neuron.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lkoop-MT9if-"
      },
      "source": [
        "# Нейрон с различными функциями активации"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IUjxg_IS31tn"
      },
      "source": [
        "В этом задании нужно будет: \n",
        "- самостоятельно реализовать класс **`Neuron()`** с различными функциями активации (ReLU, LeakyReLU и ELU)\n",
        "\n",
        "- обучить и протестировать этот класс на сгенерированных и реальных данных\n",
        "\n",
        "**Достаточно реализовать ReLU и ещё одну из двух.**"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!gdown 1hpO1lqvbdZ6K5zO66erNcYMr2rv_EFF1\n",
        "!gdown 1_1YvZgUAgjKuRKPkkVnJlzQJy7rgkqBR"
      ],
      "metadata": {
        "id": "wMpiutPV8r_w",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2466d56e-10c6-4f8c-8991-4b20eab5d089"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1hpO1lqvbdZ6K5zO66erNcYMr2rv_EFF1\n",
            "To: /content/apples_pears.csv\n",
            "100% 43.6k/43.6k [00:00<00:00, 35.7MB/s]\n",
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1_1YvZgUAgjKuRKPkkVnJlzQJy7rgkqBR\n",
            "To: /content/voice.csv\n",
            "100% 1.07M/1.07M [00:00<00:00, 96.8MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p-OYlV519igB"
      },
      "source": [
        "from matplotlib import pyplot as plt\n",
        "from matplotlib.colors import ListedColormap\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "from sklearn.metrics import * \n",
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t1VCt_rDHum3"
      },
      "source": [
        "RANDOM_SEED = 42\n",
        "np.random.seed(RANDOM_SEED)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cgpFOaPm9igG"
      },
      "source": [
        "В данном случае мы снова решаем задачу бинарной классификации (2 класса: 1 или 0). Мы уже выяснили (в ноутбуке про `logloss`), что плохо брать для классификации квадратичную функцию потерь, однако здесь для простоты возьмём её:\n",
        "\n",
        "$$\n",
        "Loss(\\hat{y}, y) = \\frac{1}{n}\\sum_{i=1}^{n} (\\hat{y_i} - y_i)^2\n",
        "$$  \n",
        "\n",
        "Здесь $w \\cdot X_i$ - скалярное произведение, а $\\hat{y_i} = \\sigma(w \\cdot X_i) =\\frac{1}{1+e^{-w \\cdot X_i}} $ - сигмоида ($i$ -- номер объекта в выборке). "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yn3F0layHunD"
      },
      "source": [
        "def Loss(y_pred, y):\n",
        "    y_pred = y_pred.reshape(-1, 1)\n",
        "    y = np.array(y).reshape(-1, 1)\n",
        "    return 0.5 * np.mean((y_pred - y) ** 2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rYoQAdXoHunK"
      },
      "source": [
        "Далее будут предложены несколько функций активации, и Вам нужно реализовать класс `Neuron` по аналогии с тем, как это было на семинаре. Сам принцип тот же, но меняются формула обновления весов и формула предсказания."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WHvdwJW9HunN"
      },
      "source": [
        "### Нейрон с ReLU (Recitified Linear Unit)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AMA7Gi6KHunQ"
      },
      "source": [
        "ReLU самая часто используемая (по крайней мере, пару лет назад) функция активации в нейронных сетях. Выглядит она очень просто:\n",
        "\n",
        "\\begin{equation*}\n",
        "ReLU(x) =\n",
        " \\begin{cases}\n",
        "   0, &\\text{$x \\le 0$}\\\\\n",
        "   x, &\\text{$x \\gt 0$}\n",
        " \\end{cases}\n",
        "\\end{equation*}\n",
        "\n",
        "Или по-другому:\n",
        "\n",
        "$$\n",
        "ReLU(x) = \\max(0, x)\n",
        "$$\n",
        "\n",
        "В (свободном) переводе Rectified Linear Unit = \"Усечённая линейная функция\". Собственно, мы по сути просто не даём проходить отрицательным числам.\n",
        "\n",
        "Производная здесь берётся как производная от кусочно-заданной функции, то есть на участках, где функция гладкая, и в нуле её доопредляют нулём:\n",
        "\n",
        "\\begin{equation*}\n",
        "ReLU'(x) = \n",
        " \\begin{cases}\n",
        "   0, &\\text{$x \\le 0$}\\\\\n",
        "   1, &\\text{$x \\gt 0$}\n",
        " \\end{cases}\n",
        "\\end{equation*}\n",
        "\n",
        "Подставим ReLu в Loss:\n",
        "\n",
        "$$Loss(\\hat{y}, y) = \\frac{1}{2n}\\sum_{i=1}^{n} (\\hat{y_i} - y_i)^2 = \\frac{1}{2n}\\sum_{i=1}^{n} (ReLU(w \\cdot X_i) - y_i)^2 = \\begin{equation*}\n",
        "\\frac{1}{2n}\\sum_{i=1}^{n}\n",
        " \\begin{cases}\n",
        "    y_i^2, &{w \\cdot X_i \\le 0}\\\\\n",
        "   (w \\cdot X_i - y_i)^2, &{w \\cdot X_i \\gt 0}\n",
        " \\end{cases}\n",
        "\\end{equation*}$$  \n",
        "\n",
        "(помните, что $w \\cdot X_i$ -- это число в данном случае (результат скалярного произведения двух векторов)).\n",
        "\n",
        "Тогда формула для обновления весов при градиентном спуске будет такая (в матричном виде, рекмоендуем вывести самим то, как это получается из формулы для одного объекта):\n",
        "\n",
        "$$ \\frac{\\partial Loss}{\\partial w} = \\begin{equation*}\n",
        "\\frac{1}{n}\\sum_{i=1}^{n}\n",
        " \\begin{cases}\n",
        "   0, &{w \\cdot X_i \\le 0}\\\\\n",
        "   \\frac{1}{n} X_i^T (w \\cdot X_i - y), &{w \\cdot X_i \\gt 0}\n",
        " \\end{cases}\n",
        "\\end{equation*}$$\n",
        "\n",
        "(напоминаем, что здесь $w \\cdot X$ -- матричное произведение вектора $w$ (ведь вектор -- тоже матрица, не так ли?) и матрицы $X$ )\n",
        "\n",
        "Почему в первом случае будет 0? Потому что в формулу $y_i^2$ не входят веса , а мы берём производную именно по весам $w$.\n",
        "\n",
        "* Реализуйте ReLU и её производную:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DCgAeho19igI"
      },
      "source": [
        "def relu(x):\n",
        "    \"\"\"ReLU-функция\"\"\"\n",
        "    return np.vectorize(lambda x: x if x > 0 else 0)(x)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nXwsy-7J9igL"
      },
      "source": [
        "def relu_derivative(x):\n",
        "    \"\"\"Производная ReLU\"\"\"\n",
        "    return np.vectorize(lambda x: 1 if x > 0 else 0)(x)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qKurn-7F9igN"
      },
      "source": [
        "Теперь нужно написать нейрон с ReLU. Здесь всё очень похоже на перцептрон, но будут по-другому обновляться веса и другая функция активации:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AM9vn3OX9igO"
      },
      "source": [
        "class NeuronReLU:\n",
        "    def __init__(self, w=None, b=0):\n",
        "        \"\"\"\n",
        "        :param: w -- вектор весов\n",
        "        :param: b -- смещение\n",
        "        \"\"\"\n",
        "        self.w = w\n",
        "        self.b = b\n",
        "        \n",
        "        \n",
        "    def activate(self, x):\n",
        "        return relu(x)\n",
        "        \n",
        " \n",
        "    def forward_pass(self, X):\n",
        "        \"\"\"\n",
        "        Рассчитывает ответ нейрона при предъявлении набора объектов\n",
        "        :param: X -- матрица примеров размера (n, m), каждая строка - отдельный объект\n",
        "        :return: вектор размера (n, 1) из нулей и единиц с ответами нейрона \n",
        "        \"\"\"\n",
        "        n = X.shape[0]\n",
        "        y_pred = np.zeros((n, 1))  # y_pred == y_predicted - предсказанные классы\n",
        "        y_pred = self.activate(X @ self.w.reshape(X.shape[1], 1) + self.b)\n",
        "        return y_pred.reshape(-1, 1)\n",
        "    \n",
        "    \n",
        "    def backward_pass(self, X, y, y_pred, learning_rate=0.005):\n",
        "        \"\"\"\n",
        "        Обновляет значения весов нейрона в соответствии с этим объектом\n",
        "        :param: X -- матрица входов размера (n, m)\n",
        "                y -- вектор правильных ответов размера (n, 1)\n",
        "                learning_rate - \"скорость обучения\" (символ alpha в формулах выше)\n",
        "        В этом методе ничего возвращать не нужно, только правильно поменять веса\n",
        "        с помощью градиентного спуска.\n",
        "        \"\"\"\n",
        "        n = len(y)\n",
        "        y = np.array(y).reshape(-1, 1)\n",
        "        \n",
        "        w = np.zeros((X.shape[1], 1))\n",
        "        w_d = relu_derivative(y_pred)\n",
        "        b = 0\n",
        "\n",
        "        for i in range(n):\n",
        "          w += w_d[i] * (X[i].reshape(1, -1).T @ (y_pred[i].reshape(1, 1) - y[i].reshape(1, 1)))\n",
        "          b += w_d[i] * (y_pred[i][0] - y[i][0])\n",
        "\n",
        "        self.w -= learning_rate * w / n\n",
        "        self.b -= learning_rate * b / n\n",
        "        \n",
        "    \n",
        "    def fit(self, X, y, num_epochs=300):\n",
        "        \"\"\"\n",
        "        Спускаемся в минимум\n",
        "        :param: X -- матрица объектов размера (n, m)\n",
        "                y -- вектор правильных ответов размера (n, 1)\n",
        "                num_epochs -- количество итераций обучения\n",
        "        :return: losses -- вектор значений функции потерь\n",
        "        \"\"\"\n",
        "        # self.w = np.zeros((X.shape[1], 1))  # столбец (m, 1)\n",
        "        # self.b = 0  # смещение (число)\n",
        "        Loss_values = []  # значения функции потерь на различных итерациях обновления весов\n",
        "        \n",
        "        for i in range(num_epochs):\n",
        "            y_pred = self.forward_pass(X)\n",
        "            Loss_values.append(Loss(y_pred, y))\n",
        "            self.backward_pass(X, y, y_pred)\n",
        "        \n",
        "        return Loss_values\n",
        "    \n",
        "    def predict(self, X, threshold):\n",
        "      return np.array(self.forward_pass(X) > threshold)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "thtFp-at9igS"
      },
      "source": [
        "<h3 style=\"text-align: center;\"><b>Тестирование нейрона с ReLU</b></h3>  "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hOuYzf_u9igS"
      },
      "source": [
        "Здесь вам нужно самим протестировать новый нейрон **на тех же данных** (\"Яблоки и Груши\" и \"Голос\") по аналогии с тем, как это было проделано с перцептроном.\n",
        "В итоге нужно вывести: \n",
        "* график, на котором будет показано, как изменяется функция потерь $Loss$ в зависимости от числа итераций обучения\n",
        "* график с раскраской выборки нейроном"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "chEeb88gHuny"
      },
      "source": [
        "***ПРИМЕЧАНИЕ***: пожалуйста, почаще проверяйте `.shape` у матриц и векторов: `self.w`, `X` и `y` внутри класса. Очень часто ошибка решается транспонированием или `.reshape()`'ом. Не забывайте проверять, что на что Вы умножаете и какой вектор (какой размер) хотите получить на выходе -- это очень помогает не запутаться."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Яблоки и груши"
      ],
      "metadata": {
        "id": "rgXN7UoA9nGj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv(\"apples_pears.csv\")\n",
        "X = data.iloc[:, :-1].values\n",
        "y = data['target'].values.reshape(-1, 1)"
      ],
      "metadata": {
        "id": "ybp8Akdi9gKz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FZDtNG6CHuop",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 554
        },
        "outputId": "e5e29425-e17e-412a-b1c0-b2deb1714536"
      },
      "source": [
        "%%time\n",
        "\n",
        "neuron = NeuronReLU()\n",
        "Loss_values = neuron.fit(X, y)\n",
        "\n",
        "plt.figure(figsize=(10, 8))\n",
        "plt.plot(Loss_values)\n",
        "plt.title('Функция потерь', fontsize=15)\n",
        "plt.xlabel('номер итерации', fontsize=14)\n",
        "plt.ylabel('$Loss(\\hat{y}, y)$', fontsize=14)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ],
            "image/png": 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nV9XGJCcD41W1Hjg+yWHAT4GbgOf2225McjawCdgKHFdVdwBM1edi75skSdIopKq23+pXxNjYWI2Pj4+6DEmSpO1KsqGqxqZatxRO0UqSJGmIDHiSJEmNMeBJkiQ1xoAnSZLUGAOeJElSYwx4kiRJjTHgSZIkNcaAJ0mS1BgDniRJUmMMeJIkSY0x4EmSJDXGgCdJktQYA54kSVJjDHiSJEmNMeBJkiQ1xoAnSZLUGAOeJElSYwx4kiRJjTHgSZIkNcaAJ0mS1BgDniRJUmMMeJIkSY0x4EmSJDXGgCdJktQYA54kSVJjDHiSJEmNMeBJkiQ1xoAnSZLUGAOeJElSYwx4kiRJjTHgSZIkNcaAJ0mS1BgDniRJUmMMeJIkSY0x4EmSJDXGgCdJktQYA54kSVJjDHiSJEmNMeBJkiQ1xoAnSZLUGAOeJElSYwx4kiRJjTHgSZIkNcaAJ0mS1Jjlc9koyV2B3wR+DdhSVVuGWpUkSZLmbMZH8JLcI8lfJPks8ENgM3AZ8O0k1yR5R5JHLFShkiRJmpkZBbwkJwBXA38CfAo4EjgAeCDwKOAkuqOBn0ryiSSrF6JYSZIkbd9MT9EeDBxSVZdNs/5C4PQkzweOAQ4BvjaE+iRJkjRLMwp4VfXMGba7DXjrvCqSJEnSvMz6LtokD1qIQiRJkjQcc3lMyleS/EOSew29GkmSJM3bXALeQcBDgK8l+e9Jlg25JkmSJM3DrANeVV1aVYcBfwr8JXBpksOHXpkkSZLmZM5vsqiqD9IdyTsTWJfkY16fJ0mSNHrzfVXZzsAGupD3JOCSJG9O8uvzrkySJElzMutXlSV5IfCIftoXuB24GPiH/u/RwKYkT62qLw2xVkmSJM3AXN5F+yLgi8DbgAuADVV1+8D6dyd5CXA63SlcSZIkLaJZB7yq2nMGzd4FvGb25UiSJGm+5nsN3nS2AIfOtHGStUmuSLI5yYlTrD8hyaYklyT5dJL7D6x7XZLL+ukPB5afkeQbSS7upwPmvVeSJEk7gAUJeNX5zEza9s/ROxU4HFgDPCvJmknNvgKMVdX+wDnAKf22TwYOBA4AHgm8OMmuA9v9dVUd0E8Xz2unJEmSdhALdQRvNg4CNlfVVf21fOuAIwcbVNV5VXVrP3sBsKr/vAb4bFVtraofA5cAaxepbkmSpCVpqAEvyV5JZtvnHsC1A/PX9cumcwzw8f7zV4G1SXZOshvwBGDwGsFX96d135jkrrOsS5IkaYc07CN4VwNfTfLYIfcLQJKjgTHg9QBV9UngY8AXgPfQ3d17R9/8pcCD6B7ncm/gJdP0eWyS8STjW7ZsWYiyJUmSFtWwA96fAP8KvGEW21zPnY+6reqX3UmSw4CXAUdU1W0Ty6vq1f01dk8EAlzZL7+hvxbwNrq7eg+a6sur6rSqGquqsZUrV86ibEmSpKVp1gFvW68jq6ozquoVVfXIWXR5EbA6yT5JVgBHAesnfefDgLfThbsbB5YvS3Kf/vP+wP7AJ/v53fu/AZ4CXDaLmiRJknZY2w14SZ4+adFXkvxDknsNo4Cq2gocD5wLXA6cXVUbk5yc5Ii+2euBuwPv6x95MhEAdwI+l2QTcBpwdN8fwL8kuRS4FNgNeNUw6pUkSVrqUlVTr+huWngL8NOqevbA8ocCb6R7NMkrgbdW1R1TdrKDGRsbq/Hx8VGXIUmStF1JNlTV2FTrtnUE7zhg58FwB1BVl1bVYcCfAn8JXJrk8KFVK0mSpHnZVsA7Fbg9ybunWllVH6R71+yZwLokH9vW9XmSJElaHNMGvKr6blU9nV88c24qOwMb6ELek4BLkrw5ya8Pt0xJkiTN1PLtNaiq9wzOJ3kh3bPlHgHsC9wOXAz8Q//3aGBTkqdW1ZeGXrEkSZK2absBbwovonug8NvoXhu2oX/F2IR3J3kJcDrdKVxJkiQtolkHvKrac/utOAN4zayrkSRJ0rwN+00WE24EDl2gviVJkrQNMzqCl2SfqvrGDNsGWFVVn5lXZQ165Yc3sulbN4+6DEmStMDW/OauvOK/je5KtZkewftikncmedR0DZLcK8lfAJuAI4dSnSRJkmZtptfgPQh4GfDRJD+jezTKt4D/BO4FrAEeDFwIvLCqzl2AWnd4o0zykiTpV8eMjuBV1Q+q6q+BPYDn070z9p7APsBWuufgPayqHm24kyRJGq1Z3UVbVT8BzuknSZIkLUGzvos2yfuSHDsw/1+SPCPJyuGWJkmSpLmYy2NSHkf3xgqS3Af4EvBPwMYkDx1ibZIkSZqDuQS8ewA39J+fBnwDuDfwDuDVQ6pLkiRJczSXgHcN3TtoAZ4O/N+quoPu7RUHD6kuSZIkzdFc3kV7OvCWJB8HnkB3V+1EXzsPqzBJkiTNzVzeRXtK97IKngS8uKqu6lcdBHxziLVJkiRpDuZyBI+qOgU4ZdLi+wHr5l2RJEmS5mXWAS/J+4BPVdVp/fx/AfYH3lVVW4ZcnyRJkmZp2I9J2W+ItUmSJGkOhv2YlNcMqS5JkiTNkY9JkSRJaoyPSZEkSWqMj0mRJElqjI9JkSRJasycAt5U+tAnSZKkEZv1TRZJ7prkdUkuT3JVkg8lecZCFCdJkqTZm8tdtG8Ankl3s8Wb6B6ZcnqS9ycZ2hFBSZIkzc1cAtkzgKdW1RcmFiR5BfAx4ETgVUOqTZIkSXMwlyN4dwNuHFxQVd8B/gfwvGEUJUmSpLmbS8D7DHDMFMuvo7uTVpIkSSM0l1O0JwJf6N9D+ybg/wErgL8CNg6xNkmSJM3BXB50fHmSQ4DTgMuArXRHAr8HPGW45UmSJGm25vqg40uAg5M8ENgP+BHwpaq6eZjFSZIkafbm9ViTqroSuBIgyYOSfK6qfnsolUmSJGlO5nKTxXSW0R3NkyRJ0ggNM+BJkiRpCZjxKdokZwAb+uniqrp1oYqSJEnS3M3mGrw9gSOAewJ3JLmSXwS+L9PdTStJkqQRm3HAq6rfAUjyW8DD++lA4G+Ae080G3aBkiRJmp25PAfvKuAq4H0Ty5LsDYzRBT5JkiSN0LwekzKhqq4GrgbOGUZ/kiRJmjvvopUkSWqMAU+SJKkxBjxJkqTGGPAkSZIaY8CTJElqjAFPkiSpMQY8SZKkxhjwJEmSGmPAkyRJaowBT5IkqTEGPEmSpMYY8CRJkhpjwJMkSWqMAU+SJKkxBjxJkqTGLImAl2RtkiuSbE5y4hTrT0iyKcklST6d5P4D616X5LJ++sOB5fsk+VLf53uTrFis/ZEkSRqlkQe8JMuAU4HDgTXAs5KsmdTsK8BYVe0PnAOc0m/7ZOBA4ADgkcCLk+zab/M64I1V9QDgJuCYhd4XSZKkpWDkAQ84CNhcVVdV1e3AOuDIwQZVdV5V3drPXgCs6j+vAT5bVVur6sfAJcDaJAEOpQuDAGcCT1ng/ZAkSVoSlkLA2wO4dmD+un7ZdI4BPt5//ipdoNs5yW7AE4A9gfsAP6iqrTPsU5IkqRnLR13AbCQ5GhgDDgGoqk8meQTwBWAL8EXgjln2eSxwLMBee+011HolSZJGYSkcwbue7qjbhFX9sjtJchjwMuCIqrptYnlVvbqqDqiqJwIBrgS+B9wzyfJt9dlvf1pVjVXV2MqVK4eyQ5IkSaO0FALeRcDq/q7XFcBRwPrBBkkeBrydLtzdOLB8WZL79J/3B/YHPllVBZwHPL1v+lzgQwu+J5IkSUvAyE/RVtXWJMcD5wLLgNOramOSk4HxqloPvB64O/C+7v4JrqmqI4CdgM/1y24Gjh647u4lwLokr6K7C/edi7lfkiRJo5LuYJcAxsbGanx8fNRlSJIkbVeSDVU1NtW6pXCKVpIkSUNkwJMkSWqMAU+SJKkxBjxJkqTGGPAkSZIaY8CTJElqjAFPkiSpMQY8SZKkxhjwJEmSGmPAkyRJaowBT5IkqTEGPEmSpMYY8CRJkhpjwJMkSWqMAU+SJKkxBjxJkqTGGPAkSZIaY8CTJElqjAFPkiSpMQY8SZKkxhjwJEmSGmPAkyRJaowBT5IkqTEGPEmSpMYY8CRJkhpjwJMkSWqMAU+SJKkxBjxJkqTGGPAkSZIaY8CTJElqjAFPkiSpMQY8SZKkxhjwJEmSGmPAkyRJaowBT5IkqTEGPEmSpMYY8CRJkhpjwJMkSWqMAU+SJKkxBjxJkqTGGPAkSZIaY8CTJElqjAFPkiSpMQY8SZKkxhjwJEmSGmPAkyRJaowBT5IkqTEGPEmSpMYY8CRJkhpjwJMkSWqMAU+SJKkxBjxJkqTGGPAkSZIaY8CTJElqjAFPkiSpMQY8SZKkxhjwJEmSGmPAkyRJaowBT5IkqTFLIuAlWZvkiiSbk5w4xfoTkmxKckmSTye5/8C6U5JsTHJ5kjcnSb/8/L7Pi/vpvou5T5IkSaMy8oCXZBlwKnA4sAZ4VpI1k5p9BRirqv2Bc4BT+m3/K/BoYH9gP+ARwCED2/1RVR3QTzcu7J5IkiQtDSMPeMBBwOaquqqqbgfWAUcONqiq86rq1n72AmDVxCrgbsAK4K7ATsB3FqVqSZKkJWopBLw9gGsH5q/rl03nGODjAFX1ReA84IZ+OreqLh9o+67+9OzfTJy6nSzJsUnGk4xv2bJlPvshSZK0JCyFgDdjSY4GxoDX9/MPAB5Md0RvD+DQJI/tm/9RVT0UeGw/PXuqPqvqtKoaq6qxlStXLvQuSJIkLbilEPCuB/YcmF/VL7uTJIcBLwOOqKrb+sV/AFxQVbdU1S10R/YeBVBV1/d/fwScRXcqWJIkqXlLIeBdBKxOsk+SFcBRwPrBBkkeBrydLtwN3ixxDXBIkuVJdqK7weLyfn63ftudgN8HLluEfZEkSRq55aMuoKq2JjkeOBdYBpxeVRuTnAyMV9V6ulOydwfe119Kd01VHUF3R+2hwKV0N1x8oqo+nGQX4Nw+3C0D/g14x2LvmyRJ0iikqkZdw5IxNjZW4+Pjoy5DkiRpu5JsqKqxqdYthVO0kiRJGiIDniRJUmMMeJIkSY0x4EmSJDXGgCdJktQYA54kSVJjDHiSJEmNMeBJkiQ1xoAnSZLUGAOeJElSYwx4kiRJjTHgSZIkNcaAJ0mS1BgDniRJUmMMeJIkSY0x4EmSJDXGgCdJktQYA54kSVJjDHiSJEmNMeBJkiQ1xoAnSZLUGAOeJElSYwx4kiRJjTHgSZIkNcaAJ0mS1BgDniRJUmMMeJIkSY0x4EmSJDXGgCdJktQYA54kSVJjDHiSJEmNMeBJkiQ1xoAnSZLUGAOeJElSYwx4kiRJjTHgSZIkNcaAJ0mS1BgDniRJUmMMeJIkSY0x4EmSJDXGgCdJktQYA54kSVJjDHiSJEmNMeBJkiQ1xoAnSZLUGAOeJElSYwx4kiRJjTHgSZIkNcaAJ0mS1BgDniRJUmMMeJIkSY0x4EmSJDXGgCdJktQYA54kSVJjDHiSJEmNMeBJkiQ1xoAnSZLUGAOeJElSY5ZEwEuyNskVSTYnOXGK9Sck2ZTkkiSfTnL/gXWnJNmY5PIkb06SfvnDk1za9/nz5ZIkSa0becBLsgw4FTgcWAM8K8maSc2+AoxV1f7AOcAp/bb/FXg0sD+wH/AI4JB+m7cBfwas7qe1C7snkiRJS8PIAx5wELC5qq6qqtuBdcCRgw2q6ryqurWfvQBYNbEKuBuwArgrsBPwnSS7A7tW1QVVVcC7gacs/K5IkiSN3lIIeHsA1w7MX9cvm84xwMcBquqLwHnADf10blVd3m9/3Sz6lCRJasbyURcwG0mOBsboT8MmeQDwYH5xRO9TSR4L/GQWfR4LHAuw1157DbVeSZKkUVgKR/CuB/YcmF/VL7uTJIcBLwOOqKrb+sV/AFxQVbdU1S10R/Ye1W+/amDzKfsEqKrTqmqsqsZWrlw5752RJEkataUQ8C4CVifZJ8kK4Chg/WCDJA8D3k4X7m4cWHUNcEiS5Ul2ojuyd3lV3QDcnOTg/u7Z5wAfWoydkSRJGrWRB7yq2gocD5wLXA6cXVUbk5yc5Ii+2euBuwPvS3JxkokAeA7wdeBS4KvAV6vqw/26FwD/BGzu23x8UXZIkiRpxNLdZCqAsbGxGh8fH3UZkiRJ25VkQ1WNTbVu5EfwJEmSNFwGPAfiHdcAAApKSURBVEmSpMYY8CRJkhpjwJMkSWqMAU+SJKkxBjxJkqTGGPAkSZIaY8CTJElqjAFPkiSpMQY8SZKkxhjwJEmSGmPAkyRJaowBT5IkqTEGPEmSpMYY8CRJkhpjwJMkSWqMAU+SJKkxBjxJkqTGGPAkSZIaY8CTJElqjAFPkiSpMQY8SZKkxhjwJEmSGmPAkyRJakyqatQ1LBlJtgDfXOCv2Q347gJ/x68ax3T4HNPhc0yHzzEdPsd0uBZ6PO9fVSunWmHAW2RJxqtqbNR1tMQxHT7HdPgc0+FzTIfPMR2uUY6np2glSZIaY8CTJElqjAFv8Z026gIa5JgOn2M6fI7p8Dmmw+eYDtfIxtNr8CRJkhrjETxJkqTGGPAWUZK1Sa5IsjnJiaOuZ0eV5Ooklya5OMl4v+zeST6V5Gv933uNus6lLMnpSW5MctnAsinHMJ0397/bS5IcOLrKl65pxvSkJNf3v9WLk/zewLqX9mN6RZInjabqpSvJnknOS7IpycYkf9Uv93c6R9sYU3+nc5TkbkkuTPLVfkxf2S/fJ8mX+rF7b5IV/fK79vOb+/V7L1RtBrxFkmQZcCpwOLAGeFaSNaOtaof2hKo6YOD28xOBT1fVauDT/bymdwawdtKy6cbwcGB1Px0LvG2RatzRnMEvjynAG/vf6gFV9TGA/r/9o4CH9Nu8tf83Qr+wFXhRVa0BDgaO68fN3+ncTTem4O90rm4DDq2q3wYOANYmORh4Hd2YPgC4CTimb38McFO//I19uwVhwFs8BwGbq+qqqrodWAccOeKaWnIkcGb/+UzgKSOsZcmrqs8C35+0eLoxPBJ4d3UuAO6ZZPfFqXTHMc2YTudIYF1V3VZV3wA20/0boV5V3VBVX+4//wi4HNgDf6dzto0xnY6/0+3of2+39LM79VMBhwLn9Msn/04nfr/nAL+TJAtRmwFv8ewBXDswfx3b/g9L0yvgk0k2JDm2X3a/qrqh//xt4H6jKW2HNt0Y+tudn+P7U4anD1w64JjOQn8a62HAl/B3OhSTxhT8nc5ZkmVJLgZuBD4FfB34QVVt7ZsMjtvPx7Rf/0PgPgtRlwFPO6LHVNWBdKdkjkvyuMGV1d0a7u3h8+AYDs3bgH3pTt3cAPzdaMvZ8SS5O/B+4IVVdfPgOn+nczPFmPo7nYequqOqDgBW0R3hfNCISwIMeIvpemDPgflV/TLNUlVd3/+9EfgA3X9Q35k4HdP/vXF0Fe6wphtDf7tzVFXf6f/x/xnwDn5xessxnYEkO9EFkX+pqn/tF/s7nYepxtTf6XBU1Q+A84BH0V0isLxfNThuPx/Tfv2vA99biHoMeIvnImB1f2fNCroLV9ePuKYdTpJdktxj4jPwu8BldGP53L7Zc4EPjabCHdp0Y7geeE5/l+LBwA8HTpFpGyZdA/YHdL9V6Mb0qP6Oun3obgy4cLHrW8r665LeCVxeVX8/sMrf6RxNN6b+Tucuycok9+w//xrwRLprG88Dnt43m/w7nfj9Ph3491qgBxIv334TDUNVbU1yPHAusAw4vao2jrisHdH9gA/016QuB86qqk8kuQg4O8kxwDeBZ46wxiUvyXuAxwO7JbkOeAXwWqYew48Bv0d3gfWtwPMWveAdwDRj+vgkB9CdRrwa+HOAqtqY5GxgE92djcdV1R2jqHsJezTwbODS/vomgP8Pf6fzMd2YPsvf6ZztDpzZ3118F+DsqvpIkk3AuiSvAr5CF6zp//7fJJvpbso6aqEK800WkiRJjfEUrSRJUmMMeJIkSY0x4EmSJDXGgCdJktQYA54kSVJjDHiSJEmNMeBJkiQ1xoAnSZLUGAOepEWX5Pwkb5m07MVJrh6Yv0uSv0lybZLbklya5MiB9XsnqSTfT3K3geX7JPlZklsm9f/fkmxI8p9JvpHk1f1rAyfWX53kpCT/nOSWJN9O8uLt7MdJSS4bmF+RZHNf125JHt9/nm7au99uTZKPJvlRkhuTvCfJbwz0e0aSjyR5eZLv9PW9q3810kSbtUk+l+SmfkzOTfLgKcZrbNI+XJbkpMnfNTB/n77PWwaWbXO/tzVmkhaHAU/SUvVXwF8DLwEeCnwA+Nf+lUqDbgWeMTD/Z8C3BhskeRLwL8BbgIcAf0L3HsjXTOrrBLr3SB5I96qx1yR56ixqPp7udXoTvkD3KqPdgaf1y3YfmK7t3wP6Wbr3fx4EHAbcHfhQksF/ow8Bfhv4nb6v3wVeN7B+F+BNfR+PB34IfHgwxM7RK9j+ay0n77ekETPgSVqqXgy8oarOqqorq+p/AZ/rlw96J/27M5PsBPwxcPqkNi8DXl9V76qqr1fVeXTB8fn9C9gnfKmqXt1/39uBd9OFvu1Kcu/+e34euqrq9qr6dlV9m+69k0zM99MdwF8AX62ql1TV5VV1CfAcuqA2eLTtDuB5VXVZVZ3b1//nSXbp+31/P32t7+N5wD59P3OSZDVdGH7jbPZb0ugZ8CSNyrH9qcZb+tN/r55YkWRX4DeB/5i0zeeBNZOWfQzYK8lDgCOBS4GrJrV5OPCySd93Ft1Rr98YaPfFSdt9cYrvm87fAOf3Nc7Gw4HHTart2n7dvgPtLqmqwdPOXwRWTLRJsm+Ss5J8PcnNwHfo/o3fa5b1DDoFeDu/PJ6D5rrfkhbQ9g67S9JCeS/wyoH5Y4BnzWC7mjR/B784ivdg4G3ArpPa3KX/rvdN0d+WmRS7LUn2Bf4UeBiwapab3wX4KL98ZBK6kDZTHwGuoxuH64GtwCa6EDhrSR4LPI7uCN6R07SZz35LWkAGPEmj8sOq2jwxk+R7E5+r6uYk3wIeDXx6YJvH0IWWyd5Jdw3bj4H1wNGT1n8ZeNDg903j4CnmL9/ONgCvBd5ZVZuTzDbofBl4JvDNqvrpNto9NMkuVfXjgdpuB76e5D7Ag4AX9KefSXIgc/83PsDfAf+7qm6681nsO5nPfktaQAY8SUvV64GTk3wN2EAX2h5LdwPEnVTVdUn+CvhuVW2dIpCcDHwkyTeBs+mObu0HHFRV/3Og3cFJXgqcQ3ejwnOAP9pOnfvQnU5+wOx27+dOpbsx5L1JXkd3RPG36ELfi6rqR3275cDpSU7uv++1wDuq6sdJfgJ8F/izJNcCe9CN39Ypvm/F4F3HdGFueZLlVTXR/hDghr626cx3vyUtIAOepKXqzcA96K4Dux9wBfC0qvrqVI2r6szpOqqqc5M8me56sRfTBZ8rgTMmNf17YH+6mwZ+DPyvqjpnO3Xu3Lf7/vZ2aJravpXk0cDfAp8A7gZcA3wSuG2g6WeAjcB5/Xe+H/iffR8/S/KHdGN2GbAZeFHfZrLJ1zVCd53hVuCkfn4X4MTtHFGc135LWlipmnw5iyT96kn3DL63VNUbRl3LZEnOAHarqt9foP5PAqiqkxaif0mLzyN4kqSpTuVK2oEZ8CTpV1xVvWrUNUgaLk/RSpIkNcYHHUuSJDXGgCdJktQYA54kSVJjDHiSJEmNMeBJkiQ1xoAnSZLUmP8fEkyxWj+nsPQAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 7.73 s, sys: 148 ms, total: 7.87 s\n",
            "Wall time: 10.1 s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sPxo2YVeHuou"
      },
      "source": [
        "Скорее всего сейчас у вас лосс -- это прямая линия, и вы видите, что веса не обновляются. Но почему?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mXUmEeUBHuov"
      },
      "source": [
        "Всё просто -- если присмотреться, то видно, что self.w и self.b иницилизируются нулями в начале `.fit()`-метода. Если расписать, как будет идти обновление, то видно, что из-за ReLU веса просто-напросто не будут обновляться, если начать с инициализации нулями. \n",
        "\n",
        "Это -- одна из причин, по которой в нейронных сетях веса инициализируют случаными числами (обычно из отрезка [0, 1)).\n",
        "\n",
        "Обучите нейрон, инициализировав случайно веса (поставьте 10000 итераций). \n",
        "\n",
        "Закомментируйте инициализацию нулями в функции `.fit()` класса `NeuronReLU` "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E821cUM0Huo8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 554
        },
        "outputId": "3ec7601d-514c-442d-ca5b-4bc4ea46ad8a"
      },
      "source": [
        "%%time\n",
        "\n",
        "neuron = NeuronReLU(w=np.random.rand(X.shape[1], 1), b=np.random.rand(1))\n",
        "Loss_values = neuron.fit(X, y, num_epochs=10000)\n",
        "\n",
        "plt.figure(figsize=(10, 8))\n",
        "plt.plot(Loss_values)\n",
        "plt.title('Функция потерь', fontsize=15)\n",
        "plt.xlabel('номер итерации', fontsize=14)\n",
        "plt.ylabel('$Loss(\\hat{y}, y)$', fontsize=14)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 2min 15s, sys: 6.17 s, total: 2min 21s\n",
            "Wall time: 2min 27s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jkWMrc9uHupS"
      },
      "source": [
        "Посмотрим, как предсказывает этот нейрон:"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "accuracies = []\n",
        "thresholds = []\n",
        "for threshold in np.linspace(0.5, 0.9, 10):\n",
        "    accuracies.append(accuracy_score(y, neuron.predict(X, threshold)))\n",
        "    thresholds.append(threshold)\n",
        "print(max(accuracies))\n",
        "\n",
        "best_threshold = round(thresholds[accuracies.index(max(accuracies))], 2)\n",
        "print(best_threshold)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RXtw7OMYODsA",
        "outputId": "a7ab384a-4a48-473f-86d4-1b07c04c05a9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.982\n",
            "0.63\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hyjfcthcHupT",
        "scrolled": false,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 520
        },
        "outputId": "9dd5497a-6a58-4e4c-8210-4184df0ce052"
      },
      "source": [
        "plt.figure(figsize=(10, 8))\n",
        "plt.scatter(data.iloc[:, 0], data.iloc[:, 1], c=np.array(neuron.forward_pass(X) > best_threshold).ravel(), s=50, alpha=0.5, cmap='jet')\n",
        "plt.title('Яблоки и груши', fontsize=15)\n",
        "plt.xlabel('симметричность', fontsize=14)\n",
        "plt.ylabel('желтизна', fontsize=14)\n",
        "plt.show();"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Голос"
      ],
      "metadata": {
        "id": "3mqBedqnKnT8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "label_encoder = LabelEncoder()"
      ],
      "metadata": {
        "id": "7hKnsKgtWceb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv(\"voice.csv\")\n",
        "data['label'] = label_encoder.fit_transform(data['label'])\n",
        "data = pd.DataFrame(MinMaxScaler().fit_transform(data))\n",
        "data = data.sample(frac=1)\n",
        "data"
      ],
      "metadata": {
        "id": "gm4sTknzKpJ5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "99ea1151-ce3c-4b56-990a-141dcf2d9f71"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            0         1         2         3         4         5         6   \\\n",
              "254   0.433168  0.711766  0.478368  0.262317  0.720885  0.544951  0.994565   \n",
              "2171  0.719788  0.179688  0.732210  0.715875  0.724003  0.076382  0.073754   \n",
              "969   0.711275  0.430318  0.724260  0.540115  0.873196  0.403839  0.036570   \n",
              "940   0.727208  0.430692  0.831712  0.534805  0.878252  0.414264  0.038517   \n",
              "331   0.560281  0.446059  0.663454  0.411420  0.719671  0.388742  0.068893   \n",
              "...        ...       ...       ...       ...       ...       ...       ...   \n",
              "1238  0.646947  0.381130  0.612289  0.517004  0.809043  0.365645  0.035292   \n",
              "466   0.682863  0.304133  0.700288  0.582486  0.785114  0.274349  0.042759   \n",
              "2169  0.720977  0.151022  0.726899  0.720113  0.709509  0.057917  0.075384   \n",
              "1638  0.730156  0.349572  0.777024  0.699832  0.791159  0.158201  0.049293   \n",
              "3092  0.601502  0.354083  0.633176  0.657260  0.634542  0.050556  0.214224   \n",
              "\n",
              "            7         8         9   ...        11        12        13  \\\n",
              "254   0.970740  0.471268  0.611698  ...  0.433168  0.227750  0.057331   \n",
              "2171  0.006827  0.443041  0.252526  ...  0.719788  0.645058  0.030899   \n",
              "969   0.001830  0.676119  0.367570  ...  0.711275  0.365841  0.192595   \n",
              "940   0.002318  0.646242  0.314970  ...  0.727208  0.390694  0.201509   \n",
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              "\n",
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            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = data.iloc[:, :-1].values\n",
        "y = data.iloc[:, -1].values.reshape((-1, 1))\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
        "\n",
        "y_train = y_train.reshape(-1, 1)\n",
        "y_test = y_test.reshape(-1, 1)"
      ],
      "metadata": {
        "id": "br0TRaMrW_Ei"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "neuron = NeuronReLU(w=np.random.rand(X_train.shape[1], 1), b=np.random.rand(1))\n",
        "losses = neuron.fit(X_train, y_train, num_epochs=10000)"
      ],
      "metadata": {
        "id": "bRfFaE6zYoKJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "accuracies = []\n",
        "thresholds = []\n",
        "for threshold in np.linspace(0.2, 0.9, 10):\n",
        "    accuracies.append(accuracy_score(y_test, neuron.predict(X_test, threshold)))\n",
        "    thresholds.append(threshold)\n",
        "print(max(accuracies))\n",
        "\n",
        "best_threshold_relu = round(thresholds[accuracies.index(max(accuracies))], 2)\n",
        "print(\"best threshold for ReLU:\", best_threshold_relu)"
      ],
      "metadata": {
        "id": "Bl2jvcIsYdUX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "36c594ed-8d8f-46ef-ceca-7f3d12c0e800"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9263932702418507\n",
            "best threshold for ReLU: 0.59\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = np.where(neuron.forward_pass(X_test) >= best_threshold, 1, 0)\n",
        "accuracy_score(y_pred, y_test)"
      ],
      "metadata": {
        "id": "5ojgtAA_kmn_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "46722a01-b832-467e-8c1b-e33e8a598b98"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9211356466876972"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 8))\n",
        "plt.plot(Loss_values)\n",
        "plt.title('Функция потерь', fontsize=15)\n",
        "plt.xlabel('номер итерации', fontsize=14)\n",
        "plt.ylabel('$Loss(\\hat{y}, y)$', fontsize=14)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "hQJCOskTfZo3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 519
        },
        "outputId": "08ad59b2-7fc7-4e7c-f4fb-d6aa280cea85"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MSNZuWl9Hupd"
      },
      "source": [
        "Есть одна тенденция: пороговая функция активации и сигмоида (обычно всё же только сигмоида) чаще используются именно на **выходном слое** нейросети в задаче классификации -- ими предсказывают вероятности объектов принадлежать одному из классов, в то время как продвинутые функции активации (ReLU и те, что будут дальше) используются внутри нейросети, то есть в **скрытых слоях**."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_X3lDHElHupo"
      },
      "source": [
        "Нужно понимать, что ReLU не может вернуть отрицательные числа."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sET710OVHupp"
      },
      "source": [
        "**Плюсы ReLU:**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HY3iXGBWHupr"
      },
      "source": [
        "* дифференцируемая (с доопределе\n",
        "нием в нуле)\n",
        "* нет проблемы затухающих градиентов, как в сигмоиде"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Nr_3XwTWHups"
      },
      "source": [
        "**Возможные минусы ReLU:**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0TAwVdRXHupt"
      },
      "source": [
        "* не центрирована около 0 (может мешать скорости сходимсти)\n",
        "* зануляет все отрицательные входы, тем самым веса у занулённых нейронов могут часто *не обновляться*, эту проблему иногда называют *мёртвые нейроны*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vj1JGXTPHupu"
      },
      "source": [
        "С последней проблемой можно побороться, а именно:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yn2taDMNHupv"
      },
      "source": [
        "### Нейрон с LeakyReLU (Leaky Recitified Linear Unit)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iCRBWooSHupx"
      },
      "source": [
        "LeakyReLU очень слабо отличается от ReLU, но часто помогает сети обучаться быстрее, поскольку нет проблемы \"мёртвых нейронов\":\n",
        "\n",
        "\\begin{equation*}\n",
        "LeakyReLU(x) =\n",
        " \\begin{cases}\n",
        "   \\alpha x, &\\text{$x \\le 0$}\\\\\n",
        "   x, &\\text{$x \\gt 0$}\n",
        " \\end{cases}\n",
        "\\end{equation*}\n",
