theil_sen.ipynb

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        "<a href=\"https://colab.research.google.com/github/Brent-Morrison/Misc_scripts/blob/master/theil_sen.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "# Abalone and Outliers  \n",
        "\n",
        "That title deserves an explanation. \n",
        "\n",
        "This note will look at the Theil Sen estimator for **robust regression**.  I'm going to use the UCI Machine Learning abalone dataset to compare this technique with Ordinary Least Squares.\n",
        "\n",
        "Robust techniques are designed to mitigate the impact of outliers in regression models.  My introduction to Theil Sen was through this [paper](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2802478) which looks at stock valuations.\n",
        "\n",
        "The Theil Sen was first applied on univariate data.  It estimates the regression slope by taking the median of individual slopes, those being fitted to each pair of data points.  If there are n data points, there will be n*(n-1)/2 individual slopes to estimate.  As stated, the final slope is the median of these. \n",
        "\n",
        "That concept is easy enough to wrap your head around.  [This](https://blogs.sas.com/content/iml/2019/05/28/theil-sen-robust-regression.html) blog on the SAS website has some nice visuals to help.\n",
        "\n",
        "Theil Sen has been extended to apply to multivariate data by [this paper](http://home.olemiss.edu/~xdang/papers/MTSE.pdf).  Scikit Learn has an [implementation](https://scikit-learn.org/stable/modules/linear_model.html#theil-sen-estimator-generalized-median-based-estimator) of this, using the spatial median which is a generalization of the median to multiple dimensions.  \n",
        "  \n",
        "The concept of a spatial median is a lttle tougher grasp.  Running some data through both the Theil Sen and OLS estimators may help with our intuition here.\n",
        "\n",
        "Let's get to it.  \n",
        "<br>\n",
        "<br>\n",
        "## Libraries and data\n",
        "\n",
        "Data is from the UCI Machine Learning repository and contains the age of abalone and their physical measurements.  Hence the title."
      ]
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn import linear_model\n",
        "from sklearn.metrics import mean_squared_error, r2_score\n",
        "import statsmodels.api as sm"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
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          "name": "stderr"
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        "id": "Rbs9WMQmyv8s"
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      "source": [
        "cols = ['sex','length','diam','height','whole','shucked','viscera','shell','rings']"
      ],
      "execution_count": 2,
      "outputs": []
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        "id": "tw1VYdAqyEPF"
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      "source": [
        "df = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/abalone/abalone.data', names=cols)"
      ],
      "execution_count": 3,
      "outputs": []
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sex</th>\n",
              "      <th>length</th>\n",
              "      <th>diam</th>\n",
              "      <th>height</th>\n",
              "      <th>whole</th>\n",
              "      <th>shucked</th>\n",
              "      <th>viscera</th>\n",
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              "      <th>0</th>\n",
              "      <td>M</td>\n",
              "      <td>0.455</td>\n",
              "      <td>0.365</td>\n",
              "      <td>0.095</td>\n",
              "      <td>0.5140</td>\n",
              "      <td>0.2245</td>\n",
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              "      <td>0.135</td>\n",
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              "      <td>0.2565</td>\n",
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              "      <td>0.2100</td>\n",
              "      <td>9</td>\n",
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              "      <th>3</th>\n",
              "      <td>M</td>\n",
              "      <td>0.440</td>\n",
              "      <td>0.365</td>\n",
              "      <td>0.125</td>\n",
              "      <td>0.5160</td>\n",
              "      <td>0.2155</td>\n",
              "      <td>0.1140</td>\n",
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              "      <td>10</td>\n",
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              "      <th>4</th>\n",
              "      <td>I</td>\n",
              "      <td>0.330</td>\n",
              "      <td>0.255</td>\n",
              "      <td>0.080</td>\n",
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              "      <td>0.0895</td>\n",
              "      <td>0.0395</td>\n",
              "      <td>0.0550</td>\n",
              "      <td>7</td>\n",
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              "      <th>4172</th>\n",
              "      <td>F</td>\n",
              "      <td>0.565</td>\n",
              "      <td>0.450</td>\n",
              "      <td>0.165</td>\n",
              "      <td>0.8870</td>\n",
              "      <td>0.3700</td>\n",
              "      <td>0.2390</td>\n",
              "      <td>0.2490</td>\n",
              "      <td>11</td>\n",
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              "    <tr>\n",
              "      <th>4173</th>\n",
              "      <td>M</td>\n",
              "      <td>0.590</td>\n",
              "      <td>0.440</td>\n",
              "      <td>0.135</td>\n",
              "      <td>0.9660</td>\n",
              "      <td>0.4390</td>\n",
              "      <td>0.2145</td>\n",
              "      <td>0.2605</td>\n",
              "      <td>10</td>\n",
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              "    <tr>\n",
              "      <th>4174</th>\n",
              "      <td>M</td>\n",
              "      <td>0.600</td>\n",
              "      <td>0.475</td>\n",
              "      <td>0.205</td>\n",
              "      <td>1.1760</td>\n",
              "      <td>0.5255</td>\n",
              "      <td>0.2875</td>\n",
              "      <td>0.3080</td>\n",
              "      <td>9</td>\n",
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              "    <tr>\n",
              "      <th>4175</th>\n",
              "      <td>F</td>\n",
              "      <td>0.625</td>\n",
              "      <td>0.485</td>\n",
              "      <td>0.150</td>\n",
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              "      <th>4176</th>\n",
              "      <td>M</td>\n",
              "      <td>0.710</td>\n",
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              "      <td>1.9485</td>\n",
              "      <td>0.9455</td>\n",
              "      <td>0.3765</td>\n",
              "      <td>0.4950</td>\n",
              "      <td>12</td>\n",
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              "<p>4177 rows × 9 columns</p>\n",
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              "     sex  length   diam  height   whole  shucked  viscera   shell  rings\n",
              "0      M   0.455  0.365   0.095  0.5140   0.2245   0.1010  0.1500     15\n",
              "1      M   0.350  0.265   0.090  0.2255   0.0995   0.0485  0.0700      7\n",
              "2      F   0.530  0.420   0.135  0.6770   0.2565   0.1415  0.2100      9\n",
              "3      M   0.440  0.365   0.125  0.5160   0.2155   0.1140  0.1550     10\n",
              "4      I   0.330  0.255   0.080  0.2050   0.0895   0.0395  0.0550      7\n",
              "...   ..     ...    ...     ...     ...      ...      ...     ...    ...\n",
              "4172   F   0.565  0.450   0.165  0.8870   0.3700   0.2390  0.2490     11\n",
              "4173   M   0.590  0.440   0.135  0.9660   0.4390   0.2145  0.2605     10\n",
              "4174   M   0.600  0.475   0.205  1.1760   0.5255   0.2875  0.3080      9\n",
              "4175   F   0.625  0.485   0.150  1.0945   0.5310   0.2610  0.2960     10\n",
              "4176   M   0.710  0.555   0.195  1.9485   0.9455   0.3765  0.4950     12\n",
              "\n",
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      "source": [
        "df.dtypes"
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            "text/plain": [
              "sex         object\n",
              "length     float64\n",
              "diam       float64\n",
              "height     float64\n",
              "whole      float64\n",
              "shucked    float64\n",
              "viscera    float64\n",
              "shell      float64\n",
              "rings        int64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 5
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hMMPJkEvyi8E"
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      "source": [
        "Drop the categorical `sex` attribute.  We are investigating a regression problem here.  Yes, we could have encoded as dummy variable or fit a mixed effects model.  I'm going to keep it simple."
      ]
    },
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      "source": [
        "df.drop(columns='sex', inplace=True)\n",
        "df"
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              "      <td>0.0895</td>\n",
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              "      <td>0.0550</td>\n",
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              "      <td>0.565</td>\n",
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              "      <td>0.3700</td>\n",
              "      <td>0.2390</td>\n",
              "      <td>0.2490</td>\n",
              "      <td>11</td>\n",
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              "    <tr>\n",
              "      <th>4173</th>\n",
              "      <td>0.590</td>\n",
              "      <td>0.440</td>\n",
              "      <td>0.135</td>\n",
              "      <td>0.9660</td>\n",
              "      <td>0.4390</td>\n",
              "      <td>0.2145</td>\n",
              "      <td>0.2605</td>\n",
              "      <td>10</td>\n",
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              "    <tr>\n",
              "      <th>4174</th>\n",
              "      <td>0.600</td>\n",
              "      <td>0.475</td>\n",
              "      <td>0.205</td>\n",
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              "      <td>0.5255</td>\n",
              "      <td>0.2875</td>\n",
              "      <td>0.3080</td>\n",
              "      <td>9</td>\n",
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              "    <tr>\n",
              "      <th>4175</th>\n",
              "      <td>0.625</td>\n",
              "      <td>0.485</td>\n",
              "      <td>0.150</td>\n",
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              "      <td>12</td>\n",
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              "      length   diam  height   whole  shucked  viscera   shell  rings\n",
              "0      0.455  0.365   0.095  0.5140   0.2245   0.1010  0.1500     15\n",
              "1      0.350  0.265   0.090  0.2255   0.0995   0.0485  0.0700      7\n",
              "2      0.530  0.420   0.135  0.6770   0.2565   0.1415  0.2100      9\n",
              "3      0.440  0.365   0.125  0.5160   0.2155   0.1140  0.1550     10\n",
              "4      0.330  0.255   0.080  0.2050   0.0895   0.0395  0.0550      7\n",
              "...      ...    ...     ...     ...      ...      ...     ...    ...\n",
              "4172   0.565  0.450   0.165  0.8870   0.3700   0.2390  0.2490     11\n",
              "4173   0.590  0.440   0.135  0.9660   0.4390   0.2145  0.2605     10\n",
              "4174   0.600  0.475   0.205  1.1760   0.5255   0.2875  0.3080      9\n",
              "4175   0.625  0.485   0.150  1.0945   0.5310   0.2610  0.2960     10\n",
              "4176   0.710  0.555   0.195  1.9485   0.9455   0.3765  0.4950     12\n",
              "\n",
              "[4177 rows x 8 columns]"
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      "source": [
        "<br>\n",
        "<br>\n",
        "\n",
        "## Independent and dependent variables.  \n",
        "The `rings` variable is what we are trying to predict.  This is a proxy for the age of the abalone.  \n",
        "  \n",
        "Don't get too caught up on the data and regression problem at this stage. I just need a real world dataset to investigate the differences between robust and OLS methodologies.  "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0VziWdi6T2oL"
      },
      "source": [
        "x = df.iloc[:, 0:7]\n",
        "y = df.iloc[:, 7:8]"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V3Re6jzYzKmA"
      },
      "source": [
        "<br>\n",
        "<br>\n",
        "\n",
        "## Let's visualise our data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M3oVsioaCG1q"
      },
      "source": [
        "Histograms of each feature."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 674
        },
        "id": "P0ZvlW6rN1eq",
        "outputId": "1fe12f22-ebbd-4444-d2f2-fbb2770da329"
      },
      "source": [
        "sns.FacetGrid(\n",
        "    data=df.melt(var_name='Attribute', value_name='value'),\n",
        "    col='Attribute',\n",
        "    col_wrap=3,\n",
        "    sharex=False,\n",
        "    sharey=False) \\\n",
        ".map(sns.histplot, 'value')"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7fa14ee3d310>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 648x648 with 8 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TA8BH5veCMbX"
      },
      "source": [
        "And scatter plots of each feature and the dependent variable."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 674
        },
        "id": "ePSIafrJlDJe",
        "outputId": "dcad8ecd-5f2f-43b8-ea8b-c7aeefde4ca1"
      },
      "source": [
        "sns.FacetGrid(\n",
        "    data=df.melt(var_name='Attribute', value_name='value', value_vars=['length', 'diam', 'height', 'whole', 'shucked', 'viscera', 'shell'], ignore_index=False).join(y),\n",
        "    col='Attribute',\n",
        "    col_wrap=3,\n",
        "    sharex=False,\n",
        "    sharey=False) \\\n",
        ".map(sns.scatterplot, 'value', 'rings')"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7fa144730510>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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GMgdQMQHITy42Sxo3qwMU3a6U/Vq15Wh/FLfDxkAsTpnbhYYsScihq+PMcgBHSyU3kvelmJpomuRAV5hDPWH8LgfTyt3MqvRxuDeS7T9zqnKXh9Of8vvinCofR/oitAdjhONJ5lb7mV87dFw2NZSzbUNzQQ5gU0PFqJ4LFTfF0c9RdziG02YjFEsSiaeo9jv58hXL+Nf/fj17fbaub+buZ/YB6cnf7VcuZ06V7yS/A8VEQ00ApzClWKyZ1fnWx04jnpSW243Euq2YgOMjq+aUJOQwqiDTKmA7iZTG5c3TR+WGM0Ut6RQjwKyvfP6DS/C5HQUTrZEk8+fvf26Nl8984BSO9A5y51P7hh2XG1bMpLE+wNH+KNMrPDQ1VIyaAETFzdDo5+j2x9/kU+fNJ5JI5VzHzWuW8p2/PINXDveS0uDBlw5xy5om3mwLMpjQuOOJt3DabeqcKoaFEoFMYaySi43JxGZ1Nq5exD3PtVhuV8p+S22LLuAYB0KOEb2vDFM6iX0qMpy4GUkyf/7+b7h4EXYbox6Xo8EoHHfSx49+jq47f4Hldbz+wgXc9dT+nDIlBFGUiBKBKHIpxVLIrI4mKbrdSKyKhhJwjAchh7JgUpTKcOJmJMn8+fsXYmzicjRQcTM0+jkqdh21vO9qlBBEcbyoCeAUphRLIbM6dkHR7UZiVTSUgGM8CDmUBZOiVIYTNyNJ5j9RcTkaqLgZGuM5srqO+b/sKiGI4nhRE8ApTCmWQmZ1ls2qKLrdSKyKzLbZeEkj23e1Zv+fbLsjZcGkKBWzvlLjd7FtQ3NO2bYNzWzf1ZpdLrU/5e//sZ2tLKoPsGl146jG5Wig4mZo9HP02M5Wqn2uguv4z//nFObX+nPKNq9tGlHfUSh0VA7gFEdXnhWzjjKrA3CgK8zhnjC+jMJxTrU/m2x+uCdMezBGJJ5kTp4a0UoRaCx32m0Eo3ECLieJVIqA20lc06gLuElp0DFwctSEpZwvEyZ9DpOiEF0FrMfI9Ao3mgYdA8fiYm61j0M9EdM4yt+XlRJf74tGFbBx/1aqY5/LTjylUeN3H5caudRzMYK40ZkS8aPb8fVG4lR4nITjKSLxFGUeOw67jUQyhc/lpDcSx+ty4HPaCEbTSuH5tX4W1gWUAERhhWnHUCrgKU4p1lFmdTRN8lb7QIGy7wNLp/GbN9sLyufX+rPbFVMELqgLMK/Gn61T5XPx8XPncudT+3Jenyw14UisthRTE5tNsLA+wML6gGW/n1vtM42joZT4ep38vjivNsC82kDR7T6wdBp7jw7wyR++lFUQ/8MljTnq5NGOKxU3xdE0aTpu+t02Xnu3P2fM+8aHVzAQS/LZn+/KeSzM3CofLpf9ZL8VxQRC/QSsGBEHu8PZwQrSCcg3P/gau9v6TcsPdoeLbqevz6/z56fPyg5+xtdW2yoU45GRxkuxbYfq96Uec83ymdnJ33D2rxg9rK7VwGCqYMz7p4d2sr8jlFN2yy/fYNd7/Set/YqJiZoAKkaElbLPylZqOEpEYx1d/Zv/2mpbhWI8MtJ4KbbtUP2+1GOquDr5WF2rcCxZsiK4Paiul2J4qAmgYkRYKfsaKoor/kaiPLZ6bbatQjEeGWm8FNt2qH5vfUxz67fh7l8xelhdK7/HUbIieFq5ul6K4aFEIIoh0ZOT2/qjNFR4aWoox2YTBflFX7piGafNruSdrhA3/vTVbPntVy5nRqUnm2yen+vypSuWcfqcyhwRydNvtbOvPURtwE1twMW+9gG272rjg8sasj+JzK3x8tWrlpNMSQaiSWoDLpKaRl3AW2CBlc8JtqaaEknsU51i4qbDPWFeOdzH5w12XndcvZJLT6nn5Xd76Q7FCceS9EbiLJoW4JJTphXkAN7++JusWT4Tuw3OnFvNuQtqsNlE9pgNFZ4cgdSsCi9/PNjDjkM9aDKtFN60ejFnzK1k93sDOTm2Myq9tHSG2PVuH1eeMRsJzKjwYLNBhddVskXkGMXQpI4fTZO82xumpSvC7tZ+FtYHsAuBzQZ1fjeheJKBwSR+t539HSFOaSgnFEuiaZK2vkF6BxOsnF3JefNq8HhUWr/CFNMYUhNARVGSSY1HdrYW2FdtWDETm01woCvMm0eDvN0+wEM7jtAbiXPH1Ss5taGMtv4oiZTki798Pcfq6gNLp3GoJ2K63eVN0wH49RtH+aeHjk0SN61upL7czWAsyby6AG6HjZ5QnJaucE6C9K1rm/j5y4f51PkLLZPYT4I11aS+gSmKCy70DzxVPhcfXjWLxdPKWDq9nLnVPp7d18G+9lBekv9KPtic2xeTSY3/eaONzz28K2f/Lofgxp++WiCQMhN2fHHNqTzw4mHe7gjxrY+dxsLaAK++mzsp3bqumbufPWZNt3lNEw+/UhhPJziGJm38GD/sGvvA5z+4BK/LTlKD7/x2Px9ZNYe7nj62XrfJ/OiZc/jRHw7ljJ9KCawwQTmBKIbP7rb+ggTxLzzyBrvb+rHZBELAZx7ayV1P7c/mFt384GtoMv2zxvU/3sGh7sHstjc/+BqHeyOW2x3sDnOwO5yd/Onb3fnUPg51R2gLxvnr+3bgstvY3RYsSJC+9bHdfPy8BUWT2EeaVK9QWFGK4KKtP8pdT+3nMw/tRAg43Bth15F+kyT/wr54uDeSnfwZ97/rSL+pQMpM2HHb9j1csLieaELjxp++SjCayE7+9Dq3PPoGa5bPzC5v2W4eTyqGRoeD3WHTPtAVjuN1Otjy2G7WLJ+Znfzp6+96eh9rls/kzqf28eenz1LnXzEi1ARQURSrJPWj/UOLOkZ7nSaPJay39UctLZMG48miSezKmkox2oxE5NEetO7D+X2xWExAoZDDStihW4cVa1u+vZhZPKkYGh2s+oAmyQpAil3L/Guqzr9iOKgJoKIoVgnj0yuGFnWM9jqbIGsJ11DhtbRM8rocRZPYlTWVYrQZichjWrlnSPu2ofZv/LWvFGGHnvFTTAySby9mFk8qhkYHqz5gF+QIQKyuU/41VedfMRzGLAdQCDEb+BEwDZDAPVLKO4UQ1cADwDzgIHC1lLK32L5UDtPJw5gDqOcwLawL0NxQzvzMQ12f2tvO6639aDI9cDXPrOD9i+p482iQw72DeJ02HDZB32CSmVUe3A4bXQNxnHYbg8kU7/VGiCc15tUGmFbupiccRwjB5kffyOYi3ba+mRmVLuLJ9ENlkZLBhMYBixzAT75vIU0z0nmIpSbkj6ccQBU/E4tScgD13Lzb1i/DaRfUl3loH4hwoGuQ27bvycZX04wKppe5iaU07DZBVyhGmduBx2nn3d4INmGjrS9C86xyhLBxuDtCucdBIpVif2cETUK5205NmYd/M/Txf7l8CQ2VXvoicZZMC6BJ2N8Z5ou/PJYnWCwHcOn0Mo70RXDb7cRTKbrDiYKcxPGUAzhaMXQicgBbOsPc8cTb2XP5rY+dRrnHSVcojt9lpz+ayHnw861rmwgOxpld4+fbz+zn7Y6QygFUFOPEikCEEA1Ag5TyFSFEGfAysAH4BNAjpfyKEOJfgCop5eeK7UvdwE4uyaTG3vYge48OFLgFXNJYx2NvtOXcRO786EqCg8mcsk2rG/n167kqXr3c77LjcdoIRlN8/Tdv5SStl3scHOqO8Oxb7Xz83Hm09kVzVcBXLieekgxEE1T7XfRH4thtNlJSsun+0hPyh1INHycjmQCq+JlgWNmd6eU94RitfdGcSdPW9c08+NIhVi+dTrnXyT3PvcNHVs3hgR2H+dhZc/nmk2/nxErA7eC/nm/huvfNx+ty5E3emrj72f3ZydvXrlrOkd5BwvEU1T4ntQE3n314F4vrA1xz9ly2PLY750NdXcDFD19oYc2K2cQSKeZU+3A5BOUeFwe6Q9y2fU+OGME4mR2PKuDRiqGxjp9kUuPJve3saQvisNlYObuctv4Ymx/dnb22N1+2mIV1fvoHk7QHo/hddr792xZ6I3FuW9/M6XMqmV+rrOAUlpxcFbAQ4pfAtzJ/F0kp2zIB+qyU8pRi26ob2MmnpTPEh+76XU4uisdp48efOotrv/9iTvm3rjmNz/x8Z0Hdr161gs+alF9/4QIW15eZbnP9hQtIZYrsNrjnuRbTOnc9tX/IsgeuP4eP3PPHgu1/tfGCsbaoOu5RWcXPxMcqhq47fwEA9z7fwnXnL8j5bxUPVrFw3fkLuPuZ/QXLd11zWjb2jK+N2379qhXc+LNXc8p+tfECAD501+8s2zQR4gdGHkNjHT/GfnHDxYtYOt16LARIacf6yt3P7M+Ow2fOrxmzNiomPCdPBSyEmAecBvwJmCalbMusOkr663mzba4XQuwQQuzo7Ow8Ec1UFMEq6ftoMFZQbvX0ej2ZPL9ckxAusk6I9J9VwrzZU/HNykpxXRiPqPiZHFjFkN6/8xP7i8WDVSzkCzj05UFDTA5axGc4niwoM4qyJrJjyHBj6ETGT77zUTH3D6MQzij+aA/GxrSNisnJmE8AhRAB4GHgH6WUQeM6mf760fQrSCnlPVLKVVLKVXV1dWPdTMUQWCV9Ty93F5RbPb3e57J+qr2/yDr9S2qrhHmzp+KblZXiujDeUPEzebCKIWMSv9l/Y109HqxiIV/AoS/73Mfiy/jaWNfvchSU5YuyJlr8wMhi6ETGT36/KOb+YRTCGfvNtHL3mLZRMTkZ0wmgEMJJOvB+IqX8Raa4PfO1u56j0TGWbVCMDvNq/Nxx9cqcG8EdV69kWUMFW9c355Q77YLb8so2rW7ke8+9w6bVjQXltX4XkXiCz3zgFNN1v3jlCI/tbGVhnb9g+298eCXLZ1XklN182WIa6wMFbW1qqDB9D/Nq/CfoLA4PFT+TC7MY2rq+me27Wnn45SNsWt3IYztb2XhJ+v9Nly4uiIe6gJvtu1qp8bsKYmzLuia272rNLm/b0Jxdvu+Flmycfu+5d9i8tim3HeuaKfPYTWNDb7fetokSPzAxYsjYLx5++QiRWIIt65pMx8Ian4vtu1rZtLqRX7xyJHvtljVUnMy3oJigjKUIRAD3kU62/UdD+deAbkMCbrWU8rPF9qVymMaO4dg5JZMau9/rp7V/kFq/m+mVbuIJyeGeCC6HDbsNyj1ONAmReJJEShKKJakrc2MXgta+QWZUekimJB0DMWoCLmw2cAobtozaMeB2MBBNUuZxUhtwMRBNcqRvkNlVXlKapCMUo8LrJJ5IUelzEU9pVPvcgOSdzjBVfgcCG12hGFU+J0IIgoMJ5tf4s6pls0T9MWYkIhAVPxOAYvGjr+sOx3DZbYRjKcq9DiLxFH2RBAG3Ha/TTm80jstmx++2o2kQTSZx2e2EYgn8biddoRjVPhd+t4OjwSgBtwOXXVDtdzEQS9LWF8XrslPtd6Fpkvf607Zwp9SV8VbnQNbCsbHWx+6jA7QPxJhW5sZhE7T2Rakvd1PuteNxONL2YsEomtTwOBx0h+M0VHg5pS7AWx0DtAWjVPtdBKMJ6gIemhrKcTjG/oekEW00SjE01ipgvY94HXYGYklC0SRVfidSSnrDSTwuGwGXA4dd8F4wSo3PRSiewC5sVHicBNwOOkKxE2FpqZi4mHaKsTQOfB9wLfC6EOK1TNnnga8ADwohrgMOAVePYRsURRiOnZOmyYLHWfz9RYtylGqbVjcys8pLXzjOl369N1t+2/pmBuNJ7v39gZIsjTatbmRRfYCBWIIbf/pqjmrRqJ685dHdWcXj1vXNPLnnPS5Z0sCtGXWj0RrL+N4W1AXGOml9NFDxM84pFj9A1r/Xqs/ral/9/w0XN/LknvdYObumoP7tO/bmxMjGSxp57d1uLl06g1sePaYE3ry2iZ/96RBvd4TYtqGZ/3g6/UiXuTVebry4sUCZ73Pa+dpv9vKRVXN4eu9Rrj5zLg++dIgrT5/Dlu2vFKiVdxzqz7bpth17+NzlS8fzo0fGdQwZ+0+Vz8Un3zcv51EwW9Y18Z8GVfem1Y05118fN42KYPUoGMVwUF7AUxgrVaKZqi+/7g0XL7JUKQKmqlxdvWamXMxXtRn3Y6VazFc8fvfaM/jbH7+cVdOdJMWiGZPWy3QqUyx+oLhyNl/tq/839mGr+nqMWNX96lUr2PizV7w9lMcAACAASURBVHNipFi86nGpq/St1Pr6fvPbNFFUwCNlrOInX/1r1U/MVN3G869fQ738JI1xivGN8gJW5DIcO6f8usVUilaq3OFYGhn3Y6VazFc89oYTOWq6iapYVEwMSrEzLKXPG/8b+7BVfb3Mqu5gRs1rrD+Uqjia0LJxZhVvgwaVsLFNKqZGRr76dziqbuP516+hXq6uh6JU1ARwCjMcOyeruvnLulLNrNxqGzNVm3E/VqrFfMVjtd+ZU28iKhYVE4dS7QyH6vPG/1V5fdisvl6W39/1cm9GzWsWI/l1japSPc6s4s1rUAkb26RiamTk95/hqLqN59/4pAR1PRTDQf0EPIXIT1ifU+WztKnS1x/qiXCoJ0zA7SA4mGBXxvKt3G2nyu8uyCmaWeUlGk9ypC+atYZbPK2M2oCT/sEUfZE4txjyBvVcluveN59gNEk8pbFiViU2IfE47fzg9y381bnz6R1M8nb7AA6bjQV1fhw2QZXPRW84RlKC0yZ4+JXDRXMAb79yOR88dTpH+gdzzsHh3khJIpjjYFL+hDVZKVUYlUxqvNDSzY5DPWgSHtvZyucuX8qpDWV0hmK09UX5xhNvWeYAXnvOPDRNoybgoXMgyoxM7KSkyMl3NcuT1XMArzxjDomkJBxLUuFzUh1wEktIgoMJ6svcxJIpgoNJKrxO+gYT/NNDO7P73bahmZmVHnojCWr8LhIpje5wgoHBOHa7PTfndl0zD+44lgN406WLEUhmVPpY2jD+nHRGk7GKn6wNXEeIxull9IYTOfaUW9c3c/czx3I4v7imib5InK6BGF6nne+/cKAgB/D2K5fzZ80NJ0KYo5hYnFwnkONB3cCOn2JepYd7IwU2VXNrvPzDJY051m+b1zbxnd8eS0r+6lXL6RqI4Xc58LsdeF02Kn0ODnZHczxIb1vfjJQatzx6zO90Xo0fn9OOsEG5x8HB7sGcG85Nly7m5UNdrFkxi75wnGhSy0mQ1m+Kn75wEQ+/cpirzpjDKdMDhGIJfE4n7QMxHDZBSpO80xliQV2AR149zAeaZmTfk9l7HKMk6kl5A5uMlCqMMqt3+5XLqfQ5+Nsfv0KVz8XfvX8BXeE4PpededV+EpqGQHCkL0IknmJhnZ87nng7x+/6gZcOsXb5TLoj8fSHnVo/Trug3OcgkZTsOtLPnBo/R/sinD63kpauQW755bH+/On3L8qJI6Nw4ObLFrOgzs/etgGWzazg3d4I2/7nTdO6d350JZVeF4d7IriddrbvfJe/PGcBTrvA77Lzbu9gzmRyvHlpjyZjOQF8am87bX2DQOYbYJ+bYDTBoZ4Iz+7t4IPLGphX46cnEue27XtyJu+zKr209kWoL/ew891+BhMa23e1jndhjuLkoCaAU5mhBB+lijyskpL15XuuPYPrTRLTzWymrjt/AfbMB1UzW6vvXnsGLx/qtVyvJ0HrSetfv2oFp84oBzB9r/nJ7SdQKDIpb2CTkVKFUVb1dAvC/L5VajxZCTDM4upHnzozp2yoYxhFH0NZyRnfS/55APP4GkPxwaSMn5bOEI+81lpQbrwuN1y8yPJaff2qFWjAZ3++s6AfKSGIIg8lApnKDCX4KFXkYZWUrC/3WCSmm9lM6ZZWVrZWfeFE0fXZ5PWMjVw4nsxJwM+vn29Fp4QiinxKFUZZ1dOFS/l9q9R4srJLNBN85MfaUMcwCgaGspIzvpf88zAc8ZjCmvZgNDu+Gf/yr6nVtQrHk9n+kt+P1LVQlIKaAE4RhhJ8lCrysEpK1petEtPNbKZ0SysrW6tKv7Poej0J2puxkfO7HAUJ+Mb6VlZ0VudEMfUoVRhlVc/4q9tI4smqj5qJQ6xizeoYRsHAUFZy+e9FLysWXypuhse0ck92fDP+5Z9bq2vldzmyY19+P1LXQlEKagI4RbCyctNtnPLXP7azlW0bcq2mNq/NtZq6bX1zzvKm1Y2kpFZgUbV1XTOReCKn7KZLF7N9VyuL6gM0z6wosHi76dLF/OSPB1hYH6DG7ypYv/GSRrbvamXzmiZ+9EILm9c2Uea151hX5de/74WWnPdk9h7Hu7WVYmwZKk6GqqfbEurWbkXjaU1uPG1d38x9L7QU2K1948MrSUmtwL4tJTW2rsvtz/l1jJZhm1Y3Zq3EFtYFuPmyQqs5ve62Dc0FFov51nAqbo6PeTV+ls2qoMbvyvkzXpfHdrYyr7bQAnPLuiZsAn70QkvBOKyuhaJUVA7gFEJXN1rZoOWv11XAh3vC+F0OkloKj9NBKPOcsOkVHuLJFO3BGHVlbsLxJBUeJ3abYDCeIqFJBuMp6spcIOFoMEaZx4HHKbDbbIRiKQbjKSp8DvxOBwOxJLFkCrfDzkA0QcDjoMrrJJrQSEqNZAr6InFqAulE6XKPk1gyCdgo9zio8DhoC8bwuRxoUsMmBOFYCrfDRn80TsDjwG2z0xOJ43c7iCVTzKrwMhBLZu2zmhoqcDhsw7LIK4FJmcM0WRkqTvLrtQej+Fz2rC2h3QZHg+n+1BdJ0NYfpdzrYEaFl0RK8m5PmIDbSVxL4bLb6RqIUVvmRggNpI2BeAK/00kwlqDC46QnEqc+4MblEHQOxPA4HYRjSSp9TiLxJB6ng85QjOllHhw2QV80QSyhMa3cRUojbfdW5sbvttMViuJzOYkmkpS5nYTiKcKxJLUBFw6bjff6B6kLuLHbBC6HjUg8SU84gc9lp8LjRENiEzY0qZHSoCccZ3q5mzKPk6Njp6SfdPGjaZLDPWH6IwniKY3eSJwaf/oaJDQt85OvxGm30xWKUV/mRiLpDafHxYDbQUpLkdIEPZE4PlfaHjDgcXBK/Qmx51NMLE64FZxinGGziaI2aGbr59f6eat9gL/7yYumj1YxKgf/7UNL2ZcI8ZM/HSp49IWx3pevWEZnKJaj6t28Zil+j5PW3sGc/W9Z18RDOw5zyZLpOfu76dLF/PTFQzmPxtiyromf/DFtg7Xxkkae3nuUj541ly888oZp2zdmFMDGfejKaOPjcU6AylExjhgqToz15tX42Xt0gE/+8KUCdX1+H9q0upElDQHaB+Js2b6nIEaMfV23iDOuv/myxbjtNr78+N6cPvzAjsPccNEiDicj/Osv0ur7VXMruHrV3AKbuCqfg289/Wb2cS7GR43o+7v98b186rz5SOArhmNtWt3I9HIPP/nTwWwbP3XefA52h3NiWcVKcfTHv7zXO5jzdIO5NV5uuGgRtzy629T+cvPaJuxCcutje+iNxHOs/vTr43fZaeuPcskp09T5VwyJ+pigKMrB7nD2Jvbnp8/KTqAgnWx851P7+PPTZxFNaNlJ3ZrlM7M3LrN6Bww3DH19WzDG/o5Qwf43P7qbj5+3oGB/33wyfRzjfjc/upu/vnAh0YTGXU/v4+PnLcg+4sWs7Xc9va9gHzc/+Bq72/qz71mve/ODr3GwO3xCz71i/GOMD6BoH7rzqX3YhY0tj+02jRFjXzdbf8cTb9MdiZv24Vse3c2BrnB23cfPW5Cd/Ol1tzy2m2QqvU4vu+WXb7Bm+cyC/XVH4tnJn7H9B7rDOW3sjsQLYlnFSnEOdofZdaSfrnDuudOvYzSh8dcXLsxO/uDY9fO5nNmx6guP5F67O5/aR1c4zq4j/er8K0pCTQAVRRmOXZGuViu1npFial8rZaSZPZbRBstoaTUcG7q2fqVyVJSGlSLWqg/pyl2r/qj32WLWbfllRkswHSs7N105mr99/rJVLGqSHOWpVT0VK9YY1b9WKu5i1y/fDs64Xt+vOv+KUihpAiiEWCiEcGdeXySE2CiEqBzbpinGA8OxKzKq1Uqtp1NM7WuljDSzxzLaYOVbWpW6j4YKr1I5KkrCShHbUGFeblTumvZ1Q58dSmWslxktwXSs7Nx05Wj+9vnLVrFoE+QoT63qqVixxqj+tVJxF7t++XZwxvW6haY6/4pSKEkEIoR4DVgFzAN+BfwSaJJSfmhMW5dBJbGXTqnihXg8xa73+jOJ2268DjudoRjVfndWQBGJp6gv83CgO8SNP301J49Od/SYU+2jPRjlmb3t/N1Fi2gPxvjFK+9yxemzc55cv3ltE9PKXHhdDrrDcSq9Tv7vk29lc5G+dtUKQHIkLwfwGx9eQZXPSVc4jsdpp70vistpY1qZBw2NcCxFdyjGYEJLv1ch2NcxwMK6AP9tcP6o8rm46dJF+FxOwrEkfo+DSDTB3b9950TkAE66JPbJjJllopldYDye4o22fo4GY3gcNjoHBumJJJlT7WNWVToudr/XzzN7O7hoST1zqn001vnpiSTZ/V4/C+sCfOXxN3OcdfrCcRx2G9997p2CHMB/v2IZfpedpCZ5eMe7nLuwhkXTyugJx2ko9+B22jjSG6Xa76QvEiOeIsfl5qtXLWdamYeOgRi1ASdOu422/iiVPhff/10LfzjQwxfXnEo4mmBhfYDW3kG+9OtjOYCf/+AS5tT4efvoALOrfdz7/DusXT4Tu13QMRDLWj8ubSjn0iXTRlOIMKniR9Mkv9vfQVcoTnfo2M/AN10yn7MXTaM9GGVmpYf3+qN89ue7cnJEvU7B4Z4oLrugeWYl3eEYHcEYKU2jvtxLuceB3Q4XLKpXOYAKIyN3AhFCvCKlPF0I8c9AVEr5H0KIV6WUp412K81QN7DSKNXGKh5P8ciu97IWUvrg8vjrbbR0h/n0hYvYsn13zj6WTi/jUHeYKp+LgXiSQ92RnAmenkxe4XHykbPmcM9z77Bm+UzsNlg5q5LH32jltDm1Ofvdur6ZSq8DKQVf+vUe4knJJ8+bS+O0MrpCcVKpFHa7nS/+0lzEsWl1IwG3Aykl9/7+gOkN889OnU5rMEpXaJDDPdEc7+It65qYUenmQGeErnCcVXOrOW9BTY4KeCglaIlMqhvYZCY/hqzsAi9prOOxN9qy/cnMhk0XIl195lxu+eUbpon92zYsoycUJSXB5bDxtf99K/vhqrE+QJXPxb72AebU+Nm6ffexyeKVy+kMxfja/76VEw/6B5nNa5uYXuHi1cNp7+76QPrDl7H/b17bxM/+lBZNbV3XzMJ6Lzc/uCt7jG98eAW1ARc94QRlHjsdA/Ecr9ovX7GMRfV+WroiWQtJXayyoM4/mkKESRU/eh+7/fE3ue5985lZ5UPIFF3hVI5o57b1zfSGYwRjKWwCGusDCCH42v/uLSqy+8aHV/LBZiXCUeRwXBPAPwH/F/g3YK2U8oAQ4g0pZfPottEcdQMrjVJtrHYc7OEv7/1TQb3vXnsGLx7oLWqPptsXWdlIAabbf/faM/hbE4s4q/Lrzl/AKdPLsrZYVjZX11+YPmZKMz/uA9efw4rZVex8t5eP3PPHgvVfv2oFbx4dyNpfjZGF0qS6gU1mSrVE/PGnzuLa7784ZD2jtdtd15xmavNWLG6uv3ABi+rLCrbbuHrRkFZuRvtFq2N/9aoVbPzZq3icNn7wiTO55nt/Kji+mbWdvv6B688xjavrL1zAhpUzRyuWJlX8GPuYfl7v++RZ/NUPXrS8nvqybuU3lOWfsoJT5HFcVnCfBM4F/j0z+ZsP/Hi0WqYYHUq1aDpqUa8vkhjSHk1PYLYSVFhtb2ZlpR/Tal+liDj0pGer9Uf70+22SsrPT6pWydNTm1ItEY8GYyXVMwqYrBL7i8WNJs23K8XKzWi/aHVso2iqKxQzPX6x92cVV0qIYI2ZsK5jwPw8mok8SrH8U+deUQolTQCllHuklBullD/LLB+QUt4+tk1TDJdSLZoaLOpV+pzZ11b70BOYi4k8hkp+zz+m1b5KEXHoSc9W66dXpNttJezIT6pWydNTm1ItEaeXu0uqZxQwWSX2F4sbmzDfrpQYNNovWh3bKJqqDbhNj1/s/VmJXZQQwRozYZ1VvzMTeeivreqqcUxRKqX+BPw6kF+xH9gBbJNSdpts831gDdCh/1QshLgV+BugM1Pt81LKXw11fPUTVmkUywEEsontM6s8/LGll7uf2ZeTp/e7t9tpqPRR6XMX5KKcMi1ASpMMxJIktfTjLP7j6WPbnz67ip++eICj/XH+/uJF7GkLosm0ldGNFzfyxJ73uPiUhoIcQBsaKSnY/OjuAmHJznd7WL20IbvOLAdwQZ2fco+T14/0M7val5NU/+9XLGNRrQ9IO4LEUilufexYHpX+YNVvPrk/KwAZowfYjkw5MkoxpOKndMxyAG+8uDEnd+5bHzuNuVV+9hwN0tIZ4sEdR3A5REEO4OY1p2K3QV25G4/DQf9ggkg8xZ1PvX0sbuZUMRCN4XI46Q7F2GrIq71tfRMNFV46BmJU+Zw5fXeoHMCt65uZW+1l57t9BGOpIXMAt21oZsn0dO5tJJak3OvEaYdIIsWrh/upDbio9LnY3xHKij2WzargosZ60wdeN04LnPQcwPEaP8mkxq/eaOOzD+/i+vPncm5jPTah8W5PLCfX9N+vWEb3QJRgLMVjO1u54aJFVPicfOlXb6ocQMVwOa4cwK8CKeCnmaKPAj7gKHC+lHKtyTYXAiHgR3nBF5JSfn04LVc3sNIxEy8ABRPDH123iiM9sZyk7q3rmrn72X3Ek5KPnzuXGZVe3ukM8ezeDq46YxbheCo7AVs1t4KPnjWPLzxybPvbr1xGLKFlH2bqcdr44ppTcdnT1nBza314HA66QjGqfC7ueGIvrX0xbl27hDKviyM9gznbbtuwjJmVLvojKQaTKTqDUeIpSTylsXJ2JT2hKN3hJN988u2cbXpCUfqjKf7U0smVZ8zJuSl/+YpllHkdlLud1Jenv/E4GhwVoUcxRnoDG5UYUvEzPIwq4ERKctdTb3H2gjrsNjh3QQ094QT/9NBrhj7XzLQyNz2hKG6Xkz1tQVIabN/Vyt9csACHzcatmT6Yjpu5OTf629Y388BLh2jti2XFHxVeB239MTbnxYPXaSMl4fm3j7L+tNnEkxqdAzFiCY05NV76BpO80xnioR1H6I3EcyYGd3x4BdUBFx3BGDUBFwc6QxwdiGMTsHR6Ge/2DuY4emxZ10S5x8G9z7ewdvlMYiktZ70+0QA40BXmcE8Yn8vBtHI3c6pHNZYmTfwYXUCmVbgIDmp865l93PD+hTiddg50hXHYbDTW5yrEt21o5je73+O8hfUsqPMjkWhS8OrhXlx2Gy67oL7cS32Zi7Pm1SgrOEU+x68CNisTQrwupVxmsd08YLuaAJ5czMQhQyWRG5O+b7h4EXYbOfXNksKt9mlMXP76VSsA+IxB3HH2/Cp2HOo13VYXaQyVYD/UezCuPwkJ0iOXD49CDKn4GRnDjRsoTM7Pr19MLLIxI9goJo7S65Uak/niACsRgdX7+vpVK9CA/R0DputPUCxNmvgxiuh++Mmz+MQPXuS68xewdHpZzphYbLy7/sIFLK4/Vt9YZ5TFN4rJw3GJQOxCiLOyexLiTMCeWUyab2LJjUKIXUKI7wshqqwqCSGuF0LsEELs6OzstKqmKAEzcchQSeTGRGOzJ/6bJSIXcw/Q64fjScJ54o6ecMJyW12kYZrAXiSp3qqNkyRBesgYUvFz/Aw3bkqJiWJiEeOylWhKr1dqTOaLA6xEBMXibzCWnGyOHyctfowius6M8EMICsbEYoIiTebWN9ZR4hvFcCh1AvjXwL1CiANCiIPAvcDfCCH8wJeHcbxvAwuBlUAb8A2rilLKe6SUq6SUq+rq6oZxCEU+ZgnGQyWR68tD1S9lnzZB1q3A73Lg9+QmpFf7nZbb+g1J6vnrhkqqt9pugidIlxRDKn6On5HETan185fz3TmsRFPGeqXu29g2KxFBsfjzuR2TyfHjpMaPUURXX3asf+WPiVbXXr+G+fX1Okp8oxgOpaqAX8r8zLsSWCGlXC6lfFFKGZZSPljqwaSU7VLKlJRSA74HnDXUNorjZ16NnzuuXpkdMDxOG8tmVfCND+eWbV3XzPZdrUBavLF1XRMep42HXz5Cjd/FptWN2frG9fr282r9fO7yJTllm1Y3UuNzsX1XK5vXNjGYSBKJJbj5ssXZffdFYiysD+TsP92eJqLJJI/tbGXjJbnrbl3bxH0vtJiWG9/D5rW5bbzj6pXZvMiJiIqhE4dV3OSXbV6T7nMPv3ykoA/Xlbm51dAHzfrkbeub+dELLcf2t7aJ//fHA2xe01QQn3q9x3a2sqDOXxCT+dtsWt3IL145ko2nWr+Lx3a2ctOliwtiV49JvWzLuiZsNrjvhRaqfa6C9zYRY+lkx8+8Gj/LZlWwaXUjT+xu5bb1zTy2s5VINJHtF2b9aPOaJn70Qgs3X7aY+jI3Thts29BccK2XzayYcNdEcfIoNQfQDVxJ2gou+xFUSrl1iO3mkZt/0SClbMu8vgk4W0r50aGOr3KYhmYoCzh9fXAwTlKDrlCMhgoPiZSkPRhNDyp2G0eDUSq8TqKJJNV+FykNWvvS1kQep42BaIqOgRj1ZW5qA3Z6winaB2LUBlz4nHa6wjHK3E7C8SQep53gYAKvy0G5x0FKk3SG4lT5nAQHEwQ8DiLxJG67nZoyJ4MxjXA8RTiWosxjx+eyE4qlGIgmmVbuJqVJ+iLp7ZIyRZnLSSypkdQk4VgKn8uOyylw2+y0D8RwO21Uep2kNElwMMHsaj+zK7280z1AXyRJTzjOrCofS6eVcaR/kO5wDJfdRiSeKmqjV+o5NzCaOUzDjiEVP0NjdS01TXK4J0x7MEYknqTG70YiGYimUxmq/S4iiSRep4OBaIJyj5OeSDwTQykqvU7iKY1YUhKKJZlW5sZpF/RFkoTiSeoCLtwOGIxD+0CMaeVufC5BJCYZTCTxuhx0DMSYWeEhJdPPs5xW7sZu09A0G8FogjK3k65wjOnlHuxC0BdNEEto1ARcJJIafYMJav0unA4bR3oHqQ248TghnoSeSIIqr5NgNEGN38VgIkUwmqTM48DvclDmEXQMJLNtD8dTdGes52oCrkx7cvt+qXExVeMnHk/xels/HcEY8+u8BAdT9EYSTC93E09pDMZTSCnxutLK8QqPk77BOJU+F4PxJH53etz0uxxoMt1v6gJuPE6BpkFnKMa0ci9NDeVKDKLQMY0hh1mhCb8k/diXl4HYEHXTRxPiZ8BFQK0Q4giwGbhICLGS9CNlDgJ/W+LxFUUoxQLOZhPMqfLxyOHeHAXi5rVNvNTSxZkLagtsrB7YcTjrkzuz0s3Vq+bkqHS3rmvG7YDP/SJ3f/fvPcQlSxqyykf902ml10E8Kdl0/6s5x3l671GuPGMO3/ntfj6yag4P7DjMp86bTySRynnsy23rm5FS4x/uP+ZLfP9LhwseibBlXRP/+ez+nMe9OO0gbLC/c4CecCLnvW5d38yTe95j5eyanP0UeyxMqbZ7x4OKoRPDUI9P2tM2UPCYE11Ze/Nli3HbbXz/hUIrwpsuXYzXacvx09UtE/W+uXXdqYAoiKsn3zzWHz/UNI1zFtbm1WniwR2HuWTJdMvHgeS/9rvsfPu3LbgcghsuWsTdz+4vaLOuSNY9ureua+LuZ/dnnwyQ/xgmo4e2fr5KiYupGj/JpMajr7/HFx4ptAbMtx2cW+Pl0xcu4h9+9mrBuHztOfNw2UVO38p/rNW2Dc1sWDFTTQIVlpT6DeAJs30zQ32DUZxSLeCs7NC+/4kz+dQPXzJVD977fEuBLZuxjtFuSi+zUjDqtm13PbU/p1xXtxmPl69wzD+erpTTt7FSPhq30zFTz1m12UrlWOo5zzCprKwmG8WuJWC6rhRlrb4uv78b++a3rjltyP7407852zQ+h1LBW7UTKBo7+YrkYlZ1+fZjVucrPy6mavwYx+B8e7589W8xVfe9z7eY9q18a0vdClMx5TkuFfALQgjTR70oTj6lWsBZ2TZ1hWKm5boaLd+WzVjHaDell1kpGHXbtvxyXd1mPF4xVSKQU7eY8tG4XTiWtFTPWbXZSlFX6jlXjH+KXUurdaUoa/V1VtuCtZrTaJHYNWAen0abOau2mbVzqNjJVyQXs6rLtx8rNS6mavwYx+D8MTX/HBc751Z9K9/aUrfCVCjMKHUCeD7wshDirYx8/nUhxK6xbJiidEq2gLOwQ0vnj5irB/X/Vopbo92UXmalYDTathnLjXZZ+v+hVMHGulZtz9/O7ylUIOvrqyzabKWoK/WcK8Y/xa7lUBZder/WX+fXM+vvxr5p1R+NFol1ZebxaYwbq7bltzNfrWy2fb4iuZhVXb79WKlxMVXjxzgGW42pQy3r47JZ38q3ttStMBUKM0r9CXiuWbmU8tCot8iEqfQTVrFkdKuEaT2f5vbH38xaTJ09v5oZFV6OBqM0VHhIaRCMxtnfEc6xg9q6vplTpnnpiWjsOxqkodJHW98gTTPL6Y8kqA64ONwdpjbgIaFp7H4vmLWCWlRfRm2ZAxs2BhPp5OWagJNwPEHXQDLnOMYcwC8/fixv5bb1zcQSKWZV++gIDuJx2mnti7KoPkClz5lJtk/RFYpSG3Azv8bHQCxFVyhGjd/FD19oKcjd+/cNy5hR6aY9GKcm4MLjFHQOxKkvc+Nx2tnzXpC7n93PtefMZVq5l3KvA6cN3moPc7uhbaOYAzhpfsIarxjjQ+/vHQNDiguAdF7WCy3d7DjUg9thw2kTzKj0sbShnNmVXh7fc5TPPrzrWP+6ohmf08FgIsWcai+DCY3gYAKX3ca2X+3J5vfpOYCP7mzl4+ctYDCeZF6NDwS09cWYVu7CaReEYhodwSiReJLBhMaMSi8zKjyEYgnCcY232/qYVV1WYM8opUaVz83r7/VnbRf1nF2XQ7BtfTMSCEWT1Ja5CcfifPOJ/axZ3sD0Si9dA7H0hEvAkd4IP3vxMBtXL2ZmhYejwVh6Mui0AYJQLN02o/PPLWtO5f4XD/N2R4g7rl7JpafU8+bRIId7BxEC7vntO9l1x5kDOCniR9MkR/rCvHigjy888jrXixORQAAAIABJREFUrJrJZU0z6MiI6KSUROIpNAmDiRR1ATdSaLjsdgYz4pvagIsjPREqfC5cDju7M9feLmBBnR+HTfDd37Zk7f1UDqAiw/CdQIQQ5VLKoBCi2my9lLJnlBpXlKlwAwPrQfEDS6cV+G3mD5bJpMb/vNHG5ww3qk2rG/n16218cFkDdz61j8X1ATZe2ojXaadzIEZLV5gj3WHOXljLf5okhOsJx7ow4+/ev4hv/3a/wZ6oCYfNRlt/NCc5fNuGZl5s6eTiJQ0kNUmF10EomiDgcfJOR4iF9WXsPRpkVpWP2w12R5//4BLiKcnjb7zHX5w9j6PB3P3e+dGV9EcSucnw65tx2iROu4OEplHrd9ETifO5h4/dqIyep5vXNvF2Wx/Ns6r5N8PNbNPqRhbU+mntG6QnksAmYPmsiqJ+pma2e6OtYhwNJnv8GOPGzDN6uBN5Xdzgcgg+84FT6A3H6QrHcdhsNM8sY39HmDueeNv0WJvXNhGJJZlV5UMiCUXjWa/r/Lg02iua7mtNEw+/cphrz52H3+UgmZIMJlMc6Arz7N6ObFwbj51IpogmUsys8nG4J1Igoir3OvjSr97k2nPm8fXf5PoIz6j04HbY+McHdmZFCH9/0aJs21fNreDvLmpk55G+rNXdjRc3smpuFTMrvFlxgzE2z55fxeyq4irgqRA/ugXcvvYQ9790mBsuWgjCxi2GD8mf/+ASBhNa1tpybo2Xf1y9uGAcvPmyxdQGnPRGkjle0Letb+alA52cOb+OpQ1lLJmmVMCKLCOaAG4H1pL2AT6YtxMppVwwig20ZLLfwHSsEqMfuP4cU/GGMWHaaltjovhd15zG/o4B4JjA4r5Pncnf/vjlomIKY8J4fgL72xYWUVbWVvn7M26n21F99aoVptZTxRLm/+r7L+Fx2vjBJ87kkxYJ8xt/9mpBgr2xzvUXLmBRfVlOu0fJ6mrC38DGM8a+P1z7P6u40YUPRjFSvv1asSR9XTRlJdTI7+PF7L/0esaYKXbs/HYb1xezVswXaZUqSnjg+nMATMeoURIhTPj4MVrAGcdds/HPeL6LXcdi4qH/d93ZrJpn+r2NYmoyfBGIlHKNTM8Q90gpF0gp5xv+TsjkbyphlRhtJd4wJkxbbWtMNNYtnYwCC138MFTCsfG/TriIRVR+Inlf3nGK2VFZWU8VS5jXX3cWSZjXXxcTqeS3e7InpU8GjH1/uPZ/xUQe+WIks2WrbfW4sxJPDcaTJe3LWC/fnrHUdhvXF7NWzBdplSpKONoftRyjlAghjdECDjAdg8ys/Syv4xDiofagOu+KoSn1++GXM/6/ijHEKjHaSrxhTJi22taYaKxbOhkFFkbBxlBCELMEdiuxRn4ieaXJcfK30/dlZT1VLGFef22VMO81WMpZCT5sgoJ2T/ak9MlAft8fjrhgKJFHfj/MX7baVo87q0R/n6uwj1v1W2O9Uo5t1k59fTFrRSuR1lDL0ys8lmOUEiGkMVrAAaZCObNrZnkdhxAPTStX510xNKVOAM8G/iCEeEepgMcOM+upO65eSVNDuWm50fLHbNtNqxv53nPvZG2FvvfcO8yr9VPjd2Vtn37w/AG2rGsytVvbeEkj23e1Zv/rllf6+kgswfxaf4Ft0bYNudZWm1Y38sPnD7B57bHjmB2vxu/ipksXc98LLcyrKdyv0y4K7Oe2rGvih88fyB4nGImxJa/O5rVN/Ndz72Rf//D5A/zz/zml4Fwtqg/ktHsiWl1NRYx938xGq9h1tIqbX7xyhMd2trLIYFGYb4lodqybL1vML145wn0vtHDb+mbue6HF1J7te8+9M+S+dPuvebX+rGWYHjNm9a3ara+/bX0ziVTS1Apu0+pGZlZ4WFjnz5Y/trM1J5bMrOZuW99MU0MFTQ3lBdZk2zak1ylyLeD0cXfrutzzpY9/xvNtNg7efNliBuNJU+u+n/zxAFvXN7N8hjrviqFRKuBxhlVidDKpsbutn7b+tMqxqaECh8OGpkkOdIU53BPG73bgsEmSmqAjmLaVsgnojSSo8DoJxZMEXI60fZTHiZSSlKbhdzkJxpLEkynKPE5CsQQBtzNtQ+RNL5e5ndhsEIlrhKJJGircxFOSvkicar+LZErSG0lQV+bC77YTT2gMxFJEk2k7rJ5wnAqfExuCzlCM2oCbgUw7ghkLLUhhFw6iyRQOOziFnWgqhaZBJKMw9jjthKIpOgdi1OuWWNKW/bmswusk4HYQjCbpjcSZWellMJGiK5RWAbvsNo70DTKjwoPTJugdTBCJpxV3SZnEZXeQSKV/Sqn0uYglUsyo9DG/triSdAgmfA7TeMcYN9PL0yrgzlCUuoAHu42sZdmsCi9vHg3S2p+2RJte4SaZlLQFo4RiSco9Tqr8DgYGUwzEkiys89ERjNMejFFb5qLc4yCWTBFNSHojcaaXe4inNPojSXwuG363g8FEAqfNQVJL4rA7snGkW8X1RhJ4nDbKPQ4cNhs9kTjRhEZtwImmpa29qv0uHHawCxsD0QRza9x0DqToCscpcztw2MDncpCSks5gHL/HjtdpJxhN4MtYiM2q9BDMWDdOK3dT5bNzsDtKhcfJYCKBz+XM7i/gtiNl2iKy3OvCYZekNEF7ZhyRpLBhR0MDaUvbjZW5sdmgJ5xgRqWHSCxFTziOz2Wnyp+2hjzYE8HvcqTPc6p0ZXYekyJ+kkmNPUf7aeuLUuV3UuZ2EI6niMRTDMZT1AbcgCSa1IjEUgQ8Dmw2idNmJxRLpsdqtzNjr2nHnRF49ITTNnHheAK/y4lDCCr9ruGeY8XkZuRWcCdqoqdIW7YtqAvkJKxrmjRVAeerg+fWeLnhokU5KlldAdsfTfDpCxexZfsr2XVb1p6K3W7jC4+8nKMwqytz8/c/OWY/9K+XL8FujzIQTVoqFo22ULesOZWA2843nnibj501l28+aa6YNKqMn957lCtPn8N3ntuftYEzs3m7+bLF/OD3B+mNxPnCny1FSgjFkgUque073+Mvz5nDnrZkju2bsZ2b1zbxHYOq+aZLF/PTFw/xF2fPzR5j4yWNfOGXb/C5y5eOqk2VYnQxi5v5tf4che+quRVcfebcHOXl1nVNgOSWR/fkxMx3frufM2ZXFliwffXKZUQTWrYsXymr7/PJNw9y8SkN2XibW+PlxosbueGnr+b0Rd2erTcS58t/voyUJgusGp0iRcdAvOCxSn6XnUqfkx/94SCXLJmejaEt21/h3PnVXL6sIaddW9Y18fjrbbR0hzNtftX0PayaW2Fi+9hEXZmTvUfDprGWr0jOt7zLj7XRtn0b75iN4fpTD4xqbN1a8MuP76XK5+KT75tnqTjftLqRGr8TTQo23v9awbiqxizFUCiN+ATgYHc4O3BAOtn35gdfY3dbf075muUzs4O2Xm/L/2fvzOPkKOv8/366q6vvmcyZDLlPjklCgIjIrogEFHdDCCII7qqsB+q6wsquxyoSCayou+Kiqz8XV9ZjVUBQBHZ1RQ7xAgxqAuFIQi5zzpE5+pg+qvv5/VFdnerqru6eZI6ezPN5veY1dTz11Leeej5PPd39/dTnoa2857zFrF05m5sfLt23fyhVfNhY225/ZBu7+hIl2/qTGXpi6eLg8+Yz5xSXrTJ3PLqdN585h1Q2z8aHX2BHb4K1K2cXX2lQ6ZgvPbadtStn86XHtvOOcxdx88NbWbtyNv3JDHc8enSfMz7rPD2xNL3xdFm9tz+yjfect5iQ7itO/irFefND5vmsfV/8+TbWrpxdcg4rxhvu/SO7+xPje6MVxhRO3rzj3EXFyR9QnMiFdF8ZZ9aunM1bXjWvjE87ehMl29aunF2cONnr/KtzFpbwbe3K2cUJnFXujke305fIFPvarr5EGR9vfmgr89qaXI/d0ZvgHecuKuFQKpvnmj9fWBbXhge3cs2fLyyL2bn+jnMXlV232U66K9ec22/68fMl3HJybbrxqdIY3pfIFCd/1rbbH9lGf+Eb4TefOYfbH3EfP+94dDsh3Vf0XLe2qzFLoV6oCeAUQL3q4GpKwmqqW2d5p8WQUzlcjy2U0x6rlsrYbgdXSfVY6TzOuJzX7KaUq2SV5YzHWcaccCpl3VSCkzejsTMUorIFWyWlZqU6nSrPaspbe3+uVOZwrDL/rf5vccd+fW5K98Fktqa6162delzicCtfiVvO+qYLKo3htcbfesbPRBU7wOnWxgqjh5oATgGMVh3sXA+6KP/cFGbOXwycymG389Syx3I7JuArtbVyUz06z1MpLqtMSHe3fatkleWMx1km4FOK4KkGJ29GY2coZWULNrf+5lx3s0N0rtvt2dzqduO/pdq1uGO/Prfz2xXzbrG5tVNntPabBuzbK3HLWd90QaV7WM/4W2v8DFexA5xubawwetQlAplsTJck9l19Cfb0JwjoHsI+DeGBvliGsF9DSsnHfriFjCG5YvUclnREWNIZZntPouj+USkHcOO65QR00D0a6Vyenb1x7t20D10T3PbmFRwYTJXlHDUHNW6496gbwMZLu8lL6I9nOBJP4/N60H1e7nzylaL13KldTfy/x3ewrSfObZetoD3qA+khls4yMxpgJJtjOGVweHiErqjOjEiA3liGjqiOkc+RykiS2Rwhn5eI30tPLI0QktZwgH0DI4R0jYc2/4mrz15AJpenKaDh17zE0lmCmkZPPE1H1E/aMGjy+4hncmSMHANJg0/+6Kjjx63rl+PzCLxeD5oHbvvJSyV5SrGRDMtmRdnREyeVzXP6nBns6ImxqCPCaxa2oeveY7m9J0QSeyPCEn/0J9IIBAMJU5Tk8YAHwb7BEbYdjnHvpn3MnuGvkAO4HI9HcuMDR3P6Pr1uObFUlplRP9lcnmd2DxSt1v7hDSdj5CSf+NFzZo7WufNpCfuLP9HObwuyYW03e/oTzGsLs/HhrWQMyd+cO5/WiL+Ea5940ynMawsTS5kikUwuT1jX2D84gl/zMpRMs6gjQjqXQyDQPB5TwOX3oWsCI5/H5xXk8oL+RIZZTX58Xg9DI1lS2RwBn5ee4RE6moIMJrPMag6Qzhr4NC8hzUMsnWNnb5yF7RGS2RxtYR8+r4dExigbFzZeupyz5kX5/d5YybX+05tOZXdfkgXtIf7zl6/w6kUdeD1wxtwZfOXx7WzaMzQWOYBTlj/2/rl/IMXHf3h0rL5p7Wn0xzMl4/V/vvNMWoJ+4ukc2XyOgNdLbzxDS8jHwaEU/2Qby/75shUsaA3SF09zfcG9ReUAKrhg9E4gjYIT+QEG5XZUlRLLr1+zlIXtYTK5PB+9b0vJQ2Q4ZZDJ5Tl1VhOtES9GzsP+gSQBXePhzX8qJKNvtQ0cyxEIPvGj51jWGeFvX7+EFw4Ol9g7jWQMOpsC5PKyaCllT0Re1hnh6lfPLxFYfGrtacxrCfLQ5n2cMa+dmx/eWjF5eeO6br7yxNGHwcZ1y7l3057iw+KGi5Yxvy3IUNIoSbZ3Tm5vXd+NkaOYAxPwebjtshUcSWaKFknz24J8/OJT2XMkyfzWEJ+1Wc99+pJuFrQHGRoxCPi83PTj58kY0lWs8qELlh6rt+aUfYA1Muwe2E6x0Ia13XztyaN97DOXreCs+TN46VCMrCFJZAzCuobPCwGfl2f3DtIe8RPStRK/W7to6DOXrcDntX5ezREO+Ljl4RdoCelcsXoOp3U1kczk+ITtIf2FK05nJJvjxgeeZ1lnhA9dsJSBZIbmkM6RRIav/WJH0Wqx7BoKk6ZFbWH+YuVJZSKQtrCpiv+ow/bQmmhVGkc2XNLN4y8dZM2ppQKR6y5YaopIzprHzQ9tLV7T4o4IM4IaX35sO5efNY+fPX+QFXNnsLQzQiYnix+uKp3rc5evLIgUACRbD8RIZHJ12Sw6MCX54xzX33BaO1e9agF7+hNEgz6+/Nh2Pvi6xUSDOi8eGmZJR4ho0EfPcIZHXzzIRaeV3nPrA8NLh2IkMwbtYR2AGWGdzojOpj2DLJ/dTHNQozmoVMAKJVATwEaF047KzXLJadVkbbfs2QK+cougSnXZLYeqWUp5C/OcSmWrxXjW/JaizVG1+i1LuYCv3DrOaelWqZ5K1nBOOyV7/ZUsuf71LaeTh+K+avF+41c7j9Xaako+wBodFm+q2Rja+9h33nU2b7/rmYp94O++/4eK9lxu/KpmZVgP16zlataI1rnPXtjial0I7uOBW192s0J048hd17yKt3396RKeVrItc7OJe/SlnoqcHIXN4pTkj3Nc/9LVZ5TYA1pjrNU233vvqzFyea79zrN12VXa+8C5i9u56s6nRtuuCtMHo7eCU5gYOBOE67VqsrbbBQtO4UMt8Uc1oUU18Ue1GO0J6LWEHNa604LNaelWqZ5KIo9qohC3JGr7vlpiFWVt1TiweFNvHzs0XNkm0BKBuAknKvGrXlFVLf5Us0a0tlezLqw2HtQrULG2u4k5+uPpo2UKbVWvGObgUMqVkye6QMFNhDSSLhe8gSk6OlK4N/XYVdr7QG+hLadDuyqMHdQEsAHgluTtXHezarILFtyED3bUK+ioJf5wi9GZgF5PgrjTgs3N0s2OStdaTRTilkTt3FctqVpZWzUO7Lypp4/NaqrcpywRiJtwwo1f9fS9evhT6xrc4qo1HrjV6WaF6CbmaIv4i8sWT+sVw3Q1B1zLnugCBTcRkr2d7W3TEfUX73W1e24X9Vl9oKPQltOhXRXGDuP2E7AQ4i5gLdAjpVxe2NYK3AMsAHYDV0opB2rVdaL9hGUlBh8eNt+KP68lVPZC5xsuWsYrvQnyhYnY0pkRmoM6ibRB1K9xJJnB5/XQHtHpj2cx8nlawzojWQOv8HDTg8+75gHdcdUZJNJGMQfw+guXMpIxv90YSGY4pStKwOctijR29MT5+i93csVZc5nVHOBPR5I8/lIPV589l4PD6WKMHVE/HVGdoWSW5pBO1jCdRjI5WZLLsnFdN/du2ltMGF81ZwZ3/243P3uhr5inNHeGTiYneOHgMPPbwqSzWYTHWyLouP3K04mnjJK8wNsuW8HQSJbP/vSl4ja3l9XevK6bqN/D136xq7iv2gurJzoHcKw4dKLypz+Rpj+eYVdfgvaIn30DSe7dtI+BZIab13Xz1Sd2FAUY3bObkRISGYOQT2NXX4LWiI7mEXRG/RweGqE16mdv/0hZrt1PnjvI+ad0srgjQntEJ5XN8UpPnHDAVyKEOndxKyNZyR/2HhWNbFzXjeb1sG9ghJaQTsAn2LRnkJDuxSMEP9t6kMvOnMudT77C286ez/ee2VOs79ULW+kdTqH7TJePTz+0tZjTeP2apcxrDSElxTQIU1jQzYsHhxECmgMafp9Wcj32HMCvPrHjqIhrVhM/+sNeXn9KV0le72cuW0E6a3BoOM3Js6IMJjM0BXR0n4dYyqiaA/iFK1bxxtNm8sT2HrYfjpdwaiJEIJPNH3uO6tqVs2kP+1jUGWF4JE3aENz9zG4+dMFS8ghiIxk6m/xkc5DO5vB6BPG0wY6eeLFP33DRMmY2+fnGL3exrSfOp9aexqxmP7kc+DXBiwdjLO6MsOaUunMrxx3OZ53KS5w0TGwOoBDiPCAOfNtGvs8DR6SUnxVCfBxokVJ+rFZdJ9IDzJkYbA2Gbzh1JnuOJNl7JEEkoLGnP1lU4c1vC/L+1y0pGZidSdvOycr7X7eEiN/L9p44T7zUw/mndDK/NcTwSJZsXvLdp/dw1avm0dnkp2f46MuUK4stlhPwefnHHxxVmv3LW1aSNvIlSsFb1y/Hr3l4aPM+Ljy1q0TAsWFtNzt64yzpiOD3waHhbJk690g8zVAqx9M7e7li9bzS5PW13dz/e9ukce4Mvvr4djKG5APnLyEnJe0R07KqP26w7XCMvDQ/Hc9vCxHWNVrDPo7EswylsoR1jWQmi0/z0juc4rTZzQggnjII6V6ePzBMNidZ1B7G5xV0NQc4rWC/dww41gfYmHDoROWP3SnB6ie3XLqckYxBQPMwOJKlLeonkTJIZHIVJ/VvO3s+LSGN/kS2+AHAEj90NQc4MJAgkZXc8vALJZPCb/92D7Nn+Lnq7AXc+MBzFT843PbmFWSMfJkI4/tP72FbT5zbrzydVDbPHY9u45rXLOCkliB98UxRWOKs79b1y4kGfPg8gpDfW/xgtnblbJoDXlojgTIBi33yOrs5QCxjoHs9xFMZ4hlZwsFPrT2NjqhOLoepgm4O8OVHtxXFWVbbfuYnLxV5/fGLT+VPA0mWdEb46uPbS1TAf7aoHV33ks9L9h5JcHg4TTJjMK81PFprxSnLn0wmx0PPHyxpZ2sS/sblJ9EznObu3+2t6n70qbWnkUhlmd0SoiPq4+BQhld64/zANjH0ez3c9ZtdXHfBMi49/aRjHafGFG7POqVMnhRMvAhECLEAeNhGvpeB86WUB4UQXcATUsqTa9VzIj3AnInBUJ4QvflPA7z1zqdqJli7JW1bCeXXnreoLEHcfswHX7+kJAm52rmcdbmJLZwiEOe5d/TEACoeWyt53ZnUX0lI4oEyYYgV17LOKH9XEJrYt+fyFNtrSWe0YpseZ2L1sZsIjwGHTlT+1OJFJSGTvZzFE6dwytpv7xuVjgWqiqKqCZKcQoovXX0GO3pidYmzvvL4jrpFGHYBi11AUI3nuTzFY5wcqzWm2Lcfo2CqEqYsf5xjOZSOhXc+ubNEDFJNDPSNX+10bWt7Xx3Ddj8u1POsU5gwNIQIZKaU8mBh+RAw062gEOJaIcQmIcSm3t7eiYluAuDm6mFP3B2Nw0c1wUKlBHF7orczCbnauSq5g7iVc00yzxhV3TtqJa/XchYYSbu7f+QLP/9V2m5vr2ouCA2Cujg0HfhTixeVhEz2ctbxbm4KTjcb57G1RFHVBEnO/SNpY1RuO/Xy1unOY527HmeSShyrOKa4tF+DCqYmlD/OsRyOjlV2x6N63I+qtbW9rzZKu9fzrFOYXEza98TS/OrR9etHKeWdUsrVUsrVHR0dExjZ+MJN8GFP3K3X4cNN2GAJFioliDsTvetN5q7kDuJWzi2BOaRrVd07aiWv13IWCPnd3T88goqOD5YTg7VczQWh0VCNQ9OFP7V4Uau/BXzubgqV3Gzsxzr3Ocu5nbeSkCLk10bltlMvb53uPE6xVaVrruaUU3FMcWm/RhdMTQR/3MZy5/2ux/2oWlvbx7FGafd6nnUKk4uJngAeLnztTuF/zwSff9KxoC3M7VeuKiH77VeuYkFbuFimu6upkHdnlnlo8342XNJdcsx1FyzlW7/ZWXH7w1v2c8NFy2gL6SX7rl+zlK8/+QrXr1lKwOfh/mf30RbWi+vWuTauK63zlkuXs6QjXLJtcWeYWy5dXrLt5nXdLOmM8N9P7eJmRx0b1y3nW7/ZSWtILzundezDW/a7Xu+GtUf3O8vb28MjKDv39WuWsqQjjNdD2fa2kM7DW/bzkTeeTHtYL2kft/szyZjWHLLz5/5n95Xdq1suNfvZdRcs5aHN+137m8WTD1+4jGQmW7b/I288mbaQzkOb93PdBUvL+s0Pf7/P5EqBA5ViaY/4y/rihku6+c8nXyHg89AW1ov7v/7kKyzqCJdw01mfdd6Az8OijjA3XLSs6hhhL3/9mqUs7gjz7d/sLJavxJP2sF48ZsMl5ZxrD5eOKR++cBnf+s1O03HItv3W9cvp7moer25wPJhQ/jjHcqtdrbHw+jVH+6m17OxvVl91jt/Wfvs41kjtXs+zTmFyMdE5gP8C9NsScFullB+tVc+JksNkKaKGRjLk8nAkkaYlpJPIZGkNB4j6NfoSaXSPaaEW1jXimRzxtMFJzQHS2Tx9iTTtEb+Z6O7zks3n8AqvqQ4O+BhOZemImBZWw2nTNqonlqYz6mdG0EtPLIuRz+HXNGKpLK1hnYDmIZHJ0xNLM7PJT8TvZWjEoD+RoTPqpynoIZmWpI0cRh5S2RwtIR9pI4dHeBgeyTIjZNajaZBI5xnJGgR9Gn1xM95EJktY95HMGET9Pox8nlxeEk/laAp60bwe+hIZmgIaPq+HZNogqGscSaRpDZsWbz6vl9iIgd/noSmgoXk89MRTzAjqxNMGqWyO1rCp7MwYkv5khohfoymgkc1JemIpOiJ+EhkDjxBE/BqprIHm8eD1ePD7PKSyOdojfnJ56I2n6IyOiXJtLHOYRs2hqcyfSirCdNrguUPDHB5OM6vJT1vYxyt9SQI+Lx1RH7kcDI1kiaUMmkM+gj4viYxBPg8jmRwzQr5iHwjqXqJ+L+msZDhlkMgYtIV1EhkDrxC0hHRSRo7eWIbmkEZQ83I4liasa7RHfaQyeQ7H0oR0Lx0RnXg6x+FYms6IHynyBDSN4ZRBPGXQGfWjeQX7BkZoDes0BzWyhiSeMYilDFpCvoK9oUE8bTAr6icn4dBwivaITsSvcSSRYSSboyOio3k8JLM5sjlpOvdE/cQzOZJpk59ej+DAUIrOiJ9sPkdY18jm86SzEiOfoymgk8zkSGZyhHQv0YBGPp+nN5YloHtoDvrwCBhIms4nbWE/EkkqmyeeNtspm8sR9vs4uSPKy70xDg2lmNUcoPvYBVOV0PD8qaZ2NYw8Lxwa4vBQmpDfS8SvkUib7k1Nfh9ZmS9+D5nNSTK5HBG/j8FkhhkhnWTGHKNCPi898TSzZwQKP6WmaYuY4+6RZIa2kJ/uk8a03Y8bVrv0xMZsLFU4NlRsdK3SxjE5mxDfB84H2oUQ+4ANwGeBe4UQ7wb2AFeO1/kbDdVsq0xF4gv81avn4/d6uO2nL1VUAdptqazXXNh9bG/5nxeKVmaV1GSW5doFp8wqbl89v7lccWtTKt52WTe7+wVfeXy7S9x7uepV85jVnOPBP+zn7EVtJYrivz1/SdFX2H7MDRct4/Bwmu8+vaesXvt1fvjCZfQMp/nGr3eVlbvl0uU0BTWe3nWQsITlAAAgAElEQVSkpJ0+fOEyvvfMHt7/uiV845evlFyvs/6N65bz8xcPFF9Bc/uVqzhzXisej2Bx5+QmKk93DlVSEX773avZ3ZvipgefL+vX+wfTfPD1i0lmciXK4NL73U0eyfV3H63z829ZyaGhVMkxbir7UgXw/BIVvJ2TldT0Tpu2v79wGb2xdFmss5oDfPep3WX91umn+7nLV3B4OF0xbienLd594HVLuO/ZvWV137yum46ozr89sp1tPXE2rO3mv5/axYWnnlTS1k6LvduvXMU5izrweASnz23h9LmT3WuOYqL4U0vtms9LtvckSpTA1n364AVL6I9nS/qRda9Mi8AXaz4Xbl2/nHmtAVbMmdFwkyuPR7CoI6JEHw0KZQU3QajHtsqu3K1H1VdJFQtUtZZyqsgseyI3paJlt1ZP3E71by2buVqqN7t60U2N6abgrKWas9f/H28/i3fe9bvivnFQqU3qqDxV+VNJRfj99766xCIQjvbXlw/FylTt1n6nnZtdDT5a+8B3//kiTp5VWS1er5K9kgLfKlNNjW7n/LHEXY0Td779LAZHjKLNmJsdmXPcmQBVZ0Pzp5baddPuI/z1N56ueJ9mBDWudWlj+zhe67nwX9e8iplNATXRUnBDQ6iApy3qsa2yq+zqUfVVUsXWspZyqlzdVK+WWrCa9ZUzbqf6t5bNXK16reVqakw3BWdRNedyffb6B5PZkn1KpdYYqKQidFoEwtH+WknVbu0vsXNzqMFd1bpV+k6tflWrX1eLtZoa3c750doe1uLEQDJbYjNWyxrPWp/ufKmldj3ksn8kbRSt35z7nON4redCXzw97e+DwuihJoAThHpsqwK+UpWdWznncqV9bsc7Va5uqldLLVjN+soZt5vFVKVj6lG9WcvV1JhuCs6ias7l+uz1zwj5SvYplVpjoJKK0M0isJZFWYmdm0MN7mofWKXv1OpX1nq1/dWU9PXUP1rbw1qcaAn5SmzG3PjsvMbpzpdaatcul/0hv1bVfnA0z4X2iH/a3weF0UP9BDxOsCcFdzUHMHKSlw7FODCYxKd5+Vwhn+OK1XOY2xKiP56mParj17x85L4ttIR03vPahfzL/71ckuvh8wi8Xg8+j+D+3+9l7elzGckYLOwIkc7miY0YhP0afzqSQCLojZsJ6qfMiqJrXjMxPqjx4oFh2qMBkqkMmk/jS49uK9pC/dniVoQwnRRmBH0cSWRoj/jZeyTJJ2zWTzet7WYgmaGrOYBfE3zvqb2cclJzMSdp9fxm3nnuIrb3xIp2ca0hnbt+s4t/eMPJJNI5vvaLHTVzAIM+T9UcwJ29iYo5gH97/hJ+sKk836lWDuA4vKm+oX/CajQYRp4XDg5xYChF2K8hMYULqWyermY/O/uSfOXx7cX+alkJPrc/zj+8YSmax8POvqM2ivPbQnzz17vZ1hPn1vXLWdwRJp4yyAP98QyzZwTY3Z9kw4NbWdYZ4e8vXIrm9ZBI52gvWBv2xzNAnnltYQaSWVpCPg4Pp/niz7dxxVlz6Yj66Wo2H/TZnCSVzTE4YpTkfX3mshX8qW+YVQvaGUxmmdnkZ1dvnJsfftFWZjkzmwIMJbMEfF5ufnhrSZ7vROYAPv7ywbpyACfA2aEh+WMf442c5MYfP1exXYZHUjz2Uj8f/+EWlnVGuPa8xYQDHpoDOsOpDPF0vuhc5BUwuzlAfzJLR9RPXyxNJKCRy8maOYCr57c3XA6gwvhiFFZ7E+8EMlaYag8wp12Vk7Cfv3wl2VyOvBQlPp23XLqclw8OcOaCDmLJNMGAjx098aKlWXtY5//9Yqc5cbm0m4DPy0cLk0WnJdYNFy1jVnOAL/zsZT74usXkEBUnWx++cBldzTqxdJ5bHn6BZZ0Rrn71/Iplb7tsBYeGUwgB0YCvxB5rwyXdtIZ9zAhpjGQkyYxBLi856Eiuv3X9cgSSZCbPf/5qZ9Ej89STmhhIZGgL+8nJPINJg5awj0TaoCnoI2vkSRs5wrqPvkSaGUGd2x95if2Daf7m3PksnRklZeTpiOi8dCjGwaE0T+/s5YMXLCOfz9Mc1OmNpQj4vIQDGoeHTFXayq4mDhZ+PhlHlVpDPsAaEYaR54HN+0tsEJ0es5+/fDlGXhQ/jAR8pl1WLp+nOegrmxR95I0n0xH1M6vJz0Ayy+GhVJk13I1/eSo+AcGAj/0DI2Xiq588d7DMS/rf3no6iUy+ZJJ3/ZqlhHUv/+8XO9E1UfTmHcnmeXpnL1eunldmsziryc8zuwcI+rzomqfkQ9+n1p5Ge1gn5NfY0xenOWS+AaCzKcDwiKmQz+YkGSNPU9DHK71x5raGkDJPSPfRFzfLxNNZ9vYn6Wzy4xUehpNpFnZE6Y2naYv48Yg8uTwcGEzTEdUx8nmaAjp5meNXO44Q0jVO64qSk5Ko30c2l59Ib9eG408l4cfnLl/J7BkBWsP+YrskRtI8s2eAWDqHB0kyK7n3d3uKAiLn2F2pv9922Qo6m3Q8wkM8ZdDV7GekkLrSGtZpj/qY2xJRk79phlFa7akJ4EShHruqu655Fe+qkMxuba9HvFArMdgST5w6K1pVyGEXUliikGriDHAXZLRFdH67s59lnVG22ayt7OXcksuteKslxwOughBrv9Oq6p5rzyEa8E2mLVHDPcAaFfXYIFYTP+xw6XPXnreIcxa18dTOfqBy//qPt5/Fs3sG6hZWVLNDrGSnVstmsdp+KO/XTss7t1icx9USkln77rn2nIo2ZpNg5dVw/KnX5uyZXf2ksjmu/c6zfPNvzuaa/3qmzI7Tfs+rPS/e9vWnS+77nW8/i017Bli/arYSf0xDjNJqT4lAJgr12FX1uSSz98fTdYsXaiUGW+KJWkIOu5BipA7RR7V6emNp03bNYW1lLzeYrJz4bMVbLTm+miDE2u/cfmgopWyJpgjqsUGsJn6o1nf64umq1nADieyohBXVzlVJrFXLZnG0/dppeVetbvu2WkIya9nNxkxxpn6bs8PD6aLQozeWKhvbnffcrQ/0x9PFZeu+DyTN/qrux/TEWDzT1ARwHFCPXZVbMntbxF+3eKFWYrC1v5aQwy6ksJ+31vkr1dMR9eMV5jndktRnhConPlvxVkuOryYIse+3b5/VHFC2RFME9dggVhM/VOs7HRF/VWu41rBvVMKKaueqJtaqVL7W/kr9OliIySmqcqvbGU+l2Oz7rJxG5/GKM/XbnM1s8heFHp3RQMWxvVZ/t54L1rJ131tC5ou61f2YnhiLZ5r6CXgUqJZwad/XGQ2wqz/O333vD7xmYSt//ZoFbN43SF7CU6+YeWlS5jmSMLjxgaP5Q7dcupwlnSGyOTDyOaT0sG8gSUjXeGjzn7jsjHls74mTy+c57aQm2sLmm/wzuTzDIwbfe3o37zh3ESMZgwXtIbxCcHg4TTSgoXsF2w7H8Xo9Jbl7H75wGS0hjf5Eljse3V41B9BKALdeNm3Ph9pwSTdtEZ2QLuiPZ0lnc/h9Xg4MpkrK3XLpch554QCr5raVJaFnDDMv8F3nLiSZLc3RuuGiZUT9XkJ+jdsf2cbbzp7PF39e+gLdOS1BAD5y35aSHKv1p8/G4xE89vJhtuwbKiZbr5jTzAUnz5yWOUwTDSd35rWE2DuQLOOSYeR5cMsB7iiIkpoDXtoiAT5p48nnL1+O1+PlZVvi/JLOCPNaQuwfHGHPkWRZknw04CMaMF0/khnJ4EiGpoCP7YdjzGkNEvX76E9kaApq5CU8s+sIeWlapl31qnn85LmDvOWsOfQlMsVzrprXzGDC4J9ccgAHkpmylz87Xw5t2Sl+xCWX9/o1S+ls8uPzePjo/VtKuHj/700xxz2b9vKucxdiSFmSP3jDRcuY2eTnY/eXxuf2Mnn7vtuvXMUbTp3Jz148XG+O0Xii4fhTz8uf9w0mGMkYDI8Y9MaztEc0dK+XjCHpiadpCmjsH0giEWwsjMnz24K8/7wl3PxwaR/57Y5e/nfrYTau6+beTXu56uz5hHwC3adN1Bim0GBQOYATiGqNDVTc131SlGd2DRSFHs4E3/ltQT69bjnxVJbtPXGeeKmHN63o4tEXD3H5mfNKBoGN65bzlSe2Fwfr2968osRF4A2ntXPhaSdx04+fryg8sR5EGUNyxeo5zG8NEdJNtfCpXU30xFLomlacPMbTObK5PE0BHz2xFC0hnZyURHStMKn04vN66E9kaY/oHBxKcsejO/j7C5cR8EJHU4g8ebyYdlWJtEFrWMfvFaRzklgqSzRgJql3RPz4NQ+HYmlaCqpjIQTZXJ5E2mBmUwCJRPN6CGoCIy8YTmVpDekMJDP4fV5aQj5OmdkEwNaDQ2WWVKMky1ij4R5gEwln289vC/KhC5aWuB9Y9yKflzy1u5dDQ9nih6P5bUE2XNJNLGUwq9nPYDLLYDJbkih/87puth8aZOXcVoZHsnTNCPHiwWEWdUT43E9fLE7A/vb8JXz1iaMfbmo57ty6fgXIHH2JLM1BvegKYk7mlvKVJ7ZzxVlz6Yz6mdUcoMmvkc7lGUoapp1awZ5xKGWYvsQRjcFkjp5YiraInwMDCXSvB5+mYeTzzG0Jks1JeuMZIgXbsCPJDHNmBIilTJu5trDOcCpLPk/h9Us6iXSWlpCftJFjIJkl7Ndo8mv4vIKhlGnr6PeaHygHRrKsmjuDu5/ZzdKZM/B64OwFrcyeEeSwQwzVIFZeDckft7bJ5yVP7eqlN5Yhnc1z04NbaQnpfPqSUxgcyZWps01HEFOs1hTwMZIt2F6msrSEdPw+wbbDCXpjaea0BJnV7Gckk2N+W5i5LcpabTpjFPxUE8DjQbWES6Divu+862zeftczNRN8nUngtQQgUJ6Abnf0qOVC4Fy3EouvK7gjWAnxlYQad779LNc319sTy6232DuTzd0S0689b1FV4YhV57EmoI8yYXas0ZAPsImCs+3d+uf/XvdaYqksQyPZin3srmtehVH4truS+4uVZO90UXCe1769Hscdy2WknmT9b/3N2bzzv55xrdtNOOJ0fKgUy7EcW218mgRHj2PFlOLPzt44+waSbLIJij74+iW8emFLVdcPy5HI6nMfvW9zmWuNfV8D3y+FxoMSgRwPqiVcuu07NFwq9Kg3CfxYXACqJRZXOt6Z8D1ic0ewEuIrJsq7CDicieVuyeZuiem1hCNWncea8KxEIJMHZ9u79c+eWIqDQylXd4T+uJlQn3Dhh5Vk7+aGU2l7PU40lstIvddQre5a/buaaONYjq02PilHj/HB4WGzD9vvlxDUdP2wHImsPpfKlrvW2Pep+6VwvFATwDpRLeHSbd+spspCD+e6Mwn8WFwA6kksrpbwHbS5I1gJ8ZXqaHERcDgTy92Szd0S0z01hCNWncea8KxEIJMHt7Z3rndGA3Q1B137XlvETKi3i5qcx1vbnf+d562XK3Zu1HsN1equ1b+riTaO5dhq45Ny9BgfzGwKVBQU1XL9sByJrD4X8JW71tj3qfulcLxQE8A6saAtzO1Xrip5gNx+5SoWtIVd963oai4meYOZUL5hbXdJuVsuXU5XYaJ4/7P7uH7NUr71m51l5Tau6+bhLfuL6wvawtxw0bJimW/9Zic3r+suqcd+/IZLSo+/fs1Sfvj7fcX8qW//ZmdJ2f9+aldZDBsu6cbI50vO66zruguW8vCW/SXb7Ms3XLSMjoi/7PgVs5uJBry0hfWy2K06rfYe6/unML5wtv1Dm/dz6/rlFe9Fd1cTOZkv9mVr/83rurnvd3sZTKbRPLDhktL9n76km0e27mfjum4e2ryf6y5YWvxvP+/Ntv1uXKnEDWe5hzab53Jy9Kkdh6vWvaA9XMYfq3/fculyOqPl3LBiaQvrfPhC92NXzG6ue3y6df3ykvFAcWHssKAtjOaFRR3hYh+5/9l9jGSyZf3aun83r+vmm7/aZY6za80+t+GSbpKZbOkYXNj3hSvU/VI4fqgcQAecal7NC4eG0iQyBnNbQqQNM4G7KaCTMfLMajYTL9Npg+cODXN4OM3MJj8zm7z0xnIMF8QOeSlBQjYniacNmoIazQEfyUyOnliajqgfyAMeemNp5rYGza/5h819rWEvA8kch4fTzGryE/R5yOZNeyzTqk0nEtCIpwz64hlmNQUw8nmGRgxCupdoQCOWMkhkcnRGdACG0wYj2Rwzo37ShplbFfF7iQQ0Utk82VwOn9dbjCGse9k3NEJnxE8qm2M4ZdAe8SOQHI6laY+YjgOmg0CegYRBOOAlpHk5kszQFNAI+LyMGDkyhiSeMmiL6Pg1D81BH3NmhNg3mKQ/niFl5MgYeVpDOtl8vuTt+s77VK8jwSQmtE+pHKZjQT4v2dWXYM+RBGFdozPqRwjzvX521a/V9idFAzx/aJiBZMYU/iSzhHQvmgd0zUueHPm8yYWOqJ9MzsArvMTTBs1BHxZXLL61hr0cGMwgpSSoa/QnMrSFdUYyBmG/j4FEhnBAQ8oculdjcCRbFGi0R/wY+Tx98QwRv0ZI93JoOEVb2M+MkJeRTJ54Oke8IGQaHMkyI+CjLeKlP5Er4XxfzCwX9Gn0xE2u+jweDgynaA5oRPwaI0aOrCGL9cVSWaJ+H1Lk8Xm8ZHN5sjlJMpOjPWKKr1qCPtPFJJamM2q6gQR1jaGRLM1BH3mZI+LXCWhe+hLpsv7t7PvO+zFJ4o560ZD8qTQGAcVtHRHTFSnkFwwl8ySzOXyaROClL56mLexnJGugeTxE/F76kxma/T4SWbP/DKdMK05d87C3f4TOqB8p8rSGAizuUM4fCqNCxc6iVdo4XVFJKer037zxL08lkc7xxZ//oVjmrmvOYt+RdIln5sZ1y7l30x72D6b5wOvMN/k77aduuXQ5//64qexdPb+5aBPlVCbObwvy/tctKSoQrW8m0tkcn/nJS8VtN1y0jJaQj7yEv/rG07ZYuvnKE0dfRfEPFy1jf4XXs2geyXV3b3U9p/MVEj/YtJeMIbn61fNLyt26fjkdUZ3r7znaRrdfuYoz57XyxPYeth+OV/Tu/djFp3Jx9ywWtFdPbD5WRa/HI1jUEVGJ02OMSvfD+ToU6/4s6ogU7d6+/Nj2iq/zaQv7yEvBp22KW+erMeyvMLFer/KVJ8pfXfSxi0+hq9nPrr5kxT739xcuY2dfouzVK1Y/v/EvT8WveUssGzeu6+bnWw9wxrz2spie3d3HGfPbufmh35ds/8GmvVy6ag665mGDjeN/f8/RNrMUoZZNmLX93992Bnv6kyXbbl2/nLXdbTy89SAf+v4fSravP302mlb6406lvq+4cOxwG4N0TRRf/3XZWXOYGdXY1Z8tjrfWfb/7d3sreqCHdS/RoI9/+/nWiq/nuX7NUpbOzLFY3TeFMYD6CdiG3f2JIqHBTLi9+aGtrF05u7jeE0sXH1jWNiNHcfJnbbvpwed5x7mLePOZ5rvD+hKZ4gPIKvOpHz9frPsd5y4qvh/szWfOKSm7duXs4gTLOnbDg1vpS2RKtt3+yDaCPq34moGjsRy9hrUrZ7OjN1ExFp9Xq3rOOx7dzpvPnFM8/zvOXcR7zltcVu7GB54nNpIr2XbDvX9k68EhtuwbKjv3F39uvvPthnv/yO7+xDHdp3qPVRh7VLofdzy6nb5Epthf7Pdn68EhbnzA7PtOLt3x6HZCuq84+YNCX3y4vP/b+7TVx60HqlXucz99CSOHa5/bZZv82WOw4u6JpYuTP2v/TQ9uZb3tNU32mNafOa8iV99x7iJ64+kiN50cT2XzfOmx7bzj3EXFiZ61fcu+obJtNz7wPM8drLx968Ghsb/JCiVwG4O27Bsilc1zzZ8vZEdPHIFWMt5a971SX7U4s6svUfLMsffHOx7dzpZ9Q2qsUxgTqAmgDfWo5Sop8dzUXZZ6sJr9lFX3sdgDVVL9uSkk7XZUbrHYFWf1qiPdFMuV1GsHh1JjovJVit7Ggtv9cFqiWffHshhztRR09KlafdFN9Vusr4KNm1W+Fi/d9teycnRud9rUucVaiU9uMTjfMlDcPqR4MN6o1ueBoq3g4Viq4n2vZftX7Y0Nyv5NYaygJoA21KOWq6TEc1N3WerBavZTVt3HYg9USfXnppCsdQ1uijO3eqzrc1MsV6qrqzk4JipfpehtLLjdD6clmnV/7HZvFfuOSx92rleyWHPri27H1+Kl2/5aVo7O7cEKNnWVylXik1sMbm8ZmNWseDDeqNbngaIKeGZTwPW+ux1v54213fkWBTXWKYwFJkUEIoTYDcSAHGBIKVdXKz+eSez25PWIX+NIIsNtP3mRq141j7ktIQI+D3kJO3sTzGk1JzBhv8ZLB4cZTudoC/s4a34LLx+K86mCC8cVq+ewuCNCWPdyYGiEtrBOJic5NFSedzdeOYD2t82PJgfwn340djmA7/vO0Twoy1qqnhzAesQck+jqcSwY86BGw6Hx4o+VBH9oKEU2l+e7T+9i7elzyRg55raEyOZN4cauvjiLOqIF9xeNlpBGTyxLMpPDyOVLLN3mtgaZ1Rzg4GC6xEHHaaFWngNounI486o+8aZT6GyqngNod9SpNwdw0+6+GjmApdvdcgDtMVXLARxMZivkAHbx8NaDZdsr5QBOcTQcfyqNQV+4YhWdTToHBlPMbw3SF8/QEdXZ0z/C/sGRUeYAbquSAxhR9m8Ko0XjOIEUyLdaStlXT/nxfIA5Sfzxi0+ha0aQnb3xiiS97oKl3LNpL1e9al6RlBvXnYbf5+XwUIpwwFfmtdsU8PK1J3cWJ5UIODQ0wjO7+njnuYs4NGQq8fLkiY3kSGQMWkI6mkfw/IFhjHyeFbOb8QpBfyLDvNYgAc3LSNbAyEMykyPs96JrHuIpg5aQj0QmR188w8ymAB0RL0cSptq4M+onqHtJZXOMZPPE0wYdYZ2ADpmsIJnNkczkCOpemvwaEln0Ew76vBwcTtEa1mkKaMRSOZIZg4XtYYaSWQ4NH7Vf83iEq03S3iMJDg+nSWaMwuS4XOVbz71rAIuqejFeD7C6ODQe/HFyx/4Bxjnx2bRnqOIEZ1lnhL86Z77jw4ppeWhZFi7uiHBgcISfPHeQ80/p5OSZUZqCPox8jpDPR28sDcD9z/6JFXNncFpXE1LC9p44Rj7P0s4ISzrCxb6eyuZpDevE0xm8wktAF/g8HoZTBvG0wYygD80rGEgYBAtWbl4PxNI5jiQydEb95KTp052XOXxezeRVxA8iT8Cn4fMI+hLZIrdShoHu1YhnsnQ1BUgbksPDKWa3mDzujacJ6ab6tzXsr6jQzedlRYtDw8hX3H6CoSH5Yx+D2sN+Xjg0zEcLns4feN0isnnTm/k1C1u59nWLyObMtzbMCGkgBUlL7TuSJaB70b2CgOZlcCRDUDe/jGgP6wT95psYQrrGzCY/81obeqxTaEyoCaATbvZg//qW0/nH+zaXWUlZ+51WZ/9+9RlVy1ezd7KWgZqWVE7rJme91ayi6jmXshYaNzTkA+x44OSO3YrQQsBXbjFotzl0O8bNstBZ57Eeb+ev10NFzlx7nsmTevileDPuaHj+bP7TAG+98ylSWdP2rVq/yuUpjsf/+pbTAdjWE+OcRW2865u/U/1JYTzQUFZwEviZEOJZIcS1lQoIIa4VQmwSQmzq7e0dlyDcEnmtJPRqyef2xNxa5avZO1nL9YgunInBznprJbTXOpdKLJ5SqMqh8eaPkztuYiCnxeCITZBRj+VhpXWrzmM93s5fN85Yyfh2uJVVvJmSGFP+WMIm89jq/co+HicyBomCQMgSEKn+pDBRmKwJ4J9LKc8E3gR8UAhxnrOAlPJOKeVqKeXqjo6OcQnCLZHXnoReTbBgfXlaq3w1eyfncj3l3OqtldBe61wqsXhKoSqHxps/Tu64iYGcFoMhmyCjHsvDSutWncd6vJ2/bpyxkvHtcCureDMlMab8sQuboHq/so/BYV0jrGt4BEUBkepPChOFSZkASin3F/73AD8Czp6MOCpZJH3kjSfj8wiuX1NuJWXlMTmtzpLpLBsu6a5Y/sMXLqtq73RzweLt/mf3VbVYc1rBfeSNJ7OwPVxSfkF7uMxiq5IlWzUrLoWpgcnmkJM7ditCOGrfZrcYvO4C0+bQ6ntff/KVMks3Zz+vtG7V6XZOe3nnusVf6/+C9jAfeePJZZxZMbuZpZ2Rku1LOiN84QplJ3giYKz5093VVLT9vP/ZfbSF9YrjeXtYL47Hn76kG48HkpksSzojPPD7vao/KUwoJjwHUAgRBjxSylhh+RFgo5Typ27HjFUOk6X4PTCUxO/1ksgYzGkJYeRzHElkiaUMogHzE1lPLE1TQMPjEQwmM4T9pghiOJUlpGsMJjOEdNM2KpMzbdMCmsZQKkuT30d/Ik1rWEfXPCQzBrrXS08sXbQ+6xlO0xLWaQ56GUyaAo2uZj9+zcOBwRSRgIaueRhMZIkGNcK6l0TGTESf3RwEAbFUBl0zY20L60R0L5l8jlxeFC20QrqXWMqgMxrA64FDw1PSCmqqY0wbdrQcGgv+2G2vupoD9MczHBpO0RH1Y+Ry+DUzaT0S0MjmcwQ1046tNaxj5Aw0SywR9ePzSqT00BfPMCOsIaSgJ2byJejzYOQlvTHTKsuvQzoLA8ksLSEfsVSWpoCP4XQWv9dLNOAlY5hWhJ0RP16PJC9NsVRrSMeQOTSPl/6C9VY8Y9quJTMGIV0jk8sR8ZuWjLER07rwpOYg812EF27iJoVxRcPzJ5PJsfXgEH3xDE0hH6mMwYygTjJrjtltEZ2A5uFIMoPmMb/583hMXnmFwOfzEPRpqj8pjBcaxgpuJvAjYSZCaMD3qk3+xgqWavFzP32xRNlb6RUT169Zyk+eO8ibVnSVvKbhhouW4fd6uO2nL5WU/fZv96Brgn+46OSi3N/+GoqRbL7M7mpWc4BwzmDLvmTJKybskn9Lcfyxi2c0qDoAACAASURBVE/l7AVtRRVtpVegvHphW10Dh91mTVlBTVlMKIfsfW5ZZ6TktT/z24L87flLikreo68POvoKIPM1REetrW5e143XAzc+sLWiEvj6NUuZ3RLkvmf3cMa8dr72ZLnF263rl/OzrXt4/cldJa9icb4G5Wjsm0f9yiCPR3D63BZOn1u6XfFmymNM+ZPJ5HhgywFusr226O/XLOOFg7Gy58d//Xo3A8kMt65fzt3P7OGtr5rP7BY/58ztUBM/hQnHhP8ELKXcKaU8vfDXLaX854k4r2Xd47TgsWykrPVU1rTbec95i8usmm5/ZBv9yUxZ2TefOadgsRYvO6Yvkalod7WrL4FXeMtspuy2P196bHuZRZqyQVOYaA7Z+5zT+m/tytkl1oOVLATtVoSprGmNFtC0Yn1O68I7Ht3Ojp540W6tkm3WjQ88z1+ds7DMjs1phab4ouDEWPNny4Gh4uQPTA7s6i+327z9kW3Fsf3GB0yr0E/9+HmMHKo/KkwKTrgXRrnBUi3Wa7PmpjB0U/S6Kb+qqcF6XWyjnKpduzJM2aApTDTsfc7Ji3r55FTiJmooefOSot2aW52DLhaMdis0xReF8cYhRx+rx2LQrogfSGZVf1SYFEybCaBdtVhJneVcd1MYVlP0VlJ+VVODdbjYRjlVu3ZlmLJBU5ho2PucGy9qrTuVuOEaSl6PoMRurVKZGS4WjHYrNMUXhfFGV4U+Vs8bGYIFRXxLyKf6o8Kk4ISeAObzkp29cX77Sh8eAV+4YlWZUvehzfvZuK5cOfv1J1/h+jWlit6PvPFkVs5p5h/fsIwvX30Gn3jTyXzxrav45bYeHtq8n8UdkbJj2sI6H76wXA22sD1MTuaKyjH7PkslZikV7cqwSsplpRxTGE/MawnxuctXVlTuPrR5f4kS96HN+8uUvbdcupyuJp0vX30Gn798Bf/x12chyRfrcyp5r1+zlCWdEX70+71sWFtZXX/r+uV896ldbFjbXba9u6u5GLvii8J4Y8VJzWy0jeMPbd7PgrZw2bPghouWFcf2W9cv59u/2WlacHpR/VFhUjApTiCjxbGoGCuJJf7lLSsZHsnS0RQgoHnoiaXpjaVZPjtK1oA/7htE93poCmi0F7590DUvf9w7aH5SC+t88kdHBRuWSONDFyxlUVsYnyY4ksgghCCVzRMNeNG8HpJpg+agTl88ja55COpeIn4vSzuaAIpKw67mANGAj0PDqRJrKKcybIrZoCmMg5PBaHA8KmC7eGrtytkEfR5WL5jBUDJHysjRGfWTMgxeOhinNaQT9mu0RXzkJDyz6wi618PclgAHhtIlCfGfvWwFc1tDHI6lmNXkx8hDfyJDxK/RFNDwCsGRZAavB3Svl4GRLDMCPpJZg7ktYea3mir24ZEMRh764mm6XKzQFF+mPBqeP6mUwZaDQ/TG0swI+cjkcqatW9J80fPcliB5KYmlcnREdeLpLAFNY2aznzkzVH9UGHc0jhXcaHEsDzA3mze7FZUFN4sne9kPvn5JVVu4e649p2gFZN+vbH0UmAIPMDc4eeTkgRsvLJvCarZYijMKdaLh+bOzN84LB4ZrWoKeNb+F933nWdXHFSYaDWUFN+5wS/6ulHTulrBrt62qZQtntwKy71fJvQpTGU4e1Sv6sMRS1RLiFWcUThQcHk7VZQk6mMyqPq7QMDhhJ4Buyd+Vks7dEnbttlXWNmcZS6TR1aySzRVOPLjxqNa6/RctN34pziicKJjZFKjLEnRGyKf6uELD4ISdAFZK/r51/XK+9ZudZQnlizrCZbY9G9eVlr3/2X1lSb12kUZ3V7NKNlc44WDnUVdzgCa/t0S45BSBWMnulk2hZYvl5M4XrlCcUThxsKAtTNTvLVqC/uMbyu0FF3eE+e5Tu1QfV2gYnLA5gFCe/G3Znx1JpNG9HvYOjLDtcIwnXuph7couTp7VRNrIM6vJz7KOKC/3xjiSSBMN6MRTWaIBH5pXMJDIFG2kuppDLGwPF106VLK5QgU0fA5TNeTzkr1HEvx+7yCf+NFztIR03vGa+cxvC9MU0GgL+8jmJYeG0oR0L0Gfl/aojpGD3niKzoifWNpgaMT8+Wthe5jFHRHFGYV60fD8yeclj718mEODI8xtC5PK5mgP++mPZwjoHiJ+Dc0riPh9qo8rTAamlwikFtxEIio5V2Ec0PAPsFpQfFGYRDQ8fxQ/FBoc00sEUgvKIUBBoX4ovigouEPxQ2EqYtpOAJVDgIJC/VB8UVBwh+KHwlTEtJ0AKocABYX6ofiioOAOxQ+FqQhtsgM4HlgJ5IeHU8xsGl0CuccjuLh7Fqdc91qVgK4wLTEa/ii+KChUhsWjjqjOPdeeQzKTG/XzSEFhMjBlJ4CVrN5uv3IVF3fPGtUkcFFHRCXpKkw7HAt/FF8UFErhxqNXL2xTkz+FhseU/Ql4d3+iSDowE25vuPeP7O5PTHJkCgqND8UfBYXjh+KRwlTGlJ0AKtWVgsKxQ/FHQeH4oXikMJUxZSeASnWloHDsUPxRUDh+KB4pTGVMygRQCHGxEOJlIcQOIcTHj6UOpbpSmM44Xg4p/ihMZ4zFMwgUjxSmNibcCUQI4QW2ARcB+4DfAVdLKV9wO8btTezKRkphimBMO+VoOaT4ozDF0ZD8saB4pDAFULFDToYK+Gxgh5RyJ4AQ4m7gUsB1AugGpUpUmKYYEw4p/ihMU4zZMwgUjxSmLibjJ+DZwJ9s6/sK20oghLhWCLFJCLGpt7d3woJTUJgCqMkhxR8FBVco/igo0MAiECnlnVLK1VLK1R0dHZMdjoLClILij4LCsUPxR2E6YDJ+At4PzLWtzylsc8Wzzz7bJ4TYM65R1Y92oG+yg6gTUylWmFrxjibWn0opLx7Dc4+KQw3Gn9FiKvWJ0UBdV/1odP402r1spHgaKRZorHgmMpaKHJoMEYiGmYC7BpN0vwPeJqXcOqGBHCOEEJuklKsnO456MJVihakV72TGOtU5NBpMpT4xGqjrmjyMNX8a7ZobKZ5GigUaK55GiGXCvwGUUhpCiL8D/g/wAnediA8uBYXxguKQgsKxQ/FHQcHEpHgBSyn/F/jfyTi3gsKJAMUhBYVjh+KPgkIDi0AaGHdOdgCjwFSKFaZWvFMp1qmME7Wd1XWdOGi0a26keBopFmiseCY9lgnPAVRQUFBQUFBQUJhcqG8AFRQUFBQUFBSmGdQEUEFBQUFBQUFhmkFNAF1QyyxcCHGDEOIFIcQWIcSjQoj5kxFnIZa6jM2FEJcLIaQQYtKk5/XEKoS4stC2W4UQ35voGB2x1OoH84QQjwsh/lDoC38xGXFOdUwlvo0GU4mbo8FU4/Gxoo5+6RdC3FPY/7QQYoFt3z8Vtr8shHjjBMTiyhEhRE4I8cfC34PHG0ud8VwjhOi1nfc9tn3vFEJsL/y9cwJi+aItjm1CiEHbvjFtGyHEXUKIHiHE8y77hRDiS4VYtwghzrTtG9N2qQkppfpz/GG+GuAVYBGgA5uB0xxlXg+ECssfAO5p1FgL5aLAk8BTwOpGjRVYCvwBaCmsdzZ4P7gT+EBh+TRg92TFO1X/phLfxvq6CuUmnZvjcL8ahsfjfJ1/C3ytsHyV1S8LY8FmwA8sLNTjHedYXDkCxCehba4B/r3Csa3AzsL/lsJyy3jG4ij/IcxX/4xX25wHnAk877L/L4CfAAI4B3h6PNqlnj/1DWBlFM3CpZQZwDILL0JK+biUMllYfQrzbfKTgZqxFnAL8DkgNZHBOVBPrO8FviKlHACQUvZMcIx21BOvBJoKy83AgQmM70TBVOLbaDCVuDkaTDUeHyvquc5LgW8Vlu8D1gghRGH73VLKtJRyF7CjUN+4xTLBHKm3b1fCG4FHpJRHCv3jEeB4nF5GG8vVwPeP43xVIaV8EjhSpcilwLeliaeAGUKILsa+XWpCTQAro6ZZuAPvxpzRTwbqMTY/E5grpfyfiQysAupp12XAMiHEr4UQTwkhxpUANVBPvJ8G/loIsQ/zvWIfmpjQTihMJb6NBlOJm6PBVOPxsaKe6yyWkVIawBDQVuexYx2LHU6OBIQQmwr3Yv1xxDHaeC4v/Mx5nxDCst+btLYp/Cy+EHjMtnms26YW3OId63apiUl5EfSJBCHEXwOrgddNdiyVIITwALdjfh0/FaBh/nx0PuYn2CeFECuklINVj5o8XA18U0r5BSHEa4DvCCGWSynzkx3YiYhG59toMAW5ORpMNR6fMHDhyHwp5X4hxCLgMSHEc1LKV8Y5lIeA70sp00KI92F+U3rBOJ+zFq4C7pNS5mzbJqNtGgLqG8DKqMssXAhxIfBJYJ2UMj1BsTlRK9YosBx4QgixGzPn4MFJSjavp133AQ9KKbOFn062YT5IJgP1xPtu4F4AKeVvgQCmybdC/ZhKfBsNphI3R4OpxuNjRT3XWSwjTI/hZqC/zmPHOhZXjkgp9xf+7wSeAM44jljqikdK2W+L4T+Bs+o9dqxjseEqHD//jkPb1IJbvGPdLrUxngmGU/UP89PrTsyviq2k0m5HmTMwE0+XNnqsjvJPMHkikHra9WLgW4XldsyvxNsaON6fANcUlk/FzAEUk9knptrfVOLbWF+Xo/ykcXMc7lfD8Hicr/ODlIpA7i0sd1MqAtnJ8YlAjpkjmIICv+1ebKeKSGIM4+myLV8GPFVYbgV2FeJqKSy3jmcshXKnALvt4/N4tE2hrgW4i0D+klIRyDPj0S51xTmelU/lP0ylzrYCoT5Z2LYR85MVwM+Bw8AfC38PNmqsjrJPMIkPmTraVWD+LPYC8BxwVYP3g9OAXxcGnT8Cb5jMeKfq31Ti21hel6PspHJzjO9XQ/F4HK8zAPwAU+TxDLDIduwnC8e9DLxpAmKpyBHg3MI92Fz4/+4JapvbgK2F8z4OnGI79l2FNtsB/M14x1JY/zTwWcdxY942mN8wHgSymN+Evxt4P/B+Gze+Uoj1OTvnx7pdav0pKzgFBQUFBQUFhWkGlQOooKCgoKCgoDDNoCaACgoKCgoKCgrTDGoCqKCgoKCgoKAwzaAmgAoKCgoKCgoK0wxqAqigoKCgoKCgMM2gJoAKCgoKCgoKCtMMagKooKCgoKCgoDDNoCaACgoKCgoKCgrTDGoCqKCgoKCgoKAwzaAmgAoKCgoKCgoK0wxqAqigoKCgoKCgMM2gJoAKCgoKCgoKCtMMagI4jhBCrBdCSCHEKbZtq4QQf2FbP18IcW6VOtYJIT5eWP6mEOIto4zhE8cS+1ihcH0Pj/KYJ4QQq8crJoXGwnTliRBigRDi+TGqa7cQov0Yj1V8O0EwXblUOG8xboX6oCaA44urgV8V/ltYBfyFbf18oCIZhRCalPJBKeVnjyOGSZ0AKijUAcUTBYWxwbTl0hjE7QohhDYe9U421ARwnCCEiAB/DrwbuKqwTQc2Am8VQvxRCPEx4P3Ahwvrry184vqaEOJp4PNCiGuEEP9uq/pCIcQmIcQ2IcTaQr0lZYQQDxc+5X0WCBbq/m5h318LIZ4pbPsPIYT3OK/zf4QQKwvLfxBC3FRY3iiEeG+hWEQIcZ8Q4iUhxHeFEKJQZk3hmOeEEHcJIfwV6n+DEOK3QojfCyF+UGhXhRME04gn3bb6tgghlhZ2eYUQXxdCbBVC/EwIESyUL34rJ4RoF0LsLix7hRD/KoR4vlDPhxznCQohfiKEeK8QIlzg1TMFnl1qK3O3EOJFIcSPgODxXJtCY2AacekpIUS3bf0JIcRqe0xCiCsKHNkshHiysK0id4QQZwkhfiGEeFYI8X9CiC5bvf8mhNgEXC+EuEQI8XSBSz8XQsw8nutoBJyQs9oGwaXAT6WU24QQ/UKIs6SUzwpzgrRaSvl3YA7GQFxK+a+F9XcDc4BzpZQ5IcQ1jnoXAGcDi4HHhRBL3AKQUn5cCPF3UspVhbpPBd4K/JmUMiuE+CrwV8C37ccJIb4IvL5ClXdX+IT1S+C1Qog9gAH8WWH7azEHmi7gDKAbOAD8GvizAqm+CawptNG3gQ8A/2aLox24EbhQSpkoDF43YA5oCicGpgtP3g/cIaX8buGh7AVmAkuBq6WU7xVC3AtcDvx3lfa6tnBtq6SUhhCi1bYvAtwNfFtK+W0hxGeAx6SU7xJCzACeEUL8HHgfkJRSnirMD2+/r3I+hamD6cKle4ArgQ2FyVqXlHKTEGK5rcxNwBullPsLfR8qcEcI4QO+DFwqpewVQrwV+GfgXYVjdCml9UGsBThHSimFEO8BPgr8g1tbTAWoCeD44WrgjsLy3YX1Z+s89gdSypzLvnullHlguxBiJ3CKS7lKWAOcBfxOmF/CBYEeZyEp5YdHUecvgeuAXcD/ABcJIULAQinlywWCPiOl3AcghPgjJgljwC4p5bZCPd8CPohtAgicA5wG/LoQrw78dhSxKTQ+pgtPfgt8UggxB/ihlHJ7oe5dUso/Fso8i8mNargQ+JqU0ijEcMS278fA56WU3y2svwFYJ4T4x8J6AJgHnAd8qXD8FiHEllFch0LjYrpw6V7gZ8AGzIngfRXK/Br4ZuFD1Q8L28q4U5g0LgceKcTnBQ7a6rnHtjwHuKfwTNMxn3lTGmoCOA4ofCq/AFghhJCYnUoKIT5SZxWJKvtkhXWD0p/zA26hAd+SUv5TtZOP8tPY74DVwE7gEaAdeC+lA0/atpyj/n4ngEeklFfXLKkw5TCdeCKl/F7hJ7a/BP5XCPE+TM44uWH9HGuP1S1OJ34NXCyE+J6UUhau43Ip5cuOuOusTmGqYJpxaX/hG86VmN8uvr8sYCnfL4R4NSbfnhVCnFUlvq1Syte47Le3y5eB26WUDwohzgc+Xe2apgJUDuD44C3Ad6SU86WUC6SUczE/LbwW85uvqK2sc70WrhBCeIQQi4FFwMvAbmBVYftczK/rLWQLX3MDPAq8RQjRCeagIYSY7zyBlPLDUspVFf7KEmyllBngT8AVmN9y/BL4R+DJGtfxMrDA9nPC24FfOMo8hflz8ZJCvGEhxLIa9SpMHUwbngghFgE7pZRfwvymbmWN+HdjfnMCZjtZeAR4nygkpTt+Ar4JGAC+Ulj/P/4/e18eJ0dZp/+81VXV9/TcR2aSSSaZXDOTBAmngECAH+wv58ohuuKB4oEmS0TdVSTkAAUBfyruuqzgoqtcoghRWG6BBYFw5L4nmSSTuY+evrur6/390V09VdVVfcyRTM+8z+czn6TreOvtqu/zvtXdz1MP8E1CUprbM5LLXwfw6eSy5hz6wjDxMWW4lMTjSPwE66GUpn2DTQiZTSl9h1J6O4AeANNhzJ39ACoIIecllwlEpS/UwQOgPfn/z5lsU1BgN4Djg+sB/Em37Knk8lcBLCQJQex1AJ4FsCb5+sIc2j4G4F0AzwH4KqU0jMQn/yMA9iDx045a0/MggB2EkN9RSvcgoal7Ifmzz4tIaPRGizcAdFNKQ8n/1yX/NUWy318A8CQhZCcAGcAvddv0APg8gEeT/X0b+f38wDCxMZV4ci2AXUkJRDN0GigD3Avga4SQD5H4Vl3Br5LvbQchZDuSN3IqrENChH8PgM0AhOS2u5OvAeDfkTBm7UVCT5vrz4QMExdTiUtA4mffTyHxc7ARfkwS5sJdAN4CsB0G3El+gXE1gLuTyz6CiUMaiW/8niSEvA+gdwzew2kHSfxSwMDAwMDAwMDAMFXAvgFkYGBgYGBgYJhiYDeADAwMDAwMDAxTDOwGkIGBgYGBgYFhimHcbgAJITaSePr3dpJ4yv3G5PJZJPE07UOEkMdJ4qGoDAwMKjD+MDCMDoxDDAyZMZ7fAEYAXEopXYxEFuGVhJBzAdwN4CeU0jlIPLLgxmwNXXnllRSJZw+xP/ZXiH8jAeMP+2N/ib+RYkw4xPjD/ibBnyHG7QaQJuBPvhSSfxSJh1UqT+5+BMDqbG319k4KxzUDQ85g/GFgGB3GikOMPwyTFeOqASSJ8OWPkIh+eRHAYQCDShQLgBMAak32vYkkAqi39fT0jGc3GRgmJBh/GBhGh5FyiPGHYSpgXG8AKaXxZCh0HRJPCs/5Ib6U0gcppUsppUsrKirGrY8MDBMVjD8MDKPDSDnE+MMwFXBKXMCU0kEknkZ+HoBiJYoFCVK2m+7IwMDA+MPAMEowDjEwpGM8XcAVhJDi5P/tAC4HsBcJEirZlp9DIheTYRJBlilae/x4+3AvWnv8kGVTDSqDCRh/Jh5YXRcWGIdOLRg/Cg989k1GjBoAjxBCLEjcaD5BKd1KCNkD4DFCyBYAHwJ4aBz7wHCKIcsUz+/uxPonPkI4JsMmcLj/2iW4sqkaHEdOd/cKCYw/EwisrgsSjEOnCIwfhYmCyAJeunQp3bZt2+nuBkMOaO3x4x9+9gbCMTm1zCZw+OvaC9FQ4TqNPTutOK0jIOPP6MHq+rSC8WeCg/FjwsOQQywJhGFM0TUU1gwCABCOyej2hU9TjxgYRg9W1wwM5mD8KEywG0CGMUVVkQ02QVtWNoFDpdt2mnrEwDB6sLpmYDAH40dhgt0AMowpZpY5cf+1S1KDgaIFmVnmPM09Y2AYOVhdMzCYg/GjMDGeJhCGKQiOI7iyqRrz116Ibl8YlW4bZpY5mRCYoaDB6pqBwRyMH4UJdgPIMObgOIKGChcT/zJMKrC6ZmAwB+NH4YH9BMzAwMDAwMDAMMXAbgAZGBgYGBgYGKYY2E/AkwyyTHG0L4CuoTCqiiaXDmMyv7epjkK9toXab4aph4lcqxO5b5MZ7AZwEmEyP419Mr+3qY5CvbaF2m+GqYeJXKsTuW+THewn4EmEo32BFImAxIM41z/xEY72BU5zz0aPyfzepjoK9doWar8Zph4mcq1O5L5NdrAbwEmEyfw09sn83qY6CvXaFmq/GaYeJnKtTuS+TXawG8BJhMn8NPbJ/N6mOgr12hZqvxmmHiZyrU7kvk12sBvASYTJ/DT2yfzepjoK9doWar8Zph4mcq1O5L5NdhBK6enuQ1YsXbqUbtu27XR347QjF6eUss1kfBp7Ab+309rJQuCP2bWd6O7AAq7JQgLjzwig586MEgeODQQnZK0yHo07DE8mcwEXCHJ1Sk3mp7FP5vc21WF0bQvBHchqkmEiIhN3JmKtMh6dHrCfgAsEzCnFMNXAap6BYWRg3GHIBewGsEDAnFIMUw2s5hkYRgbGHYZcwG4ACwTMKcUw1cBqnoFhZGDcYcgF43YDSAiZTgh5lRCyhxCymxCyLrn8DkJIOyHko+TfP4xXHyYTxsMpJcsUrT1+vH24F609fsjyxDcETRUw/pjX/IwSB6tbhoyY6vzJdb5gc8DUxri5gAkhNQBqKKUfEELcAN4HsBrAtQD8lNJ7c22rUF1YY42xdEoVgsB+EiHvE8r4k4C+5meUOPDC3i5Wt1MLjD8jQLb5gs0BUwqGF3TcvgGklHZQSj9I/t8HYC+A2vE63lSA4pQ6t6EcDRWuUZGUiYQnNhh/EtDX/LGBIKtbhqxg/Mk+X7A5gOGUaAAJITMBnAHgneSibxBCdhBCHiaElJjscxMhZBshZFtPT8+p6OaUAhMJFw4Yf4bB6pYhXzD+GINxiWHcbwAJIS4ATwH4Z0rpEIB/BzAbwBIAHQDuM9qPUvogpXQppXRpRUXFeHdzyoGJhAsDjD9asLplyAeMP+ZgXGIY1xtAQoiABPl+Ryn9IwBQSrsopXFKqQzgPwGcPZ59YDAGi9+Z+GD8SQerW4ZcwfiTGYxLDOOWBEIIIQAeArCXUnq/ankNpbQj+XINgF3j1QcGc3AcwZVN1Zi/9sKcTSVjHcs10WO+TicYf4xxKuuW1WfhgvEnO0bCJSPkE1HKuDSxMJ5RcB8H8FkAOwkhHyWXfQ/A9YSQJQAogKMAvjKOfWDIgHzid8baMcYcaFnB+GOCU1G3rD4LHow/OWC0EWy58IRxaeJi3B4DM5YoVBv+ZEJrjx//8LM3NKJhm8Dhr2svHNHgMdbtTXCwMPvThJHW2RSrz4kOxp8Jilx4wrg0IXBqHwPDMLkw1o4x5kBjOBUYaZ2x+mRgyI5ceMK4NHHBbgAZcsJYO8aYA43hVGCkdcbqk4EhO3LhCePSxMV4agAZdJAkGbs7vBgIRuAUBQwEo6gqsqOppgg8n/u9uJGgFkBqmUPkEY3HUea0pq0bqQBXcYzpdRwjdYyNdXv5gomSTx1kmeL4QABd3gh6AxHUFjtSNZ/tOiic6fCGUeNJcIXjCI70BtDWH4BT5FFVZMWMUuPrN9I6O131yeqSIRvUNVLjsSEuA90+47mg0m2DhQN6/BGIFg7BaDyvujLjn/r42Xhyusf6scRk4yfTAJ4iSJKMp7e34+evHMR1S2fgZ68cTJFhy+pmrF5cm9NNoJmgVuQJvvH7D1PL1l7aiMe3HcMPli9EVKJjIsAdyyi68Wgvn+OeYlHylNUwyTLFG4e6cXIwgo3P7tbU/MqWaXhpf7fpdVA4c9vTu1Lrf3z1InCEw7eeHN5n3bJGNFa5cOm8KsPrN9I6O9X1ycTyppiy/NFDXSMlDhE3nFePn758MONc8L2r5iMUk/GTlw7kVVdG/NuyuhnFDkHT/gOfPgOzylzo8Zvz5HSN9WOJAuenYQfZDeApwvbjA7juwb/jxgsa8NCbrWmC2MdvOheLpxs+lF4DM0HtTRc14GcvH9Isu/GCBlg44MHX0483lQW4p0GUPGUnsNYeP/acHMKtf9iedr7/+8Zz8E8PvWN6HRTOqNevXTbHsJ5vuqgBq5fUFnRNM7G8KaYsf/RQ18jNl8wxnEv0c4EZZ7LVlRH/zOaaqVCjBc5PZgI5nejwJoSwhMBQENvpzU0QayaolXX38cqxZGp8vKkswGWi5FOHrqEwAhHJuOazXAeFM2qY1bNMUfDXj9UlQzaoa8RsLtHPBSOdA4z4ZzbXTIUanYz8ZDeApwg1Hrvmietq614mhgAAIABJREFU2AQO1Z7cBLFmglr9N9A2gQOlgIUYH28qC3CZKPnUoarIBqeNN675LNdBzRkFZvXMERT89WN1yZAN+hrJZS4Y6RxgxD+zuWYq1Ohk5Ce7ARxDyDJFa48fbx/uRWuPH3Lyo5IsUxQ7eGxa1Yxnt7dj7aWNmpvBzauaIVOq2ccMZvE9i+o8mmVrL23E1h3taKnzZI37Mev3aM/B0V4/DnePTbtjCRaBNHbIVjszy5xw2yzYsKIpreYXTUuvzbs/uQh9gQhae/xYUOXGltXNmvVzKl247xrtPuuWNWJelRscSfRnLOt5NO89X7C6ZFBDX1+SJIMjwF1rWmATODz1/gmsW6adS+5a04KFNUWaZWVOEbdcNjfvumqqKUrj35bVzVhQXWTa1qni3unAZOQn0wCOEcwEolcsqMILe7uw/omPMLfShZsumg2rwKHYLmAgGIPbxuO+F/ZhW5s3r5QCvaAWULuALYjFZZTqXMBGAtyxFLbmIlCeKILZUyxKnpQaplxrR5JkfHhiAKFoHAOBGESeg8PK4cI5lQCG6zYWp/jBn3eirS+UauuyeZXY2zWETm8Y1R4bmmo8KRfwsf4ALBzBvo4h/PqtNgwEo4Yi+PGou/EShE8Gsfw4YFLyJxOM6mvL6mb8/JWDiEoU1yytw9wqN5pqihCXKfZ1+XCgy4cnt52AyBNsXtUCwUJSLuBefwTCKFzAnd6E67XLF8Zdf92L5YtqYeGAM6YX4+MN5RBFS6GbJHJCAfOTmUDGE2YC0cdvOtdQSPvXtRcCwGkXlY6lsDUXgXKBCGbHGpNyAsu1dsYrLSAfQ9RY112BC8ILDZOSP5lgVl83XtCAX7x6KPX6VM4jZqYQxcDIODGhwUwg4wkzgaiZkLbbF54QotKx7EMuAuVCFswyaJFr7YxXWkA+hqixrruJwF2GyQuz+iJE+/pUziNmc5liYGScKDywG8AxgplAtMZjLhydCKLSsexDLgLlQhbMMmiRa+2MV1pAPoaosa67icBdhskLs/pS/2B3qucRM1OIYmBknCg8sBvAMYKZQLSpxtyEMRFEpWPZB3VbRgLlQhfMMmiRa+3kst1I6jBXQ9R41N1E4C7D5IVRfW1Z3YytO9pTr0/1PGJmCmmq8Zj2mXFiYoNpAEcBfSzMjBIHjg0E0e0Lo7poOKJHEeJ2DhmbMNSi0jqPHXu7hjTRO0pCSK4xNLJMcaw/gK6hCAJRCfWlTtSXJvrWF9BGAs0ocaCtP5iI1bLyqHKnx2oZxQHpU0vU21S4rLBwQLFDRFxGxifETxFMWg2TUa3NKk9cZ3W9ZuKAUVvReBxFVgHBWDy1b4fXOObwWH8Aff4owlIckZiM+lIHLBaCDm8YbhuPYDSOQERCqdOaMSJRw4UsEXNKfwtUEF5omLT8yQR1fVW4bLAQihODYfjCEqqKrGiu8UAULQDSx+gFVW6c8IZyiixTjmM0N5wYDGq4Pb3YnjJl1ZbYYeUtOD4QTPGlrjh9DuwP5hdDN9ni1iYIDE8gywIeITI5nmaWOXN2Q3EcQUOFCw0VLtPondWLa8FxJKc2ZZnilf1dONjl1zhwt6xuxmPvtuHS+dWpGLr6Mju+eWmj5nj3X7sEM0qHP7Fl6pNyE2i+TTF4nsPsSiYAnszY0+HL6H7PxxG4p8OHu5/fmxaXuG5ZI37zdsLpq8QcfvfKBbhiQRUO9fjT6v2+a5bAZePwxsFBzXKziEQjLmSLmFNzl4FhrKHU18wyp+mYrswNeq4pjmG1o96Ie8o8ZsS5LaubEYxIuOu5fWkcbqlF2nyk5osyBxq1m2kcmApO4okE9hPwCHG0L5AqUiAhdl3/xEc42hfIuC4Tdnd4UxOQst9tT+/C7g5vzm0e7QtgxwlvaqBQt3PD+Q0pIgLA8kW1acfTt5mpT/lswzA5YVaXuzu8eXNAaWv5olpNnYZjMn768kH848fqEI7J+NkrB7F8UW3qOEb1/q0nP4IvFE9bruy744S2f0Zc+OnLB7HjhDcrbxkYxhOZxnSzueG2p3dh+aLa1Gsz7mXi3G1P70JvIJrzHKfmS6Z2M40DI507GUYGdgM4QmRyPI3UDZXJZZWP49Is+icU1UZy5eLUzeb8ynUbhsmJkbjfs7VlVpeKA1K9TYfXvN4DUeMIOqOIxEyxWszFyHA6kWlMzzQ3GDmGjdrOxDkzR30mB756fb5Pg2BO4lMLdgM4QmRyPI3UDZXJZZWP49Is+schGkdyZWozm/Mr120YJidG4n7Ppa1MDkjl/4nj2E3r3WlS75kiEvWvJ0PEHENhI9OYnmluMHIMG7WdiXNmjvpMDnz9+nzGAeYkPrUYNxMIIWQ6gN8AqAJAATxIKf0pIaQUwOMAZgI4CuBaSulAprYmogkkk1YBQEr/oDwx/cwZJSiy8+AIlxKiK6YRRexa57HjmZ0ncdvTu1DiEPGF8+sxt7oIVKYocVnRNRTCnpNDeHVfN5YvqsG86iJEJBmlTgEVbiukeOLTl0wpjvQGsOUve1N9u2tNC2IxCXVlTnR4w7DyFniDEditAr7/p50pHdSmlc2ISHFUFSXMHgCyagCj0Tj+vOMkfvBn7TYrW6blLEQ2O8dmBoMCQ94dLhT+5JKAo67BM+uLEZNoQjhu5RGKSrCLPCrdCQH5C3u78PSHx/BP5zagwxuCQ+TxxoFOrDpjOnr9UZQ6BNhFC/Z1+DC70gVL0myyo92L+188oKm/WWVOHOgawi9fb03xcGFNEX71xmF86cLZiMvAt54c1gDefEkjbv9zugbw4sZKDU+VOlaL1fXmlAKs0YmMEZ3MseLQ6Z5/9Lpu9dwgxWVUFtlwvD+Ie/5nnyah49F3j2JRXSkq3FbMqnAAlKDXH8GMUgesFguO9gXgEC2wCRZ0+xNGjU1bd6d0g1tWNyMci+M/3xjmz9L6UpzfUAaOI3huV2eKPzaBw/rL52JGqQOXza9KadaNNID3XbMETdPc6PCmzwv5aACzmUWYmUSDU5sEQgipAVBDKf2AEOIG8D6A1QA+D6CfUvojQsi/ACihlH43U1unm4BmyOQClCQZf9nVge8+tSNNgH7d0hl4ZV8nPnV2fZoB47J5lTjQ48OR3gBODIQMBezfuKQRoeiwOLe+zI