4HoAAAmsSURBVGaKXHvT4+R6a/GX9fXi37meSuvEY+WrU1Kv+WbFTRzfI4qVGZe6yQ1783p838DoBhYlavESUeKqF7IZY3FR6Y+y7h5CoXKmZtZrXaUNV3/tu2i3QvKFq/6czJgyiNozQFoav6uYkSI4MyTE8ejKbpypd9Tn+fdmZ9m2I7ki6f+Z/6IHcEOnrTm2GYtIRr8klg37n7yehHZZ0ynR1Q671ePEaE/dPqEWVn9JDS1rIWMM8/bPsf8LB+/iYCcJokofF2fszO0AxqK39iriaXPz7DhWvinBVqlv8g62PJEq78rN7KUHb2FyiFzQE4fJmnOho4qnyN5rrRyD8lf43yV1b8LgeisA2J0mKa7unz8LQPU4c92cWq8WX6Bph24FwPdIMkcdi5uXvE+KyAP8Mi64rStI0fTQgKlEficFurVmij2DJ7SueMXvpuuuuwCYV2N5nvuFLvdyWmtYy8qN+Y61+wFYEvA7dMq6f8yqXtR8Xq5TnvB3ALhU1odq3gUrkfAEdkxswrLq+a+L5wkF5Qb+idYsz480sBK2yLXPGtlZfPmCLCmkhVWSjYXkw6fpPEVRFEVRFBcwpRJVGNhaSvvqiBlzuL1kMgB/pEhkG/9aXXzJW7Y3A9aast7aE5W+zfHa1i6i6Lx3p7S/Dw7IWdQbvyOEaNznolzqR0mh7qgxM9PWnAqUwYzQ1eAQlG6PewCAvfsrOF9/rolYXfQpl+GSXLdOS/m/vimMEZuPFT/Uhz6i3lyoZO45vne4OFoPHjmPZiUTAejxpKQlqy81lwJlsOZkBAALI3+UDUsuv7+xFmL0vKeIeVtUqfQTntP0YEtO5vAkhynoe3nv98HEicSOltS7n0XSup1KZTSpbL4k27rFPgFAjTFp2ExsmJoZIyXrN/wgFsfSD/vS5P5QfaQ5z9OC0LxJzoxFZloOeBIAfzxHtfeWw+JsblnRYyzd/xwKiMlmfnjVr8NrCyQLVd9R0fPNuaqwrnBNt1WJUhRFURRFcYFrRony8pVQM2FsPQDeaSsFubeXTHYqUCOfFsM43x9NrkJFR9Bsgawn1twvNcfr3o61AwcHZBjbGcW7Tz8/CIBaP8a5tT5hbDUjeZ2hPqU5yviTbTm/k8XLi9JecgxX1HGob3Xy/vydaRcI+T2lUMbqCVSvfcjdQ8gVSylpm0+fJXVvHUsf5frZwwEIN6kCZZDeQ5oaooc8leO1Eidlfln+Pxtnq8njah//B0DksXUe25BSeokU9tetKw0DU3rM5JZs15hoHz+ifcTGwuowWDWWvGm6qSvBfc8DUP2ALHHjSevLXbr1BgCW1ZzpvD62/VzO1zAPLCg3islnV1/l3NZjb6sc2/wXeo4CZVB5pai8s/tXAaBH2QNMfk+yLp1uFzuY6Fficqig1pgoAHYMt3KTrxzlP1Lkt/7JYx2AwlWiTBlEhc2UosbXO9fj1WD50T/95QIAztt8mfiaLCboezrjUnZxoMjN8c6uL2HFBX/eGdRD9jd58GSQ8GQw3wUVfO2mZhsfouKTso5VmQNSOOju5E+buXJh+u2RsTT7QSTY4D8l+Av4NOfFylqnJjteFvfrba0+yfNzW29+EADfMQF4/2Ku7sOiZI8jtRnNXjePJCvbx0mku72meFvV/a0PkW/mbAwwI2l7Je0YOTjxsvsZPU+eGjhlxp4iE49qr0vHwLgJzRg4RFJ81VrvA+DHWt879z9nk+aAuwfK5Cxw6T+kpXju5GV355yrt0fMloDRE49v5kDJcCPf2TWjwNwIqMCzFhuGjFUapk/oCECP16YQU0KC+q2dJZga0rwlsdOkGaRqL1lAu1+ITOBbl0zm1wviD/XUjz0BiFpX+MGkpvMURVEURVFcwJRKlOH/czo1IxV0V8nzjkfn6Th2ar6fkeBw031nUD+PUaAMAjd7Qbes21480oBDF8XXZu1f4gkVukKE9CATzg7DXxS1qeeLLYgm/yLA9Lh4wkVg5G5uznO/UniGNUVmTj0mM6UKaw6TvmN3gd9Xol5GMW/wSvO4Qnt5y2Uj/v2biesgXjs/XxC9JvKdi9iSk902NuXKsJ09S7U3svpY5Pb7K+n4DXtS6i4/2m4ThcNnl7nU3YKQ3LGx41GG71pmBQpEhTrS1PMUqOwEfSj3krsS+9Fr0kIAupaR8oaJIavhLfF5MwrQbZk08EELHgcgZpqozQVbbffKUCVKURRFURTFBUypRBns7BXOPZODAZgWKYXieRmkpThWG6//qbRVR0ySomvf456lQoHUDN3zaf1sW+2ArMAela1N9VqaHV5LWKOlhf6ZF8XobXdKMHO/vS3f973b/VMA2vn/BY427MCv/gbMcax3jmoIwPZ73yPJ0STwfjepVbP/u8Vt41KU/OjRSFQ3C17Yxonpoj1tnzuHVGTsHhPjUZYG+VHip1i+clgXfXrT/QD4PHeY//aLAXOzKMlSxP4q67JGfrCfGgelbjbNkZkqCkwdRNm2bIfb5XH3R8T1uFyv/Syp9R0AMSvFp6TMmpJYHO0jYTNF+vPEIkHl2iI9QRbPntOgNgDhv6Sypc+Uy70lCy8erc+vkyQVGHDJPE7DpWtmpBmbLhgGQOQG84xPUbJjjQwHYGQFMZWzYedciNz+At02Ktcxuu0iWuUsJnd6QnlgR15+pB87BoDPcvmX5RCFuOk7tji7LIsidZcbms5TFEVRFEVxAVMrUZkpN8cx050D9yCprgg2unFEilIwDB+TXc1K0N7SNNd9UpvXZfd94mUSM1qKz9OPnyAgzbzeNW3iOhM1QhZNNkOaUVHywsuW8wyd8cpkAEaukMUADcsLT8JYC6/NkHrObddSCs8TUCVKURRFURTFBTxGiVIUT8d+meJG71/+IuoXeVxcuXxXqXjfdudjVaAUTyBt1x4AXj12IwCtSm9n6EeygkXogfwtWBQlLzSIUhRFUf4niK0nqybEUocqSKeeTgSUq0HTeYqiKIqiKC7gZbdrHK4oiqIoinKlqBKlKIqiKIriAhpEKYqiKIqiuIAGUYqiKIqiKC6gQZSiKIqiKIoLaBClKIqiKIriAhpEKYqiKIqiuIAGUYqiKIqiKC6gQZSiKIqiKIoLaBClKIqiKIriAhpEKYqiKIqiuIAGUYqiKIqiKC6gQZSiKIqiKIoLaBClKIqiKIriAhpEKYqiKIqiuIAGUYqiKIqiKC6gQZSiKIqiKIoLaBClKIqiKIriAhpEKYqiKIqiuIAGUYqiKIqiKC6gQZSiKIqiKIoLaBClKIqiKIriAhpEKYqiKIqiuIAGUYqiKIqiKC7w/1dPj6GheZpPAAAAAElFTkSuQmCC\n", "text/plain": [ "<Figure size 720x720 with 100 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import keras\n", "from keras.datasets import mnist\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n", "\n", "plt.figure(figsize=(10,10))\n", "for j in range(10):\n", " for i in range(10):\n", " digit = x_train[y_train==i][j]\n", " plt.subplot(10, 10, 10*j+i+1)\n", " plt.imshow(digit)\n", " plt.axis('off')\n", "\n", " \n", "# using the full data set will take a lot of time so we are only going to se a subset\n", "x_train = x_train[:5000,:,:]\n", "y_train = y_train[:5000]\n", " \n", "\n", "# data preparation (images should be 28 x 28 x 1 tensors)\n", "img_rows = 28\n", "img_cols = 28\n", "x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)\n", "x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)\n", "input_shape = (img_rows, img_cols, 1)\n", "\n", "# normalize images to have values between 0 and 1\n", "x_train = x_train.astype('float32')/255\n", "x_test = x_test.astype('float32')/255\n", "\n", "# convert the y vectors to binary vectors\n", "y_train = keras.utils.to_categorical(y_train, 10)\n", "y_test = keras.utils.to_categorical(y_test, 10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercise 2 (Create and train a fully connected neural network)\n", "\n", "Before we create our convolutional network we want create a standard fully connected network just like the ones we used before to classify the digits. Then we will be able to see the improvement when we use convolutions.\n", "\n", "**a)** Create and train the network" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WARNING:tensorflow:From c:\\users\\dany\\miniconda3\\lib\\site-packages\\tensorflow\\python\\framework\\op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "Colocations handled automatically by placer.\n", "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", "flatten_1 (Flatten) (None, 784) 0 \n", "_________________________________________________________________\n", "dense_1 (Dense) (None, 100) 78500 \n", "_________________________________________________________________\n", "dense_2 (Dense) (None, 100) 10100 \n", "_________________________________________________________________\n", "dense_3 (Dense) (None, 10) 1010 \n", "=================================================================\n", "Total params: 89,610\n", "Trainable params: 89,610\n", "Non-trainable params: 0\n", "_________________________________________________________________\n" ] } ], "source": [ "from keras.models import Sequential\n", "from keras.layers import Dense, Flatten\n", "from keras.optimizers import adam\n", "\n", "\n", "model = Sequential()\n", "model.add(Flatten(input_shape=input_shape))\n", "model.add(Dense(units=100, activation='relu'))\n", "model.add(Dense(units=100, activation='relu'))\n", "model.add(Dense(units=10, activation='softmax'))\n", "\n", "model.compile(optimizer=adam(0.001), loss=keras.losses.categorical_crossentropy, metrics=['accuracy'])\n", "model.summary()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WARNING:tensorflow:From c:\\users\\dany\\miniconda3\\lib\\site-packages\\tensorflow\\python\\ops\\math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "Use tf.cast instead.