        "\n",
        "где $\\alpha$ -- маленькое число от 0 до 1.\n",
        "\n",
        "Производная здесь берётся так же, но вместо нуля будет $\\alpha$:\n",
        "\n",
        "\\begin{equation*}\n",
        "LeakyReLU'(x) = \n",
        " \\begin{cases}\n",
        "   \\alpha, &\\text{$x \\le 0$}\\\\\n",
        "   1, &\\text{$x \\gt 0$}\n",
        " \\end{cases}\n",
        "\\end{equation*}\n",
        "\n",
        "График этой функции:\n",
        "\n",
        "<img src=\"https://cdn-images-1.medium.com/max/1600/0*UtLlZJ80TMIM7kXk.\" width=400 height=300>\n",
        "\n",
        "Подставим LeakyReLu в Loss:\n",
        "\n",
        "$$\n",
        "Loss(\\hat{y}, y) = \\frac{1}{2n}\\sum_{i=1}^{n} (\\hat{y_i} - y_i)^2 = \\frac{1}{2n}\\sum_{i=1}^{n} (LeakyReLU(w \\cdot X_i) - y_i)^2 =\n",
        "\\begin{equation*}\n",
        "\\frac{1}{2n}\\sum_{i=1}^{n} \n",
        " \\begin{cases}\n",
        "   (\\alpha \\cdot w \\cdot X_i - y_i)^2, &{w \\cdot X_i \\le 0}\\\\\n",
        "   (w \\cdot X_i - y_i)^2, &{w \\cdot X_i \\gt 0}\n",
        " \\end{cases}\n",
        "\\end{equation*}\n",
        "$$  \n",
        "\n",
        "Формула для обновления весов при градиентном спуске:\n",
        "\n",
        "$$ \\frac{\\partial Loss}{\\partial w} = \\begin{equation*}\n",
        "\\frac{1}{n}\\sum_{i=1}^{n} \n",
        " \\begin{cases}\n",
        "   \\alpha X_i^T (w \\cdot X_i - y), &{w \\cdot X_i \\le 0}\\\\\n",
        "    X_i^T (w \\cdot X_i - y), &{w \\cdot X_i \\gt 0}\n",
        " \\end{cases}\n",
        "\\end{equation*}$$\n",
        "\n",
        "* Реализуйте LeakyReLU и её производную:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EvvL-J5Tg733"
      },
      "source": [
        "def leaky_relu(x, alpha=0.01):\n",
        "    \"\"\"LeakyReLU-функция\"\"\"\n",
        "    <Ваш код здесь>"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "An5RxMX_g736"
      },
      "source": [
        "def leaky_relu_derivative(x, alpha=0.01):\n",
        "    \"\"\"Производная LeakyReLU\"\"\"\n",
        "    <Ваш код здесь>"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4pVZT9RZg738"
      },
      "source": [
        "Теперь нужно написать нейрон с LeakyReLU функцией активации. Здесь всё очень похоже на перцептрон, но будут по-другому обновляться веса и другая функция активации:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UM7qpfxPg739"
      },
      "source": [
        "class NeuronLeakyReLU:\n",
        "    def __init__(self, w=None, b=0):\n",
        "        \"\"\"\n",
        "        :param: w -- вектор весов\n",
        "        :param: b -- смещение\n",
        "        \"\"\"\n",
        "        self.w = w\n",
        "        self.b = b\n",
        "        \n",
        "        \n",
        "    def activate(self, x):\n",
        "        <Ваш код здесь>\n",
        "        \n",
        "        \n",
        "    def forward_pass(self, X):\n",
        "        \"\"\"\n",
        "        Рассчитывает ответ нейрона при предъявлении набора объектов\n",
        "        :param: X -- матрица примеров размера (n, m), каждая строка - отдельный объект\n",
        "        :return: вектор размера (n, 1) из нулей и единиц с ответами нейрона \n",
        "        \"\"\"\n",
        "        n = X.shape[0]\n",
        "        y_pred = np.zeros((n, 1))  # y_pred == y_predicted - предсказанные классы\n",
        "        <Ваш код здесь>\n",
        "        \n",
        "    \n",
        "    def backward_pass(self, X, y, y_pred, learning_rate=0.005):\n",
        "        \"\"\"\n",
        "        Обновляет значения весов нейрона в соответствии с этим объектом\n",
        "        :param: X -- матрица входов размера (n, m)\n",
        "                y -- вектор правильных ответов размера (n, 1)\n",
        "                learning_rate - \"скорость обучения\" (символ alpha в формулах выше)\n",
        "        В этом методе ничего возвращать не нужно, только правильно поменять веса\n",
        "        с помощью градиентного спуска.\n",
        "        \"\"\"\n",
        "        n = len(y)\n",
        "        y = np.array(y).reshape(-1, 1)\n",
        "        <Ваш код здесь>\n",
        "    \n",
        "    \n",
        "    def fit(self, X, y, num_epochs=300):\n",
        "        \"\"\"\n",
        "        Спускаемся в минимум\n",
        "        :param: X -- матрица объектов размера (n, m)\n",
        "                y -- вектор правильных ответов размера (n, 1)\n",
        "                num_epochs -- количество итераций обучения\n",
        "        :return: losses -- вектор значений функции потерь\n",
        "        \"\"\"\n",
        "#         self.w = np.zeros((X.shape[1], 1))  # столбец (m, 1)\n",
        "#         self.b = 0  # смещение (число)\n",
        "        Loss_values = []  # значения функции потерь на различных итерациях обновления весов\n",
        "        \n",
        "        for i in range(num_epochs):\n",
        "            <Ваш код здесь>\n",
        "        \n",
        "        return Loss_values"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IgTac_vFHuq8"
      },
      "source": [
        "**Плюсы LeakyReLU:**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iAw-tzuLHuq9"
      },
      "source": [
        "* дифференцируемая (с доопределнием в нуле)\n",
        "* нет проблемы затухающих градиентов, как в сигмоиде\n",
        "* нет проблемы \"мёртвых нейронов\", как в ReLU"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CMUGymwyHuq_"
      },
      "source": [
        "**Возможные минусы LeakyReLU:**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uQyqD8-LHurA"
      },
      "source": [
        "* не центрирована около 0 (может мешать скорости сходимсти)\n",
        "* немного не устойчива к \"шуму\" (см. лекции Стэнфорда)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e7k07EGyHurB"
      },
      "source": [
        "### Нейрон с ELU (Exponential Linear Unit)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dWH3Zk2zHurB"
      },
      "source": [
        "ELU -- не так давно предложенная (в 2015 году) функция активации, которая, как говорят авторы статьи, лучше LeakyReLU. Вот формула ELU:\n",
        "\n",
        "\\begin{equation*}\n",
        "ELU(\\alpha, x) =\n",
        " \\begin{cases}\n",
        "   \\alpha (e^x - 1), &\\text{$x \\le 0$}\\\\\n",
        "   x, &\\text{$x \\gt 0$}\n",
        " \\end{cases}\n",
        "\\end{equation*}\n",
        "\n",
        "где $\\alpha$ -- маленькое число от 0 до 1.\n",
        "\n",
        "Производная здесь берётся так же, но вместо нуля будет $\\alpha$:\n",
        "\n",
        "\\begin{equation*}\n",
        "ELU'(x) = \n",
        " \\begin{cases}\n",
        "   ELU(\\alpha, x) + \\alpha, &\\text{$x \\le 0$}\\\\\n",
        "   1, &\\text{$x \\gt 0$}\n",
        " \\end{cases}\n",
        "\\end{equation*}\n",
        "\n",
        "Здесь в производной использован постой трюк -- сделано $- \\alpha + \\alpha$, чтобы вычислять было проще.\n",
        "\n",
        "График этой функции:\n",
        "\n",
        "<img src=\"http://p0.ifengimg.com/pmop/2017/0907/A004001DD141881BFD8AD62E5D31028C3BE3FAD1_size14_w446_h354.png\" width=500 height=400>\n",
        "\n",
        "Подставим LeakyReLu в Loss:\n",
        "\n",
        "$$Loss(\\hat{y}, y) = \\frac{1}{2n}\\sum_{i=1}^{n} (\\hat{y_i} - y_i)^2 = \\frac{1}{2n}\\sum_{i=1}^{n} (ELU(\\alpha, w \\cdot X_i) - y_i)^2 = \\begin{equation*}\n",
        "\\frac{1}{2n}\\sum_{i=1}^{n} \n",
        " \\begin{cases}\n",
        "   (\\alpha (e^{w \\cdot X_i} - 1) - y_i)^2, &{w \\cdot X_i \\le 0}\\\\\n",
        "   (w \\cdot X_i - y_i)^2, &{w \\cdot X_i \\gt 0}\n",
        " \\end{cases}\n",
        "\\end{equation*}$$  \n",
        "\n",
        "Здесь вам нужно выписать самим град спуск для весов. Брать производную \"в лоб\" некрасиво и неудобно. Нужно воспользоваться **правилом цепочки**, оно же **правило взятия производной сложной функции**:\n",
        "\n",
        "$$ \\frac{\\partial Loss}{\\partial w} = \\begin{equation*}\n",
        "\\frac{1}{n}\\sum_{i=1}^{n} \n",
        " \\begin{cases}\n",
        "   , &{w \\cdot X_i \\le 0}\\\\\n",
        "   , &{w \\cdot X_i \\gt 0}\n",
        " \\end{cases}\n",
        "\\end{equation*}$$\n",
        "\n",
        "* Реализуйте ELU и её производную:"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from math import exp"
      ],
      "metadata": {
        "id": "4pgm3iNCmRKC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mc6fsB5HzR2Q"
      },
      "source": [
        "def elu(x, alpha=0.01):\n",
        "    \"\"\"LeakyReLU-функция\"\"\"\n",
        "    return np.vectorize(lambda x: x if x > 0 else alpha * (exp(x) - 1))(x)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lMSIjprszR2T"
      },
      "source": [
        "def elu_derivative(x, alpha=0.01):\n",
        "    \"\"\"Производная LeakyReLU\"\"\"\n",
        "    return np.vectorize(lambda x: 1 if x > 0 else elu(x, alpha) + alpha)(x)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P5p1eDNGzR2V"
      },
      "source": [
        "Теперь нужно написать нейрон с LeakyReLU функцией активации. Здесь всё очень похоже на перцептрон, но будут по-другому обновляться веса и другая функция активации:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3cxpveqozR2X"
      },
      "source": [
        "class NeuronELU:\n",
        "    def __init__(self, w=None, b=0, alpha=0.01):\n",
        "        \"\"\"\n",
        "        :param: w -- вектор весов\n",
        "        :param: b -- смещение\n",
        "        \"\"\"\n",
        "        self.w = w\n",
        "        self.b = b\n",
        "        self.alpha = 0.01\n",
        "        \n",
        "        \n",
        "    def activate(self, x):\n",
        "        return elu(x, self.alpha)\n",
        "        \n",
        "        \n",
        "    def forward_pass(self, X):\n",
        "        \"\"\"\n",
        "        Рассчитывает ответ нейрона при предъявлении набора объектов\n",
        "        :param: X -- матрица примеров размера (n, m), каждая строка - отдельный объект\n",
        "        :return: вектор размера (n, 1) из нулей и единиц с ответами нейрона \n",
        "        \"\"\"\n",
        "        n = X.shape[0]\n",
        "        y_pred = np.zeros((n, 1))  # y_pred == y_predicted - предсказанные классы\n",
        "        y_pred = self.activate(X @ self.w.reshape(X.shape[1], 1) + self.b).reshape(-1, 1)\n",
        "        return y_pred\n",
        "\n",
        "    \n",
        "    def backward_pass(self, X, y, y_pred, learning_rate=0.005):\n",
        "        \"\"\"\n",
        "        Обновляет значения весов нейрона в соответствии с этим объектом\n",
        "        :param: X -- матрица входов размера (n, m)\n",
        "                y -- вектор правильных ответов размера (n, 1)\n",
        "                learning_rate - \"скорость обучения\" (символ alpha в формулах выше)\n",
        "        В этом методе ничего возвращать не нужно, только правильно поменять веса\n",
        "        с помощью градиентного спуска.\n",
        "        \"\"\"\n",
        "        n = len(y)\n",
        "        y = np.array(y).reshape(-1, 1)\n",
        "        \n",
        "        m = X.shape[1]\n",
        "        w = np.zeros((m, 1))\n",
        "        b = 0\n",
        "        w_derivative = elu_derivative(y_pred, self.alpha)\n",
        "        for i in range(n):\n",
        "            w += w_derivative[i] * X[i].reshape(1, -1).T @ (y_pred[i].reshape(1, 1) - y[i].reshape(1, 1))\n",
        "            b += w_derivative[i] * (y_pred[i][0] - y[i][0])\n",
        "        self.w -= learning_rate * w / n\n",
        "        self.b -= learning_rate * b / n\n",
        "    \n",
        "    \n",
        "    def fit(self, X, y, num_epochs=300):\n",
        "        \"\"\"\n",
        "        Спускаемся в минимум\n",
        "        :param: X -- матрица объектов размера (n, m)\n",
        "                y -- вектор правильных ответов размера (n, 1)\n",
        "                num_epochs -- количество итераций обучения\n",
        "        :return: losses -- вектор значений функции потерь\n",
        "        \"\"\"\n",
        "#         self.w = np.zeros((X.shape[1], 1))  # столбец (m, 1)\n",
        "#         self.b = 0  # смещение (число)\n",
        "        Loss_values = []  # значения функции потерь на различных итерациях обновления весов\n",
        "        \n",
        "        for i in range(num_epochs):\n",
        "            y_pred = self.forward_pass(X)\n",
        "            Loss_values.append(Loss(y_pred, y))\n",
        "            self.backward_pass(X, y, y_pred)\n",
        "        \n",
        "        return Loss_values\n",
        "    def predict(self, X, threshold):\n",
        "      return np.array(self.forward_pass(X) > threshold)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Яблоки и груши"
      ],
      "metadata": {
        "id": "x6zgyx_1s8Zw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv(\"apples_pears.csv\")\n",
        "X = data.iloc[:, :-1].values\n",
        "y = data['target'].values.reshape(-1, 1)"
      ],
      "metadata": {
        "id": "Lfm3o0ottAdO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "\n",
        "neuron = NeuronELU(w=np.random.rand(X.shape[1], 1), b=np.random.rand(1))\n",
        "Loss_values = neuron.fit(X, y, num_epochs=10000)\n",
        "\n",
        "plt.figure(figsize=(10, 8))\n",
        "plt.plot(Loss_values)\n",
        "plt.title('Функция потерь', fontsize=15)\n",
        "plt.xlabel('номер итерации', fontsize=14)\n",
        "plt.ylabel('$Loss(\\hat{y}, y)$', fontsize=14)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "mNjLdHUttCUb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 554
        },
        "outputId": "4a626b79-878a-45f3-c661-d7f800ea5cc0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 2min 22s, sys: 7.11 s, total: 2min 29s\n",
            "Wall time: 2min 23s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "accuracies = []\n",
        "thresholds = []\n",
        "for threshold in np.linspace(0.5, 0.9, 10):\n",
        "    accuracies.append(accuracy_score(y, neuron.predict(X, threshold)))\n",
        "    thresholds.append(threshold)\n",
        "print(max(accuracies))\n",
        "\n",
        "best_threshold = round(thresholds[accuracies.index(max(accuracies))], 2)\n",
        "print(best_threshold)"
      ],
      "metadata": {
        "id": "QwXvnwHKu7FZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a3ac2d10-e22e-4329-b975-8dfd5c1a4264"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.986\n",
            "0.63\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 8))\n",
        "plt.scatter(data.iloc[:, 0], data.iloc[:, 1], c=np.array(neuron.forward_pass(X) > best_threshold).ravel(), s=50, alpha=0.5, cmap='jet')\n",
        "plt.title('Яблоки и груши', fontsize=15)\n",
        "plt.xlabel('симметричность', fontsize=14)\n",
        "plt.ylabel('желтизна', fontsize=14)\n",
        "plt.show();"
      ],
      "metadata": {
        "id": "pvokGBThweTB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 520
        },
        "outputId": "5f56b08d-a136-4aeb-8a0a-0150885aa86e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ],
            "image/png": 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lPR4/DQ0dod/dcPHJJ8exWt3k5ppQq1UIIUhNjcVojGHNmrLzrihKespGCLfbxz//eYTcXGNIeHSdjDU1VjZtMnPttZNG2MqhJzs7gXvuWcAf/vAFZrMVIYJ/h6uumsCqVeMHtS9FUfjkk+NkZyeEvZeVFc/69ZVce+2kYbnRTJ2awcMPr+DnP9/Arl0nSEzUM21aOhkZ8VRX21iyJJ/c3NERqlCrRT8XxvAwzNkye3Y2d901j7/+9QDV1VYUBYqKErn11pJuOU9nisfj57XX9rN5sxkhwO9XyM01cueds3vNZeuNxkZ7Z0g3mPPo8wXYt6+BGTMyueuueed1Toykd9rb3TzyyFacTh9XXVVMQ4Odkyfbsdk8zJuXw9e+NmVERLDfH8xxfe+9o3g8ARRFYe7cbL7+9WmYTEOfz1taWktqavhDX2KinupqKxaL67wqNpGibIRobLTj9fp79QQlJurZv79h1Isyvz9AQ4MdCCbu93XznjIlnSeeWMnx4214PH7y8kxhoauBEAgouFw+tNpwB7BOp8bp9KEoMFzXvTFjknnllWtYt66CDz44RkeHB4/Hz9e+NoVLLx03arwQixfn8/rrZSQkdP9O2tpc5OWZSE6O/AVz3rwcZs/OoqXFiVotSE42ROzv9dpr+9mw4Tj5+cEnc0VRaGlx8thjn/LQQysGdO69+up+nE5vWEh39+56tm+vYenSwojYKjl3+OyzOiwWNwUFQeGfm2skN9eIzxdgz556Ojo8Yb+x4eBvfzvAe+8dJScn6CDw+wPs2nWCurp2fv7zCwcdrRgsKpUgEOj9oU9RiPhDX7QjRdkIEROjIRBQUBQl7Gbj8fiJjx/63Keh5IsvTvLqq/toa3MCgtRUA7fcUtJnUrhGozrrpE61WsW4cck0NHSEhdtaW50UF6cM+w9co1FxxRXjueyycbhcPmJi1N0adI4GlizJZ8uWasxmCxkZ8Wi1KpqaHHi9fr773XlDJi7VahXp6XER3Wdbm5NNm6pCggy+9FCbzRZKS2tYubL/6k6bzc3+/Q1hns7gfgxs2FA17KKsutrK+vWVHDnSTFKSnhUrxjB7dvZ5d0OLZvbvbyQ+PrwhtkYTfDA4caKdCROGV5RZLC7WraukoCAx9HtQq1Xk5ZmoqrKwd2898+blDqkNixfn849/HArL921pcTJuXPIZPaCPZkbX3WEEsNs9bN1azd/+doDNm80Ri7enpcUyZkwSTU3d83EURcFicbF0aUFEjnMqgYDCoUNN/PnPe3nppd3s3n1ySHJgDh1q4plndgCQn59Ifr4Jn0/hySe3c/RoS8SPdyrXXjsJm82N3f7l99TR4cFmc3PNNROH9Nj9oVIJYmO1o06QQTDX6Mc/XsyVVxbT0eGhvr6DqVPT+cUvlo666qiTJztQqUSv30N8vI7Dh09/fnaNxOlN8Oh06m7n3nCwb18999+/kdLSWhQFTpzo4NlnP+Pll/f06YGQDD/x8Tq83t6HvCsKQ+6R6g2z2QLQ6+/BYNCwf//QjzFctqyQ9PR4qqutuN0+/P4A9fUduFw+brxx6qiJKEQK6Snrh4qKVp56ajt2uxetVoXPF2DNmv3cc89CiovP7mYkhOD222fyyCNbqa62YjLF4PUGsFrdLFqUx8yZWRH6FEF8vgAvvPA5O3bUEhOjQaUSbNpUxfjxydxzz8KIViW+9dYhTKaYbq54ozEmlEf3wx8uitixejJ5chp33TWPv/xlHy0t1tCxv/vd+eddFU8kSUiI4atfncxXvzp5pE05K4KNUnsXKm63j6Sk0+fQJCXpMRpjcDi8YaPAWludQ1oh6nL5cDi8GI0xaDQqvF4/f/zjbpKTDSHvemyslsREPZs2VbF4cZ4cKB4lLF6cx5Yt5rB2GBaLi9TUWPLzB5bPGEmC/dJ6/z34fAEMhqGXCEZjDD/96QV88EEFmzZV4fH4mTUriyuvLB6Rv8lII0VZH7jdPlavLkWnU5OW9mUIxWZzs3p1KU88sfKsZzPm5hp54IHlbNlSzf79DcTH61iypICSkoyIe1S2bDGzfXsNY8YkhZ48FEWhoqKNd945wo03TovIcbxeP8eOtYbyJk4lJSWWAwcaew3ZRpK5c3OYOTOLurrgyJKcHCMazejzUEkiT36+iczMeFpaHN1C3D5fAI8nwKJFef1sHUStVnHttZP4wx8+JyfHiF6vCXm4hRBcfHHkKkS76Ojw8OabB9m6tZpAQCEuTsuqVcUUFJjo6PCE3bxUKkFMjIbS0lopyqKEiRNTueiiIj7++DgJCTr0eg0WiwuNRsVdd80bkVDzuHHJGAxa7HZPtwfzQEDB4/Ezb17OsNhhMum54YYp3HDDlGE5XjQjRVkfHDjQREeHJ6xfmNEYQ319O2++eYA5c3IYNy75rCqtkpIMfOUrE/jKVyacrcn9sm5dBenpcd3EkBCC7OwENm6s4rrrJvfaZXqwqNUqdDo1Pl9412qv14/BMDxDxjUa1XnT600ycFQqwbe/PYfHHtuG2RxslOp2+3C7/Vx99UTGjEka0H6WLMnH7w/w5psHaWy0oygK2dkJfPe783ut/j0bvF4/Tz65jaoqS+gBw+n08uqr+5gxI7PPwhWtNtiSRRIdCCG45ZYSZs7MYsOGKqxWFwsW5LJ0aWHEcycHik6n5pvfnMVvfvMZNpsbk0mP0+nFYnFx8cVjGTt2YL8HSeSQoqwPrFZXWBuAQEChrKyR/fsbaGpysHFjFQaDljvumE1JSeYIWTow2tpcvZYda7VqvN4Abrc/IqJMpRIsXVrAxx8fD3t6P3myg6uuKj7vcgQk0UVBQSIPPXQR27fXUF7eQlKSgUWL8hg7NmnA56YQguXLi1i8OJ/6+g40GhVZWUMzjaOsrJHKyjaKir68QRoMWgoLE9mzpx5FCXr6enqD7XZvRKctSM4elUowfXoG06eP/Pfi8wUoL2/B6/Vz220llJe3cOxYK7m5Rr7xjRJmzcqW1+oRQIqyPkhNjQ07IY8caebYsVZiY7WMGZNEVlYCdruHZ57Zwf/8z7IB9zgaCcaOTcJstoYJM7vdQ2Ki/qxDsady5ZXFlJU1cfx4G8nJBhQlWPWWn58o5xZKooLERD2XXz6eyy8fXC+8nuh06iHPe9m7t6FXD7NarUKrVTFpUhr79jWEeh4GAsFKvqysBGbNimxuquTcoLrayrPP7qClxYEQwZYUY8cm8eMfXzBqe4KdONHOli3V1NXZyMkxsmRJfsS91sOBFGV9MGlSGhkZ8TQ22klPj8PnC3DsWCtabTA81+VujovTYbG4WL++kttvnznCVvfNlVcW8/DDW4mP14VmQHq9furrO/jWt2ZHNJ/BZNLz3/+9hNLSWrZtq0EIwdVXT2D+/NyIir/RgsPhZcsWM5s3V+Pz+Zk9O5sVK4p67ZIvkfRErw+O4emd4JSDKVPSWbu2HLfbjqLAnDnZ3Hjj1GGb9yoZPTgcXp58chuKQijFQ1EUamps/OY3n/Hzn1846jxkn31Wx+9/vwuVKnhPPniwiQ8/PMadd85m/vyhbekRaeQvtg80GhX33LOAZ57ZgdlsweHwYrO5SU+PY8GC3G6J+ImJeioq2kbQ2tMzaVIad9wxm7/8ZR8ejw8QoZE2F14Y+WqxuDgdK1aMYcWKMRHf92jCbvfw2GPBQc+pqbGo1So++OAYW7aY+elPl0SkQ70kemhtdeJweElNjY2YIJo9O5v33z8WVrXndHrR6dRMmJBKSUkmF188hrY2J7Gx2kE1Ia2oaGXzZjMnT3YwZkwiF15YOCo9DOcCfn8AsznYGiInxzgkPbp27z6J1eqmsPDLnFshBFlZ8Rw/3kZlZRtjxyZH/LhDRXu7mz/84QvS0mJDHuWkJANOp5c//nE3kyaljapeZ1KU9UNGRjwPPLCco0dbqa624vHsZMqU9LDcDafTNypurhdckM+cOdlUVLTi9ysUFSWOSAfp84kNG6qoqrJ2ywfKzzdx8mQ7f/3rAe6+e8EIWieJFM3NDl55ZQ9lZU2oVMGHussvH89VVxWfdSX1uHHJXHhhARs3VpGSEovBEKzaczh8fPvbs0PiT6dTk5ERP6h9r1tXwWuv7UenUxMXp6Wyso2PPqrkrrvmRbwtz2ijtdVJS4sDk0k/LIn4R4+28Pvff05razCkKARccskYrrtuSkSrx2trbb0Wp3Uds6nJMapEWVlZY69FZAaDFq/XTllZ44CqqqMFKcpOg1qtYuLEVCZOTOXIkWb27WsgJ+fLTt5+fwCbzcWKFUUjaOXA0es1TJkyckNvzzc2baoiPT08TJmREc/evQ1hpeiSgdHQ0EFdXTt6vYbx45MjUqRypjgcXh55ZCs2m4v8fCNCCDweP3//+0E8Hj//+q9nV+YvhOC222YwZUoa69ZV0trqZPLkNC67bNxZNe9taOhgzZoysrMTQjdpk0mPw+Hl+ec/Z/Xqy87Y29fe7g7d/AsLE0dV02S73cOf/7yPzz6rQ6US+P0BpkxJ55vfnDlk+VaNjXYef3wbsbHaUEjR5wvOpNRq1RHtD5iSEttn03BFgYSE0XU9stu9fb4nBMM+YP1skaJsENx883QefzxYmm4waEK9jS67bFxUVNNIhp/WViefflrNkSMtpKQYWLKkoFsVn8fj7zWPTghCVXOSgeN2+3j55T1s316LShUclJ6QEMN//Mecs2oOXF/fQV2dDb1eQ3FxyqBE3s6ddTQ3O7qFg4IFAIl8+GEFl1027qzDJ2q1igUL8liwIHJP/Lt3nwSUMK9JbKyWpiY7hw41Ddpb5vcHePvtw7z33lEgWLGemKjn3/99FpMnR//DoKIo/O53uzh4sIncXGPoHDtypJknn9zO/fcvG5Keh5s2VeH3B0hM/LJ5sUYTHHf0wQfHuPzy8RHLx509O4s33jgQ1vy4tdVJcrJh0H3tbDY3dXUjJ8B7jjs7FUWBvLy+349GpCgbBElJBn75y6Xs2VNPWVkTcXEa5s7N6daQVXL+UFHRyuOPb8Pj8ZGQEEN5eQsbN1ZxzTWTuPrqCQghmDkzi61bq8MuHDabm+zs+FGV63AmtLU5OXmyA4NBQ0FB4lkXlKxZU8a2bTXd9tXe7uapp7bz0EMrBh1mcrl8/OlPu9mxo66byPv2t2cPWESUlTX2Oqs2ePNWqK21MXly9E2T6Ojw9CswzqTH2fvvH+Pttw9TUJAY2rfN5uapp0r51a+WR32umtlspayskYICU+iaLoQgJ8dIVZWFAwcaB93+SFEUKivb2LGjjvZ2NxMnpjJ3bk43QXT4cDMmU/g0Ca1WjaJAU5M9Yn0XbTY3eXlG1q4tx2DQkJdnRKtVEx+v43vfmz9g0Rns03eIdesqQu2jEhP13Hnn7GFtWFxcnMKYMUmYzRZyc4OeakVRqKuzUVSUOOqaJ0tRNkhiYjTMn5876io6JJHF7w/wu9/tQq/XkJkZzONJSgp6vt5++zAzZmRSWJjIpZeOpbS0lqYme6jNis3mprXVyTe+MeOcFfMej581a/azcWMVKlWw5D4zM54775zTzaM0GKxWF5s3m8nPN3UTdwkJMVgsLrZurebaaycNap+vvrqPHTvqut2E29vdrF69gwcfvGhAIi8uTtdvOOhsmksPJePGpbB2bXnYckVRUJT+PRC94Xb7WLu2nNzc7hM0jMYY2tvdrF9fya23lpy13UNJXZ0NIej1d6nRqKisbBuUKFMUhb/97SDvvluOTqdGp1OzfXst77xzhB/96ILQ+ZWYqOfkyY6whzRFUfD7AxHzkm3aVMXLL+9Bo1Exa1YmZrON2tp2br55Ol//+rRBHeedd8p5990j5Od/KcCtVhdPPLGNBx64KHRdPFvsdg/bt9dSWhr0ji9alMeCBbmh0LpKJbj77vm89NJu9u1rCF1vpk3L4JvfnDkikxLOhtET6JdIogS/P8C6dZUcONCEy+Xr1q5Ao1Gh0agoLa0BICsrgZ/85AJycoxUV9uorrai06n4/vcXMGNGdDccPhtef72Mjz8+Tm6ukbw8EwUFidjtXh5//FMsFtcZ7bOpyYFK1fvw5ISEmEEPu7dYXHz6aQ15ecZuN+GEhBj8/gBbt1YPaD8LF+bidPrCZmrabLA4C4cAACAASURBVG4SE/UUFUXnZIkpU9LIzTVRV2cLeTr8/gDV1VZmzswctChrbXXi9fp7HaydmKjnyJHmiNg9lPSXQ+f3BwZdGHXoUDNr15aTn28iJ8dIWlochYWJ2O0eXnppd2i9ZcsK6ejwhJ1DDQ12JkxI7Tbq70xpbXXyyit7ycyMJyfHSGZmAvPn5zB/fg6ffVYX1iy9P5xOLx98cJTcXFM3AW4y6VEU2Ljx+FnbC0GR98ADm/nzn/fS0uKgsdHOn/60h0ce2Yrd/mWumMmk5557FvLoo5fwX/+1mEceuZh7713Yq/cx2pGeMolkEDQ22lm9upSDBxs5dqyVkyeDyebz5+eEkoBjYtRYre7QNgUFifz0p0uwWFz4/QGSkgyj7ultMFitLjZurKKgwNRNQCUnG6iutvDpp9WsWlU86P3Gx+vw++l1dqrT6R30jaupyY4QfYu8Y8daB7Sf4uIUVqwYw8cfV2A06kMzDQF++MNFUZvkrtWq+cEPFvKnP+1h//5GVKqgZ2/x4nxuumnaoL24BkNw2HvP1h0QDIWeSWi5tLSWLVvMeL0B5szJ5sILC7rlXUWayZPTiInRhOVbud0+hBCDfpDatMlMXJw27BzIyIjnyJHmUB/MqVPTWblyDB99VIler0GrVWO3e0hKMnD77TMi8tn27KknEFDCRLPBoKWhwc7Bg03MnTuwWZfNzQ58vkCvXmCTKYZDhwb3gNQX//znERobO7pVrycm6jl+3MK6dRVcc013z3h6etyIjayKFFKUnQd05TRs2VJNa6uT8eOTWbw4n+Tk0dm5eTB0ffa6unYMBg2TJ6edcbWj3x9g9epS2tqcFBenYjZbMRpjcLv9bN9eyyWXjEGrVeNweHvNYxjKm0k0UV/f0afYiY8P5t6tWjX4/WZkxFFcnExVlaVbCxqfL4DL5WPJksH120tIiCEQUHoVeQ6Hl7S0gTX3Dc40nE5JSQaffHIci8XFzJmZXHRRUdS3yklKMnDvvQtparLT3u4hOdlwxudpYqKekpJMDh5s6pY7FggoWK0uli8feOjS6Qw2OC0vbyU52YBaLXj77cN88slxfvrTJUN24+0am/fccztpbXUQHx+D3e7B5wtw660lvY6q64/WVkev3jchBGq1qtPbE5xJfNNN05k7N4ft22tpb3czZUo68+bl9JqveCbY7R7U6r6EtoLTOfAcwthYbZ+/HZfLF5Hker8/wJYtZrKzw/eVlRXPJ58cDxNl5wJSlJ3jKIrC2rXlvPnmIWJi1BgMGg4caOTdd4/yX/+1aFT1oxksHR0ennvuMw4dagYUQBATo+aOO2Yze3b2oPd39GjQM9aVcJuXZ8JstmIyxWCzuTlxoh2DQYvJpGfu3MHv/1zhdBfsM20rIITgW9+axWOPBSug9XoNXq8fv1/h+uunMG7c4M7loMhLCRN5Xq8ft9vHhRcWDHhfKlXQizJaQ9JpaXERCZHdfPN0Hn002Cw5Pj6Ya+dy+Vi2rIhZswb+m9i82Ux5eWu3AfEJCTGcOGHjr389wF13zTtrW/ti1qwsHnxwOZs3V1NdbSEzM54LLyw4o0T74uIUKivbwsJoXVXXp/7NhRBMmJA6ZInpRUVJ+P3hIcpg2FIMKlydkhLLlCnpHD3a0u23EwgotLd7WL787FtE+XwBvN5Ar0JSp1PT1NR3K4zRjBRl5zjV1VbeeusQeXlfJt8mJRmwWFz89rc7eeyxS6I2vHK2vPLKHo4cae6WxO1weHnuuZ08+OBFg64Ea2lxdHtdUpKBSiUwmy04nV6qqiwsW1bIHXfMPq97j+XmGsnNNdLS4uzmWfD5goPvFy8+fVsHp9PL3r31tLQ4yciIZ9q0dGJiNKSlxfHAA8v54ouTlJc3YzQGBfCZzJ3tT+TdcMPUboJgtOF0emludmAwaAft3TkbUlNj+Z//WcbOnXXs399IXJyWBQtymTAhdVAh+w0bqnr1hmVmJvD55yfCwouRJisrgRtuOLv+cgBLlxby0UeV2GzuUBJ/V97eFVeMi5gXbCBMmpRKQUEitbVWcnKMoZmXXdXBg819vPXWkk4B3kZ8fAxerx+n08fFF49h2rSzbxGl06kpKkqktdUZ9iDX3Oxg6tTob7FyJkhRdo5TWlqHRiPCypwTE/WYzVYqKtooLj7zBpTRSkuLg127TpKXZ+rmrYmN1aJSCTZvNvO1r00d1D57hnXUahUzZmQycWIq5eUt3HTTNK69dtI5W1E5UIQQ3HnnHB599FPM5qDHxO324Xb7ufrqiaf1aB071srTT2/HbveGKqkSE/Xce+9C8vNN6PUaFi3Ki0iX7i6Rt2fPScrLWzEaY5g7N7tbg+jRhN8f4J13ynn//aP4/QECAYWJE1O57bYZg+72f6bExmpZurSQpUsLz3gfTmfvoqtL2AWrXaN/jm56ehw/+MFCnn/+c6qrrQgR9CZdfHFRRBvCDgS1Ojg68E9/2s2+fY2o1eD3K8ybl8Ott5YM+rqVnh7Hr361jJ07T4TawgQFeEpEroFCCK69dhKPP74t1LIDggU0DoeXr3xlwlkfIxqRouwcx2p1odP1/jULEbz4nYu0tjpRq0WvT+fx8TpqaqyD3ueECamkpsbS0uLoNkzc6/WTnh7HpZeOO+8FWRe5uUYeeugiduyo5dChZpKS9CxalHfann4Oh5enn96OTqfuFtppbXXy9NPbeeyxSyLevV+v10S8MetI8dZbh1i79gi5uSZ0OnUop/LRRz/lV79aPiDPTDB86w89wIwEJSWZbN9eEyaObTY3GRnxo2o83IQJqTz22CUcP96GyxWcaTlS+byJicEqxaYmO1arm+Rkw1nZEhenY9myQpYtK4yckacwbVoG3/vefF59dR/V1cFrdmqqgR/+cOGgU288Hj/79tVTVtaIXq9hzpycbo2+owUpys5xJkxIYdu2mrDlXTk/0d7M8UwxmfT4fIFe85rsds8Z9dDRaFTcffcCnngiGO5SqwV+v4LBoOHuu+cPayiiP1wuH2VljVitLtLS4pg8OW1IupCfDqMxhksuGcsll4wd8DZ799Zjt4dXUiYnG6iqsnDoULOcnnEKJ0+28/nnJ7Ba3WRlxfPee0e79Y0KDppOwGy2sHNnXb+5Pna7h3feOcLGjVV4vcHu8l/5ygSWLi0Y9hvXpZeOZfv2Gk6caMfh8GCzedBqVajVKu67b/Goq17WaFRhI7F8vgCVlW2h4ePDKdQilUM4HMyZk83MmZmcPNmBSiXIzIwf9Pdvs7l58sltmM0W9HotPl+A998/xkUXFXHLLSVRdT5JUXaOM2dONm+/fZiGhg7S04NVPn5/gJoaG4sX542aH+ZgSU+PY/r0jNC4lC7cbh8+X+CMQyu5uUYeffRi9u1roL6+g5QUAyUlmVGTQ3b0aAvPPLOj27y39PQ47r13YcSaOQ4lzc2OfirEOOMeZ+ci69ZVsGbNfoQItrdobLRz7FgbaWmxaDTdz8f4eB379zf2Kcq8Xj9PP72dY8fayMlJCLVkePHFL7DZ3MMeKsrJMXLddZP5yU8+xmZzo9GoUKsFY8Yk93t+jBYOH27m97/fhdXq7hy5prB8eRE33ji1X09wXZ2Njz8+Tnl5C8nJei66aAzTp2dElagYCtRq1aD75p3KG2+UUVtro7DwyzzRQEBh/fpKJk9OG3ArkOFAirJznLg4Hffdt5gXXvi807ujIhBQuPDCAm66adpImzek/Nu/zeTpp7eHvFpd/ZNuv30m+fmDTwzvIiZGE1U/4i46OjysXl0amkHXRWOjnWeeKeXBBy+K+qKO9PS4XivEIBhuPx/auAyE48fbePXVfeTmGkM3cb1ew8GDTXzxxUkuuCC/m3fL4/H3O2i6rKyRo0dbKSxMDG0XF6cjP9/EO+8cYfnywmENGbpcPt555wjLlhWErllxcTpcLh+/+90unnxy5agKYZ5KfX0HTz65jYSEGAoKgtchvz/A+vWVxMRo+iwwKCtr4Omnd6BSBcOQlZUWdu/exsqVY7n55ukjEoZTFAW73YtWq+q1aXA0YLd7KC2tDYsKqVSCpCQD69dXRtX1PDr/ipKIkpWVwC9+sZQTJ9rp6PCQkRE/JD2z/P4Ahw83U1nZhsGgZcaMzGGp/OrKFdizpx6tVs3cuTlMnJhKYqKeX/xiKYcONXP8eBvx8TpKSjLP2Rv7F1+c7DX0l54eh9ls4ejRViZOjO45cCUlmRiNMbS1da+46hpTFe32DxdbtlQTE6Pu5lUxmfQkJxs4ebKDjg5PSLQEAgoul48FC/oeDbdnTz0Ggzbsxq7VqgkEFI4ftwxr2PjAgcbOXnHdz+XYWC2NjXb27KlnyZKBtywZKvz+AGazFY/HT3Z2woBm2W7cWIWiKN3WVatV5OUZWb++giuvHB/meff5ArzwwhckJsaEvte4OB3JyUFRMX9+7rAXbO3ZU8+bbx7sHE0lmDcvh+uumzys1b4DweHwAqLXB9LYWC0tLc7hN6ofpCg7T+gaqjtU2GxuVq8upaKiLeSVeu21/dx449RB5RQNlo4OD08+uY3KyrZQf6xPPjnO/Pm53HHHbDQaFVOnpncrn/Z6/ezefZJt22rx+YKdwufNyxnSEvvhoL6+vZ85i4K2tui6+PSGXq/h3nsXsnp1KVVVllBOYFpaLPfcs7DP3LjecgeHErfbx+HDzaGHnOFOGG5stGMwdD9fVSrBnDnZrFtXgdlspaDAhMvlo73dw4oVY/oVtBqNKmzET899DycdHR76mvqjVgus1pEPYx892sILL3xOc7MDIQRCwMqV47juukn9eqTLy1swGnsfPh4IKDQ3O8JEWWVlG+3tnpBnrQuVSqDTqdmxo3ZYRdmuXSd49tkdJCcbyM83EQgo7Np1giNHmrn//mVRNd7IZNITE6PG7faFefMsFlfU9RaUokwSEf7yl32YzZZuvW48Hj+vvrqfMWOShqxJ7d//foiqKku3MRyKorB9ew2TJ6eFVQW53T5Wry7lwIEmjMYYVCpBWVkj69ZV8KMfLY6qi8lgyciIx+PpfTA2KKNmokBhYSKPPXYJBw82YbG4SEkxMGlSeLFCsON3Ne+9V05Dg52srHhWrSpm8eL8IRUR5eUtPPvsjtDsPUWBceOS+e535w3b+TN2bBKHDjWF9W9KSjIwa1YWK1aMoa7ORn6+iWXLCpk2LaNf0ThnTjbr11eGiVuXy4dWqxp0Y96zJSMjnr7M9fsVcnPPPP0gEjQ22nn88W3ExmpDTWV9vgDvvluOViu49tq+210kJxuor+99+HhXmLYnHo+/z79H1xSR4cLvD7BmzX7S0mJDXju1Oth81my2sGWLmSuvjJ52FTqdmlWrxvP662UUFiaGBLPD4cXl8rFy5dA5Dc6E6E4wkYwK2tqc7Np1IswTp9OpiYlRs2FD1ZAc1+Pxs3VrdViuQNCzEsdHH1WEbbNxYxUHDjRRVJRIamosyckGCgsTqa/v4O9/PzQkdg4Xs2ZlYTBosNnc3ZY3NdnJyIgfVf3odDo1M2ZkhgRFT0GmKAp/+cs+XnzxCwKBoJDzegO88MLn/PWvB4bMLovFxVNPBVt2FBQkdv4zUVXVxvPPfz6ooc5nwwUXBIVne/uX37WiKFRXW1m+vIhvfWsWv/zlMu6+ewElJZmnFakTJqQyf35up0fGjc8XoKnJzokT7dx8c0m/g7qHguLiFPLzTZw40R76myqKEipYmjIlbVjt6cmmTVX4/YFuDzoaTTAE+cEHFf2KpOXLg8PH/f5At+UnT3YweXJar+G/riT3nttAUFwMZyPVpiYHra3OXnP6kpMNfPbZiWGzZaBcdtk4Vq0aT11dO9XVFsxmC+3tbr7znblR1yRaesokZ01XBVFvF/64OB0nTrQPyXFdrmAlZW/VSgaDptdKvfXrK8nIiAvzGmRnJ/DppzXcdNP0fkKA0U1CQgz33BMM/bW2OjuruiAtLZa7754f9Un+g6Gurp0NG6ooKkoKnXcJCTHExmr58MNjLF9eOCTNUktLa3G7fd0qWbtSAw4ebKKurv2sqsQGSlpaHPfcs4Df/nYXra2WUHf22bOzuOWW6YPen0oluOOO2UyalMq6dRW0tDgYOzaZVavGM2nS8AsglUpw990L+O1vd1JR0YoQAkVRyM838Z//OTfiveoGy6FDzb16RbVaNX5/UND2NZZp8uQ0rrhiPO+/fxSdTh3ydKWmxnLbbb0PH09M1HPJJWN4//1j5OYa0emCoc76+qBInTUrK6Kfrz+6ql97SxkIBJSovM6o1Sq+9rVpXHrpOMxma8j7G43FCdFnkWTUkZxsQFGCT3E9f5Dt7W5KSoYmQTg+XofJFBwY3NPl39bmYvz48JBLR4eX5OTwi6lGo8LvD+Dx+EetKIOgh+HJJ1eyf38jbW3BEUVTpqSN+E0s0hw61ASEPwh0VeqVl7cMiSirqbGG5XJBUJipVILmZsewiDKAyZPTeeqpSykvb8Hp9JKdnXBWeaMajYrly4siMrcwEiQnG/jZz5ZQXW2lpcVJYqKeoqLEEaky7Eliop6Ghg4gPASpKEq/+alCCG64YQpz52azY0ddaPj47NlZvZ5bXVx//RT0ei0ffHC0swcjTJ+ewS23TO93u0iTmhpLbq6RtjZXWNFUa6uTq66KntBlT5KSDGc8e3e4kKJMctYYjTEsXpzHli3V3eZMdnmyhuoir1IJrr56Ai++uJv8fFNIeDidXux2D1dcMT60blfp9tixiVRUtIXdsIOdwuOIi4uuZH+n08vWrdVs3mzG4/EzZ042F11U1G2iQE8MBi3z5kVPifdQoCj0mWMzlGRkxONy+XqxJ3gzNpmGt02DTqc+Z2cAQlDAdIWJo4llywrZtauOlJTYbg8GDQ12iotTT9v/UQjB2LHJg8q11WhUXHPNRC6/fBwtLcFigJHIExVChOZeulw+0tJi8XoDNDTYGTMmiYUL+67ylZweKcokEeHrX5+GzeZm796G0DKtVsUdd8w+q55gp2Pp0kJstmAn8mD1mEJMjIZvf3sOEyYEq82OHm3hjTcOUFHRhs3moqrKyqxZmSGvgsvlo7nZzne+My8qnsK7cDi8PP74p1RUBBuCqtUq3n//GJs2mfnZz5aQlXVuTmMYCJMmpaIohHrPdeH3BxBChHVPjxQLFuTyz38exun0dvNONDXZycszdesPJzl3mTo1nYsvHsvHH1ei12vQ6dS0t3tITjZw++29hyAjhV6vGfHZrOPHp/DLXy7l/fePhdqpXH/9JC66aMyweu3ORcRwJaYOFXPmzFF27do10mZICHoLzGYrNTVWYmI0TJ6cNmyjhzo6PFRVWVCpBGPHJoVyBSoqWvn1r7cQG6sNudoPHWri0KFmpkxJIzZWh16v4V//dTJLlxZGlSh7//2jvP56WbfKUgiO1pk4MY177lkwQpaNPIqi8NJLu9m4sYrMzHji4nR0dHhoaOjgssvGcdNNg8+rGig7dtTyhz98gd+voNEIvN4AaWmx/OAHi0bF1ARJZFAUhSNHWti+vYb2dg9Tp6Yzb15O1Ixbk0QvQojPFUWZ0+t7UpRJzmUee+xTzGZLWDihttZGUVEiN944LZQ4G2386Ecf4fcrYRf5QEChpsbKb35zxXl9A/D7A2zYcJz33jtGa6uTlJRYrrxyPEuXFg55X622NidffHESq9VNXp6R6dMzojJpWCKRRB/9iTJ5FZGcs3g8fg4dauo1fJqVFU9VleWsyqHr6ztoarJjNMaQn2+KuJfN4/H3eqPvOozPF14efz6hVqu4+OKxrFgxBp8vgEajGjZPZ1KSgRUrxgzLsSQSyfmDFGWScxaVSoRaBfQcYhwMPZ1Z6XZ7u5sXX9zN3r31qFTB/efnm/iP/5gb0fDVzJlZbN5cFdYo02Zzk5kZP+xJ5dGKEOKcqy6VSCTnJ1KUDYLmZgfr11eya9cJtFoVS5YUsHRpQa8dmHvidvsoK2ukoaGD5GQDJSWZ51xCZG2tjW3bamhstFNUlMjChXkjOmdSo1GxYEEuO3fWhSXG1td3cMklg/d0KIrC7363iyNHmkPeMUVRaGy088QT23jwwYsi1mhz5cqxbNtWE5r7KESwWWhLi5Pvf39BVOW/jQbq6mzs2nWCjg4PxcUpMuQokUiiDplTNkBOnmznoYe24HQGm/z5/QpNTQ7y8038+MeL+xVmtbU2nnxyG21trpBnJS5Oy/e/v2DIqsSGm40bj/Pyy3vRaFTo9RocDi9arZof/nDhiH7GxkY7DzywGYfDQ1paHIoS/N6Skgz8938vGXTPmqoqC/ffv7Fb648uzGYL3/72HObPj1xJuNls4dVX93P0aCtCBHsE3XDDFGbPzo7YMc51FEXh3XeP8uabB1GpBFqtCrfbT2ZmPPfdt3hEHxzKy1vYsOE4J050kJdn5KKLiqKuw7hEIoksMtE/Ajz9dCmHDzeFtSGoqmrjuuumcOWVxb1u5/MF+NGPPsLj8XfrLWW1unC7/TzxxMpRPwi7oaGDn/zkYzIy4rp5HqxWF4GAwhNPrBzR8FJzs4OPPqqgtLQWIQRLluSzYsWYM+rxU1pay/PP7+q1b9KJE+2sWFHEDTdMjYTZIRRFwWoNjr5JTjYM+3DoaKCx0c6GDVUcPNiEyaRj+fKiAY0PgqDweeihzeTmGrudhydOtFNcnMIPf7hoKE3vk48+quAvf9mPwaAhPl5He7sbt9vPN785iyVL8kfEJsmXKIqCx+NHrVadcaqDRNIbMtH/LLHbPezf39Brp+60tDg2barqU5QdOtRES4szrH+RyaTHbLawZ89JFi0a3RfgnTuDs856hoK6PmN5eQtTpoxcg8vU1FhuvHEaN9447az31V9zWa/XT3Ozgwcf3Byq+Lz00rFccEH+WY0eEUL0KiADAYUdO2r54IMKmpuDfbJWrRrP1Knp51Ros6Kilcce+xSfLzhrsKXFwZ4921m6tJDbb595WmG2aZMZg0Eb9mCQlRXPgQONNDXZT9vsM9K0tDhYs6aMuDgtdXU22ts9mEwxZGXF88oreygpyQgbWC0ZPg4caOSttw5RWdmGVqti0aI8/uVfJkZ9N/jzkY6O4P3ZZnOTlZXA5Mlpo1pES1E2APx+pXPOV/h7Go2q3+GzwfmLvXsj1WoVTU2OCFk5clitLrTa3n8EQoh+/z6jjQkTUklIiMFmc3e7abrdPurq2unoqCIrK4HMzHgcDi9//OMXHDvWyr/928yICiVFUXj++V2sWVOGzxfAaIzBYnFx4EAjt9xSwsqVYyN2rJEkEFB44YXP0es1oRtiQkJwBM+mTWbmz889bUf7xsYODIbeqlgFQgQLN4ZblO3b18DJk+2cPNmBWi3Q6dRYLC6OH7eQlZXAwYNNLFggO6MPJa2tTsrLW1AUhXHjkkPnwO7dJ1m9egcmUwwFBSb8foWtW6s5eLCZX/ziwl4HcUtGhn376nnuuZ24XD5UKoGiBOcY33PPgmH/TUeK0Ssnh5GEBB05OUasVnfYe01NDmbP7nsYbHKyoc+bsd8fICNjdJ44p1JUlITb7Q9brigKgYByTnWe1+nU3HXXPOx2L9XVFpqbHdTUWKmpsaHVqhg/PoXkZANqtYqEhBiKipLYvLma48ctEbVj0yYzTz9dis3mQlEUmpsdlJU10tHh4fXXy7DZws/V0UhNjZXGRkeYh0KlEsTFadm6tfq0+xg7Non2dk+3ZVari9LSGrZsqeH++zfx8st7aG4evgekxkY7FRVtxMfrSEiIISZGg9EYQ3y8jmPHWmltHf0Pa9GKoii8/fZhfvjDdfz+97t4/vnPue++j3j99eADzquv7ic11RC6dms0KvLyTDQ12Qd0vkmGh9ZWJ7/5zU6MxuB1tqAgkcLCRFpaHDz33E5Ga2qWFGUDoGuAbFubE6vVFZpz19hoR6NRsXLluD63nTAhOAetqcnebXlbm5P4eB0lJZlDbf6QM2tWFikpBhobO0I/BEVRqKmxMX16Bjk5kRFlgYDCgQON/OEPX/DMM6V88slxOjo8p98wwhQXp/Dwwyu47ropTJmSxqpVxdx00zTS0+PCmtCqVAKNRrB3b33Eju/zBVi9ejtarYrERAMxMZrO4ex6KivbsFpdHD7cHLHjjSQejx9VH1cprVaN3X7673/p0kKA0LlisbjYtMnM8eMWJk1KJT/fxNat1Tz44GZaW52RMr1fPB5/r61a1OpgNa/TGT5fUxIZduyo4623DpKdnUBhYfBGnptr5N13y/nnPw/T0uLo1RuWnGxgx466EbBYcioWi4v33z/KT3+6nsOHm3C7/d0EWGZmsAdlVVVkH4SHCxm+HCDTpmVw770LeeONA1RXW4Gg4Pr616f125tKo1Fxzz0LeOqp7Z0niQIIjMYY7r134TnRFkOv1/Bf/7WY3/1uJ2azFbU6WGE6e3Y2t98+IyJhu0BA4U9/2s2mTWZiY7VotSr27Kln7dpyfvzjC0hPH16PY3KyodvA8507+75YCyHw+yPX6PX48Tba2z29CkC1WkVzsyOixxtJsrMTUKkEXq8/LCesvd3NtGmnz1XMykrge9+bx/PPf05Li4P9+xtxOLyMG5fM9OkZaDQqcnONVFdbWbeugq99LbKFGr2RkhKLyRSD1RoMg3dVZdtsbpKS9CQlDf+g6fOFd98tJzU1tlvekVqtIiMjnvXrK0MP3T2vW70tkwwvVVUWHn/8U5xOL1VVVlpanGzeXMXEiWlMmJDSmZIgUKtFZ+rQ6EOKskFQUpLJ9OkZWCwu1GrVgBNxs7ISePjhi0NJ/yZTDFOmpA/LaB+73RMSSkVFSUN2zMzMeO6/fxk1NTba292kpsaSkRG5Rqp79tSzaVMVhYVJocTulJRYTp5s53//d++IVdB10dXGwO8PdEvqVxQFrzfA1KkZETuWy+UjKSmY8N7zRqFSgdcbYOzY5Igde/iBjgAAIABJREFUbySJi9NxxRXF/P3vB8nLM6HTqVEUhYYGOyaTnoUL8wa0n+nTM3nqqUvZu7eeBx7YzPjxyRiN3YVPRkYc27bVDIsoGzMmiYkT07BandTVtXf2u4P8fCMmk578fDnYfChQFIXaWluvUz7i43U0NztIT4/DanWHFde0tjr7LOiSDD2BgMLvf78LtVpFfn4ibref1lYnRmMMhw83k5ERR1KSAUVR8PuVUVuUIUXZIBFCnNGXrdGomDZtYDfmmhor69dXUl7eSkqKgRUripgyJZ0jR5o5dqyV+HgdM2Zkkpoay2ef1fHuu0epq7ORkRHP5ZePY/HifISA9947yttvH8bvD7p2DQYNt902g7lzcwZt/0AQQvR6sYsEn3xyHKNRH1Zpl5kZrKBrbXWOaL+plJRYLrtsPGvXHgkNyHa7fZw40U5JSSbFxZHr1ZaTYyQ+XkdaWhyNjcExT2q1Cq/XT1ubixtvnDrsnsOh5CtfKUajEbz77lG8Xj+BAEycmMI3vjFjUEnXMTEapk7NIDU1dsSTtYuLU5g8OZXKyjamTEnH4/Gj06lpanIwYUIKY8fKXmVDgRCCtLRY7HZv2NxYp9OL0RjDbbfN4IkntuN2+0hNjcXrDdDQ0EF+fuKAHwIkkcdsttDQYKegIHiPyc01cvhwMz5fAJVKUFNjIynJQH19B2PHJoXWG21IURZl7NtXzzPP7EClCrZBMJstPProp7jdPozGGLRaNX5/gDVrysjNTaCy0kJaWiw5OUY6Ojy88MLnVFVZyMsz8vrrZeTnm0JhH4fDy3PP7eRnP9OPuqa1Vqu71075QghUKoHT6QVG9sno+usnk5Zm4P/+rxyz2Yper+bqqyeyatX4iPYWS042cNFFRXz4YQUJCTFUV1vx+QL4fH5KSjL50Y8uiNixogG1WsVVV03gkkvG0tRkx2DQkpoae/oNeyE2VsvEiWlUV4cPqW9osJ/RlIczQaUSfO978/nzn/exa9eJUPhy/vwcbr55+lmFyQIBhdLSWt5//yj19R3k5ppYtWocs2dny/AbcPnl43nppd3Exn7pdVcUhRMn2vn616cxeXI6P//5hfzf/5Wzb18DBoOGq66awMqVY0d9T8nRjMPh7ZZfajBomT8/h88+O4HD4aWurh2jMYbcXCP/+Z9zR+25LpvHRhFer5977/0wlLjdxfbtNVRUtHH55eNCDWgtFhdr15azcuXYbl6RQEChqspCTIyaxER92KSBxkY7xcUpfP/7C4bnQ51CW1swVGMwaCgsTBxU7641a/bz8cfHw3rFud0+2tpcPPPMZVEzMicQUHC5fMTEqM+qP1l/eL1+3nrrIOvXH8fvD+Dx+Jk8OY1///fZ55SXbCg4fryNhx7aglarIi0tjkBAob6+nbi4GH7xiwu7NXkeDtranFgsLpKSDGfU0PhUFEXhtdf288EHx0hNjeX/s/fe4W2W9/7/69FeluS9Z+zE2TshkwQSVkLYLZTy/dHSAh1AS8fp4ZzSc5WettBSRjedlBY4ZSRACAESICF7Tzse8d5TsqwtPc/vD8UiiuzETmxLdvS6Lq6L2LJ0y350P5/7M95vg0FFT4+bri4nd9wxlXXrJg3Tqscufr/I3/9+hE8/rUMQQBBAFGHhwky+8pU5l62PqsPh5eTJNnp63KSmGpg8OWnE9q+LoavLyXe/+wFZWcaQQ67X6+fo0VaWL89hzZqJFBcPvO7A1HMXMplAcXESJlNkejdj4rFjhNOnu7HbvSE3BafTS2urHaNRRWOjLfi9nh43MlnAwunsm7BMFmgqr6vrDfN7BIiP11BZ2TWs63a7fezZ08D27bW43X5mz05jxYq84Fq9Xj+vvHKCjz+uDmrJxMdr+drX5lFYOLjep5Ur8/noo2qsVlfwg+TziTQ09PC5z02NmoAMAn+DkT5RK5Vy7rxzOmvXTqK93Y5er4oFY4MkPz+eH/5wOW+9Vcbhw83I5TIWL87hxhsnjnpABoHPwnD1vzQ22vjwwyry8z/LAsXHa4mLU7NhwymWLs2JaJk/GpDLZXz5y7NZtaqA48fbkCSJqVNTyM83j9nsyqVSUtLGb36zP0RTMiMjjkcfXXTRWenhJiFBy9KlOWzbVkNOjgm5XBZ0O8nPj+fBB+cP2OfdV13aurWKQB5KQiaTcfvtU7j++sKo+rtHz50sBh5PuNaXy+VDEAj2DJ2NQiHDZgvXo5LLAxMoPp8YpmzsdPowmYavn8bt9vHss3s4ebKdhAQtCoWMd9+t4OOPa/jP/1xKZqaR118vYcuWKnJzTcETjMXi4he/2MlPfnLVoET+0tIMfO97S/jTnw5SV2c9c8IVuOWW4pApyMsNg0EV1hsT48Lk5pp5+OGF+P1isAQ+mkiSRF2dlUOHmvF4/EyenDwsSuQnTrQhCIS9H4VChihKlJV1xPqiCOwdubnmfu3SLjcsFhfPPrsXq9VFc3MvDoeX+HgNDoeH3/xmHz/60ZVRE7Tcffd0ZDKBbdtqgiX/zEwjDz4477yDd1u2VPH++5UhhxWv18+rr54gI8PArFkDa42ONrGgLIroa5I/e4KvTzLD4/GHZEISErR4vWJY+lWSAieA1asLOHmyLWSKK2DGbee+++YM25p37arn5Mn2kFOmwaCitbWXf/3rOF/72jy2bKkKnmz6MJs19PS4+fTTOm69dfKgXmvixESefHI1tbUWPB5/sOE9RoyLZSjlmc5OBy6Xj+TkcD26oSBJEq++epLNmyuQy2XI5QKbNlVSVBTPt7+9KKzlYCgEgsyBvy+KY7tdJcbws3dvAydPtmGzedDrleh0SqxWN62tvfT0uKmpsZCfHx2DJ2p1YFjtppsm0dLSi16vIjvbeN6g0e8XeffdiqC8Th9KZaDF5913K2NBWYz+MZs1rF5dwHvvVZKVZUSlkqNSyYmLU9Pd7QzRQ5PLBdLT45DJAr0AOp0Sp9NLU5ONxYuzufvu6Tz99G6qq7vR61WIooTD4WXhwiyWLh0+r81t22pJTAx3LUhJ0VNa2s7p090A/WYAjEY15eWdQ3o9mUyImg1irOP1+ikr6wzaDE2YEB81J+JoornZxosvHqWsrAOZTECtVrBuXaDx+2IybIcONbNpU3lIX6UkSVRVWfj3v0/ypS/Nvui1Tp6cjCiGa2r1BWODbReIcflw5EgL7e0O0tMNwWtGp1OiUsmprrbQ2NgTdXvuUEr+TqeP3l5Pv2V7o1FNQ4N1uJd3ScSCsijjc5+bik6n5L33Ks+M/0vcdNMk3G7/mQAmID6rUsn5xS9WY7O5effdCmpr7cTFqbnzzmlce20hCoWMxx5bxtGjLRw+3IJSKWfBgsxhb950Or39Nsb2ifj1lU36E150uXyXfX9LpKipsfDcc3vOCCwKgEReXqCkN5r6Pl6vnxMn2jhwoAlBEJg3L4Np01KixlDYanXxs5/twOPxk5NjQhAEXC4f//rXMSRJ4vrrh146//DDKsxmTcjnUBAEMjLi2Lmzns9/ftpF9yTm55uZNy+DffsayciIQ6NR4HB4aW62cc01E4ZVO3Ck8PsDEgexA8Lo0GfJdu7vWy4X8PslLJaxbdmm0ShQq+W43b6w3mO73RN1HpmxoCzKkMtl3HRTMddeW0h3txOdTonJpAn2oDQ09KBWK5gyJTm4cV91VUHwgjv75K5WK1iwIIsFC0bO2HjWrDS2bq1CpwvVhLHbPZjNGoqLEyksTKChoSfkhuD3izgcXpYtG76s3fnw+0VOnGhj9+4GPB4/c+akM29eRr8yG+Od3l4Pv/zlLuRyIaSnprHRxm9+s4///u/lo3JDdDq9PPfcXkpL24PX8qef1jJtWgoPP7wwKoY3duyoo7fXHdIGoNEoyM42sWHDKVauzB/yNdQ3mHEuCkWgcbkv830xCILAAw/MJTfXxObNlcEhoXvumcnVV+df1HOOFqdOdfDmm6WUl3eiVstZuTKfNWuKIq4pN97JyzMjkwn09nrw+QJOIDqdEo/Hj8GgJC0tuoKWoaJQyFi9egJvvXWKvLzP2mxEUaK93cEdd0yN8ApDifyuF6NfNBpFiJH3+RpTZTIhYnZNV19dwPbttbS320lK0iEIAna7h5aWXh58cD4KhZz77pvDk0/uoKbGgsGgwuPx4XaLrF0bGF8eabxeP7///QEOHGjCYFAhkwkcOtTMe+9V8v3vL47YWPRgGAlrl4MHm+jt9ZCXF3otpacbqKrqprraEnQoGEk2b66ktLQ9ZKOUJIkTJ9r48MPTrF0befmGo0dbw5T/IWBM7/WKNDfbhlzayc+P5+TJNjSa0KyV2+1DqZQP2ilkIJRKOTfeOIkbbijC5fKh1SpHfZBhqBw61Mxzz+3BaFSTm2vC6xV5//1KTpxo47HHlsX0wUaQhQuz0OuVVFd3c7ZCVmKilrlzM8aFO8iaNUXU1lo4cqQVhSKgACCKEtdcM4Errhi5pMXFEAvKYlwSKSl6fvCDpbz00jFOn+5CEAK+ng88MI/FiwMXe1qagSeeuIr9+xspKWnHaFSzaFH2qPUw7dxZz/79jRQUfPZ6SUk66uqsvP56ybAOPgwHfr/Ip5/W8d57FbS12cnMNLJ27UQWLswclt9XINvaf8kZBDo6HCMelEmSxIcfVpGRERfyngRBIC3NwAcfVI16UOb3izQ1BSyP0tMNyOUy9HplMHtwNn3+iBfT8H/ttRPYv78Rl8sXzLKJokRjo43bbps8bFZogfVH/yCM3y/yz38eIzlZHxzcUank5OaaqanpZu/eBlaujO4s31imu9uJKEqkp8chlwcCFkEIlDUnTkwYs3ZFZ6NWK3jkkSuorOyitLQduVzGjBmpFxwSiASxoCzGJZOba+a//msZXV1OvF4xzOwXAhOZK1fmR2Rz3bKliuRkfdiHLyMjjl276rn77hlRU8aUJImXXjrK1q01pKbqyckx0dPj5re/3UdT0+RBT6qej+RkXb/yK2dWcMmZmsHg8wXK18nJ4RpIGo2Ctjb7qBpAHzrUzEsvHcVqDfTPmM0avvjFGSxfnsuBA81hwyzd3S4yM41kZMQN9JQDUlSUyFe/Opd//OMoXq9IoE8Urr46nzVrLj9vxebmXiwWV78WbWazll276mNB2Qjy/vunWbYsl+ZmG9XVFvx+CbVazqRJidhsnnFjxC6TCUycmDislncjQXTciWKMeQRBiIjw5mCwWl399qUEengIyVhEmro6K598Ukt+vjlYcjKZAs4MGzeWc+WVuZf8e54/P5PXXisJ613q7HSQlKSjqGjkyxUKhYzsbGO/xs/d3S7y80dvErS0tJ3nnttDcrI+GBj09np47rk9fP/7S1i0KIvduxswm9Wo1Qq6u51nyvKzL3qNS5fmMGdOOmVlAe++vDzzqDQct7fbqarqRqGQMXFiYlT0a13oVzjU0mtjYw87dtTT0mIjN9fMkiXZUdfMHS302Uvl5ppISNBSXJyEzyeiVMqRyQRqay14veKwZW9jXJjouBPFGHecLY7pcHiZMiWZadNSBrQw6dscens9pKYaLtlu5myKi5M4frwtRFIEAsGaQhEuyhtJTp4cWPxTkiTKyjpZvPjSgrKAm8J8fv/7/bS321Eq5Xi9foxGNQ8/vHBUrFUEQeCmm4p57rk9aLUK1GoFHo8fh8NDd7eT++67eFmIobJ+/SmMRnWI5p3BoMLl8vHOO+V873uLmTcvnW3baunp8TB/fiYrV+ZdsoOCTqdk9uzR0Ufy+0Vefvk4H31UHfyaXC7j7runRzwLlZZmID5eg83mDgsSu7ud3Hxz8aCfa8eOOv7yl0PI5TK0WgVHj7by7rvlPPzwQqZPTx3upY95BEEgNVVPb6+HuDj1Gd28wOff4fBiMmlQKqNjEvpyIRaUxRh2JEnitddK2LSpApksIIvx4Yenyc01853vLA4rjzU324JG6n02TMuX53LXXdOGZQLv+uuLOHAg0NxuMKiQJInS0g4OH26hqCiB73//Q6ZOTeGee2ZEXDJAFAfOHAzWp9Zu93D0aAudnQFtuxkzUsN+j3PmpPOLX1zDoUPNdHQ4yMoyMnt22qgOjMydm869987ixRePUl7eSWenE5lMYNasVJxO36iUTfx+kfLyTnJzw0tniYlaTp3qQBCEEZ9iHmk2b67kgw9Ohyiau90+/v73I6SmGpgyJTlia5PLZXzxizN49tk9uN1+EhO1wSGKnBwzCxdmDup5urqc/O1vh0lNNQQz34mJgazn7363n2eeue6SM+Jeb0CaqLfXQ1qaISiTMpa54YYi/vznQ+j1quC1EfCD7eWee2aM+fc31ogFZaOMKEo0NPTg94tkZhr7TQv7fCJ1dVYkSSIryxgV0gBD4fjxNjZuLA+xVQKor7fyyivHeeCBz3xY7XYPTz65M0QHyu8X+fjjgNH2cDThFxTE88gjC/nb345QV2elttZKXZ2F2bPTmDw5cDOqqOjk5z/fwY9/vDKiJZ0pU5J5/fXwqcs+O6ALlRZLS9t5/vm9OJ0+5PKADUl8vJbvfGdRmBeq2azhqqsilyXp0yVbv/4U2dlxzJ6dTmKiFqfTy29/uw+fb96wCh33h0wmoNEo+i3ReL0iGo3iguW1aMfnE9m0qYLMTGOYZI7BoOK99yojGpQBzJ6dzmOPLWPDhlOUlnag0Si4/voibrihaNAHhcOHmxFFKSzwMhhUdHY6KClpZ86ci89Mnj7dxa9/vQ+r1YUgBD5bkycn8fWvz4+KMvDFsmxZLnV1VrZurQ5e630H40juD5crY+tuP8Y5ebKNv/3tCF1dTiDQ0HzHHVNYsSIveAM+cKCJF188gt3uDT7m85+fyvLluWPmxLJlSxVGozqsDJaREcfevY184QvTg5vYvn2NWCyuEHkGuVxGbq6ZHTvquemm4mExxJ01K52nn06ltLSdH/94G3PnFqPTfVauSk+Po7bWwt69jaxaVXDJr3ex5OcHMgO7djWQkWFAq1XS2+uhtbWXG24oOm9vjM3m5rnn9qLXK0Myfh0dDp59dg8/+9mqqBFl7ePTT+uw2z1MnfpZaamvjPLaaye54oqsEV2zIAisXJnP5s0VYXIzTU021qwpGjOfu4Gw2dw4nb5+rx2TSUNtrSUCqwpn0qQk/uM/liKKUtDbdihYre4BS++SFNDFu1isVhe//OUuNBpF8DqRJIny8k5eeOEgjz66aMxeJzKZwD33zOSqq/IpKWlHkmDy5CSysqJvMvFyILp26HFMba2Fp5/ejShK5OSYyMkxYTSq+etfD7N7dwMAZWUd/PrX+9BoFCGP+ctfDnPwYHOE38HgaW93oNWGx/tyuQxBCJQT+igv70SvDz8JBxS9AzfG4UKhkKHRKDAa1SEBWR9xcWqOHWsdtte7GARB4KtfncsXvjANp9NHTY0FSZK47745fP7z0877swcPNuN2+8JO7UlJOjo6HEO2tBoNDh5s6nfkXqdTYrMFgtGR5oYbCsnKMlFd3U13t5PubifV1Rays00Xpdgfbeh0ygF7J+12z7AceoaTi1Xzz883DyhfAoToPg6VvXsbcLn8IZqGgiCQlWXk+PG2Yd2nIkVmppHVqydwzTUTyM4e+2XZsUosUzZKvPdeJUqlLKSfSqNRkJpq4M03S7niiizeeaccg0EZoi2k0ShITNSyYcMp5s5NHxMflAkT4tm/vzFMI8nj8SOXy0JuwiaTGrd74Eb7/oK7S0GlkjNQa5bX60eni/xHQqGQcf31RVx3XSEejx+VSj6ov3tHh/28WaWApVJ0oVLJ8fsH1gEbjcxeXJyaxx5byt69jezeXY8gCNx662QWLMiMmCjzcKJWK1ixIpcPPgj0dfZdS36/SFeXk7vvnh7hFQ4P06alkJZmoLnZRlpawMdRkgLtIsXFSeTnhwtvD5aaGmu/e5EgCMhkAW2/c9sDYsS4GCJ/B7pMKClp79fn0WBQUVdnpafHzalTHaSnhzeaG41q6uqsQXXuaGfVqgJ27KjD6fQG19vXS7du3cSQno8rrshm8+bT+HxiyA3YYnGRkKAddhHT7GwTSUk6LBZXyISnJEnY7V6WLBkd26fBIAjCkPoJ09Li+s0U9JGYGH0ikMuW5fLCCwcxmzVhOmBZWcZLnnAcLFqtkhUr8lixIm9UXm+0ueWWydTX91BS0o5SKUcUJURR4rrrCpk3b3CN9NGOUinnu99dzAsvHKSiohOZLODdOHt2Gl/+8sXLl0BAJNvl8iGKEm63D4VCFpwklyRpTPeUxYguYkHZKKHXB7zEzr3J9mUJ1Go5cXEq3G4/Ol1odqBPN2YgOYloIy/PzDe+MZ+//OUwbW12BCHQ07FyZR433RQ63p6ba+Lmm4tZv74UtVqBVqugt9eDUinne99bPOzyDDKZwFe/Oodf/GIXvb0Bf06324fF4mb58hymTk0Z1tcbTWbPTkOvV4UFnK2tvaSnx1FUFH2iifPnZ7BjRx0lJe0kJ+tQKuV0djoAePjhhWMiMzwW0GqVfPe7iykr66SkpA2lUs6sWWnjYnrwbJKSdPznfy6lqcmG1eomKUk3LIH9woWZ/PGPBzh2rDW4Z2dnm0hJ0ZORYQyzLIsR42IRBjtmPywvJgjXAc8BcuDPkiT9/Jzv5wAvAuYzj/mBJEmbzvec8+bNkw4cODBCKx4+tm6t4sUXj5Kfbw7ZBBsaepg/P4MHHpjHxo3l/PvfJ8MeU1trYdWqAu6+e0Ykln7RuFw+yss7cbt95OaaB9wcJUni9Oluduyoo7PTQVFRIkuWZI+oGG1LSy/vvVfB9u21yGQCq1blc+utU8bcpOu5VFV189xze+jpcSNJgaBfrQ4Mi8yenR51/UMQkGb49NM6Pv64GofDy/TpqVx77YRYOSjGgNTUWILeqXFxaq6+Op+lS3NG7OD67rvlPPfcXhoaepDLZSiVAjabh4QELa+8cvuoeMVeDvTpVXZ3Byol6emGcXVo6EMQhIOSJM3r93ujFZQJgiAHyoHVQAOwH7hLkqSSsx7zAnBYkqTfC4IwBdgkSVLe+Z53rARlHo+f55/fy7FjrWcmEwWsVnfQOzIhQYvL5ePZZ/dQUtJOXFxAM6anx01urpnvfW/xmEqR2+0eNm+uZOvWapzOgHjsTTcVR43FxfHjrfzmN/twu/3BTF5amoHvfGfRmFf/9nj8lJS0s29fI1u3VqHVKhCEQMZx5cpcvvCFGVE3hRkjxmA5fryVZ57Zg1IpIyFBi8fjp63Nzpw56Tz00MJhv7btdg/33ruB5mYbFosbh8OLWi1n6tRktFoFX/vaAhYvzh7W14xGJEmittbKvn2N9PZ6mDIliVmz0ofNDaWry8kf/3iAsrLOoJzP5MnJfPWrc8aF/+bZnC8oG820wAKgUpKkqjOLehW4CSg56zES0Hc8NgFNo7i+EUWlkvPIIws5fLiZnTvr8Xj8rFs3iSuuyAo2xGs0Cr7znUUcOdLC7t0N+P0iCxZkMnduRtTYAA0Gp9PLU0/torbWQnq6gaQkHTU1Fn7600/59revYObMtIiur6vLya9/vQ+TSR0yjNDSYuO3v93Pj3505Zg6nVmtLk6f7kYQAr6KBoMKs1nD7t315Oaag1ZKoijx4YfVGAzqYfHQjBFjtPH7Rf72tyPEx2uCh9Q+vbXDh1s4dqz1krTI+mPHjjqOHGklIUFDYqKWxMTAAfr06W6mTUvh8OHmcR+USZLEG2+UsnFjOXK5gEolZ/v2WtLSDHzve4svuarh94s888weWlt7yc01BYc0Kio6efbZvTz++PJRcRqJBkbzTp8J1J/17wZg4TmP+R/gA0EQHgL0wKr+nkgQhPuB+wFycqKnMftCKJXyCyqDK5Vy5s/PZP78sdt8u3dvIzU13eTnf5bST07Wo9G4eemlY0yblhLRD9jevQ14vf6w6dDUVAO1tRZqaiwha48UdruH/fubKC/vxGTSsHBhZnDDgsBG+dZbZbz9dlnw33K5jM99biq1tRZUKnmIt6VMJpCdbeT99yu54YaiMRXoj3f8fhG73YtGo4j5DJ6H+vqefs3LBUEgLk7Frl31wx6UffRRNXK5EDJkpdUqEUWJ06e7uPLKvGF9vUjTN8ygUsmD+3RpaQdvv10WJgje1GTjxReP8uijiwb93Lt317NpUwVtbXYyM42sWRPYixoarCFagYIgkJlppKbGQnl5Z1Doe7wTbbvyXcDfJUl6WhCERcBLgiBMkyQpZKRMkqQXgBcgUL6MwDpjnIfdu+v79a6MiwtMkTY395KVFbl+oebm3n4DEkEI6CMFDLEjsLCzaG628dRTO+nudqHTKfF4fGzaVM6tt05m3bpJCILAjh11vP56CXl55mDJxuPx889/HkUul/VbhlWp5Ph8IhaLK8wLdCzjdvvYv7+JnTvr8PslFi7MDMlCRyuiKLF1axUbN5Zjs3lQKGSsXJnHzTcXj4lJ69HmfNPFcrkMj2d4fWz9fpHm5l6MRnVQnqYPrVZBU1MvCxaM3QP02YiixPbttbzzThnd3S40GgWrVxdwww1FfPJJDXq9MuwwnZ5u4PjxVjo7HRfMlkmSxL/+dYwPPjhNcrKetDQDFouL55/fe8GevOZmWywoGwEagbNzvFlnvnY29wHXAUiStFsQBA2QBLSNygpjDAuSNHQ17tEkIyMOl8sX9nVJCsgE9CddMppIksQLLxzE5fKFTHX5fCLr159i6tQUJkyI5+23y0hLM4T00KhUcuLjA56Ner0qJFMGgZuMJNGvYO9YxeHw8qtf7aKioisorfHSS8f48MMqfvCDpcNqbj/cvPFGCW+/XU56uoH4eC1er5/336+kpsbC9763JNb7dw5ZWQFrOrfbFzaU09PjHvYsWUCWRs706ckcPNiCx+NHo1Hg84nY7Z4zvqFJw/qakWLDhlOsX18a9PR0u31s2HCK6moLNps7bC+BwO9HLpfR2+u5YFBm59uVAAAgAElEQVTW2Ghj69bqEP9VszlgeP7xxzXExanJzjaFWIEBZ/ar6D5cDSej+YnfDxQJgpAvCIIKuBN4+5zH1AFXAwiCMBnQAO2juMYYw8AVV2TR3e0M+3pvrweTSd2vFttosnBhJkqlPMRZAKC11U5+vrlfc+rRpKnJRk2NJWxaVaGQoVLJ2bGjDrfbT0eHA4MhfLMymTRoNAp6elxhwqxNTTYWLswcU0MjF+Kjj6qoqAiUy+PjtZjNGvLyzLS12dmw4VSklzcg3d1ONm2qIC/PFLzhKZVycnPNnDrVQWlpbOs7F41GwW23TaahoQe7PfD59ftFGhqspKYahj1rJZMJLFuWiyQJXHllLqmperxeEbVaTna2kQcemINCMfbLzd3dzjN+xeZgAKRWK8jLM3P0aAs6nRKr1R32c32Zy8FMdZ882YYgEGJ6XlLSxpYtVTQ29nD8eAvvvlse4uLhcHjRaORMmzZ2pYqGyqgFZZIk+YBvAu8DpcC/JUk6KQjCjwVBWHfmYd8BvioIwlHgFeBeaTQ1O2IMC1dckUVWlpHaWgterx9JkujqctLebufuu2dEvGEzPl7LI48spLfXQ02NJfhfUpKOb3xjQcSzfL29ngGtZrRaBZ2dDlQqOVqtErc7POPncHgpKkrguuuKqK/vob7eSnNzINDLyDBy553nt2saa2zdWk1aWnipNiMjjp076/q1F4oGqqsDnpPnfh76RIMjbfkVraxaVcD998/D75eoq7PS1GRjwYIsfvCDJf1mcy6VG2+cSEKCFqvVzZQpySxfnkNBQTxTp6Zy442Thv31IsHp091IEmGZWUEINPUbDIFD3NkHWVEM/P5XrcofVCbr3ANiZWUXp051EhenJi5OxaxZ6bjdPrZuraamxkJdnZWuLicPPjjvssqUjWpP2RnNsU3nfO3xs/6/BFgymmuKMfzodEr+4z+W8s47ZWzbVovb7aOwMIH775/DtGmpF36CUWDq1BR+9atrOXmyDZvNTWqqgeLipIgHjBAYOOjTGDt3PTabm6KiRGQygdWrC1i//lSIrp0kSbS29nLvvbO46qp8Fi3K4tChZhyOgCzJ9Omp466R3G739rtpy+UCXq8YFF+ONs4t05yNKEpRueZoQBAEli3LYcmSbHp63KjV8hHtv4uP1/LDHy5n27Zadu2qRxDg1lsnc+WVeSG2eWOZPq/h/pAkSE7W8q1vLeSPfzxIZ6cDQQhIVqxYkcdtt00Z1GsUFycjSZ+1iVRUdGI0qs+8rsDEiYlMnJjI0aMt+P0iN988hcWLs0fN1SNaiLZG/xjjBKNRzd13z+Cuu6bj90fnTVGnU0bllKvZrGH58lw+/ria3Fxz8OZtsbhQKhUsWRJozbzhhiJOn+7m+PHWoKen1+tn0aJsrrwyD0EQyM+Pj4pJ0pFk6tRkTp3qIDU1tCxutbrJzjZG7ZRpUVECCoUsrD9KFCW8XnHY+6PGGzKZMGr9giaThnXrJrFu3fjIjJ1LUVECMpkQNswgihI+n8isWekUFibwq19dS2VlF263n6ws45DEqPPzzSxYkMmePQ0YjWp8PhGVSqK7282ECfHBVozZs9ORy2XcfHPxBZ5xfBKdu1WMcYNMJiCTRV9AFu3cddc0fD6RnTvrzmj2QEKCJkQTSK1W8O1vX0FZWSdHj7Ygk8mYPTuNwsKE82Zhxhtr107kyJEWenrcwcyF3e6hq8vJvffOjHg5eiD0ehV33z2dv/71CEajGpNJjcPhpaPDwYoVeUyYML6D6RjRQ1ycmjvumMI//3ks2Jdpt3toa7OzdGlO8FpUKuUXPQUpCAGLu+xsI2+/XUZvrweVSs7MmakhB0eXy0dWVtywvK+xyKjaLI0EY0XRfywjSRKNjTZOnGjD5xOZPDmJgoL4qL3ZjSfa2+00N/ei1SooKIiPivJqNHL8eCsvvniUrq7AgElcnIo775zGokXRL+pZUtLOpk0VVFd3k5io45prJrBoUVbsbx1jVJEkiSNHWti4sZzaWiuJiVquu66QZctyh30KuE8s9tSpdrKyPhusEkWJ6upuHnlkIfPmRV8VY7iICpulkSIWlI0soijxyivH+fDDKgQBBAH8fpg3L50HHpg37vqTYoxdRDHgmydJEunpcTE5iRgxopiuLidPPrmT1lYber0Kn0/E6fSxYkUe9947a1xn+6PFZinGGGTPngY2b64kL88cPLlLksT+/U3k5FRw002XZ90/RvQhkwkRFSWOMbq0tdlpaOhBpZIzcWJi7IA4xkhI0PI//3MlBw40cexYK3q9ioULM5k0KWlcB2QXIhaUxTgvmzdXkpSkCymlBOwv4vjgg9OsXTsxVmaJESPGqOHx+HnppaPs2FEHCICETqfk/vvnRtxXN8bQ0GqVLFuWy7JluZFeStQQu5vGOC+trb39yg2o1QocDi9OZ7hOVowYMWKMFG++WcL27bVkZ5vIzTWRm2tGp1Py/PN7aWzsifTyYkQpNpubTz6p4dVXT/Dxx9X09ISL4UYDsUxZjPOSlWWko8NBfHyo9ZDT6cVoVKPVxi6hGDFijA52u4ctW6rD7Hj0ehUWi4uPPqrmnntmRnCFMaKRiopOfvWrPTidXlQqOV6vn//7v5M88sjCqPPUjGXKYpyXG24oorvbFWIE3NdQfcMNRbHSZYwYMS4JSZIY7MBZZ6cTkPod4jAa1VRVdQ/z6mKMdVwuH88+uwetNmAblZERd8ZOKpBddTi8kV5iCLE0R4zzMmdOOnfcMYU33yzl7H1z9eoCVq0qiNzCYsSIMShsNjcHDzbT0WEnPT2O2bPTR8SOaKi0tPSycWM5e/Y0IAiwaFE2a9dOPK+Cu8Ggwu8PHAzPbQZ3OLwUFCSM9LJjjDFOnmzD4fCSnBx6XcXFqensdHL8eCsLF2ZFaHXhxIKyGOdFEARuvHESS5bkUFbWgd8vUViYQFpaZE3FxyJ+v4ggCJf1ZFGM0aWkpI3nn9+H2+1DoZDh84no9Sq+851FEXV6aG628ZOfbMfj8Qf3kl276jl0qJkf/nB5mDtDHwkJWmbOTOXkyTYyMz+btPX5ROx2LytX5o3C6scfFouL06e7EASBoqIE4uLGh30UgNXqYqBErEwm0N3tGt0FXYBYUBZjUCQkaMeEEGc0Ul3dzfr1pzh2rBWFQsbixdmsWzdpSBYlMWIMld5eD88/vw+9XhlyiOrudvLcc3t56qnVEZOReOutMrxeMSSwysoy0tjYw8aN5dx335wBf/b/+/9m8tRTu6ipsaDRKPD5RLxePzffXMzkyUmjsfxxgyRJrF9/io0by4MlZLlcxh13TOGaayaMC4HwlBTDgL6eoihFXYIhFpTFiEqam21s3FjOvn2NyGQCS5bksGZNUdBiaKxQWdnFz362A7VaTk6OCVGU2LWrnuPHW3n88SvDBihixBgujhxpweXyhd104uO11NZaKC1tj4iEhN8vsn9/IxkZ4VY6qakGdu9u4Mtfnj1gQBAfH9C3Onq0hVOnOjEYVMybl0F2tnFcBBGjyfbtdaxfX0purjnYp+fx+PnXv46TkqJn9uyx779aXJxEaqqB1tbekAxsW5ud5GQdU6fGGv1jxDgvjY09/PjH29i/v5HUVANJSTq2bavhiSe209npiPTyBo0kSbz66gl0OgUpKXpkMgGFQkZWlhGLxcWWLVWRXmKMcUxnpwO5vP8gRZKkiEoCSBL9BlCCwKCa/tVqBQsWZPH//t9Mbr11Mjk5plhANkQkSeKdd8pITTWEDE6oVHLi4zW8/XZ5BFc3fCgUMh59dBGJiTpqaizU1gb+MxrVPProIpTK6BIdjmXKYkQdb7xRiihKZGR8VtrIzjZRX2/lvfcq+eIXZ0RwdYGyUE2NBUGACRMS0Gj6/xj19no4fbqLnBxT2PdSUvTs3t3AHXdMHenlxrhMSU83IIr9BziCIESsfC6Xy5g7N51jx1pJTw/NlrW22lm4MCsWYI0Cbrefzk4HubnmsO+ZTBrq6iwRWNXIkJKi58c/XkllZRddXU7i4zUUFiZEpXpALCiLEVV4PH4OH24hOzvcLic11cCuXfURC8okSWLTpgrWrz915mYnoVTK+eIXZ7JsWU5E1jScSJJEba0Vm81NcrKetDQDHR0OurudxMdrYz1wY4yZM9MwmTR0djpCyv6trb2kp8cxcWJixNZ2003FHDvWRktLb3DasrW1F7lcYO3aiRFb1+WESiVHp1PidvtQq0NDAYfDS2Li+GqtkMmEiF7zgyUWlMWIKgKli4FO94F+lEixc2c9r756gpwcUzDl7XL5+NOfDpKYqGXKlNDeBINBRVFRIk1NtrCApr3dwZo1RaO29gvR1GTj97/fT0ODDZks8L6cTh86nQK1WoEoSsyenc69987CaBw/k1njGbVawXe/u5jnnttDba0VkJCkQEP9ww8vjGiWICvLyA9/uJz1609x+HAzAPPmZXDzzcX99prFGH5kMoHVqyewfn0peXnmYHZSkiRaW3v50pdmR3iFlyexoCxGVKFWK5g6NYWqqu4wvaLWVjuLF0dmAlSSJDZsOEVqqiGkB0GjUWA0qnnnnbKwoEwQBO68cxo//emnwYyA3y/S0tKL2azh6qujQ+fN6fTyy1/uwuXykZMTyFBu21ZDU1MveXkmFi3KRpLg6NEWnn9+D489tjwm6zFGyMoy8rOfraKsrAOr1U1iopaiosSo+PtlZRl56KEFQWHq/gRhY4ws119fSFVVN0ePtqBSyZEk8Hr9LFmSw/LlMT/KSBALymJEHbffPoX//d/ttLb2kpysP3Nys6NWyyOWXXI6fXR1OfvtD4uP11BZ2dXvzxUUxPPDHy7nrbfKOHKkBblc4Mor81i7diJms+aS1tTUZGPz5koOHmxCqZRz5ZW5rFpVMGSNoUOHmunqcpKXF+gt6ehwYLG4SU830NbmwGbzYDSqyc42cfp0NxUVnUyaFJMeGCsoFDKmTk2J9DIGZKwHYzabm8ZGGyqVnNxcU1T2KQ2EWq3gW9+6grKyDo4da0UmE5g1K43CwoRR7+uTJInm5l68Xj/p6XERk2uJNLGg7BKwWl3Y7YHa+7k1+RgXT16emf/6r+W8+WYpx4+3IghCsLQxkKjkSKNSyVEq5Xg8/rDNwun0ER8/cICVm2vm4YcXBqfKhmOzq6218NOffoooSiQnBzJwb71Vxv79TTz22DIMhnAT+YGoquoOGVawWl3BdQpCYGChr2QpSVBf3xMLymJc9vj9Im+8Ucr771cCAc2rxEQdDzwwl6Ki6O9d6kMmE5g8OTmiHpCVlV389a+HaG7uRRAENBoFt902mauuyr/shj5ikcRF0NXl5J//PMbhw83IZAJKpZwbbihizZqYF+RwkZdn5tFHF+Hx+BEEIj62rFDIWLkyj82bK0lJ0VFba6W+vgcAlSowcn0hhnNzeeWVEygUsrOsQ+Tk5ZmprrbwySc1Q2qWjo/X4vH4g/8+93etVIZe05G26OnpcXPyZNuZcquJgoL4y27jHqs4nV4OHmzm6NEWNBoFhYUJtLc7aGnpJSfHxKJFWWF2ONHK22+X8847ZSEaXxaLi1/8YhdPPLEyYgfIsUZTk40nn9yBTqcMSpu4XD7+/vcjKJXyy66MGgvKhojT6eWpp3bS2ekgO9uETCbg8fh57bWTOJ0+Pv/5mMTBcNKXlerqcnLgQCOdnU6ys03MmTP6/n3r1k3i2LEWNmwoQxQl1Go5Pp+IwaBi27Zali/Pu+SS5GDo6XFTVtYxgNSGju3ba4cUlM2fn8Ebb5QGp7BSUwOaana7B7VaEZzcc7l8KJUypk2LXClsx446/v73I/h8IpIkIQgCU6Yk881vLoh4sBjj/HR1OXnqqZ20tPRiMKhobOzh+PE2UlP1zJyZxuHDzbzzThkPPbSAGTNGX9R2KDidXt57r4LsbFNI+dVs1tDb6+aTT2pj94JB8uGHp5EkQoS0NRoF6elxvPlmKUuWZF9WyY5YUDZEDh1qpqWlN9h/A4HAIS/PzAcfVHLddRMwmUb+xnw5ceBAE3/4wwH8fjFYQjSZ1Hz3u4vJzg4PTEYKnU5Jfn48EycmBg2RMzLiSErS0dBg5b33Krjrrukjvo6BtKcgUIroa5weLKmpBu69dyZ///tRZDLQapVkZBiorOxm5sxUenrc2O0efD6R+++fG7Hpy9paC3/+8yHS0gzBcqskSZSWtvPPfx7j/vvn9vtzXq+fEyfaOHQo0NM3b14GkycnXVYbfTTw6qsn6OhwkJdnxuXyUVtrJSVFR2+vB4/HR3a2Cbvdw+9+d4BnnrkWrTZ6g+yODgeiKPXb92QyaTh1qj0Cq7p0rFYXnZ1OjEb1qEngnDjR1q/8hk6nPCPJ47qs5HhiQdkQOXGiDb0+fLPo2+Dr63tGPCizWl2Ul3ciihITJiSM6wu2s9PBH/6wn8REXcgm3dnp4Pnn9/Lzn68atZur3y+yd28j06alhDUnp6XFsW1bLXfeOW3ES2kmk5rMTCNWqzssM9fR4eCaawqH/JxXXplHYWECu3fX097uYO3aiWRnGzl6tJWGhh6ysowsX55LVla4ftxo8fHHNahUspD+N0EQyMoysmdPA5///NSwz57D4eWZZ3ZTXt6JTqdEFCU++qiaOXPS+frX519UM3F3t5OmJhtarZK8PHNUTDJGOzabmwMHmoLXT0tL75mgRoHfL1FVZSEry4Rer6K93cHJk+3Mm5cR4VUPjFarxO+Xgtnas+krq48lXC4fL798nB076hCEwMFvxoxU7r131ohbwen1KqxWV1gQ3nf4VKsvr4b/WFA2RHQ6JV5v/5kISWJEJ0YkSeK99yp5440SRDGgOQRw3XUT+Nznpo3Lm8OBA02IImEf2MREHbW1FioquiguHp2mc0kKBGb9WdfI5UJIX9ZIEpDamMovf7kbUZSIj9cgSYEbnU6nZNWq/It63sxMI7ffHlpyudipvZGQOaivt2IwhGfp5HIZMhl0d7vCgrKNG8upqOgM02E6dKiZTz6pHlIA6/H4efnl42zbVoNMJiCKEklJer72tXkUFMRf2psb5zidPgSB4B7l8fiCJtEKhQyXyxfyeIfDO9pLHBJJSTqKixOpru4mLe0zXTVRlLDZPKxYkRe5xQ0RSZL4058OcvBgM9nZRuRyGZIkUVLSztNP7+ZHP7pyRHt6V67M489/PozRqA4JcFtaepkxI3XI0+RjnVhQNkQWLsxiy5aqYPmqj95eD3FxqmHfnCsru9i8uZLTp7txu71UV1uYPTsNlSrwp/P7Rd59t4LkZH3U6F4NJ+3tjrBG87Ox2UbPv0+hkDF5cjK1tZawZuT2dgezZqWNWsP5tGmpfP/7i3nttRJqaqwIAsyZk87tt0+JqGl7Y2MPb775mSDo3Lnp3HxzMZmZl55hy8w00tDQE1Y+FUUJUSQsa+j3i3z0UTUZGaFG1YIgkJqq54MPqoYUlP3f/53go4+qQ2QPursDfVI//enVJCSMLwX0PrxePydPtlNa2o5Wq2TOnPQhm3/Hx2vQaBS4XD40GgUmkyZ4qHS5fGHXx7km6tHIvffO4sknd1JTYyEuToXH48fl8nH11flMn54a6eUNmsZGGwcONIUcXARBIDPTSE1NNydPtjNr1sj1+C1alM3Bg80cOdKCyaRGoZBhsbgwmzV84Qsj3w4SbcSCsiFSVJTAVVfls3VrNSaTGo1GgcXiQhQlvvWtK4Y1M7BnTwN/+MMBtFoFZrOG3bvr6e52olLJgwGAXC4jPT2Od94pZ+XK/HGXLcvONuJ2h2egJCmQKRztSa1bb53M//7vp0H/NAg0MHu9fm66adKormXKlBQefzwZh8OLXC4b0INztGhs7OGJJ7YjSVKwTHXsWCvHj7fx+ONXXrJS+4oVeWzfXhtiCyNJEg0NPcyfnxEWlHk8ftxuX7/Za41GQXNz76Bf22p18cknNWE6VPHxWurrrezcWceNN47u33806Olx8/TTu6mttaBSyfH7RTZsOMWaNUXcfvuUQQdmSqWcG2+cxL/+dYycHBPJyXri4lR0dTmQyWRMmBCPJEk0NvZQWBhPYWHCCL+zSyc11cCPf7ySffsag20tixdnU1ycFHXTwJ2dDpxOH8nJujD5poaGnjPyN+FrVirlVFR0jmhQplLJeeihBRw+3Mynn9bhcvlYvXoCS5fmXJbuIbGgbIgIgsA998xkxoxUtmypxmJxsnRpDldfXTCs/TZ9I8Gpqfpg6c7j8ZOSoqemxkpurjl4MtfplLS3O3C5fONuAm3u3Axee60k6L8IgRtxU5ONoqIEcnNHt3ejsDCBH/xgCS+/fJy6OisgUFBg5q67pvdr7DvSCIKAXj94TbKR5K23ypAkKcRkOj09jqYmG2+/XcaDD867pOcvKIjn3ntn8o9/HEMUpWD5q7AwgXvumRn2eI1GQUqKHpvNHVYCsVhcTJgw+Bt/W5sdoN/+RYNBRVlZJzfeOIQ3M0Z4+eXjNDRYQwab/H6Rd94po7g4aUgZoWuumYDT6ePdd8uRJInsbBN+v0Ryso6eHjcWi5tp05L5ylfmjJnDpcGg4qqr8rnqqotrGRhpWlp6efHFI5w61YlMFgiAbrxxEtddVxj8HavVcgaKIf1+aUT2lz6h2L59PT3dwIIFWSxYkDXsrzXWiAVlF4FMJjB7djqzZ6eP2GtUVnbh8fhDeqn0eiU+X6CnqampJxiUud0+tFrFuGyINBhUfPe7i/n1r/dSW2sNNqEWFibwjW8siMiJdNKkJP7nf1ZgtQZKpyaTOupOxqONKEocONBEZmZ4Niw1Vc++fY3cf//cS77ZrliRz8yZaRw/3orD4SMvz8zEif3bBgmCwE03FfO73+1HrVYEM2Yulw+r1c0DDwxeNqRvSGCgxu7ziQePVXp63Ozb1xj2N5XLZcTFqdm6tWpIQZlMJnDLLcWsXl1Afb0VpTKggN/aasdicZGYqA0J6GNcGj09bn7+8x1B+zRBEHC7fbzyynF8PpF16wKZ3eLiJNRqBQ6HN+RQ7/X6kSSJOXOG9z5nsbh44YWDlJS0I5cHejMnT07mq1+dM+JDBWOBWFAWpQQapUOlDwoLEzh4sBmlUobPF/heIOVv45ZbisftiH9enpknn1xNeXknNpub5GQ9+fnmiAZCgiCMiibZWOJ8fw+ZTBjwND5U4uO1LF+eN6jHLlqUhdXq4s03S/H5RARBQKWS85WvzBmS3lpGRhy5uWba2uwhnqx+v4jL5WPZsvEncNnb60EQ+s8O6nRK2tocF/W8BoMqRD0+K8t4wSpDX/l41656ZDKBRYuyWbEi77Isbw2W3bvrsVpdIRl8tVpBTo6JjRvLWbWqAJ1OiVar5IEH5vLrX+9DJhOIi1PhcHhxu/3ceee0Ye3v8/tFnnlmD01NPeTmBoRiJUmioqKTZ5/dw+OPXzlu72ODJRaURSm5uSYkSTgz7Re4SLOzTXR1OTlxoo2sLCP19VZ8PpHZs9NYs2bwp/6xiEIhCzP8jhE9yGQCCxdmcuBAIxkZoTfYlpZerrgiKyJBtCAIXH99EcuX51JdbUEmEygoiB9y/50gCNx//1yefHIHtbUW9HoVbrcPj8fPunWTKCqK/h6ooRIfr0Eul+H1+sOm76xWNwsWZI7KOrq6nGf6OB0kJekQRYn160vZsaOOxx5bFjscDcCxY639Bq1KZUD0uqnJFuzdmz07nZ/85Co++aSGmhoLqan6oEzOcFJe3kldnSUkUPxsqMBCWVnnZb/Px4KyKCU+Xst11xWycWM5mZlxaDQKRFEiIUHDjTdOClpPTJuWQmFhwpjpwYgxflm3bhJHj7bS2NgTtJhpaelFo1Fw442RPTTo9apLdiJITzfwla/M4fXXT1Jba6WoKJHbbpvMpEnR19g9HGi1SlatKmDjxvIQPTan04vH42fVqtGZ9n7nnTIsFmfIjVyvV1FXZxk1weaxiF6vGlC+CcL1vzIy4kZ82rGx0Racuj0XQQgMHcSCshhRy+23T8ZoVPHuuxW0t9sRBIFly/K4444pl512S4zoJy3NwOOPL+fddyvYvbsegMWLs1mzpmhUfQD9fhG/v3+19YtFFCVefvkYH35YhUIhQ61WUFHRxb//XcKjjy4akgH8WOKqq/LYu7eBjz6qRqdTkpSkw2hU841vzA9p/h8pRFFix466fnvN0tPj+OSTmlERbB6LLF2aw5499SQn60J+P93dTlJS9MMiUzNUAp+T/qMySYK4uPH5ORoKsaAsipHLZVx/fRGrVhXQ0+MO1v9jxIhWUlMNfPnLs/nSl2YBw2vCfiG6u528/XYZO3bU4fOJTJiQwK23FjNlyqV7dR461MTmzacpKIgPyUrX1HTz73+f5Mtfnn3JrxFtlJV18Mwze3C7fRQWxtPZ6cLl8vLIIwuZP390SpeiKOHzSQMINsvOmwm6XGlt7aW0tB2vV2TKlBROnmzDbNagVivo7naiUMh46KGFEamuTJuW0u9QgcPhRa2Wjyl9t5EiFpSNAZRKeUQFQWPEGCqjnbmw2dz87Gc76Ox0kJ4eh1wu0NZm58knd/Ltby+6ZJ2lLVuqSEjQht3IMjKM7NpVz513ThtXcjQul4/nn9+LTqcMNnrn5YHd7uGvfz3MlCnJo5KtD/SSJlFT059gs52ZM1NjWbIzSJLEG2+UsHFjRfBrohjQDDSbNdjtXubNy2DlyrxRzVyfjcGg4oEH5vHb3+6jszMwMBJwbxD4+tfnDSnjLEkSJ060sXVrFR0dTiZMiOfqqwvGnMXVucSCshgxhoH2djvbt9dSVtZJYqKW5ctzo1JEcryyY0cdbW32kJJaQoIWhULGyy8fY8aM1EvKDLS3O/oNuhSKgCXNuSf/c7FYXOzcWUdpaQcmk4alS6NTZLSPkpJ2HA5vWCCk16vo6HBw5EjLqE2c3nJLQLC5u9sZbOrv7lJq/pAAACAASURBVHYFhyxiBDhwoIkNG8qC2o19QuZVVd0sWZLD2rUj19fZ1manoaEHjUZBUVHCeW2Z5sxJ56c/vZpdu+ppbLSRmRnH4sXZQwoUJUnitddK2LixjLg4NTqdkt2769m+vZZvfnMBc+dGr2/qhYgFZeMUt9vHgQNN7NpVj88nsmBBJldckRU1QqPjiYqKTn75y134fCJGo5q6Ois7d9azdu1E7rhj8KrnMS6evXsb+7U56vt7tLXZL2m0v6AgnuPH28Keo89d4HzSDHV1Vp56aicOh4e4ODWVlV18+mkt1103gbvumh6V10dvr2fAhmyZTMBicY3Ya3s8fg4damLXrga8Xj/z52fy0EPzeeutMurqegCJ3FwzDz20gPz8i7O1kyQJj8ePQiEbNxIMGzacoqPDQWlp+xnRVyWTJiWRkRHHe+9VcP31hcP+Xj0ePy++eIRdu+qD8hZxcWoefHDuedsGUlMN3HLL5It+3bo6K5s2VZCbaw6+J70+IOXxl78cDpZJxyJjc9UxzovT6eVXv9pNeXknJpMGmUzgpZcCTco/+MHS2Aj5MOL3i/zhDwfQ6ZRBM2yTqc+TtJy5c9OHpBwf4+IYKLAJ2HFJl6yRdu21hezb14TT6Q32dfr9Io2NNm6/fcqAQwV9Zs8AOTmhqvibN59m9uz0EM2ukcbvFzlxoo09expxu33MmZPOvHkZYRIhKSn686q8j1STuMvl49ln91BS0o7JpEYmE/jHP46QlhbHf/zHEiQpMKVnNmsuKpiVJIn9+5vYsOEUzc02VCoFV1+dz5o1RWP6wOr1+vngg9O43X5MJjVyuSx4MJ8+PRCgOJ2+YR9IefXVE+zYUUdu7mfTuTabm2ee2cNPfnLViJVJDx5sRi4P2Ax6PH7sdg9KpRy9XklHh4Py8s4x2582Po4IMUL4+OMayss7ycsLWDGZzRry8sy0t9t5883SSC9vXFFdbaGryxkMyPqQy2WoVHL27GmI0MouL5Ysyaaryxn2davVTVqaIUTw9WIoLEzgwQfnYrG4qK21UltrpaGhh2uumcANNxQN+HONjTYaGnpITAzN4snlMnQ6JTt21F/SuoaC1+vnt7/dz9NP7+bIkWbKyzv5858P8cQT27FaQzNfEycmkpNjoqnJhnRWyqytrZfkZN0ly4sMxMcfV1NS0k5+vpnERB3x8Vry8uJpb7ezfv0pEhK0xMdrLzq7uHVrNb/+9d4zKvcmEhO1bNpUwdNP78bjCffYHSscPdqC1+tHq1UEM0dqtQKTSc3x420olTK02uHNwdhsbrZtqyE72xg8/ADExakRRYlt22qH9fXOxuHwIpMJlJS0s3lzJZ9+WseWLVVs316L3e4Z03/LWKZsHLJlSxWpqYawjSs9PY5du+r54hdnDKtcwFigvd3ORx/VcOxYC3q9ihUr8pg/P+O8vQ+DweXyDXiDUKnk9PS4L+n5YwyOxYuz2bYtIHyZnm5AqZTT0RHwg73//jnDUiJctCibWbPSKC/vxOcTycszX3AAx+n0nnEzCH99tVoeFgyNJDt31nPgQCP5+fHB9SQl6aivt/L66yXcd9+c4GNlMoFHHrmC3/52H6dPdyOTCUGj+W9+c8F59w+Px09NjQVRlMjNNQ1pYvyjj6pJTdWPyN7ldHp57bWTZGUZg6UtlSpg9XT6dBdHj7aM2lTpcHP4cAsTJsRTUdGNWq0IZq1kMgGHw8uMGalDLl06HF7a2uzodMowWQ0IlBCrq7s5erQ12LpRXJxEeroBozFQph8piosT+dOfDmKxuDAaA5lBSZKw2dy0ttrHdDUoFpSNQxwOL0lJ4TcLuVzA75fwev2XVVBWU2Ph5z/fgdfrJz5eS29vL7///X727cvgm99cEGyIvRgyMuKQJCnEeaEPu907qqWpyxmdTsn3v7+EDz44zccf1+B0epk+PZV16yZRUHBxfUf9odUqmTlz8JOcfT1oPp8Ydp3ZbG6Ki5OGbW0XYsuWKpKSBg547r57RkgZMyFBy3//93Jqa610djowmzXk58efd2Biz54GXnrpKE6nDwClUsbtt09h1aqCQQXGvb2efnsDA3uXiMdz8XtXba0Vr1cM6zUSBAG9XsWBA01jNiiTy2UkJemRy+WUl3eeKfNKgEBqqi4oNj4YfD6R9etL+eCD04iihChKTJqUxJe+NCtYjvR4/PzjH8eoq+shLU2PXK7E7fazZ08Dc+akodEEArmRoqAgIeg93Hc9iqKE1yuSkKClsrJrzLaNxIKycciUKcmUlraH1fN7etykpxvG1ej+hZAkib/97TAKhSykSdts1nDoUDOHDjWxYEHWRT9/QoKWlSvz2LKlmpwcU3Aar63NTkKClvnzx+4U0FgjLk7NbbdN4bbbpvRrHB6pNa1eXcC771aSm/vZ9dHZ6USrVbJkSfaorcVqdfUrYxFYUyDre25vmSAI5OWZByUUe+pUB7///X5SUw3BqU2328c//nEUg0HFokUXfq+TJ/e/d9lsHtLSDOj1F793ne9ykCQJmWzsdvPMn5/Btm01TJ6cRG6uifZ2O6IIer0SSZKYODFx0M/173+fZPPmCrKzTSiVciRJoqammyef3MkTT6xEr1dx5EgzLS028vLMdHc7iYtTo9EoUChkHD/exuTJSUMKBIdKR4eD6dNT6Opy0tRkQxAC3roTJyaSkqLnxIk2rr22cMRefySJBWVRgtfrp6ysk54eN8nJOiZMCLVOcrl8NDT0oFTKyM42nfe0unbtRA4fbqanxx2cCnM4vHR0OPnWt2ZExc1qtGhrs1NXZw3TrhEEAZNJzfbtdZcUlAHcddd0lEo5W7ZUIQjg90NhYTxf+cqcMd08PJY53zXe3m7n00/rKCvrICFh5OVLbr99KgAffliFJAUCgMxMI/ffP5f4+PCs0MVi///Ze+/4Nstz///9aMuSLXnvmb0T4uw9CZtAacs6PV/oKdCW0J62tL8uTgelJcChm0IplNMCHZCySoFAyHISYmeQxImdeMh727K11/37Q7GIIieNE9uSnef9evVVIsvSJVm6n+u+7uv6fOwe3n+/hm3banA6fcycmc4110wI2RNNnpwy4ASp3e7BZNJespr6m29WYjRqwjZ9Wq2K1FQDr7124oL8Twdau5xOL+3tdjZtWnBJf6OCAjM6nSpsWAOCfw+73cuCBaOzSgbBjficOZmUlTWRnm4kN9dEd7eL3l43995bfMGTiFari61bq8OmGiVJIiMjntraHj76qJFVqwrZv78Jo1HDnDkZlJTU09PjQqVSEAgEjxAXLcobVCI4WPoTwHnzsnG5gh60er0KtVpJS4ttVBvVy0lZDFBb28PPf743NGYuBOTnm9m0aT5JSXreeaeKLVuOh9SrExP13H337HOOHBcVJfK1ry3i+ecPU1dnBYI7pnvvLR7V+i0Xg8fjP2dPj1qtPC1ceGmo1UpuvXUG1147kfZ2BwaD+vT02uWT/I4WzpYvsViC8iXXXTeRT31qeORLVCoFn/3sDK65ZmLICzQnJ2FIn8vp9LJ5cwm1td2kpxsxGDR8/HErZWXNPPjgYiZNSuGqqyZQWtqEzeYJTeF5PH5aWmx8/vNzL1kuoaKiY0DZkeD73IPT6fu3VfqiokT++78X8fzzh0Jrl16v4r/+ay7FxZe2dmm1Ku64YyZPPVWKyaQlMVGPy+WjpcXGjBlpwza8MBIolQq+9KV5fPhhLe++W0Vrq53x45O49tqJg/KSbGzsCz3e2RiNGsrL21m1qvC0/EVQhmLVqkKam/vo6HCEkr916wqHdf3LyUkgI8NIV5eTpCR9qMIbCAQ1A5csyRu25x5u5KQsyjgcXh5/vASFQgoz3G1u7uOXv/yI5cvz+POfPw5rTrVaXTz++F7+539WkJs7sHrx9OnpPProOpqa+vD7A2RlxV9yU/toJD3diFarGvBopqfHxcqVBUP2XPHxWtmTNIYJypeUDShf8uablVxxxfDKlwzn52PPngZqarrDdLsyMox0dzv5v//7mB/9aBVFRYls2rSA5547hMViRZKCCeNtt81g+fJLv4iZTDpcLl9Eddjr9aNWKy+4F2zGjODa1djYRyAgyM4eurVr8eJczGYdr71WwalTnSQkaPnsZ6ezZk3hJfWWxgJqtZJ168axbt24i34MrVYZpk/ndgdPaFpabDgcXtLTDfj9AebPz2Lv3gZSU+NQqYKnN7m5JpxOLzabh4KCoevjHAiFQuILX5jLo4/upq7Oismkxe32Y7N5WLu2iClTRq5Xc6iRk7Ioc+BAMzabJywhg2DzbU1NNzU13WRmxoeVn00mHXa7h/feqz6v555CIZGTM/Kms7GERqPkppum8Mc/HiIrKx69Xh3q+TIY1CxbNnp3VDKDIyhf4oj4rp0pXzJam4N3764bsEHebNZRV2elvd1BWpqBOXMymTEjndraHvz+ALm5piHrMV2/vog//enjsOlOgKamPtatKxpU0qNUKobNLmfq1NRBVY+Gg7Y2O+Xlbfh8gvHjk8jPN8VEZb1fRslqDR5H7tpVh9PpQ61W0NvrpqysmaeeKuWuu+YwdWoq5eXtZGYa0elUdHU5sVqDx6UjMUhWWJjIj3+8mp07607r2ulYsSKfqVNTY+K9vFjkpCzKNDb2nnMX6PUG6O11D6hanZQUx7FjbcMd3phgzZpCFArYsiWoeB0ICCZPTuHOO2cNaU+PTGzjcvnO2Ys52uVLfL6BBxuCDdDBycV+VCoF48cPffK5cmUhR4+2c+hQCwaDGoVCwmbzUFSUyPXXTx7y5xuNCCHYsuUEb7xRcboiFZyQnD8/m89//oqoT8UrlQruuWcujz1WwuHDLTgcXnQ6FR6Pn2nT0pg+PZW9exuZOzeTr3xlIVu3VvHuu9W0tdmZMCGZe+8tZtq0kTsGTk6O48YbJ3PjjWPn8yUnZVEmJSUOr3dgoTuFQkKjURIIiIiLidvtixAslRkYSZJYvbqIZcvyQ30PA1UVZMY22dnxBAIDy5c4HKNbvmTevCxeeaU8osG5r89NYqLuksVzLwSNRsmmTQs4erSNffsa8fsDzJ2byezZGaPW8maoOXiwmS1bjpOfbw5VDoUQ7N3bQEaGkZtuunjroaFi4sRkHnxwMZ///Ovo9Wri4tQUFJhDWmVJSTo++KCWhQtzufbaSVx77aSYmXYeC8jflCgzd24WL798DLvdE9aL0dnpIDPTSE5OAkePtoXZmggh6OhwcPPNU6MR8qhFrVaSmRkf7TBkokRiop5VqwrZurU6Qr4kMXF0y5csX57Ptm21NDT0kplpRKGQsFrddHU5eeCBBSPm76hSKZg9O4PZsy9cy2200tXl5OTJTiRJYsKEpAuqur/9dhWJifqwo1xJksjOjue996q47rqJMdH7Gx+vpagoacAjZK1WFVFVlhOyoUNOyqKM2azjS1+ax69/vZ/2dgdqtQKvN4DJpGXTpgXExan56U93YbH0EB+vxe8P0NfnYd687FE9wi0jEw1uvXU6Go2CrVtrADFm5EsSErR8+9tLefXV46EqVV6eibvums3MmYNLkAIBQWVlJ21tduLjNUydmipXus6g/wjyzTcrQ9ZCkiRxww2TuP76SedNUJqb+zCZIoc9tFoVbrcfu92L2Rz9pCwpSU9cnDpCPgSgu9s5rBpklzvSmb5mo5Hi4mJRWloa7TAuGavVxYEDzXR0OMjOTmDOnIzQl8Fu97BvXyMHDjSj16tYvDiXGTPSR/20ULQJBAQHDzbzwQc1dHe7mDQpmbVri4bNbFkmdrDZPCELmYFsfUYzHo8fny+AXq8a9Ovq7HTw5JP7qK+30t/vZDRqeOCBBUyYMHy6U6OJXbvq+N3vSsOOIH2+ABZLD1/60nwWLjy37uEjj+ykudkW0T7hdvuwWt38/OcbYqJSBvDee1W88MJhcnNNoV63nh4XdruHH/5wlXzqcAlIklQmhCge8GdyUiZzOSKE4I9/PMT779dgNuvQ6VT09LgIBOCrX13A9Onp0Q5RRmZEEULwgx9sp6XFFqY3ZrW6cLn8PPro2ste8kUIwbe+tRWfLxDxXlitLuLi1Dz88Jpz/v6+fQ385Cc7yc5OIDlZH/JsrKnp4aabpsRUw7oQgrffPsVrr50IaWSmpsZx991XDKsw7OXA+ZIyuSYtc1lSUdHJtm21YV5+BoOGvj43v//9QR57bL1ciZS5rKiu7sZi6YnoIzKZdPT0WCktbWLVqsIoRRcb+HwBWlvtA9pO9YsRDzSYJYRg+/ZaXn75aEjOSKmUKCpKJC3NwKJFOVxzzYSRehkXhCRJXH31BFatKqCpqQ+NRkl2dsJ53WRkLh05KZO5LNm7twGdThWxwMTHa6mrs1JT0x3145pAQGCx9OB2+8nOjr/sqxRjDb8/QHl5O7t31+Fw+Jg1K50FC3JCavsjTVeXMyShcTZarTKk9n45o1IpMJm0OBzeCH03h8NLcrJ+wKSlrKyZP/whqJW4dGk+V1zhpb7eSmenkzvumMmaNcOrgH8p6PXqUavfNxqRkzKZyxKHw3veSpjHM7BMyUhRWdnJM8+U0dnpRJKCu9YNG8axceOUEZukG800Nvby/vs1HD/egcmkZfXqAubOzYqZ987vD/D73x9g9+56jEbNaSPnVv75z5N861tLQ4beI4nJpONc7Sxut4/09JGPKdYIfg/H8+KLRygq+kQkNxAQtLTY+NznZkX8jhCCf/zjBMnJn9gBxcWpmTQphY4OB6WlTaxdWzSir0MmdomNFUpGZoSZMSMNu90TcbvPF0CSpHPaV40ELS22kD9jXl7QviQjw8hrr1Xw5puVUYtruGhvt3PsWBt1ddZzJgWDoby8jYce+pCdOy34/QGamoKWZc89d5BAIDZ6aMvKmti1q47CQjNpaQaSkvTk55vp6/Pwf//3cVRiGj8+iczMeNrb7WG32+0e1GrlJXtPjhXWri1i0aJcLBYr9fXB/1ksPSxdmsfKlZHHux6Pn4aG3gFNspOS9FRWdo5E2DKjBLlSJnNZUlycxRtvVNLY2EtmZjwKhXTa562P66+fOOACOlJ8+GENgUAgTBy431/un/88yZVXjo/w8RyNOBxennvuIKWlTSgUCvz+APn5Ju67b96AxtYXQrACdZCEBG3obxgXp8Zs1rFjRx2LFuWOqOL4ufjgg1oSE/URR1YZGUaOHGmlp8eF2Tyy4tAKhcT998/n8cf3UFvbg1Ip4fcLtFolX/7yPNn94jRqtZL77itm/fpxHD3ahiTB9OlpYZWzM1GpFGi1SrzeQIRiv9vti+paIxN7jP6VXSaq9Pa6sVpdmEy6UbW46PVqvvnNJfz5z0c4dKgFSZJQqxV8+tNTufrq6DbcHj/eOaBbg0ajxOcL0NHhGFJP085OB21tdoxGDTk5CSPS2yKE4Omnyzh8uIXcXBMKhYQQgvZ2O48+upuHH14doY90IVgsVrq7XeTnh1c6FQqJuDg1JSUNMZGU9fW50WojpQ8UCgmFQsLp9I54UgZBz92f/GQNH3/cQlOTjcREHbNnZ8j9jGchSRLjxyddkF2VUqlg1apC3nnnVJjvqhCC5mYbn/70tOEMVWaUISdlMheF3e7hpZeOUlJSjySBELBkSS633jpjyAyOz4cQgvr6Xnp6XCQm6i4qmUhOjmPTpgVYrS4cDi9JSfqYEMk0m7V0djqIP0sGSAhBICDQ64cmRqfTywsvHGbv3kYUCvD7BQUFZu65Z+4laxA5HF6EEOcUZG1q6uPQoZYwI2ZJkkhLM1Jb28OBA80sWTJ4s3iPx8+5PgYqlQKn0zvoxxwOpkxJ5cMPayPeH5fLh1arIjk5LkqRBZP/4mJZmHooue66iVRUdFBd3U18vBYhBH19HqZNS5X7yWTCiP4VSGbUEQgIfvWrjzhxooOcnASUyuDR065ddXR1Ofn61xcPa7Wls9PBb39bSlVVF5IUrLD0m+FejKelyaSLKR/RlSsLOHiwmaSk8EmulhYbU6emDtkF+9lng0eHeXmfVKra2mxs3lzCj3+8+qKSa4ulh7/+9RjHj3cghGDixBQ+85lpFBUlht2vudkWGmA4G51ORXV196CTMiEEPp+f7m4nBoOapKTw40GbzcPMmbGhP7dmTSHbt9eGqswAXq+fxsZebr99ZtSNqWWGFoNBw7e+tZRDh1r46KNGlEoF8+dnMWtWRsyIxY42/P4A+/Y18t57VXR3uxg/PokNG8ZfUPUylpGTsjGEw+HF4fBiMmmH9YteVdXF8eMdYVUOpVJBXp6JY8faqarqHrYvhs8X4Ikn9tLRYScvzxRKympre3jyyb089NCKmJmwu1hmzcpg9epCtm2rRa9Xo1YrsNk8JCbq+dznZg/JczQ391Fa2jRgpcpiCVaqli4dXFLU0NDLww/vPN3/FjxebWzs5eGHd/Dd7y6nsPCTxOx8CZ/X6ycxcXBJcmurjd/8Zj91dVZ6e93885+nyMmJZ+HCHLRaFS0tNlJTDTHjb5mZGc83vrGEZ545QF2dFUkKfoduuWUa69ePi3Z4YXR3O2lpsREXpw5952QGj1arYsGCHBYsOLfiv8yFEQgInnvuINu3W0hOjkOvV3HsWBv79zfypS/NZ/780VvplZOyMUBfn5u//vUYe/Y0IIRAq1VxzTUT2LBh/LAkKDU1PUBklSP476C21nAlZcePt9PU1BvWmyFJEllZ8VgsPVRUdDJ1auqwPPdIoVBI/Md/zGbBghxKSuqx2TzMmJHO/PnZQ6Zh1dxsQ6EYWJNKp1Nx6lTXoJOy11+vQJIgLe0T6YSUlDja2uxs2XKC//7vRaHbJ0xIwmTShVWKIHj8GAiIQR2feTx+Hn98D319bvLyTOTlmUhP7+LQoRbefbeaWbPSueKKTG67bUZM+VtOnJjMz362lvp6Kx6Pn+zshBE5+r9QPB4/f/7zx+zYYUGhkAgEICvLyD33FA9oVC0jM1KcPNnJzp11YeLf6elGHA4vzz9/iFmz0mOiFeViGJ1Ry4Twev088cQeLBYr2dnxKJUKXC4fL798lN5eN7feOmPInzPY0zSwtIAQ0rBOBjY1nVvAUghoaekb9UkZBBOzKVNSmTJleF6LXq86p/yEx+Mf0DT5fAghKCtrIisrshctJSWOI0da8Xr9oQquWq3kS1+ax+OP78FqtWIwqHE6ffh8Af7jP2YOavry2LE22trCVdYnTkxm3LhEKis7+fKX58Vsj5RCIYVtMGKJP/3pY7ZvryUvzxTa3HV2Oti8eTc//vHqmDryl7m82L+/Ca02Uvw7Lk5Ne7udqqruUXsdkJOyUc7Ro21UV3eHHQ3pdCoKCsy8+24VV145ftB9Vn5/gP37G3n//aBR98SJyaxfPy500Zs+PQ2FQoHH4w/rfXG7fbjdPmpre6ip6WHChCRmz84Y0h1LfLzmPFpWAqPx3MlEsOcogFKpuOytQiZMSB6wUuX1BitVF1P+V6kUBAIC5Vkn50KI0FTh2TE88sgaSkrqqanpITXVwOLFOYPWiGto6EWpjPx7KpUK9HoVTqdv0K/lcqery8nOnRby881hf7fk5Dgslh727Wtg/frxUYxQ5nLG7fYP+J2H4MmJzxcY4YiGDjkpG+UcOdI2YGVKqVQgSRIWS8+gkrJPzurrSEoKGnWXlTWxd28DmzbNZ/bsTBIT9dx550yef/4QOp2K+Hgtvb0uqqq6UauVvP9+DWq1gq