43-autoencoder-anomaly.ipynb

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      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import tensorflow as tf\n",
        "\n",
        "from sklearn.metrics import accuracy_score, precision_score, recall_score\n",
        "from sklearn.model_selection import train_test_split\n",
        "from tensorflow.keras import layers, losses\n",
        "from tensorflow.keras.datasets import fashion_mnist\n",
        "from tensorflow.keras.models import Model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ErGrTnWHoUYl"
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        "## Third example: Anomaly detection\n",
        "\n",
        "## Overview\n",
        "\n",
        "\n",
        "In this example, you will train an autoencoder to detect anomalies on the [ECG5000 dataset](http://www.timeseriesclassification.com/description.php?Dataset=ECG5000). This dataset contains 5,000 [Electrocardiograms](https://en.wikipedia.org/wiki/Electrocardiography), each with 140 data points. You will use a simplified version of the dataset, where each example has been labeled either `0` (corresponding to an abnormal rhythm), or `1` (corresponding to a normal rhythm). You are interested in identifying the abnormal rhythms.\n",
        "\n",
        "Note: This is a labeled dataset, so you could phrase this as a supervised learning problem. The goal of this example is to illustrate anomaly detection concepts you can apply to larger datasets, where you do not have labels available (for example, if you had many thousands of normal rhythms, and only a small number of abnormal rhythms).\n",
        "\n",
        "How will you detect anomalies using an autoencoder? Recall that an autoencoder is trained to minimize reconstruction error. You will train an autoencoder on the normal rhythms only, then use it to reconstruct all the data. Our hypothesis is that the abnormal rhythms will have higher reconstruction error. You will then classify a rhythm as an anomaly if the reconstruction error surpasses a fixed threshold."
      ]
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      "source": [
        "### Load ECG data"
      ]
    },
    {
      "cell_type": "markdown",
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        "id": "y35nsXLPsDNX"
      },
      "source": [
        "The dataset you will use is based on one from [timeseriesclassification.com](http://www.timeseriesclassification.com/description.php?Dataset=ECG5000).\n"
      ]
    },
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      "source": [
        "!wget http://storage.googleapis.com/download.tensorflow.org/data/ecg.csv"
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            "--2023-09-18 09:37:51--  http://storage.googleapis.com/download.tensorflow.org/data/ecg.csv\n",
            "Resolving storage.googleapis.com (storage.googleapis.com)... 172.217.204.207, 172.217.203.207, 142.250.97.207, ...\n",
            "Connecting to storage.googleapis.com (storage.googleapis.com)|172.217.204.207|:80... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 7877383 (7.5M) [text/csv]\n",
            "Saving to: ‘ecg.csv’\n",
            "\n",
            "\recg.csv               0%[                    ]       0  --.-KB/s               \recg.csv             100%[===================>]   7.51M  --.-KB/s    in 0.1s    \n",
            "\n",
            "2023-09-18 09:37:51 (66.6 MB/s) - ‘ecg.csv’ saved [7877383/7877383]\n",
            "\n"
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              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-3fcaf85d-3749-4afa-9c38-7bae38c172ec button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ],
      "source": [
        "# Download the dataset\n",
        "dataframe = pd.read_csv('http://storage.googleapis.com/download.tensorflow.org/data/ecg.csv', header=None)\n",
        "raw_data = dataframe.values\n",
        "dataframe.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "UmuCPVYKsKKx"
      },
      "outputs": [],
      "source": [
        "# The last element contains the labels\n",
        "labels = raw_data[:, -1]\n",
        "\n",
        "# The other data points are the electrocadriogram data\n",
        "data = raw_data[:, 0:-1]\n",
        "\n",
        "train_data, test_data, train_labels, test_labels = train_test_split(\n",
        "    data, labels, test_size=0.2, random_state=21\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "byK2vP7hsMbz"
      },
      "source": [
        "Normalize the data to `[0,1]`.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "tgMZVWRKsPx6"
      },
      "outputs": [],
      "source": [
        "min_val = tf.reduce_min(train_data)\n",
        "max_val = tf.reduce_max(train_data)\n",
        "\n",
        "train_data = (train_data - min_val) / (max_val - min_val)\n",
        "test_data = (test_data - min_val) / (max_val - min_val)\n",
        "\n",
        "train_data = tf.cast(train_data, tf.float32)\n",
        "test_data = tf.cast(test_data, tf.float32)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BdSYr2IPsTiz"
      },
      "source": [
        "You will train the autoencoder using only the normal rhythms, which are labeled in this dataset as `1`. Separate the normal rhythms from the abnormal rhythms."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "VvK4NRe8sVhE"
      },
      "outputs": [],
      "source": [
        "train_labels = train_labels.astype(bool)\n",
        "test_labels = test_labels.astype(bool)\n",
        "\n",
        "normal_train_data = train_data[train_labels]\n",
        "normal_test_data = test_data[test_labels]\n",
        "\n",
        "anomalous_train_data = train_data[~train_labels]\n",
        "anomalous_test_data = test_data[~test_labels]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wVcTBDo-CqFS"
      },
      "source": [
        "Plot a normal ECG."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "ZTlMIrpmseYe",
        "outputId": "902b2290-807a-4db6-bb33-f0cc5ed51692",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.grid()\n",
        "plt.plot(np.arange(140), normal_train_data[0])\n",
        "plt.title(\"A Normal ECG\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QpI9by2ZA0NN"
      },
      "source": [
        "Plot an anomalous ECG."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "zrpXREF2siBr",
        "outputId": "0af5474f-e297-4f94-8761-5249c24ca196",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.grid()\n",
        "plt.plot(np.arange(140), anomalous_train_data[0])\n",
        "plt.title(\"An Anomalous ECG\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0DS6QKZJslZz"
      },
      "source": [
        "### Build the model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "bf6owZQDsp9y"
      },
      "outputs": [],
      "source": [
        "class AnomalyDetector(Model):\n",
        "  def __init__(self):\n",
        "    super(AnomalyDetector, self).__init__()\n",
        "    self.encoder = tf.keras.Sequential([\n",
        "      layers.Dense(32, activation=\"relu\"),\n",
        "      layers.Dense(16, activation=\"relu\"),\n",
        "      layers.Dense(8, activation=\"relu\")])\n",
        "\n",
        "    self.decoder = tf.keras.Sequential([\n",
