02c_ActiveLearning_Solution.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Kopie von Kopie von ActiveLearning.ipynb",
      "provenance": [],
      "machine_shape": "hm",
      "authorship_tag": "ABX9TyOwinZKJ0hetmuAv7YHIluh",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "TPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/nicolaiberk/Imbalanced/blob/master/02c_ActiveLearning_Solution.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Intro to Active Learning with `ModAL`"
      ],
      "metadata": {
        "id": "WZDdC4uVRU9l"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "One way to address imbalanced data problems is to actively sample the most informative cases. This method is called Active Learning - active because the classifier 'decides' which cases should be annotated next. This notebook mainly builds on a [paper](http://www-personal.umich.edu/~wmebane/active-learning-approaches-4-18-2018.pdf) on Active Learning published in Political Analysis, from which the graphic below is taken as well.\n"
      ],
      "metadata": {
        "id": "yVXvyuSHTfsf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![grafik.png](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAA3YAAAILCAYAAACkZg4mAAAgAElEQVR4XuydCbRVxZnvP2cUREFB8JKHCIbBIELLgjyBBA0ISCS5YoPhmghIC4+XBsMgHcMYtYmAwOIhEEFMhMBNkEhkRqAFuoVgM4gyydQRZBAZFPFGHB7/Teqy77nn3LN3nT3V3v9ay6XIrulXtfepf31ffXXJN+eTMJEACZAACZAACZAACZAACZAACRhL4BIKO2PHjg0nARIgARIgARIgARIgARIgAYsAhR0nAgmQAAmQAAmQAAmQAAmQAAkYToDCzvABZPNJgARIgARIgARIgARIgARIgMKOc4AESIAESIAESIAESIAESIAEDCdAYWf4ALL5JEACJEACJEACJEACJEACJEBhxzlAAiRAAiRAAiRAAiRAAiRAAoYToLAzfADZfBIgARIgARIgARIgARIgARKgsOMcIAESIAESIAESIAESIAESIAHDCVDYGT6AbD4JkAAJkAAJkAAJkAAJkAAJUNhxDpAACZAACZAACZAACZAACZCA4QQo7AwfQDafBEiABEiABEiABEiABEiABCjsOAdIgARIgARIgARIgARIgARIwHACFHaGDyCbTwIkQAIkQAIkQAIkQAIkQAIUdpwDJEACJEACJEACJEACJEACJGA4AQo7wweQzScBEiABEiABEiABEiABEiABCjvOARIgARIgARIgARIgARIgARIwnACFneEDyOaTAAmQAAmQAAmQAAmQAAmQAIUd5wAJkAAJkAAJkAAJkAAJkAAJGE6Aws7wAWTzSYAESIAESIAESIAESIAESIDCjnOABEiABEiABEiABEiABEiABAwnQGFn+ACy+SRAAiRAAiRAAiRAAiRAAiRAYcc5QAIkQAIkQAIkQAIkQAIkQAKGE6CwM3wA2XwSIAESIAESIAESIAESIAESoLDjHCABEiABEiABEiABEiABEiABwwlQ2Bk+gGw+CZAACZAACZAACZAACZAACVDYcQ6QAAmQAAmQAAmQAAmQAAmQgOEEKOwMH0A2nwRIgARIgARIgARIgARIgAQo7DgHSIAESIAESIAESIAESIAESMBwAhR2hg8gm08CJEACJEACJEACJEACJEACFHacAyRAAiRAAiRAAiRAAiRAAiRgOAEKO8MHkM0nARIgARIgARIgARIgARIgAQo7zgESIAESIAESIAESIAESIAESMJwAhZ3hA8jmkwAJkAAJkAAJkAAJkAAJkACFHecACZAACZAACZAACZAACZAACRhOgMLO8AFk80mABEiABEiABEiABEiABEiAwo5zwGgCX575TIr+5wP5+5GjVj+uqnaTVLi9ntF9YuNJgARIgARIgARIgARIwC0BCju3xPh8JAgUHTose0b+Ro6/8aZcVv4aKfetPKtdRR8cki8/+VSq3HePfOe3EyLRVjaCBEjAGwJFBz+U48tXyecffCj473I1bpZK371Lbmx7jzcVsBQSIAESIAESMJgAhZ3m4H3yyScyb9486dGjh2YJzKZLAKLuv39UIF8XFckdL/0/ua5p4xJF4e83tussX3/xhXxv10bdapiPBEoQwDtfsWJFUgmBADZr9o+fYok6iLirv3WzJeog7k6+9bacWr9R6o/7NQVeCGPDKkmABEiABKJDgMJOcywmTJggM2fOlK1bt2qWwGy6BDZ2+Gcra9PFfyyziP9seq9cdvXV0nzNQt2qmI8Eigk8/vjjMm3aNBIJgcC2Xv0tSzzEGwRdajqzfZds69VPaj3RR6p17hRCC1llXAisX79emjdvHpfusB8kQAIJI0BhpzHg2Llv2bKl4N9jxoyRzp07a5TCLDoE9j03Uf429WVpvnaxlMurXmYRsNytb9lB6vxqgNToUaBTHfOQgEVg+fLlAmE3dOhQWukDnhMHX5olHy1bLQ1fnCCXV7w2Y+0Qfmsb3i3f/c+lacVfwM1mdYYSePjhh+XBBx/k77qh48dmk0DSCVDYacwAWOsmTpxo5YRrFq12GhA1s6xv1dE6U1P3NyMclfDuv/SXz8+7a2Wz7jkqjA8llgAWe9jJ5/se7BSAWNvY/qHzom6iVGhQN2vlR+YtkMN/+os0LpyR9Vk+QAKpBPCO411v0KCBLFq0iIBIgARIwDgCFHYuhwxWuvvvv18OHjxYnJNWO5cQc3gc7pXVH3pAbh3cr7gUuGZ+deas9edy1W+SO22LOuz242xOy23/mUOtzJpkAtu3b7feeZX4vgc3G06tf9t6f50KNQjBt+5ux/c9uCGKVU2wysM6jwRhB4HHRAIkQAImEaCwczlayiXLnq1GjRqydu1alyXxcRA4dOiQDBgwQM6cOWOdX8rLuxDdMlP6j1sbn1/kvVQiYArcr7767IKwQ4RMu4g7vXGzbO7SQ76/bzOBk4AWgUGDBlmBklTi+66FUSsTNmYQAfO24YOL88MKj2i4KqV+D2Dhqz/uaUcWPq1GMVMsCaRu4OCc3Zw5c2LZVxM6dWbX+3J49p+Km1q920NSoe5tJjSdbSSBUAlQ2LnED2GHHwBY7l599VXp3r27VQKiYzJinkuY5x9v3bq1JeyqVasmWECvXr26zEIg4hAgwemZOVrs3I8Jc1wkAMs8rHV43+0JmxBt27YlKpcE1EbO0aNH5aabbpK5c+eWWcL7I5+zImDa33ecsz22cEVxPgRUsUfGRaAVWPV5BYLLwUn446kbOMCBDVts5DC5J7Br1y7r97x3797uM5/P8W6fX8hHi98oznt9s3+Sxn+cqVUWM5FAkghQ2GmONhZ88MWnpU4T4D+yLVy4UDp27Chvv/22I2EHt8urz0fFc3pHHc/Y5TY+Sc8NQYeNHKQhQ4ZInz59pGbNmtYmDt203M+O5557Tk6cOCGjR4+WZs2ayfz588u00uN6A5yZQ+AUpwmumLDipYue6bQMPpcsAuqIBf5ddP4anUsvvVSuvPJKa/MGrtdM7ghA1HXr1k3KlSsnR44ckUmTJkn79u1dFZJqsbu+eVOp0qGNqzL4MAkkkQCFneaoU9hpgkuTDbv4+fn5WRd5yHps4TJ57/8OvhD5LsFRMd26sHo3WsktCZY7LPIo6HKbA5i7SHjnN2zYUGZhbs/M4Uze+yN/I02XXHThyq21zJ00Ar/+9a+tzQbeUas/8hB2/fr1k6VLl0qLFi3k3nvvlZEjR+oXyJwkQAKOCVDYOUZV8kEKO01wabJhFx9p8OCL52jKKh1Wuy8++lju3rgy42PqEvMrq9zgS0RMLE5nz54t99xzj9x1113ewXBYklsXVofF8rEyCFDYeTc9MH8hkp28O3CtvPb2unJL/z5ZRSCerXJfa8eu2t71yN+S8L1BYI/PPvvMkQurv62Jd+kUdt6N79SpUwVRxBcsWCB162aPautdzeaWlKsLq7k9Z8u9IkBhp0mSwk4TnAfCDkXg2oO/nz+ngyAJVTveV6JUWPV2DPiVXHrelabp0nlZLXs6PYEbGe46gribMWOGowWqTj2Z8igX1pdeekleeeWVrGcTvaxbpyy099ixY0bvglPY6Yx8+jxdu3bNer5O5VRXHpR1+TieQfTMovNXm7hx23Tao7At5HADrly5srX55cSF1Wm/+FxpAhR23s2Khg0bWoGnKOqcMYWow73IsBjv2bNHy4XVWU18Ks4EKOw0R5fCThNcmmw4X4fkZPfenn3XkyPkw7nz5ZLLLpMrbqgsX58/G/H1F1/I13//Qm7u8mPHd9257QkWeTgzgHNC9gWX23Jyfd6NC2uudWXKryyXcE/EWcl0CZYGPIcfq927d0dehGbqK4WdN7MI73vfvn2zumHaazuzfZe1WVOhQb3zgVE6yfXNL1jJIejwd3C/vPz8ucdsl5jr9iAqFnK8RwUFBZF/h7DhhPOTrVq1cuyJoTs2XuejsPOOaJcuXaSwsNC7An0uacmSJcXzFb9pQbedLqw+D3BCiqew0xxoCjtNcD5kw5UGH69eI1feeINcWbVKKQueD1VaRcIKhUVAtgAQftXv1oXVj3Zgwfvtb39btmzZYlkw07nTQvxCBCshmu1clR/t9KJMCjsvKOZWBqLcfrRstZxav9EKjgJhd0HsPSDVOnfKrfAyctuDPLkVpV42Cta6yZMnu94E86oNdsvliBEj0rYDrOCCh8ixP/jBD+SNN97Ieo2NV+3zohwKOy8oXigDm7Vq49a7Uv0rCUIUv2mI5FmnTh3LahZGwvszduxY6y7FKFs727VrZ/2unzt3Tnbu3BkGKtaZhgCFnea0oLDTBBejbGEvsqIg7GCNwwIObdm7d6/135kSLHom7uCr/lDYRevlhdtlkJEvo2AhV+9bWCOB+mvXrm2dLe7Zs6ds3bq1VFPsHhhuzlKG1afUeinsojIS4bUDwmr69OmhidKwXViVSyhG4LrrrpN169aVGgxYN4cOHWoxgsDDeogBcsKbs/aaKew0x4HCThNcjLIpS1RYXdJ1YfW6vco9bNasWRl35rGLj7/Pdm+Z123zsjwKOy9pmlcW5jAWOLA+h5F0XFi9bqeyXDpxCYUIrFSpUmi8dPtOYadLLh75IGrwrQ/TWgahhIiiYSVYLnGmd8qUKZblsiwWSgRic9ftlRZh9S/u9VLYaY4whZ0muBhlC3v3PCooweGHP/xhxjN2aCeDp0RltNgOXQJRsJDrtt3rfE4scdjBN9HtmsLO69liVnkQKojkCVETVoLFbtu2bWFVX1wvrHJPPPFEmW6WuM4C7Q2TV+igItYACjvNAaGw0wTHbGUScHKGJWoIseDNdu0DgikcP37cuEAKdta02EVt5gXbHrybhw8fDu18W7C9zVwbLIc4/5PN+u4m8mlU+oZ2UNhFaTSCbwvcMHGONQrCKvjel6zRiUtonz59KOrCHqiU+insNAeEwk4TnItsWEAMGjRITpw4YZn4w3KBctHknB91coYl50o8LsCJsPO4ylCKo7DzHzvE0+bNm8u0/vrfimBrgAA6ev7qlvLly1uW7agntHH79u1lbtJgHJctW2bk9SYUdlGfgea2D6IR1u4oB0Sx03US1bRWrVqW26gpfTJ39jhvOYWdc1YlnqSw0wTnIhsWPAMHDrR2yOvXry87duxwkdvMR+1nWPLz8410ZTKTfPZWU9hlZ5TLE+rcFqKsrly5MrSIdLn0wW1eFUFSvfeIxpfp2hC3Zfv1PDbcjhw5Evl26vafwk6XnPN8EDjqTHbQVwo4b6W3T8L6hUAkeHdwXVLUz6PBJbVTp06MduntNAikNAo7TcwUdprgNLKpO5FM2M3W6F7aLOrerKgv8rzqrwnlUNj5O0oQDKtWrbIsQZj32Njo0aOHv5VGoHQIWiT0N8yrDCKAIhJNoLDzfxjq1asnCxYssC7jxhmtJJzPQj8RfAlWMKSkCFr/ZxNrSCVAYac5JyjsNMG5zOYk4qLLIgN7XJ2XQ4XZzqPYG+X0DEtgHWFFFgEKu2AmQtj3QwbTy5K1wDuhSZMmRp9BDYObH3VS2PlB9WKZsAT94Q9/sELjJy3whoogOW/ePLou+jvNEl06hZ3m8FPYaYJzmQ3iaPbs2UYueGB1wCW++Ii/++67js/PODnD4hIjH/eAAIWdBxAdFAFrdVlXZzgoIpRH1EbO/v37rXM0bs4EM8JuKEOWtlIKu2DGQrljprsjLZgW6NcCgbZ69WrrInM3CUK2oKDAdT43dfBZEqCw05wDFHaa4Fxmgxvm8uXLXVm8XFbh2+Pq3IzbPsT9DItvwH0umMLOZ8D/KD7s+yF1e6kCH8GV1O2Z4EaNGqW97Fu3LcynT4DCTp+dm5w4Ow/WUT9rltoniLpu3bpJuXLl5MMPP5R9+/Y57raTYCSOC+ODJJCBAIWd5tSgsNMEl7Bs2MXH2Zn58+dnvLw7YUiM7S6FXTBDB0u3qedp7efl3Nzhhnx5eXnBAGYtZRKgsAtmguAS7okTJxrnkghL49atW61zgRCnjz32mGMLHCx2Tz31lHFiNpgZwVq8IkBhp0mSwk4TXMKywfpQuXJlI11JEzZUWbtLYZcVER84T8DJ5d0EFV0CFHbBjA3uP+vfv79xwk7RgcDDReYIAsNQ/8HMGdbijACFnTNOpZ6isHMOTp09QajfadOmOc8Ygydxx1uDBg1iGxo8BkPkuAsUdo5RJfZBfOtGjRqVuO9cnAacwi5Oo+lfX5xc3u1f7SyZBDIToLDTnB0Uds7BNWvWTIYOHWoFREiauINLWbVq1SyXDSazCVDYORs/iBucN/vss8/kkUceScSVBYqM/V46Z7T4VNQIUNg5HxGcN8MZM9POyTnvYeYneV7OC4osww8CFHaaVCnsnIOD1QoBBfDvTZs2GRkIxXlv+WRcCVDYORtZiDps5uAOuiQGBeF5OWfzJKpPUdg5Gxl1gfWNN97oOoiIsxqi/RTu4tu5c2e0G8nWJZIAhZ3msFPYuQOHSI89e/aUxYsXM0iAO3R8OiIEKOycDYQSNnjnBw0aZIUFZyIBUwhQ2DkbqeHDh1sP4j46uiU6Y8anSCAIAhR2mpQp7NyBQxCRzp070yXRHTY+HSECFHbOB4PRYJ2z4pPRIkBh5248EERk+vTpgo0cJhIggfAJUNhpjgGFnTtwFHbuePHp6BGgsHM+Jgwi4pwVn4wWAQo7d+MBl0RGhnTHjE+TgJ8EKOw06VLYuQOHS7rvu+8+126YPK/ijjOf9o8AhZ1ztjhPu3fvXkaHdI6MT0aEAIWd84HAOTuE/MedbkwkQALRIEBhpzkOFHaa4FxkgxjE9QhFRUVW1K3Ro0e7yM1HScBbAhR27njqbMrAnWvVqlVyzz330G3bHW4+7REBCjvnICHscMRi27ZtzjPxSRIgAV8JUNhp4qWw0wTnIlvXrl1l4MCB1gIvidH1XKDiowEQoLDzFzKEYIcOHaRbt24yc+ZMeeONN1xb+P1tIUtPAgEKO/9HGYFX5s+fL1dddRXP5vmPmzUkjACFneaAU9hpgtPIhvDpp0+f5jUJGuyYxTsCFHbesUxXEqx177zzjnVNQuvWrWXMmDG02vmLnKWnIUBh5++0UFa+efPmWda+/Px8K7ImEwmQgDcEKOw0OVLYaYJzmQ2LvREjRggu/mUigTAJUNgFQx8u2MuXL+dGTjC4WUsKAQo7/6cEImn27t1bWrRoIffeey+Fnf/IWUOCCFDYaQ62KcJO3SV19uxZufPOO40LZoD2HzlyRDp27Kg5UvHJZvpYmj4Spgg7uDRu3rxZGjdubKQrIy43h5tWXl6e6VMm5/abPpY5AwihAAq7YKB36dLFCrDEaxKC4c1akkOAwk5zrE0RdrhmALtiEEb169eXHTt2aPY4nGzYvd+wYYNxgtQPWvYrI3jm0A/CZZdpirCDMHrwwQdl9uzZMmPGDOPcGRFRc/DgwcEPcMRqVHcBmjyWEUPqqDmmCDt1f1y5cuVk3bp1jvoWpYewLsH3qW7dulFqVihtMX0sQ4HGSjMSoLDTnBymCDvVPbgy4uNBl0bNAY9QNozhrFmz6KoW8JiYIOwgBiZNmmRFkMXZ1Nq1axsnkhA0ae7cuQGPbvSqs48lmDRp0sS4sYwe1ewtMkXYqfvj+vXrZ3Vq6dKl2TsXoScQFA2bTxR2IqaPZYSmFZtyngCFneY0ME3Y0b1Jc6Ajlg2LvYKCAkvY0VUt2MExQdjZN3KwQKVLY7BzxI/asJHDsfSDbPoyTRF26pxanz59rOsGTLPaqfYHN7LRrUmxgHsqUmFhYXQby5ZFngCFneYQmSTsIAawM0b3Js3B9jEbXE1feeUVq4bVq1dnrYljmRWRbw+YJOy4kePbNAi8YEQI5UZOcNhNEXYgolz4aPkKbn74VRPGEu853VP9IpyccinsNMfaJGGHHd9x48Y5Eg6aOJhNk4BatMF97uTJk1nPEtKlVhO0B9lMEnZww5w2bZoHvWYRXhJQ1reioiJp37695TKbLXEssxHy9u9NEnaw8Dz66KPWXGKKFgHc1bdz507H1jecOXzqqac4ltEaRiNbQ2GnOWwmCTvNLjJbAAQQEQxnDXCGpm3bttYdXkzRJGCSsMN8wmYO3XWjNZcQxArXt+CddxoAiWcOgx1Dk4Rdu3bt5Ec/+pF1dQBTdAhA1K1cuVIaNmwoy5Ytk3379mVtHMYS5yUp0rOi4gNZCFDYaU4RCjtNcMxWigCiAG7atIkBIyI+N0wSdhFHmfjmwQp3+vRpvvMRnAkmCTu478HrgwFIojWRMC41a9a0RBoCo8Byly1xLLMR4t87JUBh55RUynMUdprgQsimXFFR9YABAyJ3J579GoMQ8LBKhwQo7ByCisBjOIt6+PDhSF71ACs9rHaMUByBiZKmCSYJu2gSZKsUAVjhrrvuOsfumCRHAl4QoLDTpEhhpwkuhGzqHBsWeoMGDYrcWUMGRwhhUmhUSWGnAc2WZfv27bJ8+XJZsWKF4PvZoEEDqVGjhiDQS+fOnXMr3JYbwqlv375Sq1Yt697OrVu3ela2FwWhfUeOHIncBpMXfYtDGRR23oxi0aHDcnzZSrmyahWp2vE+bwpNKQUuj7jnNqoujHDFRMRSJhIIkgCFnSZtCjtNcCFmi6r7E6wLSDwPFeLkcFA1hZ0DSGke+eSTT2TixImWqMNl2zhLCkEHoYfvKP6uefPmMmbMGL0KUnLBtRkJUYCdnmPzpGKHhaB9e/fuZXAbh7yCfozCLjfiu54cIUdeWyTfnPtSLit/jXz9xRfyddHfLYH3nRfGynVNG+dWwT9yL1myxHrH8/PzrcjS+HOUXFLRnpdffpnWOk9Gm4W4IUBh54aW7VkKO01wIWXDtQJYWPLi45AGIAbVUtjpDSIWyhBxEG4QdOnShAkT5NVXX5W1a9fqVZImF995z1AmqiAKO73hhoVuy8O95Nz56M51/32YVGx8h5TLq15c2Lv/0l8+WrZKqtx3j3zntxP0KrHl2rVrl+V9g8AxsIxhwyRKgUfQPnzXpkyZknNfWQAJuCFAYeeGFoWdJq3M2WCtgiXts88+k0ceecS3qJBY5FWtWpXuT56PYHIKpLBzP9bYTMFCedGiRVKxYsXiAvDep1qo8R2A8Bs6dKj7itLkoIuzJxgTVwiFnd6Qb+zwz1bGhi9OLCHo7KVB/K1v2UHq/GqA1OhRoFdRSi6cYztz5oxxF7R70nkWQgJpCFDYaU4LWuw0waVkQyjvgoICS3D56TbF6wS8Ga8kl0Jh5370wQxCDa6WKkFwlS9fXj744IMS59/gsonn58yZk9Gy57QFOMe2atUqy1WLiQTcEKCwc0PrwrP7npsof5v6sjRfuzijqFOlHnxplrw/aoy0PuDN2VenUSfd94o5SMBMAhR2muNGYacJLiWb2rkP4hL1dFYCb3rBUpJAgMLO3ShDqLVs2bKEeIPgGjt2rOUSnW6z5eGHH7YCIdiFoLtaLzxtr0cnP/MklwCFnfux/8+m90r1hx6QWwf3c5R5fauOUqFeHU9cMnFJe2FhoaN6+RAJJIEAhZ3mKFPYaYJLkw2CC1a7WbNmMYCId1hZkscEKOzcAV2/fr0VGAUWuNSU6Z3HmRSk/v37u6uMT5OARwQo7NyD/I9bG0vjwpeKA6Oc2fW+nFi9rrig/9W7e4lCt3Tpaf35zsIZ7itLyUFhlzNCFhAzAhR2mgNKYacJLoOwGzVqFKPEeYeUJflAgMLOHVScr0NAlGnTppXKCNdr3OV21113lfg7nIWF6PPqnJ27FvNpEhDrTCjOf/bo0YM4HBDAubm37m5XwrXy3T6/kI8Wv1Gc+/pm/ySN/ziz+M9w3Tz8p7/I3RtXOqiBj5AACbghQGHnhpbtWQo7TXBpsiGa1YkTJ2T06NHeFcqSSMBjAhR27oCmc8VUJQwZMiTt+44AKupKBHe18WkS8IYAhZ17jqtvaVRC2H20eIUcfX1JcUE3/bC9VOnQpvjPuBLh5FtvS/M1C91XxhwkQAJlEqCw05wgFHaa4DJkw5mY1N17b2tgaSSQGwEKO/f8cMYO1xyknpnDpeS4WNievAye4r6lzEECFwhQ2LmfCW/WbSr1xz3t+CJynLGr9N27pO5vRrivjDlIgAQo7PyYAxR2flBlmSQQXQIUdu7HBufsYIXbujV7BDyvrztw31rmIAGxXDB37NghvXr1ojumwwmBM3Of7TvgyLXy9MbNsrlLD0+vPHDYTD5GAokgQIud5jBT2GmCYzYSMJQAhZ3ewA0aNMi6oBxn7dJdUA5LHYKsQAQi0Ir9vju9GpmLBPQJdOrUSd555x2rgHLlylnX8IwbN46BvbIghdXuWz0LskbGRATN8rfe4kngFP1RZk4SiC8BCjvNsaWw0wTHbCRgKAEKO/2BQ1AUuLipqwwaNGgg+IZCzCHACv6MgCkUdfqMmdMbApinX331lRw+fFhWrlxp/TdS1apVBXcw8ix4es6wxG166FGp0KCuNF38x1IPHVu4TN4f+Zx8XVQkTZfOy3rfnTejyVJIIHkEKOzKGHMsOmbOnClt2rSRzp07C6K8YcdZLUoeeOABadGiRfJmDXtMAiERuPnmmwWBN8JIFHa5UYfVDt9QnK3Df+NbirN3+L7mem9dbi1jbhK4SCD1jB2Cey1ZskQOHDhgPXTJJZdYa4B0UV2TzhERMrc83Es+/9sHcvX/+paUq36TheTT7Tvlq8/Oyo0/+J7UGf4kRV3SJwr77ysBCrsy8Kod5nnz5gnchfAxR2rbtq2129yhQwe55pprfB0gFk4CJHCRAMKQw8LjR1L3rsFqBMsS3nd8A1To/caNG8u5c+f8qDqxZX7zzTel+o6FMxMJKAK///3vpUmTJmmBYINA/S7jN9n+O61LsKzgKTgHSitedrKw3h2Zt0A+P/CB9fCN97U+/8+9FHTZ0fEJEsiZAIVdGQhxWa66KFcFAUCENyXsHn74YVm7dm3Og8ACSIAEwieARRvebwg7nAtD2P0VK1YUCztEcrziiivk8ssvD7+xhrutD2gAACAASURBVLfgiy++kNOnT8tll11m8QRXuLzh/3/99ddy/fXXk7PhY+xV859//vm0wg4CDAnirnv37pYFGL/Tud4/5yQqJlyLX3nllWIrHtpx++23S+/evQV3NLpNEKT4J90ZVLdlRe15uGB++Mof5fSWd+Trv38hl19bQa6oVIlRMaM2UGxPbAhQ2JUxlPbdQDyGHw0kuA3xjF1s3gF2hAQsArDM491WiyuIOySIPSS6YnozUbAohtUVVlFskqUmtYkGSylc4JlIIB0BuzUdm7CffvqpJxeLOxF29vZgQwhzFsIMCRtD+I4gWFBZyR40SOXFugLvhPIYMHnk4Za5rVc/+Wzn+3J90yZy8yP/LBUb3yHHl62Uz3btkSOvLZKrbrpJ7pzzIi15Jg802x45AhR2mkNCYacJLku2Q4cOyezZs62nBg8e7E8lLJUEHBKwb+5Q2DmEVsZjWABj4Zwt+iUWuohGuGjRomJXu9xrZwlxIoCNGIggFXAH4g7/HYTFLh3HhQsXyqxZs+Svf/2rKBdjBFyB8Ettk3oP4BWgNpNUP3AOFe8I+qbcwE0ct40d/lmKPjgkd7z0/+S6po3TdgHXJJzauEm+v2+ziV1km0kgkgQo7DSHJQxhh53u3bt3W7vYcb3Mu2vXrpbbzd69e62+rl69WnOEmI0EvCVAYZc7TzBMtdThnT969Ki0b9++xGaOWuDS3T137izBOQG3Frt0JacGXIHLMX6zBw4caJ3Lh9iDRU+dD0xXBo56qGixzlsfjScPvjRL9jw9TpqvXZzVGofrD66sckPaSJrR6A1bQQJmEaCwyzJe2LHHHUvKVQI7aCoqZpBn7CDqsNCB6EGkTlygGseEiIcIJ/32229b55wo7OI4ytn7BMstAqWElexuVaoNQQs7vPNY/FWpUkVgDTA94VuKdxpWOJWwAMYmDvpZv379Ut81WO0g7HgNgumjb077vRB2qrf4jo0aNapEwBWcJ8V5XZzRKyvhG4RvTjYBGEWyTu+0Q9vhsrm+ZQdpXPhSRsteFPvINpFAVAlQ2GUZGXtABfujuVjs8LFHcrNwxcKuWrVq1q4ffhQQMjyuCXzAneGk4zrCZfcLY4/AGlu3brUWP0Fap7GYQv0QEvgHYkO5UQUt7HBnFly7BgwYILfccovx92fBdQ7fLXVmEbNAfQvx3/n5+aW+a9g8U3ffJfNtSEavsYmBpONGCbdG/B4jYdO1LCuYE5peCjt7fdjEwNnSY8eOFf9vBFzB2UBYrBE4CMcPEIBFJbwzCOCU7byek34F9QyE2lt3t5PWB7Y6rnJ9q44MpuKYFh8kgbIJUNhlmSH4sKofDPu/dYWdctE4e/as1mWnWHRWOh9RKs6XpIIREs/YJfPzhahy2MiwW3OCIgGrOFK6oB66wg6LVgiaXr16uRKpYAAWyj3Z9PcBHCDk0p0bgohNdcXEOMDCh40sBlEJ6g0Ivh54abz77rvWRqdb93s1pzBH1O9zrpEl/RJ2aB/OASLB48Z+bQI2kRo2bCg7d+60vFVUwkZTy5YtrU0uUxKiYO4Y8Cv53q6NxU3eeN+DcuZ8wBSVvvPCWKnSoU3xn3HWDunOwhmmdJPt1CCAe5+PHz8uXbp0kZEjR2qUwCxOCFDYZaGkLtRVj+Gwcy6umFikjRs3zvoR07G8YQEUd/fEXHZvnUx69YwS2ekWlG7K4bPeE4AAKCgokEceeURrF1+3RdiwgZjApdlqoaguz9YRdlikjR07VtuFGnN0zZo1sXDFxLcUFot01odM3zUd5rpjz3zhEMAcx6YF3nncDetGxOC3ApswuYo5e891hR0Wq3ArRrKLM3vZ2JjFGkJtHDm5NgHCDsGGvOyjnyOdTti92+cX8vHKi1dDNZjwbAlh9+6/9JfPD37Ic3Z+DkzIZffp00dOnDghhYWFcuutt8q+fftCblF8q6ewyzK22DHD2RD8Y/8B0bXYqep0LG/4sYAFMc7WOvBR54l07gNy+qqijqlTp1p14RzP4sWLXbnGOq2Hz+kRwPtRu3btwK22eK/xrquExZRy7dIRGfa5jI2cyZMnu7La2TeC9EhGJ1cm6wMW9HA3nTt3bonGqjNGOJPnxRk7ZRn66KOPXI9DdCjGtyX43rdq1crVO4/NAmzEqM0X3Gen/luXlI6w27Vrl2VV3rZtm/V+41uRziKRqWwsetetWyeXXnpp8Xn+cuXKWTzgamrSOVMdV0wEUKn+0ANy6+B+usNWIh9+2w8fPkyrkCc0vSkE74hKeD/27LlowfWmBpaiCFDYZZkLynqEBZ59tzkXYafCIqcuZLJNSyyAJk2aFHthl42DF38PlvjwqzOLbhfcXrSBZWQmoHbxw2Ck7llTEemUqNARdvaNHJwbdPvOY7ELC5eb87hhMHNaJwQ7zi3279+/RBbldmr/n+oCaq9CviurYBguvk75JPU5+0abDgNsAngh/lG3jrBDviVLlljuxHA3u/fee9OKCuWKmTr/hw8fLj/5yU+kbt261maj02sTdFgFkcdt8BScyWvyp5c9CZ7Srl07OXPmjHVOG2MxZcqUILrMOhwSgMsxx8UhLM3HKOyygMOHWH2EsTuoLh/NRdhh4Yj7bfy0SGnOh8Rl07GcJg5SCB3Gu4EgAkG/I7DW4T1HEAdlqfcieIqO2zWww0pfvXr12Ag7ZYWDWEt3jlFNNXgmIBqxV9Y6+xSGFRR16wTqCOFVSESVul4a+B3GPFHJHphHF5yusEN9EBXXXXed5W6WLuGbgoBAqe6mWOhiwZsqQjp16iR/+9vf5NSpU1ZxuDahXr16kQ8shusO3h81xlEAFdx3h9R08R91h6xEPrjEgj8sROCHc4tM0SGAzfRMrsrRaaXZLaGwyzJ+cIPADwd277EoUT8cuQg77BjD1zjuLpUmvBoqUIcJbWUb/SegxBzcqvC+Q2B4IezUNR7+9yD6NYAxNlQgrhDx0m5pAXMsrPEMLJVenyuitS6a8wPCDq6Ibn8T7Wfs7O9uLr3MVdgtXbq0zOpRPtYP2SJdYu2hrgdB5MzUaxOwOQwrtFtmubBxkxeC7eze/XLnrN+mtcTBZXNbr37WM81W/SXrfXdu6sazEBBNmzalxc4tOB+fh1X7mWeesd51Jv8IUNg5ZIsfDXsY5VyEncMq+VgABCjsAoBsWBVq4YVmw7KkxIWuK6a6yyrbQs4wTDk3F5ztUYfV2UaIPQhrr1zr7A2FsLvnnntcnXPMuaMswDcCsK7jtxjzBSIP5+vCOGOnOghrWjYLETYvYLVDOzO5GSuLdTrLdurl56gb1yZE8XogBEX5aNkqqdS8qdx4X+vz/9wrn2x+R479ZYl8/OZ/yqVXXilNl87zXNThzCLOO1JA+PbqaRcMt2NGxNTG5ygjhV0ZmPCjgcWG/TJwLDpyiYrpaFT4UGAEsBgwySVLN3R+YEANr0hdTK66YRcXusLOcCS+N18FqMJ31Q8xZ+8ALBwI1BK0i6/vEBNcgYpcDXfnstx7nSLKxWIH9z+ck3OS1MYGRCnO0GIDCesN3FuH9wCiLtudfLB8I2puUVGRVSXy4ZxflKx4iJL54St/lE+375QvPz0jl151pVxTu5bc8P27PQuWksqbwVOczEA+E1cCFHYORzb1cDYtdg7BJfAxv85