Решение_Математика_в_машинном_обучении.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/vn322/Cod_future/blob/main/%D0%A0%D0%B5%D1%88%D0%B5%D0%BD%D0%B8%D0%B5_%D0%9C%D0%B0%D1%82%D0%B5%D0%BC%D0%B0%D1%82%D0%B8%D0%BA%D0%B0_%D0%B2_%D0%BC%D0%B0%D1%88%D0%B8%D0%BD%D0%BD%D0%BE%D0%BC_%D0%BE%D0%B1%D1%83%D1%87%D0%B5%D0%BD%D0%B8%D0%B8.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Математика в машинном обучении"
      ],
      "metadata": {
        "id": "8s7QfAg6XhEF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Машинное обучение требует  знаний следующих разделов математической науки:\n",
        "\n",
        "- аналитическая геометрия и линейная алгебра;\n",
        "\n",
        "- математический анализ;\n",
        "\n",
        "- теория вероятностей, комбинаторика и статистика.\n",
        "\n",
        "Специалисты придают разный вес этим разделам, как например на [рисунке](https://tomcircle.wordpress.com/tag/gradient-descent/) и здесь мы остановимся подробнее на дисперсии, которая является основным понятием в статистическом анализе."
      ],
      "metadata": {
        "id": "XIWJsobpXdBX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![image.png](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAjAAAAF7CAIAAABD5CYKAAAgAElEQVR4nOy9abMc13nn+X+eczKztlt3AXABEBvBReIi0jbdsiTT0eqW7fE2trvt0ITbr+YT+Bv5xXQ4ZiZsjxyO9irv3bYWkpIoUSYpkiABEsDdt6pbS+Y553nmRWbVrbsBFyCWC+L84gZZqCUrK7Pq/PPZ6e23337++edFhJkRiUQikchDIopQJBKJRI4FUZAikUgkciyIghSJRCKRY0EUpEgkEokcC6IgRSKRSORYEAUpEolEIseCKEiRSCQSORZEQYpEIpHIsSAKUiQSiUSOBVGQIpFIJHIsiIIUiUQikWNBFKRIJBKJHAuYiB72PkQikUgkEi2kSCQSiRwPOE6diEQikchxoFIjIlLVh7srkUgkEnmciTGkSCQSiRwLor8uEolEIseCaCFFIpFI5FgQLaRIJBKJHAuiIEUikUjkWGAf9g5EIpHjguy7J16xRh4k8fsWiUQikWNBtJAikcea/VZRJPKwiBZSJBKJRI4F0UKKRCIVZbOWyUKQSfspXr1G7jdRkCKRCBSKHUGKtYmRh0O86IlEHgsGfQeFCzstK4MiKFShCihBCSCAdHSnKgTVn2r1/PJvfL+g+ieA3HlM3F/4MH4jwc4LVSABkw+VqOz8RR5PoiBFIp9xyvW9VkvK26UwTCoTMJIfhezusexDJR1urCH7IAIDLqgxFgADCmUgtabaiPciMARDEIEq2EBCtWM60iSKq9FjT/wKRCKfcYihAmKAkCaVAWTMkfxyqTGlSqXGHPYcBlwAQM6FoPACKGFkPA3yYIwlQuG0cEoEZgQPZqjCez82xaJhFIkxpEjkM44EMGPQC2+/8/b65lqWZbNzcydOnDh9+uRtX+sV3/3OdwB86ctfPkyRCqfE5Jz/l3/5lycvXXry8qUsSwofQkAI4cqVK/V6/clLl75TbudLX3rvgysi8rnPf05FXfAArLWqB0euVDX223x8iIIUeayRiTyyO3YXKAO4kwwAHeUN7OHgd75bg2H8umqzQlBge9D7v//f/4eIZufmOp3O9PT07/+335+fnw8UpmrJwIuIAFLLUgAE9IauXksYeOONH5w4MfeFL7xsjM2yVFWtJQJ4FCvKvSSJyQv/v/7t253t7sXLTw6LYBPDIpvd7rvvvjs3Ozs/P//a66/Pz8//zM/8zNs/edc5d+bcE2+8/nq32/2VX/kVKBvLvWHRaqSFD6WvL3eeSK1Nohw9PkRBikTuCh2pyP5c6UNfQqB9gqR8v5PamHeSCL70pS+9+OKLCwuLf/VXf/mDH/zglZ99pbPdrdXSjY2Ndrs9d2Lmxo0bH310bTgcPvfccxcvXiyKwlqzsbHx9ttvLy8vX7p06dy5c9PT08vLy9euXet2u8z87LPPXbp4VkSYaGN96zvffm04HD55+eJTT15uNhqXL1+emZnZ+biqT12+PBwOB/3+Wz/+cb/Xm56ePnP2LIBWq8WnTzdqdn2zc3Ph5vlz59vtVgwqPFZEQYo85uyseHdWc7NbRY4S/yA+aMO7t3MfwyjKADeb7cuXn261pqenvzUY5Fc++Oib//C3rWbL+fz8+fOXnrz4+muvFYVnNm+88cZv/Mb//vnPf244HL777rudTmcwKL73xg9+/Td+/aWXXn7nnfd++OabQUKn03n9te/93n/7vZmZ2aIoXn/j9ZWVtW63+7/+Fb/39f/j4sVLb7z+vbNnn5j/2hmAy78333wrTZO5uVMrK2ubm+tv/vCHTywvB5Htbve3fuu3kjPzN27c+PM///Pf/u3ffvqZy7WsFjXp8SEKUiRyAHJED97I4JHbK4kavo0pdJ/USAXjKMy1a1ffefudn7z33tra+le+cirP8431jRdffPGFF56r1Wr/8A//UK83f/VXv9psNr/xjW/8z//5L+fPn8+y7OLFS1/72temWtPf+ta3vvOd75w/f/7ll16ammpZY69eu/p3f/f3H3740csvtQA8/9yLv/Zrv9bv9//yL//ytddfP3nyZK/X7253J/dnOBwwc7PZuHTxYrvd+p3/+l8bzebCzZt/+qd/euXKlbNPnH7tte8COHXqVCOruRD48HyKyGeMKEiRxwLVSjrGEfLKi2V2hGdSD7zb8a057/dvZ/Tv6tU+eNwGYaLJHQBAzADSJAFAXD4KaKUf4zTo8j1Vq30kpjutXSWGqHa2tt/693+/ubjovf/pV37mp3/2lXffeafZar780svPPf+5jfWtlZWVL3zhCy+++AVVPP30s2+8/sZ2t0cwZ06fvXTpqXa7feXKlddef63b7W5tbX3rW98aDoYA8jxXlSAyOzd76cknL1y8aJh/9NZbS0s3O72eE19r1LN6TVRFNa1nzKZwRbPVak1N9QeDU6dON5tZltbnTpx68823pqenl5ZWf/7nf35ublYAVVHduTaICQ6fbaIgRR4LDlzIgoQdfVEunBs/5CYExvudEhzVvSkDo/sPzFbYvw8yur3zci9hvIesPH40ScqfZylCSlQqGlQUVTrFkVZnVWiAKmVZ9vJPv/zFn/u5ZqMxPz+fZclgOARQq9X6vaGIWJuIiARV1TRJB8NhrVYjJjbcajUB2CQBsHBz4V//7V9PzJ149dVXG/XGH/7hHzrnrTGbm5vWGGs4iLaazRU2ANI0DT7keS6qqprnhfMuQVIURZZlKup96PXyZrPx3Oef+6d/+se//dued+7JJy8ZYwAwR/PoMSIKUuQR4u47q00KhnNBgoiIqHgpa2aq+/c8X/a9duJ2mNyHo1y5E3QcMiq9d6XwOCcASrUR3dkHqYy4SpCMsdYSERORlvWrfCRNUoVzUNVarX7+/PlnnnmmfLs0re5kw+12zVi+ePHCRx999O9vv1Wr1X787z86d+6MIoj4bnd448Ynwetbb/3omWeebbfbg8Gg0Wy22+3l5WWbWCbyIRRFsbB488OPrqytrV/58P3LT14GIEF88HmelzuT57mqqqorimajWbjhJ9evzczMzM/PP/PsU9/61v965913//N//uqlS5cMIUCJ6eDMxMhnkShIkUeJCc/bnb2wKHYsnkE/V9UQgldRApQn5KS6XUpFJRu8Izy6L3th0tahW0aJVPan2JX3CwCvApLJZ/pSbMo7A0LwIcAYawwTkTGm6kC392jwnmiUCnwBFZPU6o1mM00JgDXwAVmWtdvtoih8QJalr7zy03/yJ+9/85vfLIpiMBj83u/93slTpxqNxg9+8IM/+ZM/KYoA4Gtfe/HyU089dfmpt9760Ucffui8KwrXaDZEwtTU1Ntvv71w82ae53MnTnzx576YpikbbjVb9XpjZmZqemaq3qi1Wo0sy+qN2vzpk8Ph8I/+6I9eeumlX/+1Xzt9+vRzzz+/vLL4/PPPl2W7BNIoR48T9P777z/zzDPl7zz6ZyPHgwOi+3t8YqoErVxYUlkbEAFz1ZzG+xC8+uCDD6pSWTyqABiVF0gO+L5zeSdP/BbCxJo48Ru54xQEIt5v2PGujyVjwSOFYsdaMjCjLUBox6JqNBvVRyFiQwBEnIgws3PSqGXr625tud/tDE6fPdHdXj93/mS9YRIDF6CKzY0tADOz7ZqlPGB7uzMYDhdu3uwPBidPnnj22c8BWFpcXl9fd86p6tzciTNnTtcye+Pm0uLiooiePj3f6/UuXLiQZdni4uJwONzY2AAwOzv77NOXRXDz5kKSJGfOnFxYWARw5syZxcXFLMtqtXqWZVevXu12O63W1JNPXtpc2/jjP/7jWq329a9/vT3dIqL9jtC4Rn22iYIUOYYcKkijun0uHW3EgEJo1IotqKr6EIL3QeAFKmXkYte6RjQZltjrBjyyIFXCcHR4V3CeJ+6f/JgTyRFj952yQTJ+28n3HVtvCmS1hJmJVVWbqSVgeS3/wfff++63/13FnL8w/3NffvnihZl6k8aCZA1AEK1swfGNIKoq1hgCgoIJZlTWS1W3ukotxx9DgaBQQdnBzgdYhggSAy9gRghaHm0fPBMlScpAUHjvmU1i6M03f/Rn3/iz3/zN33zllVeIDzaM4hr12Sa67CLHkAPjQyokCrBqmckMVGriCvVS2UDeuSBSRV+UKy0h7I733Ofdv0dMpE+wwEINKIA8ACURDUBZ8soqBKYwCGUGgGGbD2EtCpel6ckTJ57e2uxdu9ptNj86f/EVJVTd5lQtEYChBCUmotJRptCEiWACEBSGoEAeyswLVhUJAsBYa5iKEFRURIjIWJsXua3XAOTF0NZqRPAC4vKKQACwQWosjVMISY1hVennrt1u/fpv/OqLX3ieDY6QIxL5DBIFKfIIweOFqij8OOV6e9CD8u7UAwIgpI94++AqGiQERtnkQRQACUiYSKBEKdQSs4KgcAWCBxHW18LWVm9leQ2avvTSF0OB7va61xUzOh5EAFFR6jUxETEhKMr09CI457y1RgJslhoAxvgQiMiyYWMEcCH4AGajpEliRSESarVaaX42a7XyjSzDi3JpyVEADABRCepVNTVZYFhwYsy5c+fOnj2bJulRUhYjn0miIEUeGrd2eE0qiQCiBIWIuCDeO+8pePEiAIhIiHYZPlVIHKP86N1B/onb+1v57GfSTbRL9u5DvJ1o5yfJ8KjCXQoUagBASFSDagAgYEvsnKYJDQbwDpvrWF7a3ljvrq50NjY6/X43TdN6bXVudmr+TO2ppy9IabKUR6s0RGQnF8NQdcSsSaxJfAiWVRUBICAp7a/RtIhklJCtIAgMgcmM4z4MEJGWfcaZFcqGxzmBRDs2kkH1mizL7vnxjDxaREGKHFPKeT2GINjJU/DOOe+D90HHiQkMAPt6Qpfpc6N/MO4w3nOsEIJqkFJcldK0EZRU1agVwWCIxYVida2/stRbWd7qbA1T25LArea5dlsGg163293qfBx45okLdRcaWhSwiTHEDCKwqWYgiVYhunE8yRpz9BlFlQ5F2ybyKYiCFDl2lLHu4NWHYJhDgIioSuG9D16DKCCkZW7C6HIce7rCyd47eHz35F06aVYdm8VUqljOzj+9V4FCLcA2s/kQ3Y4f9NziQm9rY7C8tN7ZHuTDQEimpqbbU3NTrdlaDSAMh8WVK1eGAz/VnpuZm81zF0zuQ7DGJMZay2x2MhqoqoUCE27X5+hIEB2fgxp5BIiCFDmOqMB7DUGGrlBVkRBksoqHoTzWknHa951y1IZ194Syq/fYaNtrsU30eB0/TW1p/6my9yKw5XzxlWtYW+ncuL60utLpbBWuQJJkzUa7darWarazLAtBG1MoChDp1Ex6Yv7U1Q83Gq22TVEUXlKfqIqIBgmBxbAxpkwZZ9qxloB7o0mRyNGJghS57xzmLPPeoYqoG8ulywghwAV470MIqsGLBwACGabRai4EIlbRqv9bZUvsvxY/wCrCXhGaKHqdWH8PKlHat3W9vZxNvDdDzWiEkjA8IEKoKp9KGZCgQsrGGsPMBPSHsAaDIQY9s93TmzdXbi6sbm3m+UCHA59lWXu6XW9k9XozsYmxxrAhwwA8HKcAkHsWghfZ7hXOo15LCLmqBu/LOqecq/iNTZIkqebI8viITvjgDnXH3fJY8cQxkOqevS+IydyRkihIkYeGtQkAharAeXiP4CESvHota1lFd8I/E2r0yLAz64irfwKA2TNDr6yFFUBhFUbEAsSCQR/dbWxsDNY3+gs3Vtc3t7udgQSemj7Rnp6an2+BpJZpklKapkRULutaHi5SkAgYapnUpFnug1cIHdwbLs9zH4J3lNrEMpdZ4Bj1jLh1B4pI5F4RBSny0KhiRUG8V19oEJQFLkLjZDYGwoQUjYpAq44+k4v9gTzURAbl0rUIYFRyWyZZO9bS/yZQ9hJUSYUAFlhSHubobSMf4uNrS52OW1xc6/aGEkDGtqbmZ2dnarWaMak1BiSGwDyyMKo+QvuaG1mbZNmwyCXgFgTvgxfxIbUJM1trdrVzxaNTwBV5ZImCFLm/qOphC5kXOBdcUXivUhpD5UtuvcFHa1UkgY6by1GpFkJgMGBFAMk0IJSdJhRry1hZ69+8sbS2utXZGrpAia23GiebU1PtdrvZTEESxAMg9kxEcrgcj+wzYzhJrHMuHCzQk/M32AcPUWYWscxsDBNpbLkdeTBEQYrcd8q+PmXtS7k2qyJ4HboQvPdBVEqTaKdTzo5f7tGSn0lIKh0aGXKKymunAgH6wyIx9SKHClbX/M0ba52Ou/LBDYGFcq1eO3HqVK3RqNfr1hg2BhCoA7whqTauDDaVKXYwDKBWqznnh8OQ5yAi3umcVPa+A8pWSeV+KkRUQvAuAEgzy2yShIjI2ttP3KDJ5rPRoorcIVGQIvcXKrOzR41+nIcPGoKToHmZy6yqu6uIHqUo0a0YqZEykQGYkZTWXyB4B0hyY2F49ePFpYWNzc1Bv5fXG7NT7dNZvVmv141BklpryRgLlD0OBPCAgPworoZRb7lbJQwyc5Kwc0U5d+nQ/a3CdbuahRdFwWxEArOBhLLLOI2ILRUi95YoSJEHhBcEL3nuglTN0ESDyri7z62u8UserLNulBF295SJDFBYVQthBfo9dLfQ62Ph5tbC0trC0rKqGuYkbZ49f7bZajXqdVgzMVtWFAXGgl6NqOCRW3PSNhI9JKZmLWdZtt3v+hBGrR/uAJFQFIHZ+ADDbKwlojRNDUiJFLqrOezedhmRyB0QBSlyn1EIwTstnPNe/Ggqq6ru6Xn6yFN5GicT6ljBVa8dwbWPwspy99rVxeWVrd52kdUbM3Pz0zNTxnKjXueEi2JoMtqfi0E7wwLHjN7lKOnpzEliXBEklEaVBeSw1hVKEPBIY2Qc2AteQpDAhr0aw1BOM2tAArpd1C8SOSpRkCJ3zGG5a+OqH5l8hsJ59c4552UnzYuJyn4/x1CQRgkIY/ZWs47HkJfzWw0RBUEQJU6IqMhRFFKrc/DY7mJlefjJx0udTn7j+ho0VUmSbOaJJ5Nmo9aYahFrPa16uNlaOumu3LE8CFS2SiJgsvdBWYFFu/x15XCKSRfoYNgjJlHZ2uwNB+1WPQUV++Zu6M4n1fFjE2eHYIihkAAJEkLhXJEkaZIku88hT+z8XqE6hic7cqyIghS5N0yqkaoyU/BwLoTgvVeR0jtHu+fcHPMF6la7V45lUmFRUTaGElW4AgCCR6fLH3ywtbK8sbba3dzY7vddLWs1m3PN5kyrNcMJc5oThzIME8Qb3vteh1ah3nlfPiKyNmGyReGh5RYYyqNeSbc+CxOP7ggzRLyqhjAsZckYGLtrMRGC2Te7NhK5NVGQIvcGGY1oq0biuRC8L910PBG3kFEq196XH+J6ehhJ3vs6400YRlIlETAUU61a8CgK5B6d7WCs6WzJwuLmJx+vbPf81mZvOCgazUaSts6fbDebU9ZYtkmSGBgoEhEKqgYGRAFVWsekDt1JG7gqm3xi/ye2Q5QklogGgwEwHrR7x+xOfWSoQCHQoLmx1grYsCVDe542sYeRyK2JghS5Z5QDQ1VVRPrb2+VixESPXohB+YCQfNViTgGGWihvd5DnGAz9sKD11d61j5du3lwZDlzQBJI26jOnzk/X6mm9nhlDzueNqZqE4KQfXDBsiIzZSV6415+gzH9TBZSIrLFEVOQT42hJdn/Gu7BmGICIAKQuBC/GWE6YTewEFLlLoiBF7oBdsSHd+W+Jd1pOa/XOC/Gkh2eM2dX/rXpACEwHG0kkD1zNlJkIyqLqpZzIYAyzlt3lAryTYYHgsN0NqyudhRtr2323vNIhkzG3Go1sZmYmTWtZlhERGMQE0sQmzuUgYRIu25SrKDH2euf2jlQ/OuPtiCqTEdWyMVOtVh8MBsaknU6nKNBsgkiDjo1PhbKw7hRMHaYn4xdUDVirhndV3AkgF0hgjE0SNnaPvu0I3jF31EYeIlGQIncJjXpsl3WvqvChnFfkgg/GJAe+6k4bbCs9hKkQosoEMklmCYx8iCBAgAd629jcHCwubKxv9NdWOt1O3u+7enO6OXWq1Wy3260kSYI4IgWULJc3qjrWqlR2tFDftvPRp/sIVb8faAiemQ2zV9nnAp24brhLeMKvKs75IBICG2PTNHZ4iNwZUZAinwoRSFDvNQSfOxe89wrQoRfZ4wEHwC7LSehgh5E++CEIJARTzqh1DkFhCJ0tLK0MO1vDpYWtXi/f2uxu93u1WjOrTZ88M92YarbbbQnivHPBERFbS6yKoOP4E8moi8Geuiu5i9qgO8L7YK011gRfACA+IIZ350xupXQ8MkHKHhxCEoInThIc0sw1EjmIKEiRT0VR+OB9UQTnXWk08UTCWNUFdffyt38m3q0zF3Z1xr5X7DULeDSFiAEo2BoUA2xvYzDQG9dX1ze7y0vrm+tD75J6vdlunzpx+nxik3ojIwvnin6+TUTGGpuUWw5j/xkAUNlY9YBPQHpAuOoeE4IxhjkJwZdz10XpfsT2FCwkXI3SEJd7aJIAUZMiRyQKUuQ27OlYIAJmCOCc5HleFFWcnK1RKa+U921iYkE6cCzdHvNoctmWic0dZd2W3a8Gqhy5UXyFy+eIQESIysZGHLz6QLWa6fcBQn8bgz4++Xjz6sfXh8NhbzBMbGatac/OtdszjXqjXq8LC8o22CQ1m04EgnYt89UYQR23T9qnAoTJT68TMTPFId25D3OyldOhFAakKqNcOKIkcYXPTK3T2epsYm4WIjCWylhQueOVd++Wh1h119nbYwWXj6rouKmRCvlQeGdtSmm2c/pk4hgcqbI38tgQBSlye/bUGAkoBPgQRO40L2sv96Ft3cG7NJFuzsNhASamhMkyGy2blCrWNrG57ldWNlZX+9c+WgQSZq41pmZm51qteqPRtNakqQWgGlQE1QgM5UP8jccILTMD7XDgVA4O791zRAIpFaLOK2DSjCnKT+SWREGK3J4qDCIQCaoqwuV88XDL+Tp3xO072d2eydbaAMb5XxNTlAhEXGs0BBBRDSYAm1tYWet1NoufvPOh96a7XbhCpqZaU1PNdrvNiWVmYzlJrDUWoiIulGpEWk2svUUd66eGmPTepRpuD/qi02w4iJj7Lw+qqhoImucelKRpGjUpcguiIEWOhAhUoUJBtPBOggSRPT6c40UVEKpiQqj0yqhCBMMBup1hfzD8+NpKZ2u4utbpdPqJaRC4NXVyenq6NVUrOykYNmVUzDlfFEPLxjBbYwCo7gjyYTNVb6slNKpDKiuHyu3ctQJVMbnJZPzdbR2Gw6HItLEkQR9YuoiKlsMsyg54UZMihxEFKXIormyEykxkVOCc5K4oXDERw6DdNZAPty0QE7E1rKohqIoxbPOhCHFWq/opMNAb4MYn3a3NwfVPlre6g34vr9cbzeb0ubNns1ojy7JavcYMYgUMa3mNL0RkLBMnKNsfVYGSHdtld4IzY1zuanY6yx1mRQXnMC7u2T0Mt+SIKpUYG0JAmPSDihAzkU1slmXD4bAoiuBhEx3t1Wjw+cS5m3z9UQZM7EpaGb2WdY8/VoPzLmjiuFGrGWPM6NH7krQSeTSJghQ5lCRJAARoqUZlzEiFRhVIx+46VyS4wgPMnBKMBKQ1zgusr4IMbt7oXftkqbs1XF3vbW0OCMnp+TOnTjQbjaxerxETkRpTNi8VIdGy8pNKxxMAaAAxjQ2jSYWQyfHgE4v4ZO++wwSlPI5VdiIBABOXyQHlsKg7Pg66tz0QERlrrbHOOSdICNbcYqzfPUBojwAzICIhzwOEksQkiS1lKTa8i4yJghS5FQEavPdeiwJSNrSmPZ1hdhaTu81Q+PSThyoIxlorakSgAii2t7C0OLi5sPHxR6uDPOS5s9YqpU9eOnNy9lSSUq3OREpMEspaUlHVsmX2RHrfzsoaZDJDbMcqCoeNB58Up8N2mw7JThw9ejdz8MpZ6eOjysTW2CxxzpeRP2OsBsX9HIc4sWWevOWCCnzhg02SeppYjpoUqYiCFDkUL+JccK7wXoODKIHp3hpGPOGn2mHvuIeD2Dt8yACAonAY9LHVzYcD/9GVj1c3ulubPV8Ya2fq9ZlTJ5v1en3mxJQxEAWCV+RApV5QJVEChEC77BnCTqccYOTIKtOXmUj22jETAjYKs7EeUPYz6S6b9MuVOeWTz/nUCBsySVKIBIDJEIVqj+6wd/ingEu7TQVehYhVvAEhtaUmRSJRkCJ7Lk4re0VE8tw5FwrvJYDIlPN/cItqlUOqXG+RgVbaHly9b6UuMh6wo6WpMlrTyZZPABBUnVNrEmZIQD6EC+h2sLi4ubHWvX79pgveOak3WydPnbOmNjU1bZIsTVNAFD4PXlVZqzbZqkoKol3h9vHgubJWpnwkYBx9Ke8vG8hCsKNJ+z9vuWVWxm71LW9XlUCjKUsTt6sXj1ohgM2EqUEMQMaCRwyAmESpbMK98ybMKiHJTG+QD3K7vY25NiuZUR+jsqXe3SvCYd8H2uuyq0S+kiWFehlo4QVpaizTqKJ6r8UcterxIQpSZD8slW0UvAsiUC0n0d1j585oweKJlYt1skloWVQ6WpGCIgSIeAlsOGNGCNhYRz7Ex59sXL++uLHW6XS2jUlbrebJuTNpvZYkSRkJY2uYAfKomtIBqkLlprk0eY7SuqBcTEW1mio0+XFGx2f3YbqXB620n0pbSo6W4igEKkuD2RhrhnnInXhhQ5WOKOEeek2PDJdv7X0ACiBFaixKTYoOvMeXKEiRPVRq5L0rvBNB2bXsKGp0F7OLJhsoTLCzHgWumvqIoDTUjOU0QXcTWx0sLQ62NnsffXQ9L8R7JElj/vRMVktmZmYazYZhE6AqUnnYRFBVs1bJCuN3UgLrnp5GOykGuz9kacbdy7Kjg6fE3uL5Ye9M2MOSzku7SQFlShKTpOlgOCgK50OWTWQF7ktAuDcc5fsQoBJEXAFYShiwI02KPI5EQYrsQgSlGnmvEkrbCEQPohnZaF2WicWRoVZhoLW7DQkAACAASURBVJU09LaxtTns9dz77y5sbebb3b4XtiZtNudOzJ2aajeD9rKaYeKiKIKphC2MNKlcxMsPU8VsRtIidJ9bnN4LxspaZjqMsiFIRW+x99UzjbFJMhhseudU0we2z7eDVcV7YfUMC/iRJgE7yejHLp8zcp+IgvRYM/bUSDk5R2hQuOB9COK9EBkiHi9892UHSqecBlUVwDAzm0AoiiKxWVAuCpMP4QO6Hdxc2Fpf2164sVYU3vm81WqdnD+d1LJ6vZXYNE1TQBlpkBDglXeS04hoMletzIou3V+7nW77Zq1OJjZUHi4FYKpkhB0m06x3VWYdabjDvv57u7Ywkad3yIkoxbWsGys/LCb2kJm9KBOyLOtsFaLabJB6PQ55+4ar8FUI0usNsiyp15hTlp3OTFXcrnp+FKfPNFGQIlWTuhBCCOIK570EH4IgseMkgrufSnSEbtYC5rwQskycgk1/GAgt59Dvyfa2Xv9k9fony9u9YnOjr8jqWXt2brreNFlmarUGqkUNgFdVkFSD6fbvyc4CrOOUubsd5/1wmLTzSojIGINbhpREpLKRmAF455zHqJ9dmTPy8FxklWtUyslMwWtReMCyISKdjJlFHgeiID3GjKRCFcFLUXjnvQsiQVRHi8BOheW4w+qhHKZZk/eXcYXdQQuGssnqTlEU8A750Kyt59evL66tdLY6PngOQaw1p8+ct1k61ZpuNGu76p9USxur2kM64H2Pwj1sGXe/GTvrAEhVPXWoBnvvwcwMYw2APM/zXJIMDzl3YNJ2VAbEsFWVonBBfJraJImz0B87oiA9rkyKhCKIOO+LwoOIyNC4xOf2TD5ND1zjdjn9tEpS8FUatBGCAp0+Otthc6PX67ob11c2Nvrbndx7bbSm6/XGdKtVb9RqjXqtZolQOCl7owlhJ+a0u57mwc+ZfZDspJgbxv6GDuNDQQJlVYEoTNnfiJ0LzgmyB73Pt4GtqpDAB++DiHhQmtgkatJjRRSkxw4Z5YiVPp4Q4MuSoyDYSV4YVcyQlFeyPLncHxIXKTu4lbdHelB14C7bwSlxkmWDPChM4SCK/hAuoNfHlQ+vb20OVhZXg6cil0ajPXfiVJYlJ+amksTYNCUiDRKcl2prWr0bKSnGeXOT5auHZ47t7P+kiJkjiJjuKuI9+PmHbWV/TwSePEp3yx7DTjVAJ8ts1RgjUFWfZixitrfdVMs2m9Y7iHgve32q+x2D95bROQqYcJmqChRKIGNB3qsf5nlQTRJrxYy9dkeKJx3UWy/ySBAF6XGEqMx/RghwzhfeORf0LrK29zMKCYyrW4gss60KmzwNcigZH7DVwdrW8Obi2s2F1f7QD/qFikltbXpm9sTMySzL0iQBgVEQQxEg0HIYk6hSpXhVK+/PtDF0a0pn3a17CxGMAbx3SWJVaDjw/R4yE9LEEikMB6k0Ufd1o3g4KIPgg0cBAGzBMDGS9DgQBelxRRE8nJc8d16Cqpa53ZMjFSoq24j23QPsuerfsZwY5AEBLJSDNxKMl0QEvR5WVuXqxzdWVrfWN7tBDFHKNj1z8qwxptms12o1ywBUdAhF1XFUjZZzI0QVSqMs8Hvbh+1oMaTjtSweUTaISDWkSZpldQlaFKIC7zwbMsaACKQiMrbVHqYaVXtQapKg8KycJMylNfXZdsU+9kRBekwJoVSjvCgKMneXZCUT/0XZSUd2RhClAEMNFN5hc0MWltY21wdLK93OpusNhlCb1tr1Wrvdnqk16oUb1pt1m0K9S9MUVFUOld1mgvcASKGqx0wRHiZlcO5IEyKYiDiIT2wiAc6JCFWhJwEZpqrdOI+mAFe9xu/zJ7gFXHrefPBl66YkAbOhT5HwGTn+REF67FCFd+JcGTfyZLgcfFAFlvj25aETdomAREiYLBQicEGzeqIBhUNnE/2B9nrF+z+5tr6+vbmxnaV1r9JstOan59I0aWRNa9PEZgHanm6BoKrCVsJwnDUn1ZsxDooZ7HfW3an7bt+IhEcJZj6SZogGDZUqken38+DFcKoaRERVRUJiGGUCPfNgMGBmY3hycuAt2J9F+ekovYdcnW2FC6oUhDixbHmn4eBku9qY+/DZIArS44UKfPCVp87L7m4FR/pJ7/KSsSUiSCgKcV6MqQU1vXXpdPPOhly/uXH9+s3BwBeFZGmz1T5bb9RbrVqa2iypoRIDVjhDGhzDljvjcVdDgB437nQJJiZma00SgisKH0LKvCPAwQuxqiozNxuNIBKC9z4YUw0bfAhnRLmsT1IV76v72DKzGRUpR2vps0YUpM8Ut87WUoUP3jk/yJ14ADwpQqMFbie5gfZ17xaafAsWoeDT4Cl4CoKtHlbXO9c+urG82un2fQhUtpG+eOlyvd7K6jUAhkFSCZEGgTpAVCWwIlDZvod3tVCIV74Hc0RBElUaGVLMxiZJMfDDQeF9I014nEVZVXNpUBWbZVThyxc+GDUafdMmKt4IAEhZtWzDCiBJLLMZ5YMeqEnxK/PIEgXpMcJ775wfFLn3gSjBvl+uVlN7RpHtSSkqE8ErF1c1f0gVwwHWV2VpcXV9s3vjxvKgVwQ1JskajbbN0ka9ntSyRqMJIIhHOXQVUBGGkMIQuBrPWnb00ZFM8ighOF4CH8DYmXYEqRAABGUFiNlaF2RQOC/Y6WdHO9MqVDXPc2aTJDbLEu+CDyEEf+Cm7ztVK1uARBXeKzQASGBhyokeD2e/IveJKEj3gDutIrm3kYqjvLtqpUZFUQTviRg6is6AiQjKUGg5emdibQpQFRWCavBeElsvnNYy0++hszVcXukvL/ZXV3rLS0tKXKs1p2dOtVrtJLNJ3RAHMmWxfcB4hpACysbacgSRBlEQwRCBVav3Jga4mp2zq94TuPcRi0ePozvrCGIIQYUIhEAwaS3zhvpFEIYSCAISxuR8XKhqCF4kGGOtNalJJRgfAkKQe2Eq7bG5x9wqnlca6woh+KLSJGNIaPStOPSVkUeJKEiffSbVyDm3twoSk7/m3XNx2DABxhBgEugAm1uyutbd2hzcuL6yvLROyILLamn71KnLxlKr1UrT1CQW5MHBWB4NRFVVHcWimSGj6XcMJlYjBNayTvfBD+Z5VLkDN1o5D6ks5jIQUG+QBy0bnI/nH+495qrqvRMRawwbTk0aCofg74km3TmjmVghAKbUJMtJTGX4jBEF6TPF/qVCAee98965wvlJNbIsDEBYSAHIznWrQmABTgwPCwxzDAssLG9vbeULCytbm73+tvceaTrbbLZnpmdrST2rJYarKISIIyZmNlTVEwEQnQgKlZnbu6fwoWw6ftgUosg9okxS6Ha7ZXb3bU1MkeBUKHApS4C9f5p0YFXZvjlVpSaRdyLGGnvAayZ9BvHS5tEiCtJtOPrwhbLvyf3enzvFOe+8z13hvKuCxQQoV5NSUca0qWqsoBZqBVxm1C4u6lanv7i8vrE5+PjG2jBXZZOl9UZzut2eaTSmiKhRS0XEi3M+WDLEaiyDhIlUqlF4k1SJEqXNdOyO1iPGXeQaMLOI9nt9PXJRV5ns4FRSm9xvTdrPgcMDgwQQOe/ZJLEy6bNEFKTboVQ4n1jrvE9TO5rU4LPEFj4QMQDvvTHMbPYN2IEoRnlKoqLGWtZdz2Egd4UxlpgIxEDhw05iKwDAHNayq9xy5UPfNY+nHMAgCue9KwpfBaV5/MKgSiTWpGx4e7ubNeqGa/lANdDKKjY3etv94sb1lU63t9np1xpN1Wy63cwajXq9lpossalNrIj4kDORMUpkiGg8UYiUSl8QsaoQE5VLGO90guDJrhDl591JnToa+xeie70y7Y/QPfxrbmttCEFk1zm9BQbjrwglSZJlGfFwMMjbjb0//wOO57gprqp3rpyDniT1YudLddQt3BPGmXghSO4KIaSp3fMDidc5jy5RkG6DKtLU5rmTIDlhMBgCyLIsdz5LbFAUhbPWjq0o2hNfJXiQIQQ1bJDnhQ++VW/sPIMAwDK7oKrBGJNaU/jgnGdma60xdOikm8N7SwZF8F5EXFEUwQsUo4xtJYCNaEKUBGIAnLR72+hs97odf/1aZ321t7y64Z06p41We2b2fGJtvVWv1dO0nhpmiIqIqiMoMxHJSIomun3Tnj4Oe46qAuHBDKL9DHMXVqYxlplUNISgYo4or9XXWyESKJCqlpMsDtSkB4OCQwCKApA0tYb5GPonIndKFKTbECSwMVmWfHzt+uvf/97y8lJRuHa7ffLkia985SvO+evXr7/wwgvNeuYFIqGyk0Zsbw8++OCD+fn5J544PRi6n7z7k6Wlpf/tl35p/AQlEBkX9J133knT2lOXL8PSyvLaa6+//lMvv3zx0oUQdg2nnvSzH7YYKRC8L1zhnS+C3++Z8cTWZoMhhgPkQ6yu9FdXNleWO93esLM5NElWq80122mz0arVao1mk4iYlE05UlWUlBlBlAlMQkSo5s1itwI91HE7jyZSXiKosEIOSTcQ9aC7bMhdVvGIivdeKBWA79DmC8GL1zRNx5o0OeTwQaY8qMpYk+q1+gN738j9IwrSbTBsAHS7vW/82Tc2OluXLl6cnZ3b2Fh/660fv/TSy4uLC9/85jezLHv66WfTNAlBvOyswqq0vLz893//Dz/1Uy9PT0+zMapqjJls4ulCSBLT6XS++93v5rn7/d///VazaaxN05SNUYVK5diqxs/RyEensEw7HhKqDCMiiMILFw5FIWCDsopImdVCWYihZmkd6xvFws31zY3uwierrqA0aTSmZk6fSWv1erPRyLKsKApjOUnYWOT9QoKUMSEipbF1w3qInyyq0R0jtOtQ3n4k4p2TGGtgJLB35TtwWeVzlNcSlU0TQtnvbqxJY6c0gLFv9sGgihCAwidJsOYw33bkkSEK0gFM/jqd+MzYlbXVj65d+8pXXv3lX/7lLMtUZHFpyZrkww+vXbly9W+++Xen53/85S9/eauz8f3vf39zc2tqqvXyyy8/99zz77///ttvv728vLS6uvrFL36x3x/keZ572dhY/7d//betra3+YPvipUvnz51/74MrxTD/0z/90/Pnz1+4cKEoCgkhMXj/6sff/va3O51OVm/89M++cuaJJ978wQ8WFhe3t7cvnDnz6qs//8T8CQB5gFchw5CykJBhGiZtBHHeSZpkRYEAbKxhaXF9fWNw7eO13sD1e4Msa5hk6sSp6Znp6Xq9nqRUugiVijQDyKt4X2Bk+CkqKy1U18Wys5ztjhbQnnCCmWiMdrtL+1s4+h4AD0tKRzOoAKMohzyVdTZCsJPjqMqUECYVrdJGJidUHXJstcwOKIQD9TshL5jAAFdZAyQT56uSFuxY5NV5VwgnxDA+eHVik4Qt1ZNsOHQhCLMR2dst/tZ1Y0c5p5NbpGoTglFsVYW9ynBQZFmWJJMzKgSjgu6Shx/9i9yOKEi3wVqbO5+mabPZeOeddy5dvHjy1KlWq3Xu3LmiKLIsm56enm7P1uo1myRLS6si+tRTl69cufIXf/EX8/PzSZJaa2ZnZ621m5tbV69+tLS09Auv/sd//ud//t4b3/u5L32pVqttd3r6BJxz3jlrTJqmIvLmm29euHDBJslf/sVfdDqdp559dqu7/eHVq9duXP/Hf/zHV1555ewTT6yubiwurM7OnAABjMSyAgHo9eACgof3EEn6/bDdGaytdrtdf/Pm2sryhuW6F5vWWqfnTyWJnZ6bs8YQs6rXoGTK2v49y8fkwiEYOWf4oFp5ji2574adZjlCYIIo68gmFqqOtJB8moWVFAkngqTIlWncL+42eWpVhHAimGmYgwics0lSuJAkibWmKBwRM+TBGUlaGXnOCZADk5rE0Ux/5IiCdBuIwGzOnj37O7/zu//jf/zl//Xf/zuAqampV1999RdeffXixYvtdvurX/3qhQsXajVu1OsnT85evXq12Wx+8MEHa2sbJ0+eOn/+4n/4Dz/36qu/QETXrn08GBSdTmdxYdF599RTl8+dO5cmNQDPff4FDeG//JffnZpqvPPOe67wAL//3pUbNxa//vWvv/wzL6+ub5jE/tt3vp2m6dlzFy5funRy9hQJOY9WA52h1gwp0B/CJBg69Pro93Dto42tjf7y4sp2N6ckU6Gp5ul6vdmamkpqWa2eEtFwOLRMaWaMMXle7Hz2alW51a96d0xrJyXrHp+G+8ykxXbYvo9msN6vfRhF4ErzlD1X0wcrJy3xOKCkolUM6c5n1rGhNE0LR8NhIQAT012t2kQ01iSFIE0Ta4w1cm+aORyNiflbKuKcAA5IsmRiLlfkkSIK0m1gQIiGw/zzn3vuxP95ant7e3l5+c033/ynf/qnixcueOecc3meE9H6Ru8bf/aN69evnzt3jtkSWQDtdts5p6LNhs1zBWCYkzT90pe/vN3b/sb/943Z2RNPP/30l7/8ZeeCBp8kNrEo/R5M1Ol0RMKlJy+RYHqqndbMy1948ZNPPvnmX/8NkXnm6edeffWrZ0+fvvrJJkjmz87VM3S2sLbq19a2F24sr632N7d88EhN0myemJqZbrTatSQN4ovgjFFAiLhez7RMg3ClP+3THjQhUBylBuCWzsl93ioBwONeFUzKpKrQYBRGR7YoCYGChnI5PqImaWndqAIwlhimyCcS5CZ62R2dsSYRcZ7nwdtaLR2G4kEbSRU7mpSYjKseeMexNDByC6Ig3QYXYAwG/eH6xsaFC5cM0/PPfb5Wa3zwwYdra5v1ep3J2MQS0/Ly0ve+973f/Z3f/fmf/4UPP/zwk08+cc4bNkmSFK7o9T2APM8BtFrNF1544eKFC9u97e9+5/XvvvbahQsXAHT7265sjZImLoTNbietZWztxvrGVGuKFcGF2emp3/7NX3OFfue7b/7jP367KLLp9vyN60tPPvXcs7253vbgo6sL167dDB7OSaM+1ay3G81mo15ng1qjAahoTkZSC0BFC/FMVNUMgcoF9EiXzFUMaRQTkpGvCeWoPt1ZcA+3Kg574EFc2B4mFWyq3tgqOl7uyycTEZiZ7mAigxz04cu543t2wMAoCaokf1ImVig86yAzlMFo4R2IbVLmTXoGlFl2WtAe+Il276QAwkyDYSGBOluYmeHJjIaRV7Dc87KsrgrU4KAIEBFZY8rT6IPf7vlGvVEmPoyHRtzjqb7jBozl5OBdsCqC13wY0swQgYh2PyeK03EnCtLtGQzc4tLSX/3VX83MzD156ZL34Uc/+uHc7OypUyfTNK3Vav/8z/+8cHPhxMmT7Xb7gw8+9F7ee++91ZV1JkrSdHZ27oc//FGj0bx44UJiE2PN2tr6j3/81qA/uHjpYpqmzUaDmOfn5xcWb/713/zNCy+8oCrNVvPUqVNpkv7rv/3r3/zNX7/44he63W5WS4owCKqt5jREDaXLS5vXr/dOnrowO/fMe+9v/vjHbxOZRm2m3Wo1G40kMZSQYZMYAonCHbBm0aRNJHf9o60i8I8+5dwFoOq0J7ozO7Ws+xEVJg4SgCNOPT/yWwNaDaZjdariEi4yLjJXZLkPLiQmhW0MYL2ylpPcj9bxWkcfACTWGiYqislcAblXFwE+eGustfA+3NcpSrtnVYzcdyQhaPDiDScJaXmAoqn+6GD+4A/+YG5urvxHnLpYMvn9dc6laZJl6fZ2791331lcXFhaWjxxYu6XfukXX3zxhWazMRwOFhaWtjpbzz777NkzZ3/01g+XFhdPn5k/NX/y+eefP3/+HDN/8sknCwsLZ86cmZqaCiG8+OILq6trH3545Z133hUJX/1P//HFF184eerEdrd77do17/3ZM2dE5NnPPXvq1PzZs2euXb165coH21vd+TPzp+dnP/row3feeW99o//SS19kanlnnnrmpwvXfPPN95O0MTNzYn7+TLs91WhlaZ2ThGyiZJTMKO+NygjE+A9VC9TDr7L3Hp/JIpjJ21xN0cHubnV3Xqx/v76HO2Kz+5PSLnY9X/c6s7R0gKmKStUQXXb/VWlwt+SQ41x6Oi00tUo6HExbaYRNbNzU9QXprogbEgNsAlnPiYB44mqimklRnpEq23JivwkAM1vv/dbmVquVXbg0U6+ByFPlZIVWRQU7L1Sm8s5bncPJxxQgGDbMJIqwx0C8pye2qqDSibFepASIeGuMMUwEmqhU52ghHXvo/ffff+aZZ46WjPu4sKu2U2AZQdHr9aC8tr5er9eJaGpqyhpjLOW5X9/czLJ6rVaz1qyurgYfmq0WgCSxWZZJkO3etnN+emZKQgBQq9V6vV6v1wsh1LLG3IlpAKrY7vYHw6FhnpmdGQ6GtXoKwDkfnOv3eo1a3VgyKQ2Hw0Ee0nR66LI33/xgYzOfP/v5b7/24dr64OTczBNnTqepMSQMTxxAfuK0sqruShEGsHdpPrh7wuSlblmwuVMOWdXG7k5wkB1nid6x6XS/XHaTM7kP+7aXg1Ox22U3ZtIkKkcZ7v9wB5os46cREatwKeVMZe3Rnm2wsFVfk/4Jm8vmJ90bPwnry6oaWnNm9on07LM9M9WzjUApT/QLLG8pduVwj207qXLqsl4vv3Llw7Nnpn7xF5+em4ZlB813H/PJKYxHOHcTh6Cs2zPWWGN98N65yWDSvW0jVLVE2pXyLqQgCVktSVKbJIYmgpkmCtKxJ7rsDqD8gpe/ZANSASmatYYQztXPlM8hYiIqGwudmT85fu3Z0zu3q5oSy7NZe3RfUv6vNdVsTTWx+/c+M92Yma66CiWtWnXDpJolrVaDy1IQplqtMQOUZs72c3NB0u+/+d7QdwPBNqg2ZY0KKzFZ0K6cMVWl2/0qD/OxTC7fpuwdURbAkECroUU8+jAqSqDx2j25rO/2cT2cWNHunLq94iQBI4EgnShzPih9QPZYVNUzUXZbKJ9R/ndn4FAghUoWiBVOVRnKRAryosERwOoTLVIdzhmXdtb7C1do4VozX+HE5Bj0XF6batfb1sMUoFGxcxnjOeDclX6zspBZiBTi1VNitodD58EMI6xCYiaHr06elyMs4hNPDxAAwUvhXateT6x1Zd89726/nTtkJHVVBtD4fmJyzgNSFu/e8/eN3D+iIB2Vcun51O3Xjtqsk0ZLIkZLwniwN1XRGlXQ+XNz3b48efk07Ik3vn+l010f5jOtrA5V1bB3rdxnG91jxgp0r4Mr95ujhDr2f6KdkYMHMTYbZHcfBIawjifvCksIPieVGihhWHHie364xXl3u7/R765xZ6vut+ucs6hxNsDWBhvanOY0HbLtByjxaHThOCHl0BOtqtZYZg4BroD3yMgctVXDHeJ8sMaUDUruazxpN1JKlfMCwNoaR0l6dIiCdDtop0PB0ThwLZj4vZPsFob9Dps9GdO7x7uMGwcBChiGsXTu3Im0hvXVrSsfLKwurSXzZ+tZAvWT7bTvfJ8/g0y67O52Ewcs3eNrBVRnazyNtywhknIE1FiHSFlgACGVBnxiQxYKI3nq8+HGAhW9wXCbisGgs5kWRVNclrKBVfHG50noFkvXG2kNrD7JDTULrgHl1YZhLds9jE/o3r0NEpjZmDSEQZ6Ld+xtsMaUdsYBeXR3f6RQeO9FrDVExGweyBz08ecVFTjvE+9TY8ct8SPHnChID4DdanQAEw3pcAs12kU5q6K73XeaqJhTc/zCc0+uLa531jYaSZNn27XMiIT9E4k+e0wKzIOPg+45wOP2AMrj1jVsIFZgVEgrC8kqEcRimMqwzn0/2HSd1X6/M9xYMihKj2eDKatxC3WjpvBFcHkISs4NVm9ymqrkVJ/N2k9ARGEBViRQCzAfnmwdfCBOjGFfaFEE71lN2ar9iNcud4BIEAnlzFnDLPIgjCQlyOikqIhzhbXM0Up6RIiCdCj7A+BH+VJPCogZbWhyq/vuuTN2Lr2h3V5fBZCQGCseT5xMP/fUhXffvl4M+qHZcpYVsEIg2SkPutfc+RbvvTU2eY72x4R2PVouu7Trxbfcw30NTneH0CeeOXnZUb2FkikHREgQCUXDMktgcTa4GhUy7NJwWwcb/cG6DLdC0cmMTrMykRiGSSzZRCmBCWqMtVY0CQxNVzZ63ZWbRW89PX0hNUnWPIEkE6oVzjlAYcPurjnjo8GgWq02KAKz8SF4761NADAzK93zrP3RGRHnJbGJMVZEgveHFfMeJT37iGkRQtURGAyGgNTrrSR2Xn0UiIL0qOK8wyhN2Kh4F2am0qcunV1d2lpYXKjXm1ljDmDAfeY7eu1J4H7A775npOlONp3+/+y9Z5McSZIl+FTNzN2DJUWCsyKoQhfpntkemZU72d80/+9O7ubIzu7O9Gz3dPc0ma4uCp48gzsxU90P7hEZyYAEEJFIVPmTFEFmRMDdwt3c1JS9B9JAQg4as48pJBg5zVhSE8bZ3nbR30u7eyaMqRg6G5qODIiMIWPIWJiYYIJypgDYm5gYymBFZ3XFej9C6G09zrKieeVOc+UKRUvMDUiRA2c91wRoECayxkgIaZqqNGBEFz9DCl8wG2sMEV1I7K6Cqua5Z864kdQm6fKjNkjvJQiUZZmWHGcKVVlqR+Mhrqzyw4e3d3f2+oNuo7XcbkdBdcqQNsHryRpcYDr6DcF86LW8/VD5BEv0qyAnfYuSnJslmJAbn9swMGHARa8Y7xX9nTAaJERRIS7kCN7GzM7BcWAQR3CRsFFiKJPyYbUeS86wUGPQ0NiF3Gb5wd7zUBRFPqD2Fdu5GrtmUXWU8dTVm72DIXhjEzbsQxiNhkFWlSks/v4WhbdWrYmjKMpzXIhNKmm/aGKTfDOpl7vLjvoOXXIcXxbLpSOEkOeebVS9SgLRyFG8hjth6cGnd7766sWWRtHdO8ZNNG8OD3hmxvs9xclg3XzMUlUgIJjxe044Q1WFOwOsQhCGdyKkYlSceJOnMuplg53xeK/f24ooi6lIoA7WGuui2HAD1ghTYCZCIBuIoSfwwAAAIABJREFUhVjADICgM7IUgUShZAFRBE6MW3Iy7m8PRgNu77dvFMnKNXLtISSHE0RlrzL0SIrIEJU5lVGaA7CWRQosOMsiElRYVZnhnAUWaZN0yodUTv7SJuWJs3Uu6ZKjNkivwOuGgE6b8PSK949/8PgZD6XHARVIkGE6QtnuI2qIRRF0rESi0cqauXVr5dGT7YODneXV1eWVBowlTFnFeEKycnj8k3UPx771y1b5M4P6s71HZ37juWB2YKcNsiSHO32c5rTXqSqHUyEQUSCAqtI1Uy7sJBXvHCmMsgh7cQgNDk7G1vdleOBHPUoHYTyULNUsjS2aVg2sZcvGOBsR2QAUANiU9kbBApAyAzxJVlFVIF6CQVoEJSU2DbLiDIzxRTYs9kZZOmxs9OL1m9pYNs21FAYaSSBVggSQx6RXbKnV2mabpUEUzOSD0hnkT/NyjqMoApAXeV7krSTxEiDBcKlA/IpTKJ3urc5OvVDtuYgUrDIpqxOwUdKgooWkRR5zdPKG/1RqTN8H1AbpPYMEKbwPckiwBpTZdV+tudy4fWf59uPVr0d7/e5uq33NkCF+aaL+PcfcI4pKh5uASWcrV32jJKwAPFRYC0ihhWcpGlJwNmI/RDEshvt+sFcM9qnIYkMJ2bYzbCMiVjaGWcmW5k3AStDZfbtWrWbHMMO8QARSQiBA1BoHaINjKWT3YHOY53E2pKVr0RWleKWQRqAowChXYrRsuMwYMZssK0JZ5HGxibcsy4w1bDjP86qXa0E6s9Xeq2wplxB8CMba2ku6vKgN0vuHoihUoDpt0j3GiKqr63T/w1t7++nu3nZn2FpaWT6jJbaqRDpZkzaLy59Dmi8mFDuMyR5cSm0DgFUAb+CNeKM+sUI6ljQ1vh8X+0V3e9jdS9NhMzLGZxxyVUmitjWRMU6pZPqzZFgIEAo4wZP3egMtS5uZ2CoRcUgaNs32sxejaDSMvY+XrxfJ8ohjRC3h0lXSibsqANJxmufwAj6u7rtY+ODZMBFZY1VFVBenVUFE0IrMPs8LY6wxp3rFNS4FaoP0nqHwXkQAiAgzzcQbGFBAQL7b41u3k+H4zq9+9adub6/RbkbWHd0F/3jco4Vgog9UukROpRSFYORO00hyp96JL7oHlA5kPNBssLP9bYSMAUdq4QyzSRpkHNgyuUCAMoEITGAohBTCb83txqQGJJ6UDZZWluJhf9Tt6l4RijzKM1q9Io0lUB5cRzn2AlAICmIl1qIQX0AEkYuKC6x8M8x5nltj4zj2wYuIhJfp+pG+BgnebB8SgJnGMFapnaTLjtogXXooSlHPLAtFno/zkhOspIsuE0Jcyl0TSRnLiRNyDnfuLT19vvrddzv0wt3/4L4EQPRY68xE0+jwpR87we6rLXG5lhlLJEElNyIRiYWyFKxjTvco68l46NN0dLBN45FmqTPSscG5yEUJGWa2CiayQlCwTggLy1s2qS6gKfffG3oHJQMeEcgSs0KC+EajsWxMnhbD8d7o+TjvPzdr19sbd1LxnppeyUaxQI1la4xIkWW5LyKOLHBokGZZ+BYBNoaNAZAXeZIkeZ5LCCLizig50Jk5Gc51tXgivwtz9I5Plc5rm3Q5URuky43DzAFEjqhwEpkTgTgGhOGtgRcbN3Dn3vrOzrDw2cF+L0makXVQgDxIaifpZdAQxn2jeUzqpCA/5jxl7zkMhnuPkPc19yhy433MlDStMUTWkGGwBVsBK7EcKWgEgEq99HX2+68eaUWnxwCEGcpqJW4SgnJIw3icPh9mg35j455pb7hkaSC5whm21hnJNcsK1UhktjLtQhG8JyI2hhcaFp58OwmhKA2VbdRispcQtUG6VDjeLTRhWIBX9RJm1GWOiQUc+ZM4BD+MG827967s7vqv/7K1vb19/fodZ6kiEZiRJ8BpXtFlSBqdXdr3Cv7Qt4dFSEwWhaGRNPJF6B+EwUHW7/rhgeWMJIiQKrXbLWMiZx1Klmu2KOsUwDNscmXmqfpKR7/CG+JwElQyRQy1AjBsQV4sM3IhMSFfCZQX6f7WoxAkugbVoLYhpsXGOefGmabjnLmV5t6ccBhKP2n+hEJHUXhvjGFma20pznIqDt2115iYZRaQjh1AJBQFwoSlAqf16NV4V6gN0nuAAEhQVQrh7MdxpvdCRAgg5CvL8ScPrj95tL21tb/UueJsZGIubRKfj6kFwEsF5S4nDvuHpjDASTPAKDuHhBRWheCNCANORybfDaPdUXe/Pxr4wX4CcdCYpdlMBKxsGMa4uGwYElUlVtEqWFTS2JWnmKmlJhyycfNp43ldlI7NRFuRBQBZgYAA8obYsViSosjH3adjyXjlRmP1JsUI1sVGR6ppkcMiCJUyfDwVs5/gtfI3bwDvPTMTkTHmLIM0l+ChgitODYUEzfNgjHufJvVPA7VBeid42Up08r28QJ5JnnlVqnbeyjjcLDIqPRgDkBAgYAaDJGB5BR99vLq12R0Mxitrq4VXQW4sIjosL1YKmPAdlB6JnWyYgwRVnShgVzhb3+gsnHff+Tpm7/A6Vf1V0xPNXpzJ8qokgJBCVSvKbSaCN/BO8lh8JHkS0jA8yA72stG++q7PB0WextYtGeMsObZC0LhdRsYC4LWUIQJK4akqb8Egqb6wlsmMim0Bx+tK+OUz4SxLcMy7VSJAK7puVSUDGJIoqACerUSRJ5+Out+Muk/W8Iukfcu3on3g2bA/9oUwrDXqQYAoWGj2/jIJzpu5OS+KcPitg4CCRpF1liWc3i17pBn5xEBmr9LZSuzlxCgvNwMoPIyXyJXNx7MHmPl8jQtHbZAuO1RRFF5EVImIofLyWD8Lm8nKx/DNhv3gg+svnhePH+809zor653YNbKijynLwysHIJfNQ5oGM+Xk60BlhGhihEBljRyVpH8MsRRIgxaeJW9bobSn4yHSQe9gi8cDHfeRj43TKDKu1TDGMnNZf0DkciVRA4B5wll7oo952tMKOrlCvsIIvS5K0drDXGNlm1mhDAkE0dxF1llpJZJ57T/5Cu0Br+XxOLT9wGZd8rdtAmEta/4IhsAMlOKEwuVlXBSYSFUkSAGwYRHWt+ujFoDpLO//8F6IoCiEmevihkuF2iBdKhzfoCmQe/HF4Z5S6eQKOIPJUjiNFEUO6+v2ww83Njd39va2Wq2Gi8ja+Kxg/Cn6qkwaKjHvU1W9F4pTM0lCrIDwzN5ZD7e6rJUdYpGqZViZ4IyIgY9C3jAF8hHysSkG6d4TpAOfDiVLIw2kYlWcI9dapihiNiKBiECsBAFz6VlSVT+HVwWUZnf3Rxgx3uIqHvGcTukwO5J9BFmGZQOCbVjNRqPh9rexz9rBbvhxo79khzdd3BTnKrIiUQoEUSNGKFRMfQtLKRKTqha+oMBJEkNEwksN0jnjzK+6vCEE9hDHMEcuYJ1PereoDdKlhgJZlhW+KMu7X+v/lh35KigyvX27/cXnd3//u2e97i6Z5spqJx+PT92qV0v/pXGHTitqqBbckmiHtYxTCiYrPkNIhdUTPOBZhVWsGidiNXeaDp4/0axXqj+YYmi0iCAgtBrN2HLsIrZJKpz6susLSZIogQADCVDzflQoyjRaSBwTDEGMYqlt0B9Lf9sFXYF2hpuN0U6rcyVPGgFWlaAEJg1lzxTnqhdQ4SJBABFxhg+dpOl5F+Sdi0hReDbOXeweq8ZLUBukS4XDLtfyWczzELwSTJmkqHC4KT7dRJlpAoUUPrQbhoGPPlj/5uunP3z/l4fxz2i5U3XAqFKpzX506fdHQ/nGmvJdY42IzJe74Zh4RHnklwYJZYYiVkBiRLhSalWAWYUpWPjIiVExVOh4aMd9zob5YL8/OAjjnpE0CpllZnbWRjaOBKxMGRmvRrwKCMQusqj6jcvxgQCZ1AvrrFLs7DeateezwaOL0/AuOYIEMEokSlAuI3jUSJbjdhhncTqM83FzsInnfyX0mht3tNFh64LhPA85AOLFtSIdQ+l2F4V3zkaRkyBZlhFXNSBH8pfz81tEVUSDF+um3A1zDqjWeF3UBunywgcUxcs62F8JVhGCAZoNA9gPPlgfDccHe/urq1dADJKJNXo5Oem7wctHUvp/PEkLGfUEYcCCWAv4jDVbidxosJsNe6G/S73N0D9QnzcbUccZy2Q5IRhFJLBacqcyga3QhP+CRMrqvJkkyoWt0W8PIWaVMrIoE8Z3YWFG0kiakUtGtP/sye//aXv17s0Hv/zfo9UrBVl1cZS0ArEnkjJneVFQ1dImsWFiUtHSnC8sf8kSxHt1Bsxvo5pZY26oDdLlRfDqCy8SJi4RaVXK9RqY2CTTTPCzn93d3trf3hz39gYraxEAEBO/euM+9VfmXd1QlQxOz3LOU1RfSoVVDLxRX/7ixLcQqBhKPkIYdZ9vjQc7+ahr/WiJPXxKxlpG3FgCGyEDWEikaoJqpatLQjPlEqdyvB3mJxZaEP22KFUzmLUsuiz9SuHKk7Qg9NOuiZbyovj6T1/tbe998ctfXrlzT6IGNyJn1KtRrmrZzzr+3PMsU5tkjPVSzPfgx88lEgCW4D1FUV3bcClQG6RLCh9Q+EJUZ7PW8kYBC1YRKsi4tVV6+PBOOnq+tf1sZe1DgiMiqEL1nXTpnwEBzQYoqynKh+WFAsAISsfIae4kN8iN+kjywdYTzQYhHYZ8UIx71vhl1iimjnXJcjuQHeWFFxKYQExkWZmIQVAikIgyzxihkwbnUtugs1HOHFEu2coDc1BWk2Q+LEfLV5tx1t39t//n/7j38ItP/tPfI4mTpFmQF3UqBsoyaaxaUMXdVOwcAMBF4Q1zYLoAd12CBA4ipnaSLgNqg/ROcJZZKRk8EaAhiAQRBZhksgoqIRwl9j78lWaaCmdsWOVtkAfExfH9D9e3N/u/+fU3L542r1+/wxGgpThq6X6V+RGpnBUhTOL7U/m72bTTy77KLJQOe3Fmv211DFJVIQEBCGCZ6LUKYDlwmRaCBlABEuJgVFD4CBoho1F3yfow3M+HuxIyOdhVn0ooHKPdcFGUWOcMiBBnYAVzrEJccQEqK2vZnVIutUw0a3OkGt/MZZ5JgJ+5fh3JG82o2VaUdoJDjaiqP+b0459x+Fmc7AMrzzK52keCjeWxpaRAJLZJo0il2V65ut7obxW97vbWX/882N9/8Mu/s+sbnZu3UoWXuBCEACURksNKwUq9cD4cH0fFFb0q4rhJ7PJcfQh2VhH4pcc5ORlnlRVnNxNUsblLEJEA74OrMkl1bd27RG2QLiNYIT6UrN542115Ga3xDA6qSZxcu9m6+Xyp1z9I4vbS6pKLJimTiibmglwlmaz8AmiVrD5C/FrWbTO8URByC8+UkWZaZCxh2SZpbz8fHPjBbjrac2Fs/ZgMmqyUgLlZstGUa3CAhTpQyfGjAuhE+kFP6Wf6kYJKBY3JdFJUNBOwUL5x4/bqUrK9c7D56PveYHjv889vMNzylSRp5YqBL8Y+d83owhKMIXhVJWJzciPzRqhoGk59S1VEREDMNcHdu0VtkC4pvJcQqozR7C74TduAqn1fnODmzY39nexf//XryCWuYeJ4KSgq7+Q0lGef9ZPeCNM6tWqTqwTPpVgAk8AG5kAgAqlBRYPE8EZzg7yMyyVUIO9rMaIiH+/tp/1ePuoj5A0OzmhEJIKo3VHrDFsAPkxaRsu4HEIVovwJ7ILPwws1of2AwGbexM0ra+tNNXvdcfrX3/66e7D18Mu/M1coilsNVkmoQBAylbN7horrvOB9MIads96TyGIZ9UQkeM9smemC2+xqHENtkC4jhBAm7pGoVtVSb3qw6W+qKoKVZXz48Y2v//pkZ/dZkkRLS20QVCrmm2llAYCzgkZzqsQ79P+oarNSVk8ihgtWD4hBHklljSId958/1awX0r5kqU8zx6ZprYlNI25ULh5xziaoQVCUfArKRFSWKsxpq/2jwuRWsnISULgGX4niZclfbL/47nf/1nux+emXf3/t488aG6uNpLE7yrwSlEPJg7HIlEsIntgZImtNnoeZKTfXkyoDAuWpk8T849+sXGbUBulSoXoYsjQX1QkNjL62NZrNPB9bNcSD7Pqa+fIXH/3qf/zxoLvT6S032i0mFfUTfe55Y2Y8M+uKWIUARkVFKKiVwCSGAkvGyCx5jPsNP5b+Qb+7E0Y98oMIRYwQWUetFhnLJQESUwBX9A2gavNeOpcVcd3E59LqjeliunAjdUYVwOJ24q/yjfiQzoMIwKg/1rVlGOdJYdmqdWpuXd1oWX6+ufnVr/9lf2vz9hc/b9+7t0xuHCQVr8TKRg/b3eb/XYhIgpQiu0SsOncnadLHBgYo+AAU1iYiUtukd4jaIF06FEXh/SIKXpkZqh6QKIlu3Fy5eXvjyZP9bDyM4wYcJh7Sq5PqbxdCFACkMJMSZFXlECLxTrzK2CC3MtaiXxQDSgdp9wDjEflxJIVliRwnkWWyaqJAFmSESiYhFrDSy1TB9UyKs58upp6Hlm1cQGBYtkaj5eXlVqPx7Pne1nffDXqDe/u9jQefdlpLrJIT5yphwVE7VRFlc7RxexFeGRGJiEhZZKlaOu013gVqg3SJUO6lCwlF4VUsiGVuxC3V2kGkQA4ynSX3i1983u3+27Nnz1zcarnWKXv58pVT2NJejtkOlcPfp3oQDGGFETECDsqikRYtHXPoSz5E3t998b0WAy2GjiUSslV5dsSxgUVuLdSyWKCUwqtq8pS45POejVJOaLYn7Aql2/QTyCGdhYp3FgAJLIhUCSAREiUJLEIIpCaKHFOct65fj3p7g+GTrf/Y+u80LJZv33Fry2i3PMXhJZexohaUad3Km13xykliYjWnEoHPC+VzVkpg6iHf+cKVt2ocQ22QLhe8D74IC+qOrwq7SRnSbJrkDjY22un4oNfrJc2GcQySCWneKzjFXwkiJQ2khxRzQGnhhCBOxYk4H6wPBkUU8sHmD8h66bgv2QDFIGaNTDAgE0dsIxM5WJdDAiFnAGxgWbkSAQLPlmofVWn9aVTQvSmUT6kyVCWwMdZaS+tJY7m93N/tPXny4k//8j9Wn/5w42efrHzwITU6GUeBOBCDSz+19H0JylTyQxAECG9DI6siWrHZzpewatLzJApoqbQrKkEDg4hqJ+ldoTZI7wBnrZEMhABfUAgMprJCjOjMR+PU40xIHSZ/T/6vqpIoMRtmBbXaGA/x87/5oD/84+72QeSSpTUHiHNNgBl52XQEAETl4zo9xRGBgNl01aHlEUMo8qGRomFZ80J86LSXfchNZOBTFEOTj00xyLq74+6epmMuUgt1ZFzErtGxDGeMZwTDBXMBiDKR1coEsRLk0HU7FMA1NB3FtISsHPTMOE+/nD9aTKz1Me0fMBET9YeD0WjJOktgIzAkRIZUAkrdQaKmW4+utFfaj354vPXNX3a3Ht3f+vzWg8/jlVXqrA9UU5KpSoXCGIVVkIpWmoGiBHkNt/SI/HHw3jnHzhjDWZbN4XKcOFdRFGWVxij1CbGJStksf1YlYe0xLQ61QbpE8AHBq0jFufxmeEl4RFRZAMNMKsGzNcur5v69qzubj3q9XtxcWVpaEnGAgN6KZZIBhl+KxYx6fnfXhdySW214MrZ3sJdnQz88SNM+xn0qxiYfMsQaG7nERc4YJmIVeOLAXDACVyGgigJzYoeml2g2M3TMGtU4DVUkqnI7qj5krtwlhSEFGJXTo4bIW41a8Z27N/b29nu93je/+Z9Zd//Wp1+071Ijbik5EAcWIVYYpWnf7xvH6o4ghIp0kZnn5SPNUkTyzL/BqwSwRYBwPYsuHLVBukTwiiL4MI9nbrpYm1kenKpiCWS0CLlx8fKqvXNv9Ycfdl9s7iW9Rrt9LtW+V1ZVkYqRzGS7vPsNbX0fenvKNj24rSbyaQpfIE/Zp1YkcsbFMZjFObaRMU5VPXRCBABRmom6nWeBeNlnTpr5n1qZw2xsU7kiAX/J51mhUA8IkzaitWsbnU5nd3vz0Z9/N+zvr+9tXvvks2h51SRJoSjYZkwFGFX9SEWhRwp+i/1NCN4YZmZjrS8W25NUFIWESIyqKGp+uwtHbZAuEYKvyBkWRLl9aEhEVbLYujTzV68nn315d3f3Dwd7+41kZWW1VVqutxoCCUs23H7CT//c6D1J/FC8BBmm4rwo2yi2zhljExdba51TYzOmnKhQ1jK/PI2WXBr28R8fzIw2nQgMz7jFR3NvngGoQlutRjtOEsuJle0XT3f2dg8O9u59+Yv2tetRnKSm5NWwQpi4FzzVinxjBBFRZcAw+zPat+cFEfEhlNVECz1RjVNRG6TLghDgg5/IhZv5Pg1ErCpl3wkRlcXfo3EPmjA1r11tffLprX///eZ4POosNePEgEII59ogHi37nsTLVIyK8eOo6DWk1yr6xByy3fbSDR+vCMeGjTIRGQEyICgHNUIAAQRV1UMOssm/ZT7r6HW5eKrTt+hQPoJ3xwhwaITKhptRmg+H4067TXoyvcZCMiV5QmRz71UQNxo3btzurKxs7u49/sNvi/Hggy9+sXz9pomSpN3OrBh2bGye+/J0fMqRX2O0hil4MWSJOXKUZVkQsdbQIfnT6x931sunmZku8EURJ1FkrB5KX9W4INQG6VJABEUuwauEUJYULWJ7xmwOJR5YWVUggKys2Tv3Nja3xr3uTrMVg+I4IT6qk/Ta54LEkXWR5SHYkCEQExtia5UjGCZARYWgxAIomKVUhBOonnrqn1p47QJgDBtjQzgkTpyFAMpHq/gJgQBmlzSb7O7EjdXVle+f/PDrrc37D7+4+8XPm5GzxIVq4b0iIppPGklVfQh28UE0IgoivihsfK7wdY35ojZIlwKqyPIsSBmsW8imjIinDYaqMhE9tQDHMW7cat7ZWv/d777r9naJV4xLrGUNUpEJHeV/m93dTwMylXp4VesLAC5ucLKccjN2Rom960jUkigumA0IQaqabfKAZdGylEKJCIcXQY9sgOsa7nnCh2CMsdYEf3qLjxIKBgAjoKpmDp6hbIGESZ0ZLrH94CZ9/cOj7//9t0WWXf3ks+Tatc7aRm7tsNAw2Uac5sect8unLPgWCTAXYZBEQuEljh2Bf3olme8YdRnJu4cIisL7opAQsEB9zGNhIi6tEamMR3kS4+HDjVu3WoPRTpqNJIAQQa3KVDL1ZcZAywIEghAUTMqskrjIRs2CoiJayqJlH7XVNYUdyCmxJ5YyUTRlslEpC7gnoTsQQHr4U2O+kBDYGCLyIZyaMqnaicqyyanuBMELKIrhIjhnXNxqLX3x6cPlpPH4z3/+3T/+4/Yf/oSdnWZeJMFHQZyI1dn7KOXP6462dJLe6gu/CpM2B5KAsODqiRqnovaQ3j3KtcA6lxfhVGt0RF5n5v3z7CYmuRgBYGCq7kJlDW7SAOtFMxJttfnjByvPnm7tbj+PowYvJ0CsKIgKUEm6WvFpmlnuhkoYuzoRKwBm5QToOLM/Hhu2UWcdxiJqiUmKQFAVELEVoOQok5ISZpJI15kOIzoRu5tdyU5aqZevc5ffqs0rR3UekDFliZ2EUNaSGBxxCRiwgaeUS9P9jEDytFfWzpE1xrXU+6vXb7j93nC3+/X/+08HT58//Lv/3LpyAwkVigxQA7VsJjwaSiioErR9JXjCQIgQiDmJ4+BDmBQHnvV0VK/MGNpTLUz11Uo3DjAwUFGP8dh3yHG9QF4s6uv9jqGKimg4LDweVbagA8CRWIQQKTi3jCtXko8+uvYff94ZDgfNRsdaB1IgvFasjBRGhbLxqLubjYfWxsEmJmqKMUEmvN6YKhERSo2IGcPJdZzkQjDrMYuenuxhnJLOYyJSmQRnmYxhcNLEmmLZxAcH3eGz53/51T9f/eDjtZv3XKNtGk3ABqo2NKUn/WbhGRUlJmMNPMKcZClmjaKqElghwasPntjUXKsXidogvWOIlOKwQSQsNII6Fb9RVVGdZXBwxqoqW7O2uvLwYevZs16vv9NsdjrtJWPLOojTMluHlcFV5I0hrGLUG83Vj/u9vTRN1ztN42Jl4wVBAdWqvfVojKjyDKjcDM++VS8HiwJNqi7P+fmT6qvl/w1QZiKycathWw3TdLqzN9h81t3cufXR7p0HnyY3bgJIYb1BsOSJlSbVd68JVYWgtEkhX0hUTVWJoCp55q2x9QS8SNQX+x1DVVRJSiMBLPSOcFXUcIq7U64sbLC+kXzy8G7hhwcH21mWlfE9giEyZ65cJFBmBSuMikFukRZpL6RjAFHSFDYe5EXPSp6/3rd4+0PUOIFyQ6Aq56ExnLg4R14hY+FsYCRLjes3rty+ur5s6flf/vjVr//5xR9/z/1uw+cuFBwC4xUpyVNBxERVm9p8A5sn26TK44cQfPALVgescQS1h/SOoaoiErzylJGgTKDMiRW0KHyz0TDW5nk+HI8BEFPZ64SqKo68KhGJKsg3mubGzdWNx8vbm/vGJPead4sCcRL5I2askmEFyWy/kFGx6iMULeOZwjgbWWOzIOxMIczExgKK6dlfF9N6rUMOuze7KDUAAKwwICLu9we9fdtpXAEd3TEov2QeSkk0BJBOmN0JxGTINBtNEzMHdYYPnj/Z3Xyx/OTRx7/429atm5GJM3ABCqoBhHP3Y5UbqdImGWuYKGLrg3+brttp/mkqcD5NhapqAIKqVVQFoDUWj9ogvWOEAO/9qV7LgvDS3aVkebZxrfHhR7d73W+H/W6aZo1mEnwOPmttEoAZEFUj6kitjIt0X3u7LHnciIyLcjVKLCpcuWJ1iuiSQmer7F8qOzI7FSY2pfy3rJUEEVavrMZxDJJ+mm5+/R+O9CMjzfv3NRsEcuos2KlOii3PAVGFBsNGgsAwMZG8VbdcjcuG2iBdKE6u6F6kCEGgIKDs6Zl6SnNdt091SmTylkw0coiK5aX4/r21x9/tvHg66h10k2QeUZQ7AAAgAElEQVSDOQoopvvl2YFNu44MSIN3Tihk4/6u9HcdJEkS42IVFrABBGUV+Rta34vnZfhxo8oh8WGhQZnBqy5z6Suc+F9HPOVS7mgqD68MJUAKhjAFBbeSVVprpiPL+1vf/ikd7F15dH/lo4/slXXhJllStS+JwgrKMrwpVauqahABDIgtW8MmyDx1kuS41H3tGV0oaoP0jhFC6R5Vi8KUIHmOi+/542OkaEQOHp0mPv3kTjr8fnPzSdJyGxur45xVQ7ncCBMfKaCWiQ6sdxAK4/GwZ3weGWNMArVQBrGSXP6q658YeKJoztDpL6ool/iyzOS1jgYgKFgIXlWChy8azajVSVZX20+fPtt6/mjY3ev3Du7/zS/aN64HsZkgEIeyiW2y+vOEWvf4CYi8CEqdpABypXYf4Sh795therrJL1yqO5WMVjXP6sWgNkgLx0l3YPbR8T6EU2vY5gTnLAAJQURO8qdNdWQPnzcJhKIduw9uN7ZetHZ2t168eNxpd8CspbwAGFBRZSJUmk0oM+HOeoMM2cAUA5+OTZIw4oCIyBLB6BmiZycigUdWollpiepM7xPO4qy70H6jI6ealnczlCPX9M7k4zxLtSjUWCKG6iHL6hG5ejKY4RCaHpkPj1xJWBCgqiYysOxVBKIcrtzY6LQa29u7P/zqn4c7W3e+/Oz6p5+TWxlzEazhOApcukQslYGc7cguST2kKhxXhYayY6GsiMMbVWZOtn1y7EJVmSSmcVYIoWnsfBiQarwKtUF6l/ABXhdbxGOMRcmX/KpqocnTWC1IS0v4+KON/b29J0+7O7tb6xvXiIxKAAlEiflY7J4hhoLqSLODdNhrGDJcapTXm8tLiKk/hImfZKEW6oFQ1dq9PqYttKj8clUgMETB1gFFo926lyREtP/86Z+6u3ub25/+7f+2dvXmCHIwGHO7DXZlt2xpKbhSnq2sEU64+8zM0ODns0uZjn8ailQVDj7A1gvlxaC+zheKQ0VVIAjyojjybsVEWXF8vUQQ9vyw1qioiJQdha8aH0+iJTk4unu3sbd388mzP25vb7aXVuI49ipQcdYAEJrJepNAvWVhX2TpMKRDYw1bh0o3nXhatfEW1YNVxquUkbtAD+Mngon4+BHMequzO4vZRMuRWXV4f6tPUNmkRBbWFchh5Nr9u0vDYbfbffbH3/vh+N5nX3bu3LnSWT7IMh+xGCsEp1LNmRlrVI4IUy9NxDrH0FxkXoRbs6FyVQ3ee4bU/tFFoTZI7wACBNU895Uk82nlTPMKTKnoedyj4yAPhYTowcdXf3i0+/jxXndv7+qNW4ZdkGJqNeWwAlsI4tOhhBGF1Kg3NgEQyh346yYjaiwWp7CaTg3PYnSAGGpAEEvKRdSI25GNrHGE/aff/m5n885nX97+4stmey1VpojBxqmQVpkhYRHFhGVohl1CpFRhFxGzMN5VEfgQHJl6Bl8AaoP0rqGTgP4h3nYzRkQShA3HcSxBSmWBcwnwlDkhEsCDxFjTbJmPP7r+/OnW5taLqNFaWVsmtpOQ/RGRCIa04ijf6o4PthMrrqzKPRZgeVNDe3LwlyE3836DBKJMHFsuojhN03GeLUWsM+EyvEFxzWR3RYAiEBOUBTBgIQg5ZS5EmF27tbzcbDV2Nx8/3/zmN/8yONi7/4u/j1Y2OmtODaVFqqpsOBCzQhhBSy75w8cjhFAUhY1dydB4DiHj1/tGFGDJIsAXQY05IdF1XrbyGudHbZB+5JBSbOL8KFsjSaAgDqzmynrz/oc3vv2u3+sOXBw3W+7wszBCwiolkbMWaTbscZ5GBpaYYN/CBtW4AJwoJ1GBqFnAGlspsJaldAxVWzAguH79mkvc5vb+/g/fD3ujax8+uPbBx/HqShJFBbMIK3HBrHoKwWHZVK4Ka0w4Tc9pXpAiIFnc4WscojZIP3KIyOsEYQTkJ4VYHFlXKDauxg8+vrW5/V2/309acbO1WlVYMSnUUMU8xyqhyNJBtxHyViMi5vM34b8WpqpOizj4TxN02Nr6JnftzHtRdTJVpqJkgodCyQYgZS6YEbqdtdUkafa6463tre+2t/qPH/38v/yXsLzsmq2CWdQYIoCKktxKZ9NF5L13YuPIjrPitBG8FapSUsAHD0AVC1OGqVGh9jcvFMfm86QvfoF34Q05IA45ucU53Lrd+uiDW7kf7O3vlnkvLWWtmQAwUUw+ktz397nIIjaO3ZQjmSf6Rm/ApFnjgiGLYwypqhKEZ0JnwpZcIyjipLW+unrvxvW1yPWfPf7NP/5f/cc/8HCYeIlFnYAVRkDHUqEkiiASmOtKzh8JzD/8wz+sra2VfyxOGu6njLIFvvwp4/MMeJE0z30QLcUWiEFTUToAIGUCnfw5j6RPUeTGcqvdkuC9L2byPaf/kE5vPJESYCbnIlWvJMYYEzU2t7u7e3vORdZGxAwDJjFEkfqmpi0Z5VuP0q0nK3HsXKQcCZnKdyoZAaAgPSa/98qfk4moV+Cymb2zrvrFg2QidDi5tmBVFEG8+DwbuYha7cgYlM09XKpMHE5LOvOrEOlkih+9VeX/KmX5oAAUpQCJgRqIVSWA1EINUdTsLDVabQnY2d7qbm3n3V47aTXbKwoGG6iSKjMpKUEBLdW9yuryKg5ADNBxjcfZ5+rYK3T8NdKZZ20iAFZ+M2sNAEMQQCETDvx6zZwn6pDdRYMABXzQIBCR2Z7UeYFNKVijbxRYP3TXghQgEAQUra/jwcc30jTf291rtDomsiAhJghIxYTUjHtIBwmz4whqMA360xwKvmtcDN6e7+A8KOu5J2ooXHU4MJjYRsn61Y1Op/Ps6ePdb/867A1vf95vXL2xfP2GsOnmGYwtZtw4NkaUcIFUkDUWitogvQMERfBeQhBVs4ANljWmJEV+S7mHKf8xwzcb9qOPVre3un/56km3240bkSUCgjOWixCyUTEehHRoGSayZOxs/8qJet0alwuzPijRSZHeV2A2hzRr0c5ybUu+uFk1LSUQUYCSYZfEkeWb16/u7+33n339571ndz992AxfmpUrbRul4kBWCEIsJQtfadoMIAFnGFSe8ju8Bd6Ypb7G+VEbpHeA4H2oag1O1nzPAWVIvRSinc8jRDLoj9bWmg8/vbu31+vtb6+tdeJGbAwxhEKOfBjSfsQgw0RGyQBaUtjN4ew15o/DVk9imrIRzhcvWcFnBO+rE5vSsDFZNuR42ay120n3YO/Js6ff/OZfRrs79z77m/aHn7D3KXNgDiRCpkAZn8MiKgNnvsnpXeo15o46h3QBmNk/liSqUB8k+LK02gBl4OLUiPcJnCOHZJkMs4QQfDhP+9FsHPzkp0s+FWYYdo2YINGjx099EZY6nUYU+dEgCqN1kx88/Qajg4ZlGzVhXLkbnSxIh8RnNAlanhOvPScvWQ6pzIGdHwscP01UrKYvwBCxaFAosbDVleWmMVD1QMmEqwxDOJqUOTEDWV82a5nZWhNETs3qVJmnkn0DUEJQDSrMykatkcRwyLP9rc2tJ8+R5S5pttstUrHG5r5Q4gAIqTEWQqBKu4/oyIWvRlKmamdfP3H9Zz/A4GkyKYiwYWOnj1M1iwmTXFKNeaD2kN4BglcJAJjeF/prEmsU8Elsr19r3ruz8d33m/3ltZit8cGoz/p7Mu7HKJxpkLFgI1PqlxqXFYpAALOxxmS5SNkiML80kqrQS11kPdyoVL9XThWhYLBhQ60Wodle6h30R8Pshz/82954dPeLny9fu8GkEVEmIGZVNoiUaXEzTiR4760zZjHNDDVK1AbpHcB7L+csN3jtQoC37R4/mfupiH8IAXmU2Ks34gfDm4+fbe5s7bfiZttpxDru7xdpdzlyzhAm6kql8FopDnBsfDUuA8odvjFQcPDeewQfPIldTBF1yadwyjCOCqITQQmelYmJEzJRo8WrSavd69F+99lfft/r79355NOrH3zMnZUoSUQNyBIrnU3SUdnFKTP960OChBAkBOV6zVwg6ot70VCgtEZMFN6r7k4mUi2i2G1caz346Naf/v3FwaZdvh4bm496O02SyARiDQjhtL76GpcQ0zRPEEiAiJbqd/M9xUverZqsZ7dBkz6JACKyxmCUFa12K4lsMOT746ePvisG3b3Nzfuf/7x144aNk4y8k0iUZtuUptGHeUmLhSA+qHN18+YCURukhUOOTmAvqqqkCKp0aszu3F4RnbbjM2ySJMnz3HtvjQnn0Ik5ogA78/FD8i4FYEkhRgjFlQ335Rcf7T7t93Ze8Mq6ZFt22F22odOIC9YCRSkaMJfntuame3voNIeCabJj6jFIALwGhfWefEGRpUlO5DjH9Uyh9hQvm6vTeF1ReOes9wFnxPGqJqJDrSOtln1VUWLjRiFYNsnalSvxsNNq7nV7O//xRx30b3/yYP3+/cbyWpo3IpvAugKFBHAAg1gRpgobhyeb+eP1IhCnzujaPM0TtUG6UJSLq1aR+vk7EUxUKVKXVXxzqlIRsCllKTQHsc/p+ob9/NN7f/qfv+1uPuWwnYSUraDMS2sQBALXnP3vC1TUGCNBJZAxEZDNMbb6RkkpJq1KMLisjgEXZIlkZWWtiEeJidtR/8m3fx3u79zr7d785PPm7Y5CMtWhSqECYqg5qTn7puB6Ll8MaoP0DlDmjs9nkE6uC6c/GEwkqsQona43dixOjW9M0s4TUjIjnQYefLi+/XU0ePZ9045axkRJs+BYYJUY8hbR+hoXBZnpXLbW+iz1IVhjVYpFRFxPzSGdqat0yjxnKAeysE0Xo6W4e4O3d3e+//1vezt7D/9TiK/dSeLERbYrWpANTDLVrDhlNPXcvIyozf47wBvyy70KZZ2riEiQ8hTzarxXlqk1IkhiKOS4toHPP7nZdrmk3RCKRmclIAoUnSrvVOOSg5mDSPC+6hPQd+wT6AyqVwSiGKV5LkLONTvtjY2NO7dvRqqbX3/12//7/zz44+/Q3TFZ1vR5LDnDg/zphudtVCLrEPIiUXtIFwQGArQIkueF0uG0Jj0Mar9l9lVUSTiOjxDlz+aE3ur4U/dIEbF0YuQ9bCzrzVU3zIrWUme727fNK6V6jaHy06euaD+tnellzYEJwNAyqQPnTFEEVRmPxxJe+wbNbY5ND1KmV6emqKKpYwAKEUCZGRDRzvJSq5Gk/cGLJ1s//PpXe08fb3z4oPPRx2CoRHARyCm7I+1pJ0Y4jS2/hP/XGBO8L3LEsTv6Tq2KNE/UBunCoSyHsW1ehD9RsuWfU7XstVA9sRLGgy5lYbkRYu2PisFoFOLmakGRwjI8ifLRWt4alw8yaViWchKWtQaql6I9Xmd0vCZFEAJAWHQiVUxMELJRtLSyFonZ3NzZ/Ho47Hdv+Wz5zu3k6pUxFUM1gnDaU3D8uTsnG70EhaXJEX5au6sLQG2QLhQL3S9Pd5SzTU7zs0nVMQ3BIFBIQz706U4x2k+sFoUXL7ZhodZCoW/FoVfjneEU/eLLAwEJVIRRMJSYlIOwIxDQXr/aSDoH/d7m5ua/7+3c/PTBzU8/at37oDBuTKwVR9+p9YFlOd8rrEv5cNUqXItGbZAuFCcedAHhfUu6SGwoaSbD7v7mi2ej7s5qZ2mQmcEwXWkzwCQEqjeP7yVEFZe8h4wqZvAAAVjAQigYzrBpxUt2CQbfPn30+M9/6B1s3uh2lz76eauzGqCBoGwUdkpMN4kuvl416ILqY2uUqA3SwjGd7CQqokWRl38eRttflmJ9NfNK+ZkAKNglcVApghCxVsx582VHE4I443yv50Z+99HjdjO5du1a2yd/+e5FOuw7TgqSOCrn1Xtgk87i+rusuZ83wVkEVVy5zgQiQ6wqIvDBO2teGo96k9sagkxo5c6c0sf8jxPtSgyFQUQkZpLQJGWGqJFMhJwY45aa6x9E1B309394uv1064tMb3z8qVltI45HKl4RJDJgIS+qgVlKJkcSCNNMKenJsU2dpFo6dnGoDdJFg/QiYs8LiS2QlLazGI0SkZ3t7dHuXityzpnYutXlTpqOycBG9aR6/1BKlgTvg1e3gBtYho7PYg86NxgQFp7mvconSQmewQo1IMX61Y1Wu73cbG8fHPzh////dh7/cO+Lh6v37iaNZqFxDvWwRywjSdULfI6h1VG7haJeOy4Ui5vNKnoB6mpEbEksQ4ps8+l3eTq4urpunWuY+MqVle9/2AEAdKBc93m8X7DOEZGoeu+hDphz4K70MOZhk8rD8Yk/y7pBQAEbuQa1yHAU69b29vff+bS/vvn85qefxmvXhEJOsbqGJxaCUuUjgUQJJCUB4yluHBHJj8hvvpyoDdLFQYVm+irer7zRIQxUfb67+ay7vdmMuBE5a8nEZgnNdsd198eRiZMoftfDrPF6MMyGrQpCCMBsZfPcCDfmaZCOggCe6Z3ICg8QJUkrie5Ebn93Z+vZ0/2dFz5P73z2RbR2PXAIxgEWVNHuAzhBMXQ6LkZU9yeL2iBdMPiM398WxOTYEnGeL6TCTZQMWabCkDpGMeoO97euNJLV5U5B6v3QuOTa9bXB4GmvdxC7KG5EM1vsWW/pvExoNRaCqec6UWsoM5F5nhPRaDQYp83lpaT6QHWvpLqVl3IpPpxlylL6MTOaRVEUXd+4utxu7PT2v/7tb3e3tm88eLh29yPTUkQJRw6J85AAlDPzrD6qUjxJVcajUSNarnNIC8L7uk9/76ACUVHVoMfpVt8jEJEh7e1v7W0+Y81aMVfFuOQZeavh1laX4zhO88zrWUaoxuXA1DJVv/AMR8N7iKqVikoxC8/IWT1I2LF1zXbn5o2b92/fSvcP/vovv/7uX3/Ne9vLWjRD7vtdDsIKAb+/D+aPBrWHdEEoGfWPvzj55T14DpQBNSAm7R/sbz177BCWOy1iJSp/QrMdrV1ZzkbddJy73CWNCY0QyUzVVu0VXX4wwMTnLb6Zl77D6+NQ5UjoUO9SiBUQKAgMVmaBAtzqrN6gpL872Pztn7g/uvmzT1q376ytXxkKhC0QCTHIn+iQLU2dgE4pe6i3XfNFbZAuFDLpY1BayMK8wPg2CUGNeic+PdgpBr1WxHHsQrkekQDCHJZXWulAXzzfzbJxFFlmJiKtaxwuPcq+UZWXlWVfZkxK2I/zgwSGKAsxAUm73Yyby1FnqdHsbj3/0+7za588vPvl3zbWr1LCOXNQOyvMVP17YvbWrUiLQ22QLghEUNUQjid45vX0q2iUOAC+KM7qrXlLWJWlZlS8eDF6/tTmo6s3rms0jdcLyPuQGWPby1FrGG9vb5PFUmsVYEChldFaxMDeBmf1G/0U+pNmMWn6YQnIcx/FBirHFuKzuKDOSbqDGd2jOdU1HObASlXBcoSsIgQLgEBc1ioYwORBDTN14ga1VUc7m1uPfvObdL/34S//c+f27WGSZGTACZSVyvDd1CYdMUCqp4sYnjW530sL/45QG6SLRlBd3AxdEI84KUiCYY+0yPZ2+5tbCXGUxFEjKbIZeXLx4CJObLsT90dxnuepSRuN1iKGVGO+MIadc3mKwvtX8ui8GWZV+BaNqY3UmS+igIeSJddOrtiNTtzY2Ts4ePrDbw4Orn3y4M7PP3dJh+IVso2CAUIgTKzJEVtctyItDrVBujic+oi/J1zBQhCjguG4u7Uz6g2uNNvWxgGkZJVBpFTuRSEuca2lxkq+tLW5N4aL4wbBYObZfpffo8YZICJrHZFJx9nMy+eyTLOr9fm9pflC+IxxnhyPqLLRpJm4eDWOZP9gt7f77W+2xwfP73/282T9HjprSFxg6w1r6WgpnJz+1WZfq6N4b4/aIF0cVKtIBenpnXeXGSSFNcA4TXe7JNJqLwloOCrIJZOABgMQ8UxFnLj19eXdzb3ReNhqLUXOVPo6h+xhNS4drDXMJs8LoEyczN9PuiS+hZeQ+4DgG53OvdVl/eZrH4qDb//y3Wh4/3Np35aIliSJhW1R1eO8+mmtrdFcUBukC0IIWkofsR7hoHsvVmeGxJGNfDHe2dl69Kgdt4ktODIuFjUgQD0IgDBEJHcmijqNex/cevFkb9Dtra4mhhmwUC+nPNrvwRX40UNVjbFEFEKRpqmLonc9ovnglFygMpiNs2pdAZEgt+7eyYa9/W05ePLot/uDaw8+vfGzh3b1ysr6xjBIDsM2UhWVkgeiVHyupMxKSzSVo8Bps/lt5vfLLeF59aQX84wtYk/9nu3T32dMaRoYYNL5BzdOkFHOE0U6Ji3yXj/rDRpR7JxjMqIksKVsOVdUrlVbkmpotZJOp6UaijxXpXcuQlrjJRBRVTXmJCXPj/WWMcCBuGAO1kSt1o1bd+7dv8c6fvTV7/7jv//X7Mkje7C/anjJcBiN6EdazHLZUHtIF4Syyk5KKcxFHJ8Jc6kBm+mLJK0IvgCBhnF/sPX0iYzGjfVOFEXKpApQSSDGR3kZRDU0m412u+gejMdp6lxizI94dTuTf2K2R+ftb/zJjp95TSZmCkEB6DQj9N4W60+r+FT01GpJUlhBAApiWFY44ojgbTu67nRra2v47NFX/fG9g+H1Bw/t8ip7NdYGmjbVnQfzunQX/8i8fOSLHU9tkH48mF/5EmO60ikI3qgsWdvf2dt9/MQy4jhmawAbTpMXZYiQKAIZ01mKl5aT3kFaFClz48dKuHLWM3rMfii9lf1YdP8pCQhGK2pRBsJ8N0+lpSvnwCI2ZedvMy/ZHKqOWrAwWTYqTCwN2/nkg854f/Ttd8/+/N/+qbuze+PhZ+t37o0lL0oNdQVDGFU964z2M1AmyeYxy7liIH9DzF0q+sJQG6R3CX67BrtZf6jykFRFy6zOa2OiFw3Wyk/iSRF5HMQMRqPHz/q7uzfWlk1klEkhTKzwE6IzBglBFGB4RdAQ4qRx9dpSmm4d9LY3rtxAwGlhxcvIbnd+X7McfbnCztoMPU3E/W22l9OjzQZ7X9sDO8EMVPJbE6iUNFelQT/vtBomNl7GIgKqSthOxoRPfsGX6NbrRH0Jb2VcT7+EBNLK4JESkWgQUSjNnKlaowkgKkoBi+oFEVEyYI6MAF477cbPf7b+1TffPvr977Ye//DhL//m+pdfRM3WKM0breUwGpGLSFUFbJFlGZGJogiAqjJRmqbKGkfx/2LvvZosS44zQRcRcdQVqUu3bgBNoNENSQyGszQOxSxtOXgZsx3bl8HY2j7zP62t7RN3dtcMxhkuyCFBEmiAJEQrdDfQ3dVdXTIzK/WV50S470Oce/OmKpWiqoD7WVpa5hVxdHi4++efC6gP3rElIACopCTi8Q5HqxPEEzEDBZCa9KEEhAQYJmzScDhMkgRGT4iC9nt9RCQm5xwDdvv9TrdjrW232yJimQHAi4TgmY2h3fPmRYbDYZqlwfuNzc0kSWaarbgtCQIYiJiBAcCrBwCKzdUA4ovjR/Q06q74z//8z+fm5vZcsClOAaIQglQ+qILixKN5rDXz7p+I6JyVIN57eqTC2NFgqPGZBkBQgkAQ0lDh+sat9z8Ybq41m2nWKoCjwjIL6fgQtP4Da/IGCCA4m1SV73X7EtRag7x3v+9xSE8JJq4lwEiA41BrFF8/+HPvqyWjj00OcvhX7jfU0UAEE4KUVeWlmpufbzQKNOJDGRU64g4c7EjxgHZFcXfhFScZhWgYdn+OM/Uo7sYHakFxAAVVVSAcjz+e3xShZrtGM6Y62gtlZVRCJCDMizyx7KvBzc8+VV8llltpZplskYEEtIZQbt+6/YO///uFhcU8z733xpiVlZW//uu/TpJsdnZuY2s7y3JC8iGIqiqoKCgSMdQrACJkCRokIBARxwOQIApgMDJXBQCMqZ2H+IT0uv23337nH/7hH5l5pj3rg5Rl9Vd/9Vebm1vPP/9iVXkfRFSZmZm9D2XlAQkJRUG17pe4s9P93ve+R8Tnzl8AQIOIRITMI/MpIRCRghISAgYNgET3e0qPcymnHtIZIeaQVOWUqAdx5SWq9KirioMcIVJgBQZvxXc31u7c+DRzpjUzi2wC7E4iu3dnrVxX/4dIqgAgs3Pt7a1et9NhgymndEiR+1OPg77RPXyFSZDe/5MPOBQcY3EaRbIFIQDudDuNpksoiEqUtKt34+Dwev+NysSiSxFq03GYA/poOx/HryMEAIDIQKKiAqAAYdexiwZVYuBUNO4PjbtOKGAso2USBhUBzpt5mnR7sLr6yU/f6ty5e+nll+YuXXJSQuLKHkKSbqyt/MtP/ulLX/ry7Oysc66qKhEpisIHX5a+yBtV6csgUUML6lydlKUXCQBQFDkB9EtPRGVZqgoRG8PGGFUofTDIxKQCIoC4G2AYDoff//7ffPD++yvLq8+/+HKe5wbw2vWbitztD6y1bBkAhlUAACJGRMsYFHZ2elmaGrZE1O8Nbt1aPn/+Ur83yPJsWAoxAkAlAQCsdZYggA76gygxw2ycQ2I+vRajU4N0piAFebA56NFw/DoPAtltDIPCoMYHI7Jx50YY9maXlprN5o5WgYDA6L3WSgRAiKQYjKFz5+dvXL+ztbXm0iVie+SXnmZM+kaHaunCYf7wSO3miDH32rkxwhGDP+J9pcAEHlQYA8Kd1RWb4ZxN2QAThFFOkY9YS937pttzCABIiAp62AE/2s07Sk0hRN8REZEC7uZ5Jkwqjj8fd0An3iCAKMwYouYkEwBYZmfsDKAxG2s3ru90trNPPlp64bn2+XM3VlfnL1/RAGVVRidGRAjJWktkfBUGg8GPfvLjrc2dzvb29vb288899+1vf/vCxaVr126+8/bbn167tr29/a1vfev1114DgH/84Q9v3LgxHA7b7fa//YM/uHz58rXPPnvnnbfmZ2eX7yzPL8x/7atfm51rj4/600+vb21tfeUrX7mzevf27ZVLly4OBgMik6YFGdcbDN/42zeuXr3a7/fn5uY+97nPffNrX799Z+Odd95+64HHxAEAACAASURBVK23kfD1117/+je+bmwSvPziF29dvfppv9f/4he/+Hu/968B5Ic/+pFzbvnOHWP52Wef+fTTa8vLywBw7vzi7/3rf33u3Hl7ajZpapDOCDTiecf7fi8t7YmCAIKgJwUUMSCm8mFnc+P2DfLDJEn6wxASI0QoNQ0PdiedCTZEhAIisMGiSFrtfHv7Tr/bazTakV68q4P3NEPiJIuIqqIaQJOiobvvhvhHTEodxR4+6l7QUXYwejCT7q+MiGRwYCFSc8xGKcAHcMopTfN+VXpUsBwC3tlYS2YWF+dmvR+O98QfsfP23j7vPpuKhApwmMbVI66m6pjpqJsRohKBCISwT6xoTzX6RC8oiLXqCjhqvycEdU5FwdpGu8jT2VbaWVhfX11bXb5wYWnrk6tX33k7fOk1XbwcAgz7fUaEgAi4ubn9ox/+6PWvff3SxSv//E//srJ896uvfyXPG9/73l9evHTFJOk//uiNq1ev/s7v/M7i0vn//nc/YOtefvlzXqDZmrnQbP7zP/3TxvrWf/ruf/rk6qd/+Zf/7dzCYqPRyPNGUTRVABGqShDxow+vXrp45U/+x3/3v/8f/+evP/zo/MVLAsQ2qYL0y+ov/q//8sH773/hlVfSvPjxT37Snp3v9od/87d/9+nVT1555QvD4fC//83flqX/8quvdvvDjdvL3/rW5bIK3/vL/1q02s8/99wbP/6n9Y31K1euNJvNZ59/kYy99MyzVVn++Cdv3L5157vf/c8zM61RJdb4IpzMdDY1SGcOpfvnDR5tYD2+hySoAiCABhAAPXqP1aC3fne4szHfKoqiCAgBSYEQhe+/NQEgQG+cabXT9k5jOOxbm2ZZ+puUsFTRIEEQZufnzl+62Gi2BUEJIVacjYjIeMQFundFWhynpi8fyA4eSr6IW6kt1l7jdNQ2irzd6fTKsgIMRZEMy528CUnCkyzno6zpfZuo7tHXOaI+4Vg5pIkCWEQkYpEgQSYNEu5VBB/vwKSHihMD6shYGSTQwMSDXqfX6XR2ti5fOL+9uQGD/s7Kyvy5Zw2zYcNsABEUDBs2XOQ5ACQu+/JrX/6P//F/2d7eXl1ZW1/b2Fjf+uW77y8tLbWaM2VVbW/13/vlr7/y+td+95vf+uBXv1q+c2c4rDY3N8qytM7NtGe/853vvP7662lqAWAwqJLEsqHlOys/+9nPvvWtbz333AsvvPDiBx98+M3f/VfILIq9Qbm2un7rzvLXvvG7f/qnf9rtdm8vrw4Gw7W1jXfffW9xfmFx4Zyx9t133rt58/aLL76cJNn/8G9e+853vrO5ufkXf/EX1z797OKFS8z2m9/41h/98R/Pzsz4UDabrQ8//NVWr4/IN2/c7vd7czOtMgQ2J599mBqkM8I4qTDmmwLKCfZDQqx7WiLtC6Q9xNg40n0NKKDAgKEqXeU3bt/pb64vXVgqGtlm5ZUoEHCoF5iC996KEIJx1J4pet3BzRsbVVUmScKMT2+lS8QehhtTVZXbnR2zvrbZ6SrWS2zVANEeHObKwIEg3iHGqfaQFACU9nc1RTzEO9lnhJjv+5iTymqn0ytLDyDMAahcWMwarczIxDR9lIdk97U834NJ71lREBGVVPVgL6FHKxUfhexiTogQ0RCFICJeVWLcOH6SJ0N2WueQ9u13JBSS0PhCqeGY/SWgXlndvL2SNxr9bm8wrKrKi8TFiIgIIKtqkCBBEFEUxfuL5y4YY4q8yWS2tzrdbn99favZbF+9em04HF66dPnSxSvXr9/8/ve/X5bl7Gx7YWHh7sqqr4I1Ns+LpaWlNLWqQAxZbofDCpA//PDDGzdu/PznP7t2/cbN28ura+sfffzJ57/wBS8AQN3uoLvTX1o8n6bW+8yahMgSme2tTp7kv/jFW865Zmvm0sUraZIjcKNoAdDMzFyjaG2sbxGxYbe4cK7ZaCUJ3Ly6+n//l/83SLW4uJimOYOCUuknk8gnialBOlNEa3QaCaTxTHfMEoQ6rqiECoyqVcl+uL1+xznr0gyZ0QeYmDse4FgEEJggy8zMXGNjozcc9q11WZZGOl6cJR55h58ECAIypTZD5s3NzXLoxzkkHhmc6EY8SMH/PgZ5vcDHKHYxmt11lDUhAwD7mCyTvhHc1z0CACVrsvX1rZ3OVghllrvE+pSXEg2Dqtz9lNapn30X/d73m0xc32gwQAlEAWWcTJ0wRQ/d8S6OMC51QERm8iJBRh7SqFUEj/JMUZiidt9HZ0ARBCUQKAIoiaICCZIYE0AXFxeLNDNAkDZWumWaFovPvfjKV7+5CowjQB1bRSRUVeecDyEEryo++Mp7VSXiRqN49dVXv/a1rxd5o9/vA8AbP/7xtU+vffc/f/fFF5//wQ9+8A9/9wNEUlWRQCPtjKoKTJwk9u7a1tWrVy9ePPfqq18OgOcvXn7jjTdu3rr+8udeTBxnqbPWpEmysbbW2xmurd7d2dq2zxsRSbP0C1945fd///dD8N4H59xwOGwWDQAggn5/uLa2Njs3VxQ5EhKTMdQf6JtvvjkcDv/s3//7Z5+5/P3vf//qxx/6EIj4lAIcU4P09ANHnVhP4hZBBQDiYEjBauUM9bfv3L71aZGmrj3vmZHZCggA4D19o0nFB4SyGgJAo5GePz97587W1vZ6klwAmrSdT65NOprcvHuMpUKaJIQ47A9UlQREhBRwdIx1u6EjSA0HMdE1IQCAAgio1yoESUxCSA4IADxV491TGSkU7PXD7n9fqPp+J9WQNIskbaVGmplLg5hOnxh0Mmp3oNwKAEAmPoCH6HGoeoVKEIAyIgMAGpRCQFFlhXHFGwqATJRb1eMcdYriQTIAKjk10bwJADIZinR1CKCgIwHF3dWAKJS1QVLyw4DIhlkYB6QVgxALm0owyVt5YyYrGs1WYYlNMfNic94wOWdff+GVmcWl/vJKGao33njjvQ/eT5LEOddozXgRH6puZzvPkiDCTElim80iSezi0vwLLzz393//D2VZpdb2+v3Lly/PtpuW+Z233rx+7dNfvvdOEF+WAyJSDSoaQ/GqGlN1G5vrd5Zv/+Ef/dtvf/v3qqBJkvly8Mt33n7t1S+JryzT+aWFKxcv/Pyn/9LvdlZXV2989skXP//y0vzc51584c03f1ZVg6WFxc3NzcuXL1+5cqXf73z80a8beXb9+mdbm+t/+Ae/D6LOMKMaBDJYZOmg1/nVe7+8+dm1D957V1RjNJJPxyBN65DOCArgfah8iKtbRZA9KcFHHRTqSQIRrHPeex/C3vXyQ4yNgKxAQgCGVbnsu6qzcvX9zz781cLiuWZzhtgGkbg614OMsX2D7fkzBhJRRL2XXm9AZIgZkXD33ScVR57C+o2AUIEEVECMfa6ZyBAZIgYgQJSa1TJReFP/7Htx1zHa/be2MUhoLFtrUBCDkheKSSlVlPiDGBf2D/sDyMTGcbOdLS60Z2eLlNmoQqjIYixFQlCu65GU9v2oTPxL+98FRQ1IAqoKBtCAkgqwaEyQIQCrGhWOBgUVIZDCiK4oBDIipe/5IZBorVjBCrJiJMtZ58pyKCKJSzRURoXiILs/gbQiCARKitZYRiTVoBCsAZdymnOWtxYvNBcuNOeXXNFEm1QE4NK03WwsLjYvXnTNNqZpBbC1vd3p9za2NtfX1jqdTqvdbjQar7766sLC3Mb65rPPXDl3fomIVlaXL168/NLLzy8tnUPUDz/86JOPr4LC8y88/8wzl0XCO+++1e3sLCzMXbp08UuvftFY9n74+pe/nCRJXSBEELzudDudTudLX/rSwsJckqZZgmxNr9t7/rlnxPtLVy5duny52Wpkedrtdpp5fnd15Qufe/m1L7+yuLTEzJ98cnV1ZYUNXb58qdVudnrdnZ3tTz69ur29+Ud/+Idf/vKrLrErKysvvPBiq93KUmrPzK+urN66faPT3VlcWHjmmWe+8MoraWpPyVTghx9++NJLL51l76zfTgTQbn846FcKRiQaJIFYLv6oOaQ6po8CAETUaBT9Qb8qy0PzCgD33w7WO0cAZEWaMtTN2z/9//6f9Vufff6lz7fbs0pYeQ2gupfZfIhtmvCQJhbUpMHeXd2+du2ONXmS5s6lzISkT6OHJKPkhABoavvlMFQViDZcaogMMiH64eAY40/+IQJagQKABTSKJigqCPGJSAoJkhrIGkl7pkAJEIY5M4JXrcZ5viNzPPs9pAPvgwh6D1iiDWBEmAAMBAvKqKjAKqQSs6ph1Nloov7pcEzcfsTgUIwCCQJZLn0lKIlBCCVNDDDSPpAYxtRYnAAOkZUwsN0mSmZm2/MLeWtWOVN2QFyBCqixhAghlM65dqvZGwwJsSxLRBQRH7wh7HW7jWaz3+8vzC+I6HA4ZDbGcGLN2sZOnje8CBNVvtra3DHERNRoNIhgWA53draccwDinCuKvKq8D2W70Zw4t/UBb+5sR9WGKqhz1jBvbOwYw0QUi/9WVlY++eRTY/jOzdtvvf3Wn/1Pf/Z73/4GAGx1BhvrG3maElG73faV75dDRAzBG2sNs7E2S3B9o+ecSzODCINBVVVVp7PFbIiImWZmZkfqD0dcmGNgGrKbYg8EgUFIhdWDH2wuL/e2t+ZbM0SjeQLlWKpqWLVns+ZGurM9gCEDgHOO8ZHLeZ8IKEKn311eXd1c3whVdeX8xVaj0W40DfAx9esiSAGsHQ4HW73uYNBvpkXukoZJTvCkkcHbd+/c/vXtwaDXaOSL87Off+6ZdpHTSUSCEcml2cbaxoc3ri6v7fQG5cLc3AtXLp+bn7GIViUaEoAYHiRFQd2tw723wSYFBQpIQCRgFGjQL9//9QdC/oufe6lhoncJSqgIKjVtQYUESJFAqTccJGkjL1pJnl9aOq9JZtMMbIpoBY0gIAIRCcjVTz56682fV2XpnFPVhYW5P/6jP2rleWc4LGyDQVvNliFqNpsAYBkjnURVSx9mZpplGawlVW0Xrl3MD0sgBglCTGmWtVqZKoRQGmsZMLHGi9vnJKiqIORZBgBEnDoqJQyHZXumKRJS5kEIRLy8fPuHP/x7VGCiz3/upd/54heQQAVarXSmdQEAqkqtBWNM3jDDUqKtrUphg0Gh1c6JgACCQpLYNLWtVj62PqUPRKfBsAOYGqSzRE0tPbnQVLxV43iq6r1Ya6uyvNd37gnFGFYXVLFakh/cvXW96g2okY2lugDGWas64TTx/SN0xiZS1qqBkS6cW2Dc2e6UPhgWoadTCnLsMXjQtbW1D69+3MyLVqPxy19/8PwzzyZZhn7ojN0ty3zIBvOTzBFjzPKNtWu3bty8eeu1V1996dnnAVjrXN59cL8PiCKI+G6vQ4jnLl28u7byxr/8c6vVKhqNBJnHGamjcjkTOmlH3d1lGbrd/tZmt2i2TKJXr924vbz8b/7VNy60ZyJPYvegccw1GDMO7rn3AIAESKJMnPYH5Vvv/Opnb735+S++EGL4EDwoiIIiBgNBSYKqoqIldmjt0sX5tGilRZPTYpAYTyYACRIgAqgogYIhBMThTv+tn775zLPPXLp4SVUt2SzJS6/OGBxFn2VEPxMAIojtz1RQVayNXAAMCgBgHQBArMmLZT2IYIh5FME2NHnwI9YGoDE2issFUCZiy6BAyF4gZfYCX339teefvbKz00nTtNlsZllSn6TRWNYiAMSkVOLq8zy+VbFOvwEAIOn4cOIlceYUpVamBunsEPk3OjJLhE9ulMqoZ/Whv7N9d5lE59pzzA4AJLZ0emTjgQLoiU1RpEuLrte7HbwPzExExE9y1O6++PXHHxlnX3j5pTxJ+/3+1s52p9dtN1sQTmQBQls7XS9gk4ytE6D4M5qwj3veUEHFv/LSi+lMkTTyO6t3bt26dXd986Xnnj2pa4IKi/NL+cwFW7T6lWdnbt+4MRxU0D7qGxOkmNEf+07lpOKJAHmAsl+9/e6vbt1Zs1mj0x+WCoKAQoIgQJ4wIFTqPXCSFC4pkkbTpFnabJq0wDSrmCvEkVpwZDTWhAgNysSWjTPJ17/y9a9+9atEaAzHJRoRP1iSXwDvreFxj9OtcQq5x/2ECAEUCZwzi4vzi4vzRExED+rMnEpjnIfD1CD9puGYvka9nANh9Wsrt7fX1oosabVaZE2oGww+6sgqAIIUQJFYiiItimxrpx+dJBO5uZNlSUf4W08mVDDPiqxooELRbK8ur/Qr3yaW4I9xGLs5Kpdmi+fOc5HvdPtRlkljJ6pJ7vJDWo9aPIIIQJppMjs7S4XbqQaklNo0c9kJ9pBkoQwJbLK2vvXx9RtXP/r4+eefnZ2bRYV9C7PxrHgoI7Hm8cT9j0LlSoJQiQ9A127cvHHr9kuvfD5dLgZhczCsfFYAgo+CQMhg2VhjyRXN+WiQKMuUTTCmRFIgHyVERAE9IEalOwFCtEFCb1Bu73Tfeued28vLL7/88nPPPTOXOWLkhyrJwUmnZ3zsE+IeezrQPsB4ozOFtRekHoUYKWqzqhcArvk298ZR1/uoL04LY59mRKc+xFXSKeJYdwkqoVIs5F2/c0fK4cz8LBGqBlWMIex6M49WUFWbGSGW2blmp9cNvgyMkCQANRHvOPv/GBETBrEHYxU8AJRlmZ6Eli4BIJpG7rrlUCTsFf8+AVExUshcAgBl6cvK//SnP8uS9Mqly4dL5j3SFqqhCLGSrK6ufvzhRxAksc5ZB+AnZYTGpdlHAQ/OmigA5Jz79cc33//Vx5eeefbchQufrVwXsiWQJ6sKAaFiqACSJGvOzWTNmTSdBcrQWmH0gD5SCQFAYycLJaVdrwXB+2GeNy5cOP/y51723i8vL//LT3/6pS++8r/+b999OGu052Id/N6J1MqjQfZaa+MaNAe2++RiapDOCof56fRgTsCRuYdYow6xzAUZUYAxllA+ElCBgVFCat3G7c9Wb9xMDRtjK1+h4RCzu6OiGkFgqCNSoyXa4fs5Kl0EAKNiIGawsSoaPDvfWFle31rrXHQNax3W1SFyGouvR8ZRkYyJAh2UWC7sAZEkgK9EEMgYCLtWdqSltv8s3Xf82OejDKo+VCEoIVtTdvtahSxNoW5mNOoIdGD8g8o9IxAASFCbJC7LvejKxsYv3nv37vrGt7/xu+28mTKHSnU0wMEipJpWcPjuT3wMCNMUwADiK194ZeHC+bfffffap59eXJx7/txsfXJ2z8OuIuLBbdXF5Tjen7qASSp/8/qN7Z11uMt3tlc+un4NHLav3Wx8oZ2ypZQpsQsLs67Ik0aunFRUgLq6fxcAANULrFEGS3BXiUAAvPhOv9Oebf2H//k/zDRbIvLf/uq/vvXWz2/fuL24uMgM1h6V5sfJrn1HP5nxnT0NhwBg39Wb+AvH/+vEZ0bfRcJ9+3P/B4onB3jI754Upgbp7IDIiOGJLrgBQFEWkXLY3dio+t3ZLM3zhAyPpayPjVis4gGEGBuF6RZpWQ0H/ZLIjtKltdb40xK1Q4WZdnvYHYiIM2Zjc5OYms2myIlZ1mG/D0lirEmSRFT75bDprLPueE3rBYCYOUmSvJFfW77+8/ff3ep1vvbNry2eW1AN4k/sXi1Vy2pY+ipvNjJn2838ow83xlOm4P11gyY/IAfeMs699PxzrsiGABURrxqw1rVnodFIGs3m3EzabKhjNRwMAdogZrKbxoGtx7dwUmaCiEqRNE3b7aYPwVqqqso5Z5iP59Yf0vjlsKM8G5xeZ4kHwtQg/aYhio482ndJgaRi8NLtbS4vD7qddGkpTVN0Vk7wLsXoA4G1rtVqdHuh2wud7naaOWDaI3D39Nikxbn5Dz6++slHHzfyfNjrXTx3vsgyGfoTGVwQ1PDdzY0b67dX19eKVvPu+noyN+9sImV1nEyPQmVskhZ2fWfjF+++9eGnH19+/tnNnc2y310oWp+7cvkB9HMfbEMabty8fmdl5fylC71e59fvvXtxca6RpgCgUYFW90hCROCeq7/77nivFA1ERsOgf/nC0sUrFyvDO+LvdrclSV77+lcvX7jMzGyNGKuGhBAIAyAg6sQ9XZPO92194m/nbOX9Rx/9+vr165cuXPR++Oabb7788stp6pxFL6z6gDXoR93PB19/4