{
  "cells": [
    {
      "cell_type": "markdown",
      "id": "667eebfe-66e2-4103-a655-e29088be77c9",
      "metadata": {
        "id": "667eebfe-66e2-4103-a655-e29088be77c9"
      },
      "source": [
        "```{index} single: AMPL; kernel library\n",
        "```\n",
        "```{index} single: quadratic optimization\n",
        "```\n",
        "```{index} single: application; support vector machines\n",
        "```\n",
        "```{index} single: application; binary classification\n",
        "```\n",
        "```{index} single: application; counterfeit banknotes\n",
        "```\n",
        "# Support Vector Machines for Binary Classification\n",
        "\n",
        "Support Vector Machines (SVM) are a type of supervised machine learning model. Similar to other machine learning techniques based on regression, training an SVM classifier uses examples with known outcomes, and involves optimization some measure of performance. The resulting classifier can then be applied to classify data with unknown outcomes.\n",
        "\n",
        "In this notebook, we will demonstrate the process of training an SVM for binary classification using linear and quadratic programming. Our implementation will initially focus on linear support vector machines which separate the feature space by means of a hyperplane. We will explore both primal and dual formulations. Then, using kernels, the dual formulation is extended to binary classification in higher-order and nonlinear feature spaces. Several different formulations of the optimization problem are given in AMPL and applied to a banknote classification application."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "id": "78facb14-0d9c-49c1-aec0-88d32750aac2",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "78facb14-0d9c-49c1-aec0-88d32750aac2",
        "outputId": "addb73c9-1a77-44e1-8649-0c1b6e66af35"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.6/5.6 MB\u001b[0m \u001b[31m13.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hUsing default Community Edition License for Colab. Get yours at: https://ampl.com/ce\n",
            "Licensed to AMPL Community Edition License for the AMPL Model Colaboratory (https://colab.ampl.com).\n"
          ]
        }
      ],
      "source": [
        "# install AMPL and solvers\n",
        "%pip install -q amplpy\n",
        "\n",
        "SOLVER_LO = \"cbc\"\n",
        "SOLVER_NLO = \"ipopt\"\n",
        "\n",
        "from amplpy import AMPL, ampl_notebook\n",
        "\n",
        "ampl = ampl_notebook(\n",
        "    modules=[\"coin\"],  # modules to install\n",
        "    license_uuid=\"default\",  # license to use\n",
        ")  # instantiate AMPL object and register magics"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "23c4641f-fc46-418e-ae48-a72747991de7",
      "metadata": {
        "id": "23c4641f-fc46-418e-ae48-a72747991de7"
      },
      "source": [
        "## Binary classification\n",
        "\n",
        "Binary classifiers are functions designed to answer questions such as \"does this medical test indicate disease?\", \"will this specific customer enjoy that specific movie?\", \"does this photo include a car?\", or \"is this banknote genuine or counterfeit?\" These questions are answered based on the values of \"features\" that may include physical measurements or other types of data collected from a representative data set with known outcomes.\n",
        "\n",
        "In this notebook we consider a binary classifier that might be installed in a vending machine to detect banknotes. The goal of the device is to accurately identify and accept genuine banknotes while rejecting counterfeit ones. The classifier's performance can be assessed using definitions in following table, where \"positive\" refers to an instance of a genuine banknote.\n",
        "\n",
        "| | Predicted Positive  | Predicted Negative | |\n",
        "| :-- | :--: | :--: | :-- |\n",
        "| Actual Positive | True Positive (TP) | False Negative (FN) |\n",
        "| Actual Negative | False Positive (FP) | True Negative (TN) |\n",
        "\n",
        "A vending machine user would be frustrated if a genuine banknote is incorrectly rejected as a false negative. **Sensitivity** is defined as the number of true positives (TP) divided by the total number of actual positives (TP + FN). A user of the vending machine would prefer high sensitivity because that means genuine banknotes are likely to be accepted.\n",
        "\n",
        "The vending machine owner/operator, on the other hand, wants to avoid accepting counterfeit banknotes and would therefore prefer a low number of false positives (FP). **Precision** is the number of true positives (TP) divided by the total number of predicted positives (TP + FP). The owner/operate would prefer high precision because that means almost all of the accepted notes are genuine.\n",
        "\n",
        "* **Sensitivity**: The number of true positives divided by the total number of actual positives. High sensitivity indicates a low false negative rate.\n",
        "\n",
        "* **Precision**: The number of true positives identified by the model divided by the total number of predicted positives, which includes both true and false positives. High precision indicates a low false positive rate.\n",
        "\n",
        "To achieve high sensitivity, a classifier can follow the \"innocent until proven guilty\" standard, rejecting banknotes only when certain they are counterfeit. To achieve high precision, a classifier can adopt the \"guilty unless proven innocent\" standard, rejecting banknotes unless absolutely certain they are genuine.\n",
        "\n",
        "The challenge in developing binary classifiers is to balance these conflicting objectives and to optimize performance from both perspectives at the same time."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "800ac3a5-be34-4600-b07d-b06352bfd923",
      "metadata": {
        "id": "800ac3a5-be34-4600-b07d-b06352bfd923"
      },
      "source": [
        "## The data set\n",
        "\n",
        "The following data set contains measurements from a collection of known genuine and known counterfeit banknote specimens. The data includes four continuous statistical measures obtained from the wavelet transform of banknote images named \"variance\", \"skewness\", \"curtosis\", and \"entropy\", and a binary variable named \"class\" which is 0 if genuine and 1 if counterfeit.\n",
        "\n",
        "https://archive.ics.uci.edu/ml/datasets/banknote+authentication"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c63ce528-f90b-4f31-8181-1d944558547e",
      "metadata": {
        "id": "c63ce528-f90b-4f31-8181-1d944558547e"
      },
      "source": [
        "### Read data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "id": "e39f2162-09b2-422b-a352-1d13ec3b5add",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "e39f2162-09b2-422b-a352-1d13ec3b5add",
        "outputId": "58b5a790-e9ee-4be8-a68b-5b04b34feff4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   variance  skewness  curtosis  entropy  class\n",
              "0   3.62160    8.6661   -2.8073 -0.44699      0\n",
              "1   4.54590    8.1674   -2.4586 -1.46210      0\n",
              "2   3.86600   -2.6383    1.9242  0.10645      0\n",
              "3   3.45660    9.5228   -4.0112 -3.59440      0\n",
              "4   0.32924   -4.4552    4.5718 -0.98880      0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-873512e2-f29a-4c6d-af2a-473309cce7ce\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>variance</th>\n",
              "      <th>skewness</th>\n",
              "      <th>curtosis</th>\n",
              "      <th>entropy</th>\n",
              "      <th>class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3.62160</td>\n",
              "      <td>8.6661</td>\n",
              "      <td>-2.8073</td>\n",
              "      <td>-0.44699</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.54590</td>\n",
              "      <td>8.1674</td>\n",
              "      <td>-2.4586</td>\n",
              "      <td>-1.46210</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3.86600</td>\n",
              "      <td>-2.6383</td>\n",
              "      <td>1.9242</td>\n",
              "      <td>0.10645</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3.45660</td>\n",
              "      <td>9.5228</td>\n",
              "      <td>-4.0112</td>\n",
              "      <td>-3.59440</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.32924</td>\n",
              "      <td>-4.4552</td>\n",
              "      <td>4.5718</td>\n",
              "      <td>-0.98880</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-873512e2-f29a-4c6d-af2a-473309cce7ce')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-873512e2-f29a-4c6d-af2a-473309cce7ce button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-873512e2-f29a-4c6d-af2a-473309cce7ce');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-5002dce9-930e-4627-bd2f-b0c98b438c1a\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-5002dce9-930e-4627-bd2f-b0c98b438c1a')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-5002dce9-930e-4627-bd2f-b0c98b438c1a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# read data set\n",
        "df = pd.read_csv(\n",
        "    \"https://raw.githubusercontent.com/mobook/MO-book/main/datasets/data_banknote_authentication.txt\",\n",
        "    header=None,\n",
        ")\n",
        "df.columns = [\"variance\", \"skewness\", \"curtosis\", \"entropy\", \"class\"]\n",
        "df.name = \"Banknotes\"\n",
        "\n",
        "# show a few rows\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "1ce2c0fb-f082-41df-9dd5-991cec6af4a8",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "1ce2c0fb-f082-41df-9dd5-991cec6af4a8",
        "outputId": "755732ec-cd95-425d-cbdc-c6e67c708f3f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          variance     skewness     curtosis      entropy        class\n",
              "count  1372.000000  1372.000000  1372.000000  1372.000000  1372.000000\n",
              "mean      0.433735     1.922353     1.397627    -1.191657     0.444606\n",
              "std       2.842763     5.869047     4.310030     2.101013     0.497103\n",
              "min      -7.042100   -13.773100    -5.286100    -8.548200     0.000000\n",
              "25%      -1.773000    -1.708200    -1.574975    -2.413450     0.000000\n",
              "50%       0.496180     2.319650     0.616630    -0.586650     0.000000\n",
              "75%       2.821475     6.814625     3.179250     0.394810     1.000000\n",
              "max       6.824800    12.951600    17.927400     2.449500     1.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8310b85d-82c4-4a3f-9277-3b27a94c9e92\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>variance</th>\n",
              "      <th>skewness</th>\n",
              "      <th>curtosis</th>\n",
              "      <th>entropy</th>\n",
              "      <th>class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1372.000000</td>\n",
              "      <td>1372.000000</td>\n",
              "      <td>1372.000000</td>\n",
              "      <td>1372.000000</td>\n",
              "      <td>1372.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.433735</td>\n",
              "      <td>1.922353</td>\n",
              "      <td>1.397627</td>\n",
              "      <td>-1.191657</td>\n",
              "      <td>0.444606</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2.842763</td>\n",
              "      <td>5.869047</td>\n",
              "      <td>4.310030</td>\n",
              "      <td>2.101013</td>\n",
              "      <td>0.497103</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-7.042100</td>\n",
              "      <td>-13.773100</td>\n",
              "      <td>-5.286100</td>\n",
              "      <td>-8.548200</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>-1.773000</td>\n",
              "      <td>-1.708200</td>\n",
              "      <td>-1.574975</td>\n",
              "      <td>-2.413450</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.496180</td>\n",
              "      <td>2.319650</td>\n",
              "      <td>0.616630</td>\n",
              "      <td>-0.586650</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2.821475</td>\n",
              "      <td>6.814625</td>\n",
              "      <td>3.179250</td>\n",
              "      <td>0.394810</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>6.824800</td>\n",
              "      <td>12.951600</td>\n",
              "      <td>17.927400</td>\n",
              "      <td>2.449500</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8310b85d-82c4-4a3f-9277-3b27a94c9e92')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-8310b85d-82c4-4a3f-9277-3b27a94c9e92 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-8310b85d-82c4-4a3f-9277-3b27a94c9e92');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-b114e9c9-e266-4fe0-8708-86ac220c9f17\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b114e9c9-e266-4fe0-8708-86ac220c9f17')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-b114e9c9-e266-4fe0-8708-86ac220c9f17 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "# get a statistical description of the data set\n",
        "df.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9e488e8c-44b9-4e4e-b1c9-ca0b01a46133",
      "metadata": {
        "id": "9e488e8c-44b9-4e4e-b1c9-ca0b01a46133"
      },
      "source": [
        "### Select features and training sets\n",
        "\n",
        "We divide the data set into a **training set** for training the classifier, and a **testing set** for evaluating the performance of the trained classifier. In addition, we select a two dimensional subset of the features so that the results can be plotted for better exposition. Since our definition of a positive outcome corresponds to detecting a genuine banknote, the \"class\" feature is scaled to have values of 1 for genuine banknotes and -1 for counterfeit banknotes."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "f75fd9e2-1c0f-44e7-9732-9fac6bc14656",
      "metadata": {
        "id": "f75fd9e2-1c0f-44e7-9732-9fac6bc14656"
      },
      "outputs": [],
      "source": [
        "# create training and validation test sets\n",
        "df_train, df_test = train_test_split(df, test_size=0.2)\n",
        "\n",
        "# select training features\n",
        "features = [\"variance\", \"skewness\"]\n",
        "\n",
        "# separate into features and outputs\n",
        "X_train = df_train[features]\n",
        "y_train = 1 - 2 * df_train[\"class\"]\n",
        "\n",
        "# separate into features and outputs\n",
        "X_test = df_test[features]\n",
        "y_test = 1 - 2 * df_test[\"class\"]"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8937474b-23b8-4c84-8732-c61c6d87bc1f",
      "metadata": {
        "id": "8937474b-23b8-4c84-8732-c61c6d87bc1f"
      },
      "source": [
        "The following cell defines a function `scatter` that produces a 2D scatter plots of a labeled features. The function assigns default labels and colors, and otherwise passes along other keyword arguments."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "0b7e5f48-308d-4020-bf61-6c605cf447c1",
      "metadata": {
        "id": "0b7e5f48-308d-4020-bf61-6c605cf447c1"
      },
      "outputs": [],
      "source": [
        "def scatter_labeled_data(X, y, labels=[\"+1\", \"-1\"], colors=[\"g\", \"r\"], **kwargs):\n",
        "    \"\"\"\n",
        "    Creates a scatter plot for labeled data with default labels and colors.\n",
        "\n",
        "    Parameters:\n",
        "    X : DataFrame\n",
        "        Feature matrix as a DataFrame.\n",
        "    y : Series\n",
        "        Target vector as a Series.\n",
        "    labels : list, optional\n",
        "        Labels for the positive and negative classes. Default is [\"+1\", \"-1\"].\n",
        "    colors : list, optional\n",
        "        Colors for the positive and negative classes. Default is [\"g\", \"r\"].\n",
        "    **kwargs : dict\n",
        "        Additional keyword arguments for the scatter plot.\n",
        "\n",
        "    Returns:\n",
        "    None\n",
        "    \"\"\"\n",
        "\n",
        "    # Prepend keyword arguments for all scatter plots\n",
        "    kw = {\"x\": 0, \"y\": 1, \"kind\": \"scatter\", \"alpha\": 0.4}\n",
        "    kw.update(kwargs)\n",
        "\n",
        "    # Ignore warnings from matplotlib scatter plot\n",
        "    import warnings\n",
        "\n",
        "    with warnings.catch_warnings():\n",
        "        warnings.filterwarnings(\"ignore\")\n",
        "        kw[\"ax\"] = X[y > 0].plot(**kw, c=colors[0], label=labels[0])\n",
        "        X[y < 0].plot(**kw, c=colors[1], label=labels[1])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "d8a9528d-0280-4454-8ae0-7aa024984cf4",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "id": "d8a9528d-0280-4454-8ae0-7aa024984cf4",
        "outputId": "918e572a-9bda-46ff-878a-58956e14ea8f"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x400 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# plot training and test sets in two axes\n",
        "fig, ax = plt.subplots(1, 2, figsize=(10, 4))\n",
        "scatter_labeled_data(\n",
        "    X_train,\n",
        "    y_train,\n",
        "    labels=[\"genuine\", \"counterfeit\"],\n",
        "    ax=ax[0],\n",
        "    title=\"Training Set\",\n",
        ")\n",
        "scatter_labeled_data(\n",
        "    X_test,\n",
        "    y_test,\n",
        "    labels=[\"genuine\", \"counterfeit\"],\n",
        "    ax=ax[1],\n",
        "    title=\"Test Set\",\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "242e1ea6-98fe-4414-ae2a-456ad1278252",
      "metadata": {
        "id": "242e1ea6-98fe-4414-ae2a-456ad1278252"
      },
      "source": [
        "## Support vector machines (SVM)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ff57181e-b224-4a40-8e04-26dd003b6c3a",
      "metadata": {
        "id": "ff57181e-b224-4a40-8e04-26dd003b6c3a"
      },
      "source": [
        "### Linear SVM classifier\n",
        "\n",
        "A linear support vector machine (SVM) is a binary classification method that employs a linear equation to determine class assignment. The basic  formula is expressed as:\n",
        "\n",
        "$$y^{pred} = \\text{sgn}\\ ( w^\\top x + b)$$\n",
        "\n",
        "where $x$ is a point $x\\in\\mathbb{R}^p$ in \"feature\" space. Here $w\\in \\mathbb{R}^p$ represents a set of coefficients, $w^\\top x$ is the dot product, and $b$ is a scalar coefficient. The hyperplane defined by $w$ and $b$ separates the feature space into two classes. Points on one side of the hyperplane are have a positive outcome (+1); while points on the other side have a negative outcome (-1).\n",
        "\n",
        "The following cell presents a simple Python implementation of a linear SVM. An instance of `LinearSVM` is defined with a coefficient vector $w$ and a scalar $b$. In this implementation, all data and parameters are provided as Pandas Series or DataFrame objects, and the Pandas `.dot()` function is used to compute the dot product."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "5620cb4c-7b31-47e7-b877-473e05a2a14c",
      "metadata": {
        "id": "5620cb4c-7b31-47e7-b877-473e05a2a14c"
      },
      "outputs": [],
      "source": [
        "# import required libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "\n",
        "# Linear Support Vector Machine (SVM) class\n",
        "class LinearSVM:\n",
        "    # Initialize the Linear SVM with weights and bias\n",
        "    def __init__(self, w, b):\n",
        "        \"\"\"\n",
        "        Args:\n",
        "            w (Pandas Series or dictionary): Weights of the SVM\n",
        "            b (float): Bias of the SVM\n",
        "        \"\"\"\n",
        "        self.w = pd.Series(w)\n",
        "        self.b = float(b)\n",
        "\n",
        "    # Call method to compute the decision function\n",
        "    def __call__(self, X):\n",
        "        \"\"\"\n",
        "        Args:\n",
        "            X (pandas.DataFrame): Input data\n",
        "\n",
        "        Returns:\n",
        "            numpy.array: Array of decision function values\n",
        "        \"\"\"\n",
        "        return np.sign(X.dot(self.w) + self.b)\n",
        "\n",
        "    # Representation method for the Linear SVM class\n",
        "    def __repr__(self):\n",
        "        \"\"\"\n",
        "        Returns:\n",
        "            str: String representation of the Linear SVM\n",
        "        \"\"\"\n",
        "        return f\"LinearSvm(w = {self.w.to_dict()}, b = {self.b})\""
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ceb989c9-4473-4164-b1d2-a3de56e148b9",
      "metadata": {
        "id": "ceb989c9-4473-4164-b1d2-a3de56e148b9"
      },
      "source": [
        "A visual inspection of the banknote training set shows the two dimensional feature set can be approximately split along a vertical axis where \"variance\" is zero. Most of the positive outcomes are on the right of the axis, most of the negative outcomes on the left. Since $w$ is a vector normal to this surface, we choose\n",
        "\n",
        "$$\n",
        "\\begin{align}\n",
        "    w & = \\begin{bmatrix} w_{variance} \\\\ w_{skewness} \\end{bmatrix} = \\begin{bmatrix} 1 \\\\ 0 \\end{bmatrix},\n",
        "    \\qquad b = 0\n",
        "\\end{align}\n",
        "$$\n",
        "\n",
        "The code cell below evaluates the accuracy of the linear SVM by calculating the **accuracy score**, which is the fraction of samples that were predicted accurately."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "f515e9c0-4556-4cfd-8509-ae645dcedb61",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f515e9c0-4556-4cfd-8509-ae645dcedb61",
        "outputId": "d6c40b22-7c97-41f4-b90d-4680dc3fa5d2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LinearSvm(w = {'variance': 1, 'skewness': 0}, b = 0.0)\n",
            "Accuracy =  86.9%\n"
          ]
        }
      ],
      "source": [
        "# Visual estimaate of w and b for a linear classifier\n",
        "w = pd.Series({\"variance\": 1, \"skewness\": 0})\n",
        "b = 0\n",
        "\n",
        "# create an instance of LinearSVM\n",
        "svm = LinearSVM(w, b)\n",
        "print(svm)\n",
        "\n",
        "# predictions for the training set\n",
        "y_pred = svm(X_test)\n",
        "\n",
        "# fraction of correct predictions\n",
        "accuracy = sum(y_pred == y_test) / len(y_test)\n",
        "print(f\"Accuracy = {100 * accuracy: 0.1f}%\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "308d9f7b-5d0e-48b5-8d6e-570463f2b440",
      "metadata": {
        "id": "308d9f7b-5d0e-48b5-8d6e-570463f2b440"
      },
      "outputs": [],
      "source": [
        "def scatter_comparison(X, y, y_pred):\n",
        "    \"\"\"\n",
        "    Creates scatter plots comparing actual and predicted outcomes for both training and test sets.\n",
        "\n",
        "    Parameters:\n",
        "    X : DataFrame\n",
        "        Feature matrix as a DataFrame.\n",
        "    y : Series\n",
        "        Actual target vector as a Series.\n",
        "    y_pred : Series\n",
        "        Predicted target vector as a Series.\n",
        "\n",
        "    Returns:\n",
        "    None\n",
        "    \"\"\"\n",
        "\n",
        "    xmin, ymin = X.min()\n",
        "    xmax, ymax = X.max()\n",
        "    xlim = [xmin - 0.05 * (xmax - xmin), xmax + 0.05 * (xmax - xmin)]\n",
        "    ylim = [ymin - 0.05 * (ymax - ymin), ymax + 0.05 * (ymax - ymin)]\n",
        "\n",
        "    # Plot training and test sets\n",
        "    labels = [\"genuine\", \"counterfeit\"]\n",
        "    fig, ax = plt.subplots(1, 2, figsize=(10, 4))\n",
        "    scatter_labeled_data(\n",
        "        X, y, labels, [\"g\", \"r\"], ax=ax[0], xlim=xlim, ylim=ylim, title=\"Actual\"\n",
        "    )\n",
        "    scatter_labeled_data(\n",
        "        X,\n",
        "        y_pred,\n",
        "        labels,\n",
        "        [\"c\", \"m\"],\n",
        "        ax=ax[1],\n",
        "        xlim=xlim,\n",
        "        ylim=ylim,\n",
        "        title=\"Prediction\",\n",
        "    )\n",
        "\n",
        "    # Plot actual positives and actual negatives\n",
        "    fig, ax = plt.subplots(1, 2, figsize=(10, 4))\n",
        "    scatter_labeled_data(\n",
        "        X[y > 0],\n",
        "        y_pred[y > 0],\n",
        "        [\"true positive\", \"false negative\"],\n",
        "        [\"c\", \"m\"],\n",
        "        xlim=xlim,\n",
        "        ylim=ylim,\n",
        "        ax=ax[0],\n",
        "        title=\"Actual Positives\",\n",
        "    )\n",
        "    scatter_labeled_data(\n",
        "        X[y < 0],\n",
        "        y_pred[y < 0],\n",
        "        [\"false positive\", \"true negative\"],\n",
        "        [\"c\", \"m\"],\n",
        "        xlim=xlim,\n",
        "        ylim=ylim,\n",
        "        ax=ax[1],\n",
        "        title=\"Actual Negatives\",\n",
        "    )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "5e138c04-fdfb-427a-b1f4-f65a930010f8",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 803
        },
        "id": "5e138c04-fdfb-427a-b1f4-f65a930010f8",
        "outputId": "a58a2001-3cd9-436f-aa0a-328e4a61e1a2"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x400 with 2 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1UAAAGJCAYAAABinBm7AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAADquUlEQVR4nOz9eXxdd33njz/PdvdFuyxbknfHS2yHxNnAIUmjEieBhrAVKDMs3aBD2sC0fUBnvhQYpsyvM+24QxnSaRmgQzqBsjQMAQMKJLWzkIisduQktmNbsuyr5Up338495/fH8ZWvrq6ku+tK/jwfDz9s3eWczzlX1jkvvd7v11syTdNEIBAIBAKBQCAQCAQVIS/3AgQCgUAgEAgEAoFgJSNElUAgEAgEAoFAIBBUgRBVAoFAIBAIBAKBQFAFQlQJBAKBQCAQCAQCQRUIUSUQCAQCgUAgEAgEVSBElUAgEAgEAoFAIBBUgRBVAoFAIBAIBAKBQFAFQlQJBAKBQCAQCAQCQRUIUSUQCAQCgUAgEAgEVSBElUAgKAlJkvjsZz+73MsQCAQCQROyYcMGPvShD81+/eijjyJJEo8++mjN9iGuQ4JmRogqgWCZ+J//838iSRLXX399Re8fGxvjs5/9LM8//3xtFyYQCASCFcfXv/51JEma/eNwONi2bRsf//jHCQQCy728kvnRj34khJNgRaIu9wIEgsuVBx54gA0bNvD0009z4sQJtmzZUtb7x8bG+NznPseGDRu46qqr6rNIgUAgEKwoPv/5z7Nx40aSySRHjhzhK1/5Cj/60Y84evQoLperYet485vfTCKRwGazlfW+H/3oR3z5y18uKqwSiQSqKm5dBc2JcKoEgmXg9ddf54knnuCv//qv6ezs5IEHHljuJQkEAoFgFXDHHXfwgQ98gN/5nd/h61//Ovfddx+vv/46Dz30UNHXx2KxuqxDlmUcDgeyXLtbTYfDIUSVoGkRokogWAYeeOABWltbueuuu3jXu95VVFTNzMzwiU98gg0bNmC32+nt7eXf/tt/y+TkJI8++ijXXnstAB/+8Idnyz2+/vWvA/Nr23Pccsst3HLLLbNfp9NpPvOZz3DNNdfg9/txu93cdNNN/OIXv6jHYQsEAoGgwfzar/0aYP0y70Mf+hAej4eTJ09y55134vV6+a3f+i0ADMPg4MGD7Nq1C4fDQXd3N7//+7/P9PT0nO2ZpskXvvAFent7cblc3HrrrRw7dmzefhfqqfrlL3/JnXfeSWtrK263mz179vA3f/M3AHzoQx/iy1/+MsCcUsYcxXqqnnvuOe644w58Ph8ej4fbbruNp556as5rcqWRjz/+OJ/85Cfp7OzE7XZzzz33MDExUf5JFQiKIOS+QLAMPPDAA7zjHe/AZrPxvve9j6985Ss888wzs0IpGo1y0003MTw8zEc+8hGuvvpqJicn+cEPfsDo6Cg7duzg85//PJ/5zGf4vd/7PW666SYA3vjGN5a1jnA4zD/8wz/wvve9j9/93d8lEonw1a9+ldtvv52nn35alBUKBALBCufkyZMAtLe3A6DrOrfffjv79+/nv/23/zZbEvj7v//7fP3rX+fDH/4wf/iHf8jrr7/O3/7t3/Lcc8/x+OOPo2kaAJ/5zGf4whe+wJ133smdd97Js88+y1ve8hbS6fSSa/nZz37GW9/6Vnp6evijP/oj1qxZw/DwMD/84Q/5oz/6I37/93+fsbExfvazn/F//s//WXJ7x44d46abbsLn8/Gnf/qnaJrG3/3d33HLLbfw2GOPzetZvvfee2ltbeXP//zPOX36NAcPHuTjH/843/rWt8o6pwJBUUyBQNBQhoaGTMD82c9+ZpqmaRqGYfb29pp/9Ed/NPuaz3zmMyZgfu9735v3fsMwTNM0zWeeecYEzK997WvzXrN+/Xrzgx/84LzHb775ZvPmm2+e/VrXdTOVSs15zfT0tNnd3W1+5CMfmfM4YP75n/95aQcpEAgEgobyta99zQTMwcFBc2JiwhwZGTEffPBBs7293XQ6nebo6Kj5wQ9+0ATMT33qU3Pee/jwYRMwH3jggTmPHzp0aM7j4+Pjps1mM++6667Za5Fpmuaf/dmfmcCc684vfvELEzB/8YtfmKZpXW82btxorl+/3pyenp6zn/xt/bt/9+/MhW5PC69Db3/7202bzWaePHly9rGxsTHT6/Wab37zm+edm4GBgTn7+sQnPmEqimLOzMwU3Z9AUA6i/E8gaDAPPPAA3d3d3HrrrYBVzvCbv/mbPPjgg2SzWQC++93vsnfvXu655555788vhagWRVFmm4gNwyAYDKLrOvv27ePZZ5+t2X4EAoFA0BgGBgbo7Oykr6+P9773vXg8Hr7//e+zbt262dd87GMfm/Oef/7nf8bv9/Prv/7rTE5Ozv655ppr8Hg8syXhg4ODpNNp7r333jnXovvuu2/JdT333HO8/vrr3HfffbS0tMx5rpLrWjab5ac//Slvf/vb2bRp0+zjPT09vP/97+fIkSOEw+E57/m93/u9Ofu66aabyGaznDlzpuz9CwSFiPI/gaCBZLNZHnzwQW699VZef/312cevv/56/uqv/opHHnmEt7zlLZw8eZJ3vvOdDVnTN77xDf7qr/6K48ePk8lkZh/fuHFjQ/YvEAgEgtrx5S9/mW3btqGqKt3d3VxxxRVzwiJUVaW3t3fOe1577TVCoRBdXV1Ftzk+Pg4wKz62bt065/nOzk5aW1sXXVeuDPHKK68s74AWYGJigng8zhVXXDHvuR07dmAYBiMjI+zatWv28f7+/jmvy625sG9MIKgEIaoEggby85//nPPnz/Pggw/y4IMPznv+gQce4C1veUvV+1not37ZbBZFUWa//uY3v8mHPvQh3v72t/Mnf/IndHV1oSgKX/ziF2cvgAKBQCBYOVx33XXs27dvweftdvu8RD7DMOjq6lowibazs7Oma1wu8q9/+Zim2eCVCFYjQlQJBA3kgQceoKurazbdKJ/vfe97fP/73+f+++9n8+bNHD16dNFtLVYu0drayszMzLzHz5w5M6dM4jvf+Q6bNm3ie9/73pzt/fmf/3kJRyMQCASC1cDmzZsZHBzkTW96E06nc8HXrV+/HrCcrfxrycTExJJuz+bNmwE4evQoAwMDC76u1FLAzs5OXC4Xr7zyyrznjh8/jizL9PX1lbQtgaAWiJ4qgaBBJBIJvve97/HWt76Vd73rXfP+fPzjHycSifCDH/yAd77znbzwwgt8//vfn7ed3G/U3G43QFHxtHnzZp566qk5aUw//OEPGRkZmfO63G/t8n9L98tf/pInn3yy6uMVCAQCwcrgPe95D9lslv/0n/7TvOd0XZ+9zgwMDKBpGl/60pfmXDcOHjy45D6uvvpqNm7cyMGDB+ddt/K3tdi1LR9FUXjLW97CQw89xOnTp2cfDwQC/NM//RP79+/H5/MtuS6BoFYIp0ogaBA/+MEPiEQi/MZv/EbR52+44YbZQcD/9E//xHe+8x3e/e5385GPfIRrrrmGYDDID37wA+6//3727t3L5s2baWlp4f7778fr9eJ2u7n++uvZuHEjv/M7v8N3vvMdDhw4wHve8x5OnjzJN7/5zdnfFOZ461vfyve+9z3uuece7rrrLl5//XXuv/9+du7cSTQabcRpEQgEAsEyc/PNN/P7v//7fPGLX+T555/nLW95C5qm8dprr/HP//zP/M3f/A3vete76Ozs5I//+I/54he/yFvf+lbuvPNOnnvuOX784x/T0dGx6D5kWeYrX/kKb3vb27jqqqv48Ic/TE9PD8ePH+fYsWP85Cc/AeCaa64B4A//8A+5/fbbURSF9773vUW3+YUvfIGf/exn7N+/nz/4gz9AVVX+7u/+jlQqxV/+5V/W9iQJBEuxrNmDAsFlxNve9jbT4XCYsVhswdd86EMfMjVNMycnJ82pqSnz4x//uLlu3TrTZrOZvb295gc/+EFzcnJy9vUPPfSQuXPnTlNV1Xnx6n/1V39lrlu3zrTb7eab3vQmc2hoaF6kumEY5l/8xV+Y69evN+12u/mGN7zB/OEPf2h+8IMfNNevXz9nbYhIdYFAIGhacrHhzzzzzIKv+eAHP2i63e4Fn/9f/+t/mddcc43pdDpNr9dr7t692/zTP/1Tc2xsbPY12WzW/NznPmf29PSYTqfTvOWWW8yjR4/OG+VRGKme48iRI+av//qvm16v13S73eaePXvML33pS7PP67pu3nvvvWZnZ6cpSdKcePVi16Fnn33WvP32202Px2O6XC7z1ltvNZ944omSzs1CaxQIKkEyTdGdJxAIBAKBQCAQCASVInqqBAKBQCAQCAQCgaAKhKgSCAQCgUAgEAgEgioQokogEAgEAoFAIBAIqkCIKoFAIBAIBAKBQCCoAiGqBAKBQCAQCAQCgaAKhKgSCAQCgUAgEAgEgioQw38LMAyDsbExvF4vkiQt93IEAoHgssE0TSKRCGvXrkWWxe/8cojrkkAgECwfpV6bhKgqYGxsjL6+vuVehkAgEFy2jIyM0Nvbu9zLaBrEdUkgEAiWn6WuTUJUFeD1egHrxPl8vmVejUAgEFw+hMNh+vr6Zn8OCyzEdUkgEAiWj1KvTUJUFZArrfD5fOLiJRAIBMuAKHGbi7guCQQCwfKz1LVJFK0LBAKBQCAQCAQCQRUIUSUQCAQCgUAgEAgEVSBElUAgEAgEAoFAIBBUgRBVAoFAIBAIBAKBQFAFQlQJBAKBQCAQCAQCQRUIUSUQCAQCgUAgEAgEVSBElUAgEAgEAoFAIBBUgZhTJRDkEUqGCKfC+Ow+/A7/ci9HIBAIBAL0kI4e1lF9Kqpf3LoJBM2I+J8pEAApPcXgqUGGxoaIZWK4NTf71u5jYNMAdtW+3MsTCAQCwWWIkTIIDgaJDEXIxrIobgXvPi9tA23IdlFsJBA0E+J/pEAADJ4a5NDJQyiyQq+vF0VWOHTyEIOnBpd7aQKBQCC4TAkOBpk+NI2kSDh6HUiKxPShaYKDweVemkAgKECIKsFlTygZYmhsiG53N13uLhyqgy53F12uLobGhgglQ8u9RIFAIBBcZughnchQBK1bw9ZlQ3bI2LpsaF0akaEIekhf7iUKBII8hKgSXPaEU2FimRg+u2/O436Hn1gmRjgVXqaVCQQCgeByRQ/rZGNZVN/cTg3Vr5KNZdHDQlQJBM2E6KkSXJbkB1L47D7cmptwKoxDdcx5jVtzzxNbAoFAIBDUgzmBFD4Vxa2gh3VsDtuc1yhuZZ7YEggEy4v4Hym4rFgokGJv914eOf0Ipmnid/gJJUOMx8c5sPmASAEUCAQCQV1ZKJDCvddN6JEQmJZDpYd0MuMZWg+0ihRAgaDJEOV/gsuKhQIpAA5sPoBhGoyGRzFMgwObDzCwaWCZVywQCASC1c5CgRQSEq0HWjENk+RoEtMwaT3QSttA23IvWSAQFCB+zSG4bCgMpABwqA5M0+SFwAvcd8N97O/fL+ZUCQQCgaBhFAZSAFa5nwnRF6L03ddHy/4WMadKIGhyhFMluGwoJZDC7/DT5+8TgkogEAgEDaGUQArVr+LocwhBJRA0MUJUCS4b8gMp8qk2kCKUDDESGhHR6wKBQCAom/xAinyqDaTQQzrJkaSIXhcIGoT4lYfgssHv8LNv7T4OnTxUk0CKhUIvBjYNYFftdToKgUAgEKwmVL+Kd5+X6UPTNQmkWCj0om2gDdkufpcuENQL8b9LcFkxsGmgZoEUC4VeDJ4arMPKBQKBQLBaaRtoq1kgxUKhF8HBYB1WLhAIcginSnBZYVft3LXtrqoDKRYLvRgaG2J//37RlyUQCASCkpDtMh13dVQdSLFY6EVkKELL/hbRlyUQ1AnhVAkuS6oNpCgl9EIgEAgEgnKoNpCilNALgUBQH4SoEggqoF6hFwKBQCAQVEq9Qi8EAsHSiP9dAkEF1Dr0QiAoRigZIpwKY2IiIYn5aQKBYFFqHXohEBQS0nXCuo5Ptb6Xcv/2q+J7S5wBgaBCcuEWQ2NDjIZHcWvuikMvBIJ8csmST40+xcsTLxNMBGlztrGzcyc39N4gEiYFAsGC5MItIkMRkqNJFLdSceiFQJAjZRgMBoMMRSKEslkCqRQmsMZmw6+q7PN6GWhrwy5fvkVwQlQJBBVSq9ALgaCQXLLkTGKG8fg4NsXGRHyC12deZyY1A8Bd2+5a3kUKBIKmpFahFwJBPoPBIIemp+nWNGK6zolkEgnwqCptksSh6WkA7uroWN6FLiOXr5wUCGpEtaEXAkE+uWRJv83PTHKGNmcbPZ4eWhwthJIhfDYfQ2NDYti0QCBYlGpDLwSCHCFdZygSoVvT8KoqY+k0a2w2ujSN86kUXkWhS9MsF0u/fMNQhKgSCCoglAwxEhqp6Y1tPbYpWHnkkiU1RSOdTeNUnQA4VSfpbBqbYhMJkwKBoCh6SCc5kkQP1ebGttbbE6xMwrpOLJvFp6okslnShoFTlnHKMmnTJGEY+FWVWDZL+DIWVeLXFwJBGeR6XYbGhohlYrg1N/vW7qu4xyWUDDERn+DZsWcZnhyuyTYFK5tcsmQmm8Gm2EjoCbw2Lwk9gU2xkc6mRcKkQCCYg5EyCA4GiQxFyMayKG4F7z4vbQNtyPbyf3+eHk8z+aNJEq8kMLNm1dsTrGx8qopbUQjrOl5VxSbLJAwD0zSxSRJOWSak67gVZTbA4nLk8j1ygaACcr0u3e5uen29hFNhDp08BJTX45Ivzl4MvMhoZJStbVu5uudq4pl4RdsUrA7ykyVbHC2cDp0mmoqSMTL0+/oJp8MiYVIgEMwhOBhk+tA0WreGo9eBHtatBECg467Se1xy4izwTwESwwmUFgXXFheKS6loe4LVQS6I4tD0NCaw1mbj+VgMCdjj8RDOZhnPZDjQ2npZpwCKXzcIBCWS63XpdnfT5e7CoTrocnfR5eoqu8clJ84y2QzxTByPzcO5yDlOTZ+qeJuC1cPApgEObD7AhpYNdLm6MEyDTlcnG1s3ioRJgUAwBz2kExmKoHVr2LpsyA4ZW5cNrUsjMhQpq3QvOBhk6qEp0ufT2HpsKF6F+GtxMpOZirYnWD0MtLVxoLUVwzRxqypbHA42ORx4ZBnDNDnQ2spA2+WdMHn5ykmBoExyvS69vt45j/sdfkbDo4RT4ZLcg3xxpsrq7A1zPBPnzMwZtrZtLXubgtVFYbKkmFMlEAgWQg/rZGNZHL2OOY+rfpXkaNJKACwhrCInzhSfgqRJKF4FSZXAhOSZJPY+O/qUXvL2BKsLuyxzV0cH+1taxJyqBRBnQCAokVyvSzgVxqFeuniFkqGyelzyxZlpmrN9M07VyUxyhoSeIJPNiL4ZAX6HX4gogUCwKKpPRXEr6GEdm8M2+7ge0lHcCqqvtFu9nDizddmQbTJGwkDxKshOmexMlsxEBsVb+vYEqxN/gYASYuoSovxPICiRXK9LIBYgEA2Q1JMEogHG4+PsW7uv5JvffHHm1JxsaNlAOBVmMj6JLMtEUpGyt9msiERDgUAgqC+qX8W7z0smkCEdSGMkDdKBNJnxDN593pJdpZw4M9IGjg0OsuEs2UiWbDSLkTHIhrNlba+ZCek6I8nkZR3/Lag9K/9/hkDQQHK9LENjQ4yGR3Fr7rJ7XPKDCEzTZGPLRmaSM7w69Sq9jl5sim3F983UOiVxtRNKhhYcIL3Yc83CSlijQLCaaRuwelkiQxGSo0kUt0LrgdbZx0shJ86mD02jtWk4tjlIvJpAD+m4trtov7u9rO01IynDYDAYZCgSIZbN4lYU9nm9DLS1YZeFz5BPSNcXLe1b6vlmoNFrbM6zIBA0KYW9LpXeROaLswuxC2xp28LbrngbV6+5mk5354q/Ma1VSuJqZzHxCTS9MBXiWSBoDmS7TMddHbTsb7F6nnxqRY5SvjhT/Sr+G/w4r3DSfmc7ti7bEu9ufgaDQQ5NT9OtafQ6HIR1nUPTVqrhXR0i1RCWFp4rQZgu1xqb4+hL5F//9V9529vextq1a5EkiX/5l3+Z87xpmnzmM5+hp6cHp9PJwMAAr7322vIsVrCq8Tv89Pn7KhY/OXF23w33ce9193LfDffxnl3vYUv7lhUvqGqZkrjayYlPRVbo9fWiyAqHTh5i8NTgos+VSr3LL2uxxpWOuC4JmgnVr+Loc1RcopcTZ3339dF7by/9n+qn50M9q0JQhXSdoUiEbk2jy2bDIct02Wx0aRpDkYgoBbxITngqkkSvw4EiSRyanmYwGCzp+VKod/llLdZYCStKVMViMfbu3cuXv/zlos//5V/+Jf/jf/wP7r//fn75y1/idru5/fbbSSaTDV6pQFAa1YqzZiQXxFEYsuF3+IllYoRT4WVaWXOxmPg8cvYIR84eqViYpvQUD7/6MAefOsiXnv4SB586yMOvPkxKTzVk/ZeTeBbXJcFqpFpx1oyEdZ1YNjtvOK1fVYlls4SFqFpSeJ5NJqsSpinD4OHJSQ6OjPCl0VEOjozw8OQkKcNo2DHUUzyvKFF1xx138IUvfIF77rln3nOmaXLw4EH+43/8j9x9993s2bOHf/zHf2RsbGzebw4FAkH9yA/iyKfclMSVTCkO0WLiczo5zXRyumJh2ggHSYhnC3FdEghWBj5Vxa0o88RTSNdxK8o8sbUaWcohWkp4nk+lqhKmjXCQllM8r5rvoNdff50LFy4wMHCpud/v93P99dfz5JNP8t73vrfo+1KpFKnUpd/ehsOXx42AQFAvCoM4/A4/oWSI8fg4BzYfWFWuXCHl9BgtFtHf6mgFqCi+v9BBAnCoDkzTZGhsiP39+2vyGdRqxMBqRlyXBILmwa+q7PN6OTQ9jXnx65CuM57JcKC1tWnDFmpBqT1G+cLTYbtU8pkTnj12+6LPLyZMCx0kAIfNhgkMRSLsb2mpyWew1DHUUzyvKKdqMS5cuABAd3f3nMe7u7tnnyvGF7/4Rfx+/+yfvr6+uq5TIFit5LszA5sGOLD5AIZpMBoexTCNFZ9oWArlOESLRfTv79/P/v79FcX3N8pBqtWIgdWMuC4JBMtLoTMz0NbGgdZWDNNkNJnEME0OtLYy0LayUw2XolSHKCc8A5kMgXSapGEQSKcZz2TY5/XS73As+vxioqhRDtJSx1BP8bx6ZXmJfPrTn+aTn/zk7NfhcFhcwARNTzNFWC/mzlSbkriSWMghSmQSPHLqEXZ376bf3z/nPaVE9Jcb399IB6kWIwYE8xHXJcFKRQ/pVaUP1orFnJm7OjrY39LS9HHgtWIhhyhhGDwyPc1uj4d+x6VrRU5gDkUijCaTuBVljvBc6vmFaKSDVOkaq2XVfCetWbMGgEAgQE9Pz+zjgUCAq666asH32e127HYR/StYGTQywrpU4VZKfHrOHVnNwirnEPX6egHIZDMMTw5zcvokU/EpkOC2jbfN+ayWiuivJL6/keWXtRoxsFoR1yXB5YKRMggOBokMRcjGsihuBe8+L20Dbcj22hZFlTJ7aKno9Nz7cu7IahZWOYeo96JwyhgGw/E4J5NJptJpkCRua2mZLQW0y/KiwnOp5xeikeWXla6xWlbNd9HGjRtZs2YNjzzyyOzFKhwO88tf/pKPfexjy7s4gaBG1Gv+U76AcqiOkoXbYv07T40+RSwTY3hi+LKYYVToEA1PDnNs4hgyMu3Odlyqa8HPyu/wLyhGFntuIRrtIFWyxssBcV0SXC4EB4PW0OBuDUevAz2sM33IEjEdd1U+/ynf+cp65ZL6gpbq3bnW5+OZcLip5yzVkkKHaDge51gshixJtGsaLlkuOqvLv4QQWer5YjTaQapkjdWwokRVNBrlxIkTs1+//vrrPP/887S1tdHf3899993HF77wBbZu3crGjRv5//6//4+1a9fy9re/ffkWLRDUiHoEEBRzvkzTZDw+zlrv2iWFW6E7k8Pv8PPY6ccYDY+yuW1zwwYAL2dZZL5DlMgkODl9EhkZA4MtbVvob+knEA3UNCxiIYSD1DjEdUlwuaOHdCJDEbRubXaelc1hA9MaItyyv6XsUsBiztcrW01+ulen021bdHBvoTOTw6+qjCaT/Ghqil9Fow0dAFyKu1Yv8h2ihGFwMplEliQMYIvTSb/DQSCdrmlYxEIsl4PUKFbUkQwNDXHrrbfOfp2rOf/gBz/I17/+df70T/+UWCzG7/3e7zEzM8P+/fs5dOgQjoL/WALBSmQxATMaHiWcCpd941zofI1Hx/nZqZ+xtX0rV625ClhcuC3UvxOIBggmguzo2FHXBLoc9S6LLFWs5ZygR049wlR8inZnO1vatrC9YztQ3WdVyXqEg1R/xHVJcLmjh3WysSyO3rnf06pfJTmatJymMkVVofMVnU4z+ZNptiad2A54gIWT4xbr3VEkiVcSibon0OUoNXWvEsoRajkn6JHpaabSado1jS1OJ9vdbuCS4AzresXHX856Gu0gNYoVdUS33HILpmku+LwkSXz+85/n85//fANXJRA0hmoDCApvxIs5Xx67B03RmEpMkcgkcGpOYGExsFD/zrnIOdqcbXR5uuasoVaiopB6lUWWK9ZyDtHu7t0ggUt10d9yKZyi2rCIRvbUCUpDXJcElzuqT0VxK+hh3XKoLqKHdBS3gupb+FazWLBFMecr26EQm4GOYzrRN2YxfApQXAws1rtzjdvNy/E4vjyxtdB2asFSvV2VUIlQyzlEuz0ekCRcsjwnnKKasIh6CseVxooSVSuCUAjCYfD5wC9+QyyoHZUGECx0I35F+xXznC+n6sRr8xJNRUnol0TVYmKgWP/OnVvu5KXxlxqSQFfPuUyVirV+fz+3bbyNQycPEYgGahYWUS/xKFjdNEsim2B1ovpVvPu8Vg+VaX2th3Qy4xlaD7QW/Z5bLNiimPPlkBVkn4p+QUeOGLOiaiExsFDvzrU+H2fOnWtIAl295jJVI9T6HQ5ua2nh0PQ0gXS6JmER9RCOKxXx07VWpFIwOAhDQxCLgdsN+/bBwACIFCdBjagkgGChG/GcwMoXPk7NSYerg2AySCQVwaW5lhQDC/XvuF91NySBrh5lkVC9WKt1WESjhvoKVg+NTGQTXN60DVgiJjIUITmaRHErtB5onX28kMWCLVr2t8xzvpyKzPq0xmt2nZA9i8dQFhUDi/XuNCqBbqnerkpcsVoItVqGRTRqoO9K4fI50nozOAiHDkF3N/T2Wm7VIes3yOzfL9wrQU0oN4BgsRvx4YlhdnTs4PDI4TnCx2v3csfmO5AkqSwxUNi/U6qoqDZcol5zmaoVa/mlgOcj5+nx9sybU9XI9QguP5a6cRXulaBWyHaZjrs6Svq+KiXYopjz1RdRMd7Yyi+9UslioFjvTqmiotpwiXrMZaqFUMsvBTyfStFjt88pBWz0elYTl8+R1pNQyHKouruh62IPicMBmQw88AAcOQLZrHCvBDWj1ACCpW7Er157NW6be47wuWvrXQxsGiCpJ6sSO4sJwFAyxER8gmfHnmV4srrI9XrNZSpXrBWKw5Se4qFXHuKp0afQDZ12Z3tV/U+NHOorWPksdONqZkwCDwQIHwljZA3hXglqiupfWqSXEmxRzPnqvKONKwbauFExqhI7i7lYIV1nIp3m2UiE4Xi8qh6hesxlKleoFROG4+k0P5qa4pVEgqxhVNUD1ciBviuBy+to60U4bJX89c69ceX8eTh+HNauhf7+ue7VXaL/QVAbFnN6lroR73R1smXblqLCx67aa+J85AvA/P6uFwMvMhoZZWvbVq7uuZp4Jl5xf1A95jKVKtaK9azt7d7L0NgQPzv1M2yKDY/Nw7RrmkAsUNHxlbOeSlnOOHpB7VnoxjV9Pk38eBz7WjuO/trNExII8lmsj6+UYIvFnC8/ck3cj3wXKz9s4cVolNF0mq1OJ1d7vcSz2Yp7hGo9l6lUoVYsPGKv2w2SxLfHxzkej9Oiqmx1OnEpSsXHV++BvssZRV8Jzb/ClYDPZ7lQ4bDlUAEkEnDiBLS0QF+f9bjDAaZpuVr796/6UkBxk1ZfSkmCK/VGvFHR27n+Lr/NTzwTx2PzcC5yjhZHC3u691TcH1SvuUyliLViPWv3/+p+Tk2fos/fR6erk4SeYDQySi+9VfU/1UM8ikTB1UmxG9dsIkv8RBy1RcXR50B2yFXPE1ppiNCO+lJKH185wRalOF+1IBe24FcU4oaBR1E4l0rRoqrs8Xgq7hGqx1ymUoRasfCIr164QMYwSJsmPXY7qiTxajKJTZbpstkq7oGqx0DflZooKH6i1AK/3yrrO3TIEk1+P4yMwMwMXHstOJ1zXzs6agmwVSqqxE1aYyg1Ce7atdcyEZvglalXiKQjNbkRr4T8/i5VVjFMA7/dTzQd5WTwJFvbtlbdH1RrcbiUWCvWs2aaJuFkmGQmid/uR5VVvDYvmDAZn6TV2Vrx8dVDPIpEwdVJsRvX1EiK7EwWz7UeZKc857WVzhNaKYjQjsawWB9fvhPqu9ZHZiJD4pUEycjSwRb1JD9sQZUk0oaBS1HImiZnkkm2Op1V9wjVci7TUkKtWHiEqSjEslmShoFTUfDIMtpFcXI6maTP4WAyna7o+OohHFdqouDq/Om5HAxcvEEdGrJEk6bBjh3Q0zP3daGQ5Wr5Vm//g7hJqz+lJME5VMcccatICjs7dnLn1jvnzY9qBPn9XelsmunkNGdCZ5CQSGfTdLm72NCyoWb9QbV0ShcSa8V61hJ6Ak3VUGWVaDqKS3MBVrLiVGIKVVarPr5aiUeRKLi6KexLkTQJ5w4n9p65v9wqZZ7QSqfUm31B5ZQSQCE75DniVlIkXDtddNzZMfueRpMLW1hjt3M8Hmc0ncYwTWyShE2Wieg6piTVpEeoluVsCwm1YuERCcNAkiQ0SUICkoaBJss4ZZnpTIbxdBpvlcdXK+G4khMFm3NVKxG73eqTyk/6O3LEcq8UxXKlQiEYH4cDB1atSyVu0hpDKUlwR84emSduf3XhV3S6O5dF3Ob3dwWiAZJ6klQ2hSZpKLLCsYljBJNBPnrNR6v6HmmkU1qsZ82pOlFQ8Ng8JPQE4VQYp+pkMj5JJpvhht4b6vZ/oFwhORoe5Xz0PBtbNs55XCQKrg6K9aXMHJlh+tA0kiKVNE9oNVDKzf5qPfZGUkoARfRIdJ64jf0qhq3TtmziNhe28Fwkwmg6TauqMqXrJAyDqGHwYixGt91eVY9QI8vZioVHOGUZ0zRRJIlNTievxeOYQBZImybhbJZb6yhWyhGTYV0nmMnQpmkkslmcysKDnpuN5lzVSsbvvySYCt0rt9sSVAONLbtqJCL2uTEsFUBhYjaduM31dz30ykO8NvUa67zrCCaCjMfGaXG04LV58dl8XLv22qr2s9hcrht7b6xpj1+xnrVwKozb7sahOmhztjGVnCIYDpIxM9yx5Q7uvuLumuw7n5yQPHL2CNPJaVodrezv3z9PSOZEl12x88zYMxw5e4SXAi/x6tSr7OzcyY6OHWiKJhIFVxn5fSnlzhNaDZRysy9EVfUsFUCRE7HNJm79qsoOt5sfBYN4FIVeux3TNBk3DNySxEQmw7s6O6vqEVqonC1mGNzo89U0iKFYeET4opAD6LkotE7E48xks+xwOrm7vb2q41uInJg8EgoxncnQqmns9/vniMl8weWQZZ4Mh3kxFkM3TVpUlfUOBztcrhWRKNi8K1sNFHOvVqlDlUPEPjeGpQIoJKQFxe3J4EmOTx5ne8f2hgurgU0DTMQmODZ+DFmW6XJ3sbd7L12eLgzDIJaJkcqmKt5+MadUkRRemXyFrzzzFR4/+zhtzrYFnatKSgaLhUf89lW/DcALgRdoT7SjSAo39N7A3dvvrktf4Y9f+zFfff6rxNIxJEnCNE2OThwlk83w9h1vn+fejYZHiaQiXNNzDVd2XcnzgecZGhsinU3T5+ury5BmQXNQzjyh1UIpaXOC6lkqgAKJBcVt/GSc2PEY7u3uZfl+vNrjoddmI26aRLJZumw29nq9+BWFyUyGXW53xY5SsXI2RdN4JR7nK+fO8fjMDG2atqBzVUnJYLHwiN9eswYkiReiUVoUhet9Pq5wOrmzo2N2XbXmx5OTfPXCBWLZrHVtSiQ4GouRMQzu6OiY596Zpsm4rtNrt3M6lSJ+0Smc0XVaNa3mA5prTfOubDWR716tcuod+yy4xGJJcEk9OU/cZrIZnh59mnORc5iYVc9NqgS7aueeHffwytQr6IbOGs8aXp95nRcuvMB0YhpN0Xhy9Em63F0VramYU3p88jinQ6fBhDZnG4qszOvxq6ZkcLHwiFs33lr3BMxQMsS3X/42E7EJut3dOFQHST1JIBbg2y9/m1s33jqnFLTd2T57nFOJKfZ078Gm2Dg6fpRj48dod7YvS5CJoLE0KlWtGSgnbU5QHYs5oUbSmCdujYxB+OkwqXMpxs1x1HZ1WQJEOm029ng8ZEwTj6KgSRKvJ5P8KhIhZRh8c3yc/clkReV6xXqcjsdinE6lwDRp0zQUSZoXxFBNyeBi4RG3trY2JKY8pOt8e2KCiUyGbk3DIcskDYNAJsO3JyZIA4dDoVn3bjyd5mfT02xzOrnB76clFuN0Msl0JsO5VIrfqJObVkvETxJBzalH7LNgPovdzNtV+zxx+/To07w4/iJ7u/eypW3LsgWI+B1+9vfv59DJQzx3/jnOhs+iyRqSLNHr6+Xw2cPWEOIK1lTolCYyCU7PnMam2HCpLlocLTg157wyyFqEqxQLj2hEVP1oeJQzM2dod7bjtXsB0BQN3dA5M3OG4YnhOe5dMBFEkzX8dj/Dk8Os861jd/du+nx9nJo5xQf2fIBdXbvqumaBoNFcjmWPy8FiTqhsl+eJ2/DTYWIvxnDvdePc4ly2AJH8kjm7LHMqmeTFaBQTuMrtxi3LFafPFfY4JQyD08kkNknCpSi0qCpORZkXxFCLBLxi4RG1TCJcjNFkkjPJJO2ahvfi/jRZRjdNTiUSPDYzQ4/NNuuS5cTshXSaiXSaLS4XW1wuZjIZpnSdG/3+po5TByGqBHWgXjODBMVZ6MY9X9yeDJ7kXOQce7v3ckPvDWiKtqw9VgObBoilY3xl6CuYpolLc7GzZSfbO7YTTATLXlN+2V6+mDRMg+nkNLIks75jPU7NGm+Q3+MHNF3/WUVIxR+OZqJz3DtVUplOTjOdnCaTzQCwo2MHbc42ejw980pGBYLVwOVY9ricLOSE5ovb+Mk4qXMp3Hvd+G/wI2nSsvZY5VyQI6EQx6JRXLLMLreb7W43miRVlD6XK93b4XZzOBTCBAzTZDqTQZZl1jscRYMYuLivlZiAN4u0wEVJktCBeDY7pz9KxUopPJtMkjJN/IrCBoeDNk2jXVWbupcqR/OvULAwoVBT92o1aqCsoDj54vb45HFMTLa0bUFTNBKZBAk9gV2xM5mYbHiAiF21c2PfjRwZOUK7s33WQYLyQk2KBTRcv+56bttwGy8EXmAqMYUma6zzrWNHx47Z9+X3+K30cJVeXy8bWjZwYvoEqqTi1JwkMgkCsQC9vl7WeNbMce9Oh06T0BMkMgmcqhNZknl67Gm6XF18dF91yYsCQbMP172cyh6bkXxxGzseY9wcx7nFiaRZN+BGwsA0TPQpveEBIrmSuQ1OJ9O6ziank5a8G/ly0udShsFDExM8FQ6TNU18qkq7opA2TUK6jibLrLPb2eFyzb4nP4ihWMlguWtYbnrtdjY4HJxIJlElCacsE85mOZ9Ksd7hYI3NNieh8HQqRVTXMbDSCg3g6UiELk3jo2vXNv3xghBVK5NUCgYHrVTBWMxKFdy3z0oVtIvhuoK5+B1+tndsp93ZTjAeZCoxxemZ06SzadLZNGu9a7Erjf++8dl9tDvbUWRlVlBBeaEmCwU0/PZVv819N9xHOBXmyZEnOTxymGAiuGCP30oOV/E7/Lx757v56nNfJZwKE0qFmE5Mk9ATAHz35e9imiZjkTESmQQnp0/S4bRKR1yqCwkJt+rGa/dWnbwouHwRw3UF5aD6VSuUol1FD+toikbseIzk6SSZ6QyyJhN6MoSty9bw759eu50em420Ycx5vNT0uZRh8MXTp/nx9DQ2ScKjqrNOyy1+Pzf6/TwZCnE4HCZ4URyFdJ3xTGZOEENhLHo5a2gG/KrKu7u6+Or584R0ndOZDOFsFhlwSBJ2SWIsncYEbJLEy7EYPlVlm6aBJJE2TdyyjFdRuHaFzHZt/k9FMJ/BQWv+VXc39PZabtUhq/+Du8RwXcF8cgEi9w/dz3h8nA5XB6qsEkqFCKfDPDP2TMNnV1UbalJKQEOfv88q6ZPgqdGnmIhPzAtiWA3hKndsvQNN0Thy9gjPX3ieiBzhunXXcXXP1cQzccYiY3S5uginwkzFp2h3tnPL+ltY71+PbupISEwlpqpKXhRc3ojhuoJyyQ8Qib8SJ3U6hWSTkGUZ+zo74cNhFLfS8O+fYpHkxUTPQjw0OcmPp6fxKAodqkrSMDiXsn62Dsfj3N7ezt2dnSBJPBUOM5HJ0K6qHGhtnS1BrHYNzcId7e1oksTXL1xgMpOhz+Fgu8vFWpuN8UyGLlXFME1eTyZJGQZXezzs8XrRDcMaWAxMZTKkCgRus7IyPhXBJUIhy6Hq7oYuq/8DhwNM03p8//6lSwGbvGxQUB+uXXstD9gfIJaJoWd1bIqNfWv3zabBLUfvUDWhJksFNIyGR3GoDgZPDTI8MUzWyKJKKjs6dsxL9Vvp4Sq5Us/d3bs5+ORBrll7Df3+fgA8Ns9sf9lHrv4ISJZD1d/SP/v+QDSwIlw5QXNSq+G6zV46KKg9bQNtGDGDc1+xUmkVl4JjvQPXDhd6UF+2+VXFIsnzRc9ChHSdp8JhbECnpqFKEposYwJTuk57JsNEOs0r8TjDsRhZw0CVJHa4XPNS/SpdQzNhl2X2t7RwJBRig8NBr90+20OmSBKGafLhnh4ius43x8dxyzKaJKEpCk5FIZBOrxhnDoSoWnmEw1bJX29BM7nfbw0YDocXFkqibPCyJpVN0evrZW/3XkxMnKoTp+YkqSdr2jtUzqynmoSaLNALC3MHAW9u20w4FebwyGHctrnpgs0erlLqOZWQUBV1NnAjR64/zG/3c9vG2zh08hCBaGBFunKC5qPa4bqidPDyRbbL+G70MfP4DFqbhtqiojitm+5aD2cuZ97TYpHkixHWdXTTxKOqJAwD70UB4ZRlguk0iiTxbCTC4XCYbk1js8tFWNc5HA7jVpQ5qX6VrqGRlHJOw7pO1jTpczhw5InGXH+YBOzyeNifTK54Z25lrFJwCZ/PEkPhsOVQ5QiFrMcXqzsVZYOXNbm48YyRmXPTXaveoWpmPVUSarJQQMNkYpItrVvw2r1lp/qVso5KBgRXur1yz2kpw7dXuisnaD6qHa4rSgcvb1SfitamISnSrKCC2g1nrmbeU7nx476L/VPTmsZoOg1YgmpS18mYJns8Hobj8bJS/UpdQyVDgivdVjnntDBSPn8f+S7UanDmhKhaafj9lrt06JBV8uf3W4JqfBwOHFjYpapF2aBgRVPv3qFazHoqh8KAhmg6imEadLm6ePfOdyMh1TTVrxrRmCNfQOVKExfbXrnntNTPuJldOcHKo5rhurUqHRSsXOo9nLkW855KJdcLFchk6AUmMxmm0mkywB2trez3+3k5Fqtpql81ojFHTkTZZZlnwuElt1XOOS21P2wlOHNLsbJWK7AYuPgb5aEhq+TP7bYE1cAiv2mupmxQsGqol0sRSoaWZdZTfkBDLlJ9f/9+BjYNkNSTNU31q0Y0FhNkpmkyHh9nrXdt0e1Vek5L/YzFyANBLal0uG61pYOC1UG9hjOHdL3h857yHZfWi31VN/h83N3RQdIwap7qV41oLBRk51Ipwtks13i9C26rknNajgvVqOHE9WBlrvpyx263yvX27y89cKKaskHBqqFevUPLNetpseOxq/aaOXNnQ2d55PVH8Nv8FYnGQkE2Hh3nZ6d+xtb2rVy15qqi26v0nDZ7f5hgdVLpcN1qSwcFq4N6DWdejnlPizkudlmuWapfSNcZTSY5crE/qxLRmC/IOmw2hiIR4obBZCbDWru96LYqOaerwYUqhdV3RJcTfn/p7lKlZYOCVUmtXYpSennqyULHU6prU9jXlPvarth5ZuwZHjn1yOyg4lAqxPaO7WiKVpJoLOY4eeweNEVjKjFlDeHNG3x8MniS45PH6fH2VHVOhRMlWA7KHa5b79Ivwcqi1sOZS+3nqQcLOS7luDb5vU3AvBK986kUL8ViXOl206ppaJI0u++lRGOh4xTMZNAkiXZV5UwyyVanE6eizG5rNJkkrKqYVD5DayW7UKWweo9MMJ9KygYFghJo1llPS7k2hWV5dsWOIilkzSypbIpz4XOE02F2de6i3dVOOpvm6MRRAHZ37y5J4BRznJyqE6/NSzQVJaFboiqTzfD06NOci1jRwu3O9tmhvc10TgWCWlOv0i+BoBnnPZXi2uSX5YV0nQvpNBLQbbcTyCvR2+h08moiwfOxGDZZZrfHA5QmcAodJ6eiYJNlDCBtmiQMA6eiMJXJMJpK8c3xcbIXyxdN02Qsk2mac9osXL5H3szUa45UYdmgaYIkQTJp/RGzqwRV0Mypcgu5NoVleU+fe5oXAy+yt3svu7t2M3RuiLgeJ+qPsrl1M8cmjiEjcyJ4Ar/dTygdWlLgFHPxnJqTDlcHwWSQSCqCS3Px9OjTvDhu7XtL2xbCqfDs0F7DNJrunAouL+o5Qyq/9Cs5mkRCwt5rx0gapMfTYm6VoCqaNVVuMdcmvywvahicSiYxAZsscz6dni3R2+PxsNPtZigS4WgsRp/DQcowShI4hS6eU5bZ4HDwdCSCW5aRgEA6zbORCABuWcZnsxHWdcbyBvc20zldbsRPqWaiUXOkHA44csTaTygEFy5Y4qq7+1KZoJhdJSiTldbLU1iWl8gkCCVDdLo6mUnOENfjaIpGu9bOmZkz3Lz+ZgBOTp9kKj5FXI+XJHAWcvG8di93bL4DSZI4GTzJucg59nbv5YbeG9AUbbbHyjANPvyGDyMhNf05Faw+GjVDykgZzByZITIUQQ/ppC+kQQJ7tx3FL+ZWCSpnpfXz5JfleRWF86kU3ZoGksRIKoUEc0r0drhcpE2TY9EopxIJemy2kgROMRevTdPourjfqUwGRZLwKQpbXa55PVu5wb0SNP05bRTiDDQTjZojlb+faBROnbJcK48H2trE7CpBVZTay1PreU/lUliWl9ATpLNpWhwtRNNRAGyKDQODdDaNburs6d6D3+4nkUlw3w330e/vL2lfxVy8u7beNZtSeHzyOCYmW9q2oCna7PtyPVsSEn3+vhqfAYFgaRo1Qyp/P0bUIHkqCSYoHgW1TRVzqwRVsxzzniohvywvns2SNk1aFAVJkohks8iSBJI0W6LXpmn02e20qyofWLOGXru95HUXc/E+unYt1/p8pAyDcDbLN86fp03T5rwvf3BvX0FgxeWMEFXNQqPmSOXvx+uF8+etfwOMjcHOnWJ2laCu1GLeUy0oLMtzqk5sio2Z5AwuzUWro5UNLRt4euxp3KobCYlANEA4HebA5gMlCypYOqVwe8d22p3tyxb0IRAUo1EzpPL3o3gVUudTaN0aEhLpsTSenR4xt0pQd2ox76kW5JfleRUFmySRNAyQJNyyTI/Nxgux2JwSvVy53y63u6x9LeXi5XqzliPoYyUifPRmITdHqjDa3O+3Hg+Ha7+fRALSaUu8OZ3WvxOJ2u9TIMgj18ekyAq9vl4UWeHQyUMMnhps6DpyZXmBWIBANIAkSfjtfibiE7Q4WpAkiTZnG12uLtZ41zCVmMIwjbJ6mkLJECOhEULJ0Ow++/x985y5wrUk9SSBaIDx+Dj71u4TJX+CZSE3Q6ow1lz1q2RjWfSwXvP9GAkDM20iO2Rkp4yRNsgmsjXfp0BQSK6PSZEkeh0OFEni0PQ0g8FgQ9eRP0A4nM3SY7cTyGS4kE6z1majx26nS9NYY7MxlclgmGbZ/UwhXWckmSSk67P77HM45jlc+WsJpNMkDWNWxO3zekXJXwHibDQLjZojlb8frxdsNiukAqx/O51idpWgbizXkOCFKCzL29i6kS1tW8ia2dkyvY/u+yjXrr2WVDZVcqliJW7cYkEfy10qKbg8adQMqfz9KF4FySZhJA0kJGSbjOJUxNwqQV1ZjiHBi5FflueRZTY5HEiAW1WRYU6JXjllipW4cYsFfSx3qWSzIc5As9CoOVKF++npgRdftP591VWW2BKzqwR1YrmGBC/EQmV51YqYwlTBcCrMoZNWr+Jd24r3KubWsrt7N+cj5+nx9tDt7m6KUknB5UmjZkgV7sfeYyf6YhRMcF/lRg+LuVWC+rIcQ4IXo1hZXm6d1QiY/FTBXoeDsK5zaNrqV7yro3i/ol2W2d/SwgaHAySJXrsdhyw3RalksyF+OjUTjZojlb8fjwc2bbLS/9xuMAwxu0pQN5Z7SPBCFIZrVDM4t1I3rpi7ZZom4/Fx1nrXlizOBIJa0qgZUvn7kT0yjk0OkEB1q5iGKeZWCerKcg4JXozCcI1qhF0lbtxCzlbGMHgkFCpLnF0OCFHVTBTOkarXzKhi+4HGzqmq1ywuQVOS7/w045DgWlKpG1fobo1Hx/nZqZ+xtX0rV625CljeUknB5Un+DKl6zalaaD9AXfdZjHrO4xI0H/nla802JLjWVOLGFXO2HpqaIqrr7HS7m6JUspm4PI+62fH7GyM0CvfTiH02ahaXoCko5r7s7d7LbRtu44XAC6tyoG0lblwxd8tj96ApGlOJKRKZBE7NCSxfqaTg8kb1N0ZkFO6nUcKmUfO4BM1BMQdmr9vNbS0tvBCNrsqBtuW6cQs5WzO6zrFolL0ez5zXL1epZDNxeR61YPlo1CwuQVNQrLfokdOPcGDzAe674b6mDF+otp9qoWG/i7lxxdwtp+rEoTiYik0xnZyeFVXLXSopEKxGGjWPS9AcFHNgHgmFONDayn19fU0ZvlBtKESxYb+LuXELOVtdNhu6aXIqkWCnLONUlNn1Xe4x65fvkQsaT6NmcQmaglJ6i5ppoG0t52ctluRXjEJ3K5PN8FrwNSbiE4zHx/n56z9ne8d2ejw9BJPBVVMqKRA0A42axyVoDkrpLWqmgba1nJ+1WJJfIcWcrYxh8GI0SsIweCoS4bVEgi0uFz02G0FdXzWlkpVy+R65oPHkZmT1zu01we+3gjnCYSGqVhBLOTrNlvS3FJUk9i3EYsN+i1Hobo2ER3gx8CIO1cFV3VeR0lMMjQ2xvWM7v7X7t1ZNqaRA0Azk5mQ5eufeSKt+leRo0uqxEqJqRVCKm9NsSX9LUUli30IsNew3n2LO1tPhMC/GYux2u/EoCq8lEgyFw2x3ufit7u5VUypZKc3zXSNY/TRqFpegrpTq6DRr0l8x6jU/q5wUwZxQOnL2CMfGj+FSXezq2sX6lvVE01Gm4lN4bB729+8XceoCQQ1p1DwuQf0ox81p1qS/YtRrflZhquBC5DtbJ+NxzqVS7HW7ucHvRzdNeux2pjIZPKrK/paWyzpOHYSoEjSSRs3iEtSVUh2dSnqLlotmcNVy7taGlg1MJ6fp8/VxIXqBx04/RjqbRpZkXJqLifhEU507gWCl06h5XIL6UY6bU25v0XKy3K5avrN1PBbDHB9ng8PB8ViM08kkacNAliRcssxEOt1U5245uLwlpaDxDAxYAsowrJI/MRdrRVHo6DhUB13uLrpcXQyNDRFKhua8fmDTAAc2H8AwDUbDoxim0bCkv1AyxEhoZN6aipHvqhVuo9GuWq+vlx5PD8cnj3N04iiyLNPqbCVtpBmNjPLs2LMNW4tAcLnQNtBG64FWTMMkOZoUs7FWEIVujkOW6bLZ6NI0hiIRQro+7z0DbW0caG3FME1Gk0kM02xY0l9I1xlJJouuq5B8V61wG4101fyqyna3m3ZV5blIhKPxOLIk0apppE2T0XSaZyORhqylmbm8JaWg8Sw0iyvnWIm5VU1NuY5Oub1FtaCSwIlmctX8Dj87Onfwo9d+hMfuwaE4iGfiGKbB1tatDE8OE0qGhFslENSQheZx6SGd9HhazK1qYipxc8rpLaoVlQRONJOr5ldVdrjd/CgYxKMoOCSJeDaLYZpsdToZjscJNVk/WqNZVU7VZz/7WSRJmvNn+/bty70sQTH8fujrs3qrHn4YDh6EL33J+vvhh615VoKmo1JHx+/w0+fva4gQyJUnKrJCr68XRVY4dPIQg6cGF33fcrpqhVy95mp6fb3YFBszyRlM02RX5y6uXns1sUxs3vlvFspxBy8nxLVp5aD6VRx9DmSHzOTDk4wcHGH0S6OMHBxh8uFJjJSx3EsUFFCNm+NXVfocjoYIgVyJoiJJ9DocKJLEoelpBoPBRd+3nK5aIVd7PPTabNhkmZlsFhPY5XZztddLLJud9xk0C+W4g9Ww6uTkrl27GBy8dPOkXsaKuSkIheY6UoWIuVXNT95n6Pc3j6NTdKlVBE4sh6u2EJ3uTvZ07yFjZPDYPDhVJ07NSSAaaLqQD6htHP1qRVybmgs9pM9xpAoRc6uan/ykv2ZxcxZba6WBE8vhqi1Ep83GHo+HjGniURScF+dUBdLppgv5gNrG0ZdCcx19DVBVlTVr1iz3MgSplCWYhoasGHW32wqpGBiwSgBBzK1qdhb4DAduuQkoMoOp/VoYGVnWEs5aBE6Uk9hXL/LLEe2KHZfmIhANNI14LaSWcfSrFXFtag6MlEFwMEhkKEI2lkVxK3j3eWkbaEO2WzdZYm5Vc1PsRnmv281tLS28EI3Om79U7dDcWlCLwIlSE/vqSX45ol2WcUkSgXS6qQRsPrWMoy+F5jr6GvDaa6+xdu1aHA4HN954I1/84hfp7+9f8PWpVIpUXqlZONycZTUrjlIcKDG3qrlZ4DO0A3fdlefoYMd/5Bn4v19ZWEA3iJUU474U5Q4QXi7qFUe/2ijn2iSuS/WjFAdKzK1qbordKD8SCnGgtZX7+vpmBZRDlhvqUizGSopxX4pyBggvJ/WKo1+MlfMplsD111/P17/+da644grOnz/P5z73OW666SaOHj2K1+st+p4vfvGLfO5zn2vwSlc5pTpQYm5V81LCZ+j3X3R0Hn64aUo4mylwolqaqRxxMZohjr7ZKffaJK5L9aFUB0rMrWpeSrlR7rt4P/Hw5GRDXYrFaKbAiWpppnLExViOOPpVFVRxxx138O53v5s9e/Zw++2386Mf/YiZmRm+/e1vL/ieT3/604RCodk/IyMjDVzxKiXnQBWKIr/fejz3W9fc3KpAwPqTTFp/j49bjwuXavko+AzjmQTBRJC42z73MywUXw6H9XdXl/V4qPTAglqFHDRT4EQtaGTIRyU0Uxx9s1LutUlcl+pDzoEqFEWqXyUby6KH9dmvvfu8ZAIZ0oE0RtIgHUiTGc/g3ecVLtUykrtRzjk7iWyWYCaDTZLmBCVUErO+ELUKOWimwIla0MiQj0pYjjj65jwTNaKlpYVt27Zx4sSJBV9jt9uxN7hEadVTjgOVm081NGSV/LndYm5VM3DxM8xMBxmWpzgzc5p0Nk1rOE2vZy3rXXbsUJMSzlqHHKwUh2e1sJrcwUax1LVJXJfqQzkOVG4+VWQoQnI0ieJWxNyqJiB3oxzMZJjMZDiTTJI2TTKGwRqbbbasrxYuRa1DDlaKw7NaWA53cFV/mtFolJMnT/Jv/s2/We6lXF7kHKhDh6xysfw5VAcOzL3JXmhulWB5ufgZjj54P2ekcbSWDtpTKmooxCPrw/RPPcNd7XfVpISzkpCDUDK0pGBqhsCJy4WV0v/VLIhr0/KQc6CmD02Dyewcqsx4htYDrXMcqIXmVgmWl9yN8v1jY0xkMrRrGqokMWMYRLJZngmHuaujoyY9TJWEHJQSitEMgROXC43u/1pVn+of//Ef87a3vY3169czNjbGn//5n6MoCu973/uWe2mXH+U6UH6/EFNNRmj/tTzx4gOsezVGy4yO7rQxcdM+pne1M54LIChHQBfbR5khB6slursUUbiSEO7g4ohrU/NQrgOl+oWYajau9fn4p0CAWDaLbhjYZJnrvF7aLpb25QIIqnEpyg05aHR0d71ohqTEWtJod3Dln7E8RkdHed/73sfU1BSdnZ3s37+fp556is7OzuVe2uWHcKBWPGFSvLCvl8h1e/EmTdIeJxmPE4+enBtAUEUJZ7khBys9unu1iMKFEO5gccS1qXkQDtTKJ2UYrLPb2ev1YpgmzovzkpKGMae0rxqXotzywUZHd9ea1SIKF6JR7uCq+kny4IMPLvcSBIUIB2rFkgsgmJQzyC1ds4/PCyCoQkD7kiY90zqpzDiO7n60aAJbNMEMEdzuuSEHVUV3LzWEukGsdFEoqAxxbWo+hAO1csmV9qUNY9ZFgvmlfdW4FCagmybj6TSdmkbCMHDKMuGLgiO/fLCa6O5mcYZWuihsFsRPFMHy0iQ3u4L5lB1AUI6AvjhY2D80xG+MnOHV2FmcNjdOxU42HqNFzdB50x34b7z0W8KKortLGULdIFbCPKfVVpYoEFSKHtKFk9WklFvaV45Lke/YnEokGI7Hscsy7ZqGBLgVhd9es2bO9ioJxWgmZ2g55jlVssZmEJ9L0bwrE6xumuhmV7AwdQsgyBss3L/rTdgeDaL98mXObexgamc/m5UOtrw4br3u4qyrigb7ljKEukE08zyn1V6WKBCUipEyCA4GiQxFyMayKG4F7z4vbQNtyPaVXwa1WqhXAEG+Y9Nhs0E8TlDXkYB2TbNeJElz3lNJKEYzOUPLMc+pVJpJfJaCEFWC5aGJbnYFC1OXAIKC2VZaIkGf4SG9fjs+m8aO9Tfj8rVZM8vyhkWX7ZyVOoS6QVQkChuEKEsUCCyCg0GmD02jdWs4eh3oYd1KCwQ67hJlUM1CPQII8h0br6IQSKfZ7nKRAdKGwUBrKxnT5IVolFvzHLFynbNmc4ZqkZRYL5pJfJZC88k8weond7Pr94OqWje5FQ6MFTSGJQfQhkIwMlLaZ1c4HDqRgHQaW1sHLkPBlZvTVzgsmjIH+xYZQh3PJAjaDZIzU3O22whyojAQCxCIBkjqSQLRAOPxcfat3QdQk+HH5VJYluhQHXS5u+hydTE0NtTw9QgEy4Ue0gkdCSE5JBSvguyQsXXZ0Lo0IkMR9FB1w18FtWepAbTlDO7NHyycMAzSpolDlvEpCkre/vKHDOcoZ7BvqQOMG0VOFAYyGQLpNEnDIJBOM57JsM/rBajJ8ONyqeUA50YhnCpB45mYgBdfhHgcDANsNli/HjZtggsXShoYK2gSKinjLJxt5XRa3wMzM+ByWV8nEiRfP0FKMsBmkvtuKMs5y9tPRlMYnjzOmZnT2Canscka6fEnuaWnq6HlbcXKKW/bcBuZbIaDTx1cltK7Zi5LFAgahZEymPj+BDP/OoPiUIgfj+NY78C1w4XqV0mOJq0eK9FftSKopGws37HxKgo2SSJpGCBJ2GQZp6Iwnk4Ty2Y5l0rNccfKcc5KHWDcSIqVU97W0kLGMDg4MrIspXfNXJa4EM21GsHlwbPPWtHbHg90dEAyCceOWTfVW7eWNDC2bojgjPKopIyz2Gwrvx/OnIErr0Q/dpSZF58mHRznQm8Lw/97mra3vYdf237HrMgoKbo7bz8nJ1/hWPwk3qSBN6xz4tp1PDN+GP2Uu6HlbcVE4ZGzR5a19K6ZyxIFgkYRHAwSfiKMbJeRHTLIEDsWA8DWbUNxK6i+5bllEqEZ5VNJ2VhhGV+P3c6L0SgmsMfl4pehEEORCArwfDTKBoeDd3d1cUd7+6zIKCUUI3+A8fl0Gp+iYAIzuo5NkmYHGDeSYqLwyMwMh2Zmlq30rpnLEhei+VYkWJ3kxIppwvAwbNtmCatk0nImolF47TX4jd9YHjFT6+CMRoizs2fh/Hno6YH+/vrsYzGq6VkqnG21cSNs2QLHjxN9/OdMqiliV6wjvaaDrUOn+FX6qwy+TytfZAwMMJOY4cVvf5HkdICAqvNSn51XWiX6YwpPjT61LKl7OVHYDImAZfeqCQSrCD2kkxxNEj4Sxt5vR7JLxI/GUXwKsksm9nIMM23Sfnd7wwVNrUMzGiHO9JBOajSFiYmj17EsIrCanqV8x8Yjy2xyOJCACV3n5XgcCVjvdKJIEieSSb56/jyaJJUtMvZ4PESyWSbTaUYMAx1oURS8qso/BQJc6/PNiYtvFDlR2Ax9X9UOcF4Omm9FgtVFoVjRdcuRuP56aGmB06dhetoq/+rthauvXnqb9RAsSzkupe6zEamG4TD8xV/AY49BOm2t6eab4ZOfbKzLl+tZ6p1bNobfbwmlXBlnsXNXbLYVkPzCZzlqdBPrbsHpsy5uimpjx+koT716pHyRYbfzL5sz/PdrYrjidvB1EnUpRNOTTAfipLKpZS1va5bSu7qlPAoETUq+YEmdTxF7KYb7SjeenR4AkqeTGEkDI2XgfaMlZJai1qJlqdCMUvfXiERDI2Uw/p1xxv95nMz5DKjgWO+g8z2ddNzR0dDkxFLLxorFdBdzbEK6zv/vzBmCmQytqor34mtVSSKs6xwJh8sWGY8Eg4SzWVo1DUnX0WQZE6u/ajiR4EeTk3xo7dqanZNyaZbSu3qlPNYLIaoE9aVQrIyPWzfcLhfcdJPlTiQSllOladDZufC2aiVYCm/yF3NcnnrK2tfwcPF9Fm6r0lTDckTbRz8KjzxirdHlsoTqd79rPf+JTzSufLGwNyr/WNxu6/w8/PDin1febKvwa8eIRia40KbS6rkkDtMeJ55IjGxoumyREUqGeCHwAmE7pFwe/E4vLkBGJpwKMx4bx8Ssxdkouu+l+r6apfSuWFkiwHhsXMysEqxK8gWLc6OTxKsJYs/HkG0ynt0eXFtcJEeSyJpM1z1di4qCWoiWQoGkh3QiQxG0bg1bl+UU2Bw2MCH8VBgjZhAbji24v/ztzRyZqTjRsBThZqQMTn/xNOP/dxwjbCB7ZVSfSvJkkgtfvYCsybTsb2lYCeNSZWN2WebhyclF+63yy/hGL4Y0SJKEI6+XyCnLRIHpTKYskRHSdV5JJPAoCoFUCp+i4FAUkoZBWNfpttt5JZEgVCfhUsrMp2YpvSsUuSYgAUnDEJHqghVOuQ5RMbHS32/1Tb36qhVO0dUFmYz12gMHFt9utTHsC4myK65Y2HF57DFLBG7ePHefmYwlAvO3tWMHvPRSeeVw5QrF//t/4V//1XpdW5u1jmTSeu33vmedR01rzNyvYr1RoZAlnA8cgGeeKenzys1IeunVI+yJniAUDxAmzcbWjSiygi2aIKqZKP7WskVGOBUmoSfw2r0kMgmSehJN1jBMg4yRwWPzICEtvaEyKGfmU7OV3vkdfhyqQ8ysEqwoynWIigkW9043kaEIsaMxHH0OjJSBmTLx3epbcpvVxLAvJMhcV7jIxrI4euc6BapfZfqxaVKjKZybnfP21zbQNmd7siKTOJPAtc01T5xFhiK07G8penzlCMWJhyYI/r8gZsZE7VBBhmwki9KikI1kOf/184SOhDCzZkPmfi1VNvZMOFxSv1Uu7OJIOMxriQTjmQzJbJZNF8v/EoaBAbRqWlkiI6zrZE2TXpuN04kEmiyTNU0M0yRhGPTabGRNs+ZuUDnhHc1WeueQZY5Eo00/r6p5ViJoXlIpy3E4eBC+9CXr74cfth5fjCKR1oBV4tfba6X/jY5aCYAHDlzqsylGoUBzOMqPYc+JMkWx9q8o1tfPPnvJccknEIBgENatm7/Pb38bHnpo7rZ+/GN4+eX5x1skGnzJNQ0OFj8Hjz9u/bulxXqtw2H9mZ62khPj8aW3U0sGBqzPzjDmfpbXXlvy55WbkaT73GTesIfepI3YudOMjJ9AnZyGwDjDG9zs3lZ+f5HP7qPV0Uqbs402ZxumaRLPxMlkM7Q6Wrmy68qau0G541FkhV5fL4qscOjkIQZPFf8syoqJbwDlrl8gWC6MlMHkw5OMHBxh9EujjBwcYfLhSYyUsej79LBONpadEzzh2uHCs8eDkTZInEpgGiatB1qXLPsrFGjlxrDnBJmkSDh6HUiKxPShaaLPRlHcCnp47vvTgTR6UMe+zl50f5MPTc7ZnpExiB+Pkx5Lz9mO6lfJxrLztr/UuoKDwXnHH34qDDJIdgnZIaM4FCSHRDaaJT2ZJjGcwNTNRbdTaxaKOL/W5ys5pjsXduGWZa7yeLBJEmdSKV5LJJjWdS6k03gUhf0+X1kiI+cC9TscrLHZ0E2TeDZLxjRZY7PR73DUxQ3KHY8iSfQ6HCiSxKHpaQaDxT+LcmLi6025a18uhFMlWJpKHaKFysNiMdizBz78YWsyeSnOV6n9OwuxWInf8LDlMh0+PNdxOXfOcoNyrwerVDEeh1OnLOcpf1vJpNUjNj4+NzgiVw5XKLZKDXrIOYThsFXqpyhWUmJbm/VvWbZe4/dbgQ85odWIIbfFeqP8fmtmVQmfV2FQw8QbW+mWFNb+8nHSYxeYaZeZvnYzG972Hn6tApHhd/jZ37+foxNHSepJ1rjXoBs6oXSItZ613LLhlpq6QUsFT+zu3o2EhImJhDRbWlfzAct1Wv9yhHoIBAtRqUOk+tRZwWJzWO6NrMnY++yo7SprPrAGe6+9NNfrokAr5igtFcO+WIlfbDiGa4eL8OEwmMyWBKbOpVDb1NnX55BskiWextPYe+yzzzv6rLCI+Ik4rl0uFKcyu++FEg0XW1fO3Zo99nAWI2YgO2TMoGm5Wi4FWZPJRDKYGRPHRgf2XrslAEtwyWrBQhHnI8lkyf1W+eKrVdNQJInDoRCBdBpFkthyMf2vXJGR7wJtc7k4lUwiAQawxeEgZhjc5PfX1A1aKnhit8eDBLPldbnzVesBy/VYe6OHJS9Gc6xC0LxUk/BWrDxsfNwSK3fcUV5i3VL9O0sFNCwlyq6+2tpOLo3O7YY777TK+cJhS7wMD1shGxcuWH+mpi6VAYJ1ftrarPfb7fPL4QrP01JrmpiAI0cuOTuvvw7PPWe5Z6kUTE5aPWiZDGSzVqKi02k973SWLjjzqTQEJK83Cij58yoMajBsGudvfgPG3g2cP/cKb9v3W7xp076qbuQHNg2QyWb49svf5szMGQC2t2/n3TvfXXM3aKHgCRmZJ0afYCw8xmRikmAiSJuzjZ2dO7mh9wYGNg2UFhNfZ5olOEMgWIpSbvwXumFX/SrefV5LgF0ULOnxNKlzKdrvaMe9y13yOooJtNz6lophX0qQea/2orgVIkMRkqNJFLdC251txF+Kz+7PyBjEh+PEXo6hh3VkRca8ykRr1ZA0Cdkp49zmJPpMlNRICke/Az2kkxnP0Hqgteg5WnRdp5OMf3+c1JmUlZx4Nkn8eBw9pJMNZjExUVwKkk2CLEhOCfd2NxISmWAGxamUPferlB6ghSiMOC+1V6gwqEGTJK7yetngdDIci/Fb3d1cW6ZDlU9OiD0VDlvDf3WdNlVlg9PJDRfL2mrJQsETMvBEKMRYKsVkJjO7jp0ez+w6SomJryfNEppRCs2xCkHzUq1DlCvpe+wxeOIJK5Ciq8sSK2536T0/S/XvLCUAlrrJ7+y0QjMKHRe329rn8eNWhLmmgapa4unll61ZW7t3X9rWzp3W18PDl8TZQqWNS63p2Wct98zvt0TcSy9ZZX6qav1JJCyB6nJZDlVHh+UqptNWmmJLC2zYUFoiYK1TC0v8vBYKaphQ08j969lapaACK4Th7Tvezq0bb2U0PApAr6+3LuKg8Hgy2QzDk8McPnOY8fg4I+ERMKHT08l4fBzXjItALMBEbIJ7dtyz7IKlWYIzBIKlqMYhAmbL+mYemyH0RAgjaqB2qURfiiK75ZJ7fooJtKVEy+x7lxBktk4bri2ueSEPk+7J2f2lRlJEX4yCCe7dblJnUnMCNwDsa+xkd2SRNGlWnC1W2rjYulKBFNknstj77ehBneTJJJmpDNlkFtkpYyZMsvEsUkzCvt6O+0o3elQnOBjESBvINhm1RcW5wbnk3K9KBvguRam9QguJr5RhsMHhqEpQwcIBDPVygwqPJ2MYDMfjHJ6ZYTyTYSSVAkmiU9MY13XUWGzW1XtPd3fN11PN2nM047yq5lmJoDmp1iHKceqUVRLW1mbd/Od6fqC0kAmYP9toMcFSSKmirNBxGRiwRMZXvmK9z+WyhJOuW67R0aPQ12eJkty2So1gX2xNN910SUSdOGGJtGDQep1hWE6Uplmlf1dfba3ppz+1xGFLi1UeeOaMFbBRiuNUbQhIMUr4vBoZ1NAIJ6jweEbCIzx3/jmm4lOsca8hnomTzqbpMDvw2/y8MvkKDs3BsfFjvDL1Cvv7988GQpSSHljv9S93cIZAsBDVOET5JE4lSI4k0do0tA5ttucHlg6ZyJETJ/mOUin9WKUKMtU/N4Ajt93QkRDRY1Fkl4x7lxv3djcxd2xe4IYe1Ol+f3fJCXwLrSs1kgITbGttJM8mCT8RJpvOYkQNzIwJbquMEht4rvLg3esFBaZ/NI3WqaG2qOgzOrEzMRybl55hVckA31IoJaa7UUENjXKBCo9nJJXiuUiEKV1njc1GPJslbRi0qSppw+C5dJp2VeVMMgmSxN0dHbNCthrnsBZrX+7QjMVonpUImpNqHaLBQSvQIRi00v5U1Ur+M01LcBw5UnrPz0L9O6VSiSiz2+HGG611trdbgsXpvFRyd+yYJRh7euZuq1CclbumK66Ab33LKgHM9Wul05YY1TRobbW+drthzRrr33v2WJ9NNGqJvz17rDXm+q0WopoSz8Uo8fNabTOScus+cvYIx8aPoSkaXZ4uerw9nJ4+jaZoBBNB0tk0F2IX2NK6BU3VyBgZDp08RCabQVO0ZUvfW22fh2B1Uo1DBFY/1tRDU+hBHed6J5IqkXw1iWRKKD6F8JFwyT0/sl2m466OimLDKxFkuf05NzjRp3Wcm5yoLdb+XDtcmGmT6LEoiVMJbD222e3Jdrmqdfne6CP6YpT0WJrY0RimaWIaJkbGQJIkZLeM4lIwoga2DhtmysTExL3HbZUHRrOWANzjhqwlgBdaTz37aBbqtypkpc1IWorcuo+EQhyLRtEkiS5No8du53QyiQacTaVQsMoCW1SVqK7z46kp3LLMQFtbzZ3Dctfe7J+FEFWCpanUIcrdrPv9lhDweCxnJRCwygHXrLEcnyuugPe9zxIOpYilUgVLIZWKMp/PElSKYgkqsI6nr896/AMfsNydWq7p7FlLiObOm2FYghSsf7tcVshHNGqdU0mC666zhFAiYa1Tkkor0cwv8UwkLr2/kp6sYizxeRWbkbSSHZHc8Wxo2cB0cpoeTw9DY0NkzSyKrGAYBmkjTSASwKk6cWgOFEmhz9dHOBXm2y9/G4/NQ7+/n15fL+FUmEMnLdfwrm0VuoYVrH+1fB6C1UulDlGuH0vxW4EKskdGlmXSgTQzj82grdGstLorHKx53xqMpFGyw1Nu8EI1gszea8fWY8NIX0o7rDRwo5R1AcRfiRN9PorWoqGHdMyQiazKVviOLiGrMrghPZpGa9VABd91PkzTxEgYyE4ZSZKWLNEs7KNJGAaJbBa7LDOZTtekj2Ypl6hU8bVSyB3PBqeTaV2nx2ZjKBIhCyhAxjCIZbP4FAWnoiBJEi2axjq73RJShsHhUKjmzmE5a2/2z6L5ViRoPioVI7mb9Y4Oq8cnJ5qmpqznZdna9uHDVmqeJNWmn2cpyhVlS7l1u3bVfk2SZJVKjo9bwslms8ROOm2dk3jcOp+qapX/TUxY57ar65LwCwRKK9H0+axtPvOMVTZYSU9WDWiGoIZa0uvrpcfTgyIrbGjZwNGJozhVJxdiFzAMg6yZZZ1rHQk9wZWdV+LUnCT1JGdmznBj343Lnr632j4PweqjUkGS68fSOjQkm4SRNMiEM+hTuiUOZAnJLhE+HCZ5OokkSRUP9S2VSgTZUm5dOYEbpa7LeYWT6Z9Mo/QoqG6V9Lk0pmSds2wyixSV0Do1stEs7l1u0hNpq0SzyzabPJgOpJcs0cz10QQzGaYyGU4nk6QNg7RpstZma+hsouUOaqg1vXY7PTYbiiSxweHgaDyO8+K5zpgmBuBSFBKGwZUuF102GyfjcZ4Kh5c9ga/ZPwsxp0pQOn6/5c6UKkhy/ViZjFX6FwzC2JglGGTZcql27rT6kX78Y+t1jZyxVA4LzWMqpZ+rEnw+qx+qs9Pal80GXq/lWimKJXxaWqx5UO96lyX6AgHrTzJp/T0+bj2+1Ofl91vbfOEFS6x5PNbfL7xgPV6vOPZVTq4/KRAL0OZsY1vbNpyqE1VSLffH7setubmy80q2d2wHYCI2AUCXq2vetmKZGOFUkVlnAsFljupXZ6PDS3r9xX4sM2PiWO8gG8ySGkthSibIYOom7p1ujJTB9I+nrdc1cMZSObQNtNF6oBXTMEmOJkuer1UpLftbsPfbyUasUj7FraD5NBSn5WwofgXFreDc4aTzXZ1493nJBDKkA2mMpEE6kCYznsG7z7vo55Xro/lVJMLTkQgGoF4cuBvOZnmm2NxHQUnkzm0gk6FN09jmcOCUZTRJwiZJuGSZdk3jSpeL7W43IV1HkSR005wXCuFXVWLZLOG8+V6XM80r9wQrn3yHp73dEmRnzlgiYe1a2LvXckIeecQSDR5PY2cslUO1/VzlkEpZPVyjo5aD5/FY8fORiCVGN260+p9SKbj7bsudqibEIxSyeq8q7cla4dQzECK/P8nv8HNj3418qP1DvKn/TTx+9nGeGH2CLncXWTNLMBoknA6zoWUDaWPuoE6RvicQ1I58h0dr17D12UicSYBhhTB49nqwb7CTfCQJNlA8SkNnLJVDNeWD5WCkDIKDQSJDEVBAj+nY19tpua2F+MtxzKyJa4cL+1o72XCW9rvbsXXZKi7RBLjW5+OBQIBYNotumtgkiX1eL+0XB/U203yielDPQIj8HiW/qnKj38+H1qwhbRg8GQ6zzm6ny2YjmMkwnslwk8/HcDy+IhL4lpOKzkIikcA0TVwuFwBnzpzh+9//Pjt37uQtb3lLTRcoWOHk3+x3dFiOVUcHvOlNljAJBi2x4PVeKluD2vXz1JpK+7mWIj8t8MgRS4hu22YJqhMnrBRAv98So2vWXBKsufNbjegLhy2BVmlP1golpacYPDVY10CIxfqT+v39dLo75wRC3H3F3WSyGR45/UjN0veWI0VwORDXJUE55N/s2zpsONY70Do0fG/yofk0MsEMekRH9aqzZWtQemR7o6mkfLAU9JCOHtYJPRkifDiM1q3R8qYWIs4IidcSSD0SziucVirgGhuKV6Hl1pbZ81uN6EsZBr12O3s9HkzAKcs4FYWkYTTdfKJaUo8o+UIW6lFKGcZsD1VhIIT7YhpjrRL4Gp0i2AgqOoq7776bd7zjHXz0ox9lZmaG66+/Hk3TmJyc5K//+q/52Mc+Vut1ClYqhTf7Tz5p9VAlEpY7FYlYZX8dHXNFVbmR7fWi0mG4pVI4H0pRLDdv2zZLQK1da/VsjYxYoRUf+5gldhZaTyWiLz82v5KerBXK4KlBDp08RLe7u+6BEMX6kxYSXCk9NZv+V036XiNEYzMhrkuCcii82c+JBjNhYtgMspEsZsZE69CQnZduZMuNbK8XObHTCGdKn9KJvhTFvs6Oc7sTWZNpeXOLVTqZyNJ3X5/Vz7XIeioRfbm+qoxpzvbxwOp3R+oVJV+Mwh6lxQIhapXA1wjRuFxU9B357LPP8t//+38H4Dvf+Q7d3d0899xzfPe73+Uzn/mMuHgJ5pO72e/qsm7U88vU7rjD6v8JBMqPbK8XtR6Ge5F5rkHhfKizZ61Bwx6PJajAEjn9/db5kiSrjLKWVBubP+8gayhE6yRqQ8kQQ2NDdLu7my4Qolbpe40Ujc2AuC4JKiF3s2/rsqG4lbllane0oo/rpAPpsiPb60W+2KlleEahSAsOBq3yyG4NtV3F0A1SZ1PEW+J49lhDhW3dNpKjSZDq45TVej5RLZ2Rerks9YySL4digRC1SuBrpGhsNBV9MvF4HK/XC8BPf/pT3vGOdyDLMjfccANnzpyp6QIFq4xiZWoOxyUBU24/UL2o8TDcYq7BDd4d3Pb0S6j586FyQSAnTlgOVc41qrdzV01PVo5aCtFqtlWCEAunwsQyMXp9vXMe9zv8jIZHCafCy14qV036XjOJxkYhrkuCaihWpiY75FkBU24/UL3IFzuOXgd6WC97WHE+xUSae4eb6EtRtG4NW5cNI2GgtWgYcYPkmSTOrU4Up9IQ164W7kgtnZFqtlWKECuMks/hV9WmKXmsJoGvWURjvaho5Vu2bOFf/uVfuOeee/jJT37CJz7xCQDGx8fxrdJSIUGNKSxTq3cIRDmORx2G4RZzDY4c+xFbzkXYvOfmSy90Oq3Sv2eesUr++vsb49zVIoijlkK0km2VIcR8dh9uzU04FcahXrp4LXcgRK36n1aCaKw14rokqAWFjku9QyDKKePLzdfKiR2g6vCMYiJt6sdT6GGd1ptbAZCdMo4NDmLPx9BTOvqMTjacbYhrVwt3pJbOSCXbKkeI5Uoemy0QolbO3EoQjdVQ0co/85nP8P73v59PfOIT3Hbbbdx4442A9dvBN7zhDTVdoOAyoh4hEJU4HvnDcAvXV0Fww0KuwUx7grPZJ1gbHMe5tv/SG9asgR07rB6qRjt3lX4GtRSilW6rTCG23r+eJ0afqFkgRDXUuv+pWUVjPRHXJUG9qEdpWyVlfLn5Wo7euTeklYZnLCTSjKRB8nSS9HgaR7+1L/d2N/qMTmo0RSaYQWvTGuraVeqO1NIZqXRb5Qqx9XY7T0QiNQuEqIZa9z81q2isFRWt/l3vehf79+/n/Pnz7N27d/bx2267jXvuuadmixMIqqYSxyM/uCH/tymhkBUkEQ6XFTO+kGvg6Ojm9c1t7Dt7DhT7pV6mYBDe//7GxLfXiloK0Uq2VaIQyxcvoVSIWDrG8OQwazxr8Nv9FQVC1IJa9z/lZmQdOnmopqKxmZMExXVJsJKopIwvN19LD+uWQ3WRdCCNqZtglreGhUSarcuG2qaSGk0h2+XZfjKtRaPjbR34bvTVLSCj1tTSGalkW6UKsXzxEspmiek6w7rOGpsNv6pWFAhRC2rd/1TrPrkczZIkWPGe16xZw5o1awAIh8P8/Oc/54orrmD79u01W5xAUBWVOh75wQ3JpOUYJRJw7JglcL7xjbJ6fBZzDcLX7UTetRteHJ7vStntyy+mSi2bXEyIltsLVsm2ShRi+eJlQ8sG2pxtnA2dZU/XHu7ZcU/dhUIxUVKv/qf8GVnVpAjCykkSFNclwUqg0jK+3HytqYem0Gd0tDaN2MsxEq8lsPXaOP+182WFViwk0vSQjmenB9duF/Hh+Lx+smoCMWpFqTfRtXRGKtlWqUIsX7xscDhoU1XOplLs8Xi4p7Oz7kKh2PmsV/9TrVIEofmSBCv6lN7znvfw5je/mY9//OMkEgn27dvH6dOnMU2TBx98kHe+8521XqdAUD7VuCc33WT1NT32mLWNRMIaiLt3ryXQyugXWso1cG+7C36tztHt5VJu2WQFCYILuh6VpBGWIMQWEy9nQvUNMlhMlBQ6mYlMgoSewK7YmUxMVtz/VKsUQVgZSYLiuiRYKVRaxmekDIyMgR7ViR6LkglmwADvPi++a3xk49myQivyhyBjMi/hsOOujrpHt5dLuTfRlTgjCwm2SrZVihBbTLycSSarPmeLsdj5zBeECcMgkc3iVJSq+59qlSIIzZckWNFR/Ou//iv/4T/8BwC+//3vY5omMzMzfOMb3+ALX/iCuHgJmoNq3JPDh2FqyhpSnM3C449bQiMUssIjTBNmZqxBvfmO1wLOzpKuQb2GCldKJWWTJSYIluR6lJtGWIIQC4dGli28YfDUIA+98hB+u58OVwcZIzMrSvb378etuQnGg0wlpjg9c5p0Nk06m2atdy12pTonqJoUQVg5SYLiuiRYKSzmEC2WphccDBJ6JIR7pxvXFS6CPw1ipky0Ng3Fo6B4FIyEwfQj03h2e2b7oXLbLiaO8ocgF0s4rNdQ4Uqp5Ca6VGekFMFWrstSihAbSSaXLbxhMBjkoakp/IpCh6aRMc3Z87m/pQW7ovBMOMyMrpM2DGyyTIuqssHprLr/qZoUQWjOJMGK9hYKhWi7+A106NAh3vnOd+Jyubjrrrv4kz/5k5ouUCComErnLxWWDQaDViqf1wsnT1oC49w5a2ixrsMVV8A73mEJsQWcnVq6BnWn0rLJEhMES3I9KkkjXEKILVd4w3h0nAdeeoDR8CgAbs3N5rbNtDvbZ0XJvrX7uH/ofsbj43S4OlBllVAqRDgd5pmxZ5bVDVopSYLiuiRYKSzlEBUTMYUlg5lgBtWjIrVIJE8ncW5wEn8lTvy1ONlQFgmJlttaaLmphZnDMwsGYhSLkm8mEZVPpTfRpTojpQi2SlyWpYTYcoU3jKfTPBAIMJpOA+BWFDY7HLRr2uz5VEyTF2IxOjWNFlVlRtc5E4ux2eFY9pS+ZkwSrGhvfX19PPnkk7S1tXHo0CEefPBBAKanp3EUHJxAsKxUMn+psGzQ6QSbzRJQr74KL710yflyuSy36vRpy9lawtmp1jVoCNWGTuRct1DIioXPE0Rlux7lOHhLCLF6hTcsRK688fvD3+eXo79ElmVUSSVgBjgXPsd1666jzdVGOBXm2rXX8oD9AWKZGHpWx6bY2Ld23xzhtVzfNyslSVBclwQriaUcokIKSwZlp4xkk8AAI2Ew+aNJkqeSmIaJbJNJnksSfDhI+Jkw2anskoEYzeZIFaPam+icMxLSdUaSyar6h8pxWZYSYvUKb1iIXHnj9ycm+GU4jCxJqEAAOJdMcp3PR5uqMppKkQX2uN2EdJ1oNotLltnjdpO9uJ3lFFbNmCRY0R7vu+8+fuu3fguPx0N/fz+33HILYJVf7N69u5brEwiqoxLHo7Bs0OmE9estkTQxYbk3DoclPHQdAgF45RW4+eaazbVaVqoNnVikH6shrsciQqyW4Q0LkV/eGEwEGTw1yExyhg53Bx6bh7SRJpyyHKg7t96Jz+4jnArT6+tlb/deTEycqhOn5iSpJ5fdDWq0GK0UcV0SrCTKdYgKSwYVp4JjvYPIUAQ9Ys2NkjQJxaagtqlkxjJk41myL2VpubmlZnOtlpNqb6JL7R/Kp5aux2JCrJbhDQuRf/zBTIbB6WlmdJ0Omw2PopA2TcK6zjPhMHe2t4NpkjIMrvP5ME2ThGHglGUkSWqKmVKNFqOlUNEe/+AP/oDrrruOkZERfv3Xfx35Yq3ppk2b+MIXvlDTBQoENaEcx6NY2aDbDYZhiQxNsx53uy0B8cILVilgfz/09FjP57ZTwVyrZafSsskci/Rj+W7bP8/1SGQSjIRG0BStpq5HsSCMRpRh5pc3ujU3kVQEWZKJpqM4FAeaoqHJGuOxcfr8fbP7d2tuMkYGr83LdHKa6cQ0GSPTFG5QI8RotYjrkmAlUqpDVKxkUGvXUFtUMjMZMC33SuvQwIT0eJrsmSxmxsTeb8feY0fSpNltVTLXarmp9iZ6sfK+/S0t8wRbIptlJJVCk6SauR4LhWDUMrxhIfKP362qRLJZZCCazeKQJDRZRpMkxjMZ+hwOeh2O2XOSc++mdZ2pTAZPk8yUaoQYLYeKz8i+ffvYs2cPr7/+Ops3b0ZVVe5aIgVNIFgxFJYN6jpceaXlvCiKlQY4Pm45Yek0qKrlVrW1Qe634pXEiZdCqTHn1VBJ2WRubYv0Y/n37591PSKpCCPhEUbDo0TTUXZ07ODI2SNVx3SXEoRRrzLMwvLGMWMMTdGwa3Yy2Qy6oaMbOqZp0uJo4cZ1N86uZ0vrFv7h+X9gIjpBXI+jZ3UcmoP37HzPnLK75WCl9ASK65JgNVOsZLDzvZ2Ej4RJnEygOBWy8SypCylkm4wpm0iqROKVBFqbhme3B1g6EKNSGpEUWOlNdCnlfTnBFslmGUkmGU2niWaz7HA6OTIzU1VMd6mphdWGNyxE4fGPXRSL9ovhFLppohsGJtCiqtzo9c6K2O9NTvJMOMyZZJKgrpMxTa5wufjF9DR3tLcvS3R5jkaI0XKoaM/xeJx7772Xb3zjGwC8+uqrbNq0iXvvvZd169bxqU99qqaLFAgaTmHZoGnC174Gr70GZ85YgkrTLIdKkmDXLisN8OhR6OuzHKxSnZ1SKTfmvBoWKJsMJUOEQ+ML31SX0I91U/9NPHPuGb47/F0CsQBuzc32ju1sbNlYk5ju5Yz/LixvbHW00u5sZzw+jomJz+ZDVVQSeoJt7dvY1LZpVgT+v9f+H8Pjw8T1OC7NhcfmQULiuQvPMXhqsCmiy5u5J1BclwSrnWIlgwCpMymMpEHyTJLMeAZZkzEzJrIk49rlQp/RiR2N4ehzYKSMRQMxKsFIGQQHgwuGYdSSYjfRYIUuLHZDXUp5300tLTwTDvPdyUkCmQxuWWa7y8VGp7PqmO7ljv4uPP5WTaNd0xhLp8E06XQ4sEkSQV1nm9PJJpeLlGGQMU1OxuO8EI2SAXyKwjqbjUQ2y1fPn0eTpGWJLi+kXmK0XCr6bv/0pz/NCy+8wKOPPjqnAXhgYIBvfetbNVucQFARuYCEUKj6bfn9lkjq77cEjM8HHR2WmIrHLaFz5ZWWeNqzx3KtTp2ySgVLcXbKIVdWpyiWaFEU6+vBwdrto5CLx59yO3j41Yc5+NRBvvT0lzj41EEefvVhUnpq7uvz+7HyyXPtDp89zFh0jFZnK1d2XsmOzh0ks0lCqRBdri6GxoYIJSv77AqdIofqoMvdVfF2Q8kQI6GRkt+XH+oA4NSc7OnaQzgZJhANcHTiKC+Nv0Q4GeatW96K3+GfjVsfi4zhc/jo9lhlgz3eHrZ3bCeeiXPk7JGKz8nlgrguCZodPaSTHEmih/SqtqP6VRx9jtnSQe8+L6pPRevQMDIG2XgWI2XgvtJN24E2PHs8GGmDxKkEpmEuGohRCcHBINOHppEUCUevA0mRmD40TXAwWLN9FOJXVbpsNo7MzHBwZIQvjY5ycGSEhycnSRnGvNfn92Plk9+PdXhmhrFMhlZN40qXix1uN0nTJKTrdF1MxAvp5X92hS6RQ5bpstkq3mYuaKOc9xUev2maGIbBVCbDRCbD89EoryYSdGga77k4bHgwGORHwSAG0GazseZi2aBXVVljtxPNZjkSDld0TlYrFcm6f/mXf+Fb3/oWN9xwA5IkzT6+a9cuTp48WbPFCQRlUW8nJyeQjhyB8+dBli1BtXev5Vr19UF7O3zgA5boqWVpXqUx5zWiZPdniX6skN3qy/Hb/ThVJy2OFjRFI5KKcHrmNH2+vqoG3tYqCKOkWVpFKBbq8Nr0a+imPiu4TNMkY2R4efLlWRHot/uRkJAlGY/NQyabIZS0RKYsyUwnp5smurxZEdclQbNSbycnJ5BCR0KkzqeQZAn3lW68e71ImoS9z47arrLmA2uw99prWppXGPUOjQvDKMf9WaofC6wyQL+i4JRlWhQFTZaJZLOcTibpcziYTKcrCmeoVQhGuYOPFzv+x0MhzmYyuGQZhySBJBHLZmlTFO7o6JgVgn5FQZIkNEnCo6pkTJOpTIY2TUMGpjOZZQ+saCYqOgsTExN05W7s8ojFYnMuZgJB1ZTTP1TJwNpyyC+Ju+IKeOIJS0hls9Ysq1y5365d1e+rkGpjzqug7Bj0RfqxwslxYpkYHa4ObIqNpJ5EUzScmpPpxDTj8XG8Nm/FwQw+uw9FUjgbOkufrw+n5pw9hnICH6opIcwPdXh5/GXOhM6wqXUTW9q2YJgGmqwxGZ/k8ZHHeevEW2fPh1N1ggkZI4NNsRHPxImkIximQaujddnDKpodcV0SNJJy+odyTs5SseaVkl8W6LrCRfiJMPY+O2bWJBPMzJb7uXe5q95XIYVR7znqHYZRycyqxfqxxtNpYtksHZqGTZJIGgaaLOOUZaYzGcbTabwVhjP4VBVFljl7UZw5LwqgcqO/qy0hzB3/4PQ0x2IxPLLMepeLdk3DME2mdJ3zmQyBdBoJZs+H8+LPz4xpYpMk4oZBRNcxsMoImyGwolmo6Ezs27ePhx9+mHvvvRdg9oL1D//wD9x44421W53g8qVc16mRTo7fD+97H3R2Wts+ccIKqrjpptqW++VTbcx5FZTt/iwSY+8zfbMpd+tb1nNs4hgmJlkjSzprRY3fuuHWihyZlJ7iyNkjnAmd4fjkcfx2P9vat7HGs4ZgMlhy/HfZIrKA/FCHx04/xtNjT7OxZeOcsIkWRwsj4RGimejs+djSvoVz0XOEk2E0WcPEJJgI0uPpWdY5VSsFcV0SNIJyXadGOjmqX6X7fd1onRqRoQiJEwkkVcJ/k7+m5X5z9lkQ9Z6jXmEYOSpxfxYLNciVx2VMk/UOB8diMUwgC1bUeDbLrQsMF16MlGFwZGaGM4kEx+Nx/KrKNqeTNXY7QV0vOfq70sHHxY7fLsv8fGaGDQ4HXkWZfV6SJEaSSc6nUmx3u2fPxxaXi3MXXTpNkjCBoK7TY7Ox3+cTLlUeFfnOf/EXf8Gf/dmf8bGPfQxd1/mbv/kb3vKWt/C1r32N//yf/3Ot1yi4HCm3fyjn5BSKC7/ferywx6da7HZLQO3YYQmqbBaGh631pVJLv79ccmV1gYD1J5m0/h4ftx6vY+lfYZ9QjuRkgJ5pHV/SXHjNfX1FB/AGYgHane1sbdtKNBXlfPQ8a71rufuKuyuO6c65S9vat7Fv7T4kSeKZsWc4ETxRVvx3TkQWOkN+h59YJjbvPCyE3+Hnqp6r8Nv9zKRm5jw3k5zBrbln15o7H9etvQ6vzUskY8Ww9/p6ee+V722q6PJmRVyXBI2g3P6hnJNTKC5Uv0o2lkUP17YfRbbLtA204drhAhWMrEFsOEZwMIiRmt9rVC25nq5MIEM6kMZIGqQDaTLjGavXq06lf4v1SCmyTFjXF+z18asqfQ5H0QG8gUyGdk1jq8tFNJvlfDrNWpuNu9vbK4rpzrlL21wu9vl8SMAz0Sgn4vGyor9zIrLQFfKrKrFsdt55WIxtLhdtqko0m53z+MxF56zHbp93Pq7z+fAqChHDQAZ6bTbe29W1bNHlzUpF3+379+/n+eef57/8l//C7t27+elPf8rVV1/Nk08+KYYsCqqnEtdpOZycwUE4fNhap89X+3LDQiqNOa+Swj6hNtlN65FnWXfsNbY7evEPf62s3rX88rgWRwvX917PFe1XcOfWO+nyzC/fKoVCd2mtdy27OnfNzr/a37+/5Jj2fBGZ7y6VW0II0O/v5+b1N/O9498D03KoZpIzTMQneMf2d9Dv76fb3Q1Y56Pd1c7tW24nlophYGBX7ZwInmDw1GDVUfMLUWyeV7HHmh1xXRLUm0pcp+VwcoKDQcKHw2jdGqpPrXm5YSHFot5rHYZRSLEeqalMhmcjEbyKwjcuXCir5wjmlge2KArX+3xc4XRyZ0fHrDtUDoXu0lq7nV0u1+zsq/0tLSXHkVc7+DiffoeDm/1+vjc1BVgR6jO6zkQmwzva2+m/eA+Vfz7aVZXb29qIXSz7s8syJxIJBoPBqqLmF6PYTK+F5nw1CxWvaPPmzfz93/99LdciEFhU0j9U7cDacml0cESut2z/fmsO1vnz1qDh/v7a7WMR8oWQ87HH2fzsKP7+bfSvfwPE4mWJyXrMPCpWoujUnPS39JcVUAHFwyZCyRDj8fGSSwjz+eSNnwTgsTOPMRIewa25ecf2d8w+Xng+nhx9ksNnD7PWvRaf3Ve3SPhiYRx7u/cC8ELghbICOpoFcV0S1JNK+oeKDe3VQ3rNY81n19jg4Ihcb1nL/hY8uz2kzqew99hx9Nd/tl5hj9S5i1Ui21wu2jSt7J6jWs88Klai6FQU+h2OsgIqoPrBx4V88uK9w2OhECMX+8ve0d4++zjMPx9PhsMcDoVYe7GPql6x8MUCOfZ6PGCavBCLlR3S0Ugq/m4xDIMTJ04wPj6OURBf+eY3v7nqhVXDl7/8Zf7rf/2vXLhwgb179/KlL32J6667blnXJCiDSl2nRjk5oRAcP26FU2zePPe5WgdH5PeWhUJw4YI1F6u7+5KQrMecqgJmb/z9uzF/chD7nh0411784ev2zIrJ0L7dhB3SrFBazPGo5cyjWrpLMFdEjoZHcWvuskoI56zN4eOzt36Ws6GznI+cp8fbQ79/vhjOnYvhieGK+7nKoTCMYzw2zt8+/beoisr+/v0Nn/FVC5r5ugTi2rTSqdR1apSTo4d0YsdjZIIZXJtdc9de4+CI/N4yPaSTvpAGCezddhR//eZU5ZN/0z+aSvHNi+5UsZ6j3R4PEsw6OouJplrNPKqluwSVDz5eaG2f3bSJsxd7qHrs9lmHqpDcuRiOxarq6SqVwkCO8XSavx0dRb3o7i3HnK9SqegMPPXUU7z//e/nzJkzmObcfgpJksgW1Gk2km9961t88pOf5P777+f666/n4MGD3H777bzyyitFk6EETUglrlO+k1MkIKEm5AucqSl46SXr7xtvtCLVc+uoZblhfqJhNGrNwDJN8HigrW1xh6ic5MQS8aclyKrQNvf/Usbj5uyxx/nBYwc536piV+wokkLWzJLKpurueNTaXaqHm9bv7y8qpvKpVST8UuSXS7Y6WhmeHOZk8CSvTL2CKqv0+/rZu2YvXe6uugi6etDM1yUQ16bVQLmuU35CYOHQ3lq6RXMEzpRO7KUY+pSO70YfsibPrqWW5Yb5iYZG1CB5KgkmKB4FtU1dtNywnOTEUvBfdE2ypjlPqLgVhcdnZjg4OgqmyYWLyXbddjv+OrsdtXaXau2kgVUKuJCYyqdWsfBLkV8y2aqqDMfjnEwmeSWRQJUk+h0O9no8dNVJ0FVLRav46Ec/Opu01NPT01RxtX/913/N7/7u7/LhD38YgPvvv5+HH36Y//2//zef+tSnlnl1gpIp1XWq92yqfAYH4aGHLJHS2wvT0/DCC9Zz111X+3LD/BJDr9cq+eu2+m8YG4OdO4uXG9bznCzgIp488xwnk6Ponh30+rp4+tzTvBh4kb3de7l23bUNcTxq6S7lqKWbVgq1dtwWIl+8DU8Oc2ziGJpsRdvrhs7RiaPYVBt7uvfUXNDVi2a+LoG4Nq0WSnGdFksIdPhrXxYXHAwy9dAUik/B3msnM50h9kIMAN91vpqXG+aXGCpehdT5FFq3hoREeiyNZ6enaLlhPed1LeQKPRuJMJpOs8PtZjyT4VQyiQl4FIU2Va2721FLdylHrZy0cqi167YQ+eJtOB7nWCyGJkk4ZRndNDkai2GTZfZ4PDUXdLWgolW89tprfOc732HLli21Xk9VpNNpfvWrX/HpT3969jFZlhkYGODJJ58s+p5UKkUqL60tXOuUOEFlLBLLPYd6z6YCS9ycOgVf/7rlTGka2GxWst2VV8K5c3DypOUc1bLcML+3