Stock Market Forecasting with Differential Graph Transformer

Stock_Market_Forecasting.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Install Dependencies"
      ],
      "metadata": {
        "id": "IroaP_EM55Oc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%capture\n",
        "%pip install torch_geometric torch pandas wandb"
      ],
      "metadata": {
        "id": "1l1soPp457nm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "import os\n",
        "\n",
        "# Create a directory in your Google Drive\n",
        "workdir = '/content/drive/MyDrive/Colab Notebooks/stock_dgt/'\n",
        "\n",
        "# Remove and recreate directory\n",
        "if os.path.exists(workdir):\n",
        "    shutil.rmtree(workdir)\n",
        "os.makedirs(workdir)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YzAaU-adD4kF",
        "outputId": "5ea9b8be-81c7-4f51-ea74-c5f9b499523b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NjKkCviZ0dFF"
      },
      "source": [
        "# Dataset Construction"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Download Dataset"
      ],
      "metadata": {
        "id": "UEE6lvSA0z74"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Clone the repository to download the S&P500 stock prices, precomputed correlation matrcies,\n",
        "# along with trained model weights for ease of evaluation\n",
        "!git clone https://github.com/AlienKevin/sp500.git\n",
        "\n",
        "import shutil\n",
        "import os\n",
        "\n",
        "repo_name = \"sp500\"\n",
        "for file_name in os.listdir(repo_name):\n",
        "    shutil.move(os.path.join(repo_name, file_name), workdir)\n",
        "\n",
        "# Remove the cloned repository folder\n",
        "shutil.rmtree(repo_name)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IFxwrsQ71FbF",
        "outputId": "f1e67512-c405-4dcf-842d-af7e7a489b8f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "fatal: destination path 'sp500' already exists and is not an empty directory.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Exploratory Data Analysis Shows Superiority of Mutual Information in Capturing Interstock Relationships"
      ],
      "metadata": {
        "id": "mVwZhqFx-KRY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "# Plot the 3 most correlated stocks to the target_stock based on corr_name with scope corr_scope\n",
        "def plot_most_correlated_stocks(target_stock, corr_name, corr_scope):\n",
        "    df = pd.read_csv(f'{workdir}/sp500.csv')\n",
        "    df['Date'] = pd.to_datetime(df['Date'])\n",
        "    df = df.set_index('Date')\n",
        "\n",
        "    target_index = df.columns.get_loc(target_stock)\n",
        "\n",
        "    corr = np.loadtxt(f'{workdir}/{corr_name}/{corr_scope}.csv', delimiter=',')\n",
        "\n",
        "    top_3_correlated_indices = corr[target_index].argsort()[-4:][::-1]\n",
        "    top_3_correlated_stocks = df.columns[top_3_correlated_indices]\n",
        "\n",
        "    plt.clf()\n",
        "    plt.figure(figsize=(12, 6))\n",
        "    plt.style.use('default')\n",
        "\n",
        "    for stock in top_3_correlated_stocks:\n",
        "        if corr_scope.startswith('global'):\n",
        "            # Plot the entire duration of the dataset for global correlations\n",
        "            plt.plot(df.index, df[stock], label=stock)\n",
        "        else:\n",
        "            # Only plot the time window corresponding to the local correlations\n",
        "            num_days_in_quarter = 64\n",
        "            quarter_index = int(corr_scope.split('_')[-1])\n",
        "            quarter_start_index = quarter_index * num_days_in_quarter\n",
        "            quarter_end_index = (quarter_index + 1) * num_days_in_quarter\n",
        "            print('Quarter Start date', df.index[quarter_start_index])\n",
        "            print('Quarter End date', df.index[quarter_end_index])\n",
        "            quarter_df = df.iloc[quarter_start_index:quarter_end_index]\n",
        "            plt.plot(quarter_df.index, quarter_df[stock], label=stock)\n",
        "\n",
        "    plt.title(f\"Top 3 Correlated Stocks with {top_3_correlated_stocks[0]} using {'Global' if corr_scope.startswith('global') else 'Local'} {'Pearson' if corr_name == 'pcc' else 'Mutual Information'}: {', '.join(top_3_correlated_stocks[1:])}\")\n",
        "    plt.xlabel(\"Date\")\n",
        "    plt.ylabel(\"Price\")\n",
        "    plt.legend()\n",
        "    plt.grid(True)\n",
        "    plt.show()\n"
      ],
      "metadata": {
        "id": "wOQvTHmp-Pdd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Global Mutual Information Captures Shared Trends Well"
      ],
      "metadata": {
        "id": "wq6NugHBLJ8P"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plot_most_correlated_stocks('AAPL', 'mi', 'global_corr')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 581
        },
        "id": "G0t3kRxQEFo1",
        "outputId": "93a46323-39dc-4a88-969e-04120741a6d1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Global Pearson Struggles with Nonlinearity in the Market"
      ],
      "metadata": {
        "id": "Gir8jsqCLTEm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plot_most_correlated_stocks('AAPL', 'pcc', 'global_corr')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 581
        },
        "id": "vqRlDukPEMMy",
        "outputId": "3b7200e1-d502-4a20-e663-a480a0c4da0f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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bMjMzbdo7efJkQaPRCBcuXDBvs/49SbZu3SoAEObNm6fY/vfffyu2S+9B3759BZPJZN7vnXfeEQAII0aMsDm2XGZmptCgQQMBgBAbGys8+eSTQnx8vHDt2jWbfe211dVz69SpU4JWqxUGDBig+H0JgqBoe2xsrNC3b19BEAThm2++ETQajTBp0iTF/q+88ooQGhoq5OXlOXx91hYvXiwAEI4dOyYIgiAsX75c8PPzEx588EHh0UcfNe/XrFkzxbk+a9YsAYBw7tw587bGjRsLHTt2tHkOad9u3bopXte4ceMEnU4npKSkOG3n77//LgAQTp06JQiCIKSlpQn+/v7ClClTVPf/8ssvhYCAACEtLU0QBEE4efKkAEBYtmyZYj/pXJ05c6aQnJwsJCYmCn/99ZdQs2ZNQaPRCLt37xYEwfJ38X//939O22otNjZW9bzr2LGj4v166KGHhMaNGzs8ltr7HhsbKwAQtmzZYt52/fp1wc/PT3jttdfM28aMGSNoNBph//795m03b94UIiIibI5p7eDBgwIAYezYsTb33bx5U0hOTjb/5OTkmO8bMWKEEBsba75dHJ+7ap8t27dvFwAIv/zyi3mb2ueSGuk9XrdunZCcnCxcunRJWLhwoRAZGSkEBAQIly9fFs6fPy/odDrhk08+UTz28OHDgo+Pj2K7q599I0aMEAAIb7/9tmLf/fv3CwCExYsX223zgQMHBADC008/rdj++uuvCwCEDRs2mLe5er5I+8p/f/ZI7631j1artXmPJP369RPatm1rvv3jjz8KPj4+Nv93CkL+WalmypQpAgDhzz//NG9Ta7f1j/Q5IAiC8MEHHwgAhOTk5EK3s2nTpsLQoUPNt9955x2hYsWKgsFgUOw3YsQIQa/XC5UrVxaqVKkinDx50m3HDQoKEgRB/Nvr2rWrIAiCYDQahZiYGGHixIlF+swj8jbsHk9Uiq1atQo6nQ4vv/yyYvtrr70GQRBsKtm2adPGXAAOAGrUqIGHHnoIa9asgdFodPp8nTt3RkJCAhYvXoznn38eer0eGRkZTh+XlpbmsNiW3Lp165Cbm4uxY8dCq7V8RD3zzDMIDQ3FX3/9pdjfz88PI0eOVD3WwIEDUalSJfPtW7duYcOGDRg8eLA5m3Ljxg3cvHkTPXv2xKlTp3DlyhW7bQsICDCvZ2Rk4MaNG7j//vshCAL279/v9LUtXrwYYWFh6N69u/m5b9y4gZYtWyI4OBgbN25UvAdjxoxRZMnHjh3r9Dmkdu7cudPc7Xz27NkYNWoUKleujDFjxth0NVXj6rn1xx9/wGQyYcKECYrfFwDVDP8XX3yBV155BZ9//rnNkIjw8HBkZGQgISHBpdcpad++PQCYuxxv3boV99xzD7p3746tW7cCEIdwHDlyxLxvYT377LOK19W+fXsYjUZcuHDB6WPnzZuHVq1amXumhISEoG/fvna7vM+bNw99+/Y1/+3Uq1cPLVu2tLv/U089hUqVKqFKlSro27cvMjIyMGfOHPOY5pIQHh6Oy5cvY/fu3QV+bKNGjRS/n0qVKqFBgwaKau5///032rRpg+bNm5u3RUREmLtgO5KWlgYAqpWsa9eujUqVKpl/HA2XKY7PXflni8FgwM2bN1G3bl2Eh4dj3759Tl+bPd26dUOlSpVQvXp1DBkyBMHBwVi2bBmqVq2KpUuXwmQyYfDgwYrPo5iYGNSrV8/8eWTdPlc++6zHW0uZ9DVr1iAzM1O1ratWrQIAvPrqq4rtUs8U689+V84XQMywF2SYwYQJE5CQkICEhAQsWrQIjz32GN59912bYTo3b97EmjVrFLUBBg4cCI1Gg99++83l5yso6fy9c+eOYvtDDz1kbrf8Rz78yF0OHTqEw4cPK177Y489hhs3bmDNmjU2+xuNRty4cQMRERGoWLGi244refzxx7Fp0yYkJSVhw4YNSEpKYtd4KnPYPZ6oFLtw4QKqVKliExBL3X+tA4l69erZHKN+/frIzMxEcnKyw2JygNhVWOouPGjQIHz66afo3r07Tp065fCxoaGhNl8wHL0mAGjQoIFiu6+vL2rXrm3zmqpWrWq3q7B1gZvTp09DEAS8//77eP/991Ufc/36dVStWlX1vosXL2LChAlYvny5zRh0+bhMe06dOoXU1FRERUXZfW7A8h5Y/74qVaqEChUqOH0eQPyS/MUXX+CLL77AhQsXsH79enz55ZeYNm0awsLCnBYMcvXcOnPmDLRaLRo1auS0TZs3b8Zff/2Ft956S/WL5IsvvojffvsNvXv3RtWqVdGjRw8MHjwYvXr1cnjc6Oho1KtXD1u3bsVzzz2HrVu3onPnzujQoQPGjBmDs2fP4vjx4zCZTEUO2mvUqKG4Lf0+rM8HaykpKVi1ahVeeuklnD592ry9bdu2WLJkCU6ePIn69eubtx8/fhz79+/H8OHDFft36tQJ06dPR1pamrk2gWTChAlo3749dDodKlasiIYNG6qOwy1Ob731FtatW4d7770XdevWRY8ePfD444+r1lewZv3eAuL7K39vL1y4gDZt2tjs52yIDgDzuazWXfjPP/+EwWDAwYMH8frrrzs8TnF87mZlZWHy5MmYNWsWrly5ohiv7Mpniz3Tp09H/fr14ePjg+joaDRo0MB8ce3UqVMQBEG1fQAUw5AK8tnn4+NjM/ygVq1aePXVV/H1119j3rx5aN++PR588EEMGzbMHNBfuHABWq3W5ncZExOD8PBwm/fVlfOlMJo2bYpu3bqZbw8ePBipqal4++238fjjj5svBC9atAgGgwEtWrRQ/I3ed999mDdvnmrROneQzl/r869atWqKdksuX77s9jbMnTsXQUFBqF27tvm1+/v7myvo9+3bV7F/QEAAfv75ZwwdOhR9+/ZFQkKCarG+gh5XItVPWLRoEQ4cOIB77rkHdevWdXtNCCJPYtBORIU2aNAgvPvuu/jzzz/x3HPP2d0vLi4OBw4cQG5urturt8szQM7uM5lMAIDXX38dPXv2VH2MvS//RqMR3bt3x61bt/DWW28hLi4OQUFBuHLlCp588knzsR0xmUyIioqymymV9wpwp9jYWDz11FMYMGAAateuXaCpedypcePGSElJwa+//ornnnvO5qJKVFQUDhw4gDVr1mD16tVYvXo1Zs2aheHDh5vHddvTrl07rF+/HllZWdi7dy8mTJiAJk2aIDw8HFu3bsXx48cRHByMFi1aFOk12CtQJg+y1CxevBg5OTn46quv8NVXX9ncP2/ePEycONF8e+7cuQCAcePGYdy4cTb7L1myxKaHiXWw4U726iIYjUbFe9KwYUOcOHECK1euxN9//40lS5ZgxowZmDBhguL1qSnse+uqunXrwsfHB0eOHLG5T6rIX9IXOSRjxozBrFmzMHbsWLRp0wZhYWHQaDQYMmSIS58t9tx77712e1qYTCbz3OJq772U0S3oZ5+fn59NrxtArMz/5JNP4s8//8TatWvx8ssvY/LkydixY4ciyLd3rlkr7vNFrmvXrli5ciV27dplDhylz3F7F6TOnj2rWmitqKTz15ULVcVByJ+2MiMjQ/Vi7fXr15Genm7To2XIkCG4ffs2XnzxRTz88MNYsWKF4vtAYY8LiOfcww8/jDlz5uDs2bPlcgpTKvsYtBOVYrGxsVi3bp3NXM///fef+X65U6dO2Rzj5MmTCAwMLFTAKFUOdpYJeuCBB7B9+3YsWbLE6TRDUptPnDih+MKTm5uLc+fOFSkokY6n1+sLfJzDhw/j5MmTmDNnDoYPH27ertaV296Xzjp16mDdunVo27atw4sN0ntw6tQpxXuQnJxcpCxShQoVUKdOHUXQYq+trp5bderUgclkwrFjxxRdltVUrFgRv//+O9q1a4euXbvin3/+sZk/3NfXFw888AAeeOABmEwmvPjii/jhhx/w/vvvO/yS2r59e8yaNQsLFy6E0WjE/fffD61Wi3bt2pmD9vvvv99pVXBXA4aCmjdvHpo0aYIPPvjA5r4ffvgB8+fPNwe1giBg/vz56Ny5M1588UWb/SdNmoR58+bZHRZSHCpUqKA6S8SFCxdsApOgoCA8+uijePTRR5Gbm4uHH34Yn3zyCcaPHw9/f/8itSM2NlaR1ZSobbMWFBSETp06YfPmzbhy5YrdHjWutMHdn7u///47RowYobigk52d7XRmjqKoU6cOBEFArVq1FL08rBXks8+Zpk2bomnTpnjvvfewbds2tG3bFv/73//w8ccfIzY2FiaTCadOnVIUi7x27RpSUlJs3teSlJeXB8CS5ZYqmr/00ks2UzCaTCY88cQTmD9/vs0QoKJKT0/HsmXLUL16dbsFNYvb5s2bcfnyZXz00Uc2bbh9+zaeffZZ/PHHH6pT077wwgu4desW3nvvPQwbNgwLFy40X+ApynEBsYv8zJkzodVqMWTIEDe9WiLvwTHtRKVYnz59YDQaMW3aNMX2KVOmQKPRoHfv3ort27dvV4yPvHTpEv7880/06NHDYTBz48YN1ezFzz//DABOx8w+//zzqFy5Ml577TWcPHnS5v7r16+bM7/dunWDr68vvv32W8VzxsfHIzU11W73OFdERUWhU6dO+OGHH3D16lWb+x1N3SW9P/I2CYKgOh2Z1O3P+gv34MGDYTQaMWnSJJvH5OXlmffv1q0b9Ho9vvvuO8XzTZ061W775A4ePKhaRfjChQs4duyYYuiBvba6em71798fWq0WH330kU3GTe2cqVatGtatW4esrCx0794dN2/eNN8nXwcArVZrrvzubBy+1O39888/R7Nmzcxdbtu3b4/169djz549LnWNDwoKcnugdOnSJWzZsgWDBw/GoEGDbH5GjhyJ06dPY+fOnQCAf//9F+fPn8fIkSNV93/00UexceNGJCYmurWdjtSpUwc7duxQzF6wcuVKm6nBrH+Hvr6+aNSoEQRBgMFgKHI7evbsie3bt+PAgQPmbbdu3XJ5KrwJEybAaDRi2LBhqt3kXcnSFsfnrk6ns3nu7777zqVaI4X18MMPQ6fTYeLEiTbPLQiC+XdZkM8+e9LS0syBr6Rp06bQarXmv+0+ffoAsP2c+/rrrwGg0J/9RZ3yDRDPdQC46667AFiy7G+++abN3+fgwYPRsWPHAk3P6IqsrCw88cQTuHXrFt59991iu8DojNSF/Y033rB57c888wzq1avn8LW/++67GDduHBYvXqzooVfU43bu3BmTJk3CtGnTnA71IyqNmGknKsUeeOABdO7cGe+++y7Onz+Pu+66C2vXrsWff/6JsWPH2kyp0qRJE/Ts2VMx9RAAp91W586di//973/o378/ateujTt37mDNmjVISEjAAw88YJ77154KFSpg2bJl6NOnD5o3b45hw4aZCzPt27cPCxYsMI9TrVSpEsaPH4+JEyeiV69eePDBB3HixAnMmDED99xzj92r7K6aPn062rVrh6ZNm+KZZ55B7dq1ce3aNWzfvh2XL1/GwYMHVR8XFxeHOnXq4PXXX8eVK1cQGhqKJUuWqGa+pdf28ssvo2fPntDpdBgyZAg6duyI5557DpMnT8aBAwfQo0cP6PV6nDp1CosXL8Y333yDQYMGoVKlSnj99dcxefJk9OvXD3369MH+/fuxevVqh0V8JAkJCfjggw/w4IMPonXr1ggODsbZs2cxc+ZM5OTkKLoO2murq+dW3bp18e6772LSpElo3749Hn74Yfj5+WH37t2oUqUKJk+ebNO+unXrYu3atejUqRN69uyJDRs2IDQ0FE8//TRu3bqFLl26oFq1arhw4QK+++47NG/e3GlWqW7duoiJicGJEycwZswY8/YOHTrgrbfeAgCXgvaWLVvi+++/x8cff4y6desiKirK6fntzPz5883ThKnp06cPfHx8MG/ePPN4WJ1OZzdIefDBB/Huu+9i4cKFNkW7XLF+/XpkZ2fbbO/fv7/dqR+ffvpp/P777+jVqxcGDx6MM2fOYO7cuTafMT169EBMTAzatm2L6OhoHD9+HNOmTVMU1CuKN998E3PnzkX37t0xZswY85RvNWrUwK1bt5wGMu3bt8e0adMwZswY1KtXD0OHDkVcXBxyc3Nx8uRJzJs3D76+vg6/9BfH526/fv3w66+/IiwsDI0aNcL27duxbt26Yp2ur06dOvj4448xfvx4nD9/Hv3790dISAjOnTuHZcuW4dlnn8Xrr79eoM8+ezZs2ICXXnoJjzzyCOrXr4+8vDz8+uuv0Ol0GDhwIAAxIB4xYgR+/PFHpKSkoGPHjti1axfmzJmD/v37o3PnzoV6nQWd8m3r1q3mv49bt25h+fLl2Lx5M4YMGYK4uDgAYtDevHlzVK9eXfUYDz74IMaMGYN9+/bh7rvvxvnz51GrVi2MGDECs2fPdtqGK1eumIfIpKen49ixY1i8eDGSkpLw2muvORyOVlibNm1C586d8cEHH9jtXi5NW9m9e3e7vWYefPBBfPPNN7h+/brd+i1fffUVbt++jZ9//hkRERH46KOPinxcrVbr9p4NRF6lRGrUE5FbqE3PdefOHWHcuHFClSpVBL1eL9SrV0/4v//7P8W0VIIgTgkzevRoYe7cuUK9evUEPz8/oUWLFk6nDhIEQdi9e7fwyCOPCDVq1BD8/PyEoKAg4e677xa+/vprm2lYHElMTBTGjRsn1K9fX/D39xcCAwOFli1bCp988omQmpqq2HfatGlCXFycoNfrhejoaOGFF14Qbt++rdinY8eOqtNLOZvm5cyZM8Lw4cOFmJgYQa/XC1WrVhX69esn/P777+Z91KZWOnbsmNCtWzchODhYqFixovDMM8+Yp5GaNWuWeb+8vDxhzJgxQqVKlQSNRmPzO/vxxx+Fli1bCgEBAUJISIjQtGlT4c033xQSExPN+xiNRmHixIlC5cqVhYCAAKFTp07CkSNH7E69JXf27FlhwoQJQuvWrYWoqCjBx8dHqFSpktC3b1/FtEnO2urquSUIgjBz5kyhRYsWgp+fn1ChQgWhY8eOQkJCgvl+tWmMdu7cKYSEhAgdOnQQMjMzhd9//13o0aOHEBUVJfj6+go1atQQnnvuOeHq1asOX6/kkUceEQAIixYtMm/Lzc0VAgMDBV9fXyErK0uxv9rUY0lJSULfvn2FkJAQAYB5OjNpX/m0SYLg2hRcTZs2FWrUqOGw7Z06dRKioqKE3NxcITIyUmjfvr3D/WvVqiW0aNFC0QZH02kJguXvwt7Pr7/+6vDxX331lVC1alXBz89PaNu2rbBnzx6bKd9++OEHoUOHDkJkZKTg5+cn1KlTR3jjjTcUf9/2pnxTm+bK+viCIE4f1r59e8HPz0+oVq2aMHnyZOHbb78VAAhJSUkOX4P8GMOHDxdq1Kgh+Pr6CkFBQUKzZs2E1157TTFlmyDYTvkmCO7/3L19+7YwcuRIoWLFikJwcLDQs2dP4b///rP5ey/olG/W56uaJUuWCO3atROCgoKEoKAgIS4uThg9erRw4sQJ8z6ufvbJp+GSO3v2rPDUU08JderUEfz9/YWIiAihc+fOwrp16xT7GQwGYeLEiUKtWrUEvV4vVK9eXRg/frxiik5BKNj5UpQp33x9fYW4uDjhk08+EXJzcwVBEIS9e/cKAIT333/f7rHOnz8vABDGjRsnCII4jR5UpsJTI01nB0DQaDRCaGio0LhxY+GZZ54Rdu7cqfoY6TxTo3YuqE35tmLFCtUpVOWWLFkiABDi4+Pt7rNp0yYBgPDNN98IgmD/nMjLyxP69+8vABA++eQTtx1XjlO+UVmiEYRiqNhBRF5Ho9Fg9OjRNl06iYhKu7Fjx+KHH35Aenq607oFRCVtxowZePPNN3HmzBnzDCze5s0338SCBQtw+vRp+Pn5ebo5RGSFY9qJiIio1JAKYEpu3ryJX3/9Fe3atWPATl5p48aNePnll702YAfENr7//vsM2Im8FMe0ExERUanRpk0bdOrUCQ0bNsS1a9cQHx+PtLQ0vP/++55uGpGqxYsXe7oJTu3evdvTTSAiBxi0ExERUanRp08f/P777/jxxx+h0Whw9913Iz4+Hh06dPB004iIiIoFx7QTEREREREReSmOaSciIiIiIiLyUgzaiYiIiIiIiLwUx7QDMJlMSExMREhICDQajaebQ0RERERERGWcIAi4c+cOqlSpAq3Wfj6dQTuAxMREVK9e3dPNICIiIiIionLm0qVLqFatmt37GbQDCAkJASC+WaGhoR5uDZUGBoMBa9euRY8ePaDX6z3dHCIbPEfJ2/EcJW/Hc5S8Hc/R0i8tLQ3Vq1c3x6P2MGgHzF3iQ0NDGbSTSwwGAwIDAxEaGsoPSfJKPEfJ2/EcJW/Hc5S8Hc/RssPZEG0WoiMiIiIiIiLyUgzaiYiIiIiIiLwUg3YiIiIiIiIiL8Ux7UREREREROWUIAjIy8uD0Wj0dFPKHJ1OBx8fnyJPK86gnYiIiIiIqBzKzc3F1atXkZmZ6emmlFmBgYGoXLkyfH19C30MBu1ERERERETljMlkwrlz56DT6VClShX4+voWOSNMFoIgIDc3F8nJyTh37hzq1asHrbZwo9MZtBMREREREZUzubm5MJlMqF69OgIDAz3dnDIpICAAer0eFy5cQG5uLvz9/Qt1HBaiIyIiIiIiKqcKm/0l17jj/eVviIiIiIiIiMhLMWgnIiIiIiIi8lIM2omIiIiIiIi8FIN2IiIiIiIiKnW2b98OnU6Hvn372t1nwYIF0Ol0GD16tM19mzZtgkajMf9ER0dj4MCBOHv2rHmfmjVrYurUqcXRfJcxaCciIiIiIqJSJz4+HmPGjMGWLVuQmJhod58333wTCxYsQHZ2tuo+J06cQGJiIhYvXoyjR4/igQcegNFoLM6mFwinfCMiIiIiIiIIgoAsQ8kHqwF6XYHniE9PT8eiRYuwZ88eJCUlYfbs2XjnnXcU+5w7dw7btm3DkiVLsHHjRixduhSPP/64zbGioqIQHh6OypUrY8KECRg6dChOnz6NBg0aFOl1uQuDdiIiIiIiIkKWwYhGE9aU+PMe+6gnAn0LFpr+9ttviIuLQ4MGDTBs2DCMHTsW48ePVwT/s2bNQt++fREWFoZhw4YhPj5eNWiXCwgIACDOY+8t2D2eiIiIiIiISpX4+HgMGzYMANCrVy+kpqZi8+bN5vtNJhNmz55t3mfIkCH4559/cO7cObvHvHr1Kr788ktUrVrVa7LsADPtRERExSctEcjLASJqebolRERETgXodTj2UU+PPG9BnDhxArt27cKyZcsAAD4+Pnj00UcRHx+PTp06AQASEhKQkZGBPn36AAAqVqyI7t27Y+bMmZg0aZLieNWqVYMgCMjMzMRdd92FJUuWwNfXt+gvzE0YtBMRERWHvBzg64aAPhB47QTgH+rpFhERETmk0WgK3E3dE+Lj45GXl4cqVaqYtwmCAD8/P0ybNg1hYWGIj4/HrVu3zN3dATH7fujQIUycOBFaraXT+datWxEaGoqoqCiEhISU6Gtxhff/RoiIiEqja0fFpSETSLkIxDTxbHuIiIjKgLy8PPzyyy/46quv0KNHD8V9/fv3x4IFC/DII4/gzz//xMKFC9G4cWPz/UajEe3atcPatWvRq1cv8/ZatWohPDy8pF5CgTFoJyIiKg65GZb1jGTPtYOIiKgMWblyJW7fvo1Ro0YhLCxMcd/AgQMRHx+P7OxsREZGYvDgwTZV6fv06YP4+HhF0O7MlStXcODAAcW22NhYVKhQodCvoyBYiI6IiKg45MnmgmXQTkRE5Bbx8fHo1q2bTcAOiEH7nj178Oqrr2LAgAGq08gNHDgQy5cvx40bN1x+zi+//BItWrRQ/Pz1119Feh0FwUw7ERFRcTBkWdYZtBMREbnFihUr7N537733QhAEh48fPHgwBg8eDADo1KmT0/3Pnz9f4Da6GzPtRERExUGeaU+/7rl2EBERUanGoJ2IiKg4MNNOREREbsCgnYiIqDhwTDsRERG5AYN2IiKi4mDItKyzezwREREVEoN2IiKi4mCQZ9pdr1BLREREJMegnYiIqDjkyce0XwecVKclIiIiUsOgnYiIqDjIM+3GXCA3w3NtISIiolKLQTsREVFxkGfaAWU1eSIiIiIXMWgnIiIqDmc2Km/LC9MRERERuYhBOxERkbuZjEDqJeU2Bu1ERERUCAzaiYiI3O3aEcu6PlBcMmgnIiJyiyeffBIajcb8ExkZiV69euHQoUM2+z733HPQ6XRYvHixB1rqHgzaiYiI3O38v+KybjcgrJq4nsugnYiIyF169eqFq1ev4urVq1i/fj18fHzQr18/xT6ZmZlYuHAh3nzzTcycOdNDLS06H083gIiIqMxJ/k9cVm0pFqC7cRJIu+LZNhERETkjCJ7pGaYPBDSaAj3Ez88PMTExAICYmBi8/fbbaN++PZKTk1GpUiUAwOLFi9GoUSO8/fbbqFKlCi5duoTq1au7vfnFjUE7ERGRu0nj2f1CgdCq4vrun4G7hniuTURERM4YMoFPq5T8876TCPgGFfrh6enpmDt3LurWrYvIyEjz9vj4eAwbNgxhYWHo3bs3Zs+ejffff98dLS5R7B5PRETkToZs4MwGcV3nCwRHies+/p5rExERURmzcuVKBAcHIzg4GCEhIVi+fDkWLVoErVYMcU+dOoUdO3bg0UcfBQAMGzYMs2bNgiAInmx2oTDTTkRE5E4ZyZZ1kwGIbQtsnwbkZXuuTURERK7QB4pZb088bwF17twZ33//PQDg9u3bmDFjBnr37o1du3YhNjYWM2fORM+ePVGxYkUAQJ8+fTBq1Chs2LABXbt2dWvzixuDdiIiIncyGWTreYCPn7jOoJ2IiLydRlOkbuolKSgoCHXr1jXf/vnnnxEWFoaffvoJEydOxJw5c5CUlAQfH0vIazQaMXPmTAbtRERE5ZYgAAkfWG4bc2VBe45n2kRERFQOaDQaaLVaZGVlYdWqVbhz5w72798PnU5n3ufIkSMYOXIkUlJSEB4e7rnGFhCDdiIiIndJ3A8cX2657RdmGcvOTDsREZHb5OTkICkpCYDYPX7atGlIT0/HAw88gKlTp6Jv37646667FI9p1KgRxo0bh3nz5mH06NGeaHahsBAdERGRu+SmK2/fPZyZdiIiomLw999/o3LlyqhcuTLuu+8+7N69G4sXL0bDhg3x119/YeDAgTaP0Wq1GDBgAOLj4z3Q4sJjpp2IiMhd8nIt6/c8Dej9mWknIiJys9mzZ2P27Nl27zcYDHbvmzFjRjG0qHgx005EROQuxvxsenRToO9X4joz7URERFQEDNqJiIjcRQrM/cMs28yZ9hyxUB0RERFRAXhN0P7ZZ59Bo9Fg7Nix5m3Z2dkYPXo0IiMjERwcjIEDB+LatWuKx128eBF9+/ZFYGAgoqKi8MYbbyAvL6+EW09ERARL0O7ja9kmZdohAEb73fWIiIiI1HhF0L5792788MMPaNasmWL7uHHjsGLFCixevBibN29GYmIiHn74YfP9RqMRffv2RW5uLrZt24Y5c+Zg9uzZmDBhQkm/BCIiIuDaEXGp87NskzLtAMe1ExERUYF5PGhPT0/H0KFD8dNPP6FChQrm7ampqYiPj8fXX3+NLl26oGXLlpg1axa2bduGHTt2AADWrl2LY8eOYe7cuWjevDl69+6NSZMmYfr06cjNzbX3lERERMVj+zRxeXK1ZZtOlnXnuHYiIiIqII9Xjx89ejT69u2Lbt264eOPPzZv37t3LwwGA7p162beFhcXhxo1amD79u1o3bo1tm/fjqZNmyI6Otq8T8+ePfHCCy/g6NGjaNGihepz5uTkICfH8sUpLS0NgFhl0FGlQSKJdJ7wfCFvxXO0hOWmQ7f6DfOVcCGkMvJk772Pjz80edkwZKcDfuEeaaK34TlK3o7nKHm7op6jBoMBgiDAZDLBZDK5s2kkYzKZIAgCDAYDdDqd4j5Xf3ceDdoXLlyIffv2Yffu3Tb3JSUlwdfXF+Hh4Yrt0dHRSEpKMu8jD9il+6X77Jk8eTImTpxos33t2rUIDAws6MugciwhIcHTTSByiOdoyWhyeS7qJK813/4n5incWrXKfLuPoIUewOb1a5DhX9kDLfRePEfJ2/EcJW9X2HPUx8cHMTExSE9PZy/lYpSbm4usrCxs2bLFpvZaZmamS8fwWNB+6dIlvPLKK0hISIC/v7/zB7jR+PHj8eqrr5pvp6WloXr16ujRowdCQ0NLtC1UOhkMBiQkJKB79+7Q6/Webg6RDZ6jJUs3Z5ridut+I4DACPNtn5PBQEYmOrZtDUQ3LunmeSWeo+TteI6StyvqOZqdnY1Lly4hODi4xOOx8iQ7OxsBAQHo0KGDzfss9fh2xmNB+969e3H9+nXcfffd5m1GoxFbtmzBtGnTsGbNGuTm5iIlJUWRbb927RpiYmIAADExMdi1a5fiuFJ1eWkfNX5+fvDz87PZrtfr+aFMBcJzhrwdz9ES4hukuKkPCAHk73t+MTo9jMrtxHOUvB7PUfJ2hT1HjUYjNBoNtFottFqPlzors7RaLTQajervydXfm8d+O127dsXhw4dx4MAB80+rVq0wdOhQ87per8f69evNjzlx4gQuXryINm3aAADatGmDw4cP4/r16+Z9EhISEBoaikaNGpX4ayIionJKbzW0yscqYyFN+8bq8URERFRAHgvaQ0JC0KRJE8VPUFAQIiMj0aRJE4SFhWHUqFF49dVXsXHjRuzduxcjR45EmzZt0Lp1awBAjx490KhRIzzxxBM4ePAg1qxZg/feew+jR49WzaQTEREVi4AKytvWGQspiGfQTkREVGRPPvkkNBqNOYMdHR2N7t27Y+bMmYqiejVr1sTUqVNtHv/hhx+iefPmdm97G6/uBzFlyhT069cPAwcORIcOHRATE4OlS5ea79fpdFi5ciV0Oh3atGmDYcOGYfjw4fjoo4882GoiIip3NBrLenis7f1Spt3IQj9ERETu0KtXL1y9ehXnz5/H6tWr0blzZ7zyyivo16+fTcG30s7jU77Jbdq0SXHb398f06dPx/Tp0+0+JjY2FqtkFXqJiIhKnCn/y0G9nkB329lJmGknIqLSQBAEZOVllfjzBvgEQCO/AO4CPz8/cx2zqlWr4u6770br1q3RtWtXzJ49G08//XRxNNUjvCpoJyIiKpWkoL12RyCqoe395jHtOSXXJiIiogLKysvCffPvK/Hn3fn4TgRa14cphC5duuCuu+7C0qVLy1TQ7tXd44mIiEoFo0Fcau1UgWWmnYiIqETExcXh/Pnz5ttvvfUWgoODFT+ffvqp5xpYCMy0ExERFZWUadfq1O/X5QfzeRzTTkRE3ivAJwA7H9/pked1F0EQFF3t33jjDTz55JOKfb799lts2bLFbc9Z3Bi0ExERFZUUtOvsZNqlYF4wlkx7iIiICkGj0bilm7onHT9+HLVq1TLfrlixIurWravYJyIioqSbVSTsHk9ERFRU5ky7nWvhmvx/t4JJ/X4iIiIqsg0bNuDw4cMYOHCgp5viVsy0ExERFZWzMe0aKdNuFbQf/h0ICAfqdiu2phEREZVFOTk5SEpKgtFoxLVr1/D3339j8uTJ6NevH4YPH+7p5rkVg3YiIqKiMuV3e7c3pl3KtJtk3eNTLgFLRonrH6Qo53onIiIih/7++29UrlwZPj4+qFChAu666y58++23GDFiBLTastWhnEE7ERFRUUlV4aWp3axpVbrHZ6coH693XxEeIiKismz27NmYPXu20/3kVeTlPvzwQ3z44Yd2b3ubsnUJgoiIyBOybonLgArq95vHtMsy7TpZgJ+dVjztIiIiolKPQTsREVFRZd0WlwF2qtGag3bBsi033bKenVo87SoqQQBuX1C2m4iIiEoUu8cTEREVhckIZKWI63Yz7SqF6JY9Z1nPuVMsTSu0lEtAwgQg/Rpw4V9g8K9Aowc93SoiIqJyiZl2IiKioshOBZCfiXbWPV5eiO7GScu6IaNYmlZoK14Gji4VA3YA+GeKZ9tDRERUjjFoJyKiskUQgJtnlAFycZK6xvuGAD6+6vto7Uz5JsnNdH+7iuLqIeVte/PPExERUbFj0E5ERGXLgXnAd3cDq14vmefLzC9CF2gnyw7YFqIzWQXv3pRpz7ih0l2fY9qJiIg8hUE7ERGVLRs+EZd7ZpbM8zmrHA/Igvb8YD3Hqlq8t2TaczOB/6sDGHOU240Gz7SHiIiIGLQTEVEZ4x9Wss93dpO4tFc5HrAN2uVztAOAwUuC9rRE9e3J/5XccAMiIiJSYNBORERli19wyT7fjhniMvWS/X3Mhejyg3ZpHLwk10u6x2vtfC3IywYS95dsW4iIiAgAg3YiIiprdH6eed6Ui/bvsy5EJ00RJ/GWTDs09u+6erDkmkFERERmDNqJiKhskVdwL8mx2NXutX+fdfd4m0x7ftAuCMCh34Brx9zfPleY8uzfZ8gquXYQERG5YPv27dDpdOjbt69i+/nz56HRaHDgwAHztjt37qBz585o1KgRLl++bN6elZWFiIgIVKxYETk5VjVdANSsWRMajQYLFy60ua9x48bQaDSYPXu2216TGgbtRERUtmj1lvUD80vueXtMsn+fdfV4m6A9HTjxN3BwIbD0GeD7NsXTRmccXeTIY9BORETeJT4+HmPGjMGWLVuQmGinLguA5ORkdO7cGRkZGdi6dSuqVatmvm/JkiVo3Lgx4uLi8Mcff6g+vnr16pg1a5Zi244dO5CUlISgoCC3vBZHOPEqERGVLfLx4WvfA1o8YX+stjv4BIgBbWCk/X00Vt3jrQvR7Zsj/sgJAqBx0F29ODjKtG/4GIAG6FBCU+kREVGJEwQBQlbJX6TVBARAU8D/eenp6Vi0aBH27NmDpKQkzJ49G++8847NfpcuXUL37t1RtWpV/PnnnwgOVta+iY+Px7BhwyAIAuLj4/Hoo4/aHGPo0KGYMmUKLl26hOrVqwMAZs6ciaFDh+KXX34pULsLg0E7ERGVLfKAOCcNMBkAbTGOc5ey59K4dTXmQnT5+0oXFnS+gDFX/TG3zgKRdYrYNgFIuQCEx7p2AcAky7S3fhG4cUocy55xXdy2YRJwz9NAQLjj4yQeAP56Dej2IVCrfSEbT0REJU3IysKJu1uW+PM22LcXmsDAAj3mt99+Q1xcHBo0aIBhw4Zh7NixGD9+vCL4P3HiBN544w20atUKCxYsgJ+f8vvAmTNnsH37dixduhSCIGDcuHG4cOECYmNjFftFR0ejZ8+emDNnDt577z1kZmZi0aJF2Lx5c4kE7eweT0REZUt2qvK2o+yxO0jZc42DoF3K9O+bA+SkA3n5Y+YcZef3zy1627ZPA765C9j2nWv7G2Xv1d0jgGG/A61GKvf5PNZSBd+eHzsCV/YAq98sWHuJiIhcJGXIAaBXr15ITU3F5s2bFfsMHz4cdevWxeLFi20CdkDMlvfu3RsVKlRAREQEevbsadMNXvLUU09h9uzZEAQBv//+O+rUqYPmzZu7/XWpYaadiIjKFuvK7MUdtEvZc42D6+Dy+zZNthR1C4gA7lxVf8yJ1UC3D4rWtrXvicuE94G2LzvfX8q0+wQAUXH56/62+134B6jVQblNEIC5A4FLOy3bfIt/nB8REbmPJiAADfbt9cjzFsSJEyewa9cuLFu2DADg4+ODRx99FPHx8ejUqZN5vwcffBB//PEHli5dikceeURxDKPRiDlz5uCbb74xbxs2bBhef/11TJgwAVqroXV9+/bFc889hy1btmDmzJl46qmnCvgqC49BOxERlR3GPCD3ju224iIIAARx3VH3ePmFg8u7gch64npghP3HJB8Xs/LO5p03ZAGr3wLSEoHgaODBby1tCY4B0pOcvgwzqRBdRC3LNr3KF6k5DwAfWvVoyE4FzqxXbgus6PpzExGRx2k0mgJ3U/eE+Ph45OXloUqVKuZtgiDAz88P06ZNM29799130axZMzz++OMQBAGDBw8237dmzRpcuXLFZgy70WjE+vXr0b17d8V2Hx8fPPHEE/jggw+wc+dO8wWDksCgnYiIyo6cNNttxZlpF2TdxB1l2o/I/rFf2mnpDeAoaAeAhY8BI1Y43ufXh4GL2yy3Gz4ANOglrleqbwnajQZAp7d9vJz0XmllXw/UMu0AkHIJCK9uuZ2bbruP18w/T0REZUVeXh5++eUXfPXVV+jRo4fivv79+2PBggXo1auXedv7778PrVaLoUOHQhAEc5AeHx+PIUOG4N1331Uc45NPPkF8fLxN0A6IXeS//PJLPProo6hQoUIxvDp1DNqJiKjskKZS0weJBd5MhuIN2qWu8YDjoN06eL1xQlwGOAnaz21x3gZ5wA4og+cQSwYCl/cAsU6mkpPeK3lwby9oz04BIA/aM2z3YdBORERutnLlSty+fRujRo1CWFiY4r6BAwciPj5eEbQDYsZdp9Nh6NChMJlM6NatG1asWIHly5ejSZMmin2HDx+OAQMG4NatW4iIUP6fbtiwIW7cuIHAEu6NwEJ0RERUdkhF6ALCLYGnycHc40Ulz7Q76h7/4Lfq251l2nW+BW+TPOAWZBcVEvc5f6zUPV5r5xjB0ZZ169oBamPzs1V6PhARERVBfHw8unXrZhOwA2LQvmfPHqSl2f7/efvtt/Hpp5/iiSeewI8//oigoCB07drVZr+uXbsiICAAc+eqF4SNjIxEQAHH4BcVM+1ERFR2SNO9+YcBOflj2+XZcHcTXMy0x7ZV3169tWW9QV+xe//5rcBDM4A/XxR7C2Tech7cy8kDbvlrd6XHgXSBQx74G2UXPV47AcR3F8flW881/8tDtse7eUq8kOJv+8WKiIioMFassD9s7N5774UgiLVmpKXcm2++iTffFGc2se4WL/H19cXt27fNt8+fP++wPSkpKU5aXHTMtBMRUdkhZdr9wy3jso3FmGlXdI93NOWbDoiobbs9pqny9qNzgZF/Ay2GAiGVxW23zhWsTfKA++Ypy7or74NUtE/ea0Ae7Gs0lu7y0rR1QH5BPhWCCbhS8lWIiYiIyhIG7UREVHZIXbb9wyxBuzcUogOA0butNmiAIFl1dVOe2K1fGnceUUdc3jpbsDZJFxLycoCkw8rjO32sSvf48BrKfaTXKX/tednKfYYusfQuyLjp/HmJiIjILgbtRERUdsjHtJd00O5oTDsA6HyAR2ZbbvuHKbPi1mPv/UPFpUGlwJsj0nFyrKe+cyXTrtI9vl4PoNuHwPA/xdtqQXuurOCcjz9QrxsQGCnetu5GT0RERAXCMe1ERFQ2XNoFJOdXZfcPE4NkwDZoFwRg72wgtApQv2fRnrMgmXZArGovsQ5mrdspXQRwdtHBJwDIy7LclgJv6yDd0XFMRrHqvNqUbxoN0G6cbbvkr11+YUHK0geEi0vrgnVERERUIAzaiYio9Lt6UCyQJpGPabcOVk+vB1aOFdcn3HKeIXfEPKZdIwa3zsgz2PJAWDqGnLn9TgrphVcHbpwEdH6AMccSrGfdVu7nKGhf+DhwdjNwzyjbdlqTLk7I2yXPtLcZLS79w8UlM+1ERF5NrWAbuY873l92jyciotLv8h7lbf9Q+0F76kXL+u3zRXteqXq8q4G/PBgOrKi8zzrolzLWzjLtUvAsFYgzGcSK899bzclur3u8yQic/FvM1m+fpnxuNRonmfb7x4hLZtqJiLyaXi9+1mdmZjrZk4pCen+l97swmGknIqLit/JVIOUi0OszoGJd9x8/oILytm+wJfC0DlYNsqJpmTeByDqFf14pcHWlazygnHc9qKL9/QDXx+RL9+v9gZxU4Pw/tu8HYH+++vTr9p9bjaMx7RXrA37B4joz7UREXk2n0yE8PBzXr4v/BwIDA6FxpdcYuUQQBGRmZuL69esIDw+HTlf4nn0M2omIqHgZ84A98eL6tATgyVVATTvzlheWwSpL4BdiyX4f+wOo0twSyObKssI5aUV7XinL7Wi6Nzl5pt1p0O7imHYpeJYuRmSliBctrNk7jtp7kJFs//mkL3TyOeql918faNnGTDsRkdeLiYkBAHPgTu4XHh5ufp8Li0E7EREVL+sM77nN7g/ac9KVt/1CLAHy3tnAtaPA0+vE27myfbOLGLRLAbOr3eO1Kt3jAyPFjH/9Xlb7ujimXbq/5XBg23dA1i3bKdgAyxzs1tTeg1tn7D+fuRCdbIzetaPi0ldWaI+ZdiIir6fRaFC5cmVERUXBYHBhlhEqEL1eX6QMu4RBOxERFa+CVDEvLGmqN4lfiLKL92XZHOnuyLRfOwYcXw7E9RVvu9w9XiXT/vw/wMXtQMOHlPu62j1eyniHx4rL9GuAIct2P3vd46WifHI3HQTt1t3jBQFYP9H2ccy0ExGVGjqdzi3BJRUPBu1ERFS8rINOg0oWuKjkxeUAIDjK/rhsRdB+R30fZ37uKnYJ3zQ5f4OLYwDl3filTHtoFaDJQNt9pfY7m19dyrSHVBaX6dftZNpVjiMIwLUjtts7jbf/fNbV40+sttyXnmRZZ6adiIjILVg9noiIipcxV3k7TyULXFQpl5S3g2McBO1u6B5vPYY+J1V9P2sR+UXvdH5i4ThH5GPaBQE4uAhIOmy7n5RpD61saVvGDXG9Xg9LAK6WsVfLqDfqD3R43X67rKvH75+rvp+5hkA6kJdj/3hERETkEDPtRERUvBxVb3eX1PygPayG2GXdN9A2aJ/aFLj3WeX0cIXNtFu77wXX9vMPBd44Y5mezRGp/TdOAmfWA8ueFW9/aHWBwJQfPPuFiQXoctOBlAviNt9gMZMPqAftl3babqvTxfEYfevu8VrZ9X95ITr/MMv6/9oBL8mGKBAREZHLGLQTEZH7ZaeKWeEa99uOpS6OTHt6frXzJ5ZZppTTWc2HmnIRWPuecltRq8dLekxyfV9nVeMlF7aJy5N/AzVa29/PPFe8VhwWcCsduJ0ftOsD7E99BwDXj9lu8wtx3C6tVaZdXihPXrVeHvjfOClm/1197URERGTG7vFERCTKuOm4AFlBzOoDzO4LHJhnW7U8N1P9MUUhjeHWB1i2OZprXCIP2tOTgTkPAId/L9hzh9ewvUDgDpd3ubaffNq54Ghx/dQacenjLytopxK03z5vu006hj1qU75JHPUguLjd8XGJiIhIFYN2IiISfXe3+HMnyfm+zkjFzQ4vth3Tfvtc0Y8vZzJZAlJ50OjKNGzyMe0J7wPntgBLRjl/XKAsY5yW6Fo7i2Ldh5b1Ne9axqwDsky7zjaTrQ8AdA6mjpPG5jceAPT9Cmg7Foi933FbrLvHRzWy3Kd18LXi4g7HxyUiIiJV7B5PRERillmq8n31IBAS457j+vjbZnhdCXJzMwEfP9cC72XPyZ7P17J+ZpPzx8rHtKdedr6/RApYgeKZwg4A+n8P/KEyVn77NPH3E1oVSLtiuSii81XOnQ7kZ9oddI+X2h7XD2g6yLV2aazmaZf/fh+abv9x26cBPT62ZOqJiIjIJcy0ExGRspu0vLp6UZ1aY9vdPDfdUjxNLi8HWDEW2PcL8FUcsPBx157j8G+WdXmm3ZWK7vLu8dYV4R2Rz4PeqL/rjyuIux6zf9+Jv4HfR8rG6GuAgAigkfVc7zpL13217vFS9t2VoQQS6ynfpMrwbV4CarZT7qvzVd4uiV4JREREZQyDdiIiAu5ctaxnpbj32Nun2W4zZNhu2/k/YO8sYPkYMeA++XfBn0sefAZGOt8/R3aBwuBigTxBsBTTe2oNMHiO6+0rCI0GiG2rfl/yceXtwAixG3yzwUCX9y3bz/9r6a1gXVsAsGTaCxO0S70NMm+Ky6BKtvvKawwA6vPHO2LMA85sKPzUfERERGUAg3YiIlIG7UXt7m3dRVvS8W1L12q1qdbUiuCpjcNW3G+VsZd3vR6+HGgxTFnRXNLwQXEpr2TvaqZdHnjKx3MXB7VAGLAEymr7Vb7Lst5+nKV7vNrvtTBBu7x6/KbPxboFABBe3XZf+RRwQMGr9e+YDvw6APhteMEeR0REVIYwaCciIvcG7VJ3aWuNB4jzlANA5i3b+9XGXDvL+qtl7CUxTcQx1lVa2N53z9P5j5cF4K5m2uX7WWeS3c066LVHHrRXbSku/cOBut2cdI8vSqbdCGz61LI9tJrtvtbvj9rFGkd2x4vLsxsL9jgiIqIyhEE7EREBabKgXS14Lgh7gVlkHUtm+uoBlcepZGH/naK8bcwTM/lShl0e/Mu7hcupbQ8Izz9ejiWb78pUdKmXgTn5WXqNrnimepOTB71+Yfb3C46yrAdGAOOOAS/vF287LEQnqzzvKilotz5emErQHtdPebugQXvKhYLtT0REVAYxaCciKs+MBiD5BHBHViBMLSNbEPa6QOv0QLV7xPVLO23vv37cdtu27yzrl3YDk6sBE8OBn7uIAWfaFfG+CjWBDq+rP6/0nHLy8e5S5txR1l6yaTJw7bC4rjZPubv5yjLtLYZaeghYu20V3IZVFYN3wBKQGzKVPQuAomXarcenq8040PldcSq5yHri7YIG7URERMSgnYioXJLGna94BZh+rzg/uUStYFlBOBq3XP1ecXlln+19WbcdH3fFK5Yx6In7xUrk6dfF28EOpqhTmztcHrRLwaegUtHemp3h+sVG3j3ex0+s0K5Gmq5P9Rj52fqMZOCrBsrfb1GC9gvblNvVsvV6f/FCQ3R+D4uCBO3W9QqKel4SERGVUgzaiYjKmyXPAN/fL449PzDP9v6cO8DZzYXvJu8oMAuOzt/HKrDPzQSyVMa5A+rzgQPAuc3A+o/Edb0/CsTH3zI9XEGmepNfAJCK2RUnefd4nZ+lW7+17h/ZP0ZABct6dgqQnmS5XZRCdNePuf4YvxBxWZBCdPI6CwBw44TrjyUiIipDGLQTEZUnJpM4r/n1Y+pd1AGxYvcvDwIbPyncc0hBu1q3dCk4tM6ayscuWxdfy83vtm5dlf7P0cCt/IrzPgUM2jUaS0Bs3WXcEfmFjN6fF+w5C0ORafcVA3e5vl8DTywD4vraP4Y8aAeUPQoKM6ZdW4hx/H75BQgLMnXb1KbK21u+LPjzEhERlQEM2omIyhN5F3R9IFD9Pvv7/vtN4Z5DCtr9QizBWnR+ACYF7dYV6m+fF5eV7wLGHlHed/g3Mch3lMHX+Ra8nT5S0F6ATLtUGb/X50BolYI/Z0HJM+0BFcQu8nK1OwF1ujg+hvVj5BceCpNp91OZQs8Z3yBxWZD32rpmwNGlwI3TBX9uIiKiUo5BOxFReSKf39vZGG5XxnirkYJr3yDg7YvA0xuAUWvEbfaC9pSL4jK8BhAUCbR6ynLfynHAtm+BjOv2n9M6MLX22ELbbeZMe5ZyPniNg6yzMTf/+QpxkaAw5Jn2oCgxIy4PsKVg2Bn5XPXyKesKFbSHuL6v+fnz25nrQrE/NdXyayEkHSrc44mIiEoxBu1EROWJFHQCYtZYfrtCTfc8h5SN9gkQu6FXa2kJ2sxzhlsF7VK36YD8iuc9Jyvv3/q144sIzoL2Br1tt0kBcV4WkJtuu12N9Nqsu6kXF0XQXlFcyt8HV4N2ea8AedX3wgTtvoXJtOc/JjcdSD4JZKda7rMe9qAmvIa4lGYLICIiKkcYtBMRlQcpl8Tx7PJgOS/HMrZ82FKgxRPueS6pwrtacThp7LR10C4FzVIQqvdXzvGd66TqeGGCaKl9hixl13tH47vNmfaSCtpl3eOl90YetDu6wCAXWtWyLs+0S13lCzKmXRryIGc9H7s1qe0XdwLT7wGmtwYA1E/6Az7fNLb0tLBHqvafleJ6O4mIiMoIBu1ERGXd0T+AqU2Apc8ou4HnZVsqsuv0hRsXrsacaVcL2vMz7Ypx1Sbg36niujxzHNvW9vGx7cR5v60VJoiWnisnXRm0mxzMvy4F7e56r5yRB+XSunx6O1eD7TBZ0J6RDCQeEDPcjn5X9si7x+t8gccWAQN+cPwY6b2WKtffSQQANLy6FJqM67ZF5+RCqyqHMhAREZUzDNqJiMq6rflB7pHflcW9jDmyTKve9a7WzkiBlWrQrjKm/dIOy7q8DU0fsX18zbZAxQa223WFqGguVVXPui0G7hLrAmhyUiE1eQa8OMl7K0jPWfXugh/n3ucs67+PBH7sCJxYbbloU5CLHvJCdM2HAg16OS9OV5Rza9BMywWLghSyIyIiKiMYtBMRlXUajWVdHiwfXWaZN1vnxqBdKhamFrSbg2tBzLADyuJkelkbgisBtTrYPt4/zPa4hSmaZw7abym73zuanz7ztvKxxU0+vZoUuDYZWPDjVG5mu00+O0BBeg7IM+2FKYQncTaWXXq9ITHMtBMRUblWgMozRERUKmlk12flXb+Pr7Csa31sx5kDQF6u65XSDVnA5zUthc4cjWkHxCyv1k8ZcOus/i11egc4t0XZTrXq5Y4CbTMNAFmgKBW9y7pt1V3fII71t26LtC9QckG7nDxoz06xTKNXWPLiewXJtPvK3n9Xx9SrBPe62b0cP0beC0RfiOn5iIiIyghm2omIyjpF0K4SmANiplUti5md4vrz7J2trEyuV8nCyquUS+PDHU23Fhhh+/gKNYGGDwCtRsn2i3TePusK6fJpyKyDfoPK1GQmoyUjr5btLw4V6wOR9YBq91gunmg0wD1PAzXuK9qxk/+zrBco016I6vEqwb02ca9yw4dhlqrygqCst2DuHs9MOxERlT/MtBMRlXny7vF2xmv7BQPVWllu++cHUFkpQHCUa0+Tckl52zrgBpTdvY8uA+4eLo6tNzfV6lqydUZbqxeD1kfnirdj7weOLAXue955+7Q6SyAIKKchM1kF7bmZtoG5fHq8kqoer/MBXtxh+764g/wCjnwIhTOK1+7CdG2A69PEbZwM9P5MeZ5qfQDf/KC9sPO8ExERlWLMtBMRlXXygM9ekbXQqkCVFsCTq4CxRyxdoJ1NtXZwETDjfuDsZttAVm3Msjzbff4fcSmv3O40aLfKxDcdBDw2H/BXmYbMmnUW3xwIZtpezFDrhi3PxhdkXvOi0vkAWi/9d+3KHOuA62PfL+0Ul/KLKDq9ZUiEs/ORiIioDPLSbwFEROQ28kB4/Ufq+0jBcM22QHh1ZUBrj9EArPsAuH4UWPa8bdflKs1VnkfWlqhG4vLONcu24Gjl/jq9Mkvr0th1OwrSPV4toyvPTGsLUa3e07pNLIaDuhi0uzr2XRrmoLhAorfMDZ/DoJ2IiMofBu1ERGWdPGi/dsT2/jYv2W5zNsXWjVPA5OrAnavi7TuJwKm14vo9TwNPbwAqqUzNBgB1u4vLMxvEZbosaK/b1Xb/gT9b1uVd6QvKOlstjbk3ZKh0j3cWtLs4P7o3aTdWfXvv/yv4saQgum431/a301PAoPVHXn/ZHO/V7xWX8vdanmln0E5EROUQx7QTEZV1zsYrB4TbbpNnodWcXg/kWWXWb50Rl8ZcoFpL+893OkFcntssLtOTxGWfL9Xb2qC3Zd1eIT1XFLUQnbyaeUHGgHu7+54t+GPG7ANun7ME2YWkN2XD0HggcHU/sPN/QF7+RRnz70MjXiBh0E5EROUYM+1ERGWdsyJm/uG225xW63bQLTrjhuPns+4CL3WPd6Xgnb1Ceq6wnjfeHLRn2l4MUBsWIGXjS3I8u7tVvss9xwmuVPCAPSj/9xvV2PaclKrXS7MPWL/XUtBuzLUE9kREROUEg3YiorLOWVdutTnHpXmxE94HNnysLDh2eS/w99v2jyevQq9myHzL+sKhwOVd4npwjOPHAUUL2h+ZAwRVAvp/L952mGlXK0SXH9jrSuF4dskTfwADfnC6W7F4fBHQ/SNg1BqgjtUwCOmCilSh3yib7g1Q1jVgtp2IiMoZBu1ERGVdgMrUa4r7w223SZXgM28CW/4PSDoEGLKBrV8BP3dxfLzWLzq+P6aZZf2/lZb1kGjbfa0VpXt8tZbA66eA5o+Lt30LOaa9NGfaAyOAu4Z45rmr3g20fUXMmj80Haa6PbC9zuvifT7WmXbpvc4P2rU6S+Cek1ZybSYiIvICDNqJiMo6qWuxPf4qmXad1fRtmTeBf6farz4vafigJUtvj4+vepd8627zaqyD64KSj0WXhgCY8my7w6sNCzBZZX+p8EKiYXx0Pq6H5l/AkX4XF3eKPTvmDRJv62QXSDiunYiIyikG7UREZdm1I8C+OY73cZRpl5iMwOU9zp8vrLpr7VIL0J0F+4B6sF9Y8rnDs1OV96lVqZcXoiP3kqZ6u3FC7Nlx+7x4W/5eM2gnIqJyikE7EVEZplUL2JtZdY9WC4TVgnbB5PwJQyu71jApSJPcP8bx/gN+BOL6Aa1fcO34rtDpLQXQslOU9xkN4jh+edEzc5ftUjjdm7cLrKi+Xd6rwdw9nkE7ERGVL6V4YB4RETmjyVUJcKzHZPuH2e4jBbMSV8eSO+uKL5FnuZ/eII53duSuR8Ufd/MNArJybTPteTnAwseB0+uAV48DQRUtVfHZPd79guwE7Vp2jyciImKmnYioLJOm1mr4APDwz0Cj/kCLocp9dCrXb62nRxOMcDjNm0Re5dsR+UWBqnd7bt5zff7Fg6wU5fZ/vgZOrBKrmR9ZIm7b8oW4jG1bYs0rN4IqqW/XqXWPZyE6IiIqXxi0ExGVZVL37podgGaPAIPn2A+Q5HxUMu1nNjh/nDyD7kivyZZ1TwXsgKW91pl2OWlYwI1T4rLNS8XbpvLIbqZdFrRLF5Lycou/PURERF6EQTsRUVkmBe3yINw6i67Gust84n7Xns/VoL1CLPD8v8C4o67tX1x886uWS0G72nRu0tzw0hABvQvvn7d74Ftx2f9/nm2HxMcP8FUZWiG/oCP9booy7R8REVEpxDHtRERlmVQFXR6oy6u017Ez53rTR4AL2yxdw7d959rzuRq0A0BME9f3LS5Sd34paFcLCAWjWM1cmkO8NM/TLmk5QvwdSxctvIF/KGBdg+Hmacs6g3YiIiqnmGknIirLpEy7zk6mve9X6o/zDwMGzQTqdrd/7CHzgYemA5XvsmzTFyBo9wbSRQZDhv19BBMws5fltqaMVI/3poAdUK+HEC27sCPVXpB6PhAREZUTDNqJiMqyPCeZ9oAKjh/vKKtctRXQYhjQ42PLtoJk2r2B3ipwVRuvbjICd65abpeFTLs38pMF7S/tAZoPAwb8YNnGTDsREZVT/OZBRFSGacxBu2zeda0OGLVO7O7tNGi3k1UOqACEROcfW3ZBoLQF7dbtrVATeGoNMLOnZZt1kMh52ouHfLrAivWA/tOV9zNoJyKicopBOxFRWWZUCdoBoPo9RTuuomiYrFiYq1O+eQu/UOVtrQ9QozUQVh1IvSRuS0u02odBe7Fwdu6Yg3ZD8beFiIjIi7B7PBFRWabWPb4grOcvl6RetKyHVbWsW08V5+0CrXoaSPOCa2T/HlMuKvdh9/jiIZ+TXY10sYRj2omIqJzhNw8iorLMqFKIriAybzrfJ7QKMGypbda6NLAeHiDNCx4QDqRcENel+dnN+/BfZ7HQOgva8+9n93giIipn+M2DiKgsK3Km/Zb69i7vK2/X7Vq443taeE3l7aCK4rJ6a+DqQXE97bJyn7JSPd7bOM20c0w7ERGVTwzaiYjKMnPQXshMe/p15e3xl8XuyQHhRWqW16hxn/J2pThxWaczsOsH2/0BQMuRZcUiqJLj+xm0ExFROcWgnYiorBIEWSG6QmbaIShvyit8lwXWrye0iris16Pk21LetX8VuHoAaDJI/X7pYsnJtSXWJCIiIm/AdAERURmlEYzQCCbxRmHHtA9ZAITVAJo9Coxc7b7GeStNfiV8rQ7oOsGzbSlv/MOA4X8Cdz+hfv+pdeLyTqL6/URERGUUM+1ERGWUv+G2uKLzBfzDC3eQuD7iT3nga5V1L3TvBCoWt897ugVEREQe4dFM+/fff49mzZohNDQUoaGhaNOmDVavtmRysrOzMXr0aERGRiI4OBgDBw7EtWvXFMe4ePEi+vbti8DAQERFReGNN95AXh7HuxERBRryK7+HVeM4bEcCIsRlg97K7YXtnUDFo8ckT7eAiIjIIzz6La5atWr47LPPsHfvXuzZswddunTBQw89hKNHjwIAxo0bhxUrVmDx4sXYvHkzEhMT8fDDD5sfbzQa0bdvX+Tm5mLbtm2YM2cOZs+ejQkT2KWRiEgnjWcva+PQ3e3Jv4D7xwB9vlBuZ6bdu1S+S1wWttcIERFRKeXR7vEPPPCA4vYnn3yC77//Hjt27EC1atUQHx+P+fPno0uXLgCAWbNmoWHDhtixYwdat26NtWvX4tixY1i3bh2io6PRvHlzTJo0CW+99RY+/PBD+PoyS0JE5ZdWyO91pPPzbEO8XXQjoMfHttutg/bwWKDaPSXTJrKlzZ9qz2T0bDuIiIhKmNeMaTcajVi8eDEyMjLQpk0b7N27FwaDAd26dTPvExcXhxo1amD79u1o3bo1tm/fjqZNmyI6Otq8T8+ePfHCCy/g6NGjaNGihepz5eTkICcnx3w7LS0NAGAwGGAwGIrpFVJZIp0nPF/IWxkMBmgF8fw06Xxh5LlaYBqNzvxPUtAHIu/F3YBGC/C9dIsCf46aAD0AwZSHPP4OqATwfz15O56jpZ+rvzuPB+2HDx9GmzZtkJ2djeDgYCxbtgyNGjXCgQMH4Ovri/DwcMX+0dHRSEpKAgAkJSUpAnbpfuk+eyZPnoyJEyfabF+7di0CAwOL+IqoPElISPB0E4jsqm4S/xEk30rDjlWrPNya0ic69TBa568bTMDq1X97tD1llaufo/65t9ATgGDMxSqez1SC+L+evB3P0dIrMzPTpf08HrQ3aNAABw4cQGpqKn7//XeMGDECmzdvLtbnHD9+PF599VXz7bS0NFSvXh09evRAaGhosT43lQ0GgwEJCQno3r079Hq9p5tDZMNgMODkgo0AgEqVq6JPn3JSAd6NNOeCgLNfAwD0/kF8D92swJ+j6deAo4BGMPF3QSWC/+vJ2/EcLf2kHt/OeDxo9/X1Rd26dQEALVu2xO7du/HNN9/g0UcfRW5uLlJSUhTZ9mvXriEmJgYAEBMTg127dimOJ1WXl/ZR4+fnBz8/2zGeer2eJzwVCM8Z8ma6/Ey7Vh8ALc/TgvMLMq9qdL78Wy8mLn+O+gYAADQQoNfpOCMClRj+rydvx3O09HL19+Z1//FMJhNycnLQsmVL6PV6rF+/3nzfiRMncPHiRbRp0wYA0KZNGxw+fBjXr18375OQkIDQ0FA0atSoxNtORORNpDHt8GEhukKRv286fhnyOKkQHQAILEZHRETlh0cz7ePHj0fv3r1Ro0YN3LlzB/Pnz8emTZuwZs0ahIWFYdSoUXj11VcRERGB0NBQjBkzBm3atEHr1uIowx49eqBRo0Z44okn8MUXXyApKQnvvfceRo8erZpJJyIqTwIMt8QVTl1WOPoAyzrnbPc8rewriymPF1KIiKjc8GjQfv36dQwfPhxXr15FWFgYmjVrhjVr1qB79+4AgClTpkCr1WLgwIHIyclBz549MWPGDPPjdTodVq5ciRdeeAFt2rRBUFAQRowYgY8++shTL4mIyDtk3UbsjU3iev2eHm1KqeUfZlnXMkD0OOugnYiIqJzwaNAeHx/v8H5/f39Mnz4d06dPt7tPbGwsq8gSEVlLuQidYIAQVAma+r083ZrSKaCCZd2YY38/Khny7vEM2omIqBzxujHtRERUdBopqPHxBzQazzamtJJ3j891bUoWKkYaedBu8lw7iIiIShiDdiKisii/cryiSzEVniHD0y0grRbQ5H9tYaadiIjKEQbtRERlkTE/aGexLvdgpt07SBehGLQTEVE5wqCdiKgskoIaFlBzD6nnAnmWFLQvfdaz7SAiIipBDNqJiMqi/Ey7wO7xVJYY8ns8XPgHyLzl2bYQERGVEAbtRERlkZRpZ/d4KqsEFqMjIqLygUE7EVFZxEJ0VNblcRo+IiIqHxi0ExGVRSxE5x5V7haXfmGebQfZMuZ6ugVEREQlgkE7EVFZkXIJuLJXXJfG/rIQXdE8+ivQ4gngqdWebglZY9BORETlBPtNEhGVBYIATG0iro/ZB13C+wAA7dkNHmxUGRBWDXhomqdbQWpSLwOVGni6FURERMWOmXYiorLg5mnL+s/doDFkeK4tRCVh2XOebgEREVGJYNBORFQWnFxjWc+yTIWV99D/PNAYohKQkezpFhAREZUIBu1ERKWdyQRsn26z+WT0AxCaDPJAg4iIiIjIXTimnYiotLu4HbiTKK53eBMIqACjIQv/3aqJWp5tGVHxaTrY0y0gIiIqEQzaiYhKu5unxGVIFaDLuwAAk8EAYdUqDzaKqJgFR3m6BURERCWC3eOJiEq7lEviMq6PZ9tBVJIEk6dbQEREVCIYtBMRlXap+UF7WDXPtoOoJDFoJyKicoJBOxFRaXfnqrgMrerZdhCVJJPR0y0gIiIqEQzaiYhKO0OWuPQN8mw7iEqSwKCdiIjKBwbtRESlnSFbXPr4e7YdRMVt5GrLOrvHExFROcGgnYioNEvcD1w7LK7rAzzbFqLiFns/0OV9cZ3d44mIqJxg0E5EVJoteMyyzkw7lQea/K8uguDZdhAREZUQBu1ERKWZVIQOYKadygetTlxyTDsREZUTDNqJiEozv1DLus7Xc+0gKinmTDvHtLtd4gEg44anW0FERFYYtBMRlWaBkZZ1ztNO5YEmP9POMe3ude0o8GNHYGozT7eEiIisMGgnIirNslPF5XNbAR8/z7aFqCSYM+0M2t0qcb+4NGQAeTmebQsRESkwaCciKq1MJiA7RVwPjvJoU4hKjHlMO7vHu1VIZcv69eOeawcREdlg0E5EVFplp1gCl4AIjzaFqMRoNOKS3ePdS34RJOmw59pBREQ2GLQTEZVWhxZZ1n1YhI7KCWlMO6d8cy9TnmX95mnPtYOI7EtPBm6d9XQryAMYtBMRlUbX/wP+ftvTrSAqeRzTXjzkPReOLvVcO4jIvm9biD/7fvF0S6iEMWgnIiqN5j/i6RYQeYY0pj0t0bPtKGvkF0FSLrInA5E3yr0jLpePAX59GJrzWz3bHioxDNqJiEqjlIuebgGRZ2TeFJdJhzzbjrJG3j0eAIwGz7SDiNSZrIpvnlkPn3kDPNMWKnEM2omISrth7MpK5ciVvZ5uQdlkHRDkZXumHUSkzqg+FaPOxCkaywMG7UREpV1sW0+3gKjk+IdZ1q0DTSo86xoBxlxxmZcDJJ8o+fYQkVKeenBe/da/JdwQ8gQG7UREpZ3e39MtICo57V61rJvYhdttrLvHS5n2358Cpt8L/PdXybeJiCykC2lW/AwpJdsO8ggG7URERFR6BEdZ1jnu2n2s572Xsnr/rRSXO/9Xsu0hIiXpb1LnB3yYCnR4AwDgl3fHg42iksKgnYioNKrdWVze+6xn20FU0rQ+lnXr7DAVnnX3+LwcZQX5gIiSbQ8RKUlBu4+fuAyJAQD4M9NeLjBoJyIqjaR/3jXaeLYdRCVNq7esM2h3H+tMe8Z1ICPZclteS8AV//0F3D5f5GYRUT6pEJ3OV1yGVAYA+Btue6hBVJIYtBMRlSaCIH4RTr0s3g6t4tHmEJU4rRbQ5H99Yfd497EO2i/vAY7+Ybm9b47rxzqyBFj4ODCrr1uaRkQADPl1JvQB4pKZ9nLFx/kuRETkNTZ/DmyabLkdHuu5thB5ilYvZp1YiM59rLvH3zwNHFyg3HbrHBBRy/mx9uYH+GmX3dM2IgIMGeJSHygu8zPtfoYUGAXOpFHWMdNORFSayAN2AAiO9kw7iDxJl99Fnpl295GGGkhdb2+cst3n2+bAnWvOj5Vxw7J+cJFybDwRFU5uprj0zQ/ag6IgQAMtTMq/OSqTGLQTEXkbQQCWPQ+selO8vW4iMKMNkHZVuV/tzmJXYaLyRipGxzHt7iN1j49qKC6v7FHfb9Onzo+VdcuyvuxZzvNO5A7mTHuQuNT5AEGVxPX0JM+0iUoMv+0REXmbhAlit9RdP4iZxH++Bq4fA76Os+zTaTwwZJ7n2kjkScy0u590AaRiA8f73Tjt+P6sFOCO1QVGqaBdyiUgO7VQzSMq96wz7YC5t50m/boHGkQliUE7EZE3MRmBbd9abufYmX+141uAb1DJtInI20gV5Dmm3X0MWeIyMALwD7dsr9oS6PWZ5XaWk0rV8wbZbsvNEHsKTW0CTGlS5KYSlUu56eJS9r9fkNbzsj3QICpJDNqJiLyJIVN5e8UrtvvU7gxoNCXTHiJvpMvvHm9k93i3Mc8B7W+ZBxoAQqsCVe623HZ2oeTybtttuemW7vY5aYCJRbOICuxOfhd4eS2b/BoU2v2/eqBBVJIYtBMReYtNnwPxPZTbji+33Y/d4qm8Y6bd/fLyM+0+/pZidAAQVh2odg8QXkO8nZtp+1hndv5POc97JotmERXY7XPiUha0a/KHomjPrmfBxzKOQTsRkTe4eUYs8HT9mPr9ARWADm8AH6ayWzwRx7S7n3kOaH/ll/+wqmLBy5GrxdvpSa6/79XuEZeXdyuLBnL8LVHBCAJwdpO4Hnu/ZXtOmmXdmFuiTaKSxaCdiMgbqHUplXvrPNDlvRJpCpHXY6bd/cyZ9gDl/Oph1cRlaFXAN1gMvm+ece2Y/aZa1n8dYFnPvFmkphKVO7fOikUcdX7K4SoanWVdqktBZRKDdiIiT7u8F1j2nKdbQVR6SGPapWnKqOikTLt8PDsgBuuAWEejUv4MFsnH7R8nppm47DcViLFTdI7d44kK5uoBcRnTBPCRDV8RZPUhpLoUVCYxaCci8rRDizzdAqLSRcvu8W4nda21DtpDYizrUflB+/X/7B9HKpIpZejr97bdJ/OW7TYisu/qIXFZublyu1RRHrD0lqEyiUE7EZEnZN4CNn5qO2/xS3uBJ/8Siz9JHvyu5NtH5M107B7vdtKYc+mCiEQKvgEgsq64vH3e/nGkCylSMbu+X9ruk8FMO1GBZKeIy5DKis2CvKu8gdO+lWUM2omIPGHZ88Dmz8V5iw8tFLf1+hyoWBeo2Q5o/6q4LagScPdwz7WTyBtpOeWb20lDDbQ6+/v4BIjLI78DU5sCl3bZ7iNl7KWgPaCC7T4c005UMOYpGZU9YYx9p8r2YdBelvl4ugFEROXSqTW22yo3s6w3HyZOvVSrQ8m1iai0YKbd/cyZdgdfDc3vex6QchFY+x4waq1ynzyroF0faHscjmknKhgpIPfxV24Pr4F0v2gE51xj0F7GMWgnIippavMcNx+qnMbFxxdo/njJtYmoNOGYdvdzKWj3Vd72C1Hezk4F0q6I60EVxaU0xl3u5unCtZGovDJn2n1t7jJp8j8PWT2+TGP3eCKikrZvjvJ2ZD3ggW890xai0kjqws1Mu/sUJmiPqK28nZYICEYgIAKoEGv/OEmHgZ+7AV83Aq7ss71fEIBFw8RhRPI544k8LeMmcN3B7AnFxV6mHYBRm/93yerxZRqDdiKiknZ8pWV9/BXgpd2WKayIyDmpm3Z5HNMuCMDOH9THkxeFfEz7vc+K63W7K/ex/pyy7umQdVtcqo1jt3Z5t5iV/6mz7X13rgLHVwAHFwDp15wfi6gkXD0EfN0QmNEamNkL+KQKcGl3yTx3np3ZHQAYpZ5HrB5fphUpaM/NzcWJEyeQl1cO/2kSERWW1F20y3uAX7B691Eisk9bjse0LxoGrH4TiO/ufN+CEKSg3QfoPgl4ZA4w8GflPtaZduvMnjloDy/Yc2elKG/n3LGssys9eYurBwBj/jl/cTtgyAD+eKFknlsKyFUy7Zbu8RzTXpYVKmjPzMzEqFGjEBgYiMaNG+PixYsAgDFjxuCzzz5zawOJiMocqWtdna6ebQdRaaUrp2PaEw8A/8l66rjz9cu7x+v9gcb9bYNv66D9TqLy9o1T4tJqWio0HyYumw5Wf27r4F8+DaZ1QE/kKWr1aG6eAnb9VLzPazQA1/8T1wMibO82d49npv2z1f/hrd8PwWgqe8NqChW0jx8/HgcPHsSmTZvg72+54tOtWzcsWrTIbY0jIipzMm7mV07WAJXiPN0aotJJGndtKmc9/ay7irtzvnNXxrRb33d2E/DNXcDB/O9+57aIy5rtlPv1/RJ44g+gl53EjjRNnEQetOekOWo1UcnJTVffvu274n3ejGQxqw8NUK2Vzd1GDce0A8C+i7fxv81nsGjPJew8W/amlSxU0P7HH39g2rRpaNeuHTSybp2NGzfGmTNn3NY4IqIyJyNZXAaEA74qUyERkXPlNdNuPZ41cb/7ju1K0C4PpiW3zwPLnhXrC1zcLm6znqpSHwDU6QwERgD1e9kew1HQns2gnbxEbob6duueJe62bmL+imApwilj0pau6vFnktMx9Ocd2HIy2a3Hjf/nnHn9amrZGypQqKA9OTkZUVFRNtszMjIUQTwREVnJThGX/uGebAVR6aaVzRdenuisgvaFj7nv2PJCdHaf33a6KbPLuwBDfvfhivXV99FogMcXAY/MVm63vvgifU4Clgud7nInqeSKh1HZYh20+4WKS2MxZrjvXAMOLXS4i6UQXekIVB+esQ3/nr6JD1ccLdJx0nPycOGm+Ds5dDkFfx26ar7vtcUHMX7poSId39sUKmhv1aoV/vrrL/NtKVD/+eef0aZNG/e0jIioLCpsoSYispCqmJe7QnTFOE7TlUx7/V5AvZ5AXD/guS3K+06tFZcanaUnhD3W07g5yrSnXnJ8rIL6qgEQ3w24ste9x6Wy78YJ5e3IOuIyx063eXdIlk0v99Ra1V0s3eO9P2ifuu4kUrPEz+2zyXZ6Lrio19Qt6Ph/m3D6+h3835oTNvcv2HUJpjI0tr1Qcwx9+umn6N27N44dO4a8vDx88803OHbsGLZt24bNmze7u41ERGWHVFTJlSmRiEidlFmSVxkvD4pzOIBL87T7AEN/U7/v9nlxqVLd2pbVF2mTAci8BSx5GohuBFyWBdQpF104XiFc2A5UbVk8x6ay6U6S8nbF+uIQleKsu5CWnz2u3QmocZ/qLpbu8aUhaD9lXq8eEVDo4wiCgMu3xeEAW0/dgE6r3tP7Tk4ewgKcXEQsJQqVaW/Xrh0OHDiAvLw8NG3aFGvXrkVUVBS2b9+Oli35AUhEZBe7xxMVnVSQbe9s9x0zOw1YMRY4/6/7julu1j0Lqqt/iS/csV0I2h2RiuKpzCNto15PIKy65bbRAJxeB5xZLxb1urjNcl9xBe2OuvoTqZGGkEiq3ysu068DSYcBk8n9z5l1S1wGRtrdpbRWjy9KMJ2SafksrBjsh1B/9WOlZpad3liFnqe9Tp06+Omnn7Br1y4cO3YMc+fORdOmTd3ZNiKissecaQ/3ZCuISrfT691/zA0fA3tnAbP7uP/Y7mK0GsN/aSdwaVfhj3dhG3D0j/xj53+5dTSm3ZH06+LSlUy7XzDwykHLDBrGXECwCniqtBCXaYnuq4otf/98GLRTAQn5QXvrF4F+U4EWTwDQABCA/7UD/p1iO/SjqKQu73r7WenSWj0+M9fo8P70nDz0+24rvvj7P5v7Lt22TL+3/exNLD8oTj/5UPMqiv1SsqyG3pRihQraV61ahTVr1thsX7NmDVavXl3kRhERlTpX9gEbPwXyVP5BmEyWqq43T4tLlblWichFxTF288ZJ9x/T3aRseFRjy7b47oU/3qzewOIRwKw+liJyDoIDh6Txvq5k2gHx4oB5FoBc22AnOBrwCQAgAKmXC9cma7my4RTastFllkqQlGlv/DDQaqR4rkvF6ABg/UfAl/WBZDd+lkiBuIOLYZbu8SrzyHsR6/HlWU6C9rVHk3DkShpmbDqDbIO479nkdLSclIAPlluK2M3faemNM7Zbfawc0w5Vw8XPsdvlPdP+9ttvw2i0faMFQcDbb79d5EYREZUqWbeBnzoDmz8HTvwl254CnFgNTK4KfBIDrHgFOPK7eF/Feh5pKlGZUBxBu+D4C6RXkLrHB4QD4bHuO+4F2ZAAl8akO2BvLms1Uhf1vFzbYnTXj1l6JLmrdoH8ONIFVCJXSb1BtLLwyXrq1ozrwNYv3fec0medg79Lgy7/QtvxFeqJAy+RnK7sCZBlcPyZ++kqS4b94i3xgsSiPZdwMyMX+y+mqD6mRkQgmlQNQ40I8feSkum970dBFSpoP3XqFBo1amSzPS4uDqdP80OQiMqZLbJ/0AcWiGNi5z8KfB4LLBhiufotH39br0eJNpGoTAmt4nyfgiqO8ajuZpSNO39omrgeUdu9z1GQTPvji22DiYJM0eYbLC5z022Ddp8Ay7Fd6fablyN290/4ADi4SH0f+Zzv/3ztejsBsSdAaThHqPhImXaNbAjJnau2+xW2t4oac6bdfg8Wg0524eByEYbLFLM/D1xR3LbXPT41y4C2n23ADVmQf/6GWGk+KsTxRUWpIF14oN58rLKiUNVGwsLCcPbsWdSsWVOx/fTp0wgKCnJHu4iISg95xubUGvHHkS7vAYHsHk9UaH2/An4d4N5jlqZMu9YHCKokrkt1MtxCU7ACbfV7AIN/AeYPLtzT+ed3Lc5OBf6ZqryvzWhgx/fiurMCW5f3Aj93UW6r2Q4Iq6rcVpSM/bxBwI1TwLClQMW6hT8OlV7SZ4Szug+h1dz3nC5k2rWCrFaDFxdYnLPtguJ2bp4JRpOgqPx++XYm2n2+0eaxUqY9z2j/wtn8ZyyFOTvUr4TIYF/UjQouarO9RqEy7Q899BDGjh2LM2fOmLedPn0ar732Gh588EG3NY6IqFQoaFfdavcUTzuIygvpS7E7Z2GwLoTmjaQx7Tq9ZdrI7JTCZYDVHuPjD2jUp06yqyhBgn+YuMxOBdKsxq23eALQu5hptw7YAdvpuY6vBM4VclrivGyxun3KBbFQITPu5ZNapl2NoWjzjyu4kGm/FVRftr93TvuWmZuHKynixbdd73Y1bx/2805k5FguOmw/c1P18edviu9pbp7lb699vYqKfepFhZjXH7u3Bj7u3xT311HuU5oVKmj/4osvEBQUhLi4ONSqVQu1atVCw4YNERkZiS+/dOM4DiKi0qCgFVvdeRWeqDySAkt3Vmq2ns7JGxllmXbpgoVgKtw80ao9CwrxfloHE/Iiec5Ir0GqPC8Z/Ks4bljKLhocZNpvnVPfLk2VBYjTxi0aCmyabNlWkKnt5F2g068V77zc5L3UMu2BKkGhO3u/SMPrHGTac/RhEELze5XkuvGCgRvduCMOf/HXa1Ep2PKZIa/8DgCXbimL6Q28W/y+dOGmuD03P9M+vE0sfh11H5pUtRQCDPEv5HSVpUShu8dv27YNCQkJOHjwIAICAtCsWTN06NDB3e0jIvJ+jr5QSpoPBQ7ME9eLYzwuUXmikXIObgzaS0X3eFmmXe8P6APFL/XZKQWfRlLtIkVMIabu1cmC9jpdgAe+df2x0lztu35Qbm+U32vTlTHtu35S354py9hl3ba9vwAXaTSZVo+3Hn9P5YPUw0KeaX92I7D5C2D/r5ZtBxcCzR4FYtsU/Tkz8s9jB/O0A4BQoRY0aVe8NmhPThd7AFQK8YNGo0GLGuHmYnJ+PpYcslTtvXujaDxwVxVEh/hhyb7L2HrqBt5YfBCL94o9cnx14mOaVw/HkStpNscpiwr96jQaDXr06IE33ngDL730EgN2Iiq/pC+Uw5crp3Kr2w1oMhAY+TfQdJBlu3W1WSIqGHOm3Y3dlEtbph2wZKrVglJn1C5SPGangJsj8vnOe3wChFd3/bHWY84BIChKduz8oP3kamDG/cAZ27GuSNyvvN34YXGZKcu0m6zmtwcACK7/zq3H1HtpF2QqpLObgBN/O9/PnGmXhU/hNYC7R1huB0WJ58vmz93TNqmwo1TDwh59/vcKLw3ar6eJ35OkQnK/PdcGFfMz7tM2nsbriw9CEATcyhAviLWpHYkH76qC2EhLrTQpYAcA3/wAvUKg5fNHU9ChPaWMy5n2b7/9Fs8++yz8/f3x7beOr6K+/PLLRW4YEVGpIS8UI/8iPGyJZV0QgA5vANFNSrZtRGVSMXSPd+exiosUfErzMgdUAO4kFi5otw5kXzkIBDnO5jk9jloQ7ohfiO02+eemVKju6DJx+Wt/oOVIoN8U8cKNIRu4uE35eCkjKe8eb1QL2iG23VlRMcA2SPfiabWogAzZwC8Pievjjjk+h6Vz3XpMu05vWe/4JrDqdWVPj6LIyB864ixol5IBXjpX+7U08W+oYrAYZOt1WjSvHoZ1x6/jbHIGziZn4PH7auDApRQAQOUwMbiPClEfy+/nI/4OOsdF4bsNpwtciqM0cjlonzJlCoYOHQp/f39MmTLF7n4ajYZBOxGVL/JCMe1fBxLeBxpaFeXUaMSq8URUdFL3eHdm2ktF9/j8TLsu/+ubVIzu1wHAK4eACgWYu906y+wXqr6fM2GyzLpUWM5VeqsZhx74FqjczHJben1ye2cB9zwNxDQBUi/b3i/NzKHItNuZ9smUB8B+gS8zg1XQbixgHRPyXvLZX37qDLx+0v6+0t+MdT0EedAelD/GPTe9aO3Kui3+XUsX5IKjHO8v/S0V9XmLyZlksQdArYqWau5+euXFj4s3M5GYKvZqaVFD/NvXatWjcT+9+D/g7hoVMP+Z+1C9Qtnvwehy0H7u3DnVdSKick8a36jzBVq/KH6ZZIV4ouJjTquUo0J0p9cDN8+K61KmvWI94MI/4vriJ8Xxta6yvuDhW8ipkYIqAi/uBPwK8Xhfq6C9kdXFTrWgHbAEJtYF4Wp3sgxRkmc6jXaCdnvbrdl0j5cF7Zd2i2Pyb5wE7nsBaP6Ya8ck+wQBOL8VqNQQCHaSYS6qGycs6+nXxCJyUn2IvBwg4QOgXnegblfXpnyTMuJF7aZ+ZqNy6Ie9v4V8gvS3lOudmfbE/MrxsZGW4NrfR/k+jl10wLwuzbMOAEcm9sQ3607ip62W+DPYzxLClqUK8Y4UeEy7wWBAnTp1cPz48eJoDxFR6SP/R67zEYsxqXX7JCL3KJZMuxdP45V4AJj7MHBwvnhbyux1ed+yT8oFm4c5dGWv8rZPEaZui4oDwgoxK4Z1fQ+dVdbbXvZfyqJbz7vebaIl057lbEw7CjCm3bp7vCxoj+8GHF4MXD0I/PG8a8cjx85uBOY8IGa+i9vvTylvy3tv7J8L7Pxe/NsTBMtnhHX3ePnFH3cF7TdOWdY7vOF8GIc50+6dY9rTssX3KDzAEoz769XDUL1OoygqF+zng3f7NsK3j7UwbyvrleLVFDho1+v1yM5mAQ4iIjO1irJEVIyKY0y7Fwftt84ob0vdc4MigV75Ba8ybwKHfnPteEufBeYPdl/7Css6u289fZzezjRX146IS3mmvfVooPJdljHt57YA+34R1+VBVb+plos+9oJ5KxrrccLsHl+8zmwQl6mXSr7WhDxol/fWkM8SYx1Aa2ThlNSNPTfdtZll7JF60PT50rWhdb7eF7RfuJmBG+ni30pqlvg3GCoL2sNk63JhAXrVonLSOHeAQbvLRo8ejc8//xx5ea592BERlWlqFWWJqPiUtzHtPgHK2/IxtfV7iENzAGDpM8BNqwBfzaFCVIkvDtaZdOtgyN7c1Bd3iEupK3DtTkCvT8VhE9XvtZwfy8eIWXlpTHuNNkCrkZbhBfbGusubZMqFVpquU2Kd4Sf3kve4cKVSf3py4YJVtQsCqZcs6/Lib9KFBEAZpAPiVImN+osXjvzDLRejUq8UvE0Saaq3yDqu7S89Z653nJvX0rLR8f82oetXmwHIgnZ/S6A+tHUs6kYFIy5G2TOxS5z6+P2YUMvnQaVg+/PWl1WFukyxe/durF+/HmvXrkXTpk0RFKQck7R06VK3NI6IqFSQulgy005UMoplTLsXZ9qtx1TLC19F1AYemi4G7ICYKbT3Rf/aUWDuwOJpY2E465JvnXkfMh9Y+DhwaRdweY8l0y4P7v1CgLFHgCmNxNuXdwMXd4rr0sUOrY+YLXch097s8q/Q3Dyq3Ch1z/fmc8bbGQ3i+68PsNzePk28ELf1K8t+uZlAWiKwYIhYM6bVSOVxbl8Apt8H1GgNDP+jYG2QXxCIagRcP6YM2o2yWQI2Tbas22TaNcDgOZbbwVHArXSx8nvFugVrk0QqQOdkLLtEkGpKHPsT+KED0HwocN9zhXtuN9hxVrzokJplQGZuHpLv5E/5Fmr5m64aHoB1r3bEgUsp6D/9X/P2wa3Up42sViEAD7eoCr1OiyZVC1k4sxQrVNAeHh6OgQO96EOfiMiTXClOQ0TuU9yZdkGAV80hlGfVHVtr1a202WAx4Ll60PFUU8tfBu5cdX/7iou8h8HgX4F6PcX13DvAz10t9+msgv+wqkC1e4HL+cH9junidukzWucDGGB/KjiZ2JubbTdm3hCX0nRcVHBzHgCuHQOeTgAqNQB2/g9Y96HtfrnpwLZvxUJ/K8faBu0H5okXtc5uBG6cFn8nsfe71gb538pdQ4CECUCKLGiX96iQhmQAtpl2a+asdxGKwhUwaIevLFt99aD4U7eb65l6NxIEAa8sPGC+/b9NZ2DKv74qzc0uVyFQ+XlWOTzAZh9AnKHs60ebu6uZpU6BgnaTyYT/+7//w8mTJ5Gbm4suXbrgww8/RECA+ptLRFQuMNNOVMJkAbW7Amz5BQDB5F1/z9ZdhNUuEIbHil/UjywRK7rX6mC7T2Hmc/ckeaY9tIplqjtr1kE7ANRsKwbtW76wbDu7SVz6hwPZqUB6ksNMqEZevRsQu0tnyLpiywM8icnEoVKOZN4CVrwCXNwu3t74KdDmJWCtnXHbhkxlYdfcDOWsA/LfwbSW4vKlvc4z3DdOAdNaies6P/HvB7CMaf9nCrBhkvpjrYerWDMH7YWcfs2QZeld42rQrjZ7w9r3gMcWFK4NRXDuhnKowrcbLNPq6VSmcAsPUP79RgQWoShmGVagT5VPPvkE77zzDoKDg1G1alV8++23GD16dHG1jYiodGCmnahkyTNd7ipUJa8k7upUYCXFep5w627jgKUA238rxSxmXq6YKVQUwyrhol6ucJS1lH+mhlS2v5/a+1H5LvvbopuIy6QjtvvIyacDAyxj8KWeD6kqQbt10TpS2j4dOL7ccvv4CrECvz05dwC/MMvtxAPK+y/vtn1M6kXn7dj9s2VdHwCE5XfJvn4cOPy7etYfAB6Z4/yiTFGLwmWliEuNzv4MCjbPqTJjTXYqcOca8Ndr4tCYEnL+ZsFet3VRuQBffpdSU6Cg/ZdffsGMGTOwZs0a/PHHH1ixYgXmzZsHE8f0EFF5JQ8YnHWZIyL3kGfW3dVFXt493sWq4iXGphiXSs+CIKu5ilMvAd+1BP7XTrwgYcgCbp1VP37zoW5pZqE4ylpGNxWDkVodxUy7PWqZ9oYPASFWjxm1Lv+4jcXlvl/Ez/DkE8CiYbZBvPWFWP/8AEoa66wWtHtR9W6vZH0uOysAeXk3YJC9p8n/WdYNWbYzKwCAPtB2mzX534tOL54TGp049GLJKMt91kNR1C4GWZOC9o2fFO4CoLlrfLjLvYgEtUz7hX+Br+qLFyh+7FTwdhRSZm7BinpqVbLvZKtA3zAvXryIPn36mG9369YNGo0GiYmJbm8YEVGpIM/OMWgnKhmKL7LFkGn3pqD9wHxg/UTltvAatvsFWgXt01oB6deAm6fFjNuKV9SP/+Rf4jRonqKWJZcERQJvnAaGLbH8zjuNt91PrZq7Vgs0eshyu8lAS+E7KWi/fhQ4txmYN0jM+P46QHkM6ym7pIsDUtCu1j3ewKDdIUcXX+Tue0FcJp9Qjg2XT/OXekX9op0rgbI8M52RLE4vaN02rQ/w1nnlRYCIWs6PLY2pT7sCnFjlfH9rBR3PDqhn2uXkRfWKWZadoD3Yr/xN0+ZOBfqGmZeXB39/ZYl9vV4Pg8HLupEREZUUeZaA3eOJSkhxZNplxzEVLFNUrP54wbJe5W6g41tAXD/b/aTu8RL568m5Y3+at5rtnFdxL056J+OD9f7KavntxokV8+t0sWyz7sYukQc9QbJppKTu8QBwYjWQkt+d2qqwnMY6aJdu5zHTXmiuXtyu0lxc3jyjvBAiv0Bjr+jiidVAjpPx5GoXV+SzEPgGAy9sE8eKO7qwpOa+58QicADwz9SCPRYApFoKBQna1TLt1jZ9VvC2FEK2Qf3zM35EqxJ5/rKqQJc8BEHAk08+CT8/y8mbnZ2N559/XjHtG6d8I6JyQ5FpZ9BOVCKKY0y7PDvnwvzdHtG4P9DWTsY8KFJ9O6DMTgJAs0e9Z672ggZEPn7AS3vEc2BieP5GO91rAyMs68GyObfl2dIKNe0/lzxoD44G4voCSYfErOX6ScDJv8X7Hv7JMuVedprtccjCeiYESc32wPmtltsV64nLC/+IPxL5+5uVP/VehZrAA9+I1d+vHhRnC/ANBLrYKW4HqFd218uC9vueE6vaA0ClOEvhPFelXxOXifsKXixz7bvi8k6S64+RF+ez57+/gE5vu37MQspSCdqbVQvDfbUdfEaRUwXKtI8YMQJRUVEICwsz/wwbNgxVqlRRbCMiKjeYaScqecUxpt0oCya8qXu8nKOiVNbd4+WsA8luHwIxTcVAx9OcVeJWo9WJ58ATf4hjjO29DvlYd5PVZ3XN9uK6o8xvfvVvY6ungXHHLOOgjTnA1i8t+8U0A6q3Ftc5DZxj9oJ262Ayws5UZbt/An7sLJ7TUjfyiDpA7U7K8edbvxYz7vIZE7bPAGb1EQu9qQ2pkJ+L8r+1AT+I0w2OWGnvVdnqLZu1INlOTxA18jphaj057HGlB4PUC+TmGbGy/J1rrh+/ALJyxddQVTZ1W/Pq4cXyXOVJgTLts2bNKq52EBGVTsy0E5U8xRdUN2TajXlW3eO9NGj3dxC0Wxeik0tLtMxb3m2iOHb3+X/s71+SmgwANhx1nPG2p05n8cce+XliPXe3VBfAXrX3fb9Ct22quB4UJU43J10EyLMaHxxe3ZLJT092qenlltFO0G5d4C0g3P4xEveJRQSlC+X++QlD+TAKwQgsGCIOJRkyT/wbX5NfD2HBECBYNlyiwxviUp5pl/+tVYgFhv5mvz1qYu8Xp108t0XsKRAV59rj5BcZarg437yrpKB9zgPiePurB4ERK9z7HACy88TvRc1rhONGeg6qhgdgdGfHU/B1axiNdcev4eG7q7q9PWUFKwIQERWF/Is+M+1EJcTNmXbrQMLoJUG7ddd/h5l2B11Pk49bqnZLRdi8RduxQIValsy3OzUZKGZmw2uIY/flpLH0ahlXAFj+knlVkKab0+V35ZePW/cLE7smS2PmDy8Gdv0gFverVQyvqbSTZ9pHrAAu7RSHHviFAJWbA1cPiHULnDm70ZKNl4J2rUpY899KMXstH8N+cbslq/7gd0CLJ8R1ecE5V6dacyS2nRi0n/8HuOdp1x6TLst+959R9DYAlosH6UnA5b1iwA6I24pBnlH8TK4WHoAd47si2N8Hep3jngBfP3oXNhy/ju6NooulTWUBg3YioqJg9XiikufuMe3W1aa9JdNu3Q5HgYR8bHhwjPgFXXJ2syXQdGXsa0nS6YGmg4rn2L6BwHN2AhMpQLOuAG/IAjZ+qtwWEiMuoxuJS/kY6yfzM5VS5vbyLnG57Hng1ZKbG7vUkC6SdHxbDCZrdbDcN/Bn4MYpoEFv8fb9Y4Bt36kf5/Q6APlT+ElZcXufBcnHbS9q5eXXK6je2jLcptlg4FSCOJ7e+iJPYdRsKy7P/+v6Y6SgPaqRa5XqXfHwz+LUb4DtDAnFIM8k/h50Wg0qBLlW5DLUX4/+LZhld4TfMImIikIa067RFqzQDBEVnrvHtFsfw1uCdus5rf2cTOskqZZfpVnK/ibus2TXvC1o9xQpaL99Xrn92jFg27eKTeZMe0xTcZiBnFRxPKiScjt7Xqm7cUpcRqqMWa9YD4jrY/n7bvWUa8eUMu32ZhE4u1l9GEStDpaCd4DYM+Pdq8DonZYLNUUhdfnPuG6/R4e19PyaCPLu+0UlL8iYk6q875+p9usMFJIxP2j34fzrbuXRoH3y5Mm45557EBISgqioKPTv3x8nTij/4LKzszF69GhERkYiODgYAwcOxLVrysIJFy9eRN++fREYGIioqCi88cYbyMvzkn+4RFS2SZl2jmcnKjnuHtNuPcWb1wTtVmOnHY1pl4uoDbx5Dnj1mLgumCwXAHxdmBqqPJC6x0uZccnVA7b7SkE7IAZ2ctI4d+sgSx4MkkV2iri0vsihRufiVIRS0J5hp57Af38B37aw3f7gd7YX2ws6m4EjfiGW+dNdLfomZdqD3dhNXKcXC+mpWfcBsDvefc8FeaaduWF38ui7uXnzZowePRo7duxAQkICDAYDevTogYwMy7iTcePGYcWKFVi8eDE2b96MxMREPPzww+b7jUYj+vbti9zcXGzbtg1z5szB7NmzMWHCBE+8JCIqb6RMO7MqRCXH3d3jBespitw0jVxRWWcHnWXaB80C6vUQxwQHRohf1is1VO4jH7dbntl7H5IO226TD0uwvnAiBXlBVkG7OzK1ZZGU1XUlOHY1aPezmrnKOuC9YKfoYmgJdMcOyW/Lnauu7W8O2t2YaQeAxxYCg2aq33fNvcM4jMb8TLuOmXZ38uiY9r///ltxe/bs2YiKisLevXvRoUMHpKamIj4+HvPnz0eXLl0AiBXsGzZsiB07dqB169ZYu3Ytjh07hnXr1iE6OhrNmzfHpEmT8NZbb+HDDz+Er6+Lf/BERIVx9ZC45Hh2ohIk7x7vjky7VWbdXdPIFZV10O7rJGhv8rD4I+dnlVln93iRPkB9e1L+Z3qFmsDt80gJqIkgeTbWOpCUitMFW2WOrXtvkMiY33tE50rQLqsGH1BBWVldTsq0P/6bONVb/xlid/RLO4HVb7p2/OISUhm4edr1Odel7vHWF4GKSqsFatuZaSGwglufSj6mndzHqwrRpaaK4ywiIsSxF3v37oXBYEC3bt3M+8TFxaFGjRrYvn07Wrduje3bt6Np06aIjrZcVevZsydeeOEFHD16FC1a2HaHycnJQU6OZfxGWpo4f6nBYIDBYLDZn8iadJ7wfCnncjOgXzQUACAERiLPi84HnqPk7Yp0jgoCpK/bBkMOUNTzPDcH8q/veQYDBC/429Fkpiq+qBmMRsBYsGBQp/FRdKs0aHyL/n6VARqtr+qXYOHqIWgAmKq3Rs4jC/HvjoPoLHu/NNAqfyfQie+nb7jiHDIZsmHk+2zDJy8HGgAGaJ2fhyaN+T011bgfxm4fQT+9pc1uhuDK4rFqdRF/8mlysx0GOiXx/1EXFAUtAOOl3TA17O98/ztJ0ALIC4h06TNI/jmqeTgeuuWjYXzoewjhNaGP7wxTo/6W81AfAs3gefD5bajiGEafIJjc+F4Y8qd800LgdxAXuPoeeU3QbjKZMHbsWLRt2xZNmjQBACQlJcHX1xfh4eGKfaOjo5GUlGTeRx6wS/dL96mZPHkyJk6caLN97dq1CAxktzFyXUJCgqebQB4Ul/g7GuSv/xv1BG6uWuXR9qjhOUrerrDn6EP5y/Xr1iFHH+ZwX2cCc66hu+z23s2rkBSW5PHikpF3jkOqYX0hsiMOFOIzptmVJEg1qPM0vlj19xq3ta80i0i/ALUJ2TQm8Qv0uau3cWT3SUAXoDhHo1MPoXX+ulHjg1UJm8TzRBDM5yQA3Lh0Etu98H+Cp/XOTocvgC3/7kC6/0WH+2qEPDyYv340PQxntx1VvMdA/jm96zSgOWvz+ODsRHSV3V7VdDoqp+5Di4vxuBlUD/+UwO+ncXIm6gLQ7f4B21Iq4lZwA4f7d046g1AAu46eR/Il19snnqN6aJp8D+GsDsAl+DT9Hnm6QMD6dbb4BbWSE1A/aTn881Jx9r9DOHbHfe/FpStaAFqcOH4Mq1I4g4IzmZkqRRJVeE3QPnr0aBw5cgT//GNn3IkbjR8/Hq+++qr5dlpaGqpXr44ePXogNNQN8zJSmWcwGJCQkIDu3btDry+B7lXklfSfDDev3zf4VQd7ljyeo+TtinqOCge00AgmdO3Suejjh2+eAo5Zbt537hsYO7wNU/vXi3bcItKc8gFOA6bKzVHlqSWoUohjaBO2ATfWAwB0ASHo06ePextZWgm9YVpyENoTKwGImXXtpR3mu2vWqYfKHbrbnKOaM/7A2SkAAG1gBPr07Ws55gHLatSdo3yvVfgcFrtOd+jSHQiPdbyzIJjf07gHX0FcRB2YbrWF9oJlCjWd3hd9+vZTf/ydJOD42+ab3fsOALSPIO9CP4TG3IU+rhZ2LALtliPA9dUAgPurAqa2js8Jn//GAgDu6fIAEKWsR5GVa8SkVf+hWdUwDLmnGoCifI72gfaf6sDmT1EnJgw13Xiurri9H7iZjKZNm6DPvdXddlwAMJgMSLiQgHui70GlQBeKGZYCUo9vZ7wiaH/ppZewcuVKbNmyBdWqVTNvj4mJQW5uLlJSUhTZ9mvXriEmJsa8z65dysqfUnV5aR9rfn5+8POzHUuj1+v55ZYKhOdMOXZ8pWW9yUCvPQ94jpK3K/w5KmbB9T4+QFHPcZUqx7otn0HXZXzRjltUJnEon9Y3GNrCvsZL282rGt8gfh7INewH5Aft2hZDAVnQrtP7m98rxTnqZxkLrwmMcPh+6rUaQOcVX7U96/pxcRnVEDCK57TeL8i1v9vhfwJ5OdBHx4m3h8wDTq0Dlj4NANDE3m//dxBSUXFT759fz6FeF5Wdi0nzIcDWLwAAOiEPOr0euLIXMGTZzgVvNABZt8S2hle1eX8W7k3E4r1XsHjvFeQYBTzdvrb5vkJ9jlaoAQDQpl8t/OeLlR1nb2Ldf2IVfz+9j9s/b+YcnoNv9n2DykGVsXbQWrce21NcfY88WjlJEAS89NJLWLZsGTZs2IBatWop7m/ZsiX0ej3Wr19v3nbixAlcvHgRbdq0AQC0adMGhw8fxvXr1837JCQkIDQ0FI0aNSqZF0JE5c/uny3rTQZ5rh1E5ZVU/NEt87R7adGw3PzZdHyLMHRPPtUUp3tTks/6YV1J3F6RMnkhOv9w5X1xVhlfo9X818Y84Pp/7imeWFoYsoAZrcWfnHTL36urU6vV7gTUl01XFlABaPaI5Xavz+w/Vh8APDQdaDkSeHGH/f0K4ExyOn7ccgZZuS5+ZkTWAaQeO8n/iQUKf+oCzO4LZN6y7Jd1Gzj8e/4Njfg6ZXLzTNhw3PK3/PFfx5GaVcTx4mH553zqlaIdR2bIj7ILX24uRCcIAr7Z9w0A4GqGi9X4yxCPBu2jR4/G3LlzMX/+fISEhCApKQlJSUnIysoCAISFhWHUqFF49dVXsXHjRuzduxcjR45EmzZt0Lq1OKKoR48eaNSoEZ544gkcPHgQa9aswXvvvYfRo0erZtOJiIos5w5wdqO4fvcIoEFvz7aHqDwyjzcvhurxkuvHgV8eAi7uLPpzFIZUPb4oFd/lwVGluKK1p8yRBRWVrMYaa+1kyK0rmssNmilOuSfJswra/34LmHEfcGB+wZtaWmXLuv7ePi8uNdqiz2IwbIk4xWFkHcf7tRgGPDDVpqt5Qd3KyEViSha6frUZn676D3N3XHD9wZF1xWXSEWWgLs1ZDwCr3gT+eF5c9w+z6f3z45Yz2HhCOQ/9ldtZqk/36/bz+HD5UeQZnVzQlC5UpV1xy4Ukg9Xz+bg5aP9sl4MLNADWnl+LwSsG43zqebc+r7fwaND+/fffIzU1FZ06dULlypXNP4sWLTLvM2XKFPTr1w8DBw5Ehw4dEBMTg6VLl5rv1+l0WLlyJXQ6Hdq0aYNhw4Zh+PDh+OijjzzxkoioPDi6zLLe4XWPF6siKpfcmWm3Nz3XvMHA2U3AzB7q9xe33HRxqS9CgFP5Lst6q6eK1p6yRv7ZHRgJBMuGVbqSabcO2n38gId/tNw25mdCc/J/j1IPrU2TC9fe0kj+93n1oLiMqON6pt2eut1spzcsJhv+u4a7JyXg/s82mLftv2Rn+jk1MWKBbeSmAxmWnsHIy7WsH/7Nsm59XgH4cu1Jm21rjiYhx6D87DIYTXj/z6OYve08lu13kkGXgnZDpmU6vcxbwK1zjh9nx5ErqYrbtzJy7exZOPuv71fcPpR8CILsYsNrm1/D8VvHMXG7bbHxssCjA20EF67q+Pv7Y/r06Zg+fbrdfWJjY7GKFTqJqKSkXBKXEbWB8BqebQtRuZUfcLmjq7G9wD/VcXXrYpfrhkx7ny+BxP1A4wFALbV66eWYRpa70voAmTcst63nY5fIfxcqwRUCKgA+/kBettg9/vIeYGZPoN04yz7+RZvtoFSRDxG4sldcxjT1TFtcNHn1cSQcvYZ5z9yHymEBeGr2Hpt9bmcUoGu6Pn94S26mcq55g6xquE8AkJefObfq5XEzXdljo2p4AK6kZOGb9afwy/bzeKux5b6T1+6Y1zefTMYjrRwUgtP7A4EVxfM+7QoQGAHMewRI3Ae8tMd5LwYARpOAdcevoVVsBfyyXdn7ID3HTg8mFxlNRuhkQ1hyrIabDF01FOF+4fi609doFd3KvP165nWURR7NtBMRlUpJh8Vl88c92w6i8sytmfaifbksNubu8UUY0x4SDYw7AvSY5J42lSXWQbv8PLDXPd43xLJuL/jW5WeR83KBNe+Ix93yf5b7r1l1ky7L5EMEruQHv1Lm2Uv9sPkszt7IwKuLDiIzV/2z4XZmAbLIUi2J3DuWIQKAON5f4ierN2H1ebT8YKLi9tQhzREV4pffDgPO3rH0GNkk60J/4FKKTZd1G8H502ZnJAOXdou/I8EkG1/v2PydF/Dcr3sx5McdWHkoEY005zG32p9oV90fQ+9zMjuAA7+d+A33zb8Pu65aio1bB+0AkJKTgre3vI0Tt0843K8sYNBOROTM9f+ABY+Jy4s7gJPi9C2Ie8Cz7SIqz9w6pt1bC9G5oXs82ScP2q2HOdnrHi8Pruzt45OfpTfmKJ9Drrx0kZcH7VL3+GjvzrRLtp+9ie82nFa9L/lOAQJDee+MNe9Y1qWLciaT8iLOfc/h67UnUPPtv/DF3//hK1nX+L9ebod7akYg4dWO5m3XZLH/phOWLPPl21nOx977yC4wxXezbE865PRlAcDMf88DAE5dT4fBKGCV3ztod2MR5ua9hkrBdnqrWNv4KfD7KEWvqUk7JiHHmINRa0eZt2XlqY/hT81NxZ1cSw+DW9ll84IYg3YiImf+fBE4sQr4/n7gl/7itvBYIIpFnYg8xpxpd0f3eG8N2t3QPZ7ssw6oO8mm+DPa6f7s429ZtzfPt9S13roQndydJOftKwvU3oNo757dSa+zXMD5ftMZ83qlED+sHCNO03YzIxepmS52kdfLespky8Z9S0F7dorlM+ixRcho/jS+zb9YMGPTGXM387iYEDSuIvbuCAvQY0wXscBdcralvedvisdsFSsO3Th9Pd1x28xBe7Zy+/mtTi9m/n3kKs7dyFC/8/Y54MK/jp8bED+/N38OHPkduLLP0iyVni5SBv2Hbj8othtNRqTkpJhv169Q3/nzlkIM2omInJG+XAlGy5izu4Z4rj1EBLeOaXele3xervhcuZnO93WHpMPiF1mgaN3jyT6boP1ty/qts3YeowFi2wIhlYGmg9X3kYJ2Y679bvDe2rvDndKTgYMLbLdbT6/nRXLzTDAYbT9TxveOw9Y3O6NJ1TAE+orjrK2nXMvJM+LtJYfQ5ctNmL9TVg9DqwVav2j7ZFL3eOkc8Q0BGvTCxJXHVNuWlKYMrGMjxYt5N/I3C4KA2/nF31rUCAcA/LH/Cmq+/Rfe+t1O5lw6V68eUG7PTgVunFJ/TL4ZsgsaABAEq0z4bRcq7MuHCMg6uzSKsFzY6f9HfxhNRmTnX1io4G9VS0ID3Mqy/J3Js+5lCYN2IiJHUi+LBVqstXmp5NtCRBZSd2a3jGl34RhfxwELhgCfVgZunnG+f1EtlNXMYPf44hFU0XabNE69Rmv7jxu+HBh72H6m3Zy9zLGdq11SRsfdKszqDeyJV26LqO3VM64cvpICQMy2y5s55N4a8Nfr8u8Tw6dcq/Hi287cxMLdl3D2RgaW7LusPHDTR2AjNz9LLWXfA8IBAGeTxe1VwvwVu7/ZU9m7r2akeDHvRn6mPT0nD3km8YJDjfyAPiN/PvlFey7haKKyujsAy7n6zxTb+246DtqTUpUXEb7qafX3lK3yfNZyZFMCyoo/ygvQnUk9g58P/wxjfm+EyIDI/2fvrMOsqro4/N6a7p4Bhu7ukhKQEsUACxMD88PEwG5sVOxCRJRQlBIERDqlOwaGYbr71vn+OLfOjekZZmC/zzPPPWefffbZd26dtddav6UYwmg2klpkr2GfXVIJZf8GxAVVjxcIBIJ6z6+3u7YFRHu+WRMIBHVDTea0VyQ8vigTjq2Ut7d9DmPfKbt/dSl0UDIXnvbaoXFvGPSEbEhaeXCH7HVsNRJMHt4XmnJun22edoNrmH1cd1nN32QRMjOb5D9tBfN/GwqS5Gr0jXkHOlx9YeZTQXafyQFgSJtISo1mNhyXP4fBvnb9AqvR7izytvuM3VgscSrFRrQb8T2rl9lqZPqEoDeabQrwX9zai3axgaTllxLooyXQW/m+s3ras0uh1GimQC/PR6tW2YTqHPllRyKvXO0knuipSgJAXrLHQ6VGExkFpXRSneJbr3d51HA/gXRx6uRgkP/7rlxJYdIP8kJBQRqkHYI5Du8Hh4gn5/Jun+z5xLYd7qM02gGSCuzOlXxDPgaTAZ0nzYkGijDaBQKBoCyy3HjUergx5AUCQd1yQdXj68BT6Bi6Xd2a1gL3qFQw/HllW2A0BI6Stz0Z7eVhfb1Mpa453Zc9Cr/eZq/R/e1oyDkL/9sDOt+qXa++YdS7Cpl1ngh9770w86kgS/Yk8frywwC0jQnkhl7xXP/5Zq7poQzn99ZaPO1G5XfPLgejvdjZaNd6yf+D/Qvsbaueg9Bm9qgLn2Dun7uLvBIjAd5aOsQFoVGraBTi/n0REeCFn5eGIr2Jn7adZflBWYTO10tDkI+rwbr9tJtUDefvlh63yx7yQ79DgWfdhaMp+ZglWOozA4CfvN4kO3y+spPV0y5JsNZSveK1KDm9xF2+u2WBqzxPuUatYVqPaeTr8/nu4HeYJTPLTytLf+eU5hDpF1nmOA0NER4vEAgEnshPsdRUVcETJ6DNGOh9jzLvUSAQXCBqMqfd6QbbLwIi2lZ/3OrgaLSX5Q0T1D80juHxDp72Dlfbj5lK5bSMc9tl48haw/xi4Ndb4evhyrbw1hdmLhUgLb+EcbM28L/5e2xtYf7exIf7sf25ETwzpr2iv1WoztnTnldif61LDW4WE8e9DwOnQdPL7G2bP4biHAD0XkGsOSIb3gWlRjTqshcHVSoV8WFyFM6bK4+xN1Eex1enIdDH1S/rnBMP2N+PVjpeAzEWdf+CVNf+FtYfTaeDKkHRFqpxGr/E4ml3Fl30JFBniT4pMNjF857v97zbrlM6T2Faz2mYPSzaelKab8gIo10gEAg8kbBRfoztCgGRcPN8GPcuOORaCQSCC0RNetqdw+NVKs/lvByvXZs4JtSqL64wz4serYMQnTUM/n97YdIc+zGjXqnYXXqRiGfpi+xpJI70u7/u51JB5m49y8HzeYo2L63nz7j1mHNOe16xPWLHxdMOclrdyJehv4MoXeYJm0faoA2s7NRpGubqhTeZJZtYHsAPd/UBIKfIwIRPN3HN7E0YrXN39rRrfey123Pd6PkA2YV63lt9jI7qBOUBy+KDjRLLvruIRXdYFrj0ls+MRqVhWJNhii4v9n9Rsd86VLkYZFWdF0a7QCAQXErknZcfI0VpN4Gg3mEznGupTrubkkP2a9dFeLzD4qDwtDcsHEu+WUOfraXirJ7NzOPwcQ/7OReL0X56vWtbh6vrtQ7Mwp2JLm3jOsd67G/PaVd+9+Q7eNqL9WWkVrQbB3daFjaKMuDE3wCUaAJsXT66sVt50wagabir3kVmoZ7oILuI3aBWEXRqJP//9yTm8N/ZHDaesGhmBMUpT9b5QJDluZ9aZ/eWW9h0IoPur64GII5M+wG11u6Z97I8D2tO+8HfKvRcrEZ7iUlezIrwjSDSL5Irml4BwDuD3+H6NtcrTvls+GcEe9vz9EO8QwBYemppxa7ZgBBGu0AgEHiiyPKD5OcqeiIQCC4wNaoe7+xpV5ftaa/rnPbyhM8E9Qur0W4otr8/rW06izFl0kO+g9BXkYMB1JBxLhPWdCBc8+WFmUsFyCnSc95JBX338yMJ8/e8UGZTj3fIaZckibwSu6e9xGhCKit1J9ghT94SLn4sz75Qd3W3ipXFaxrmXqTS31vLhqeGsfWZ4ajVKka2j1Ecv+O7HZxML4DwlsoTtb7QfCiENJX3nUr2zfzrKABNVKk0VTuEz5tN9jB4q6Oj0PKe3vG1/BjRBkLiPT8Zs2y0ZxTJCwoGy/7MwTNZOH4hVzS7wuWUaP9ovhjxBTq1jitbXElGsXzu9we/93ydBoow2gUCgcAThenyo19o2f0EAkHdYwuPl2TtiU2z7NExlcVFPV5Vee+2s0p4dRE57Q0Xa8ix3p6ba3sNvT14nMvIH25QOAvvtRphX6ioh+xIsIue/f3YYLY8c3mZBjvYw+Mdc9oTs4oxme1GuiTJiu4eceMMmJcYVtFp24h3MNq/vLUngT5aHhwmG+JNwvyIsZSN69/S9Xpfrj8FQU6LA1pveZHQWlf+iNJj3SjEh8aqNDZ4P8q1mo0ORyT7IlQjSwSJc7ncHrfBxB9c5mGw/FlTSZ5Y/wQAWSWycJ5GraFtWFvUHtKSOkZ05O+Jf/PKgFcU7bmlFSg514AQRrtAIBA4k3tOFghKtqjfRrS5sPMRCARucPC0L3kIVj8P34+r2lDO6vEqVdnh8Vs/hW0O3sN9v8KrkXBoSdWu7w7HG1SR035BkCSJ1GIwmyuZgmENhbfm9IKD0e4hb7n4IqktbXQSIwuMcd/vApNfYuB/8//jpT8OAnBTn3haRQUSG1y+gr+1/Ju1TvnOhCwGv7POpZ9bMTpg5YFkHlxw1KU9NaQ7AP8bXnHRvniHnPYhbSPZ+8IVPDnKNaWvT/Mw3p3YVXm9/BJo1EvZ0ZrPHmGZQ5FScb6VlMhG72nuJ2PNgY+zGO0lOWAokb33IKcFOJW+y1WruTy+EQ/GRCIZ9VCYaQuPrwxhPmEuJd5m7phZ6XHqM8JoFwgEAkdOrYcPOsIroZC6X26L635h5yQQCFxxzGk/vkrezDpVtbEqHR4PrHjSvr34Hnkef82QXWw5Z6uvaq/wtAuj/ULw/ZazvLFHy+z1lXxf+VhybDd/bGlwEDb05Gm/GHLas07DhneVbc6e3HrC/O2JLNlznqQcWbDMnSfaE10aya/vK0sP8euORF5desh2LMzfC61F9X3LqUyMJrNd9M3C1Lm7WbY/mWORoxTtScXyws7QthUvVRYX4svNLU3Mvqkb3loN6jIU56/v2ZiEt8bx1rWyOvzh5DzZq/7IHiStD8bG/cHL4rn3CZEfS5Te6glJTq+vI3nn5MeI1vbvr3/eAKsonHewXYjRwl5vL3I0Grb4+rI3/wympJ0Vet6eeHvQ27btP07+Ua2x6hvCaBcIBAJHTqxW7sf1gOAmF2YuAoHAM9Z7U0mi2jnm7sLjK+Lddjb2c8/Cmlfgw86w8f3qzcmxSoUw2uucFfuTeWOF7A39aG0F1a+tOBvm3kF2DQadL+j8Xc9piEZ7URZs+VQ21vWFMKuba5/4fnU+rYpwJqtQsd+vRcVD07vH21Pmnlq0j73n7IZtz6ahGC2RGVPn7qLzS6u47rPNtuN/H7KnQVyReLttW2rUm4wCObUgIsBJ0b0c+kZJjOwQVeH+47rIQnOpeaVymbqw5rzSbC7dTkyxlY3DN0R+dFKED9e7V5SX+1qiRQJj7FoOmz6yH7eKEY63t50OtC+W/FeQwJkc+wLZn+FDK/iM7IxtMZYwH/m17B51cTlchNEuEAgEVkrzYY9SdIW719SNUrRAIKgcjjnt1f2Mmp3CWFUqpfjbFa+5P8+5xBHYjfU1r7geqxQOz6kmatELyuS7Tad5etE+/th7nmOp+dz/0+6qD+aslO4tq2kn5RSz8USmPecXILS5/FjSAPNvt30Ofz0L342RDXd3OJcUqydkFyo1KML8Kq4b0aVJsMdjt/RVCq0VG0zsPZdLiUEWprt7jtKTXBQgC76tDRhLqdFMoI+W2ODa1QAI9NERGSi/LgkZ8uLFdwf0FODHlxssRrM1WkSfDyZ7+lCBugJVAPw9LCBYFx973gEv5fLZlS/xrr99cfJESTrnCmRvfftSPc2omgCntSycpxruDRVhtAsEAgHI4axvNpbLrziiFl+TAkH9xFE9vrpGu1NOOyp7aS6w5yg7U5RZtkFdVWE8UC5EeMqDFtQIuUUGXv7zEPN3JPLIz//xw+aE6g3o7Gm3CHKN/3gjk7/ZxpKTDhEaUR3kx8TtkFSNhYILwTmLAZqfDNkejPZ6iMFkZtn+ZEWbVlPx3/ogHx0TezZ2e6x9bJCiRrqVvGIDZzKLXNp/7/Yl3DiPKXtl7Ry90VypuVSV5hFytMfpDGXEQZCPxVD2sS9M/LrpgG1bJ+nLHljrI4fAj3y1zG4phSnMPvitou1ESSZZJbLifJjJBMZyruVpChY9EqPL93rDRtyNCgSCS5dDf8COb+Tt0xtcjw9+0rVNIBDUD2yednP1Pe3O4fEqFfhH2Pc9hacXZ5UtIPZ+e0h3FZyq2JwsiwFXvFava1xfDOSXKr2uv/2nDAEO869keoLzIkvj3gBkFcpGSJbkcLztaGg7FoU2Q0PBsWzZL5Ndj99awfrcdcxvu+2v74xx7fntgQGVHmPm9V347s7eLu3h/l4snOo6Xp831rDBUhu9RYQ/j1zeCoA9Ob5IbcdiXXi8tV/TSs+lKrSMlI32U+mFCqHFIB/Le12jo1glC8h9umKX7bhGkj8rx5tPhmfPw1NOizVWIcKBj5R5/YMZB13aTumzeD5dLn8XYDbDnrmQcaLiT8qCViUb7SaXtKeGjSj8KRAILk2Ks+HXW+XtlsPspUlC4uGhXbLnQOSyCwT1F5uhXgM57S5CdE5Guycl+aIs+GJI2WN/OwqmJ1R+TiZL6awWQyt/rqBSFOuVr3+R074nFXBJkli46xyJ2cU8OqI1Kut70nGRRa2F4S8ozltv7srN0hq8tRpoPhhSLQaMc7m0+k5ZUSbPZ9Q7LYbFu8/x7abTHEjKA+DpMe24e1CLKo2lUqkY1jYKL63aVq/9uh6N0WrUdIgLItBHS36J0tP709YzgJyz3tEiZvfP0XTF++3RkXVTraZpuGy0n80qIrfYvmgV4G3/rstXBeArFRNOHpIkoTKbCDfJpXDPtZhEay9/8HKjz2Cl2SBIsDhEet6hOHQwU2m0e5klStT2/0OWxhKt8ElPuPYr6DKpws9NY9EDudg87cJoFwgElxaFGbDxA8g4bm9b/SIctqiM9rhNDu0KrZvVboFAUEUcPe3VxW14vEOOq2N4/O1L4Ycr5e3887L4XFlUtZSX1WPlKTRfUGPkl7q/uf/85m5MnbeHQr2JZxbv4+Y+TencWDa2lu9PZvrCfbZzx3aOoV2MxVh3DI8f/xE0H0xmgd0g/8fcjZ6ln3Pg6ZGy4JfVuDUrPf61wfpj6TQO9aVlZED1BzOVEb5cjwx2SZJIySvhvVXHbGrxANd0r76yvd6hFvu7E7vYtqMCvV2M9iMpsthgdLAPvZvJYmlp+aVsOSmHhOs0Kreh9bWB1aN+Mr2AkR/8a2v/aM1xooK8uaF3POle8USVpNNanURSTjFeG97Gmq2u1lXge+mWBbJAoVeAi7bBlvNbFPtxRiMJXvb3zFNZDt+bi++BlsPBv2IK/xrVxWm0i/B4gUDQcNkyG/6c5uolK4vPBsCWT+D4X/a2ww5lQSLb19j0BAJBbWLNaa8BITpnw1/lpB7fbhzEdIY+90LzQTBwmtyecoBaw5rPqam4QJagasxxk8PeMS6IQa3t0RY/b09kxhL59ZYkiQd+2q0w9hXeeod8YLxk4/i1ZYcV4xt1AXaFbut7zVS7RkbCkf8Imjua1z/4gBJDDYQOl2W01xPeX3WU5s8sp/+baxUGO1Repd0d9w1pgZdWzfx7+9kjLYD3J3WjRYQ/3eNDXM6JD/MlxFeHtTqbVZwu2FenGKM28feWDdt953JtqvUARrPE9EX7MZrMZGrk0nMR5HI0JZ+o3R/a+qkrspio85UjlnQ+Lt/Rx7KPKfajTcr3Yzu90wJWsbJefFlYc9ovtvB4YbQLBIKGy1/PwK7v4GAF8+aS90FBatl92o6t/rwEAkHto/C013R4vFOddp0vTN0IY9+R92MtHrVd31VsfHdhxGtehY97QmGm+/7C015nHDif59IWE+SDl1aNTm1/7azlsFLzXMPYz2U7GISOnnadL2azxHIn4TOdo8ip9b1Wy0awtOAOuqtP8K3Xu5zLdhVFqzTWhaVmg6D9eHt7kHuRtgvB0n3JHo9pyqhpXlGeHt2OPS+MpF8LpRe4a5MQ1j4xlIVTB3BDL2WqXZNQP9RqFR3jlCr0Qb51F53gqyvbo5+cW0K+Sq7Zfpn6ALlpyogiXz8/+067K+3bYRVLN3A2qCMcjPYeJSWuJ7ir1OEBIUQnEAgE9QmTwyrsoilwYHH555xc69p203z7du+7hVq8QNBQSLd4Lo/9VQMl39yEx6vLuKmN6Vr+mM0G2bczlF4lzGbY8C5knoDDSzzMx2IsaoWnvbbp6VB320qov/x/1zq9tbIL9ZxML3Dp//DP/zH7H4tolpNwYKHeSKkljPrzyT0BOSTfFlpdB+HxJxLP09yUYNvPzHFdqKg0aYfkx643wQ1z4flMOf94Sj0S1PPw1aDT1IxHW6VS4eflOdtYo1bRNkYpTHidRXn+xyl9FO0hdWi0+3uXnSH97abT5EuyYd5fc4hr141QHPcLdFhwuGEu/G8v9H8IbnPzfeaEJEkKo32QTyyOsU5fpqTBoCeg03X2xkqkGQmjXSAQCOoTJU43HAvvBH2Ra71lR06ts283HwLPpUBgrL0tumPNzlEgENQ+Wz+l2p525zDKXncpjW5n3HmTOl2v3O/ssF+arzyWddK+7S+HoLLja1h0t7wgaXTwNAlPe61TqHe9uQ+zGO06pzvltUfS3BrtADNXWioFOObvSmZbbrNOo2JEe3sN6xUHLF5gW3h8LRjtWz+H36biv2SKornvvA7w13NVH1eS7GXerKHLGq0sGBZc/VzxmsBgMttEBLVqFZ/d0oP3JnalT7MwtwrvtUXzSLtY29+PDUZnKekW4ufFgJZ2D31NhOtXFGej3VlB/7tNCRzJ8WwmBvo7aCKoVBDaDEa9Lov5loNj/fSpXafyZsxwHL+BvSUgKA7GvGNvrITRbs1pTy9O58dDP1b4vPqOMNoFAkHDpDDdte3NRvD1cFnR2ZmcRDj1j7x9zZdw+x9yyKtj7qGvq7dFIBA0AKrrNLOGx/e+G+5aBX2nQkRruH8zPOGm5JBarQwJBbjqY+V+sEOIcEGaHAZvXWw8vd5+bPcc+XHZ47B/Aez7VVmfWFN3N/KXKlb17shA+/861E822gfHmmkc6mtr33Qig1Ppcm3rRiG+eMS6sNOkr81oD/TRKWpw/2/+HjYcT7frFtSG0b5yOuz9mdiMza7HtnxS9XEdF5bU9Ud0zsrus9l0evEvWx77Hw9dxpjOsVzXszG/Tu1P1yYhdTaXAS3DmdAtjleu7kirKKXX3dF4HtE+us7m1CrKbnS/fV1nuseHsurRwfg7COHtNHtWsrdGolQFRy/7bR1uIzi+P6MK5XSNeIPlM2AyyMJzHa+V9yuR065xiJK6mLztwmgXCAQNkxN/y49N+tnbJDOc3w3bv5RvehfcCS8Fy38fdpL7xPWArjfYz3H0tNfGDZNAIKgDaiin3SsA4vva02SiO0JApPtzHPOWVWrw8oPRb9nbWg63b6fsl2u2f9wTDMVwZLn92PFV8neUlczjdoNIrRMpO3VAoUVQ7toedg+xNXx6ZCOJdY8N4ud75N+af4+nk5ApG+0PXd6KW/p68Cw+sBWmnwG/MFt99mBL+PN9g+2RGrd+s732wuPLijyrLqUO0QY9bqu961SRVQdTbSkJAF7aC/c58tZq+PDG7tzWv5nLsSQHLYTRnWPqbE4B3lreurYzIztEM7KDfN020YHse2kUj1vKzh2Qmpd5flVxNKQ1Kg006snIomK+S05l3nmL7lDT/vKj1ZlSCU97sdH+Px3fcnwZPRsW4pdAIBA0PCQJ9syTt7tMgninMLd/3oTXIuGgmzx359AtnQ+EtZS3mw6s+bkKBIL6j9XzU1YeuzPeDuGhVk9pj9ug6WUw/EU5ZLT7rXL7mY1y3fXCNFkQ8+Qaz+MWZ9trtGuFl70usHra+zUPZ2znGFQqGNAyQtGnR9MQADIK9KTkyosqAd5aQvyUXmbJKjqo9bapw1vD6ZtHyGHSzSKcaltbcnBrfOG4NLdmx3NEbzHadf7yglU9YdeZbAa+tZY5WxIU7d4X0GgvixNp9sUPaxm2uuLGPvF8dVsvWyoIyDn4Dw9vTcJb42gVFciQ0vd51TBZeeI966gOjuHxWrUWvPxRqdT0Kikl2LrQFN5KfqyC0d46pDWNAhrRN7YvEb4R5Z/QQKif72CBQCAoi5T9kHZQDhvtdC3c+BNM+Kxi514+w7Xtvn/h0YP1Jg9PIBBUkmoL0VmMdlVljHaHMFdrCLuXP9y5DAY9Ju8HWMJdT9vrILPkgbLHLckDozDa6xJrTrufl4ZZN3Znx3Mj6BCnFJPz1mpsNbStnnOdRuVi3KfnuyrLp1naYoNlfQLnGul/HMiQN2raaK+IobP9q6qNrZejDfDyL7tfLSNJEkv3nedAkrxA8dC83STlFNsWYqzUV6P9joHNALiqa9yFnYgb/n5sCH+9cgffmOxVdSSNNzTqUa1xHcPj1dYqIAFOqQE6y0JQFYx2L40XS69Zylcjq/jerqfUz3ewQCAQlMWK6fJj0wHyF7pfGHS7GUa8XPZ5Ez6X81Sd8Q5Q5p8KBIIGRg2px6srEfLp5WB4eTKuA6Jc2zLd5Mg7UpJrN9pFPnudUFQqGxH+3lq0GrVHQTBreHumxWjXqtUMbBXByTfG0ixcNjL2ncvlsV/38PSifZjNstc9r9igON8xnxhgzTE5X9dsMrAzIYtSYw3Vl3Zj6BiCnUKet31etbFLcuRH78Ayu9U2Kw6k8NC8/7jt2+3MWnOc5Fw35cJAoSVQn3hsZBu+vaMXb1/X5UJPBYCDGQdZeXqlbd9Hp1GsiapqIKrCJTwewE9ZMs920SoY7SB78Ouq5n1dUT/fwQKBQOAJkxHOWgR1rOFTVrrdYt9uO1auq/zkSblmrE8ItBlVZ9MUCAR1SHVvzmzh8ZW4LXI0VpxvOK34e8iHL4vCdOFpryMkSeLfY+mk5suGnp9X2ZEW1vBlk8UY11ry3jVqFYNay6/1zL+OsHh3EvN3JLLf4v3NdTLaw5xEvAzIi0XnMnK5/vMtPLv4QLWfG+BS23qa/gFUfk6Cq7oyxPTKIvuM/BjSpOx+tcysNccBOfrh/dXHXI57adUMbhNJqF/9E8sD2Si+vF00vuW89+qKG5fdyJP/PsmaM/YUnleu7sTcIEv1gQlVXORxwBoer1ap7Ya1X5i9g+OCqLX9xN9ytYPkfdW+fkNFGO0CgaBhcWajfXvIdOWxgEh4KVf+u+lniOkM/hEwdQM8tEP5oyAQCC4iqutpt+RRVjU83t9D3qQ7T7uunHDi/BSR015H/HMsndu+3Y41Db08ca2oIOXr4eXgvR3ZQQ7vPZZqz1FOzSvBYDKz84zsSY8Ospfvu7KLXQTViKVEVa587qLd5yr7VNzj4J2cqp/G37qhqL0DnPrkAJBTpLfXja8IWafkR3flD+sISZI4nVFYZp+f7+nLnLv6XHRe19rApscALDy+0LZ9a7+mTH7sfXjmHLQdXe3r5OvlEpiOue0M/J/8GBAj369Zcazqs+UT+GKQUgTxEkIY7QKBoGFx8Hf5scdtnlWdnfELc3/zLBAILg5U1bydqUp4vKPR7pyPacXfzfeOwYORcbfFs1WYbs8X1lS9rJKgfLaeyrRta9QqRck3dziWfgNlyHW/Fq7RFvuTcll7JI3ErGIiArwY1dGuDv7JzT349GY5N9jqaddRQ+Wpzu+BWd1h1/cA7DG35EDwEObf2w91eEtFV6kwnZScYrq9spqJX2yp+DWsNdpDPSuM1zYFpUaFQryVjdOH0S4mkMahvrSJvrDh+w2FQkMhj69/3La/O3W3a7m0GkqFeG/Xe66NrUbAw7th2j65RrsVx7K8VioZKn+xUHW9foFAILgQJO+VH1uNuLDzEAgE9QfvQFmZvapURT3eMYQzwkM946BY9+3O3LMWYrvLiwZmI+Qkyu1an7LPE1SLEF/7ooi3Vl2uN9a5Lrs1PB7clxT7eK1dv6Bfi3CXEOhxXWLRm7qyaMF+AHTUUC77oimyJ9ziDT9sjueqrnF0ahQM5zoouqqMJfz1nxxivjcxp+LXsHnaL5zRnponR6T4e2loHxvEzjOyMRfsq2PF/wYhSaBWCw97RZhzaA6rz6y27RcZi0guTKZJYM2nP2w+v9n9AacFJcB96pHRVezRRtZpyDkDzYdUP22qniE87QKBoOEgSZB1Ut52zmcXCASXLo7eGIcQzwpTJfV4R6PdjcAlVNwz1ainnE9vXQgosniARXh8rfHB6mO8vfKIbT/Ur/yohiZhShEur0qImzmXhrPio9XYwuO1NWW0F6QrdnMJwGjJwyeqvUv3otwMgilAWxlPf85Z+TG0WRUnWT2K9Ebe+Ut+/ZqG+9O/pd2489VpUKlUwmCvBJnFmS5tJUb3on7VRVOZ79mAKPB1Sm00FLnvK0nwxWCYc7WyYsdFgjDaBQJBw6E4W1ZWhgsakicQCOoZjka7vuwcV7fYwuMr42l3MMgj21b+mkOmg3cQTF5sb7OKglm/54TRXiss25fMRxYBMysVUe8e2SGapuF2w93R014eVhE6Z3o1C0Mv1XB4vFNYc5IUzvFUOY+YqA4u3WNT1rLX516+082s4Phme4hyVcQWa4Bh7/7DXwdTAYgL8aF3M7thV1+V4uszuaW5Lm2bz2+25Z/XJOrKpjM1HaDc97SYUJAKpXnydsr+yk+sniPe1QKBoP5iLLWv5gOcXCs/hjaDGig7IhAILhIcw8irku9oFUSqjNHu41DHO8xNWGd59LoLpp+BVsPtbdbnYQ31FyXfaoWX/zyo2L9jQDMua+1BTNABPy+tQkBO66HaQIfYIJe2mCD3qQ6Rgd5c17sZAP6qYkaqd+JHNT2cTkbRLnMb2lvn5B8O13/LkUGzSJZkQ3dCyscADNJUULVen2//zPiEVG+uVWBHQpYtNB7kUn2DWkdwz6DmPH+l66LEpUZmcSZFnrzRHsgudf3efHfnuwz4eYCb3jJrz67l6Q1PV/palfK0g8silEdPuzVlA6A4q3LXaAAIo10gENRfFtwBH3a2l/jY/YP82OWGCzYlgUBQD3FUIa6K0V6V8PjAGBj3Plz3DejKyD2/6RcIbw1Xfaxs9wlxLTFn9bSf3So/VsWDLygTs1kivUCZE9ujaaiH3q50jLNHdXg75bHPndKXK7vE8u0dvV3Oa+fGkLehlr3wkao8vvJ6n/d1n1V4Pm4JjFHshkc34oFhDillna5D1+U6SiRX77+1nF2ZWD9jWp+y3/u1xOHkPMX+yfQCVCoVz43rwJTLLu0ovPMF5xn661DuXnV3pc7Ls3ioPx3+aYXP+d+6/7Hs1DK+OfBNpa7VOaJzpfoz/AWlqOecq+Uw+E/6QNphSD0kp4Q4Gu35qZW7RgNACNEJBIL6y9Hl8uO2z2HwE5YcJRV0n3xBpyUQCOoZ1Tbaq6AeD9B7Svl92o6W/wzF8MfD9nZ3xo7V056fIj9e4BrYFyP5pUYX2YPOjdwoVHtgTKcYpg5pSX6JwUVN/rLWER499tYa7+6Q1MpjozU7PPSsIE6fgSlX9HQpZxcT5EMiyjz+AsmHb9ae4IqO0XbPvDsMxfKjVznlC2uJjAK9Yv++wVWIdLlImXd4HgD7MyoWHm4wGdBpdLYw+CAv19fdZDahKSMKKaUwpVJzbBnSki3JWxjTfEzFTojuCE8cg1Uz5LJvYBclnt1PfmzUC3IdSiXmnKnUnBoCwtMuEAjqJ47qoFmn4KdJ8nZ8fwiJvzBzEggE9RPJQcDLarCYzfJCX4lrrqbH8ysTHl9ZdL4V72OdT3k13S9BEjIKWXmgckaCI3nFBpe2pmEVT7dSqVQ8PaYdr1/TuVK1v/29Pb+3zG4Wi7IL9W56VhDL7+cmc2em6R8gMsTVEPP31lKCcrFAh5EP/j7GmI82eBy6xGBi87FkeacWSxL+czSNbadcxdHOZhYxy6JH8PZ1ndnw1DBFysKlSnZJNisTVrI52YMyuxs+2PUBPeb2oPMPnTlXIBu87ox2g9n1M+OISaqcgKJ1vGZBzSp+kkoFgx73fDxpJxQ4fC8kbIDVL1ZNmLSeIox2gUBQP3EUkzq7BTItokG1eVMtEAgaDv0elB91fu497bu/hx/Gw3djyx+rKuHxVSEwruzjziXeKmLoX0KYzRJD3/2HqXN3KWqsV4ZcJ6O9b/OwWlEZX/G/QYp9Z0+3I706tnNpO5tVuTxhRySL0f6k/h6WSJe5zbEHCAtWRhhUpOTcx2uPM3OZxYtbw0Z7YlYR7/x1hK/+PcUd3+3ghi+3oneqwz77H3sZvaFto2gS5lepxZOLlTtW3cGT65/kePbx8jtb+PbAty5tgV6BzL9yPhPbTLS1lWe0myVzmccdyS3N5ZejvwCgU3uOPnGLXxiMn1Xx/ps+hISNlbtGPUYY7QKBoH7iSR205bC6nYdAIKifdL5efvQLVxrtJTlQkgf7F8n7qRUQ17KG+2prz3MIgKac8HtnI10YIwocDfWdCVUTmrJ62ltHBfDPE0P5/s4+NTI3Z5qFK6Mk/Msw2js0b4QhuJmibfI326p2YUlCZZa99Hp0+HtpPRq1EZHRin21SkJN2QbYgp3n8MJixNWw0T7h0018uu4kry8/bGtLyJQX8BfvPseI99dTqLcvLER7EPe7mFlwbAE7U3Yq2iRJIrEgscJjSJLEB7s+cHss0CuQjuEdmdFvhq2tJo3230/8btuurIcegGaXlX08uhNEdbTvV6WaSD1F5LQLBIL6ifUm2pmB0+p0GgKBoJ5iDSk2GeRQeCupB+HdNmD08B3iDmsuZFDjmpufO8rzLDkb7VXJz7+IWbjLnrN6JrNqnmirpz3IV0eziNpLP/DRKf1iunLKkOl8A8EhkyO/pIrl30z2sHo9Wor0nsfxi2wOp5RtOoyU4oXRZHZbOq1fi3Ay98tjGlRaKukrLZNMNykBx1MLaBMdyGO/yjnMJ9IKAHhw2KWXx74nbQ+vbHkFgP237+dM3hkOZxwmR8px21+SJLcLNon5iW697Fq1Fh9LtI9apUatUmOWzGQWZxLmYy+pl1aUxo4Uu+7CsexjVXo+hYYqGNThLeGOZfD9OPfHfUOV6vIXUXSm8LQLBIL6iTtPe9/7L6ovYIFAUA2sRrvZqPS0719QOYPdWAr55+Xt2hZ+05Rj4jiHx7epoFDTJcDy/cks/i/Jtn8uu2KvcWJWEacz7MaB1Wj3VDe9plCpVPjqKvF7pXPNq6+QkrvLSXbDtxQdZQ7R4Sq5hGrfqbYmrSVE/uftZ12655cY+GPveVs9+RKz/PwkSaKwtIZqzDtxKDmXnCJXY95Rxf9SIa0ozbadUZzBxD8n8uSGJ3kv7z23/T15wHNKc9y2+ztpaFjPf3jtw4r2ycsn8/SGp237p3NPV9jbXuJwb3dzu5srdI4LzS6DyYvcpzP5hYNXgH1fX1C1a9RDhNEuEAjqJwYno12lgf4PXJi5CASC+ofVAHY22iuD2QyfOJTn8o+s/rzKojKe9pB4CKjl+TQgHvhpt2K/yFB+aK3JLDFo5jqGvfsP646kkVtkIMNS7i0ioJZTIYDYkEqEb7sx2h3zuf87m82n605gNJXzXjcqPe1l0nQA/G8vXPG6relqjSxk9vySg6w6qBT8+3WnHOngbTHa9ZI8/jt/HaXji3/R7OllbD9dtbQFTwsU/53NYWeCa8RJp0vQaPdzeI8M+3UYxW4WJ8e3GG/b9hR+nqe3l8zz0djfo7e0u8Vt/6SCJJtRnlWSRXJhskufjUkVyx0vMMhG9O0dbic2oBoCgq1GwGOHXNsNxXDFa/Z9fdW1IeobwmgXCAT1k5Ic+TGiDUz5Gx7aIVTjBQKBHWvUjdmkVI93h8mDFzD7tLI0UG3nkFfG0x4Q7bnfJcJHfx/nwZ92U2p0fX2LKuDZLXAIMb/z+x10fWUV766SQ3kjArxrbqIeaBRSCSFBp/JpGkw2o12SJK6ZvZl3/jrK4t1J7s62cTAxHQCDpEFCzR0DmpV/bYcIthnaubZt52tZ/+dWT3uppOW9VUeZ/c9JW5/7flTmW1eUQqcw/uaW1IXNJzPZeCLDpX9UUO2/fvUNTTlCmZ9c/okiF91odv8ZsZZ3axbUjL+u/4smgU3oH9uf+7vd73Hso1lHAdidutvt8aUnl5Y5NytFltB1PzeLVJUmMEZedHJErYHYLtBmtLwvctoFAoGghsk6LdcwTtgAGm8wWUq++QRDk95lnysQCC49bOHxhvI97aV5svKwS3u+w04diL6VZ7Q7pgX5hNTqVOo7aXklfPC3JVfW4aX54IauPPrLXo6nFVBqNOGtdW/ImM0SB897LvcXX4kyb1VlymXN2XA8g97NQsvv3Go4HLEbPv4U89bKI0y5rBn3ztllaz+dWYYRYtQT8/MIUMle9n+eGFqxvH2HxSo/lb3cakSgMhrB6gu3Gu1pRWY+XntC0Se7yEBhqbFM4T13FJWabFOZc1cfejcLo93zKwH4fnOCoq+vToNPZVIPLhKcBeEmtpnI6dzT7EyVF0rahrVF61A+0OppzyjO4L7V93FVy6u4vePt5JXKnvaWIS0J8wnjzwl/olaV7cedvWc2LUJaeAyDzyrN4pP/5BrqD3Z70KP4ofU5eNWUiGFoM+X+yFflx/bjIbS5XOP9IkF42gUCwYUj7QhsmS0//nqbbLCD3WAHyKh4+RKBQHAJoa5EeLwnQTdHL4xPHYTbuqnHrSC+v307ok3tzqUekJxbzBvLD5OWX8L+c7n8uiPRFibtmL++bJ8cjuvvpVHkMredsdKlJJiVR+b/x81fe1ZgH9ym9lMPhraNYunDl/FdRRTqe90Fj9sFvfwo5eftZxnx/r+ccsjJ/+yfk55D0Jc9SrhKXog6JjUh1L8KhpF/FE+PbgvYDWkrx85l8JXuPaZoVwCQVereMOv44l+sP5ZeqcsWWLz4QT46BrWOxEenIczD/KcOufRE6AAMJrvR3j6sPS/0f0FxPNovWuGN35i0kc/3fs7b29/mWPYx3t35LmAPjw/0CgRAo9a4NbJ7RPWwbf9z7h++PfAtcw/PdekHcC7/HF/s+4Iv9n3Bz0d+9vgcrN7/Spd7qygRreTH7pNhzFvQbGDtXOcCIIx2gUBQt2Sdhp8mwt8vwWcD4K9nYHZfSNnnvr81xEkgEAgccRSiM5cTHm9Nt3HG0Wj3C6+RaZVJYEzZx2M627cjL36j/eU/DvHlv6fo8/oaxn+ykacW7WPDcdnY+81NGHiRwURUoDIs+u/DqS79MgpKWbpPmXfrWIr9qdFtiatM6Ho16NQouMwa7QoCo22LR44eb2cmfbHFpc1sluA/u0F1vf7Fil8XoMuN8mNhGoMTPgKg2EE3oMRgIvbkfEZqdtFJnQBArtlzzv5D83ZjKC//3gGrkJ2/l93o/Ob2Xoo+Q9pE8ta1nXn48lYVHvdiQm8p5dc1siu/XCnXOn+wy4P4qHy4o8MdqFQq2QC3hKY89e9TfLrnU1YmrLSNsT15O7P+k2udB3kFlXm9r0d9zVUtr1K0WY3u1qGtFe1JBfbP65vb33Sbbw92T7u2vAXMyjBkuvw44OGy+zVwhNEuEAjqlvm3wPFVsPED1zxUv3B4JgnGvAPdJsPwF2HkyxdmngKBoH7jWEnCQ+6mjRIPYdJ6h/D4a7+s/pzKY9Qb0GwQXP+d++PeDjfR4a3d97mI2OGm1voTC/bRZsYKjqbmuxzrFBdMiJ8XX9za09Y2a81xPvvnpGy0WkhyUJb31qp5cXwHVk4bTIylrvfg1vVY4M+i4O2HmwoqZfDtptOKfRMaNOpKpHx0sBtnHRLmALDiQApXf7qJvBIDCZmFBJuVESsFkrzwcdfA5uyaMYJQP7v3NL/EyPL9yTbhv7LIKChl2i97AGU9+66NQxTaA9FB3tzYJx51ZZ7XRYTV4A3QBdg8410ju/Js0LM80u0RWz9NGVV2pqyaYtsO9i47ukin1tEm1P3i4YPdHmTFtSv4cqT7783N5zeX+Rxq1NM+5Gm4fzOMuLjvF4XRLhAI6o6CdEg7qGxzFFsa9SZ4B0Dfe2HCpzDosfI9UwKB4NLE0VNjLMcwKM5x315kMRrbjIHGvdz3qUkCY+COpdDpWvfHHUP0Iy5+oz3Qx9XbllFQ6jbkfUibSD6+qTsAozrGMK6zrDx9JCWft1ceYfkBu2f9TJYsdtW1SQhHXxvDnQOb0yY6kOX/G8Sax4fQqVE9Vh73knPt/fD8nvbWut6+v7dKWSv7wxu6Ve66HtJD9ibm8M/RdPKKjXijzKnOR56rl1ZNeIC3S8rB/+bvYfDMdaTnl/35fP73A7ayfI5Gu1qtYvH9A2z71kWXSxW9pZyfzkkbwzkfXauqmBd7XAsPtc4duK71dW7bQ7xDaBzYmGh/94KZ3+z/hu3J29mRsoPr/rjOVte9VsLj1Wo5d/0iLwkshOgEAkHdkX7Yte3BbbDlUwiKg6431P2cBAJBw8TxxtVk8NwPZCE6d1iV48Oa18ycqovOB26YK+foB0Rd6NnUKoeT80jILL8cU6ifjkdHtuG2/s0U7R0bBbFsv91Q/35TAld2iQPgt91yabK+zZXig2H+Xh7zpOsNFlVtP1WpXfnNiVZRAS5tbaID2JDaiUGaAxRJ3kzo3qhy123SDzpdBwcWYVIr/0eJWUXojWYXo71Ikr3gOo3s9W0R4TqvIr2JPYk5jOzguRrCkRR7VEWjUGXaQny4Hx/d2I2tpzK5f+ilGRZvxWa0l2PwatQaKCNjKMY/htXXr67QNQO8AvBSe9lC862EeIfIY/m5d6zsz9iv8Oq/s+Mdfh3/a+2Ex18iiP+YQCCoG4qyYOtnyrZeU8A3FC6f4f4cgUAg8ITjTZ+pHE+7US+XfdM43fbknJUf61M5yfbjy+/TwCksNTLmow0ej4f7e9EhLoirusYxsVcTt326Ng5R7B9Pk+s/55UYbCJoN/WpR69rRbGUfvN18rQPah1Bp0bBfPbPSUqsueaph+DQErhsGvmlRjKR0yv0g5+h0tr4Gi2Mew8OLEJj1qPDiMFiJqw/ls7201ks9lKG4G8ztwdAq5Y9vTHB7suw5ZeUvajmWKM90E0e/tXdGnF1t0ouQlyEFBkt5dK0Zb+65ZWGG9e8fA+7I84GO0Coj1wRoaKl26z9aiU8/hJBGO0CgaD2kSSY6eDJ6nMfNO0vROYEAkHVcbwxLS88/tx2+PtFGPIUXPaovb0+Gu01jCRJZBXqCa+DuuQVZdvpTLftT45qy8BWEXRpFFxu3nLXJiGK/dxiA4/8/B9/7D0PyDXSm1ek3Fl9w8ua0658T3fRJnLfuTe5wiuRd0osgltzr4P885CfTG7ReFsptpBAV493xa5tP8+PEnKR97efziKaLHqo7eXdtpnbsUNqB4DW4mn39XJvVuQUeTba0/JKOJtlj7i4tkfjqs39EsBa4zzAq+zX16oOb+XpPk/z1va3bPsT206s1HWHNB7C+nPruaX9Law8vZI+MX0I87FHsSyZsISrf7+6zDF2pe7iVM4pmwK+MNorjzDaBa6k7Af/KFnFVCCoDpIkF111qD0LyKU4YrtcmDkJBIKLA7UaVGo5lLy88Pj9C+THv1/yYLQ3rZUp1geeWriPBbvO8cNdfRhSB2XOKoLB5D7u+8FhFQ9/DvDW0rlRMPuT7CKDVoO9QWPxSPo6qcc/eVoONe6uhmcNs4A7ZIMdYPcPbJHm4qWxeOA1VTSINDrQeIOplACKbUY7QAu1Uo1/sWmQfcoWo93PQ+30EqPnWO3NJ+0LOJ9P7kEfp5QGgZ0CgxxNUp6n3ZHxLcZzS/tbaBzQmIfWPkTP6J40Cqhc1MIrA1/hbN5ZukV146neT7nk0LcIbsGPY37k7e1vcyb/DPl6VxFJgG8OfGPLaRfh8ZVHCNEJlGSfgc8vg/fbX+iZCBo669+BNxrBhvfhl8n29idOCINdIBDUDFZvjXN4fFAFvHWH/4Qii8EQ4j4Eu6FjNJlZsEvO717yn2sJtQvFkWT5pj4myId5d/cF4P6hla+9/cnN3fn6NvcCgkk57ktO1XssnvZrOoV47NJMcl2c8FI5GMbV8WJ6y4a6v0qpXh+DUunfms8O9vD4Xs1C3Q5ZYvBc+s2azx4X7MPoTrGVn+8lRKHBItanq3gEidUjPqTJEBaOX+hR7b28MbpFdQNcRe+sdIvqxs9X/szs4bM9jpNbmmsLtRee9sojljkEStIOyY+SCQozwD/iws5H0DDJTYJ1r8nbaxxKcLQcDgH1w9MjEAguAtRa2WB3LvmmrYDYmONiogfl7IZOQqa9Dn1BaTll8eqIUqOJudtkAcDpY9oyoFUE/z0/khC/yt/ENw33p2m4P69O6MTzvx9QHLvBQy58vcfiaY/18eyd3m9uTh+zRIo6jkZmN9EFVfW0g7xoUJSJv1PJuY6W2uxWirAb7WZJjpwI8fNizwsj8dZq+Pd4Or/tTmLlwRRKDZ6fy8HzcqTEQ5df/NUSqkNKYQp/n/kbkIXkKopjKHzbsLY1Pi9nukV1448Jf/DZ3s8oNhRTZCzi9o638+CaB0nIS7B52v29GmDqygVGGO0CJxxyyP59Vzbix70vl6n54UqI6w5XfnDhpidoGOz7xbVt8iJoPrSuZyIQCC5mPIVYaitQGkqtLb++ewPncLI9TLW4DMOpLtl0IoP0/FKig7wZ11lWew+tpqL7rf2aKoz2t6/rzMgODbRcqKXkm49DTnvrqAAkTTNU2QkABKiKkH6+yb3BDlCYXvXrWz47Xig/Gz3VxxX7Jdhfs6xCu1BZiJ/cPqpjDPvP5cJBKHVTwg9kvYUDlvSGzvW5DF894IeDP1BkLMJP68dljS4rs++PY35k6amlPNz94XJrsdcGzYObM3PwTNt+WlEaAIn5iXhZKhMEegXW+bwaOiI8XqDEsSzOts/g9Hr4pCe82QjO/wc7v4UMhy/uXT/A4vugxEM5HcGlScp+17Ym/VyVmwUCgaA6ePpO0VTACAy1iGO2v6rm5lPPOJJi/20u0tcPo/18juzB7do4BC839caryn2DWwDw/JUduKF3fP0v7eYJS+izj2T3dN8zqAUqB92GzuoEtMdXeB6j5fCqX18re9C9VbIh/tktPfBGT0eVUjm+fWN71Jzeg1FurSdf4mHBKL2glOwiA2oVtImponjeJUJKYQoAU7tOLdcQ7xbVjRn9ZlwQg90dkb7ye8UsmSkxye/rIK+gCzmlBom4gxYoKcktv8+q5+USXd6B8Ocjctvpf+U85c4TofP1tTtHQf3HeZW/w9W2PDmBQCCoMTx62stRSk/eC5mWBehmg8ru24BJybV7azMLSll3JI3ezcMIcFNWq67IKJDnFB5Qs0b1E6PaMr5rHB1iG7gxYPG0e0sleGvVlBrNdIgLApNr2S0rnxvH40sJt3fxh/EfgW9I1a9v8bS/MLolWU3606d5GO0mBeL1hwm8g2zOneeu6srXn6QCoDe5N9p9LMJ0RXoTZrPkUhFg9SH5/DB/L7y1ZZcpu9TJLJH1NyorIlcfUKmUr7uX2otgr/qxoNCQEEa7QElhRvl9jq2Q/xzJPy//HVvZcI12kxF+vx9KcuC6r+UcR3d1fQXuWTEd/MLlkkrORvvot9yfIxAIBNXBk9Felqf93E44s8m+r/Ot2TldAIr1Jt5bdZSrusXRxaF+eZ5DfeyEzCLu/H4HAD/d3ZeBrS6MZs2mE/J9RqOQmv2/6zRqOl0MIdaWnHaNsYhvbu+NwWyWn1cZRvsmc0ea9L4Srulc/etr5AWvVsX7QNsC0jNoXmRJPWg6QL7PA1QOC2OlHoTmrGkPf+w9z6mMAv548DKb4X4qvYDnfpPHzSjw/NwEMpnFstEe7ht+gWdSNe7tci9f7pNF8D4f+Tm66uguXKIIa0SgJD+57OPB8ZB7tuw+ZhOo6/mKqSTJUQVnNsF/P8Fl0+QQ//2/ysffioeml8GZjdBqJLQbK/d3LBUksJN9BrZ9Lm8PeAQK5Pwlrv0KwlpAUNyFm5tAILh48fRbo/WGST/KJd6yTiqPfe0UOnwRGO1vrjjMnC1n+HrjaRLeGmdrzyt2Xwrvlq+3cfS10VXybkqS5OI5qyi5RQZ2JGQDcF1PUY/bLdYa3Hnnuay1ZWHFZCyzrOFnT96Ff0hUzVzfaoxv+UT+A2gzWn5sOtBmtBPUCJDvB1tHu4+kiwu2a0scSMojs1BPZKA8/jt/HbUdu168F8olq0RW73esj96QeKDrA0xuP5lQH/cVBgTlI4x2gZ0zm2H3D8q2nnfIBtmpdTDocTi3w9Vo73gNHPzNvp+bCKHNqjcXsxkSt0JMZzkMv6bZPcce2g9wdBmMeEnZ58xG+fHEavkPoNP1sPY1NHnnUQXfUfPzaqg4ijllHodiS2mYFsOEWrxAIKg9PJUN0nhDh6vkvw872+uxu+MiUI7f4lDr2pH8Es9Ce99vSuC+IRUvs7bqYAr3/rjLtt+zaSif3Nyd2OCKLXrkFhuYvf4EANFB3hU+75IjxuItT9oFXw6DwU/CssfBUOTS9WPjBPI638VzodE1d313Io5WQ73ZQJi6EYyl4BfGnw9dxr/H07mtfzO3Q7VzSlXIKzEQGehNfomBFQfkHO1Xru7ITX3ia27+FyGlplJbjfaG6mnXqDXCYK8mQohOYGfZ4/bt+zbA1bNh9NtwywKYngDDX3C9uYnuDN1vVbalH7Nvr30d/vwfpB4CfSEVZuGd8N0YWHxvpZ9GuRRmKA12K/++V/65OWdh33zUCf8Sle9GbO1SxeBQD/dzi6qpdxD4NcwVYYFA0EDwmNPuEB5fniidJRy5plm8+xzD3/uHk+kFtTK+I44ic44h8Y7bzvy49Qwms1Sh8U1mSWGwA+w6k81rSw9XeI7vrTrKF+tPAdC/RcM0POqEsBb27fO7Yf5NcvqhG7aYO3D9kB41e/2y9CBiusqLCo17AdC5cTAPDmvlUVAwzN9LcSzTEgZvrc0OMLFnE3QaYY6UhTU0XqfWEagTquuXKuJTcqkjSXB2K3x/pb1G+w1zZVG57reAzkeu9+lrWR1zXoEd8za0vBzGvAPBlpqo/86E0gIwlMjbu76Hz/rDL07GfVlzOvS7vH10Ofz7jv1Y0i6YfwtYyp5Uib9fVO73uE1+1Ft+RMaVYbw7qKL76SuQ/3+pYCxxbWs3rv6nSQgEgoaNx5x2b/fb7vCpHeGyx37dy8n0Qp5ZVLsLvCUGEyl59u/gIw5l3tyFx696dDDBvjrOZRezMyGrQtd4fskBt+3L9iezdJ+HsmMOGExm5mw5Y9uf3K9pha57SaLz9RxB4oRR0tAqqoZFXgPKCLOvgsbPmw559tb3itV47x4fgq9Xw7lPOJBxgIOZB+v0mqWmUib+KddaD/UJrXJqiqDhI4z2S5n8FHi/PXw7ChI2yG3RnaDdlZ7P6XOv7F3veSfctkQOlVKpoO+9cONP4BMih9Dv/AbmXK089+Qa1/HOboV328DvD8i58CCXlnNk7Wuy0u/GD+Cry+HIUvioK/wwXl4YqCyZlvzGNqPhhWwYMl15vFEveeHCHSvtfQNKysn/v5RwE7ZHfL+6n4dAILi08GREOBrz5Rka0Z1qbj5uSMuvwu9UJXht2SGFx3zSF1sYPHMdaXkl5Jcqw+MfG9mGNtGBtprYidnFVIR52zynFzw07z+Px6zcM2enbfvewS3o1UxEYXlEpfKcsnHF64pdIxo06ho24qLau28f9UaVhruuZ2M+uKErAHO2nOHXnYkcTpYV6MMbQFm+49nHeXbDsxzOPMxNy27ixqU3UmoqLf/EGuJw5mHy9PL/q0Vwi3J6e0YyuxcLFDQchNF+KbPja1fhuWHPyT8YnmjSB+7fCOM/hBZDlcdiu0Kfe+TtdW/IOemOqLWyF91sD+Pjt6lQkAp7frIb6/sXuF53wZ2yoJAjp/+151lVhlJLqGKfe0CttoipOBDdEdqPh5dy4cZ5HocJLzjq8dglh7vFk0Y9634eAoHg0kLrKS/aIezbr4xQ7D73lf2bVwMUe6hRXVPM3epqUJ/NKqLPG2uQLP+Gro2DaRzqy+0DmgEQZREDS82r/ILChG5xtIjwV7TtSczx2P9oSj7/HLVXFHlmTLtKX/OSw8vfta3ZIOh3v6LJoKoFaaqojq5tjx+F/g9WecgJ3RrRNFxOQ3lq4T4+WiOXW+zXANIknt7wNH+e+pNJSyfZ2qw102uLhccW8vuJ39mUtIncUnsp5hf7v1jGWe6RJIkzt9/B0R49yZrzY01OU1DHCCG6SxnnUPf/7a2+gJw1RN4xXHrqRvhmFBgKYd4NsuBdv6myCmn2aXu/s1sgsi1snS3vT/hMLsEGruq/Vjy1l4XeYrRbFVpVKohsD+mHod+DcjqAlXbj4OYFMG+ivc03DIqzCC5JxFCYDiFCGZ2SHPmx6WVyaJ2xFKI6XNApCQSCSwBPyu+Sg9F+7Vew/UtIPShHajnizjiqYdR1FM46qHUEG467pm15adT8/uBA9CazTS0+3mJAvfPXUR4c1qrMcY0mMyqV/C+dMa49dw+SvX0bjqdz6zfbAZjw6SaFar0jD87bbduePrqdCO+tCO6EE3V+csqZWgdmOe1h5qRaWByPclpUie4MgTHVGlKlUuHllLceF+xjW0SqzyTmJ7q0rTm7hqtbXo1KpapxNfekgiRe3vKybT/YW466GBA3gMaBlVfZN2VlUbRtGwDZv/5C2G0VTFUV1DuEp/1SxtH78OD26hvsYPshsdH7Hlm0pO998v7xv+Tc8X/fgR8nKPuumgGrHVYRmw6E8bPKvp61tFiF5maW7zqcjXaAa7+Q8/Ivn+F6XpsroNst9n2HnHftd6Mqfv2LmVRLjld4S5j4Hdw0T+SzCwSC2qciRrd/BAx71r2n0Kt2ROgc0Wpqz0hdecAeLffcOPdhzUG+WlQqlaK824CW9hrtGQVlh/qm5pciSaDTqLhrYHNbe9cmIYp+nkTvch3y6u8fWnG1+ksbNwKB1ve6w3u2faNa8FR7ByrvD8sSpqsEZkn5nLrHhzYIAbp2Ya6RIR/s+oChvw5l+K/D+WrfVxzKPFRj1ytySje0etojfatWiUd/6pRt21xQCUFoQb2j/n9aBLWHZMlvaTZI9nDXBK1GKPethttAN2rtViLa2Ld3fmPfDm4CzS6z7+v8YMLncO3X0OVGua2iivRFWTC7H3zaBwotYXqOpeRiu8p5+Z5u4BzTCJr0RQqTbzxU5dWsv5g5swXWvQlbZsOmD+U2x9dLIBAIahuPNdbLMHoU59eOp90xx9xPVztBjcm5xUyda/diB/vqaBvtqiwd5OMqataneRi+Ovn3edr8PYx4f71H4z0lV857jw7yQe2QP+2nUy7MdnlpFbd+s4375+7iqEUdPDGriPR8edyN04dV5ukJnLHen3g75Lt7EmKsLrcsdLhuzXxGXD6RDSTgosSd0K4Fo2Rk1n+zuGHpDTV2Pb1J77b9ssaVu7+SJImi//4j57ffbW2m3FzPJwjqPcJov5SxfhFVM+xJQWgzmLYfAi0h4zFd5EffUOg+2bV/n/tgymp7fyttRsv55mEtsH2zT/weut0EXSZCXHe5raJG+4rpkHEUMhzK0QVUoq5px2vkx/4PQXAjTOM/BkDyrWUxHbMJlj8F+xeW37eiSJKsD/Cfg9he4nZ4KVj+W12BnKlT/8B3o2H9W/DXM/b29lfV3DwFAoGgPDyVa5PcGe1uVLZrydO+/bRdlT0qqGY8lc445okDRAf60Lu5ax3ktjHuS0QNbiN72zeeyOBEWgHfbDzttt/5HPleIc6prrpWo+ayVhGKtg3HM1hxIIVxszZwKr2AQTPX2Y41ChF12auF9f3rWO1AUzGV+Urj+LkKrRml/8GtlZ7iZfsahphvvj6//E41SInJ8nnzj+Pjyz+mVUgr/LR+9IruVeExTLm5nLn1Vs7cdDO5ixfb2qXiYiSD5zKQgvqNMNovZay1tWso9MlGSDxMXgjXfQPdbra3d54EQY0hMFbe1/nJyu2+IdB2jHKMka/KjyoVTN0A138HbRxC0a0rv+6MdklS1g03m+HwH8o+/pFyObuK0v1W+N8+uOI1+RJW8brSan6Zn98Dm2aByej++NHlsP0LWDRF3jcUQ3o1BPBOrJFL5m38AJZYQkXNZrt2AMhe87LSDiTJtTIAyBEQlfmfCgQCQXVx1max4k58zq3RXsPlsoCzmUXc9JVdiNXd+kFN4Fib/b7BLVCrVfSItxvt7WICuaJDNK9OcK+O3zZGWequWG+ixGBixu/72eiQG7/DUhYuNsT1f/3jlD48O9Y1fNholrj8vfW2fa1aJXLZq4vVkHZUlq9gabjKX8vhtQ5t7rlfJXhyVFseudyunzCwVf0XoQPIN9jv8yJ8I1h81WK3/cxSzaizlxrlyJQArwCGNhnK/Cvns2biGiJ8I8o5007So49RvHOXvUFtN/eE0d5wEUJ0lzKWLwbP6rvVILqj/OdIiyHwmCX3OfuMXCbM3/KlHdvV3u+5FGXIY0xn+c8RT0a72Sznyp/bCQ9ulRcQCtNd64g7LiZUBJVKudps+fFUmQ1gMlRttdtshi+HyNteftD7btc+xdn27aIsmOnw4/nECQjwkOOUuB1MemW4uqEY5l6r7Jd1Go6ugMwTyvZ3W0PHa+HgYuh5B4z/SG6XJLmknzPx/eUICIFAIKhLnLUzGvWCpJ3Qx833qdvw+Jr3tA9+Z51iX28q+2a+oNSIr65ypbsSs4o4YimbFeSjZfpo2XCe0K0RBpOZHvGhtHYTKu/IhG5xzLKoeINsWM/deoa5W88yd+tZEt4ah8kssXy/rJQ9plOsyxgqlYqm4eWHT695fEiFn9ulhv5cEkXbthF8zQRUVuOqzWjX6jjW97p3HXjaHe8La0LvCPD31vLYFW1pHObHLzsS+fCG7jUybk1TYixhy/kt9Ivrh7fG2+Zp/3bUt7QJbYPkYRWuyFBEQA0sAp7OkyNefDTywom3xhtvTcWda5JeT+HmzbZ9bVwsLZct42j3HvJxowcnkaDeI4z2SxmjxRt9IbyjzuFWXW6Q1VJju5aRo+iA9YvRKipnJXU/nLas7n92GTx1EvLOuZ5/+QuVn7Pi+g43KfpCOVqgspy2eyFI2g293fRxXEV/z0l34NDv9hJ7CRvh19vl9IFWI2GOJUz9mSTwtvyvDjupJgPM6iZHHQAMeVoWbFr+hLx/0LKavOt72Whf+Sxs/VR5fmQ7+SZ51GtlP1eBQCCoDVROAYM3/yo/+rvx4rkz0Gv49+9cdpFLm6EMoz2joJR+b6yhb4swfrq7X4WukVtsUISdX9O9kS3XXK1WcUPv+AqN0yJSaWAU6o2cyrAvhP+59zyxwT5kFJQS6KPl8nZRbsdxzpkf0ymGFQfsJbE2Th9G49DaF/xrCOjPnCHrhzmET70PXZT8/zw5QtYCUgcEEDTqCrnjxO8h5QB8M8J1EMd7pFoLj3f4XARXXrG8LCb1asKkXk1qdMya5Kv9X/Hlvi8Z02wML/R/weZB7xLZBW+Nt0ejvcBQUG2jvchQxFvb3wJA7fzdVkGK9+61bTf/bTG6Ro1Q+dhfT+Fpb7gIo/1Sxlpb21N4YV2i84Hhz1e8v9VodlTZlCRY9rh9vzRXNoYLUl3P11Tzra/xwowGNSZ5DlUx2hO32beLslyP/zcXdv1g33cWJ3F8Xt9bSu1s/lj+s6IvkI323x+EPXNxS2G6vAhy2TT5f2g12h3JT3E12Ic+A0Ofdj+mQCAQ1AXOnnatt32h0qWvWk7bOrsFdnwttxnLVk6vLGcz7b9JzcL9OJuej9HkOT5+xf5kjGaJTScyKzT+1lOZ3PjlVkWbj1fZlTqKDx7k3P0PEPnoo4RcM8Fjv/R8PQWl9hv6h3/+j34tZN2W4e2i8NK6NyIc1fFPvD4GrUbNyfQChr+3nmBfHTFBFbvHMBcXkz1vHkgSfn374dvZfVh/Q+bkqNEAZM+bR+jkyUQ9Yb9nKTl40G6063yhidNKvvVeTePl0FZLOgGOAo3hZZcEvNiYd3geACsSVnBT+5sA8FJ72bzdKpWKWcNmkVWSRbR/NPf/LacXFhuL3Q9YCZ769ynbdodw92VzJbOZjE9nYy4oIOLhh8HbS3E8a+5PAIRMvB6f9g4VJbRaMBqFp70BI4z2SxWTEYothmJ9MNori1U8qLQA0o7IPyqL7nIN3c5NlA13xbllhwxWFJPGG7WpqOJieM4UOdyknV4v57fHdZMN532/2HPOPWEV1Svr+oZiyE5QGuzPpciLBJ/2sUcqjHjJvnp/73p72L6Vz51USwc/JQx2gUBw4XH2RpXnnep8vfxnNdoDXUO+q0OJUc4z79I4mOcicvH57jmWDJwEuFd+1jsY9CazVG6I/C87XGtGO9e/dub8E09iTEsj+ZlnyjTa/z7susC99ZR8nzC8vWfh1m5NQujdLJTmEf5oLXNpGRnAoVdGYSoqJvvzzwgePx6veM8RAAWbNpE4RZnS0Gr9enTR7r37DRFJr1x4z547l9w//7Ttlx4/jmQ220PkAa6eDUsekLetv9GO3vXa8rRrtHDPWlkM16+WBXfrEamFqRQY7BGcT6yXnRiBTveNw+LtlRAifSNJL06nxFiC0Wzk/V3v0yemD0ObDK309defs0dgTu8z3W2f9FmzyPz8CwCyfvgB/8svB8tijyRJFG7ZAkDIpEmK81Q6HZLRKDztDRghRNfQMZvg6Ep7neyK8tN1cGCRvF2RcPT6hjUEKf88zO4LK6fDoSX2440tK9Tn/4Nd38nbAx6G6E5w2+81MgWD2rLY8d0Y957y8nA02g1Fcr65vhAO/wm/3ef5vA4T5MeM47D9Kzi13nPfWd3go67KNp0vBDeCuyz5cuNn2cPsQV4AcRa3sZbJU+tg6ka4/LmynplAIBDUDSonL3NFQ0rvXCmXEI3rVqPTKdbLobQ+Og1BLz2Bl9nI1Zt+9dhfb7SHzu9JzHHbJy2vhOxC2eDLK3a94Y4PKzv03FxgN0Ikg4Gcxb9hTJe/08P9vTydpqB9rOfFbp1GzYKpA5h5vfK3xs9LS8mc78n4+BNOXjGK/HVySL9kNJI1Zw6pb75F+uzZSGYzRVu3uoxrTLmw6uLJL73E2esnoq2BMlmS0UjusuUu7WaHsQvWrSNvqVMaW/dboOXlgAo6XSe3OXraa1Pcr1FPaNKn9savh3x/8HvFflqRLMrrbLQ7YvXAl5pKeX/X+/x46EceXvtwpa+dp8+zbY9tPtZteLypoIDMr79RtBWuXYv3uXMYMzM5M/lWzHnyON5tlSmVKp18XyfphdHeUBGe9obOv+/CP2/ItTpvW1LxOtmn/rFv17R6fF3gLChk9ZqAfCOWdVL2um/5RG6L6Swr0l9Rcz9wem0QfoYs2aDd9BGMfLlyA1iN9rHvwrrX5f3Ug3Dib9e+3W6BvT/LtVO9AuR89rRDylB27yAozXM915GbHW4eYzrDS25uRrwD4M7l8s3vorsh26EMUMthrqKAAoFAcKFwDo933vdE0/7yXw1TbJA97T4ONcy9zEaSc4uJDXZdIM8psntf/9iTRM+mypJtX284xWvLDtMkzJf1Twwjr0S+4X7z2s40CfVDp1G5nOOMYzhs6ptvkj3vZ/wvu4z4r7/iiVFteWHJAQxlhPADRFUwxN2Zwm32NLCkxx6n2c/zOD3hGkWf0iNHyV+1yuXcvOUryPn9d2KeeQaVV8UWF6qLWa9Hf/Ik5qIicub/AkCLN96kICqa0NGjyjnbPSWHD3N60g1g8XCG3z0Fr+bNSX5uhkvfnF8XEHyVU+nUWxbKC/veFsOxId6zNQCMZiOrz6x2eyzW33NEjo8lWnVL8hZWnraLBx7OPEypqZRuUd0qdP1Zu2fZtu/oeIfbPkXbt4PRiDYulha//caJkVdgzssjfM0acvUGinfZFePVTp8ZlVY2+SSjMNobKsLT3pAxlMjlwADMRtjwnvvaMqVOYm16J6GcvPO1M7/axNfDTUpwE+h6o6twSnirGl+RLtU6rLzmuhG7K49Ci9Ee2hyiLLlLSbvtNdmHPA03zYdHD8GE2fBCFrQaLovFuaPzRLnv6LfBWpLOkfEfKcvmlUWTPtC4l90bb6W5UAAWCAT1iMqGx9cyVqPd18ElUqLRkZTtPt81Pd+eU3/aIR9+26lM+r+5hteWHQYgMauYtUfSyLV42uPD/LisdQR9W4TbQtI94Wi0Z8/7GYDCjRsBuKlPPMdeU5ZcvXdwCwD8LLnyfZuHEehdeR9P8f79CiNCKi52MdgBhcEe9cTjeLVsCcihvzk/zydn0aJKX7uqJD/9NKevuZYzt0xWtGd/9ZVLX8lspvTkSSRz2dUBchb/ZjPYAUJvvZXga6+l0ayPCLvzThrPdtCL0br5P6s1doMdai8k/hJnzdk1pBalEuodyo5bdjC+xXjbsSFNPN/7JBfKESGz98wmvTjd1j5p6STuWHkHGcUZnk5VsPDYQtt2kEOFAEmSyFm0iNwlSyg5JH8f+PfujSY4mGbzfwaNhoBDh8n+4gvbObFvvOEyvs3TLsLjGyzCaG/I7JmrDLE+uRZeDrHXKDcZYPmT8GZjWD8T/n5Zrs+d65QT1xBz2nW+cjk3Z7reJBvnOidPfGBcjU8hJaSnfefAQnnBJPtMxYvyWl87vzAIsajpr5wOBkuOesthcv36YIsBbl10CG1u7+9ITCe5b7+p7hc13Bny5REYAw9sk98jPW6Hfg9UfgyBQCCoLaoaHl8NSk+dJvXNtzDl5SGZTMpjFqO99257xFSGbwjnPBntBaVM3fc7L279lo1HUymxnP/95gSSc5WlSu+es5PTFnX3JpVQY3cnPKVrbF/YVqlUNkMd4KlRbUl4axyHXhnN6TfHMv/efpWqsZ67ZAn5a9eSMHFSmf2Cr1OWIA29+SZCJ09G5SSslT3v5zoxNIzp6eQtX2Fv0Gjw7Scr+utPn3ZRDc/88itOjbuSEyNGkPTUUx6Nd6uxZEUXHY1KpSLoiiuInv4UgZdfjl/fvgAUbd2KISmp7IkG1ayauwBSClN4Y5ts6N7Q7gZ8tD74O9xHdgzv6OlUCg2edYVMkonUQjdiyG7oEtkFgCCvIOL87fesKS+8SPJzMzg//WkyPpGjR7Uxsuffu0ULAp0iM1r8+Qch17oujtneh8Job7AIo70hY/XIdr/VnucMcPB3+XHjB7D9S0CSw683vg9/vyQLkFnpdB30ubdu5lvTRLZzbes7VX509ihf9miNXz4h4nIkx9yyLZ/AR13k/3V5ZJyQ8/FBNqadS+Dp/OR8Mneo1XD7n3DDXNkbD3J6RKfr7X2chWN6TbHkxVWBqHbwbDJcNUu+tkAgENQXFMakqtZyfCVJIn32bDK/+ZZTV15J1g8/cKxPX05fc63CKLZ6wpslHLK16dVapv2yR+FVP5FWwGf/nGT/iWSuPrWRfimHaJF7nnGzNrAjIYujqflu52EwSTQL9yM+vHpGu1fz5op9x9x2R8+9SqWqlMFefOAg56c/zbkH7EKqEQ88gE+XLrb9qCcep/2Rw8S9/jqacLk0X+zrrxPzwguofXyQipWLFaXHj3OkcxdyFv+GZDTa8vFrAv3Zs6S++SbG7GzOP/Os4ljItdcQ+4lcjUUqKiLr228xpKahP3eO9E8/Jf3DDwEwnk8m748/Kd6zx+01TJl2T2vrzZvc9gl2EAjMW+U+RNtGz9uh8yS45suy+wnKpdRUytxDcxm5cCRZJbI20Q1tbwDAKNk/N61CPCvo39zu5jKvsfSUm3K7biiyVEN6Z/A7ts+cJEk2LQhHvJrYF258OjksKKjVeLVyP1dbeLww2hss4g68oWI2wbmd8naP2+Fqh/Aqq/Da6X/LHuPKD+H6b6tWrqw+ENHGtc1am9cnCG6xhNR5BXgOKa8mpgkOIXOrLPlp/75T/onWXPugxhAYLUcIhLW0H386sewQuNCm0H48DH4SRr0B9/0rP2crMfYbJG6YC1e+X/FcT3cIY10gENRHHL/XasnLXnr8OCmvvELGrI9Je+cdcPColh47Ru6SPzDly0a2NQw+JMtep9zbJN8kP7N4H6UGE+biYka8v563Vx6hZcpJW79AfREn0wuZ+PkWTqXbvXdNwpS58EPbVlJR3d1NuklpyHduFFy5MZ0oPX2ahFsmk3D99S7HIh58gPB77kYdHEyjjz4i/G67SnzTH38k9s03CXbwDOpP23VUfLvaxe2Sn32WI506c3zQYA63a0/B+jJEWCuAJEmcHD2GrB/mkPb2TIr371cc9+vTF7W3N2aLsZP2zrucvOIKTo4YScbHn7iMZ853v9BiXWSIe/sttGHuldi1lsULgLS336Z43z7PE9d6w3VfQdcbynx+gvJZcHQBb+94W9EW4SvfL+odyuyWVX/9mb7PMHv4bI/H5x72UG7XiSKjbLT76eQFOUNyMieGDsOUoQyv18XHE3jFFbZ93169kNRq1CEhtN2x3eMimzpQTrEwObxPJZMJQ2paheYnuPCIO/GGiCTJJbnMlh/iuG6yeNg0yw9O4jbZqLcqyns7/RjHdIHJi6DXnXU25VohuIly/1qnnLNWw2HSHJi6ofa8L1aV+sqgL4S98+XtURavfGhTeGAr3LkCZqRXvI68Rgv9H4Rop9Ct2G72bf/Iys9RIBAIGgKq2jfak19+mZyf53s+/txzpL7xJm8sP8zi/5JQm034ZdqNdh/Lzf/fh9P4586HODZgIBFFOQCMP2X3vIaUujf6pgxsjrdDjfR7HELZq4pkVIb1D2gVwYvjO/D9nVX4TQMyPv5Ekb8efP11+PbqSdOf56HSaAgaOZI2W7fY65Bb8G7RnJBrJrg1NNR+fqDxvNiceN9Ukp58ClNB1cquJj83w7YAk/v77zYl98hp/yPikYcJsgjPmX3tiyZSaanrQBYcIwDyVq/m1Pjx5C1fjjFdNro0EZ6dB9bweCvZZbzfBDXHvnTPiyO3driVGP8Y3rjMNT/cmUGNB/HhsA+5q9NdvDPY1XFz6/JbMZjL9nBbPe2+Wl9yFi3mxLDLMabKofVBV42nzc4dNP1pLi2XLUUTZHfSeLVowZlp/6PJvHmo/f3djg2gCZPTJk3Z2ba28888w4khQyjavdvTaYJ6hFCPb4hkJ0CKxUAPb233yAY41FFN3iPXYVdp4PEj8IZF+dInRDZiLwZCHIz2GWmuiqoqFXS4unbn4B8BwfGQe1bZXpwtL5a481D//TIYi8EvQjk/rRc0HVAz83I04iPbeu4nEAgEDRlHQ7060URlYDhfvlhr+tp/+FIaCECjwgxUDrnu3ka7xy5+5z9IwI+rXmP6wKl0zThhO9bCF5wDYb+/szdD20bxqkWQDqBRSCXLtGo04JR77y5k/s6BzV3aPJG3ejVpb88kbuZM1P7+5C23lzOL//47/C254I5UNMw+6MoryVu6lKjp0zFlZ1NchkGR9+ef+PXoTuhNN5U5piRJGNPSMaYkkzrzHYKvuorcxYtd5+jlRcTUqfYGgwF1GYZ69IwZFG7eTMHateQsXETQVVeh9vIi+4c5lB4/QdJjj9v6aiM8L6CrvbwImTiRnAULANf0BUHNsObsGnam7OTRno/ipfFC5xTReH/X+23b7cLasfr6clIVHBgeP5zh8cORJIlIv0j0Jj33rpbTT/ek7+FQ5iG6Rnb1eL7V0+6TlEnyc/ayuo0/+ZjAESMA8OvpPm1SHx2NrknZWgfaENloN2bJaQCGpCTy/vgTgIzPPif+K5FuUd8RRntDJN+hdulNP9u3td5yHW2zAbZ+Lre1GgFefjD0Wbk03JUf1O1ca5PWV0CbMbJReqFKoKjU8Mh/8Gq4sv3tZjD0GRj6tLI9O8Gu+B/arPZqrEa1hyHT5UUaT0r7AoFA0NCpg/B4lZtx/QcNonCDfQFcm5uDt1FPsL6AzzfIpZvUwcGYc3Nt4fHOIqVvb/pcsX/9xp/5ZoLyprxxqGygv3ltZ55auI/7h7akMhRu3+5isANIJlejvTIkPfwIAGduvlkhtNZs4UJ8O3kW7aoIsa+9Stitk/Hp0gXJYEATHkbg0KHkrfwL/8sG4tW0KeefftpmcBQfPIjX5s34D/C86J35xZe2HHRAERXgiGPovhW13r7oEvnYYxRu2UzRFrmuvHerVpgLCylYu5biPXvI+eVXQm++ieJDh1zG0UaWnaYX+eg0m9Hu6EkV1AyphalMWzcNgL6xfYn1j+WPk38Asvjbt6O+LTN3vaKoVCp6RvfEZDYR5Rdlq/WeV0ZJXrNkpthYTItkiaKJU2ztYXfdZTPYq4vGkpphys6heM8eEm60L3TpT52qkWsIahcRHt+QSN4HXwyB78fJ+/EDIKK1so+3Je9mv6Ued4/b5MchT8Fjh6GTUq21QaPRwc3zK18fvcbn4WHt6583Xdt2/WDfHlUBwbqqolLBsGehv1B7FwgEFzF1EB5v1rt6WuO/+pLYN+3f8Wok7tu/hOe2z0GtL0Xt70/Uo9MA0EkmIouy6ZRZ/o1x7xSlsedvKbU2sWdjVvxvEI+NdKPl4oHiffs4e9vttv2w228jbqYlf9dQPaPdEauwVcjE6/Hp2KHa46l9fPDt2hWVSoXay4vQiRPRRkYSdutkvJs3R6VW02jmTEJvvRWA3IWLOHvXFHKXLvM4pqPBrsAh/D7szjuJfORhly5pV14JyGW0Iu69h6j//c92zKtJY0ImTbTtF27div7UKaQip9K6Wi2akJAyn7c2LIzA0aMB95EQgupxIPOAbTulMIWj2Udt+w93f5i2YW3R1GC0jkat4ZcrfyE+UK50VJbKfIlRFl98br59gS30lluIevKJmpuPNTw+K4tcy4KXFUNSEucefZSEm27m5LgrOdy5C1lzfqyxawtqBmG0NxT0hfDLLXLYu2QRwWk13LWfYy3PwDi5ZBjIRlxQzZc9E1iI9RDylO9Q6iPnrKzgD7Lqa7xr+KBAIBAIKoFjtJJz+bcaQiqRjfZmixbiP3gQMS+9CEDINRNosWwp+8PlHPMxZ7bRJuccADEvvUjwtfZF8jmrXuedjZ/Z9veHuw9/nvbfAlSSXeguwGK0q1Qq2scGoSunJrsjOQ7h316tWhL9zDM2tfbqGIXmYtfydVHTpxP76quVUpqvLl7xyrKvae+9p9gv2LSJs3dNQX/mjNvztbGxNJ1jX0iPeupJt/1yBg6g6epVtjJaPh07EnjFFUQ8cD+6Ro3QhoYS8fBD8pgRERTv3esyhiYgAFUFBF1tCt9GofBdXXJKcnhq/VOMXjSa347/xh8n/rAdO5d/jny9XUPiqpZXuRui2kT4RjDwoJkpK02sPPIHSQVyOb/s+b+QNdcuUFdoKKT9WYlAS+EEbXQ0Mc/PqNHPky5aTqHN/f13sufNczmev2Ilxf/9h/7kSTAYSH3jDfTnyik/KKhThNHeUEjapTQA2451X6otzEGgpu3oWsvxEzhxxzK4fIZr+xmH8i4bP7RvN+5V61MSCASCix5FeHztGIxWVXBtaCjxX35J6I032o55t2zJ/LauC+j+Awag0ulQebumbj098D5Wx9sF37xbt8Z/yGAAwkrziS3MtI/jVXYWo7moiMLt20l+8SVSXn2NUgfl9dKjx2zb8V/JQq0qrRzK7lxfvjIU7XQNLdfF1b1TIGSiUqneZBGSAzAXFpI45W4KN2/m5Ogxbs+P//ILfHv0IPzuKcS+VsaCg1qNLibGtqvS6Wg86yMiH3nE3sXHBwCppNit0a5r1KhCz0mU5aoZJEnivr/vY0XCCpIKknhh8wusTVxrO77+3HpbuPr1ba63KbbXBtf8lMCo/yTue3QdD80aRfbBvaS89BKpr73O0Z69KN6zhyJjEVduty/WxX/3XY3PI3DECEXpRZC/p5ov+d3jOWfvuIP0WR/X+FwEVUMY7Q2F5oPlsl63/wnPnJNz2X3c5Dw5lkELFRXORz0AAJ2lSURBVEImdYZ3oHLBxMqJv2HDezB7AOz8Rm4LbQbhlctLFAgEAoEbHEPi3YTHGzMyyF22rMqe5ZJjdsNXZTHMHEnJLWF3lFLsM/j669CGh6NSqfBp397lnAyfYFbH96bpvJ8Iu/024n/4nvgvvrDdUH/z99uEluQR6qdDrS57ISL17Zmcve12cn75heyffiLZUmvcXFhoK2HW8u/V6GJlMVqVVl7kqKon11xURP6av13aywv9rg3UTq+HVFSEuUR2VWb96BDa66Ql0HzJ7zRbsADv1q1RqVREPfEEQdddy8NrHubhtQ9jMld+QUPlLc/FXFJK8R5Xoz3i/qkubW7HseoDiPD4KiNJEtPWTeNQpquugJWEvARm75XLtAV7Va/cYZlzcdBDAHhjjomU6+yLfubCQpKeeJIiQxGBxfL7NPyee/BuUfP372o/P1vKjpXo557Fp21btNF2IeuAyy+3bRvOnSNj9mwkhzKX9Zm8lSs5de215K91rW1/MSCM9oZEVDvZeHcMgXdG56AqK7y5dYu7L7U9P8GaVyDtoL3tJlHKRSAQCGoEx5B4p8iynIULOX7ZIM4//gT5a9biiewFCzjasxdnJt+KudCedypJEucefMg+vJ+rN279MVlkakd0OwCMTZoS99prtuM+nTsr+h8Ib06yfzioVPj16EH0M8/YancHDB1i6zc6YRuto8r4rbc+x19+UewX79lD2nvvUbBhAxiNaCMj8WpsV5VWWXO4jZU3TIsPHuRYv/7kzJevGTltmu2YJqj8udYG8d9+g3e7drb97J9+wpSfT/qnskGmcnrNIh64H5+2bfHt3EnRnl6Uzj/n/uGfxH/YmLQRgIMZB+kxrwebSzaXOw+1r2y0GzMzKD0hVwSIe/stAkeOpPXGDRUWE1PprJ52YbRXla3JWxVedSs3tL2B1devpmlQU0V7kHfNif5JJhM5ixZRvHcvksFA4tTyF2sMSUkU6QsIsGSd+A8cWGPzccavTx+Cxo4lYPhw2u3fh3dL2YEU9+YbRE2fTrv9+2gy+1NiX1dqLpnzPIvo1Seyf55P6aHDnHvgAQypqeWf0MAQRvvFRoth8mPbcTVXPkxQMfIqkPvTbbKs7C4QCASC6uNoqKuV5ZvSP5pl2y5Y59nzkr96NebCQop27iR/7Toki2e29NhxDImJ8tABAS6eXYB/j8s1uPMffoqwKXfRfp5SvMlRmO2+y5/gyUEPYvaQthZx33227W6dm/HKBKUKuyk/n+yffybj8y/I/uVX0hzE1Xy62sNeM7/6mgyL0apxViy3hsdXMvy6YOMmEq673uY5VPn4EGzJ8QbKrA9dm/gPGECL33+z7ae98y6lR46AwYAmIoJYi/6Arf+gQW7HsSp8Azy09iEkSeLGZbJHdHnJ8nK971ZPe/Gu3SBJ6OLiCL76ahp/PAttGfXZXbDltAujvaqcKzin2G8S2ITHez7OjH4ziPGPIas4S3F8ZPzIKl8rf+1a8tfZvzMyv/mW5OdmkHDDjRzp3IXCzVsA8OnRnW0DlVWG1neyRNFIEoYtOwmyaBdqQmrP86/SaGj0/ns0+fQTRdUH/wEDCL/zDltb4MgR6BwW+3IW/+YyVn2jYNMmivfts+0X7dh5AWdTO4iSbxcbLYbCQ7sgTITG1zmOwnLXfQOLprj26eNaTkYgEAgEVcQxJN43RHHIu21bjOnpgCy+FPP8DLfGpSk7x7Z9/sknSX72Wdps3YIhyX7z3+iD913PM0tsOiEb7X17tSX6+v4ufRzD440xcVAkR2S9NN5VZV2l0RA4ahT5f/3FyHaRhMXYPYCSJHHugQcp2rHD5TxNeDjN5s8n4YYbKbHctJYePy4fC1R6EdX+sufZ7Kxu7gF9QgJZc+aQPe9nRXvTH75HFxVFzEsvYczKdBGFq2uinnictHdlIbozt8pVc7zi421pAQBBY8fg6xT5YGXR8UWK/a3JWxX7p/NO0z7S84K7LdLAYrx5t23rsW9ZqKq4qCKw46f1U2z/OOZHwn3tBvO0ntN4deurPN3naSa1nYTOabGvohT99x/nHngQgMhHHyV8yl1kfOw+/zt2xgxuadeagV93p3E6ZAdCRrCKyw4a0UgQ/Kx9gdGrSZMqzacm0QQF0erv1RxuJ7/nc379lfC77rzAs/JM4ZYtJE5R3l+bsjI99G64XFBP+7///sv48eOJi4tDpVLx+++/K45LksQLL7xAbGwsvr6+jBgxguOWHyIrWVlZ3HLLLQQFBRESEsKUKVMoKCiow2dRz1CpIKKVEKC7EMT3g7tWwRMnoPP1oPFSHn94N8R1vzBzEwgEggZM4ZYtpH34oauAmmN4vI+Th8rJO3r6+oluBdhM2dmKfclgoHDHDoxpsvc1YPhwAtx4aDMLSskpMqBSQdcmIW7n7d2mDaG33kr43VP46YHBvH5NJ9Y9MZTbBzRz29/q6SpYv54zt91O6enTFO/dy4nhw90a7ABBo65ApVIR/+03hDt468E1bN1a/9tcUFCuYXj+mWc5OXqMi8Ee9fR0fLvKFVNCb7yByAcufGnRsLvucmnThITg26sXUU8+QfycH2j0/vs2oTdHJEliV6pSXO/e1Uqh37tWu47viDYqSrkfE+2hZ9lYX39HT7tkMHDuf9M4NmgQZ269TSG4J3DFWj5tUKNBrJ20VmGwA0xqO4k9t+7hlva3VNlgByjcsMG2nbt4MUc6dbZ9phzfD41mfYRPhw7o1DqKvVUcb6wiI1j2sq/tqtSsyIz1v2BRK+4IufEGeUOlwlxURPGePUiShCRJ5CxaTOG27bV2bWNWFqYKhuVnz//FpS31jTc56+Z7oSFzQT3thYWFdO3albvuuotrHUqjWJk5cyazZs3ihx9+oHnz5jz//POMGjWKQ4cO4WMJU7vllltITk5m9erVGAwG7rzzTu69917muSlnIBDUOvF97dtaHzBZREguf16IzwkEAkEVOXunfPOlDY8g7NbJ9gOOZbScjHZzcYliX3/6NKUnTpL17TcU/beH5gt+RR0Q4GK0A+QtW07en3ItY21UpNs56U2y19xLo/ZYik2lUhHz3LO2/eYRZd+QW422wn9lg+DUmLHK476+eDVrRunhw4B8Ux3xoOzt0wQEoPJSGiGa0DDFvjrQbsQf6dyFxp/NJnDYMJd5mEtLyf3NNSS2yTdf49+nj6Kt2FjMgYwDdIvqVi0jqDqo1GrC772XzC+/tLWZi4pQqVSET3ET9eZAUkESCXkJZfYpMJTtDNJGKt8j7vQPKoLKW17sl0rsZfWK9+0j/6+/AChKz+BY3360/GslXk2buh2jIbM+cT1+Oj96x/Quv7MHSkzy595f54+/zv3nrSbqsRsz7J5cx7KCUdOnE3bLzQCovLxcznPk69FqRu6xLyQmDXAjaHwBiXr8cfJX/oX+9GmO9ugJyIthfv36kb9yJQDtjxyusesVbt9O6dFj+HTswJmbb7G1+/XvR/wXX9j+n6b8fE6MGIk5N5foZ5+1fT58OnTAq1lT8pavkMfbvIWiXbvw69mzxuZ4IbmgnvYxY8bw2muvcc0117gckySJDz/8kBkzZnD11VfTpUsX5syZw/nz520e+cOHD7Ny5Uq+/vpr+vbty2WXXcbHH3/M/PnzOX/+fB0/G4HACa1DqZ/QZhdsGgKBQNCQMeXb6ym7lNPSOuSZ+4QoDhX/9x8AgaNG2dpS33yT3CV/YDh7ltQ33+JIx04K8TkrVoMdQBPsPsfUYJJDob0qUTu9PMq7yW/x+2+0+G0xbXZsp92B/cS+9BLacLsn0aeDMuzef4BS20btNP65+1295MX7D1C0bZuiLWr6dFos/ZOAgQMVubAlxhKu/O1K7vrrLn47fmHzXiPun0r43XYDPXj8lRU6b1+6nFLQNtQ1pH3B2AW2bbPkWUFbExpqy0cHUPtWzWi3pjOY8u2LBAY397NJTz1VpfHrM+lF6Ty09iHu+uuuMv/X5fHVPrm8YXaJ62JcTeKsDG8l7PbbUHl5uf0s39P5HtqEtsFLbVmcUan45Er5++OfzipyrhlcexOuAprAQIKuGq9oM+Xk2Az2mubsbbeT+vrrCoMdoGjLVjK//8G2n/3TPMyWiJPUN96wtTee/SlxM2cqSiwW/PNPrcz1QlBvc9pPnz5NSkoKIxwUN4ODg+nbty9btmzhxhtvZMuWLYSEhNCrl10lfcSIEajVarZt2+Z2MQCgtLSU0tJS236eJfzCYDBgEHlEggpgfZ+U9X7RGoqxBj4ZGvUF8d4S1CEVeY8KBBeSirxHJZOJZAeVckNaqqK/Shdgu5ExeQVithxzDCEOvuN2DGlplPz3H0Vb7bnKuU4peZ7wGTjQ7RyLS+Sbdq1GVWOfM0nj2QMYes89qOLi5Gv5+GCWJJffFe+BA4l571200dFIkoRP164ucwsYNYoCi2cKlP//vN9+J+2FFxT9wx54gKDJt7j0Bdh4bqNNxC0hN+HCft9otYT+738E3XorJQcO4DdokMf5ZJVkka/Pp2lQUz7a/REAPSJ7MLzJcGbvk0X8bmt/G3G+cahQISGRXpBOmE+Y2/EAtNFRGJNkA9uQkVGl/4Vk0Rww5uXZzs9yE/qrP3nqovtuT85Ptm1nF2VzJu8M7cPao1VXzlTJLJE94AZz7d7Tm0pKXNqiXn4Jo8kEbtJwAO7vfD/3d76fpIIkFp9YzHeHvuPfzmq2tVVR6qXi77YTKz3n2v6t9+7WDeb86PF4Xb0P099/H7+RIzAXFpHuIMJpJfS++yA8HKMk0XTlCkx5eeT+9BMh995b7z8rFZ1fvTXaU1JSAIiOVuYFRUdH246lpKQQ5ZxHpNUSFhZm6+OON998k5dfftmlfdWqVfhVMaRJcGmyevVqj8eu1ttXypdv2F0X0xEIXCjrPSoQ1AfKeo/6nD1L/CZ7ya2CfftZvnSpLSw+qPgs1uDuE+fSOLJ8OQC6zEyscqxrExJoVFREWYHppbExeCfL9w0mXx80xSXkd+xIyk03cuz8eXDj7TxXCKDFbNCz3HLd6hKRmIg7szBt3DiOtWgOFb1OkqWaiZt5a/r0pqWD0b78zz/BsljQ+Ltvcb4L2to03u11i83FrCyxe9yOnTzG8uSa+T/UCCtWuG0uNBfycf7HFEgFNNU0JdMkG3mliaWoNPYc47OnzrIueR0BqgDypXwWr1pMnDbO4+X8R4yg0Q9zAEjau5cdVXhP+B89SiMg78ABMidMwO/kKdux3F69KGrditif52PUV/09pzIakdzk9l9oThtP27bv/P1OThtPM9ZnLAN8KlYJySAZ2FZqjxDpVNipxj6X7ohLPEsAUNChPbr0DDJHjuCYj0+FP6MtpBY01jTmnOkcpV4qHgh4gK1rt5Z/ogdq7bfeZCJ82FBKGzemoEMHGn37Hf4WfTFJra6R/7EmPx+VwYBjckD66NEUt2xB7I9z0Vmcqweemk7g/v0u5+f07+/++7FpU3D4rquvFFVQGLT+fWrrgGeeeYbHHnvMtp+Xl0eTJk244oorCAqquXqNgosXg8HA6tWrGTlyJDqd+xw+6VgUqsI0pJgujB071m0fgaC2qMh7VCC4kFTkPVqwahUpgLZRHKaMTDQlJYzo1AmvZs3kDnlJSAdnoNJAq049adF3LNnffUfm+x8AoAkLY+yVV5Ky/l8Kjh51e40mC37Fq3Vrzk2+VS6JNOcHVOryQ973JObAvu0E+vsydmzNhLVmHj9OtoPAlZU+TzyBNiLczRlV4/z6fyn6918Aeu3aTeSM5yj+7z/On05Q9NNERrr9/UovSmfU76MUbZGNIhnbv/7/1i06sYiC7fKi+hmTPRf53lH3EuUXxbKly0jIS2DyoMl0Du3Mp4s/Jd+UT6serRjSaAgqlcr9wGPHklZcTN6vC2j77LN069a10nMrjooi6Yc56PLybIaKlU7PPYsuLo5TP89HrdczevBg1AEBlRq/aNs2zk+9n/Bp0wi9/bZKz682WXVmFWySt60G/PKS5bx27WsVOn/h8YWs3GFfRLpm2DUuNdlrkvN/LqUIaHHTzQRNuLpKYww3DqfYWIxWrSXQK7D8E9xQJ7/14+0h8qYhQyhcu5a0Gc+jMpsZM2aM58+EByS9nvylS/EbNAi1ry+nBg1G5eWFZDke/ug0WllF5KZOJf31N8idP9/FYG+y4Fc0ERGVK6tYD8mroOBevTXaY2JiAEhNTSXWoWRHamoq3bp1s/VJS0tTnGc0GsnKyrKd7w5vb2+8vb1d2nU6nbi5FVSKMt8zl02Dfb+iuu4b8b4SXDDE95qgvuP4Hi0+eBCVVotP27ZkfP6FLQzSt0sXjMkpFO/Zg/HIUfxbtwagNC2fIwtjCWtTSPQYHygqshnsAGpfX3Q6Hd5Nm+JJSsw7PBydjw/NF/wKUOEbUMmiXO+l1dTYZ0zj4+u23Sc6qkILCRUl/tNPyF+zlqRHHyVv0SL0hw9TcuiQ7bh327b4du1K+JS73D43Z4MdZAGwhvBdk1ac5rY9yDcInU7HvHHzOJlzkm5R3TAYDOiQn9Nj/z7G0MZD+Xi4+7JeAHEvv0zMY4+hCQmp0txMYe7D76OeeooAS/lAdWAg5vx8yM5GFxpa5niSJMllDyUoOXiQ8xal/8x33yX48mF4t6g/wmen80+7bdejVwjKJeYlctequ5jeezojmo7gYMZBPtj9AduS7V72KN8omoc2rxHBOY9YFP61lu+YqqDT6Qikasa6u7Hq4vOnCwtDN3IkaTOeB+D8HXcS8/wMRXnL8shetIi0l+SIZ01wMBiNiooJoePHK56Lzo0YaNhddxHgoYRjQ6Oir9sFFaIri+bNmxMTE8OaNWtsbXl5eWzbto3+/eVaqP379ycnJ4ddu+ylOtauXYvZbKZv374uYwoEdUr/B+G+9XIJPoFAIBB4pHDLFs5MvpWE667n9NUTMOXlKfIWfdq2tXnXDSn23Nf0z78GSUXW0QDbOI6o/WQj2K+PezXq8PvuQxcnhzyrVKpyDfaCUiNfbzjFuFkbOJIie0c8KcdXBV2sq8PBt0ePGjXYQVapDxo9yqZA7miwB0+YQIslvxP7ystuFcozijPcjllkrFiIZ0VZcmIJL21+icXHF9fouKlFqW7bfbXyeyXQK5BuUd1s7TqV/Yb6n3P/UGoqdT7VhkqlqrLBDnIFAHeEOXjFreXEjGnuFx8cyfr2O04MHsKJIUM451SaL2/psirPsywkScJUydLLC48t5It9X7g95vx+G/vbWFIKU3j0n0fpP68/Ny67UWGwA7w44MXaNdgBw7lzAC4VGy4FHEsnFu/ezZnb76jUa57751LbtrsShionI1bnlCodOnmyQnTyUuGCetoLCgo4ceKEbf/06dPs2bOHsLAw4uPjmTZtGq+99hqtW7e2lXyLi4tjwoQJALRv357Ro0dzzz338Pnnn2MwGHjooYe48cYbiYvznHckEAgEAoGgfmAuLLSVdLNyrI/DwrtaTcCwYeQulo23gvXrKdywkehnnwHsRnbxyRTydx1XjKOyqHj7dOiA2t8fTWgoukaNKNq2DW1sLFGPTqvUXO+fu4sNx2Uj4oUlBwHQaSsXGloW3q3si7xqPz/iZr6N/2WX1dj4LjgJ3/n16UPU9LKVydcnrlfstwltw7HsY2Uas5XhcOZhTuWeYsamGQAsOr6IViGtkJBoHdIanVqHTlN5Q8lkNnHz8ps5lCkvUNzZ8U6+O/id7bgnwTOrp93KqZxTtA+vuFexMqgdKhV4t26FJEkEjR6DyuF10kZFoj95EmNqKsbsbPJXrca7ZQv8HESZAcx6PTkLF3q8Vsbs2QSNGY23JWqlpkh76y2yfphD0x/n4Nfbc+k2g8mAGTMnsk/w8ha7zlTrUPk1tr5OGcUZHsPc3ZXiGxA3gMGNa0eF3azXk/HZZ2jDI+xGexnikRctTka1OS+P4/0H0G7/vnJPLd67l2IHZ6tbnDQXAi6/XLEfM+O5is3zIuOCGu07d+5kmEN9UGue+e23387333/PU089RWFhIffeey85OTlcdtllrFy50lajHeCnn37ioYceYvjw4ajVaq677jpmzZpV589FIBAIBAJB5Ul50rORqGsaT+MPP8SnbVvyLZozxTvlG76zU+5WeNATXpmvPFmlIvjqqwDQhobSYsVy1N7eSHo9qe+8Q4iHCjOeWHsk1WawK+ZYg552n06d8O3Vk+Kdu4h7ZyaBw4fX2Nju8OvRA/3Jk7b9xp9+gibQc7husbGYo9l2bYCWwS25o+MdPLvxWY5nH0eSpErntzpilsxMWjrJpf2W5fYSUF0juzJnzBzUqsr931edWWUzBAGGNhlKm7A2PLPhGTpHeA6zdfS0AxzNPlprRrsmIIDIadMoOXiQ8Lun4NvVNS9eFyV7HQ1paaS99Ra5S/4AoM2O7bbXrmjHDs7efQ9SadkLKacnTqLdnv+qNWd9YiLa8HBbbfosixhf9oIFHo323NJcrvztSnJKc1yO3dr+Vq5pfQ23r7id3Wm7FZ72bw98W+58rmldsc+1ZDaTPX8+xTt3EvnY43g1buSxb9HOnaS88Qalh1xrknu3dS0VeLGjciNkKBkM5K9dS6CTge1M3qpVLm1+ffsqykyqdMpyedrQUIKvuYbc334j7I47qjbpi4ALarQPHToUSZI8HlepVLzyyiu88sorHvuEhYUxb9682pieQCAQCASXDJLRSP66dQQMHGi7Aa9tvFJSKXIQXmuxYjmnxtjFzAKHXW7LldQ65fuaMjM9Gojxc37Av08fRZvOodpMo5kzKzXPA0m53PX9TrfH/LxqztOm0mhoNndutY3fihL56DTyli3DbFEvVvt71tgvNBQydvFYskqyAHis52Pc2elOtpyXUxLy9HnM3DGTyR0ms/bsWq5oegXR/tEex3PHF3vdh0g7sjd9L2fyztA8uHm5fR1xNP7ahralW1Q3eqh60C2yW5ml3DQoX9+jWe4FDWuKiKn3lXncHh6fTmlCgq39WG/5/d564wYKt21XGOzx339PxqefUrRjh834AZAsZcuMWVmcvfMu/Hr2IMap5F9ZFG7Zwtkpd6MJD6PlihXoT9vz0ov37vV43qHMQ24NdsAWRRHhK4uLpRelA1BkKOKDXbJeRZBXEC8NeInH/nnMtn95/OWMajaKyxpVLDKlcPMWUl95FQBDahpxb7+FV+PGLv0yv/2ONA/fF/5DBrs952LHU7pO2sx38O/fH7Wve20OU0EBWd/ICy+xb71J8PjxqDQa8lavVhrtblIOYl99hYDLh+F/Cac/11shOoFAIBAIBHVH1ty5pL31Nn79+9H0u+9shqOk12NITrblNxszMsieNw9DUhIxL7+M2iH6rbL4JJ4FwLt1a5r+PA9NQADt9u3l/IwZqL19CL/3Hltfx9BxO+4NW12cZ69ZVdh4wm7w9WsRxtZTWbb96KCqP39P1IXBDvJCSKt//yXlhefx6dipzNz5M3lnbAY7QLC3HMrtrbEL+849PJcjWUfYmbqTmTtmMnPwTMY0H1OhuRQaCvl6/9cV6rs3fW+ljXZrzjrAG4PesHnqGweWbXQFqpWRB8eyj1XqujWN1pLfa0xNxZSZ5XI89Y03bDoNVvz79cW7TWvy//6boLHjUPv6kD3vZwAK/v2XnMW/UXr0KKVHj1bKaM/9408wmzGlZ3Csl9KrbjhzFlNOjkuO/6mcU9y7+l6Xsd647A12Jm5hZJwcgWsNiT+WfYwCfQFvbn/T1vedwe8woNEA9t++H4PJgFatrdRnRjIYSLz7btt+8a5dnBwxkvD77rOlzJgKClBptR4NdoCgUaMrfM2LmVb/rOPE0GHoExI42r0HbffuQe1G8Nua4gQQMGiQLbXA10lQzp0nX6XVEjRyZA3PvGFRb4XoBAKBQCAQ1B05C+T816ItWzncrj1Hu3Yjf80ajg8azMlRozncrj3F+/Zx/LJBZMz+jNwlf1C0Y0e1rqnLko0O3x49bCJcKi8vGs2cSeyrryi869poN15bDzfquugot+1V5a0VRwC4Y0Azfrq7X42OfaHRBPjT6P33CZ9yl8c+fyX8xUNrHlK0hXrLyuWORjvAzlR7RMK6xHUVmkORoYjP9nyG3qz3PE+Vhv6xshDx29vf9iiIVxFahVRcIHaQ9yBe6PsCX4yUowCOZh8tM0q0ttFalLTzV62y5VU7UrD+X4zZ2bb9MMvrqg0LI3TSJDQB/kQ9/jgqLzkEOfm5GeSvtJdKk8zmCs/FnXHlSMbnX1B6XKkz8VeC+7rZ4xpfweSXt5J042QkSaJLZBcAfjvxG/1/7s8fJ+U0gCCvIAY0stdu12l0lV7kyvYQoZv5xRfk/bUKQ2oqJ0eM5PhApdfer29fWv61ksAxo/Hr38+WfnMpE/3ss+hiYvDuYE8ZOTl6jOI9CFBy9Cipb8gLL5rgYLTh9hKWzt/tNS28ebEg/isCgUAgEFwCFGzcxMmx40j/+BOSnniS4oMHbcdMBQWoA5Sh0ZJez7kHH1Ko+yZMukHRJ/Gee11uziqDd0qK/NiyZbl93apyu7lZj33tVRf14apQajSRVagnIaPQ1tYxLgiNWnnNyf1qrxZ0fWDN2TU8sf4J0ovTFe1Ng8t/3itOr+D+v+/HYDLY2o5mHWXq31Pp+WNPXtsq1+D+/cTv/HDoB1ufdwa/w3Wtr2N8C3t96I03buTTEZ/SNKgpBYYC9qWXL3plRZIkPt/7OQBjm4+tVD68n9qPCS0n0DO6JyDnY+eWuipe1xU6hzLIVnx79iT6uefQRkZiLiwkf9VqQC4VF/3kky791f7+tPhTNoKN6crX1VxY6NLfGeuiRXl9s77/noQbb1J8h+xO223bnthmom279OhRjMnJlB45gjk312a0O1PdKBRJksj4yh7R0WLFclqt/weVJaQ79bXXSP/wI0w5Obbnp42Kot2B/TT94Xu8mjal8Qcf0PS77y5NEToLgVdcgVfTpoRcdy0A2lD7AqsxOZnj/QdQvG8fxqwscv/4g9NXT7Adb/KlMg1GpVIRPUMWnox+5unan3wDRYTHCwQCgUBwCWANB8349FMA8pYuJfz+qUjFJeQuWYKpisb32bum0OI3e9ij/uxZUGso2rGDwMuHyXV4PaDNzgHAq3mzcq+jdiOQZs3FtuLVrBkh119fsYmXwxUf/MuZTPv43lo11/aQQ6lfHN+BOVvO8MKVHegRX3at7IaMWTIzbd00t8eaBDYBKLe01sakjexO203fWDkX9eP/PmZT0iYAfjn6CzP6zeDPk38qzhndfDSjm4+m2FhMhF8E3SK7EeAlR2I0D27OmbwzlfK0H885biv11jq0amrp3hpvgryCyNPnMefQHO7vdj/JBck0CWxSZ+kMgEvoe/C11xL3xusAlBw4QO6SJXIdd0Ab7jlX36tpU1R+fkhOn6HML78i8uGHbJ54Z7Lnzyd15jvEPPes7fMXNHYs/gMH4tu9O9qwULJ++omMjz8BZMP+vR/vJ6NLY57q/RR70+Vc94WBjxO4JpErtkcSlFlCWot3bdfQn0sirFNHt9e/tf2tHp9TRTBlZWHKyACVirb/7bal97Rau4bj/QdgTE+35fxbif/h+3KjCi41Gn30IWBfRIl58QXO3H4HxmR7SU7nRV6QxUV93NRXD5t8C2GTb3FpF9gRnnaBQCAQCC5RMj/7nKzvvy/XYI97+y3Ffvh9drGs0sOHSfvgQ0qOHiVn0WJOXjGKkyNGkPzMMyQ/7zk/Vp+QgI/lBs8xVNIT7gwjU5Yyp7fRrI/KHacimM2SwmAHeH9SN5uX/c6BzVn3xFCGtavZMPz6hrMxPaHVBEAuj6ZTy9EMbUPbcmsHpSEV66/0BueW5rLo2CJGLRzF+nPKknEms4kDmQfcXt9X68tjPR/j8ni7InW0nxxK++vRXxmxYARrz64t93n8dlw2wvx1/kzpVPX6zlZxtK/2f0W/n/ox7rdx/Hr01yqPVxU0ToKMVoMdXJXM/QcNKnOskOuuc2nL/OorshcscNtff+YMKS+9jFRURPJzMyjavh2AwBHDCbnuWrxbNEcTEkL4XcpUi/Sje1lxegXDfh1GsbGYvsWxmJ99m9y58wg+lowqM1uRapNw/fWce/hhngyQleCndp3KA90eYMnVS7i7891UlZJjx2wh79roaIUeh7vFxYChQ2nyzdd4N6+cfsKlgEqlUnwne8XH03rdWpp841mXIvCKK2i5fLkIf68iYtlIIBAIBAIBvj16EPPii5y96y5CJk0k8pFHyP3td1TeXgSPG0fBvxvIX7OGlsuWoo2LI3TSRE4MHwHIuaC5S5ZgTEtTjJm/ahWlp0/bbnoL1q9HfzYR/4EDOXfLZFs/TQWMdncU79mj2Pdu1qxK4ziTklei2G8a7sfYzjE1MnZD4Y+Tf9hqpQPc0PYGnuv7HDe0vcFmOIN88/5U76cY1mQYd/0lG2uT2k6i1FRqC0lffGKxzbvuTEJegmL/nSHvlDmvrpFd+eXoL7bSc89vel5h1FtZmbCSvWl70aq1zD08F4AuEV2q5RUP9w3nVO4pAFv+/WvbXuOqVlcphO5qE5VKhTogAHNBgYvav3fbNrZtTXi4S8UFZwJHjiD3jz/w7dwZ71atyPr+ewByf19C6E03ce7Bh9BGhBP7qqyynveXslyXp6oDal9f2uzcyZrbRtPkUAaBxUoNgKnSEGBumXPLX/03Aw41YsdfO/DRVl/sUZIkTl91tW3faEnNsaJSq/Hp2JGSgwdBp6PV6lXoYi6tz3xNEDBwIK3+WUfi1PspPXIElZcXKi8vAoYNI27m23UalXKxIYx2gUAgEAguBbRaMBrdHvLp2JGmc39EpVbTeuMG241VyLX2msdx78xEKi623aDrGjVCFx+P4aysAO98E2zl1JixNPn6a/x69yLxvqkuxwOvucZWxqq6eArprSg/bTvD2sNp7D6rjDyYc1efS+pm02A28NzG52z7Hw77kOHxcs34ThGd3J7TO6Y37w99H71Jz9jmY1GpVGQWZ7Lg2AKPBjvAhCUTbNtP93ma0c3KVuTuH9cfb403pSa5pFmePo+dKTvpFdPL1qfEWMKT611zuatr/HkSoBu9aDQrrl2Bn04ulVhoKESFyrZf0zT56kvS3nnXJf/Xp43daG/510rn01zw79OHttu2AnK9davRro2MRJ9whoJ1spBg1PSn0QT4U7R1q9txnEtElppK8Q7w53ycD00OQUCx/Vi7RAn/uWUb7FYMSUk1YrCbcnI4ff1ERVvMq67lpON/+AFzYWGNC1leauhiYmjxuz3FoK5KWF7siPgEgUAgEAguATzVXg+58Qaa/TzPFrLo6eZKpVa7eNSazP4UPzd1c5t88TmNP/nYtp94992cuHy4S7/MYcOIfuXlCt/QNfrwA/wH9K9Q36rw3G8HWHMkjewiu3DayA7RNA33XL/8YiRfn6/Y7xTu3lB3ZmTTkYxrMc72ep7NO+vSx0fjw1dXfMW4FuNcjk1qO6nca0T4RnBvF2XJsDv/upODGbKwolky0/un3u5OdXvNyhDu6z4iJKski8NZhwF5weDq36/mxmU3YpYqrsReGfy6d6fZvJ9cSmVpIyNp/PlnxH/7ja0aQ0XxatKEuJlvA7I4nVRit7QNSecoPX6cws2b3Z7r+L3w1b6v6D23N2MWjeGY+TwAzVIlxjQeybjsprwy12Tr22bbVkImTiToqvFEPPwQ7Q4fImisvUSgVw2FpafOfMemtK8JCaHZooVutS80Af7CYK8FhMFeMwhPu0AgEAgEFyl5K/9C5eWFVFqCOS/PbZ/Yl16q8vjerVrR9IfvSbhlMsW7dgEQdOWVBAwZ4lI6ypSZaduOeOB+VOERHPeunGc8aPRogkaP5viQoRhTUxXHqhpib0VvdDWw/n1yGI1C6ybsuT4gSRIvbn5RYbT3iu5FtL+bcnsV4J4u97AtZZtt/48JfxDuG06QVxD9Yvux7NQy27Fukd1sefLlcXO7m1l9ZjVHso7Y2m5cdiNbbtrCocxDLv37xfbjgW4P0D2qe5Weh5Wn+zzN+sT1lJhK3B43mU08vv5xm+hdQm4CzYKbsS15Gx3CO9hq29cmgUOHVvlcL0t6iTEjQ6H4bkhMJOkJOXJBGxtLzPPPc+6BB2zHHY32Wf/NAuBcwTm8m8jGWvtz0OmF7YrvAF1cHJrgYGKdPN5RTz5J3qrVHqOCqoJjabwWy5eVmzYgENRHhNEuEAgEgjrFkJqKJigIte+lYwxdCLJ+nEvq66+7Paby9UUqLib0tuopMVsJv3sK5/buxadjB9tNuEqtpvmS3xWlfgB8u3Uj8pFHMBgMSMuXV+l67pScfZxEuCpLRkGpYn/m9V2ID6+d8Ob6yunc0/x2Qqmc/d7Q96o8Xt/Yvvx9/d8sO70MjUpD82Cl57RJYBMS8xMBz2H37gjwCmDBeFks7aXNL7Ho+CIAvj3wreIae2+TlcorU+KtLCJ8I/hu9HfctOwml2N3rLyDq1tezb/n/rW17cvYx7HsYzz575N0DO/I/Cvn18g8agttpFwD3piejsFhUUx/NhGpRF6oMCYnu5RffGjXszwd/CoLjikF7E7Gqcjv3orA/04oDHa1nx/xc+a4nYMuNpZmP80l4YYbkQwGt308kTXnR1LfeIPIRx8l/J67bdFD1t+a2NdeFQa7oMEijHaBQCAQ1Bk5i38j+dlnAbnEjnP5IkH1MWZnU7RtG2nvv+9yLPyee/Dt0R3/fv0oOXzYJby2qgQOG0b7A/td2n3atqXdoYMkPf44hRs30XzhAryaVr+uuVezZhiSkhRt3u3bVWvMtHyl0T60bWS1xmuIFBuLFfttQtsQ5lM9IyfaP5q7Ot3l9tgHQz/gluW3cF3r63i4+8NVGv/5fs/bjPalp5baRPIqW4+9okT6en5fLDm5RLF/Ove0LUXgYOZBzJK5VuZUU2ijo20LeslPP2NrLzlkj14Ivv46fDq0x6tlS/QnT/L6JDV78/dx9ZKrXcbTqXX0nvs7RzraF2TiZr5N8FVXlTkPlU6OuJD0+krNP/WNNwBI/+ADVFot5sICgsaOxVwqf7ZV3tXPjxcILhTCaBcIBAJBnWAuKrIZ7AAZn33uEhoJcohuyb59eLdpc8l64ws2bqLk0CHC755SqfI4xqwsTl89AWN6OgB+vXvjP3gQ6e+9T+xrryryOP169KjxebtDpVbTyLKAUFO5jbGvvGxTrgc5ND58StVLeQGsPWz3LE7oFkdU4MV5g28wG1ChQqt2vQXcnbZbsW8011yIsjvahrVl5+Sd1RpDo9agQoWERHJhMsmFchnBxoGNa2KKLkT6VXwx55/Ef+gdY8+v35u+t9oh+rWJSq3Gu0ULWUHdgbylS23bMc8+i9rHh5bLlvLa1tfYe/QXl3HCfMLoGd2TqV2notJoaDx7NoUbNxDx8MNoQ0PLn4fVaK+Ep91UUKjYT3tHrkKQv3adrbSbyse7wuMJBPUNYbQLBAKBoE7ImD1bsW81LJ1Je3smWd9/T+ittxLz3LNu+1zMSGYziXfLtYg1IcGETnIvziWZTJiLi1H7+ZG/+m80ISGcf+op2/9V7e9P7Kuv4NWsGRH33FNn83dHdYz1wlIjfl4axRi6Ro1s2/4DBtDkyy/chsxXlCK9ka83ngbAz0vDOxO7Vnms+syp3FNc/fvVBHkFsfr61Qp18z9O/sHMHTMV/StjoF5Ifh73M5NXTFYsMvSNcRVIrAnUKjVvDXqLlMIU/HR+6E163t35rqKPl9oLvVnPqdxTthJxINe9r89GO4Bvzx4uRruVoKvGKwQtVbh+rpsFNeOtQW/RMaKjrS3w8mEEXj6swnOoitFetGO72/bSI3bdA8e67AJBQ0MY7QKBQCCodXKXLCFr3s8A6JrGYzhzluIDBxSlYCRJIm/ZclvZoewff7wkjfa8P/+0bWd8/AnB48ah9vcnZ+FCzEVFhN12G4bkZE5PmoQpPcPtGBEPPUToDZNsOaoNCYPJzKn0Qry1an7adoavNpymY1wQX9zak8ahdoNBExKCKSeHgGHDqmWwAyzfn0KRXla13vviFeg09TeEuTpc/bscwpynz2Nv+l76x9mV+B1LvAF4a7x5ureypFh9pWNER74b9R1Pb3iaQkMhj/Z8VOHhrmmcVeibBTXjobUP2fbbhbdjX/o+l/P2pe9j3dl1DIuvuAFb10Q+8j/MuXnkLlmCJiQElY+PrZxjwOAhir4Gs6tR/fbgt+kQ3qFac7CWbqxoeLypoID0Dz8CIPCKK1B5eyu+R+3jCk+7oOEijHZBrZLy+hsU7dhB2G234T9wAKAS5TQEgkuMwq1bOT/dfvPfeNYsEibdgCkjA/3Jk3i3aoVkMpH1/Q+2kEYrksFg87pcCugTE0l+/gXbvjE9nbyVfyEZ9KS89DIAksGIqSDfo8Ee/dxzhN06uU7mWxu8vuww329OULQdPJ/HZW+vY/kjg+gQFwRA8yW/U7x7N4EjR1bpOpkFpfx1MJUBLcN5YsFeW/vFarCbzCbF/o6UHQqj3ZEnej3B7R1vr4tp1Rjdorqx8rrya5PXBkOaDGHpNUu58rcrAWgc0Nit0X40+yiPrHuEL0d+6fF/X1ukF6Xz9IanGddiHNe2vtZjP02AP3Fvv0XMSy+C2cyZO+60Ge1B48Yq+upNSqPaV+tLfGB8tefqmNMumc1lpghlfP4F6R9+aNvXRkQQ/czTbo1255KVAkFDQhjtghqlaMcODCmpBI+/EnNpKdk//gigyGNtvWVzhXKaHDEXF2MuLhaqnwJBPab05ElSX3+DkEmTCLx8GIkPPoSk1yuMqqjp0/Fp2xbfHt0p2rKVoh078G7VivQPPyLzq69cxtQnJsqf/YhItwt+prw8CjdvIWDwII91yOs7ktlM9o8/oo2JpWDdOiS9Ht9ePfFq2pTcRYtJnTkTs0P5JeeFDWeCx19Z21OuUSRJ4ofNCXy76TRnMovK7Pv1xlO8P6kbALroaHRjxpTZ3x3ZhXrO5xYzbtZGl2NPjqqeAn195lj2McX+yoSVJBcmcy7/HI/0eERxrKEZ7PWBpkF2gcUSYwlNg5pyJu+M275bk7fWidFeaiolrzSPcN9wFh5byPaU7WxP2U6MXwwDGg0o81yrnkjw+Csp2b8fr6ZNbVFRBrMBnVqH3iwb7U/3eZrh8cMpMZYQ4FW5+vDucFSnT33jTaIee9Tt97skSQqDHcC7TWtUOh1R06eT9e23tnQh31498elYvQgAgeBCIox2QY2RPmsWGbM/A0ATHMT5p6a77XfugQeJe2cmXo0rLhJzZvKtlBw8SMjE64l55ZUaETNyDMsVCAR2DKlp5C39k5CJE9EEBVX4vNQ336Jw82YKN29WtBdtk+s0q4OCCL/zDgB8OnSgaMtW9AkJGDMzFQZ707k/kvrmW5QcPEjmN9+Qu2gx3u3b0+K3xS7XTHrySQrX/4s2LpbWa9dW4dleeIq27yD1zbcUbZEPPIA+KYncRYsVBrsz0c88bTs3YOhQAoYOcSnHVN/5bvMZ3lx5zOPxED8dOUWVK/3kCb3RzPWfb+ZkeqHLsXl392VAq4gauU5lkCSJvel7aR/eHm9NzYfv/nnyT07lnqJZUDMAOoZ35GDmQRLzE23l1u76y67u/sWIL2p8DpcaJaYSvhz5JaMWjXJ7vLYF/gBSClOYtm4aBzMPEuIdQk5pju3YwuMLyzXarYRMnIjaz4+A4cMBWJWwisfXP07XyK4E6GQDXafWEeMfU2NzV+l0NnX67LlzUft4E/HAAwrD3ZCURJLTfWbMiy8QMnEiAOF33kHopIkc7dkLgCaffiru+QQNmoszBkxQ5xiSkmwGO0DivfdhyskB5HrAwdfaQ7GK//uPkyNGok9MrNDY5pISmyhKzoKFJP2/vfsOj6L6Gjj+3ZZN7xVIgBB67733JigiIIoiKgjYELCL2EDFLr4WFH5ioQkCSpUOIr33FgIhvdfNZnfeP5ZssmwSEgiQwPk8j4/ZmTszdzaXzZ659547aRLmzMJ7Y0wpKUS9/TbpW7YUe86EH3/idJu2pG+z72kR4l4X9/nnxH48i4uPjirdkjvXDL+9VtWrI28AdIFBABivRHGlwNJCYRs34NyiBQ6hoQCk/GEJ1A0nTqCYzQDkXLzIibr1uPLa62RssayJnHsliqwjR0te13LEcOqkzWuNny9OLVrg0c92KKrG05NqC3632ebWowduPXvi3LIlVb76Eq/hw295fcuSSaHYgL13/QAOvtWLL4Y3ASDiOj3x17Nk32W7gH3e6JYcm977tgbsOaYc1oWvY9X5VTT6uRGPrn6UeUfnlfl1zief57XtrzHnyBxWnrMMF67mUY0wz7BCy09pMaXEwZyw1yqwFQAP1HyASq6V6BLcpdBypxJPYTSV7kGUWTGTlpNWorJR6VH0XNKTYwlXvzsVCNgB1l9cz5s73mR3VOHJ2wpSOznh+eCD1hGSi04tAiyZ8Hdc2QGAg8ahpLdRYpXef8/6c8KcHznVqjWRkyah5OaSEx7O2e49yNq3DwCX9u2ptXsXXiNGoNJo8uvu4kKVb/+PKt/MRuPhUeZ1FOJ2kqBdlInMAwcL36HRUHX+fCp98D6199ku63KuZy+yT5267rmvXYs3bfUaot9/n+xTtl/0ot99j9Ot25C8YCGXxo6zOc6clUVuUpL1fLEff4w5JYWMnTtLcHdC3FtSli0DwHDqFOf6DyhxBl+tn/3wdaemlkzJHg88gGPtWvllfX0ASFu/noxt2yxl7r/fum671tc+gDIlJqLk5HCudx9QFFKW2va8hw8dSk54eInqWl5k7j9g28uu0VD541moHRxQu7jgPSa/B7Tq77/h1KQJ6gJfPrV+flT56kuqzv+5Qs79P5aU3/Pl7qjlm5G2y9AFuFuyPdcNsoz4OBmdhtmsYDIrN3S9AxFJNq9nP9yMLrX9cdGX3cDDledWcjzheLFlPtrzES9teYmXt+X3FP549Mciy19Ku8SzG54tdJ50cX4/mf+QZ1e0ZcSLq86V9zu8X2j5rsHlN0FaRfB1969ZOGAhvar2AiwPQZy1zjxW7zG+65k/gmFX9C6+OvhVoeeIzYxFUezb95s73qTLwi7MOTLHLj/BtQ7EHrhuXf88+ydj1pVumURFUaztqCAHddkH7U5NmhBccMpUbi6pq1YT+/HHRM+YYd3sP3UqwXN+KHJUmFuXLrh161bm9RPidpOgXZSY4fwFEn78kdT16+32Ga9csdvmOfRBah/Yj1MDy7IfahcXav67A/eBA61lLgwazKVnxpOx2/5pb/r2HZyoU5fz/e3nZ6b8sZQLgwaRudfyICD6/Q9I+vVXmzLZV5f5MKWlca5Xb8717EXW4cMk//lnyW9aiHuQNijI+rPx0iVONmyEKT3drpzh/HnM2dnW19cu4VZz21aq/f4bYRv+IWj62zb7ru31cGnXlkozPrC+du/dy+56WYePcGn8BLvtecM2Ac716Uvc17MxZ9gPf77TzBkZGKOiiHjyKVLXrCHzwAEuPvywZadOR/UVy6mxehUubfKXqvJ9ZjzaoCAcwmrgUNUyZ9atSxfr/rwsyxVRYkYOhxMtQft9jStxaFov+jYI5K0B9Wgf5sP9TSvzWLtqAIT6uuCgVZNuyOWrjWep99YaNp6MKebshbsQn98uxnSoTr+GZTekF+B4wnFe2/4aw/4aVmTgnmvOZWEha1tn5Wbx8Z7C8xU8uupRNl/ezMhVI0nISihxfU4knrDb5uXoRT2femwfbj/SzMVBEnXdDCetE/V86lmHYYe4h7BjxA4mt5xMu0rtbLLOn0q0dFoYzUZiMixteeHJhXRf3J3vD39vLWdWzHyy9xNWnFtBjjmHL/Z/wZ9n/yz0+mbFzLnkcyQZkgrdX5xMY+Z1HwYU1p6AWzKtA0BXKchuW+L/fraOsAJw69lDhr2Le4LMaRclknXwIOHDR1hfK5/Mwq1bN5ScHIzR0cR9+ikAvs9OJPv4CZxbtLDOXS1I6+1NpRkf2GT1TN+0ifRNmwjb8I917d2Yjz4m8aefbA9Wq3Hr3p20Ag8N0jZuwpydn/CuoOQlf+DWvTvpW7dag4lL4yeg9fHJL2S69fPKhKholEKmn1x66mmqfDObuE8/I2XFChSDAQCXTh0J+f57sk+etI5cqf7nMvS1alkz/hZcUztPwd5iXdUQKn/2mc1+TYFklU5Nm5J14ACXx4+3bgt47TUM586Rc+EClT/+iMsvvmj9Ihf/9dfkXLxI5Y9t15w2Z2Vx8dFRgGWepkNwFVza3Z6hwDmXI7kweDDmqw8/MrbbBkxV/zcPx1q17I7TuLoQunIlKq3G+n76T51C9qlTV1fkqJgUReG+b3YSk2q5p9qBbtYv3k90qM4THarblNdq1NQJdOPw5RQ++8cyyuqJeXsJn2m79FZxMgy5HLyUDMDmyV2o5lv2AWreHHGAH4/8yCddPrHZ/95/77EmvOgM5z8f/5kpLaeQmJ1IXGYctb1rk5aTRkJ2fqA+c/dMPu5cfDJCsARwZ5LO2GzrVbUXj9d/HAAPvf1w4bw5yqLsaNX5X7ULrmsemR7J69tfZ3vkdhKzE/Fx9LH+nr8++DVjG48F4EzSGeYdm2dzzkNxhxhSa4jNNkVR+HD3h/x28rci69LQtyE6tY79sftttu+J3sOzG5+liX8TZnaYaW0b7Re0Jy0njb/v/5sQ9xDiswpfscLHyafQ7TdLGxBQ7P6QeXNxCA6+JdcWoryRoF3YyD5xgktjx+HStg1ejzxKwg8/4PPkGJuAHeDKS5NBq4Vc26DXa/hw26C4ECqtFv8pk4n9eJbN9qi3phHy4xxM6en2ATsQ9O47uPfpQ7SbGxk7d5IbFUXi3Lk2ZSvNmkXmrv9IXryE9E2bMJw/T8qfy637TfHxmOLz/+goRgnaK4qM3btRcoy4dmh/p6ty1zNnZQHg3q8vqatWA5ZcFGfa2geJGVu3caJOXetrt759cKxT57rXcAgJQe3igmI0UvXnn+163gsuzePepzdZB/KHe7p2747Xo4/Y9K6EfPcd6du2c+mppwDLWudB70y3ZkAGy+ib7KOWee/RV/9f5+iRm17j+1qm9AzULs6oVCqyjhwl7ssvrVMAClPp449wbtasyP0aV9vgUuvjQ+ify8qsvnfCqZg0YlIN1tftSzCfPMzPlcOXi07KV5yolCzazrAkKqzs6XRLAnaAK+n5o84Oxh60/pydm824f8axL2af3TFjG43F39mfd/9717rtvj/vI8WQwqIBi3jor4dsyq+7uI7g/cF0qtKJ6h7VSTemU9nV/sFYqiGVzNz8B3BatdbuIUJBapX6lsxNFvnGNx7PX+f/AuBi6kWb7PIFH8wUlDeyQq/R0z+0P0vPLGXZ2WW80/4dm3Kf7P3ELmAP8wzjwVoPMvvAbGZ2mkmHyh346ehPNkF7Vm6WNRHhjsgddFzYkQ87fkhDv4bWOfT9l/VnZseZbIu0fI5Vca3C5fTL1nP4ON6aoF3j6oprly6kb96Mxs8XrbcPhqvTKj0GD8alTZtbcl0hyiMZHi9sXJ74LLmxsaQsX0H40KGkrVtH+EPD8gsUSPBxbcDu0q7tdQP2PD5jxlDnxHGqr1iOc8uWAGTs2MHF0aO5OOJha7ka69fh0rkTGh8fXLt1Q+3iQqUP3sfv+avL01wz78tjQH+8Hslfn/h8v/7FfllWTMUPBRMlp+TmYjhzptC5eCWVfeoU0R98QOJvv1lzEACYMzOJGPUYl558slwOe76bKLm51uRzAW++iVvPHqU63q9Ab3hxNG5u1PhnPTX+WY+ukN4Ujbc3jvXq4VivHp4Fkqs5t2xJlS+/KHQ4pGvHDtTckd+DbTh7zmZ/YXPzM3buxHx11ICSk0PCjz+RfbroxGjXk7pmDadbtOBk3XrEf/sdEWPG2HwGufe37Rn2mzQJjwJThu4FF+IzePa3/Icwi55uRZNgz+sedzwqtVTXyTWZMeRaPuM/X5/f49w+7NYEGFm5WTZBU2xWLJ0WdOJU4ik2X9pcaMD+edfPGdd4nDWBGUB6TjopBsvDiWsDdrD0oP9w5AceXf0oT657kr5/9LUmmSuoYNKy0Q1Gs3jAYrsyS+9byrNNn2XpfUtZO2Rtqe5XlF6wezB/3/93qY7JSyLX2K8xTf2bWrcX/Ft7PuU8/zv+P7tjX2/9OiPrjmT7iO10qtIJtUrNgzUftJmD3urXVnbHvbztZZacXmKz7ZVtr/D3eUvda3rVZEH/BTxc52GeavgUwe63rrc7+Nv/o86J49TcssVmOUvPYfb/NoS4m0nQLqwMZ8/aJX0ryLlNG8tw02uSHXmOGE7gtLcInjOnVNdTqVQ41qpFyI9zUOkt86Eyd/6H4Yzly5X/lCk4BAcT/O231Ny6xWZtd62Pfa+M36RJADjWro1rV9tkOo4NG9rMe82j5JbNMkIC4r74kvMD7+PyM+Nv6GFIysqVXBh8P0k/zyfmnXc507Ydycv+RDGZuPT0WGu5olYOEDfPbDBweeKz1tdqFxdcOnYstKy+Xl2qLV6Mvl5+L7vPM+PQ16xZ4utpvbzQ+dsnrwNQqdVUW7KYaksWo9brqXPkMCHz5hL84xyb7MB25yzw4DDumiH31z5oBMuw/8vPPUfmgQOcbNSY2I8/5sJ9g274gV7U62/kX//zzzGn5geaTk2aUPmTWahd84cgV7Q11cvC+3+f4EysZZrAxHommpYgYAfwdrHvBV68t/BVSHJyzYS9vprab6xhx9l467B4gGe7lbyNlsbqC6uJzojG29EbfydLu04yJPH8pueZsnWKXfnJLSbTPaQ7WrUWL8f8v2/Pb3q+0PP/cd8f1PWua7PtZOJJFBRe2/4a84/bThNLNVranr+zP5OaTyLMyz5jfE2vmjzd6GlqetUs0yW7RNG8Hb1LVC7XnMuhuEPEZVmm93noPawJ7sDykCjP0tP5STlfafUKTf2b8k67d2gRaFnuTK3K/7rv6ejJ3w/8zYyO+cncCvPTUfsRj3lcda7U963Pq61f5blmz5Xofm6GSqVCpVbj9eijOLdtg1vPHjg1aXLLrytEeSLD44VV3BdfWn9WOTmhZGXZ7K/88Udo/fxw69WLnIsXMaWkFDuks6RUDg74PfccsR/bztFzv/plVqVS2fbwk595Oo/fC8/jUyDLst+LL5C+aZP1tWP9egRMnUrahg1oAwLI3LOH+K++LvRLvLgx6Vfn6KZv3kzWocM4N2tabHnDhQtEjH4Ct+7dcevVi+Qlf9iNnIh69VWiXn3VZlupliATJWbOyOBUi5aW34FGg8/ox1E7OOA5ZAhKtoHkP5dhOJ6fhCh49mx0QUGELrVfO72s5M3hBsu6vaUdCnnt6hRFZcHP2LLVJrERQNznX+D9xGibh4XFMWdlkbRwYZEjQQJee836meY+oD/JCxai8fZGF2SfaOlulZZt5MsNZ/jnhCXp1icPNkQbef0s13neHFCPvl/YjpyasuQwQ1vY9/JFp+QnSBw5Jz/b9YhWIQR7O9uVv1mJ2YlM+3caAMNrDyciLcI6DDoyvfCH4UNrDbX+7ObgZv15d7R9Yta/7v+Lqu5Vi00w9tGej+hZtac1+E7MSrScW+dW5DHi9nPRlWxqxrC/hnE6KX/Uj6feEyetEw5qB3LMOSRmJ+KscybXnMvK85aRFh90+ICBNQYysu7IYs8d6BLIgNABHIg5wKLTi4ot29ivMYfiDtlsy3sYcLup9Xqqzp17R64txJ0mQbsALMOsMv77D4Bqixeh9fUlNzYWx0aNSN+wAbAsLQSWOen6GjXK9Preox4FwKlpE+I+/QyXDu2L7IED++WgXDt3tul9c6xVC9/xz1jXjnft3Bm1s7N1GKrhhCX4UHJleHxZMOfkYDh71vo6++gRa9BuSkvDlJJC+tatODVogFOjRmQdOUL4UMvQtqRff7XJ/F992VI0Pj6c7dS5yGuJwim5uaBWW4NdldFI9Cuv4t65Ex733UdufDyXX3iBrL37cOvTh8qfzCL76FGSly4Ds9n60ETr7Y3/5MmWc2g0eI96FO9Rj5K+YwfZR47i1rNHuQ42A956k5h33rVZYg6uvj9XufXtQ9rqohOCJfzwA1kHD1J1/s8lumbsrE+s7dixfn3cenQnedmfuPXsgc/o0TafWf6TJ6NxdcVj8OBS3FXFlmsyM3LOLuuc9LahPgxsFMjqogd32Skq2N58KpYutW3/XqRkFf6Apl7QrQlgt17Of+hzf837MZgM1qC9oGru1QhPDSfUIxRnXf79FOwJLUwV1yqAJbiPzoguslzPJT3Z9fAunHXO1uHNDXwblOpexK2lUql4osETLD69mB97/UgNzxokZSfRY4ntVKSCATtYgnaVSkWIewhnk89yNvksVdyqEJ8VT2J2IlqVlj7V+5SqLm+2fZMGvg2YtXcWqTmWkRnVPapzIeWCtUyrwFZ2QfvA0HtrSo8Q5YEMjxcAmFNSMKdZ5r/pa9dGFxSEU+PGqFQq3Hr0wK1H6ea1lpZKp8NnzBM4N2tG1V/m4ztuXLHlNb6+6GvmD/XTFDKX3vuJJ0CtRuPri0v7a5KXXU08pUhPe5kwJSVBgV7MmA9mEPH001x+9llOt2zFuR49iXnnXcIfGkbi/F+sAfu1dFWqWNqfvz+hK1dYtzuE1bBOy5Ce9sKZs7I417sPFwYNxpyRgWI2471hI+l//82VqZa1oBN/nk/WXsu82rQ1a8g+cZKIMU+SvHAhyYvz57v6TpxY6DVc27fHd9zYMn9oV9byHihkH7ddniivp925RQuqfPYZtf7bicbbdqhq3lQdgMw9ewpd6s56vqsPORRFsXnw5DViOL7PPEPYurUETJli95BR4+qK/+TJ6MPshyvfrZbsu2wN2OsGufPNyGalXqbJWVf4tIjH5+6x21ZU0N6v4a152PTmjjcBS+KvQJdAqrpXZdl9tskC+1bry7JBy1jQfwFL77MfofJi8xcLPffIuiPRqC33rlXZ9rV46D34ue/PPFI3P5fLrL2z+OrAV2y8tBEVlgBRlC8vNn+RHcN3UNenLg4aBwJcAniu6XNUcqnEK61eKfQYT70nAPV86gFwIuEE55LP8cw/zwDg6uCKTq0r9Nji3F/zfrYP387o+qPx0Hsws+NMm/31ferTuYrtQ3SdpvTXEULcHOlpFwDWL6YqR0fUFWDdX5VKhXv//sR9/gVg6Rm8lsbVlbCNG0ClsrsnleZq0C5LvpWJwgLpjK2FJwCMef9968+uPbrj1rUb8bNno5jNBP/fN9ZeYn3NmtQ5YVnnWKVScbZbd4xXrliXGiuMMSoKNJpiR2ncrbKOHLHmpDjV3DJ0seCjrIIZ3vMYIyOtS5DlCf7hB1wqeIZ+rZ/l929KTiblr79JXrAAtZsb+rCrDxt0ln//Gk9Pgr/7jvBhw/AcOhTf8eMxnD3DpTFPWs+VGx2NppDgOuHHH0n4YQ6O9eqBtkAwqdXi3q/frbu5CkBRFFQqFfHpBhQFnl9wgH/PWTJgT+ldmwldLe+nsYjpCkVRq1UEujsSnZpdbDlFUfjlv4t228990A+NumzXc47NjKX74vx8Kb2q5c859nex/RwaUXcEWrWW+r71Cz3XEw2e4ETCCbsl4Zy1+T3yk1tMZsy6Mdwfdj99qvWhnk89PB09aerflLPJZ/kv6j8Wn85/ADewxkBCPUNv6h7FrXHtQ6unGj3FU42eIiErgZm7Z9qV93T0BCxB+4pzKziecJx1F9dxNtkyyi3DeONJWlUqFZNaTOLF5i/a1aueTz2a+Dehy6IuN3x+IcTNk6C9gjNnZBD97nvkXLqEW7duuHXvhkO1agDE//ADcZ98ir5WLarOt19SyeY8V3vZ1W4VZ41WzyFDSPj+B/S1axe5ZJMusPDEOqqrX9qRJd/KRF4PptrdHcdatcjcu9e6T6XXE/T++1y5Oty6oICXX8YhOBj3Af1RqdWorklyWPDLg+rqg5esw0cwZ2bh0qa1dV/mvn1EvjiJ3NhYUKmo9NFH91yCL+OlwhNyXUvj5YXazQ1jRASRz+cnvAp49RV0wSG4duxwq6p42zjWq4v7fQNJXbHSpt3l5bko2M6cGjag1n87UbtZ1gnXBfhTc+e/RIwaheHMWaJef4OqC363aYuKoliXrMz491/rdrdevSzLzDmX/ZzpiiDXZGbUT7vZezGJnFxzoWUGNqp0U9f4c0J7LiVlMvG3/TZLxhW05XQca45Fo9OoqObjwpnYdKr6OJd5wK4oCi9vfdn62kHtwOj6o62v3XRuBLsFcyntEu4O7iUapt6nep9i13FvFdSK