BzSCWvKPc4nbmqQzg4UFeJQSFCQUE/E4ThZCEjlNMCg19tYyxLnnEW2BAyjNgr78ICZpH1iZQQiCAre2qTVanT71cZab9gfGEqI6anLJJHC5Yvnu91ut9O9cXdtZmbm2SuX1QshntSxmMTs3N4qB+XS/MLO1nYCsFA0kqh09ojjC2BgoiRhYlENjWbyzDMXKt9fv3tnrazKmfnLSzNNeyIcX1HVZrO4cbv/wXtvl2XZyOxXvvjKTCMd9ccTwaOn6sOiVlH7Ino5CtQfDNUEpARMcX7p0hdffw0St3TpUtJoAbEAAaEQCETGnU6KYpDeP2qBSNaYsizv3Ln92adXy7J66aWX/+RP/jiWQ0Se9JPaUOZpwlSp4YwQ00C9YdXvDULUdxx3cLkfjsoh7VO6ZGKXpYPBQCeoxg9l81h9BiHrd1be/cX7P/nxrKXF+YWsUShyLTp5AOGBNzCZIVD0giBKCM4L72z5zz5d9h7PLy0N+oMkjTEQD/EsPfFOUiAISGHEX4h0YahFomCsFHCUK3Nftq0iDEIISJ5iZwQlVIdsFBmRxn2Pjhhn8vQhjONLXiGw0aLIilYx1EGlfqjeiwCAZU7IGB8Ka3nvYuKQHJLuf2W037uVrUMRD4jj04JokJDqUwQAUWv1wDnZVQEfnUap9X+EAhpBAuOAuNPrUeoac3Oz58+ns3OdqqrQuDQTsjLKCQGhAI0SbTo+MXsN3n7hvt2QHSKg7Ox0GnlqrXVJQoip213Tn5S828OuL570x+NhMPWQptiD7s4GV73++hqXw0Z7wTknQKJBj5QjeljUWWhSAgRRT2SarbzVbqzd7Wxt7hSNhmqol0a7NZJPNEgh1FIDh8xucvD1h4SoWiZSZKh70DEgATIpPmIstbZJ1hpjAXBYpNYrJgEDaFT1RhVjMHZwmPjKIwKZGIGi3FzkJEcOBoKqPpCTpwQogiIIAY0nCkjKCSc5OjO7eM6kSXNurjk/XyHaYSAik2b9YTXSgKDYATwm3e7H5jsEogqqzWajWeSIyIboEM7fFMfC1CCdEeLqOdbGPmprzHthrL1DTCEcHl6bxLjuOu5VXd+nYh0Od3aWP/ssZ2qkibE2AARFOkp0+T6HMjHR7AvTY92rRtQTuZnZ9vZWr9vrFo1GCGqQgJ6mqB0qUJxVJ7WCTug6MyAgmlGuQCXWICk8QKzpSKAgapLaJGUCb41hUSsgdd+m+BkgFQGJCnWPVjcZeX3E0TszpECgPFmBg3j0/bqr2iCj3sSBIAB4JpO1OC2SvAl5ViwsVETg0soVZek9VF4JlQWrOBCLxAhQNOoBUB5prSOiRERcXw7FeBmmsaWTwdQgnTli6+9TCEOpyiPRGUSANYZENGQQNu6u7KytXmi1nUsBQBSUMOi9i8QffqsY45jCQAEqa2Vuobm2vNXtdPI8V1VUc1I9Nc4ANJEi3+sMHfdCj4jaArG8URSiOGht4B/tBEVeiVjL1nIIVSiHGkQkaBAmQqw9vt2UDcCjHYuKKoJBhrG0OQJOmFLBsR4CHPDDCGK6CAmAAkkgqggCGo9mfmbOFu202bJFU7LUV1UlWpUegEohRYhcfIDdYGDt6x0n34MSQlAFMIYQcOoinSimBunUsfcJm4ywPOhDrocVV+J+NgECACmRkKLct6v0iN8pShrZTeqDldKvLXduXjMkeZYTkSrF1TkcycE4uKGDns2kJleMYkWVgcjsEMZhq20k4LBnup1+6VNHjtACelA5SL46eCoeL6IQ1KFBuUkG1JEp2geeHMOoRd+j93iMFEcUQMmSxDKKLxHBkFFQQQ4oIBLrcgAg+iV0oCoo4t5xyJj1QURBoBBg5IvH6xW9rqC1IGJtonAkO6BKiGQskd3q9sgwWQ7G+NS4RiNpNI3Lk7SBNuMkqYiqSpCcNQYAQgAyDADe+6hdUOe6amsOioBRyG3k1E4c0cQ/9Z8MAKK1F+ecAyPEZtz9Jf7Cw+770Xl4ODwFQepTw9QgnSl27/ZjZ0cOykiL6v17g+5HrVMSWwDYUOGgv3VnmRFdlgJGMuspPSCjYiMAABiWO81WVpZ5t7tR+SGRsWgRSal8KorMjzKKJ8UTGvu+4eTIR0liY/umQ3tGnCBIR0FArFtAQW3qqC71mQgYjHJxHFR9WXkQTTPK87TRoiI3zQamzqaZgAHkgMYTi2JQVEVURERF2VPYtDt4bB326LeTMcYYMmwwkksAxwuOKcnuRDA1SGeEcbaGCOVYxYyHA5Fia1EkPIKhffh+kSogkXoj3oqsr66urazONWfTPFMiQFIiQhTQQ1fl9+vSFwuJJgs+DqiDx9CTKhtqNPI02xkM+tYkquZpYn0eFYM9qUOYHH/PeXnwe0lGO1RfjjRNiRRGzYGOwIiidjxexiHjxtDjqEHR6O6qadweMbABYwLbbGbGZoVpNW2W27wICEKsQF40AASNak37OW73cSJ3zdIhQcIj95nImHHyCCf8ot9mr+YkMTVIv72gUaxJFVjEiXfBr99ezl2ysLBA1oSRSyd4vBjRAyTMmLnyQ2I7P9++fWuz8n1isminPKbTQ93dFkDC4/BB62CgSq2uSgokUeEUaaBisixrzeRFI23PirVgXcWmUgAhEVUMgUCAAFFBENGgQUTcW/F6PzzEgTMxMxljxtZoMoH0FK2dnmRMDdIZId74bIxLwMtg7CRNehjH6nuHaNiIBsMcPMBubcpEr5rJWPnoURRBIu8H/TaG7t2VnZVVByQSkCkoCIzK6OO3JqpD6jHv7Y49gCmKZ8AQA4BxtLjQEi83bqyHKiwtXiilOtQJm1Az+63AXg26h8Uh026jaBBz7CrJdAAAIABJREFUdFkTZ1VUpE49Kj26L3Twm5M1jjpm7433DMWLBtBKKCiTY7Zp2mqnjdw0CpsVaBJKUiUKdUVanROKvbvGJUIEyIYMcRCRCVn6yfBmvah6pBNJiEFCCKiqUU0V94TpYrn3RODxUTYyxdQg/WZBRAgRCUnxvryGOJvHykSDqMNeZ3UldAczeSN1SVAVppPp5nNf4FhQ1QNSq5W3mv1uV7e2NvOWOwWS/G87nHOxBzYSGWYAkb3z+NlAEARZCX2QEjFpzWXNmWZrDvKscizOgk0EKBArjKxR/D0yRoSRgIGoyKO+P6d0FGd8cn47MTVIZ4q4XkOko5R4Hhn106L6APw6gAlrBABWyRL7Xm/15o3QG7QX5pM087GshvAUEl5HIZK8Jctds5mX5bDX62WNZIJVURfVAsA0av/wqL10GBkkxMowI4qIip75hKukCMoGnUtd6jiZOXfJJE2bNYZMwqFiEGASAiBUigxSiN2JSBQFyasSoYhoYi0xi9cQwriu7mQxeninUeRTxNQg/aZBREblt/exeVFGjFSI1EEVBp3tu7eNZTKOLBM9rhILGQx7jUZaVlTe3Rn0+lkjVVVE1b38iCkeDbU1AjHMSNXIN7rPt44l4aqCddkwKYJgrUHnyXhjXHO2Nb+QNdo2awc0FRqPIhQAgioJEtVe0Z6dEQASEhIdaRE98GLskSFIilM63WliapDOGvzADYUfEISxtN5HVTMFjZKbhBhEAPQgSxgVqC5FNKheh9vO6I3rH/vuVnOmAZnzgCKKCoohiqzwnqdwQu/rECYcw258Y783s3deG40zrlFCAABrTJYWIv211btbW0PC+bSRqoZJtxKfEnbTZHuIo/JAx2khcZ+5MVoBhfGpFh9M6hjBMDP6fYTPcS3B4TVVB/NDk7p2B65FXYekAAil75vEBcTu0JOzJi1MWnDemjl/SdOEjSsJB7ETLoAgoSrH84WkCCgoGAtcFSDUqno0prpBORw656KHLRB2z8vBc77nKA6ev4NadgCRBUpIseCsTiEJ7n5+v7rSw/YimiJiapDOGswnJQp3JBARCe/nIAEpxXINhzBcu7O5fFuDz5oNcKyiAoo46lp26pBJGi4xEYUkw4XF1upqbzgckkVrXT2X6FOmBf4EYDfUaa0xlo1BJBUNCkEg+ke7l/kRnaG9dEoFH6LvQoxkOG10Q6nEydxs4NS1ZxvtRcwLbzLP7Jlo38pGaUKBiSZuwsmw7S6Y6scqyAkHw2ugwAN1AZ/iWJgapDMFIyiflqq6qkxWOCLSfWPppILgU4L1u2srd27n1jaLhjU2KKjKqZXE3hMoAOJDxSyzs61uT3rdEvpgrRvv9TSG/6gQY9FaII5CUPIIZ3IfF3TPemWk2C0IGlRElIjZiHXUKowAJ3nanGnOLlToPKBXVlE295voD5ifaL1GEToCADbMRFXlgw/Ip3Lf1oJ+pzH0FCNMDdJvIOpqjHtMNjV9VgDEaDnYuNvdXHNIeZIY5gfJKJwWUFQA0EsQJMoKt7A48/HW9dD1RVEQw97ox9RPemDgSLzOAVvASOkXVQj7iPuRTv0gTtIB11nGLyqSGtvt9U1ikK0mGRdzzZnZNCuC8pATIYtsLKFXgV3TAjAhE/xQiPp7ACAS+HQMEoyEjQ7dv2lU7kQwNUhnDQTIsqzf70s4mSRsHCQ+xgoeAEJAZpYQebyy+wSNWkjESQcUGDxr6cKgc/u2RcyygsgqUJSTmVSfi+oAI52xA0IvR+JBnlOpxxytrwMKUgAgDTQ/2+6dW1pdXe3ubLWaM/VSGkfCM5MjTLEHhxBAkNA5MiZQXOvjkR7S+LIeaplGVmf3lViLKgjEDIRBMSi3z11CmzXaM+1zF3ouqcgEqBkNOuI1ALJCXTaHe/yeA8dzwFDte6WqKh88Ee95IkRhlL07jtYcIjpnnbMAoKC7PageSxThNxdTg/QYEOV4vPeq+riC0lo/oZ7Vd++ubq/c0WGZsIUJJekz3yeKop+gtXilgoBIkaedLAk++OCtsXXI7l4zzBQR+3vvIipbIFZAH5kwqg8nF664xxrFoQeDPjEjIhCGANYUriiypLDFjM0aWWsWG62yHFY0ViGiSEiAmvLwVF1HOYQnMcVJYWqQHgOYiJlUg8gpchyISb0STeaZ90AQWIXA3711fbC11coy55J7R/NPx1DtZyghUWwXi2RUfNFMi266tbUzGAxM4RBjgaQHhINC4L892g0PC0RMUmstMAsShiCEUfP0Qdf4+7NHoxcFgQitdegcuMIWzbw9Z4sZT4nLm5gWPaIhhyhsipHyNxYl3bOweNBnIfolDy8lfEw8VYbz6cTUID0GqCoRGmPK0j+2fSBkRSveit9aWTWAM61ZRApj72MiaPMgdkj1EH75MUCReqdQEttWO/chdLpD76OTFDXIpXaqDgtPTbEXgiRZlhERgGiM5h4w3vfgfE9CMfYoAgEKBJA2wKWcFZykxcJ5Sgub5+zy4SBA2ggOB5Xq6BqNmOXHvV5nbo1qCAIe0WpkiuNjapDOAJNPDgGAYawqAABrzGnoIKiokgJAZCiMXx8bDEFUIBB1pV/96BPtVSll7dm5SoWUZGLFHKvSj8rk7g4oAidik/Zo3xEoAXoicAnlud3c2tne2cwLcM5h3YV6bJPiDslh4/w2w4AKgA8SssLkuUMKEluHj+Sxa69kdOr2XWuZUPse9EtmIiIg9AQByKMNYJqL54r2XN6a4yQtDZRIQwTwSmkSLKqAiKe6oJQB9t1LD1pPticzNKJCMDEREZOoxgLfe/SRPM49YdgYY5CAEEBxLFrPU9rdiWJqkB4rUEDxNBg6Ua+BmMUf7oSpqpZl6GyXWxudjZ2lRsuwCxDG8t4Pi32k8+NhgrxOAQmZKUmSNE2HA93Z6TKVRTu1Ns6l4xlt6iTtQ7ToAADOOefoqGxNXO/vmchHFp1GrWMJMc0LQVBVD9hXSYrmzMxi0mgljQV0Gbh0CFgRBAwAoKJpYhWh8lWQ6qQd6P3cBwkSJBynxPheQGHDRLR7CAfbkU1xEpgapDPGoX35jtusbx9URQOwMQBgDKkqKOlkj20QCSE3SL68+eknVTloz14SZrhfp4kRtWg0zsRTeWxrdNjXURCIGIzBJEmI3KDf90GdVaKSGo4dwr6KetjtsTSdMcZw1jl3+AUaW6MoJTW6SWq1HhQiBEDyoBVIIFBjAnNzfs61Zlrt2aSYLWOjvKABMYAicKSSGzaqIvdpBnEyd37tHlHdXOtExpyEMYb4eJLrUzwApgbpMSA+6Ke6okekuCYVUdX97F5SSEG46ofOTtXtNpo5WK5AgfjEVV+PCxRAj0TMdtAvfQUqKAYHgxLQZ7jPT5ricDgXw5z3OUsjubnIya4dLEEKCAFJLblGnrcaXKStpfMVkYDpkQpAAAgMABKXM+MyOO9DkHDi7tFBnJJAw3jP97hHU5wapgbpTDG+oy1xhViWHvhEL8GB3AkiHpa7lkQDl4Nr778n/W671Q4sooHZPkRvM4BRripu5ZQmHYmSE8QAGqphn5hJbVmWaZYiIjODwpl3TniawMxs2FhmFl+V+94dd/uW2JcbWZgVjBcIiorWOEuJbTZb2WwLU2eypCLqIgcgIBYgoMgvR1UFVgU1yFmaBgnRPTqNG4MQvQ/WWGM47L1rJ+uTHsSp2eNJ146+jO/tECoAIMZdLaOpYTo1TA3S4wExMzPxQ83/j4Ko/B3zwbtbBwE/DJ2tcmsz9Hp2cR6dlVhZ+2ShFi5DCsyysNgCpU43iPaQdWenG4LHdm4sIwIxTc3SCHvksZ1z1lhElSNSLIL1pz1CQO0PvRpC45SdGpvMzc3OzyeNvFIJBANiYRZiwRjfo1hSRBOSD4hY8/hOkwtHTHXFa6jN3olfflVlwwAy7TpxNpgapMcDJrTWVpUcNUccHyqKjAyIgEE1gKoSIsZakJR0fW1le2UlMybPMuNcKRJU6VHL/k5hFTxZmQRI1dL5RpLw+mYX1LRbC5ubW1s7m2trgyJLksQmqZ3qtxyKNE2ttUwhiFfRQ5N9HiEQBKSAJLkzaZEULZM304U5sLZkHoQAxqAhIqNAojgh710z2+IdQFhr00mQE2wIcVCpwbBBxCAio3jd3r60x92cirAxTEA8LYY9I0wN0mMDERlmL6dYioSiaJCARJVClGcBACDwOuzsrNwO5bDZapjEgSEJoCr0xEzqexe8giwJu6XzjbmlpgQq8vbsdrp8FzY3N8uqF5SGJSVJZpPkOJ3gf2NABCKxf0+wDolF1YOUUbU6fmbMXVaAQKYiCkgVmUZ7MWnONOfmTLPZCRJs7ELsAACUAoACCWAdHz6MuYd0Ki3y9h8jExP74IPIia+HxlqQxDTKU05x6pgapFPH5BQv9W+NtSDERP7+vcbvvwWA0SwTO9AQKCASMZIqIIrWj5doUBCSSnbWb3/ya2uoMTsHhKWEcDABpbuMtRh6Gc31e4Luh+7TURPEUfPU5OelqpiJiIkosqcABJCCDgCJiZig0kHW5Odb7X4v/+zarc3NnaqURgNm0twaE0IQEZ3owPEkZKRPh5R8BHcOUUkNhTx3iROAoYRKpSQADQBsCdkLCBAQCpiKEtdsF0XDNIq81QaXYGIDGzA+xvNG5Pq6LEnr9r4AIGO+PwGIIhErgKgGDcdcHNSEgjrup7CX7e1cJkFEPIBM3oc04QM+7Bkf5YkIQFCBVAxag6MyqilOGVOD9BggqgBIREQMUJ3ehqJSGSIyoKoSCokAela/dvtm1evNFLnNco8UJECsOHnccXJVNaa+LfcyhqWmJiIgEmClAKomze2VZ87leb650el2+2t37xaNRpZlzrnhcDg58FkexeOGgApqyUzWBKBSoQzqCQCUvEgQHzSYtFA2bBO2STFz3hbNpJELG7UMhgKRB1EyMsoD0Z6+RDAZU93V9Rg1DRqtJE4XqnJatUfTyrbHgalBejwgBCEw5hRDAaqiiqLCSBpD4KKMwurT4Ndv3aqGw3x2IWnkflQ8+yQIotS9M0Yp8YPlTXVbmihygyiiaeYWl2YbjcbqysbGRq+73SUlTBk03t5RhSj8lswvUWoh+D6AJ3TG7rK9gwqoQTbMFsmWhrNmqzU/nzRn2DWErDACQkASwoAgAPKwrARmZJrM65ws9nZCgiByeryJcTJsijPD1CCdNRSUMLb9mRQ+BVVFwJPNf4hqZJ4poqKAeJbgsDTifW/QyHKbp6VKrDp/UnJHAMwU6t4Z91ZDJwAhJAFtNPI0jeX0m1ub3a2trcFg0GjNTKgVPGH1VacLCb4i45mdMRz1T5UI0FQluqxwjSbn+dzMLDiHmfM2U5sokCAEEF+bLhx7zIj30hOJnlCcu+Ni4n7FsCd1kGfhhE1xlpgapLMGAgIox8pBxsS6waBUFUIUUBilUh+1KHzPrKFRQQhJScEgBW+gMr3O+vXPVm/caLqk0ZopJ1gVGNnh93vG6f4ppCPxIEtO72vjwWwmy5s45rRiH/PYjU0VICho6QMoNZrG2aW5uXJ1dW1jY6ffHyRJkRUNa/kxdvp4LEDLLnWcZhWRMaZSqqQ0Ls3bM1ljJpuZSVottcmQqSJQpEp0LMStdUFSHGkyQzl5d9U3gcpIHwGQiKkmfJ+8QZr0lRnBWBNLbuXkVX2hzsUSOueci/p7OnY0997/U77DSWJqkB4z2Bhm8b5SVTiFKTOAqggQMgNQcD5Av7u9cscINJsNjxqolo150uJZ4xX3/T4Y1/Exx27SPDUmTdKl2bn2zRurvhpubVV5lqUZEf8WRWCStEgLy0lSIZRgbCPPk8SlWWtmwaQFJ2nJHNhWRIFAAHRvuDa2HBZ8oAWEihJTPL9yJu4RMRGiD0HC6Tm+QoRERLUI7dQVOwtMDdJjhnMYvAnBn1DwYc9coAiiooBEhKqG0YoMOjtrNz5jlVar5UGFif3+b0U8yVpwk/OnoIzaxYmxBgmJ2dhMZaHTGXR6ZX/YQUqS1MZJ83Ht8yE4yJk+tk557KW73e9jnpHL1JmZc4uuyFyRqyFTNIRMQCoFA5JEqQUQBaWaork7FI25m0efMyRERKKaF+l9UD31SBoR1e2Q927oJCLeu1eEOB4VwNQcnRWmBunxgw2xIV9VUZz/ZJluEpuwBQAggwzIod/dXr1bpJlzru89ORs/9jTrF48nETEkAKQaLNL8YpFmxnX6a2vDshoCQJIQ8RNBAX9kHJhzJebJBEFi13kUQVDTdLMLM+eXslYzbTfUGUwsGe4OSwQktmBZgoyKWwkgHOSVjbnO9zYwte+JiEgip8garTfHRIiV96fqHkFNatCoJvRblYF8jJgapDPFKD6NMFp7aixTVV/5obMpAAgSjhaqD7bii5PIUR8lQSJBRErTHHa21q7fqrY6sy+dg4AOST0GOHwReHDrB2l4B6eqyen+4NR/nLXzUYaEJryK/mDAZCwbYCj9sNECl9pGMbu63B/0Zb23ZRjTLHPWIkUW1eSYZxK2POAV7SGU7DnEXT7h+FoIQtxPQYGoAqUmoAloKjKBQFjZ2ivPXDp3YSltNYDQJ84jQEARVbICgEFUABFpfMj1eYidXGM/1rjhWr4JAMIRKyVH9ef7/d6eAz1wD9+799HB9dDBT1a+SjiB6CSFACfjFR22M0REzIgEIAGYcffaPMXrmScdU4P0mIEIiGoMmZNVWR1BEUAFhQgUGbSs7t66Y5mzNI99BULscxcXwhNSm08vgvggnomQgFBTA86lxuRbG8O1jY2dzs6wHLaarbxoGuYgJxUsPQHcw0NFHc+8Ul8sJAHwEJs+WI9OTYZpajLLDtPFRTMzy0kyDBUiBYKofhMVVEfmZ3/ryIOoS1IneHSH7BsiAPgQQhDm02VrxghazFSdoC7RIRtSsYyIoHK4GsUUp4GpQXrMQARminrMJ0BN2vfkIKASACB4kgD9cu3W7c2NzWajYdNECZFIVfat+Cablz+9CCICihhXutBoOmPIZZpv2rW17cGgZK7UITHEQNep1VfeGxJr0uI/NP4VsT9PVut7AAAhK6EwV0yCqWChmIHJTZ7b3BkTXGvGZAkQATIQII7Ym4hHLPAf/earFU6rcAamPbaIVVV/WOdJOcA/eHjzuHsejGFE0KlGwxliapAeP2Lw3RiqynDi3WNV1RAyBNSq6mzd+PQjJG202kCop5aqvfeC+mwQVSpUNYSAAIhlmpk0a83MtrM8W7mzubW1Ya1Js8Q5NtYi4WO0SXCY+Z+kEsRaIkFSMALkgUSoRJPPnyvZoeQErlQKNjEmocybPBeLigSKcsq5QUQK3qvqqRZ6RxDX4UER2VPHd7LAKAgy+nuKs8LUID1mIAIRWxYI7CnWxccH4GSebRQlwywlyWC4tb5681phKG82iFiRNJZ34ERSK6ZzjzGDTS6TxzbpvmvnB9G4eyjszQ4JaAUaFIwhs7gw0ywad1c3V1dXJQy50bRsYKQCMLknZzkVxWDpOO8iIyM02g3ywALk1QpZ43KTFqlLQ9oWdAJOwRCQJ2Hm8+cXXdME8uCD0n6yyvi/o6bzuNGRjlxdGQsAcuAbTMyGB/0hAMBIS378Lk2MVm964nfEgztVsbYJkVU1eH+Kyx0UREpTNymveOIrxSkOxdQgPX4gKTMFjrp2J3rrK4ECKWio/HBn5fpVGA7zvMizbDIuV4eN9MTyw+O542SGOxnEyKQHAGsNIl+8NDc337j68Wfb29vD4bBoFEwcScxwQi0MjoNojUIUWQAaVAImMWlhbK6coc3R5Z5diZlHA2oUASkAqi2MaxhhCBLqxYaeSvQ1WqPxiToNCzHKEmnkltexQZGTLobdD2PQMENN6Zh6SGeHqUF6/EBAIuZTmL1RgRA1eF/1pd9ZvfGZDnvtpaW0KHrVIfTcE5Q0PqhB9zhwcB8EKSCCQSAOxObZ565sbnR2dnY2NjayLLHWOptGs3S4TUKBMbNLx5xp2NO96VFn/9goL/pGniAgKMameaS2YNuios2uUWEaKK3YBXAeWNQAAGkJUAGWScZ5AUECYJ1uItmdvB9k1yat11F057E18t6f9pUWESAyjMysqhJOtyc6IhlrjaWxbNJTXSfwdGFqkJ4IIAIzRBV90t0oBx7bayFEFMDSQ68P3V7TOZf8/+y9WZcc13UmuodzYsqh5sJMgABJkLAtUpQlkZLtlgd5aN++Xqt7+S/0o3+SV9+Hvk+3r93murZpy5Y1mZIlirYokeIAgCDGAgpDDTlEnHP2vg8nMiuzBrAAVBWKdn4rF5CVQ8SJkxFnx56+LzWJgYFBih1Ie4iH8549dagGazmgQlCAqj2VFsX8/fv84MGqc5WIOBestYlNiAlGbRIOxEMARg6wfjKsxgYYEXMbqUffsFJKqGHcfRQEiSILggbAeDQeyaMJSIHAYVLMHifbwKSpJvMeBZKARsCIcPRvBcmQprkpCquw8eOy1gHAJ3eSEFEGykNDa6T7n/SXIESEiExcluXeukdjjcAqQGrYGmZVOHzsJf/+MTFITwGDVSrmiwgGmaQizfr9KiiQbnTIb7rdflTjwZxR2Wlxev3aUv/28rGpmSLNVjsdoLhSAQ0SFXGBHFuBt/aRIMD40rbt0jDK+hP9jG29jd2ExR41dDY+XQG2YxlwVZ+QLRMwipTG0KIppqeyO7fv37+31um6PG8UDUgSy9ZGNSaFIHWzpA4aSAemCCjqYgjJ4JXh6CF2rUZF8ZqhHASUQFDjzKJPyJfdBxC6zEmazPkk60MippHkM5BkmmfepI4bjqwA1TX8QIoCKkgEoKgKqP1+d3Z+qt1soAKKCkVuOmAlGFawjKXodyj13tIMG8s92ACoMJs0TSWEoMI1FcgItnpXo2fL5hfGsNO5rcRAjGS8qFeFnZ3vhxvdnTzXwbcEAAxyYqyheFXW4x04ZIP9PgGX4wQPx8QgHRYg1rx2we9lV3i/02uAVKurK7eWEsF2XpBhGJBf7re46kOs0VOEqgb1okioSMjgmRI25uSpo42itXx/rdvpra6uNpuNhrVpmvb7/Xoxwodl+IaroYwF9IAASGXApy6MijRg0SYkAOl3TLlq3KrlfL0TQoPTY/OhsaCmFTjzzBUmFaZ1//LoT4a1XwUogJKmtijyNLNIVRxPXRMxiAQ+oZOkKsSEiKriQ9D9IVHdCmOY2QBA2H+WPGOYxlqpBFBA6TA7/f+eMDFIhwWIkCQmeHF7R6pPABZDwbB+7/7Sp582SNJWTibR4GGHEudHYYh4GKJ4RL3NQxmCVw1BIZokJGUAa+zcwnTaSO8tr96/36lKb02FgeNlQiqAgrShq6QQxQ/jgdKoBYKN+3EiEFShmB5HTyIA4DAACguwCPbums791K0RV6Dcw+5Uo+kbjVJzD9aDCWpEUWGnUFVMvEuWJ0UjSxIIWpfrbbJAQ2dRcezPXUKCGGuZSIJUVRWFzXdMtu0dIlEeAISwTe/R3oLZmEPGd/gfChODdIiACGzIGHJub5wkVMkZqOyt3r6hVS/PM0UIoMik+6OfNsTovfMhK7cbg6oKKKgQcL9cz7P2jG0YNo1G686d+6urq0Tr07PzMGRXUiTwAABMtbY6AOhmStSN0mr10Y9B8AQC4KPIQ6fXEQQjEETYOXCuqjybQFlWAlVJ0lGoED0YwuQhfgGSgiiAEEijkaVZrdc69KTwyUrsRgNyMXlDTM75ELwSG2JEGpWK3w/EfqNwIBK0xFE9C5AUhucwxvmcOEn7jolBeuoYrjaECIbZWBP2qOedQKz40Fmp7t8rEKamW5SaQKBCihuLSFywxvpFHrr30T4VHFQibRudG621e1rl1Dsvx8OUgAKoqk8MB98RoDw3zWKqWeRLd5aXlu7dvbPUbE7lWREkgACztakRcQoqUU0KQeP/WjczDebSm4zE94MrwYuIV1eJD8FL5cVjdLwgAWuTaeLUYb7sqDj1TEetS4tKEtVEorGrZy8+HWELJFURCZXNcKrVzCyAAu/8A+7e9417CSqESEyIVKRp8N77oKppmg4mb1/CaN6HSB9ujQWAIBLCRu/R3p5FwQdiIkJjDMdmqomH9JQwMUiHC8Ro2ASje+IkkUoC4e7S9VuXLy1MtfM8VUNegleCfeasq4XGP0dXNkqsqlIggECG0lwXj0y3281rV2+trj7odjsLc/PBew8KpbeGRSsWAQBiMCkDgDgvIsE5kVhQEPr9MohoEFXtrq9bssZatk1TNJKkYbLcWHb9HlSdbn+t77U9f1Zmj65z7sAqGEGguk9sG5CCehHxTJhnaV4QEqj6QeXCk97Rq+ogLkcbZKYHlToCAMRa0kJEEGm/hS0wpvfgCSr3J3gyTAzS4QIRsCH2PDBIu11Z6tzPlp4Y3+907i/7zlrWXsibOVoMokF1axCt9pNGXtnvkoftsHWlO5g4ych+0SOyqLLB3JgkTV7IT99eure60r1/b9lykiQJp+xBJFSkAujVS5rlIVTqXAjB9ct6owir3VLYAFplk8wvUpqbos1pUyjHpIFZDtaEbif4nvTXgvM6day0uTdFEAtKBBLlJFjttlMRQhXEm8TMzDYbDSACRRkUXwwjTo85KYiIXFsjQlTVICISDowaalTSYn+s0UhgmchYuys2os/PXdbnDhODdOhADMZsVqZ5NCgBCkpg9VR27934tJEmrUYD2biAMhEbA4DxEGU0xjigTUIUUGfYIKgweJDjx2anWvmt67fX11bApRisTYxIpSCAHhE76+veS+mc806AgFCAAprW8WeUM7Q5Wiu2QJNKmgbOAjf6aAIRKEGzRSo8VVmFEoxDFrTDyi55KJdaECHCLDeNArBu5XzSzP+ovanJaYmQ0DkfrVGUiH3CvXwmkJAj+e9+WaPhnmr1o1qtfIKnh4lBeorY/pImBLaapFRVfpz7eVh5PHgBB98W3IlSAAAgAElEQVTQ0URU5FcpQQP6XufetdWlq/N5yjZ1nj1TQADV0bu8sWqr0fzElnd3ujUc1tRtCeZsPsbRZSVJbAgSghMJbMz2e9gHN03GKNXiegcEwIEBwBCBggRR9QFEtWcpI5akScmJmds3l3vr3f5q8CnnedatOhIEiMu+os1NNl0ayNozaWsqbbfFFiVkARIloyqARpAEIaDxZAOOXX0EGUSpEIymqK6pwx1YDaMRVUEgNQZsEuN6pUCAwc/7eJOnovEkIuI0TVRBQvA+yKAQBmmssm7UVdors5EkSaxlcM6HEWJv3D54+RgY4dwjSFJKU4Mb2ToEAK2PK3IIbXxl/JyeVDrsJSYG6dABARCJ2Rgjld8ltd2gsQViZZAwIDgfuuud5SUDwVquvEPIw2EKN4QgqoqEtEdMslsxrnS+8eLguQyaVYEFet21FA2mqWUuq4rJkkE2tlf2e91uWToJ4GFdE3GVryRZ6/QrwSRvNVoz7WQKTIFZ1kiLkKSaJD1OHCZ9zYJYgMgQVe81IHiy21uLwasImzpIt/eTkDAxPDNT2BQAw4Z7VHfPPDLiLzJUJRcRFR2tcDuY4pQYJBRV2edyUAAhBkQlBtqeGWVzGHyC/cPEIB1GMGGS2BD8o14GMQeOISSEwZfV6oMbV65qkLzRBJtsXGsouwmEP2qfyqMihBA5M5GHa9yWnNlowGqHFXasOmM8wDV6BFuctUG/qgICoGgrSUiBQ0CRzEtZrq90u52y6vmq68p+CALQqfqNqdmieZSzmU9vrXRKY/NZmj3J7dmAhowhYwOBF/AqQRMPGaCpg2BDib14D66bR/uwo9v2t0AhCmxgbi5JU4BN7inKdtQIn2GlapJv4ljkFq2RiBxAIcOoqYtBQvFBwr7uVwCAOaosMW/8RBEPEy182CcmeFxMDNJhBMaOSzYJQzV+Qe7Ebhel2wQAEFS9QTTe+fVu78GqIaI0SRp5FTaaJXdzIe0UltkriAQiRgDD7LbTW9vtdnauGNxurgQV7ECXm3Qo+oBlr+yWZdnrl6ULlS+d7/mq67xtFtlU+9j8XNFuJY18auF40pytsNW4svyzD65/emvlxtLKjC8aramZYoqIfAhKoKqgRjQBNArDrEws5JO90qLKiyRNQl6AYfChzoGNciHuEqN5IyLGQam3FxfL6g5Y4yrKU0VLSPu6U5ToDkYK1Ul+9eliYpAOHeLFRwCM6BE3ltpBt9AOqwzVNKkqU81cH9zLfHnlyqX+ysrJxbmkUVQSAoDKoEt/zAvZ/oLfTXPrQxqPIO5n45MwOLIaXKu2q/M+FmJsu4vPBorgoIVTAWKkJ75DVK+hhADAAiyIQUgEvaBot9vr98v19U6/32cyQUQUHYgzppifO3pksTkz05qfSYpm2iwCkQ+knHkqxBQnfuWIOXU2v3jtvV9e/WRldQYLl8N0u+nR80BhSjzpoJZ4OFOCABC2Pd6dcyTbzD+qL3vrp08dNwa8D6ABMMS+pXjeDCzTTr/v5jMpkrYNfaPKlxjr2ocb0X33m4nYGiNBfAjB+1h3vkfblq1/MnG73QIAAI1n4cZItvvOBPuKiUE6vKjLmwQkPMJ9m6CU/W7boHY763fuFCYpipYaU4mwEsEj6B4djITExrJYrwaPvALUYT4mrFnjiJgFJd7Zh+AFNE1T53zVL0Pl+mvr6oI4LwJ37q8BMTIB0/TszGxrenZhvpie5plpSdM0LyhNKg0C1CUICmBSpMyTDZwgmSa3zv/qC4vPnPmXf/nwwcra/curi/MLM9Ozqc2ssRqCoOB+3na32nmzZRBANNATLJ6xkCH6CtEaOe9FAh940pFiRV81VsuwDxAAIFLaD92XCR4XE4N0SEEExrAXkOBjQ+LDIUACIggMEkKFoVy9e3vt7p0pmzaLIjBFJe+Ntr8d1pnRmAyPSmY+YhJ7p/toGktFbLsWPIoVxOH9O4AGVDBeY7OqkDCRc069V+8fLC0758qyrLzrln0iMsaITRtnz+RTs/OLC43ptkkSm+V5o4F5Yy1AQFMRAoASgtY5dlCjYCReOAi5BWZupHnzN1/+6KOlq1ev37t9rbO20mrOTLXnsjxDEMBNsxHHTCM5pI0iyZ0PdVDeMupKorTaeaM5VgP2eBgtZBhaoyfZ4OOMAQkAatq6/bVGG3tMbHIAO5pgl5gYpEMNZggM4ARgNxEtAhASysmGzoOV23ekWzaOTCGZx6uJGksY7EVV1ZhuxaZihIdtfljpMCaCtzVvhABVZ50lijzJvfv3S1f1qrKqqtw2BCkgBLInz5/NWs2pmWnTmoLmgpiErVVCYXRkKsIAUHGqYOqmUGZC9Kqk0VulOnKlnhFzy1kCmYWGPbI4Y999/+MbN5a9C5XThYUjaUKkI0Lk4/Oxpa7hs/qgx8o6BMA3mwkBiASIDWbDmNPAbtHONx9jUzewRgDwVKxRRHSP/J4S3kdsDXQTITFlqYWH3qJNcJCYGKSnjG00TeN/qIhomdAacSH4qN42uEfeYTsUKBEq0EKg6x99klmbtpvBsnowGzIyg4T+UCJnVL4I6ueqG4G9TTx1Y1xqW/NPKIPNA41f5RvjVhoVrAPY5jmBICIaUIUgohLiBlSFFJIAILH9SeNog/cuhKhH3in7Pd9fK3uNqXbaKhrFkVY+PzN7bGZxXhO2zQYmjImtyJSUOaqvgljtMfBmTDR5IgASAAAJSQddQSgDjQmINLUFczoNzebM4rFXfvqzK9dv9m7ffxAgm8rTmalWknGnu0ZRvAEZAEQjRbdGB6yeNx2mLR7mJiKiiC9Df2bGLCwkoo5ASev8U+11EgIwg8B40nH0ORNXVQUASZJE0wsxdifykEjd4D4AYe/qMCMjQwxRex+835AzfsIEEspG996gXlHiHpkwS1LCwc2NIsA2Wks08v8kn7TfmBikwwsiEEFjyBhURQlKKnUp3bafV0AAIwAAVz/4uLu6dmx6lvM8EKjo4zHXRSP0qPE62G5BJR2zN7pd+QMqAEpd9oaCIfiqAoBYlEukKigK6ENGaVX1Vzvr/X6/dJULWlVVKV6YOc3S2fl2ljx7dL41P9OcmULIimIBuADLolKhOoaAIMwOk7AxktEhjSyEWlN9R2bvwYthSC9LIAp9IsyyZC5Jf+u3nv/oYvXRxeWrV5b6q6YqZ2Zm2kWaBZFYrYakgKMJMwJ41LYhyXNqT2WACugfz32tqgoRrbVJYqPUkAR5GpG6DWsURLx3Q4rxvStn2LpTYGZmfkI29An2FhODdEgRu1SiTbJJ4n25sRTu0PMYU/qs3q2vLN9ZMkhJYtM0DQq7FwgY9ZaeEEMLtBGxwm2coSFvAqkQCkalBhQCQQRjEEVUPZSBmMpOub661u9XZem8iAviVDhP06LROjo/22g2F47bRrOYaieNHBMGa8gwqAkhdR69SEBWJmEURgUaX/PGKCt2eL7dYSKAkogEcASkak4eT1rt4/PTyXvvXl69L65fHls8Zi0jClKQyJURRUg12uFdk0WhACigzxvJ/EJzeFYgDrToH6VY0VprbL0IRGu072XW43gq1ggAiNBaa8y+VpxM8MiYGKTDjFgIRKNOEtQL/fjnEJBIg2MNVn3VWVu9e8dabjab8JQEWwfNPRIFvLeDCNZmKZYoRyE7ViEQ1loLNVSh0ytX19f6vWptbQ0JmYyiAUyz9vT83Hw21WrMzNgiy1pNUzR92ghohNEzBYKgGoKAGoOGrBUEQQgEYXOhwZNiKO4u4EjcVLNoFJCZmdlWdvnjO7euLX+88mBudr7ZbBatDEGIjAzzFqoP16LdAMY2JldkZqqdJebRzM8mJEmSJBYAqsohYrRGj721x8PQGqnqgVkjgNo92tddTPAYmBikQ4codD3GtcXkE+NDGbxnMNveTRNZVzpmD1X3/tI1119JUYlIRXWXix0AjFivrcG63btN0RoNIiFS9ktkMtYiogRRlbqeChEIE0AiRYVQOVc558rUYLfX63W6/X6/XzqH6EQdYDY/X7Rb7bmZRmsuy49krZm81RTGClANVYb7zEIc6hmMjVkUE1kCPJyEaJZGa9+fRG1AaKMdFRUIITOg2jcEi3Nmrt08eax5+WLrg/ev3r93q7PanJmfXVxc7FUlIpI1hAogoPJZHtKG5QYQa6DIKEsAUREQxrw8hA1hPYUNDngAAESQIMZwrIMvy3KoC77977tFWXjUkD/GtA1NTiRHMMyxBzYOY7/tULTfxlBirCH8jN99B46MCTvD/mFikA4zahEBATCGrCVVlu2qYUkBRUmBJFTdB0tXL4Fzc9OzRBxUnkr50DAPBABZagmRCAFESEIQCS7KGBBirN/orDyoqqosy6pyKysPkC2RAeJ8er41PT2zsJhPt1tH5sUSJlY5A51WTPtMHtUBAqEQAmGA0YZQGjLSypbVdifOi8cECgEIULRJoB5BACokMztTFC/Nt5vZB+/fvLO0dv3ap+vrvYWFeZMkoCQgoHVBwa6idiigLknTLMFIefRIZoGJ8yxDJL//cuC7QbRGPoQDy10R4cQ7OrSYGKRDCwEAAQEgBUVEY2wIIMFvtwBRkEAMFNza6v31B3cNhFa7YKYwUgTBtWgeAAy5w+PzQRoHtk2XPE7H+qiHhIgqElyFSIxKqpXrq3ed9XVfun6/V1XO+VKRPJiAZv6Zc83puZmFo8XMjOQFFnnabJg8pSJzIGVwPpCGXIQIIMQKwJimQkAlAOBI2qA66N2pb/8H+SrZKv70+IgF1pEeWxE0WkFhrACAM7O68kDEPHu2uTD//Mcf3f3owxu3bl3t93vtqdn2bDvLEiUIKohEw8qJehKl3v7gZxEUAlGUNLVpxoACIIA1RezYb7oD2ETRcQk+bHWJtlro/eZlOGBrBABEmKYpM+H2xQyT6u+niYlBOrSgYfmvqgKgMaRqQggSthY1CAokquy8dLqr9+6enplTooC7dQJihZ7gIESDAKrjC9ag/VR05Eoec79qVSGpCeIoSvkg+KoSH5xzItIry8r1e71u6VwVvDEmsZba7WOLR5JGq9GeNY329PyicopJJiYtDQeDJXPFJB4DWq8cENEwqRGIxd+ywRs0MlQchh8H5upgUDe1CAFK1S8JlVKf2NBq8YVfmZuZKX760w/u3Lm/uiI+VItHj4BBQgSSgIpIpFJbmnpzo9WJtcNnE2NTRHC7bDMagon6/Z73gZgMP/3Lf1+t0WhErqZwJGYmNjSpZTicePpn5ASbIADDtqL6pbjQIxrD1phKvI7f3KMCOJczJF5ufPwJVQERKc+9SpR3QyIAiCxntNUTil1BOvAmADSyHmtskREAAq1zV6ohBA8oiKBay2MgEgNSpBENAiJVv9QgwQfxfmVlpeq7qtd3oCv9TmNqOms306l8ZmpqZmF+emFebY7tObW5MVaResiKhMgCEIgEAAKoAAorsWEyCEFRB1OFgECgKnXGKy49OjJ/pAo6aps3+UZ1edpYoy5tencnIG10t3Is5hY/+BYhapqmAADq0gSMxTNns/nFX33/l7c//PD29RtXSldNzc62povE6npnPU0ahoBwSPReRx0VaBgCXZibn53NXVVSKoC6MeyRAgfDRlW8VxHJciYiw4aY1tfXAYAYAMSHKu6hPkwYurQb8azRKalViZ/MriMhgBCxMUxEVeVqwtmH1VM8WtZmqx2qIWos50nKsKMVH+PMmGSPDhwTg3RoMdIiSqCgKooIxnIQCX5MQJNAGAUr/+DGjQdLS628yLK8Ei+4q0to9ALWwZouCJvKf+OdO5IYBAAI4mMzJilI8LH+odsve+vdfr/yXoLX0rvgFQlNWmRTs+1G48zcTGt+vj03I2TSqYbaBCyXanqYOkoAgAG3JXIVBAgIqigKhECAiIKDASMCMKpqqElLhxTg0TEaUMrWMztcVZ+knGEcoz2t2wkUoAiUiGBTbNv0xZeONVuty5fvXrx0o9Nfmy6npqZzkybBAzERU113N+4KR9vXauWJAVER74zZ5hJWVR88IiZJwoYJVVQrV0n/6fAvbEKk1hbVISfWgRSCirWcJikxIW5Y3x0/PTE/TwMTg3SoMSzziU4ACBjD3oUAfvzmVRqJxaq3dOva2uqDZ+amG42i9EENjYbdmBSGOaQwamzGHAYZZJ2EhrfcYsCzAqig+iJLvK98cOq16gVXhcp7J/7+yopXccGLgLFtmzfz2aYpioWTxyjP86lW0WwjEyUWjQWCDmClwQsIkjIhKiHrDkuFQh1IDBBAEYWUEAGBEHVDspaJYchTvotlbq/qGhANgCgi1fKztTpRNOqjxYqqCuinZ5PWdOvoydb0Qnr58tLa6r27d2lmZmFmOkNrUIHZDC0rDEwRgAf0rTYkKXhPGOsHNzCIVSJSpAIyTIiiEkLw3osI7VSEDzDc104dWHs1V8aw1IoWur/a5DDmNaZpyjxKbb9tDnFS7fA0MTFInxtEm0RAxnIQE7zf4PIBCWW3d+9W2VlJWTOboApZE+u9tt2aktSsKuMvxqVzmA3imglUWGBAJi137ywNKuK8OuMq6fb7PV9l062sNTU7M5022vNHzpqskTWbXGQ+sY5AiCuuU2JewIkiG2CM9dmkEEkoYnxqJ8SK7bpxRwCISDF2V0YCIVDdVkou1tTt8z0vRSv0meu2Bl95RUqmp/HLXz42v9B+74OrVz+5f+fOEijNTk8TEAAx1zULg+8JgEfwiQVXQp5bV1Y77aIochHwwTsf+v0uMRlmkyT+QEhLHw4iAhGvB8YKEbmyojJs5MfSCQ3Q4cTEIB1q4OY/EUiNYVV1qME5re2HgOvkJMvXryUIRxfnBSEgBwIcWZ0HWf26bCFmZ2raurpjHjOTBOd8WakLGBSDQ1GQ4H0gww/WVnu+Wnpwj1NbqbDNQUxjZv7o4nw+3W7MTRftVtZuKKcCDaHUIfQJPJJivQBgfcMfPT4EiRwN4xUHO6/omwsTRASAgeL4oVb8rCnv4qENA3eooDggKKqxIzfgjthBwXZMOyr+i3EYALA5+ImIbK2qiIohev75xsLiixePL//iF1fv3L68+qC1OL+wOD/rFYnRmhQRy17lq367zceOLiIBoK96FRJIGBB1D5QjDBsiWO90h7szNgEAURD/NFfh2HVETP1+97M/vQvsJtwq4gGADaZpwhSDhQCbckuPirHE2hNsZ4ItmBikzxkQkA0hJgCgPgQNAEAgoezdvXrFl52ZRhYD4IQoGKuJtnTRKoAEGtRKxBdVVVHKsq9exAfxvruyhsGL8yGEtX6pQI7B5sXCsy/YokjazSRtTM0e57QweYqJ1YSD5dKwBw6QRI0Gxc1igCNrQQyPjEkw7KYWTnE8lKQCQEOzOlDCRhCNRXgP3dbTyRQgeAAl8ICgmDUadP78/LHj8++8c+njX169eWO97K0eP3GCODHIXgUAkMGmmKRADAxCAAToRZjYGmsMI1KQUFXV0+LqfgiGeSPYX0nyTRCorZG11tbWaO/ShhPsOSYG6fMHBERWkxrnGJ2oKioYlRufXvb9zvTcSQShh62zggpUeh7c1Ftrvfeuqnxw/X6/V5W9XrdXlZX3RGCtFZPMnD1DeWN6dt42m/PHTlaAJknQ5mxqWQdFcBA8qGCs16MhJykMqq5JYegqAcCIoFz95LGzFAMHZSOJEg2T1JWB+7MG1d4SjhTow/DfHb+kEm8TAgZEISDR0rCVzMwk+KWvnF2Yb3/43ifXPr1cluXU1PT03FSWJUxkTNpsZdNzibXAwASIKiY6h4SiquIliA/+gOXGdwPDHHNGXg7MIG2yRjBijeog3tO6HZlgJ0wM0ucPGhs9iaxl9UElsIqsrfuV1aa1iWECCSCsACKgfuiRoGJcQ1kAnR/GspbuLFVV1S37pfOV95gYk6fYLo7Mz2WtRnt6Km1PJ+0FMZlNU5M1HGCWF8i2dKFTBkWCWPCGHBRHkvAbY5aBTVJQGK9/q0ntdsDYWyML/bap5+gqDf/EGCtDDhJ2SEzJeJfPZ2F74rhHXWEp6pfTQGcdNDBSZgiQ7QwsfGl+cbZ58ePrv/zg02q5t7LeWVw4urq+lhWUFdDrpo0slhkKqTTaeVVCv19VVRVrFoxhpHjIhwjEJD6o6oH2wIKkaRKtERFvtkYTHD5MDNKhwyYJtpFXBEYWPwRI8rTX6zJoJnj9o8tu6e78fKtIbJpaFwS8x+DFlxaUiAxRr9vzIQTvNYA4KUvXKaue86u9ftpsJUU7nWnMzc6l7db0wnw23bKNXA2BNU7JQyZkPYAAEZkysEXCJEV0PoiqImBQ3eDwHj+oQfWFxCKn2rMY9LGOOEbjRww72JHxQuHRFS66fYM3iRCFBAkbaea9DyGoaGxgqss3ELeN4YyR+O04mFE9isHWNg90S+UICiCLxpYcwcjxoAJaClA7y5yHZ84k03OnTp6Z+8W/Xb91y3300a1SyaZ478FqIy+m2rZoMaOIDw8erIEaADCGQe1g7Jtdz5G9P8z60sixPDmIGGp2YJYQHaQtv+8u8KgxNhrwdCgoI6WWOTYq6MZvPdC12rKvXQ3o0cYzwe4xMUifVxAIg5lrFtVK7/61a6s3bxRE8+12q8gQhAANKAkSmbLqdTu9XlV2OuuV9845F5RtZtNG0ppu5PnxhaPtubnWzLxnzmdnAxEkSSAoUUsJHlWJEdLh2orIJBg8WFSwlih472XEGg2xOQSnJCN3prsh0ty1KsM2UBUBUlEkLMuSYpu+wSAiIuI9wDZST9uJL2zXV/SkGPHkFACEEBmClD1EMCZMTaV5Np0l0//yw2vXrq5XFREm95c77/38k9Mnn08YmHri+4D5Ho1nXxCtEQD4EPa9wnsAVUVU4ppKfOz3VAR8knNqgv3FxCB9jkAy4DojEAYBdbjeSzvrVy5dunf7pgWHiP1ur++8BAgevNf7KytVcJV3/eCTRp5NNVvtVtpoFNNHTaPVmJpOipbJcrAZpxkAdlQDkhcog/eMkCQx92PDhvmob3QlOAecmEhU6f1jysRtxVij7pPVaquKgmgARBIQIgJEJooiPCISFT32AtHris9Hmpp3DA1tfl3qVipRBGIiy0Rw6hn48MP0xs171MuqTpkl2O9U4kAcKJEGZHN4syAb1sgfgDUanU9FAmssMRjmOEHjvzPtlRc4wd5ivwzStnLXT7K1J9nOE379IDEcqo5whg64CAYpF4jtQQKd7r2rl6vle7m4xBAFs7K67lfCelm5oM6DF0jzImlPT7Wai43G/MnjNs/ydpOLQpKWkAViIVsBeUAvFBQVKc1zAvJlT+suGFEErne6Mc5ok8Ajm5o82VVh01EcEiDSgFXaVarWWhyYpcF6BQDgqh3beh4Rn9nmv72VqiXZCRM2ikQMiYHZWZOmlTguu5X69WZjIbFgLBCxkHkaWle7xYg10oMS5YoTq4iGGGJlXf2GKO23tsUET4y9N0ibzrx+r8ryZPTJLiEioDEKDaCoulG8NLrWPTQkDhCD81EyhjZe2eV3HwNx49tu+TP3G4t7h/I0AEAjJdKqOiihHtRJl/0P33vXL99+8eixxbnZyx8trzvlZqMPydTRhfnFxaRotprtotXOWk0hFktqWAx54oDGo1EwACRgFAiIFAgRK6eKwZJVBC8Sr2gkBeVBV9PgIldVCeCBDRMRkkjYop8zjs+a8if9SYZFEDSiLqEqQ8lcQhTnAUCo5oiDgbEvijyeZqpSVW64zUHRGkFNxVZ/PngFrLWdIkbMMG15BZxzoxscXilpkhMTEwdxqhpFNIjAB6g8nHtupt89+tEHN2gaTpw48txzR4oCVHqiXQABSHZRlHFwXhQRiwRmk6aJL52IeA0xarr3+xpw7228hAAALrgkNWmaGoOotW+0xRodXs/yPzL23UNK09oIDa3RQ5ZsAFhbW2u1WgDgvTBh1HLDLbT62zXjP3w8e5mJ3Mm0DF/feKIQJKgqE3/mDdrwMFVAZHMF8Vafw7musXBz+ebRZnri2VM37t7FrHn+i1+209PUbHMzR5uEoETYI6MIakgYhVCQApJKbFQyELm5gUA3OPlr1qLhNb+DxyPRfnpgw9ZYjyEarYPXHt08MHzk5sder4xRJiKqGVEBAMCP0CyNUQgyxnK/gYEZPejaAI6aK+bhdtCY+nIgRAASVeeciI/qhUos4HtVAE5mZvHXv3zy3DPzjUbWbEKWAYIDjIQLh25VFQmJTRCxLCvxPnpG+y67F1FHRyVNU2PsQexxgr3G/uaQEBEUnAsAYC0DQFConAshZFlmBtqoo8iKJgB0+1WSJCGoKJAC4pbeDtz0/84gEAFVjcNgNvhZ3/3MaMuQD0wAVGvjMTQYOIizSVAkZMvRqRn6DtvuN24HoyoOgQLqGD81IOCwpyfu3DbS9uKUN/rzTy/amdbp179qi5nGwkloTKlNfQKBQAQ3tIIQgUgQREUQAmHN/T1KF12v4nV7E7CSRuKdjRDiJvhYuBYgSxIA8IoAEEsGHq+4di918x5l+4goIr2+iyFHwrqE3dq6vx8RWTdcpaHg6CaPZ3yboy9unFlDCh/RKFkoQRwMEvIA4CUgAEKFmLTbaLw2m8AGKlchKgopRd9oY5sDtSeAp5chYTaRICMEXyuY7OOvOazZE6Q6imwMJWliLW/sefgT7PeJNcETY38Nkqo653vdHgDkRZ6mVhW8dzEeEm0SDZb1mtKDcG29f+ny5cXFxampKQMsgETQ6/WHm83zbLcDiP8RqBfnvAu+1WpuLL+PejhSOzECIAL9fn9tba3X6+Z5kWVFkecAYOwwVgPE2O30HqysdLvd558/+/CNI4JldEHv3XvQ6XSPHz++sV/VSIA9uKBIwQAINKdOPf+SYXvpgw+ul+7XXno+LeaE0mAyT+RJFEBt/ROPiy+IAAx1CwayAgIQjV40JDo6hTDK8lbaeG8AACAASURBVDmC+GLUnyYEY0kDHlTCYM8QB1xVVU3CE5dUjeKvUFUVDG44GDYCd3FBHKUr3erFjrrFw5Ddpm3WWgcbHyQAYFQyLKASuv0y2BRidqkokn6/pxt9x4fLSUrTpCyrEDwRqx4UVV19ugoAGJskScKEooAjJ/kEnwvwn/3Zn83OzsY/9iQRjYgCCFg/FOD7//yD//F//Q82fOLk6dTS9eu3/uf//L//6Z++89prrxvLPgAoqAAoMEEIcOvWnb/8y79M02x+buE73/ne2vra7Ox8I0/++Z9/9L3vff/06TNpkhEjIzgnEtQyBoGy9MYQAaz3XQgKSD4IM3oFQFCla9ev/+Vf/VXRbDXbU84F70UELFPpBAivfnr9pz/916n2dJJmTFA5FcAQFLmWO3BBFZEQgoCILi8vv/HGG3/3rX94+6f/+sMf/eiDDz+ampqZnplNLAJCUPABRIGN/e53v/ejH//4pQsXiixRhABgEPpOiLGsgigqogD4oILY77ufvvPTv//Wt06fOZ0XhTEsgArovTeGGcArKEDpIGGrkASTLTzz/MIzZ585/2vp7IJtTplGe73qy0CBjIBwyK4GkR8aAaO4DsVXotBE/Fj9UEQgUiSgSJMG9QMAEZCGv+8QCuCcBAVrmTmyXgfQ8c1u9xjrn4VxJ/CxTr/hc8X4wJGHjtAO6dDo1sYmSlszxRkaGcfIxKioqtRQEQ1BQpAQfBjAi/og8eF8GDxUdOOBREj1rMZ5jNtHJFACxaFzRUBEbJAJybBlg855weHR6fAB8TE+H/vaNaOioECgiTV5ljXypOyXGjwBYHxvOExQhDi8R/6Ft/u0bNxioQAIozJhmtg8TQ0TQjzXN30Vh//QpJnoUGLfy769D1eufPrJJ1fefvunv/Zrr7hG42fvvvvOO+/Mzc6ura2pNquqSmxGZFR1raqYSBW8136vv7Ky8vbbPzl58vjxY8e6abq4uJhlGRGtra1leZ6mSZqQC3Dj1vLc7GxVVbdv3yaiyrv5+fksNcS0vBLlyEiCkDGvvvrq8ePHJUi/31tf75S9frvdPnp0viz9xYsXf/D978/NzhJzu91U1bXV9Tt37rRaxfT0TJalUN9Ho6revr30v/7X/7u0dOtrv/Fbc3Pzq6vrH3304c2bS8eOH+t0odfrqWiWZa1Wi5iqqmI23nsEWFntLC8vJ0kyMzNjbba+vl6W5fTMTJ5ZL7K21m02G2VZ3r17V1Wd93fv3Wu1ms2i6JZuba2XpilbKwHu3r3/IDUMOrcw3ZdgZhYhShXFAI41GjbfnI4SmJJCgC2UpvGtkedaS2KPhd10+0ZSgNiQ6b2II2JrDVjrnXvqyaTdIEZ7cG+qXGjHP3f0ZrYhbK3pyQeTR4hBNYQgjoEZnjYRQ6xTICZESgypqnOuLAfSvQeWRKyldQVAiQ2zGdRBAcBnix5NcNiw7wZJVZvN5unTz6ytrb79zjsXLlz4xfvvzczNAjFbe+Xqpz/4wQ++8Y3fOXPm1PVrS3/zt3/3+9/8pnfiRRXpyrWr//buux9fvnjl2tWvfvWrRVFcvnz5+KmT3/72t0+cOPHV116rPF68ePGtt976oz/6o1/84hdvvfUWAKRp+vz587/5W78ZfHjzzTfXuuuoIEFefPH8hx99BMxnzpz+67/+m0sffQwAzWbjtddee+7suZ/9/N133v3Zaqdz/vwLX3vt9aosv/+DH9y/dy+Iv3Dhwh/8wR8Yw0VRAEAAufTJJx98/OF/+6//7cKFX220WwDway9/QUTuPXjwxhtv3Lt3D5Hn5+Z/53d/59kzz3oVL6Fb9q8uLX/rH7516