1bq0lLM/Dgg0tISRma5vSZMzPQaFQRNid2uwetVsW0aQPvjg4fbmHLlhPU1vag06lYs6aQq6+eEFPHWSOJSqU4Z6XqtttmkJ09OMkNSZJYujSPDz+sjUiqWlpsLFyYM+BRusmkIy/PRF2dldrabgwGNUajZlCaWCaTDr9/4ERdkqTL9m98KbS02JCkyEQawGjUUFHROaqSssrKTv7616PU1/cyYUIyGzdOlq2DRjEzZ6axc2dtxO1B8W8G9CYdLchJ2ShHq1Wd84IEDNpUu6Ki4/RZ/Sc7ZL1ejd3u4dlnD/LEE2mo1UpWrSokN9fEtm01NDT0YTZr6ehwMmVKStiQQXNzH88+e4BvfnPpxb3AszAaNdx3XzG//vV+OjocpyshXpRKBfffP3/AC/CuXXU8/XQZiYk68vNNeL0B3nrrJOXl7XzrW0tHbe/BpTJhQjI/+UmwUlVd3U1KShxLluRe9HHaNddM5ODBFurqekhLMyJJ0NZmx2DQcMMNkyPuHwgIXnjhEB98UIPBoEGrVVFR0cm//nWKb35zyQVXzGbPDk6wnZ2o9/W5iYtTj9pjjGgSF6c+Z0Xa5fJFRUPtYnnmmTI2by5BCFCrFWzbVsvf/naM731vOddeOyna4clcBLNmZZCfn0hdnZWsrPiQ3E1TUx833jh5VGlmns3leTUaQxQXZ/LPf1YSCIiwXa3T6UWjUQ7aVHvfvsYBjboNBg2dnU6qq7uZNCkFIEw88dlnD5Ca2hsx9ZmRYeT48Q7a2uxhDeCXwpw5mTzyyBr27m2gqamP7OwEFi7MGbAa5/H4eemlI2RmGkNipBqNkoICM9XVPRw82MzChblDEtdoJClJz7XXThyyx/re95azdWs1u3bVIUTQkmbdunED/m2OHWvjgw9qKCj4pFnXbNbR3m7n2WcP8tBDKy5o0i8hQct99xXzm9/sp6PDjk6nPv35V/GVrywYE+4HI01eXtDaq7PTESbB4vMF8HoDF6UhFw0+/riVxx4rITFRF/r+CyHo6nKyeXMJxcXZF+0eIRM9NBolX//6Iv72t3JKSuoRQhAXp+bOO2eNet03ebUaJk6e7OTtt09RVdVFUpKedevGsWBB9pBPQxYVJbJ6dSFbt1aTlKRHr1fT0+PC5fJx773Fg74gud2+c8bY35M1EO3tjgEV2CVJQqmU6OtzD1lSBpCaauC66/79Lre+3orT6RvQ4Dk+XsO+fY2XYrJhZAAAIABJREFUdVI21CQm6rnllmnccsu/VynfsaMOo1EbsQFISQn2LbW02C5YxPaKKzL56U/XsndvA62tNnJyEpg/P1u2BrpIFAqJe+8tZvPmEiwWK0aj+nTPaICNGydTWDg6jodefPEIkkTY2iRJEmazjpYWG3v21LNx45QoRihzscTHa7nrrjnceut0nM6gXdVgT4ZiETkpGwb27KnnqadKMRg0mM06urtd/Pa3+ykvb+fuu+cMqc6PJEnceecspk5N5d13q+nqcjBzZjpXXjnuonomZs5MZ8+ehojbfb4ACoV0zlH4ceMSOXmyM+JYw+8PIARD1lMmM3bo7XUNKJLa38s02Ab9lJS4Iav6yUB+vpmHH17Nnj0NVFZ2kpioY/Hi3HN6PMYijY2R1XsgtPFsbbVd8nMIITh1qosdOyx0dDiYMCGZpUvzhnQTKnNu9Hr1RVmyxSpyUjbEuFw+/vjHw2RkhB+XJSRo2bnTwooV+YM+Uvx3KBQS8+ZlM2/epY/9z5mTSXZ2PPX1wbP6fomNxsY+Nm6cfE4PvGXL8nnnnSpsNk9IS0sIQV2dlZUrC6I2Pp+XZ8JgUEf0GwVtTtwsWiQLOUaLadPSqKw8HpHIe71+FApJPlaKAUwmHRs2jGfDhtHT1H8m48Ylsn9/E0KIsEQyEBAEAgyJD+obb1Tyyivl6HQq9Ho1lZXBvsivfW1RqNVDRuZCGf21vhjj1KkuPB5/ROauUEhoNEpKS5uiFNmFodOp+MY3ljB/fjaNjX3U11ux2TzcfvsMbrwxslm7n4wMI1/5ygJcLh8WSw91dT1YLFYWLswZFq20C0WtVnL77TNpabHR2elACIHL5aO21srEicnMnp0Ztdgud5YuzSMuTkNHhyPUVO71+qmrs3LVVRNiSkhVZnRyyy3TiI/X0t3tCn3GAoEAra02Jk1KvmR1/bo6K6++epzcXBOZmfGYzTpyc00YjRp++9tSvF7/ULwMmcuIEa2USZK0Afg5oAR+L4T46QD3+TTwPwTVSQ8LIW4byRgvlWDP1cBTS0qlArc79r+kZrOOe+4p5o47ZuJweDGbdRdk2zR9ejpPPHEllZWduFw+cnISYqLasXBhDgkJWv7xjxOcPNmJwaDhppsms379ONljMIokJen55jeX8MwzZdTVWUNaZhs3TuaGG+SpOJnB4fcHqK3tweHwkpkZT0pKHFOnpvLtby/lscdKQsLTQsCkSck89dQ1l/z937+/EZVKEdHLlJCgpa7OyqlTXcMmAC0zNhmxpEySJCXwa2Ad0ADslyTpdSFE+Rn3mQD8f8ASIUS3JEmXXlseYYL6KEHxurO/qC6Xl1mzYsMQ+UIwGDSD1njSaJRMnx57f7apU1OZOjU14hhDJrrk5Zn44Q9X0dTUh8vlIyPDKOuKyQyamppufvObUjo67CgUEn6/YOnSPO68cyYbN05h2bJ8Skrq6Ox0MmVKKgsWZA/JOtDb60atPveBk8slCxfLDI6RrJTNB04JIaoBJEl6GbgBKD/jPv8F/FoI0Q0ghGgbwfiGBLNZx9VXj+e11yrJygr2lfl8ARobeyksTIzJhOVyQk7Izk9zcx/bt1uore0hPd3AihUFFBUl/vtfvAQkSRq0WK2MTD/d3U4efbQEjUYR0tgLBAS7dtWhUEjcddccUlLiuP76c7df/DucTi+7dtXx4YcWPB4fc+dmsWZNIRMnJrN9uyXi/oGAQAghf65lBs1IJmXZQP0Z/24AFpx1n4kAkiTtJnjE+T9CiH+d/UCSJH0B+AJAXl7s6eXcdNNUEhJ0vPFGBR0dDhQKiZUrC7j55qkXdAx4NoGAoKqqi64uJyaTjgkTkobFaFzm8ubw4RZ+8YuPkKSgXEhNTTfbt1u47bYZrF8/LtrhycgMSElJPW63j/T0TybDFQqJ5GQ9L774MX19biZOTGbhwpyLkkhxOr1s3lxCVVUXKSlxKJUK3nuvip07LXz1q4tISYmjpaWP9HQjkiTh9weoq7OyZIk8gSkzeGJt+lIFTABWAjnADkmSZgghes68kxDiaeBpgOLi4nPL2UcJhUJi/fpxrF5dSF+fG71efdEClu3tdn75y4+or7ciSRJCCNLTjWzatGBA82cZmYvB5fLx1FOlJCXpQseHJpMOrzcovjtrVjrp6dHvD5SROZvKys7QxHc/zc19fPRRE3a7m717G0I2a1/5ygKmTh3cacXOnRZOneoKqxjHxZlobbWxZctxHnxwCc8+e4DKyi6USggEYPXqQj772elD8vpkLi9GMilrBM5U6cw5fduZNAD7hBBeoEaSpEqCSdr+kQlxaFGpFJckXun3B/j5z/fR0WEPs75pb7fz+OMlPPLIWrlRXWZIOHGiA5fLF5F4qdVKJEniwIFmrrpqQpSik5E5N8nJcRw/3g4E11q320dpaRN6vRIhNGRkGDGZdPT1ufnlLz/iiSeuHJSu1fbtFlJTI3UW09IMlJe3o9Op+Na3ltLaasdm85CaGhc1CSCZ0c9InoHtByZIklQoSZIG+Czw+ln3+QfBKhmSJKUQPM6sHsEYY4qKik4aGnrJyAiviKWmGujsdPLxxy1RikxmrOFy+TiH1eFpRwbPyAYkI3OBLFmSi9sdCLmNtLTY8PsFXm+AhARtyAcxPl6L0+mjvLx9UI/vdvsHVIrv708NmmAHdfXGj0+SE7Lz0NnpoKmpD48n9lUIosWIVcqEED5Jkr4MvEOwX+wPQohjkiT9ECgVQrx++mfrJUkqB/zAN4QQnSMVY6zR0eE458+USonm5ktXo5a5dE6c6OCdd6poaLCSmRnP+vXjmDYtdVQNFWRnBxP/gaZTvd4AEyYkIYSgpcVGb6+b5OQ42aVhDOF0eikpqWf7dgteb4B587JYubKApKTYt6kqKkrkU5+awt//Xo5araCtzY7d7iEpSU9xcVbE59lmG9wG44orMnj//Rpyc8PdTHp73aSmGkaVOXu0aGrq44UXDlNR0YFCIaHTqbjxxsmsXVs0qtbJkWBEe8qEEP8E/nnWbd8/478F8N+n/3fZEx9/bmkAvz8g+/rFAO+9V8Wf/vQxBoOG+HgNVVVdPPpoM5/+9PRRZfmTk5PAnDkZHDzYQm5uAkqlAiFEyPA9M9PIo4/u5sSJDpRKBX5/gHnzsvnc52bJEhajnP5G9lOn+hvZJd58s5Jt22r5zneWxYTW4PmQJIlrr53IjBnp7N1bz5Ejbfh8AebOzRqwvWOwvZFr145j58562trspKbGIUlBL9+ODgebNi2I8G49F16vnxMnOrDbvaSnGygoMF8WCUlPj4tHHtmFz+cnL8+EJEm4XD5eeOEwgYDgyitHp1vEcBFrjf4yZzB1aioJCVqsVldYSdxm86DTqZk9OyOK0cn09Lh46aWjZGcnhBZ/vV5NYqKeV14pZ8GC7AGN0GMRSZL4whfm8uKLR9i9u/601lOAGTPSueOOGTz55D46OhyhRTUQEJSWNuFwePna1xZdFheXscr27RaqqsIb2Q0GDc3NffzlL0d54IGFUYzuwpAkiYICMwUFZm6+2c/3vreNri5HaCJSCEFzs438fDMTJw7O5i4tzcC3v72UF188yokT7UiSREqKnk2bFlBcnHVBj3HqVBe/+MU++vrcQFDAduLEZL70pXlj/rhz1y4LdrsnzDdZp1ORk5PAa69VsHJlAVqtnIr0I78TMYxWq2LTpgX87//uwWLpQaNR4vH40WiU3H///IiJI5mRpby8nUBAROzG+/tPjhxpY/XqwmiEdlHo9WruvvsKbr55Kp2dDhIStKSmGjh4sJmmpr7TwshBFAqJ3NwEjh5tw2Kxhv1MZnTx4Ye1A24e0tONHD7cit3uGVXVULVayde+tohf/3o/tbU9KBTBTcTEicnce2/xBVe2ziQ318Q3v7kEq9WFzxc8pbjQx7FaXTz+eAk6nSo0sCWEoLq6m9/9roxvfGPxmN7UHDzYgskU6Zms1apwuey0tNjCBtkudwaVlEmSlAhcBeQBYd9SIcQPhzAumdOMH5/Ez362jrKyJpqaglo4c+dmjvnd1WjgfJZaEJwCG42YzbqwPpmamp4BVcslSUKSiEjYZEYXLpdvQMkeSQomD15vIApRXRqpqQYeemgFFouVnh4XSUl6cnMTLjn5uZh196OPGiMmm4OCyfGUl7fT2NhHTs7YFZk1GNS0tdkjbhdCIASygsBZXHBSJknSQuAtwA2kEpSzyDz971pATsqGCaNRw4oVBdEOQ+Ys8vNNgBTRHN+/2IwfnxS94IaQ+HhNaLJtIPR6ueA+mpkzJ4OdO+siEoO+Ps9pOYnIKsdooP9IM9rU1VkHlOCQpKDXa0eHY0wnZcuW5XPwYCtJSfqwdbKry0lu7uD8kdvb7XR2OjGZtGRkGMdkhXEwq+lm4M/AA0AvsBqwAy8Bzw59aLGPEAKHw4tGo7wopX6Zi8Pl8mGx9CBJEvn5pqj1I+TlmSguzmL//kZychJQq5X4fAEaGnqZPj1tzCRlc+Zk8tJLR3G7fWHvtd3uQa9Xy4bLo5z168dRUlJPR4eD5GR9WCP7Aw8sGJMXvpEkLc1wTg9MIcR5B7rGAnPmZDJ/fhYffdREYqIOjUZJd7cTtVrJXXfNuaDPl83m4fnnD1FW1oRSGfQ2nTQphf/6rytITh5bU+CSOJc40dl3lCQrME8IUSlJUg+wSAhxXJKkecCLQoioKEsWFxeL0tLSEX1OIQT79jWyZctx2tsdKJUSy5blceONU0KaODJDjxCC7dtrefnlYyGdG51OxW23zWDp0ujYbbndPrZsOcEHH9Tg9wdQKCSWL8/nU5+aOiiByljnww9reP75w6hUCgwGNTabB0mS2LRpPjNnygMno53a2h7+9KePqarqQpIkkpL0fPaz0y+4kV3m3NTVWdm06W1MJi1paYZQf157ux2zWc+PfrTqovrcRhM+X4Cysia2bavFZvMwc2Y6q1YVXNAglBCCzZtLOHGinZwcEwqFFJLnSU6O4wc/WDnqiiKSJJUJIYoH/NkgkrJ2YMnppKwCeEAI8S9JkqYQ1BmLyphZNJKybdtqeO65Q6SmxhEfr8XnC5yWDojnO99ZftGWSjLn58CBJp58ch9ZWfGh99jp9NLcbOPBB5dE1ezd5fLR2+smPl4zppKxM6mrs7Jzp4XWVjv5+SaWLs27ZOulzk4HbW12DAbNkPT8yFw8Qgi6u4ON7CkpcWM+URgJysqa+P3vD1Bf30tlZQeBQFATMD/fTEpKHF//+mLZLu/fUFPTzQ9+sJ38fFPE+lBb28MDDyxgzpzMKEV3cZwvKRtM9nAAmAdUAh8CP5YkKR24A/j4UoMcLbjdPv7yl2NhiYFKpSAvz0RtbQ+lpU1Rq9qMZYQQbNlygqQkfVjSq9erMZt1vPbaiagmZTqdaswn43l5Jm6/feaQPJbL5eP//u8wJSX1KBQKAoEAeXkm7rmnWL5IRYn+CtnljtXq4tSpLoBLUui3WHr41a/2k5oaR3FxFtOmpdLQ0Et9fS+TJiXzta8tjvqa4fX6sVisBAKCvDxT1OMZiKamPiSJATdsKpWCmpqeUZeUnY/B/AW+A/Svlt8FXgB+STBJ+39DHFfMUldnxePxD/jhjY/XyEnZMOHzBaiv7z3dXB9OYqKOU6e6BlSjl4lNnn/+EHv3NpCX98lxRHu7g0cf3c3DD68eVRIMMmMDIQSvv17B669XEAgET5AUComNG6dwzTUTBr22vP9+DWq1gri4YOVcr1czYUIyBQVmLBYrF3pKNVyUljbxxz8ewm73AqBWK/j0p6exenVhTK2j/e/fQPj9gTHXMnTBSZkQovSM/24nKI1x2aFUntsuNBAQA0oHyFw6SqUCvV6Nx+OPaOx3uXzEx2tjaiGROTetrTb27WsIO46QJIm0NEOo2ixPG8v8O4QQVFV1c+hQM36/YMaMdCZNSj7vGn0+9u5t4O9/Lyc/3xzSGvR6/fzlL0dJTY1jwYKcQT1eVVXXgJOrarUSvz9Ad7craq0OFRUd/PKXH5GWFhdqlHe7fTz//CGMRs2gX+twMmVKKnq9OkIvz+32oVBIzJkztnpa5QxikOTlmYiP12C3h/unCSGw2TwsWJAdpcjGNgqFxNq1hTQ394XtMPuVutevL4pidDKDobnZdlrjLDKJjotTc/LkZWt3O6oIBAQWSw8VFR0hpfqRwu8P8PvfH+BHP9rB22+fYuvWan72s1088cSec046no/+KllqqiHMfFytVpKSEscbb1QO+jFTU+NwOLwRtwcCQckcgyF6vadvvnkSo1EdluRotSpSUw1s2XI86lW8M9HpVNx3XzHd3S7q6610dTlpbOylpcXG5z43e8xNXw5Gp6yG8yhlCiEui6uiSqXgc5+bzS9+sQ+73UtSkh6320drq42ZMzOYNWtsZe2xxFVXTeD48XZOnuzCaNQgRFCWYdq0VNauHRft8C4ZIQQVFZ3s2GGhu9vJxInJLFuWP6zG3/2L70hWGePi1Odc9D0ev+zpOgqoqenm6afLaGmxo1AEPz/r14/jppumhCU1w8XOnXXs2GGhsDAxNJAghODo0TbefLOCT31q2qAez+8XEcryTqeXzk5naNIvEBCDGn5Ys6aIAweaSUrSh1XvGht7KS7OiqoAeEVFB+npkbN5CQla6uqsOJ2+8x4bjjQzZqTzk5+sYdeuOiyWHjIzjSxZkhdhEj8WGExP2a9O/78E/Bh4Argst7RXXJHJd7+7nDffrOTEiQ7i4zXcccdMVqwoGHWjuaOJuDg1Dz64lMOHW9i/vwmFAubPz2HGjLRR/74LIfj738t5881K9Ho1Op2KyspO/vWvKr7xjcX/VvOsq8uJzeYhOVl/Qf1YLS023nijgn37GgFYsCCb666bNCLm0+PGJZKUpKenxxXmHOD1+vH7A8yfL1ebY5nOTgePPlqCWq0I9Xj6fAHefLMShYJBJ0QXw7vvVpGWZghLkiRJIisrnvffr+HGGweXHCqVEvHxWpxOLzqdihMnOqis7ESI4GsTQvDqq+XcfPPUC97ATJ+exnXXTeKtt06iVEqoVArcbj+5uSZuv33GoF/zUJKQoMXl8kWsFV6vH5VKMextOP0anyqV4oJ1JtPSDNx005RhjSsWuGBJjLBfkqQ+YJYQonroQxoc0ZDEkJEZak6d6uJHP9pOXp4pbFfd0+NCpVLws5+tHbBXprvbyR//eJjDh1tRKgEk1qwp5Oabp57TvqS5uY8f/3gHXq8/JGnR0mJDo1Hyve+tGJHErKqqi82bS3C7fRiNGlwuHx6Pn898ZjpXXx0VyUOZC2TLluO88UZlmME0BC/obW12nnxyw7APatxzzxukpRkG/E5YLFZ+9aurBh3DW29V8pe/HEOlkigtbcZs1iFJ0N3tYvr0VJRKBffcUzyoQS4hBHV1VsrKmnE4vEydmsr06WlRtxZ6551T/PnPRygsNIclmRZLD2vWFHHHHUMzZT0QBw8288orx2ls7EOhkFi4MJubb556WU3+DpUkhozMqKS3143X6x+UifBIs2dPPVqtMuIiYzbrsFis1Nb2MG5ceLXM7faxeXMJbW12cnMTUCgkfL4Ab799Crvdy+c/f8WAz/X66xV4PH6ysz+xdsnOTqCxsZfXX6/gC1+YO/Qv8CzGjUvikUfWUFJST1VVN0lJepYsyaWwMHHYn1vm0jh+vOOcDeyBgKC11U5R0fAmZfn5ZlpabBEXcrvdg9msvagG+nXrxlFV1c0zz5QhRFBF3u8X5OQkMG5cEg6HlzfeqBhUUhZ0HTHHnOH2ypUFHDnSxpEjrRgMGhSKoItDQYGZG26YNGzPu29fA7/+9X6SkvTk5SUQCASF2CsqOnnooRXEx4+tScqLQU7KZMYsTU19vPjiEY4dawtpMH3609Ni8nist9eNRjPw11GhAKczsnn5449bI8zAVSoFBQVmdu+u4/rrJ5GWFt430r8IZmdHaoFlZBjZu7eBz3/+ihFJXhMT9VxzzcSL/n0hggmAzxcgPd0w6o+wRwtms46Ght6I24UQBAJiRHqRrr12Io89VoLRqAlVnXy+AC0tNv7f/5t9UZ9fjUbJF784j23balAqFUhS8DvR79kYH6+ltrYHvz9w0ROesYJWq+IrX1nIkSOt7NnTgN8fYN68LObMyRw22zq/P8BLLx0lPf0TVwOlUiInJwGLpYfdu+vZsGH8sDz3aGIwjf6/OOOfGuCh09ZLAAghNg1lYDIyl0Jnp4Of/GQnPl9QlLTfz++Xv/yIL395XkyNfENw7Lu0tCmiqd/vDxAIiAGTqBMnOtBqIxMRhSI42Vhfb41IysYKVVVdPPfcIRobg8KSBoOaW26ZyrJl+bI0yjCzfHk+e/c2RKj+t7c7KCpKHLCBfKiZMSONO++cyV/+cgyfL3B6mhduuGESK1cWXvTjKpUShYWJKJVSxPGnw+HFbNbFbLV9sKhUCubMyRwx4dWWFhu9ve6IY28IbtD27WuUkzIGVyk7szOxBDizhhs787MyMsCHH9bidHrDpnP6tcz++tdjFBdnxdRud/78bN54o4LWVhtpaQYkKXgUWVdnZe3aogEnEg0GDT5f4ByPKAbc8SoUEvPmZXHwYEuEcn5rq53587Nj/qLT1NTHz362G51ORV5e0JrJ6fTyzDMHUCqVLFmSG+0QxzTTpqWybl0RW7dWo9er0WiU9PW5iY/XcvfdV4xIUixJEuvWjWPRolxOnuwkEBAUFSVe1OSuEAKLxUpfn5vUVANXXTWeF144HDHZ2dzcx513zpST/otEqVQgBAOKfAcCApVKfl9hcOKxq4YzEJnRQ1eXkx07LBw+3IJOp2LFinzmzs2KqeOj0tLmARtHjUYNdXVWOjudMVVFMho1PPjgEn7/+wNUV3eHFrANG8Zzyy1TB/yd4uIsXn+9Ap8vEDZpZrd7TquHDzyxecMNk0NHn/1VjdZWO0qlxPXXD18/yVDx3ntVBAIi7O+r16vJyDDyyivlLFyYHVMJ91hDkiTuuGMmxcVZ7N5dT2+vm+nTU1m4MHfE1dWNRs0lVXqam/v47W9LqauzolRK+P2CmTPTmDcvm7KyZpTKYKIQCAiWLMlj9erhV35yu30cPNjMwYMtaDRKFizIYcqUlFH/mU5PN5CRYcBqdYdNXUPwmrJx4+QoRRZbXNLhsSRJiUKI7qEKRib2aWzs5ZFHduFweElM1NHZ6eQ3vyllzpwMvvzl+TGTmGm1SlyuSOFGIQRCiBHRUhosmZnxfPe7y2lpseFweElLM5y38TU3N4Hrr5/EP/5xAoNBTVycmt5eN36/4IEHFpyzNyQrK57vfW8F//jHCUpLm4BggnfjjZNHhe/kkSNtJCcPXDmsq7PS0+Mac4KSsYYkSUyZksqUKanRDuWicTq9bN5cgsvlC7lL9GudTZ2aykMPLefYsXYApk1Li5hUHA56e91s3lxCfb0Vg0GN3y/YscPCggU5fOELc2Ny3bpQJEnic5+bzebNu3G5fCQn6/F6A7S22hk/PinmWkqixWB6ysYDTwFKgl6Xm4GbJUmqBa4RQhwflghlYooXXjiM3x8I6wtITNRx8GAz+/c3sXhxbBwdrViRz3PPHYoQaOzocDB+fBKJidETbjwfkiSRmXlhiZEkSWzcOJnJk1PYtq2Wjg47s2dnsGpVITk5Cef93ayseL74xXn4/cHjz9G0C++3XNGflZf1K6VHW25AZnRw8GALXV3OsEEZSZLIzTVx9Gg7n/nMdK67bmQrx3//ezlNTb1hMQkh2LOnnhkz0li2LH9E4xlqJk9O4fvfX8Fbb53kyJFW9Ho1t9wyhdWri2LSDD0aDOZd+AXgAZqBN4BTwGzgYeCnwA1DHp1MTNHV5aSysjOiUVOSJBIT9Xz4YW3MJGWLFuVSUlJPZWUnKSlxqFQKOjudqFQK7rhj7PSFSJLE1KmpTJ16cRWL0ZSM9bN6dQHPPXeI+HhN2N+xtdXGrFnp8li9zAVRU9M94KBM/9BAS4ttyBTjhRB8/HEr779fTWenk/Hjk1i7tijs8V0uH7t310VsyiRJIiUljvfeqx71SRkE5Uy++MV50Q4jZhlMUrYYWAJUATbgbiHEx5Ik/QB4aziCk4kt+g1gB0po1GrlgD5v0UKnU/G1ry1m9+46tm2rxeXysXJlAevWFYUEU2VGJ4sX51Ja2sSRI22YzTpUKgXd3U5MJh233jr9vL/r8fgpL2/Hag0ecU6enDKqj4RkLh6tVkV7uwONRjmghuFQSXsIIXjxxSO8804VCQla9HoVe/bUs3NnHQ88sCBkzedy+RCCAT+Per0aq9U1JPHIxDaDSco0QJ8QwiVJkhPoOH17GxD7jSgyl0xKShxxcWqcTm+EOGN3t5Mrr4ytcWadTsWaNUWsWXNZ2LJeNmi1Kh54YCGlpU3s3GnB7fazenUhy5blnddPsKammyef3IvV6g5NgKWnG/jqVxeNiIuBTGwQCAjefLOSLVtOcORIG6dOdaHXq5gzJ4OMjHisVhcJCVomTkwekuerru7m3Xeryc//xK3DYNBgt3t4+uky/vd/N6DRKImP15CQoMXh8EYkhN3dTqZNSxuSeGRim8FsEWuB/qvb1NP/BsgFGoYuJJlYRa1WctNNU2hs7MPlCoqZCiHo6AjuNletKohqfDKXDxqNksWLc/nmN5fy/e+v4NprJ543IXM4vDzxxB4kSaKgwExhYSIFBWb6+jw8+eTeUG+dzNjn/fer+dvfjlFQYGLJklwUCgm73cP27RaOHWvD6fQN6dDS/v2NaDSKiFYBg0GD0+nl1KkuINhKcMMNk2hu7sPr9YfuZ7d7cDp9XHVVbG16ZYaHwVTKvgjUAwgh6s+4PRl4ciiDkgnSr1je71EYCw3MK1cWEAgIXn31OG1tdoQQFBSY+c//nE1qauxITMjInMnhwy309XnCGqghaHJssfRUyospAAAgAElEQVRQUdF50X15/47ubidHjrTicASn/CZNSqG93c6//nWKjz5qQqWSWL48n7Vri86bWMpcOj5fgNdfryArKx61WklBgZmkJD0NDVaamvrIyDDywx+uuii9s3Nht/vOe0Tu8XySgK1cWYDN5uH11yvw+4PynwaDmvvvnx9hsyYzNhmMTtmH57j99SGLRiZEdXU3zz13kIaGXiRJQq9X8alPTWXlyoKoNqlLksSaNUUsX55Pa6sdjUZJamrcmGmcl7l0HA4v1dXdSBIUFSVelA/hUNPSYjvvhbGryzksz7tjh4UXXjgUUp0XAlJT4+jpcSGEIC3NGDpO27u3ke98Z1mEhpPM0GG1urDbvWGSKQkJWqZOTaOoKCnkkTuUTJ+eys6dlojb/f4AQhA2OCVJEtddN4nVqwuxWKyoVAoKC80xIzUkM/wMRhJjYHfj0wghDlx6ODIQvID87Ge70GiUIYsgp9PLH/5wEKVSwfLl557ACQQEbrcPrVY1rMrsarXy38ouyFxeCCHYurWav/2tPOQ0oFIpuPXW6VHfTKSmGs7jfsCwJEIWSw9/+MNBMjONYZpx//rXKZTKoCJ9P/n5ZiyWHrZureZTnxpYLFjm0tHpVEgSA/pXuly+AQWnL5XZszPIyoqnoaGXrKx4FAoJj8dPQ4OVK68cP+BzGgyaYavcysQ2gzm+LOUTO6WzV1dBUL9MZgjYurUav1+E7eb0ejWZmfG8+upxFi/Ojdj1+/0Btm2r4a23TtLT48Jo1LBhw3jWrx8n77JkRoTS0iZeeOEwubmm0FG72+3juecOYTbrRsxjbyBmz85Ar1fR2+sOU53v7HSQlKRn8uSUIX/O7dstqNWKsITM5wvgcnkRQopo6E5PN7Jjh0VOyoYRg0HD/PnZ7N/fSE7OJxUqIQSdnc5zumdcClqtim98YzF//vMRDhxoRqEI9o9t3DiV666bOOTPJzO6GUxStougLtlPgReR/S6HjSNHWgfcPcXFqensdNDVFWkR9NJLR3nnnSoyM43k55txOr385S/HqKuzcu+9xfLxosywIoTgtdcqSEmJC+t91GpVJCbqeP31yqgmZUajhq9+dRFPPrmXri4nCoWE3x8gMVHPV7+6aFhkMRoaejEaw02thRCAhEIRrMycmZT1xyQzvHzmM9OprbVSW9sTUs13OLwsXpzDwoXDoyqfmKjny1+eHzo+TUrSy2KpMgMymJ6y5ZIk3QQ8AtwIfFUIsXvYIruMMRg0dHc7gfBenKBFEBGCh62tNrZuraaw0Bw6stTr1RQWmtm3r5ENG8ZTWJg4UuHLXIb4fAEaGnrJz48U2zSbddTUdA9oRDySTJyYzGOPrefw4RY6O51kZhqZPj3tnHZUl0pubgLV1d1hzfsqlQKzWUdrqx29Pvx529rsLF2aNyyxyHyC2azj+99fTllZM4cPt6DVqli0KIcpU1KHteUDwGTSycMcMudlUKuREOJVSZJeB74E/EOSpJ3Ag0KIU8MS3WXKqlUFPPNMGQkJ2gjF8mnTUiO+1P0j1WcvKP3K1MePt8tJmcywolIpMBg0uN3+iAqAy+XDZNLGRLU2Lk7NokUj4zqxcmUBH3wQFC4+8z1JSYnD7fbjcHjR6VQIEUzI1GolGzbIsgcjgV6vZunSPDkJlok5Bl2zF0L4hBA/B8YTtFoqkyRJlsQYQhYuzGH27Exqarrp6nLS2+umrs6KVqvi9ttnRtz/313sLsRKx+320dvrJhCQT6VlBo8kSaxbV0Rzc9/pI7ogQgiam21hTe2XC7m5Ju65Zy6dnQ5qa7upqenGYrFSXJzFb35zNRkZRurqrNTX9zJ5cgrf/e4yWcRWRuYyZzDTl30M3EemA+4HvjJUQV3uqNVK7r9/PgcONLNjhwWn08fq1QUsXZo/4JTYpEnJSFLwCOnM3phAQBAIiPMqQff2unnllXJKSurx+wVms46NG6ewdGluTFQ2ZEYPGzaMp6Kig/Ly9pAMhtPpZdasdNatuzxdFRYuzGHatFSOHWvH5fKRk5PAuHGJSJLE7NkZOBxeFAopJmRDZC5vurqc7Nxpoby8nYQEHcuX5zFtWtqwH+nKhCOduas97x0l6T85T3O/EOKPQxTToCguLhalpaXReOqY4tVXj7Nly3GSk+NISNBis3lob3ewbl0Rd945sAG3y+Xjxz/eQVNTH1lZ8ahUCmw2D62tNm6/faZ8lDJMCCGwWt0oldKYM8/2+QKUl7dTWtqEQiExd24mU6emjkrjcxmZywWLpYef/nQ3brcXk0mH2+3HZvOwZk0hd945S07MhhhJksqEEMUD/Wwwjf7PD1lEo5DWVhsWixWNRsmkSckxt7PduHEyOTnxvPXWSRoaeklPN/KFL1xBfr6ZLVtOYLW6mDAhmSuuyAxNfJWVNdHQYKWg4JN+M6NRg0Zj4tVXj7NiRX7Mvc4LJRAQdHU5USolzGZdzFT9jh1r4+WXj9LQECw8T56cwq23zggTkBzNqFQKZs5MZ+bM9GiHIiNz2SKE4MiRNv75z5PU11tJSzOwYcN45s3LjkiwhBA8/XQZKpVEevonjhcpKXG8/34NxcVZsu/mCDKY40s/kCmEaBvGeGIOj8fPCy8cZteuOvqv6xqNkrvumsOCBcMzPn0xSJLE/Pk5zJ//SUxvv32S739/G0qlhEajZPt2C0lJeh58cAkZGUYOHmzBaIys1Gg0Sny+AHV1ViZNGnr9puFCCEFDQy8ffdTEhx/WYLd7EUIwblwSt98+I+rDDuXlbWzeXILZrCMvLyi8a7FYefjhnfzP/6wgMzM+qvHJyMiMTny+wOlhEQUpKXG8804VL754BJNJi9mso7vbxa9+9REbNozntttmhG1S6+qsHD/eQSAQoLa2h/R0AxkZ8aeHd9Ts2lUnJ2UjyGCmL2Oj1DDCbNlygh07LBQUfCI34XR6eeqpUjQaJZ2dwUb8wkIz06enxYxQa01NNy+/fJScnISwmFpbbfzud6V8//srUKnOr4s0mo6c2trsPPVUKWVlTRw71o5GE/S1mzUrnZYWG488souHHlpBdnZ0XAiEEPztb8dJSNCG9QWmpRlobOzl7bdPcdddc6ISm4yMzOhECMHOnRb+9rfy0CY0MzOeqqouxo9PCq39ZrOS+HgN771XzfLl+eTmBivzfn+AP/3pMEeOtJKQoEWhkKir6yUhoYslS3LRaJT09rpH/HV5vX4qKjrp63OTmmoI9WFeDgxWoOeyGs1zOr28/341ubkJYSVfvV5NZ6eT++9/mwkTklAqFfh8ATIyjHz964tJSYk7z6OODLt21aHRKCOSxLQ0AzU1PTQ29rFoUS4lJQ0R+lF2uweDQR1h3hyruN0+Nm8uobfXRXe3i5SUOLRaJc3Nffj9ARYuzKG5uY9//esUd999XrewYcPh8FJb2xOqkJ1JaqqBsrKmMZmUeb1+Dh9u4dChFpRKBcXFWXKPWYzQ2NjLzp11NDb2kpNjYtmyPLKy5GrtaGLXrnqeeeYAmZnxJCfHIYTg2LF2jhxpJS/PFLb+K5UKFAqJjz9uDSVlpaVNHDnShtGowWDQoFBIGAxBj9Bjx9pISYkb8SpZTU03P//5Pnp6XEgSCBH00L3//vlD7ksaiww2KSs9fYwZgRBizI1XdXe7CAQCEYlNb6+b6upu4uM15Od/krg0N/fxu9+V8u1vL4t6Vt/R4RiwH0ySJJRKCZvNw7Rpacydm0lpaTNpaXHodCq6upw4nT7uv3/+sKicDweHD7fQ0WEnMzOe3l53SBMrIUFLa6ud3l43KSkGDh5siVqMSqUitMCc/dHw+yM/Y2MBu93D//7vHk6e7CIuTo0Q8OGHtcydm8V99xWPydc8Wti7t4Gnny47fRFWU17ezjvvnOK++4qZNy872uHJXAB+f4BXXiknI8MY6hOWJAmTSUsgADU1PUyfHp5Q9U/p97N1aw3p6Ub8fqio6MBk0qJUKjAaNZw61UV2djxLloyMrh+AzebhscdKUKuVoaKAEIL6eiu//vV+vvOd6F9bh5vBJmWPA9bhCCQWMRo1BAKR5rX19Vb8/kCEiGtGhpFTp7poauqL2jFZP+PHJw1o19Qvk5GWZkClUvDFL85j5846tm6txmp1M2VKKldfPYHx45OiFPngqarqRq1WolBIYQlP/5e3r8/z/7P3nvFtlvf+//vWnpbkvVecOM7eOwQySCkQymoppZTxe50eTqFAC+W0PdBXT+lglNKeQltOobR/4FAoI8yEFSCJyXCms+O9tyxZe13/B4oFRnZiJx6yo/czS5buS7bu+/5e3/H5oFTK+9j/jDYajYK5czM4dKg1qnestdXJFVcUj9HKRo433zxORYWV/Hxz5H8hhGD37kamT09hzZrY2ce1tzupqrKiUMgoLk6OskeaSNjtXp5+ei+pqfqIqK3FosXt9vPUU3soKUmZ0J9/uGhq6uG99yopL2/FYFBx0UUFLFuWM2rXmc5ONz093kjWqxeLRXuqUuDoE5QJIQgEQpSUfG50brW60WqVlJQkI0lhIXIhwr+r0Si47baFo+pAUFbWhMvlJy/vcxtBSZLIzDRSUdFFTU33mPcGjzRDCcoE8OL51OifkKCOmNd+8Ytvs3kRQlBY2PfLIUkSMpmEzeYla4w3m8uW5fDWWyex2TyRk0oIQV2djWXLciLBmlIpZ/XqAlavLhjL5Z4TJpMmotGWkWGkpcXRx3RaqZTR1ubkqqtKzuk4QggcDt+p7MLQb1rXXjuNEyc6qK+3kZysIxQStLe7yMgwsnZt7AQow0EgEGLLllqysox9draSJJGWZmDz5sqYCMoCgRAvvHCQLVtqIo8pFDJuuGE2q1blndN7CxHeAMVaqfbgwRb8/lCU84JWq8Tvd3DoUNuIeUDGEl5v4FRpvRWFQmLBgixmzBicLldFRRcPPbQNgMRELT09Pp5+ei9lZU3ceefiUckCq1TySAD1xXPMYFCRlRW+DrrdfrRaJT5fkMZGO7Nnp/fZcE+ZksSePU1kZBiZNi2FoqJEnE4fwaBACMG0aSn9HXrEaGiw92t71ntv7ehwxYOyLzCxc4YDcP31M2ludlBT0x2ZSvT7Q2RlJZCS0rd3TAhBMChioqcsKUnHPfcs5YkndlNb240kSYRCgoULM7nxxtljvbxhZcGCTP71ryN4vQGmTUuhs9ONzeZBLg9nzhwOH/n5lnMKPI8da+ef/zxCTY0VgOnTU/nGN6ZH7VJPR3q6gZ/97EI++KCKXbsaUShkXHNNCatW5U84vTKvN4DPF+z35qTVKmhrc47BqqJ5552TfPBBFfn5lsjN2OsN8PTTe0lP15/V9LHHE2DTpgref78Kp9NHXp6JK64oZu7cjJgovTid/qgSei9CgMvlG90FjQF2u5dHHinl2LF2mpsdtLc7CQZDzJuXwcMPr+sjE/RlhBD87W/70OmUkR4nrRaMRhUHD7awZ08TS5aMfMnPbNYwdWoy1dVW0tI+d4IQIlwJWb26gIYGO+3tLlQqOZdfPoXLLy/uE3RefPEkPvus4VQfsQqVSo5Mpqa2tpvrr5856i0GKSk6vN5Av88JIfpsticqQwnKbuY8Kl32kpCg5v77L+DQoTaOHetAp1OSm5vAE0/sxun0R9L8vXIM8+ZlkJqqP8O7jg6TJ4cNmCsqunC7/WRkGCekjUtqqp4bb5zF3/9+ALlcxqxZqVRWdtHV5WHJkmyuvHIqK1bknlV2C+DEiU4eemg7RqM6oidWWdnFL3+5lZ/9bGhSFsnJOq67bgbXXTfjrNYyXlCp5KjVclpaekhLM/QJRqxWD5MmjX153O8PsmlTBVlZfQd51GoFBoOKd9+tGHJQFgiEePzxHRw50k5mppHkZC02m5ff/W4Ht9wyl4suGvuMdE6OaUA7NUmSxrz1YjR4+eXDnDzZSXV1N0II0tMNCCHYv7+V739/E0888dUBN1zNzQ6amnqitAUlScJs1rJ1a92oBGUAN9wwi1//eit1dTYslnDFwGp1M2tWGnfeuQSZTMLl8qPVKvoNsPLzzdxxxyKeeWYfnZ3uyOe44opi1q8fffHwhQuzePnlI7hc/kifHIR7pFNS9OOqreZsGUpQ9iaglySp34hDCNE1PEuKPRQKGXPmpDNnTnrksTvvXMyf/lRGZ6cLCO8wS0qSY26CTqGQMXXq+NEaO1suvLCAoqIktm2ro63NyYYNxSxblkNS0tlnLYUQ1NR088gj23E6fWRkfF6KS0szxKUsBmDnzgZeeKGc+nobx493kJysY/78TFJS9Ljdfux2L5dfPmWsl4nd7sXjCfS7iTKZwtmCoVJe3srRo+0UFHzeR2c2a9BqFbz44iGWLMkec0Hm4uIkCgst1NfbyMpKQJIkhBA0NvZQVGRh8uSkM76HEIKysibeeuskjY12UlP1XHJJEcuX58a8+rvb7ae0tB673YMQIpKlliSJxEQtTU09vP76ce64Y1G/rw8EQkiS1G/WUy6X8Hj6z/SMBJmZRn7+84v45JMa9u1rQa2W8/WvT2fhwqxIb9uZskvz5mUwY0YqVVVWAoEQubmmMctIJSZq+Y//WMiTT+6mvd2JQiHD7w9hNmv4/vcXx1wrwEgwlKCsfYDHJcL9ZufVKNWMGWn89rfrOXq0HZfLT2amsU9Dc5zRJzs7YdgyUL1adHv2NLNjRwN6vZKKCivz5mWQnR3OJKSm6ikrm5hSFmfL/v3NPPnkblJT9SxYkInZrOHgwVbee6+KOXPSSE7W8d3vzu/TbDxW6PUq5HIpyjMWwiW+L5aEBktZWRM6nTLqOqBWK/D7Q1RXd496n86Xkctl3HXXEp5+eh/l5a3I5TKCwRBz5qRz881zBxVUbdx4nFdeOUJSko7MTCMOh4+nntpDdXX3gLZusYLbHUCIcMbry20DCoUMIQT79jVHDXj1kpERnnbs7df6It3dHtatmzSi6/8yiYlarryyhCuvPPueWZVKHjOb93nzMnj00YvZs6eJri43OTkJzJ6dPuabmdFiqD1lVwMTNiM2VHon6uJMPP7v/w5x4EAreXkmDh9uIyFBTSAQoqysCaNRhcmkIRgU40Y2ZDQQQvCvfx3FYtFGSsWTJyeRl2emoqKLyZOTeOCBC/pt5B0LNBoFK1fmsWVLNbm5pkggEQyGS0A33jhryO8Zw7FIH0wmDT/4wVLa2pxYrW4sFu2g2y46O1288cZx8vLMke9/QoIag0HFRx9Vc9FF+UPqtRxtEhLU6PVKgsHoEq7HEyA5WXfa/6NSKeeaa0p4+ul9pKcb0OtVhEKClhYHZrNmVCUkJipmsyYmBoHGgqFeHbefT9OXcc5P7HYv27bVkZOTgFwuIz3dSFtbeFctkwWoqelm9ux0WlsdXHbZ2JfhYgW3O0BDg528vL43ZJVKzpQpSbS3u2ImIOvlmmum0dBg58SJTpRKGaFQeFjnkkuKmD8/c8jvt2BBFlu31kVNxHm9AZRKWdTE9liTmqofcg/siROdhELRG5JeSZpDh9piOihTKGRs2FBMWVkzdrs34rDh9wcJBEKYTGoWLsw6bals1ap8FAo5r756lLq6cKv17NlpfPObM0dVQiLOxGOoV8hZkiS1Ak6gWQjhHoE1jRt6R6orK62YTBrmz884q5JHnNiiq8uNJH1uMzVtWjKdnS5sNg8KhYz2die1td2kphomnJTFuaBUypDLpVMZxL6pBp8viE4XWwEZgE6n5L77lnPsWAdHjoTtuebOzSAnJ+GsSnAzZqQyY0Yq5eVtZGYaUavldHd76Opyc+utc6NkKMYjAw0JQDhT2F8GKtZYs6aQxsYeHn54O01NdrRa5SnBUhPJyXq+9rWpp329JEmsWJHL0qXZdHd7UKnkE26COs7YMNQrxGY+7yETkiSdBJ4WQjw67CuLcdranDz6aCltbc6IVMbLLx/h29+eyerV8Rv1eMZkUiNE+OYjk0kYjWpWrcqjstLKiROdmM1aNmwoZs2awvNiRHuwKJVyli3LobS0PipT0trq5Nprz00nbqSQy2VMn546LHYyCoWMO+5YzHvvVfL++5W0tPgoKDBz881zmDNnYrQ69E7A9Z4fvfRKAo11z9xgkMkkbrppDqtW5fHCC+UcONCK0ahi+fJcLr108qA313K57JyGieIMnUAgRFWVFZ8vOKZDCSPFUIKyAsIBmRIwAZnAEuDnkiRJQohHRmB9MYkQgqee2oPd7unjD+nzBfnHPw5SVJQUNS4dZ/xgsWiZPz+DvXubI8GFXq9i6tRkjEYV99+/6rwYzT4brrqqhOPHO6mt7cZs1hAKCaxWD0VFiefNZkWjUbBhQzGXXz4FIYj5acShkpYWzhBv3lwZaXr3eAI0NfWwZEk2BQXjwzMXoKDAwk9/egFChLN7sTygECc83fzXv+7FbvchSeHM7Pr1RVx9dcmEmcwcdFAmhKjt5+E3JEk6DvwUOG+CsqamHioru6ICL5VKjkIhY9u2Oq6/fuYYrS62CYXCMhMOh4/UVP2o6aYFgyFOnuzCbveSlKSlsNBy2gvwt789m44OF9XV3cjlYeFdSZK44YZZ8YDsNFgsWh54YBWlpXXs2tWEXC5xzTXTWLgwa0KU7oZCWDZhrFcxMnzzmzNIT9fz1lsn6ehwodMp+frXp7N+/aRxGdiMxzWfb9TX23j88R1YLNpI32ogEOLNN0+g1Sq4/PKJYVU3HFfJ/wOODMP7jBvsdu+pptboE1mnU8aMWnms0dho58knd9PU5EAmC/eezJuXwa23zj1rYdfB0NBg5w9/2El7uzPibFBQYOaOOxZHeYP2kpCg5r/+6wKOHu2gqsqKTqdk9uw0UlJiQxg4ljEYVFx8cREXXzz64pNxRge5XMbatZNYvboQrzeAWq2YcBnBOLHFBx9UIZNJfXxZFQoZ2dlG3n77JBdfPCnmBonOhiHl+yRJ0kiSdI0kSfdJktSbo84BKod/abFLSoo+Yuz9ZRwO37hK348WTqePhx/ejs3mJS/PRE6Oibw8E/v3t/DXv+4dseN6PAEefbT0lMmtmdzc8HGbmnr4/e93nrZpWS6XMWNGKhs2FLN2bWE8IIsT50vIZBJarTIekMUZccL9vNGTrWENwCBWq2cMVjX8DDookySpCDgK/Bn4JdBbw7kNeHj4lxa7JCfrWLgwi/p6W6QXAaCnx4tCIWP58twxXF1sUlbWhM3m7eMLKkkSOTkJ7N/fQlNTz4gc98CBFrq7PVHHzcgwUlfXTUVFXHYvTpw4cWIdi0XTr1tCb4Lki7ZM45mhZMoeB94H0oAvSmG8AVw0nIsaD9x00xxmzUqjttZGba2NmppuvN4gd921OCYMyWONqiorWm10arnXrqSlxTEix21s7EGpHPhr3t4eLzXHiRMnTqyzenUBNpsnqrrR3NzDvHkZE2YKcygF2GXAEiFE8Eu9VHWEJzHPK3Q6JXfdtYSGBjvNzQ60WgXFxckRv7E4fUlM1OL1Bvt9TgjQ60dml9Nr0jvQcePaQnHi9EUIQUVFFzt3NuJy+ZkxI5V58zLOu0GNOLHFvHmZrF5dwJYtNajVCpRKGU6nn/R0A9/61tDdN2KVoZ5l/d05cwHbMKxlXCGEYOfORt55J2zIm5Zm4CtfKWLFitg35B0LFi3K4vXXj+HzBfsErlarm8REzYhNNM6bl8ELLxzC6fT1GSbo7vZgNmsoKYkNv7c4cWKBUEjw/PMH+eCDKlQqOUqlnNLSOtLTjfzoR8sHHIyJE2ekkckkvvOdOSxZksOuXY24XD5mzEhj/vyMCeWLKX2xJ+q0vyhJLwJOIcStkiT1ALOATmAjUCWEuHXkljkwCxYsEGVlZaN+3FdfPcprrx0lOVmH0ajG4fDR3u5k3bpJMW/IO1Zs2VLNP/5xAJlMQqNR4HKFDX3vvXcZBQUjZz+zf38LTz65G78/HBB6vUH0eiU//OGymLO9iVUCgRD79jWzdWsdbrefuXMzWL48J24pM8HYv7+F3/62lIICS5/NZWOjnZkz0/j+9xeP4erixJkYSJK0RwixoN/nhhCUZQJbTv1YCOwDioBW4AIhRPswrHXIjEVQ1tHh4kc/ep/MTGMf/7dQSFBXZ+PBB1eTnZ0wqPdqbu5hx44GOjtdFBRYWLgwa8LUxvujsdHOjh0NdHS4KCgws3hx9qjc2K1WN2VlTbS1OcnOTmD+/Mw+o9VxBsbvD/Lkk7vZs6cZk0mNQiGju9uDyaThxz9eEbcWm0D89relVFd3R/XFhkKChgY7jz22vt8JuDhx4gye0wVlQxGPbZIkaQ7wTWAe4SGBp4DnAe9wLHS8cPx4B6FQaEBD3sOH2wYVlH3ySQ3PPrsfmUxCrVawfXs9r756lHvvXd7HKWAikZWVwNVXTxv141osWtatmzTqx50I7NzZSFlZUx/BXZNJQ3NzD889d5Af/nDZGK8wznDR1eXudyCn99rmcvnjQVmcOCPIUCQxrhJCuIUQzwghbhdC/IcQ4q/ANGDPyC0x9jhTcvF02le9tLQ4+PvfD5CebiAnx0Rqqp68PDMKhYw//nEXwWD/zenjkbAn3sT5POcbW7bUkJSkiyrJp6cbOHy4HZtt+PSB3G4/27fX8fe/H2DjxmMjJpUS53OcTh82mwchBFOmJNHdHf3/9PmCKBSyeE9ZnDgjzFAa/Z+RJClNCPEnAEmStMCDnIc6ZeGmdKlfQ14hoKTkzIa8u3Y1IklEKRBbLFpqa8P6WcXF47sJPRAI8fHH1bzzTgVdXW7S0/Vcdlkxy5fnxHvuxhFOp6/fqeJeGyGPJ4BpGKxeW1ocPPzwdqxWN2q1Ap8vyOuvH+f662fEs5wjQHNzDy++eJiDB1uAcJC9alU+oZDA4fBFyvvBYIiGBjtXXlkSn8CME2eEGcoZtgZ4S5KkLGAr8CegCVgghDivbJbS0w2sWVPAe+9VkpFhRKdT4nb7aWrqYcEB6aoAACAASURBVNmynIgv1+no6nIPKJ8hSRJOp3+4l31agsEQtbU2/P4gOTmm0wrxBQIhPJ4AOt3ASt5CCJ59dh+fflpHWpqevDwTDoePv/yljJaWHq65ZvpIfZQ4w8yMGal8/HFN1HfC5fKj16tISjp3XT4hBH/60248nrDzQi8+X5AXXiinuDg5yms2ztnT2eniV7/ais8XPt8lKWwf98IL5Vx66WS2b6+ntrY7snm65JIiLr98yhivOk6cic9Qesr2SJK0HNgE/Bi4G/gfMdhJgQnG9dfPJCPDwNtvn6S21onBoOK662Zw8cWDM+QtKDDz8cc1UY/3lvrS0kbP0ufQoVaefnofNlu4NVAul7jiiql89auT+wRdXm+At98+yfvvV+LxBEhIUHPZZVNYs6YwKjirrbWxbVs9+fnmyHNGoxqdTsk771Rw0UUFw3IzjzPyrF1byKef1tLV5cZi0SBJEh5PgObmHm65ZW5Ub+XZUF9vp67OFhV4qVRyFAoZpaV15ObOPOfjxAodHS7cbj/JyboxGef/6KNqnE5/n7+3yRT+3+7d28wjj6yjpqY7ErTF+8jixBkdhpSLFkJUSZK0FHgLuAT4K+AaiYXFOl805O3V3hqKPtmCBZm8+upROjpckUknIQT19TZmz04nM9M4UkvvQ12djcce24HFoolcoP3+IP/85yE0GgVr1xYC4T65J57YzYEDLWRmGlGrw5IW//jHATo6XHzzm31vmIcPtyFJRP1N5HIZQghOnOhk6dJ4UDYeSE83cN99y3nmmX3U1dmQpLCkyY03zubCC/OH5RgOh+9UM3n0OaTRKGhrmxiXmdZWB88+u59jxzqQySTkchmXXDKZDRumIJefe3A7WMrKmvp1HklIUFNXZ6Onxzfu2yfixBmPDDooO6VN1psVUwBqoEOSpACAEGJwGhATjF7NraGi16u4997l/PGPu6ittSGTQTAomDMnnf/3/+aNWs/Ve+9VolDI+ijbK5VyMjONbNx4jAsvzEehkHHiRCcHDrSSn2+OrE2nU5Kfb2bz5krWrZvU5yIvBAz0EYQ487DEcOJw+NiypZpPPqklEAixcGEmF188KW4wPgQmTUrkwQdX09zswO8Pkp5uiOqHPBdSU/URD7svB/JOp39C6Mk5HD4eemg7LpeP3FwTkiTh8wV59dUj+P1Bvv710Svpq1RyXK7oFonevtjhyH7GGVuEENTW2qiutqJUypk2LSU+qDEOGMpV9Q4+D8q+AywC/guwDveizheysxP41a/WUF1tpafHR2qqftQyZL0cPdqOxRJdmtBqlXR0uCJm3keOtKNUyqKCRblchiRBZWVXn6BsxoxU/vWvI1E32WAwhCRJTJmSNHIf6gv09Hj57//+hBMnOklM1JKYqOWjj6opLW3gpz9dGfl7+3xBGhrsyGQS2dkJ8ZtSP0iSNGLfz+RkHYsXZ7NzZ0MkYAGw2TyoVHKWLcsZkeOOJrt2NdLV5e4jd6NSycnLM7N5cwWXXFI0arZfK1fm8vzz5VHH6+hwMXlyYrxcOc7xegM89dQeysqacDr9+P0BtFoV3/nObL7ylaL4oFUMM5SesmcBJEn6ObAKuFQIsWmE1nXeIJNJTJo0eIsht9vPsWMd+HxB8vLMpKefm3CnyaQ5pU3Ut68lFArvmHuzgHK5NKDUhxBElV7y8kwsW5bD1q21kWGInh4vbW1ONmwoHhXTdiEEjz32GRs3HkevV1JXZ0Mmk5gxIxW/P8hLLx3mG9+YzgcfVPHhh9Vf6H1TceONs5k//7yzdB1TbrxxNj5fkL17m5HJJIQAk0nND3+4dELs8A8dasNojBYs7t0ANDb2MHXqaAVleZSWNlBdbSUlRY9CIaOjw4lcLudb35o4vXvnK6+/fpxt2+poa3NitXqQJIlgMMR//dcWEhO1LF6cPdZL7EMoGKTz+HFay8uRZDIy5s7FMmlw/dkTjaGUL2XAX4CLgHeBZyVJuksI8eJILS5OX3bubOCZZ/bh84WAcNC0fHkO3/nOnLM2Ql+zpoA//7kMk0nd5wRoauphwYLPVe9nzUrj1VePRmW+fL4gcrksKvMlSRK33DKXggIz775bQU1NN2lpev7t3+azfHnuWa31TAgRdlTo7HRjNmvo6HDy4ouHSEzUoNGEg86wXVALc+em88ILB3nvvQpOnOhCo1GQnm5g/vwMgkHBH/6wk5/8ZGW8r2YU0emUfP/7i2lq6qG5uQedTsnkyUkTJmup1yvx+YL9PicEqNVndw6fDVqtkh/9aDmffFLDxx/X4PEEWLkyj/Xri855oxdnbPF4Arz/fiV1dTZ8vmDk2i6EoL3dyc9//jFvv/2tmAl4Ah4Pu/74R1rLy5GrVCAEJ99+m5xly5h7663I5KN3XsQCQylfvgGkAsuEEG2SJF0HPC5J0reBfxdC1J/pDSRJ+grwe0AO/FUI8ZsBfu9q4F/AQiHE6BtbxiA1Nd386U9lpKXpI1mtUEiwbVsdCQkavvGNs+tHWbw4mwMHWtmxowG9XolCIaOnx0damoHrrpsR+b38fDMXXZTPhx9Wk5KiR6dTYrd76e52c/31s/q1hlIoZKxbN4m1awsJBgVyef+N3MNBV5ebJ5/cTUVFFzJZOKtXWdmFTCZDofj8pFYoZGi1Cj7+uObUZJ+c5GQdarUcq9XNZ581cOGF+RgMKt5888SggjKPJ8DWrbVs2VKDy+Vn1qw01q+fRFbWmdss/f4g3d0edDplH8P085nMTOOol/FHg2XLcvj009qojU13t4ekJG0fKZDRQKdTcsklk7nkksmjetyxwOsNcPBgK9XVVhIS1Mybl0lq6sTsKe3p8dLR4cTtDvQpQ0uShMWipa7OTmWl9ZTe5pnxORzUfvop9Z99BqEQWUuXkn/BBagThqeN/OQ779BaXo45Pz9yfxChELVbt5I0ZQr5F144LMcZLwwlKJMDFwohXABCiBclSdoMPAYcBk77H5IkSQ48AawDGoDdkiS98WWNM0mSjMCdwM4hrG1CYbN5+PTTWnbubEShkLFyZS7Hj3egVsv7lBl7+58++KCSDRumnNVovUIh47vfnc/KlbmUljbg8fiZMyedhQuz+uhSSZLEt789m6KiJDZtqqC93UVenolbb53LrFlppz2GJEkoFBLt7U62baujoiLcf7ZyZR6TJlnOOVALhQS///0Ompsd5OWZIqn6zz6rJxgUOJ2+Pv6aQgi6uz3MmpWG3e7FbA5LARiNarq7PbS3O0lO1nHsWMcZj+31Bnjssc84dqyDlBQdWq2CHTvq+eyzeu67b8WAF75QSLB5cwVvvXUCjycAhAPkb3xjetzke4JSXJzM6tUFfPBBFQkJajQaBd3dHuRyGffeu2xI09txBk97u5NHHimlrc2JSiUnEAjy0ktHuPHGWVx4YcFYL2/YMRrVuFwB+lOr8vmCaLVKmpt7BhWUeWw2tv361/S0tKBLSkKSJI6+8gp1n3zCip/8BK3l3AZwQsEgle+/jzEzs899QJLJ0KemUrFpUzwoOw2XCyECX3xACGEFbpYk6f8bxOsXARVCiCoASZJeBK4Aviw8+wvgIeDeIaxtwtDRERZ1tFrdJCZqEQL+8Y8DVFV1M29eetTvK5VyQiGB1eo5a70juVzGzJlpzJx5+uBKLpexYkUuK1YMvfx4/HgHv/3tZwSDIYxGNSdPdvHxx7Vce+00Lrvs3EQpjx/voK7O1ifTIJNJJCRocDi8eL1BHA4fen3479PdHdZjmz49hd27mwiFwlk8ICKimZCgHpRh+e7dTRw71kFBwedTqZmZCXR1uXn22f384hcX9Rt0vvrqETZuPE5WVgIpKXqCwRC7djVSV2fj/vsvGNbJxjixgUwW3tjMmZPOxx/X0N3tYdGibC68MC9u6j5CCCF46qk92GyePgMWXm+AZ589wKRJieTkTCxRYo1GweLFWbzwwqHIhhPCQ1Y+X5CCAsugs/IV776Ls60NS35+5DGVwYCtvp4Tb77J7BtvPKe1Br1egl4vCnV0pUWp0+FoaTmn9x+PDLpZ48sB2Zee+2gQb5EFfLHE2XDqsQiSJM0DcoQQbw92XRONV145gt3uJS/PjNGoJiFBTUGBBa/Xz5Ej7XR3e/B6P/9XBIMhhGBQAcRYEQiE+NOfyjAYVBEhysxMIzk5CfzrX0eor7ed0/u3tTmjJDYkSWLSJAt+f4iiIguJiVrsdi82m5dQSFBSkkR6upG8PDM9Pb7I64QApVJGc7ODtWvPvIveurU2Iqj6RSwWDY2NdtranFGvsdu9vPtuBXl55i8MUsjIzk6gvt7G/v3n34XofEEmk5g9O50771zCz352Id/4xvQRCci83gAul7/fbMn5RFNTDxUVXVF9cmq1AqVSxvbtdWO0spHle99bREqKjs5ON93dHrq73TgcPgoLLWRmGpg27cxWgEIIaj7+GEN6dDLAmJFB7datiNC5eRorNBo0ZjM+Z/R10muzYc7LO6f3H4/EzHb81CDBY8BNg/jdfwP+DSA3d2SaxscCrzfArl2NUf00brcfkNi9u4m2NicymYz8fBPTp6fS3NzD4sVZ/fZ0fZlwo6cLhyMsvzFagdzJk53Y7d4otXaZTMJm8/C3v+3niiuKmTYtBaVy6E2dBoOqX020oqJEKiq68PmCFBZayM424nIFmDkzlepqK06nj+LiJDo6XFitbuTysG6U1xtk5sw01qwpPOOxewcdvkzYF1IiEIi+aNXWdg+oBaXTKSkvb4256ag444PWVgevvHKUsrImhBDk51u49toSpk1LHeuljQl2u3dAUWKtVklLS3QwMBFITzfw+OPr+e1vP8Nu96LTKdBqVRgMKr73vUWD1tYM+nzIFNG/K8nlhAIBRCiEJDv7QRxJJqN4wwb2Pv005ry8yLGCPh+e7m7m3nrrWb/3lwl4vfgcDlQGQ7+ZuVhhNIOyRuCLYkPZpx7rxQjMAD4+dQKlA29IkrThy83+QoingKcAFixYMGG2goFAiFCIKF2vzz6rx+sNkJioRZIkQqEQhw610dzs4CtfKYpS0++PtjYnzzyzj+PHOyIXqdWrC7j22mlnFQgNhd6eqS/S0+Pls88a6Opy09bmorraitms4e67lw7K4zAUElRUdNHV5UajkaPRKOjp8fbRXfJ6A8ydm84tt8zl2LEOJEli/vwMpk1L4eDBVp54YjdCCKZPT6GuzkZ7u4v16/O57roZzJ2bMaiJ1vnzM3nllSNRQbHL5cdoVPWbBTndNGEwKOKlyzhnRWeni1/+cisej5+sLCMymURnp4uHHy7lnnuWMmPG6dsTQiFBW5sTSQqL+cbKdN65kJysO40osa9PSXOisWRJDv/zPxZKS+tpbu4hL8/M0qXZg7a3kySJ1Jkz6Tp5En1q36De3dlJcnFxvwHbUMm74AJcnZ2cfPttEAIhSchkMmZ9+9ukzZp1zu8f8Ho5vnEjVR9+SCgQQKZQULhuHVM3bAhPe8YYo3n13w1MliSpgHAwdh1wfe+TQggbEBl1kyTpY+Ce82n6UqdTkp1txGbzolbLqaqycvx4B01NDtRqOQkJatLS9MjlYReBQEBw661zz5glc7n8PPTQdhwOb0SYMxAIsWlTBT5fkJtumjOinysrK6HPhTEcaDYQCITQaBQUFyeRm2uiq8vNY499xkMPrT1tYNLe7uQPf9hFQ4MNSep1D5Do6fFhtXpQq+V4vUHkconvfW8Rc+aks3RpX/HRuXMz+NWv1rB9ex319XbWrClg+fJcMjKGNvW3cmUuH35YRWOjnYyM8I2wV4/tu99d0G8ANmlSIlqtAqfT16e3IxQSeL0BFi3KinpNnDhn4sMPq3E4fH02NYmJWmQyiRdfPMwvfpE6YKC1b18zzz9fTmenC0mSSEvTc8MNs5g+fXxn2FJS9CxYkMmePc3k5CREPn9PTziDtnz5+BclPh3p6QauuqrkrF8/9Yor+PTBB3FbrWjM4QDWa7PhczgoueqqYVmjJJMx7eqrKVi9GmtVFZIkkTRlCirDuZf1hRDs+ctfaNqzh4SsLOQqFUGfjxNvvomzpYWF3/tezG0+Ri0oE0IEJEm6HdhMeJLzGSHEYUmS/hsoE0K8MVpriVUkSeLaa6fx4INbqajoIhQSuFx+nE4fdruIeGxC2A0gOVlPc7PjjI2qe/Y00dnp6rMrVChk5Oeb+fTTWi6/fMqImoOnpupZujSb0tJ6cnNNdHS4cDr9KJUSWq2SjIzwyZeYqKWmppvy8jYWLOhfuDUYDPH44zvp7HT2aezv6HCh0Si49NIiWlpcZGQYWLw467SfKzVVz5VXnv0FC8Liuz/+8Ur++c9D7NvXghCQkqLl9tsXDRhcqVRybrllLn/84y7sdi8Wixa3209Xl5sLL8xn8uTRcTs4n7HZPGzdWse+fc1oNAouuCCPefMyRjxrPJLs2tVISkr0991kUlNfb6e724PFEi3Ce/RoO48/voOkJB15eWaEENhsXh59tJT771817i2ubrppDh5PgPLytkibg06n5K67lsSt1s6ApbCQZffeS/nzz2Ovr8fndOJ3ONAkJ1P+/PPkr15NzrJlyJVnN2T2RbQWC9r584dh1Z/TXV1N8969feQ25CoV5vx8msrKsNXVxVzf2qjWSYQQ7wDvfOmxBwb43QtHY02xxqxZ6UyZksjRo+0oFLLIpGBKio5gMByYpabqqa+3D1pw8ujRjj7yFr30pvMbG3tGNCiD8IVRrVbw6ae1tLY6cDrDO/r58/veCGUyibY2x4Dvc+JEJ01N9ihNp+Rk3SmBWgOXXlo8Yp+jP1JT9dxxx2KcTh9+f4iEBPUZ5Q3mz8/kgQdWsXlzJSdPdpKaauD662eyYEFmXBphhGltdfDrX2/DbvdgMmkIBgVPPLGbOXPSuf32RWctxDzWnM51Axjwe/XKK0dJSFBHMu6SJGE2a/D5gmzceJy7714yIusdLfR6FT/4wVLq6+00NYVFiYuLk+JtAoMkpaSEi37xCxp27mTX//wP+sxM9ElJeOx29j79NC379rHo9tuHpZQ53HRVVgJEZcN6f+6qrDy/g7KJgNcboK4uPC2Yl2ce9gt4IBCiudnBFVcU43D42bSpArlcQqGQIZeDzeYlLc2AUimjrc0xKHHThATVaZTExVkZqg8VtVrBTTfN4Wtfm8onn9Tw/PPllJQkR50soZA4bYDY3u4a0MxcLpdobh44oBtphiL+6vMF6ex0oVbLmT8/kwULMpk8OTHmUukTkeefL8ft9pOb+3lgb7Fo2L+/mZ07G1i5MrYu0oNl5co8/vWvI1Hfw44OF0VFif3q3/l8QSoqusjLi862JyfrKC9vRQgx7r+XkiSRm2saVL9qnH4QgmOvvII+NRWNKfw3VGg0qBMSaN6zh5YDB8j8UpYr4PVS88knVH/wAb6eHpJLSphy2WVYCs88QDVcyJRKvni7CPr9ONvaCLjdeGw2YvFbHQ/KhsDWrXX83/+VRxrXNRoF118/86x0uwZCCHFKN0t2SixWgUKhp73dhUwWbgS32TwEg+EG9cEEVIsWZfPuu5UEg6E+k4J2uxeTSTOq5QmzWcMll0xmy5YarFZPH09Dm82DwaA6rRhtQoK630lLCP9txoNHYk+Pl0cf/YyaGitarZJgMMTmzRVccEEeN900p99pzjjDg83moby8NarkL0kSSUk6tmypGbdB2YUX5lNaWh+xNFMoZLS3OwGJ66/vfxhILpdQKmUEgwKFou+J5feHhUbHe0AW59yxNzTg6uzE9CW1A0mSUJtM1G3b1icoC/r97PzDH2gtL8eQmoomMZH2o0dp3rePJXffTfo5NPA729txtraiMhgw5eWd9vuZOn06MkkiFAjgtdup/+wzQn4/IhTC73Jx+OWXSSouJiErdvp441f/QXLgQAv/+797MBpVkR2X0ajiqaf2cPDg8OlKKZVySkpS6OhwoVYrUKnCDf55eSaUSjlms4b8fAuzZ6cNOhgsKDBz+eWTqauz0dLiwGbzUF9vw+Hwcdtt/TejjyQqlZy7716CJIXto+rqbNTUdOP3h7j77iWnFcGdNi0Fo1GNzebp87jD4UOjUTB7drSmTqzx8sthbbaCAgvp6QayshLIyzPz8cc17N7deOY3iHPW+HxBJEnqt5SnVMpxOHz9vGp8YDCo+PGPV3DFFcV4vUG6ujwsXJjFz362asApw15B6KamnqjnmpsdrF6dP8KrjjMeCAUGlClFkssJePpej1v276etvBxLQQEqgwG5UokhLQ1tYiL7//Y3QsH+Kzenw+92s/vJJ3n/Rz/is9/9jo9//nO23H8/9saBr5n6lBSmXnUVXZWVVG/ZAkJEJi6zFi1CJpez4/HHT/v5Rpt4pmyQvP76cSwWTZ+AQatVYjZr2LjxOLNmDV8wcPXVJfzyl2FV/+LiJPbta0GlCns0rlqVh1Ipp63NyVe+UjSo95Mkiauvnsb06al88kktXV1uli3LYdWq/DHzf8vLM/PQQ+soL2+ltdVJSoqOWbPSzuhKoFLJufPOxTz22GfU1najVMrx+8MDEN///uKYFtGFsOZcaWl9lBadTCaRmKjlgw+qWbJkbCfC3G4/J050EgiEyM83j3i/4WiSmKjFaFThcvmj+iytVjerVuWPzcKGCaNRzZVXlgxpgOWKK6Zy+HA7NTXdp1xEBF1dbnJyTFx88aQRXG1s4/UGsNvDMjtqtRy3O4BSKRvXwyBni/HU5GKgH/V9r81G8eWX93msvrQUdUJCVBZLbTRiq6vDXl+P+QsuAYNh39NP07RnD6acHCSZDCEEro4Otj/8MGt+9StU+v7vZVMuuwx3VxddJ08iUyhQJSSQMWkS+tTwNHJ3bS0dx46ROmNGv68fbeJB2SAIBkNUV1v77buwWDRUVlr71cE5WyZNSuQ//3MFL754iJ6esNCrzealsNCMzeZFq1Vw++2LmDRpcIayEA7