        "      layers.Dense(16, activation=\"relu\"),\n",
        "      layers.Dense(32, activation=\"relu\"),\n",
        "      layers.Dense(140, activation=\"sigmoid\")])\n",
        "\n",
        "  def call(self, x):\n",
        "    encoded = self.encoder(x)\n",
        "    decoded = self.decoder(encoded)\n",
        "    return decoded\n",
        "\n",
        "autoencoder = AnomalyDetector()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "gwRpBBbg463S"
      },
      "outputs": [],
      "source": [
        "autoencoder.compile(optimizer='adam', loss='mae')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zuTy60STBEy4"
      },
      "source": [
        "Notice that the autoencoder is trained using only the normal ECGs, but is evaluated using the full test set."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "V6NFSs-jsty2",
        "outputId": "ca94f12f-397d-4c99-f2d8-ac39aa4eb5ab",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "5/5 [==============================] - 12s 128ms/step - loss: 0.0579 - val_loss: 0.0531\n",
            "Epoch 2/20\n",
            "5/5 [==============================] - 0s 31ms/step - loss: 0.0557 - val_loss: 0.0513\n",
            "Epoch 3/20\n",
            "5/5 [==============================] - 0s 31ms/step - loss: 0.0526 - val_loss: 0.0495\n",
            "Epoch 4/20\n",
            "5/5 [==============================] - 0s 22ms/step - loss: 0.0484 - val_loss: 0.0477\n",
            "Epoch 5/20\n",
            "5/5 [==============================] - 0s 22ms/step - loss: 0.0441 - val_loss: 0.0451\n",
            "Epoch 6/20\n",
            "5/5 [==============================] - 0s 32ms/step - loss: 0.0400 - val_loss: 0.0427\n",
            "Epoch 7/20\n",
            "5/5 [==============================] - 0s 26ms/step - loss: 0.0362 - val_loss: 0.0409\n",
            "Epoch 8/20\n",
            "5/5 [==============================] - 0s 20ms/step - loss: 0.0328 - val_loss: 0.0394\n",
            "Epoch 9/20\n",
            "5/5 [==============================] - 0s 21ms/step - loss: 0.0304 - val_loss: 0.0381\n",
            "Epoch 10/20\n",
            "5/5 [==============================] - 0s 25ms/step - loss: 0.0285 - val_loss: 0.0372\n",
            "Epoch 11/20\n",
            "5/5 [==============================] - 0s 32ms/step - loss: 0.0270 - val_loss: 0.0363\n",
            "Epoch 12/20\n",
            "5/5 [==============================] - 0s 25ms/step - loss: 0.0258 - val_loss: 0.0353\n",
            "Epoch 13/20\n",
            "5/5 [==============================] - 0s 21ms/step - loss: 0.0249 - val_loss: 0.0346\n",
            "Epoch 14/20\n",
            "5/5 [==============================] - 0s 22ms/step - loss: 0.0241 - val_loss: 0.0341\n",
            "Epoch 15/20\n",
            "5/5 [==============================] - 0s 20ms/step - loss: 0.0235 - val_loss: 0.0336\n",
            "Epoch 16/20\n",
            "5/5 [==============================] - 0s 30ms/step - loss: 0.0229 - val_loss: 0.0328\n",
            "Epoch 17/20\n",
            "5/5 [==============================] - 0s 21ms/step - loss: 0.0224 - val_loss: 0.0325\n",
            "Epoch 18/20\n",
            "5/5 [==============================] - 0s 34ms/step - loss: 0.0218 - val_loss: 0.0322\n",
            "Epoch 19/20\n",
            "5/5 [==============================] - 0s 49ms/step - loss: 0.0213 - val_loss: 0.0318\n",
            "Epoch 20/20\n",
            "5/5 [==============================] - 0s 39ms/step - loss: 0.0209 - val_loss: 0.0315\n"
          ]
        }
      ],
      "source": [
        "history = autoencoder.fit(normal_train_data, normal_train_data,\n",
        "          epochs=20,\n",
        "          batch_size=512,\n",
        "          validation_data=(test_data, test_data),\n",
        "          shuffle=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "OEexphFwwTQS",
        "outputId": "2f7e011e-3e42-4871-f5bc-d0083dd7157c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x799be222e170>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.plot(history.history[\"loss\"], label=\"Training Loss\")\n",
        "plt.plot(history.history[\"val_loss\"], label=\"Validation Loss\")\n",
        "plt.legend()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ceI5lKv1BT-A"
      },
      "source": [
        "You will soon classify an ECG as anomalous if the reconstruction error is greater than one standard deviation from the normal training examples. First, let's plot a normal ECG from the training set, the reconstruction after it's encoded and decoded by the autoencoder, and the reconstruction error."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "hmsk4DuktxJ2",
        "outputId": "00186140-bd73-4f63-d602-53c77d3bb087",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "encoded_data = autoencoder.encoder(normal_test_data).numpy()\n",
        "decoded_data = autoencoder.decoder(encoded_data).numpy()\n",
        "\n",
        "plt.plot(normal_test_data[0], 'b')\n",
        "plt.plot(decoded_data[0], 'r')\n",
        "plt.fill_between(np.arange(140), decoded_data[0], normal_test_data[0], color='lightcoral')\n",
        "plt.legend(labels=[\"Input\", \"Reconstruction\", \"Error\"])\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ocA_q9ufB_aF"
      },
      "source": [
        "Create a similar plot, this time for an anomalous test example."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "vNFTuPhLwTBn",
        "outputId": "10b40268-5d48-4f1d-e85d-2fbc4aebb87a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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vJza0iB2Ho5n1atX2kjlwQP8zylpCdEjFm+GleGuNHIlGVTXlBEC1/4V+8cUXmTRpEhMnTqR79+689dZbhIWF8f7771d4/Pvvv092djb/+9//GDJkCO3atWPYsGH06dOn1o0PNiaTiSlTpvDss8/y0EMP8cgjjzBjxgy6devG+eefz88//0xKSgoAzZo1Y968eRQUFDBs2DD69+/Pu+++6xuCuPvuu5k6dSp/+9vf6NWrF7Nnz+aHH34ot5LmVFwuF5MnT/Zdv3PnzrzxxhsAtGrViscff5wHH3yQhIQEpkyZctJzvf/++zidTvr378+9997Lv//973LPn+r9TJo0iS5dujBgwACaN29e4UqdVq1a8csvv7Bq1Sr69OnD7bffzi233HJC8BFCiEDwDtGM6LibMEv5YBAdYmPqWUsA+NdTFpzOU5dwP9V8ESitwppbEsrR/Q13ea+myvZpn4LdbicsLIyvv/66XDXM8ePHk5OTw/feO13GhRdeSFxcHGFhYXz//fc0b96c6667jmnTpmE0Giu8js1mw2YrTXl5eXkkJyeTm5tLVFRUuWNLSkrYu3cvKSkphISEVPWtCFFl8ndMCFEXTjtN39Tu9Uu/54Z+G054vthhotsLU8kpCeX3L7I4b1zzk57v3XfhtttgVKedfHX9Z5Ue1+X5qWQURLL8m1TOuLxNbd9GteTl5REdHV3h53dZ1eoZOXLkCC6Xi4SEhHKPJyQkkJ6eXuFr9uzZw9dff43L5eKXX37hkUce4YUXXjjhN+OyZsyYQXR0tO/LO09CCCFEgB06BG++GbiFAo1UaqoeRAyam/M776jwmFCzk0u6bQXgi1mn3hrkZAXPyvKuqNm11V6NFtcvvw+ku91uWrRowTvvvEP//v0ZN24cDz/8MG+99Valr3nooYfIzc31fR3wDowJIYQIrAkT4M47cV9wgV40Q1TJjz/qfw5KPkB8eOWFOS/vuRmA/y1sht1+8oGLkxU8K6tdnD5vZM+uhrtqsFphJD4+HqPRSEZGRrnHMzIyKi1GlZSUROfOncsNyXTr1o309HTs9opTmtVqJSoqqtyXEEKIAFu3Dv74AwDDkiW4b7hBXyIiTumnn/Q/L+xaca+I19nt9hIfVkh2URhzvzx5/aPSfWlyTnpcu1j9+T37Kp4a0RBUK4xYLBb69+/P3LlzfY+53W7mzp3rqxR6vCFDhrBr165ydRx27NhBUlKS3zZQE0II4QfPPw+AKzERpWkYvvoK90MPwdat8Mgj0Ls36hST2ZuqTZv00HZG8v6THmcyKsZ09wzVfHzyYRW9xsjJJ7BC6YqaPYesVWprIFR7mGbq1Km8++67zJo1i61bt3LHHXdQWFjIxIkTAbjpppt46KGHfMffcccdZGdnc88997Bjxw5+/vlnnnrqKSZPnlx370IIIYR/7d+P+uorAIouvZSSCy8EwPDMM9C9O/z737BxI9rMmbi3bg1kSxuckhJ9qg1Ae8+QycmM7aEP1Xy/pDm2koqHVtzu0qW9pwwjnlojezMjqcaalXpV7Qqs48aNIysri0cffZT09HTfbrDeSa2pqanlajokJyfz22+/cd9999G7d29atWrFPffcw7Rp0+ruXQghhPCvl15Cc7lwtG+POykJe1ISWk4OIUuXogwGnJ06YcjOxpiVhfvttzG8/HKgW9xg7N0LSmlEWmzEhRWf8vghbfeTEJFPRkEkv32azphbTpwGkZEBDoeGQXOTFJl/0vN5e0YO5UZRcrSA0Pjq7X9TH2pUDn7KlCmV1pUouyW81+DBg1mxYkVNLiWEECLQsrNR//kPGmA/80zfw7aRI3F264Y7Lg4VFoZl5UpCf/0V7aefQMKIj3dfmHZx2VUq1G006EM1764ayJefOhhzy4nHeOeLtIzMx2Q8eW9HfHgR4WY7hQ4Lezbm02NEwwsjUpZSCCHEyb35JlphIa6EBJwdOpQ+rmm4WrdGefblcnTvjgKMu3fj3rat4nM1Qbt363+mxOVU+TWX99gEwI/LWlBceOJWGFUpeOalaaUranZvPnXPTCBIGBFCCFE5pVDvvAOA7cwzT7oHl4qMxNW2LQDut9+ul+Y1Bt4w4q2GWhWDkg/QMjKPfJuV3z/LOOH50hojVauq6q01snvHqfcTC4Tg27XXw52biyqqfC13XdPCwjBER9fb9YQQol7s24eWmooyGHB49qY6GUf37pj270f78Ud46aV6aGDD5x2m8c7dqAqDAc7rtJNZ6/oz52c7l04q/3x1ekagNAjt3lvzDV39KSjDiDs3l/zXX6fKWx/WBZOJyClTqhxIJkyY4Nv0rqzRo0cze/bsum6dEELUzOLFALhatoQqlGNwdO9OyK+/+oZqDF27+ruFDZ5vmKYaYQRgRIc9zFrXn7krT5zjUVrwLKdK5/KuqNmd2jBLagTlMI0qKqrfIALgdFa7J+b8888nLS2t3Nfnn39e4bEV7VJbWdG4U6np64QQTdASffM27/DLqajISFxt9P1PZKhGX4K7Z48+wbQ6wzQAw1L2oKHYlt6MA9vKr5ipbs9I9xb6UM/6vc1wuxve8t6gDCONhdVqJTExsdxXbGwsAJqm8eabbzJmzBjCw8N58skneeyxx+jbty//+c9/ym3alpqayqWXXkpERARRUVFcffXV5arkVvY6IYQ4JU/PiLMae4Q5evQA0Idqmri0NLDZNEwGV5WDg1dsWAn9Wh4G4PevyveqVLXgmVfflmmYDC4y8iPYu756PTT1QcJIA/bYY49x2WWXsXHjRm6++WYAdu3axTfffMO3337Lhg0bcLvdXHrppWRnZ7Nw4ULmzJnDnj17GDduXLlzHf86IYQ4paws8KyK8fZ2VIWjW7fSVTVbtvipcY2Dd4gmOTr3lEtwKzKigz7h5Pc5pXM9iorgyBH956qGkVCzk96J+oa2y34/eV2SQJAwEkA//fQTERER5b6eeuop3/PXXXcdEydOpH379rTx/ENgt9v56KOP6NevH71792bu3Lls3LiRzz77jP79+zNo0CA++ugjFi5cyOrVq33nOv51QghxSt4hmubNfct3q0JFReFq1w4AdxOfxOpbSRNXvSEar+Ht9RPM39AMl0sPM97Kq5EWG9Ehtiqf6/TkgwAsX9bwNsyTMBJAI0aMYMOGDeW+br/9dt/zAwYMOOE1bdu2pXnz5r6ft27dSnJyMsllulC7d+9OTEwMW8uUZD7+dUIIcUqeMOKs4nyRsuynnQaA4dtvUa6GuZy0PvhW0tQwjAxMPki42U5WQTh/LjgKwM6d+nOto3OrVETNd67WeopZ+Vd4jdriTxJGAig8PJyOHTuW+4qLiyv3fEWvqem1hBCiWrwraaoxROPl6NYNFRKCITsbt2dPm6bIt5ImpmbzNKwmF0Pa7QPg92/zKSyEv/9df65/60PVOteA1nrPyF8H4yk6VlKj9viLhJFGrlu3bhw4cIAD3n47YMuWLeTk5NC9e/cAtkwI0agVFKDWrQPAWYMwgtmMvVcvANSbb9ZlyxoVbxhpW82VNGUNb693r/yxyMJ99+nTeBIj8nl85JxqnadNTC4JEfk43UZW/X60xu3xBwkjAWSz2UhPTy/3deTIkWqdY+TIkfTq1Yvrr7+edevWsWrVKm666SaGDRtW4TCPEEJUyYoVaC4X7uhoVExMjU7hHaoxLluGSkurw8Y1Ht5lvTUdpoHSSazzNyfx7rugaYq3L/+WZuHVK+2uaXC6p3dk2QLpGREes2fPJikpqdzXWWedVa1zaJrG999/T2xsLEOHDmXkyJG0b9+eL7/80k+tFkI0CbWYL+LlTkrClZSE5nLhaoITWfPySle9VLfGSFldm2eRFJmHW+kf2VPPXsqw9vtqdK6BnkmsK1abfY999x3MnKnXRAmUoAwjWlgYmOq5uKzJpF+3ij788EOUUid8bfMso1NKMXbs2HKveeyxxypcltumTRu+//57CgoKyMvL46uvviIhIeGUrxNCiEp554tUo75IRez9+gGgffYZqIZXbMufvEM08WGFRFprXmxS02BEB/1kp7c+wIPD5tf4XKd7JrGu2hGH263IzITbblNMmQL/eSNwvSVBWQ7eEB1N5JQpsjeNEELUhMOBWrECjdr1jADYe/Ui5PffMR46hOuPPzCed17dtLERKF1JU/siYw+PmE+bmFwm9l+D2VjzLoyyxc/2bTjKA08148gRjR4J6Vw3KAPoU+u21kRQhhHQAwkSDoQQovrWrkUrKsIdGoo7Pr525woNxdG1K5ZNm1AffghNKIz4VtLE5dT6XK2i83lw+MJanyfU7KRXYjrrD7di6v1Gvp8PJoOLN8f+D4v5jFqfv6aCcphGCCFELSxYAKAXLjPU/mPC2aEDANry5bU+V2PiW0lTw2W9/uKdxPr9/BgA/j5sMb2TMk7yCv+TMCKEEKI8Txip7RCNlzMlBQDDvn2ozMw6OWdjUNuCZ/7incQK0DsxjalnLQ5ga3QSRoQQQpRyOFDelTSeEFFbKiYGV1wcmlK4vvuuTs7ZGOze7VnWG9uwekbOaJOKQXNjNrh487L/1WoOSl0JmjCimtgsbVF/5O+WaFLWrEErLNTni9ThFhLeYKN++63OztmQ2e2Qmqp/X5tlvf7QOjqPr6//lJ8nfkiPhIbRU9Xow4jRaAT0jeCE8Afv3y3v3zUhglodzxfxcnmHalatqrNzNmTr14PLpREXWkRiZEGgm3OCczruKTdcE2iNfjWNyWQiLCyMrKwszGYzhjr8n0cIt9tNVlYWYWFhmOq7do0QgTBfr2Hh9Oy6W1e85zMeOoR73z4MdXz+hsY7V/f05IPV2syuqWr0/7pqmkZSUhJ79+5l//79gW6OCEIGg4E2bdqgyb8oItjZ7ailS/X6InUcFlREBK4WLTBmZuL69lsMU6fW6fkbGm8YaUi9Dw1Zow8jABaLhU6dOslQjfALi8UiPW6iaVizRq8vEhZWp/NFvJwpKRgzM+GPPyDIw8iyZQrQGNg6NdBNaRSCIoyA/ttrSEhIoJshhBCNl2eIpq7ni3g5U1KwrlyJYe3aOj93Q3LwIBw8qGHU3JzW6nCgm9MoyK97QgghdN76In6az+Fs2xalaRgzM3Fv2eKXazQE3iGaHgkZhFscgW1MIyFhRAghhG++CPgvjBAaiispCQD3t9/65xoNgG++SBs/zRdxOjHu3QvFxf45fwAEzTCNEEKIWli1Cq242G/zRbycKSmYDh+GefPgn//023UCadky/c+Bnh1y64ohKwvLunWY//wTQ1ERrmbNKLz5ZlR4eJ1eJxCkZ0QIIYQeDvAUJ/PjyjGXp8S8tmmT364RSCUlsG6dXiixLsOIZdUqImfOxLp8OQbPjvTGo0cJ++wzsNnq7DqBImFECCGEvsKFuisBXxlX69YAGLOycO/d69drBcK6deBwaDQPL6BtbE6dnFPLySFkzhwAHJ06YXvwQZyzZ6MiIzEdOkTYV1+B01kn1woUCSNCCNHU5eejPBMdvDvs+osKC8MVHw+A+5df/HqtQPAO0dRlsbPQ2bPRHA6cbdtiWLQI64wZmEaPht9/R1mtmHfvJvTnn+vmYgEiYUQIIZq6RYvQnE5csbGo2Fi/X86VnAyAWrTI79eqb97Jq4PqaIjGtGMH5m3bUAYDzn/8A2Niou857Ywz4OuvAbCsX9+oh2skjAghRFPnGQLwd6+Il9MTRrT16+vlevVFKVi+XJ8vcnpdVF51OAj19B7Zzz4b6/jxJxyiXXwxqlkzAAxHj9b+mgEiYUQIIZq6epov4uXtGTHu3YvyTMYMBvv3Q1qahsngol/L2hc7sy5ejCEnB3dUFIbnnkOzWis+sGtXQJ/Q2lhJGBFCiKYsLQ02b0ZRurOuv7mbNcMdGqoPDXmCUGOnFNx/v/59/1aHCDXXbkKpVlCA1TPmYxs/HtOAAZUf26ULAIYjR2p1zUCSMCKEEE2ZJwy4kpJQYWH1c02Dwbeqhrlz6+eafvbqq/DNN2A2unhq9G+1Pp91+XJ90mrLllj++c+Tb9TpDSPSMyKEEKJR8g7R1NN8ES/vUA2rVtXrdf1hxQq4/359rsi/R/1O/9a1G6LRCguxeO6L88YbMbZocfIXdO4MyDCNEEKIxkgplHfyavv29Xpp7yRWw9atKKXq9dp16ehRGDdO4XRqjO2xmdsG1j5cWZYt03tFkpIw33ffqV9Qtmekkd5LCSNCCNFUbd2KlpaGMplKeyrqiatVK5SmYcjNxb1xY71eu6643XDTTZCaqtE+7iivXvJDrWuLaIWFWL29IjfcgDEh4dQv6tABZTSi2e1o+fm1a0CASBgRQoimyjtE07YtmM31e22LBZenZob69df6vXYdefZZ+OUXCDE5mHX1f4kKsdf6nBbPXBFXYiLmqVOr+CILeDY3bKyTWCWMCCFEU+RwwJtvAvU/X8TL1xvj2S24MVm4EB5+WB8SefbCX+mVmFH7kzqdvl4Rx/XXlytwdipaI1/eK2FECCGaorffhm3bcIeFYT/ttIA0wRtGtA0bAnL9msrIgGuuUbjdGtf0+ZMb+9VN8TZDbi6a3Y4ymzHdc0/1XtzIV9RIGBFCiKbm2DHUY48BYDvnHAgJCUgzfJNYDx1C5eQEpA3V9dtvcPbZkJ6u0a15Ji9c9HOd7UGj5eYC4I6KOvUKmuM18jBiCnQDhBBC1LN//xvt6FFczZtj79cvYM1Q0dG4w8IwFBXhXLAA09ix9Xbt/fthwQKYPx8OHID+/eGss2DIEPBUVz/h+Hvvhf/9T/85ISKfWVd/RbjFUWdtMuTlAeCOjcVYWbXVyniW9zbWOSMSRoQQoinZtQv12mtoQMno0WA0Bq4tmoarZUsMu3ahFi+GeggjdjtceinMnl3+8Xnz4LnnwGhUzHzFyf9NLp3Qm5kJAwcqMjM1jAY3/zdoFdOGLSA6pG43pvOGkQrT0Kl4e0ZycsDpBFPj+nhvXK0VQghRO//4B5rDgaNjR5wdOwa6NbhatsS8axesXVsv15s+XQ8iRoOb01oe4qyUVNrFZLP2UCuW7W/LrqPNuOdejaHDnHTrqX9E3nMPZGZqdInP4oOr/kv3hCy/tE3zhBGVlFT9FycmoiIi0AoKMGRn467uME+ASRgRQoimoqAA9b//6b0i554b6NYAer0RAMP27bU+V3q6Puxy2WUVT4NZuBCeeUYBGh9c9TVjum31PTe+/zqUgis/uZ65uzty05W5LN8Uza+/whdfgEFz8/bl3/ktiIA+gRVAee5JtWia3juydi2Go0cbXRiRCaxCCNFULFiA5nDgjonBXY1lo/7katkSAENGBu7MzBqfp6gIhg2D666DQafZ2Lat/PM5OXDjjQqlNG7ot75cEPHSNHh1zA9EWUtYsz2af96bxx136Mt37xqygr4t02rcvqrwDtNobdrU6PW+5b2NcN6IhBEhhGgqPBMlHJ06UWdLQGpJRUbijoxEUwp3LXbwnTYNduzQv/9rq5X+fV289x83mzfre/FNnAgHDmikxGbz9PmVF1lrFZ3P0xfo9+mZmVEcOqRXV31w2Pwat62qvMM0Wk13T27EK2okjAghRFPxm76bbKCKnFXG2zuialj8bM4ceP11/fs3x37H0JQ9FNmM3DrJQM+eMHKkvgrGaHDzzuXfEmE9+QqYa/v8yejOpcNGr1zyI6FmZ43aVmV2O4biYgC0Tp1qdo7jwojh8GGs8+Y1ihLxMmdECCGagt279ZU0BgNOT+nwhsLVqhXm7dvR1ldcPKygAP76C/bs0ZfYpqZCq1YwahR07AgTJ+rzQCYNXMW1ff/i6t4beWnJWby67EyMmpuEiAJaRBRyy8C1nJ586JTt0TR45ZKfuOVrK8M77OXslP11/I5P5B2iURYLWk3mjEC55b3WBQuwLlqE5nZj2r2bwokTG/QKm4bbMiGEEHXH0yviSk4OWJGzynh7RrSdO1FKoWkaf/0FL70Eq1bB1q36XI/jTZ+uL8V1uTQ6xB3lsZH6MI/RoLh/6GL+dvbiGo9GJUYW8PPEWTV+T9XlHaJxR0VhiI6u2Uk8PSqG4mJCFiwAQBkMmA4dImTOHEouuKAumuoXEkaEEKIp8MwXaQjLeY/n8ixlNR45gjs1lRWH2zJ6tCI/35skNFpG5tEh/ihtYnJpGZnLziPxLNjbnpziUIyelS7HFyBrINNiqsRX8CwmBqPFUrOThIej2rRBS01FhYRgu/FGzMOGYbzhBqwrV+Js0wZnjx512Oq6I2FECCGCnd2OmjcPDXA0wDCiwsNxx8RgyMnhr7eWMOr1NhQUaAxus5+7hyzjtJaHSIgsPOF1LrfGn2lJhJoddGvhvyW39cG3rDc+vnYneu45HO+8g/vOO7GOHYtmMKDWrUN78UXCfviBwvBwvXcskMXuKiBhRAghgt3SpWiFhbjDw3EnJAS6NRVytmyJJSeHb55PpcCpMTRlD59f+8VJy60bDYrTWh2ux1b6j3eYhpoUPCt7nquvxnz11eUfe+YZ1NKlaCtXEvHhhyiLBWfr1jhOOw1Hz561ul5dkdU0QggR7LyraDp2BEPD/Gd/g6E/AL2d6xjefjdfXPt5ne770tD5SsHXdPLqyZhMaP/7H65zzkGFhKDZ7Zj37CH0hx/q/lo11DD/VgohhKg7DXRJL4DbDU/OG860TRMBGGpaxufXfkGYxc9LaRsYXxipYcGzU0pMxDh3LuTm4vz+ewA0ux1cLv9cr5pkmEYIIYLZoUOwYQOKwIYRu9OAzWXC7jRS6LCw60gztma1YO6uDszb3ZFI9A/jBOdh8uy5KHN4wNoaCJpnzojWvr1/r2OxYDr//NIH7HYIDfXrNatCwogQQgSzL78E9CW9KjwwH/DPLBjKc4uG4nRXPGnSbHTxzHVrUcs7ou3ahfHwYZw1LfzVGNntGEpKANDqY4KxxYIymdCcTjS7HSVhRAghhF999hkAjl69AnL