ml-finance-python

python scripts for finance machine learning
git clone https://9o.is/git/ml-finance-python.git
notebook.ipynb

(96627B)
      1 {
      2  "cells": [
      3   {
      4    "cell_type": "markdown",
      5    "metadata": {},
      6    "source": [
      7     "## Strategy Definition"
      8    ]
      9   },
     10   {
     11    "cell_type": "markdown",
     12    "metadata": {},
     13    "source": [
     14     "Now that we have learned how to access and manipulate data in Quantopian, let's construct a data pipeline for our long-short equity strategy. In general, long-short equity strategies consist of modeling the relative value of assets with respect to each other, and placing bets on the sets of assets that we are confident will increase ([long](https://www.investopedia.com/terms/l/long.asp)) and decrease ([short](https://www.investopedia.com/terms/s/short.asp)) the most in value.  \n",
     15     "\n",
     16     "Long-short equity strategies profit as the spread in returns between the sets of high and low value assets increases. The quality of long-short equity strategy relies entirely on the quality of its underling ranking model. In this tutorial we will use a simple ranking schema for our strategy:  \n",
     17     "\n",
     18     "**Strategy**: We will consider assets with a high 3 day average sentiment score as high value, and assets with a low 3 day average sentiment score as low value."
     19    ]
     20   },
     21   {
     22    "cell_type": "markdown",
     23    "metadata": {},
     24    "source": [
     25     "## Strategy Analysis"
     26    ]
     27   },
     28   {
     29    "cell_type": "markdown",
     30    "metadata": {},
     31    "source": [
     32     "We can define the strategy above using `SimpleMovingAverage` and sentdex `sentiment` dataset, similar to the pipeline we created in the previous lesson:"
     33    ]
     34   },
     35   {
     36    "cell_type": "code",
     37    "execution_count": 1,
     38    "metadata": {},
     39    "outputs": [],
     40    "source": [
     41     "# Import Pipeline class and datasets\n",
     42     "from quantopian.pipeline import Pipeline\n",
     43     "from quantopian.pipeline.data import EquityPricing\n",
     44     "from quantopian.pipeline.domain import US_EQUITIES\n",
     45     "from quantopian.pipeline.data.sentdex import sentiment\n",
     46     "\n",
     47     "# Import built-in moving average calculation\n",
     48     "from quantopian.pipeline.factors import SimpleMovingAverage\n",
     49     "\n",
     50     "# Import built-in trading universe\n",
     51     "from quantopian.pipeline.filters import QTradableStocksUS\n",
     52     "\n",
     53     "\n",
     54     "# Pipeline definition\n",
     55     "def make_pipeline():\n",
     56     "    # Create a reference to our trading universe\n",
     57     "    base_universe = QTradableStocksUS()\n",
     58     "\n",
     59     "    # Calculate 3 day average of sentiment scores\n",
     60     "    sentiment_score = SimpleMovingAverage(\n",
     61     "        inputs=[sentiment.sentiment_signal],\n",
     62     "        window_length=3,\n",
     63     "    )\n",
     64     "\n",
     65     "    # Return Pipeline containing sentiment_score that has our trading universe as a screen\n",
     66     "    return Pipeline(\n",
     67     "        columns={\n",
     68     "            'close_price': close_price,\n",
     69     "            'sentiment_score': sentiment_score,\n",
     70     "        },\n",
     71     "        screen=base_universe,\n",
     72     "        domain=US_EQUITIES,\n",
     73     "    )"
     74    ]
     75   },
     76   {
     77    "cell_type": "markdown",
     78    "metadata": {},
     79    "source": [
     80     "For simplicity, we will only analyze the top 350 and bottom 350 stocks ranked by `sentiment_mean`. We can create pipeline filters for these sets using the `top` and `bottom` methods of our `sentiment_mean` output, and combine them using the `|` operator to get their union. Then, we will remove anything outside of our tradable universe by using the `&amp;` operator to get the intersection between our filter and our universe:"
     81    ]
     82   },
     83   {
     84    "cell_type": "code",
     85    "execution_count": 2,
     86    "metadata": {},
     87    "outputs": [],
     88    "source": [
     89     "# Pipeline imports\n",
     90     "from quantopian.pipeline import Pipeline\n",
     91     "from quantopian.pipeline.data.sentdex import sentiment\n",
     92     "\n",
     93     "from quantopian.pipeline.factors import SimpleMovingAverage\n",
     94     "from quantopian.pipeline.filters import QTradableStocksUS\n",
     95     "\n",
     96     "# Pipeline definition\n",
     97     "def  make_pipeline():\n",
     98     "    # Create a reference to our trading universe\n",
     99     "    base_universe = QTradableStocksUS()\n",
    100     "\n",
    101     "    # Calculate 3 day average of sentiment scores\n",
    102     "    sentiment_score = SimpleMovingAverage(\n",
    103     "        inputs=[sentiment.sentiment_signal],\n",
    104     "        window_length=3,\n",
    105     "    )\n",
    106     "\n",
    107     "    # Create filter for top 350 and bottom 350\n",
    108     "    # assets based on their sentiment scores\n",
    109     "    top_bottom_scores = (\n",
    110     "        sentiment_score.top(350) | sentiment_score.bottom(350)\n",
    111     "    )\n",
    112     "\n",
    113     "    return Pipeline(\n",
    114     "        columns={\n",
    115     "            'sentiment_score': sentiment_score,\n",
    116     "        },\n",
    117     "        # Set screen as the intersection between our trading universe and our filter \n",
    118     "        screen=(\n",
    119     "            base_universe\n",
    120     "            & top_bottom_scores\n",
    121     "        )\n",
    122     "    )"
    123    ]
    124   },
    125   {
    126    "cell_type": "markdown",
    127    "metadata": {},
    128    "source": [
    129     "Next, let's run our pipeline over a 3 year period to get an output we can use for our analysis. This will take ~3 minutes."
    130    ]
    131   },
    132   {
    133    "cell_type": "code",
    134    "execution_count": 3,
    135    "metadata": {},
    136    "outputs": [
    137     {
    138      "data": {
    139       "application/vnd.jupyter.widget-view+json": {
    140        "model_id": "",
    141        "version_major": 2,
    142        "version_minor": 0
    143       },
    144       "text/plain": []
    145      },
    146      "metadata": {},
    147      "output_type": "display_data"
    148     },
    149     {
    150      "data": {
    151       "text/html": [
    152        "<b>Pipeline Execution Time:</b> 2 Minutes, 45.56 Seconds"
    153       ],
    154       "text/plain": [
    155        "<IPython.core.display.HTML object>"
    156       ]
    157      },
    158      "metadata": {},
    159      "output_type": "display_data"
    160     }
    161    ],
    162    "source": [
    163     "# Import run_pipeline method\n",
    164     "from quantopian.research import run_pipeline\n",
    165     "\n",
    166     "# Specify a time range to evaluate\n",
    167     "period_start = '2014-01-01'\n",
    168     "period_end = '2017-01-01'\n",
    169     "\n",
    170     "# Execute pipeline over evaluation period\n",
    171     "pipeline_output = run_pipeline(\n",
    172     "    make_pipeline(),\n",
    173     "    start_date=period_start,\n",
    174     "    end_date=period_end\n",
    175     ")"
    176    ]
    177   },
    178   {
    179    "cell_type": "markdown",
    180    "metadata": {},
    181    "source": [
    182     "In addition to sentiment data, we will need pricing data for all assets present in this period. We can easily get a list of these assets from our pipeline output's index, and pass that list to `prices` to get the pricing data we need:"
    183    ]
    184   },
    185   {
    186    "cell_type": "code",
    187    "execution_count": 4,
    188    "metadata": {},
    189    "outputs": [],
    190    "source": [
    191     "# Import prices function\n",
    192     "from quantopian.research import prices\n",
    193     "\n",
    194     "# Get list of unique assets from the pipeline output\n",
    195     "asset_list = pipeline_output.index.get_level_values(level=1).unique()\n",
    196     "\n",
    197     "# Query pricing data for all assets present during\n",
    198     "# evaluation period\n",