LB6HdBpaWqzyv+lpSCSKlxvW85wUuog2G8nz54iefJnwm3bg7+4nkUkQSobodHUyk5zBNM2GOB71cJcaTa0dt4XIibfx2DhnZs7gs/lwqA7OcAabYqPF3sKZmTNsbdtKOBWuqaCrF816XYLyr03iutS85M+EWshtqfdcqhx6SCd+Kk7g6wHSU2kkTUK2ydj77LiudJE6lyJ+Mo7WptW03DC/tywbz2KmTZQWa0hsZjpDNpEtWm5Yz/NS6ArZJInRVIrheJydLhedmsYL0SjdmgaSxFg6zU6Pp+5uRz3cpeWg1q7bQuTE23g6zZlkEp+i4FAUzqRS2CSJFlXlTDLJVqeT8MXeqmaak1XRSq6//npOnDjRdBevyclJstks3bnf5l+ku7ub48ePF33PF7/4RT73uc81YnmCSvD7F3Z96h0Wke/4PPUUPP+8JVI2bLAEzvAwbNsGHR3w/vfD9u21LTfMd4W8XkvIJZPWczYbOJ3zyw0bEaAxMACZDHz723DmDOgpZHccj+Qgls6QyCZIZ9O0OFqIpqMk9AROzdkwx6PR7lKtqYfjVkhOvP3zy//MdGKabk83ST05W5opSRJxPc5IaISUkaqpoKsXzXpdgvKvTeK61Pyo/uKla40Iish3fEJPhYg9H8Pea8e5wYmRNkgMJ3Bsc+Dp8ND1/i7c2901LTfM7y1TvAqSTcJIGkhYok5xKvPKDRtxXgba2sgYBt+emOBMMknKMIgbBg5JIpLNkjZNWhRL/E1nMiSy2Ya5HcvhLtWaerhuheTE2z9PTDCt63TbbCQNA/vFygPp4vDhkVSKlGnWVNDVgopWcu+99/Lv//2/58KFC+zevRst109ykT179tRkcY3g05/+NJ/85Cdnvw6Hw/T19S3jigQlU0lKYDnkHB+/3xIRTidMTlr9TOvWWa959VW44YbaCyqY7wr19MCLL1r/vuoq6/jGx62hw7nfZNf7nIDlImqadR5uvBGj1cv0ycfofvooqt1B7Iat2BQbM8kZXJoLp+oEal/CtlpplOM2sGmAWDrGV2a+wnhsnBZHC/v79gMwPDlMOptGUzRu3XjripiTJa5LgmagkoTAcsk5PopfgQzITpnMZAbFo2BfZ/1iJPFqAv8N/poLKpjfW2bvsRN9MQomuK9yo4etckP/Tf7ZOVyNOC92WUaTZTyqyo1+P15V5bHpaV6Mx5EkCZskkTQMkCRssoxTUWpevraaaZTrNtDWRiyb5StjY4ynUrRoGvsv3rcMx+OkDQNNkri1paXp5mRVdDbe+c53AvCRj3xk9jFJkjBNc1kbgjs6OlAUhUAgMOfxQCDAmjVrir7Hbrdjr3NymqBO1HM2Vb7jo6rW8OF16yxhMjYGra2g69brrriifsN383vLPB7YtMkq/3O7Lbesvd0KzBgash7bscMSPfWc15U7N/390NWFC+g0dnHmtafxvvAy9t19+O1+zoTOsLd7L5IkEYgGal7Cttqpt+NmV+2858r3gAQ/fu3HrPOuo8vTRSgZQjd13tj7Ru7Zcc+K+bya9boE5V+bxHVp5VLvuVT5jo+kSkiKhG2djfRomtRYCq1Vw9AN9JCO8wpn3Ybv5veWyR4ZxyYHSKC6VascsF0h+lKU8FAYxa3g2uFCsdd3Xlcu5KDfbp9NqNvpdjMUifBaIsGGi306JnCV2024DuVrlwP1dt3sssx7urtBkvjx1BTrLn6eIV1HB97o83FPZ2dTfmYVrej111+v9Tpqgs1m45prruGRRx7h7W9/O2BF7D7yyCN8/OMfX97FCWpPPWdT5Ts+F3uHuFiLzblzlmOlqpZDdeedtTumQor1luXW9+STl4YPd3Zajx0+bAmtQKB+87qKuGE7OrYjp9KEThxl6sLrbOzZyJa2LWTNbN1K2AS14e4r7satueeUG959xd0rZjZVjma9LoG4Nl1O1HsuVb7jY5omkk1C1VQkSSJ1LkVmMgMquLa7aL+zvUZHNZ9ivWW59YWeDM0OH7Z12tDDOuHDYZR2BT2g121eV7GEuh0uF2nT5Fg0iipJbHI4kAC3qmJcLB9rNrdDYHF3RwduWZ5Tbnh3e3vTzabKp6Lv4vXr19d6HTXjk5/8JB/84AfZt28f1113HQcPHiQWi80mLglWGfWaTZXvgnV1wfr1cOyYJaw2brSS95JJuPvuS71L9aRYb9nwcPHeqXTaKgkcHq7PvK4iDqGmaFxp7yW5tY31N30Ab1fvknOqGkUzrKGZWQ0BH9Dc1yUQ16bLiXrOpZrjhHXZcKx3EDsWQ9ZknBuduHa6MJMm7Xe3z/Yu1ZNivWXx4XjR3ikjbeC/yU9sOFaXeV3FEuo0WabPbqddVfnAmjX0XnSAlzs0IqTry76GZmclhnxUtLr+/n5uueUWbr75Zm655RY2Fw5AXUZ+8zd/k4mJCT7zmc9w4cIFrrrqKg4dOjSvQViwSig1JbBcCl2wTZtgZgZee81yaNrbL8WU56jDXKgFWap36sYb4fbba7aeOcJkEYfQceAAvf27Li1nGUMjUnqKwVODDI0NEcvE6j4ra6Wz0gM+mvm6BOLadDlRSkJgpRQ6Yc5NTvQZncRrCWy9NrR2bTamPEet50ItxlK9U74bfbTd3laz9RSKk8US6na53bPvW66b85RhMBgMMhSJELuYXlfPWVmrgZUU8iGZhVMSS+Cb3/wm//qv/8qjjz7KiRMnWLduHTfffPPsxWzr1q31WGtDCIfD+P1+QqEQvloNcBWsTIrNe9qxA66+2iq3q2YuVLUCLBSCgwetXq98pywQAMOA++6ribArJkxu8O7gFvcu7C8es9ywes8Hq5CHX32YQycP0e3uxmf3EU6FCcQCHNh8oG6zsgTVUc3PX3FdElwuFJv35Nrhwnu1F1unraq5UNUKMD2kM3JwxOr1ynPK0oE0pmHSd19fTYRdMXGyw+1ml8vFsXic4VisKUXLw5OTHJqeplvT8F0cWhy4KPrqNStLUD2l/gyuSFTlc/78eR577DF++MMf8q1vfQvDMJZ9cn01iIuXYB5LCaCHH740F8rns14bCFgld4VzoWo5mDe3366u+b1T1c6junjMPxl/kh+OH6bb3U2r5KLt8WfxHX2N7Y5eNvftKS4ym4BQMsTBpw6iyApd7kuiMxANYJgG991w34p2ZVYrtfr5K65LgsuBpQTQ5MOTs3OhVJ9qpfIFrB6mwrlQtRzMO7vfLm1e71Q186jyXakjMzOz4sSlKDwXifBqIkGvzcYej4cdLhdXe7102mxN43KEdJ2DIyMokjQbpAEQSKcxTJP7+vqaZq2CuZT6M7jiTy8ej3PkyBEeffRRfvGLX/Dcc89x5ZVXcsstt1S6SYGgOanlrKxaDuatRz9ZnuhLzkxhm3mJN16xjtjN21n3y2H6njvHuMfDK844a80MzsOHrf022WygcCpMLBOj1ze3PLJRs7IEy4O4LgkuJxaalQXlz4Wq5WDeWveUFbpSiixzJpFgm8tFl83GS9Eoo+k0HkUhbppkTJPD4TBuRWGLy1XRPutBsSANoGGzsgT1p6JP741vfCPPPfccO3bs4JZbbuFTn/oUb37zm2ltba31+gSC5qacuVC1Hsxbj36yPNEX72knNa2z41dnmTBdtJ4+T7zNh9niJJGYJtHqwanYazdUuIb47D7cmptwKoxDvXQBE7OyVi/iuiQQXKKcuVC1Hsxb656ywWBw1pXqdTg4m0xyPB7Ho6q0ahqnk0l8ioJDkpjJZvEoCvaLqXH7W1qaRqgUC9IAxKysVURFBabHjx/H7Xazfft2tm/fzo4dO8SFS3B5kp+El09uLpRpwsjIpRLCWGz+rCi/33q8cBul4vdDX1/1oqZA9DncLWTaW5j2aXQdPYUtFCPtcZDIJLApNhyqs/q11wm/w8++tfsIxAIEogGSenJ2Vta+tfuES7UKEdclgeAS+SmB+eTmQmFCciQ5W0KYjWXnzYpS/SrZWHbeNkpeg1/F0eeoSlDlZk91axpdNhsOWabP4cCvqpyIx5nOZEgbBk5ZJmEY2CQJpyzjV1Vi2SxhvbK114NckEYgkyGQTpM0DALpNOOZDPu83qYRf4LKqUhUTU1N8fOf/5wbbriBn/zkJ7zpTW9i3bp1vP/97+fv//7va71GgaDxhEKXxNBi5JLwAgHrTzJp/X3+vCWovvY1+NKXrFCJJ5+8NJi3cF/5g3lL3XetKRB9Ls3J+pYNTGppkskoumSQDk4SSYdZ37IBl+as7VDhGjOwaYADmw9gmAaj4VEM0xCzslYx4rokuBzQQ/qsGFqMXEpgJpAhHUhjJA3SgTTp82lM0+T8184z+qVRRg6OEH4yjGyXFxRgszOoStx3LcmVzOW7OE5ZZpvTyUw2SzCTQZYkJjIZItks6x0OnIrStO7PQFsbB1pbMUyT0WRSzMpaZVQdVGGaJr/61a/427/9Wx544AHRECxY2VQSJFHsPaZphUasXTs3vKK9HaamiodLDAzULsSiEookCmayGU6+8hTnQ6OMrvOy7eUA/v5tbF7/BrRorHbBGHVEzKlaOdTq56+4LglWG5UESRR7j2ma6OM6trW2OeEVSrtCdipbNFyibaCtZiEW5bJQuMO5VIoT8Tj9DgevxuOMptNsdTq52uslls3Oxqg3a6KemFO1sqhrUMWzzz7Lo48+yqOPPsqRI0eIRCLs3r2be++9l5tvvrniRQsEDaVYql8lQRKFvU05h2rt2vIG89YyxKISisyf0kIhtmdb6PuNt7HlTVfT9vizuF8chvMXaj9UuE6s9PlLgtIQ1yXBaqFYql8lQRKFvU2YcP5r57Gttc3rnTLTJr6bfMSH4/PCJWoZYlEuC82eCuo67+/uZn9LCxPpNM9GIgzH41xIpXArStO7Pytp9pKgdCr6RK+77jre8IY3cPPNN/O7v/u7vPnNb7YGggoEK4GF3Khrr60uSCKXEjgyUv5g3lqHWFTKAomC7oEB3HY73LMFfq2BQ44FghIR1yXBSmchN8p3ra+qIIlcSmByJLloeIX/Rj/tt7fPEXS1DrGohJw4GopEGE0m54gm+8X+qS0ul3B/BMtORd91wWBQlCAIVi4LOUITE6Un+S1GfnhFfnRqfv9RYUx7OSmCNaJoWVwpiYKLRcwLBMuEuC4JVjoLOULpiXTJSX6LkR9eYXNcKqXL750qjGkvJ0WwVhQ6dXZZ5q6ODva3tCwqmoT7I1huKvru8/l8zMzM8J3vfIeTJ0/yJ3/yJ7S1tfHss8/S3d3NunXrar1OgaA2LOYIvfKK1U+0mBgqhSJldHN6p3Jf54uWUoRYsWOpwDFK6SkGTw0yNDZELBPDrbnZt3YfA5sGsKv2S8cghJNgBSGuS4KVzGKOUOKVBLIiLyqGSiEXXjF9aBpM5vVO5b7OFzSlCLHC46g0Rn2pvjEhmgTNTkXfnS+++CK33XYbLS0tnD59mt/93d+lra2N733ve5w9e5Z//Md/rPU6BYLasJgjFInAzp3wq18tLIZKZaHBvDfdBA8/XDyMYikhlqOSMI08Bk8NcujkIbrd3fT6egmnwhw6afVu3bWteQMnBILFENclwUpmMUdIj+i4drqI/iq6oBgqlYUG87bc1MLkw5NFBc1SQgwqC9IoZDl7twSCWlCRqPrkJz/Jhz/8Yf7yL/8Sr9c7+/idd97J+9///potTiCoOUs5QnfeCZ2d88VQuWEMC5XRPfzwwmEUCwmxwn1XEWgRSoYYGhui291Nl9ty6hyqA9M0GRobYn//fhHqIFiRiOuSYCWzlCPUfmc7Wqc2TwzlRFKpLDSYd/LhyQUFzUJCLH/f1QqiZujdEgiqpaLv0GeeeYa/+7u/m/f4unXruHDhQtWLEgjqxlKleV1dS/cUlbu/3PvPnoVHHrG+XiiMYql9l7KNRdYbToWJZWL0+uY6dX6Hn9HwKOHU/7+9O49vqzwTPf6TjlbLkrzEcmzsELJAQhNoglO2cAPEHzDNMCwFOiUta2lLoTQFOrRzp3S7QDsFGkrntnSDdi4dWgZaOk3r3JibUsJS4kICISshi53FjmNbkmVrOdK5f5xIeLdka/fz/Xz8cXQsnfNKjvX68fO8z+uToCqHpP375Mm8JArZRKV5Fo9l1GBoKteLPz54MEjvi70obmXMgGa8a6teFe8mLwabAcWpYLQZUw6IcrF2SyRHGoAkb1KvjtVqxTd8A1Ng9+7dVFVVTXlQQmRUMhmhdK4pipfrvfgibNqk71Xl9cKCBWA2j2xGMdq1RztHby8sXDj6OcbgsrpwmB34Qj5spg8mL2/Qi8PswGWVhf65kNQ6NzEumZdEoUsmIzS8kcRUxEv2el7swbvJi7nSjOpVcSxwYDAbRgQ0o107Fopx7HfH6P1rL4pNoX9nP7aTbZQsLEkpIEp17ZbIvFAsRkt3N61+P4FoFIei0OB0JrouipEm9b/0H//xH/nWt77Fb3/7WwAMBgMHDx7kvvvu42Mf+1haByhE2iXT4S6d4uV6brceDIXDsG2b/rXFi5NrhDHaOd59V//aGWck3UzDbXPTUNtA895mNE3DbXPjDXrp7O+kaW6TZEdyZPA6t0p7JV39Xbyw6wVg4nVukt3SybwkCt1YpXmZEi/ZU9wKlkoLsXCM/m39AJQuLk0qoOlu6cb3qg+j1YjRZgQjBN4NAGCptiQdECXTRENkV0t3N809PVSbzdTZbHSGwzx77BiBaJTrqqsnfPx0zHBN6lk+8sgjXHPNNXg8HgYGBlixYgVHjx7l3HPP5YEHHkj3GIXIjGx0uBvebbC3Vw+GjEZ4770Pyg/Ha4Qx/Bxerx6UGY2wd6/+OJ8v6WYajXP0jFzr4Vbafe04zA6a5jYljovsiq9zq7RXcrTvKAd6DxCOholEIzz9ztMsq12Gp9STuG88gLKZbJLdGkTmJVEs0pmNGsvwNUxqr0rg3QAGo4GB9wYwuU1EvdFxA5r4OayzrBisBvq39aO4FIwlRgLbA2hhjcorKpN+Lslk6kR2eFWVVr+farOZcpOJHf39HAgG6VFVfnT4MBgMXDFjRiJjNTiAshmN0zbDNamfWrfbzYYNG3jllVfYunUrfX19LF26lMbGRjRNS/cYhShcw7sNLlyof967F44fh/7+iRthDD/HggX65/fe088xMJBSMw2rycqqU1exfNZyyXDkgfg6t95gL3u69+CyuCizldEX7mNn107+tOdPfGLxJ0YEUJqm0dnfSa2zVro4IvOSEKkYvoapZGEJAMG9QcLHw8T6YxMGNIPPYS4364/fHyQWjBELxXCe50wpIMp2pk6MzaeqBKJR6mw2dvT3824ggEtRqLZY6AyF+PPx4ziMRhorKkYEUJqm0amq1Fos1Nls+FSV5h69acmqGcXdxXFS/1u/973v8eUvf5nzzz+f888/P3E8Go3yyU9+kv/8z/9M2wCFKGjDuw2azXq5ntutB0Nr1sCsWamfY/Fi/Rz9/cmdYxRum1uCqTzgsrpQDAo7u3ZiU2xYTVbMihmT0YTb6mbX8V28sOsFXj74cqINfmdfJxve38D8yvl8eOaHAeniKPOSEMkbvobJaDZSekapnqEaiFK3pg7bLFvy5/BYKF1cSsm8EoJtQYxmI56rPEm3Ux9y3ixk6sT4XCYTDkWhMxzmvf5+zIDVaCSkaZSZzZxkteqBVCzGy17vkBLBDT09zLfb+XBpKQA2iwUNaPX7WV5WVtSlgJPKw33ve9/j5z//+ZBj0WiUf/qnf2LLli3pGJcQxSHebbCjQ/8IBvXPPh+sXJlcMDTWObze5M8h8pbNZCMYCbKnew/v9bzH9mPb2XN8D72hXk6tPJX+SD+vt7+eaINvM9kotZZiVswcHzjOQGQgcS63zU0gEsAXGtmwodjJvCRE8uJrmCIdEcIdYWLBGOGOMFFflPKV5RMGVGOdQ/WpaCEN13KXBEYFzG0ycabDwWteL9v7+9kfDvN2IMD7AwPUW614LBa6IxFe9/moNpvxWCzYjEZKFQUzcFxVGYhGh5wvEI3iU9XcPaksmNT/+HXr1nHJJZfgdru55pprUFWV6667jp07d7Jx48Z0j1GIwpbs/lOZPofISy3vt+AP+/GUeBiIDBBSQwTCARZ5FjGzdCZd/V34w35mOmYmHmM32XFanPSF+hhQB7Cb7cD07uIo85IQqUnHGiZZB1XEDAbMBgOKwUA0GsVgNGIwGNDQ11CpgD8SYablg26NdkXBaTLRp6oMxGLYFQVO3N+hKLiKOEsFkwyqli1bxnPPPceVV16JxWLh5z//Oe+99x4bN26kOomOIEJMK+noNpjtjoUiK+JNKmaXz8ZutrOlYwt2kx0DBvxhP39r/xul1lKO+I+wv2c/H/J8iAUzFmA325lRMoPuYDf+kJ8Sc8m07+Io85IQqUnHGiZZB1WcvKrK1r4+lpeVMctuZ1tfH2VmMwZNY0d/P/sGBihVFI6Ew+wPBvmQw8EChwO70cgMs5luVcUfjVKiKHhVlc5IhKby8qIu/YNJBlUAF198Mb/61a/42Mc+xsKFC3nppZeYUeQL0ISYknR0G8xGx0KRNYM3Yy63lQOwv3c//ZF+Ovo6OMl1Eos8i6iwV/B2x9u8cfgNwtEwda46nFYnl829DIPBIF0cT5B5SYjUpWMNk6yDKi6DG1WcaTZjNRjYHwzSH4vREQ5zksXCotJSKsJh3u7r4w2/n3AsRp3NhtNk4rLycn1uCgZxKApN5eU0VhR/9jLpn4Crr7561ONVVVWUlZXxmc98JnHs+eefn/rIhBCiyA3ejNnj8LC4ejHzKubxXvd7hKNhzqk7h1pnLVUlVVgUC9s7t7OtcxsV9gpWzV9F45xGgmpw2nZxlHlJCCHSL96owqeqeCwWFpeWMq+khPf6+wlrGue43dRarVSZzVgMBrYHAmwLBKiwWFhVUUFjRQXBWEz2qRqLe4y/jl966aVpG4wQYpK8XikLLECjbcbsC/noC/fhcXjwOPT9qcyKmTOqz6DeVc++3n188oxP8iHPhwC9Rf50C6biZF4SIn+pXlVKAguU22SiwemkuacH7cRtn6rSF43iOdGYAsBsNHJGaSn1Viv7Bgb4pMfDh050/bMajdMmmIpL+tk++eSTiX8PDAwQi8VwOBwA7N+/n9///vcsXLhQJjMhsikUgpYWvYFFIKA3sGho0BtYWDO7AexB70GO+I9Q46xhlls6EE7WaJsxXzb/Mt7peAdfyIfNpHfhGogMcCxwjHJbOXWuulwOOW/IvCRE/omFYnS3dONv9RMNRFEcCs4Gfc+qybRYT9XBYJAjoRA1ViuzbBN3MRSji5frtfr9iTK+yyoreaevD5+qYjsRWA1EoxyLRCg/0VZ9OptUCHnFFVdw9dVX87nPfY7e3l7OOecczGYzXV1dPProo9x+++3pHqcQYjQtLdDcDNXV+ubAPp9+G/TGFhngC/p49LVHeenAS4mNaFecvIK7z70bl236dZ2bqrE2Y3aYHTTvbSYSjXCk7wjvHX+P3lAvC2csZNPBTTTOacRqymzgXEhkXhIiP3S3dNPT3IO52oytzobqU+lp1jd/nbEqc2scfarKowcP8pLXm9iIdoXbzd2zZhV917lMsBqNrJoxg+VlZUPK+BxGI809PUQ0jSMn9rHqjUZZaLezqbeXxooKrMbMB8/5aFLP+s033+SCCy4A4L/+67+orq7mwIED/OpXv+IHP/hBWgcohBiD16tnqKqrwePRNwb2ePSP1lb96xnw6GuP8vzO5zEajdS76jEajTy/83kefe3RjFxvunDb3NS76xOlfI1zGmma28Se7j20Hm4FYFntMuZXzKd5bzMt77fkcrh5R+YlIXJP9ar4W/2Yq81YPBaMNiMWjwWzx4y/1Y/qzdw+RY8ePMjzx49jNBiot9kwGgw8f/w4jx48mLFrTgduk4l6my1RytdYUUFTeTl7+vtp9el7Ii4rLWV+SQnNPT20dHfncrg5NanQvb+/H6fTCcD//b//l6uvvhqj0cg555zDgQMH0jpAIcQYfD695K9uWCmY263vZeXzpX191UHvQV468BJVjipqSmsA9D2SNHjpwEsc9B6UUsA0sZqsLJ+1nE0HN1HrrKXeVZ/Yj0oxKrQebmX5rOXTdj3VcDIvCZF7qk8lGohiqxtaBmZymwi2B/U1VhlYX3UwGOQlr5cqs5maE2Vp9hOfX/J6ORgMSilgmliNRpaXlbHJ56PWaqXeZsN+IjOlGAy0+v0sLyubduupYJKZqnnz5vH73/+etrY21q9fzyWXXAJAZ2cnLpeU/wiRFS6XvobqxF+KErxe/XgGfhaP+I8QiAQos5YNOV5mKyMQCXDEfyTt1ywU3qCXNm8b3mD6MoS+kI+oFtU3BlYHGIgMAHpWKxAJ4Av5JjjD9CHzkhC5Z3KZUBwKqm9oRkr1qigOBZMrM79oHwmFCESjlA37Rb7MZCIQjXIkFMrIdQuBV1VpCwbxqunLEvpUlWgshsdiYSAaZSAWA/SsViAaxZfGaxWSSf3vvv/++7n++uv50pe+xMqVKzn33HMB/a+DS5YsSesAhRBjcLv1phTNzaBp+m2vFzo7oakpI10Aa5w1OMwOekO9iawJQG+wF4fZQY2zZsRjtnVu4/3u95lTMYdFnkVpH1OuhdQQLe+30Hq4NbHGrKG2IS1rnqyKlUO+Q7QeasWsmLEoFmaXzabCXoHD7MBllWAhTuYlIXLP5DbhbHDqa6g0/bbqVYl0RihvKs9YF8AaqxWHotCrqokMFUCvquJQFGpGady0ra+P9wcGmGO3s+hEx7piEorFaOnuptXvT6wxa3A607LmyWo0cigUotXvx2wwYDEamW2zUWE241CUabuGbVLP+pprrmH58uUcOXKEM888M3F85cqVXHXVVWkbnBBiAo0nNnptbdVL/hwOPaBqzMwGsLPcs1hx8gqe3/k8aHqGqjfYy7H+Y1y94OohpX/H+o7xxeYv8mr7q4TUEFaTlfPqzuOxpseoKq3KyPhyoeX9Fpr3NlPtqKbOVYcv5KN5r94sZNWpU2sWsvnwZnxhH/1qP5XmSmLEeOPwG3hKPHyu4XNS+jeIzEtC5IeKRr1rnL/VT7A9iOJQKG8qTxzPhFk2Gyvcbp4/fhzQM1S9qsqxSISrKyuHlP4dC4f54u7dvOr3E4rFsBqNnOd08tipp1I1KCArdC3d3TT39FB9oiufT1Vp7tEbhqya4qbom30+fNEo/bEYlSYTMeANvx+P2cznamunZekfgEHTNC3Xg8gnPp8Pt9uN1+uVkhFROLK4T1Wy3f+u/6/rWb93PW6bG6fFiT/sxxv0cuncS/n1Nb/O6BizxRv0svb1tShGJbGnFEBHXwcxLcaac9aMGfh4g95xN+2Nn1tDo6u/iwO9BwhHw0SiEWY6Z7L20rV4Sj2jnLlwyfvv6OR1EYUo2/tUJdv97/pt21jf24tbUXCaTPhVFW80yqVlZfx6UXFUU3hVlbVtbSgGQ2JPKYCOcJiYprGmvn7MwMerquNu2hs/twZ0RSIcCAYJaxqRWIyZFgtr588fcs1ikOx78PQMJYUoNm531jb9ddlcfOOib4y7T9W2zm282v4qbpubKoeelbKZ9b8Uvtr+Kts6txVFKaAv5CMQCYzYN8ptc9Pua8cX8o0ImJItFxx87lpnLfMr5jOgDmDAwPGB44Si03eNgBAi/5nc2d3012Uy8Y05c8bdp2pbXx+v+v24FSWRlbJZLBAO86rfz7a+vqIoBfSpKoFodMS+UW6TifZgEJ+qjgiYki0XHHzuWquV+XY7A7EYBuB4JELoxPqq6Wh6NpIXQkzZLPcszq47e9Ruf+93v09IDeG0OIccd1qchNQQ73e/n61hZpTL6kIxKLR52xJNJEDPMo215ileLqgYFepcdShGZdQW6S6rC4fZkWhGYTfbqbBXEI6GZT2VEEKMYZbNxtlu96jd/t4fGCAUi+EcFlA4TSZCsRjvDwyMeEwhcplMKAYDbcEgA9Fo4rj3xBqz0dY8xcsFFYOBOpsNxWAYtUW6y2TCoSiJZhR2RaHCbCasadN6PRVIpkoIkQFzKuZgNVnxh/2JDBWAP+zHarIyp2JODkeXHiE1xKaDmzjoPciOrh2UWcuYVzmPmtIauoPdNM1tGpGl8ga9tB5updpRnSgXtJlsaJo2okW62+amobaB5r3NaJqG2+bGG/TS2d856rmFEEKMb47djtVoxK+qeobqBL+qYjUamWO3j/PowhCKxdjU28vBYJAdAwOUKQrzSkqosVjoVlWaystHZKm8qkqr30+12Zwo3bNZLGgwokW622SiwemkuacH7cRtr6rSGYmMeu7pRDJVQoi0W+RZxHl15+ENejkWOEYwEuRY4BjeoJfz6s4ritK/eMZpfsV8ltUuA6D1cCt7uvdwwawLOK3ytBHt1eMlfcOzTGO1SI9vABzTYrT72olpMZrmNtE4JzONSIQQopgtKi3lPKcTbzTKsXCYYCzGsXAYbzTKeU5nUZT+xTNO80tKWHbi+bT6fOzp7+cCt5vTSkpGtFePl/QNzzKN1SI9vgFwTNNoDwaJaRpN5eU0VmSuGUkhmL7hpBDFIJkGFWloYjFRU4XRPNb0WKL7X2egE6vJyqVzL+WxpsfSep1cGJ5xqnXVcnrV6ezv3U+7r523jrxF6+FWHGYHC2csZGntUqpKqoaU9NlMtiHnG62kz2qysurUVSyftbwgXhchhIDkmlSko5HFRE0VRvPYqacmuv91hsNYjUYuLSvjsVNPTet1cmF4xqnWauX00lL2DwzQHgrxlt9Pq8+HQ1FYWFLCUqeTKotlSEnf4AzeWOWCVqORVTNmsLysrCBel2yRV0CIQhIPkKxW2LxZb6UeCOit1Bsa9Fbq8f04QiFoaRn/PhMIHe9k07Y/sdm/i25rNKU9mKpKq/j1Nb9Oap+qTO71lAmjNaiwm+30R/rZ27OX2WWzqXHW8OaRN/nTe3+izlnHGdVn0FDbwJnVZ/Li/hdTKulz29wSTAkh8tLg4MhoM9Ld0o2/1U80EEVxKDgbnFQ0VmC06sVRsVBswvtMpDMc5k9dXewaGCB6Yi1PsnswVVks/HrRoqT2qcrkXk+ZMFqDCrvRSH80yt5gkNk2GzVWK2/6/fypu5s6i4UzSktpcDo50+HgRa83pZI+twRTQ8grIUQhGB4gHTqkB1dnnQV1dfq/m/W9kVh1Ym+klhb9WHX12PeZ4HoH1v8a7fAOznGW4V00j+1nlqS8B9Miz6IJy/0yuddTOsUzaRraiIzTQGSA3cd3U2Yro85Vx57uPRzyH6LUXEp/pJ9INELz3mZWzl5J09wmWg+30u5rx2F2SEmfEKLgjBYcaZqG2qliqbVgq7Oh+lR9I2Bgxip9b6Tulm56mnswV5vHvM9Y4kHOrzs6hqwXKlGUlPdgWlRaOmG5Xyb3ekqneCZNgxEZp4FYjN0nXqs6q5U9AwMcCoUoVRT6YzEimkZzTw8ry8poKi+n1e+nPRjEoShS0pciCaqEKASDA6QZM/Tgqr8furqgthZsNtA0/fjy5fpjWlv1+3tO7GU0/D7jlQK2tBD64wu0hY6g1tbgipiY8cYeLIoFdUn1iKYKU5FK84ZcGS2Tpmkah/2HExmnNl8b3pCXhtoGAA70HsBlcWE32+kZ6KHUWorVZGVrx1bWnLNGSvqEEAVteHAU7gzTs6EH+3w7pR/WgxWLzQKavhFw2fIyQP+3udqMxWMZ9T7jlQK2dHfzwvHjHAmHqbFYMAF7+vuxGAxUWywjmipMRSrNG3JltEyapmkcjkQSGae2YBCvqtJwYn+lA8EgLkXBrij0RCKUKgpWo5GtfX2sqa+Xkr4pyL/cpRDFwuuFtjb981TPMzhAisXAbIbKSjhwAOItYN1uPYvl8+kfgYC+jmqwwfeZ4HoDlS56XWbMDifBCif9FU6qdhygSrWM2lRhsgY3bxiIDNA90M1AZGDM5g25MFob9M7+TjwlnkQTCbPRzIIZC6h11jKgDhCOhrGZbAxEBrAoFuwm+5Dn5La5qXfXDwmovEEvbd62EQ0uhBAiXVSvSrAtiOpVJ77zOOcYHBwZbUaUUgXMoB5XiQ580Mbb5DYRDURRfXqZYDQQxeQa+sv64PuMJR7kuBQFs8GA88TmvU5F4UAwiMVgGLWpwmQNbt4wEI3SHYkwEI2O2bwhF0Zrg96pqnhMpkQTCbPRyIKSEmotFgZiMcKahs1oZCAWw2I0YleUIc/JbTJRb7MNCai8qpoIzsTYJAQVYiyTbfCQhrVMQ8QDpLoT63fsdrBY9OAqHNaDKrtdH6/D8UEg5XDojx28V8fw+4xzPctMD5YBC8HIAGark3CpndKOXkLdx3BUOdO2T5LL6sKqWHnj0Bt4g17C0TAGDNhNduZVzsv5fkzjZdJiWoybl9yMAQMuq4tNBzfRvLcZl8WF0Wikq7+LGDEWVS3CbrbT0dcxakOKQltTJoTInck2eEjHWqbEGE4ER7a6D+YXxa5gcppQ+1RiAzEUu5IYr+JQEoGU4lBQfaqeoRr0nAbfZzTxIMdjsWA5ERQ4FQW70UhvNMqxSARnGvdJcplMWI1G3vD58KoqYU3DoGnYFYV5dnvO92MaL5MW0zRurqnBcOJ5bOrtpbmnB5eiYDQY6FJVYsCikhLsRiMd4fCoDSkKbU1ZrhXVKzJ79mwMBsOQj+985zu5HpYoNKEQrFsHa9fC44/rn9et048nI16qpyh6IKQo+u2WlokfOxqX64MACfQAavZsvfQvEgGDATo6oLNTD97cbv2joUE/3tEBweDI+0xwvZL+MCeXzcYX9uEL+TH6+uhVIrQbfDTUNqStZM1tc6MYFN7ueJu+cB/+sJ/dx3ezqW0Tmw9vZtPBTYTUJF/7DJioDboBQyLjFG+BblEslJhK6Av3Uees45SyU+jo66Czv3PU1y7ZDYFFYZK5SaRDLBSja10XbWvbaH+8nba1bXSt6yIWiiX1+Hi5nkExYKuzYVAM9DT30N3SPfGDhzG5TIngKM5oN2KeYUYLa0T9UWLBGOGOMJHOCM4GJya3HgQ6G5xEOiKEO8Kj3mcs8Q514ViM2TYbvmgUfzRKXzRKJBbDF43S4HSmrWTNbTKhAG8HAvRFo/hVld0DA2zyetns97Opt5dQLLnXPhMmaoNugETGKd4C3WIwUGIw0BeNUmexcIrdTkc4TGckMuprl+yGwEJXdJmqb33rW9x2222J206nM4ejEQVpKg0ehpfqQWprmUYTD5Cam/XzuN1QUaGf3+mE48f1oKupSc+GxcX/3doK7e2j32eC6y2cUYHRdSqdh3cT6/Zy6OwFXHjGFWltquANeolqUc6sPpNdXbs4GjiK3WRndtls0OCFXS8AuWtYkUob9MEt0I8FjvHm0TfZcWwHRwNHx2xIUQhrysTUydwkpmoqDR6Gl+tBamuZhosHRz3NPaDpt1WvislpovyycgwGA8H2IIpDobypnIrGD5odxP/tb/WPeZ/RDN50tsJs5lSbjd0DA3hVlQUlJVxRWZnWpgpeVSVqMHCmw8Gu/n6OhsPYjUZmn6j+eOH4cSB3DStSaYM+uAX6sXCYN/v62BEIcDQUGrMhRSGsKcs3RfdqOJ1OZs6cmethiEI11aBoeKlenNutBzY+3+T2ihotQPrc52DZMj2DNlqJotWqB4HLl6dexnjieubWVhaF3QRPOgd/02ksvvSjuN2e1Mc/Dl/IRygaYpFnEV39XZTZyyi36ZNyz0APLqsrqeAiU3tcuW1uGmobaN7bPGobdIA2b9uQ68ZboM+rnDfhuEZrzx4/R7uvPbH+ShQ2mZvEVEw1KBqtXA/0YCjYHtTLCVPcK2q04KhiVQUVjRXEgrExSxSNViMzVs2gbHlZymWM8V/8W/1+3CYT57jdnGa389HKysQv/uniU1VC0SiLHA66VJUyk4lysxkD0BOJ4FKUpIKLTO1xNTjIHK0NOkBbMDjkuvEW6PNObAA83rhGa88eP0d7MJhYfyU+UHSvxne+8x2+/e1vM2vWLK6//nq+9KUvYRrnmx4KhQgNKuvyjbeAXxS/qQZFg0v1Ul3LNJ6pBEjxcsApXM/mcmGb5MbBE4lngrr6u4hpMcpt5ZgVM/6QH4tiwVPioWuga8zgIhvrkeLZpcFt0FfOXkkkGmHt62vHve5Ee0yluiGwKEypzE0yL4nhphoUDS7XS3Ut01jGC46MVuOEgVK8HDAV2dx0Np4J6opEiGka5SYTZoMBfzSKxWjEY7HQFQ6PGVxkYz3S4CAz3gZ9pdtNRNNY29Y27nUn2mMq1Q2BRZEFVXfddRdLly6loqKCV199la9+9ascOXKERx99dMzHPPTQQ3zzm9/M4ijz1GSbMhSbqQZFo5Xqeb36Wqampqm/tpMJkPL8evFM0Au7XiASjdAX7sNkNOEL+1hUtYhQNDRucJGNPa4Gl/XFs07xphRTve5EmTDJUhW+VOcmmZd0k23IUIymGhSNVa4X6YxQ3lQ+pdd3MsHRVGVj09l4JuiF48eJxGL0GQyYNA1fNMqikhJCsdi4wUU29rgaLciMN6WY6nUnyoRJlmokg6ZpWq4HMZ6vfOUrfPe73x33Pjt27GDBggUjjv/iF7/gs5/9LH19fVjH6Lo22l8E6+vr8Xq9uCabVSgk6e5UVwzWrdODIo9nZFA00ZoqkNd0EuLZpqffeZqdXTtxW92cWnkqM0tn0h3spmlu06iBijfoZe3ra1GMSmI9EkBHXwcxLcaac9ZkJChJ93Wl+5/O5/PhdrsL4v03k3PTdJ+X0tmlrph0revS11R5zCOCoonWVIG8rpMRzzY93dHBzv5+3CYTp9rtzLRa6VZVmsrLRw1UvKrK2rY2FINhSFliRzhMTNNYU1+fkaAk3deV7n+6ZOemvA8z77nnHm666aZx7zNnzpxRj5999tmoqsr+/fs57bTTRr2P1WodM+CaFqbSlKFYTbbBQ9xUSvVSVSQZxngmaFntMv6050/sOr6LqBbFaDCO2twhLlfrkdJ93dEyYZKhym+ZnJum+7w0lYYMxWyyDR7iprKWKVXFkmWMZ4KWuVz86fhxdg0MEI3FMMKozR3icrUeKd3XzWa5ZTHI+1emqqqKqqqqST12y5YtGI1GPJ70LqwvGpnoVFcM0hUUZbJ0rkizYZ5SDzctuSnpphO5Wo+UqetOtP5K5A+ZmzIj3V3qikm6gqJMlusVazbMY7FwU01N0k0ncrUeKVPXzUa5ZTEomlfotdde429/+xsXXXQRTqeT1157jS996Ut88pOfpPxEFxQxTKY61RWLbK9fSkWRZxiTDS5ytR5J1kGJZMnclJpMdKkrNrlYw5SsYs8yJhtc5Go9kqyDyq2ieXWtVivPPPMM3/jGNwiFQpxyyil86Utf4u6778710PJXpjrVFaJ0l9FlsixPMoxDjNaZb7ySwUK/rigsMjelJhNd6gpZusvoMlmWJ1nGoUbrzDdeyWChX1cUUVC1dOlSXn/99VwPo7BkulNdIUh3GV02yvIkwzhErtYjyTookQyZm1KTyS51hSTdZXTZKMuTLONQuVqPJOugcqdwC1xFejQ26gFULKb/Qh6LpdaUIdO8Xmhr0z9nQryMTlH0IEVR9NstLflxvtEMzjAONh0zjIO4bW7q3fVZD2xydV0hilVFYwXlTeVoMY1gexAtpqXUkCHTVK9KsC2I6lUzdo14GZ1BMWCrs2FQDPQ099Dd0p0X5xvN4CzjYNM1yxjnNpmot9myHtjk6rrTmbzS0102O9WlIhsZn3SX0WWrLE8yjEKIIpbNLnWpyFYThnSX0WWrLE+yjGK6k0yV0LndUF+fP7+QZyPjEy+jG57Zcbv148MzQdk+33jyPcOYJ7xBL23eNrzBDGU6hRAZY3KbsNXb8uaX8Wxke+CDMrrhmR2T20Q0EB2RCcr2+caT71nGfOFVVdqCQbxq5rKdIvvy451KiMGylfFJd6OObDb+GJ5h1DQwGCAYLOiW6ukim+kKIdIpm00Y0t2sI5vNP4ZnGdEAA8SCsYJuqZ4usplucZOgSuSfbDViSHcZXfx8L7wAvb1QVQXh8NTK8ibqImizwaZNRbdf1VS1vN9C895mqh3V1Lnq8IV8NO/VW86vOrXwW84LIbIrm00Y0l1GFz/f8ReOo/aqmKvMaGFtSmV5E3URNNqM9G3qK7r9qqaqpbub5p4eqs1m6mw2fKpKc4/ecn7VjMJvOT/dSVAl8k82Mz7xcrnWVj1gczgmX0YXCkEkogc3776rHzv5ZLjuutTPl+yasiLfr2oyvEEvrYdbqXZU43HomU6byYamabQebmX5rOXSVEIIkZJst3qPl8v5W/0E24MoDmXSZXSxUAwtoqEGVPre7QPAdrKNquuqUj5fsuvKin2/qsnwqiqtfj/VZjOeExvz2iwWNPT258vLyqSpRIGT757IP9lsxJDORh0tLfDii7BwIZx5pj5enw/MZr0sr7Mz+fMnEyzJflWj8oV8BCIB6lxDM51um5t2Xzu+kE+CKiFESrLdhCGdzTq6W7rpfbEXx0IHzjOdhDvDRH1RjGYjsWCMcGc46fMnEyzJflWj86kqgWiUOtvQbKfbZKI9GMSnqhJUFTj57on8lM4MUjLc7qkFIKMFOGVlcOgQ/PrXeoleNJpceV6ywZLsVzUql9WFw+zAF/JhM30weXmDXhxmBy7r9Gw5L4SYmnRmj5Jlck+t8+FoAY6pzEToUIiOX3fg3eRFi2pJleclGyzJflWjc5lMOBQFn6pis3yQ7fSqKg5FwSUBVcGT76DIT/na6n0sYwU4R4/Cjh1QW6t3V0ymPC/ZYCmbZZIFxG1z01DbQPPeZjRNw21z4w166ezvpGluk2SphBCTkq+t3sczVoATOhpiYMcA1lortvrkyvOSDZayXSpZKNwmEw1OJ809PWgnbntVlc5IhKbycslSFQH5Dor8NtUM0kTijSDi3fMmG7yNFuB0d8OWLXr5X2WlfjyZ8rxkgyXZr2pMjXP0jOamg5s45D9Eua2cprlNiePeoBdfyIfL6pIgSwiRkqlmj5KhelVC7SE0NGx1k28rP1qAE+mO0LelD8xgqjRhtBmTKs9LNliS/arG1lihZzU3eb0cCgYpN5tpKi+nsaICr6riU1VcJpMEWAVKvmtieoo3gnj9ddi+XQ9ESkth0SK48MLUu+cNDnAiEb3s75VX4OBB/bzPPw9nnaWff6LyvFSCpWyXSU5RroMZabUuhMhnsVCM438+TueznQT3B9GiGpaZFqquraL6muqUO+cNDnC0iEboUAjvK16CB4MopQpdz3fhPMuJY5FjwvK8VIKlXJRKTkXWAxqDIfER0TT+3NXF1kBA2qwXOAmqxPQUbwTR3Q07d8LAgN7+/NAh/XYkAldemdo544HM00/D3/6mn9Pt1gOdY8f0dVWKoq+Vmqg8L9lgqUDKJLMdzAxuqV7jrEm0VN98aDPHg8el1boQIi91t3Rz5OdHCHeGIXoiY3UwRP+Ofgb2DDD7q7NTDqzigUzH0x34/uZDG9AwufWsU+RYBO8mLwbFgKXaMmF5XrLBUqGUSmZ736jBLdVrHA58qsrPjxwB4GyXS9qsF7j8+x8uxFRMtK9T/D6trfrX48GP06k3kgiH4cgR+O1v4aKLUgtQrFY9uGlp0QOnsjL93IcP618LheDtt2HBArjiivHPnWqwlOkyySnK5r5RY7VUD0aCvHTgJc6fdb60WhdCZM1EezoNvp9vk49oIIoBA6pfxVhixGg1Eu2P0v3f3ZSeXornOk9K1zdajZQtL6O3pRdLtQVzmZnoQJTw4TAGq4FYKEbf2304FjiovKJy3DGmGixlo1RyKrK5b9RoLdU1k4m+aBQD4FQUbEajtFkvYPKdEsUh2X2d4INGECYTHD+u39dm04Oq/n49gDlwQM8QpRqo+Hx6IGW3Dw2EOjv14/39cN55yZfn5XmwlIxs7xs1Vkt1s2ImEAlgNpqHHJdW60KITEh2T6c41acS6YmgRTU9sLIZUGwKWlRDUzU0o4bvdR8Vl1akHKioPpXoQBSj3YjiUhKPD3eGiQ3EiPZHcZ7nTLo8L9+DpWRke9+o0VqqD0SjGAEMBgZiMeyKAkib9UIlxZqiOMTL+RRF75qnKPrtlpaR9403gujrG3o8EtEDram8gblcUF6ur4UaGNDHcdJJ+ibAJ50EF1wAV12V2nqtAhcPcoa3Mnfb3AQiAXwhX1qvN7il+mCRaASH2UEkFhlyXFqtCyEyIb6nk0ExYKuzYVAM9DT30N3SPer9TS4T5nIzWkQjFophNOu/omkRDQCz24ymaqg+NeWxxM+NBrGBGChgPcmK7WQb1pOslF9QjucqT8qlhYUsHuQMb2XuNpkIRKP41NRf5/EMbqkeZ1cUYoCmadgHlRtKm/XCNH1+ekTxGr6vk82mf/Z49ONe79D7xxtBBIN6EwmfT79PPMPk9cLs2SNbmifD7dZL9hwOvZ16T4/+0d2tdwBsbExf5snrhba2kc8vz4wV5GQqmIm3VO8IdNDR10FQDdLR14E37GXFySvwhrxDjnf2d9JQ2yBZKiFE2gzf08loM2LxWDB7zPhb/ajekb+wm9wmXMtdKC6F6EBUzy71658NZgOWGgumStOkWpInzu1QCB8No/boWTG1W8VcaaasMT0b8qpelWBbcNTnl29GC3IgcwFNvKV6RyRCRzhMMBbDp6qUKoo+jmiUYCxGRzhMZyRCg9MpWaoCI98tUfgmswluvPzO54PNm/UAy+XSAzKXC669dvLBT2OjnvV69lnYv18/NmcOXHdderrypVLqmAdysW9UvHV66+FW2n3tOMwOmuY2ccGsC3j54MsjjsfvL4QQ6TDZDXArGivQIhrta9vp39VPLBjDXGWm5LQSTE69+95kg5/4ueOdBQFsc2xUXVc15a58qZY65oNc7BsVb6ne6vfTHgziUBRurakBTWNrIJA4Fm+zLgqLQdM0LdeDyCc+nw+3243X68U1TTdPLTheL3znO6CqemBlt+vHOzogFoM1a8YOkDo74U9/gq1b9UxVebmeaUpHgOL16kEd6ONKV4Zq3Tq9tLG6Wg8AfT79uTY1jb2hcI7lqpX5WC3cc93aXYxO3n9HJ69L4VG9Km1r29AiGkqpoq9lsiuEO8JoMY36NfXjBkfhzjDH/usYgXcDGBQDpkpT2oKUdO2BNVjXui56mnswV5sxuUz6+rAOvdX6WJsJ54Nsd/+LG62Fu+xTlb+SfQ+W75oobKGQ3qr84EHYsUPvuDdvHtTU6CV3E22C6/HATTcl1zUwVZloMjG81BGS21A4x6wmK6tOXcXyWcuzGsy4be5RrzPWcSGESAejzYimafRs6MFgNmBymhKlexWrJm40YfFYOOnzJyXdOTAV6W4yMbzUEUhqM+F8YDUaWTVjBsvLyrIa0LhHuc5ox0Rhyc+crBDJijeomD8fli3Tj7W2wp49qW2C63ZDfX1eBiRDxEsdh/+lxO3Wj/vS2/Qh3dw2N/XueglohBBFrbulG7VTxX6qHcWpEOmLMLBnAJPHlFKpncltwlafnmxSpsRLHYev9TK5TUQD0Uk11sg2t8lEvc0mQY2YEvnfIwrX8KxNbS2cfrrevMFs1rM2ebjGaErinQt9Pj1DFef1TryhsBBCiIyLZ24stRZKPaV6y/KBKNG+KAaDgVgwlrfrjCbD5NI3ElZ9qp6hOkH1qhNuJixEMSmen2ox/YyWtbHbYdYsfc+pPM/aTEq8c2FHh/4RDOqfOzv14/meaRNCiCI3PHNjtBsxV+ilcYWSuUmFya2v94p0RAh3hIkFY4Q7wkQ6I1NqrCFEoZH/6aJwTdesTbyksbVVb4ThcKRW6iiEECJjpmPmJl7S6G/1E2wPojgUypvKp9xVUIhCUnw/2SL/ZKIJBHyQtWlu1hs1uN36tTo7J25QUcisVr3L3/LlmXldhRBiGshEEwj4IHPT09wDmn5b9apEOvVueMWYuTFajcxYNYOy5WUZeU2FKATyP15kTjb2U5rOWZtMdBcUQogil409laZr5ibdnQWFKCTyP19kTrwzX3W1vk+Tz6ffDgTg3HPTk2GRrI0QQogUdLd0J/ZUstXZUH0qPc09xAIxXOe60pJlkcyNENOP/ISLzBhtPyVFgV274Ec/gldegYqK9GWuJGsjhBBiAqPtqWRWzPTv6ufQjw7R+0ov5gpz2jJXkrkRYvqQ7n8iM0brzLdzJ+zfD6qqB1SKomeuWlqGPtbr1duie71ZHbIQQojiNtqeSoGdAUL7Q2iqhrnCjEEx0NPcQ3dL99DHelWCbUFUb3F17xNCpIf8+URkxvDOfAMDekBlsUBJCZSV6e3PNU3PaC1frt8v02uwMi1TTTmEEEJM2fDOfLGBGMH9QQwWA0qJgqnMhGJXQNPXQ5UtL8NoM2Z8DVamZaophxDiA/KTJTJjeGe+WAx6esBohJNP1gOq+P3a2/VAZNOm0ddggb5uKl95vXDsGLz5JuzYUbgBoRBCFLnhnfm0mEakJ4LRaMR2sk0PqE7cL9geRPWp9G3qG3UNFsCMVTNy+XTGpXpVwsfC9L3ZR2BHoGADQiEKhQRVInMGd+Y7fhzMZjjpJFi48IP7xPeUimesBq/BstmGZrLyLfMzuLvh22/rweGpp8KSJdDfXxgBoRBCTDODO/Opx1WMZiPWk6yULCxJ3Ce+p1Q8YzV4DZbFZhmSycq3zM/g7oZ9b/cRbg9jP9WOc4mTaH+0IAJCIQpRfr0TiOIyvDPfa6/Byy9Dd/fIPaUMBj3DU1c39ByDM1n5FlTFuxu63XoQVVqqj7WsDBYvzu+AUAghpqnhnfm8r3nxvexD7VZH7CmFAaKBKLY625BzDM5k5VtQFe9uqLgVtH4NpVQh3B5moGyA0sWleR0QClHI5KdJZF68M5/Ho2elRttTKhgcugYrLp7JGtzwItOSWRc1uLuhyaSXN86YoT+P/fth3rz8DgiFEGKai3fms3gsKA5l1D2lYsHYkDVYcfFM1uCGF5mU7Jqowd0NDSYDWkzDNMOEFtQI7g9SMq8krwNCIQqZ/DSJ7BlvTymrdegarOGZrGwEJalsVhzvblhXp4/XYtEDKrtdXzs2MACRSPYDQiGEECkZb08po9U4ZA3W8ExWpoOSVDcqjnc3tNXZ0DQNg8WgB4Z2hUhPhOhAFC2iZTUgFGK6kFWKIvvcbqivHxkoNTbqAVQspmd4YrEPMlnZEC/nUxQ9WBqr5TsM7W5ot+vNN3w+vWGF0Qh9fXpA2NAgWSohhCgAJrcJW71tRKBU0VhBeVM5Wkwj2B5Ei2mJTFamxUv5DIoBW51tzHbviecwqLuhYlewnWwj6osSORbBYDQQ7YsS6YzgbHBKlkqINJOfKJE/xstkZdpomxWP1yhjeHfDOXOgtxf27NEDMrM5uwGhEEKIjBgvk5VJo21UPFGTjOHdDe1z7Ki9KgN7BrDUWTCYDVkLCIWYbiSoEvknvgYrW7xefWPi48f1tVDDxzLWuqjB3Q2PHoX58+Ef/xGWLoWqKslQCSFEEYmvwcqWUHuI8JEw9jn2EeMYb03U4O6GoaMh7PPtVP5jJc6lTixVFslQCZEh8pMlpq/Ba6i6u+Gdd/T1UOeco2eaYPxGGbnMrAkhhChK8XVU3k1e+t7po393P44POXAscGAwGyZskpGrzJoQ0538lInpK76Gqroa5s7VM1Vbt+pfW7Ys+UYZyWTWkukoKIQQYtqLr6MyV5sp/VApfW/34X/DTywcw1ZnS7pJRjKZtWS7CgohJiY/QWJ6Gm0N1bnn6p/b26G8HCorU18XNTx4SqWjoBBCiGlt+DoqU7kJg8VAYHuAwLYAlgpLymuiRgucUu0qKISYmARVYnoa3BI9zmyGj3wE9u6F66+HBQuSzyqNFTxFIvDii3rwVlenX7e5WX/MqlXpf15CCCEK1uCW6ABGs5HSM0qx1lsZ2DeA55MeSj9UmtS5xgucBmfDbHU2VJ+qN7cAZqyakbHnJ0Qxk6BKTE+DW6IP32y4oiK1gAqGlhLGg6cXXtBbq59+enIdBYUQQkxrg1uiD95sWAtrWGusiWArGWMFTrFAjMCOQEpdBYUQE5Mcr5ie4i3ROzr0j2BQ/zyZvaWGlxLabPpnlwsOHPig6cXgawcCeuAlhBBCnBBviR7piBDuCBMLxgh3hFPeW2p4GaHRZsTisWD2mPG97iPSHRnR6MLkNhENRFF9aiaemhBFr2CCqgceeIDzzjuPkpISysrKRr3PwYMHWbVqFSUlJXg8Hr785S+jqvLmIMaQrs2G46WEwzsExrNTXV1Dj4/XUVAIUVBkbhLplo7NhuNlhKMFTpqqYVAMI4KniboKCiHGVzA/OeFwmGuvvZZzzz2Xn//85yO+Ho1GWbVqFTNnzuTVV1/lyJEj3HDDDZjNZh588MEcjFjkvXS1RB+rlDAUgtmz9SCqo0M/d7IdBaeRWCxGOBzO9TBEFpjNZhRFyfUw0krmJpFu6WiJPlYZoepVMVWaKFlYgu9lH2h6oKV61aS7Ck4X0WiUSCSS62GILEjX3GTQNE1Lw3iy5qmnnmLNmjX09vYOOf7nP/+Zf/iHf+Dw4cNUV1cD8OMf/5j77ruPY8eOYbFYRjnbSD6fD7fbjdfrxSWZBJGsdev0NVUez9DgaeVKvfxPuv+NKhwOs2/fPmKxWK6HIrKkrKyMmTNnYjAYRnytkN9/Mzk3FfLrInKna12XvqbKYx4ROMWbVUj3v5E0TePo0aMjfpZFcUvH3FQ0f4547bXXWLx4cWLSArj00ku5/fbbeffdd1myZMmojwuFQoRCocRtn6xzKS7Z2h8qXjLY2qqXEjocH5QSWq2yQfAoNE3jyJEjKIpCfX09RuP0nsiLnaZp9Pf309nZCUBNTU2OR5Qdk5mbZF4qbtnaGypeLuhv9RNsD6I4lERAJRsEjy0eUHk8HkpKSkb9JVsUj3TOTUXzE3T06NEhkxaQuH306NExH/fQQw/xzW9+M6NjEzmQ7f2hJiolTGaD4GlGVVX6+/upra2lpKQk18MRWWC32wHo7OzE4/EUXSngaCYzN8m8VJyyvTdUMoFTMhsETyfRaDQRUFVWVuZ6OCJL0jU35fRPw1/5ylcwGAzjfuzcuTOjY/jqV7+K1+tNfLS1tWX0eiJL4i3OFUVvca4o+u2Wlsxe1+2G+noJoJIQjUYBki7NFcUhHkDn81qFXM9NMi8Vp3iLc4NiwFZnw6AY6GnuobulO6PXNblN2OptEjwlIf6+JH/om37SMTfl9Cfsnnvu4aabbhr3PnPmzEnqXDNnzuSNN94YcqyjoyPxtbFYrVassraluAxvcQ6yP1Qek9KK6aUQvt+5nptkXio+w1ucg+wNlc8K4X1KpFc6vuc5/QmuqqqiqqoqLec699xzeeCBBxKpO4ANGzbgcrk4/fTT03INUSDiLc7r6oYed7v19U4+nwRVQogxydwk0i3e4nz45r0mt4lge1Avz5OgSoiCVjArww8ePMiWLVs4ePAg0WiULVu2sGXLFvr6+gC45JJLOP300/nUpz7F1q1bWb9+Pf/6r//KHXfcIX/xm24GtzgfTPaHEkVm9uzZrF27NtfDmNZkbhLJGNzifDDZG0oUo+k6NxXMT/H999/PL3/5y8TteMekjRs3cuGFF6IoCn/84x+5/fbbOffcc3E4HNx4441861vfytWQRa643XpTiuZmveRP9ocSRWrz5s04HI5cD2Nak7lJJMPkNuFscNLT3CN7Q4miN13npoLbpyrTZD+QIpHt7n8iZcFgkH379nHKKadgs9kmfoAoCuN93+X9d3TyuhSHbHf/E6mTeWn6SsfcJD/FojjFW5yvWQNf+IL+edUqCaiKlDfopc3bhjfozcr1/H4/q1evxuFwUFNTw/e//30uvPBC1qxZA+j7DN17772cdNJJOBwOzj77bP7yl78kHv/UU09RVlbG+vXrWbhwIaWlpTQ1NXHkyJHEfQafL+7KK68c0kBheImFwWDgZz/7GVdddRUlJSXMnz+fP/zhD0POsW3bNi677DJKS0uprq7mU5/6FF1dXel6aYQQY4i3OK9fU0/dF+qoX1PPjFUzJKAqYl5VpS0YxKuqE985DWRuyi35SRbFTVqcF7WQGmLd7nWsfX0tj7/xOGtfX8u63esIqaGJHzwFd999N6+88gp/+MMf2LBhAy+//DJvvvlm4ut33nknr732Gs888wxvv/021157LU1NTezZsydxn/7+fh5++GH+4z/+g7/+9a8cPHiQe++9d8pj++Y3v8l1113H22+/zUc/+lFWr15Nd7fesrm3t5eLL76YJUuW0NraSnNzMx0dHVx33XVTvq4QIjnS4rz4hWIx1nV1sbatjcfb21nb1sa6ri5CsVhGrytzU25JUCWEKFgt77fQvLcZxahQ56pDMSo0722m5f3M7Ufm9/v55S9/ycMPP8zKlStZtGgRTz75ZGLfrYMHD/Lkk0/y7LPPcsEFFzB37lzuvfdeli9fzpNPPpk4TyQS4cc//jENDQ0sXbqUO++8kxdffHHK47vpppv4xCc+wbx583jwwQfp6+tLtPT+4Q9/yJIlS3jwwQdZsGABS5Ys4Re/+AUbN25k9+7dU762EEIIaOnuprmnB8VgoM5mQzEYaO7poaU7c3uSydyUe/JnEiFEQfIGvbQebqXaUY3HobeqtplsaJpG6+FWls9ajtuW/gzl+++/TyQS4SMf+UjimNvt5rTTTgPgnXfeIRqNcuqppw55XCgUorKyMnG7pKSEuXPnJm7X1NTQ2dk55fGdccYZiX87HA5cLlfivFu3bmXjxo2UlpaOeNzevXtHjFkIIURqvKpKq99PtdmM58Tm9jaLBQ1o9ftZXlaG25T+X79lbso9CaqEEAXJF/IRiASocw3dj8xtc9Pua8cX8mUkqJpIX18fiqLw97//HUVRhnxt8IRhNpuHfM1gMDC4b5DRaGR4H6Fkdnof7byxEyUnfX19XH755Xz3u98d8biampoJzy2EEGJ8PlUlEI1SN6zZgdtkoj0YxKeqGQmqJiJzU+ZJUCWEKEguqwuH2YEv5MNm+mDy8ga9OMwOXNbMdEmbM2cOZrOZzZs3M2vWLP2aXi+7d+/mf/yP/8GSJUuIRqN0dnZywQUXTPo6VVVVQxYHR6NRtm3bxkUXXTTpcy5dupTnnnuO2bNnY8rBpC6EEMXOZTLhUBR8qortRKYK9AyWQ1FwZei9V+am3JM1VUKIguS2uWmobaAj0EFHXwdBNUhHXwed/Z001DZkLEvldDq58cYb+fKXv8zGjRt59913ufXWWzEajRgMBk499VRWr17NDTfcwPPPP8++fft44403eOihh1i3bl3S17n44otZt24d69atY+fOndx+++309vZOaex33HEH3d3dfOITn2Dz5s3s3buX9evXc/PNNyfq7oUQQkye22SiwemkIxKhIxwmGIvREQ7TGYnQ4HRmLEslc1PuSVAlhChYjXMaaZrbREyL0e5rJ6bFaJrbROOcxoxe99FHH+Xcc8/lH/7hH2hsbOT8889n4cKFib0tnnzySW644QbuueceTjvtNK688sohfz1Mxi233MKNN97IDTfcwIoVK5gzZ86U/hIIUFtbyyuvvEI0GuWSSy5h8eLFrFmzhrKyMoxGmQ6EECIdGisqaCovJ6ZptAeDxDSNpvJyGisqMnpdmZtySzb/HUY2WRQiO9K5yaI36MUX8uGyunKyjioQCHDSSSfxyCOPcOutt2b9+oVENv9NnbwuQmRHujf/9aoqPlXFZTLlZB2VzE3JS8fcVLiFi0IIcYLb5s5qMPXWW2+xc+dOPvKRj+D1evnWt74FwBVXXJG1MQghhMhv7iwHUzI35ZYEVUIIMQkPP/wwu3btwmKxcNZZZ/Hyyy8zY8aMXA9LCCHENCZzU+5IUCWEEClasmQJf//733M9DCGEECJB5qbcKpzVX0IIIYQQQgiRhyRTJYqX1ws+H7hc4M5+8wIhhBBiMNWrovpUTC4TJrf8CiZEMZGfaFF8QiFoaYHWVggEwOGAhgZobASrNdejE0IIMc3EQjG6W7rxt/qJBqIoDgVng5OKxgqMVikaEqIYyE+yKD4tLdDcDIoCdXX65+Zm/bgQQgiRZd0t3fQ092BQDNjqbBgUAz3NPXS3dOd6aEKINJGgShQXr1fPUFVXg8cDNpv+2ePRj3u9uR6hEEKIaUT1qvhb/ZirzVg8Fow2IxaPBbPHjL/Vj+pVcz1EIUQaSFAliovPp5f8Dd+cze3Wj/t8uRmXEEKIaUn1qUQDUUyuoSsuTG4T0UAU1SdBlRDFQIIqMTleL7S15V/mx+XS11AND568Xv34ODthCzGd/f73v2fevHkoisKaNWuSeszs2bNZu3ZtRsclRCpUr0qwLZhX2R+Ty4TiUEYET6pXRXEoI4ItIcQHCmlukp9kkZpMNIFIZ5c+t1sfT3MzaJp+2+uFzk5oapIugKIo7N+/n1NOOYW33nqLD3/4w2k552c/+1luvvlm7rrrLpxOZ1KP2bx5Mw6HI3HbYDDwu9/9jiuvvDItYxIiWeluBJHOLn0mtwlng5Oe5h7Q9NuqVyXSGaG8qVy6AIqiMd3nJvlJFqmJN4GortabQPh8+m2AVatSO1emuvQ1NuqfW1uhvV0/b1PTB8eFEAmRSIRQKERnZyeXXnoptbW1ST+2qqoqgyMTInnxRhDmajO2OhuqT9WDGGDGqhlJnydTXfoqGisA8Lf6CbYHURwK5U3lieNCiKEKcW6S8j+RvHQ3gchUlz6rVQ/w1qyBL3xB/7xqlbRTL2ZZLkeNxWL827/9G/PmzcNqtTJr1iweeOABAN555x0uvvhi7HY7lZWVfOYzn6Gvry/x2AsvvHBECcOVV17JTTfdlLg9e/ZsHnzwQW655RacTiezZs3iJz/5SeLrp5xyCgBLlizBYDBw4YUXJr72s5/9jIULF2Kz2ViwYAH/+3//78TX9u/fj8Fg4De/+Q0rVqzAZrPx9NNPJ/76d/HFF2MwGPjLX/4CwKZNm7jggguw2+3U19dz1113EQgEhowzXmIxe/ZsAK666ioMBkPithCZls5GEJnq0me0Gpmxagb1a+qp+0Id9WvqmbFqhrRTL3LZLkeVuemDceZibpKfZpG8dDaByEaXPrcb6uul5K+YhUKwbh2sXQuPP65/XrdOP55BX/3qV/nOd77D1772NbZv386vf/1rqqurCQQCXHrppZSXl7N582aeffZZWlpauPPOO1O+xiOPPEJDQwNvvfUWn//857n99tvZtWsXAG+88QYALS0tHDlyhOeffx6Ap59+mvvvv58HHniAHTt28OCDD/K1r32NX/7yl0PO/ZWvfIUvfvGL7Nixg4suuihx3ueee44jR45w3nnnsXfvXpqamvjYxz7G22+/zW9+8xs2bdo05nPZvHkzAE8++SRHjhxJ3BYi09LVCCIbXfpMbhO2epuU/BW5WChG17ou2ta20f54O21r2+ha10UsFMvodWVuGimbc5P8VIvkDW4CYbN9cHwyTSDiAVpd3dDjbrdesufzSTAkJpbOctQk+f1+HnvsMX74wx9y4403AjB37lyWL1/OT3/6U4LBIL/61a8S9dw//OEPufzyy/nud79LdXV10tf56Ec/yuc//3kA7rvvPr7//e+zceNGTjvttERpQ2VlJTNnzkw85utf/zqPPPIIV199NaD/1XD79u088cQTibECrFmzJnEfgN7eXgAqKioS53vooYdYvXp14i+X8+fP5wc/+AErVqzgRz/6EbbB7wF8UG5RVlY2ZExCZNrgRhAWmyVxPNVGEPHgzFY39P+2yW0i2B7U11hJMCSSkK5y1FTI3JT7uUneHUTyxmsCccEFH2SqkgmG0hmgielpeLYT9P9LmqYfX748I4H5jh07CIVCrFy5ctSvnXnmmUMWyJ5//vnEYjF27dqV0sR1xhlnJP5tMBiYOXMmnZ2dY94/EAiwd+9ebr31Vm677bbEcVVVcQ97HRoaGia8/tatW3n77bd5+umnE8c0TSMWi7Fv3z4WLlyY9HMRIpPGagQRagvhOi/5uSRdwZmY3oZnPAH9/5Omr6krW16WkeBc5qbcz03yDiFSM7wJhNUKlZXwzjv6sWSbTUiXPjFVOcp22u32KT3eaDSiadqQY5FIZMT9zGbzkNsGg4FYbOzSkXht/E9/+lPOPvvsIV9TFGXI7cET63jn++xnP8tdd9014muzZs2a8PFCZNOQRhD7g4Q6QqBB39t9BA8Ek2o2IV36RDrkKuMpc1Pu5yZ5hxCpiTeBWL5c/6X1tdfg5Zf1bEFVVWrlV6N16bvgAjjtND3AksBKjCdH2c758+djt9t58cUX+fSnPz3kawsXLuSpp54iEAgkJodXXnkFo9HIaaedBuilCEeOHEk8JhqNsm3bNi666KKkx2CxWBKPjauurqa2tpb333+f1atXT/r5xS1dupTt27czb968pB9jNpuHjEmIbIk3gihbXkbn7zqJvhrFOsuKyWVKqfRqrC59rmUugm3BtLRYF8UtVxlPmZvGlq25Sd4ZxOTEA54dOyZffjU4QDt2DN58Uz9fKhkvMX3lKNtps9m47777+Od//mcsFgvnn38+x44d491332X16tV8/etf58Ybb+Qb3/gGx44d4wtf+AKf+tSnEuUVF198MXfffTfr1q1j7ty5PProo4m68WR5PB7sdjvNzc3U1dVhs9lwu91885vf5K677sLtdtPU1EQoFKK1tZWenh7uvvvulK5x3333cc4553DnnXfy6U9/GofDwfbt29mwYQM//OEPR33M7NmzefHFFzn//POxWq2Ul5endE0h0iF0IIR1lnVSpVeDgzPVp2K0GvFt9nHoR4fS2mJdFK9cZTxlbsr93CTvCGLy0tUN0O2GXbv0jFe626uL4tbYqAdQsZie7YzFsrIn2de+9jXuuece7r//fhYuXMjHP/5xOjs7KSkpYf369XR3d7Ns2TKuueYaVq5cOeSN/pZbbuHGG2/khhtuYMWKFcyZMyelvwQCmEwmfvCDH/DEE09QW1vLFVdcAcCnP/1pfvazn/Hkk0+yePFiVqxYwVNPPZVoc5uKM844g5deeondu3dzwQUXsGTJEu6///5x9wp55JFH2LBhA/X19SxZsiTlawoxVenqBBjv0ufb7MtIi3VR3CoaKyhvKkeLaQTbg2gxLSv7ksncNLpszU0GbXgB5TTn8/lwu914vV5c0ixhfF6v3sJaUT7IVAF0dOi/3K5Zk1y2IF3nEQUlGAyyb98+TjnllBHdelLm9epBvMsl/1fy3Hjfd3n/HZ28LslTvSpta9swKIZEpgog3BFGi2nUr6lPOlOQznOJwpDWeQn9/5DqU6VstACkY26STJWYvHj5VUeH/hEM6p87O/Xjyf5ym879r8T0JHuSCSH4oPQq0hEh3BEmFowR7ggT6YzgbHCm9ItturJeYvqSfcmmF/kui6kZrdlEquVX0l5dCCFEmozVbCLV0itpsS6ESIW8I4ipGd4NcDLlV9JeXQghRJoMbzYx2dIrabEuhEiFvCOI9HC7pxb8pCPjJYQQQpxgck99HUu6sl5CiOInQZXID+nIeImCJL1yphf5fotCkq6slygs8j41/aTjey7vDCK/TDXjJQpGfCf1cDg85Z3gReHo7+8H9M0YhSgU6ch6ifwXf1/q7++XeWmaScfcJO8QQoicMJlMlJSUcOzYMcxmM0ajNCMtZpqm0d/fT2dnJ2VlZYmgWggh8oWiKJSVldHZ2QlASUkJBoMhx6MSmZTOuUmCKiFEThgMBmpqati3bx8HDhzI9XBElpSVlTFz5sxcD0MIIUYVf3+KB1ZiekjH3CRBlRAiZywWC/PnzyccDud6KCILzGazZKiEEHkt/gc/j8dDJBLJ9XBEFqRrbpKgSgiRU0ajMS071wshhBDpoiiK/BFIpEQWMQghhBBCCCHEFEhQJYQQQgghhBBTIEGVEEIIIYQQQkyBrKkaJr75l8/ny/FIhBBieom/78rGm0PJvCSEELmT7NwkQdUwfr8fgPr6+hyPRAghpie/349bNgFPkHlJCCFyb6K5yaDJnwSHiMViHD58GKfTmZcbvvl8Purr62lra8PlcuV6OJMmzyO/yPPIL8XyPCC156JpGn6/n9raWtkMehCZl7KnWJ6LPI/8Is8jv6T6PJKdmyRTNYzRaKSuri7Xw5iQy+Uq6P/QcfI88os8j/xSLM8Dkn8ukqEaSeal7CuW5yLPI7/I88gvqTyPZOYm+VOgEEIIIYQQQkyBBFVCCCGEEEIIMQUSVBUYq9XK17/+daxWa66HMiXyPPKLPI/8UizPA4rruYjRFdP3uFieizyP/CLPI79k6nlIowohhBBCCCGEmALJVAkhhBBCCCHEFEhQJYQQQgghhBBTIEGVEEIIIYQQQkyBBFVCCCGEEEIIMQUSVBW4devWcfbZZ2O32ykvL+fKK6/M9ZAmLRQK8eEPfxiDwcCWLVtyPZyU7N+/n1tvvZVTTjkFu93O3Llz+frXv044HM710JLy7//+78yePRubzcbZZ5/NG2+8keshpeShhx5i2bJlOJ1OPB4PV155Jbt27cr1sKbsO9/5DgaDgTVr1uR6KCk7dOgQn/zkJ6msrMRut7N48WJaW1tzPSyRJTI35YdCnpsKfV4CmZvyUSbnJgmqCthzzz3Hpz71KW6++Wa2bt3KK6+8wvXXX5/rYU3aP//zP1NbW5vrYUzKzp07icViPPHEE7z77rt8//vf58c//jH/8i//kuuhTeg3v/kNd999N1//+td58803OfPMM7n00kvp7OzM9dCS9tJLL3HHHXfw+uuvs2HDBiKRCJdccgmBQCDXQ5u0zZs388QTT3DGGWfkeigp6+np4fzzz8dsNvPnP/+Z7du388gjj1BeXp7roYkskLkpfxTq3FQM8xLI3JRvMj43aaIgRSIR7aSTTtJ+9rOf5XooafGnP/1JW7Bggfbuu+9qgPbWW2/lekhT9m//9m/aKaeckuthTOgjH/mIdscddyRuR6NRrba2VnvooYdyOKqp6ezs1ADtpZdeyvVQJsXv92vz58/XNmzYoK1YsUL74he/mOshpeS+++7Tli9fnuthiByQuSn/FcLcVIzzkqbJ3JRrmZ6bJFNVoN58800OHTqE0WhkyZIl1NTUcNlll7Ft27ZcDy1lHR0d3HbbbfzHf/wHJSUluR5O2ni9XioqKnI9jHGFw2H+/ve/09jYmDhmNBppbGzktddey+HIpsbr9QLk/es/ljvuuINVq1YN+b4Ukj/84Q80NDRw7bXX4vF4WLJkCT/96U9zPSyRBTI35b98n5uKdV4CmZtyLdNzkwRVBer9998H4Bvf+Ab/+q//yh//+EfKy8u58MIL6e7uzvHokqdpGjfddBOf+9znaGhoyPVw0ua9997j8ccf57Of/WyuhzKurq4uotEo1dXVQ45XV1dz9OjRHI1qamKxGGvWrOH8889n0aJFuR5Oyp555hnefPNNHnrooVwPZdLef/99fvSjHzF//nzWr1/P7bffzl133cUvf/nLXA9NZJjMTfmtEOamYpyXQOamfJDpuUmCqjzzla98BYPBMO5HvEYa4H/+z//Jxz72Mc466yyefPJJDAYDzz77bI6fRfLP4/HHH8fv9/PVr34110MeVbLPY7BDhw7R1NTEtddey2233ZajkU9fd9xxB9u2beOZZ57J9VBS1tbWxhe/+EWefvppbDZbroczabFYjKVLl/Lggw+yZMkSPvOZz3Dbbbfx4x//ONdDE5Mkc1N+kbmp8MjclHuZnptMaTmLSJt77rmHm266adz7zJkzhyNHjgBw+umnJ45brVbmzJnDwYMHMznEpCT7PP7f//t/vPbaa1it1iFfa2hoYPXq1Tn/y3ayzyPu8OHDXHTRRZx33nn85Cc/yfDopm7GjBkoikJHR8eQ4x0dHcycOTNHo5q8O++8kz/+8Y/89a9/pa6uLtfDSdnf//53Ojs7Wbp0aeJYNBrlr3/9Kz/84Q8JhUIoipLDESanpqZmyHsTwMKFC3nuuedyNCIxVTI36WRuyrxim5dA5qZ8kem5SYKqPFNVVUVVVdWE9zvrrLOwWq3s2rWL5cuXAxCJRNi/fz8nn3xypoc5oWSfxw9+8AP+1//6X4nbhw8f5tJLL+U3v/kNZ599diaHmJRknwfofwW86KKLEn+ZNRrzPxFssVg466yzePHFFxMtj2OxGC+++CJ33nlnbgeXAk3T+MIXvsDvfvc7/vKXv3DKKafkekiTsnLlSt55550hx26++WYWLFjAfffdVxCTFsD5558/om3w7t278+K9SUyOzE0yN2VLscxLIHNTvsn43JSxFhgi4774xS9qJ510krZ+/Xpt586d2q233qp5PB6tu7s710ObtH379hVkh6X29nZt3rx52sqVK7X29nbtyJEjiY9898wzz2hWq1V76qmntO3bt2uf+cxntLKyMu3o0aO5HlrSbr/9ds3tdmt/+ctfhrz2/f39uR7alBVih6U33nhDM5lM2gMPPKDt2bNHe/rpp7WSkhLt//yf/5ProYkskLkpfxTq3FQM85KmydyUbzI9N0lQVcDC4bB2zz33aB6PR3M6nVpjY6O2bdu2XA9rSgp14nryySc1YNSPQvD4449rs2bN0iwWi/aRj3xEe/3113M9pJSM9do/+eSTuR7alBXixKVpmvbf//3f2qJFizSr1aotWLBA+8lPfpLrIYkskbkpfxTy3FTo85KmydyUjzI5Nxk0TdPSk/MSQgghhBBCiOknvwtrhRBCCCGEECLPSVAlhBBCCCGEEFMgQZUQQgghhBBCTIEEVUIIIYQQQggxBRJUCSGEEEIIIcQUSFAlhBBCCCGEEFMgQZUQQgghhBBCTIEEVUIIIYQQQggxBRJUCVFg9u/fj8FgYMuWLbkeihBCCAHI3CSEQdM0LdeDEEIkLxqNcuzYMWbMmIHJZMr1cIQQQgiZm8S0J0GVEAUkHA5jsVhyPQwhhBAiQeYmIaT8T4iM+clPfkJtbS2xWGzI8SuuuIJbbrmFvXv3csUVV1BdXU1paSnLli2jpaVlyH1nz57Nt7/9bW644QZcLhef+cxnRpRYRKNRbr31Vk455RTsdjunnXYajz322JDz3HTTTVx55ZU8/PDD1NTUUFlZyR133EEkEkncJxQKcd9991FfX4/VamXevHn8/Oc/T3x927ZtXHbZZZSWllJdXc2nPvUpurq60vyqCSGEyCSZm4TIDAmqhMiQa6+9luPHj7Nx48bEse7ubpqbm1m9ejV9fX189KMf5cUXX+Stt96iqamJyy+/nIMHDw45z8MPP8yZZ57JW2+9xde+9rUR14nFYtTV1fHss8+yfft27r//fv7lX/6F3/72t0Put3HjRvbu3cvGjRv55S9/yVNPPcVTTz2V+PoNN9zAf/7nf/KDH/yAHTt28MQTT1BaWgpAb28vF198MUuWLKG1tZXm5mY6Ojq47rrr0viKCSGEyDSZm4TIEE0IkTFXXHGFdssttyRuP/HEE1ptba0WjUZHvf+HPvQh7fHHH0/cPvnkk7Urr7xyyH327dunAdpbb7015nXvuOMO7WMf+1ji9o033qidfPLJmqqqiWPXXnut9vGPf1zTNE3btWuXBmgbNmwY9Xzf/va3tUsuuWTIsba2Ng3Qdu3aNeY4hBBC5B+Zm4RIP8lUCZFBq1ev5rnnniMUCgHw9NNP80//9E8YjUb6+vq49957WbhwIWVlZZSWlrJjx44Rfw1saGiY8Dr//u//zllnnUVVVRWlpaX85Cc/GXGeD33oQyiKkrhdU1NDZ2cnAFu2bEFRFFasWDHq+bdu3crGjRspLS1NfCxYsACAvXv3Jv+CCCGEyDmZm4RIP2nPIkQGXX755Wiaxrp161i2bBkvv/wy3//+9wG499572bBhAw8//DDz5s3DbrdzzTXXEA6Hh5zD4XCMe41nnnmGe++9l0ceeYRzzz0Xp9PJ9773Pf72t78NuZ/ZbB5y22AwJGrq7Xb7uNfo6+vj8ssv57vf/e6Ir9XU1Iz7WCGEEPlF5iYh0k+CKiEyyGazcfXVV/P000/z3nvvcdppp7F06VIAXnnlFW666SauuuoqQJ8c9u/fn/I1XnnlFc477zw+//nPJ46l+he6xYsXE4vFeOmll2hsbBzx9aVLl/Lcc88xe/ZsaZUrhBAFTuYmIdJPyv+EyLDVq1ezbt06fvGLX7B69erE8fnz5/P888+zZcsWtm7dyvXXXz+iG1My5s+fT2trK+vXr2f37t187WtfY/PmzSmdY/bs2dx4443ccsst/P73v2ffvn385S9/SSwovuOOO+ju7uYTn/gEmzdvZu/evaxfv56bb76ZaDSa8piFEELklsxNQqSXBFVCZNjFF19MRUUFu3bt4vrrr08cf/TRRykvL+e8887j8ssv59JLL038pTAVn/3sZ7n66qv5+Mc/ztlnn83x48eH/GUwWT/60Y+45ppr+PznP8+CBQu47bbbCAQCANTW1vLKK68QjUa55JJLWLx4MWvWrKGsrAyjUd5GhBCi0MjcJER6yea/QgghhBBCCDEFEsYLIYQQQgghxBRIUCWEEEIIIYQQUyBBlRBCCCGEEEJMgQRVQgghhBBCCDEFElQJIYQQQgghxBRIUCWEEEIIIYQQUyBBlRBCCCGEEEJMgQRVQgghhBBCCDEFElQJIYQQQgghxBRIUCWEEEIIIYQQUyBBlRBCCCGEEEJMwf8HXw7BNfVOFxQAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x400 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "scatter_comparison(X_test, y_test, y_pred)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1fd259a1-b42b-4bed-aa47-a602a68347b3",
      "metadata": {
        "id": "1fd259a1-b42b-4bed-aa47-a602a68347b3"
      },
      "source": [
        "### Performance metrics\n",
        "\n",
        "The accuracy score alone is not always a reliable metric for evaluating the performance of binary classifiers. For instance, when one outcome is significantly more frequent than the other, a classifier that always predicts the more common outcome without regard to the feature vector can achieve. Moreover, in many applications, the consequences of a false positive can differ from those of a false negative. For these reasons, we seek a more comprehensive set of metrics to compare binary classifiers. A [detailed discussion on this topic](https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-019-6413-7) recommends the [Matthews correlation coefficient (MCC)](https://towardsdatascience.com/the-best-classification-metric-youve-never-heard-of-the-matthews-correlation-coefficient-3bf50a2f3e9a) as a reliable performance measure for binary classifiers.\n",
        "\n",
        "The code below demonstrates an example of a function that evaluates the performance of a binary classifier and returns the Matthews correlation coefficient as its output."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "b3b67b1e-60f3-4484-a09f-6b9a29f83d03",
      "metadata": {
        "id": "b3b67b1e-60f3-4484-a09f-6b9a29f83d03"
      },
      "outputs": [],
      "source": [
        "def validate(y_true, y_pred, verbose=True):\n",
        "    \"\"\"\n",
        "    This function calculates and displays the sensitivity, precision, and Matthews correlation coefficient\n",
        "    (MCC) for a binary classifier based on its true labels (y_true) and predicted labels (y_pred).\n",
        "\n",
        "    Args:\n",
        "    y_true (array-like): A list or array containing the true labels of the samples.\n",
        "    y_pred (array-like): A list or array containing the predicted labels of the samples.\n",
        "    verbose (bool, optional): If True, the function prints and displays the calculated metrics and\n",
        "                              confusion matrix. Defaults to True.\n",
        "\n",
        "    Returns:\n",
        "    float: The calculated Matthews correlation coefficient (MCC).\n",
        "    \"\"\"\n",
        "\n",
        "    # Calculate the elements of the confusion matrix\n",
        "    true_positives = sum((y_true > 0) & (y_pred > 0))\n",
        "    false_negatives = sum((y_true > 0) & (y_pred < 0))\n",
        "    false_positives = sum((y_true < 0) & (y_pred > 0))\n",
        "    true_negatives = sum((y_true < 0) & (y_pred < 0))\n",
        "    total = true_positives + true_negatives + false_positives + false_negatives\n",
        "\n",
        "    # Calculate the Matthews correlation coefficient (MCC)\n",
        "    mcc_numerator = (true_positives * true_negatives) - (\n",
        "        false_positives * false_negatives\n",
        "    )\n",
        "    mcc_denominator = np.sqrt(\n",
        "        (true_positives + false_positives)\n",
        "        * (true_positives + false_negatives)\n",
        "        * (true_negatives + false_positives)\n",
        "        * (true_negatives + false_negatives)\n",
        "    )\n",
        "    mcc = mcc_numerator / mcc_denominator\n",
        "\n",
        "    if verbose:\n",
        "        print(f\"Matthews correlation coefficient (MCC) = {mcc:0.3f}\")\n",
        "\n",
        "        # report sensitivity and precision, and accuracy\n",
        "        sensitivity = true_positives / (true_positives + false_negatives)\n",
        "        precision = true_positives / (true_positives + false_positives)\n",
        "        accuracy = (true_positives + true_negatives) / total\n",
        "\n",
        "        print(f\"Sensitivity = {100 * sensitivity: 0.1f}%\")\n",
        "        print(f\"Precision = {100 * precision: 0.1f}%\")\n",
        "        print(f\"Accuracy = {100 * accuracy: 0.1f}%\")\n",
        "\n",
        "        # Display the binary confusion matrix\n",
        "        confusion_matrix = pd.DataFrame(\n",
        "            [\n",
        "                [true_positives, false_negatives],\n",
        "                [false_positives, true_negatives],\n",
        "            ],\n",
        "            index=[\"Actual Positive\", \"Actual Negative\"],\n",
        "            columns=[\"Predicted Positive\", \"Predicted Negative\"],\n",
        "        )\n",
        "        display(confusion_matrix)\n",
        "\n",
        "    return mcc"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "02311db9-9ce2-4354-abf4-87fa01482eea",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "02311db9-9ce2-4354-abf4-87fa01482eea",
        "outputId": "44f1e1b3-6dcd-4fd5-b58d-bcb7fab8a584"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LinearSvm(w = {'variance': 1, 'skewness': 0}, b = 0.0) \n",
            "\n",
            "Matthews correlation coefficient (MCC) = 0.736\n",
            "Sensitivity =  89.3%\n",
            "Precision =  86.9%\n",
            "Accuracy =  86.9%\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                 Predicted Positive  Predicted Negative\n",
              "Actual Positive                 133                  16\n",
              "Actual Negative                  20                 106"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-277f6bad-2547-4987-8979-08860b9f86d9\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Predicted Positive</th>\n",
              "      <th>Predicted Negative</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Actual Positive</th>\n",
              "      <td>133</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Actual Negative</th>\n",
              "      <td>20</td>\n",
              "      <td>106</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-277f6bad-2547-4987-8979-08860b9f86d9')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-277f6bad-2547-4987-8979-08860b9f86d9 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-277f6bad-2547-4987-8979-08860b9f86d9');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-f6580e52-c078-41ee-981f-90656031451b\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f6580e52-c078-41ee-981f-90656031451b')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f6580e52-c078-41ee-981f-90656031451b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x400 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x400 with 2 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1UAAAGJCAYAAABinBm7AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAACx1klEQVR4nOzdeXib5ZXw/++zaLMsS96TYCchCyQQthC2EtakJTRlgNBStinQedu37ZQO0GXamSlL2l8pL1MaCjPQoS2UNjPAUJbSgAsBAoRCS8rSAgGSkMWGxI5jWZK1P8vvD1uqd8uybMvy+VwXV2tZep5bsqJb57nPfY5i27aNEEIIIYQQQoi8qJM9ACGEEEIIIYSYyiSoEkIIIYQQQogxkKBKCCGEEEIIIcZAgiohhBBCCCGEGAMJqoQQQgghhBBiDCSoEkIIIYQQQogxkKBKCCGEEEIIIcZAgiohhBBCCCGEGAMJqoQQQgghhBBiDCSoEqLIKIrCDTfcMNnDGOD000/n9NNPz+m+V1xxBXPnzh3X8QghhCi8Yp2DxsOmTZtQFIVNmzZN9lBECZCgSpS0//zP/0RRFE444YS8j/HRRx9xww038MYbbxRuYGO0a9cuFEXJ/qdpGrNnz+b888+fsHEW4+sihBDFZDrMQb/5zW8G/P6GG25AURTa29snYXQD/ed//if33nvvZA9DlDgJqkRJW79+PXPnzuVPf/oT27dvz+sYH330ETfeeGNRTWgZF198Mb/61a/4xS9+wSWXXMKzzz7LiSeeOC5jfeqpp3jqqaeyPw/3utx999289957BR+DEEJMJaU+BwGsXbsW27YnexjDGiqoOvXUU4nH45x66qkTPyhRciSoEiVr586d/OEPf+DWW2+ltraW9evXT/aQCm7p0qVcdtllXH755fzwhz/k17/+NclkkjvvvLPg53I6nTidzpzu63A4cLlcBR+DEEJMFdNhDjr66KP5y1/+wiOPPDLZQ8mLqqq43W5UVb4Oi7GTd5EoWevXr6eyspLVq1fz6U9/esgJrbOzk2uuuYa5c+ficrloaGjgc5/7HO3t7WzatInjjjsOgCuvvDKb7pC54jV37lyuuOKKAcfsv/8olUpx3XXXceyxx+L3+/F6vZxyyik899xzBX3OZ555JtA9mWf87//+L8ceeywej4eamhouu+wyPvzwwz6P27dvH1deeSUNDQ24XC5mzpzJueeey65duwZ9TiO9Lr33VKXTaaqqqrjyyisHjDccDuN2u/nGN76RvS2ZTHL99dezYMECXC4XjY2NfOtb3yKZTPZ57NNPP83y5csJBAKUl5dz6KGH8i//8i95vW5CCFFo02EOuuiiizjkkENyXq364x//yKpVq/D7/ZSVlXHaaafx0ksvDbjfpk2bWLZsGW63m/nz5/PTn/40m1LY2z333MOZZ55JXV0dLpeLww47bMBFxblz5/L222/z/PPPZ1+/3nNZ7z1VX/3qVykvLycWiw0Y08UXX8yMGTMwTTN725NPPskpp5yC1+vF5/OxevVq3n777T6Py2V+FaVBn+wBCDFe1q9fz5o1a3A6nVx88cXceeedvPrqq9kJCqCrq4tTTjmFrVu38vnPf56lS5fS3t7Ob3/7W1paWli8eDFr167luuuu44tf/CKnnHIKAB/72MdGNZZwOMzPfvYzLr74Yr7whS8QiUT4+c9/zllnncWf/vQnjj766II85x07dgBQXV0NwL333suVV17Jcccdx0033URrayu33XYbL730Eq+//jqBQACACy64gLfffpurrrqKuXPn0tbWxtNPP82ePXsGLTgxmtfF4XBw/vnn8/DDD/PTn/60z2rXo48+SjKZ5KKLLgLAsiz+7u/+js2bN/PFL36RxYsX89e//pUf//jHvP/++zz66KMAvP3223zqU5/iyCOPZO3atbhcLrZv3z7o5CyEEJNhOsxBmqbxb//2b3zuc5/jkUceYc2aNUPe99lnn+Xss8/m2GOP5frrr0dV1WxQ9OKLL3L88ccD8Prrr7Nq1SpmzpzJjTfeiGmarF27ltra2gHHvPPOOzn88MP5u7/7O3Rd5/HHH+crX/kKlmXxj//4jwCsW7eOq666ivLycv71X/8VgPr6+kHH+NnPfpb/+I//YMOGDXzmM5/J3h6LxXj88ce54oor0DQNgF/96ldcfvnlnHXWWdx8883EYjHuvPNOli9fzuuvv56dO0c7v4opzBaiBG3ZssUG7Kefftq2bdu2LMtuaGiw/+mf/qnP/a677jobsB9++OEBx7Asy7Zt23711VdtwL7nnnsG3GfOnDn25ZdfPuD20047zT7ttNOyPxuGYSeTyT73CQaDdn19vf35z3++z+2Aff311w/7/Hbu3GkD9o033mjv37/f3rdvn71p0yb7mGOOsQH7N7/5jZ1Kpey6ujp7yZIldjwezz72d7/7nQ3Y1113XXYcgH3LLbcMe87+z2m41+Xyyy+358yZk/3597//vQ3Yjz/+eJ/7ffKTn7TnzZuX/flXv/qVraqq/eKLL/a531133WUD9ksvvWTbtm3/+Mc/tgF7//79w45ZCCEmw3SZg2655RbbMAx74cKF9lFHHZUd8/XXX9/nM9qyLHvhwoX2WWedlb2Pbdt2LBazDz74YPvjH/949rZzzjnHLisrsz/88MPsbdu2bbN1Xbf7f22NxWIDxnbWWWf1mVds27YPP/zwPq9HxnPPPWcD9nPPPZcd50EHHWRfcMEFfe734IMP2oD9wgsv2LZt25FIxA4EAvYXvvCFPvfbt2+f7ff7s7fnOr+K0iDpf6IkrV+/nvr6es444wygu0TsZz/7We6///4+S/e/+c1vOOqoozj//PMHHKN/msFYaJqWXaGxLIuOjg4Mw2DZsmW89tpreR/3+uuvp7a2lhkzZnD66aezY8cObr75ZtasWcOWLVtoa2vjK1/5Cm63O/uY1atXs2jRIjZs2ACAx+PB6XSyadMmgsHg2J7oEM4880xqamp44IEHsrcFg0GefvppPvvZz2Zv+9///V8WL17MokWLaG9vz/6XSWvMpKpkVtgee+wxLMsalzELIUS+pssclDn2v/3bv/Hmm29mswn6e+ONN9i2bRuXXHIJBw4cyH62R6NRVqxYwQsvvIBlWZimycaNGznvvPOYNWtW9vELFizg7LPPHnBcj8eT/f+hUIj29nZOO+00PvjgA0Kh0Kifi6IofOYzn+GJJ56gq6sre/sDDzzAQQcdxPLly4Hu9PPOzk4uvvjiPnOVpmmccMIJ2blqIuZXUTwkqBIlxzRN7r//fs444wx27tzJ9u3b2b59OyeccAKtra0888wz2fvu2LGDJUuWTMi4fvnLX3LkkUfidruprq6mtraWDRs25PXBn/HFL36Rp59+mmeeeYY///nPtLW18a1vfQuA3bt3A3DooYcOeNyiRYuyv3e5XNx88808+eST1NfXc+qpp/L//t//Y9++fXmPqz9d17ngggt47LHHsnujHn74YdLpdJ+gatu2bbz99tvU1tb2+e+QQw4BoK2tDehO0Tj55JP5P//n/1BfX89FF13Egw8+KAGWEGLSTac5KOPSSy9lwYIFQ+6t2rZtGwCXX375gM/3n/3sZySTSUKhEG1tbcTjcRYsWDDgGIPd9tJLL7Fy5Uq8Xi+BQIDa2trs3tp8n9dnP/tZ4vE4v/3tb4HuFM0nnniCz3zmM9lAN/N8zjzzzAHP56mnnsrOVRMxv4riIXuqRMl59tln2bt3L/fffz/333//gN+vX7+eT3ziEwU511BXEk3TzOZdA/z617/miiuu4LzzzuOb3/wmdXV1aJrGTTfdlN0HlY+FCxeycuXKvB+fcfXVV3POOefw6KOP8vvf/57vfve73HTTTTz77LMcc8wxYz4+dG9o/ulPf8qTTz7Jeeedx4MPPsiiRYs46qijsvexLIsjjjiCW2+9ddBjNDY2At1X/1544QWee+45NmzYQFNTEw888ABnnnkmTz31VJ/XXgghJtJ0moMyMqtVV1xxBY899tiA32cueN1yyy1D7t8qLy8nkUjkfM4dO3awYsUKFi1axK233kpjYyNOp5MnnniCH//4x3lfZDvxxBOZO3cuDz74IJdccgmPP/448Xi8zwXAzLF/9atfMWPGjAHH0PW/fb2eiPlVFAcJqkTJWb9+PXV1dfzHf/zHgN89/PDDPPLII9x11114PB7mz5/PW2+9NezxhkvBqKyspLOzc8Dtu3fvZt68edmfH3roIebNm8fDDz/c53jXX399Ds8oP3PmzAHgvffey6bPZbz33nvZ32fMnz+fr3/963z9619n27ZtHH300fzoRz/i17/+9aDHH21qyqmnnsrMmTN54IEHWL58Oc8++2x203DvMbz55pusWLFixOOrqsqKFStYsWIFt956Kz/4wQ/413/9V5577rmCBJpCCJGP6ToHXXbZZXz/+9/nxhtv5O/+7u/6/G7+/PkAVFRUDPv5XFdXh9vtHrSnV//bHn/8cZLJJL/97W+ZPXt29vbBKhqOdr668MILue222wiHwzzwwAPMnTuXE088ccDzqaury2m+Ge38KqYmSf8TJSUej/Pwww/zqU99ik9/+tMD/vvqV79KJBLJLutfcMEFvPnmm4P22MikMHi9XoBBJ6758+fzyiuvkEqlsrf97ne/o7m5uc/9MlcMe6dF/PGPf+Tll18e2xMexrJly6irq+Ouu+7qU478ySefZOvWraxevRrormrU/+rg/Pnz8fl8A8qY9zbc6zIYVVX59Kc/zeOPP86vfvUrDMPoc+UPuieyDz/8kLvvvnvA4+PxONFoFICOjo4Bv89c/RxuzEIIMZ6m8xyUWa164403ss8v49hjj2X+/Pn8+7//e5+9Shn79+/PHmPlypU8+uijfPTRR9nfb9++nSeffHLE5xQKhbjnnnsGHN/r9eY8V0F3CmAymeSXv/wlTU1NXHjhhX1+f9ZZZ1FRUcEPfvAD0un0kM8n3/lVTE2yUiVKym9/+1sikciAq2QZJ554YrYJ42c/+1m++c1v8tBDD/GZz3yGz3/+8xx77LF0dHTw29/+lrvuuoujjjqK+fPnEwgEuOuuu/D5fHi9Xk444QQOPvhg/s//+T889NBDrFq1igsvvJAdO3bw61//OnsVK+NTn/oUDz/8MOeffz6rV69m586d3HXXXRx22GGDTjCF4HA4uPnmm7nyyis57bTTuPjii7Ml1efOncs111wDwPvvv8+KFSu48MILOeyww9B1nUceeYTW1tZsqfPBDPe6DOWzn/0st99+O9dffz1HHHEEixcv7vP7v//7v+fBBx/kS1/6Es899xwnn3wypmny7rvv8uCDD/L73/+eZcuWsXbtWl544QVWr17NnDlzaGtr4z//8z9paGjIbiQWQoiJNt3noEsvvZTvfe97vPHGG31uV1WVn/3sZ5x99tkcfvjhXHnllRx00EF8+OGHPPfcc1RUVPD4448DcMMNN/DUU09x8skn8+UvfxnTNLnjjjtYsmRJn+N+4hOfwOl0cs455/B//+//pauri7vvvpu6ujr27t3b5/zHHnssd955J9///vdZsGABdXV1AzI4elu6dCkLFizgX//1X0kmkwMuAFZUVHDnnXfy93//9yxdupSLLrqI2tpa9uzZw4YNGzj55JO544478p5fxRQ1maUHhSi0c845x3a73XY0Gh3yPldccYXtcDjs9vZ227Zt+8CBA/ZXv/pV+6CDDrKdTqfd0NBgX3755dnf27ZtP/bYY/Zhhx2WLenau7Ttj370I/uggw6yXS6XffLJJ9tbtmwZUM7Wsiz7Bz/4gT1nzhzb5XLZxxxzjP273/1uQOlx2x59OduRPPDAA/Yxxxxju1wuu6qqyr700kvtlpaW7O/b29vtf/zHf7QXLVpke71e2+/32yeccIL94IMP9jlO/+c03Osy2PPKvA6NjY02YH//+98fdLypVMq++eab7cMPP9x2uVx2ZWWlfeyxx9o33nijHQqFbNu27WeeecY+99xz7VmzZtlOp9OeNWuWffHFF9vvv//+iK+HEEKMF5mDbPuee+6xgUHbXrz++uv2mjVr7Orqatvlctlz5syxL7zwQvuZZ57pc79nnnnGPuaYY2yn02nPnz/f/tnPfmZ//etft91ud5/7/fa3v7WPPPJI2+1223PnzrVvvvlm+xe/+IUN2Dt37szeb9++ffbq1attn89nA9nXpn9J9d7+9V//1QbsBQsWDPk6PPfcc/ZZZ51l+/1+2+122/Pnz7evuOIKe8uWLbZt5z6/itKg2HYOLbCFEEIIIYSYJOeddx5vv/12tvKeEMVG9lQJIYQQQoiiEY/H+/y8bds2nnjiCU4//fTJGZAQOZCVKiGEEEIIUTRmzpzJFVdcwbx589i9ezd33nknyWSS119/nYULF0728IQYlBSqEEIIIYQQRWPVqlX8z//8D/v27cPlcnHSSSfxgx/8QAIqUdRkpUoIIYQQQgghxkD2VAkhhBBCCCHEGEhQJYQQQgghhBBjIHuq+rEsi48++gifz4eiKJM9HCGEmDZs2yYSiTBr1ixUVa75Zci8JIQQkyfXuUmCqn4++ugjGhsbJ3sYQggxbTU3N9PQ0DDZwygaMi8JIcTkG2lukqCqH5/PB3S/cBUVFZM8GiGEmD7C4TCNjY3Zz2HRTeYlIYSYPLnOTRJU9ZNJraioqJDJSwghJoGkuPUl85IQQky+keYmSVoXQgghhBBCiDGQoEoIIYQQQgghxkCCKiGEEEIIIYQYA9lTJYQQQgghSoJt2xiGgWmakz0UMUVomoau62PezytBlRBCCCGEmPJSqRR79+4lFotN9lDEFFNWVsbMmTNxOp15H0OCKiGEEEIIMaVZlsXOnTvRNI1Zs2bhdDqlkqgYkW3bpFIp9u/fz86dO1m4cGHezeclqBJCCCGEEFNaKpXCsiwaGxspKyub7OGIKcTj8eBwONi9ezepVAq3253XcSSoEqIXI2RghA30Ch3dL/88hBBCiKkk31UGMb0V4n0j3xqFAKykRcfGDiJbIphRE82r4Vvmo2plFapLPqCFEEIIIcTQ5NuiEEDHxg6CTUEUTcHd4EbRFIJNQTo2dkz20IQQQgghRJGToEpMe0bIILIlgqPegbPOiepWcdY5cdQ5iGyJYISMyR6iEEIIIUqQbdt88YtfpKqqCkVReOONN0Z8zK5du3K+b7E6/fTTufrqq4e9z7333ksgEJiQ8RSCBFVi2jPCBmbURK/omw2r+3XMqIkRlqBKCCGEEIXX1NTEvffey+9+9zv27t3LkiVLJntIE+Lhhx/me9/7XvbnuXPnsm7duj73+exnP8v7778/wSPLn+ypEtNSn4IUFTqaV8MIGzjdzj730bzagGBLCCGEEKIQduzYwcyZM/nYxz422UOZUFVVVSPex+Px4PF4JmA0hSErVWJasZIW7RvaaV7XTMvtLTSva6Zzcyfeo7ykW9OkWlNYCYtUa4p0WxrfMp9UARRCCCGmkZBh0JxIEDLGN1Pliiuu4KqrrmLPnj0oisLcuXOB7tWr5cuXEwgEqK6u5lOf+hQ7duwY8jjBYJBLL72U2tpaPB4PCxcu5J577sn+vrm5mQsvvJBAIEBVVRXnnnsuu3btGvJ4mzZtQlEUNmzYwJFHHonb7ebEE0/krbfe6nO/3/zmNxx++OG4XC7mzp3Lj370oz6//8///E8WLlyI2+2mvr6eT3/609nf9U7/O/3009m9ezfXXHMNiqJk+4v1Tv97//33URSFd999t885fvzjHzN//vzsz2+99RZnn3025eXl1NfX8/d///e0t7cP+VwLSYIqMa0MVZBCQaFyVSW2ZZNoSWBbNpWrKqlaOfKVFCGEEEJMfUnLYkN7O+uam7m9pYV1zc1saG8naVnjcr7bbruNtWvX0tDQwN69e3n11VcBiEajXHvttWzZsoVnnnkGVVU5//zzsYYYx3e/+13eeecdnnzySbZu3cqdd95JTU0NAOl0mrPOOgufz8eLL77ISy+9RHl5OatWrSKVSg07vm9+85v86Ec/4tVXX6W2tpZzzjmHdDoNwJ///GcuvPBCLrroIv76179yww038N3vfpd7770XgC1btvC1r32NtWvX8t5779HU1MSpp5466HkefvhhGhoaWLt2LXv37mXv3r0D7nPIIYewbNky1q9f3+f29evXc8kllwDQ2dnJmWeeyTHHHMOWLVtoamqitbWVCy+8cNjnWShyCV5MG/0LUgDd6X42dL3ZRePVjQSWB6RPlRBCCDENbezooCkYpN7hoMHtJmwYNAWDAKzuCVIKye/34/P50DSNGTNmZG+/4IIL+tzvF7/4BbW1tbzzzjuD7rnas2cPxxxzDMuWLQPIrngBPPDAA1iWxc9+9rPsCtA999xDIBBg06ZNfOITnxhyfNdffz0f//jHAfjlL39JQ0MDjzzyCBdeeCG33norK1as4Lvf/S7QHfS888473HLLLVxxxRXs2bMHr9fLpz71KXw+H3PmzOGYY44Z9DxVVVVomobP5+vzOvR36aWXcscdd2T3Yr3//vv8+c9/5te//jUAd9xxB8cccww/+MEP+rx2jY2NvP/++xxyyCFDHrsQZKVKTBu5FKTQ/TruRrcEVEIIIcQ0EjIMtkQi1Dsc1DmduFWVOqeTOoeDLZHIuKcC9rZt2zYuvvhi5s2bR0VFRTZI2rNnz6D3//KXv8z999/P0Ucfzbe+9S3+8Ic/ZH/35ptvsn37dnw+H+Xl5ZSXl1NVVUUikRg2pRDgpJNOyv7/qqoqDj30ULZu3QrA1q1bOfnkk/vc/+STT2bbtm2YpsnHP/5x5syZw7x58/j7v/971q9fTywWy+flyLrooovYtWsXr7zyCtC9SrV06VIWLVqUfa7PPfdc9nmWl5dnfzfScy0E+eYopo3xKkjRp+iFBGNCCCHElBM2DKKmSYPb3ed2v67TkkgQNgz8+sTM8eeccw5z5szh7rvvZtasWViWxZIlS4ZM1zv77LPZvXs3TzzxBE8//TQrVqzgH//xH/n3f/93urq6OPbYYwekzQHU1taO23Pw+Xy89tprbNq0iaeeeorrrruOG264gVdffTXvMukzZszgzDPP5L//+7858cQT+e///m++/OUvZ3/f1dXFOeecw8033zzgsTNnzsz3qeRMvgGKaUP36/iW+Qg2BcHu/tkIGaTb0lSuqhx1QGQlLTo2dhDZEsGMmmheDd8yH1Urq1BdsggshBBCTBUVuo5X0wgbBm7n3y68hgwDr6ZRMUEB1YEDB3jvvfe4++67OeWUUwDYvHnziI+rra3l8ssv5/LLL+eUU07hm9/8Jv/+7//O0qVLeeCBB6irq6OiomJUY3nllVeYPXs20F0M4/3332fx4sUALF68mJdeeqnP/V966SUOOeQQNE0DQNd1Vq5cycqVK7n++usJBAI8++yzrFmzZsC5nE4npmmOOKZLL72Ub33rW1x88cV88MEHXHTRRdnfLV26lN/85jfMnTsXfYL+Xr3JNz8xrVStrCpYQYqhil50bOwYh5ELIYQQYrz4dZ1lPh+t6TStqRQJy6I1laItnWaZzzdhq1SVlZVUV1fzX//1X2zfvp1nn32Wa6+9dtjHXHfddTz22GNs376dt99+m9/97nfZ4OfSSy+lpqaGc889lxdffJGdO3eyadMmvva1r9HS0jLscdeuXcszzzzDW2+9xRVXXEFNTQ3nnXceAF//+td55pln+N73vsf777/PL3/5S+644w6+8Y1vAPC73/2On/zkJ7zxxhvs3r2b++67D8uyOPTQQwc919y5c3nhhRf48MMPh63Wt2bNGiKRCF/+8pc544wzmDVrVvZ3//iP/0hHRwcXX3wxr776Kjt27OD3v/89V155ZU4B21hJUCWmFdWlUrO6hsarG2m4qoHGqxupWV0z6pWl/kUvVLeKs86Jo85BZEsEIyQNg4UQQoipZGVVFasqK7Fsm5ZEAsu2WVVZycoceioViqqq3H///fz5z39myZIlXHPNNdxyyy3DPsbpdPKd73yHI488klNPPRVN07j//vsBKCsr44UXXmD27NmsWbOGxYsX8w//8A8kEokRV65++MMf8k//9E8ce+yx7Nu3j8cffxxnzyre0qVLefDBB7n//vtZsmQJ1113HWvXruWKK64AIBAI8PDDD3PmmWeyePFi7rrrLv7nf/6Hww8/fNBzrV27ll27djF//vxh0xJ9Ph/nnHMOb775Jpdeemmf382aNYuXXnoJ0zT5xCc+wRFHHMHVV19NIBBAVcc/5FFs27bH/SxTSDgcxu/3EwqFRr1MKqaPRHOClttbcDe4Ud1/+4dqJSwSLQkarmrA3ege5ghCiP7k83dw8roIMbJEIsHOnTs5+OCDcbvHNv+GDIOwYVCh6xO2QlVMNm3axBlnnEEwGMx7/9NUM9z7J9fP4On3ThGiAMar6IUQQgghJpd/mgZTYmzkHSNEHgpd9EKIwWSultqAAtP2qqkQQghR7GR2FiJPmeIWkS0REi0JNK+Wd9ELIXpLWhYbOzp4JRLhna4uOgyDKl3nMK+XEysqWFlVhWsC8sOFEEJMP6effjqyO2j0JKgSIk+ZoheB5QHpUyUKamNHB03BIJ3pNG2GgVNR2G8Y7Ewk6OypYLS6pmaSRymEEEKIDLnUKcQY6X4dd6NbAipRECHDYEskgl/T6OxZoZrpdBLQNEKGQYWmsSUSIWRIhUkhhBCiWEhQJUQejJBBojlR0NLp43FMMfWEDYOoaeJQFFKWhacnzc+jqqRsG6eiEDVNwhJUCSGEEEVDLq0LMQpW0qJjYweRLRHMqInm1fAt81G1smrUva6gO5BK7U8ReS1CbGusIMcUU1uFruPVNNK2jVNViVsWPk0jblk4FYWUbePVNCqkYIUQQghRNGRWFmIUOjZ2EGwK4qh34G5wY4SN7gqAQM3q3Pe49A7Ouv7SRaolhWehB99SH2bMzOuYojT4dZ1lPh9NwSABXWdXMkmXaZK2bWa7XIRNk1WVlVIFUAghhCgichlciBwZIYPIlgiOegfOOieqW8VZ58RR5yCyJTKqtL1McGanbayYhVaukfwwSfyDeN7HFKVjZVUVqyormevxUKfrWLZNra5zsNvNqspKVlZJhUkhhBCimMilTiFyZIQNzKiJu6Fvp23dr5NoSXRXAMyhWEXv4EzRFWzLxlHrwIyZJHYn8Cz0jPqYorS4VJXVNTUsDwSkT5UQQohp7YorrqCzs5NHH310socyLJmdhciRXqGjeTWMsIHT7czeboQMNK+GXpHbP6c+wZkNqlPFiluoHhWz08SKW9hpe1THFKXJL0GUEEKUtNNPP52jjz6adevWTfZQJt2uXbs4+OCDef311zn66KOzt992221Tom+WpP8JkSPdr+Nb5iPdmibVmsJKWKRaU6Tb0viW+XJeUeodnKkeFfdcN2bYxGg3UFQFM2KO+pjFKmQYNCcSUv5bCCGEyJNt2xjTeB71+/0EAoHJHsaIJKgSYhSqVlZRuaoS27JJtCSwLZvKVZVUrcx9j0v/4MxzsAdngxOzy0QpU1CcyqiPWWySlsWG9nbWNTdze0sL65qb2dDeTtKyJntoRWm44HMqBKZTYYxCCJGriWpxcsUVV/D8889z2223oSgKiqKwa9cuNm3ahKIoPPnkkxx77LG4XC42b97MFVdcwXnnndfnGFdffTWnn3569mfLsrjppps4+OCD8Xg8HHXUUTz00EPDjmPu3Ln84Ac/4POf/zw+n4/Zs2fzX//1X33u09zczIUXXkggEKCqqopzzz2XXbt2ZX9vGAZf+9rXCAQCVFdX88///M9cfvnlfcbb1NTE8uXLs/f51Kc+xY4dO7K/P/jggwE45phjUBQl+7x6P+//+q//YtasWVj9vk+ce+65fP7zn8/+/Nhjj7F06VLcbjfz5s3jxhtvHPfAdGpfBhdigqkulZrVNQSWB7r3O1Xoea0mZQKmyJYIyX1JPAs81JxTQ/nScpy1zim/QrWxo4OmYJB6h4MGt5uwYdAU7K5ouLpGKhpmJC2LjR0dbIlEiJomXk1jmc+XLUQx1O9canFcDxtu/MUyRiGEyFWh26aM5LbbbuP9999nyZIlrF27FoDa2tpssPLtb3+bf//3f2fevHlUVlbmdMybbrqJX//619x1110sXLiQF154gcsuu4za2lpOO+20IR/3ox/9iO9973v8y7/8Cw899BBf/vKXOe200zj00ENJp9OcddZZnHTSSbz44ovous73v/99Vq1axV/+8hecTic333wz69ev55577mHx4sXcdtttPProo5xxxhnZc0SjUa699lqOPPJIurq6uO666zj//PN54403UFWVP/3pTxx//PFs3LiRww8/HKfTOWCcn/nMZ7jqqqt47rnnWLFiBQAdHR00NTXxxBNPAPDiiy/yuc99jp/85Ceccsop7Nixgy9+8YsAXH/99Tm9jvmYUrPeCy+8wDnnnMOsWbNQFGXAhjXbtrnuuuuYOXMmHo+HlStXsm3btskZrChpul/H3ejOO/jJBGeNVzfScFUDjVc3UndhHWULyqZ8QBUyDLZEItQ7HNQ5nbhVlTqnkzqHgy2RiKxm9JIJPjVFocHtRlMUmoJBNnZ0DPu7XI33ClIhxjjVybwkROnIVOZVNAV3gxtFUwg2BenYOD6faX6/H6fTSVlZGTNmzGDGjBlompb9/dq1a/n4xz/O/Pnzqcqh6msymeQHP/gBv/jFLzjrrLOYN28eV1xxBZdddhk//elPh33sJz/5Sb7yla+wYMEC/vmf/5mamhqee+45AB544AEsy+JnP/sZRxxxBIsXL+aee+5hz549bNq0CYDbb7+d73znO5x//vksWrSIO+64Y0DK3gUXXMCaNWtYsGABRx99NL/4xS/461//yjvvvAN0B5QA1dXVzJgxY9DnXFlZydlnn81///d/Z2976KGHqKmpyQZwN954I9/+9re5/PLLmTdvHh//+Mf53ve+N+JrMFZTKqiKRqMcddRR/Md//Megv/9//+//8ZOf/IS77rqLP/7xj3i9Xs466ywSicQEj1SI3Iw1OCtGYcMgapoDmtP6dZ2oaRKWoAoYPvjcHAqxORzOOzCdiPRLCZ67ybwkRGkoZNuUQlm2bNmo7r99+3ZisRgf//jHKS8vz/5333339UmzG8yRRx6Z/f+KojBjxgza2toAePPNN9m+fTs+ny97zKqqKhKJBDt27CAUCtHa2srxxx+fPYamaRx77LF9zrFt2zYuvvhi5s2bR0VFBXPnzgVgz549o3qel156Kb/5zW9IJpMArF+/nosuugi1J0PizTffZO3atX1egy984Qvs3buXWCw2qnONxpT6Jnf22Wdz9tlnD/o727ZZt24d//Zv/8a5554LwH333Ud9fT2PPvooF1100UQOVYhpq0LX8WoaYcPA3WvpPmQYeDVtQLBVikKGQdgwhi2Bngk+G9x9S/T7dZ0PEwlQFGZ6vQN+15JIEDaMYasCTkT65XDjz2WMpULmJSFKQ6HaphSSt98coKrqgCp46XQ6+/+7uroA2LBhAwcddFCf+7lcrmHP5XA4+vysKEp231JXVxfHHnss69evH/C4zOpSLs455xzmzJnD3Xffnd0XtWTJElKpVM7HyBzHtm02bNjAcccdx4svvsiPf/zj7O+7urq48cYbWbNmzYDHuvvNWYVUMjPezp072bdvHytXrsze5vf7OeGEE3j55ZeHnLySyWQ20gUIh8PjPlYhSplf11nm89EUDGL3/BwyDNrSaVZVVpb0F+3R7DEaLvisdDhAUfIKTPuvIAG4nU5sYEskwvJAoCB/AwmeRybzkhBTR6HapoyW0+nENM2c7ltbW8tbb73V57Y33ngjGxAddthhuFwu9uzZM+z+qdFaunQpDzzwAHV1dVRUVAx6n/r6el599VVOPfVUAEzT5LXXXsuWRj9w4ADvvfced999N6eccgoAmzdv7nOMzB6qkV4Pt9vNmjVrWL9+Pdu3b+fQQw9l6dKlfcb73nvvsWDBgryeb76mVPrfcPbt2wd0/1F7q6+vz/5uMDfddBN+vz/7X2Nj47iOU4hS1Xv/zsqqKlZVVmLZNi2JBJZts6qyMluAoVSNZo9RJvhsTadpTaVIWBatqRRt6TTL/X6WV1QM+rtlPt+wQdFEpV8ON/6RxjhdyLwkxNRRqLYpozV37lz++Mc/smvXLtrb2wdUtevtzDPPZMuWLdx3331s27aN66+/vk+Q5fP5+MY3vsE111zDL3/5S3bs2MFrr73G7bffzi9/+cu8x3jppZdSU1PDueeey4svvsjOnTvZtGkTX/va12hpaQHgqquu4qabbuKxxx7jvffe45/+6Z8IBoMoigJ074Wqrq7mv/7rv9i+fTvPPvss1157bZ/z1NXV4fF4aGpqorW1lVAoNOyYNmzYwC9+8QsuvfTSPr+77rrruO+++7jxxht5++232bp1K/fffz//9m//lvdrkIuSCary9Z3vfIdQKJT9r7m5ebKHJMSIJqrcay4G27+zsaODlVVVXN3YyFUNDVzd2MjqmpqSrgg31B6jCk3jmWCQPYPsoRku+Mw3MO29gtR/fIVeQZquwfN4k3lJiMlRiLYpo/WNb3wDTdM47LDDqK2tHXZ/0VlnncV3v/tdvvWtb3HccccRiUT43Oc+1+c+3/ve9/jud7/LTTfdxOLFi1m1ahUbNmzIlivPR1lZGS+88AKzZ89mzZo1LF68mH/4h38gkUhkV67++Z//mYsvvpjPfe5znHTSSZSXl3PWWWdl0+1UVeX+++/nz3/+M0uWLOGaa67hlltu6XMeXdf5yU9+wk9/+lNmzZqVTZsezJlnnklVVRXvvfcel1xyyYDX6Xe/+x1PPfUUxx13HCeeeCI//vGPmTNnTt6vQS4Ueyq0KB6Eoig88sgj2br1H3zwAfPnzx/Qhfm0007j6KOP5rbbbsvpuOFwGL/fTygUGnKJU4jJMpHlXnPZFwSwob09u3+nQtcJGwatPal+q2tqcj7OVNecSHB7SwsNbjduVSVtWWyNxdiRSHAglWJ5IMCKQGDQVMDhXqN8Xr/M36TO4RiQfjkeJe0L9Tee6p+/Mi8JMXkSiQQ7d+7k4IMPHvO+GSNkjKltiujul7V48WIuvPBCvve97032cEY03Psn18/gknmnHHzwwcyYMYNnnnkmO3mFw2H++Mc/8uUvf3lyBydEgWTKvTrqHbgb3Bhhg2BTdwGCmtX5f1nuPYGYPjXnfUHD7d95JRwmallsjUanRQ+j/nuMtsZivB2NoioK1Q4HZao6ZLEI/zDByHC/G0pmpWhLJEJLIoFX08Z1BSmfMU4HMi8JMTXpfgmmRmv37t089dRTnHbaaSSTSe644w527tw5YBWplE2pd0xXVxfbt2/P/rxz507eeOMNqqqqmD17NldffTXf//73WbhwIQcffDDf/e53mTVr1oDu00JMRf3LvQLdm2nt7ibCgeWBUU8Cg618vbfQ5qmjDGq9zhErxw1XAe75YJCWZJL5Hs+ENQCezFWx3gU64pbFjkQCVVGwgAUeD7PdblpTqYIWixiKS1VZXVPD8kBgWqwSTiaZl4QQoju979577+Ub3/gGtm2zZMkSNm7cyOLFiyd7aBNmSs2yW7Zs6dOZObPB7fLLL+fee+/lW9/6FtFolC9+8Yt0dnayfPlympqaxrV8ohATZTzKvfZf+eoKpmj/fZCFCQ/OVeXA8JXjhqoA15pK0WEYLPZ6x7UCXcZoqu7lI9dgLbMS9EwwyIFUimqHgwUeD4t6yuIWqtx4ruORFaTxJ/OSEEJAY2MjL7300mQPY1JNqdn29NNPH1CfvzdFUVi7di1r166dwFEJMTHGWu61f474YCtfZo1GtBNq3jbo+piJVdHd2X2oYGCo8ukfJpNU6Xo2oOp9//HoYTRefZlGG6xlVoiOKC8HRaFMVZnd68vzWItFjHfwKEZP5iUhhBAwxYIqIaazTLnXYFMQbLKBUbotTeWqyiFXqYYqblF2aNmAlS+3qqFW6Bj7DNSIlQ2qhgsGBtu/88mqKv4ai01ID6Px7MuUb7A22+1mRSBAUzBIaypVsF5dE9HUV5Qe2XQvppMpWn9NTLJCvG/k01WIKSRT1jWyJUKiJYHm1UYs9zpUcQsrag1Y+fJoKnNSDra5DEIuk3JLGzEYGGr/jrenAt14NwAebl/XWFbFxhqsFbpYxEQ19RWlYyKrhQox2TINcGOxGB6PZ5JHI6aaWCwG/O19lA+ZgYWYQlSXSs3qGgLLAzldeR6uuEV0a5SyxWWEXwz3WflqjOhYH6vkjz5lVMFA//07uQYVYy0uMdS+rrGuio01WOudCrg3mWSmy9UnFXCixyOmn+Gqheb6GSLEVKFpGoFAgLa2NqC7t1Km8awQQ7Ftm1gsRltbG4FAAE3T8j6WfJIKMQXlWu51pOIWvqU+NK/WZ+Wr9uwqDl1ZxUmaNaZgZ7gKdCHDYH8qxWuRCFtjsTHtDxpqX9dYV8VGG6z1Dw6TlsVj7e28Eg5j2DbVPePMd//TeAWPojQNdUHFTtu0rm8lvDmMZVqyeiVKyowZMwCygZUQuQoEAtn3T75kFhZiihtuv8RIxS2ctU7KFpQNetXaj1qQlY/eK1i9Cy38pauLllSKhR4PS30+YqaZ9/6g8ejLlGuwNljxiKO8XrZ0dfF0MIgTKNd1gg4Hrel0Xs9vNOPJ13Rp0jxdDHVBJbU3RezdGK5ZLtyzC9frTohioCgKM2fOpK6ujnTP560QI3E4HGNaocqQmVOMG9kcPb5y2S+Ra3GLiWp0mCm04Nc0YpZFuabxYTJJQNc5srw87/1B49WXKZdgbbDiEXd99BEfJJM0ulzUOhzELYuWVIqGPJ/faMYzWlJRsDQNdkHFjJvEtsfQAzruRjeqWx1zrzshipGmaQX5kizEaMinpyg42Rw9MYbbL9H7inPFcRWk96eJvxcnEcmtuMV46F1oQVcULNvGr2l0mSY74nEWejxj3h9U6L5MIwVrgxWPsDWNsGmSME38uo6uKPh6Jvf2dJpKhyPv5zcewaNUFCxNg11QSTYnMTtNyo8rR/Wofe6bb687IYQQ3eTTUxRcrl/2Rf6GK0CRueKsutU+wa2iKZQdVkbNJ2uyj5lIvQstpCyLoGGwO5lEAVKWRZ3TyVy3u2D7gwqZzjZUsDZY8Yi4ZeFQVXRFocswKOsJtjyqyoFUCl1Rxvz8ChU8SkXB0ta/WqjiUPAs9uCa6epzv1x73QkhhBiafIKKgsrly75cCR27kQpQGGGDrs1dA4Lb6J+jOGudkxLc9i600JpKkbAskpaFQ1HQVJW3o1E60mm+NGvWmL7IT2Q622DFIzyqigaUaxpx2yZsGHhUlXbDIA2cWFExboHKaAPJlkSCvckkB/crPywVBUvDYNVCOzd3EmwKomhKzr3uhBBCjEw+QUVB5fJlXybusRupAEUmiC2m4DZTaOGxAwfYFotxkMtFRzpNWzpNQFXxaRoVmsZxFRVjOs9Q6WxRy+KkioqCFmIYrHhEuCeQc6sqVQ4HBwyDjlSKtG1zdmUl545DSl0mkNwcChHsSTFc7vcPCCQzQZdLVXk1HGZzOMxfo1Hej8c5zOtlcVkZDlWVioIlpveeyXx63QkhhBiZzJiioEb6si/pJYUxUgEKFIYMbmM7YkTfjeJd5J3wwGplVRX702ne7upCVRTqnE6O8vmocziwgKhhkLSsvI8/WDqb5nDwXizGnR9+yEudnVQ5HEOuXOWTMjhY8Yh/mDEDFIU3u7qoTqfRFIUTKyo4t7Z2XIo/PNnezs/37SNqmiiKgh2P81Y0StqyOK+ubsDqXUsyScQ0OdbnY4nXyxvRKFsiEVK2TaPLNS5NmkVxGG2vOyGEELmRT1JRULlWmxNjN9wVZythDQhurbRF+E9hkh8mabPb0Kv1CS8g4lJVzq+t5b1YDMO2meF0sjOR4M2uLoKGgUNReDkcps7pzCv4GGyP07vRKLuSSbBtqhwONEUZUIhhLCmDwxWPOKOyctzLlIcMgwf372d/Ok29w4FbVUlYFq3pNA/u388ZVVVs7uzMrt5VOxzdz9OyOJBOc6TPh1NVeSsa5e2uLqp1fcwVBUXxm6iKn0IIMV3IJ6ooOEkvmRjDXXFWXeqA4Db8pzDRv0TxHuXFs8AzaQVE/LrOcr+fpmCQ17u62JNM4lAUFEWhweXixVAIb0+gMlr99zjFLYtdiQRORaFM0wjoOh5NG1CIoRAV8AYrHlHoaoSDaUkk2J1IUO1w4Os5l0NVMWyb3YkEW7u6+qzedaTTOFQVv6qytScN84jychrdbj6Ix7lsxgwO93rHdcxCCCFEqZGgShScpJdMrKGuOPcObmM7YiQ/TOI9yov/RD+KQ5nUPVYrq6qImiZ3fvQRtmVR5nBwmNvNIq+XjnR61JXneqft9d7jZNk2wXQaVVWZ43bj6Slt3rsQAzC1K+ApyrC3d1lWn9U7HQim0wQNg7RtA7C4rIwqh4OZTicNLtfgxxNCCCHEkIr4m4KY6iS9ZHL1Dm6j70Zps9vwLPCgOBSsuIUZN1FdKqn21IQXEHGpKif5/WwOh6nWdQIOB56eNLvRVJ4brEDDCT4fKwIB3uzq4oBh4FBVDnK5WFxWln1c70IMg6UMjnYck6nB5WKu2832RAJdUfCoKnHLojWVosHlYobT2Wf1blcySdyyiFsWHlVFVRT+FIlQ53CMufKiENL0XQgxXcknnhAlTvfr3UUpqnXSHWnSB9IkdiWwUhZ2ysY5yzkpTZkrdJ1qXUfrCQQyRlN5bqgCDf8wYwZXNzYSNgxeDoV4MRymoyc4ChnGgEIM/cuij3Yck8mv63ymro6f791L2DAI0b0SFbcscDr5zf792LbNRz237UgkqHE4AChTVRTA21N9cayVF8X0JU3fhRDTXXF/WxBCFESmgMhHd31Eui2No8aBoiuYIRMzbBJ+NTzhvasGK0c+WMAzlFwKNDS63d0pfYrCK+Ew+9PpAYUYxjqOYnB2dTUORWFzOMwbkQgRVeX48nKW+nzETJOP0mnqesq9H0ilqHY4OD0QYI7bjWHbKMCBdHpMlRfF9CZN34UQ013xf1sQQhRExXEVtK5vxYya2IaN4lTwLfPhqHZMWu+qwcqR51p5bqQCDS2JBO6yMjZ2dLA1GsW0LHRFYXFZ2YCqfmMZRzHIVCA8orycdc3NHKtpzO5JZyzvKcxh2TafnzkTFIUyVc3+HqA1lZoSq3KiOBWq6bukDgohpjL51BJimrCSFq4GF+VHlYMNqkdF82hYCaugjZlH0+tpuHLkIxqhQAOK0qeq3/yyMsKGwYvhMF5N61PVb0zjmAC5vqYKoPf0/+otsz/Mr+usCARoCgZpTaWm5KqcKD5jbfouqYNCiFIgM6gQ00SmMbOdtrNXk6FwjZnH0uspn9LjQxVoaE+nWeB249O0UVf1y2Uc+TQIzvd4o31N+5eU732OzErUVF+VE8VnrE3fJXVQCFEKJKgSYpoY78bMhej1NBr9CzR0ARZQ53Dwmbo6FChoVb+xBI0ZvQMot6qOeLzRvqa57g8r5lU5MfWM5bOlUKmDQggx2eSTSohpZLwaM4cMY1J6PfUu0JApqb68ooKVVVUkLKugVf3GEjQOFpDZtk2bYTDL6Rz0ePm+prmuRE1EY2IxfeT72TLW1EEhhCgW8kklxDQyXo2ZJ6vX03B7oVyqWrCqfnsSCZ7p7MSvaXkFjf0DsrZUiqeDQRZ6PBxdXj7o8fJ9TYt9f5goTfl+tow1dVAIIYqFfFoJMQ0VujFzLnt5xtNQqy65rtr039eU+dmlqrwaDvNMMMjmUIhqh4OQYbDI68WhKDkFjYOtOJVrGg7ggGEQN008mpZ9HjtiMd6NRpnpco3pNZWVKDEZRvvZMt5pyUIIMVHk00oIMWbF2utppFWb/ml5LlVFA0xFIWmafJhMEjZNDvd6qXY6SVkWb8ViABxRXp5TgDPYipNH0/DpOl2GQdyy8GgaacviT+EwHyaT2G1tVOt6tmlvMb2mQhTaeKUlCyHERJJZWQhREMVcVW6oVZv+aXl/Cof5SzTKUV4vR5SXsyUSIWZZdJkm891u3o5GURWF7fF4d5BjmiMGOIOt4nlUlRqHgw7DIGKalGlan3Mv8HgIG0a2aa9l20X3morpZTx7SPVOHUy0JFBQcDW4sBIWqbaU9K0SQkwJ8iklhCiIqbaXp39aXtw0CRkGtQ4HnYZBzDRxKArVus7uRILT/H4AdiQSHEiliFlWTgHOUKt4Pl3n7MpKFEVhRyzGh8kkR3m9nOj341CU7B4ry7a5cuZMFCj611SUnonqIWUlLTo3dxLZEsEIGaT2pUABV70LzS99q4QQxU9mZyFEQeW6l6fQ/Z5Gq39aXtyySNk2gZ60PACnqmIBKdvGAI4sL8ev68RNk6sbGpjdr4jEUAZbxVtdVZWtUvhuNIrd1sYCjwdHr6bGmT1bCtCY47mEKKSJ6iHV+zxWl0XigwTYoJVr6FW69K0SQhQ9CaqEEBOqEP2eCqF/Wp5HVXEqCp2GQZmqUulwMNft5k+RCF5VRQFaUynCPSl/uQZUMHKVwkVeL9W6PmmFPoQYzET1kOp9Hs2nkdybxFHvQEEh9VGK8sPKpW+VEKLoyTq6EGJCZfYxaYpCg9uNpig0BYNs7OiY0HFk0vJa02laUymUnmp++9NpArqOAlQ5HNQ5HMxwOjmQTmPZ9qj2NIUMg+ZEglDPypdf12l0uweszPUfS8KyaE2laEunWebzScqfmBSZHlL9y5rrfh0zamKEjYKfx4pb2Ckb1a2ielSslIUZNwt+TiGEKDSZqYUQE2aymgQPpX9a3sFuNwvcbkxFyabpfWnWLI6rqCBpWTmnKuazGjdcoY/JTpUU09NE9ZDqfR7Np6E4FayEhYKC6lTRPJr0rRJCFD35dBJCTJjJahI8lKHS8sYaxPSvKhg2DJqC3XtCVtcMvickM5YjysvZm0wy0+Wi3uksilRJMT1NVA+p/udxzXTR9ZcusMF7tBcjLH2rhBDFTz6dhBATZrKbBA+lf3GNsTTOzXc1brDVLdu2aTMMZjmdOQdnQhTSRPWQ6n0etVzFPc8NCuheHduypW+VEKLoSVAlhBh3vVd+irFJcCHluxrXf3WrLZXi6WCQhR4PR5eXA5ObKimmp949pMarT9VQ5wHG9ZyDGc9+XEKI0iafGEKIcTPY6stRXi8rAgHe7OoqyYa2+azGDba6Va5pOIADhkHcNPFoGjB5qZJietP9ExNk9D/PRAU2E9WPSwhRumRGFkKMm8H2Fj0TCrGqspKrGxuLsvjCWPdTDdXsd7jVuMFWtzyahlvTOJBKETSMbFA12amSQpSiierHJYQoXTIrCyHGRS57i4qpoW0h+2cNV8lvMP1Xt9KWxbZYjP09ZdWf7exkUVkZM51OOgyjZFIlhSgGE9WPSwhR2uRTQgiRl5FWdIqt0t9I8qnYN5Thmv0Opv/qVnMyyV+6unCrKkeXl5O0LLaEwywqK+PS+vqSSZUUohhk+mS5G/p+Vul+nURLonuPlQRVQogRyKeEEGJUcl3RKdZKf4MZr/5Zo6kimAmUNodCvN3VRZmqcrjXyxy3my7T5EA6TbmuszwQkHLqQhTQRPXjEkKUNvmkEEKMSq4rOvnsLZosxbCqllndmuvxEDQMGl0u9qVSPN/ZScqyUBWFMlVlfypVVK+dEFPdRPXjEkKUNrncKYTIWf8VHbeqUud0UudwsCUSIWQYfe6/sqqKVZWVWLZNSyKBZdsTVukvZBg0JxIDxjSY3qtq/Y8x0atqDS4XM51O3o3FeCsWQ1UUKh0OUrZNSyrFa5HIhI1FiOmiamUVlasqsS2bREtCemMJIUZNLr8IIXI22hWd0e4tKoR8Ck4U06qaX9dZ7PXyREcH5ZqGW1GImSaWbbPQ42FrLEaoyPajCTHVDdWPywgZpNpS0rdKCDGiklqpuuGGG1AUpc9/ixYtmuxhCVEy8l3R8es6jW73hAQCmfRETVFocLvRFIWmYJCNHR3DPm4yV9X6W1peToPTiVNV6TRNbOBwr5elPh9R0xzw+heL0awOTicyN00dul/H3ehGdau0b2ineV0zLbe30LyumfYN7VhJa7KHKIQoUiV32eXwww9n48aN2Z91uZorRMEU04rOYMZScGIyVtWGUut0cmR5OWnbplzT8KgqHk2jNZUquiIfUNhy9KVK5qbiYoSMPitS/UnfquI31p6CQhRayb0LdV1nxowZkz0MIUrWaHswTaRCFJwYTcW+8dI7eHWpKmWKQmtPz6piCF77K2Q5+lIlc1NxsJIWHRs7iGyJYEZNNK+Gb5mPqpVVqK7uCwDSt6q4jfYijgRfYqKU3Ltr27ZtzJo1C7fbzUknncRNN93E7Nmzh7x/MpkkmUxmfw6HwxMxTCGmrGJa0elvKpVxH0kxB6+9jVc5+lIzmrlJ5qXxk8sKlPStKm65XsSRFXQx0UrqXXXCCSdw77330tTUxJ133snOnTs55ZRTiAxTLeumm27C7/dn/2tsbJzAEQsxdU3kPqlcZVZ4WtNpWlMpEpaVXeFZ5vMV1VhHkgler25s5KqGBq5ubGR1TU3RfRnIrA72D1j9ul7U+78m0mjnJpmXxkf/FSjVreKsc+KocxDZEsEIdb9Xe/et6v946Vs1uUZTgTbf/bVC5Euxbdue7EGMl87OTubMmcOtt97KP/zDPwx6n8GuCDY2NhIKhaioqJiooQpR1CYifaJQ55CrkxMrZBisa25GU5TsShVAayqFZdtc3diY898zHA7j9/tL/vN3pLlJ5qXxkWhO0HJ7C+6G7kIUGVbCItGSoOGqBtyN3atT7Rvau1e06hwD+lbJnqrJ05xIcHtLCw1uN+5en+cJy6IlkeCqhgYa3e6Cfi4JkevcVNLvqEAgwCGHHML27duHvI/L5cLlck3gqISYOiYiQCn0OYo5PbEUFXvxkmI00twk89L46L0C5XT/7Yv2YCtQmf5UkS0REi0JNK8mfauKQK4p3sXQ0F1MPyV92barq4sdO3Ywc+bMyR6KEFPSRKRP5HOOXEp3F2N6YqkqpnL0U4HMTZND9+v4lvlIt6ZJtaawEhap1hTptjS+Zb4++6Qyfasar26k4aoGGq9upGZ1TbaYhZgcuaZ4F1NDdzF9lNS76hvf+AbnnHMOc+bM4aOPPuL6669H0zQuvvjiyR6aEFPORBQgGO05SiW1r9SqUcnq4PBkbioeo12B0v3S9LfY5FLER1bQxWQoqXdVS0sLF198MQcOHKC2tpbly5fzyiuvUFtbO9lDE2LKmYj0idGeY6qX7i6VoHAoxVCOvhjJ3FQ8MitQgeWBYftUieKV60WcqVJBVZSOkvokuf/++yd7CEKUjIkoTz6ac5RC6e6pHhSK/MjcVHxkBWrqG+kizlhW0HtnEwDjtgJfalkL0538BYUQg5qI9InRnGOqbzyeCkGhTPBCdDNChqxklYjRrKD3ziYIGQb7UikUoN7lwl/AzIJSz1qYruSTQggxpIlIn8j1HFO9sW8xB4UywQvRzUpadGzsILIlghk10bwavmU+qlZWSZGKaaB3NkGXZfFBIoENlGsaVbpesMwCyVooTcX9LUQIMakmogBBrueY6huPizkolAleiG4dGzu6+1PVO3A3uDHCBsGm7n8L0p+qtPXOJvBpGnuTSeodDlAUPkqlOKy8vCCZBVMha0HkRy67CCFGNBHlyXM5x1hLd+dSin28jFQKGJiUsfWf4N2qSp3TSZ3DkU2BEWI6MEIGoc0hFLeC5tNQ3SrOOieOOgeRLRGMkPxbmGpG85mfySao0HXilkXKtnGrKh5VJWVZxE0Tv64TNc0BpdpHo/d5eivEscXkklBYCFGUBtvfk+/KWbGktw2W6rgiECBtWaxrbp6UsRVzWqIQE8VKWux/ZD+dL3SiuTVi78Zwz3FTtrgM3a+TaEl077GS/VVTQj6f+b2zCXyahlNRSFgWKApOVcWjabSlUhiAPYaxFXPWghgb+csJIYpKLpPhaEt3b+zo4LEDB3ArCuWaRtq2JyW9bbCgcHNnJ02dnZOWeicTvBDdaX/hP4RRXSqqWwUVom9HAXDWO9G8GnrF5PxbkKIZo5dPSnP/FPOZLhd/6erCBo4sK2NLOMz78TgNTif37N2b98WvqZ7KLoYmfzkhRFEp9P6etlSKX7W28k40StQ0AahxOJjv8fDKJOWvZ4LCYsitlwleTGdGyCDRkiC8OYxrtgvFpRB7K4ZWoaGWqUTfiWKnbKrPrZ7wgKbQRTMmIjgzQgbJliQ2Nu4G96QEgWP5XO2dTVCuqsxzu1GA/YbB3lSKhR4PS30+YqY5pnlJemiVJpkthRBFYzyCjCfa23k1HMa0bXw9j21Lp+kyTZKWNanpbcWSeicTvJhuegcsyb1Jon+N4l3ipfywcgASuxJYCQsraeH7WHcgM5JCBy0jFc3I9XwTUdHQSlq0PdRG2/+2kd6bBh3cc9zUXlhLzdk1E1o5MdfP1VxTzEOGwbrmZpZ4vczuOWa5po1pXpqIIlBi4slfUAhRFEKGwbvRKAcMgwUeT5/f5RtkhAyDN6NRLMCtqpRpGgCqohA2DNoMY0y58SOde6TJslhS7wab4KF7lU8me1GKegcsnoM9xN+PE30jiupUKT+inLIFZSSaE6gOlbrz64YNCgoRtPQPkIyQQWRLBEe9A2dd92eD0+0EG8KvhLGiFtGt0SHP1/t4nZs7865omEvgZiUtdt20i7b/acMKW6g+Fb1CJ7Ejwb6f70N1qASWByYshXGkz1WXqrKhvT3nFPOwYaArSvZCX0YhLn6NNpVdFDf5SwohJlXvPVQd6TR/jUYJptOc6PfjUBQg/yAjbBjETROfphG3LBKWhUNRsGybtG1Trqoo4/h8RtogXWypd35dx62qRVHUQ4hcjXaFaLCAxXuYl8iWCNG3orgb3VhJCztpU3FGxYjHHEsZ9qECsrJDyzCjJu6Gvqstul8n+HyQZEsSz3zPgPNVrazqczxVU4nvjlN2SNmA4CyyJUJgeWDQ5zeaQHH/Y/vpeLwDO22j1+igghkx0QIaZsRk7717CW0OYZv2hPT9Gulz9dVweFQp5sVy8UsUP5khhRCTKrOHSlMU5peVcZDLxZvRKK+EQgPKjo82yKjQdSodDqp0nSpdx7ZtYqZJ2rap1HWWlJcXfELs/Xwa3G40RaEpGGRjR8eg9x9rmfhCG+34hZgsVtKifUM7zeuaabm9heZ1zbRvaMdKWsM+zggbmFGzT+GJssVllB9ZjpWyiH8Qx7ZsKldVjpj21z9AG20Z9kxApmgK7gY3iqYQbArS9VoXmlfDCPd9fKo1hdFh4DrINej52h9r73M8K20RezdG6qNUn+Pofh0zag44/kjj6tjY93PACBmEXwmDCopLQXWraG4Nxa1gdpmk2lPEt8axDXvY4xTaUJ+rx1VUjLqFxEjtMGSlSWTIO0EIMWkG20N1UkUFAC3JJJXxONW6nneQ4dd1lvv9vBWNkkinmeF0Ytg2IdNkltPJ6X5/QSfEkfaEHVFejkJ3OV4Fsql1xZJbXwyFM4TIVb4rRHqFng1YnO7u97nqUHE1utCrdWZcNgNXgyu3Va+eAG2wFaWRyrAPl+IX3RqlbHEZ4RfDYJNNCUx+mESv0rP3z1CcSnfw1JbCNdOV/b27sbtYRGx7jLLDy9A8WvbcQ1U0HG5cmdWt7HMPm1hRC9WtYnfY3ataZRqqQyUdSWOnbdwHu3E1uLoDwBxWyQphqD1LzYlEXvtYZd+pyIXMjkKISTPYhmKHqnJ8RQU7YjEuqatjkdc7pi/yK6uqSFsWD+7fz+5EAoBFHg+fqasr+IQ41AZpFfhDKMRHySTt6TQdhkGVrnNYeTkn9qTWFUNufbEUzhBiJLl88R/qC7vu1/Et83UHYD0BS6otRfLDJNVnV+M93JvzOAYL0DLjG6kM+0gBmW+pD82rEdkSIdGSQPNqVH2yithfY9nzWWmL2NYY0XeiGGEDVVOxj7ZxVDpQHAqqR8VziIeuV7tINidxz3ZjhAzSbWkqV1UO+hoNO65dCdoeaSO5O9ldOXFPgti7MYyQgdlhYmOjlWkoTgVMUDwK3kVeFBTSHWk0jzbqvl+57E8dSv/P1XxT+aSwhMiFvCOEEJNmuAmuyuEYc0AF3ZPheXV1nFFVRUsiAYpCg8s1LhNi/+eTtiy2xmK82NlJWzpNczIJikKtw0GbYVAWj9OaSrE/leL8urpJn6Rl74CYKsayQgRk0/o6n+8k9IcQVpeFXqfT9dcuVK+a856fwQK0kYKW7GNHCMictU7KFpQNKPLQ7m3Pni/ZnKTrL11gg/cIL8ndyT4FNwBcM1yYi00Uh5INzoZLbRxuXMnWJOYfTFyzXRgdBokdCdIH0pgJE9WjYsdtzJiJElVwzXHhXeLF6DLo2NiBlbJQnSp6QMcz1zNi36/xaNo+1n2sxXDxSxQveWcIISbNRBZq8Os6/vLygh1vqHP0fj7NySSvRyIcMAxmOJ3ETJOUZVHjcODXNN6LxXBrGm93dfFeIsHyiorsF4axXJ0t1Pgnu3CGEEMZywpRb/EP4iSaEziqHDhqHNk9PzBykYmMTHDSe0Upl/1YuQZkur9vAY7McUObQ3S93YVapuI93It3kZeoNzqg4IbRYVB/SX3OFfiGGleyOQk2OGc5SexJEP5DGDNlYnVZ2GkbvN1plDih/OhyfEf5QIPgE0EctQ70gI7RaRDdHcU9f+QeVoXuWZghqXxivMgMKYSYVKU2wWXGvTkU4u2uLhyKQp3DwUyXi12JBA6gI50mZVnsS6VY4PHgUFXSlkVTMEjasnCo6qRV3yu1v4coTWNZIYLu/VgHHjuA0WHgmeNB0RUS7ydQbAWtQiO8OZzznh/VpVKzuiavsuH5BGSZ83nmejCCBp55HvRA9/nKFpdhp2y63u4i/kEc50xn9niqSx3TuCo+VkHXX7pIfZQi+lYU27axLRsrbaEoCqpXRSvTsLosnDVO7KSNjY33SG93emCX2R0AHukFszsAHmo847m/U1L5xHiRd5EQYlKV2gSXeT5zPR6ChsFMp5MtkQgmoAGWbZMCWlMpPKqKW9PQgMaeK7EP7t9Pua4z2+Uq6NXZ0Y6/VP4eonTlu0KU2Y+l+bsLKqjlKqqqkmpN0fl8J44Zju5qdYe6mXHxDKyElfMKz2gLL4wlIHM1uHDOdGKl/lbtMN+CG7mMCyD2XoyuN7pwBBwYIQM7ZKPqKjY2iqGg6ip4IdWSwlHpAB0qjq/Atm2suIXqUVEUZcQUzf77O+OWRdw0cakq7alUQfZ3SiqfKDR5NwkhikKpTXANLhcznU40RWGu281bsRgeTWNfKoVl25i2zUEuF3HLYklZGR5VJaEo7E4kOMnvn/Tqe6X29xClJ9+AJLMfy1HjQHEqWAmLdDiNccDoDg5UBcWlEH4xTGJXAkVR8m7qm6t8ArKRVutGU3Aj13F5DvUQ/H0QbaaG7tVJfZjCVrpfMzNhonQpOGodmF0m3sO9pPanulM065zZyoOp1tSIKZqZ/Z0d6TQH0ml2JRKkLIuUbTPL6ZSeeaIoybtSCCHGQe/eJlUOB4e43XhUFR2o0DT8PV8alpSVscjb/eVnfzoNkA2oeh8rapqEB+mhIsR0p/v1bOnwnO7fsx/LTtu457gxO0ySHyWxFRtUsA0b72FerKRF8Mlg9/0msMfSaFStrKJyVSW2ZZNoSeTcXytfgeUBXLNdmJHuVD7Nq+GocKB5NBRFQfNraF4Nz2IPtZ+uxbfMR7o1Tao1hZWwSLWmSLel8S3zDfv3ynx+/jkS4U+RCBagKwpxyyJsmrwaDo/L8xNiLOQypBBiWhvPghC99yf5dZ2T/H6umDGDk/1+XgqF+EM4TJ3TiWnbdKTThE2TuW43Katv81KpvidE4fRe4XFUO3A2OonvjoPVXYSh/KhyXHNdJJ5JgBO0cm1CeyyNxljSB0fDSlp0bOwgsiUCGhhRA9ccF4EVAWLvxLBNm7LFZbhmuTDDJtXnVuOsc+adoglwXEUF61tbiZomhm3jVBSW+XxU9zTqlb55otjk9W6Mx+PYtk1ZWRkAu3fv5pFHHuGwww7jE5/4REEHKIQQ42E8yvX2N9z+pNluN7U9Xw4yBSHOra4mbds809lZsOp7k1FFcDLIvCRGo/eXfWeNE/ccN44aBxUnV+CocJDuSGNEDHSfnk1bg9xLtk+0fNIHc2GEDIywQejlEOEXwzjqHQRODhDxRIhvi6PMVPAc6umuCjjDiebTCJwRyL6+Ywn6kpZFg8vFUeXl2IBHVfFoGgnLkr55oijl9W4899xzWbNmDV/60pfo7OzkhBNOwOFw0N7ezq233sqXv/zlQo9TCCEKarzK9Q5msP1JQwVcScvCoShjrr43EUFjMZF5SYxG/y/7maDBjttYTgszYmKnbRw1DlTP3/69jLZk+3jJBDsTsTJlHDDo+msXroNceBZ5UB0qgVMD3amTcZPGqxu793MNM558gr7Mvqq0bfdJiZaVe1Gs8ppZX3vtNU455RQAHnroIerr69m9ezf33XcfP/nJTwo6QCFKiREySDQnMEKyN2Yy9S/X61ZV6pxO6npWjkITuHfJr+s0ut3ZoCsTbF3d2MhVDQ1c3djI6pqaUQdCmaBRUxQa3G40RaEpGGRjR/HsBykkmZdEPjL7sWrPre2zN0lxKlSeXYnu00e9H2g8WUmL9g3tNK9rpuX2FprXNdO+oR0raY384FHo2NhBsCmIoino1TqWYZHckyS2NZa9j7PeiaIroIx+X1sueu9LbU2lSFgWrakUbek0y3y+Ua9ShQyD5kSiIJ/vhTyWKB15vftjsRg+nw+Ap556ijVr1qCqKieeeCK7d+8u6ACFKAW9r/qNdxUpMbL+5Xoz/LpeNGklY6m+N549XoqVzEtiLAZLU1PdavZze7T7gcZLJthx1DtwN7gxwsaomxWPJFNy3lHvwFnnxIpbOAIOrJhFYncCz0IPmkebkFW7QvTNK+Sq/ViONV1SsaezvP6qCxYs4NFHH+X888/n97//Pddccw0AbW1tVFRUFHSAQpSCiZgIRe4yaSVhw8BdRGklhZp0p0LQWGgyL4lC6J+mNt5FIEaTxtc/2AHGpXhGpuS8u6H780P1qLjnuom+EcVIGhidBmbYzLnR8lgUom9eIVO98znWdEvFns7y+pdw3XXXcckll3DNNdewYsUKTjrpJKD76uAxxxxT0AEKMdVN1EQoRmeOy8UfIpGCFYQYi0JPusUaNI4nmZfEeBmPIhD5ZC/0D3Z6j6+QxTMyJeeNsNE9VwHeRV6MToNkS5J0RxpHlWNCV+3yXbkv5Kp9vseayP27YnLl9a/v05/+NMuXL2fv3r0cddRR2dtXrFjB+eefX7DBCVEKJmoiFCPrHbyETJOoYbDVMJjhdOLX9bwKQhRCoSfdzF6EpmCwoEFjMaevyLwkppJ8shcGC3agu5mubdhgF2ZsQzUVdgQc1JxTQ8VJFeNWIKPQCrlqn8+xpmMq9nSW919yxowZzJgxA4BwOMyzzz7LoYceyqJFiwo2OCFKwVATYbFUkZpOegcvc91uqnSdPckkR5aXc35t7bhPboMFJeM16RZiL0LGVElfkXlJTAX5Zi9kgp0Djx3A6DRwVDmIvhMlvi2Os8HJ3nv2Fmyv7nD9pYphH3CuF3gKuWqfz7GmYyr2dJbXX/LCCy/k1FNP5atf/SrxeJxly5axa9cubNvm/vvv54ILLij0OIWYsoa66jcR+ejFbKJXPYYLXnYnEuN67uGCkv6TbtyyiJsmLlWlPZXKe9ItxF6EjKmQviLzkpgq8s1esJIWVtrC6DLoeruLdEcaLPAt81FxbAVmzCzYXt2Jaio8WqO9wFPIVft8jjUdU7Gns7wuN7zwwgvZ0rWPPPIItm3T2dnJT37yE77//e8XdIBClIKqlVV9yvXalj3pVaQmS9Ky2NDezrrmZm5vaWFdczMb2ttJWoUtCdxfJnjpP4n5dZ2oaRIex9K4Gzs6eOzAAbpMk2qHo09588yk25FO89euLjZ2dPBcMMiTBw7wYTI55pWg/iXbR6uYys8PR+YlMVX0zl7obaTshY6NHYSeCeE9zEvVJ6pwVDnQfTqOKgdauYazzolWoRF8JkhiT2LAsfNp5zEepdLHIp9WESurqlhVWYll27QkEli2nfeq/WiPVeiy8KK45fXXDIVCVPW8gZqamrjgggsoKytj9erVfPOb3yzoAIUoBcV61W8yTNaqx2RdMWxLpVjf2kpLKgWAV9OY73ZT3ROULA8EWObzcddHH9GWTlPjcKArCiHTJGyavBoOT+pq0FRJX5F5SUwV+WQv9E8ZTHek0ct1lIBCYlcCz1wPsfdixLbFMEMmCgqBFQECpwTofLGzJNp55JsqXchV+3yOVchUbFHc8npXNTY28vLLL1NVVUVTUxP3338/AMFgEHe/iVcI8TfjUUVqKpnMTbvjVbxhKJn0xkf27+eP4TCqoqADrcCHiQTHV1RQpeuEDYPjKipY39pK1DQxbBunorDM5+sTeE1W4DJV0ldkXhJTyXB7lgYzWJlzxamABVbcov2JdhIfdGdBqE6VxIcJOjZ0EH41jHnALIl2HmO9wJOpIJhp3DuW4Go01QgLGdSJ4pbXX/Xqq6/m0ksvpby8nNmzZ3P66acD3ekXRxxxRCHHJ4QoIZO96jERVwx75/x3pNNsDAbpNAxqnE7KNY2UbRM2DF4Nh/lkdTUVPYFVg8vFUeXl2IBHVfFoGgnLmvTVoIkORvMl85KYSkabvdC/4JHm0XDPcRPZEsGIdPeNUhwKmlNDr9JJf5TGjJmYfzUJnBYoiXYeY73AM9kFd8bS0F1MDXn9db/yla9w/PHH09zczMc//nHUnjfjvHnzJHddCDGkwSbFuGnSnEziUJSCrnoMVghjIq4Y9k5v9Oo6EdNEBbpME7ei4FBVHIpCWzrdZ6+TV9NI2zY+TSNoGAQNg7RtF8Vq0FRIX5F5SUxFuWYvDJYy6Kh2oAd00p1psLtXrxw1DrAh1ZbC3G1ip21cs124ZrpQHEr2WFOxncdYL/BMhYI7YmrL+1/TsmXLOPLII9m5cyfz589H13VWr15dyLEJIUpM70kxYpo0JxK0pFJ0mSaLPR42d3aO+aphLlcjx+uKYf/0xo96gkWXw0HatjFsG8OysIGArnOSz5cdzwK3m5/t3ct+wyDWkwbo1jQurKnBPclly6dK+orMS6KUDZYyWHtRLeHNYeI74mgeDTNmktyXRHWq2KqNoivE34vjqHJQfkQ5MH7tPIyQMe57hvO9wCP9osREyOsdFIvFuOqqq/jlL38JwPvvv8+8efO46qqrOOigg/j2t79d0EEKIYpDIcqgnxII8Go4zG/a22lNp/GqKovKyjjY4ynIVcPJvBrZP72x0uGg2uGgLZ3GBio0DV1ViVsWh3g8zCsrywaBj3d0sDUWI2ZZlGka5ZqGYtu8Ho2ysaOjKK6kFnP6isxLotQNljIIkNydxEpYJHYnSLelUR0qdtpGVVTKDi/D6DSIvhXF3ejGSloFb+dhJS06NnZMSDGMfC/wTHbquZge8nq3f+c73+HNN99k06ZNfTYAr1y5kgceeKBggxNCFIdClkF/sbOTj9JpKh0OlpSVsdjrJWHbhAxjzGW6C13+O7OhOdfH9U5vhO69UUd6vYR7yum+FYvx12iUcDrNp3rSVTLl1j9KJqlwOKh3OvGqKjNdLhZ5vcRMk83hcNGULi9WMi+JYpdvWfP+epc5z6QF6hU6jhoHVtrCjJlYSQvvEi9Vq6ooP7IcK2UR/yA+Lu08OjZ2EGwKomgK7gY3iqYQbArSsXHoMudjNdpWEf0/mzOKreCOmNryehc9+uijPPDAA5x44okoipK9/fDDD2fHjh0FG5wQojgUavUnE/T4NQ2PqhLQNByqSsQ02ZVI0Oh2j6nhbaGuRua7oXmwnP9t8TgG3atUXk3Dtm3SwDs9wVrm9VAUBRUo13XStk0onabO4UAFgum0XEkdgcxLoliN90pOJkAKbQ6R3JtEURW8S7z4jvKhOBRcjS70ap0Zl83A1eAqaGpe/1LvUJzFMKZKwR0xteX1Ltq/fz91dXUDbo9Go30mMyHE1FfIXPRM0FPjcOBUFBKWhUNV8agqwXSatlQK3xiuGlboOpqqsqcnQPP0BECjvRo5liCyd87/O11d7E4mmefxsMDjwbJtHKpKezrNS6EQn+rqyr4enp7PznRPSfWYZRExDCy60wjlSurwZF4SE2k0+4cyKznjVda8d1pg2aFlhP8QxtXowjZt0h3pbLqf93DvmM/VX/9S7xnFWAxjKhTcEVNbXu/0ZcuWsWHDBq666iqA7IT1s5/9jJNOOqlwoxNCTLpC5qJnUjDSts0ct5u3o1FswITuUuOmyRl5bhhOWhabOzvZHY/zbiyGX9c5xONhhstFh2HkfDVyrEFk75z/54NB/tTVxcFud59iEwFdpzmRoMuysq/HgrIyPuxZpXMoCjbQYRjMdDpZXlEhV1JHIPOSmAijXXWayJUc3a9Tf3E9jloHkS0R4tvjKLqC/xR/QdP9+pyzX6n3jPEqhjEWU6Xgjpi68no3/eAHP+Dss8/mnXfewTAMbrvtNt555x3+8Ic/8Pzzzxd6jEKISVTI5q+9UzCqHQ4WlpWxPRajs6f637nV1XlfNcysLh1SVka5rrM9FuPVri4WmyaX1NfnfNxCBZF+Xedonw+/ptFpGHh6vXadPa/dIT2FKjKvx/EVFbwaDrPfMKhQVRqcTj5TVydXUnMg85KYCKNddZrolRzVpVK1sgozahJ+JYxlWkS3RlG96rgUjhis1LsRMgpeDGM0RiqoVMwFd8TUlte7avny5bzxxhv88Ic/5IgjjuCpp55i6dKlvPzyy9JkUYgSU+hc9N4pGAFN44SKCg71ePhkTU12ZWi0+q8uzXK5OLysLNv/ankgkHOZ9kIGkbPdbk7z+3n4wAGge4Wq0zDYn06zprqa2W439T3n2BKJUK3rnFVVRbQn7c+lqmyPx9nY0TFuDSoH+wJSiCqPE03mJTHe8ll1moyVnI6NHYRfDOOod6BX6AVPN+xvsFLvhS6GkYvJbu4rRN7/mufPn8/dd99dyLEIIYpUIXPRxyMFY7DVJY+mMdvtHnWKYqGDyGtnzwbg+VCI5p7Xbk11dfb2/q/Hy+EwL4ZCzOrZRzVeJeEH+wJyVHk52DZvRqNT8kuJzEtiPOWz6jTRKzkTXTgis7cssDxA+RHlJPcmcc104Z7tHvnBBSbNfcVky/tflmVZbN++nba2Nqx+ZZVPPfXUMQ9sLP7jP/6DW265hX379nHUUUdx++23c/zxx0/qmISYynINhPqvcAy34lHIFIxCri5BYYPICl3nhnnz2JNIsDeZZKbLxWz3wC8cmddiazQ6IQ0q+38BaUuluKOlBb1nZW8qfikp5nkJZG6a6vJddZqolRwjZBB9N0q6I03Z/LK+Yy9wumHvvWVGyCC1LwUKuOpdaP7x61M1FGnuK4pBXu+wV155hUsuuYTdu3dj23af3ymKgmmaBRlcPh544AGuvfZa7rrrLk444QTWrVvHWWedxXvvvTdoZSghRO6GCoT6r3q4VBUNMBWF5ASseBR6dWk8VtNmu92DBlO9TVSDyt5fQCp1na2xGDsSCd6Lx9EVhdluN0eVl1M3hb6UFPO8BDI3lYLRrjr1rhDYv2lvIVeL+gQ4Bwyif41iHDCoOKkC1aFmx1LIdMPee8usLovEBwmwQSvX0Kv04feZjaJyYq6kua8oBnl9u/nSl77EsmXLeOutt+jo6CAYDGb/6+gYv2Zvubj11lv5whe+wJVXXslhhx3GXXfdRVlZGb/4xS8mdVxClLLMqoemKDS43exMJHj4wAF2xeM0uN1oikJTMMjGcfx8WFlVxarKSizbpiWRwLLtMZfLHW2DybGaqAaVmS8gFT0B1dvRKKZl4VFVFOCtaJStsRjQ/RpETXPAmIpNMc9LIHNTqahaWUXlqkpsyybRkhi0ma6VtGjf0E7zumZabm+heV0z7RvaUd1qtmlvIXVs7ODAYwcwIyauBheuBhfRv0QJvxzGSlikWlOk29LdTYILcO7eKYaaTyO5N9mdbjjDSeqjFLpPx1HnyK5iZQz1uljJ0TeR70+a+4pikNe7bNu2bTz00EMsWLCg0OMZk1QqxZ///Ge+853vZG9TVZWVK1fy8ssvD/qYZDJJMpnM/hwOh8d9nEKUkv5pF3HTJGQY1DocdBoGNkzIikcplMudqAaVmS8gbakUuxMJKjQNt6axO5nEqSgEdJ3diQQLPR7CPSuNxf6lpFjnJRj93CTzUvHq3RNqqNWW8e5LlWGEDGIfxGi9t5XUgRSKQ0F1qrgaXZQtKSP5YZLYjhiOKkdB0w177y0zYyZ2ykYLdDcwTwfTmHFz0HTD8Xxd+n92OhWF/ek0YdPk3OrqKTcXiKkpr5WqE044ge3btxd6LGPW3t6OaZrU19f3ub2+vp59+/YN+pibbroJv9+f/a+xsXEihipEyei96gEQtyxStk1A10lZFvGetKuJWvGY6NWlQhuPFbf+Ml9AWpJJgoaBrqokLAuXouBUFJSe5sPNySRt6TTLfL6ifz2LdV6C0c9NMi8VP92vD7rq1L9QhOpWcdY5B125yVfvFZ8P/uUDOjd3YkZMHBUOFFUhvjWO5tMoP6Kc+kvqaby6kZrVNQXb39R7b5nqUVGcClbCwopbqE4VzaMNSDeciNdlZVUVK/x+tkaj/La9nZdDIaKGQdq2SVpjXw0TYiR5zZJXXXUVX//619m3bx9HHHEEDoejz++PPPLIggxuInznO9/h2muvzf4cDodlAhNiFPoXifCoKk5FodMwKFNVPJoGSBpGriZqxW1lVRVR0+TOjz6iLZkk4HCw3O8HYGssRsqycCgKZwQCU6JPlsxLohhMRF+qzIqP5tcgDapHJd2eRivXcB3kAiD+fhz/iX68i7wFTzfsv7fMNdNF11+6wAbv0V6McPc+M/8pfoxwd7A0Ea+LS1VxqCrlus5Jfj91Ticpy+KZzk4cijIliu2IqS2vd/AFF1wAwOc///nsbYqiYNv2pG4IrqmpQdM0Wltb+9ze2trKjBkzBn2My+XC5XJNxPCEKEmDpaz5dZ3d0ShHeb0oQGsqVfAUtlI33g0qXarKhfX1oCg8eeAAB7lc1DmdhAwDA/hYRQXn19ZOmb9Xsc5LMPq5SealqWu8+1L1XvFRdAVFU3Ae5CTVkiL5URJHpQPLsDBCBp5DPePWfLd3RUO1XMU9zw0K6F69Ox2wWqPrr12Et4TRvBpli8vQXOPbryuTij6757MsY6oU2xFTX17vrp07dxZ6HAXhdDo59thjeeaZZzjvvPOA7hK7zzzzDF/96lcnd3BClLD+JcgPdrtZ4HZjKsqYS5KL8XVuTQ1eVe1TPv7c6uop05sqo1jnJZC5aToZ775UvVd8bNtGcSroDh1FUUh+mCTdngYdyhaVUf3J6gI9q4EG21uWGV/o5VC2+bCz1okRNgi/GEar1jBajXHr1yUVAMVky+vdNWfOnEKPo2CuvfZaLr/8cpYtW8bxxx/PunXriEajXHnllZM9NCFK1lApa8P1qZooxTCGYlYKBT6guOclkLlpOhnPvlR9VsLqnLjnuIm+HUV1qHgO9lB2WBl2wqb63Ops89/xpPsHFuqIbY0N2nzYSln4T/ET3Rodl35dhe5XKMRo5fUOmz17NqeffjqnnXYap59+OvPnzy/0uPL22c9+lv3793Pdddexb98+jj76aJqamgZsEBZC5C7XwKR/ytp4p7ANp3/vrPHulTXVTebfqhCKeV4CmZumk1wqBOar/0qYZ54Ho9Mgvi2Os8GJo9qRbbybMR59oYYy0t6pipMqqDqralzGM1HVU4UYimL375KYg1//+te88MILbNq0ie3bt3PQQQdx2mmnZSezhQsXjsdYJ0Q4HMbv9xMKhaioqJjs4QgxqaZyYLKhvZ2mYJB6h4MKXSdsGLT2TK6yYbk4jeXzV+YlMV30bvZrRs3sniXfUh/OWmc2UBnsfpmAa6hKgGMNwIyQQfO65u69Xr1WylKtKWzLpvHqxnEL7EKGwf5Uite6utgajU65OUsUr1w/g/MKqnrbu3cvzz//PL/73e944IEHsCxr0jvXj4VMXkL8zVQNTEKGwbrmZjRF6bNhuTWVwrJtrm5slKuWRahQn78yL4npYKQAqH1De7YvlF6hd1fla+3ew9S/L1Q+AdhQsuetcwzYOzWWflRDZUwMdvFvcVkZS30+ap1O+awXY5brZ3De77RYLMbmzZvZtGkTzz33HK+//jpLlizh9NNPz/eQQogi0r+pL4B7Apr4FoJsWJ6eZF4S08lg+5ky+veFgr/tbYpsiRBYHujz2EI25i30nrKRMiY2dnRkL/41uN2EDYMXw2G8msaCsrK8zilEPvL6VvGxj32M119/ncWLF3P66afz7W9/m1NPPZXKyspCj08IMUmmcmAiG5anH5mXhPib0fSFGm0ANpJC7ykbLGhqCnYHfMsDgSl78U+UnrwSTN999128Xi+LFi1i0aJFLF68WCYuIUpM78Ckt6kQmGQ2LLem07SmUiQsK9sra5nPJ5NsCZJ5SYi/6V0lsLdMXyhsSDQnsimEZtQc0CtK9+uYUXPAMXIeg1/H3egeU0DVP2PCrarUOZ3UORzZNhBR0xwwH/l1nahpDpi/hBhPeQVVBw4c4Nlnn+XEE0/k97//PSeffDIHHXQQl1xyCXfffXehxyiEmARTPTBZWVXFqspKLNumJZHAsm3plVXCZF4S04ERMrLB0HAyVQLTrWlSrSmshEWqNUVqbwrbttl7z15abm+heV0z4ZfDqC51yAAs24Mqx3MXUiZjYqigCUWZshf/ROkZc6EK27b585//zB133MH69etlQ7AQJWQqV//LkD5VU0ehPn9lXhKlJp9CEoM9xrZtjDYD5yxnn+IVWrWGecActLhE1cqqghWxGK1cig5t7uykKRikzuEYUEa9mAsqialjXAtVvPbaa2zatIlNmzaxefNmIpEIRxxxBFdddRWnnXZa3oMWQhSXUmgMO9X7L4ncyLwkSsVgVf3yKSTRf28TNuy9Zy/OWc4Be6fslE3FKRXEtsYGFJcoZBGL0cql91Qm+yCTDujVNMlKEJMir28axx9/PMcccwynnXYaX/jCFzj11FPx+/2FHpsQokhIYCKKncxLYqobajWq4riKMRWSyFQJTDQnhi1e4T/JT/VZ1X0CukIXscjHSEFTKVz8E6Uhr3ddR0eHpCAIMcWNtcmjEMVE5iUx1Q21IpTan8q5kt9wehevcLr/lkrXe+9U/zLto6kiOF5yDZrk4p+YbHm9+yoqKujs7OShhx5ix44dfPOb36SqqorXXnuN+vp6DjrooEKPUwhRIIVs8ihEsZB5SUxlw60Ixd+Lo2rqsMFQLjLFK4JNQbAZsHcq83Pvi225BGL9n8d4XayToEkUu7zenX/5y19YsWIFgUCAXbt28YUvfIGqqioefvhh9uzZw3333VfocQohCmQy8+OFGC8yL4mpbLgVISNiUHZYGV1/7hoyGMrVUI15A6cEaN/QPujFtpECMZCLdUJAniXVr732Wq688kq2bduGu1dj0E9+8pO88MILBRucEKKw+l8NVd0qzjonjjoHkS2RCS2VK0QhybwkprKR+kpVf7KaylWV2JZNoiWBbdnZQhKjkSle0Xh1Iw1XNdB4dSM1q2vofLGTYFMQRVNwN7hRNIVgU5COjR1Urawa8dyZi3WDPV6I6SKvlapXX32Vn/70pwNuP+igg9i3b9+YByWEGB/FkB8vhibl3/Mn85KYykZKzXPWOftU8htrel3vvVOJPQk6n+lE82tDFqMY7txGyCC0OYTiVtB8WvfFugkuZiFEMcjrXe5yuQiHwwNuf//996mtrR3zoIQQ42O0+fFiYpRCP7DJJvOSmOqGSs3rvSLUv5DEWGRS9oLPBAltDuGodmCEDLyLvCgOZcDFtsHObSUt9j+yn84XOtHcGrF3Y7jnuClbXCYX68S0k9ds/Xd/93esXbuWdDoNgKIo7Nmzh3/+53/mggsuKOgAhRCFk7kamm5Nk2pNYSUsUq0p0m1pfMt8MvFNko0dHTQFg2iKQrXDQZdp8tiBA2zsGDl1JmQYNCcShIzpnbop85KY6oZKzRuvPUmZlD2tTMNZ7cRO2cTeihF9NwrkdrGtY2MH4T+EUV0qqlsFFaJvR4ltjcnFOjHt5PUv9Uc/+hFdXV3U1dURj8c57bTTWLBgAT6fj//v//v/Cj1GIUQB5ZIfLyZOyDDYEolQrevsS6XYHArxl2iU7bEY61tbaUul+tw3E0AlLYsN7e2sa27m9pYW1jU3s6G9naRlTeKzmTwyL4lSoft13I3ucb3I1Xt/rXu2G/d8N7Zlgwrx7XESexIjXmzLHMM124X3cC9WzEJBQS1Tib4TJdmclIt1YlrJ653u9/t5+umneemll3jzzTfp6upi6dKlrFy5Etu2Cz1GIUQBZa6GFio3X4xN2DCImiadpsm2WIwKTSOgaXQpCu/GYjxx4AAX19cPSA+0bZs2w2CW00mD203YMGgKdldxXF0z/ao4yrwkRO76768tW1wGQGJHgtSBFFbMGvFiW+9jOCod3Y/flcBKWFhJC9/HfHKxTkwreX2TuuWWW/jmN7/JySefzMknn5y93TRNLrvsMv7nf/6nYAMUQoyPQubmi/xV6DqaovBuNIpbUXCpKg5VRbdt/LrOe/E4j7W382IoRL3DQYPbTVsqxdPBIAs9Ho4uLwfA7XRiA1siEZYHAtOu0IXMS0Lkrv/+WtWhUn5kObpfx4ybNFzdgHu2O/dj1DkpP6KcsgVlJJoTqA6VuvPrpJy6mFbyerffcsst/PznP+9zm2maXHTRRbzxxhuFGJcQQkwLblUlYZpsSyTYnkjwTizGtliMTsPgEI+HmGHwSjhMvcNBndOJW1Up1zQcwAHDIG6a2WP5dZ2oaRKehvurZF4SIndD7a81wyaVKypHDKiGOoYRNrCTNhXLK+SinZh28nrHb9iwgU984hP4/X4+/elPYxgGF154Ie+++y7PPfdcoccohBAla2NHBxHLok7XiVsWSdMkaposcTiY4XLRnk4TSaeZ4fxbtUaPpuHTdboMg7hl4dE0oHvPlVfTqJhmq1Qg85IQo5VLtcGJOIYQpSKvmfe4447jN7/5Deeddx5Op5Of//znbN++neeee476+vpCj1EIIUpSpkjFXLcbj6ryRjSKR1FQFIWIZfHHUIhyTWNvKsWuRILDvV4Web14VJUah4MOwyBimpRpGiHDoC2dZlVl5bRL/QOZl4QYrULsr5U9ukL8Td7v/DPPPJP77ruPCy64gMWLF/P8889TMw03RwshRL4yRSoa3G4qHd0bvXclEsRMk9ZUioOcTpaUl1OVSvGXri7+FImQsiwa3G58us7ZlZUoikJLIoFX01hVWcnKqul7hVjmJSFGrxD7a2WPrhCjCKrWrFkz6O21tbUEAgG++MUvZm97+OGHxz4yIYQocRW6jlfTCBsGdU4nR5SXs6CsjO2xGCnb5kS/n1kuF7UOB05F4Z1olLeiUaqcTlZXVbGyqoqEZRE2DCp0fdqtUMm8JIQQoljkPAP7/f5Bbz/rrLMKNhghhJhO/LrOMp+PpmAQu+fnsGHQZZrU9RSmAHCoKkeWl9PocrEzHueyujoO76n651LVaRdMZci8JETxMkKGpASKaSXnd/k999yT/f/xeBzLsvB6vQDs2rWLRx99lMWLF8tkJsQ0sSeRYG8yyUyXi9nukStFicFl0vW2RCLZNL6zq6v5a1cXYcPA3RNYxU2T/ek0lT1l1YXMS0IUIytp0bGxg8iWCGbURPNq+JZ196ySEuuilOV16eDcc89lzZo1fOlLX6Kzs5MTTzwRh8NBe3s7t956K1/+8pcLPU4hRJEIGwa37tnD86FQthHtaX4/186ePS2rzo2VS1VZXVPD8kCgTxqfV1VpCgZJ2zZ7Uym2x2J0miaLPR42d3aysqoKlypfUDJkXhKiOHRs7CDYFMRR78Dd4MYIGwSbuhuT16yWPY6idOU1I7/22muccsopADz00EPU19eze/du7rvvPn7yk58UdIBCiOJy6549PHzgAKqi0Oh2oyoKDx84wK179kz20KY0v67T6HZnU/lWVlWxqrKSbbEYW8JhAI4rL2dhWRlNwSAbOzomc7hFR+YlISafETKIbIngqHfgrHOiulWcdU4cdQ4iWyIYoenXQ09MH3ldVo7FYvh8PgCeeuop1qxZg6qqnHjiiezevbugAxRCFI89iQTPh0LUOhzM7ElL8/T87/OhEHsSCUkFLBCXqrI8EGBzOMwsl4vGnrLrAJqisCUSYXkgMG33U/Un85IQk88IG5hRE3dD33lA9+skWhLde6xkf5UoUXmtVC1YsIBHH32U5uZmfv/73/OJT3wCgLa2NioqKgo6QCFE8dibTBI1TQL9vsgHdJ2oabI3mZykkU2+kGHQnEgQMgp3JTZsGJiWRZ3TSdw0iVsW0L2qFTVNwgU811Qn85IQk0+v0NG8Gka472eTETLQvBp6hQRUonTl9e6+7rrruOSSS7jmmmtYsWIFJ510EtB9dfCYY44p6ACFEMVjpsuFV9PoNIzsChVAp2Hg1TRmulwDHvNWVxcfxOPM83hY0lOxrpQkLYuNHR1siUSye8yW+XwF2fPkUlU+TCbZEongUBScqspct5sqhwOvpsketl5kXhJi8ul+Hd8yX/ceKrv7ZyNkkG5LU7mqUlapREnL69396U9/muXLl7N3716OOuqo7O0rVqzg/PPPL9jghBDFZbbbzWl+Pw8fOAB0r1B1Ggb702nWVFf3Sf3bn0rxT++/zx8iEZKWhUtV+ZjPx22HHEJtr4BsqtvY0UFTMEh9T1W+sGHQFOzelL16jI1nXw2HCZsmMcuiWtexgD9FItQ5HHxp1ixJ/etF5iUhikPVyu6KppEtERItCTSvRuWqyuztQpQqxbZte7IHUUzC4TB+v59QKCQpI0IMItfqf5e89Ra/7+zEr2n4dJ2IYRAyTc4KBPjvJUsm8RkUTsgwWNfcjKYo2Z5SAK2pFJZtc3Vj45CBT8gwhm3amzm2DbSn0+xOJEjZNmnLYobTybqFC/ucsxTI5+/g5HURU5H0qRKlItfPYHmXCyFGpULXuWHevGH7VL3V1cUfIhH8mpZdlXI7nZBK8YdIhLe6ukoiFTBsGERNc0DfKL+u05JIEDaMAQFTrumCvY89y+ViocdD3LJQgAPpNMme/VVCCFGMdL8EU2J6kSYnQoi8zHa7OcHvH7Ta3wfxOEnLwtcvoPDpOknL4oN4fKKGOa4qdB1NUWhOJIibZvb2UM8es8H2PGXSBTVFocHtRlOUQUukV+g6Xk3LFqPwaBpVDgcp25b9VEIIIUSRkVlZCFFw8zweXKpKxDC6V6h6RAwDl6oyz+OZxNEVRtKy2NzZyZ5Egq3xOAFNY0FZGTOdTjoMg1WVlQNWqUKGwZZIhHqHI5u653Y6sWFAiXS/rrPM56MpGMTu+TlkGLSl04MeWwghhBCTR1aqhBAFt6S8nI/5fIRMk/2pFAnLYn8qRcg0+ZjPVxKpf5kVp4VlZRzX83y2hMNsi8U4xe/n0LKyAeXVMyl9/VeZhiqRnmkAbNk2LYkElm2zqrKSlVWy4VsIIYQoJnKpUwgxopGKKgzmtkMOyVb/a0ulcKkqZwUC3HbIIQU9z2Tov+I0y+XisPJydsXjtCSTvB6JsCUcxqtpLC4rY6nPR63T2Selr/cK3lDpgi5VZXVNDcsDgSnxugghBORWpEIKWYhSI+9iIcSQxtKDqdbp5L+XLMmpT9V49noaD4MVqPCoKjHTZEciwVy3m5kuF69FIjzR0UGD08mR5eUs8/k4yuvlmVBoVCl9fgmmhBBFqndwpLpVOjZ2ENkSwYyaaF4N3zIfVSurUF3dn+VW0hrxPiOZKhfgxPQi70QhxJAK0YNpSXn5iOl+49nrqZAyE7kNA1ac4pbF+z17qxpcLrbF43yYTFKuacQsi7Rt0xQMsiIQYFVlJVsiEVoSCbyaJil9QogpZ7DgyLZtjDYD5ywn7gY3RtjobgQM1Kzu/izv2NhBsCmIo94x5H2GMtUuwInpRYIqIcSgRlNUYSqcZywGm8ht2+ajdDq74tScSBAyDJb19LDYnUhQoWl4NI1gOk25puFSVd7s6uLqxkZJ6RNCTGn9g6NUW4rg00E8Cz2UH919Ic3pdoLd3Qg4sDwAdP9/R70DZ51z0PsMlwo4VS7AielJwnohxKBGW1ShEOeJmyYd6TRx0yz4ecZisDLobYZBna5ni0g4VJVFZWXMcjqJWxYp28atqsQtC6eq4tG0Ps/Jr+s0ut19AqqQYWSDMyGEGA9GyCDRnMAI5f85Y4SMPsGR6lbRyjVwgHHAwIz/rcWE7tcxoyZGuDtN0Iya6BV955Xe9xlK/wtwblWlzumkzuFgSyQin5ti0snlUSHEoEZbVGEs53GpKn8KhwkZBinbRrFtPJrGAo9n0vsxDbeSZtk2V86cidLzPDZ3dtIUDFKhaaiKQrthYAFLysrwqCqtqdSgr52ktAghcpVvgYdC7GXKjqEnOHI3/G1fqebR0H06RpeBFbfQPFp2vJpXywZSmlfDCBvdK1S9nlPv+wwmn2brQkykkpqt586di6Ioff774Q9/ONnDEmJKyvRJak2nae0pi96aStGWTrPM5yvY5OXXdTTgL9EoXaZJxDB4Px5ncyjEq5EImzs7SVpWQc6Vj5FW7BTIrjhlSqA7FYUyRaHLNGlwOjnY4xn2tcu1IbCYmmRuEoVgJS3aN7TTvK6ZlttbaF7XTPuGdqxkbp+PmXQ9RVNwN7hRNIVgU5COjaP/nNEr9GxwlKF6VBw1DuyUjRkxsRIWqdYU6bY0vmU+dH93EOhb5iPdmibVmhr0PkPp3xA9o9AX+oTIV8m9A9euXcsXvvCF7M8+n28SRyPE1JYpnjCeRRVChoGpKBzl9fJeLMa+VAqPqjK352rkYwcOAJOXLz+aFbveJdD3p1K81tXF1miUfcnkkK/dVNhTJsZO5iYxVmMp8NA/XQ9Gt5epv0xwFGwKgt39sxEy0H06lWdXoigKiZYEmlejclUlVSv/9rmX+f+RLZEh7zMYaYguil3JvQN9Ph8zZsyY7GEIURImok9S2DBImiZLvF7aDYOArlPpcKAAwXSaCk3LKbgYrxK7I03kAM2JRJ/zZkqgL+hpADzcuCSlZXqQuUmMxViDosHS9aA7GEq0JLrTCUfZK2qw4KhqdRVVK6uwEtaQKYqqS6VmdQ2B5YFRpzFOxIU+IfJVcjP1D3/4Q773ve8xe/ZsLrnkEq655hr0Yb6QJJNJkslk9udwODwRwxRiShnPPkmZlaD2dBrLtqnUdRyKQsQ0cfZsRG5PpYYMLiZiP9JgE/kKv5+0bbOuuXnY84702k3U3jUxuUYzN8m8JPoba1DUO11vtHuZhjJccKS61BEDpUw64GhIQ3RRzErqnfi1r32NpUuXUlVVxR/+8Ae+853vsHfvXm699dYhH3PTTTdx4403TuAohRC9ZVaCHjtwgLRl0aUo6LZN2DRZUlZG0rKGDS4mosTuYBN5pijFWM8rKS2lb7Rzk8xL3fItyFCKxhoUDZWul25LU7mqckyvbz7B0VhJQ3RRjBTbtu3JHsRwvv3tb3PzzTcPe5+tW7eyaNGiAbf/4he/4P/+3/9LV1cXLpdr0McOdkWwsbGRUChERU+/GSHE+MqsNq1vbeXdWAy/rnOIx8MMl4sOw2BVZeWggUrIMFjX3IymKNn9SACtqRSWbXN1Y+O4TLyFPq9U/+sWDofx+/1T4vN3POem6T4vFbJKXSlp39DevaeqzjEgKBppTxXI6ypEvnKdm4o+qNq/fz8HejaqD2XevHk4e32xyXj77bdZsmQJ7777LoceemhO55tKk7oQpaYtleKJAwd4Lx7H7FmhGi64aE4kuL2lhQa3G3ev3ycsi5ZEgqsaGmjst1epEMbrvOO1L2yqmEqfvxM5N02l16UQssFDvQO9QscIG6Rbcw8eSlWhgqKJWAGUVUZRSnL9DC76d3ptbS21tbV5PfaNN95AVVXq6uoKPCohxHioczq5YubMnIOLydqPNF7nlZSWqUPmpvFR6Cp1pWQsBR56G890PVkNE9NZyXwyvfzyy/zxj3/kjDPOwOfz8fLLL3PNNddw2WWXUdlToUsIMTXkGlxM1n4k2QclciVz0+iMR5W6UjMZe5hyNZay70JMdcX5rzIPLpeL+++/nxtuuIFkMsnBBx/MNddcw7XXXjvZQxNCjKPJKrErpX1FLmRuGp3xqFI3lRU6jW480/JklVFMdyXz7l66dCmvvPLKZA9DCDHBJqvErpT2FbmQuWl0xrNK3VRS6DS6iUjLk1VGMd1JgqsQoiT4dZ1Gt3vCA5vJOq8QpapqZRWVqyqxLZtESwLbsqlcVZltNjvZjJBBojmBETLG7RyZNDpFU3A3uFE0hWBTkI6NHUVxvMH0XmXsbbquMorpR97hQgghhCgahSrIUGgTVYSh0Gl0E5WWJ6uMYrqTlSohRMkKGQbNiQQhY/yuKAshxofu13E3uovmy/hErPbA39Lo+q/s6H4dM2oOWAma6OMNp9hXGYUYT8XxSSWEEAUkzXSFEIU0kUUYCl2sYyKLf/RfZcQGFLASlpRUFyVPgiohRMnZ2NFBUzBIvcNBg9tN2DBoCnaX9V1dI2V9hRCjM5FFGAqdRpc53oHHDmB0GjhqHdgpe0xpeSNVEVTdKl2bu6RflZhWJKgSQpSUkGGwJRKh3uGgrqcxr9vpxKa7/PnyQECKSgghRmWiS71n0uUiWyIkWhJoXi3vNDoraWGnbYyoQdfbXQC457ipvbB21MfLdV+Z9KsS05F8sxBClJSwYRA1TRrcfa8o+3WdlkSCsGFIUCWEGJWJLsJQyGIdHRs76HymE+9iL76jfKTaUphhE9WhYiUsUm2pnI+fS7Ak/arEdCXvaiFESanQdbyaRtgwcDv/dkU5ZBh4NY0KCaiEEHko5OpRrnT/2CofDhbg6AGd5IdJWv+7ldDmELZp55Sel2uwJP2qxHQl72ohREnx6zrLfD6agkHsnp9DhkFbOs2qykpZpRJC5KVYS70PZ6gAJ7kvSXxrHNcsF+7G3NLzcg2WJjpVUohiIbsFhRAlZ2VVFasqK4mZJu90dREzTVZVVrKyqvuKspRaF0LkayJKvRshg+jbUbre7hpTk+HBGvKmO9J0vdEFDtCrdVS3irPOiaPOQWRLZMjz5drcN5MqmW5Nk2pNdacYtqZIt6XxLfMVfSAqRL7knS2EmDAhwyBsGFTo+sSsGCnK3/5DSq0LIYqblbQ48OQB2v63jcSuBLZp45zhpPYztdR/un7UlfN67wWz0zbJD5OEXgqR2JNAK9dof7gd37E+vEu8I6bnjWZf2WSkSgox2SSoEkKMu4kOZnqXVJ/p9WZLqr8aDnPANKXUuhCiKHVs7GDvz/eSakuB2b0KlNyTJLY1RnxbnLnfmTvqwCoTyLSubyX8xzB23Eb3d686pfenCW0OoWgKznrniOl5uQZLUzFVUoixkne4EGLcTWTfqKFKqicsi+dDIU72+6XUuhBiwozU06n3/cKbw5hREwUFI2KglqmoLhUzZtLxeAflh5VTd2HdqM6vulQCywN0buzEWe/EEXBgxk1SH6VQXApW0qLrL114F3mpPrd62DGONlgaa6ENIaYSeacLIcbVRPeNGqqkukNRiJomjp5UwAwptS6EGA+59nTKMMIG6WAa27S7Ayu3gubWsE0b27CxVZvwK2GqzqoadaBihA3MuInqUdEqtOzjU20prLiFGTPxfcyXc3qeBEtCDCSbCIQQ4yoT5PQvZe7XdaKmSbjAxSJ6l1TvLW3beDWNtG33uV1KrQshxkOmp5OiKbgb3CiaQrApSMfGjkHvr1foOCod2GkbK2mhOrq/otnp7s8sh9+BbdgDCkXkInNsbLDiFmjgOsiFe44b10EuKk+ppO78ulGnFgoh/kb+9QghxtVQQc54BTOZkuqt6TStqRQJy6I1lSJkmpzm9xMyzT63t6XTLPP5ZJVKCFEw/Xs65VJhT/frVCyvQKvQMONm9+pSrPt/FYeCc6YTvVrPqyR59thejdS+FEawe1XM6DBwVDsIrCxMQ14jZJBoToypYqEQU5V8ixBCjKvJ6BuVKZ2+JRKhJZHAq2msqqzklECAFzs7B9yeub8QQhRCvg1wq1ZWYadtWta1EHsvhpWwcNQ6KDu0DN2nj6kkeebYmcqCAO55bmovrB1zVb7RpjoKUYokqBJCjLuhgpzxCmZcqsrqmhqWBwIDSrgPdbsQQhRKpqdTqi2FVq5172XyaCM2wFVdKrXn1eL/mJ/9D+0n+nYURVPQq/VskJKvzLErz6gk2ZLExsbdUJh+W5lUR0e9A3dDbs2EhSg18m1CCDHuhgtyxpN/iPMMdbsQQhSC6laxbZvg00EUh4Lu07Ope1WrRy404axzctBXDsq5cuBoFLrIRP9URwCn2wl2d+n1wPLCpBYKUexkTVYIMWH8uk6j2y0BjRCipHVs7MBoM/Ac4kHzaaS70sS3xdHr9FGtNul+HXdjYVaTxksm1bH/6pvu1zGjZl6FNYSYior3X6kQQgghxBSTWblxznJSXlfeXbI8bmJ2mSiKgpWwSmqfUSbV0Qgb3StUPUZKdRSi1JTOv2ohhBBCiEnWf+VG9ag4qrpT40px5Ub3d+/3SremSbWmsBIWqdYU6bb0mAprCDHVSFAlhBBCCFEgvVdueivllZuqlVVUrqrEtmwSLQlsy6ZyVeWYqwoKMZWU3r9sIYQQQogRjEcRCPjbyk2wKQh2989GyCDdlqZyVWVJrtyoLpWa1TUElgfG5TUVYiqQd7wQQgghpo2J6KmUWaGJbImQaEmgebVpsXJT6MqCQkwl8s4XQgghxLQxVE8lK2pRcVJFQVZZZOVGiOlH/oULIYQQYloYrKeSQ3MQey/Gh3d+SOdLnTiqHAVbuZKVGyGmDylUIYQQQohpYbCeStF3oyR3JbENG0eVA0VTCDYF6djY0fexIYNEcwIjVFrV+4T4/9u7/7io6nx/4K/5xQww/FJAQVAkxZ9YKOb115qFgtdcKFOvTaHV6t4WS0xS2kIzU9vSdc3bo7TdhdrV8HYr915JvOgNTbIEFdIkVAIHC0HE+CENzI/z/YMvs4z8cIAZzgy8no8Hj4dz5sw57zMy5817Pp/zPmQb/PqEiIiI+oU776lk+sUEXakOEhcJZG4yyL3lkLnKAKH5eijvGd6QqqR2vwbL3uzVlIOI/omfLCIiIuoX7uzMJ5gE6G/pIZVKoRqmai6o/v96ums6GGoNqD9Z3+41WADgO99XzMPplKHGgKYbTag/W4/bhbedtiAkchYsqoiIiKjfaN2Zz3DTAKlCCuUQJdzGuJnXabmnVMuIVetrsFxULhYjWY428tO6u2H9t/VoutYE1zBXeER4wNhgdIqCkMgZOdaZgIiIiMiO7uzMV3OqBrVf1sJQbWhzTylIAONtI1RBKotttB7JcrSiqqW7ocxLBqFBgEwtQ9O1Jvzi/QvU4WqHLgiJnBk/TURERNTvtHTmc/F3gcxd1u49pUw6k8U1WC1aRrJaN7ywJ2uviWrd3VAil0AwCZD7yiHoBOhKdXAb4ebQBSGRM+OniYiIiPqtzu4pJVVKLa7BunMky95FSVdvVNzS3VAVpIIgCJC4SJoLQ1cZ9Lf0MP5ihKAXerUgJOoveJUiERER9XtyLzlUwao2hdKAqAHwifGBYBKgu6aDYBLMI1n21jKVTyKTQBWk6rDdu/kYWnU3lLnKoBqmgrHWCP0NPSRSCYz1Rugr9fCI9OAoFZGN8RNFRERE1IHORrLsqb0bFd+tScad3Q1dQ11h+NmAXy7/ApcgF0gUkl4rCIn6GxZVRERERHfRcg1Wb2m81oim8ia4hrq2iaOza6JadzdsvN4I15GuGPjrgfCY6AEXPxeOUBHZCT9ZRERERA6i5TqqmpM1qD9fj4ZLDXAf5w730e6QKCR3bZIh1sgaUX/Ha6qIiIiIHIS5Jbq7DOpxapgaTKg7XYe6b+vQVNFk9TVRHV0j1pqhxgBdmQ6GGoOtD4Oo3+FXF0REREQO4M7rqOQ+ckhcJLh98TZuX7gNlwEuXb4mqr127F3tKkhEd8eiioiIiMgBtG6JDgBShRTqCWoog5X4peQX+D/hD/U4tVXb6qxwahkNUwxSQBWkgqHW0NzcAoDvfF+7HR9RX8aiioiIiMgBtG6J3vpmw0KTAGWA0lxsWaOjwsl024Tbhbe71FWQiO6OY7xEREREDqClJbq+Qo+miiaYdKYuXUfV4s5phFKVFC7+LlD4K1D7dS301fo2jS7kXnIYbxthqOX1VUTd4TRF1ZYtWzBt2jS4ubnB29u73XW0Wi3mz58PNzc3+Pv748UXX4TBwJMDERHZB3MT2ZotbjbcMo2wvcJJMAiQyCRtiqe7dRUkos45zSenqakJixYtwtSpU/GXv/ylzfNGoxHz58/H4MGD8dVXX6G8vBzx8fFQKBTYunWrTWMxGo3Q6/U23Sb1LwqFAjKZTOwwiKiHHCk3Ud9gi5boHU0jNNQYIB8oh9sYN9R+WQsIzYWWocYAfaUePjE+nPpH1E0SQRAEsYPoirS0NCQmJuLnn3+2WH748GE8/PDD+OmnnzBo0CAAwHvvvYf169fjxo0bcHFxaWdrbdXW1sLLyws1NTXw9PS0eE4QBFy/fr3Nvom6w9vbG4MHD4ZEIhE7FCKH0Nn519HZMzc58/tC4qnKqGq+pspf0aZwamlWwe5/RHdn7Tm4z3wdcerUKYSHh5uTFgBER0fj2WefxXfffYeIiIh2X9fY2IjGxkbz49ra2g730VJQ+fv7w83NjX8MU7cIgoCGhgZUVlYCAAICAkSOiIjspTu5qSt5iZxPey3O7aFlumBdXh1013SQucvMBRVvEExke33mE3T9+nWLpAXA/Pj69esdvm7btm3YtGnTXbdvNBrNBdXAgQN7Fiz1e66urgCAyspK+Pv7cyogUR/VndxkbV4i59Lb94aypnCSe7GYIrIVUcd4k5OTIZFIOv35/vvv7RrDSy+9hJqaGvNPWVlZu+u1XEPl5uZm13io/2j5XeL1eUSORezcZG1eIufS0uJcIpNAFaSCRCbBrcxbqD5abdf9yr3kUAWrWDwR2Zmon7C1a9di+fLlna4TGhpq1bYGDx6M06dPWyyrqKgwP9cRpVIJpVJp1T4AcMof2Qx/l4gck9i5qat5iRzfnS3OAd4biqivEfUT7OfnBz8/P5tsa+rUqdiyZYt5OhUAZGVlwdPTE2PHjrXJPoiIqO9jbiJba2lxfufNe+Vecuiu6Zqn57GoInJqTtPiRavVIj8/H1qtFkajEfn5+cjPz0d9fT0AYO7cuRg7diyefPJJFBQU4MiRI3jllVeQkJDAb/z6gbS0tA7vEdOaRCLBwYMH7R4PEfUPzE1kjdYtzlvjvaGI+g6nKao2bNiAiIgIbNy4EfX19YiIiEBERATy8vIAADKZDIcOHYJMJsPUqVPxxBNPID4+Hq+99prIkYvvgQceQGJiothh2NWSJUtw6dIl8+NXX30V9913X5v1ysvLMW/evF6MjIj6MuYmsobcSw6PSA/oK/RoqmiCSWdCU0UT9JV6eER6cJSKqA9wmk9xWloa0tLSOl1n2LBh+Pzzz3snoD5GEAQYjUbI5U7zK2HB1dXV3FGvM51dX0dE1FXMTWStzlqcE5Hzc5qRqr6mxmBAmU6HGoPh7iv3wPLly3H8+HHs2rXL3LWqtLQU2dnZkEgkOHz4MCZNmgSlUomTJ09i+fLliIuLs9hGYmIiHnjgAfNjk8mEbdu2Yfjw4XB1dcW9996L//qv/+o0jpCQEGzevBlLly6Fu7s7hgwZgnfeecdiHa1Wi9jYWKjVanh6emLx4sXmC7oBoKCgALNnz4aHhwc8PT0xadIk87fBraf/paWlYdOmTSgoKDAfc8sfPa2n/02bNg3r16+3iOHGjRtQKBQ4ceIEgOb7xSQlJWHIkCFwd3fHlClTkJ2dbcU7T0RE9E8tLc6DE4MR9FwQghOD4TvflzfbJeoj+EnuZY0mEzKqqvCnsjLsvnYNfyorQ0ZVFRpNJrvsb9euXZg6dSpWrFiB8vJylJeXIzg42Px8cnIy3njjDRQWFmLChAlWbXPbtm348MMP8d577+G7777DmjVr8MQTT+D48eOdvu6tt97Cvffei3PnziE5ORmrV69GVlYWgOZCLTY2FtXV1Th+/DiysrLwww8/YMmSJebXazQaBAUFITc3F2fOnEFycjIUCkWb/SxZsgRr167FuHHjzMfcejutt5eeng5BEMzLDhw4gMDAQMycORMAsGrVKpw6dQrp6en49ttvsWjRIsTExODy5ctWvVdEREStscU5Ud/ET3QvO1pdjcxbtzBIoUCQSoVagwGZt24BAOb7+tp8f15eXnBxcYGbm1u7U99ee+01zJkzx+rtNTY2YuvWrTh69CimTp0KoLm18MmTJ7Fnzx7MmjWrw9dOnz4dycnJAICwsDDk5ORg586dmDNnDo4dO4bz58+jpKTEXPR9+OGHGDduHHJzczF58mRotVq8+OKLGD16NABg5MiR7e7H1dUVarUacrm80+l+ixcvRmJiIk6ePGkuovbv34+lS5dCIpFAq9UiNTUVWq0WgYGBAICkpCRkZmYiNTUVW7dutfp9IyIiIqK+iyNVvajGYEBeXR0GKRTwd3GBSiqFv4sL/BUK5NXV2X0qYHsiIyO7tP6VK1fQ0NCAOXPmQK1Wm38+/PBDFBcXd/raliKs9ePCwkIAQGFhIYKDgy1G0caOHQtvb2/zOi+88AJ+85vfICoqCm+88cZd93c3fn5+mDt3Lvbt2wcAKCkpwalTp6DRaAAA58+fh9FoRFhYmMWxHj9+vMf7JiIiIqK+gyNVvajWYMBtoxFBKsv7VHjJ5bim06HWYIBXLzeKcHd3t3gslUotpsMBgF6vN/+7pU1wRkYGhgwZYrGevdsDv/rqq3j88ceRkZGBw4cPY+PGjUhPT8cjjzzS7W1qNBo8//zz2L17N/bv34/w8HCEh4cDaD5WmUyGM2fOQCaTWbxOrVb36FiIiIiIqO9gUdWLPOVyuMtkqDUYoHJxMS+vMRjgLpPB004FlYuLC4xGo1Xr+vn54cKFCxbL8vPzzdcujR07FkqlElqtttOpfu35+uuv2zweM2YMAGDMmDEoKytDWVmZebTq4sWL+Pnnny1ukBkWFoawsDCsWbMGS5cuRWpqartFlbXHHBsbi5UrVyIzMxP79+9HfHy8+bmIiAgYjUZUVlaapwcSEREREd2J0/96kZdcjkgPD1To9ahoaoLOZEJFUxMq9XpEenjYbZQqJCQE33zzDUpLS1FVVQVTJ00xHnzwQeTl5eHDDz/E5cuXsXHjRosiy8PDA0lJSVizZg0++OADFBcX4+zZs9i9ezc++OCDTuPIycnBm2++iUuXLuGdd97Bxx9/jNWrVwMAoqKiEB4eDo1Gg7Nnz+L06dOIj4/HrFmzEBkZiV9++QWrVq1CdnY2rl69ipycHOTm5pqLsvaOuaSkBPn5+aiqqkJjY2O767m7uyMuLg4pKSkoLCzE0qVLzc+FhYVBo9EgPj4en376KUpKSnD69Gls27YNGRkZnR4rEREREfUfLKp6WdSAAYjx8YFJEHBNp4NJEBDj44OoAfa7T0VSUhJkMhnGjh0LPz8/aLXaDteNjo5GSkoK1q1bh8mTJ6Ours5i9AYANm/ejJSUFGzbtg1jxoxBTEwMMjIyMHz48E7jWLt2LfLy8hAREYHXX38df/zjHxEdHQ2gudX5P/7xD/j4+OBXv/oVoqKiEBoaigMHDgBovoHmzZs3ER8fj7CwMCxevBjz5s3Dpk2b2t3XwoULERMTg9mzZ8PPzw8fffRRh3FpNBoUFBRg5syZGDp0qMVzqampiI+Px9q1azFq1CjExcUhNze3zXpERERE1H9JhDsvoOnnamtr4eXlhZqaGnh6epqX63Q6lJSUYPjw4VDdcU1Ud9QYDKg1GOApl/f6dVRiCAkJQWJiIhITE8UOxWHY+neKyNl1dP7t7/i+EBGJx9pzcN//a95BefWTYoqIiIiIqK/j9D8iIiIiIqIe4FAJ9YrS0lKxQyAiIiIisguOVBEREREREfUAR6qIiIiIeoGhxgBDrQFyTznkXvwTjKgv4SeaiIiIyI5MjSZUH61GXV4djLeNkLnL4BHpgQFRAyBVctIQUV/ATzIRERGRHVUfrcatzFuQyCRQBakgkUlwK/MWqo9Wix0aEdkIiyoiIiIiOzHUGFCXVwfFIAVc/F0gVUnh4u8Chb8CdXl1MNQYxA6RiGyARRURERGRnRhqDTDeNkLuaXnFhdxLDuNtIwy1LKqI+gIWVf2AIAhYuXIlBgwYAIlEgvz8/Lu+prS01Op1+yqJRIKDBw+KHQYREVnJUGOArkznUKM/ck85ZO6yNsWTocYAmbusTbFFRM6Jn+R+IDMzE2lpacjOzkZoaCh8fX3FDsmhvPrqqzh48GCbArK8vBw+Pj7iBEVERFazdSMIW3bpk3vJ4RHpgVuZtwCh+bGhxgB9pR4+MT7sAkjUR/CT3A8UFxcjICAA06ZNEzsUpzJ48GCxQyAiIiu0NIJQDFJAFaSCodbQXMQA8J1v/ReJ9urSNyBqAACgLq8Oums6yNxl8InxMS8nIufH6X8i6a0pCsuXL8dzzz0HrVYLiUSCkJAQAM2jVzNmzIC3tzcGDhyIhx9+GMXFxR1u59atW9BoNPDz84OrqytGjhyJ1NRU8/NlZWVYvHgxvL29MWDAAMTGxqK0tLTD7WVnZ0MikeDYsWOIjIyEm5sbpk2bhqKiIov1/vGPf2DixIlQqVQIDQ3Fpk2bYDD88z37/vvvMWPGDKhUKowdOxZHjx5tM21v/fr1CAsLg5ubG0JDQ5GSkgK9Xg8ASEtLw6ZNm1BQUACJRAKJRIK0tDQAltP/pk2bhvXr11vEduPGDSgUCpw4cQIA0NjYiKSkJAwZMgTu7u6YMmUKsrOzO3wPiIio52zZCMJeXfqkSil85/siODEYQc8FITgxGL7zfdlOnagP4ae5l5kaTajKqELZn8pwbfc1lP2pDFUZVTA1muyyv127duG1115DUFAQysvLkZubCwC4ffs2XnjhBeTl5eHYsWOQSqV45JFHYDK1H0dKSgouXryIw4cPo7CwEO+++655GqFer0d0dDQ8PDzw5ZdfIicnB2q1GjExMWhqauo0vpdffhk7duxAXl4e5HI5nn76afNzX375JeLj47F69WpcvHgRe/bsQVpaGrZs2QIAMBqNiIuLg5ubG7755hvs3bsXL7/8cpt9eHh4IC0tDRcvXsSuXbvw/vvvY+fOnQCAJUuWYO3atRg3bhzKy8tRXl6OJUuWtNmGRqNBeno6BEEwLztw4AACAwMxc+ZMAMCqVatw6tQppKen49tvv8WiRYsQExODy5cvd/oeEBFR99mqEURvdOmTe8mhClZxyh9RH8RPdS+z1RQFa3l5ecHDwwMymcxiOtvChQst1vvrX/8KPz8/XLx4EePHj2+zHa1Wi4iICERGRgKAecQLaC4uTCYT/vznP0MikQAAUlNT4e3tjezsbMydO7fD+LZs2YJZs2YBAJKTkzF//nzodDqoVCps2rQJycnJWLZsGQAgNDQUmzdvxrp167Bx40ZkZWWhuLgY2dnZ5mPbsmUL5syZY7GPV155xfzvkJAQJCUlIT09HevWrYOrqyvUajXkcnmn0/0WL16MxMREnDx50lxE7d+/H0uXLoVEIoFWq0Vqaiq0Wi0CAwMBAElJScjMzERqaiq2bt3a4baJiKj7WjeCcFG5mJd3tRFES3GmClJZbt9LDt01XfM1ViyGiKgDPDv0oju/BQPQnACE5nnW3jO8e+2EffnyZWzYsAHffPMNqqqqzCNUWq223aLq2WefxcKFC3H27FnMnTsXcXFx5mu0CgoKcOXKFXh4eFi8RqfTdTqlEAAmTJhg/ndAQAAAoLKyEkOHDkVBQQFycnLMI1NA8+iUTqdDQ0MDioqKEBwcbFEM3X///W32ceDAAbz99tsoLi5GfX09DAYDPD097/YWWfDz88PcuXOxb98+zJw5EyUlJTh16hT27NkDADh//jyMRiPCwsIsXtfY2IiBAwd2aV9ERGS9jhpBNJY1wnOa9ed6WxVnRNQ/8QzRixzpW7AFCxZg2LBheP/99xEYGAiTyYTx48d3OF1v3rx5uHr1Kj7//HNkZWXhoYceQkJCArZv3476+npMmjQJ+/bta/M6Pz+/TuNQKBTmf7eMcrUUePX19di0aRMeffTRNq9TqVRtlrXn1KlT0Gg02LRpE6Kjo+Hl5YX09HTs2LHDqte3ptFo8Pzzz2P37t3Yv38/wsPDER4ebo5VJpPhzJkzkMlkFq9Tq9Vd3hcREVnPohFEqQ6NFY2AANR/Ww/dVZ1VzSbYpY+IeoJniF7kKN+C3bx5E0VFRXj//ffNU9lOnjx519f5+flh2bJlWLZsGWbOnIkXX3wR27dvx8SJE3HgwAH4+/t3eQSoMxMnTkRRURFGjBjR7vOjRo1CWVkZKioqMGjQIAAwXzPW4quvvsKwYcMsrrW6evWqxTouLi4wGo13jSc2NhYrV65EZmYm9u/fj/j4ePNzERERMBqNqKysNL+nRETUO1oaQXjP8EblZ5UwfmWEcqgSck95l6bZd9Slz3OyJ3RlOpu0WCeivolnhl7kKN+C+fj4YODAgdi7dy8CAgKg1WqRnJzc6Ws2bNiASZMmYdy4cWhsbMShQ4cwZswYAM0jOG+99RZiY2PNTTGuXr2KTz/9FOvWrUNQUFC34tywYQMefvhhDB06FI899hikUikKCgpw4cIFvP7665gzZw7uueceLFu2DG+++Sbq6urM10+1jHqNHDkSWq0W6enpmDx5MjIyMvDZZ59Z7CckJAQlJSXIz89HUFAQPDw8oFQq28Tj7u6OuLg4pKSkoLCwEEuXLjU/FxYWBo1Gg/j4eOzYsQMRERG4ceMGjh07hgkTJmD+/Pndeg+IiKhrGq82QjlU2a1p9q2LM0OtAVKlFLW5tfjx3R9t2mKdiPoenhF62YCoAfCJ8YFgEqC7poNgEnr9XhVSqRTp6ek4c+YMxo8fjzVr1uCtt97q9DUuLi546aWXMGHCBPzqV7+CTCZDeno6AMDNzQ0nTpzA0KFD8eijj2LMmDF45plnoNPpejRyFR0djUOHDuF///d/MXnyZPzLv/wLdu7ciWHDhgEAZDIZDh48iPr6ekyePBm/+c1vzCNSLdMDf/3rX2PNmjVYtWoV7rvvPnz11VdISUmx2M/ChQsRExOD2bNnw8/PDx999FGHMWk0GhQUFGDmzJkYOnSoxXOpqamIj4/H2rVrMWrUKMTFxSE3N7fNekREZB+26gTY0qWvNrfWLi3WiajvkQite0QTamtr4eXlhZqaGouCQKfToaSkBMOHD7f6ep7O2PJu7fRPOTk5mDFjBq5cuYJ77rlH7HA6ZevfKSJn19H5t7/j+2I9Q40BZX8qg0QmMY9UAUBTRRMEk4DgxGCrc64tt0VEzsvaczDPBiKRe7GYsoXPPvsMarUaI0eOxJUrV7B69WpMnz7d4QsqIiKyPVtOs3ek5lJE5Ph4NiCnVldXh/Xr10Or1cLX1xdRUVHd6uxHRER9Q0fNJro6zd5RmksRkXPgGYGcWnx8vEUXPiIi6t/ubDbR3Wn2jtJcioicA88IRERE1OfYYpq9rUa9iKjvY1HVRezrQbbC3yUiIsdmq1EvIur7eGawkkKhAAA0NDTA1dVV5GioL2hoaADwz98tIiJyTGwuRUR3wzOElWQyGby9vVFZWQmg+d5MLTeYJeoKQRDQ0NCAyspKeHt7QyaTiR0SEREREfUAi6ouGDx4MACYCyuinvD29jb/ThERERGR82JR1QUSiQQBAQHw9/eHXq8XOxxyYgqFgiNURERERH0Ei6pukMlk/IOYiIiIiIgAAFKxAyAiIiIiInJmLKqIiIiIiIh6gEUVERERERFRD/Caqju03JC1trZW5EiIiPqXlvMub4xtiXmJiEg81uYmFlV3qKurAwAEBweLHAkRUf9UV1cHLy8vscNwGMxLRETiu1tukgj8StCCyWTCTz/9BA8PD4e8uW9tbS2Cg4NRVlYGT09PscPpNh6HY+FxOJa+chxA145FEATU1dUhMDAQUilnp7dgXuo9feVYeByOhcfhWLp6HNbmJo5U3UEqlSIoKEjsMO7K09PTqX+hW/A4HAuPw7H0leMArD8WjlC1xbzU+/rKsfA4HAuPw7F05TisyU38KpCIiIiIiKgHWFQRERERERH1AIsqJ6NUKrFx40YolUqxQ+kRHodj4XE4lr5yHEDfOhZqX1/6P+4rx8LjcCw8Dsdir+NgowoiIiIiIqIe4EgVERERERFRD7CoIiIiIiIi6gEWVURERERERD3AooqIiIiIiKgHWFQ5uYyMDEyZMgWurq7w8fFBXFyc2CF1W2NjI+677z5IJBLk5+eLHU6XlJaW4plnnsHw4cPh6uqKe+65Bxs3bkRTU5PYoVnlnXfeQUhICFQqFaZMmYLTp0+LHVKXbNu2DZMnT4aHhwf8/f0RFxeHoqIiscPqsTfeeAMSiQSJiYlih9JlP/74I5544gkMHDgQrq6uCA8PR15enthhUS9hbnIMzpybnD0vAcxNjsieuYlFlRP75JNP8OSTT+Kpp55CQUEBcnJy8Pjjj4sdVretW7cOgYGBYofRLd9//z1MJhP27NmD7777Djt37sR7772H3//+92KHdlcHDhzACy+8gI0bN+Ls2bO49957ER0djcrKSrFDs9rx48eRkJCAr7/+GllZWdDr9Zg7dy5u374tdmjdlpubiz179mDChAlih9Jlt27dwvTp06FQKHD48GFcvHgRO3bsgI+Pj9ihUS9gbnIczpqb+kJeApibHI3dc5NATkmv1wtDhgwR/vznP4sdik18/vnnwujRo4XvvvtOACCcO3dO7JB67M033xSGDx8udhh3df/99wsJCQnmx0ajUQgMDBS2bdsmYlQ9U1lZKQAQjh8/LnYo3VJXVyeMHDlSyMrKEmbNmiWsXr1a7JC6ZP369cKMGTPEDoNEwNzk+JwhN/XFvCQIzE1is3du4kiVkzp79ix+/PFHSKVSREREICAgAPPmzcOFCxfEDq3LKioqsGLFCvztb3+Dm5ub2OHYTE1NDQYMGCB2GJ1qamrCmTNnEBUVZV4mlUoRFRWFU6dOiRhZz9TU1ACAw7//HUlISMD8+fMt/l+cyX//938jMjISixYtgr+/PyIiIvD++++LHRb1AuYmx+fouamv5iWAuUls9s5NLKqc1A8//AAAePXVV/HKK6/g0KFD8PHxwQMPPIDq6mqRo7OeIAhYvnw5/v3f/x2RkZFih2MzV65cwe7du/Hb3/5W7FA6VVVVBaPRiEGDBlksHzRoEK5fvy5SVD1jMpmQmJiI6dOnY/z48WKH02Xp6ek4e/Ystm3bJnYo3fbDDz/g3XffxciRI3HkyBE8++yzeP755/HBBx+IHRrZGXOTY3OG3NQX8xLA3OQI7J2bWFQ5mOTkZEgkkk5/WuZIA8DLL7+MhQsXYtKkSUhNTYVEIsHHH38s8lFYfxy7d+9GXV0dXnrpJbFDbpe1x9Hajz/+iJiYGCxatAgrVqwQKfL+KyEhARcuXEB6errYoXRZWVkZVq9ejX379kGlUokdTreZTCZMnDgRW7duRUREBFauXIkVK1bgvffeEzs06ibmJsfC3OR8mJvEZ+/cJLfJVshm1q5di+XLl3e6TmhoKMrLywEAY8eONS9XKpUIDQ2FVqu1Z4hWsfY4/u///g+nTp2CUqm0eC4yMhIajUb0b7atPY4WP/30E2bPno1p06Zh7969do6u53x9fSGTyVBRUWGxvKKiAoMHDxYpqu5btWoVDh06hBMnTiAoKEjscLrszJkzqKysxMSJE83LjEYjTpw4gf/4j/9AY2MjZDKZiBFaJyAgwOLcBABjxozBJ598IlJE1FPMTc2Ym+yvr+UlgLnJUdg7N7GocjB+fn7w8/O763qTJk2CUqlEUVERZsyYAQDQ6/UoLS3FsGHD7B3mXVl7HG+//TZef/118+OffvoJ0dHROHDgAKZMmWLPEK1i7XEAzd8Czp492/zNrFTq+APBLi4umDRpEo4dO2ZueWwymXDs2DGsWrVK3OC6QBAEPPfcc/jss8+QnZ2N4cOHix1Stzz00EM4f/68xbKnnnoKo0ePxvr1650iaQHA9OnT27QNvnTpkkOcm6h7mJuYm3pLX8lLAHOTo7F7brJbCwyyu9WrVwtDhgwRjhw5Inz//ffCM888I/j7+wvV1dVih9ZtJSUlTtlh6dq1a8KIESOEhx56SLh27ZpQXl5u/nF06enpglKpFNLS0oSLFy8KK1euFLy9vYXr16+LHZrVnn32WcHLy0vIzs62eO8bGhrEDq3HnLHD0unTpwW5XC5s2bJFuHz5srBv3z7Bzc1N+Pvf/y52aNQLmJsch7Pmpr6QlwSBucnR2Ds3sahyYk1NTcLatWsFf39/wcPDQ4iKihIuXLggdlg94qyJKzU1VQDQ7o8z2L17tzB06FDBxcVFuP/++4Wvv/5a7JC6pKP3PjU1VezQeswZE5cgCML//M//COPHjxeUSqUwevRoYe/evWKHRL2EuclxOHNucva8JAjMTY7InrlJIgiCYJsxLyIiIiIiov7HsSfWEhEREREROTgWVURERERERD3AooqIiIiIiKgHWFQRERERERH1AIsqIiIiIiKiHmBRRURERERE1AMsqoiIiIiIiHqARRUREREREVEPsKgicjKlpaWQSCTIz88XOxQiIiIAzE1EEkEQBLGDICLrGY1G3LhxA76+vpDL5WKHQ0RExNxE/R6LKiIn0tTUBBcXF7HDICIiMmNuIuL0PyK72bt3LwIDA2EymSyWx8bG4umnn0ZxcTFiY2MxaNAgqNVqTJ48GUePHrVYNyQkBJs3b0Z8fDw8PT2xcuXKNlMsjEYjnnnmGQwfPhyurq4YNWoUdu3aZbGd5cuXIy4uDtu3b0dAQAAGDhyIhIQE6PV68zqNjY1Yv349goODoVQqMWLECPzlL38xP3/hwgXMmzcParUagwYNwpNPPomqqiobv2tERGRPzE1E9sGiishOFi1ahJs3b+KLL74wL6uurkZmZiY0Gg3q6+vxr//6rzh27BjOnTuHmJgYLFiwAFqt1mI727dvx7333otz584hJSWlzX5MJhOCgoLw8ccf4+LFi9iwYQN+//vf4z//8z8t1vviiy9QXFyML774Ah988AHS0tKQlpZmfj4+Ph4fffQR3n77bRQWFmLPnj1Qq9UAgJ9//hkPPvggIiIikJeXh8zMTFRUVGDx4sU2fMeIiMjemJuI7EQgIruJjY0Vnn76afPjPXv2CIGBgYLRaGx3/XHjxgm7d+82Px42bJgQFxdnsU5JSYkAQDh37lyH+01ISBAWLlxofrxs2TJh2LBhgsFgMC9btGiRsGTJEkEQBKGoqEgAIGRlZbW7vc2bNwtz5861WFZWViYAEIqKijqMg4iIHA9zE5HtcaSKyI40Gg0++eQTNDY2AgD27duHf/u3f4NUKkV9fT2SkpIwZswYeHt7Q61Wo7CwsM23gZGRkXfdzzvvvINJkybBz88ParUae/fubbOdcePGQSaTmR8HBASgsrISAJCfnw+ZTIZZs2a1u/2CggJ88cUXUKvV5p/Ro0cDAIqLi61/Q4iISHTMTUS2x/YsRHa0YMECCIKAjIwMTJ48GV9++SV27twJAEhKSkJWVha2b9+OESNGwNXVFY899hiampostuHu7t7pPtLT05GUlIQdO3Zg6tSp8PDwwFtvvYVvvvnGYj2FQmHxWCKRmOfUu7q6drqP+vp6LFiwAH/4wx/aPBcQENDpa4mIyLEwNxHZHosqIjtSqVR49NFHsW/fPly5cgWjRo3CxIkTAQA5OTlYvnw5HnnkEQDNyaG0tLTL+8jJycG0adPwu9/9zrysq9/QhYeHw2Qy4fjx44iKimrz/MSJE/HJJ58gJCSErXKJiJwccxOR7XH6H5GdaTQaZGRk4K9//Ss0Go15+ciRI/Hpp58iPz8fBQUFePzxx9t0Y7LGyJEjkZeXhyNHjuDSpUtISUlBbm5ul7YREhKCZcuW4emnn8bBgwdRUlKC7Oxs8wXFCQkJqK6uxtKlS5Gbm4vi4mIcOXIETz31FIxGY5djJiIicTE3EdkWiyoiO3vwwQcxYMAAFBUV4fHHHzcv/+Mf/wgfHx9MmzYNCxYsQHR0tPmbwq747W9/i0cffRRLlizBlClTcPPmTYtvBq317rvv4rHHHsPvfvc7jB49GitWrMDt27cBAIGBgcjJyYHRaMTcuXMRHh6OxMREeHt7QyrlaYSIyNkwNxHZFm/+S0RERERE1AMs44mIiIiIiHqARRUREREREVEPsKgiIiIiIiLqARZVREREREREPcCiioiIiIiIqAdYVBEREREREfUAiyoiIiIiIqIeYFFFRERERETUAyyqiIiIiIiIeoBFFRERERERUQ+wqCIiIiIiIuqB/wfabidEqRqTvQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "def test(svm, X_test, y_test):\n",