5L//sxYwFI8o9ZjK4aBd7jG5JR+nevpjLLrQxcOIQtJsHQRMMI+UKnrVuXT8XDQ+H3Fw0hwNVP1c8KQkjQggRrHbsgLVrUZqGIwD1JTYcTuKeHy8BYOo5K3nsrVZYIywYLUa00DAMYcctYz39dPj0U4yHg2OFTFV5h2jc0dEYoqLq56JhYZCbqw/TNAASRoQQIlh9/jmgzxWp7yGazIJwrv9iHCVOM6M772TGrGQsrVue/EUDBgA0uTBSruCZ2Vw/F42IAEBzNIwVSxJGhBAiGCnlCyP1OUSTkR/Ol3/14YM1/TmUF02nZkf4+L0SLK2rMOzSrx/KYMCQn4+Wl4eqr16CAPMN09S24Fk1aJ5wqknPiBBCBIDDAfX122cgrV8P27ejTCYcXbv6/XIHcqJ4+LfR/LytKy6lV41oFlbIVzO20vyss6t2krAw6N4dNm3S5400kTBSVwXPqsXTM0ID6RmROiNCiKZBKbj5ZlR8POrrrwPdGv/zTlzt0gWsVr9dRimYtfY0znzjTn7Y2h2XMjCw9QFenbicrXNT6XPrWdU6n3b66UDTGqrxloL3S8GzyjSwnpEahZGZM2fSrl07QkJCGDRoEKtWrar02A8//BBN08p9hTSwHSOFEE3As8/CBx/ov4Vecw3qp58C3SL/cbtRX3wB4Ndy3zuymnH5xzdwz4+XkG+3Mig5lVVvLWP55mjuen8wzc/ohlbd3eqaYhjx9oy0bVt/F/WGkcbaM/Lll18ydepUpk+fzrp16+jTpw+jR48mMzOz0tdERUWRlpbm+9q/f3+tGi2EEJVasUKfe3DVVbBvn/7YnDmof/wDAFdCAprLBZdfjpo/P3Dt9KeFC9EOHUJZrX7ZpfdwXiT3/HAxZ7xxJ/P3dCDE5OCpyxazcImR0//vzNqtCCk7iVU1hEWn/ucLIykp9XdR74Tmxtoz8uKLLzJp0iQmTpxI9+7deeuttwgLC+P999+v9DWappGYmOj7SmigGzUJIRoxpeD111FDh8KGDWhff43q0gU1bRrqmmvQ3G7sp51GwaRJODp10n8jvPBC1OLFgW553XvxRQDsPXvW+fyY91f3p/+rdzFrXX/cysAFXbez8j9rePCrwVjb1MEwQ+/eKLMZQ1ERWk5O7c/X0NlsaJ6CZ4b6rK3SmIdp7HY7a9euZeTIkaUnMBgYOXIky5cvr/R1BQUFtG3bluTkZC699FI2b9580uvYbDby8vLKfQkhxAmKimDLFvj5Z7j2WrjrLjSHA0e3bjjbtUOz29GefRYtOxtny5YUX3ABmEwUXX21/nxJCZxzDurdd0vP6XTC99/DTz81zt/MN2+Gn35CaRr2wYPr7LRKwYz5w5j688UUO82ckZzKH08v4aeVSfQePxjNVEfrIaxW8Kz+aQpDNb6VNFYrWstTLH2uSw1smKZaf3uOHDmCy+U6oWcjISGBbdu2VfiaLl268P7779O7d29yc3N5/vnnOfPMM9m8eTOtK6k0N2PGDB5//PHqNE0IEczsdpg3D777DjZuRGVlQVaWr1iUlzIYKBk1CvugQQCYN28m5PffQdMoGjeutJfAbKbwuusI++47zFu3wm23odauRevUCfXaa2ieoWR1wQVoH3wAjak397nnAHB27Yq7mktF7U4DRoPCaCgfwtxumPbrBby7eiAAD41axr9mdcCUWL3JqVWlDRwI69ZhOnwYZwCKtdUnX42RqKj6K3gGDW6Yxu9LewcPHszgMun8zDPPpFu3brz99ts88cQTFb7moYceYurUqb6f8/LySE5O9ndThRANyf79MHcu/PEH6pdfygWPslMildWKOyYGd7Nm2M44A1eZXU8dPXvqlUfdbjAety+KxULRVVdhXbSIkAUL0N5+23dud2goms2G9uuvqC5d4J130K6+2o9vto4cOID69FM0wDZkSLVeunBPCld/eh12l5HokBKahRVhNrpwuIwUOcyk5UehaYrnrljIfe+d5t8PziZU/EwLRMEzaNw9I/Hx8RiNRjIyMso9npGRQWJiYpXOYTab6devH7t27ar0GKvVitWPS9GEEA3Y+vVw/fWwdavvIQ1wR0T4hl9URAQqLAx3RASEhMDJVmxo2olBxMtgwDZ8OK6EBMK+/x53ZCS2wYNx9OqF4ehRwr77DmNGBowbh3vXLgyeSbAN1ksvoTmdONu1w1WNPU7ySixM/t+l2Fz6R0JOSSg5JeU3TzMbXLxz8wJuemUwhrCwOm32CcqGEbcbDMFbhcKQn69/06xZ/V64MVdgtVgs9O/fn7lz5zJ27FgA3G43c+fOZcqUKVU6h8vlYuPGjVx44YXVbqwQIsjl5sIVV8DevShNw9WqFc727XF27Kh/uPrpQ8nZrRt5XbqUO787MZGCSZMImTcP67JlGB5+WN87ZPJkv7Sh1o4dQ73zTo16RR6dM4qDedG0i83mtw8PUGyO4WiGE0exA7NZw2yGdm3dtD7zbDSLxT/tL6tHD30Ohc2GITu72sNNjYnmCSOqvocCG/swzdSpUxk/fjwDBgxg4MCBvPzyyxQWFjJx4kQAbrrpJlq1asWMGTMA+Ne//sUZZ5xBx44dycnJ4bnnnmP//v3ceuutdftOhBCNm1Jw++2wdy/umBgKJk2q3/1UKgo6JhMl550HgHXZMrS77kJFR6ONGAHz58PKlfpyzHHj6rdgVUVefBGtsBBXQkK1lvMu2J3Ch2v7A/DmPX/SecyIU7yiHphM0K8frFiB8fDhoA4jhkBUX4UGt5qm2mFk3LhxZGVl8eijj5Kenk7fvn2ZPXu2b1JramoqhjL/Ux87doxJkyaRnp5ObGws/fv3Z9myZXTv3r3u3oUQovGbNQu++AJlMFB0xRX1vrFbpTRNDyQ2G9a1a1E33XTCKht1//0wbBja5ZfDGWdAnz7gcsEPP8BHH6E2bIBx49D+/W+95HldW78e9fTTaEDJ0KEnH7YqI99m4a4fxgBw2+C1jPr7oLpvWw1p3jCSkUHDGEjwD2/PSL2H2cY8Z8RrypQplQ7LLFiwoNzPL730Ei+99FJNLiOECGapqTBnjv69w4G6/359iGHECFwNbcK6plFy0UVodjuWjRv1IaSkJFxt2mA8fBhTaiosWKB/AcpiAYsFraBAfznASy+hvv0W7aOPYOhQfS5Ebi6kp8PBg/pXZCRcdtmJc1y2bAGbDVq3hvj48mHDZoObbkJzOvU5NVX4RS81J5pP1/fl0/X9OJgXTduYYzz9Spj/54JUh2d5r+EkBTWDgW/OSJmJ1/WisQ/TCCFErW3fjjr99NLfCtE/sJ0pKdWe71BvDAaKL7sM+xln4IqLg9DSCZ7asWNYNm3CuH8/xkOHMBQXg92OOzoae9++uOPiCPnjDwz798OwYaiYGMjNRaugjol72DAM33yjT2jMz4fJk+Hjj33PK4sFBg5Ee/RRGDkSpk+HTZtwh4dTfPHFlfaK2JxGft3ehY/W9WP+7g4oz5qkZmGFvHf/BmJPbwDDM2V5a40ct2AiqLjdpYG1ffv6vXZjH6YRQohaKSiAyy9Hy8/H1awZbs8qAhUSog+HNOSVEwYDrgq601VsLLazz4azzwalMGRng82GOzHR934cXboQ+vvvWNatK1dZVFmtuKOjcUdGYkpNxbBwIapXL7Rnn4XHH4ddu1CahgoLw1BYqH94LFkCo0ah+vWDP/9EA4ovuaTCoS23G55ZOIz/rD6do0Wlzw9rv4cJF6dzxW0JRHQbVue3qtY8e+oY8vKguLhc+AsWWkEBmlIoTcPQrl39XjwYhmmEEKJKdu2CKVNg0CC4916IiYFJk2DLFtwRERROmICKjAx0K+uWpvkCVjkhIRSPGUPJ0KFoDgcqNBQVEqJP1vQwpKcT9uWXGNPS4MYbAb0YVtEVV+Bq2xacTgw5OVhWr8aydi3a+vUA2Pv0wdm1a4XNeWTOKGYu12s9JUXmcf3gbdx8i0aXC7thiKjn38arIyYG1bo12sGDGDMz9fcfZLxDNCoiAi0mpn4v7lnaK8M0Qojg98AD8Ntv8NtvqBdfhHPPRfv+e32S6tVXB18QqQIVE0NlRebdiYkU3HabXhl2xw4c3bpRNGZMaa+AyURhdCKzO9zFqpIYumz7hThXFrtjr2G8cwtWk6vc+WYuP8MXRF66dhG3/7sd1pTTq7+TbqD07g1BHEa8w5TuqCiM9T1h29sz4nbrWyDUVTn/GpIwIoTwjy1b9PLtgCs+HuORI/qeL0DJqFHlKqWKMkJDKbr2WrSCAlREhG8OiNOl8cHaAcyYP5zsYu9E04v0PxbAzA0j+Oc58zit1WFiQ4uZv7s9D/82GoDHL1nC3e/2x9BQVihVkdarF/zyC4YgnTfi25cmNrb+A2LZvwsOh4QRIUSQevppAP23+6uuwrRtG9aVK3G1bOnbO0aU2psdy5J9bWkWVkSLiELiQotwHDFS7DCzPyeGGfOHsy2rBQCtonIZ1WMfF4y0c8yYyPSX4tifE8ukb6844bz/d+Ya/vFB90YXRIDSSaxBuqLGV/AsEHVULBaUyYTmdKLZ7agAz8mRMCKEqHt796I++0xfqnvWWWAw4OzevUrLToNNZkE432zqydjum0mKKqjwmOyiUC78YAJp+Sff7yUutIh/jl3LHU+0wdq+t++36ev+pnjuoSw+/sLM0fwQ8mwhAFzT9y9e+TwJU7O4un1T9aXsihqlqlw/pbHwLesN1EaM4eH6qi6Ho9Khw/oiYUQIUfeeew7N5cLRoUOFq0+aiiOFYVz0wQR2Ho3nlSVD+Pzaz+nXKq3cMUrBvT9eTFp+FIkR+bSMLSAzL5RjRWFYTE7CzA7CzA5G99zHI09YaX7WWSd06UdEaDz+WnMee1WBzYY97xhFx0qITmpXvzvB1rUuXVBGo75pYV4eKjo60C2qU95N8gJWvTcsTK910wAmsUoYEULUrfR01Pvv670iZ58d6NYETF6JhSs/uZ6dR/Uu+PSCSC74YCJvjv0fl/Xc4jvusw19+GFrd0wGF18/vo4hU4ehlEIVF4PLhWa2gjkCzXjqrnxN0yAkBGtICNYWfntr9cdqhc6dYetWjBkZOIMsjPh6RqqxqWGdakCb5TXgBf1CiEYnJwf+7//QbDacyclBuQKiKkocRq7/4ho2pLWkWVghi15exvln51HiNDPx66uY9M1l/LClK5szWjDt1wsA+McFKzhzir7yRdM0DGFhGCIj0UJC0CrbdbgJ0Hr3BoKzEqsvjATo/xOtARU+kzAihKgb8+ejeveGH35AGQyUnHNO0I3xV4XTpXHz11eyeF8KkRYb3/9rNWfdPZif5kdx7yT9w+e/G3tz01fjGPLmHRTYrQxuu59/zGxbPzviNjbBWonVbkcrKQHA0KFDYNrgrTUiPSNCiEbPZoO//x117rloBw7giouj8OabcaWkBLpl9c7thrt+GMMv27tiNTn56sGlnHnfUDRNw2iEl96JZMmcQu6+Yj/tmuUCEB1SzAdPp2FtG6Cu+oYuSFfU+Aqemc1oLVsGphENqGdE5owIIWpu40a44Qb46y99jkj//pSMGqWP9Qepg7lRtIrKO6HTRyn45++j+PzPvhgNbmZNXsToR4aiHVfefsjIcIaMDOdlBVtWHCO8KJ22IwbW4ztoZLwb5mVl6TshB8mQla/gWWRk4CYZN6AwIj0jQoiaeeUV1IAB8NdfuMPCKLzmGkouuSSog8jzi86m50v3ceOXV1PiKP1QVErf/+WNFfqcjzduWsDVzwxBO0khKU2DHoNjaXdutxMCiyijbVtUeDia243h6NFAt6bO+HpGoqLQAvX/jLf2jAzTCCEapc8/h3vvRbPbcXTuTMGdd1a6N0qw2H00jmcXDgXgp23dGPfZdeTbLBwpDOO6L8bx9ILhADx9+SJunXlG4D5ggo3B4Ns0L5jmjXiX9aqK9jGqLw2oZ0SGaYQQ1XPgAOqOO9CAkrPOwnbuuUE/UVUpmPbr+dhdJnonpbHnWDwL97bnog8mkFEQQUZBJBajk6cuX8p97/bFEBZ26pOKKtN694aVK4NqRY2vZ6R588A1ogHt3CthRAhRdW43jB+PlpuLs1UrbCNGBGUQsTsNuJVGiFnfeO7nbV34Y1cnLEYnn8zYS1HnFlw4ysZf6UkAdG2eyQePbmfQ/52JZjYHsunBqV8/AEypqdgC3JS64p0zQlJS4BrhHaaRnhEhRINlt8Pvv8O6ddC9O5xxBnz1lb6E12ym+PLLg2YyoVdmQThvLD+D91afjt1l5LKem7ih3wYenH0+AHePWEPP605HMxtZtMLA/43Lpk/sHp56LYrovk23wJvfna/ff2NqKlpRESoIep58PSOBrFDcgIqeSRgRQpS3bRu89BLq66/RsrMrPKRk9GjcgRzrrmPHikKYsWA4H607jRJnac/GF3/25Ys/+wLQOjqHfz4T4+v56NFDY8mmOJQKwI6rTU1KCqpbN7StWzHt3o3Ds8KmMfPu2KslJweuEQ1omEYmsArRFC1dioqJQU2bVv7xzEzUWWfBO++gZWfjDg/H3qsXrsRElOcD19GlC/b+/QPQ6LqnFHy9sScDZ07mnVWDKHGaOb31Ab56YAmL/3uQG847RIhJ/4f6uQnrierX5YRzSBCpH9qYMQCYduwIcEvqgFK+YRotkPV4ZJhGCOF3+fnw889QXAzjx+urEkBfxnfbbWi5ufDss7jPOgvDJZfoz917L9rRo7iaN6f4ggtwtWtX+jq7HcPRo7ibN2/U80TySixsz2rO1qwW/LClG3/s6gTo8z6eu2Mb59/VA1P8WQCcdSW8kuXk8OrddB82UIJHIF18MTzzDKZduxp9vRGtuBjNpc9HaghhRFbTCCHqVm4u/PgjfP01avZsNJs+3c+9eTOG55/Xj3n1VdhSulEbEyagdu5EW7ECPv8cpWkUjx174m67FgvuQE62qyWl4OHfRvlqgXhZjE4eGLWKh15MJKzr0BNeF9fcRNyFASrXLUoNHoyKicGQk4PxwAE9KDdS3mW97rAwDPGn3gDRbxrQMI2EESEaO7dbn1j62Weo337z/ZajAe7oaAy5uRheeAF3r14YzjsP9dhjaEDxhRdiWbECY3Y27quugl270AD74MEnBpEg8Pqywb4gkhiRT9eELHq0y2fSjcX0vu50WQXT0BmNcNFF8OmnmHfsaNRhxDd5NTISQ2Rk4BoiwzRCiDrz0EPw7LOAHkBczZrh6NEDR/fuuBMSCJkzB+uyZWiTJqHOOAOtoABn69bYBwzA1bIl4e+9h2HePADcMTGUDB8euPdSR0ocRsxGN0aDAvSluY/OOQ+AZ65YyN/