    199     "asset_prices = prices(\n",
    200     "    asset_list,\n",
    201     "    start=period_start,\n",
    202     "    end=period_end\n",
    203     ")"
    204    ]
    205   },
    206   {
    207    "cell_type": "markdown",
    208    "metadata": {},
    209    "source": [
    210     "Now we can use Quantopian's open source factor analysis tool, [Alphalens](https://www.quantopian.com/lectures/factor-analysis-with-alphalens), to test the quality of our selection strategy. First, let's combine our factor and pricing data using get_clean_factor_and_forward_returns. This function classifies our factor data into quantiles and computes forward returns for each security for multiple holding periods. We will separate our factor data into 2 quantiles (the top and bottom half), and use 1, 5 and 10 day holding periods:"
    211    ]
    212   },
    213   {
    214    "cell_type": "code",
    215    "execution_count": 5,
    216    "metadata": {},
    217    "outputs": [
    218     {
    219      "name": "stdout",
    220      "output_type": "stream",
    221      "text": [
    222       "Dropped 1.5% entries from factor data: 1.5% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
    223       "max_loss is 35.0%, not exceeded: OK!\n"
    224      ]
    225     },
    226     {
    227      "data": {
    228       "text/html": [
    229        "<div>\n",
    230        "<table border=\"1\" class=\"dataframe\">\n",
    231        "  <thead>\n",
    232        "    <tr style=\"text-align: right;\">\n",
    233        "      <th></th>\n",
    234        "      <th></th>\n",
    235        "      <th>1D</th>\n",
    236        "      <th>5D</th>\n",
    237        "      <th>10D</th>\n",
    238        "      <th>factor</th>\n",
    239        "      <th>factor_quantile</th>\n",
    240        "    </tr>\n",
    241        "    <tr>\n",
    242        "      <th>date</th>\n",
    243        "      <th>asset</th>\n",
    244        "      <th></th>\n",
    245        "      <th></th>\n",
    246        "      <th></th>\n",
    247        "      <th></th>\n",
    248        "      <th></th>\n",
    249        "    </tr>\n",
    250        "  </thead>\n",
    251        "  <tbody>\n",
    252        "    <tr>\n",
    253        "      <th rowspan=\"5\" valign=\"top\">2014-01-02 00:00:00+00:00</th>\n",
    254        "      <th>Equity(2 [HWM])</th>\n",
    255        "      <td>0.003898</td>\n",
    256        "      <td>0.015298</td>\n",
    257        "      <td>0.048514</td>\n",
    258        "      <td>6.000000</td>\n",
    259        "      <td>2</td>\n",
    260        "    </tr>\n",
    261        "    <tr>\n",
    262        "      <th>Equity(24 [AAPL])</th>\n",
    263        "      <td>-0.022028</td>\n",
    264        "      <td>-0.030234</td>\n",
    265        "      <td>0.001813</td>\n",
    266        "      <td>-1.666667</td>\n",
    267        "      <td>1</td>\n",
    268        "    </tr>\n",
    269        "    <tr>\n",
    270        "      <th>Equity(62 [ABT])</th>\n",
    271        "      <td>0.010188</td>\n",
    272        "      <td>0.026926</td>\n",
    273        "      <td>0.040026</td>\n",
    274        "      <td>4.000000</td>\n",
    275        "      <td>1</td>\n",
    276        "    </tr>\n",
    277        "    <tr>\n",
    278        "      <th>Equity(67 [ADSK])</th>\n",
    279        "      <td>-0.007310</td>\n",
    280        "      <td>0.037157</td>\n",
    281        "      <td>0.088122</td>\n",
    282        "      <td>6.000000</td>\n",
    283        "      <td>2</td>\n",
    284        "    </tr>\n",
    285        "    <tr>\n",
    286        "      <th>Equity(76 [TAP])</th>\n",
    287        "      <td>-0.003790</td>\n",
    288        "      <td>0.001270</td>\n",
    289        "      <td>0.016851</td>\n",
    290        "      <td>6.000000</td>\n",
    291        "      <td>2</td>\n",
    292        "    </tr>\n",
    293        "  </tbody>\n",
    294        "</table>\n",
    295        "</div>"
    296       ],
    297       "text/plain": [
    298        "                                                   1D        5D       10D  \\\n",
    299        "date                      asset                                             \n",
    300        "2014-01-02 00:00:00+00:00 Equity(2 [HWM])    0.003898  0.015298  0.048514   \n",
    301        "                          Equity(24 [AAPL]) -0.022028 -0.030234  0.001813   \n",
    302        "                          Equity(62 [ABT])   0.010188  0.026926  0.040026   \n",
    303        "                          Equity(67 [ADSK]) -0.007310  0.037157  0.088122   \n",
    304        "                          Equity(76 [TAP])  -0.003790  0.001270  0.016851   \n",
    305        "\n",
    306        "                                               factor  factor_quantile  \n",
    307        "date                      asset                                         \n",
    308        "2014-01-02 00:00:00+00:00 Equity(2 [HWM])    6.000000                2  \n",
    309        "                          Equity(24 [AAPL]) -1.666667                1  \n",
    310        "                          Equity(62 [ABT])   4.000000                1  \n",
    311        "                          Equity(67 [ADSK])  6.000000                2  \n",
    312        "                          Equity(76 [TAP])   6.000000                2  "
    313       ]
    314      },
    315      "execution_count": 5,
    316      "metadata": {},
    317      "output_type": "execute_result"
    318     }
    319    ],
    320    "source": [
    321     "# Import Alphalens\n",
    322     "import alphalens as al\n",
    323     "\n",
    324     "# Get asset forward returns and quantile classification\n",
    325     "# based on sentiment scores\n",
    326     "factor_data = al.utils.get_clean_factor_and_forward_returns(\n",
    327     "    factor=pipeline_output['sentiment_score'],\n",
    328     "    prices=asset_prices,\n",
    329     "    quantiles=2,\n",
    330     "    periods=(1,5,10),\n",
    331     ")\n",
    332     "\n",
    333     "# Display first 5 rows\n",
    334     "factor_data.head(5)"
    335    ]
    336   },
    337   {
    338    "cell_type": "markdown",
    339    "metadata": {},
    340    "source": [
    341     "Having our data in this format allows us to use several of Alphalens's analysis and plotting tools. Let's start by looking at the mean returns by quantile over the entire period. Because our goal is to build a long-short strategy, we want to see the lower quantile (1) have negative returns and the upper quantile(2) have positive returns:"
    342    ]
    343   },
    344   {
    345    "cell_type": "code",
    346    "execution_count": 6,
    347    "metadata": {},
    348    "outputs": [
    349     {
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    352       "text/plain": [
    353        "<matplotlib.figure.Figure at 0x7f4083b460b8>"
    354       ]
    355      },
    356      "metadata": {},
    357      "output_type": "display_data"
    358     }
    359    ],
    360    "source": [
    361     "# Calculate mean return by factor quantile\n",
    362     "mean_return_by_q, std_err_by_q = al.performance.mean_return_by_quantile(factor_data)\n",
    363     "\n",
    364     "# Plot mean returns by quantile and holding period\n",
    365     "# over evaluation time range\n",
    366     "al.plotting.plot_quantile_returns_bar(\n",
    367     "    mean_return_by_q.apply(\n",
    368     "        al.utils.rate_of_return,\n",
    369     "        axis=0,\n",
    370     "        args=('1D',)\n",
    371     "    )\n",
    372     ");"
    373    ]
    374   },
    375   {
    376    "cell_type": "markdown",
    377    "metadata": {},
    378    "source": [
    379     "We can also plot the cumulative returns of a factor-weighted long-short portfolio with a 5 day holding period using the following code:"
    380    ]
    381   },
    382   {
    383    "cell_type": "code",
    384    "execution_count": 7,
    385    "metadata": {},
    386    "outputs": [
    387     {
    388      "data": {
