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"outputs": [ { "output_type": "execute_result", @@ -6618,7 +3882,7 @@ } }, "metadata": {}, - "execution_count": 2 + "execution_count": 3 } ] }, @@ -6627,7 +3891,9 @@ "source": [ "# Initialize GitHub authentication (token)\n", "\n", - "GitHub's API doesn't allow anonymous release downloads. We can use a read-only token." + "GitHub's API doesn't allow anonymous release downloads. We can use a read-only token.\n", + "\n", + "https://docs.github.com/en/rest/authentication/authenticating-to-the-rest-api?apiVersion=2022-11-28" ], "metadata": { "id": "OQTe2IE95P7c" @@ -6651,9 +3917,9 @@ "base_uri": "https://localhost:8080/" }, "id": "vyKWa35bkFcG", - "outputId": "10f15782-6ed1-4669-aa9b-b349408b4578" + "outputId": "afd6da24-fb42-48e7-bece-8697870b2cb8" }, - "execution_count": 3, + "execution_count": 21, "outputs": [ { "output_type": "stream", @@ -6673,14 +3939,16 @@ "\n", "No full AE campaigns with post-exploitation, just the Dropper itself.\n", "\n", - "The set contains security agent telemetry (aka sysmon lohs) of 1000 documents, some malicious and some not.\n", + "The set contains security agent telemetry (aka sysmon lohs) of 1000 documents, some malicious and some not. These are imbalanced datasets.\n", "\n", - "## Objectives\n", + "https://github.com/norandom/log2ml/releases/tag/lab\n", "\n", - "1. Which ones are malicious?\n", - "2. How does the VBA Excel malware behave? \n", - "3. Can ML help to find out?\n", - "4. Can Tpot autogen the ML models?" + "## ML development Objectives\n", + "\n", + "1. Classify: which ones are malicious?\n", + "2. Explore: how does the VBA Excel malware behave? \n", + "3. Evaluate: Can ML help to find out?\n", + "4. Engineer: can Tpot autogen the ML models?" ], "metadata": { "id": "oYVNy4rNojZc" @@ -6819,7 +4087,11 @@ { "cell_type": "markdown", "source": [ - "# Download pre-trained BPE Tokenizer from the project" + "# Download pre-trained BPE Tokenizer from the project\n", + "\n", + "The BPE tokenizer is used for the Sysmon messages, not for the entire JSON or CSV files.\n", + "\n", + "https://towardsdatascience.com/byte-pair-encoding-subword-based-tokenization-algorithm-77828a70bee0" ], "metadata": { "id": "Uyn6jyZvMsQI" @@ -6898,86 +4170,15 @@ { "cell_type": "markdown", "source": [ - "# Download pre-vecorized data from the project" - ], - "metadata": { - "id": "rTw3shjR33yL" - } - }, - { - "cell_type": "code", - "source": [ - "# File name to search for\n", - "file_name = \"lab_logs_blindtest_activity_sysmon_1000samples_july_28_2024_filtered_vectors.parquet\"\n", + "# ETL and labeling\n", "\n", - "# Get the download URL of the specific file\n", - "# download_url = get_specific_file_from_latest_release(github_token, repository_name, file_name)\n", - "download_url = get_specific_file_from_tagged_release(github_token, repository_name, \"lab\", file_name)\n", - "print(download_url)\n", + "This uses Polars as an ETL (Extract Transform Load) tool.