From 11ded2d57333167de09ccdc718a1ee41d1d69132 Mon Sep 17 00:00:00 2001
From: Alexander Nikitin <1243786+AlexanderVNikitin@users.noreply.github.com>
Date: Fri, 15 Dec 2023 21:26:32 +0200
Subject: [PATCH] v0.0.4

---
 README.md                  |   2 +-
 setup.py                   |   3 +-
 tsgm/metrics/metrics.py    |   2 +-
 tutorials/evaluation.ipynb | 137 ++++++++++++++-----------------------
 4 files changed, 57 insertions(+), 87 deletions(-)

diff --git a/README.md b/README.md
index 62508be..e38f0ba 100644
--- a/README.md
+++ b/README.md
@@ -37,7 +37,7 @@ pip install tsgm
 
 - [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1l2VB6eUwvrxyu8iB30faGiQM5AKthc82?usp=sharing) Introductory Tutorial "[Getting started with TSGM](https://github.com/AlexanderVNikitin/tsgm/blob/main/tutorials/GANs/cGAN.ipynb)"
 - [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1Vw9t4TlI1Nek_t6bMPyKcPPPqCiXfOK3?usp=sharing) Tutorial on using [Time Series Augmentations](https://github.com/AlexanderVNikitin/tsgm/blob/main/tutorials/augmentations.ipynb)
-- Tutorial on [Evaluation of Synthetic Time Series Data](https://github.com/AlexanderVNikitin/tsgm/blob/main/tutorials/evaluation.ipynb)
+- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1hubtddSX94KyLzuCTwmU6pAFBgBeiEB-?usp=sharing) Tutorial on [Evaluation of Synthetic Time Series Data](https://github.com/AlexanderVNikitin/tsgm/blob/main/tutorials/evaluation.ipynb)
 - [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1wpf9WeNVj5TkUcPF6EavVx-hUCOfyvUd?usp=sharing) Tutorial on using [Multiple GPUs or TPU with TSGM](https://github.com/AlexanderVNikitin/tsgm/blob/main/tutorials/Using%20Multiple%20GPUs%20or%20TPU.ipynb)
 
 For more examples, see [our tutorials](./tutorials).
diff --git a/setup.py b/setup.py
index a20fab3..d26e82c 100644
--- a/setup.py
+++ b/setup.py
@@ -44,7 +44,7 @@ def read_file(filename: str) -> str:
 
 
 setup(name='tsgm',
-      version='0.0.3',
+      version='0.0.4',
       description='Time Series Generative Modelling Framework',
       author=author,
       author_email='',
@@ -66,6 +66,7 @@ def read_file(filename: str) -> str:
           "seaborn",
           "scikit-learn",
           "prettytable",
+          "antropy==0.1.6",
           "yfinance==0.2.28",
           "tqdm",
           "dtaidistance >= 2.3.10",
diff --git a/tsgm/metrics/metrics.py b/tsgm/metrics/metrics.py
index c7266a4..a7c3edf 100644
--- a/tsgm/metrics/metrics.py
+++ b/tsgm/metrics/metrics.py
@@ -275,7 +275,7 @@ def __call__(self, d_hist: tsgm.dataset.DatasetOrTensor, d_syn: tsgm.dataset.Dat
         X_all, y_all = np.concatenate([X_hist, X_syn]), np.concatenate([[1] * len(d_hist), [0] * len(d_syn)])
         X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(X_all, y_all, test_size=test_size, random_state=random_seed)
         model.fit(X_train, y_train, epochs=n_epochs)
-        y_pred = model.predict(X_test)
+        y_pred = (model.predict(X_test) > 0.5).astype(int)
         if metric is None:
             return sklearn.metrics.accuracy_score(y_test, y_pred)
         else:
diff --git a/tutorials/evaluation.ipynb b/tutorials/evaluation.ipynb
index 3d04ac9..2e2bc24 100644
--- a/tutorials/evaluation.ipynb
+++ b/tutorials/evaluation.ipynb
@@ -85,7 +85,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-12-14 16:08:19.585715: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
+      "2023-12-15 21:20:18.765228: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
       "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
      ]
     }
@@ -95,11 +95,8 @@
     "%autoreload 2\n",
     "from IPython.display import SVG, display, Image\n",
     "\n",
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
     "import numpy as np\n",
     "import functools\n",
-    "import random\n",
     "import sklearn\n",
     "import tensorflow as tf\n",
     "from tensorflow import keras\n",
@@ -117,7 +114,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2/2 [==============================] - 4s 91ms/step - loss: 4031.7565 - reconstruction_loss: 3640.0479 - kl_loss: 0.3930\n"
+      "2/2 [==============================] - 5s 97ms/step - loss: 4010.0732 - reconstruction_loss: 3641.8901 - kl_loss: 0.1948\n"
      ]
     }
    ],
@@ -211,28 +208,19 @@
    "execution_count": 5,
    "id": "a7b0621c-4c2e-42fc-96a7-ef8ed60bd8df",
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "dist_metric = tsgm.metrics.DistanceMetric(\n",
-    "    statistics=statistics, discrepancy=discrepancy_func\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "1f2f4d0a-fbaf-459c-903f-9f1992548e22",
-   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "12.795391\n"
+      "12.869374\n"
      ]
     }
    ],
    "source": [
+    "dist_metric = tsgm.metrics.DistanceMetric(\n",
+    "    statistics=statistics, discrepancy=discrepancy_func\n",
+    ")\n",
     "print(dist_metric(d_real, d_syn))"
    ]
   },
@@ -247,7 +235,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "id": "2fa3b069-9a8b-436a-8433-ed8fadd06843",
    "metadata": {},
    "outputs": [
@@ -255,7 +243,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "1.009191274642944\n"
+      "1.0090577602386475\n"
      ]
     }
    ],
@@ -276,7 +264,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "id": "7556587b-2fae-4f8d-8133-0331f14adc3a",
    "metadata": {},
    "outputs": [
@@ -284,26 +272,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 1/10\n",
-      "5/5 [==============================] - 1s 51ms/step - loss: 0.0000e+00\n",
-      "Epoch 2/10\n",
-      "5/5 [==============================] - 0s 51ms/step - loss: 0.0000e+00\n",
-      "Epoch 3/10\n",
-      "5/5 [==============================] - 0s 49ms/step - loss: 0.0000e+00\n",
-      "Epoch 4/10\n",
-      "5/5 [==============================] - 0s 52ms/step - loss: 0.0000e+00\n",
-      "Epoch 5/10\n",
-      "5/5 [==============================] - 0s 53ms/step - loss: 0.0000e+00\n",
-      "Epoch 6/10\n",
-      "5/5 [==============================] - 0s 50ms/step - loss: 0.0000e+00\n",
-      "Epoch 7/10\n",
-      "5/5 [==============================] - 0s 51ms/step - loss: 0.0000e+00\n",
-      "Epoch 8/10\n",
-      "5/5 [==============================] - 0s 50ms/step - loss: 0.0000e+00\n",
-      "Epoch 9/10\n",
-      "5/5 [==============================] - 0s 53ms/step - loss: 0.0000e+00\n",
-      "Epoch 10/10\n",
-      "5/5 [==============================] - 0s 50ms/step - loss: 0.0000e+00\n",
+      "5/5 [==============================] - 1s 61ms/step - loss: 0.0000e+00\n",
       "2/2 [==============================] - 0s 9ms/step\n",
       "0.525\n"