7cO34+7gbhdcvd76dQb+aTtseWrLqSW9HVFOuOvdi92fl4zwaMJRErMTrduN5ptPrJvXplbdv4qXt71M/9D+BLlaRqesfmA1r29/nUfrPXrT1xFClJ4E7RWUoijkxsZyceQjGC9b1srM2rePuM8/p+a2rRijooj75FMADKdPc7p1G2ofOoi6wNDPgkxXg3aNa8VJWKP18yNs4wab4awldnX+e258PMYrV9BVurkvkve6vJ52ld6Bqr/MRzGZSFv/D4ohG9du3dC4udkF7RovL3SVLWsLF9UuC8oJDwcg5r33AKi24Hcy9x8g9qOPrqmMwpUpU0j67TeC3n8Pfei90cuUm3D1y5tGY5nXrtEQNXAATTt3JiovI7xOR9imjaT8uZzot9+2HqvS6fB+7LHbX+lbRKVWEzRtGqkr7JfBAlBpbR8OadxtvyRrvbzwGHw/sR9/TNahQ2QfPYpTw4YAGKOjSf7jj0LP6z/5JTSenjd/AxVQhiGXVu//Q0ZO0XlCpvSuTYjPzT3QCPRwJNDDkU8famKTYA7ye/jzhsv3aRDEu4Pq8/POi9zf1H4d85thVsw0/rmxzbYl9y3BUetofa1SqVg0YBF7ovdQxa1KiYYuN/dvbrctb8mvPB76wh/AV/OoRg2PGpxLsSx1+E33b4osK8qvotqJn5Mlr1Ad7zpoVBris+Jt9pdlMB3sHsxv/W3XfK/iVoX/9bVfVk4IcXtI0F5BRU6aVGgSJcVoJP6778k6dMhuX/Tb06k044NCz5cbbUlsU9J11suLG/2CnPelPfvYMc52607lTz+554e03gxr0H61B1Ol0eDep7dNGbeePUlbvx7XLl1wbGhJSFcwO/j1uHbvbk2KCBA+fESx5bMOHCDh+x+oNNN2WRvDhQsYTpwg88BBfMY8UeRojIrGlGAZfuwz+nH8J0/GaDRyatUqXDp3pva+vWTu24dLu3aotFqcm9uu+hC6qnTrBlcEapeiMzRfO6KjMN6jH7euaGE4e84atF95+RUyd1mCRafGjXEfOBDD6dM4NWqIQ0hIGdS84olJzab1BxuKLfNkh+rWYfFloX2YbZ6AnecSeObXfUztXce6zVGrxtPZgee6l/0Sb3nrVefZ9NAmfJ3slyJ1dXCla0hXu+1F8XT05POun2NWzEzabFnGNCm76Izx13qx+YtM3DiRMM8wOlYpfLlGUXEMqz2Myq6VUVBoE2RZPcNZ50x9n/ocjj9sLTen1xxaBra8U9UUQtwGErRXQGaDwSZg9xjyALqAQOK/+QaAxALLYYRt2kjEk0+Rc+4cKcuW4VA1BN9x41AUBcOJE6jdPdBVrkTq1fM5hJXvBFNl5dqpApGTXsKclYXnkCF3qEYVm3V4vK7ofAiBb0/D65FHcG7VstQJqACC3nuXnNGPE/HkUyjZtnNanVu0wP/lqai0Wq5MnYrhjGWOX94olDzG2FjO981/OJMbG0uVLz4vdV3Ko9xES0+7xtv+wZvaxQXXTp2srx0KJJJTOTvjEGy/ZNbdrCRBu0qtxnPEcJJ/X0D8N9+QunIF/lOnWgN2sAT27n1Kl635bhOblk2vz/Izp7/Rvy4jWoWw7Uw8bWv48O2Wc/x3PqFMA/bCjPjBsvrJa8uOWLe91s8+j0NZiEiN4LXtr1lftwlqU2jAfqO6h1jmyAc4BxCTGVOq4LtzcGcWD1xMoPPd8TDyXrV2yFqiM6JpFlD4srreTvl5fCq7VqZ1UOtCywkh7h4StFcg8T/8gGO9esR9+pnNdvfevXHt1AnfZydysm4963a1uzu6oCBq/P0XJxo2AqORuM+/IHPvPhyqVydp/nzULi54PfqIda6nPvTeCNrzhmUXFD39Hdz79UPt5HQHalSxWXvai0liqPXxuamRHFovL7QtWlB96R+krl6NvkYYukpBaAMC0QXkJ3wKXbmStM2buTzuGcxZWTbniJ1pm9zHeOXKDdenvMnradf62CdlvJZKrabOieOkrV9v7UG+G+kqVcJ45Qoeg+7D+4kxXBg0yJIorn/JRtXoq1cHLPkCjJcucWHw/YDloV/1ZUtlWg0we+NZa6b2D+5vyMOtLaMN+jSwBI0v96lT5LG3Uu0AN7xcyj6p6vnk8wxaPshm24yOM4oofXMWDFjA4bjDdpm7r6eO9515z0XZqeRaiUquRX++uOry8w/JFAgh7g0StFcQGf/9Z52jXlDIvHk4t24FWObPuXbpQvrmzQCYU1Ot5ZybNbP2EGVs307G9u2WMhkZJHz7nbWca7eSD+OryByqVcWpRXNyzpwlcPrbRL89HVNyMrGffobfxAnFJu0T9koStJcVfWgofhMmFFtG62eZ+5d97BiGM2dwqFGD9C1bSF212qacc7PCezEqouJ62gujUqlw79Xr+gUrsJB5c0leuhTvUaPQentT9+SJ6x9UQF5Sz2u59eopATvw5YYz/G+nJUv7iFYh1oC9LJkVM2eSzpCWk0ZMZgydq3TG1eH6CVOreN2ah6/rLq6zeb1jxA7cHYpPHHajfJ186RbS7ZacW1RsBYP2vKXghBB3NwnaKwinRo3wGDSIlOXLAXBp1w6fcWNxadXKplzwt/9HyvLlRE1/h8DXX7dur/TRh0S9/oY1WC9MzZ3/ovXyujU3UM6o1Gqqzp+Pkp2N2smJhDk/YkpOJmn+fDL37SV0qf0auncTU3o6aheXGxqmXpjcRMucy5IMO74dHGvVAq0WcnM5P/C+IsspuXfP6gGl6Wm/VziEhOD/wgs3fLy+duE9lu4DBxa6/V5yPi6dT9eftr4uyyB5d9RuLqVdYnDYYJ7b9BxbL+cPv+8S3IWvun0FwOrnO9L3i8KXljwcmVJm9SnoUlr+Kg0/9f7plgXsQhSn4DJ+vare3Q9fhRAWJc8CJe4otbMzQTNn4Na7N05NmlDl66/sAvY8HoMGUXvfXjyHPGDdpgsIIGTOD/hNsiS2cWzYkIA33rDud+nY8Z4J2POoVCrrUHjHhvlL8RiOn+Bcn75EPDGG7FOnizq8XDOlZ1hXBMiTtmEDFx4cyok6dTndoiVxX3xRJtcyZ2UR//XXADi3bFEm57xZKp0O1872Q0rV7u7UWLMaj/stw5yVUq4TXV4pZjO5SZYHJxpvCdrLii7AH4eqVe22OzVqdAdqUz5k5uQSl2ag2ydbrNtUKuhU06/MrjFm3Rje3vk2D/31kDVgz1urfPOlzRxPOA5A3SB3HmhWeFb412/RfPaELMvDsXfbvyuJv8QdM6jGIIbWGsrHnT9mSC3JxSPEvUB62isQlUpV4qRZRfWg+jz+GI51auPSti1otWQdPoQ5NQ3/lyaVYU0rHr/nnkNXqZJ1CkJOeDg54eFcuPoApLhM1KWRm5iIWq9H5eQEikL2iZMk/DgHv2efQx9avUyuoZhMnOnQAcVkos6+vShmMwk/zCF+9mybchn/7oSb6IXMk7J8BcbISLT+/viOG3fT5ysrgW+9xYW9ezGlpODWpw/eox5FHxqKxtMTfQ1LL0VFD9pzk5KI++ILkhcstG7TStBepkJ+/pmE777FpWNHLj8zHrWHB2pHx+sfeBdKyzbS+ePNJGbkWLf9+mRrage64et6A0tvFuJc8jnrz6eTLA9Np7WdxpCaQ3hx84tsiNjAv1f+pa53XVQqFZ8MbczS/ZHWY+aObklCeg6DmpR8+sKmiE34OPnQyC//YUxydjIuOhd0GsvooVxzLj8c+YEdV3YAliRxQtwpzjpn3mr71p2uhhDiNpKg/R6jcnCwySJd+do1ru9RWi8vfMaMIWXZn+ScP2+z79LYcVT9Zf5NXyNz714iRj+B2sUFU3KyzT7jpcvoa9bEpU1r3Pv2vaG54Rn//osupCpqR701u/rpdu3R+HhjvBhhV76owMNw/gKowHDyJKbUNDzvH2xXnyuvv0724SNUmjWLlD//BMC9b98ye7hRFnQB/tTa9V+h+/KG8Ve0oN2Unk7SL7+QeeAAudExGM6cAbPZut+pWbPbklegIjgTk0Y1Xxd0mpsbUKYL8CfwLcuX4+pL/7inRzIcu5JqDdhVKvj+0RZ2S6/djOzcbAYvH2y3fXDYYFQqFTU8a7AhYgPzj89n/vH5zOw4k7aV2tqU7VLLr1TTfiJSI3hu03MAHHnMknn+eMJxRq0eRdfgrnzc2bLs3y/Hf+Gbg99Yj2voe/cmcBRCCFH+SNAuxFUqtZrQ5X+StmkTmExEvmgZfZC5dy85ly/jUKXKDZ1XURQuPzPemiDw2oAdIPvoUbKPHiVl2TJiZsyk1n87Lcfm5BD50mScW7bEe9SjdsflXLzI+fsGoRgMADg2boTf+PHW/eb0dMzp6dbX1Zf+gTEqissTJlqPsanHqdNcGDIECs71VoHXQw9ZX5pSUkj5wzLn/8Kg/CzKXiMfLsG7UU5UwKDdGBtL9FvTrO3oWm49exD07ru3t1J3UEK6gYOXkqkV4Eawt7PNvoV7Inj5jyNM7BrG5N61y+yajvXqXb/QXcBsVlCp8kdsmcwKry09wsK9lvncob4uzB7ZjLpBZTufOzw1vNDtWrXlq0plV8tQ+MRsS9LFcf+M49CoQ7g4aMjIMQFFjzIrSsE56uvC19EysCXfH/4eg8nAmvA1vN3ubVx0LhyKO2RzXEmS4QkhhBBlRYJ2IQpQ6XS49+qFYjLZbE/69Td8xz6NYjaj5OSQW8IEZorJRPTb04sMtABUjo42646bkpMxJSej8fQkZdUq0tavJ239erugPWP3biKeGGMTYGcfOsylsYUPUQ/9ayX6sDByryYsM+fk2JVJXrjQNmAHso8eg/yYnaxDh7iWU4vmOISUfeboW6Wse9pzExJQ6R3RuJbdSIO4r2eTvnUrwbO/JvHn+ST88IN1n75mGA7VqmHOzMJnzBM41AizWfbubpeSaaTbJ1tIyTLi4aTDz01P21Af3hlUn53nE3j5D0uP6debznIuLp1vRjYrs6SLdzujycyAL7dzKiaNekHuPNw6hB+2nediQqa1zEMtg0sVsBvNRtJz0lFQ8HYsfKSCoij8eORHAPyd/JnbZy5DVgyhf2h/a5kglyCbY8yKZZTJK33r8ObyY3g5lz4RZq45//PupS0v2e1v81sb1g5Zi4Ji3RbiVnE+64QQQtwdJGgXohAqjYawLZs527kLAIlz55I4d27+fidHXB98EKVv32LPk7JiJcmLF9tsC3j1FdJ37MC9bz+cGjVE7ehIblwcKStWkvTbbwBknzyJS5s2mK5mZQcwZ2fbDGlP+u13uwC7KD5jx6IPC7PU3cEy91S5JmiPfucdyzmBSh9/hGLMJeq110hetAi1szNej4wkfetWYt7J783VeHvjMXAAvhMnlqge5YVKa/lyn75x402fK+O//4h4fDT6unUJXVZ2qw7kJfc707GTzXaNjw/VFi++Z+dVA/x15Ip1bfCULCMpWUbOxlqCwl/+s50KsvpoNNVfXYWbXkuW0cSLPWux5XQcsx9uhp9b2czDvptcTMjgVIwlieXxqFTe+POozf56Qe480sY+Od+1rqRfYdbeWRxPOE5kev6c8+3Dtxe6rvSa8DWsCV+DRqXhi25fEOIewrbh29Br8n9HtbxqoUJlE0ADPNKmKu5OOqp4OV972uvaH7v/umV6/9Hb+vOYBmMYFDaomNJCCCFE2ZOgXYgi6AIC8Hn6aRK+/95un5KVTaX5v5Ds44v/uLFFniNvvjdAze3bUOn1aNzc8H7sMdtrVa6MNiDAGrRHPD4ax0aNMF65Yi2TczGC2A8/xLF+PTSenqStWQNAwFtvYkpItEs0l6faooU22a7Vesuc57zh8RFjx5KxJX9JJV1wMO79+6NkZxP12msAJM6bR+K8eTbnDdvwD7rKhWduLu90lfOTVBmjotAFBRVT2tILmBsTgzYgwKbHtuB7ZzhRujXAi7yW2UzMzJl22z2HDkXj5YXHwAH3ZMCekmXk7RXHCPZ25q/DVwotUzBgf7pTKN9vzc9PkWawPOD6eO0pAObvDGdSr7IbOl8RZBtNpGYZ8XfPbz9XkrOISsmmWYgnWUYTn60/U+Txrap7s/DpNgBEZ0TjqHHEQeOAs84SLF9Ku8SPR37kjzN/FHmODgs6sG7IOoJc8//NZRoz+XiPZe74042epoGvZTUPR61tO/dx8rEL2BVFQaVSMahJ6T+LErMT+f3k7yUu3zqwNS80f6HU1xFCCCFulgTtQhTDa+RIm6Ddf/JLuPfvz5U33yJz+3bSN/xTZNCeuWcPmbt2gU5H2JrVaH2LT9ikCwqi0scfcWXKVACyDx+22Z83fzzj339t6zh8OCq1GseGDYidNQuvoUPRBgYR+fzzAHbD1vMSlRkvXybqzbdsAnZ97dpUW7gAlVqNytmZwOnTiZ42za6uQe+/X2EDdgDnli1R6fUoBgMJP80l8PXXii2fvGAB0dPfAcB3wgSyjx5FX6umzXsH+QFEaWUdOkT8t9+RvmlToftVzs4Evj0NlUZT6nPfLZbtv8yyA/k9tm56LX8914GXFh1i78Ukm7Kv9q3D2M41bIL2a5X34fKHLycz/tf9fDik0U0nezOZFS4lZjLgq+2kG3JZ+0InXB21rDkazbt/WZZPe657TfZcSGTnecv0mcFNKvFIm6os2XeZZlW96FE3AAetmdScVB5a+RBXMiwPTgJdAnmk7iO0rdSWz/Z9xvbI7detT68/evFgrQfpGdyTb9K+4Y3FluVHnbROPFb/sescbSvFkIKno2epjsmz9fJWsnKzqO1Vm+oe1VkTvsa6T6vS0qlKJzZeyh+N81Sjp27oOkIIIcTNkqBdiGLoAvwJePUVYj78CP9JL+Lz5JMA+Lz4ApnbtxealT1Pysq/APAcPLjEAa57377WoL0kvMc8gUptyY7t1qULbl26AGA2GHBs1AittzcaT0+bYwpmeC84dD/4xzm4tGtnE8x4DXsIjYcHkVeXhvMZOxa3rl1watKkxHUsj1QqFf5TphDz3nskzZ9Pxs5/CfnpJ3T+tvPCsw4dInXdOhLnzrNuyxvRkL5lC3aMRihl9nZTejqXxj2DKSnJbl+Vb//P+ju9F5nMCptPxfL3kSi2n4m3bm9Xw4e376tPVR8XFo1tS1p2LilZRo5eSaF1dW98ri4/9kb/urz39wm0ahV1g9w5EpliPccXG87g4aTjiQ5ls9RiWTl4KZll+y/zv50XARg5ZxcbXupMDb+SJz7LzMlFo1ahVqn453gM7/19gsjkLOv+ef9eYO2xGJul277ckN/D3jjYk08eaoKCiTlnvuHdYzt491jh14rOiGbW3lmF7vui6xd0C+nG2vC1OGmdmHNkDgdiDwCw5PQSlpxeYlN+eJ3huOiKzwvxUK2HWHR6kfV1ZEbkDQXtueZc5hyZA0CnKp14utHT6NQ6Vp5fCYCnoyeTWkyyBu2zu8+mdVDrUl9HCCGEKAsqRVGU6xe7u6WmpuLh4UFKSgru7mWbDVfcHcyZmaid8+dLZickcKF9BwBqHzxQ6HDl8IdHkrV/P5VmzcJjQH+7/UXJOnaMxJ/m4jv+GXSVK6Pk5HCmS1eUzPxEUJ5DH8R94EBcWrUq9b0oikL09Ok2a3u79+9P5U8K/+INkBsXh8bHx/qA4G6Qm5TEmbbtrK89hjxApfffRzGZSPp9ATHvvVei89RYt5ZzvSxzXmvt3VuqZHTmjAwS5s0j/quvrdu0QUF4DLoPnyefuqnEdmvPr2XytskAjGs8jmG1h7E3ei/uDu60q9zuOkeXD9OWH7UGrwB6rZrVz3cktBQBbE6uGQetpd3Gpxto8d4/NvuPTu+Nq758PL/+dss5Zq4+abd9QKMgvn64WYnOse9iEiPn/Ee20Xz9wkXIe0jw4qYX+Sfin+sfcI3f+/9uHeJe0MnEkwxdOdRue0OfhrzX4T2qe1S/7ggIg8lAfFY8U7dM5XD8YYbWGnpD61Xvid7DE2ufAGDNkDXWzPQN/2dZyq2ya2XWDFlDRGoEbg5ueDl6lfoaouIzGo2sWrWKfv36odOVPtGhELeatNGKr6RxaPn4piJEOVcwYAdQu7lh1mpR5+aSuWcvrh072OxXFIWcc+cA0NcILdW1nOrXtw2gHR2pvngR8bNn49ajB+79+t3YTVylUqkIevttPB8cSuyHH+L50FA8Bg4s9hitn99NXbM80np5Ebp6Fef7Wt7PlD+WWpeyK0rw99+RsWsXyYsW49SkCYHT3kJXKX9+/MURI9DXrIn3qEeLHY1gSk0l+p13Sf3rL+u20j7cAUjOTsZD74FKpSLDmMGvJ36lmX8zMnMzrQE7wLeHvuXbQ98CoFapWTl4JSHu5T8D9j8nYq0/u+m1/DG+XakCdsAasAP4uur53xOteOyn3dZtuy8k0K1OwM1X9gYlZuTQ/8ttRKVkF1nmnxMx5JrMaItYc37tsWjGzt9X7HWq+7rwQo+aPL/goM12tT4SrfthcuJ7MLxTFn/HfsC5jE/IJKjQgF2n1tGrWi+ebfoszlpnBi8fbF2CDWBA6IBCA3aAOt51+Ov+v3DVueLt6E1yVjKb1m1iYO+BJf6yqdfoqexamYg0yyinxacXlypoz8rNYlfULmtyvEZ+jawBO8DQWkNZfHoxzzezTC+qCP9OhBBC3P0kaBfiBqhUKoy+vuijo7n01FMEvfcuWYcO4/nQQzg1bICSnY0pxTIUVxccfNPX09eoQeVPP73p8xTk1KA+Vef/XKbnrGj01asTumoV54t5EFJt0UIy9+3HqUF9nFu2xLVTJwKmTCm0rOHMGQxnzpC6ahWODRtS5asv0QUGkr51K9HvvIspKQmXjh2tSQTzqHQ63Lp1LXG9zYqZJaeX8O5/pV+X3ayYWXhqIVNaFn4P5YXJrBCTaglkt0zpQlWfsllOr1NNX1pW82JPuGU6wvYztzdoN5rMfL/1PN3q+FPJw4mRc3bZBOx1At34+uFm9P58K5U9nYhIzCTbaObfcwl0qmX78Cw5M4cP15zk992Xrr2M1ZcjmpKUkcPwVvafQ7UD3Lji/RUAet8t/H31GUnBpc96Ve3FJ10+YdvlbTjrnGke0NzmHCsGr+BY/DHG/mPJ7fFKq1eKvf+q7vmZ5111rmhUN5anIdmQXKryiqIQkxnD6gur+XRf/mdpY7/GNuXeaPMGYxqOsQnkhRBCiDtNgnYhblBi504ELbTMrYx6403AMkfcc/gw/J591lruXsz0XZE4VKuKQ1gNcs6es25TOzsTunoVGjc31M7ONtn3Syr7yBHOdumK3wsvkPr33xgvXwawCdgdGzXCuWkTnFu1shvNUZT4rHjGrh/L6aTT1y1bU1uTapWqsT5ivc32n4//zOgGo/F1urkEZyVlNJl576/jhCdkMqhJJQY3qYxabTsMWlEUIpOzuBCfwaoj0fy+29KTqlGrbmgpr6KoVCrmjW7FL/9dZMbqk/y04wJmReHt++qX2TWKM29HOB+vPcXHa0/RsaYvJ6JSAQj1c2Fo82AGNg6iipczp97tg0ql4pU/DrN432VG/bSbv57tQIPKluXSck1mnv39ANsKzPXPs/GlzlT3dUFRsHuf3Ry1pBmy6VrXiX5tYnhnZ/H1zVverGOVjoXu99B70K5yOw6POnxbk/uNbzyebw59U+LyPxz5ga8OfGW3vZp7NZvXapVaAnYhhBDljgTtQtygzKvrnl8recHC/PniajUqrfwzK89UajVV58/HlJSMPrQ6ismEYjCUOIguyHPYMDyHDiX8wQet2+I+/7zQsiHz5uLSpk2pzm80G3li7RNcSLlgt89Z68zklpPpEdIDD70HplyTZZ5bh358qvsUo9lIZFokA/+0TIXouqhrmQdaOblmPlxzEqPJTPOqXpyPy8BBq7Yuswaw5XQckxYd4o9n2tG8qhfZRhPrjsfw3O8HCj2nv5sejbpsg0EXvZYRrUOYcXX++Lx/w3mue028XUqXRLC0IpOzeH9V/tKAeQH3t480p0+DQJuyeUPhW1X3ZvE+ywOfAV9t5+uHmzKgUSU2nIy1Cdin31efh1oEo1GrrFMCrv3VGk1Gpt5v5rODM9irZLC3QMDePKA5g2oMYlDYIPbH7Gdr5FactE50qGw79acotzsb/4g6I6xBu9FsRKcufnh9YQE72AftQgghRHkk0YQQN8jk7o5Lt25kbNxYZBmVJAWpELReXmi9LImmVBoNqlIG7P6vvEz2seMEvPoKakdH6p48QdxXX1szzQO4dOxI0PS3MUZH49S0aamDnNScVNr/3t76ukdIDyY0mUAl10po1Vp0ap3NOU2YbI7XqXVU86hGlypd2Hx5MwCR6ZFUcatSqnoUxWRW+GLDaX7cbnmg8HOBBHKFGfJ//6JWgfk6qVCbhnjavI5Kj8LP2Q+t+ub+fLk72v7b/PtIFF1r+/HiwoMMbxnCkOZl874U9HwhDyZmPtDQGrAfiD3A8rPLic2MZVzjcTTya0SbUB+b8hN/O0DfBkHsv7rM3eCm/jzaRU2AsxYnh+KHmrf+rTVGs9Fu+9/3/20zd7tFYAtaBLYo9f3dTnlrw4NlnrrO4cY+a8O8Cn/4KoQQQpQnErQLcRMCZ33MxX79UQwGKn04k0tP267Zrirl8l+iYvJ5/HG7bX7PTsStR3cydu1CX6OGZQi8Xm+TuK6kFEXhze1vWl+3CGjBR50+QqcpfaDydru36bKoCwBj1o5hTq85BLvbznfONZn5bXcEqVlGnukSZu3pPpd8jsfWPMajdR9lbOOxZBozcdI6oVKp+GHbeWZvOnft5ajh50JiRg4TuobxZMdQNp2MZfS8PYB9wD6qbVWm9K6Nq16L0aRwJjaNMP/8xHM7r+zk6fVPM6TmEJ5v9vxNZ/ReMq4tD35r6W5euCeCN/88CsCe8KQyD9pXH4myric/olUwbUJ9qOrjQpNgTxRF4ZcTv/DRno+s5R21jnza5VOCvZ35ckRT5u/byZ7TWkBLjddWodKm4FztZzZkR7Lh6oyLnlV78mkXy3xto9nIOzvf4UDsAdpVasdj9R8rNGD/odcPFTLZmk6tQ6PSYFJMZOdm4+5Q/MovHnoPUgwpNtscNY54O3rfymoKIYQQZUKCdiFugkqnI/Svv8BsQu3mhscDD5CyND8DuTm76IzQ4u7nWLcujnXr3tQ5cs25rAtfZ10vekKTCYxrPK7QsgnpBrycHWzmMRuMJubtvMR3W8/TuZYfYzpUp2+1vqwOX82VjCv0W2ZJwtfcvzUXz/TgYpQbCqCoDGgcL7HuRHP+N7oZ8YZIZu2dRYohha8Pfs3847+TYkgkwCmYDFMiSlJPdJ5Qr7Ijvz40CWOuGg8nnU1dojOiOZq1mLWThvHm0nPsDrdkHb+vcSWm9qltM3fdQauifiUPckw5TNzwPFXcqrD50mYA/jjzB3+c+QOwPMD4stuXuDm4lfq9bVHNm+n31WfaimMcjUy12ZdtNOGoK3mStFyTmT3hSdQLcsfDWYeiKGw+Hcczv+zDaFIwXX1C8VTH6rzev57NsVsvb7UJ2AHWX1zPJ3s/oYl/E8wuaZzUvI1bXTAbPTAbAkGVg8Yp0u6YrZe3sid6D/OOzbNuv5h6kd9P/m59HeoRysyOM6nrc3Nt805SqVQ4ah3JMGaQnXv9z1mjyfLA4suuX9KhcgeWnV1GpyqdbnU1hRBCiDIhQbsQN6ngWtqVPngfn9GPc37gfZYNRvueLSFKIj4rnrd2vMW2yG3WbR5KQ5q4DWXw7B2oVPBwqxCGtggmPt3A5MWH2HwqDoDPhzWhXagns4+reX7nBuvxf+y/zKojUXz1xH2sDl9tc719sbvAYxc6c2ty4rvhXOUXNE6XuMAcOi22r19KTgKoICbbkjAO12U4usJ5Mzyyeje/9PsFtTp/pMm+mH08vuZxwLJU3aJxb5KSacTD2Xa0QK45l8zcTL479B0ZxgxrcF6UvTF7mbl7JpOaT8Lb0bvU0w6aVy28t37M//YwvksY7cNKlqzvu63nrXP36wS6cTI6rdByE7vWtHn924nfmLF7BgABzgFMaDKBt/61LGE279g8OGZ7vFqXglpn22PcPaQ7GyIsv+cJGyYUW8/qHtVZPnh5ie6pvHPUWIL2rNysYsuZFTOZuZmAZYk3nUbHQ7Ufuh1VFEIIIcqEBO1ClDF9zZo4NWtG1v79d7oqooLKMGbQdZHtEnC5mVW5fOlBhp/8z7rtQEQyX208y+WkTJuh5i8sPHj1J/t1vbOMJmYsy6VZ3Wbsj81vo4pJj0pjwMFrFw5eu26q/udSzjHt32lMbjGZ2Qdnk23KxqyYrfsXnV7Eq61ftQvYd0Tu4IVNL5BtKrrntHVQa0bWGcnq8NWsvmB58LDi3ApWnFsBgJvOjW4h3Wjo25AHaj1w3QRlDSp7MLJ1CL/uiqBOoBtNgj1ZsOcSO84msONsAsvGt6NpSPHD8KNSsmyS7V0bsFf1cSYxI4cpvWtb73n6zukoisJf5/+ylvuq21fU9q7NruhdbL20lTRj4YF/Hk8HP7aNsIzA+DfyX+uyawVtG7aNpWeX8tm+z3DWOjOxycRiz1mROGotK3MU117A8u8pT8G58EIIIURFIUG7ELeAU6NGErQLG4qiXLcXWFEUkg3JDF4+2LotN60uxtSGtA/qyRZzgt0xEYmZ1p8HNq5EVHKWde60g1phWMsQHmgeTNMQL37bFcFry45wLjaDiIRhfDXmMV5beozE+FBQZ+JW+50S3UtmxBOYc3xwDvkRtUMixtRGaPRX8NSGkK49iFkxs+7iOtZdXFfkOUauGsnPfX9Gr9FzNP4o/zv2P9aEr7ErN6LOCLwdvbmQcoEglyBeaP4CAF1DutI1uCtTt061KZ9mTGP5ueUsP7ccrVrLkFpDrns/7w5qwLjONaji5cR/5xNZsCd/3fP7v/n3uuvEP/tbfoK5+pXcOXYlldHtqlEt0MiwZnVw1DoSmR7JD4d/YF/MQA7GHmTJ6SU25/i2x7fW4eozO84ELKMO5h6dS2pOKt1DupNpzKRpQFMmb5lMTEYM77Z/13p8I7/8ZQmntJhC3+p98XO2rOv+RIMnGFl3JHqN/rrvRUXipHUCsOtpz87N5uWtL9MqqBUj645k0SnL0pz+zv44amQJTiGEEBWPBO1C3AK+458h68AB3Pr0udNVEbeZ2awQlZrN/otJ1PBzpYq3E+/9dZyl+yOpHehGx5p+tA71plNNP2uCt9jMWCZsmECKIYWojCjruQzxXciJs7ShH6a24o/9l1m45xLPdKlBDT8XfvkvAo1ahVatYnyXMDycdZjNCj/tuEByhoEqGacZMqAuuqurGIxoFUxatpEZq09iNCmM+z4LCEWvVXPgrft5a+cu1oavBeD11q8zvM5w5vx7hPdXHwRFi3PI9yiKlufa9WZc59oM/NaFc9ERmDJqAiqe6lGTPs3MDF05tND3JtAlkPtq3Mf3h7/neMJxWvzSAmets3Xo8rVG1BnBa61fK/K97lu9LzU9a7L07FK2Xt7KxVTbjPVv73ybMK8wGvs1LvZ3plarCPa29MC2reHDwbd68u2W83y7xZJYb9uZeHQaNRfiM2gW4mXN0m42K8SnG6wPSeaObsnB9F+JOPoTS5KAJPjurBudgztbe9SvHe6vUWl4vc3rtK/cnmtp1VqeavSU3fbZ3WfbbXN1cOXbHt9yMfUiI+qMsHtAdLcF7IA1AM+b056dm42j1pENERvYeGkjGy9tZObumdby4xuPv+1L0wkhhBBlQYJ2IW4Bjbs71RYuuNPVECVQkh7wkjoamcKby49yICK50P3HrqRy7EqqNRh8o39dWtROZfTa0eSacwvUSYUhejDG5NbotWq2Te2Kg1bNiFYhjGiVn+n77fvq211DrVbxZMdQjEYjq1adttmnUqkY27kGKw5d4diV/MRrDzSrjLODlpdbvkyAcwD9QvtR38dy7ifaNMDb0YvmVb0I9BiKTq1GrbYMu//98QHc9/UOIsmiRVUvnuwYisFsO996WO1hTGk5hXPJ56jiVgV3B3eiM6Ktw9mvDdintJjCx3s/BixL0l1PmFcYU1tOZWrLqSiKwumk0+yL2WedJ/7uzndZct+S65zFlqezA6/0rcOZmDQ2nIzljatZ5QHaNTnDEcOPNNCPYudB24RybUN9eO63n2y2pRnTbIbAF/Ru+3dpE9SGQJfAQveXVvvK7QsN/u9WecPjV55biQoVz296ntdav0ZqTmqh5ZsGNL2d1RNCCCHKjATtQoh71r9n43l4zi70WjWNqnjQuZYfiRlGlh64TC1/NzrW9OXZ7jWve54L8RnsCU9k6pLDRZaZ+UBDftsdweHL+UHte38fw/3MhygaS8Cek9wCY3JLzIYAMFsCkoldw/B3L9shveM61+DZAmuGP9PZsla1n7MfU1pOsSmrVqt4oFnhy5/5uOrZ8Uo3MnNycdRqUKtVuOLDe+3fQ6/V4+fkR0PfhjhoHKjnkx/gvtv+XVSoWH7OkhBt27BteOg9AMuDhbygvYZnjVLdl0qlorZ3bWp61WRP9B7+ifiHU0mnmLxlMk82fJI63nWue45LqZcIcAnAQeNA7/qBbDgZa7P/iOFHAI4afkbr+igqbTqKosHB9Qwtf3vFpqyX3oskQ5L19azOs/hi/xdcSrvEhCYTGBw2uFT3J2zlDYvXqDVM3ToVk2Li3f/e5dF6jxZaPtgtuNDtQgghRHknQbsQ4p50Li6dh+dYEq4Zci3Lde0Jzw+wdocnsjs8kSHNq1DJ08nu+KwcE7/tjmD5wUibQDzP/U0r46rX4qBVM6V3bRx1GgY0rsTOi6c5EneMRTuzicuKRdFYjs0MH4cpqxotq3lxMiqNNEMu1XycGd6q7NfQHti4EkciU/h+63kmdg0jxOfmknM5O9j+KRkUNqjY8mqVmvc6vMfrbV7HUeNoN9Jh+aDlrLqwisfqP3ZD9VGr1HzW9TO6LepGXFYca8PXsjFiI/sf3Y+iKBxPOM6vJ35lUNggWge1BizZ+p9a9xRnk89S36c+CwYsoEFlj2tObJvwzCl4fpF1ODzqMNmmbF7c9CI7ruxgQpMJ9Kzak9aBrVkTvoYBoQNu6N5EvkfqPcKr214lMSsRjSp/eb75x+1/L881fe66SQmFEEKI8kqCdiHEXSvdkItaZRtUZhtNbD0dx9Pz99mVH9ykEldSstl9IdG6rd3Mjeg0KgLcHUnJNDLnsRZczDjCh7s/IT01CCXXFcegBHIzamHKCKNvMxXPd2nCtitbqexamb7V+1rP5eyg5rOjk7mUdgn8IC9UVmc1ZEDtdnxwf0Nc9LfnY/m1fnV5rd+dXac7L5HYtUI9Q5nY9OaznD9c92G+2P8FAEazkRMJJ3jor/ylvuKz4mkd1Jpccy4f7fmIs8lnATiWcIxJmycxqMYg6lZL4kS4F6F+LkQ5zinRdR+p+wgqlQonrRPf9vzWZp+noyfD6wy/6XsT4O3oDUBCdgK+zr6kpdhm2x9aaygtA1vSMrCltawQQghREUnQLoS4KyiKwumYdDacjOFifCYrDl0hy2jCxUFDFS9nmgR7suNcPLGpBnJMluXHHHVqtk7pyvn4DFz1Wpue1bk7LjB95XEAjCaFy0mWobjDftiOS41ZqHXJOHiHW8vrPC2rBWxLh20FpjD/36H/w9fJlz3Rewqttxo1Xw94ho5VZL5tWRvTYAwNfBvw1DpLMreCATtAXFYcaTlpdFnYhRxzjs2+9RfXs/7ienCCCX0/JMzLl7cOWIL68Y0n0q5yG57d8Kx1+HtD34bM6DgDPyc/WVbsNvFx9AEgMTuRUI9QLqRcsNn/UouXcNEVnfVfCCGEqCgkaBdCVDjRKdmciklj+opjnI/PwM9NT0K6wWat8jwZOSZOxaRxKsa2Fy7AXc/sh5vh7+5Y6Jzxx9tVY8+FRFafOINicgNApUtE778KtS65xHW9kHLBLph4odkLdK7SmaMJR6nvU5+aXtefNy9KT6VS0SaoDaPrj2busbl2+88mn6Xd7+2sr59u9DRN/Jqw6PQiNl/abN3+c/jLEG752UXrwbjGT6NSqfhz8J+cTz4PQD2fehKs32Y+TpagPSk7iUR9os2+MM8wCdiFEELcNSRoF+IuZzKbOBh3kCvpV9gfu5/ItEhaBrZkYI2BZZa1+nZQFIXVR6P5ZN0pzsVl2OyLz0xA7RSLg1MEZoMfuel1aVJNw+Re9UjNUPHRvvdJVc5jMpt4oNI0HmvZgmq+riiKQnRGNOsvrmdt+FqiM6L5vf/vLDq9iHPJ59ir2o5rraxC6zO4xv282+EdzIqZDGMGv5z4hRRDCuObjOd4wnG89F4sO7uMHZE7CE8NJ8A5gNZBrelXvZ81w3eYV9gtf98ETGg6wSZof7/D+7y+/XWbMlNaTGFU/VEAdKzSEYPJwMzdM+3WU5/U4jnrHHxvR2+8A2XY9Z3ipfdCrVJjVsyEp4bb7AtxK/tcEEIIIcSdIkG7KFS6IRfXG5xbW9gSWumGXPRaNVq1qlTLa5nNCmr17V1X16yYSTWkcjDuICpU1Petj6+T722tQ1mac2QOXx/82mbbzqidfHngS77r8R3tKuf3NO6J3sOpxFMMqzOsXCVtOhCRxKx1p9hxNsG6zdvFAVfXJBy99xLFartjzgHPbLv6QnX1PzUsiX2WJX9DI79GnEk6Y81Anafb4m7XrU+gSyAj6lrmJatVatwc3Him8TPW/W2C2gDwSqtXCj1e3F56jZ5dD+9iT/QeOlTugEat4Ur6FWYftKx3/kqrVxhZd6TdMZOaT2LF2RXkmHOo7VWbBQMWoFXLn83yQqPW4Kn3JDE7EbNittknvexCCCHuJvLtQ9hQFIVpK47x886LAPw5oT2NKnugVqvIyTWjVoFWoy702LOx6by06CCRyVl8ObwprUN9iEzK4ued4czZnj88uEttPx5qEYyXswOtq3tjUhSyjSbcHPODREVR+HrjWb7bep4Xe9ZiTIfqNtdKy0ljxbkV3FfjPtwc3Gz2RaRGcC75HF1Dupb4viNSI9CpdeyL3ccHuz4gLcd2KPXA0IE09mtMrpLLynMrUaOmn7lfic9fWtm52ejUOk4nnaaOd53rPuhIMaSw8NRCBoQOoJJrJev2rZe32gTs3YK7sePKDgwmAwBj/xlb6Pk+3PMh7Sq1w9fJl8fqP0Ytr1plcFell2PKYfOZSJ6ed9y6rVMtP6YNrEeS6SSj10664XMfjit6ebaCtGot9bzroVVrLcF5k2esa5iLisNZ50zn4M7W1+Maj2Nc43FEZ0Tj7+xf6DFuDm4svm8x84/PZ0SdERKwl0O+Tr4kZucPjX++2fP8efbPG155QAghhCiPVIqiFDIL9N6SmpqKh4cHKSkpuLu73+nq3DHfbTnHnO0XiEsz2O0b0SqEtceiSczIoXV1bwI9HIlMyuJsXDo1/V05GZ1GWnbuTdehcbAndQPdiErJZsvpOOv24+/0tmYANytmJmyYwPbI7fQP7c/MjjP54/QffHngS5svbx90+IABoQNIzUlly+UthLiF4OPow4aIDSw4tYDp7abTOqg1CVkJdFvcza6n5noa6Brw89Cf0eluvkc6KTuJDGMGlV0rsyFiAy9tecmmPveH3c/UllNxdXC1bssx5XAm+Qw7Infw1YGvANCoNDTya8SB2AM251er1Pz38H/WbN37Y/bz2JqSf6ldMnAJueZc9Bo9oZ6hqFXqQkdUlAWTWcGsKEQkpjFq3RBSjHHkxPUgJ6ktD/Y4yM6YTVRyrcTJxJPWY4Ldgnmu6XP4O/sT4BLAlfQrBLsF4+Pow5WMK7jqXPFx8mF75HZ+Of4L4anhmBUz7Sq1o0twF6q4VsFZ54yPkw8HYg+QkZNBt5But+T+bhej0ciqVavo169fmbRRIcpaWbTRcf+MY0fkDuvrw6MOV+h/t6J8kc9RUd5JG634ShqHStCOBO1ms8KOc/E8+uPuW3aNiV3DMJrNRCVnY1IU1h6NJrewrGFF15JhbTyZ1q8Nj/z9GGdSj5ToKC+9F2nGNHLNRTxQyAlCYwzG5JJ/7/U82vBwvWG8tfMlzNgH8o18G3E43tJL+3yT53my8ZOluA+L1JxU/j7/N+svrqeBbwOWnFpCmjGt2GP8nf2Z0nIKDXwaEJsZW+KgW4WKmR1n0i/UdmTAlfQrzDs2j2C3YExmEz5OPkRnRJNsSGZb5Da75GmFaRfQk7pOg4nKPo6vqg39G4RSr5I7ydnJuOvdUavsR2WcSDiBTq3DbPTixJVsEtJz0GnV+Lk68OeBK2w4GYPRpKBxPo9z1e+LvX5jv8Z83vXzCj194VaRP+SivCuLNvrattdYeX4lYBkS/9/D/5VlFcU9Tj5HRXknbbTik6C9FCpq0K4oCuuPx/D0/H2E+bvy4ZBGNAvxLHEvQ4Yhl1E/7WbfxSTrti61/fhieFM8nCz/8DeciGHM//Za96tVYFZApUlH53qe+l6NaRzsxb7YPXgprfjsoaZ8vekshy4lo9Wo6RDmS7+GQYT5u9pc22gyk5SRQ2RyFtlGM+mGXJYduIxWrWbFoSsAjO9Sg1A/Vz5ae5h0n8/QOEaX6L7qufbgQtZuskypJSpfUE5CBwyx/bFMgL5KZaB3xz1czj7Mc82eo3e13kzaNIn1EesJcQvh7wf+LvH54zLjiM+KZ+z6sdalooryTrt3SM1JZdbeWcWWq+xamWru1Qj1DOXPs3/aDe1fMnAJtb1rl7iOBY34awRHE46W/ACTM84OWjJNqQwOG0zPqj1ZfOoPtkVuwaSYeKrBU/xw9AcAzLmuZF6YiJLrCmhBnY3aIQ6N8wXU2jR0nntRaQpPAgfwceeP6R7cHZ1G/kgVRv6Qi/KuLNrowdiDPLr6UXRqHQsGLLhjU3nE3Uk+R0V5J2204pOgvRQqYtD+++4IXl1aeG/zuM41eKBZZf46HMWRy8m4Oep4ulMoAe6OaNQqvF0cuJSYyRPz9nAmNt16XIC7nt+eakMNP1cyjZk4aZ1QqVREpWRxNDKVHnX9UalUnIiJ4qE1vQq99oQmExjXeFyp7iXFkMKS00sIcAnA38mfZ9Y/hwo1TQLq8UyTZ9h6aTs/HZtjc0xOUktMmTVwqrzAui3I+Cinz1rmGqsdL+NU+TfUDpbh8jqVI37qVlwxbcWY3JycpLa4VLdNzmYJ2AcUWc9nutTg5T51AEjMSKTzEsv82Gru1ZjbZy5H4o7QxL8JCgrejvYZpTOMGQxcNpC4rPxh/ypUOGgccNY646erw7n4VELd6qLP7ETXWiE83akG8VnxLDy1kE0RmziVdMrmnJ2qdGJ299nW13n/nLNys3h126v4OvnyZts3i7wno8nM3vAkLiVlEpuajbuTjtoBbrQO9SEl00hsWjavrdzKgbh9gBlMliWtVLokHAP/KvK8paU1BZKrjgGV/cfRqHqjCPMM42zyWQKcA3i03qMy/LUE5A+5KO/Kqo1eSrtEgHMADhqHMqydEPI5Kso/aaMVnwTtpVBRgvZLiZks3neZC/EZrLzaG11Seq0aQ65lqHeYvyvx6QaSM43W/b8/U5c3dj+Fg9qBqIwoTIoJAJ1axxddvyDEPYSx68cSmR553WtdO6fwYupF1lxYQ6cqnajtXZus3Cyctc7sjt7N1stbWXhqoTUxWnFUig6zSUduRm2yox4AdQ4uoZ+gGL3JDJ+ATe84AAoOPpsBFTkJXQATan0cZkMAoMLVLZp3BzXFzS2RD7b8RvjZjphz/Pn7uQ5U83Hh990RHLuSyrID+fesUaswmRVaV/ci3ultYpWoQuv6dKOnebbps9bXay6sYcrWKTZlWgd0pqP7JGLTslh2IIoryfbvwcDGlZjauzbB3pZgOS0njaTsJIJcgshVLHPMCxuCfj1ZOSZ+2nGBj9eeKnR/3oiKogR5ONKvYRBt66URl3OeELcQPtnzBWdSTqCYtajUttMRchLb4+CdP+9UyXVDpS16OoBWrSXXnEvf6n15u+3bsv71DZA/5KK8kzYqyjtpo6K8kzZa8UnQXgoVIWj/91w8j8zZZRNIVfNx5s2BtWlWzQmzScvBiAzG/G+fzXFVfZy5mJAJgNZjD1qXcxhTG2NKr2st06/jYbbF/1bqOj3X9DnSjem0r9SeyPRI3vr3Leu+gaGWNcBPJp5kW+S2Ys5Scl92/ZKuIV2tDxx6fLoF1JmgaEDRW8utmNgeDycdigJ9vthKttF2Xno1H2e+e7QFtQPzs84bck0cuZxCoyqeOGhtg+ColCzazthoVx+d5384Bv1ZZH0XDlhIPZ96XEq7xNCVQ8kwWtYW91E3wkkJ4cTJFmB2LNG9v39/A0a2rmqzTVEUjl1Jxc1RS4i3M2bF8lChONlGExmGXEb88B+nY/JHWQS6O9I0xBNDrpktp+MwXROxV/Z0YtvUrsUuv2c0GzGajKw4EM+yU2s5nPYXZqM3Xfwf5v+G9WDZqfXMOTyPMNcWfNr7eRIMcaTmpLI2fC2nk07jqnOljncd+lXvh5+zHzmmHOk5uwnyh1yUd9JGRXknbVSUd9JGK76SxqGyfk0F0byqF35uejJzTPSuH0iorwth1S7x5n8PkrYrv8eyVfu61HN4goW7Ymnb5Ayh/np2R+3jQko4qCy9nzqPg7hFf8hLPRuwMfpXtsblB+wdK3dkeJ3hRGdEczjuMMvPLberi5fei1davWKX2Gz5ueXsi7E8NMhLDHQ9XnovelfrzcutXsZgMuCkdbL2HB+KO8RPR37icvplWgS0oGOVjgD4uurxcnagbpA7J6LAxUHDS71q069hEIEetkHwrtd6YMg14eVsCf50RSxXp9dqaFHNfkg7QJCHE6uf78iM1SfJNpo4H5dBfLoBY3IbjMmt0XlvxclvC4o60+a48f+Mp01QB/6+YHkPVajIiulLeGI7Cv7Tq+ThiIteS9c6/rzatw5ZRhPODloW773ElCWWhHdrjkbbBO2KovDe3yf48epSeg5aNTq1ipoBbrQP86FLbX/C/FzJNSsYck38ey6Bn7Zf4Hx8Bjm5+Q8xPJx0LB3fjhp++TkH4tIM7I9IorKnE28tP8rlpCy+HNGk2IAdLKMydGodw1uFMLzVU2QYRnMmNp3GVTxQqVQMqduLIXXzp1UEagMJdAkscg6qBOxCCCGEEEJITztQMXraAVKyjHg46biSfoUJGyZwNvnsDZ+roU8jjiTYrlP974h/7dY8j8+KZ+W5lbg6uNK/ev9ihymnGFLYH7OfN/99kxRDinV7z6o9+bTLp6QYUjibfBYvvRf7YvfhqHFkQOiAG56fnJyZw//+vUjn2n40Cfa8oXPcqHMxKXT/bHuh+zSux3EO/tlmm2LWkXH+BRSjDwCtqntTw8+F8V3CrEPfC7M3PJEHv91pPaZRZQ8uJWWy9ljMTd9Duxo+/PZUm2LLKIqCyaygLeJhhyi/5Om7KO+kjYryTtqoKO+kjVZ80tN+l4nJiGHx6cX8cOQH6/rdapWaLlW68E77d4hIjeCVba8QkRZhc9wjdR9BQWHb5W02+64N2JcPWm4XsAP4OvkyusHoEtXRQ+9B15CubA/ZTmpOKoqi4ObgZu0599B70DygOQChnqElv/kieDo78HyPmjd9nhsR4u1Mc18z++Ltg1lTeh1yM6uidb4IgDnHh6zIYShGHxpW9uCzYU3ssukXpVEVT3xdHYhPz2H3hUR2X8hfh16lgqm969Cuhg+HI1PwdXFg6YFI1h+3D+grezrRsLIHvRsEEOzlzC//XWRYy5DrXl+lUqHVSNI3IYQQQggh7hQJ2isIg8nAd4e/s76u5FKJr7t/TU0vS9Da0K8hfz/wN4fiDvHBrg/IMeXwXNPn6BrSFYCXW75MRFoEZ5PO8sLmF6zn6RLchQ86fFBowH4z3B3K74iFsjKqppkFz/dBp9OhKAoZOSamrziGs4OGhKyXOa58ipPGne8GfYnZpMOkKFT3dSnVNRy0an57qg3z/g1n+5l4IhItQ/A9nHQsGdeWmgGW31vjqyMN+jYMwpBr4mxsOrM3nSXQ3YlJvWrh4qCxGdFQ1FQAIYQQQgghRPkiQXsFEewWzJCaQ7iQcoG6PnV5qflLha5P3divMQsHLLTbrlKpqOpelaruVTnw6AFOJZ7C18mXAJeA21H9u55KpcJVr+XjoY0LbO1YJueuFeDGB/c3BCAn10xUShZVfYoO/vVaDfUrefDNyOZlcn0hhBBCCCHEnSNBewWhUql4u93bZXIurVpLfd/6ZXIucXs5aNXFBuxCCCGEEEKIu4tklxJCCCGEEEIIIcopCdqFEEIIIYQQQohySoJ2IYQQQgghhBCinJKgXQghhBBCCCGEKKckaBdCCCGEEEIIIcopCdqFEEIIIYQQQohySoJ2IYQQQgghhBCinJKgXQghhBBCCCGEKKckaBdCCCGEEEIIIcopCdqFEEIIIYQQQohySoJ2IYQQQgghhBCinJKgXQghhBBCCCGEKKckaBdCCCGEEEIIIcopCdqFEEIIIYQQQohy6q4J2mfPnk21atVwdHSkdevW7N69+05XSQghhBBCCCGEuCl3RdC+cOFCJk2axLRp09i/fz+NGzemd+/exMbG3umqCSGEEEIIIYQQN+yuCNo//fRTnnrqKUaPHk29evX49ttvcXZ25qeffrrTVRNCCCGEEEIIIW6Y9k5X4Gbl5OSwb98+Xn31Ves2tVpNjx492LlzZ6HHGAwGDAaD9XVqaioARqMRo9F4ayss7gp57UTaiyivpI2K8k7aqCjvpI2K8k7aaMVX0t9dhQ/a4+PjMZlMBAQE2GwPCAjg5MmThR4zY8YMpk+fbrf9zz//xNnZ+ZbUU9ydli9ffqerIESxpI2K8k7aqCjvpI2K8k7aaMWVmZkJgKIoxZar8EH7jXj11VeZNGmS9XVkZCT16tXjySefvIO1EkIIIYQQQghxr0lLS8PDw6PI/RU+aPf19UWj0RATE2OzPSYmhsDAwEKP0ev16PV662tXV1cuXbqEm5sbKpXqltZX3B1SU1MJDg7m0qVLuLu73+nqCGFH2qgo76SNivJO2qgo76SNVnyKopCWlkalSpWKLVfhg3YHBweaN2/Ohg0bGDx4MABms5kNGzYwceLEEp1DrVZTpUqVW1hLcbdyd3eXD0lRrkkbFeWdtFFR3kkbFeWdtNGKrbge9jwVPmgHmDRpEo899hgtWrSgVatWfP7552RkZDB69Og7XTUhhBBCCCGEEOKG3RVB+7Bhw4iLi+Ott94iOjqaJk2asGbNGrvkdEIIIYQQQgghREVyVwTtABMnTizxcHghbpZer2fatGk2uRGEKE+kjYryTtqoKO+kjYryTtrovUOlXC+/vBBCCCGEEEIIIe4I9Z2ugBBCCCGEEEIIIQonQbsQQgghhBBCCFFOSdAuhBBCCCGEEEKUUxK0CyGEEEIIIYQQ5ZQE7eKeNGPGDFq2bImbmxv+/v4MHjyYU6dO2ZTJzs5mwoQJ+Pj44OrqypAhQ4iJibEp89xzz9G8eXP0ej1NmjQp9FqKojBr1ixq1aqFXq+ncuXKvP/++7fq1sRd4na20bVr19KmTRvc3Nzw8/NjyJAhhIeH36I7E3eLsmijhw4dYsSIEQQHB+Pk5ETdunX54osv7K61efNmmjVrhl6vJywsjHnz5t3q2xN3idvVTpcuXUrPnj3x8/PD3d2dtm3bsnbt2ttyj6Jiu52fpXl27NiBVqst8nuBKH8kaBf3pC1btjBhwgT+++8/1q9fj9FopFevXmRkZFjLvPjii6xcuZLFixezZcsWrly5wgMPPGB3rieeeIJhw4YVea3nn3+eOXPmMGvWLE6ePMmKFSto1arVLbkvcfe4XW30woULDBo0iG7dunHw4EHWrl1LfHx8oecRoqCyaKP79u3D39+fX375hWPHjvH666/z6quv8vXXX1vLXLhwgf79+9O1a1cOHjzICy+8wJNPPikBkSiR29VOt27dSs+ePVm1ahX79u2ja9euDBw4kAMHDtzW+xUVz+1qo3mSk5MZNWoU3bt3vy33J8qIIoRQYmNjFUDZsmWLoiiKkpycrOh0OmXx4sXWMidOnFAAZefOnXbHT5s2TWncuLHd9uPHjytarVY5efLkLau7uDfcqja6ePFiRavVKiaTybptxYoVikqlUnJycsr+RsRd62bbaJ7x48crXbt2tb6eOnWqUr9+fZsyw4YNU3r37l3GdyDuBbeqnRamXr16yvTp08um4uKecavb6LBhw5Q33nijyO8FonySnnYhgJSUFAC8vb0ByxNLo9FIjx49rGXq1KlDSEgIO3fuLPF5V65cSWhoKH/99RfVq1enWrVqPPnkkyQmJpbtDYi73q1qo82bN0etVjN37lxMJhMpKSnMnz+fHj16oNPpyvYmxF2trNpoSkqK9RwAO3futDkHQO/evUvVzoXIc6va6bXMZjNpaWnFlhGiMLeyjc6dO5fz588zbdq0W1BzcStp73QFhLjTzGYzL7zwAu3bt6dBgwYAREdH4+DggKenp03ZgIAAoqOjS3zu8+fPc/HiRRYvXszPP/+MyWTixRdf5MEHH2Tjxo1leRviLnYr22j16tVZt24dDz30EGPHjsVkMtG2bVtWrVpVlrcg7nJl1Ub//fdfFi5cyN9//23dFh0dTUBAgN05UlNTycrKwsnJqWxvRty1bmU7vdasWbNIT0/noYceKrP6i7vfrWyjZ86c4ZVXXmHbtm1otRICVjTyGxP3vAkTJnD06FG2b99e5uc2m80YDAZ+/vlnatWqBcCPP/5I8+bNOXXqFLVr1y7za4q7z61so9HR0Tz11FM89thjjBgxgrS0NN566y0efPBB1q9fj0qlKvNrirtPWbTRo0ePMmjQIKZNm0avXr3KsHZCWNyudvrbb78xffp0li9fjr+//w1fS9x7blUbNZlMPPzww0yfPt36fVRULBK0i3vaxIkT+euvv9i6dStVqlSxbg8MDCQnJ4fk5GSbJ5sxMTEEBgaW+PxBQUFotVqbD8i6desCEBERIUG7uK5b3UZnz56Nh4cHH330kXXbL7/8QnBwMLt27aJNmzZlch/i7lUWbfT48eN0796dp59+mjfeeMNmX2BgoN2qCDExMbi7u0svuyixW91O8yxYsIAnn3ySxYsX203rEKI4t7KNpqWlsXfvXg4cOMDEiRMBS8eSoihotVrWrVtHt27dbu0Nipsic9rFPUlRFCZOnMiyZcvYuHEj1atXt9nfvHlzdDodGzZssG47deoUERERtG3btsTXad++Pbm5uZw7d8667fTp0wBUrVr1Ju9C3M1uVxvNzMxErbb9U6DRaADLH3QhilJWbfTYsWN07dqVxx57rNDlMNu2bWtzDoD169eXqp2Le9ftaqcAv//+O6NHj+b333+nf//+t+aGxF3ndrRRd3d3jhw5wsGDB63/jRs3jtq1a3Pw4EFat259a29S3Lw7mQVPiDvlmWeeUTw8PJTNmzcrUVFR1v8yMzOtZcaNG6eEhIQoGzduVPbu3au0bdtWadu2rc15zpw5oxw4cEAZO3asUqtWLeXAgQPKgQMHFIPBoCiKophMJqVZs2ZKp06dlP379yt79+5VWrdurfTs2fO23q+oeG5XG92wYYOiUqmU6dOnK6dPn1b27dun9O7dW6latarNtYS4Vlm00SNHjih+fn7KI488YnOO2NhYa5nz588rzs7OypQpU5QTJ04os2fPVjQajbJmzZrber+iYrpd7fTXX39VtFqtMnv2bJsyycnJt/V+RcVzu9rotSR7fMUiQbu4JwGF/jd37lxrmaysLGX8+PGKl5eX4uzsrNx///1KVFSUzXk6d+5c6HkuXLhgLRMZGak88MADiqurqxIQEKA8/vjjSkJCwm26U1FR3c42+vvvvytNmzZVXFxcFD8/P+W+++5TTpw4cZvuVFRUZdFGp02bVug5qlatanOtTZs2KU2aNFEcHByU0NBQm2sIUZzb1U6L+qx97LHHbt/Nigrpdn6WFiRBe8WiUhRFKZs+eyGEEEIIIYQQQpQlmdMuhBBCCCGEEEKUUxK0CyGEEEIIIYQQ5ZQE7UIIIYQQQgghRDklQbsQQgghhBBCCFFOSdAuhBBCCCGEEEKUUxK0CyGEEEIIIYQQ5ZQE7UIIIYQQQgghRDklQbsQQgghhBBCCFFOSdAuhBBCCCGEEEKUUxK0CyGEEPe4xx9/HJVKhUqlQqfTERAQQM+ePfnpp58wm80lPs+8efPw9PS8dRUVQggh7kEStAshhBCCPn36EBUVRXh4OKtXr6Zr1648//zzDBgwgNzc3DtdPSGEEOKeJUG7EEIIIdDr9QQGBlK5cmWaNWvGa6+9xvLly1m9ejXz5s0D4NNPP6Vhw4a4uLgQHBzM+PHjSU9PB2Dz5s2MHj2alJQUa6/922+/DYDBYGDy5MlUrlwZFxcXWrduzebNm+/MjQohhBAVjATtQgghhChUt27daNy4MUuXLgVArVbz5ZdfcuzYMf73v/+xceNGpk6dCkC7du34/PPPcXd3JyoqiqioKCZPngzAxIkT2blzJwsWLODw4cMMHTqUPn36cObMmTt2b0IIIURFoVIURbnTlRBCCCHEnfP444+TnJzMn3/+abdv+PDhHD58mOPHj9vtW7JkCePGjSM+Ph6wzGl/4YUXSE5OtpaJiIggNDSUiIgIKlWqZN3eo0cPWrVqxQcffFDm9yOEEELcTbR3ugJCCCGEKL8URUGlUgHwzz//MGPGDE6ePElqaiq5ublkZ2eTmZmJs7NzoccfOXIEk8lErVq1bLYbDAZ8fHxuef2FEEKIik6CdiGEEEIU6cSJE1SvXp3w8HAGDBjAM888w/vvv4+3tzfbt29nzJgx5OTkFBm0p6eno9Fo2LdvHxqNxmafq6vr7bgFIYQQokKToF0IIYQQhdq4cSNHjhzhxRdfZN++fZjNZj755BPUaktKnEWLFtmUd3BwwGQy2Wxr2rQpJpOJ2NhYOnbseNvqLoQQQtwtJGgXQgghBAaDgejoaEwmEzExMaxZs4YZM2YwYMAARo0axdGjRzEajXz11VcMHDiQHTt28O2339qco1q1aqSnp7NhwwYaN26Ms7MztWrVYuTIkYwaNYpPPvmEpk2bEhcXx4YNG2jUqBH9+/e/Q3cshBBCVAySPV4IIYQQrFmzhqCgIKpVq0afPn3YtGkTX375JcuXL0ej0dC4cWM+/fRTPvzwQxo0aMCvv/7KjBkzbM7Rrl07xo0bx7Bhw/Dz8+Ojjz4CYO7cuYwaNYqXXnqJ2rVrM3jwYPbs2UNISMiduFUhhBCiQpHs8UIIIYQQQgghRDklPe1CCCGEEEIIIUQ5JUG7EEIIIYQQQghRTknQLoQQQgghhBBClFMStAshhBBCCCGEEOWUBO1CCCGEEEIIIUQ5JUG7EEIIIYQQQghRTknQLoQQQgghhBBClFMStAshhBBCCCGEEOWUBO1CCCGEEEIIIUQ5JUG7EEIIIYQQQghRTknQLoQQQgghhBBClFP/D0ngVcBeSeZvAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Mutual Information and Pearson Perform Similarly Well on a Local Scope (the Length of 1 Fiscal Quarter)"
      ],
      "metadata": {
        "id": "hQi4BHLALhc0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plot_most_correlated_stocks('AAPL', 'mi', 'local_corr_38')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 720
        },
        "id": "4c0m35WzGEdh",
        "outputId": "a286a360-5fc3-4abd-83dc-6abed024de7d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Quarter Start date 2024-07-03 00:00:00+00:00\n",
            "Quarter End date 2024-10-03 00:00:00+00:00\n",
            "Quarter Start date 2024-07-03 00:00:00+00:00\n",
            "Quarter End date 2024-10-03 00:00:00+00:00\n",
            "Quarter Start date 2024-07-03 00:00:00+00:00\n",
            "Quarter End date 2024-10-03 00:00:00+00:00\n",
            "Quarter Start date 2024-07-03 00:00:00+00:00\n",
            "Quarter End date 2024-10-03 00:00:00+00:00\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_most_correlated_stocks('AAPL', 'pcc', 'local_corr_38')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 720
        },
        "id": "6xvYLO-_GRW5",
        "outputId": "d523a91f-68f1-4152-90c2-56235522fb6b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Quarter Start date 2024-07-03 00:00:00+00:00\n",
            "Quarter End date 2024-10-03 00:00:00+00:00\n",
            "Quarter Start date 2024-07-03 00:00:00+00:00\n",
            "Quarter End date 2024-10-03 00:00:00+00:00\n",
            "Quarter Start date 2024-07-03 00:00:00+00:00\n",
            "Quarter End date 2024-10-03 00:00:00+00:00\n",
            "Quarter Start date 2024-07-03 00:00:00+00:00\n",
            "Quarter End date 2024-10-03 00:00:00+00:00\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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RgGKrx2dmZiq1a9dWAKVKlSrKiBEjlAULFhRaXbCovJb03Dp58qSi1WqV/v37231eiqLY5f3Kqppz5sxRNBpNgSYRzzzzjOLu7q7k5uZec/+utmzZMgVQjhw5oiiKovzyyy+K0WhU+vXrpwwePNiWrmHDhnbnen61zSurdBZXPb5Lly52+/Xcc88pOp1OSU5OLjafy5cvVwDl5MmTiqIoSmpqquLo6Kh88MEHhaZ/7733FCcnJyU1NVVRFEU5ceKEAigrV660S5d/rn711VdKXFycEhkZqaxevVoJCwtTNBqNrUr/zVSPL6pZRocOHeyO13333Vdo1eIrFXbcq1SpogDK77//bpsWGxurGI1G5fnnn7dNGz9+vKLRaJR9+/bZpiUkJCje3t4F1lmYklSPb9y4seLv768kJCTYph04cEDRarXK8OHDbdOGDx+uaLXaQptM5J8j2dnZBb4XZ8+eVYxGozJjxgy7aVxH9fhrfdZpaWmKp6enMnr0aLtlo6OjFQ8PD7vphV3T8q/zV34W+cdt6NChdmmTkpKKPadu5Jr+zTff2Kbl5OQogYGBysCBA+3Wm5+2JEwmk+Lj46NMnjzZNu2hhx4q9HepqOrx+VauXKkAypw5c2zTbrZ6/J49exRA2bBhg6Io6vkTEhKiPPPMMwXSAsqoUaOUuLg4JTY2Vtm1a5dy7733KoAye/Zsuzxd+Xtd2ONmqsePGzdOCQ0NtZ3r69evVwC776ai2DctOn36tOLg4KBMmDDBNv/q4221Wm2fbUBAgDJ06FDlk08+sfuNzbd+/XpFp9MpOp1Oad26tfLSSy8p69atU0wm0w3vlxDlkZS0C1HBrVmzBp1Ox4QJE+ymP//88yiKUqBkonXr1rYO4AAqV67Mfffdx7p167BYLMVur1OnTmzYsIFly5YxZswY9Hp9iUp+U1NTr9nZ1pU2btyIyWTi2WefRau9fJkaPXo07u7urF692i690Whk5MiRha5r4MCB+Pn52d4nJiayefNmHnzwQdLS0mylDgkJCXTv3p2TJ08W2rttPicnJ9vrjIwM4uPjadOmDYqisG/fvmL3bdmyZXh4eNC1a1e7Uo9mzZrh6urKli1b7I7B+PHj7aqrP/vss8VuIz+fu3btslU7//rrrxk1ahRBQUGMHz+enJycYtdR0nPrp59+wmq1MnXqVLvPCyi0w7R33nmHZ555hrfffrtAkwhPT08yMjKK7KG5KO3atQOwNfXYvn07zZs3p2vXrraS9uTkZP79919b2hv1xBNP2O1Xu3btsFgsREREFLtseHg4d999t61mSn6V5aJKp8LDw+ndu7ftu1OzZk2aNWtWZPrHHnsMPz8/goOD6d27NxkZGSxatIi77777enfzhnl6enLx4sUSlxBeqV69enafj5+fH7Vr17brFf23336jdevWNG7c2DbN29ubhx9++KbynS8qKor9+/czYsQIvL29bdMbNmxI165dWbNmDaA2c/npp5/o27dvocc3/xwxGo2274XFYiEhIQFXV1dq167NP//8c8P5vNZnvWHDBpKTkxk6dKjddUan09GyZUvbdQbsr2nZ2dnEx8fTqlUrgELzN2bMGLv3Tk5OGAwGtm7dWqCJT77rvaa7urryyCOP2N4bDAZatGhRoHf8rVu3XrMm0ZXWrl1LQkKCrdYKqDVXDhw4UOJmR1fmDyAtLe26lruW8PBwAgICbDXF8keXWLp0aaG/zQsWLMDPzw9/f39atmzJn3/+ycSJEwv8RrRs2ZINGzYUeNzsCBK5ubl8//33DB482Haud+7cGX9//2uWtlerVo1hw4bx+eefExUVVWgajUbDunXreP311/Hy8uK7775j7NixVKlShcGDB9s1qejatSs7duygX79+HDhwgHfeeYfu3btTqVKlEjV5E6KikKBdiAouIiKC4ODgAgFxfpXBqwOJmjVrFlhHrVq1yMzMLFG73ICAALp06cKgQYP49NNP6dOnD127di2yem8+d3f3Ev/Byc9z7dq17aYbDAaqVatWYJ8qVaqEwWAodF1Vq1a1e3/q1CkURWHKlCn4+fnZPV577TUAYmNji8zb+fPnbX/mXV1d8fPzo0OHDgAlqm548uRJUlJS8Pf3L7D99PR027bz9/Hqz8vPzw8vL69itwPg4eHBO++8w7lz5zh37hwLFiygdu3afPzxx8ycObPY5Ut6bp0+fRqtVku9evWKXee2bduYNGkSkyZNsmvHnu/pp5+mVq1a9OzZk5CQEB577DF+++23YtcbEBBAzZo1bQH69u3badeuHe3btycyMpIzZ87w559/YrVabzpor1y5st37/M+jqIAlX3JyMmvWrKFDhw6cOnXK9mjbti179uzhxIkTdumPHj3Kvn37aNu2rV36jh078uuvv5KamlpgG1OnTmXDhg1s3ryZgwcPEhkZybBhw25qf6/XpEmTcHV1pUWLFtSsWZOxY8cW27wh39XHFtTje+WxjYiIKLQ5TnFNdEqqqOsPqOd+fHw8GRkZxMXFkZqaWmCoqqtZrVY++OADatasidFoxNfXFz8/Pw4ePHhTVZSv9VmfPHkSUIOoq68z69evt7vGJSYm8swzzxAQEICTkxN+fn6262Zh+bv6mmo0Gnn77bdZu3YtAQEBtG/fnnfeecfuN+F6r+khISEFbvhdfR5cr8WLF1O1alWMRqPtu1S9enWcnZ2vq+04qMNpAiW+EV0ci8XC0qVL6dSpE2fPnrXlr2XLlsTExLBp06YCy9x3331s2LCBjRs3smvXLuLj45k9e3aBG6e+vr506dKlwOPKm/c3Yv369cTFxdGiRQtbfs+ePUunTp347rvvsFqtRS776quvkpube8227UajkcmTJ3P06FEiIyP57rvvaNWqFT/88EOB5kjNmzdnxYoVJCUl8ffff/Pyyy+TlpbGoEGDbP3YCFHRyZBvQoibMmjQICZPnszPP//Mk08+WWS6OnXqsH//fkwmU5EB9o26sqSouHn5fyReeOEFunfvXugyRf35t1gsdO3alcTERCZNmkSdOnVwcXHh0qVLjBgx4pp/Uq7c/rVKIq6sFVCaqlSpwmOPPUb//v2pVq0a4eHhvP7667dkW9dSv359kpOT+fbbb3nyyScLBAD+/v7s37+fdevWsXbtWtauXcvChQsZPnw4ixYtuua677nnHjZt2kRWVhZ79+5l6tSp3HXXXXh6erJ9+3aOHj2Kq6srTZo0ual90Ol0hU4vrsRv2bJl5OTkMHv2bGbPnl1gfnh4ONOnT7e9X7x4MQDPPfcczz33XIH0P/74Y4EaJg0aNKBLly7F7sONKGqYOYvFYndM6taty/Hjx/n111/57bff+PHHH5k3bx5Tp06127/C3OixLc/efPNNpkyZwmOPPcbMmTPx9vZGq9Xy7LPPluiaUZRrfdb56/32228JDAwsMN/B4fLfvwcffJC//vqLF198kcaNG+Pq6orVaqVHjx6F5q+w6+2zzz5L3759+emnn1i3bh1Tpkxh1qxZbN68+Ya+b6V9HuQPm5idnV3ojeslS5bwxhtvFHmOXy2//XRp3SjavHkzUVFRLF26lKVLlxaYHx4eTrdu3eymhYSE3LLveknk/4Y9+OCDhc7ftm2bXf8yV6pWrRqPPPIIn3/+Of/73/+K3VZQUBBDhgxh4MCB1K9fnx9++IGvv/7a7jwG9SZQ8+bNad68ObVq1WLkyJEsW7bMdkNeiIpMgnYhKrgqVaqwcePGAmM95/eom9+hS778EpgrnThxAmdn5xsKGPN7TS+uxKhv377s2LHDrhOuouTn+fjx41SrVs023WQycfbs2Zv6o5K/Pr1ef93rOXToECdOnGDRokV2Hc8VVpW7qD9/1atXZ+PGjbRt2/aaNxvyj8HJkyftjkFcXNxNlTZ5eXlRvXp1u057isprSc+t6tWrY7VaOXLkiF2V5cL4+vqyfPly7rnnHu69917++OOPAmNKGwwG+vbtS9++fbFarTz99NPMnz+fKVOmXPNPcrt27Vi4cKGtOmmbNm3QarXcc889tqC9TZs2RQYExR2PmxUeHs5dd91V6B/I+fPns2TJEltQqygKS5YsoVOnTjz99NMF0s+cOZPw8PAim4XcCl5eXoWOEhEREWF3jgK4uLgwePBgBg8ejMlkYsCAAbzxxhu8/PLLODo63lQ+qlSpwqlTpwpML2zaja4f1OvP1Y4dO4avry8uLi44OTnh7u5ebAdYy5cvp1OnTixYsMBuenJyMr6+vqWS56tVr14dUG+CXes6l5SUxKZNm5g+fTpTp061TS/sd6Ik23z++ed5/vnnOXnyJI0bN2b27NksXrz4ll7TS2LFihVkZ2fz6aefFjjmx48f59VXX+XPP/+09YZ/LRaLhSVLluDs7Fyi9CURHh6Ov7+/bSSPq/O+cuVKPvvss2v+ZtxOGRkZ/PzzzwwePLjQzmUnTJhAeHh4kUE7qKXtixcv5u233y7xdvV6PQ0bNuTkyZPEx8cXekMqX36TlaKq4AtR0Uj1eCEquF69emGxWPj444/tpn/wwQdoNBp69uxpN33Hjh127RQvXLjAzz//TLdu3a4ZzMTHxxdayvHll18CFNtmdsyYMQQFBfH8888XqAYMapX0/JLfLl26YDAY+Oijj+y2uWDBAlJSUujdu/c1t3Ut/v7+dOzYkfnz5xf6Y36tJgL5x+fKPCmKUmiv1S4uLkDBofAefPBBLBZLodXTc3Nzbem7dOmCXq9n7ty5dtv78MMPi8zflQ4cOEB8fHyB6RERERw5csSummpReS3puXX//fej1WqZMWNGgZK5ws6ZkJAQNm7cSFZWFl27drUbWurqYaa0Wi0NGzYEKLYdfn6197fffpuGDRvi4eFhm75p0yb27NlToqrxLi4uxQ5heL0uXLjA77//zoMPPsigQYMKPEaOHMmpU6dsPaznD001cuTIQtMPHjyYLVu2lGrv48WpXr06O3futOu5/tdff+XChQt26a7+DA0GA/Xq1UNRlAK9hN+I7t27s2PHDvbv32+blpiYeN1VnIsSFBRE48aNWbRokd158O+//7J+/Xp69eoFqOfm/fffz6pVq9izZ0+B9eSf+zqdrsD3YNmyZdfsO+Nmde/eHXd3d958881Cj3n+da6waxqU/DoDkJmZSXZ2tt206tWr4+bmZvvO3qprekmHfFu8eDHVqlVjzJgxBb5LL7zwAq6uriU6fywWCxMmTODo0aNMmDDBNnTmzcjKymLFihX06dOn0O/6uHHjSEtLK1fts1euXElGRgZjx44tNM99+vThxx9/vOY1u3r16jzyyCPMnz+/QPO6kydP2o08kC85OZkdO3bg5eVlK2TYsmVLob8z+X1PFNbMRYiKSErahajg+vbtS6dOnZg8eTLnzp2jUaNGrF+/np9//plnn33WVuKS76677qJ79+5MmDABo9HIvHnzAIqttrp48WI+++wz7r//fqpVq0ZaWhrr1q1jw4YN9O3b1zZGcFG8vLxYuXIlvXr1onHjxjzyyCO2NnX//PMP3333Ha1btwbUKuIvv/wy06dPp0ePHvTr14/jx48zb948mjdvbtdB0Y345JNPuOeee2jQoAGjR4+mWrVqxMTEsGPHDi5evFjk+Ml16tShevXqvPDCC1y6dAl3d3d+/PHHQku+8/dtwoQJdO/eHZ1Ox5AhQ+jQoQNPPvkks2bNYv/+/XTr1g29Xs/JkydZtmwZc+bMYdCgQfj5+fHCCy8wa9Ys+vTpQ69evdi3bx9r164tUenchg0beO211+jXrx+tWrXC1dWVM2fO8NVXX5GTk8O0adOKzWtJz60aNWowefJkZs6cSbt27RgwYABGo5Hdu3cTHBzMrFmzCuSvRo0arF+/no4dO9K9e3c2b96Mu7s7jz/+OImJiXTu3JmQkBAiIiKYO3cujRs3LnZopxo1ahAYGMjx48cZP368bXr79u2ZNGkSQImC9mbNmvHpp5/y+uuvU6NGDfz9/Ys9v4uzZMkS2xB6henVqxcODg6Eh4fTsmVLwsPD0el0RQYz/fr1Y/LkySxdurTQoR2Ls2nTpgKBFqg3YIpqo/3444+zfPlyevTowYMPPsjp06dZvHhxgWtMt27dCAwMpG3btgQEBHD06FE+/vhjuw71bsZLL73E4sWL6dq1K+PHj8fFxYUvv/ySypUrk5iYWOKaEu+//z7Ozs5207RaLa+88grvvvsuPXv2pHXr1owaNYqsrCzmzp2Lh4eH3XfnzTffZP369XTo0IEnnniCunXrEhUVxbJly/jjjz/w9PSkT58+zJgxg5EjR9KmTRsOHTpEeHh4gdoJpcnd3Z1PP/2UYcOG0bRpU4YMGYKfnx/nz59n9erVtG3blo8//hh3d3dbG3Sz2UylSpVYv379dY31feLECe69914efPBB6tWrh4ODAytXriQmJoYhQ4YAt+6aPnz4cLZt23bNavP5wyZe3aFmPqPRSPfu3Vm2bBkfffQRer0eUGuP5TdRyczM5NSpU6xYsYLTp08zZMiQEvULMmLECBYtWsTZs2cJCwsrNM0vv/xCWlpakdeGVq1a4efnR3h4OIMHDy52mzdLo9HQoUMHtm7dWmSa8PBwfHx8aNOmTaHz+/XrxxdffMHq1asZMGBAkeuZPHky3377LcePH6d+/fq26QcOHOChhx6iZ8+etGvXDm9vby5dusSiRYuIjIzkww8/tN1wGj9+PJmZmfTv3586depgMpn466+/+P777wkLC7uttZGEuKVuY0/1QohSUNjwXGlpacpzzz2nBAcHK3q9XqlZs6by7rvv2g1LpSjqMDFjx45VFi9erNSsWVMxGo1KkyZN7IY0K8ru3buVBx54QKlcubJiNBoVFxcXpWnTpsr777+vmM3mEuc/MjJSee6555RatWopjo6OirOzs9KsWTPljTfeUFJSUuzSfvzxx0qdOnUUvV6vBAQEKE899ZSSlJRkl6aooXmKG9rq9OnTyvDhw5XAwEBFr9crlSpVUvr06aMsX77clqawId+OHDmidOnSRXF1dVV8fX2V0aNHKwcOHCgwVFNubq4yfvx4xc/PT9FoNAU+s88//1xp1qyZ4uTkpLi5uSkNGjRQXnrpJSUyMtKWxmKxKNOnT1eCgoIUJycnpWPHjsq///5b5NBbVzpz5owydepUpVWrVoq/v7/i4OCg+Pn5Kb1791Y2b95sl/ZaeS3puaUoivLVV18pTZo0UYxGo+Ll5aV06NDBNnyRohQ+JNKuXbsUNzc3pX379kpmZqayfPlypVu3boq/v79iMBiUypUrK08++aQSFRV1zf3N98ADDyiA8v3339ummUwmxdnZWTEYDEpWVpZd+sKGHouOjlZ69+6tuLm5KYBtOLMrhy26UmHnydUaNGigVK5c+Zp579ixo+Lv728bmqpdu3bXTF+1alWlSZMmdnkobvim/O9FUY9vv/32msvPnj1bqVSpkmI0GpW2bdsqe/bsKTDk2/z585X27dsrPj4+itFoVKpXr668+OKLdt/vooZ8K2zIrKvXryiKsm/fPqVdu3aK0WhUQkJClFmzZikfffSRAijR0dHX3If8ocsKe+h0Olu6jRs3Km3btlWcnJwUd3d3pW/fvrYhBa8UERGhDB8+XPHz81OMRqNSrVo1ZezYsUpOTo6iKOqQb88//7zte9y2bVtlx44dBfbreod8K8lQXVu2bFG6d++ueHh4KI6Ojkr16tWVESNG2A37efHiRaV///6Kp6en4uHhoTzwwANKZGSkAiivvfZageN29VB58fHxytixY5U6deooLi4uioeHh9KyZUvlhx9+KJCfm7mmP/roo0qVKlUKpC3ur+zs2bMVQNm0aVORab7++msFUH7++We79eY/XF1dlZo1ayqPPPKIsn79+kLXUdj5O3DgQMXJyanAPl6pb9++iqOjo5KRkVFkmhEjRih6vV6Jj49XFOXyb3lxrjUMXWHnUVpamgIoQ4YMKXKdMTExioODgzJs2LAi02RmZirOzs624TWLunYqivq5AnafeUxMjPLWW28pHTp0UIKCghQHBwfFy8tL6dy5s91vtKIoytq1a5XHHntMqVOnjuLq6qoYDAalRo0ayvjx4wsd4lSIikqjKBW4dxchxHXRaDSMHTu2QHVnIYSo6J599lnmz59Penp6sf0WCHE7BAQEMHz4cN59992yzkqJrFmzhj59+nDgwAEaNGhQ1tkRQlxB2rQLIYQQokLJ7wAzX0JCAt9++y333HOPBOyiXDh8+DBZWVm2pjkVwZYtWxgyZIgE7EKUQ9KmXQghhBAVSuvWrenYsSN169YlJiaGBQsWkJqaypQpU8o6a0IA6vCWqampZZ2N61JRagQI8V8kQbsQQgghKpRevXqxfPlyPv/8czQaDU2bNmXBggW0b9++rLMmhBBClDpp0y6EEEIIIYQQQpRT0qZdCCGEEEIIIYQopyRoF0IIIYQQQgghyilp0w5YrVYiIyNxc3NDo9GUdXaEEEIIIYQQQtzhFEUhLS2N4OBgtNqiy9MlaAciIyMJDQ0t62wIIYQQQgghhPiPuXDhAiEhIUXOl6AdcHNzA9SD5e7uXqrrNpvNrF+/nm7duqHX60t13aLikPNAlAU57wTIeSDKhpx3AuQ8EKXvTjunUlNTCQ0NtcWjRZGgHWxV4t3d3W9J0O7s7Iy7u/sdcWKJGyPngSgLct4JkPNAlA057wTIeSBK3516ThXXRFs6ohNCCCGEEEIIIcopCdqFEEIIIYQQQohySoJ2IYQQQgghhBCinJKgXQghhBBCCCGEKKckaBdCCCGEEEIIIcopCdqFEEIIIYQQQohySoJ2IYQQQgghhBCinJKgXQghhBBCCCGEKKckaBdCCCGEEEIIIcopCdqFEEIIIYQQQohySoJ2IYQQQgghhBCinJKgXQghhBBCCCGEKKckaBdCCCGEEEIIIcopCdqFEEIIIYQQQohySoJ2IYQQQgghhBCinJKgXQghhBBCCCGEKKckaP8PsqSlkf7Hn+ScPYtitZZ1doQQQgghhBBCFMGhrDMgbg+ryUT6tm2krvqV9K1bUUwmALSurjjWrYtj/fq2hyGsChpt+bufk7l3L7HvvIvGaCTglZdxrFOnrLMkhBCiDFkzM4mZNYucM2cJnvUmhsqVyzpLQgghRKmToP0OplitZO7eQ+qvq0hdtx5raqptnkNwEJb4BKzp6WTu3k3m7t22eVoXl8uB/F13lXkgnxsfT+y775Hy88+2aWcHDsJ72DB8x41D5+pSJvkqTblJSSR9u5iUn3/G84FB+I4ZU9ZZEkKIcs0cGcmFcePIOXIUgIiHH6Hywq8w1qhRxjkTQgghSpcE7Xeg7OPHSV21ipTVa8iNirJNdwgIwL13bzz69sFYpw7k5pJz+jTZhw+TffgwWYcPk3PsONaMDDL37CFzzx7bsvaBfH6JfNgtDeQVi4WkpUuJ+3AO1rQ00GjwHDQQS1o6ab/9RuLXX5P6228ETH4Fty5d0Gg0JV63+dIlMnb9jdbVBX1AAA4BATj4+qJxuL1fidy4OBIWfk3S0qUomZkAxH00F9cOHXCsW/e25kUIISqKzH/2cXH8eCwJCei8vND5eGM6dZqIYcMJ/fILnOrXL+ssCiGEEKVGgvY7hDkykpRfV5O6ahU5J0/apmvd3HDr3g2PPn1xbn43Gp3u8kJ6PY516qjVzAcOBEDJD+T/PWwL5rOPFxHIOztjrFcXJ7uq9WH227hBWfv3EzVjhq0ExbFePQJfm4pTo0YApP/en+gZMzFfvMil8RNw7diRwCmvoq9Uqch15sbHk/rbOlJXryZr376CCTQadL4+6P3zgvgAfzWg9/PHISAAfYD6rHVzu64bBIUxR0aS8OUCkpcvtzVVMNapg9bVhaw9e4me+TpVwhff9HaEEOJOk/zjCqKmTQOzGWPt2oTO+wStiwvnRz9B9qFDnB8xktD583Fu2qSssyqEEEKUCgnaKzBLcjKp69aTsuoXsvbstU3X6PW4duyAe5++uHbsgNZoLPE6NQ4OONaujWPt2jBwAJAfyJ+5HMQfPkz2sWNYMzPJ2rPXftvOznkl8vVswbyhatUSB/K5SUnEvf8+ycuWA6B1d8fv2WfwGjzYbh2u7dtTbdUvxH82n4SvviJ961ZO79qF39in8X70UTR6vXqMUlJI27CB1DVryNi5C/I73tNo1BsAioI5NpbcuDjIzcUSF48lLh4OHy76GDk5off3x8Hf3z6498977e+Pg58fGoOhwLKmiAjiFi4k5edfIDcXAKdGjfB5agyuHTqQGx3N6V69yfrnH1JXrcKjX78SHTchhLjTKbm5xL77HomLFgHg1rUrwW/NQuuiNpGqvPArLowZQ9aevZx//HFC532CS6tWZZllIYQQolRI0F7BWLOzSd+6lZRVv5L+++9gNqszNBqcmzfHvW8f3Lt1Q+fhUWrbVAP5WjjWrgUD+gN5gfyZM2QfPmIXyCuZmWTt3UvW3r0k5S/v7IyxRg10Hh5onZ3Vh4tLgdeW1FQSPvsMS0oKAB79++P/wvM4+PgUmi+tkxP+zz2LR98+RE+bTuaePcS+N1ttFz50KBl//En69u2XjxHg2LAhHr174dajB/qAANt0xWrFkpCAOSaW3NgYcmNjMcfEkBsTS25MDLmxMZhjYrGmpqJkZWGKiMAUEXHN46bz8ckL4gPQ+vkRdOwY5w8ftt04cG7VCt8xT+LcsqWtRF0fFITvmDHEffABMe++i2vnzuhcXW/ocxNCiDuFJSWFSxOfJ+PPPwHwHTsW37FP2zXR0rm6UvmLL7g4bjwZf/7JhSeepNKcD3Hr1Kmssi2EEEKUCgnaKwjT+fPEf/oZaRs2YE1Pt0031qmDR98+uPfujT4w8LblR+PggGOtWjjWqgX97wfUNuimM2fIOnz4cjB/9ChKZibZBw+WeN3G2rUJnDoF52bNSpa+Rg0qf/sNKT/9TOw775Bz8hQxM2Zenl+rFu69euHeuxeG0NDC90erxcHPDwc/P6DotpDWrCz7gD425orXaoBvjosDsxlLQgKWhARbFX+3vHW4duiAz5gncW5SeNVN75EjSFmxAlNEBPEff0LA/yaV6DgIIUR5Ys3OxpKURG5iIpakZCxJiXbvrWmpOPgHYKhaFUNYGIaqYWotpauaBeWcOcPFp57GFBGBxsmJ4FmzcO/RvdBtap2cCPl0HpcmTiR94yYujp9ApXffwb1nz9uxy0IIIcQtIUF7RaHRkLJyJaD2/O7Ruw/uffuoQXM5odHpMNasibFmTbj/fiAvkD97lpwzZ7BmZGLNzLj8nJmpPjLU14rJhFunzng9NPS6O4TTaDR49r8f144diJ87l6zDh3Fp3RqP3r3V/JQSrZMThipVMFSpUmQaxWpV/5jG5AX0sXHkREVy+shRGo0bi2vDhtfehsFAwKuTuTD6CRK//RbPgQNKdR+EEOJ6KVYrlpSUQoNvS2IiluQkchOTsCQlYUlMJDc52da55vXQurioAXxYGIaqVdG5uxE392OsaWk4BAcR+sknxXbSqTUYCPngAyJffoXUX3/l0vMvYM3MwjOvyZcQQghR0UjQXkEYQkPxe34izk2a4NS0abkcR70wGp0OY40at20IHgcvLwKnTr0t2yqKRqvFwccHBx8fHOvVA8BsNrNrzRqMJewR3rVdO1y73Ev6xk1Ev/4Glb9eKJ3SCSFKjTU7Ww2uk5KKDsSTkvLmJ2FJTr7cJ8j10Otx8PRE5+2NzssLB28vdJ5e6Ly90bq6kBsVTc65s5jOnsN88SLWjAxbk6srOTVrRshHc4psLnU1jV5P8NtvoXVyJHnZcqImT8aalYX3Iw9f/z4IIYQQZUyC9grEd/Toss6CuI0C/vcyGdv/IHPXLtJ++02qdwohbkjWvn0Ef72IC4u+wZKsBulKVtYNrUvr5qYG315e6lBr3t7ovDxx8PbOC8bz5uUF6VpX1xLfcFRMJkwXLmA6d06toXX2LObzF3Bq3Bi/8eMK7dzzWjQ6HYEzZqB1diZx0TfEvP46KStW4NqxI64dO+B4110V5ga4EEKUJmt2Nim//EJudDTWjAwsGRlqzdeMDKzpV7zOzERfqRIefXrj3qtXXjNSURYkaBeinDKEVMLnidHEz/2YmLfexrV9e1svyUIIURLp2/8gctw4XHNyyLl6Zn4p+JXBt5d33nv74Fvn5YWDp+d1B87XQ2MwYKxeHWP16qW3To0G///9D62bO/GffEL2kSNkHzlC/Lx56Hx8cG3fHtcOHXC5p610+imEKNcUq5WMP/5AsVpx69jxhtdjSUnhwtNjydq7t/jEgCUxkexDh4h5+x1c2rbFo19f3O69F62z8w3nQVw/CdqFKMd8Ro0iZeVPmC9eJP6z+fg/P7GssySEqCDSNm/h0jPPoJjNpNepTc1x4zD6+t5QKXhFptFo8Bs3Fq8hg0n/fTvpW7eS8eefWBISSFm5Uu0vRq/HuVkzXDt2wKVVK4w1alx33ypCCHErKBYLqb/9RsJn88k5eRIAzwcfJODVyWiv80aqOTqaC6NHk3PyFFo3N9z79Ebn6qqO5OSS/5z/cEbr5ETm3r2k/rKKrAMHyNi+nYzt29E4O+PW5V48+vbDpXUruV7eBnKEhSjHtI6OBLzyCheffpqEr7/Go39/jNWqlnW2hBDlXOr69Vya+Dzk5uLS5V5OdOpE4w4d0Ov1ZZ21MuPg64vngP54DuiPYjKRuXcv6Vu3kb5tG6Zz58jcuZPMnTsBdahSpwYNcGrUCKfGjXBq1KjE7emFEKI0KGYzKat+JeHzzzGdOweA1tkZa1YWyT/8QM7x41T66CP0Af4lWl/O6dOcf3w0uVFROPj7E/rFF+pwzsVwrFMH74cfxhQRQcqqX0n55RfM58+T+ssqUn9Zhc7XF4/evXDv2w/H+vX+EzeDy4IE7UKUc66dOuLSoT0Z234n5o03CP3yC7kgCiGKlLJ6NZEvTQKLBfdevfB743VYv76ss1WuaAwGXFq3xqV1awJe/h+mc+dI37aN9N+3k7V/P9aMDDJ37SJz1y7bMvrQUJwaN1YD+UaNcKxTG81/+CaIEBWVJT2D5O+/x1C9Gq5t2tzSZj83wpqTQ8qKFSR88SXmyEgAdB4eeD06HO+HHybr4EEuPf8CWQcOcHbQQELmzMG5adNrrjNz3z4ujnkKS0oKhqpVqfzlF+grVbqufBmqVMFv3Fh8xz5N9oEDpPyyitQ1a7DEx5O46BsSF32DoXp1PPr2xb1PHwwh17d+cW0StAtRzmk0GgJfeYUzf+0g488/Sd+0CbcuXco6W0KIcih55U9ETZ4MVise999P0Buvk3sjvb7/xxjCwvAOC8P70UdRLBZyTp8m68ABsvbvJ+vAAUynTmO+cAHzhQukrloFgMZoxPGuu2wl8U6NGpe4xKu8UhQF86VIHHx90Do6lnV2hLgloqdOIXXNWgC07u64demCe8+euLRqWaY34qyZmST98AOJC74iNy4OAJ2vLz4jR+A5eAg6V7VfI9d27ai6fBkXx40n58QJIoY/SuDkV/AcMqTQQp20LVu49NxElOxsHBs1JPSzz3Dw8rrhfGo0GvUGZuPGBLz8P9L/+IPUVatI27QZ0+nTxH34IXEffojT3c3w6NsP9x7d0Xl43PD2hEqCdiEqAEOVKniPeoyEz+YT8+YsXNq2RevkVNbZEkKUI0nLlhE99TVQFDwfeIDA6dPU3tElaL8uGp0Ox1q1cKxVC68HHgDAkppK1sFDZB3YT9b+A2QdOIA1NZWsvXvtOnNyCA6ylcQ7N26MsV69625zejspZjPZR46QufcfMvP2xZKcjM7bG5/HH8froaESvIs7SuqaNWrArtOh8/bCEhdPyooVpKxYgc7TE7euXXHv2QPnFi2uq522YrVizczEmp6ONS0NS3q6+jo9HUtamtoje3o6lvS812lpWDPSsaRdkS4lBcVkAsAhMBCfxx/Hc9DAQr+DhsqVCVv6HZGTJ5O29jeip88g699/CZw6Fa3RaEuX/OMKoqZOBYsFlw7tCfngg1LtQE6j1+PWqRNunTphSUsjbf0GUlatInPXLrL27CVrz15iXn8d144dcO/bF9eOHcv1NbE8k6BdiArC98kn1XZEkZEkfPEFfhMmlHWWbglLcjJaNzc0Ol1ZZ0WICiMxPJyYma8D4PXwwwS8Olma0ZQinbs7rve0xfWetoD6B9107pwtgM/av5+ckyfJjYwiLTKKtLW/AeofWsd69S6XxjdujENQUJl9NtaMDLIOHCBzz14y//mHrAMHCg7/p9FgSUwk9p13SFy4EJ8nn8TzwQfkj7ao8HLj4oiePgMA3yefwHfsWLWTtbVrSVu3HktiIsnLlpG8bBk6b2/cunXFWK3aFUF3XjCeH2hfGXRnZICi3HQe9aGh+DwxGs/77iu22r7W2ZlK779P4l13ETv7fVJ+XEHOiZOEfDQHh8BAEj7/grgPPgBQa17NnHFLaxLo3NzwHDgAz4EDMEdHk7p6NSk//0LOiROkbdhI2oaNaN3dce/RA49+fXFq2lSG3bwOErQLUUFonZwImPQ/Lj3zDPGffgY6Hb5jxlTo4FZRFExnz5K5Zw9Ze/eSuWcv5kuXcO1yLyFz50rQIUQJJHz9NbFvvQ2A98iR+L/0onx3bjGNVouxWjWM1arhOaA/oLaTzf73kF0gb0lKUl8fOGBb1sHPTw3i89rHG+vUueXDzSlmM3Eff0LCV1+B2Ww3T+fhgVOzZjg3a4Zzs6YY69QhdfUa4j/5BHNkJDGvv07CggX4PjUGz/79pR2/qJAURSHqtWlYUlIw1q1r+//k0qIFLi1aEDh5Mpm7d5O69jfS1ucF8Eu/v/4NOTiovbG7uaF1dUXn4nL5tZur2kO7qytaN1c1nasbWlcX2zL64ODr+l+n0WjwGTUKx7p1ufTcRLIPHeLswEG4tGlD6q+/AuAz+nH8Jk68rb8L+sBAfEaNwmfUKLKPHyfll19I/XU1uTExJP/wA8k//IA+OBj3vn3x6Ne3VIf6vFNJ0C5EBeLWrSteDw0lacl3xM/9mKy9ewl+990K06uxkptL9tFjZO7NC9L3/oMlMbFAuvSNm0hdvQaPPr3LIJdClG+WtDRyTp4k58QJsvbtI+XnXwDwGfMkfs88IwF7GdG5uuDSqhUurVoBee3DL1xQg/Z9atv47GPHyI2Ls5U65dMHB2OsWRNjrVp5j5oYq1YtlQ6yTBcvEpnXaRWoVfidm91tC9IN1asXKO3yHDgAj759SF6xgvhPPyM3Koroqa+R8MWX+I59Go++fSv0DeP/IkVRMEdEkP7Hn2QdPIDWxQW9vz8O/gE4+Pvj4O+PPsAfrYfHHXkNSfnpZ9I3bwa9nuC33irw3dI4ONg6pwyc8ioZu/4mbcMGrGlpeUG3yxVBdl4A7qoG4bbXbm5oDIYyOX4ubdoQ9uNyLo6fQM7Ro7aAPeDl/+H96KO3PT9XcqxdG8cXX8R/4kQyd+8m5ZdVpK1bp9YcnT+fhPnzcaxXD/d+ffHo3RsHP78yzW95JUG7EBWIRqMhcOpUHBs2JHr6DDL+2sHZ+/sTPPs9XFq0KOvsFWBJTc37w7qPzH37yD5wEGtmpl0ajdGIU8OGON3dDOdmd5O5Z3de2/03cWnb5qY6SxGiIlNMJnLOniXnhBqg55w4QfbJE+RGRhVI6zthPH5PP10GuRRF0Wg0GCpXxlC5Mh59+wJgzcoi+/DhKzq5O0hubCzmyEjMkZGkb9t2eQUODhjCquBYqxbOrVrhcd99dm1VSyJl9WqiX5uGNT0drbs7QTNm4N6je8nybzDgNWQIHv37k/z998R//gXmCxeI+t/LJMz/HN9xY3Hv2VOqt5ZjlrQ0MnbuJOOPP8n44w/Mly4Vu4zGYLAF8Vo3V7QGAxq9Ho3egMaQ96zXq8FpCZ6tOi1Op0+Ttf8AuU5OeevQq80t8p6vTH8rmKOiiHnjDQD8xo8vdpgzjV5v1xymojCEhBC2JJzoGTNJ37KFgFdfLVeFHxqdznZj0zp1CulbtpDy8y+k//EH2UeOkH3kCLHvvKv2yfLqZKnVcxUJ2oWogDzvvx+nu+7i4rPPYjp1mvMjRuI3YQI+T4wusz9QalX3c+of0X37yNq/j5yTpwqk07q749ykiS1Id7yrvl1bSZcWzUnbuBHTqdPEvvsewW++cTt3Q4hyIX37diIn/a/QmigADgEBthJZl1atcG3X7jbnUNwIrZMTznffjfPdd9um5SYlYTp1iuwTJ9QaFCdPkXPiBNa0NEynTmM6dZrUNWuJm/MR3sOH4zV0CDp392tux5qZSfTrb5CyYgUATk2bUundd657iCcArdGI9/DheA4aRNKSJSR88SWms2eJfP4FNXgfPw63Ll3uyNLZikZRFLIPHVJHmvnjT7L27weL5XICvR7npk1xbtkCcnMxx8aSGxNLbmwsuTExWJKTUUwmzBcvYr54sdTyFQpc+vyLYtPpPD3Rh4ZiCA1BHxKKPjQEQ0gI+tBQ9IGB19UxXD5FUYia/CrW9HQcGzXE57GRN7AHFYfWyYngWW+iKEq5/k5qHR1x79kT9549yU1MJHXtWlJ/WUXWgQMkf/89ubGxVPrg/RvuCFNRFDJ37MClTZtSznnZkaBdiArKWKMGVX/4gejpM0j5+WfiPvyQzL17CX7n7dtSOm3NyiLr0CG12ue+fWr7zeTkAun0VSrj3LgJTk2a4NSkMcaaNa95Y0FjMBA0YyYRDz1EyooVePTra6tuKsSdTrFaSZg/n7iP5oKioHV1vVxdOq9Hc2PNmjJ8zh3EwcsLh+bNcW7e3DZNURRyY2LU2hWHD5O0bBm5kVHEffABCZ9/jufgwXg/Ohx9QECB9WUfOcKl51/AdPYsaDT4PjUG36efvqGA50paZ2e1N+shQ0j85hsSF35NzokTXBo/Acd69fB7ZgIu7duX60DhTqYoCpHPv0DqmjV20w1hYbjccw8u97TFpUWLa/Ycbs3JITcuntzYGHJjY7FmZKKYTSgms/psNqOY7J+tJhPkPdvPN6vzc3JIS0rCxWCA3Fz75U0mu87bLMnJWJKTyT50qGDmdDr0wcE4N2uG38Tn0PuXbHjF5O+/J+Ovv9AYjQTPeuumvwcVRUX6Hjp4e+P98MN4P/wwaZu3cOm550jfsoULj48m5NN56Nzcrmt9uUlJRL38Culbt1Lp/dm49+p1i3J+e/03zlwh7lBaZ2eC3pqFc4vmRM+YScb27ZztP4BK78/GuWnTUt2WOSoqr5q7GqRnHzsGubl2aTQGA44NGuDcpLEapDdufEPt7Z2bNsFz6BCSv1tK1GuvUe3nn2XYIXHHs6SlETnpf2q7S8Bz8GACJr8ivXb/B2k0GvSBgegDA3Ft3x6fxx8ndc0aEr5cQM7JkyR+9RWJ336LR7+++IwahbFaNRRFIenbb4l99z0UsxmHgACC33kHl5al23RK5+qK39NP4/3wwyQsXEjSN9+SfeQIF54cg1OjRvg9MwHn1q1LdZuieMnf/6AG7Ho9bh07qoF627YYQkpeu0JrNGIIqXRdyxTHbDazZs0aevXqhb6Q6s5Kbq4a3Gdnq01FLlzAdOGi+nzxAuYLaqm/YjZjvnCBlAsXSNu0Cf+XXsRz0KBrBqem8+eJeeddAPyfn4ixWtVS2y9xa7h17kTlL7/gwlNPk7lnDxGPPkrlL74o8X/JjJ27iHzpJXJjY9EYDFjS029xjm8fCdqFqOA0Gg2eAwfieNddXHrmWUznzhExbDh+48fh3qMH+sqVr7vKvGI2k33smK0teta+/eRGRxdI5+Dvj1PTpmqQ3rgxjnXrlkrHSQD+EyeSvmkz5ojzxH/2Gf7PPlsq6xWiPMo5eZKL48ZjiohAYzAQOHUKnoMGlXW2RDmh0evxuO8+3Pv1I33bNhK+/JKsPXtJ+XEFKStW4npvZxSzmYxtvwPgeu+9BL0+85bWutJ5eOD/7LN4P/ooCV9+SVL4ErIOHOD8Y6Nwbt4cr7HSx8LtknPmLDFvvQWov50+I0eUbYaug8bBAY2DA1onJxy8vHCsXbtAGsVqJTc2lpxTp4n78EOy//2X6ClTSf1lFYEzpmOsWjAYVywWIl95BSUzUz0fH3nkduyOKAXOzZtT5ZtFnB/9BDlHjhLx0MOELlhwzZtJSm4ucZ98QsJn80FRMFSrRqX3Z+NYp85tzPmtJUG7EHcIx9q1CVu+nOjXXiN19WriPpxD3Idz0Dg7qz131q2DsU4dHOvWxVizpl3JdW5S0uVq7vv2kfXvvyjZ2fYb0OlwrFPHVs3duUmTWzresM7NjYApr3Jp/AQSvlyAe69eONa6ducxQlREqWvXEjn5VZTMTByCggj5aA5ODRqUdbZEOaTRaHDr2BG3jh3J3LePhC8XkL5pE+kbN6nzDQb8/zcJr6FDb1v1WAcvLwJefFEN3j//guTvvydz924yR4ykUs2aZIeGoi/lml/iMsVkIvLFF1Gys3Fp0xrvR4eXdZZKnUartdU8cWnVksTFi4mb8xGZu3dz9r778X36aXxGPWbXcVniN9+StWevWiNx1pvSYWIF41ivHmHhizn/2ChMERFEPPwwlRd8ibFGjQJpTRcvEfnCC2ofDoDHoIEEvvLKNZuCVEQStAtxB9G5uhD83ru4tG5F0vc/kHPiBEpmpi0Yt9FqMVSriqFKGKbTpzGdO1dwXR4e6jjCTfLaoze467ZfAN27diWly72kb9xE9JSpVFkSLsMMiTuGkptL7PsfkPjVVwA4t2pFpfdn4+DtXcY5ExWBc5MmOH/yMTmnT5OwcCG5kZH4T/pfsT1j3yp6f38CX52Mz6jHiP9sPsk//ojLyZNcfOhhXDt1wm/CeBzr1i2TvN3J4j7+hOzDh9F5eBA0a9YdH5xqHBzwGTECty5diJ42nYw//iDuww9JXbuWoJkzcGrYkJzTp4n74AMA/CdNwhASUsa5FjfCEBZGle+WcH7UKEynThPx8COEfj4fh3r1bGlSf1tH1JQp6tB8rq4EzZh+x7Rhv5oE7ULcYTQaDZ6DBuE5aBBKbi6mc+fIPnqM7GNHyTl6jOxjx7AkJtp6Jc5nqFFd7dU9L1A3hIWVix//wClTOLNjJ1kHDpC0dCneDz9c1lkqFyzp6ZCbi87Ts6yzIm5AbmIil56bSOauXQB4j3oM/+ee+890kiRKj7F6dYJff72ss2GjDwoiaPo0PB59lINTpuCxbx/pW7aQvmULbt264Td+HMaaNcs6m3eEzN27SfhC7ZU9cMaMQjsmvFMZQkII/eJzUletIubNWeQcP865IUPxHvYImf/sQzGZcLnnHjwffKCssypugj4ggLDFizn/5JNkHzhIxMjHCPrgAzQmE7HTZ5C6fDkATo0aETz7vTv6Bo38OxDiDqZxcMBYowbGGjXw6NsHyOuVOC6OnGPHMJ07hyEsDKeGDctt8KcPCMDv+YnEzJhJ3Psf4HbvvegDA8s6W2XKkpzM2QEDMcfF4Tt6ND5PPnHd4zeL20MxmTDHxmK+FIk5Sh2LOzcqivTft5MbE4PG2ZngN9/AvUePss6qEKVKHxpCzIMP0OC1qSTP/5zUNWtIW7+etA0bcO/dG79xYzGEhZV1NissS2oqlyZNAkXBY8AA3Lt3K+ss3XYajQaPfv1wueceYt56i9RfVpG46BtAHV426I3XK1Qv6qJwOk9Pqnz1FRfHTyDjr7+IHDuWKh4epCYkgEaDzxNP4Ddu7B0/rrsE7UL8x2g0GvT+/upwKe3bl3V2SsRryBB1/M79+4l+/XVCP/641NZtSU1F6+xcoUo4Y956G3NkJADx8+aRumYNgdOnl3ov0aJ4lvR0+4A8MhJzZBTmyEjMUVHkxsbaDWl0JUNYGCEfzy20jZ4QdwpD1apUmv0ePk8+Qfzcj0nbsIHUX38lde1aPO67D9+nny7V3sr/K6JnzCQ3Mgp95coEvPJKWWenTDl4e1PpnXfw6NuP6NdewxwVReCUKf+pmgd3Oq2LCyGffUrkS5NI++03DAkJ6Pz8qPTO27j8R0arqDj/UoUQ/1karZbAGdM5O2Ag6Rs3kbphA+5du97QunITE8n8+28yduwkc+dOTBERoNdjCAnBUKWK+girYnvtEBRULpoJ5Ev//XdSfvoJNBr8JowncckSTOfOcf7RR/EYMAD/F1+4pT1G/5coViu5cfGYIy+RG5UXiF8RkJsjI7GmpRW7Ho3BgD4oCIfgIPTBweiDgjGEhuB6bxd0ri63YU+EKHuOtWoRMvcjsg4fJv6juaRv20bKihWkrFqF58AB+I4Z85+vRVVSKat+JfXXX0Gno9I7b8t1JI9ru3uotnYNuXHxciPoDqQ1GKg0+z1iqoZxbs9eGr/3Lo7/oRszErQLISoEx1q18Hl8FAmfzSdmxkxcWrVC5+ZW7HKW9HQy9+whc8dOMnbtIufYsYKJzGZMZ89iOnu2wCyNwYC+ciiGKmGXg/q8wN7B3/+2BvSWtDSipr4GgPfw4fg+9RReDz9M7Oz3Sf7+e1JWrCB961YCXv4f7n363LZ8VXS5SUlk/P47pogI+6A8OhrM5mKX13l44FBJDcbVoDwvOM8L0nXe3uXqxo8QZcmpfn1C539G1v79xH30ERl/7SB56fekrFiJ55DB+I4ejYOfX1lns9wyX7pE9PTpAPg+9RROjRuXbYbKmfyx5sWdSaPT4fP00+xaswbdf6zTVgnahRAVhu9TT5G29jdMERFcmvg8jnfVB7MZ5cqH6fLr3NhYsv79FywWu/UYa9XCpXUrnFu2wvnuZljT0zFFRKiPcxG21+YLF1BMpgKd9uXTODpiqFy5QOm8vkoVHPz8Sr0tXey775EbHY2+cmX8nn0GAJ27u9rp0339iJo6FdOp00S++BIpP/2M7+T/dpXJa7GkZ5C+eRMpq1eT8edfkJtbeEKdDocAf1sJuS0or5T3HBSE1kVKuYS4Xk6NG1P5q6/I3L2b2DlzyNqzl6RvviX5h2V4P/Iw3qNGSa2hqygWC5cmTcKano5T48b4jnmyrLMkhLhNJGgXQlQYWqORwBkzOP/oo2Rs307G9u0lWk5fuTIurVrh0qolzi1b4uDjYzdf5+6OPji4QLsoxWLBHBWVF8ifuxzMn4vAdOkSSnY2OSdOkHPiRMG8Ojujv7JkPj+wr1r1hv6IZuzYQfIPPwAQ9PpMtE5OdvOdmzal2ooVJHz1FfHzPiXjzz/J7D8Ar86dULp3hzu8g5aSsGZnk77td1LXrCF961aUnBzbPGOdOjg1amRXQq4PClJrU1Sg/g6EqGicmzenyrffkvHXX8R99BHZBw6S8OUCkpZ8h9ejw/EZORKdu/tNbye/E1ZLcjLG6tXLzfChiqKQGxmJxtkZnafnNW/2Jny5wDb2ePC778i1SYj/EPm2CyEqFJeWLQh6802y9u1DYzCg0esvPwyXX+PggM7NDeemTdFXurGqchqdTm3rHhIC97S1m6eYzZgjIwuUzpsiIjBfuoQ1M5Oco0fJOXr0qpVq8Hl8FH7PPVfiKtPWjAyiXp0CgNdDQ3FpUXiHcxqDAd8xY3Dv0YOoadPJ3LkTv7W/keDvT9DLL1//AbgDKFYrGX/8QerqNaRt3Ig1I8M2zxAWhnvv3rj37oWxWrUyzKUQ/20ajQbXtm1xadOG9K1biZs7l5wjR0n49DOSwpfgM3IEHvfdh8bJCa2jIxqjscjrp2IyYTp/npwzZzCdOYvp7Blyzp7DdOYM1vR0QL1RUOn92WVWDd908RKZu9QmW5k7d6kdVqJ2tqUPCUEfEoIhpBL6SiF57ythTU0lbu5cAAJefRVDaGiZ5F0IUTYkaBdCVDieA/rjOaB/meZBo9fbStC5qhN+xWTCdPGSfel8XnBvjowk4YsvMUWcJ/idt9E6Oha7rdj3P8B86RL64GD8Jj5fbHpDWBiVF35FwpIlxM18neRF3+DcsCEevXvf6O5WWDFvvElSeLjtvUNQEO69euLRuzfGunVlOCAhyhGNRoNbp064duhA2saNxM+dS87JU8TN+Yi4OR/ZpzUY0Dg6ojUa1WdHI1aTCfPFSwWaRNlotWh0OjJ37+bsgIFUmvMhzk2b3vL9MsfGkrnrbzJ27SRz5y7MFy/aJ3BwgNxcrBkZ5Bw/Ts7x40Wuy617dzz6339rMyyEKHckaBdCiFKmMRgwVquKsVrVAvNSfvmFyMmvkrZ+PREx0YR+8gkOvr5Fritzzx5b0Bk4c0aJewnWaDR4PPggx7f/gffWrUS9OgVjjRo41q59YztVAWUdOkTSkiUAeA4dgkffvjg1biydwglRzmm0Wty7dcPt3ntJXfsbCfM/I+fsObu+JxSTCcVkwlrI8loXFwzVqmGsVhVD1WoYqlXFWK0a+sqVMV+8xMUJ4zGdOk3E8EcJeOklvIY9Uqo38HKTksj8e7damr5zF6YzZ+wTODjg1KABzq1a4tKyFU5NGoOiYL50CfPFi5guXsR8Me/1JfW1NTUVfWgoQdOnyc1GIf6DJGgXQojbyKNfP/RBQVwcN57sAwc5N3gIofM/K3SsbmtWFpGTJwPg+cAgXNu2LZCmOPHdu1HJZCLrr7+4OG48VZf9gM7T82Z3o9xTrFaiZ74OioLHff0Ieu21ss6SEOI6aXQ6PPr0xqOPWktIyc3Fmp2DkpONkp2NNSdHfc6bhlaHISwMB/+iOwI1VqtK1e+/J2rKFFLXrCXmzTfJOnCAoJkz0Do731A+LenpZO7enVeanjdKiaJcsSMaHOvVywvSW+LUtFmhN2CN1atjrF698G2kpqpNAwyGG8qjEKJik6BdCCFuM+fmzamy9DsujBmDOeI854Y+RMhHcwp0hBf30VzMEedxCAjA/6WXbmxjWi2B77zNxSFDMV+4wKUXXyL0s0/LTSdMt0rKTz+TffAgWmdn/J4vvkmBEKL80zg4oHN1gJscl1zr4kLw7Nk4NW5MzDvvkrp6NTknjlPpo48wVi1YQ+pq1qwssvbtI2PnLjJ27ST738MFRympWQPnlnkdoDZvjs7D46byXBqd8QkhKi4J2oUQogwYq1YlbOlSLo4dR9Y//3B+9BMETZ+O58ABAGTt30/iokUABE6fVqIx6Yui8/Ag5OO5nBsylIzt24mbOxf/Z58tjd0olyxpacTOng2A79in0fv7l3GOhBDljUajwXv4cBzr1+fis8+Sc/IU5wY9QNBbs3Dv2tUurWIykXXwIBk7d5G5cydZBw6gmM12afRVKuOSH6S3aHHNZk9CCHG9JGgXQogy4uDlReWFXxH1ymRSV68mavJkTBfO4ztmDJGTXwWrFY/7+uHWseNNb8uxTh2CZs4k8sUXSfhsPk533YVbly43vxPlUPzHn2BJSMBQtSrew4aVdXaEEOWYc7NmVP3xRy5NnEjWnr1cGj+B7NGP49a1q61398x//kHJyrJbziEwEJdWrXBu2RKXli3QBweX0R4IIf4LJGgXQogypDUaCX7vXQxVKhM/71MSPptP6uo1mC9cQOfrS0ApDtXm0bcP2f8eInHRN0RO+h9hy36444Y6yzl1isS8jvsCXnlZ2n8KIYql9/enysKFxL43m8RFi0j44ksSvvjSLo3O21stRc8rTddXriwdwgkhbhsJ2oUQooxpNBr8JkxAHxJK1NSpmC9cACDwtaml3mmc/wsvkH3kKJm7d3Nx7DjClv2AztW1VLdRVhRFIebNNyE3F9fOnXFt166ssySEqCA0ej0BL/8Pp8aNiJ4xE8ViwblFc1xatsK5ZQuMNWtKkC6EKDMStAshRDnhOaA/+uBgol6bilvHjgXaVZYGjV5PpQ8/4OzAQZjOniVy0v8ImfvRHTEMWtqGDWT8tQONwUDAy/8r6+wIISog9549cevRA6zWO77DTiFExVHx/6UJIcQdxKVVS2qsW1eq1eKv5uDjowbqBgPpmzaRMH/+LdvWjVByc0la+j0ZO3eWeBlrVhaxb70NgPeoxzCEht6q7Akh7nAajUYCdiFEuSJBuxBC/Ac5NWhA4GtTAXVoufjPPsOUVy2/LClWK1GTJxM9bRrnR4wkctL/sKSkFLtcwoKvMEdG4hAUhO8TT9yGnAohhBBC3B5lGrR/+umnNGzYEHd3d9zd3WndujVr1661zc/Ozmbs2LH4+Pjg6urKwIEDiYmJsVvH+fPn6d27N87Ozvj7+/Piiy+Sm5t7u3dFCCEqHM+BA/EcMhgUhbgP53C6azfO3Hc/cR/NJfvIERRFua35URSF6NemkfLzL6DTgUZDys8/c7pPH9I2bSpyOdPFSyR88QUAAS+9iNbJ6XZlWQghhBDilivToD0kJIS33nqLvXv3smfPHjp37sx9993H4cOHAXjuuedYtWoVy5YtY9u2bURGRjJgwADb8haLhd69e2Mymfjrr79YtGgRX3/9NVOnTi2rXRJCiAol8NVXCZz2Gs6tWoFOR87x48TPm8fZAQM5fW8Xot98k4xdf6Pc4puhiqIQ88abJC9bBlotld59hypLwjFUq4YlLp6LY8dx6fkXyE1KKrBs7Ntvo+Tk4NyypdoWVQghhBDiDlKmHdH17dvX7v0bb7zBp59+ys6dOwkJCWHBggUsWbKEzp07A7Bw4ULq1q3Lzp07adWqFevXr+fIkSNs3LiRgIAAGjduzMyZM5k0aRLTpk3DIEP9CCHENWkcHPAaMgSvIUOwJCeTtnUr6Zs2kb79D8yRkSR98y1J33yLztMT106dcOtyLy5t2pRqabaiKMS+9x5JixcDEPTGG7j36gVA1ZUriP/4YxIWfEXq6tVk7NxJ4NSpuHfvBkDGX3+RtmED6HQEvPKK9O4shBBCiDtOuek93mKxsGzZMjIyMmjdujV79+7FbDbTpUsXW5o6depQuXJlduzYQatWrdixYwcNGjQgICDAlqZ79+489dRTHD58mCZNmhS6rZycHHJycmzvU1NTATCbzZjN5lLdr/z1lfZ6RcUi54EoC9d93rm44NK7Ny69e+OXlUXmjp1kbN5MxrZtWJKTSVm5kpSVK9E4OuLcpg0u93bGpX37mx6WLuGTT0ha8BUAflOm4NKn9+U8a7V4TZiAU+fOxE6ZgunUaS498wwp3brhO+klol5/AwCPwYPRVasq37FCyPVHlAU57wTIeSBK3512TpV0PzTK7W60eJVDhw7RunVrsrOzcXV1ZcmSJfTq1YslS5YwcuRIu+AaoEWLFnTq1Im3336bJ554goiICNatW2ebn5mZiYuLC2vWrKFnz56FbnPatGlMnz69wPQlS5bg7OxcujsohBAVncWCU0QErv8exvXIYfRJybZZilZLVtUw0uvXJ71efXK9PK9r1V5btuD3m3oNj+3bh+R77ikyrSY3F+9Nm/HeuhWN1YpVp0NrsZDr4sK5F57HKtdvIYQQQlQgmZmZPPTQQ6SkpODu7l5kujIvaa9duzb79+8nJSWF5cuX8+ijj7Jt27Zbus2XX36ZiRMn2t6npqYSGhpKt27drnmwboTZbGbDhg107doVvV5fqusWFYecB6Is3IrzTlEUTMePk755MxmbNmM6cQLn02dwPn0G/19WYaxbF5fOnXG5tzOGGjWuWV09+dtvic8L2H2efZYaox4rPgP9+pFz9CgxU6ZiOn4cgKAXX6DOwIGlsn93Irn+iLIg550AOQ9E6bvTzqn8Gt/FKfOg3WAwUKNGDQCaNWvG7t27mTNnDoMHD8ZkMpGcnIznFVUvY2JiCAwMBCAwMJC///7bbn35vcvnpymM0WjEaDQWmK7X62/Zh38r1y0qDjkPRFko7fPO0KABrg0awDPPYLpwgbRNm0jbuJGsf/aRc/QoOUePkvjJJ+grV8bt3ntx63IvTo0b2417nPTdd8S/8y4AvmPH4jfmyZLvT8OGuCz7gcQlS1CysvB58EE0WhnBtDhy/RFlQc47AXIeiNJ3p5xTJd2HMg/ar2a1WsnJyaFZs2bo9Xo2bdrEwLwSlOPHj3P+/Hlat24NQOvWrXnjjTeIjY3F398fgA0bNuDu7k69evXKbB+EEOK/whAais+IEfiMGEFuQgLpW7eStnETGX/+ifn8eRIXLiRx4UJ0Pj64de6E6733khsbS/T0GQD4PD4K33Fjr3u7GoMBnxEjSnlvhBBCCCHKnzIN2l9++WV69uxJ5cqVSUtLY8mSJWzdupV169bh4eHBqFGjmDhxIt7e3ri7uzN+/Hhat25Nq1atAOjWrRv16tVj2LBhvPPOO0RHR/Pqq68yduzYQkvShRBC3DoOPj7q2O8DB2LNyCD9jz9J27SR9K3bsCQkkLxsOcnLltvSew0bht/zz0uP70IIIYQQ11CmQXtsbCzDhw8nKioKDw8PGjZsyLp16+jatSsAH3zwAVqtloEDB5KTk0P37t2ZN2+ebXmdTsevv/7KU089RevWrXFxceHRRx9lxowZZbVLQgghAK2LC+7du+HevRuK2Uzm7t2kbdxE2qZN5MbE4DlkMAGvvCwBuxBCCCFEMco0aF+wYME15zs6OvLJJ5/wySefFJmmSpUqrFmzprSzJoQQopRo9Hpc2rTBpU0bAqa8Sm5sHPoA/7LOlhBCCCFEhSA99wghhLhtNBqNBOxCCCGEENdBgnYhhBBCCCGEEKKckqBdCCGEEEIIIYQopyRoF0IIIYQQQgghyikJ2oUQQgghhBBCiHJKgnYhhBBCCCGEEKKckqBdCCGEEEIIIYQopyRoF0IIIYQQQgghyikJ2oUQQgghhBBCiHJKgnYhhBBCCCGEEKKckqBdCCGEEEIIIYQopyRoF0IIIYQQQgghyikJ2oUQQgghhBBCiHJKgnYhhBBCCCGEEKKckqBdCCGEEEIIIYQopyRoF0IIIYQQQgghyikJ2oUQQgghhBBCiHJKgnYhhBBCCCGEEKKckqBdCCGEEEIIIYQopyRoF0IIIYQQQgghyikJ2oUQQgghhBBCiHJKgnYhhBBCCCGEEKKckqBdCCGEEEIIIYQopyRoF0IIIYQQQgghyikJ2oUQQgghhBBCiHJKgnYhhBBCCCGEEKKckqBdCCGEEEIIIYQopyRoF0IIIYQQQgghyikJ2oUQQgghhBBCiHJKgnYhhBBCCCGEEKKckqBdCCGEEEIIIYQopyRoF0IIIYQQQgghyikJ2oUQQgghhBBCiHJKgnYhhBBCCCGEEKKckqBdCCGEEEIIIYQopyRoF0IIIYQQQgghyikJ2oUQQgghhBBCiHJKgnYhhBBCCCGEEKKckqBdCCGEEEIIIYQopyRoF0IIIYQQQgghyikJ2oUQQgghhBBCiHJKgnYhhBBCCCGEEKKckqBdCCGEEEIIIYQopyRoF0IIIYQQQgghyikJ2oUQQgghhBBCiHJKgnYhhBBCCCGEEKKckqBdCCGEEEIIIYQopyRoF0IIIYQQQgghyikJ2oUQQgghhBBCiHJKgnYhhBBCCCGEEKKckqBdCCGEEEIIIYQopyRoF0IIIYQQQgghyikJ2oUQQgghhBBCiHJKgnYhhBBCCCGEEKKckqBdCCGEEEIIIYQopyRoF0IIIYQQQgghyikJ2oUQQgghhBBCiHJKgnYhhBBCCCGEEKKckqBdCCGEEEIIIYQopyRoF0IIIYQQQgghyikJ2oUQQgghhBBCiHJKgnYhhBBCCCGEEKKckqBdCCGEEEIIIYQopyRoF0IIIYQQQgghyikJ2oUQQgghhBBCiHJKgnYhhBBCCCGEEKKcKtOgfdasWTRv3hw3Nzf8/f25//77OX78uF2ajh07otFo7B5jxoyxS3P+/Hl69+6Ns7Mz/v7+vPjii+Tm5t7OXRFCCCGEEEIIIUqdQ1lufNu2bYwdO5bmzZuTm5vLK6+8Qrdu3Thy5AguLi62dKNHj2bGjBm2987OzrbXFouF3r17ExgYyF9//UVUVBTDhw9Hr9fz5ptv3tb9EUIIIYQQQgghSlOZBu2//fab3fuvv/4af39/9u7dS/v27W3TnZ2dCQwMLHQd69ev58iRI2zcuJGAgAAaN27MzJkzmTRpEtOmTcNgMNzSfRBCCCGEEEIIIW6VMg3ar5aSkgKAt7e33fTw8HAWL15MYGAgffv2ZcqUKbbS9h07dtCgQQMCAgJs6bt3785TTz3F4cOHadKkSYHt5OTkkJOTY3ufmpoKgNlsxmw2l+o+5a+vtNcrKhY5D0RZkPNOgJwHomzIeSdAzgNR+u60c6qk+6FRFEW5xXkpEavVSr9+/UhOTuaPP/6wTf/888+pUqUKwcHBHDx4kEmTJtGiRQtWrFgBwBNPPEFERATr1q2zLZOZmYmLiwtr1qyhZ8+eBbY1bdo0pk+fXmD6kiVL7KreCyGEEEIIIYQQt0JmZiYPPfQQKSkpuLu7F5mu3JS0jx07ln///dcuYAc1KM/XoEEDgoKCuPfeezl9+jTVq1e/oW29/PLLTJw40fY+NTWV0NBQunXrds2DdSPMZjMbNmyga9eu6PX6Ul23qDjkPBBlQc47AXIeiLIh550AOQ9E6bvTzqn8Gt/FKRdB+7hx4/j111/5/fffCQkJuWbali1bAnDq1CmqV69OYGAgf//9t12amJgYgCLbwRuNRoxGY4Hper3+ln34t3LdouKQ80CUBTnvBMh5IMqGnHcC5DwQpe9OOadKug9lOuSboiiMGzeOlStXsnnzZqpWrVrsMvv37wcgKCgIgNatW3Po0CFiY2NtaTZs2IC7uzv16tW7JfkWQgghhBBCCCFuhzItaR87dixLlizh559/xs3NjejoaAA8PDxwcnLi9OnTLFmyhF69euHj48PBgwd57rnnaN++PQ0bNgSgW7du1KtXj2HDhvHOO+8QHR3Nq6++ytixYwstTRdCCCGEEEIIISqKMi1p//TTT0lJSaFjx44EBQXZHt9//z0ABoOBjRs30q1bN+rUqcPzzz/PwIEDWbVqlW0dOp2OX3/9FZ1OR+vWrXnkkUcYPny43bjuQgghhBBCCCFERVSmJe3FdVwfGhrKtm3bil1PlSpVWLNmTWllSwghhBBCCCGEKBfKtKRdCCGEEEIIIYQQRZOgXQghhBBCCCGEKKckaBdCCCGEEEIIIcopCdqFKC+yU+D39yByf1nnRAghhBBCCFFOSNAuRHmQHgtf94bNM2FRP0g4XdY5EkIIIYQQQpQDErQLUdaSIuCr7hB9SH2fkwLfDwNTRtnmSwghhBBCCFHmJGgXoizFHoOvekDiGfCoDCNWg4sfxB6GVc9AMcMiCiGEEEIIIe5sErQLUVYu7oGFPSAtEvzqwKh1EHYPPPA1aHRwaBnsml/WuRRCCCGEEEKUIQnahSgLp7eobdezkqDS3TByLbgHq/PC7oFuM9XX6ydDxI6yy2dpurgHtr+v7rMQQgghhBCiRCRoF+J2O/IzLHkQzBlQrRMM/xmcve3TtHoa6g8Aay4sexTSossmrzdLUeDcH/DNffDlvbBpOvw8rqxzJYQQQgghRIUhQbv4b7Ja4PxONaCM3AfxJyE1ErKSwZJ7yzar2fctLBsBFhPUuw8e+h6MroUk1MB9H4N/PUiPgR8ehVxTyTaSeAaWjYRPWkL0v6Wa/xJTFDi1ERb2VHvFP7MVtA5qtf9jv8Kx1WWTLyGEEEIIISoYh7LOgBAllpEAy0eAzgB954BHyA2uJx6Wj4SzvxedRmcEgwsYXMHgnPc6/70L6J0vv7ab52yfzuACehfQGqgRsxqHfd+r62/6KPT5ALS6ovNgcIHBi+HzjnBhJ6x/FXq9U3T6rCR1nPdd88FqVqd9/zA8sRWcvK73KN0YqxVOrIXf31VvhoD6eTUZBm2fgb1fwx/vw5oXoWp7MLrdnnwJIYQQQghRQUnQLiqGrGT49n6IPqi+/6wdDPgcana9vvVE7lOHU0u5oAbeHiHq0GqmdPXZmlfKbsmBrBzISiyV7OuB+vlv7nkO7n1NLU0vjk91dT+/GwJ/z4dKzaDRYPs0uSbYswC2vX25vXi1TpB4GpLOwcoxMOQ70N7CijVWi1rtf/tsiMkr3XdwgrtHQpvxl9vrt38RDq9Q87VlFvR489blSQghhBBCiDuABO2i/MtJg/BBasDu7AselSDqgDqt3fPQ8RXQleBU3r8EVj2rBuTe1WFIOPjXtU+Ta7ocwJsy1Hbnpisf+fMy7dPlvzZfPT3vgYJF4wCdp6Br9+z17X/tnmqw+/u76jBwAfUgsIFaBf3oKtj4mlolHsCvLnR7HWp2gcj9sKAbnPgN/pitrqO0WcxwaLkarCecVKcZXKHFaGg1Flz97NMbnKH3bFg8EHZ9Cg0fhODGpZ8vIYQQQggh7hAStIvyzZQJ3w2Fi7vB0VPttM23JqybDLu/UIPF87tg4JfgHlT4OixmWPcK/P25+r5WD+g/H5w8C6Z1MICDd8GO4W6GomDOSmXdunV0b9Wfa1SIL1rHl+HSP3B6E3z/iFq1fts7cD6vZ3kXf+g8GRo/cvkGRnBj6PM+/DwWNr8BwU2hxr2ls0+5OepNkD8+gOQIdZqjh9qBXosnrn38anSBuwbCvz/Cr8/C45uu3UxACCGEEEKI/zAJ2kX5lZujBqjntoPBDYatgMC71Hm934MqreGXCRDxB8xvpwbu1TraryMtRu347fxf6vsO/4MOk25tVfGraTSgd8aiNaIoCqnZZlIyzaRkmUnONJOcZSI5732WyUKvBkHUC3a3X4dWp+7f5x3UquXf9lenOzhBm3Fqe/HC2oc3eUS94bH3a/hxFDyxDbyq3Pi+mDLhn2/gzznq+PKg1n5oMw7uHgWO7tdePl/3WXByo9pcYfeX0PLJG8+TEEIIIYQQdzAJ2kX5ZDHD8sfUkmW9Mzy8TG3PfaW7BkJgI/hhOMQehm/uV0uk27+gBrkXdsMPwyAtCozuaul6nV63bRdiUrPZdz6ZAxeT2ReRyL8XdEzctRGLVbnmcgv+OMtXI5rTurqP/Qxnb7VjugXdITcbGg2BzlPU5gLX0vMdiDoIkf+ox+qxdaB3vL6dyUmDPV/BX3MhI06d5hYEbSZAsxFqtffr4RYAXV6D1RNh00yo2/dyu3chhBBlIytZrcXmXw9q9ypZ3ytCCCFuOQnaRfljtcDKJ9WhwXRGGPqdWqpeGN8aMHoTrH1JLQHe+qZaql6zu9rW22IC39pq+3Xfmrcsy+k5uRy8mMyBCynsv5DEgQspRKdmX5VKA6gBu6Nei6eTAU9nPe5Oejyd9Hg66zkdl8HeiCRGfv03Xw5vzj01fe1XEdQInt4BilXtpK4kHIzw4Dcwvz1E7VePVb+PSrZsVrLarGDnvMud3HlUhnuehcYPX3/wf6VmI+HAd2pNgLWTYPC3N74uIYQQN+fYGvVGalqU+r5WD+j1HniGlm2+hCht0f/CqgnQ4smCnfuKMhWbmo2Hsx6jgzSbvJoE7eLWyklTq3NnJUHAXcW3Fbda1Srv//4IWr0ayOVVeU/NNjNn40lcjQ6M71wDB11eFXe9E/SbC5XbqH84zmxVH6CW4N7/aakOLZZrsXI8Jo39F5I5cCGZ/ReSORmbjnJVAbpWA7UC3Ggc6kmDYDeSzhzk/h6d8XV3xlFf+MUo22zhqcV72XI8jlGLdvP58LvpUOuqzty8q15/pj1DYdAC+HYA/LMIQu6GpsOLTp+RoAbqf38OOal5262udvzX8EHQ6a8/D1fTatWh++a3h6O/wPG1aqd7Qgghbp+MePVm7r8/qu89QiEtWu3E9Ox26Pyq2oRJ+h4RdwJFUfvTubQXfhqjDq9bt09Z5+r2s1oh4k8wZ6l9PDl5qX1HOXmWzn+8EsrJtfD32US2HItj64lYzsRlUD/YnaVPtMLN8fbloyKQoF3cHKtVvSufdC7vcVZ9Tsx7zoy3T+9dHUKaQ2hz9dm//uWO0xRF/eOwfzFotGqQWas7ALvPJfLs0v1cSs4C4HBkKnOHNsHJcMWfiMZDIbiJWgU84aT6R+OeiTdVvU9RFC4mZXHgYjL786q6H7qUQrbZWiBtsIcjjSt70ijEk8ahntxVyQMXo7pvZrOZNbEHCXB3RF9EwA7gqNfx2bBmjA3fx8ajMYxetIf5w5rRqY7/De+DTfXO6jHZPBNWv6D2QB/cxD5NWrRaBX7PV2pP+KD2SN/+Bajfv/T/tAXUh9Zj1Tby+WO3G1xKdxtCCCEKUhQ1UF/7EmQmqL+7bcarzcySItTA5vwOWPcyHPxevckqo32Iiu7wSrWGH6i1Fpc/BsNWQljbss3X7WQxq8MB/7u88PkG17wA3ksN4l18wS0Y3ALVpoxugWoTSbeg628eCVxMymTr8Ti2Ho/lz1MJZJktdvMPR6YyZvFeFo5ogcHhNvZBVc5J0C6KZ85Sf8CvDsiTzqrTLTnXXt7JWy3pTo5Qxw5PPA0Hl6rz9M5q4Bhyt1oV+59FgAbu/wzq3YfZYmXuppN8vOUUVgUqeToRn57DxqMxPPzlThY82hwvF8PlbfnXgaf+guxk9SJznVIyzWqAnleKfuBiMvHppgLp3IwONAr1pFGoB41DvWgU4oG/+01UFb+C0UHHvIebMv67f1h3OIYnvt3DvIeb0bVewM2v/J6JcHEPnFgL3w+HJ7eptR+SL6iB8z/fXP48gxqpw8TV7n1rO+7rMEn9EU0+D1tnqUPWCSGEuHVSLqk10078pr4PuEutsVapqfrevw6MWAP7voENU9WmVV90UkcI6fgyGF3LLOtC3LDcHNg4TX3d/kW1mvyJteooRY+tVQsS7nS5Jlg+Um2CqnVQ9zkrWX3kpKhpTOnqI/Vi8etz9Cg8oLe9Dsbk6Mue8ylsPRHHlmOxnIxNt1uFv5uRjrX96FTbHy8XA6O+3s2fpxJ4afkB3n+wMVqt9K0BErQLUO+2Z8QXEZSfu9y+rSganVr92qsqeIWp1be9wvLeV1G/0ACZieqwZRd3w8W/4eJe9QIR8af6yNfnA2g0mIiEDJ5Zup/9F5IBGNC0EtP71ed4dBqjFu3hn/PJDPzsLxaNbEGo9xV3+nQOJQrYc3ItHI1Ks1VxP3AhmTPxGQXSOWg11A1yp3GoJ41C1VL0ar4ut/QiYnDQ8vFDTXl26X5WH4riqcV7+fihpvS4K/DmVqzVQv/P4POO6ue7bAR4Vlbblltz1TQhLaDDS+rQbLejEyKDC/R+H8IHwY550OBBCGp467db0aVFqx0SeoWVdU6EELdKUgQsfUgt8bp/nnq9vhmKoo4msmGq2vRJq1ev922fVYc8vZJWq3Y0WqunWtr+74+w42M48jP0nm2rCSdEhbFrvlqA5BYE9zwHaNTReC7shMUDYdT6m/+OlWfmbLWD5pPrQWdQ+zu6slmi1QLZKWqT1uxk9TkrWe2AODVSjQfSoi+/Nmeq6bNTIO5okZvVKRqq4YmL4sXdihcxem8cPILwr1SV6tVrUqVKMBr3ILV0X6Nh3iPNGPX1bn7aH0mghxP/61nnVh+ZCkGC9juVoqgl5FlJkJWY95ykBs5ZSWpVOFuV9nPqHbVrMbrnBeJhVwXlYWr7N10JTiVnb6jZRX2AWrU+4WReEL9bvePZ5BGUZiNYvucC0345TIbJgpujA2/0b0C/Rmrv4neHebN8TGse/epvzsRlMODTv/h6ZHPqB3uU6NBcTMrks22nWbbnIjm5Bau5V/FxtlVxbxTqSf1g9yLboN9Kep2WOUPUO4yrDkQydsk/fDSkCb0bFjEefUk5eaq90H/ZBc5uuzy9anv1znNYu9vfY3DNrlDvfjjyk1olc9QGaT95LTFHYGEPtYrb0ztvbhg/IUT5lHIRFvVRayGB2v/HgC8v/4ZeJ0v8abJXjMMlMm8I1Ep3w30fg3/day/oFgCDvoJGQ+HXiZByHpY8qDaZ6vG2Ol+I8i4jAX5/T33d+dXLTfGGfgcLe6lB57cD1BF2XHyKXk9FZcpQaxSc3aYOFzx0idps8kpanfpfvbj+p0CNM3JSITUK0qLITYkk8sIZ4iLPkZ14CZecWAI0SfiRjIPGSiBJBGqSLi+fDhzPe+RzcAL3IDo0fZS3BgzkheUH+WzbaQLdjYxoewP9Od1hJGivKHJzIOHU5aD7ymDcNi3Zflpx1dbtaMC9Ul5QHnZFUF5VDdKdvEo/kNNqwa+2+mjyCKBWT3/lu32sPqiW7reo6s0HgxtTydPJbtGaAW6seLotIxb+zbHoNAbP38nnw5rRpkbRJeznEzKZt/UUy/deJDdv2DUvZ72t9LxRqCeNQzztq9uXMQedlg8ebISDVsPKfZeYsHQfuVYr9zUuZpi34gTeBfd/Aj+Phypt1GC9csvSyfSN6vEWnN6sdg7z9+fQ6qmyzU95lXJRrZWQnVeN7c850Of9ss2TEKJ0pUbBor5qwO5VVb3ZGrlP/e53eEltVlTCG5uKOYtzP79JpX8/xQUzWYqB962D2ZR0P5VWpRLqfYjK3s6Eejmrz95OeDoX8jtYsyuM3ak2Y9oxT23WdGozdJ0GTUfc2mZUQtysbW+rtTsDGqg3oPI5e8MjP8KCbmpB0pIH4NFVd1b/Otmp6o228zvU9uoPfQ9h99zcOjUaYkxGtkW4sPWEO9tPmEjL8QSa5s+mcagnnWv50KWyltrOGWjToyAtMq+0Piqv5D7vkZUEuVmQeAY2vsagzrnEdL+fd9cdZ/qvRwhwd6Rng5sstKrgJGivKFIuwqdtrn85rUNeRxLe6rNz3rOTF3hWuVxy7hF6c8N33SSrVWHHmQReWHaAqJRsHLQanutaizEdqqMrohp6oIcj3z/Zmie+2cOus4k8uvBvZj/Y2FYin+9MXDqfbDnNT/sv2cZIb1vDh/Gda9Kyqjeacj4OrYNOy3sPNEKn1bB870We+34/uRaFgc1Cbm7Fdw2E+gPKzzi87kFw71RY8wL89jI4OMLdI8s6V+VLVjIsHgSpl8A1ENKjYd+3akeBMs69EHeG9Fj4pp/659WzMoz4FVz81OvingVq8HFxt1rqXkyJ4Jmdq3Da8BJVLZEA/KE0ZKr5Mc5Y/SExhzOJhd/cD/Fyok11H9pU96V1dR8C8vtsMbio/Y40eFAdMityH/z6HBz4Hvp+WHypvRBlIf6U+t0B6P46x2IzmPLTvzjqdQS6OxLk6UTtxp/Qbeej6C/txfzdMBweXorm6iYjFVFWklr1/9JeMHqoNyhCm9/QqnItVvZfSGbL8Vi2HIvjSFSq3XxvFwPta/rSqY4/7Wr64V2gEOyqzo+vZM5Sg/d/f4TNr8PmmTzdy5PIli0J33WeZ77fj6+bkeZhJagFcIeSoL2icPICZ1/7oNvpitfOhU3zVu+olZOgzGpViE7N5lx8BucSMolIyOBsfAYRCZlEJGbYemSv6uvCh4Mb0yjUs9h1ejjpWfRYC57/4QCrD0Ux4bt9xKZm83i7apyKTePjzaf45UAkebE6HWr5MeHeGjSrUrG+9DqthncGNsRBq2Hp7gs8v+wAJ2LSeLF77ctD392IcnJu2Nw9CmKPqj+uvz6rVr1q+0xZ56p8MGfD0ofVKnxuQWoTghWj1Tvnf82FHrPKOodCiJuVkQDf3AfxJzC5BPN7iwUc/ycbd6doWreYTvXQFmh+fU6tlTS/HTywqNA/4JcizhC9fCLN0rYAEKN48VfN5+nc/0k2OOmJTs3mfEImF5IyuZCoPs4nZnIhKYu4tBwuJmXxw56L/LBH7Yiqhr9rXhDvQ8uqPngFNYTHN8HfX6gjklzYCZ+1g3uehXYvlGkhgBAFbJiq9ttTszvple5hzEfbOZeQWSBZE81zLDG8gdPZTfwycwAfuU0kwNMZP1cjfm5G/N0c854vv3d3cii/hT8Z8fDt/RB9SI0Phq0s0QgQFqtCpimXLJOFtJxc9p9XA/XfT8SRmp1rl7ZRiAcdavvTqbYfDUM8iyxoK5beCbyrqTU/c3Pg93fRrHmBmQO+JDYtjA1HYnh80R6Wj2lNmHcJri+Kov4/qnIDBZ7llATtFYWzN7x0uqxzUYDFqpCcaSIxw0RChvp85UOdlkNsag7nEzMLbUOez0Gr4YG7Q3m1d13bUGkl4ajXMXdoE/zcjHz91zleX32UXw9GceBism3s9Hvr+DP+3po0LsGNgPJKq9XwZv8GuDvp+fz3M8z//QwHLiYzd2hT/NyMZZ290qHVqh0cObrDHx+oP7TZqWr7s/L6o3g7WK2w8kmI+EPtX+Lh5Wrnj+1fhMUDYM9CdWQAV7+yzqkQFVdmonrN0WjApyb41gLfmmqttJL024I6TKjFqpCb/7BYMVsUcq1Wci0Fp+XkWrmUlMW5hAxiY2N4/MwEquWeIUbx5MHEF4j4JQ6Is63fz82XAZU+ZmzcDNxTz6Es7Imm+5vQYjRoNCSlZbHzh3dod/5TKmmysCga/vQZSI3Bs+gfcHno0EqeTlTydKI1BUvq03Ny2XMukR2nE/jrdAL/RqZwKjadU7HpfLMjAo0G6gW55wXxA2gxuicuG/8Hx9fA7+/CvyvUDmVD75w/y6ICO/cHHF8NGh1K1xm8uvIQ5xIyCfZw5NmutYhNzSYqJf/hzovJz/Oh9W36abYTlezOrPiHr7l6g4PWFtRfHdBf+d7X1XjDw5eZLVYuJmYSfe4o2ed24ZCbiUdAFQJCquNXqTpaZ8+C/5HSotUbgHHHwMUfhv9Mrm8dzsSkcSQylcORKRyPSScly0xmTi6ZJguZJvX5Wv/VPZz0tK/lR6fafrSv5Yev6y34/9lpslpDYPeXaH96ko8HhTM03ZN/ziczYuFuvh9dTE2BtBi14Of4GrUPp7p9Sz+PZUCCdmHHlGslKdNEQnp+0J1DYoaJpCuC8iuD8+RMk60UuyQctBoqeztTxceZKj4uVPV1oYqPM1V9XQj2dEJ/g6XGWq2G1/rWI9DDkbfWHrP1ON+tXgAT7q3JXZVK1kldeafVanilV10ahXjy0vID7DyTSJ+525n3cNMKV3ugSBoNdJmmBqebpsP299QS9x5vV8w2k4qi3jU2pav7YbWCT/WS34RQFLXn5iM/qT09D16s9kkAaicywU0h8h+1V+eu02/ZbghxOymKQnpOLjGpOZgtVvQ6DTqtFgetBgedBgetNm+aBn1GJIbMeLShzW54e7mZKWR8eR8eiQcLzDPjQJQ2kIvaSkRoKxFBMGeVIE4rlUiwuuQF4moQbrZcxw/iFdzI5FvDm1TTniFOcech02QyXKrQ3NeZUG9nolOy2RuRRFxaDvOPORLOq7yt/5ze/A1rX+T8gc3843c/tQ68RU/OggZOG+qg6fMB7RteX/DsanSgY21/OtZWg/yUTDM7zybkBfHxnIhJ53BkKocjU/li+1l0Wg2NKo3l0Vod6HHhfYyJp+GbfugaDsGgtLuh4yFEqbBaYd0r6utmI1h23oWf9p9Gp9Xw0dAm3F1oVet2mPaGwqqnedJhNb2rWEnQ+ZKSayDJrCfB5EBcjo7YbB0JJgcyLUYyU4ykpRiJURzJwkAWRnLQA/a/817O+kIDer8rAvuEdBNn4zO4FBODJvIfvBIPUDX7KI00J6mqSbu8sis6cMvCkRRDAGaXIHReIbj4heF2ciXaxNNkGP2ZH/Ih25YncSxq3TUD8qtpNOCs1xHm60Kn2v50quNHoxDPm6vhWdIN93xX7bvn0DKMK0bw9QM/cP8qF87EZ/D4N/8wIq+T//zfiri0HGJTs9EfXUn9AzNxNKeQq3Hg4tlThN0hrXY0iqLc2C/MHSQ1NRUPDw9SUlJwd3cv1XWbzWbWrFlDr1690Ov1pbruG6EoCgcvprDhSAxxaTm2kvD8YDztqmovJeXhpMfHxYB33sPH1YCX8+XXvq5Gqni7EOzpeMu/7L/9G82fp+J5qGVl6gaV7ud5o27FeXAqNo0xi//hVGw6DloNr/auy6NtwspvNa0bsftLWP0CoKgdx/T7uMQlXjfNYoactMsPU3re61TISS96mm16at77dLCa7dftX1+t9n/XANAVcz78+RFsmKK+HrgAGgyyn398LXw3RG0K8+whu15fy9v1R5SN4s6DLJOFgxeTOXQphZxcKxoN6DQatBoNGg1oNWpwrNWA5qrX6jzy0qrTdVe8zl82fz1ajQZtXnpFgYSMHGJSc4hJzb7iob7PNFmuuV9arDymW8sLDj/gqDGzymck1nteoGOdADycSna+n45LZ8Xfp+m4ZyzNlUMkKa4stnQhTBNNdU0UVTVROGlMRS6fqLhyRgnmjDWIM4r6OK0Ec14JwHxFuYjtBoNWq9500GnRa9Xnqm5WZqROpUrWv5gMnpzvt4zAmk1xvarGWbbZwr7zyew4k8DO0wnsu5DIMNbyssMS9JrLxypN40L03ZOo2XPcLRmFIzYtm51nEtlxOp6/TicQcUU1YzcymWT4gYe0G9CikKV1RdvrLYzNHvlv15b6DyvT36EDS9VaagY3zj60nV4LjpNltvBi99qM7VTj2sv+OUeteXODLGjJ0RjJVIxkKOpzJupzFpdfZ1/xOgc9NTSRNNGeopbmIlqNfYhmwoFLjjVJ03nhnBWNtyUWb03Roz9dsPrxkPkVLiiXR3hwMeioG+RO/WB36ga54+tqxNmgw8mgw8XogJNefXY26DA6aMv2P6XFrDYLPLkOjB5ED1hOn2WpxKfn4GVQcHFxJj49h2yzFR9SmKlfSC/d3wD8aw3jefMYhvTpwchy3vN8SeNQCdr5bwTtqdlmft53ie/+vlCg44iraTXYgm9vFwM+LsYCwbiPiwFvV3Wal7PhhkvI/ytu1XmQkZPLSz8etPW2369RMLMGNLiu5gXl3oHv4aenQLFAnT7q0EMOpVQdK+G02hNyamTB4Dw3u3S2cSWDq/ojlD+yg0dlaD0Wmg4rvKfag8tgxePq625vQJtxBdMoitqWNOYQtH8JOk+2zSov1x9Rtq48DxwcHIhIyGTfhST+iUhm34Ukjkal2TrpLG/cjA4Y9TosedXLzXnPVZSLvKufT1PtKbv0C3O786Z1OC2r+dG1XgBd6wUQfNXoI+k5uaw5GMUPey6wLyKeT/Uf0k23lwwc+a7OJ3jWbGULsh004JoTg3vGOVzTz+KcehbntLM4ppzGkBFZZL4VjQ7FKwx8aqDxrYnGt2Zelfuaaqdy+X+ETRkQ/gBE/AmOHmqv1UGNSnRsMk257I1I4vz+zfQ4+jI+1gTOh/Qj5MH30LrfvmHYLiZlsuO0WhL/5+l4YlJzaKI5ySz9l9TRXgDgsGMTDjV+jbsaNKVukPuNt3sVt4fFDAd/UKsoe4SozbE8Qu3P3RIqtd8hixnObVdrnFVpW3zNO1MmfHw3pF7C3Gkqff5pzvGYNO6p4cs3j7VAW9w5qChq9erI/ep45KaMK56zrpqWCeYM9fnqm/Q3Id0pmCz/phjCWuJWvTXa4IZ2/3/MFivnoxO4dP4k8ZfOkhkfgTX5Ao6Z0WRbdXxvHIhPperUD1aD9HpB7oT5uBS/7+WJKVPtSO/8X+Dix4ney+m/NJqMK27q9tTu4g39V3hr0shFx28+w/in8mP4uLvQrqYvDUM8yy7/JSBB+3W4U4N2RVH453wS3/19gV8PRto6ejM4aOleP5DaAa545wXkVwbjHk76ivWFrgBu5XmgKApf/XmOWWuOkmtVqBXgymePNKOan2upbudWUhS1k8Lj0WmcicugXU1faga4XU5wbA0sG6EGu9U6wZDwmx+O5dI/6vBJmQnXTufgCEY39WFwVavtG93A6Fr0NKMbGNwuvza6qum0urx2Wgtg12eQkddW1ckbWjyhPvJ7gz6zVe0p3mqGVmOhx5tF5/HwT7DsUbVn2OcOqX/+kaBdqM7FpfLuD1vJdA7gwMVUEjMKlhz7uxlpUtkTTycDVkXBoigoClgVBauidiSqvlbf57fdtualURTy3l9ezpKX1smSTpDlIkHmS4RYLlLJepEQyyV8lQSOGRqw2/d+kgLbEujhjL+7kQB3RwLdHfF3N+JsuOoGpCUXdsxF2TILjSUHxehGTueZxCUlE7pzGsD/27vv+KiqvI/jnynplYR0Qu8dQZqgiJQAogK7Fqy77roq+ohlXXHdta69rFhWXSPorgVdsQCKIAhYAAGpAZEqLQUS0ttM5j5/XDJkSAJJSMiEfN+87mvu3HvmzLnhZDK/expzy4Zxr+MmnMdaunsmhDK6Wyw9E0JZuCWNBZtTKSwtw4KL53xeY7LtO8qsvrimfoxPx/Nr/oMtLTBv/GXuMGenztwBR34xj5VW3/qFf9jxAD5rN+xfbX6GXPcpJNSxi39JnjnrfGSHur2+nhiGwZ4jBfywK5OVv6TSccdb3GKdi7/FQbHhw0znJOb4XMaADjEM7dCSoR0i6RgdfHb1EGssLhcsvM/sfTX0dhjw+7r1TDu4zlwONiOl8jmbH4QlmAF8WKIZ0FcM6kMTKk1CeFp/h1xl5pj0LR/Dts/Nv58ALbvAkFuh95XVT3q44hlzFvKwRP7eejbvrEmnZbAvX9wxnOiQBpwoscxRIZA/MdgvNAP+qoJ9x7GbAeGtodW5kDAAQup2881Z5iKv2El4oM/Z8btVnAOzJ5gT6oW1ZvfEj/jvil+YMKgLXTf8g6Adn5nponvApH/V+Mant1DQXgtnW9CeXVjK3J8O8sGaffySfvyLQ+eYYK4a2JpJ/RKqXoNVGsyZqAdr9mYx7d2fyMgrIdjPzkOX9KBNZCCO8kmPylw4ylyUVth3ugxslmNdNY+NE7VZLeZ+he6bdtvxbp3l6cz98jGmx1/vY7Oc9I9ETqGDn9Ny+SU9j5/T8vglPY/taXkeM5KG+tv58OYhdI2t8Pu4exm8P9X8w9ZqIFz9kbl2cV3sWgpzrjW/WMf1MbuquwPv8kD82P6puq/XlaMINrxrzvx+dK95zCcQzrnOHKv+vxuhNA96TIIpb528VcHlglcHw5Ht5qR95//ZfAsF7c3a/qxCXl22k4/WHsBZoSXd12alR0Io57RuQb/W4fRr3YL4MP/T+3JX5oTsX+HIjmPB6w7I3Gk+FmSc+vUt2kL/30G/ayCoZdVpMrbBp7eaczgAdBxtLjMWdmz5y00fmr1yXE5+jRzGDNvdrNxfRFXfctpHBvJi+Pv0OjgHLDbzRmCXcXW58soMw1y6yP2zqBDQZ+8HTiiQb7A5q3PiwPp5fy/hcDiYv+ALunVsTfDS+2iVtQqA7a5WzHD8gZ+MzgC0DPZzz0w/tENLEiMCzo5A40wyDPjyXvjxjePHonvAuKegXQ3nFSgtgG8eh1WvguEybya3vwByDprLDuelUqnuViUoqkJAn0hZSBzrdmbQ78JL8IlsB4GRJ2+td7ngwI9moJ7yqefnR1CUuZJK6bGx3YEtzUkYz/2D5+dGXjq8dA6U5rP+3GeY9G0CFgu88/uBDO+kCVubpPzDMCsJMnditOzMT0EXcs6RuVgKDpuf4cPvMnsbNsFl+hS018LZELQbhsHqPVl88OM+vtiSRumxiSb8faxc3Dueqwa25pzW4fpD2EjOVD3IyCvmtvfW8+OerAZ7j5qwWS3YrceC+grBvKPM4Eh+1esC26wW2rc0J3Xac6SAqBA/PvrTENq2rNCivn8NvDvFvOsa1dUc417b9UY3/w8+udlswW53gTmxm38jzn3gKoOtn5mz5aedMAlW2+Hmmqo1GQ6w6UNzCbiACHNsu1+wgvZmal9mIa98s5OPfzoerHcKdfHb87pxbrtIuseH4mevw1hnwzCXEMrcWTkwP7rHXFKpOsExx1qXOx5vZfYPN7+Yb/wASnLMdDZf6HaJ2UrYZqj55b7MAd//E5Y/DWWlZo+SpCeg79TKX/5/WQQfXgfOIkgcTOal/2HJnhIWbU1nW2ou53WM5PIBifTf/SqWFc8AFpj8BvS+vPY/j7pwFJmt6+UBffZ+80ZdqwFn5v3PII/PH7sdNn+EsfA+LIWZGFhYEjSB+3ImccTpOXwhIfzYGvEdIxnSviWxYVo+7pS+fsj8G4LF/N1JmXu8VbrHJBj9qNkaXp1d38C8O8wbbwC9Ljd/xyoGwmUOcyhZzn4ziC9/zK7w3FF5GbVK7AHHW+iPBfaEJ5oB+N4VsOUTyD1wPH1AC/MzoecUaDvMvLnw0ztmb7Wc/cfy9Ic+V5q90qI6m9eybjYl0X05N/0+cktc3DqiA/cmda3NT1W8TfY+eCsJcg8ePxbVFS77FySc03jlOk0K2muhKQftmfklfPzTAT74cT+7jxS4j3ePC+WqQa25tG88of76st7YzmTw5Chz8c+vf2H+plSsluPBs4/teBBtPjdb1Q3DcC8/VN4i73QZJ+y7PGZHLis/5jLqNBa2VYsAusSE0Dk2hK6xIXSOCaF9VBB+dhs5hQ6ueGMlP6flkRAewMe3DPX80pa2Bf4z6fjd93OuN2ebD6xqFtgTrH4dvvwLYJhfZCa9Xn/j40+XYcDub+C7f8Ke5RDTE25YUPPeBGVOeOVcMyAY/Sic938K2puZXzMLeHnpTuauP+j+vRzeqSXTBwbju+pluvUZgD0kxmzpCow0v5D7h1fuxeEoNuvRiYF55g7zhll17AEQ2dEzMI/saG4nuzFWWmgGGWvfMrvmlmvZBfpeBSmfQOpG81jnJLj4nxAad5IfxEp47wrzRkBMT/PGV0js8fM/vAyLjs39MP5Zs6VO6l2Vnz+FWbDob7DhvwAYwbHs6P8A8x0DWbk7k/X7sj16hQC0jwpiaIdIhneK4vxOUQT41v/kek3aimdh6aMAFI15lnVRk3AVHqHtphdJ3PUBFlw4bf783OEPpLS9nhKLL2UuA38fG2Hk02frMyTsnQuAIzie7JFPY+k8hiBfO/4+tZiIzDDMGwUVA/qc/biO7iP71y20sOZjyU+vWV6+IdDtYugxGdqPqLr1tMxprqqy8mU4tP748Q4jzZ55hov7wp7mg/RW9G/Tgjk3DW74Wc/PEmVlZTgc9Tc2v15l7cGYexMUZ+Pqey22wTd7feu6j48PNlv1n1sK2muhqQXtLpfBD7syeX/NPhalpLmXmAnytXFJX7NVvVdCmFrVvcjZHDy53GsSnxj0Hw/0y5dEAmgTGUjIKW4kHc4r4bev/cDezEI6Rgfz4Z+GEBFU4UO54Ig5q+uGd83ngZEw+hHoM7XqbuSGYY5t+/ZZ8/m5fzS7DTbAzMr14uivZstkdWP1qvPTf+Dz28w1WadvwoH9rK13ctzeIwW8/M1OPqkQrJ/fOYo7LupE/4QgXMmjsaZuqPrFFqvZkhUYaT7mpVbdhfv4C8yWsRMD85adICT+9JdlPLQB1s0yJ2F0HL8RjX84jHvabBGvyd+2tC3w38mQn252vb/2U4hoZ7bQfX67mWbk3+D8e06vvFKtk/7d2/OtuY5y5rGJBDsnwfhnKQiIY+2vR/lh1xFW7spk88Ecj+EN/j5WLugcRVLPWEbWYpWAtJxiVu4289yelkenmBB3d/wm3ZK/6l/mOHZgfuw07j4w3GNJr26WX3nI520GWX8GzNnEH3New1euAYyz/sgjPrOJsuTgMiy8UzaaZ5xXUMDxng9tIgOZ0CuOiX3i6RobUqfvlR71wOI61lp/oHKLfW4qRHc1W9Q7jq753z/DgF9/gJWvmBPHHfvs+rnFCJJSbyIswIcv7hhOwgkTUkplhmGQlpZGdnZ2YxflpAxXGcVFRfgHBjWZWCc8PJzY2Ngqy6ugvRaaStCekVvMR+sOMGfNfvZlHe+C1KdVGFcNbM3FfeIrLREj3uFsDtobyoGjhfz2tZWk5hTTKyGM9/44qHKw/+sPsOBuyNhqPk8cDBOeO76OOZh34xfcaX5ZB7jwAfOLehP5oD+RYRis2HGE5xdtp7C0jL9O6OZeT5kyB8zsZ34JGvc0jnN+r3rXhOUWOziQVUR2YSlHCx0cLSw9Yd9BVkEpmw/muIP1CzpHcceoTpzTuoWZyaK/wQ8zKbUFYm83DGvxUfOmV2HW8S7pVfELqxCYVwjQI9qDzxn48lucC5s/hA3vmUH32CdqPylT1m6zV87RveZNsEE3my2ShsucqGv0o032c6ApOOXfPUcxfPuc2a3b5QCfIHP1i4F/ck+gllPoYPWeTL7feYSvt2VwMLvI/XK71cKQDpGM7RHL+JaHiVj7AmTtgV5TONz1alYecrFyVyardmeyp0JPxBOVt+QP7dCSwe0jPW8QezHn2newzzdvQL3gmMKLZVMAaB0RSHigz/ElBq0wtGQFU7P/TUSZOfnpYZ94ohzm6gf7bYk87387a41OFJaUUVhaRpGj8nKLHaKCmNgnnot7x9MxuuYT3Z7R7z9HdsKqV8nav5WLf72KQ7Tk9Wv7M7ZH7KlfK6SmppKdnU10dDSBgYFeGxC7XC7y8/MJDg7Gero3ihuYYRgUFhaSkZFBeHg4cXGVe4kpaK+FphC0r96dydQ3V7u/mIX42bmsXwJXDkykR3xYfRVXGoiC9rrZmZHP5a+vJKuglEHtInj79wPx9zmhdbzMYY5t++YJs2XOYoPBt8CI+8Bqh4//AD/PN1sUL34B+t/QKNdSHzYfyOHJhdv4fqfnjPcTesXxt4u7my1Ga940b2SExOO4dQ1fLFqietfElDjL+PeK3by0dKdHq9nJXNglijtGdaZvYvjxgzuXmK3NwI/t/o9+U//uWQ+cpWZ31sIj5ioKhVkQHG22nNdhaSevlJcG/5nsORP2OdfBxJlnx/V5sRr/3cv42Wx137fSfB7Xx/z/ie/rkcwwDFIO5fJVShoLt6SxIyOfTpYD3Gn/n3tt5nIFhh8flo0guWwcB4xorBbomRDG4PaR9IgPZVtqHit3HWHzwRxOHOHVLS6UoR0iiTvWAm8YYFC+aoK5bxxbQcFcKaHCMY4fP9lrDI6vulAuKsSPxIhAWh/bWlQz83dGbjFrF7xJ0vYHsGLwhnMCT7uuZlyveK4f0ob+bVpUHWyVFpg3SL6faa7EYrXDsLvMm9gnDBNzuQzyip2s2HGYeRsPseyXw+65kgC6xoYcC+DjaBN58pVczvT3n7ScYsa9uIKjhQ5uGNqWhy7p0eDveTYoKyvjl19+ITo6msjIyMYuzkm5XC5yc3MJDQ31+qC9XGZmJhkZGXTu3LlSV3kF7bXQFIL2YkcZQ55YQvuoYK48N5EJveMqL4UjXktBe91tPpDDVf9eRX6Jk4u6RvPatf3xqWpcWs4BWDjDXBYGICQOQuPNMbI2P/hNMnSbeGYLX0/2ZRby7KLtfL7RbBnxtVm5dkgbAGb/sJcyl0Gwn527RnfmunNjsL98DuSl4hz3HAvSolTvmpBVuzP56yeb2XXYbBmMCPIlIsiXFoE+hAeajy0Cfd374YG+dIgK8lwiEczlv/51HhRkUHbO75hvXNh860HRUXjvSti/6thqDMneOzTmLFKrv3suF6x/xxz2VJxj3mQdfCuMmGEumXmiIzvI/+oxgnZ8hgUDl2Hhc9cQVrm6c51tMd2t5oRqLqxkJI4l+MK7CG5feXb+nCIHq3dnmkvU7cpke3pefVx6vQj2s9OqRYA7iG/VIoB1+7Ip2TKfV+wv4GMp43/WMewf/BhXD25DdGgNu5Nn7TEnZO06AWK61+glecUOFm9NZ97GQ3y744jHvAO9W4Vxce84JvSOr7IL+pn8/vPtjsPMmLuZA0eL6BEfytxbh9Zt0s1mqLi4mD179tC2bVsCArx7KEFTDNqLiorYu3cv7dq1w9/f83dVQXstNIWgHSCroLTJdNsSTwraT8/q3Zlc99aPlDhdXNo3nhcu74vVWk0r2Y7F8MU9x5dR8wuFq943Z51tYjLzS3hp6U7eXf0rjjIDiwUu65vAXaM7kxgRCEDKoRz++skWNuzPBqBHfChvdPqRhNWPYIS1Zl67hxg34ZIq693RglJW78lie1oe53WMZEDbGkzmJw0iM7+Ex7/4mY9/MmdNbhnsy98u7s4lfeJr30XR5YL3fgs7v4aobjh+t4gvFn/TvD9/nKWQvhni+p3+uHupkTr93ctLh69mmKsKgDl/woTnoPNY83nWHnMVgU0fmMMcALpfyuH+d/JlRgsOZhfRr1U4w2xbCF73L9i15HjebYaZwyI6jam2DhzOK2HV7kxW78kkr9iJBbBYLFgsYMF8tFbYNzfLsXTmceuxYxw7Zq14/oS8LJjnXYZBem4J+7MK2ZdVSFpucZXlO8+6mbd8nsXP4mB/4kRirpuNr8+ZbcDJLizlq5Q05m1M5YddRzx6KvRv08IM4HvFuW8inInvP9mFpTy2YBv/W2d+fiaEB/DuHwZ5rj4jJ1UetFcVVHqbphi0n+znW9M4VE21TYgCdmmuBrWP5F/XnMNN76zjsw2HCPG38+ilPasOZjqNhltXwfcvwr5VMOZRiO115gt9GgpLnbz13R5eW76b/BJzGa3hnVpy37iulYbD9IgPY+4tQ3l/zT6e+vJnUg7lMiq1HWuCWhCcs49WWSuBSwAzMPxxTxar92SxancmP6cdb1V64WuY2CeeGeO6Eq8Je84Yl8vgo3X7eeLLn8kudGCxwNSBrbl3bFfCAuv4BXfVq2bAbveH37x1Zsagezu7LyT0b+xSyKmExJh1ts9VMP8uyNkH710O3S8D/zBz8tHypQU7j4ML74e43kQB13WomFEcdB9tTki48mXY/BH8+p25texsrkDQ9rxKbx8V4sfEPvFM7BPf8NdaFZcLykooLi4iLSuHQ5m5pGflkH40l7Iju/jj4RfwdTmg68Uk/na2e+z/mRQe6MsV57bminNbcyS/hC+3pDFv4yHW7M1i3a9HWffrUR6Zv5VB7SK4uHc8o7q2PHWmdWQYBl9sTuPBz1M4kl+CxQLXD2nLPWO7aI4nOeuoRotIkzCyawzPX9GXOz5Yz39X7aPU6WJ091i6xoaQEB7g2fLuE2COaW9khmG4l8VzugzKygxKysrIzC/lcF4JR/JLOJxXcnz/2PND2cXuYL1HfCgzxnVjWKfqv/hYrRauHtSGsT1ieXzBNuauP8hLRUnM8HmfhIPzeGTe1az+NZdf0vMrvbZzTDCtI4JY8rPZ9fHrrencOqIDfzy/feX5A6Re/ZKex18/2cyaveZ6yt3iQvnHpJ7HJ5Gri0MbzDWbAcY+bnZ/9dale0Sq02k0TFsFy56Ala+aS3uV63ARXPhXaFWDmzCxPWHSa+ZKAT++DmtnwZFf4D+XweR/Q4/LGugCaiBrj7nM566l5qSqRdlgmBPA+QNtj22VdBxl3thohID9RC2D/bh2cBuuHdyGtJxivticyrxNh1i/L5tVu7NYtTuLB60WOoVYKYg5yITeCXW/GXmC9Nxi/vbpFhZtNZeR6xgdzFNTetG/jXqMydmp8X/jRURq6JI+8eQVO/jrJ1v4cO0BPlxrdoUL8rXRKSaELjEhdIk9vrUM9qPEWeaeZftoQSlZhebj0WPHsgtL3evUu4PrY0vUlbmqOO46ftzlwvN82QnnT2PwUasWAfx5bBcm9o6vfijACVoG+/H8FX35zYBWPP6JhaN584gpSyNv7Yf84jKHB3SNDWFQuwgGt49kYLsIIoPNCYi2HMzh4XkprNl7lOcW/8Kctft5YEJ3xvaI8doZZJuSYkcZR/JLyCooJTO/lFW7M0n+bg9Ol0Ggr407R3Xmd+e1Pb11hEvy4X+/N2fi7noxDPh9/V2AyJnmGwRjHoNel8NX94PNFy64F1oPrn1eYQnmsqDD74HPboVt8+CjGyD/aRh0U70XvUpF2bD3WzNI3/UNHN1z6tfYfM05WezHHtsOg4kvVpo4zhvEhvnz+2Ht+P2wdhw4WsiCTWYAv+VgLj/nWLn/0xQenLeV8ztFcXGfOEZ1iznl8q9VMQyDD9bs5/EvtpFX7MRutXDriA5MG9lR49ebuZUrVzJs2DCSkpJYsGBBlWnef/99rrnmGm6++WZeeeUVj3PLli3jwgsvdD+Pjo5m2LBhPPPMM7Rv3x6Atm3bMn36dKZPn95g11EdBe0i0qRcPagNkUG+fLklje1peew6nE9BaRkb9me7x3WX87Nbazz79plisUBEoC9RIX5EhfjRMth8jAr2o2WIL1HB/rQM8aVDVHDVE+7VwNAOLZk7PYl1/72OIXtf5aGgjxl/8c0M6BhX7TCbnglhfPinIczblMrjC7Zx4GgRN/93Hed1jOTBiT3ofOJEZ82co8zF0YJSjuSXkllQQmZ+KZkFpWTml++XcCS/9FiQXkJBaeUllABGd4/hoUt6VJ7AqSATvrjbXJ980J8gutupC/XlvZC1C0IT4JKXNDu6nB3iesMN8+snL/9Q+O3b8MWfYW0yfPlnyE8zW+Lr+/elzGFOhFoepB9ce3wcPpizt7caCB1GQvsRENbKDNLLA3SbT5P9HW7VIpA/XdCBP13QgR1p2fzz4xXsKAnjl4x8lvycwZKfM/C1WxnZJZqL+8Qxsmt0tZMrG4ZBkaOMrIJS0nKKeW7RL6zcba6g0qdVGE9O6U23uPqdj0qapuTkZG6//XaSk5M5dOgQ8fGVh7kkJydz77338vrrr/Pcc89VOX5/+/bthISEsGPHDm666SYmTpzIpk2bKs36fqYpaBeRJiepZxxJPc21Lh1lLvYeKWB7eh7b045t6Xnsyyp0B+xWC7QI9KVFkC8Rgb60CPI5NiO3ufn5WLFZLditFmxW67HH45vd/Xgsna2a4+XPbZ7HK56zWSw1bjk/Hb52KwMuv4+iF94n3JHOmLxPIGj6SV9jsVi4pE88o7pF869lu3h9xW6+35nJuBe/5drBbbhzVOd669robVwug+wiB5n5Je5APKs8KD8WiGcVlHLkWICeU1T7Lue+NiuRwb5EBvsSHeLPlecmMqaq9YPzD8M7l0DGVvP5ullml+Aht5qPVX2R3/SROd7XYjW7/Qaqi6hIlaw2c3K70DhY+pi5VnxemtmCbTuNzzfDgKzdx4P0PSug9ITZ6CM7mUF6hwvNVnO/s/9maNvIIMa0Mvjn+KHsySpm/sZDzN+Uyu4jBSxMSWNhShoBPjZGdosmMsjX7BVXWEpWgcPdO670hJvv/j5W7hnThd+d1w7bGfh72lyV3zBpDAE+tlr18svPz2fOnDmsXbuWtLQ0Zs+ezf333++RZs+ePfzwww98/PHHfPPNN8ydO5epU6dWyis6Otq9pvrf//53rr76anbu3EmXLl1O+7pOh4J2EWnSfGxWOsWE0CkmhIt7Hz9eWOokI7eE8EAfQv19zkig7HV8AtkW9xvO2fdv+PZ56HctBJ16/dVAXzt3j+nCb/sn8o8vtvJVSjqzf9jL5xsPcc+YLlxxbuJZ80UpI7eYf3+7m/dW76u2Nbw6VgtEBPnRMthcli0y2I/IIN9jz/2IDDb3I4P8iAj2JcTPfuovIXlp8PYlcGS7uWxhQn/Y/oU5C/auJRDVDQbfAr2vAJ9jLQRZe2D+neb++fdWOcGWiFRgscD5f4bgGJg33bzhlZ8Bl79tdsuvqcIsMzjftdQcn569z/N8QITZit7hQmh/IYQn1udVNDmdY0K4a0wX7hzdma2puczbmMr8TYc4cLSIBZtST/paX7uViEBfercK44EJ3WkdGXiGSt18FTnK6P73rxrlvbc+MrZWS1t/+OGHdO3alS5dunDNNdcwffp0ZsyY4fE3d9asWUyYMIGwsDCuueYakpOTqwzaKypf/q60tLRuF1KPFLSLyFkp0NdO25b6iNsfcR79ildiydgCK56GcU/V+LWtIwN5/doBfLfjCA/PS2FHRj73f7KZd1f/ykOX9ODcJrxE3IGjhby+fDdz1u73aMUJD/QhMsgMtMtbxd37FQLxiCA/wgPq+WZQzkF4e+KxLu6t4PrPIbKDuXzh6tfhp3fg8DaY93+w5BE490Y453r4+EazRa/1EDMQEZGaOec6CIo2x7fvXAyzL4arP4Kgaib+dJbCgTXHg/SDPwEVJi+x+phj7jtcaLaox/bREoNVsFgs9IgPo0d8GH9J6sLGAzks3ZaOgdkrLiKocs+42ra8SvOSnJzMNddcA0BSUhI5OTksX76cESNGAOYycbNnz+all14C4Morr+Tuu+92L8NWldTUVJ599lkSEhIavZUdFLSLiJzdLFbKRj2M/b0psOZNGHiTGQjWwrBOLfnijuH8Z+WvvPD1L6QcyuW3r63kkj7xzBjflbiwprOk2J4jBfxr2U7m/nQQ57GZAge0acG0kR0Z1rFlnecROG3Z+8yA/eheCGsNN8yDFm3Ncy3aQtIT5ooIP71jBvA5+2H5U+YG5nJYk//tFTNKizQpXZLg+nnm0nKHfoLkMXDNxxDRzuzyfmTH8SB9z7fgKPB8fVS340F6m6G1a6kXLBYLfRPD6ZsY3thFkRME+NjY+sjYRnvvmtq+fTs//vgjn3zyCQB2u50rrriC5ORkd9C+ePFiCgoKGD9+PAAtW7Zk9OjRvPXWWzz66KMe+bVq1QrDMCgsLKRPnz58/PHH+Po2/rLb+usuInKWM9pdAB1Hmy1JXz8EV/yn1nn42Kz8flg7Lu0bz7OLtvPBmv18vvEQi7emM+3CDvxhuHcvEfdLeh6vfLOTeRsPuWf1P69jJLdd2InB7SMatwXn6F6YPdFck7pFW7h+ftXdaP3DYOjtMOgW2PY5rHzFnNwKzInnmnnXW5E6SzwXblwE/5ls9nRJHmMuO7d7GeQe9EwbFHWsy/uxCeRCG2lNd5EGZrFYatVFvbEkJyfjdDo9Jp4zDAM/Pz9efvllwsLCSE5OJisry93dHczW902bNvHwww9jrdAj5ttvvyU0NJTo6GhCQrxn3gnv/58QEZHTN/oRc0z0ts9h36q6LZsERAb78cTk3kwd2IaH5qWw7tejPLvo+BJxY7p71xJxv6TlMnNRCvO3ZrmPjewazbQLO9K/zWmsh15fMneZY9hzD0BEB3OW7FMFATY79JxsbgfXgaPInNRKROquZSczcH/3t5C+2RznDuZM7m2GHAvSL4SYnuryLuIlnE4n77zzDs899xxjxozxOHfZZZfx/vvv89vf/pbPPvuMDz74gB49erjPl5WVMWzYMBYtWkRSUpL7eLt27QgPDz9Tl1BjCtpFRJqDmO7Q7xqze/WiB+DGxae1nFCvVmH87+YhfL7xEI9/sY39WUX86T/rGN6pJX+/uDudGnmJuKMFpbzw9S+0WPNPXrZ/xDW+3fg59lLOHX8DPdrGNWrZ3I7sMLvE56VCy85mF92QKmaTP5mE/g1TNpHmKDQOfrfAnFXe7mcG6W2Ggk/TGQIk0pzMnz+fo0ePcuONNxIWFuZxbsqUKSQnJ1NcXExkZCSXX355pUaF8ePHk5yc7BG0n8rBgwfZsGGDx7E2bdrQokXDNgToVqGISHNx4V/BJ9CcSGnrp6edncVi4dK+CSy9ewTTLuyAr83KtzuOkPTitzwyb2udlkU7Xc4yF2//sJcRzy7ji5WbuNn2GQCDrdu4IeNJerx3Lsy7Aw6sNcerNpaMn2HWeDNgj+4ONyyofcAuIvXPPwzGPwNjHoOOFylgF/FiycnJjBo1qlLADmbQvnbtWu666y4mTZpUZS/AKVOm8Pnnn3PkyJEav+ezzz5Lv379PLYFCxac1nXUhFraRUSai5BYGPp/sPxJc2x7lwlgP/3JVYL87Px5bFcuH5DIYwu2sXhrOm99v4fPNhzknrFduHzAmVki7tsdh3l0/lZ+Sc8H4JmwxQSUlEJcX+h2Maz/rzl+fN1sc4vqZvY+6H0FBEfV/I0MA5zFUFpoTkrlKILSAnAUHj9WWmg+r3SsyNzf+x0UZkJML7jusxotxSciIiLHzZs3r9pzAwcOxDjFzfnLL7+cyy+/HIARI0acMv3evXtrXcb6oqBdRKQ5GXo7rJtlBq9r3oQht9Zb1m0ig/j3dQNY8cthHp6Xwq7DBcyYe2yJuIk9GNBAS8TtPVLAYwu28fW2dABaBPowY0QUv/l2oZngwvuh81gYdjf8+j2s/w9s/cxcPm3RX+HrB6FzEoQmVA6uy4Pv8qDcUWQ+Gq6TlKiG4vrCtZ9AYNNdOk9EREQanoJ2EZHmxC/YDGLn3WEuF9b3Kgio33FY53eOYuH083n7h728+PUOthzM5TevreSyvvHcN64bsWH+9fI+ecUOXv5mJ299twdHmYHNauG6IW2YflFnwn543Ayu4/pAp2OT01it0G64uY1/BrZ8DD/9x1zm6ef5dSuEzQ98A8En6NhjoLnkk09Ahf3A42l8AsxjAS2gyzgtDyUiIiKnpKBdRKS56XsNrHrNbGn+9jlz7GY987FZ+cPw9lzWL4FnFm7nw3X7+XTDIRZtTWfahR25bkgbQvx96pT3/qxC3lm5lzlr9pNb7ATMGwV/m9DNnACvMAt+fMNMfMFfqp5wzz8MBvze3NJTYOvn4HKcEGgfeywPtCsG3+UButV7l7kTERGRs4OCdhGR5sZmhzGPwru/gdWvw7l/MNcHbwAtg/146je9uXpwax76PIWf9mXzzFfbeXHJDi7oHMXFveO4qFsMwX4n/3NkGAY/7Mpk1vd7WfJzunsOufZRQTwwoRsXdok+PsnM6tegNN8cL95l/KkLGdPD3ERERES8kIJ2EZHmqOMoaHcB7FkOSx6F3yQ36Nv1bhXO/24eyqcbDvLKNzvZdbiAxVvTWbw1HT+7lZFdo5nQO46RXaMJ9D3+p6mw1Mncnw7y9g972ZGR7z5+fucofje0LRd0jsJacZK7omyzFwHABX8+rWXtRERERLyBgnYRkebIYjG7xb9+Pmz5n9lNvO15DfqWVquFyee0YlK/BLan5zF/YyrzNx1ib2YhX25J48staQT42BjZLZqxPWLZfCDbowt8kK+N3/RvxXVD29IhKrjqN/nxDSjJMWeG7zqxQa9HRERE5ExQ0C4i0lzF9YY+V8LG92H2eGg/AgbcaE6QZqvbePOasFgsdI0NpWtsKHeP6UzKoVwWbDYD+P1ZRSzYlMqCTanu9G0jA7luSFt+M6AVoScbB1+cCytfMfcv+LM58ZyIiIhIE6egXUSkORvzD7NL+S8LYfcycwuOhXOuM7fwxAZ9e4vFQs+EMHomhHHv2C5sPpjDgk2pfLM9g/jwAK4fUkUX+Oqs+TcUZ0PLztD9sgYtt4iIiMiZoqBdRKQ5C4qEqR+Y67ave9tcwzw/DVY8Dd8+C53Gml3nO17U4DOlWywWercKp3ercGaM71a7F5fkww8vm/vD79Gs7iIiInLWUN9BERExZ48f9SDcuRV+MwvaDgfDBb98Ce/9Fmb2NZeHy89o7JJWbW0yFGVBRHvoOaWxSyMiIiJSbxS0i4jIcXZf6DkZbpgP09bA4GngHw7Z+2DJI/B8N/joBtizAve6a/Wt4Ah8eZ+5HF2Z49TpSwvh+5nm/vB7zCXtREREpNlYuXIlNpuNCRMmeBzfu3cvFovFvYWEhNCjRw+mTZvGjh07PNLOnj2b8PDwM1jqmlPQLiIiVYvqDEmPw90/w2X/glbngssJKZ/A2xPh5QHmxG+FWfX3nju/hleHwOp/wZf3wr/Og13fnPw162ZB4REIbwO9L6+/soiIiEiTkJyczO23386KFSs4dOhQpfNff/01qampbNy4kccff5xt27bRp08flixZ0gilrb3Tao4oLS1lz549dOjQAbtdLRsiImclnwDoO9XcUjeZQfKmDyFzJ3x1v9kC32OSOfN8qwF1WxvdUQxLHoZVr5rPIzuZ3d2PbIf/XAbdJpqT5rVoc8LriuD7F8394Xc36Kz3IiIizYZhgKOwcd7bJ7BW3yXy8/OZM2cOa9euJS0tjdmzZ3P//fd7pImMjCQ2NhaA9u3bM3HiRC666CJuvPFGdu3ahc3m3XPh1CnSLiws5Pbbb+ftt98G4JdffqF9+/bcfvvtJCQkcN9999VrIUVExEvE9YaLX4DRj5iB+9q3IH2LuWzcxvchphcM+J3Z4u0XUrM807fCx3+AjBTz+cCbzPydxfDNE7DmTdg2D3YshmF3wnl3mDcSAH76D+SnQ1gi9LmqYa5ZRESkuXEUwuPxjfPe9x8C36AaJ//www/p2rUrXbp04ZprrmH69OnMmDEDy0kCf6vVyh133MGkSZNYt24dAwcOrI+SN5g6dY+fMWMGGzduZNmyZfj7+7uPjxo1ijlz5tQ4nyeeeIJzzz2XkJAQoqOjueyyy9i+fbtHmuLiYqZNm0ZkZCTBwcFMmTKF9PR0jzT79u1jwoQJBAYGEh0dzZ///GecTmddLk1ERGrCLwTOvRFu/g5u/Br6TAW7P6RvhgV3wXNdYd50s2W+OoYBq9+AN0aYAXtQFEz9CMY/YwblAS1g/NNw87fmxHjOYlj2BLwy0AzinSXw3QtmXsPuNMfji4iISLOSnJzMNddcA0BSUhI5OTksX778lK/r2rUrYI5793Z1amn/9NNPmTNnDoMHD/a4g9GjRw927dpV43yWL1/OtGnTOPfcc3E6ndx///2MGTOGrVu3EhRk3l258847WbBgAR999BFhYWHcdtttTJ48me+//x6AsrIyJkyYQGxsLD/88AOpqalcd911+Pj48Pjjj9fl8kREpKYsFkg819zG/gM2fmC2vmfuMLvRr5sFCQPMZeN6TALfQPN1+Rnw2TTYsch83nE0XPYqBEdXfo+YHnD9PHMs/aIHzEnx5lxjzhSfdwhC4qHfNWfumkVERM52PoFmi3djvXcNbd++nR9//JFPPvkEALvdzhVXXEFycjIjRow46WuNYxPqnqxF3lvUKWg/fPgw0dGVv1gVFBTU6qIXLlzo8Xz27NlER0ezbt06zj//fHJyckhOTua9995j5MiRAMyaNYtu3bqxatUqBg8ezKJFi9i6dStff/01MTEx9O3bl0cffZS//OUvPPTQQ/j6quVFROSMCIyAIbfC4Ftg77dm8L5tHhxca25fzYC+V0Nsb1j8Nyg4DDY/GPOo2SX+ZH8/LBZzVvvOY83W9e9nQtZu89ywO8Hud2auUUREpDmwWGrVRb2xJCcn43Q6iY8/3pXfMAz8/Px4+eWXT/rabdu2AdCuXbsGLWN9qFPQPmDAABYsWMDtt98OHL878eabbzJkyJA6FyYnJweAiIgIANatW4fD4WDUqFHuNF27dqV169asXLmSwYMHs3LlSnr16kVMTIw7zdixY7nllltISUmhX79+ld6npKSEkpIS9/Pc3FwAHA4HDkcNlheqhfL86jtfaVpUD6QxNGq9azXE3EalY934Ptb1b2PJ2X98ojnAiO6O89LXIbob1HRIk8UXhv8Fel6BbfnjUOagrNeVoN+taunzRxqD6p2A6kFT4XA4MAwDl8uFy+Vq7OKcVHnruGEYlJaW8s477/Dss88yevRoj3STJ0/m3XffJSkpCaDStblcLmbOnEm7du3o06ePx/n6/hm4XC4Mw8DhcFSa8K6mvxt1Ctoff/xxxo0bx9atW3E6nbz44ots3bqVH374oUbjB6ricrmYPn065513Hj179gQgLS0NX1/fSuvlxcTEkJaW5k5TMWAvP19+ripPPPEEDz/8cKXjixYtIjCw5t0xamPx4sUNkq80LaoH0hgav951hnaPEp27mbaZS4nO3cLelheyNe5yXGv3AHvqlq3vZebj4lMsCSeAN9QDaY5U7wRUD7yd3W4nNjaW/Px8SktLG7s4NZKXl8eCBQs4evQov/nNbwgLC/M4P2HCBN58802GDRsGwP79+wkKCqKoqIitW7fy2muvsXbtWubMmUNBQQFgzqVmGIa7Qbe+lJaWUlRUxIoVKyrNu1ZYWLMZ+usUtA8bNowNGzbw5JNP0qtXLxYtWsQ555zjbvWui2nTprFlyxa+++67Or2+NmbMmMFdd93lfp6bm0tiYiJjxowhNDS0Xt/L4XCwePFiRo8ejY+PliJqrlQPpDF4X727GJiByzBobbHQurGL00x4Xz2Q5kD1TkD1oKkoLi5m//79BAcHe0wy7o0MwyAvL4+QkBDef/99LrroIhITEyulu+qqq5g5c6a71fyyyy4DIDAwkDZt2jBixAjefPNNOnbs6H6Nv78/Foul3uPB4uJiAgICOP/88yv9fGt6g6DOi6t36NCBf//733V9uYfbbruN+fPns2LFClq1auU+HhsbS2lpKdnZ2R6t7enp6e519mJjY/nxxx898iufXb48zYn8/Pzw86s8/tHHx6fBPlAaMm9pOlQPpDGo3gmoHkjjUL0TUD3wdmVlZVgsFqxWK1ZrnRYXO2PKg3CLxcL8+fOrTTd48GCPrvQ18fvf/57f//73p1/IE1itViwWS5W/BzX9vajT/8oXX3zBV199Ven4V199xZdfflnjfAzD4LbbbuOTTz5h6dKllSYB6N+/Pz4+PixZssR9bPv27ezbt889dn7IkCFs3ryZjIwMd5rFixcTGhpK9+7da3tpIiIiIiIiIl6jTkH7fffdR1lZWaXjhmFw33331TifadOm8d///pf33nuPkJAQ0tLSSEtLo6ioCICwsDBuvPFG7rrrLr755hvWrVvH7373O4YMGcLgwYMBGDNmDN27d+faa69l48aNfPXVVzzwwANMmzatytZ0ERERERERkaaiTt3jd+zYUWUrdteuXdm5c2eN8/nXv/4FUGkNvVmzZnHDDTcA8MILL2C1WpkyZQolJSWMHTuWV189PvuwzWZj/vz53HLLLQwZMoSgoCCuv/56HnnkkdpfmIiIiIiIiIgXqVPQHhYWxu7du2nbtq3H8Z07dxIUVPP1/GoyvsDf359XXnmFV155pdo0bdq04Ysvvqjx+4qIiIiIiIg0BXXqHn/ppZcyffp0du3a5T62c+dO7r77bi655JJ6K5yIiIiIiIhIc1anoP3pp58mKCiIrl270q5dO9q1a0e3bt2IjIzk2Wefre8yioiIiIiIiDRLde4e/8MPP7B48WI2btxIQEAAvXv35vzzz6/v8omIiIiIiIg0W3Vep91isTBmzBjGjBlTn+URERERERERkWNqHLTPnDmTm266CX9/f2bOnHnStP/3f/932gUTERERERERae5qHLS/8MILXH311fj7+/PCCy9Um85isShoFxEREREREakHNZ6Ibs+ePURGRrr3q9t2797dYIUVERERERERqWjlypXYbDYmTJjgcXzv3r1YLBb3FhkZyZgxY1i/fr07zYgRI5g+fXqlPGfPnk14eHgDl7xmaj17vMPhoEOHDmzbtq0hyiMiIiIiIiJSY8nJydx+++2sWLGCQ4cOVTr/9ddfk5qayldffUV+fj7jxo0jOzv7zBe0jmo9EZ2Pjw/FxcUNURYRERERERFpZIZhUOQsapT3DrAHYLFYapw+Pz+fOXPmsHbtWtLS0pg9ezb333+/R5rIyEhiY2OJjY3l2Wef5bzzzmP16tWMHTu2vovfIOo0e/y0adN46qmnePPNN7Hb6zwBvYiIiIiIiHiZImcRg94b1CjvvXrqagJ9Amuc/sMPP6Rr16506dKFa665hunTpzNjxoxqA/+AgAAASktL66W8Z0KdIu41a9awZMkSFi1aRK9evQgKCvI4P3fu3HopnIiIiIiIiEh1kpOTueaaawBISkoiJyeH5cuXM2LEiEpps7OzefTRRwkODmbgwIHu46+++ipvvvmmR1qn04m/v3+Dlr2m6hS0h4eHM2XKlPoui4iIiIiIiDSyAHsAq6eubrT3rqnt27fz448/8sknnwBgt9u54oorSE5O9gjahw4ditVqpaCggPbt2zNnzhxiYmLc56+++mr++te/euQ9d+5cHn/88dO7mHpSq6Dd5XLxzDPP8Msvv1BaWsrIkSN56KGH3F0MREREREREpGmzWCy16qLeWJKTk3E6ncTHx7uPGYaBn58fL7/8svvYnDlz6N69O5GRkVXOCB8WFkbHjh09jkVHRzdYuWurVrPH/+Mf/+D+++8nODiYhIQEZs6cybRp0xqqbCIiIiIiIiKVOJ1O3nnnHZ577jk2bNjg3jZu3Eh8fDzvv/++O21iYiIdOnTwmiXcaqtWLe3vvPMOr776Kn/6058Ac+r8CRMm8Oabb2K11nr1OBEREREREZFamz9/PkePHuXGG28kLCzM49yUKVNITk4mKSmpkUpXv2oVae/bt4/x48e7n48aNQqLxVLlWngiIiIiIiIiDSE5OZlRo0ZVCtjBDNrXrl1Lbm5uI5Ss/tWqpb2qGfR8fHxwOBz1WigRERERERGR6sybN6/acwMHDsQwDAD3Y3WWLVtW5fEbbriBG264oa7Fq1e1CtoNw+CGG27Az8/Pfay4uJibb77ZY9k3LfkmIiIiIiIicvpqFbRff/31lY6Vr4knIiIiIiIiIvWrVkH7rFmzGqocIiIiIiIiInICTfkuIiIiIiIi4qUUtIuIiIiIiIh4KQXtIiIiIiIiIl5KQbuIiIiIiIiIl1LQLiIiIiIiIuKlFLSLiIiIiIiIeCkF7SIiIiIiIiJeSkG7iIiIiIiINFkrV67EZrMxYcIEj+N79+7FYrEQHR1NXl6ex7m+ffvy0EMPuZ/v2bOHqVOnEh8fj7+/P61ateLSSy/l559/dqdZvnw5I0eOJCIigsDAQDp16sT1119PaWlpg16fgnYRERERERFpspKTk7n99ttZsWIFhw4dqnQ+Ly+PZ599ttrXOxwORo8eTU5ODnPnzmX79u3MmTOHXr16kZ2dDcDWrVtJSkpiwIABrFixgs2bN/PSSy/h6+tLWVlZQ10aAPYGzV1ERERERESaFMMwMIqKGuW9LQEBWCyWGqfPz89nzpw5rF27lrS0NGbPns3999/vkeb222/n+eefZ9q0aURHR1fKIyUlhV27drFkyRLatGkDQJs2bTjvvPPcaRYtWkRsbCxPP/20+1iHDh1ISkqq7SXWmoJ2ERERERERcTOKith+Tv9Gee8uP63DEhhY4/QffvghXbt2pUuXLlxzzTVMnz6dGTNmeAT+V111FYsXL+aRRx7h5ZdfrpRHVFQUVquV//3vf0yfPh2bzVYpTWxsLKmpqaxYsYLzzz+/bhdXR+oeLyIiIiIiIk1ScnIy11xzDQBJSUnk5OSwfPlyjzQWi4Unn3ySN954g127dlXKIyEhgZkzZ/L3v/+dFi1aMHLkSB599FF2797tTvPb3/6Wq666igsuuIC4uDgmTZrEyy+/TG5ubsNeIGppFxERERERkQosAQF0+Wldo713TW3fvp0ff/yRTz75BAC73c4VV1xBcnIyI0aM8Eg7duxYhg0bxt/+9jfee++9SnlNmzaN6667jmXLlrFq1So++ugjHn/8cT7//HNGjx6NzWZj1qxZPPbYYyxdupTVq1fz+OOP89RTT/Hjjz8SFxd3Wtd9MmppFxERERERETeLxYI1MLBRttqMZ09OTsbpdBIfH4/dbsdut/Ovf/2Ljz/+mJycnErpn3zySebMmcP69eurzC8kJISJEyfyj3/8g40bNzJ8+HAee+wxjzQJCQlce+21vPzyy6SkpFBcXMxrr71Wux9wLSloFxERERERkSbF6XTyzjvv8Nxzz7Fhwwb3tnHjRuLj43n//fcrvWbgwIFMnjyZ++6775T5WywWunbtSkFBQbVpWrRoQVxc3EnT1Ad1jxcREREREZEmZf78+Rw9epQbb7yRsLAwj3NTpkwhOTm5ypnd//GPf9CjRw/s9uOh8IYNG3jwwQe59tpr6d69O76+vixfvpy33nqLv/zlLwC8/vrrbNiwgUmTJtGhQweKi4t55513SElJ4aWXXmrQa1XQLiIiIiIiIk1KcnIyo0aNqhSwgxm0P/3001VOEte5c2d+//vf88Ybb7iPtWrVirZt2/Lwww+zd+9eLBaL+/mdd94JmK303333HTfffDOHDh0iODiYHj168Omnn3LBBRc03IWioF1ERERERESamHnz5lV7buDAgRiGAeB+rOj111/n9ddfdz9v2bIlL7744knfr1+/fvznP/+pY2lPj8a0i4iIiIiIiHgpBe0iIiIiIiIiXkpBu4iIiIiIiIiXUtAuIiIiIiIi4qUUtIuIiIiIiDRzVU3YJqevPn6uCtpFRERERESaKR8fHwAKCwsbuSRnp/Kfa/nPuS605JuIiIiIiEgzZbPZCA8PJyMjA4DAwEAsFksjl6pqLpeL0tJSiouLsVq9u/3ZMAwKCwvJyMggPDwcm81W57wUtIuIiIiIiDRjsbGxAO7A3VsZhkFRUREBAQFee2PhROHh4e6fb10paBcREREREWnGLBYLcXFxREdH43A4Grs41XI4HKxYsYLzzz//tLqbnyk+Pj6n1cJeTkG7iIiIiIiIYLPZ6iXIbCg2mw2n04m/v3+TCNrri3cPBBARERERERFpxhS0i4iIiIiIiHgpBe0iIiIiIiIiXkpBu4iIiIiIiIiXUtAuIiIiIiIi4qUUtIuIiIiIiIh4KQXtIiIiIiIiIl5KQbuIiIiIiIiIl1LQLiIiIiIiIuKlFLSLiIiIiIiIeCkF7SIiIiIiIiJeSkG7iIiIiIiIiJdS0C4iIiIiIiLipRS0i4iIiIiIiHgpBe0iIiIiIiIiXkpBu4iIiIiIiIiXUtAuIiIiIiIi4qUUtIuIiIiIiIh4qUYN2lesWMHEiROJj4/HYrHw6aefepy/4YYbsFgsHltSUpJHmqysLK6++mpCQ0MJDw/nxhtvJD8//wxehYiIiIiIiEjDaNSgvaCggD59+vDKK69UmyYpKYnU1FT39v7773ucv/rqq0lJSWHx4sXMnz+fFStWcNNNNzV00UVEREREREQanL0x33zcuHGMGzfupGn8/PyIjY2t8ty2bdtYuHAha9asYcCAAQC89NJLjB8/nmeffZb4+Ph6L7OIiIiIiIjImdKoQXtNLFu2jOjoaFq0aMHIkSN57LHHiIyMBGDlypWEh4e7A3aAUaNGYbVaWb16NZMmTaoyz5KSEkpKStzPc3NzAXA4HDgcjnotf3l+9Z2vNC2qB9IYVO8EVA+kcajeCageSP072+pUTa/Dq4P2pKQkJk+eTLt27di1axf3338/48aNY+XKldhsNtLS0oiOjvZ4jd1uJyIigrS0tGrzfeKJJ3j44YcrHV+0aBGBgYH1fh0AixcvbpB8pWlRPZDGoHonoHogjUP1TkD1QOrf2VKnCgsLa5TOq4P2K6+80r3fq1cvevfuTYcOHVi2bBkXXXRRnfOdMWMGd911l/t5bm4uiYmJjBkzhtDQ0NMq84kcDgeLFy9m9OjR+Pj41Gve0nSoHkhjUL0TUD2QxqF6J6B6IPXvbKtT5T2+T8Wrg/YTtW/fnpYtW7Jz504uuugiYmNjycjI8EjjdDrJysqqdhw8mOPk/fz8Kh338fFpsP/8hsxbmg7VA2kMqncCqgfSOFTvBFQPpP6dLXWqptfQpNZpP3DgAJmZmcTFxQEwZMgQsrOzWbdunTvN0qVLcblcDBo0qLGKKSIiIiIiIlIvGrWlPT8/n507d7qf79mzhw0bNhAREUFERAQPP/wwU6ZMITY2ll27dnHvvffSsWNHxo4dC0C3bt1ISkrij3/8I6+99hoOh4PbbruNK6+8UjPHi4iIiIiISJPXqC3ta9eupV+/fvTr1w+Au+66i379+vH3v/8dm83Gpk2buOSSS+jcuTM33ngj/fv359tvv/Xo2v7uu+/StWtXLrroIsaPH8+wYcN44403GuuSREREREREROpNo7a0jxgxAsMwqj3/1VdfnTKPiIgI3nvvvfosloiIiIiIiIhXaFJj2kVERERERESaEwXtIiIiIiIiIl5KQbuIiIiIiIiIl1LQLiIiIiIiIuKlFLSLiIiIiIiIeCkF7SIiIiIiIiJeSkG7iIiIiIiIiJdS0C4iIiIiIiLipRS0i4iIiIiIiHgpBe0iIiIiIiIiXkpBu4iIiIiIiIiXUtAuIiIiIiIi4qUUtIuIiIiIiIh4KQXtIiIiIiIiIl5KQbuIiIiIiIiIl1LQLiIiIiIiIuKlFLSLiIiIiIiIeCkF7SIiIiIiIiJeSkG7iIiIiIiIiJdS0C4iIiIiIiLipRS0i4iIiIiIiHgpBe0iIiIiIiIiXkpBu4iIiIiIiIiXUtAuIiIiIiIi4qUUtIuIiIiIiIh4KQXtIiIiIiIiIl5KQbuIiIiIiIiIl1LQLiIiIiIiIuKlFLSLiIiIiIiIeCkF7SIiIiIiIiJeSkG7iIiIiIiIiJdS0C4iIiIiIiLipRS0i4iIiIiIiHgpBe0iIiIiIiIiXkpBu4iIiIiIiIiXUtAuIiIiIiIi4qUUtIuIiIiIiIh4KQXtIiIiIiIiIl5KQbuIiIiIiIiIl1LQLiIiIiIiIuKlFLSLiIiIiIiIeCkF7SIiIiIiIiJeSkG7iIiIiIiIiJdS0C4iIiIiIiLipRS0i4iIiIiIiHgpBe0iIiIiIiIiXkpBu4iIiIiIiIiXUtAuIiIiIiIi4qUUtIuIiIiIiIh4KQXtIiIiIiIiIl5KQbuIiIiIiIiIl1LQLiIiIiIiIuKlFLSLiIiIiIiIeCkF7SIiIiIiIiJeSkG7iIiIiIiIiJdS0C4iIiIiIiLipRS0i4iIiIiIiHgpBe0iIiIiIiIiXkpBu4iIiIiIiIiXUtAuIiIiIiIi4qUUtIuIiIiIiIh4KQXtIiIiIiIiIl5KQbuIiIiIiIiIl1LQLiIiIiIiIuKlFLSLiIiIiIiIeCkF7SIiIiIiIiJeqlGD9hUrVjBx4kTi4+OxWCx8+umnHucNw+Dvf/87cXFxBAQEMGrUKHbs2OGRJisri6uvvprQ0FDCw8O58cYbyc/PP4NXISIiIiIiItIwGjVoLygooE+fPrzyyitVnn/66aeZOXMmr732GqtXryYoKIixY8dSXFzsTnP11VeTkpLC4sWLmT9/PitWrOCmm246U5cgIiIiIiIi0mDsjfnm48aNY9y4cVWeMwyDf/7znzzwwANceumlALzzzjvExMTw6aefcuWVV7Jt2zYWLlzImjVrGDBgAAAvvfQS48eP59lnnyU+Pr7KvEtKSigpKXE/z83NBcDhcOBwOOrzEt351Xe+0rSoHkhjUL0TUD2QxqF6J6B6IPXvbKtTNb0Oi2EYRgOXpUYsFguffPIJl112GQC7d++mQ4cOrF+/nr59+7rTXXDBBfTt25cXX3yRt956i7vvvpujR4+6zzudTvz9/fnoo4+YNGlSle/10EMP8fDDD1c6/t577xEYGFiv1yUiIiIiIiJyosLCQqZOnUpOTg6hoaHVpmvUlvaTSUtLAyAmJsbjeExMjPtcWloa0dHRHuftdjsRERHuNFWZMWMGd911l/t5bm4uiYmJjBkz5qQ/rLpwOBwsXryY0aNH4+PjU695S9OheiCNQfVOQPVAGofqnYDqgdS/s61Olff4PhWvDdobkp+fH35+fpWO+/j4NNh/fkPmLU2H6oE0BtU7AdUDaRyqdwKqB1L/zpY6VdNr8Nol32JjYwFIT0/3OJ6enu4+FxsbS0ZGhsd5p9NJVlaWO42IiIiIiIhIU+W1QXu7du2IjY1lyZIl7mO5ubmsXr2aIUOGADBkyBCys7NZt26dO83SpUtxuVwMGjTojJdZREREREREpD41avf4/Px8du7c6X6+Z88eNmzYQEREBK1bt2b69Ok89thjdOrUiXbt2vG3v/2N+Ph492R13bp1IykpiT/+8Y+89tprOBwObrvtNq688spqZ44XERERERERaSoaNWhfu3YtF154oft5+eRw119/PbNnz+bee++loKCAm266iezsbIYNG8bChQvx9/d3v+bdd9/ltttu46KLLsJqtTJlyhRmzpx5xq9FREREREREpL41atA+YsQITrbinMVi4ZFHHuGRRx6pNk1ERATvvfdeQxRPREREREREpFF57Zh2ERERERERkeZOQbuIiIiIiIiIl1LQLiIiIiIiIuKlFLSLiIiIiIiIeCkF7SIiIiIiIiJeSkG7iIiIiIiIiJdS0C4iIiIiIiLipRS0i4iIiIiIiHgpBe0iIiIiIiIiXkpBu4iIiIiIiIiXUtAuIiIiIiIi4qUUtIuIiIiIiIh4KQXtIiIiIiIiIl5KQbuIiIiIiIiIl1LQLiIiIiIiIuKlFLSLiIiIiIiIeCkF7SIiIiIiIiJeSkG7iIiIiIiIiJdS0C4iIiIiIiLipRS0i4iIiIiIiHgpBe0iIiIiIiIiXkpBu4iIiIiIiIiXUtAuIiIiIiIi4qUUtIuIiIiIiIh4KQXtIiIiIiIiIl5KQbuIiIiIiIiIl1LQLiIiIiIiIuKlFLSLiIiIiIiIeCkF7SIiIiIiIiJeSkG7iIiIiIiIiJdS0C4iIiIiIiLipRS0i4iIiIiIiHgpBe0iIiIiIiIiXkpBu4iIiIiIiIiXUtAuIiIiIiIi4qUUtIuIiIiIiIh4KQXtIiIiIiIiIl7K3tgFEKmNrOIsipxFtPBrQYA9AIvF0thFqpP80nzSCtJILUglrTCN1PxU0grSyC7J5pIOl5DULqmxiygiIiIiIl5AQbt4LZfhYk/OHtZnrGd9xno2ZGxgX94+93k/mx8t/FvQwq+F+VhhP9AeSIGjgAJHAfmOfPId+RQ6Csl35JvHSvMpdBYCYLVYsWLFYrGY+xYrFo7vu49ZLFipvO9x/oS8ytNZsJCan8rsL2aTXpBOniOv2uv+9uC3FDoLmdxpcoP/jEVERERExLspaBevUewsJiUzxSNIzy3N9UhjwYKP1YdSVyklZSWkFaSRVpDWSCWug+zju2F+YcQGxhIXFEdsUCyxQbHszd3Lpzs/5aEfHsJutXNJh0saragiIiIiItL4FLQ3E2WuMpyGkzJXGQ6XgzKjzOO8BUu1z0/WBf3Ecx6vq7DvcDk4XHSYjMIMDhceJr0w3WP/cNFhMosyMTA88vO3+dMrqhd9o/rSL7offaL7EOITQpGziKziLI4WH+VoyVHzscJ+kbOIIJ8ggn2CCfI1H4N9gj2OBdoDAbNF38AwHw3z0cXx/TKjzNzH5ZGm/DXu8ye8rjxNmasMZ5mTzZs2M2rwKFqFtSI2MJZAn8BKP0/DMPCz+TFn+xz+9v3fsFvsjG8/vtqfv4iIiIiInN0UtDcRhwsPM+PbGe6A2+ly4nQ53fuVjh8L0h0uB2WuskrBsLdqGdCSftH93FuXiC74WH0qpQv0CSTQJ5BWIa0aoZS153A48N3uy5C4Ifj4VL6echaLhfsH3Y/T5eTjHR9z/3f3Y7PaGNt27BksrYhI81ToMIdN+dv9sVqa11y9LsNFVnEWhwsPExUYRcuAlo1dJBEROUZBexPhdDlZnba6sYtxWsL9wokOjCYqMIqYwBiiAqKIDow29wPN/Uj/yCY7uVx9sVqs/H3I33G6nHy26zPuW3Efdqudi1pf1NhFExFpMIZhkFWcRXphOukF6aQVppFekE6+I5/SslJKXaXmY5k5PMq97yrBgoUwvzDCfMMI8wsj3C/c4zHML4wgnyCyirM4UnSEw4WHzceiwx77Rc4id3n8bH742fzwt/sTYA/A3+aPn92PAFsA/nZ//O3++Nn8PM8d2/c4d0K68teW79utDftVzDAMCp2FZBVnkVWcRUZhBukF6e6fc3rh8c3pcrpflxiSaPZwi+pDv+h+dAjv0OxuZIiIeAsF7U1EuH84Tw1/CrvVjs1qw8fqg91i7tutdmyWY8eO7dutds+tQtry/fLu6ye2whvG8eeVzlV8fkLjfcVzJ77OihUfW/UtzOLJarHy8NCHKTPKmL97Pvcsv4d/jvgnFyRe0NhFqxXDMMgtzXUPhcgsznQPGzixnhmGgYGB1WJlSNwQ4oLjGrHkImcPwzAoNoopKSvBbref9o1RwzBwGs5KAXRJWYk7sPYIqiuePxZ8l5SVUOwsNgPICsGjw+Wop6s+fSVlJZSUlVSaW6W+lc/V4mPzMR/LtwrPfW2+HoF+pUe7P8DxYWPFR839kqNkFWVR6iqtcVla+LfgaPFR9uftZ3/efj7f9TkAIT4h9I7u7R6u1qtlryqHeYmcDcp7njjKHNisNqwWKzaLDZvVhs1iPrdb7B6TEdcnwzAocBS4h35ml2STVZxFdnE2FouFVsGtaBXSioTgBIJ9g+v1vcU7KWhvIgLsAQ02tvnE8ewnPpXGYbPaePS8R3G6nCzcu5A7l93JSyNf4ryE86p9TYGjgLVpa1mVuorUglR6tuxJ/5j+9IzsWe83TUrKSjhcaM5TUGkrynAH6sVlxbXOO8AewH0D72NSx0nNvueFSG1lFmWSkplCypEUtmRuIeVICpnFmTw25zGsFiv+Nn93C3CAPcC972/zx2W43EF1dcF3qasUl+FqkLJbsNAyoCUxgTHEBMUQExhDqF8ofjY/fK2++Np8zX3b8X0fqw+GYZBTmkN2STY5JTnuzf28NIf80nxa+LegZUBLogKiaBloPkYFmF3By7uEW7BQXFZMsfPYVmG/qKyIEmcJxWXFFDmL3Dcfip2ezyumO/Fc+bHym9sGhnmzo4aBdV352/xp4d/C42cbExhDdFA0sYGxxATG0DKwJT5WH3JLc9l0eBMbMjawIWMDm45sIs+Rx/cHv+f7g98DYLPY6NyiM/2i+9E32gzkY4NiG/QaROqDYRj8mvsrhwoOeXyPKe95k16YTmZRJk7DeerMjnEH8tbjgXx5UO8R6B87b7NUfSzfke8O1Cv2fDmZcL9wEoIT3EF8q5BWtPRvSaGzkPzSfPIceeSV5rn380vzySvNo9BZSAu/FsQExRAXFOeeGLn8UTflvIuCdhEvZrfaeXz44zhdTr7e9zV3fHMHL1/0MoPjBgPmBH+bD29mVeoqVqWuYvPhzR5/ZJbsWwKYX9b6RPWhf0x/BsQOoFfLXu6WmRNVHNdY3gpWPolgemG6+3h2SXaNryPML4yogCgiAyKxW+1Yyv9ZLO6bRhYsYIH0gnS2ZW3jwR8eZMWBFTw45EFa+Leo409QpOnKLs4mpzTH3QvF3YPJwKO3SkZRBlszt7LlyBZSMlNOuqKGy3BR6Cx0L3lZH+xWu7srecVAuuKxisF2+Xl/m78ZoAfFEBsU6x425Q29shr6y6phmIF6sbMYh8uBo8xhPpZvZQ5zXpoyh/smyqluFrgMFxH+Ee4lUCP8I2jh14KIAPOxNtcU6hvKsIRhDEsYBphD9H45+os7iF9/eD1pBWlsy9rGtqxtvPfzewDEBMa4g/ieET0rTXor0lgMwyAlM4VFvy5i8d7FHMg/cMrXlPeCcRmuUwbwZUYZZUZZvfcWCrAHEO4X7rGsscPl4GDeQQ7kHyC7JNu9pWSm1Ot7h/qGEhcUR5BPkEcvyUoTL1cxCbN7/yQTOVd13sAgMSSRgbEDGRg7kAGxAwjzC6vX62qqFLSLeDkfqw9Pn/80dy27i2UHlnH7ktu5oecNbM3cytq0tZW+fCeGJDI4bjCJIYlsOryJdenrOFpylNVpq815ETaaefZq2Ys+UX3MFvOiw+6A/HDR4Rrf3fW1+hIdGH3SLSogqtobBFVxGS7eTnmbmetnsmTfEjYe3shj5z120h4GzdHOozspLiume2R3jTM9S5S5yth8ZDPfHvyW7w5+x9bMrXXKx4KFdmHt6NmyJ90ju9MlrAu7f9zN6DGjceKk2FlMobPQHQCWB37FzmJsVlu1LdruxxPOq/7VnsVicd/UaArsVjvdI7vTPbI7U7tNBSCtIM0M4g9vYH3GerZnbSe9MJ2FexeycO9CAHzwYd6SeZwTcw79ovvRO6o3ob6hjXkp0kQZhkFGYQZlRhlRgVFVTlJc1Ws2H9nMor2LWPzrYg4VHHKf87P5kRiS6P6e4v7OEhhFdIC5X97QUK58xaAyV5nHfnnAfuIxl+HC6XIeP17VsWN5OQ3zWJA9iHD/cFr4tSDcP5wAe8BJr7HAUcCBvAMcyD/gDuQP5h8kqyjLXDHJ11w9KcQ3hBDfEPd+sG8wgfZAsoqzSCtII7Ug1eMx35FPbmlugw8PqsrO7J3szN7Jez+/hwULXSK6cG7suQyKHcQ5Mefgb6n5d8qziYJ2kSbAx+bDcyOe445v7uC7g9/x2sbX3Oda+LVgUNwgBscNZlDcoEoz6huGwe6c3axNW8u69HWsTV/L4aLD/JTxEz9l/FTl+1mwEBkQSVSAOWlgxQkEK+6H+obWe/d1q8XK73r+jiHxQ/jLir+wO2c3N399M1d3u5rp50yv1Q2Ak8kpyWHxr4v5/uD3OA2ne9yor9XXvV8+rtTX6nv8sfzcsfGm5QFMTV9jt9R9THFOSQ4Ldi/g052fsi1rGwBxQXGMazeO8e3G0yWiS738bOTMySrO4vuD3/PtwW/54dAP5JTkeJwP9jHHKpb3RKnYQ6V8P8Q3hO4R3enRsgc9InvQLbIbQT5B7jwcDgcHLQcJ9gk+6eoVIrURGxRLUrskktolAebM+1uObGF9xno2HN7AxoyN5DnyWJu+lrXpawGz/nYI7+DuTt83qi+JIYkaBnWG5ZTkUOwsdvfmKF+F6MRHC5ZKcyeVd/U+cb6kivMplY/9tltqF2YYhsHRkqPsy93H3ty9Ho/78va5J4q0YCEqIIrYoNhKW1xQHKVlpXy972sW/7rYo+dRgD2A81udz+g2oxmeMLzWPWrKu73X5IbBmRLkE0SXiC71/vc/rzTPHcSXlpW6/+64x/Af27dYjh2jwn6F8+40eO5bLJbKxy1WXIaLbZnb+DHtR35M+5E9OXv4Oetnfs76mf9s/Q9Wi5VuLboRURRB6MFQzo0/t9mM6bcYFWeDaqZyc3MJCwsjJyeH0ND6vQPscDj44osvGD9+vL4sNWP1VQ9Kykp4bNVjHC46zODYwQyOH0znFp1r1dJlGAb78/azNn0tWzO3EuwTXKl1PDIg0iv+KBU7i3l+3fO8//P7AHQM78iTw5+s8x+nQkch3+z/hi/3fMn3h76vcY+C+lTeYtU/ur+79elkXb9chotVqav4ZMcnLNm3xN31rvxGQIGjwJ22Y3hHJrSfwPh244kPjtfnTz0qn3ytYjdmp+v481JXaZVdnD2eVziWU5LDqtRVbDmyxWPizhCfEIYmDGV4wnDOSzivXpbdUj2QxlBSWsLb898mtFsomzM3syFjA/vy9lVKF+kfSd/ovvSN6kvf6L50j+yOr823EUp8dipzlbEze6d7WMOGjA0czD94xt7fZrFhMSz42n3dEylXDOrLg3yLxUJ6YTp5pXknzctqsdaqC3qgPZALEi9gTJsxnJdw3ilbrsW7HC48zJq0Ne4gfn/efo/zVouVrhFdzeGfMQPoH9O/yXWnr2kcqqAdBe3S8FQPTs+3B77lb9//jcziTHysPvxfv//juh7X1ehmRWlZKd8e/JaFexaybP8yj4nxOrfozNi2Y4nwjzADr7JSd2BVHoSVz4btcfxY2tKyUpwu50nTlpaVVlpNoSILFjq26Mg50efQP6Y/50SfQ0xQDAfyDvDZrs/4bOdnpBakutN3adGFSZ0mMaHdBPzt/qw4sIIv9nzBigMrPL7I9IvuR1KbJNgOv734t8223u3O3s3K1JUUOgopcha5t+KyYoocRR5dxN3/d9UE3g2la0RXhiUMY3jCcHpH9a73JcD0+SONoap6l1mU6W6FX5+xnpTMlEq/W75WX3q07OEO4vtG9yXCP6IxLqFJKnQUsunIJrPHQ8YGNh3eRL4jv1I6u9XuXnWofEUiH5uPx3Ew5zMo38q7czuNqo/Vl7igOFqHtqZtaFvahLZxb/HB8dgsNnNpyIJ0j+7caYVp7ueOMgfnJZzHmDZjGJowtMkMQ5FTSytIY+WBlXz606dk+GZUmpvAgoVOLTq5A/gBsQO8/vNDQXstKGiXhqZ6cPqyirN48IcHWbZ/GQAJwQmE+4W7lzsqXzu54trIhwsPs3TfUvIcx+/ctw5pzbh24xjXbhwdwjuckbI7XU53QJhTksOGwxv4Kf0n1qWvY2/u3krpowOjySjMcD8P9Q1lQvsJTOo4iW6R3ap8j5ySHJbsW8KC3QtYk7bGfaPAho1LO17KTX1uIiE4oUGuz9vkl+bz1d6vmLtzLpsOb2qw9ykfAlHdMl2VzlU45mfzo09UH85LOI/owOgGKyPo80caR03qXUlZCdsyt7kDzA2HN5BVnFUpXZvQNnRu0dmc7T/QHH/cMqAl0QHmcK0Th2qVuco4WnKUI0VHyCzK5EjREXO/OJP80nz6RfdjZOuRTa5F7kROl5Nd2bvYfGQzW45sYcuRLezM3llpAsBAeyC9o3q7Jwns3bJ3vXcpNgzDPW67YqBfUlrC4qWLGX7BcCw2izvAL3OZk7ZVDPqjAqJIDEmst2Fwcnaq+NmSWZrpHvq5Ln0de3L2eKT9U+8/cVu/2xqppDVT0zhUY9pFpEmI8I9g5oUz+XjHxzy95mkO5h+scRe/6MBoktomMb7deLpHdj/j4yfLx/mBOZN+69DWXNLhEgCOFB1hfcZ6dxC//eh2MgozsGBhcNxgJnWaxMjWI0/ZUhDmF8bkTpOZ3Gky6QXmZFDzd83n56M/M3fnXD7f9TmXdLyEP/T6A4khiQ1+zWeaYRj8lPETn+z4hEW/LnKPfbRZbAyOH0xMYEyVS51VXO7Mz+ZX4yC8vDuniNSdn83P3ZoOx4dvrc9Yz/qM9Ww8vJGd2Tv5NfdXfs39tdp8fK2+RAVGEegTSFaRuT79yZYl/GTnJzyy8hEGxQ9ibJuxTSKANwyDA/kH2HJkiztI35a5rcplVeOC4tzzBvSL7kfH8I713oPnRBaLxezujt3j75XD7iDcGk5iSKJuGkq9iw2KZUL7CUxoPwEwv1OtS19nzuOUsY5zY89t5BLWHwXtItJkWCwWftP5N1yYeCE7s3dSUlbi7trssZ7ysUe71c75rc6nf0x/r53humVAS0a3Gc3oNqMBs5V4W9Y2WgW3Ii44rk55xgTFcH2P65naeSqvfv4qW4K3sCptFXN3zOWznZ9xcfuL+WPvP9ImtE19XkqjOFx4mM92fcanOz/1+FLfNrQtkztNZmKHifUyLlxEGp7FYqF1aGtah7bm0o6XAmYvok2HN7Evbx9Hio6Y62kfW+nkcNFhckpyKHWVVrqJa8FChH8EkQGRtAxoSaS/+Wiz2lh+YDk7ju5wrzv/yMpHGBQ3iDFtxzAycSTh/uGnLGv5sn0FjgIKHeYyioUOcytwHj924vkCR8HxtBUey2cUr3I7tiRWVYJ9gukR2YOeLXvSq2UverTsQWxQ7Gn/X4g0RS0DWjK27VjGth3b2EWpdwraRaTJiQyIJDIgsrGL0SCCfYPr9c5wW3tbbh15KylHU3ht02t8f/B7Ptv1GfN2z2N8u/H8sfcfaR/Wvt7e73RlF2ezLn0d6zPWc7TkqHtYQWlZ6fG5A8qPuUrZl7vP3RU0wB5AUtskJneaTJ+oPmoJFzkLhPmFMbzV8GrPl5SVcLjwMEeKjlDoKHT/fQj3C6+2dfmOc+5gT84eFv+6mEV7F7H96Ha+P/Q93x/6nkcsZgCfEJzgGWBXDMKPHTvT69D7WH3oGtGVni17ure2oW299qa0iNQfBe0iIs1A3+i+vDbqNTYf3szrm15n+YHlzN89nwW7F5DUNok+0X0ItAe6u467H23+7uPlx+qzm2XFrmxr09eyM3tnrfPoF92PSR0nMbbt2Fov4SMiTZufzY9WIa0qLXd6Ku3C2nFT75u4qfdN7M3Zawbwvy7i56yf+eHQD7XKq3yoTZBPEIH2QAJ9jm32QPexIJ8gAn0CPdKVHwu0B2K32t1LZJVvNovNveyaBQuhvqH42NTFXKQ5UtAuItKM9IrqxcsXvUxKZgqvb3zdXAJv75d8uffLGudht9rNL6m2AAJ8zMD+xGDfPV68wrnyzWW42Hh4I2vT11aaNAbMpev6x/QnITjBXN/+2DhyX5uv+3n5JHCxQbFn5Rh9ETlz2oa15Y+9/8gfe/+Rfbn7+Gb/NxQ6CwmyB3kG3xWC8fJjAfYAbFZbY1+CiJzlFLSLiDRDPSJ7MHPkTH7O+pn//fI/jhYf9Vj+zL00WoX98hnpnS4neaV55JEHRadXjorLswyINZdo8fblWUTk7NU6tDXX97i+sYshIuJBQbuISDPWNaIrDwx+4JTpyiddqiqgL3YWU1RWxbGKa6JXOOZ0OekS0cW9jqq3z9osIiIi0pgUtIuIyClZLBb8bH742fwUZIuIiIicQZpuUkRERERERMRLKWgXERERERER8VIK2kVERERERES8lIJ2ERERERERES+loF1ERERERETESyloFxEREREREfFSCtpFREREREREvJRXB+0PPfQQFovFY+vatav7fHFxMdOmTSMyMpLg4GCmTJlCenp6I5ZYREREREREpP54ddAO0KNHD1JTU93bd9995z535513Mm/ePD766COWL1/OoUOHmDx5ciOWVkRERERERKT+2Bu7AKdit9uJjY2tdDwnJ4fk5GTee+89Ro4cCcCsWbPo1q0bq1atYvDgwdXmWVJSQklJift5bm4uAA6HA4fDUa/lL8+vvvOVpkX1QBqD6p2A6oE0DtU7AdUDqX9nW52q6XVYDMMwGrgsdfbQQw/xzDPPEBYWhr+/P0OGDOGJJ56gdevWLF26lIsuuoijR48SHh7ufk2bNm2YPn06d95550nzffjhhysdf++99wgMDGyISxERERERERFxKywsZOrUqeTk5BAaGlptOq9uaR80aBCzZ8+mS5cupKam8vDDDzN8+HC2bNlCWloavr6+HgE7QExMDGlpaSfNd8aMGdx1113u57m5uSQmJjJmzJiT/rDqwuFwsHjxYkaPHo2Pj0+95i1Nh+qBNAbVOwHVA2kcqncCqgdS/862OlXe4/tUvDpoHzdunHu/d+/eDBo0iDZt2vDhhx8SEBBQ53z9/Pzw8/OrdNzHx6fB/vMbMm9pOlQPpDGo3gmoHkjjUL0TUD2Q+ne21KmaXoPXT0RXUXh4OJ07d2bnzp3ExsZSWlpKdna2R5r09PQqx8CLiIiIiIiINDVNKmjPz89n165dxMXF0b9/f3x8fFiyZIn7/Pbt29m3bx9DhgxpxFKKiIiIiIiI1A+v7h5/zz33MHHiRNq0acOhQ4d48MEHsdlsXHXVVYSFhXHjjTdy1113ERERQWhoKLfffjtDhgw56czxIiIiIiIiIk2FVwftBw4c4KqrriIzM5OoqCiGDRvGqlWriIqKAuCFF17AarUyZcoUSkpKGDt2LK+++mojl1pERERERESkfnh10P7BBx+c9Ly/vz+vvPIKr7zyyhkqkYiIiIiIiMiZ49VB+5lSvlR9Tafcrw2Hw0FhYSG5ublnxQyHUjeqB9IYVO8EVA+kcajeCageSP072+pUefxZHo9WR0E7kJeXB0BiYmIjl0RERERERESak7y8PMLCwqo9bzFOFdY3Ay6Xi0OHDhESEoLFYqnXvHNzc0lMTGT//v2EhobWa97SdKgeSGNQvRNQPZDGoXonoHog9e9sq1OGYZCXl0d8fDxWa/ULu6mlHbBarbRq1apB3yM0NPSsqFhyelQPpDGo3gmoHkjjUL0TUD2Q+nc21amTtbCXa1LrtIuIiIiIiIg0JwraRURERERERLyUgvYG5ufnx4MPPoifn19jF0UakeqBNAbVOwHVA2kcqncCqgdS/5prndJEdCIiIiIiIiJeSi3tIiIiIiIiIl5KQbuIiIiIiIiIl1LQLiIiIiIiIuKlFLSLiIiIiIiIeKmzJmh/4oknOPfccwkJCSE6OprLLruM7du3e6QpLi5m2rRpREZGEhwczJQpU0hPT3ef37hxI1dddRWJiYkEBATQrVs3XnzxxWrf8/vvv8dut9O3b99Tls8wDP7+978TFxdHQEAAo0aNYseOHe7zy5Ytw2KxVLmtWbPmpHkvW7aMc845Bz8/Pzp27Mjs2bM9zq9YsYKJEycSHx+PxWLh008/PWV5m6rmWg9SU1OZOnUqnTt3xmq1Mn369EppZs+eXSlPf3//U5ZZTq2p1zuAX375hUsvvZSWLVsSGhrKsGHD+Oabb06Z96ZNmxg+fDj+/v4kJiby9NNPe5xPSUlhypQptG3bFovFwj//+c9T5tlUNdd6UFxczA033ECvXr2w2+1cdtllldJU99mWlpZ2ynLLyZ0N9e6nn35i9OjRhIeHExkZyU033UR+fv4p89bnz3HNtR7o86fheHudmjt3LmPGjCEyMhKLxcKGDRsqpTlV+arjjZ8tZ03Qvnz5cqZNm8aqVatYvHgxDoeDMWPGUFBQ4E5z5513Mm/ePD766COWL1/OoUOHmDx5svv8unXriI6O5r///S8pKSn89a9/ZcaMGbz88suV3i87O5vrrruOiy66qEble/rpp5k5cyavvfYaq1evJigoiLFjx1JcXAzA0KFDSU1N9dj+8Ic/0K5dOwYMGFBtvnv27GHChAlceOGFbNiwgenTp/OHP/yBr776yp2moKCAPn368Morr9SorE1Zc60HJSUlREVF8cADD9CnT59q04WGhnrk/euvv9ao3HJyTb3eAVx88cU4nU6WLl3KunXr6NOnDxdffPFJv9Tk5uYyZswY2rRpw7p163jmmWd46KGHeOONN9xpCgsLad++PU8++SSxsbE1Km9T1VzrQVlZGQEBAfzf//0fo0aNOmkZtm/f7vEZFB0dXaOyS/Waer07dOgQo0aNomPHjqxevZqFCxeSkpLCDTfccNJ89fnjqbnWA33+NBxvr1MFBQUMGzaMp556qto0pypfVbz2s8U4S2VkZBiAsXz5csMwDCM7O9vw8fExPvroI3eabdu2GYCxcuXKavO59dZbjQsvvLDS8SuuuMJ44IEHjAcffNDo06fPScvicrmM2NhY45lnnnEfy87ONvz8/Iz333+/yteUlpYaUVFRxiOPPHLSvO+9916jR48elco2duzYKtMDxieffHLSPM8mzaUeVHTBBRcYd9xxR6Xjs2bNMsLCwmqcj9RdU6t3hw8fNgBjxYoV7jS5ubkGYCxevLjavF999VWjRYsWRklJifvYX/7yF6NLly5Vpm/Tpo3xwgsvnLS8Z5PmUg8quv76641LL7200vFvvvnGAIyjR4/WKB+pu6ZW715//XUjOjraKCsrc6fZtGmTARg7duyoNm99/pxcc6kHFenzp2F5U52qaM+ePQZgrF+/3uN4XcvnrZ8tZ01L+4lycnIAiIiIAMw7PQ6Hw+MuXNeuXWndujUrV648aT7leZSbNWsWu3fv5sEHH6xRWfbs2UNaWprHe4eFhTFo0KBq3/vzzz8nMzOT3/3udyfNe+XKlZXuLI4dO/ak19ScNJd6UFP5+fm0adOGxMRELr30UlJSUuolX/HU1OpdZGQkXbp04Z133qGgoACn08nrr79OdHQ0/fv3rzbvlStXcv755+Pr6+s+NnbsWLZv387Ro0drVL6zWXOpB7XRt29f4uLiGD16NN9//3295Cmemlq9KykpwdfXF6v1+FfSgIAAAL777rtq89bnz8k1l3pQG/r8OT3eVKdqoq7l89bPlrMyaHe5XEyfPp3zzjuPnj17ApCWloavry/h4eEeaWNiYqrt9vfDDz8wZ84cbrrpJvexHTt2cN999/Hf//4Xu91eo/KU5x8TE1Pj905OTmbs2LG0atXqlHlXlW9ubi5FRUU1Kt/ZqjnVg5ro0qULb731Fp999hn//e9/cblcDB06lAMHDpx23nJcU6x3FouFr7/+mvXr1xMSEoK/vz/PP/88CxcupEWLFifNu6p8K75vc9Wc6kFNxMXF8dprr/Hxxx/z8ccfk5iYyIgRI/jpp59OK1/x1BTr3ciRI0lLS+OZZ56htLSUo0ePct999wHmfC0ny1ufP1VrTvWgJvT5c/q8rU7VRF3KV/46b/xsOSuD9mnTprFlyxY++OCDOuexZcsWLr30Uh588EHGjBkDmONmpk6dysMPP0znzp2rfN27775LcHCwe/v2229r/d4HDhzgq6++4sYbb/Q4XjHfm2++ufYX1cyoHngaMmQI1113HX379uWCCy5g7ty5REVF8frrr9e6bFK9pljvDMNg2rRpREdH8+233/Ljjz9y2WWXMXHiRPeXpR49erjzHTduXJ2vrblQPfDUpUsX/vSnP9G/f3+GDh3KW2+9xdChQ3nhhRdqnIecWlOsdz169ODtt9/mueeeIzAwkNjYWNq1a0dMTIy71VWfP7WjeuBJnz+nrynWqZpoSp8t9Xc7w0vcdtttzJ8/nxUrVni0TsbGxlJaWkp2drbHHZf09PRKEwhs3bqViy66iJtuuokHHnjAfTwvL4+1a9eyfv16brvtNsC882QYBna7nUWLFnHJJZcwaNAg92sSEhLcX3bS09OJi4vzeO+qZkecNWsWkZGRXHLJJR7HK86KGBoa6r6uE2dBTE9PJzQ01N2tqDlqbvWgLnx8fOjXrx87d+6scx7iqanWu6VLlzJ//nyOHj3qrlOvvvoqixcv5u233+a+++7jiy++wOFwAMe7LFb3+VN+rrlqbvWgrgYOHFhv3V6l6dY7gKlTpzJ16lTS09MJCgrCYrHw/PPP0759ewB9/tRCc6sHdaXPn5rzxjpVEzUpX5P6bGnwUfNniMvlMqZNm2bEx8cbv/zyS6Xz5ZMR/O9//3Mf+/nnnytNRrBlyxYjOjra+POf/1wpj7KyMmPz5s0e2y233GJ06dLF2Lx5s5Gfn19t2WJjY41nn33WfSwnJ6fKCchcLpfRrl074+67767Rdd97771Gz549PY5dddVVzXYiuuZaDyqqbiK6EzmdTqNLly7GnXfeWev3EE9Nvd59/vnnhtVqNfLy8jxe27lzZ+Mf//hHtdddPllLaWmp+9iMGTMafbKWxtJc60FF1U0EVZVRo0YZkyZNqlFaqV5Tr3dVSU5ONgIDA086cZg+fzw113pQkT5/6pc316mKTjUR3anKdyJv/Ww5a4L2W265xQgLCzOWLVtmpKamurfCwkJ3mptvvtlo3bq1sXTpUmPt2rXGkCFDjCFDhrjPb9682YiKijKuueYajzwyMjKqfd+aznD45JNPGuHh4cZnn31mbNq0ybj00kuNdu3aGUVFRR7pvv76awMwtm3bVqPr3r17txEYGGj8+c9/NrZt22a88sorhs1mMxYuXOhOk5eXZ6xfv95Yv369ARjPP/+8sX79euPXX3+t0Xs0Jc21HhiG4f4/7t+/vzF16lRj/fr1RkpKivv8ww8/bHz11VfGrl27jHXr1hlXXnml4e/v75FG6qap17vDhw8bkZGRxuTJk40NGzYY27dvN+655x7Dx8fH2LBhQ7X5ZmdnGzExMca1115rbNmyxfjggw+MwMBA4/XXX3enKSkpcdfNuLg445577jHWr19f49mAm5LmWg8MwzBSUlKM9evXGxMnTjRGjBjh/j8v98ILLxiffvqpsWPHDmPz5s3GHXfcYVitVuPrr78+Zbnl5Jp6vTMMw3jppZeMdevWGdu3bzdefvllIyAgwHjxxRdPmq8+fzw113pgGPr8aSjeXqcyMzON9evXGwsWLDAA44MPPjDWr19vpKam1rh8VfHWz5azJmgHqtxmzZrlTlNUVGTceuutRosWLYzAwEBj0qRJHv+xDz74YJV5tGnTptr3rWnFcrlcxt/+9jcjJibG8PPzMy666CJj+/btldJdddVVxtChQ2tz6cY333xj9O3b1/D19TXat2/vcc3l56u6ruuvv75W79MUNOd6cKoyT58+3WjdurXh6+trxMTEGOPHjzd++umnWr2HVO1sqHdr1qwxxowZY0RERBghISHG4MGDjS+++OKUeW/cuNEYNmyY4efnZyQkJBhPPvmkx/nyO+AnbhdccMEp825qmnM9aNOmTZXlLvfUU08ZHTp0MPz9/Y2IiAhjxIgRxtKlS0+Zr5za2VDvrr32WiMiIsLw9fU1evfubbzzzjs1unZ9/hzXnOuBPn8ahrfXqVmzZlWZ94MPPljj8lXHGz9bLIZhGIiIiIiIiIiI1zkrZ48XERERERERORsoaBcRERERERHxUgraRURERERERLyUgnYRERERERERL6WgXURERERERMRLKWgXERERERER8VIK2kVERERERES8lIJ2ERERERERES+loF1ERERERETESyloFxERaeZuuOEGLBYLFosFHx8fYmJiGD16NG+99RYul6vG+cyePZvw8PCGK6iIiEgzpKBdRERESEpKIjU1lb179/Lll19y4YUXcscdd3DxxRfjdDobu3giIiLNloJ2ERERwc/Pj9jYWBISEjjnnHO4//77+eyzz/jyyy+ZPXs2AM8//zy9evUiKCiIxMREbr31VvLz8wFYtmwZv/vd78jJyXG32j/00EMAlJSUcM8995CQkEBQUBCDBg1i2bJljXOhIiIiTYyCdhEREanSyJEj6dOnD3PnzgXAarUyc+ZMUlJSePvtt1m6dCn33nsvAEOHDuWf//wnoaGhpKamkpqayj333APAbbfdxsqVK/nggw/YtGkTv/3tb0lKSmLHjh2Ndm0iIiJNhcUwDKOxCyEiIiKN54YbbiA7O5tPP/200rkrr7ySTZs2sXXr1krn/ve//3HzzTdz5MgRwBzTPn36dLKzs91p9u3bR/v27dm3bx/x8fHu46NGjWLgwIE8/vjj9X49IiIiZxN7YxdAREREvJdhGFgsFgC+/vprnnjiCX7++Wdyc3NxOp0UFxdTWFhIYGBgla/fvHkzZWVldO7c2eN4SUkJkZGRDV5+ERGRpk5Bu4iIiFRr27ZttGvXjr1793LxxRdzyy238I9//IOIiAi+++47brzxRkpLS6sN2vPz87HZbKxbtw6bzeZxLjg4+ExcgoiISJOmoF1ERESqtHTpUjZv3sydd97JunXrcLlcPPfcc1it5pQ4H374oUd6X19fysrKPI7169ePsrIyMjIyGD58+Bkru4iIyNlCQbuIiIhQUlJCWloaZWVlpKens3DhQp544gkuvvhirrvuOrZs2YLD4eCll15i4sSJfP/997z22mseebRt25b8/HyWLFlCnz59CAwMpHPnzlx99dVcd911PPfcc/Tr14/Dhw+zZMkSevfuzYQJExrpikVERJoGzR4vIiIiLFy4kLi4ONq2bUtSUhLffPMNM2fO5LPPPsNms9GnTx+ef/55nnrqKXr27Mm7777LE0884ZHH0KFDufnmm7niiiuIiori6aefBmDWrFlcd9113H333XTp0oXLLruMNWvW0Lp168a4VBERkSZFs8eLiIiIiIiIeCm1tIuIiIiIiIh4KQXtIiIiIiIiIl5KQbuIiIiIiIiIl1LQLiIiIiIiIuKlFLSLiIiIiIiIeCkF7SIiIiIiIiJeSkG7iIiIiIiIiJdS0C4iIiIiIiLipRS0i4iIiIiIiHgpBe0iIiIiIiIiXkpBu4iIiIiIiIiX+n+k5aKVfr6YbgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Construct Temporal PyG Dataset"
      ],
      "metadata": {
        "id": "yHdXqx-H2yhI"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xjihcgYj0dFH"
      },
      "outputs": [],
      "source": [
        "# Copied from PyG temporal rather than imported because the library has dependency issues with PyG\n",
        "# https://pytorch-geometric-temporal.readthedocs.io/en/latest/_modules/torch_geometric_temporal/signal/dynamic_graph_temporal_signal.html\n",
        "\n",
        "from typing import Sequence, Union\n",
        "import numpy as np\n",
        "\n",
        "Edge_Indices = Sequence[Union[np.ndarray, None]]\n",
        "Edge_Weights = Sequence[Union[np.ndarray, None]]\n",
        "Node_Features = Sequence[Union[np.ndarray, None]]\n",
        "Targets = Sequence[Union[np.ndarray, None]]\n",
        "Additional_Features = Sequence[np.ndarray]\n",
        "\n",
        "class DynamicGraphTemporalSignal(object):\n",
        "    r\"\"\"A data iterator object to contain a dynamic graph with a\n",
        "    changing edge set and weights . The feature set and node labels\n",
        "    (target) are also dynamic. The iterator returns a single discrete temporal\n",
        "    snapshot for a time period (e.g. day or week). This single snapshot is a\n",
        "    Pytorch Geometric Data object. Between two temporal snapshots the edges,\n",
        "    edge weights, target matrices and optionally passed attributes might change.\n",
        "\n",
        "    Args:\n",
        "        edge_indices (Sequence of Numpy arrays): Sequence of edge index tensors.\n",
        "        edge_weights (Sequence of Numpy arrays): Sequence of edge weight tensors.\n",
        "        features (Sequence of Numpy arrays): Sequence of node feature tensors.\n",
        "        targets (Sequence of Numpy arrays): Sequence of node label (target) tensors.\n",
        "        **kwargs (optional Sequence of Numpy arrays): Sequence of additional attributes.\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(\n",
        "        self,\n",
        "        edge_indices: Edge_Indices,\n",
        "        edge_weights: Edge_Weights,\n",
        "        features: Node_Features,\n",
        "        targets: Targets,\n",
        "        **kwargs: Additional_Features\n",
        "    ):\n",
        "        self.edge_indices = edge_indices\n",
        "        self.edge_weights = edge_weights\n",
        "        self.features = features\n",
        "        self.targets = targets\n",
        "        self.additional_feature_keys = []\n",
        "        for key, value in kwargs.items():\n",
        "            setattr(self, key, value)\n",
        "            self.additional_feature_keys.append(key)\n",
        "        self._check_temporal_consistency()\n",
        "        self._set_snapshot_count()\n",
        "\n",
        "    def _check_temporal_consistency(self):\n",
        "        assert len(self.features) == len(\n",
        "            self.targets\n",
        "        ), \"Temporal dimension inconsistency.\"\n",
        "        assert len(self.edge_indices) == len(\n",
        "            self.edge_weights\n",
        "        ), \"Temporal dimension inconsistency.\"\n",
        "        assert len(self.features) == len(\n",
        "            self.edge_weights\n",
        "        ), \"Temporal dimension inconsistency.\"\n",
        "        for key in self.additional_feature_keys:\n",
        "            assert len(self.targets) == len(\n",
        "                getattr(self, key)\n",
        "            ), \"Temporal dimension inconsistency.\"\n",
        "\n",
        "    def _set_snapshot_count(self):\n",
        "        self.snapshot_count = len(self.features)\n",
        "\n",
        "    def _get_edge_index(self, time_index: int):\n",
        "        if self.edge_indices[time_index] is None:\n",
        "            return self.edge_indices[time_index]\n",
        "        else:\n",
        "            return torch.LongTensor(self.edge_indices[time_index])\n",
        "\n",
        "    def _get_edge_weight(self, time_index: int):\n",
        "        if self.edge_weights[time_index] is None:\n",
        "            return self.edge_weights[time_index]\n",
        "        else:\n",
        "            return torch.FloatTensor(self.edge_weights[time_index])\n",
        "\n",
        "    def _get_features(self, time_index: int):\n",
        "        if self.features[time_index] is None:\n",
        "            return self.features[time_index]\n",
        "        else:\n",
        "            return torch.FloatTensor(self.features[time_index])\n",
        "\n",
        "    def _get_target(self, time_index: int):\n",
        "        if self.targets[time_index] is None:\n",
        "            return self.targets[time_index]\n",
        "        else:\n",
        "            if self.targets[time_index].dtype.kind == \"i\":\n",
        "                return torch.LongTensor(self.targets[time_index])\n",
        "            elif self.targets[time_index].dtype.kind == \"f\":\n",
        "                return torch.FloatTensor(self.targets[time_index])\n",
        "\n",
        "    def _get_additional_feature(self, time_index: int, feature_key: str):\n",
        "        feature = getattr(self, feature_key)[time_index]\n",
        "        if feature.dtype.kind == \"i\":\n",
        "            return torch.LongTensor(feature)\n",
        "        elif feature.dtype.kind == \"f\":\n",
        "            return torch.FloatTensor(feature)\n",
        "\n",
        "    def _get_additional_features(self, time_index: int):\n",
        "        additional_features = {\n",
        "            key: self._get_additional_feature(time_index, key)\n",
        "            for key in self.additional_feature_keys\n",