e7okhLkWGXRoZ7ZJL4NNPMe3cCaNGBbo5NeYrBR8TgzGQw00yTCOEqBMffugLIrYhQ7D37o27RYtyczpKRo7EcOQI5h07YPFifRjmwgvBYMDVujW2oUMJWbgQgOJLLoFGvnPsz9u6cPu3l2E2uji34y4GtD7E43+ci0LjlkHrmPp2L4zN4gLdTFETo0ejTCaMWVkYsrNxxzXO/46GhlB9FRrU0l5ZTSNEQ5CfD57txKts0SLUbbcBUDJsGCXnnYc7IeHEyaUGA0WXX47LU+nRPmAA7jK7hNqGDsU2aBDFo0bhDNRW5nVAKXhj+SBu+GIc+XYr2cVh/Hdjb6b9egFFDgsjOuzmtU+aYZIg0njFxMCQIUDjXlVjPHhQ/6Zt28A2RIZphBCAvirgvvtQM2eiud2okBCIjoYePdAGDoRBg6B1a/0fC4cDCgvhyBHIykLNmIHmcGDv0QPbsGEnv05ICIXjx2PasePEGg1GIyUXXOC/91gPcopDeHLeCN5dre9+O3Hgeq7/v0hmzzHx+5JwokyFfPFBEaEdG399iqZOGzMGFi7EvGMH9jPOCHRzqs9mw7R3r/79xRcHti3eYRq3G5zOgDZFwogQgWKzwQ03wNdf4+3L0EpK9B6SjAzwzOOojAY4W7ak+NJLS5ffnoSKiMBx2mm1b3cDkZoTzfurB7BwbwobDrdEoaFpiifGLGbaf3pgim/GuTfDc4Byxp50B13RiFx6Kfztbxj37kU7dgwVGxvoFlWLadcuNJcLV1wcxtGjA9sYb88IBHznXvm/U4hAyMuDyy6DefNQRiPFl1+Oo0MHtJISDIWFGNLTMR06hPHQIbTiYpTRqK8mMJlwh4ejwsNxR0frvxk28jkeNbE9K55LPhxPZmGE77GuzTOZfs1Grn568Akb1UkQCSIdOqBGjECbPx/LmjXYzjsv0C2qFvO2bQC4+vXDEug5IxYLymRCczoDPolV/g8Vor4tXw4TJ8L27SiLhcJrrsHVvj0AKiQEV0wMrlatcARJldO6ti0znjGz9CDSvUUGU6/Zz7kXhdHmjDYYos4NdPNEPdDuvhvmz8eyfj224cOhsSzLdrkw79wJgPIWGgy08HDIzQ34JFYJI0LUl+JieOQR1IsvoimFOyKCwuuuKzeZVJzc1szmjJl1E1mFEfRKTOO3TzJIOndgoJsl6tvFF6NatsRw+DDmzZtx9O0b6BZViXH/frSSEtxhYRgvuyzQzdGFhenFEqVnRIgmIDcXzjoLNm3SC4v16UPJ6NFBsfuovykFK1KT+c/q0/lhS3ccbiO9E9OY/UkGSef2DXTzRCCYTHDnnfDPf2JZvbrRhBHvEI2zRw/Mgdwgr6wGsrxXwogQ/qaUPiyzaRPu8HCKL70UZ+fOgW5Vg1dgM/Pfjb35z+oBbM5I9D0+NGUvX76XR+KIvoFrnAg47dZbUY895ptb1eCrBivlCyPq/PMbTME9zRtGpGdEiCD30kvw3Xcoo5Gi665r+P9oBtjRwlCeXTSMzzf0Ic8WAkCoycFV/bZwxy02Bl7TBUN0ADcXEw1DQgJceSV88QWW1aspbuD/XxnS0jDk5aHMZgxXXx3o5pRqILVGJIwI4U9LlqAeeAANKDn/fAkip7AiNZlbvr6CQ3nRAHSIO8qk83Yy4a4oWpzRE60R79Qq6p52113wxReYN22iZNSoBj3s6Rui6dQJU9euAW5NGQ1kszwJI0L4S2Ymatw4NJcLe8+e2AcMCHSLGiy3G15ffiaP/3EuLmWgY7MjvHDnNi64vSPmlo2wsJWoH4MHo3r1Qtu4EfP69dg91VkbEq2ggJB58zCvWweAe/jwhrXUvIHsT9OA7ogQQcTlguuvRzt8GFd8vL7nSwMZI26IHpx9Pu+sGgTAlb0389Y7Gs0GnRXgVokGT9P0Zb6TJmFZswb74MFVKgBYLxwOLKtWEbJoEZrNBugT14333hvYdh3PO0wT4J6RBvJfTYgg869/wR9/oMxmiq6+GqzWQLeowVq0t50viLw4biGfz02k2aDuAW6VaDSuvRYVGYnx2DFMu3bVyyW1wkL9WhV9gLtcWNasIfLVVwmdMwfNZsOVlETJk09iWrwYU6dO9dLGKpOeESGCxJ9/wpw5MGIEnHYa/PYb6okn0IDiMWP0XXRFhQrtZu7+QS/+dPOg9dzz3gAMZUtUC3Eq4eFw883wyitYVq/2/0o1t5uwTz7BlJaGOywM+2mn4ejTB8PRo5j27MG8fTuG3Fz90Oho7Jdfjulf/yKkdWv/tqumZM6IEEFAKbj6avDsIKpat4bCQjSlsA0YcOKmdKKcf887h33H4mgdlcszL1sliIga0SZPhlde0fd9qcl+NUpVeRjVtG0bprQ0AAxFRYQsWULIkiXljnGHh2O/8EKMjz2GtVu3BrOMt0Kepb2ymkaIxmzjRtixA2UwgNGI5tka3JmUREmgN8Fq4FamtuatFfrwzGu3r6PZoOGBbZBovDp1Qp17LtrcuVhXr6bk7LOxrlmDaetWnF26YBs8uOI9nFwuQn/6CfPWrdgGDsQ2ZMjJh1TdbkLmzwfAdt55mK64Al5/HeOmTbji4nB164YaNgzDVVdh7dOnYYcQLxmmESIIfPMNoC/XK7riCky7d2NMS9NXzjSW/TICYHtWPLd+cwUKjWtP+4sx/xzQOP7hFg2WdvfdMHculjVrsKxZ4/twNR0+jGXNGkpGjNArtXonuLpchH73HZZNmwAIWbRI33hv+HDs/fvrG1Mex7xpE8asLFRICNqjj2I86yz4v//DnZ2NZjBgiYmpp3dbh8oM06gANkMmsApAX1r5zjtw4YXw9ttQUKA/rhRs2ACPPAJff13z82dnQ1bWiY/Pnw8dOiiioxWxsYr4Zoq7prhrfqH65gkjjm7dwGLB2a0btnPOQUVFBbhhDdfcXR047z+3cCA3hnax2bz4AhgiIwPdLNHYXXQRqlUrNLsdzW7H1aIFtiuvxN28OYb8fMJ++IHIV1/FumgRWk6OL4gogwH7mDG4ExMxFBUR+ssvRLz9NsbU1PLnd7mwLlgAgP3cczGfeabvKUNcHIbGGERAip6JumW3w7RpsHmz4o03NDp2LH3O6YQ//tADR2KiXriwRYvSX9z//BNuvx1WrNB//vVX+PvfXFx2uca69QY8vzigaYr//dfOmCsq7sa02+Gvv/T94LzXXb4cfv4Zli9XhIYoZv+qOHuY/hvHgQNw1VWKo0fL/0b8+kyNkUMKuPTaiOMvcVIuF3z+uf79DTdU66U1s307bN6MMhhwdOlSDxds3DLyw/n8z778a+45uJWBwW1S+eqdIyQOPy3QTRPBwGiEWbNwPPEEriFDMN91F9bERLDZcP3rXxheeQVDTg4h8+YRMm8eAMpopOTOOwl5/nk0TcM1YwaGZ5/FmJlJxPvvY+/bF3v//qjQUEx79mDMzsYdGor26KNoDWUJcW01kJ4RTSkVyOtXSV5eHtHR0eTm5hIlv3FSUKBvtOj9fyE3V6+K/Mcf+s9xkXa+/dHMsGEau3bBTTfpoeB4zeLcJCRqbN8OLpdGpMXGdf02MHdXB3YdjfcdZzU66RB/lC0ZCUSG2FmxxkT3HvrF09Lgs8/0ay9apCgqOnlXe3SYnYVL9dcPG6a3q0/SYf5zxbdoKP6z+nTeWnkGrZsVsnlPGFFRVeu6X7gQ7r5bD0MArz9XxOT7/VyN8amn4OGHcXTsSFG9pJ+G73BeJN9u6sHqg62xGF2Emp1oKFYdSGZrVumqouv7/8lbH0UQ0b1DAFsrmhJVUIDrjTfQPvgA47ZtKIOBksmT9SBSZj6JysrCPWkSxu+/r/A8JWPHYv322+AZVvz1V7jwQlyJibh+/RVLHW86WNXPbwkjDck33+gJY+zYSmd2z5wJ996rSGju5oqrDIwerfHgg/o8ynCLnbYxx9iSmYDZ6GLSbfDhLANFRXrQSGmWTVZBOJkFEbhU+VR/afctzDh/Ni2j8lFKr/0we0cXujXPZEz3LYSZHYz9+EaW7W9Hh5aF/L44nHffhVdeURQXl7a1WVghsaHFvp87NMtmdKcdDE3Zy5TvL2XFgTYkxJYw+hIrH32kEWUtYdH/vU27uBwAiuwmBr9xJ/tzYplyw1Fe+7jZSW9ZTo7eq/Pll/rPISYHJU4zRoOb2T85GXlBBZPW6kr//rBuHUWXXIKjf3//XacRWLy3Lc8sHMbSfe1QVPx3V0PRKzGdCefs4q6Xu2FqHl/hcUL4m3vDBpwHDmAePbpcECl3zNy5qKlT0VJT0YqL9XohzZvj/v13zI1kl+AqWbQIhg3D1awZrj/+kDByMkEfRpTSJ2U8+aT+44ABaG++CWXKh7vd+jDMa8+XMIYfcGBmFx3ZRUdKCCUhIp8vr/uMzvFHuP27y/hha2nRqLPa7eWNsd/TJibXd67s4jAyPcEkJrSYvi3TT9nMI4VhjHhnEgdyY8o9PqDVQS7ruZnh7ffQvUVmpSvkcoqtXPjBRLZkJvge+/iaL7mk67Zyx83b1Z7LP7kRg+ZmxSI7p58VUuH5UlP1OS6bN4NBc3PzgLU8NHw+//htNF/+1YfYcBsr1prp3MUP3al790L79ihNI//++1FNeElqTnEIPV68j0KH/o/6GW1SufCMLKxRoRQVa9hs0KtjESMuCiOhXxtZvisaFaUU2GyoggIUYIwPshC9bh307487MhLnokUBCyMyZyTQlIIHHoDnn9d/NJnQ1qxBDRwI11yDdscdlPQfwk3jNYq//onN3EsH9pQ7xQFjW6KSYrDujsfpaMOHV37FjIUj+GxDX+4YvJLJfeZh2bUTp6EdKioKgwHiw4uIDy+ie0IFs0orER9exOfXfs7o926h0GGhe4sMHjl3Hud33lGlJfoxoTa+vfETRr93M/tzYpl85vITggjAOR33cFWvv/jvxt5cfAl06qF3GEVEwNChMHq0HqguvliRnq6RGJHP59d+Tr9W+tr/Vy75kT3Zcaw+mMzo4UWMGhNGWBhERUHv3nDGGVDZfnXz58Pvv+vZsOyeW0rBTz9BTAyceSYYPRNXXW3bNukgAvDxun4UOix0bZ7J928doOPorhjC2wS6WULUCU3TICQELaTiX4oavQaytFd6RgJJKbj3Xnj1VQCKL7wQR9euhPzxBxbv5AcgJ6I1fxWkMJTFALgjIlAxMRiOHEErKTnhtK5mzbAPGICzXTssGzZgWb8ezW7HHRZG0VVX4Uqp3fbrWzKak5oTw3mddmE0VP+vT3ZRKGsPteKcDrsrfX1WQRiDZk4mu/jk8z66t8jgy+s+Izkmr9zjGfnhnPvuJA56dn89XqskF/fcZ+D++zVfkNq4Ec44Q5/38uAdx5jxRmnhpA8+gBdv3khXttEpIo27jW+QmLud4gsvxD5wYDXefXBxujT6vno3B3NjmHnLMu78z5mnfpEQouE4eBCSk/WJ+GvXyjDNyQRtGJk9Gy64AKVpFF98cbl5B8aDB7GsXo152zbfJksuzYRj2FkYn30W04AB4HTi3rMHtWwZasUKtA0bMG7YUGHCVWazPlta0yg5/3zsAweiHTuGMS0NTCacDXA1SGpONH+lJaLQcCuN9PxI5u1uz5J9KRTaLQxvv5tZV/+X6BBbha9Pz4/gu809yLdZKbKbyS4OY/3hlmzOaIHbM2dm+rRiHns6lJwcfVRs9279tSEmJ9t3QJsUE3l5cH3bxfyYM7Tc+V0Y2HnrIyS1DpKJbDXwv83dmfDfq2gWVsjelWlE9ux46hcJIRqOY8cgLg4A+6pVWE4/vU5PL8M0jcHSpQA4+vQ5YQKkq3Vrilu3JjXHwgMvd6E7W7h9qkarp/5eOuHKbMbYpQt06QITJwKgcnJwvfQSfPABxgMHcHTsiOv66zGPH492zz0YfvyR0F9/JeSPP8rtRVB8/vnYz2hYW7W3icn1zXPx+r9Bq7A7Dew8Gk/X5lkn7ZlJjCzgjjNWnvB4od3Mu6sG8tgfI3n8mVAMxmLWbAxl925Ijs4hMTKf1QeTeejOTD79tQVPPAETcl4GwBnbjIyIFJYc6cGXxWOIWNuO11r/WKfvuyFzujRMxtJ7/qanguotZ28iokfT7SESotEqO8xcXFz5cX4mPSOBdPHF8PPPJ+3q/3ZTD27++kp6JqTz564IDBFVrL2hFO4jRyAsrHTCoFK4n3gC7bHH0JRCGY24Y2MxHjmC0jSKxo3D2bVrHb25hu+VJWcy/Y/zfD9bTU5+u/k9lNIY8e5tAHz6oY2Hb81kpzMFEy5s772H9eabWbYMhgwBo8HNqskz6dAsO1Bvo1643BovLD6bFxadzVnt9vH0BbPJK7Fy7n8mYTa42PHzJtqd3yfQzRRC1IAym9GcTuyzZ2Op420sqvr5HSRVWxontX49AK7ExEqPWbRXn98xrHt61YMIgKZhaN68/MoFTcPw6KOwfTuOL7/EtW0bhgMHcI8fj6YUYd98g/HQIbSCAszr1xPy88+YPBvABaN7zlrGI+fM9f38wsU/07dlOv1apXF17z8BuH6ClYnOdzHhwtm2LeYrrwT0SawXjnbgchuYMX94pdewOU8sKd0QFdlNOFwV/3OQlhfB2I9u5Kn5I7C5TMzd3ZEz37iD2769HIDL+2yl7Tnd6rO5Qoi65P2cCGDPiAzTBEpmJtrhwyjAlZBQ6WGL9rYDYMRQZ51dWuvUCXOnTqU//+c/uPfvx7BgAeEffABOp69ShHX1aux9+1J8/vkQhLPJ/zZ0CR2bHcXuMnJV702+xx85Zx4/bOmOy6m4jXcAcF52GSFlkv2TT5v55Tf4ZnNP7jt7CT0SMnG6NFYdTGburo78sasDf6a1pGdCOk+d/xtDU/b5Xut0abiUAavJVW/vtTK/bu/M7d+NxagpJg5Yyy2nryYpMp/tWfHM3tGF15YN5mhROOFmO49evpoF+7rw68p4dmfrNWDumVRYaa0GIUQjEBYGubloMkxzckE5TPPbb3D++biaNaPgrrsqPORgbhQ9X7oPo+YmY9Uumg3o7L/25OfjHjgQwzZ9qa0rKQl369aY1qxBUwp3dDRFY8fWeiVOYzJj/jD2LUznC67FHRmJe+1aTGVCHMBVlzv5+jsTQ9ruo2Ozo/y0rStHiype6ntx162c1W4fi/amsGRfO4ocZq7qtZG7zlxWrSXW1VXZ7uhuNzy9cDjPLhxW7nGTwUVSZH65ejK9EtP45Mnd9Bo/GM1o5IcvCvjXwzb6xu/jnd87YoiueNWSEKLhU126oO3YgfPTTzFdd12dnltW0zR0Tz8NDz2EvWdPij1d/8f7fENv7vjfZfRvdZDVu+L9v849