\n", "\n", - "if download_url:\n", - " local_file_path = file_name\n", - " download_file(download_url, github_token, local_file_path)\n", - "else:\n", - " print(\"File not found.\")" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 85, - "referenced_widgets": [ - "48125358d93747af8d60adf49cb4bed3", - "ce66e5dab6a7433498679f44d7922fde", - "037cca2c146a429498219af57498f92d", - "a4dd416d3a3f400a958713c9e1338830", - "c98d82d69aa5436e86a00b3b24c71dc8", - "3e842df16ae04f7c9fa0500251ea6070", - "125527f8c490490f93c88276e8da441e", - "38ea9e2fdf934b299dbf47b478f454cd", - "25123bc15acb4025a468103612181d4b", - "38eadb841c66408ca51a85054a2da63c", - "283ef3c448dd4fb0baaa1621123a5ced" - ] - }, - "id": "WdRLtddt378k", - "outputId": "58f22716-5958-4cf4-a2d4-eff7384b87cc" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "https://api.github.com/repos/norandom/log2ml/releases/assets/182698628\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - " 0%| | 0.00/3.29M [00:00 tpot_sample_size:\n", + " X_train_sample, _, y_train_sample, _ = train_test_split(\n", + " X_train, y_train, train_size=tpot_sample_size, stratify=y_train, random_state=42 + run\n", + " )\n", + " else:\n", + " X_train_sample, y_train_sample = X_train, y_train\n", + "\n", + " # Create preprocessing pipeline\n", + " preprocessor = Pipeline([\n", + " ('quantile', QuantileTransformer(n_quantiles=1000, output_distribution='normal')),\n", + " ('pca', PCA(n_components=n_components))\n", + " ])\n", + "\n", + " # Fit preprocessor on training data and transform both training and test data\n", + " X_train_preprocessed = preprocessor.fit_transform(X_train_sample)\n", + " X_test_preprocessed = preprocessor.transform(X_test)\n", + "\n", + " # TPOT classifier with memory-optimized settings\n", " tpot = TPOTClassifier(\n", - " scoring='recall', # Use recall for single-label classification\n", + " scoring='recall',\n", " verbosity=2,\n", " generations=5,\n", " population_size=20,\n", - " random_state=42 + run\n", + " max_time_mins=30,\n", + " max_eval_time_mins=5,\n", + " random_state=42 + run,\n", + " config_dict='TPOT sparse',\n", + " memory='auto',\n", + " n_jobs=n_jobs,\n", + " periodic_checkpoint_folder='tpot_checkpoints',\n", + " warm_start=True,\n", + " cv=3,\n", + " early_stop=3\n", " )\n", "\n", - " # Fit\n", - " tpot.fit(X_train, y_train)\n", + " # Fit TPOT on the preprocessed data\n", + " tpot.fit(X_train_preprocessed, y_train_sample)\n", "\n", - " # Predict and calculate recall score\n", - " y_pred = tpot.predict(X_test)\n", - " recall = recall_score(y_test, y_pred, average='macro') # 'macro' for single-label multi-class\n", + " # Clear memory\n", + " del X_train_sample, y_train_sample, X_train_preprocessed\n", + " gc.collect()\n", + "\n", + " # Predict using the preprocessed test set\n", + " y_pred = tpot.predict(X_test_preprocessed)\n", + " recall = recall_score(y_test, y_pred, average='macro')\n", "\n", " # Update best_tpot if this run has better recall score\n", " if recall > best_recall:\n", @@ -8517,9 +5915,13 @@ " results['best_pipeline'].append(pipeline_str)\n", " results['n_features'].append(n_features)\n", " results['runtime'].append(time.time() - start_time)\n", - " results['pipelines_tested'].append(tpot.evaluated_individuals_)\n", + " results['pipelines_tested'].append(len(tpot.evaluated_individuals_))\n", "\n", - " print(f\"Run {run + 1} completed. Recall Score: {recall:.4f}, Features selected: {n_features}, Pipelines tested: {len(tpot.evaluated_individuals_)}\")\n", + " print(f\"Run {run + 1} completed. Recall Score: {recall:.4f}, PCA Components: {n_components}, Features selected: {n_features}, Pipelines tested: {len(tpot.evaluated_individuals_)}\")\n", + "\n", + " # Clear memory\n", + " del tpot, X_test_preprocessed\n", + " gc.collect()\n", "\n", "# Convert results to DataFrame\n", "results_df = pd.DataFrame(results)\n", @@ -8531,148 +5933,73 @@ "# Save results to CSV\n", "results_df.to_csv('tpot_recall_results.csv', index=False)\n", "\n", - "print(\"\\nResults saved to 'tpot_recall_results.csv'\")" + "print(\"\\nResults saved to 'tpot_recall_results.csv'\")\n", + "print(f\"TPOT sample size used: {tpot_sample_size}\")\n", + "print(f\"Number of PCA components: {n_components}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ - "a336f820e5124f5d9e344bb8b0d12ee5", - "150475d8c96148eaa9d5230b85283e64", - "19f3e10b3dd94c71b9add7ee1d8f4c13", - "5aa33fe21abc482c997975ec5712d0da", - "0b51be1ad43c43fa89041fea2b062768", - 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"0967c0fe1d9a4048b4e7b4266a75d893" + "e596dc64bc2145feb8039da7c626250d", + "4f8021ec3c87454e856aa23426dd92f7", + "abaf96bfaed843918b225cf759ab5f5d", + "647e90686f1d49d8b6512e9144287cb7", + "5ccef87811f84ba69ff589e4dd0a0db1", + "3811f9b18eb94a998715a71e1d80c902", + "cd69e99568cc440fb9c70c0e1327c740", + "d6aeb9c1304c478aa713252980655782", + "20a9e1000946406dbe45e5c0b92c88ab", + "50d69c11c64942e7a8551479dbf89ba5", + "e9a9ed09603a4cf38180372257723d1c", + "33d9616f5fc34716b62b6b76940de17f", + "99c1326110284c378d90c411f95d2c41", + "7d7228aabd874fe89402185e323a6d56", + "20f007cefbca4a38a9887914f9b8f908", + "44390d56d7734686b6dab24db04abd1c", + "4f9eee0acf654464bae4a250bc78aec3", + "bbc3470e99a74d9385884ce080281aed", + "0a9b4564bbc24e11aed04594e38190fb", + "122fdca0c85449e0a3cad8828bcb67df", + "6ad9c2cfa49c4b0f9843d9afb3ef4bcc", + "34a265dfe03b4c31bad5c098d2a53a57", + "66157afab32248dc8d040eebee0718d3", + "740ad9c72d404dcea2affe9118a039e8", + "4a9497a6f861409fbe56f64dabcc5aab", + "8c143fc49ac0499a9144d4705ee42fff", + "883189238c3f4928b35158622649598a", + "46e1ce81b2e54ee5936120bc892e8ddd", + "ec201971aa684b97acc1a9ca17f9d180", + "c7900ee411fb4d719907d466ebd9c96f", + "71ac2675223046a893072a3ffc361d05", + "743e80c2b3bb4269a095f6be3b7292ae", + "92088f9533bb48ccb63bf9328304b0e3" ] }, "id": "usxaQgkSbEND", - "outputId": "7ace5c69-7761-48c4-ab9f-ff2623cf9355" + "outputId": "fa47c4f5-3209-44b5-8de9-3999ac71a173" }, - "execution_count": null, + "execution_count": 15, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", - "Starting run 1/10\n" + "Starting run 1/3\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ - "Optimization Progress: 0%| | 0/120 [00:00" ], - "image/png": 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Pu7u7Y9q33W7XX3/9pWvXrqlWrVpp9uPt6NWrl1atWqXGjRtry5YtGj9+vBo0aKBy5cpp69atjvWWLl0qm82mMWPGpFtvyuO7hg4d6rT86aefliR9/vnnTu1pjevMfoYpn1fKlPyb+eCDD+Tm5qYOHTo42rp27aovv/zS6bKOpUuXqmjRonryySfTPdYUHTp0UGBgoOP1yZMntXv3bvXo0UOFCxd2tFetWlXNmjVzesRZwYIFtX37dp04cSLNelPOZK9evVqXLl265fHd6OLFiwoMDFRgYKDKli2rYcOGqV69elq+fPkd3cyxX79+Tts3aNBAycnJ+v333yVdPy5JWrlypZKSkm77fQDgZpheDgDZpF+/furUqZPc3NxUsGBBx3XJKVICdWJiouMfgjfKSDC/HUuXLpW/v788PDwUEhLiFCpjY2MVHx+f6prZFHFxcU6vw8PDM/y+v//+u0qUKJHqeCpUqOBYntF9lypVyul1SggIDQ1N1X7jtegrV67UhAkTtHv3bqfredP6x/6N7yNdD1Mp+zxz5owSEhJuOX04NjZWP/30k1MI+qcb+zUt5ibXBs+cOVN33XWX4uPjNX/+fH399ddO4+3gwYMyxmjUqFHpPsoqLi5OJUuW1KFDh5zCX3refvttvfzyy9q/f79TgMnMmLiVqKgoRUVF6dKlS/rhhx/04Ycfavbs2WrdurX279+voKAgHTp0SCVKlHAKkjf6/fff5ebmprJlyzq1BwcHq2DBghkae5n9DFM+r4yEyHfffVe1a9fWn3/+qT///FOSVKNGDV29elUff/yx+vXrJ+n6pSB333238uW79T/pbjyGlGO8++67U61boUIFrV69WhcvXpSfn59eeuklRUdHKzQ0VJGRkWrZsqW6d++uMmXKOPY9dOhQTZs2TYsXL1aDBg3Utm1bPfbYYxmaWu7t7e24xOaPP/7QSy+9pLi4OKc/iN2OG7+vKX/4SPm+NmrUSB06dNC4ceM0ffp0NW7cWO3bt1e3bt2cvi8AcCcI3QCQTcqVK3fT67ArVKigTz/9VD/99FO6N8f66aefJCnds723q2HDhipatGiay+x2u4KCgtJ9bM+NgeNO/5F8Mzfbt7u7e4bb/xlWN2/erLZt26phw4Z64403VLx4cXl4eGjBggVpPvs5vfe5WQBOi91uV7NmzfTMM8+kufyuu+666fZFihRJ9ceDf6pdu7bjTHv79u1Vv359devWTQcOHFD+/PkdN/oaNmxYujMTbgykN/Puu++qR48eat++vYYPH66goCC5u7srJiYm1c3bsoKvr68aNGigBg0aqGjRoho3bpy+/PJLRUdHZ2o/GT2LmtbYy+xnmPJ5pfddSxEbG6sdO3ZIuv5740aLFy92hO7MuJPvZufOndWgQQN98sknWrNmjaZMmaLJkydr2bJlatGihSTp5ZdfVo8ePbR8+XKtWbNGgwYNUkxMjLZt23bLa/Dd3d2dfj9GRUWpfPny6t+/f5o39rtRcnJyuvtNyz//ALJkyRJt27ZNn332mVavXq1evXrp5Zdf1rZt25Q/f/5bvjcA3AqhGwByiNatWysmJkbvvPNOmqE7OTlZ7733ngoVKqR69eplW10RERFat26d6tWrl+WBOiwsTOvWrVNiYqLT2e79+/c7lltt6dKl8vb21urVq53ObC1YsOC29hcYGCh/f3/t2bPnputFRETowoULt31DvPLlyzvuLn8rKeG3SZMmev311zVixAjHGUoPD49b1hAREXHL41myZInKlCmjZcuWOQXZtKZ4Z7WUPy6cPHlS0vV6V69erb/++ivds91hYWGy2+2KjY11zKyQpNOnT+v8+fMZGnuZ/QxTPq9/vl9aFi9eLA8PDy1atChVaNyyZYteffVVHT16VKVKlVJERIS2b9+upKSkDN9UL0XKMR44cCDVsv3796to0aLy8/NztBUvXlz//e9/9d///ldxcXGqWbOmJk6c6AjdklSlShVVqVJF//vf/7R161bVq1dPs2fP1oQJEzJVW/HixTVkyBDHjRHvvfdeSdfPVJ8/f95p3atXrzo++9t177336t5779XEiRP13nvv6dFHH9UHH3ygPn363NF+AUCSuKYbAHKIunXrqmnTplqwYIFWrlyZavnzzz+vX3/9Vc8884ylZ5Nv1LlzZyUnJ2v8+PGpll27di3VP4Azo2XLlkpOTtbrr7/u1D59+nTZbDanf8xbxd3dXTabzelM2ZEjR/Tpp5/e1v7c3NzUvn17ffbZZ06Pp0qRcoatc+fO+vbbb7V69epU65w/f17Xrl276fvcd9992rNnT6rHW6WncePGql27tmbMmKHLly8rKChIjRs31pw5c9IMLP98rFKHDh30448/prqj9z+PJyUc/vOM//bt2/Xtt99mqL6MWL