FwZ3vwIED1uLl2WefZVQpj+eW2vGfOXNmcclhXlDucfdYnIEE8M7jDsTy5cvH4soLL4cAropI9ii2Xlx9kIuwc9s/tB39gFUfawp8b2DJc7vJoYLPvPfee8VNgBUvPz8/541LtfGSqyXULZtcnofFDsmkwCluNgVyYcO88SdAYZdljBE8BRYd5Z6FM3bYWaOwi//LodND7KAibdmyxbqvyMtLpSE87rvvPqt8/GBjwcfkLQG813i/sZiBJcB+MTYtdt6yjktpflnRsdjH9wSLdloaS88WJejwzqoEQZTrucwghZ0f7wBcNWfPnl3i2gRc6aPuz3VSJ75/iC0A0Qm+4Ir/xr/xu2aSl4uT/ob9jIrk+eGHH1pXscDqykQCugQo7LKQw0ceHzm4eSg3IewKUtjpTrl450PEPYS0h9Wub9++vogv1NGkSRNPRWO8R8V57yDmsDBU/8Z7rqLiUtg555iUJyHqMFfwPsLSa3fb94IBviMIAoF3fuDAgTwbmAIV7GFJwr/hwoiN11zdeU0XdgoR5s7YsWOt36KvvvrK+t8IuILNgrKEGQQcGIArRJzdHVT9HcrCpem5svbiHQmqDETbxGbCVVdd5XlUR0RExZlAWBr3798v2QLwBNVn1mMmAQo7B+OmAqdgkYcPGf6hsCsJDj8WsFLB1z81lLMDxLF7ROeyXScQ8CONQ/IqPLiTPHzGOQFs4iASHTZysPixX3dCYVeSo18ux85HK/wnVdhuiC91T57XreKVLJmJ4v3ERitENc6/q6tKchmDuAg7O4PUgCuZrk1Q65ps15GoyLUQd0lIOLM3ffp0S9DhXUf/vbaqwRWzW7duGS+3TwJn9tEbAhR2WThC1KmLcvGoOtBMYXcRnF1sYBFSu3btRFuT8CO6adMm15dRO3mlwXrVqlWJ5uuEkx/PUNhdpOqny7EfY+d3mX6KL0buzTx6+B2GqOvevbv1EP6c61mwOAo7O0HM1ZUrV5aw4qlrE/B3alMLeSA2IJ5hTXrqqadKiBlE9mzTpk1i3DLBAgl3440fP95zYQd3zFq1ahl1LtDv7yrL1yNAYZeFG3bvIeZSXQ4o7EqDw5mQgoKCxEedg3vWHXfc4YvbFBjj/ISXZ/f0Ph3xzIX3Gu+8SvaFC4XdxTEPwuXYpBmmewG9kz76WbaT+qP+DNwDVTRbiLpc3QPjLuzUeOJ36pVXXrECcqlUrlw56//BKoUE90MkXOOA3509e/YUPwvmLVu2TJSHTqaL5L14R+CGCbd/p1FVvaiTZcSTAIVdlnHFfTI4c6MOZKt/U9iVBsezXxeYwGKHRPFl3kcTrlxI6cKmU9iVHk+/XI5NmjnYbJk0aZJvIeaV+5dJTIJqK8QJzjVC/CJB2CU9eIoOe1jqVq9eLefOnbOyq2sTcJ7sJz/5ifX/YKlKvaMPQVnWrl2bs5jWaXMYeSDs/LobD2VjY7x3795hdI11xogAhZ0DYWePPgiXD95jlx4aBA3FTIy+DgnsCjZssGMPSx0S3nUVPIDCruSE8NPl2KSp57ewM4lF0G1VUatztdLZ250Ui13qWKHfp0+ftsRb6rUJsNTBepd6sTTcMXG2MVf316DnjW59Ti6g1y2b+UjAKwIUdllIwt0Arh7YBcSPBy12mYHhDp433njDumyViQRMJABhp+7HQvvxvqtFC4VdyRGFtQRR9njZt4kzPR5thoUdYfnV7zI2XnMVGUkVdhDJSCoKsJNrE+CKiQAquVpJTZmNS5Ys8fxsnSl9ZzvNIUBhl2Ws1EdeRcPEB4wWO3MmOFtKAm4IYCMHgkUtcFREXPyZwq4kSbocu5lZfNYUAkkVdhDJr776qkybNq3EUCHGwJtvvim4Y81+bcLdd99tBWFhFGxTZjbbmRQCFHYOhB12AdVFnbiwmBeUJ+X1MLOfQ4YMsc5LqEhnZvYinFZjcQNxp+6vwyIPCxsKu3DGg7WSQFkElBiBt4gKnGK/d02HXlKFnQqGAgucnSGsVFj/wA0z9doE8L399tutK3hUwBUd5sxDAiTgHQEKuywsVThlfNhwWae62JPBU7ybhCzJOwK4327cuHGWsENAgcmTJ6f9wcW5oM2bN9ONLgW9sth9+umnFj/8GZs5FHbezVGW5C0BbOS8++678tlnnyUuIjFEGM54wcqOgEewsKv3VZdyUoUdeEEoo/8IiFJWUpy/+OKLtNcm6LJnvrIJ4D69w4cPlzrrSG4kYCdAYedgPkDEqXDKaieLws4BOD4SOAEINnz41YXJY8aMSSvscC6qQoUKVkS5GTNmcLfVNlLYxFmxYoV1VhSLRBWYga6YgU9nVuiAgLoOAZs6s2bNSnt/Jr4Lo0aNskpLdbVzUEVkH4HAgPsgfo+R8L3LNZBKkoUdGOK6F3DFPEl3dg7fRzwDQY3vY7prE2DFQ3RHnr/17tXBPXfHjx8XXElRVFRkXZbORALpCFDYZZkXOFCMxS9cPZBgscMPB4UdX6goEyjrwmS1yMMPN6+oKD2KWLhgYYME9y5GxYzyTGfbFAG887Vr104bmVhdS7F3717B+w8RGKcES5MXd9iBSdKFHRhArIEDrKBY++AbiG8iNryUgE7n8oo5aL9XUF2bMHr06DhNt1D6gqikhYWF1qXx2GS03ymoGgS3WfvRgdQopqE0nJUGToDCLgty+xkb+6MUdoHPVVboggAWctkWb9jx69u3r8yfP5+RTP/BFgtELF7gdo2EhQmFnYuJx0dDIYCzT2vWrMn4zquraOyu2qE01ONKITbwG62sdLAi8Yydd5DxPQRjtbkNoacihJdVi7Ie//Wvf5VvvvnGehRWvPz8/OLjLN61MlklwRunadOmMmXKlFIdx9899thjlrX01ltvlX379iULDntrEaCwyzIRlM+5ckmAqweDp/DtiToB5Z5VVjvxDEVdSUL2IEmp7OiKGfVZn9z2wcJyxx13lOlSDUsdFtaZzt2aSA+iDsHNvAy3T4udtzMBmwpwlz127JhVMFwJcbH5wIEDeQTAJerhw4dbkUizXZKOs3h4z7dt2+ayBj4eBwIUdllGUR3OTvXbp8UuDtP/Yh/gQrJ7927rjpo4XLKOhZ4K9JNppNDnOJ238WJG4r0GF7VQhPhVHCnsvCDMMvwgAAt9NmsIgqxUrlw5Ft83xRAbMfhHWem8cMeksPNjhop1JuzFF1+0hIn92gRGb3bO2x6hNFMuuGp26tRJFixYIHXr1nVeOJ+MDQEKOwfCDj7maqGHHxCesYvN/Lc6gh1FnD2ByKlTp451Z08SLll34q4Zr5HO3htY6PF+p7vkmMIuOz9TnoDI2bBhg9XcTAGGTOmL03bG8UJ5dam2+n3Ge5ur9Y7CzumM0n8uyGsTYOVCisN5M/QFls90bphqNCDshg0bZp3HY0omAQq7LOOOhZ6KuIVHVZQ8Wuzi9cLATQkJu95qwRevHrI3TgioMzuw1CFhoahEHoWdE4JmPAMrAa4EsW/qmNFy/VaqM7dxilSorjnIVczZqVLY6c8xnZzwkPDLioczZzfeeKMVTRKBhZIgdvr06SMnTpxIRF915lsS8lDYZRhlhJPFxwALPXXVAR7F4WFa7OL5amCxFxdXzHiOUDC9UhExVW0MnhIM96BrwWYOFpXZXBiDbhfrc05g3rx5MnPmzGIrHc7bpbO2Oy+RUTHdsPLyWT+uTVCRJOHCiCMWSTlzBqsd3TC9nJ1mlUVhV8Z4wSpnv8NO7eBT2Jk1yZ22lq6JTknF8zksLCDicNBfpTZt2libOUi02MVr3Mu6HiBePWVv3BCgxc4NLX+eTb02AQFXWrVqpXUmHCIHnlaIAI1okUwkEHcCFHZljLD98lO4eqj7QZCFrpjxejVwsHvEiBFZrwiIV6/ZGzsBWOaxaWO30OPveUF5POeJugIgnr2Ld6/wjqoItupuNfQY1x3QYhefsU93bULVqlUtS3u24GCKAqx2SElww4zPyLMnuRCgsMtAD66YcOuAmIOog8sHfkDmzJlj5aCwy2XaRTMvfkTidP4kmpSj2yps5AwaNMgScvhvLBLtiwda7KI7djotg+t1UgKn6PAxIQ/eU/wDS3uu99ep/tJiF82RTw24ctlll0m9evWsDVmcpcuUEPq/Zs2a1jELJhJIAgEKuwyjfODAAXnttdekf//+xU9g0QehR1fMJLwa7GPSCGAX2H5GB3+2X3hMYZe0GcH+RpkAXKex2Qp3afVvp1acsvpFYRflURfB2dhRo0Y5DriCYCKw8sUhKma0R4atiwoBCrsMIwG/7JMnT5Zw64DVTu0MJs1i9z8njsijL4+SLYfel9OffybffPONXH7pZVLl2utl/ENPSJd/ujcqc5rtIAEtAljQ2d2t4e6FxaPa3EmqsDvw8WHZcvB9uf7qCvL9bzfRYstMJOAlAbybeF9hcVUJG6/YiMk1QiaFnZcj5W9ZQV6b4G9PWDoJeEeAwi4Dy1OnTsmTTz5Z6m+Va2aShF3hf6+U7r/7tVx1xRUy/P7H5Md3fk9qVq4m6/ZulUXv/peMXT5bGubVlk2//J13M5MlkUDABJTFzl4tLAFK7CVJ2J36/IyMXDhdXn5rkVx/zbVyZ43bBP/vP3Zvkh81anV+M6e/3HJD9YBHiNWRwAUCygUTQTFUKusOSjfcKOzc0IrOs/h+r1mzRnCMBgmeVXC/HD16dHQayZaQQAAEKOw0ISdF2MFSV2doZ+l4Rwv58+OZP5A3D+lokfxw9EJNosxGAuESgEU+NXAKdv+TFhUT1jls5EDQzfzpr0oJuAmrCuWJP02QP/f+jSXymEggaAJ4T7GQt1vn8P8efPDB4vdVt00UdrrkopEPXhbz58+X9957r7hBt99+O681icbwsBUBEKCw04ScFGF327CH5Npy12S1xikBOLBtN/n3Tn00qTIbCUSXQBIsdrDK/Xjqk/L92xrL8I6PZRwMiL/Wz/8fWf2LFyxrHhMJBE0Av8GpCVYaFcVWtz0UdrrkopdvyJAhgjvs1IZdLtcmRK93bBEJpCdAYac5M5Ig7OCCWfDScNnz63mW66VKcMFsOba3zH3s6RJn67CT/4t5E+XrF/5LkyqzkUB0CSRB2OEdfvO8uyWscfaEb0He9TdKi9qNiv/3a1vXWJa7/U/Pj+6gsWUk4JIAhZ1LYAY8rq5NwLVGX331ldVit9cmGNBNNpEELAIUdpoTIQnC7rFZz55f5G2W90f9qQSlTMLOmlB9vivfTHlLkyqzkUB0CSRB2NX6Vb6sfmJyKffLSgPaSK0bbi5lucfzEIG02kV33rJl7ggkWdh1/u0v5aMzJ+XNX0xxB82gp3WvTTCoi2xqwglQ2GlOgCQIO7hhfu/bjWV6wS8tSv+2YIose2+9fFp0VvZ8dFDqVKlhuWned3vzYvfLa/71+zLzZ0MZJVNzXjFbdAnEXdjBDbPWUz+Wk8+vKDUImYQdLHY1zwdR6X/PhUuAmUjAdAJJFnZVB3eQk599IucmrzN9GB21H+c0V65cWcKKh/stGXDFET4+FFECFHaaA5MEYdfk2Z9ZizYVNOXH04acj4r33/L3c+fk83N/l6uvuMqKlHln3m3WWRukK/q2OP/fk0u4bGkiZjYSiBSBuAs7RLwcuWiGZbFDUtFw8d9FX35hXXGi3vmT4y6IP0TNfPP9zVaQFSYSiAOBJAq7eZtXWUPX8/fPypm/n5XCXk9bf+7c+J44DGnWPiDgyiuvvCK4v1glBFzJdvl51oL5AAmEQIDCThN6EoQdLHS/O79wS410mckVEwFU4JrFM3aak4rZIk0g7sIu1WKn7q7EoKzb+45UKHe1tYlzffmKxZs9uBIBqaxAK5EeVDaOBFIIJFHY4QhFuvSbH/eVwW0LEjVHeG1CooY7lp2lsNMc1iQIOyXU1gyYUsICl0nYwaK3Yf+7vPJAc04xW7QJxF3YgX6lX7SR/c/82bqM3J4yuWIigubPvns/rz2I9tRl61wQSKKws1vszn5RJHMeG2URq3tTTWl4c20X9OLzKAKuTJ06ldcmxGdIE9MTCjvNoU6CsAMauGMe+eTjUmINblrNa91eHC0TYq/1831lVo+RPF+nOaeYLdoEkiDsuv/+aUvU4QJye8I9ldUq3lAieApcN/H85qd+X0oIRnsk2ToSyEwgicJO0UjaGTun7wECrsyePbvEtQmNGjWScePGSV5entNi+BwJBEKAwk4Tc1KEHfAgIEq9ajUz3mWHEOm/fG2KNLulQfFZO02szEYCkSWQBGEHd8zGz/w0a6RLPId77OCCyUvKIztl2TANAkkWdoiKufvY3+SdX83SIBf/LLgu4cUXX2TAlfgPtdE9pLDTHL4kCTtlucOlxN+rc6c0r93wfHjzb8uKHRus6xAOfHxYeDG55kRiNmMIJEHYYTBgiWs9vq9ltUsX7RJ/j2iY3/92k1KWPWMGkw0lgQwEkizsOCmcE0i9NuGSSy6RBg0aMOCKc4R80icCFHaaYJMm7IAJ7pdT33xVdp3f0Tt19sz5C4uryHfyasuE8wtA+wXmmkiNyXbo0CEpKCiQs2fPSpUqVQS++EzxJ5AUYYeRhEWu++9+bYm8H935PbmlcjXr/+HP+DdEX5IsdXjnly1bJnfccYfcdddd8Z/sCe4hhV2CB1+z67w2QRMcs/lCgMJOE2sShZ0mqthlGzJkiFSuXFkGDx4s8LNfvHgx/exjN8qlO5QkYad6D2s8LPVbP9gt111zrWWlu+X8FSipwVXiPvwdO3aUVq1aWedsZsyYQXEX4wGnsIvx4GbpGoKlvPbaa3L8+HGB26XbxGsT3BLj834QoLDTpEphpwkuRtlU1Cxa7GI0qGV0JYnCLhkjW3YvYa2bNGmSdWkxNnWQeIFxfGcGhV18xzZbz+rVqycLFiyQYcOGCd77dev0L2qHFW/9+vXFAVcqVqwo7du357cj2yDw73MmQGGniZDCThOcLRt2tzZs2CC9evUycge8WbNmMn/+fFrrcp8KRpRAYWfEMPnWSOzg9+3bl++8b4SjUTCFXTTGIYxWwGLXu3dv6dOnj2zbti0nYafaj43fWbNmWWsdlXD5eX5+vvTo0SOMbrLOmBOgsNMcYD+FHT4Er7/+utSuXdty94tjwsHjNWvWWB+3MWPGyI4dO4zqJhZ5q1atiu34GDUYATWWwi430NjIwUYIkolW7tatW1vfKp6xy20eRD03hV3uI7Rr1y6pW7du7gWFUMKSJUtk6NChltu1133gtQkhDGgCq6Sw0xx0P4UdLEH4sOAHBv/G+Y64JeW/jkUS+mvfS+qGqgAAIABJREFUzTKhr2r33rR2m8A2qm30S9hhLo0dO1b2798f2/cdYwphtHr1asud8eTJkzJt2rSoDnXadmFRFteNNqMGwufGUtjlBrhdu3bWGbWPP/5YfvrTn8rIkSNzKzDg3F26dLG+VbDc+ZV4bYJfZFkuCFDYac4Dv4Qd/LqxU4QFRNeuXaVJkyaxXkzAD71SpUpafufKxeHMmTNGWgA0px6zhUTAL2GnNnIaN25sWbDjulmA9xWbVPiutW3b1jg3JLSbFxKH9PIFWK1fwg6WIPyunzt3Tm655RZZunRpgL0Krips1kK4wK1x8uTJlkujSSkIYWfnwWsTTJodZrSVwk5znPwSdqo5SrTEfSGhdvF1hgF54buOwAYHDhyQuXPn6hTDPCTgiIBfwg6LICyG4Kq4fPnyWM9jWOt031VlJcdg4d1nABNH05YPuSTgl7CDJatfv35WAI06derInj17XLbMrMfxTcM3U8diB1fOP/zhD1p5c6UEAY6EcQo68dqEoInHsz4KO81x9VvYJSEwhz3anM4wKAsAdrw2bdrk+YIY7RswYIDAIjhixAierdEZpBjl8UvYARFES8+ePWN/dQasXrobMFj0/PCHP7SsfvXr1zfuXG6MXoVYd8UvYaegeRmYI6oDgT7CtVzHKglh9fOf/1zatGlj3R25b9++qHbTt3ZlujYB7qFxPJrjG8iEFkxhpznwfgs77Gzj45aXl6fZwuhny1XYoYfqygGc1/GalXIZw6XEiIYXtIscL0WO1hz2U9jhhxwp7lHSlJU9l3fVz2tG8M4fPXpUbrrpJm0BGq1Zy9a4JeCnsIMlqnPnzjJv3jzPA3O47aefzw8fPlx+8pOfaPURLpyHDx+2rHUNGzb0hRXGAed9/TxH5xXfdNcmNG/e3Lgzyl7xYDnZCVDYZWeU9okghB1djbIPDqx1DRo08GUXS1kEwwqUgkUwFpiIGAoXnrgv+rOPdrhP+Cns7Jfeh9tLf2vHZgVSLsLOC3GYrpd2V1h4TOB8UJARMMFm1KhRcvr0aZ7l83callm6n8IO1qi//OUvMmXKlBB76H/VcDutVatWTv2EOFy5cqUnVw6k9hjvdbly5awgL+PHjw/F7dLtKKjjOX/961/lm2++sbJXrVpVIPy4NnBLM97PU9hpjq/fwk6zWYnLZnfP8qPzWGwVFBSEEuYcfYMlEuJ179693KHzY4BdlOmnsEMz1Fk7F01K3KNeWPnLgqaEJ4LYBH1HpQqWdeONN1rvetAeAombTBk67KewgzVq+vTp1rvOVDYBdVm411cOwFqHjVK4iSJQSuXKlXMSoG7HUVlt1QaXjrtqasCVyy67zNqEGjhwYKCbUW77zueDIUBhp8mZwk4TnMfZ7NcmeFy0VRx2ydatWxdaoAYlLBEkJhcrhx9sklam38IuaTx1+qvuwvPzHjycYQnjLAv6hp13vPNxjo6qM+5B5vFT2AXZD9PrgugqLCz0rRuwnj7xxBOyYMECLZdR3YbZrZm33nprTmcIlZUfls2vvvrKahKseAwupTs68chHYac5jhR2muAMy4adMaSw7q/CLj4shjwwHf7EobALfwyCaAHcYsNyg1cbOQjaxHc+iNEuXQeFXTjc7bXCqtWtWzdfLZuwCPbv3z+0c3ZKWO7cudMT4KlWPBR6++23M/CbJ3TNKoTCTnO8KOw0wRmWDQstHOQO8qyNHRE+1viBo7Uu/IlDYRf+GPjdArzvHTp0kK1bt/pdVdry/QwME0qHDKyUws7AQdNoMixnOm6QGlWlzeKXqykqS702oWLFitY5wrA2rLxixnKcEaCwc8ap1FMUdprgmM0VAXyge/XqFZqwdNXYmD9MYRfzAY5A95ISHTUCqDM2gcIuyqPjXduwWTt79uxA3TBV62GRHDZsmK+upqiL1yZ4N19MKonCTnO0KOw0wTEbCRhKgMLO0IEzqNk4M/zOO+8wyl2IY0ZhFyL8hFQdRhCd1GsTEBW0VatWDMoWwzlHYac5qBR2muCYjQQMJUBhZ+jAsdkk4IIAhZ0LWHzUOAK8NsG4IXPdYAo718guZKCw0wTHbCRgKAEKO0MHjs0mARcEKOxcwOKjRhNId20Czv6NGDGCxz8MHlkKO83Bo7DTBMdsJGAoAQo7QweOzSYBFwQo7FzA4qOxIMBrE2IxjMWdoLDTHE8KO01wzEYChhKgsDN04NhsEnBBgMLOBSw+GjsCvDbB/CGlsNMcQwo7TXAeZcNdUxs2bJDy5ctbl4gz+U8AEbamTZsmVapUSSRzCjv/51hZNWBXmdd+hDsGSaidwi4Jo8w+OiGAgCtr1qyRoqIi63Fem+CEWvjPUNhpjgGFnSY4D7Ihchx8wCHocIlvfn4+o8h5wDVbEc2aNZP58+fLpEmT5OTJk4mLpkVhl22G+Pf3WGBs2bJFPvnkE+nevbsMHjzYv8pYskUA39dx48bJkSNHpFOnTom5A4vCLtwXYPjw4YJLuwsLC8NtSIJqR5TO1atXZ2Su7td87733iqng8nOuvaI5SSjsNMeFwk4TnEfZ1O5969atZcyYMTzo6xHXsopRQrpr167SpEmTxC2uKewCmGQZqsAGDuYfNnUGDRpkLUKY/CWA93zgwIHWt7VRo0ahXdruby9Ll05hFzTxi/V16dJF8NvesGFDWbdunWzbti28xiSkZgjpRYsWSdOmTa0L2/fv319mz+Gqifv/sMmGhGsT8H3AJhA9KqIxaSjsNMeBwk4TnIfZsItfu3btxAkMDxG6Lgof9U2bNsncuXNd5zU9A4Vd+COId75SpUqJsR6FT1wEbu8HDhxIzDtPYRferOvTp49MmTJFcIE3rMSw3DH5SwDWupo1a0r79u0tQe1UTGOTbezYsdZm21dffWU1smrVqoJvdI8ePfxtNEsvkwCFneYEobDTBOdhNnxAcOaLKTgCYN6rV69EWkgp7IKbZ+lqSvKmQljkk2ghpbALa7ZdrBdWYliQIPKYgiHQrl07qyJY7dwmXpvglpi/z1PYafKlsNME52G2JLkHeYgtp6LgEpfUO24o7HKaOjlnhitmtWrVErmpkDM8zQIg7HDGDu99UhKFXbgjDQvS9OnTLUsQU3AE3FjrymoVNn9XrlxZwoqHIzOjR48OrjMJr4nCTnMCUNhpgvMwG6PkeQjTYVFgjpREX3oKO4eTxKfH6IbpE9gyisVO/N69exPlGUFhF/w8s9cIYXf48GEZOXJkuA1JUO1LliyRl19+2dOANYii/corr1hu3Coh4EpSN4aDnE4Udpq0Kew0wTEbCRhKgMLO0IFjs0nABQEKOxewfHgUwm7r1q10w/SBbaYicaZx2LBhngo7e128NiHAwTxfFYWdJm8KO01wzOaYAHa8li9fXhyZznFGPugLAQo7X7CyUBKIFAEKu0gNBxsTIwK8NiGYwaSw0+RMYacJjtkcEUAkOtwVh7vjnn32WdmzZ4+jfHzIPwIUdv6xZckXok9u2LBBypcvb13twBQOAQq7cLgnqVZYyOrWrZukLpfqK69N8G/4Kew02VLYaYKLUTbc83T06FG56aabPA8FDmvdfffdZ9HCJaBY8DGFS4DCLlz+Uajdr3O9KBfuSuquSF78G95oU9iFxz4JNSP65PHjx+Xjjz+WF154wbpmIMkJQXJefPFFBlzxcBJQ2GnCpLDTBBeTbPagArCqzZ8/35eAIohG16pVK97VF4F5Q2EXgUEIsQnYyEHC+R8EBUBIdi8TFjgo034xuJflsyxnBCjsnHGK61OwpvXr18/qnk7o/2xc8I7jXcedfbgzDhexM10gkHptwiWXXCINGjRgwBWXE4TCziUw9TiFnSa4GGVTESKxu+6HsMPHHxGk4uaWBW6jRo2S06dPy7hx43wRxH5MMwo7P6iaUyY2WfAu+nmfHiN/hj8fKOzCH4MwW6Du0Nu/f79lWfPj2gWIx86dO0vfvn2ld+/eYXY3snXz2gT9oaGw02RHYacJLmbZcD/LgAEDfLnnCT8oq1atip21DhaJJk2ayI033mhZPlavXm3ErKCwM2KYfG0kNiUKCgp8e+eVePS1EyEUjkXa7t27fXFb97o7FHZeEzWrPFjScDE6onNOnjzZsqp5neCOWatWrVhG/vT6/GDqtQmw4u3bt8/rIYlVeRR2msNJYacJLkM2LJg2b95s3AXEWLBMmzbNWxj/KA1Mli1bJj169PCl/LAKxYcafVKLZAq7sEaC9bolgPe9du3avm22wK07budpYeWcNWuWdQ4ZG2Fjxozx3I3V7TiW9TyFnZc0zSxLWdRgnffjDFyXLl0sr5W4BVDBJedXXXWVnDhxwhLFXrPD9xeePviWMGUmQGGnOTuCFHb4YTx27FjsFvh29PjBRxCSHTt2yIwZMyL9w6/aDWHCwCaaL9D5bH5aO/VblTknLXbeUTXVHReRK3/+85/75j7sV3AW70ZOryS72zoWfF6fT9RrVfpcFHZe0jSzLAivypUr+2ZRa9GihWX5j5MbJsRwz549rTOD4IdUWFho5gQwvNUUdpoDGJSwww4FfhQrVKggZ86cid15K4VfWb6wcEIaPXq05sgwmwkEYLXDuUSTzg8GJeyScH+hqe64DGyi/3VRYz548GD9QgLISWHnHWRl+Tp37pw0atTImIX+8OHDpXr16rESXt6NatklqTGfN29e7CySQTHMtR4KO02CQQk7CB2IHIi7Dh06WBHZ4pqUax7cdvLy8uLaTfbrPAETrdBBCDt1gSuC5jzyyCPyxhtvxPJdsLvj0uqdjE8ChJ0JLlRBCTtYbeBWhuTHOa4ozCr7WbJbb73VmLNROF+HFCeLWlDzIY7WyKDYeVUPhZ0myaCEnWpeEsLeo4/4kOLfTPEmgMAw77zzjlHuxUEIOxWBDa5qJpxHymWWqrvbIGKj7JqXSx+Z9yIBzGcTztMGIeyWLFlindOEoMPcx7dl5MiRsZwusOAg2MUTTzwhO3fujGUf2amLBOCGSRfMcGcEhZ0m/yCFnXLNMmG3UxOnlU3t4udSBvOSgF8EghB2qu1JCHsP6+Trr7/uW/Ahv+YBy9UjoO7p08sdXK4ghB16o6IHIuCEX0E6gqNWdk316tWTBQsW0DUvKgPiYzswn+mG6SNgB0VT2DmAlO6RIIWdiW5rOlixmO3Vqxd373XgMY/vBIIUdqZYN3KBjo0cpLhFfc2FCfOGTyAoYYeewm0NC2GE149rgoAdNmwYrThxHWD2K3IEKOw0hyRIYccFkOYgMRsJeEggSGGnztZ62PzIFQULzpEjR+h6HbmRSXaDghR2SXBbw3m16dOn+3LRd7JnKntPAukJUNhpzowghZ1mE5mNBEjAQwJBCTucPZs9e7Zvd6V5iIRFkUDsCAQp7EwKKBK7gWaHSCCmBCjsNAeWwk4THLORgKEEghJ2huJhs0kgFgSCFHaxAMZOkAAJRIoAhZ3mcFDYaYJjNhIwlACFnaEDx2aTgAsCFHYuYPFREiCByBGgsNMcEgo7TXDMRgKGEqCwM3Tg2GwScEGAws4FLD5KAiQQOQIUdppDQmGnCU4jGy62PXr0qLRv3963c0dB1KHRdWaJEAEKuwgNBptCAj4RoLDzCSyLJQESCIQAhZ0mZgo7TXAus+F+n71791p3XdWpU0f27NnjsoTsjwdRR/ZW8ImoE6CwC2aEcO3Jli1b5JprrpExY8b4cv1JEHUEQ4u1eE2Aws5ropnLGz58uPWXfl7OHkQdwRFjTSSQnQCFXXZGaZ+gsNMEp5ENUQKR8vPzZcOGDRolZM8SRB3ZW8EnokyAwi6Y0VF3+Nk3XLyuOYg6vG4zywuGAIVdMJzvuusuKVeunBQVFUnTpk19ucsP9wQi+VlHMLRYCwk4J0Bh55xViScp7DTBaWbDQsxPV0w0q2PHjtKqVSvf3D01u85sESFAYRfsQOB9xGaOnxeYB1FHsNRYW64EKOxyJegsf7t27WTp0qWyZMkS6zd327ZtzjK6eCqIOlw0h4+SQCAEKOw0MVPYaYLTzKZ22DWzO8qGRd7ChQsdPcuHkkeAwi64MYerZO3atX3dZAmijuCIsSavCFDYeUUyezm7du2Szp07S9++faV3797ZM2g8EUQdGs1iFhLwjQCFnSZaCjtNcBrZ4CaJRZifoiuIOjS6ziwRIkBhF9xgwA0Tu/h+piDq8LP9LNsfAhR2/nBNV2qXLl2s/11YWOhbpUHU4VvjWTAJaBCgsNOAhiwUdprgNLNB1MGi5mcKog4/28+y/SVAYecvX3vpzZo1k/nz50teXp5vlQZRh2+NZ8G+EaCw8w1tqYKnTp0qNWvWtI5Z+JWCqMOvtrNcEtAhQGGnQ43CTpMas5GAuQQo7MwdO7acBJwSoLBzSir35/r06SNVq1b1NSomLHb16tXztY7cSbAEEvCOAIWdJkta7DTBMRsJGEqAws7QgWOzScAFAQo7F7D4KAmQQOQIUNhpDgmFnSY4ZiMBQwlQ2Bk6cGw2CbggQGHnAhYfJQESiBwBCjvNIaGw0wTHbCRgKAEKO0MHjs0mARcEKOxcwOKjJEACkSNAYac5JBR2muCYjQQMJUBhZ+jAsdkk4IIAhZ0LWHyUBEggcgQo7DSHhMJOExyzkYCHBNavXy8rVqyQ7t27S40aNTwsuXRRFHa+4mXhJBAJAhR2kRgGNoIESECTAIWdJjgKO01wzEYCHhKAsHv44YetEnv06OGrwKOw83DgWBQJaBJo2bKltG3bVoYOHapZQtnZKOx8wcpCScAVAfy2N2/e3FUePnyBAIWd5kyAsMvPz5enn35aswRmIwESyJXA7t27Zdy4cSWKUYs+ry14FHa5jhbzk0DuBCDs8Pvr12YOhV3uY8QSSCBXAo8//rhs375dHnzwQenfv3+uxSUqP4Wd5nB/8sknMn78ePnwww81S2A2EiCBXAmcOHFC3n777RLFVKxYUTp37iz9+vUT/LdX6d5775WGDRvKDTfc4FWRLIcESMAlgT/+8Y9y5syZErnU++7FZg6FncsB4eMk4AMBCLvly5cXl+y3R44PXQitSAq70NCzYhIggVwJ2F0xIeJw1g4/AF4KOtVGWAlef/11ueqqq3JtNvOTAAloEpg0aZKcOnWqOLfX7/0LL7wgkydPlssvv1yzhcxGAiSQK4GzZ8/Kl19+WaqYadOmWa7YTJkJUNhxdkSKwEsvvSQbNmwoXpjjHAV+uOfNm2dZYbxKqAfluREAEBGvvvqqjBkzxqtmsJwcCWBMBg0aZAk6t+OZY9XM7gEBeD4ot7oGDRp4UGL6IlCPm3fdt4aw4JwJKFdMWOf8eu8xX5hIgATCI/DEE0/IqlWrihuA83ZYD/r5OxFeb72tmcLOW54sLUcCEyZMsHZj8PLCv3rmzJnWy4zFHxZm+MHFYh7P4P/hz2r3Bv9f/Rl58TxM+fh7LALw98iDP6sy8Dyexd/jn3T5VJeQF2462DFiig4BLtqjMxZuW4L3beLEidKsWTMruin+jfMU2HiB5dWrhHowT9wcxse3A23D98dNPq/azHLSE8BGTv369bmRY9gEwTutBLM6M+V1gAy1UeR28Y92qDWAYVhj21y4YmJcMJYUdO6GmcLOHS8+7TMBu7BDVQhYASEFgdemTRvr3zhMC4EFyxn+jLNU6sOMPMoCoBZyWJzNmTPHip6IDwQ+4FgcoFz8G+WpRaX6YcBCDn+3du3aEj3Gx4bCzudJwOITQwCCC1ZwFeFQve+w0CuBBxiHDh0qfv/xvuPHXlnQ8f7ifUUevPuffvqpVR7KhjizizLkwwITz6grMvBnVUdqpEX8HfJQ2CVmSrKjPhHAbyd+s/He4jdZ/Y56cS7SvrmHb4CbMtUGMjYLvNxM8gljYoql2NYfago7fXbM6QMBu7DDx1r9GChhh48wPr5KYGHhhucgzPBhVgnWOnzcYZ2DoIOwQ9lq0afyq38raxzEIxLc+lQ+ezcp7HwYdBaZWAKpwg7vKIQU3ne8sxB6+Ley6qkNF4g7CEL8W72T6n3FhgwEmvpmKNEHyGoBiPcbZS9atEjg2od/ow68/3YRR2GX2KnJjntMwP7biU0YvIvYsMEGC969vLw8653Gn3fs2GH9WW3u4Fn8P4hBvOdqYwd/Vt8QWPvxe682e/EdUAIP3wOVD2sG5LNb9SAi1NrC426zOBIInACFXeDIWWFZBJSww4ccH3mINfWxxqIrk7DD8/hQ4x+IOizIUoUd6lULNXz88XFXFgLlpql+CCjsOE9JwH8CqcIOiy/s6mNRBkGnFoN2gYVnsPjD9wGLObURo6z46ll8C/D/8N1QLmBYHKpoqWrTSFnvsdhUZameU9j5PwdYQzII2IUdxBU2U5WwU+8ufodxxh5CTG3q4P3EO4zNW/x/9W1QZeA3G3+H7wbKxG+7+i5g7YCy8b1Q77kScfYQ+hR2yZiDSeklhV1SRtqQfuIDi4/0tddeW3zWDosy5VKFD7fagYf4sp+dwSJMuWyp83jqWXzg8fcqFL79nJ4KqYvy1Lk9ex12dF4HcTFkWNhMEvCFgDpjp9yh1S69sr5lEnbYlMHfKZGGxV2qsFPWObsAxHP4s6oPC0VVB4WdL0PMQknAIqDeM7yXKuAVfushutTZdbvAsm+8KBdp/H7j/2PTB8/iHwg09R1RlnrlvYPfdGwW499KEKZuJqFtFHacpHEiQGEXp9FkX0iABEjAMALYWMGCDf8o9ygsvlQAJfxbWdzwjD3gAsSYstSrPOpZtUmjysT/h7BL/f/2fOoZhdBer2FY2VwSiBQBuDxjgxXvGyxwOFKhRBdcMWFxw9/h/cbfQYjh/2FD1u6aqTZ91BlalInNGiRs9KAs9W/8P2wW2Y9vKKue/Tytvd5IQWNjSECDAIWdBjRmIQESIAESIAESIAESIAESIIEoEaCwi9JosC0kQAIkQAIkQAIkQAIkQAIkoEGAwk4DGrOQAAmQAAmQAAmQAAmQAAmQQJQIUNhpjgbPXmiCYzYSMJSAOotlaPOzNhvfNHVmjfe2ZcXFB2JKAGe6EGwD5znjmhBxEmdPcYaNiQRIIF4EKOw0xxOHfvHh54WWmgCZjQQMIgDRo8Jvu7n81qAuWgFKGjVqZC1queAzaeTYVq8IqPcckRrj/NuOQCbo69atW71Cx3JIgAQiQoDCTmMg8EHEhxGJH0YNgMxCAoYRgCULUdriLnq44DNsYrK5nhJAhFaE08embZx/23G9ACyTa9euLb7E21OQLIwESCA0AhR2GuhhrUNIXSTsbGOxx0QCJBBfArDWwRUzKQs+LGrj7IoW35nKnuVCAKH0EW4/7r/tSsBy/ZLLbGFeEogmAQo7l+OiXDVwFwpS3Bd6LvEE/viZXe/L53sPFNd7de1bpELd2wJvByuMLwG1CFI9xP1HcXXTUpZJXAAc1XN2RYcOy98/PFI84a5r2ji+k489C4xA6nse59925XaN+95wwXcUU9HBD+XIvAXFTbux7T1SoUHdKDaVbSKBSBGgsHM5HKkf/7jv7LnEo/X4rl27pG5dvQ/2R4tXyLt9BhTXe8UNlaTFpje12sFMJJCOgH0XP+6bOUEs+A4dOiSHDx+Wu+66S2vCbenSU06u31ic9/bJY6Rqx/u0ymImElAEUt/zuP+2R93t+vjyVbKt10XRWX/cr6Va506csCRAAlkIUNi5nCLpPv6ILrVo0SKXJfFxEGjXrp0cP35cPv74Y3nhhRekffv2rsDAYvf+0GeL81Sod5vcNuqXrsrgwySQiQDcsvDOp6Y4uzAhgAqsddOmTfN8YkDUFRQUSPny5eWDDz7QOsd0bOEyObn2reK21fzXx6VcXnXP28oCk0MAGxo4d4YEN+RrrrlGbrvtNqlfv35srfPqnB3drpMzz9nTZBCgsNMcZ7gsbdiwgdHjNPmpbNi1f/vtt6VPnz6ybds2WbduXY4lMjsJ+EMAAg+uS1F1UfSq1wgSA88EP4JHIGADNnIGDx4szZo1k/nz50teXp5XTWc5JJAzAQgezMm4ulsrQH67XdutbZW+e5fU6FGQ89iwABIggewEKOyyM0r7BIWdJrg02eCKiQA0ffv2ld69e3tXcMxLysWFNeZofOleUoSdCg4FLwR4I/iRFi5cKOPGjZPVq1f7UXwsy8zVhTWWUHzoVFKEnXK79uvM8Mb2DxWPzvXNm8ptwwf7MFoskgRIIJUAhZ3mnKCw0wSXJhvcMa+77jopLCz0rtCYl9SiRQspKirSdmGNOR5fupcUYafOEfu14MPg0FrnbopC1OXn50uVKlW0XVjd1Zjcp5Mi7DDCfrpdJ3cGseckEC4BCjtN/hR2muDSZOvSpYuMGjVKO4CKdy1xXhKE1enTpy1BGob7aMOGDS3XVbqwOh+zXJ9MirBTO/lt27b15ZwdxuG5556z3DFNSbAwqjNYuOcsaDc9urAGN1OSJOwwl3GO2A+36+BGLF410YU1XuMZRm8o7DSpU9hpgkuTDSIJARVMccOEmNq/f78sXbpU6tWrJwsWLAhFlJriwjp16lSZPHmynDt3LjRWXszWpAg7sEJfcW+fHws+ZX3CGWVTkrIwor2wnIXVdghMvE/4d5RT165d5ejRo3LTTTfJ3Llzo9zUUm1LkrALwu3apME/s32XnLJF3EUUzssrXhtYF7785FPZ3KVHcX10YQ0MfawqorDTHE4KO01wMckGUYXUqVOn0MQKXFhr1aolU6ZMCY2qslxeddVVVhCcdEmJXywilCAOrcE5VJwkYaci5q1du1Zq1KiRA7V4ZR0yZIgcOHAgNLEStgsr3vERI0ZYgwqBm85yCesi3Hkh6Fq3bm0FGNO92iKM2ZMkYReE23UYY6hbJ+7N2zFgaHH2xoUvyfXN9a5l0W2DSfns9wyWq5FHVhEZPAo7zYGgsNMEF6NsWKw89thjoVkaw3ZhheVg+vTplqAri8WSJUusayyiIESsjXuUAAAgAElEQVRzmX5JEnZc8JWeKZjniBg6a9asUCJ5wtI5e/bsUF1YYYmDd0XHjh2tqwB27NiR9pVCW5X4My3yaZKEXRBu17l8c8PIC6uZPQVpsQujv+nqxOXwdstlhQb10l4Ov7rmHcXZcYF8wxcnRKULiW4HhZ3m8FPYaYKLUTYIqzADvkBMYRHi9u4/L4fAbrkcP358xrbE4SxgkoSdWvAhWi0sLkxiuT8eO3Ys8PN1ir26AzAKkUSdRDWF+GvVqlWoQlRn3iZJ2IGPn27XOvyZJ3wCp9a/XcIlFBFN011XAQFoT+Vq3Bx+49kCobDTnAQUdprgYpINggZumDt37oxJj/S7AYHZtGnTMl1CIYIfffTRUEWofg8v5EySsEN/W7ZsKRB4fpyzy3UswsiPQBOVKlWS0aNHh1F9ZOpUZySzWeLAy49L7v0GkTRhR7drv2eUmeXbRRuslkm0XJo5ckJhpztwFHa65OKTz6975Pwq1y/yOGeXLTIooniafk9h0oSdWvBB2FWsWNGv6WNUuRA1flyo7le5fsBFWydNmlSmwDUxQI5ilTRhR7drP94SlkkC4RGgxU6TPYWdJjgX2SBwhg0bJpUrVw41QIiLJuf8KM6hYVH0+eefS7du3WTkyJE5l+l3AQiOks1yqVw269at63dzfCs/acJOLfjgigmXzCASXPxef/116dWrl1EBN3TZKPfK8uXLG3M/Ha6qOHHiRFbLpTp7q8smrHxJE3Z0uw5rprFeEvCHAIWdJlcKO01wLrIpFz/c11ahQgXreoG4J/QZCyIEHME9X+h71JNpFkZdnkkTdmrB169fP+nfv78uNsf5MO9h1VUufLgiw6Roio47ansQfR47dqwVQRKBSXB3YNB35Om02yQLo9v+JU3YgQ/drt3OEvfPvz/yOVGBWa69vW7aM2vuS2UOEihNgMJOc1ZQ2GmCc5FNBSexR190kd3oR+HeeO+99xphsTMatIvGJ03YqQUf3DAXLVrkgpTeoxA5R44csSIu4h+E1Y+7sFOknJ5b0yPLXG4IJFHYBe12/dbd7USd4cJdbY0LZ7gZIiOfxcXjRQcvRItFlMn6435tZD/Y6OgToLDTHCMKO01wLrOpS7jh/hNm9EeXzS5+XMeaZb8AXbde5vOeQBKFHcL741uH+weDSmHfFRdUP+31mHjfWxicgqgzicIuaLfrgy/NKrZeIZIiLgJnIgES8IYAhZ0mRwo7TXAus5l89xmsbkVFRdZ5FLiVORWmsFBioWfyeTSXw2zE40kUdvjOQdzNmTNHmjdvHsg4NWrUyMhInMqV9JprrhGcmcN5QacJG1dwvWYKn0AShV3QbtfhjzJbQALxJUBhpzm2FHaa4FxmGz58uLWgdCqKXBbv6+PqvBxcSpGc3nlnspj1FWjIhSdR2KkF39ChQwM7+2VqmHxYGhHoCQIN4nTx4sWOI2g2a9ZMNmzYEPIMZ/UgkERhh37jnF2DBg2MvKIiyJmLc3I4L6dSlftaCy7nZiKBqBCgsNMcCQo7TXAus0HYVa9eXXr37u0yZzQeV66kpof6jwbNcFuRRGEH4hAp2FwJ6k4yCCST74qDpQ5WdzcWu3BnNmu3E0iqsINlHi6ZvLey7PcBwm5j+4eKH6r+UCe5pX8fvkQkEBkCFHaaQ0FhpwkuYdkYBCU+A55UYYd+b9++nQs+h1PZrbXOYbF8LCACSRV2YbhdBzSkrIYEEkWAwk5zuCnsNMElLBsCoUyZMiVhvY5nd5Mq7CZMmCATJ06UtWvXSo0aNeI5uB71CufsVq1axfNyHvEMo5ikCrsw3K7DGF/WSQJxJ0BhpznCFHbOweGsWbly5axQ5nv27HGeMQZPNmzYUEyN6BkD/J52IanCTkXMc3rODuIGbl1nz56VO++8MzAXTk8HW7Mw1ffVq1drlsBsYRNIqrAD96DdrsMea9ZPAnEkQGGnOaoUds7A4aLtZ555RtatWyd0S3TGjE9Fk0BShZ3aycfl2U7O2eGMXOfOna076EyNcBnNGchWBUEgycLOjds17qHbP/6iNwqDiAQxO1kHCWQnQGGXnVHaJ1588UUZM2aM5OfnG33QX7P7rrOpICIQxAzj7xofM0SAQFKFHdDff//9cvDgQVfn7GCp3rRpk8ydOzcCo8cmkIAzAkkWdm7criHsNnfpUQy11hN9eB+dsynGp0jAVwIUdpp44ZY0a9YsKzfcDDt16kSBVwZLuCQyMqTmZGO2SBBIsrDDYvell15yfM7u0KFDUlBQYH0j8/LyIjF+bAQJOCGQZGHn1u3aCU8+QwIkECwBCjtN3soVE25K+BhS4JUNEne5Ob3HTXNImI0EfCWQZGHndsHHICK+TkUW7iOBJAs7t27XPg4DiyYBEtAkQGGnCS71jB0u1aXAywyzTp06iQucojm1mC2iBJIs7NSCD2fn4IKeLcG6N3/+fN7llg0U/z5yBJIs7DAYOm7XkRtENogEEkyAwk5z8DMFT4mawMOCDHdQVaxYURo0aKDZ23Cy4Vxez549rcoRfIWJBMIkkGRhB+4tW7YUfE/8vMBYCcJWrVrxyoAwJ3uC6066sHPrdp3gqcKuk0AkCVDYaQ5LtqiYYQu89evXCz7QWIjh7ikl8BDZDucDTbiPql69etK/f3/rnE6FChVk6dKlmqPFbCSQO4GkCzu14IOww0aR1wnumyNGjLAib3bo0EEWL17M83leQ2Z5WQkkXdgpt2tY5mGh9yMh8IpKl1e8VvAPEwmQgDcEKOw0OWYTdqrYoAUeBBwuE4aww4cZAs6+CMOOOH64nN5JpYnHk2zDhw+XkSNHCv69cuVKWu08ocpCdAkkXdipBd+cOXOkefPmuhgz5oOwQ8I1Ca1bt7a+X/hvJhIIkkDShZ1bt2udsVnb8G758pNPraw3tr1HGr44QacY5iEBEkhDgMJOc1o4FXZBCzz8KCH169cv4646Ptzwo4e4gwUvygn34P385z+XRYsW8ZqEKA9UAtqWdGGnFnz4tsCS7lfCZhiSkzvz/GoDy00uAZOE3ZaD78stN1SX66+u4OmA+e12fWTeguL2lquRJ9c35waOpwPIwhJNgMJOc/jdCrsgBB521PGjBBGkrHQ4p4aF2JkzZ0pYvGDRwwJq7dq1vrhVaWItlQ3RNCtXrixTply8CNWrsk0rJ9NYmtYPU9trkrC7eUhH+dl375d/79THU9xY8OGsrp+iq1mzZrJhwwZP221qYQsXLpTXX39dwKRHj4t3hpnaHxPabZKw6/77py2kM3/6K0/R+u127WljWRgJkEAJAhR2mhNCV9j5KfAg1Nq0aVPCLx6uTE2bNpUTJ05YQVS2bdtW3ONBgwZJ/fr1I71ggLB79NFHpX379pojFZ9sGEtYWrHoxT1h9rGMTy+j2xOThN0VfVtIxztayJ8fH+0pUHwzsIHkZwCVrl278lLz86OGdzw/P18mT55s3QGKf9M11dPpnLYwCjux3nGsJ/xyu/Z/FM2qQbmlotU8b2jW2EWxtRR2mqOSq7DzQ+BhNx0fYntglD59+ljWrqlTp1oLA7sY8KoPmggdZUO7cd6mbt26jp6P80NljWWc+x2VvkVd2K3bu1UWvftfFq6xy2dbLlqd/+keuenaytL/ni6eYJwwYYJ1hhdeAX5F2cU5YFqnLgi7ZcuWWSwgduE2Ty6eTOMyC4m6sDv1+Rn58dQnrT4c+PiwnDr7qdz5rW9bf/5z79944pYZlNu1/6NpRg0b2z8kZ7bvshpboUFdabrkT2Y0nK2MJAEKO81h8VoUeRFkpVatWrJ///5SPYILX6dOnWT8+PElLF+w4GEHHos0JjMIYCwRqey5556jFTPgIYu6sCv875Xy8IyhFpVvvvlGLrnkEuu/L7vkUjk32ZvrQtSCz4TgSwFPD9+qw7u+adMmWjF9I1yyYBOE3ciF061Gv7Z1jSXkvv/tJtafh3d8zBNhh7KCcLsOaEgjX83x5auKg8nAYoeAMkwkoEuAwk6TnNfCTjUjF4HXqFGjtGfmYOlBSj2nBneLV1991dfzMpp4E5MNQu0Pf/iDFf3TSeKZQyeU/Hkm6sLO3mu/XDFRB74ziIrp5zk7f0YwGqXi3NyxY8ccW99wvg6Xvefl5UWjAzFvRdSFnR2/X2fsUEcQbtcxn0rsHgmEQoDCThO7X8IuF4GXaeGJ6wKQUsUD3KqQ/Ixwp4k3EdmUJRWupnC52rdvX9Z+YyyrV68uvXv3zvosH/CWAIXdBZ7gAGu/n+fsvB256JQ2ZMgQOXDggFx33XWye/duWb16ddbGYbOPIjorJs8eoLC7gDIIt2vPBo0FkQAJFBOgsNOcDH4LOx2Bhw/xjh07Si0CcE6tZs2aJVz34FIFVwssGPy4k0oTa6Ky4SoHRCeF4EZQBCwosgWJSTeWiYIWYmdNEnb/tmCK3P+d/y0tajfynJha8CGirv08r+cVxbBACDtc3wLrGyyfTsQxXDEHDx4cQxrR7JJJws5PgnS71qcL18r94y9G8r5t+JO80kEfJ3O6JEBh5xKYejwoYedW4CFqIq43yHY/nbrvDmdlmMIlALE2ffp0URc0h9sa1p6JgEnCzs9RVBHzeM5OnzKscLVr16Zg00foW04Ku4to6XatN80QCMUu7Ko/9ADPzemhZC4NAhR2GtCQJWhh51TgHTx40AqJ371797QultiFg+88/g1rnbrvThODEdng8ggrwwMPPJDVIhZGhxo2bGjNJ0b+DIO+8zop7C6wUjv52DyKqosgImvC1XH0aG+ve3A+WzI/iTN22MzBv5miR4DC7uKY0O06evOTLSKBbAQo7LIRyvD3YQk7JwIPCy/8OMHND26WuKsOacWKFQLhB9GHyIpJEHXoN4RTixYtrP5PmjQpcuIOAVEKCws1ZyKzBUWAwu4iaWwe4VvixJUwqPFR9UDUIdhIq1at5Le//a3s2bMn6CaUWR8s8++8847j4CmRanwCGkNhd3GQ1UXldLtOwMRnF2NDgMJOcyjDFnZOBB4CHOAfnLtDwuXlEHN+3T+lidLXbLDWDRs2zBJOEFBIURNROF9HN0xfp4EnhVPYmbHggyWsWrVq1rlVBCZyEqDEkwnisBC0D99lnptzCCzgxyjsLgKPstt10cEPBfe/qfStngVyS/8LEcCZSCDJBCjsNEc/KsLOicDT7GKsssH1Ce6YCxYsoMtjrEY2uM5Q2JVe8I0ZM8ay/kcx4RwbUlTdRaPIjG0Sy9sFwW14GXy03a6//OTTEufYrr29rlTr3IlTmAQST4DCTnMKRE3YUeCVPZD16tUrdUG75tAzW0IJUNhdHHh1zg6iDuIuiimK1roocmKbShKgsCvJA9Gz8b5H0e2ac5cESKA0AQo7zVkRVWFHgZd+QHmOTXOiM1sxAQo7cxZ8cG3GNzqKwVP4SkWbAIVdaaGLc6s8ZxftecvWkYAiQGGnOReiLuxMEXhndr1fPAIV6t6mORrZs7Vr104mTpxIN8zsqPhEBgIUdukXfNjJdxqIqejQ4RKFlMur7st8O3TokBUoicLOF7yxLpTCruTwqnN2btyucf4Nd7mpdGPbe6RcjZtjPW/YORKICgEKO82RMEXYRVngQdRtbPtg8QhccUMlabHpTc0RYTYS8JcAhV1JvvgG4uqUOXPmWNF3naQtXXrKyfUbix+9ffIYqdrxPidZ+QwJBEKAwq4kZuV2jftx+/fv72gMTq1/WzZ36VH8bP1xv+b5N0fk+BAJ5E6Awk6ToWnCLpPAw9UHYUVng7Db8a9Dikfg6ltrynemPK85IsxGAv4SoLDLfcF3bOEyOfaXJcUF1Rn+pPhltfN3NrD0uBKgsCs9sjrn7BDcxJ4ur3htXKcM+0UCkSJAYac5HKYKO9Xdjh07ynvvvWf9EW5U3bp1C03gaQ4Bs5FAoAQo7ErjbtSokWWtY+TJQKciK/ORAIVdabjqPjs3btc+DhGLJgESKIMAhZ3m9DBd2KHbOIdSUFAgBw4coMDTnAfMlhwCFHalxxqumDiDw4h5yXkP4t5TCrvSI6zjdh33ecL+kUBUCVDYaY5MHISd6joFnuYkYLZEEaCwKz3cuBsSQYkWLVokDRo0SNR8YGfjSYDCrvS4qnN2Q4cO5f1+8Zz27FWMCFDYaQ5mnIQdBZ7mJGC2RBGgsCs93CpiHhd8iXoVYt1ZCrv0w0u361hPe3YuRgQo7DQHM47CjgJPczIwWyIIUNhl3slv27Ytz9kl4i2Ifycp7NKPMb5/27dvp9t1/F8B9tBwAhR2ZQwg3A+Q1B1N+Kgpd6M4CzsKPMPfajZfmwDeefudbPZ3nsKOCz7ticWMkSKA32+86z16XAjJjwu41X9T2KUfKrpdR2oKszEkkJEAhV0GNF9++aX827/9m/Xxx0IPl3Pig4+7XPDnJAg7Cjx+OZJEAG6Fr776qvXOt2nTxlroITgI3n0kCrv0s0FFzFu7dq3UqFEjSVOGfTWQAN7zgwcPWnMV7zt+02fOnFn8nlPYpR9Uul0bONnZ5EQSoLDLMOwnTpyQVatWSefOna0fAQQIQMJZkqCF3ctvLZLfrV8sBz4+bP1zyw3V5fvfbiI/a97B+ndQiUFWgiLNesIgkLpxA4G3Y8cOCrssg8EFXxizlXXqEsCmLDxvlPfN448/bm3mzJkzxyqSwi49WRVAhW7XujOP+UggGAIUdmVwxg8AhB0SPmrYscfHPyhht+Xg+zJy4XQ59fkZGX5/T0vQ4R+Iu//YvUlGLpohP2rUSsY/1D+Y2fKPWijwAsXNygIisH79emuxp1wx8Wfs5Ks72mixK3vBh2+lsm4GNGSshgRcE8BvOTZr7VFc4WbYv/+F31EKu8xIec7O9XRjBhIInACFnSZyv10xIeZaP/9/5GffvV8ePf/P9VdXSNvSH0990vr/f+79G82e6GejwNNnx5zmEaCwyzxmLVu2tDa/eJ+defOaLS5JgMIu84yg2zXfFhKIPgEKO80x8lvYPfGnCVbL7Na4bR/ulV1H/0c6N76nRKsbP/NTGd7xMct6F0aiwAuDOusMmkAYwm7d3q3yrUo3Sc3K1YLurqv61IIPws4efMZVIXyYBCJAIAxh99rWNXLq7KfWJm6UE92uozw6bBsJXCBAYZdlJii3DRUhs3nz5lYOP4UdXC0h1vY/8+cSlrrvPd9H1ry/Rb6Z8laJVqvnTz6/ItR5nU7gPfPMM9KxY8dQ28XKScANAbhpIRomEty1VECQMIRdpQFtpNYNN8umX/7OTRcCf1Yt+OCKqdzXA28EKyQBFwTwG75ixYXfTLzjOD+PFIaww0YufsfD8LxxgcyyyuM+O7du119+8mmJai6veK2bavksCZCACwIUdllg4WB1/fr1i3ehVUhkP4Uddu9+dz5gSupHPpOwQxcq/aJNKSHoYh54+miqwKtatapMnjxZ7rrrrjLr+dvUmbL/+ReKn7m564/ltlG/9LRtLCz6BIYPHy6LFi2Spk2bypQpUwJtMAQdFnaIiqkWfAgWgERhl3ko1IIPEQbVWSUnA7elS0/5bN+B4kfv3rjSSTY+EyMC+L0YNWqUnD59WgYOHJj1d8KLrmO+4j1XwdDsZeYq7HD+/fprrpU7a9yWtakQc0g4L4//nvnTX1l/xln6qCYdt+uDL82S90c+V9ylpkv+JBUa1I1qF9kuEjCaAIVdluFTH//Ux3SF3dSpU2XWrFlSVFQkb7/9dtraJ6wqlP85/5FXbpjXPfEDOftFkXz9zdfn//lGLr/0MivfM516y+C2BdZ/tx7f1wqwEmSUzGwzH/1DuPgDBy4s3LIJPAi7vaMvuKAiXf//2fsSOJvK///395eyr1+UUAoRpkEkW0KWTJksRUVlqchXtixfshZfy79BJdsgpSxlGTWWbIksGWTNvo6YLGPXlOr/fM69z51z7z137rnnnnPXz3m9vDDzrO9n+7yf57M8VhVVFszyVg3/PoIQWL58uSJsffHFF4iPj8f48ePx9NNPB6yH5DCFXuy0Xp2MErsBAwZg3bp1aNWqFfr16+e1L/O3r0GXL0cr6a7cuqH8nT9nbuS8Mzt+Hf2t1/zBSkACH6lhEinX+xGxu/Lzbkfyege36c3K6SIEgbZt26Jq1arK6zidt1u3bg1Iz6QXXKpMrT7sL7Hr8Nn7ilqlnte3f3WtqdnX4+8vCllyZ0Tt+vfUX3Hhu7WOvt7TOh78aheQaR4yldCrrfrlNkeJe0OmbZHWECZ2XkaUXuzUG7/0+maU2JUvXx5JSUlK7Bz6VqxY4dYC8oRJH9nN0ffI++1w7uolpN+4itt//4UieQsqP3/h0Yb4uE0f5d/kRIUcrQTLzi4rGH0leIFaZAcPHsSxY8cCShwC1bdwrUc9JrRWAk3s6MWO1rxUua5Ro4aD5BkhdhT4eNGiRfj2228VFaZly5ahePHiWQ7PyUvnlPVMH3nGJVJH6pj5c+TGut6ZL9qhNsZ0iUP74vHjx0OtaY720OuQN/xDtvER2jAZHJzGpmXLlgEhdvLFTkJKxM4sVUxfiB2tb/pcz3x6sfPkMC3Y04DVroM9AuFZPxH7Pa9nenB/+IP3QASfP/MRYGLnBVNpW6c+AOjfRokdvdh16dIFbdq0UVRPtIidEVXMB95tiXW9JoXsLR9hFkoEj8aBXFwXLlxYGdqNGzeav7q4RMMING3aFNevXw/KuHha80aIHQmrZ8+eVdTLiCTqUUlWgxYuNnZyTyRyRyFhJDE2PAEsyEiEnfZcik3Itr8WAOxHkbROaHzobAyUTTatc7rIIfs6aUdLXTDyYkderMkunj56raP/S3XKnYM+80rSwsXGjvpnVO3aj+nBWSMAAXq1vbwlUyOjwOPVwa921gwsEzsvuEoCR8nUt/dGiR2VI9UxZ8yYgXLl3PXM6VB4YFAL0IGg1rX3ZGNHRJBu/Ch9OHyhQPCIONCrKan5Pfjgg8rLHX+hgwC91h04cCAoDSLCT+pg8hbfDOcpUt1MjypmuBI7KfDRy4e0RQ7KAHqolMZg3rx5yuspqcPTv/kLDQToTBg2bJgyNoH66BKC7OclWfEnjh2d2Z8Ku3j6yD6ePtKgoa9ngzZeu0R5qQw9ab0WFoAEpHZNqrMyxmcAqtRdxbmvk3B+5TpHenoZYrVP3fBxwghAgImdl0F0DVwq1TX8IXZdu3ZF8+bNs1QBJDu7JEHY6BUuq0/eFJLRdSjZ1+lZG6FA8Ojl9OjRox7tHfX0g9OYjwC9cHmyQTW/tswSSc2ICArZ2El1Lal+beTFTpYsSYWVbQ+FskndlF7rQlHgI3ykY6f27duHJPkMhTEMRhtIHZO+QF0I0NqmOtVkLhiqmMHA2ow6iRTTXhmKcSvJUcvZr5Ic3awyfyYTOzMGncsIGwSY2HkZKlIPkYLdxIkTHXr4/hA7ei167rnnFLWTrD5yiEJ69p6MsEk/v8Ps9xRCp453Fzazz97QYBE8suciAX7Pnj3hBlnEt5c8Yw4fPjzg/SSBT65zcqRCqlpS2PSH2JF6WSBfIwIOnL1CwogwC0WBj5pI+3nBggUxerTNOQ1/oYEAnQHnzp0LmBom9ZrmKmnh0Eev83KdG1HFVKNI2jMnhJ2s9HAZGgib2wqSf0JZ7drc3nJpjEB4IcDEzst4kZCycOFC5fa+Q4cOivoBff4QO/L8R+p/WmqYrs2hQ4Je70hFo54gcORGmdwirxculUkFs4f4ebiob3hbGoEmeDQOJLwHg0B4w4J/HzwEaE4QuSOhj4Q96THPH2InHUQEr1eBqZk0HAi7DRs2ONktBaZ277WQh9Lu3buzAxXvUEVFCnp1IlVrea5Tp/0ldtEAXKirXUfDGHAfGQFPCDCx84DM7du3kS1bNkXdQP3JmFb+EDtfpyO9zCX9vB7fH96pGGaT3R3FyCEd/lCKd0OE04z2BIrg0avQmjVrguKgw9c5wOmtR4AucYjEyeDkVGOwA5Rb32tza5Ae80LZzq5du3YBfRkyF2EuzV8E6OKGyBwROPkFO0C5v30KRv5QV7sOBiZcJyMQCggwsfMwCrdu3cLFixfdiF0gApSHwsTQ2wYy+p4oXhSJ1MnXRAq5QK+L/r4kBorg6e1rsNOR053ExETFWyTZBvJLo7kjIgU+9WUOCXzBDFBubg+tL03e5BNmoWpnZz0K5tVAtplpaWm4dOlSyKq3mtfb0CiJX+z0jUOoq13r6wWnYgQiDwEmdl7GlIQ8ElJIxYhu74PxYheK046ctpB9H/1N9n3quDukIkpkTwZp9fcVL5wIHr0CygDNZjv/iImJUVSA6aPg3cHyGhmK89GsNlFwciIn9HI3a9YsRf3aDK+YZrUvHMqJi4tTgryHqp2d2RgS+aLYfXoD0OutX+0pkuqgsydQzkX0tjFc09Eal2qYtM7JO6Y/XjHDFQd/2h3qatf+9C3QecmTJ4UDoI88eJbo2C7QTeD6IggBJnZeBpNupUi4I0GF4h/5G6A8EuYOkTkKoPxk2Sro0bCtxxg9ZBtIBO/4+4tM6XY4EDx1APr8+fNj/vz5pvRdXQg536FPKwai6ZVFWYFS2CM7MVrras95/tjYRROM9OJBuIWqnZ2ZYzF27FjFqy69ThI5oDPC7E8G7vY1BqLZ7Yik8ojYkSMdqYLp6iGTgtgzibaNOJ339LkGTA91tWuz5+vlLSmOIin+mpkx2Ha26YTr+23hfYjY1fxxhdnN5/KiCAEmdl4Gm15I6LAmmxG1A4RA2tiF2nwkhy5k96f21rnn16N4bHRHfN5hKFpXaeBoMhFAerEz02tnKBM8GYC+Tp06oBe2yZMnmzp8MgYiB1Q3FVZHYTLEgYxZqV7zTOycMf9v0mSs3LcFOwbOdvpFNAl8RLrk17JlSyX+odkfeVSlsplomIustKUlTRz6t3SgwqqYzjh/Lxy1kYfufyZvdvpFNKld3756DRtiajv6f0/reFB8PMsfYgwAACAASURBVP4YgVBEgImdl1GhzUuqZ1FSitFEXzQTuwfebanE11OrWH69cy2enzYIY1p0Q7/GmWoEMtj68ZGLPb7sGV0Y5D7+gw8+wIkTJ5QiihYtilC41aY4hRRCwQryRcTu7NmzbF9ndNLoyKde83Sjz6qY2qDVT3gLW0/sx80Pv9cU+CiUiNRw0AF7WCch8vXEE0/A1wD0ejpNnjw5PIMepHxLQxcQRMRpvdMLHatiauPnidhR6mhSu1a/2NGrWp4K5XybcJyaEQgQAkzsvABNsVrI3iZv3rxKSrn5Ryuxk7Hzdg76zEZwBaH7/tBOnLtyEQt3rsMTZSsjpngZEVuviuPljm77hsZ1siyAeqgRPFKV7NGjR5YB6I2ubyr7gQceMP0l0Gh7Ii0f3dzTjT3d3tOaV9vV8oudbbRJxZq+Sd9/rThNGtequ/L/FpXr4f5C9yj/rlu3riIwR4udnZVxCq1S8Yy0tetLf2idk22dDEpOeWVYE36xy1znu4RmDtnKk938a8ILN33xwjkaOUijL5rUrn2ZX5yWEQgmAkzsvKBPxE7r1jlaiR15wVwvwi7I4Kv0ekfCneuXL0duXBm/Wvlxr68m4H6hjumvl0xvCyVUCB6pYc6YMUNXnEJvfeLfBxYBusUnAU++zKtrZ2JnQ+PObnVw+++/3AaG1K3lGpcCHxE7KTAHdiQDVxupY/bp0wfz5s0LXKVck18I0KUD2dGqiZ0skImdDQl51tP5Tq92kti9+ngzxyWtVLsmGYle6PljBBiB4CPAxM7LGJCBNQkmUjiRB0G0Eju6uZstyJ2rfd3yvZvRf/EkRRXz6Uo1FVRj7i2t/N3hs/dRTzhakQeD1dM+2ASP1CXr16/PxM7qgbagfFK7psscUr+kNd+oUSMHyWNilwn4yUvnFAdKB86dxC/DMgmNfLGTAt/cuXM1SbIFQxfUIqMlAH1QQTaxciJ2tM7lR2tdXuAysXMGOitVTGlnF01q1yZOQy6KEbAEASZ2XmAlQU/9SXubaCV20mYuPWGVEy6ebOwokZZNniWz2aXQYBO8QPSR6zAfAU9rnomdM9aebOwolRT4SCVZqq+bP1JcIiNgHAGaoxS7ksJIyBAnVBoTO/3EjlJGm9q18RnHORmBwCDAxM4LznQTS7r4dPNMt9AcoNxG1EgV80kRhFx+5BWz+Sd9sfStcY6XOvod3fbRzb4rEQzM9LbVwgQvkGiHd13yJl/e4Ms4ltQrJnbOY0u2dqSWvfjN0ZqDTgIf2ShyoPLwXhOR2HrXdU4x2dh5iueRpgtd13AHMnU0qV1H4lrgPkUeAkzsvIwpbfj0Skc2N/RKR8SOhL5ofbHzhazRYUC3+kOf6ewwtg7mEooWgnfw4EEcO3bMEuctwRy/QNRN65rWO93kk6BHQotUv2Zi59sIkKobEeNocaDiGzrmppZhF8i7I3/eESBiR2ud/nZd5/xi5x0/dQrCkdZ6tKhd+4YOp2YEAo8AEzsvmNMmT97xSNgj18gcoNwGGMWyI3u7Wa8ORuUSZd1QJO+ZlMbsGHZmLJFIJnhk30eXEYULF8aVK1eUsAv86UdAfZNPFzgUz45UtehjYqcfR0pJ85AcVCQnJztihPlWAqfWgwDZgdNap3irL7/8siUhF/S0I9zSkDbOqlWrlLmptqVlYufbSLLatW94cWpGwGoEmNjpQJhunemjVzvpRCWaX+wkZOQ1ixyjkFOUWEHuiOARoSMXyaSC2aNBG8s9YeoYPo9JIpHgUQy95s2bK691FCDdE7Ejz50kDNLfZgdR92dMQiUvrW8S+GTQYiZ2vo+MFPjoxZODa/uOn94cbdu2VTxy0n42Z84cTe+c9Du69Dl//jwWLVqkxG3jz2YLSp/acysTO99nBqtd+46ZrzlSZ87B+ZXrHNlipk8AxdPjjxFwRYCJnZc5QSpZdKsnHSpIexEmdpnAEcEjMkekjsgdhTagODfqAOahuvRofD/77DOsWbMGf/zxh9LM/PnzIzExEdWqVQvVZnttV5s2bXD06FGkpKS4pSXyd/z4caxYsQIPPvigorbJnw0BEvToJp9eP+ijm3zpxptf7HyfJbGxscqFGNvZ+Y6dLzlIFZNe7lq2bKlJoslLL5G+s2fPKmpz69ZlCoi+1BMpadVeMYnUcYBy/0aW1a79w09Pbldi9/AH7yFHiXudsv6e+ivoj/woiDqTPz3oRlYaJnZexpM2LK0gpkzswnshUIBaUhOjA16q2y1ZsgQLFy7EpUuXlM4VKlRIEUjDjeCRjR2RkazUMCkNffHx8Thw4EB4D6aJrad1TZ9Uv6R/y9t8Jna+A02Y0VpjOzvfsfMlx4ABA5Tko0drO7KhCx7ax+jSgjRQoj3mnrzA0fLYyi92vsw8W1pWu/YdMytyEPk7PHyso+gq82eiwOPhe0FtBUbRUCYTOx3ErlWrVg7hTqpmMbEL3+VBr68kcHbo0EEhQK4BlEltafjw4bhw4YLSyVKlSim2leFC8JYvX46lS5d6VbEkVU1WxXSex9KpggxQLuPZUSomdr6veSnwbdiwQbFT5s8aBIjYde/ePUsVSyJ3nTp1wrJly1gVUwyDjFFLI/Lwww87XjqZ2Pk+R+lMJXVMVrv2HTuzc1zfb7u0pY9e9PjFzmyEQ788JnY6iB0J/uRAhf7mcAehP6m9tZAEdFKxU9v9kP0JqSkRoZPf+PHj8cknn+D27dthRfCoL6R2tXHjxiyhIFLnLY03LCPt9/SaQa+2REJozdNFDjtPMT7KMlA5C3zGMdSTk0gKaR5kZcs4duxYxakS2ztCeUWmdU5xFuUnL/iY2OmZce5pWO3aGG6cixEwGwEmdjqInfSEqU7KL3ZmT8XAlEfjRjaTapsfsjnbtm2bIvSQrYpahZEEJhLwt2/fjhMnToQVwcsKUVLFpNt7JnbOKJHNJX3yxU79W36x832NSgcqRI7Zzs53/MzMwcQuE016YaL4tKS1QR+ROiZ2/s02Vrv2Dz/OzQiYhQATOy9IxsXFOVSIZNBiysLEzqwpGNhyyGaSbralQwyqvWnTpsrNLXmSLFOmDI4cOeJolHzBIaHU1YtmuKlouiJNL3tdunQJ7ACEeG10k08CiiR27DzF/wGjPZQE6VCxs7uybSfOfZ3k6Ng9reORv3oV/zsa4iXQpRVpJYSLSrmVcNKFA9lYy4+cp8iXTH6xM4a8DFQeKmrXt69ew842HR2dKdmpHWit88cIRDoCTOwMjjATO4PABTkbCZn0Aqt2Yy+bRASPPGLOnz/f0UoSAMh2QC2URhrBC/KQhE31/GJnbKhCTeBzdTBwb9uWKDdmmLHOca6wQ4AuGeiSli5x5Ef/l2cCEztjQxpqatdE7H7pM9jRmYI1q6FEx3bGOse5GIEwQoCJnYfBIs+IN2/eVFxDqzd/DncQRrNbo6meiB29XlGIA9fwAFrEThbLBC+854Jr6+myhgQ8UtGSHzlO4nAH/o2zFPjoQkX9Uu5fqf7l/v3MWacCchQv5l+BnDtsECBCR+tcxqelhtP/5dxkYmdsKFnt2hhunIsRMBsBJnZZIEobFd3u0d+uNjf8Ymf2VAxMeZ4ObfIkSfZVaucp1CL6GanszJ0712MDmeAFZuwCUYtc8yTouXpx5Bc7YyMgBT5SdVOHjjFWGudiBPxHQManpZLo8katwcHEzji+pN1C6z1U1K6N94RzMgLhiwATOw9jR54Q//vf/yrEjgQ8OgjotY7DHYTvZKeWk/t1Cj7t6siBXuwojh0F7VZ/lP7atWu6BFImeOE9N2iN0ws9OfqgW31a92rHSUzsjI8vC3zGseOc5iJAL3XkQIscp9D5Lr1jsiqm/ziHmtq1/z3iEhiB8EOAiZ2HMTt58qTiKVGqZ9AtFAl9rIoZfpNc3WIaR6mOqeX5UJ2WhHvyipmcnOwW6y4rFJjgheccobEmIie948lg5ayK6f94SoGPbvJd40b6XzqXwAjoR4DOcXKWJV/kXc92frHTj6VrylBUuzbeG87JCIQnAkzsPIzb0aNHkT17did1LDoQ5A0+q2KG54SnVtPLDAnxpF6p5USF0khSRwKAUbsgJnjhNUdIoFOrCpLAN3PmTPTs2VPpCL/YGR9P2i9p/6Q15+1CxXgtnJMR8I6A6zqnHHQeyEtbJnbeMfSUQqpd07kp903jpXFORoARMIIAE7ssiN0333zj9NutW7c6bK2Y2BmZbqGTR612Ry7tSdiU9lWkmkM3j6SqY0YwXyZ4oTPuWbWEiBuFwlB/ajVcJnbGx5EFPuPYcU5zEZDq+OpS6fVOXuowsfMPb1a79g8/zs0I+IsAEzsPCJKNnauHREoqb5uZ2Pk79YKfX77IEGEnokeHO6mJ0RgTqXN1nuFvi5ng+YugtfnplZbmhPpTu0FnYucf/rGxscra4kDl/uHIua1FgImdf/iy2rV/+HFuRsBfBJjYGUSQiZ1B4EI4Gwn1gbD/cSV4FStWxLBhwzhwcAjPDWoaEzv/BohUMeklnD3m+Ycj57YWASZ2/uHLatf+4ce5GQF/EWBiZxDBaCZ2Y7+bgzk/rcDud+cYRI+zEQJM8MJrHkQzses8ZxQaPVwDbR5taHjQSAWOQoeQMyJPtq2GC+eMjIBJCEQzsRv+bSJOXDqHWa+8axhNqXZNqq1mmDIYbghnZASiFAEmdgYHPpqJXb2Ervjh8M/4Z/Jmg+hxNjUCTPDCYz5EM7Er2KcRHvj3vdgxcLbhwZIe81jgMwwhZwwAAtFM7Hp9NQE/px7Gul6T/EKa1a79go8zMwJ+IcDEziB80Ujs9vx6VEHrP/P+n0Lsdg+2vdjF3FvaIIqcjQle+MyBaCN2J8XNvfwqj2yPwrkLYHXPj5Qf3V/oHp8HTt7kU5xAtrPzGT7OECAEopHYTVg7X0E3adcPOHHxLHo0aKP8/7WacSiQM4/PyNNeSTbLrHbtM3ScgRHwGwEmdgYhjEZid8dbtfD3P/+4IfZE2cpY33uyQSQ5mysC/IIXmnMi2ojd/O1r0DZRWyXL6Gs9C3yhObe5VZkIRCOxe+DdlgoAl29eU/4ukCuv8vfiLmNQuURZn6cHq137DBlnYARMQ4CJnUEoo5HYkW3dqfQ0LBAC3/lr6ej2ZGsFvScfqoLWVRoYRJKzeUKACV5ozY1oI3aEvrzJH7hkMooXKOJY8z3tN/q+jpD0mLdhwwbTvc762hZOzwhoIRCNxE7iYJYqJqtd89piBIKHABM7g9hHI7GTULGNncFJYzAbEzyDwJmcLRqJnYTQDBs7KosFPpMnJRdnOgJM7Py3sWO1a9OnJRfICOhGgImdbqicE0YzsaOXu5HLP8WV8asNosfZjCDABM8IaubliWZiVz/hLRTInQ+L3xztF6BS4GvdujXGjRvnV1mcmRGwAoFoJnZkX0d/nnyoqt/Qstq13xByAYyAIQSY2BmCDYhmYmcQMs5mEgJM8EwC0sdiopnY+QhVlsnr1q2rBIJnxwpmosplmYVANBM7szCkcljt2kw0uSxGQD8CTOz0Y+WUkomdQeA4m2kIMMEzDUpdBTGx0wWT10RS4CNily9fPq/pOQEjEEgEmNiZgzarXZuDI5fCCPiKABM7XxGzp2diZxC4CMh28OBB9OjRA9evX8eMGTNQrly5oPaKCV5g4GdiZw7OUuAjVUxSyQyHj9bYnDlzUKpUKYwe7Z86ajj0N5rbyMTOnNFntWtzcORSGAFfEWBi5ytiTOwMIhY52erUqYOYmBgULVoUixYtwp49e0Kic0zwrB0GJnbm4CsFProc6dmzpzmFWlxKjRo1MGnSJHTr1g1vvvkmOnbsaHGNXHywEGBiZx7y4aZ2ffuqLdyD/LLls4V94I8RCCcEmNgZHC1+sTMIXARkGzp0KIYPHw56uYuPj8eBAwdCqldM8KwZDiZ25uFKAl+JEiUwd+5c3YVe338Qv6eeAQlfeSqUF38C81J+5swZrFy5UiFzbdu2RdWqVdGvXz/d7eaE4YUAEzvzxivc1K53tumEy1u2KQAQqau750fzwOCSGIEAIcDEziDQTOwMAhch2YjUvfzyy4qB+NNPPx2SvWKCZ+6wMLEzD8++ffsqDqiOHz/utdDLW1Kw5/UeyFHiXvGnuCJwXd9/QPw5iLJD+6FEx3ZeyzAjgVTHnDdvnhnFcRkhigATO3MG5vtDO/DBp5Oxd+5qTJ06FY0bNzanYAtLufDdWnF59KuD2N3TOt7C2rhoRsAaBJjYGcSViZ1B4OzZpkyZgsTERFy6dAlNmjTB5MmT/SswwLmp/UuWLMGKFSsCXLPv1THB8x0zrRxM7PzDkVQYDx06pBRClyIjR45UXuwef/xxzYLpZe7w8LEKiXv4g/fdXuiI2NHvbwsPm9WXf+Vf43Tkrl+/vhKioVq1ajpSc5JwRSAQxI4Cgfdo0Aal/l0sXGHy2u5PNyej95cJyP/tMcUmPVzUrr12jBMwAiGOABM7gwNkFbGjlyC1Q4FQsd8yCJPHbGSjRhjSF4rqjN76S+qY9JFKZrh8TPD8GymriJ0cl9OnT+ONN96IWDU/slPbunWrYqOWJ08exT518ODBHu3VUmfOQfrmFEHq3lNe6Tx9v/QZrKhnxkyf4N8Ae8k9YMAAdO/eHcWLF7e0Hi48uAhYTewu37qOgr0bYV2vSabEiwsuWu61/5x6WPkhvdgN/zYRD22+hgfKlsa8z74ItaZyexiBiESAiZ3BYbWK2BFhIJut+fPno3z58khKSgq610WDEOnK1qZNG5ANy8aNG3WlVyciErxu3Tp06dLF57z+Zli+fDlOnjwZlLr9bTsTPGMIWkXs6CWIPC7S17JlS4X8ROpHa71du3bo06cPBg0apLzWkZqW60evcaR+SS9x3hwYEKnb9vTzilpm4cYNLINu7NixyksjEzvLIA6Jgq0idhT4m0jd5ZvXUH98N4x/vqdC7ArkzBNRL3fUNyJ18iu85wpypf2O4Z99hNdqxoXEGHMjGIFIRoCJncHRtYrYyebIl7tIfbGjfhI5ohv7lJQUQ6NAr37Zs2fHxYsX8cknn4SsrZuhzgUgExM830C2itjJVtBL1pUrVxDJNlxq5yOE5/79+zUDldNr3a3TvypkTc937usknF+5zvJXOz1t4TThjYBVxG7C2vkgFUzX77nYJ7C4y5jwBk3VekleZwtVTOrz/2LbYsyw9zF99kw89UT9iOknd4QRCFUEmNgZHBmriR291o0fPz6iyQrZqdFn5MWNSOHEiRMVGzcZfsAKOz0i2MGOU2dwiurOxgRPH1RWEju63Bg2bBhoLCL5o1cv6VFywoQJyhresGGD4iFT/e15vSeKPd/c6QWOyN4fFy4qye4q/G8npyn0are5dlNTvNjRWLAdXSTPwqz7ZhWxo1rlq12Vka8oZK5yibJKYyLR1o5s7IjInhy2ELGxsVmqXatHhNY5XdLIj1Ssvb3aR+9s5Z4zAu4IMLEzOCusJnakokjqmJH8+aOGKXGRL5s0HmYTsKZNm+LChQuKgxcSRo0Q0HAaPyZ4WY+WlcROErpnnnkmnKaMz20lAW/GjBkKcZKByrXs7Ei10tVhyvpy1fH37xlKnf+64w48eWynU/0bYmqj5o8rDAuBpCZKr6b0kb3jrl27fO4fZwh/BKwkdoROpNvYyRlAtnakktlTOImhde9J7dp1xjCxC/81xD0ILgJM7AzibyWxI7ISFxeHI0eOGGxd9GQjdUwKGmwF6aKXQLL9o5dFsoEyYgfoz0hQ/aSalz9//oDWzQRPe9SsJHbkmIO+0aNH+zNlwiqvDFRObtBd7ewontQDvbqiwOOZHiivbNuJjLTfHH0s+kwTx7/JJu+XPu/65R2T5j2tcRoDafcYSHs6+Wp748YNtG/fnoOgB2k2B4LYkVORV4W9mXyxC1JXA1ZtVmrXAWuEqiLSCJBf3orlUKpn12A0g+tkBCxBgImdQVitJHYGmxSV2bp27WppqAQZr46IdiA9YMpwECTsEXklFbZAx8tjgue8pKwkdlG5eEWnaV2lpqa6vY6dmGALf6JX4CIbO+lB018sad6TcB9oJzb0WksXVFWqVEGzZs34xdDfgTSY32piZ7BZYZ0tK7XrYHSMNALkV+Dx6rptec1oK8XJoxAu6vpZ1dQMZLkMiQATO4NzgYmdQeBMzkakx0oHM6QuSl8w1GKJVNJH4SCCaW/JBM82aZnYmbx4RXEkRM+cOdPNzo6EHxK+yCsmBSbP6iP7up1tOiovfGZ4xaSwDBSKIZCvddQ/UgWlOoNFLM0f3fAskYmd+eOWldq1+bWFdokUBF39YkjhXDgQemiPWbi1jomdwRFjYmcQuDDLRsRuxIgRptvv6YXBSscwetsg00U7wWNi5+uM8Z4+K4GPgo8TwfMWn45e98iDJglIZnykFhsslVgidxTyIhjE0gzsIqEMJnbmj2JWatfm1xb6JdK+Jj96reMXu9Afs3BqIRM7g6PFxM4gcGGWjRyo0A1+INUw1RBJO79Qgk2+KPz2m83eqWLFiopHx0j3JMjEzvxZKAW+1q1bY9y4cW4VUPBxUluKmT7R7eWOXurUwcnNEI6IWAVTDZJUr9euXRuxQerNn0Hml8jEznxMqURPatfW1MalMgLRiwATO4Njz8TOIHCczScEKOwFBawPxY9U6MjpRbQQPCZ21szCunXrggieJy+U5CWPXu/yVCgn/pRXbrcvb9kGInYlO7VT1JjMIHXW9M63UqPRiY5vCFmfmomdNRh7Uru2pjYulRGIXgSY2BkceyZ2BoHjbD4hQPH6Au00xacGisTRQvCY2Pk6M/SllwIfEbt8+fJ5zHTlpx24deq0QujI4QHZ3kUKoVN3Wtra6UOPU5mNABM7sxG1lSfVrullnl7o+WMEGAFrEGBiZxBXJnYGgeNsEYtApBM8JnbWTF0p8M2dO1eJdaX+iMTZ4lqtVQgd2aYQoSNiR85SvDlWsabFXGokI8DEzprR9aZ2bU2tXCojEH0IMLEzOOZM7AwCx9kiHoFIJXhM7KyZulLg69GjB3r2zIwvdXlLihKbrtjz8Yq3S/lCR+SOPMuRembZof1QomM7axrGpUYlAkzsrBt2b2rX1tXMJTMC0YMAEzuDY83EziBwnC1qEIg0gsfEzrqpSwJfhQoVHIHKJXEjpylkW6f1yaDkZoU5sK53XHI4IcDEzrrR0qt2bV0LuGRGIPIRYGJncIyZ2BkEjrNFHQKRQvCY2Fk3dfv27avY4JCdnS9x6XyJd2dd67nkSEKAiZ11o0lyE611LbVr62rlkhmB6EKAiZ3B8WZiZxA4k7KRU5EtW7YELQyBSd0Iq2IoYPq6devQpUsXQ+0Od4LHxM7QsOvKNGHCBEycOBHJycnIt2UHru07qDsuHalk3pk/L0r17KqrLk6kHwEKbUJebzt27Kg/U5inZGJn3QB6Uru2rkYumRGIPgSY2BkccyZ2BoEzIRuRul69eiE2Nhbbtm3DsWPHTCiVi8gKASJ15MmM1OV++uknHD9+3DBg4UrwmNgZHnKvGaXAN3jwYFTfvFvY1TVX7Ork99u3K/HHb+cd/1fb1ZHa5tmvlnoNZO61EZzACYGxY8fihx9+QPHixXHo0CHlUicaPiZ21o6yq9q1ujZay+dXZs4zdpBk7Vhw6ZGJABM7g+PKxM4gcCZkGzp0qFIKBQ2nOG9JSUkoV07bDseE6rgIgQCR6aVLl2Ly5MlKIPLOnTsbfrmTgIYbwWNiZ+1SoIsa8or52t6TqDJ/ppPHyy1PPINbJ085GlD/5G7Hv0l1c3Ptpqi750drGxhlpb/55psYMmSIQuxobDzFGYw0WJjYWTuiarVr15qI2B0fP9nx44c/eN+jja21rYye0l0xLzu0v/A6XC16AIjAnjKxMzioTOwMAmditjZt2oBiPm3cuNHEUrmorBCYMmUKEhMTkZKSYhpQ4ULwmNiZNuSaBRG++/fvR+K9Dwk1TGeB7sq2nbi2Z58jn/rFjuzsdrbpiJo/rrC2gVFaOhG8ggULYvTo0VGBABM7a4dZrXZNGiD8BRcBckKlJtOu2hLBbR3XbgQBJnZGUBN5mNgZBM7EbDExMdizZ4+JJXJR3hCw8oU01AkeEztvs8O/30uB7/PW7VDq4XK6wxiwKqZ/uGeVm2zs6DKH/o6Wj4mdtSOdmpoKUscktetost20FlUunRHIRICJncHZwMTOIHAmZSObL1ITmj9/vkklcjF6EKBXUqsxJ4I3btw4/P7770qTKlasGBKCJRM7PTPEeBoZqPydV17DYz/tRfXlX+kq7Jc+g5Gz5L3sPEUXWr4lopf53bt3R5UAzsTOtzliJLVUu546daqR7JyHEWAEskCAiZ3B6cHEziBwJmUjYtepUydWwzQJT73FBPKVlJw3TJs2DX/99VdIEDwmdnpnibF00oFK48aN0eXybRRpUt/rq520DyGbvGz58hqrmHN5RIBe6kjVPVrUMAkIJnbWLwipdh0tdpvWI8o1MAKZCDCxMzgbmNgZBI6zMQI+IhAqBI+JnY8DZyB5XFwcSFVr+4aN2BBTG+QVz1MYg3NfJym2IWSPx8b+BsDmLJoIMLGzfmLIQOUbNmxAiRIlrK+Qa2AEoggBJnYGB5uJnUHgOBsjYBCBYBM8JnYGB86HbGqB7558+UFqlr+nnhGvdw0EeauulHR9/wER3iBJvNDlQ9mh/dhrng/4clLvCDCx846Rvymk2jXb2fmLJOdnBNwRYGJncFYwsTMIHGfThQCFdCBbtoyMDLzyyisciF2FWrAIHhM7XVPXr0RS4CMbS4qbSB+pW5LntvTNNk+seSqUU4ge/R1J6pdt27bF9evXcfr06agJLeDXZLEoMxM7i4BVFatWu2Y7O+vx5hqiCwEmdgbHm4mdQeAiKBuRrwMHDljiTIRs2WiOUfD1fv36sfdPjXkTaILHxM76xSsF7vv0RQAAIABJREFUPvKWR7f5ofRRaJUvvvgChQsXNt2ZCDkpGTZsmOIkiAge2Rmyx8DgjD4Tu8DgTp4xab1Hm51d6sw5uHX6VwVkcvqkDt0SGOS5lkhHgImdwRFmYmcQuAjJJmPoEQEj5wJWhV1o2rSpgtiKFRyjy9PUCRTBY2IXmMUbqgJf/fr18fTTT2PhwoVo1aqVcuFi9kfksWXLlli0aJESGJy/wCPAxC4wmEernd3ONp0UdXL6cpS4V7f338CMCtcSCQgwsTM4ikzsDAIXIdm6du2K5s2bK4IexXajlzuzP6pj27ZtpgYDN7uNoVSeK8H76KOP8Mwzz5jWRCZ2pkGZZUFS4KOb/HzCji5UvgEDBijeIWmeHT16FFaokNF87dKli6nzNlTwC5d2MLELzEhpqV0HpuasayG7XlL/ll/dPT+GQrO4DYyAbgSY2OmGyjkhEzuDwEVYtjp16oBe7SZPnmx6zygwMH0k6EXSR6+QJ06cwB9//KG8epjdPxK8ly9fjnXr1pkKGxM7U+H0WBjtrX379sXcuXPx+OOPB6ZSnbWQymS3bt0se1GT5FFnc8IiGb1CtmvXTmnrzZs3sXXr1pBuNxO7wAyPVLvu0aMHevbsGZhKddRCpE7a81JyctDEHyMQTggwsTM4WkzsDAIXQdmIeCUmJlr2okYEKH/+/JbY8AVzGOQLJ+E3Z86csIlFyMQuMLMmVAU+6v2bb76JZ5991rIXtYcffhi//PJLYIAOUC1kN/jNN98oL5zh8CLJxM6aifHp5mQ8V7keCuTM46ggVNWurUEg8ks9MWEy/rxyTelo3orlcE/r+MjvdAj2kImdwUFhYmcQOA/Z6OXrypUrCpEhm7Vw+OhVaMuWLeyx0uBg0Zg3bNgwbPBjYmdwoDWySWch58+fV5ykuKrMxsbGKq91Vqg7+tMLelEjb53VqlXzp5iozCtf7ugyJ5TtBwNN7L4/tAMFcuVF5RJlI3peFOzdCLNeHYznYp9w9DNU1a4jeiAs7Nzm2k1x+6qN2OWpUB5V5s+wsDYu2hMCTOwMzo1AETt61ZgwYQLuvPNOyxx0GITAtGzkXXLNmjUKoaPXnPHjxyu2a6H+0diQHZAVapih3nd/20f2g8ePHw8rpzCBInb0KnTo0CHUqFFDsemKxE++3FSpUkVxFuKqnkeqmGSDE2oe80jN9+WXXw5pYhKq84Wcz1AYi1AnxYEmdh0+e18ZslmvvBuqQ2dKu7SIXSirXZvSaS6EEQgCAkzsDIIeKGJHRCcpKQlDhgxRXrQi1TviwYMHlZGIj49X+luuXDmDI8PZwgEBIvMvvfRSWI1zIIjdzJkzFUIzb9480KvVsmXLIpJE0OsNvdqQmt4HH3zgZg9Jl1kTJ05EcnIyKlSoEA5TmtvoBYFwsR9kYmfeVCb1S0lc1aU++VBVrOs1SQl3QPscByo3D3MuiRFgYmdwDgSK2NGrEDmXIHurBx54IKJfh6x0RGJwmDmbRQiEmxomwRAIYkf1kJpisWLFIt7tfVau/aXHPBb4LFqAQSg2XOwHA0HsLt+6jgcGtXCMAv1f2p4dH7nYyQ4tCENlWpUnLp7Fz6mHlfI6zH4PPRu0QWzJh5T+EbmjL1TVrk0DgQtiBAKMABM7g4AHithR80htjeKkhYvtmUFIQcJ+pPfRKDacL/gIBIrYUU9JVfGJJ56wJFZa8JG0tYBe6/bv36/ZR3mTT4G6Q83OLlTw43ZYg0CgiB3Z1tE3ce185e8egvTQp7ZBs6aHwSlVSxWTWkL7Ku0DoaZ2HRyUuFZGwH8EmNgZxDCQxI5e68glcDjYnRmEU8lGYQOsCvTtT7s4LyMgBRBah4FwwU92dpFOaMhe7dKlSx7tCFng43UXDAQCQezU/YoWG7sWU/or5FW+1EkMpNr1hg0bUKJEiWAMOdfJCEQUAkzsDA5nIIkdGZvTYRPpxI68TEZ6Hw1ON84WAggE6sWOVBSJ2NGLVqR/0tZOq5/SYx4LfJE+C0Krf4EmdhPEi92Vm9cw9JnOoQVEgFrDatcBApqriRoEmNgZHOpAEjvpWIQdihgcLM7GCJiAQKCIHTWV7OxC3XugCZBmWQQLfFYjzOV7ulAgxz4dO3ZkgAKAAKtdBwBkriKqEGBiZ3C4A0nsDDaRszECjICJCASS2JnY7LAtSgp8FDeO3OTzxwgEAoFAv9gFok+hXkdcXBxSU1PZzi7UB4rbFxYIMLEzOExM7AwCx9kYgTBFgIld4Aeubt26ikt0dqwQeOyjtUYmdoEfeVa7DjzmXGPkIsDEzuDYMrEzCBxnYwTCFAEmdoEfOCnwEbHLly9f4BvANUYdAkzsAj/krHYdeMy5xshFgImdwbFlYmcQOAPZyMbw2LFjljpWCUQdBrrOWUIIASZ2gRsMchxTpUoV7Nu3T3EkQ6qYpJJp5ifrIHsq/hgBiQATu8DOhSW7fsCwRVNw5bPNyhpntevA4s+1RR4CTOwMjikTO4PA+ZiNYvht27YNOXLkQJ48ebBixQofS/CenOqg+Hn58+e3rA7vreAUoY4AE7vAjFD9+vVx991345dffsGHH36I1157TQn30rNnT9MaQHECaT+hOmbMmBH1jmpMAzYCCmJiF9hBJGJHoRDq7M3GateBhZ5ri1AEmNgZHFgmdgaB8zEbBS0nwYs8gpYpUwZHjhzxsQTvyamOQYMGKS+CVtXhvRWcItQRYGIXmBFq27Yt5s2bBxnn7scff1TiW82dO9e0BrjWMXr0aNPK5oLCGwEmdoEZv083J+PyrevYlXoY9O8Wf5XBzlUb8c2671CpVFlTGnF4+Fhc33/QUVaV+TNMKZcLYQRCGQEmdgZHh4mdQeAMZqMg7fSiNn/+fIMleM8WiDq8t4JThCoCTOwCNzIU365du3aYM2cOKIAx7bfHjx83tQHqOlgd01Row7owJnaBGT4KzP79oR24LGL4EcErdSsXsP4Y2vR+A6O7/9eURpyYMBnpm1OY2JmCJhcSLggwsTM4UkzsDAJnINuUKVMwadIk7Nmzx0BufVkCUYe+lnCqUEWAiV3gRoZe1Bo3bqzEEqO9tm/fvsqL3eOPP25aI9R1mFYoFxT2CDCxC+wQSlXMK2NWIjY21nS168D2hmtjBIKPABM7g2PAxM4gcAayEemir0uXLgZy68sSiDr0tYRThSoCTOwCNzIDBgyAVI+U8ewGDx5satBodR2B6xnXFOoIMLEL7AhJYvfP5M2g8CYVKlTA1KlTA9sIro0RiCAEmNgZHEwmdgaBM5CNSNe6dessVcOkOpYsWWKJcxYDXeYsIYgAE7vADQq9pr3zzjsOpyZ0k0+vdWYKfK51BK53XFMoI8DELrCjc+LiWfws7Oyei31CeZmn0AcctzKwY8C1RRYCTOwMjicTO4PAcTZGIEwRYGIXvIEj7Pfv388CX/CGIGpqZmIXvKG2Su06eD3imhmBwCPAxM4g5kzsDALH2RiBMEWAiV3wBo4cqEycOBEbNmxQPGTyxwhYhQATO6uQ9V6uVWrX3mvmFIxA5CDAxM7gWDKxMwgcZ2MEwhQBJnbBGzhSz6JA5Wbb2QWvR1xzqCLAxC64I2OF2nVwe8S1MwKBRYCJnUG8mdgZBI6zMQJhigATu+ANnLzJJ0+ZZtrZBa9HXHOoIsDELrgjw2rXwcWfaw9/BJjYGRxDJnYGgeNsjECYIsDELrgDFxcXh9TUVLazC+4wRHztTOyCO8Ssdh1c/Ln28EeAiZ3BMWRiZxA4zsYImIgACfqBsrliYmfiwBkoigTumTNnsp2dAewiKYvVa56JXXBnC6tdBxf/UKmdCH7r1q0Ddr6HSr/NaAcTO4MoMrEzCBxnYwRMRIA2/4ULFypBbekQsPJjYmclut7LZoHPO0bRkELGOmvVqpUSxN7sj4md2Yj6Vh6rXfuGV6SmJptq2vMpzA2tdavP90jCkYmdwdFkYmcQOM7GCJiIgFTbkUV27NhRIXn58uUzsRZbUUzsTIfUpwKlwEcH/Lhx43zKy4kjBwEidvRqRx8Fs+7QoYOpQh8Ru2zZsiE+Pj5yQAuznnTv3h1paWlYsGBBmLWcm2sWAu+//z42b97sKM6KtW5WW0OtHCZ2BkeEbhKGDh2KQoUKGSyBszECjIC/CJw/fx70x/UjctezZ09/i3fK/9prr+HEiRPInTu3qeVyYfoROHbsGO68806ULFlSfyZOGVEIHD58GH/++adTn0joI7JPf/v7rV27FiNHjkSOHDn8LYrzG0Tg8uXL+Pvvv1m+MohfJGQ7ffo0rl275tQVurAlz8j8epf1CDOx82MF0K0h3SLzxwgwAsFB4IsvvsCXX37pqJw2fqmWacWrHQXJ5o8RYASChwC9ytNrjvxIVYvWPP3NHyPACEQGAlIVk3pDdvTyZd6Kcz0yEMvsBRO7SBvRMO/Pli1bQMIzLV55K0MEmv5v5oKmOny93SUST38C5awjzIcyIM2Xqpgk1DVq1EiZM2bOk4B0IoorofVE2g/0N61HKZzTPmCmoC4v4HydG1bsPVE83KZ0nVQx5Xyh23tf93FTGsGF+IwACep0dtKakvaRtM5p/Hxdl1lVTvuJL7aXNJfIKRO1w5d8PgPAGXxCgOYLjQ2f6z7BpiRmYuc7ZpzDQgRkEGLa8GfNmqWo10hSZ+bmb0Rw7Nu3r9Jztu+xcAL4WDQd4jQvzCQBPjaBk/uBAAl5tM7p8KbLllWrVmHu3Lkwsj6zaoaRSxmaW9Q2fg3yY4AtyEpCOK13JnQWgGthkXS2yxiU8pyn6ojsSU0ISdhpX1CPL+0HlE6mlRc1NA/o35Sffkdngbx8lT+XZ4PUsKKfq88L+j/5TKCPXoP5Cw0EaFzMlPlCo1eBaQUTu8DgzLXoREC9+ZNgRR4PSehTb+gk/NGN3y+//OIgWrTx089pMyDiRQbw9NFmTk/4dEgQMatRo4ayedPv6baXNnTKR+XT/6VHNPoZ5VPf4FHZ9HsmdjoHk5MxAl4QkMSO1h59tEZp3clLHXnLT4IbrXlJtGhd0mvtmTNn8PDDDysvtXLN0zolAZL2D7lXSEGQ9gEqgz7aC6gc9UuCa/BzeZPPFwc8lRkB/xBwPdtp7dP6pfU+ceJEZR3T/+UrDf0sOTlZWde0VkkWoPVJ8Swpj3ptU1m0Rukcl3sE1SfLpnJkPpIb5J4heyQ1hZjY+TfGnDs0EGBiFxrjwK2wI6De/EmYo82diB3d3Ej1SdrAKR3d7JMgSEIh/U2Cn7y5o02ffk751GkoD33yxpAOAUnw6Odbt25VDgM6ICifWtBjYsfTlBEwFwFXYkdkjciXFOLkfiB/Tmuf1iW9osmXPkpLly1yTyBBkPYM2gOkACfVPak+GRuJBD0S+MjbKe0Lsg71ZQ4TO3PHm0uLXgTUZzutKzqbiWTReUtrls5aIlj0hxxfUXpa55LYybQkE8hXfUorL3Xo/JcXN8WLF1fOcHkBRH9TOZSPZATaD9TOtZjYRe+8jMSeM7GLxFEN4z6pN38StOiTKha0IdMGrSZdtFnTwSCFPZle/bImCRrlp3+TMCeJHW3wdEsnBT8idnSYUB1S4JNwMrEL44nFTQ9JBFyJHZEtEvCkoCf3AzXBop+REEdrlQgcffJFnvJSWinU0b9JIKR0tH6lcEi/p3LUhFCqY6k9rjGxC8lpw40KQwTkWlabWRBJy4rY0XqmM1utJSPPZTUJJDjo55ROXvjQxY4khvQ3lSOJHV0GSS0BysvELgwnFDfZIwJM7HhyhBQCtPnTLbsU2kjIknZUWRE7SkNqHHRTJ2/n5GEgSR+lIcGOfi7JHv1N9dHPpSomE7uQmhLcmAhGgIgdrXkiXrTmZSBaKQR6InaS/OXNm9dB4uSalsROqnpRGiJ+0n5Hqn3RiwFd6khBkYldBE807lrQEZCq0tQQqRVDa45e4eXLG61Zec7T7+j8p/8TWaOznV7ZaH3TupVpqTzaO+gcp7OfznnKR+lIJqC9heqQ+aTDJvUFDpUl1TmDDhQ3gBHwEwEmdn4CyNkZAUaAEWAEGAFGgBFgBBgBRoARCDYCTOyCPQJcPyPACDACjAAjwAgwAowAI8AIMAJ+IsDEzk8AOTsjwAgwAowAI8AIMAKMACPACDACwUaAiV2wR4DrZwQYAUaAEWAEGAFGgBFgBBgBRsBPBJjY+QkgZ2cEGAFGgBFgBBgBRoARYAQYAUYg2AgwsQv2CHD9jAAjwAgwAowAI8AIMAKMACPACPiJABM7PwHk7IwAI8AIMAKMACPACDACjAAjwAgEGwEmdsEeAa6fEWAEGAFGgBFgBBgBRoARYAQYAT8RYGLnJ4CcnRFgBBgBRoARYAQYAUaAEWAEGIFgI8DELtgjwPUzAowAI8AIMAKMACPACDACjAAj4CcCTOz8BJCzMwKMACPACDACjAAjwAgwAowAIxBsBJjYBXsEuH5GgBFgBBgBRoARYAQYAUaAEWAE/ESAiZ2fAHJ2RoARYAQYAUaAEWAEGAFGgBFgBIKNABO7YI8A188IMAKMACPACDACjAAjwAgwAoyAnwgwsfMTQM7OCDACjAAjwAgwAowAI8AIMAKMQLARYGIX7BHg+hkBRoARYAQYAUaAEWAEGAFGgBHwEwEmdn4CyNkZAUaAEWAEGAFGgBFgBBgBRoARCDYCTOyCPQJcPyPACDACjAAjwAgwAowAI8AIMAJ+IsDEzk8AOTsjwAgwAowAI8AIMAKMACPACDACwUYgYohdRmoqVh/8A0XL3YfqJbIFG1euXwOBjPQzSLv2F3L++z4Uye0bRDcOrMXyvZeUTIUqPY0G5X0swLfqrE+dkY4zadfwV85/4z5fwbC+dSFSww2cP3URt+7Ii7uLF0T2EGlVKDTjxvlTuHjrDuS9uzgKmg5MBlK3rcaWk7dEV3Pi/sefEnuq6ZV4gTEU2hAKI21rgz97Z+j0wkNLQmwvtHZthfxocAMDhkAYnG8htjbdhyYMMAzYfMqsKPSJXcZNIQBn4C8P4OT8d0FBEk6iz8gzSMgQibLnwfpBj+CJIIAZjCpth5B2zXfkvRvFzZf6DHdz39jKqNR/F9ov+QefxesvJmPt2yjZ8COcz10E9xf4A1VHH8eidgX1F2BlyowtGFG7KRIuPYfE9Z+idUmdle0bi8qV+mNX+yX4xxcwdBYfGcmS8Mq/nsPnsWOw9+d+qBgZnTKlF0mv/AvPfR6LMXt/Rj+TgTk2tQEqdFkH5C+Oe+7Kh5cXzkT2XgbmuB89DYU2+NF807Ma3Tv1NiRjywjUbpqAS88lYv2nraF3G9Nbfpbp3PbCDGwZURtNEy7hucT1+FT3pmpKa2Dl2jKnhaFdyrG57fFU129QaMBqpAyoFtqNDWrr3M+3kMMuKHKKL+ufZQStKRz6xG7/flSedxm7PCzA9vVr4bP6ZzBs7EkMvy4S5cmHlH6V8GhQF6zeyjNwYvsJjNl4GcuJnJUsihMvP6A3s5LOdghpZ4kdsxc/my31+dQ658RGhZNlnXMgbkZRDNx8CiMfpzKPIWnwaCTf/wamdc7q4EhB4hvT8Gv8YAyJs0hUyViLPqWbIeFyPczYvRIdH9QJkO4N8zSSR7yHpHu99VWj3pREvDHtV8QPHgKruu9eqx/tdSvM7E3bw3w4nYwR7yXhXjFXspxOOofW1GQextA64fMgxlcvj94pzfH5pSQo9ydG57gGEKeTR+C9pHvFvOwMzyvX2jaYOj4BKszo3qm3eRlr+6B0swRcrjcDu1d2hN5tTG/5WabTIHZr+5RGs4TLqDdjN1bq3lR9bE3A15aP7QtqcuP7+LGZTfBIp/Uo0HsZjn7QwCdNC337Q1CBMbFyDWLnB3YmNiyzKN1yipm1Z0Br/WvPDbNlBDP7EbyyIoTYCQBvXMOB1Fv4d4miPqv5BQX+Q7+g9tx0bFI/RZa6G/90LO1Tc6wT8Hxqhq7ExoQTKeS1wde/z0MrRSPMvpi9vXadnooG93XBpRAjuApYujfMtXi7UEN89IzvL3unxcvLfV0uWfKq43nAjbfXvUyTN21P80G8CBcSL8LP+PiSrGvS+5nI0xhat+6XoXOOOMwoMxK79g7EI3623zX72rcLoeFHz2DJP5/B86O9tW0wuUsBKc7Y3hmQpvlfie690P+q1CUEfm2Z235rSzNzH9ffUn37g/7yQjulyeebFZ0N0trU6or23AgDDK0YFy9lhhWxi638IH5ueY9Gl25i98ZfcUhRxcyNhnWKQSrqZaSfw48bL2Dexb/EY14O1Mp/hz3/HXioygNCcDmLlTtv4Jr46b0PlkEt5cFMq7zMnynpil7CmrWpmH/1LnR8ujweL0T5bC9ws/b8jrN33olGFe5F8yoFtG+sDh1As0VXsB//4MLNv3GDsltB7Ow60neRXRsOYO3yvbiU80HUjauKu6nOG+exc+t6HBXmazmLV0GtqqWdbXbE709d/MNmy3Pbnh+FUL5hbVRS1Dxv4MDa5SDzt0LlG6J2Jc+2UB6FE9HGozs2YeeZWy5tIP3p7ZjYrB5G7muNWSc/QANl9MQr2f0d8HXrWTgpbgTJDujf9xWB2upOsUnZMxnN6o0EBq3HsjdKuaTLQPrRHdi08wxuif6UrlcDVbK0dbPpcv9B6q25zmPb6i04easoYp+tgcIX0nDtL9c2qMrPWRyVYoojl33mKTaGJ1SqmNOfwN4f1+CAK4YK9skufdVjVyXqPpOGPZObwdb9ZbB1X2XPp8IchUqjXo0qzhciTvPmBDZ/n4Izt3KieJVaqFrawxjraO+N8zuxdf1RXBJjlmVZClaqTXvr6/hHYlS6HmpUcR5vJXkWffI0H3LkvIXfk/vg/g5fo/Wsk1Cmk5NNnxj3nVux3rZAUKVWVZR2Um9WzYuCtzXXgsM+tFB5NKxdSadNXNZjqCZ23XJvxvcpYh6L9lV7siZKuZmfqvqgNdZyR1XGPAk9aW1VHIT1y95AKQWLXLgp5pPTHFftC7nOb8PqLSdxq2gsnn2iLLKLcuR8ztxTbDgl97kfHb7OXMtu6uI62yAKU1TQ3ex1tWxCPO4vGkeJul839+LHNQfEXPW0P6hwddoT7eU67Z2qNVTtSdR0HySxFcu14V6f9t4p5sjeH7GGNg7NuWk7k2gvuPaXxr6htO+WmO60p5HJg7vN740T9rnlioEH2xtP9mmZfdM4JzSER1ebQvl/txFzsb/NCsPMvEFYW/az1oG3Yw+R46M6P3ycN85neDU8WbOU01mYFSY2XO9Szk+xeSl27DmLVUBMyZ+yOHf0zHv7vLLLCI6zU+yRJzZ/jxTlrFe31cP+cMcd+OsvcfutYYtum2vuZ79tnL2vjSznpJBr1G3WknE87+tqWeFm5tnudm5pkBL1mtSNnWpmpx/Fjk07bWd1pRixrh1Ch0dbfl/Xpq1E5zPlsTL/tlek3me8yVj2uX8XySU0/UiOzIkH68ah4l1qfwxZnR0rVOYa3ZBbc27ZZAPa3xQ5OFuqXX4TGDn24kybbud5qXFGhMGPIoTYncPYhGPof1kgXqAA9vauoNjjpP24G/VWX8dBTQO9OzGmbXX0E9RKqnra1Dpp1LTKy/xZtcpF0OzoBYy49o9ImwOf96yKdrnPYvyUE+h9kX4mv3+hXLli2PxyKQfRdJ8TR/HKkDQo2pRWEDv7oYn2vXH3igR8d17UY7dZKryqDxq1SMCeG7lRpHg2XD1zBSjeBonrZqNdWbuzhKRX8K/nFqB59zdwcNpHOCFoakaGYNDZqyFh9Sgc7/wsPjoh/it+liF+Vy1hO7b10jb60RJOMg7Pwav1O2P+hRwofk8eXD93Br8X7oT52xMRf7d94/O6kNq7vQDIupyz2tNl7MbUVs3QI/mMYkeU7/YZnBcY1Bq6AouG1bERXrfP1pYFzbvjjYPT8NFBwkCUd6U/DtaohP67VG2g8ps/hS7f3USRiqVQIPUIDl+hWwfbp9gYlrETu9jGiLuwHskXVBiO+Qkb+z2C7Ar2rnq2euyq9mFsZWqTSyfsL5wZu6eiVbMesHU/H26fOY8buWth6IpFGFbH3nv7vLlUPw4FfkrGoWxFkOP387iSkR3FO83H9sR4d5yybG8aVvVphBYJe0RdRVA821XYplsi1s1uBzndnFtsH/+C5RCT6wT2XM4nLifOi7ECctdKwKa1vfCIfZp665On+fDf9p/jf24Q22360lahT6MWSNhzA7mLFEe2q2dwBcXRJnEdZrcTBEZprMu8cFoLqzHqeGc8a1sgYt3QsknA9m29dNgLZj2GNmJXEvXjCuCn5D24nd2+LouIebhHvIbJSZy2EcNaNsXwTUCR+wsDF04KBGshYdNa9JLgSdClgK0eBGWviEOyMp9Uc9xlX7AthyW40v8imtfvhO+u5nes5yvZ6mHy9l7YVF7YS7pMSTd1cZ1teGJeSxR7cTHKu7zGZ6x6C8UaT0aFj49iY7cHkfX+orXMbWsutnEcLqxPxuV8QuI4L9aHEJVrJWzC2l5iXYpsx5O64vlXZ2G7mMP5ixOuYm5kFBFre4+wH7aD78CoG47PFPvs7ez2fdIlHdKwcVhLNLUNEgrjAk6KfVpdn/veuQ8zm9RHp++uivrvQZ7r58R6yoZ6k7fj+y7lnDq2fVgZVBueKtp2RbQt0wnOsUl1UPo/+9H1u7P45N6JLja/6UIdqqZQhzyIbI423Ubx/yzFLx81Rl4PN/luL8nHk9D1+VcxywaU0rczYi8sIubKHmFbrCClUZZrf7XXr8gr1tMBsZ7K6cAwE5QgrC1lq7DPLac5K9uitba8zZsMHJ7zKup3no8zGZlneLaYd7Hsx/fwRF6980qcSb3vxoqE78TeIMSDV5/FrtnfuK5URfPj+SN6571YQ45+Zu6R3Y7PFPvpbWS374eZ80D7rI8dNg4P/q8vFpd3tbO2vyjep2V/7WVt6JmTfu3rkrDZz3aP55ZS59y3AAAgAElEQVQGsXObI3qwo6HKwO6pzfFUl+9ws0hFlCqQiiOHr4ifOoQOd1t+PThorXNxLr5dj+S+HChbpgQun9innMm2zy6flNEjY9nnPtqj990rkGATTpV5Fpes9sfgYW4o8+uIjdiVrI+4Aj8hWXNuZe4xDjn4auZe3GnJIlT/pKWQ165mzstOyTiY2CwLuV3j7AihH4UVsatYoTiW1cqXCV/OXILp00GlRcQy7e6yF8yPta0K4uCKU+iY+jcgXtOeK5kLnRtURNx134ld9jv+hQxBFvPn/D8UyJcPyd3ux8lPdyPumChb3DDFPVwAzf68hmHCS+d5/B+6P10NH9b05KkzMMRuF5GugSuxeEg9KA7ujgk1xQpdsC5fe3y9YzpaiR/e2DEKT9YahJR83bHm9IdoQOmksJ69HLovXo1xTwvRetYLKNNxqY3I2csscn4WXijTEUtzibyXRF6NSe4mnAjbnbdLCjVDsbBlGzIOf4KnY7phU7O5OLuorVhYGgefXvUAzYNU6G+/XVKog11FtZGb8P3AqsidkYqFr1dF68+vov6U/Vj7ppaFSebmUqTFTPzweQfYHHO6t++00JMvK2wMmsw8ggUdSghhbjdG1YjFiDtVQr1DgC2Cxh9/g3mda6Dg5YVo+3BrzL9JhFEI54S/3r5q4K2pricxv1oNIzd9j4FVcyMjdSFer9oan1+tjyn710LpvqxXjHunz5Zg4gvlBU67MbbOY+ifkksIiUeFkKjhwMZDe6UzjHztv8aO6a3EHLyBHaOeRK1BKcjXfQ1Of6hliyExFxiN+QozetDcTRNTMkaQmvOZgoPePmnOB+qqllOfY5jaoAK6rMuH9l/vwPRWYhxv7MCoJ2thUEo+dF9zGh/aFojtYBFroVz3xVg97mk41oJC5AZi5eIhqFfkPGa9UAYdl+YSeS+JvPpOAU8ql9K2Nnu57vhqxf/wbKls2D2qBmIH7VIJVPY+bIpxjDXSVqNb7Ub4BEORcmSYhi2ylmqLJ+GTaFoRtJj5Az7vIOaH+J/NHraEsIfdJ+xhBT4Cs1mfXkSzbo2EEK9RjiYMOtqQIdLkF7g7CXwZWPVWMTSeXAEfH92IbsX17C8uDZD7XZHGGPPVDPSoJ8ZdvGK+EiPqOp95qZKxZSTazi6L90bFK5oLGan2/a/oQGw+NRKKKbB67/xqBf73bClk2z0KNWIHYZfKIZBtbWxCjNyPBElZ3a02Gn0CDE05gmHCYNxtji7rjBxxM1Bi4GbsE4bHYvfGjlmf4mKzbmjkejNlX5MHiHgLMmWjdscwqU5p/Gd/V3x39hM0OuLizOngeFQv3xt7mn+Ok0ntxNiJG+3liVhbvDNeoQsBvcROOJca2XY2yr43CvGkzSH2Wts6UNlM6yB2zqOULuZZOTHPgE7JB5HYrKA4zrxj6DrVAr62fCJ2Ym3RmZvFvJFOxa7KPYbO8AMLMOmn8ujxyiM4owMTB2EWF7UDVy7GEJrvyrGm7dzLl3nvSuxse+RXWPG/Z1Eqm+1MHLRLfVGptT/INV3IsRaUcbSrz8sLHKex9bY29MxJv/Z1neeWWiNFOgfzQOy8Ynd6JpqU7YT1TWbiyIIO4pwURI/OgxF3ImH7NmjetevBQWMe/NCnKOol5EPfDbuEPCB2/TSb3LK2pSRDemWszAsWxzlp976sdZFlu6x2vcRXn79ZzC0neUtc4HerCnF7jti643CUTjEp02Ej+sXWxbijQhY6JWQhi1wz6Dv9jacKK2Ln1k3HC5cGsTu0H9XnXEaKyNSm3mOY11AQq737UGnBFSFe2F/ZSH1S5ZxF74sdBFlrX688pje0q1meOoAGiZcg/MihSc0YrHg6r/jXbSxM/AmtTwmfKBVK4lRbTzPEDGKnMQGk/Zmc0KWdBbktg+5DzVG/ocXcs1jUNlNAl7e7jp/bN5qKI3dh70C7xU36HMQXao+lFdV2OOmYE18I7ZeKl7wDYiNxvjRWGui6WNOVW/dlaObSBkU4nNMOyb8nopnZxE623QUPpM9Dy2IvYnEJLwJv9haYe3YRMiFzP4y0BIbdoyoJoTtnJjZyXNp8jd/ntXKo67rlNZnYpc+JR6H2S1F6aAqOkMRo/2xjsRgl5M891btlEO6rOQq/iRut38WNltunmW8LBt1XE6N+c8VuO4aVqYbhqa4/l6V60J+XY2i/rS+qt0++EDvZzxbygsHepu3DUKbacKQ6fm5vo+ZaqIiRu/Yic9nYsK+WcEC8amssEI1lnLXw6Vy+mzBmb6uQiJzG+uD46hAyu4d1qoNUUTvtWGZ3wcdG7MqLfm8V/XYNkWAisRNEw0bi7su0Ic1YhbeKNcbkCh/j6MZuKKhrf3EB3cMckevG89hlYGHbHGg9X7X/ae2dbvuZfQ24Em07sRKDpMwVT8SuvNiXt4oJlnUwCjuJS1FdGB2bhDql/4P9Xb/D2U8aIbvrurXXf1TsT7+I/clNi0EvsdOY0xkL2yJH6/mZ68BHYpcmcI0hTQbHhZA+DH0jdhasLR+JndOZ6zZv0jGvZTG8uFh1ieLUQX2YyHnleh7ov1D0PO/diJ3THql1oeZhf7DvY3+IS4xTNu9pgteRra79Asf1HtZO7PStDRtobnNSki5D+7q+c6ucL8TOG3Zac0tcIlUSl0g5fThvvK9NbfKt7H1LOtlkNt0yliyrtDNp15AVtS7QbSOndf5qzC1Necsul5xW+28ApGzcKfl3cWmksYGFwY8il9hlHMdb485i8h9AaUGs9glidXn5dtyzWVyj35ELyUMrC9IgPiPELl9+7HqnYqZjgXU/41/rborC/mV7xfuXbeRv//k3zvwpVDOzVLE0g9hVzLSjkpNO6qRrHsAaXuccsrRNXaSk3EQ1DyPtjcubQwdX4cQmBObCo61bo6rq8Sd9x9f4entpu+Bp8oudfdMvqjokbF2X9ai8ATotYE9Guu7tsx06eVRePDOEHJxfvDI1yfQ0qFcwMpnYuXsYtXdS1iNu6C+JG/qCnurNELdzOcSroqcQBFr57EJiiixbhatUKcz0eKoG3RPm4ra3krjt3We7vbvTzWuqhz75QOxs5CcFzT+/hCSn0Br2NpWULzM+rAUP9Wd1TmRN7FzUcl2wl30o/dTraKB2tnt8LaavThWvHVoHl2/EzlWVMmP3eDSo1RubhGpzTLshGNX/dTRy2N2aSeyEMGZXu7zPrvIl/195yinx6l7S/nrobX9xQd/TGNmFpH1qh003hN3csgX4as5iLNr+C84J3eIMqYpE2uh6BHm5Nko/hdedBwlrp69Gqv0CxV3bYTfGN6iF3puEkmhMOwwZ1R+vN/Jsv2lTu0xxqGNKNUzH67HbuhUv450fRZsZQl+7eF10e28Mur+gst/Uu38p8N4QdlXLsOCrOVi8aDt+Eer2pJnumDu+EDv5egqVVolODF3XWcDXlp75oMiqelQ27Q6GPGnI6MTEo917VueOz/Nee4/U/yrj8rqc3S6Q32e7wHHTrxGaJd7Xhpc5qUW6lKHRCDPj6ZXN7Yx0PrfifSF2LmW5YWd/wcyjkmsyRLvyC1muidsZpl4Jvq7N00KT5T6hyaKWk+z7+iX7mahbxvJ8HuifGzrnluZ81q4/EhxVhRWx8+w8RdvG7tiqnXh8wy1Fbzz3nf+nEK0MQb6qVS6FjeK2S7nlNELsVHZ8yhLxQOwcyyfLMAZmELss7K58mNBKe11fZCwkdrZNsqAbsbPhVg5tx/RBg4ImEzuPwrW3F0f9xA7HhFrEI0ItokQnfDb3HZTcPBDx3RaT3lCm3rZewchkYueRfLu8gpUzk9hl0Yesb8f0ETuhC6kd0821Tz4QO8+bu3x9tN9O+iUAZEXpbL/zR/h03Ma7Ejul5Dyo3yMBL7qZw/pH7GxyvCA8Cz7Cu+9NxdrjgnjUGooVi4ahzt3mEjtyoqR4jFXsbHrgV+UFr67j8kTf/uIyBjqJXZrdPvlQAUF6Bg5Du2dicPjdomijji2oR0B3aFS4Ejtbu/LU74EEMUja85GEsgX46N33MHXtcXc7WXXX7B5hNynqmDFIJDXMUyq1Uc01Sg4oVmH6mGEY9dV2Z/tSvfuXtFM9VAB1uw3EsHbPIObwuyjaRmWDpZvYSTXsfJmqvtRHnRi6rraAry0988G26G12nlna4nnaG+291ImJr8TO2LzXKXxnoapt0yj5Hh3JHjTvCEVrpIz9Akd7F81ibeiZk37t6/rOLVOJnVCtntnkEXRaX0KYTszFOyU3Y2B8NyyGOKcOilc0rbC/enDQWJsyNMof9cZg7sRGuDX1DbSesAcx0r+CbhmLiZ13CcBYiogmdmmCcMWsEz4P78mOglf/BArmRIvy96BbvaKZXqNUxM5BHDNOod/4VIyjRzgHidMmjwrsqjIyVTH1DognYncbqTuP4svj2dC8SWm7PZd7md5eybRVKuQLkjo2nK1sN5UjC4md7VA5ZTPeb+RJmchkYuf6MuWA1FVgdxMDPATL1mhfuvDaWbMZPr+rCopePYvreUqjSdchGNzZbt+oFkZcQjZYrYopD3K3lyhXQu9NFdPJXkeFlVY+aQ+ltj+yzTYv6rteVFrst5jCU4wS+N5rn3wgdvK20+1l15Us+iUAeN8j/BE+ZR+q2R2JeK9NkSo15rlnG7usY2Vm2lHaVHxz2J36uDs7cm6bzjaITLaLgfuETd0IHKzWENOezFSd1be/uKDiRRXT1t80G6HM0xcbDo9FHYefKZcLBj0Culwb1Ty8PjjkdC070My2O+yjPaqS29faBmFTlxKL/5XuglNqFV0vF0iZdrh2telftW2wXOer1F7ou+GwsMdRO+TyndilCRXOh4UKZyGHXaG9/zoxdNvRPVwIaa45F3wMrS0980FZgnqInXz98aBhohMT34id/SLF53nvP7GTphLLXliC7x/sg5rD79NtA+W6Nq7YNWqynJN+7ev6zq2KZr7YibNUcXb0+V2oUvQqzl7Pg9JNumLI4M6oZ7dbc53/Rtcm9omL6/pv4eR9VfD7hbNAsVi82msM+pEdvlq2cdPQcZWxmNjpO5N9TxXBxE46T7kDvRqXw+DqBbTdjKts8YRLHAwVziTS9qdjyhW7d0s9xA4XMenDg/iP8GyIO7Kh02PF8E7FPLh1/Cym/JYDo59/0HevmFt3o2jydeW1MXuJIjj7RlnNMowROyFSi8OjtLgVzKb2TmZ38jFoV22b8wHScbCQ2EnSeaDaGPwkDFndTHKU+ayx+O23z+s0VPucloBUn3KyY7OXd6AaxvwkDGXtlUqhQbBMm82J21rS/2JnU4HLjknHNuItT/Hm9d54S9VHFx174a1Bcdn7W9HH8VR1u8G7S5ttdn370Obr3zHPFgDQcbvtjHmasA96WNgHIZNky/aJH17/6gX7RUhmOo/OUzTbmy6mUWnxqpbNyWtghnQkUduTUCv154dh+/ahqKp0wW4UrnYSYm+r1z5pzodM+wpnO1JRd2nhMCOb2stkZt21HWTJH1VMe6gQVMLTDeyHop4xVJalxiul65xK1+qDt0NCJ6nyQIDSdmxGermamRdRdsyPKCqFDe12aC72S7rWWtb2N/e1aIGfFy/Dk2p7Xc154aX/0i5u2HZsH1rVtg849kWpGWHHSL3/ZBzGhw0fQo8fVdoTugR07bXh2ko3ATxtBzanl0NNmwcn8dkF/SPyJdm9n/LVo1mLyli8+JSzXYvr3BH92bY7Jx5x7C0utlSl7CrZDpVkAZN0wqRyMmObp2ryITw5ftgQD/X40TdVTLuDhvn3CbvurSK+otMGrQ9DV0Q090cr15bcf1TzxkE6Mly9Ynp7sQNs9vBHEetkZylcw5/5A/mKAys09lyv80om0NzHjc57vcROzjGt/cFuUzuzNKqVT0HK/XaTAa3l7GVt/KlnTppB7Cp6ObfMJHZ21dvsk45ho0ehwxkso2tTMedY0Amrrk7CU5rbqV4Zyxdi52lu6JxbPmiuuau9H8YP3+zCtQfrIq6qts90b6dqoH8fwcQuHTMnHUCnNHX4ATu82e7Ac5WKYUrL+4RR+EVM/fgQuvymSnfHnWhxz99YfEa4vtRF7MShtvcAmiddwneZXu1lZXj/+ccwKMbT0Hp4sfvhZ+RYfdPmrrZoIZz6T3louV8xSuyEKyO7d8GrwoSiGwZ2vBubE0Zgzh5nN9tmEjtp8xP71nJ8ldBUuLgXgvLYOnisfwqykZ1I73g8eHMzJv+/qTj54mq7tzetxW83DD8qvCUOHYv4f07h/s5DEOcKkHSmcFN4dkwYgIeO3EaDhM6I2S1u2R/rj5RsMWg3pDdqps3EqEkbcMERZkFrrPQTO+kUpnDdlujUtiViitrLU8cZ00vspPe6H537WmdjSxR4Sah3wrP3JmlzdLNcJyQMeAhHbjdAQucYF8xrIm3mKEzacAGF1WEMZPtEDbljmqNPlya4Po/SnUE+9WWAG1R2ewiX9sbdJfAjz4JXiysqWR3v3oyEEXOwx5PrfaVcO+biX9nJzmdgR9y9OQEjxCS97XQZ4DqPPPTJw3yoZnck8aPwhDh0bDz+OXU/Og+Jw112Jw1XXeoWfuhVoRb8IHYpQ1Gm+gjhlaugR0+Z2mNYTR+xE7hJRxO2PryNmrmOIenjUVhcXJBpxduh6+cHsbvyLV67/1nMvm1bV/EP3sSaEf0wZQ95ArV5EbXZdv2IIo2HYmz8Pzh1f2cMcVu4OtugNF3uBTRJVM5BlN/p2V9c+m8nY6IwsS++irfblsaeKbQv3kY1GYZErEnpMZX2n9eL7sGHoybhJ3Gxl5FRPtOZiy5ipwyS09p4u2YuHEv6GKMWFxcXbEloJwbJee+siYNv3o9nZ99W7Bh7xz+Im2tGoN+UPcjl0cOsqEc6iKJDxdX+x2U/2vv/qqFa370oTGfD2zWRa++neHd0Mi7UmoL9a98Udk2nhepXWRFugUzwXsUL1c5h9bSVOJcrl4gOkenQxuENl8b79aLY86HYQ2xAZYaqkPZgsW9h+VcJaCpinzgLV2niZf9+4ZgrAyXrvYqmD92lGrTH8Ma0zqimA0PXmR74tfUD+hSth4TzZH/6Bp76KwULFh3AH/lECJfzvhM7unCweSkWhgtP98fbL+TGrpkfYvZPZTBh//foktv7vPKscq61j7dD6qs2T8G+zXudwrcYoCz3B7sTlaNibbo6fcsc2yv49rWs14bNW+g65MtqTppB7Ozn1qtvt0XpPVM0zi1pJ9kcE1dPw9tEGnTa67mNm31tLytcFy07tUXLTKFDFavNeQUYXZu2C4VziGn+Btq1qIUH89jLVcVpzdAlY/lC7DzNjR2amlRu+PhB7OTei+yeL820JMZg/iz0id2pYxix9hpSBUolHrhPuOPVUhZOR/JXp5BEsTRy58Vg8UJWUnilPJy8C49u/QP/dydwhZyYOH0UhuBxEYaADtZTmPTtbxh3XgQxz5MTw5tWQIPzh9D/oFDfdJSnVYdLkTd+w4Jvf8XU0xk4evv/ULpwTrR7tARe8hSkXMl+BomfXsBP9M+ihTGtmbhqU2SS81g4/yT6/PZ/+G/TR/BmJe1wCSmJb2DaTyUQP1iD2FA5p5Mx4r0kpD4m0nWu5tpgHFgwFv3Hz8Yu8aKep3QTdB0yGJ2ly2NKnZIoDs6fUCJ+sEoAS0HiG9PwU4l4DBYCsORTXttCOt3PvY6FZytgwMplsIVbEm601yfivRGTsfLodWoEarV6EwN72tyIiw4gecR7SEq1H+D2HmQcXoiBXYZiIeUpVh3DPvkKr1VxX0ppGyfhnb7jsEHpXxuM+XSMQgAzUtcj8b0RmLzyKK6jmIjd0wtj+r3gUeVVAKHZZ832pS1H5+otsLGoXVVBadZNEUKM4sU1xozdK9HxTu1x0cLQua+xePejJHQuuRGTBvbE4Jm3MUDEfemnGTpQxDGa9A76jtuAs8KmqnSbMfh0DI2XC+auqhTUXMeL3TjMr7AB42fvwlktlVKN3UuzvTT1bhzAgrH9bWVRe7yoikjMf43rgnqpUzFi8kocve5prHT0iZa65nwQrwgLB6LL0IWifNIseRcfJQlhUWnyAoztPx6zbQvEXaXWw7zQXAuua0m8iiwc9Rb6j1iNWhTbMF4DTIrPpTGGaVrr3sNad+oDzfWW7fD226+hoUaQbO15rrEGNfcFigO7F6umf4Txn9FYEWSt0Gd0L7wkX36ozwO7YOhC+7p79yPhpMN1X9Jaa9r7ACF2LGkwRienAVU74qMu5Ppf/XnbX1wwly92vaah6415tv1Ba32I+ICT3umLcbSxFKuLtwYNwku5xJqedyNzL9bEyEM/nNYGzcGWaPf223itoT3YtOveWUoEgV81HR+N/8yxb7bqMxq9XqpuC2ej+WVgy5TumLkDuDtuAN4ThNDxuc0dm43SrNHTbHNfzJu6bw3CoG7PZO6RrhgMHYJuBdag18zfVOeRev7ay3hJOC8bMQ83HGcKxbh8Dq8vPIsKA1ZimTgYTiePwHtJqXhMnDOdq9nng2afVOeCNwzd8gd6bYn1oTq3lLUxrh/qnf5YzN/7bQTV45nrYd4IzY31ie/Z90aaN6+i18iettASVJYXTJxxdgbI47nj87zXPjs1685yf5Cv0p68KEvhwNva0DMnfZBx3Na51DTphWldb2Cex3OLYs+1xUv/24nrLafhREJjDXlLL3ZpWN65OlpsLIoqv18Q56vtu0lxS4Ujq8YzdmNlR1c3M3pwcF+bGbsnoGGdUbhasSiunhWbPH23LyvOo1C8N5Yd/UAJleVdxvK8p+ufG9CUy9zya56N2vW75RWqpy3ajMC5JtOx5AMK2xP6X+gTO6MYCjfjZRZexVEXD5YZ4uePip/vE+VmhjcwWgnnYwRcEbDf5v/xgZvHLnkbqQQo1xTifUMzbVZT3NM1l0v4Bd/K8JjaD6ctJrUgKorJECrIZesuQkd7vLKo6HQod9KA59JQ7g63jRGIGATkq5Qjxm2o9syLYxsLmm17ffsDH0gTGlmH1EZxseU33AR7zNg5rV1NVjTCzxiuhDP6i0DkEjupykg2b1ULo9NDBZHj4m9I2pmO4WkikHh2cWvYVYQ8oFh2/DECpiFg39RFoNBzKzqobnduYH3vh/Hk+LvcYrb4XjW9MHXDsy/PQd4RP4k4m97iWPleg/44RgbK5iwKAmkbx+KVlv2xs+kS7BFMPxxuAiN+6JjYRfwQcwfDEQEPHlFDsiuBJ3Y2k5wmmHluBTqoDpIb63vj4SfH4y6XWKaGYbNf+F7quwGHhRmNQzlAvBxPjy+JN7738ppquGLO6AsCkUvsMs5i6sxT6HH2L5udmurLnT83ZsdXRKsy2uqNvgDIaRkBZwTSsaxrDFpOETZrdvuUotePYRXZPmz4A/WmrMbSN/0nYvvmDsX3977urDZr5lDwi52ZaGqWlb72A0y4EJfpTczyGrkCrwgwsfMKESdgBAKHAHlzLYn/rL+NK1eyCfPmTVjby//z09r2B57YpS/ripiWU4SfALstddHrOLZqJj6cvQF/1JuC1Uvf9OCczlck9uHj2jXQfRMc9r15ftuDz6d8gKWHSqD7N+vxYSO+ovQVVbPTRy6xU5C6jfQzv2HngZu4RP/Nlh2VKhRF+SIejRDMxpfLi0oEhF3PtqX4cu4qHLGroBer3BbPPV8DVYpIL3YhDky60MlfcwjX7n0UrWt5cu0Z4n3g5jECviJwfBO+3v4r8j7UUMSi1LLn9rVATs8IMALGEaCzdDW2nBTmW9WeRE1Nu2DjpVuT8zg2fb0dv+Z9CA1FbLlA7SIZqduw9Mu5WJUpdKDtc8+jRpUimeG9TOkweXOejbnzfrbb8uVBmUbPo9VTVVFa8YvAX7ARiHBiF2x4uX5GgBFgBBgBRoARYAQYAUaAEWAErEeAiZ31GHMNjAAjwAgwAowAI8AIMAKMACPACFiKABM7S+HlwhkBRoARYAQYAUaAEWAEGAFGgBGwHgEmdtZjzDUwAowAI8AIMAKMACPACDACjAAjYCkCEUbsbiA9PRsK+m3AmY7dK9fg0LV78WjrWmDXEZbOwYgvPCM9HbcLFjTZgNk82JQA1gmrbYbQJZ7DkCHNUMK84nWUZHC9mengxcyyYKXxvJVl6xiqgCQxax8PSGO5EkbAHQFT9xN/Aeb9NSsEj2/6Gtt/zYuHGjZBsPwlpe9eiTWHruHeR1sj/HyVGZxf/k5rzu8RgbAidjcOrMXsufPwsyKBkieeF/FS8+oooTji2Y6RFWrj3V9E3Lrki0hs5o93nn0YW7kS+u9qjyX/fAYTYknzFIxSBNIXtkXJ1vNxu1oCtm/rhYqhhoM96OvibDFo/lIlXC/UDstGN8uMTxOQ9hpcb2aGZDCzLFjp7trKsgMy2F4qMXMfD0J/bhzA2tlzMc92SAHFKqPti6+iQfkw8YYbBMgiskpT9xN/EeL9NSsEbTHgYjFm78/oF6QDet/YyqjUfxfaL/kHIqRpmH0G51eY9TKcmhsmxC4NG4e1RNPhm3AjdxFUrPEUSt9cjTVbzuN28Xb4YsvnaFUiHWsHNEPHb+/BkPmL0dGvBcoTNRQm8Xe9S+GNAwOwclkXlAuFBhlpw76ZaNFmBM49M1MQpgYBc32su6n2uF0VR+7C3oGP6M5mbkKD681M4cnMspjY6ZseB6egWZPRKD/tBBIayyxm7uP6mmFWqhs7xqLxE/2x6UZ25C9bG/VKpmHz1n04fyM3ao35Ad/1qxqyr/ZmYRBp5Ryc0gxNRpfHtBMJcExRPZ00dT/RU2FWaXh/VdD5rjdKvXEAA1YuQxeVQBHOxC7QMpL2ejA4v/yd1pzfIwJhQez2ja+OR3unIF/jj7FiUTdUtV9+0kHabno5TJkcD3NDIvJEDf6aOYjx1cuj959jsPfnfqH30hV8gExpQWjcFBpcb2YKT2aWxYDCb/cAACAASURBVMRO39xc1hk54mbghbC8pXbpYtpCtH24NebfrIaRm77HwMxDCqOerIVBKbnQ5utfMK+VuSeVPqA5lVEElnXOgbgZL/iuuWPqfmK09TIf76+ExEEhR5bv/afby1z4ErvAy0ja68Hg/PJ3WnP+MCZ2GavwVrHGmAyhFnlUqEVmEe3RTVfarueeV+gtx2bY7Yjy1EePhBcVopCRvhc/zv3KpjaTpwyav9ENzygqM54nqlodlIJOv/hqA6i1bG6c2IwFs2Zhs1JkIzz/QjMfgmradZXziqDQsRlYMDYBq8/mQf0eCXiRGqxW83FT8bHnzRmLZx/9C6smTcNSEaiS2vDiS81R3aavqnwKTtdE8MwnC2F3YgK+2HUd5dqOQZ8GAtyMVGxb+iXmrjqC6wKTRi++hObVS2Sq5mWkY++q6Zi5VPwexVC57fN4sXYlqM0as8RINSaP3rkNX06frmBVrObreP0lu1qtkmY9Zvd+G3PxIj5MaClqgo/65zdwYvMCzJq1WdiOFUPNDh3QKPdBbDmU12E3qa3X7smGSR2Uk/r9Il5tUN5xA28ri8qORcaCsUhYfRZ56vdAQtO/tAN9ZzmWNNYnsHnBLMyyTSQ0ev4FNKtZyscbf3WbXVWXbfPg24974+25p1G731foWV0vxh506t2CO6vm86N3YtuX0zHdNth4/fWXVHPSw3qjufbjXHxlD4RarGYHdHihJhwxalXC0/WBd2DStKUiILx7P+W8z3JeehTEsh53pWz1mlD2kRrY3PhJjCpk4FJCPS+01p+KNO76tj62y/WjsRfR/rZq+kxlH1BUAp9/EbUrFVSp2VLw36X4cu4qbdyU8bwmbE+eRKHdiUj4Yheul/v/7X15QFXV9v/n/R5PS03FwgYcQ58mohVYjpk4UGA4Tz01Z1EcwSkox8QhxZHEAfwq5qyoBQUq5gBagiWKaYqaSkaWKIp5zff6rXPOPveee+459x4mhdr3r8Jz9ln7s9dee629prbo6fZP3Hpom3+s3k9W8pXtwZ6Mh0UZtHsxesxPNvMedQUXG/re0sl5Ua6fllwjhmb0+sAt+3MLP/gPQ2BHy16lzaWQDVJz3Z6+Cr4qgMKQNr0OvGZkosXyTBwJfNF6hIsRaOk2Cslu05B6YTo86V8t8sIT/zq+EatXMzk1dCjeVcpbmb/M+4DoVc/HkSwxNB8Bk3hs3SbwgiBy/DFoaHs0NAt2SS7eERq3u2Xjc/P54o9hgR2tzkBDn4M+7905sx8JZ3Lw5Iut4PcqM4RN53Hos5P4xak6mnZ+HdXEtc7Bi63aoeoVC35quSzRUhA+fxPdapZB0uIemJ/