        "    y_pred = svm(X_test)\n",
        "    print(svm, \"\\n\")\n",
        "    validate(y_test, y_pred)\n",
        "    scatter_comparison(X_test, y_test, y_pred)\n",
        "\n",
        "\n",
        "# train and test\n",
        "svm = LinearSVM({\"variance\": 1, \"skewness\": 0}, 0.0)\n",
        "test(svm, X_test, y_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7e324c80-14de-4545-9257-e38d9a5b2ab7",
      "metadata": {
        "id": "7e324c80-14de-4545-9257-e38d9a5b2ab7"
      },
      "source": [
        "## Linear optimization model\n",
        "\n",
        "A training or validation set consists of $n$ observations $(x_i, y_i)$ where $y_i = \\pm 1$ and $x_i\\in\\mathbb{R}^p$ for $i=1, \\dots, n$. The training task is to find coefficients $w\\in\\mathbb{R}^p$ and $b\\in\\mathbb{R}$ to achieve high sensitivity and high precision for the validation set. All points $(x_i, y_i)$ for $i\\in 1, \\dots, n$ are successfully classified if\n",
        "\n",
        "$$\n",
        "\\begin{align}\n",
        "    y_i (w^\\top x_i + b) & > 0 & \\forall i = 1, 2, \\dots, n.\n",
        "\\end{align}\n",
        "$$\n",
        "\n",
        "As written, this condition imposes no scale for $w$ or $b$ (that is, if the condition is satisfied for any pair $(w, b)$, then it also satisfied for $(\\gamma w, \\gamma b)$ where $\\gamma > 0$). To remove the ambiguity, a modified condition for correctly classified points is given by\n",
        "\n",
        "$$\n",
        "\\begin{align*}\n",
        "y_i (w^\\top x_i + b) & \\geq 1 & \\forall i = 1, 2, \\dots, n\n",
        "\\end{align*}\n",
        "$$\n",
        "\n",
        "which defines a **hard-margin** classifier. The size of the margin is determined by the scale of $w$ and $b$.\n",
        "\n",
        "In practice, it is not always possible to find $w$ and $b$ that perfectly separate all data. The condition for a hard-margin classifier is therefore relaxed by introducing non-negative decision variables $z_i \\geq 0$ where\n",
        "\n",
        "$$\n",
        "\\begin{align*}\n",
        "y_i (w^\\top x_i + b) & \\geq 1 - z_i & \\forall i = 1, 2, \\dots, n\n",
        "\\end{align*}\n",
        "$$\n",
        "\n",
        "The variables $z_i$ measure the distance of a misclassified point from the separating hyperplane. An equivalent notation is to rearrange this expression as\n",
        "\n",
        "$$\n",
        "\\begin{align*}\n",
        "    z_i & = \\max(0,  1 -  y_i (w^\\top x_i + b)) & \\forall i = 1, 2, \\dots, n\n",
        "\\end{align*}\n",
        "$$\n",
        "\n",
        "which is **hinge-loss** function. The training problem is formulated as minimizing the hinge-loss function over all the data samples:\n",
        "\n",
        "$$\n",
        "\\begin{align*}\n",
        "    \\min_{w, b} \\frac{1}{n}\\sum_{i=1}^n \\left(1 - y_i(w^\\top x_i + b)\\right)^+ .\n",
        "\\end{align*}\n",
        "$$\n",
        "\n",
        "Practice has shown that minimizing this term alone produces classifiers with large entries for $w$ which performs poorly on new data samples. For that reason, **regularization** adds a term to penalize the magnitude of $w$. In most formulations a norm $\\|w\\|$ is used for regularization, commonly a sum of squares such as $\\|w\\|_2^2$. Another choice is $\\|w\\|_1$ which, similar to Lasso regression, may result in sparse weighting vector $w$ indicating the elements of the feature vector that can be neglected for classification purposes. These considerations result in the objective function\n",
        "\n",
        "$$\n",
        "    \\min_{w, b}\\left[ \\lambda \\|w\\|_1 + \\frac{1}{n}\\sum_{i=1}^n \\left(1 - y_i(w^\\top x_i + b)\\right)^+ \\right]\n",
        "$$\n",
        "\n",
        "The needed weights are a solution to following LP:\n",
        "\n",
        "$$\n",
        "\\begin{align*}\n",
        "\\min\\quad  & \\lambda \\|w\\|_1 + \\frac{1}{n} \\sum_{i=1}^n z_i \\\\\n",
        "\\text{s.t.} \\quad &  z_i \\geq 1 - y_i(w^\\top x_i + b) & \\forall i = 1, \\dots, n \\\\\n",
        "& z_i\\geq 0 & \\forall i = 1, \\dots, n \\\\\n",
        "& w\\in\\mathbb{R}^p \\\\\n",
        "& b\\in\\mathbb{R} \\\\\n",
        "\\end{align*}\n",
        "$$\n",
        "\n",
        "This is the primal optimization problem in decision variables $w\\in\\mathbb{R}^p$, $b\\in\\mathbb{R}$, and $z\\in\\mathbb{R}^n$, a total of $n + p + 1$ unknowns with $2n$ constraints. This can be recast as a linear program with the usual technique of setting $w = w^+ - w^-$ where $w^+$ and $w^-$ are non-negative. Then\n",
        "\n",
        "$$\n",
        "\\begin{align*}\n",
        "\\min\\quad  &\\lambda \\sum_{j=1}^p (w^+_j + w^-_j) + \\frac{1}{n}  \\sum_{i=1}^n z_i \\\\\n",
        "\\text{s.t.} \\quad &  z_i \\geq 1 - y_i((w^+ - w^-)^\\top x_i + b) & \\forall i = 1, \\dots, n \\\\\n",
        "& z_i \\geq 0 & \\forall i = 1, \\dots, n \\\\\n",
        "& w^+_j, w^-_j \\geq 0 & \\forall j = 1, \\dots, p \\\\\n",
        "& b\\in\\mathbb{R} \\\\\n",
        "\\end{align*}\n",
        "$$"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ece58bfb-1d65-402a-ae55-65e57a3f6c0a",
      "metadata": {
        "id": "ece58bfb-1d65-402a-ae55-65e57a3f6c0a"
      },
      "source": [
        "### AMPL implementation\n",
        "\n",
        "The AMPL implementation is a **factory** function. The function accepts a set of training data, creates and solves an AMPL model for $w$ and $b$, then returns a trained `LinearSVM` object that can be applied to a other feature data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "id": "89afe53b",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "89afe53b",
        "outputId": "f5168acc-799d-41bd-f01c-17759810f842"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LinearSvm(w = {'variance': 0.2395364623336089, 'skewness': 0.05899041681482269}, b = 0.02663639314215303) \n",
            "\n",
            "Matthews correlation coefficient (MCC) = 0.777\n",
            "Sensitivity =  95.3%\n",
            "Precision =  85.5%\n",
            "Accuracy =  88.7%\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                 Predicted Positive  Predicted Negative\n",
              "Actual Positive                 142                   7\n",
              "Actual Negative                  24                 102"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6aaf5a3a-ae4a-4f61-97f2-ea3dab2384b2\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Predicted Positive</th>\n",
              "      <th>Predicted Negative</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Actual Positive</th>\n",
              "      <td>142</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Actual Negative</th>\n",
              "      <td>24</td>\n",
              "      <td>102</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6aaf5a3a-ae4a-4f61-97f2-ea3dab2384b2')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-6aaf5a3a-ae4a-4f61-97f2-ea3dab2384b2 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-6aaf5a3a-ae4a-4f61-97f2-ea3dab2384b2');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-14da8ce3-ba38-42fe-af8c-42391ee6d9ae\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-14da8ce3-ba38-42fe-af8c-42391ee6d9ae')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-14da8ce3-ba38-42fe-af8c-42391ee6d9ae button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x400 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x400 with 2 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1UAAAGJCAYAAABinBm7AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAACwbklEQVR4nOzdeXxb5ZX4/89dtFmWJe9JsJOQBRIIWwhbCWvSJjRlgNBStimhM+237ZQO0GXamSlL2l8pQ0uhMAMd2kJpMwMMZSkNpBAgQCi0pGwFAiQhiw2JHce2JGu/y+8PW6p3y7Iky/Z5v168WsvS1SNZuY/OPec5j2Lbto0QQgghhBBCiJyo4z0AIYQQQgghhJjIJKgSQgghhBBCiDGQoEoIIYQQQgghxkCCKiGEEEIIIYQYAwmqhBBCCCGEEGIMJKgSQgghhBBCiDGQoEoIIYQQQgghxkCCKiGEEEIIIYQYAwmqhBBCCCGEEGIMJKgSosQoisJ111033sMY4PTTT+f000/P6r5r1qxh9uzZBR2PEEKI/CvVOagQNm3ahKIobNq0abyHIiYBCarEpPZf//VfKIrCCSeckPMxPvroI6677jpef/31/A1sjHbt2oWiKJn/NE1j5syZnHfeeUUbZym+L0IIUUqmwhz029/+dsDvr7vuOhRFoa2tbRxGN9B//dd/cc8994z3MMQkJ0GVmNTWrVvH7Nmz+fOf/8z27dtzOsZHH33E9ddfX1ITWtpFF13Er3/9a375y19y8cUX88wzz3DiiScWZKxPPvkkTz75ZObn4d6Xu+66i/feey/vYxBCiIlkss9BAGvXrsW27fEexrCGCqpOPfVUYrEYp556avEHJSYdCarEpLVz507++Mc/cvPNN1NbW8u6devGe0h5t3jxYi699FIuu+wyfvjDH/Kb3/yGRCLBHXfckffncjqdOJ3OrO7rcDhwuVx5H4MQQkwUU2EOOvroo3nzzTd5+OGHx3soOVFVFbfbjarK12ExdvIpEpPWunXrqKysZNWqVXz6058eckLr7OzkqquuYvbs2bhcLhoaGvjc5z5HW1sbmzZt4rjjjgPg8ssvz5Q7pK94zZ49mzVr1gw4Zv/1R8lkkmuuuYZjjz0Wv9+P1+vllFNO4dlnn83raz7zzDOB7sk87f/+7/849thj8Xg81NTUcOmll/Lhhx/2edy+ffu4/PLLaWhowOVyMX36dM455xx27do16Gsa6X3pvaYqlUpRVVXF5ZdfPmC8oVAIt9vNN77xjcxtiUSCa6+9lnnz5uFyuWhsbORb3/oWiUSiz2Ofeuopli5dSiAQoLy8nEMPPZR//dd/zel9E0KIfJsKc9CFF17IIYccknW26k9/+hMrV67E7/dTVlbGaaedxosvvjjgfps2bWLJkiW43W7mzp3Lz372s0xJYW933303Z555JnV1dbhcLg477LABFxVnz57N22+/zXPPPZd5/3rPZb3XVH31q1+lvLycaDQ6YEwXXXQR06ZNwzTNzG1PPPEEp5xyCl6vF5/Px6pVq3j77bf7PC6b+VVMDvp4D0CIQlm3bh2rV6/G6XRy0UUXcccdd/DKK69kJiiArq4uTjnlFLZu3crnP/95Fi9eTFtbG7/73e9obm5m4cKFrF27lmuuuYYvfvGLnHLKKQB87GMfG9VYQqEQP//5z7nooov4whe+QDgc5he/+AUrVqzgz3/+M0cffXReXvOOHTsAqK6uBuCee+7h8ssv57jjjuOGG26gpaWFW2+9lRdffJHXXnuNQCAAwPnnn8/bb7/NFVdcwezZs2ltbeWpp55iz549gzacGM374nA4OO+883jooYf42c9+1ifb9cgjj5BIJLjwwgsBsCyLv/u7v2Pz5s188YtfZOHChfz1r3/lJz/5Ce+//z6PPPIIAG+//Taf+tSnOPLII1m7di0ul4vt27cPOjkLIcR4mApzkKZp/Pu//zuf+9znePjhh1m9evWQ933mmWc466yzOPbYY7n22mtRVTUTFL3wwgscf/zxALz22musXLmS6dOnc/3112OaJmvXrqW2tnbAMe+44w4OP/xw/u7v/g5d13nsscf4yle+gmVZ/NM//RMAt9xyC1dccQXl5eX827/9GwD19fWDjvGzn/0s//mf/8n69ev5zGc+k7k9Go3y2GOPsWbNGjRNA+DXv/41l112GStWrODGG28kGo1yxx13sHTpUl577bXM3Dna+VVMYLYQk9CWLVtswH7qqads27Zty7LshoYG+5//+Z/73O+aa66xAfuhhx4acAzLsmzbtu1XXnnFBuy77757wH1mzZplX3bZZQNuP+200+zTTjst87NhGHYikehzn46ODru+vt7+/Oc/3+d2wL722muHfX07d+60Afv666+39+/fb+/bt8/etGmTfcwxx9iA/dvf/tZOJpN2XV2dvWjRIjsWi2Ue+/vf/94G7GuuuSYzDsC+6aabhn3O/q9puPflsssus2fNmpX5+Q9/+IMN2I899lif+33yk5+058yZk/n517/+ta2qqv3CCy/0ud+dd95pA/aLL75o27Zt/+QnP7EBe//+/cOOWQghxsNUmYNuuukm2zAMe/78+fZRRx2VGfO1117b5xxtWZY9f/58e8WKFZn72LZtR6NR++CDD7Y//vGPZ247++yz7bKyMvvDDz/M3LZt2zZb13W7/9fWaDQ6YGwrVqzoM6/Ytm0ffvjhfd6PtGeffdYG7GeffTYzzoMOOsg+//zz+9zvgQcesAH7+eeft23btsPhsB0IBOwvfOELfe63b98+2+/3Z27Pdn4Vk4OU/4lJad26ddTX13PGGWcA3S1iP/vZz3Lffff1Sd3/9re/5aijjuK8884bcIz+ZQZjoWlaJkNjWRbt7e0YhsGSJUt49dVXcz7utddeS21tLdOmTeP0009nx44d3HjjjaxevZotW7bQ2trKV77yFdxud+Yxq1atYsGCBaxfvx4Aj8eD0+lk06ZNdHR0jO2FDuHMM8+kpqaG+++/P3NbR0cHTz31FJ/97Gczt/3f//0fCxcuZMGCBbS1tWX+S5c1pktV0hm2Rx99FMuyCjJmIYTI1VSZg9LH/vd//3feeOONTDVBf6+//jrbtm3j4osv5sCBA5lzeyQSYdmyZTz//PNYloVpmmzcuJFzzz2XGTNmZB4/b948zjrrrAHH9Xg8mf8fDAZpa2vjtNNO44MPPiAYDI76tSiKwmc+8xkef/xxurq6Mrfff//9HHTQQSxduhToLj/v7Ozkoosu6jNXaZrGCSeckJmrijG/itIhQZWYdEzT5L777uOMM85g586dbN++ne3bt3PCCSfQ0tLC008/nbnvjh07WLRoUVHG9atf/YojjzwSt9tNdXU1tbW1rF+/PqcTf9oXv/hFnnrqKZ5++mn+8pe/0Nrayre+9S0Adu/eDcChhx464HELFizI/N7lcnHjjTfyxBNPUF9fz6mnnsp//Md/sG/fvpzH1Z+u65x//vk8+uijmbVRDz30EKlUqk9QtW3bNt5++21qa2v7/HfIIYcA0NraCnSXaJx88sn84z/+I/X19Vx44YU88MADEmAJIcbdVJqD0i655BLmzZs35Nqqbdu2AXDZZZcNOL///Oc/J5FIEAwGaW1tJRaLMW/evAHHGOy2F198keXLl+P1egkEAtTW1mbW1ub6uj772c8Si8X43e9+B3SXaD7++ON85jOfyQS66ddz5plnDng9Tz75ZGauKsb8KkqHrKkSk84zzzzD3r17ue+++7jvvvsG/H7dunV84hOfyMtzDXUl0TTNTN01wG9+8xvWrFnDueeeyze/+U3q6urQNI0bbrghsw4qF/Pnz2f58uU5Pz7tyiuv5Oyzz+aRRx7hD3/4A9/97ne54YYbeOaZZzjmmGPGfHzoXtD8s5/9jCeeeIJzzz2XBx54gAULFnDUUUdl7mNZFkcccQQ333zzoMdobGwEuq/+Pf/88zz77LOsX7+eDRs2cP/993PmmWfy5JNP9nnvhRCimKbSHJSWzlatWbOGRx99dMDv0xe8brrppiHXb5WXlxOPx7N+zh07drBs2TIWLFjAzTffTGNjI06nk8cff5yf/OQnOV9kO/HEE5k9ezYPPPAAF198MY899hixWKzPBcD0sX/9618zbdq0AcfQ9b99vS7G/CpKgwRVYtJZt24ddXV1/Od//ueA3z300EM8/PDD3HnnnXg8HubOnctbb7017PGGK8GorKyks7NzwO27d+9mzpw5mZ8ffPBB5syZw0MPPdTneNdee20Wryg3s2bNAuC9997LlM+lvffee5nfp82dO5evf/3rfP3rX2fbtm0cffTR/PjHP+Y3v/nNoMcfbWnKqaeeyvTp07n//vtZunQpzzzzTGbRcO8xvPHGGyxbtmzE46uqyrJly1i2bBk333wzP/jBD/i3f/s3nn322bwEmkIIkYupOgddeumlfP/73+f666/n7/7u7/r8bu7cuQBUVFQMe36uq6vD7XYPuqdX/9see+wxEokEv/vd75g5c2bm9sE6Go52vrrgggu49dZbCYVC3H///cyePZsTTzxxwOupq6vLar4Z7fwqJiYp/xOTSiwW46GHHuJTn/oUn/70pwf899WvfpVwOJxJ659//vm88cYbg+6xkS5h8Hq9AINOXHPnzuXll18mmUxmbvv9739PU1NTn/ulrxj2Lov405/+xEsvvTS2FzyMJUuWUFdXx5133tmnHfkTTzzB1q1bWbVqFdDd1aj/1cG5c+fi8/kGtDHvbbj3ZTCqqvLpT3+axx57jF//+tcYhtHnyh90T2Qffvghd91114DHx2IxIpEIAO3t7QN+n776OdyYhRCikKbyHJTOVr3++uuZ15d27LHHMnfuXH70ox/1WauUtn///swxli9fziOPPMJHH32U+f327dt54oknRnxNwWCQu+++e8DxvV5v1nMVdJcAJhIJfvWrX7FhwwYuuOCCPr9fsWIFFRUV/OAHPyCVSg35enKdX8XEJJkqMan87ne/IxwOD7hKlnbiiSdmNmH87Gc/yze/+U0efPBBPvOZz/D5z3+eY489lvb2dn73u99x5513ctRRRzF37lwCgQB33nknPp8Pr9fLCSecwMEHH8w//uM/8uCDD7Jy5UouuOACduzYwW9+85vMVay0T33qUzz00EOcd955rFq1ip07d3LnnXdy2GGHDTrB5IPD4eDGG2/k8ssv57TTTuOiiy7KtFSfPXs2V111FQDvv/8+y5Yt44ILLuCwww5D13UefvhhWlpaMq3OBzPc+zKUz372s9x2221ce+21HHHEESxcuLDP7//+7/+eBx54gC996Us8++yznHzyyZimybvvvssDDzzAH/7wB5YsWcLatWt5/vnnWbVqFbNmzaK1tZX/+q//oqGhIbOQWAghim2qz0GXXHIJ3/ve93j99df73K6qKj//+c8566yzOPzww7n88ss56KCD+PDDD3n22WepqKjgscceA+C6667jySef5OSTT+bLX/4ypmly++23s2jRoj7H/cQnPoHT6eTss8/m//2//0dXVxd33XUXdXV17N27t8/zH3vssdxxxx18//vfZ968edTV1Q2o4Oht8eLFzJs3j3/7t38jkUgMuABYUVHBHXfcwd///d+zePFiLrzwQmpra9mzZw/r16/n5JNP5vbbb895fhUT1Hi2HhQi384++2zb7XbbkUhkyPusWbPGdjgcdltbm23btn3gwAH7q1/9qn3QQQfZTqfTbmhosC+77LLM723bth999FH7sMMOy7R07d3a9sc//rF90EEH2S6Xyz755JPtLVu2DGhna1mW/YMf/MCeNWuW7XK57GOOOcb+/e9/P6D1uG2Pvp3tSO6//377mGOOsV0ul11VVWVfcskldnNzc+b3bW1t9j/90z/ZCxYssL1er+33++0TTjjBfuCBB/ocp/9rGu59Gex1pd+HxsZGG7C///3vDzreZDJp33jjjfbhhx9uu1wuu7Ky0j722GPt66+/3g4Gg7Zt2/bTTz9tn3POOfaMGTNsp9Npz5gxw77ooovs999/f8T3QwghCkXmINu+++67bWDQbS9ee+01e/Xq1XZ1dbXtcrnsWbNm2RdccIH99NNP97nf008/bR9zzDG20+m0586da//85z+3v/71r9tut7vP/X73u9/ZRx55pO12u+3Zs2fbN954o/3LX/7SBuydO3dm7rdv3z571apVts/ns4HMe9O/pXpv//Zv/2YD9rx584Z8H5599ll7xYoVtt/vt91utz137lx7zZo19pYtW2zbzn5+FZODYttZbIEthBBCCCHEODn33HN5++23M533hCg1sqZKCCGEEEKUjFgs1ufnbdu28fjjj3P66aePz4CEyIJkqoQQQgghRMmYPn06a9asYc6cOezevZs77riDRCLBa6+9xvz588d7eEIMShpVCCGEEEKIkrFy5Ur+93//l3379uFyuTjppJP4wQ9+IAGVKGmSqRJCCCGEEEKIMZA1VUIIIYQQQggxBhJUCSGEEEIIIcQYyJqqfizL4qOPPsLn86EoyngPRwghpgzbtgmHw8yYMQNVlWt+aTIvCSHE+Ml2bpKgqp+PPvqIxsbG8R6GEEJMWU1NTTQ0NIz3MEqGzEtCCDH+RpqbJKjqx+fzAd1vXEVFxTiPRgghpo5QKERjY2PmPCy6ybwkhBDjJ9u5SYKqftKlFRUVFTJ5CSHEOJASt75kXhJCiPE30twkRetCCCGEEEIIMQYSVAkhhBBCCCHEGEhQJYQQQgghhBBjIGuqhBBCCCHEpGDbNoZhYJrmeA9FTBCapqHr+pjX80pQJYQQQgghJrxkMsnevXuJRqPjPRQxwZSVlTF9+nScTmfOx5CgSgghhBBCTGiWZbFz5040TWPGjBk4nU7pJCpGZNs2yWSS/fv3s3PnTubPn5/z5vMSVAkhhBBCiAktmUxiWRaNjY2UlZWN93DEBOLxeHA4HOzevZtkMonb7c7pOBJUCdFL0DAIGQYVuo5fl38eQgghxESSa5ZBTG35+NzIt0YhgIRlsbG9nS3hMBHTxKtpLPH5WF5VhUtO0EIIIYQQYhjybVEIYGN7Oxs6OtAUhQa3G01R2NDRwcb29vEemhBCCCGEKHESVIkpL2gYbAmHqXc4qHM6casqdU4ndQ4HW8JhgoYx3kMUQgghxCRk2zZf/OIXqaqqQlEUXn/99REfs2vXrqzvW6pOP/10rrzyymHvc8899xAIBIoynnyQoEpMeSHDIGKaVPRbQ+XXdSKmSUiCKiGEEEIUwIYNG7jnnnv4/e9/z969e1m0aNF4D6koHnroIb73ve9lfp49eza33HJLn/t89rOf5f333y/yyHIna6rElNS7IUWFruPVNEKGgbvX/gRBw8CraQOCLSGEEEKIfNixYwfTp0/nYx/72HgPpaiqqqpGvI/H48Hj8RRhNPkhmSoxpSQsi/VtbdzS1MRtzc3c0tTE5s5OjvJ6aUmlaEkmiVsWLckkrakUS3w+6QIohBBCTCFBw6ApHi94+f+aNWu44oor2LNnD4qiMHv2bKA7e7V06VICgQDV1dV86lOfYseOHUMep6Ojg0suuYTa2lo8Hg/z58/n7rvvzvy+qamJCy64gEAgQFVVFeeccw67du0a8nibNm1CURTWr1/PkUceidvt5sQTT+Stt97qc7/f/va3HH744bhcLmbPns2Pf/zjPr//r//6L+bPn4/b7aa+vp5Pf/rTmd/1Lv87/fTT2b17N1dddRWKomT2F+td/vf++++jKArvvvtun+f4yU9+wty5czM/v/XWW5x11lmUl5dTX1/P3//939PW1jbka80nCarElDJUQwoUhZWVlVi2TXM8jmXbrKysZHkWV1KEEEIIMfENduF1fVsbCcsqyPPdeuutrF27loaGBvbu3csrr7wCQCQS4eqrr2bLli08/fTTqKrKeeedhzXEOL773e/yzjvv8MQTT7B161buuOMOampqAEilUqxYsQKfz8cLL7zAiy++SHl5OStXriSZTA47vm9+85v8+Mc/5pVXXqG2tpazzz6bVCoFwF/+8hcuuOACLrzwQv76179y3XXX8d3vfpd77rkHgC1btvC1r32NtWvX8t5777FhwwZOPfXUQZ/noYceoqGhgbVr17J371727t074D6HHHIIS5YsYd26dX1uX7duHRdffDEAnZ2dnHnmmRxzzDFs2bKFDRs20NLSwgUXXDDs68wXuQQvpoz+DSkA3E4nNvBGVxdXNjayNBCQfaqEEEKIKSh94bXe4aDB7SZkGN0XXoFVPUFKPvn9fnw+H5qmMW3atMzt559/fp/7/fKXv6S2tpZ33nln0DVXe/bs4ZhjjmHJkiUAmYwXwP33349lWfz85z/PZIDuvvtuAoEAmzZt4hOf+MSQ47v22mv5+Mc/DsCvfvUrGhoaePjhh7ngggu4+eabWbZsGd/97neB7qDnnXfe4aabbmLNmjXs2bMHr9fLpz71KXw+H7NmzeKYY44Z9HmqqqrQNA2fz9fnfejvkksu4fbbb8+sxXr//ff5y1/+wm9+8xsAbr/9do455hh+8IMf9HnvGhsbef/99znkkEOGPHY+SKZKTBnZNKTw6zqNbrcEVEIIIcQUUkqdgLdt28ZFF13EnDlzqKioyARJe/bsGfT+X/7yl7nvvvs4+uij+da3vsUf//jHzO/eeOMNtm/fjs/no7y8nPLycqqqqojH48OWFAKcdNJJmf9fVVXFoYceytatWwHYunUrJ598cp/7n3zyyWzbtg3TNPn4xz/OrFmzmDNnDn//93/PunXriEajubwdGRdeeCG7du3i5ZdfBrqzVIsXL2bBggWZ1/rss89mXmd5eXnmdyO91nyQb45iyihUQ4reTS8kGBNCCCEmnvSF1wa3u8/tfl2nOR7PXHgthrPPPptZs2Zx1113MWPGDCzLYtGiRUOW65111lns3r2bxx9/nKeeeoply5bxT//0T/zoRz+iq6uLY489dkDZHEBtbW3BXoPP5+PVV19l06ZNPPnkk1xzzTVcd911vPLKKzm3SZ82bRpnnnkm//M//8OJJ57I//zP//DlL3858/uuri7OPvtsbrzxxgGPnT59eq4vJWvyDVBMGX5dZ4nPx4aODuyen4OGQWsqxcrKylGfLBOWxcb2draEw0RME6+mscTnY3lVFS5VksBCCCHERFEqnYAPHDjAe++9x1133cUpp5wCwObNm0d8XG1tLZdddhmXXXYZp5xyCt/85jf50Y9+xOLFi7n//vupq6ujoqJiVGN5+eWXmTlzJtDdDOP9999n4cKFACxcuJAXX3yxz/1ffPFFDjnkEDRNA0DXdZYvX87y5cu59tprCQQCPPPMM6xevXrAczmdTkzTHHFMl1xyCd/61re46KKL+OCDD7jwwgszv1u8eDG//e1vmT17Nvo4XOSWb35iSlleVZW3hhRDNb3Y2N5egJELIYQQolDSF17HuxNwZWUl1dXV/Pd//zfbt2/nmWee4eqrrx72Mddccw2PPvoo27dv5+233+b3v/99Jvi55JJLqKmp4ZxzzuGFF15g586dbNq0ia997Ws0NzcPe9y1a9fy9NNP89Zbb7FmzRpqamo499xzAfj617/O008/zfe+9z3ef/99fvWrX3H77bfzjW98A4Df//73/PSnP+X1119n9+7d3HvvvViWxaGHHjroc82ePZvnn3+eDz/8cNhufatXryYcDvPlL3+ZM844gxkzZmR+90//9E+0t7dz0UUX8corr7Bjxw7+8Ic/cPnll2cVsI2VBFViSnGpKqtqariysZErGhq4srGRVTU1o84slVLttRBCCCHGLp8XXnOlqir33Xcff/nLX1i0aBFXXXUVN91007CPcTqdfOc73+HII4/k1FNPRdM07rvvPgDKysp4/vnnmTlzJqtXr2bhwoX8wz/8A/F4fMTM1Q9/+EP++Z//mWOPPZZ9+/bx2GOP4ezJ4i1evJgHHniA++67j0WLFnHNNdewdu1a1qxZA0AgEOChhx7izDPPZOHChdx555387//+L4cffvigz7V27Vp27drF3Llzhy1L9Pl8nH322bzxxhtccsklfX43Y8YMXnzxRUzT5BOf+ARHHHEEV155JYFAALUIFUSKbdt2wZ9lAgmFQvj9foLB4KjTpGLqaIrHua25mQa3G3evf6hxy6I5HueKhgYa+9VlCyGGJ+ffwcn7IsTI4vE4O3fu5OCDD8Y9xvl3qq+V3rRpE2eccQYdHR05r3+aaIb7/GR7Dp56nxQh8qBUaq+FEEIIkV/+KRpMibGRT4wQOch30wshBpO+WmoDCkzZq6ZCCCFEqZPZWYgcpWust4TDNMfjeDWt6LXXYnJKd5Z8ORzmna4u2g2DKl3nMK+XEysqpMOkEEKIgjn99NOR1UGjJ0GVEDlKN71YGghM6dprkX/pzpKdqRSthoFTUdhvGOyMx+ns6WC0qqZmnEcphBBCiDS51CnEGPl1nUa3WwIqkRfpzpJ+TaOzJ0M13ekkoGkEDYMKTZMOk0IIIUSJkaBKiBwEDYOmeDyvX2wLcUwx8YQMg4hp4lAUkpaFp6fMz6OqJG0bp6IQMU1C8jkRQgghSoZcWhdiFNJrXbaEw0RME6+mscTny3mNS9Aw2J9M8mo4zNZoNC/HFBNburNkyrZxqioxy8KnacQsC6eikLRt6TAphBBClBiZlYUYhfRal3qHgwa3m5BhsKGjAxjdGpfewdmbXV00J5PM93hY7PMRNc2cjikmh96dJQO6zq5Egi7TJGXbzHS5CJmmdJgUQgghSoxcBhciS+m1LvUOB3VOJ25Vpc7ppM7hGPUal3RwlrJtopZFuabxYSLBB7FYzscUk8fyqipWVlYy2+OhTtexbJtaXedgt1s6TAohhBAlSC51CpGl9FqXhn47bft1neZ4nJBhZJU96B2c6YrS/YXZ4SBqmuyOx5nv8Yz6mGJy6d9ZUvapEkIIMVWtWbOGzs5OHnnkkfEeyrBkdhYiS+m1LiHDwO10Zm4PGsao1rj0Ds5syKyb8agqnaZJzLJIyboZQXfALkGUEEJMXqeffjpHH300t9xyy3gPZdzt2rWLgw8+mNdee42jjz46c/utt946IfbNkvI/IbKUXuvSkkrRkkwStyxakklaUymW+HxZf/ntHZx5VJXZbjch06TNMFAVhbBpjvqYpUo6GgohhBBjY9s2xhSeR/1+P4FAYLyHMSIJqoQYhfRaF8u2aY7HsWx71Gtc+gdnB3s8NDiddJkmZYqCU1Em/LqZhGWxvq2NW5qauK25mVuamljf1kbCssZ7aCVpuOBzIgSmE2GMQgiRLSNoEG+KYwQLe05bs2YNzz33HLfeeiuKoqAoCrt27WLTpk0oisITTzzBsccei8vlYvPmzaxZs4Zzzz23zzGuvPJKTj/99MzPlmVxww03cPDBB+PxeDjqqKN48MEHhx3H7Nmz+cEPfsDnP/95fD4fM2fO5L//+7/73KepqYkLLriAQCBAVVUV55xzDrt27cr83jAMvva1rxEIBKiuruZf/uVfuOyyy/qMd8OGDSxdujRzn0996lPs2LEj8/uDDz4YgGOOOQZFUTKvq/fr/u///m9mzJiB1e/7xDnnnMPnP//5zM+PPvooixcvxu12M2fOHK6//vqCB6YT+zK4EEXWf61Lrmtc0gHTlnCYfYkE8zwezq6pYXF5ObVO54TPUOWrS+JkN1yLfiCv7fsLId9bDAghxHiyEhbtG9sJbwljRkw0r4ZviY+q5VWorvyf02699Vbef/99Fi1axNq1awGora3NBCvf/va3+dGPfsScOXOorKzM6pg33HADv/nNb7jzzjuZP38+zz//PJdeeim1tbWcdtppQz7uxz/+Md/73vf413/9Vx588EG+/OUvc9ppp3HooYeSSqVYsWIFJ510Ei+88AK6rvP973+flStX8uabb+J0OrnxxhtZt24dd999NwsXLuTWW2/lkUce4Ywzzsg8RyQS4eqrr+bII4+kq6uLa665hvPOO4/XX38dVVX585//zPHHH8/GjRs5/PDDcfZaapH2mc98hiuuuIJnn32WZcuWAdDe3s6GDRt4/PHHAXjhhRf43Oc+x09/+lNOOeUUduzYwRe/+EUArr322qzex1xMqFnv+eef5+yzz2bGjBkoijJgwZpt21xzzTVMnz4dj8fD8uXL2bZt2/gMVkxqfl2n0e3OOfhJB2dXNjZyRUMDVzY2ckFdHfPKyiZ8QJXPLomTXTr41BSFBrcbTVHY0NHBxvb2YX+XrUJnkPIxxolO5iUhJo/2je10bOhA0RTcDW4UTaFjQwftGwtzTvP7/TidTsrKypg2bRrTpk1D07TM79euXcvHP/5x5s6dS1UW1SuJRIIf/OAH/PKXv2TFihXMmTOHNWvWcOmll/Kzn/1s2Md+8pOf5Ctf+Qrz5s3jX/7lX6ipqeHZZ58F4P7778eyLH7+859zxBFHsHDhQu6++2727NnDpk2bALjtttv4zne+w3nnnceCBQu4/fbbB5TsnX/++axevZp58+Zx9NFH88tf/pK//vWvvPPOO0B3QAlQXV3NtGnTBn3NlZWVnHXWWfzP//xP5rYHH3yQmpqaTAB3/fXX8+1vf5vLLruMOXPm8PGPf5zvfe97I74HYzWhgqpIJMJRRx3Ff/7nfw76+//4j//gpz/9KXfeeSd/+tOf8Hq9rFixgng8XuSRCpGdsQZnpSjdiKN/kw2/rhMxTUISVAHDB5+bg0E2h0I5B6bFKL+U4LmbzEtCTA5G0CC8JYyj3oGzzonqVnHWOXHUOQhvCRe8FHAwS5YsGdX9t2/fTjQa5eMf/zjl5eWZ/+69994+ZXaDOfLIIzP/X1EUpk2bRmtrKwBvvPEG27dvx+fzZY5ZVVVFPB5nx44dBINBWlpaOP744zPH0DSNY489ts9zbNu2jYsuuog5c+ZQUVHB7NmzAdizZ8+oXucll1zCb3/7WxKJBADr1q3jwgsvRO2pkHjjjTdYu3Ztn/fgC1/4Anv37iUajY7quUZjQn2TO+usszjrrLMG/Z1t29xyyy38+7//O+eccw4A9957L/X19TzyyCNceOGFxRyqEFNWvrokTmRBwxixPHS4Fv0fxuOgKEz3egf8LptW+8Uov8zXFgMTncxLQkwORsjAjJi4G/qe03S/Trw5jhEy0P3FPad5+80BqqoO6IKXSqUy/7+rqwuA9evXc9BBB/W5n8vlGva5HA5Hn58VRcmsW+rq6uLYY49l3bp1Ax6Xzi5l4+yzz2bWrFncddddmXVRixYtIplMZn2M9HFs22b9+vUcd9xxvPDCC/zkJz/J/L6rq4vrr7+e1atXD3isu9+clU+TZsbbuXMn+/btY/ny5Znb/H4/J5xwAi+99NKQk1cikchEugChUKjgYxViMks34tjQ0YHd83PQMGhNpVhZWTmpv2iPZo3RcMFnpcMBipJTYNo/gwTgdjqx6V7DtzQQyMvfQILnkcm8JMTEoVfoaF4NI2TgdP/tnGYEDTSvhl5RmHOa0+nENM2s7ltbW8tbb73V57bXX389ExAddthhuFwu9uzZM+z6qdFavHgx999/P3V1dVRUVAx6n/r6el555RVOPfVUAEzT5NVXX820Rj9w4ADvvfced911F6eccgoAmzdv7nOM9Bqqkd4Pt9vN6tWrWbduHdu3b+fQQw9l8eLFfcb73nvvMW/evJxeb64mVPnfcPbt2wd0/1F7q6+vz/xuMDfccAN+vz/zX2NjY0HHKcRk1Xv9Tj66JE5Eo1ljNFyL/qV+P0srKnJq31+s8st8bTEwmcm8JMTEoft1fEt8pFpSJFuSWHGLZEuSVGsK3xJfwbJUs2fP5k9/+hO7du2ira1tQFe73s4880y2bNnCvffey7Zt27j22mv7BFk+n49vfOMbXHXVVfzqV79ix44dvPrqq9x222386le/ynmMl1xyCTU1NZxzzjm88MIL7Ny5k02bNvG1r32N5uZmAK644gpuuOEGHn30Ud577z3++Z//mY6ODhRFAbrXQlVXV/Pf//3fbN++nWeeeYarr766z/PU1dXh8XjYsGEDLS0tBIPBYce0fv16fvnLX3LJJZf0+d0111zDvffey/XXX8/bb7/N1q1bue+++/j3f//3nN+DbEyaoCpX3/nOdwgGg5n/mpqaxntIQoyolFpYD7Z+Z2N7O8urqvo04lhVUzOpO8INtcaoQtN4uqODPYOsoRku+Mw1MO2dQeo/vnxnkKZq8FxoMi8JMT6qlldRubIS27KJN8exLZvKlZVULS/cOe0b3/gGmqZx2GGHUVtbO+z6ohUrVvDd736Xb33rWxx33HGEw2E+97nP9bnP9773Pb773e9yww03sHDhQlauXMn69esz7cpzUVZWxvPPP8/MmTNZvXo1Cxcu5B/+4R+Ix+OZzNW//Mu/cNFFF/G5z32Ok046ifLyclasWJEpt1NVlfvuu4+//OUvLFq0iKuuuoqbbrqpz/Pous5Pf/pTfvaznzFjxoxM2fRgzjzzTKqqqnjvvfe4+OKLB7xPv//973nyySc57rjjOPHEE/nJT37CrFmzcn4PsqHYE2GL4kEoisLDDz+c6Vv/wQcfMHfu3AG7MJ922mkcffTR3HrrrVkdNxQK4ff7CQaDQ6Y4hRgvxWxhnc26IID1bW2Z9TsVuk7IMGjpKfVbVVOT9XEmuqZ4nNuam2lwu3GrKinLYms0yo54nAPJJEsDAZYFAoP+rYZ7j3J5/9J/kzqHY0D5ZSFa2ufrbzzRz78yLwkxfuLxODt37uTggw8e87oZI2h0r6Gq0Iu+jmqysCyLhQsXcsEFF/C9731vvIczouE+P9megyfNJ+Xggw9m2rRpPP3005nJKxQK8ac//Ykvf/nL4zs4IfKkUA0Ien8pdqtq1oHbcOt3Xg6FiFgWWyORKbGHUf81RlujUd6ORFAVhWqHgzJVHfJv5R8mGBnud0PpvQ9aczyOV9MKmkHKZYxTgcxLQkxMul+CqdHavXs3Tz75JKeddhqJRILbb7+dnTt3DsgiTWYT6hPT1dXF9u3bMz/v3LmT119/naqqKmbOnMmVV17J97//febPn8/BBx/Md7/7XWbMmDFg92khJqJCNCAYLPNl2zathsEMp3PEwG24DnDPdXTQnEgw1+Mp2gbA45kV692gI2ZZ7IjHURUFC5jn8TDT7aYlmcxrs4ih5GuTajEymZeEEKK7vO+ee+7hG9/4BrZts2jRIjZu3MjChQvHe2hFM6Fm2S1btvTZmTm9wO2yyy7jnnvu4Vvf+haRSIQvfvGLdHZ2snTpUjZs2FDQ9olCFEshWlj3z3y1JpM81dHBfI+Ho8vLgeEDt6E6wLUkk7QbBgu93oJ2oEsrdFlktsFaOhP0dEcHB5JJqh0O5nk8LOhpi5uvduPZjkcySIUn85IQQkBjYyMvvvjieA9jXE2o2fb0008f0J+/N0VRWLt2LWvXri3iqIQojrG2sO7/RXywzFe5puEADhgGMdPE07Oz+1DBwFDt0z9MJKjS9cxxe9+/EHsYFaoscrTBWjpDdER5OSgKZarKzF5fnsfaLKKYa+pEdmReEkIIARMsqBJiKst1/6ehvogfWlY2IPPl0TR8uk6XYRCzrExQNVwwMNj6nU9WVfHXaLQoexgVcl+mXIO1mW43ywIBNnR00JJM5m2vrmJs6ismH1l0L6aSCdp/TYyzfHxu5OwqxASSSwOCob6IRyxrQObLo6rUOBy0GwZh06RM00YMBoZav+Pt6UBX6A2AC1EWCWMP1vLdLKJYm/qKycNKWLRvbCe8JYwZMdG8Gr4lPqqWV6G6JLMpJpf0BrjRaBSPxzPOoxETTTQaBf72OcqFzMBCTCCjbUAw3BfxrZEIC8vKeCEU6hP4+HSdsyorURRlVMFA//U72QYVY20uMdayyKGMNVjrXQq4N5FgusvVpxSw2OMRU0/7xnY6NnTgqHfgbnBjhAw6NnRnNgNLA5K9EpOKpmkEAgFaW1uB7r2V0hvPCjEU27aJRqO0trYSCATQeip0ciFnUiEmoGwbEIz0RXyxz4dX0/oEPqt6Np6NW9aYgp3hAsCgYbA/meTVcJit0eiY1gflWhY5ktEGa/2Dw4Rl8WhbGy+HQhi2TXXPOHNd/1So4FFMTkbQILwljKPegbOu+/PidDuxUzYt61oIbQ5hmZZkr8SkMm3aNIBMYCVEtgKBQObzkyuZhYWY4IbL9Iz0RbzW6WReWdmggY9LVfOS+egdAPZe3/VmVxfNySTzPR4W+3xETTPn9UGF2Jcp22BtsDVrR3m9bOnq4qmODpxAua7T4XDQkkrl9PpGM55cTZVNmqcKI2RgRkzcDX0vqCT3Jom+G8U1w4V7Zt/sVc0qWZcnJjZFUZg+fTp1dXWkes63QozE4XCMKUOVJjOnKBhZHF1Y2XSCy/aLeLFab6fXd/k1jahlUa5pfJhIENB1jiwvz3l9UKH2ZcomWBtszdqdH33EB4kEjS4XtQ4HMcuiOZmkIcfXN5rxjJZ0FJyc9AodzathhAyc7u4LKmbMJLo9ih7QcTe6Ud1q9+9sCG8JE1gakHO1mBQ0TcvLl2QhRkPOniLvZHF0cWTbCe64igr2p1K8F4sRztMX8Vz0Xt+lKwqWbePXNLpMkx2xGPM9njGvD8p3cDhSsDbYmjVb0wiZJnHTxK/r6IqCr2dyb0ulqHQ4cn59hQgepaPg5KT7dXxLfN1ZKLv750RTArPTpPy4clSP2ue+8eZ490UwCaqEECIncvYUeTfc4mgpL8mPbDrBuVW1TwZCUxQOKyvjkzU1A/aPKobe67uSlkWHYbA7kUABkpZFndPJbLc7b+uD8lnONlSwNtiatZhl4VBVdEWhyzAo69VZ8UAyia4oY359+QoepaPg5Fa1vPvCSXhLmHhzHMWh4FnowTXd1ed+RtBA82roFfK3FkKIXMkZVOTVUIujpbwkv7LpBLe5q2tABuIvkQi1Tue4ZCB6r+9qSSaJWxYJy8KhKGiqytuRCO2pFF+aMWNMX+SLWc422Jo1j6qi0b2Rcsy2CRkGHlWlzTBIASdWVBQsUBltINkcj7M3keDgfu2HpaPg5KC6VGpW1fTp9Ne5uZOODR0omoLu1zGCBqnWFJUrK+XcLIQQYyBnUJFXQy2OlvKS/BqpAUU601BKGYj0+q5HDxxgWzTKQS4X7akUrakUAVXFp2lUaBrHVVSM6XmG25frpIqKvDZiGGzNWqgnkHOrKlUOBwcMg/ZkkpRtc1ZlJecUIKBNB5Kbg0E6ekoMl/r9AwLJdNDlUlVeCYXYHArx10iE92MxDvN6WVhWhkNVpaPgJKP7/7autX/2SvNqVK6szNwuhBAiNzJjirwabHE0SHlJvo3UgEKBITNZO6JR3o1EWOD1Fj2wWl5Vxf5Uire7ulAVhTqnk6N8PuocDiwgYhgkLCvn4w9WzqY5HLwXjXLHhx/yYmcnVQ7HkJmrXEoGB2se8Q/TpoGi8EZXF9WpFJqicGJFBefU1hak+cMTbW38Yt8+IqaJoijYsRhvRSKkLItz6+oGZO+aEwnCpsmxPh+LvF5ej0TYEg6TtG0aXa6CbNIsSsNg2Su50CWEEGMnZ1KRV4MtjpbyksIYrhNc3LIGZLJSlsWfQyE+TCSwW1vHvG9SLlyqynm1tbwXjWLYNtOcTnbG47zR1UWHYeBQFF4KhahzOnMa02Blke9GIuxKJMC2qXI40BRlQCOGsZQMDtc84ozKyoK3KQ8aBg/s38/+VIp6hwO3qhK3LFpSKR7Yv58zqqrY3NmZyd5VOxzdr9OyOJBKcaTPh1NVeSsS4e2uLqp1fVwamYji6p29EkIIMXZyRhV5J+UlxTHcl3mXqg7IZP05FOLNSISjvF7meTzj1uXNr+ss9fvZ0NHBa11d7EkkcCgKiqLQ4HLxQjCIt+e1jVb/ssiYZbErHsepKJRpGgFdx9OrPDJdBpmPDniDNY8oRqv65nic3fE41Q4Hvp7ncqgqhm2zOx5na1dXn+xdeyqFQ1Xxqypbe8owjygvp9Ht5oNYjEunTeNwr7egYxZCCCEmGwmqRN5JeUlxDfXFvXcma0c0yoeJBEd5vZzo9+NQlHFdY7W8qoqIaXLHRx9hWxZlDgeHud0s8HppT6VGPabeZXu9g0nLtulIpVBVlVluN56e1ua9GzFA6a0/GxVFGfb2Lsvqk73TgY5Uig7DIGXbACwsK6PK4WC600mDyzX48YQQQggxpBL+piAmOikvGV+9M1nvRiLYra3M83hwKAoxyyJmmrhUlbZksuhd3lyqykl+P5tDIap1nYDDgafXhsXZdp4brEHDCT4fywIB3ujq4oBh4FBVDnK5WFhWlnlc70YM2XRSLOWgqsHlYrbbzfZ4HF1R8KgqMcuiJZmkweVimtPZJ3u3K5Ho/vtbFh5VRVUU/hwOU+dwjLnzohCy6bsQYqqSM54Qk5xf11ng9VKt67SnUhxIpdgVj5O0LJK2zYwc1y+NVYWuU63raD2BQNpoOs8N1aDhH6ZN48rGRkKGwUvBIC+EQrT3BEe9G3qkA4jhOimWegc8v67zmbo6frF3LyHDIEh3JipmWeB08tv9+7Ftm496btsRj1PjcABQpqoogLen++JYOy+KqUs2fRdCTHWl/W1BCJEX6W6Bd370Ea2pFDUOB7qiEDRNQqbJK6FQ0feuGqmD4UgZk2waNDS63d0lfYrCy6EQ+1OpAY0YxjqOUnBWdTUORWFzKMTr4TBhVeX48nIW+3xETZOPUinqetq9H0gmqXY4OD0QYJbbjWHbKMCBVGpMnRfF1CabvgshprrS/7YghMiL4yoqWNfSQsQ0MWwbp6KwxOfLdIMbj7VDw3UwHMlIDRqa43HcZWVsbG9naySCaVnoisLCsrIBXf3GMo5SkC71PKK8nFuamjhW05jZU85Y3tOYw7JtPj99OigKZaqa+T1ASzI5IbJyojTla9N3KR0UQkxkctYSYopIWBYNLhdHlZdjAx5VxaNpxC0rr2uHRrPX03AdDEc0QoMGFKVPV7+5ZWWEDIMXQiG8mtYnMzemcRRBtu+pAug9+3/1ll4f5td1lgUCbOjooCWZnJBZOVF6xrrpu5QOCiEmA5lBhZgi0u3GU7bd50t3vtYOjWWvp1xajw/VoKEtlWKe241P00bd1S+bceSyQXCuxxvte9q/pXzv50j/jSd6Vk6UnrFu+i6lg0KIyUCCKiGmiEKvHcrHXk+j0b9BQxdgAXUOB5+pq0OBvHb1G0vQmNY7gHKr6ojHG+17mu3fuJSzcmLiGcum7/kqHRRCiPEmZyohppBCZSmChjEuez31btCQbqm+tKKC5VVVxC0rr139xhI0DhaQ2bZNq2Eww+kc9Hi5vqfZ/o2LsTGxmDpy3fR9rKWDQghRKuRMJcQUUqi1Q+O119Nwr8elqnnLzO2Jx3m6sxO/puUUNPYPyFqTSZ7q6GC+x8PR5eWDHi/X97TU14eJySnXTd/HWjoohBClQs5WQkxB+c5SZLOWp5CGej3ZZm36r2tK/+xSVV4JhXi6o4PNwSDVDgdBw2CB14tDUbIKGgfLOJVrGg7ggGEQM008mpZ5HTuiUd6NRJjuco3pPZVMlBgPo930fSylg0IIUUrkbCWEGLNS3etppKxN/7I8l6qiAaaikDBNPkwkCJkmh3u9VDudJC2Lt6JRAI4oL88qwBks4+TRNHy6TpdhELMsPJpGyrL4cyjEh4kEdmsr1bqe2bS3lN5TIfIt19JBIYQoJTIrCyHyopS7yg2VtelflvfnUIg3IxGO8no5orycLeEwUcuiyzSZ63bzdiSCqihsj8W6gxzTHDHAGSyL51FVahwO2g2DsGlSpml9nnuex0PIMDKb9lq2XXLvqZhaCrmHVO/SwXhzHAUFV4MLK26RbE3KvlVCiAlBzlJCiLyYaGt5+pflxUyToGFQ63DQaRhETROHolCt6+yOxznN7wdgRzzOgWSSqGVlFeAMlcXz6TpnVVaiKAo7olE+TCQ4yuvlRL8fh6Jk1lhZts3l06ejQMm/p2LyKdYeUlbConNzJ+EtYYygQXJfEhRw1bvQ/LJvlRCi9MnsLITIq2zX8uR7v6fR6l+WF7MskrZNoKcsD8CpqlhA0rYxgCPLy/HrOjHT5MqGBmb2ayIxlMGyeKuqqjJdCt+NRLBbW5nn8eDotalxes2WAjRm+VxC5FOx9pDq/TxWl0X8gzjYoJVr6FW67FslhCh5ElQJIYoqH/s95UP/sjyPquJUFDoNgzJVpdLhYLbbzZ/DYbyqigK0JJOEekr+sg2oYOQuhQu8Xqp1fdwafQgxmGLtIdX7eTSfRmJvAke9AwWF5EdJyg8rl32rhBAlT/LoQoiiSq9j0hSFBrcbTVHY0NHBxvb2oo4jXZbXkkrRkkyi9HTz259KEdB1FKDK4aDO4WCa08mBVArLtke1piloGDTF4wR7Ml9+XafR7R6Qmes/lrhl0ZJM0ppKscTnk5I/MS7Se0j1b2uu+3XMiIkRMvL+PFbMwk7aqG4V1aNiJS3MmJn35xRCiHyTmVoIUTTjtUnwUPqX5R3sdjPP7cZUlEyZ3pdmzOC4igoSlpV1qWIu2bjhGn2Md6mkmJqKtYdU7+fRfBqKU8GKWygoqE4VzaPJvlVCiJInZychRNGM1ybBQxmqLG+sQUz/roIhw2BDR/eakFU1g68JSY/liPJy9iYSTHe5qHc6S6JUUkxNxdpDqv/zuKa76HqzC2zwHu3FCMm+VUKI0idnJyFE0Yz3JsFD6d9cYywb5+aajRssu2XbNq2GwQynM+vgTIh8KtYeUr2fRy1Xcc9xgwK6V8e2bNm3SghR8iSoEkIUXO/MTyluEpxPuWbj+me3WpNJnuroYL7Hw9Hl5cD4lkqKqan3HlKF2qdqqOcBCvqcgynkflxCiMlNzhhCiIIZLPtylNfLskCAN7q6JuWGtrlk4wbLbpVrGg7ggGEQM008mgaMX6mkmNp0f3GCjP7PU6zAplj7cQkhJi+ZkYUQBTPY2qKng0FWVlZyZWNjSTZfGOt6qqE2+x0uGzdYdsujabg1jQPJJB2GkQmqxrtUUojJqFj7cQkhJi+ZlYUQBZHN2qJS2tA2n/tnDdfJbzD9s1spy2JbNMr+nrbqz3R2sqCsjOlOJ+2GMWlKJYUoBcXaj0sIMbnJWUIIkZORMjql1ulvJLl07BvKcJv9DqZ/dqspkeDNri7cqsrR5eUkLIstoRALysq4pL5+0pRKClEK0vtkuRv6nqt0v068Od69xkqCKiHECOQsIYQYlWwzOqXa6W8whdo/azRdBNOB0uZgkLe7uihTVQ73epnldtNlmhxIpSjXdZYGAtJOXYg8KtZ+XEKIyU3OFEKIUck2o5PL2qLxUgpZtXR2a7bHQ4dh0OhysS+Z5LnOTpKWhaoolKkq+5PJknrvhJjoirUflxBicpPLnUKIrPXP6LhVlTqnkzqHgy3hMEHD6HP/5VVVrKysxLJtmuNxLNsuWqe/oGHQFI8PGNNgemfV+h+j2Fm1BpeL6U4n70ajvBWNoioKlQ4HSdumOZnk1XC4aGMRYqqoWl5F5cpKbMsm3hyXvbGEEKMml1+EEFkbbUZntGuL8iGXhhOllFXz6zoLvV4eb2+nXNNwKwpR08SybeZ7PGyNRgmW2Ho0ISa6ofbjMoIGydak7FslhBjRpMpUXXfddSiK0ue/BQsWjPewhJg0cs3o+HWdRre7KIFAujxRUxQa3G40RWFDRwcb29uHfdx4ZtX6W1xeToPTiVNV6TRNbOBwr5fFPh8R0xzw/peK0WQHpxKZmyYO3a/jbnSjulXa1rfRdEsTzbc103RLE23r27AS1ngPUQhRoibdZZfDDz+cjRs3Zn7W5WquEHlTShmdwYyl4cR4ZNWGUut0cmR5OSnbplzT8KgqHk2jJZksuSYfkN929JOVzE2lxQgafTJS/cm+VaVvrHsKCpFvk+5TqOs606ZNG+9hCDFpjXYPpmLKR8OJ0XTsK5TewatLVSlTFFp69qwqheC1v3y2o5+sZG4qDVbCon1jO+EtYcyIiebV8C3xUbW8CtXVfQFA9q0qbaO9iCPBlyiWSffp2rZtGzNmzMDtdnPSSSdxww03MHPmzCHvn0gkSCQSmZ9DoVAxhinEhFVKGZ3+JlIb95GUcvDaW6Ha0U82o5mbZF4qnGwyULJvVWnL9iKOZNBFsU2qT9UJJ5zAPffcw4YNG7jjjjvYuXMnp5xyCuFhumXdcMMN+P3+zH+NjY1FHLEQE1cx10llK53haUmlaEkmiVtWJsOzxOcrqbGOJB28XtnYyBUNDVzZ2MiqmpqS+zKQzg72D1j9ul7S67+KabRzk8xLhdE/A6W6VZx1Thx1DsJbwhjB7s9q732r+j9e9q0aX6PpQJvr+lohcqXYtm2P9yAKpbOzk1mzZnHzzTfzD//wD4PeZ7Argo2NjQSDQSoqKoo1VCFKWjHKJ/L1HHJ1sriChsEtTU1oipLJVAG0JJNYts2VjY1Z/z1DoRB+v3/Sn39HmptkXiqMeFOc5tuacTd0N6JIs+IW8eY4DVc04G7szk61rW/rzmjVOQbsWyVrqsZPUzzObc3NNLjduHudz+OWRXM8zhUNDTS63Xk9LwmR7dw0qT9RgUCAQw45hO3btw95H5fLhcvlKuKohJg4ihGg5Ps5Srk8cTIq9eYlpWikuUnmpcLonYFyuv/2RXuwDFR6f6rwljDx5jiaV5N9q0pAtiXepbChu5h6JvVl266uLnbs2MH06dPHeyhCTEjFKJ/I5Tmyad1diuWJk1UptaOfCGRuGh+6X8e3xEeqJUWyJYkVt0i2JEm1pvAt8fVZJ5Xet6rxykYarmig8cpGalbVZJpZiPGRbYl3KW3oLqaOSfWp+sY3vsHZZ5/NrFmz+Oijj7j22mvRNI2LLrpovIcmxIRTjAYEo32OyVLaN9m6UUl2cHgyN5WO0WagdL9s+ltqsmniIxl0MR4m1aequbmZiy66iAMHDlBbW8vSpUt5+eWXqa2tHe+hCTHhFKN8YrTPMdFbd0+WoHAopdCOvhTJ3FQ60hmowNLAsPtUidKV7UWcidJBVUwek+pMct999433EISYNIrRnnw0zzEZWndP9KBQ5EbmptIjGaiJb6SLOGPJoPeuJgAKloGfbFULU538BYUQgypG+cRonmOiLzyeCEGhTPBCdDOChmSyJonRZNB7VxMEDYN9ySQKUO9y4c9jZcFkr1qYquRMIYQYUjHKJ7J9jom+sW8pB4UywQvRzUpYtG9sJ7wljBkx0bwaviU+qpZXSZOKKaB3NUGXZfFBPI4NlGsaVbqet8oCqVqYnEr7W4gQYlwVowFBts8x0Rcel3JQKBO8EN3aN7Z3709V78Dd4MYIGXRs6P63IPtTTW69qwl8msbeRIJ6hwMUhY+SSQ4rL89LZcFEqFoQuZHLLkKIERWjPXk2zzHW1t3ZtGIvlJFaAQPjMrb+E7xbValzOqlzODIlMEJMBUbQILg5iOJW0HwaqlvFWefEUecgvCWMEZR/CxPNaM756WqCCl0nZlkkbRu3quJRVZKWRcw08es6EdMc0Kp9NHo/T2/5OLYYXxIKCyFK0mDre3LNnJVKedtgpY7LAgFSlsUtTU3jMrZSLksUolishMX+h/fT+Xwnmlsj+m4U9yw3ZQvL0P068eZ49xorWV81IeRyzu9dTeDTNJyKQtyyQFFwqioeTaM1mcQA7DGMrZSrFsTYyF9OCFFSspkMR9u6e2N7O48eOIBbUSjXNFK2PS7lbYMFhZs7O9nQ2TlupXcywQvRXfYX+mMI1aWiulVQIfJ2BABnvRPNq6FXjM+/BWmaMXq5lDT3LzGf7nLxZlcXNnBkWRlbQiHej8VocDq5e+/enC9+TfRSdjE0+csJIUpKvtf3tCaT/LqlhXciESKmCUCNw8Fcj4eXx6l+PR0UlkJtvUzwYiozggbx5jihzSFcM10oLoXoW1G0Cg21TCXyTgQ7aVN9TnXRA5p8N80oRnAWNAyaEwmwbRoKXDI+3BhyPa/2riYoV1XmuN0owH7DYG8yyXyPh8U+H1HTHNO8JHtoTU4yWwohSkYhgozH29p4JRTCtG18PY9tTaXoMk0SljWu5W2lUnonE7yYanoHLIm9CSJ/jeBd5KX8sHIA4rviWHELK2Hh+1h3IDOSfActIzXNyPb5itHRMGFZPNjayv+1trI3lUIHZrndXFBby1k1NUUts872vJptiXnQMLilqYlFXi8ze45ZrmljmpeK0QRKFJ/8BYUQJSFoGLwbiXDAMJjn8fT5Xa5BRtAweCMSwQLcqkqZpgGgKgohw6DVMMZUGz/Sc480WZZK6d1gEzx0Z/lksheTUe+AxXOwh9j7MSKvR1CdKuVHlFM2r4x4UxzVoVJ3Xt2wwUc+gpb+AZIRNAhvCeOod+Cs6z43ON1OsCH0cggrYhHZGhny+Xofr3NzZ84dDbMJ3BKWxQ27dvG/ra2ELAufqlKh6+yIx/nFvn04VLWowcNI51WXqrK+rS3rEvOQYaArSuZCX1o+Ln6NtpRdlDb5SwohxlXvNVTtqRR/jUToSKU40e/HoShA7kFGyDCImSY+TSNmWcQtC4eiYNk2KdumXFVRCvh6RlogXWqld35dx62qJdHUQ4hsjTZDNFjA4j3MS3hLmMhbEdyNbqyEhZ2wqTijYsRjjqUN+1ABWdmhZZgRE3dD32yL7tfpeK6DRHMCz1zPgOerWl7V53iqphLbHaPskLIBwVl4S5jA0sCgr280geKj+/fzWHs7KdumRtdRgbBpEtA0wqbJPXv3sjkYxLTtopxPRjqvvhIKjarEvFQufonSJzOkEGJcpddQaYrC3LIyDnK5eCMS4eVgcEDb8dEGGRW6TqXDQZWuU6Xr2LZN1DRJ2TaVus6i8vK8T4i9X0+D242mKGzo6GBje/ug9x9rm/h8G+34hRgvVsKibX0bTbc00XxbM023NNG2vg0rYQ37OCNkYEbMPo0nyhaWUX5kOVbSIvZBDNuyqVxZOWLZX/8AbbRt2NMBmaIpuBvcKJpCx4YOul7tQvNqGKG+j0+2JDHaDVwHuQZ9vrZH2/ocz0pZRN+Nkvwo2ec4ul/HjJgDjj/SuNo39j0PBA2Dl0MhVMClKLhVFbem4VYUukyTtmSSrbEYRs8aq2KdT4Y6rx5XUTHqLSRG2g5DMk0iTT4JQohxM9gaqpMqKgBoTiSojMWo1vWcgwy/rrPU7+etSIR4KsU0pxPDtgmaJjOcTk73+/M6IY60JuyI8nIUutvxKpAphSmV2vpSaJwhRLZyzRDpFXomYHG6uz/nqkPF1ehCr9aZduk0XA2u7LJePQHaYBmlkdqwD1fiF9kaoWxhGaEXQmCTKQlMfJhAr9Iz909TnEp38NSaxDXdlfm9u9GN7teJbo9SdngZmkfLPPdQHQ2HG1c6u5V+7R1OAyVk0timsl23ifhMyjQNh6oSTqVI2TYHu900uFzdAVeRzidDrVlqisdzWscq605FNmR2FEKMm8EWFDtUleMrKtgRjXJxXR0LvN4xTbzLq6pIWRYP7N/P7ngcgAUeD5+pq8v7hDjUAmkV+GMwyEeJBG2pFO2GQZWuc1h5OSf2lMKUQm19qTTOEGIk2XzxHyqY0f06viW+7gCsJ2BJtiZJfJig+qxqvId7sx7HYAFaenwjtWEfKSDzLfaheTXCW8LEm+NoXo2qT1YR/Ws083xWyiK6NUrknQhGyEDVVOyjbRyVDhSHgupR8RzioeuVLhJNCdwz3RhBg1RrisqVlYO+R8OOa1ec1odbSexOYAQNIvsSLIlEqfYZTHMZvDvfYuvxGqpLxQQ8isICrxcUhfZUCo+mjfp8ks361KH0P6/mWsonjSVENuQTIYQYN8NNcFUOx5gDKuieDM+tq+OMqiqa43FQFBpcroJMiP1fT8qy2BqN8kJnJ62pFE2JBCgKtQ4HrYZBWSxGSzLJ/mSS8+rqxn2SlrUDYqIYS4YIyJT1dT7XSfCPQawuC71Op+uvXaheNesmE4MFaCMFLZnHjhCQOWudlM0rI7A00GfNWJu3LfN8iaYEXW92gQ3eI7wkdif6NNwAcE1zYS40URxKJjgbrrRxuHElWhKYfzRxzXRhtBuktsYoM1Mw06AyoLH0JRtFsXjpJJtZLheLvF66DION7e0kLQunqhLQdWZ7PCOeTwqxaftY17GWwsUvUbrkkyGEGDfFbNTg13X85eV5O95Qz9H79TQlErwWDnPAMJjmdBI1TZKWRY3DgV/TeC8axa1pvN3VxXvxOEsrKjJfGMZydTZf4x/vxhlCDGUsGaLeYh/EiDfFcVQ5cNQ4MmuHYOQmE2np4KR3Rimb9VjZBmS6v28DjvRxg5uDdL3dhVqm4j3ci3eBl4g3MqDhhtFuUH9x/YDgbLTjSjQlwAbnDCfxPXFCfwxhGBbOiMkR+6B9poWpqNRsUqg6zsvsGT404PGODmodDgK6TqdhsDsSYW4We1jle8/CNCnlE4UiM6QQYlxNtgkuPe7NwSBvd3XhUBTqHA6mu1zsisdxAO2pFEnLYl8yyTyPB4eqkrIsNnR0kLIsHKo6bt33JtvfQ0xOY8kQQfd6rAOPHsBoN/DM8qDoCvH34yi2glahEdocGraEsDfVpVKzqibroKW3XAKy9PN5ZnswOgw8czzoge7nK1tYhp206Xq7i9gHMZzTnZnjqS51TOOq+FgFXW92kfwoSeStCLZtY9k2asLCYypUGxrJKo1pTRaB151sabTBtjnS6yVoGHSZJmWqypFeLybdGfChAqtCru+UUj5RKPIpEkKMq8k2waVfz2yPhw7DYLrTyZZwGBPQAMu2SQItySSenk5ZGtDYcyX2gf37Kdd1Zrpceb06O9rxT5a/h5i8cs0QpddjaX4N1aGilquoqkqyJUnnc504pjmwDRv3oW6mXTQNK25lneEZ7aa/YwnIXA0unNOdWMm/dTvMteFGNuMCiL4Xpev1LhwBR3dnw5CN7VAxHaDHFGwULL8K25O4uxwkvHB8RQW2bROzLDyqiqIoI66p6r++M2ZZxEwTl6rSlkzmZX2nlPKJfJNPkxCiJEy2Ca7B5WK604mmKMx2u3krGsWjaexLJrFsG9O2OcjlImZZLCorw6OqxBWF3fE4J/n94959b7L9PcTkk2tAkl6P5ahxoDgVrLhFKpTCOGBgY6OoCopLIfRCiPiuOIqi5Lypb7ZyCchGytaNpuFGtuPyHOqh4w8daNM1dK9O8sMkTsWmq1xBj5soQYXQoQ5iSZPj8fK2ozsAqnM68fRsvt6STI64RjO9vrM9leJAKsWueJykZZG0bWY4nbJnnihJ8qkUQogC6L23SZXDwSFuNx5VRQcqejpgeTWNRWVl3d2xgP2pFEAmoOp9rIhpEhpkDxUhpjrdr2dah2d1/571WHbKxj3Ljdlukvgoga3YoIJt2HgP82IlLDqe6Oi+3zB7NY2nquVVVK6sxLZs4s3xrPfXylVgaQDXTBdm2EQtU9G8Gi6vA6+mYWsKB+ZpJGo1Zld7OGt2bc77O6XPn38Jh/lzOIwF6IpCzLIImSavhEIFeX1CjIVchhRCTGmFbAjRe32SX9c5ye9nzbRpnOz382IwyB9DIeqcTkzbpj2VImSazHa7SVp9Ny+V7ntC5E/vDI+j2oGz0Ulsdwys7iYM5UeV45rtIv50HJyglWvdm+xm2bK9mMZSPjgaVsKifWM74S1h0MCIGLhmuQgsCxB9J4rTtKk5soyG2S60NpNpp1bjrXSy3Mp9jeZxFRWsa2khYpoYto1TUVji81Hds1Gv7JsnSk1On8ZYLIZt25SVlQGwe/duHn74YQ477DA+8YlP5HWAQghRCIVo19vfcOuTZrrd1PZ8OUh/2TinupqUbfN0Z2feuu+NRxfB8SDzkhiN3uuxnDVO3LPcOGocVJxcgaPCQao9hRE20H16ZsNcyL5le7HlUj6YDSNoYIQMgi8FCb0QwlHvIHBygLAnTGxbDGW6gudQT3dXwGonuqrh+2Qg8/6OZY1mwrJocLk4qrwcG/CoKh5NI25Zsm+eKEk5fRrPOeccVq9ezZe+9CU6Ozs54YQTcDgctLW1cfPNN/PlL3853+MUQoi8KlS73sEMtj5pqC8bCcvCoShj7r5XjKCxlMi8JEajf4YnHTTYMRvLaWGGTeyUjaPGger527+X0bZsL5R0sFOMzJRxwKDrr124DnLhWeBBdagETg10l07GTBqvbOxezzXMeHJZo5leV5Wy7T4l0ZK5F6Uqp5n11Vdf5ZRTTgHgwQcfpL6+nt27d3Pvvffy05/+NK8DFGIyMYIG8aZ4d9ckMW76t+t1qyp1Tid1PZmjYBHXLvl1ncZee7akg60rGxu5oqGBKxsbWVVTM+pAKB00aopCg9uNpihs6OhgY3vprAfJJ5mXRC7S67Fqz6ntszZJcSpUnlWJ7tNJtiSx4hbJliSp1hS+Jb5xy1JZCYu29W003dJE823NNN3SRNv6NqyENfKDR6F9YzsdGzpQNAW9WscyLBJ7EkS3RjP3cdY7UXQFlNGva8tG73Wpo12TNZigYdAUj+fl/J7PY4nJI6dPfzQaxefzAfDkk0+yevVqVFXlxBNPZPfu3XkdoBCTQe+rfoXuIiVG1r9db5pf10umrGQs3fcKucdLqZJ5SYzFYGuTVLeaOW+PpmV7IaWDHUe9A3eDGyNkjHqz4pGkW8476h0465xYMQtHwIEVtYjvjuOZ70HzaEXJ2uVj37x8Zu3HcqypUoo9leX0V503bx6PPPII5513Hn/4wx+46qqrAGhtbaWioiKvAxRiMijGRCiyly4rCRkG7hIqK8nXpDsRgsZ8k3lJ5EP/tUmFbgIxmjK+/sEOUJDmGemW8+6G7vOH6lFxz3YTeT2CkTAwOg3MkJn1RstjkY998/JZ6p3LsaZaKfZUltO/hGuuuYaLL76Yq666imXLlnHSSScB3VcHjznmmLwOUIiJrlgToRidWS4XfwyH89YQYizyPemWatBYSDIviUIpRBOIXKoX+gc7vceXz+YZ6ZbzRsjonqsA7wIvRqdBojlBqj2Fo8pR1Kxdrpn7fGbtcz1WMdfvivGV07++T3/60yxdupS9e/dy1FFHZW5ftmwZ5513Xt4GJ8RkUKyJUIysd/ASNE0ihsFWw2Ca04lf13NqCJEP+Z5002sRNnR05DVoLOXyFZmXxESSS/XCYMEOQLIliW3YYOdnbENtKuwIOKg5u4aKkyoK1iAj3/KZtc/lWFOxFHsqy/kvOW3aNKZNmwZAKBTimWee4dBDD2XBggV5G5wQk8FQE2GpdJGaSnoHL7Pdbqp0nT2JBEeWl3NebW3BJ7fBgpJCTbr5WIuQNlHKV2ReEhNBrtUL6WDnwKMHMDoNHFUOIu9EiG2L4WxwsvfuvXlbq9u75Xz/9WSlsA442ws8+cza53KsqViKPZXl9Je84IILOPXUU/nqV79KLBZjyZIl7Nq1C9u2ue+++zj//PPzPU4hJqyhrvoVox69lBU76zFc8LI7Hi/ocw8XlPSfdGOWRcw0cakqbclkzpNuPtYipE2E8hWZl8REkWv1gpWwsFIWRpdB19tdpNpTYIFviY+KYyswo2be1uoWa1Ph0RrtBZ58Zu1zOdZULMWeynK63PD8889nWtc+/PDD2LZNZ2cnP/3pT/n+97+f1wEKMRlULa/q067Xtuxx7yI1XhKWxfq2Nm5pauK25mZuaWpifVsbCSu/LYH7Swcv/Scxv64TMU1CBWyNu7G9nUcPHKDLNKl2OPq0N09Puu2pFH/t6mJjezvPdnTwxIEDfJhIjDkT1L9l+2iVUvv54ci8JCaK3tULvY1UvdC+sZ3g00G8h3mp+kQVjioHuk/HUeVAK9dw1jnRKjQ6nu4gvic+4Ni5bOdRiFbpY5HLVhHLq6pYWVmJZds0x+NYtp1z1n60x8p3W3hR2nL6awaDQap6PkAbNmzg/PPPp6ysjFWrVvHNb34zrwMUYjIo1at+42G8sh7jdcWwNZlkXUsLzckkAF5NY67bTXVPULI0EGCJz8edH31EaypFjcOBrigETZOQafJKKDSu2aCJUr4i85KYKHKpXuhfMphqT6GX6ygBhfiuOJ7ZHqLvRYlui2IGTRQUAssCBE4J0PlC56TYziPXUul8Zu1zOVY+S7FFacvpU9XY2MhLL71EVVUVGzZs4L777gOgo6MDd7+JVwjxN4XoIjWRjOei3UI1bxhKurzx4f37+VMohKoo6EAL8GE8zvEVFVTpOiHD4LiKCta1tBAxTQzbxqkoLPH5+gRe4xW4TJTyFZmXxEQy3JqlwQzW5lxxKmCBFbNoe7yN+AfdVRCqUyX+YZz29e2EXglhHjAnxXYeY73Ak+4gmN64dyzB1Wi6EeYzqBOlLae/6pVXXskll1xCeXk5M2fO5PTTTwe6yy+OOOKIfI5PCDGJjHfWoxhXDHvX/LenUmzs6KDTMKhxOinXNJK2TcgweCUU4pPV1VT0BFYNLhdHlZdjAx5VxaNpxC1r3LNBxQ5GcyXzkphIRlu90L/hkebRcM9yE94Sxgh37xulOBQ0p4ZepZP6KIUZNTH/ahI4LTAptvMY6wWe8W64M5YN3cXEkNNf9ytf+QrHH388TU1NfPzjH0ft+TDOmTNHateFEEMabFKMmSZNiQQORclr1mOwRhjFuGLYu7zRq+uETRMV6DJN3IqCQ1VxKAqtqVSftU5eTSNl2/g0jQ7DoMMwSNl2SWSDJkL5isxLYiLKtnphsJJBR7UDPaCT6kyB3Z29ctQ4wIZkaxJzt4mdsnHNdOGa7kJxKJljTcTtPMZ6gWciNNwRE1vO/5qWLFnCkUceyc6dO5k7dy66rrNq1ap8jk0IMcn0nhTDpklTPE5zMkmXabLQ42FzZ+eYrxpmczWyUFcM+5c3ftQTLLocDlK2jWHbGJaFDQR0nZN8vsx45rnd/HzvXvYbBtGeMkC3pnFBTQ3ucW5bPlHKV2ReEpPZYCWDtRfWEtocIrYjhubRMKMmiX0JVKeKrdooukLsvRiOKgflR5QDhdvOwwgaBV8znOsFHtkvShRDTp+gaDTKFVdcwa9+9SsA3n//febMmcMVV1zBQQcdxLe//e28DlIIURry0Qb9lECAV0IhftvWRksqhVdVWVBWxsEeT16uGo7n1cj+5Y2VDgfVDgetqRQ2UKFp6KpKzLI4xONhTllZJgh8rL2drdEoUcuiTNMo1zQU2+a1SISN7e0lcSW1lMtXZF4Sk91gJYMAid0JrLhFfHecVGsK1aFip2xURaXs8DKMToPIWxHcjW6shJX37TyshEX7xvaiNMPI9QLPeJeei6khp0/7d77zHd544w02bdrUZwHw8uXLuf/++/M2OCFEachnG/QXOjv5KJWi0uFgUVkZC71e4rZN0DDG3KY73+2/0wuas31c7/JG6F4bdaTXS6inne5b0Sh/jUQIpVJ8qqdcJd1u/aNEggqHg3qnE6+qMt3lYoHXS9Q02RwKlUzr8lIl85Iodbm2Ne+vd5vzdFmgXqHjqHFgpSzMqImVsPAu8lK1soryI8uxkhaxD2IF2c6jfWM7HRs6UDQFd4MbRVPo2NBB+8ah25yP1Wi3iuh/bk4rtYY7YmLL6VP0yCOPcP/993PiiSeiKErm9sMPP5wdO3bkbXBCiNKQr+xPOujxaxoeVSWgaThUlbBpsisep9HtHtOGt/m6GpnrgubBav63xWIYdGepvJqGbdukgHd6grX0+6EoCipQruukbJtgKkWdw4EKdKRSciV1BDIviVJV6ExOOkAKbg6S2JtAURW8i7z4jvKhOBRcjS70ap1pl07D1eDKa2le/1bvUJrNMCZKwx0xseX0Kdq/fz91dXUDbo9EIn0mMyHExJfPWvR00FPjcOBUFOKWhUNV8agqHakUrckkvjFcNazQdTRVZU9PgObpCYBGezVyLEFk75r/d7q62J1IMMfjYZ7Hg2XbOFSVtlSKF4NBPtXVlXk/PD3nzlRPS/WoZRE2DCy6ywjlSurwZF4SxTSa9UPpTE6h2pr3LgssO7SM0B9DuBpd2KZNqj2VKffzHu4d83P117/Ve1opNsOYCA13xMSW0yd9yZIlrF+/niuuuAIgM2H9/Oc/56STTsrf6IQQ4y6ftejpEoyUbTPL7ebtSAQbMKG71bhpckaOC4YTlsXmzk52x2K8G43i13UO8XiY5nLRbhhZX40caxDZu+b/uY4O/tzVxcFud59mEwFdpykep8uyMu/HvLIyPuzJ0jkUBRtoNwymO50sraiQK6kjkHlJFMNos07FzOTofp36i+px1DoIbwkT2x5D0RX8p/jzWu7X5zn7tXpPK1QzjLGYKA13xMSV06fpBz/4AWeddRbvvPMOhmFw66238s477/DHP/6R5557Lt9jFEKMo3xu/tq7BKPa4WB+WRnbo1E6e7r/nVNdnfNVw3R26ZCyMsp1ne3RKK90dbHQNLm4vj7r4+YriPTrOkf7fPg1jU7DwNPrvevsee8O6WlUkX4/jq+o4JVQiP2GQYWq0uB08pm6OrmSmgWZl0QxjDbrVOxMjupSqVpehRkxCb0cwjItIlsjqF61II0jBmv1bgSNvDfDGI2RGiqVcsMdMbHl9KlaunQpr7/+Oj/84Q854ogjePLJJ1m8eDEvvfSSbLIoxCST71r03iUYAU3jhIoKDvV4+GRNTSYzNFr9s0szXC4OLyvL7H+1NBDIuk17PoPImW43p/n9PHTgANCdoeo0DPanUqyurmam2019z3NsCYep1nVWVFUR6Sn7c6kq22MxNra3F2yDysG+gOSjy2OxybwkCi2XrNN4ZHLaN7YTeiGEo96BXqHnvdywv8Favee7GUY2xntzXyFy/tc8d+5c7rrrrnyORQhRovJZi16IEozBskseTWOm2z3qEsV8B5FXz5wJwHPBIE09793q6urM7f3fj5dCIV4IBpnRs46qUC3hB/sCclR5Odg2b0QiE/JLicxLopByyToVO5NT7MYR6bVlgaUByo8oJ7E3gWu6C/dM98gPzjPZ3FeMt5z/ZVmWxfbt22ltbcXq11b51FNPHfPAxuI///M/uemmm9i3bx9HHXUUt912G8cff/y4jkmIiSzbQKh/hmO4jEc+SzDymV2C/AaRFbrOdXPmsCceZ28iwXSXi5nugV840u/F1kikKBtU9v8C0ppMcntzM3pPZm8ifikp5XkJZG6a6HLNOhUrk2MEDSLvRki1pyibW9Z37HkuN+y9tswIGiT3JUEBV70LzV+4faqGIpv7ilKQ0yfs5Zdf5uKLL2b37t3Ytt3nd4qiYJpmXgaXi/vvv5+rr76aO++8kxNOOIFbbrmFFStW8N577w3aGUoIkb2hAqH+WQ+XqqIBpqKQKELGI9/ZpUJk02a63YMGU70Va4PK3l9AKnWdrdEoO+Jx3ovF0BWFmW43R5WXUzeBvpSU8rwEMjdNBqPNOvXuENh/0958Zov6BDgHDCJ/jWAcMKg4qQLVoWbGks9yw95ry6wui/gHcbBBK9fQq/Th15mNonNitmRzX1EKcvp286UvfYklS5bw1ltv0d7eTkdHR+a/9vbCbfaWjZtvvpkvfOELXH755Rx22GHceeedlJWV8ctf/nJcxyXEZJbOemiKQoPbzc54nIcOHGBXLEaD242mKGzo6GBjAc8Py6uqWFlZiWXbNMfjWLY95na5o91gcqyKtUFl+gtIRU9A9XYkgmlZeFQVBXgrEmFrNAp0vwcR0xwwplJTyvMSyNw0WVQtr6JyZSW2ZRNvjg+6ma6VsGhb30bTLU0039ZM0y1NtK1vQ3WrmU1786l9YzsHHj2AGTZxNbhwNbiIvBkh9FIIK26RbEmSak11bxKch+fuXWKo+TQSexPd5YbTnCQ/SqL7dBx1jkwWK22o98VKjH4T+f5kc19RCnL6lG3bto0HH3yQefPm5Xs8Y5JMJvnLX/7Cd77zncxtqqqyfPlyXnrppUEfk0gkSCQSmZ9DoVDBxynEZNK/7CJmmgQNg1qHg07DwIaiZDwmQ7vcYm1Qmf4C0ppMsjsep0LTcGsauxMJnIpCQNfZHY8z3+Mh1JNpLPUvJaU6L8Ho5yaZl0pX7z2hhsq2FHpfqjQjaBD9IErLPS0kDyRRHAqqU8XV6KJsURmJDxNEd0RxVDnyWm7Ye22ZGTWxkzZaoHsD81RHCjNmDlpuWMj3pf+506ko7E+lCJkm51RXT7i5QExMOWWqTjjhBLZv357vsYxZW1sbpmlSX1/f5/b6+nr27ds36GNuuOEG/H5/5r/GxsZiDFWISaN31gMgZlkkbZuArpO0LGI9ZVfFyngUO7uUb4XIuPWX/gLSnEjQYRjoqkrcsnApCk5FQenZfLgpkaA1lWKJz1fy72epzksw+rlJ5qXSp/v1QbNO/RtFqG4VZ51z0MxNrnpnfD741w/o3NyJGTZxVDhQVIXY1hiaT6P8iHLqL66n8cpGalbV5G19U++1ZapHRXEqWHELK2ahOlU0jzag3LAY78vyqiqW+f1sjUT4XVsbLwWDRAyDlG2TsMaeDRNiJDnNkldccQVf//rX2bdvH0cccQQOh6PP74888si8DK4YvvOd73D11Vdnfg6FQjKBCTEK/ZtEeFQVp6LQaRiUqSoeTQOkDCNbxcq4La+qImKa3PHRR7QmEgQcDpb6/QBsjUZJWhYOReGMQGBC7JMl85IoBcXYlyqd8dH8GqRA9aik2lJo5Rqug1wAxN6P4T/Rj3eBN+/lhv3Xlrmmu+h6swts8B7txQh1rzPzn+LHCHUHS8V4X1yqikNVKdd1TvL7qXM6SVoWT3d24lCUCdFsR0xsOX2Czz//fAA+//nPZ25TFAXbtsd1QXBNTQ2aptHS0tLn9paWFqZNmzboY1wuFy6XqxjDE2JSGqxkza/r7I5EOMrrRQFaksm8l7BNdoXeoNKlqlxQXw+KwhMHDnCQy0Wd00nQMDCAj1VUcF5t7YT5e5XqvASjn5tkXpq4Cr0vVe+Mj6IrKJqC8yAnyeYkiY8SOCodWIaFETTwHOop2Oa7vTsaquUq7jluUED36t3lgNUaXX/tIrQlhObVKFtYhuYq7H5d6VL0mT3nsrSJ0mxHTHw5fbp27tyZ73HkhdPp5Nhjj+Xpp5/m3HPPBbpb7D799NN89atfHd/BCTGJ9W9BfrDbzTy3G1NRxtySXBTWOTU1eFW1T/v4c6qrJ8zeVGmlOi+BzE1TSaH3peqd8bFtG8WpoDt0FEUh8WGCVFsKdChbUEb1J6vz9KoGGmxtWXp8wZeCmc2HnbVOjJBB6IUQWrWG0WIUbL8u6QAoxltOn65Zs2blexx5c/XVV3PZZZexZMkSjj/+eG655RYikQiXX375eA9NiElrqJK14fapKpZSGEMpmwwNPqC05yWQuWkqKeS+VH0yYXVO3LPcRN6OoDpUPAd7KDusDDtuU31OdWbz30LS/QMbdUS3RgfdfNhKWvhP8RPZGinIfl353q9QiNHK6RM2c+ZMTj/9dE477TROP/105s6dm+9x5eyzn/0s+/fv55prrmHfvn0cffTRbNiwYcACYSFE9rINTPqXrBW6hG04/ffOKvReWRPdeP6t8qGU5yWQuWkqyaZDYK76Z8I8czwYnQaxbTGcDU4c1Y7MxrtphdgXaigjrZ2qOKmCqhVVBRlPsbqnCjEUxe6/S2IWfvOb3/D888+zadMmtm/fzkEHHcRpp52Wmczmz59fiLEWRSgUwu/3EwwGqaioGO/hCDGuJnJgsr6tjQ0dHdQ7HFToOiHDoKVncpUFy6VpLOdfmZfEVNF7s18zYmbWLPkW+3DWOjOBymD3SwdcQ3UCHGsAZgQNmm5p6l7r1StTlmxJYls2jVc2FiywCxoG+5NJXu3qYmskMuHmLFG6sj0H5xRU9bZ3716ee+45fv/733P//fdjWda471w/FjJ5CfE3EzUwCRoGtzQ1oSlKnwXLLckklm1zZWOjXLUsQfk6/8q8JKaCkQKgtvVtmX2h9Aq9uytfS/capv77QuUSgA0l87x1jgFrp8ayH9VQr3ewi38Ly8pY7PNR63TKuV6MWbbn4Jw/adFolM2bN7Np0yaeffZZXnvtNRYtWsTpp5+e6yGFECWk/6a+AO4ibOKbD7JgeWqSeUlMJYOtZ0rrvy8U/G1tU3hLmMDSQJ/H5nNj3nyvKRsp4NvY3p65+NfgdhMyDF4IhfBqGvPKynJ6TiFykdO3io997GO89tprLFy4kNNPP51vf/vbnHrqqVRWVuZ7fEKIcTKRAxNZsDz1yLwkxN+MZl+o0QZgI8n3mrLhAj7HisCEvfgnJp+cCkzfffddvF4vCxYsYMGCBSxcuFAmLiEmmd6BSW8TITBJL1huSaVoSSaJW1Zmr6wlPp9MspOQzEtC/E3vLoG9pfeFwoZ4UzxTUmdGzAF7Rel+HTNiDjhG1mPw67gb3WMKqPoHfKpbxVnnxFHnILwlTMeBOBHTHDAf+XWdiGkOmL+EKKScgqoDBw7wzDPPcOKJJ/KHP/yBk08+mYMOOoiLL76Yu+66K99jFEKMg4kemCyvqmJlZSWWbdMcj2PZtuyVNYnJvCSmAiNoZIKh4aS7BKZaUiRbklhxi2RLkuTeJLZts/fuvTTf1kzTLU2EXgqhutQhA7DMHlRZPnc+jRTweaPKhL34JyafMTeqsG2bv/zlL9x+++2sW7dOFgQLMYlM5O5/abJP1cSRr/OvzEtissmlkcRgj7FtG6PVwDnD2ad5hVatYR4wB20uUbW8Km9NLEYrm26Cf0h1sqGjgzqHY0Ab9VJuqCQmjoI2qnj11VfZtGkTmzZtYvPmzYTDYY444giuuOIKTjvttJwHLYQoLZNhY9iJvv+SyI7MS2KyGKzLXS6NJPqvbcKGvXfvxTnDOWDtlJ20qTilgujW6IDmEvlsYjFa/ffl6h/w6X6d5VZ39cGWcJjmeByvpklVghgXOX3TOP744znmmGM47bTT+MIXvsCpp56K3+/P99iEECVCAhNR6mReEhPdUNmoiuMqxtRIIt0lMN4UH7Z5hf8kP9UrqvsEdPluYpGLkboJToaLf2JyyOlT197eLiUIQkxwY93kUYhSIvOSmOiGyggl9yez7uQ3nN7NK5zuv5XS9V471b9N+2i6CBZKtt0E5eKfGG85ffoqKiro7OzkwQcfZMeOHXzzm9+kqqqKV199lfr6eg466KB8j1MIkSf53ORRiFIh85KYyIbLCMXei6Fq6rDBUDayKaXrf7Etm0Cs/+so1MW64fblEqIU5PTpfPPNN1m2bBmBQIBdu3bxhS98gaqqKh566CH27NnDvffem+9xCiHyZDzr44UoFJmXxEQ2XEbICBuUHVZG11+6hgyGsjVUKV3glABt69sGvdg2UiAGcrFOCMixpfrVV1/N5ZdfzrZt23D32hj0k5/8JM8//3zeBieEyK+R9vwoZqtcIfJJ5iUxkY20r1T1J6upXFmJbdnEm+PYlt1nXVG20qV0jVc20nBFA41XNlKzqobOFzrp2NCBoim4G9womkLHhg7aN7ZTtbxqxOdOX6wb7PFCTBU5ZapeeeUVfvaznw24/aCDDmLfvn1jHpQQojBKoT5eDE3av+dO5iUxkY1Umuesc2a1rmg0z5d+fHxPnM6nO9H82pDNKIZ7biNoENwcRHEraD6t+2JdkZtZCFEKcvqUu1wuQqHQgNvff/99amtrxzwoIURhjLY+XhTHZNgPbLzJvCQmupG63EF+1xWlS/Y6nu4guDmIo9qBETTwLvCiOJQBF9sGe24rYbH/4f10Pt+J5taIvhvFPctN2cIyuVgnppycZuu/+7u/Y+3ataRSKQAURWHPnj38y7/8C+eff35eByiEyJ/01dBUS4pkSxIrbpFsSZJqTeFb4pOJb5xsbG9nQ0cHmqJQ7XDQZZo8euAAG9tHLp0JGgZN8ThBY2qXbsq8JCa6oUrzCrUmKV2yp5VpOKud2Emb6FtRIu9GgOwutrVvbCf0xxCqS0V1q6BC5O0I0a1RuVgnppyc/qX++Mc/pquri7q6OmKxGKeddhrz5s3D5/Px//1//1++xyiEyKNs6uNF8QQNgy3hMNW6zr5kks3BIG9GImyPRlnX0kJrMtnnvukAKmFZrG9r45amJm5rbuaWpibWt7WRsKxxfDXjR+YlMVnofh13o7ugF7l6r691z3TjnuvGtmxQIbY9RnxPfMSLbeljuGa68B7uxYpaKCioZSqRdyIkmhJysU5MKTl90v1+P0899RQvvvgib7zxBl1dXSxevJjly5dj23a+xyiEyKNs9/wQxREyDCKmSadpsi0apULTCGgaXYrCu9Eojx84wEX19QPKA23bptUwmOF00uB2EzIMNnR0d3FcVTP1ujjKvCRE9vqvry1bWAZAfEec5IEkVtQa8WJb72M4Kh3dj98Vx4pbWAkL38d8crFOTCk5fZO66aab+OY3v8nJJ5/MySefnLndNE0uvfRS/vd//zdvAxRCFIbs+VEaKnQdTVF4NxLBrSi4VBWHqqLbNn5d571YjEfb2nghGKTe4aDB7aY1meSpjg7mezwcXV4OgNvpxAa2hMMsDQSmXKMLmZeEyF7/9bWqQ6X8yHJ0v44ZM2m4sgH3THf2x6hzUn5EOWXzyog3xVEdKnXn1Uk7dTGl5PRpv+mmm/jFL37R5zbTNLnwwgt5/fXX8zEuIYSYEtyqStw02RaPsz0e551olG3RKJ2GwSEeD1HD4OVQiHqHgzqnE7eqUq5pOIADhkHMNDPH8us6EdMkNAXXV8m8JET2hlpfa4ZMKpdVjhhQDXUMI2RgJ2wqllbIRTsx5eT0iV+/fj2f+MQn8Pv9fPrTn8YwDC644ALeffddnn322XyPUQghJq2N7e2ELYs6XSdmWSRMk4hpssjhYJrLRVsqRTiVYprzb90aPZqGT9fpMgxiloVH04DuNVdeTaNiimWpQOYlIUYrm26DxTiGEJNFTjPvcccdx29/+1vOPfdcnE4nv/jFL9i+fTvPPvss9fX1+R6jEEJMSukmFbPdbjyqyuuRCB5FQVEUwpbFn4JByjWNvckku+JxDvd6WeD14lFVahwO2g2DsGlSpmkEDYPWVIqVlZVTrvQPZF4SYrTysb5W1ugK8Tc5f/LPPPNM7r33Xs4//3wWLlzIc889R80UXBwthBC5SjepaHC7qXR0L/TeFY8TNU1akkkOcjpZVF5OVTLJm11d/DkcJmlZNLjd+HSdsyorURSF5ngcr6axsrKS5VVT9wqxzEtCjF4+1tfKGl0hRhFUrV69etDba2trCQQCfPGLX8zc9tBDD419ZEIIMclV6DpeTSNkGNQ5nRxRXs68sjK2R6MkbZsT/X5muFzUOhw4FYV3IhHeikSocjpZVVXF8qoq4pZFyDCo0PUpl6GSeUkIIUSpyHoG9vv9g96+YsWKvA1GCCGmEr+us8TnY0NHB3bPzyHDoMs0qetpTAHgUFWOLC+n0eViZyzGpXV1HN7T9c+lqlMumEqTeUmI0mUEDSkJFFNK1p/yu+++O/P/Y7EYlmXh9XoB2LVrF4888ggLFy6UyUyIKSK+J05ibwLXdFdWnaLE4NLlelvC4UwZ31nV1fy1q4uQYeDuCaxipsn+VIrKnrbqQuYlIUqRlbBo39hOeEsYM2KieTV8S7r3rJIW62Iyy+nSwTnnnMPq1av50pe+RGdnJyeeeCIOh4O2tjZuvvlmvvzlL+d7nEKIEmGEDPbcvIfgc8HMhOk/zc/Mq2eiV8jVyNFyqSqrampYGgj0KePzqiobOjpI2TZ7k0m2R6N0miYLPR42d3ayvKoKlypfUNJkXhKiNLRvbKdjQweOegfuBjdGyKBjQ/fG5DWrZI2jmLxympFfffVVTjnlFAAefPBB6uvr2b17N/feey8//elP8zpAIURp2XPzHg48dABFVXA3ulFUhQMPHWDPzXvGe2gTml/XaXS7M6V8y6uqWFlZybZolC2hEADHlZczv6yMDR0dbGxvH8/hlhyZl4QYf0bQILwljKPegbPOiepWcdY5cdQ5CG8JYwSn3h56YurI6bJyNBrF5/MB8OSTT7J69WpUVeXEE09k9+7deR2gEKJ0xPfECT4XxFHrwDm9uyzN6en+3+BzQeJ74lIKmCcuVWVpIMDmUIgZLheNPW3XATRFYUs4zNJAYMqup+pP5iUhxp8RMjAjJu6GvvOA7teJN8e711jJ+ioxSeWUqZo3bx6PPPIITU1N/OEPf+ATn/gEAK2trVRUVOR1gEKI0pHYm8CMmOiBvpOiHtAxIyaJvYlxGtn4CxoGTfE4QSN/V2JDhoFpWdQ5ncRMk5hlAd1ZrYhpEsrjc010Mi8JMf70Ch3Nq2GE+p6bjKCB5tWkRFxMajl9uq+55houvvhirrrqKpYtW8ZJJ50EdF8dPOaYY/I6QCFE6XBNd3VPmJ1GJkMFYHR2T5iu6a4Bj3mrq4sPYjHmeDws6ulYN5kkLIuN7e1sCYeJmCZeTWOJz5eXNU8uVeXDRIIt4TAORcGpqsx2u6lyOPBqGhWSpcqQeUmI8af7dXxLfN1rqOzun42gQao1ReXKSslSiUktp0/3pz/9aZYuXcrevXs56qijMrcvW7aM8847L2+DE0KUFvdMN/7T/Bx46ADQnaEyOg1S+1NUr67uU/q3P5nkn99/nz+GwyQsC5eq8jGfj1sPOYRap3Oop5hwNra3s6Gjg/qernwhw2BDR/ei7FVj3Hj2lVCIkGkStSyqdR0L+HM4TJ3DwZdmzJDSv15kXhKiNFQt7+5oGt4SJt4cR/NqVK6szNwuxGSl2LZtj/cgSkkoFMLv9xMMBqVkRIhBZNv97+K33uIPnZ34NQ2frhM2DIKmyYpAgP9ZtGgcX0H+BA2DW5qa0BQls6cUQEsyiWXbXNnYOGTgEzSMYTftTR/bBtpSKXbH4yRtm5RlMc3p5Jb58/s852Qg59/ByfsiJiLZp0pMFtmeg+VTLoQYFb1CZ851c4bdp+qtri7+GA7j17RMVsrtdEIyyR/DYd7q6poUpYAhwyBimgP2jfLrOs3xOCHDGBAwZVsu2PvYM1wu5ns8xCwLBTiQSpHoWV8lhBClSPdLMCWmFtnkRAiRE/dMN/4T/IN2+/sgFiNhWfj6BRQ+XSdhWXwQixVrmAVVoetoikJTPE7MNDO3Bw1jyDVP6XJBTVFocLvRFGXQFukVuo5X0zLNKDyaRpXDQdK2ZT2VEEIIUWJkVhZC5N0cjweXqhI2jO4MVY+wYeBSVeZ4POM4uvxIWBabOzvZE4+zNRYjoGnMKytjutNJu2GwsrJyQJYqaBhsCYepdzgypXtupxMbBrRI9+s6S3w+NnR0YPf8HDQMWlOpQY8thBBCiPEjmSohRN4tKi/nYz4fQdNkfzJJ3LLYn0wSNE0+5vNNitK/dMZpflkZx/W8ni2hENuiUU7x+zm0rGxAe/V0SV//LNNQLdLTGwBbtk1zPI5l26ysrGR5lSz4FkIIIUqJXOoUQowolwXHtx5ySKb7X2syiUtVWREIcOshhwz5mJGaN5SK/hmnGS4Xh5WXsysWozmR4LVwmC2hEF5NY2FZGYt9Pmqdzj4lfb0zeEOVC7pUlVU1NSwNBCbE+yKEEJDdnCGNLMRkI59iIcSQrIRF+8Z2wlvCmU5/viU+qpZXobqGT3TXOp38z6JFWe1TVci9ngphsAYVHlUlaprsiMeZ7XYz3eXi1XCYx9vbaXA6ObK8nCU+H0d5vTwdDI6qpM8vwZQQokT1Do5UtzrinDGWeWWw55SATJQK+SQKIYbUvrGdjg0dOOoduBvcGCGje1NHoGZVdnswLSovH7Hcr5B7PeVTOpNmw4CMU8yyeL9nbVWDy8W2WIwPEwnKNY2oZZGybTZ0dLAsEGBlZSVbwmGa43G8miYlfUKICWew4Mi2bYxWA+cM55BzxljmlXwEZEIUigRVQohBGUGD8JYwjnoHzrruwMHpdoLdvaljYGkgL1cIR9O8YbwMlkmzbZuPUqlMxqkpHidoGCzp2cNidzxOhabh0TQ6UinKNQ2XqvJGVxdXNjZKSZ8QYkLrHxwlW5N0PNWBZ76H8qO7L6T1nzOAMc0r+bjQJ0ShSFgvhBiUETIwI2afDX2he+8RM2JihIwhHjk6vZs3xEyT9lSKmGkO2bxhPAzWBr3VMKjT9UwTCYeqsqCsjBlOJzHLImnbuFWVmGXhVFU8mtbnNfl1nUa3u09AFTSMTHAmhBCFYAQN4k1xjGDu55n+F91Ut4pWroEDjAMGZuxvW0z0njPGMq8M9pzOOieOOgfhLeExvR4h8kEujwohBqVX6GheDSNkdF9J7GEEDTSvNmBSzFWFruNSVf4cChE0DJK2jWLbeDSNeR7PuO/HNFwmzbJtLp8+HaXndWzu7GRDRwcVmoaqKLQZBhawqKwMj6rSkkwO2pBioq0pE0KMn1zXE+WzdC4dHLkb/rauVPNo6D4do8vAilloHi0z3t5zRq7zymDPCd0BWbw53v2eyPoqMY4m1Ww9e/ZsFEXp898Pf/jD8R6WEBOS7tfxLfGRakmRbElixS2SLUlSrSl8S3x5m7z8uo4GvBmJ0GWahA2D92MxNgeDvBIOs7mzk4Rl5eW5cjFSG3QFMhmndAt0p6JQpih0mSYNTicHezy0JJO0plIs8fkGlPtluyGwmJhkbhL5YCUs2ta30XRLE823NdN0SxNt69uwEtmdH9Olc4qm4G5wo2gKHRs6aN84+vNM74tuaapHxVHjwE7amGFz0DljLPPKYM8J+b/QJ0SuJt0ncO3atXzhC1/I/Ozz+cZxNEJMbFXLu5snhLeEiTfH0bwalSsrM7fnQ9AwMBWFo7xe3otG2ZdM4lFVZvd01nv0wAFg/BpWjKYNeu8W6PuTSV7t6mJrJMK+RGLIhhQTYU2ZGDuZm8RYjWU9Ub7XyKaDo44NHWB3/2wEDXSfTuVZlSiKMuSckeu8MtRzplpTVK6slCyVGHeT7hPo8/mYNm3aeA9DiElBdanUrKohsDRQsPa1IcMgYZos8nppMwwCuk6lw4ECdKRSVGhaVsFFofa48us6S3w+NnR0DNoGHaApHu/zvOkW6PN6NgAeblyDtWdPH6M5Hs+svxITm8xNYizGGhQVonRusOCoalUVVcursOLWkHPGWOaVYlzoEyJXk26m/uEPf8j3vvc9Zs6cycUXX8xVV12FPswXkkQiQSKRyPwcCoWKMUwhJpR02UYhpDNBbakUlm1Tqes4FIWwaeJUVeqcTtqSySGDi2KsR0pnl3q3QV/m95OybW5pahr2eUfaY2q0GwKLiWk0c5PMS6K/sQZFhVgjO1xwpLrUEeeMXOaVYlzoEyJXk+qT+LWvfY3FixdTVVXFH//4R77zne+wd+9ebr755iEfc8MNN3D99dcXcZRCiN7SmaBHDxwgZVl0KQq6bRMyTRaVlZGwrGGDi2LscdW7rC+ddUo3pRjr846UCZMs1cQ32rlJ5qVussHr34w1KCpk6VwhL7qV0nMKMRLFtm17vAcxnG9/+9vceOONw95n69atLFiwYMDtv/zlL/l//+//0dXVhcvlGvSxg10RbGxsJBgMUtGz34wQorDS2aZ1LS28G43i13UO8XiY5nLRbhisrKwcNFAJGga3NDWhKUpmPRJASzKJZdtc2dhYkKAk388r3f+6hUIh/H7/hDj/FnJumurzkmzwOri29W3da6rqHAOComz2aJL3VYjcZDs3lXxQtX//fg70LFQfypw5c3D2+mKT9vbbb7No0SLeffddDj300KyebyJN6kJMNq3JJI8fOMB7sRhmT4ZquOCiKR7ntuZmGtxu3L1+H7csmuNxrmhooLHfWqV8KNTzFmpd2EQxkc6/xZybJtL7kg+Z4KHegV6hY4QMUi3ZBw+TVb6ComJkACXLKCaTbM/BJf9Jr62tpba2NqfHvv7666iqSl1dXZ5HJYQohDqnkzXTp2cdXIzXeqRCPe9I669E6ZC5qTDy3aVuMsnXeqJCls5JNkxMZZPmzPTSSy/xpz/9iTPOOAOfz8dLL73EVVddxaWXXkplT4cuIcTEkG1wMV7rkWQdlMiWzE2jIxu8jqyU1xONpe27EBNdaf6rzIHL5eK+++7juuuuI5FIcPDBB3PVVVdx9dVXj/fQhBAFNFhnvsH2g5oszysmFpmbRqcQXeomsnyX0RWyLE+yjGKqmzSf7sWLF/Pyyy+P9zCEEEU2WGe+YmSKxut5xcQic9PoyAav3fJdRleMsjzJMoqpTgpchRCTgl/XaXS7ix7YjNfzCjFZVS2vonJlJbZlE2+OY1t2SW3wagQN4k1xjKBRsOdIl9EpmoK7wY2iKXRs6KB9Y3tJHG8wvbOMvU3VLKOYeuQTLoQQQoiSUaobvBarCUO+y+iKVZYnWUYx1UmmSggxaQUNg6Z4nKBRuCvKQojC0P067kZ3yXwZL0a2B/5WRtc/s6P7dcyIOSATVOzjDafUs4xCFFJpnKmEECKPZDNdIUQ+FbMJQ76bdRSz+Uf/LCM2oIAVt6Slupj0JKgSQkw6G9vb2dDRQb3DQYPbTcgw2NDR3dZ3VY209RVCjE4xmzDku4wufbwDjx7A6DRw1Dqwk/aYyvJG6iKoulW6NnfJflViSpGgSggxqQQNgy3hMPUOB3U9G/O6nU5sutufLw0EpKmEEGJUit3qPV0uF94SJt4cR/NqOZfRWQkLO2VjRAy63u4CwD3LTe0FtaM+XrbrymS/KjEVyTcLIcSkEjIMIqZJg7vvFWW/rtMcjxMyDAmqhBCjUuwmDPls1tG+sZ3OpzvxLvTiO8pHsjWJGTJRHSpW3CLZmsz6+NkES7JflZiq5FMthJhUKnQdr6YRMgzczr9dUQ4aBl5No0ICKiFEDvKZPcqW7h9b58PBAhw9oJP4MEHL/7QQ3BzENu2syvOyDZZkvyoxVcmnWggxqfh1nSU+Hxs6OrB7fg4aBq2pFCsrKyVLJYTISam2eh/OUAFOYl+C2NYYrhku3I3ZledlGywVu1RSiFIhqwWFEJPO8qoqVlZWEjVN3unqImqarKysZHlV9xVlabUuhMhVMVq9G0GDyNsRut7uGtMmw4NtyJtqT9H1ehc4QK/WUd0qzjonjjoH4S3hIZ8v281906WSqZYUyZZkd4lhS5JUawrfEl/JB6JC5Eo+2UKIohmpY1TeKcrf/kNarQshSpuVsDjwxAFa/6+V+K44tmnjnOak9jO11H+6ftSd83qvBbNTNokPEwRfDBLfE0cr12h7qA3fsT68i7wjlueNZl3ZeJRKCjHeJKgSQhRcth2j8qV3S/XpXm+mpforoRAHTFNarQshSlL7xnb2/mIvydYkmN0XohJ7EkS3RoltizH7O7NHfc5MBzIt61oI/SmEHbPR/d1Zp9T+FMHNQRRNwVnvHLE8L9tgaSKWSgoxVvIJF0IUXDHb6w7VUj1uWTwXDHKy3y+t1oUQRZNtht4IGoQ2hzAjJgoKRthALVNRXSpm1KT9sXbKDyun7oK6UT2/6lIJLA3QubETZ70TR8CBGTNJfpREcSlYCYuuN7vwLvBSfU71sGMcbbA01kYbQkwk8kkXQhRUsdvrDtVS3aEoREwTR08pYJq0WhdCFMJoM/RGyCDVkcI27e7Ayq2guTVs08Y2bGzVJvRyiKoVVaM+ZxohAzNmonpUtAot8/hkaxIrZmFGTXwf82VdnifBkhADySICIURBpTtG9S8p0f06ZsQcsOh5rHq3VO8tZdt4NY2Ubfe5XVqtCyEKIZ2hVzQFd4MbRVPo2NBB+8b2Qe+vV+g4Kh3YKRsrYaE6ur+i2anuc5bD78A27JzOmeljY4MVs0AD10Eu3LPcuA5yUXlKJXXn1RWkHFuIqUL+9QghCirbjlH5km6p3pJK0ZJMErcsWpJJgqbJaX4/QdPsc3trKsUSn0+yVEKIvOmfoc+mw57u16lYWoFWoWHGui84mdHu/1UcCs7pTvRqPadzZubYXo3kviRGR3dWzGg3cFQ7CCzPT8WAETSIN8XH1LFQiIlKvkUIIQpqNB2j8iXdOn1LOExzPI5X01hZWckpgQAvdHYOuD19fyGEyIdcN8CtWl6FnbJpvqWZ6HtRrLiFo9ZB2aFl6D59TC3J08dOdxYEcM9xU3tB7Zi78hW7GZEQpUiCKiFEwRW7va5LVVlVU8PSQICQYVCh65lM1FC3CyFEvqQz9MnWJFq51r2WyaONmKFXXSq159bi/5if/Q/uJ/J2BEVT0Kv1TJCSq/SxK8+oJNGcwMbG3ZCf/baK2YxIiFIl3yaEEAU3Xu11/UMETUPdLoQQ+aC6VWzbpuOpDhSHgu7TM6V7VatGbjThrHNy0FcOKsjefvluMlHsZkRClCrJyQohikb367gb83NlVAghSlX7xnaMVgPPIR40n0aqK0VsWwy9Th9VtmkinDOL3YxIiFJVuv9KhRBCCCEmmHTmxjnDSXldeXfL8piJ2WWiKApW3JpU64x6NyNyup2Z2wvVjEiIUjV5/lULIYQQQoyz/pkb1aPiqOoujZuMmZt0M6JUS4pkSxIrbpFsSZJqTY2psYYQE40EVUIIIYQQeVLsbSRKQdXyKipXVmJbNvHmOLZlF7QZkRClaPL9yxZCCCGEGEEhmkDA+GwjMd7GqxmREKVEPvFCCCGEmDKKsadSsbeRKBX57iwoxEQin3whhBBCTBlD7alkRSwqTqrIS5ZFMjdCTD3yL1wIIYQQU8Jgeyo5NAfR96J8eMeHdL7YiaPKkbfMlWRuhJg6pFGFEEIIIaaEwfZUirwbIbErgW3YOKocKJpCx4YO2je2931s0CDeFMcITq7ufUKI/JDLJ0IIIYSYEvrvqWTFLOK74ihOBa1MQw/oaB4N7O71UIGlAVS3WvA1WIVWqKYcQoi/kX9ZQgghhJgS+nfmsy2bVEcKVVVxz3J3B1Q994s3xzFCBl2buwZdgwVQs6pmPF/OsIygQXJ/kq5Xu4hsjUzYgFCIiUKCKiGEEEJMGb078xkHDFSHiusgF2ULyzL3Se8plc5Y9V6D5XQ7+2SySi3z07u7YdebXSSbk3gO8eA7xocZNSdEQCjERFRaZwIhhBBCiALq35kv+FKQ0AshjHZjwJ5SKGBGTNwN7j7H6J3JKrWgKt3dUPNr2FEbrVwj2ZwkFohRfkR5SQeEQkxk8q9JCCGEEFNOujOfs86J5tUG3VPKilt91mClpTNZvRteFFK2a6J6dzdUdAXbstFrdOy4TXxXnLJ5ZSUdEAoxkcm/JiGEEEJMWcPtKaW61D5rsPpnsgodlIx2o+J0d0N3gxvbtlGcSndg6NFIdaQwYyZ2yi5qQCjEVCGrFIUQQggx5el+HXeje0CgVLW8isqVldiWTbw5jm3ZmUxWoaVL+RRNwd3gHrLde+Y19OpuqHk03LPcmP9/e/ceFlWd/wH8PcwMw224qKAgKFLgFQ2F/Im6ZqHgmgtl6toU2kXbwhKTlC54ydS2NNfcnlIrsF0Nt61sVxIXXdEkS1AhDUIlcLAQJIyLxmVmzu8PH2YduTgwM5yZ8f16Hp7HOefMOZ/vkTkfPvP9nu+p06LlcgskDhJoG7RoqWqBMlzJXioiM+MnioiIiKgDnfVkWVJ7Dyq+1SQZN89u6BzkDM2vGvx27jc4+jtCIpf0WEFIdLthUUVERER0C633YPWUpotNaK5ohnOQc5s4Orsn6sbZDZsuNcE52Bm9/9AbytFKOHo7soeKyEL4ySIiIiKyEq33UdUerUXD6QZcO3sNrsNd4TrEFRK55JaTZIjVs0Z0u+M9VURERERWQj8luqsUbsPdoLumQ/3xetR/V4/mymaj74nq6B6xG2lqNWgsb4SmVmPuZhDddvjVBREREZEVuPk+KpmXDBJHCa4WXsXVM1fh2Muxy/dEtTcde1dnFSSiW2NRRURERGQFbpwSHQAc5A5wG+kGRYACv5X+Bp9HfOA23M2ofXVWOLX2hsn7yuHk7wRNneb65BYA+kzvY7H2EdkzFlVEREREVuDGKdFvfNiw0CxA4avQF1vG6Khw0l3V4WrR1S7NKkhEt8Y+XiIiIiIr0DolektlC5orm6Fr1HXpPqpWNw8jdHBygKOPI+Q+ctR9U4eWmpY2E13IPGTQXtVCU8f7q4i6w2aKqrVr1yIyMhIuLi7w9PRsdxu1Wo3p06fDxcUFPj4+eOGFF6DR8OJARESWwdxE5maOhw23DiNsr3ASNAIkUkmb4ulWswoSUeds5pPT3NyMWbNmYdy4cfjggw/arNdqtZg+fTr69euHr7/+GhUVFYiPj4dcLse6devMGotWq0VLS4tZ90m3F7lcDqlUKnYYRGQia8pNZB/MMSV6R8MINbUayHrL4DLUBXVf1QHC9UJLU6tBS1ULvGK8OPSPqJskgiAIYgfRFWlpaUhMTMSvv/5qsHzfvn24//778fPPP6Nv374AgPfeew/Lly/H5cuX4ejo2M7e2qqrq4OHhwdqa2vh7u5usE4QBFy6dKnNsYm6w9PTE/369YNEIhE7FCKr0Nn119pZMjfZ8nkh8VRnVF+/p8pH3qZwap2sgrP/Ed2asddgu/k64tixYwgNDdUnLQCIjo7G008/je+//x5hYWHtvq+pqQlNTU3613V1dR0eo7Wg8vHxgYuLC/8Ypm4RBAHXrl1DVVUVAMDX11fkiIjIUrqTm7qSl8j2tDfFuSW0Dhesz6tH48VGSF2l+oKKDwgmMj+7+QRdunTJIGkB0L++dOlSh+9bv349Vq9efcv9a7VafUHVu3dv04Kl256zszMAoKqqCj4+PhwKSGSnupObjM1LZFt6+tlQxhROMg8WU0TmImofb3JyMiQSSac/P/zwg0VjePHFF1FbW6v/KS8vb3e71nuoXFxcLBoP3T5af5d4fx6RdRE7Nxmbl8i2tE5xLpFK4OTvBIlUgiuZV1BzoMaix5V5yOAU4MTiicjCRP2ELV26FPPnz+90m6CgIKP21a9fPxw/ftxgWWVlpX5dRxQKBRQKhVHHAMAhf2Q2/F0isk5i56au5iWyfjdPcQ7w2VBE9kbUT7C3tze8vb3Nsq9x48Zh7dq1+uFUAJCVlQV3d3cMGzbMLMcgIiL7x9xE5tY6xfnND++VecjQeLHx+vA8FlVENs1mpnhRq9XIz8+HWq2GVqtFfn4+8vPz0dDQAACYOnUqhg0bhkcffRQFBQXYv38/XnnlFSQkJPAbv9tAWlpah8+IuZFEIsGePXssHg8R3R6Ym8gYN05xfiM+G4rIfthMUbVixQqEhYVh5cqVaGhoQFhYGMLCwpCXlwcAkEql2Lt3L6RSKcaNG4dHHnkE8fHxePXVV0WOXHz33HMPEhMTxQ7DoubMmYOzZ8/qX69atQp33XVXm+0qKiowbdq0HoyMiOwZcxMZQ+YhgzJciZbKFjRXNkPXqENzZTNaqlqgDFeyl4rIDtjMpzgtLQ1paWmdbjNw4EB8+eWXPROQnREEAVqtFjKZzfxKGHB2dtbPqNeZzu6vIyLqKuYmMlZnU5wTke2zmZ4qe1Or0aC8sRG1Gs2tNzbB/PnzcfjwYWzevFk/a1VZWRmys7MhkUiwb98+jBkzBgqFAkePHsX8+fMRFxdnsI/ExETcc889+tc6nQ7r16/HoEGD4OzsjFGjRuGf//xnp3EEBgZizZo1mDt3LlxdXdG/f3+88847Btuo1WrExsbCzc0N7u7umD17tv6GbgAoKCjA5MmToVQq4e7ujjFjxui/Db5x+F9aWhpWr16NgoICfZtb/+i5cfhfZGQkli9fbhDD5cuXIZfLceTIEQDXnxeTlJSE/v37w9XVFWPHjkV2drYRZ56IiOh/Wqc4D0gMgP+z/ghIDECf6X34sF0iO8FPcg9r0umQUV2Nv5SXY8vFi/hLeTkyqqvRpNNZ5HibN2/GuHHjsGDBAlRUVKCiogIBAQH69cnJyXj99ddRVFSEkSNHGrXP9evX46OPPsJ7772H77//HkuWLMEjjzyCw4cPd/q+N998E6NGjcKpU6eQnJyMxYsXIysrC8D1Qi02NhY1NTU4fPgwsrKy8OOPP2LOnDn696tUKvj7+yM3NxcnTpxAcnIy5HJ5m+PMmTMHS5cuxfDhw/VtvnE/N+4vPT0dgiDol+3evRt+fn6YOHEiAGDRokU4duwY0tPT8d1332HWrFmIiYnBuXPnjDpXREREN+IU50T2iZ/oHnagpgaZV66gr1wOfycn1Gk0yLxyBQAwvU8fsx/Pw8MDjo6OcHFxaXfo26uvvoopU6YYvb+mpiasW7cOBw4cwLhx4wBcn1r46NGj2Lp1KyZNmtThe8ePH4/k5GQAQEhICHJycrBp0yZMmTIFBw8exOnTp1FaWqov+j766CMMHz4cubm5iIiIgFqtxgsvvIAhQ4YAAIKDg9s9jrOzM9zc3CCTyTod7jd79mwkJibi6NGj+iJq165dmDt3LiQSCdRqNVJTU6FWq+Hn5wcASEpKQmZmJlJTU7Fu3TqjzxsRERER2S/2VPWgWo0GefX16CuXw8fREU4ODvBxdISPXI68+nqLDwVsT3h4eJe2P3/+PK5du4YpU6bAzc1N//PRRx+hpKSk0/e2FmE3vi4qKgIAFBUVISAgwKAXbdiwYfD09NRv8/zzz+PJJ59EVFQUXn/99Vse71a8vb0xdepU7Ny5EwBQWlqKY8eOQaVSAQBOnz4NrVaLkJAQg7YePnzY5GMTERERkf1gT1UPqtNocFWrhb+T4XMqPGQyXGxsRJ1GA48enijC1dXV4LWDg4PBcDgAaGlp0f+7dZrgjIwM9O/f32A7S08PvGrVKjz88MPIyMjAvn37sHLlSqSnp+OBBx7o9j5VKhWee+45bNmyBbt27UJoaChCQ0MBXG+rVCrFiRMnIJVKDd7n5uZmUluIiIiIyH6wqOpB7jIZXKVS1Gk0cHJ01C+v1WjgKpXC3UIFlaOjI7RarVHbent748yZMwbL8vPz9fcuDRs2DAqFAmq1utOhfu355ptv2rweOnQoAGDo0KEoLy9HeXm5vreqsLAQv/76q8EDMkNCQhASEoIlS5Zg7ty5SE1NbbeoMrbNsbGxWLhwITIzM7Fr1y7Ex8fr14WFhUGr1aKqqko/PJCIiIiI6GYc/teDPGQyhCuVqGxpQWVzMxp1OlQ2N6OqpQXhSqXFeqkCAwPx7bffoqysDNXV1dB1MinGvffei7y8PHz00Uc4d+4cVq5caVBkKZVKJCUlYcmSJdixYwdKSkpw8uRJbNmyBTt27Og0jpycHLzxxhs4e/Ys3nnnHXzyySdYvHgxACAqKgqhoaFQqVQ4efIkjh8/jvj4eEyaNAnh4eH47bffsGjRImRnZ+PChQvIyclBbm6uvihrr82lpaXIz89HdXU1mpqa2t3O1dUVcXFxSElJQVFREebOnatfFxISApVKhfj4eHz22WcoLS3F8ePHsX79emRkZHTaViIiIiK6fbCo6mFRvXohxssLOkHAxcZG6AQBMV5eiOpluedUJCUlQSqVYtiwYfD29oZare5w2+joaKSkpGDZsmWIiIhAfX29Qe8NAKxZswYpKSlYv349hg4dipiYGGRkZGDQoEGdxrF06VLk5eUhLCwMr732Gt566y1ER0cDuD7V+RdffAEvLy/87ne/Q1RUFIKCgrB7924A1x+g+csvvyA+Ph4hISGYPXs2pk2bhtWrV7d7rJkzZyImJgaTJ0+Gt7c3Pv744w7jUqlUKCgowMSJEzFgwACDdampqYiPj8fSpUsxePBgxMXFITc3t812RERERHT7kgg330Bzm6urq4OHhwdqa2vh7u6uX97Y2IjS0lIMGjQITjfdE9UdtRoN6jQauMtkPX4flRgCAwORmJiIxMREsUOxGub+nSKydR1df293PC9EROIx9hps/3/NWymP26SYIiIiIiKydxz+R0REREREZAJ2lVCPKCsrEzsEIiIiIiKLYE8VERERERGRCdhTRURERNQDNLUaaOo0kLnLIPPgn2BE9oSfaCIiIiIL0jXpUHOgBvV59dBe1ULqKoUyXIleUb3goOCgISJ7wE8yERERkQXVHKjBlcwrkEglcPJ3gkQqwZXMK6g5UCN2aERkJiyqiIiIiCxEU6tBfV495H3lcPRxhIOTAxx9HCH3kaM+rx6aWo3YIRKRGbCoIiIiIrIQTZ0G2qtayNwN77iQecigvaqFpo5FFZE9YFF1GxAEAQsXLkSvXr0gkUiQn59/y/eUlZUZva29kkgk2LNnj9hhEBGRkTS1GjSWN1pV74/MXQapq7RN8aSp1UDqKm1TbBGRbeIn+TaQmZmJtLQ0ZGdnIygoCH369BE7JKuyatUq7Nmzp00BWVFRAS8vL3GCIiIio5l7IghzztIn85BBGa7ElcwrgHD9taZWg5aqFnjFeHEWQCI7wU/ybaCkpAS+vr6IjIwUOxSb0q9fP7FDICIiI7ROBCHvK4eTvxM0dZrrRQyAPtON/yLRUrP09YrqBQCoz6tH48VGSF2l8Irx0i8nItvH4X8i6akhCvPnz8ezzz4LtVoNiUSCwMBAANd7ryZMmABPT0/07t0b999/P0pKSjrcz5UrV6BSqeDt7Q1nZ2cEBwcjNTVVv768vByzZ8+Gp6cnevXqhdjYWJSVlXW4v+zsbEgkEhw8eBDh4eFwcXFBZGQkiouLDbb74osvMHr0aDg5OSEoKAirV6+GRvO/c/bDDz9gwoQJcHJywrBhw3DgwIE2w/aWL1+OkJAQuLi4ICgoCCkpKWhpaQEApKWlYfXq1SgoKIBEIoFEIkFaWhoAw+F/kZGRWL58uUFsly9fhlwux5EjRwAATU1NSEpKQv/+/eHq6oqxY8ciOzu7w3NARESmM+dEEJaapc9B4YA+0/sgIDEA/s/6IyAxAH2m9+F06kR2hJ/mHqZr0qE6oxrlfynHxS0XUf6XclRnVEPXpLPI8TZv3oxXX30V/v7+qKioQG5uLgDg6tWreP7555GXl4eDBw/CwcEBDzzwAHS69uNISUlBYWEh9u3bh6KiIrz77rv6YYQtLS2Ijo6GUqnEV199hZycHLi5uSEmJgbNzc2dxvfyyy9j48aNyMvLg0wmw+OPP65f99VXXyE+Ph6LFy9GYWEhtm7dirS0NKxduxYAoNVqERcXBxcXF3z77bfYtm0bXn755TbHUCqVSEtLQ2FhITZv3ozt27dj06ZNAIA5c+Zg6dKlGD58OCoqKlBRUYE5c+a02YdKpUJ6ejoEQdAv2717N/z8/DBx4kQAwKJFi3Ds2DGkp6fju+++w6xZsxATE4Nz5851eg6IiKj7zDURRE/M0ifzkMEpwIlD/ojsED/VPcxcQxSM5eHhAaVSCalUajCcbebMmQbbffjhh/D29kZhYSFGjBjRZj9qtRphYWEIDw8HAH2PF3C9uNDpdHj//fchkUgAAKmpqfD09ER2djamTp3aYXxr167FpEmTAADJycmYPn06Ghsb4eTkhNWrVyM5ORnz5s0DAAQFBWHNmjVYtmwZVq5ciaysLJSUlCA7O1vftrVr12LKlCkGx3jllVf0/w4MDERSUhLS09OxbNkyODs7w83NDTKZrNPhfrNnz0ZiYiKOHj2qL6J27dqFuXPnQiKRQK1WIzU1FWq1Gn5+fgCApKQkZGZmIjU1FevWretw30RE1H03TgTh6OSoX97ViSBaizMnfyfD/XvI0Hix8fo9ViyGiKgDvDr0oJu/BQNwPQEI18dZe07w7LEL9rlz57BixQp8++23qK6u1vdQqdXqdouqp59+GjNnzsTJkycxdepUxMXF6e/RKigowPnz56FUKg3e09jY2OmQQgAYOXKk/t++vr4AgKqqKgwYMAAFBQXIycnR90wB13unGhsbce3aNRQXFyMgIMCgGLr77rvbHGP37t14++23UVJSgoaGBmg0Gri7u9/qFBnw9vbG1KlTsXPnTkycOBGlpaU4duwYtm7dCgA4ffo0tFotQkJCDN7X1NSE3r17d+lYRERkvI4mgmgqb4J7pPHXenMVZ0R0e+IVogdZ07dgM2bMwMCBA7F9+3b4+flBp9NhxIgRHQ7XmzZtGi5cuIAvv/wSWVlZuO+++5CQkIANGzagoaEBY8aMwc6dO9u8z9vbu9M45HK5/t+tvVytBV5DQwNWr16NBx98sM37nJyc2ixrz7Fjx6BSqbB69WpER0fDw8MD6enp2Lhxo1Hvv5FKpcJzzz2HLVu2YNeuXQgNDUVoaKg+VqlUihMnTkAqlRq8z83NrcvHIiIi4xlMBFHWiKbKJkAAGr5rQOOFRqMmm+AsfURkCl4hepC1fAv2yy+/oLi4GNu3b9cPZTt69Ogt3+ft7Y158+Zh3rx5mDhxIl544QVs2LABo0ePxu7du+Hj49PlHqDOjB49GsXFxbjzzjvbXT948GCUl5ejsrISffv2BQD9PWOtvv76awwcONDgXqsLFy4YbOPo6AitVnvLeGJjY7Fw4UJkZmZi165diI+P168LCwuDVqtFVVWV/pwSEVHPaJ0IwnOCJ6o+r4L2ay0UAxSQucu6NMy+o1n63CPc0VjeaJYp1onIPvHK0IOs5VswLy8v9O7dG9u2bYOvry/UajWSk5M7fc+KFSswZswYDB8+HE1NTdi7dy+GDh0K4HoPzptvvonY2Fj9pBgXLlzAZ599hmXLlsHf379bca5YsQL3338/BgwYgIceeggODg4oKCjAmTNn8Nprr2HKlCm44447MG/ePLzxxhuor6/X3z/V2usVHBwMtVqN9PR0REREICMjA59//rnBcQIDA1FaWor8/Hz4+/tDqVRCoVC0icfV1RVxcXFISUlBUVER5s6dq18XEhIClUqF+Ph4bNy4EWFhYbh8+TIOHjyIkSNHYvr06d06B0RE1DVNF5qgGKDo1jD7G4szTZ0GDgoH1OXW4ad3fzLrFOtEZH94RehhvaJ6wSvGC4JOQOPFRgg6ocefVeHg4ID09HScOHECI0aMwJIlS/Dmm292+h5HR0e8+OKLGDlyJH73u99BKpUiPT0dAODi4oIjR45gwIABePDBBzF06FA88cQTaGxsNKnnKjo6Gnv37sV//vMfRERE4P/+7/+wadMmDBw4EAAglUqxZ88eNDQ0ICIiAk8++aS+R6p1eOAf/vAHLFmyBIsWLcJdd92Fr7/+GikpKQbHmTlzJmJiYjB58mR4e3vj448/7jAmlUqFgoICTJw4EQMGDDBYl5qaivj4eCxduhSDBw9GXFwccnNz22xHRESWYa6ZAFtn6avLrbPIFOtEZH8kwo1zRBPq6urg4eGB2tpag4KgsbERpaWlGDRokNH383TGnE9rp//JycnBhAkTcP78edxxxx1ih9Mpc/9OEdm6jq6/tzueF+NpajUo/0s5JFKJvqcKAJormyHoBAQkBhidc825LyKyXcZeg3k1EInMg8WUOXz++edwc3NDcHAwzp8/j8WLF2P8+PFWX1AREZH5mXOYvTVNLkVE1o9XA7Jp9fX1WL58OdRqNfr06YOoqKhuzexHRET2oaPJJro6zN5aJpciItvAKwLZtPj4eINZ+IiI6PZ282QT3R1mby2TSxGRbeAVgYiIiOyOOYbZm6vXi4jsH4uqLuK8HmQu/F0iIrJu5ur1IiL7xyuDkeRyOQDg2rVrcHZ2FjkasgfXrl0D8L/fLSIisk6cXIqIboVXCCNJpVJ4enqiqqoKwPVnM7U+YJaoKwRBwLVr11BVVQVPT09IpVKxQyIiIiIiE7Co6oJ+/foBgL6wIjKFp6en/neKiIiIiGwXi6oukEgk8PX1hY+PD1paWsQOh2yYXC5nDxURERGRnWBR1Q1SqZR/EBMREREREQDAQewAiIiIiIiIbBmLKiIiIiIiIhOwqCIiIiIiIjIB76m6SesDWevq6kSOhIjo9tJ63eWDsQ0xLxERicfY3MSi6ib19fUAgICAAJEjISK6PdXX18PDw0PsMKwG8xIRkfhulZskAr8SNKDT6fDzzz9DqVRa5cN96+rqEBAQgPLycri7u4sdTrexHdaF7bAu9tIOoGttEQQB9fX18PPzg4MDR6e3Yl7qOfbSFrbDurAd1qWr7TA2N7Gn6iYODg7w9/cXO4xbcnd3t+lf6FZsh3VhO6yLvbQDML4t7KFqi3mp59lLW9gO68J2WJeutMOY3MSvAomIiIiIiEzAooqIiIiIiMgELKpsjEKhwMqVK6FQKMQOxSRsh3VhO6yLvbQDsK+2UPvs6f/YXtrCdlgXtsO6WKodnKiCiIiIiIjIBOypIiIiIiIiMgGLKiIiIiIiIhOwqCIiIiIiIjIBiyoiIiIiIiITsKiycRkZGRg7diycnZ3h5eWFuLg4sUPqtqamJtx1112QSCTIz88XO5wuKSsrwxNPPIFBgwbB2dkZd9xxB1auXInm5maxQzPKO++8g8DAQDg5OWHs2LE4fvy42CF1yfr16xEREQGlUgkfHx/ExcWhuLhY7LBM9vrrr0MikSAxMVHsULrsp59+wiOPPILevXvD2dkZoaGhyMvLEzss6iHMTdbBlnOTreclgLnJGlkyN7GosmGffvopHn30UTz22GMoKChATk4OHn74YbHD6rZly5bBz89P7DC65YcffoBOp8PWrVvx/fffY9OmTXjvvffw0ksviR3aLe3evRvPP/88Vq5ciZMnT2LUqFGIjo5GVVWV2KEZ7fDhw0hISMA333yDrKwstLS0YOrUqbh69arYoXVbbm4utm7dipEjR4odSpdduXIF48ePh1wux759+1BYWIiNGzfCy8tL7NCoBzA3WQ9bzU32kJcA5iZrY/HcJJBNamlpEfr37y+8//77YodiFl9++aUwZMgQ4fvvvxcACKdOnRI7JJO98cYbwqBBg8QO45buvvtuISEhQf9aq9UKfn5+wvr160WMyjRVVVUCAOHw4cNih9It9fX1QnBwsJCVlSVMmjRJWLx4sdghdcny5cuFCRMmiB0GiYC5yfrZQm6yx7wkCMxNYrN0bmJPlY06efIkfvrpJzg4OCAsLAy+vr6YNm0azpw5I3ZoXVZZWYkFCxbgb3/7G1xcXMQOx2xqa2vRq1cvscPoVHNzM06cOIGoqCj9MgcHB0RFReHYsWMiRmaa2tpaALD689+RhIQETJ8+3eD/xZb861//Qnh4OGbNmgUfHx+EhYVh+/btYodFPYC5yfpZe26y17wEMDeJzdK5iUWVjfrxxx8BAKtWrcIrr7yCvXv3wsvLC/fccw9qampEjs54giBg/vz5+NOf/oTw8HCxwzGb8+fPY8uWLXjqqafEDqVT1dXV0Gq16Nu3r8Hyvn374tKlSyJFZRqdTofExESMHz8eI0aMEDucLktPT8fJkyexfv16sUPpth9//BHvvvsugoODsX//fjz99NN47rnnsGPHDrFDIwtjbrJutpCb7DEvAcxN1sDSuYlFlZVJTk6GRCLp9Kd1jDQAvPzyy5g5cybGjBmD1NRUSCQSfPLJJyK3wvh2bNmyBfX19XjxxRfFDrldxrbjRj/99BNiYmIwa9YsLFiwQKTIb18JCQk4c+YM0tPTxQ6ly8rLy7F48WLs3LkTTk5OYofTbTqdDqNHj8a6desQFhaGhQsXYsGCBXjvvffEDo26ibnJujA32R7mJvFZOjfJzLIXMpulS5di/vz5nW4TFBSEiooKAMCwYcP0yxUKBYKCgqBWqy0ZolGMbcd///tfHDt2DAqFwmBdeHg4VCqV6N9sG9uOVj///DMmT56MyMhIbNu2zcLRma5Pnz6QSqWorKw0WF5ZWYl+/fqJFFX3LVq0CHv37sWRI0fg7+8vdjhdduLECVRVVWH06NH6ZVqtFkeOHMFf//pXNDU1QSqVihihcXx9fQ2uTQAwdOhQfPrppyJFRKZibrqOucny7C0vAcxN1sLSuYlFlZXx9vaGt7f3LbcbM2YMFAoFiouLMWHCBABAS0sLysrKMHDgQEuHeUvGtuPtt9/Ga6+9pn/9888/Izo6Grt378bYsWMtGaJRjG0HcP1bwMmTJ+u/mXVwsP6OYEdHR4wZMwYHDx7UT3ms0+lw8OBBLFq0SNzgukAQBDz77LP4/PPPkZ2djUGDBokdUrfcd999OH36tMGyxx57DEOGDMHy5cttImkBwPjx49tMG3z27FmruDZR9zA3MTf1FHvJSwBzk7WxeG6y2BQYZHGLFy8W+vfvL+zfv1/44YcfhCeeeELw8fERampqxA6t20pLS21yhqWLFy8Kd955p3DfffcJFy9eFCoqKvQ/1i49PV1QKBRCWlqaUFhYKCxcuFDw9PQULl26JHZoRnv66acFDw8PITs72+DcX7t2TezQTGaLMywdP35ckMlkwtq1a4Vz584JO3fuFFxcXIS///3vYodGPYC5yXrYam6yh7wkCMxN1sbSuYlFlQ1rbm4Wli5dKvj4+AhKpVKIiooSzpw5I3ZYJrHVxJWamioAaPfHFmzZskUYMGCA4OjoKNx9993CN998I3ZIXdLRuU9NTRU7NJPZYuISBEH497//LYwYMUJQKBTCkCFDhG3btokdEvUQ5ibrYcu5ydbzkiAwN1kjS+YmiSAIgnn6vIiIiIiIiG4/1j2wloiIiIiIyMqxqCIiIiIiIjIBiyoiIiIiIiITsKgiIiIiIiIyAYsqIiIiIiIiE7CoIiIiIiIiMgGLKiIiIiIiIhOwqCIiIiIiIjIBiyoiG1NWVgaJRIL8/HyxQyEiIgLA3EQkEQRBEDsIIjKeVqvF5cuX0adPH8hkMrHDISIiYm6i2x6LKiIb0tzcDEdHR7HDICIi0mNuIuLwPyKL2bZtG/z8/KDT6QyWx8bG4vHHH0dJSQliY2PRt29fuLm5ISIiAgcOHDDYNjAwEGvWrEF8fDzc3d2xcOHCNkMstFotnnjiCQwaNAjOzs4YPHgwNm/ebLCf+fPnIy4uDhs2bICvry969+6NhIQEtLS06LdpamrC8uXLERAQAIVCgTvvvBMffPCBfv2ZM2cwbdo0uLm5oW/fvnj00UdRXV1t5rNGRESWxNxEZBksqogsZNasWfjll19w6NAh/bKamhpkZmZCpVKhoaEBv//973Hw4EGcOnUKMTExmDFjBtRqtcF+NmzYgFGjRuHUqVNISUlpcxydTgd/f3988sknKCwsxIoVK/DSSy/hH//4h8F2hw4dQklJCQ4dOoQdO3YgLS0NaWlp+vXx8fH4+OOP8fbbb6OoqAhbt26Fm5sbAODXX3/Fvffei7CwMOTl5SEzMxOVlZWYPXu2Gc8YERFZGnMTkYUIRGQxsbGxwuOPP65/vXXrVsHPz0/QarXtbj98+HBhy5Yt+tcDBw4U4uLiDLYpLS0VAAinTp3q8LgJCQnCzJkz9a/nzZsnDBw4UNBoNPpls2bNEubMmSMIgiAUFxcLAISsrKx297dmzRph6tSpBsvKy8sFAEJxcXGHcRARkfVhbiIyP/ZUEVmQSqXCp59+iqamJgDAzp078cc//hEODg5oaGhAUlIShg4dCk9PT7i5uaGoqKjNt4Hh4eG3PM4777yDMWPGwNvbG25ubti2bVub/QwfPhxSqVT/2tfXF1VVVQCA/Px8SKVSTJo0qd39FxQU4NChQ3Bzc9P/DBkyBABQUlJi/AkhIiLRMTcRmR+nZyGyoBkzZkAQBGRkZCAiIgJfffUVNm3aBABISkpCVlYWNmzYgDvvvBPOzs546KGH0NzcbLAPV1fXTo+Rnp6OpKQkbNy4EePGjYNSqcSbb76Jb7/91mA7uVxu8FoikejH1Ds7O3d6jIaGBsyYMQN//vOf26zz9fXt9L1ERGRdmJuIzI9FFZEFOTk54cEHH8TOnTtx/vx5DB48GKNHjwYA5OTkYP78+XjggQcAXE8OZWVlXT5GTk4OIiMj8cwzz+iXdfUbutDQUOh0Ohw+fBhRUVFt1o8ePRqffvopAgMDOVUuEZGNY24iMj8O/yOyMJVKhYyMDHz44YdQqVT65cHBwfjss8+Qn5+PgoICPPzww21mYzJGcHAw8vLysH//fpw9exYpKSnIzc3t0j4CAwMxb948PP7449izZw9KS0uRnZ2tv6E4ISEBNTU1mDt3LnJzc1FSUoL9+/fjscceg1ar7XLMREQkLuYmIvNiUUVkYffeey969eqF4uJiPPzww/rlb731Fry8vBAZGYkZM2YgOjpa/01hVzz11FN48MEHMWfOHIwdOxa//PKLwTeDxnr33Xfx0EMP4ZlnnsGQIUOwYMECXL16FQDg5+eHnJwcaLVaTJ06FaGhoUhMTISnpyccHHgZISKyNcxNRObFh/8SERERERGZgGU8ERERERGRCVhUERERERERmYBFFRERERERkQlYVBEREREREZmARRUREREREZEJWFQRERERERGZgEUVERERERGRCVhUERERERERmYBFFRERERERkQlYVBEREREREZmARRUREREREZEJ/h/+3yAAGjJ7egAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "def svm_factory_lp(X, y, lambd=1):\n",
        "    \"\"\"\n",
        "    Creates a linear support vector machine (SVM) model using linear programming.\n",
        "\n",
        "    Parameters:\n",
        "    X : DataFrame\n",
        "        Feature matrix as a DataFrame.\n",
        "    y : Series\n",
        "        Target vector as a Series.\n",
        "    lambd : float, optional\n",
        "        Regularization parameter. Default is 1.\n",
        "\n",
        "    Returns:\n",
        "    LinearSvm :\n",
        "        A trained linear SVM model.\n",
        "    \"\"\"\n",
        "\n",
        "    m = AMPL()\n",
        "\n",
        "    m.eval(\n",
        "        \"\"\"\n",
        "        set P;\n",
        "        set N;\n",
        "\n",
        "        param lambd;\n",
        "        param X{N,P};\n",
        "        param y{N};\n",
        "\n",
        "        # Decision variables\n",
        "        var wp{P} >= 0;\n",
        "        var wn{P} >= 0;\n",
        "        var b;\n",
        "        var z{N} >= 0;\n",