dKlOMg//s9//N577yHhH/7hH5pgfvz229euXfv6b3zdiQ+gAfQX77/3/vvvf/Ob30zz/ObS0ptv/v2f/umfOuf+7u++de3aDWOoWWS///u/99xzZ4l5cMeHAJBlWWd9ff8Kr3fMwURCIFUJPurbguUtNumzF/39aEMd2/7I+B9e+LCVV2D02McNM+3w/PGBI9YoPvESeGeepbFOnB0+s4fziYjWWGIilaDqnQsiXGsTH5g1Gg6GmMhaQ0QTO/T5xb4bJER0zh0/fqLVav3kJz++d+9ukiQvv/zyO++80+ms93q9X/7yw9df/7oI9Pr9u8vLy8v3Z2dnVDVN8hPHT548efLs2bO/8qsvPf/882+//fa1a9fKsnTO/fCHP3zxxZdmZqZ/9KMfdTqdNE2PHj329a9/vdFovPveL7773e+ePnN6bnbukyuf9Mr+r7x0YXFxMc+LS5cu/fpXvsJsXnrppWPHjhrk7//g+3//998699/Pnjx58szp069/7fXTzzzDRH/75ptFnv/eN7+5tHTzrbfeOnXq1Fe+8pXhQS3dvt1qts6ePdtut23CCtCaa6+VIiJf+tKrWdq4fuP6v/3bv73zzjvHjh5F5BACk/nxT3783nvvffUrXz1x4sTNWzeJaW19bW1tDQDKsrxz587S0lKSJEXRyLIs3nLeunmz2+0axH7ZvXNnaWVl5dKlS59++ulrX/3a1HTrwb27eZEibY6BWGvYGNniJG3F0EnayPrskRkLwQOAMQTWBh8OG7Xatoh+0uFczOo+XlUUiXmRp97sFfUurKFep4yvHKg1GgcxRfeIxqmkJvh8Yd8zoohorW02m+fPv7C+svrP33vr/HPnF2YXW0U7Twtx6vqVesgMhCqUvX68zKKQZLPZPHb85Nz8/Je//NXFxaPMFgCMMb/+xVe7a+uXL168ce36lStXnnvu+TwvpqenVlfX3nrrrRtXr127ejVULrXWsHn9q6/9yZ/8yTe+8Y12u22tjYlrVbl+/cYHH35w787yyv37zrljR44eXVg8+8zpl86/2Ot3Ll36yPnqypXLt2/frqrqwYMHIkFEBcAg+7IyxBokT5gBQCEANFJq5gUEfPvHb3/4/ofXPrl25+Yd8cqApBQqf/Xy1eNHjv3ub//2q6+88jv/6RtnzzwLQVDU9UsC6qytlb0eBHF9J04ZWJysra4yoih019YJILUWRNcerFy5fBlFL1w4Pz83DwB+JFsOAIiQ57mxFokApX6Mg4EYCJFIAbWu1qJhb+YTdDLGpBMAhOCryokIIxpiBnzUOux9AG16jOZ1hh86wEbOuD/aeGyHmLYCAEJUkUaRAoCKjoanHorRQ94bGGuTxCJSr+9iEm3rNO4FZNODND6AFCSIcx4A8qJoNVuxUv8hbOLxWxMcZhxEyK7X61ZVdfz4iZdfeeXq1esvv/LKe++9F4IvyzL2cgPUIimtVnNYX0uIzjlXVVmWAUBuAZFE1NrkxIkTU1NTP3/33SuffMLM586dffDg/htvvNHpdE6deubUqVN3794NIagqMdnxjoQiy27evPHX/99fT7XbL73wIgN+8MEHvV7PGlOWZZZlWZY45xCx2WxkWWatee21144fP+Z9SJJ6tPPz86urq71ed71fMlGaWAJ4sNr/7ve++8O3/uXcuedPHD/R6XSH1z8RlWXZ6/fn5qbzPLcMkqZlWcY6YwCw1uT5GClZJF4biLypsXZ9fb2qyl//0pfW1zqXLn3y5ptvZrn5kz/5Ly+cf150TAYUMRbZp2VZiuyKsmUY+t/zlhEVjaZRQlDVyodDm1WKAzukgxtBEGA2AP5p6R4xG2sNIjnn5ODkJDYDEa01SWL4obrsE3yOcBBcdkQsIu12+3d++7cfrKw0m4V3ZZRJmJpqFY3i/v3llZXj9+/fvX37togT8SI+qM+LAkBWVlZE/IP14H1prTGGsyx75ZVX/uEf/7Gzvv4b/+m3jh07du3ateXl5S+8/PLXXn99aen297//g3ipICIbjndGqEAK/V6/LMtep/N//h//5flz577zHffhhx9Gn6yqqjt37hw/ccJaOzMzOzU1/eqrrzYaxdra2tTUdJqmw9P+xInjzWbjBz/45z+enc2y7O7d+6urq/1+/+LFi81m8zd/8+t5nl/59FNXDewESrvdbjSy5eXlTz755MiRI6urq81msyiKmzdvPnjwoCzL5eW7o5NWlmXUKFpbW19fW1tZeYCIIgERv/71r3/pS1/+5Mrl//2//5+LFy+ePvNMmqa1IRkJvEWb5KrKhzC0SSoSsyB7lMDfFVQVRIhZQmA2EPyhtUmfCwQf4p3c6C3dgWGTNdpWMP6ARmJMklhrGSe13f9ecBA5pHa7vba25lx17Pixufl5a5mNsdYiUaPZLPL0n/7pH997772bt26trNxH0pFCYllYWHj33X/t9zuvvfZaVVVpmnof2Jiz58596x/+oaqqc+fOpWm6sLDAbH769k9v3rixvLx869bNyKDFxKmxMFINnNqkNdXu9/tv/s3f/mRu9uLFi6pKxPMLC2zMd77znTt37px77tlTp079/Ofv3r69NDs7d+vWrd/7vd99+eWX4xIuALOzc1/72te+973v//mf//mRI0fW13v379979dVXz5179s03//7b3/52VVW//OUvzp07Nwg1gGj4whde/pu/+eu/+Iu/mJ+f7/f7r7322uLi4s9+9rM33nhDVW/cuLG4uDicN+/DmTNnAOCv/uqvjh458v7771eVt9b++Cc/+fDDD6anZ1ZWHiwsLDxz+pkksSIBttwkDmxSAtEm7RnD8SNbMuc9wEBmnJhhYpOeCEEkMZZJawH3A0S0RsxUlgdkjR5iawjRWk4SDqKfB892gs/G3vchAUBsb4kPCdLrdk+eOPHs6WdVMUk5eLXWtlut02fOTE9PHT16dL3TMdacP//ChQsvvXD+hXa7nabJiZPHTxw70mo0Go1Gv9c7dvTYsaPH5mZnnj1zupEnRaPRajXPv/jCixdeLIqiaBQzM1OdtTXDfO65sy+//IULF15aXFxIE3v69DOL83OE6F3VbBTnX3xhcXHhyOJCr9dt5PlLL75w4aUXnz1z5tixo41Gbozxwb/4/AuvfOHlqampXrfb7XSef+65c2fPNooiEnEqQKNRLC4ePXHiuATp9/oz0+0vvvLySy++eO7Zs8bw2srK9FT7woXz51947tTJ46py7OjR06dOnX7mmSOL871u11XlkcXFr3z5148fO9aemu50OrNzc1/84hdPnTr17NmzaWLm5mbOnnt2enpqZnaGiIqiuHDhwpkzp59//oVmswEAa2v3G43866+/fv6FF7I0TchA3RY0TvSHwIaAUFSj5gJi7DGKDUKAWhNAbPQTwbDFBQe9IxsbFEAFQKWN1pyRBSM2moyeRYgbn1RAUbDGoDFkmIg2aoQ3trClS2m/8dBdbjeGkY/iDo8d96WP/6CNliMiCkGMMZtCdjTsZhpk8gaPjesRHxHeBwRkNsymSDNGDD74yqsEVB00T23samvjmY7NzMN+Vay/PNJkFY8rKjuKoA7rd4QNF1luLWPdVBQfCgAM4/Mw6KGLQxj00wEM+t5GH5PupKcL/Oijj5577rl90jxWVe9l+DwSWRKTcz6GfYmJCGLqO01TkeBCsEniXQCAIuHKaRCJSXJi8t7lqY1cyuvdfpLYoGqt8SFY5n6vVFWTWhUlZkMIAF4URYg41q0VWQIApfO9Xj9JkiRJ4mmqCiEE57yqFmlGDBIi04RTVTZs2MQUctB4WkNQUIX19fU8z2hAVee9L/vOWAsgaWaD9wDEzCJgGbyASIiFG8wcQlDkXq/HxlhjiFGCOl/FVnMfQsrcLasgkqZpdBAlaAgOSZlN1SuLoogEaWMlvyPLPCE4BVf5qqoieVLMnI8KtY07K5uLrscpHgAAxvTRR0tvYyPnZoM0jkiyPahbCQO97Qkejk1mHhHTNOn3y1GbtB9Fd0SMiHWSJv5mg4qPbd2jrY7v7vl6NogTN41h5BUJgIhsKE3TPDVbjlgAgMfm6mG733oAk2TU08W+c9kxcb3iIASQWBeUpgZq9rZQVZKmlgAqHxApS5kBPEiR2NJ5a00CNQmeKqSp7ZUuTS0AWGsAILHGhWCYK+fzPCWAfghsDCG4EADAMBOxCBBhkSU+srWJNptNQ7V1iWR3RNzIWQBQoCxdYi0x+oCJSUarmeIZXvlgDDNCUeRJpNkXcM4xU7uV+6jN4D0bA4qxwgLYGAIPLBKImBGAuXRhupl7AO8VAFKDxKmqGgBkdgp5mngFRhBmAEgMkkkAwIMOrRE8VMguftdYK6Fmeo0/x1NBCF5wEL1DBDYTm/SoiOcVIhhmt886Q4ZZVSM1lMbQ625L+/YFiMjGpKm1dqs1muBzjwPIIYEPAQCsMT7oUE7MCxgCIAbDLigxErEhKCUQcZrYSoUH6mOVr70K5yRPLQAEBWtNCOpFnfMZsxgTBttJEDxAFoXjAJzzxhjGwU4BUrZV5XuqIpJlqSEAQBdqsjpRICIkCF6HjpFzIfLDRiSGXVCvYgwPtb9MtFEAlesXaSZE3jsAMmZDdac+agAAYASDBAAhgDV1eMsiKGLP+cwaQBCAeDiqmiAAQCkq4gyy4d0uDcRolMUqOAgHXNM8QH1DjTUrNiJBlPPc2SZFR+qgB3roEURIhRCICfxnf/6xgUixIkYkhCAoGqkZiCj2mR082NDEGv07xv6G7GCk43KXG9/pNl9H5BAQcNiMHdMQo8XKkcBq1PUe3SbusMA96rGPLZQjUYnRGNVuZnUnVoLdzAOPhMJ3HCds6AJ5B1VVuaDe+52CE2GsrXBzyG6w0RE60a0BloeG7MLI5+O7UWUaEX0Isds/+r6ItMuc+WhzSfgPkNyO53yWZWy41+t95iU2Oj878TzE2ZYgopqmKROxYVXo9bobnxkLBW+/obF2rkdcTkZDdvWYUWBwvPEwW41mkkRK8S0KAINNjB7v2JzU23y0UU1wkDg4CfM9bCz/D00NsqXq6LPXbKxtEjMkSSKVB4AQts8BHCRq1VkBATDMiGisJQmj706wFQP120C7dpEfDlVVDXH+I+eTigYfDol7OgzxEiIbHN59bm+TJvg84+AM0pPiCVoNxrjInnwkWzE2tgO4ARuu1I+2HiFGm2SqCgD08W3SSCHD1vkcvQff5g5kyxfiGJwXRIpmiRCDyNPq+vy8wDtnmBHpyVUe4p0iETMRMcWWiTj/8a3dW6Ynkxza/mwkQmYyJmUijiTpEy/n3yk+PwZpghpP4DcgIAIjJGicE9h/P2n3G/c+MGtANMy1IhHRxCzthKGTtFcbzLI8huycGyOAOAxOEhEys7XEhqiW5NizrroJDhUOojH2kT6/c9nl9tup78h0t/v6/9s79+c4riu/n8e9PS88BL4kgiRIiZRI6mFaj7UtxZFoJ4qzScWbSiW1f8W69g9zKmVXTFq2ZdmbNWVZlFbSyjItyrJIhQ+IpAASwGCm+95z9ofb3dMDzJAAiSdxPwWyBoPume7G4H773HPu96zVtOFavdew813ZdRAYnHmq5niw3CmMLYzABkiJFDJU50GHur8I9Id/ZYfAoUfXP4St/Gobw+GuPBXPbAhDa4NcllRVVaIRWYEAQOg41Wo12u2O985Yq9mwQoOyaSQhQpkrYiZjbJJYa6HblSxz3rvQV2Lgq1Sjn2qubvUTBIM/b8XrS8gEQVGmW08sERlC1Hw5U7n/ss8XDnpyydus9mgjG0eMkHYSlV7OzABKggYAsnWzI1t+f30XiSp/FErDSTXkDsIiGC+IMnT5y47Fe2UmVRY/vC9FubF4RShzRaEwwWUuTdW5TEVFdVi1wsYTfvXGmiSxiEAUA6OHnyhIm8563Pbfu6MQErABSwRAIthrD7GiJEBloeIqlxKuMOulKt6LIJU1eEy5F3l5RlGZAEDEExGxuizje93wE6JgL1cEAOIlc1kImEqb9tXyYHmjKpUKTNJCjRLmuF51pxAFaecSahwAjHeuFCQCWa3GrB+qIgJarKJFQgQMZgEAkFel72BZCsYJREwoqrp8rmp5xFPNFfXqxQk59BzfMjBxUKMQGEV2CNtekNZj7dTW5m7ne68/XYQ8/wDBFYwB2AA3amlGwSTJCVA5xCvBkqo5Bei/I17+jmurD9UEuyWDFVkqls32ydKgjNfSo+vfZm2Hu7U1oxl8LXudZPOK7Xyz6hoga614AfXBd6rwTBHvnHhRVVARUKh0HtkQll7/peeIQojEbIxJjLUMROD71yCu5eEMWMm0li8fWS3bXpAiD46xCGABJMsyZvR+S9RWLcd5R4ikHGQpaBLkS9x2YrQkquClKNqmsuhDVEMjSiYOtSEA4MWLl2DRLRvdgnBlBDUiToIaGQzhUayz3DlEQdqpVIIdCpqEFgAyLwCyZTVJVGWJLPm8X8kmdorbRES8KhEVSSEAAEBRVQktxnMfW59b3vXMDLceQY2sNbaiRhIXwO4koiBFAIImGQYAST2AAw0j12Yf1hBEFcQj9mygy0E2TEzBINtpxGG+UduYUoaZqZwKQ0ZCFNXg6iveQ+WCbCWHwL57iKBGxlomjqmjnUkUpE1noyeti79zhCI/VI4KxnAClDl06gFyu3SqVMYJLs8B9Ag5m7s78j3IULh0HYx6KMynS+fWRqMmIs55FFHnQhCFSGGKD5ABQEF6o/MKZvn6c1H3zprdK4+1nHvkiqDfO06KSSxmJkFUsGxsDbyAKqiCdy60v1LRJfKc58wQYf0/ecPaSfSeQSn9Hpm4ZhNjkJmCA3K5G+XH+sDHG/NDW54oSJE+jEEAA0ouy9igigBsnRvqAYQBt3RKbLfbzMYwW2spqXnxInk5GTHlJyJEFU3aFvjKhCQiMHOYpwOgEE8AgAh455z34mVYmLhVKNynmMJEHVljrSE2hte4biGynYiCFOkDc00iAEukIuKcijh8AC/BjSHPkXgBcKpqVDPnicgYJmYVDTl9gOA8s800iSst6tkEMQr2G6gqLgNPmKapePHbymyJjSEia601JvS0hdi5dQcTBSnSRxgIgiYJQZYBYBZSy5t8ZENY4lQULIiyLEvTtJHURKTbdaLaqNcJkY3NZWm7aVKtVut9gyKSl8pBXvntguJu2vEtY/gNjFTNea2xiTHWUunCEHtE7GSiIO1oludDgkcZAlhGJTBkrOUs891uV1WXu8ktX6UUWJ5P2gB6pQ1MAJC5DADCQLfY6ZSPR5otyiuhRbQ3r+UyKV5nwHzXkibiAOCdA+g77eo24S1kyDq5/l5Zudl2+dNw/Fg4NKpqaJSXd/+Civ1g5VcyOjqyuNhJ05SIluj0wGNYD/ryXgNW+QgAgAIihlnHxFBicu881Or+UZJ2IlGQIksIIx0BKgISg2EkNgAQNGlzD25VVIf7KouLbSbOi8UrC2ONzf8c8sij6Asn2Lf+FArFCoXWQZyC+BWmRgC9+/0hclWRH2vNwG1AVES886GGu3zfYW5TIoqIS9RoC1Kv1xGDfxElJi/XUL1bVBXZIURBipRI/+Mw2ubT+kmyYk0K/WTx3tVrG0/Iu4gT8YWiYO9PwNgBTnuIWK9bgL6lMKpFMYVo9UzzNaqVx1UfpmqlnE167+vySA6gUsYNAJL5sIgVAPKSwrAQuBqVVg5VvCciJk6zlGjrVk1ba4mIGKmoX9jaAhrZOKIgRYYhgJSvnCUAAwkaQOnTJKVeRLV96C0hVdKKDLuifcOSKTvpAixtzY4AwCa4fhIAMBsACI0bKJ9qA6gEM8Ggp7xy3W5WvJcCSBmBlWkhqEzZrfC8RJWJPfmgTCvca4PIZTs/R1VVQaE4MRfpIwpSpCSs96hGNloOh0ShkVKSJNTpdJz3Lgv37CZsmGuSUsgqoVD4biNPYCD97qJBFqqDde9xkAREQiRVKXdU56AwsMFiik9VXLrUvTQ8rPYT8opVpRlIeY3LIyzmGqU44jLeAujPzVQnEl3mTMMkNulydxPyRrpcAssTl1rNEudG4yZvSQ6gApVrVT2vHWdRGQGAKEiRe9FbnogIDECWAZMsc4Y5LHkBCHl22C5xEhIBDG1LuFpDvL7ah5BbqsypKW5cRkdVQt6LeXW97dcFFBVFVGJAJGOZGSisOso/LnGeLrKUKEiR1YGAzAYR1WqWSZY65yqNSjFPPm3NHFIe34SpsHxSi9bckrUqUboOw261srEaVXgRL94YY63Nsmz5jutLXkFX/e0LElhjicFag1jEglGJIkOIghRZHQpKiEAMAIj5whfnXBkkkfZGTNQ17N62XjxMTuFlafiGMaAyDkOYKAqCaIjBWqZiai72oY/chShIkVWAAAiooByy+mxMjVMkR+yceicAoKQGyXuFcC+vD7QO5sH1LMQreX2aFKexDERa2ZRj0XipP89UsHSu7N59xZceRnWHlcy89dX4eec5t/7mNV8nO6wqu19jQpclAZRWvWEsW8uIlZ2xf/uoT5EKUZAiDwQRJXkFsw+tdkR0uySTHjIIUVWcD03NqVcyvoGoKhtKksSwqSWECIwgUXciKyMKUuSBIFAoNImYxWvmvXOi9xXa5O4G6zyMDp2dW8nbbsncWImoohfDDESCFFYvbST1esKGrGVjwABIHsGVkhTvVCJ3IwpS5D7BvCG6AGiIk4jIe8Aso9R3u1kcfTaejQ2SBmhzs2UBgRBQwQMAKsboKLJioiBF+lhZX7Rqb558eyJSUCJs1izU7B0g5z0DMFG1dYIss4mjvlU41WcACm+9+6MI0gh6bbAFALivw1P1tFYX/ayonnudR2MZFImSIDErIiJQv2ysNqdFMujzgIIIgPkSK8m8sTZJ2Bhjem1r82rLfrtU7T0s3mF1BxR5qImCFFlTirq6eiNJu857DwDBmBpCDRht+aq7yL0QVVABECJFpGarboxhg1WDvkjkPoiCFFlrUEGRGeoNk6bovUvYiBfnnTh/94/cxlRg968TityVQVEjAQIqEVlriLheswBAAxrrbel8W2QLEgUpsj4gGAKuc5oSG3SZYIqIJL6/RjjGS+tPXxppZSX45WykaL+JW9ddtwAAEKVJREFUX/F0khhrrU2MQRAAURAF0mBTEX+pkfskClLkgejPABR+awisAAoIUEtQBCyjqSfeg4ikaeq98yIITISgFfc2LYyIyldcFsI82MokeuBX2AY47wAgSRJE7Ha7fT+T5dk7gMqdQbCeq+7hi06AltiyMYaZySaImJv2ERSOdAgEeO/AqOp697D/LiKrIgpSZN0hAkVURUQFMIWHqVdBEVV8oJWzkYGI5O0qCLFaVLLCS11GSAjIRIjEiIkhZmMMIcFwt7w4TRe5f6IgRdaHENoUwQgiIIJiMJCzzMZ7F7xZXdEHL8rSGhJWKYfL3i8Sq6s7YEYistYaJMuEBJTbASqAFLNzq6yUi7/nyBCiIEU2DkQggMSiseh94px3WYZevQSb6qUD1cYbDTw0qKgXMUxICCtYHVva+eRTesW3DGgYE0NsKNrQRdabKEiRNaa4W0YYVLNQ3qszAxEbQ8Y5l3kvIB6ceFUFzaeMCLF69513Wqp2e4j32hUKwQgzoiouA7SWWNGXjWj7Llh/u1mR8M8bY4JleGLYWEVUIkBUwOKKY6hcyIOtofGRFiuRyje5+/aRHU8UpMimEUzDw3pK58A7Re9ExDmHiKGtaJzGuz8QUXwwbCBmDhdz2MYERARArCrKxiaJDaaoBIyCqAAKqoI01GA1ElkLoiBFNpOQhCACa4EJSazzHlG9iIhUWqQvp3efHWOmgaiqF8fGkgKSqg+tqqrXs2iJq5LUagBClABAkKKQK0KlVS7Wunf8Uz2CGC1FqkRBimw+IfeOAMBIbBCRvBNPXkTED+qNXey4HfotbRaIKOK9I2JiYlVVkcFFDShESGyNKdoWVlGMgVFkY4iCFFlHVt5fSBUAgQGYwRCX4+bcQrsUpE4nLffy4piIDBOzc5WyZq3mKe6ds1iJnj3IaLz5eolSs3UHGSp4dZnPEBER2ZharcZEbIgYeMiS2eJJhNXW50GspousmihIkS0H5otlQRWazWbpg8fGeOcyl3mnKuIhpJ08UfwY3w2XZc57ES+qxhrDhg0TsbVMnFv+RO2IbAXiX3Jka9AfhoQJvGIZTW4DQETeIGXgWUMWPniBo/TWMEUvokEIohrLrGRqSWgpCwDGrESHYsYnsnFEQYpsSUrTtOqcFyobYlMDxW7XiddMvHgPossS7zvCL6Aq4gMWCWFoj8dsDAMgIhMpIRKUlusDFn9FIptHFKTIJtO3bmn5T/tK6BRCDIRYb4SPrgWAzmLqXJZlHgCMMSLee1FFa2xl114fJsJeNqTabwkH9v7ZAIb2YRr8PJcigj1vOtVQRwdEZAxZa9iQNQYpr1MIMaRW1r2upKReVhkVLd96R9waRNaIKEiR7Ug5yhEA1BuJd2SMOJeRMd4TkRRFZTmIpCq0/Zc1oYKvtOcoxQkRbZKwQcOGiIzBIEXb/4wjO4goSJFtjQAAKREby5B5Q4ZEwItXwTR15TYiEsKCECmV+2NvuShVI5XqDNgD5aWG7NuXMlMYHkgMeN4QhsZRQV+x+L9eqzEBcTX9FolsJ6IgRR4eEkOA4BkQWUmJknIGyTnvvfPOC6qqlKXkFeEZWiPe83lb/RB/l5VSVaefQXsOneuq1WrFQ6k8hsTcpwTdZfnxWlEtw18h0W93BxIFKbKFWD5sUSXECCIyYDVMGW4oamjPQxCWNZU/SQyLsHPee68ipfCkaVa+TOYyBCbE0GddVfvipIqPUahE50E9GMoi9XwvBNA8iMl94BBJQbzPN0Gp5rrStFhrhWJN788TiZjZWhtqE4gpX02MPQkdNtyrAiJ0u5m1lgi8V1Vh5ixzNZu/BQF0K98CgBNRVWLi5aa3AOW+YS8BCPOjROAVOp2s1bDlK5cE5VOFXlxayWYhVdpeIAKAdwoAvHqhVck/FVHSthdRkCIPEdhXNVaqURiUgp2rCKnkA5+oGsulhFBKWtYIYNECtfdqRUlFWRowOIjRyha9vfseIxCXL07E4SARABqmBsVcHJveYE5ERBg2RcQlUnR3RNQ5x0yzszPG2JGREUTudlNmmr0z32y0VLWbLnov0GwEmZEiQMFBJ6kKphBLY8zCYuadu3rt2qFDhzKiGzdujIyM1OtWl5VO5L8IBMC+gFQFVCHk/KjwikBELsanuzW1r8x+BgvELbAaOXKfREGKPFRgtf473HdXxIARkFEJgr5wMW+mqgBoiVXVg6qq+KU9G1QJeoLUFwb1b6YAIIWwUREVlfuG/I9RLNXOWovhdp76ckKEBCBIFYnF3v8rxzICmPn5+TNnziKb06dPH9i/j23y/vvvv/322y+88MI3v/nN8+fPL7QXTp9+tZOF40FEBAUuWs6r5mElIoVxP3VOEYy1QHhrduYXv/rlyy+/vH//5Jmfnz1+/Pi3vvWtWs0AgBaVgKWocOX4y5cX8cF9wxYTrSHqUlW9mxwBgJS/iEKwubxKq3aXiGwqUZAiOwvqCUP+LQDk1jiGwsIcryBil+wYgqdSkIZpUqgsV+l7PoiKFo9JwVI5LIu1vWGzOvYqSBlaUZ/HXPXF761OqfMClHk59/t3bt68uX///lartbDYPfPGL/7wzu8f3b//5DNP7977aLM9h2gMc2exU4oiY15AAQCqGSIRESJmWdpuLypCo9Gq1+uI+NVXX9VqtYWF+StXrhw5csQ5Nz8/Xx7D6OhoeOBFUFRFqq55iAgg7Xbb2gSRE8MA4Jyfm5vvdBYnJnYtOaM8EiovR27cAUSKiLValKHtShSkyJZj+EzU6kKD+/EYKOaUQljSV8VAWPyXbzfM464qUtVxM0Qd+WPpPStDWozj0PNd6XUI782GASB1ktSbj00efP+Dj46ffOazz7+Ymb2z/8DBbua8YqeTztyeW2x3bty4ce7cubm5OWPMk08++dq/f3V2ZubcuXNXrlwBgMf27/+bb38bAD788INLly4DwOHDU9/97qudxdTa+sLC4tjoI8bUiCyiOf/u+5cuX/bOOe9feOGFp59+WlV++ctfZWnqvPPOH3n88e9859utZvLpxS/ee+/89SvXGs3Gs8888+yzT9cbjQ8//PC9995bbLf37t378ssvTx0+/Kc//Wl6evr06dMAePnylxcuXPh3r7yy2EnPv3v++rVpUd2zZ9frr7+eJEnMHG1TohdIJDII1MFfUPkasg1S78sQlF+MSpB/9bbpZz1OpZA7UqQXX3yx3W5/8NG/fvzxx8+dOrXn0ceACRH/eOHPn138Ynbmzlu//u1X0zdPfePFw1NHZ2butBcWf/vbf37//Q/27n300NQUE92+PfvrX7/5wQcfnDhx/MiRw2+++ea7f3gXAFyW1euNEAuK+CxNx8bGHnv00aeOH8/S9OzZM1/furUwv3Du3Lnr09NjY2PE9POzb3x28fMbN+787P+euXZ1+jvfeeWRsYmzZ3/xxaUvP/3zxZ/85Cfj4+PfOHXqytWrb7311s2bNy9fvvzJJ5+0FxZU9fKlSx9//DEAfPAv//Lee+8//sSxgwenarVGryoksg2JEVIk8pBDpceFypEjRwDgpz/96cTExA9/+He3btxEQTZJqzmycGeBTK3dcWkqzPbo0Sfr9WT2zvynn/3l1PMvfv/739890bpx8+u5hfkLF/68Z89ua60TqTeb129cP/T4oa7vAikZowipc4tp97EDj7a77WvXryrrrZlbM3duj46OAvLzL7700ksvTU9Pf/rZFzNf315c7F648Olrr712Z27B1hvz7c7ly9e8ulq9+R9ef31kpJXUaz/72c/+/9Uri2nXqwqCIJha0u50FtPu17OznSzrZumhw1N79uxqjraq5x6d+LYXUZAikR0AAoEACiKePHnyN7/5zcnjLz8yOkoA4rw1Vry41DcaI9979Xvv/OHtH//4fzcbjZf+5qXDh490Oume3ftqtZoq7JqYuHHrpnNZmmYfffQRAIyOju7Zs8c7r6qdTqoqXnyapp1O58c//j+3b389NXXYGuu9c94RmWarOT4+niRJo9EyyN1ut9vtGjZXr05/efmKtfbw1JGJXRPvvffu2NhYkthmq7F//6QxxjlPiN47VTXMnU4ny9JGo/niSy9en77xu3/+XZqlTz559L/98L+2Gvs2+3JH7pMoSJGHlmF3xCu5Uw6zZyE5PqDH6srfd1OTGeHNQ4oKVWqGQdyJp47944/+YffuPY1Gc+HO7cnH9pGCuMzWjCGYPLD/f/zdf595bfb//dM/vf27c4/t3ZcY+9fPPz/13DPiWUXVQbPWnDow9bf/5W/J8OLi4tjo2FdffdVIGiCSdb1m2kgaX9/4uj03/5/+4w9OHD/xl7/85fPP/lpjy4CkoF4aCbhuJ0lYxY20WmOjrZe/9dLjTzzRqNezzNXq9tJfP7906fOFufnxkdHrV6/WbJIwJ8a6brq40G7VGwt35hI2Wbe7Z2L33//P/zU7c/vTixff+f2561euHpzcV5x+TCVtM6IgRSIPOYbAiQAKiHdpt2aTp44dq9XszNezSc14n7m0YxlF0hs3ps+dO5cYOzU15cWNj48ePDh58uTJjz788MzZs6Ojrenp688///wTR4/+8Y8fI+KuvXu+/PLysWNP7tmz2zlfrzcM8/j4OCLW6/W5+bkLF/587dr1P33ySRZSOyiZc9YY56HRrM8vzAHIM8+efPf8H3715hsnr54cGxv76F8/+s8/+MHRo49/+ukff3727P7JyfPnzx+YnDx06BAivvHGG2fPnBkbG3vnnXfYGCJ669e/mZ2deeLxY51OuzXSrDdy64qoRtsR/tGPfrRrV15VGVc1RyLLWW3D2M36Kxp2nM57y1yvN5zLTpw8sXfXRGIZFUR8ktjDU4cmJsbZ8N49uw8enGwvzi8utGdmbtXr9e997/ShqUOP7ttnDHc67U6nMzV18PiJ48ePPzUy0rp2/dpip91sNk6d+kar1Ww0apOTB3btmgDVg4cO7Nu3t9Vq3bw5naadgwcPHjv2xONPHHnkkXFrkxPHn2o0aqrSaNSnpiYPHz5waGoyzRZv3prudBf27d397HPPHjw4Of7IxNWrV2dnZ6cOHXrllVcmDxxoNpu7d+26MzfHxjz11FPHjh594ujRNOsuLnZu3LzlvX/11e8+99yzRINHsji6bX3w4sWLx44dC8WpUZAikeWs1udts5Lnw44zy1Jm473zIgYpSZLwvKqmaUrGBM+kRs12vO92uyg6M3Nn18REkiTO+ySxaZplWUaINkmMYcsw306dc0DovR8ZGQGAmZmZ8fFxAGovtJutpooCyu3btxuNRlivWqsl3vtawgSQinKox3NZYmwmqaq224siMjo6Ih6IWBUWFjp37twZGxtrNZuqaix1Olmn0wGAsbHRLHNJYgCg00mzzDcbjVoCXocuHI5FDVufKEiRyEPMgA5TK98U+tdUiVPorW3qjRW+dLVQqvpKVCmd95a+fiWuW7KauDzU1Q5LuQVG9LLbhsQcUiQSARji+V0dz61F0H43PwAYYjJ7l9cZus1SMVuNK11YpRxd7LY5UZAikciKKZ0m4sgfWQfitGokEolEtgQxQopEHjLuv9neSu5P1/oe9q6h1iriMOxtX/EbjGwvYoQUiUQikS1BFKRIJBKJbAmiIEUikUhkSxBzSJHIQ0b1LvP+80mRyMYTI6RIJBKJbAmiIEUikUhkS/Bv/WRa2KfKd/MAAAAASUVORK5CYII=)"