cApO2jQOJR+ON6xXy5H7YC9rnnFkwkayP34ptwpkrpJj4D8LQ9qoztVB6xd9dvkrn0MF1wlkH9Fkajq6SQoHulNBmMeyS4X8pAqsE3cZKH1RwdJHsNeF4t+iJgu7TBx/hpUF7VKGYdvakAx3Jnuw1tj/VT0n7f7fmfvjJkrp0Pwwv6J7zbDcqGqlbyfFrX2PXsat4yNbFQoEJ5w99hpO/5KfmhQk5p5OxaRtL3SKdY+DAnla6uF09mM4ISxN2W32vYBg+urdKvseO3epWDTmKK7Ob2kXG5uaFCdebXs3xv1NpuPvMc6jcbAYOb3sPZfaOQet3luEcKsHTxxeVTu1B0iUnvPfZj/i/jtc0cuxMSF/pj3YBibjn4Y93iZRjG/fgVDkyOE+RwUnnjykpGG6+4fj1mVbo6lsN2fuEMV/EnFPpmNLQyKIyAXzTC83/dwppd5/Bc5WbYcbhbeidsxL+7QKQeE/IhRI/jj2nypEgOEUx2cLhx9694grXB1m4VdUbbWtfxcHk87gNL8z75ggmNZKMOxmn5s0vIOVkOdR8pir6rT+NWfX2Ykzrd7Ds8jNo1ZUKaFyLx87Dv8Jj3jc4MqkRKYQXEU2K1+BEwMP/XTTFCWxNSMPDHrHIiulMSDrGCOo1qVEH1W5dRsaNPLgMjsO5Nb5wFkO0puLQjzeQV7YSXJ+rDCeiu6tVyJY9PLNpjh4UN38D5V2aom2zP3Aw4TQe0iB5eZbbV21vlUYOkykdK/3bISDxHpv3MWzccwrl+u3CKQqIF9EXY+Rvwqv5/3Aq7S6eIZqbzTiMbV6b8HLDyThJz/7JgudN6Q7W0pSEYDdfhP/K1iF7H/YkXcKLc04h3RgjETOmY5F3cwSlPEQlTx/4VjoljvGw3mh8dnAp2hPRQhjHoOifkXXbRDjVxDPljGKsoyiwsM7G807jOzFZQc3PNVCn2i1czqB1dRmMuHNr4Cte1GiM9+0CeLWaiLSHlVC3hS+8/jhEvJgFeM3DN0cmQWRlWXkqXx7l84Cq3m1R++pBJJ+/bf1cPvhSuU4Cho7WXXhmfsvXMDkVLPzuEvYn/YSHZU0w1c+fYWdKXwTv5kFIoTlbZNJD1Bv9GQ4ubc+iEhh/lqU54yGcGvqg9b+OimHpeS4WWYSL0fBpNBiJYPLixFYkpD1Ej9gsxHSuROBlY++Y1nhn2WU806orfKtdQ/zOw/jVQ4GvvJ7Nm+NCykmUq/kMqvaLwqLfeqDDigoIOXoFFpGcg82kLfWJ9UHMzd1wj/JCq4kkGyrVRQtfL/xxaCek5ZNkyUEN3kMDKez6rE3OC8mVRd5oHpRC43nCx7cSTu1JwqWH9TD6s4NYKjCzJNjwj84xaCzT614Lla9dgMgO4Wk4Pl68zkNSsBt8w39l887GPmGsF+fgVPoUGBLTNqJH5l9/ce59bS4fc7ChUxX02+OO2SdPQ4h4VsuLGnWq4dZlIWzThXLEz1GOOBskm8nkc8Rfin3s9N5n+PH/OuIJR7LEyLFzfRcGNOmNdVlOcGnaFp2rXhLlmxU/mS8UmqP5hRScLOeOWpWv4YIEbj7zhx3w3r14DKnnhyiMxv6rS+FNe/3iSm80CEiBT8yP2N2X1puttYurK3J/vYuGPq3xr6P7cUw4RxRyueB8PhodYhYh+ucs3DaVhwvxPolHYtEExCvj+PTwlWWTC53Hub/irtY5R+9e3zUATXqvQ5aTC5q27Yyql4Rz3XoORaZXKGsG/K3k6zlE+vpg6qEfaX9RmLQr6YGSQoHLFAMu6UPOcHV9gKxb5eBeqzKuXZD0pvC04xDFhiA5imKvCZKXeNeD5NQNSitq2rYZ/jiYgNP3aXyTyWLYOdqTt/R1pH7p9mVvwapPJCKo1iCd/fCQ6csuhGEufr2rd87T3JnefZnpyNfi6Vz41UPST+sdkpw4FUJw9Mps0i/Zz0RnXqXOiGkeiStJw1HdoUz7Fgu8WmEinXdCSLyv1x84RGdbllE9+HUD575DGh7vAyXesJOV717b71MYi32W1DPsTsIF/bafwOpuzPMkF4wo1wtb0tehp+jNIgv96Dk4N3uVFCgNRTNtOup4zUCu4tAw/RCBtxuNwnfD9uPmUm8mIFpgeeYRSJe22ThxPBfuTeoaLEYhfxd0OG3HidXdzIVhptfxwoxcheJm+gERbzfCqO+GYf9NOvzMNLugS/QhxAyUbqhlIZLbIQrnEwaJm0LCCSjrNRspX4Ww0FammMWzTSZqznnYO7I2KXENEXklCcNz54tGypURibj+SXtxTqbzx5FRsQnEi1UDGJmV8bJeCNn3OWa3FCziYwh1b4awTKViVPAiETmbycPXJxYVlRhmf4lhTd7G6quWgjhGDTvpNj5XYUSb8EPE22g06jsM238TtPRMUTspLBy2n1hNOZ+MV208QWlwuJZMaVHe/mefOI5c9yaoa0gqm3As1B3Nwq6ZFWnhNTMvdNmE6zt7i/l+BQvFzKdhd7IsvEL24fPZLWlvybRlwj/mJilpIhUaFykXsTsiFfUG9GTeABPx4vPEi8BohrmZl1y6IPpQDAaKrnPZqM9Fh6jzSBhEHJ8fvlQY4EbWXXqGcJ6dgq9CpBwq0/mlaPvvsUhunA/DTt4D15SXMJa5dNl0HTt7C1jJhh3tn4RYTG0tyDQyfOa3xGtkXVYMOohfFr7B1vUKRiRexyftxZ2K88czULGJIN/IYyTukXjFpQ3t9r0jUZsAbhh5BUnDCTfGhyQorMMLk8agSttlqKC8bJNlqk8Mbu7uC+eLuxGRWg8DejJPmTnygpR1UV7p855ajpuOhcK9WRiuKY36bMLBgw773C7YdH0nJGgkww7EDzHk4eorbJbsDehUsx/2VJWVBYZfi+XIPBIISUyfwPFcdzQxtrk0Tmw2pvtsnDwdAq1M1fSwhmgcmmFW3OR9V9YrBPs+nw1JDErzzPRnGEI2limMc0s61vWUzq+8s0dxzrkZyVwDssSQfkF7cv0amLyHoDWTWxcjWsJtVDLayLwg8x2dpV1i6Ca8r3CmZZPBWpMM1qoqI9/+R43wniyrKhKPZUzIxlC3ztjqpjAg2VoL+CXETpXoNl+yVETQwV9A26BwfG4ncschrLLcd3TO0b5fv8YE7yGt2Vl/EREt3TAquY105orbUDivi0KvUBaD+5vJV7PeY1skRdaHXLrEUARXX/GMzd7QCTX77YHFoVBEe02WkxWVekI2vhzWBG+vvqrw2OVjT6rPGQOy1yH/aj6gF8km/93BOc/mHq+8PMyjiLzaZMw1lIy2X8Tz9Kbi3KJtTXu9Esl1H7O+4Jj6i7sjkFpvAHqycDoTnW3PS8qDqKsLP209WFAXHOt7jil4vE+UGsPOSLUgXcNOeYgT3lfp9q9GwAHtsBlxPWwZOGlMFbRd1kBhtAnPXcVK7xoIuCkpcNniM07ot+UbrOiZ37A55XeVQpz+zhSpBqowH2keN1nMuN6mS0dYw8YIzeiF7fc3Q7CN5Rsqi9InggLvGgH4TmG0iVCwb3cU8mBelZ5JIYPwYGIwXle1lTCCkbtOyJttnHtBDTu2JgdUGGqsqTHDLgljqrTFsgYKRVBcegmLm8w7JY9lE4qlnq+RtcyWFGenflvwzYqeltBDw7KC0YwRSLz+CUS9XvzZKg2PxrBTVZfV8+zZrUJLoRWf9kWVvtth9gjqhU+mh6Fh41Bk9NqO+5u7IdnA3rXlSyPrni3xhg3OBeBdxhd0opkvTaQlY2F8beTbSp2xTZTj9QTleDHjwlmUDSlkcB5EYvDrVqHSZrn1nZo/2Jw7Mu8yWydnNU2gi5gadBEDy82qrKz7mg1QFbNSuOqnfaug73aLYqXHe2pZIMkVWB32EjQq48OGr7RkOZujUz9s+WYFeprjeg1vLo0HHa+3PNcuG29hZ59KOhcqqnGYjDmgOr/MBBiRJQUsIpb39Qd4velsZJgvOrTnmH/5weSzI94jo3FH75fQfUsNdOlyF7GxFcjb+TV5O5kw01xrUgKpCvETVIVYKgblLJ3Pjr6ly+eFyB0yfM6p2SkPX3/wOprOtlwCSPxfFHqFvSrff3H5SjDr5dJp/r0g57aBvaanexrZR0b3pI2A0pC9BZN2jgw7++e8NPfvbOS4xN8dpTxWdt6dMZ85Jlq3SnSxIUWC2EZDGJuJKedT9KWK39sVRrCmHgwj576BhTZGVrE9VWoMO8vtvj4Wuoad4hZeeFt6rrqdW0Y1A8v/bwnJkKm496vg3mcMTWEzwe27IJxCKcrX9sbwDz/CaGVOkMNl1N448qaXw+XMw9z7FT9S6Ilk9OofQmpcNAWZfANayZXCP4U4BfZ7eAs/Z91GfdGAqUPhqN3gOzaO3NoULtYjBNMnD0V7MW/HIEaGDzzHypI2nBTG8wSF8ZSzeAak52zxMWTYmcP9XChkVQjGMa88fhVCRRlv6Qpm1XyNrSWFKgW3R5dwCocqXxvewz/ER6Ot48PtspL8TWbYKJ18UvJzObPXy8iBYvut/HrsCmjYUT7D5xGLsGh9LL6m8E1QyGVeXp5jw86qeIkf4sTWJQ72rpovjax76EU0qR+EVLPRJSOVf97Vj0xQ87Pe2Ko1EcJIu/libBzFP1L4Yo+Q6ZhszpmSvX6WMGeJ8oe4JYSeySGkOsqz8KR0q/mAyVDZ2+9r8Z7RM3lnKc9t0SKsj/2aQq2F5cuj9cuvYSfPy3IxZeZHFqZfTr6FNWTYCeFAwWjfJRynqFplbe/h+PCj0ZBz/xyKaM0HGJ7VVSFEimePhdYgD7rlRt6Q7GHzqa6ThmBMlhickZBbTbkxH62KwsFvs3DfiXzPtNdMRW7YGeQ9gezstXjruUFIoP90Ezx3FPdrlmV6vGm1j4H+/yCvriKcP398/mgMOxPlHG1c/RFWRR3Et1n3IUGvCMsrJr1CyNn/u8hXYd0LY9gV1V5Tn7/y7tSUBwXck8KYjmSvQamgeqxwhp2EvyIUlo3+8JaQDlKfOShUZ0k5qcZG9JuWKCNjtFME3ucRWLRoPWKFysQUS1Oe5FmejWGn8uAaOfdLQT+KEm/YQT68bW6NbZc3f4adJd/BseIqM7QlidrqnSdfRCs/KcRJCHnKOb0Xq+eFYOYGIU+hA6IOJBhsv2DfsJMLXFjT+yQlkPtRWI7eppPzOywMbM+wq95nKcLFzGLrX9XG7+ANOVRJLOyxDJM/jBDzZuoFxePowmexWlSgHWBU7IadnpJVSMOuxSRsE6qLqH5ygn9+DTv7ayl9RCp+MQ8hMzeQEupCoYUHKLTQwG2RHcNO9n7I4Ywl1bCT883SKvthSuQsBLZ5BS77WA6VnMOn57HLofC7KhR+Jwpx2bDLJ1/KY9tb97LrpPxJc+iczBxFadjJ3lf5okJvbOZFU4Qaiwe8kKS/bDI+jBByDOohKP4oFnofkhTe6pbCRFZsXZUKML1BoXZ2DDs5vPWBYHRMuEzFCPogvucu3KZDTwwNFXPi0lDZbwoiZwWizSsu2KfKnTPmsbNj2MleznwadmxziUWg5oXMxAYhp4lC1Q9QqLqB3aWhV7DqdKnqKAH5UTmKwJFRq1pbB61IzFECrPCR9rlgQA2isNYhnr0QdevfCFi2EhM6vwq3n5aocoOLymMny2cHvEdk59DZX4+qptKdgCX/2rzFVLJA/juFs9agcNarokHKDLsC83nxG3bZu4fAs1cUbv07AMtWTkDnV93w0xKtnmZFq1f83eSrwB5FYdgZObft7Tg9p4KNLCzwnjQmew1IBY1HisKwq24pXmN96KDxO2+IYbBS2GQ03hSiP6pOo+ilVdJ/i/H2Bn5yfYG0yvCbEolZgW3wiss+6cwzatg50PcMUPFYHyn5hp05N4NubL+nUEI71aKNGnZS2dvUAoRiVsG01AuYLpQ1M/DLprCQlygsRBnXa/81nY3DFJgq01JxQffjOu9q5LZoCjgW9pOiyL9yOEXaQGGvU5jnSSkfoO7HgkvdAUb5Nezs5K1o0ycrWepCBvqGnXX+plqBYYp1FUtFO63vGjXs5NBW+2up+oJcSl0oJsDyk+yvjdoYkJ8u6lBMlRfFaIilgeekm035Fo/Rr35Ph5fU8fSSMZtfvjSy7sybZs7fMmudNoeIw72kNlLMS2YwFNMmZNP6iyYKT32dwlNPit5FX8QLIWopivw0LQLtGXayB/zuNLrUuYg3O3+OQeZ8PoaLqniMWu7oeSn1QjHNuZWM1oKFYqonKof8KXI3HS6W7QMyLY0pBPBrqo5ilQor86miKqYhj925RZJH2EEoZr5kicbcpPPwFEV+3KbID73c4KIy7JiR64j3qIDUmOoU5vzCbES+sZgMTmF9rlI+s/1QTGuegBSK6ehbunxu51LBEY8YOufYWXWKIhpuUxiaGXr7zaqLQq/428lXWi9Jrthe6OcnFLNo9lqqIr9cYiS1PMjXnrTSkYzJXkfsq/3vevvBWASPFIqZAku+uA4Vss7aKga7ao5B51VvWkWCOKSdOYOkKDP5qs5Wfmkb+kbOfYcUPPYHSr5hRxBJFbEOUPUtS6K5WML44Bz06hQHvySpb5BRw44GhHeDABywSmAVSqp+hd89fNDI2ZZRNRP4iTZTzg3cK+dCOSx0U7J+Cc6+FmhO2KQKFuLt4V1FwmaBDDvNwgrix3HjXjm4iLluFq/ix98nYIKYNGopqlBNEcqizdByInxFRZEQgdo83KArUxcXGo9CQiK2l0Enc6K37A2UDLsBWRpFDqwwEiWYTZVI4Su2NMnz0as0p4+kXHrcbeJhnKSiEgISeSfC8GbzUKSaFGGBNt7gbBwJ7Yh2YakwmW92tAuRiJ7ZG/dQzkUqH2/YsDOwlqb09Vhy9jUEyoUn5Jymu0YNu+IungJypEshneY8zewjCO3YDmGpJkuopF7xAQOGnU3BAApL+WL8m/BdkWkbitniY3yfMEEqsmIuoFCNwgQzqGoj+Y+0im845Esj6y4XtwD8oy9g60CpkMm1HUPxaneqelacxVOqD8VnKcvQUSx2YSmy4s+qBmbvjcD2Mp0wRCyuQj/Zi8nCRsuw4gAVrSoI0j6RNrvUVsOuYSfnuN2Fl9dZpGYOUuRz2hYoMV37AuPf9MWKTEXoi06oYWGLp1iqsooqk3VhHuKP9UvO4rVAuSgPFRgWwyTvsvBkCoOe9yG2ZbfEqDn9peqrRn6yIZLrisGfHkMEK9QlzHtiuy5Ydq6iVbVLQ4Yd5cSu9G6AgAMkkxXFv0znD+Gr3z3gU++cVHTKquCO+lxwTLxEi7LQTh5OLH4H7cYfQE6Rh2JaClPo856893IlY+6V/TZVMmXerD70M6RQfo60DeSCOnRm/Ej5OHQJLBfBKCifO07b0MHX0DnHePOKItc17wQWv9MO4w/ksBSL4tEr/nbyVXFGq9N6DBl2Bs5txzuNnmCFvDLdJuLwyfloKSknrNelJfzW2J7U0pGMyV7T+R0I+5iCnH0mIqSb0eJ+emlMxgw7cyEr0rvlSvIiZnk3yCvvAkHFlH9yqH/16lfxy5tyJIghhM3nlqXeAZ3JX4zHm74rkOnIY2cu7mZdeE6t7xmk5LE9VioMO0FxOTK9K96akWIpf89yv5w8ghC7d6FYvt2wYSeeAVIIhFRmuB3KfR9PZdLv47WIDBwaWUa66TtA/zZjM+KnelMFQVXJ7Z5UAS9TKkPfeFkmkt+7hilNOmDeOSfK26C2CG55UjuEh9Ylc+2vtH7oh3Up9J5UyTJTKtPdeBkyk0dRdTf5XeELlE9EZaub/XGQSpyrS7/rhyQIB6MYkkNlr+U5nNiagLQyA5GQuQI1d3ZEqwFxyBVKjgvzPyu0Q8hCRblNgSOMRlENOkMHnoSSbKCVp/YS/i6ZeDo4Dct8DWhayhL05hLhvxAsQo6PwrDLYWW1b1Dcd12p7cKFMo3h8f9SkKrsQaZqHdCTLhEyxVYWjbEsMxnStHRuWTXma38t38O1KU3QYd45OFGepn87N+SJrS0eKkq2G5AXBtodiGqvHt0OPmEOk6L8lbpiqfYLKNPYA/8vJRVV1O0O1EVRbAwGOVTNst+EEGwhDCvXVWgd4owfiA/PVngGZbKyUEMVinlByL1DVXj7e+B2PPGryPJyiw5hIg72rrCAcqEKao8xY3M8poo9Ha1bRmitu1mZvCHka/nD43Y8qLMGnnC6jdt18lEVU6AyH+0OttKchf4dDc2tWpQl0s9hXcdWGBCXK5bIF+g+K5SVzlIaF2QMDvFEr6gsC5+JLRHKYGBCJlZ0eMqhYWcu7EK0O1tdXuXQ8tUjwz8XrkIrBecfqC3KWVR4pgyysmpYGgTLFeLgSu1VPPHHjfZYs3cULhS23YHVTa21TPU+NAVNOszDOSfKXaUWJm55UuuSh3LJftlLRnMyUrBLuU1M5zfgvTZDsCVLbh9yT8rDLesKvyXx2DHc4skzZtiJB5VZJgsyvV257xFPLWzuvxaBjEMjUc2KZ7TOBQOyIoM8g57kGXQSWmM0xF2SbckPKqMi7bUbxWDYiRcRdnhv8XPLRO/yFfp2JoX2CgFYspeqinxBKVdApX8rK7TUaF0dl/bTeUgh65YWQMLcC8fncoVlYmR09fwDN9qvwd5RBgJ2DZ5zGeQt9aToISehhVLDu3SmJ+NB5Yq0T25I/Edh04XXK7h8ZQqFWNk8szzxub8LMp8ORtoyX3xpI2/Eg1GjTZGyHU0B95risl3IfZZbt/ziVAkPb99GT7FegvB9I3tSS0fah7d3NnIoe2XdCmWp7dB9ajtkQEwIj2jvB2hUtaaHNS4GLXq3LH+lllllBiYgc0UH0Kkj/eQIFNr9VkX+jNAp63V0ySa07XL+gc61sxXwTJksZNWwnMl6obmGzn0jdDzGZ0qJYSchJOSLxG/dhr1CB1WN5th6Dcrv2DQ8lMYTkpb3bNwkjadq+C3faFyv1gkfTvUz985QNskV3unUkYw4c0NZR01eHa20TuNn+TVlc0xh/p06kuLPmnorbqa3fD8Av24SGjNqN2u2wUlJlrJBudAstTl9o4elCbuRxpd2MWINytVrokkTSx4Wm1pTw9C3BgxF10ZG46ylYgDyu/7DOiNrZD0y1q0LeUiJ63KTdKkB9p8nWQN36tln+ZqyYaXALp3QkRRDufG7drNzQRIKzdZ/gA0P2l1LFa8LzVIHyUVqHPGQ8t/tNygXBbXYWP1OPpu/i7uHmlsrGitTA/iezf7ESbmJsbhORhuZCy0CpBtEy37TGP/VP5BGTYp/F5okC+Obm92/jSqX1mGT0Mhcs6k3kx9nk7BO3BeC+FDvXfLY7wjDxwnXUa3Th5jqJ3fLsb/ukmASihCsYo3Apebrz1z5DGl3pIbbBjlWJhJJ6zZhs0Ck5lzkRtGt4G46xJqLazVQddyg1bpxs/C55sTTPeDNSkRLDb9/stOUWV5fjcb2yr3LmsO++kcaPjv5OzU8F6Ii5OnKjcSFJRmAASObgzrhIu2np6yek2C2rJ9+g3I1vRo8KOQI6zaEpkvEiHlY/0sLTAzpZrC9iGLPKZvV059tG31Lz2rvO2ltf2KN2s18YyWTNdbagSwxIjHEYgur9kiN6sUG2S64tO8wLjpLJr8JggAAIABJREFUTZwBbdoKJz/2MP615r1rX+/Csav/YrnjMvXUOiiO6PndGQ182qJBEsuxm/0VVtf9GpuE5t6qM9wyb2WD8vzyuVSEQWxaLZy5AwZgpIiHg5/hc04xviznXS5h3+GLcPbsDulThdUruHyVVkt5phCfvzUAQ7s2wi0teVPAc9sRW2jS4T8MgS0e4AjpCU+Z15wVQLG7J4UpWes5oo5UL8+i++jJXpIZiR8OhM+iulI1SmOEi7xoux8qiw3gf7ijaiCuc35Y6d3E881Jh+7hzdrimOnQ8GYbplHS7a31ulfxRxo1Of/dcibb1YOtGpTb6nv5IOWxPFqqDLvHglCp+WghEr1LzRwLQyjHpzDo8Xc5AhwBjkCJQcBBmHCJoZMTwhEokQiw3OJrsyz9PEsSnTZtD0oScSWfFm7Ylfw1MkghN1zsA8XxMchI/DGOAEeAI1CyEeCGXcleH05dCUaAQvW7tMXwvTUxJyUJ4w0nEhf/lIQc7wdOFykVoDVCTzVH5JkkDKdMCf7LHwLcsMsfXiX4aW64cMOuBLMnJ40jwBHgCBQVAtywKyok+Th/OwSEQoHJ+K+nJcWmZEAg54ISNZQHGRS7FwuF4hn8l28EuGGXb8hK6guUU5OVjTv/fRJP12BV7UoqqY+FLo7PY4Gdf5QjwBHgCBQ1AlRJ78pvv+OfTz0LV7EqNP9xBDgCpR0BU04Wsu8ATz3rSpXmS/tsHh/93LB7fNjzL3MEOAIcAY4AR4AjwBHgCHAEOAIcgSJBgBt2RQIjH4QjwBHgCHAEOAIcAY4AR4AjwBHgCDw+BLhh9/iw51/mCHAEOAIcAY4AR4AjwBHgCHAEOAJFggA37IoERj4IR4AjwBHgCHAEOAIcAY4AR4AjwBF4fAhww+7xYV8CvpyIoFrDsLPBFCTEB6CeFkWJQag1bCcaTElAfIDmE8U8DwM0FjMFxT/8OUT6+mDuma5YdTkcHQr9wSLAzJSOld18MTYui9q6UpGqvrtwPaYTnio0bY4HOBfpC5+5Z9B11WWEFx4Mxx8srifExuV7cKPNu3i3STXo54JLjXM3HnCBf2BHyP3Bi4sse+MmBtXCsJ0NMCUhHo9luz+KSZ+LhK/PXJzpugqXi5zBSs5aPgoo7X+DyzUlPn8ZuVYYxjIsEwvzESPvFsEZaeQzxfJMcdJenGMXCxjFP2ixnhfFQz437IoH12IalVV2LPM0ariUL4Jv7Eb/f3RGTON5OP3dJLhrjfjYy0oboLEIkHi8QxR1q4rCY5azuSue7xOLaiM/w+eTXXHnVi00aeRctDCZcpBFJbDKPF0DSnbOmP8yGk4+iX67/sT6TkX7yUc52vVoH7wwOBFwm4rjF2bAS/i4WM3vgarq1yEEV22N8BtAl423sLNPpUdJptW3dvf/BzrHNMa8099hkqZAeGykFd2HM+bj5YaTcbLfLvxZ5AxWctay6AAr6Eh/U7mGPNy48hseqCp2/lXkmlFuyLtxBb89eArPujqbL7U0ZaLRAYv0ucKfkUVKDg0mXarZH1W67CxO2otnbC1eKGr8im28Yj0viodqbtgVD67FNGo6who2RuirRaWQGNjE3LArprVUDlvyFKBjoTXQLOwuRu+/iaXexQRBehgaNg7FqyoD7i+jAJmu4fi+Y7jj9ja8mRvOtKM3nuh+1sZwyjubhC8yn0LTdk1Q7TGWeeaGXeF5vaSsZeFnUtgR/qZyzbQDvZ/ojrPzTuM7xe3IX0auGWILE3b0fgLdz6oujTVkoqHhivwhA7pPkX/T/oCXUrYj7Sf5meNY3GM+kqv3wdJwumRlf37Bszua1y5O2otjbB1eeMT4Fvhz3LArMHT8RUMIsE1XZDfNBjYxN+wMrUzhHip5CtAjUfAZb6k9c39lBUiaG0qsR+yRrHvhNkvh3y6FB3XhJ/04RvibyjXGX7TJ/8aGHVt72IkGehwsaf6mAd2nxNJXnLQXx9glnRccLHQpPC9KgcdOivk9OmIDIirMhX9wHLJMHRD1UwIGPW/C+R3j0H9EDI7dyKNEIBc07bcC6xd3Q13x1pvF+Jd/H+t6fIfgYWuRdtuEspXq4u2QaEROagmprz177vm5+LL7cfTsEY5TeS4IOvgLFr4BZB+Zj4BBYfji/G2YylZC3bdDEB05CS2ll5F3Yi0Cho3HtjT6d5SHS9MumPbxAgTKD5jOY8e4/hgRcwwSmU3Rb8V6LO5WVwpRYDG8z8/9Ev2vvI+BM3fhkvBc7c5YuGszhjeip8Rctw349ccbyKN51nymHL1oyYUxnd+Bcf1HIOYY/btIQz+sWL8Y3SQgxF/2kQhMGBcq0Unz8Bw4Ca8eDsVqJ8ehmO7jV6H/pTmYueuSavwHiBvticDPnsaIHUcx2dOySUxJU+A5aDMahR3FxnflOyf536V1Tey9EBFVNljmrMYGtoJGO1fBNjbcdO0LhL0XiIVJAs1lUaluCwyeE4kwGXer/Uy5MV+E4b3AhUgSwBfWucVgzIkMQ70vfdFxYSbaLvwWUd0U4YgXo9HFeyZ+HLQJJ6ZWkfLkRB76HsOGzsPhLIHXCJttcQh76QTC3h2KeYcpZ03APnAb4ha2Z/wnK0Dd8fGWZ7BZl09FbsPZrRMwcBTjeQ1+hAZmxrGQ9sLUQz8SrxJmrs+hspPMZ4TRwSUYPZTtBU0+s7dfLYCLYScbfsWPtCHKu9SExM5SrudDForZa9VXeDV+oJnnandeiF2bh0PYDhaetr831SJb/G5ibyyMqIINA2dil7jR1HJDeMvIXPV5Rtx3VrH5NUVcP0r5GVm3wXClz7A8L2Vu238ujCb++AxPj9iBo1YbKh6j643EZ83DcHTju3SL60j+qWcvzev8jhAEvB+FZEGe0RrW7jwVa5eNRWtyFdoadlLO2KIpcxCVfB4kPkWe7rE0Gmv6N2IhVg5wyDuBtQHDMH5bmvi+IP+6TPsYCwJl+WtNp+nacWxcNAVzopJxXvogPHssRfSa/ua1l2TA85j7ZX9ced8ej7D5Tlwp7evytdF5anf8PvljJNi5IBPX4+gIbIioipUDRmPDKfbuwl3Y3PchNgYMwOgNp0RZqOZLmzxFI/JdPAa0ckuLT651+GGCDp8lYYrnIGxuJPCZJ3aXYLlGh69B3iqsXLOvI8gcLK7hRyn4WdrkeK6yk7DJxdxp1+KWawK/rq2NT/sOw1r5jLc6Z2Q9wL7MlM6KsVgi7nf5DGAzFOVVC5z9PAKzZy9ArKhvCNvKG8ErN2JGe1KKRH7/CCk/Z+E2KsH1ucoQURDCCGsq81tfwLK2HbEwsy0WfhsF5dGasbw9/Bb8hkGbTmBqM1F5wfyAQQj7gtFU922EREdikqxjaYk6eidiwkTMiJX0LkFuDVy1AQt61qddq2HAiPl/szF7QaykTwp72zsYKzfOgDAt8eeA3xzqglp0av7NnoEl/9t0fBbyM6ab9QVJ11hoJlYQ9w50T5tvFzEu7U5joB4v2OTPG9UJSQeyx3/CnAzKXHH6jjDihp1hrs3Hg4zRypZFWad/Y+D8qWhb80W08nNH9vyWeG3yKdQavB6bpjYF9k/COyO24EHfOJxb4wtnyAqzsEc9ELBsJYbVv4b1Y/phcSrQJvIMkoa/SP/InjtL38AzaDflIwxo+CIav/MGKpLi4NE5BugwD5uW9IZrVjQGd5qBtJfCkXZ8PNxziD43ylNz6oLohMVoi3RETwlAXNt4HJlEig8VoZjf8jVMPlULg9dvgkTmOxix5QH6xp3DGl8yFBjjnKU5olYnzJzVH+6XozBwUixuuE1D6oXp8LyUgu2fL0fQmE242mISto1rQnQ/hX+39UG9q/PR8rXJOFVrMNZvmoqm2I9J74zAlgd9EXduDYRPmNKlZ1KdmmPC2gV4t9pZbAiajHASzDCQY0diEa5+UxA5qxPK7ZuF3gJtLqOx/+pStDg0Es93WIEq01JxYbps2Zmwd+Tz6LCiAZZnHkGgALPVj60r/a2sqx+mRM5Cp3L7MKv3JMTecKEQwKsUAiho8LaCRtujo35OXlMvhMTFYLhrDvYvGIgPH8xDGuXVyHLaTJK8Bl4hiIsZDtec/Vgw8EM8mJeG9R7b0dJtFFK7bML1nb2Jr6TfxYiWcBt1BiMSr+OT9hcw/+WGmCzyUC30DV+ILk/GInhEFM6Vq4d6TtdQptt8zOn4JGKDRyDqXDn2njBHo3xqQrrI86mo2GEaouf2xtPfLcBA4RsV+2HXqfXoJE6sMFjkID1hPz6d1QPzk6ujz9JwdH1e4rNnD0l7IbfeYKxYOwEv/7YZUwbNQGKuF+Z9cwSTRItLb7++aoW5EHby+fIgjNl0FS0mbYPEzv9GW59G+IkpQALP1es5E7P6u+Ny1EBMir0BNwWPZZPHz+7e1JAykuEiMh38pkRiVqdy2Dertzi2y+j9uEpxp8Is5LHtztUezwgLYXUgvKGBK33oBU90b17b2qCqsxcjn++AFVXY3mfzMNF8KxHxzSOvkNyqynjBnvxTA2DhHyePACxbOVZcww8D1qBKBPE50Wxj2OXEY0i9rviyfiBCpvdFR+fzmNmnP/FvRfPFl1l+ae2dTmVoTDfC3AldohOwuC2QHj0FAXFtEX9kkpWRLlGbg/gh9dD1y/oIDJmOvh2dcX5mH/SPOoeKQQfxi3DTJuwYkUfOQhKZjnkk17UXZq2fjHb39mLuhKnYeo4MRjuGnYQD7eWyFdF6SgTGuGdgaSDx+g031Kv3M+7WmYBFI9yRsTQQMxJvoMXyTBxhQs4GQyPy3TwndW5pYfayOKiYT3hWa23eStfms72SPG8gzun3EizXcvLBW4WVa/o6guLuFDnpCdj/6Sz0mJ+M6n2WIryrcKH5Ajy7N8e9YpdrEr++EjgdH3i7YN90Sc/psuk6dvaWTi2HMtN0DKHuzRCW2wHLd0Wj87Nn8UlvP4Sda4WpK4fDoybJq0q70MTzQ5h6TMWHI/3hcW8PxvaZTOcAXbifpwv3CulI2P8pZqlCCcUwwnvW+a3SGZpqRaP5PLxC+sXNpfDOpj3gQToWlRWbt2kJertmIXpwJ8xIewnhaccxXisXWH4nl8n53k/juyXDMT61Lw6JckdDr1jUBJ4fmtBj6ocY6e+Be3vGos/kROR2iML5hEGoTrLJriy750AX1DiP9P9kwLATXqZLsqDpH8DbZR+m91sMQhKbru+EuNxGdE8bAooYl8/ewvUjOrxQW/1xgzphxiL7/FddIffs6dTC541gxA27fHGuwYflxXZDyNEMzG7KrutzNpPC2QfxPjH4cXdfpjSayJiqi1Yfv4KYm7vR11lWmH0Q/fOXGChr89kb0KlmP+ypGoKjV2aTISQ/Vxb+MT9id1/5wTRMr+OFGaDnMug59unstW/huUFniJ4rmP2UJKjuTU/DD9NetZmTVIQiHj7KcU2kBNdthY9ficFNot1ZZpzGs3Hy6xCm7ORgQ6cq6LfHC+FnSXgJBSk1GSwHm+nw6BPvg5gfac6MdBMJr7qtPsYrMTdpPpCeia1mjaHWrZXNXuuPf5AyWX3iYZwno0KCwIRjoe6Ug5VJeAnjX5RwuskEsfgIU05fjsSVpOEkFHU2cfWJOHyejE6GrelYKNybhSHTn2FTYMOO8U33Lbi7rSfdvTn4sbDA7lvuYltP9dMM49jmiLyShOHiZC4ioqUbRp2R5yzzUGPMPvk1QkQjx0QHQSVSEE1oPPskvg6RPByygl415CiuzCZL38x/Dvj0ajR86g5GYjUVP27ohJr99qCa2ehRC+d8YkEU2XpuWFGIXH9rPmPrdc1s9OrsVw349UIu5b8rMUMO7dkqtGe9wnGWLlTqwcDeFKBV/aR5VcfEw+fpwsXMdJIyk0lzE+WGwbna5Rnt/aoX6mj9d/lSpAqmpV6AdFci8xLjwQpG5J9q8kxmxlaULmTEexNNfKyLp+Tl5MDJ2VIAwcQU/woy/9rFge2Le9OR9sM02EpIDcbIy0GOkzOcZfpkWVJBlteyYXfSal/p8sg1a57VlqPWdEjr4Yxe27/H5m6SUJX50rnXdny/uZt05sgy2SyvNPaOQflu7MIqn3vZAY+mTa8Drxk3Fbm0Mu+9zGRdSZZr+eetgss1PR1Bg3910heKX65Z8ysd0qhBZ+kvg+Nwny66YURm3huDKm2X4QU6r07TeSX+kqS/tRLPetFiQE7OQzg7W85JyUBLZvqA8IyOYaLWYS5G2F6asmeu0EXbTbpok3