6vfWIO7nh2d2rbzauaMRCIYCgSB0eAumIbhsJx3PvCfs0E11DhRGtPAIQAbpuAzVv3pN3oAUjFASlxXGpn8vO7O/Hwm4dxw/kNCEclTC914IzaYrxysGfEYl4zM0uBCoJHcgNYMPwx44EsJz6I7O0cwoEuH17b143li6dpbtSUmv7MOfVoqHDiglnleHZXh+bDxKaVTfjFa4dSE9OGFU2wgOIXfzuM65bOwOPbjuFfr1qAYoeArqEIDvf48eS2ExgIRnHP1YsQiMSxUcWNDSua8NT7x/DVi+dgZqkzFaP12r5uXNVSg+mlDhTZeEwvcWB6icPQzGJ0g6s2pxRgjU5kjPQGcEw4NBHmH1mmaOsLoNsfRvdQJG1uuH35AhDCaep886pmPPDqQXhsAq4/px4bn91tGNGpNlj9YPlCVLitmF3uwvRiO95s7UGHN6KZF5T6f+1gN3ac8EKmAEeA+jIHHn2nDV+8YHbqi5AjvQEc7vUjGpPR2huAJMtorHTh3hf2GxpUlHFf3e6iOk+aESvbjSIzk6Th9EbBEUL+DOCB5N/FlNKOJEFfo5TOy7TvRCBgvsgU5fPQm6245+rF+M4ftqetV6J9nv6oHQ++nh6lpuyvjrsyi11TxwatXTbHsL2bLmpAPLnIqA0l5iff95kpAi8X12Rrj9/0HBRgtNCoR5xC5U8u8YDqmr6osQL/9NA7GWvZJnC49+rF2Nvpw0Nvtqb2f/CzZ+Km376v2des7pV9zOr0posasHpJ4sNPPvWttFuANTqRMSYz9kg5NJHmn+3HB/Dyvu60ms5U5/Oq3am5Jttcoa99szE41/rPJ9Iu0/b5xkuyWLo0nL4oOELITABnAHgHQBWltCO5qhOJr+eN9rmJELKNELKtp6fnVHRzTJFJmBuOyQhFjAXqioDWTPSr7K8W52YTzQPpondlG5km9jdrI5uRYyxNAPp2zfo81QTBhcyfXOIB1TXdmaOgPRCVUvsp//YHYmn7ZuORWZ3qxez69Wb7Ke1OtRqd6MiXQxOFP3qYmZ4y1bl6rsnFYKWufbN2c63/fA0qYxUvycwkuWHcbwAJIS4ATwH4Z0rpkHodTXz9aPgVJKX0QUrpUkrp0oqKivHu5pgjkzDXJnBwWI0F6oqA1kz0q+xvJM412laBWXscgUZcr1+fzcgxliYAfbtmfZ5KguBC508u8YDqmq7JUdDuFPnUfsq/pU4hbd9sPDKrUyMxu3q92X5Ku1OpRic6RsKhicIfPcxMT5nqXD/XZOKXvvbN2s21/vM1qIxVvCQzk+SGcb0BJIQISBDvd5TSPyYXdyW/dlc0Gt3j2YfTBaNYnLWXNmLrjnasvbQRj7zVmharo472aanzpEWpKftvXtWMcqeYWvfs9nbcsaJJs+2mVdrYoDKniFuvmKfZZt2yRjRWurB1R7thdJs65ief95ktAi/X82d0DqZStNBk4I8+HnD95XMNa3r95XOxqM6DlmmeNF5sWtmkqeUNK5oQjMRSXNq6ox33X7sEvAXYoOPB7EpX2jFvuWxuah+jOl23rBGL6jwZY7aM9lP3ZarU6ETHZOCQGk01RZhT6UobF8td1rQ6V6Lj/vP1wyleGI3z65Y14o8fnDCsfbMxONf6zyfSzmz7kcRLsli63DCeJhAC4BEkxLb/rFr+YwB9KgFuKaX0O5naOh0ajJE6iNT71XhskOIJx6PIc7ALFnCEoMcXQalTTD2PrD8QRVWRFVaeYDAkQYpT8BzgtArwRyT0BaKodFshxeOw8TwkKmMwKMFt4xGR4nBZeVQWWTHgj6Ejedx5FW7s7/Gh05t47bLyOOkNJdy/oRjcNkET3ROIJH4+6/JFUO4S4RJ5hKTcXI3qGKJpyWO1J4/lCyeOJVMZdp5HMJZbHJByLhUXcDAqYcbUcgFPOP6MlBPRaBw7TnrRORTG9BI7QIEuXwQeu4ChcAzFdgHFDgsGgnH0+iOY5rEhFqfo9kVQ6bbCLnAISzKC0TiC0TgqXFbEaRyUcugLRFDltqHSI+LkQAT+SKLehsIxVDitmFfpxqE+PwaCMYSicZQ6RYRjEqZ57PBHJJxMRmd57DyO9AYg8hxsvAXlbjHlqjeKsQMSAnclNs5ptUCKyyCEaKK09O5hAMyZmD9GagIZEw6N9fwzGneqMiYOBqMIxWT0J+eGYExCmcOKYCyOHl8EFW4rrDyHoVAMAs/BF5bgsQvwhWMotouISHH4InG4rRY4RR6+iAR/JBExRykwGIqhvsQBQSDo8kbQG4jCY+dRU2RHvUH9F9kt8Ifj8EckFNkEeMNRlDltWFDlxvHBkCZqVB0Xp+aTet40ihDVPxVieokDESmOEwMhw/g7dTQri2o89VFwHwfwWQA7CSEfJZd9D8CPADxBCLkRQBuAa8exDyPCSB1E+v3M4qX0kVafPrseg8FIUtsUx2PvHcOXLmiAPyJp3FqbVjbBJlrwnT8MO4u3rG7G+bPK8dL+bsP+Lp5u7og6a2YpOI6gzmPH0zsGNI/A2LCiCY++04YD3f6s0T2KG1JxmD323rE06/+G5U345euHskYTqcFxBDPLXZhZPiVFuxOKPyPlhCTJqUcbKfttXNmESCyOtY99qFn2b2qn7/ImvLq/A5fMq8EvXz+UVk/q7evL7Pj6xXOw4Zndmr41TfMYOngvm1eZ1qctq5tR4RZx4yPbDJ2S2RyGD3z6DE20nNKmOo7LaJsp7kwcb0woDgGjizpTHLInB0IIROOa+rz1inloswRx13P7Mo/DK5qwaeueVE1+76r5cFh5wzlK5Am++ok5Gmfx/dcuSXPGG81zibltD26+pBG/eFUbSTej1KmJUMzlnJg9FWL95XPx6/89CpEnhrGmVzZVT1XTR044ZS7g0eBUfwM4UgeRfr9cHFeKa+reqxfjQLcPD76ecDVaOJi6dhX3r7Lsv288x9A5masjatvRfsP977l6MdY++mHG927k8lTeU67OrymAgg+zHyknth8fMHXZ6utYXx//8dkz8ZXfvp+1nsx4ZuZUNOPLvVcvxjce/dC0vUx8yuTCzObEn0I8GCkKnj8KRuNOVZ6MAGSeG/IZhzPVLWD+ZAg1r7K5+7ON+7mck0xPhcjUV8atFE6fC7jQMFIHkX6/fCKtAhEp5bgyiqpStjeK5jFzTubqiDLbPxSVsr53I5dnLq5k5sgqLIx1vKFRHevrYzAYy6mezNabRhSavJdAst6zxVcZnYtMLsxs2zAeTB2Mxp2quHKzzQ35jMOZ6jZXXmVz92cb93M5J9mejJFv5BxDAuwG0AAjdRCZ7ad/bRRp5bTxGsdVJteufln1KB1RZs5Lu8hnfe9mLs9M7ztbmwwTD2Mdb2hUx/r6KHYIedWTfr2ZU9Gs3p0ir3mtX5+JT5lcmNm2YTyYOhiNO1Vx5eYyN+TKm2x1mw+vjNrIZdzP5ZxkezKGWR8YtzKD/QSsglqcK8UpbvvzTo12oWmaG51e81gyfSoGpTLiMvC9ZNSaWl+haAA/Ot6Hz53fgIFgDC6rBcf6gnjof4/g65+Yjf5gTKN32LyqGXbRgluf3J5adteaFiyu9WB35xAOd/vxRDIB4a41LfjYjGLMKHWmRPiKwUOwEHQORTCzzAErb8GJgSAogI3PDscAbVzZhOd3dqC1L4DNq1pgFzmIHIcefwRWnoPTyqPMJaLW4xiRBvC+a5agvsyeEvA21RSl0kXMrkkBi+YL/ics/RP6LQRo0T2hXy3SjsbjKLIK8EVi6BqK4vtPD3PgzjUtEDjgO0/t1NT2QCCCX7+V4EY2DeCW1S3o94cxFInjQOcgrmiqxc9fOaCKrCqBy8qj1x9BsUNEMCrBLiReTyu24cRACN9WaWnvWtOCEiePQERGvz+K2hK7JhZLST84NhBEXyCCk4NhTcrPA58+A8FIHN95SqvPVTSA9WV2/OgfF6HDG9aMB0wDmBMKnj8AhpNxOoZwsNuXGqvv/uQi/N/mGk26kn7MAxI/gx7u9SMYjaPTG07TAIoWgof+9wg+ddYMFNkFPPj6YXz67Hr8/t22FC8+NqMEAEWXN4KBYBTTS+2ISFRTkxtWNMEbioIDUOK0alJ57v7kIsypdOJYXwguqwUgBL5QDBTA/i4fZAq4RAvmVLmwr8OHpmlFaB8IotMXRZHVggXTPAhFpVTUKM9zkCQZb7X2YceJQdSXOdExGMSCGg/OayjTnBMjDeC3/888/OqNIxk1gIxbAE53EshocCpuAI2EqHd/chFqi20ocVhxYjCAPSd9WWPJwmEJz+zqSBkq6svsCQs+b0FVkQ0cAfZ0DoHKQCwuISYTjfnirjUtmFFqQ/tgGF3eMGqKHQhFJVQW2fDMhyfw/vFB/MuVCxCOxdEXiMAmWDSZvz9YvhDVRVb89KWDONDtx8OfPxMnBiJpBo8XdnXgvDnlmliuzaua4bbxcFl5/ObtVqxaMh1xmeK+F/enTcDrljWi2mNDqVPAxxsqUm6rGo8NbX0h7OvworrYgU5vCC21nuQnVJJyce7tHNJMwPp8YbNrUqCkLvgJLJfoJWWA1n8AqC+z4/blTQjH4uAIwY+e34uoRHHDefWo8djR2jsc3bZ5VTOml9oRikoIRGQEYzGUOm1o7fal6mlOpUvzYWXzqma8uOckLp5XjU2qyCr1hy39B5Atq5tRXWTFR8e9aKhw4e7n96bWKeasmy6ajUA4hvk1Hpw7s1RjtKovs2PzqhYIFoJKtw1H+vzYvHWPJjP13JmlOOENoT8QQXvyhrHEIeKapXWYW+XGguqiQnW1n2pMSv78YPlCDIVieOy94fhAAIYGo0iM4ltPJj5kf+0TDQjH4qm5ocpjw/G+AGQKCLwlFfn2hfPrUV/uRK8/qolyU/PiX66cj5piG3afHALPcWisdOFHKi78v+sWAyDY0zEE0cKhyMZrjCY/ffkg5la68Jlz6zUmLPUx1i1rxHM7O3BVS41m/lTy4vUGRrNIRUmS8V5bP/r8UQQiEvqDUcwqd6LcJaLUaWWu38xgN4CZkEmICphH4uhFpmaGiv++8RwsnVmqOc4jXzwLX9FFV9kEzjDSyiZoTRk3XdSAOZVuwzg5Jd7tF68ewm++eJZhW4rA3mhfAIjLSBlRzMTEN13UgLmVbiycVqQR62YT9JoZA/Sxc5MozqfgJ7BcopcUjpjVy71XL8atqno1E4+ra1O/jdk+ZtGKesOVWoyu8CSbgD1TZJwyPrBYqnHFpOWPuj7Naklt1MhkuACQxhUzM6E++k1tHsnl2Or//+z6M7Jyz4yfZoYso0hFxqNRgZlAMiGTEDWfWDIzgXnXULqAfMAguiock02Xq00ZMoVpnJwS7wbAMB4rHJMxaLJcERmrjShmAluZAoGolCbWzXaeTMX53vzFwQynBrlEL2Wrl0BUW69m26lrU7+N2T5mXNAbrtTrlDrPJmAPxzJHG7JYKoZsyBaJlqmW1HNPpno14ko2c5LCA7O2zY6t/n8u3DPbJlP0o54jjEdjD3YDmEQmIWo+sWRmAvOqonQBuVF0lU3gUGKyXG3K4Eh6xI96nfLFrtkxik2WcwSp/dXv2Wxbp8iniXWznSczY4A+do7F+Uwc5BK9lK1enKJxvepf62szl33MuKA3XKnXKb8Ome2n/tesZjPFxbFYKgYF2SLRMtWSfu7J1k6mfY22NzKP5HJs5f+5cM9sGzMDo1GkIuPR2IP9BJxEJo0TALyyvwttfUH8+H/2p9bfd80SXNWs1Sjs7x7CnpM+jXB206pmnNtQgj5/FHE58S1hqUOAyANdQ7GUeNZCgDmVLlS6regLRLGvc3j5zHInfv3mERzo9uPuT7agwmVFhzeCEoeAjVt3IypRXLO0DjNKHahwiWjtCSAWl/Gx+hIc6Q2m9efvh3owf5pHowFct6wRTtECQghCUQnTih1w2Xg88MoBXDq/WqMBvH35QpS5RFg4gtpiG/zhOLp9EZS7rbBaCN5u7UcgGjc0C0iSjKe3t6c9iPfMGSWptAULl/imUG/GeeDTZ2BWmQvdvpGbQsbKWJJHOwX/E5YZPxRjRKc3DIEnONQdwC//lm7auHNNC1xWC2QK/Oi5vZoHOP/ba4c0IvUypwX+CIU3FIPLyqM/EIXIc7AKHKB8I0GAtr4AQtE4Zle68Ju3juDyhTX4yUsHNDX62LvHcKDbn/ag6S2rm2EXLLjnf/al9VXRIK1dNhcneodw9uxKeEMxeOwC7nthH9oHI7jhvHrMLHOi1Cmi0m3F4V4/vvH7D9PGDrMHRxtpWfUpQnEZo6pzo2tYoIaqSckftU5OPdekaQCvPwOBSBzf/eMOzK104bqzZ2g0fXeuaQFPZISjMio9dvT4IvCFJVQWCShx2nCsL6jRxq6/fC62bj+Ji+dXYmaZE2UuEbf/eRc8NgFfu3gO9nYOwcpzEDiCmmIHrDyHB18/hCuba1DmsuJobwCv7utOafrmVrpw/Tn1mgdGq9/b5lXNqPaIONwdxI+e36cZ87NpAJXxReHEng6f6RjUNRSG08pDisvoC0RRlEy7mlFaMHU+nmAawGxQBkgjEWk0GsfzeztxqNsPmSa+JWusdOGqppqUk0m5qVGE3rPKnajx2MBxFPc8tx+fPHOGhiR3f7IFvf6o5qbylsvm4sU9Hbh26QzcrhLVblzZhIGgsTPr3qsXIxSLa5atv3wurBYOP3x+X6o/M0oc6PFH4LbxKLILKHeJ2Nfhg9smoMwpwipwsPIc2vqD+P6ftKaRacVWiBYL3m8bQCgmY+uOdnzmnHq8fagXVzTXaN7XxpVNeHLbMWxr85pOdkp8nBJV1+2PaCZQ9QCimHHKXVbDASAfU8hYGUvybKfgJzAgnR8zdIkANoHDv145H8FYHA0VTlS5bRgMRSHLwEaVo/bO1S1wiBysvAWPvXcUn5hXrZnQNq1swkt7O7BkepnGSKJPJVAmii+cPwvzqt2gNI5onOCDYwOIy8DWHe24+ZJGDAYi2LqjAxfPr8TsChecogUd3jB+9WYrli+qhcdmQVNtMQKRGJxWHrvbvfCG44n9L27ES3tP4oU9vUkt0yLIMsW//FHr7G+scmFOhUsTF2d0c2cmUFfXU7YkkpFeuwI2VE06/lS40uMF1XPN/7b24sPjg7ALFlg4gt+906Zx9yofmOZXF+HhNw9j5eJaWAWLxoixaWUTfvHaoeEvB5Ljf0OFE0MhSTNf3LmmGVGJmt7EbVrVrEn02LSqGdGYhMGQhGhcxrkNpeAIQZ8/igq3FZFYHF2+CI71B1MGr3uuXoT2ZIoJR4CWWg+Wza8CMBwD5xAtiMXllKlDP74oXwAoMXFG2+gNKI1VLs0XEFMU7AZwNMhmXMiUeADA0LBh9hT2TIJ2IF20btaOWdrCQ2+2pvqlX68X6mdbbmYmUQwryutMQt3RCKSztZ3rsfIVEufZzqSYwPTIdN0UAXefP4LPPvxu2jYPf/4sfPG/3jM1jOjrKlvawE0XNeC8hjJ84b/eM60j5bUZv8zMV//x2TPxuYffA5CZa6uX1I5YjG6UqDPaGjVrf6zaPIWYlPwxg3ouMTNeKFDqO5vZI9u22ZJszNrSm6qW1pdgW9sAgNz6M5J5IRdjiN7kMhpuThIwE8hokM24kCnxwMywYSbQDUXNRbXZhLr6Yxu1oe6Xfn3ARKyrF/Ary7MZVpTXmYS6oxFI5ysCHishMRMkZ75uyrnoHIoYbtPrj2i21a/Xm5SymTVkCvT4jI+lN3+Y8cuslgeDsdTrTFwbzbU3StTRH2Os2h+rNhnGB+q5xMx4oUCp71ySaDJtm4tZxKiMCLNQAAAgAElEQVQt9WuZJoyNmdJK8kmDyjUhJFu/R8vNyYycbgAJIbMJIdbk/y8mhKwlhBSPb9cmFrIZFzIlHpgZNswEug4TsXy+Ql+ztAV1v/TrnTbjY5sJ+M1MJnZdqkImoe5oBNL5ioDHSkjMBMmZr5tyLqqLrIbbVLiGlxutNzMpmR2LI0CF2/hYevOHGb/MzFfFDiH1OhPXRnPtzRJ11K/Hsv2xaJNhfKCfS7LVhd6wp1+Xy7a5mEWM2lK/5ghQ4hQyppXkkwaVa0JItn6PlpuTGbl+A/gUgDghZA6ABwFMB/D7cevVBERTTRG2rG7WTFpbVjejqcZjun7dska01HqwqM6DR95qTTwQWrW+ocKJ9ZfP1Sxbe2mj4bbrljXijx+cwLPb27FxZfZ21l8+F2UOMa3trTvasW5ZIypcVpQ7xbR9BAvBumWNacvddgvuv3ZJWp9+/eaRtL5uXNmE37zVmnp9/7VLUk+zN8LMMqdh23/84IRmf6PtsrWdy7HybWMs2ylkGJ0DpcaUc9FS48GmlVpebFrZhD99cAwbljfh2e3tWHtpY9r63/39iGb5s9vb0+pMOdb6y+diUZ0HFgtN4+CGFU3YuqN9uO1VzXjkrVbDY8qUph1j08pm/O7vR1Kv51S6cN816bW6qM4zqmuvPpdPvX8ijYOjrS1Wr4UD9VyirgWjulA4UOYU0+aFTSu1ta/e9pbLtPOF0TL1GLxpVbOmLT2v1i1rxKxyJ3hLwvRX5hTT+rpZ18ZI5gX9PtnmjrHg5mRGThpAQsgHlNKPEUK+DSBMKf05IeRDSukZ49/F0xMFp7jkJEnGjpNedA6FUV1khVO0oMcfRSQmo6HciVkVLsgyxb6uIXhDMQQiEpwiD54nqHTZUF/mhCxT7D7pRZcvjGKHiKFwDEU2AaGYhCKrAH80Dn9EQoVLRFSKgxACwWJBrz8Cl5WHyBMQQhCT4rBwFvQHoih3iYjG4+AIB39YQpGDByjBUCiGUpcIKS4jHIvDLiZclGVOEYGoBCvPwS7w8EUk+MNSwslLAIlSVLpsqHHbsLtzCB1DYZQ5RTgEDhRAfzCKqiIbrBYLegORlIi5xx+BhRD0+hOpJHbRAp4DZEoQlWRNlJH63M4ocaCtP4i2/gCcIo9qjxVSHOjxZxZIGxkRFAdYrs7GbIL8fOslh3YmjYZJz5Fpbhv2dfvgj0gIRCW4RB6lLgE8l4gYdFl5cBwQiiZkBCUOEb5IDEVWAUV2C7yhOAaCMZQ4BAyFYnCIluQneIKBUBTlDhH+aBy+sIQKtwiOAMGojGA0jmKHAJlSDAZjqCqywiFyGAhKkOIUwWgcTqsFIk8gEC7FsVKXAA4EPb4ISp1WBGMxuEQBNj6xTTAqwWVL9KXEIaKySMTx/lDiePZEtF2l25aKUHSIPJxWC0KxOMqc1lQNZHPcmsV9KfVUXZRwASti97F0ARdgUsKk4Y8C/fVXj4cuKw+31YK+QAy+sIQajw1OkUePPwK7wCEQjSMuy6mxvdQpwhuKocIlIhan6PFHUO6yIhiVYCEcRJ6A5zgMhqLw2EU4rRwCkThi8URUqT8iocwpQoYMUA5DoRjKXCLilKJrKIJKtxVSPA6Rt8AXlmDhCDw2AcGYBJlS2HgegWji/0o/K1xW2EULBgIx+CMSKt1WCDxBhzcMl8jDJlhQ5hJTDl11jGQgIqHcbUVESqRGUQp0+zJHsCrnUuQ5SLIMb1CCx85cwCoYngDeaKEBYoSQ6wF8DsCK5DIhw/YFByOXnD5Grb7MjpsvnqNx5953zRLwFqC1J6Bx7d13zRKcVV8GAGkupbWXNuKVfZ24+swZ+MqzH2jcS9OKbRgKSbjzr3s12390vA/LFtRoXF76R1usv3wuKtxWbHliT2rZumWNeLe1D1c01+CXfzuEL13QAH9E0vR148omEFDEZRnb2gY07rANK5pgIRQ/eekQBoJRTWSbLNM0V67agbVldTPOqi8FoH20QX2ZPS230citNbM8XbTLcQQNFS40JG+8R+JsVLcxGoxVO4UC/fleWu/BDefPQqc3nHqckJFbVxsFldj3ioXluGzBNNz+zC5NHQYjBPe9dBCfOmsGGsrs2N/tT9W8Ef/0j5t4/L22lPtcqf0rWxK8MXLXrr98LoptPPp0udv3XbMEg6Eo/umhd1KPufjmo8MudfUjLD736/THUug5n+tjYfT1NLty7GprqtXrRIX++huNhxtWNOGXfxse2+9ak4gd/PYfdhjW8dpLG7HlL8dw48dnIRqnWP/Edg1Hqots+N07R3Ht0ukAOPzitYNpjz/atLIZK5qrIQgW/Omjds08oDjuP312PewCh7ue24e5lS584YJZ6PQOGea/6+cnNVf1j3p57WB3Ws6v8kSMT51dnzHjl+MIZpY5sa8z/QkRZ80sZTd/GZDrT8BfAHAegDsppUcIIbMA/Hb8unXqcbQvkCoeICEeleJI3fwBwPJFtanJR9nmW09+hJhEU4WrXn60L2DY7s9eOYgbzm/AHc9q2/rpywdxuCeAHn8kbfvPnDsrNREqyzc8sxvLF9WmXt//4gEc6Q1olv305YP4/AWzsPHZxLY9/khaXzc8sxsOUYAUR4r0yrqNzybW/ePH6hCOybjt6V3Y3eE1PWc/fflg2rb67ZYvqk0RWr3fjhOJbUdzzdY/8VFebTDkDv35vuH8Bhzq9qdu/oDEtd1oUNdfumi2pu4+c+6s1M2fst2GZ3aj2GnF8kW1+OnLB1HksGpq3oh/6nr7wZ934YbzGzTrPn/BMG/+8WN1abV//4sH4LAKhvzdccKLcEzGly6anfaebnt6F3ac9BrW3+4O4+VKXbK6ndrIZTxUxmvl9ff+tBMHu/2mdfyzVw5i+aJa9AaiuPeF/WkcOdIXwA3nN8AhCrj9mV1Yvqg2dbOmbHf7M7uws3MIuzu8afOA0v5PXjqA3kA0xYsjvYkvPoza089Paq4q7Sl82XHCm/aefvLSAdxwfkPauTHiCuPUyJDTDSCldA+ldC2l9NHk6yOU0rvHt2unFkZuIn2MmmnElYlzNpNzNVOMm5E718yhaBZvZbRvJrdYICqZxsYFopLGVaU4n3NxYHV6089Bpmi50+HoZcgN+vMdikhp9WQa1aZz3prGIAZjqTb0rt5MLkj1cTTtZYiVU7Yxc7grPMw3xipTbBzA6naqI9fx0Ghsz7Z9Jpd6KCql5iqzNrqGIqb1q3bcA1r+5/oe9K5ihS/5PhFDzxXGqZEhVxfwTkLIDt3fG4SQnxBCysa7k6cCRm4iM4er/rWZczaTczVTjJuRO9esL0ZOLP0y9b5m7iynyJsewynyGleV4nzOxYFV7TE/B0bv/XQ4ehlyg/58O6y8aT3pX+udt6YxiA4h5ew1c/XqX6vrTe8+1x/HrL7N6lF5n0brzWKsMsXGAaxupzpyHQ/NogszbZ/JpW4Xec1cZbRdVZHVtH7Vjnsgnf+5vAe9qzjBF/OoVTPHvp4rjFMjQ64mkHsAxDHs/P0UAAeATgAXUEpXGOzzMIDlALoppc3JZXcA+DKAnuRm36OU/jXb8cfLBKKPXtLr2dQaQEV3Mc1jR2uvH08kn25+55oWzCl34kC3X6OZuGtNC0qdAopsPLqGorhF1e4tl83F+229uKJpWpoOrrbEjrhM8a+qpIFbr5iHBTUutA9EcPszw0kjDeUudHpDeOTtNog8wb9etQBSnKLMLSIYkWAXLSnx+pHeIB549aCpBrDCLaLELuJQT0DzPjavakZdiQ1xmUKSKay8BeFYHE4rj2K7BTtP+vH9Pw33dcOKpoQoORhFtceGc2eWgee5rJqX9ZfPRUttwlGdj5mjQNINRtSZseLQWEVZHesP4INjg/he8nqbaQC/fvEcjVb1rjUtqC4SQIgFA4EoHCKPcjeP7qEYPjoxCJkmXL5fv3gO7DzBb/7ehq9d3AgQioGAhJ+/cgDLF9XCLnBYWFOkSRXRawCLbBYc6gngiW3HcfMljbALBOEYxQ+SHP7Cx2dq4g/vXNOCWaUOtHtDIITgwb8dxoFuP+5a0wKXlQcB4IskxPOChUPnUAQO0QKnlUNdsRMHunygAE4MBBGKxtFS58HFjZUj1gBOsLqdKCh4/gDD801fIIKTg2F896kdhuNhfZkdd6xsRjgah03gcLw/iHKXFRYLwe6TQ/DYBHgcgmb8vOWyufj9u21Yu6wRUpxq+KfMK9VFVvQHY7BaOPzi1YO4dH41Ht92bDiGcXoJOAuFSxTgi0h4v20gxc3rls7A49uOYd2yuXBaLdh9cgh1JQnzyvGBMB54NV1TmEkDuHlVM0SeoMJtQ22xDUf7Athz0ofH3juGT501A9NLHCAEcCTnsG89uT11bjavSugh1fOEPknnmqV1mFvpxoKaojTTyBTFyJNAFBew0TJCyE5KaYvBPhcB8AP4jY58fkrpvfn0fLxcWGmZi7qYGcUFvKdzCAd1N3ibVjYjHJNAKfDD5xOC2K9fMgeHuv1oqHDh7uf3pgp/48omeINRDEUSETizK5zgCMG9L+zHNWdOR6Xbigq3FSLP4altx/H+8UHcsaIZR3v9qCtxIBiT8N2ndmJupQvfuLQR3lBM05fEIwMsuPXJYeGvIrL97Lkz8du/H8W3rpiHumI7+gJRVLqsGApL8EVicNsEdHmDoOBSk+QN59VjWrEdh3v8qRgfxWCivjHdsKIJT71/DOc0VMDCAWdML0ZfIILbnt6dNrEBSHPvtvUHcSzpeusLRPHPj+c/IRaIs3GkE9iYcGi0/DEcXKvcWFBdhOnFdhzo9mEgFIM/LMEmcAhG46l8a44ANUVWOG0CvvOHHakbx2uWzki7SZxf7UL7YBi9/mhKc7e03oNPnT0Ttz29U7NtsYOH2yogLlMMBGPw2Hnc/+L+lAFkw4omPPpOGw50+3Hb/12AKrcNMoBYXMaBZN+cogXVRTb8q+oDzKZVzZheYsO//HE4f3rTyiYIPKep/U0rm/HEtmHDiVrUblTv+UbDMWhQ0PwBjI0f6huZGSUOHOkLoLU3AF84lvbFQKlDQDROcedf96LEIeLmS2aj2xdJcWxOpQudgyH8+q3ElwEbVzYjlLyBdFgtONoTwIZn92jG7umlVnQNxTQf4G+5bG7K5KH+kFTq5CHLBP/+2kFcvrAGv3+3DV88fxaCsTgee+9YKlJxQY0HezuH0FDhgk0AZJmg0xvGUDgGWaaoK3XAJliw8dnhD3FK1Oiscid2nhjCsf6g5guKTSubMBiMQgbgtgma2Ej9Byv9h1T24SqFUd0AbgfwZUrpu8nXZwH4FaV0cabHwRBCZgLYOhFvAPOJRjLbVh0ppcT0mEVbGcXm6GPYbrqoAXMq3Vj76IepfeZVayPkzOKAMsW+Kf/+943nYOnM0rTYOn2b+R4j2/vKFjdV4DFVuWDEI89YcGi0/Ml2fdTrf3b9GYYxa+q6MNvmPz57Jt5vG9BESJnV4n994Wy8dbg3tSyXGCsAOdX5vVcvxjeSMYZG/VeW6eMO1RF4k6RuJwoKmj9AbmPc9uMDON4fMozcVEd3ZoqE04/FcRmmMXFm8Z5Gta7EJKrnlEzxc2Zxow9cf4Zp1Oj0Ujte3tdt2iaQHoOqP4dTYC4ZKUYVBfclAA8RQo4QQo4CeAjAlwkhTgA/zLMj30hqCB8mhJSY9paQmwgh2wgh23p6esw2GzHyEY2aGjlUAlVF0JqvoFe/TBGxK/voBeiZDBRGx1T/2zVkHFunbzPfY2TbJpsQlwl480ZWDo0lf7JdH/X6TOYmBWbbDAZjOZtKenzhlGEq1xirXOs8oDKSGPVfWaY3nKgj8BgmNE4pf4DcxrgOb9jUUKg2B+Yzx2Q0/uXAVWWZ2kio/JuJd/o+K8gUNZrJDGIWg6o/h2wuyQ+5uoDfS/7MuwTAYkrpIkrpu5TSAKX0iTyO9+8AZifb6QBwX4ZjPkgpXUopXVpRUZHHIXJDPqJRUyOHTqCajxjWyOihCHXV+5gJ0I32NTqm+t+qosyxdSM9RrZtsglxmYA3L+TEobHkT7bro16fydykwGybYoeQs6mkwm1LRU7lE2OVS9tOkU9bZlTXesOJOgKPYcLilPMHyG2Mq/HYTQ2FenNgrnNMJmNIpmPpl6ljEpV/M/HOqM8AMkaN1njsOXFZvy6XaDjGSWPk+hOwFcAnAcyE6uHRlNJNWfabCdXX77mu02OsfwI20gqoNRmV7kQSRYc38UT+YEzC3g6fRpexeVUzQtFhDWCJQ8SXLpyF37x9VCOGrS+zY8PyJuzpGEJYkvHs9nZ8/vxZECwEW/6yN6WpmlnmRI3HBl9EgixT2AUOsThFKBZHRJJTx75iYTlWLZme0lhZCNCQ1BR+O6mxsgkcbl++EOVuK+JxilInD4BDty+C6iIrRB7oD8Tx4fEBOEQLRAuHUqcVR5LmFpEn+MYljRqd4caVTXBaeY3OUP+w0i2rmxGOxbHlL3tT733jymaIloQbWJ3aUem2gSPAkb4APDYB7YMh3Pfi/pQg+az6UpzXUAaOI6aJCV1DYThEHtG4NoUhl+s/mv1HgDH7CSvXdWqMlj+SJOMvuzo0ovU717QgGpPhtPKoLhJx0hvBsf4gqoqs6A/ENBqcjSubYBct+E7yIbZfvnBWqt5e3deNi+dXYnaFC7XFNvgjMciU4N9ePYhzGipQUySitsSBD48Pm0XWLZuLcpcAgbcgEJEgcBza+gK467l9KT7NrnCh2C4gEpdBKYWFIzjeF4TbLqb0hPVldnz1ojnYuHVYi7hldTOml9jxnad2oK0vlBLk+8IxdA6GEI1TROMyltQV47H3jmJnux/XLK3D9BIH+vwRTC91oLm2CFI88UijTPWVLS0kE4ySJPJNxDFrN5XIYJK8cBpQ0PwBhjWAdz+/NzXGfXxOGWy8BQPBCJyigN5kwtPR3qBm7F23rBG1xVa4rCJ6/BGUOkUMhSR8/+mdpmPx7csXoqHCjlCUIiLJsPIc7kjq7urL7Lh9eRMiUhwcIfjhc8N6dUUD+ND/Hhk2h8wogWChADhYOILBYAw0+e2i08rjUJcP5W4b2voCmFPpwq/eOIw1H5uOORUOWDgOPb4obAKHHl8YM8qcON4f0ry/H1+9GCVOAcFIHA7RgtYev0aD+MM1LSh3ifCGJdh4Dpv/Mhx0oNf3GZ1nZS7heS61zXhwZ4JjVBrA5wF4AbyPhBsYAEApNf0GL7nfTGj1FzWU0o7k/28BcA6l9FPZjj/WUVZ6QXtzrQehaDw1wSmkG04vOKhx3nZ4Qyk304YVTWiocGAgEIUkAwe6fHCIFtQVOyDyHHwRSSOy3bK6GcUOAVaewC7wONoX1NxYKmLymy+egye2HcO2Ni+W1nvw9Ysb0TMUgt0maFyXNoHDd6+cj7/saMc3Lp2LoXAMh7qHzRubVixEHESTzLBpZROe2HYM7YORtCfKb1rVjMFABFt3dGD1GbUoc1lxcjAEh2DBw28NDwqL64oRjcXgtCUGI54jKHJYMM3jwJG+IA52DsGpE+xuWd2Mn79y0NAVtmnlQlgsFs25uv/aJYmb0d8PJzA88OkzEJVoWrKKWoCfibxG5p989h8hxlLDlDeHRsMf/YDqsVlQ7rZpnMB6Q8fdn2yBPyzBKlhQ7BCxeetuRCWKL5xfjxKnNTX4J5I9GjVpIEpKzjVLZ+DfXjuU5izcsroZj72bbr746ifmoKHcjuMD4TQB/bRiG3779lFsa/OivsyO2/5hIcJSHAQEvnAU00ud6PFFcKQvkOJNws0uwB+O4/tP7zRMX9iyuhkcAb73p+Hj3XrFPIgWopnAjOprNE5go3313BqJ8F2WKV7Z35WWyDABRPQFyx8FskzxxqFunBgIY/PWPal6MkrQuOeTLajx2NGTjNfs9YXBcZymrr931XzUlThSEZ0OgUNIktHvj+JQjx87jw+mEnCUfX6wfCEqXCJ6/VFsUo3LP1i+EOFoPPULUamTR1t/WDNnKDX8mXPqYbVwePitIylnsBFH3zrYg8bqIk0dKTepUYmm5tJyt4A9J32a+Uz9JImaYjva+gKpLxUS48si1BbbUGryoUp9nvU1DGBcuFMAGNUN4K5cvqnT7fMogIsBlAPoArAh+XoJAArgKICvKGTMhLG8ATQSia5dNsdQeKo2eaiXG4lt51S607Y1a/feqxfjQLcPS+tLcNNv3zds/6E3W1Mic0X0aySSV+9jJMo1E93ec/Vi7O/0ZRQTZzO2KOdHLbZ//KZzcd2Df8/ZDKO8NjtXekGy2Xa5CvDNRMLjLOAfqYtxTDg0Gv7oz5degG5m6DCqr1zNRuq6yrWGHnqzNaOoXTFXAdoauvmSOTmJ5PMxRmUyZBkZZ9Tb5VJ/mWpYfV7yreXWHj+e/qjd8FycZhF9wfJHgf7cZhtb1UakTLUXl5FqBxg2SZjx0owj6jE80zaKuUN9XKO+/cdnz8RXTOY2dY1m64/ZeG9Wj9lqGMC4cKcAYMihXLOA3yKEtFBKd+Z6NErp9QaLH8p1//GCkUjU9CnkJoJVI7Gt0baZUjdkmp40om4/HBsWmSuvBwLpInn1PkbrzES3oWS6R6b3l83Yophg1GJ7xWCS75PhMz3BXo1swuNuXzgjcTOll+Sy/6nEROBQttQCM44Y1VeuJgx1XeVaQwqvzGpIbdhQ11C2dBz1drnUp9kyfX1lEqtnq79sCTz5tKVv1+xcTCRe5IqJwB8F+nObbWxVG5Ey1Z66HWU5YM5Ls/lAvX+mbYyOa7RtrslV2fqTbz1mq2GaxTCWrf3JhlxdwBcAeJ8Qsj/pntpJCNkxnh0bLxiJRE2fQm4iVjcS2xptmyl1gyPmaQiKiNYo1SCTSNZonZnoVm02yfb+zLazJ00waoGu2mCSS9vK60xPsFcjm/B4pIYTJuA3RrbUAjOOZKqvbK/V5qpca0jhlVkNqbmkr6FMPFUvz6U+zZbp62s0YvVsCTz5tKVv1+xcMF6MDkbnNlONGxmR9K8Vk4d+DAbMeWk2H6j3z7SNur4z9b8kx+SqbP3Jtx6z1fB4cadQkesN4FUAGgFcAWAFEk9XT0v/KATMLHPi/muXaIq3pc6Ttmzdskb85+uHsW5Zo2b5HSuasHVHu2a7WeVOPPJWK9Zeqt12TqULm1c1a5ZtWNGEsCSh0m1FXJaxaWWTZv3aSxuxdUc7Nq1swm/eagWQEL5vWtmM//77EcyucKb16dYr5mHrjnZUuK3Jh0IPrwuGY9iwQnsMpe2n3j+R1tYG1ftLHLcJz25vT3tvG5Yn2li3rBF//OAEbEJCO9FUU4T7r11iuM+W1c1p507Zt8wppvX9/muXYFGdJ+u1Us7Z/dcuSZlE8rn++ew/1aA/X89ub9dcp0feasVGXQ1vNKmvZ7e3Y8PyJs1r/b5rL23EI2+1YsMK87pT15By7dYta0QwGkvj27pljZhd4UxxSak1hXdPvX8CZU4xjQfrL5+rac+IKxtXNqHcKWqW3XLZ3LRlRvVlVIe51p/RvnpujaSWZ5Y50VLnSXufjBejh/7cKvVkVON3rGgCx0HDE6O6LnOIqbli6452TY3+5+uH07i1blkjwjEJt1w2N225Mg7fesU8BCOxtBpQanj95XNR5hBT/Tbk6Iom/NebRzLOLTaBw7f/zzwEw+nHUvdndoUL6y/X9ve+a8zrMVsNjxd3ChUZNYCEkCJK6RAhpNRoPaW0f9x6psJoRezquLe4nHDnOa08YpKMoXAMpU4rZCqDIwSBSBwuqwVhKRGMXVdsQ5wCA8EoPHYBvf4Iyl1WSPE4eM4CXyQGl01AKCrBYxMQiMbhC0sod4lw2ywYDEno80dR7rIiGpeS6QVAty8Ct41HsUNAKBpHjz+CMqcVwWgMbpsIwQKEYjJiEkUoJqHUKcImWBCIxDEUjqHIJmAoFEOpS4RgSTizwrE4qjxWUJlgKJz4Ct7jEBCKxuAQBXhDMRQ7BHCEoMcXQbFDQDgmwcJZIHCAYLHAH4nBLvKQKcVgMIbqIiuC0TiicRlOkUd/MIoSuwhvOIZiuwAKGZRy8IZiqYiseBzo9kdQ4bIm25FQ47GisdyF3V0+dHjDqHRbE+c5JkGSCao9VsQkimMDQbitPOyiBUMhCS5b4jqFYolzF4jGUeLk4QvH0euPoCrp2C6yiznHxx3pDaCtPwCnyMNpTUTbGQmKxxCnVU08Ev7oeUMp0OuPYDAYQ4lTACEEfb4o7FYLyl0CfOE4ovE4RM6CLl8EVUVWiBYOvf4IiuwCfOEY7KIFdt6CXn8UdtECjgOcIg8pTtHtC6PMZYUvnEinsVoIgjEZ/ogEj11AXyCCcqcVIk8wFI4jKsXhsYvoC0RgFyxwihbYBAsGQzEEIhKKnQKoDHhDUvKbc4LeQBTFdgEDwRjcNgsEiwX9gSg8dh42nkMkTuELx+AQefjDEqo9NhBC0T4Yhl2wwGVNfCvT54+iONlm51Ci36AybDwPjgOiEkXnUBiVRVZwIBgIRlHiECGDosxpRZ3Hjn1dQ+gYCsMlJmpded88RxBICvLVMVd6Z259acK5qE7WUb/W17Ikydjd4UWHN4wajx1NNUUpV6T+uivHCkYlzMjBBTwaN3OOKGj+TCu2YTAQQ8dQGFVFVoACJ4fCqPXYEY3LqfHcF47BbRUQjMVg43n4IhLcNh6hmAQbb4GVt6AvEIXHJiTGYaeIaFyCnRcwEIjCbU9sK3IJbrmsPGQZ8Ecl+MIS3FYeVoGA5wiicaTmMn8kBoHjUGQX4I9ICEQS800sLsMficNjE+ANx+CxC7BaOASiMfAWC7qGEk+VsHBAjz8Kp8ijyMYjGpfR4U2sEywcenwRuGw8IpKUmIfCMZTYBEiUYiAQQ12JDcGojF5/BJXuxD7tgyGUOkW4bYnxQZnPShwC7IIFPf4InFYevnAMxXYRcZqIKSWEIiolxpNyV94BiTsAACAASURBVKJv3qCE+rJEHQMYHv+tPKrcVtQVZ+bOKajvU4ERaQB/TwhZAaAXCcGsuhEKoGFMujZO0Dt+9S4+tQtVcR9++aLZaOsP6lyzCyFTgm8+mnCjGuWd/nBNCw50+TVuJn0W4oYVTbCQMG5/Zo+mD07Rgn//WysGglF876r5KHZIGAzGEIjGU/01cltuWNGEgWAEAm/B/3vpAKISxdc+0aDZT/n09vi2Y7jx47PQ2hPAT146kLbuU2fN0PRDfW42LG/CUx8cw5cubMBgUMK6xz7SvMcnk27l+jI7vvqJOYbusS9f2ID9Xdo4PcVp+YnZFXjlYI/GmaU+vjqeyOg6Jr4pLJlM2cGnFUaxVfrs5o0rmxCJxfHmoW5cvnAaXtxzEssW1KRlkKr5pdSZftnNlzTCbbXg23/Yji+ePwuEhNJq+NYr5qHPH009OulLF87Cj//nQ82xFLevkbt9/eVzYRcsKQ4r+9QW2/CH99tw9qwK/PbvR9Mcjer3sG5ZI6o9NsTlxAeS7zyljZBb0VSNv+zpxG1P7zKsU2WMue6s+rTHfLisPCgNatzD91+7BFcsqMJrB7tNnblqnVJDhctQtyRJMp7e3q65fltWN2P14tq0m0COI5hZ7sLM8tz0T4xT6VCfk7mVLlx/Tr1mTNywoglOgeDt1r7UEyZuvmQ2gtE4fvdOW1oNqsfvUExOjd/1ZXZ87RNzcMezHxiO5xVuEQAxdcVfOr8a658YztjVj93KuPvN54Y588M1TYjGkTYPPfpOG7zhWJqrf/OqZlgFDt/8rw81Y0fHQChtPDebo6cV20AIwX0v7Dc9N9ctnZF6eoBR3w50+w2fIHH/tUswo9Rpyp3/z96bx8lt1/f/L91zz16zh3e962t97doOsZPQACHgOITWR26gQAr8aKBA49aBL1CS+AqUcoQflCPQEpq0CSR8gSRO2zQhZ8GExDmxnfha39713jszmhnd3z800koaSTN72N5d6/V4+OGV9JE+kub91kc7+359njM9vs+YC3gyNd5vAK1uuXL4nBCjuw9JoMSV5HTSuh3Lz53qdD25YaYUFabryZD1eH6urv29GSjF1X54nkrQPcZ5VHpvjG1Wt/JY+v7W9SvQkODw0Xte9P1sDGelVx8TcU+eBcfXtPoGo5zzFxiN25Vt1fjUv79c1vVnjTO3dd+6fgXe7MmAKr6P+DnC/c5nQX3c093u5dg1rqGc69hov7A+jv29mZJj3feJi3FTMY7LOZzdzg1AyTEfvPntnnisSuPWiX60HnvFbE8YU0U6Szk1bfPH67ltoNWMWDGej34x6HyG+j1vK3HFjwcxOp5ZJfyc8tZ+/c4BgOk89rper9wyxqexOoqdn2Wl+0xRueZQpTWALxf5v9NKVrdcOUdhQdJdv26uJOc6t2OVw1EZy16YKavryQ1v5enqKjqKDVTOWF3CVieX9TxK7o3ofm+MbV73pVzfvCijJy2U/WzKYZAqQf0EmKDKVM75C4x+Jobbr5zrzxpnbuv4omvYD+1WLgYMt+9YHbtOzJXfNagazJxztjttieNyDme383A7ph8eq9K4daIfjf17RiYe90FOlaoSNOJQTrLFivEZj+UZ6te2IPmj3pxxWGnOjGdWCT+nvHUfv/2tzmOv6/WbkQDwdxR7aabHd6UvgJcA+ANBEIemkwvY6fix/mwsW91PEY52dSV5OZWsKoewMZa9MFNW15Mb3srT1VV0FJdzTfkhgazuLi98Vph1vzfGNq/7Uq7vKEujMcGV/WzKYZAm4p48Xxxflaqc89dYtjrZ/Rzt1p+91hl54Yd2KxcDTpSi3/7WdW6YK69rIAmYOeds1+CIY7djOfGR1uO6HdMPj1Vp3HqhHxuTE4/7IKdKVQkasTpizxfrZzzWZ6hb2xDjj3rzikO3tlaNZ1aJck75cjljzQ2/6y03I8F48mimx3elfwJuc1uvadrRST8jF433T8BWisEHL2pFIsyYs7B//NI2tDcmUBAVJCM0ogyN4bwEDSoGedlGOvji+5fY8DxeNYB9WcGsAXRDwP3NuxdgVhWHXUdHsVafumw+kmEax4fy+O3eHtyyuh00RUJRNPCiAo4mISoantp7Civn1Nn6/Mb1yzErGUbPSAF1cQ6qqoAggaP9BWxxqcO79cpFyAmK67YPXtSK2TVhUASJTEFCQzKEZ9/sQTzCYX4qBmgqWqojODFUQFcRGacTPEapIh+/tA1VUQ53PFJKNtm0ZiFEWcPtj+y2UVV0swyBQ30FGz7s9rVLkSlIyIsKFtTHQBLArb98wzaD/g0rZyMV59CUDCHMUmBJEgQB8KKCpmQIsqKbSqIsjYaEXuz7xJunbfUc/3TdcvxFZ5NrQbxXTI2jIHha/QlLllW8dHQQA1kRvCBDkBXUxjjsO50BTZKYl4qCJgnUxVgwJIkDfVk8safyGkCDsNNaE0GEpcAxJDRNw6FeHnPqouAFBWGWwv6eNH62U9//81cuQlWExrHBPCIshZoIix88e9Ak0yxpShRfziiEGQonhgvo6tNRc2uXN6GjOWkawE6nC3ho13HcfNl8tNaEEGEYHB7I4Z+f3u9aA/jff+rG5Yvr0VoTQV2MQ5Qj0Z+RcHSAx38Vt81PxdBSHUZ/VsDGX+g1x3/z7nno50UT2dhaG8H/7D6FNUtnldQAtlSHQRIE9p/O4Jm3evH+ZU1oq40iGaLBMgSGeRl/OjViPjf+bvVC1EQZcAyNGEchL+m4udbqCI4O5nB0UEcsMhQBWdVwZCBnyy+vGkBgbDEuyyp2dg1g19FB89zOAFFnWuWPtW7sz+bWlBA5vnn9CtREGaTzMiIsiRhHQ1BUnBwq4PvPHHCtc3v6rR586rIFGClIOD6YM5Gd1trcttowvnjVEnT1ZTG/PgaOJjDIS7YawDvWLkVtjMVTe7vxvs5mvH5CH4teONSHG1e14o5H7VjExmQILx4eBEeTSIZotDfGcXQgb3vGb9/Qifo4i/v/eARXLJ1l2/bVa5YhEaJx53/uNUlCHc1VyBQkhBgSNEligBfB0STSeRGSCnOMvmFVCxakYqhPcMgKMmRFAy/K+MpvSseXT1w6F4qmoTbGoatvdHzaur4Dj/+pG384PIgff+RCDPISvmzLg2VY2VqFNg/jxwAv4NRwwUYJGwux50ybR8bQh/vKSl4Az7Um4gK2ckyNF7/GqjBODuVtxaaGYcMwUhhcRo6msPnRPTbGaFMyBEGSISnA3u40WmujECUJjckIsgUZKjQM5SQbiuar1yxDTZTG3/zHq7aXnN+8chxrljbhgReP4tY1i5AuSMiJis1MYhSrx8MUOJrRXVUchYGsaEvY7Rs6wdEEvv3kfnNgXN5SBQIa9p/O4r4/HMVfdKZw6YIG7O1OY15dFLKqIhFmEeFIvHZsxNbv9g2d+P4zXui2DiQjLH718lF87B3zcGq4YBa/m/epyFHWNGDLjj1Ihhh87r3tGMlLdjNI0WRyybwUwgyJpbMS2FrkVoYYvYA/wlJoq4ngcD+PBQ0xHB/M2x6qm9YsRCrO4cu/dsd2bVzdjvaGGC6bn8ILRwax6+ggFBV47I3KB6wJFARPmwHMDQfWVhsuYUNbmaGffOc89GUFzK0NY1ZVFL2ZAqojLI4N8hjkJVzYWg1elMHRJAANvWkRt7m8AI3kJRvyaePqdjRXh1EdppGTVAzyErbu0HPx769YAEUjSorWH3jxKD797gVmUfqtaxZhOCeWmEq+ds0yvHCoF29rrcPWx/aYjOK5qQhYisKrx4YBAFVhGhxDl7ywmXmwoRM/sOTIlnUdaEpyyEsq8qJiG2w2rVmI2hiLx147hXctTOnfwGkAy5DY/thek9P62fe02wZRJ+dVN5Np+M5vD5rmlAhDYccbJ/HBi9tsJhQDNfbgrmM2tuulc2vBspTr519pjLu1HesvVBVq2uSPIStv/vHdJ/GRt89DX0b/RX1fTyn6zIpIW9qUQCLMYPfJEbRUhUFRhBn71rFDUVSQJIFMQUZLdRiiotlQmtvWd2B/zzDe0d6AV48Pm8+7TWsWQlK0ElNQKs7i5eKXE1GWwqyqEL74q1LecDLE4G/f246cpOBQnwU7ur4DLxzqR0ttFK01ESRDDH78/EFc/bYWhFka33tq9BcsL3PME7u78Wfzaz2/SPjwJW1YOisBWVERYWikBQkxlsbRwVwJ9q6lOoz7XziMdy9qhKaq4Bga33/mAG5YORuNyRCODebMc7fGuJsJbvuGZWAoouIXubNhHhljH+fnC6BbQbufGQIoj9P58UdX4sXDQ65YKz/Dgx8iytgP8N53ZVu1iYLzKyZ3K7g1zB0P/PUl+MS/vVSyXzkkj3PZ+rMfSu9gb6biwmUrfs7tugDY7oHXtfsdY/XieteC+DNsIpk2A5gbSqkSFFVB8seqGfE6VhOEM+7L9WMtCN9XxlTiZlxxxnIl5jFnjpQriDcMW24orXL9OY9jNadYC/vLocb8UFqVxvhZLJCfNvljlXF/nM/4SsyC1mdZuZyyft7jiXGjbSX4OeM8/RCQt/z8VVuMuhlcxoKQtPZtmBVXL65HPMTY7q/f86mSezUZmEbnZ38mc2OMfUzIBDJt5VbQ7meGsBaaehWVDuckT6yV3/H9EFGVFMAP50ZRcF7n5lVwaxSy92fcDRflkDzOZevPnii9YrG8233y6qdcIbAfDq8Sk4BXQXxgItHlhlKqBEVltCuHVfSLW6/YtcZ9uX6sGMVyOTXsYlwZa7y65Ui5gnjrc6ZSPJ4bftJpTrHe23KoLq+YHUuMnw/5MBEZ96eSscHt8wXK5FQFqMJKYtxoWwl+zjjPcoYLa4y6GVzGgpC09m0ct2ekUHJ/ne2tzye33HC2N+J2MuL6bOTGZPQx418Ax4J+qxSnUxVhbO2s2/yO74eIMop8/fatioyi4LzOzavg1ri2VNzdcFEOyeN2j8oZT8IsPabCZb/tRiFwtQ8OrxKTQFNy8hFcM6UgGPBHKTmX3Qqzy2EV/eLWK3atcV+uHydG0S+nqlyMK2ONV7fYLVcQ73zOjCc/nMdxw1H6Hd8PpVVp+/MhHyYi6/0pNza4fb6GPHOqAlThWGK8EvyccZ6VICCNGHUzuIwHIWk1KzYmQ67319reisrzyg3r8mRgGg2djdyYjD5m/AugG8pqQX3MFevkhtNxw6jd/8Jh/OrlE/jC+xbZjmvgq2oipWipL7xvERakorZ1Bl7H+H9OXRSpOFeCvtm4uh1za6O4/4XDmFOno+Du3dllw2qFGL1mz9mHgaozUDe/eeVYCX5u+4ZOaJpacs7bN3ij26w/Nya4UuRdERWXinO2/oz75Gxrw8+5YI/qoizmp6L4t98dRm2ULblHm9YsxNw6/drdsF0bV7djeUsSHU2lKLmJILhmGjbIDaXk9pkYyDMrCsoPq2ZgFe/d2YUt60oRVQvqY6i3/HJijV3jM7ditNz6MfJo87oO/Ovzh7Dj9ZOYn4q6tjXy2JlDTnyjE1/njH3juWFss/bthuKqi7L49SsnTKSeE6Xltp8ToWU9jrFcG2Fx784uE9Pnhxrzi9mxxPj5kA8TkXF/rNg0t7HBDZG2oD7mG+tOVKnbM2/LOvcYn1MXLcFubnbBz/nFoRsCcksx9q0x+dgbJ1EbZbF9gz3W3cZWa+44t1mRjwvr4+hoStrurxvezujfwEtu88E6Tham0fnZn8ncmIw+ZmwNoNUdUx2lkckrOJ0RTATZSF6CrAIFUUFVhAEvSohyjI6HE2Q0xjmIsoa0oGN6+jICUnEOHAMIEiArChhKR/NUR3SE02BOQiLMQNMUkKDQmxVQF2MRZkiIsoaRvI57yksyEkVsXE6UdbSWpuPc4mEaLEWiNyMgFWOhaEBfRkBNlEWUoyArGgayImJhGoKko3WGclLxHDSomo6Bi7JFfFaMA0FoIAkSHA1Isu6S5UUZUZYGTRHgKBJZUUKEYcCLMvKiglScg6LphfcGrmqAFxFmKCTDNHKigr6sbpShSQIsTUCQNPSkBdQn9Hs8yEvIizLqYhxIgsBgTkCY0ZFAUVbH6tXGOHA0IMjAcF5CTYTFUE7vh6YIMCQBpfhnwFSchSgrIAkSOVFGmKUxnJNMJ2mYpiDIKvqyAlqqw+BoCieGcogUXcCtNXrxroHFGuAFJEKsif+ytvFyVxnrvbBBHpo2NUyqqqGrL4tTIzmEaBr9vJ4zgiyDJCjwgoxEmNFRiBSFvoyAWVW6mSEjyChICqqjLBRVQzovIxGmkQzTSOcVDOd1JBovyqiNsOBFBQNZEQ0JDskwhQFexkDRfEWRQJimUZBV9FmwibwgIyPo+TPIi6iN6hiodF5HVcmqAo6m0Z8VkQjTiBbxcBxDIZOXEQ/TkBSjjX5tJEGgL6vn8UjxHCVFRaagoDqiO2kzgo6fS4QY5EQZLD16L0byet4DCjSVhCArCDP6vauLcWAoAieHC2iIc6ApfR5BhtT7b0hwIAD08yIiLI0IQxVxcDq+K8rRCDMkKFLH6oWLLuf+rKC3ZykwlI5izEsK5tREIcgKjg3lTKzWAC/oiDFRRpRjIMoqGpN67KqqVoKIA1ARNs6Il3Hkg23f8ToYz5bGmj/GNcVDNNJ5CemCjFSMAwgN0HTaSkFSMciLqI9zCDMkssVcqIuxYCkSQzkRsZD+PI6xtBlzRnz3ZfRnXEFSTSznSE5CiKXAUoSOYMvq6MOqCIV0XkFPWo+3MKMjPzMFxUTN1UY5SEoRYRjXsaUUQYKh9FiLcTRqogx4QcFQXkJdlAUvyMhJ+jFYigRNApKql+mEWQoRlgJFALKqAiCREUTEOT1vwwyFRFiPyZG8jBhHI87RyMsSCFBQNB21aoxtg7yImigLRVXAUpSOoszq+DsSBEbyEpIRRsfqcQxiIT3vMwUZEZZGlKVQF2fRlxbRky6gpSoMQdHn7mxMcljWmATLUiXY2KGcAIYikXPgGccSC+PJjUo1hj5cV5ZDwU1LVYLi+fkfj4KlCXzgojb87S9eLYuk2by2Az/7fRduWNWKAz3DWNhYhTuK7mBrWyeyrRw2zsux+mLXAK7sbLKdtxMtZ8XgPLjrGD5z+QL8ctcxvHdxo20agS3rOlAdoSDIwMnhfAli699fOIJPX7YAdz+/x+Y0tDoPDedjcxVXMl3A1vUdCLMU/s//HbXKG9PD7Do6ghBD4hvXLUNvRsS3nthnu6c//V0XblzVht++eQoXz03hFguqy+rsLXcPbl+7FH2yWNYRpaoannjzNP7p8Tddp/xob4jh8vb6kulirMfywgZNd1mnTfKaiuK6la24+7mDuO7CVtM964Yf3LRmIUI0CQ0cjg7YHXrb1i1Fz4hgxnZbbRifvXyBLab+6bplEGQVdzxiz9tfvVwa3zZs4boO3P3caBz/w/sXl+CzPn3ZAmx9bBSfZbTZ6IJI/Jt3L8CPig7Nj79jji+qyyv/rDiqf7x2GSRZxR2PvuK63Zl3m9YsRFWYBkmS+OenS6cI2b6hE3lRtiHknPi6+/6gP+uc1/39v3wbhnNSiRu0KsLgcw+86ptHhsabDzMRsVUOPWr8pWWAl2wOex3nVjqWOGN1dGx5xRzX7n7uoOtzzMB6sjRhyy035NuXr1qMg728zZm8ZV0HNE3F1sfeLMm/dcubcajXjib8wvsWgSl+blac6Ny6CHpGBPxs52F89O1zbM//javb0ZgI4f9/ar8Z79s3dOLJvafwnkVNuPv50mszHPB3PLrXF7d43cpW2zWWmyVi24ZONCRYfOrf7fnhho6rNEbPxlgx0T5m5J+Ajwzw5of2ycvmm4EA6EWSW3fswScvm4+bLp1nTu9w7YUtZkBYfzb3eWwPbrp0HjY/ugdXdDSbCeVsa7Qxltcub7YtFyQVhwdGk82tr+8+dQAfe+fckvPe/OgerF3ebC5/7+kDWLu82fx/86P6ORoJY7TbsmMPEmEOB/uyJX1964l9WLu8GVsfsx976w778nefOoBrL2zBTZfOM6/del4He7O2dXcUz8VYPtjHm8nvvKd3PLobH3773JLtdz25H4f7+YruwRsnRszP3Ni26aHXcGSAd40N47457/sbJ0awp7uyY800+d2b7z19ADddOg9bd+if2dbHRuO/nxdL4uquJ/ejnxeRCLEl8R/hGFtsr13eXBJTh/p48+XPWGf07fa5XXthi2vc9vOi+fJn9GWcu1cba1xtKR7v2gtbcFdxeiW3e+OXf8bzpiCpONzPl1yrdbvz/O96cj8iLIPbHt7t2vftj+xGPy963g/jZ7frfuPEiPnyZ6y77eHdeOPEyBmPfesz+kz2czZlvSa35/rtj+xGhGVs640Yc9vH+ZlZxxZjXPN6jvXzovm53+EYj5zjykBONMcjY92WHXvQbSHbWPNvIFea79/8n33o50WzX+M8ZAX4+uNvYe3y5pLn+3efOoDDA7wt3m9/RB8LjPHILZciLON5j63PKa+xxG2/Ox7ZjUxeKcmPmRajTs3IF8BKUDx5UfZ0BpXDOPVlvBFzzv7KYePG4t4qSO6uXOv/XtfbnxXKOie9HL/W5bG4j/MWV1k5J5sXSszLOe1crhTzU4lzbCJO4emscvfG+KyceePnyu5zcZ1PBK3ohVLzcuVW4uqtJC/KuWoniqOyuifd8JHlYrbc/RjLdbsdb7JjfyY6iCtBj/Kid+yXc4RbY8v4uVK3udcxgbHFgdvMDtb2Tnd6JbHrjPdyaEbDrTwe3KLffk5M60QRjNNBM/IFsBIUT5ilfZ1BXvuEGBKpuDdizqs/q5wuLLf2bu6tEOPuyrX+79V/XYwr65z0cvxal8fiPrYi4vycwiGGtOG4yh3X7bwqxfxU4hzzQmfNdHdjuXtjuA6dMeDnynZznU8EreiFsPJy5Vbi6q00L/zuzURxVFb3oxs+slzMVnI/Kr1ut+NNduzPRAdxJejRaBkEm982a2xZf/b6DL0+94nEQcRnZgfDyW/tt5LYdcZ7TRk0o9WtPJZnRLlZIpwu6IkiGKeDzpgJhCCIewCsBdCraVpncV0NgAcBzAFwBMCNmqYNlTvWRFA8f97RgHUXNOPA6QzmpmIQJBXxMI1kiIasqjgykLfNnO9WA9hWG8a2DZ2QVRUxljFNCvt60vjDoQFc0dFoUj/cagA/e3k7fvCs/meiuiiDjuYkBrMiZFXD6XQBYZbCNgs1ZOPqdhzoSeMvVoyiena8ftI8jjFj/OzqCAayAsIMhXt2HsZn39OOh146ihtXzkaE0wvmoyEaDEUgyhIY5BXPGsDPXL7AJKHcsKoF8+ti6B7J495i/dCX378E+09nUROhwdI07nh0tG7ItQZwQyd+u/cUntjbjxBD4pvXL4coa/jKw6P1fFvWdeCVo/24+m2tJgHFiqsyXGZ3PbnfxL41JkJQNBX/vvMols2uQmtNBP0ZAUtmxcELagmVYFZVCLVRzmbi8Kpzq7QGcBwa306TlEOV5o/13nzynfOQF2XMrolC1TRUF4u+WZrE/S8cxnsWNdlqAAuyascgruuApKhoiHMYykl49fhoHH/hfYvAC4qZI6vakvjM5e3Y253G/PoYKIIASQLZgoIv/foNWzwYNXZWssWSpgR+9MxB7O/NYtuGTgzzAn62U4/bbes7zG8i+7ICnnnrtC0/Q0xpnWCIGa0l+sx72rH3VBoEAdRFWfzwuUNjrgHctqETOUHCz3Yewd9dsbBYA2hFbi0DS2s4OSSgKRnGUE7/1jQvKVjalIAoyyAIEiM5sYRKcufVnSAI4Cu/GaXwzK2Lgi/IyIkyUokQkmEa3cMFRDkGhy0Yx2/fsAKCrJYg4sZSAzheTQbFoJzOVf4YNYC3XtmOREg3AaoAcoJumOjqy+Jr//2WSaaqinL4gYVQYaDemqs4/NWlc3GgNwuaJLG8JYHejIiHXjqKv37XfAiyihBL4dhAzlZb94/XLkNjIoTejIC6KAtZVfHyMT3/YiyFttoI3uzJmIjC5mQIGVHBN/9n9BjfvH454iEGrxwbMvP20+9eYNYA5iR7ze+WdR1IJVgMZASTUHP72qWYWxfBYFbEv+08jOtXttowpBtXt6MpGcJ3fjtaA/iP1yxDKs5gMCfj5FAedTEOJ4ZyZsx+9ZplaKkOo2e4gFiIwpGBnO28DUJKMsKUUEyinD5GedVn1k9yDeAUk7sz5Ay+AF4GIAvgPkvyfQPAoKZpXycI4ksAqjVN+2K5Y43nBfDpfafRM5wHQ1P44bMH3QtQkyH8775eXNnZhL3daYQZHY3UUh1GdZTFn06MgCCAZJjF3c8dxF9e3GYbJDaubsesqhD+/Q9HcMm8FCgSeNvsajC0BoakcTpTAAkCT7/Zg5Vza/GT5w+Zhg3rIGIMmEf7efCigqowjRBL24qzjQANMRSODeRLBoHmqhB+9vsufOzSuTgxJNhe0Las60B1lEFjnEFBBvKSiqwgg6NInM4U0FwVwQ+e2Y/VSxpNXrI1cViaMPmLbbVh/MOfL4GqAqKi6jxYikCEoZARFBwfzCHE0rhvZxc+dMkc1EYZUCQBXpDRmxZMNipJAIsa4lA0zXxxbKsN40tXLcGB3ixkVUVdlEVrbQSDvGQbpAw03refHH1w3HXjBbhySQOODeVwOl2ApGi4/ZE/2bZbUT9HBngM8gIYksRgTvR0AU+Sg2u8A9ik5NBY8keWVbx6YgjdIwX0jBRsheHGi86n370As6s50BSN7pE8ZiU5nBoR0DNSQEt1GOmCbPI8nQ/a29cuxYMvHgNLE/i7KxZhMCdClFVbrBsmqPVva8bhft6MlwX1MdRGGciqhoGsPSa2b+jEUPHFbygnmjHf1cfbzVzrOvDMW9340CVzkS1ISMU5CJKCfT0ZNFZF0DOSx5LGBGRNwUBWxm0Pl/ahAmipioAkgZooqzsQwwzyogySJJEXZVRHOJxOF3DQw3QRogAAIABJREFUgsr62jXLMK8ugpGCBFWFDc/16XcvcDVdDeVEfOO65UiGaeztzuAXL42++HY0JZEuiPj+MwdtrHPrfTS4y87Bbsjykrx1fSdEWUVDgkNHUxIkSZxx9yIwcQdjOZ2r/NnZNYCRvABNI1wxhFvXd6AhwaE/K+K2h3djYX0MH7i41fbZGfF7dCBny8Hv3rgcI4Jqw6R947rlJsYtylKIcLSnCdENNXjn1Z2Ih2jsOZWGqgEJjkJ1lLONMTpmFEglwsgWZNREWGQFGbwo4+hAzozVr12zDFGWwp7utBn3d17dCYokcP8LR/CpyxboX6CwNLqHc2irjaKrOO6RBLC4MY7hnIi8pNpe7O68uhOtNWGcHC7YTIHfuG4ZUgkOp4YKOGpBu33pqsXISwrykoILWqrww2cP4OSwgBtWtWBhQxzVEQZvnBgx+13WnMR7Ftbj2FDOFo8AzkounAWd3RdAACAIYg6AxyzJtw/A5ZqmdRME0QTgWU3TFpU7zlhfAA1EioF/8UPFrGyr9kXllEMqWZFMxjonAs15DK9j/fijK/FX97zki956x/w6/NXPXizZ9pOPrsQfDw9hSWPcFen2retXoDbG4g9dA56oK79+DUSXV5uffHQlbnbcR2P9cF4uQcK5HdtYZ8VseSHq3O77ZKJ8Jlnjf3OchBwaKwpu76k09nt8XkbsGp+LNdadqKtKMGpeaDg3hJURD4kw7RprTpyWH97wp7/rwj0fuwgvOPLBaPOzj12Ej7sgEyvBvvnhu751/Qrfe+uFXfTLAz/8odf9dfZ1DnOjEk2r/Pnz7/2vGVvA+DFubp/59z/0Ns/n++d+/moJYs157Ik8460otnLITS8caTnM4S2rF3jeM6+c9Bp7vHCNXii8KZ4DE5VrDp3tGsAGTdO6iz/3AGjwakgQxM0EQewiCGJXX1/fmDoxinErKZQdzvmjcsoVf7sVsToLZZ3H8DR+5CRbe7e+ejPuxdNDRTydF9KNF2VXI0gl5hdrMbBXm0EPE8dQTkJe8C8cdq6zFRH7mE6c930yUT5TWBXl0Hjz53S6AN7n8zI+f95iCLGinioxOFk/X0/cokc88aLsGWsl5gkfvGFB8jdGuZlX3PpwQ1f5GWN4nyJ6P+yi0zxgrC9X2F7ONGMsz5DcqERnPH8Kkor+jOCLIawE4+b2mfs934HyJsSJPOOtKLZKj2VdVwnm0O+e9WXdc9LLQFipMcxocx7lgKlzZgLR9K8ePb9+1DTtJ5qmrdI0bVUqlRrTsY1i3EoKZasi5VE5Yy1idSuUdR7D7VgGYs6vr/q4e/F0dXFfrwL7KEv7GkHK9evV3liu8TBxVEcYRDj/wmHnOlsRscf1uN33yUT5TAf55dB486chEUI05P15GWaHqMUQYm1bicHJ+vl64hY94inK0p6xVmKe8MEbhhgSKZ988EImVop988NtVYoD8yqmt7YpV9hezjRjLM+03KhEZyp/jPjxwxBWgnFz+8z9nu9AZSbE8T7jrSi2sR7LDRtpPa4hv3uWirnnpJeBcKzGsPMxB2bkn4BVVcN/7+7Bc/u6seFtszHAi5AVraSOrCkZAkkADEWBo0mwDAG+oEJUVDQmOWQLMkgCGOAlPPDHI7j2wtklZo3FTTEURJ1EYBgu+tIFxEIM0nkJ/byIZIhBIszg+88cwF9e3IYHXiydTHbb+g7sOtKPVCKKZIhCbSxkM0xsXN2OluowZiUZHB4QbbVJ29Z3YklTFCN5BZmCTj3YumN0MtxtGzrRUs2BoSjzm5EwQ+mUhXTerDd0mzT781cuAksR5kSzbbVh3LpmEQ72Zc0i4vn1McQ5Cj1pEXc/d9CsUbqgpQqKpuDHz3Xhw5fMQU+6YKuFue0vliARovF/fmW/TusktnUxFryg4OuP2ye6nVUVwl0+NYCyouE2jxrAc6Bp8yesfF7CqyeHkRFkZAUFX7HkjFEDeMt7F6KpigNJaCAJCgNZERGWwvFBHhoIZAUZv3jpmGtd2teu6cQgLyJdkLGipQr/+fpJLJ6VtNU5bVqzEEmOAkFRtlqlbRs6Mbs6BJYGjgwItnNzTqC8eV0HalxqqAzT0+evXISGeAjDeRGxEI1MQQKhkchJCmZVcYgwNPb1Zm19bFvfgR8UjVIfv7QNCxsTGORFDPEiGIrAT39/GGuXNyPMkOhsTuLYAG/Lmy3rO8ELMkI0iZ50AXf+55ue52/Ng2/fcAFiIRJ7T2VKJt+lCQL/+PhbnhPKe9UAfv+ZA765MQZKx9nQtMkfwwjy7FvdeGd7g2sNoFEn15QMoSDrfyXhBdk2Pn392uVoTHLoHingSD+PZ97qxeWL6zG/LopYiMG2x0af79+4bjliHI3XTgyjKcGBpiizfs8wIRo14c7lEKMbLygS5nPYbWL2zes6IMkKamIcqiMMukcKqAozyEsKvmAx/9159TJEWdI0mSQ4CitmV0EukoGiHIUj/bw5WbSRU6YJ5NplyBfpIM6YbapicXxQsI1PW9Z1oC7O4sBp+8TUm9YsxM9+f8QyOfxoH05zR1ttGNs3LANDEVMh1s+UpkQN4DcBDFgKcGs0Tfs/5Y4z1hdAUVTw+8N96EuLthnQt2/oNPFKmqZBlFVsfFAPAqt713iY/uIl3azx9Fs9uObC2fjJ84fMl5sVLVWApmAgp9gHqfWd2HWkD2/18Pjw29tsxbe3r12KTEHG4X4eEZZCS1UEGjQ0JkJgKA1d/QXzIdBWG8aX378EWUFGiKYQYkk8sfsUVs2pww+eHX3JetvsakRYDV399sTYvqETLE2AIkiomoKRvIKsIJckSYgmURfn0J8REA3R4AuyzajRXh/D7OoweFFBOi+jMcHhzZ6M7UX4S1ctRoQhQZIAQVK2QfPOqzsxuyaMgqQgyuroH0FSMacuqiO2ClLx6/dRbFZXPw+OptA9nENzdQSdzQmIsoYj/Tw4hkSEpUAQwIuHh8wi3pVtVa4u4OaqEGosLuBzpMkcwMacQ5XmT6EgY8fubtz+yG4b4SMV54p4KBVRlsKhPh7P7z+NNUtn2QrFN65ux7xUFBGGRE9aNI9zw6oWtNZEkMlLkFTN9kK2bUMnljTG0M9L2HNyBIsa4sjLiunWu2FVC+anYjg1nDdfiAzKjGG8umB2FQazBeRFDbEwjcP9PJ59qxc3XdoGgiBwdCBni+e8KGMoJ9sMXW4vkL96ebSPC1ur0VzFYiivoGe4UOKm//YNKyDKKr7siP2GOIfhvATBYXS58+pONCVDGMpJiIdoVIcZCLKKAV5EPESjICnYcyoDWVWxvCWJy9vrcWI4h9NpATlRxuzqCGiKQH9WAEuROD6cB0OSUFQNmYKElpowkiEGJ4byaK4OI0RT6MsKkBQN33tqn3ldq9pqcOm8WhvqbQpSOqZF/hgqFGTs6RmGrKpQNQKKqkGUNQzlRCRCDO78r71IhhjbL9tttWHcsbYDB3qz0DQNzdVhmznOSZP66jXLwJBAMsygr2gmsf5i3RAPISvKZi5cvrge8+piqI0x2HdqCPPqq/DaiVET0q1XLkIqxqEvI4AkdOOiYY5kKRKJEI2f/v5wyRcXm9YshKJqyEsKljQmkBUkyIpOEHGbIcD6ZUb3cB5/ODSA61bOhgYNTYkQNEJDtiCjKqyjItMFGYkQjfv+0IV3tTdgTm0YFElhMKdj7kgSOlYuxKAnXUC2oKApyYGiCJwY1J3CzVVhKCrQly01dwzyAk4OF/DFX70xVWL9TOmsu4B/DuByAHUATgPYDOBhAA8BaAVwFLoFf7DcscaagLuODKJnpOBaLGsU3zqLo63Fs5WaNv7t4xfjYy6GjHs+dhF+f3CgZB+v4lNn8bxzm1HI6lUYX65g3a+w9ubL5gEAFtTHPY0a37p+Bd7syQCA53n6Ffl+4/oVuOXnr5rLXsW2lZo33NpN8cLe8boYJyWHKs2flw4P4KP3vGjLAef9NAquvWLx5svmYUF93NV44GVIsMZvOeOI3zIAm6HJK1a9zr1cH36F/X65bT0vt/6s9+bBm9+OD/zkhTHF8UTyZiLtzqKmRf4Y2nVkEDlRRjov2ww/1th2mjWA0RxxMxV5xc+ixrHl2reuX4GaGItPuIwX93zsInO9mznQaxy0xrExnhgmkXLjhdO0tXRWAvNSMbx+fMg1D6xjsrXPqy9oHldsTsFYP1NyzaEzxgLWNO1DHptWn6k+DfUUC9ndCj1NmoGjwNaNClLOtNHnYcgYyApjJg6UKwwvSN7Frl7Fsca+RlGuWxtjm59Rgy8Wuvtdg1+Rr5UIon/bV3BNLj/zhrW9Wzu/wt7pmshnO4d6LOgnr89yqGia8iO3eNIwPNZb47eSQnG/ZetxvGJiLJQd67JhGrH2Y6iS/PXbZjyP/Cg0XnE8kbyZSLuprnM1BvUUS1AESfE0RfkRYyo1bviRmbzMP7woQ81ortv6PfKwEgqO8bPTTFJuvHCemxFjXnngpFYZxxpvbM6UWB+vzpkJ5EyqqVjI7lboaZAnnEW4XlQQ5//W7V6GjNoY57qPX/F3ucLwEONttPAqjjX29SusNWZv9zNqRFnadiyv47hds3HPrctexbaVmjfc2gWFvRNXY4JzzQHrcnXRNOUVi0YsuW3zWu+MX79YLrds3dcrJsZC2XGep1cuVZK/ftuM51JTcuwGponkzUTaBXJXUyKEmijjaqYyfi5HjHHb5lz2IzN5mX+iLO1pcKrzyUO/cdAax8Z4YshvvHAj3hgx5kVjcqPzGObI8eh8j/UZ+QK4bFYSDEVgy7oOW+BuXtuB+3Z24Zb3tuPenV3Ytr7T3H7vzi6TPPGrl09g4+p27Hj9JG557+j/1mPd8t52PLnnJLatt/exbX0HHn7lmHkM67aaCKvX3TmO89gbJzGnNoovvG+RbdvG1e349SsnzHb/8cJh8xzNa1rXgXRewGbHtVr3rY2yqItxJeezac1C1EVZzK2L4t6dXaiJsCVttq7vQEGW8etXTmDH6ycxLxV1PU5thHW9T9s2dOK+nV3m8l03XmDWYDg1pzaKu268wLa/W3u3dstakhXtG8hby5qS2L6h05YDzliTVRWb1izEf7xw2GxrbN+4ut2MJWccbFzdjn95/pDrMdN5wcxFt363re/AY2+c9FzeWly27vurl08gFS+N+e0bOnH/C4dLzm/zOvsxncvbN3Tily8dw5y6qOtx56WiuPPq0vth5M3mtfb83LRmoZmfxnPprhsvQEfT2ON4InkzkXaB3LVsVhKKpoIhgTl1UVtMGj//y/OHSp7Zm9d14F+fP4Qdr5+05daO10+6tn3sjZOux/HLNZoCfvPKsZJxZOv6Dvzfl47ZztUYq5zjodc4s3F1O1IxvV7Y2K82WjrmbVzdjgX1sZJ8i4cpM8Y6mhIl+WSMlc4+l7ckxx2b53usn1ETyGRpLCie40M8MnkZgqyiIOvGhzBDIcbSyAgSQgwNUZFBEiTiHI10QYaoKIhxDIZzIqoiLGRFQYih0ZcRUB1jQGgETqcFNCR0k0JGUJAtyKiLsVChggCJrCAhxjLo53UzQ06UURfjkBcV9KQF1Mc5cDQBAgQygoycqKA6opsiwqzeV1NS/63j5HCh+G0MhVPDeSQjOmWApijURBhkBRn9WRH1cQ4kAYwUJNREWBRkFQNZUf+mgtQLxOtiepvBnIhEmIEgqciJCmKcbqRgKQocQ2KQF6GqGuIh2iSF1ERYcAyJgqxgJCcjwlKoizFQVGCw6FxrTHBQNf34yZB+PRGWxlBOQkOCw5KGBE6M5CueSb1SOoBbO2DKztp+Tk+i0vzJ5UUcLM7KP8iLaEhwoAgCp0YKqImyiIdopPMS0gUZNTEGLEmiNyMgzNBIhChoGsBLCgRZQYShkSnISEQYyIoMiqAwnJfQEOcgqyp4UUGYoTFQzBeKVCErJIZyAhrjIQiKiv6siFScgwYFFKE72LOCjESYBlX8GxBJEpAVBRRJoT8roCEegqgoyBQUNMQ5qJqGrKiAF2TURkfzNS/KYGkKmYKMGEchEdZd+yN5Gak4C4YiMZLX/1ScDNPgaBIZQUJVmIOs6DMFiIoGXpBRE2VRFaGQE1RkBQU5UUEqzkLT9D8x1cY4SIoCiiSRLciojrCgSAI96QLqYhxoEkiEWRuucKxxPJG8mUi7s6Qpnz+qqqGrL4sjAzxqYywkWUNfVn+mUwSBkYKEvKgiFWcgq0BOlBFlafRmBD1+wjR4y1jBUiS6RwqIcTSSYRqKpqE3IyDC6jNNaJoGjqaQLkhIxTjkRAWnM4JupNA0nM4IaEyEQBDAqeECUnEOiZD+zB/JK0gXJFSFGQzwIqIcjQRHQ1JVZAUJYYZGOi+jOspAUlSk8zKqIgwIEOBFCVGOwUhOQpSjEKIp9PP6eXEMCb4gIxaiAejjT3WEQYimkBXlYq7RYGkCUZZGVlDQnxWQinGIhygM52WbC1eWVezpHkFvWkAywmAkLxXvFYNjg7kSetN4NcVi/Uzp7LuAJ0uVJuD/HuyFVHwonxoulEyJYLgIDc7ndStbcfdzB0unZNnQiR88ozN3nVMrON2CW9Z14Om3unHFklk2a/229R1gaRJf+rV9qgqK0PAfLxzD+5c1mS5jJ4/WOM/N6zpAQcMdO/Zajttp8oA//o45NnfVP7x/MTiGsrnFnNOqzK2LoDctmNNTGG0iRZ7wBy9qxX1F/u/fvrfd5i5z4tSe3ne6xH4/Qx1UE9WUH8ByeRG/3ddX4m61Tn3ixco2viETZAX/8r9dJTG9eW0H7n5+NGe+cf1yFETFNs3E1vUdePlIP/5sfn3JdEFfvmoxZE2zoaE2rm5HlNXRjT96rgssTeDT715gc8JvXd+B+184ipGCVLJt+4ZO5EXZ1dloTDMEwMYTdebmrCoO75iXwrMHel3z4MolDZPNlD5fNaXzx5h27NZfvuY6ldamNQvBUSTu2XnYnFXiugtbsfWxPSV55pwGyC2ujemYPnhRK5qSIQiSgs073NGL1pj97gdWYDAnY+uOPa5tN6/rwM//eBT7e7OmU5ciCZwYytvGma3rO0CRQF9GLEHc3f/C6P4xjsa//q4Lt69dClHWXPnrM4y3O5U1s18ADYRVIkxj19Ghso7acpi4Stx7xrKfI9KJxPnW9SugAmX7tiKgPld00JY7Nz8nYqVoNcNh5XXtVtzaw6+dnMrO26mkKT2AAcCLhwfwu4P9FTlZ/RBQleKe3PrxQrN5tTcch0pxtZcD3emqrOR8rW5Gt+swcml2TRhPvdXren7jcfQGctWUzh+rk9TL3WuNtUrxfOXGIAPLBsAXzWYc14qR80MH3vLzV81zXljvjRb1mmXDur+ijs4c4TbeTfHZG2aSXHNoxtQAGgirQV6qyJFXDhNHEJU7Ev0ckc51vChX1Le1faXnVsl1l0Orlbt2K24tQOrMHJ1Oe2PRnPFQDotYLme8+vFCs/k5CY0+/Rzo4zlf49he12HkZveIdx74OXoDzRxZnaRerlxrrHm1cXOhV4JlK4dmM47Lu8x04WxrzNhgHNsPPVfJ/lYn8FjGrCBHzo5mzAuggbCqiTIVOfLKYeIqce8Zy14oGjckTpSlx9R3lKV9t1tVyXWXQ6uVu3Yrbi1w3s4c6fV+leeCX7yXyxk/1NNYHLaG49AvXv1clX7n63QzOq/DyE29xsv9GONx9AaafrI6Sb1cudZY82pTibPdus0tTv2O63z2l8sXkvBHz1Wyv3OGi0pzO8iRs6MZ8SdgVdVwdIBHP1+AICvgBQ2DvB1LtrK1ChGOAi+oKEgKFEVFbZxFTlBAkiT6swKiHI3jAzzaG+L4wTMHcHJYmNI1gJ9811xbXdREagDrExwGMgJm10bxo2cOYqQgBTWAk6cp/ScsWVZxdDCNPd08ekYKuP+PR02k2cq2agzwIuqiDDQQODaYQ32CgyRrONKfRVNVBEcHeHTM0ikAJ4Z0Y8OJoRwe2nVCj+NzXAPI0gQ+eHGbLZbL1QA2V4cBTbNhCq3UgxUtVQizBC5uqwtqAM+8pnz+PPXWaRwd4DG/PoahnITbHt6NP5tbg0+8cx6G8xJmVYWgqhr6siKqIvpL0vHBPCIsje7hHBqTYXz7yX1TqgZwdk0EtVEGh/tzNvJTpTWAyTCNHz8f1ABOEc3MGkDri8jhvjTetbABI3kJLEVAUmFySNtqw7ZEaqsN4++uWIieEftg8/dXLMQDLx7F3763HSGaRCrBgcSoC5gkgHRBRl5SUBtlkZd0d199nMFwTkFf0dWUEyWcHMojFdd5j/VxDixNQJRVnBgqIBlmEGEp04HcnxVQF+WgQUNPWkBNhEWI1bmqHK0j2qqiDEI0iayggC8i1Y4N5lATYREtusUIEsjmFQiKgoaEzjoe5CWEWd0JDagIMTTyklLEUDFQVBX/8JtRbu7XrlmGC1ur0FIVwbGhnKc7SlU1HBvkTTxVa00Uc+tmpINqopqyA5gsq/jvPd3ICTJYmgBXdABusQw4X75qMQRFxzk5MYnWh/nW9R34oaWQ3cAACsUcGc5JoAgCWVHC3Loo8qI+sXQqrjvvh3MKMgURc+qiEBUNBVFBVYQFL0qIsDRokkB/VkSYpSDKKrqH85ibiqIgqqiJspAUGaIC8IKC2hgLigRG8hKGczK++9R+85fBC1urUROlUZD1PmRVRZxjMJyTwDEkRFnF3c8dxF+/az7mp2LoywqIchSOD+Zx6y9fLxmoAHjmwXniMDzTmrL5o6oaXjjch/6s/rz/xUvH8IlL5yLKkVA1AncU0aJOw57zF/Oaosu1IRlGc1UIBIDTGf1LCYYEFI1Ab1qPoZykIMSQSHA0hgsiVBWgSQI5UUUyos/ZOpyTUF903I/kZWgaIKkyZldHkZdUDPEimpIhqJqG06bzmEB3Wu8zzlE4PpTHFyxIxgWpGOpixZkhJBUkAcgqkClISIZHHcMMReL4II/m6gg6ZiXQWmNHrzEUiZyomI5fY1uQI2dUM/MF0GpGsKKaADuCxln06oepMYpVjWLdmy+bh7fPq3UtULcWy5Yzd3iZQvb3ZjyLba3nYN23EsOHG25nGmCfZqKm7AD2+vEhPPVWr22dnwmjUiyUsexV8G6NTb/idaspaUmje44YeDq3/cshFo3lcoaNIE/OqaZs/nT1ZXFiKGcaD42YtRoDy5kzrPnw0991lcSz1/5eRgzr+OVlgHIaIg3jhrHvf/x/l+AjP/2ja5+GKbGS6wryY8poZppArGaE/oxgFsU6i0vd8Dp+Re9GQatRzGoc26vAlxdls71fYblzHS/KvsW21nOwqhLDhxtux1lc64fCCTTzZZgYvPIGsK+rFAtlLHshqayxWa7Q3SiG98oRA0/ntn+5PDGWyxk2gjwJ5KbT6YLNeGjErNUYWClCzW1fv/29jBjW8auSvq3GDaN9j0e8W02JlRw7yI+prWn/Amg1I6Tio6gmr+JSq/yK3o2CVqOY1Ti2V4FvlKVt+B2/QmDruihL+xbbWs+h0nO39ucsLA6wT4GsakqGbTlTiQnDL8ad8eaFpHKLTbdj+RWxG+0MPJ3b/uXyxFj2Qk9ZTU9BngRyqqGIfXMaHZyoxEqf1SHG21ToXPYyYljHr0r6trYz9m3yiHc3U6LfsYP8mNqa9i+Ac2qjWNaSxMbV7fjNK8cwvz6G2iiL2qgda+bE6ex4/aQN0wPArAF87A297X07u7BxdTvqoix++dIxzEu549rm1kWREyTcu7MLm9d1uCJzPn/lIsyti9rWbV7XgZwoYX4qCoYiSs5lS/Ec7ry6E80WR2GIIbGgPuaK2LEictoduJ0A+xTIqY6mBBbUxzArGUJtlEUqzpXEuBVh6IeF2rzWjk/7+ysW4t6dXSVIxo2r27EgFTXbuqGuDOyTFfOWE6SSdlvWdUAp4unc9p9TF8Xnr1zke546gi3hmwdBngRy05zaKGgKmF8fs+WFFdtpxaoB7s/q2ghrxvswL9ja73j9pA1bauxDUyjBMRpxv72I4HRDK1r7NvBz1n3vuvECLJuVLEGx3Xl1J+JhynZeXvjDID+mh6Z9DaCqajjUl8VQrgBAxzclwzo6LRFiIMgqJFVDXlRQH2fBiwoyBRnJsP6NXUFSICn69mSEQbogIRlikBEkMBQFjiHAkiSG8xKqwwwUDchLCnhBKf72RIEhCfBFtJQgFTFqRYTccE5CLEQjL8mojbAQFR3TUxtlQVMATZIgoBs1OJoEx5BIFyTEOAYFSUaEoRHmdMxWXtTRdq01UbTVRHBiOIfTaQHpgoTqCIMwQ5m4oIYE52riAPSC29PpQlCEe/Y0ZWuYVFXDkf4M+rKSGfuKpoAmKQzwIqoiDEgQyEk64q8/qyOmNAP/F9axUCGGQpgloWrAEC+iOsoCUAGQJpIwW8ypZJgGAWA4L4MXZMRDNJIhWv+TUVYvSAeAnrSgF53TJIbzImoi+npeVDDEi6iL6XjFqgiDkbysm7OKiMWMIKE2wqEqyqAvI4AhSQzlRERYGhpUhGgaOUmxoadUVcPhfh7HBnlXzFRg6DhnmrL5A4y66IdyKmRVxx72ZQXMrg4jL6k4nRZMY0dPRkAiRCNSfFZHOd3cpOPdgKGchFiI0lGKgp4ftTE9lzSNNNFxMY6CIGsYyYuIhxgMZAXURjnkJB0xp0EFNBJZUUKc05Fv1REGJEFgKCcVvznXx6Bjg3kkQgxEWUFzVQTzUjEbiq1npIDGZAgdTUmQJGHLkcYkB1kB+rIFpGIhUCTQkw7yYwrK9YOg3VZOFxkInkdeO4bVS5rww2d1rNuDu47hLy9uwwMvHi3FUjmmcfmn65ZBUjTc9vBuT7dWlNVZoTlRwaaHRl2Am9d24FevHMP6Fc1IRpgS/JzVUXz1imaQJIGn953GYFbA8cEcfvGSfp7f+a29v/aGGFa11ZRNntaaKPZ2Z0os9O9eOLqrom1BAAAgAElEQVTvvFTMLMJVVQ2P7+lxtdxb2wU6P6SqGv73QC+6RwT86LnR3PnEpXORkxRXp++mNQshyCq+/Os/+U47wdIEPnP5AtuURAaC8YZVrSXrjWktmqs4HOzlbS5kA7vWlxXwuQdeHfN0EbOro55x79x33+nSfDLakSQR5Ekgm1RVw+8P9aEnLeCpN7txxZIm0/nrN4XYqrYkblzVio0PvlZ2+pbb/mIJOJrC7Y+MTmPkdNx//spF+Prjb+GLVy3BZe015i80j+/pwWfuf9G1jzuv7kQqzmLjL+zxbsQ3TZNYMbsaK2bbr9cvRwBgTl2QH9NF0/pPwEcGeNz6y9fw4bfPxeZH92Dt8mZ87+kDWLu8Gd/57X5z2ShULUgqtu7Q2xnLh/p4c36way9sMV/+jO3ffeoA+nkRXf08VBX2Yz22BzddOg/9vIhDfbyZXMZ24xxue3g39nSP4MgAjzdOjKCf1+dPMs7T2d8bJ/S2lVy/kYjG/psees1z37G2DzSzdWSAR6agYMsOe+4M5Ebj05k/dz25H4f7eTNfnDH/3acO4NoLW7B2ebP5kmds+97TB3DTpfNc169d3ozvPnUAVRHOfPkztm/dsQeZvII3ToyMK3YrjfsgPwKNVUYObX50Dz789rnm3JZuuWEde266dJ5vWyOPdCOFYL78GduN8c5Y/tYT+7B2ebMtXq3x7NbHbQ/vRiavjCnegxyZWZrWL4CGM89wTTkdimPFUpXDQrlh2fJFF285R3HPSMF0LPuhcYz+KnFPjdWZGDgZA1ll4BOdOVNJfAL+LkA/1JRfngx6YBUNt7xz/WTmSZAfgcYqaw6NxfmbrwDLZrSt1MluHMfNue7nJHau84v3IEdmlqb1C6DhzLM6rrz+N+R0AHo5HK3LBm7HzQEVZmlf17HhqmpMhkzHsh8ax+ivEvfUWJ2JgZMxkFUGPtEZi5XEp3XZud0PYeXlCjbyxOmeNLYbbnnn+snMkyA/Ao1V1hwai/PXiYPza1upk93IIS/nuldeOdf5xXuQIzNL5+QFkCCIIwRB/IkgiNcIgvCuri2jObVR/PgjFyLEENi+oRMvHOrDHWuXYsfrJ/H3Vyx0dyquszsA56WippPJy61VF2Uxry4KkrQPjJvX6i7d2iiLeSlvR/GdV3eioylpOpYNh7Jxns7+lrckK3JPjdWZGDgZZ44mI4fm1EYxu5rDnRs6bTlTE2E9nb6b1iw03ex+DsMdr580XZDGtlve2457d3a5rn/sjZPYuLodwzmhxDW8eV0H4mEKy1uStvX/dN1yDPACuvqyUItfD6qqhq6+LP5wqN9cX2ncB/lx/miyxqDGGIuWqhDuvLoTrx7px7YNnZ65YR177t3ZhW0Wl7CfUzcV50rcvoY73lj+/JWLTAevm3PdrQ+nq7eSeA9yZGbpnLiACYI4AmCVpmn9lbT3cmHl8xL+c28P/vnpA/jcexZAVmHyf+uiDDqak8gKEsIMjZG87nxiKQKKqrt2ayKjDsMwQ2M4LyEeosBQFAZ5ERGWAkUCLEWhIMsIM1TR8SQgFecgyDIAEomQ7mrMywokWUNOVFAVZpCT9D6WNiVB03rCGPi0gayIgqxA0zTEQ0yxv1LnYTmN1ZkYOBnPiSb9Bo8lh7zyJ5Mv4Jn9A0jnJeQEGalECIf7eSRDDJbOikNSNCiqijBLY4gXEQsxYCgCNKk7Fg2XsCSryIkqohyFCEuhPysgHmIQYUhIioaBnIjqMIuRgoQYRyPO0RguSEXUm+68Zyl9INIASLIClqbRmymgLsqhIclhdrXdwS4pGm5/ZBRfWI69a+xbLu6D/JiSmpL5Y4w/tz28Gx9a1Ywls6rx0K6j+MQ75kHRNDQlwxBlBVlBQZSjEGUpjOR1jGh1hEWYJVAQNfRmBcxKhFBQFGQLSnFeQUKf0YGjwVIEwqw+w0RvRkeNRjndpV+QVDQmOIiKipoo54rrNOK5IR5CpiCh2+HqHWu8BzkyLTV1UHCT9QL44uEB3HTPi77Ip5sv0zFSbgg2P/SaFWfz4M1vx4rZ1eO82kCBpuYA9uLhAfzuYL8NYeWWP06coBd6zYqzOpM544VlK4dzCzRtNWXz56Z7XkRBUvHAX1+CT/zbSyWxd8/HLnJd70QRWsebAKUW6AxoSqHgNABPEATxMkEQN7s1IAjiZoIgdhEEsauvr8/1IKfTQknhulVWA4dzvRdGx4pzM9b1jAQFroGmnHxzqNL8qcTw4Sw2d6KqnG3PdM54FaKXw7kFCmTRpOSPEW/9GcE19gay7uvdDBxuPwexG+hM6ly9AL5T07QLAbwfwGcJgrjM2UDTtJ9omrZK07RVqVTK9SANCc61cN2Q1cDhXO+F0THQa9ZC9sZkUOAaaMrJN4cqzZ9KDB/OYnMvVJUVZ3Umc8arEL0czi1QIIsmJX+MeEvFOdfYq425r3czcLj9HMRuoDOpc/ICqGnayeL/vQB+A+Di8RynszGOuz9yId6zsA4XzanGHWuXmsnWVhvGd268AAtSMVwytwar2pIARgvZGZooKUY3TB3WAlzDwBEo0FTSZOTQksYoLltYgx99ZCUSHIXNa5eirTaMz75nAW5ZvQDf+cAF6JiVwB+79G9AjOLzkbzoioBKxTib6elMyasQvRzOLVAgQ5ORP52Ncdz78VV48FOXgKF0I6I1f3744QsxkC3gOzdegLbaMAB3I6ITCxeg1AKdLZ31GkCCIKIASE3TMsWfnwSwTdO0x732cavBUFUNrx0fwIkhAT3pAu56cj+qIyxuWNWCpU0J8KKCr/zmT2Yx+LYNnWip4qBqBKIsZSJzpCKarT7GQVYVpGJhCIqCk0N5s1DWMHAECjROTWoN01hzyKuGaThfwBO7+3DHo/oks6vakvjgxXNw28OjebNpzUK0VIUR4UhEGAYUBVAECVmVAY1EX1ZELKQbO0byIqoi7FnJGa9C9KBAfUZqyuWPQdEZyknozQi468n9WFgfwwcubsX2x/aa+WNQbjauXojaKAOCIBAP0VBUoD+rGzqqIwx6s0KAUgt0JjU1TCAEQcyD/hsXoKPoHtA07at++7glYFdfFn0ZAb8/1F9i/rhl9QJXQ8iPP7oSf3XPS+ZyUGAb6CxpsgewMeVQJUXsAPDZ9yzwNIIsqI/jlp+/aq4LcifQWdSUy5+uviz2nkpjf2/GHGu88scwWN182TysXlwfGAoDnQtNDRawpmldAFZM9Din0wX0ZwVX84eXIWQ4J9mWezOFYBALNO00eTlkL1D3M4LkLcSAIHcCTWdNRv4YBJBKSFLGelUDekYKNrZuoEDnUtP2b5sNiRDq4yFX84eXIaQqwtiWgwLbQOezrEXshrzMHWELMSDInUDnuwwCSCUkKcMYRRIIDIWBppSm7QvgnNooGErD/FSshN7hNnP6tvUduP+Fw+ZyUGAb6HzXwsYotq0fzZMdr5/EZgeFY+Pqdiyoj+G+nV3muiB3Ap3vmlMbRZyjMLcuao4/brQNg3Kzac1CtNfHAkNhoCmlczIR9FjlVcMkyyq602n0Z1TwogK+OON6hKWgQYWi6rOp18c5zKrmIIhAXzYosA101nVOA80rf1RVQ2+ax7EhAX1FwoCgyAjRtE6m4WjEWAp1CRaiFOROoHOmKZs/Xf1pjOQU5CV9/KmK0CAJAiN5CYkwg5wog6FIJMMMFjckAkNhoHOlqVEDOFlSVQ1v9Q6jqy+PYV4ELyr47lMHTPfVl65ajJaaMP68s8k2WM2vD+qWAgVSVQ37eodxoCeH4ZyeP7f+8nUzf756zTKsXtxgG7CC3AkUaFR5QcTe7qzr+PO1a5bh8oX1wQtfoCmtaRudRwZ45AoaDvZm0c+LZvIBesHt1x9/C2+cGMGRAf4cn2mgQFNPRwZ4ZHIqDva5589XfvMn7OkeOcdnGSjQ1NWenqzn+PMPQf4Emgaati+Ap9MFnM4UTNSbl3sxQOkEClSqSvInQCAGCuQtA6UY5E+g6app+wLYkAihIaG7gP0wcIFbMVCgUlWSP4FjMVAgbxkoxSB/Ak1XTdsXwDm1UUQ4AvPrY6iNsiXuqy9dtRjLW5KBWzFQIBfNqY0iHiYxP+WeP1+9ZlngWAwUyEcdjTHP8edrQf4EmgaatiYQkiSwuL4KgqigpSoMUVbxLzetQl5UUBtlURtj0VoTuBUDBXITSRJor0tCLOZPQVHwr8X8aUhwWBogEAMF8lWYY1EXZdCY4KComjn+NCY5LG0M8ifQ1Ne0fQEEgGNDOXz4p7tK0Dv/dcu7MKcucCwGCuSnY0M5fOBfXnLNn2DwChTIX0cGeHzi3peD/Ak0bTWto/R0uuBafBsYPwIFKq8gfwIFGr+C/Ak03TWtXwAbEiHX4tvA+BEoUHkF+RMo0PgV5E+g6a5p/QI4pzaKu268wFZ8G2CqAgWqTEH+BAo0fgX5E2i6a1rXAALA0qY47v34xciJMlprophbFxg/AgWqVEH+BAo0fgX5E2g6a9q+AKqqhsf39GDTQ6+Z+J27brwAc+uC374CBSqnIH8CBRq/gvwJNBM0bf8EfGSAN5MP0ItvNz30WoB+CxSoAgX5EyjQ+BXkT6CZoGn7Ahg4sAIFGr+C/AkUaPwK8ifQTNC0fQEMHFiBAo1fQf4ECjR+BfkTaCbonLwAEgRxFUEQ+wiCOEgQxJfGc4zAgRXofNZEcyjIn0Dns4L8CRQIIDRNO7sdEgQFYD+ANQBOAHgJwIc0Tdvrtc+qVau0Xbt2laxXVQ1HBnj0Zgqoj4cwpzZwYAWakprUoBxrDgX5E2iaK8ifQIEmJtfAPBcu4IsBHNQ0rQsACIL4BYANADxfAL1EkgTmpWKYlwqwb4HOK01KDgX5E+g8VZA/gQLh3PwJuBnAccvyieI6mwiCuJkgiF0EQezq6+s7aycXKNA0UNkcCvInUCBPBfkTKBCmsAlE07SfaJq2StO0ValU6lyfTqBA00pB/gQKNH4F+RPofNC5+BPwSQCzLcstxXWeevnll/sJgjjq06QOQP8knNtU1ky/xpl8fY9rmnbVJB5vTDl0HudPcF3TS17XFeTP5Gm6nGtwnpMr1xw6FyYQGnoB7mroSfcSgL/UNG3PBI65S9O0VZN0ilNSM/0aZ/r1TaYmO4dm6r0Prmt66Wxd1/mcP9PlXIPzPDs6698AapomEwTxOQD/A4ACcM9EXv4CBTrfFORQoEDjV5A/gQLpOicsYE3T/gvAf52LvgMFmgkKcihQoPEryJ9AgaawCWSM+sm5PoGzoJl+jTP9+qayZuq9D65remm6Xtd0Ou/pcq7BeZ4FnfUawECBAgUKFChQoEDnVjPlG8BAgQIFChQoUKBAFSp4AQwUKFCgQIECBTrPNK1fACcK9J7qIgjiHoIgegmC2H2uz+VMiSCI2QRBPEMQxF6CIPYQBLHxXJ/TTFW5fCEIgiMI4sHi9j8SBDHn7J/l2FXBdX2MIIg+giBeK/775Lk4z7GoXO4Tur5XvOY3CIK48P+xd+dhclV1/sffp/at9y1Nls7WCdCdBYiIiIgJIGhIgrI6I6MyE53RSTSKOiiETccFcWBkVGZkBEcREASSnyhMANFBkLAkJGwJWbtJL+n0WtW1n98ftaSqum51ddLVXd39fT1PP6m699xzz62myEnV+dzvWI/xWORxXecopXpTflfXj/UYjUyU908e49wQ///tdqXUFqVUQzGOM6Xdx5VSWik1brdbyWesSqnLUv4e+9VYj/GYaK0n5A+x+P47wFzABmwDTh7vcY3yNZ4NnArsGO+xFPAa64FT449LiN2fa1L9HovhJ5/3C/BPwE/ij68A7h/vcY/SdX0K+NF4j3WE15XzvQ98BHicWJH3M4AXxnvMo3Rd5wCbx3ucWcY1Id4/eY7zQ4Ar/vgfi3Wc8XYlwLPA88CyIv7dNwKvABXx57XjMdaR/kzkTwCTBb211kEgUdB70tBaPwscGe9xFJLW+pDW+uX4437gDbLUhhbHLZ/3y2rgnvjj3wArlFJqDMd4LCbl/wfyeO+vBu7VMc8D5Uqp+rEZ3bGbwP9Pmyjvn2HHqbV+Wmvtiz99nlgllLGW7/v2ZuC7gH8sB5chn7H+A3Cn1robQGvdMcZjPCYTeQI4bEFvMbHEvzI5BXhhfEcyKeXzfkm20VqHgV6gakxGd+zy/f/Ax+Nfef1GKTUzy/6JZjL//+99SqltSqnHlVJN4z2YuIny/hnpfxdXE/skeawNO874soaZWuv/N5YDyyKf13QBsEAp9X9KqeeVUqNZurBgxuVG0EJkUkp5gIeAL2qt+8Z7PGJS2QTcp7UOKKU+S+xTmuXjPCaR3ctAg9Z6QCn1EeARYl+viVGmlPpbYBnwwfEeSyallAm4jdjyjYnAQuy/03OIfaL6rFJqkda6Z1xHNYyJ/AngiAp6i+KllLISm/z9Umv98HiPZ5LK5/2SbBOvl1oGdI3J6I7dsNelte7SWgfiT/8LOG2MxlZIk/L/f1rrPq31QPzx7wCrUqp6nIcFE+f9k9d/F0qpc4FvAKtS3htjabhxlgDNwDNKqX3E1rk+Nk5BkHxe0xbgMa11SGu9l9ha9qL/h8tEngC+CDQqpeYopWzEFt0+Ns5jEiMUXyPzM+ANrfVt4z2eSSyf98tjwN/FH18CPKXjK5qL2LDXlbE2bhWxdaYT3WPAVfE08BlAr9b60HgP6ngppaYl1s0ppU4n9ndUMfwjZKK8f/J5P5wC/JTY5G+81qrlHKfWuldrXa21nq21nk1sreIqrfXWYhtr3CPEPv0j/g+WBcCesRzksZiwXwHrKVDQWyl1H7H/qKqVUi3ARq31z8Z3VKPu/cAngdeUUq/Gt10b/9e/GCVG7xel1E3AVq31Y8Qm4r9QSu0mtlD/ivEbcX7yvK51SqlVQJjYdX1q3Aacp2zvfcAKoLX+CbE6th8BdgM+4NPjM9KRyeO6LgH+USkVBgaBK4rhHyET5f2T5zi/D3iAB+Nz7QNa61VFOM6ikOdY/wCcr5R6HYgA12iti+EfLjlJKTghhBBCiClmIn8FLIQQQgghjoFMAIUQQgghphiZAAohhBBCTDEyARRCCCGEmGJkAiiEEEIIMcXIBFAIIY6RUmpgvMcgxEQl75/xJRNAIYQQQogpRiaAQggRp5T6jlLq8ynPb1BKfVMptUUp9bJS6jWl1Oosx52jlNqc8vxHSqlPxR+fppT6o1LqJaXUHzIqkwgxacj7Z2KRCaAQQhx1P3BZyvPLgHuAi7XWpwIfAn6QKFc2nHid638HLtFanwbcDXxrdIcsRNGQ988EMmFLwQkhxGjTWr+ilKpVSp0A1ADdQBvwQ6XU2UAUmA7UxbcPZyGxovZPxv/OMwMTvmavENnI+2dikQmgEEKke5BYTdppxD7R+Btif5mdprUOKaX2AY6MY8Kkf6OS2K+AnVrr9xV0xEIUD3n/TBDyFbAQQqS7H7iC2F9iDwJlQEf8L68PAQ1ZjtkPnKyUsiulyoEV8e1vATVKqfdB7CstpVRTwa9AiPEj758JQiaAY0QptUYppZVSJ6ZsW6qU+kjK83OUUmfm6GOVUurr8cc/V0pdMsIxXHssYz8eSqnZSqkdIzzmBqXUV+KPR3ydQhwPrfVOoARo1VofAn4JLFNKvQZcBbyZ5ZiDwAPAjvifr8S3B4n9RfhdpdQ24FXA8D0uxEQn75+JQ2mtx3sMU4JS6n7gBOAprfXG+LZPAcu01l+IP78BGNBa35rleIvWOpzy/OfAZq31b0YwhgGtted4rmOklFKziY2zeQTH3ED8dTiW6xRCCCFEbvIJ4BhQSnmAs4CriX00jlLKBtwEXK6UelUp9TXgc8CX4s8/EP/06ydKqReA7ymlPqWU+lFK1+cqpbYqpd5WSq2M95vWRim1Of7J4ncAZ7zvX8b3/a1S6q/xbT9VSpmP8zqbUvrbrpRqjO8yK6X+Uym1Uyn1hFLKGW8/Tyn1+3i8/0+pn44KIYQQonBkAjg2VgO/11q/DXQppU6Lf7R9PXC/1nqp1vq7wE+AH8af/yl+7AzgTK31hiz9zgZOBz4K/EQplbmwNklr/XVgMN733yilTgIuB96vtV4KRIgt1k2jlPphfEKX+fP1LKf5HHB7vL9lQEt8eyNwp9a6CegBPh7ffhfwz/F4/1eA/zAavxBCCCFGj6SAx8aVwO3xx7+OP38pz2Mf1FpHDPY9oLWOAruUUnuAkXyCtgI4DXgxHq93Ah2ZjbTWXxpBn38BvqGUmgE8rLXeFe97r9b61Xibl4DZ8U9FzwQeTLkllH0E5xJCCCHEMZIJYIEppSqB5cAipZQmdh8jrZS6Js8uvDn2ZS7g1BjH6YcMDbhHa/0vuU6ulPohsZt3Zvq11vo7aSfX+lfxr6s/CvxOKfVZYA8QSGkWITbZNAE98U8LhRBCCDGG5CvgwrsE+IXWukFrPVtrPRPYC3wA6CeWlkrIfD6cS5VSJqXUPGAuscj8PmBpfPtMYl8RJ4Tid1YH2AJcopSqhdhEVSk1JJ6vtf5S/GvjzJ/vZLZVSs0F9mit7wAeBRYbDVxr3QfsVUpdGj9WKaWWjODahRBCCHGMZAJYeFcCv83Y9lB8+9PE7n30qlLqcmATcHEiBJJH3weAvwKPA5/TWvuB/yM2wXwduAN4OaX9XcB2pdQvtdavA98EnlBKbQeeBI63xuJlwA6l1KvE7t5+7zDt/wa4Oh7v30lsraQQQgghCkxuAyOEEEIIMcXIJ4BCCCGEEFOMTACFEEIIIaYYmQAKIYQQQkwxBZsAKqUc8aoQ2+IVIG6Mb5+jlHpBKbVbKXV/vCKGEEIIIYQYI4X8BDAALNdaLwGWAhcopc4Avkus2sV8oJtYebScLrjgAk3sHnfyIz8T8UcIIYQoKgWbAOqYgfhTa/xHE7sp8m/i2+8B1gzX1+HDhwsyRiGEEEKIqaigawCVUub4PeE6iN1n7h1i1R/C8SYtwHSDY9cqpbYqpbZ2dnYWcphCCCGEEFNKQSeAWutIvNTXDGIVKfKuVau1vktrvUxrvaympqZgYxRCCCGEmGrGpBaw1rpHKfU08D6gXClliX8KOANoHYsxTHXRqGZfl5f2Pj91pQ5mV7kxmdR4D0sIIYQQ46BgE0ClVA0Qik/+nMB5xAIgTxOrj/tr4O+I1YwVBRSNan6/s40ND7yKPxTFYTVx22VLuaBpmkwChRBCiCmokF8B1wNPx+vMvgg8qbXeDHwN2KCU2g1UAT8r4BgEsK/Lm5z8AfhDUTY88Cr7urzjPDIhhBBCjIeCfQKotd4OnJJl+x5i6wHFGGnv8ycnfwn+UJSOfj9zazzjNCohhBBCjBepBDIF1JU6cFjTf9UOq4naEsc4jUgIIYQQ40kmgFPA7Co3t122NDkJTKwBnF3lHueRCSGEEGI8jEkKWIwvk0lxQdM0Tlz3ATr6/dSWSApYCCGEmMpkAjhFmEyKuTUeWfMnhBBCCPkKWAghhBBiqpEJoBBCCCHEFCMTQCGEEEKIKUbWAApg6pSKmyrXKYQQQuQiE0AxZUrFTZXrFEIIIYYjXwGLKVMqbqpcpxBCCDEcmQCKnKXiJpOpcp1CCCHEcGQCKKZMqbipcp1CCCHEcGQCKKZMqbipcp1CCCHEcJTWerzHMKxly5bprVu3jvcwxl0hE6yJvid7qbhxus7J90IKIYSY0CQFPEEUOsE6VUrFTZXrFEIIIXKRr4AnCEmwCiGEEGK0yARwgpAEqxBCCCFGi0wAJwhJsAohhBBitBRsAqiUmqmUelop9bpSaqdSan18+w1KqVal1Kvxn48UagyTSaESrNGoZk/nAH955zB7OgeIRos/FCSEEEKI41OwFLBSqh6o11q/rJQqAV4C1gCXAQNa61vz7UtSwDGjnWCV0mhjRl5MIYQQRaVgnwBqrQ9prV+OP+4H3gCmF+p8U0EiwXrG3Grm1niOe5ImwRIhhBBiahqTNYBKqdnAKcAL8U1fUEptV0rdrZSqMDhmrVJqq1Jqa2dn51gMc8qRYIkQQggxNRV8AqiU8gAPAV/UWvcBPwbmAUuBQ8APsh2ntb5La71Ma72spqam0MOckiRYIoQQQkxNBZ0AKqWsxCZ/v9RaPwygtW7XWke01lHgP4HTCzkGYWy4YMlYBEQkhCKEEEKMvYJVAlFKKeBnwBta69tSttdrrQ/Fn14M7CjUGERuJpPigqZpnLjuA0OCJWMREJEQihBCCDE+CpkCPgv4E/AakFhodi1wJbGvfzWwD/hsyoQwK0kBj709nQN85I4/pa0RdFhN/G7dB0atjNpYnKNIyGxWCCFEUSnYJ4Ba6z+T/S++3xXqnGL05AqIjNbkbCzOIYQQQoihpBKIyGosAiISQhFCCCHGh0wARVaFqjwy1ucQQgghxFAFWwM4mibaGsBExY72Pj/1ZQ7CEc2Bbh9um4W6UjuzKo8GLRLt6kqzV/YwapPv9lkVLg50+3KeY7jryKfySD7XcrznmMAm3QUJIYSY2Aq2BnCqSk22VrhsfPr9s7ntybeTKdf1KxpprPNwTmMtT7zRnjMBa5SSPf+kuqzHZm5vqHLyz8sb+eYjO44pZZuoPDLcerzjSfPmew4hhBBCjB75CniUpZZX+9ipM5KTP4gFHG7fsovtLb3sPNQ7bBk2o1JtRsdmbl+5eHpy8md0jtG+5kKeRwghhBCjQyaAoyw12aoUWVOuUQ2Heocvw2aUkjU6NnO70flHu9SblJQTQgghJhaZAI6yzGRrtpSrSUF9mXPYBKxRSra+bGTbc51jNEiaVwghhJhYZAI4yhLJ1oYqJ26bmY0XNaWlXNevaOTk+lJOqisZNgFrlJJtqi/La/umba3csqb5mFK2RiXasm2XNK8QQggxsUgKuADC4Sj/b8chvvbQdipcNq56XwMzK1147Bb2dg7w87/s42sXnMT5J9VxoNuXMwFrlJLNth3gqWyn1qUAACAASURBVLfa2d7SS1SDScFpDeXMKHfTOZB/ynak4ZMLmqYBTIU077GSF0IIIURRkQlgARiVOFt79lwiUbjz6d0FKXk2WqXVjPq5f+0ZXH7X81OhdNtokwmgEEKIoiJfAReAUSgiqmPBjMTzYg1jjDR8ImEPIYQQYmKRCWABGIUiTAoSH7gWcxjDOHwyfHBFCCGEEMVPJoAFkC0UsX5FI9VuGw+/3FKwkMRohTGMwyelEvYQQgghJgFZA5inbKXOAMPyZ9GoZu9hLweOePHYLTitZjoHArjsFupKjpaDO9YxuGwWojqKSSl8wciQMR3xBrCaTfiCEWpLHFjM0NYbwBsM01DpZk517vNnhkxmVbg42O3jsDeAPxTFGwhzQpmTk+tLsViO/98Rx1pKboKYNBcihBBicpBScHnIlor90SdOIRjWhuXPTCbFvFoPc6rdWRO1sypH9qlZ5hgaqpx87uz53Lh555Dzz65y82Zbf3rbD87nxk1D2xpNslJLtEWjmqfeaufd7kG8wQi3b9mV7OcHly7lwub8SsuN5PUdSck6IYQQQoyMfAWch2ylzra3DF/KzejYYymTltnPysXTk5O/zH6ztt2UvW2+597e0sthbzA5+Uv08+UHj7/km5SSE0IIIcaWTADzkC0VG9X5lVkrVDI3V5m3kbTN99xRnf81j5SUkhNCCCHGlkwA85AtFWtW+ZVZK3QyN1u/I2mb77nNKv9rHikpJSeEEEKMrYKFQJRSM4F7gTpAA3dprW9XSlUC9wOzgX3AZVrr7lx9jXcIxGgNYCCk+fKD6dvmVHno6E8PiqRW5zArWDSjjOUL65IVPQ4c8dLeF8AbCFPustIzGKTK7aCpvhSTSbGvy0tbrx+nzcSAP0KXN4jbZiaio3zx/m3J83/74kXMq3bTFwgy4I/yRlsfUQ2ldjNlLhsbHzu6BvC7H1/MR5vrsVhMycDK/iNe3DYLdaXpIZXEGsC2Xj/9/nDaGsDvfnwxJ5Q7qHLbh4Rg8g11FGINYJGFSmQhoxBCiKJSyAlgPVCvtX5ZKVUCvASsAT4FHNFaf0cp9XWgQmv9tVx9jfcEELKnYp/Z1ZGc2JXazVR67HzzkR0jKp/21Fvt7GofSJtUfencBfzqr/v5yvkLMSkTX37wVRbUerjyvQ1pQY6bVjUTDIdp6w9iUlDlsnH3c3v5+7Pm4rCaue7R1LEswWk188rBHiJR2Ly9NVmOLnN861c00ljnSU5SIVbe7n/fbOdAl5f6cheBcISaEjvXP7qD/V2DQ65rpBM6o5J3x/q7KrJQiUwAhRBCFJUxuw2MUupR4Efxn3O01ofik8RntNYLcx1bDBPATJnl0j7/ofn87M97RlQ+DeCRV1u569mhx1191lzMJpL77rjyFL76m21D2n3vkiWsu+8Vw+MS1q2Yn/U8RuNbe/Zc1iydnizxlu/1Jq5rNErSHavRKok3imQCKIQQoqiMyRpApdRs4BTgBaBOa30ovquN2FfE2Y5Zq5TaqpTa2tnZORbDHJF8gxa5yqclwhXZ9iuVHroYDISzthsMhnMel2B0HqPxRTVpIYzjCaGk7hsL431+IYQQotgVfAKolPIADwFf1Fr3pe7TsY8fs34EqbW+S2u9TGu9rKamptDDHLF8gxb1ZcYBh0S4Itt+rdNDFy67JWs7p82S87gEo/MYjc+kSAthjEYIZaxCHeN9fiGEEKLYFfQrYKWUFdgM/EFrfVt821tMoK+AjcIEqevMKlw2Pn1mAxVue9q6u29fvIjTGsrZd9jHa629NFS56fUFmF9XQt9gGLfNjMkEu9oH+Pbjb1LhsnHV+xqYWemKrelz2whFNX/de4RKlxWnzZIW5LhpVTP/+8a7NNaVYzbBqbPKQYM/HMVuMXHDpp3J9XnXXngiLrslbY1iIsARiWjebu+nwm1nMBim0m3DZIIShzUZ7ohGNc/t6WLr/iNENbzd1sOHm6fzjd++lnVt43iuwZM1gEIIIURuhQyBKOAeYoGPL6Zs/z7QlRICqdRafzVXX+M1ARxuIpFI8L58oIdrf/saFS4bly6bwbwaD+/2DPL4a4e4cFF9WsBj40VN/OSPu5MTs/UrGplW5uCEMjsHjvjTJpDrVzTitpn58R/30O0L8oNLl1BbYqdzIMD0MieNNW5+/0YH16ZMwtavaOTev+yn2xdk40VNVLisOKxmDnR5aah2MaPcTUe/n1BEc92jrxEMa/7xg3OHVPi45sML+a8/7aXbF8xa9eTm1c3c/+J+3ju3BrMJljVUcubcqmRZuNEMdRzr7248z59BJoBCCCGKSiEngGcBfwJeAxILsq4ltg7wAWAWsJ/YbWCO5OprvCaA+YQJjNpcfdZcgKxBiavPmsudT+9OPl979lwW1JbwlSwhj7VnzyUShTuf3o3DauJ/rn4vy2ZXArDtYHfWAEei/2zHZ4Y0Pv+h+VlDI6n9GAVIMq9jHEMWxU4mgEIIIYpKwWoBa63/jPFffCsKdd7RlCtMkJjoGLVR6uhjo32J51ENXoOQR1ST1ld739Egg1GAI7V95vEd/X50SiDEKDSS2s9w+7O9LkIIIYQoXlIJJId8wgRGbRIfrObal3huUuB2ZA95mBRpfdWVHj13fZlz2HNnHp8tpJEriJLP/myvixBCCCGKl0wAc5hd5ea2y5YmJz+JNYCJCh9GbW5Z08zm7a089FIL61c0pu3beFETm7e3Jp+vX9FItduGLxDiK+cvTGub2Pfwyy2x0MfqZhafUJY8d1N9KbesaR5yTKL9hvMWpB2fGHvqmB96qYUqt23IODectyB53KIZZYbXaPS6CCGEEKJ4jdmNoI/HWK0BzEz8zqpwsf+IL1kizW03MxiKUOW2p+0rc1ixmhXeYCR53IFuH32DQUDR5w8xGIxQXWInFI5gMpno8YVw2cy47Wb8oQj9/jAehwW72Ux/IITHbsEfjuILhHHZLLhsZhbUuninc5BOb4BSh5V+f4hqj51QOEpbf4DaEjvBSBilTZQ4LGjgiDeIy26hvsxO90CI9n4/VW47Ea2xW8yEIhGqPXa0ho7+AH3+EGVOKy5bbFyVGdea+jrYzGa0jqKUwhe/9nzCFsdapq3IyruNZDyyBlAIIURRKdgawIkmM/HbUOXkn5c3pt02JZGwtVlU1n2NdZ7kJGBWhYtHDnSntblxVRMPbj3A1v29NFQ5+eKKBbx+KMBtT76dbHPthSdS5rLyxqH+tFTuhvMW0NI9yL1/2cvyE6dxx1O7hoyr2xdk3fJGnnqzjStOb0g7902rm7nz6V3J9PG65Y3cv/UAX7vgJE6dFQuVvNHWPyTxvHRGRdZScUavQz4l347lFi3FdmuXYhuPEEIIMRLyCWBcvqXOcqV7U8unGSV0E6XbjNK361bMB7KncteePZf5tSVZS8KlJn+/d8mSnG1Sn//sz3tylm8zKhWX63XIlQY+1jJtxVbebYTjkRmhEEKIoiJrAOPyLXWmlPG+1PJpRgndROm2XCXbjFK3UW1cEi416Ttcm9Tnw5Vvy5U0zlUOzsixlmkrtvJuxTYeIYQQYiRkAhiXb6mzXOne1PJpRgnd1NJtRiXbjFK3JmVcEi51XMO1SX0+XPm2fJLGmftypYGPtUxbsZV3K7bxCCGEECMhXwHH5bMG8LqVJ9PvD4GGOTVu9nR6qXTZqPDYcNvM9A6GWFhbgtms6PYF2d3hTavscfPqZgaDYX7zUgsXLqpnQZ0HUGxr6SGqYdO2Vq5+/xzKXFba+9LXBn79ghNZUOfhUF+ASpeVg0d8eIMRBkMRlswop7XHR9dAkOllDvzhKE6bJe3cRmsAr1t5MrMr3Rzs9mG3mtnR0kNfIMKmba187YKTOP+kuiFrADde1MR9L+yn1x/iCx9qHFL+7tRZ5cyqPFoyLzNY88Qb7Xz392+wcvF0zCZ4T0Ml751dSUvvoGGgYrTW3KWOx2WzEIxEkuXuRtrPCMYjXwELIYQoKjIBTJFZPiyR5m3vO1o6bX/XYNrkMFG/NzWwsX5FI4+/doi/PWMWpQ4bg+EIew97eXBrC92+YDIMkhnmuHl1M4FQmNoyB/5ghN2dXqIaTArm1bi57cm300rIpZaJS0zoPn/OfO58ZjfBsObSZTOYVemirdfPM2+18/cfmEcgFMFsMtHS48MEVHrsaZPcRD8bzlvIR5vrMZkUT73VzvaW3uRYqt02qjx2Kt02fvDEm8lycCdNK+XHz+zm7Y4BbrtsadbJ422XLeXchbU8/nobX3tou+FkO9tk6njLu2WbtKWGYY51MpnHeGQCKIQQoqjIBDAPuQIiRmGRRBAjEbQw2p8tXDFcabbE88wyb4lzZQt7pJaGu2PL7iHXkXme4cIha8+OhUASfWU7l1GAJHO70ThGO+CRq2xf4noLFCiRCaAQQoiiImsA85ArIGIUhBgMhtOCFkP25whq5Ft6LbPMW+Jc2cIeqcdku47M9sOFQxJhFaMx5gqQZG4/ljDJschVtk8CHEIIIaaSvCaASql5Sil7/PE5Sql1Sqnywg6teAwXEMm2z2U7GsTIuj9HUCPf0muZZd4SoY5sYY/UY4yuI7OfXOEQkyJrX6nnMgqQ1JflF7gZ7UBFrrJ9EuAQQggxleT1FbBS6lVgGTAb+B3wKNCktf5IQUcXN1YhkL2Hvbzb68NttTAYjhAIRWmoctNQ6UquZVtQ62H9uY1YzSa8gQg2i8JqNnHdozuS6/NuXt2MJorFZOJwfwC3w8pdz76TDD0snVHOr1/cx9KZVWlrAL907gIUmllVbnzBCLdveZtLT5tJTYmdaaUOIjrK2+0D3PfXA1zxnlnUldpx263sPzzAzEo3T795iNWnzOTdHj8Oq5l7ntvDipOm8fhrh1i5uJ7GuhJ8wQgD/iClTjveYJhqt52Nm3YYhkMOHPFhMSvePNTH5u2HOOfEWmZVupLj+ewvXs56Q+pvX7yI0xrK2dUxwLvdg8yocOENRqgtsbPkhDKe2tWZFrj5/Icauf7R/G8onfm7y6cix2ivARwB+QpYCCFEUcl3Aviy1vpUpdQ1gF9r/e9KqVe01qcUfoiFnwAmJgbf/f0bfObMOfhCkbRQRyLQ0NrrY1tLL63dg0OqdNSXOTjUM0ivP8Lm7a185sw5hLXm+394iwW1Hi4/fRY3b349LVFc47HhtFo4PBBgeoWTtj4/X/1NLBixrKGMK987m2/89rW0CZbbZqbSbafHF+DOZ44GQF492MW5J9Vz/WM70yqPNNa5aen2J8ecLbRyy5pmqj02XNZYKnZ6uYvXD/Xz5QePTpSuvfDEIcniW9Y0c9qsCtr7/dR4HJgU7DzUx9vt/Ty4tQWbRXHNh0/k4BHfkPOtWnQCB3sGeaMt1v6ZNzs458RaFtSVcNK0UuZU518ebiTp4PQUsJlQJErlMaSAR0gmgEIIIYpKvhPAF4B/A74BXKS13quU2qG1bi70AKHwE8BEOCBXAON36z5Avz/Eljc7DKt0JAIZEKvokWhnFHJIPSa1PRgHIxLhi9RzOawmfvrJ0/jsL14a0v6uT57G1v3dw47l1kuWcPIJpcyt8WQNS2SOL3Hc/WvPYMnMirTXMfUajF7P+9eeQYnDetzVPYqtQogBmQAKIYQoKvmGQD4NvA/4VnzyNwf4ReGGNbYS4YBcAYyOfj+Hev05q3Skhi9S2+WqHJI4JrPfXMdknssfitLtDWVt3+0L5TUWbzCcDEFkC0sYXXdb79HgRLawTK7jRqOahlTkEEIIIUYurwmg1vp1rfU6rfV98ed7tdbfLezQxk5qOMAogFFb4qC+zJmzSkfqh6mZ7YY7xqjfbMdknsthNVHptmZtX+Gy5jUWt82SDEFkC0sYjW9a2dHgxEiPG41qGlKRQwghhBi5fFPArymltmf8/Ekp9UOlVFWhB1los6vc3HbZUjZta6XSZWP9isa0BO9tly1ldpWbpvpS5td6huzfcN4C5lS52by9Nbmt0mVjw3kLcFhNPPRSy5Bj1q9opNpt4+GXW3BYTcyv9XDthScm22za1srNq5uzHpN5rnXLG/mf5/dy46qmtPYbL2qixxegrtSePH+2sWy8qIkSp5nZVe601yO1TZXbNmQ8t6xppqm+bMjrmHoN2V6vxHHZzpN4rUf6uzuePoQQQoipJt81gN8DIsCv4puuAFxAG3CW1vqiLMfcDawEOhJrBZVSNwD/AHTGm12rtf7dcOcv5BrARPr3wBEvbruFcCSCw2rBF4zgDUSYVmYnEtV0DQQpdVlxmBXhKPhCEQaDEUqdFgZDYUrtNkwm6OwP4rKZsZoVbocZXyBKe1+AaaV2rGYTrT2DVHlslNotdPlCeANhqtw2vMEQbrsFpRVt/QGq3DbKnGa8gSjt/QGqPTbMJjArE8FoGBNmjniD1JbYY2MJRKj22PCFIvT7Y33arSYCoShHfCFOKLMzEIjQ5Q1yQpkDs1L0+kMMhiLUldgxmRTBsObwQIATyp2UOiy09wXo84cod1pjSWSPgx1tfbT1+akvdVDlsXGoNz15m62aysFuH+/2DjLgj1BfbqfEbqUtpTTcgW7fMVf3SPwOj6dCyBgoqsEIIYQQI0oBZ9umlHpNa70oyzFnAwPAvRkTwAGt9a0jGWShJoDZEqSJRO81v9lOhcvGp98/O60m77UXnohSCq013mB6Wvjm1c3c/+J+tu7vjd3a5Jz5aancxG1SbBbF5z44nxs3Hd33pXMX8Ku/7ueK98wybJO4Zcnly2Zx/9YDfPHcBXT2p9cMTrT5p3PmU+W28YX7XmFBrYcr39uQ7Kuhyjmk7xtXNfEfz+xOKzWXuKWLUVm3zDbD3UZltOr5TkCT+uKEEEJMPPmGQMxKqdMTT5RS7wHM8afhbAdorZ8Fjhzf8AprX5c3ORmBWHjgtiffZlfHAP5QlI+dOiM5uUrsP+wN0jkQ4LA3mJz8JfZd9+gOrjozltJduXh6cvKX2H/7ll187NQZrFw8PTn5Suz74f++zcrF03O2ueOpXaxcPD35597D3iHjS+zb+NhObBYT/lCUvz97Xlpf2fre+NhOVi6ePmSs/lCUDQ+8ys5DvUNeq8w2+7q8I3698zlOCCGEEKMr3wng3wM/U0rtVUrtA34G/INSyg386wjP+YX4GsK7lVIVRo2UUmuVUluVUls7OzuNmh2XXGXOYmMYmmBNpHCN0q2DwbDhsYmk8XAl2PJtk6tkXCIBDAwpO5er72zPc5V1S20zXPJWErtCCCFEccg3Bfxi/GvepcASrfVirfVftdZerfUDIzjfj4F58X4OAT/Icc67tNbLtNbLampqRnCK/OUqc5b6PJVZHf3JdqzTZjE8NrNUWrZ9I2mTq2RcIgEMGJadMxpbtrEalW9LbTNc8lYSu0IIIURxyHcNoB34OLFScMkZjtb6pmGOmw1sznbD6Fz7Mo32GsBEaKDLG+BQj5+vPrQ9rUpFicPCt3/3Ble8ZxaVbhsbH9tJhcvGpctmMKfaTX2ZDX8IDg8EOHjExwNbW+j2BfnWxYvw2M04rGYOdvkoc9nY1dFPND5Zq3Lb+PEf92Rd33fLmmbKnFbKnFY6+gPUltpoOeLn2pRKILesacYfDFNf7uIPO1r5+LJZ+AIRXjnYQ1THUreJ9YGf/1Aj82ocHOoJ4Q9HqC9z8MMn32Lr/l6WNZRxxemz+eYjR/u+aVUzdz6za8gaQJtF8a2LFxEMRZMl4f77udj2r19wEvu7fMyodGI1KerLnZw8rRSLJfu/K3KtAQSylnMzKvOWb/m3IlG0AxNCCDE15TsB/D3QC7xELA0MgNba8BO8+HGzSZnkKaXqtdaH4o+/BLxXa33FcOcfzQlg5iTk/JOrueL02bx6sIdIFDZvb2XdigW4rWb+NV4aLhCO4HZYuXnz60MCFYmJ2YwKJ7f+4U2WnziNO57alTVA8u2LF2E1K1q6B5Ml3Sxmhc1i4j+e3pU8NhHU2HDeAt7p9BLVsXv/VbttaA13P7eXL5+3gM6BIN//w1tp/bttJgIR+PPbbZw2u5qNKSGUm1Y3U19m48ARf1pt4qb6Ml7c28H7G6fxysFubGYTNrNido2HwWCEr6VMkNevaGRujZseXyit70T45J+XN7JmyfSck8DMxC6QdWKYLXiSa3sRh0mKclBCCCGmrnwngCMu+6aUug84B6gG2oGN8edLAQ3sAz6bmBDmMpoTwMzSYXdceQpf/c02wzJtP/vzHq4+a26yfJpR+1svWcIbbf3JdrlKud2xZXdy24+uPIWv/GZb2jkgdym4SBTDEmtXnzWXO5/ebTjOuz55GmuzlIz73iVLhrQ3Kv926yVL+EqWvhPXkFoe7lh+J4n+7l97Bpff9Xze24us/FsqmQAKIYQoKpbhmwDwnFJqkdb6tXw71lpfmWXzz/I9vlAygwiZAQlIL9OWGqrI1d4bDKe1y1XKLZU33l9m++HKx+UKgOQa5xGDknGDwaHtjc7hzdI29Rraev0smUnejMIhRsETo+0d/f5inQAKIYQQRSXfFPBZwEtKqbfiCd7XlFLbCzmwQskMIhgFJBLfJKZWmMjV3h0Pf+RT/i2V22EZco7hjh8uAJJrnEYl41y2oe2NzuHO0jY1fJJaHi4fRuGQ+jLniLZLmEQIIYTIT74TwAuBRuB84CJiFT6GVP+YCDJLh93z3B5uylLibNH0MjZta2Xd8sbknw6rif989h02XjS05JrFTFq7bCXXbl7dTLXblrbNalbctKop7ViI9ZVZ2m39ikaqXDY2b29lbo2baz68cEj/iRJx9zy3Z8jxN8RLw2WO60vnLsj6OiyaUcYPLl06ZAxWi0qWuUtsX7e8kc3bW4eUhzuW30liTV9TfemItkv5NyGEECI/OdcAKqVKtdZ9SqnKbPu11mNyo+djXQMYjWoOHPHS3hfAGwzTUOlmTrWbaFTz+qFe3u31U+q0UF/qoNsXomcwiMdu5Yg3SInDgtNqptcfosxhRWtNMKLp9gWZVupgMF5yrbbETlRrjniDlDqsmE2Kbl8It80cLxMXKwVXV2rHZlZ09Adx282UO630+cN0eYNUe2yU2C14g2F6fGGcNjMWE1S4rPT5I3T7QrHHgyGcNjMeuwVvIExER7FbLMn+y5xmenxhIlFNnz/M9HJH2vmViqJR2JSJgVAEXyCC02bCZTVjNZsIRCL4Q5oBf5jqEhtOmwmPzUoootnf5cVuNWO3KmpcdkxmRVtvgMFgGI/DykAghMduRaOpctuTJd7yTekalXMb6fYiVbQDE0IIMTUNNwHcTOyTvgix0EbqX2Raaz23oKOLO5YJYDSqeeqtdna1D6SVa/vBpUuxWxVf+NUryW3XXngitaV2egfDacnW2CduVoJRTb8/nNbPuuWNPPVmG5ctm8X18dvEXPW+hrQ2ucqrpe7LVjbumx89CbvFzI+e3sXly2Yl08GJTxwfeulAWmo48QlgNBpl46bsaeWNK5t46OUDrFoyncF49ZFE4viL5y6grdc/pLRdqcPEQFDzjZTb0axf0UhjnYflC+uSk7HMFO8ta5r596eO3lamyFO6hTYlL1oIIUTxyvkVsNZ6pY7NEF/XWs/VWs9J+RmTyd+x2tflZXtL75BybV9+8FW2t/SmbTvsDRKOkJz8JbbfvmUXLnvsvnyZ/dzx1C6uOnNuctL2sVNnDGmTq7xa6r5sZeM6+gNc9+iOZOm31H03btrJVWfOHbL9ukd30BoPSGSWf/OHoty4OXbcYW8wOflLnH/vYW/W0nalTnty8pd6HdtbepMl3LKVePvmIzvSrl1KvgkhhBDFI981gC/F6/9OGO19fsMUa2YSN6qPpnEz23qDYeOybynHHEt5tcRjo5Jz2dLB2c6d7dqM9g9muZ5cqeLOgYDheRIl3IxSvJnXLiXfhBBCiOKQ7wTwvcBflFLvTJQUcF2pwzDFmvktpFmlp3FT27ptFsN+MpO2Iy2vlrkvc0y50sHDpZeN9jsNrsfoGms8dsPzJFK3RinezOuTlK4QQghRHPK9EXRDtu1a6/2jPqIsRmMNYKKU2/waD7MqnQyGInR7Q5Q4rbhtZnr9QQYDUd5sP1q6raHKRYXbhtcf4WC3j1+/eCBZPeOkaaX89pUDnHvSCVz/2A4W1Hr4pw/N5/VDfcmybJ8/pzGtvNq1F55Inz9MMBJl6Yxyfv3iPhrryilzmKn0ONJKs+VaA/itixcRiUQIReHmza8nt//rxYuYXulkb6eXCpeNcDTKhge2DVkDeNmymdSVOuj3RxgMRahyx8IrHf1Brnt0R9oawGqPhS5vmG8+cnT7+hWNzKl2s3xBLS29g3R5A/iDYTSKI94QlW4rER3lhsdeT1sDeHJ9CZ0DAWxmE75gZEg4ZIKVdxuJSXERQgghJo+8JoDj7XhTwN3eILs7vVz36I6sYY31KxqpK7UR1SptorPhvAW4bOZYWbRqFweOBNImabesaWZnSzcfWFhLe18wLXBx3cqTKXXEyr292+Onym2lZzCcVtv3ptXN3Pn0rmQQ5F8uPImI1piVosRhoWvAj0mZ0Wgq3DZePdADQKnDwrcffzM5qW2odOELRPA4LfzLw+n9n1Bm492eANMrnIQjUVp7BvE4rLR2D6a9Bl86dwFPvn6Iq8+aRzgaxWEx89Nnd3Pl6bOxWuBwf5DaUgdoONjtY8sbbVxxegPffGQH75tTyQWL6tMCNDeuakKhaenxc8rMcuxWxb88vGPIZDa1FrBRneBJMAmc8BcghBBicpnUE8CEbQe7k6XDjEqsGZU3W3t2LOuyrKEiawm1n37yNF7a3521ZFri2Eh8c7bzJkq3pR4zv7aE3R39yT5Tx2w0/p9+8jQ+azC+v7v7xeQ1RiGt78yx/OzPe5Ll5u58endyTImyeIljUsdxz2fek9e5U0vlpbb73boPAGQtB1fE5d1GQiaAQgghikq+peAmTSkp1QAAH2dJREFUtNTSYUahCqPyZolQhVEJtR5faNiwSWrwI7NNZlAiqmMBjtQ+8ykx15NjfKnXiM5dRi4xhtQxp5bFS0h93p3nuY3G3tHvRxuMScq7CSGEEKMv3xDIhJZZOmwk5c1MCkwKwxJq5S5rzrBJonSb0XkzgxImFQtwZPY53PjLc4wv9Rqz9Z06lsQYUsecWRYvcxy5XpvUcxv1UVviMAySSHBECCGEGH2T8ivgRJigyxtAoejxBbGYTWx8bAfBsB6yBvC6lSdz4jQP+7sG09boXbfyZCpcVspcVoKhMMEIWEwKs8mEPxShzGmhxGGhdzBMZ3+QvYcHePrNDs45sZbGWg+1JXZ6BkO4rBasFoXNbKLXH8JmNtHeF2BamZ3BYITD/UHcDgtuuwm33UIwFLvxdHWJjVA4SudAkJoSO3s6+jGbFHVlTl492JMMm3z27Hls3dvFR5acgM1s4og3RF2pDRT0DYZxWs2UOSyAiodQYtVFUtc73ry6mSqPFQ2Ew1FcdjP+oGYwHGFaqQNNlJbuQPKegA1VTj7/oUaufzT7GsCbVjWB0vR4QyysL2UwGMGkFOFolLfa+nlgawvdvqCsARRCCCHGwaSbACaqUnz3928MCRxct/JkvP4Q82o99A2GMJlMvNM5wDNvdnDhonp+/eIBLj1tJvVlDircNm7ctDOZYr39iqUEwtG08ERDlZN//OB8boiHPxIVPe58ZjefPGM2tz7xVlrQpMptRaOGVBtJVAe5eXUzg8FwMuCRrbJIMBzlW797Iy2I8sTOd/nEexvo6Atx/WPZgy43rmoiEIrw7cffTI71xlXNdA0E2X/Ey4PxCdmG8xZQ4bLS7Qtx25Nvpx2/5Y1DNNaVYzbBKTPLue+v+5LPz26sQmtFe1+A6hI7d/95N6+1DgwZx7rljdy/9QBrz57H3Go372moxGIxJX93E6i820hMiosQQggxeUy6CeCezgE+csefkoEGo6BDamgiM1iRLWjxoytP4e2M8ITRcUbnNgqaJIIgicDFHVt2G4Y9EvtTt33vkiXUeGx8+ucv5gyKZB67bsX8rGEQo3F+75IlrLvvlSHjTjz/n6vfS6XblgxzGI0jNWyyZun0qbDGTyaAQgghisqkWwOYqEqRqzKHPxRNCy7kCjgkeAPZK2hkez7SoElm4MJoDKn7U7cNBsN09gcMr8foWKMwiNE4B4PhrONOPG/v86dVBRnud5BaTUQIIYQQY2fSTQBTwwS5gg6ZwQWjgEOC22Ecnsj2fCRBk2yBC6M+Mr8RTVT3qCmxD3s92SqgjGScTpsl7XlmgKWudGiYY7iwiYQ8hBBCiLE36SaAs6vc3HbZUjZta2Xd8sa0Cdm65Y1s3t7K+hWN/Pef97LxoiYcVhMPvdTC+hVH227a1spNq5rSjrWaFfNqPUPa3XBRU9rzjRc1sWlbK185f2Ha8etXNOILhrgxo9/1Kxp5+OWWZBCj2m3LOqbEOrzU0mwOa6y6x73P7aFvMMBNq5pzHpvoO7Ftbo17yDg3nLcAXyDEhvMWDDn+3uf2JJ/fsrqZzdtbk89vWt3M4hPKkq+/0ThSfweLZ8TaCyGEEGJsFWwNoFLqbmAl0KG1bo5vqwTuB2YD+4DLtNbdw/V1LCng/V1ejvgCBMOaI94g9WUONJrD/UGcNjMeu4Uyh4mOgTDdviDTy50Ew1EODwTx2C2UuyxoDb5ghFBEEwhHKHNaCUcjmE1mugaCeBwWSh0W0NAfiKVrSx02AuEQTouVgWCYqNY4rGZ6fCEq3TbcNjM9gyFCkSgldgtHvCEcVhMum4VAOIzTasEbjBAIR6h02fAGI/T7w9SU2LCZTXT7gjhtFjr6AtSW2tFEQZsIR8OUOGz0DYYZCISpcFsxK0W3N4TdaqLcaSWqod8fxhsMU+OxY7coBoIRQmHNYChCqdOC2aTo94codVjQWtHRH6Cu1A5Anz+Ex26lxxcbs8duoc8fwm2z0DytlEMDAdr7YgEOswkODwRwWM14AxF8wTCVbhv9/hBWs5m6UjuzKidNyGM4U+IihRBCTByFvBH0z4EfAfembPs6sEVr/R2l1Nfjz7822ieORjU73u2lJZ7YrXDZ+PsPzOH7f0hP5U4vd/DMmx1YzHD63JohZeAaKp3sOexLS/1+7oPz00q+bThvATUldv7tf9/mE6c38OTru/n4qbO4cfPLOcvOHR5IT9mmpoG/+dGTYiGKJ14ZkiKOasUNm14esr3bF+aH/3u0v0Ta9or3zOLx1w5x4aL6tHH868cW0dkfSBvDhvMW8N//t49uXzB5/NXvn8NgKMK/P7WLT5zekHaO9SsamVbq4Fcv7OP8phPSXr8ffeIUgmE9WW/rIoQQQkxoBU0BK6VmA5tTPgF8CzhHa31IKVUPPKO1XjhcPyP9BHDbwW62vNmRtZRaQiIVe8bcKgA+E0/Qpu7PTMPm6idRKu17lyzhq/FjRlp2LpGqHWk6N1d/mWNKMDpHaiL56rPmYjbBXc8aJ5vXnj2X0xoqhpSCM+p/kpR2GymZ8QohhCgqY70GsE5rfSj+uA2oM2qolFqrlNqqlNra2dk5opMc6vUbllJLSKRQuwYCHE5J0Kbu9wbS07C5+knsG0w5xjANHMidBjZM5xoclytdnEjvZu7PVQ4u9XGiXa5r7/ENLQVn1L+kfoUQQojxN24hEB376NHw40et9V1a62Va62U1NTUj6ru+zJmzlFriuUlBlcc+JEGb2O92ZE/DZusn8dhltwx7XqN+Ex/GGqZzDY7LlS5OrC/M3J+rHFzq49R2Rtde7hpaCs6of0n9CiGEEONvrCeA7fGvfon/2VGIkyys8XDyCaX8xydO5d+vPIX3zq7g2xcvGpK+nV/rwWZW+ILhIfu/dfEiZlY4+I+/OZV1K+bzheXzef6dzrTUbyK5e9b8amZXubjzE6eyadtBNq7Mni52WE1svKiJGo+Vm1en93P9ypNZPKOMay9cyLKGCm5Z0zwkhTuzwjFknNd8eCG+YIgvnZue2l23vJEX9nTyw8uW0to9yA8vX0pDlTO5f3a1e0jSd8N5C3j45RYaqpzcdtlSnFYTi6aX8f1LFrNpW+uQc6xf0cicKje/fH7vkPEumlGWTAMntt122VJJ/QohhBBFYKzXAH4f6EoJgVRqrb86XD8jWQMYDkfZ8lY7/f5wWijh2gtPZF6thx5fCKfNjNtmZn+Xl+sfez0Z8Nh4URPeQOymyrMqXXQOBNMCH9+6eBGVbguvHOglqsGkYF6Nm9uefDtZMu6WNYuYWWlDaTOdAwHKnBZea+3DG4zEPnF02bj7ub1c/f45zKx00eUNcuCIL1mKLRG++MyZc/CFItSU2OnxBSl1WJJfqR72BpPnn1/rYXqZne0tfcyrLeHNtj5mlDsxm2AgGE3W7nVYTXz74kVUuC1YTGY8djOBcIRgOJbWLXda2d3Rj9lkotRp5bpHj75213x4IcFwFLOCpbMq6B0M4bCaKbFbAE2p08asChcHun1pZdyAyVrabaSm5EULIYQoXoW8Dcx9wDlANdAObAQeAR4AZgH7id0G5shwfY1kArjtYDcHjwxmDUWsPXsu82tLeKutPxluMGpjAsM+MkuxZZZE+/mnT+dT//3XYcvRDRfeSA1x7O7oB7KP+dZLlvBGW/+w5ewyAyuJ86eOM1f5tkQ4ZIoGOY6HTACFEEIUlYLdBkZrfaXBrhWFOifEAiBGYYmohsFgOC3ckLVNIJx8nm1/5rbMkmid/fmVo8sVBskMcSTOaxQCyaecXWZgJREeSW2ba8yJxx39fpkACiGEEBPYpKsEUl/mNAxLmBTJcmZGIQWTApfdkrOPzG2ZJdFqS/IrR5crDJIZ4jCr3KXbjM6VbfzJ86eEQ4YLrqSGQyTIIYQQQkxsk24C2FRfitWsuHl1eihh/YpG5te4ufe5PTz0Ugs1JfYhAY2bVzczv9bDPc/twQTJUnGp++tL00ux3biqKa0k2g0XNfH712Kl5IYrR+cPhbOGNzZvb01r95/PvkOV20aV25Z1zL5gaMi5Nm1rTYZRUl+DKpeNzdtbuebDC7FaFOtXNKYdmy24klquToIcQgghxMRX0BDIaMl3DWA0qmnp8dLeG2AwFCur1tEfoNJtw2UzA5q2vgCVbitklDnr6A9Q47ETjkZw26z4Q1F8oRBOq5Uub2wfKlZ27fBAkJL4p3c2s4k+f5h+f5hppXaiQHufn5oSO6FwBIfFgjcUYTAYpsJloz8QK4XmsZkxmxXd3iAOq4UjvgBVbjsDgVi5tYFACJfNgtVkoq3PzwllDhQwGI4QicJAIEx1vC5wZ78fp9VCIBzBabVwxBekwmUjFAljt1joGQzhtJqxW014/WHsVjPTSh3MrHDR0uOjvS+APxTGabPgDYSZXeUmqqFzwE+NJ1bWra1vygc5joe8YEIIIYpKIUvBjaloVLP1wGH2H/bzo6ezly1z28w8/WYHH26u54aUdG9qGbbvfGwRwbCf6x87un/d8kbu/vMePn7arLRU8E2rmnhg6wG27u/NWiZu48omfvLs7mRCODGGH/9xD92+IDetauLOZ9L3J8ax8aImfvLH17PuW7+ikTnVLra19KSVt7tlTTNlTs0X708vv3b+SXU88UY7G+5N395Q5WZ2tYfZ1dnX882rPbrdqI0QQgghJp5J8xXwvi4vkYjiukd3sHLx9OTkD2LBhdu37OKwN8inzpqTnPyl7vvYqTPwh6LsOexNTv4S++94ahdXnTk3OblLbL/+sZ1cdeZcAFYunj5k/42bd7Jy8fQhY0ic6/rHhu5P7Ltxk/G+27fsIhwhOflLtPnmIzt4rbU3bduGB15l56HeZE3e1O37uryF/aUIIYQQoihNmglge5+fznhJt1wJ2G7v0LJl+ZRhGzRI7A4GY4nh4dKzqWPILLeWrf1w+3IlnTO3Her1S1k2IYQQQiRNmglgXakjraSbUQK20j20bFk+ZdgyS7wltidSxUbnzEwIJ1K4Rvvz3TeSlHJ9mSNrW0nzCiGEEFPTpAiBRKOa/V1evKEQrd1+FAp/OEJr9yA1JXamlzuIRCEQjlDmtBIIRwmEonR5g3QNBHClrMv7/iWL8YeiaZUw1i1v5Kk32wqwBrCZO5/ZlWMNYPb1gYk1gK09/ixrAK38832vZF8D+ED6GsALmqZJoGNsyIsshBCiqEz4CWA0qnnqrXbe7R5k8QwPb7f7ufOZXXzyjNnc+sRbVLhsXPW+Bn79Yqy8mlLgDUa4fcuu5GTo5tVNlDqt2Mwmbti0kzKHlX86Zz5KQanTii8YpsRhJRyJYDaZOTwQSxZHohEsZjN9g2FcNjM2