MSkpSKCk5s5Lz2RAMhggEQqhU8kGXOzQaBQsXDj1tPGlSIg89tI69e5tpbu4hJUXP/PkZ48IzsldlfSDh2M5O9xis6nNKS+v5+9/3n+pBlADB2rWFXHfdjAlRVpXLZVx1VQn/+797yc5OQKNRIETYpkySJNauHb1+l1jBbNZw//0XsH17PTt2NCBJEpddNoWlS3P6HRCKZYLBEHa7F41Gcda2cz5fkNdfP8aHH1bh94fo6nIRDIYwm3VoNApWrszlyiun9tFEnOgo1GpKrr6aA//4B8aMDJQ6HSIUwnHKEzN7Sd9hkFAgcFpR2cFmymz19VR98AEt+/dTX1pKxrx59PavSJKEPiUFa00NTbt3D+iRKUkS6oQE0ufMISG7H1FuIXBbrYNaz2gQD8oGgUwmodMp8fmCURM7Xm9wQEX5c2HKlCTuv/8Cenp8SFL4i1VT041CIaOw0BIzE2I2m4eNG4+zdWstfn84s3L11SVn9NE8VwwGFRdcMP56fxIS1Gi1SjyeQFQ/oNXqYebMsdOFOnmyk7/8pYyMDGNkbcFgWM/ObNZw6aXn5lEaK1xwQR7BoOCVV45ELLqysozceefiKDue8wWjUc1XvlI06Ox7rCGE4NNP63j99aPYbOGJ9SVLsvn616djNg9+syaE4K9/3cvOnQ1kZSXQ2GinoiK86c7ODrFgQQYff1zNyZNd/PSnK0dlSCpWKFy7FrlKxbHXXsPV0QFAxrx5zLjuuihNscwFC2grL0eb2DdxEPB4UGg0mHLOXA1oOXCAnb//PZJCQfCUGn/Djh0kTppE6syZkc2/UqvFWll5WuNyQ3r6gIGgJElR6xxLzp9v1DnQu4PeuPEY+fmfG08LIWhudnDllVNHpBlWkqQ+wrAzZsSWkKPT6eM3v9lOa6uDjAwDCoUMq9XNI4+Unlan63xGqZRz6aWTeeGFcvLyzJGMmdvtx+sNsH795zfF6mor27bVRabnli3LGdFS4qZNleh0yj43GrlcRlZWAm+/fZKLL540IUo3vW4WK1bk0tLiQKmUkZ5uiDe0j2M2b67k+ecPkp5uIDfXRDAYYufOBqqqrDzwwKpBZ/zq6+3s2tVIfr6ZYFBw+HA7ZrMGuVyirc2JwxGe2q2psbJ3bzPLlk1s8dkvIkkS+atWkbtiBR6rFYVGM6DAa9aiRVRu3oytrg5jZiYyhQJvTw/Otjbm3nrrGZX0gz4fe556Cm1SEiq9HldnJ5JcTtDno760FHdXFynTpqFLSSHo86GxRA+rBTwe2o8eJeB2o7FYUOp0eLq7IyK4EB4a0KWkkDx16rn9cYaReFA2SC65pIijR9s5caIz0rfkcPgoLk5m/frzs+9ix44Gmpt7IkbadXW2yATk3/62j3nzMkZ9iGA8sH59ET09PjZtqgA+lyW57baFFBWFd2zvvHOSl146jFIpQ6sN+2G+9dZJ7rln6YiJy1ZVWfstAWs0CtranNjt3gnVX6ZSyeMSCRMAt9vPa68dJTs7IVLJkMtl5OSYqKnppqysadBZ9ZqabiAsAWKzuQkGQ32uYZ2dLhITtRgManbvboyJoEwIQdDrRa5SnZMP5WCRyeXokk8vxaTUall+330cfeUV6ktLEaEQupQUFn3ve2QtXnzGY3RVVOB3udCnnGq3EQJXe3vYa1OS6K6rw221Ys7LQ2OxRL1ny8GDlP3pT58PIAiBMTMTt9VKd01N5Pd0KSksuftuZPLY2WzGg7JBotUquffe5Rw40MKuXeFpj0WLspg9Oz1mSomjzc6djRgMKrZvr6ery3VqfD58YTtxopODB1uYN69/Zf7zGZlM4utfn87FF0+itjZcki4qSozcUBoa7Lz00mGysoyRzFRiohabzcOTT+7mkUcuHrZg1273UlHRhRACg0EZ6cf5IoFAKFLCjxObBIOhSHvF+SY+3NBgx+8P9SsGazSq2LNn8EFZ+Lz6vGfpy/T2VQ7UFzqa8eG/SQAAIABJREFUCCGo376dY2+8gau9HaVWS+HatUz+6ldRaMa+v1ZjMjH3lluYdcMNBLxeVIbBZ6ODfn+kd0yEQjTv3YsxIwNnWxtBrxeEQJLJaD9yhJU//WkfSQtHayu7/vAHNBYLhrRwG40QAnt9PWlz5pC3YgXu7m70yckkl5QMixvBcBIPyoaASiVn4cKss2pOHyx+fxC73YtOpzzrRtXRQiaTqK+30dXl6mPfolYraG528PbbJ+NB2WkwmzWYzdFTu7t2NZ7Sj+ob7JtMGurqbFRUdDF16plFg0+HEIJ33jnJq68eIxQKAWGR0Z4eHytX5vZp6m9stLNqVX7Mfx8HQ0uLg4MHW/H5gkyenMjkyUnjOojxegO8885J3n+/Crfbj8WiZcOGYi64IG9cfy6/P0h9vR2AnJyE05bNTzeAEgyKISn3l5QkI5NJ+HxBTCY1SqUcny8YCcDS0vQIIXA4fFF+uqPNiTff5PBLL6FPS8Ocl0fA6+XYxo10VVay9Ac/iJnsj1ylGrLxtyknB4QgFAzitdnwu1xozGZUBgM9TU3o09IiXpZfzg7WbduGEKLPNGYoECDo93Pg2WdxtrZSuGYNiUVFMReQQTwoixnCAqKVvP32CdzuAJIEy5fncu2102J2ymfp0myee+5glCG61xsgMVFDVZW1X+mBOKenu9szYPZVksLlmnNl9+4mXnzxELm5psgNLzPTyJYtNezZ00xGhgG5XIbPF6Sw0MI110w752OOJUII3njjOK+/fgwIZ0F6BZjvuGPRuAw4QyHBk0/uZv/+FjIzjaSm6nE6fTz99F46Opxcc830sV7iWbFzZwPPPXcQpzP8PdfplFx//cwBS4W5uSZMJjU9Pd4+18re4GkoJUaLRct1183guecOYjSqKClJZseOBoSA2bPDWZfq6m5mzkwd06Ecr93OsY0bMeXlRQILhVqNOT+f9kOH6Dh6NGYkHs4GbWIihWvWUPHee+GsnyQhhMDvcmHMyiJ/1SpkCgWujg58PX019rqrq/v0ugW8Xuq2b8drtxPy+7E3NLD/73+nZssWlt9337AYnw8n8aAsRnj55cO8/fZJsrISSEnREwiE2Latlvp6Gz/5ycqYbLBevDgbvV5FT48Hk0mLXC7hcvnx+0MsXZqNxxPA5wvGg7IhMnlyIp9+Whv1uBCCYFBEaZydDW++eZzkZF2f75VSKWfx4izc7gBr1hTg8wWZPj2VGTNSx7xUc64cOtTGK68cJTfXFPksQgiOHGnn5ZePcOONs8d4hUPnxIlO9u9v6TN8pNeryMsz8/bbJ1mzprBfA/JY5siRdp54Yjfp6YZI/6Lb7efPfy7DYFD2qwepUMi4+eY5PP74ThwOH4mJWrzeIO3tLhYsyBjygNS6dYVkZyfw3nsV1NfbufLKqREnFSHgW9+ayYUX5o/pNdlaVYUIhaIyPZIkIVeradm/f1wGZc62Nrw9PehTUsJTnXo9xzZuxO90RvrCUmfOjPhs+pxOUqb33XwYMjLoOH4crcVCKBikqawMV1sb2qQk/E4n+pQUVAYD3TU1nHz3XaZfe+1YfNQBiQdlMYDV6mbz5kry882RVLxCISM310x1tZVDh9qYOzdjjFcZjUaj4KabZvPaa+ExdKczbJZeXJyMQiHDZFJHZdHinJn58zN57bVjtLU5SEnRI0lho+m6OhuLF2eRlnZuOzshBA0N9n6b3E0mDd3d3Xzta1NjciNwtrz3XiUJCeo+waUkSWRnJ7B1ay3XXjtt3GXLystbUSqjdQF7P2NFRdeItlqMBK+/fgyTSd1nI6fVKrFYNLz22rEBRbpnzUrngQdWsWlTBUePtmM0qrn11rksX54z5A2FJElMm5bCtGkjo+k4LHzhfx7werFWVtJdU0PQ70eh0UQFKrGOq7OTfc88Q/uRIyBJ+Hp6MOfnM2n9elY/+CDH3niDuk8/xTJpEnKlEiEEztZWNGYzWQsX9nmvvJUrqXr/fWx1dbQcPEh3TQ0yuRxnezuG9HQU2vBGxZiZSfVHHzHtmmtiavI6HpTFADU13eEdTj+9EWq1gvLy2AzKIKwGvm9fC2q1nMRELZIk4XD4aG11cNddS8Z1X8tYodMp+dGPlvOXv5RRU9ONXC4RCsGKFbnccMPZe8b1IkkSFosWtzsQlcV0u/0YDKpxnxn7Mi0tDvT66KBLoZARCgl6enzjLigLXy/EgM+Pt3NPCMHJk539bhbMZg3V1d34/cEBNwv5+Wb+/d8XjPQyY4LEoiJkCgU+h4PGXbvw2u1hOyO1GmdbG7WffMKkiy8mpWTwzg5jRcDrpfSRR3BbrRizsmjZuxd7YyMt+/ZRt20bKVOnUnzFFUy96iqq3n8fIQQiGCRp8mTm3nprVPnRlJtL0Ve/ykc//WlYRsPjwR8MIpPLcVuttB44QMa8eciUyvB0phB9gtyxJh6UxQAKRdgyoj+G2qg62mRmGvnP/1zBP/5xgNracHBpNmu4/fZFzJ8fb/I/W9LTDTzwwCoaG3twOMKuDsNptr5+/SSee+4ghYWWL+nu9fD1r8+IqZ3jcJCba+Lo0Y6owMvvDyKXy8ZlRnf27DTeeON4lJuIzxf+TFOmjIx0ykii16vweoNRE8B+fwiNRjEhXCWGA5Vez4xvfIOtv/kNjrY2DGlphPx+fE4niZMmYczOpvz557noF7+I+XO55cABepqakKtUnNi4EWdbGxqLBV1KCgG3G7XJxJFXXmHZPfcw5dJLw5OmOh26lJQBP1vA7SZ97lzaDx1CplCg0GhQaLWETumcmfLyQAhSSkpGRUZkKMTu3f48YvLkJNRqBW63v89NIxQS+P1BFiyIzSxZL4WFFn72s1V0droJBEKkpOhG9OJZVWXlrbdOcPRoB0ajirVrC1i1Kj+mg9ezobe8NhKsXl1ARUUXu3Y1oVSG/1d+f4j58zMnpN/hxRdPoqysCZ9PExmi6C3jXnrplHGpzF5YaGHFihw++aSW1FQ9Op0Sm82L1ermW9+aFbMDQgMhSRJr1hTw+ut9RboBmpt7+OpXJ4+77N9IUrBmDYdffpmAy4XXbkep1ZI+ezamvDwkmQx7XR2u9nb0qbElOv5lOo8dw1pVhcdmw9naikypxNPdjauzE5lSGZbDyMzk6GuvMfOb30ShVqNLTj5tsNl18iQBtxu5Wk3S5MnY6utBCBRaLT6Hg6ayMpKnTGHq1742ip90cIy/K9EEJNybNYc//7kMjUaB2azB7fbT1eVhzZp8Cguj1YpjDUmSSE4eeWHRAwdaePzxHWg0ikhD73PPlXPgQCt33bVkQvVBjSRKpZzbblvIunVdHDjQEpkuG+8SEQNRXJzMd74zm+efP0QwGIo8vmRJNl/7WuyoeQ8FSZK4+ea5FBUlsWlTBS0tDnJzTdx88xzmzOm/9yrWWb++iMOH2zh5sgujUY0khbX0CgstfPWrk8d6eTGFJEkY0tJQGQyojf0M/5yaWBxt3F1d2BsbUWg0WAoLzyjN4ersDHtopqbiam8Pq/+7XAQ9HgRh1X1rdTXVW7bQXV0NkoRMJsOQlkbA40Gfnk7R+vWkzZ4dCdS0SUnYamtRJyQgUygwZmXhbGsj4PEQCgZxtrdTvGEDddu34+roIGPevJjQdgOQxuKfNpwsWLBAlJWVjfUyhoWKii42b66gstJKUpKWdesKWbAga0LeJM+GYDDEPfe8h0wmRY2+V1d3c8cdi8ZdY3Oc0cVu93LkSDuBQNinNSvLGPPlnfMNrzfAvn3N7NgRFuleuDCT+fMzx2U2c6Q58dZbHHnlFcx5fcVxvXY7MoWCtb/5zaiV54J+P+XPP0/tp5+Ge7RCITQWCwtuu42kyQMH1J88+CDlzz2HNjmZ7qoqgj4ffrcbSSZDBIMYs7Jwd3YiU6kovOgiJLmcpj17kCSJgrVrQQi8NhtTr7ySkiuvBKD14EFeuvZadMnJkQlVIQSujg4EEPR6yV2xAqVWi9/txpCayrJ77/3cQWCEkSRpjxCi3wbI+Lc8higqSqSoaFG/z7ndfvbvb6G93UVqqo7Zs9PHXWPyuVJfb8dm80Y1Avd6hJaW1seDsjinJSFBzZIl2WO9jDinQa1WsGRJDkuWjL2FUayTd8EF1H7yCbb6eowZGUhyOR6rFY/NxtK77x7VfqnDL71E9UcfYc7PjxzXY7NR+uijrH7wwQEDHrlSSdKUKXTX1iLXaHB3dYVff6rc6LZakSkUGNLT6Th+PGzZlJyMz+HA2dJCyrRpaEwmjm/cSM6yZRjS0kidOZOC1aup+uAD1EYjkkxGKBAIly9tNhKyszHn5SGCQSS5HEdLC/ueeYYV9903an+vgYgHZeOAysoufve7HTgcvlOTeAKDQcUPfrCUgoLYL20OF6HQ6SfNAoHQgM/HiRNn6Pj9QQKBcJP9+ZpRDIUEO3Y0sGlTBW1tTnJzTVx66WRmzUob87+JOiGBFT/5Ccc3bqRu+3ZCfj+JRUUsuO22c5q8DAWDWCsrw2KtmZln7Evz9vRQ89FHmHJz+wSCGpMJr91O3datlFx1Vb+vTZ89m46jRzHl5WGtqsLd0REuUSqViGAQJAl9RgbqhAQczc2o9HpkcjkKjQZnezspENYtkyRaDx7EsG4dfqeTZffcg89ux9HaikKrRZ+aSldFBb5TOmjVH36Iz+FAplBgLiwkcPgwzra2Me/BiwdlMY7HE+Dxx3egUMjIz//c3b6728PvfreDRx5ZN+Ea3AciK8uIRqPA4wlElTLsdi8LFsSnPeMMDx5PgP37mzlwoBW1WsHixVkUFyefN60EVqub1147RmlpPcH/n703jZOrLtP+v+dUndqXrt73LUlnI/u+gyQgojiiMgooDjKOD6OPoqMzjIg+jjMoIIiMjDMjzzgPOIL8MYCRLYYEQvZ96aT3vXqvfV/OOf8Xp7ug6E4MIelOYn/fwCepnPM7tZy66/5d93XJChUVTj75ydksWHB5atXOF1VVefrp42zd2kp+voX8fAv9/WEefng3t946jxtumHydm9nlYuEXvsD8229HkWX0xvMf8EiGw3Tv2cPJZ57RukiiiKoolK9axcI77jij7ioyOKgVUvqx30VGux1PU9MZz1mxejUtr75KKh6ndOlSEqEQcb+fVDiMvbycuNeLOTcXOZHQOl2RCOlEAiWdxllZmXWsQHc3bz/wQOZ8jooKcqZNI9jdjaoo6I1GnBUVeBobkaxWjE4nqizjaWxEZzAQ9/unirIpzs7Jk4OEQsmsggw0356ODj8nTw6yYEExLS1eYrEUpaX2D2wueqliNOq55ZY5PPnkEQoLrdjtRtJphd7eEKWl9qmtyykuCIFAnAcf3I3bHcRikZBlhTfeaGft2kq++MVFV7wtQzic5IEH3sbjiVJSYkenEwgEEvzkJ3v4yleWs3z5pfE58/li7NrVTUuLl7w8M2vWVFJTk3NBu1fd3UHeeKOdmhpXpiDPzTVjtxt47rlTrF5dgd8f5w9/aObYsX5MJj1XX13Npk3TsNneX97jB0XU68ctit6NqigMnTpF965dJKNRihcupGz5ciSzmcYXX+TU735H74EDCKKIoNNlhPL+zk7S8Tgrv/a1cY9rsFpRZBlVVcc8/+l4HHNu7hnXZHQ4WPsP/8Cxp55i+NQpzC4Xkf5+SpctwzV9Ol07dxL3+1FSKRRVzejLlGQSQafD19ZGTk0NyVCI1ldfxVJQgGOkWIt5PKRiMdb+wz/grKzk+P/8Dzvuvx9UFXXUWiMvD53BgL+jgzfuu4/8mTOp2biRitWrJyUbc6oou8TxeKJn9bU7cWKQp58+jt8fBzQfvBUryvirv1p02QljFUVFEDjrTXXDhmpMJonNm0/T1RVAFAXWrq3kk5+cPRXnNMUF4dln6+nvD2X9EFIUlZ07O5k/v/CctU7xeJoTJwYYHo5SUGBl3rzCy6KrvWtXF4ODkazrz8kxIUkiv/nNCZYsKZn0wrStzcdDD+0ikZCx2QzU1w+ybVs7n/rUHD760boLdp7Dh/uQZWXMPViSdCiKytatbbzySjN6vUhBgZVUSubFFxs5fLife+9d+yfvSclwGPeBA3iamjC7XJSvXDmm+3OhUGSZI08+SdfbbyNZrYh6PQPHjtH8hz+QP3s2zVu2oKgqepOJZCRCfHCQcH8/zspKFFnm4L/9GzXXXEPR/LEG1taiInKnTyfY04OtqCjrnKlolKr168+6NltxMWu+9S1iXi+pWIy+I0c48PjjNPzud8iJBDGfD50kYcrNxWC3kwwGsRQUYM3Pp//oUVLRKMloFFWWSScSJMNh7KWlWPLzCQ8M0Pzyy8z/3OdoefVVzWjXbkdnMJCOxfC0tKCm0xhsNlKRCMlIhMO//CUDx4+z7O67JzzY/dK/Q/yZU1hoPeNYczyeZsuWJioqHFRVaTdQVVXZt8+NJOm4667FE7nU86alxcsLLzRQXz+IwaDj6qur+chHZuB0jm2VC4LAypXlrFhRRjicxGDQXRZfdJc7oxNxJ04MYjLpWb68jLq6vEnX1FxoIpEk+/b1jMkXFUWBvDwLW7e2n1NR1t7u45FH9hAKJRFFTQeak2PinntWZj6rlyr797txucZ+9qxWA11dAfr7w5SVXRz/vHNBlhWeeOIARqP+XbsCZlIpmeeeO8X8+UXjpgK8H4LBBJs3n+bpp4/T0RGgvd3P7Nn5lJa+M62rqip/+EMTdrsxY+w8KjNpb/exa1cXmzad2fMv6Haz68EHNY8xiwU5kaD55ZeZ8+lPU3fjjR9o/ePRe+gQnTt34qqtzVyD0eGg8623OPbUU9iKi7XpREXJxDUpqZRW4JSUEBkY4NB//Ac3PP74mM+9IAgsvusudv34x/g7OpCsVuRkEiWVou6mm8ibOfOc1mjOzcUMdGzfjjk3F3t5OXqjkXQiQcsrr6DKMoVXXYUiy0SHhkhGIsjJJKqq4m1qwmCzIer1BLq6GDp1iorVqzHn5tKweTNtf/wjgydOAJAMhZDMZhAE5FgM0WDAWlSEKEkYHQ4Mdju9Bw8yePIkxQsmNhd36tvsEmfu3EJycy14PNFMQC/A8HCUWCyFy2XOKl4EQaCy0snu3d3cfPPsC+oCfzGorx/k4Yd3Y7FIVFQ4SacVtm5t49ixAe67b/0ZtwAEQbjszDHPhNcbo75+kERCprbWdcG3YD4ofn+chx7aTU9PEKtVIp1W+OMf29iwoZovfGHhFaWzisXSqCrjdoJMJj1+f+xPHiMeT/PII3vQ6bJ1oF5vjEcf3cuDD27KGNheioiiwNmckib79W5r8+HxxKiqyi68JEmHJIns3dvzgYqyWCzFj3+8i76+ENOn5zIwECadltm3z82iRcXU1LhQFJVkUgYY91x5eRbefrv7jEWZqqoc/MUvUNLpLDsLOZXi1HPPUTBnDq6amvO+hvFof+MNzK7sBI/eAweIeTzIySRGu510PE6gsxNVUUjHtPd6OhbD6HBkIpwiIwkC78VWVMQ1//RP9B44wHBDA0aHg/KVK8mpqXlf97Po8DBt27bhmj4906WKjJjgqkBOVRXWwkIUWUZOJIgMDzNUX49ktWKw2zP/JhWL4d6/H0teHqG+PpyShKWwEDmV0hodqoo5Lw9UFUEUScfj2Es1XbIgCBisVnr27JnwouzKFkdcARgMOr7xjVUYjXo6Ovx0dPjp7PSj1wsUFFjw+WL09YWyDDFFUUAUYXAwMokr/9Ooqsqvf30cp9NEQYEVURQwGHRUVjrp7w/z9ttdk73Ei4qqqrz2Wgvf/OZr/PKXR3j66WP84Adv8tOf7iUeT0/28jL85jcn6O8PUVOTQ2GhldJSO1VVOWzf3sHBg+7JXt4Fxek0YrVK4z7/Hk8Us1nPf/7nIf793w9x8KCbVEoe87gTJwYIhZLk5GR3m3Jzzfj9cerrBy/a+i8Ea9ZU4vONLT4DgTiFhZZJ16zGYukzSjoMBh09PUFaWrx4PNHzOv7+/W56eoJUVjopLLRSUmLP5MSeOjVEOJygvd3H2rVVmM3SuAWHKApZ9+T3EuzuJtjTgyU/P+vPdZKEKEl079p1Xms/G4lAAN27hgDifj+RwcGM2D3Q1UXc79c6T4qiXdfItfnb29EZjUg2G3IiccZzGKxWqq++mqVf/jLzbr01qyt3rvja2wGytg0NViugFUvhwcHM30sWC4Hubox2O67aWpLhcObfSGYzyVCIgRMnyJ81C1EUEXU6Tcg/kp85Ot0pJ5NYCwuzXg9BFEmf5VovFlOdssuA8nIHP/rRRk6fHsLni+P3x3j55RYaGz0MDETo7AxgsUisWlWO3W5EVVVkWZ1woen7ZXg4Sl9fhMrKsVsheXlmdu/u5sMfnj4JK5sYGhqG+fWvT1BR4cgkEaiqytGjAzz//Cluu+2Dh49/UMLhJAcP9o67nedymdi2rZ3ly68c3y9J0vHRj9bx9NPHqarKyQSzBwJxjh4doKLCgccTQxAEdu/uoq4uj3vuWZWlHRoejp61mzRewXOx8HpjvP56K3v39iCKAqtXV7BxY+2YgvHdrFxZzptvdtDW5qO42EY8nqa11UsiIfP1r6+csLWfiZISG6qqjsn8TCTSHDrUS3u7j/r6QRRFZcmSUu64Y8H76qofOtSH06k9XhAEli0ro7nZQ2urj2AwztBQlNtvn8+HPlTDd77jIxhMjMlO9XiifPKTZ7akSIbDmph+nIJFP+LVdaEpmD2bzrfeyhQ4iUAAVVEI9/ejpNOkYjHkZDJj2iqPCOlFoxFBp0MnSUgmE9ZxumQXElGn472tWsliwV5WhqepKctyIxEMoqbTOCorMefm4mlsxNvaimQ2Y7DbSQSDCHo9BbNna12+oSHMLhc6SSLU10cyHEZVFMx5eVSuWZN97HCYksUTLwGa6pT9CWKxFPv3u3nllWYOHeolkZicDoZeLzJvXhGLFhXz8sstWK0SixeXYLFI2O0GUimtva4oKkNDEWpqXJSVjRO9cQmhqpx1iOFK549/bMNmM2RFQwmCQEWFgx07OojFUpO4Oo1oVFvDeNt5ZrMer3fiCoyJYtOmaXziE7Pp6wvR2al1pltavBQWWpg3r5CiIhuFhVaqq3Nobvbwhz9kj/sXFlrP6qmXm3vx48gAhoYi/OAHb/L6662YTHokSeTll5v5p39686yvm8mk51vfWsPNN8/m5MlBXnmlhaGhKDk5Jn7723oeeWTPpHZyCwqsrFxZTmenP/M8K4rCtm3thMNJFiwopqLCSUWFkyNH+vjZz/ad9fV4L6NekKPo9SKzZxfwkY/MYMWKcr573zpm2wZ46/v3U9K0mfptu+k63YGiKMiyQk9PkNxcM+vWVZ3xHLbiYlRFQVXGdtOSkQi50y/8j9HajRsBzdAVtGnNZCRC3O/HWVWF3mhETibRmUwgiiiKgtHh0IxWa2oI9fVR97GPfSDLjXMhb+ZMREka06Uqmj8fs8sFikKgqwt/ZydKOs3iv/5r4n4/zVu2EPN6ScfjRD0e/B0d6AwGiubNyxR1BoeDeCCQ8S0rmjePovnzcdXWoqS197QiywS6u3GUlFC6dFzT/YvKVKfsLLxj2ppAFMWMWPeb31xFRcUHE5KeL4cO9ZFIpCku1rYQ5s4toL5+aCQjLsKJEwPU1Lj40peWXFK6pPHIz7dQVGQlGEyMEfV7PDE2bqydpJVNDD09Qez2sd1MvV57rwUCiUlPbcjJMWE06kkk0mMGKvz++BXpDSeKAjffPJtNm2rp6gqg14v8/OcHxhioCoJAaamDN95o55OfnJPp2lx1VSE5OSa83liWplPThZqZO3diolxeeqmRcDg7AaOy0klXV4CXX27m9tvP3InVNJ4OcnPN/OVfzs0U5aqqcvLkIJs3n+azn5130a/hTNxxx0L0epFdu7oRBK0jmEik+fCHp2emzkVRoLzcQXOzl5YWL3V1eed07FWryjl0qI+8PHPW6x2NpsjLsxDc8yodr7+KtbCQhfMLMNtCbD9+mBNDAXJrqlm5spybb5497qDSKObcXCrXrKHr7bezDFdjPh+S0UjF6tXn/+ScAXtpKav/7u84/J//SaCrCzmdJhEIYHa5tK27vDxS0SiKoiBZLOgkSdNcAXIySf7MmUy7/voLvq73YrBaWXD77Rx+8kkt19PpJBWNEvN6Wf7Vr1J99dWE+voI9/Ux3NjI6c2bad+2TdOBORyIggCqijSSCWrKySHu92PKyaFyzRo8jY342ttJhkLkrlnDwjvvJBEM0vjCC0Q9HgDKV65k7qc/rQ0DTDBTRdkZiMVSPProXiRJpLr6Hdf8UbHuj3+8cVLCrwcHw1nnnTEjj6IiG253ELc7yHXXTeOOOxZeFvYQoihw223z+MlP9pBKKeTlmUmnFfr6QhQWWlm79uKMhl8qlJU5aGgYHlN4pdMKoiiM2RKZDAwGbTvvN785SVWVM7OdF4kkSSbls06XXe7Y7UbmztWMJMPhsRoxAEkSicXSpFJypmg1GvV84xureOSRPXR0+DOPzcszc889qybkviHLCnv29FBcPLZbXlJi4623Ornttnln/eH22mutuFzmrC6pIAiUldnZvr2Dm2+ePWmTzyaTni9+cTGf+MRsBgcjHDrUy+uvt47Zphy9vq6uwDkXZYsWlWR+7BYVWTEatY5wNJrirltr6PqfZ8ipqclonmZNdzCjKs1w51E+/MPP4Cw5t+29UbPXnr17UWSZRCCAKSeHVd/4BqacizOhmz9rFhsffJC+w4dp/sMfMDgcxHw+Ldi7sBCjw0EiGMRSXEwyGESVZRRBQDIYmPGRjyBOUGxT1YYNWIuKaH7lFQIdHVjy86lYvRq9ycRwQwNxn4+mLVvQm814GhoQ9HqUZJJUJILeZCKdTBJ3u4lIEkG3G1NuLoVz5uCsrMRaWIhksTD9Ix+h7mMfI9TdjcFq5doHHiAdj6M3mSZRIEgCAAAgAElEQVSlGBtlqig7A8eO9RMOjzVtzc0109Hh59SpoUlxty4qsmWmfkZxOIw4HAVYLBIf+lDNZVGQjXLVVUXce+86Nm9u4PTpISRJ5EMfquWjH627YqYrz8SmTbUcPNiLy2XK0pR1dwfZtKn2knkdr79+GuFwktdea2FkaAmLRc9Xv7pizOfjSmX69Fz6+kJZE9CgWSeUltrHTFNWVjp58MFNnDo1hNerTU7PmVMwYT/kVBVkWR1X23aukWQDAxGs1rHvQUnSIcsKkUhq0u1ocnPN5OaaGR6OnLXAHO86zoQk6fj611fyxhvtbNvWztBQlDlz8rnxxjqEtoMMwxjvKp2kx6BXCbS1nHNRpjeZWPKlL2HJzeXEM88g6rVO7L6f/YzZn/gEM2688aLsdiQCAY4//TSpaJSShQsZamhAjsUI9/eTO306MZ+PkNuNqNdjyskhGQqRDIXoevttZt98c2ZC8WKTP2sW+bNmkYxE2PfYY7S8/DKCTkc6kaB3/36KFy1CZzSiptNIZjM6h4NUNIpksxHv7UWVZUSzGZ3JhJpOM3DsGPbyckqXLKFi7Vp8ra289rWvZbR1lsJClvz1X0/KluW7mSrKzsDwcBSd7swfiFGz1olmyZJSnnnmJIFAPKs9PjgYpqTEzowZ5/Zr8FKiri6Pv//7NSMmjcKkj9xPFLNm5XPbbfN45pmTKAoIggoILFxYxKc+NWeyl5dBpxO55Za5XH/9NLq6AkiSjtpa1yVt63ChuemmmTz44C4sFinT2Uwk0ng8Mb7ylfE7TgaDjoULJyeWSK8XueqqQlpbvRQWWrP+bmgoypIlJX/yC7+iwkFbm4+CguyviUQijSTpLqlBonnzitDrxTERbPF4GkkSMx3Pc8Vo1HPDDTPGxCi1tJ5FCKuq42rEzkb/sWM0/v735M+cic6gPZ9yKsXJZ5/F5HJRuWbN+zreudC2bRuJUIicykpMOTkEuroQbJocRtDpMLlcxH0+jE4nkYEBEASMdjt9hw/z6j33cNMvf4nRPnF65ZPPPIO3pQVndTWCIBDs6UFvNjPc2IijrIxUIoGcSCDqdIg6HdHR6UxJQkml0BkMGHNyiHm9hPv7WfTTn9L51lvs/9d/JTI4mPEB9ba24mtt5cZ/+zfyZkxefNaU0P8MFBRYkeWziXUnp705GkSeSilZFhl2u5GvfW3FZV3Q6HTiZb3+94sgCFx//XQefvg6vvjFhdx++wK++931fP3rKyckjSEcTlJfP0hDw/CY7ut4OJ0m5s0rYtas/D+rggw0ndiXvrSEYDBBV1eArq4AXm+Mz39+AcuWXZq6uptvnkUiITM8HEVVVVRVGwKSZYWPfexPm3l++MPTCYWSWe8NRVFxu0Ncf/30S+o94HSa+Ku/WsTAQBi3O4jfH8ftDjIwEOHOOxddMClA/syZWvH1nunAUQuJ9/tl3vT732NyuTIFGWi2GNbCQhpfeumMxuEfBPe+fVgLNF2jwWqlcu1aJIuFdCJByO0mMjBA1TXXYM7Lw5yXh3NkstFaVET/0aPsf/zxi7Ku8UiGw3Tv3o29rOwdfzXtFyyJYJDeQ4dI+HzI6TTR4WGS0SjpREKbHk0kSMfjhNxuPI2NJAIBOrZvZ+9jj3H4yScJdHejN5sxOZ2YnE6MTieBri72P/74hFzbmZjqlJ2BBQuKcTiM+HwxXK53CjAtMsXC7NkTI9Ydjxkz8nj44euorx8kEIhTUGBl9uyCjN5nisuLvDwL69dXT9j5VFXl979v4qWXGjNTZmazni98YeFUfuhZWLu2kmXLSmlv96OqKtXVOZM+iHE2ampc3HvvWp599iQtLV5AoK4uj898Zu45mavOmVPA7bfPH+nkqoDWyV23rpKPfnTyg7jfy5o1FVRWOnjrrU56eoKUlzvYsKGa8vILlz7grKqibMUKrVAoLUUym0lFo4T6+qjduBFb8djOqJJOEw8EkMxmJEv29re/vR1H+VhLGaPdjr+zEzmZvODTjqMh46OYcnKo2rCBZDhMoKsLUa/HYLWSCofHaNsMNhuepiZ8ra0XZUL0vSSCQeA9nmV2O9HhYeJ+P8LIWkkkQFVJx2Ka95hOp+26jOaBqipKKoXeaqXjzTcZPHECc25uVralONIl7Ny5UyuyJ0g/916mirIzYDJpYt1HH9XEuqNBqwUFFu65Z9WkF0Amk54lSy7NX+iXC5FIku7uYCYaZbJf04lix44OnnuunspKZ0bjFI2m+PnPD3DffWamTz9zePCfO0ajnlmz8v/0Ay8Rpk/P5R//cR3hcBJBEN7XlqMgCFx33TRWrCjj9Olh0mmF2lrXGM+6S4mKCudF9fcbjRNyVlbS8sorRIeGMNjtzL/99ozlxCiqotD+xhs0vPgiqUgEBIHyFSuYe8stmWLHnJtLKhbLeIeNkk4kMhOQHxRVUfC1tREZHMRgt1O+ahWNL76YdU5BEEjH45QsWoTR6aTxxRfHbG8nw+FMykCor29CijJTjpZuoqTTiHo9cirFwLFjJAIBbWtSFEkEAsipFAJkPNZ0ej16iwUlmUTU6TTvzkQCa3ExeXV1dO7YgaVgbGNltGBNRaMYbJNjkjxVlJ2F6uocHnpI60j5fLEJF+tOcXFQVZWXX27mhRcaMlvUdruBO+9cNCnDGxOJLCu8+GIjJSX2rPexppXS8/LLzfzv/71iEld46aOqKvF4GlEUJl3ofi580Egyp9PEypVXjkHwB0UnSdTdeCMzbriBdCKB3mgct6vStGUL9c89pwVj5+Vpk5b79uHv6GDD/fejN5mYdv31HPt//y/L+V5VVUK9vcz6i7/4wN2amM/Hvp/9DH9HR+bPJLMZvcnE0OnTyMkkqVgMFAVrURHzvvUt9CYTbVu3kohEkGw2UFWSI1ONuTNmEBkYGNPx+6CkYpohs96UPeEsWSxUX3MNra+/Tk51Nd6WFmI+H3qLJVOkqaqKTqfDlJOD3mwm1N+vdc3i8UxBpqQ0v8X8mTMxWCwY7HZiXi/Su4YWVFUlGQ7jrKzM2k6eaC79O8okYzDoWLSoZLKXMcUFZMeOjozFw2hhEg4n+elP93H//eupqXH9iSNcvoRCSYLBxLjbVy6XmeZmzySs6vKhqcnDb39bT2urV+uaLC7hk5+cTUnJpds9muLiIIjiGa0TkuEwjS+9pH3Bj3S7RJ0OZ0UFvo4Oeg8donLNGqqvvprhhgZ6Dx5EEEXNwFRVKVqwgBk33PCB1qeqKgd+/nOCPT1Z+Zpxvx9fRwcJn4/I0BCCKGJyOlHTaeLBIMXV1Wx68EFe+uu/JhkMIkoSudOm4Zo2DVVR0JtMFMy5MINI/o4O6p97jqFTpxCAgrlzmXvLLTgr37FDmvOpTxH3+XAfOMDA0aMIBgMCYC0sJB4IZAooVZbRGY0YbTZti9PjIR2LIYgiBpsNs8uFo6wMVVXJnzmTkNtN3O9HlCRQVeRUCoPVytxbbpnUouzPY79miilGkGWFF15ooLQ0u1NksxkwGERefbVlEld38TGZ9Ge0RIjH0+TkXNoB9pNJQ8MQDzywk4GBMJWVTsrLHZw4McA///NOhoYuTM6sxxOlvV2L7vlzQVFUWlq8HDzYS1ubb8JE5BeTQHc3qqKMu/1osFrpP3oU0LpuC++4g7yZM/G3t2umrqkUrmnTNC0UkIpGGTp9Gk9TE3Iyec5r8Hd04G1pGWNhoTebGT51CltpKXNvuYU5n/oUtZs2YS8v5+ATT5CKRsmrq2PjAw9QvGgRhfPnYysp0XRcPh9L7747qxiNDA4y3NBAuL//fT1H/s5O3vrnf8bf3o6zshJHRQXe1lbe+uEPCfb0ZB6XikSwlZZqGZ2qSk5lJdXXXJMZrhB1usy2JYKA0enEaLdTunQpeTNmULJ4Ma5p09AZDPg7O2l9/XVyZ8xgwR13YCspQW8yYbDbyamuZtp11zHnk598X9dxoZnqlE3xZ0UgkCAUSmYNb4ySm2umsfHK7hSZTHrWrKlg586urG7Z6GTeX/zFrElc3aWLqqo8++wp7HZjZvJaEKCkxE53d4CtW1u59dbz1zL5fDF+9aujHD8+kJlAXreuis985qoJmcSdLAYGwjz++H7c7qCmHVJUampy+MpXlo/xhLucGC2oxkOV5cw2nZJOs/dnP8Pf1kbl2rWIej3pRIKGF14gGQphLSig4YUXUNJp1JEtOXtpKXqDAWdlJbWbNp1R2xUdHh43XzPc36+FcL8nxshgtRIdGmKwvp6yZcuoWL2anOpqunbtItTbi7Oykso1a7RAbzQR/uEnn2Tg2DEEnQ5VUSiYM4fFd92lxSH9CRo2b0bU6zPHQxCwFRUR6uuj8aWXWHb33YR6e9n5wAOkIhFMLheW/Hy8ra04y8txVFYyMFLcyuk0Or2e/JkzMefm0vXWW1gKC7GXleFtbSUZCiFZLPja2pCsVuI+HxGzmZVf/zqBri5URaFk8WKKFyyY1C4ZTBVlU/yZoUXlaB2z9+Y5xuPps0ajXAii0RQdHX5EUaCmJmdSNEmf+tQcOjr8tLf7sFoNyLJCLJZm1aoKVq+umPD1XAxSKZnu7mAmame8IQ6/P87Bg70MDUUoK3OwZEkJVuv4N+RwOElHh2/cbd+CAiv79rnPuyhLpWQefngPg4NhKiqciKKALCts395OJJLkb/92+Xkd91InlZL5yU/2EA4nqKrShO+qqtLbG+LRR/fyf/7P1eNmrl4O5FRXY7DZSIbDWYJxVVVJRaOUr9SC3YdOncLX0oKzqipTPOmNRlzV1dQ/8wx6s1k7lt2Ot7mZgWPH6D1wgLKVK4l6vXTv3cviO++kasOGMWswOhzjdh3lRAJ1JEpplEQoRHhggHBvL8MNDZQuWYIgithLS5n76U+POYaqKBmt2ujaVVXF09TE3kceYcP3vz/GYPfdKLLMwLFjOCrG3m+shYX0HjyIqqocf/pplFQqs51ZvGgRXW+/Taivj5IlS0iGQlo0laJQsXYtubW1JCMRypYvZ9qHP6xNlHZ20rFjB+b8fARRJO714j5wgGQoRMeOHdRs3MjsT3yC0qVLL4lowqmibIo/KywWiRUrytm3rycrv1RVVYaHo9x88+yLcl5VVXnttVZ+97vTpNMyqqoViJ/73IIJL4TsdiP/+I/rOHasnyNH+jEa9SxfXsasWflXhE/czp1dPPvsyUyYut1u4I47FrJ48Tva0GPH+vn5zw+QSikYDCLJpMxvf1vPN7+5alxN4dmeF01ofP7Fw4kTg7jdwax0BJ1Omwg+cKCX3t7QJT3xeL6cPDnI4GAk67oFQaCkxE5Hh5+mJs+kWg99EHSSxMI772T/Y4+RCIUwu1yk43FiHg/lq1dTMFu7zwydPo3OYBhTDKSiUdwHDwLQe/CgFofk92vWDokEHdu3kzdjBnmzZnHsqacoXrQIoyPb+iN3+nSsBQVEh4ex5L8zMTx6PvuIvmp4ZGsUNC3c6eefJ9zfz/KvfOWMmjlvSwvelhZyqqszfyYIAo6yMvydnQyfPk3hVVed8fkRBAE5ncbb2koiEEBvMuGoqMDkdIKqIuh0xP1+hk6fztKXWQsKKFu+nN6DBxk6cYLcmTNRGhq07qHRiL+zE51ez+pvfYvihQsB2PH971Mwdy6Bzk6CIzqyUcsLf0cH/o4ODjzxBHIySfU4xe1EM1WUXQYoikpTk4fOTj9Go57584smzbz2/RKPpzl2rB+3O0RurpnFi0smPdPxL/9ybsZ412YzZCJjVq4sZ9Wqi1Mg7dnTw69/fZyKCmfGdDMWS/GLXxzE5TJN+JePVoiVs3z5lTVVd/hwH//xH4coLbWRn691AsLhJI89tpd7712HXi/S1xfiF784SGmpPasz5vfHeeyxfTz00KYxE9ZWq4E5cwpoa/NRVJQ9Kj84GOGmm/60GeuZaG72jGvEKggCgiDQ3R24Iosytzt0xmJXVVX6+8MAtLX5MJslFiwouqy2NEsWLmT9/ffT+tpreBobMblczPn0pylfsSIzVakzGDRx/wipaBRPaysd27eTDAYx2u0o6TT+jg7SsRimnBwMVityKkUqGqV3/35c06YxdPo05Suyp6ZFnY4VX/saux9+GH9Hh+Zwn04j6nRUrl2rFSeqynBjI0aHg1QkgqO8nML58xmqr6dh82bm3XrruNcW6u3NdOGUdBpGtF2gvW/9XV1nLcqCbjehnh6CbremFZNlvC0tFMydi2Q2U7luHcGeHrzNzQyePIkgiuRUVeGqrcVRVobJ6SQRDrP27/8eS34+Q6dOEXK7MblclCxerBV3QHhggNbXXyc6PEzU40GRZVAUzWpjxP7C29REzbXXUv/b31KxatXU9uUUZyccTvKzn+2jqcmDIGiZdqIo8LnPzeeaa2ome3lnxe0O8pOf7MHrjaHXi8iywv/8zwm+8pVlzJ8/edYTTqeJ++/fwK5dXeze3Y3DYeTaa2uZPTv/omyXqKrKCy80UFhozfryNZslHA4jv/9902XbEbiUUFWV3/3uNHl55ixTV5vNwOBgmLvv/gNVVU76+8M0N3uZPj2XxYtLMq9JTo6Jjg4/DQ3DzJs3Nr/wM5+5ih/+cCdud5DCQi3xY2AgTGGhlWuvrT3vdVutBtLpMycqXKmaMpfLlDEvfi+yrPLcc6eIRlOIorY19vTTArfeOo+NG8//uZ5oXDU1LP3yl8/49yWLF9P44ososkx0eBj3vn0E+/pIhcOoikI6mdSyG2Xt/TFqTaGTJCSLhUQoRMjtPuMAgKOsjI0PPEDf0aMEu7ux5OVRsngxiixz6N//nVPPP4+SSmX0ayVLliCKIvayMjp27GD2zTePsakAzaoiGQrRtXMnUY8HBAFHeTn5s2ahKkomhmnU3/PdqIrCwSeeIKe2lmQ0ipxMYrDZUNJp3Pv3U7VuHcULF7L/8ceJ+/1IVis6ScLT1ETQ7aZq/Xpifj+1116b8U17b0EKms3G7gcfRJQk5JG4JTUWQ06nSYxozMSRQYxUNIqcSBDq7c3q/k0GV+an/Qri178+TkuLh6oqZ+bNnUzK/OpXR6msdDJt2qVp9CnLCo89to9kUs7anohEkvzrvx7gwQc3kZNzcfVbZyKVknnxxQb++Mc2zcNG0bICy8sdF2VNsViaoaFIRjfzblwu05QNxQUikZBxu4NjdF/JpMzp08NEIinWr68iGk1htxvo7w9z+HBflgeXIHDGyceKCiff//4GXnmlhQMHepEkkRtvnMGmTdM+UPd3yZISnn/+FKmUnNWhi0SSmEw6Zs68fMxq3w8LFhRjMumJRJJZHctgMEFPT4DKyhxqanKy7ntPPXWM6uqcK8bg2FlZSe1119G8ZQsDx4+jN5tJhUIIOh0GsxklnSYZCmkPFgRUWSYVj+McSQHQm83EhofPWkjoTSYqVq6EER3bKGvvvRdfWxsqYHI6MdhsmedaJ0kospwpAt+LoNMxWF+PZLFonS5FwdvSwtDp07hqaogOD7PtO98h1NuLtbCQGR/5iDbIoNPh7+wk3N+Ps6oKS14e3pYWAj09CIC9rIyyVato27oVQafLbFWOhqPH/X4Gjh3DWVVF7bXXnvW57T9yhKjHQ8nixfjb21FlWbvfp9Okk0nSsRhGu11LARhJOBDOooObKKaKskuYQCDOvn1uysudWb82DAYdJpOe7ds7LtmirLnZO24hYrUaGB6OcfBg76T94v3v/z7GW291UlHhQJJ0KIrKkSN99PWFuf/+DQgC9PS8IxL/oN0zg0GHwaAnmZTHbFNpNhSTU5xeaej1IpKkI51Wsoqb3t4QsVg6UzjZ7QZUFZxOI/39YUKhRJa5akGBdcyxRykpsXPnnYu4885FF2zdJSV2Pv3puTz77ElMJj1Wq4FgMIGiqHz1q8uv2E6ZzWbgb/92GY8/vp/h4ShGo55EIq1lSOZZqK7OOcN9r/2KKcoEQWDeZz+Lkk4z3NgII9ua4khM0GgRMfpYVZaRLJbMFmbc78deWjpuVNO5nLtg7ly8LS1jAsbTiYQW5D1O8LiqqjS+9BIlixbhaWoi7vUS9XpJx2LalKgss/Vb39IsKWbOJBkOc/iXv8Tf0cGCz39eSzcYuU7JYqFo/nyK5mtDMtHhYZKhEIP19VgKCgj6/egMBkK9vehNJsQRrdmN//Zv70xtngFvczN6kwmT00nZ8uW0b99OOh5HkWXNqNZiQTAYiAwNEfd6cVZV4Sib/Ji5K/PTfoXg98cRRWFc3YXNZsDtDk7Cqs6NQCAOjK8XMRhEBgcvjK/T+6W+fpCnnjqGLCu43UFqanIoLXVQXu6ko8PPU08d48iR/oxI3Ok0cscdC1i48PwNhPV6kWuuqeK111qzilRNNxPh85+/eLEwf4pRXcilMHX0QdHrRdavr2Lbtras59nrjZJKydTWagL+4mI7JtMQsZhWAITDSWw2rXNWWemclC/8G26YzowZuezY0cnAQJilS0vZsKGKsrILl9t4KTJvXhE//vFGDhxw098foazMTlGRjZ/+dM8Z73u9vaFJWOn5IadSDNbXM3j8OJ7mZnSSRG5dHdUbNmAv0e4pgiCQU1lJwdy5RIeG0EkSyVgMBEHTnilKRg+m6nQkfD56h4YQAGNODku+/OXz/vxOv/563j5yBJPT+Y4Jq6IQcruZ/YlPjKuvSgSDBLu6yJs5E3tZGW2vv65FeBUXIxoMhN1ucmfMYLixEXtpKUaHA4PVSsf27dReey224mJURRk3XzIZieCqraVnzx4GTpzQDGENBsy5uaRjMexlZeTV1WWZ4b6bdDxO35EjDNbXM3zqFFGPB2tREfmzZzNw4gSKLKPKMkoqhajTIcdiiJLEYH0967/3vUnLu3w3U0XZJYzLZUZR1HHtG0KhJHPnnv2XwmSSn29BVdVxNQWJhExZ2cQLl9vbfXz3u2/Q1xcmP99COJzkwIFeSktDLFtWRjCY4L/+6yhr1lRkicR/+tN93Hff+g/0Zf2xj82kqclLa6sXq9WAoqhEoymWLClhw4bqC3SF505XV4CXXmrkyJE+JEnHhg1V3HDDjMu+a/fxj8+ksXGYjg4fDodJi6wJpbDZDNTUaIWaXi+yenUF+/b14PPFGB6Okkho2+xf/erySZlAFQSBGTPymDEjb8LPPdm4XGauu+4dr61gMIGqjm9bc6nf995NoLubvY8+Sv/Ro/g7OzNB33l1dbT/8Y+s+NrXKJo3DwBbcTHRoSGC3d1Yi4tJd3aipNPoJAkZbSBATiYRdDp0RmNGg6WmUpx+/nmq16+neMGC973GgjlzWPD5z3PyN7/RRPAjVK5bx4yPfnTcfyMIAuq7/l/Q6XBNm4YgCJq+bGS9qViMQHc3hXPnasWOIDBYX8/066+nfOVKevbswVlZmSmEYl4vBquV6g0b2PuTn6Akk5hz37nnGqxWfK2t1G7aNO66Yl4vux58kHB/P3qzmUQgQP+RI8iJhDadarejyjLxQIA0mimtoNejGwmVb9+2jfy6uimh/xRnxuEwsnp1Bbt2aUafo8VNIpEmmZS55prqSV3f2aitdVFTk0Nvbygrgsbvj2O1ShMepq6qKk8+eQSTScJikUa2EbXtELc7RHl5iLY2LxUVzjEi8Wg0xe9/38g996w67/NbrQb+4R/Wcvx4P4cP9yOKAitWlDFnTsGEezG1tnp54IG30etFSkvtyLLK1q2tHD7cz333rbvoXm0Xk1G7j4MHe9m/341OJ3LDDdN59tn6LFG5w2Fk0aISotEUd921iKIiG7W1riuiY3i543AYWbOmkrfe6szS0sbj2n3vQx+6NAeckuEwge5uRL0ee2kpex55hLjfT8zrxVFWhiCKpKJR/B0dlK9axcFf/IIPP/ooOoOB3BkzCPX2EurtRWcwoDMaScdipBMJrWhRVXKqqkgnEpplhCBgLCxEstmIe70c+b//lw8/+uh5dXpqN26kdNkyhhsaUNJpXDU1Y1IA3o3BbidvxgyCbndmQnj0NUrH41peJlphFvN68TQ3ExkYIBEK4WlspHbjRhbecQcAPXv3ZqYgbYWFLL37bi36yGgkMjhIsKcHcWSoQW82ozMazxjSfvzpp4l6PBl9na2oCBVw79uHqNdrEVFmM1aTKVPw6U3aD7dEIIB73z4s+fnMv+229/0cXkimirJLnFtvnYfPF6O+fijTddLrRb74xcWXdEajIAh85SvL+dnP9tHZ6UdVNRG102nim99cjc02sb9G+vrCme3K5mZPRlQtCAImk57WVh/hcIpp08Y+p3l5Zk6dGvrAazAYdCxdWsbSpZOnW1BVld/8RtMujXYDdTqorMyhs9PP9u0dk+bq7/PFSCZl8vIs45q9nitms8S6dVWsW/fOFofVauBXvzqaeb1jsRRWq4HvfW/DuAMYU0wun/3sVfj9cY4fHwBUQECSRO66a3HW4NBEEQ8E6Nm3D19rK5bcXMpXr8Y5YnyqKgqNL71E05YtKIqSCcNORSLozeZ3tiHRNFRxv1+b9ksm8TQ1UXjVVXTv2qUVNFYrSiqlhZyPiM5Lliwh2N0NqoqtuHiMd1hapyPY3U14YCCzJfp+MTmd404wjocgCFz12c/y9gMPkBrRaI1adJhyckjH46iKQioSIeb1EvN40BmNJAIBOnbsQNDpWPo3f8PSv/kbZt98M+H+fiSLBVdNDXIyybb77mPo5ElSsRhKKoUSjZKKRDA6nRTOm5cR5We9Pn4//UePjtHW5dfVodPrUWSZkiVL8DY3k47H8Xd2ko7HMdjt6CQJW3Exrtpa2t94g1kf/3iW4e9EM1WUXeJYLBJ/93eraWvz0dUVwGTSM3du4aR7fZ0LeXkWvve9q2lp0UT/druR2bPzx3hAvZdoNMWxY/0MDkYoLLSycGFxVvfqfEilZERRQJJ0LF5cwr59bkQxjcmkz/wCr67OwWIZe55EQp7wIvJiEQwmaG31ngBHAwcAACAASURBVNGZfteurgkvyvr6Qjz11HFOnx5GFLXu5M03z2b9+qoL1rnasKGauro89u1z4/FEqalxsWxZaZbAf4pLB7NZ4p57VtLR4ae7O4jRqGPOnIJJeb187e3sfvhh0tEoktVKOh6n+ZVXmH/77dRu3Ejbtm2cev75rPDxwZMnGTp1SsuwHKd7lYpGM5FKiixzevNmcmpqiA4OojMatWJElgm53QzV16OOFD6JQICc6uoJ2WLztbfja2/H6HBQMGtWVqHiqqlh/Xe/S9OWLXhHxP4Fc+eSO306nqYmhhsaiPt8mAsLkaxWkqEQuTNmUHDVVbj37qVkyRIqVq7EWlCAteAdO6DGF1/EO5LxaXQ6M8MOcjyOOS8Pf1vbuJOmyUgkq/h9N9aiIvRGIyvvuYfffupTeJqaEPR6EATCfX2IkkT5ypWZaKyoxzNVlE1xdgRBYNq03As6aamqKgcP9vLyy8309oYoLrbzkY9MZ/nysgu6hSOKAnV1edTVnZtWpq3NxyOP7CEcTqLXi6TTCjabgW98Y1VGqH0+FBfbMBh0JBJpSkrsXHNNNe3tPny+OBaLns99biE2m8Sbb3aOyYQcGAjz6U/PPe9zX0pouv6zudNP2FIArTv2L//yNqmUTGWlQ9OlRFP88peHURT1gnrxlZTYp7I9LyMEQaCmxjWpOwKKLHPg5z9H1OtxvktcLieTHP/1r8mrq6PxpZewl5ZmbauZc3MRRJFkJJJlDjuK3mhESadxlJcT9/tJBAIUzplDx+AgqCp6sxlfWxuCKCLH45SvWsVQfT3xQIBgTw85NTUIgkAqFiPm8yHodOx55BEq166l5pprxrj7vx8C3d1s/fa36R1JFJAsFlzTprHqa1/LinNyVlSw7H/9L+bfdhv7Hn8cb3Mz4f5+DA4HjspK0vG4VlSl0xTOn09ubS2CKGLKzaVjxw7NpuNdyMkkbW+8gajXozebtSLYYkEURVRJIjY8jNHpxJQztlNqyctD1OuRk8kxBWu4v5+SxYvp3r0be0kJyWiUYFcXOp0Oc3ExoiTh7+jAWlSEqiiTWpDBVFH2Z8vmzQ1s3nyavDwLhYVWAoE4jz++n5tumsktt0xOARKPp3n00T3o9WLWFoXfH+fRR/fw0EPXnbc9gNGo5y/+YhZPP32c0lI7TqeJBQuKGRyMIEk6brttHqIo0NTkpb3dj9NpRFVVAoEEs2blX1aGlWfD6TRSWenA54uPSYUYHo5y440zJnQ9O3d2EYkkqKx85/W2WCRKS+08//xp1q6t/JOd1SmmuFj4WluJeTxZBZkiy0Q9Hnxtbbz94x8TGRykYM6crH9nLSzE6HCQDIUw2u0kQiEMNhvpeBxRkkjH41SuW4etqIhkWEsuMNhslC5dSveePUQ9Hm3702jEXlJC8aJFpONxBo4dIxEMEg8GQVUJdnYi2WyZgYGGzZvp3rWLdf/4j+MWL6Bp33oPHcp4iJUuWZJ5bHR4mC1f/jKepiZsJSVaHFIigbe5mV0PPYS1uJj8mdnpFUaHg3X33ou3tZVQby+S2Yw5L4+3/vmfcZSWamHlskwiFELU6xEliWQgMGZdqViMYE8Pw42Nmr2GopAaHtasMCQJUaejaN48JLMZRZbp2rmTlldfJTri05ZXV0ff0aO4qqsRdTqSkQju/fsJ9/aipFIc++//xllVRdnixcjxOCanU9OzqSrhgQECXV0UzZuHJW9yh22mirI/Q4aGIvz+941UVeVktDs5OSbsdgMvv9zM+vVVFBdP/K+FkycHCYWSYzQjOTkmOjv9nDw5yNKl5z8gcN1109DpRF54oYHh4SiKojJ7dgGf+9z8zNThffetY/9+d0Ykvnp1OYsXl44bg3M5IgiaK/qPfrQLWVbIz7cgyyp9fSEcDuOEi6iPHOnD5RobGWY2SwwNRRkYiFBefmXbQkxx6TK6LTZKOpGge88eEj4fcipF/5EjRIaGkFMpihcsyOwyiHo9RfPnM3TqFI6KCrwtLYTcbnRGI4VXXcW0665j7l/+JaAVYyWLF9O2datmoiqKiKKY6WcXzpuHKIqUr1yJpaCA7rffJuH3A+AoL6fq6qszhYTBasXf2Unzq68y7zOfGXM9vrY2dv/kJ6QiEXRGI3IySf2zz7L8q1+laN48Wl59FV9rK7aiosy266h5bKi3l5ZXX80UZYosIyeT6E0mBFHUsjhnaD/q5FQKg8WCnEoRbG/H09iYMW8VdDoW/tVfjVmbv7MTX2srktlM0mDAYDRqZrmxGEanE2d5OZLVSk5NDUeefJLOnTuxFhZiKykhMjREuL8fRVFo3rIFBUj4/UhmM9XXXou9uJjB+nrc+/YxYDYjShKJYBCjw6FZkIRCmHJyWPiFL3ywN8wFYKoouwxQVZXOzgB9fSEsFolZs/IxGs//pWtoGEZR1DFiap1O+9Vw6tTQpBRlPl+Ms+2c+nyxD3R8QRDYuLGWDRuqMmaV7+0Wmc0SGzZUT4pNxSiKotLS4uX48X5UFebPL2LGjLwLZtUwc2Y+3/nOOn73u9OcPj2EKAqsWVPJTTfNHLdAupiYTHo8nrGvq2anApI0+b5BU/z5Yi8p0Ty1Roashk6eJBEIYHK5iPv95NXVYcrJYai+HltRUUZor6oqcirFtQ88gMFmIzLiP+asqMBeVjbGlLX66qs5+ItfAFqRJttsJCMRxBFz07y6OkSdjvyR/y758pc5+uST2EtLM1qod6+5c8eOMUWZnEyy97HH0EkS1nfpspKRCPv/9V+57qGH6N69W+tKjRxTkWWSoRCJUIh0LEbX22+z+K67aH3tNdr++EfS8Tgml4uZH/sYVRs2ZCUCzPz4x9n5L/9CdGhIy5rU6UjFYqTCYfoPHybm9WZZXjRv2ULujBn4OzqQzGbSsRg6gwG90Ujc6yV/5kyqr7mGZChE965duGpqMhoyvdGIv72duM9H5fr1+Ds6iI94lFny8gj19ZEIBFDSaVLRKDnV1SSDQVRVxZybi6uujlkf/zhtb7yBJTeXkiVLMvmZE81UUXaJEw4neeKJA5npP0HQtnfuvnvZefv1yLJyxuJHECCdHjvdMhEUFFjPqGlS1bM7rb8fJEmXZdNxKZFKyfzHfxxi//7eTEGyZUsTS5eW8uUvL71gW3nTp+fy7W+vyQxATLQtxygbNlTxxBMHcLlMWVpGrzdGVZWTwsIL85pPMcX5YCsupnTpUnoPHcJWVKQFaNvtpKJRdEYj9tJSbMXFhAcH6Tt4EHHlykx3p3LtWqrWr88EdZ+NoVOnKFmyBFVRCPf1abomQcDkdBIdGspscXbu3El0eJh0NMpwUxPlq1bheI99haDTIadSqKpKMhymbetWOnbsINTXR7Cnh/L3aLkMVivRoSH6jx5Fsmqft1Fz10BXl5arKYqkYzE8jY38f5/9LEa7HWdlJdbCQs2x/8kniXm9zL755sxxK9etQ7JYEPR6reOoqhjsdipWriQRDtO5cyezPv5x7Xyqiqe5meKFC9EZDHibm1EVJXPdktXK4i9+kbqPfpTTL7yAKElZov6hU6dQ0mkkq1XTpBkMWIuKiHm9+Nra8Le3YykoIDSylakqCpbCQqJDQ6STSQRV5eivfpUx6T3xm9+w7O67KVl04ZI7zpWpouwS57/+6wgNDUNZfj2hUILHHtvHv/zLtRlbg/fD9Ol5qKrWkXl390VzdxeYOXNy9tTnzi0gL8/M8HA067qGh6Pk5ZmZO/fKD+3evr2dvXt7sjyzVFXlwIFeamtbufHGugt6vsnWay1ZUsrChSUcPdpHbq4FSRLx+WLodDq+8IWFU75hf6aoqoqvrY2Yx4PR6SR3+vRzKm7GYzTsW9TpMOflve/31KI770QF2rZuJeb3o8gyRoeDsmXLMqLyqvXrCXR0YC8txVpYSMXq1RTMnn3OvmFBtxuT04k5N5fcadMy19+1axepcJiON9/E196Omk5jLy3F09JCMhSifds2ihctIhWJZCwpDDYbFatWkYpG2fWjHxFwu7EXFyNZLMQ8HjrffJPKtWuzulSiXk/U46HmQx/StmeDwYxth95kIp1IoDcaya2ro//IESpWr0Zv1CZhDTYbOSYTTVu2UPOhD72jTxscxFZURMmiRZqeTKfDYLcjCAKiXs/A8eOZokwYKUDlZJKiefPInTaNmM+neYuZTCjpNDNvukl7PmU5a0tZSaczxXIiFAJVRTQYQFEwWK14mppAUdCZTBgdDsIDA0Q9HkzJpKZvC4WwXHUVlvx3MmZT0SgHn3iCjQ8+iNk1sYMmU3sDlzCDgxEOH+6joiI7+9JuN5JOK+ze3X1exy0rs7NuXRXt7T5iMS1OKB5P097uZ8WKsnHtEiYCSdLxjW+swmzW09npp6PDR2enH7NZzze+sWrSC4iJ4NVXWykpsWe93oIgUFJi47XXWjOxSFcKkqTjq19dzpe+tDTjT3bttbX80z9dMyl+VFNMPjGvlzd/8APe+uEPOfiLX/D2j37EG9/5DkG3+30fy71/P1u//W223Xsvr3/727z5gx/ga29/X8eIDA1pXlsGA4xkUf7/7J13YGR1ufc/50zvM8mk92y2996XRZamF66IKF7bi/eKyAUEUVFfUK++7wVUvIKIBQX11StepEhzRZdll3V7Lymb7KYnk0wymV5Pef84k8OGBFjaLqz5/AUpk1/OzOY88zzf5/v11tZizo8gU6EQnS++SDwQYOTECQaPHtU0X2+g+HNXVJBNJgGtIA21tjJ45AgWpxODxcLIyZMomQwFU6fiKC7G6vVicjhIDQ3RvmkTicFB5GyWUFsbfXv24KmpoXPrVqK9vfhqazFarVhcLm2b0WjUIoxO+VuiSBKuigpq1q2jZs0a/fdWFIVMLIaczVK2aBGgbWOGX3ENRaNRO/eJE/rHDGazriGz+XxY3G7975qczY7bcqzfsIF4fz+qqmKy23FXVOCurCSXSFC/YYNe4BbPm4eSzernH00iGP1/q8+Hu7JS23oVRaRslnQkQri9nVQohNFs1sxoHQ7806cjiOKYgmz0d5QlSd9APZNMdsrexQwNJRFFYcJ3dg6HiY6O8Jt6XEEQ+F//awFlZU42bmxjcDCB3W7iqqtmccklDWe1O1FR4ebOOzfQ1BQkHE7j9VqZObPoLZmJvldQFFUf270Sm83EwEACWVYxGs+t7pHJZGDNmmrWrKk+20eZ5CyjKgo7772XeCCgRfDk/xYlh4bYcc89XHDnnXqH5vXo27eP3fffj6O4GE91NaqqkhwaYtudd7L+W98a41qfiUYZOHKEXCKBu6pKv1kngkG23XUXosGgbViqKj179tC2cSOO4mJ89fWMnDyJqihUrVmDvaCAbCLBnp/+FMFoPG1D1uo1a2jbuJFcKoWUShFsbMTidpOJx3GVlxPu6NC6WcGgNhIUBL0oFAwG5HQaVZZxlpRQ0NDA8Wee0Tpvp2wS2goLsXg8ZONx0iMjmlmtzUZyaAhbQQGl8+djtFhYd8cdlCxYwOY77gA0u4mSefNwlpURbGxENBi0ZIEJOPXe4SgpwVtbSzIYHFP0qKpKOhKh9hR7DYD6Cy8k2NjI4LFjmPNj1GwiQcHUqUz7wAf0r/NPn07x3LkMHDmiaeryViSJgQFK5s/HZLNhtFrx1tUxeOyY1h3r7dU2P41GPFVVmolvJKIvYkyEwWwmMTh4Ws/f28lkUfYuxu22IMsT50emUhIlJW9ejG80inzgA9O45JIGUikJm8141nRFr8RoFJk7t+RsH+OMI4oClZVuotHMuKijaDRDWZkTg+HcKsgmmeRUQm1tRDo7xxmE2v1+wp2dBA4donLZstd9HFVVaXz0Uex+v96REQQBe2Eh0d5e2jZuZOFnPgNA944dHPjlLzXtVD4yyFdfz4qbb6bjxReRs1mcVVXI2SzxQABB034Q7+8nGQyiSBIN738/9vw40GS3Y7Ra2fuzn+GtqcFZWvq653WVl7PkuuvY9+CDDDU3k8t3zaxeLxaPh0hXF4IoIqXT2kjRYiEXj+tdqNr3vQ+zw4HBYkEQBMKdnSReUQwJgkDl8uX07NpFvK+PSGcnqbxHWtGsWRz81a9ouOQSvDU1zPnIR+jPd4lO7Wg5S0sZOHQI35QpY84vZ7OIBgOF06aN+XkLr7mGbXffTaS7WxtP5nKkw2GqVq2i5BVZnaMGr4NHj9KzaxcoCuVLl1I6f/4Y7zFBFFl2440cf/ZZjvz2tww1NyNnMpqlRm8vrvJyrF4vnpoaVFUll0iQiceRUynsRUUY85Yaar7raXG5JrzHypnMuISAM8FkUfYupqLCRUNDAd3dkTHC9ExGQpYVVq+euMJ/IxgM4jnjVn8ucNll0/jRj3Zjt5v0ca0kKQSDCT7/+aWTGqt/IFKpHOFwGpfL8g/zb/S1OhOiwUCspwdOoyjLRKPEBwbwVI/vvtr9fgKHDgEQ7elh+z33kAwGyUQiIAh4qqpQJIkDDz9MMhjUNUWhkyfJxOP46uuRczmSQ0MIoojBaCQ9MgLkPcD27tWilBIJNn7xi1SvWsWCa67B7HAQHxgg2NSEKkkUTpuGu6pK/zddsWwZ/pkzeeH224l0deGuqMBWWMjIyZOYHQ5NLyUI5OJx0uEw6XAYKZPB7HJpZrWv+NvgnzGDoeZmvesEWsFYunAh2fwWZyYapXL5cow2G/3799O7axcrvvhFSubMYeaHPsSen/wEt8mkdycNZjN2vx+DxUI2Htdjo1KhENVr13L0979HymYpW7yYsoUL8VRXc/63v03n1q0MHD6M2eWiZt06yhYunFAjaDCZKFu48HUF9kaLBXdFBSabjfqLLsLq8RDp7qZ/717aNm6kdMECbbHA6STS2al/vZzNkolEEAwGCurrMdntOEpLiff3j+mcpsNhzTdu8eLXfa293UwWZe9iBEHgc59bzPe+t52OjjAmk+ZwL4oC11yzcNK/6Rxk2bIKrr56Dk880ZQP0BYQBPjwh2excuWZf9c2yZknk5F4/PEmXnihXQ9RX7WqiquvnoPDcW4XZ6/lRK/I8hhx+mshCALZeJzU8DBWr3eMbYSSy+ni8YO/+Q0Dhw9jy3ekUFUtBDuvh/LW1SHnNN1tpKNDL3BEo1Efk6mqSryvDymdpnv7dk0v5XaDIOCtrqZ//36kTAZ3VRVtzz035pxV+YJtNA3A4nJRf+GFND/+uN7lcpWVYXY6yaXTpEMhYpKEaDSSzWdDjhq8jvqJqaoKqsr0yy8nnt+4dJaVIYgi6ZERwp2dCAYDg4cPa/YbmQzFc+fiKisjE41y8OGHufC736UyvyzQ+Mc/al1EtDDyS++7j0wsRtvGjcT6+nBVVmKwWunatg2z04kgipquraqKVV/+MvbCQmZecQUzr7jitJ6700FVFBofewxHaaluL+KtrsZTWUmorQ0hH0DuqazUguK7ukiHQtgKC6lZuxaDyaRtugYCrPjCFzjw0EOaTk4QQFWx+nysvPXWs+LuL7zXhcNLlixR954FMd6ZJJuVOXw4QHt7GLfbwuLF5W9q63KS9w6RSJq2thCqqtlXjJrbTnJuo6oqP/nJXnbt6qGy0o3JZECWFXp7YzQ0FHDbbavfNTKDdwI5m+X5L39Z28Y7xZE+m0iQiUS46Pvf1wu3dDiMlM9EPDXiKHD4MAcfeoienTtJjYxgdjopmjULX309giAQam3FU11NMhTi+NNPI+dyuCsrxwjR0+EwNr+fxddey/Gnn8ZXX0/rc89p7vJ5t3iL242rvJzAoUOIokjJ/PkEDhzQnPyTSZwlJVQuX46qqvTt3YvRbKZozhy9QzS6YTnnox9l2j/9k37+VCjEpq9/HaPVql+DcGcnbc8/Ty4e1wpBWcZkt2Pz+ZDSaXwNDVQsXYoiSUS6u6latYoln/sciWCQpscfp3f3bi1CyOEg2tNDamSEbCKB2eFASiZRFIXa887D4nYT6erivG9+E28+xUBKp4l0dyMaDHiqq8f5onW8+CL7f/lL/fqOEunqonrNGn1M/HaSCoX4y5e+hDffCZVzOaI9PUR7epBSKWJ9fcz52Mc0j7NwmI4XXwRBIBEIYPX5sHg8mJ1O5n3ykyz41KdQFYXQiRMkg0HMLhf+GTPGvKbebgRB2Keq6pKJPjfZKXsPYDYbWLKkgiVLKs72USY5Q3g8VhYvfvPpBZOcHVRVpbU1xPbt3UQiaWbO9LNiRRVu9+mJ03t7Y+za