        "        }\n",
        "        return additional_features\n",
        "\n",
        "    def __getitem__(self, time_index: Union[int, slice]):\n",
        "        if isinstance(time_index, slice):\n",
        "            snapshot = DynamicGraphTemporalSignal(\n",
        "                self.edge_indices[time_index],\n",
        "                self.edge_weights[time_index],\n",
        "                self.features[time_index],\n",
        "                self.targets[time_index],\n",
        "                **{key: getattr(self, key)[time_index] for key in self.additional_feature_keys}\n",
        "            )\n",
        "        else:\n",
        "            x = self._get_features(time_index)\n",
        "            edge_index = self._get_edge_index(time_index)\n",
        "            edge_weight = self._get_edge_weight(time_index)\n",
        "            y = self._get_target(time_index)\n",
        "            additional_features = self._get_additional_features(time_index)\n",
        "\n",
        "            snapshot = Data(x=x, edge_index=edge_index, edge_attr=edge_weight,\n",
        "                            y=y, **additional_features)\n",
        "        return snapshot\n",
        "\n",
        "    def __next__(self):\n",
        "        if self.t < len(self.features):\n",
        "            snapshot = self[self.t]\n",
        "            self.t = self.t + 1\n",
        "            return snapshot\n",
        "        else:\n",
        "            self.t = 0\n",
        "            raise StopIteration\n",
        "\n",
        "    def __iter__(self):\n",
        "        self.t = 0\n",
        "        return self"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9D-3UwVo0dFG"
      },
      "outputs": [],
      "source": [
        "# Copied from PyG temporal rather than imported because the library has dependency issues with PyG\n",
        "# https://pytorch-geometric-temporal.readthedocs.io/en/latest/_modules/torch_geometric_temporal/signal/static_graph_temporal_signal.html#StaticGraphTemporalSignal\n",
        "import torch\n",
        "import numpy as np\n",
        "from typing import Sequence, Union\n",
        "from torch_geometric.data import Data\n",
        "\n",
        "\n",
        "Edge_Index = Union[np.ndarray, None]\n",
        "Edge_Weight = Union[np.ndarray, None]\n",
        "Node_Features = Sequence[Union[np.ndarray, None]]\n",
        "Targets = Sequence[Union[np.ndarray, None]]\n",
        "Additional_Features = Sequence[np.ndarray]\n",
        "\n",
        "class StaticGraphTemporalSignal(object):\n",
        "    r\"\"\"A data iterator object to contain a static graph with a dynamically\n",
        "    changing constant time difference temporal feature set (multiple signals).\n",
        "    The node labels (target) are also temporal. The iterator returns a single\n",
        "    constant time difference temporal snapshot for a time period (e.g. day or week).\n",
        "    This single temporal snapshot is a Pytorch Geometric Data object. Between two\n",
        "    temporal snapshots the features and optionally passed attributes might change.\n",
        "    However, the underlying graph is the same.\n",
        "\n",
        "    Args:\n",
        "        edge_index (Numpy array): Index tensor of edges.\n",
        "        edge_weight (Numpy array): Edge weight tensor.\n",
        "        features (Sequence of Numpy arrays): Sequence of node feature tensors.\n",
        "        targets (Sequence of Numpy arrays): Sequence of node label (target) tensors.\n",
        "        **kwargs (optional Sequence of Numpy arrays): Sequence of additional attributes.\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(\n",
        "        self,\n",
        "        edge_index: Edge_Index,\n",
        "        edge_weight: Edge_Weight,\n",
        "        features: Node_Features,\n",
        "        targets: Targets,\n",
        "        **kwargs: Additional_Features\n",
        "    ):\n",
        "        self.edge_index = edge_index\n",
        "        self.edge_weight = edge_weight\n",
        "        self.features = features\n",
        "        self.targets = targets\n",
        "        self.additional_feature_keys = []\n",
        "        for key, value in kwargs.items():\n",
        "            setattr(self, key, value)\n",
        "            self.additional_feature_keys.append(key)\n",
        "        self._check_temporal_consistency()\n",
        "        self._set_snapshot_count()\n",
        "\n",
        "    def _check_temporal_consistency(self):\n",
        "        assert len(self.features) == len(\n",
        "            self.targets\n",
        "        ), \"Temporal dimension inconsistency.\"\n",
        "        for key in self.additional_feature_keys:\n",
        "            assert len(self.targets) == len(\n",
        "                getattr(self, key)\n",
        "            ), \"Temporal dimension inconsistency.\"\n",
        "\n",
        "    def _set_snapshot_count(self):\n",
        "        self.snapshot_count = len(self.features)\n",
        "\n",
        "    def _get_edge_index(self):\n",
        "        if self.edge_index is None:\n",
        "            return self.edge_index\n",
        "        else:\n",
        "            return torch.LongTensor(self.edge_index)\n",
        "\n",
        "    def _get_edge_weight(self):\n",
        "        if self.edge_weight is None:\n",
        "            return self.edge_weight\n",
        "        else:\n",
        "            return torch.FloatTensor(self.edge_weight)\n",
        "\n",
        "    def _get_features(self, time_index: int):\n",
        "        if self.features[time_index] is None:\n",
        "            return self.features[time_index]\n",
        "        else:\n",
        "            return torch.FloatTensor(self.features[time_index])\n",
        "\n",
        "    def _get_target(self, time_index: int):\n",
        "        if self.targets[time_index] is None:\n",
        "            return self.targets[time_index]\n",
        "        else:\n",
        "            if self.targets[time_index].dtype.kind == \"i\":\n",
        "                return torch.LongTensor(self.targets[time_index])\n",
        "            elif self.targets[time_index].dtype.kind == \"f\":\n",
        "                return torch.FloatTensor(self.targets[time_index])\n",
        "\n",
        "    def _get_additional_feature(self, time_index: int, feature_key: str):\n",
        "        feature = getattr(self, feature_key)[time_index]\n",
        "        if feature.dtype.kind == \"i\":\n",
        "            return torch.LongTensor(feature)\n",
        "        elif feature.dtype.kind == \"f\":\n",
        "            return torch.FloatTensor(feature)\n",
        "\n",
        "    def _get_additional_features(self, time_index: int):\n",
        "        additional_features = {\n",
        "            key: self._get_additional_feature(time_index, key)\n",
        "            for key in self.additional_feature_keys\n",
        "        }\n",
        "        return additional_features\n",
        "\n",
        "    def __getitem__(self, time_index: Union[int, slice]):\n",
        "        if isinstance(time_index, slice):\n",
        "            snapshot = StaticGraphTemporalSignal(\n",
        "                self.edge_index,\n",
        "                self.edge_weight,\n",
        "                self.features[time_index],\n",
        "                self.targets[time_index],\n",
        "                **{key: getattr(self, key)[time_index] for key in self.additional_feature_keys}\n",
        "            )\n",
        "        else:\n",
        "            x = self._get_features(time_index)\n",
        "            edge_index = self._get_edge_index()\n",
        "            edge_weight = self._get_edge_weight()\n",
        "            y = self._get_target(time_index)\n",
        "            additional_features = self._get_additional_features(time_index)\n",
        "\n",
        "            snapshot = Data(x=x, edge_index=edge_index, edge_attr=edge_weight,\n",
        "                            y=y, **additional_features)\n",
        "        return snapshot\n",
        "\n",
        "    def __next__(self):\n",
        "        if self.t < len(self.features):\n",
        "            snapshot = self[self.t]\n",
        "            self.t = self.t + 1\n",
        "            return snapshot\n",
        "        else:\n",
        "            self.t = 0\n",
        "            raise StopIteration\n",
        "\n",
        "    def __iter__(self):\n",
        "        self.t = 0\n",
        "        return self"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PGE29GpL0dFH"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import torch\n",
        "from typing import Union\n",
        "import glob\n",
        "from natsort import natsorted\n",
        "import random\n",
        "\n",
        "# Fix random seed for ease of reproduction\n",
        "seed = 42\n",
        "random.seed(seed)\n",
        "torch.manual_seed(seed)\n",
        "\n",
        "# Dataset loader for SP500 stock prices\n",
        "class SP500CorrelationsDatasetLoader(object):\n",
        "    def __init__(self, corr_name, corr_scope):\n",
        "        self._read_csv(corr_name, corr_scope)\n",
        "\n",
        "    # Load a global correlation under the name corr_name\n",
        "    def _load_global_corr(self, corr_name):\n",
        "        return np.loadtxt(f'{workdir}/{corr_name}/global_corr.csv', delimiter=',')\n",
        "\n",
        "    # Load a local correlation under the name corr_name\n",
        "    def _load_local_corrs(self, corr_name):\n",
        "        _correlation_matrices = []\n",
        "        corr_files = natsorted(glob.glob(f'{workdir}/{corr_name}/local_corr_*.csv'))\n",
        "        for corr_file in corr_files:\n",
        "            matrix = np.loadtxt(corr_file, delimiter=',')\n",
        "            _correlation_matrices.append(matrix)\n",
        "        return _correlation_matrices\n",
        "\n",
        "    # Helper function for reading a correlation with type corr_name and scope corr_scope from CSV file\n",
        "    def _read_csv(self, corr_name, corr_scope):\n",
        "        match corr_scope:\n",
        "            case 'global':\n",
        "                self._correlation_matrices = [self._load_global_corr(corr_name)]\n",
        "            case 'local':\n",
        "                self._correlation_matrices = self._load_local_corrs(corr_name)\n",
        "            case 'dual':\n",
        "                # Stack global and local correlation matrices for dual correlation\n",
        "                global_corr = self._load_global_corr(corr_name)\n",
        "                self._correlation_matrices = [np.stack((global_corr, local_corr), axis=-1) for local_corr in self._load_local_corrs(corr_name)]\n",
        "            case None:\n",
        "                # None uses identity matrix as correlation\n",
        "                # Infer dimension from a global correlation matrix\n",
        "                global_corr = self._load_global_corr('pcc')\n",
        "                self._correlation_matrices = [np.eye(global_corr.shape[0], global_corr.shape[1])]\n",
        "\n",
        "        if corr_name == 'mi':\n",
        "            # Normalize MI to [0, 1]\n",
        "            max_mi = 0\n",
        "            for matrix in self._correlation_matrices:\n",
        "                max_mi = max(np.max(matrix), max_mi)\n",
        "                # MI shouldn't be negative\n",
        "                matrix[matrix < 0] = 0\n",
        "            for matrix in self._correlation_matrices:\n",
        "                matrix = matrix / max_mi\n",
        "\n",
        "        df = pd.read_csv(f'{workdir}/sp500.csv')\n",
        "        df = df.set_index('Date')\n",
        "        data = torch.from_numpy(df.to_numpy()).to(torch.float32)\n",
        "\n",
        "        # Round data size to nearest multiple of batch_size\n",
        "        self.days_in_quarter = 64\n",
        "        num_quarters = data.size(0) // self.days_in_quarter\n",
        "        num_days = num_quarters * self.days_in_quarter\n",
        "        data = data[:num_days]\n",
        "\n",
        "        # z-score normalization with training data following GERU\n",
        "        train_days = int(0.8 * num_quarters) * self.days_in_quarter\n",
        "        data = (data - data[:train_days].mean(dim=0)) / data[:train_days].std(dim=0)\n",
        "        data = data.numpy()\n",
        "\n",
        "        data = data[..., np.newaxis]\n",
        "\n",
        "        assert(not np.any(np.isnan(data)))\n",
        "        self._dataset = data\n",
        "\n",
        "    def _get_edges(self, times, overlap):\n",
        "        # Construct a fully-connected graph\n",
        "        def helper(corr_index):\n",
        "            return np.array(np.ones(self._correlation_matrices[corr_index].shape[:2]).nonzero())\n",
        "\n",
        "        if len(self._correlation_matrices) == 1:\n",
        "            _edges = helper(0)\n",
        "        else:\n",
        "            _edges = []\n",
        "            for time in range(0, self._dataset.shape[0] - self.batch_size, overlap):\n",
        "                if not time in times:\n",
        "                    continue\n",
        "                corr_index = max(0, time // self.days_in_quarter - 1)\n",
        "                _edges.append(\n",
        "                    helper(corr_index)\n",
        "                )\n",
        "        return _edges\n",
        "\n",
        "    def _get_edge_weights(self, times, overlap):\n",
        "        # Edge weights are the correlations between stocks\n",
        "        def helper(corr_index):\n",
        "            w = self._correlation_matrices[corr_index]\n",
        "            # Flatten the first two dimensions\n",
        "            return w.reshape((w.shape[0] * w.shape[1],) + w.shape[2:])\n",
        "\n",
        "        if len(self._correlation_matrices) == 1:\n",
        "            _edge_weights = helper(0)\n",
        "        else:\n",
        "            _edge_weights = []\n",
        "            for time in range(0, self._dataset.shape[0] - self.batch_size, overlap):\n",
        "                if not time in times:\n",
        "                    continue\n",
        "                corr_index = max(0, time // self.days_in_quarter - 1)\n",
        "                _edge_weights.append(\n",
        "                    helper(corr_index)\n",
        "                )\n",
        "        return _edge_weights\n",
        "\n",
        "    def _get_targets_and_features(self, times, overlap, predict_all):\n",
        "        # Given previous batch_size stock prices...\n",
        "        features = [\n",
        "            self._dataset[i : i + self.batch_size, :]\n",
        "            for i in range(0, self._dataset.shape[0] - self.batch_size, overlap)\n",
        "            if i in times\n",
        "        ]\n",
        "        # predict next-day stock prices\n",
        "        targets = [\n",
        "            (self._dataset[i+1 : i + self.batch_size+1, :, 0]).T if predict_all else (self._dataset[i + self.batch_size, :, 0]).T\n",
        "            for i in range(0, self._dataset.shape[0] - self.batch_size, overlap)\n",
        "            if i in times\n",
        "        ]\n",
        "        return features, targets\n",
        "\n",
        "    def get_dataset(self, batch_size, split) -> Union[StaticGraphTemporalSignal, DynamicGraphTemporalSignal]:\n",
        "        # Returning the data iterator where the train is designed for many-to-many predictions (each day predict next day's price)\n",
        "        # while the validation and test are many-to-one predictions (many past days predict tomorrow's price)\n",
        "\n",
        "        self.batch_size = batch_size\n",
        "\n",
        "        total_times = list(range(0, self._dataset.shape[0] - self.batch_size, self.batch_size))\n",
        "\n",
        "        # We do a 8-1-1 split for train-validation-test. Since the test set is one year apart from training,\n",
        "        # It is much more challenging to predict.\n",
        "        if split == 'train':\n",
        "            times = list(range(total_times[int(len(total_times) * 0)], total_times[int(len(total_times) * 0.8)]))\n",
        "            overlap = self.batch_size\n",
        "            predict_all = True\n",
        "        elif split == 'val':\n",
        "            times = list(range(total_times[int(len(total_times) * 0.8)], total_times[int(len(total_times) * 0.9)]))\n",
        "            overlap = 1\n",
        "            predict_all = False\n",
        "        elif split == 'test':\n",
        "            times = list(range(total_times[int(len(total_times) * 0.9)], total_times[-1] + self.batch_size))\n",
        "            overlap = 1\n",
        "            predict_all = False\n",
        "        else:\n",
        "            raise ValueError(f'Invalid split name: {split}')\n",
        "\n",
        "        _edges = self._get_edges(times, overlap)\n",
        "        _edge_weights = self._get_edge_weights(times, overlap)\n",
        "        features, targets = self._get_targets_and_features(times, overlap, predict_all)\n",
        "        dataset = (DynamicGraphTemporalSignal if type(_edges) == list else StaticGraphTemporalSignal)(\n",
        "            _edges, _edge_weights, features, targets\n",
        "        )\n",
        "        return dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-9rcAGsl0dFH"
      },
      "outputs": [],
      "source": [
        "# Helper function to get the dataset for a correlation\n",
        "def get_dataset(corr_name, corr_scope):\n",
        "    loader = SP500CorrelationsDatasetLoader(corr_name=corr_name, corr_scope=corr_scope)\n",
        "\n",
        "    lag_size = 64\n",
        "    # Train dataset has double the batch_size because it's trained under many-to-many prediction.\n",
        "    # During test time, the model is used for many-to-one prediction given batch_size previous days.\n",
        "    # Hence, we need to have a larger training batch_size than the lag_size during test.\n",
        "    train_dataset = loader.get_dataset(batch_size=lag_size * 2, split='train')\n",
        "    val_dataset = loader.get_dataset(batch_size=lag_size, split='val')\n",
        "    test_dataset = loader.get_dataset(batch_size=lag_size, split='test')\n",
        "\n",
        "    train_samples = list(train_dataset)\n",
        "    val_samples = list(val_dataset)\n",
        "    test_samples = list(test_dataset)\n",
        "\n",
        "    return {\n",
        "        'train_samples': train_samples,\n",
        "        'val_samples': val_samples,\n",
        "        'test_samples': test_samples,\n",
        "    }"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "93mt0lVh0dFH"
      },
      "source": [
        "# Differential Graph Transformer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9-REadVV0dFH"
      },
      "outputs": [],
      "source": [
        "# Adapted from reference implementation of Differential Transformer, included an optional A input to MultiheadDiffAttn.forward()\n",
        "# https://github.com/microsoft/unilm/blob/master/Diff-Transformer/multihead_diffattn.py\n",
        "\n",
        "import math\n",
        "import torch\n",
        "import torch.nn.functional as F\n",
        "from torch import nn\n",
        "\n",
        "class RMSNorm(nn.Module):\n",
        "    def __init__(self, dim: int, eps: float = 1e-6, elementwise_affine=True):\n",
        "        super().__init__()\n",
        "        self.dim = dim\n",
        "        self.eps = eps\n",
        "        self.elementwise_affine = elementwise_affine\n",
        "        if self.elementwise_affine:\n",
        "            self.weight = nn.Parameter(torch.ones(dim))\n",
        "        else:\n",
        "            self.register_parameter('weight', None)\n",
        "\n",
        "    def _norm(self, x):\n",
        "        return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)\n",
        "\n",
        "    def forward(self, x):\n",
        "        output = self._norm(x.float()).type_as(x)\n",
        "        if self.weight is not None:\n",
        "            output = output * self.weight\n",
        "        return output\n",
        "\n",
        "    def extra_repr(self) -> str:\n",
        "        return f'dim={self.dim}, eps={self.eps}, elementwise_affine={self.elementwise_affine}'\n",
        "\n",
        "\n",
        "def init_method(tensor, **kwargs):\n",
        "    nn.init.kaiming_uniform_(tensor, a=math.sqrt(5))\n",
        "\n",
        "def repeat_kv(x: torch.Tensor, n_rep: int) -> torch.Tensor:\n",
        "    \"\"\"torch.repeat_interleave(x, dim=1, repeats=n_rep)\"\"\"\n",
        "    bs, n_kv_heads, slen, head_dim = x.shape\n",
        "    if n_rep == 1:\n",
        "        return x\n",
        "    return (\n",
        "        x[:, :, None, :, :]\n",
        "        .expand(bs, n_kv_heads, n_rep, slen, head_dim)\n",
        "        .reshape(bs, n_kv_heads * n_rep, slen, head_dim)\n",
        "    )\n",
        "\n",
        "def lambda_init_fn(depth):\n",
        "    return 0.8 - 0.6 * math.exp(-0.3 * depth)\n",
        "\n",
        "\n",
        "# Differential Graph Attention with multiple heads\n",
        "class MultiheadDiffAttn(nn.Module):\n",
        "    def __init__(\n",
        "        self,\n",
        "        embed_dim,\n",
        "        depth,\n",
        "        num_heads,\n",
        "    ):\n",
        "        super().__init__()\n",
        "        self.embed_dim = embed_dim\n",
        "        # num_heads set to half of Transformer's #heads\n",
        "        self.num_heads = num_heads\n",
        "        self.num_kv_heads = num_heads\n",
        "        self.n_rep = self.num_heads // self.num_kv_heads\n",
        "\n",
        "        self.head_dim = embed_dim // num_heads // 2\n",
        "        self.scaling = self.head_dim ** -0.5\n",
        "\n",
        "        self.q_proj = nn.Linear(embed_dim, embed_dim, bias=False)\n",
        "        self.k_proj = nn.Linear(embed_dim, embed_dim // self.n_rep, bias=False)\n",
        "        self.v_proj = nn.Linear(embed_dim, embed_dim // self.n_rep, bias=False)\n",
        "\n",
        "        self.lambda_init = lambda_init_fn(depth)\n",
        "        self.lambda_q1 = nn.Parameter(torch.zeros(self.head_dim, dtype=torch.float32).normal_(mean=0,std=0.1))\n",
        "        self.lambda_k1 = nn.Parameter(torch.zeros(self.head_dim, dtype=torch.float32).normal_(mean=0,std=0.1))\n",
        "        self.lambda_q2 = nn.Parameter(torch.zeros(self.head_dim, dtype=torch.float32).normal_(mean=0,std=0.1))\n",
        "        self.lambda_k2 = nn.Parameter(torch.zeros(self.head_dim, dtype=torch.float32).normal_(mean=0,std=0.1))\n",
        "\n",
        "        self.subln = RMSNorm(2 * self.head_dim, eps=1e-5, elementwise_affine=True)\n",
        "\n",
        "    def forward(\n",
        "        self,\n",
        "        x,\n",
        "        A=None,\n",
        "        attn_mask=None,\n",
        "    ):\n",
        "        bsz, tgt_len, embed_dim = x.size()\n",
        "        src_len = tgt_len\n",
        "\n",
        "        # Project input x into query, key, and value\n",
        "        q = self.q_proj(x)\n",
        "        k = self.k_proj(x)\n",
        "        v = self.v_proj(x)\n",
        "\n",
        "        q = q.view(bsz, tgt_len, 2 * self.num_heads, self.head_dim)\n",
        "        k = k.view(bsz, src_len, 2 * self.num_kv_heads, self.head_dim)\n",
        "        v = v.view(bsz, src_len, self.num_kv_heads, 2 * self.head_dim)\n",
        "\n",
        "        q = q.transpose(1, 2)\n",
        "        k = repeat_kv(k.transpose(1, 2), self.n_rep)\n",
        "        v = repeat_kv(v.transpose(1, 2), self.n_rep)\n",
        "        q *= self.scaling\n",
        "\n",
        "        # Compute attention weights by multiplying query and key\n",
        "        attn_weights = torch.matmul(q, k.transpose(-1, -2))\n",
        "        attn_weights = torch.nan_to_num(attn_weights)\n",
        "        # Apply attention mask\n",
        "        if attn_mask is not None:\n",
        "            attn_weights += attn_mask\n",
        "        # Calculate attention scores using softmax\n",
        "        attn_weights = F.softmax(attn_weights, dim=-1, dtype=torch.float32).type_as(\n",
        "            attn_weights\n",
        "        )\n",
        "\n",
        "        # Calculate the lambda used for differential attention\n",
        "        lambda_1 = torch.exp(torch.sum(self.lambda_q1 * self.lambda_k1, dim=-1).float()).type_as(q)\n",
        "        lambda_2 = torch.exp(torch.sum(self.lambda_q2 * self.lambda_k2, dim=-1).float()).type_as(q)\n",
        "        lambda_full = lambda_1 - lambda_2 + self.lambda_init\n",
        "\n",
        "        # Optionally condition the differential attention on a graph prior A\n",
        "        attn_weights = attn_weights.view(bsz, self.num_heads, 2, tgt_len, src_len)\n",
        "        attn_weights = attn_weights[:, :, 0] * (1 if A is None else A) - lambda_full * attn_weights[:, :, 1]\n",
        "\n",
        "        # Compute output embeddings by mixing values based on their attention scores\n",
        "        attn = torch.matmul(attn_weights, v)\n",
        "        attn = self.subln(attn)\n",
        "        attn = attn * (1 - self.lambda_init)\n",
        "        attn = attn.transpose(1, 2).reshape(bsz, tgt_len, self.num_heads * 2 * self.head_dim)\n",
        "        return (attn, attn_weights)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "K0G6q4eQ0dFH"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "from torch_geometric.utils import to_dense_adj\n",
        "\n",
        "# A normal feedforward layer\n",
        "class FeedForward(nn.Module):\n",
        "    def __init__(self, hidden_size, expand_ratio, dropout):\n",
        "        super(FeedForward, self).__init__()\n",
        "        self.linear = nn.Linear(hidden_size, hidden_size * expand_ratio)\n",
        "        self.linear2 = nn.Linear(hidden_size * expand_ratio, hidden_size)\n",
        "        self.relu = nn.ReLU()\n",
        "        self.dropout = nn.Dropout(p=dropout)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.linear(x)\n",
        "        x = self.relu(x)\n",
        "        x = self.linear2(x)\n",
        "        x = self.dropout(x)\n",
        "        return x\n",
        "\n",
        "# Wrapper code for MultiheadDiffAttn with layer norm and feedforward after the attention\n",
        "class Attention(nn.Module):\n",
        "    def __init__(self, d_model, num_heads, expand_ratio, dropout):\n",
        "        super().__init__()\n",
        "        self.mha = MultiheadDiffAttn(embed_dim=d_model, num_heads=num_heads, depth=0)\n",
        "        self.ln2 = nn.LayerNorm(d_model)\n",
        "        self.ffn = FeedForward(hidden_size=d_model, expand_ratio=expand_ratio, dropout=dropout)\n",
        "\n",
        "    def forward(self, x, A=None, attn_mask=None, need_weights=False):\n",
        "        x1, attn_weights = self.mha(x, A, attn_mask=attn_mask)\n",
        "        x = self.ln2(self.ffn(x1) + x1)\n",
        "        if need_weights:\n",
        "            return (x, attn_weights)\n",
        "        else:\n",
        "            return x\n",
        "\n",
        "# Differential Graph Transformer = temporal attention + spatial attention\n",
        "# Spatial attention may optionally receive an adjacency matrix for conditioning.\n",
        "class DGT(nn.Module):\n",
        "    def __init__(self, in_channels=1, out_channels=32, num_heads=2, num_layers=2, expand_ratio=1, dropout=0.1, T=128, N=472, use_spatial=True):\n",
        "        super().__init__()\n",
        "        self.T = T\n",
        "        self.N = N\n",
        "        self.d_model = out_channels\n",
        "        self.num_heads = num_heads\n",
        "        self.num_layers = num_layers\n",
        "        self.input_proj = nn.Linear(in_channels, out_channels)\n",
        "        self.time_embedding = nn.Embedding(T, out_channels)\n",
        "        self.stock_embedding = nn.Embedding(N, out_channels)\n",
        "        self.use_spatial = use_spatial\n",
        "        if use_spatial:\n",
        "            self.spatial_attns = nn.ModuleList([Attention(out_channels, num_heads, expand_ratio, dropout) for _ in range(num_layers)])\n",
        "        self.temporal_attns = nn.ModuleList([Attention(out_channels, num_heads, expand_ratio, dropout) for _ in range(num_layers)])\n",
        "\n",
        "    def forward(self, x, edge_index, edge_weight, need_weights=False):\n",
        "        N, T, D = x.size()\n",
        "        assert(D == 1)\n",
        "        assert(T <= self.T and N == self.N)\n",
        "\n",
        "        # Compute initial node embedding for the graph transformer\n",
        "        # Node embedding incorporates current stock prices, stock embeddings, and time embeddings.\n",
        "        x = x.permute(1, 0, 2) # T, N, D\n",
        "        x = self.input_proj(x)\n",
        "        stock_embs = self.stock_embedding(torch.arange(N).unsqueeze(0).expand(T, N).to(x.device))\n",
        "        x += stock_embs\n",
        "        time_embs = self.time_embedding(torch.arange(T).unsqueeze(0).expand(N, T).to(x.device))\n",
        "        x += time_embs.permute(1, 0, 2) # T, N, D\n",
        "\n",
        "        x = x.permute(1, 0, 2) # N, T, D\n",
        "\n",
        "        # Iterate through each layer of DGT\n",
        "        for i in range(self.num_layers):\n",
        "            # First apply temporal attention to learn temporal dependencies\n",
        "            temporal_causal_mask = torch.triu(\n",
        "                torch.zeros([T, T])\n",
        "                    .float()\n",
        "                    .fill_(float(\"-inf\")),\n",
        "                    1,\n",
        "                ).expand(N, self.num_heads*2, T, T).to(x.device)\n",
        "            x = self.temporal_attns[i](x, attn_mask=temporal_causal_mask, need_weights=need_weights) + x\n",
        "\n",
        "            # Next apply spatial attention (aka differential graph attention) to learn interstock relations\n",
        "            if self.use_spatial:\n",
        "                x = x.permute(1, 0, 2) # T, N, D\n",
        "                A = to_dense_adj(edge_index, edge_attr=edge_weight)\n",
        "                # Encountered more than one adjacency matrices, e.g. dual correlations\n",
        "                if len(A.size()) == 4:\n",
        "                    A = A.reshape(A.size(-1), A.size(1), A.size(2))\n",
        "                x = self.spatial_attns[i](x, A, need_weights=need_weights) + x\n",
        "                x = x.permute(1, 0, 2) # N, T, D\n",
        "\n",
        "        return x"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A5zY_vud0dFI"
      },
      "source": [
        "# GRU Baseline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "y9rQkiLU0dFI"
      },
      "outputs": [],
      "source": [
        "# Gated Recurrent Unit baseline for comparison\n",
        "class GRU(torch.nn.Module):\n",
        "    def __init__(self, in_channels: int, out_channels: int, num_layers: int):\n",
        "        super(GRU, self).__init__()\n",
        "        self.rnn = nn.GRU(input_size=in_channels, hidden_size=out_channels, num_layers=num_layers, batch_first=True)\n",
        "\n",
        "    def forward(\n",
        "        self,\n",
        "        x: torch.FloatTensor,\n",
        "        edge_index: torch.LongTensor,\n",
        "        edge_weight: torch.FloatTensor = None,\n",
        "    ) -> torch.FloatTensor:\n",
        "        outputs, _ = self.rnn(x)\n",
        "        return outputs"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l__wiYS40dFI"
      },
      "source": [
        "# Driver"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "shaDjKvu0dFI"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import torch.nn.functional as F\n",
        "\n",
        "# Common drive for all models\n",
        "class Driver(torch.nn.Module):\n",
        "    def __init__(self, gnn, corr_name, corr_scope, node_features, hidden_size=32, **kwargs):\n",
        "        super(Driver, self).__init__()\n",
        "        self.recurrent = gnn(in_channels=node_features, out_channels=hidden_size, **kwargs)\n",
        "        self.linear = torch.nn.Linear(hidden_size, 1)\n",
        "        self.corr_name = corr_name\n",
        "        self.corr_scope = corr_scope\n",
        "\n",
        "    # Run on the provided graph (specified with edge_index and edge_weight) and temporal signal x (past stock prices)\n",
        "    def forward(self, x, edge_index, edge_weight, hidden=None):\n",
        "        device = self.model_device()\n",
        "        if hidden is None:\n",
        "            outputs = self.recurrent(x.to(device), edge_index.to(device), edge_weight.to(device))\n",
        "        else:\n",
        "            outputs = self.recurrent(x.to(device), edge_index.to(device), edge_weight.to(device), hidden)\n",
        "        # Use final linear layer for regression\n",
        "        return self.linear(F.relu(outputs)), outputs\n",
        "\n",
        "    # Get the model name for display and saving model weights\n",
        "    def model_name(self):\n",
        "        arch = self.model_arch()\n",
        "        if arch == 'GRU':\n",
        "            return f'{arch}'\n",
        "        elif arch == 'DGT':\n",
        "            name = f'{arch}{\"\" if self.recurrent.use_spatial else \"_no_spatial\"}'\n",
        "            if self.corr_scope is not None:\n",
        "                name += f'_{self.corr_name}_{self.corr_scope}'\n",
        "            return name\n",
        "\n",
        "    # Get the model architecture for display\n",
        "    def model_arch(self):\n",
        "        return self.recurrent.__class__.__name__\n",
        "\n",
        "    # Set the device for the model\n",
        "    def model_device(self):\n",
        "        return torch.device(\"cuda\" if torch.cuda.is_available() else \"mps\" if torch.backends.mps.is_available() else \"cpu\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XgwYifus0dFI"
      },
      "source": [
        "# Evaluation on Price Regression with RMSE and MAE"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yI5sg5TL0dFI"
      },
      "outputs": [],
      "source": [
        "import math\n",
        "\n",
        "# Root Mean Squared Error for evaluation\n",
        "def rmse(y_hat, y):\n",
        "    return math.sqrt(F.mse_loss(y_hat, y).item())\n",
        "\n",
        "# Mean Absolute Error for evaluation\n",
        "def mae(y_hat, y):\n",
        "    return F.l1_loss(y_hat, y).item()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "L7FQLMzi0dFI"
      },
      "outputs": [],
      "source": [
        "import wandb\n",
        "\n",
        "# Helper function for inference\n",
        "def infer(model, snapshot):\n",
        "    X = snapshot.x\n",
        "    batch_y_hats, _ = model(X.transpose(0, 1), snapshot.edge_index, snapshot.edge_attr)\n",
        "    return batch_y_hats[:, -1]\n",
        "\n",
        "# Evaluate the model on eval_dataset and calculate RMSE and MAE\n",
        "def eval(model, eval_dataset):\n",
        "    model.eval()\n",
        "    with torch.no_grad():\n",
        "        y_hats = list(map(lambda snapshot: infer(model, snapshot), eval_dataset))\n",
        "        ys = [snapshot.y for snapshot in eval_dataset]\n",
        "        y_hats = torch.stack(y_hats, dim=0).squeeze().to(model.model_device())\n",
        "        ys = torch.stack(ys, dim=0).to(model.model_device())\n",
        "        eval_rmse = rmse(y_hats, ys)\n",
        "        eval_mae = mae(y_hats, ys)\n",
        "        return {'y_hats': y_hats, 'ys': ys, 'rmse': eval_rmse, 'mae': eval_mae}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "W_Q0NEde0dFI"
      },
      "outputs": [],
      "source": [
        "# Helper function to get a model based on the input configs, optionally loads the weight if load_weights=True\n",
        "def get_model(gnn, use_spatial, corr_name, corr_scope, lr, load_weights=False):\n",
        "    node_features = 1\n",
        "    if gnn == DGT:\n",
        "        model = Driver(gnn, corr_name, corr_scope, node_features, num_heads=2, use_spatial=use_spatial)\n",
        "    elif gnn == GRU:\n",
        "        # GRU does not support any correlation\n",
        "        if corr_name != None or corr_scope != None:\n",
        "            return None\n",
        "        model = Driver(gnn, None, None, node_features, num_layers=2)\n",
        "    if load_weights:\n",
        "        model.load_state_dict(torch.load(f'{workdir}/models/{model.model_name()}_lr_{lr}.pth', weights_only=True))\n",
        "    return model.to(model.model_device())\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I2ICXl7m0dFI"
      },
      "source": [
        "# Training"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Note: Training took 3 hours on a T4. You can skip the following code block and run the evaluations directly as our checkpoints are already downloaded.** In case you are training, you can also set `track_with_wandb` to `False` if you don't want to track with Weights and Biases."