i+LIQIiOfj8LppUQjUaxmM0c8QZx2y0oNCaToq03lj6+57k9NNaVYzbBsoYK7FYTvb4wlW4b/f4g/pDGGwzjsVmoKrHR7Y2XsbOaqS61EQprDvX66Q+EqS2x01RXyp/3HmZ7y9FSdYtnlLF8YR0gZdnGkbzQQgghisqEnwDu6RzgkVdj9+E7a141V2WUYEuUNrv6rLmY49+CGpVTMyrLliiBZtTm1kuWUOK08NlfvJT1+Myyc0bl1hIl1vZ0DvCRO/6UtYzbmqXTDatwGB0npdvGnUwAhRBCFJUJvwawvc9PVMfCG+1ZSrClllVLtDMqpzZcCTSjNt5gmJ4c4ZLMcxoFRhKhjPa+7KGNqCZncMPoOAl7CCGEECLVhJ8A1pU6kmXS6kqzl2BLPB6unNpwJdCM2rhtFspzhEuMzpn5PBHKSL2O1P0mRc7ghtFxEvYQQgghRKoJ/xVwMBjh5ZZuotEoNouZiI7gMFvp8gV59WAPLpsZk1L88oX9hmsAb1nTTF2Jnfb+AAeP+Hhga0sybJEIXnxrzSI0UTr7gxnrB5s5odzB2239zKv1sPPdPrzBCGYF08scdPlC1JU6cNnM/Ovjb7C/a5CGKif/+MH5aTejToQyAA4c8fLygR5u3/I2KxdPx2yCJTPKcdoUZ8ypMVy7F41qfr+zTcIexUdefCGEEEVlQk8Aw+Eoj2xr5Ymd73LuSfVs3XeYsxprae3xp03Sbr10CS6rmf1dXk6eXko4opMp4H5/iAq3jX95+LVk++tWnkwwFCEUiVLlsXOo18+DLx3kmg+fSG2JjUBYMxiIUOmx0tEXYMOD25Jhk9Tz3rS6mTufPpry/dbFiyh3WTgyEGB2tYdoFHzBMLMq3cypjtXXTUzg3jenko8uOYFvPrIjbaK6Zsl0LBbjD26jUS1hj+IjvwAhhBBFZUJPALcd7Obyu57np588jc/+4iXu/tR7eH5PV9aQx9qz53LHlt1DAhhGgYzU8Efq40Q/mccO10+2fjPDGakhjjuuPIWvZgmc3L/2DJbMrBilV1aMEZkACiGEKCoTeg1gosJForrH4f6AYcgjUSEjM4AxXAWPzMeplTayhU2M+snWV2Y4IzXEYVR5pK1XAh1CCCGEOD4TegJYX+bEYTUlq3vUlNgNAxep34LmE8jIVpHDqNJGPv1k6ysznJEa4jCqPDKtTAIdQgghhDg+E3oC2FRfyi1rmvmf5/dy06omfvvyAebVuFm/ojEtDXzTqiaq3TYcVhMPvdTChvMWJPdv2tbKxoua0tqvX9HIwy+3DHl88+pmGms9acfesqY52e+Q865uZvP21qz93nbZUmZXudOuZ3aVm9suW4rDauI/n31nyLhuWdNMU31Z4V9YIYQQQkxq47IGUCm1D+gHIkBYa70sV/tcKeCeQT9vt3nRRFGY8IfDeGxWBuJfoVa4jlby8AbCDIai1JbY8IUiDPgjuGxm7FaFQnHEG8JjN2O1mDjiDVLttmNS0Nrjp6bETonDDEAoovEFI9SVOphV4eJAt4+Ofj91JQ76/SEO9fqZVubgpLpSWnoH6ej3U+NxYDZBW1/ucEZqiKO+zEGPN0RbX6y/pvqynAEQUbRkDaAQQoiiMp4TwGVa68P5tDeaAPYN+vn9jk4e2Lqfj586ixs3H72tyrrljdy/9QCXL5vF/VsPcMV7ZjG93MG9f9nH1v29OKwmrvnwQv7rT3uTt3yZUeHkwqZ6mWSJ0SYTQCGEEEVlQs903mzzcv1jO7jqzLnJyR/EwhJ3PLWLlYunJ/+8fcsudnd6uerMuck23//DW3zs1Bn4Q1Fu37KLXR0D7DzUO56XJIQQQghRcOM1AdTAE0qpl5RSa7M1UEqtVUptVUpt7ezszNpJe18AfyhqmJhNLQOXSPAOBsND2iQeRzWSshVCCCHEpDdeE8CztNanAhcCn1dKnZ3ZQGt9l9Z6mdZ6WU1NTdZO6krtOKwmw8Ss1ul/mhQ4bZYhbRKPTQpJ2QohhBBi0huXCaDWujX+ZwfwW+D0Y+ln4TQ393zmNKaXO7h5dXNyEthQ5eS2y5bitJr4t8uX8sKeTtavaGR+jZt7n9sDkFwDmJr2baz1SMpWCCGEEJOeZfgmo0sp5QZMWuv++OPzgZtG2k80quno87P/cIDrH3uJCpeNtWfP5eT6UgYC4bR6uN++eBHzql2YzSa+dN5CvP4INaU2bBYTDZUu7FYzFS4rJ9aVSgBECCGEEJPemKeAlVJziX3q9//bu7cYu87yjOP/x5PY0zixMY4xJtA4FlEhJBwSK+JQDmlaiLiwW2FFpiBICwK10FxwBQIJmgqJgwgVBwkQTUWjNserUAWlEaFw00AMoUkcMHWcQGKCj5DYph4znpeLvSbaM8yM7Xhm7b1n/3/S0qz1rb22n/k80rxaa7/zQacA/Y+q+uRc18zUBbxr32H2HxrjXf/6gymf/7v2yhfPuBTcZ7a8gmtvuv+Z4+nLsEkLyC5gSVJfaf0OYFXtAl5xuu+z5+mj7D889gfNH7MtBTe9+WPvoaMWgJIkaSi1XgDOl7UrRhlZEkbPXDKl4JtcCm76HcDpzR/Tl2GTJEkaFgP7gbf1q5ez6qwRrtt08ZTl0tavXj5lqbfJJdS6mz9mWoZNkiRpWPRkJZBTNdNnAI8dO862XxxgzTlLOXjkOPsOj3Hu2cvY85sjPH/Vco6NT3BkbJzVy5ey4o9GGMkI+4+MzbkMm7RA/GGTJPWVgX0E/MAvn2J8Ap586hjvu/GHf/DI98a/vZzXX7huyjUv5py2Y0qSJPWdgS0Af/X0UcaPd+5eztT0sefpsV7EkiRJ6nsDWwCuWzHKkaazd6amj7UrlvUqmiRJUl8b2CaQS16wkqpifGKCf9z0silNH9dtuphLXNFDkiRpRgPbBAKdRpBHDx7id+Nw+Nhx9h8aY+2KZVyybiWjowN7c1OLj00gkqS+MtBV0hlnLOGRff8/Zdm3669+JUuXjvQ6miRJUt8a2EfAAI8dOPJM8Qed5o8P3fpjHjtwpMfJJEmS+tdAF4B7nj46Ywfw3kNHe5RIkiSp/w10Abh2xegzzR+TXOZNkiRpbgNdAP7xqrP49NtePqUD2GXeJEmS5jawTSATE8V//WQP19+9g/f86QZGlsDG85/Lazesdpk3SZKkOQxsAdjdAPLl7+wEOncA77z29WxYc3aP00mSJPWvgX0EbAOIJEnSszOwBaANIJIkSc9OTwrAJFcl2ZFkZ5IPP5v3WL96Oddf/UobQCRJkk5R60vBJRkBfgb8BfAEcB/w9qp6eLZrZlsKbmKieOzAEfYeOsrzzhll/erlNoCoH/lDKUnqK71oArkc2FlVuwCS3AxsBmYtAGezZEnYsOZsmz4kSZJOQS8eAZ8HPN51/EQzJkmSpBb0bRNIkvcl2ZZk2759+3odR5IkadHoRQG4G3hR1/ELm7EpquprVbWxqjauWbOmtXCSJEmLXS+aQM6g0wRyJZ3C7z7gr6tq+xzX7AN+Psfbngvsn8+c86yf8/VzNlgc+fZX1VVthJEk6WS03gRSVeNJPgjcBYwAN8xV/DXXzHkLMMm2qto4jzHnVT/n6+dsYD5JkhZCT5aCq6o7gTt78W9LkiQNu75tApEkSdLCWCwF4Nd6HeAE+jlfP2cD80mSNO9abwKRJElSby2WO4CSJEk6SRaAkiRJQ6avC8AkVyXZkWRnkg/PcH5Zklua899Psr7r3Eea8R1J3tKjfB9K8nCSB5J8O8n5XeeOJ/lxs93Ro3zXJNnXleO9XefeneT/mu3dPcr3+a5sP0vym65zCzp/SW5IsjfJQ7OcT5IvNNkfSHJp17kFnztJkk5LVfXlRudvBD4CbACWAv8LXDTtNX8PfKXZ3wrc0uxf1Lx+GXBB8z4jPch3BXBWs/93k/ma48N9MH/XAF+a4drnAruar6ua/VVt55v2+n+g8zcj25q/NwCXAg/Ncv6twLeAAK8Gvt/W3Lm5ubm5uZ3u1s93AC8HdlbVrqo6BtwMbJ72ms3AN5r924Erk6QZv7mqxqrqUWBn836t5quq71TVb5vDe+kse9eWk5m/2bwFuLuqDlbVr4G7gfleyeJU870duGmeM8yqqr4HHJzjJZuBf6uOe4HnJFlHO3MnSdJp6ecC8Dzg8a7jJ5qxGV9TVePAU8Dqk7y2jXzd3kPnjtGk0STbktyb5C/nOdup5Htb8wjz9iSTazT31fw1j84vAO7pGl7o+TuR2fK3MXeSJJ2WnqwEMmySvBPYCLyxa/j8qtqdZANwT5IHq+qRlqN9E7ipqsaSvJ/O3dQ/aznDydgK3F5Vx7vG+mH+JEkaSP18B3A38KKu4xc2YzO+JskZwErgwEle20Y+kvw58FFgU1WNTY5X1e7m6y7gv4FXtZ2vqg50Zfo6cNnJXttGvi5bmfb4t4X5O5HZ8rcxd5IknZZ+LgDvAy5MckGSpXSKgOndnncAk12WW4B7qqqa8a1Nl/AFwIXAD9rOl+RVwFfpFH97u8ZXJVnW7J8LvA54uAf51nUdbgJ+0uzfBby5ybkKeHMz1mq+JuNL6DRT/E/XWBvzdyJ3AO9quoFfDTxVVU/SztxJknRa+vYRcFWNJ/kgnV+eI3Q6QLcnuQ7YVlV3AP8C3JhkJ50P7G9trt2e5FY6RcE48IFpjw/byvdZ4Gzgtk5vCr+oqk3AS4GvJpmgU4R/qqrmtYA5yXzXJtlEZ44O0ukKpqoOJvknOkUawHVVNVdDxELlg87/6c1NYT9pwecvyU3Am4BzkzwBfBw4s8n+FeBOOp3AO4HfAn/TnFvwuZMk6XS5FJwkSdKQ6edHwJIkSVoAFoCSJElDxgJQkiRpyFgASpIkDRkLQEmSpCFjATjkkhzudQZJktQuC0BJkqQhYwG4yCT5VJIPdB1/IsnHknw7yY+SPJhk8wzXvSnJf3YdfynJNc3+ZUm+m+SHSe6atoKIJEkaMBaAi88twNVdx1cD3wD+qqouBa4APpdmaZITSXIm8EVgS1VdBtwAfHJ+I0uSpDb17VJwenaq6v4kz0vyAmAN8GvgV8Dnk7wBmADOA9Y24yfyJ8DFwN1NzTgCPLkQ2SVJUjssABen24AtwPPp3BF8B51i8LKq+l2Sx4DRadeMM/WO8OT5ANur6jULmliSJLXGR8CL0y3AVjpF4G3ASmBvU/xdAZw/wzU/By5KsizJc4Arm/EdwJokr4HOI+EkL1vw70CSJC0Y7wAuQlW1Pck5wO6qejLJvwPfTPIgsA346QzXPJ7kVuAh4FHg/mb8WJItwBeSrKTzM/PPwPaWvh1JkjTPUlW9ziBJkqQW+QhYkiRpyFgASpIkDRkLQEmSpCFjAShJkjRkLAAlSZKGjAWgJEnSkLEAlCRJGjK/B+gzyitCSp3mAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 648x648 with 7 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6-PGrxhN0hQn"
      },
      "source": [
        "With the exception of the height attribute, there are no obvious outliers.\n",
        "<br>\n",
        "<br>\n",
        "\n",
        "## Ordinary Least Squares estimation\n",
        "\n",
        "I'm using `statsmodels` for the OLS estimation.  It produces a nice summary result."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XmD57WTWak69",
        "outputId": "930aa4c3-e203-43a6-ca4d-10e9d2567db7"
      },
      "source": [
        "x = sm.add_constant(x)\n",
        "ols = sm.OLS(y,x).fit()\n",
        "sm_pred = ols.predict(x)\n",
        "\n",
        "print(ols.summary())"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:                  rings   R-squared:                       0.528\n",
            "Model:                            OLS   Adj. R-squared:                  0.527\n",
            "Method:                 Least Squares   F-statistic:                     665.2\n",
            "Date:                Mon, 23 Aug 2021   Prob (F-statistic):               0.00\n",
            "Time:                        20:13:27   Log-Likelihood:                -9250.0\n",
            "No. Observations:                4177   AIC:                         1.852e+04\n",
            "Df Residuals:                    4169   BIC:                         1.857e+04\n",
            "Df Model:                           7                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "==============================================================================\n",
            "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "const          2.9852      0.269     11.092      0.000       2.458       3.513\n",
            "length        -1.5719      1.825     -0.861      0.389      -5.149       2.006\n",
            "diam          13.3609      2.237      5.972      0.000       8.975      17.747\n",
            "height        11.8261      1.548      7.639      0.000       8.791      14.861\n",
            "whole          9.2474      0.733     12.622      0.000       7.811      10.684\n",
            "shucked      -20.2139      0.823    -24.552      0.000     -21.828     -18.600\n",
            "viscera       -9.8297      1.304     -7.538      0.000     -12.386      -7.273\n",
            "shell          8.5762      1.137      7.545      0.000       6.348      10.805\n",
            "==============================================================================\n",
            "Omnibus:                      933.799   Durbin-Watson:                   1.387\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             2602.745\n",
            "Skew:                           1.174   Prob(JB):                         0.00\n",
            "Kurtosis:                       6.072   Cond. No.                         131.\n",
            "==============================================================================\n",
            "\n",
            "Warnings:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "glGSHwVO1WrC"
      },
      "source": [
        "<br>\n",
        "<br>\n",
        "\n",
        "## Theil Sen estimation\n",
        "Now to Theil Sen, using Scikit Learn."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2kuZyTMxVbNq",
        "outputId": "c2a64ae7-31d8-4d24-8a24-3e36c65f00b7"
      },
      "source": [
        "# Create regression object\n",
        "ts = linear_model.TheilSenRegressor()\n",
        "\n",
        "# Fit the model\n",
        "ts.fit(x, np.ravel(y))\n",
        "\n",
        "# Print the intercept, co-efficients & R-squared\n",
        "for i in zip(x.columns.tolist(), ts.coef_):\n",
        "  print(i[0].ljust(10),' :', '{:>9.2f}'.format(i[1]))\n",
        "\n",
        "print('R-squared'.ljust(10),' :', '{:>9.2f}'.format(ts.score(x, np.ravel(y))))"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "const       :      2.08\n",
            "length      :     -6.41\n",
            "diam        :     11.16\n",
            "height      :     23.40\n",
            "whole       :     13.17\n",
            "shucked     :    -24.88\n",
            "viscera     :    -13.46\n",
            "shell       :      7.02\n",
            "R-squared   :      0.51\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sNehy4mc15cK"
      },
      "source": [
        "## Model comparison\n",
        "\n",
        "Lets compare the regression co-efficients and model fit."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "F9rGY7vs2DEk",
        "outputId": "6395eb47-1b95-4cf3-87cb-e69e3f9a7355"
      },
      "source": [
        "ts_coef = zip(x.columns.tolist(), ts.coef_)\n",
        "ts_coef = pd.DataFrame(ts_coef, columns = ['Variable', 'TS coef'])\n",
        "\n",
        "ols_coef = ols.params.to_frame(name='OLS coef').rename_axis('Variable')\n",
        "ols_coef.reset_index(inplace=True)\n",
        "\n",
        "coef = ts_coef.merge(ols_coef, on='Variable')\n",
        "coef['Diff in coef'] = coef['TS coef'] - coef['OLS coef']\n",
        "coef.round(2)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Variable</th>\n",
              "      <th>TS coef</th>\n",
              "      <th>OLS coef</th>\n",
              "      <th>Diff in coef</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>const</td>\n",
              "      <td>2.08</td>\n",
              "      <td>2.99</td>\n",
              "      <td>-0.91</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>length</td>\n",
              "      <td>-6.41</td>\n",
              "      <td>-1.57</td>\n",
              "      <td>-4.84</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>diam</td>\n",
              "      <td>11.16</td>\n",
              "      <td>13.36</td>\n",
              "      <td>-2.20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>height</td>\n",
              "      <td>23.40</td>\n",
              "      <td>11.83</td>\n",
              "      <td>11.57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>whole</td>\n",
              "      <td>13.17</td>\n",
              "      <td>9.25</td>\n",
              "      <td>3.92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>shucked</td>\n",
              "      <td>-24.88</td>\n",
              "      <td>-20.21</td>\n",
              "      <td>-4.67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>viscera</td>\n",
              "      <td>-13.46</td>\n",
              "      <td>-9.83</td>\n",
              "      <td>-3.63</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>shell</td>\n",
              "      <td>7.02</td>\n",
              "      <td>8.58</td>\n",
              "      <td>-1.56</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Variable  TS coef  OLS coef  Diff in coef\n",
              "0    const     2.08      2.99         -0.91\n",
              "1   length    -6.41     -1.57         -4.84\n",
              "2     diam    11.16     13.36         -2.20\n",
              "3   height    23.40     11.83         11.57\n",
              "4    whole    13.17      9.25          3.92\n",
              "5  shucked   -24.88    -20.21         -4.67\n",
              "6  viscera   -13.46     -9.83         -3.63\n",
              "7    shell     7.02      8.58         -1.56"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0V6XP_tD1W55"
      },
      "source": [
        "The fit models are similar with the sign of coefficients in alignment.  \n",
        "  \n",
        "We can see how the OLS estimate of the height coefficient is dragged down by the outliers.  The Thiel Sen approach, by virtue of the estimation methodology explained above, places less weight on these outliers.  This is to be expected with a robust technique.  \n",
        "\n",
        "The r-squared values are similar at around 0.5.\n",
        "<br>\n",
        "<br>\n",
        "<br>\n",
        "## Inserting outliers\n",
        "Below, we amend a subset of 200 records in order to transform these into outliers.  100 records are multiplied by 5, and 100 records divided by 5.  These artifical outliers represents around 5% of our data (there are 4,177 records in total)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "buuJN97yl_S9"
      },
      "source": [
        "df1 = df\n",
        "\n",
        "df1.iloc[0:100, 0:7] = df1.iloc[0:100, 0:7].multiply(5)"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wyyo6EIN6Ct8"
      },
      "source": [
        "df1.iloc[4076:4177, 0:7] = df1.iloc[4076:4177, 0:7].div(5)"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "efTpYH1sxPXj",
        "outputId": "9a3c8607-d059-463c-868d-2d8e5c0725a4"
      },
      "source": [
        "df1.iloc[:, 0:7]"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>length</th>\n",
              "      <th>diam</th>\n",
              "      <th>height</th>\n",
              "      <th>whole</th>\n",
              "      <th>shucked</th>\n",
              "      <th>viscera</th>\n",
              "      <th>shell</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2.275</td>\n",
              "      <td>1.825</td>\n",
              "      <td>0.475</td>\n",
              "      <td>2.5700</td>\n",
              "      <td>1.1225</td>\n",
              "      <td>0.5050</td>\n",
              "      <td>0.7500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.750</td>\n",
              "      <td>1.325</td>\n",
              "      <td>0.450</td>\n",
              "      <td>1.1275</td>\n",
              "      <td>0.4975</td>\n",
              "      <td>0.2425</td>\n",
              "      <td>0.3500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2.650</td>\n",
              "      <td>2.100</td>\n",
              "      <td>0.675</td>\n",
              "      <td>3.3850</td>\n",
              "      <td>1.2825</td>\n",
              "      <td>0.7075</td>\n",
              "      <td>1.0500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2.200</td>\n",
              "      <td>1.825</td>\n",
              "      <td>0.625</td>\n",
              "      <td>2.5800</td>\n",
              "      <td>1.0775</td>\n",
              "      <td>0.5700</td>\n",
              "      <td>0.7750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.650</td>\n",
              "      <td>1.275</td>\n",
              "      <td>0.400</td>\n",
              "      <td>1.0250</td>\n",
              "      <td>0.4475</td>\n",
              "      <td>0.1975</td>\n",
              "      <td>0.2750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4172</th>\n",
              "      <td>0.113</td>\n",
              "      <td>0.090</td>\n",
              "      <td>0.033</td>\n",
              "      <td>0.1774</td>\n",
              "      <td>0.0740</td>\n",
              "      <td>0.0478</td>\n",
              "      <td>0.0498</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4173</th>\n",
              "      <td>0.118</td>\n",
              "      <td>0.088</td>\n",
              "      <td>0.027</td>\n",
              "      <td>0.1932</td>\n",
              "      <td>0.0878</td>\n",
              "      <td>0.0429</td>\n",
              "      <td>0.0521</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4174</th>\n",
              "      <td>0.120</td>\n",
              "      <td>0.095</td>\n",
              "      <td>0.041</td>\n",
              "      <td>0.2352</td>\n",
              "      <td>0.1051</td>\n",
              "      <td>0.0575</td>\n",
              "      <td>0.0616</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4175</th>\n",
              "      <td>0.125</td>\n",
              "      <td>0.097</td>\n",
              "      <td>0.030</td>\n",
              "      <td>0.2189</td>\n",
              "      <td>0.1062</td>\n",
              "      <td>0.0522</td>\n",
              "      <td>0.0592</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4176</th>\n",
              "      <td>0.142</td>\n",
              "      <td>0.111</td>\n",
              "      <td>0.039</td>\n",
              "      <td>0.3897</td>\n",
              "      <td>0.1891</td>\n",
              "      <td>0.0753</td>\n",
              "      <td>0.0990</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4177 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      length   diam  height   whole  shucked  viscera   shell\n",
              "0      2.275  1.825   0.475  2.5700   1.1225   0.5050  0.7500\n",
              "1      1.750  1.325   0.450  1.1275   0.4975   0.2425  0.3500\n",
              "2      2.650  2.100   0.675  3.3850   1.2825   0.7075  1.0500\n",
              "3      2.200  1.825   0.625  2.5800   1.0775   0.5700  0.7750\n",
              "4      1.650  1.275   0.400  1.0250   0.4475   0.1975  0.2750\n",
              "...      ...    ...     ...     ...      ...      ...     ...\n",
              "4172   0.113  0.090   0.033  0.1774   0.0740   0.0478  0.0498\n",
              "4173   0.118  0.088   0.027  0.1932   0.0878   0.0429  0.0521\n",
              "4174   0.120  0.095   0.041  0.2352   0.1051   0.0575  0.0616\n",
              "4175   0.125  0.097   0.030  0.2189   0.1062   0.0522  0.0592\n",
              "4176   0.142  0.111   0.039  0.3897   0.1891   0.0753  0.0990\n",
              "\n",
              "[4177 rows x 7 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "huvFXr096r2V"
      },
      "source": [
        "x1 = df1.iloc[:, 0:7]\n",
        "y1 = df1.iloc[:, 7:8]"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rNhRfEJY6CMe"
      },
      "source": [
        "## Visualise data with artificial outliers\n",
        "\n",
        "The plots below replicate those shown above, except using the amended data containing artificial outliers. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 674
        },
        "id": "pPz390is2giL",
        "outputId": "239eaa78-a1b0-4ecd-c3fe-ec59623e6541"
      },
      "source": [
        "sns.FacetGrid(\n",
        "    data=df1.melt(var_name='Attribute', value_name='value'),\n",
        "    col='Attribute',\n",
        "    col_wrap=3,\n",
        "    sharex=False,\n",
        "    sharey=False) \\\n",
        ".map(sns.histplot, 'value')"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7fa1424f6290>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 648x648 with 8 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 674
        },
        "id": "nx2lUV4C6PRO",
        "outputId": "b10f1d48-aeef-4eb3-ad89-381b30c41838"
      },
      "source": [
        "sns.FacetGrid(\n",
        "    data=df1.melt(var_name='Attribute', value_name='value', value_vars=['length', 'diam', 'height', 'whole', 'shucked', 'viscera', 'shell'], ignore_index=False).join(y1),\n",
        "    col='Attribute',\n",
        "    col_wrap=3,\n",
        "    sharex=False,\n",
        "    sharey=False) \\\n",
        ".map(sns.scatterplot, 'value', 'rings')"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7fa1417eae90>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 648x648 with 7 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qzwkusspa5Jx"
      },
      "source": [
        "Naturally the data becomes a lot more skewed.  The scatterplot highlights the artificial outliers very clearly.\n",
        "<br>\n",
        "<br>\n",
        "\n",
        "## OLS estimation (with artificial outliers)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YvzB_mIk6_eV",
        "outputId": "6ed5038c-0c26-4323-a595-dc35ae932d56"
      },
      "source": [
        "x1 = sm.add_constant(x1)\n",
        "ols_o = sm.OLS(y1,x1).fit()\n",
        "sm_pred= ols_o.predict(x1)\n",
        "\n",
        "print(ols_o.summary())"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:                  rings   R-squared:                       0.334\n",
            "Model:                            OLS   Adj. R-squared:                  0.333\n",
            "Method:                 Least Squares   F-statistic:                     298.8\n",
            "Date:                Mon, 23 Aug 2021   Prob (F-statistic):               0.00\n",
            "Time:                        20:13:35   Log-Likelihood:                -9967.2\n",
            "No. Observations:                4177   AIC:                         1.995e+04\n",
            "Df Residuals:                    4169   BIC:                         2.000e+04\n",
            "Df Model:                           7                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "==============================================================================\n",
            "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "const          9.5868      0.084    114.707      0.000       9.423       9.751\n",
            "length       -12.1602      1.733     -7.018      0.000     -15.557      -8.763\n",
            "diam           6.8125      2.292      2.972      0.003       2.319      11.307\n",
            "height         6.8685      1.711      4.014      0.000       3.514      10.223\n",