      ]
@@ -322,7 +291,7 @@
     "print(\n",
     "    discr_metric(\n",
     "        d_hist=Xr, d_syn=Xs, model=model,\n",
-    "        test_size=0.2, random_seed=42, n_epochs=10\n",
+    "        test_size=0.2, random_seed=42, n_epochs=1\n",
     "    )\n",
     ")"
    ]
@@ -343,7 +312,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "id": "8ec81387-e242-4280-909f-7a3c36167045",
    "metadata": {},
    "outputs": [],
@@ -385,7 +354,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "id": "ae4e5e67-e5eb-4b6d-8223-b477c7b09af4",
    "metadata": {},
    "outputs": [],
@@ -403,7 +372,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "id": "5217664a-8eb6-4200-a2cd-72153933e817",
    "metadata": {},
    "outputs": [
@@ -411,29 +380,29 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "4/4 [==============================] - 2s 52ms/step - loss: 0.6024 - accuracy: 0.6400\n",
-      "4/4 [==============================] - 0s 15ms/step\n",
+      "4/4 [==============================] - 2s 51ms/step - loss: 0.6264 - accuracy: 0.6100\n",
       "4/4 [==============================] - 0s 16ms/step\n",
+      "4/4 [==============================] - 0s 14ms/step\n",
       "(100, 2)\n",
-      "4/4 [==============================] - 4s 122ms/step - loss: 0.6547 - accuracy: 0.4900\n",
+      "4/4 [==============================] - 3s 99ms/step - loss: 0.6739 - accuracy: 0.5500\n",
+      "4/4 [==============================] - 0s 27ms/step\n",
       "4/4 [==============================] - 0s 27ms/step\n",
-      "4/4 [==============================] - 0s 26ms/step\n",
       "(100, 2)\n",
-      "4/4 [==============================] - 5s 189ms/step - loss: 0.6707 - accuracy: 0.4800\n",
-      "4/4 [==============================] - 1s 40ms/step\n",
-      "4/4 [==============================] - 0s 41ms/step\n",
+      "4/4 [==============================] - 5s 150ms/step - loss: 0.6674 - accuracy: 0.4800\n",
+      "4/4 [==============================] - 1s 44ms/step\n",
+      "4/4 [==============================] - 0s 42ms/step\n",
       "(100, 2)\n",
-      "4/4 [==============================] - 0s 51ms/step - loss: 1.2168 - accuracy: 0.5000\n",
-      "4/4 [==============================] - 0s 15ms/step\n",
+      "4/4 [==============================] - 0s 49ms/step - loss: 0.7769 - accuracy: 0.4700\n",
       "4/4 [==============================] - 0s 15ms/step\n",
+      "4/4 [==============================] - 0s 17ms/step\n",
       "(100, 2)\n",
-      "4/4 [==============================] - 0s 106ms/step - loss: 0.7013 - accuracy: 0.5000\n",
-      "4/4 [==============================] - 0s 28ms/step\n",
-      "4/4 [==============================] - 0s 28ms/step\n",
+      "4/4 [==============================] - 1s 124ms/step - loss: 0.7031 - accuracy: 0.5300\n",
+      "4/4 [==============================] - 0s 26ms/step\n",
+      "4/4 [==============================] - 0s 30ms/step\n",
       "(100, 2)\n",
-      "4/4 [==============================] - 1s 181ms/step - loss: 0.7127 - accuracy: 0.5000\n",
-      "4/4 [==============================] - 0s 44ms/step\n",
+      "4/4 [==============================] - 1s 144ms/step - loss: 0.6934 - accuracy: 0.5000\n",
       "4/4 [==============================] - 0s 44ms/step\n",
+      "4/4 [==============================] - 0s 43ms/step\n",
       "(100, 2)\n"
      ]
     },
@@ -441,16 +410,16 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 3/3 [00:00<00:00, 10494.51it/s]\n"
+      "100%|██████████| 3/3 [00:00<00:00, 13812.20it/s]\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "0.0"
+       "0.3333333333333333"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -482,7 +451,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "id": "d5ff295f-5bfc-4c46-9ce8-b969487517c7",
    "metadata": {},
    "outputs": [],
@@ -497,7 +466,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "id": "80b2c173-db89-4249-81dd-91b0277e335f",
    "metadata": {},
    "outputs": [
@@ -505,15 +474,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "4/4 [==============================] - 2s 55ms/step - loss: 0.6840 - accuracy: 0.5700\n",
-      "4/4 [==============================] - 0s 14ms/step\n",
+      "4/4 [==============================] - 2s 68ms/step - loss: 0.6465 - accuracy: 0.5700\n",
       "4/4 [==============================] - 0s 14ms/step\n",
+      "4/4 [==============================] - 0s 16ms/step\n",
       "(100, 2)\n",
-      "7/7 [==============================] - 0s 70ms/step - loss: 0.5860 - accuracy: 0.6800\n",
-      "4/4 [==============================] - 0s 14ms/step\n",
-      "4/4 [==============================] - 0s 14ms/step\n",
+      "7/7 [==============================] - 0s 64ms/step - loss: 0.5349 - accuracy: 0.6700\n",
+      "4/4 [==============================] - 0s 19ms/step\n",
+      "4/4 [==============================] - 0s 15ms/step\n",
       "(100, 2)\n",
-      "0.06000000000000005\n"
+      "-0.12\n"
      ]
     }
    ],
@@ -549,7 +518,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "id": "00e3c9cc-672b-4337-a8b5-31b651961726",
    "metadata": {},
    "outputs": [
@@ -560,7 +529,7 @@
        "<IPython.core.display.Image object>"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 13,
      "metadata": {
       "image/jpeg": {
        "height": 512,
@@ -584,7 +553,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "id": "db528d98-69ff-4b02-8204-f9cee0361499",
    "metadata": {},
    "outputs": [],
@@ -604,7 +573,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 15,
    "id": "e6fe5c89-e7ce-4eed-958b-586c1b094cf4",
    "metadata": {},
    "outputs": [],
@@ -625,7 +594,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 16,
    "id": "e4051772-44cb-4a0c-9460-103e2b5ab7fc",
    "metadata": {},
    "outputs": [
@@ -642,7 +611,7 @@
        "1.0"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -666,7 +635,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 17,
    "id": "2353d941-db44-4280-9843-0a48afb099f3",
    "metadata": {},
    "outputs": [
@@ -677,7 +646,7 @@
        "<IPython.core.display.Image object>"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 17,
      "metadata": {
       "image/jpeg": {
        "height": 512,
@@ -693,7 +662,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 18,
    "id": "ef877399-1eff-4965-8527-51cbbb61c029",
    "metadata": {},
    "outputs": [
@@ -711,8 +680,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "295.39627947142617\n",
-      "1784.2491965382226\n"
+      "302.1819545165017\n",
+      "1790.309182347125\n"
      ]
     }
    ],