1UNtrfdlI06DSFWVm5aWYVpahpk169y1hjGYzaz4whfYfs89hDs7MZjNWp6hycTSf/93LG43icFBDv/udwwcPowgihitVqrXrgVFoWfXLnp27KBwxgyqVq+ma9s2cqkU/fv26bYOsUAABAF7UZHmS5XLaf5VkqQ74xvtdhKBALXr1hHv66N/3z4sTqc2sjQaMZhMlM6fj8luJ3TiBJlIRIv0CQS0kabZTNGMGbpWKdbbS+GMGWNGdoIg4K6ooPXPf6bh0kv1z5ldLmZffTVHfvtb0vlcS4Cy+fNRVRXRaMTi8WD3+xENBoKNjYRaW7H5/RjNZhouuYRZV14JgKOoiCWf+xwLr7kGWZLY8f3vI4giuWRSz6Y05UejodZWyvLjulPzOo1Wq+7UPxEn//Y3HEVF48anrvJyurdvZ+6//IvexXu7MJjNCKqKqijIuRzd27aRjsUwWa0kQyHSkQg927dTtXo1Vq8Xd3U13X//O6osI5rNWianLJMcHCQ+MEDXtm0MHj6Myemkdv3607YyeSeYLMommWSSSd4GVFXl0UcbefbZ41gsRiwWAwcPBnj66VZuu231a8oNJEmhpWWILVs6CIdTKIpnzFKHIAgYjQKtred2UQbgq6/nwrvvpm/fPm30VlJC+ZIlWL1eMrEY2+66i2w8jqeqCkEUifb1sfn22/HW16NKEtlEQhOj19dTc955RDo7CXd0EOvpYd4nP0nX3/+Ob8oU5ExGy6PMZDAYjSSHhrRRp8FANhbDUVqKs7SUZTfcQN++fRx95BFannpK0ydVVaGidfA8VVWkXS5GTp7EYDbjKCrCZLczeOwYRqsVT3U1uVRqQod4o9VKfGAAKZXC7HRy4q9/5eCvf42cyWCy20FVqb/gAqZcdBF//+53QRBIDg4y1NxM8NgxQItaKl2wgFW33ELB1KljNGSjGMxmFElipL0dT3U1zrIy4oODmGxagofJbifW309RJoPBbMZd8XIDQFUUho8fZ6ilBYPZTOn8+WP1V5HIuIQCyNtkKIpWDL/NRZnZ6aR43jyGmps1PWAshsXp1Owx8i796UiE0IkTFE6dSryvj4KpU0mPjOCpqqJk/nzMTifdO3bQu3s3ZpcLq9dLMhRi9333UblyJYs/97k37Y33VpgsyiY5a6iqysBAglgsQ1GRY3JEN8l7mpaWYZ599jjV1R59xFhYaCcYTPDgg/v41rfWT7io0d8f47/+ayfBYIJgMElz8xCDg0lWrKgcs4WrKCo22zs3Unk3YXY6x1kmAPTs3ElyaAhXRQUIAqqiEDxyBKvPR2p4GEEQNNG7KBI+eRJ3RQVFM2dSNHOm5k6fy2HJG5gaLBYcxcWoikIyGCSXSjF8/DhWjweT3c7iz34WyOvH7Hay8ThlixYR6e5muKmJoaYmKlesoHrdOvr37qVk7lxObtqEOe8qL5pMuiWDzesds0E4ipROa19vMvH3736XfT//OSabDSH/M4tmzeLEX/5C2aJFFE6bRvMTTxDt6cHsdGL1eFAVhXBXFwaDAW9d3YQF2SiCwaAFcCsK7ooKRtrayESjmm4q33WK9vQw/1Of0ouoXCrFrvvuY6ipSQsWVxSO/eEPTLvsMmZ+6EMIgoB/2jQGGxtxlozdmM8mEvqY8JW/cyYaxexy6UXhG0WRZcoXLaJ72zb69uxBRTPgVWUZY17rZ7BYCJ88id3v1wx/HQ6MNhvFc+boG5fhjg6sXu+YQHlbQQHdO3ZQsXz5pNB/kn8cgsEEv/jFfo4fH0YUBVQVVq+u4uMfn4fVeu68LFOpHIcOBejri1NQYGPRorLTHmVN8t5i27YubDbTOM2X32+nqytCX1+Mioqx3TJJUviv/9pJIpGlpsZLaamTvr4Y2azMjh09bNhQj9GoLfiM5qD+o5KNxznw0EMMHD6MeOgQJptNMxdNpbB6vaQjETAYUCQJo8WCYDQS6e7Wb8oIgrZFmNdRC4JA0axZhI4fR1EURIMBg9mMlE5jLyqiZt06QCtM9jzwAFaPRyvyZs1CSqe14iIeJ9bbqzvpl8yfz8DBg1qouNFIamSERDDIqi9/meY//Qmrx6NrslRFIdbXx+yPfISWp5/m8O9+h9Xn0wurXCpF4MABSubPp+VPf2LGFVfw97vuAlHUbDHsdsx2O0K+MxQ4eHDCQnYUo8VC+eLF9B88iLu8nKo1awgeO0a0t5dsPI5/xgyWXHstVXnzWICmxx4j2NSEt6ZGf0OhyDLNTz5JwZQplC5YwJRLL6V982bNlFdVsfv9OEtKSASDLPrXf9W7TXI2S/MTT3By0yYUWUYQRWrWrmXWVVe9oeIsHgiw84c/JD4wgKKqJIeHIV+M2woLMdlsJAYGSAwOYsyHrsuZDOmREXxTpmgLHfnXUy4ex1lWRiYW0zZ/8+e3uN10bt06WZRN8o9BOi3xve9tJxJJU12tpRXIssJLL3WRychcf/3Ss33Et4Wurgj33LOdSCSjb87+/vdHuOGGZf+QPmznOuFwGotl/LhDELRs0WQyN+5zTU1BBgcTenqBxWJk/vwS9u/vJx7P0tUVwek0E49nueqqWZSWnvltsLNJJhYjHQ5jtFrZ88ADDB47RioU0jo7+U6H0WbTBfHemhpCra0YzGZEgwElvzUY6+ujatUqKpYt07RFea1XJhrF7HYjms3ayLKoCF99PQaLhc4tW5hz9dUEGxuR0mm9EyQIAiabDZPNlI0BMQAAIABJREFURiYa1Q1dAXy1tdgLCoh0dZGJx7F4PKz73/8b/8yZIAgcf+YZ7Wfnf7/RfMyNt9wCeSf7UUw2G+lIhEwsxlBLC+2bNyNls0iplP77i0YjlStW4K6qInDgwGsWZQAzr7ySoeZmIl1d2IuK8M+cidXrxV5UxHnf+MaYSCEpnaZjyxbcFRVjOryiwYDV66Xt+ef15QYpmyXa0wOKoo1xTSZWf+Ur1OTPo6oq+3/xC3p27cJdWYkhnzHZ/sILxAcGWHXrrael41IkiR0/+AHZeBxvTQ3Dra1Y3G5949TqdiOIIq7ycuIDA/jq6xENBr1D5q2tfbm4lCRkWSaXTNK+aZNWrOc/Zy0owFH85rKl3yqTRdkkZ5yDB/vH3IhAEzPX1HjYvbuXD31o5nv+5iNJCvfeuxNVZczvGY9nuf/+3XzvexdNdszOMWbMKKSxMYjPN/Zd/2iXa6LX9PBwalx0Vk2NF5fLwp49faRSORYvLmfDhrpzXkt2KlI6zdFHHqHrpZdQ0XIUQydPImcymqWF3Y6A1k2KBwJa7JDdjn/mTLKxGOGODjLxOIqqMnD4MIXTpjHryitR0TRrQy0tuCsrCbW2aqMus5na9ev1jUM5m6XjxReZ9eEP60XQRIgGg/Z4TU1YvdpyhsXtpnjOHG3z0+HAP3MmgiAw68orqVm7lmBTE4osUzh1Ku7KSiKdnaCquk2DcIqOyWi1kggEMNbUcOL557EXFGDxeJDSaU3kLknkkklURcFwGkkHzpISzvvmNzn5t7/RvmkTyeFhCmfMYOYVV4zTvGXjcVRZnnAL0exwkAgECLW2cvyZZ3RvsXQ4jKooKLLMcGur/vXRnh56d+8eUxSJRiOemhqCx44RamsbYzz7agSbmkgMDurmwvFAAEdxMYmBAaRMRhuZulx69NKqL3+ZuvPPp23jRg7/7ndk43EsLhdyNksqFEI0mUgEAtj9fr0oVFWVaGcnSm78m6gzwWRRNskZ5/jx0IQjSkHQPLl6e6Pv+aKspWUoH5k0Nr/R6TQzPJxk374+zj+/7iydbpJ3gtWrq3n22TZCoRQFBVphJssKXV0RLrmkAZdr/E3T47FMqDMrKLAxfXoh1123mBUr3rpJ9HsJVVXZ+9OfEjh4EHdlJaLRSLS7m1hvL0a7HavPRyYaRTQaMVqtZONxYn19zPzgB0FVtW5SfrNQQNM2OYqLOfbHP9KzYweqopAeGSE+MEC0txdnSQmlS5aMEbePbn3KuRzOsjL9XK98rhRJ0nIZBYFgYyOO4mJEk4nU8DCqorDshhsQBIF0NErbc88RbGrCXVlJzbp1uCsrtQUOmw1VVfHU1jJy4sQYKxA5l0NVFCwej7YU0NeHnMvpHTVVVUkEg1h9PqpWrTqt6ztqNCtns1g9HhKBAHt+9CP8s2ax/Kab9FGixe3Wx7nJ4WEinZ0okoSzpASj3U7J3Ll0b9+u24SMPnZiYIBwezu9+a7YrCuvJNzRQS6Z1DMzbYWFugWJIIoMt7ZSOG2a5vkWCJCJRrEXFo7LpIwPDpKOxRg4cgQ5k9FeB2YzrspKzYssXxQaLBaKZs3CP306AFMuvhiL203zk08S7uzEaDYz7QMfQM5mGTh0SCuGRVFLAEgmsXg8xAMBFFk+42L/yaLsLTI4mODQoQDptMSUKQVMn154TvsIvR243RayWflVP38uaMqi0Ymz4UCLkQoGk2fwNJOcCXw+G7fdtpqf/WwvnZ1h/QZ+8cVTuOqqWRN+z+zZxXg8FkZGUmM6bNFoBrvdxLx5rx/Rc64R6eqiZ+dOEEUChw+TjUYZPn4cKZVCymQQRBFrPsNRlSSsXi/Fc+eiqird27cT6+mhcPp0ShcswOxwoMgyh3/7Wxz5LU5BEEiHw1p4tqqSicfp37cPOZvFV6e9UcpEo9iLizFarfjq6ymcNo3QiRN6ITVaPLjKyyldsICSuXPp2raNk5s2kUsmqVi+nIaLL8ZdWUnPrl38+cYbSY2M6Hqyo488wsJrrmHuxz+Oo7iYgoYGwh0dJIeGSI2MYLRYtIzIkREa3v9+nEVFpEIhShcupGfnTpRsVo8LyiUS+GfOpHjOnNO6vv0HDtDy9NN4a2vHeKYNNTXR9NhjzPvEJwCtMK274AK2f/e7+galIIoMHjuGnM0y/9Ofpn/fPt3tX1VVBg4eZKS9XdNy5XKc/NvfCBw4gMFioX//fkz5pQIB8NTWUpq3+TBaLKRCIfb9/OcMtbRoBZIsU750KQs+/WnMTieqqtL54ov079mjFYxWK5lIhHQ4TEFDA67ycvwzZ+IuL0fOZrF4PHpBLQgCVatWUblyJVIqpY23jUbaX3iB0oULCbW1oSQS2uZmYSGl8+eTGhnRt2LPJO/9u99ZQlVVNm5s43/+pxFQEUUBWVaZOrWAL3xhxT9MLMqbYenScp58splcTtajhABisQxOp5lp0wpf47vfGxQWvrq5by6nUFExfoV8kvc+tbVe/u//vYDu7giplERZmXNcjumpmM0GbrllJffco6V2GI0isqxgs5m45ZYV2O3n3ralnM0Szo/sPNXV4+wS2l94QbuBOxzaTXdkBFVVUdFurqmhIdIGw8tbg6pK2aJFrP3613n285+ndMGCMaO4TCSijbbicUSDgVBbGyMnTuAsLSUZDKICotnMwMGD+lZjIhhk6fXX64X1shtv5MAvf0ng0CF9g7Fw2jQWX3utNt4zmajfsIH6DRvG/C7xgQH+fMMNSOk0rvJyBEFAkWUSg4Mc/NWvKF24kOLZs1n4mc/w97vvxltTQ87vJ9rXhypJzPvEJ1jzta9x/JlnaPvzn/FUVVF73nkMt7WRGhpCNJkomDqV5TfddNodnZPPP69bf4wiCAKOkhJO/O1vzLzySr1b5iwr08ap+c4avDx27Nmxg9L587VEhIICkkNDjHR0YPX5QFWRJYnCqVMZbmtj8OhRDPm8zmw8jgokhoY0gb7Ph7O0lGc+/3li/f24KyvxVFZitFrp27ePXCLBks9/nm133cXRRx4hl0ySS6WwOJ04SkuR0mlGTpzAUVKipxIIosjCz3xmXGdTEIQxuj13RQXJUIiChgZyiYTWfbXZtK1YpxPjm9wOfSuc0aJMEIRLgHsBA/ALVVXvesXnvwj8GyABQeAzqqp2nskzni7Hjw/zyCNHdddt0Aq1EydC/Pd/H+Haa8/81sZ7hYoKN1ddNYtHH23EbDZgt5uIxTKIosAtt6wYU6i9V2loKKCqykN/f2xMbunISAqXy8zChWVn8XSTvJOIojBubP1a1NZ6+e53L+TQoQADAwmKiuwsWFB2ThZkPbt2ceg3v9F0WoBoMjH7ox+l7vzzNeF9LEbLU09hMJu1rMhAALPbjSJJZJNJlLyPFoAqywgGAyabjUhHh6Y1s1jGaaNS4TCiwYCUSumeW2aXSxOAW604SktJDQ8jZbP07t5N2cKFzP/kJ6lcsUJ/DIvLxYqbbyY+MEBqeBiLx6MXWRMhpdMEDh3iwEMPEevvx3PK9uLozw02NfHc9ddTsXw5teefz5qvfY3BI0c0Gw2XC29dHaETJ9j01a8CEO/v13IuvV4qlixBVVUinZ3Url+P4xVjvtciMTAwxjojm0gQbGwk3tdHJh5n8+23M+/Tn6Z03jy6tm2jYvlyxHy+p2AwaNmXZjNDzc3M/uhHMTscJIeGNLNfkwlUlXQkgq++XtfEoarkUikykQgGsxmD0UgumaTzxRdZ+eUvs/kb32Dg8GGsPh9DTU0MNzdTtmgRAMefe47AoUOE2tq0pQS/n0h3N+lolFwyia2oSMvwXLECo9lM+fr11F1wAa6yl//GqopCYnAQQRSxn2J02/CBD7Dr3nuxuFx6UoSqKMT6+5nz0Y+e2z5lgiAYgB8DFwI9wB5BEJ5SVbXxlC87ACxRVTUpCMLnge8CHz1TZ3wjbNrUPiY0GrQqvKLCza5dPVx99ZxJIfdr8P73T2XGDD9bt3YSDCZpaKhhzZpqiotf3WfnvYQoCtx003LuvXcXnZ1hQNMK+3xWbrll5Tl5w53kzWOzmc557dhQczN7HngAZ0kJjiJtaUHKZDj48MNYnE4qli0jcOgQJrsdg9lMNj9OEgQBg8mE0WpFSiZ17Y+qqth9PpxlZYgmE6G2NgDC7e1YCwp0zZKSF3XbfD4i3d3I2ezL3RJVxVVWRsWSJSSCQbKxGBf/8IdI+QUCR1HRmFghZ0nJOD+uVxLt6WH7979POhymd/ducokEI3nPNLPTSS6V0uOAcpkMqqpy9JFH6N29m9Vf+Qr1GzYw1NLC9u99D0EUsRUWImeziEYj/Xv24GtoQMx7hlWuWMGcj33sDT0Pnpoaho8f165nOk3XSy9pFhs2G4giUjbLju9/nxU330wuHsdgMmEwmzE5HLptiKb/1SxGVt92G/t+9jMSg4Na4SzLFEyZQtHs2aiqqllPJJNYXC4cfj+pUAg5k8Hq9aLkcrQ+9xyOvMZstFgcLc6dpaVkYjFGWltRRu0qXC4Kpkwhmw9m99XVYSsoYO3Xvoavvn7M7yrncux/8EEOPPwwmWgUi9NJ2ZIlrLr1VgqnTaN88WJmXXklzX/6EyiK3o2tPe88Gi6++A1d17eLM9kpWwa0qap6EkAQhEeAfwb0okxV1c2nfP1O4BNn8HxviEAgPmE4sMEgIgiaJmSyKHt1BEFgypQCpkw5vYDh9yJ+v53/+I/1tLYOMzSUxO22MGOG/5zoBE4yyRul5amnsLhcY8ZHRosFR1ERTU88QfnSpWTCYYwWC6ULF2rh3rmc7gw/6tRudrlIh8NYPR5KFyzAN2UK4fZ2dt17L8nhYUZOnMBkt2P3+7EVFBBsbCQTjWJyOOjft0/zr7LZtGJLFDVxfj5g3GSzsfMHPyB04gSCKGK22ylbvJhsMkkukaB49mwtumcCd37QPLx23XcfiiThra0l3NlJMhhEMBiI9vbiq68nMTCgC9ydpaWYHQ7MdXWMnDxJ9/bt1J1/PgceflgbpeZF/6PXZOTECRouvVTf3Hy9AnEiGi65hP4DBzCn0wSPHCE1MqJr9IpmzsReWIjBbObII49QOm8eBx5+WB8hj9p2FM2Zo19jg8nE+d/5Dv6ZM2l67DEKp00bM5I22e1kOzrwVFdjMJn051/OZklHo8T7+3FXVOgbk6qqkhgY0DuhotGod0fjvb1Y3W5EoxGrR7NTGtWH2YvGbierisLmb3yDY3/4A1afD2dJCblUis4tW4j19PD+Bx7AW1PDjA9+kOo1axhqbkZVFHxTpoxZ+jjTnElFegXQfcr/9+Q/9mr8K/Dnd/REb4Hqag+x2HgxtyQpgDDpTj8JoHXMpk/3s3p1NXPnlkwWZJOc8ySCQVqeeoo9P/kJLU89RSIYBNDc8k/xwRrF7HIRDwSQ0mnclZWoioKnqoopF16oFz9mpxN7YSG2ggLclZW4KyuZ+v73458xA0EUGThyhEwiQcXy5drYSxQZaW+n/YUXEM1mqlauxGS1YjCZEM1mLa8yGqVk7lxMNpvmZt/bS7izUxs3VlfjrqxkuLWVLd/+Nieff55oTw/HHn2UzbffTrS3d8LfPdTaSjIY1LcGCxoaMNhsKPktytEQ9dENSv8pNhD2wkI6t24l1t9PYnBwzBYmaG9krT4fsd5eyhcvflMFGYC3tpba88+n7c9/pnv7dhIDA4Ta2pCyWdz5gG+Ly6Vp9yIRIl1dIIpY3G6sXq9mF7J5MzXnnafbZQiCwPTLL8dTXa1tQOYtRORsFrPLhcEydstYzY84lVyO1NAQwcZGYn19JIeHySYSKJKEaDJpEVgmE0aLBXthISpaGLn+OLJMJhKh/oILxkU9BZuaaH3uOWz57pogipgdDux+P6GTJ2l+4omXr73fr3vGnc2CDN6lQn9BED4BLAEmdMITBOFa4FqA6vyL6ExzwQV1bNvWRTot6duCqqrS0xPlfe+rmxT6TzIhyWSO3bt7OXCgH6vVyKpVVcyZU3zObeyqqkp/f5xEIktxseM1xe6TnDsEDh5k9/33oyoKJrudvj17aHnqKZbdcAMWtxs5k0G0j12CUSRJG5GZTBTNno2juJj4wACO4mKmXHwxPTt3IqXTOEpKkFIpEoODFE6fji0fHh7p7ETOZCiZPRtRFCmaNQtffT1d27cTVVUqly/HVVZGKhTSjE2tVqLd3bgrKjBYLES6u1FlGbvfj5zN6qPVeCBArL8fR0kJ8UCAknnzsHo8DLW0sPmOO1h2000Uz549xscrHYnoBqQArrIyimfPZvDoUbKxGKlQiFwyicnhYMrFF4/pGgp5s1tFkphIqaaqKlIqRaSri0h3t74JerpkEwkaH3uMjs2b6duzB1mWsRYUYLRatecmr6mrXb9ef176du9myoUXMnj0KJloFNA0ce5Tiq/RM1hcLtZ89asc/NWvGGpu1rtcS667juPPPEPXSy/pCwSqqqJks0jZLGa3G4ffj6CqWoSU3Y4iy6j5HMuqtWsZOHAAAGdpKUoupxVvsRjZWIyCadMonD5dt7UYpXv7dqR82sOpjHbeurdvZ8XNN5/29TtTnMmirBc4VTRRmf/YGARB2AD8b+A8VVUn9BVQVfXnwM8BlixZMrGr3ztMXZ2Pf/u3RfzqVweRZUX3Fly0qIyPfGT22TjSJO9yQqEUd921jcHBBC6XGVlW2bGjh2XLKrjuuiUYjedGYTYwEOfBB/dz4kRIj9Bat66Gj31sDhbLu/J94CRvAVVVGTl5kmBzM/sffBBvdTW2gpdlCdlEgj0/+QkNl1xC8xNP4K2rG1NMxHp7mXLxxbp2a9WXvsTuH/+YSFcXgiBQ0NCAIst4a2sxmEyMnDyJyW4nHgggZ7MkBgcpmjVrTLbkqHeWyeFAzmi3EVtBgX6uSFcXs6++mszICKLJROnChez96U+1MWmecGfny2OzQICTmzaRGh5GNBqR0mmyiQTuigpW3HwznnxzwO73jzEDFkSRiqVL8VRX0793L566Oq2QaGjAUVSElMnoXcJcMsm8j38cV1kZRpuNXCqlFzG5VIr+ffsId3TgmzKFzXfcQUFDA0s+/3nsha+9rZ5NJOjbt4/9Dz5IamQEe2GhNv7zeon29elJBkaLhXQ4TCofW2T3+4kNDJANBsmmUiiyjNnh0LJCBwbY8+MfE+3pYe6//IvuB+YsLWXNV79KIhhESqWwFxVhstmoXLGCTV/7mh6CLqVSDLW24igqQjAYyMRi2Px+jDYbsUAAAEdJCVMvvRRLflzZu2sXcjZLQUMDoePHURWFqjVrcBQVsevee6k57zwWXnPN2GQAQRjj1H/qa3YiU9x3A2fyL+QeYKogCHVoxdjVwL+c+gWCICwEfgZcoqrq4Bk825tizZpq5s8voalpiExGoqbGS1WV+w29e5nkH4dHHjnKyEhqjMN/UZGd3bt7WLCglDVrzk7X9+0kmcxx991/J5XKjYnQ2ry5nWxWntxKPsfIpVLs+fGPGTx6lEhvLyOtrYTb2ylduBBf3nXd7HBoI73CQornzmXwyBHNzkIQyMbj+OrqmH7ZZfpjOoqLWf+tbxFubycdDmMrKBizvZiJxejft08zfi0tRc7lOPbII+POZsuP+l4ZBK7IsuZbtXLlmJGX2eEgNTKi//9ooZQcGkLJ5TTn/Hy0kcXjwVVWpsf+bLj7bowWC776erw1NcT6+/XtP0EUtcJk5Uo23H03Q01N7LrvPjLRKIPHjqHKMnImgyAIdGzdSs26dcy66ioOPvQQjuJiTA4HPTt3Eh8YwF5URNmiRRjMZiLd3ey45x7O//a3xywjnMpQSwu77r2XaG8vwcZGTHY7oePHtTGu0YirrIxQWxvJoSGMdjvJoSFan30Wo81Gzfr19O3cicnh0JYm0EbQosGAo7QUe3Ex6XCYv991F2tvv52CKVNefg5foe9ylZUx/5pr2Paf/8ng4cOk8trBgiVL8FRX07dnjzaWFASMVivusjL8s2frG5HO4mLKFi0i1t+PnMnga2ig+JTP2/1+OrdswT9jBlI6rev2rF4vmVhsjAZQkWWkVIqpp7zm3k2csaJMVVVJEIQbgL+gWWI8pKrqMUEQvg3sVVX1KeB7gBN4NP8PsEtV1cvP1BnfDC6XhWXLzu4MepJ3P/F4lr17+6isHBtILQgChYV2Xnih/Zwoyvbt6yMUGlt4ahFaXrZv7+aKK2ZQVHRubNhOAkd//3v69u4lNTJC+ORJMtEouXSajs2bMV922RirBimTYeUttzBw5Ag9O3fq5qClCxboBqSjCIIwbpNuFIvLpY/YQCvSmh9/XBsLnjIOtBcVaQXRKfYPqqoS6eqi7n3vG6dBqjnvPA788pf61qbV42Hg8GFdnK5IEiarFTmXIzU8jMFiwWSzEe7oYPDIEd2YdtmNN7Lrvvs0Lzbth2LxeFh5662YHQ7KlyxhwWc+w1++8AXNMd5kwldXR9Hs2WSTSXbffz/r/+M/MJpMND7+OINHjhDr66Nw6lSK58zRr5WrrIxwRwfBpiZK5s4dd52y8Tg7f/hDTPmlBovbjcXlIp7JEB8cxFZYiMFkwllcjLWwkOHmZnKpFK7yclRVpeWJJ0iFw5gdDkSTiXQkopvaJoNBqlauxObzoUgSzU88waovfelVXyfhjg4O//rXFM2aReWKFfTu2kUsECCYt/+oXruWTCRCLpUiHQqx+qtf1dz3Ozr0LpfN52Pxtdey54EH8FRXj2l+CPmt0b9+5SsUTpumJxEokkQ2HkeRJMxOpxZMHolQuWzZmDcC7ybO6CxBVdXngOde8bFvnPLfG8Z90ySTnAOk0xKgCf9ficViJBJJn+kjvSMcPz6MzTb+z4ooCoiiQG9vbLIoexUikTR79/bR1xejtNTJ0qUV7+qFoUwsRseWLUS6upAyGaw+H7lEQrOziMfp3raNGR/8oP71rvJyRKNRz0l8u7C4XCy57jr2PPAAiaEhTFYruXQag9HI+d/5Du2bNhEOa7Y0qCoVy5Yx56PjnZaqVq6kd/duBg8fxlZYqGU4ShKi0YjZ7Sadj04avcGnw2FMNhuCKBIffHmwYy8sZP03v0morY1EMIjF5cI/c+aYcZmcyVCyaBGukhIEg0H/nNnpJNzZSbi9narVq6lcuZKWZ57h2B/+QOHUqePOLIgi0Z6eCYuywMGDSKkUzpKSlzdY0bpKiWBQ37oUDAZNU1dUhJLLIRoMWLxeksPDgJZuEDx2DNFkwux0osgyRotFHwXb/X4Gjx59zUii1ueew2A26x0rT76baLTZCDY14Swr07JDDQbMDgelCxZQtnAhw62tJIeGsHo8FE6fTrSnR7fjOBU5m2WosRGLx6Pnl4IWFTXU1IRgMJAYGMDkcLDsU59i8Wc/O8ar7d3EpMBjkknOAF6vFZfLTDKZG+dRFgqlWL783Oi2vlaElqoyYcE2CTQ1Bbn33l364lAmI/PHPzZx003LmD27+Gwfb0IykQjJUIhcIoHV59N0OhaL5nlltRIPBMgmEmQiETxVVfhnzHjHzlK2aBEb7rqLnp07iQUCuCsqqFy+HFtBAdMvu4yh5makTAZPVRWu8vIJH8NgNrPiC1+gd/duOl58kWhPD8Vz5qDIMumREeRcDkRRt4EYDaxWFWWcrksQRQqnTXvVkO14fz8mi2VcmoH2zVoM1OjjuMrKXlX/pCoKZtfE6SCJYFAfazpLSxlubkZVVc2ZP288GxVFBJMJs82G3e/XdXip4WFy8TiCwaAVjUYj2WQSs9OJp7oa9ZSwbjXf7Xst2c7gkSP6Ygbk/d5KS4n19ZHNu/xn43GUXI4Vt9yiF3f+6dMhr1cb/T7RaETO5cZck3ggQC6ZxD9jBrH+fhRJwuJ2a9u6VVUsvf56imfPxmizvevlRZN/ISeZ5AxgNIr88z9P5+GHD1JV5cFs1v7oRKMZJEnhwgunvM4jvDdYvrySZ59tRZKUMYsLkUgaj8dyzvnS5XIy0WgGh8P8pjNbU6kcP/rRbhwOE6WlL+fsxeNZ7r9/N9///kUTeiKebSweD5mREYT8jV8QBNyVlZozfCymBU23tFC5ciVLPve5d9wd3e73M+2f/mncx41WK6ULFpzWYxjMZqrXrKF6zRr69+9n13334ampIR0O07lli+Znlo9/MrtcpCMRzE4nJfPmvaGzuquqkLZuHfdxVVUR8vmLoxTPno3BYiGbSIzp7uSSSW1JYf78CX+Gs7QURdI69FavF299PcHGRl275SwvR0qn9RDx0oUL6d+/Xw9sN9rtyGmtg29xu7WopUwGVZKw+Hy6Vm+kvR2r18sLd9yByWqlet06zV3/lJG0yeFAOaWQEkSRimXLGGlvJ3j0KFI6TdmiRTRcfDHevBZxIkx2O1MvvZTGxx/Xfc9UVSU+MICST2t45TWw+/1IqdSY0fa7mcmibJJJzhDnn19HOi3z5JPNSJKCqqr4fDZuvXUl1dUTm1G+16iqcnPFFTN4/PEmLBYjNpuReDyL0Shy662rzpkNU0lSeO65VjZubCOTkRBFgbVra/jwh2e94bSGY8eCJJO5cWkWTqeZoaEkR44MsmJF5dt5/LcFi8tF0ezZtG/erOdQ5pJJEEVEsxlfbS3Lb75Zj1B6q2SiUaI9PRgsljFh2u8UxXPm4CwtJR4I4CwtpXb9erq2byfW24vV6yUdDmvxS7fcom9Jni7lS5bQ+Oijmq2EoqDIsuYNNjKCt65uTGFisttZ+u//zu4f/Yjk8LC+vYggsPT668dp40YpXbAAs8tFpFuzB7X7/drYdWREu4Y1NRROnYrd76d140YtCBxNi6YqCgLaWFAwGpGyWa0wjEQYbm3FXVlJ765diBYLkY4ObalBEBAMBgIHD9Kzcycrbr5ZL8zKFi9m5w9/SHJoiFzepqJswQIMFgtL//3fWfzZz572tZt2+eUgCLQ+95xmOKso2PJ5m0aFjBJhAAAgAElEQVSrVTuHKKLIsmYIq6qUzJuHt7ZW35J9NzNZlE0yyatw8uQIf/3rCdrbwxQXO7jwwnrmzCl+0zcYQRB4//uncv75tfT0RDEaRaqrPeeUR5kgCFx++XRmzy7mpZc6GR5O0dBQwJo11fj97413qqfDf//3Yf72t5NUVLixWBxIkrZh2tMT5bbbVr+h5/S19ISCMPbzqqpqup/XGRedKZbfdBN9e/eSDAa1LUVJ0kw6XS5SoZBmPDqBJcEoqZERul56if4DBzDZ7dSsW0f54sVjtgkVSeLYH//Iyb/+lVwySSoUwmSzseS665hy0UVjLRDeRgxmM6u+9CX2/uxnhNraEESRohkzsPn9VK1Ygbu6mtL5899wQQZg9XiYccUV/O2227Q4qTzFs2dz/re/DapKJhbDYLFobv7z5rHhzjvp3rGDaE8PrvJyqlauxFH86qNt0WDAWVrKkd/9Djmb1QX6npoaGi6+WCuk85QvWcLJ55/HVVGhGevG4wiqqnmNGQyaFYjJhMnhoKChAUEQiPX1IaXTCEYj8UAA0WjU/MUUhdTICBXLl1O3fj2JwUHannuOkfZ27bVrNhMfGKD12Wcpmj2bDXfe+YaunWgwMOODH2TKRRcRDwQw2myc+Otf6dy6ldCJE4gGA3Iuh5LNgtGIwWAgcPgwwaYmFl5zDTXr1r3h5+tMMlmUTTLJBOzY0c3Pf74Ps9mAx2PlxIkQBw/288EPzuSKK2a8pRuizWZi6tTX9hZ6LyMIAg0NBTQ0vP6oUlVVgsGkbjL7bhzTvZLBwQSbN3dQW+vTFzdGC+yWlmGamoaYM+f0dWAlJc5Xq1lQVSgtdSJlMpz4y1848fzzZONxnGVlTL/8cipXrDirxVnRrFms/spX2Pp//g8GqxW7y4VoNCKKIuXLltHxwguULVgw4fgw1tfHS3feSTaRwOb1khoeZvf991M6fz7LbroJY3481vynP9H6zDNI6bSebynncvz5xhuZ/bGPsfZrXzutwmg0jDw5PIzV68U/ffqrWkmMYvf7Wfv1rxPr7SUTjWIrLHzTTvqnEh8YoOmxx6heuxY5m0Ue7UTF4+z7+c/JRCKkQiEEUaRq9WpmfuhD2P3+N7Qx2PzEEwwfP86MD32I1NAQuWSS7p07Aa0DdiqW/Ai2YOpUAgcPouRympbMbNZsKux2DEYjzpISqlevxuJykUul2P+LX2DxeHCeUhyOBno3Pf44devX0/LUUwRbWvBUVaEqCplIRPNyE0VUWWa4tRVnaekbvoYmu13f0D3xl79gsFhQFQU5b1+CqmIQBBzl5dgLCjA5HBz81a8onjNnjI/eu43JomySSV5BMpnj4YcPUlLi1HVCdrsJn8/GU0+1sGxZxThri0neOIODCR566AAtLUOIorZRtWFDHVdeOetdHUfV0aGJsF+5SSsIAmazSGNj8A0VZTNm+CkrcxEIxPIFmva4AwNxiosdzJhewJ4f30/g4EFc5eXY/X4y0Sh7fvxjUsPDE+qo3k7kXI5gYyPpcBh7YSH+GTP0YkYQBKZcdBFHH3mEXCqFnNcoeevqsLhcJIeGOPnCCxMWZYf+3//TMiKrq5FzOUJtbYycOEH39u0MHD7MshtvpGjWLE785S8YLRYCBw9qrveSpOcddm7Zgn/aNOZ94rVjkhODg2z/wQ8IHjuGnMlgsFopmDKFlV/8Ip6q1w6CH9XKvZ10bt2KIsu4XrEgMHT8OId+/WumfuADeKqrUSSJrm3bGGlrY+3tt592Vy6XStH2/POY7HYykYgm4jcaSYfDDB0/TujkSSoKClAVhZGTJ+nfvx97cbG2WFBRQWJggFy+g2cwGMilUqCqmB0OfVyajcVQZVnrSJ2CIIoYTCZCra2oikLHiy/qAeSjViOgFW+pUIiOF1+kZu1a/ftHt0RPtwOaiUaJdHdjdjpxlZfrHVuj1YosSdrzne84qqpK/4ED1F9wwWk99tlgsih7F9DSMsQzz7Ry/PgwHo+Fiy6awrp1NboYfJIzS3PzELmcPE64bTSKiKLAgQP9k0XZW2TUZDaRyOgms5pOq41sVuFTn5pYvPxu4LV0cYqiYrG8+r9bTSM2QDYrU1/vo6GhAKNR5JZbVnD//bvp6oogCAKqqlJR4ebGG5cROdFK4NChMU74Frcbo81G8xNPUHPeea+qK3qrhDs72fnDH5IeNVUVBBxFRaz84hf17kYumcRWUEDpBMWN0WYjfUpW4SipUIih5mat8JBlenbuJDU8jNnpxFZYSKitjV333cfUSy/VtUGJwUHtRp13aVfRRPwdW7Yw88orX7VgUWSZF7/1LdpfeEETvosiRouFaFcXmWiUDzzwAHI2S9/evUS7u3GUlFC+ZMnrOuVPhKooDB49Ste2bWTjcYrnzqVq1apxAeZDzc3jPqZIEsMtLRjtdv15Fo1GPFVVhDs66N+3j+o1a07rHJ1bttCzYweGvKZLNBgomjNHs5Xo7SXS1UXx7NkEGxsJNjZiLyykasUKIj09RLu6sHo8OEtKSI2M6FumgiCMCf0WDAYQRXLJJCPt7QiCgMXjweJ2o8qynnOqSNKENhYIAgiCXvzF+vpoeeopenfvRhAEKpYvZ/rll79uFy3a24uzpIRQJIKqKHpkl8FkQslmNUuT/IKEIIpk4/HTuoZni8mi7Cyza1cPDzywB6fTTFGRnXRa4je/OcSRIwPcdNPyc0pv9F4hl5NhwvQ5MBgEUinpzB7oHGTPnl5CoSQ1NS+bzBqNIjU1HjZvbueyy6bh871xrc6ZYNq0QoxGkUxGGhMbJcsKsqyycGHZuO9RVZWNG9t49NFGFEXJF14we3YRN9ywjKIiB9/85nra20cIhVL4fDbq67Xx6JFNhzC+ItAZ0G46qkqore1t9f0aRUqn2fGDH4CqjhGeJwYH2fGDH3DBf/4notGox/bI2ew49/x0ODymCzKKnM3qN+r44CCpoSEs+U6KQVEQDAbclZW0bdyoddFaW/XMwlGyySSJgQGUXI5sPP6qRVnfvn00Pf647jkGkM27vffs2EHLn/5Ex5Yt5JJJjHlLj8ZHH2Xp9ddr4eZ5cskk/fv3E2prw1ZYSMXSpWMKBkWWOfDww3Rt3YrZ6UQ0mRhqaqJt40bWfPWrusM/gNXnI97frzvSg5abqSoKoigivsICw+x00neaRdlQSwuHfvMbBINBD+JWJInAgQP4p0/HYDIhpdN0bt1KMhikdOFCChsatM5SXiMmZbM4bDYK8n5t0e5uUiMjY2woBEFAyWvVDFYroigSDwRIBoOYHA481dUc/u1vMbvdSJnMOBuLbDyOxeWibOFCYn19bPnOd1BlGVc+ELx3714Chw5x3h13vGZhZjCbMTudFM+bx+DRo0jZrGZfglY4nmpCrCoKvrq6172GZ5PJO/5ZJJuV+fWvD1FS4qSoyIHRKOJ0mqmr83LwYIBjx4Jn+4j/kNTUeDVBtTI+VlWSFGbM8E/wXZO8EZqbhybcUjQYtG5kT0/0LJzq9HA6zXziE/Pp7Y0xOJggk5EIhVJ0dIS56KJ6qqrGd1Gbm4f4/e+PUlbmpLbWR02Nl5oaD8eOBfmf/zkGaOPQKVMKWLq0goaGgpfHo6I4Jk/xVAR4xzRlgUOHyEQi4/Q3juJiEsEgQy0tgNatmnrJJUR7erRtuDyjW3x173vfuMe2FRZicjjIpVLE+vr0xYVcMkm4s5NodzftL7xArLdXt3U4ldEgbIPJpN3c3a/eud7/4INkEwlMDgdGq1XvHsX6+ogPDrL9e99DNBrx1tTgLC3Fk8/u3POTn+h+YdHeXjZ9/evs/+Uv6dm5k+Ynn2TT179O+4sv6j9n4PBhOrduxVtbi6O4GJvPh6emBjmb5eDDD495DuvWr9d8uU65XoIgICWT2pbkK4xNRztAp8Pxp5/G6vPhq6sjE4vp0VCKJNG+eTOpUIiK5cs1jVf+545eE0EUMTscuhO+kN9kNLtcWhGmKLpuq3fPHqw+H8782HO0IyZnMuSSSSIdHXRv304mHCYbjxPp7ETKF3GZWAwplcI3ZQq173sfzU8+qRVk5eWI+eUCd3k5cjZLy9NPv+bv662txebzYff7qd+wgbKFC3GVl2s+ZUVF+OrrUVWVaG8vnspKimbNQkqnGfz/7L15fFwFuf//PnNm35KZZJLJvjVpm+5L6EJLS8uOiAKXCypeEQRFX4r6u4ryUrzuC+pVrytfvSpfEQQtq6VASze6QNM1bfZ9mySTzL7PmfP740wOHZKyKAXk289/TTOTM2eW88zzfJ735+RJxk6ceMd1zs51yt5G9fb6iMVmrsILgoDZrOPgwSEWL/7nTaXn9MbkdltZt66S3bv7qaiwo9OJSFKG4eEQNTUOGhtdr30nZ5Asyyge17d/c+7tlNWqf1XI7D/K/HqrtGFDFW63haef7qavz09JiZUPf3gxK1aUzlokPfdcD1arPscrJwgC5eV29uwZ4LrrGs+45OBesoSurVvVQmRaUjKJoNHgnDPnzX+AKB0x4UzYCVlWgquzarjqKlLxOD3PPpv9b1nBRdx556x+LFGnY/4113Dkd79Ti65EKESgvx80GvIqK9GIIv7+fvJE8WWiviwjoBQpRocDKZmkcN48klku2isLl1QsxsSpU2i0WnUsnAgGyaRSyJkM6ezozVpaqtDtsz4mndlMZGKC0cOHqd64kUO/+AVSKpVDi08nEhz7wx8obGjAVlpK386dGO32GV4oS1GRSqafzoR0LVhA3WWXqX45Ua8nHgyiNZtxvoLcL8syyUiE8jVrzvhcSakUwy++SP+uXZx8+GGcdXU4amtJhsNEp6ZAlgmPjiLLMkWLF5NXUUHY40FvsTDV2UleRYUSw5SXp+AlgHQsRjwQAFnG6HCQV1lJydKlBIeGQKNB1OtZcN11THZ2EhgcRJYkZCA6nWKQhQVb3W5MBQX07dhBbGICGSUyqf7yy1n1mc9gcjoZOXRI7ZCdLqvbzfDBgyy/9dYzfvnQiCIrbr+dfffeC7KMraQEvcWC58gRBFEkHgwqo+SFC1l2yy0MHTjAsfvvR0qllC81Gg3z3vc+6q+88h2x0fzO/uR7lyuTkc/4Ipj22JzT26P/+I8l5OcbeOaZHiRJeR7WrCnnxhsX/UOsrVgsxdNPd/Hssz1Eoynq6hxcffVcFi9+41tH7watXl3OM890I0mZnBF9MJggP99Iba3jbTy616e5cwuZO/f1dU2Hh0NYrTOLLikaZrKzk6e/epjymkJqLrwQV2NjzudC4dy5lK5cychLL2F1u9GaTApNf3KSJTfdlIM2eC3JskxoeJjQyAhak0kZZ+lnLwano4bOJGP+y6NnjVbLohtvZM5llxEcHETU63HU1b1qd6d640bkTIbm3/yGyc5OJbbIaMReXo7WYFCo7Hl5aDQanLW1yDU1eNvaEFCKJk3WMzTW0sKzX/gCOrOZhve8JweTMR1ortFqyaRSpJNJdbNQOXANxvx8/L29mAsKcjhWGq2W6OSk0rkbGckpyABlpKzRMLh/P43XXksiGFQ7TqdrutuUjsVyfrboxhspW7mSoQMHSIRCFC1YgEan4/B99xEeG8PkdBKbmmKyowOr263kir4i3xOU4vzgT3/K2PHjGB0ONKKIt72dQH8/Feefj5RMMtnZSSIYRG+zUZR9fZkLChR8RDpNKDtKtRYXo7dYSCcSVF1wgbJQodORDIVw1Nay/stfBpQCetudd6K32ylduZLCefPU4/McOzYjqcBaXEzFunXUbN6sBsGbCgqU7ps0+5ezN6LCuXPZ9I1v0Pv883jb2pCSSQrmziUjSaTCYYqXLGHxhz5E2OOh+Te/wVZaqh6jlErR8tBDGOz2dwQu41xR9jaqqiofrVZDMinNMPVHIklWrJjpTTmnt0Y6nch11y3gyisbCAQSWK36WS+qr0fJpMSPfrSfjo5JSkttuFxmJiai3Hvvfm6/fSXnn//q21/vRtXWOrjiinqeeqoTi0WH0aglGEwgiho+//k17zovZXm5nZMnx3NGtmGPh/79LxJJiGhTGbynxhl58UUa3vteGq+9Vi3MBI2GlR//OL1z59L99NMEBgbIr6pi8U035XieXkupaJTm3/wGz9Gjqlleb7XS9MlP4po/f8bvlyxdis5kIhEM5owHA4ODJMJhQqOjGB0O7OXl6rGaHA610/JaEgSB2uxF+rm77qL98cexlpYi6nSkYjGV8q4zmxk7fhz30qW4GhuZbG8nGY0SGhpCZzYrxPusT+r4n/5EIhxmwXXXAcpChLmgAFtpKeHRUVLhMIgiSBIZSUJvtytjTZOJqa6unKIsk0phr6hQunBn+PKsNRqJTig2E1djI11PPz1j6ULKms1fyRQTBGHWKCar203nU0/Rt3MnU11dWEtK0JpMHP3975Xg7//8T+yndZWGX3wRz/Hj5FdXkwgEsLjdBPr6yMgyYydOUJXFbvh7eymYM0ctWEW9nuLFixl44QViU1NIySSpaBRrWRnm4mKlmyhJyNEorsZGlnz4w3hbW5GzHsPpQtGYl4feakVvtSpk/WQS2yxUfq3RSDIYnOHpmt747H3+eUSdDpPDQX51NQa7nbDHQ9mqVa+rg2V1u1l0440M7NnDofvuw+p2o7daFUTHyAh7vv1tTE6nsiRzWtEo6nRYi4tpe+wxKtetO2vcu9erc0XZWZIsy3R1TdHW5kWr1bBwYRHl5facF5fZrOOaa+bzpz8dp6jIgs1mIJWSGBkJUVvrYMmS/ze7KO8kmUw6TKY3Rmh/pY4eHaW9fZKamnz1+Xc6TZhMWh544DhNTaXvyE3bZFIimZQwm3Vv+rhVEASuv34BixYVs2tXH1NTMdaurWDDhuoZ4/x3gy6+uJaXXhpWv4BlJInhQ4fxp82c16glzwmgdA86n3ySsqamnM6MqNMx55JLmHPJJTPGmK9XR3//ezzHjpFXVaXePhEKceDHP2bzt7+NuTC366czm1n92c9y4Mc/VqJ5NBp83d1EJyZwNTbS8uc/A1C5bh1LP/KRWZlfsizj7+3Fc+wYyDKuxkYKGhpyLnzTxPqJ1laSoZDqYytZtgyr203M56Nq/Xr8fX0MHzoEskw6kSAZCmHMzycVjSLm5aE1GsmvqqJr61bqLr5YKRYsFqouuIBkJIIgisrCgCgiCwI6g0Hxwg0OEg8E1C1DQAnBdjgoWbpUpe6/8rynolGCg4O4ly1DSqWo3riRrq1bmezoUPxM2e3NwOAgC66/XilKIhFCw8OIej32iopZUwmcdXUs+Pd/x3P0KPVXXJFTQES9Xl76n/9h07e+pZ7Dvl270Igivdu3k4pEkGWZ2NQUGlEkGQoRHBoiEQqhs1hmFIB5VVUU+f0458whEQphdjpZcP31lKxcydC+fcSmpiicN4+o18vOe+5R+WYarZbipUsZ2r8fjSgqxU82V1POZHBkR+qZdFpFVCSCQfJeUazJskzbli14W1uJ+3wIGg1xvx9fdzeO+nqsxcVviM02DRq2ud1qR3E6P9Tf34+3vZ2ypqYZt5sOgk/FYm97UPm5ouwsKJmU+MUvXuLIkVFEUTHpPvhgCxdfXMsHPrA45wJ36aV12Gx6HnusjYGBAKKoYfPmWt73vnk5m13n9K+rF18cwWbTz7iQmkw6JiaiDAwEXhdo9a1SKJRgy5Y29uzpR5JknE4TV189j3XrKt5Uz4UgCDQ2uv4pj96/iubOLeSmm5bwwAMnkCSZhN/HeECmsU7LhsaXf0+TDYAeOXRoxrhsWv/IcxD1ehl+6SXyKnKfQ4PNRtznY3DfPua+970zbldQX88l996L59gx+vfsITw6ytyrr1bjc+RMhr7du7GXlzPnsstybpuRJI7+/vcM7N6NkPV0tT/2GMVLl9J0xx052YiO2loK581DZzLNCI1O+P3UbNpEaHRU2eSUZXy9vSSCQTSiyPCLL1K7eTOCRqMUhtmwbWM2j7Jq40bGW1qQJYmE308mk0Gr1+NetgxHbS220lL6d+1CSqfVBAJzURGrPv1ptEYjVrcb97JljB07hj0LQB07doypri6kZJJkJELnk09Set55JMNhvG1tpGIxNBoNjjlzWH7rrdRffjntjz9O+xNPIEsSyDImp5Plt92mhG6fpsjEBC98//sMHTyoZFZWVZFfXY2o12MuLMTf34+vp0f1EkYnJxk9fBit0aiOlA02G6GxMeRMhryqKmo3b2aspYWBPXvIq6hA1OvJpNMEh4cpbWpi3V13qQWit72d7XfdRSxbJB27/34iExNUrl2rFjpSMsnQ/v3Ubt7MeEuLct5QMj0X3HADweFhZVu1uZlMOo2UTJKRJMaOHaNm40alw+j10vbYYzTfdx95FRVUXXABgcFBQsPDpBIJvK2tlDY10b93L8b8fIx2O4Xz5r3qUkdkfJxkOIzJ4UBKpQgNDyseOMDkdJIIBknH4+itVjLpNFM9PYwfP07U6wVZ5rkvfYml//EflCxf/rb5y85d9c+Cnnyyg8OHR3M6I5KUYdu2btxuG8Fggl27+kgmM6xcWcLll9fz3e9eTCyWQq8X39HgzP8XlUxKvPTSMLt395NISCxfXsL69ZWvG9nwWp2NM23WvZqmpmLs2zdIf7+f4mILa9dWUlr6z7OqEok09967j4GBAKWlNnQ6kXA4yW9+c4hwOMHll9e/9p2c06y66KJaVq4s5dSpCUZOnGJKv5/6RssMmr+Y9fC8mYpMTCBoNGp3JRWNMtXdTWh4GCmRAEGgeuPGWS94OrOZijVr6HzqKQrnzcsppqa7EJ1///uMyKOBvXvp27kTR02N+nNZlvEcPUrn3//O/Pe/P+cxL/rABzj0y18qI6a8PDLpNKGREaUQyr5H8ioqSEYi+Pv7ScfjCkg3S7+f7vTJoIJSm++7j4lTp1STv7OhgXQsRsny5SonTCOKOObMYfEHP4jF5cJgt+Ooq8vpYi2/9Vaaf/MbpRjr6SE8PIyc3f6MTU7i6+mhZ8cOSpuamHv11STDYdKxGIlAAEdtLT3PPcfJhx9WQ7RBKSB2feMbXPSd76gA2+DwMHu//W08x48rXj9ZZuLkSQJDQ1StW4eo1yNoNCROe31otFrS0WjO2Fij1WLJQobP+9SnMDkclDY1YSkspPuZZxSTuyBQvWEDjf/2b+pjDY+Nsf+HP0RnsahfCnw9PcS8Xvy9vbgWLFCeL70ei8uFt72dzd/9rhLDJIqYCgpIRSLsu/deTvzpT4gGAxqtFoPdTmlTE97WVk488AClTU0c/OlP8XV3kwwGmerqYqqri9KmJoz5+YyfOEEqGuXUww+TCAQQtVqKFi5Eb7ez+KabqNm4cdbX+XRRnk4kGNy3j7jfr3YaQ8PDiAYDwaEhHHPmMLR/P5OdnaSjUWRAb7PR8+yz+Ht6WPnxj591KPOZdK4oe5OVTmd49tluyspsORdiUdRgsxm4557naWgooLjYitUqcPDgEC++OMyXv7w+h9n0TtHkZJRnnunh4MEhNBqB88+vYPPmWvLzja9943eBkkmJn/zkACdOjOFwmNBqNWzZ0spzz/XwpS+to6TktQuhVavKOHRohIKC3A5ALJbCaNS+4ee9vd3Lj360n1Qqg9ms48iRUZ56qpOPfnQ569f/c4G7R4966OvzU1Pz8ge81apHr89jy5Y2NmyofsOB2+f0svLzjaxdW0Gk3sizBx4EOQNCroclHY9T2Nh4hntQJGcyeI4do3fHDuI+HwUNDdRs3pzjNTpdBrtdHcGlIhH6d+9GSqXQWyxKgdbZyZ5vf5t1X/rSDKjptMIeD7bSUvXf0zmH076qdCKRwwrr3rYNSxaXMC1BELCVltLz7LPMfe97cwqfijVr0JnNtP7tbwT6+xH1emovuoi5V11F26OPosl2EINDQ6STSSIeD4lAAIPdro7VUtEoOqOR/Joa9n73u4RGRsirrFSo/BUVBIeGSIZCxHw+FXehEUWW3XwzNZs2nfHLk95iYfWddzLZ3s6zd92FyenE192tFEKCQGxyEq3RiK+rC1txsTImzMtD1Otp27KFyPg49rIyRJ2ORDDIWLY7k4rFePzWW9l4zz2Ur1nDyb/8hYwk4aipYSxbmIl6PTG/H39vL86GBmRJUrc4QfFqaU0mBfuRBc9KqRSpSIT8qipik5OYHA5147X+yisV07/FMmNpoH/nTjKZTM5rIO73YyosZKq7G2dDg1pU6m02ggMDyJKUEz2lt1opmDuXkhUrMNjtaHQ6zAUFCNmlir6dOxnYswdTYSEGu105FqsVKZViaP9+QFkiSQQCJAIBbKWlpKNRQh4PFXV1HP3d77CXls4YxQKYXS7sFRUMvfiiMgbPFqqyLCtpEwUFxAMBurZuJeLxkIrHFZ6bxYK9vJxMOk1kYoJTjzxCxfnnv25/5Jupc0XZm6x4PE0iIc06evR6I3i9US6+uE79WWmpnfHxCA8+eJIvfvH8t/JQX1Pj4xG++c3dRCJJXC4Lsizz5JMd7N8/xN13r39b4Z6JRJpYLI3Vqv+HtiFfr/btG+T48TFqax3qB7bNZsDjCfHAAyf4/OfXvuZ9LF1aQn19AZ2ditFfrxfx++NMTcW4/faVb8hPlkxK/OxnL2Kx6LHbX+5YJBJpfv/7I8yfX/hPBX8fPjw660KDXi+STmfo7/czf/67f9x4tmVxuahct47+XbuwV1Qg6nRqZqCtpAT3kjMnGsiyzPH776d7+3aMWbL/wN699O/axerPfY6ibDfjdOmtVpBlerdvV/lY5oICMpKkBG0vWMDokSPs/NrXaLzuOkqWLZtxwbaWlCgh2Xo93tZWAoODIMtozWYcNTU5HTRQunOzQT+1BgOReBwpkUDzir/hXrKE4sWLVXP8dNGWV1mpeM7CYYz5+RgAWZKIer0kIxGkdJqwx0M6Hue8T30KX08Pgf7+HOitIAgqHUF6R3gAACAASURBVL/pjjtIxWLEfT4K582jYO7c1xxXCYKgjBALCpjq7ERvsSBoNCq3azqrc6qrC2d9vRIplJ+Pt60NncWCxWgkFY0ysGePggzJy0NnNhP3+zn0q1+RikYZP3ECe3m5Em3V2ko6HkdrNKK3WPD19aE1mylauDAHH5FXXk7x4sWERkaUvExBQJMdzwqCMON51BoMaF2zv4e97e0YX9Et1RqNSoJCtqAXsyPSTCqFaDTOumHr7+3FWlIyo8DXiCIxn490PE4yEiEZiSiFtMWiFqzT28CJUEg9j9psXJQUj6OzWOjetm3WokwQBBre8x6O/O53gLJZqTObFbCxTkd0fJxUIoFWrycZjSJLEo4sDkTQaNCIIvFAgGQkgretjYpXwZCcLZ0ryt5kmUxaLBYdsVhqhkG8q8s3q4nZ5TLT1jZBKJTAZpu5Uv126dFH24jFUlRUvPzGqqrKZ2DAz9NPd3HjjYve8mOKRlM8+mgrO3f2k04rnaIrrqjn0kvrzsrG3s6dfRQWmmd8YBcVWWlpGScQiJOX9+pdQ71e5HOfW8PTT3fx3HMKEqOmxsEttyx7w0iMtjYv4XCS6urc7prBoCWTkWluHuHSS/9xbpVWq5kVmjutd9tW5NupJR/+MDqLhZ5nn1XBm+6lS1ny4Q/PKHBO12RHBz07duCorla7UHqLhUQoxOH77uOSe+/NMd0HBgZ44fvfJxWPkwyH8fX0oNFqSUWjGOx2bGVlDB88iJRKERweJu7zYcjLY+3nP5+zjVh/xRUc/MlP8Pf1kU4k0Gi1iqnc60VnMjF6+DClK1eqv59XWUnU653RbUhGIhiyxvzZJAjCjMfvqK0lNjWlEOqnNz0LCkjH46SiUSLj4zRccQVzLrsMR20tnVu3nnGLTs5k6Ni6ldDgILIsK1ufbjcrbrstp4g7XdHJSYZffBFfVxeh4WFifv/LjyuLvMik0xiyBUAmnVa3SKe7M972diVD1OdTi9WMJKG3WrEUF3Pqb39T/GbZx1+xdi3DBw8S9/vJSBJSIoHOaKRk+XLiPp8K9a1cv56BvXspX7uWTNa7pbdYiHq9WN1urCWvf4vf6HQS9nhyxtiO2lrGTpxAk2WTASqMteGqq2Y9z1a3G39//4yiLJ1MMtnejpRMYnI6yWQL61Q0ir2yUnn8KDFcOosFXfY1Mp3+ICWTGOx2fP39SuGehQ5Pa+TQIQ796lfqGDMyMYGo1VK8ZAmhkRH0djtCJIK5sBApmSQ2OUkiuzByuuQsJPft0LlP2DdZoqjhiivqGRkJ5VzcUimJWCxNXd3shm6FlvzGvUVnS+l0hoMHh3C7ZzKQ3G4bu3f3v+XHJEkZfvrTgzzzTDcul5nKyjysVj1//vMJ/vznlrPyNyOR5KydrOkA7TMBUF+p6U3bn//8Cn772/dyzz0b/iFGWTSamuFBmpZOJxIIJN7wfZ6uVavKiURSM3xu0WgKk0k7oxg8p39c6USCVDisFhbG/HwlJ/E1RibDBw+qjKzTZbDZiAcC+Hp61J/JmQwv/fKXIAi45s2j7tJLsRQXYyosRM5kKF60iPDoKFqTCWN+vjL6q64GWebgz36Ww5AqX72a/JoagsPDBAcH8XV34+/pIR2Po7NaOf6nP+X8fv2VVxLo72f08GFGDh0iMDBAKh4n7PGc8WL+SkXGxzn6hz/w7Be/SCaVIubzqeywybY2MskkRqeT+NQUJcuXq5E609uAsyk4OMjwgQMKvb+iQsFehMPs/d73iJ4GxJ3WSHMzz33xi5x6+GHGT54k6vUSHh0lEQgAqJmPUiKB1mJRfVSyLBPxeChfvZpAXx+jhw8THR8nnUgQ6O8nPDamUO1rahQ2WHa8Nn2/JoeDuksuoWLtWrRGI4b8fJLhMMf++Ee2ff7zCgVflimcN485V1yB99Qphpub8Rw5wkhzMwjCrNBVWZYJDA4yuH8/o4cPK2HjWVVv2DAjacBRW4vBYkEQRRLBIOGxMfx9fRTOnUvDlVfOeo6rNmwgHY+rcUfTGtq3D63BoGZkmhwOHHV1SMmkCreVkkmcc+ZgzgamTx+zLMtodDo8R44wuHcvT9x2G7u+/nU8x48DEPP5OPTrX2MuLMRZW4ulqIiixkbyKiuZ6ugAjYZMKoXJ6cRWVqZ4LLVakuGwOv6WUiklK9NqPWtQ5tfSuU7ZWdAll9QxMRFhx44+9WcajcB731vPxER0xu/7/XEqKuw546i3W5mMjCTJs6IQRPHtAdu2tXlpa/NSVfXyAoXRqBQK27f3ctllc/6p0d1sWrLEzc6dfZSX53Y9I5EkeXkGnM43NsIVBAFR/Me3etxuK7I8+/JAMilRXZ1HNJpix44eduzoI5FIs2SJmyuuqH9dIeoLFrhYvryE5uYRioosGI1afL44kUiKT36y6R2J7vhX1PiJEzz1qU8RGh5GZzZjdDjQWywc/NnPWPqRj1C7efMZb5uMRGZkI05rupswLX9fHxGPh7ysaTudSKjRRIJWi7e9XY3wSQSDKn3f5HQqhU9Hh8owm45B0pvNildIq1U8TKJIoLeXTCJBZHwcW0mJsqF4/LiKZEAQlDGe2czqz3zmjEbt0xUaHWXPt75FKlusaE0mtTBAEHDOmaN4qUIhjA4HL/3yl5gKCiior6d48WI0okgqFkNnMqnvl3ggQGh0lIarrlK7iYIgYHI6CQwM0L97d84CQtzvV5cPpseA1RdeSMcTT+Dr7VWLQFGnw15eTsLvxz5/vjpKLVmxgonWVgrmzkXOZEgElMB5BIHI2BjFS5ZgKy1VCkhZZv5119H8618DYMjLA0EgODxMIhCg/sor1c5TJp3m1F//irWkhLKmJqSU8kVKTqeR0mkEUURrNM4YXabjcZrvu4/RbNEGiml/xcc+RunKlRQtXJiTNKDR6UhFIpStXs28q6/G19NDRpIoXbkS14IF+Lq76d+5k4jXi3POHKo3bsRWUoKzro5FN95Iy0MPKeNUrVYZF/v91Fx0EZ4jR1SYrdZgwFZejpxKUb1hA3KWnycIAv6+PvR2O8lgEIvbzWhzM6HRUWouvBBbWRmxqSn233svTXfcQTISIZNOozOZKJw/n4E9e9BotWiNRmUbVq+HTEZdJpjq6iIZDpOKREjFYkraQyCAvaKCms2bc3xyb6XOFWVnQaKo4cMfXsqll86hu9uHRiMwb14h6XSG//qvnYyMhHC7rQgC+HxxgsEEt9++4h0R8TAtvV5k/nwXAwN+XK7ckev4eIQVK0rPcMuzp5MnJ9DpNDODmUUNINPT43vTi7KLLqplz55+JiejOJ2KUT8WSzE2FuFjH1vxlo/zqqryaGx00d7uVbl3siwzPh7B5bLQ0FDA97//Ar29PoqLrZhMRg4dGuHFF4e56651r4neEEUNn/xkE3v2DPDMM91MTcVpaHBy5ZUNNDQUvEWP8t0tX08Pz335y4THxrCVlSGlUsR9Pka8XirWrePkX/5C5fnnn3G8V7xoEcMHD8JpvqBMOk3I4yE4PKyCQEW9nlQ0ql58fb29jB07BiiGeDmTIeH3Yy4uRhOJKIXO6RE/mQxDBw7QuXWrsrG4bBmT7e2Ier3K4JqWzmolMDCgdjY8R4/St2OHQpRPpYhlI5KmvUSvp0vWtmULUjJJXnk5chYhkYxE1CJMZzarywbOOXOQkkm6/v53Cj7zGYx5eSy75RZ2f/ObBAcHlTBsrRZTQQHOhoZZWVSGvDy8ra1wWlE2evgwGUnKKW70FgvzrrmGrq1bIesNMxcWostGJNnLyxF1OkqWL0dvtbLza18jr7JSoeXbbIyfOIHOYlG2TK1WBI2GyPg4BQ0NVK5di8Fm4+RDD6nnM+7zUX3hhTmjQI1Wi7mwkBMPPEDHE0/Q8pe/qNBVR20tWoOBwNAQJx54gKY77lBv1/Lgg4w2N+ew6lLRKC/94hdc+PWvYy8vp/Haa8mvqmL8xAkykkTRggWUNjWht1iozhbT03yxtkcfVcaMJhO927fTu307q+68k+KFC5lz2WW4ly5lpLmZZDiMxeXi2B//iDEvj/LVqxk5dEhBUQiCMvq12bjoe98jEQzy0s9/jsFmQ6PVEh4ZwexyIUsSodFRSleuVDpdgoDJ4UBrMHDigQeoWLNGLbTNhYWUrV7N2LFjatC7FI9TtX69uiRRtX49Y/n5eA4fVnJaJYmiRYtYdvPN1F588Wu+Ps+WzhVlZ1HFxVaKi3PHf3fffQF//espmpuVVm1dnZM77ljJvHnvPPP0ddfN51vf2qMWJAATE1FkGd7znpkmy7MtnU6DJJ15xHs2DP9ut5W77lrH739/lIGBIIIAFouOW25Zzrp1bz2JXxAE7rijid/+9jBHjngQRWXsXV2dzyc+0URz8yi9vb6c7cnSUhtTUzHuv/8YX/vaxtcs/nU6kU2bati0qeZVf+/drGQ4zFR3N6DAPN9IlNFrqW3LFuJ+v0JAz24QglJY9Tz3HGVNTfj7+2fwq6ZVsmIFlieeIDQyolLVB/buJebzkVdezuHf/Y7WLVtY87nPobfZSEajxHw+xo4fR2+1Kh0ui4Xg4CAyEB4Zwb5yJcWLF6s0eimdZvzUKRLBILbycjRaLScffpjJrq5ZIbFkMmh0OrTZ7cve7dsxZLMktQaDurUpZzKMHjlCPBA445YnKGOk0/MQBUGgdOVK+nftUkCkPh+JLK3d1diIMT+fTCqlPmcAwYEBtAYD9spKxZRuMChFztQUmUwGzSsKQymRQP8Kk3t0amrWx6vV63EvXUrjddepm60F9fU526kAk52dypgsuyRQ1tSELEkKokQQlLzPwUG0ej2LP/QhAAoaGig97zzCTzxBIhRS4LWSNKM7npEkup9+GqPDgdFuR9TpmGxvJzA0ROmyZZgcDkYOHVITGRLBIP179mB/BatOZzYjaDT0bN+OubCQzqeeIh2PI8syJcuWUbx48YwiNjAwQPvjjysZpdnzY7DbSYbDNP/qV1z6ox8pSxEuF8WLFysFVGEhpx55hHQigdZopOL88wl7PAQHB0kEgxQ0NHDqkUfw9fRgdDgoW72a8z79aSWD0++nLfv3pvEhpx9/1OtFNJlyAMAmp5Py1atJJ5Pqa+F075hoMGB1u1n7n//JsltuQRBFdCbTrEDft1LnirK3WG63lU9+8jwSiTSSJGMyad9RHbLTVVfn5EtfWsdDD52ku3sKUDp+N9yw4HWNwt5sLVtWwmOPtZPJ5I5VE4k0oqg5a52cmhoHX/vaRiYmoqRSEkVFlreVJWe16vnMZ1bj8YTxeqPY7QYqKpSu2Z49AxQUzOwWOhxGBgeDeL3RGZ3Pd7NkWemgnjgxDsCiRUU5m7Sz/X73tm2ceuQRtesjaDTMv/Za5lx22T/1Xk0nFL/fWEsLerud4OAgqVgMrdGY9ZQq4y3P8eM5OYmvlM5k4vwvfIHj99+v+LWyo6iy886joL4eQaMhNDrK47fcgq20lNDgIIN796q8KEBFRJSvXk33M89gLSpSiyRZlhk7dgwplaJwwQLSsRgarZb8qio8R48Sm5oi5vMpBZ4okopGkVIpCurr0RqN+Pv7GW9pAY0Gk8ORi8TQaNBoNIqH7lWKMmQZOZPJua3BZqNm0ybifj/pZBJHXR328nL1flKxmOrHi4yP0/n007gWLMi5yGYkifbHH2f4wAHSiQRSMom1uJj8mhoS4TDVGzbkHEZetqBLJxIE+vqU4G1ZVqKlRFGJSao/M7vP6nYrz206rWyTarWUr15NcGgIz7FjWFwu6i6+mJrNm7G4XGQkiZd+/nM8x45hKynBWlLCZHs7g/v24V66VN04lGWZ0eZmdFarsrmr1SJoNEiJBMHBQaIejzIaNBoJDg/jstuVVAaYtegw2O10bd2KJjuGtRQVKSPoEyd44XvfY8PXvpaDOxl+6SUEUZxRsOqtVqKTk0x1dZGKxTj+f/+v+qXDmJ+Ps74ez5Ej5FdX4+/rYyzbjUsEAgQGBxk5dIjK9etJx2J429qo3riRZTffjKDRMNnRoUJqZ5Nr/nwGdu8mNDpKeHRUfa7SsRiuxkZWffazHM+CcMn6DQsaGlh2yy3EfT5SsRi2kpIZpv+3WueKsrdJ/yq0/vr6Au6+ez3hcBJBEP7h/Mc3Q1VVeWzaVMOzz3bjdJqwWPQEAnFCoSQ337z0rB6bIAjvuPgft9s6YxEjnc7M6gOcLibeScskZ1vpdIb77mvm4MFh1ce3ZUsbq1eX8bGPrZi1szpy6BDH//QnlXoOStfmxAMPYC4ooOy8897wcUx1dXHq4YfxtrerFxdrcTHxLGNLzbgEBFFUOile76vep7mggNV33knv88+TTiQoqK9XL5ByJsNURweBwUHyqqqovvBC2rMeKCmdxpiXhzEvj7JVqzDm5VG8eDEyEOjvRwaQZZKRCPbycnqyoFFkWTFQ19czeugQjro6wiMjJONxZSxntVK1YQMv/c//MHHqFFM9PUTGxvA6nZStWqVuKqYTCaR0mpHmZtKxGM66OooWLpwRii7q9bgWLMDf25uTGakzm7GVl5NJJnPQH3ImQ8zrpfHaawFUr9yZuh7e1lZEkwlZkggMDDB6+DArPv7xGTgR9+LF6CwWurZtQ85k0JvNyIDnyBFMBQU5W4qyLJMIBhE0GrXjaLDZqL34YjqffJK8qio0Wi2CKKK32ahcu5YLv/nNnO1Ub1sbnmPHyK+uVl8XrvnzGTtxgonWVvKqqtAaDKSiUcIeDxXnn08iEFD4a+Ewcb8f0WRSUBBWK7HJSY7+7/+y6ZvfVI41u1WYjETwdXcTGR9HNBiUkPtscoLqtdNosJeV4e/vZ7S5mcp169TjTIbDrxo2P37qFB1PPIHObFawF5EIMb+f0MiIOtL0HD2qdqZ0JhMmpxMpmWTi1CmqLrgAY34+/bt3U3n++RTOm0f5mjWMnTihbp2efiwGux3nnDms+fzn2XLTTQQGBtBZLMqXj7IydCYTwwcOcMkPf4j31ClSsRj2sjIEUWTvd79L2ONRvgDIMtUbN7Lwxhtf9fGdTf1rVAbn9LZKEIR3BKpDEAQ+9KHFzJ1bwLZt3Xi9UWprnVxxxZxz7KyszjuvjC1bWmcUqKFQAqfTdNa6ZLFYiuHhEHq9SHm5/U3PyvxHtGNHD/v2DVFb+/JiiCzL2Z85ZkWHtD/+OOaCgpwiQdTpMBcW0v7442+4KJvs6GDvd76DzmIhr6oKOZPB19uL5+hRtAYD6XhcpbQnw2E0BgOF8+bhP22D8tWUjsfRWyw5HYvIxITayZrmfbkWLCA0MkIyFEJvNqs+L73Nht5i4YKvfAUpkVDyDwsLee6uu5g4efJl71Ymg39ggEQ2rmiqqwvXvHmYXS5SkQi2sjIls7Cnh7yqKkxOJ73PP4+UTDK4bx+1F12ERqtlJJtd2frXv6LRaunIkuUdtbXqKLJ640ZMTifFS5bQ+fe/M9XTg7OujkQkQqC3F0GrpWjRIny9vYqJP5MhnUhQuX495adzpWbpaoZGR0mEQtgqK4mMjipoj2zHp3PrVs771KdyOF1aoxH34sUM7N6txDB5vaQTCQxWK4a8PNoefZSVt9+Ot72dlj//GX+/spVeUF/Pog98gPzqahqvvRZZlul99lm16LWWlLDittswORzKa6Knh2QkwsALLyibtacdu6OujnggwGR7O97WVoxOJ+loFKvbTX5VlYo5ift8aLPnQ5ZlkqEQ7iVLiIyP4zl2jLKmJtzLltG/Zw++7JhXazIpo/quLkxO56xeP0EUOfHgg4yfPInF5aJ8zRoK582jf+fOGb873V0ebW4mFYkwdvw4ZDKkk0m1+2spKqJm0ybSiQQ2txsEgeEXX0TU69HodMR9PpKhkBocPnTgAIXz5lG6ciW927cz1dODze1WoLpTUyRCIVZ9+tNq19ZeUYFrwQKkZBJddikFYLylhYjHo76HYz4fO+6+W+0Cg9JJ7XnuOQSNRh0nv9U6V5Sd07+UNBqBVavKWbWq/O0+lHekLrigiuef72V4OIjbbUWjEQgEEkxNxfj0p1e96cVSJiOzdWsnjz3WTjqdQZYV7t6tty5/WxcDZFlm69YuSkqsORc4QRAoKbGydWsXl1xSl/N/ciZDMNtdeqUMdjuB/n4ykvS6PSeyLNPy4IPorFbMWWO8IIqUnXceoeFh5EwGU0GBwkoKBhFEEVthId7WVpy1ta/6t3y9vXT+/e/079qFt7WVTDqtdGJEUY1UkjMZdCaTQuzv6FAKt0wGncWC1mBg9MgRIuPj1F58MY7aWvVcpBMJJdBap1NGY7KshHb7fEjptIILqK0lODSEvaKCxTfdhKWoiN1f/7pqIDfY7ZSvXs3o4cPEpqbwHDmiRCelUpStWqVuRHqOHGGspYXBfftw1NYy0txMx1NPYXO7CY2OYikqwtvaysDu3Up6gMWC3mIhNjlJ5bp1FC1YgMFmo7SpSR3dglIUCTDjHAayRVNiakoZQZ4W/xQaGeHQL3/Jui9+Medcjx0/jnvFCoYPHkRvsykjSVEkNDzMsT/+kYrzz+fAD3+I3mZTuW7B4WH2fPvbbPjqV7GXl7PohhtouPJKFT9iLy9Xtgv7+zn0i18QGR8HQWCqu5tMOo3Z5VI7NRpRpHTlSnRGI5Xr1+PIdhebf/1rBaabl0dBQwOh0VGk7EjWYLfjrKujoKGByPg43tZWypqaWPyhD9Hy0EMqlDYVjYIsU7x0KZOnThEaHc1JhYhNTTG0b5+y2JFd1Gh/4gnmve99mJxOxddYUqKOaAMDA1SuW0fvjh1MdXcrRv3RUcXrpdEgpdMc++MfWfqRj5BfVYW5oEB57FkJWeZbOpHAAOrWJijQ27X/3/9H17Zt9O7YQXJsDNf8+cy9+mrVf+nv60MQBPX9Bsr7Ojw2xsSpU+z+1reYf801VK5bx+ALL5COx3NYfBpRJK+qit4dO2i46qpXH7GfJZ0rys7pnN5Fys838uUvr+eRR07x0ksjyLJMWZmdm29eypIlb5yL9lp69tluHnywhYqKPBWX4ffH+cEPXuDrX7/wdcVQnQ1lMjI+X5yqqpkfqmazjvHxCJmMnIsnyRYT6Xg8xz8D2Y6Uzfa6tganlQyH8fX25nzog+IJm3P55XQ88YTKG7OXl2PIz0dA8Ub5+/poe/RRdRw3rVQ0Ss+OHRz7wx8wOZ3kV1cz2dnJ8EsvER4bo3zVKkStVrmo2WyYXS4mOzqQ0mklZqi/X9l+zD7eZDTKso9+NKc4DXs8WFwu4n4/qViMRChE2OMBjQYkCSmZJDAwQNGiRaQiEUqWLsXb1qYa2qdlLS6m7pJLmOruxjV3LtayMoYPHFDPbXhsjLFsxmEinVb4XhoNEydPIup0NF5/PXI6rXRRDAam7zkdixHo76dtYoLoxAQbvvKVGUsRVreb2osvpuPJJ5EliVQkooxoh4YUTEKWxv/yUy+gs1gYbW5WjfHq8xiLMdHSokQVnd5BNRgIDQ9z+L771G3KaVlcLkKjo3Q8+SQrP/5xQBllTo81ARLBIPt+8AMEjUb9IiAaDPQ88wyjR45QflpXNpNKYXQ4WH7rrerSyaIPfpD9P/yhErtUVIS9tFSBEIsiNZs2qWNRKZVCazQSGBggPDaGc84cdCYT0akpRL0em9uNzmwmODDAZGenWpRlJInhgweRZZnixYsxFxYSHBpi4uRJBvbupTgb+J4Mh9GazQjAnMsuY/411ygMNUkikt26nV4AEXU6kpLE4IEDOGtrMRcUKAshZLtsgqCMibOLBalIRP07oIyv57///cx///tnxQJpjUZON2jImQyjhw8TGBxESiRIRaO0bdlC97ZtGJ1O9LaZn08aUVSRJeeKsnM6p3P6p+VyWfjEJ5q4+eY06XQGi0U348PrzVAqJfH44+2Uldlz+GUWi47Ozkk+97ltNDWVsXZtOcuXl76ljDNR1FBSYiUUSs7g/wWDCdxu6wyciSAI1F16KS0PPaSEaJ828gyPjtJ4/fVv6DxOe1Rmk9ZopHrjRkIjIySCQVLxOLHeXpBljPn5pNNpOp96iprNmwkODBD3+xlvaWGkuZmhAwdUYGnJ8uVUb9jA4L59+Lq6FH9QNsi69Lzz0Igi/p4eohMTZFIpZEnCYLNRUF+vEvdTkUiOr0nU69GZTFSsXcvo4cMEBwZAlhWiu9Go0PhNJsZbWnDNn4+vpwe9zTYrrHXaL1S8dCm+7u4ctIS3rY1kMIjOYkESBHRGo2JMzxZOyVCIUJbRpbdalQghUcTsdJKOx4n7fPTv2sXDLS0svOEGltx0Uw6Rv2bTJtoefRTvqVNKx0yrRWc0Ep2cVP1L6WSSZCikjNay3Z74K4qyvPJyhl54YQYZX4rHMebnM3r4MDWbNs147BaXS1nCOIOGDh4kmc2nVG9TVISjvp6p9nYcNTVq4Hp0cpLFH/xgzhZw8aJFrP3CF2h95BEmu7qQUins5eW4ly5Vi5qMJBEeHaVz61a6nn6aRCiEt7WVyvXrKXpFvmrRokVMtrURHB7G5HAQHB4m6vXiWrAAc2Eh/v5+PIcPo7da0VssiHo9eosFrdHIyk98gvyqKvRWq0LRNxgIj40p3drTnvPpLp7Bbifm86kFsLO+nonWVuV8V1aqCwpWt5uS5ctnnDtZlvH19DCwZw+xqSkKGhqoWLtWYcUFAsoXi6Iiwh4PgYEB9DYbaY1GiQMzGomMjzPe0oK5sHBG4oScXTLRmt6eGMFzRdk5EYkk6e72qYiOt9PMf05vnozGs/v2npyMEY+nc3xq8XiavXsH8PtjmEw6CgvNHD3qobHRxZ13rj7rx3S6rrpqLr/85UuYzTrV1J9OZxgfj/CJT6yc9TZ1F1/MeEsLA3v3Iup0GPLykDMZ3EuXMueSS97Q39dbLLjmz8ff359jVo9NTTFy6BDW0lKFn5RMIqCwlYz5+YgGA8GBAaIeD9s+TaARbgAAIABJREFU9zmQZYJDQ/h6epSugiBgLiwkHY8zuG8fNRdeSN0ll+Btb8dSVMSyj34Ub2srXdu2Eff7lS5BllRuy+ZsetvaVNaY+IpII6vbjb2igpjPR+H8+fh6e5HTaUSjESmRUI4xCxWN+XzImQyOmhpsJSVEJiZywrLT8bjSpcqOvqYLIoDo+LjSfcsWupps/qcgikjJJMlwmODwsPLvadRBlvieDIeVTokootHpmDh5kj3f+Y4yLiwrQ5ZlpZtYUMD8665TNzlTsRhHf/c7vK2tCKJIOhZDyMYHmQsKGDtxgtFDh8grf9keUXnBBbQ8+CCpaFS9UE+PCQvnziUwNKQe9+mahvKeSVNdXTPgroIgUNbUBNlN3FQkQl5VFYtvumnW4qSosRHXV75COhZj/ORJDv3qV8R9PpBlUtEogYEBEuEwjro69BaLus05sHdvTjcNQNRqWX3nnUipFJMdHRgdDlyNjbiXLUPOZJg4eVKFBkuJBFIigX3uXPz9/YQ9HlyNjbQ/8QRtf/sbyUiEdCxGOpEgk0yis1jISBKiwYDBbsficlG8aBGhkRHVlG8tLiYdi6kdudKVK1l4440zutbT0Vhtf/ub8iXEaGTs+HH2//jHmBwO0vE4Pc89h85sRjQa1U5p8ZIlKvvP7HIpOBq/n0xxcc6IO+r1Yi8vV0HKb7XOFWVvULIsMzwcYmoqRn6+UUUR/CtKlmW2b+/loYdaVP6XRiNwzTXzufzyOf+yj+uc3hoZjUre5umIkrY2L5FIEotFj9Wqp6DAjNNp4tSpCZ5/vpfLLz8zPuDN1po15Xg8YZ58siMb06Jc/6+5Zj5r185kzMmyTN/OnUy2t6PRaol6vUQnJ1nwb//Gio9/HHE2PtdraMENN7DnW98iNDKCpagIX1+fMsKzWLCVlNDX0UEiGCQv66+Zlt5qZbylBWdDA47aWsZbWrCVlBAPBIhMTCiEe6ORdDKJr7eX4kWLsJWUkF9dTemKFZQsX07RwoU8/9WvorfbSWfN+NNFgJzJMNrcTP2VV2IuLMw5ZkEQWHrzzbzw3e8SHhlBazSSDAaRsiPR6RGcnMmQkSTya2oQNBqaPvlJ9v3gB/j7+xGzhu2pri70djttf/sbUiLB6JEjJMNh3EuXIur1SqxOIoHObFYKm2wIeSYbID2dgSmn00q3Lmvmni7ktCYTAqCzWECW6fz731nxsY/hbW+na9s20okEXq2W/OpqNU5JZ7GQnpxESiRAFNVcRY1OR8HcubQ99hjla9aoRPfy887DvWQJ4fFxFe9gsNspWb6cZCRC1fr1hD2eHC8WKEsF9ZdddsbXhtnpVI7hFdKIIrayMjZ85SvKuX0dQek6s5mypiZsJSX07tiBr7ubvKoqIhMTpMfHGdizB1DYXfl1dXhPnmS8pYWq9esVz5XHgyEvj+oLL8RSVISo0xEYGOD5r34VgGQopGA9TuvATeNHDHY7I4cOYXG5OPXww+RVVmIrKyMyPo6vp4d0Oo2YTqudRovLhUano+y886hcv57J9nYVR2HOjs11ZnPOqPd0Bfr7aduyBXtFhbrgEhweJjQyQioSoe6SSyior2f85EkmTp2ioL4e99KlOePl6bD40qYmhg8cQDQYEA0GJZfVYmHl7be/bde/c0XZG5DPF+NXvzpEe/skGo0C7Zwzx8knPrFyVjbUO11Hj3r44x+PUl7+sh8olZL4859P4HSaWL36nJn+nM6s/HwjixcX09rqpbTUhiRlGBgIYLXqCQQSLFyodIcEQcDttrJ9+1tblAmC8gXjwgur6ehQMg3r6wvOGI01euQIx+6/n7yKCtUHJqVSDB04gHvZMirPP/8NH0N+VRUb7rmHjiefZOjAATyHD+NauBDXvHlojUZMDgehoSEi4+MYbDbVsxSbmkKTRSekolHk7AXR5HAoKIpwGIPNpozjJiYAxaNUsmKF+tiLlyzBXFjIwuuvZ6S5mdDwsBqenU4mkeJxFn3wg7NefBw1NVz4jW9w7P77CQwNqQZsS3ExgiCQTiRIRiLMu+oqdVRmLytj83e+Q9+uXRz93e+Y7Owk5vcjpdNKCHdJCXnV1SopXm+xKAHa2YigVG+vMkLMZJQRqsVCfnU1U11dRLMpBbIsK8WBKCKIIpZsx3B6y260uZnQlVey99vfJjg8jMXlQkql8Bw7RmBggIwkYXI60ZvNhMfGFESFICBl77N40SKFIXb0KHMuvRRQio4FN9xA25YtmJctU7xvOh3h0VHyKytZfttt7Lv3XvwDA0qXMBuEbXG5qMvex2wqX7uWzq1blbSB0zpq0clJbG53Dhbj9cpeXs6SD38YAM+xYxz8yU+U5ICsNyoZDhPz+3EtXKhiU+RMBq3ZTDoW4/mvfAWNTkfNhRfS8N73UrRwIROtrar3Ss524LQmkxqqPt0R7N62Te2kgTI+Tvj9JIJBNUvTWlysZkqWNjUh6nQULVyY8xhOjzfKSBKjR47Qv3MniWAQV2MjsUBAZb6BMhL19/ZicblU2K7J6aRq/Xo18cTkdKrIkukgc1GvZ+nNN1N70UUM7ttHIhCgYN48Klavzhlfv9U6V5S9TklShv/+74OMjASpqspTn+zBwQA//OF+vv71C98UonwslqKlZRy/P05RkYXGRtdZA5U+/ngHDocpx+uj04m4XBYee6yNVavK3hXdsmRSort7imRSoqoqn/z82eNrzumN64MfXMx3v7uX/n4/RqOOaDRFOp2hosJOWdnLH2x6vcjERORtOUaHw/S6tnU7n3gCk9M5A4dhKS6m/fHHqVi7VonZmpqi57nnVG9Xxbp11GzaNKspeOLUKdofe4zJzk5S0SjWkhKKFy5ULyimwkKFuh8MEvV6MTocSIkEmSxPTKvXK+HWZPNONRoM+fnE/X60BgOZdBqtyURgcBBbSUkOsiPs8agbk0ULFyrMqb4+MqmU0jUzmXL8TK+UpaiINZ/9LMlQCH9/v0Kf7+sjlU4jyzKVa9ey/Lbbcm6jNRiULUW7XRkJFhYS8XiUIPLxcQVlkDXtN3360+z40pdIRl/OA85kMmgEgZLly4l6vciShEanU7b3sp+5ciaDlMlgzs9HSqUw5OUpHrFEAlGvp+XBB9HodBiycFsh23GLTk6SikaV8ZnbjUanUzJEs0HX08We2o07TfPf/370VisdTz5JYmICBIHy1atZcP31GPPy2HjPPfRs387Q/v0IGg3zrr6amk2bXvXinldRwaIPfIATDzyARqtFzPLHDFYrK++4Y9alkqjXy+D+/QT6+7G43VSuXTsjRYDsa6XtsccQ9XoVTgyKUT4TCpEKh6nZvJkL7r6brm3b6H7mGWwlJejMZqRkks6nn8bf18eK22/n6P/+L2PHjpFJp4l6vZgKCijLFlTTeZEV69Zx8s9/zjHOG+12Fvz7v9O/axfBkRHya2ow5eej0elY9elPn7ETpr4WJIkjv/0t/Xv2YMzPR2sw0P3ss0x2dORsSCcjEci+N4CcEHTnnDmMHjmCv78fX08PiUBAwYWEw9RfcQXIMgX19a8KAH6rda4oe53q6Jikv99PdfXLtF9lvd5GX5+f1tYJFi365wJMOzsn+e//PkAkklSLPpfLwuc+t+ZN32KTZZm+Ph+VlTMvJDabnoGBAKlU5l8+gPrIkVH+z/85TDSaUj+YLr64luuvX/CW51a+G1VUZOG//msjBw8Oc+TIKD09eRQVWaipceTgNyYnoyxYUPQq9/T2SpZl/P392LMRLslIRAVkGh0OggMDSMkkiWCQ3d/8JolQSO2KtD/+OEP79rH+7rtzaOBDBw7w0i9+gSEvD3t5OZNdXerKfvmaNcr6fXk5kx0dagSM0W7HXFSksqOM+flKh8rlIjo5qY4P82tqCHs8RCcnsRQXU7F2LY3XXovOZCKTTnP0D39gYO9eolNThE6cQG+z4WpspOL889Wg57JVq864iDAtQaNh1Wc+Q/Ovf423vZ2ihQvJpNMUzJ1L0x13zPBMTXV14e/txVZaykgySXxqClmWMVitpONxBFHE5HQy2dGhAGCzvqJ0NhBaI4pYsyT7C7/+/7N33vFxFPa2/27vVaveqyXLlptcwRhjYwMm+YSEdlMuJCSk3RSSvE9eGrl5gVQSkpuE5KVAXholJCFAAGODcQPbsi1cZMuS1euutNred2feHzMaLEumJBhI4vMfWNrdmd3VnDm/8zvn/zDV1UV8aoqixYvxd3cTGRmRlA7Zd6ZSqylbuRKVSkXU66Vm40b6tm/HXl5OKhyWvFFy3ZPeYiEyNobBbievoUEij3Lx9ZkXciGXU9ScM89D3ebN1GzYQDIUQmcyzfCDmdxumq+7jubrrntNn7vaTZvInz+foX37SE5N4a6ro2T58jkJi6+jg/0/+AG5XA692cxYezvdf/sbS2+5ZUawK0hevqmuLpw1NURGRmbcMOjMZoL9/az45CfR6PX079iBUw62BWnRw1lZyWRnJ+HhYVZ/5jNERkcZ3r+fY/ffL3kK9XpSkQgxnw+T203XY48x9MILCNkshS0tSpWTzmSiav16gv39tH7kI5jcbjyNja8qmHV6y/PMuBa91UoiEMB39CjuWinSZpocCoIgdZGeee5UKurkgvVkMIjGaESt1VK6YgWpSIT2e+9lxcc//pres/ONC6TsVWJsLALM/QdMpYLh4fA/RMqi0TR3370Pg0FDfv5L5suJiRg/+ME+vvGNDa8riVCpVLhcJhKJLGbzzC9IKpXDbNafly7JNxKDgyH+53/24/GYFTN6LifwxBPdWCx63v72ubsFL+C1wWYzsHFjDRs31rB+fTU//OE+UqksJpP0uYpEUiSTWbZseevcjZ4NlUqF0eUiFQ4zdfo04aEh6cYIqecwr6EBjU5H5yOPkInHcZ4Rc+GsrCQ4MMDpp55iwY03AtJI5chvf4vJ7ZYu3ioV1oICZTstMj6OCilZPh2JkJyawuhy4a6vlzKk9Hp0LS2Sx8Vmo2DBAgZ27yY6Pi6NjgoLMVitNN9wA8s+9KEZ3YTdTz7JwHPP4ayuxlpUxOCePSRDIcba25WlgaQ8Vpw4cYL6q66iZuNGpYPQnJ+PraREuRCaXC4u+vznichl50anc0Z/oiiKTMmxHL5jx4hNTEjqjRzqqhi1VSpJzdLppJT8p57CVV1N4cKFxP1+qZTabsdgsxEaHFS6KC35+TgrKylcuBBREAgNDjJy4ICiPMb9frzPPYeYy6G324n6fNjLy/E0NiLmcgT6+qTXmc2il0vDrUVFWIuLicokLROL4a6vJzQ0hLOqivympjk/J2qtdob370ykYzGpPkpehHi1sJeV0XzttbP+f8znU0ZxepuNtnvuQS+fn2lkk0na77uP/PnzMbndZFMpTj3+uESS9u5Fa7GQjcelrUd5QzYdi2F0u6nZsEEqYJeP60xMd3V6jxyR/IolJTRdcw0ly5Zx+qmn8B0/jt5mw11XJzVVqFQUtrQwsHs3IwcOkAwG8cyfTyYeJzI8TNO73jWrwuqVMLh7t0Kaz0R+YyP+zk6CfX04q6vRWSwYXS4io6N45s1TyLIgG/zddXUE+/qkGyaVCoPNJhH206c5+LOfEfP5mP+ud1GwcOFbYjJ0gZS9DIaHw+zdO8T4eIRUKkcymZvz50SRWWv3rxXt7WPE45lZVT75+RYGBoJ0dflf99T6TZtq+f3vj81KPB8djfDOdza9JVLZ/xFs29aDTqfBYjljHKVRU1Zm54knutm8ufafpu7qnwXLlhVz662tPPDAMSYmpBGQ02nktttWUVvrfoXffmMgiiL+ri6GX3iBVCRCwYIFlC5fTt0VV7DjK18hm0wqW46CHDxpmpwkEQgwvG/fnOMiW3ExA7t2KaRs8PnnGdm/HyGbBSQlJX/BAqyFhUycOEH344+TS6fRW63ozGasTU2otFr83d0s+/CHqV6/nqjXy/4f/pCJjg7Cw8NkEgmEXE4at8ybR/Vll1GwYMHMbsdsltNPPYWttFQpA69cu5bg4CCTnZ0M79+Ps7qamk2bMFitZBIJDv/qVxz6+c8lVU5W6Auam1l2660zuzLljbRAXx9H/t//Izo+jqOykqjXy3h7u6SeBIP4T52SlMTCQsLDw4jy+EzM5UhFo5JnqqSExNQUBocDo1Y7w0ckLWWIs6qXQFKsnFVVWAoKGDl4kITfT6CnB2dVFZ7GRoRcjmB/P7l0mvI1ayhsacFdX086EiE+OUnFJZcQ7O0lPDSEZ948JU1/OtqheOlSFr3vfXOXrp8DyVCIjgcfZHj/fkCKO2m4+mrqNm9+Tbl2yuMFg7Tfey/eY8eU2i1LQQHJYHDWmE1rNCpZXJbCQnbdcQdjhw6h0enIZjLkAgFMHo8y2tUYDJjy8mh573vRmUzS459DLZ1re9RRUcGyW28lFYnQu307z991lxQeKyucxUuXSmHA+/dLaf5ykKv3yBFy6TSB3l5p5H/RRZSvXj1r+1TIZhWjfzoel8bLZ0Gj11O4aBEGh4PQ4KCkrsoJ/waHg2B/P5lEArVGQ+2mTfQ/9xwjbW1oDQb0VitGl4vQ4KBybJOdney96y6a3vEOmt75ztf8fr3euHBFOgd27x7k3nvb0WhUmExaQqEUR454MRo1VFW9pGTF4xn0eg0tLf/Y6NLrjaLTnfsLPDV17oLivxcbNlTT1eXn0KFRDAYtKpUUadDSUsCVV86uoPlnQ3f31Jz+MYNBSyaTIxBIzuqOvIB/DCqVirVrK1i5spSRkTAajZrSUttbZlQsCgJH//AHerdtQ2s0otHpGDt8mK7HHmPR+98PQC6Xk9bl5Y1DvdVKJp2mb8cOKZzzHFU0uXQagPDwMAfvuYd0NIpFJhvpaJTBXbskz042K+VzWa1kolEQBMrXrMFRXk6grw97SQnmvDzMeXnUbNzI4V/+EldtLZb8fMweD1Gvl1QoRH5T06zE/+kogjNjKTR6PXl1deiMRgb37KFCHmGC5JkLDw2RCoWov/pqyXgvCPiOH6ftnnu46POfn6Ee9G7fzpHf/hat3Jc4sGcPkydPUr1hgxReWlhIcGCA5NQUlsJCdGYz2WQSIZslHYuhy2Qw5eWRjkRQ63RKBIL6jHOaDAaxFhdjKy1VAnvPNsNrjEZ0RqNUFSVndwmZDCa3m/I1a+h79lns5eU45cyrdDSqjBi1RiMDO3cysn8/ZStX0vrRj5Lf1IS1sPA1l1FnUyleuOsuwqOj2EpKUGs0ZJNJjv3hD6RjMUUBm64+eqU2CCGXY98PfkBoeBhHRYVEZmXSFRkdxV03eyterdEQHBjg2P33E+zvx+zxoDUaMTidTJ0+TTIQwOBwoHc40JtMUgPCsmWIsp9Ko9ORTaWUTdfp15vLZCiaI4YjODAgLTb09pKQfXqB06elwvT6eoRcjsHdu7GVlVG8eDFqvZ7up57i5J/+RNVll6E1Gjn629/Sv2MHF33+85KCJwj0bt/OqcceIxOLgUoljSr9/lk5YtOq38ZvfYvE1BSZeBxLQQEJv59dd97JZGenEnFy4i9/IR0OK4Qtm0gwtHev9NmyWhGyWSz5+RidTk49+ihlq1bNedP1RuICKZsDfn+c++5rp6jIoigpeXlmVCrYv38UQRCx2QwkElnUahUf+9jyf7gbsrDQSiYjnPPfz7Ux9o9Ap9PwX/+1gs7OSQ4fHkMURZYsKaapyfOWuYj+I8jLMzE4GJo1nhUEKR7BYjn/hbP9/UGefrqH7m4/breJjRtrWLas5J9ehXwl6PUaqqtdr/yDbzB8HR30Pv20tNkmEwGzx0NkfJwX772X/PnziXm9eI8fl2IrSkvJJZN429vZ9tnPYisvJxkIULR48QxyFvP5lM3HU48+it5uR2+1SkZ1rRad2UwqGpVUN6cTvcWCpaBAin/I5Zg4cQJ7aSkGm42RtjbK16whHY3Ss3UrJa2tMwiJs7JS6s+U+wzPhM5sRqPXKxtmCb+f4MAA6WiU+OSkZLI/46IeGR+XFgsEgdNPPYVGp5OS89VqwiMjzL/+etw1NcoxHvvDH7CXlpIIBBh/8UWpLSCdpvfpp5l/7bXorVZKV65k6PnnCQ8OYisvJzo2RjIQwCaPDEW5FLtizRoG9+7Fd/QojooKqcswGESt0bDyk5+URsoOB43veAcdDz6IpaBA2kZNJBjZt4/E1JS0xWm1EvN6iYyMULxsGY7ycgoWLiTu9UrHIoq46+tpee97FUVu3tvfzry3v/3v+gyJgsDo4cP0btuG99gxAj09lMpBvSCpV87KSk4/+SSly5czsHMnA7t3I2QyFCxYQOM11+CurZ31uIHeXtruuYeTf/mLUl1klxVPZ00Nk52dkofwrAgTIZslJndy5lIpZdNSazDgqKwkNDBALpMhMjKCp6kJV00Ne775TfLnz2fZrbey8D3vof3ee5VKruDAAJlYjJrLL5/VRiHkcrT95CeoVCoshYWEZL+akM0y/uKLGF0uAqdPozWZ0MpbqlOnTpEOh0lFIpx+8kkclZXkNTQQHh6mZ+tW5l97LZ2PPMLJv/wFm3xDMq14+ru60JpMyjg97vcz0dFB6apVBPv78TQ2otZoSEUiHPjxj9GZTNRu2oRKpSLQ18fgnj246+sVcivkcqi0WqLj46TDYVCrpZGzywVqNWPt7RdI2VsR7e3jkjn1rNFWdbWLdFpg2bISjEYtxcU2Vq8ue11KnpcsKcZsPk4olMTheEndmZiIUVBgOW89gmq1ivnz85k//1+v0Puyy6r5wQ/24XKZZpCg0dEwra0l571k/fDhMX70o/3o9RqcTiNjY1H+53/2s2FDNTfdtPgt4V/4d0P/jh2ST+UstctaWMhERwciEB4dxVFRIfVI+nzE/X5pzAPYSkoYO3SIdDRK+cUXSxeKyUlEQaDh6qsRRZGxQ4ewyZU34+3t6EwmNAaDFKYZj2MoL5+hTKi1WlKhEKlwWFJT5NFZcGDgnAGkOrOZ8fb2WaRMo9NRvWEDpx57jGw8zqQ8SkxHo6QiEbR6PaMHD1K8bBkqlYpUMEg2lSI6NobR6VQ8XSqNhkBvL71PP41brgkaPXwYURSJ+XyMtLWhM5mkbUWTiVQ4TO/27dRfdRXmvDxqL7+csfZ2ajZsYOzwYUYOHMDocJCJxSRFy+Nhqrsbu0ygdEYj6UiE2o0bqVq/fsY4s+Hqq7Hk59P56KMKwRBFkeLWVrxHjqCROzqniYG1qAhbURGlK1bQ8La3odZqldHs2cjE41K8iMMxQyk6F0RR5NgDD3D6yScxuVwkAgFFBS1etkwhMWqtlmwyyY7bb5dCe+UR32RXF09/9rMsuvlmajZsUPxhPc88w87bbyfY3086FiMZDDJ54gQFLS1UrVuHyeVCb7cT6O3FnJenjJmnk+sRBEkxPOMYU5EI0dFRiYxksyB/VoVMBo3BwFRPD+GREa64+270Fgvb//f/JjQ0hMFux15RwdTp0+z6+tdZ+6UvKb7AQE8P8YkJHJWVaPR6qR5p+jOrUjHS1iZ5+ESRsF5PZHycyPAwKrVa2iIWBHKpFCP79+Our6fv2Wep3byZrr/9bcaygVqjwV1bSy6VQq3XExocJDw0RHhoCHt5OZGxMZ7+zGfIJJPkNTSQy2SIT05SIn+uQdo+NrlcxLxeJYolHY1KG5iCgJjLYSstZfTQIcx9fTgqK+fMjXujcYGUzYFwOHVOpcho1NDaWsJFF1XM+e9/L6xWPbfdtoof/GAf/f1BVCqU7ctPf3rVG6ZcCYLI3r2DPPFEN15vjOJiK1u2NLB6ddk/HYlYsqSYyy+v5emnexAEEZNJSy4nUlJi493vXnhenzudzvGrXx3G4zErnjaTSYfTaWTHjn4uuqiC+vo3r7D73xXJUEhJ9U6GQoT6+0lFIhjsdlQ6HWok87TR4SCXyZCQ+wGnRyaIInVXXMHQ3r1MnjiB3mbD09BA84034igvl2Ir5IuPs7ISrV7PZGcnqVAIIZ1Gb7NRuW4dAzt3kkunZ/imBEEgHY1KW5FI/qmX9fvM4bkCSQUaf/FFjj/wAKloFEHO99IZjUraeXxykpqNGyVFbGgIIZNByGSIT0wQ83oxeTxoDAaG9+1j2a23olKrSUejqNRqyeQt1+zozGZSkQgavZ50LEZ4eBiXrKy5a2tZ9elP4+/uZvcdd6DSavEeOYJaPp+RsTFSkQh6s5l3/PrX5/RfiYKAq6aGRe97H52PPELP9u3EvF4ysRipcBi9HH2h1moRczniExPk0mmKly6dMcY9E+lolI6HHmLo+ecVUlG3eTMNb3vby5r0Q4ODitKq1miUaiidycT4kSNYi4uV34+OjWFwOChatAiQSuR9x4+TTSTY+d//Tddjj9H0zndStno1e7/zHRLBICaPByGXQ2s0IuRyiororqkhv6kJS0EBoYEBxWvmqq2l9SMf4fSTTzLV04PJ5SIdiaAxGomOjSnhuEImg8HpJDE1RcLvx1ldjSgIdD36qLR53NND1OejZNky1Ho9E8ePE5uYUEaK626/nXlve5sUPyG/T3qrFWd1NYGeHvQ2G0ImQ6CnBzGXw+TxYM7Pl0hvJILebpe6UTUayTag1zPV04PWaFRuPuby8Zk9HgpbWihobuaFu++msbWVdCxG/44dhEdGlMUPk9stBQMbjTNyz7LJJIlgkEQggNZgkMhYNgsaDeb8fGVpIx4IIAgCefPe/OWvC6RsDlRXO8lmZ48Sp1PBz1fJcn19Ht/73maOH/cRCCTOe07ZXLj//mM89dRp8vMtlJfbCYdT/PSnbQwPh7n++uY37HW8HhAEEbNZSzabY3w8SjYrsnp1GZ/+9MrXJassmcxy5Mg4g4Mh3G4TS5cW43JJd5Q9PVMkEplZKqparUKv17B//8gFUvYGIRWJ0L9zJ0N79jBx8iTpaBRbURG+jg6lYic+OUkmHmfJLbfgO3qUpKwgTft8vyh9AAAgAElEQVTELAUFykXDkp9P0ZIl1F1xBfVXXTVjG06lUlG5di19zz4rJZuXlEgju1yO0fZ2UoEAeouFosWLGdm/f4ZXKuH3U7R4sXIRd9XUSCOlRGJG1YwoimQTCUrOUsmmoTUYlDHV1OnTCNOeIdlDl4pEmOjoQGs0kpE38/Q2m+L9EkWRdH8/5oIChSga7Hby6uroCIfJplJKxILJ7SYZCoEoordaCQ8P46isJDIyQvONN0qF18XFaIxGAr29aAyGGZuiQjZLZGwMX0cHhQtn3iiJosjw88/T8fDDxCcnGW1rQ2s2S2NMeUEi7vcTHh7GVlKiRCNERkcpXbWK/Oa5/14JuRz7fvhDAj09kg9MqyWXTtP5yCMkAgGW3nLLOT9LY4cPS6qPPKq0l5ZK5nWNBlEQiE9OYisuVtSuwsWLAWlMPN7ejkEea6dCIQwOB8cfeICp3l7CMrFQyUXYmXhcGUf2PfMMWrln8rI77pCWFvx+jA6HtNChUlF5ySX0PfMMnsZGhvfvJzUxIQW25nIS4ZH9ezqTiZwcHKy32UiGQpz8058wulzoLRZG2tqUjlG1Wo3OZCIyMsIzX/gCvmPHWPz+90vKmyCQlAN9UauJeb3E/X7UOp3kA5Tf22wigVqvJxuPo1arlboxlVpNLpnE7PG8LAkWcjkp++7gQaxFRWRTKfqfe46o16so3tPqq85qZer0aRyVlZLK7fUSHhmRjttsJid3wupsNnKpFMlgUPIzyu9dNpEgr6HhnK/ljcI/v3HoPGDBggKKiqyMjUWUO1UpKDZMU5OH6urXZgZ9LTAatbS2lnD55bUsWlT0mgmZzxfj8OExTpyYIJOZe1v0XBgZCbN9ey9VVU7sdsl74nAYqapy8uST3fh8L4V/iqJIMplFEF4+5+jNxB/+cJRHH+2ipsbNpZdWc9ll1UxNJfjRjw6QTr+2c3M2RkbCfOEL2/nJT9p4+ukefve7o/yv/7WNQ4dGAWR/4NzKolarJpXK/kPPfwGvDslQiN133MHJhx9GyOWwl5URHBig+8knScdixCYmCI+MkAyFyGtoYOLECfIXLMBdXy8lodtsOKuqpERwQZgxVtNbLHPmSdVfdRVGl4vgwADZZJJcKkVkbAxXZSUlra2EhoawFBRQsXYtOpOJmM+Hq66Olve+l5Wf/KRykdIaDCz6z/8kOj5OzOcjl8mQCocJ9PZSunLlOWMb4KVaHIPdjpDNojObJWXHYpFIkUYjKXjhMPaKCoWcIVcOTV80w4ODSjdmfnMztuJiqWFA/ruokfPVNLIKlwqHiYyMSNuHcsWQye2maPFiwiMjiko5Hc2gNRhwVlUxuGvXrGMY2ruXtp/9DFEQmOrult6rwUFG9u8nPjGBSq3GLnd55tJpEsEg2USC2iuuYPVtt53zYj9x4gT+rq4ZNT0avV56HXv2EPV6z3lec+n0jI5LU14ezspKKaQ3lSKbTBIZGyM+MUFhS4tC3qZkb9SZapBGp8NSUED/s89KI0WdTiJDWq2Ugp/NIqpUpEIh+p97jtKVK6X+yqIiCpqbsZe9NL1wVlXR8r73ScSivh6t0UgumUSt0VCwcKESLaGSu0YFQSAmN0HoTCap1mp6xJ5IIGazaI1GKWBXq0VrNtP7zDOEh4YobGlhaM8eBnbvJub1ojMaEWRSY7DbFaI+2dkpqahaLUI2K5nt5e/L9A1AxcUXSzcfZrOkwp2BaaJUunIlsfFx9BYLU93dSk6deroiS63G4HCQDASUke7Y4cOgUikjaZ3FopTcT4+ThWyW2Pg4qUgEZ1UVeQ0NiMK5fd1vFC4oZXNAp9Pwuc+t4ec/P0RXlx+NRqpUWrKkmA98YMlbcoyXTuf4zW+OsHfvoOI3sNkMfPjDy151aOfJk5OIIrNGpRqNNEY5dWqS/Hwze/cO8eijnUxMxDGbdWzcWMOVV9a/oWXTr4TJyTg7dgxQVeVU/GRqtYrycgf9/QGOHh2ntbX0FR5lbkxNJfjIRx6ntzcgNyCYaWz0YDLp+OlP2/j2ty9XQnlzOWHW+UwkskoF0QWcX5x+8kliExM4q6oAiUh55s2jd/t2sqkUeosFEdCp1UR9PiJjY4jIZdlAJpFAGwqh1miwl5djysuT/nCL4qx6mGmY3G4u+fKX6d2+naG9exEFgdLly6m69FIsBQV0PvKINDYTBMpWr6bpmmsolQNQz0bZqlWY3G66n3gCf3c3RqdTCQt9ubiF/KYmhFyObDIJoDy2kM2SleMR1BoNqXBYUdA08nhnusJGzGalx0gk0BoMaHQ61n31q3iPHiU+MSF1VwKehgY8zc34T56k8ZpraLzmmlntBnVXXMHJP/9ZMlfLpmuj00nxsmWIgiCpbWdAyGY58fDDWAoKGJcT2bVGIyq1mqTcfpCWM6h0Fgsly5aRDAapvuwylp3VMnA2Jk+dkvxQZ51vlVqNCgj2988g3zPO6/z5nH7iCWlMLRPYosWLMXs8jL/4IjqzmaJFi6jdvBnfsWOc/Mtf0FssJAIBJV5kOnLlzKT96e1dIZslJ2+o5lIpMokElsJCajdtYrStjZb3vEchtmejZuNGCltaGD14kKneXnq2bqVw0SLEXE6KJpkmHKIoNRzI76HOZAKNhkwsphDz6TDdaUKvt1jIxGIM7NrFvHe8g+P33w+iiJDLkUunJa+k00k6EsEubzbG5RgZjcGAzmrFWlxMSn7/dSYTBQsWUL1+PRqdjqW33ML+H/2IVDiMyeUiGQqR8Pup3rABz7x5OCsrGT9yRMrpMxiYdn6JggByxmAyHCY2OYn/1Ckp+NloxOhwKB2r04XoOpMJR0UFxlAIW2kphS0tkhotL8m82XjrXEXfYvB4zHzhCxczOhohHE6Rl2eelSH2VsKDDx5n9+4BKitfIiGRSIq7797HHXdc9qqiHwRB5Nx8UyKmjz3WxR//2EFBgYXKSiepVJa//KWTnp4At932xnnfXgmDgyFUKubccjQadZw4Mfl3kbJIJMXnP7+Nzs5JioqsqNUqgsEUu3cPsmZNGYIABw6McOWV9WzeXMfjj3dRVmbDYNCSywmMjkYoK7OzeHHR63GYb1nE4xlisTQOh/FNbYXo37lT6eibRnxiQlKOtFoccmSCKIr4T51CpdPhKC+XyonDYbLxOOGhISyFheQ1NpKJx4mOj1O2ejW5bJZgf7+kuJwVdWByuWi+7jrym5s5+tvfMrJ/P8P792MpKKDlve9l4XveQyoSwX/qFMPyv5WuXEnJ0qWzLgx5DQ2veaxSsnw5rupqgv39iqol5HIkAwHUOh06k0ny4cTjEnGTFQeNTgcqlRRVkZ+Ps6qKiLwEAGDOy+PKH/2I3XfeiUqjkbbi1GqiY2MUL1tG8w03kAqFCMs5ZdNqlaO8nLyGBgx2u+Slmx5zqVSEhoaovOSSme+R368QxvjUlKSkRCKKSifKQbT+7m70FosUP3H99TS87W2zzsVkZyddjz/O5KlTGGw29DabMpY+GyK87Dgtv6kJd309Uz092EtLpdFnJkMum2Xlpz/NYrl3MpdO4+/qYur0acYPH5Yyt2SVTMhmKV2xQukQtRYWYrDZGNq7F5VWq5jip5XOSpnMBwcHmerpoeAcY1mQxuz1V10lkZBMBu/RozjKyyleupTe7dsRIhG0BgO5bBYhm8VeWYlKFMlvbsZ75MgMpUjI5RBzOcwej+JhTMfj9Gzdis5kwmC3KwoUajUarZZATw+ZWAy91SqNrXU6ya+VySgVV/bycjR6PfOuvhqzvElavHQp626/neP33y+p2NEottJSAj09jB85Qs2mTVIOnEqlKLeCICCkUhjdbhKTk2RjMSlmJh4n7vdTuGAB7pUrGdm/X8kdjPv9JPx+ZVnEUliIKIrE/X5WvvvdbwnB5QIpexmoVCpKS+2U/n2CyhuGSCTFc88NUF7umEFCbDYDwWCSnTsHuOGGV/aDNTZ6EEWJnJ35ONMjyuJiG7/5zREqKhzKWNVg0FJd7eTYMS+dnZNvmSqdlyMCmUwOs/nv++jv3DnA2FgUq1WvEFCLRUcyqeLYMR/z5+fj80mhqdde24TVquNvf+smlZKk+RUrSrnxxgX/sqG1kUiKhx7q4IUXhpUN5iuvrOOqq+rfcMIuiuKscZOQyynGdJVKhc5sRqVWE/P5QK0mG4+TCoWkKIKyMtKJBJl4HI1Gw9CePVRcfDGFCxfiffFFxg4eRBRFjE4ni9//fopaWmY8/1RPD89/97sYHQ7scuZUMhTihe99j5Wf+hRdjz2mmKQBxg4dwt3QwJrPfGZWqOZrhd5iYcOdd/LILbcw/Pzzkr9IEKRAWaNR8fZ4GhvxySGllvx8cqkUuUwGvcVC3RVXkI5EZikzhQsXctmdd9L16KP4OjrQmc00vfOduOvq2P2NbxCWC8x1ZjNN115L1bp16Mxm6q64gs5HHpmxZZeYmkKj080iZWqtVtr0nJxELY+noqOjqLRa1Gq1chx6ux2NTsf6O+7ANUeP5/D+/VISvtWKtaiIZCAgeZLGxtBZLFjy85ULcTaZRK3VvqzZW63Vsuq22zjx8MMM7t4tlXkbjTRfey3Vl10mFbCn07T/8pdMdHaS39xMeHAQ79Gj+L1eyTO4ZAkml0vxv033ZP7tv/6L3m3blJG3zmKhZOlSHGVSb6sKEHOvznahUqlo/fCHOSLfEBhsNhzl5aQTCRwVFVg8HvRmM8lQCFd1Ne66Oqlb8umnlS1dRBFbSQlak4lUMIilqEgapT7zDIlQCL3FQnxyklxGqrDTORzSGDEUIhEIkAqHSYVC6G02SlpbSU5NkQyFSIVCrPzkJ2m+/nrl9aajUQb37KHjoYdAFHHW1uKurUXIZtl3992s/sxnWHrLLez8+tcJ9Pai1uul7DmXC7VGQ3hkBJ3ZTMH8+RQsXEjP1q3E/H7yNBpMeXkkAwH0NhsarRZPU5M0opaJa2JqiiXvfz8lra2v6tyeb/xrXhn+zSAlp4tKLVIikWFqKiHl/Bi19PRMvarHKS+3c/HF5ezaNUBRkQ2zWSqYHhuLsHFjDbGYvMV1ls9NpVJhMGh58cVxmpsLEASR/v4gsViaoiLr6xIZ8lpRX+/GaNQSj2dm5JTlcgK5nPh3jy737RumuNjK8HBYGWEAGAwawuEU4XCaigppTKHRqNmypYHLL68lGExiNuuwWt98efx8IZPJ8f3vv8DAQJDSUjsajeSde+ihDsLhFO95T8srP8jrCJVKRfHixfg6Ol5Sy+SkeLVWK1VxyO9fMhxWMq2m76oB9LLfpmbjRuI+HzWXX87x++/HXlam+FVSkQj77r6bdbffjqu6Wnn+U3/9KzqTaUYgqdHhQMhkeP6uu9BotTjP+HlTXh5T3d2c3rqVpmuu+buPWxRFhvbsofOvf8Xs8WApLCQ2Pi6pQPJFShQE8hoa8DQ1kU0mGW1rU8a5Rr1eyVybrjg6G+7aWlbddpvy39HxcXZ89atSNpZMQDOJBO2/+hUajYaKtWtpfMc7EAWBnq1bJfVOVipW3XYb5rw8yYCeTqOzWDC53bjr6pQxr1qrlUrDczkEQVDULrVaTf78+Ux2dMwiZbl0miO/+Q3WwkK0JhNT3d1MnDghmdRDIU49+iiu6mpKV6wgE4+TicdZ9qEPoVKp8HV0AC8tW5wJvcXC4ptuovn666WgU6Rsukduvpmpnh7JX5ZOk9/cTOHChZSuWEHR0qX0P/cc6WiUZCDAxMmTUrRERQVGt5vBvXtxVVVRftFFTJ46RX5jI86qKgxyLlsmHkfIZpUxfC6TwXf8OGOHDqFSqylpbSW/uXmGYqszm2n98Idpvv56EnKsS+/27Uppuq2sDI1Op2zL2svLqVq/nv5nn5Xe++JiNFotyWBQSsO32Yh5veQ1NjLe3q4sbER9PlLBIKa8PGUsG/N60RgM0g1AURExr5eSFSuwFhSQSSSYPHVK+vuJRMj2fPOb9O/ciZjLSan8fX1EhoepWLsWU14eHQ89xGV33sk7f/c7nv3ylwkODIAoEvF6CQ8MoNbpKG1tpUD28U03T/i7uihdsYLRgweJjo6Sy2Rwz5tHaWsr9Vu2kInFJLVYjoV5szPK4AIp+5eAzaZHEKRC1s7OSbq6XiJhiUSWzZtrZhCIc0GlUnHzzUsoK3Pw5JPdTEzEcDqN3HTTItavr+bYMR+iOPdjiKKIRqNicDDET3/axvh4FLVaRS4nsmpVGTfdtEjpQnwjYDBoueWWJfz4x23odGocDinsNxxOcuWVDVRWzi5if7Ww2fS43SaCwSQ220velFQqh8kkRaacCb1e85Yefb9e6OiYoLc3MCM01mDQUlXl5Jln+rjiijry8v4xBei1ouHtb2fsxReJ+/2Y3G5Qq9GaTFIwbGEhqVBIiQxQ63SYrVY0Z5ixp8mDWqNBpdHQ9eij0kX+jEwrg81GJhaj+4knlHJjURTxHjuGQy44PxNGt5vebdtoPIt4qVQqbCUl9D3zDI3veMc5v6/T1UDJUAhzXh6OykrlZ73HjvH8XXcxtHfvjBBRS2Eh6WiUXDJJKhCgbPVq8pubUalUVFx8MdlEApVOh62oCEtBAZl4HJVKxbJPf/pVVQX1bNuGKAjSOZahM5mwFhVx4k9/omzNGtQaDc3XXUf9lVcSGR1FazRiLysjFYnw4q9/zeCePQhypVDTNdfQ8t73MtnZqRS2awwG1LJSpNXrUVss1G7eTDaZJBkMznpNocFBpd0g6vXi6+jAYLej1mgkL5W8fTrV1UXdlVdSc/nljB85wr6bbkKlViseowU33kjVpZfOej90JhMqtZrdd9yBr6ODKTk0ddpnFezrIxkMUrVuHXqrlerLLmN43z5lpFywYAEarZYnP/5xjC4XJStWSD2qMpmwFhVJFVvyBqGzspLORx6h4e1v5+A99zB56pSkqIoi/c89R9GSJaz4+Mdnjb9NLpeSir/sQx9iwY03kgqHMTqdBPr66HjgAcKDg5I/sLFRUnEff5zuxx8nm0xiLSqi/KKLEDIZksEgBrtdqdLSW61YPB4Sk5OER0awyb4xc34+U93dIIqIuRwqoxHf0aPYNm3CYLMxeeoUpx59VLoR6eoiNDwsjWsdDilXzuEgLW8Jl65cSWhwkFQohKWggCt++EOG9+1jcM8eUuEwowcPUrp8+Qx1uWjxYmnpYniYaHk59tJSbEVF1F99NSWtrdiKi2m75x68x45J25eiSMcDD1C/ZQvzr732TR1jXiBl/wLIz5eiM3btGmRgIIjTaUStljxguZxIf3+I9vYxli595bsArVbNFVfUsWlTLel0DoNBo3xA6+rcaDQq0uncjPGgKIqk0znq6/P4znf2olJBZaWkDgiCyL59wwB85CNvrDy8bFkJ//3f69i+vZeengDFxTY2bKimpaXw7/7SrVlTzkMPdbB8eQkHDowQCEhG6nQ6h8Wi44tfXHveQ2nfqjh+3Dfnssf0osjAQOgNJ2XOykrWfuELHPv97wn095NLpTDl5ZGNx9Hb7Xiam9HodPi7uwn09OCqrSXm8ykXtkw8jikvT/FcZeLxWR41kMz90+XO09AaDMrW2ZkQZD+Leg5TsUYey5yp4p2JyNgYbT/+MeHRUSWl3F1Xx/KPf5zxF1/k0M9/jvfIESyFhaTCYcLDw5jy8rCXlZGYmlJGk9HxcQrkCIpkMEjpihUsueUWKSk/GMQzbx4VF188g2S9HLxHj875s3qLhdDQEImpKSUzTG+1Kh65TCLB89/5DpHxcWzFxdLmYSTCgZ/8hCUf+ACbv/99dn/jG3Q/8QSIItbiYilvKpejeNkyTC4Xgb4+NAYDfTt2oDUYyG9uxuhwzPBHBU6flhLmZSVJrVZLfq1LLiHu99N8443s+da36PzTn9DJm4rTfaUv3neftJSwZMms4xtrbyc4OEjc75fIvskklXkbDGRkj9NUTw9FixZJcRfj45QsX66Qde/Ro8ox55JJyRN25ZUM7NpF1+OPozUYMLpcFC9dir2sjN5nnmFwzx5y6bTUSnFGZ/FYezt9zz6rbL2eCwabTdmCLFywgIKvf51kICDlj8mZbaWtraz78pelFgi9HpPbzTNf+pKyFFJ+0UWMt7cTn5yUth9tNvLmzSPm9ZIIBMhEIlKchckkBSMHg+jtdkJDQ9Iot6uLvmeeAaSxsTk/H73VOiMuRWe1Eh0fV/x/0yNvnclE9fr1VK9fTy6T4fEPf5jI2JgSfWErKUFrNFLQ0kLBggU033ADOrOZ/KYmZRR/7IEH8B47NuMcCrmcpJ7W1FAiK8VvBi6Qsn8R3HzzYh56qANBEInHM5IJUoDmZg8FBRYee6z7VZGyaajVqlkXWKtVzw03NPPb3x7B5TLhcBhJJDKMj8dYtaoUvz9BLJZWCNn041RUONi3b5h3vavpDR9lVlY6ueWW2f1tfy/Wrq1g584BfL4oq1eXEQ6nGR0NIwjwta9dSl3dv2/2mMGgIZc790r59Hj9jYa7ro5Lbr+d0UOHaPvxj7EWFGC025VoBHddHaWtreTV1xP1eomOjZGORhEFAZVKhaexkfDwMM3XXUfnX/+KkMvNMvZnUynFGwaS6lV16aWc3rp11vgv5vVS0NxMKhSa1euXDASkWpg51KlsKsUL3/semXhceUxRFAkODPDCXXcR9fnQW61o5E3JTCKB1mQiHYlIG5RGI0ank0w8TsznY/TQIdQaDZaCAtZ+8Yu4qqtnZYW9WugtFmKTk8QnJ8kmk5KCUlCgEMdzpeWPtrURGhnBJY/lQCINGp2OE3/8I+Vr1rD5+99n8c03s+MrXyHQ14e1sBB3fT06i4WRAwfwnz5NsK8Pc0GBFGqr1bLoppuk6iM5o23aRzi97BAdHycRCCg1TW0/+Qk9W7di8njQmUyIokgqFGLs0CGKFi+m69FH5yRlXY8/ju/4cYJ9fWiMRkwuFzqzWVHu1Go1kdFRihYtIub1ImSzyohs+r3T2+1kEwlCg4NYCgqkrtJ584h6vTRecw1ag0H5PDgqK+l48EGlSmgaKpUKW1ERPU8//Yqk7GyoVCrCo6N0PPggkeFhRCC/sZEF7373DKU3r6GBwT170MuxKhUXX0w6FiMTi5FNJrnqnnt49otfpPORR7DU1iJkMsq2pJjLEff5GNi1i4TfP/3EqFQqctksiakpSb1zOLDLHrppxLxeCltalI7T0NAQA7t2ERkZkZoJensJDw9L262CgK+jg6JFi8jE46z4xCeouOiiGY+XTaXo37FjRqQISC0CJpeLnqefvkDKLuAfh9Wqp7HRg16vYXIygcGgobTUht1uQBRhaCj0yg/yKrBxYw0ej5nHH++iry+I223i5psXcckllfzyl4dn9UyCRMw0GhXj49E3xV/2esJmM/DFL17ME09089xz/aTTOdasqeCaaxqprX11qsK/KpYtK+Fvf+uetSiSSGTQ6zXnrSrs1UDIZjn6m99gdLmUuIa8hgZSoRCxiQlWfOIT6K1W+p97jpN/+YtUj2Sx4CgvR8hkpM2+LVuITUww9PzzMy5WoigS9/lYdPPNM56z/qqr8B49SrCvT1Hb4n4/1oICFn/gAxz++c+VNHiQVLlkMMiyD394zmPwHj1K3O+fQfKm+xHHDh9Go9NhLS4+46AF1CoVglotjZosFkwuF+66OvqeeYa4z4fJ4yGTSCjHdGaO1rRvyXv0KBqDgdLWVly1tXOqzK7aWo794Q/SCEmqI0FrMuGqraVk2TIlDuJsjB48OGfWm9ZoJObzER4exl1bS9GiRbzr/vt58de/lvxBPh++I0dIx2Ko9XpSkQipcJiCBQswl5TQfu+92MvKaL7hBl687z40Oh3peJz4xISU/C7nfcUnJ/F3dZFNJJTnnT6veptNCREO9PXNsoAMvfACfc88QzaRUCJG4n6/ZJKXR+KCKKKVFdbI2BjO6uoZhF7M5ZSk+zM3QpPBICo5Z2sGQZd7QzPy6z37nIWHh5XXGRkbY/iFF6RImMpKylatmrNw3dfRwQt33YXR6cQu10QFBwbYfeedXPrf/60owzUbNjC4a5fUgCG/Z1qDgajXS/O116IzGrGXl4NajcFqRcjlSPj9Un4YkEsmSchZYkoUB6BJJKS4Cp1O2pycmsJgtZJNpaSsMZOJBTfcIJ3z55/n8C9+gUqrRWcy0bdjB0Img9ntViJF4uPjTHV3k9/cTO+2bVjy82dsL0+Hzc5ZXyaryG8mLpCy84hwOMXUVAKbTX/exzZ6vQa73YDNZqC8fKZfKhpNkZf3+hSaq1QqliwpZsmS4ln/5nabSSbnDkUVBFGpG/pnh8Nh5D/+YyE33rgAUZw7duNsJJNZ/H4p12069f9fDdXVTjZsqObpp3twuUyYzTqCwSTJZJaPfKT1NefYTZuvjU7nnBUsrwX+ri4pJPIsQmN0OkmGw4wdOkT9VVfRsGULDVu2IAoC4eFhcuk0tpISxa8y/13vIiArMwaHAyGXIx2JULRkCZVr1854ToPdztovfYnBvXsZ3rsXURSp2biRiosvxmCzoVarOfr73xOfDvE0m1n+sY+dM/IgPDw8S6E742DIJJMYXS4lpkFvsxGTvWGiKJLLZDA4HIwePIgpL4/azZtRazTkMhmOP/CA4s0pXroUW0kJL9x9N4G+PikcNJul58knqVy3jsU33zyDKKQiEQZ375a2G4NBtLLXKhkIMHnyJJd/+9vnfF/UOt3LBnae+Tx6i4UVH/84UZ+P526/ncLFi5k8eVKqyFKrpVqi48ellHiDgb5nn2XJLbdgsFo5+H//L6efekrymBUWYvZ4pN+RlavJU6dQa7Vk4vEZIzSV3H/qOmPMBdISwdHf/Q5PYyNjhw+jNZvJJZNoDQayyaRUdxSNkg6HMbvdZFMpWj/6UU48/LDS4qBSqTAXFJCUIyPMZ1RCTWeZna2YqjQaKZV/jo7GZDCIq41wSYIAACAASURBVKYGlUrF4O7dtN93n0RqjEZG9u+n869/Zc1nP4u7rk75HVEU6XjoIQwOxwzCZikoIDw8zOmtW1l8000A2MvKWHnbbRz++c8JDQ4qn7t5W7ZQv2WL9DOlpRisVpKhEEImQzadRpSPR5Rz84xnHZfeaiUZDCrZda7qaiKjo+SSSRbdfDNL3v9+zB4PyVCI9nvvxVJYqJB2tUYj+c9iMTzz5zOyb5+0rOJy4aqrI+73s+db3+LiL3yBvPp66flsNiko94z+2WmkI5EZyzdvBi6QsvOAZDLLAw9IuWEgEZJFi4q46aZF5+2CrNGoufzyWv7855NUV79UvisIIj5f7HUd4Z0Lq1eX8eST3WQyuRkbmn5/nMJCK1VV568J4c2AFB758j+Tywk8/ngXTzzRTSYjIIrQ0lLA+963CI/njfVXnW+oVCre854Wmpo8bNvWy+RknJaWQjZvrn1NKmIyFKLjwQelXCJRRGexMO9tb6Nm48ZXZTqfC5mz0sLPhEanm2UUV6nVSrn0mTA6nVzyla8w0tbG2KFDqHU6Ki66SEpvn4M46i0W6jZtonr9eiKjo6jUauWiX75mDUWLF+M7fhyNXk++7G87F4xOpxRtMQcMNpsyci1atIiRtjYlHT4te5tsJSVEvV6yySTlsvFeyGYZbWsjMjpK4PRpkqEQPVu3IsjxGWduk4qCQN+OHXjmz6d81Srl/4+3t5PLZKhct47I2BihgQGEbJaipUtRAalQiHPlCpWtWsVoW5tEks74Mk1XO821KJGNx8mlUlgKC5k8dUr5TEwnvIeGhnBWVREeGpJijVasoLi1lb/efDPDBw6g1mqJjo8jCgIGh4PQwABRr1cab2azmPPycNbUSEn0uRyZaJTaTZtmvIbQ0BDZRAJHeTnR8XGEgQFJgZGzvZLBICWysrj84x/HXVND1OtlaO9ehvbuxdPUhNnjkYKMt21DYzBgLytTxqZ6q1XxJJ75ucomErjq6hDlZYJpv2I2mSQZCLD0Qx8iNjFB+333SeTlDNKRDIXY/6Mfsemuu5TPWToaJTQ4OOdn3Zyfz2hbm0LKQPKgbf7+9wn09pJLp7GXl88ICs6fP5+C5mZifj+jbW2Kad9YXa20Q0y/t9NQazTo5AWbabXYVVvLwne/m3lXX628v95jxxDkpgGQxpDwUnxKeGgIo9OJwW6XGhGSScx5ecSBE3/8I2u/+EVA+r7XXXEFJx5+GFd1tfL4uXSaZCj0mse/rzcukLLzgF/+8jBtbSNUVDjQaNQIgkhHh4/vfe8FvvrVdeety/LKK+vo6wvw4otedDrJXJ3NClxySSVr176+BepzoaLCwQ03NPPggx1otWoMBg3xeAaLRc/HPrb8VSlK/2r4059O8thjpygvd6DXaxAEkc7OSb797b187WuXzjnu/WeGWq2itbX0744cyaZSPP/d7xKR19PVGg2ZRIIjv/0t6WiUpne+8+96XGtRkbQJNscWci6VwinHArwa6Mxmqtato2rdulf82el4iuMPPaQQQ5PLRctNN5EKBDjx5z8rZKqwpYWF7373OdPki5cu5dgf/kAmHp+xaZYMhTC6XDRecw2djzyCtbCQyrVrmThxglQ4jMVsxlVTg6WggMnOTkpXrFCiFfzd3US9Xkx5eYqqo9brOfHQQ5SsWDHj+VVqNSa3m75t22aQsqjXK0VWaDQ4ysowOZ0EensJDw2RSSQY3LuXvHnz5lT5ihYvpmDhQnzHjikkIu73k00kWPmpT81JdNOxmLQdaTRK76lMIAGl4zEdjeJpbFR+R61Wky/7+HwnT0rqnFqN/+RJyduk0SjxE/HJSXLpNOaCAhJ+P0a7nclTp3BUVlKwYMFML5daTeny5VgLC5ns7CTm85FNJrGXl7P0gx+kesMG9BYLx+6/n95t25Qg395t2zDn5WEpKsJZU4MKmDp9GqPdjrW4mHW3306wr4+OP/4RnbwtnAqHEXM5LvnSl0j4/Zx4+GGEbFY57qUf/CCFCxfS/cQTiDBLBTI6HAQHBvB3dSlqrHJ+51gsOVfpvVqrPWeYsaepCXdDA5HnnsNeXq6MOdORiLToodGQjkbJJpMKMcymUuiNRmU5xV5SgsZopPPPf8Z79CirPvUpDDYb2Xh8xnPpzGZpyUxWJBN+v0IuRUFALxM/k9uNv7t7Rodsw5YtxCcmGNyz56X3UqNh0fveR/78+XMe2xuFC6TsdcbISJi2tlGqql5Sq9RqKYS2vz/A8eO+OUd/rwcMBi2f+tQqurv9HDvmRa1Ws2hRITU1rjdsxffKK+tpbi7ghReGmZqKU1fnZuXKMuz2f7+NxHA4xdatp6msdComd7VaRXGxjYGBIG1tI6xbV/WmvkZBEOnq8nPgwAjJZJZFiwpZvLjoTQu3HWtvJzw8rJAGkLatnJWVdP3tb9RcfvmcHqRXgr28nIIFC/CdOIGjvFwZ6cUmJjDl5VEsF0e/3hg7dIhDv/gF1qIiTG43qXCY0OAgj33oQ5jz8ihYsEAKEhUEJjs72fPNb3Lp1742q6YIpIvq8o9+lLZ77pG24uRRmdZoZPVnPoO7rg5Lfj6nHn2UdCxGXkMDrR/9KA1XXw1ISsChn/2MkBzuKooigd5e5XyKyONEeWsu1N+P56yLr85sJj41M/fQWlSEINfyJKamGNy7V/KUGY1kYjFOPfYYQjbLsltvnUXMNDodKz/5Sfp37qR32zbik5Pkz59P/ZYtuGtr5zyn1sJCBJkw2MvKFJO3Sq4H0tvt5FIpqtevn/F7rpoaDv/iF0rTQGhoSCJJKpVETuT6H7VORyoaRRRF8ufPp6S1lUBvL3u/+13mXX0186+7Dkd5OdozyuKdVVXKZzbQ18fyj32MspUrARjcu5fTTz2Fs6oKtUaDq7qaTDJJz9atxCcmcMvnOBuL4a6vZ/XnPodWryd//nzc9fX079hBzOejaPFiqtevV4zwFRdfTKCvD5VKhaumRlGQEoHAORVXFTNVY53JRNHixUycOIGteOZ1Kebzvea8PLVGw+rbbmPi5Eki4+NkVCpEwFFRgaOykqHnn5f8YrIqNa0sguRjq7r00hnhvcG+Po7+7ncs/+hHZ6l5Gr2exNSURNJB+Txb8vOVOBYApiuxzlDZ1VotSz/4QeqvuopAXx9qjQZPU9Oc37s3GhdI2euM4eEwUufrbBKk02no7vafN1IG0kV/3jwP8+Z5zttzvBIqKhxK9+O/M4aHwwBoNCpGRsJ0d08RjaZxOAx4PGaOH/e9qaQslxO47752du0axGjUotWqef75IcrL7Xzuc2twOObu2PtHIeRySoXO2cGc3iNHFBVIyGal4FC5FBkgNDBwzs7JMxEeGcF79CjZZBJPYyOeefNo/chHOPyrXzHe3o5Ko0HM5XBUVLD8Yx87Z5/gPwJRFDnxpz9hystDYzAwevAgkZERhFyO0OCg4n1x19VJYZ7FxQQHBhjau5f6q66a8zGLly5l47e/zciBA0S9Xhzl5ZQuX04uk+H0U08R83qpu/JKChYuxJyXNzNM1GSiZtMmXrj7boUICpkMaouFVCgkRQnI/Zdao5FUJDLr+ZPBIAVnKQnFS5dy/IEHSASDjB0+jFo2YadjMcweD0WLFjH0wguUr15N0RzkV2swULdpE3VnjQjPBbPHQ9nKlQzv20fBggVk4nESfr80zpIz5Vr+8z9neKcA0vE4ermGR2W1kpR7GFUqFVqZ4GUTCTKJBNlYjOKlS5XNPQMoHaTTFVYt73kPB3/6U2l5xOUil04TGRvDXVc34zi7HnsMRJGYz4fR4UBnNhMZGSGXyZBNpXCUlaFSS5ONyc5Oerdto2HLFmnzd948POdoGNCZzXP6D13V1fRu2yb1nCaTaPR6pf9RFMVZkS7N113H7m98g9DQkFKpFJ+YwFZSQtVZxPbVwGC303rrrRz+5S8xywRp+vtVeckljBw4gCU/n5GDB8nEYhjcbokwCQLBvj7JlyarXPbSUkYOHGDhf/wHeQ0NuGpqCA4MYCsuZuTAAUx5edINgUqFRq+XFne8XpquvVYhptHxcYqXLp1zA9hWUvKWCIw9ExdI2euMl1MYslkBs/lfw+x+Aa8MvV6DKEqhql1dfsxmHSaTlmg0zeBgiJKS1674vJ5oaxth584BqqtdM0bLQ0MhHnqogw996PVdCxdFkb4dOzj117+Sli/4ZatX03z99codqtZgIB2PEz58WClR1tts5Dc1SRfcl/FcTT/HiYcflnKt5AvuqUcewTN/Pis/8QlWfepTRMfHiU9OorfZlPT584HpnkxHxf9n78zD46rr/f86sy+ZLclkX5ukTdp03wulhULZfhRZZBMERZRFxAURvF5FBb2KyPWqiFcEQQVBUDYpW1uglBaaNm3TNm2TNPueTJbZ1/P745w5JE3SpoUuV87reXiekszyzXxn5ny+n+X9LqC3tpbhtjZMTifxcFjx2+zZvRuTw6HYHpmcTjq3bx8TlImiSF9trSQpMDBA+rRpTL3wQqxuNx3btlH1u98p5ab4O++gs1hY+s1vYnW78dTXA5I0SNbs2ZSsXk3jm29KWTGQsoWpqYochqDRYMvJYbC5eZT8RzQYJBYIUHJIz43BamXpN77Bu/fdh6+rC4PVqtgE5S1ejEYuDTa/9964QdmxMPv66xETCdq3bsVZWKg0j1dcfjmlq1ePq5kW7O0lb8kS/D09DDY3I8bjiKIoTUsilbws6elEfD4CPT1SRi4Ww9/bS0IenECrpaOqCldxMfnLlqFPSWH/iy8y0NCA3mKhfM0aSs87j6DHQ9vmzbRt2ULN009L5TdZmkNvsSiekgnZ9F2r0SAIAinZ2TS8/jplF1xwzO/LjJkzCXo8tG/dqpQf7bm5mFwuMmfOlCYkR2DLyWHFD37AwbfeouPDD9Ho9VRcfjnFK1cqMhRHS87Chez75z+lrKsckImJBKGBAWZfdx2Zc+aw9eGHJZ0woHnjRmKhEIlYjK6dOyk4/XQlu6XRaqUSvdPJkq9/nerHH6f57beV91rWnDk4i4qIBAL01NQQHh4m6PFgtNuVcnIikWD7o4+SOWcOWbNmnRLG4xOhBmWfMOXl6RiNY+19olEpRTt//vHLkqmcWhQVOdFqBWpre0lLsyiBj8kkYDbraG4ewuMJkpp6cqYx33yzcdS6kuTk2NiypY3PfW7WMfe8BT0eggMDmJxOLGmSFEbdq6+y+29/w5adjdnlIhGPSwrnjY2s+P73JcHHykref+ABNAaDVFYTBOKRCC2bNpE6deoRT7XdO3ey/6WXlFIRfBTQ7Pn735l93XWkZGWNKwD7SaPV66XpxnCYgYYGpcSWlIwQtFp0RiOe+nolKBvZyJxEFEX2PPMM+19+mYjfTzwcpvmdd9j30kssvv12qh55BHNq6qg+s+DAAK/J9kXJUqVGo6H80kuZec015C9dSvvWrVjcbjqrq8maPVt53ngkgs5kIm/xYpo2bEBrMGBJT8ecmsr8m29WpthGklpayml33UXQ48Eka3VZ3e6Per10ujE9QR8HvdnMwltvpaK7G19XlyQamptL7549NG/ciNnlImvu3FGlbkd+Pn379pE5a5bkJODzMdTYSCwSgUQCb3s7moICSdPMaERrMFD/2mvScIXcC6YzmchduFB5zKxZs8iaNYtEUtpCEGjdtIntf/wjYa+XrupqwkNDBHp70ZpMGFJSpGycbARvzcgY1TenN5sVPbPDDX0cjn0vvIDWYJA06fx+EokE3TU1pGRlsfiOO8btH7O63cy8+mpmXn31MT2nmEgo04xJMdlld91F1cMPM9TcDBoNYiJB3pIlzL7+erb86leSb6X8/jDa7YpPZlDuKdRbLFJ5M5FQ9PyMdjtL7riDtKlT2f7oo6SWlIx639tzcujYto3w0JB08BNFErEYvbt3I2i1NL/3Hq6iIpZ+61vH1AZxIlCDsk8Yk0nHl788n9/85kO0WgGbzYjfHyEUinHFFTPIzj413wgqnzw6nYZFi3J5550mfL4wJpOeWCxBKBRjypRUUlIM7NvXx7JlYyfMTgQDA8HDKPBL+mJHG5SFvV52PvkknVVVkkJ4IkH2ggVUXHYZ+154AUdBgXKx0Wi1OPLzGWxqomPbNgpOO41Afz9Gh0OyBIpEFLXzQF8fsWCQtbffTtbcuUy//HLs40z01b/xhiShMaJsFx4extvZycb776dxwwYKly9n2kUXKYHQ8UJrMJC/bBkH162TyrBJNXn57zfINj7hYanMnZy8KzhEWmOgoYG9f/87Q62tisG6GI8z3NbGm9/5Dvbc3DEG5hG/n949eyg+6yzldUpKXyTLf6klJVReeSUHXn6Z/S++iF/2IoyGQiQiEaJ+P1qjkYHGRjz19RSuWHHYnhtncTEp2dlY0tLGZCLCQ0NkyrIJx0p4eBhfd7ei2i4IAimZmaRkZjLc3s6G732PoMeDRqeTghqjkYVf/SpGq5XQ4CD2ggKpdO7zSaUvh4Oo2y3158m+mv3792PJyCC9ooLO6mr0ZjOGZCZNFPG2tzMgZx5HktzboMfD9scew+J201NTQ3BgQDlYJGIxyVvVZiMiipIdUzQqDSrI9w97vVgzM49ZAibQ10fj+vW4p0+XzNDb2ujctg2jHOy8+6MfkTV3Lotvvx1L+sdvcUnEYhx86y3q1q4l4vVisNkou+ACpqxahSM/n7Puv59BeTLVmpmpuDpEfb5RQaeruJghOXuJIEjBmCgy3NYmScgconPnKi7GaLePed8nrbvm3HADGp2OqkceIb28fFTWcbClhdrnn2fOIbqCpwpqUHYcmDcvmx//+EzefruJxsZBKirSWbGiiLKyEycuKooig4MhtFrNp7LJ/lQhN9fG3LnZRKMJ+vsDpKQYKC52kpWVQnPzIImE+Ik8jyiKDA2FlYPAZCgtTWXXrm6yskaXKILBKFar/qjeN0MtLRx86y12Pvkk0WCQrDlzlInHzm3b6D9wAFEWiDwUQ0oKTe+8Q3h4mF1/+QvOKVPQGQwMNjYSHBwkMjSENTNTOoE7HPTV1rLxvvtY8YMfjMl4+bu7R+lMhQYHad64UeobsliwpKXRtnkz7Vu2UPHZz2JJTSW1rOy4NfhWXHopvXv3KvIAgkZDPBTCVVJCLBQi6PFIWlVDQwT7+siaO5fseaPla5rfe4++AwfQWyyjtKTi0Si9e/aMe+Lv379f6Q9LotXrsaSnc+Dll5UmdEEQmLZmDUUrV+JpaABBYPdTTxH2eunfv59gfz/2nBxEJKHX9f/5n5z27W+TN2L6MonebGbamjXs/tvfsOfmKiVaX1cXJqdz1MTm0RCPRNj9t7/R9PbbgPRedxYVMf+mm7Dl5JCIx/ngV79SrIeS+Hp6ePGGG0gvL1deC43BQH9trVS+TkmRMoBpaYqGls5k4vR77qFtyxYaXntNKd+JoihNEGZm4u/tJdDXN25Q07l9uyTu6vcz2Nws+WvKfV3JHi8SCTQaDTqTCZ3JRGhgQCll+nt6WPCVrxxV6XKgsZGG11+Xpj97e+mvqyMRi2FOTaV3714laxb2ehU5kC3//d+s/OEPJ9a9mwSiKLLjT3+i6d13lex3NBCg5q9/xdvezpwvfGGMrEqSzNmzqV+7Vnl9TU4n2fPn01FVRTwSIdDbS7C/n4zKynGzd6llZaRkZuLv6Rl1uEr2qmbPm8fmX/xijNQKSNm0lvfeo/Kqq45LL+nHRQ3KjhO5uXY+97lZJ+W5a2q6efrp3XR2+hAEmDo1jWuumak2358Epk1Lx2TSU17uGPXlIAVjAiUlronvPElqarp55pk9tLcPAwLTp6dz1VWVY0SED+X880v58MN2/P6IIuwbiyXo6PDxuc/NnLR0S2d1NR/++tdSL05vLzqLhbYtW0grK8M9Ywb2/Hx6ampAo+HQFYmiiKeujs7t2/HU1Skir/bcXPKWLqX9ww/R6vWSdc3QkGSFkpXFcHs7dWvXMvcLXxj1eM7CQvr27VO+bHtraxHkCyCy1dBwWxud27fTu28faaWloNFQcemlSh+Xv7ubRDxOSlbWx7pogXSxWfnDH6I1mah/9VVMTifWsjKp7yoUoq+uDldREWank4pLLyVvyZIxgaunvp5ELDZmKEKr1yvipqNe00SC8PCwpHp+SCbBaLdLGYkRMhLJn2fPnctgU5MkZqvREOjvx+T8aIpcbzYroqnZ8+ePWWdocBCzy0X23Ll019RIjy+KuGfMYNa1106o6n8kap56isYNG3AUFCjm0f7ubt772c9Ydf/9DLe1Kar1I1+Dnl27iPp8iuZcMkDU22yklpVhSU/H5HBglku8IOmPJaJRQgMDaAwGvB0d6C0WSejV7SZ73jyCHg+B/v5xg7Lg4CAanY6BpialZy/ZCylotWiNRsnDMyuLjMpKunbsYLijg4i8zopLLiF/hC3QyLLoeHTt3MmW//5vNDod3s5O+mpr8ff04G1vR2+xSHIvclAkJhIIOp00UNLUhKeubpRsyKGEhoborqkh6vfjKCggfdq0Ue8Zb3s7LXI5MPlzvcWCs6iI5nffpeTcc8fNZgMUn3kmjevXE+jrU15/q9tNekUFhcuXK71vI70pR6LRall8xx1s/uUvGWxqUloC9HJ/o9FmIzg4OOqAptxX1jWLyt8HpxpqUPZvxu7d3fziF++TmmqmoED6EmxtHeL++zdy770r1PLpCWbKFBfz52ezbVsHOTk2jEYdoVCMjo5hVq4s/tj7kdxvl8usBN0HDw5w//0b+eEPV5KZOXGjbnGxi9tvX8Rjj1XT3x+U20wELrmknNWrx5cjGEnS8PqDX/0Ks3zS1+j1GKxWRLOZ/vp6bLm5mF0uDCkphIeHifh8o5qHfd3d9NfVMeXss5WSVEdVFYG+Prp27SLQ14fR4SAWCqG3WBR/SWtGBgfffJOI10t3TY1kUrxqFQWnn07ntm3EwmE0csBisNkIDw2RXl6Ot72dvtparBkZxEIhHIWFxKNR9jzzDNFQiM6qKvxdXSAIGO12Kq++WskqHSsGq5UV//mfmJxO9r/wAu2yKC6CgLuigtO+8x1pkGEckv1MocFBtHq95G05ojFdbzajt1jw9/YqpaHhzk58HR0gCLS+/z6u4mLSpk5FazAQCwYxOhzjmp2DJCaK7NeoMxpHewPqdIjxONFAgKGWFkWyQhRF6teuZe9zzymZuUQ8Ts6CBcy85hplXcdC0OOh+d13cRYWKhd+QRCwZmQw2NxM+9atStA0En/STkmetkzez5adja+7G53JNK7kxnBbG7XPP6/ILOjNZoIDAxisVoIeD03vvIPJ6ZywAd5ZWEgiGiXq82FOS8Pf3Y3GYCAWDiPIkhuxYBBbbi6ppaUYUlKY+8UvSuspLZWmYkWRlvfe48Arr+Dr6sKcmkrJ+efjzM+no6pK8ohMTSURjVLz179izcggHo8z3NKCLSeHaCCg9AVGfD6pSd5uB1FUZC+SAx4TBWWt779P9WOPSZONchuCq6SEJV//uhJc9+3fL72uh7oOaDSISC4aEwVllvR0lt9zD9WPP85gU5NycJp/000Un3XWpDKFtuxsVv3kJ/Tu3Yu/uxuT00nGzJnK4SW1pITe2toxun/RYBBDSoraU6YyPl5vmOHhMA6HiZSUjzcRIooizzyzRzELT+J2W2lrG+b11xu44Ybjo8ekMj6CIHDzzQt45ZUDvPlmA+FwHKtVz5VXVnLuuaVHfoDDkNxvp9OE0/nRfmdmptDePszrr9fz+c8ffr/nz89h5sxMDh4cIBqNU1joPGLZUiknvfMOocFBunfuxFlUhCUjQxHHTJ7uvR0dmF0uBI2GaZ/5DC3vvEPE58PkchGTgyBbXp5ShrTl5GBxuwn09DDY2Iig0SjiqvlLlypf1r7ubjqrqzE6HKRkZRGPRKj9xz9wFRcz4+qrqX3uOWLhMBGvFzGRwFFYSNrUqTSuW4chJWXUhUQrS25svO8+ilaswC5PZEb8frb+9rdoDYZxzahHEhocpGfPHqWEdugJX2c04iwowOJ2SxkfvR5rRgZiPM7mX/6Ss3784zGl2LDXy5aHHqJn925iwSDD7e1o9HpS3G6MTifh4WFSsrJYdNtt0sBEczP+7m769u/HLNsI6cxmPPX1BHp7yT/9dHxdXVReffWEFz1rZiai7K84Zt+jUUzJqUbxo7J7965dUskyP1/JnomJBB1VVaSVlU0o7zEZvHJwOZ6Tg8Fqpa+2lpLVq8esN+L1giAgxuNjLr4mu51YODwqkAVJJ8tTX0/uokWYXC4Csm9jPBwmIoqkZGYSkpv22z74gOnjCBlnzpqlBIwarRZzaipBj0f6XMgSJFqDgdQpUxhua2PO9dePsefa+/zz7H/hBayZmTgKC4n4/bz7ox+RiMdxT5/OUHOz8tkQ43G8nZ0EPR5FbDkprZLMvvm7uiCRkMr0cvlbgAkzl0OtrWz7wx8wp6ZKE71ypnqopYXqxx9nyR13SI8hy3iMh3S+O3xg5SgoYMX3v0+gt5d4JKIMPfTt20fbli1E/X4yKivJWbhw3IwXSJ/drNmzx/1d2QUX0Ll9+yjR2EQshrejg9mf/7zSt5fs5RQTCUyuE6fpORFqUHaSCASi/O1vu9m0qRWQxO2WLy/giitmYDYf29SNzxehrW143DKl221h27YONSg7CRgMWi69tII1a6YpU7lJMdmPg9c78X6np1vYtq3ziEFZcn3l5ZNv+t355JM0b9woBRc6HXqrldDAgFQyEwTFGkaj1Ur+d6EQGp2OiksuoWjFCurXrqX/wAGMDgdp8kTlyCm9/KVLGWhspHvnTnQWCzq9npwFC5QLiiiKtH/wAc6iIox2u1LacxUXM3DwIEUrV3LuQw/Rt28fJpeLsOwJmIjFFC/N0NDQqF4Xb1cX0UBA8huUv5QNViuJtDRqn3+erDlzDH6agwAAIABJREFUJvyyPrhuHTV//euowCBz9mwW3nKLUh6JBgI0vPkm7unT0er1iHL2K9DbS6C/nz3PPcei224b9Ry7/vIXBpubyZozh0BfHwONjSRiMYbb20mJx7EXFJBWWkrJ6tVMW7OGvtpaNtx7L1POOQdLejqd27bh7ehAo9Uy3NFB5/btlJ53nhScbtiARqfDLdv9JLG63eQtXsz+l19WspMgZRc0Oh2WtDQSsdgoIc/6V1/F6HCMKmcKGg323Fzq1q6lZPXqY25c15nNMIE3ZiwcxuhwYHG7EbRaOrZvx1VcjMnpRGs0KrIch07sChoNc264gaYNGyRpDFEkFgjQsW0biXicvtpaQMoaRrxedCYTsVAIX08PVreb3IULqX/1VUpGCBlHg0GG29rQ6vUs+cY32PTzn3Pg5ZfRmkxYUlOlgEOvJ9jXhz0/H3NaGvMuuYScEZOcAIH+fur+9a9RxuWxYJDQ0BCIIlGfD39PD7acHIL9/QR9PlJycxlubSU0OIjebMZot2PPy5N6szweEEVyFy9WMtGhwUGMdvuE2dnG9esZbG6me+dOErEYYa9XGqrIzcXb2cmMK67Alp2Nu6JC+ryPkE0BObsrZ4GPRDLrCVIgv+PJJ2lavx6d2YxWr6ejqooDr7zCad/5zlFnXFNLS1lwyy3sfOIJxV9WEAQqLrmEKatWAVI/Xs1TTzFQX48oCNiysqi8+mpFHuZkoAZlJ4FEQuQ3v/mQ2tpe8vLsaLUaYrEEGzY00dcX4JvfXHpM0bpWK13Yxpl4Jh4XT5pKu4qETvfJDl0kA7uJ9vtY7LyCHo+kEq7RkDZ16pgTqr+3l5ZNm5RykkkOYvQpKZIAaV4e3rY2BI1GMiEWRXzd3cy78UbJA8/hYOGttyqPt/Xhh+netWtUOUij0+EsKsJgtXL63Xez6ec/V/qmtCYTg42NkmCox0PDG2+AKGJxu8mcORNzaiotmzZRtHKlYn/z7n334e3sVE7iQY8Hndk8SlzU294uZfQOeSGNdjtDLS1E/f5xS1b9Bw6w68knseXmflRWFEW6d+xQJDiSr1sy6yAmEnTKOmwgZaCqH30Ug8XC7OuvV3SZOrZuxZ6XhyAIFJx+OlqDgUBPDxHZZiljxgyW3HGHEjhpTSaMNhs2OeOWu2gRwf5+vF1dRLxeMmfNQqvX8+6PfyxN/cViaE0mSs87D0dBgeTlWFDArOuuIxYOs+NPf2K4vV2aQLRaSauoIOjxsOArXxk1XTkk668dStI0OhoIHLGfTBRFZQo3FgjgnjGDzJkzcRYVYU5PJzQ4OOo5ErEYiUgErV7PG3feSdTvZ6i5mb69eyUz6pISBK2WrDlzRq01qRNWtHIlJeecQ9++ffi6u9nxpz/hKCoiIvtOgpT91BgMWDIyiHi9iq+jRqslNDjIUHMz7hkzqF+7ln0vvKBYHplcLuZ+6UsUn3kmO598UurtM5tBFMmeP5/Zn//8R/Io47yfgFFyLl07dhDo6ZHEaTs6JIkZhwOjy4W3q0sJnkMDA9J7XPYAzV+6lEQiQdf27SSiUXydncSjUfQWC8u+9a0JtbrqX30VX2cnRrsdnyzPATBQV4eg0/HW3Xez6ic/wZ6bS+l551H7z39KgePgoHRASklhzvXXT2gXNhFdO3fStH69lGWWD2mW9HSGOzrY9ec/s/Sb3/xoH4eHpeBS/k6ZiLzFi8maM4eBhgYSsZhykANJYPq9n/5UcoaQDxnh4WE2P/ggy+66a4xI8olCvUqfBBoaPOzd20th4Uencp1OQ2Ghg5qaHhobB5ky5egbwC0WPXPmZLF3b++YXqXubj+XX37kk4vK/x0sFj2zZmWxf3/fmAnK7m4/n/3s5PdbTCTY+/e/U/faa0rpQdBqmXnNNaPsarzt7aMsS5KNvQMHDyLodGg0GgpXrKBrxw6MDgfTLr6YkrPPHtfwGKBk9WraPvhA0TgClFH4svPPx9vRQSIWY6i1lc7t26WG9PnzlUZ1k8MhZZ0GBmjeuJHsefMUs+ah1lYEjYbl3/0uB/71Lzq3bZOsjnw+SfhTDmaSyvaOcfprRHlSbiLR2oNvvYVOlrZIIggC9vx8mt95h+mXXSb1wlmtUllQtoIZamlRSiURvx9LWhqNGzbgLC6m+MwzpayjRvPR62w2k7d0KV3bt+Opr5d6v2TzbbtsG3XoRV4QBCzp6VjS0wn09RHo72eopUWSCOnoACTLnYbXXyd7/nwlE5E2dSqLv/Y1pl50EbXPP0/ntm1oDQacRUWUX3LJmHKRNSOD0MAA2kMCs6Rhts58eB0+URSpefppGl5/Xcqw6nQ0rl+PIz+fpXfeyYKbb+b9Bx5gsKVFEmENhSRT9aVLOfCvf2HLzsaem0vatGlSRvHgQdzl5Zx2111se+QRBpqaJEV7uXS46I47lHJWRmUl8WgUQavFVVhI5/btyrqSrgZavR6Tw4FrhPYdSIeHxnXrqHnqKWy5uYp4bywY5IOHHmLlvfcy5eyz6du3T1LuLyiYsMdqIjz19Xjq6xFkxfp4JCIFP/E49rw8TE4nkeFhjE4nvo4O4tGo0rdpzcxkuK2NVT/5CXqLBV93N/bcXLLnzZuwJy7o8eDt6lICakRR6oMLhZRBm6Z33uGlm26i8sormbJqFbX/+Icixqu3WNBotQw0NirTrCAdPFree4+Db71FaGCA1LIypl544SgPzcb166Vg9ZBStS07W5EX0RmN1Dz9NK2bNknl6USCvMWLmXXttRP+TTqjcVw/y7pXXwUYlSk2ORyI8Th7//533N///kkpZapB2Ung4MEBYKwVU1Lksalp4JiCMoArrpjB/fdvpLV1iPR0C/G4SG+vn/x8B2eeOXY0WeX/NldeOYP773+X1tYh3G4r8XhC2e+VKye/3wfXrWP/K6+MEl2NhcPsePxxrBkZip1LUuZgJBkzZ6IxGOjZuZOw10t4eJgZn/0ss669dtwMykhSS0uZd+ON7HzyyVEinflLl5KSnU3V73+PLTubKatWIYoigb4+Wt57D4PNNsYYOmnz4yop4bWvf12SHwAMNhuzP/95Ft5yC/FolG2//z0tmzYhxuOKgGvRmWcS9HjwNDQwLHtDJoOd/GXLxrVoAan/ZryG4eSEV2hoCL3FgiU9Hff06Xjq6vDU1yvrFxMJ4pEIrilT0Fss1K1dS/GZZypimXHZIzDQ30///v0kYjHFiQBRpOqRR4h4vZSedx72/Hz0VisRv39UhjNZKo0EAvi7u6XMlc0mZV5k26eBxkZyFy6UpmHr69n55JMsvPVWTvv2t6WgNR6XJD3GuUiVnnceH/7mNxhstlEZnuH2dsovvviIIqjdO3fS8NprOAoLRwU9Q62t7P7b31jwla9w1v3307ppE/0HDmByuShcvpzmjRvRGY3KhT85wWdOTVX2JXXqVA6+8QbRQIC08nIW3HLLmAyIv6dHUdPXmc3SgIDVisFmw9vVRWhwkLSyMmXIJOL3ozObcRQU8MGvf000GKRx/Xolo2TLyUEA3n/wQWZfdx0ZlZWTVpBPKytTSoKJWIy+2lqlBA9S5jYWDhPx+4n4fBjtdtIrKug/cACt0Uiwrw9LRgaOggJ8nZ2UrF4tuQOM05M3HsPt7aRkZRH0eIjKf2c8GlUst3QWCymZmcSCQRrXraN10yaMTucoj8zkRHXTu+9Suno1iXicqkceoX3rVqxuNyaXC09dHe/efz8Lb7lFkVcJDw6OOw0pyAfEiN9P9WOP0VNTgz0/X2qPiMdp+/BDAv39LL/nniP+nUrPqyDQtX07lnFKoiaXi8HGRkXA9kSjBmUngfEEOz9C+FhlxuxsG/feu5J16xrZurUdvV7DlVdWsmJFoSJ7oPLvQ06OtN9vvXWQrVs70Ok0XH75DFauLBozOOLv6aFl0yaGmpuxZmVRePrp2PPySMTjHHjlFWzZ2aMuijqjEaPdTt2//qUEZamlpZgcDsLDw0oZQKPVkj51KjqjkcW3307atGlHpftVeMYZZM+bR29tLZ76evpqa+nasYPqxx7DWVw86qJrsFqV6bWQx0M0GCTQ10ciEiEWiRDo6WHnn/5E8VlnKdm5iM/HB//zP5zxH/8hidcajZLsgdynkz59OiXnn8+6u+8m2N8veUbq9Qy1tGBJT2flvfdOuHZ7Tg49e/eO+fJOXqBHlu3m3HAD7/3kJwT6+qRBh2AQMZEgraxMuTgMtbQgiiKGlBTclZVs/e1vlcb74MAAOoMBo90uiWfabOiMRvY+/zyFZ5yBRq9n2kUXseNPf8Kcmoo5NZVYOIyvqwvnlCl0VVcT8fmUgC80OIig1aI3GgnIZUa9xYI9L4+OqiqCHg9m2ZdQe5iLXe7ChZSdf75USpYzdmIiQfbcuZRNQjD24JtvYnQ4xsiP2HJyaP/gA2Zecw2WtDSmrVkz6vc1Tz+tBEoj0Wi1xEIh3r3vPnRGI7mLFyNoNAT6+vjw17/m9O9+d9TkpSUtDUQRrV5PwWmn0bF1q/TeEAS0ej06oxFXSQnxcJjgwADxSISFt95Ke1UVB996i4jXizktTVLQDwZp3bSJRDxOx7Zt9O/fL7kdyCK/R8KSnk7pBRew/8UXASlTa7TZpClOvZ5oIEDE60XQ6/F1dZG3dClpsnfq0m98g7wlSxhqaQEgvbx8jMn4kdAaDBgsFjJnzqS+o4NYKCRNr4oiGp0OsyzMLMZiWDIyqHvlFSouu2zUYySD46YNGyhdvZq+2lo6tm7FVVysBPXWjAwMgQA7n3yS7Hnz0BoMpJWX0/T222MyXknB5IjXS09NDY7CQuVxkgLUnro6+vbvn7CPrXfvXva98AL9dXUYrFaKV61C0GjGdU5IZuEF7ceTwzlW1KDsJDBzZiZarUAkEsdg+GjjI5E4Op3AjBkfT2k8Pd3ClVfO4Morx5rVqvz74XZbufrqmVx99cTNqd01NXzwP/8jeUlarfTs3k3D668z74tfJHP2bOnC4hqbnTU6HJIOkIxGp2PBLbew+cEHCQ0NYbTZJE/EUIiKSy8lZ+HCY0r5G1JSSMRi1MuCnVqTiUggQF9tLRGvV3lcQaORyjjhMLmLFlH32mvEgkE0er0UhIRCCFotrZs2kbdsGebUVMWE+sDLL6OzWGj/4AMyKislIddoVLIs+uc/0RmNGCwWRRg0Z9EiBEGQ/BVHiJKOZMo559C+dSvxaPSjyUO5/Fq4YgVRv18SuHS7sbrdnCn3twX6+zHabFIJSi5jhoeHlWbs5nffZffTTxMaHESMxSQrIACjcVQDodZgQIzF2PXnP9MlG7DHQiG8HR2EBgYwp6Ux/fLLKVyxgmcuuWTUfZOyIUnT99DgoCQvodGAIBAcGBjXQ/JQBI2GyquvpnDFCrp37SIRi+GuqJD6uibxXgj094/RYAO5r0oQJNPqcYIvW04O3o6OMb9LxOMMt7biLCoa1eRvldX79z77LKffc4/y84zKSgw2m9K3VrhyJeHhYQJ9fWTPm8fsz3+eZnnSOGv2bKasXk3T+vXUv/46vs5O9FYr/u5uvB0dRAMBJRsjJhIMt7fj7+lh089+xrm//OWkBh6mX3opKZmZVD/2mDSp7HRidrmkbJVGg9ZsJurzEZGb9juqqihZvZrKK69Eo9ONK/UxWVJLSjA6HBgFAWdREYloFG9HB4JOhyCKymHCVVqKVpZISYx47ydJBpAAze++S6Cvj8HmZgDseXk4CgrQy5+1gYMHSS8vp/iss2jasGHUgS/ZujDjs58l0NuLKIrjVpgEjYaBxsZxg7K2Dz5g629/i9HhwFFQQDwclnoA5QygPT9fkhAxGiVx3e5uchYsmDA7frxRg7KTQGqqmauuquQvf6nBbNZhsxnweiUrpuuvnz1K3kDl0004HKO6upMtW9oBWLIkl7lzs48qmxoLhaj63e8wORzKKTSZRal+/HHO+slP0Oj1Sg/QSKKBgOJdmSR92jTOuu8+mjduxFNXhyUtjYIzziBt6tRj7sGIhcPsfOIJUrKylB4WrV4vlZDa2wkUF2N1u9GZTJicTin40umwpKYqulvBgQFiwaDknRcOEwkGMdlsZMyahS0nh47t29Hq9aNO2kmx2mgwiCUtDXNqqmTFMzSE2enE6HDQuH490y+/fNy/LW3aNGZecw17nn1Wmr6URSwdhYWSDdJddyHIjdfFZ5/N9MsuY8HNN7P9j38cVSpOqrkvvO02PA0NbH34YaKBAO6KCuKRCMPt7USGh5U9HGxuJksuDQ82N+Pv7ydj+nSsbrcUrHR2YnW7WfmDHyjTg+nl5dSvXasMZ+iMRoLBoGT9Y7crmQExkQBRHDdInwhBELDn5h51zxSAq6SEjqqqMdnGpM2WaYJ1FJ95Ji3vvTfqfZssmwoaDfa8vDH3Mael0V9Xp2QFQSrJL/n619ny0EOSZpZsY2VyOln6zW/iKi6maMUK5THaPvyQ5o0bsaSnY7TbiQaDxMNhoqEQxOMIOp2UWTIYMLtchIaG6Kyupm/fPjIqK0etJzQ0RN++fcSjUSxpaaRNnYpGq6Vw+XKy5s7ltTvuwNfVJfWQ5ecTCwZJJBIEZJN0f08PGdOn07VjB9sffZS5X/zipEulg01NNL3zDr7OTuz5+ZIcTF4e8778Zbb88pekZGYqIrjxaBSr261YQjmLiqTSv8VCdMSUbpJAXx/5S5cSGhpi/0svMdjYqMip9O7Zw1BTk2InlohLvtC27GyW3Xkn2/73f5VsH4LAtIsvpuzCC+nYunVCmY1YKISnro6GN96Q5G/KypRD166//EX6Xhmx386iInr37mWorY3WzZvRGo2ShInLhXvGjDHZvxOJGpSdJM45p4TCQifr1jXS3j5MaWkqq1ZNobT0xFkxqZzaBAJRfvGL92lo8CjWSdXVnZSWpvKtby2btC9lb20t0WBwjNejzmhEFEV6d++m+MwzqZf7epLBh5hIEOjrY/rll495TGtGBtMn+OJKNt4jCKMU4Q/HQEMD8UhEyZjoTCasbjdBjwdBp8Pb3i5dFEQRa1YWiUhEkoiQ/QOjgQA6g0EShrRapbKTwYDGYKCjqorsuXOVE3VyPYlYjIGDB6VeKHlaFKQMjc5kwlNfT96SJcrPx0MQBErPO4+chQvp2b2bsNdLIhpl/4svSqUV+fVMxGLUv/oqiUiEWddei6+ri/rXX5dO/tIDKYr+2x99VCmdJUU1UzIzGQyHCXu9kjeo3OMTGhrC29lJ+cUXK2VejVaLyeGgddMmXvnqV9EIAuGhIRKJBBGfj4GDBxXtuEQkgjktDaPdjiUtTbLramvDWVQk9d0lErinTye1pGTcfp1YOEzbli20bNxIPBIhe/58ilasOGIv4UhKzjmH1vffHxUoiYkEw21tlH/mMxNmLFJLS5l93XXUPPWUkkERZT0uncEwrtYaoihlsg55T6aWlHDOAw/QtXMngd5eLG43WbNnj8rgxUIhYuEwB996C7PLJU0aWq0EBwak55KDCzEWkzJ8Pp9SivZ3d+OXZRlACqS3PfII+156iXg4jCElBdeUKaSXl7P4jjtIycyUJn7tdlr/8Q/0KSmSI4DBQKS/HzEWw56fT2hgQAoKYzEa3ngDU2oqlVdcccTXvPHtt9n5xBOK2LOnoYGDb73FgptvJm/xYs788Y9p3LCBg2+8QdeuXQR7e6VDktVK1pw5im5ZxSWXMNDQIGWZrdaPPvtAybnnUvfqqwg6HVqzWdlHndFIaGiI3r17sWZmjspCp5eXc84DDzDY1EQ8EsGem6tkzdwzZqDV6UYNEAD019fTXlWFmEjQVV0NokjatGks/trXpOlfeZDmULxyeTZn/nyG29qIx2KIgD0//7j74h4ONSg7iUydmsbUqWPfLCoqAGvX1nHw4ADFxR9lCtLTLTQ0eHjttXouvXRy05XJMkKSWCiEr6uLWDis+C9Ou/hiBpua6K2tRWc0kpAnEotWriS1rIz6N94gNDiIs7CQrNmzJ7Qn6dm7l91PP423rQ0RcBYVMetznxslPzEe411AM2bOpGXjRsLDw0QDAUKDgwQ9HnIXLmTmNdew44kn2P3002h0OjJnzWKwqQlzaipRvx+QSq1avR6t0UjXjh0svO02Wt9/X3n8eCQCiQRanQ7NIRdxrcEgGaH395MxY8YRA0tLWhoCcODllxlqacFTX4/RbicejUqaUzodjsJCGjdsYOpFF1F51VUUr1qFp64OkDJuyQtHUi5juK0NQ0oK5tRUyffSbifo8Sg2VIG+PoZaW3FNmTJqP3zd3bRv2UIkGMTb2UkiGpUkTqZNI3fJErp37JCESCsrsefnM9jYSEp2NsNtbZJFk89H//79UtZIENj3j3+QPX8+C26+eVQWJhYK8f6DD9K/f7/Ue6bVsu+FF2hcv57l3/3upCURnEVFLLrtNrb/8Y8E+vqU8t+Us88e00cGHxmDd1ZXEwsGmXPDDcTlnkJXcTHp06ax97nnqFu7dpT1EkjSJJkjVN9Hojebx/XnDA8Ps/f552mVg9TumhpcJSWklpSQiEbRGY2jPmPJTFk0GCQ4MIAlNVUJzkAKBt750Y/o2LoVvdWKRZb8aK+qwtPQQHdNDVPXrKHqt7+VgvxIhGBfH6H+fqyZmVjS0tCZzfg6O4n4fMq+Rf1+hn7xC/IWLx7zd48k6PGw68knScnKUgIlk8NBNBik+o9/JGPGDGzZ2cy65hpmXXMNsUiE6j/8QRqssFiI+P2EBgakwOeOO+jetYs9zzwjZbdEEXt+PovvuANbTg5Nb79NxowZRH0+QoODyjCI1mCgt7aWeTfdNEZ2R6PVjluCNdpszLnhBrY/+qjU+2az4evspP3DD8leuJD0adOU90d/fT07//xnilauxN/XJ03RGgzYcnIUd4Zgfz+OwkLSy8sVZwNRFOnft4+hlpbDvobHEzUoU1E5BRFFkXXrGsnOHjvmnZ1t4623DnLJJeWTsyPJyVEyBN7OTjrlUyWCQMTrpd7loviss1j27W/TV1tLd00NGr2e7Llz8XV2su7uuxHlRuh4JIIlLY1l3/72GAX6/ro63n/gAUm8Um6yD/T1sfGnP2XF979/2C+5pDbRyN4so81G0Zln0v7hh9hlNfyKyy4jb/FitAYDp915J77OTumUnpJCT00N1sxM+vfvJ+r3S028RiM6WVCz8qqr6KutVXqHtAYDaDTEIhFM8ih+UpojHomgM5uJ+v1Mu/jiI77GXTt3sv3RR7Hl5uJtb8fkdKIzGunauROt0Yg9N1fSj9JoGG5rw+xyKX1mIzm4fj2d27dLsgayPVTE78fqdpOSlaU0IRusVhwFBZRecAG1zz2n3D8Ri9FRVSVNEQaD+Lu70ZvNiCAJ7hYXU7B8uRRIOZ2UnH02uYsXE/H5iAYC+Lq6aHj9dVxTpiiZMVEU6aiqov6110YFSc0bN9K/fz/OEQ3cRpsNb0cHe555hsVf+9oRX7ckOQsWkFFZKZlpR6M4CgvHzW6Iosje55+n7pVXlGbsRDSKa8oUlsiehyD1+rV98AFDLS2kZGUhaDT4enrQCALTP/vZSa8rFg7z/gMPMNzeLinmyx6Tndu2odFosGZm4uvultai1yPGYoiiiE4OXoN9fQiCgNnlIkt2hTjwr38R6O+X9tFiIdjfj7+nh0QiQUielG197z3M6ek48vMRNBqpnyyRgERCKs3L+2V2udBotXi7u4nHYvi6unj5y1+m4pJLmHPDDeMenrp27pTWeEgGUm824+/poXfvXnIXLVJ+rjMYWHDrrUxds4au6mpikQju8nLSKyrQaLXkL11K7qJF0jCCTieJ+Y6YLNYZjeQvW0Zvba00yJJIKDZXpeefP+m9ACiQh5Ma169nqLWVQH8/2fPn45YDMpCy1468PFo2bqRv3z48Bw6gNRgQNBp69+4lY+ZMSCSIR6NjStzJ9/FgU9OnIygTBOE84FeAFnhUFMX/OuT3RuBJYD7QD1wpimLTiVyjisqpQCIhEghEcbvHjmQbDFoCgSiJhIhWe+SgzFlURHpFBV07dtC/bx86iwWNTkfE55MuWFotW3/3O8743vfIqKxU+l58XV1UP/YY1oyM0ZmYnh62PvwwK3/4w1FBYe1zz2GwWkf1IlnS0vBFoxx4+WUWffWrE67RkJLCtDVr2PP3v5OSlSUZdss9M0UrV7L8nnvG9MqM7AUKejxEw2FFW8men08iHkeUZTZsOTkYUlJYeNttvP/AAwy1tKC3WjE6HAw1N5O7eDFGm03RQ4r6/eQvX87Sb36TtLKyI77G+196CZPLhc5kkprvZQkJvdlM//79SgM/ojhhljE0NETNX/9K1pw5tG/ZgjEzE0SRiN/PUHs7Ub8fW14eqx94QJERiEej1K9dq2hTBfr7peyN2Yyvq0uxWgKICQKR4WEG6utJr6ig4rLLKD333FFreOvuu7FmZo4qVQqCgC0nh4Y33qDswguVPrjG9euxZmSMORikZGXRtWPHGGmOI6EzmY6opN5TU8P+l17CeYh8xkBTE3ueeYZ5X/oSAGaXizP+4z+of/11qbQajZK7aBFTL7xwjML/4eisrpayJiPcH9wVFfg6O+muqSFt6lTJAUCWM0oIAhp5UCIhZ6/iDgdn/fjHaOUm/46qKnQGA4IgEI9E8Pf2ojUa0QoC8XAYgHgsJpU/Ewks6emKJ2k0GEQUBEU+wuh0MtTaKpXcjUaQpTRaN29Go9cz78Ybx/xNUb9/4sOcnOE7FEEQcOTn48jPH/duGq12XOeEjBkzGGhsxCqXgzMqKyWXgOFhnAUFymtyNDiLipj7xS8C8O599xH0eMauV6Ohv64OURTJX7aMjqoqqW9Mr6eruhqTyyUJLU8wnXokGZfjycf3epkkgiBogd8C5wPTgasFQThU0e1GYEAUxVLgIeBnJ2p9KiqnElqthpISFwMDY/uZBgZClJamKg4OR0IQBBYuSicHAAAVR0lEQVTddhsGq5WwfMIODw1hcjrJX7YMW04OAw0NDLe2jrpf25YtAGOCCKvbzXBrK0PyNBVIpcC+/fsxj5PdsLjddFZXT+iTl2TqRRcx78YbEeNxBpubCfT2MmXVKpbdeeeEzctpZWWc8/OfM+cLXyBfNg5Pr6jAnpuLs6AAZ1ERerNZasT2+0ktKWHVT39KxWWXkVZWxuzPf57Z116LTjaNTi0rI728nBXf/z4XP/ropOxWkoKwyYZ0e14eYjwuiYkajYSHh6Wy4PAwJqcT15Qp4z5O7969iPE4tuxsMmbOlMzDbTbMaWkYLBYyKiu54rnnlIAMpIvHvC99iUB/P97OTiJ+v3Sh7+5GZzCgHZkNEQQEuScn0N8/7msa6OsbN5DSmUyK0XWSmGy9dCiCRgOiOOq2nxQH33oLk90+Rj7DnptL2+bNUvAiY05NZebVV3Phww+z5g9/YP5NNx1VQAbQVV2N/hCJBqPdTv5pp0klw2AQBAGNToerpISMGTMwyrIRBosFc2oqqx98kKkXXaTcX6vXS+VyUVT0v0YGwclssSiKUq+k0YizsBC9fFARZVsje36+FMSJIoJWS1y2nUpEozgKCmh57z2Ccn/XSJzFxeO2CyR77cYbkDhWyj/zGaX1QBRFxekj6vdTPkLb7Fix5+URHrHnSZKK/46CAhwFBZJYtDy1mojFSCsrI2PmzDHfSfFIBEGjGVds9kRxIjNli4B6URQPAgiC8DfgYmDviNtcDNwr//s54DeCIAjikb7NVVT+DfnMZ8p54IH3MZl0SlN/IBBlcDDETTfNO6rHMqSkkD1/PolEAr3ZjM5kkoy55ROzoNEoX2JJfN3d46qxC4IAsoSD8jO5lCQmp89GIMbj6IzGI5ZaBUGgaOVKCs84QxLoNBonNUlmSEmhaMUKBhsb6d69m4jPp9wvHolgzczElp2Np76erDlzMLtcTB2hn5W0+BloaECj15MxY8Yole8jIQgCJodDsrsxmzG5XKROnYrnwAHQaBATCbzt7Wh0OpZ+61tjAookSW0zkJrYbTk5ks2N7B8654tfHLdPK2v2bFbeey8Nb7xB57Zt6IxGUnJyGGpulnq05JJnUp09kUgQD4XGTAKC1OQc9HjGTF5G/H6lJJskc/ZsWt9/f8zEZdjrxeJ2H5VW3WTx9/SMK+ip0WoRQckYflJo9Xop23oIVreb3EWLmPW5z3FwwwZqn31WyvokEpJh/JQpmNPSKP/MZ5h6iCF74fLl7Hv5ZSlb7fcrQwfxSASdyYRGpyMeiZAYYQyvM5mw5eSg1euZfcMNNK1fj6+ri6DHI00vhsOSc0RKCia5pCloNAR6e8fsZXp5Oc7iYoZbWxXf2eRghbuiYsJDw7GQWlrKsjvvpOavf1UOfdaMDJbeeecoNf9jpWjlSprefntU878oigw2N2PNyFB6B1OysrBmZiImEoSGhnDk5+OuqGDPs89isNkwpKQQ8XqJ+HzMuu66oxpU+aQ5kUFZLjDyKN4GLJ7oNqIoxgRBGALSgL6RNxIE4cvAlwEKJrBvUVH5v87MmZnccssCnnqqhr4+qZHYatVzyy0LmDnz6HzlABz5+Yqq9khEud/s0CyXs7BQyZaNd/uRgYtGp6PgtNOki/QhJ21fV9eYMtnhEDSacXWpjoRGrydr9mylb0sQBGy5uaRkZTHc3j5hpk4QBFzFxaMMyo+WKWefzZ5nn1X6q9zTp2Nxu+navh3XlCmUnn8+RStWHHaqK9nDkpwk1FssuIqLpYnI5mbSD3MRcxYWMv+mm+Cmm6h65BHq1q5F0GqxZmTg6+6WAmOTCTQaIkNDzLzqqnENnqf+v//HlocekrTi5BJOIh7H19XFnC98YVRGp/Tcc2nbvJlAf7/U6C9bRgV6elj0ta9NWkX+aHAWFdG1Y8fE8hmf8MU0d/FimjduHKOPFfH70VssFK5YQf6yZWh0Orq2bZP8R+UpREdBwbiTyyXnnktHVRURn4+w10ssFJIyNIKAKSuL9PJyWjdvJjw4qBxwRFEk5PFgzcyk8sorSS0tZc8zz6C3WPD39mJJTVWyoK4pU6TPaCKBfpysp0arZcnXv87OJ56gs7pa0arLXbSIWddd94lbC2XMmMFZ99+vmIJb0tM/sfeGs6iIeTfeyI4nniAejUrZYPk59WbzRwcSPnIGiPr9OIuLKbvwQpzFxTS8+Sbe9nbSp01jyurVkzJSP578n2z0F0Xxf4H/BViwYIGaRVP5t2Xp0nwWLMihpWUIgIICxzEZjQPkLV3K/hdfHJNN8HZ04K6oGFPayVuyhH0vvDBKzDEpjJpRWTnm9tMuvpie3bsZbG6WAjbZFiklK4uS8847pjUfDVmzZ9Pwxhs4i4pGZW/i0SgaQfhYoppHYopsbt1dUyOdzgWBaCDAtDVrWPKNb4w77Xco9vx8chctou2DD7Dn5aHV60nEYlIGY8aMI06wJpnzhS+gNRjY/OCDJEbojRntdnQWC6nFxSwYYQo/kux586i86ipq//EPEokEycvz1AsvpHjlylG3teXkcPrdd7PjiScYamlBEASMdjsLbr2V3IULJ7XWo6Vk9WratmwZnRmRszxT16yZsF/vWHFPny7tyZYtik5eUg9v0Ve/Kk336vWc8d3v0rltG62bNysBTu6iRePuu8npZPn3vkfjunU0vPkmTRs2oNHpyJg1SzoYCAK+jg68ej2hgQFFYsKamcnZ//VfuIqLseflSYLIL75IoK9PGlBBMqE3ORz4urtxFRdPWK41ORws/trXCPT3ExoclBwgjkKX7mgRBOG4yUwULF9OxqxZ9O7ZQzQQwFFYSGpJCdv+8AfatmzBUVCgBJoRuZ+ucPlyBEEgY8YMxa3kVEE4UZVBQRCWAveKoniu/P/3AIii+NMRt3ldvs1mQRB0QBfgPlz5csGCBWJVVdXxXbyKyr8J3bt3UyULkyZxTZnC4q99bdwsQ/+BA3z4m99IpUq5UT29vJyFt946ykIoSWhwkMYNG2jbsgVBoyH/tNMoWrHimDJfR0siHmfLQw/RXVODLTtbMZT29/Qw44ormDair+e4PH8sRs+ePXRs3YoI5MyfL/keHkXTcDwSYd8//8nBdetIxOMIGg2FZ5zB9Msvn1RgN5KePXvY9POfE49EJMeEaBSdycSi228/4oUoNDhI/4EDku5Xaelhy7lJT9JELIY1I2PC8uwnRevmzex4/HEpMyItgILTT2f29dcflwbtRCxGy6ZNkpm27INZdsEFkw6Sj0RwYIAdjz9Od02NJOCbSFBw+ukUrlhB186d+Do6cJWUUHzWWWMmJv29vdQ89RRNGzYogyaxUAizy8Vpd901ZkL600Q0EKDq97+ne+dO5Wc6s5kFt9xC1qxZJ3FlIAjCNlEUF4z7uxMYlOmAA8AqoB3YClwjiuKeEbe5DZgpiuLNgiBcBVwqiuJhlfDUoExF5eiIBoP07t1L1O8nJSuLVNk7byKSTfxRv18Re/ykSxyfFLFwmPq1a2l4801igQAWt5tpa9aQf9ppp+yaxyMWChEeHsZgsx11MDaSaDBIV3W1ovCfNXfuCQmQjzfRQEBRwncWFU1aE+1Uxt/b+5GP5lH24w21ttL+4YeEBwdJmzaN7HnzToqZ9qmGKIoMt7bi7ehAZzKRXl7+iWdTj4VTIiiTF3IB8N9IkhiPiaJ4vyAIPwKqRFF8SRAEE/BnYC7gAa5KDgZMhBqUqaioHIoo6xBpZekBFRUVlVOFwwVlJ7SnTBTFV4FXD/nZ90f8OwRMXtlPRUVFZRwEjeakGQqrqKioHCsnTKdMRUVFRUVFRUVlYtSgTEVFRUVFRUXlFEANylRUVFRUVFRUTgHUoExFRUVFRUVF5RRADcpUVFRUVFRUVE4B1KBMRUVFRUVFReUUQA3KVFRUVFRUVFROAdSgTEVFRUVFRUXlFEANylRUVFRUVFRUTgHUoExFRUVFRUVF5RRADcpUVFRUVFRUVE4B1KBMRUVFRUVFReUUQA3KVFRUVFRUVFROAdSgTEVFRUVFRUXlFEAQRfFkr+FjIQhCL9B8nJ8mHeg7zs+hcvSo+3Lqoe7JqYm6L6ce6p6cmpyIfSkURdE93i/+zwdlJwJBEKpEUVxwstehMhp1X0491D05NVH35dRD3ZNTk5O9L2r5UkVFRUVFRUXlFEANylRUVFRUVFRUTgHUoGxy/O/JXoDKuKj7cuqh7smpibovpx7qnpyanNR9UXvKVFRUVFRUVFROAdRMmYqKioqKiorKKYAalI1AEITzBEHYLwhCvSAId4/ze6MgCM/Iv/9AEISiE7/KTxeT2JNvCoKwVxCEXYIgrBMEofBkrPPTxpH2ZcTtLhMEQRQEQZ0yO85MZk8EQbhC/rzsEQThqRO9xk8jk/gOKxAEYYMgCNXy99gFJ2OdnyYEQXhMEIQeQRB2T/B7QRCE/5H3bJcgCPNO1NrUoExGEAQt8FvgfGA6cLUgCNMPudmNwIAoiqXAQ8DPTuwqP11Mck+qgQWiKM4CngN+fmJX+eljkvuCIAg24A7ggxO7wk8fk9kTQRDKgHuA00RRnAF8/YQv9FPGJD8r3wOeFUVxLnAV8PCJXeWnkj8B5x3m9+cDZfJ/XwZ+dwLWBKhB2UgWAfWiKB4URTEC/A24+JDbXAw8If/7OWCVIAjCCVzjp40j7okoihtEUQzI/7sFyDvBa/w0MpnPCsCPkQ4uoRO5uE8pk9mTm4DfiqI4ACCKYs8JXuOnkcnsiwjY5X87gI4TuL5PJaIovgt4DnOTi4EnRYktgFMQhOwTsTY1KPuIXKB1xP+3yT8b9zaiKMaAISDthKzu08lk9mQkNwJrj+uKVGAS+yKn+/NFUfzXiVzYp5jJfFamAlMFQdgkCMIWQRAOlylQ+WSYzL7cC1wrCEIb8Cpw+4lZmsphONprzyeG7kQ8iYrK8UYQhGuBBcCKk72WTzuCIGiAXwI3nOSlqIxGh1SOWYmUUX5XEISZoigOntRVqVwN/EkUxQcFQVgK/FkQhEpRFBMne2EqJx41U/YR7UD+iP/Pk3827m0EQdAhpZr7T8jqPp1MZk8QBOFs4D+ANaIohk/Q2j7NHGlfbEAl8LYgCE3AEuAltdn/uDKZz0ob8JIoilFRFBuBA0hBmsrxYzL7ciPwLIAoipsBE5L/osrJY1LXnuOBGpR9xFagTBCEYkEQDEgNly8dcpuXgOvlf18OrBdVobfjyRH3RBCEucDvkQIytUfmxHDYfRFFcUgUxXRRFItEUSxC6vX7/+3dWahVVRzH8e+vua71kpBZoRAEmUIDNNA8EFRkUUkZDTYTFWlqA2SDZVIZlERlpZJRYYWNVj5UNj9kNEiJeC2yaKAs1NIs7d/DWod2u3Pu2Tdv9+7y94HzsNdZe+3//26Q/1lrb9fwiFjQN+FuFKr8+/U0aZYMSf1Jy5mf9maQG6Eq92UZcCSApN1JRdl3vRqllT0LnJXfwtwfWBERX/fGhb18mUXEOkmXAvOATYEZEfGxpInAgoh4FphOmlruJD0keFrfRfz/V/Ge3A70A57I71wsi4jhfRb0RqDifbFeVPGezAOOlvQJsB4YHxGe6f8XVbwvY4EHJI0hPfQ/yj/2/12SHiP9QOmfn+W7HtgcICLuIz3bdyzQCawGzum12HzvzczMzPqely/NzMzMasBFmZmZmVkNuCgzMzMzqwEXZWZmZmY14KLMzMzMrAZclJmZmZnVgIsyMzMzsxpwUWZmZmZWAy7KzKyyvO3IWElLJK2V9KWkyZIGS4ri/paSbspt4wptIWmdpIGFtn6SVubv+ue2Ufn4xdL1z87tz5diulLSUklrJC3MG9QXr9nqM6rQ51JJcyWtlvR5aYy2+Uk6rJhDbrtb0vzC8XxJd5dyGpf3CG0cbyJpgqQv8t94oaQTSucMlPSIpOU53g8kHV74uzX9FP62P3V5o82sT7goM7PuuAWYAEwG9gBGAF+UO0naCRgNrGkyxjekTZgbTgdWNun3C7CPpEGFtov4+8bAN+fxLgGG5NimSTouf79j4QNwcuF4dmGcG0l73u0J3A/MUotN1Nvkt6EuB8YDVwHDgKeAOZL2zNfuAF4DBgMn5j4T87mz+TO30aRNyMv5m1lNee9LM6tEUj9gDDA6Imbk5k7gHUmDS90nkQqEo5oMNR04X9KkiPidVGg9SNp/rmg98DBwAXCtpGHAAOBlYPscUwdwBXB0RLyRz/tM0r6kIm1uRHxTyAHgh2JbwZyImNaIX9LhpMLmjCZ9m+XXKNC2btK/O8YBUyLi0Xx8naRDcvsZpCJ2AHBARHyf+ywtxyFpBbC+Ra5mVkOeKTOzqoYAW5KKopbyjM5JpBm1ZhaSZruOyTNRHcD8Fn3vB86VtBmpeHuAtGlzMaatgJck/dT4ABcDu1ZJquCdJsdDyp26yG8J8CswUrn6a+HCUqyTCmNvBwwE3iqd82Yhlr2AjwoF2T/Rka+/Ki/7TpW01QaMZ2Y9wDNlZtbTppBmer7uojaZRiqyviUVXk1FxGJJi4GRwKnAUODWQpfGD8vjgWWl03/rfuiVNM0vIn6QNAa4E7hR0npgC+Dt0vmzSUulDeeR8msn2nepbDVpmVbAbsBMYAWtC2kz6wUuysysqkXAWuBI0qxQM8cBuwPD24z1OKm42YRUFAzrou80UuE2LyK+LRV6n+SYBkXEK+0SaGN/YEbpeFGpT5f5RcQ9kmaSZrtEWpLdpdRtRUR0Ng4kLS+cv1LSV8CB/HVG8iBSrgDvA2dK6r8Bs2VRiGGJpLmkGTgz60MuysyskohYJekuYLKktcDrpGe79gEab0mOBy6LiNVtxloj6QJgm4hY3vVqH3OAQcALLWKaAkzJS4avA/1IBdXvEdFyFq6JkyS9S1pKPYVUfO5X6tM2v4hYQ37GKz/XVS7K2rkdmChpCfAe6Tmyg4G98/ePAlcDz0i6mrQUPBRYFRGvVr1IXq5szJQdATzWzTjNrIe5KDOz7rgG+JG0zLUzaflxVuH7TuChKgNFxNMV+/3KX5csyybkOMYB95Le5PwAuK3K+AU3kN7MnAp8B5wTEe+W+lTObwNMBbYlxb8DsBg4OSI+BIiInyUdCtwBPEdaIl1Megmjqg7SCwFByvV54KaeSsDM/hlF9ORjCmZm/z35//AaERFP9nUsZrbx8tuXZmZmZjXgoszMzMysBrx8aWZmZlYDnikzMzMzqwEXZWZmZmY14KLMzMzMrAZclJmZmZnVgIsyMzMzsxpwUWZmZmZWA38AFgUbqGwET7YAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Голос"
      ],
      "metadata": {
        "id": "rkCwt2Qrs-bJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv(\"voice.csv\")\n",
        "data['label'] = label_encoder.fit_transform(data['label'])\n",
        "data = pd.DataFrame(MinMaxScaler().fit_transform(data))\n",
        "data = data.sample(frac=1)\n",
        "data"
      ],
      "metadata": {
        "id": "z63Q95BNx04A",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "123c007a-fbba-4dbe-e02a-0f7883e44b20"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            0         1         2         3         4         5         6   \\\n",
              "2051  0.660364  0.297893  0.676673  0.677498  0.710152  0.102851  0.079609   \n",
              "1044  0.747046  0.439345  0.828905  0.569704  0.912316  0.411017  0.039718   \n",
              "1285  0.667503  0.502074  0.684395  0.523221  0.870822  0.419102  0.015834   \n",
              "2906  0.905879  0.193256  0.897889  0.893716  0.906623  0.068600  0.067251   \n",
              "46    0.563054  0.493792  0.488822  0.448597  0.787388  0.415768  0.083202   \n",
              "...        ...       ...       ...       ...       ...       ...       ...   \n",
              "2088  0.726840  0.146906  0.748908  0.715069  0.739543  0.092293  0.056249   \n",
              "2702  0.726299  0.226267  0.689206  0.681790  0.759392  0.146147  0.060920   \n",
              "331   0.560281  0.446059  0.663454  0.411420  0.719671  0.388742  0.068893   \n",
              "1136  0.677415  0.463854  0.661570  0.544769  0.862408  0.388536  0.027166   \n",
              "1107  0.706728  0.389725  0.716230  0.558434  0.851516  0.363764  0.037196   \n",
              "\n",
              "            7         8         9   ...        11        12        13  \\\n",
              "2051  0.008263  0.623511  0.464446  ...  0.660364  0.576899  0.070254   \n",
              "1044  0.002449  0.734211  0.477129  ...  0.747046  0.486549  0.192118   \n",
              "1285  0.000470  0.897188  0.742612  ...  0.667503  0.405114  0.191643   \n",
              "2906  0.005804  0.403480  0.216055  ...  0.905879  0.729381  0.192356   \n",
              "46    0.011830  0.796251  0.658806  ...  0.563054  0.136729  0.032367   \n",
              "...        ...       ...       ...  ...       ...       ...       ...   \n",
              "2088  0.003366  0.431027  0.227391  ...  0.726840  0.655469  0.072594   \n",
              "2702  0.004875  0.541476  0.225966  ...  0.726299  0.515357  0.215891   \n",
              "331   0.005676  0.690060  0.546806  ...  0.560281  0.265416  0.207017   \n",
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              "\n",
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            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = data.iloc[:, :-1].values\n",
        "y = data.iloc[:, -1].values.reshape((-1, 1))\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
        "\n",
        "y_train = y_train.reshape(-1, 1)\n",
        "y_test = y_test.reshape(-1, 1)"
      ],
      "metadata": {
        "id": "uaTahmnNx6bm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "neuron = NeuronReLU(w=np.random.rand(X_train.shape[1], 1), b=np.random.rand(1))\n",
        "losses = neuron.fit(X_train, y_train, num_epochs=10000)"
      ],
      "metadata": {
        "id": "4Eb3kVdVx9R5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "accuracies = []\n",
        "thresholds = []\n",
        "for threshold in np.linspace(0.2, 0.9, 10):\n",
        "    accuracies.append(accuracy_score(y_test, neuron.predict(X_test, threshold)))\n",
        "    thresholds.append(threshold)\n",
        "print(max(accuracies))\n",
        "\n",
        "best_threshold_elu = round(thresholds[accuracies.index(max(accuracies))], 2)\n",
        "print(\"best threshold for ELU:\", best_threshold_elu)"
      ],
      "metadata": {
        "id": "Fn_ljOEVx_d1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8d54126c-7245-438b-8ba6-65efb947bf7f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9263932702418507\n",
            "best threshold for ELU: 0.59\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = np.where(neuron.forward_pass(X_test) >= best_threshold, 1, 0)\n",
        "accuracy_score(y_pred, y_test)"
      ],
      "metadata": {
        "id": "otrXgoRJyBgF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cc9dd0c7-5c85-4e1f-e377-77a849e7bfbc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.92534174553102"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 8))\n",
        "plt.plot(losses)\n",
        "plt.title('Функция потерь', fontsize=15)\n",
        "plt.xlabel('номер итерации', fontsize=14)\n",
        "plt.ylabel('$Loss(\\hat{y}, y)$', fontsize=14)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "qWSOHpM-yF_q",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 519
        },
        "outputId": "00207e5d-91c9-4cbd-b45a-2a3975d4b1b2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MYiGj1wnHur4"
      },
      "source": [
        "**Плюсы ELU:**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xOGAh06uHur5"
      },
      "source": [
        "* дифференцируемая (с доопределнием в нуле)\n",
        "* нет проблемы затухающих градиентов, как в сигмоиде\n",
        "* нет проблемы \"мёртвых нейронов\", как в ReLU\n",
        "* более устойчива к \"шуму\" (см. лекции Стэнфорда)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r4doD89fHur6"
      },
      "source": [
        "**Возможные минусы ELU:**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AO4bvkKGHur6"
      },
      "source": [
        "* не очень хорошо центрирована около 0 (может мешать скорости сходимсти)\n",
        "* вычислительно дольше, чем ReLU и LeakyReLU"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Сравнение нейронов"
      ],
      "metadata": {
        "id": "aIdeghXU0LSh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Сравнение происходит на датасете \"Голос\""
      ],
      "metadata": {
        "id": "jS-uMuVx9lIz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = data.iloc[:, :-1].values\n",
        "y = data.iloc[:, -1].values.reshape((-1, 1))\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)"
      ],
      "metadata": {
        "id": "_USpY49hFXuz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "neuron_relu = NeuronReLU(w=np.random.rand(X.shape[1], 1), b=np.random.rand(1))\n",
        "neuron_relu.fit(X_train, y_train, num_epochs=10000)\n",
        "\n",
        "neuron_elu = NeuronELU(w=np.random.rand(X_train.shape[1], 1), b=np.random.rand(1))\n",
        "neuron_elu.fit(X_train, y_train, num_epochs=10000)\n",
        "\n",
        "df_comp = pd.DataFrame(index=['ReLU', 'ELU'], columns=['precision', 'recall', 'f1-score' , 'accuracy'], data=0)\n",
        "df_comp.loc['ReLU'] = pd.Series({\n",
        "    'precision': precision_score(y_test, neuron_relu.predict(X_test, best_threshold_relu), average='weighted'),\n",
        "    'recall': recall_score(y_test, neuron_relu.predict(X_test, best_threshold_relu), average='weighted'),\n",
        "    'f1-score': f1_score(y_test, neuron_relu.predict(X_test, best_threshold_relu), average='weighted'),\n",
        "    'accuracy': accuracy_score(y_test, neuron_relu.predict(X_test, best_threshold_relu))\n",
        "})\n",
        "\n",
        "df_comp.loc['ELU'] = pd.Series({\n",
        "    'precision': precision_score(y_test, neuron_elu.predict(X_test, best_threshold_elu), average='weighted'),\n",
        "    'recall': recall_score(y_test, neuron_elu.predict(X_test, best_threshold_elu), average='weighted'),\n",
        "    'f1-score': f1_score(y_test, neuron_elu.predict(X_test, best_threshold_elu), average='weighted'),\n",
        "    'accuracy': accuracy_score(y_test, neuron_elu.predict(X_test, best_threshold_elu))\n",
        "})\n",
        "\n",
        "df_comp"
      ],
      "metadata": {
        "id": "Xnl5duUR2vnC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "outputId": "981f5b17-3cd6-4780-aba8-706d5aa92605"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      precision    recall  f1-score  accuracy\n",
              "ReLU   0.926068  0.925342  0.925251  0.925342\n",
              "ELU    0.925594  0.925342  0.925294  0.925342"
            ],
            "text/html": [
              "\n",
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "    .dataframe tbody tr th {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>precision</th>\n",
              "      <th>recall</th>\n",
              "      <th>f1-score</th>\n",
              "      <th>accuracy</th>\n",
              "    </tr>\n",
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              "  <tbody>\n",
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              "      <th>ReLU</th>\n",
              "      <td>0.926068</td>\n",
              "      <td>0.925342</td>\n",
              "      <td>0.925251</td>\n",
              "      <td>0.925342</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ELU</th>\n",
              "      <td>0.925594</td>\n",
              "      <td>0.925342</td>\n",
              "      <td>0.925294</td>\n",
              "      <td>0.925342</td>\n",
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              "      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-ef1383d9-4390-454a-b858-9028b89ded7c 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-ef1383d9-4390-454a-b858-9028b89ded7c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    }
  ]
}