        "        var w{p in P} = wp[p] - wn[p];\n",
        "\n",
        "        minimize lasso: sum{i in N} z[i] / card(N)\n",
        "                            + lambd * sum{p in P} (wp[p]+wn[p]);\n",
        "\n",
        "        subject to hingeloss{i in N}:\n",
        "            z[i] >= 1-y[i]*(sum{p in P} w[p]*X[i,p] + b);\n",
        "\n",
        "    \"\"\"\n",
        "    )\n",
        "\n",
        "    # Use dataframe columns and index to index variables and constraints\n",
        "    m.set[\"P\"] = list(X.columns)\n",
        "    m.set[\"N\"] = np.array(X.index)\n",
        "\n",
        "    m.param[\"lambd\"] = lambd\n",
        "    m.param[\"y\"] = y\n",
        "\n",
        "    m.param[\"X\"] = X\n",
        "\n",
        "    m.option[\"solver\"] = SOLVER_LO\n",
        "\n",
        "    m.solve(verbose=False)\n",
        "\n",
        "    w = pd.Series([m.var[\"w\"][p].value() for p in X.columns], index=X.columns)\n",
        "    b = m.var[\"b\"].value()\n",
        "\n",
        "    return LinearSVM(w, b)\n",
        "\n",
        "\n",
        "# Train and test\n",
        "svm_lp = svm_factory_lp(X_train, y_train)\n",
        "test(svm_lp, X_test, y_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "562ba753-f1e8-46f0-810e-9a826e8d811b",
      "metadata": {
        "id": "562ba753-f1e8-46f0-810e-9a826e8d811b"
      },
      "source": [
        "## Quadratic programming model\n",
        "\n",
        "### Primal form\n",
        "\n",
        "The standard formulation of a linear support vector machine uses training sets with $p$-element feature vectors $x_i\\in\\mathbb{R}^p$ along with classification labels for those vectors, $y_i = \\pm 1$. A classifier is defined by two parameters: a weight vector $w\\in\\mathbb{R}^p$ and a bias term $b\\in\\mathbb{R}$\n",
        "\n",
        "$$\n",
        "\\begin{align*}\n",
        "y^{pred} & = \\text{sgn}(w^\\top x + b)\n",
        "\\end{align*}\n",
        "$$\n",
        "\n",
        "If a separating hyperplane exists, then we choose $w$ and $b$ so that a hard-margin classifier exists for the training set $(x_i, y_i)$ where\n",
        "\n",
        "$$\n",
        "\\begin{align*}\n",
        "y_i \\left( w^\\top x_i + b \\right) & \\geq 1 & \\forall i \\in 1, 2, \\dots, n\n",
        "\\end{align*}\n",
        "$$\n",
        "\n",
        "This can always be done if a separating hyperplane exists. But if a separating hyperplane does not exist, we introduce non-negative slack variables $z_i$ to relax the constraints and settle for a soft-margin classifier\n",
        "\n",
        "$$\n",
        "\\begin{align*}\n",
        "y_i \\left( w^\\top x_i + b \\right) & \\geq 1 - z_i& \\forall i \\in 1, 2, \\dots, n\n",
        "\\end{align*}\n",
        "$$\n",
        "\n",
        "The training objective is to minimize the total distance to misclassified data points. This leads to the optimization problem\n",
        "\n",
        "$$\n",
        "\\begin{align*}\n",
        "\\min\\quad  & \\frac{1}{2} \\|w \\|_2^2 + \\frac{c}{n} \\sum_{i=1}^n z_i \\\\\n",
        "\\text{s.t.} \\quad &  z_i \\geq 1 - y_i(w^\\top x_i + b) & \\forall i = 1, \\dots, n \\\\\n",
        "& z_i\\geq 0 & \\forall i = 1, \\dots, n \\\\\n",
        "& w\\in\\mathbb{R}^p \\\\\n",
        "& b\\in\\mathbb{R} \\\\\n",
        "\\end{align*}\n",
        "$$\n",
        "\n",
        "where $\\frac{1}{2} \\|\\bar{w}\\|_2^2$ is included to regularize the solution for $w$. Choosing larger values of $c$ will reduce the number and size of misclassifications. The trade-off will be larger weights $w$ and the accompanying risk of over over-fitting the training data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "7fdf300f",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "7fdf300f",
        "outputId": "67b1b207-b4e7-41ce-d552-774be3806b21"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LinearSvm(w = {'variance': 0.3033555525912086, 'skewness': 0.099369295011854}, b = -0.08537894540894428) \n",
            "\n",
            "Matthews correlation coefficient (MCC) = 0.714\n",
            "Sensitivity =  88.6%\n",
            "Precision =  85.7%\n",
            "Accuracy =  85.8%\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                 Predicted Positive  Predicted Negative\n",
              "Actual Positive                 132                  17\n",
              "Actual Negative                  22                 104"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b49f162f-7160-42ea-9376-bfffe99d82d4\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Predicted Positive</th>\n",
              "      <th>Predicted Negative</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Actual Positive</th>\n",
              "      <td>132</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Actual Negative</th>\n",
              "      <td>22</td>\n",
              "      <td>104</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b49f162f-7160-42ea-9376-bfffe99d82d4')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-b49f162f-7160-42ea-9376-bfffe99d82d4 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-b49f162f-7160-42ea-9376-bfffe99d82d4');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-ccbfb41e-240b-445e-94ac-c4716cf2463d\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ccbfb41e-240b-445e-94ac-c4716cf2463d')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ccbfb41e-240b-445e-94ac-c4716cf2463d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x400 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x400 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "def svm_factory_qp(X, y, c=1):\n",
        "    \"\"\"\n",
        "    Creates a linear support vector machine (SVM) model using quadratic programming.\n",
        "\n",
        "    Parameters:\n",
        "    X : DataFrame\n",
        "        Feature matrix as a DataFrame.\n",
        "    y : Series\n",
        "        Target vector as a Series.\n",
        "    c : float, optional\n",
        "        Regularization parameter. Default is 1.\n",
        "\n",
        "    Returns:\n",
        "    LinearSvm :\n",
        "        A trained linear SVM model.\n",
        "    \"\"\"\n",
        "\n",
        "    m = AMPL()\n",
        "\n",
        "    m.eval(\n",
        "        \"\"\"\n",
        "        set P;\n",
        "        set N;\n",
        "\n",
        "        param c;\n",
        "        param X{N,P};\n",
        "        param y{N};\n",
        "\n",
        "        # Decision variables\n",
        "        var b;\n",
        "        var z{N} >= 0;\n",
        "        var w{P};\n",
        "\n",
        "        minimize qp: c / card(N) * sum{i in N} z[i]\n",
        "                            + sum{p in P} w[p]^2;\n",
        "\n",
        "        subject to hingeloss{i in N}:\n",
        "            z[i] >= 1-y[i]*(sum{p in P} w[p]*X[i,p] + b);\n",
        "\n",
        "    \"\"\"\n",
        "    )\n",
        "\n",
        "    # Use dataframe columns and index to index variables and constraints\n",
        "    m.set[\"P\"] = list(X.columns)\n",
        "    m.set[\"N\"] = np.array(X.index)\n",
        "\n",
        "    m.param[\"c\"] = c\n",
        "    m.param[\"y\"] = y\n",
        "\n",
        "    m.param[\"X\"] = X\n",
        "\n",
        "    # Solve QP with the interior point method\n",
        "    m.option[\"solver\"] = SOLVER_NLO\n",
        "\n",
        "    m.solve(verbose=False)\n",
        "\n",
        "    P = m.set[\"P\"].members()\n",
        "\n",
        "    # Extract solution\n",
        "    w = pd.Series([m.var[\"w\"][p].value() for p in X.columns], index=X.columns)\n",
        "    b = m.var[\"b\"].value()\n",
        "\n",
        "    return LinearSVM(w, b)\n",
        "\n",
        "\n",
        "# Train and test\n",
        "svm_lp = svm_factory_qp(X_train, y_train)\n",
        "test(svm_lp, X_test, y_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f1731fd3-fb15-432b-8792-7a8beb898df1",
      "metadata": {
        "id": "f1731fd3-fb15-432b-8792-7a8beb898df1"
      },
      "source": [
        "### Dual Formulation\n",
        "\n",
        "The dual formulation for the SVM provides insight into how a linear SVM works and essential for extending SVM to nonlinear classification. The dual formulation begins by creating a differentiable Lagrangian with dual variables $\\alpha_i \\geq 0$ and $\\beta_i \\geq 0$ for $i = 1, \\dots, n$. The task is to find saddle points of\n",
        "\n",
        "$$\n",
        "\\begin{align*}\n",
        "\\mathcal{L} & = \\frac{1}{2} \\|w\\|_2^2 + \\frac{c}{n}\\sum_{i=1}^n z_i + \\sum_{i=1}^n \\alpha_i \\left(1 - z_i - y_i (w^\\top x_i + b) \\right) + \\sum_{i=1}^n \\beta_i (-z_i) \\\\\n",
        "\\end{align*}\n",
        "$$\n",
        "\n",
        "Taking derivatives with respect to the primal variables\n",
        "\n",
        "$$\n",
        "\\begin{align*}\n",
        "\\frac{\\partial \\mathcal{L}}{\\partial z_i} & = \\frac{c}{n} - \\alpha_i - \\beta_i = 0 \\implies 0 \\leq \\alpha_i \\leq \\frac{c}{n}\\\\\n",
        "\\frac{\\partial \\mathcal{L}}{\\partial w} & = w  - \\sum_{i=1}^n \\alpha_i y_i x_i = 0 \\implies  w = \\sum_{i=1}^n \\alpha_i y_i x_i \\\\\n",
        "\\frac{\\partial \\mathcal{L}}{\\partial b} & = - \\sum_{i=1}^n \\alpha_i y_i = 0 \\implies \\sum_{i=1}^n \\alpha_i y_i = 0\n",
        "\\end{align*}\n",
        "$$\n",
        "\n",
        "This can be arranged in the form of a standard quadratic program in $n$ variables $\\alpha_i$ for $i = 1, \\dots, n$.\n",
        "\n",
        "$$\n",
        "\\begin{align*}\n",
        "\\min_{\\alpha_i}\\ & \\frac{1}{2} \\sum_{i=1}^n\\sum_{j=1}^n \\alpha_i \\alpha_j y_i y_j ( x_i^\\top x_j ) -  \\sum_{i=1}^n \\alpha_i \\\\\n",
        "\\text{s. t.}\\quad & \\sum_{i=1}^n \\alpha_i y_i = 0  \\\\\n",
        "& \\alpha_i \\in \\left[0, \\frac{c}{n}\\right] & i = 1, \\dots, n \\\\\n",
        "\\end{align*}\n",
        "$$\n",
        "\n",
        "The symmetric $n \\times n$ **Gram matrix** is defined as\n",
        "\n",
        "$$\n",
        "    G = \\begin{bmatrix}\n",
        "        (y_1 x_1^\\top)(y_1 x_1) & \\dots & (y_1 x_1^\\top)(y_n x_n) \\\\\n",
        "        \\vdots & \\ddots & \\vdots \\\\\n",
        "        (y_n x_n^\\top)(y_1 x_1) & \\dots & (y_n x_n^\\top)(y_n x_n)\n",
        "    \\end{bmatrix}\n",
        "$$\n",
        "\n",
        "where each entry is dot product of two vectors $(y_i x_i), (y_j x_j) \\in \\mathbb{R}^{p+1}$.\n",
        "\n",
        "Compared to the primal, the dual formulation appears to have reduced the number of decision variables from $n + p + 1$ to $n$. But this has come with the penalty of introducing a dense matrix with $n^2$ coefficients and potential processing time of order $n^3$. For large training sets where $n\\sim 10^4-10^6$ or even larger, this becomes a prohibitively expensive calculation. In addition, the Gram matrix will be rank deficient for cases $p< n$.\n",
        "\n",
        "We can eliminates the need to compute and store the full Gram matrix $G$ by introducing the $n \\times p$ matrix $F$\n",
        "\n",
        "$$\n",
        "    F = \\begin{bmatrix} y_1 x_1^\\top \\\\ y_2 x_2^\\top \\\\ \\vdots \\\\ y_n x_n^\\top \\end{bmatrix}\n",
        "$$\n",
        "\n",
        "Then $G = FF^\\top$ which brings the $p$ primal variables $w = F^\\top\\alpha$ back into the computational problem. The optimization problem becomes\n",
        "\n",
        "$$\n",
        "\\begin{align*}\n",
        "\\min_{\\alpha_i}\\ & \\frac{1}{2} w^\\top w -  1^\\top\\alpha \\\\\n",
        "\\text{s. t.}\\quad & y^\\top\\alpha = 0 \\\\\n",
        "& w = F^\\top\\alpha & w\\in\\mathbb{R}^p \\\\\n",
        "& 0 \\leq \\alpha_i \\leq \\frac{c}{n} & \\alpha\\in\\mathbb{R}^n \\\\\n",
        "\\end{align*}\n",
        "$$\n",
        "\n",
        "The solution for the bias term $b$ is obtained by considering the complementarity conditions on the dual variables. The slack variables $z_i$ are zero if $\\beta_i > 0$ which is equivalent to $\\alpha_i < \\frac{c}{n}$. If $\\alpha_i > 0$ then $1 - y_i (w^\\top x_i + b)$. Putting these facts together gives a formula for $b$\n",
        "\n",
        "$$\n",
        "\\begin{align}\n",
        "b & = y_i - w^\\top x_i & \\forall i\\in 1, 2, \\ldots, n\\quad \\text{s.t.}\\quad 0 < \\alpha_i < \\frac{c}{n}\\\\\n",
        "\\end{align}\n",
        "$$\n",
        "\n",
        "This model is implemented below."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "id": "2d727961",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "2d727961",
        "outputId": "96d90359-533d-4fd5-b883-da3caa02485c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LinearSvm(w = {'variance': 0.36280668370788516, 'skewness': 0.12460912162931281}, b = -0.10998536976946749) \n",
            "\n",
            "Matthews correlation coefficient (MCC) = 0.707\n",
            "Sensitivity =  87.9%\n",
            "Precision =  85.6%\n",
            "Accuracy =  85.5%\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                 Predicted Positive  Predicted Negative\n",
              "Actual Positive                 131                  18\n",
              "Actual Negative                  22                 104"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-072424f9-0008-4ee7-903c-d35f91f182d1\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Predicted Positive</th>\n",
              "      <th>Predicted Negative</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Actual Positive</th>\n",
              "      <td>131</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Actual Negative</th>\n",
              "      <td>22</td>\n",
              "      <td>104</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-072424f9-0008-4ee7-903c-d35f91f182d1')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-072424f9-0008-4ee7-903c-d35f91f182d1 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-072424f9-0008-4ee7-903c-d35f91f182d1');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-eb221f34-72d1-415e-908f-560543710ea1\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-eb221f34-72d1-415e-908f-560543710ea1')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-eb221f34-72d1-415e-908f-560543710ea1 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x400 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x400 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "def svm_factory_dual(X, y, c=1):\n",
        "    \"\"\"\n",
        "    Creates a linear support vector machine (SVM) model using the dual formulation\n",
        "    and quadratic programming.\n",
        "\n",
        "    Parameters:\n",
        "    X : DataFrame\n",
        "        Feature matrix as a DataFrame.\n",
        "    y : Series\n",
        "        Target vector as a Series.\n",
        "    c : float, optional\n",
        "        Regularization parameter. Default is 1.\n",
        "\n",
        "    Returns:\n",
        "    LinearSvm :\n",
        "        A trained linear SVM model.\n",
        "    \"\"\"\n",
        "\n",
        "    m = AMPL()\n",
        "\n",
        "    m.eval(\n",
        "        \"\"\"\n",
        "        set P;\n",
        "        set N;\n",
        "\n",
        "        param y{N};\n",
        "        param F{P,N};\n",
        "        param C;\n",
        "\n",
        "        # Decision variables\n",
        "        var w{P};\n",
        "        var a{N} >= 0 <= C;\n",
        "\n",
        "        minimize qp: 1/2 * sum{p in P} w[p]^2 - sum{i in N} a[i];\n",
        "\n",
        "        subject to bias:\n",
        "            sum{i in N} y[i] * a[i] = 0;\n",
        "\n",
        "        subject to projection{p in P}:\n",
        "            w[p] = sum{i in N} F[p,i] * a[i];\n",
        "    \"\"\"\n",
        "    )\n",
        "\n",
        "    # Use dataframe columns and index to index variables and constraints\n",
        "    m.set[\"P\"] = list(X.columns)\n",
        "    m.set[\"N\"] = np.array(X.index)\n",
        "\n",
        "    # Model parameters\n",
        "    C = c / len(X.index)\n",
        "    m.param[\"C\"] = C\n",
        "    m.param[\"y\"] = y\n",
        "    m.param[\"F\"] = X.mul(y, axis=0).T\n",
        "\n",
        "    # Solve QP with the interior point method\n",
        "    m.option[\"solver\"] = SOLVER_NLO\n",
        "\n",
        "    m.solve(verbose=False)\n",
        "\n",
        "    P = m.set[\"P\"].members()\n",
        "    N = m.set[\"N\"].members()\n",
        "\n",
        "    # Extract solution\n",
        "    w = pd.Series([m.var[\"w\"][p].value() for p in X.columns], index=X.columns)\n",
        "    a = pd.Series([m.var[\"a\"][i].value() for i in X.index], index=X.index)\n",
        "\n",
        "    # Find alpha closest to the center of [0, c/n]\n",
        "    i = a.index[(a - C / 2).abs().argmin()]\n",
        "    b = y.loc[i] - X.loc[i, :].dot(w)\n",
        "\n",
        "    return LinearSVM(w, b)\n",
        "\n",
        "\n",
        "# Train and test\n",
        "svm_lp = svm_factory_dual(X_train, y_train)\n",
        "test(svm_lp, X_test, y_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3743af5d-f42b-41a1-b2eb-4c9e17cecefa",
      "metadata": {
        "id": "3743af5d-f42b-41a1-b2eb-4c9e17cecefa"
      },
      "source": [
        "## Kernelized SVM\n",
        "\n",
        "### Nonlinear feature spaces\n",
        "\n",
        "A linear SVM assumes the existence of a linear hyperplane that separates labeled sets of data points. Frequently, however, this is not possible and some sort of nonlinear method is needed.\n",
        "\n",
        "Consider a binary classification done given by a function\n",
        "\n",
        "$$y^{pred} = \\text{sgn} \\left( w^\\top \\phi(x) + b \\right)$$\n",
        "\n",
        "where $\\phi(x)$ is a function mapping $x$ into a higher dimensional \"feature space\". That is, $\\phi : \\mathbb{R}^{p} \\rightarrow \\mathbb{R}^d$ where $d \\geq p $. The additional dimensions may include features such as powers of the terms in $x$, or products of those terms, or other types of nonlinear transformations. As before, we wish to find a choice for $w\\in\\mathbb{R}^d$ such that the soft-margin classifier\n",
        "\n",
        "$$\n",
        "\\begin{align}\n",
        "y_i \\left( w^\\top \\phi(x_i) + b \\right) & \\geq 1 - z_i  & i = 1, 2, \\ldots, n\n",
        "\\end{align}\n",
        "$$\n",
        "\n",
        "Using the machinery as before, we set up the Lagrangian\n",
        "\n",
        "$$\n",
        "\\begin{align*}\n",
        "\\mathcal{L} & = \\frac{1}{2} \\|w\\|_2^2 + \\frac{c}{n}\\sum_{i=1}^n z_i + \\sum_{i=1}^n \\alpha_i \\left( 1 - z_i - y_i \\left( w^\\top \\phi(x_i) + b \\right)\\right) + \\sum_{i=1}^n \\beta_i (-z_i) \\\\\n",
        "\\end{align*}\n",
        "$$\n",
        "\n",
        "then take derivatives to find\n",
        "\n",
        "$$\n",
        "\\begin{align*}\n",
        "    \\frac{\\partial \\mathcal{L}}{\\partial z_i} & = \\frac{c}{n} - \\alpha_i - \\beta_i = 0 \\implies 0 \\leq \\alpha_i \\leq \\frac{c}{n}\\\\\n",
        "    \\frac{\\partial \\mathcal{L}}{\\partial w} & = w  - \\sum_{i=1}^n \\alpha_i y_i \\phi(x_i) = 0 \\implies  w = \\sum_{i=1}^n \\alpha_i y_i \\phi(x_i) \\\\\n",
        "\\frac{\\partial \\mathcal{L}}{\\partial b} & = - \\frac{c}{n}\\sum_{i=1}^n \\alpha_i y_i = 0 \\implies \\sum_{i=1}^n \\alpha_i y_i = 0\n",
        "\\end{align*}\n",
        "$$\n",
        "\n",
        "This is similar to the case of a linear SVM, but now the vector of weights $w\\in\\mathbb{R}^d$ which can be a  high dimensional space with nonlinear features. Working through the algebra, we are once again left with a quadratic program in $n$ variables $\\alpha_i$ for $i = 1, \\dots, n$.\n",
        "\n",
        "$$\n",
        "\\begin{align*}\n",
        "\\min_{\\alpha_i}\\quad  & \\frac{1}{2} \\sum_{i=1}^n\\sum_{j=1}^n \\alpha_i \\alpha_j y_i y_j  \\phi(x_i)^\\top \\phi(x_j) -  \\sum_{i=1}^n \\alpha_i \\\\\n",
        "\\text{s.t.}\\quad & \\alpha_i \\in \\left[0, \\frac{c}{n}\\right] & i = 1, \\dots, n \\\\\n",
        "& \\sum_{i=1}^n \\alpha_i y_i = 0 \\\\\n",
        "\\end{align*}\n",
        "$$\n",
        "\n",
        "where the resulting classifier is given by\n",
        "\n",
        "$$y^{pred} = \\text{sgn} \\left( \\sum_{i=1}^n \\alpha_i y_i \\phi(x_i)^\\top \\phi(x) + b \\right)$$\n",
        "\n",
        "### The kernel trick\n",
        "\n",
        "This is an interesting situation where the separating hyperplane is embedded in a high dimensional space of nonlinear features determined by the mapping $\\phi(x)$, but all we need for computation are the inner products  $\\phi(x_i)^\\top\\phi(x_j)$ to train the classifier, and the inner products $\\phi(x_i)^\\top\\phi(x)$ to use the classifier. If we had a function $K(x, z)$ that returned the value $\\phi(x)^\\top\\phi(z)$ then we would never need to actually compute $\\phi(x)$, $\\phi(z)$ or their inner product.\n",
        "\n",
        "Mercer's theorem turns the analysis on its head by specifying conditions for which a function $K(x, z)$ to be expressed as an inner product for some $\\phi(x)$. If $K(x, z)$ is symmetric (i.e, $K(x, z) = K(z, x)$, and if the Gram matrix constructed for any collection of points $x_1, x_2, \\ldots, x_n$\n",
        "\n",
        "$$\n",
        "\\begin{bmatrix}\n",
        "    K(x_1, x_1) & \\dots & K(x_1, x_n) \\\\\n",
        "    \\vdots & \\ddots & \\vdots \\\\\n",
        "    K(x_n, x_1) & \\dots & K(x_n, x_n)\n",
        "\\end{bmatrix}\n",
        "$$\n",
        "\n",
        "is positive semi-definite, then there is some $\\phi(x)$ for which $K(x, z)$ is an inner product. We call such functions kernels. The practical consequence is that we can train and implement nonlinear classifiers using kernel and without ever needing to compute the higher dimensional features. This remarkable result is called the \"kernel trick\".\n",
        "\n",
        "### Implementation\n",
        "\n",
        "To take advantage of the kernel trick, we assume an appropriate kernel $K(x, z)$ has been identified, then replace all instances of $\\phi(x_i)^\\top \\phi(x)$ with the kernel. The \"kernelized\" SVM is given by a solution to\n",
        "\n",
        "$$\n",
        "\\begin{align*}\n",
        "\\min_{\\alpha_i}\\quad & \\frac{1}{2} \\sum_{i=1}^n\\sum_{j=1}^n \\alpha_i \\alpha_j y_i y_j K(x_i, x_j) -  \\sum_{i=1}^n \\alpha_i \\\\\n",
        "\\text{s.t.}\\quad & \\sum_{i=1}^n \\alpha_i y_i = 0 \\\\\n",
        "& \\alpha_i \\in \\left[0, \\frac{c}{n}\\right] & i = 1, \\dots, n \\\\\n",
        "\\end{align*}\n",
        "$$\n",
        "\n",
        "where\n",
        "\n",
        "$$\n",
        "\\begin{align}\n",
        "b & = y_i - \\sum_{j=1}^n \\alpha_j y_j K(x_j, x_i) & \\forall i\\in 1, 2, \\ldots, n\\quad \\text{s.t.}\\quad 0 < \\alpha_i < \\frac{c}{n}\n",
        "\\end{align}\n",
        "$$\n",
        "\n",
        "where the resulting classifier is given by\n",
        "\n",
        "$$y^{pred} = \\text{sgn} \\left( \\sum_{i=1}^n \\alpha_i y_i K(x_i, x) + b \\right)$$\n",
        "\n",
        "We define the $n\\times n$ positive symmetric semi-definite Gram matrix\n",
        "\n",
        "$$\n",
        "G = \\begin{bmatrix}\n",
        "    y_1 y_1 K(x_1, x_1) & \\dots & y_1 y_n K(x_1, x_n) \\\\\n",
        "    \\vdots & \\ddots & \\vdots \\\\\n",
        "    y_n y_1 K(x_n, x_1) & \\dots & y_n y_n K(x_n, x_n)\n",
        "\\end{bmatrix}\n",
        "$$\n",
        "\n",
        "We factor $G = F F^\\top$ where $F$ has dimensions $n \\times q$ and where $q$ is the rank of $G$. The factorization is not unique. As demonstrated in the Python code below, one suitable factorization is the spectral factorization $G = U\\Lambda U^T$ where $\\Lambda$ is a $q\\times q$ diagonal matrix of non-zero eigenvalues, and $U$ is an $n\\times q$ normal matrix such that $U^\\top U = I_q$. Then\n",
        "\n",
        "$$F = U\\Lambda^{1/2}$$\n",
        "\n",
        "Once this factorization is complete, the optimization problem for the kernalized SVM is the same as for the linear SVM in the dual formulation\n",
        "\n",
        "$$\n",
        "\\begin{align*}\n",
        "\\min\\quad & \\frac{1}{2} \\alpha^\\top F F^\\top \\alpha -  1^\\top \\alpha \\\\\n",
        "\\text{s.t.}\\quad & \\sum_{i=1}^n \\alpha_i y_i = 0 \\\\\n",
        "& 0 \\leq \\alpha_i \\leq \\frac{c}{n} & \\alpha\\in\\mathbb{R}^n \\\\\n",
        "\\end{align*}\n",
        "$$\n",
        "\n",
        "The result is a quadratic program for the dual coefficients $\\alpha$ and auxiliary variables $v$.\n",
        "\n",
        "$$\n",
        "\\begin{align*}\n",
        "\\min\\quad & \\frac{1}{2} v^\\top v - 1^\\top \\alpha\\\\\n",
        "\\text{s.t.}\\quad & y^\\top \\alpha = 1 \\\\\n",
        "& v = F^\\top \\alpha & u\\in\\mathbb{R}^{q} \\\\\n",
        "& 0 \\leq \\alpha_i \\leq \\frac{c}{n} & \\alpha\\in\\mathbb{R}^n \\\\\n",
        "\\end{align*}\n",
        "$$\n",
        "\n",
        "Summarizing, the essential difference between training the linear and kernelized SVM is the need to compute and factor the Gram matrix. The result will be a set of non-zero coefficients $\\alpha_i > 0$ the define a set of support vectors $\\mathcal{SV}$. The classifier is then given by\n",
        "\n",
        "$$y^{pred} = \\text{sgn} \\left( \\sum_{i\\in\\mathcal{SV}} \\alpha_i y_i K(x_i, x) + b \\right)$$\n",
        "\n",
        "The implementation of the kernelized SVM is split into two parts. The first part is a class used to create instances of the classifier.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "id": "1d3f892f-19c9-433a-aa64-65ef734a4395",
      "metadata": {
        "id": "1d3f892f-19c9-433a-aa64-65ef734a4395"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "\n",
        "class KernelSVM:\n",
        "    \"\"\"\n",
        "    Kernel Support Vector Machine (SVM) class.\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(self, X, y, a, b, kernel):\n",
        "        \"\"\"\n",
        "        Initialize the Kernel SVM with weights and bias.\n",
        "\n",
        "        :param X: numpy array or list, training data.\n",
        "        :param y: numpy array or list, target labels.\n",
        "        :param a: numpy array or list, alpha values for the support vectors.\n",
        "        :param b: float, bias value.\n",
        "        :param kernel: function, kernel function to be used in the SVM.\n",
        "        \"\"\"\n",
        "        self.X = np.array(X)\n",
        "        self.u = np.multiply(np.array(a), np.array(y))\n",
        "        self.b = b\n",
        "        self.kernel = kernel\n",
        "\n",
        "    def __call__(self, Z):\n",
        "        \"\"\"\n",
        "        Compute the decision function.\n",
        "\n",
        "        :param Z: pandas DataFrame, test data.\n",
        "        :return: pandas Series, predicted labels.\n",
        "        \"\"\"\n",
        "        K = [\n",
        "            [self.kernel(self.X[i, :], Z.loc[j, :]) for j in Z.index]\n",
        "            for i in range(len(self.X))\n",
        "        ]\n",
        "        y_pred = np.sign((self.u @ K) + self.b)\n",
        "        return pd.Series(y_pred, index=Z.index)\n",
        "\n",
        "    def __repr__(self):\n",
        "        \"\"\"\n",
        "        Returns:\n",
        "            str: String representation of the Linear SVM\n",
        "        \"\"\"\n",
        "        return f\"KernelSvm(b = {self.b})\""
      ]
    },
    {
      "cell_type": "markdown",
      "id": "83eebe9d-9561-4a46-8a35-c4028c3a7739",
      "metadata": {
        "id": "83eebe9d-9561-4a46-8a35-c4028c3a7739"
      },
      "source": [
        "The second part of the implementation is a factory function containing the optimization model for training an SVM. Given training data and a kernal function, the factory returns an instance of a kernelized SVM. The default is a linear kernel."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "id": "2a3087a0",
      "metadata": {
        "id": "2a3087a0"
      },
      "outputs": [],
      "source": [
        "def svm_factory_kernel(X, y, c=1, tol=1e-8, kernel=lambda x, z: x @ z):\n",
        "    \"\"\"\n",
        "    Creates a linear support vector machine (SVM) model using the dual formulation\n",
        "    and quadratic programming.\n",
        "\n",
        "    Parameters:\n",
        "    X : DataFrame\n",
        "        Feature matrix as a DataFrame.\n",
        "    y : Series\n",
        "        Target vector as a Series.\n",
        "    c : float, optional\n",
        "        Regularization parameter. Default is 1.\n",
        "\n",
        "    Returns:\n",
        "    LinearSvm :\n",
        "        A trained linear SVM model.\n",
        "    \"\"\"\n",
        "\n",
        "    # Convert to numpy arrays for speed improvement\n",
        "    n, p = X.shape\n",
        "    X_ = X.to_numpy()\n",
        "    y_ = y.to_numpy()\n",
        "\n",
        "    # Gram matrix\n",
        "    G = np.zeros((n, n))\n",
        "    for i in range(n):\n",
        "        for j in range(i, n):\n",
        "            G[j, i] = G[i, j] = y_[i] * y_[j] * kernel(X_[i, :], X_[j, :])\n",
        "\n",
        "    # Factor the Gram matrix\n",
        "    eigvals, eigvecs = np.linalg.eigh(G)\n",
        "    idx = eigvals >= tol * max(eigvals)\n",
        "    F = pd.DataFrame(eigvecs[:, idx] @ np.diag(np.sqrt(eigvals[idx])), index=X.index)\n",
        "\n",
        "    # Build model\n",
        "    m = AMPL()\n",
        "\n",
        "    m.eval(\n",
        "        \"\"\"\n",
        "        set Q;\n",
        "        set N;\n",
        "\n",
        "        param y{N};\n",
        "        param F{N,Q};\n",
        "        param C;\n",
        "\n",
        "        # Decision variables\n",
        "        var u{Q};\n",
        "        var a{N} >= 0 <= C;\n",
        "\n",
        "        minimize qp: 1/2 * sum{q in Q} u[q]^2 - sum{i in N} a[i];\n",
        "\n",
        "        subject to bias:\n",
        "            sum{i in N} y[i] * a[i] = 0;\n",
        "\n",
        "        subject to projection{q in Q}:\n",
        "            u[q] = sum{i in N} F[i,q] * a[i];\n",
        "    \"\"\"\n",
        "    )\n",
        "\n",
        "    # Use dataframe columns and index to index variables and constraints\n",
        "    m.set[\"Q\"] = list(F.columns)\n",
        "    m.set[\"N\"] = np.array(F.index)\n",
        "\n",
        "    # Model parameters\n",
        "    C = c / len(F.index)\n",
        "    m.param[\"C\"] = C\n",
        "    m.param[\"y\"] = y\n",
        "    m.param[\"F\"] = F\n",
        "\n",
        "    # Solve QP with the interior point method\n",
        "    m.option[\"solver\"] = SOLVER_NLO\n",
        "\n",
        "    m.solve(verbose=False)\n",
        "\n",
        "    # Extract solution\n",
        "    a = pd.Series([m.var[\"a\"][i].value() for i in X.index], index=X.index)\n",
        "\n",
        "    # Find b by locating a closest to the center of [0, c/n]\n",
        "    j = a.index[(a - C / 2).abs().argmin()]\n",
        "    b = y.loc[j] - sum(\n",
        "        [a[i] * y.loc[i] * kernel(X.loc[i, :], X.loc[j, :]) for i in X.index]\n",
        "    )\n",
        "\n",
        "    # Display the support vectors\n",
        "    y_support = pd.Series(\n",
        "        [1 if a[i] > 1e-4 * C else -1 for i in X.index], index=X.index\n",
        "    )\n",
        "    scatter_labeled_data(\n",
        "        X,\n",
        "        y_support,\n",
        "        colors=[\"b\", \"y\"],\n",
        "        labels=[\"Support Vector\", \"\"],\n",
        "        title=\"Support Vectors\",\n",
        "    )\n",
        "\n",
        "    # Find support vectors\n",
        "    SV = [i for i in X.index if a[i] > 1e-3 * C]\n",
        "\n",
        "    return KernelSVM(X.loc[SV, :], y.loc[SV], a.loc[SV], b, kernel)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "512c77bb-ed3c-45e3-a10c-7e6e15183327",
      "metadata": {
        "id": "512c77bb-ed3c-45e3-a10c-7e6e15183327"
      },
      "source": [
        "### Linear kernel\n",
        "\n",
        "For comparison with the previous cases, the first kernel we consider is a linear kernel\n",
        "\n",
        "$$ K(x, z) = x^\\top z $$\n",
        "\n",
        "which should reproduce the results obtained earlier."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "id": "3fabde16-fff3-4b1a-9a2f-ef6ad42100df",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "3fabde16-fff3-4b1a-9a2f-ef6ad42100df",
        "outputId": "ea73d0a9-7da9-4691-f5e7-749b2e8b209d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "KernelSvm(b = -0.11015200409218262) \n",
            "\n",
            "Matthews correlation coefficient (MCC) = 0.707\n",
            "Sensitivity =  87.9%\n",
            "Precision =  85.6%\n",
            "Accuracy =  85.5%\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                 Predicted Positive  Predicted Negative\n",
              "Actual Positive                 131                  18\n",
              "Actual Negative                  22                 104"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d08653ce-d8bb-4621-ad4c-589472f41bdd\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Predicted Positive</th>\n",
              "      <th>Predicted Negative</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Actual Positive</th>\n",
              "      <td>131</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Actual Negative</th>\n",
              "      <td>22</td>\n",
              "      <td>104</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d08653ce-d8bb-4621-ad4c-589472f41bdd')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-d08653ce-d8bb-4621-ad4c-589472f41bdd button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-d08653ce-d8bb-4621-ad4c-589472f41bdd');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-8729a2bc-66c5-4fe0-9e89-9dca1d683ca2\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8729a2bc-66c5-4fe0-9e89-9dca1d683ca2')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-8729a2bc-66c5-4fe0-9e89-9dca1d683ca2 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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