            "whole          5.6037      0.755      7.418      0.000       4.123       7.085\n",
            "shucked      -10.1362      0.872    -11.629      0.000     -11.845      -8.427\n",
            "viscera        1.3970      1.227      1.139      0.255      -1.008       3.802\n",
            "shell          7.0807      1.184      5.978      0.000       4.759       9.403\n",
            "==============================================================================\n",
            "Omnibus:                      729.658   Durbin-Watson:                   1.183\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             9803.518\n",
            "Skew:                           0.417   Prob(JB):                         0.00\n",
            "Kurtosis:                      10.459   Cond. No.                         124.\n",
            "==============================================================================\n",
            "\n",
            "Warnings:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NcwhDhtdbELv"
      },
      "source": [
        "## Thiel Sen estimation (with artificial outliers)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NhePpBIK7uJH",
        "outputId": "9b5de4fe-c849-4748-8d8f-1164e3f44daa"
      },
      "source": [
        "# Create regression object\n",
        "ts_o = linear_model.TheilSenRegressor()\n",
        "\n",
        "# Fit the model\n",
        "ts_o.fit(x1, np.ravel(y1))\n",
        "\n",
        "# Print the intercept, co-efficients & R-squared\n",
        "for i in zip(x1.columns.tolist(), ts_o.coef_):\n",
        "  print(i[0].ljust(10),' :', '{:>9.2f}'.format(i[1]))\n",
        "\n",
        "print('R-squared'.ljust(10),' :', '{:>9.2f}'.format(ts_o.score(x1, np.ravel(y1))))"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "const       :      2.76\n",
            "length      :     -9.97\n",
            "diam        :     11.31\n",
            "height      :     19.35\n",
            "whole       :     12.91\n",
            "shucked     :    -23.39\n",
            "viscera     :    -12.50\n",
            "shell       :      9.30\n",
            "R-squared   :     -0.62\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3gA6ytYtbOQO"
      },
      "source": [
        "## Comparison of models"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zd6gbvGo-FI5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "550a716c-7132-434b-b0ce-7304015da0f8"
      },
      "source": [
        "ts_coef = zip(x1.columns.tolist(), ts_o.coef_)\n",
        "ts_coef = pd.DataFrame(ts_coef, columns = ['Variable', 'TS coef (outliers)'])\n",
        "\n",
        "ols_coef = ols_o.params.to_frame(name='OLS coef (outliers)').rename_axis('Variable')\n",
        "ols_coef.reset_index(inplace=True)\n",
        "\n",
        "coef_o = ts_coef.merge(ols_coef, on='Variable')\n",
        "\n",
        "coef = coef.merge(coef_o, on='Variable')\n",
        "\n",
        "coef['Diff in coef (outliers)'] = coef['TS coef (outliers)'] - coef['OLS coef (outliers)']\n",
        "coef['TS coef (diff)'] = coef['TS coef'] - coef['TS coef (outliers)']\n",
        "coef['OLS coef (diff)'] = coef['OLS coef'] - coef['OLS coef (outliers)']\n",
        "coef.round(2)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Variable</th>\n",
              "      <th>TS coef</th>\n",
              "      <th>OLS coef</th>\n",
              "      <th>Diff in coef</th>\n",
              "      <th>TS coef (outliers)</th>\n",
              "      <th>OLS coef (outliers)</th>\n",
              "      <th>Diff in coef (outliers)</th>\n",
              "      <th>TS coef (diff)</th>\n",
              "      <th>OLS coef (diff)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>const</td>\n",
              "      <td>2.08</td>\n",
              "      <td>2.99</td>\n",
              "      <td>-0.91</td>\n",
              "      <td>2.76</td>\n",
              "      <td>9.59</td>\n",
              "      <td>-6.83</td>\n",
              "      <td>-0.68</td>\n",
              "      <td>-6.60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>length</td>\n",
              "      <td>-6.41</td>\n",
              "      <td>-1.57</td>\n",
              "      <td>-4.84</td>\n",
              "      <td>-9.97</td>\n",
              "      <td>-12.16</td>\n",
              "      <td>2.19</td>\n",
              "      <td>3.56</td>\n",
              "      <td>10.59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>diam</td>\n",
              "      <td>11.16</td>\n",
              "      <td>13.36</td>\n",
              "      <td>-2.20</td>\n",
              "      <td>11.31</td>\n",
              "      <td>6.81</td>\n",
              "      <td>4.50</td>\n",
              "      <td>-0.15</td>\n",
              "      <td>6.55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>height</td>\n",
              "      <td>23.40</td>\n",
              "      <td>11.83</td>\n",
              "      <td>11.57</td>\n",
              "      <td>19.35</td>\n",
              "      <td>6.87</td>\n",
              "      <td>12.48</td>\n",
              "      <td>4.05</td>\n",
              "      <td>4.96</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>whole</td>\n",
              "      <td>13.17</td>\n",
              "      <td>9.25</td>\n",
              "      <td>3.92</td>\n",
              "      <td>12.91</td>\n",
              "      <td>5.60</td>\n",
              "      <td>7.31</td>\n",
              "      <td>0.26</td>\n",
              "      <td>3.64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>shucked</td>\n",
              "      <td>-24.88</td>\n",
              "      <td>-20.21</td>\n",
              "      <td>-4.67</td>\n",
              "      <td>-23.39</td>\n",
              "      <td>-10.14</td>\n",
              "      <td>-13.26</td>\n",
              "      <td>-1.49</td>\n",
              "      <td>-10.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>viscera</td>\n",
              "      <td>-13.46</td>\n",
              "      <td>-9.83</td>\n",
              "      <td>-3.63</td>\n",
              "      <td>-12.50</td>\n",
              "      <td>1.40</td>\n",
              "      <td>-13.89</td>\n",
              "      <td>-0.96</td>\n",
              "      <td>-11.23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>shell</td>\n",
              "      <td>7.02</td>\n",
              "      <td>8.58</td>\n",
              "      <td>-1.56</td>\n",
              "      <td>9.30</td>\n",
              "      <td>7.08</td>\n",
              "      <td>2.22</td>\n",
              "      <td>-2.28</td>\n",
              "      <td>1.50</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Variable  TS coef  ...  TS coef (diff)  OLS coef (diff)\n",
              "0    const     2.08  ...           -0.68            -6.60\n",
              "1   length    -6.41  ...            3.56            10.59\n",
              "2     diam    11.16  ...           -0.15             6.55\n",
              "3   height    23.40  ...            4.05             4.96\n",
              "4    whole    13.17  ...            0.26             3.64\n",
              "5  shucked   -24.88  ...           -1.49           -10.08\n",
              "6  viscera   -13.46  ...           -0.96           -11.23\n",
              "7    shell     7.02  ...           -2.28             1.50\n",
              "\n",
              "[8 rows x 9 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3oDKgs3Uc29R"
      },
      "source": [
        "## Observations\n",
        "\n",
        "### OLS model\n",
        "The r-squared is now much lower for the OLS model. From 0.5 on the original data to 0.3 on the data with the artificial outliers inserted.  This makes sense in the context of the outliers increasing the variance of the data.\n",
        "\n",
        "The difference in coefficients is substantial, for example viscera has increased from -9.8 to 1.4, and length from -1.6 to -12.1.  Interestingly, shell has changed by a much smaller relative amount, 8.6 to 7.1.  \n",
        "\n",
        "Why is this?  Why has the sign of the coefficient on viscera changed?  \n",
        "\n",
        "It appears there is excessive multicollinearity in the original dataset, and this has given rise to spurious or unstable coefficients.  A small(ish) change to the data has given rise to large changes in coefficients.  Going back to the original data reveals an average pairwise correlation of around 0.9.\n",
        "<br>\n",
        "<br>\n",
        "### Theil Sen model\n",
        "\n",
        "The Thiel Sen model coefficients are much more stable and have not changed sign.  The largest change, the length coefficient, has almost doubled from -5.7 to -10.9.  This variable has the largest change in coefficient in both the Theil Sen and OLS models.\n",
        "\n",
        "The r-squared for the model with outliers has decreased to -0.56 (yes, negative).  This is because the model in aggregate has worse predictions than simply predicting the baseline, the average of the y values.  This result makes sense in the context of the solution that Theil Sen implements; if outliers are not taken into account when coefficients are derived (remember it is the median of all slopes across all data points), these will result in large prediction errors.  \n",
        "\n",
        "Below the r-squared is calculated for data points including and excluding the artificial outliers.  The values for this are -0.27 and 0.51 respectively.\n",
        "\n",
        "It should be noted that by virtue of the method of inserting outliers, the correlation structure of the data has been preserved.\n",
        "<br>\n",
        "<br>\n",
        "## Conclusion\n",
        "\n",
        "This has been a quick little piece of analysis.  What have we learned?\n",
        "\n",
        "1. Multi-collinearity can lead to unstable regression coefficents  \n",
        "2. The r-squared based on models fit using robust techniques may be spurious if data is bifurcated similar to that artificially induced above  \n",
        "3. Theil Sen regression is another string in your bow.  \n",
        "\n",
        "\n",
        "\n",
        "<br>\n",
        "<br>\n",
        "\n",
        "---\n",
        "\n",
        "<br>\n",
        "<br>\n",
        "\n",
        "## Scratch workings\n",
        "\n",
        "### R-squared information"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h2mDp_zKV5TW",
        "outputId": "ebcaf861-7a3b-4d8b-ae5b-ad99903898bd"
      },
      "source": [
        "# r-squared - OLS with outliers\n",
        "ols_o.rsquared"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.3340788540256908"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JxkEZwisWIeh",
        "outputId": "a74a4fe8-bff1-443d-caa7-bbfcfb87bd87"
      },
      "source": [
        "# r-squared - TS with outliers\n",
        "ts_o.score(x1, np.ravel(y1))"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-0.6166942787878218"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oE-hhRAUXD-f",
        "outputId": "2c37221e-b321-445e-cfda-aa6e425abe6d"
      },
      "source": [
        "# r-squared - TS on ALL data points\n",
        "ts_o.score(x1, y1)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-0.6166942787878218"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D1scRd3hzvaV",
        "outputId": "51b11040-30b2-4fd6-dfd1-02a5b6abb2ca"
      },
      "source": [
        "# r-squared - TS on data points that ARE outliers\n",
        "ts_o.score(x1.iloc[np.r_[0:100,4076:4176]], y1.iloc[np.r_[0:100,4076:4176]])"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-29.564854211258634"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JwRU0rYA0-Vo",
        "outputId": "a33722d2-f1c0-451b-b2f9-bfcc3ee3da3a"
      },
      "source": [
        "# r-squared - TS on data points that ARE NOT outliers\n",
        "ts_o.score(x1.iloc[np.r_[101:4075]], y1.iloc[np.r_[101:4075]])"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5081364119559657"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kmRQcbYDv00f"
      },
      "source": [
        "### Average pairwise correlation of original data set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 266
        },
        "id": "wxQ0mzp3jmP1",
        "outputId": "b5ee5db0-698f-41de-f9db-9f8850faf16c"
      },
      "source": [
        "df_corr = df.iloc[:, 0:7].corr()\n",
        "df_corr"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>length</th>\n",
              "      <th>diam</th>\n",
              "      <th>height</th>\n",
              "      <th>whole</th>\n",
              "      <th>shucked</th>\n",
              "      <th>viscera</th>\n",
              "      <th>shell</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>length</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.997471</td>\n",
              "      <td>0.962519</td>\n",
              "      <td>0.861553</td>\n",
              "      <td>0.827044</td>\n",
              "      <td>0.850503</td>\n",
              "      <td>0.868906</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>diam</th>\n",
              "      <td>0.997471</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.964844</td>\n",
              "      <td>0.871345</td>\n",
              "      <td>0.835328</td>\n",
              "      <td>0.858122</td>\n",
              "      <td>0.879775</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>height</th>\n",
              "      <td>0.962519</td>\n",
              "      <td>0.964844</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.869090</td>\n",
              "      <td>0.827839</td>\n",
              "      <td>0.857939</td>\n",
              "      <td>0.880715</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>whole</th>\n",
              "      <td>0.861553</td>\n",
              "      <td>0.871345</td>\n",
              "      <td>0.869090</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.977039</td>\n",
              "      <td>0.973434</td>\n",
              "      <td>0.971785</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>shucked</th>\n",
              "      <td>0.827044</td>\n",
              "      <td>0.835328</td>\n",
              "      <td>0.827839</td>\n",
              "      <td>0.977039</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.955973</td>\n",
              "      <td>0.915816</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>viscera</th>\n",
              "      <td>0.850503</td>\n",
              "      <td>0.858122</td>\n",
              "      <td>0.857939</td>\n",
              "      <td>0.973434</td>\n",
              "      <td>0.955973</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.928095</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>shell</th>\n",
              "      <td>0.868906</td>\n",
              "      <td>0.879775</td>\n",
              "      <td>0.880715</td>\n",
              "      <td>0.971785</td>\n",
              "      <td>0.915816</td>\n",
              "      <td>0.928095</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           length      diam    height     whole   shucked   viscera     shell\n",
              "length   1.000000  0.997471  0.962519  0.861553  0.827044  0.850503  0.868906\n",
              "diam     0.997471  1.000000  0.964844  0.871345  0.835328  0.858122  0.879775\n",
              "height   0.962519  0.964844  1.000000  0.869090  0.827839  0.857939  0.880715\n",
              "whole    0.861553  0.871345  0.869090  1.000000  0.977039  0.973434  0.971785\n",
              "shucked  0.827044  0.835328  0.827839  0.977039  1.000000  0.955973  0.915816\n",
              "viscera  0.850503  0.858122  0.857939  0.973434  0.955973  1.000000  0.928095\n",
              "shell    0.868906  0.879775  0.880715  0.971785  0.915816  0.928095  1.000000"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IbajE01mshYP",
        "outputId": "f5b461d3-a253-4659-9af3-9d762c8fe1dc"
      },
      "source": [
        "np.nanmean(df_corr.values[np.triu_indices_from(df_corr.values,1)])"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.901673180385633"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "po4eS8qQ7cou"
      },
      "source": [
        "### Average pairwise correlation of outliers data set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WwE0tqVTwc1X",
        "outputId": "11d1fb4a-5a00-41e0-b4ff-1a4cd4644edb"
      },
      "source": [
        "df_corr_o = df1.iloc[:, 0:7].corr()\n",
        "np.nanmean(df_corr_o.values[np.triu_indices_from(df_corr_o.values,1)])"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.901673180385633"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "22Buj-Q0v6IY"
      },
      "source": [
        "### Attributes associated with regression model objects"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R5LnTi82WUpU",
        "outputId": "8c3424f4-70ff-4059-c730-eda8fd8d8d57"
      },
      "source": [
        "dir(ts_o)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['__abstractmethods__',\n",
              " '__class__',\n",
              " '__delattr__',\n",
              " '__dict__',\n",
              " '__dir__',\n",
              " '__doc__',\n",
              " '__eq__',\n",
              " '__format__',\n",
              " '__ge__',\n",
              " '__getattribute__',\n",
              " '__getstate__',\n",
              " '__gt__',\n",
              " '__hash__',\n",
              " '__init__',\n",
              " '__init_subclass__',\n",
              " '__le__',\n",
              " '__lt__',\n",
              " '__module__',\n",
              " '__ne__',\n",
              " '__new__',\n",
              " '__reduce__',\n",
              " '__reduce_ex__',\n",
              " '__repr__',\n",
              " '__setattr__',\n",
              " '__setstate__',\n",
              " '__sizeof__',\n",
              " '__str__',\n",
              " '__subclasshook__',\n",
              " '__weakref__',\n",
              " '_abc_impl',\n",
              " '_check_subparams',\n",
              " '_decision_function',\n",
              " '_estimator_type',\n",
              " '_get_param_names',\n",
              " '_get_tags',\n",
              " '_more_tags',\n",
              " '_preprocess_data',\n",
              " '_set_intercept',\n",
              " 'breakdown_',\n",
              " 'coef_',\n",
              " 'copy_X',\n",
              " 'fit',\n",
              " 'fit_intercept',\n",
              " 'get_params',\n",
              " 'intercept_',\n",
              " 'max_iter',\n",
              " 'max_subpopulation',\n",
              " 'n_iter_',\n",
              " 'n_jobs',\n",
              " 'n_subpopulation_',\n",
              " 'n_subsamples',\n",
              " 'predict',\n",
              " 'random_state',\n",
              " 'score',\n",
              " 'set_params',\n",
              " 'tol',\n",
              " 'verbose']"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G4lSsZWa96qC",
        "outputId": "d9843008-244c-41d8-af39-2db26b342992"
      },
      "source": [
        "dir(ols_o)"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['HC0_se',\n",
              " 'HC1_se',\n",
              " 'HC2_se',\n",
              " 'HC3_se',\n",
              " '_HCCM',\n",
              " '__class__',\n",
              " '__delattr__',\n",
              " '__dict__',\n",
              " '__dir__',\n",
              " '__doc__',\n",
              " '__eq__',\n",
              " '__format__',\n",
              " '__ge__',\n",
              " '__getattribute__',\n",
              " '__gt__',\n",
              " '__hash__',\n",
              " '__init__',\n",
              " '__init_subclass__',\n",
              " '__le__',\n",
              " '__lt__',\n",
              " '__module__',\n",
              " '__ne__',\n",
              " '__new__',\n",
              " '__reduce__',\n",
              " '__reduce_ex__',\n",
              " '__repr__',\n",
              " '__setattr__',\n",
              " '__sizeof__',\n",
              " '__str__',\n",
              " '__subclasshook__',\n",
              " '__weakref__',\n",
              " '_cache',\n",
              " '_data_attr',\n",
              " '_get_robustcov_results',\n",
              " '_is_nested',\n",
              " '_wexog_singular_values',\n",
              " 'aic',\n",
              " 'bic',\n",
              " 'bse',\n",
              " 'centered_tss',\n",
              " 'compare_f_test',\n",
              " 'compare_lm_test',\n",
              " 'compare_lr_test',\n",
              " 'condition_number',\n",
              " 'conf_int',\n",
              " 'conf_int_el',\n",
              " 'cov_HC0',\n",
              " 'cov_HC1',\n",
              " 'cov_HC2',\n",
              " 'cov_HC3',\n",
              " 'cov_kwds',\n",
              " 'cov_params',\n",
              " 'cov_type',\n",
              " 'df_model',\n",
              " 'df_resid',\n",
              " 'diagn',\n",
              " 'eigenvals',\n",
              " 'el_test',\n",
              " 'ess',\n",
              " 'f_pvalue',\n",
              " 'f_test',\n",
              " 'fittedvalues',\n",
              " 'fvalue',\n",
              " 'get_influence',\n",
              " 'get_prediction',\n",
              " 'get_robustcov_results',\n",
              " 'initialize',\n",
              " 'k_constant',\n",
              " 'llf',\n",
              " 'load',\n",
              " 'model',\n",
              " 'mse_model',\n",
              " 'mse_resid',\n",
              " 'mse_total',\n",
              " 'nobs',\n",
              " 'normalized_cov_params',\n",
              " 'outlier_test',\n",
              " 'params',\n",
              " 'predict',\n",
              " 'pvalues',\n",
              " 'remove_data',\n",
              " 'resid',\n",
              " 'resid_pearson',\n",
              " 'rsquared',\n",
              " 'rsquared_adj',\n",
              " 'save',\n",
              " 'scale',\n",
              " 'ssr',\n",
              " 'summary',\n",
              " 'summary2',\n",
              " 't_test',\n",
              " 't_test_pairwise',\n",
              " 'tvalues',\n",
              " 'uncentered_tss',\n",
              " 'use_t',\n",
              " 'wald_test',\n",
              " 'wald_test_terms',\n",
              " 'wresid']"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    }
  ]
}