\n", "Train on 5000 samples, validate on 10000 samples\n", "Epoch 1/10\n", "5000/5000 [==============================] - 1s 148us/step - loss: 0.9077 - acc: 0.7618 - val_loss: 0.3834 - val_acc: 0.8898\n", "Epoch 2/10\n", "5000/5000 [==============================] - 0s 82us/step - loss: 0.3189 - acc: 0.9108 - val_loss: 0.3324 - val_acc: 0.9054\n", "Epoch 3/10\n", "5000/5000 [==============================] - 0s 85us/step - loss: 0.2253 - acc: 0.9374 - val_loss: 0.2892 - val_acc: 0.9132\n", "Epoch 4/10\n", "5000/5000 [==============================] - 0s 77us/step - loss: 0.1785 - acc: 0.9486 - val_loss: 0.2663 - val_acc: 0.9186\n", "Epoch 5/10\n", "5000/5000 [==============================] - 0s 78us/step - loss: 0.1402 - acc: 0.9616 - val_loss: 0.2461 - val_acc: 0.9244\n", "Epoch 6/10\n", "5000/5000 [==============================] - 0s 74us/step - loss: 0.1175 - acc: 0.9692 - val_loss: 0.2282 - val_acc: 0.9318\n", "Epoch 7/10\n", "5000/5000 [==============================] - 0s 74us/step - loss: 0.0905 - acc: 0.9772 - val_loss: 0.2382 - val_acc: 0.9270\n", "Epoch 8/10\n", "5000/5000 [==============================] - 0s 76us/step - loss: 0.0755 - acc: 0.9812 - val_loss: 0.2275 - val_acc: 0.9326\n", "Epoch 9/10\n", "5000/5000 [==============================] - 0s 76us/step - loss: 0.0622 - acc: 0.9858 - val_loss: 0.2405 - val_acc: 0.9279\n", "Epoch 10/10\n", "5000/5000 [==============================] - 0s 75us/step - loss: 0.0503 - acc: 0.9898 - val_loss: 0.2360 - val_acc: 0.9317\n" ] } ], "source": [ "h = model.fit(x_train, y_train, validation_data=(x_test, y_test), epochs=10, verbose=1, batch_size=64)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**b)** Plot the accuracy on the training and validation sets" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x2b217762d30>]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(h.history['acc'], label='training')\n", "plt.plot(h.history['val_acc'], label='validation')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**c)** Create and train a convolutional neural network" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WARNING:tensorflow:From c:\\users\\dany\\miniconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n", "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", "conv2d_1 (Conv2D) (None, 26, 26, 32) 320 \n", "_________________________________________________________________\n", "conv2d_2 (Conv2D) (None, 24, 24, 64) 18496 \n", "_________________________________________________________________\n", "max_pooling2d_1 (MaxPooling2 (None, 12, 12, 64) 0 \n", "_________________________________________________________________\n", "dropout_1 (Dropout) (None, 12, 12, 64) 0 \n", "_________________________________________________________________\n", "flatten_2 (Flatten) (None, 9216) 0 \n", "_________________________________________________________________\n", "dense_4 (Dense) (None, 128) 1179776 \n", "_________________________________________________________________\n", "dropout_2 (Dropout) (None, 128) 0 \n", "_________________________________________________________________\n", "dense_5 (Dense) (None, 10) 1290 \n", "=================================================================\n", "Total params: 1,199,882\n", "Trainable params: 