@@ -754,13 +723,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 19,
    "id": "86db3d71-96b1-467d-8819-a698883a53c5",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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U09PTmFKdSlJXVwcAeOCBB/Bf/+t/TeXwDOOOhQNERER2oAS9l18GXnlFSlIRKkV2VF8PaHT6SKl9++R+zfDwww8DAN5++234NQL4T37yk7DPPf300wCAX//611hcXEztAA3CgEdERGS00KBXXm71aOLi9QIHDsgJbWYoLpb7M2sW8+WXX0ZVVRX6+vrwb/7Nv/lq+hUAOjo68IMf/CDsa/bv348XX3wR/f39eOGFFzSrf3Nzc/jxj3+s2WLFCpyiJSIiSpXsbMf1wgPk2LCjR4Hf/S61x5V5PMADD5h7TFleXh5+9KMf4Zvf/Cb++q//Gr/4xS9w6NAhTExM4LPPPsPzzz+PU6dO4datW8jKyvrq6/7u7/4OU1NTeP/997F9+3bcc889aGpqQjAYRF9fHy5duoTl5WW0t7ejoqLCvG9IByt4REREFGbXLqC6OrX3UV0N/KFTiKkee+wxnDp1Ct/5zncwMTGBX/7ylxgYGMB/+A//AT/60Y8wPDwMr9eL0pAzhAsLC/Hhhx/iJz/5CZ544gncunUL7777Lj755JOvmie/++672LJli/nfkAZPMJjqo4SJiIjIDKurq+jq6kJzc3PYaQqJaG8Hfv7z1OwVyciQI3937DD+tpPxxRdf4JFHHkFLSwsuX75s9XA0xfJ7ZgWPiIiING3eDGzfnprb3r5dbt8Ko6Oj6O3tDfv81atX8b3vfQ8A8I//8T82e1iG4ho8IiIi0pSdDRw5Aty4IaezGSU3F7jvPiBkiZuprl27hq997WvYtWsXNm/ejNzcXPT29uL8+fMIBAJ48skn8ad/+qfWDM4grOARERGRrro6OWHCSIcOAbW1xt5mPJqbm/Enf/In8Hq9OH78ON5991309PTg6NGj+G//7b/hvffeQ0aGs2tgzh49ERERpZTHI33q2tqAiYnkb6+0VG7PyvaA1dXVjmlYnChW8IiIiCiisjLgwQeTD2Uej9zOxo3GjIv0sYJHREREUW3fDvT3A/Pzid9Gfn7qNm3Qegx4REREJJR+KBrrz/LzgeefN3k8lDBO0RIREbmEJ9E5VL8fmJsDJieB1VVjB0UpE+n3zQoeERGRSygv+H6/P7ZGx34/sLQk4S4V3YwpJVb/EMIZ8IiIiNKAx+NBfn4+5ubmkB3pDFwGO0dbWFhATk5OxIDHKVoiIiIXKSoqwsTEBPxawU2Zip2YAKanrQl3gYCES0pIMBjE1NQU8vPzI16PFTwiIiIXKS4uxvz8PPr6+lBSUoKCggJkeDzA8jIwOxs91AUCqVmHFwgAKysSMD0ezY0cFNnS0tJX4b2srCzidT3BYDBo0riIiIjIBMFgENPT07h79y7m5uaAYHBtWjZawCsoMDZ8Kfe9vCz/DQaBzEzZlktx8Xq9KC4uxsaNG5GZmRnxugx4RERELhYMBr/6wOIicPMmcOYMMDAgVbVQXi/w1lvGnCO2siKN886dA3p71wfLbduAl19O/j7SiMfj+eojFqyPEhERudi6UJCfD+zaBTQ1AX19wKlTwK1ba0EvGJTp01h24OpZXpbbPHMG6OnRrhgGg8ndB0XFgEdERJRucnOBnTuBxsb1QS8ZgcDabekFOzINAx4REVG6Cg16N28Cp08nflteL1BSAmzeDIyNAePjhg2T4seAR0RElO5yc4EdO4CGBlk7NzsrU62lpbHfht8vrVfm54FXXpGK4KlTEvbIdAx4RERE6Wp4WMJdcbH8e3VVKnmtrcDXvhZbwPP7ZcPGuXNAZ6dU8e67Dzh8GNi6FejuZtCzAAMeERFRuhkeBq5dA7q6gBdekA0Pvb0S7IaGZBNENOpgt7wcfp2SEgY9izDgERERpQsl2F28KFOxDQ1Ssbt0ydhgp6YOeiMjSX8rFBkDHhERkdupg11VlYS5Y8eA8+djazqcSLBTU4Le3bvxfy3FhQGPiIjIrUKD3cyMVOw8HuDECeD6dZmaveee2G5rYAD44gtphaJukByvoqLkvp6iYsAjIiJym9FR4OpVqc7NzKx9fnVVNlXk58txZPEcZtXYCJSXS8A7eVKmdJMNepQyDHhERERuU1gI1NRI1W1+XoIdIP/OyAB27wYOHpS1d15v7Lebny9fqzRIZtCzLQY8IiIit8nJAZqbgfr6tX50fX0S9Px+OSM2IwPYuxdoaZFqn/q82EgY9GyPAY+IiMitogW9oSHgiSfkNAvlJItkgx6PKLMFBjwiIiK30wt6iqwsYNu2tbYp8R5ZFhr0+vtlIwdZyhMMxrPCkoiIiBxvcVGC3qVLwEMPAZWV6y9fXpa1exs2WDI8Sh4DHhERUbpaXJR1c3l5Vo9kzdxcbH35KCIGPCIiIrLe5KQcnba8LFVFSgrX4BEREZF1lGB3+jQwPg4cPWr1iFyBAY+IiIjMpw52ZCgGPCIiIjIPg50pGPCIiIgo9RjsTBXH+SRERERECVhakvNxL18GJiasHk1aYMAjIiKi1FhdlcbJ778vzZRffx144QU5J5fNkFOKU7RERERkrNVVYGAAuHABaG+XCt6+fUBBgZx929QkR6K1tspxaezYZjgGPCISy8vyDpuIKFFawU4Lg17KMeARpbuZGeDGDTkg/OBBq0dDRE4Ua7BT0wp6nLo1BAMeUbpSgl1rKzA8DDzxhNUjIiKnSTTYqYUGvclJY8eYphjwiNKNOthxSoSIEjUzAwwOSvUtWrgLBOR6NTX6y0EKCuSDksaAR5QuGOyIyGgbNsjRYtu2AR0dwNmzwPT0+uusrsrnuruB/n7grbcsGWq6YcAjcjsGOyJKtfJy+dixYy3oTUxIsBsakv9/4AFgZcXqkaYNBjwiN1taAjo7gWPHgKkpq0dDRG5XXi5VvfJyeVP5wQfA2JjVo0pLDHhEbpadDRw6BDQ0yALoc+fCp0+IiIywsgLcugWcOQP09AD5+cBzzwF37gAnT1o9urTDgEeUDpTpk507GfRCjY8DfX2ye489AIkSd/MmcOoUcP362jTs1JR8bNgAPP88kMHIYSb+tInSCYOeGB9fO/Q8J0cCHhElrrwc2LMHmJ2VtimBwNplStDLzAQ2brRqhGmHAY8oHWkFvWBQ2h3cuQPs32/1CFMjNNgpvbYqK60dE5Eb5OUBu3YBjY1SFW9tDQ963GBhKgY8onRWXi7vqKuqZKfbO+9IuwO3BTytYEdExosl6JEpGPCI0lUgIBW7ixeBa9dk+mR21upRGYvBjsgaekGPTMOAR5Ru1MFucVE+r/zXLXp6gM8+k8aqRGQNddDr7LR6RGmDAY8oXegFO7favFleXK5cAS5dAubmrB4RUfoKDXrcTWsK/pSJ0oHfD1y+DHzxRfo0PPZ4ZG1hZaXskmXQI7JeXp7VI0gbDHhE6SAjA7jnHqCsDDh/Xo4ScnsFT8GgR0RpiAGPKF34fEB9PVBTAxw4wKCnBD0iIhdiwCNKNwx664NeT4/VIyIiMpwnGAwGrR4EEVlodVU2XyhBb+9e4JlnrB6VeZSnQI/H2nEQERmIAY+cZ2kJyM62ehTuowS9sTGp7BERkWMx4JFzjI9LD6XSUmDHDqtH417BIKtZREQOxzV4FNnUFLC8DGzaZN0YlGB35oycRvDyy9aNJR0w3BEROR4DHmmbmgK6u4FTp4BHH7Um4KmDHREREcWEAY/WCw12o6OJ3cbICFBSAmRlJfb1DHZERERJYcAjYVSwu3ZNDpR+6aX4Ax6DHRERkSEY8NKdkcHuwgVgZiax6dzZWTkj9eRJOVaLiIiIEsaAl65mZ4GuLglURgW7RN25I5W74WHglVeA/n4Je8ncppMMDwP5+UBhodUjISIil2DAS1f5+UBFBbB5s5zJOT8f+9caHezOngWmp2VK99FHgccfB3bvlvtwc9AbHpbvsacHeOEFBjwiIjIMA1668njkqKrqajm54PJlOZszUtBLVbDTUlkpH24MekqwU76noiKrR0RERC7DgJfu4gl6GRlAbi6QmZnYfcUS7NS0gp5TqYMdERFRijDgkYgl6G3cCNx/P9DcLOv3zpwBJiai3/bEBNDWJtePNdipKUFvzx7nbcJgsCMiIpMx4NF6WkHP611/nXiDnrLer7xc1vslE9AqKhL/2ijGx2XvidZd5uQkcIN37gBXryY/pU1ERBQnBjzSFhr05ua0rxNr0MvOBrZtA+rrgVu3pCVLX5+tKnGLi8CHHwLXr6//vNcLPP880NKSwI0WFEg63LBBfoaBgBFDJSIiiooBjyLzeCSoRKIOejduaF/PxkGvp0faAaozWCAAfPklUFcnOS0ueXmydrCpCejtBVpbgcFBBj0iIko5BjwyjhL0tm+PvBFDL+hZZHpaQtzqqvbld+7IcsSHHkrwDhj0iIjIZAx4ZLzS0tiupw5658+ndlw6rl6VfRCRnDoFbNkiM9YJ0wt6REREBmPAI+uFBj2Tp2vn52WDbzSzs3LEblIBT6EOeleuGHCj+hYWtJdGFhQAxcUpvWsiIrKIJxgMBq0eBJGVLl0CfvWryDOmGzcCb74Ze3EyLvPzstYxNzcFNy7dWd5/P/zze/cC3/gG4POl5G6JiMhC3uhXIXK35mYppunxeGT9XUrCHSAVvRSFu4kJWV+4tBT+cekScPNmSu6WiIgsxoBHaS83F3jgATkKV0tjo4RApwkGJcSNj2tfvrwMHD8uU7hEROQuDHhEABoagH37ZE1a6EdJCfDgg1Jkc5rBQWlNGElvb3jvPyIicj5usiCCrEN75BHgyJHwy5y6EeH6de0jhUMFAnLQxo4d+hVMIiJyHgY8oj/Iz5cPt9i+HTh3Tvv4NYXPBxw6xHBHROQ2DHhEDrayIu1b1Hvhc3KkpcuRI8DHH+t//dat0t+PiIjchQGPyMF6e4G335agF6qyEnjtNWDPHuDyZWB0NPxrc3Lk4JGcHHPGSkRE5uEmCyKHmp0Fjh0LD3eAnMxx7ZpsEnn4YZl6zstb/7F/v/SWJiIi92EFj8ihOjuB/n79y1tbgc2b5ZCQ8vLwy/PzAS/f4hERuRIDHpEDjY1J9S7SOTQzM8DZs8DTT8uULbnfyIh28+raWoOO2SMix2DAI3KgsTFgejr69fr65HobN6Z8SK4XCGg3hc7KAjIzzR+Pmt8PnDwpR9Opbd8OvPCCHPtMROmBAY/IgerrpTlzb2/k6917L8OdUXp7gQ8+CK+atrTIOker9fXJukst3d3ysXu3qUMisq+FBVnjUl4ui5VdiAGPyIHy8uSEjcFBOXJMS00NsHOnueNyq/l5Odbtzp3wy06dklYzNTXmj0sxNydnDmttuAGA1VW5vLbWuY27iQyxsCDvhk6dkumNt96yekQpwyXWRA7V0CBtULT4fBIAi4rMHZNbXb8urwla5uaA06f1w5UZOjuBW7ciX2d4GGhrM2c8RLazsAC0twM/+5n0lurrk3UXLsYKHpFDZWQAhw8DQ0Ph4aKmhg2MjTIxIdWvSK8FbW0y/dncbN64Qk1PR95wo5iclO+Du6cpbYRW7G7dcn2oC8WAR+RgVVXAG2+Efz4jg8ePGaWjQza1RLKyIhscGhqs2ciwdy9w5YqEUT1FRcCBAwx3lCbSONgpGPCIHM5t07CBgPTwU5++kZUFHD1q/hqyqioJbUtLka+3dat1u1Q3bgQeegj49a/1K3lHj7JdDqWJwUFZNNvRkZbBTsGAR0S20t8PfPEFsLgYfllJCXDffeaOp74e2LdPCgF6Kiqs36G6fbtUELXWClZVccMNpZHq6rVFyJcvyy6pNMSAR5SgqSlpnaGumJSV8QiwRC0tyVSnVrgDgBMngKYmCVRm8flkarO9Hbh7N/xyr1eqZxs2mDcmLXl5Mg6tnnwHDnD3LKURj0dCXlWVrF+4fDktgx4DnlXm5+UZmRzr8mXgk0/CP19TA7z2GlBYaP6YnK6nR3as6rl7Fzh3Dvj61yV4maWiQqY4f/e78Mu2bpUPO9iyhZtriL6S5kGPAc9sMzPyKjY2BjzxhNWjoQQNDUlrDL3L2tulyTDFbnpadquurka+3qVLMt3Y1GTOuBS7d2s3jS4qAnJyzB0LEcVBL+iFGh6Wc/6OHLFmjCnAgGcWJdi1tsqBkS0tVo+IEuT3S7ibndW+PBiUqcTGRmDTJlOH5mg+X2w7f30+2SVstsJCVmWJHE0d9Do65J3lhQtyxl9ZGQOeYy0uyi/YzK1u6mAXS7MqsrVIR0IppqbkOePJJ9mWIlYFBbIuWquvX6iDB+VEBiKihHg88sSckwN8+qns7AIk4LlIegS8xUV5Vb56FXj8cXMCHoOda01PSxUvmtFROUYsnabvgkHtdiIZGbFV3ZqagF27ZBpWS2mp7Gj1eJIaJhGlq+FhyQIXL+pPw7iEuwOeEuxaWyWhmzG/wmDnes3NUkFS3vRpyc6WRfnpFO4AaT/13nvh6+