    389       "image/png": 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H1dFVarOGBBHZik1nSGRmZuK1115DaWkpFAoF9u7dizlz5iAsLAzz5s3DqlWr\n8OyzzwIAbr/9dkRERGDp0qV48cUXcf/990Or1WLVqlW2DJGIiIiIqN80eg1Kmkrg69L6BdpY5Vg7\nR2Qdrg7tCYlmXbMdIyGiocymCYnY2Fhs2rSpy+OTJ0/Gtm3bLF5zcnLCW2+9ZcuwiIiIiIisIkeV\nAxNMAIAQzxD4ug6Nmb3mCQkWtSQiW7Hbkg0iIiIiosEusyJTascqh8ZyDcBy288WfYsdIyGioYwJ\nCSIiIiKiftAatLikuiT1xwWOs2M01mWekGjWc8kGEdkGExJERERERP2Qq8qF3qQHAAS6B8Lfzd/O\nEVmP+ZINjYFFLYnINpiQICIiIiLqh8xKs+UaQ2R3jTbOCmep3aJvgSiKdoyGiIYqJiSIiIiIiPpI\nb9QjpypH6g+V3TXaOMgd4CBzAAAYRSN0Rp2dIyKioYgJCSIiIiKiPrpcfVn6kO7p4Ikg9yA7R2R9\nFoUtDSxsSUTWx4QEEREREVEfXay8KLWjPKIgCIIdo7ENFrYkIltjQoKIiIiIqI8K6wqldoR7hB0j\nsZ1r60gQEVkbExJERERERH2gN+pRrakGAAiCAH+nobO7hjnznTY4Q4KIbIEJCSIiIiKiPqhqqpJ2\nnfB18YVCprBzRLbh7NA+Q4JbfxKRLTAhQURERETUB+Xqcqk9FItZtjGfIaHRMyFBRNbHhAQRERER\nEQCtQQuTaOrxvIrGCqkd6BFoy5DsykXRXtSSCQkisoWhOb+MiIiIiKiX9EY9dmbvxKnSU3BVuCJG\nGYNYZSxG+Y+CTOj4/V1ZY5nUDnQPhLZOO5DhDhiLJRtMSBCRDXCGBBERERHd1L67/B0ySjIgiiKa\n9E3IKMnA/53+P2w+vbnDuaIooqShROqHeoYOZKgDymLJBmtIEJENMCFBRERERDctURRxtuxsp8dy\nVDkW23sCQHVzNVoMrVtgujm4wdvZ2+Yx2ov5tp+cIUFEtsCEBBERERHdtMrV5VDr1AAAR7kjHp/6\nOEb4jpCOH7562OL84oZiqR3qFQpBEAYmUDtwcWANCSKyLSYkiIiIiOimdbn6stSODohGhHcEUqJT\npNeyKrMsPoybz5gI8wobmCDthEs2iMjWmJAgIiIiopuWeUJipO9IAECwR7CUbDCKRmRWZgJoXd6R\nVZklnR/pHTlwgdqBeVHLFn2LHSMhoqGKCQkiIiIiuinpjXpcrb0q9Uf4tS/ViAuKk9oZJRmo09Rh\nf/5+NGh73BpNAAAgAElEQVQbALTWj4jyjRq4YO3AYttPgwaiKNoxGiIaipiQICIiIqKb0tW6q9Cb\n9AAAf1d/+Lj4SMfGB46XtvwsrCvEGwffwP68/dLxsYFjO90SdCiRy+RwUjgBaJ0d0lbMk4jIWob2\n36JERERERF3Iq86T2iP9R1oc83T2xLTwaZ1e56xwxvTw6TaN7UZhMUuChS2JyMoU9g6AiIiIiMge\nLlVfktpt9SPMzR0xF5erL6OqqQoyQYbhvsMxPmg8xgaMhauja4fzhyJnB2fg/0+MYEKCiKyNCQki\nIiIiuumodWqUNZYBgJRsuJaLgwueTnwa9S31cHN0k5Yv3Ey40wYR2RITEkRERER008msyJTa4V7h\nXSYb5DI5fF19ByqsG475ko1mfbMdIyGioYg1JIiIiIjoplLaUIrdObul/mj/0XaM5sbGrT+JyJaY\nkCAiIiKim4Zap8aWM1ssdte4WQpU9geXbBCRLTEhQUREREQ3BVEUse3sNtS11AEAnBROeCDhAYtZ\nAGTJWdH+3rCoJRFZGxMSRERERHRTyKvJw5XaKwAAQRCwePxiBLgF2DmqG5ubo5vUZg0JIrI2JiSI\niIiI6KbQlowAgEkhkxATEGPHaAYH8yUbzTomJIjIupiQICIiIqKbQmFdodQe4TfCjpEMHq6O7QmJ\nJl2THSMhoqGICQkiIiIiGvKMJiOK6oqkfoR3hB2jGTzcHNqXbDTpmZAgIutiQoKIiIiIhryyxjJp\nZw0fFx94OXvZOaLBgTUkiMiWmJAgIiIioiHvat1Vqc3ZEb3n4uACQRAAtO6yYTQZ7RwREQ0lTEgQ\nERER0ZB3tZYJif6QCTK4KswKW3KWBBFZERMSRERERDSkiaKIgroCqR/hw4REX5gXtmRCgoisiQkJ\nIiIiIhrSajW10g4RLg4uULop7RzR4GJeR4I7bRCRNTEhQURERERDWmljqdQO8QiRaiJQ71jstMGE\nBBFZERMSRERERDSklTWWSe0QzxA7RjI4cckGEdkKExJERERENKSVNrTPkAj2CLZjJIOT+ZINVZPK\njpEQ0VDDhAQRERERDWnljeVSmwmJvjPflSSrKguiKNoxGiIaSpiQICIiIqIhq1nXjAZtAwDAQeYA\nfzd/O0c0+Az3HQ5nhTOA1gKh5jNOiIiuh80TErm5uZg/fz62bNnS4diRI0eQlpaGJUuWYM2aNdLr\nr7/+OpYsWYK0tDR89913tg6RiIiIiIao6uZqqe3n6geZwO/j+kohUyAmIEbqZ1Zm2jEaIhpKFLYc\nXKPR4OWXX0ZiYmKnx1955RWsX78eSqUSS5cuRXJyMlQqFfLy8rBt2zbU1dXh7rvvxvz5820ZJhER\nERENUdUay4QE9U9sYCzOlJ0BAFysvIgFoxYMyH0vVFzA0cKjUGvVmBExA1PDpw7IfQc7rUGLT85/\ngqt1V+Egd4CDzAGOCkcYTUb4u/njtuG3cfkS3RBsmpBwcnLCunXr8OGHH3Y4VlRUBG9vbwQGBgIA\nkpKScOzYMSxbtgzx8fEAAC8vL2g0GoiiyO2ZiIiIiKjPapprpDYTEv030m8kHOQO0Bv1qGqqQqW6\nEkp3pU3veeDKAey7tE/qf5X1FZwVzogLjrPpfQc7o8mIjac24mrd1dYX9JbHK9QVyKnKwSOTH8Ew\n72EDHyCRGZvOWZPJZHB0dOz0mEqlgq+vr9T39/dHZWUlBEGAs3PrGrUdO3Zg9uzZTEYQERERUb9c\nu2SD+sdR7ojR/qOlvq2XbTS0NGB/3v4Or++4sAM/F/1s03sPdkcKj7QnI7pgMBmw9exWaPSaAYqK\nqHM2nSHRnWur8147C+L777/H559/jo8++qhX42VkZFg1PhoYfG5DA5/j4MdnOHjx2Q0NfI62c+7q\nOahaWreqLLtShowK27zXN8MzlDfIoVK1vpffNX4Hz1pPm9zHJJrwY9mP0u4oLnIX6Ew6GEUjAGB9\n1Xqc8j2FKf5TrPbF5VB5fi3GFmzL2waDaAAAxPnEYZzPOOhNeuhNeqgNavxU/hN0Jh1UUGH13tVI\nCkoaMrVVhspzvJnYLSERGBiIqqoqqV9RUYGAgAAAwMGDB/Hhhx/io48+gru7e6/GmzRpkk3iJNvJ\nyMjgcxsC+BwHPz7DwYvPbmjgc7Qdk2jC7obd8Ne37qwxe8pseDpb/0P0zfIMxxnG4WL6RRhMrR92\no8ZEwdfVt4er+qZJ14Rt57ah3qke/k6tz215wnKEeYVh0+lNKK4vBgAUoxhxAXGYETHjuu85lJ7f\n4auH4V3nDQBQuimxMnEl5DK5xTljK8biP2f/AwCoQx1OGE7g3nH3ItA9cMDjtaah9ByHmu4SRXZL\nhYWGhqKpqQmlpaUwGAxIT0/HzJkzoVar8cYbb+D999+Hh4eHvcIjIiIiokEuvyZfmpLu7ugODyf+\n2/J6OCmcMNx3uNS/XH3ZquPXaerw3s/vIb8mX3otITgB0f7RcHd0x39N+i+L3T4ySvhtuDmdUWex\nnOWWiFs6JCOA1gKlcUHtdThKGkrw76P/xunS0wMSJ5E5m86QyMzMxGuvvYbS0lIoFArs3bsXc+bM\nQVhYGObNm4dVq1bh2WefBQDcfvvtiIiIwI4dO1BXV4dnnnlGWsbx+uuvIygoyJahEhEREdEQ07Yr\nBADEBcWxLpkVjPQbiVxVLgDgcs1lq+56sefSHtRqaqX+vJHzkBSVJD03J4UTFo9fjFfTX4XBZECF\nugJ1mjp4u3hbLYbBShRFfHr+U6lmiqPc0SLpcK208WkIdA/Ej/k/wmAywCga8VXWVxjlPwrujr2b\noU5kDT0mJPR6PaqrqxEUFITs7GxkZ2djwYIFcHV17XHw2NhYbNq0qcvjkydPxrZt2yxeW7x4MRYv\nXtyL0ImIiIiIOqcz6pBZ0V54cULwBDtGM3SM9Bspta/UXLHabng1zTW4UHFB6i+LX4bYwNgO5zkp\nnBDpEynNzshV5XIrUAB7cvdYFBpNGZ0CJ4VTl+fLBBmShidhrHIsNp/ZjOrmauiNehzIP4BFMYsG\nIGKiVj0u2Xj++edx5swZVFRU4Omnn0Zubi5eeOGFgYiNiIiIiKhfsiuzoTPqAAABbgEI8Qyxc0RD\ng9JNKX2D3qxvxvmK81YZ92TJSano/Ui/kZ0mI9qY7/aRo8qxyv1vVCbRhCu1V3Ch4gL0Rn2n52SU\nZODQ1UNSf0bEjF4naZTuSiwcvVDqHyk8YpEYIrK1HmdIVFZWYuHChdiwYQOWLVuGhx9+GA899NAA\nhEZERERE1D/myzXig+O5XMNKBEFAfHA8Dl89DAD4OutreDl7IcI7ot9jiqKIc+XnpP608Gndnh/t\nH41dObsAAHnVedAb9XCQO/T7/jcqk2jC2hNrUVhXCAAI9QzFw5MehouDi3SOKIoW26OOCRhjkWDo\njTEBYzDSb6Q062TbuW0Y7Tcafq5+kMvkqFBXwEHmgDHKMYgPjh8yO3LQjaHH3yadTgdRFPHdd98h\nKSkJANDc3GzruIiIiIiI+kWtU+NS9SWpHx8Ub8dohp45w+dIBUI1eg02ntoo1S7oj5KGEql2hLPC\n2WIGRGf83fzh79q6A4fepMeV2iv9vveN7GzZWSkZAbS+T19lfWVxTrm6HHUtdQBa37u08Wl9ThgI\ngoC08WnwcfEB0JrkyFHl4EjhERwsOIhcVS4yKzPx6YVP8cHxD6A1aK/zJyNq1+Nv69SpUzFp0iQE\nBAQgKioKH3/8MaKiogYiNiIiIiKiPjtffh4m0QQAiPCOsPrWlDc7Zwdn3B9/P9wc3QAAWoMWG09t\nRKW6sl/jmc+OGKscC4Ws57r70QHRUvti5cV+3fdG1qhtxHeXv+vw+oWKC1A1qaR+TlX7kpVR/qO6\nrRvRHXdHd6ycttJiF5POFNcXY+Opjahvqe/XfYiu1WNC4re//S3S09PxzjvvAADmzZuHV155xeaB\nERERERH1R3ZVttRmMUvbCPcOx4MJD0IutG4rWd1cjfd+fs8iudAboijifHl7HYrudoYwN1Y5Vmpn\nVmTCYDL06b43srYET9uHflcHVwzzHgag9f3akLEBWZVZMIkmi6VJ0f7RnY7XW+6O7liesBxPTn8S\nd8fejdToVNw2/DakRqdiZsRM6byrdVfx7tF3ca6sb8+aqDM9ph8vXbqETz75BPX19VKhGQB4/fXX\nbRoYEREREVF/mC8fGO473I6RDG2hXqFIG5+Gzy58Br1JD51Rh+3ntsMkmnqdCCqoK0CDtgEA4Obg\nhhF+I3p1XYR3BLycvVDfUo9mfTNyVbkWSYrBymAy4D9n/4OyxjIArbth3DvuXjgrnLHu5DqIooi6\nljpsPrPZ4jonhVOPsxt6K8QzpNMisO5O7th7aS9EUYRGr8H289tRranGbcNvs8p96ebU4wyJZ555\nBp6enpg+fToSExOl/4iIiIiIbjQGk0FaUy8IArxdvO0c0dA2Pmg8fjXtVxbLYg4WHOz19bmqXKkd\nGxjb6/oHgiBY1Ab5Ie+HIVHb4IfLP0jFJQHgzjF3IiYgBpE+kVg8bjFcHVw7vS5xWKJFsUtbmBU5\nC49OflSqNQEAB/IPQKPX2PS+NLT1OEPC398fTz311EDEQkRERER0Xeo0ddKsXk8nz17VI6DrE+wR\njJVTV+JvB/4GURRRoa6A1qDtVT0D8w/fPRWzvNYtEbfgaNFR6I16lDeW442DbyBxWCISwxPh6tj5\nB/cbmSiKOFV6SurPHTEXk8MmS/244DiM9BuJfZf34UTxCen1ALcAzIqYNSAxRvpE4unEp/HB8Q9Q\noa6A3qTHmbIzSBzGL6ypf3pMQd566604dOgQdDodTCaT9B8RERER0Y2mbbcGABbf5JJtuTm6Icg9\nCEDrB+ui+qIer1Hr1BZLE6J8+lY438PJA7OjZkt9jV6D/Xn78c6Rd1BcX9ynsW4EVU1VUOvUAAAX\nB5dOl0K4OrrirrF34VdTf4VYZSymhk3Fr6b+Cs4OzgMWp5PCyWJr1uNFxy2W9hP1RY8p4/feew9q\ntVrau1kURQiCgKysLJsHR0RERETUF+YJCV8X7q4xkIZ5D5MSDIV1hRjpN7LLc0VRxNcXv5Y+yIZ5\nhfXrQ3VSVBI8nDxw4MoB1DTXAGhNdKzPWI9HJz/aaS2EG1VeTZ7UHu4zXPr81Zlh3sOwbMKygQir\nUxOCJ2BP7h7ojDpUNlUiryav2+dN1JUeExLHjx+HTNa3vWyJiIiIiOyhRlMjtZmQGFgR3hH4uehn\nAEBhfWG35x4sOIjMykypb76LQ18IgoDJoZMxMWQizpefx87sndDoNdAatPjP2f/g2ZnP9rouhTmT\naILe2Fqo01Hu2O/tNK+VVZmFHFUOIrwjEB8cbxGb+fKVG70Yq5PCCZNCJ+Fo4VEAwOGrh5mQoH7p\nMSHx4IMPYtOmTQMRCxERERHRdTFPSPi4csnGQAr1DJXaZQ1lXZ6XV52HfZf3Sf1bht2C2MDY67q3\nTJAhPjgege6B+OD4B9AZdajV1CKnKgdjlGN6vD67KhsHrhxAdVM1dEYd9Ca9dEwQBCjdlPBy9oKb\noxuUbkoo3ZUY5TcKcpm81zFmVmTiP2f/AwA4UXwCWZVZWBq/FIIgQKPXWCQkBsOH+8RhiThWdAyi\nKCJXlYuqpioEuAV0e41JNKG4vhgthhaEe4XbvBAn3fh6TEiMGTMG77zzDhISEuDg4CC9zp02iIiI\niOhG0zZtH+AMiYHm5+oHR7kjdEYd1Do1GrWN8HDysDjHaDLi0wufSks1hnkPw8LRC60WQ5BHEKaH\nT8dPBT8BAI4XH+8xIXFVfRWnzpzqsg5CW6HOCnWF5b3cg/DI5Ed6VUBTo9fgs8zPLF7LrMzEhowN\nmD9yPiqbKmEwGQC0Jnb83fx7HNPe/Fz9EOMfg6yq1qX8289tR9r4NHg7e0szShpaGpBfmw+NXgO1\nTo2zZWelZVUKmQKzImdhZsTMAa2BQTeWHhMSbbUiTp48Kb0mCAITEkRERER0QxFF0XKGBItaDihB\nEBDkEYTCutblGqUNpYgOiLY4p7ihGA3aBgCAq4MrlsYt7dMsg96YEjYFB68ehCiKuFR9CTXNNRbb\nkppr0bcgvSwdHr4eHY45yh3hIHdAs76502RFuboc6zPWY0nckh4TCD8X/dzptqR5NXnIO55n8dr4\noPHdjnUjmRk5U0pIlDWW4V9H/gWgdYcbuUyOupa6LhM9BpMBP+b/iCOFRzA5dDJmRMyAl7PXgMXe\nFYPJgEuqS2jWN8PVwRXODs4I9wrnjj020uO7yuUaRERERDQYtNUOAAAHuQPcHd3tHNHNJ8QzpD0h\n0dgxIZFfnS+1xyjHwNPZ0+ox+Lr6YqTfSFxSXYIoiki/ko67xt7VoZaEWqfGrpxd0Jq08IAHPJ08\nsWLiCvi5+sFB5iAVldQatChtLIXWoEWdpg75NflS/YuyxjKsPbEWj015rNOkhNFkxI/5PyL9Srr0\n2t1j78bVuqsWW3y2cXFwQUJIghXfDduK9IlE4rBEqZZEm7akU2cc5Y5wc3STZkpoDVocvnoYZ8vO\nYuW0lQOSSFTr1ChvLIe3szd8XX0hE2QQRRHnK85jV84uNGobLc73dvbGiokrEOge2Ol4OqMOTbom\neDt7d1uMlDrqMSGxbNmyTt/ULVu22CQgIiIiIhq8RFFEo7bRJh80e3LtDhv8YDDwQjzad7XorI6E\nxU4SNizcOC1sGi6pLgEAMkoyUKGuwL2x90LproTeqMf+vP04WnQUemN7rYjk0ckI9gjuMJaTwsli\nS9Lpw6bjRPEJfJP9DQwmA9Q6NTad3oSV01Za1ESo1dRi69mtKGkokV7zd/XHhJAJmBw2GbMiZ+HA\nlQM4V34OJtEEAPhFzC8GXSJt4eiFcFI4obCuEPUt9ahvqZeWnwCtzznALQCOckd4OHkgLigObo5u\nOF16GocKDqGyqRJAa5Lgpys/4c6xd9o03sNXD2PfpX1SjDJBJr3nXSVS6lrq8NHJj/DU9Kcs/m4z\niSb8mP8jDuQfgFE0wtPJE4nDEjElbArrY/RSjwmJZ555Rmrr9XocO3YMrq49r5MiIiIiopuLKIrY\nkLEBeTV5iAmIweLxi622O0FvVGuqpTbrR9iH+Qf60sZSi2N6ox5F9UVSf7iP7RIS0QHRGOk3UioU\nWVxfjH8f+zeWJyzH6dLTOFN2xuL8mIAYxAfF93r8KWFTEOAWgI8zPobepIeqWYU3Dr6BKaFTMCVs\nCrxdvLH5zGaUN5ZL10T5RCFtfJo09V/prkTa+DQkj0pGQW0BPJw9LBIfg4VCpsD8kfOlvkk0obyx\nHJVNlVC6KbvcenVS6CRMDJmIU6Wn8Hnm5wCAU6WnMGfEnA61R6zlTNkZ7MrZZfGaSTR1SES4O7oj\nwicCWoNW+h1q0jXh0wuf4qFJD0kzKnZm7cTx4uPSdQ3aBuy9tBfpV9KRMjoFU8Km2OTnGEp6TEhM\nnTrVoj9jxgw89thjNguIiIiIiAanrKos6Rvw7KpsrD2xFg9OfNBmHy6uZT5DgvUj7EPproRCpoDB\nZECtphbNumap6GNhXaH0rXSAW4BNZ9HIBBlWJKzATwU/4ce8H2EUjTCYDNiQscHivCCPICQ4JuDe\nCff2eUZNpE8k7hhzh/RhWmvQ4tDVQzh09ZDFeXJBjvmj5mNmxMxO7+Hp7Im44Lg+/oQ3LpkgQ4hn\nSJeJCHOCIGBiyEQcLz6O4vpiGEwGHC08igWjFlg9rhZ9C77J/kbqywU5nB2c0aRrsohnathULBi5\nQCq0mVedhw2nNkAUReTV5GH7ue2IVcbics1lZJRkdHovrUGLLy9+iTCvsE5n3VC7HhMSRUVFFv2y\nsjJcuXLFZgERERER0eB0sOCgRb+ssQwfHP8AKxJWQOmutPn9q5vbZ0hwy0/7UMgUULorUdrQOjui\nrLEMI/xGABi45Rpt5DI5bht+G8Yqx+LD4x+ixdBicTw+OB5p49Jw6tSpfi/vmRgyEUaTEelX0lHf\nUt/pOcmjkzEjYka/xr8ZCIKAWZGzsPXsVgCtBUBnR8226uwqk2jCrtxd0Og1AFoTlk8nPg0nhRMM\nJgMaWhrQYmiBp7NnhyUzI/xGYHbUbKTnpwMALlRcwIWKCxbnTAiegLvG3oWz5WeRnp8uJUdPlpzE\nHTF3WO3nGIp6TEg8+OCDUlsQBHh4eOCpp56yaVBERERENLiodWqpmKG5Wk0tPjzxIR6f8rjNkxJV\n6iqpHeAaYNN7UddCPEKkhERJQ4mUkMivaS9oOcJ3xIDFE+geiJmRM/H95e+l19wc3LAoetF11xkR\nBAFTw6dicthkXFJdwsmSk8iuypZqQiQOS8Qtw265rnvcDMYqx8Lf1R+qZhVaDC24UHEBk0InWWVs\ntU6N7ee2W/z+zR0xV0p4KGSKLndhMT+/uL5YWr5hLto/GvfE3gO5TI7JoZPh4+yD9RnrAQBnSs8g\nZXQKd+joRo/vzNq1azFihOVfGGfOnOnibCIiIiK6GV2tvSq1h3kPQ1JUErad2wadUQeNXoMf83/E\nfXH32ez+oiiioqlC6ndVDZ9sL9w7HCdLTgIAclQ5uDXqVjRqG1HcUAyg9UP8QNdKuDXyVlSqK3Gu\n/BwA4PYxt8PN0c1q48sEGaIDohEdEA21To38mnx4OA3OmhD2IBNkmBw2GXty9wBorSVhjYREfUs9\nPjj+gcXsldjAWMQH975eSFt8KxJW4GDBQVyqvgQXhQu8XbwR6hmK+OB4ix1chvsOh4+LD2o1tWgx\ntCC/Jh+j/Udf988yVHWZkGhoaEBdXR1efPFFvPnmm9LrLS0teP7557F3794BCZCIiIiIbnzmsyMi\nvSMRHRCNByY8IH1TmF+TD1EUbbbzRYO2Qdry08XBZcDqVlBH0f7REAQBoijiat1VVKorcbXuKkRR\nBABEeEdIdSUGilwmx+LxizEtfBoUMgXCvMJsdi93R3fEBQ2dmhADZULwBOy7tA8m0YSC2gKomlSd\nbqXaFz/k/SAlIwRBwNzhc5E0PKlffw/JZXIkDU9C0vCkbs8TBAFjAsbgSOERAK31dJiQ6FqXCYnT\np09j48aNyMrKsli2IZPJMHPmzAEJjoiIiIgGh6t17TMkInwiALR+U+jq4IpmfTPUOjWqmqpstmyj\nQt0+OyLALYBbftqRh5MHIrwjUFBbAFEU8c6RdyyOj1WOtUtcgiAg0ifSLvemnnk4eWC0/2hkV2UD\nAE6XnbbYvaOvtAYtzpefl/r3jb8P44PGX3ecvRETECMlJHKqciDG2C4ZO9h1mZCYPXs2Zs+eja1b\nt2Lp0qUDGRMRERERDSIm0YSyxjKpH+4VDqB9an5mZSaA1qKGtkpIVKorpTaXa9hfQkgCCmoLOrwu\nE2R2S0jQjW9iyEQpIXHk6hH4u/pjQvCEfn2Yv1h5ETqjDgDg7+qPcYHjrBprdyJ9IuGscEaLoQV1\nLXUoV5dzt40uyHo6ISUlBX//+9/xu9/9DgCwf/9+1NTU2DwwIiIiIhocVE0qaTtHL2cvi7X5Ub7t\na+g7K3ppLeYzNILcg2x2H+qdSSGTsCx+GUb7j5bW18sEGe4ceye3ZKUuRQdEw9OpdTtYnVGHTy98\niq3ntkq7Y/SWKIo4WnhU6k8MnTigMxTkMjlG+Y+S+m1JFuqox4TEn/70JwQHB0vbf+p0Ojz//PM2\nD4yIiIiIBodydbnUvnZ2QttsCQBSUUNrM4kmXKlt35Z+ILaUpO4JgoDYwFg8OPFBPD/7edw3/j48\nnfg0JodOtndodANTyBRYnrDcImmVWZGJzy581qdxCuoKUNJQIo1prR07+mJMwBipzYRE13pMSDQ2\nNmLFihVwcHAAACxcuBAtLS09XEVERERENwvz+g3Xzk4I8giStryraa5Bs67ZqvfWGrQ4WnhU+gbV\nw8kDAW7c8vNG4u7ojrjgOJtv+0pDQ4hnCJ5OfBpTwqZIr2VVZfV6hpUoitiXu0/qxwfHw93R3epx\n9sR8dlBxfTEatY0DHsONIFeV2+3xHhMSOp0Oer1emuKiUqnQ3Gzd/5EQERER0eBV3mg2Q8LDcoaE\nQqZAkEd7ksIasyREUcSJ4hNYd2Id/nbgb9iVs0s6NsJ3BIvHEQ1yTgon3DX2LkwIniC99k32NzCa\njN1ep9FrsDN7JwrrW5MXCpkCSVFJtgy1Sy4OLojwjpD6Oaocu8RhTz8X/YyNpzZ2e06PCYn7778f\nv/zlL3H58mWsXLkSd955Jx555BGrBUlEREREg5dGr7FYLtFZQUnzLRaL668vIWESTfgq6yt8efFL\nXKm9Ar1RLx1zkDlgchiXBBANFXNGzJFmWJU0lODTC59C1aSSilUCrbOkzpSdwZ7iPfhb+t/wc9HP\n0rFbht0CX1ffAY+7TUxAjNQ+VngMDS0NdotloOVU5WBn9s4ez+tyl402qampmDhxIk6fPg1HR0f8\n9a9/hVLJ6VZEREREBBwrOgatQQugtZJ9ZwUlw73CcQzHAABF9UX9vpfOqMP2c9s7rMdWuimREJKA\nCcET4Ons2e/xiejG4ufqh3kj52FP7h4AwLnyczhXfg5A6ywKmSCD1qCFSTRB1aSCv4u/dO0I3xGY\nN3KeXeJuExMQgz2X9kAURZQ1lmH1sdVIG5cmFbw0mowwikY4yByGzMyuhpYGnCs/h+8vfw9RFHs8\nv9uERF5eHi5fvozx48cjJSVFen337t0WfSIiIiK6+WgNWhy5ekTqJw1P6vQf1WGe7TMkSupLIIpi\nn/7xLYoizpefx77L+1CrqZVeH6sci/kj5yPALWDI/GOeiCzNjJiJisYKnC47bfF6WyL0WqGeoYgP\njse08GmQy+QDEWKX/N38kTI6Bbtzd0MURTTpmvDxqY+ROCwRGr0GFyouwGAyQBAEuChcEO0fjeTR\nyfBw8rBr3P2RWZGJ9CvpKG0otXi9p111ukxIbN26FRs2bMDo0aPx0ksv4e9//zvGjBmDP//5zygv\nL+dhAV4AACAASURBVGdCgoiIiOgmd7z4OJr1rbXFfFx8EB8c3+l5fq5+cHFwgUavQZO+CbWa2l5P\no65vqcfmM5s7/CP31shbsWDUAiYiiIY4QRBwz7h7MMx7GC5UXEB1czUatY0wiu31JII8ghCFKNw9\n8274ufrZMdqOZkTMQLBHMLaf2w61Tg0AFluSAq1J12Z9M06XnUZBXQGenP4kXBxc7BFun7XoW/B1\n9tc4W3a2wzEfFx88POlhFGQVdHl9lwmJL774Al999RVcXFxQVFSERx99FAaDAQ8++CCWL19uleCJ\niIiIaHDSGXU4WHBQ6s+Omi1VlL+WIAgI9QzF5erLAFrrSPQ2IfF55ucWyQhXB1ckj0pmrQiim4hM\nkGFq+FRMDZ8KoPUDfNvOOg5yBzjIHZCRkXHDJSPaDPcdjqcSn8LGUxtR1lhmcUwQBIulDbWaWnyT\n/Q3SxqcNdJh91qxrxoZTGyz+jpYLckT4RGC0/2hMDp0MFwcXFKCgyzG6TEg4OTnBxaU1KxMeHg4X\nFxe89957CA4Ott5PQERERESD0qmSU2jSNQEAvJy9kBCS0O35w7yHSQmJK7VXEBcc1+M9VE0q6RpB\nEHBr5K2YFTlr0HxzSES2IQgCXB1d7R1Gn3g4eWB5wnLsOL8DVU1VCHQPxLyR8xDhHQGjyYhTpafw\n5cUvAQBnys5gRsQMhHiGXPd91Tq1xdbIMkGGQPdAxAfHw1Hu2K8xRVFEQV0BdmbttNj2OSE4AanR\nqX16Nl0mJK6d/ubp6clkBBEREREBAPJq8qT2jIgZUiX8rkT5RElt8105unO0qH1ac7R/NBaMWtDH\nKImIbhxezl54bMpjHV6Xy+SYEjYFl1SXkFmZCQD47vJ3WJGwwuJzuUk0IVeVi6qmKjTrmqEz6TBO\nOQ5RvlEdxgRakxFrj6+FqlnV4diRq0fw6JRH4ebo1qefIa86D99d/s6iQLEgCLhrzF39mrnW5f85\ntFotioqKuuyHh4f3+WZERERENDSomtr/gRvpHdnj+eFe4VDIFDCYDP+PvfuOjuo88wf+vdMkjdqo\noFHvEgI1JCQhECCqwYCxwZjibifY/iXxrpOzidebbHx2Y6/jnDjV3dhrY2yaC5FjuugIBKhLqBfU\ny6hrJE29vz9meWGsjjS6Ks/nHM657525d55hJM3Mc9/3edCibkG3pnvYwm1tvW24XnOdjRO8E8YV\nLyGETHWrg1fjZstN8DyPElUJTpSewCKfRXCycUK/rh/7svcNSOim16Rja/hWxHrGDjjfiZITgyYj\nAKBZ3YwDuQfw7MJnR12Lp7qjGp9mfgojb2T7RJwID0c8jAUeC8bwTO8YMiHR0tKCp59+2mw9y1NP\nPQXAlAFJTU29pwckhBBCCCHTm5E3oq2vjY1dbV2HubeJVCyFj6MP+zB95OYRPLbgsUHrTugMOhzM\nO8iK1vk6+iLUNXSCoieEkKlJaadEgncC0mvSAQAXqy7iYtVF+Cp80djdCK1BO+AYnueRcjMFc13n\nms120Og1yGvKY+MIZQT8nfzRom5h569oq0BGfQbivEY3s+FMxRmWjJCIJFjgsQDL/JeN6j1gKEMm\nJM6cOXPPJyWEEEIIITNXR18H9EY9AMBOZgcridWojkvwSWAJiaKWIhwrPoaNYRsH3O9U2SnUdtYC\nMF0IWz93PXXTIITMCutC1qG6o9qs+GV1R7XZfebNmYc5dnNw5dYV6Iw66Iw6XKu9hpWBK9l9ClsK\noTPoAAButm7YGbWT/R21ElvhQtUFAMDxkuOY6zp3xFajtzpuoVRVCsD0d/kniT+B0k457uc7eClk\nQgghhBBChnD3FOCxXBmLco/Ccv/lbJxWnWbW/q5X24vvi75HWnUa27dx7kb4KfzGGTEhhEwPVhIr\nPJ/wPHZE7kCIa8iAZOyKwBV4POZxrAtZh4fCH2L7r9VcM1vdkN2QzbajPaLNzrMyaCWcbJwAAH26\nPvw17a9ILU9Fr7Z30Jh4nsfR4qNsHOUeNSHJCGCYGRKEEEIIIYQMprW3lW27ysc2Vfe+kPvQ2teK\ngiZT4bajxUcx13UuClsKcab8DPr1/ey+wS7BSPRJnJigCSFkmpCKpYjyiEKURxRUahVOlp2ERq9B\npDISC70WsvtFKCNwtOgo1Do1ujRdqGqvQoBzALo13axDEYAB9R1kYhkenPcgPs38FIApKXGm/Ayu\nVl/Fj+N/PCDZkN2QzWatSUQSrA1eO2HPlWZIEEIIIYSQMbnXGRKAaarvIxGPwMvBC4CpHsVbl97C\n0eKjZskIP4UftkVso6UahJBZzdXWFY9GP4pnFj6DOO84s7+JEpEEEe4RbHy+6jzaetvwXdF3bLZE\ngFMAFDaKAecNcQ3Bo9GPwkXuwvb16nqRUphiNtNCo9fgZOlJNk7yS2KzKybCiAmJzs5OvPnmm/i3\nf/s3AKbaEm1tbSMcdUdJSQnWrl2LL774YsBtaWlpeOSRR7Bz5068++67ozqGEEIIIYQI6+4OG2Od\nIQGYrv6tD10/6G2uctOH793xu0dc00wIIbNdtEc02y5VleKtS2+xGWgAEOs1sPvGbeHKcLyU9BIe\niXyEFRiuaq9Cek06eJ5HfVc9UgpT0KXpAmCqGZQckDyh8Y+YkPjP//xPeHh4oLbWNEVDq9Xi5Zdf\nHtXJ+/r68Nprr2Hx4sWD3v7666/j7bffxv79+3HhwgWUl5ePeAwhhBBCCBHW3Us27r66NhYBTgEI\nmxPGxhzHIckvCS8ueRHhynCaGUEIIaPg6+hr9rf0bkHOQYjxiBn2eBEnwgKPBYj3jmf7viv6Dq+e\nfhXvXH3HrBbFfSH3jbqI8WiNmJDo7u7Gk08+CalUCgBYv349+vv7RzjKxMrKCnv27IGbm9uA22pq\naqBQKKBUKsFxHFasWIGrV68OewwhhBBCCBGWzqBDR38HAFMSwVnufE/n4TgOjy14DM8ufBZbw7fi\npSUvYcPcDZCIqMQZIYSMFsdx2BG1AxHKCMilcjjZOMHdzh0Ryghsj9o+6uTuupB18HTwZOPbbZdv\nC3YJRqzn0LMt7tWIf/G1Wi10Oh17IiqVCr29g1ff/CGRSASZTDbobSqVCs7Od97AXF1dUVNTM+wx\nhBBCCCFEWG19bWx9scJaMa4EgogTIcglaKJCI4SQWUkmlmFX9K5xncNKYoVnYp/BidITyGnIgc6o\ng43UBiEuIQibE4YIZYRFZq6N+A7y2GOPYdu2bWhpacELL7yAvLw8/PrXvx73A99dKOP2mKbmEUII\nIYRMbWb1I8ZY0JIQQsjUJZfJsSV8Cx6Y9wD69f2QS+WstoSljJiQuP/++xEbG4usrCzIZDL893//\n94Qsp1AqlWhpaWHjpqYmzJkz557Pl5GRMe6YyOSj121moNdx+qPXcPqi125mmE6vY3ZrNlQqU1LC\nXe8+rWK3JPp/mN7o9ZsZ6HWcfkZMSCQnJ2PTpk148MEHMXfu3Al7YC8vL6jVatTX18PNzQ3nzp3D\nW2+9dc/nW7hw4ch3IlNKRkYGvW4zAL2O0x+9htMXvXYzw3R7Havyq+AK08yI+LB4LPSdPrFbynR7\nDYk5ev1mBnodp67hEkUjJiQOHTqEY8eO4Te/+Q20Wi02b96MTZs2QalUjvjABQUF+P3vf4/6+npI\nJBKcOHECq1atgre3N9asWYNXX30Vv/jFLwAAmzZtgp+f36DHvP3223BwcBjDUyaEEEIIIZag6qUl\nG4QQQibGiAkJd3d3PPPMM3jmmWdQW1uLjz/+GGvWrEFeXt6IJw8PD8fnn38+5O1xcXE4cODAmI4h\nhBBCCCHCmYiWn4QQQggwioQEAJSUlODEiRM4efIkFAoFfvvb31o6LkIIIYQQMsX06fqg1qoBABKR\nBAprhcAREUIImc5GTEisX78eNjY22LhxIz766CO4u7tPRlyEEEIIIWSK+eHsCOqQRgghZDxGTEi8\n/fbbCA4OnoxYCCGEEELIFGZWP0JO9SMIIYSMz5AJiZdeegl/+ctf8KMf/cgs+83zPDiOw7lz5yYj\nPkIIIYQQMkWo1HcSEi62VD+CEELI+AyZkPjNb34DAPjyyy8H3NbX12e5iAghhBBCyJREMyQIIYRM\nJNFQN7i6mt5kfvvb38LLy8vs38svvzxpARJCCCGECKFb0w29US90GFPK3TMkqOUnIYSQ8RpyhkRK\nSgreeecd1NfXY8WKFWy/RqOBm5vbZMRGCCGEECKIM+VncKbiDFzlrng+4XnYSG2EDklwPM9Ty09C\nCCETasiExObNm7Fx40b8+te/xosvvsj2i0QiKJXKSQmOEEIIIWSy5TflI7U8FQDQom7BtdprSA5I\nFjgq4XVruqE1aAEANlIb2EptBY6IEELIdDfkkg0AEIvF+P3vfw+FQgGO48BxHDQaDbZv3z5Z8RFC\nCCGETKrLty6bja9WX4XBaBAomqmjRd3CtqnlJyGEkIkwYtvPPXv24P3334dWq4VcLodGo8EDDzww\nGbERQgghhEyqXm0vajprzPZ1abpQ1V6FIJcggaKaGvKa8ti20o5myxJCCBm/YWdIAMDx48eRlpaG\n6OhoXL16FX/84x8REhIyGbERQgghhEyq0tZS8Dw/YH+xqliAaKaOXm0vsuuz2TjWM1bAaAghhMwU\nIyYkbG1tIZPJoNPpAACrV69GamqqxQMjhBBCCJlMOoMO5yvPs7GvwpdtF7fM7oRERn0GdEbTZ0EP\new/4KfwEjogQQshMMGJCwtHRESkpKQgNDcUrr7yCd955B83NzZMRGyGEEELIpDlddhpNPU0AAKlI\niq3hWyEVSQEAql4VCpsLhQxPMEbeiPSadDZO9E2k+hGEEEImxIgJiTfffBOxsbF45ZVX4Ofnh/b2\ndvzpT3+ajNgIIYQQQiZFZVslLlffKWZ5/9z7Mcd2DmI8Y9i+fxb9Exq9Ztjz8DyPPl3foMs+pqvi\nlmK097UDAORSOaLdowWOiBBCyEwxZFHLmhrzgk4qlQobN260eECEEEIIIZNJo9fg64KvWRIhxDUE\