9+fZrtKdcep0yT7tChg4wxGjduXLr1tmzZUpJS3WF82rRpkpTqrvVpyexn+MMPPyggIECVKlW66X5Tpmc/8sgj6tixo9PP8OHDJUnvv/++41jPnj2b6jv0z2NNT/HixVW9enW9/fbbTt/jPXv2aM2aNY4+Sk5OTvXYr6CgIJUoUcIx/hISElIdb5UqVeTm5pbhMXqjJ598Ur6+vnrxxRcdbREREamulZ87d266Z7pv5dy5c6n6KeVO+7dbNwDciDPdAJCDvPPOO3rggQfUrl07devWTQ0aNNCVK1e0bNkybdq0SY888ojjH93ZpVGjRurfv79iYmK0e/duNW/eXB4eHoqNjdXHH3+sV155RR07drytfbdp00ZNmjTR888/ryNHjqhatWpas2aNli9frsGDBztdW26VVq1aadq0aXrwwQfVrVs3xcXFaebMmSpbtqxjOn9mTZo0SWvWrFGjRo3Ur18/VahQQSdPntTHH3+sLVu2qGDBgho+fLhWrFih1q1bq0ePHoqMjNTFixf1888/a8mSJTpy5MhNpyG3a9dO48eP11dffaXmzZtnqK7hw4erU6dOWrhwoZ544gnNnDlT9evXV5UqVdS3b1+VKVNGp0+f1rfffqs//vhDP/74o2O7JUuWqFOnTurVq5ciIyP1119/acWKFZo9e7aqVaum1q1ba9myZXrooYfUqlUrHT58WLNnz1bFihV14cKF2+rHtI45PDxcbdq0UUREhC5evKh169bps88+0z333KM2bdpIkpo0aaLHH39cr776qmJjY/Xggw/Kbrdr8+bNatKkiQYOHKhq1aopOjpac+fO1fnz59WoUSN99913evvtt9W+fXs1adIkQ/2Zmc9w7dq1atOmzU2ntG/fvl0HDx7UwIED01xesmRJ1axZU4sXL9azzz6r7t2765133tHQoUP13XffqUGDBo5++e9//6t27drd9BimTJmiFi1a6L777lPv3r0djwwLCAhwPA87MTFRISEh6tixo6pVq6b8+fNr3bp12rFjh15++WVJ1x/nN3DgQHXq1El33XWXrl275jhTn5H7AaSlSJEi6tmzp9544w3t27dPFSpUUJ8+ffTEE0+oQ4cOatasmX788UetXr36llP20/P222/rjTfe0EMPPaSIiAglJiZq3rx58vf3d/zRAQDumAvumA4AeU5GHhk2ZcqUDO0rMTHRjB071lSqVMn4+PiYAgUKmHr16pmFCxc6PfrmRnfyyLAzZ87cct25c+eayMhIR01VqlQxzzzzjDlx4oRjnbCwMNOqVas0t0/rkWHGXD/eIUOGmBIlShgPDw9Trlw5M2XKlFTHKinNRxalPBroxkf+pHdsKY8n+qe33nrLlCtXznh5eZny5cubBQsWOLbPSA03Pq7IGGN+//130717d8ejusqUKWMGDBjg9FitxMREM3LkSFO2bFnj6elpihYtaurWrWumTp1qrl69mup9blS1alXTu3fvDPWHMcYkJyebiIgIExERYa5du2aMMebQoUOme/fuJjg42Hh4eJiSJUua1q1bmyVLljht++eff5qBAweakiVLGk9PTxMSEmKio6PN2bNnjTHXH081adIkExYWZry8vEyNGjXMypUrTXR0tAkLC0vVj7fzyLD333/fdOnSxURERBgfHx/j7e1tKlasaJ5//nmTkJDgtO61a9fMlClTTPny5Y2np6cJDAw0LVq0MD/88INjnaSkJDNu3DgTHh5uPDw8TGhoqBk5cqTTI9SMufm4zuhnuG/fPsej3G7mySefNJLMoUOH0l1n7NixRpL58ccfjTHXH3n3/PPPO44jODjYdOzY0bGPW/0OWrdunalXr57x8fEx/v7+pk2bNuaXX35xLL9y5YoZPny4qVatmilQoIDx8/Mz1apVM2+88YZjnd9++8306tXLREREGG9vb1O4cGHTpEmTWx6vMWl/J1McOnTIuLu7O75fycnJ5tlnnzVFixY1vr6+Jioqyhw8eDDdR4bd+D3YuHGjkWQ2btxojDFm586dpmvXrqZUqVLGy8vLBAUFmdatW5vvv//+lnUDQEbZjMnknV8AAECOsGjRIg0YMEBHjx5N9473yBkGDx6sr7/+Wj/88MMdPQILAJD7ELoBAMil7Ha7qlatqq5du+r55593dTlIx59//qmwsDB99NFHTFkGgH8hQjcAAAAAABbh7uUAAAAAAFiE0A0AAAAAgEUI3QAAAAAAWITQDQAAAACARfK5uoCcyG6368SJEypQoACP9QAAAAAApGKMUWJiokqUKCE3t/TPZxO603DixAmFhoa6ugwAAAAAQA537NgxhYSEpLuc0J2GAgUKSLreef7+/i6uBjlBUlKS1qxZo+bNm8vDw8PV5QBZivGNvIzxjbyM8Y28LDeM74SEBIWGhjryY3oI3WlImVLu7+9P6Iak6196X19f+fv759gvPXC7GN/IyxjfyMsY38jLctP4vtUlydxIDQAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACL5IjQPXPmTJUuXVre3t6qU6eOvvvuu3TXXbhwoWw2m9OPt7e3Y3lSUpKeffZZValSRX5+fipRooS6d++uEydOZMehAAAAAADg4PLQ/eGHH2ro0KEaM2aMdu7cqWrVqikqKkpxcXHpbuPv76+TJ086fn7//XfHskuXLmnnzp0aNWqUdu7cqWXLlunAgQNq27ZtdhwOAAAAAAAO+VxdwLRp09S3b1/17NlTkjR79mx9/vnnmj9/vkaMGJHmNjabTcHBwWkuCwgI0Nq1a53aXn/9ddWuXVtHjx5VqVKlsvYAAAAAAABIh0tD99WrV/XDDz9o5MiRjjY3Nzc1bdpU3377bbrbXbhwQWFhYbLb7apZs6YmTZqkSpUqpbt+fHy8bDabChYsmObyK1eu6MqVK47XCQkJkq5PVU9KSsrkUSEvShkHjAfkRYxv5GWMb+RljG/kZblhfGe0NpeG7rNnzyo5OVnFihVzai9WrJj279+f5jZ333235s+fr6pVqyo+Pl5Tp05V3bp1tXfvXoWEhKRa//Lly3r22WfVtWtX+fv7p7nPmJgYjRs3LlX7mjVr5OvrextHhrzqxlkUQF7C+EZexvhGXsb4Rl6Wk8f3pUuXMrSezRhjLK4lXSdOnFDJkiW1detW3XfffY72Z555Rl999ZW2b99+y30kJSWpQoUK6tq1q8aPH59qWYcOHfTHH39o06ZN6YbutM50h4aG6uzZs+lug3+XpKQkrV27Vs2aNZOHh4erywGyFOMbeRnjG3kZ4xt5WW4Y3wkJCSpatKji4+Nvmhtdeqa7aNGicnd31+nTp53aT58+ne412zfy8PBQjRo1dPDgQaf2pKQkde7cWb///rs2bNhw007w8vKSl5dXmvvOqR8wXIMxgbyM8Y28jPGNvIzxjbwsJ4/vjNbl0ruXe3p6KjIyUuvXr3e02e12rV+/3unM980kJyfr559/VvHixR1tKYE7NjZW69atU5EiRbK8dgAAAAAAbsXldy8fOnSooqOjVatWLdWuXVszZszQxYsXHXcz7969u0qWLKmYmBhJ0gsvvKB7771XZcuW1fnz5zVlyhT9/vvv6tOnj6Trgbtjx47auXOnVq5cqeTkZJ06dUqSVLhwYXl6errmQAEAAAAA/zouD92PPPKIzpw5o9GjR+vUqVOqXr26Vq1a5bi52tGjR+Xm9n8n5M+dO6e+ffvq1KlTKlSokCIjI7V161ZVrFhRknT8+HGtWLFCklS9enWn99q4caMaN26cLccFAAAAAIDLQ7ckDRw4UAMHDkxz2aZNm5xeT58+XdOnT093X6VLl5YL7w0HAAAAAICDS6/pBgAAAAAgLyN0AwAAAABgEUI3AAAAAAAWIXQDAAAAAGARQjcAAAAAABYhdAMAAAAAYBFCNwAAAAAAFiF0AwAAAABgEUI3AAAAAAAWIXQDAAAAAGARQjcAAAAAABYhdAMAAAAAYBFCNwAAAAAAFiF0AwAAAABgEUI3AAAAAAAWIXQDAAAAAGARQjcAAAAAABYhdAMAAAAAYBFCNwAAAAAAFiF0AwAAAABgEUI3AAAAAAAWIXQDAAAAAGARQjcAAAAAABYhdAMAAAAAYBFCNwAAAAAAFiF0AwAAAABgEUI3AAAAAAAWIXQDAAAAAGARQjcAAAAAABYhdAMAAAAAYBFCNwAAAAAAFiF0AwAAAABgEUI3AAAAAAAWIXQDAAAAAGARQjcAAAAAABYhdAMAAAAAYBFCNwAAAAAAFiF0AwAAAABgEUI3AAAAAAAWIXQDAAAAAGARQjcAAAAAABYhdAMAAAAAYBFCNwAAAAAAFiF0AwAAAABgEUI3AAAAAAAWIXQDAAAAAGARQjcAAAAAABYhdAMAAAAAYBFCNwAAAAAAFiF0AwAAAABgEUI3AAAAAAAWIXQDAAAAAGARQjcAAAAAABYhdAMAAAAAYBFCNwAAAAAAFiF0AwAAAABgEUI3AAAAAAAWIXQDAAAAAGARQjcAAAAAABYhdAMAAAAAYBFCNwAAAAAAFiF0AwAAAABgEUI3AAAAAAAWIXQDAAAAAGARQjcAAAAAABYhdAMAAAAAYBFCNwAAAAAAFiF0AwAAAABgEUI3AAAAAAAWIXQDAAAAAGARQjcAAAAAABYhdAMAAAAAYBFCNwAAAAAAFiF0AwAAAABgEUI3AAAAAAAWIXQDAAAAAGARQjcAAAAAABYhdAMAAAAAYBFCNwAAAAAAFiF0AwAAAABgEUI3AAAAAAAWIXQDAAAAAGARQjcAAAAAABZxeeieOXOmSpcuLW9vb9WpU0ffffdduusuXLhQNpvN6cfb29tpnWXLlql58+YqUqSIbDabdu/ebfERAAAAAACQNpeG7g8//FBDhw7VmDFjtHPnTlWrVk1RUVGKi4tLdxt/f3+dPHnS8fP77787Lb948aLq16+vyZMnW10+AAAAAAA3lc+Vbz5t2jT17dtXPXv2lCTNnj1bn3/+uebPn68RI0akuY3NZlNwcHC6+3z88cclSUeOHMnyegEAAAAAyAyXhe6rV6/qhx9+0MiRIx1tbm5uatq0qb799tt0t7tw4YLCwsJkt9tVs2ZNTZo0SZUqVbqjWq5cuaIrV644XickJEiSkpKSlJSUdEf7Rt6QMg4YD8iLGN/IyxjfyMsY38jLcsP4zmhtLgvdZ8+eVXJysooVK+bUXqxYMe3fvz/Nbe6++27Nnz9fVatWVXx8vKZOnaq6detq7969CgkJue1aYmJiNG7cuFTta9aska+v723vF3nP2rVrXV0CYBnGN/IyxjfyMsY38rKcPL4vXbqUofVcOr08s+677z7dd999jtd169ZVhQoVNGfOHI0fP/629zty5EgNHTrU8TohIUGhoaFq3ry5/P3976hm5A1JSUlau3atmjVrJg8PD1eXA2QpxjfyMsY38jLGN/Ky3DC+U2ZI34rLQnfRokXl7u6u06dPO7WfPn36ptds/5OHh4dq1KihgwcP3lEtXl5e8vLySnP/OfUDhmswJpCXMb6RlzG+kZcxvpGX5eTxndG6XHb3ck9PT0VGRmr9+vWONrvdrvXr1zudzb6Z5ORk/fzzzypevLhVZQIAAAAAcNtcOr186NChio6OVq1atVS7dm3NmDFDFy9edNzNvHv37ipZsqRiYmIkSS+88ILuvfdelS1bVufPn9eUKVP0+++/q0+fPo59/vXXXzp69KhOnDghSTpw4IAkKTg4OMNn0AEAAAAAyAouDd2PPPKIzpw5o9GjR+vUqVOqXr26Vq1a5bi52tGjR+Xm9n8n