i2bJElp9GqmRkZsnaxu1uOAAvl8UiFT2sdHBHROiMjMh2wf7/8O42CncKdAU8d7KKt2jbK6qo8oM6dY7hzscJCCRpvv61fyWtpkZ5k6WRlRXrFDQ2FXzY1JcG4sTH67dTUAE89JcdqhfL5pNcbEZGukZG1IFdXJyX/jg7g888l5KURdwU8q4KdQnkFqq2VBmmtrVLSYNBznc2bZSfnlSvhlxUVAYcOmdvGww56e2X3sJalJdl4UlkZW1XznnuMHRsRuVxosJuZWfu8xwPs2CGNKq9ckfUf6ukBl3JHwLM62Knl5wN79siCIgY9V8rMlM1Wc3PhJ+Hs2pV+R0LNzgLHjkVem9jdLR979pg3LiJyOb1gF8rjkZ2zlZUyvZImQc8dAW9yErhxQ37RVoe7UAx6sVtdlZ9NXp4tdjJpHV/o8axf3F9bC/zRH5k3Jjvr6Ym8JhGQn+mJEzJ1zXYjRJSUWIKdWpoFPXcEvKoqaTW/d6/8sq9dAxYWrB7VGq2gx94ZYnUVGBgAzp+XKuyrr1o9Ity+Dbz/fvh7ha1bgUceceevrrNTdv2G8nqluW8sR1xt3CjZPNrzZH29/DkQESVkdRVoa5M1dWNjid2GXtBzGXcEPEBejWprpYnhvn32D3p37lg9GmuFBruODlmkZYMtp34/cPYscOtW+GUTE7Ib1G3nzI6OyhFc6k0NgPxZ3Xdf9NuoqQEOHwY++0z/OsXFciaqGwMyEZnE55N1MEVF0my0vV27N1Ms1EGvr8/QoVrNPQFP4ZSgZ/Y5S3ahFexs5OZN7Y0TgDyETp6UYrGZvbJj0d8fPq2ckSHBK5JAQP5EtMIdIFOqTU3yPUfi8UgB/epV/TfVR4/yZA8iMoDPJ2s9amulDYqRQc9F3BfwFFpBb3Awsduam5MHTmmpkSNMLzYPdoCcP338uDQn1tPVJcs9d+40b1zRDA0B//AP4a2dioqAN96IHM6UX4meu3el68/Xvx59V3BpKfDQQ8CvfhUeNmtq5E03EZFhUhH0XMS9AU8RGvSGhmT7Y6zm5mTN3MmTwKOPMuAl6s4dmfe8dMmWwU7R0xO9Qr+6KiGwrk6O1rKa3w+cObN+jXF19VqFsaND/gS8XhlvaOVxeVke2tGK25cuSaCNpei8bRvw2mvh+4gKCrixgohSRC/opTn3BzyFEvRiERrshobk1Yo7XxNXVibzd4GA/abLQxQXSwCKNryKClssFwSwdgKforhYQuhvfyv/9vmkBVRREfDww9KfT7GyIlXLaFZXY8/leXnS0NjOZmcltKpbumzcyBYuRI6mDnqJztq5RPoEvFhoBTtKnhPWRUKqcgcPSj83PYWFEpJiOVc11ebnZawrK2ufq6wE3n13/Y7YlRWpwB0/LidJKF1o8vPlRI6hofW3obZ9uzR2dovOTuCjj8L/vAsLJeRVVVkzLiIyiBL06uqsHomlbPAyZQMMduaIJeitrkqJxYL5T49HhnXtmv6mg/vus08AGB1dfyzYpk2yPlC9QXt6WsLg5KTMXDz++NpO1qYmWRun1yEgN1e+56ys1HwPZhsbk1Cs9Sc+MyMrCZ5+OnUBvqtLjm0L5fNJAM/LS819EqWtNN+yn94Bj8HOGlpB78oVSSIdHdJwzqIFbmVlslHgN78JfzhUVEhfOLuorpZgcOmShISSEuD//b/w623atPbjPH9eKnJKq5eMDODee+WECa0edocOxb6ywe6CQQm4euEdkIfhrl3SDsdod+7I72d6ev3nPR75/e3bZ/x9EqU9v1/++ONZf+8S7g94IyPyyqdVgsjIkMVU+fnyDBvpnCUyntcrc4r79kmp6Px5+X2l4tU1Ds3NwAsvhAe84mI5ztAuMjPXwllRkVQe1SEtK0uCoPJGdmFB8nRoL7+aGmngrO795/PJmbBu2VjW3x95xzAgG0+OH5cqrZEVtUBA7lsd7gB5nB07Jr8T7uMiMojfL09q7e2yFiWWju0u496ANzIi3a4HBoCXXtK+Tna2VIvq6uTZ//RpmeNi0Es9v19+N+fOyaKolRUpNdlgXV5BgfS8dILqamkwfPKkVOZOnVq/nk692zczUzZdqN17r3y42cpKbCcZLi0Z/xRw65YEaz1jY1KJffRR9wRqIksowe7cOeD6dXkCfPBB8+5/fl7GUFRk3n3qcF/AU4LdhQvSxCuWzqoMeuZRB7vQpnMjI9aNy6GUBsPd3VKQvv9+efgC8rAuL19//V270rfHdkODBPdz5/Sv4/PJa4GRz82LixLAFxcjX+/MGWkz45YpcSJTqYNdpIam8VJuK9Ji5Pl5WfJ19qwsdGbAi9HqavQuq+pglwgGvdSJFOwoKaWlwMsvy5/JwYPypzI/L+EvtJ1Lfr5U6dJwKQoACcCHDslGB72zyVOxY3hpKbanpOVlWxSwiZzF75fX67NnUxPs+vtl/cuDD2qvoVCCnbKWP1pWMZG9A97MjASs/HwJXlqMCHZqWkGP8ybJmZuT39XgIMNdCijLS0pKgAcekCPG1A4flinddFZVJbuCf//78MuUHcNGH0NXXCy/k3ffjTxFvHOntLEhohiYEezOnJEO+Hl54dO86mCnHN/DgBeFEuxaW4HhYeDVV8Ovc+eOBLvz540LdmqhQS/a/ApFVlwMHDkic1BdXRKaJyasHpXreDwS5LSaDW/Y4Kz3Kbdvh/fn83hkCjOZ72P3btlJqy7Mb9oUvW1Wf788Lak34OzaFblJ8tat8tHZqX15Xp78eaRrdZUobsPDEu46O42bZVMHO60GoXrBzobsFfDUwS5S2xKfT+bDzUjL2dn2O13eqUpLpUzS3MyglyLFxc7fMDYxAfziF+EPjZwceb8Xugs4Xhs2AM8+G//XLS3Jc3pbW/hld+5IdVRvF2xOjqyP7OvTPhXk8GHZzUxEMaqtBb71LWOWUy0vSxVweFjyh9Y7y5UVmap1QLBT2CPgxRPsFBs3AkePyqKZzk5J3JEaXAHyLDw/z3kQO2DQIx3BIHD58vrTOBRzc7JTuLLS/ObLN27oV+DGxmTMjzyiX12sr5eQpz49KTMz+XY0q6vy+qR+XcrJkcqhkyq3RDFLdt28EuyOHZMM8fDD2lU7j0duc3FR3qE5INwBVge8RIKdWixBb2pKfvHnz8szLANemLExqUirVVen+LQXraDHV6O0NjQkf8Z6OjvlaUOr3Uuq3L0rrwGR1tCdOSOvNXq7YL1eaYOSCjdvSsVTvQwpPx94/XXuzCWXizfoBYOSOU6fBt5/X0JepF2vgYAs0j14UO6nu1tyi9a7UBuxLuDNzsoz4qlTsZ9kHolW0BsYkFeLzz+XExJIUyAgSxlaW8Mva2wEXnnFhGOUQoOeQ94dkfFWVuQ5V+tUDYXfD3z5pUxpFhaaM66Ojujnls/NydgrK809q3hhQZoza60xV8ZUUcH1fZQGtIJe6NmASrC7elUaT87OyuLbnBz5Y4llyVdxsWOCnnUBr6BAyqGbNxvbOmPjRgkJPp90Fu3tlWlZ0hWpCevNm/Kr2b/fpMGwlX9aGxuTN97RDA/Lc6pZAS8vT55SojVKLiw0fxPd9evyNKenvV02gGhtvCFypdCgNzAgf5S3b68PdgqvVxbmVlVJcMvJiS2POCDoWTtFm5EhJaLaWmN6pN25I89myplAxcXS2TRSOSDNLS5KSw29TcLBoFRL6uslO5Mzzc9r/xkUFdlr/1BFhTRuPn488vW2bDG35cvWrXKfXV361ykpkTdCZq4wmJyUv89IRe+VFZlerq627IhnImtkZ8u7rrNn5SM02Kl5vVJgeP75+PKIOuhF6qRuMntsskg26C0uyinhx46tP+xxelr78Ef6Sne3fEQyMSFver72NS6Pc6JgUGYqtJ53nnxSApVdeL1yNHFbm/6eqexsWUob2sQ51XJyZAXIzZvaK0o8HmmTVVZm3pgAWcas1SVqenr9nqXJSVmz2NDA3bqO4/ebO+fvNkVFUp1TjsKMVobXyiOxnLIUGvRswl6PGr2gF01Ojqy9y8iQMumdO4lt2EjG7KzMLzlsA8fsbGxL3qam5O+CzzPOMzgoAU9rpcKXX8osRklJcvcxOal9CkN5efxrv8rLpTHw//t/2n/G+/Yl1yYlUfX1ct+nToVfVlcnT0Fmq6kJH9PKirRjCQ3IBw5IAePWLeCFF8zfgUwJGB+X+ffqamse8GZZWJANDKmi5IOGBnmHduqU/DeeoDc4GPsTmY16VNnz5Vod9Lze6F9TVCTzI1u2yHZQs4Le7OzaTuCaGscFvJ07pTp3+7b+dZQjrhjuUm9sTJ5/1KG7rk5eyOO1siK3p7cMdXRUit8PPxz/bSuWluRkCPXaOZ8P+Pa3pbd1vHbskNUW6iUtOTkSVlK9zi0Q0J7NOXBAluuon1ZqaqyZ/vT5ZEwdHWuTFRMT69eVNzTI8orubrlOd7c0ZiabGh9f6ygwPQ1897tWjyg1JiflwRgMygtMqiUT9BoaUj++FLD3S7YS9OJhVtALDXa3b8vtO/AcqOJimVr6xS/0H+dswmqOQECWj2q1COnulofXpk3x3WZvrwSlSE6dkj+XRH/HPT1SaNd6/Chrv/Lzo9/O6qr8Sa2urp3IoRxDXV4u1/F4zNmHc+uWVBDVQXvbNpnWttFpRKiokOnj99+XYsjNm2tPdz6fLK1QdgCvrq7tQLZRoYGA9cFOKb/GUtwA5BfulPUzSrA7dUre0T7+uLn3rxf0XMjeAS8ZekEvWVrBzuG2bpUXLq1OMmVlskbLKc8dan6/diUmJ8fcNVyx6O+XgKdlelpWLHz967E/5/v9Ehaj9fycm5M1b4kEvOlpCQx6bw5u3ZLwd+BA9Nu6dQt4553wNW6FhcAbb8gyGjMsLkqzeq0NcbOz8trQ1GTOWGK1c6c8zXV3r5/N27RJQmro9Pnt2/L7vv9+88dJGrSCXawWF2U+Pjvbfg9KNXWws5o66F26ZPx9LC3JH9+GDcbfdgzcG/AU6qCXKBcGO0V2tlQAtL6lPXuc3bmkuxv48MPwz99zj0xL2iW4KsdgRTry+OLF+A6kz8iQcN7TE3mdZU5OYtOogASFSNP7waBU8ZRpQj2Li7JzVmsDw8yMrB97+mlzlgkoQUnL0pLsOq+sTO2yoXgVFUm/yvPn5USNUFr9+7Sa9ZPJjAh2ra3yzvCVV1IyREPYLdiphQY9o17Xl5YkNJ4+LVMRDHgppgS9eFuw+P3yR3jsmOuCXaj6evet452ZkV+b1gloyrSkXTr837gh66kjCQ0XsVYft22TVpORdkonumnh7l39/omhJiakOhcp4F2/Hrn/3ZUrsm5sy5a4hxmXqanIFUlgLQC2tKR2LPHKyJDfdWtr5FP/ysvtN/a0MzkpT0IXLsSXttXBLtr6MSvZPdipGTGlExrs+vokPxw6lPztJih9Ap4i3u1jGRmSAg4ftm6HLiUk0ukD8/PyvJOKDv+Tk9qVqE2b9KdXfT65LNrzdWZm7FO0gDxnPfCAvBZojam4WKZP47lNhfKe6Xe/i3y90tLIAXJyEvjii8hVxuVlqfBVVaX2VJWeHmmiHEkgINXWpib79ZUrK5M1tb/5jfbTlNcrletkd01TkkpKgCeekMrRmTPywNMLeh6Ps4IdsBZ0nBDulpbkjyWZgKcV7Gwg/QJeIqzaoUsJGx2VQBDpV9TeLpUMIzv8z88D770XHiwzMqQ9hd70alOTTL+qp9dC5ebKaW7xvkepr5cpaa31fQ88EP/GjVDRdmF7PMBDD0Wu3sXarnJ0VK6XyoBXWSkbQqL1Rt+2LbaNI1bYvl1+J1rrxjdvTnw6ngyWlSWvKcqxWuqgl5kpC2NHRoBPPnFGsFNkZ8vUQEODlOdPnZIpaTtZWpKf6eXL8q4okYBn02CnYMCLB4OeY7S1rW8VocXvl19hY6NxfcGuX9df83bypP70amamdAro6dEPF4cOJTal7PPJGkutablk11cWF0uA+/nPtV976uujB+jaWlnrqbfBRHH4cOo3WlRXy+/h00/1r7NxowRmu6zfVMvPBx55BLh2LfyyvXvtt7ko7ekFvd27pU3Y22+bcBh4ipSUyB/Utm32CXpKsDt9WtaF5ORIwIv3Nmwc7BQMeIlQgt7mzfKHeOqUMTt0yTANDfImUmtaUuHxSAXKqHA3MRH52Kju7rXnbS01NRJiPvss/LLSUnlDnGio2LAhdet8t26VvnVaffAefDB6pSsjQ77vri79k4TMWjfm8cj9XLmiPbOkVCTtvvFo82b5IAcJDXpKo39l90xbW2w92+zKDkFPHeySCWWBgHz4/bYu8DDgJUNZwLRlizxgeCyabdTXSwZvbdW/Tm2tBBOj6IUChdKDrLZWuweZx7N2bJg6JFZXr5/mXFnR3i+UlWX8msJosrKkN5zWSRaxHt1VVSXTzx99FH6Z1yuhyqx1Y6Wlcn+/+lX476Gx0dgpfaJ1hodlLUJLizxR9PdL9W7Pnvia89qVVtBLNSODnSI3V148GhttvTaSAc8IxcWSJrRe4Wi91VVZoLayktItkV6vZO/2du3cnZEh1aXCQmPub3w88vo5xfCwvDnXazJbWgo8+mj022lrkx3Cag88kNiJF8kyokK4Z480ZlZPUW/caP66seZm4M03wz9fUODc2TKyseFhmVO/eFEe7C0taxU9RSKnMNhVaNCbmUnNfaQi2Knl5Ng66DHgGclOjbHsRgl258/L9tannkr5XW7aJIHn/ffDq+g7dhg7hVVaKgHl88+jjynZU0GUqWCtauGxY1K9tPsUopYNG4BXXw3/XXm95lclc3NT35KFaF2wiyXouO0UhpIS40rzq6uy8FqZ6piaknUxAwOpXyNn06DHgEeppQ52kTr5GiXk2J5du9aOuQpVVGRsaPB4ZOH91av6S0uUqcZkKl3BoFQK9aaCx8bk8kcese8mgEiMWg+pZ3VV+2eXk8Oju8hE8QY7NXXQS/Ufjp2trkqIu3BB3kEfPSqfr6iQQkJLi2wrv3pV/1Buo2gFPQufiBnwKDWsCHbT0zLHV17+VZmsoMC8XmWha7e01t1u2SIbEpIxOKh9Vm2o06flfuzSxNlObt4EfvnL8DfWTU3At76V3q+TZIJkg52aEvTSUWiwa2+XKdnHHtO+7v33yztwK4Jequ8rAgY8MpZVwU5pWzM9Dfyjf5T6+9TR3CxBrr9//eczM+WNZTKz+MGg/Fij9Wibn5fr1dQ4s4qXKgsL0hvx7t3wy9rbZXfzzp3mj4vSwOqqBIvPP498zAhFpxXs1AIBac556ZJs5nj5ZXlCrKoyP+hZ2JeIAS8dLS+nplSxsCBb+0+cMOddS2iwGx2VBGRxCSYvT85M1XrOqahI7rY9Hlk3eOlS5KUdXq9cz+nhbmVFuxKa6K/4+nUp8GpZXZX1izU1Mn1PZCifT9aLFBbKwcrXr/NA4HjFEuyCQQl2Fy6sBbjs7LXLvd7woNfXF/9YFhdlOiA7O/bDwS3AgJdORkZkemDLFlm7YbTcXNm6unHj2iLTSOdPJUor2NlIpFMbkrV1q/z6uroiXyfZqeBkLSxob5aI9c3swoKcCDI6uv7zubkylRrvuuzJycg9CgEpPLe3A0eOxHfbRDHJzJR3XkpDYwa92MQa7GZm5Od69Wr4E4daaNC7fXt9CIxECXbK5pbXXov/+zERA146GBmRB/3FizK/l8rtgXl5Ms+lLP41MujZPNiZISdHpnpv3tR+nsvOlsutPK1gagr49a/DmxZv2CDhLJY1kT098l5E62Fz5YqcpxqPW7eiP+cDUoDesYMbLiiFGPS0BQLah2IHAhLylpbCnxCUYDcyIocN1NbG91qjBL1o1MHOJm1QonF/wFMeHGb3WbCD0GCnLOhN5FT5RBgd9MbGZA7t8uXUVAUdJFIT53375HIrXbsWfqoFIAGrsxM4eDDy109NAV98of9rVjaRVFfHPqaaGqn6TU5Gvl5LC8MdmUQv6KUbpUI3OLi2AzaU+ud05oy0P5makk0rw8OpC8cODXYK9wa8QAAYGpKS7j33JHeiutNoBTur6AW9eJWVAU88IdXH1lb53aZhBQ+QjH7vvdrTsKWlSWb42Vn5/WzbJt2g43T7tv7pIcGgZPSGhsgnXFy9Gvnkv9lZCXnPPhv7EMvKZIfzb36j/7ApL5dehkSmUgeYdDn2Uj31Gm1mSf1zOn1adpMtLUlvKiPDl8ODncJ9AU8JdsouGa9XAl46sFOwU1MHvUTCWUGBlFiammS1fBoHvdJSg5sZz86u/UyBhI6O8PulCBHpoTc5KQ/Pxx/X3gQyMiLP29FcuyZ/1k1NsY+vuVleG27dCr/M65VpX7OOQyMKowQYGy/aT8rUlLw+FxdHX1MXSWjQu/fetaDX1yebWZKd4ZmZAU6elEqhw6fN3RPw1MFOOTYs2knnbjExId/7+fPmtCZJlBL0kvkjZNAzTmiwU36G8cx9hhgakvVx0Zw/LxsKte6mqEgqaVqtTEIVF8c/lVpQIMfTvftu+Bvyhgbzj0MzXKp2x5O5zFpGY5apKVlUe/KkvIv64ovEgp2aVtArKZFQduVK4rdfWChPFLW18rw4MODYZUHOD3h6wS7dlJZKWWTHjrV3RnYOekY8iWkFPb0jHmg9rWCXpA0bJJwNDka+XkWF/mkeublyvNzAgP7zs3IiSCIVzKYm7TNmc3Ot3ZiSlKkpWbuVlyfN/IjsIDTYdXfLWrmKCim/Gyk06E1NScjbs0d2TEVqNxBJXp68Cw09kcKBQc+5AS+WYOf3O3buPCE+n6ywr6mRVfhOCHpGCA16fX3uewdspBQEO0VRkbzxfecd/T+7rCy5Tl6e/u00NMiv8+xZ7csbGxOvtmVmuuiEDyXYnTolb2y+8x2rR5RepqdluUltrTMPf04VJdi1tsqai7ExeV6en0/t63Fm5tq5lI2N8nsZGJCNeYlyeNBzbsC7c0deAa5dC58n9/vlj290VF4p0k06Bz2uktfX0yPN4BJdAxmDzZvl5KS2Nu3L9+yJ3oLR5wMOHZI33+qp2qwsqfBFCohW6uyUH3Moj0f+DCsrDboTdbAjc01Py4v82bOyNMbCk3NsJTTYKT2JvF7Z4JiXJ58zcwlBRsZa0Iu0ls7vj75bSy/o2ZxzA15lJfDMMxLgTp+WvgwLC2t/fFNTDp5zMYhW0OvsTGh3JLlAfb2sgVHaDKRgAXF2NnDffVIkVBfVCwqAw4dje/hVVkpBSj1N6/Olpke3ESYngY8/1t4EubIiT1dJ/ekx2Flrelp2b3Z1yWtMVhbX/QLyLqy7W6Zi9ZpNKoeCb98uU6gXLkRfaGuUjAztPzxlp+zoqEwrxEId9Gz+Wmrv0UWTlSVbqysrpUL13nvyBOj2SlW8lKBXXS1bCaemYmvuSO6i10/K4KBXVwe89FL4bExWljSOj1U8O2RTZWAg/HXI45HvUd2wOVJ7l6tXZXlcQj3GGeysdfeu/L10dgKffSbl6X37rD8uxi5ycmTHU2mpvMvx+/Wvm5cHfO1rEpLa2swNegp1C5R9++K/DSXo2TzgOzvgLS2t/aL6++XV44/+SLbpxbKdL10o5/NduSLrER55xOoRkZVSHPQ8ntQelmKWu3eB998P3zTi8UgPvtCGzUND8jSkZ3lZegBWVcU5vTw3J29eT5ywX+sjt9MKdpHCS7pSCi2h/el6eiL/rCoq5CM06KVaKnrb2fzAb2cGvNBg19e39osaHJRq1fbtciZqW1t6nmChUAe7uTmrR0R2ohf0CIDkKq0dweqGzX6//OjUR7Op9fXJ7F5cBYP8fOD++6WcqfS4jHZHlLyhISkUfPghg12skg16t2+nZlwuaVqcCGcGvNFRebLr7w//Za2urgW9lhbphmrzeXLDMdhRPNRBL9HWAi4yMgIcP65/eWjDZiC2jdseTxIbvCsr5WPPHga9VAoNA+3t8jPOyGDAi0cyQc9IaRzsFM5MPrW18mS3f/9ar5vl5fXXWV2VBTHV1dK/IR0w2FEyQoOez2f1aCyzuirFm2hLg86dkyWt9fVru34jzaI2NRnQTJlBL3UCASmzfvGFPI9mZ8svt6xM1j4ODXF9dzy0gp5Z/H5Zt/r552m9btWZAQ+Qd1VNTWsPHr2gly4WF2Udw/HjfMKn5DhsWUMwKG/Q1a2pMjPl6SFeCwvRmzUD8ic3MSEZoKpKdg///vfa11Xau+Tmxj8eTVpBj5Lj9Uqj+OrqtXYfd+7Iokl10CMJUcFg9OeL0KCX7OkVscrIkBm8qirJBWfOSNk9zTg34CkY9ERODrB3rxyz0toqr1A23+GTjKtX5c12KK9X2nAovS4pPfT3Az/7WXhbluJi4I034p/5KSiQMBapYTMgTzmhGyl375bi+fBw+HX37k1Re5fQoOe0czNXVqT5bbxnzqVaUZHMDm3Zoh/0qqvluXZ+3urRms/vlz+69nZpLxLrG8KsLPOP0isrk4/m5rQMes4PeAqtoDc+bvWozJWfL0/0oUd3uTDojY5KvzGtv1OvF3jqKR5mkS6WlmSJjdZBNtPTMtX61FPxzzhHa9icmSmvbaGtUjZskDV56jcegAS8lM56G9ZF2QQrK2ubevbvt1/AU0QKehUVwDe+IaHB5jspDaMEu7NnZfpTOdzZCdI06Lkn4ClCg97AQPptsABcHfQCAZmJ1vvbvHhRZlkaG80cFVmlt1e6WOi5eBHYuTP+x0Okhs2AbPrT6tO3bZsB6+zcKjTYKW15EulBZjatoOfxSMPeI0dc8bwakTrYOXl2TCvouZh7049yTEk60wt6DjYwIFUZPYuL0lC9spIHmbjdzIycvBZpc97SkrSQKy+Xypr6ujk5UqnTUlcH3HuvvKaF8nrl8w5bqmgdrWDnRKFBL7Slh1sreG4KdmqhQc/FmzDcG/BojTroOeSgZLWlJQlv0TaydXfLG+3du80ZV6yWluT5Uv2GPy9v/cEiN2+G78b0eGTa0LBF+i4wPq5/ckSowUF52P/qV+HZIj9f1ulpHezi8cjJblqzUEaFu9VV7cdzZqb5y5UM55Zgp1ZU5O7ODG4OdmpK0HMp9wS8YFAeiNnZVo/EvpSgp145PjEhycHm6cHrjW3G3eMxt8tHMCiL69Wbl7OyZCOX8pDs7gZ+8YvwH39lJfD667IUaWIC+M1vwt9Uer1yNmtLS+q+j1jMzUnIVj/nK7NVZq59rKmR6dfLlyNfb88emY3Ryhdzc9K94dlntUObz5fax1Jvr/TSVYf+lhYJl47k1mCXLm7flt9dVxf7/zmc8wOe0vvt2jV5tq+ttXpE9qe8Yk1MyB9xVxfwrW/ZPuBlZkqI6OmJvHlt+3apdpmlv1+OtFKv1fL5JFPv3CkL/o8d096VOTwsD9/775ewojVjEAhIe67aWglTZpiZWf/87vHIerfjx8MDSXa2dCQwc1VEZqZMlfb06Ld8LCmRDY+//KX+7bS1SbW3uTklw9Q1Py+PCa0q5KlTMhPoyCOjl5flm7t7lwHBierqgE2bgFu31k6L4u/RkZwb8NRNfZeX5ZWUolOC3enT8v9mJQYD1NRIK5TPP9e+PDdXFsebNb21tCRLG7UW4q+uygt4ba0EuEgn8bS2Ahs3Rl7zqxzg8tBDyY87mtlZmdIM3Yje0AC8957sFlWfp6qsdTN77aPyePjss/DLPB5pQHz2bOR18Csr8nuqrl6/KzbVOjtlOl6LUll87jkH7hNTZgoaGyUcnDwpvePcvhnBTbKzZbdQfT2DnoM57alD/7QGrniOTh3sHMjjkdPnrl7V7oJz6JC5RdwbNyLv4hwakstbWyPfzuSkNMktKIh8AIlS2amuTmy8serslMqY8ppcVibLcbq65A3+5s3ha8u7u+Vjz57Uji2UxyMtSLSquqWlEgC/+CL67UxNyc/drIA3Pi4bRCJlnrY2+Vk6dlduQQGDntMx6DmacwIej+FKnAuCXajSUuDJJ8M3BCvhz6xNbTMz+tOuisxMCR+3b0thQ8/Kiryg33df5Pucn5fp3lQGvLEx+b6U12GPRwLeT34i/759W/6tbl8WCEhoqa2VKp9ZSkuBV18N3zvk80k1cd8+eV2K5P77jT8KM5KOjuh/iisrkonq6x2+tJhBz/m0gl60s/zIcvYPeAx2iXNZsAu1Y4d8WGlpKXoj++VlCYANDZF34+fmSmiNduLU1q3a/deMEqnPoLKBQtlkl58fPn3o9VrTZDpS5e3AAQlU09Pal1dWSl87M1VXy2tmtJObtm51eLgLpRX0yFlCg97AADvK25z9fzvj4xLszp9nuItXICAffKecEmVl0kIjWsWwokJOU4i0iqChQYLIpk3618nOBo4eTe0at5ER4NKl9Z8LBmUjQOiuzomJ8FYuPp9cx24dJCoq5OemxeeTNY1mH6ZQXy/V5kgqK+3X6scQStB7/XVXt6hwtexsWSti5qJVipv9A15ZmZQ23nhDnhFd83bWBGVl8sr25puSMBy0mcIptm+PfKB9SYmsEdu8WX9tmnLsVUmJnIGqt0Fk/34JBqlUWqp9HxMTEpSU7zU/P3yjRXOzPOfb0c6dUvGtrV3/0dKy/jxZs/h8wMGD+mHYquBpqoIC2VlERClh/ylaQJ7tGhrkGfnAAanmdXREn98gYbdz+MbGpLRfWmrdGAyiHMf49tvh7b48Hgls5eXy78OHpUKmvl5j41p7kYYGeeFX767MzJSHfqpnRCId0TU0BHzta8CPfxy+Lszs3cvxKioCXnvN6lGsp1QWf/e78Mu2brUmeBKRe3iCQQfO362urp1ZpQS9zEzgu99lH7xYjI2tD3olJfKzS/XK+LEx+X1dvAg8/3zk0peDrKxI2xb1rt7cXOCxx9bPYmitMlCfWqDMrIcys3lzMAh8/LFstFBraJCxVFauX39XXCzBlEty4jMzI6Ffrago8nQ9EVE0zqjgqfl88gy4fbvMf12+LFsVKTbqip76sE2jKcHu7FnpR+G4xl6RZWYCTzwR23Uj7aRVWLVRQeHxyM7Ta9fCC72jozLj78gGvDZUWCgfdtPVpd3+Z/9+vocmcgrnvdJOTq7tDPX5pPLU2Cg9M+w6P2RXStDbvj01axvVwc6uAgF5XHE90FfKyoCXXtKedk51D75YTE5Kzz31/MOmTeaepuFGd+8Cn36q3Zh7YQH49rf5VEvkBM4JeKHBTpkLU+YwfL7Urz53M6ODjZOC3dCQTBkXFgKPPGL1iKLy+8OnbwGpIhrd/y9VVbpbt7R71sUzY3/5soQQtaoqWWvn6s0JKdbWpn/qSmenNPe2