gU1rpL9lq89dwgnDGfodbrIL1zTXF5byPpbAvHUTGvnIckd8zTYmd8snwuOOA3RreeLqjBKXb+ZMCwc+6F7d9vBhNPtNxCXvwvGBx3nxwGdEVmRPe0PQGKHhcjeqZIB5uzQ53QAP8ZlLmGMOKGXf5Y19jTbLHdyeg5TUYPeymHlNkqpMz6r87Esg6Co1/6PUwcDbeh30pGV1O9GP+rWOldAwEH2qgOL9oo92mjyAoFE4xPTNqPfYF1LOReiEC7tvPxkiDoenyLMdXbYlluPfRcGIF5A9qillkwyMaZP1ZnLoOFzIdIHO2God8yRUWHcfRufq2quckKr/9qZC7rIIY8MCAw2m0ovhUE39QsMZl7iwpDXeGrXBi9HDsm7EkjEIWvtfeKAGT//rLoXbA16/S/nYQxVQhPyAZTQT12F2mNG9AaV0TToOVY/UEnNDRrihqcd3ElvBsE4EBFugFbvhofdGpoUSyFo8zqFpvcj1fp+RoB+I7N32KckefsT/KcsU9oKms2622MT3selHi+sSp3AyxZf4vZ86rGLJ9YCCJWXR1RVhLIy3GbPJ4Wm4AV9MmQ5629X7X2uiPDzjr3TjUnI3tTVEKsf3qGVdKYKiDdRVJwyzGFyNFcHfCMlgFhzLAjmtn+MXvBTTT/SnRrTRdZwmUQ3foYkH/Wc5dlprtSaTOIj/BY3o2zSP/mNL5NmIsPlqUhR/Z42cXBRFOpTtjmkveVwq7nDUBbi4C0fwRQBdGz6d/g9LcJmPvBMqTlWPaWISOIFefJIEPsPhliZp41cFBrrpOmLLSVT4bktsYeMzQnulAqSrlGgltUrM+MSMT1T9oTRkz+mi/ktGVTyZBr+eetgss1GQeVjqDFwQ4Mu0cm19R8bkRmPjVWvMi1OmPYe8+Gn8Vx8YZZ/glVus/g26MnkbrxI0yOzVS8Z9CwA9PFUiyeJom3bjIdjjyINejS7YlJ2L8vEBYt7AIi2rXF/JfkC2DlQrB3rtpbKzuGk1Ct+cy3OHoyFRs/mozYTPmiywG/mWjv6OqC9kWd7b8aMOxUkVbWe9Kg7ml7QupXSi8oLvk17GwiyNQ6oQH+M6RTG8TIwHmR39Ut7udLQY6dNoPLTKwNUFl2a6GfvG0t4HWeYwJabxG8mKAzXTuINe8HI1TIIaGYcs8es7F4QSDl4ClC7LQGKTsYcfcpVNIQE9IAWs/Jf9MhsqxwWzf+tFTeW63gGNlweoadmhbZg9pTUvyTRUW5FfOAaJ5+OgJEvRYFNewETfQstk4NxJSVSZSzKMTLD8fHlDOnzDtUUpZ3diumBk7BSiEnj3JxvId/jMgwOV+TeYgqSPmIFVd6o0bABXb4CKMUhwJkbWD5xem1AZAPMpVxpRSQ+cTCRgHSLaIjC0f58LN3IFnzQaEMO4N7U815eoaVFS2QvNQ2BjSF4khedItHyy7PaOxXw4Yd2JqWkcIxG4phmFvhy8KqjMk/Y3grn9LMsRPyKoV8xJ+cKO+U8k3d87B92R5cVYQy2sWBKuEdXPM+gkO3UY4zpR559sDsxYocZNUiCfJ0yWghb+4nOFFerG9Hd+RtX4Y9V/Np2DEesTFkDRzUJdewK2q5Jkd89MSu23Qhlaz2zpRwuZZP3iq4XMtHIZjCGHZFKdfUfG5k7AFnpdSSGuRhTyYPe/lsuuTzIO91RUX0gJTn3VfI7XpIF6dtO6NDpTOYGZNcAMOOPEviJVUCC8f8SQr9vSuHoVvC8zRVHHMEh5UUc9zGSSfHbuuEvhi2Ng0P6YK3becOqHRmJmKSFREMDvhNXxfMrypfWMPOoO5pxLATdIdC4WJUJ9B7Tr33DPCfIZ3aIEYGzov8rm5xP19qDburomJ9gEJl7lOojF59cD1hfA6LmtRHUKraY2ftbZFvza3CEuytSN5l7I8YLsZlS7lxg5EqegYd3PIZYkIdw455jg7YGG0KQuVnbISggQ2np9DHD8ETflEoZ/ZYsVCxrb4U4z0JpxrTLZscaqqJmd63KafnCT9ElSusx07xUbppOr17Irr3p1w0ZQy6zlqack5j98TuYl4PnTbmvDpJmb5LB9xx1JlJCr7scRXHKQ4FyJpPfeMlnm9j4wVVe8ns30YawcJGAVJ5aC3QqW+yDfAUe7lQhh2jx/DeZN/UM6zihzwBv6hykscOknIre6P152r5F02eKZRhR4FTYjhRBSqu9zXqzaOQXnFvSfkTxuSfNYPL79jyj+U5NT4mwrm6gEWXaByKGSg1T7dz0OntHekLebi8PwLDhXwcylWzhJkq9yq79aZ9Gn0oBgOlD0rK3sl8GnaMR+6ZQ9HMjOewj12JNuxkuIpIrkmh4dKlwaRTjSmcS05lKEVyzQhv0XQKLtcekWFXlHJNvU8Njn1jey+49diKO1RUytUpF1n3n4HfknjsGC6lElwk3atBQAo8QhIQO7W11J7FRk8w6rGjd5WXwpPP4XW6hL5rTitg3poXrCO27CvGeh4e5Vs6US0pHghJiMXU1tXEuepXCXYgy2x0QaqTYJ9o1b8W1rCTo9IMeJitvlwcuBjVCYzphLWM8J8hndogRtywyxfnGnzYvoA4S7kNlhw79ZA6wpiFnySbPRo6z8nhT6qcJvuEs54dW6RbHsnLclaVu6cawRAT0juygdZGmbcmJ3Wr8kisPsFu/39RPWMTwqcxM03DTs7DuEf1B25T0QWWeSeGK0bjzXFk0C6Ogpciadt2ZO11lUMe75nD4Gyf01RQWcx9gp1CMOlhDdE4NMNg42t1iKFwoklhSz+PGwf3xYuRYVUsphgMOzWfynziE42fvxxoLkYiY0bVWFg4lWNB6ggLmwNN3gtVrXMiZUySWyxH5pFAvGjEC8yYQc/QMBSSVqC9qXNQyzlc98hwELwWwhyEsEeHc1VztYpnNPa1Vcinoj+gpgKRNh11KLmkwuzlaL5gFKI7KsJg2dj25Z+KPjZepnmtNHalKgTXOizKqGGksXeUtpvYy2+LhkdUsOGknOWbVrkzBTTs5DB1N+ucVMghmg6Lp6gatRdzKOZjk2uM/6PfHIfBqYsR5aUsElW65JrUJ1KHt7QUdcNyLR+Gnc4l2COXa2r5Y0RmsmJwKSMO4+jIGvj9v//EU8+6WqUlSLJKzjtjG9tmb+gYV5q6jsVrvHzuRYwaf1fRDkbOLVbXB7Cnhck58coCZern1Wck+39Frqzwhr5hJ41nn9+sdUFFS0MDum9hDTtLgSnruhGOPl0cuGSr0mv0aDCmE35phP8M6tRyES67GHHDzhHTFOTf9Rhcqp7mF5ULr5A4bAuh3DYn8szsXY1td/wwo49QKkkWxuXhERCNdTP98O+8YwjrQVWeUitSuCarSqnjbRGozRAqJQVRFUK6sU6KepfytB7i8tGtWJtSE+OCvfGABJpn4E2MWbccgW1rwenalwh68218cnMEEq9/gvb3hKpy5IGiyoEhcdsQIjxDHqG9q7fhjt8MSGRaV4qSUbIVKkxY3vNH9IWt6Fn2Vzyo6EplNoegHnnPcoXKZ9tCKIfFCTmn92L1tjvwm9EH4ifkeXRYjs82D0HD21QKeHgfTKaKbkaqYgqVK2cKc2xZCZc2Dob3oFj6HqsMal5WKVxx7rWylF5t8S5orzpbV6E64cx1WDK0KapkbcVg70GIJaws1a7U3igajcX8X208Eft2jEX93EQEC1VAqb2APJc6QhXQNzbAa9VGhAn5cg/PYuk7L2PsAS+NKp1StcA3Nnhh1cYwdGrojIdnl+Kdl8figJdssAizkA8gKqpoqoYp5sIWIqfYeBXEv7JKaFZ5Fbo5dhKfbgzrhNq0PrZ8qub5lqh0aaNDzOrlCwtptbQONPVe0KbRsVFp5gfZo+IfjQtbe6Lsrw9Q0dUZF7Qw0yqi42Bv2mbYyfMSKrzOxLolQ9G0Sha2DvbGoFiSI+FplEMilVhzPNdyjnlGY1/LyrsbFSM6Ocsdt3LLwNWFim+rcxolKiSeukIJ/TlAb7H6qhydYET+qXeeHFZF+W6jqd3GjA547uEZbA3qhyS//YjpLVfFdKcCQKepAJDFM9iYikMlT3PHg9O7MbG7UBVTrir5ln0cdlSj4i+BuDlmHZYHCjL6Gr4MehNvf3JTURVWQad8edVYCgVzf2DxnpuQT48dq7ApnBHCfL+cQ6FVl9T0y9mw1lg9Do/d45NrzDs89xrJNZjDfSVESq5cayRUPswPbxVKruXDsJN52I0uwU7OgvutXJRxdcHlYpdrqosIDfnjSJ9xPkcVB+sHIXtoLBJnt0d9kk3qn3w51WtLOtb1dMZ1sxdeOH7l3n2yN8SNClWdxCz3W8gt4wqXy9q6juQ1/hzlSNDlNFCeuaIwpiqIQUitSF78pCi8K5zPl49i69oU1BwXDG8NQW+uAl5RqO65AQM8nfDzkSUI+OBfmPVVKBXCU+sVsiHaC1vS16Gn83VL9BUYrs/Y57dp94ba1QW9zHqaWm/S0pAKb9hBrGjsQPe0+XTR4/JJ+19YTQsVL9iwljGd0BD/GdWpjWAkj2UvKk5byX1sfy21oZgiYmL/iW54L1LoJ8R+ZCi0GrcKG+f6opr5UHKDh8fPOCX0IhJ+gjGhCC3QO7ykh7NxZP5g9J4q9M+TP0IKeN+lWPvJIDKQlL3S2PCsd5ncT0TsMdftPUTK3xdJaIVxZHTM9a2WD8OOHl3eAq+PTmHzdWYdSNQqAAAe6ElEQVTVzKR+Vt3ei6T+e2Yg4NpqHBkqcyF8QpjH3uD26BIuYyW1L+h0awY+ueuoj91X8B/cCGkbLBiU9whC7N6Flv4u7LNydayyNoUn1DzONrFzPXiUu4xTMrjUliIodq+iF4tcLIB6wplDqkhBHeKJXlHUE05cz0pUkjwKHQ51R+hNNhdVvxnx60IPK6EPFQspUVJk3X9G+herPoLsYdk7lmPjGSwCBahCPwTk7rTwiQ2favO8YHQv3LMFga/KklKF2ZLnrHo9OcJC+HdtQ0NjLwjrtS0eYW9LoSuGCvKYgc/A8havY3QKY1pnKfz2WYMKkKO96fmUmufkeTmjnkc5XD7F+IdqpnoExWKvua+gzr5XzdUhz2gdLnSQjPDoikiZ35n3/YSmYSe31EgGnNlFkTLq3KH8s52/lswUZNHk1RsRQmuYJYa5HKBeiyz/l8pBL/JujiB5jcQ+cH3wv6lh2EP5tH9SPq1dHOrdwBdh7yFwoZDnyuix6eOopJMuWRZ5o3mQLOOkPnt9/jcVYXsoD4wVJjLk/RCGJfpXdvPF2DjFWgeMRcO4MGx6U6Jf6/dYDDuS0Y9Lrpk972WZ19pSaabwF1bFJdciGuWTtwoj1/Jh2AkXCiM80DVS5jkp5aPlJq0caS0F3r7OoS/XHBt2jmVmNvaPboV2y8+rtgX1s/MciKhN1Bu34pcY4tkLUUyGif3hQl6nnpufQNmUPSd+BDy6Rpr1JjEEvOUm7TBoOWqCLrCUrUNkIoRewoN7T0WcRQmjDlZ9sXTtJxikBQi9aPuOoLctw/9FDqTcQVu9Inv3EHj2imL0SvMNeZ1684rT+g6T6lyzy2+VrfrmSpTLfWwFXVCqwBgLk0sQDv6yEFLjFr1fERh2orrgQPe0+XzR4yJUD9bkBZuiegZ1QrrQcch/Rg07IxjJRXHIByLX1bC7dCXgH0uBYWcAJaqeduW33+nBJ/F0DRdFaXtrYdzuxhX89rttaIGBL9AOoUpJ2XfwX+i8r0uDZfQ88fsCmU+jhsZNmCE6BEbMyUL2nf9qjJOHG1d+g/SJGtD6hP67Br4uY2CHfjkRWhmiqT2yUnCNxQtZ2bjzX/X6sTd1vivjqTdX8W1H66YizozPP5/Cs+Q9ssneZN7CKurKlAbgM/qINC8HfCrzmx6dWpjlEwtdes3j6KyX0YlKC0SV1YS11+dZh8PlY15KhX3sC9I+MsY/+nN1yDM2E5D3aQFlkXo8A7LHlgQmM234R14P5Xzlv9mn1z4OjmWTkkZ5rH8+9Sxc7VWzdcgc0gOyrCiq8Qx+tkCPPRa5xsJWhabt1lVvCzQFzZeKTa7RNaejc8/QLIpUrhUBz+VDrhman/UG09RnJE/XXLh9egLLmvw/UZcQfr8ljEbbYXtgyVeVMXdwBjg6p/JFuOWsML6P7ZwvWmekTK9dHc0Bv+nKY4mWh5W1dbN8QZHPh/OlexY3Lno6i1VUjgOd0Lzni0IHsT4jNPVztqbG+S6fC1TEj/81DDtdUPJzy1bEyP4dh6Pb8bDXGyP0J/0+WRZY8hGyV2KwzMaO3i+h+y7q8XSG+tnZNF0vMYRyQnQQcJQzwYHjCPz9ECAvadjrlH/8E0WAXMVSrQaHfz9Q/pYzlopIWTzjZhBYeGmKuhDR3xIlPuniQaA06oTFg0RhR+WGXWER5O9TIk4cFm7KwE+xCxB+DFSc5BQVVJGbvOsBVJo2cSrWTEvCzbPRmLr1MjzmfYMjk6QKYfxXuhDghl3pWi9ObXEicBVxCzch46dYLJAEN05RaKojyV2cFPGxHy8CcmNvc92C8uRlyjyCJf07YUbaS5j3zRFMasRPvse7Sn/Vr5cmnbBkr8Ff3LA7h0hfH8w90xWrLoejQ8lei9JLXepcNOseievPt8LEuXMwhJUKtj+hRATVGoadDaYgIT4AyranJQ6Iq9sx4O0J+Apu6DY1AjN71leE+5Y4ajlBdhBIDKqFYTsbYEpCPAJKNNPxZeQIFDcCqZjbrDsirz+PVhPnYs4QVrq+uD/Lxy+5CAh92paMxtCwL3D+NisqQG0PmrYdgQkLQnR7wJbcCXHKSg8CpUgnLOGg/sUNuxKOPiePI8AR4AhwBDgCHAGOAEeAI8AR4AgUAQLcsCsCEPkQHAGOAEeAI8AR4AhwBDgCHAGOAEfgcSLADbvHiT7/NkeAI8AR4AhwBDgCHAGOAEeAI8ARKAIEuGFXBCDyITgCHAGOAEeAI8AR4AhwBDgCHAGOwONEgBt2jxN9/u2/NgLmPkRAael/8tdeED47jgBHgCPAEeAIcAQ4An9dBEq/Ybe7P/7ROQaNi7Fh9KNY/oz5L6Ph5JNUcfpPahXwKL74GL6RMR8vN5yMk1RW+898TLIosSnKsewimBOPER5dEZklVRZzHpGI65+0z2eLBI0+jA75vYAlgx2Omw9+KcqxCsgzNtQWJU35gOLRPXoJu4OHYOkvPTF76XA0dX50X+Zf4ghwBDgCHAGOAEegZCDADbtHvg6XkLI9DXf+3RY+jSzaV3EaHDnpCdj/w1Pw7N4ctR/5fBUfLKCSXpTYFOVY9qC8utIbNQIOoMWic9g/zhX3cpzg7Jzf/j/csEMBeeavbNhp7ueczej6fB/Eml7CR9+dQWjjx7nR+bc5AhwBjgBHgCPAEXgcCHDD7lGjnrMBnar0w48qD2NxGhzHQmugWdib2PXnejxWZ2ABlfSixKYox7LHOkljqqDtsgoIOXoFs5sWlMm4YccNO1ve0dvPppwsZD+sjBou5QvKcPw9jgBHgCPAEeAIcARKMQKlxrATlZY7/wWefNpacbEKsaqDnKxs3PnvP/HUs67QdpDk4caV3/A77D1TgBXNu4Erv/0uJFPhWVdn/ZA7ZtzArmEn0/gknq7hot0MW87fcvQ9msru/v9A55h+dgw7E8PNzvfsQSLPXb026nfsGHbm9dWYj5Ux9lYOsrLv4L92562Pn65hZ3T95DmZ8+e0MZMwb0zL/B0mudsDT8ZeKw+viA07M80avG+1j2qxPWKPH2S6HY2lmrwRvlXy081VaFI/CKkGwnd1ZYQAf77mZ1mTJ5+uAU07ycg86LMyTbrj6LBG3o0rEMSJjbwztJ/Vg+ZD5jmaV373SQFEKX+FI8AR4AhwBDgCHIGCIVDyDTvTeWwY4oOADZeQx+ZYvnZnLNy1GcMbUWgbU9jch85Gq2NhiDzFnirfHOEpSRgvPCP+TDi/Yxy6vRcJ+RGUdYXfwj3YEvgqGU9XsdK7BgIO+CPm5m70FaMkTTR8JVLQTfCPuYnd0h+B+CF4wi8KzSOvIGl4GRyZ3gsd5x7EbSmdCmUreSJwWxwWtn/WalVko8J6qSSDqw7LsfOZOA9OG6cijuVmlXUdjC1pa9DJPFQeTkT0gn9wHNgjNA0/LInfIeFh9WP5Vqq/SvmIF9D/H53x+YhoLL35IYZsyaLZCnMPw09tGmLySZUhqJWjZLqGL0J80SP8lGVtPIIQu3chVFOXKLAx7Ey4dnAN3g8Oxba02/R96Ve+dgA+PbrCPGcZtxb9ApC707J+tvOm8b4IgW+PcMsal/dAUOxe81rYGnbZhtdPoo6enz8Yvada8IfwjW3xCHu7Ghn0sjGmBF3DwCPsDq55H8Gh25AmMw5xYe2AT3F0RSdIy100hp3p2kGseT8YodvSzDxKICPg06NYITMWW9/q/oPRKG2Dmf/EuSnwE6jKOxGBXv7BlmeEfbQkHjuGN5IuNDTz2YzwbTb2BvuhR4RMZ1m4+rXGM3GJ9vMys49g/uDemBon8DDjIeLDbfFheLsa2xMG52c6vwFDfAKw4ZJZ2qB254XYtXk4pO3leB6iUf/5CEQvvYkPh2wR96n/wvn4LXgSkttE4krScFQ3s8cxhNZohrAy05B6YSLKb52KwCkrkWT+fllUaj0HhxLG0/ft7Wdg/svqfetI5klEmOld/ScWKeRj+ebhSEkSvsv43qCcU4kb/r8cAY4AR4AjwBHgCDwiBEq8YXc12gd1Byei/vQ0JE97Fbi8Hx+P345XIpnizxQ2MgfQfMJmLB/3Ciomv49mvWJwQ6FE5ZAxVo+MsVyvEMRtC0HLSpewcbA3BsXmwis8DcfHu5PdIRQwOUsFTG5TARNBm0nCmCptsSyHjDXyGNymgh/CX6VQqCqSN+a3YFRtHY6HvbYgfV1PuNw7jY1jPsStiZsVRqW0muLt/e5xqDlwO9xDDyJ+WC36q+QVucwMO8HY7BW+DbM71UD2us7wDk1FudH7cXOptzhGxqIm8Aw6BY+QBMRObQ2XG19g/Ju+WJE7GvuvLoW3lW0n3NSnYYlva8zO6I61Py6EMIpUofFL0bCLof8vW28w1m+aiqZVBW/jT1hioyCK2p+qSE0O2bf1QJCiS/QhxAysD5xYjHfajUfKy5E4Q8rri2om1jDs0sNawjfFD5GzAtHmlXK4uvpdNB+2B/cUeJsNYjIyAqI3IszPFTfjxkpr7BaCoxmz0ZTmLa8xEYRDMQNRHyew+J12GJ/yMiLPJGE4EWRj2B0yvn4W/FNRkb6RFPUuat8+grAefghLrYjBceewxrec6P3cPa4mBm53R+jBeAyrpeHVMqUjrKUvUvwiMSuwDV4pdxWr322OYXvuKfiviAy79DC09E2BX+QsBLZ5BeWursa7zYdhzz0y3m9TeK7AM/I+Eoy0meuwPLApyp9cAl/vUKQKBv+PdNkhWJsZi9DEMwinPEKQEDsVrV1u4Ivxb8J3RS5G77+KpQIDahh2RvhWeoaw7bAcuzYMQOO8dKyb0gNBwqWDrscuA4uaeCKI8O8SnYSod2vj9pEw9PALQ2rFwYg7twa+wn2MofldRbRPXQxOrI/pacmQxM3HGL/9FUQyY9vIPCRvrbixMHj9JkxtWpUiCMohrmcV9NvTBpFXiBdly+5YKGo0C0OZaam4ML0advT2xAznWVg5oTNerZaHYx92xJsfn2TFoQRPqt5+vmBj2BmReWbDTqCXLsMmbF6Oca9URPL7zdAr5gbaiJdXRGw+98kjOr/4ZzgCHAGOAEeAI8ARUCBQ4g07SRG/iYmHz2N+S43iE/JN/MTDOD+/JQuBJKW5YWOEZsheJ1lBboHlmUcQKFscpr0Y+XwHrMAIJF7/BO2/lZSsu7IhRQpxw8aRqNwCSD7TGftvkuEke1FukkFxhQwK9n2f6J/x5UBrD50mp+lU55MNDqtxTDvQ+4nu2NJ4Hk5/NwnuMr0NliPzSKDZcDLt6I0nuieSYn2TFGsba0rjJl9U55hhp8JEy0skaX/Wht3FCLR0G4UzqmqP6WEN0Ti0qrXyKpNkKMeOeTDukqEq4m0xxnptv4/N3Swe2L0jn0eHFWDzvoiIlm4YdYatpfyYuIahqMoUVBvDLj/rZyLMKpExXE71DYZFcgvLuhgPxVSsF1PyzfxXRB47LT6ULifuWnhG9nzPPonTIY3Mr1yMaAm3UclMwa8KCfMGqn0k8WmivG/UvGKEb1uo9qK8fg54xkTfqkRWVDkVH8p0t1ieiSPChjc0v1xpr9yciMPn58NG3BiZBzGsbNiZv83QzNncFc/3iWWefsmyk9ahDKalXsB0T42VYjm5e8yXVBrGvvia+u8GZZ5ohwuGaHVrGcv2TYZsUOdnn/AjliPAEeAIcAQ4AhyBx4JAiTfsZA9BqlNtdJ6wFLPGtkdDZfKcpqGkUnKuroR3jQAcsAmDkpUad8w+eRohjZiHroZkSFUUKhuOrYbtnwL/6f4VgoVCGPWk4ieHZSWWytoPqeeHqNxK8By4CMsm90SzWnaKFzgw7KzbHahK1yeNQZW2y1Chz1KEd33ewjDXdyJozCbUkW/XrVhJTxHUK4uv87yKbnPVx0nbMK6J4oPHF6PH/DPaRqaukk6exW+/Rtzez5GSfAhbE4RQPEsoqF5eXM6GTqjSbw95XM/iOK2PuMYtJmGbNUFY3GM+zrD1shkrP+vH8KfJmT2o0sxlzHph+/3NEGxPw4Yd5Sx9+3Uc9n6eguRDW5EghKSavVNF47GTVyfvxrf4Om4vPk9JxqGtCWIIqJnf9NoBMGPzl8FxuL/mCcmDXaEPloaTkWJe9uvYGTQGm+qwMEP1WEb4li5QhEuVq2rPnAPDTipSIxv3Cj6U3+u1Hfc3d0NZQ/PzZR7xVDhRuPeEpbMwtn1DS66ukXmQd0t37eXKlc3lcEx2iVGFXdzI5FOeW+aJFCR8uRtHjuzDniQKQ5cvd/QuXtR/NyzzdHhVjXt+9sljOcr4RzkCHAGOAEeAI8ARKPmGHa2R6dpxbFw0BbNWJuFSHuUhdZ6H7RsC8apgPxkx7Owohydm/Bue088zBVfOqWtOHqcdKD+KQqcoWPHm8jx0I6OB/ogvngslD8FW9DSHaxINeWfxecRsTA8T8qWASp6B2LRLkd+j5LPCGHbs3bKVXPFcZScb7m0y4zC2vWfJ3rE2OtTFUwpn2MkGUnmXmnimnJqUCui3/jRmvaH6u8Y6ZB+Zj8G9Kafw1gvw/k8A+vlXQ/rk/2CR2duqET4pD6vE0i9O6pFX3gU1bQlChX7rcZoI0jQSja6fnnFAeVdbe1RAr+2WXDrHhp0lV+/WC974T0A/+FdLx+T/LILZS1JUHjtzDtotvOD9HwT080e19Mn4z6IMx4ad1ZpB8vKWrQTX5yrDhgObzMDhbe+huhonI3xbOVS7H6UDw04X57yt6FGBDG3ZINJbO628z+MbsWgKhUMKBhXlInaetx0bhDxcI/Og/ae/9kr5QuGYWVKEQA3Zq6jIA/7ByRM+YwehX+t72NJ2omUeRg07wzLPoGEn7Dmj+4SfqxwBjgBHgCPAEeAIPBYESoVhZ0ZGKDgxswt8wijvTA69MmLYyeFM5ltv89U45bQ8ge5bLAq57AXqtX0XKvynM07OIW/Q+IdSiFbNGOxtGIL2YXW0Qw1JwT+7th/eGBSLG4qwPKuVLYxhx7wnVEMfVwzX0C8ej52Mk1VRGUcsbOMFYL236k9HWvI0yVDXUFz1PHZS2GeGVNiGDDvBk7rHnwzx3X2h15/ZfrsDB+sne68U+X/SlNWhv449dnJYnpw7Kk1d3cC9KDx2OdhM3t0+sXLemORN1gtJlQrrWCpZSmG+W1Bd5DlIhT7AwpD11lvN40b4lj2TX4+dFMr4iyIvkRGlE0pof37WfSmEojMzu/hQ/mQ5jEi8jk+emikaYo72nz2j3rR3JJ6n+OFWxLPvf9+YaK9hCWtNm446XjNwk+XrSnVf1BcwBkMx8yHzNOm1a1AbkHOOZAH/d44AR4AjwBHgCHAEihyBkm/Y5eUgx8lZ0bpApegYMeyo4qVcFGH2ya8p5JIl8GRTWGVNMgaqCRXppkNMcWEhTBdaCIl1V9CP9SETFciFddDG6wAO3FWETlHYVM5Dos8cfamneLG1swpt8zUvqLbBoVbqDiG4amuEPyRPxPcU8mcgpY9KimBDJ6FogxfCzx7H+HryJ/U8dvLzykqOJqSHvU5GlFzEgRT/q9HwqTsYifVn4+TXIaxyngP+VCuL7P/vUWGcH4RKFcLPdAIzPD0x3ZHHzkRhbO5kZFzrgKjzCRhUXWeNVSTZ4Jyv9dPG30Rr6k4K/7Uum3B9Z2/RqHTksZPouEdFOn4Qi3RIU58BT8/pReyxY/x4j4znH8h4lr6EEzM8yVPtyGOXTbxTk3inIoIO/oKF5IE9FFwVrcMfknfye8p31GFAmz1pgG/N+YvkWc6kgi7MMjcdmYS6rT62DdGU19Vc1GM7vqeQS4kiE+WuuZPRdA1dNlGYaG8aTFNO2M4vLycHTs6KdiVW7/1maP/ZXXs5T6/VOIzLWIzFNRRVMtm3um+5i209mUBRex5197Na7hiXeYYMO4f7RGqp8EAszKSRC13kRxcfkCPAEeAIcAQ4AhwBNQIl3LA7h+UtGmPCj+0whSr6DWr0JM5vGIkeoQdQRawiR6aYIcOOVL10Kobw2mSqlNcB06LnovfT32HBwBGIOleRbvtPURVMWUlVlKt3thTwAMuvoQKZzHsh3O7fQeIIN/jEPIeAZSsxtq0rfj/2AXpQtUalkm8FupxnU84fq/bPQ8OfbuI5v+a4x6pi2s2xo4Gyab4eVCwilypZrlg7AW1df0f65nDMPtoGn8YOsq1ESe+kTa8DrxmZaDxxHzb3+Qd+/Ocr8Gl0SAqrs/FiWrw5Lh2mIWJME9yIDUbQhssEogn1zR4dMvaoWM1rk4UqhtMQPXcQGj2Zhf2rP8Cqf4Xiq7netr38ZMPOJwKXdoxErQfMY1exC6ITFqPZ/f1YMnw0a1lhm2MnlN4XcX75N2yeMggzEqmi6bxvcGSSVGZfvcYCv2TtX40PVv0LoV/NFSuGmovURFzCjpFPIzmf66fG/+XfNmPKoBlIzPXCvG+OYBK7NHBk2MkeO6G6ZsLiZri/fwmGj2atHGxy7HwQcWkHRgq5m4zfq4/fR97kthq9GpnBXn089p2cg7bO95jHTqgamYDFze5j/5LhGB0ptahQ59iJLUCmUKXO3k/juwUDMSLqHCoSPaeoIqy4Q7JpfA/im1yq+LhiLSYIPJ++GeGzj6LNp7EYJBQm0qDRMd9a+Mmp+QRsXj4YT3+nhYlahFEF0v4eVPwjF/UGr8DaCS/jt81TMGhGIlXAnYdvyDAUl4TRZHd+55ajReMJ+LHdFKrSKvAztVoZ2QOhB6qYi5s4nodjo17MC1x1D2VpP/ko26gwj901oXJvzHBUuRCNDwPmSm0lFPtUez//ZFMkyajMc2zYeTuUc3dZ9WKTSxAO/rIQ6ihsfvRyBDgCHAGOAEeAI1D8CJRww46U9fNfYsnMGVgQeww3BE2Ucl68h3+MyLBuqCsobIlBqDVsJxpMSUB8gOyOOodIXx/MPdMVqy6HowPDMe/sVkwYOAoxx26QUkv9oeq+jZDoSExqae15SJvXDN1WXAfe+QTnlvlKBoopHqPrjcRnUOWPZZ/A2vkTMScqGeeFfmSUf+TZYzYWLwiEalhGBSmwK3ujc/AuyhcUHn8by0/Eo1WiL3zmnkHXVZcRLhOMRATVGoadDaYgIT4A0uyE3m9LMHpoGL44L/R+o3m4voI+s5Yi9N0mkNt2WbFO9l4E+/VAhNgrrjxcJ+3BtXkPNcaWSUzHyt6dEbxL6B1IOY3ewYiIeAnb35qC61Y4U0jW1gkYOCoGx6TFgYt7e4ycOwcTO9bXaKyeQYb66xidksfKt7sje28w/HpEiIU8hP5/PWYvROergQje3MG8duciBWxqYtJ8IIZ9q2ylung7JBqRk1oyL41Eu/UaE0Uu7mg/ci7mTOyI+oITJIOU99dHIyWPeSSfyf/6WeNPc27aDyvWL0Y3kSGlX2JQLQzb2QBTEuJhZkvrRVH0bKM19OyB2Qs742pgMDZ3WIXLjAkylrfA66NTLMUzqIjFCI+uiCRl3/oSQB6c2lCM8EDXSEWLAOX6M/5c2PkqAoM3o4PMb+I+OoUB09qTwch4i/IVm/ZbgfWL2V6T2YNCFJeMHoqwL86LffGEnM9X+szC0tB30URgQE0ajfCtkHcYgP4z5b3hiYGrpqP26lH4xMOCiY1YFEK0l4zG0LAvpD2oRbeh+VHfty+XYOaMBYgVZYQgbrwx/ONIhHWryy4qHM/D4dqnzUOzbitwHW2x8NsodDPHDVvLBvHbc6fi35S3OCdPIQM097NrgWWeJr3nIuHrMxdnujLcHcg5U1Iw3HzDcctzGb5OHgVVW/riP8n4FzgCHAGOAEeAI8ARQIk37PgacQQ4AhwBjgBHgCPAEeAIcAQ4AhwBjoB9BLhhxzmEI8AR4AhwBDgCHAGOAEeAI8AR4AiUcgS4YVfKF5CTzxHgCHAEOAIcAY4AR4AjwBHgCHAEuGHHeYAjwBHgCHAEOAIcAY4AR4AjwBHgCJRyBLhhV8oXkJPPEeAIcAQ4AhwBjgBHgCPAEeAIcAS4Ycd5gCPAEeAIcAQ4AhwBjgBHgCPAEeAIlHIEuGFXyheQk88R4AhwBDgCHAGOAEeAI8AR4AhwBLhhx3mAI8AR4AhwBDgCHAGOAEeAI8AR4AiUcgS4YVfKF5CTzxHgCHAEOAIcAY4AR4AjwBHgCHAEuGHHeYAjwBHgCHAEOAIcAY4AR4AjwBHgCJRyBLhhV8oXkJPPEeAIcAQ4AhwBjgBHgCPAEeAIcAS4Ycd5gCPAEeAIcAQ4AhwBjgBHgCPAEeAIlHIEuGFXyheQk88R4AhwBDgCHAGOAEeAI8AR4AhwBLhhx3mAI8AR4AhwBDgCHAGOAEeAI8AR4AiUcgS4YVfKF5CTzxHgCHAEOAIcAY4AR4AjwBHgCHAEuGHHeYAjwBHgCHAEOAIcAY4AR4AjwBHgCJRyBLhhV8oXkJPPEeAIcAQ4AhwBjgBHgCPAEeAIcAS4Ycd5gCPAEeAIcAQ4AhwBjgBHgCPAEeAIlHIEuGFXyheQk88R4AhwBDgCHAGOAEeAI8AR4AhwBLhhx3mAI8AR4AhwBDgCHAGOAEeAI8AR4AiUcgS4YVfKF5CTzxHgCHAEOAIcAY4AR4AjwBHgCHAE/j9MqFtb7l3o0gAAAABJRU5ErkJggg==)"
      ],
      "metadata": {
        "id": "illiNfQdT259"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We use a highly imbalanced dataset of tweets that were annotated using crowd-coding from the replication materials of [this paper](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3026393). The tweets are classified according to whether they discuss the refugee movements in 2015. We will use the `modAL` package to implement Active Learning.\n",
        "\n",
        "We start by importing the packages and loading the data."