JwfnBx/g7tYN01lnYYiIwD13Llx/PYaMDH0lzoUXYq/j2dYNlVJgePtjotJ3Y7vgAiw//3xCCeht26BHD4XbXfp4TEgxI7vsZfTQQgaeE8mbn8Xx7jfxuNyVj4qO7rSDZy78lXaxOeUed7gMGDRVoyXUf6UlMO3XC1hzsDXNwwtJiMynRUQBkRY74RY7e7LjWLxPD5e3DV7LOZfH8tr7kSzd2hwAi9HJ0Pb7uPisbG6+P57I7u1PvEcuF1oFu5sKIRqR/Hy9uFtkZJ2XuZfVNA1ddeaL9MxEC2nt/zbFxGC6775yDxnOPRd27cJ93XX6Spyff8Zw9Cglo0aVbo4TpIwZ6USm70YZDHDXXRX+T9q1Kzz8oIuP38pnROf9XH6Jg3PHJRCS0tW3kdabY2HyejuP3ZdN9qFihvXNZuQoA6pZAi++ZOB/SxP4bWdnNn+QwA/jZ9G+2TEAVh1ozfivrqLEaeLKnpu4tu+f9Gt5+JQF5vJtFmbMH85bKwf5ljAfzo/icP6J/xCEmBy8fMNiJr3cH0N0NOPuhw1L80ldcYCzhpuI7Z2CZq58ua4EESGCQGRkJZs41B/pGQmUzp1h504Kb7gBZ8cT/7FXCnq9dC8H86L5ccZaLn4wwHufKIX7H//A8PTTADi6dKHo8svBWvEOvsEg5KefsK5Zg71XL8yrV6Od5L0qhwNMphr9VrHtrxLGXuxi+4FwWkbm8eOEWaxITea+ny7G7ir/+0K35pncNWQZV/XaiNnoBiCvxML8PR1Yc7A16w615M+0JArselsv67mFRx4owRYax6H9DjLTXBTkKwoKwGFXXD4ql/4TT0czye8lQoi6J8M0DVl+vl6bHMj7+98rLCe+52gsp712N2aDi6wN+4ju1TB2N3XPmoV2661oTieuxEQKr7sOFQz/TY7ncBD1wgtoJSXYnngC6z//6dfLZWTAiMFFbN0bRqTFRr4nTFzcbRu3ToLPvo/kh6UtKHGaAWgdncO1ff5k/eGWLNzTHoe7fA9FSmw2L92+kUum9ZX5HEKIgJFhmobMU+rdHRlZ6b4m3iGa05MPEtUlud6adiqG8eNRKSmoSy7BmJ5OxLvvUnjddbiTkgLdtDpl3rYNraQEd3Q0xvHj/X69hARYsCKMEYOL2LJHL4E/7bzlPPFhCuaWiVx6H+QcdfLGv9N4+b1oDubG8Nyi0omnnZod4ewuhxnQz8nAwSZ6DWuOpfWwyi4nhBANioSRQPDOFznJB/jife0AGNb7KJqlbX20qsq0oUNR69fjPuccDPv3E/H++9jOOAP7oEGo6tRCacDMGzYA4DjzTCyt62G+DtCihR5Inrg3g7NbbueK6aeVqy0T08zEP15K4t4nFf95JoO5PxdzWtssLrvSRI/RbTHG9a6XdgohRF1r2mFk6VLIytK3cT3JRNI6V4XJq6sO6L0hw4YHelpRxbT27dH+/BP3BRdgWL6ckMWLsS5bhr1vX2zDh6MiIwPdxBrTcnIweTepueWWOp9dfjLNm8OrnyYAldeeCQvTuPvxBO5+HKBdPbVMCCH8J7iXQ5zK3/4Gl12Ge/Hi+r3uKcJIscPEwVx9nL/X4Ab8oR4djWHJElzvvYerY0c0lwvr2rWEf/ghWmFhoFtXY5Y//0QDnO3aYT7//EA3Rwghgl7TDiOeuh22OXPq75p2O2qTXumzsmGavdmxKDSirCW06BhTf22rCYMB4803Y9yxA9cPP+Bu3hzj0aOEffop2CreTbdBU8o3ROM6//zy5fSFEEL4hYQRQGXX4yZnW7agORyokBBUTEyFh+w6qpfZ7hh/FGNcbP21rTY0DeMll6AtWoSKisJ0+DDhX3wBzrorY18fjPv3Yzx2DGWxYLjttkA3RwghmoQahZGZM2fSrl07QkJCGDRoEKtWrarS67744gs0TWPs2LE1uWzd81Y0rc/f4MsO0VQyF2G3N4wk5DW6olJa167wxx+okBBMe/cS/umnGLL8V+q8TimFdeVKABx9+mDqLRNChRCiPlQ7jHz55ZdMnTqV6dOns27dOvr06cPo0aPJzMw86ev27dvH/fffz9lnn13jxtY5TxjR6vO3940bgZOvpPFuQNapXSMc5gC000+H779Hmc2Y9u4l4o03CPn5Z7SCgkA37aSsixdj3roVpWm4b7qp0QVBIYRorKodRl588UUmTZrExIkT6d69O2+99RZhYWG8//77lb7G5XJx/fXX8/jjj9O+/Yn7WwSMt2ekPsOIJ7S5T7IEdvfROAA6VV6Fu8HTRo2CtWtxnXkmmlJYV68m8tVXCfn9d7T8/EA37wTmDRsImTcPANuVV2K99dYAt0gIIZqOaoURu93O2rVrGTlyZOkJDAZGjhzJ8uXLK33dv/71L1q0aMEtt9xSpevYbDby8vLKfflFIHpGcnIAUCfZ9M47Z6Rz98a98lrr1Qvj0qW4f/gBd/v2aHY71mXLiHzlFUJ++SWwE1xLSjBkZGA8eBDzhg2E/vADALbhw7G8+67/NyUUQgjhU61PuyNHjuByuUhIKF8DISEhgW2ereePt2TJEt577z02eFYoVMWMGTN4/PHHq9O0mglEz4g3jISGVvh0bomVrEK916Rzn7D6apVfGS65BC6+GNcnn8ATT2DcuRPrqlUY09IovP760v8O9cS4bx/hn32GZreXe9zeuzemTz+V8ulCCFHP/LqaJj8/nxtvvJF3332X+Pj4Kr/uoYceIjc31/d14MAB/zQwED0jx/QdWSvrGdnjGaJJiMgntl1cvTXL7zQN4403Yty+HddXX6HCwzEdOED4xx9DcXH9NSMvj7D//hfNbkdZrbijo3HFx2MbOBDt888xtmxZb20RQgihq1bPSHx8PEajkYyMjHKPZ2RkkFhBAa/du3ezb98+LrnkEt9jbre+06jJZGL79u106HDiBnBWqxVrfewGG4CeEZWTg0blYcQ7RNO+WTZaVMPZk6bOaBrGq65CtW+PGj4c06FDhH/0EcVXXom7WTP/XtvpJOyrrzAUFuJKSMD100+Ye/VCM5uxGpr2KnchhAikav0LbLFY6N+/P3PnzvU95na7mTt3LoMHDz7h+K5du7Jx40Y2bNjg+xozZgwjRoxgw4YNJCcH+MPWM1TSkHpGvGGkU1IBWhB/QGr9+8PixXpNkrQ0Il97jYhXXyXk118xnGJlVk2FzJ6N6eBBVEgIzuefxzJgAJrVGtT3WQghGoNqz5CcOnUq48ePZ8CAAQwcOJCXX36ZwsJCJk6cCMBNN91Eq1atmDFjBiEhIfTs2bPc62M8hb6Ofzwg6rtnxGZD8w5JVDZMk+1ZSZPiqJ82BZDWty9q2TJcN9yA4a+/MGZnY1y5Esv69RTeeCOuugqrSmFdvBjrmjUoTaNk8mRCrruubs4thBCi1qodRsaNG0dWVhaPPvoo6enp9O3bl9mzZ/smtaampmJoLL9p1veckdxc37en6hnp2KleWhRwWo8eGNevR2Vl4fz8c7S33sK4dSvhn3xCwfjxuGs7h8PhIPSHH7B46rvYLrgA6xNPSG+IEEI0IJpSSgW6EaeSl5dHdHQ0ubm5REVF1d2JZ82CCRNwdOyIeefOujtvZbZvh65dUVYreQ89dMLTSkHbp6eRZwth/Vdb6HtVd/+3qaEpLMQ9dCiGdetwh4ZSOGEC7oTKd7AF0HJzUVbrCb1N2rFjhH39NaZDh1AGA7arrsLy1lsYKinDL4QQom5V9fO7cReyqK367hk5RY2RI4Vh5NlC0FB07N1EN2gLD8cwfz7uIUMwbNpE+AcfYBs2DPvpp4OpzF9XpxPztm1YVq3ClJqKMhpxduyIo1cvAMzr12PavRsNcIeGYps6lZBHHkGrj4nRQgghqkXCCNTfnJEqTl5NjskhrJWfV5Y0ZFFRGBYtwn322Rg2byb0t9+wrlypL78tKsKYmYnx0CEMhYUAKEBzuTBv3455+/Zyp3KmpOB6/HFCbrgBrZK9gIQQQgSWhBEC0DNSScEzbxjpEH8MLTylftrUUMXGYtiwAdcLL6DNmIEhJ4fQ338vd4g7IgLHWWfBlCmYwsNR//kPht9/B4cDx8CBcMstmC+8ENNJSu8LIYQIPAkjUH89I6cII77delsVym/xACYTxmnTUFOm4Hr0UdTs2Xotks6dUb17Yzz/fCydOpXeq+HDAXAXFmIJC5N7KIQQjYSEEfSeEaWU/z+8TjFMs9u7rLe9y7/taGS08HCML7wAL7xQpeMN4U10vo0QQjRSTXt9Y9mekfroHTnFBFZvz0iXLvIbvRBCiKZDwgienpH6CCPenpEKVnS43WUKnvWUFR9CCCGajqYdRrxzN+q7Z6SCOSM7jsRT4jRjNTpJ6Rnp/7YIIYQQDUTTDiNlV9M46qH8+kmGaebs0kuunpWyH2vLFv5vixBCCNFASBjxUEVF/r/eSSawztnZEYDRZ2Sjmc3+b4sQQgjRQEgY8fIU0PInVUnPSL7NwvL9bQG4cIwEESGEEE1L0w4jZjPejXlUPYSRyoZpFu5JweE20j7uKF3PbeP/dgghhBANSNMOI5pWut+Jv4dplCodpjluAuucnfp8kVE9UzHGxfm3HUIIIUQD07TDCKA8YcTvPSOFhWguvZhZ2Z4RpUrDyAWjpNiZEEKIpqfJhxFfz4i/w4i3V8RggDITVLdktuBwfhShJgcjLpNeESGEEE1Pkw4jH38Mx5yemh7+HqYpO1+kTNl5b6/I0Pb7iOgs80WEEEI0PU06jLz+OmSUxOg/1GcYKeN375LewcfQTE17qyAhhBBNU5MOI3FxUIIeDlRxsX8vVsHk1ZxiKytT9d6QC8ZKCXghhBBNU5MPI8Xo4UDzdxipoGfksw19cSkDneOz6Dy8rX+vL4QQQjRQTT6MBKpnZP+xaJ6cdw4At4/ahiEmxr/XF0IIIRqoJh1GYmNLw0h99owoBff8eAmFDgtntt3PlGc7+vfaQgghRAPWpMNIuZ6RkhL/XqxMGPl0Q18W7OlAiMnBW48fwtwqyb/XFkIIIRowCSOeMIK/w4hnmCZfi+Lh30YB8PBFK+l5/UD/XlcIIYRo4CSM1FcY8fSM/H6gF7klofRreYj7X2ory3mFEEI0eU06jJSbM2Kz+fdinp6RpRldAPjHhAOEpCT795pCCCFEI9Ckw0i5OSP1VPRsV3ErQkwOzhsX79/rCSGEEI2EhBFvGCnw7940yhNGjhHLsPb7iOomdUWEEEIIaOJhpOwwjT2vfoZpcojhgiHH0MpslieEEEI0ZU06jFgsoMx6GXZHkcN/F3I60fLzAb1n5OIrQk7xAiGEEKLpaNJhBMAQrgcDh1357yK5ub5vW7coJuXsdv67lhBCCNHINPkwYozQy7O77G7/XcQzX6SAcEZ2T8UQFeW/awkhhBCNTJMPI+ZIPYy47S6/XcNxJAfwDNFc5LfLCCGEEI1Skw8jlijPahqn/8LIliWl1VcHX9nGb9cRQgghGqMmH0as0Z7JpA6n367x58IcALQwK5a2rf12HSGEEKIxavJhJCTGU4HV5b8wsnuN3jMSHmVA0zS/XUcIIYRojJp8GAmN08OIweWfpb3Z2VCUlgNAbJxfLiGEEEI0ak0+jIR7wojR5UC5635FzbJlEEMOANZm4XV+fiGEEKKxkzDSTA8jZrcdHHXfO7J0KcSiD9MQL/vRCCGEEMdr8mEksrknjCgbyln380aWLCntGSEmps7PL4QQQjR2TT6MRLXQw4hVlYDdXqfnttlg9WpV2jMiYUQIIYQ4QZMPI9EJehix4MB1LKdOz712LdhsGvGGbP2B2Ng6Pb8QQggRDCSMJIb6vi88lF2n516yRP+zjfGg/k3z5nV6fiGEECIYNPkwEtHM6vv+WGpenZ576VIIo5AEx2EAtD596vT8QgghRDBo8mFEM5twYAIg52B+nZ1XKVi6VNGZHQC4Q0MxdOpUZ+cXQgghgkWTDyMAdk2fN5KXXlxn59y+HY4e1ehj2AiAu3lzDDJnRAghhDiBhBHAbvSEkay6CyPe+SLDI1cD4G7dGs0gt1sIIYQ4nnw6Ak6THkaKjpbU2Tm9YaSfeRMAqkOHOju3EEIIEUwkjAAusyeM5NRdBdalS/U/2zl2AaB161Zn5xZCCCGCiYQRwG3Rw0hxQd3sTXPsGOzaBUacROWn6Q/27Vsn5xZCCCGCjYQRQFn1MGKro1Ga/fv1P/uHbkJzu1AmE8bevevm5EIIIUSQkTACaCF6GLGXqDo534ED+p+DQzYA4I6PxyAFz4QQQogKSRgBtDA9jDhsdRNGUlP1P/uaPMt6k5LQLJY6ObcQQggRbCSMAIZwvSS801Y3c0a8PSOd2Q6AOyWlTs4rhBBCBCMJI4ApXO8ZcdrdKFX73hFvz0gb+279m65da31OIYQQIlhJGAHMkXoYcTvcYLPV+nx6z4iiRaE+k1Xr1avW5xRCCCGClYQRwBKlhxHN5cSeV/sqrKmpikTSsTiLUZqG4bTTan1OIYQQIlhJGKE0jIRQwrGM2vWMuFxw6BB0YysA7thYDK1a1bqNQgghRLCSMAIYwkrDyNGM2lVhTUsDl0ujp7YZAHdCAlpYWK3bKIQQQgQrCSMAIXUXRryTV08z68t6VZs2aJpWq3MKIYQQwUzCCJQLI8eOuGp1Ku+y3h6GLQCozp1rdT4hhBAi2EkYgXJhJLuWYcTbM9LBuRMArWfPWp1PCCGECHYSRqB8GDlau1MdOADhFBDnzARAk5U0QgghxElJGIHyYSS7dqdKTYV4jgCgTCYM7drVsnFCCCFEcJMwAhCql4MPpZisbGOtTnXgAMShJxoVGormObcQQgghKiZhBMr1jKRnmWp1qtRURSzHAFAhIb4dgYUQQghRsRqFkZkzZ9KuXTtCQkIYNGgQq1atqvTYb7/9lgEDBhATE0N4eDh9+/bl448/rnGD/aJMGDl8tObhobgYjhzRSntGwsPRjLXraRFCCCGCXbXDyJdffsnUqVOZPn0669ato0+fPowePZrMzMwKj4+Li+Phhx9m+fLl/PXXX0ycOJGJEyfy22+/1brxdaZsz0huWI03y/Mu600y6vdChYfXSfOEEEKIYFbtMPLiiy8yadIkJk6cSPfu3XnrrbcICwvj/fffr/D44cOHc9lll9GtWzc6dOjAPffcQ+/evVmyZEmtG19nyoaR/EhcBUU1Oo03jLS1puvfREbWReuEEEKIoFatMGK321m7di0jR44sPYHBwMiRI1m+fPkpX6+UYu7cuWzfvp2hQ4dWepzNZiMvL6/cl1+VCSMuZSBjT0GNTuOtMdLKkgGAio6uk+YJIYQQwaxaYeTIkSO4XC4SEhLKPZ6QkEB6enqlr8vNzSUiIgKLxcJFF13Ea6+9xnnnnVfp8TNmzCA6Otr3lZycXJ1mVp8njISi79h7aE9JjU7jDSNJpiz9GwkjQgghxCnVy2qayMhINmzYwOrVq3nyySeZOnUqCxYsqPT4hx56iNzcXN/XAe/4h794w4imh5BD+2u2P423mc00T+W0uLhaN00IIYQIdtVaxxofH4/RaCQjI6Pc4xkZGSQmJlb6OoPBQMeOHQHo27cvW7duZcaMGQwfPrzC461WK1artTpNqx1PGLEqGwCHUmtWEt7bMxKrjnm+ia1104QQQohgV62eEYvFQv/+/Zk7d67vMbfbzdy5cxk8eHCVz+N2u7HZbNW5tH95wogRF0acHE6r2Wm8PSMRrlz9m2bN6qBxQgghRHCrdoWvqVOnMn78eAYMGMDAgQN5+eWXKSwsZOLEiQDcdNNNtGrVihkzZgD6/I8BAwbQoUMHbDYbv/zyCx9//DFvvvlm3b6T2ihTmCyEEg6nVb82iFJ6wTPQCHXqE2C15s3rqoVCCCFE0Kp2GBk3bhxZWVk8+uijpKen07dvX2bPnu2b1JqamorBUNrhUlhYyJ133snBgwcJDQ2la9eufPLJJ4wbN67u3kVtlRkSCqGEw1nmap/i2DEoKtIAMNkK9QcljAghhBCnpKmaVviqR3l5eURHR5Obm0tUVJRfrqFCQtBsNlpzgNgkMxsPJ1RyoIKCghNqiGzYAP36QVLYMQ4X6RNXnevWYerXzy/tFUIIIRq6qn5+y940XmWW96blRqBclUxivfVWVPPmqB07yj3snS/SNaJ05Y8M0wghhBCnJmHEyzNUE0IJR4vCKTlaSeGz5cvRbDac331X7uE0z6TXDmGHAE9PS0SE35orhBBCBAsJI16hoQBEGvQQcnh3YYWHKW812OP24vHWfGtj9VRfDQlB85xTCCGEEJWTMOLlGaZpGabXCDm0p5Klx7meZbvZ2eUe9oaRVib9G3dYGFgsdd9OIYQQIshIGPHQvGEk1BNGUp0nHuRyoRV4hm9ycso95Q0jiaYjgL5jr6ZpfmmrEEIIEUwkjHh5wkhCSA4Ahw64TzymoMw8Em8PiYc3jDQ36GGE8PC6bqEQQggRlCSMeHnCSAtPGDmcVkGvRtkAckIY0VdIx6EP3yg/LUEWQgghgo2EES9PGGlu0SeopmVUUIXVO3kV0PLzfd8rVdozEqU8IUV27BVCCCGqRMKIlyeMxHnCyKEjFUw+LRtGCktX2+TnQ3Gx3pMS7vSEEdkkTwghhKgSCSNe3jBi1ns80o+FnXhMmaEZrbgYb/Fab42RKGsJZluR/kNMjN+aKoQQQgQTCSNenpogMSZ9kmp6XgTq+J2Fy/aMFBWB53nvEE2LiAK04mL9B9mxVwghhKgSCSNenp6RKKMeRvLtVnLTjqvCWjaM2Gwoz1BNaRgpLA0j8fH+ba8QQggRJCSMeHnCiEXZiLKWAHBod3H5Y45bQaMy9Gqr3jCSEFmAVqK/VvalEUIIIapGwoiXJ4xoTieJkfq8kYPHV2Et0zMCoDwpxNczEpZf2jPSooX/2iqEEEIEEQkjXp66IJrdTpInjBw+cNzOvceFEbKygDL70oRmobn1YmlaQoL/2iqEEEIEEQkjXt4wYrP5ekYOH1Lljzl+mOa4MNLaon+jjEa0uDg/NlYIIYQIHhJGvLwVU0tKaBmlh5FDacfdnuN7Ro7opd+9YaSlWd/JV4WEoIVVsDRYCCGEECeQMOJVQc9IWuZxVViPDyPH9E310tL0HpQWnn1pVGiob+M9IYQQQpychBGvioZpjhwXKDzDNG7vJnjZ2bhcvqkjxGtHAVBhYWgmk//bLIQQQgQBCSNeZcJI25gcAPYfjUS5y+ze6+kZcXurq+bkkJUFbreGQXMT7dmXRsmOvUIIIUSVSRjxKhNG2sXqwy8ZBZEUZpTZEO/4MJKb65svEh9WhKHEUwo+MrJemiyEEEIEAwkjXt4JrDYbMdYiYkL0eiG7/iwNI95hGuXdkTcvr3wpeE/BM+U9lxBCCCFOScKIlydgaAAOh693ZPdWT+Ezp1Pfj4bSnhGtoKB89VVvwTPZsVcIIYSoMgkjXlYrymwG9KGalLhsAHbv8BQ+yy/tIfGFkcLCisOIt+dECCGEEKckYcRL03xzPfR5IzkA7N3nuUXeIRqTyTdBVSsq8i3rTQgvwCA79gohhBDVJmGkrDKTWFNiPT0jByz6c57Jq8pqxR0aqh9XXEzaIb3npEVEmZ4Rqb4qhBBCVJmEkbK880ZsNtp65ozsy/Qs0y0TRpQ3jDgcHDmkzylJKDOBFdmxVwghhKgyCSNlaGVW1KTE6WFkf3Y0zsKS0mEaq1WfX+J5Tclh/bhyPSMSRoQQQogqkzBSVplhmlZReZgNLuwuEwe35paWgg8JAYMB5Sn3bj+ih5SEsFw0m95LorVoUf9tF0IIIRopCSNlecNISQlGg6KNpxLrro1FpcM0nhDiHaqxFOlhJNF0xHcaLTGxvloshBBCNHoSRsoq0zMClNYa2eEsP0xDaSiJ5RghJgdR7hz9cYsFg1RgFUIIIapMwkhZlYWR3ZT2jFj01TXenpFYjtEiogCDt/pqaCia5zkhhBBCnJqEkbKODyOeSax7U03lVtNAaRiJI1tfSeOZvKpCQ8FzjBBCCCFOTcJIWd7VNHY7UNozsudw6InDNGV6RhIiCzFkZQHgjotD07T6bLUQQgjRqEkYKeu4nhFv4bN9R6NQnjBCBXNGEiIKMB4+DIC7a9f6bLEQQgjR6EkYKeu4MNLWUxI+uygMZ5beS3L8apo4smkXm+0LI9rAgfXZYiGEEKLRkzBS1nFhJNJqp3l4AQD2DE8Y8fSMOCx6ZdZYjjGy1UaM2XovijZ0aL02WQghhGjsJIyUVabOiJd33ogrR9+11xtGthckA5BgyKKn40/9mNhYjN2711tzhRBCiGAgYaSsMuXgvVI8YcRQpPeQeMPIyiOdAGhpzsSU5pkvkpxcWlJeCCGEEFUiYaSsssM0St99xrthnsVWvmdkfpreAxLHMYyHDgHg7tlTVtIIIYQQ1WQKdAMaFG8YUQocDrBYSIk7hhk7FrfeW6JCQsjID2d1tt4zEurMRx1266+XyatCCCFEtUnPSFnh4SiDfkvKVmGNJL/0GIuF+Xs6cIxY/TiXC0NeHgowyORVIYQQotokjJSlaeDZV6ZsrZFo9BojLpMFjEbm7epAARG4NKPvpe7mzTF27lz/bRZCCCEaOQkjxztueW9iZAFnttgBwFF3LPk2C/P3tAc0nGGlG+K52rbFEB5e780VQgghGjsJI8eLjtb/9IQRTYN/D/sRgGx3DGM/upGswggiLDbMLWJ9L1O9etV7U4UQQohgIGHkONpxPSMAzU36ipp8Ill7qDUAZ7Xfj9aiRenrBg2qx1YKIYQQwUPCyPEqCCPe7xOaO32PnTcoFy0uDgBlMGAYNqweGymEEEIEDwkjxztJGElKcvP4P4oZ1O4w19wSAZ4w4k5IwNiuXb03VQghhAgGEkaOV1EY8ZaHDw/n0SdDWb4niZZnd4NmzQBwpaSgeTbQE0IIIUT1SNGz41UQRryTWVVEhP6ct8rqLbfgWr8edcst9dpEIYQQIphIGDleBfvT+IJJZGT5Y3v3xrhoEUaEEEIIUVMyTHM8b8+I3e57qNIwIoQQQohakzByPG8Y8c4ToUwYkR15hRBCiDonYeR4J1lNI2FECCGEqHsSRo53ktU0WkxMIFokhBBCBDUJI8erYAKrbzVNbGwFLxBCCCFEbUgYOd5Jhmk0CSNCCCFEnZMwcryTzRnxVFwVQgghRN2ROiPH84YRlwscDlBK/x58e9EIIYQQou5IGDmep8oq6D0ihqNHAXCHh6MlJQWqVUIIIUTQkmGa4xmNpWXfbTZMqakAuNq1wxAfH8iWCSGEEEFJwkhFvJVWbTaM+/cD4D7ttNI9aYQQQghRZySMVCQ6GtDri5gOHNC/Hz48cO0RQgghgpiEkQponjBiOnAAzWZDWSwYRowIcKuEEEKI4CRhpCKeFTWmHTsAcLZpg7FNm0C2SAghhAhaNQojM2fOpF27doSEhDBo0CBWrVpV6bHvvvsuZ599NrGxscTGxjJy5MiTHt8geMKI8dAhANy9eqEZjYFskRBCCBG0qh1GvvzyS6ZOncr06dNZt24dffr0YfTo0WRmZlZ4/IIFC7j22muZP38+y5cvJzk5mVGjRnHI80HfIHlrjXh/Hjo0YE0RQgghgp2mlFLVecGgQYM4/fTTef311wFwu90kJydz11138eCDD57y9S6Xi9jYWF5//XVuuummKl0zLy+P6OhocnNziaqPnXPvvRdeeQUAZTDg/OsvzD16+P+6QgghRBCp6ud3tXpG7HY7a9euZeTIkaUnMBgYOXIky5cvr9I5ioqKcDgcxJ2kmqnNZiMvL6/cV70qc8NcrVph6tSpfq8vhBBCNCHVCiNHjhzB5XKRkJBQ7vGEhATS09OrdI5p06bRsmXLcoHmeDNmzCA6Otr3lZycXJ1m1l7ZMNKtG5rFUr/XF0IIIZqQel1N8/TTT/PFF1/w3XffERISUulxDz30ELm5ub6vA55aH/WmbFfSkCH1e20hhBCiianW3jTx8fEYjUYyMjLKPZ6RkUFiYuJJX/v888/z9NNP88cff9C7d++THmu1WrFardVpWt3yhBEFGEaNClw7hBBCiCagWj0jFouF/v37M3fuXN9jbrebuXPnMnjw4Epf9+yzz/LEE08we/ZsBgwYUPPW1hdPsHInJmLs1SvAjRFCCCGCW7V37Z06dSrjx49nwIABDBw4kJdffpnCwkImTpwIwE033USrVq2YMWMGAM888wyPPvoon332Ge3atfPNLYmIiCCizA65DcrQobiffx5n8+ZYw8MD3RohhBAiqFU7jIwbN46srCweffRR0tPT6du3L7Nnz/ZNak1NTcVgKO1wefPNN7Hb7Vx55ZXlzjN9+nQee+yx2rXeXwwGDH/7GwEcKBJCCCGajGrXGQmEeq8zIoQQQoha80udESGEEEKIuiZhRAghhBABJWFECCGEEAElYUQIIYQQASVhRAghhBABJWFECCGEEAElYUQIIYQQASVhRAghhBABJWFECCGEEAElYUQIIYQQASVhRAghhBABJWFECCGEEAFV7V17A8G7l19eXl6AWyKEEEKIqvJ+bp9qT95GEUby8/MBSE5ODnBLhBBCCFFd+fn5REdHV/q8pk4VVxoAt9vN4cOHiYyMRNO0OjtvXl4eycnJHDhw4KRbGwczuQdyD0DuAcg9ALkHIPegrt+/Uor8/HxatmyJwVD5zJBG0TNiMBho3bq1384fFRXVJP/SlSX3QO4ByD0AuQcg9wDkHtTl+z9Zj4iXTGAVQgghREBJGBFCCCFEQDXpMGK1Wpk+fTpWqzXQTQkYuQdyD0DuAcg9ALkHIPcgUO+/UUxgFUIIIUTwatI9I0IIIYQIPAkjQgghhAgoCSNCCCGECCgJI0IIIYQIqCYdRmbOnEm7du0ICQlh0KBBrFq1KtBN8osZM2Zw+umnExkZSYsWLRg7dizbt28vd0xJSQmTJ0+mWbNmREREcMUVV5CRkRGgFvvf008/jaZp3Hvvvb7HmsI9OHToEDfccAPNmjUjNDSUXr16sWbNGt/zSikeffRRkpKSCA0NZeTIkezcuTOALa5bLpeLRx55hJSUFEJDQ+nQoQNPPPFEuX0zgu0eLFq0iEsuuYSWLVuiaRr/+9//yj1flfebnZ3N9ddfT1RUFDExMdxyyy0UFBTU47uonZPdA4fDwbRp0+jVqxfh4eG0bNmSm266icOHD5c7RzDfg+PdfvvtaJrGyy+/XO5xf96DJhtGvvzyS6ZOncr06dNZt24dffr0YfTo0WRmZga6aXVu4cKFTJ48mRUrVjBnzhwcDgejRo2isLDQd8x9993Hjz/+yH//+18WLlzI4cOHufzyywPYav9ZvXo1b7/9Nr179y73eLDfg2PHjjFkyBDMZjO//vorW7Zs4YUXXiA2NtZ3zLPPPsurr77KW2+9xcqVKwkPD2f06NGUlJQEsOV155lnnuHNN9/k9ddfZ+vWrTzzzDM8++yzvPbaa75jgu0eFBYW0qdPH2bOnFnh81V5v9dffz2bN29mzpw5/PTTTyxatIjbbrutvt5CrZ3sHhQVFbFu3ToeeeQR1q1bx7fffsv27dsZM2ZMueOC+R6U9d1337FixQpatmx5wnN+vQeqiRo4cKCaPHmy72eXy6VatmypZsyYEcBW1Y/MzEwFqIULFyqllMrJyVFms1n997//9R2zdetWBajly5cHqpl+kZ+frzp16qTmzJmjhg0bpu655x6lVNO4B9OmTVNnnXVWpc+73W6VmJionnvuOd9jOTk5ymq1qs8//7w+muh3F110kbr55pvLPXb55Zer66+/XikV/PcAUN99953v56q83y1btihArV692nfMr7/+qjRNU4cOHaq3tteV4+9BRVatWqUAtX//fqVU07kHBw8eVK1atVKbNm1Sbdu2VS+99JLvOX/fgybZM2K321m7di0jR470PWYwGBg5ciTLly8PYMvqR25uLgBxcXEArF27FofDUe5+dO3alTZt2gTd/Zg8eTIXXXRRufcKTeMe/PDDDwwYMICrrrqKFi1a0K9fP959913f83v37iU9Pb3cPYiOjmbQoEFBcw/OPPNM5s6dy44dOwD4888/WbJkCRdccAHQNO5BWVV5v8uXLycmJoYBAwb4jhk5ciQGg4GVK1fWe5vrQ25uLpqmERMTAzSNe+B2u7nxxhv5+9//To8ePU543t/3oFFslFfXjhw5gsvlIiEhodzjCQkJbNu2LUCtqh9ut5t7772XIUOG0LNnTwDS09OxWCy+//G8EhISSE9PD0Ar/eOLL75g3bp1rF69+oTnmsI92LNnD2+++SZTp07lH//4B6tXr+buu+/GYrEwfvx43/us6P+LYLkHDz74IHl5eXTt2hWj0YjL5eLJJ5/k+uuvB2gS96Csqrzf9PR0WrRoUe55k8lEXFxcUN6TkpISpk2bxrXXXuvbKK4p3INnnnkGk8nE3XffXeHz/r4HTTKMNGWTJ09m06ZNLFmyJNBNqVcHDhzgnnvuYc6cOYSEhAS6OQHhdrsZMGAATz31FAD9+vVj06ZNvPXWW4wfPz7ArasfX331FZ9++imfffYZPXr0YMOGDdx77720bNmyydwDUTmHw8HVV1+NUoo333wz0M2pN2vXruWVV15h3bp1aJoWkDY0yWGa+Ph