      ],
      "metadata": {
        "id": "SOAe947ephXR"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "cYjWdVjJ0dFI",
        "outputId": "dfa35f72-50ba-402d-c6df-0688f52a32ca"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "        window._wandbApiKey = new Promise((resolve, reject) => {\n",
              "            function loadScript(url) {\n",
              "            return new Promise(function(resolve, reject) {\n",
              "                let newScript = document.createElement(\"script\");\n",
              "                newScript.onerror = reject;\n",
              "                newScript.onload = resolve;\n",
              "                document.body.appendChild(newScript);\n",
              "                newScript.src = url;\n",
              "            });\n",
              "            }\n",
              "            loadScript(\"https://cdn.jsdelivr.net/npm/postmate/build/postmate.min.js\").then(() => {\n",
              "            const iframe = document.createElement('iframe')\n",
              "            iframe.style.cssText = \"width:0;height:0;border:none\"\n",
              "            document.body.appendChild(iframe)\n",
              "            const handshake = new Postmate({\n",
              "                container: iframe,\n",
              "                url: 'https://wandb.ai/authorize'\n",
              "            });\n",
              "            const timeout = setTimeout(() => reject(\"Couldn't auto authenticate\"), 5000)\n",
              "            handshake.then(function(child) {\n",
              "                child.on('authorize', data => {\n",
              "                    clearTimeout(timeout)\n",
              "                    resolve(data)\n",
              "                });\n",
              "            });\n",
              "            })\n",
              "        });\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mkevinxli\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Using wandb-core as the SDK backend.  Please refer to https://wandb.me/wandb-core for more information.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Tracking run with wandb version 0.19.2"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Run data is saved locally in <code>/content/wandb/run-20250119_200232-5lp1cu5y</code>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Syncing run <strong><a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction/runs/5lp1cu5y' target=\"_blank\">GRU_lr_0.01</a></strong> to <a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/developer-guide' target=\"_blank\">docs</a>)<br>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              " View project at <a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction' target=\"_blank\">https://wandb.ai/kevinxli/cs224w-stock-market-prediction</a>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              " View run at <a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction/runs/5lp1cu5y' target=\"_blank\">https://wandb.ai/kevinxli/cs224w-stock-market-prediction/runs/5lp1cu5y</a>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "GRU epoch 0 val/rmse: 1.0151258562542624 val/mae: 0.5934833884239197\n",
            "GRU epoch 10 val/rmse: 0.7394664072011641 val/mae: 0.24550306797027588\n",
            "GRU epoch 20 val/rmse: 0.7674437349427956 val/mae: 0.2417578399181366\n",
            "GRU epoch 30 val/rmse: 0.6855584602789891 val/mae: 0.13805679976940155\n",
            "GRU epoch 40 val/rmse: 0.6683109812157823 val/mae: 0.1362270712852478\n",
            "GRU epoch 50 val/rmse: 0.6567212184121727 val/mae: 0.12936756014823914\n",
            "GRU epoch 60 val/rmse: 0.6512660415562784 val/mae: 0.1538236290216446\n",
            "GRU epoch 70 val/rmse: 0.6393448902919666 val/mae: 0.1312980055809021\n",
            "GRU epoch 80 val/rmse: 0.6378874321394541 val/mae: 0.14947465062141418\n",
            "GRU epoch 90 val/rmse: 0.6279125062956136 val/mae: 0.13595153391361237\n",
            "GRU epoch 99 val/rmse: 0.6195823465564757 val/mae: 0.11029697954654694\n",
            "GRU lr: 0.01 test/rmse: 3.1166079945745024 test/mae: 0.4076187312602997\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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              "<IPython.core.display.HTML object>"
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            "text/html": []
          },
          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
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              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<br>    <style><br>        .wandb-row {<br>            display: flex;<br>            flex-direction: row;<br>            flex-wrap: wrap;<br>            justify-content: flex-start;<br>            width: 100%;<br>        }<br>        .wandb-col {<br>            display: flex;<br>            flex-direction: column;<br>            flex-basis: 100%;<br>            flex: 1;<br>            padding: 10px;<br>        }<br>    </style><br><div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>▁▁▁▁▂▂▂▂▂▃▃▃▃▄▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▆▇▇▇▇▇▇▇██</td></tr><tr><td>step</td><td>▁▃▁▅▂▃▃▃▃▂▇▇▆▃▂▅▆▃▇▆▁▁▅█▄▁▇█▃▇▃█▁▃▇▆▂▇▃█</td></tr><tr><td>test/mae</td><td>▁</td></tr><tr><td>test/rmse</td><td>▁</td></tr><tr><td>train/loss</td><td>█▄▂▁▁▁▁▂▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>val/best_mae</td><td>▁</td></tr><tr><td>val/best_rmse</td><td>▁</td></tr><tr><td>val/mae</td><td>█▃▃▁▁▁▂▁▂▁▁</td></tr><tr><td>val/rmse</td><td>█▃▄▂▂▂▂▁▁▁▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>99</td></tr><tr><td>step</td><td>14</td></tr><tr><td>test/mae</td><td>0.40762</td></tr><tr><td>test/rmse</td><td>3.11661</td></tr><tr><td>train/loss</td><td>0.01211</td></tr><tr><td>val/best_mae</td><td>0.1103</td></tr><tr><td>val/best_rmse</td><td>0.61958</td></tr><tr><td>val/mae</td><td>0.1103</td></tr><tr><td>val/rmse</td><td>0.61958</td></tr></table><br/></div></div>"
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              " View run <strong style=\"color:#cdcd00\">GRU_lr_0.01</strong> at: <a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction/runs/5lp1cu5y' target=\"_blank\">https://wandb.ai/kevinxli/cs224w-stock-market-prediction/runs/5lp1cu5y</a><br> View project at: <a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction' target=\"_blank\">https://wandb.ai/kevinxli/cs224w-stock-market-prediction</a><br>Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
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              "<IPython.core.display.HTML object>"
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              "Find logs at: <code>./wandb/run-20250119_200232-5lp1cu5y/logs</code>"
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        },
        {
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              "<IPython.core.display.HTML object>"
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              "Tracking run with wandb version 0.19.2"
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              "<IPython.core.display.HTML object>"
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              "Run data is saved locally in <code>/content/wandb/run-20250119_200257-53m3csrj</code>"
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        {
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              "<IPython.core.display.HTML object>"
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              "Syncing run <strong><a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction/runs/53m3csrj' target=\"_blank\">GRU_lr_0.1</a></strong> to <a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/developer-guide' target=\"_blank\">docs</a>)<br>"
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              "<IPython.core.display.HTML object>"
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              " View project at <a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction' target=\"_blank\">https://wandb.ai/kevinxli/cs224w-stock-market-prediction</a>"
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        {
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              "<IPython.core.display.HTML object>"
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              " View run at <a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction/runs/53m3csrj' target=\"_blank\">https://wandb.ai/kevinxli/cs224w-stock-market-prediction/runs/53m3csrj</a>"
            ]
          },
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        },
        {
          "output_type": "stream",
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          "text": [
            "GRU epoch 0 val/rmse: 1.147122415157888 val/mae: 0.7995038032531738\n",
            "GRU epoch 10 val/rmse: 1.0865229942462133 val/mae: 0.5747919082641602\n",
            "GRU epoch 20 val/rmse: 1.1592675603968394 val/mae: 0.7387993931770325\n",
            "GRU epoch 30 val/rmse: 1.1101862130896667 val/mae: 0.624571681022644\n",
            "GRU epoch 40 val/rmse: 1.0454624382105593 val/mae: 0.5586006045341492\n",
            "GRU epoch 50 val/rmse: 0.9844211083164526 val/mae: 0.4778263568878174\n",
            "GRU epoch 60 val/rmse: 1.0663733844094228 val/mae: 0.5754823684692383\n",
            "GRU epoch 70 val/rmse: 1.0399111386996494 val/mae: 0.5042101740837097\n",
            "GRU epoch 80 val/rmse: 1.014945169480599 val/mae: 0.48927828669548035\n",
            "GRU epoch 90 val/rmse: 1.0538482717935054 val/mae: 0.5330270528793335\n",
            "GRU epoch 99 val/rmse: 0.969964189409132 val/mae: 0.4710395634174347\n",
            "GRU lr: 0.1 test/rmse: 3.527882413405376 test/mae: 1.0555206537246704\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<br>    <style><br>        .wandb-row {<br>            display: flex;<br>            flex-direction: row;<br>            flex-wrap: wrap;<br>            justify-content: flex-start;<br>            width: 100%;<br>        }<br>        .wandb-col {<br>            display: flex;<br>            flex-direction: column;<br>            flex-basis: 100%;<br>            flex: 1;<br>            padding: 10px;<br>        }<br>    </style><br><div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>▁▁▁▁▂▂▂▂▂▃▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▅▅▆▆▆▆▇▇▇▇▇████</td></tr><tr><td>step</td><td>▇▅▂▄▁▄▇▂▇█▅▃▁▁▃▃▄▁▃▃▃▁▇▁▅▅█▅▅▆▁▇▆▇▂▅▅▂▄▅</td></tr><tr><td>test/mae</td><td>▁</td></tr><tr><td>test/rmse</td><td>▁</td></tr><tr><td>train/loss</td><td>▆▂▂▅▅█▅▃▅▂▃▄▆▆▂▁▁▂▁▃▁▃▅▂▃▁▂▂▁▁▁▃▁▁▃▃▁▁▁▂</td></tr><tr><td>val/best_mae</td><td>▁</td></tr><tr><td>val/best_rmse</td><td>▁</td></tr><tr><td>val/mae</td><td>█▃▇▄▃▁▃▂▁▂▁</td></tr><tr><td>val/rmse</td><td>█▅█▆▄▂▅▄▃▄▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>99</td></tr><tr><td>step</td><td>14</td></tr><tr><td>test/mae</td><td>1.05552</td></tr><tr><td>test/rmse</td><td>3.52788</td></tr><tr><td>train/loss</td><td>0.18069</td></tr><tr><td>val/best_mae</td><td>0.47104</td></tr><tr><td>val/best_rmse</td><td>0.96996</td></tr><tr><td>val/mae</td><td>0.47104</td></tr><tr><td>val/rmse</td><td>0.96996</td></tr></table><br/></div></div>"
            ]
          },
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        {
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          "data": {
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              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              " View run <strong style=\"color:#cdcd00\">GRU_lr_0.1</strong> at: <a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction/runs/53m3csrj' target=\"_blank\">https://wandb.ai/kevinxli/cs224w-stock-market-prediction/runs/53m3csrj</a><br> View project at: <a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction' target=\"_blank\">https://wandb.ai/kevinxli/cs224w-stock-market-prediction</a><br>Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
            ]
          },
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          "output_type": "display_data",
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              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Find logs at: <code>./wandb/run-20250119_200257-53m3csrj/logs</code>"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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              "<IPython.core.display.HTML object>"
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              "Tracking run with wandb version 0.19.2"
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              "Run data is saved locally in <code>/content/wandb/run-20250119_200322-yd1xc1ur</code>"
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              "Syncing run <strong><a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction/runs/yd1xc1ur' target=\"_blank\">DGT_no_spatial_lr_0.01</a></strong> to <a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/developer-guide' target=\"_blank\">docs</a>)<br>"
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              "<IPython.core.display.HTML object>"
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              " View project at <a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction' target=\"_blank\">https://wandb.ai/kevinxli/cs224w-stock-market-prediction</a>"
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              "<IPython.core.display.HTML object>"
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              " View run at <a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction/runs/yd1xc1ur' target=\"_blank\">https://wandb.ai/kevinxli/cs224w-stock-market-prediction/runs/yd1xc1ur</a>"
            ]
          },
          "metadata": {}
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        {
          "output_type": "stream",
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          "text": [
            "DGT_no_spatial epoch 0 val/rmse: 1.0516432732541026 val/mae: 0.6799300909042358\n",
            "DGT_no_spatial epoch 10 val/rmse: 0.5059577816720363 val/mae: 0.17544981837272644\n",
            "DGT_no_spatial epoch 20 val/rmse: 0.4525906931368216 val/mae: 0.1393440067768097\n",
            "DGT_no_spatial epoch 30 val/rmse: 0.4252298266622955 val/mae: 0.12889644503593445\n",
            "DGT_no_spatial epoch 40 val/rmse: 0.400435394017216 val/mae: 0.118231400847435\n",
            "DGT_no_spatial epoch 50 val/rmse: 0.36498747859667224 val/mae: 0.09809175878763199\n",
            "DGT_no_spatial epoch 60 val/rmse: 0.3594312209229434 val/mae: 0.10356435179710388\n",
            "DGT_no_spatial epoch 70 val/rmse: 0.3336577240219205 val/mae: 0.11112850904464722\n",
            "DGT_no_spatial epoch 80 val/rmse: 0.32225048145201995 val/mae: 0.08382288366556168\n",
            "DGT_no_spatial epoch 90 val/rmse: 0.31846378229186056 val/mae: 0.08361205458641052\n",
            "DGT_no_spatial epoch 99 val/rmse: 0.3056713383695309 val/mae: 0.07731342315673828\n",
            "DGT_no_spatial lr: 0.01 test/rmse: 1.4920656643804655 test/mae: 0.1991715133190155\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<br>    <style><br>        .wandb-row {<br>            display: flex;<br>            flex-direction: row;<br>            flex-wrap: wrap;<br>            justify-content: flex-start;<br>            width: 100%;<br>        }<br>        .wandb-col {<br>            display: flex;<br>            flex-direction: column;<br>            flex-basis: 100%;<br>            flex: 1;<br>            padding: 10px;<br>        }<br>    </style><br><div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>▁▁▂▂▂▂▂▂▂▂▃▃▃▄▄▄▄▄▄▅▅▅▅▅▆▆▆▆▆▆▇▇▇▇██████</td></tr><tr><td>step</td><td>▃▅▃▇▃▃▄█▁▃▆▆▄▃▃▃▅▅▄▆▄▆▅▃▅▇▃▆▇▂▄▅█▄▅▆▃▃▆▆</td></tr><tr><td>test/mae</td><td>▁</td></tr><tr><td>test/rmse</td><td>▁</td></tr><tr><td>train/loss</td><td>█▂▂▂▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>val/best_mae</td><td>▁</td></tr><tr><td>val/best_rmse</td><td>▁</td></tr><tr><td>val/mae</td><td>█▂▂▂▁▁▁▁▁▁▁</td></tr><tr><td>val/rmse</td><td>█▃▂▂▂▂▂▁▁▁▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>99</td></tr><tr><td>step</td><td>14</td></tr><tr><td>test/mae</td><td>0.19917</td></tr><tr><td>test/rmse</td><td>1.49207</td></tr><tr><td>train/loss</td><td>0.01076</td></tr><tr><td>val/best_mae</td><td>0.07731</td></tr><tr><td>val/best_rmse</td><td>0.30567</td></tr><tr><td>val/mae</td><td>0.07731</td></tr><tr><td>val/rmse</td><td>0.30567</td></tr></table><br/></div></div>"
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              "<IPython.core.display.HTML object>"
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              " View run <strong style=\"color:#cdcd00\">DGT_no_spatial_lr_0.01</strong> at: <a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction/runs/yd1xc1ur' target=\"_blank\">https://wandb.ai/kevinxli/cs224w-stock-market-prediction/runs/yd1xc1ur</a><br> View project at: <a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction' target=\"_blank\">https://wandb.ai/kevinxli/cs224w-stock-market-prediction</a><br>Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
            ]
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              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Find logs at: <code>./wandb/run-20250119_200322-yd1xc1ur/logs</code>"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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              "<IPython.core.display.HTML object>"
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              "Tracking run with wandb version 0.19.2"
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              "<IPython.core.display.HTML object>"
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            "text/html": [
              "Run data is saved locally in <code>/content/wandb/run-20250119_201021-13einxap</code>"
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              "Syncing run <strong><a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction/runs/13einxap' target=\"_blank\">DGT_no_spatial_lr_0.1</a></strong> to <a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/developer-guide' target=\"_blank\">docs</a>)<br>"
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              "<IPython.core.display.HTML object>"
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              " View project at <a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction' target=\"_blank\">https://wandb.ai/kevinxli/cs224w-stock-market-prediction</a>"
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              "<IPython.core.display.HTML object>"
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              " View run at <a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction/runs/13einxap' target=\"_blank\">https://wandb.ai/kevinxli/cs224w-stock-market-prediction/runs/13einxap</a>"
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        {
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          "text": [
            "DGT_no_spatial epoch 0 val/rmse: 0.7455923182958474 val/mae: 0.5306437015533447\n",
            "DGT_no_spatial epoch 10 val/rmse: 0.2376273673249165 val/mae: 0.1642322540283203\n",
            "DGT_no_spatial epoch 20 val/rmse: 0.2251414837651074 val/mae: 0.15359073877334595\n",
            "DGT_no_spatial epoch 30 val/rmse: 0.2587245212901415 val/mae: 0.1711944043636322\n",
            "DGT_no_spatial epoch 40 val/rmse: 0.26055001023906893 val/mae: 0.17343495786190033\n",
            "DGT_no_spatial epoch 50 val/rmse: 0.2182623673329115 val/mae: 0.13900776207447052\n",
            "DGT_no_spatial epoch 60 val/rmse: 0.17033703281725954 val/mae: 0.09737835824489594\n",
            "DGT_no_spatial epoch 70 val/rmse: 0.21870207261523666 val/mae: 0.1400461047887802\n",
            "DGT_no_spatial epoch 80 val/rmse: 0.23271536528880676 val/mae: 0.1374342441558838\n",
            "DGT_no_spatial epoch 90 val/rmse: 0.2509344043279461 val/mae: 0.1355830729007721\n",
            "DGT_no_spatial epoch 99 val/rmse: 0.19533162976995996 val/mae: 0.12171226739883423\n",
            "DGT_no_spatial lr: 0.1 test/rmse: 0.4328992281618065 test/mae: 0.18165773153305054\n"
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              "<br>    <style><br>        .wandb-row {<br>            display: flex;<br>            flex-direction: row;<br>            flex-wrap: wrap;<br>            justify-content: flex-start;<br>            width: 100%;<br>        }<br>        .wandb-col {<br>            display: flex;<br>            flex-direction: column;<br>            flex-basis: 100%;<br>            flex: 1;<br>            padding: 10px;<br>        }<br>    </style><br><div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>▁▁▁▁▁▂▂▂▂▂▃▃▃▃▄▄▄▄▄▄▅▅▅▅▅▅▅▅▅▆▆▆▇▇▇▇▇███</td></tr><tr><td>step</td><td>▃▄▄▇█▆▄▄▁▇▅▃▂▄▇▁▄▄▆▄▁▅▄▆█▂▄▅▅▇▆▃▄▅▅▅▆▆▃▄</td></tr><tr><td>test/mae</td><td>▁</td></tr><tr><td>test/rmse</td><td>▁</td></tr><tr><td>train/loss</td><td>█▆▅▂▂▂▁▁▁▂▂▁▁▂▂▂▂▁▁▁▁▁▁▁▁▁▁▁▂▁▁▁▂▁▁▁▂▂▁▁</td></tr><tr><td>val/best_mae</td><td>▁</td></tr><tr><td>val/best_rmse</td><td>▁</td></tr><tr><td>val/mae</td><td>█▂▂▂▂▂▁▂▂▂▁</td></tr><tr><td>val/rmse</td><td>█▂▂▂▂▂▁▂▂▂▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>99</td></tr><tr><td>step</td><td>14</td></tr><tr><td>test/mae</td><td>0.18166</td></tr><tr><td>test/rmse</td><td>0.4329</td></tr><tr><td>train/loss</td><td>0.02425</td></tr><tr><td>val/best_mae</td><td>0.09738</td></tr><tr><td>val/best_rmse</td><td>0.17034</td></tr><tr><td>val/mae</td><td>0.12171</td></tr><tr><td>val/rmse</td><td>0.19533</td></tr></table><br/></div></div>"
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              "<IPython.core.display.HTML object>"
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              " View run <strong style=\"color:#cdcd00\">DGT_no_spatial_lr_0.1</strong> at: <a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction/runs/13einxap' target=\"_blank\">https://wandb.ai/kevinxli/cs224w-stock-market-prediction/runs/13einxap</a><br> View project at: <a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction' target=\"_blank\">https://wandb.ai/kevinxli/cs224w-stock-market-prediction</a><br>Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
            ]
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              "<IPython.core.display.HTML object>"
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            "text/html": [
              "Find logs at: <code>./wandb/run-20250119_201021-13einxap/logs</code>"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
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              "Tracking run with wandb version 0.19.2"
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              "<IPython.core.display.HTML object>"
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              "Run data is saved locally in <code>/content/wandb/run-20250119_201642-t8d39ll0</code>"
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        {
          "output_type": "display_data",
          "data": {
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              "<IPython.core.display.HTML object>"
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              "Syncing run <strong><a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction/runs/t8d39ll0' target=\"_blank\">DGT_lr_0.01</a></strong> to <a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/developer-guide' target=\"_blank\">docs</a>)<br>"
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          },
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        {
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              "<IPython.core.display.HTML object>"
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              " View project at <a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction' target=\"_blank\">https://wandb.ai/kevinxli/cs224w-stock-market-prediction</a>"
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        {
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              "<IPython.core.display.HTML object>"
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              " View run at <a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction/runs/t8d39ll0' target=\"_blank\">https://wandb.ai/kevinxli/cs224w-stock-market-prediction/runs/t8d39ll0</a>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
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          "text": [
            "DGT epoch 0 val/rmse: 1.1663328215372715 val/mae: 0.866641104221344\n",
            "DGT epoch 10 val/rmse: 0.5661705842233048 val/mae: 0.18316423892974854\n",
            "DGT epoch 20 val/rmse: 0.4797872657038466 val/mae: 0.141910582780838\n",
            "DGT epoch 30 val/rmse: 0.46390838440108234 val/mae: 0.15902476012706757\n",
            "DGT epoch 40 val/rmse: 0.4005524843141996 val/mae: 0.12455122172832489\n",
            "DGT epoch 50 val/rmse: 0.40952463109017445 val/mae: 0.10992292314767838\n",
            "DGT epoch 60 val/rmse: 0.42137112373146624 val/mae: 0.13949576020240784\n",
            "DGT epoch 70 val/rmse: 0.38506452694684307 val/mae: 0.21908588707447052\n",
            "DGT epoch 80 val/rmse: 0.3309199979114579 val/mae: 0.11764570325613022\n",
            "DGT epoch 90 val/rmse: 0.33019609995222 val/mae: 0.11783812195062637\n",
            "DGT epoch 99 val/rmse: 0.3275464839014915 val/mae: 0.11910999566316605\n",
            "DGT lr: 0.01 test/rmse: 1.473704888039203 test/mae: 0.2925158739089966\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<br>    <style><br>        .wandb-row {<br>            display: flex;<br>            flex-direction: row;<br>            flex-wrap: wrap;<br>            justify-content: flex-start;<br>            width: 100%;<br>        }<br>        .wandb-col {<br>            display: flex;<br>            flex-direction: column;<br>            flex-basis: 100%;<br>            flex: 1;<br>            padding: 10px;<br>        }<br>    </style><br><div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>▁▂▂▂▂▂▂▂▂▃▄▄▄▄▄▄▅▅▅▅▅▆▆▆▆▆▆▆▆▇▇▇▇▇▇▇████</td></tr><tr><td>step</td><td>▇▂▃█▆▅▅▄▅▁▁█▇▅▁▅▇▃▄▃▇▁█▃▃▃▆▁▅▃▆▇▃▃▂▇▆▆▄▅</td></tr><tr><td>test/mae</td><td>▁</td></tr><tr><td>test/rmse</td><td>▁</td></tr><tr><td>train/loss</td><td>█▄▅▃▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>val/best_mae</td><td>▁</td></tr><tr><td>val/best_rmse</td><td>▁</td></tr><tr><td>val/mae</td><td>█▂▁▁▁▁▁▂▁▁▁</td></tr><tr><td>val/rmse</td><td>█▃▂▂▂▂▂▁▁▁▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>99</td></tr><tr><td>step</td><td>14</td></tr><tr><td>test/mae</td><td>0.29252</td></tr><tr><td>test/rmse</td><td>1.4737</td></tr><tr><td>train/loss</td><td>0.01151</td></tr><tr><td>val/best_mae</td><td>0.11911</td></tr><tr><td>val/best_rmse</td><td>0.32755</td></tr><tr><td>val/mae</td><td>0.11911</td></tr><tr><td>val/rmse</td><td>0.32755</td></tr></table><br/></div></div>"
            ]
          },
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        },
        {
          "output_type": "display_data",
          "data": {
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              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              " View run <strong style=\"color:#cdcd00\">DGT_lr_0.01</strong> at: <a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction/runs/t8d39ll0' target=\"_blank\">https://wandb.ai/kevinxli/cs224w-stock-market-prediction/runs/t8d39ll0</a><br> View project at: <a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction' target=\"_blank\">https://wandb.ai/kevinxli/cs224w-stock-market-prediction</a><br>Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
            ]
          },
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        {
          "output_type": "display_data",
          "data": {
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              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Find logs at: <code>./wandb/run-20250119_201642-t8d39ll0/logs</code>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "<IPython.core.display.HTML object>"
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              "Tracking run with wandb version 0.19.2"
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          "data": {
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              "<IPython.core.display.HTML object>"
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            "text/html": [
              "Run data is saved locally in <code>/content/wandb/run-20250119_202832-hzbqy1h9</code>"
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        {
          "output_type": "display_data",
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              "<IPython.core.display.HTML object>"
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            "text/html": [
              "Syncing run <strong><a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction/runs/hzbqy1h9' target=\"_blank\">DGT_lr_0.1</a></strong> to <a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/developer-guide' target=\"_blank\">docs</a>)<br>"
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          },
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              "<IPython.core.display.HTML object>"
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              " View project at <a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction' target=\"_blank\">https://wandb.ai/kevinxli/cs224w-stock-market-prediction</a>"
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        {
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              "<IPython.core.display.HTML object>"
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              " View run at <a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction/runs/hzbqy1h9' target=\"_blank\">https://wandb.ai/kevinxli/cs224w-stock-market-prediction/runs/hzbqy1h9</a>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DGT epoch 0 val/rmse: 1.2528722189421675 val/mae: 1.1105965375900269\n",
            "DGT epoch 10 val/rmse: 0.6066736844813188 val/mae: 0.146450012922287\n",
            "DGT epoch 20 val/rmse: 0.6511204379429594 val/mae: 0.33825546503067017\n",
            "DGT epoch 30 val/rmse: 0.544522374873031 val/mae: 0.2150142341852188\n",
            "DGT epoch 40 val/rmse: 0.6574977185024812 val/mae: 0.3009336590766907\n",
            "DGT epoch 50 val/rmse: 0.4153857768839304 val/mae: 0.1313445270061493\n",
            "DGT epoch 60 val/rmse: 0.36706583564109163 val/mae: 0.13257576525211334\n",
            "DGT epoch 70 val/rmse: 0.33499947151099696 val/mae: 0.1202550008893013\n",
            "DGT epoch 80 val/rmse: 0.3908646420679214 val/mae: 0.1295650452375412\n",
            "DGT epoch 90 val/rmse: 0.37920721626738946 val/mae: 0.18798206746578217\n",
            "DGT epoch 99 val/rmse: 0.41624913355877485 val/mae: 0.2747070789337158\n",
            "DGT lr: 0.1 test/rmse: 1.628739163178983 test/mae: 0.23096409440040588\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<br>    <style><br>        .wandb-row {<br>            display: flex;<br>            flex-direction: row;<br>            flex-wrap: wrap;<br>            justify-content: flex-start;<br>            width: 100%;<br>        }<br>        .wandb-col {<br>            display: flex;<br>            flex-direction: column;<br>            flex-basis: 100%;<br>            flex: 1;<br>            padding: 10px;<br>        }<br>    </style><br><div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>▁▁▁▁▁▂▂▂▃▃▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▅▅▅▅▅▆▆▆▆▆▇▇▇▇█</td></tr><tr><td>step</td><td>▅▁▄▅▄▁▃▁▅█▁▇▃▃▇▆▇▂▁▁▆▆▃▄▂▁▃▂▃▃▅▅▆▇█▁▁█▄▂</td></tr><tr><td>test/mae</td><td>▁</td></tr><tr><td>test/rmse</td><td>▁</td></tr><tr><td>train/loss</td><td>▃▂▃▃▃█▂▂▂▄▂▃▅▄▂▁▁▁▁▃▁▃▃▂▁▃▂▆▁▃▃▁▂█▂▂▃▆▁▃</td></tr><tr><td>val/best_mae</td><td>▁</td></tr><tr><td>val/best_rmse</td><td>▁</td></tr><tr><td>val/mae</td><td>█▁▃▂▂▁▁▁▁▁▂</td></tr><tr><td>val/rmse</td><td>█▃▃▃▃▂▁▁▁▁▂</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>99</td></tr><tr><td>step</td><td>14</td></tr><tr><td>test/mae</td><td>0.23096</td></tr><tr><td>test/rmse</td><td>1.62874</td></tr><tr><td>train/loss</td><td>0.08559</td></tr><tr><td>val/best_mae</td><td>0.12026</td></tr><tr><td>val/best_rmse</td><td>0.335</td></tr><tr><td>val/mae</td><td>0.27471</td></tr><tr><td>val/rmse</td><td>0.41625</td></tr></table><br/></div></div>"
            ]
          },
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        {
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              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              " View run <strong style=\"color:#cdcd00\">DGT_lr_0.1</strong> at: <a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction/runs/hzbqy1h9' target=\"_blank\">https://wandb.ai/kevinxli/cs224w-stock-market-prediction/runs/hzbqy1h9</a><br> View project at: <a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction' target=\"_blank\">https://wandb.ai/kevinxli/cs224w-stock-market-prediction</a><br>Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
            ]
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          "output_type": "display_data",
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              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Find logs at: <code>./wandb/run-20250119_202832-hzbqy1h9/logs</code>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "<IPython.core.display.HTML object>"
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            "text/html": [
              "Tracking run with wandb version 0.19.2"
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              "<IPython.core.display.HTML object>"
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            "text/html": [
              "Run data is saved locally in <code>/content/wandb/run-20250119_204024-epf8kmbr</code>"
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              "Syncing run <strong><a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction/runs/epf8kmbr' target=\"_blank\">DGT_mi_global_lr_0.01</a></strong> to <a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/developer-guide' target=\"_blank\">docs</a>)<br>"
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              "<IPython.core.display.HTML object>"
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              " View project at <a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction' target=\"_blank\">https://wandb.ai/kevinxli/cs224w-stock-market-prediction</a>"
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              "<IPython.core.display.HTML object>"
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              " View run at <a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction/runs/epf8kmbr' target=\"_blank\">https://wandb.ai/kevinxli/cs224w-stock-market-prediction/runs/epf8kmbr</a>"
            ]
          },
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        {
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          "name": "stdout",
          "text": [
            "DGT_mi_global epoch 0 val/rmse: 1.0012687740399335 val/mae: 0.6714795827865601\n",
            "DGT_mi_global epoch 10 val/rmse: 0.5359844843341077 val/mae: 0.31612247228622437\n",
            "DGT_mi_global epoch 20 val/rmse: 0.502615406391655 val/mae: 0.2863154113292694\n",
            "DGT_mi_global epoch 30 val/rmse: 0.3854373161695279 val/mae: 0.23913811147212982\n",
            "DGT_mi_global epoch 40 val/rmse: 0.19174504949229512 val/mae: 0.11173715442419052\n",
            "DGT_mi_global epoch 50 val/rmse: 0.19272254341589656 val/mae: 0.12769179046154022\n",
            "DGT_mi_global epoch 60 val/rmse: 0.17119214568601335 val/mae: 0.1005631685256958\n",
            "DGT_mi_global epoch 70 val/rmse: 0.15980018018186004 val/mae: 0.10093577951192856\n",
            "DGT_mi_global epoch 80 val/rmse: 0.14929510504782809 val/mae: 0.08889802545309067\n",
            "DGT_mi_global epoch 90 val/rmse: 0.14006206637412835 val/mae: 0.08726727962493896\n",
            "DGT_mi_global epoch 99 val/rmse: 0.19063454393040083 val/mae: 0.11815609037876129\n",
            "DGT_mi_global lr: 0.01 test/rmse: 0.46510339746130386 test/mae: 0.11491794139146805\n"
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              "<br>    <style><br>        .wandb-row {<br>            display: flex;<br>            flex-direction: row;<br>            flex-wrap: wrap;<br>            justify-content: flex-start;<br>            width: 100%;<br>        }<br>        .wandb-col {<br>            display: flex;<br>            flex-direction: column;<br>            flex-basis: 100%;<br>            flex: 1;<br>            padding: 10px;<br>        }<br>    </style><br><div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>▁▁▁▁▂▂▂▂▂▂▂▃▃▃▃▃▄▄▄▄▅▅▅▅▅▆▆▆▆▆▆▆▆▆▇█████</td></tr><tr><td>step</td><td>▅▇▁▆▅▅█▅█▇▁▇▂▃▅▂▃▆▇▄▃▅▅▄▃▇▁▅▇▅▅▃▂▄▇▃▁▃█▇</td></tr><tr><td>test/mae</td><td>▁</td></tr><tr><td>test/rmse</td><td>▁</td></tr><tr><td>train/loss</td><td>█▆▃▂▂▄▂▂▂▂▂▁▁▁▁▁▁▁▂▁▁▁▂▂▁▂▁▁▁▁▁▁▂▁▂▁▁▁▁▁</td></tr><tr><td>val/best_mae</td><td>▁</td></tr><tr><td>val/best_rmse</td><td>▁</td></tr><tr><td>val/mae</td><td>█▄▃▃▁▁▁▁▁▁▁</td></tr><tr><td>val/rmse</td><td>█▄▄▃▁▁▁▁▁▁▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>99</td></tr><tr><td>step</td><td>14</td></tr><tr><td>test/mae</td><td>0.11492</td></tr><tr><td>test/rmse</td><td>0.4651</td></tr><tr><td>train/loss</td><td>0.01955</td></tr><tr><td>val/best_mae</td><td>0.08727</td></tr><tr><td>val/best_rmse</td><td>0.14006</td></tr><tr><td>val/mae</td><td>0.11816</td></tr><tr><td>val/rmse</td><td>0.19063</td></tr></table><br/></div></div>"
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              " View run <strong style=\"color:#cdcd00\">DGT_mi_global_lr_0.01</strong> at: <a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction/runs/epf8kmbr' target=\"_blank\">https://wandb.ai/kevinxli/cs224w-stock-market-prediction/runs/epf8kmbr</a><br> View project at: <a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction' target=\"_blank\">https://wandb.ai/kevinxli/cs224w-stock-market-prediction</a><br>Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
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              "<IPython.core.display.HTML object>"
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              "Find logs at: <code>./wandb/run-20250119_204024-epf8kmbr/logs</code>"
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        {
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              "<IPython.core.display.HTML object>"
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              "Tracking run with wandb version 0.19.2"
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              "<IPython.core.display.HTML object>"
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              "Run data is saved locally in <code>/content/wandb/run-20250119_205235-kok5k8bg</code>"
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              "Syncing run <strong><a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction/runs/kok5k8bg' target=\"_blank\">DGT_mi_global_lr_0.1</a></strong> to <a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/developer-guide' target=\"_blank\">docs</a>)<br>"
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              "<IPython.core.display.HTML object>"
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              " View project at <a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction' target=\"_blank\">https://wandb.ai/kevinxli/cs224w-stock-market-prediction</a>"
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              "<IPython.core.display.HTML object>"
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              " View run at <a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction/runs/kok5k8bg' target=\"_blank\">https://wandb.ai/kevinxli/cs224w-stock-market-prediction/runs/kok5k8bg</a>"
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          "text": [
            "DGT_mi_global epoch 0 val/rmse: 0.8252649402022962 val/mae: 0.6091215014457703\n",
            "DGT_mi_global epoch 10 val/rmse: 0.628280778802649 val/mae: 0.20826566219329834\n",
            "DGT_mi_global epoch 20 val/rmse: 0.4549073647948406 val/mae: 0.13138806819915771\n",
            "DGT_mi_global epoch 30 val/rmse: 0.43879581035654247 val/mae: 0.20296455919742584\n",
            "DGT_mi_global epoch 40 val/rmse: 0.3909421777483479 val/mae: 0.0946093201637268\n",
            "DGT_mi_global epoch 50 val/rmse: 0.37161972516393743 val/mae: 0.10006465017795563\n",
            "DGT_mi_global epoch 60 val/rmse: 0.5672059300478544 val/mae: 0.4679655432701111\n",
            "DGT_mi_global epoch 70 val/rmse: 0.6988751083794231 val/mae: 0.4168681204319\n",
            "DGT_mi_global epoch 80 val/rmse: 0.6391157421886711 val/mae: 0.21604809165000916\n",
            "DGT_mi_global epoch 90 val/rmse: 0.6124751874218606 val/mae: 0.2516571283340454\n",
            "DGT_mi_global epoch 99 val/rmse: 0.8388722322998409 val/mae: 0.17682863771915436\n",
            "DGT_mi_global lr: 0.1 test/rmse: 1.6498760436920556 test/mae: 0.1788974553346634\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<br>    <style><br>        .wandb-row {<br>            display: flex;<br>            flex-direction: row;<br>            flex-wrap: wrap;<br>            justify-content: flex-start;<br>            width: 100%;<br>        }<br>        .wandb-col {<br>            display: flex;<br>            flex-direction: column;<br>            flex-basis: 100%;<br>            flex: 1;<br>            padding: 10px;<br>        }<br>    </style><br><div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>▁▁▁▂▂▂▃▃▃▃▃▄▄▄▄▄▄▄▅▅▅▅▅▅▅▆▆▆▆▇▇▇▇▇▇▇████</td></tr><tr><td>step</td><td>▂▁▅▁▃▅▆▇▃▅▅▅▅▇█▂▁▃▂▅█▄▃▇█▆▂▂▆▃▄▅▆▇▁▅▃▁▂▅</td></tr><tr><td>test/mae</td><td>▁</td></tr><tr><td>test/rmse</td><td>▁</td></tr><tr><td>train/loss</td><td>▃█▇▃▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▂▁</td></tr><tr><td>val/best_mae</td><td>▁</td></tr><tr><td>val/best_rmse</td><td>▁</td></tr><tr><td>val/mae</td><td>█▃▂▂▁▁▆▅▃▃▂</td></tr><tr><td>val/rmse</td><td>█▅▂▂▁▁▄▆▅▅█</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>99</td></tr><tr><td>step</td><td>14</td></tr><tr><td>test/mae</td><td>0.1789</td></tr><tr><td>test/rmse</td><td>1.64988</td></tr><tr><td>train/loss</td><td>0.03201</td></tr><tr><td>val/best_mae</td><td>0.10006</td></tr><tr><td>val/best_rmse</td><td>0.37162</td></tr><tr><td>val/mae</td><td>0.17683</td></tr><tr><td>val/rmse</td><td>0.83887</td></tr></table><br/></div></div>"
            ]
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        {
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              "<IPython.core.display.HTML object>"
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            "text/html": [
              " View run <strong style=\"color:#cdcd00\">DGT_mi_global_lr_0.1</strong> at: <a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction/runs/kok5k8bg' target=\"_blank\">https://wandb.ai/kevinxli/cs224w-stock-market-prediction/runs/kok5k8bg</a><br> View project at: <a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction' target=\"_blank\">https://wandb.ai/kevinxli/cs224w-stock-market-prediction</a><br>Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
            ]
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              "<IPython.core.display.HTML object>"
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            "text/html": [
              "Find logs at: <code>./wandb/run-20250119_205235-kok5k8bg/logs</code>"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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              "<IPython.core.display.HTML object>"
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              "Tracking run with wandb version 0.19.2"
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              "Run data is saved locally in <code>/content/wandb/run-20250119_210454-kctbq2yn</code>"
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              "Syncing run <strong><a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction/runs/kctbq2yn' target=\"_blank\">DGT_mi_local_lr_0.01</a></strong> to <a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/developer-guide' target=\"_blank\">docs</a>)<br>"
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              "<IPython.core.display.HTML object>"
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              " View project at <a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction' target=\"_blank\">https://wandb.ai/kevinxli/cs224w-stock-market-prediction</a>"
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          },
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        {
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          "data": {
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              "<IPython.core.display.HTML object>"
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              " View run at <a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction/runs/kctbq2yn' target=\"_blank\">https://wandb.ai/kevinxli/cs224w-stock-market-prediction/runs/kctbq2yn</a>"
            ]
          },
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        {
          "output_type": "stream",
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          "text": [
            "DGT_mi_local epoch 0 val/rmse: 0.9002928111801067 val/mae: 0.5393624305725098\n",
            "DGT_mi_local epoch 10 val/rmse: 0.6887280160542051 val/mae: 0.4005378186702728\n",
            "DGT_mi_local epoch 20 val/rmse: 0.4494036915762458 val/mae: 0.2704002857208252\n",
            "DGT_mi_local epoch 30 val/rmse: 0.2686673014951199 val/mae: 0.14062510430812836\n",
            "DGT_mi_local epoch 40 val/rmse: 0.2844522879445983 val/mae: 0.20511041581630707\n",
            "DGT_mi_local epoch 50 val/rmse: 0.16961481588168448 val/mae: 0.11053712666034698\n",
            "DGT_mi_local epoch 60 val/rmse: 0.17120601223983523 val/mae: 0.10645116120576859\n",
            "DGT_mi_local epoch 70 val/rmse: 0.18160796128140974 val/mae: 0.1133645623922348\n",
            "DGT_mi_local epoch 80 val/rmse: 0.1421013135967102 val/mae: 0.09032121300697327\n",
            "DGT_mi_local epoch 90 val/rmse: 0.15679185611236277 val/mae: 0.09798093140125275\n",
            "DGT_mi_local epoch 99 val/rmse: 0.14165065177569927 val/mae: 0.10277072340250015\n",
            "DGT_mi_local lr: 0.01 test/rmse: 0.3241913680497987 test/mae: 0.13397999107837677\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<br>    <style><br>        .wandb-row {<br>            display: flex;<br>            flex-direction: row;<br>            flex-wrap: wrap;<br>            justify-content: flex-start;<br>            width: 100%;<br>        }<br>        .wandb-col {<br>            display: flex;<br>            flex-direction: column;<br>            flex-basis: 100%;<br>            flex: 1;<br>            padding: 10px;<br>        }<br>    </style><br><div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>▁▁▁▁▁▂▂▂▂▂▂▂▃▃▃▃▄▄▄▄▄▄▅▅▅▅▅▅▅▆▆▆▆▆▇▇▇▇██</td></tr><tr><td>step</td><td>▂▁▇▇▁▅▅█▇▃▃▂▃▂▁▇▂▅▃▃▁▃▃▅▅▅▅▇▇▇▅▂▁▆▅▁▃▇▇▄</td></tr><tr><td>test/mae</td><td>▁</td></tr><tr><td>test/rmse</td><td>▁</td></tr><tr><td>train/loss</td><td>█▂▁▂▂▂▂▃▂▂▃▂▄▂▂▃▁▂▇▅▃▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>val/best_mae</td><td>▁</td></tr><tr><td>val/best_rmse</td><td>▁</td></tr><tr><td>val/mae</td><td>█▆▄▂▃▁▁▁▁▁▁</td></tr><tr><td>val/rmse</td><td>█▆▄▂▂▁▁▁▁▁▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>99</td></tr><tr><td>step</td><td>14</td></tr><tr><td>test/mae</td><td>0.13398</td></tr><tr><td>test/rmse</td><td>0.32419</td></tr><tr><td>train/loss</td><td>0.01362</td></tr><tr><td>val/best_mae</td><td>0.10277</td></tr><tr><td>val/best_rmse</td><td>0.14165</td></tr><tr><td>val/mae</td><td>0.10277</td></tr><tr><td>val/rmse</td><td>0.14165</td></tr></table><br/></div></div>"
            ]
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              " View run <strong style=\"color:#cdcd00\">DGT_mi_local_lr_0.01</strong> at: <a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction/runs/kctbq2yn' target=\"_blank\">https://wandb.ai/kevinxli/cs224w-stock-market-prediction/runs/kctbq2yn</a><br> View project at: <a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction' target=\"_blank\">https://wandb.ai/kevinxli/cs224w-stock-market-prediction</a><br>Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
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              "<IPython.core.display.HTML object>"
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            "text/html": [
              "Find logs at: <code>./wandb/run-20250119_210454-kctbq2yn/logs</code>"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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              "<IPython.core.display.HTML object>"
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              "Tracking run with wandb version 0.19.2"
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            "text/html": [
              "Run data is saved locally in <code>/content/wandb/run-20250119_211716-cxxigb15</code>"
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              "Syncing run <strong><a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction/runs/cxxigb15' target=\"_blank\">DGT_mi_local_lr_0.1</a></strong> to <a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/developer-guide' target=\"_blank\">docs</a>)<br>"
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              "<IPython.core.display.HTML object>"
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              " View project at <a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction' target=\"_blank\">https://wandb.ai/kevinxli/cs224w-stock-market-prediction</a>"
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              "<IPython.core.display.HTML object>"
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              " View run at <a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction/runs/cxxigb15' target=\"_blank\">https://wandb.ai/kevinxli/cs224w-stock-market-prediction/runs/cxxigb15</a>"
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        {
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          "text": [
            "DGT_mi_local epoch 0 val/rmse: 1.037091673504184 val/mae: 0.5196841359138489\n",
            "DGT_mi_local epoch 10 val/rmse: 1.060045773759421 val/mae: 0.7239880561828613\n",
            "DGT_mi_local epoch 20 val/rmse: 0.9378771341252035 val/mae: 0.2847558259963989\n",
            "DGT_mi_local epoch 30 val/rmse: 0.7247933619443653 val/mae: 0.2686854898929596\n",
            "DGT_mi_local epoch 40 val/rmse: 1.01728409456467 val/mae: 0.28426408767700195\n",
            "DGT_mi_local epoch 50 val/rmse: 0.9637774857635276 val/mae: 0.25843414664268494\n",
            "DGT_mi_local epoch 60 val/rmse: 0.7276040272222197 val/mae: 0.1767144352197647\n",
            "DGT_mi_local epoch 70 val/rmse: 1.1755456022944113 val/mae: 0.3743884861469269\n",
            "DGT_mi_local epoch 80 val/rmse: 1.273515897806638 val/mae: 0.3659111261367798\n",
            "DGT_mi_local epoch 90 val/rmse: 0.8872466343557628 val/mae: 0.31716471910476685\n",
            "DGT_mi_local epoch 99 val/rmse: 1.2860957830452633 val/mae: 0.24827918410301208\n",
            "DGT_mi_local lr: 0.1 test/rmse: 3.4772281995201415 test/mae: 0.6231410503387451\n"
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            "text/html": [
              "<br>    <style><br>        .wandb-row {<br>            display: flex;<br>            flex-direction: row;<br>            flex-wrap: wrap;<br>            justify-content: flex-start;<br>            width: 100%;<br>        }<br>        .wandb-col {<br>            display: flex;<br>            flex-direction: column;<br>            flex-basis: 100%;<br>            flex: 1;<br>            padding: 10px;<br>        }<br>    </style><br><div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>▁▁▁▁▁▁▁▂▂▂▂▃▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▆▆▆▇▇▇▇▇▇████</td></tr><tr><td>step</td><td>▇▁▇▃▁▁▇▆▄▃▇▄▃▇▁▂▁▂▂▅▇▅▅▇▁▇▇▁▃▅▇▅▁▁█▁▇█▅█</td></tr><tr><td>test/mae</td><td>▁</td></tr><tr><td>test/rmse</td><td>▁</td></tr><tr><td>train/loss</td><td>█▅▂▁▁▁▁▁▁▁▃▃▁▁▂▂▁▁▁▁▁▂▂▃▁▁▂▂▂▁▂▂▄▁▂▃▁▁▁▁</td></tr><tr><td>val/best_mae</td><td>▁</td></tr><tr><td>val/best_rmse</td><td>▁</td></tr><tr><td>val/mae</td><td>▅█▂▂▂▂▁▄▃▃▂</td></tr><tr><td>val/rmse</td><td>▅▅▄▁▅▄▁▇█▃█</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>99</td></tr><tr><td>step</td><td>14</td></tr><tr><td>test/mae</td><td>0.62314</td></tr><tr><td>test/rmse</td><td>3.47723</td></tr><tr><td>train/loss</td><td>0.03594</td></tr><tr><td>val/best_mae</td><td>0.26869</td></tr><tr><td>val/best_rmse</td><td>0.72479</td></tr><tr><td>val/mae</td><td>0.24828</td></tr><tr><td>val/rmse</td><td>1.2861</td></tr></table><br/></div></div>"
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              " View run <strong style=\"color:#cdcd00\">DGT_mi_local_lr_0.1</strong> at: <a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction/runs/cxxigb15' target=\"_blank\">https://wandb.ai/kevinxli/cs224w-stock-market-prediction/runs/cxxigb15</a><br> View project at: <a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction' target=\"_blank\">https://wandb.ai/kevinxli/cs224w-stock-market-prediction</a><br>Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
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              "<IPython.core.display.HTML object>"
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              "Find logs at: <code>./wandb/run-20250119_211716-cxxigb15/logs</code>"
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          },
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        {
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              "<IPython.core.display.HTML object>"
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              "Tracking run with wandb version 0.19.2"
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              "Run data is saved locally in <code>/content/wandb/run-20250119_212916-ua5u9agb</code>"
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        {
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              "Syncing run <strong><a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction/runs/ua5u9agb' target=\"_blank\">DGT_mi_dual_lr_0.01</a></strong> to <a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/developer-guide' target=\"_blank\">docs</a>)<br>"
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              "<IPython.core.display.HTML object>"
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              " View project at <a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction' target=\"_blank\">https://wandb.ai/kevinxli/cs224w-stock-market-prediction</a>"
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              " View run at <a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction/runs/ua5u9agb' target=\"_blank\">https://wandb.ai/kevinxli/cs224w-stock-market-prediction/runs/ua5u9agb</a>"
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          "text": [
            "DGT_mi_dual epoch 0 val/rmse: 0.7391773013740808 val/mae: 0.45560505986213684\n",
            "DGT_mi_dual epoch 10 val/rmse: 0.5032932189825671 val/mae: 0.3045569658279419\n",
            "DGT_mi_dual epoch 20 val/rmse: 0.39251623092792287 val/mae: 0.2444455772638321\n",
            "DGT_mi_dual epoch 30 val/rmse: 0.37159131473368523 val/mae: 0.22902081906795502\n",
            "DGT_mi_dual epoch 40 val/rmse: 0.2654297756394218 val/mae: 0.16484399139881134\n",
            "DGT_mi_dual epoch 50 val/rmse: 0.25267701065461456 val/mae: 0.15037177503108978\n",
            "DGT_mi_dual epoch 60 val/rmse: 0.2815375579938595 val/mae: 0.17735306918621063\n",
            "DGT_mi_dual epoch 70 val/rmse: 0.1762174650948065 val/mae: 0.11425253003835678\n",
            "DGT_mi_dual epoch 80 val/rmse: 0.16801417091504559 val/mae: 0.1077650710940361\n",
            "DGT_mi_dual epoch 90 val/rmse: 0.20235126105842394 val/mae: 0.168687105178833\n",
            "DGT_mi_dual epoch 99 val/rmse: 0.1216295180214235 val/mae: 0.0851675346493721\n",
            "DGT_mi_dual lr: 0.01 test/rmse: 0.2598891143975098 test/mae: 0.09928729385137558\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
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            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<br>    <style><br>        .wandb-row {<br>            display: flex;<br>            flex-direction: row;<br>            flex-wrap: wrap;<br>            justify-content: flex-start;<br>            width: 100%;<br>        }<br>        .wandb-col {<br>            display: flex;<br>            flex-direction: column;<br>            flex-basis: 100%;<br>            flex: 1;<br>            padding: 10px;<br>        }<br>    </style><br><div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>▁▁▁▂▂▂▂▃▃▃▃▃▄▄▄▄▄▄▄▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇▇████</td></tr><tr><td>step</td><td>▃▃▂▄▅▅▆▇▅▂▅▇▂▆▁▅█▇▃▄▅▅█▆▇▃▅▂▃▆▃▇▆▇▃▅▃▃█▅</td></tr><tr><td>test/mae</td><td>▁</td></tr><tr><td>test/rmse</td><td>▁</td></tr><tr><td>train/loss</td><td>▄█▃▂▁▃▅▃▃▃▁▁▁▁▁▃▂▁▁▄▁▁▁▁▁▁▂▁▁▁▁▁▁▁▁▁▁▄▁▁</td></tr><tr><td>val/best_mae</td><td>▁</td></tr><tr><td>val/best_rmse</td><td>▁</td></tr><tr><td>val/mae</td><td>█▅▄▄▃▂▃▂▁▃▁</td></tr><tr><td>val/rmse</td><td>█▅▄▄▃▂▃▂▂▂▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>99</td></tr><tr><td>step</td><td>14</td></tr><tr><td>test/mae</td><td>0.09929</td></tr><tr><td>test/rmse</td><td>0.25989</td></tr><tr><td>train/loss</td><td>0.0151</td></tr><tr><td>val/best_mae</td><td>0.08517</td></tr><tr><td>val/best_rmse</td><td>0.12163</td></tr><tr><td>val/mae</td><td>0.08517</td></tr><tr><td>val/rmse</td><td>0.12163</td></tr></table><br/></div></div>"
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          },
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        {
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              "<IPython.core.display.HTML object>"
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            "text/html": [
              " View run <strong style=\"color:#cdcd00\">DGT_mi_dual_lr_0.01</strong> at: <a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction/runs/ua5u9agb' target=\"_blank\">https://wandb.ai/kevinxli/cs224w-stock-market-prediction/runs/ua5u9agb</a><br> View project at: <a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction' target=\"_blank\">https://wandb.ai/kevinxli/cs224w-stock-market-prediction</a><br>Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
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              "<IPython.core.display.HTML object>"
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              "Find logs at: <code>./wandb/run-20250119_212916-ua5u9agb/logs</code>"
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          "metadata": {}
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        {
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              "<IPython.core.display.HTML object>"
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              "Tracking run with wandb version 0.19.2"
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              "Run data is saved locally in <code>/content/wandb/run-20250119_214329-yctt35b7</code>"
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              "Syncing run <strong><a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction/runs/yctt35b7' target=\"_blank\">DGT_mi_dual_lr_0.1</a></strong> to <a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/developer-guide' target=\"_blank\">docs</a>)<br>"
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              "<IPython.core.display.HTML object>"
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              " View project at <a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction' target=\"_blank\">https://wandb.ai/kevinxli/cs224w-stock-market-prediction</a>"
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              "<IPython.core.display.HTML object>"
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              " View run at <a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction/runs/yctt35b7' target=\"_blank\">https://wandb.ai/kevinxli/cs224w-stock-market-prediction/runs/yctt35b7</a>"
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          },
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        {
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          "text": [
            "DGT_mi_dual epoch 0 val/rmse: 0.6942267996085015 val/mae: 0.43968915939331055\n",
            "DGT_mi_dual epoch 10 val/rmse: 1.513055097374729 val/mae: 1.2570449113845825\n",
            "DGT_mi_dual epoch 20 val/rmse: 0.21284556130644008 val/mae: 0.12287840992212296\n",
            "DGT_mi_dual epoch 30 val/rmse: 0.29924503240868056 val/mae: 0.218398317694664\n",
            "DGT_mi_dual epoch 40 val/rmse: 0.25668211417240455 val/mae: 0.18500438332557678\n",
            "DGT_mi_dual epoch 50 val/rmse: 0.31338376011452096 val/mae: 0.2572592794895172\n",
            "DGT_mi_dual epoch 60 val/rmse: 0.29370700643298053 val/mae: 0.24092897772789001\n",
            "DGT_mi_dual epoch 70 val/rmse: 0.2481150796189882 val/mae: 0.1967838853597641\n",
            "DGT_mi_dual epoch 80 val/rmse: 0.24141548534122279 val/mae: 0.17510837316513062\n",
            "DGT_mi_dual epoch 90 val/rmse: 0.2549060211723098 val/mae: 0.1469665765762329\n",
            "DGT_mi_dual epoch 99 val/rmse: 0.18624654659487494 val/mae: 0.13340604305267334\n",
            "DGT_mi_dual lr: 0.1 test/rmse: 0.6633853826118248 test/mae: 0.21146634221076965\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<br>    <style><br>        .wandb-row {<br>            display: flex;<br>            flex-direction: row;<br>            flex-wrap: wrap;<br>            justify-content: flex-start;<br>            width: 100%;<br>        }<br>        .wandb-col {<br>            display: flex;<br>            flex-direction: column;<br>            flex-basis: 100%;<br>            flex: 1;<br>            padding: 10px;<br>        }<br>    </style><br><div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>▁▁▁▁▂▂▂▂▂▃▃▃▄▄▄▅▅▅▅▅▅▅▅▆▆▆▆▇▇▇▇▇▇▇▇▇████</td></tr><tr><td>step</td><td>▇█▅▃▃▁▃▃▃██▃▃▁▁▁▃▅▇▃▅▃▁▇▃▅▅█▃▅▄▃▅█▁▇▃██▃</td></tr><tr><td>test/mae</td><td>▁</td></tr><tr><td>test/rmse</td><td>▁</td></tr><tr><td>train/loss</td><td>▆▃▃██▂▂▁▂▁▁▁▁▁▁▂▁▁▁▂▆▂▁▂▁▂▂▂▁▂▁▁▃▂▁▂▂▂▁▂</td></tr><tr><td>val/best_mae</td><td>▁</td></tr><tr><td>val/best_rmse</td><td>▁</td></tr><tr><td>val/mae</td><td>▃█▁▂▁▂▂▁▁▁▁</td></tr><tr><td>val/rmse</td><td>▄█▁▂▁▂▂▁▁▁▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>99</td></tr><tr><td>step</td><td>14</td></tr><tr><td>test/mae</td><td>0.21147</td></tr><tr><td>test/rmse</td><td>0.66339</td></tr><tr><td>train/loss</td><td>0.02027</td></tr><tr><td>val/best_mae</td><td>0.13341</td></tr><tr><td>val/best_rmse</td><td>0.18625</td></tr><tr><td>val/mae</td><td>0.13341</td></tr><tr><td>val/rmse</td><td>0.18625</td></tr></table><br/></div></div>"
            ]
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              "<IPython.core.display.HTML object>"
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              " View run <strong style=\"color:#cdcd00\">DGT_mi_dual_lr_0.1</strong> at: <a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction/runs/yctt35b7' target=\"_blank\">https://wandb.ai/kevinxli/cs224w-stock-market-prediction/runs/yctt35b7</a><br> View project at: <a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction' target=\"_blank\">https://wandb.ai/kevinxli/cs224w-stock-market-prediction</a><br>Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
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              "<IPython.core.display.HTML object>"
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            "text/html": [
              "Find logs at: <code>./wandb/run-20250119_214329-yctt35b7/logs</code>"
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          "metadata": {}
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              "Tracking run with wandb version 0.19.2"
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            "text/html": [
              "Run data is saved locally in <code>/content/wandb/run-20250119_215741-8x4pwb05</code>"
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              "Syncing run <strong><a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction/runs/8x4pwb05' target=\"_blank\">DGT_pcc_global_lr_0.01</a></strong> to <a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/developer-guide' target=\"_blank\">docs</a>)<br>"
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              "<IPython.core.display.HTML object>"
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              " View project at <a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction' target=\"_blank\">https://wandb.ai/kevinxli/cs224w-stock-market-prediction</a>"
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              "<IPython.core.display.HTML object>"
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              " View run at <a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction/runs/8x4pwb05' target=\"_blank\">https://wandb.ai/kevinxli/cs224w-stock-market-prediction/runs/8x4pwb05</a>"
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        {
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          "text": [
            "DGT_pcc_global epoch 0 val/rmse: 1.066074697074617 val/mae: 0.777685821056366\n",
            "DGT_pcc_global epoch 10 val/rmse: 0.577958830588208 val/mae: 0.35283103585243225\n",
            "DGT_pcc_global epoch 20 val/rmse: 0.3581205532869752 val/mae: 0.18000569939613342\n",
            "DGT_pcc_global epoch 30 val/rmse: 0.28523552458925105 val/mae: 0.15971672534942627\n",
            "DGT_pcc_global epoch 40 val/rmse: 0.19334186127848263 val/mae: 0.13116315007209778\n",
            "DGT_pcc_global epoch 50 val/rmse: 0.23222657822078718 val/mae: 0.10899177938699722\n",
            "DGT_pcc_global epoch 60 val/rmse: 0.23714674886052958 val/mae: 0.1874692142009735\n",
            "DGT_pcc_global epoch 70 val/rmse: 0.1837740175451858 val/mae: 0.09711305797100067\n",
            "DGT_pcc_global epoch 80 val/rmse: 0.18948618610215193 val/mae: 0.12731781601905823\n",
            "DGT_pcc_global epoch 90 val/rmse: 0.1741502755572811 val/mae: 0.0949571430683136\n",
            "DGT_pcc_global epoch 99 val/rmse: 0.27686721534635217 val/mae: 0.14365161955356598\n",
            "DGT_pcc_global lr: 0.01 test/rmse: 0.6430229572051165 test/mae: 0.14795534312725067\n"
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        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<br>    <style><br>        .wandb-row {<br>            display: flex;<br>            flex-direction: row;<br>            flex-wrap: wrap;<br>            justify-content: flex-start;<br>            width: 100%;<br>        }<br>        .wandb-col {<br>            display: flex;<br>            flex-direction: column;<br>            flex-basis: 100%;<br>            flex: 1;<br>            padding: 10px;<br>        }<br>    </style><br><div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>▁▁▂▂▂▂▂▂▂▃▃▃▃▃▃▄▄▄▄▄▅▅▅▅▆▆▆▆▆▆▆▇▇▇▇▇▇▇▇█</td></tr><tr><td>step</td><td>▃▃█▄▁▆▃▅▃▅▅▃▅▇▇▃▄▁▇▅▇▆█▅▇▅▄▆▁▂▅▇▃▇▆▂█▅▃▅</td></tr><tr><td>test/mae</td><td>▁</td></tr><tr><td>test/rmse</td><td>▁</td></tr><tr><td>train/loss</td><td>▄▁▂▂▂▂▂▁▁█▄▂▂▁▁▁▁▁▁▁▁▁▁▅▃▁▁▁▁▁▁▃▁▂▁▁▁▁▁▁</td></tr><tr><td>val/best_mae</td><td>▁</td></tr><tr><td>val/best_rmse</td><td>▁</td></tr><tr><td>val/mae</td><td>█▄▂▂▁▁▂▁▁▁▁</td></tr><tr><td>val/rmse</td><td>█▄▂▂▁▁▁▁▁▁▂</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>99</td></tr><tr><td>step</td><td>14</td></tr><tr><td>test/mae</td><td>0.14796</td></tr><tr><td>test/rmse</td><td>0.64302</td></tr><tr><td>train/loss</td><td>0.03702</td></tr><tr><td>val/best_mae</td><td>0.09496</td></tr><tr><td>val/best_rmse</td><td>0.17415</td></tr><tr><td>val/mae</td><td>0.14365</td></tr><tr><td>val/rmse</td><td>0.27687</td></tr></table><br/></div></div>"
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              "<IPython.core.display.HTML object>"
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              " View run <strong style=\"color:#cdcd00\">DGT_pcc_global_lr_0.01</strong> at: <a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction/runs/8x4pwb05' target=\"_blank\">https://wandb.ai/kevinxli/cs224w-stock-market-prediction/runs/8x4pwb05</a><br> View project at: <a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction' target=\"_blank\">https://wandb.ai/kevinxli/cs224w-stock-market-prediction</a><br>Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
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              "<IPython.core.display.HTML object>"
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              "Find logs at: <code>./wandb/run-20250119_215741-8x4pwb05/logs</code>"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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              "<IPython.core.display.HTML object>"
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              "Tracking run with wandb version 0.19.2"
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        {
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              "<IPython.core.display.HTML object>"
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              "Run data is saved locally in <code>/content/wandb/run-20250119_220906-bpsv7yp4</code>"
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        {
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              "<IPython.core.display.HTML object>"
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            "text/html": [
              "Syncing run <strong><a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction/runs/bpsv7yp4' target=\"_blank\">DGT_pcc_global_lr_0.1</a></strong> to <a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/developer-guide' target=\"_blank\">docs</a>)<br>"
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        {
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              "<IPython.core.display.HTML object>"
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              " View project at <a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction' target=\"_blank\">https://wandb.ai/kevinxli/cs224w-stock-market-prediction</a>"
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        {
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              " View run at <a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction/runs/bpsv7yp4' target=\"_blank\">https://wandb.ai/kevinxli/cs224w-stock-market-prediction/runs/bpsv7yp4</a>"
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        {
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          "text": [
            "DGT_pcc_global epoch 0 val/rmse: 0.8879743584397527 val/mae: 0.5402513146400452\n",
            "DGT_pcc_global epoch 10 val/rmse: 1.8407399450916289 val/mae: 0.2869521677494049\n",
            "DGT_pcc_global epoch 20 val/rmse: 1.6265783714084423 val/mae: 0.20345649123191833\n",
            "DGT_pcc_global epoch 30 val/rmse: 1.4992064125145774 val/mae: 0.262802392244339\n",
            "DGT_pcc_global epoch 40 val/rmse: 1.3776486635383223 val/mae: 0.39483073353767395\n",
            "DGT_pcc_global epoch 50 val/rmse: 1.6133821100291263 val/mae: 0.1863420605659485\n",
            "DGT_pcc_global epoch 60 val/rmse: 1.7617123216735893 val/mae: 0.224675253033638\n",
            "DGT_pcc_global epoch 70 val/rmse: 1.9495031726475758 val/mae: 0.42596253752708435\n",
            "DGT_pcc_global epoch 80 val/rmse: 1.72775696738175 val/mae: 0.18614937365055084\n",
            "DGT_pcc_global epoch 90 val/rmse: 1.7126741696633032 val/mae: 0.20532815158367157\n",
            "DGT_pcc_global epoch 99 val/rmse: 1.6333274211906297 val/mae: 0.270331472158432\n",
            "DGT_pcc_global lr: 0.1 test/rmse: 3.1046794152118564 test/mae: 0.8780212998390198\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
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            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<br>    <style><br>        .wandb-row {<br>            display: flex;<br>            flex-direction: row;<br>            flex-wrap: wrap;<br>            justify-content: flex-start;<br>            width: 100%;<br>        }<br>        .wandb-col {<br>            display: flex;<br>            flex-direction: column;<br>            flex-basis: 100%;<br>            flex: 1;<br>            padding: 10px;<br>        }<br>    </style><br><div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>▁▁▂▂▂▂▂▂▂▂▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▅▅▅▆▆▆▆▆▆▆▇▇▇▇█</td></tr><tr><td>step</td><td>▇█▃▁█▅█▃▃▁▃▁▃▃▅▁▅▂▆▇▇▄▅▅▅▃▃▁▇▁▃█▃▂▅▁▃▁█▇</td></tr><tr><td>test/mae</td><td>▁</td></tr><tr><td>test/rmse</td><td>▁</td></tr><tr><td>train/loss</td><td>█▅█▂▁▁▁▁▁▁▁▁▁▁▁▁▁▂▁▂▁▁▁▁▂▁▁▁▁▁▁▂▁▁▁▁▁▁▁▁</td></tr><tr><td>val/best_mae</td><td>▁</td></tr><tr><td>val/best_rmse</td><td>▁</td></tr><tr><td>val/mae</td><td>█▃▁▃▅▁▂▆▁▁▃</td></tr><tr><td>val/rmse</td><td>▁▇▆▅▄▆▇█▇▆▆</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>99</td></tr><tr><td>step</td><td>14</td></tr><tr><td>test/mae</td><td>0.87802</td></tr><tr><td>test/rmse</td><td>3.10468</td></tr><tr><td>train/loss</td><td>0.05394</td></tr><tr><td>val/best_mae</td><td>0.54025</td></tr><tr><td>val/best_rmse</td><td>0.88797</td></tr><tr><td>val/mae</td><td>0.27033</td></tr><tr><td>val/rmse</td><td>1.63333</td></tr></table><br/></div></div>"
            ]
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        {
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              "<IPython.core.display.HTML object>"
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              " View run <strong style=\"color:#cdcd00\">DGT_pcc_global_lr_0.1</strong> at: <a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction/runs/bpsv7yp4' target=\"_blank\">https://wandb.ai/kevinxli/cs224w-stock-market-prediction/runs/bpsv7yp4</a><br> View project at: <a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction' target=\"_blank\">https://wandb.ai/kevinxli/cs224w-stock-market-prediction</a><br>Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
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              "<IPython.core.display.HTML object>"
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            "text/html": [
              "Find logs at: <code>./wandb/run-20250119_220906-bpsv7yp4/logs</code>"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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              "<IPython.core.display.HTML object>"
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              "Tracking run with wandb version 0.19.2"
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              "Run data is saved locally in <code>/content/wandb/run-20250119_222056-uzdblt4s</code>"
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            "text/html": [
              "Syncing run <strong><a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction/runs/uzdblt4s' target=\"_blank\">DGT_pcc_local_lr_0.01</a></strong> to <a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/developer-guide' target=\"_blank\">docs</a>)<br>"
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              "<IPython.core.display.HTML object>"
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              " View project at <a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction' target=\"_blank\">https://wandb.ai/kevinxli/cs224w-stock-market-prediction</a>"
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        {
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              "<IPython.core.display.HTML object>"
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              " View run at <a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction/runs/uzdblt4s' target=\"_blank\">https://wandb.ai/kevinxli/cs224w-stock-market-prediction/runs/uzdblt4s</a>"
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          },
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        {
          "output_type": "stream",
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          "text": [
            "DGT_pcc_local epoch 0 val/rmse: 0.8985436418280034 val/mae: 0.550024151802063\n",
            "DGT_pcc_local epoch 10 val/rmse: 0.6056835654869687 val/mae: 0.4801201820373535\n",
            "DGT_pcc_local epoch 20 val/rmse: 0.3533077155504064 val/mae: 0.2916525900363922\n",
            "DGT_pcc_local epoch 30 val/rmse: 0.15869354246609046 val/mae: 0.10822608321905136\n",
            "DGT_pcc_local epoch 40 val/rmse: 0.1591691908856734 val/mae: 0.11498145759105682\n",
            "DGT_pcc_local epoch 50 val/rmse: 0.13670301346813016 val/mae: 0.08955355733633041\n",
            "DGT_pcc_local epoch 60 val/rmse: 0.15843628549956776 val/mae: 0.09703715145587921\n",
            "DGT_pcc_local epoch 70 val/rmse: 0.12820453820853162 val/mae: 0.08339686691761017\n",
            "DGT_pcc_local epoch 80 val/rmse: 0.24204782334404729 val/mae: 0.20554694533348083\n",
            "DGT_pcc_local epoch 90 val/rmse: 0.17953268928714503 val/mae: 0.12237368524074554\n",
            "DGT_pcc_local epoch 99 val/rmse: 0.11312804879247905 val/mae: 0.07187668234109879\n",
            "DGT_pcc_local lr: 0.01 test/rmse: 0.2941033328303634 test/mae: 0.08675127476453781\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<br>    <style><br>        .wandb-row {<br>            display: flex;<br>            flex-direction: row;<br>            flex-wrap: wrap;<br>            justify-content: flex-start;<br>            width: 100%;<br>        }<br>        .wandb-col {<br>            display: flex;<br>            flex-direction: column;<br>            flex-basis: 100%;<br>            flex: 1;<br>            padding: 10px;<br>        }<br>    </style><br><div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>▁▁▁▁▂▂▂▂▂▂▃▃▃▃▃▃▃▄▄▄▄▅▅▅▅▅▅▅▅▆▆▆▆▇▇█████</td></tr><tr><td>step</td><td>▃▃█▅▂▆▃▃▁▅▅▃▇▃█▁▃▁▅▅▅█▅█▅▁▅▅▅▇▂▅▇▁▄▇▇▃▃▁</td></tr><tr><td>test/mae</td><td>▁</td></tr><tr><td>test/rmse</td><td>▁</td></tr><tr><td>train/loss</td><td>█▇▄▄▆▅▂▅▂▃▂▁▂▂▁▂▂▂▂▂▁▂▁▁▁▁▂▁▂▁▁▂▂▃▂▁▁▃▁▁</td></tr><tr><td>val/best_mae</td><td>▁</td></tr><tr><td>val/best_rmse</td><td>▁</td></tr><tr><td>val/mae</td><td>█▇▄▂▂▁▁▁▃▂▁</td></tr><tr><td>val/rmse</td><td>█▅▃▁▁▁▁▁▂▂▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>99</td></tr><tr><td>step</td><td>14</td></tr><tr><td>test/mae</td><td>0.08675</td></tr><tr><td>test/rmse</td><td>0.2941</td></tr><tr><td>train/loss</td><td>0.01151</td></tr><tr><td>val/best_mae</td><td>0.07188</td></tr><tr><td>val/best_rmse</td><td>0.11313</td></tr><tr><td>val/mae</td><td>0.07188</td></tr><tr><td>val/rmse</td><td>0.11313</td></tr></table><br/></div></div>"
            ]
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        {
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              "<IPython.core.display.HTML object>"
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              " View run <strong style=\"color:#cdcd00\">DGT_pcc_local_lr_0.01</strong> at: <a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction/runs/uzdblt4s' target=\"_blank\">https://wandb.ai/kevinxli/cs224w-stock-market-prediction/runs/uzdblt4s</a><br> View project at: <a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction' target=\"_blank\">https://wandb.ai/kevinxli/cs224w-stock-market-prediction</a><br>Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
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          "output_type": "display_data",
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              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Find logs at: <code>./wandb/run-20250119_222056-uzdblt4s/logs</code>"
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          },
          "metadata": {}
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        {
          "output_type": "display_data",
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              "<IPython.core.display.HTML object>"
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            "text/html": [
              "Tracking run with wandb version 0.19.2"
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            "text/html": [
              "Run data is saved locally in <code>/content/wandb/run-20250119_223303-f3qg6gdm</code>"
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            "text/html": [
              "Syncing run <strong><a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction/runs/f3qg6gdm' target=\"_blank\">DGT_pcc_local_lr_0.1</a></strong> to <a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/developer-guide' target=\"_blank\">docs</a>)<br>"
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          },
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              "<IPython.core.display.HTML object>"
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            "text/html": [
              " View project at <a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction' target=\"_blank\">https://wandb.ai/kevinxli/cs224w-stock-market-prediction</a>"
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          },
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        {
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              "<IPython.core.display.HTML object>"
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              " View run at <a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction/runs/f3qg6gdm' target=\"_blank\">https://wandb.ai/kevinxli/cs224w-stock-market-prediction/runs/f3qg6gdm</a>"
            ]
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        {
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          "text": [
            "DGT_pcc_local epoch 0 val/rmse: 0.6162145164131906 val/mae: 0.46609604358673096\n",
            "DGT_pcc_local epoch 10 val/rmse: 0.7962484327387693 val/mae: 0.25552991032600403\n",
            "DGT_pcc_local epoch 20 val/rmse: 0.761364233218597 val/mae: 0.2903038263320923\n",
            "DGT_pcc_local epoch 30 val/rmse: 0.6128863138177298 val/mae: 0.22324974834918976\n",
            "DGT_pcc_local epoch 40 val/rmse: 0.620641483729527 val/mae: 0.20450033247470856\n",
            "DGT_pcc_local epoch 50 val/rmse: 0.656507418871679 val/mae: 0.1447349190711975\n",
            "DGT_pcc_local epoch 60 val/rmse: 0.762964804266313 val/mae: 0.1745220124721527\n",
            "DGT_pcc_local epoch 70 val/rmse: 0.7329783042621677 val/mae: 0.16009120643138885\n",
            "DGT_pcc_local epoch 80 val/rmse: 0.7164827744808504 val/mae: 0.1376502513885498\n",
            "DGT_pcc_local epoch 90 val/rmse: 0.7559001428615346 val/mae: 0.20387840270996094\n",
            "DGT_pcc_local epoch 99 val/rmse: 0.7303876780843128 val/mae: 0.18701553344726562\n",
            "DGT_pcc_local lr: 0.1 test/rmse: 2.506897376109701 test/mae: 0.5010432004928589\n"
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<br>    <style><br>        .wandb-row {<br>            display: flex;<br>            flex-direction: row;<br>            flex-wrap: wrap;<br>            justify-content: flex-start;<br>            width: 100%;<br>        }<br>        .wandb-col {<br>            display: flex;<br>            flex-direction: column;<br>            flex-basis: 100%;<br>            flex: 1;<br>            padding: 10px;<br>        }<br>    </style><br><div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>▁▁▁▁▂▂▂▂▂▂▂▂▃▃▃▃▃▃▄▄▄▄▄▄▅▅▅▅▅▅▅▆▆▆▆▆▇███</td></tr><tr><td>step</td><td>▅▇▅█▇█▁█▃▇▂▃▇▃▃█▇▃▂▇▇▁▅▁▃▃▆▁▄█▃▃▄▂▅▃▁▇█▃</td></tr><tr><td>test/mae</td><td>▁</td></tr><tr><td>test/rmse</td><td>▁</td></tr><tr><td>train/loss</td><td>█▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>val/best_mae</td><td>▁</td></tr><tr><td>val/best_rmse</td><td>▁</td></tr><tr><td>val/mae</td><td>█▄▄▃▂▁▂▁▁▂▂</td></tr><tr><td>val/rmse</td><td>▁█▇▁▁▃▇▆▅▆▅</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>99</td></tr><tr><td>step</td><td>14</td></tr><tr><td>test/mae</td><td>0.50104</td></tr><tr><td>test/rmse</td><td>2.5069</td></tr><tr><td>train/loss</td><td>0.01632</td></tr><tr><td>val/best_mae</td><td>0.22325</td></tr><tr><td>val/best_rmse</td><td>0.61289</td></tr><tr><td>val/mae</td><td>0.18702</td></tr><tr><td>val/rmse</td><td>0.73039</td></tr></table><br/></div></div>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              " View run <strong style=\"color:#cdcd00\">DGT_pcc_local_lr_0.1</strong> at: <a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction/runs/f3qg6gdm' target=\"_blank\">https://wandb.ai/kevinxli/cs224w-stock-market-prediction/runs/f3qg6gdm</a><br> View project at: <a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction' target=\"_blank\">https://wandb.ai/kevinxli/cs224w-stock-market-prediction</a><br>Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Find logs at: <code>./wandb/run-20250119_223303-f3qg6gdm/logs</code>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Tracking run with wandb version 0.19.2"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Run data is saved locally in <code>/content/wandb/run-20250119_224536-pioxs823</code>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Syncing run <strong><a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction/runs/pioxs823' target=\"_blank\">DGT_pcc_dual_lr_0.01</a></strong> to <a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/developer-guide' target=\"_blank\">docs</a>)<br>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              " View project at <a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction' target=\"_blank\">https://wandb.ai/kevinxli/cs224w-stock-market-prediction</a>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              " View run at <a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction/runs/pioxs823' target=\"_blank\">https://wandb.ai/kevinxli/cs224w-stock-market-prediction/runs/pioxs823</a>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DGT_pcc_dual epoch 0 val/rmse: 1.0027066199069214 val/mae: 0.531448245048523\n",
            "DGT_pcc_dual epoch 10 val/rmse: 0.5686480912932805 val/mae: 0.30941909551620483\n",
            "DGT_pcc_dual epoch 20 val/rmse: 0.3920950693278707 val/mae: 0.14852195978164673\n",
            "DGT_pcc_dual epoch 30 val/rmse: 0.42255946547839507 val/mae: 0.16893088817596436\n",
            "DGT_pcc_dual epoch 40 val/rmse: 0.36900113008165863 val/mae: 0.13784931600093842\n",
            "DGT_pcc_dual epoch 50 val/rmse: 0.39474134233486063 val/mae: 0.17326299846172333\n",
            "DGT_pcc_dual epoch 60 val/rmse: 0.34443931390973087 val/mae: 0.1496657431125641\n",
            "DGT_pcc_dual epoch 70 val/rmse: 0.4169347218709851 val/mae: 0.26017606258392334\n",
            "DGT_pcc_dual epoch 80 val/rmse: 0.16448492209415183 val/mae: 0.12450996786355972\n",
            "DGT_pcc_dual epoch 90 val/rmse: 0.205356155120654 val/mae: 0.12474433332681656\n",
            "DGT_pcc_dual epoch 99 val/rmse: 0.1836568034230089 val/mae: 0.10215266048908234\n",
            "DGT_pcc_dual lr: 0.01 test/rmse: 0.29678211515858255 test/mae: 0.15739485621452332\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<br>    <style><br>        .wandb-row {<br>            display: flex;<br>            flex-direction: row;<br>            flex-wrap: wrap;<br>            justify-content: flex-start;<br>            width: 100%;<br>        }<br>        .wandb-col {<br>            display: flex;<br>            flex-direction: column;<br>            flex-basis: 100%;<br>            flex: 1;<br>            padding: 10px;<br>        }<br>    </style><br><div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>▁▁▁▁▁▂▂▂▂▂▂▂▃▃▃▃▄▄▄▄▅▅▅▅▅▅▆▆▆▆▇▇▇▇▇▇▇▇██</td></tr><tr><td>step</td><td>▁▇▄▇▃▃▇▇▂▁▅▃▅▃▁▁▃▇▇█▁▃▅▅▄▃▃▁▅▅▁▇▇█▃▇▁▁▂▃</td></tr><tr><td>test/mae</td><td>▁</td></tr><tr><td>test/rmse</td><td>▁</td></tr><tr><td>train/loss</td><td>█▂▁▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▂▁▁▂▁▁▁▁▂▁▁▁▁▁</td></tr><tr><td>val/best_mae</td><td>▁</td></tr><tr><td>val/best_rmse</td><td>▁</td></tr><tr><td>val/mae</td><td>█▄▂▂▂▂▂▄▁▁▁</td></tr><tr><td>val/rmse</td><td>█▄▃▃▃▃▃▃▁▁▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>99</td></tr><tr><td>step</td><td>14</td></tr><tr><td>test/mae</td><td>0.15739</td></tr><tr><td>test/rmse</td><td>0.29678</td></tr><tr><td>train/loss</td><td>0.01558</td></tr><tr><td>val/best_mae</td><td>0.12451</td></tr><tr><td>val/best_rmse</td><td>0.16448</td></tr><tr><td>val/mae</td><td>0.10215</td></tr><tr><td>val/rmse</td><td>0.18366</td></tr></table><br/></div></div>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              " View run <strong style=\"color:#cdcd00\">DGT_pcc_dual_lr_0.01</strong> at: <a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction/runs/pioxs823' target=\"_blank\">https://wandb.ai/kevinxli/cs224w-stock-market-prediction/runs/pioxs823</a><br> View project at: <a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction' target=\"_blank\">https://wandb.ai/kevinxli/cs224w-stock-market-prediction</a><br>Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Find logs at: <code>./wandb/run-20250119_224536-pioxs823/logs</code>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Tracking run with wandb version 0.19.2"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Run data is saved locally in <code>/content/wandb/run-20250119_225959-6w2qd34h</code>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Syncing run <strong><a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction/runs/6w2qd34h' target=\"_blank\">DGT_pcc_dual_lr_0.1</a></strong> to <a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/developer-guide' target=\"_blank\">docs</a>)<br>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              " View project at <a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction' target=\"_blank\">https://wandb.ai/kevinxli/cs224w-stock-market-prediction</a>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              " View run at <a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction/runs/6w2qd34h' target=\"_blank\">https://wandb.ai/kevinxli/cs224w-stock-market-prediction/runs/6w2qd34h</a>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DGT_pcc_dual epoch 0 val/rmse: 0.5137868252199914 val/mae: 0.3425385653972626\n",
            "DGT_pcc_dual epoch 10 val/rmse: 0.6532990547679005 val/mae: 0.16436786949634552\n",
            "DGT_pcc_dual epoch 20 val/rmse: 0.5517294817377448 val/mae: 0.1413598507642746\n",
            "DGT_pcc_dual epoch 30 val/rmse: 0.5629765558138379 val/mae: 0.13847942650318146\n",
            "DGT_pcc_dual epoch 40 val/rmse: 0.7624036790638924 val/mae: 0.2591487765312195\n",
            "DGT_pcc_dual epoch 50 val/rmse: 0.7292081594014849 val/mae: 0.3468876779079437\n",
            "DGT_pcc_dual epoch 60 val/rmse: 0.8175930905783602 val/mae: 0.6307493448257446\n",
            "DGT_pcc_dual epoch 70 val/rmse: 0.9565153327287272 val/mae: 0.2979082763195038\n",
            "DGT_pcc_dual epoch 80 val/rmse: 0.7688234433703265 val/mae: 0.20415645837783813\n",
            "DGT_pcc_dual epoch 90 val/rmse: 0.6282031708086251 val/mae: 0.21568505465984344\n",
            "DGT_pcc_dual epoch 99 val/rmse: 0.6459992769815988 val/mae: 0.15335515141487122\n",
            "DGT_pcc_dual lr: 0.1 test/rmse: 1.0980833044153202 test/mae: 0.5658656358718872\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<br>    <style><br>        .wandb-row {<br>            display: flex;<br>            flex-direction: row;<br>            flex-wrap: wrap;<br>            justify-content: flex-start;<br>            width: 100%;<br>        }<br>        .wandb-col {<br>            display: flex;<br>            flex-direction: column;<br>            flex-basis: 100%;<br>            flex: 1;<br>            padding: 10px;<br>        }<br>    </style><br><div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>▁▁▁▁▁▂▂▂▂▂▃▃▃▃▃▃▃▄▄▄▄▄▅▅▅▆▆▆▆▆▆▇▇▇▇▇▇███</td></tr><tr><td>step</td><td>█▅▆▅▄▅▇▇▃▃▃▁▃▃▃▂▇▅▃▇▃▅▆▂▅▅▂▅▄▃▄█▃▅▃▁▇▇▆█</td></tr><tr><td>test/mae</td><td>▁</td></tr><tr><td>test/rmse</td><td>▁</td></tr><tr><td>train/loss</td><td>▇▂▃▁▁▁▃▁▁▁▁▁▁▃▂▂▂▂▃▁▁▂▁▂▂▂▂█▃▁▃▂▃▁▁▃▂▂▂▂</td></tr><tr><td>val/best_mae</td><td>▁</td></tr><tr><td>val/best_rmse</td><td>▁</td></tr><tr><td>val/mae</td><td>▄▁▁▁▃▄█▃▂▂▁</td></tr><tr><td>val/rmse</td><td>▁▃▂▂▅▄▆█▅▃▃</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>99</td></tr><tr><td>step</td><td>14</td></tr><tr><td>test/mae</td><td>0.56587</td></tr><tr><td>test/rmse</td><td>1.09808</td></tr><tr><td>train/loss</td><td>0.03013</td></tr><tr><td>val/best_mae</td><td>0.34254</td></tr><tr><td>val/best_rmse</td><td>0.51379</td></tr><tr><td>val/mae</td><td>0.15336</td></tr><tr><td>val/rmse</td><td>0.646</td></tr></table><br/></div></div>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              " View run <strong style=\"color:#cdcd00\">DGT_pcc_dual_lr_0.1</strong> at: <a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction/runs/6w2qd34h' target=\"_blank\">https://wandb.ai/kevinxli/cs224w-stock-market-prediction/runs/6w2qd34h</a><br> View project at: <a href='https://wandb.ai/kevinxli/cs224w-stock-market-prediction' target=\"_blank\">https://wandb.ai/kevinxli/cs224w-stock-market-prediction</a><br>Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Find logs at: <code>./wandb/run-20250119_225959-6w2qd34h/logs</code>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "import wandb\n",
        "import os\n",
        "\n",
        "# Train a model with the input configs on train_samples for num_epochs under the learning rate lr\n",
        "# You can pass track_with_wandb=True to trace the entire training process with Weights and Biases\n",
        "def train(gnn, use_spatial, corr_name, corr_scope, train_samples, val_samples, num_epochs, lr, track_with_wandb):\n",
        "    os.makedirs(f'{workdir}/models', exist_ok=True)\n",
        "\n",
        "    model = get_model(gnn, use_spatial, corr_name, corr_scope, lr, load_weights=False)\n",
        "\n",
        "    optimizer = torch.optim.Adam(model.parameters(), lr=lr)\n",
        "    best_val_rmse = float('inf')\n",
        "    best_val_mae = float('inf')\n",
        "    eval_per_epoch = 10\n",
        "\n",
        "    if track_with_wandb:\n",
        "        wandb_run = wandb.init(project=\"cs224w-stock-market-prediction\",\n",
        "                   name=f'{model.model_name()}_lr_{lr}',\n",
        "                   config={\n",
        "                       \"corr_name\": corr_name,\n",
        "                       \"corr_scope\": corr_scope,\n",
        "                       \"learning_rate\": lr,\n",
        "                       \"epochs\": num_epochs,\n",
        "                       \"architecture\": gnn.__name__,\n",
        "                       \"use_spatial\": use_spatial,\n",
        "                   },\n",
        "                   reinit=True,\n",
        "                   )\n",
        "\n",
        "    for epoch in range(num_epochs):\n",
        "        model.train()\n",
        "        train_loss = 0\n",
        "        for step, snapshot in enumerate(train_samples):\n",
        "            optimizer.zero_grad()\n",
        "            X = snapshot.x\n",
        "            y_hats, _ = model(X.transpose(0, 1), snapshot.edge_index, snapshot.edge_attr, hidden=None)\n",
        "            loss = F.mse_loss(y_hats.squeeze(), snapshot.y.to(model.model_device()))\n",
        "            train_loss += loss.item()\n",
        "            loss.backward()\n",
        "            optimizer.step()\n",
        "            if track_with_wandb:\n",
        "                wandb.log({\"epoch\": epoch, \"step\": step, \"train/loss\": loss.item() })\n",
        "        train_loss /= len(train_samples)\n",
        "\n",
        "        if epoch % eval_per_epoch == 0 or epoch == num_epochs - 1:\n",
        "            result = eval(model, val_samples)\n",
        "            val_rmse = result['rmse']\n",
        "            val_mae = result['mae']\n",
        "            print(f'{model.model_name()} epoch {epoch} val/rmse: {val_rmse} val/mae: {val_mae}')\n",
        "            if track_with_wandb:\n",
        "                wandb.log({\"epoch\": epoch, \"val/rmse\": val_rmse, \"val/mae\": val_mae })\n",
        "            if val_rmse < best_val_rmse:\n",
        "                best_val_rmse = val_rmse\n",
        "                best_val_mae = val_mae\n",
        "                torch.save(model.state_dict(), f'{workdir}/models/{model.model_name()}_lr_{lr}.pth')\n",
        "    if track_with_wandb:\n",
        "        wandb.log({\"val/best_rmse\": best_val_rmse, \"val/best_mae\": best_val_mae })\n",
        "        return wandb_run\n",
        "\n",
        "\n",
        "def run(args):\n",
        "    config, num_epochs, track_with_wandb = args\n",
        "    gnn, use_spatial, corr_name, corr_scope = config\n",
        "    dataset = get_dataset(corr_name, corr_scope)\n",
        "    # Do a grid search over learning rate. We found that models are sensitive to lr so we need to try different options.\n",
        "    for lr in [0.01, 0.1]:\n",
        "        wandb_run = train(gnn, use_spatial, corr_name, corr_scope, dataset['train_samples'], dataset['val_samples'], num_epochs, lr, track_with_wandb)\n",
        "        # Test\n",
        "        best_model = get_model(gnn, use_spatial, corr_name, corr_scope, lr, load_weights=True)\n",
        "        result = eval(best_model, dataset['test_samples'])\n",
        "        test_rmse = result['rmse']\n",
        "        test_mae = result['mae']\n",
        "        print(f'{best_model.model_name()} lr: {lr} test/rmse: {test_rmse} test/mae: {test_mae}')\n",
        "        if track_with_wandb:\n",
        "            wandb.log({\"test/rmse\": test_rmse, \"test/mae\": test_mae })\n",
        "            wandb_run.finish()\n",
        "\n",
        "# List all the model variants for the experiment\n",
        "model_configs = [(GRU, False, None, None),\n",
        "                 (DGT, False, None, None),\n",
        "                 (DGT, True, None, None),\n",
        "                 (DGT, True, 'mi', 'global'),\n",
        "                 (DGT, True, 'mi', 'local'),\n",
        "                 (DGT, True, 'mi', 'dual'),\n",
        "                 (DGT, True, 'pcc', 'global'),\n",
        "                 (DGT, True, 'pcc', 'local'),\n",
        "                 (DGT, True, 'pcc', 'dual'),\n",
        "                 ]\n",
        "\n",
        "num_epochs = 100\n",
        "track_with_wandb = True\n",
        "\n",
        "if track_with_wandb:\n",
        "    wandb.login()\n",
        "\n",
        "_ = list(map(run, [(config, num_epochs, track_with_wandb) for config in model_configs]))"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Results Show Local Mutual Information Performs the Best, Followed by Dual Pearson"
      ],
      "metadata": {
        "id": "AvlH4SXnPK7X"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Function to test a bunch of models given by model_configs on the test set\n",
        "def test(model_configs):\n",
        "    results = {}\n",
        "    for config in model_configs:\n",
        "        (gnn, use_spatial, corr_name, corr_scope, lr) = config\n",
        "        eval_dataset = get_dataset(corr_name, corr_scope)['test_samples']\n",
        "        model = get_model(gnn, use_spatial, corr_name, corr_scope, lr=lr, load_weights=True)\n",
        "        if model is None:\n",
        "            continue\n",
        "        results[config] = eval(model=model, eval_dataset=eval_dataset)\n",
        "    return results\n",
        "\n",
        "# Test each model under its best learning rate based on validation performance\n",
        "model_configs = [(GRU, False, None, None, 0.01),\n",
        "                 (DGT, False, None, None, 0.1),\n",
        "                 (DGT, True, None, None, 0.01),\n",
        "                 (DGT, True, 'mi', 'global', 0.01),\n",
        "                 (DGT, True, 'mi', 'local', 0.01),\n",
        "                 (DGT, True, 'mi', 'dual', 0.01),\n",
        "                 (DGT, True, 'pcc', 'global', 0.01),\n",
        "                 (DGT, True, 'pcc', 'local', 0.01),\n",
        "                 (DGT, True, 'pcc', 'dual', 0.01),\n",
        "                 ]\n",
        "test_results = test(model_configs)"
      ],
      "metadata": {
        "id": "xlwKYtG4PhYp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Test the models and show a table of results\n",
        "test_results_df = []\n",
        "for config, result in test_results.items():\n",
        "    (arch, use_spatial, corr_name, corr_scope, lr) = config\n",
        "    test_results_df.append({'Architecture': arch.__name__,\n",
        "                            'Use Spatial': use_spatial,\n",
        "                            'Correlation': corr_name,\n",
        "                            'Scope': corr_scope,\n",
        "                            'RMSE': result['rmse'],\n",
        "                            'MAE': result['mae']})\n",
        "\n",
        "test_results_df = pd.DataFrame(test_results_df)\n",
        "test_results_df = test_results_df.sort_values(by='RMSE', ascending=False)\n",
        "print('Test results sorted in descending RMSE (Lower the better)')\n",
        "display(test_results_df)"
      ],
      "metadata": {
        "id": "qX7TIllclzN1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        },
        "outputId": "8afde05b-f7db-4424-c921-b084467e30d1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test results sorted in descending RMSE (Lower the better)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  Architecture  Use Spatial Correlation   Scope      RMSE       MAE\n",
              "0          GRU        False        None    None  3.116608  0.407619\n",
              "2          DGT         True        None    None  1.473705  0.292516\n",
              "6          DGT         True         pcc  global  0.643023  0.147955\n",
              "3          DGT         True          mi  global  0.465103  0.114918\n",
              "1          DGT        False        None    None  0.432899  0.181658\n",
              "4          DGT         True          mi   local  0.324191  0.133980\n",
              "8          DGT         True         pcc    dual  0.296782  0.157395\n",
              "7          DGT         True         pcc   local  0.294103  0.086751\n",
              "5          DGT         True          mi    dual  0.259889  0.099287"
            ],
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              "      <th>Architecture</th>\n",
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              "      <th>7</th>\n",
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              "        document.querySelector('#id_c6abb6c9-774e-4a19-946a-c37573a17e51 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('test_results_df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "test_results_df",
              "summary": "{\n  \"name\": \"test_results_df\",\n  \"rows\": 9,\n  \"fields\": [\n    {\n      \"column\": \"Architecture\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"DGT\",\n          \"GRU\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Use Spatial\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Correlation\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"mi\",\n          \"pcc\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Scope\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"global\",\n          \"local\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RMSE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.9432741552197175,\n        \"min\": 0.2598891143975098,\n        \"max\": 3.1166079945745024,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          0.2941033328303634,\n          1.473704888039203\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MAE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.10462648465800446,\n        \"min\": 0.08675127476453781,\n        \"max\": 0.4076187312602997,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          0.08675127476453781,\n          0.2925158739089966\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KTzU4f_F0dFI"
      },
      "source": [
        "# Visualize Results"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xDyg3IUk0dFI"
      },
      "outputs": [],
      "source": [
        "df = pd.read_csv(f'{workdir}/sp500.csv')\n",
        "df['Date'] = pd.to_datetime(df['Date'])\n",
        "df = df.set_index('Date')\n",
        "stock_names = df.columns\n",
        "stock_lookup = {name: i for i, name in enumerate(stock_names)}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3qwQR-z00dFJ"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "from cycler import cycler\n",
        "\n",
        "# Plot regression results on test set\n",
        "def plot_regression(configs, labels, fig_name, stock_name, test_results):\n",
        "    stock_index = stock_lookup[stock_name]\n",
        "    plt.clf()\n",
        "    plt.figure(figsize=(10, 6))\n",
        "    colors = [(0.650, 0.120, 0.240, 0.6),  # red\n",
        "              (0.122, 0.467, 0.706, 0.6), # blue\n",
        "              (1.000, 0.498, 0.055), # orange\n",
        "              (0.580, 0.403, 0.741, 0.6), # purple\n",
        "              ]\n",
        "    plt.rc('axes', prop_cycle=cycler('color', colors))\n",
        "\n",
        "    for config, label in zip(configs, labels):\n",
        "        result = test_results[config]\n",
        "        ys = torch.tensor([y[stock_index] for y in result['ys'].cpu()])\n",
        "        y_hats = torch.tensor([y_hat[stock_index] for y_hat in result['y_hats'].cpu()])\n",
        "        x = np.array(range(len(ys)))\n",
        "        plt.plot(x, y_hats, label=label, linewidth=1)\n",
        "\n",
        "    plt.plot(x, ys, label=\"Real\", color='green')\n",
        "    plt.legend(fontsize=14)\n",
        "    plt.title(f'Predicted vs Real {stock_name} Stock Price on Test', fontsize=20)\n",
        "    plt.xlabel('Days', fontsize=16)\n",
        "    plt.ylabel('Normalized Price', fontsize=16)\n",
        "    plt.tick_params(axis='x', labelsize=16)\n",
        "    plt.tick_params(axis='y', labelsize=16)\n",
        "    plt.savefig(fig_name)\n",
        "    plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Local Mutual Information Outperforms Global and Dual"
      ],
      "metadata": {
        "id": "Cymu6slGN3fj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plot_regression([(DGT, True, 'mi', 'global', 0.01),\n",
        "                (DGT, True, 'mi', 'local', 0.01),\n",
        "                (DGT, True, 'mi', 'dual', 0.01)\n",
        "                ],\n",
        "                ['Global MI with DGT', 'Local MI with DGT', 'Dual MI with DGT'],\n",
        "                stock_name='AAPL', fig_name=f'{workdir}/sp500_AAPL_MI.png', test_results=test_results)"
      ],
      "metadata": {
        "id": "vnTk76ijNpuK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 601
        },
        "outputId": "6e9fbd7a-2dfe-44c1-9645-59a3899ba214"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Dual Pearson Outperforms Global and Local"
      ],
      "metadata": {
        "id": "TQpB3LkkOFTq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plot_regression([(DGT, True, 'pcc', 'global', 0.01),\n",
        "                (DGT, True, 'pcc', 'local', 0.01),\n",
        "                (DGT, True, 'pcc', 'dual', 0.01)],\n",
        "                ['Global Pearson with DGT', 'Local Pearson with DGT', 'Dual Pearson with DGT'],\n",
        "                stock_name='AAPL', fig_name=f'{workdir}/sp500_AAPL_PCC.png', test_results=test_results)"
      ],
      "metadata": {
        "id": "u9f7Wu6iNoIF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 601
        },
        "outputId": "df692e24-bf65-4358-fe9b-d19a7e9b7067"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],