1,199,882\n", "Non-trainable params: 0\n", "_________________________________________________________________\n" ] } ], "source": [ "from keras.layers import Conv2D, MaxPooling2D, Dropout\n", "\n", "model = Sequential()\n", "model = Sequential()\n", "model.add(Conv2D(input_shape=input_shape, filters=32, kernel_size=(3, 3), activation='relu')) \n", "model.add(Conv2D(64, (3, 3), activation='relu'))\n", "model.add(MaxPooling2D(pool_size=(2, 2)))\n", "model.add(Dropout(0.2))\n", "model.add(Flatten())\n", "model.add(Dense(128, activation='relu'))\n", "model.add(Dropout(0.5))\n", "model.add(Dense(10, activation='softmax'))\n", "\n", "model.compile(optimizer=adam(0.001), loss=keras.losses.categorical_crossentropy, metrics=['accuracy'])\n", "model.summary()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train on 5000 samples, validate on 10000 samples\n", "Epoch 1/10\n", "5000/5000 [==============================] - 33s 7ms/step - loss: 0.7657 - acc: 0.7624 - val_loss: 0.2617 - val_acc: 0.9207\n", "Epoch 2/10\n", "5000/5000 [==============================] - 32s 6ms/step - loss: 0.2693 - acc: 0.9190 - val_loss: 0.1453 - val_acc: 0.9547\n", "Epoch 3/10\n", "5000/5000 [==============================] - 34s 7ms/step - loss: 0.1703 - acc: 0.9512 - val_loss: 0.1198 - val_acc: 0.9621\n", "Epoch 4/10\n", "5000/5000 [==============================] - 36s 7ms/step - loss: 0.1388 - acc: 0.9586 - val_loss: 0.1267 - val_acc: 0.9617\n", "Epoch 5/10\n", "5000/5000 [==============================] - 36s 7ms/step - loss: 0.1102 - acc: 0.9670 - val_loss: 0.0888 - val_acc: 0.9717\n", "Epoch 6/10\n", "5000/5000 [==============================] - 35s 7ms/step - loss: 0.0881 - acc: 0.9728 - val_loss: 0.0909 - val_acc: 0.9719\n", "Epoch 7/10\n", "5000/5000 [==============================] - 33s 7ms/step - loss: 0.0679 - acc: 0.9778 - val_loss: 0.0877 - val_acc: 0.9730\n", "Epoch 8/10\n", "5000/5000 [==============================] - 31s 6ms/step - loss: 0.0556 - acc: 0.9814 - val_loss: 0.0838 - val_acc: 0.9735\n", "Epoch 9/10\n", "5000/5000 [==============================] - 32s 6ms/step - loss: 0.0500 - acc: 0.9836 - val_loss: 0.0887 - val_acc: 0.9737\n", "Epoch 10/10\n", "5000/5000 [==============================] - 34s 7ms/step - loss: 0.0437 - acc: 0.9848 - val_loss: 0.0859 - val_acc: 0.9739\n" ] } ], "source": [ "h2 = model.fit(x_train, y_train, validation_data=(x_test, y_test), epochs=10, verbose=1, batch_size=64)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**d)** Plot the accuracy on the training and validation sets and compare it to the results from the fully connected network." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(0.7, 1)" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x360 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(10, 5))\n", "plt.subplot(1, 2 , 1)\n", "plt.plot(h.history['acc'], label='training')\n", "plt.plot(h.history['val_acc'], label='validation')\n", "plt.ylim([0.7,1])\n", "plt.title('Fully connected')\n", "\n", "plt.subplot(1, 2 , 2)\n", "plt.title('CNN')\n", "plt.plot(h2.history['acc'], label='training')\n", "plt.plot(h2.history['val_acc'], label='validation')\n", "plt.ylim([0.7,1])" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.1" } }, "nbformat": 4, "nbformat_minor": 2 }