4jEbjCSslMjIySExMDFCr/G/KlCn89NNPzJ8/n9atW/seT0xMxG63k5OTU+74YLofa9euJTMzk9NOOw2TyYTJZGLhwoW8+uqrmEwmEhISgv4eJCUl0b1793KPdevWjdTUVADf+wzm/y/+/ve/8+CDD3LNNdfQq1cvbrzxRu677z5mzJgBNI17UFZV3m9iYuIJE/udTifZ2dlBdU+8QWT//v3MmTPH1ysCwX8PFi9eTGZmJm3atPH9+7h//37+9re/0a5dO8D/96BJhhGLxUL//v2ZO3eu7zG3283cuXMZPHhwAFvmH0oppkyZwnfffce8efNISUkp93z//v0xm83l7sf27dtJTU0Nmvtx7rnnsnHjRjZs2OD7GjBgANdff73v+2C/B0OGDDlhSfeOHTto27YtACkpKSQmJpa7B3l5eaxcuTJo7kFRUREGQ/l/9oxGI263G2ga96CsqrzfwYMHk5OTw9q1a33HzJs3D7fbzaBBg+q9zf7gDSI7d+7kjz/+oFmzZuWeD/Z7cOONN/LXX3+V+/exZcuW/P3vf+e3334D6uEe1HoKbCP1xRdfKKvVqj788EO1ZcsWddttt6mYmBiVnp4e6KbVuTvuuENFR0erBQsWqLS0NN9XUVGR75jbb79dtWnTRs2bN0+tWbNGDR48WA0ePDiArfa/sqtplAr+e7Bq1SplMpnUk08+qXbu3Kk+/fRTFRYWpj755BPfMU8//bSKiYlR33//vfrrr7/UpZdeqlJSUlRxcXEAW153xo8fr1q1aqV++ukntXfvXvXtt9+q+Ph49cADD/iOCbZ7kJ+fr9avX6/Wr1+vAPXiiy+q9evX+1aKVOX9nn/++apfv35q5cqVasmSJapTp07q2muvDdRbqraT3QO73a7GjBmjWrdurTZs2FDu30ibzeY7RzDfg4ocv5pGKf/egyYbRpRS6rXXXlNt2rRRFotFDRw4UK1YsSLQTfILoMKvDz74wHdMcXGxuvPOO1VsbKwKCwtTl112mUpLSwtco+vB8WGkKdyDH3/8UfXs2VNZrVbVtWtX9c4775R73u12q0ceeUQlJCQoq9Wqzj33XLV9+/YAtbbu5eXlqXvuuUe1adNGhYSEqPbt26uHH3643IdOsN2D+fPnV/j///jx45VSVXu/R48eVddee62KiIhQUVFRauLEiSo/Pz8A76ZmTnYP9u7dW+m/kfPnz/edI5jvQUUqCiP+vAeaUmVKDwohhBBC1LMmOWdECCGEEA2HhBEhhBBCBJSEESGEEEIElIQRIYQQQgSUhBEhhBBCBJSEESGEEEIElIQRIYQQQgSUhBEhhBBCBJSEESGEEEIElIQRIYQQQgSUhBEhhBBCBJSEESGEEEIE1P8DG0xZinsMH0kAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "encoded_data = autoencoder.encoder(anomalous_test_data).numpy()\n",
        "decoded_data = autoencoder.decoder(encoded_data).numpy()\n",
        "\n",
        "plt.plot(anomalous_test_data[0], 'b')\n",
        "plt.plot(decoded_data[0], 'r')\n",
        "plt.fill_between(np.arange(140), decoded_data[0], anomalous_test_data[0], color='lightcoral')\n",
        "plt.legend(labels=[\"Input\", \"Reconstruction\", \"Error\"])\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ocimg3MBswdS"
      },
      "source": [
        "### Detect anomalies"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xnh8wmkDsypN"
      },
      "source": [
        "Detect anomalies by calculating whether the reconstruction loss is greater than a fixed threshold. In this tutorial, you will calculate the mean average error for normal examples from the training set, then classify future examples as anomalous if the reconstruction error is higher than one standard deviation from the training set.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TeuT8uTA5Y_w"
      },
      "source": [
        "Plot the reconstruction error on normal ECGs from the training set"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "N7FltOnHu4-l",
        "outputId": "aba66886-2061-4c9f-c00a-7ddb4a364f81",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "74/74 [==============================] - 0s 2ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "reconstructions = autoencoder.predict(normal_train_data)\n",
        "train_loss = tf.keras.losses.mae(reconstructions, normal_train_data)\n",
        "\n",
        "plt.hist(train_loss[None,:], bins=50)\n",
        "plt.xlabel(\"Train loss\")\n",
        "plt.ylabel(\"No of examples\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mh-3ChEF5hog"
      },
      "source": [
        "Choose a threshold value that is one standard deviations above the mean."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "82hkl0Chs3P_",
        "outputId": "46f2289d-2fe0-4342-bae3-ce9d8ea96989",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Threshold:  0.031422224\n"
          ]
        }
      ],
      "source": [
        "threshold = np.mean(train_loss) + np.std(train_loss)\n",
        "print(\"Threshold: \", threshold)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uEGlA1Be50Nj"
      },
      "source": [
        "Note: There are other strategies you could use to select a threshold value above which test examples should be classified as anomalous, the correct approach will depend on your dataset. You can learn more with the links at the end of this tutorial."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zpLSDAeb51D_"
      },
      "source": [
        "If you examine the reconstruction error for the anomalous examples in the test set, you'll notice most have greater reconstruction error than the threshold. By varing the threshold, you can adjust the [precision](https://developers.google.com/machine-learning/glossary#precision) and [recall](https://developers.google.com/machine-learning/glossary#recall) of your classifier."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "sKVwjQK955Wy",
        "outputId": "63623c0a-f21d-4f7d-a7e1-c5b35e10c880",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "14/14 [==============================] - 0s 3ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "reconstructions = autoencoder.predict(anomalous_test_data)\n",
        "test_loss = tf.keras.losses.mae(reconstructions, anomalous_test_data)\n",
        "\n",
        "plt.hist(test_loss[None, :], bins=50)\n",
        "plt.xlabel(\"Test loss\")\n",
        "plt.ylabel(\"No of examples\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PFVk_XGE6AX2"
      },
      "source": [
        "Classify an ECG as an anomaly if the reconstruction error is greater than the threshold."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "mkgJZfhh6CHr"
      },
      "outputs": [],
      "source": [
        "def predict(model, data, threshold):\n",
        "  reconstructions = model(data)\n",
        "  loss = tf.keras.losses.mae(reconstructions, data)\n",
        "  return tf.math.less(loss, threshold)\n",
        "\n",
        "def print_stats(predictions, labels):\n",
        "  print(\"Accuracy = {}\".format(accuracy_score(labels, predictions)))\n",
        "  print(\"Precision = {}\".format(precision_score(labels, predictions)))\n",
        "  print(\"Recall = {}\".format(recall_score(labels, predictions)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "sOcfXfXq6FBd",
        "outputId": "2907135e-db55-41db-89b9-27c8f75336f3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy = 0.941\n",
            "Precision = 0.9940828402366864\n",
            "Recall = 0.9\n"
          ]
        }
      ],
      "source": [
        "preds = predict(autoencoder, test_data, threshold)\n",
        "print_stats(preds, test_labels)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HrJRef8Ln945"
      },
      "source": [
        "## Next steps\n",
        "\n",
        "To learn more about anomaly detection with autoencoders, check out this excellent [interactive example](https://anomagram.fastforwardlabs.com/#/) built with TensorFlow.js by Victor Dibia. For a real-world use case, you can learn how [Airbus Detects Anomalies in ISS Telemetry Data](https://blog.tensorflow.org/2020/04/how-airbus-detects-anomalies-iss-telemetry-data-tfx.html) using TensorFlow. To learn more about the basics, consider reading this [blog post](https://blog.keras.io/building-autoencoders-in-keras.html) by François Chollet. For more details, check out chapter 14 from [Deep Learning](https://www.deeplearningbook.org/) by Ian Goodfellow, Yoshua Bengio, and Aaron Courville.\n"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "autoencoder.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}