ukUREUVn/4CnFezIniYn5dXhzBnnBLtr16Qs8eCDVo8qqmBQ1sV1dKz/vNcLfOMbzniPMzgI/Oxn4WsRi4pk6jeWZsODg/J0oOX2baC9PXrDaNI2MiJHzulZXZVm1jU19pxaJqI19g14DHbOk58vq+8rK+UVXD2/ZzWtYOcgSrDRaq588qT82O08deb3S/bX2mhy9y5w7hzw9a9H3kzi98vPIFJLzBMnpBm0mSdTuMHqqvwOoh1QMDQk7+OOHDFnXESUGHsGvN5eOT1c7yRusqesLGmEVlcnRx2cOSObX6wOeg4PdoCciNHaqn9yRleX/Kh37jR3XPHo7ZUfv55Ll2Tqb/PmyLfR1hb5fmINi7SexxP7kd48+pvI/uwZ8JqapMfE1avyojw7a/WIKB52CXouCHaK3t7wqdlQq6vAF1/IBgg7rj+bm5Pp5UgPgaUlqb5VVcmfv97txHLO+dSU3JcjAt7Kii0Sk9cru6fb2iK3yayvlw34RGRv9u1aVVkpvSfeekvWR/FIFOdRgt4LL8jK9x07zH0hW12VF8+FBfl/h5qfl+ATLdjcvi07S+1ofFx/4X6owcHIKzKUYzAjyc4G7r8/tUe6GWJ4WGYqbLQEpbxcmnPrbdhRTl3h0zGR/dmzghdKWdO1Zw8rek6lruidP2/O/WZmSjVYud+zZ6Xn3/KyOfdvkNxc+RairVjIybFvp5fqamDXLvnz1bKwIAF2925pkzg0JJ/ftGl928T8fAkYQ0P61cB77rH5hpPh4bXnskDAdltSd+yQ6fL+/vDLdu2SPykisj/7BzyFVtAjZwkNemZO12ZkODroKY2H29rkTFg9+/bZN9hkZMhRbd3d4e/P/H75vFLc/bu/W/uaF14IP9+2qUmCxqVL4fdTVCQnathyajY02Ck/BPWBvjZQUCAnsNy4EX7Zrl2u61NO5FrO+1MNDXo8vcKZsrKs2e7p4KBXViaVq9/8Jry5LyCnzJlxVm0yamqAe+8FPvlk/efHxyW4vvyynECoHDG9tCTr9qqq1uegjAzZwbmwEP6zaG624e5ZrWBnc01NrNQROZ3zAp5CCXoOPEqXYjM8vPZir/B4JCgkVaHRCnoOKEs0N0uFTj1V6/HIofVOOLu0pUUytZJzFhfld7x3r1SO7txZf/2+Prn+Pfes/3x1NfDGG6YMOXEODHZkQ36/vM7ZYCMOOYv9X9WiMbp9P9nC1BTw7rvhL/jZ2cCrrxpUXQgNepHmPm2ioEBaf0xPr/+8EnqdoKREKnXKSRadnRJMPR7ZYKEWDEpj3bo6oLTU3LECwMyM9ubrkpIor7cjI9Kc8Nq12Lb9Eqn5/fIGtL1d3sFt2GD1iMhhnB/wyJWuXpXXSLXFRdlRWlmp30ojbhkZzih/QSpXdjgLNhlFRfLfqSkpno6ORr7+2JhM3Zod8Px+4PPPw9vTeDzAs8/KEc66KiqAZ56RXSOnT0spkkGPYqEEu3PnpMFlfr4EPKI4MeCR7dy+LU199fT0yKL8lhbzxkTG27BBNob8/veRr1deHt85tUa5eVPOvNVaonnsmFRNI7YLyc6WefWGBrkxBj2KRB3slAdefr614yLHYsAjW1GOs4q0ZCkQWJu246yFs+3eDVy5IsvVtHi9wEMPyZSomebngePH9fffDAzIzNnhwzHcGIMeRaIX7IiSxIBHtjIxod2eQW1sTKb2GPCcbcMG2R387rvavaibmqS5sdm6uiSD6QkGJQA2NkqFMSZaQU9rHQKlBwY7SjEGPLKVTZuA/fuBTz+NfL3GRudsLKDItm6V3KMOVBkZcqqCYWstYzQ/L1VkZSOInqkpWS4Qc8BThAa9/n5H7ODWFAxyk1syhocl3HV0sJpLKeHQZxZys717ZdpubEz78qwseeG3YY9YSkBOjuwOXlwMvyzu8GSAvDxpyzI0FLkLU2GhZLSEZWdLuk1QICC7zNVjzM5O8YYUpfI0O8uFsMmorQWee04WoirndTPokYEY8Mh2Skpk3dWvfqVdRWlpSfKFlWzHblPtO3fKSRlarVsU990nTZitMjAA/Pzn4TN7VVXAiy+mYG2+ekrxwQcNvoM0pIR8pScngx4ZiAGPbKm5WT7Ui++zsoBDh5w7q0XOUFgobzLeeUf7tbayUjaIWGV5WdrsqXsiAkBvr/QXPHDAoDvjWrHUY9CjFODLJNlSbi7wzW+GH1nr8Zi/o5LcZXVVe+pV/aZh82ap5Kn74Hm9UryysurY0yNZS0swKG1cGhqAjRuTuBMGO/NpBb3JSatHRQ7FgEe2VVho9QjsJxDQflPv88V/fNv8vHYFqKAg/Gd/546si1QHo/p6qbQ6xfIy8N574Sek5OTIcqjQNw9ZWcDXvibn3qpZsTZQcfeuBDitXceKiQk5Ie2xxxLYB8FgZ73QoDc4yCkLSggfNUQO0t0tO4zVQevQIfmIR1sb8PHH4Z8/fFiCjRIMAgF5rT91Kvy6mzbJh93W0Onp6ZGgqhWOLl8GHnlk/edKS605Ii2SoSH5iObqVdmwFFcYXVoCLlyQBMnzc5M3MyPrTBLt9ZOdLaVkogQw4BE5xOysvO7evh1+mTIlF+uL+eio9HHTOmf17Fl5PVJOj7h1S6pBWpTK3kMPxXa/VopW+Tp9Woomdm+/U1UlawC1Hgehdu8GysrivPHsbHmnUFm5VsFbWkp4rGlrZkYaera2yrsfK5o5UtrzWj0AIrcLBmXWS/0Rrc+aWmenzJxpmZoCzp+P7TYDAQlsekt75uelWre8vHb2b6TX+FOnYqsoWa29PfKu2Lk5WfJk93XtxcUSqCNNyZeUSPeNhNrUZWRIo8nnnwdef122rWdnJzjaNDMzI9uvf/IT4Je/lBQeqdcOUQqxgkeUYjdvynmr6uf5lhbg/vtju42xMak+RXqtuHAB2LEjeguZgQEJg5F0dMj03vKyTAtHMjsrVb9nnrHvUqHRUdl1Gs21a/J9231WbMsWKQqpN4AAEuoefDCB6p2aEvRqa+VBw4qevtCK3fAwQx3Zgk2fjoncQamAaVWOZmbkKK7Kyui3c+VK9M10i4tSTaupiRy02tq0p2ZD+f2yJi3WPrx+v71f0woLZfp6Zka78qVUVAsKgKIi88cXr+xseXMwNKTdB2/7dgPvTC/oEYMd2RoDHlEKdXfrV8BmZqTy9Y1vhAcyvx/46COZ4cnIkIBy9aqEkG3b9I/vamiIXkXbuVOmaLVOjlBkZMgarsZGCaE9PfrXzc8H7r0XyMyMfL9WyslZO/ZMvYMWkGVSnZ0y9Zl05cskdXXAa6+Ffz47W4Kq4dRBL503YQQCEnK//DL6kSdEFmHAI0qRqSl5/o+0Lu7yZWDXrvApwZs3ZRpVqc7U1EiwO3lSzqdvaAhfX1VTI7cVTV2dNME9cUL/Otu3y5iysyUY9ffrd8s4fNj+GxMAqeB1dAAffhh+2ZNPypS5k1q+eL1AdbUFd6wEvXTm9cofYTAo1bv+/vgX1RKlGAMe0R+cORM+lerzAUePJtYwtqNDwlgky8sStGpq1taxz8/LervQQHX7tpyP2tYmYywtXT+V6PPJuqtYegd6vcD+/XJbU1Phl+fmSu83ZTz19RJ+zp0Lv25ZmaxZc8KZ8z098v1mZ4cvI2ttlTWEKal8kTvl5ko5vLER6Otj0CPbYcAjgsyyfP659qxTfv76vnCxKiuTaUv1aRxqtbXSVFfR1SWvF6ECAdko8NBDwG9/KyEvP39tPZlScYtVeblU5rT64B06tNYiBZCCzeHDEgzVM1GbN9uvT5yWyUmppublyc9KvVPW45ENFjt3ynQuUcwY9MimGPAo7fn90gNNb0nR2bMydVdbG9/tNjYCe/bI7lY96grYxIT0p9Na0jM+LtO03/ueXH/LlrUqXmlp/J0s9uzRnlotLAwPs5WVcnSc0WZn5TVR/f0WF0vl0ChXrkhABvQDaW+vrJfcs8e4+6XU8vtl7aQ6sOfkGLzRJBZ6QY/IIgx4lPb6+qR6o2d+Xp6rv/Wt9ZW2aJTK1/Xr2uHR65WKXGjgyMuTHmZKGFG7fl3+W1goVcWqqtjHo5abq79Zwyzt7XJ0mDrgVVXJBoLi4uTvw++XNjPRrK5qT1mTffX1Sbs5dZU8Px944w2L1oaqg97goDwIR0acsViVXIMBj9Kast4t2jRqR4esQ4u3KlBdLevZtKZCGxvDG9zn5Miav5s3I7cbu+++5MKdmebnZZ2hetduRYVMm2pVK2/fljWCsfYJjCQjQ3b59vRIM2M95eWyc5icIdLf7tycVOWfe87C3oy5ufIHXlAgjTDn54EXX7RoMJSOGPAorfl8sU1t+nyJtwHZu1eqdOogU1oqFTu1ujo5hUDr7FdApkudFESuX5eAF7okqbxcAtzAgP6U6cmTsfcJjKamRtYWfv659uVKNbWkJPn7cou+vvC1oIAUpyoqzB5NuM5OeSOkp61NptstOSXM75fK3fnz8u5waclZW7TJFRjwKK1lZ69VzCL1hWtpiX5ChJ7i4vimGn0+4OBBeV2Yng6/7KGHpG+bE0xMhLeK8fkk1L3/voTmwkLt8Hz3rrw+fv3rkY/lioXHI7uQr13Tnq7dvJnHhYaanZWqs9YSsrt3rT+1ZHxcv/qrWFmRCl9VlYm7o7WCHZFFGPAo7dXVSdsQvaOsioqk+pNsyIjHpk3As8+Gr93zemVzhRMEg9LnTx2oMjMlTM/Py3XGx/WrdLdvy3Xz85MfT2kp8PDD0uRZTWmCTKKjQ6qrWq5elQqylY/DubnI0+2KyUm5XsoDHoMd2RADHjlGMKjdeSDZ4OX1SuPf9nbtRfb332/MNGG8nF5RGhqS3oJqi4tSUdu5Uypqt25JiFZPV2dkSPAyItwp9u6VD7sIBLTPo8/OlmlsK4yO6u/kBqQ/o1IZ01piYIaaGqnInj4d+Xr33Zfi6eRAQMqcDHZkQwx45BjnzoW3HPF4ZDdpstWE8nKZClQfY+XzOWu9m514vfKhZXBQpsa7u+V3qHW9HTvi6+3nRP39wNtvh58SUlEBvPJKbI2rjRQISIUz2rnHfX3Sr3HfPhMGpSF0GcPdu9rXMWWtqtcrpd+yMtkhxYBHNsKAR44wOipVA60K2/HjxlQTdu6UDzJGVZVUUH7/+/DLVlZk+vnIEZnCVTcXzsuTy+JpS+M0S0uy+USrhc7AgFSU773X3DEFg5HXoobSO7rOLBUV8ibhd78Lv0xZq2pEm52oNm2Sj+3bZefH2bPhi2eJLMCAR7YXCEjlTq9HWV+f7NS85x4zR0Wx2L1bmgwPD4dfNjwsU7A3b4Y3Vt62Lf7G0k7T3S0fWoJBCX+NjZIdzOLzyXrTri5gZkb/etXV9ngztGuXNKhWr8crKwO2bjV5MAx6ZDMMeBS/YFAWDhUWmjKHpCxx0RMIyI66ujpnHJtlNr8f+OST8Onn3FzgiSdSW+XYsEHOyP3FL8LXT2ZlyTTsE0+Ef53X64zzbRN1965UpFdX9a8zNSWP+6ee0p/qToWqKll3+uGH2pdnZEh1zOzpYy1FRcDrr4evF7T0sRMa9Do6ZG0Jgx5ZgAGP1iwuynxRWZn25Uqwu3xZymYvvZTyZ/mVFekHF23aaGxMFuw/9FBKh+NIvb2y2UGrIWxNjUyjptLWrcBjj4UvTyoslCqdmbuT7eL2be2qplpHh6w1M3vDxe7d8meuNcZ4zz02gy3fDChBb8cO+UVGW9hIZDAGPJL0dPOmJKm9e8MDXmiwu3JF5kNMevuemSnTQW1tka+XkWHdrkM7m5uLfFLHiRPSTDiVOw1zcqSKZ0fz89rHwhUUABs3pu5+a2ok3EY7qlTrz9EMxcWyeamjI/yygwfdvTbScErQGx+3eiSUZhjw0llosLt5U+aLWlrWLtcKdhbYs0eGoJ5iDJUOOy4T0dkpbUj03L0rM0hGNBN2os5O4De/CZ/i27kTeP752E45SURBgYTet9+WKXQtpaWyrtSq6tT27fEfzUcRpPIdA5EGBrx0pBXsQtkk2Ck2bJCp13ff1e6DV1QkuVRrwXpNjUk76WxofFyqd5G6/QPApUuyWL2x0ZRh2YZyGoLWY6qzE7hxI7UbCTZvltu/ciX8Mo9HHvPMBESUKAa8dBIt2AEyZ/Xxx7Jt1eJgF2rrVul1d/16+GUPPAC0tspaM7Unn5TL01FOjsykT0xEvl52tr1PcZibk7556qBaVCQbAhIRDEqw1fvZrK5KOK6pkftJhcxMaQUzMBDecqSyktUzIkoOA146iCXYKbKyZDHb0FD065ooN1cW6ofOIAOy9m5mRv/Q8dZWqZQkGgScLD9fpgGHhvTX4AHSS8wOh8fr0ZtGra+XZsCJHEM1MCBdLCIZHJRedEeOxH/7saqp0d4Fmp1t3SkRROQODHjpwOeT4JaXJ/8fKbRlZADNzdJz5Nat2EKhSaqqwoPaxIS0ANGaZgMk/J09Czz9tLWHo1ulqUl2RGqdvwrIz3PXLu3LRkeloKtWWZm6tWlqY2P608z9/dKv7cCB+G+3v1/7e1O7dk3WwakbMRvF4zG3zx0RpY80fMlLQ5mZ8kpfVyevbOfOyStjpFb0ubkyR1Rfb7ugp1Cm2dSH2atduSIhxsrD0a2SkQEcPixT2+oZd58PePhh7SnIhQXggw+Anp7wr/nOd8w5vi0YlGCqN40aDMoauvr6+HeaNjdL65hInSt8PqnepSrcERGlEgNeOsnIMC7o2cDyskyjxXK90dH0DHiATAM++2z4yQQZGfo/k+vXZZOBunLm90uoqq1N/eYV5SEayeSkhMDHH49vt2lZmUxf//a3+ptQtm614DQEIiKDMOClIyOC3uXL5o1XR3a2dNy/dSvy0CsrpY1KOotnN+jkpP7uUkCa3167Jmv3UmlsTCqJ0fT1yXRrfn58t799u1SAtdrI5OTI92fWVDQRkdEY8NKZVtCLdiZSaNCzQfv4hgbZeKFX6fH5pFKzYYOpw3K0y5e1m/+GOnlSNq9UVqZuHFu3SpProSH96/h8sks63nAHrPWie//98Crerl3yZ0FE5FQMeLQ+6EU6YTyUTfpqZGTI4eidnTKtqK743HOPTFHa0ciIdjBtalqruAWDEnDUASQrKzWL82dmZOdoLNcbGkptwCsqkl5w77yjv/SzuTm5qffNm4E/+qPwz2dnm3v+KxGR0RjwaE1GBlBSYvUo4lZVJUGut3d97vR4pALU22u/b8vvl0X+Wq06btyQ4FRSImsMf/az8LN4y8qAV181vjJZWCgbC37968gNksvLJYim2pYtEuK0QmdurkzRJ3NslkMf8kREUTHgkeMtL0sY0WqXMTQkR5wlstMylSItYxwbk8vuv1/6+GkVVW/flnVwqWjivH27TH339Wlf7vXK7lszglFWlvwcpqbC1wQ2N8tmDyIiCseAR47X2wucPq1/pieQ2E7LVJmfl95ukTaGnD4tAaqzU/86yjo4o5s45+XJ2rTBQe0GyZs3A9u2aX/t5KR225rS0sSP3aqr055GzchIv2nUwUHgww/Dp6y3b5ffmR0e30RkDwx45GizsxKWIoU7QNa6NTdLJc9q16/rV8cUExPAe+9J1fH2be3rzM7KNO8zzxjfxLmxEdi7N/x8X2VTg1ZvuEBAuui0toZfdu+9wNe/Ll8fL4/HNks+LaVM62t1KZqclOBt1/WmRGQ+BjxytGBQv51HItczQyxVp7k5WegfaR0cIGFxbMz4zQ63b0tgCA3EHo9U4fTu69YtOcJYy6VLsnHEjHV7pltZkWbiKdbbK9PyWubmpOr77LOmDIWIHIABjxytsFCmpiLttASA/fvtUb0DpP3Hli0SzvRUVwP79klFLJI9e4zfTTszI6dYDAyEX/bUU9pVosVFmTJeWtK+zaUl4MQJmU52zckQIyMyh755c8oXA87NSaU60pnC167JCSPNzSkdChE5BAMeOd7mzbIGqa1N+/LiYgl4RqzX8vtl2lJdDczJkXHEIjdXmujeuqUfiB58UNaenT8fvlZvfl7uv6RE1sLduSPVtYqK+L8fLR0d+ieE6PW/6+kJn85V6+mRUNvSYsw4LTMyItt6z5+Xvjyx/uKT0N2t3ZA5lN8PHD8uATyRvoBE5C4MeOR42dnAfffJFJa6D57HI2vGjKpy9fUBP/95eCWlqAh4443Yp0rr6/UrdJs2SWWuoCC8ifPiogSwxUXglVeA3/1O1uLl5AAvv5z8hovRUQkJelPDMzPS2uUb31i/7u/GjejHFAcCcr09exy6GSA02N29K58zaT60qEh2FOu9IVBUVLioQkpESWHAI1eoqwMefTR8B2dmZnzHdEUSaZrs7l0JYt/4RmwbCXw+4OBBedFWh6n6+rX+docOyaJ6pQ/e9LQEq+3b5WuVadT5eQleTz+d+IaLQECyy9RU5OtdviwnPYQWrg4ckOA5N6f/dXl58j07LtzduSPl4dBgZ7JIbwgURUXy801kIwsRuQ8DHrmCxyMNelOpq0t7B6Pi8mUJk7HO2G3aJK1bIqmqkobGwaBki/PnZbrZ45HT5UJduSLBK9GTHZaW5JzZaFZWZNdmqJoa+fl/8on+1x0+7LBdnjYIdgrlDUFHh4R8LUePGjdNT0TOl2ZdpIgSMz4OfPll5F2tykYC9TRxssrLZfdqV5fkjRs3ZD2bem3e8rJMr87PJ3Y/ubkynR1t1rG6Wnshf0uLjFXLxo1y2ohjqndjY7It9fhxy8OdYtMmCXFaqqok3BMRKVjBI2dYWLC0Gdrdu7Ed0zs6KtczeqhKMSmamzelCpfouv/GRlkjp9fuJCNDzoctLAy/rKRELvvyy/DLHnhAmh07RlmZNO7buVOaz/X0RN7CapJdu+Sxpd7kU1oqU7RERAoGPLK3sTEpXeXnSwnIInV10oLi4sXI17vvPuPblgBSGWtultnCSGprk5umy8iQqdTr12XzhtqOHZHDo94UcXZ24mOyTGamfDP19bKF1QZBr7BQGlATEUXDgEf2pAS7M2dkwddzz1k6HCX4dHdrBx9gbZpsYkI2P6inczdtSnyXq3L/XV3695+ZKRW0ZFtkVFfL+a/t7es/r6xzzMqKPE6jT9WwnA2DHhFRNG57KianUwc7G6mpkSO3tDYS+HxrU5cffyxLt9Sam4EXXki8jUVVlVQIP/pI+/Ldu2WK1QgPPKC93ssxa+hSQSvoRWtOR0RkEQY8sgcbB7tQLS3SC0+9kWLTJnntHxjQn0bt7paPPXsSv/89e+TEgomJ9Z/PzZXwaWT1LK3DXCShQa+/n2eDEZEteYLBaKddEqVQrMHuueekT4QN+P3h068ejyx8f/fd8KnNUBUVwOuvr/W5S8SdO+Gzgx6PTK0mamVFNoio5eTEtjlieFh76ri6WvrfGe3OHdnZrFZR4bDNHEREKcIKHllnYUHKYTav2qnpVcna2yWrRjIyIhW4Bx5I/P5TsYnj5k0Jp37/+s83NgLf/nbkXcFzc8D774f3CPR6geefN35vzPIy8MUXwNWr4ZcdPQo88YQxx9IRETkZnwbJOrm5clTDW29JS4qSEqtHlJTR0ejHdQHA7dvhQcpK8/NyQsfcnPTyC/24fl32E0TS2am9FC0QkJYp6unkZPX2SsNfLefPhzeAJiJKRwx4ZL2NG2XbpsOD3p490j4tklSslUtWZ6f+CR2BgFTL9I4vGxuTcKi30GNsTE74MGohyMyM3J9eQF5cBE6ejH5mKxGR2zHgkX04POiVlspO2kjTgwcPSk89u4jlhI47d7SnQ4NB4NKl6BW606eBwcHkxqno6Fg7f1dPLFVHIiK3Y8Aj+3Fw0Nu2Tb9VSWmpHBhvp92pAwOxTaFevhx+BurcnBybFs38vITEZK2uyvRrtGrg6iowNJT8/REROZmNJoqIVJSg19xs/AGvKZKXJxso1OfEAsCBA9GncM1WVydjGhuLfL19+4Di4vWfKyiQX88vfhF+dFaoqipg69akhwqfb63ZdKTzdjds4GkPREQMeKTZbiIz00ZnW27caPUI4rJli/ZxXnaq3CmUaeVf/Uo/pFVWShNlLVu3yofe7mGfD3j4YeMeS7W1Ms2tdd4tID/jo0dTs9OYiMhJGPDS3NCQtMdQV5yqqqTFRaT2GKTPjmFOjzKtrDXdqpzQoa7eKXJyJFDdvKm9saG5Wfts2kR5PFJN1Gr2DEjfvZ07jbs/IiKnYsBLY36/LIDXanA7MyOL1TnV5X7KtPLoaPju1Kam6NOr9fXS7UZdxfN65Wi1SGfXJmLjRgmdp0+HX6YcF0dElO54kkUa6+4GfvYz/TPTy8uBN95w1B6HpI2MhH8uM9P9pyP4/drr8HJyYjt1Y2lJu3VJXl5qqpmBgPb9ZWSwyTEREcAKXtqam5N1THrhDpCKzpUrsoYqHQwNAe+8I73UQpWXAy+9FFtlaGFB+2daWGjvaduMDFlrp3bunDRmDpWZKb38QoN/drZ8mMXrNb4ySETkJgx4aWpgQPv0AbULF2RNU3l56sdkJb9fTkzTWtd165YcQ3bvvdFv4+OP5aSFUD6fHKVrp/53sRgaAj79VPuM2bw8mQ6l1NKrVPp88kFEpIcBL01t2iTtMbTW34XautX905OAHImr1cwXkL5rx4/LRoRIuzP7+qTxr1YFr7UVqKhIrurU1hYeQDMygJYWID8/8dvVoqzP1Ap3AHDqlGyeqK429n5pvZ4e4JNPwnv/HTwoLWOIiPQw4KWpkhKZen33Xf32GEVF8kLi9kqBchZrpOnq6Wk55/Spp7TXeEWb8u7slF2qO3YkNsbhYeD992XzSyiPR0LjgQOJ3a6e3l7ZqapndlYqnt/8pr2OXXOT2Vl5XKqnyAH5fEMD28EQkT4uR05j27Zp92tTHD2qvS7LbW7fjm26+to1/RMZuroi34bfLwFQHdBisboKnD2r/bXBoLzYa/UyTNT8vFQsIwVeQCqefX3G3a8TBIPhH6kS6TGlvOGI1GCaiNIb33unsZwcaY8xMBDeB6+yMn36iVVUADU1cgxWJNu3a69FnJ6WKdhoL/ZDQ1IZi7f1zM2bclSYnokJ4OJF4LHHjNnI4fHEVrX1eNJrx+rVq/J7VnvgAeP/VsbGJLhHekxdvCgVYb2j8YgovTHgpbn6emmFopaVpd/c1m0KCmTDwD/8g/aCdkB2wR46pB18CgtlPZpWixX19eKdUltYkGqa1tFnoc6elabCRmzkyM2V0NLfH/l+9+6VacJ0MDUlFVit3/GxY/IGwciTX65ejX5G8OKirIWsreU0ORGFS6P336TF55OQp/5Ih6nZUE1Nkasw99+v/zPxeoH9+6P3C4x0G3pWViKfu6rw+6OHwHg0NESuNBYV6QdeN7p2TT/ADw7KLmsjVVXFFtoaGhjuiEgbAx4RpLfbkSMSXLKy1n/U1OifxaooL5eql94UaVUVsGtX/OMqKpLqolaQysmRcTU3SwuX1VVpXt3drX1sWDx8Pglwer3/EgmrsVhdlXV96g+tjQZmuX1be2o21IkT0Su48di8Ofq0b6KPKSJKD3zvR/QHNTXAa6+Fr3uKdbp6xw5pk6JeyxftPNdotm6VDTEdHeHj/eADWYjf0rIWxnw+4MUXk3/xr6yUXcPqjSU+X+qCxc2bcrqKOqBu2gS8/rr5p6oEg9LsOdrmmOlpWRP39a8bc7/KG46eHu0Krs8nu+CNnBYmIndhwCP6A48nub5uBQXSNkT9gpzs7WZlScWsr2/tlI0NG4C7d6Va19S0vtK2uirrxWpqkl9H2dKS3NfHY2FBKmFa1cc7d2RdmtnNlT0eqZR5PJE3PHi9xlc0a2qk193nn4df1tws6z6JiPQw4BEZKFVrF+vqJNwoU5UlJcDbb8v9VVSEX//2bWmMfP/9qRlPKly/Lr0C9VjVXHn7dqnMRmqD09goVVYjeTzAvn0SMNXhsrSUR7URUWSeYDCVnZyIyGirq9L0+OzZyNcrKgLefFM7ANrN5CTwk59EP1ll/35rmit3dUmg1uoNmJUFvPoqK2pEZC/cZEHkMDMzslYtluuNjaV+PEYYG4utWXNvb/T2IanQ1KS/7rClJX3axRCRc3CKlshhNmyQqdff/CbyurD6euc0wa2tlRDV0xP5egcOWHM8V2am7JLWqtLF2tIkbY2MyAJLpzwYiVyCT0tEDqSsC9Or5GVmypq9/Hxzx5WoWJorl5ebu+lDbdMmnv0al5ER2Rlz8aJs+WXAIzIVAx65zuKitK1Qy8+Xna5ukJ8vAW5oSHtd2O7dxr+eBoPSqkW9yzUzU1rEqHv1+f3A3Fz47eTkANnZ4Z9XmitrrS30eiUjmN0mxTILC5J6nSg02MV6+HIgIAswnbBglMghGPDIdTo6gN//Pnz68uBB485rNYPeYfbK+a+NjcA994RX8TIypPGx0dOG/f3Ar38t2SNUdrZkkc2b13/++nXgo4/Cv4cDB4AHHwy/faW5slYVr7LS+F2qtjQ/v9bd+ZlnrB5NfBINdoODwIUL8oB12vdMZGMMeGR/fn/MaWV8XHrAaVWOzp6VkFBfb/D4UuTcOXmtVHvsMQlTGRnAk09qn59r9NTs0pK0KVGHO+WyEydkLZpSdLp7V85o1do40doq49dqd1JZKQ2N1TIypPLnWkqwa20FBgacNZ2ZbLBra5Oy+759qRwlUdphwCP7untXVt1nZ8d0dEIwCFy+rL8bc2FBXj8rKrSnCO1kdFQC0tRU+GXHj6+Fqexsc76XGzeAzs7Il1+/vnZ+bXu7vH5rmZ0FzpzRb3eyYUPSw3UOdbALBKweUewWFuQP7vhx+VuNhVawI6KUYMAj+1GCXWurHGHw7W/H9GWDgxIcIunslDAS7ZxPKwUCwPnz2uEOkFYhoWEq1WZmJGxqVQoVgYBUTuvqZHr1xInIt3n1qqwT3LrV2LE6hpODnSI3V/6QsrPl+xgZ0V5T4PFI+bW/n8GOyEQMeGQf6mAXZw/u9nbtcztDra7K1Oe2bfZtbaG8DuoJDVNmbDqYndXetKJ1vdlZOT4t2vVXVuTX3NAgmzTShhuCXaiiIpla3bxZ3jmpg15JiZTMz5yRv2kGOyLT2PQljtJKksFOsX27VL601okpfD45DcHocKe0+lKrqopvCnVlRda6RXsdHB2VQsgDD8Q3zkRUVcmmjU8+iXy9Awfk/NSVFTndQa/dCSBFnZ070yzcBYNSZj592h3hLpQ66J08ufa3HAzKH5yXffWJzMSAR9YxKNgp6uokZBw/rn+drVuNP1JqdhZ4773w3aw+H/DSS/FNB2dmykaDtrbI18vIADZujH+siWppAa5c0T9KrKREXt+93sjtThR1dRLIUyoYlNSdl5fiO4qRxyOl45oad1XxQoUGPeVvu6sLeOEFOUD38mV5IEUrtRNR0viWiqwxNydP9h99pL92J04ej1Tn9KYtc3LkBAijd2N2dWkfRL+6KlOpsa4/V7S0SFPfSHbsCG9LkkolJdJ3T6sI4/FIJVEZs9LupKhI+7YyM6VNSsp6EgaD0iDwgw/kv3aTlyebhl57Td4B1Ne7r7pVVCR/jG++CXzrWxJia2qAb3xDPnfkiH2CN5FLsYJH1sjPl1TQ1GTou/qyMgkPH34Yftn+/ca3SBkbkw0Ievl0aEiqcffdF/ttlpRIU99339Uu7uTlyetjVpZ2OxivNzU9crdtk40Rk5PrP19YKIEzVGWlhOkPPgi/nV275NduuGAQuH177fE0N2d8udZIStBrbHR3RW///rX5eo9Hgl51tZR5WdEjShkGPLJOip7sd+2SgKFWWGhsoSQYlOPCJiYiX+/ECQk08TTp37ZNKnTd3eGXHTokZ7eOjwO//GX4er3ycuC554wPebm5wIsvxn79PXu0v+eiIoPX3mkFOyfRCnpuq+hlZa3/t9bfPhEZigGPrGdw0MvNlZtLtbt3ZXo2lusND8cX8HJygMcf126FUlsr/714UXbcqo2PS0XNrDYqegoL5SNlnB7s1EKDnh2nllMh9G9fb4EnESWEAY/sQy/o2VRxsUyV/uY3kZcQVlUlNiVZVSUfWvr7pd2LFrPbqJjObcFOLS8v/RoEejzApk1Wj4LIVRjwyH7UQS9Sh12LNTdLkNLaZAHIhoOHH9bfcJCI5WVppRKpwDk6Ktnn4YeNu1/bmJmRLcsdHe4Ld0REBnHZQg9yFSXoNTRYPRJdBQWyqUNvTVlzs/Hr/AcGJNtEc/asTA1bYmoqdcG8qEh2cLz5psxjp9XZZkREsWEFjyhJmzdLa5Pe3vWf9/kkh6jXlyerpEQ+oi1Zqq62YIp2akp2hnR3A88/n9rjQsrL5WPHDkm8587pn+9GRJRmGPCIkpSRIYWklZXwy4ycmlVEa6MCrPX8i+cUjaQowe7UKUme8ewoSRaDHhFRGAY8IgPk55t7f1u36rdRAeREj7o6EwaiDnZW0gp6RERpigGPyIFyc4GjR2U37dLS+ss2bJDesiltpWanYKcWGvTc1DSYiCgODHhEDtXQALzySniLlqys6EedJczOwU4tZT8EIiL7Y8AjgnTeUBd7fL4UnpdqAJ/PgpO45ucl2LE9CRGRrTHgUdobH5cNC+q+chs3ykZQO4c801VXS/dlniNKRGRrDHiU1oJBySkDA+GXTU7KWv1Dh8wfl605+MD427eBL74Ir9Zu28bfMxG5CwMepbXBQeDMGe3LgkHg+HE5GrSszNRhOYPDgp7fD5w+DbS3h192+7bsOjazuwsRUSrxJAtKWysr0Y/8mpwELlzgZsyIlKD3jW/I6RJHjsh5qjZz8yZw9ar2ZXfvSleV1VVzx0RElCoMeOlictLqEdjO0FBsR35dvGjhkV9OEhr0nnxSdoHYxPw8cOyYdjNqxaVL+mcKExE5DQOe2w0PAx99JCmF1iktBTZtin692lrZcEEx8nhk2tboM9qScP060NcX+TpLS8CJE8DioilDIiJKKQY8t1KC3Y9/LKWL5WWrR2Q7hYXAQw9FLjSZfuQXpUReXmzH4hYXp/b4XCIis/CpzG2Gh2Wh0aVL0tyNImpqArZvB9ratC/ftw+orzd1SJQCTU3Arl3yZ6GnsFB20jLgEZEb8KnMLRjsEpKdDdx3H9DbCywsrL9swwY50zWlR379weKitO9Q/+oKC4FHHmEFMVkZGcC998ohHHo9mu+/H6isNHdcRESpwoBnF8EgMDIS/ysMg13S6uqAV18N3ymbmRnbGj0j9PTIjl71Lk6fT9YA7tplzjjcrKZGNvh+8kn4ZZWVwO7d5o+JiChVGPCsFgxKE67Ll6W08OKLsX/tygowOwvcuRNefqKYeTzS684qU1PAl19qt+hYXZXLampkfZirLS4C09MpbUbX0iLVUPX5vRUVafDzJaK0woBnldBgd/my9HHYujW+28jMlK+pr5f+DqdPAzduSEdXcoxr1yK3Ybl9W9YI3n+/eWMy1eKiNKk7fRrYvDmlAa+kRKp4RERux4BnNq1gl6ysLAY9hxoeBlpbo1/v5EnZKOCqNWKhwa6vT8qVDQ1Wj4qIyBUY8MySimCnxqDnOJmZ8hFNVpat2solRyvYOc3IiMytb99u9UiIiDQx4KWaGcEOkLVLMzOyIl8r6NnoVAFas3Gj9OL79a/D14UpvF7gwQelMXO8xsbkxA61igoLzl1dXJTH46lTzg52V69K4/B77mHAIyLbYsAzg8cjASsV/Tamp6X3w6lT8oJTW7t2WWjQGx83/r4pnLIVN47fdXOz/Ipu3tS+vKFBrhMvv1+mf8+eDb+spQX41rdiqx4mbWlJvjm3BDvuViciB2DASzWPB6iqksVTe/astTSZnU3udkOD3Z078jm9ElBWloyBUicQAAYHga4uaayXnx/zl+bnSxVvaCj8rNSsLKne5eXFP6Rbt6RorKW9XR6OphSgsrLkmywrk2p2qqrYqTAyIrtgLlxgsCMiR2HAM4tRQW96WpqmtbauBTuyjhLsLl6UIFBYKAEvTo2NwFNPhZ+Dmpub2L6DhYXIJ9T5/XJ5TQ1QUBD/7cfF45E7qq4G9u6V1Hnlir2DHoMdETkcA57ZEg16ocFudFS/WkdC6RFYUpKa21cHOyWZFRYmdHMZGcDhw8YNr6tLZkMjGRiQSp6R9xuRE4LeyoqM68svZRMFEZFDMeBZRSvoaVUKFhfl1frYsfiC3epqem6sWFkB+vuBM2eAnTuND3h6wc5mRkbCT+ZQCwalTUsgYM5xbF+xc9DLzJQjLQoKZPnDzZvOXDNIRGmPAc9qoUFPa8o1J0deCPftk9AyORn59lZXpTTT0wN87Wty++kgNNh1d8u/d+ww7vYdEuwU+/bJMKen9a9TUAAcPGhyuAulFfRM2fURRU6OLE5saFjbHMKgR0QOw4BnFx6Pft+KsjL5aG6Wap5W0AsEZErygw9kyre8XAKe22kFu1S4dQv44gugt9fw6XGtmcDMzLj2aYTZtAl44AHg/ff1h3vkiGQry4UGPTtNizLoEZGDMeA5iVbQGx8H7t6VubbycqksLS1ZPdLUMyvYKRobpWndjRuyDnJ42JCgNzwM/PKX4b+yqirguedkk0Widu6UrD84GH7Zpk3SKsVWPJ7UrZlMhl7QIyKyMQY8JyorkxfC8nIJOO+9J0khHSoLZge7UIWF0mtw82ZDgt7qqvSo0zqHdnpaAloyIaywUNqvHDsWftmRI8CGDYnfdlpSB727d60eERGRLgY8p1HW2F24IFsgc3KAp5+WKducnOhr9JxudVVC3fJy9F0EqWJQ0Lt5U79PXSAgGznr6pILYjt2aDdJtmzdnRsoQY9HABKRjTHgOUkwKJsnPv98bd5taUnKPcXFUtUbGbF2jKlmp3VRWkEvxnEsLADHj+v3qQNkz82VK1KFSwbDXIpk8OmTiOyLz1BO4vFIOaa8HLh+XcKNcgTZ9HTkLZNuY9egd+tWTImqv1/2a0Rz7pxU4crLDRgnERGlDQY8JyopAe69F9i2LTzopRu7Bb3du2O6ank5UFoKjI1Fvl5Tkz33HRARkb0x4DkZg94araBn4x6AJSUy9fqrX+kvJSwokFMmOBNIRETx4kuHG2gFvXQVGvTm5qweTUTNzVKh6+nRvtw2feqIiMhxGPDcJDTo3bpl9WislZMjHzaWmyvNiPv7wzdb2LJPHREROYYnGOSp9URWWV2VgKeWlZXi6p3fz7lfIiIXY8AjSidTUzKNX1ICbN1q9WiIiChF+BaeKB0owe7UKdm6+/rrVo+IiIhSiAEvHc3MyGkYO3daPRJKNXWwIyKitMCAl05mZtZOXMjMZMBzMwY7IqK0xoCXDkKDnXJmal2d1aOiVGCwIyIiMOC5m1awI/eamQEuXJBwt7ho9WiIiMhCDHhuxGCXngoLgUcekd2xFy4AbW0MekREaYoBz02CQaC7G/jiC9lEwWCXfrxemX6vqQH272fQIyJKU16rB0AG8niA+nqp4mzbxka2djI/D4yOmnd/StB79lngzTeBAwdsf7IHEREZhwnAbbKzZYquvl6OKzt9WqZr/f7IX7ewIAGRIcBY8/NAb69Mlx8+DJSXm3v/ehU9IiJyNQY8t8rKii3oLSwAfX3AlSvAY48x4BklNNgNDgKBAHDokHXjUQc9j8e6sRARUcox4LmdVtAbGJDT7NvbJYD09wMFBVaP1B20gp2dKEGPiIhcjQEvXShBr6ICuHYN+PBDma6zWwBxKrsHOyIiSisMeOlCmYpVKnbLy9yEYYSFhbWWNAx2RERkE3yFdzt1sFMCCMOdMbKyZHp740bgzh1gacnqEdnLwgKQm2v1KIiI0g5f5d3u7l2pLI2NsbqUCj4f0NAA1Nau7VJtb2fQm5kBenrk6LRHH7V6NEREaYcBz+0qKmRDxe7dslP20iVgbs7qUbkPg55Qgt2pU3KKyoEDVo+IiCgtMeClA48HqKoCKiuBlhYGvVRK16CnDnY8RYWIyFIMeOmEQc88WkHP68KDYxjsiIhsiQEvHWkFvd5eq0flTqFBb3bW6tEYh8GOiMjWGPDSWWjQGx6WY84oNXw+oLjY6lEYw++XZtmnTzPcERHZFAMerQU9olhkZAA7d0pVUukByKBHRGQrDHhElJjCQuCee4DNmxn0iIhshgGPiJLDoEdEZDsMeERkDK2g5/FYPSoiorTkCQb5NpuIUmBmRk5QaWqyeiRERGmHAY+IiIjIZVzYeZWIiIgovTHgEREREbkMAx4RERGRyzDgEREREbkMAx4RERGRyzDgEREREbkMAx4RERGRyzDgEREREbkMAx4RERGRyzDgEREZIRCQo9mIiGwgw+oBEBE5WiAADA0Bly8D2dnA449bPSIiIgY8IqKEBALA4CBw6RLQ1gbMzwP33mv1qIiIADDgERHFRwl2ra3Ap58CxcVSuSMishEGPCKiWIQGu48+Anp6gKws4J57rB4ZEVEYBjwioki0gp3fL5dlZVk7NiIiHdxFS0SkJxAAbt8GuruBa9eA/v61cEdEZGOs4BERqSk7Yy9elGC3tARUVQF/9EfA1avA+fOyqYKIyKYY8IiI1AYGgC++kOnYYFA+d/s2MDIC1NUBe/ZI0Gtvt3acREQ6PMGg8uxFRERYWpJdsbOzQF8fcPKkVPNCnyq9XqCiQj6WlyUILi1Jm5RnnrFs6ERECgY8IiIAmJyUtXY+H3DgwNrnIwW98nLgH/0j+drz54GCAuDJJ00fOhGRGqdoiSi9KcHu1Ck5auzpp9dfXlAgU7KNjeFBLxgEPB6goQGoqQHGx634DoiIwjDgEVF6Uge7aLSC3vLy2uUZGTJlS0RkAwx4RJRe4g12aqFBr79f1uMREdkMAx4RpYdkg51aQQGwc2fyt0NElAIMeETkfouLwI0bwIkTEvSIiFyOAY+I3C8nBzh4UKZVOzuBM2cY9IjI1RjwiCh9bNwIHD0KbN/OoEdErsaAR0Tph0GPiFyOAY+I0pdW0CMicgGeZEFEpBgfB+bmgPp6q0dCRJQUBjwiIiIil2GHTiIiIiKXYcAjIiIichkGPCIiIiKXYcAjIiIichkGPCIiIiKXYcAjIiIichkGPCIiIiKXYcAjIiIichkGPCIiIiKXYcAjIiIichkGPCIiIiKXYcAjIiIichkGPCIiIiKXYcAjIiIichkGPCIiIiKXYcAjIiIichkGPCIiIiKXYcAjIiIichkGPCIiIiKXYcAjIiIichkGPCIiIiKXYcAjIiIicpn/D8k8uc332k07AAAAAElFTkSuQmCC",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -780,7 +749,7 @@
    "source": [
     "## Citation\n",
     "\n",
-    "This blog post is a part of the project TSGM, in which we are creating a tool for enhancing time series pipelines via augmentation and synthetic data generation. If you found it helpful, take a look at our repo and consider citing the paper about TSGM:\n",
+    "This tutorial is a part of the project TSGM, in which we are creating a tool for enhancing time series pipelines via augmentation and synthetic data generation. If you found it helpful, take a look at our repo and consider citing the paper about TSGM:\n",
     "\n",
     "```latex\n",
     "@article{\n",