      ],
      "metadata": {
        "id": "LS6EEAFDUhdo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# start by loading old numpy version and restart notebook\n",
        "!pip install numpy==1.23.1"
      ],
      "metadata": {
        "id": "AdKfZKtLKH-m"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## setup and load data\n",
        "\n",
        "# load relevant packages\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from copy import deepcopy\n",
        "from scipy import sparse\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.linear_model import LogisticRegression as LogReg\n",
        "from sklearn.metrics import accuracy_score, f1_score, recall_score, precision_score\n",
        "from IPython import display\n",
        "from matplotlib import pyplot as plt\n",
        "%matplotlib inline\n",
        "\n",
        "# set seed\n",
        "np.random.seed(42)\n",
        "\n",
        "# define vectoriser\n",
        "vec = CountVectorizer()\n",
        "\n",
        "# load data\n",
        "corpus = pd.read_csv(\"https://www.dropbox.com/s/gv56nu1ptrp63ps/annotated_german_refugee_tweets.csv?dl=1\")\n",
        "# corpus = corpus.sample(10000)"
      ],
      "metadata": {
        "id": "Uj5eISTcRoTc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "corpus.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "WtFE5Ne97kWz",
        "outputId": "231e6505-3483-4515-bd8e-c0e5f86c2b12"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Unnamed: 0    tweet_id  annotation  \\\n",
              "0           0  1393164707         0.0   \n",
              "1           1  1550399954         0.0   \n",
              "2           2  1910339412         0.0   \n",
              "3           3  2069362297         0.0   \n",
              "4           4  4504479876         0.0   \n",
              "\n",
              "                                                text  \n",
              "0                            \"@mayers glückwunsch!!\"  \n",
              "1  \"http://twitpic.com/3io4f - Ein neues Netbook ...  \n",
              "2  \"Städte- und Gemeindebund fordert rasche Verso...  \n",
              "3  \"Kennt jemand ein gut sortiertes Online-Archiv...  \n",
              "4  Ehemaliges Sanatorium im Harz, 33 Zimmer, Schw...  "
            ],
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              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>tweet_id</th>\n",
              "      <th>annotation</th>\n",
              "      <th>text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>1393164707</td>\n",
              "      <td>0.0</td>\n",
              "      <td>\"@mayers glückwunsch!!\"</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1550399954</td>\n",
              "      <td>0.0</td>\n",
              "      <td>\"http://twitpic.com/3io4f - Ein neues Netbook ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>1910339412</td>\n",
              "      <td>0.0</td>\n",
              "      <td>\"Städte- und Gemeindebund fordert rasche Verso...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>2069362297</td>\n",
              "      <td>0.0</td>\n",
              "      <td>\"Kennt jemand ein gut sortiertes Online-Archiv...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>4504479876</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Ehemaliges Sanatorium im Harz, 33 Zimmer, Schw...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ecc6d8d5-be26-4949-9eb9-62f914b10d45')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ecc6d8d5-be26-4949-9eb9-62f914b10d45 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ecc6d8d5-be26-4949-9eb9-62f914b10d45');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "corpus.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sF-PdlqGVa1w",
        "outputId": "4b1c1f8b-facd-403c-9ca3-a4aec6fc90d6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(24420, 4)"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The corpus contains the text 24,420 tweets and a variable called 'annotation' containing the label of interest. We drop the remaining variables and split the data into a train and a test set:"
      ],
      "metadata": {
        "id": "uA5rBVdcVgsm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## we only need two variables: outcome and text\n",
        "corpus = corpus[[\"annotation\", \"text\"]]"
      ],
      "metadata": {
        "id": "c-3fxRih7tr9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# general train/test split for assessment of performance\n",
        "#     and vectorize\n",
        "\n",
        "X_train_raw, X_test_raw, y_train_raw, y_test = train_test_split(\n",
        "    corpus.text,\n",
        "    corpus.annotation,\n",
        "    test_size=0.20,\n",
        "    random_state=42\n",
        ")\n",
        "\n",
        "# reset indices and vectorize text\n",
        "X_train_raw = X_train_raw.reset_index(drop = True)\n",
        "X_test_raw = X_test_raw.reset_index(drop = True)\n",
        "y_train_raw = y_train_raw.reset_index(drop = True)\n",
        "y_test = np.array(y_test.reset_index(drop = True))\n",
        "\n",
        "del corpus"
      ],
      "metadata": {
        "id": "xC_lB9JDa8F8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Active Learning in Python"
      ],
      "metadata": {
        "id": "278mO7cqRZgG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Cold Phase/Initialisation"
      ],
      "metadata": {
        "id": "1VCOHJc4SKwI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "To start, we need a small random sample. This is necessary to train an initial classifier making uncertainty estimates about our unlabelled data. The size of this starting set will depend on the rareness of the outcome - it has to come up at least once. Let's sample 500 cases for a cold start:"
      ],
      "metadata": {
        "id": "iQSxI91xRgOk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# draw random initialisation sample\n",
        "n_initial = 1000\n",
        "\n",
        "rng = np.random.default_rng(42)\n",
        "initial_idx = np.random.choice(range(X_train_raw.shape[0]), size=n_initial, replace=False)\n",
        "\n",
        "X_start = X_train_raw[initial_idx]\n",
        "y_start = np.array(y_train_raw[initial_idx])\n",
        "\n",
        "# drop starting sample from training sample\n",
        "mask = np.ones(X_train_raw.shape[0], dtype=bool)\n",
        "mask[initial_idx] = False\n",
        "X_train_raw = X_train_raw[mask].reset_index(drop = True) \n",
        "y_train_raw = y_train_raw[mask].reset_index(drop = True)\n",
        "\n",
        "X_train = vec.fit_transform(np.array(X_train_raw))\n",
        "y_train = np.array(y_train_raw)\n",
        "X_test = vec.transform(X_test_raw)\n",
        "\n",
        "\n",
        "print(f\"{sum(y_start)} positive cases out of a set of {len(y_start)} in the starting set ({round(sum(y_start)*100/len(y_start), 2)}%).\")\n",
        "pd.Series(y_start).value_counts().plot(kind='bar', title='Class distribution in starting set')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 322
        },
        "id": "3KMnaNufRUJO",
        "outputId": "25736cdc-2601-4f98-f8c4-48a129c351af"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "24.0 positive cases out of a set of 1000 in the starting set (2.4%).\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff200768590>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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dMfQlqSOGviR1ZOzQT7JTkr9KclkbPyTJdUnWJflMkl1a+65tfF2bvmLcbUuSZmc+zvTfDtw6Mv5+4KyqOhR4CDi1tZ8KPNTaz2rzSZIW0Fihn2Q5cBzwsTYe4OeBi9ss5wEntuHVbZw2/ag2vyRpgYx7pv8h4DeBJ9v4fsDDVfVEG98ALGvDy4D1AG365jb/VpKsSbI2ydpNmzaNWZ4kadScQz/J8cB9VXXDPNZDVZ1TVauqatXSpUvnc9WS1L0lYyz7CuAXkxwLPBfYE/gwsHeSJe1sfjmwsc2/ETgY2JBkCbAX8MAY25ckzdKcz/Sr6t9X1fKqWgGcBFxVVW8ArgZe02Y7BbikDV/axmnTr6qqmuv2JUmz90xcp/9u4LQk6xj67M9t7ecC+7X204DTn4FtS5KexjjdOz9UVdcA17Th24Ejppnne8Br52N7kqS58RO5ktQRQ1+SOmLoS1JHDH1J6oihL0kdMfQlqSOGviR1xNCXpI4Y+pLUEUNfkjpi6EtSRwx9SeqIoS9JHTH0Jakjhr4kdcTQl6SOGPqS1BFDX5I6YuhLUkcMfUnqiKEvSR0x9CWpI4a+JHXE0Jekjhj6ktQRQ1+SOmLoS1JHDH1J6oihL0kdMfQlqSOGviR1xNCXpI4Y+pLUkTmHfpKDk1yd5GtJbkny9ta+b5IrktzW/u7T2pPk7CTrktyc5PD5uhOSpO0zzpn+E8A7q+ow4EjgrUkOA04HrqyqlcCVbRzgGGBlu60BPjrGtiVJczDn0K+qu6vqxjb8HeBWYBmwGjivzXYecGIbXg2cX4Nrgb2THDjnyiVJszYvffpJVgAvBa4DDqiqu9uke4AD2vAyYP3IYhta29R1rUmyNsnaTZs2zUd5kqRm7NBPsgfwv4B3VNW3R6dVVQE1m/VV1TlVtaqqVi1dunTc8iRJI8YK/SQ7MwT+p6vqs6353i3dNu3vfa19I3DwyOLLW5skaYGMc/VOgHOBW6vqgyOTLgVOacOnAJeMtJ/cruI5Etg80g0kSVoAS8ZY9hXAG4GvJrmptf0WcCZwUZJTgTuB17VpnweOBdYBjwJvGmPbkqQ5mHPoV9UXgcww+ahp5i/grXPdniRpfH4iV5I6YuhLUkcMfUnqiKEvSR0x9CWpI4a+JHXE0Jekjhj6ktQRQ1+SOmLoS1JHDH1J6oihL0kdMfQlqSOGviR1xNCXpI4Y+pLUEUNfkjpi6EtSRwx9SeqIoS9JHTH0Jakjhr4kdcTQl6SOGPqS1BFDX5I6YuhLUkcMfUnqiKEvSR0x9CWpI4a+JHXE0Jekjhj6ktSRJZMuQNIza8Xpl0+6hEXjjjOPm3QJY/NMX5I6suChn+ToJN9Isi7J6Qu9fUnq2YKGfpKdgI8AxwCHAb+c5LCFrEGSerbQZ/pHAOuq6vaq+j5wIbB6gWuQpG4t9Bu5y4D1I+MbgJeNzpBkDbCmjT6S5BsLVFsP9gfun3QR25L3T7oCTYDPzfn1wpkmPOuu3qmqc4BzJl3HYpRkbVWtmnQd0lQ+NxfOQnfvbAQOHhlf3tokSQtgoUP/emBlkkOS7AKcBFy6wDVIUrcWtHunqp5I8jbgT4GdgI9X1S0LWUPn7DbTs5XPzQWSqpp0DZKkBeInciWpI4a+JHXE0Jekjhj6HUiyb5J9J12HpMkz9BepJC9IcmGSTcB1wFeS3NfaVky2OkmTYugvXp8B/jfwI1W1sqoOBQ4E/oThO4+kiUtyQJLD2+2ASdfTAy/ZXKSS3FZVK2c7TVoISX4K+CNgL576VP5y4GHg31bVjZOqbbEz9BepJBcCDwLn8dSX3B0MnALsX1Wvm1RtUpKbgH9TVddNaT8S+OOqeslkKlv8DP1Fqn3NxakMX129rDVvAD4HnFtVj02qNmkbr0TXte5IPQMMfUkLLsnZwI8B57P1K9GTgW9W1dsmVdtiZ+h3KMnxVXXZpOtQ35Icw9avRDcCl1bV5ydX1eJn6HcoyXuq6oxJ1yFp4Rn6i1iSFzH9mdStk6tKenpJ1rQfU9IzwOv0F6kk72a4Hj/AV9otwAVJTp9kbdI2ZNIFLGae6S9SSf4GeHFVPT6lfRfgFq/T17NVkjdV1ScmXcdi5Zn+4vUkcNA07Qe2adKz1XsmXcBi9qz7YXTNm3cAVya5jacuiXsBcCjg5XCaqCQ3zzQJ8OsYnkF27yxiSZ4DHMHWb+ReX1U/mFxVEiS5F/gF4KGpk4AvVdV0r1I1DzzTX8Sq6kng2knXIU3jMmCPqrpp6oQk1yx8Of3wTF+SOuIbuZLUEUNfkjpi6EtSRwx9SerI/wf+J7WDzpJhsAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We have a few tweets about the refugee situation in 2015 in our dataset. It's not much, but it is sufficient to initialise our active learner."
      ],
      "metadata": {
        "id": "pYqybuuJTJiy"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We will use the `modAL` package, which integrates well with `scikit-learn` and provides some great [documentation](modal-python.readthedocs.io/)."
      ],
      "metadata": {
        "id": "T3ytCM9tWy03"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# first install modAL package\n",
        "!pip install modAL"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "csPNj3j0aRYY",
        "outputId": "cdb37d0f-0378-4261-a482-62806394f686"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: modAL in /usr/local/lib/python3.7/dist-packages (0.4.1)\n",
            "Requirement already satisfied: numpy>=1.13 in /usr/local/lib/python3.7/dist-packages (from modAL) (1.21.6)\n",
            "Requirement already satisfied: pandas>=1.1.0 in /usr/local/lib/python3.7/dist-packages (from modAL) (1.3.5)\n",
            "Requirement already satisfied: scikit-learn>=0.18 in /usr/local/lib/python3.7/dist-packages (from modAL) (1.0.2)\n",
            "Requirement already satisfied: scipy>=0.18 in /usr/local/lib/python3.7/dist-packages (from modAL) (1.4.1)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=1.1.0->modAL) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=1.1.0->modAL) (2022.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas>=1.1.0->modAL) (1.15.0)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.18->modAL) (1.1.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.18->modAL) (3.1.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# import modAL\n",
        "from modAL.models import ActiveLearner\n",
        "from modAL.uncertainty import uncertainty_sampling\n",
        "\n",
        "X_start = vec.transform(X_start)\n",
        "\n",
        "# initializing the learner\n",
        "learner = ActiveLearner(\n",
        "    estimator=LogReg(max_iter=1000),\n",
        "    X_training=X_start, y_training=y_start\n",
        ")\n",
        "\n"
      ],
      "metadata": {
        "id": "4av5diQVTpaD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The 'learner' is initialised with the starting data - time for the *Hot Phase*!"
      ],
      "metadata": {
        "id": "TgmRr_GdXY2o"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Active Phase"
      ],
      "metadata": {
        "id": "3s_y5kYgUFqA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### General logic"
      ],
      "metadata": {
        "id": "XU3w5x357RnZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We ask the estimator which is the sample it is most unsure about:"
      ],
      "metadata": {
        "id": "OyY5XMJ-XgFU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# query for label\n",
        "query_idx, query_inst = learner.query(X_train)"
      ],
      "metadata": {
        "id": "jdqoE61WpynV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_raw[query_idx].item()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "ea-bd9PdAfQ1",
        "outputId": "8b721f62-fa68-427e-f379-ea8535087161"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'RT @Marvin_Ronsdorf: Die niederländischen Nationalspieler können das Auswärtsspiel gegen das @DFB_Team in Hannover am 17.11. sicher gar nic…'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "And provide the sample to the learner with the relevant label:"
      ],
      "metadata": {
        "id": "35meEDtFaUHf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        " y_train[query_idx]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uoaUMu9YjE11",
        "outputId": "283fd818-38a9-40a2-9254-239352e779eb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.])"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "learner.teach(X_train[query_idx], y_train[query_idx])"
      ],
      "metadata": {
        "id": "WIdMVTqVaK15"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we need to drop the observation from the training set in order to look for the next most informative one,"
      ],
      "metadata": {
        "id": "wIYNGakLYAMQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# delete coded example from training sample (takes a bit due to transformation)\\\n",
        "mask = np.ones(X_train.shape[0], dtype=bool)\n",
        "mask[query_idx] = False\n",
        "\n",
        "X_train = X_train[mask] \n",
        "y_train = y_train[mask]"
      ],
      "metadata": {
        "id": "irb80ZT4X_UM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Supply the new label to the classifier and ask for additional labels:"
      ],
      "metadata": {
        "id": "NT4-4-W_Xnv-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "query_idx, query_inst = learner.query(X_train)\n",
        "print(X_train_raw[query_idx].item())\n",
        "learner.teach(X_train[query_idx], y_train[query_idx])\n",
        "\n",
        "# delete coded example from training sample (takes a bit due to transformation)\\\n",
        "mask = np.ones(X_train.shape[0], dtype=bool)\n",
        "mask[query_idx] = False\n",
        "\n",
        "X_train = X_train[mask] \n",
        "y_train = y_train[mask]"
      ],
      "metadata": {
        "id": "s-Mq5t3dAz-N",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "90a9a17f-2bbd-4d82-d766-ba50919383bc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Aber egal was du tust, ich wünsche dir nur, dass du glücklich wirst... auf deine Art. Egal was war! #lasttweet #nächtlichesgetwitter\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Of course, this could be done more efficiently in a loop:"
      ],
      "metadata": {
        "id": "B-s1kTEWahLb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(5):\n",
        "  query_idx, query_inst = learner.query(X_train)\n",
        "  print(X_train_raw[query_idx].item())\n",
        "  learner.teach(X_train[query_idx], y_train[query_idx])\n",
        "\n",
        "  # delete coded example from training sample (takes a bit due to transformation)\\\n",
        "  mask = np.ones(X_train.shape[0], dtype=bool)\n",
        "  mask[query_idx] = False\n",
        "\n",
        "  X_train = X_train[mask] \n",
        "  y_train = y_train[mask]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ytla6hsIan1g",
        "outputId": "4ba7d27a-d133-4a58-edff-2fab73e578e0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "@bonsai__ ich habe dasselbe mit sky vor :D wünsche gute jagt ^u^\n",
            "Der @KlonBlog verlost einen Saugroboter von #Vorwerk da muss ich mitmachen! #Gewinnspiel #KBGewinnspiel https://t.co/mR7dEbkA9k\n",
            "adsnbfner #KCAArgentina #RuggeroPasquarelli\n",
            "Ich habe die Aufgabe \\Ein vorteilhaftes Ding\\ abgeschlossen! http://t.co/gRovTL5Urv #ipad #gameinsight #ipadgames\n",
            "RT @Iris_Rohmann: #Kölner gegen #Pegida und Co. https://t.co/dwwqqXDWPp\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Annotation by Hand"
      ],
      "metadata": {
        "id": "Iu_MvJHJ7Vvk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The common use case won't be the supply of existing labels but the use of Active Learning for informed annotation. The script below provides some text and asks for the appropriate label:"
      ],
      "metadata": {
        "id": "Oqm8gjX-bRWM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# we can also annotate by hand - check rubix for more info\n",
        "n_queries = 3\n",
        "for i in range(n_queries):\n",
        "  \n",
        "  # get most informative label\n",
        "  query_idx, query_inst = learner.query(X_train)\n",
        "\n",
        "  # print text\n",
        "  display.clear_output(wait=True) # clear any existing text\n",
        "  print(X_train_raw[query_idx].item())\n",
        "\n",
        "  # display questions and text field\n",
        "  print(\"\\n\\nIs this a tweet about refugees? ('1' = yes, '0' = no)?\\n\")\n",
        "  y_new = np.array([int(input())], dtype=int)\n",
        "\n",
        "  # update model\n",
        "  learner.teach(query_inst, y_new)\n",
        "\n",
        "  # delete coded example from training sample (takes a bit due to transformation)\\\n",
        "  mask = np.ones(X_train.shape[0], dtype=bool)\n",
        "  mask[query_idx] = False\n",
        "\n",
        "  X_train = X_train[mask] \n",
        "  y_train = y_train[mask]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "elxqrv2Kt2MP",
        "outputId": "1641cd39-1fbf-435b-e66f-b4edd88d5e62"
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "RT @tagesschau: Pro Asyl zu CSU-Plänen: \\Populistisch und unrealistisch\\ https://t.co/FHDHsGrqvB #ProAsyl #Flüchtlinge #CSU\n",
            "\n",
            "\n",
            "Is this a tweet about refugees? ('1' = yes, '0' = no)?\n",
            "\n",
            "1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Batch Sampling and Comparative Performance"
      ],
      "metadata": {
        "id": "RLWOb6DP7Zk6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "As the looping over single observations takes ages, we can also use batch sampling to query a certain number of uncertain samples rather than a single one. This way, we can annotate several at the same time, but also give away a bit of learning - instead of updating with every newly supplied label, we only do so every 25 labels or so."
      ],
      "metadata": {
        "id": "rRuMpvHpccma"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We first prepare a classifier that is trained on random data which we can compare to our batch learner. We create lists to track performance of the classifiers while they run."
      ],
      "metadata": {
        "id": "kZXxEztpfmEH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# train classifier on random sample for comparison\n",
        "\n",
        "# draw sample\n",
        "X_train_passive = X_start\n",
        "y_train_passive = y_start\n",
        "\n",
        "# define and fit classifier\n",
        "passive_clsfr = LogReg(max_iter=1000)\n",
        "passive_clsfr.fit(X_train_passive, y_train_passive)\n",
        "\n",
        "# assess performance\n",
        "y_pred_passive = passive_clsfr.predict(X_test)\n",
        "accuracy_passive = [accuracy_score(y_test, y_pred_passive)]\n",
        "recall_passive = [recall_score(y_test, y_pred_passive)]\n",
        "precision_passive = [precision_score(y_test, y_pred_passive)]\n",
        "f1_passive = [f1_score(y_test, y_pred_passive)]"
      ],
      "metadata": {
        "id": "unyBVPbe2sp5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "We define the active learner with a batch size of 10 for uncertainty sampling. This means the classifier will always return the 10 most uncertain predictions, which are then fed back with annotation to get the next 10. This is of course less informative than updating the classifier every iteration. "
      ],
      "metadata": {
        "id": "22dmQjaef8Yt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# define learner for batch sampling\n",
        "\n",
        "from functools import partial\n",
        "from modAL.batch import uncertainty_batch_sampling\n",
        "\n",
        "# Pre-set our batch sampling to retrieve 50 samples at a time.\n",
        "batch_size = 10\n",
        "preset_batch = partial(uncertainty_batch_sampling, n_instances=batch_size)\n",
        "\n",
        "# Specify our active learning model.\n",
        "batch_learner = ActiveLearner(\n",
        "  estimator=LogReg(max_iter=1000),\n",
        "\n",
        "  X_training=X_start,\n",
        "  y_training=y_start,\n",
        "\n",
        "  query_strategy=preset_batch\n",
        ")\n",
        "\n",
        "# generate deepcopies from training set that can be used up\n",
        "X_train = vec.fit_transform(np.array(X_train_raw))\n",
        "y_train = np.array(y_train_raw)\n",
        "X_test = vec.transform(X_test_raw)\n",
        "\n",
        "X_pool = deepcopy(X_train)\n",
        "y_pool = deepcopy(y_train)"
      ],
      "metadata": {
        "id": "4AU9JiKX4LHf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# performance of initialised active learner\n",
        "accuracy_scores = [accuracy_score(y_test, batch_learner.predict(X_test))]\n",
        "recall_scores = [recall_score(y_test, batch_learner.predict(X_test))]\n",
        "precision_scores = [precision_score(y_test, batch_learner.predict(X_test))]\n",
        "f1_scores = [f1_score(y_test, batch_learner.predict(X_test))]\n",
        "training_progress = [n_initial]"
      ],
      "metadata": {
        "id": "yYZwhMby4nnh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# this bit takes a while\n",
        "learner_scores = [batch_learner.score(X_test, y_test)]\n",
        "\n",
        "sample_size = 2500\n",
        "n_queries = (sample_size - n_initial) // batch_size\n",
        "\n",
        "for i in range(n_queries):\n",
        "  display.clear_output(wait=True)\n",
        "\n",
        "  # all that matters are the next few lines\n",
        "  query_idx, query_inst = batch_learner.query(X_pool) # obtain uncertainty estimates for full training sample\n",
        "  \n",
        "  X, y = X_pool[query_idx], y_pool[query_idx]\n",
        "  batch_learner.teach(X=X, y=y)\n",
        "  \n",
        "  ## assess performance\n",
        "  training_progress.append(n_initial + (i+1)*batch_size)\n",
        "  accuracy_scores.append(accuracy_score(y_test, batch_learner.predict(X_test)))\n",
        "  recall_scores.append(recall_score(y_test, batch_learner.predict(X_test))) \n",
        "  precision_scores.append(precision_score(y_test, batch_learner.predict(X_test))) \n",
        "  active_f1 = f1_score(y_test, batch_learner.predict(X_test))\n",
        "  f1_scores.append(active_f1) \n",
        "  \n",
        "  print('Active F1 after query {n}: {acc:0.4f}'.format(n=i + 1, acc=active_f1))\n",
        "\n",
        "\n",
        "  ## delete newly annotated from training pool\n",
        "  mask = np.ones(X_pool.shape[0], dtype=bool)\n",
        "  mask[query_idx] = False\n",
        "\n",
        "  X_pool = X_pool[mask] \n",
        "  y_pool = y_pool[mask]\n",
        "\n",
        "\n",
        "  # random classifier performance\n",
        "  \n",
        "  ## draw sample\n",
        "  rand_sample = np.random.choice([i for i in range(len(y_pool))], n_initial+(i+1)*batch_size, replace=False)\n",
        "  X_train_passive = X_pool[rand_sample]\n",
        "  y_train_passive = y_pool[rand_sample]\n",
        "\n",
        "  ## define and fit classifier\n",
        "  passive_clsfr = LogReg(max_iter=1000)\n",
        "  passive_clsfr.fit(X_train_passive, y_train_passive)\n",
        "\n",
        "  ## assess performance\n",
        "  y_pred_passive = passive_clsfr.predict(X_test)\n",
        "  accuracy_passive.append(accuracy_score(y_test, y_pred_passive))\n",
        "  recall_passive.append(recall_score(y_test, y_pred_passive)) \n",
        "  precision_passive.append(precision_score(y_test, y_pred_passive)) \n",
        "  passive_f1 = f1_score(y_test, y_pred_passive)\n",
        "  f1_passive.append(passive_f1) \n",
        "\n",
        "  print('Passive F1 after query {n}: {acc:0.4f}'.format(n=i + 1, acc=passive_f1))\n",
        "\n",
        "\n",
        "  # the remainder is only for plotting\n",
        "\n",
        "  with plt.style.context('seaborn-white'):\n",
        "    plt.figure(figsize=(20, 10))\n",
        "    plt.subplot(2, 2, 1)\n",
        "    plt.title('Recall')\n",
        "    plt.plot(training_progress, recall_scores, marker = \"o\")\n",
        "    plt.plot(training_progress, recall_passive, color = 'red', ls = \"--\", marker='+')\n",
        "    plt.xlabel('number of queries')\n",
        "    plt.ylabel('recall')\n",
        "    plt.subplot(2, 2, 2)\n",
        "    plt.title('Accuracy')\n",
        "    plt.plot(training_progress, accuracy_scores, marker = \"o\")\n",
        "    plt.plot(training_progress, accuracy_passive, color = 'red', ls = \"--\", marker='+')\n",
        "    plt.xlabel('number of queries')\n",
        "    plt.ylabel('accuracy')\n",
        "    plt.subplot(2, 2, 3)\n",
        "    plt.title('Precision')\n",
        "    plt.plot(training_progress, precision_scores, marker = \"o\")\n",
        "    plt.plot(training_progress, precision_passive, color = 'red', ls = \"--\", marker='+')\n",
        "    plt.xlabel('number of queries')\n",
        "    plt.ylabel('precision')\n",
        "    plt.subplot(2, 2, 4)\n",
        "    plt.title('F1')\n",
        "    plt.plot(training_progress, f1_scores, marker = \"o\")\n",
        "    plt.plot(training_progress, f1_passive, color = 'red', ls = \"--\", marker='+')\n",
        "    plt.xlabel('number of queries')\n",
        "    plt.ylabel('F1')\n",
        "    display.display(plt.gcf())\n",
        "    plt.close('all')      \n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 652
        },
        "id": "j5oI7got2Gr8",
        "outputId": "43f24c1c-5e1c-4b75-80fd-5234b5ac0728"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Active F1 after query 15: 0.3333\n",
            "Passive F1 after query 15: 0.0145\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x720 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}