nCd4JAIC1wWtR0FQAtU6Njv4OnKs4h3Wh6wY9T2V7JY4UHIGqVwVfhS8eW/AY7GR2k/Y8LOVqzVW2\nHecVB6lYKmA0hBBCZpIhExJPPfUUOI4bNMPPcRzVkSCEEELIjJDTkGM2A2Dr/K1sSYJcJse60HX4\npuAbAMClW5ewwHPBgC4T7X3t2Je1D/36fgBAdUc1/nr5r0gOSMYin0XT9kt8t6YbZa1lAEyf/xJ8\nEgSOiBBCyEwyZELizJkzkxkHIYQQQoggbn/hBoDkgGQ4WDuY3R7rGYuMugzc6rgFI2/E8ZLjeCr2\nKbP7pBSmsGTEbb26XhwrOYbLty5jZ/TOaVkIsr6rnm37OPjAycZJwGgIIYTMNEMmJG771a9+Nej+\nP/zhDxMeDCGEEELIZDLyRlS0V7BxqGvogPtwHIcH5z+Iv6X9DQBQ0VYBnUHHZj0UtRShRFXC7rvc\nfzlyG3PZrIsuTRcO5BzAL5b+YtrNlGjobmDbHg4eAkZCCCFkJhoxIbF48WK2rdPpkJ6eDm9vb4sG\nRQghhBAyGeq76tGn6wMAOFg5YI7tnEHvp7RTws3WDc3qZuiNetR01iDAKQDpNen4vvh7dr8FHgtw\nX8h9WBW0Cjdqb+B0+Wn06frQpenC9brrWOK7ZFKe10Rp7Glk2572ngJGQgghZCYaMSGxZcsWs/H2\n7dvx/PPPWywgQgghhJDJUthyp5VnkEvQsO0s/Z380aw2tT4vbytHTkMObtTdYLfbyeywNngtAEAi\nkiDRNxEG3oCjxUcBAOnV6Vjss3hatcxs6LprhoQ9zZAghBAysUZs+2k0Gs3+1dXVoaqqahJCI4QQ\nQgixrKLmIrYdNids2PsGOAWw7XMV58ySEV4OXvh/i/4fHK0dzY6J946HTCwDAKh6VWZLIKY6jV6D\n1r5WAKalKG52bgJHRAghZKYZcYbE/Pnzzbpt2NvbY/fu3RYPjBBCCCHEkpp7mtmSBIlIghCXkGHv\nH+gSCBEngpE3mu1f4LEAD81/aND6EDKxDOFu4chqyAJg6ujh6TA9lj5UtVexz38e9h7Trv4FIYSQ\nqW/EhERRUdFIdyGEEEIImXZOlp5k28EuwbCSWA17fzuZHcKV4chrzGP7XOWu2Bq+FWKReMjjIt0j\nWUKiRFWC++feP87IJ0dleyXbvnt2CCGEEDJRRkxINDU14eTJk+jq6mJZcgD42c9+ZtHACCGEEEIs\nQWfQ4WzFWbP6EasCV43q2MW+i1lCQiaW4aHwh4ZNRgBAgHMAJCIJ9EY9mtXN6OrvGtBadCqihAQh\nhBBLGzEh8dxzz2H+/PlQKpWTEQ8hhBBCiEXojXqk16TjYtVFdGu62f6FXgvh5eg1qnP4KfywK3oX\nmnqasNBzIRQ2ihGPkYll8FP4obytHABQ1laGWM/Ye3sSk6SzvxN1XXUATPUj/J38hQ2IEELIjDRi\nQsLR0RFvvPHGZMRCyLTE8zxae1th4A1wtnGmNbaEEDIF8TyPg7kHcbP5ptl+P4UfHgh7YEznilBG\nIEIZMaZjglyCWEKivLV8yickMusz2czYIOcg2EhtBI6IEELITDRiQmLt2rVISUlBTEwMxOI7UxI9\nPadHQSZCLKmrvwuH8g6xaa0cx0FhrYDSTokkvyQEOgcOeWyPtgc5DTno7O9Er64XeqMe/gp/xHjG\njLiOmRAhtfa2gud5OMudIeJGbNZEyJSQ15Rnloywt7LHMv9lSPBOmJREcrBzME7CVLOirLUMPM9P\navvPHm0PTpaeRH5TPnieh8JaAWe5M5R2SiT6JJotIdEZdLhRe6eDyFRPnhBCCJm+RkxIFBcX47vv\nvoNCcWdKIsdxOHfunCXjImTK0xq02Ju116yFG8/zaO9rR3tfO0pUJXg0+lHMc5s34NjC5kIczj8M\njV5jtj+vMQ9p1Wl4fMHj1F6NTEknS0/ifOV5AICDlQMSfBIQ7x0PO5mdwJERMrR+XT++L/qejWM9\nY7F53uZJndHm4eABuVSOXl0verQ9aOppgru9+6Q8dnVHNb7M+dJsmUqzuhnN6mYUtRThSvUVzHeb\nDx9HH3T0d6CyvRId/R0AALlUPuj7GCGEEDIRRkxI5OTk4Nq1a7Cyoiu2hNym0Wvw7c1vzZIRtlJb\n9Op72RRXI2/E4fzD+HnSz2FvZc/uV9lWiQO5B6A36gc9d2tvK/Zm7cWLi1+kmRJkSslpyGHJCADo\n0nThdNlpnKs4h83zNmOh10IBoyNkaGcrzqJH2wPANDNi49yNk768TsSJEOgciPymfADApVuX8HD4\nwxadJdGt6UZWfRbOVJyBzqAb8n5agxbZDdnIbsgecNvKoJWQiWUWi5EQQsjsNmJCIiIiAlqtlhIS\nhABo6mnCxcqLyG/ON/tw9+C8B5HgkwCdQYfazloczj+Mzv5OU+Ki4Fs8HvM4RJwI7X3t2J+7nyUj\nFNYKxHnHwd7KHt2abpyvOA+dUYf2vnZ8X/w9toZvFeqpEmJGb9TjROmJIW/7x81/INglGI7WjpMc\nGSHD0+g1uF53nY03zN0Aa6m1ILGEu4WzhERWfRbsZHZYF7Juwh9HrVUjpTAFN5tvwsgb2X65VI6t\n4Vvh7+SPtt42NPQ04HLVZTSrmwc9T5BzEBK8EyY8PkIIIeS2UbX9XLVqFYKCgsxqSHzxxRcWDYyQ\nqaa5pxnvpb834CpTgncCEnxMH9ikYikCnAPwcPjD+CTjEwBAsaoYb5x7A1YSK7T3tbPj7GR22B2/\n26xCu7ONMw7lHQIAZNRlwNHaEUv9ltJMCSK47IZsdPZ3AgBsZbZ4aclLKGktwemy02jva4eBN+BC\n1YUxFwckxNKyG7LZ8jhXuSsilZGCxRLpHoliVTGbiXCx6iKsJdawh/0IR46ewWjAF9lf4FbHLbP9\njtaOeGbhM5hjOwcA4OXoBS9HLyz0XIiG7gYUtRShs78TtjJbeDp4wsvBCwprxaTWuSCEEDL7jJiQ\neOGFFyYjDkKmNJ7n8V3Rd2bJCKWdEvHe8Vjks2jA/YNcgrDMfxkuVl0EAPTqetGr62W3izkxdkbt\nHNAuLtojGoUthazH/ZnyM0ivSceqwFVY5LOIPhgSwdz+mQSApX5LIZfJscBjAWwkNtibtReAKYm2\nOnA15DK5UGESYobneaTXpLPxIl9h/45yHIeHIx6GRq9BYUshAOBU2SkslS+dsMe4UHXBLBnhp/BD\nrFcsIpWRgya3OY6Dp4MnPB2oWDkhhJDJN2JCwmAwTEYchExpeY15qGirAGBaB/x07NMIdA4c9oPt\nupB1EIvESLuVBq1By/Y7Wjtia/hWBDgHDHrc5rDNUKlVrD6FWqvGd0XfwcAbkOSXNIHPipDR0Rq0\nqGqvYuNoj2i2HeoaCnd7dzR2N5oq89fdwPKA5QJESchARS1FaOppAgDIxDLEegjfLULEibAjagc+\ny/yMdWgq6CjA/bh/3Ofu7O/E+Yo7dV7WBK/BysCV4z4vIYQQYikjJiTeffddtq3T6VBWVobY2Fgs\nXrzYooERMlV0a7pxtOQoGyf6JCLIJWjE4ziOw9rgtVjuvxwd/R2QiCSQiWWwldkO2ypRLpPjJ4k/\nQWZ9Js6Un2HT5E+VnkLYnDC4yF3G/6QIGYOq9ipW98TN1s2sTgTHcVjiuwTfFHwDALhacxVJfkkQ\ni8SDnouQycDzPC5WXcSpslNs3wKPBYLVjvghqViK+0Pvx7vpps9Y5d3lUGvVsJXZjuu86TXp0BlN\nM/k87D2QHJA87lgJIYQQSxqxgfznn3/O/h04cAAnTpyAq6vrZMRGiOD0Rr1ZqzQ7mR1WB60e0zms\nJFZQ2inhIneBvZX9sMmI20ScCHFecfh50s9ZWzidUYcjN4+wLh6ETAae53Gh8gIbB7sED7hPlHsU\n+yLV2d+JgqaCSYuPkB/SG/X4IvsLnCg9wQo62spsp9zMHS9HL3g5eAEwdWW6dOvSuM7H87xZl4yV\ngStH9X5DCCGECGnM71QuLi6oqKiwRCyETCk92h4czD2I6o5qAKYrwdsitk3qFTapWIqt87eypSEV\nbRXIqMuYtMcnJL0mnU0rF3EixHoNnPIuFUuR6JPIxidKT6C2s9asuj8hkyW9Jp3VZwAAX0df/DTx\np3CycRIwqsHdnSS5Wn0VKrXqns9V0VbBZtTJpXLMnTN33PERQgghljbiko1f/vKXZuvkGxoaIBJR\nxp3MXDzPI68xD/8s+ifUOjXbvz5kPUJcQyY9Hi9HLyzzW4YLVaar1EdLjmKe27xxT+0lZCRtvW04\nXnqcjZf5L4OHvceg903wSUBadRr6dH3o6O/Ae+nvwUZqgzDXMKwMWklLjcik0Og1ZjUUErwTsCls\n05RdQhTuFg53e3eoVCpoDVp8kf0FHgp/CD6OPmOe3ZBVn8W2oz2iIRGN+BGPEEIIEdyI71ZLlixh\n2xzHwc7ODklJVFiPzFzpNen4rug7s32JvomCFpRcFbQKBc0FaO1thUavQWZ9Jpb5LxMsHjLz8TyP\nbwq+YZ1l3GzdsCpo1ZD3t5PZ4aH5D2F/zn62r0/Xh6yGLGQ3ZsNf4Y8FHgsw320+deEgFpNWncYS\nyQprBTaGbZyyyQjA9Llq6/ytuFl1EwDQrG7Gh9c+hJXECgFOAVgdtHpU3S/a+9qR35zPxrGewhfv\nJIQQQkZj2IRETU0NtmzZwsZ9fX1obGyEjY2NxQMjRAhG3ohzlefY2NHaEQ/Oe1Dwqa9SsRTJAcms\ncOD12utI9EmEVCwVNC4yc1W2V7KlGrdbFY50xTVCGYEfx/0YGXUZKGsrY7VXeJ5n5ztSeATeDt4I\ndQ1Fok8iJSfIhOnR9uBS1Z06DKuCVk2LWQJejl5Y4b4C+fp8tsxJo9egqKUI5W3lSA5IxiLvRUP+\nrtR01ODz7M9Z8lBppxxyJhMhhBAy1Qw5H/DKlSvYtWsXuru72b6amhrs3r0b+fn5Qx1GyLRWqio1\nK2D5r0v+VfBkxG2R7pGwlpjqV7T2tuKTjE+g1qpHOIqQe1PTWcO2Yzxi4O3oParjApwDsC1yG15e\n/jJ2x+9GsEuw2bI/nudR01mD1PJUvH/tffRoeyY8djL78DyPlMIU9Ov7AQCuclfEeMYIHNXoBTsE\nY3f8bkQoI2BvZc/26ww6nC47jT9e+iOKW4oHHFfYXIiPb3zM3gskIgk2zN0wbEtqQgghZCoZMiHx\n9ttv45NPPoG9/Z03xtDQULz33nv4y1/+MinBETLZ7i4YGesZCyuJlYDRmJOJZWb95Ks7qvHBtQ/Q\n2tsqYFRkpmrobmDbvgrfMR/PcRz8nfzxzMJn8PLyl7Fh7gb4KnzNvii19rbiYO5B6hxDxi2nMces\nu8vGsI3TrsOEr8IXu6J34eXlL+P5hOfN6q5o9Bp8nv05vs7/GnWddQCAXm0vDuUdYm0+5VI5nln4\nzKCdcAghhJCpati5jKGhoQP2hYSEQKPRWCwgQoTSo+0xq8w+WDcBoS31XwqO43Cs5Bh4nkdrbyu+\nzPkSP0v8GV0RIxOqoetOQmK807/treyR5JeEJL8k9Gp7cb3uOk6VnQLP86hoq0B6TToSfRNHPhEh\nd+F5HqWtpbhUdQnlbeVsf5xXHEJdB35+mS44joOvwhf/suRfkNeYh5OlJ9Gl6QLP88isz0RmfSaC\nXYLh6eAJrUELAHCyccLTsU/D1ZbashNCCJlehrx8oFYPPRW8o6Nj1A9QUlKCtWvX4osvvhhwW1pa\nGh555BHs3LkT7777Ltv/xhtvYOfOndi1axfy8vJG/ViEjEdOQw5bv+ur8MUc2zkCRzS4JL8k7Ira\nxdZGN3Y3orazVuCoyEyi0WvQ2meaecNxHNzs3Cbs3HKZHMkByVjuf6fd4fHS42jrbZuwxyCzw7GS\nY/gs8zOzZITCWoENczcIGNXEkYgkiPGMwXMJz8HX0XyWUllrGS5UXmDjlYErKRlBCCFkWhoyBfhS\nvgAAIABJREFUIREeHo79+/cP2P/RRx8hOjp6VCfv6+vDa6+9hsWLFw96++uvv463334b+/fvx4UL\nF1BeXo7r16/j1q1bOHDgAF577TX87ne/G+VTIWR88hunT4XycGU4otyj2Di7MVvAaMhM09TTxJZR\nzJHPgUwsm/DHWBW0Cko7JQDTOvmUohRaukFGrby1HJdvXWZjESdClHsUfhz/4ym11G4iONk44bmE\n5/BCwguI9hj4+cvBysHs/YAQQgiZToZcsvHyyy/jxz/+MY4cOYLIyEgYjUZkZmbCzs4OH3zwwahO\nbmVlhT179uDDDz8ccFtNTQ0UCgWUStMH0pUrV+LKlStoa2vDmjVrAABBQUHo6uqCWq2Gra3tvTw/\nQkals78T1Z3VAEwfbMPdwgWOaGQxnjHIrM8EYEqmbJq7iZZtkAnR2N3Itt3t3S3yGBKRBA+HP4z3\nrr1nmnqvKsWxkmNYEbCCOm+QYfE8j+Olx9k42CUYD81/CE42TgJGZVkcx8FH4QMfhQ9CXEKQUpgC\nrUELmViGxxY8Rh2XCCGETFtDJiQcHBxw6NAhXLlyBaWlpRCLxbj//vsRHx8/6pOLRCLIZINfWVOp\nVHB2dmZjFxcX1NTUoL29HREREWb7VSrViAmJrv4uOFg7jDo2Qu6W33RndkSQS9C0+ELk7+QPW6kt\n1Do1erQ9aOhuGFW/ekJGcndBS0u2D/Ry9EK8Vzyu1V4DAFy+dRk36m5gddBqLPFdQgk2Mqiq9irU\nd9UDAKQiKbZFbDPrTDHTxXjGINglGGWtZfB38p/RiRhCCCEz34gNuhcvXjzkkovx+OHU3KGm6vI8\nP6oPpY42jhMSFyEA8AyeETqEMfsf/I/QIZAZSIifq1fx6qQ/Jpm+/gv/JXQIhBBCCBnGjRs3hrxt\nxISEpSiVSrS0tLBxU1MT3NzcIJVKoVKp2P7m5ma4uo5cqOmD9A/wXMJzFomVWEZGRgYWLlwodBho\n623DW5feAmBarvHvyf8OW9n0WCKU05CDQ3mHAJgKcT6f8PykxzBVXkdy7+5+DY28Ef995r+hM5ha\nCf578r9b/Oozz/MoaC7AqdJTUPWa/v472TjhpaSXWPHWH96/qKUINZ01MPJGGIwGOMudEeUeNW1+\ndyfKbPv90+g1eOPcG6zV5YuLX7TYsqLJNNtex5mIXsPpjV6/mYFex6krIyNjyNsEa9Lt5eUFtVqN\n+vp66PV6nDt3DkuXLkVSUhJOnDgBALh58yaUSiXk8pGnz1d3VqOrv8vSYZMZpqG7AZ9kfMLGgc6B\n0+oLTYhLCJtBVNNZg15tr8ARkemurbeNJSPsZHaTMhWe4zhEKCPwk8SfwFZq+v1r72vHjdqB2XQj\nb8Th/MPYl70P5yvP42LVRaRVp+GfRf/E39L+htbeVovHS4RT3FLMkhFKO+WMSEYQQgghs5lFZ0gU\nFBTg97//Perr6yGRSHDixAmsWrUK3t7eWLNmDV599VX84he/AABs2rQJfn5+8PPzQ3h4OHbu3Amx\nWIzf/va3o3osnufxZc6X+FHcj6i4ExmVwuZCHMo7xPq4cxyHpX5LBY5qbOQyOXwcfFDdWQ2e51HW\nWoYoD6q2Tu7d7RkKAFgXjMliJbFCcmAyjhYfBQCcrTiLGM8Ys64JZ8rPIKchZ9Dje7Q9+Cr/K+yO\n3w0RJ1i+nVhQfvOdej+R7pECRkIIIYSQiWDRhER4eDg+//zzIW+Pi4vDgQMHBuy/naQYq5rOGnyV\n/xV2Ru2kYmhkWM09zdifsx8G3gDA9EVoe+R2hLiGCBzZ2IW6hrIOISWqEkpIkHG5e4aBq+3Iy+Um\nWoJ3Ai5VXUKXpgs92h4cLT6Kh+Y/BI7jUNleiXOV59h9A5wCEOoaih5tD2sBWd1RjYtVF5EckDzp\nsRPLMhgNKGstY+P5bvMFjIYQQgghE0GwGhKWkt+Uj9Plp7E2eK3QoZB7wPM8+vX9sJHaWPQxvi/+\nniUjnGyc8ETME5N+NXiihLiG4HT5aQBAaWvpqAvBEjIYlfrODAkXucukP75ULMW60HU4nHcYAHCj\n7gbKWsvgauuKqvYqVgA50DkQzy58lv2s20hs2O9BalkqwuaETdvfaTK4Wx23oNFrAJj+brvZugkc\nESGEEELGa8bMaU30TWTbFyovoFvTLWA05F5Utlfib2l/w2tnX8NnmZ+hobthyO4r41GsKmZX2TiO\nw2MLHpvWX1w8HTxZAqdH24NmdbPAEZHp7O4lG67yyZ8hAQDR7tFm0/E7+jtQ1loGvVEPALCR2mBb\nxDazxFtyYDJ8HH0AAAbegH3Z+8yuppPpTaPX4FjJMTYOdQ2lxCshhBAyA8yYhMTGuRvh7egNwFT0\nLLcxV+CIyFi097Vjb+Ze9mW6RFWCt6+8jT9f/jNOlp5kV8XGy2A0sPXpABDvFQ8Pe48JObdQRJwI\ngc6BbExfwsh4CD1DAjAlCrdFbEOU+8DlR7YyWzwa/Sgcrc1bPYs4EbaGb2VdOdp62/C/Gf+Lz7M+\npwT1NMfzPL7O/xr1XfUATK91nFecwFERQgghZCLMmCUbtz+g1HbWAgCy6rOwxHcJXUGZJr4r/I4V\nl7xba28rzleeR1lrGZ5d+CyspdbjepyMugy2Rt5aYo3VwavHdb6pItg5GAVNBQCAS1WXEOsZa9Fl\nL2Rm0ug16NKYuhWJOBGc5c6CxSIRSbAjagfuC7kPXZou9On6AJjqRtxd5PJubnZu2Bq+FUduHmF/\nT4paivBl9pd4LuG5Ub0fGIwG3Ki7geqOathb2SM5IJl+lwR2quwUCpoL2HjzvM3wdPAUMCJCCCGE\nTJQZM0MCACKUEezqWEN3A6o6qoQNiIxKZXslilXFAExXRtcGr0WEMgIysYzdp66rDgfyDsDIG+/5\ncXQGHc5WnGXj5IBk2Mns7j3wKSTSPZK1K+3SdOF4yXGBIyLT0d0FLZ1tnKdEpwonGyf4KfwQNicM\nYXPChkxG3BbtEY1fLP0FYj1jWQKiurMaRS1Fwx7X2tuK1PJU/Pnyn5FSmILshmxcrLqIvVl7WRtU\nMvnqOutwvvI8Gy/xXYJ473gBIyKEEELIRBL+0+YEspHaINojmo0vVl4UMBoyEp7nkd2QjX1Z+9i+\nBR4LsCJwBXZF78J/rPgPs+KkpapS7M3ai9zGXJSqSlHXWcfWlI/GxaqL7OqvnczOrO7IdGcjtcED\nYQ+w8e1CgISMhVn9CAE6bEwUeyt7PBzxMJb4LmH7TpedHrImzcnSk/jTpT/hTPkZtPe1m912u2sH\nEcbdyy+DXYJx/9z7BYyGEEIIIRNtRiUkAGCZ/zK2XawqRnVHtYDRkKFoDVqkNqTicN5h9Ov7AZiq\n668OurOEQiqWYkXgCqwIXMH2lapKcTD3ID7N/BTvpr+LP1z4A67XXh+2+KVGr8FXeV8htTyV7VsZ\nuNJsBsZMEOkeiXBlOBsfuXlkwmpvkNnBrOWnQAUtJ9LygOXs97yxpxF5jXkD7lPcUmx2BR4wJfjs\nrezZ+Gr1VZolIQCe582WaizxXTIlZu0QQgghZOLMuHf2ObZzzAqhHc4/jMbuRot0ayD37nTZaVR0\nV7Cxk40Tnl34LJxsnAbcd03QmiFnM6i1ahy5eQRHi48O+hqr1Cr8/crfkdWQxfZ52HsgzntmFkR7\nIOwByKVyAKZCoVTclYxFq/quhMQ0niFxm53MDot9F7Nxanmq2bIvjV6DlMIUNvZ19MXOqJ14efnL\n+NXyX7G/R2qdGt8UfDOmGVlk/Bq6G9iMFSuJFYJcggSOiBBCCCETbcYlJABgTfAas0rrf7/yd7x+\n7nUczjsMtVYtcHSkV9uLa7XX2Hih10K8uPhF+Cp8B70/x3HYNHcTnln4DBb7Lka4MhxBzkGsZgIA\npFWnmc2AAP6vMnvB12ZTsGM8Y7A7fjf7+Zhp7K3skeSXxMa3i7wSMhpToeXnRFvmvwzWElMxXFWv\nCjdqb7DbTpSeQEd/BwDAVmqLx2MeR6R7JKRiKUScyOx3KbcxF59mfMqKaxLLu/t9Yt6ceTP27zYh\nhBAym83Id3cXuQs2hW3CkZtH2L4+XR+yG7LR1teGH8X9iD7YCCi9Np1Nf3a3d8eW+VtGrH7PcRyC\nXYIR7BLM9ukMOhzOP8y6S5ytOItmdTOi3KMQ6hqKyrZKtmRHzImxLXLwNoIzjY+jD9tu6G4QMBIy\nnRh5I1rULWwsVMvPiWYjtcFS/6U4XXYaAPCPwn8gvykfHMeZ1VnZELbBLMkJAIt8FqG5p5l9Ma5s\nr8SeG3vwQsILkIqlk/ckZqFuTTeyG7LZmApZEkIIITPTjP1WHu8dDyuJFTLrM1HbWcuuat0uULYy\ncKXAEc5OOoMOV6uvsvFSv6X33JpVKpZiR+QO7DPsQ4mqBABQ0FTAEhR3i/OOmxXJCMC0JOW2pp4m\nGIwGiEViASMi00FmfSar52IrtTWroTDdJfklIachhyVcytvKzW4PdwtHtHv0gONEnAib522Go7Uj\nTpWdAgA0djcivykfMZ4xlg98FjDyRtyovYESVQkkYgnsZfaQSWS42XTzTuLazh1+Cj+BIyWEEEKI\nJczYhAQARLlHIco9CjzPI7U8lbV8PF95HrGesXC0dhQ4wtknpyEHPdoeAIBcIkeke+S4zicWibEr\nehf25+xnSYkfkkvlsyoBJZfJobBWoKO/A3qjHs3qZrMkBSE/ZDAacL78rtaKfkvuOVE4FcnEMuyI\n2oH/zfjfAcv2fB198XDEw0M+X47jsCJwBfRGPXsPyWnMoYTEBOB5HgdyDwyaRL7bhrkbZtTPIyGE\nEELumNEJids4jsOqoFUobClEY3cjdAYdjpccx46oHUKHNqvwPI9Lty6xcaRT5IQsnZGJZXgy5kk0\n9TQhvykfGXUZrL0nx3HYEr5lRl3tHQ1PB0+2Nr6+q54SEmRYhZ2F6DD+Xy0Fma1ZIciZwsPeA79a\n/iu09raiRd2C9r52uMpdMXfO3FF1bljotZAlJMpby6HWqgcs8SBjk1adNmIyYmXgSipmSQghhMxg\nsyIhAZim3m6auwl7buwBYCpQluSXBG9Hb4Ejmz1KVCVsyrSVxArz7OZN2Lk5joO7vTvc7d2R5JeE\nU2WnoNVrschnEXwUPiOfYIbxsPfAzeabAKiOBBmeRq9BZmsm7JzsAADJAcmwklgJHJVlSEQSKO2U\nUNopx3ysk40TfB19Ud1ZDSNvRLGqGLGesRaIcnbo1fayBA9gWjYTOicUGr0G/fp+GHkjwlzDZuXf\nb0IIIWQ2mTUJCQAIcA5AuDKcXZG5UHkBjy54VOCoZo+M+gy2HecVB1mPzCKPYyO1weZ5my1y7unC\n08GTbVNCggznas1V9Bv6YQc7OFo7IsE7QeiQpqx5bvNQ3WkqlFvUXEQJiXvU1d+Fz7I+Y7WdXOQu\n2BG1g2rdEEIIIbPQjGz7OZxVgavYdkFzAc5WnAXP8wJGNDuo1CoUtxSz8UKvhQJGM/PdvUSjobuB\nfsbJkLLr73QyWBm4krpHDCNsThjbLm0tZUUXyeg19zTjg2sfoLG7ke1bH7qekhGEEELILDXrEhLu\n9u4Idwtn49Nlp3Eo7xB9sLSg2s5afHj9Q+iNegCAt6P3PU2ZJqPnYOUAW6lpfbtGr0Frb6vAEZGp\nqE/Xh2Z1MwDTsrZoj4GdJsgdc2znsHaoWoMW+U35Akc0vfRoe7Dnxh5W30bEibA1fCvmu80XODJC\nCCGECGXWJSQAYEv4FgQ53ymSlduYiy9zvqSryBZQqirFxzc+ZpXtpSIp1oeuFziqmY/jOHg4mM+S\nIOSHajpr2LaHvQdkYssso5opOI5DnFccG1+pvkLvG2OQVZ/F3gtkYhmeiHmCZssRQgghs9ysTEjY\nSG3wVOxTZmulS1QlqGirEDCqmUej1+Bg3kFoDVoApvabz8Y9iwCnAIEjmx087amOBBne3QkJKvA7\nOrFesaw7UF1XHTLrMwWOaHrgeR5Z9VlsvDFsI0JdQwWMiBBCCCFTwaxMSACAWCTG5nmbzYqS3V3x\nm4xfYUshK1pmb2WP3fG74avwFTiq2ePuOhL13fUCRkKmquqOarZNv5ujYyezwyKfRWz87c1vcbzk\nOC37G0FDdwOaepoAAFKxFJHKSIEjIoQQQshUMGsTEoBp+u2qoFWsB31leyUq2ysFjmrmyGnIYduJ\nPolws3MTMJrZx6zTRhfNkJgpeJ5Hek06DuUdwrmKcyhvLYdGr7mn89R21rKxryMlJEZrddBqONk4\nATD9P16suoh3rr5DtVqGcfdMknC38BnbWpYQQgghYzOrExKAqbf8Ao8FbJxalkprgidAV38XylrL\n2DjKPUrAaGYnF7kLqwnQo+1BV3+XwBGRiXCh6gJSClOQ05CDU2Wn8EnGJ3jr4luobBtbMrVF3YJ+\nfT8AwFpszb5gk5FZSaywO343gl2C2b4WdQu+KfhGwKimLr1Rj9yGXDaO8YwRMBpCCCGETCWzPiEB\nAMkByWazJIpVxSMcQUZyo+4GjLwRAODv5A9nubPAEc0+HMeZLduo6qgSLhgyIVRqFU6VnRqwX61T\nY3/u/jElnao77yzXUNoowXHchMQ4WzhaO+Lp2Kfx0PyH2PtHVXsVW5ZA7shrzINaZypm6WjtiEDn\nQIEjIoQQQshUQQkJAK62roj3jmfjY8XHYDAaBIxo+uB5HrmNufjHzX/gyM0jOFN+BmfLz5rV47h7\nvTWZXEEud7rJlLeWCxiJ8GbCzKdrtdfMnkesZyzkUjkAQK1V42DeQZYIHElVWxXbVlpTG957wXEc\n4r3jMc9tHtt3tfqqgBFNPTzP49KtS2yc4J3AEjiEEEIIIRKhA5gqVgWtQnZDNjR6DVS9Khy5eQSb\nwjbROtdhGIwGHM4/jLzGvCHv42TjRD3mBRTkHIQz5WcAAGWtZTDyxmn7ZYDneZS3laOjvwN+Cj/M\nsZ0zquNKVCVILU9FXVcdnG2c4evoiwDnAER7RLNuCdOBzqAzW4f/VOxTCHUNRWVbJT7O+Bg8z6Oq\nvQonS0+y1ro8z6Nf3w8RJzL7W6bRa1DQXMDGXrZek/dEZqBEn0QUNJn+P7Pqs7AqaBXsrewFjmpq\nqGirQGN3IwBTMcu7u1sRQgghhEyfT+MWZiezw8rAlThechyAqQBXeVs5Hpr/ELUmG8KpslPDJiNE\nnAjbI7dPqy99M42Pow+sJFbQ6DXo6O/AF9lfYHvk9mmXaNPoNdiXvY+15uU4DhHKCKwKXDVssdTL\nty7jaPFRNm7tbUVrbyuyGrJQ3VGNLeFbLB77RMltzGVda5xsnBDiEgIACHAOwJqgNWwpx8Wqi8iq\nz4JMIkOfro8d42DlAD8nP9jKbFHdUc3a8brKXeFq5SrAM5o5ApwC4OXghbquOuiMOqRVp2FdyDqh\nwxJcZ38nTpSeYONYz1jIZXIBIyKEEELIVDM9L5VayGLfxQh3C2fjzv5O7M3aa1ackZhUtlfiYtVF\nNg5XhmNT2CYsD1iOGI8YJPom4kdxP6JWggITi8RI9Elk46KWInx842N0a7on5PxG3oib7Tfxfvr7\nSClMQa+2d0LO+0Pf3vyWJSMA05X/vMY8/O3K33Aw9yB6tD0DjslvyjdLRvzQjbobuNVxyyLxWsK1\n2mtse5HPIrOaD8kByWaJ0x5tD9p621gyAgC6NF3Ia8zD1eqrqO+60wY2xjOG6keME8dxSA5IZuP0\nmnT06/oFjEhYtzvB/DXtr6jrqgNg+j9a4rtE4MgIIYQQMtVQQuIuEpEEu6J3YXvkdrYum+d56rzx\nA73aXnxf9D0bz3Wdi11Ru7DYdzHWhazDtshteCDsAfg7+QsXJGHWBq/Fcv/lbFzXVYe9WXsnpE7K\nP4v+iUvNl1DTWYP0mnS8f+39e2pBOZzqjmqzmTi2Mlu2fbuGyWeZn0Fn0AEA+nX9uFF3A1/lf8Xu\n56vwxS+X/RLPJzxv1hnhdNnpCY3VUuo661iLTolIgljPWLPbOY7DzqidiHSPHHCsVCwdcpaSr8IX\nS/zoS+JEmO82ny0j0ug1uFoze2tJnCo7hZTCFLO/BWuD18LVlmbiEEIIIcQczaX/AY7jEO0RDT+F\nH/58+c/QG/Wo7qxGVUcVApwChA5PUDzPI7M+E8dLjqNXZ7oSLhVJ8eD8B+kK6xTGcRzWha6DwkaB\n74q+A8/zqO+qR2p5Ku4Lue+ezmkwGnCl+grSa9LN9rf2tuJ4yXFsnrd5Qn4majtrsS97HxtHKCOw\nK3oX6jrrkFqeyjri1HfV47309+Bo7YiKtgrojXp2jIvcBU8seAJymRwKGwUenPcg/nz5zzDyRlS0\nVaCusw5ejlO3hoLeqMf5yvNsHKmMNEvK3GYlscLOqJ1YH7Ieffo+SEVSWEmsYCezAw/Ta97Y0wiN\nXgOtQQsXuQsilBHTtqbIVMNxHJb5L2OtP9Oq05DokwhrqbXAkU2utt42XKq6U8TSVe6KLeFbKEFN\nCCGEkEFRQmIIChsFFngswI26GwCAC5UXZnVCgud5HMw7OKBmxOrg1XC0dhQoKjIWi3wWQWfQ4VjJ\nMQDA+crzcJG7YKHXwjGdp6y1DP8s+ida1C2D3n6t9hr69f3YEr4FMrHsnuPNb8rHV3lfQWc0zXyQ\niqRYG7wWAODl6IUnY5/EpapL7Pk09TQNaLkol8rxRMwTZuvWneXOiHSPRE5DDgDgSs0VbHPcds9x\nWlJjdyO+yv8KDd0NbF+Cz/BFARU2CiigMNvHgYO3oze8Hb0tEicxifaIRmp5Kjr7O6HWqvGny3/C\n6qDViPOKg1gkFjq8SZFangoDb5p95evoi2fjnoVULBU4KkIIIYRMVXRpbBjL/Jexq7wlqpJZ3V8+\nrynPLBnhZOOEJ2KewDL/ZQJGRcZqid8SsyULR24eGVONlPLWcnya+alZMsJJ5oRXVryCeXPutD7M\nbczFh9c+RFd/1z3FWaIqwYHcAywZIZfK8fTCpwdM+U7yS8Ka4DUDZmN4OnhiXcg6/MuSfxm0G8fd\na9nzm/InfJnJePE8j2s11/Be+ntmyYgo9yj4OPoIGBkZjkQkwaqgVWys1qqRUpiCd66+g8r2yhm/\n9K++qx45jTlsvH7uekpGEEIIIWRYNENiGK62rpg/Zz5rj5dRl4ENczcIHNXk69Z0m9WMiHKPwkPz\nH5p2nRqIqfPJo9GP4qPrH6GhuwFG3ohvCr7Bvy751xFfT57ncbrsNPtSZSWxwsrAlbBqMS0L2Bm9\nE98Xfc+KLzZ0N+Bg3kHsjt89phh5nseJ0hPscVzkLngy5slB159zHIeVgSsRqYxEVUcVJCIJfBx9\n4CJ3GfYxvBy8oLRToqmnCTqDDnlNeYjzihtTnJZS2FyI4yXHoepVsX1SkRRrQ9Ziie8SWh41xcV5\nxUEikuBk6Ul09ncCMM3e2XN9DwKdA7E9cvuMbAmq0Wvwdf7X7Pc2bE4Y/BR+AkdFCCGEkKmOZkiM\nIM77zpeUnIacCSkEOJ209bbh04xPWRcDO5kdNs/bTMmIacxKYoUnY56ErdRUh6CzvxN/S/sbchtz\nh72CW9leierOagCmK8E/S/wZlvkvY1PRJSIJHpz/IB6c9yCrS1DVXjXmThYFzQVo7G4EYCrIuDt+\n94jF8FxtXRHnFYcFHgtGTEYApkTG3YUhM+syxxSjpRQ0FWBf9j6zZIS7vTt+uvinSPJLomTENLHA\nYwF+nvRzrAtZZzZDoKKtAh9d/whqrVrA6CaO1qBFiaoEqeWpePvq22jsMf3eSkQSantKCCGEkFGh\nhMQIgl2C4WDlAMDUSu/2bImZrq6zDt8UfIO/pv2VfcjkOA7bI7fDRmojcHRkvBysHbAh7M5sn47+\nDhzMPYhPMz8dcvnCuYpzbDvWMxbOcudB75fgk4AYzxg2vlh5cdD7DYbneZwpP8PGi30WW+xqcrRH\nNEuc3Oq4hcr2Sos8zmgZjAacKD3BxlKxFHFecXgu/rlBl52QqU0qlmJ5wHK8uPhFs9aqrb2t+Mvl\nv+B02el7XtIkNCNvRHpNOv544Y/4LPMznCk/g7beNnb7A2EPwM3OTcAICSGEEDJdUEJiBCJOZFb0\n71zFuWn7IXI0ujXd2HN9D95NfxcZdRmsW4GYE+Ph8IcR5BIkcIRkoizwWICt4VvZTAnAVLDyXOW5\nAfetaq9CeVs5ANPvxEi1Q5b6LWXbhS2FQxbA/KGcxhxWq0UmlmGp/9IRjrh39lb2mOs6l40/ufEJ\nLt+6LNg6/zMVZ9Da2wrANIvl50k/x5bwLTQbaZpzkbtgW8Q2bI/czvb16npxtuIs/njxj8iqzxIw\nurHr0/Xho+sfIaUwBWqd+UwPMSfGprBNZjMLCSGEEEKGQzUkRiHRNxGXqi5BZ9ShqacJf7r8JyT7\nJ2Op/9IZVbCL53kcyjs04Eqxt6M3Hgh7gCr0z0ALvRZivtt8nCo7xVp4Xrl1BQneCXCycQJguhp6\ntPgoOybaPXrI2RG3udm5Yd6ceShsKQQAHC85jscXPD7skoNebS+OFR9j48W+iwdtbzmR1oasRVVH\nFfp0fex51nbWYnvk9kldHpHbmGs2AyU5IJm618wwUe5RMBgNOFl6El0aU1LbwBvwTcE3UNgozLo4\nGXkjMuszkdOQgx5NDxxtHBHhFoEoj6hxda4ZL71Rj71Ze1HdUc32OVo7ImxOGHwVvgh0CoSDtYNg\n8RFCCCFk+qGExCjYyeywOng1jpccBwDoDDqcLj+N8rZyPBv3LJv2Pd2lVaehoq0CgGl5RrR7NBJ8\nEuDr6Etr12cwG6kNHgh7ADWdNajvqofOqMOB3APYFrENXf1duFB1AXVddQBMxRVXB68e1XmXByxn\nCYmiliJcqbli1t0CMH3xKmstQ2V7JTJqM9gVVwcrByQHJE/gsxyc0k6Jnyb+FAdyD6C2sxaAKTkQ\nrgxHhDLC4o8P/N/yqPxv2DjENYS618xQMZ4xiHKPQmFLIVLLUtGsboaRN2J/9n48HvNtbiTMAAAg\nAElEQVQ4FNYK1HXV4ULlBVavBQCa1c0oVZXieOlxxHvHY3XQakhEk/v2XdNRg+Olx82SEauCVmG5\n//IZlZgnhBBCyOSihMQoLfNfBk97TxwtPspqKlS2V+Lbgm+xed7maf+BrLmnGSdLT7Lxcv/luC/k\nPgEjIpOJ4zhsDtuMD69/CCNvRG1nLf5y+S8D7pcckMxmTozEV+GLJb5LkFadBgA4VnwMeY15cLdz\nh7u9OwDgavVVNKubBxz74PwHJ22pgpONE3bH78a3Bd8iuyEbAJBalor5bvMtnmzUG/XYn7uftTd1\nlbtiR+SOGZPkJAOJRWJEKCPg7eCNd9PfhVqrhlqnxgfXPhjx2D5dHy5UXoBaq8bW8K2TEC3Q0deB\nr/K/GjBzbn3oekqcEUIIIWTc6FPvGAS5BOGni39qduU2sz4Tf037K+o66wSMbHx4nsf3xd+zehGe\nDp5YFbRK4KjIZPNR+GB96Pohbw9wCkBy4NhmLawLXQdPB08AptkQ1R3VuFZ7DSmFKUgpTBmQjJCI\nJHgg7AGEzQkb+xMYB4lIgo1zN7Lp8M3qZlypvmLxx63uqEZ7XzsAwFpijSdinqCisbOEwkaBR6Mf\nHTKZzXEckgOS8ZNFP8GGuRvMEoGZ9Zms1oolGYwG7MveNyAZschnkVmdGEIIIYSQe0UzJMZIxImw\nNngt2vrakNeYBwBo72vH3qy9o2pPOBXlNeahrLUMgOlD8NbwrZM+HZhMDUl+SbC3ssf5ivNo72+H\ns40zvBy84KvwxQKPBWO+ci8RSbArahcO5h1kSyJ+SCqSItYrFsEuwfBX+EMuk0/EUxkzuUyO5IBk\nnCo7BQA4XXYac13nWvR3uqG7gW2HK8On5d8Pcu/8nfzxs8Sf4WTZSZSoSsCBg7u9OzwdPBHlHgU/\nhR8AwMvRC4t9F2Nv1l6UqkrB8zzOVZzDjqgdFo3v0q1L7GdUxIkQ7R6NZQHLoLRTWvRxCSGEEDJ7\n0LfOe8BxHHZE7kCgUyCOlRyD1qBFj7YHf7/ydyT5JSE5IHlKV8Y38kZUtlWivK0cxapiNHY3stsS\nvBPgYe8hYHREaFHuUYhyj5qw8znLnfFCwgvo0fagobsBjd2NbGaEi9wFUe5RcJG7TNjjjcdS/6XI\nachBs7oZWoMWh/MP4/mE5y22hKKh605Cgn7vZidXW1c8Gv0o6+4yVL0eESfC2qC1KFWVAgDym/Kx\nrm+dxeJSqVVmLXjXBq/F8oDlFns8QgghhMxOFk9IvPHGG8jJyQHHcfiP//gPREZGsttOnz6N999/\nH1ZWVtiwYQMee+wx8DyPV199FSUlJZDJZPiv//ovBAQEDPMIwuA4Dgk+CXCWO+OzzM9g5I3QG/U4\nX3keuY25eC7+uSlXbZzneeQ05uBk6Ul09ncOuN3JxgnrQiz3AZfMXhzHwd7KHvZW9gh1DRU6nCFJ\nRBI8EvkI3k9/HwbegNrOWqTdSrNY+9GGnjsJidt1NcjsNJrCwV6OXgh0DkRFWwWMvBFp1WlQYuJn\nKxh5I74u+NpsGZ8lW/ASQgghZPayaA2J69ev49atWzhw4ABee+01/O53v2O38TyP1157DXv27MG+\nfftw9uxZNDU1ITU1FT09PThw4ABef/11vPnmm5YMcdyCXYLxXPxzZi0x2/vacbbirIBRDe5k2Ukc\nzjs8IBkh5sQIcQ3B07FPT+mZHYRMBk8HT6wIXMHGx0qOIaUwBTqDbkIfR2/Uo6WnhY097GiGBBnZ\n3bUbrtdeh8agmfDHuFJ9hXXTEHEibJm/hQqtEkIIIcQiLDpD4sqVK1izZg0AICgoCF1dXVCr1bC1\ntUV7ezscHBygUCgAAImJiUhLS0NrayuiokzTxX18fFBXVwee56d020kfhQ9eSHgB6TXp+K7oOwBA\nVn0W1gavFWw9/N00eg3OVpzFxaqLbJ+tzBbhbuEIdglGsEswJSIIucsy/2UoaC5gy5nSa9JR3VGN\np2Kfgr2V/YQ8RnNPMwy8AYBpdpK11HpCzktmtlDXULjZurFlRUWdRViCJSMf+H+0Bi3EnBhikdhs\n/+0WvHmNechtzGX7VwSuYIVpCSGEEEImmkUTEiqVChEREWzs4uIClUoFW1tbODs7Q61Wo7q6Gh4e\nHkhPT8eiRYsQGhqKzz77DE8++SSqqqpQW1uL9vZ2ODs7WzLUceM4Dot8FuF63XU0djdCZ9QhtzEX\nib6JgsZ1o/YGTpadhFqrZvtCXEOwK2oXJSEIGYJULMXuuN349ua3yG/KB2AqQHm85DgeiXxkQh6j\nvruebXva0xc+MjocxyHJPwnfFnwLAMhrz4Naq4atzHbY4wqaCnCs5Bjr6mIjtYFcKoetzBbWEmvU\nd9WjR9tjdoy7vbtZVylCCCGEkIlm0TmYt4t03T2+e6bDm2++iVdeeQUvvvgifHx8wPM8li9fjqio\nKDz++OP4/PPPERQUNOA8UxXHcYjzimPjwpZCAaMBchpy8O3Nb82SET6OPtgRuYOSEYSMwFpqjZ1R\nO7ExbCPbV9BcgP/f3p1HR1nfexx/z5KETPaZkISsJCGbgUgIa9gEAQWvh6oUFUEpNvfcW6wtPVwX\ntF5rb+XidvSW2+ItStErIihQDxW5hFLAguwhQAhgWMOSbZJA9kky9w8OU3ArW2bI5PP6K5NZ8n3y\nnPnMM9/n9/x+za03Z4j85Sts9AjW5Rpy9fr26EugbyAADa0NvLb5NdZ9tY5GR+M3HltZX8kn+z9h\nyd4lrmYEQKOjkaqGKk7WnORw5eFvNCMiAyN5OOthrbgkIiIiHcrg7MBv+/PnzyciIoLJkycDMGbM\nGD799FMslm9exvDGG2+Qnp7OhAkTrvj92LFjWbdu3ff+nV27dt28om9QnaOOJUeXAGDAwKO9HsXP\n5P4v/06nk+XHl1PTUgNAgDmAAeEDSAlOuaUvfxG51Xz9vTS6x2h6Bfe64df99OSnnGu8eEnI3TF3\nEx8Yf8OvKV1HUU0RX5R9ccXvAswBjI8dj6PdwVfnv+JU/SnOO85f9Wv6Gf1IDUklOSiZ7t2667NC\nREREbpqcnJxv/X2HnvoYOnQo8+fPZ/LkyRQVFREZGXlFMyIvL4958+bRrVs3NmzYwIwZMyguLua9\n997j5ZdfZtOmTWRmZl7V3/quDfSEIkMRpbWlAATGB97UJRSv1sHyg5jtZsIJx9fky1MjnsLfx9/t\ndXyfXbt23VL7Ta5PV9iPF8IukF+SD0CbtY2c229se51OJ6trVhMeEA7AnYPu9OiqPF1hH3qbHHLo\nV9aP9za/hzn47x/lf63768UfzOAb4ks44a77+kT1YWLGRPzMfjQ6GqlvqafeUe8aRZdsTb7lPie6\nCr0HOz/tw85N+887aD/eur5vAEGHNiSys7PJzMzkoYcewmQy8cILL7By5UqCgoIYM2YMkydPZsaM\nGVgsFmbNmkVoaCghISE4nU4eeughgoOD+c///M+OLLFDpIanuhoSR+1H3d6QcLQ5WHN4jet2/5j+\nOsgUuQG3Rd7makh8VfUVre2tNzSUvaqhipa2FuDiBLM3a6JM6VoyIzOZ1HMSpmjTd64E42PyIdma\nzKC4QaTY/j5CLsA34B/OOyEiIiLS0Tr84tBf/OIXV9xOS0tz/Tx27FjGjh17xf0Gg4G5c+d2dFkd\nKsmaxF9K/gJc/PLiTk6nkz8V/Ymqhirg4sRlI5M0KZnIjYgIiCDMP4zqxmqaW5s5UX2CZFvydb/e\n5RNa9gjqoaHxct2MBiP9ovsRGRDJZ4c/43j1ccxGM30i+5AdnU1CWILmgRAREZFblo5SOkBcSBy+\nJl9a2lqobqzG3mDHaum4VUKqG6spKi/i7PmzHK85fsXEZXel3OWa/ExEro/BYCCtexpfnvwSgEOV\nh26oIXH5hJZaYUNuhpiQGPIG5OFoc2A2mtXkEhERkU5BDYkOYDaa6RnWk8OVhwEosZd0SEPidO1p\nNp/YzP6y/d+6EklOTM4Vq36IyPVLC/97Q6K4opgJaRP+wTO+m1bYkI7iY/LxdAkiIiIiV00NiQ7S\ny9briobEgNgB1/wa9gY7fzv5N0prS/Ez+REZGEmbs40GRwP2Bjunz5/+1ucZDUaGJQxjbMpYnSUT\nuUkSwxJdI5+qGqqorK90TUp5LZxOJ2fO//2SDY2QEBEREZGuSg2JDpJkTXL9fLTqKO3OdowG41U9\nt7yunE3HNrH33F7ane2u35fYS77zOcnWZDIiMogKjCIqKEqTWIrcZD4mH3rZelFUXgTAwYqDDA8Y\nfs2vU9lQ6VrVwN/HH5vFdlPrFBERERHpLNSQ6CBRgVEE+Aa4llXbeHQjo5JH/cPnbT25lT8f+vO3\nXoLxdUaDkayoLIYmDCU6WGdZRTpaRkSGqyGxv2w/w3tee0OipOrvjcXEsESNYhIRERGRLksNiQ5i\nMBjIjc9l3VfrAPjL0b+QbEsmPjT+O59zofkCaw6tuaIZkWRNYlDcINrb26lpqsHH5EOATwD+Pv5E\nBUVpuUARN8ronoHJYKLN2UZpbSkFZwu4Per2q24qOJ1OiiuLXbcvH0klIiIiItLVqCHRgUYkjuBw\n5WFO1Jyg3dnOsn3L+OmQn+Jn9vvWx289uZU2ZxtwcZnB+zLv+94Ghoi4l7+PP71svThUeQiA5fuW\ns+XEFsanjScxLPF7n9vc2swn+z/hSOUR1+/UkBARERGRrkwNiQ5kNBj5YZ8fMn/rfJpam6hurGbN\n4TX84LYffOOx7c52dp3e5bo9ptcYNSNEbkF3p95NaW0p9Y6L80CcPn+ahTsWYrPYsPhYMBvNBHcL\nJsQvBFuAjWRrMgYMvLfnPcrqylyv0zuyNxEBEZ7aDBERERERj1NDooOF+YcxMWMiH+37CICCMwXc\nm34vJqPpisedqD5BXUsdAAG+AWREZLi9VhH5xyICI5g1bBabjm1i68mtONodAFQ1VFFF1VW9Rm58\nLuPTxmv+CBERERHp0q5u2Qe5IVk9sgjzDwPA0e7g7IWz33jMvrJ9rp97R/a+6hU5RMT9/H38uSv1\nLmYNm0VKeMpVP89sNHN/5v3ck36P3uMiIiIi0uVphISbJIQmUN1YDcCJmhPEhsS67mt3trO/bL/r\ndp/IPm6vT0SuXUi3EKb3m05NYw0NjgZa21tpaWuhtqmW2qZaTtae5Jj9GK3trRgNRh7o/QBZUVme\nLltERERE5JaghoSbxIfGU3C2ALjYkBiaMNR13/Hq49S3XLwePdA3kISwBI/UKCLXJ9Q/lFD/0G+9\nr6WthdLaUgJ9A4kI1JwRIiIiIiKXqCHhJgmhf28yHLMfo93Z7hqyXXiu0HVf7yhdriHiTXxNvlpN\nQ0RERETkW+ibr5tEBkYS7BcMQIOjgRM1J4CLSwFe3pDQ5RoiIiIiIiLSFagh4SYGg4G07mmu24cq\nDtHc2syaw2tobm0GINwSfsVIChERERERERFvpUs23Ci9ezo7SncAsPn4Zr489SWONofr/oFxA7UM\noIiIiIiIiHQJGiHhRim2FGwWm+v25c2IlPAUBsUN8kRZIiIiIiIiIm6nhoQbmYwm7km754rfWXws\n3J95P49lP4bZqAErIiIiIiIi0jXoG7CbpXVPY8rtUyixlxDoG8jAuIEE+gZ6uiwRERERERERt1JD\nwgMyIzPJjMz0dBkiIiIiIiIiHqNLNkRERERERETE7dSQEBERERERERG3U0NCRERERERERNxODQkR\nERERERERcTs1JERERERERETE7dSQEBERERERERG3U0NCRERERERERNxODQkRERERERERcTs1JERE\nRERERETE7dSQEBERERERERG3U0NCRERERERERNxODQkRERERERERcTs1JERERERERETE7dSQEBER\nERERERG3U0NCRERERERERNxODQkRERERERERcTs1JERERERERETE7dSQEBERERERERG3U0NCRERE\nRERERNxODQkRERERERERcTs1JERERERERETE7dSQEBERERERERG3M3f0H5g7dy579+7FYDAwZ84c\n+vTp47ovPz+fBQsW4Ofnx4QJE3jkkUdoaGjg6aefpqamhtbWVmbOnMmwYcM6ukwRERERERERcaMO\nbUjs2LGDEydOsHTpUkpKSnj22WdZtmwZAE6nk//4j/9g1apVhISEkJeXx5gxY8jPzycpKYlZs2ZR\nXl7OY489xpo1azqyTBERERERERFxsw69ZGPr1q2MGTMGgOTkZM6fP099fT0A1dXVBAcHExoaisFg\nYPDgwWzZsoWwsDCqq6sBqK2txWq1dmSJIiIiIiIiIuIBHdqQqKysvKKhYLPZqKysBMBqtVJfX8/J\nkydxOBxs27aNqqoqJkyYwJkzZxg3bhzTpk3j6aef7sgSRURERERERMQDOvSSDafT+Y3bBoPBdXve\nvHk8++yzBAUFERcXh9Pp5NNPPyU6OpqFCxdSXFzM888/z8cff/wP/9auXbtuev3S8bTfvIP2Y+en\nfdh5ad95B+3Hzk/7sHPT/vMO2o+dT4c2JCIjI10jIgDKy8sJDw933e7fvz8ffPABAG+88QYxMTFs\n376d4cOHA5Cenk5ZWRnt7e0Yjd89mCMnJ6eDtkBEREREREREOkKHXrIxdOhQ1q5dC0BRURGRkZFY\nLBbX/Xl5edjtdhoaGtiwYQO5ubkkJCRQUFAAwOnTpwkICPjeZoSIiIiIiIiIdD4G59evq7jJ3njj\nDbZv347JZOKFF16gqKiIoKAgxowZw7p16/jv//5vLBYLP/7xjxk9ejQNDQ3MmTOHqqoq2tra+PnP\nf87AgQM7skQRERERERERcbMOb0iIiIiIiIiIiHydroUQEREREREREbdTQ0JERERERERE3E4NCXGL\n9vZ2T5cg0qXpPdi51dTUeLoEERERkZtODQnpMJWVlYwZMwa73Y7RaETTlXRe+/bt83QJcgM++ugj\nFi1aRF1dnadLkWu0ceNG/uVf/oWioiJPlyI3YNu2bdjtdk+XITdgxYoVbN++naamJk+XItdo/fr1\nzJ49m4KCAhobGz1djtyAkydPeroE6QBqSEiHsdvtlJaW8u6773q6FLkBW7Zs4ac//SmrV68GdKa9\nM9mzZw95eXns3LmTO++8k8DAQE+XJFepqqqKZ555hg8//JDp06eTm5vr6ZLkOpSUlPDcc88xf/58\n6uvrPV2OXCOn04ndbufJJ5/k//7v/9i4cSP79+/3dFlyDVasWMHixYvJzs7m7Nmzaih1UqdOnWLm\nzJn88pe/5IMPPqC1tdXTJclNZPZ0AeJ92tvbMRqN+Pv78+CDD/L5558zevRo+vXrR1tbGyaTydMl\nylVwOp0YDAYsFguhoaGsXLmSESNGEBwc7LpPbl11dXW8//77DB48mMcffxyAxsZG/P39PVyZXI1j\nx45x4cIF5s6dS1hYGE1NTTQ3NxMSEuLp0uQqbdmyheeee445c+YwduxYT5cj18FgMNDW1gbAggUL\nPFyNXI+2tjZGjx7NI488gsPh0GjdTmrTpk0kJCQwa9YsSkpKMJv1FdabmF588cUXPV2EdH4rV66k\noqKChIQE1xfVzZs3ExERwR133MHChQu57777MBo1KKezuLQfd+/eTXp6OqGhoXz55ZeuM7VqSNx6\nWltb2b17N2FhYQQEBGC322lqaqJnz54sWrSIVatWUV9fT0hICEFBQZ4uV75m5cqVlJeX07NnT6Kj\noykoKKCuro79+/fz2muvceDAAQ4dOsSAAQM8Xap8j/b2dgwGAzabjTVr1jB79mxMJhOfffYZlZWV\nRERE6GD6FnYpR202G2azmUOHDnHixAmysrJYtGgRH330EU1NTYSFhWnU2S3o8uNRuHjZm6+vL+fP\nn+f5559nz549nDhxgn79+nm4Urla7e3tLF++nLFjx5KQkMDevXspKysjKipKJzm9hBoScsNqa2t5\n6qmn6NatG5GRkVitVgBaWlooLi7mwQcf5A9/+APvvPMOvXr1Ij4+3sMVy7epqqpiypQpWK1WkpOT\nXb8/efIkR48eZcqUKSxZsgSTyUS3bt0IDQ31YLXybV588UU+//xzoqKiSEhIICUlhUWLFrF+/Xq6\nd+/O8OHD2b17N5s2bWLMmDGeLlcucylH/f39CQ8Px2azERISwoIFC2hvb+epp54iKSmJDRs2UFFR\nQVZWlqdLlq+5lKE2m42EhAT8/Pxobm7mV7/6FSUlJRQVFbFt2zaOHTtG9+7dXZ+Vcmv5eo6Ghoay\naNEiampqCAoKYvDgwezatYvt27dzxx13eLpcuczlx6Pdu3fHZrPhdDp5++23CQ8P5yc/+QkJCQls\n3LiR6upqMjMzPV2yfIvLj0cTExMxGo0cO3aMpUuXUlNTw/bt2/nb3/7G6dOniYyM1PGoF9Dparku\nFy5ccE0MtGPHDuLi4jCbzezevds1vPHw4cNUVFTwyiuvYLFYABg6dKjHapbvd+bMGRobG9myZcsV\nk6/V1taSk5ODxWLBbrfzxhtvaHTELaSlpQW4+J48fvw4WVlZHDp0iHPnztGtWzemTJnCoEGDeOKJ\nJxg5ciQzZsygoaFBE0PdAr4rR/fu3Utrayu33347U6dO5eGHHyY2NpasrCzuuOMOampqNOz4FnR5\nhtbW1gLw6KOPEhAQQFxcHK+//jrPPfccZrOZAwcOeLhaudy35WhxcTHnzp3D39+fiRMnsnbtWkaP\nHs2IESOYMmUK58+f59SpUx6uXL4rRwsKCnA4HAwcOJD09HQOHDhATEwM/fr1Y+jQodjtduXoLery\nLK2urgZg2rRpAJSVlTFv3jxmz55Nc3MzBw8e9GSpcpNohIRck7a2Nl555RWWLVvGzp076d27N+np\n6dx///1UVVVx+PBhAgIC6NGjB62trSxcuJAhQ4bw8ssvs3XrVkpKShg8eLCnN0MAh8PBF198gdPp\nJCwsjOLiYoYPH8727dtxOp1kZGRgMBg4deoUL7zwAn/5y18YN24cDoeD+Ph413BI8YyysjJ++9vf\nsm3bNnr06EFUVBS9e/cmLi6OwsJCnE4nKSkpJCQkkJ2d7Zrb5ciRIxw5coQHHnjA05vQZV1Ljqam\npmKz2Vz779133yUtLU1n9m4B35ehAKmpqZhMJgYPHkx2dja+vr6Ehoaybt06IiMjSU9P13w8Hna1\nOdq7d2/Wr19PSEgIaWlpnDt3jn379nH//fdr/3nI1eSoxWIhOjqa9PR0Vq9eTWxsLCEhIXz44Yck\nJycrR28R/yhLMzIyMJvNOJ1OVqxYwbRp0wgPD2fjxo3YbDbS0tKUpZ2cGhJyTTZv3kxRURGvv/46\nu3btcp3liY+PJzQ0lAMHDlBbW0tiYiIJCQk88MADruudc3Nz6d+/P35+fp7cBOHiMp4zZ86kvr6e\nxYsXEx0dTU5ODqmpqVitVpYtW0ZOTg4hISFcuHCB6OhonnrqKXJzc/Hx8eHkyZP07dvX05vRZdXX\n1/Pss8+SkZGBv78/a9eupb29nYEDBxIVFUVJSQmnT5/GZrNhs9lcTaX9+/ezatUqcnNzycrK0ge4\nh1xtjiYnJ9OtWzc++OAD3n//fd5++23S09N56KGH8PX19fBWdG1Xk6H9+/cnODiY4OBg9u/fz86d\nOzl37hz5+fnk5ubSs2dPvf886GpzNDQ0lPDwcBITEykoKODTTz/ls88+Y9CgQfoc9KCrydHz58+T\nkJBAVFQUVquVw4cP8/vf/564uDimTJmiHL0FXO3xaFBQEJmZmezZs4ejR49y5swZNm3axKBBg0hM\nTFSWdnJqSMg/dODAARwOB8HBwXz22WcYDAaGDx9OSkoK586d49ChQ9x2223YbDYaGho4fvw4VquV\nuro6QkJCMJlMOJ1O/P398fPzc034JZ6zcuVKUlNTmT17NlarldWrV9OzZ08iIyOJi4tjx44dnDp1\nikGDBhEVFUXfvn3x8/Ojra2N5ORksrOzPb0JXVJFRQUBAQGcPXuWtWvX8tJLL9GvXz8aGhooLCwk\nJCSEyMhILBYL+/fvx2w2k5KSQkhICMnJybS0tJCXl8eQIUMATUzqTteTo6GhobS0tJCdnc3AgQMZ\nNWoU99xzD76+vmomedjVZGhpaSmDBg0CLn75XbNmDXv37uXJJ5/UxKQedK056uvrS0pKCsHBwQwb\nNgybzcaDDz6o0Z4ecL3How0NDaSlpTFgwAByc3MZN26ccvQWcbVZeun9NnjwYAwGA9u2bSMvL4+B\nAwd6eAvkZlBDQr5TXV0dr776KkuXLuXEiRMUFBTwwAMPsHTpUkaOHEn37t0BOHr0KM3NzaSkpJCU\nlMQXX3zBH//4R/Lz8xkyZAhWqxWDweAKfYW/+1VUVPC73/2Oc+fOER0dTV1dHUVFRYwaNYpevXpx\n8OBBTp8+Tc+ePQkICCA1NZXly5cTHBzMxx9/7Jpkz2g0uvafPsjd5/Dhw7z44ousX7+eI0eOMHr0\naNauXUtQUBCJiYkEBgZy+vRpSktLyc7OJjw8nJaWFtauXcuCBQuorKxk/Pjx3HbbbZoV3s1uJEcX\nL17M559/ztChQ4mLiyMsLMx1zbPee+51Ixm6bNky+vbty7333suECROIiIjQfvSAG8nRt99+G7vd\n7mrSa/lk97rRHF23bp3reDQgIEDvPw+6kSxdvnw5MTExZGdnM2LECLp376596SU0qaV8p0OHDlFe\nXs7y5cv52c9+RlFRESdPniQ7O5tly5YBkJKSQkBAAPX19cDF5ZXy8/O59957Wbly5RWrNYhnFBUV\n8c///M9YLBaOHDnCwoULaWxspHv37hQWFgIwceJEiouLXZNZxsbGUltbywsvvIDVaiU1NfUbr6vw\nd58333yTkSNHMm/ePOx2O3/84x958MEHWbNmDXBxfyUlJXH+/HnXZHr5+fkcPnyY6dOn88QTT3iy\n/C7tRnN01apVV+To5c1dcY8bzVCbzUZcXJxrebpLowS1H93rRnL0scce48knn8BXyN0AAAwlSURB\nVPRk+V3azTge7dWrl+v19P7zjJuRpWlpaa7XU5Z6DzUk5Dt99dVXVyxpFRYWRmRkJMOGDWPPnj0U\nFhZisViw2WyuWW6joqJYsmQJeXl5AK4VN8Rz9uzZw6RJk5g5cybjx4+nvr6ejIwMWlpaKCwspK6u\njqSkJMLCwlixYgUA7777LpmZmXz88cfMmDHDw1vQdTmdTk6dOkVERATDhw8nODiY9PR0fH19SU1N\nxWg08tFHHwGQlZXFtm3bMJlMnDlzhuzsbFasWMHEiRM9vBVdm3K087vRDH388ceveD2jUYde7qQc\n7fyUo97hZh+PKku9hy7ZEJdLnca2tjaMRiNJSUmute7b29tZsWIFd999N6mpqdTW1vI///M/9OzZ\nk9WrV5OTk0NmZibh4eFYLBba29sBhcWt4PTp0yQnJxMVFUVUVBTz589n2rRpGAwGjhw5wpkzZ+jT\npw+NjY0YjUb69u1LUlISd911FwEBAbS1takD7SEGgwGLxULv3r2JjIwEYN26dQQGBjJy5EhCQkJ4\n8803GTx4MGVlZRw/fpzc3FyioqLIzMx0nZEV91GOeh9laOemHO18lKPeSVkq38Xs6QLk1mE0Gqmr\nq3NdY375NZLFxcWEhoYSFRUFwNSpU7Faraxfv57c3Fx++MMffuO1xP0uLQ14+fwO48ePd91fUFBA\nZGQkgYGB5ObmEhISwm9+8xv27dtHYWEhc+fOBSAkJMT1ejoYc5+2trYr/t9OpxMfHx/XQTRcXKZu\n1KhRAAwYMIBHH32UDz74gIMHDzJr1izXtbTiGcrRzk0Z2vkpRzs/5WjnpyyVa6GGhFxh9uzZ3Hvv\nvdxzzz1XdCD379/vmpn/D3/4AwEBAUyZMoUJEya4HnMpfMT9Lv3vv+v/f+n+oqIisrOzMRgM+Pj4\nkJyczNtvv01xcTEvvfQSPj4+VzxP+9M9Lh1Am0wmGhsbOXjwIP369fvGWYDS0lKam5vp168ftbW1\nrFu3joceekjvvVuMcrTzUYZ2fspR76Ic7ZyUpXI9dMlGF/T1GWlPnTrl6kBWVFQQGxtLXFyc67EG\ng4G9e/eyadMm1q1bR3NzM5MmTSIoKOiKx2gIledc+t9v3ryZV155hSNHjtCnT58r1tg2GAxs3LiR\n1NRUqqur+dWvfoXT6SQnJ8c14dql4ZHiXpf+54WFhfziF79g3bp1+Pn5ERMTQ7du3VzDV6urq9m0\naRNOp5O33noLX19fBg4ceMXqJ+IeylHvogzt/JSjnY9y1PsoS+V6aIREF3P5UMaWlhaqq6t58skn\nmT59OuPHj6e1tZWjR4+Sm5t7RYf5zJkzOJ1OHnnkEdeavwp+z7p8OFx9fT1vvvkmDoeDqVOnsnjx\nYj788EP+6Z/+yTWssaWlhZMnT/LFF18QERHBY4895jrLcImGw7mH0+nE6XRe8WH7s5/9DIvFwm9/\n+1tKS0tZvXq1axK2S4+rqqriyJEjfPHFF8yZM0er2HiIctQ7KEM7N+Vo56Yc9R7KUrlRGiHRBbS0\ntHDmzBlCQkIwGo00Njby1ltvsWzZMvr06UNubi6FhYVs3LiR+++/n6VLl3L33XdjMplcZxR69erF\nww8/TExMDKDhcJ50+TJHLS0tmM1mmpqaeO2117j99tuZNGkS8fHxFBQUYLFY6NmzJwaDAZPJxJEj\nR+jduzfPPPPMN846SMe71PG/tP9KS0vZu3cvCQkJ+Pj48Mknn/CjH/2IuLg4ioqKqKioICYmxnX2\np1u3buTk5PDoo49itVo9vDVdi3LUeyhDOzflaOelHPUuylK5WdSQ8HJ2u52pU6dy+PBhRo0aRX19\nPb/85S9JTU2lb9++vPnmm9x9991MmDCBFStWYLfbaWpqYvjw4ZhMJlfIX/ogv/xAQDzj0v9+2bJl\nvPbaa9TW1mI0Ghk8eDAffvghkydPJioqim3btlFWVsaQIUNwOByYTCYGDBjA7bffDmj9Zndqa2vj\nrbfe4vjx4yQmJuLr68vvfvc7Fi5cSGtrKx999BH/+q//ysaNG7lw4QJ9+/YlMDCQHTt24HA4SE9P\nx2Aw4O/vT3R0tKc3p8tRjnoXZWjnpBzt3JSj3kdZKjeLWopezmq1EhMTw7Fjx8jPz8ff35/s7Gz6\n9+/Phg0bsNvt/PnPfwZgzpw5REVFkZ+fj8Ph+NZg0BAq99u5cyd5eXm8+uqr7Nq1C4A1a9ZQWFjI\nvHnzsNvtvPfee2RmZpKcnMyrr74KQEJCAuXl5QDfmBzo68NcpWOtWLGCLVu2UFBQwIkTJ6irq6Oq\nqooFCxbQr18/Dh06xLJly3j++edZsmQJFy5cICMjg/j4ePz9/V3X2YpnKEc7N2Wod1COdm7K0c5P\nWSodRSMkvMyZM2fYsWMHsbGxmEwmWltbqampwWq1UlRURE5ODomJiSxYsIAf/OAHTJs2jddee42A\ngACio6MZOHAgpaWlmM1mkpKSPL05XVpLSwuvv/46n3/+OZMnTyY2Nhaj0UhsbCyrV68mKyuLbdu2\nUVBQwMyZM0lMTKRHjx78+te/pqysjH379l0xrPFy6kK7V2ZmJpMnT2b//v2UlZURGxtLQkICCxYs\nYN++fTz66KN8/PHHTJ06lX379vHll19y55130rt3b1JTU7W/3Ew56h2Uod5FOdq5KEe9h7JUOppa\nUl5m1apV/OQnP2H+/Pm0t7djNpupqqqivb2dAQMGsHTpUqxWKxs3bmTIkCHExcXRv39//vrXv1JU\nVERTUxNNTU1kZGR4elO6vKqqKkpLS3nnnXcYN24co0aNYsiQIRgMBlJSUvi3f/s3YmNjeffdd8nI\nyGDNmjXcdttt5OXlUV5ezu9//3vXhE/iWa2trQDceeedfPXVV5SWlpKQkICvry8vvfQS48aNAy6u\npz5kyBBGjx4NgNmseYc9QTnqHZSh3kU52rkoR72HslQ6mlLay0yfPp3y8nJWrVqF2WzmRz/6ERMn\nTmTu3LmMHDmS3bt3U1ZWxsSJE3nkkUcwm83ce++9TJw4ER8fH/Lz87HZbNhsNk0u42FWq5XS0lJW\nrVqFxWLh6NGjlJeXc/78ef793/+djIwM14HW//7v/3L06FHGjx/PpEmTmD59Ojt37qR///4e3gqB\nvx8QZ2VlsXXrVnbu3ElTUxMNDQ3s2rWL06dP8+Mf/5jKykomTZrk4WpFOeodlKHeRTnauShHvYey\nVDqaLtnwMj4+PlitVioqKvDz82P37t04HA7i4+Pp2bMn7e3trF+/nmeeeQaA++67j5EjR7quxYuL\ni2PEiBGYzWaFv4eZzWbCw8N577332Lx5MwkJCQDU1NRQUFDAz3/+c1auXMnixYu5cOECM2bMIDw8\nnMDAQCIjI4mLiyM0NNTDWyGXXJq0KSYmhuXLlzN27FhCQ0P505/+xLlz55g2bRrZ2dmeLlNQjnoL\nZaj3UY52HspR76EslY5mcGqWH6/T3NzM+++/j9FoJC0tjaeffpr4+Hjmz59PfX09S5Ys4fHHHyc8\nPBzANdGTAv/WVFFRQffu3WloaMBisQAwceJE3n//fYKDgykpKXGto66zCLe28vJyIiIi+M1vfkPv\n3r2ZOHEidXV1BAYGero0+RrlqPdQhnoX5WjnoRz1LspS6SgaIeGFzGYzQUFB5OfnM3XqVKKiotiw\nYQN+fn6MHDmSYcOGuYLkUmAoNG5dAQEBOBwOunXrBsA777yDn58fd955JyaTybWOutbivrWVlZXx\n8ssv8+mnn3L27Fnuu+8+wsPD8fX19XRp8i2Uo95DGeo9lKOdi3LUuyhLpaNohISXcjqdLFmyhOrq\nap544gmKi4vp0aMHISEhgMKiM2loaOC//uu/qK6u5uzZs6SmppKXl0dkZKSnS5NrZLfb2b59O6NH\nj9YBdCegHPUOylDvohztXJSj3kNZKh1FDQkvVlZWxvLly5kxY8Y3OtDSuZSVlbFnzx6io6PJysoC\n9CEu4g7KUe+gDBXxHOWo91CWSkdQQ0KkE1L4i4hcP2WoiMiNU5bKzaCGRBegLrSIyI1RjoqI3Bjl\nqIh8GzUkRERERERERMTtNMZGRERERERERNxODQkRERERERERcTs1JERERERERETE7dSQEBERERER\nERG3U0NCRERERERERNxODQkRERERERERcbv/B2BAwD9a2MzcAAAAAElFTkSuQmCC\n",
    390       "text/plain": [
    391        "<matplotlib.figure.Figure at 0x7f407eeaa198>"
    392       ]
    393      },
    394      "metadata": {},
    395      "output_type": "display_data"
    396     }
    397    ],
    398    "source": [
    399     "import pandas as pd\n",
    400     "# Calculate factor-weighted long-short portfolio returns\n",
    401     "ls_factor_returns = al.performance.factor_returns(factor_data)\n",
    402     "\n",
    403     "# Plot cumulative returns for 5 day holding period\n",
    404     "al.plotting.plot_cumulative_returns(ls_factor_returns['5D'], '5D', freq=pd.tseries.offsets.BDay());"
    405    ]
    406   },
    407   {
    408    "cell_type": "markdown",
    409    "metadata": {},
    410    "source": [
    411     "The plot above shows some drawdown periods, and this analysis does not yet take into account transaction costs or market impact. With a cumulative return of only about 3% over four years, it is not a very promising strategy. At this point we really should conduct a deeper analysis using Alphalens and then iterate on our strategy idea. But for the sake of this tutorial, let's continue with our strategy as it is.  \n",
    412     "\n",
    413     "Having defined and tested a strategy, let's use it to build and test a long-short equity algorithm. The rest of the tutorial will cover the Algorithm API and will take place in the Interactive Development Environment (IDE)."
    414    ]
    415   }
    416  ],
    417  "metadata": {
    418   "kernelspec": {
    419    "display_name": "Python 3.5",
    420    "language": "python",
    421    "name": "py35"
    422   },
    423   "language_info": {
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