      ],
      "metadata": {
        "id": "M03gstMTYPxs"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Некоторые сведения из статистики и теории вероятности"
      ],
      "metadata": {
        "id": "OBfN0qbZcul-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Если мы попытаемся определить точность попадания в игрового персонажа броском снаряда (который был смоделирован ранее), то то после нескольких атак можно сделать заключение о точности (хорошо известного параметра в компьютерном спорте - Aim). Обращаясь к теории вероятности можно представить каждое из попаданий в качестве случайной величины. Действительно, мы не можем гарантировать попадание и всегда есть вероятность промаха большего или меньшего.\n",
        "Для начала по определению классической вероятности можем определить вероятность попадания отношением количества попаданий в интересующую нас точку к общему количеству бросков.\n",
        "\n",
        "${\\displaystyle \\Pr(A)={\\frac {n}{N}}}$\n",
        "\n",
        "Итак рассмотрим двух игроков.  Каждый пытается выполнив 4 броска попасть в персонажа (NPC). Мы рассмотрим отклонение от центра \"мишени\". Если игрок попал точно, то отклонений равно 0, если правее мишени на 5 единиц, то результат равен 5, а если левее, то -5.\n",
        "\n",
        "|index|попытка\\_1|попытка\\_2|попытка\\_3|попытка\\_4|\n",
        "|---|---|---|---|---|\n",
        "|Player\\_1|12|-6|7|-8|\n",
        "|Player\\_2|22|-16|17|-18|\n",
        "\n",
        "«Снайперское» математическое ожидание равно , однако и у «интересной личности»:  – оно тоже нулевое!\n",
        "\n",
        "Таким образом, возникает потребность количественно оценить, насколько далеко  рассеяны пули (значения случайной величины) относительно центра мишени (математического ожидания). Ну а рассеяние с латыни переводится не иначе, как дисперсия."
      ],
      "metadata": {
        "id": "DG1K26sC0m2t"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Теперь нам надо понять куда попадёт игрок при следующем броске. Не зная этого точно мы можем прогнозировать попадание и для этого используется математическое ожидание, которое означает среднее (взвешенное по вероятностям возможных значений) значение случайной величины. \n",
        "\n",
        "$${\\displaystyle \\mathbb {M} [x]=\\sum \\limits _{i=1}^{\\infty }x_{i}\\,p_{i}}$$\n",
        "\n",
        "В нашем случае, это значит, что было сделано четыре попытки, а значит вероятность попадания в каждую из точек попадания  было равно одной четверти (0.25). Чтобы найти математическое ожидание (сделать прогноз) нам нужно сложить результаты разделённые на количество попыток. \n",
        "\n",
        "$${\\displaystyle \\mathbb {M} [Player_1]={\\frac {12}{4}}-{\\frac {6}{4}}+{\\frac {7}{4}}-{\\frac {8}{4}}}={\\frac {12-6+7-8}{4}=1.25}$$\n",
        "\n",
        "Как можно заметить математическое ожидание для подобных ситуаций равно средену арифметическому значению. И в результате прогноза мы получили ожидаемое отклонение при следующем броске от \"центра мишени\".\n",
        "\n",
        "Второй игрок промахивается на большее значение, но давайте рассчитаем математическое ожидание для него и увидим, что оно не отличается от презультата для первого игрока.\n",
        "\n",
        "$${\\displaystyle \\mathbb {M} [Player_2]={\\frac {22}{4}}-{\\frac {16}{4}}+{\\frac {17}{4}}-{\\frac {18}{4}}}={\\frac {22-16+17-18}{4}=1.25}$$\n",
        "\n",
        "Но результаты каждой отдельной попыкти у игроков отличаются тем что они на разное расстояние отстоят от центра. Первый более меткий и его результаты укладываются в отрезок $[-8;12]$ радиусом 20, а второй менее и его результаты описываются большим отрезком $[-18;22]$ радиусом 40. Таким образом первый игрок в два раза точнее второго.\n",
        "\n",
        "Для определения разброса значений случайной величины относительно её математического ожидания была введена дисперсия.\n",
        "\n",
        "\n",
        "$${\\displaystyle D[х]=\\sum _{i=1}^{n}{p_{i}(x_{i}-\\mathbb {M} [x])^{2}}}$$\n",
        "\n",
        "Формула похожа на математическое ожидание, только мы вместо точки попадания, которая характеризовала удаление от центра мишени, мы используем удаление от среднего значения или мат. ожидания возведённое в квадрат. Если не произвести возведения в квадрат, то в результате мы получим 0, так как все точки равноудалены от среднего значения. \n",
        "\n",
        "$${\\displaystyle Сум.отклонений [Player_1]={\\frac {12-1.25}{4}}-{\\frac {6-1.25}{4}}+{\\frac {7-1.25}{4}}-{\\frac {8-1.25}{4}}}={\\frac {10.75-7.25+5.75-9.25}{4}=0}$$\n",
        "Дополнительно вводится поправка (несмещённая оценка дисперсии, именно так будут считать методы в библиотеках Python) и делить нужно на $n-1$, что в нашем случае равно 3.\n",
        "$${\\displaystyle D [Player_1]={\\frac {(12-1.25)^2}{3}}-{\\frac {(6-1.25)^2}{3}}+{\\frac {(7-1.25)^2}{3}}-{\\frac {(8-1.25)^2}{3}}=95.58}$$\n",
        "\n",
        "Но сама величина дисперсии, как отклонение от среднего не позволяет судить об этом отклонении в единицах его измерения, так как мы имеем квадрат этих отклонений. Поэтому вводят другой показатель рассеяния (дисперсии) случайной величины -  среднеквадратическое отклонение или стандартное отклонение.\n",
        "Среднеквадратическое отклонение измеряется в единицах измерения самой случайной величины и используется при расчёте стандартной ошибки среднего арифметического, при построении доверительных интервалов, при статистической проверке гипотез, при измерении линейной взаимосвязи между случайными величинами.\n",
        " \n",
        "$${\\displaystyle \\sigma ={\\sqrt {D[X]}}={\\sqrt {{\\frac {1}{n-1}}\\sum _{i=1}^{n}\\left(x_{i}-{\\bar {x}}\\right)^{2}}}}$$."
      ],
      "metadata": {
        "id": "VqpoPol86hgJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "В результате мы можем охарактеризовать наш набор данных следующими описательными статистиками."
      ],
      "metadata": {
        "id": "Xkz2wMWDc15t"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "|рез.|Player\\_1|Player\\_2|\n",
        "|---|---|---|\n",
        "|мат.ожид.|1\\.25|1\\.25|\n",
        "|станд.отклон|9\\.78|21\\.19|\n",
        "|min|-8\\.0|-18\\.0|\n",
        "|max|12\\.0|22\\.0|"
      ],
      "metadata": {
        "id": "Zd84ePydDyFX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Задание"
      ],
      "metadata": {
        "id": "edWAV3jAdBme"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Рассчитать по приведённому алгоритму описательные статистики (мат.ожидание, стандартное отклонение и минимальное и максимальное значения) для следующего набора данных.\n",
        "\n",
        "На основе вычисленных описательных статистик оставить краткий рассказ о имеющемся наборе данных"
      ],
      "metadata": {
        "id": "8-_SmsbpdD81"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "|index|попытка\\_1|попытка\\_2|попытка\\_3|попытка\\_4|\n",
        "|---|---|---|---|---|\n",
        "|Player\\_1|-28|15|-23|14|\n",
        "|Player\\_2|48|-24|17|-58|"
      ],
      "metadata": {
        "id": "awsQH31QC2vN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Решение и код для решения"
      ],
      "metadata": {
        "id": "w413quYWEDV7"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "|рез.|Player\\_1|Player\\_2|\n",
        "|---|---|---|\n",
        "|мат.ожидание|-5\\.5|-4\\.25|\n",
        "|станд.отклон|23\\.19|46\\.41|\n",
        "|min|-28\\.0|-58\\.0|\n",
        "|max|15\\.0|48\\.0|"
      ],
      "metadata": {
        "id": "wLOQufS-DAHF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd"
      ],
      "metadata": {
        "id": "SMe8J4SR3jrE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "list1 = [12,-6,7,-8]\n",
        "list2 = [22,-16,17,-18]\n",
        "\n",
        "\n",
        "df1 = pd.DataFrame([list1,list2], index=['Player_1','Player_2'], columns=['попытка_1','попытка_2','попытка_3','попытка_4'])\n",
        "df1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "DbJspOBg3nJj",
        "outputId": "7cbb78d8-422b-4f63-b4c8-5b25337e1406"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          попытка_1  попытка_2  попытка_3  попытка_4\n",
              "Player_1         12         -6          7         -8\n",
              "Player_2         22        -16         17        -18"
            ],
            "text/html": [
              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>попытка_1</th>\n",
              "      <th>попытка_2</th>\n",
              "      <th>попытка_3</th>\n",
              "      <th>попытка_4</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Player_1</th>\n",
              "      <td>12</td>\n",
              "      <td>-6</td>\n",
              "      <td>7</td>\n",
              "      <td>-8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Player_2</th>\n",
              "      <td>22</td>\n",
              "      <td>-16</td>\n",
              "      <td>17</td>\n",
              "      <td>-18</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "            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",
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              "  "
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        }
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    {
      "cell_type": "code",
      "source": [
        "df1.T.describe()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "npBxr2nx5Hak",
        "outputId": "59388d15-b59e-4ce7-96c5-131e62e90cfc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Player_1   Player_2\n",
              "count   4.000000   4.000000\n",
              "mean    1.250000   1.250000\n",
              "std     9.776673  21.187654\n",
              "min    -8.000000 -18.000000\n",
              "25%    -6.500000 -16.500000\n",
              "50%     0.500000   0.500000\n",
              "75%     8.250000  18.250000\n",
              "max    12.000000  22.000000"
            ],
            "text/html": [
              "\n",
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              "      <th></th>\n",
              "      <th>Player_1</th>\n",
              "      <th>Player_2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1.250000</td>\n",
              "      <td>1.250000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>9.776673</td>\n",
              "      <td>21.187654</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-8.000000</td>\n",
              "      <td>-18.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>-6.500000</td>\n",
              "      <td>-16.500000</td>\n",
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              "      <th>50%</th>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>8.250000</td>\n",
              "      <td>18.250000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>12.000000</td>\n",
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              "            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",
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          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "z =0\n",
        "for i in list1:\n",
        " print((i-1.25)**2)\n",
        " z += (i-1.25)**2\n",
        "\n",
        "z/3, (z/3)**0.5"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I60Ke7yJStSE",
        "outputId": "0bc144a7-719c-426f-861a-e1e8c555dd27"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "115.5625\n",
            "52.5625\n",
            "33.0625\n",
            "85.5625\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(95.58333333333333, 9.776672917374976)"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    }
  ]
}