48+dO6e+ffvq1KlTKlSokCIjI7V161ZVrFjRsc6KFSscoV2SunTpIkkaM2aMxo4dmz0HBgAAAACAcsCN1AYOHKiBAwemuWzTpk1Or6dPn67p06ffdH89evRQjx49sqg6AAAAAABun8uu6QYAAAAAIK8jdAMAAAAAYBFCNwAAAAAAFiF0AwAAAABgEUI3AAAAAAAWIXQDAAAAAGARQjcAAAAAABYhdAMAAAAAYBFCNwAAAAAAFiF0AwAAAABgEUI3AAAAAAAWIXQDAAAAAGARQjcAAAAAABYhdAMAAAAAYBFCNwAAAAAAFiF0AwAAAABgEUI3AAAAAAAWIXQDAAAAAGARQjcAAAAAABYhdAMAAAAAYBFCNwAAAAAAFiF0AwAAAABgEUI3AAAAAAAWIXQDAAAAAGARQjcAAAAAABYhdAMAAAAAYBFCNwAAAAAAFiF0AwAAAABgEUI3AAAAAAAWIXQDAAAAAGARQjcAAAAAABYhdAMAAAAAYBFCNwAAAAAAFiF0AwAAAABgEUI3AAAAAAAWIXQDAAAAAGARQjcAAAAAABYhdAMAAAAAYBFCNwAAAAAAFiF0AwAAAABgEUI3AAAAAAAWIXQDAAAAAGARQjcAAAAAABYhdAMAAAAAYBFCNwAAAAAAFrmj0H358uWsqgMAAAAAgDwn06Hbbrdr/PjxKlmypPLnz6/ffvtNkjRq1Ci99dZbWV4gAAAAAAC5VaZD94QJE7Rw4UK99NJL8vT0dLRXrlxZb775ZpYWBwAAAABAbpbp0P3OO+9o7ty5evTRR+Xu7u5or1atmvbv35+lxQEAAAAAkJtlOnQfP35cZcuWTdVut9uVlJSUJUUBAAAAAJAXZDp0V6xYUZs3b07VvmTJEtWoUSNLigIAAAAAIC/Il9kNRo8erejoaB0/flx2u13Lli3TgQMH9M4772jlypVW1AgAAAAAQK6U6TPd7dq102effaZ169bJz89Po0eP1r59+/TZZ5+pWbNmVtQIAAAAAECulKkz3deuXdOkSZPUq1cvrV271qqaAAAAAADIEzJ1pjtfvnx66aWXdO3aNavqAQAAAAAgz8j09PIHHnhAX331lRW1AAAAAACQp2T6RmotWrTQiBEj9PPPPysyMlJ+fn5Oy9u2bZtlxQEAAAAAkJtlOnT/97//lSRNmzYt1TKbzabk5OQ7rwoAAAAAgDwg06HbbrdbUQcAAAAAAHlOpq/pBgAAAAAAGXNbofurr75SmzZtVLZsWZUtW1Zt27bV5s2bs7o2AAAAAABytUyH7nfffVdNmzaVr6+vBg0apEGDBsnHx0cPPPCA3nvvPStqBAAAAAAgV8r0Nd0TJ07USy+9pCFDhjjaBg0apGnTpmn8+PHq1q1blhYIAAAAAEBulekz3b/99pvatGmTqr1t27Y6fPhwlhQFAAAAAEBekOnQHRoaqvXr16dqX7dunUJDQ7OkKAAAAAAA8oJMTy9/+umnNWjQIO3evVt169aVJH3zzTdauHChXnnllSwvEAAAAACA3CrTofs///mPgoOD9fLLL+ujjz6SJFWoUEEffvih2rVrl+UFAgAAAACQW2U6dEvSQw89pIceeiirawEAAAAAIE/J9DXdO3bs0Pbt21O1b9++Xd9//32WFAUAAAAAQF6Q6dA9YMAAHTt2LFX78ePHNWDAgCwpCgAAAACAvCDTofuXX35RzZo1U7XXqFFDv/zyS5YUBQAAAABAXpDp0O3l5aXTp0+naj958qTy5butS8QBAAAAAMiTMh26mzdvrpEjRyo+Pt7Rdv78eT333HNq1qxZlhYHAAAAAEBululT01OnTlXDhg0VFhamGjVqSJJ2796tYsWKadGiRVleIAAAAAAAuVWmQ3fJkiX1008/afHixfrxxx/l4+Ojnj17qmvXrvLw8LCiRgAAAAAAcqXbugjbz89P/fr1y+paAAAAAADIUzJ8Tfevv/6q7777zqlt/fr1atKkiWrXrq1JkyZleXEAAAAAAORmGQ7dzz77rFauXOl4ffjwYbVp00aenp667777FBMToxkzZlhRIwAAAAAAuVKGp5d///33euaZZxyvFy9erLvuukurV6+WJFWtWlWvvfaaBg8enOVFAgAAAACQG2X4TPfZs2cVEhLieL1x40a1adPG8bpx48Y6cuRIlhYHAAAAAEBuluHQXbhwYZ08eVKSZLfb9f333+vee+91LL969aqMMVlfIQAAAAAAuVSGQ3fjxo01fvx4HTt2TDNmzJDdblfjxo0dy3/55ReVLl3aghIBAAAAAMidMhy6J06cqP379yssLEzPPvusXnrpJfn5+TmWL1q0SPfff/9tFTFz5kyVLl1a3t7eqlOnTqq7pP/TwoULZbPZnH68vb2d1jHGaPTo0SpevLh8fHzUtGlTxcbG3lZtOVFysrRpk/T++9f/m5zs6orytuRk6auvbPr665L66isb/W0xxnf2YnxnL8Z39mJ8Zy/Gd/ZifGcvxnf2ynPj22RCUlKS2b17tzl+/HiqZbt37zZnz57NzO6MMcZ88MEHxtPT08yfP9/s3bvX9O3b1xQsWNCcPn06zfUXLFhg/P39zcmTJx0/p06dclrnxRdfNAEBAebTTz81P/74o2nbtq0JDw83f//9d4Zqio+PN5JMfHx8po/HakuXGhMSYoz0fz8hIdfbkfXo7+xFf2cv+jt70d/Zi/7OXvR39qK/sxf9nb1yU39nNDdmKnRboXbt2mbAgAGO18nJyaZEiRImJiYmzfUXLFhgAgIC0t2f3W43wcHBZsqUKY628+fPGy8vL/P+++9nqKacGrqXLjXGZnMegNL1NpstZw7E3Iz+zl70d/aiv7MX/Z296O/sRX9nL/o7e9Hf2Su39XdGc6PNGNfd/ezq1avy9fXVkiVL1L59e0d7dHS0zp8/r+XLl6faZuHCherTp49Kliwpu92umjVratKkSapUqZIk6bffflNERIR27dql6tWrO7Zr1KiRqlevrldeeeWWdSUkJCggIEDx8fHy9/e/4+PMCsnJUunS0h9/pL3cZpNKlpT27pXc3bO1tDwpOVmqWFE6fjzt5fR31qK/sxf9nb3o7+xFf2cv+jt70d/Zi/7OXhnp75AQ6fDhnNPfGc2NGX5OtxXOnj2r5ORkFStWzKm9WLFi2r9/f5rb3H333Zo/f76qVq2q+Ph4TZ06VXXr1tXevXsVEhKiU6dOOfZx4z5Tlt3oypUrunLliuN1QkKCJCkpKUlJSUm3fXxZ6auvbPrjj/Q/LmOuB/KAgGws6l+M/s5e9Hf2or+zF/2dvejv7EV/Zy/6O3vR39nLGOnYMWnjxmtq1ChnPDUro1nRpaH7dtx333267777HK/r1q2rChUqaM6cORo/fvxt7TMmJkbjxo1L1b5mzRr5+vredq1Z6euvS0qq5eoyAAAAAMBlvvxyty5eTOd0eDa7dOlShtZzaeguWrSo3N3ddfr0aaf206dPKzg4OEP78PDwUI0aNXTw4EFJcmx3+vRpFS9e3Gmf/5xu/k8jR47U0KFDHa8TEhIUGhqq5s2b55jp5X5+Nk2bduv1PvvsmurXzxl/+cnNtmyxqU2bW3896O+sQX9nL/o7e9Hf2Yv+zl70d/aiv7MX/Z29MtrfLVpUV6NG1bKholtLmSF9Kxm6pvunn37K8BtXrVo1w+tKUp06dVS7dm299tprkiS73a5SpUpp4MCBGjFixC23T05OVqVKldSyZUtNmzZNxhiVKFFCw4YN09NPPy3pemcEBQVp4cKF6tKlyy33mZOv6T5+/PrUihvlxGsccjP6O3vR39mL/s5e9Hf2or+zF/2dvejv7EV/Z6/c2N8Zzo0ZuSubzWYzbm5uxmazpfmTsszNzS3Td3z74IMPjJeXl1m4cKH55ZdfTL9+/UzBggUdjwF7/PHHzYgRIxzrjxs3zqxevdocOnTI/PDDD6ZLly7G29vb7N2717HOiy++aAoWLGiWL19ufvrpJ9OuXbs88ciwlLv53XhHv5x6N7/cjv7OXvR39qK/sxf9nb3o7+xFf2cv+jt70d/ZK7f1d5Y+MuzIkSMZ/rkdr732milVqpTx9PQ0tWvXNtu2bXMsa9SokYmOjna8Hjx4sGPdYsWKmZYtW5qdO3c67c9ut5tRo0aZYsWKGS8vL/PAAw+YAwcOZLienBq6jUn7uXWhoTlvAOYV9Hf2or+zF/2dvejv7EV/Zy/6O3vR39mL/s5euam/c8Ujw3KqnDi9/J+Sk6XNm6WTJ6XixaUGDXLOFIu8KDn5+l0Sv/xyt1q0qK4mTfLR3xZifGcvxnf2YnxnL8Z39mJ8Zy/Gd/ZifGev3DK+s/SRYStWrMjwG7dt2zbD6+L2uLtLjRu7uop/D3d3qVEjo4sXj6tRo2o58guflzC+sxfjO3sxvrMX4zt7Mb6zF+M7ezG+s1deG98ZCt3t27fP0M5sNpuSk5PvpB4AAAAAAPKMDIVuu91udR0AAAAAAOQ5bq4uAAAAAACAvCpDZ7pvdPHiRX311Vc6evSorl696rRs0KBBWVIYAAAAAAC5XaZD965du9SyZUtdunRJFy9eVOHChXX27Fn5+voqKCiI0A0AAAAAwP+X6enlQ4YMUZs2bXTu3Dn5+Pho27Zt+v333xUZGampU6daUSMAAAAAALlSpkP37t279fTTT8vNzU3u7u66cuWKQkND9dJLL+m5556zokYAAAAAAHKlTIduDw8Publd3ywoKEhHjx6VJAUEBOjYsWNZWx0AAAAAALlYpq/prlGjhnbs2KFy5cqpUaNGGj16tM6ePatFixapcuXKVtQIAAAAAECulOkz3ZMmTVLx4sUlSRMnTlShQoX0n//8R2fOnNGcOXOyvEAAAAAAAHKrTJ/prlWrluP/g4KCtGrVqiwtCAAAAACAvCLTZ7oPHz6s2NjYVO2xsbE6cuRIVtQEAAAAAECekOnQ3aNHD23dujVV+/bt29WjR4+sqAkAAAAAgDwh06F7165dqlevXqr2e++9V7t3786KmgAAAAAAyBMyHbptNpsSExNTtcfHxys5OTlLigIAAAAAIC/IdOhu2LChYmJinAJ2cnKyYmJiVL9+/SwtDgAAAACA3CzTdy+fPHmyGjZsqLvvvlsNGjSQJG3evFkJCQnasGFDlhcIAAAAAEBulekz3RUrVtRPP/2kzp07Ky4uTomJierevbv279+vypUrW1EjAAAAAAC5UqbPdEtSiRIlNGnSpKyuBQAAAACAPCXTZ7ql69PJH3vsMdWtW1fHjx+XJC1atEhbtmzJ0uIAAAAAAMjNMh26ly5dqqioKPn4+Gjnzp26cuWKpOt3L+fsNwAAAAAA/yfToXvChAmaPXu25s2bJw8PD0d7vXr1tHPnziwtDgAAAACA3CzTofvAgQNq2LBhqvaAgACdP38+K2oCAAAAACBPyHToDg4O1sGDB1O1b9myRWXKlMmSogAAAAAAyAsyHbr79u2rp556Stu3b5fNZtOJEye0ePFiDRs2TP/5z3+sqBEAAAAAgFwp048MGzFihOx2ux544AFdunRJDRs2lJeXl4YNG6Ynn3zSihoBAAAAAMiVMh26bTabnn/+eQ0fPlwHDx7UhQsXVLFiReXPn19///23fHx8rKgTAAAAAIBc57ae0y1Jnp6eqlixomrXri0PDw9NmzZN4eHhWVkbAAAAAAC5WoZD95UrVzRy5EjVqlVLdevW1aeffipJWrBggcLDwzV9+nQNGTLEqjoBAAAAAMh1Mjy9fPTo0ZozZ46aNm2qrVu3qlOnTurZs6e2bdumadOmqVOnTnJ3d7eyVgAAAAAAcpUMh+6PP/5Y77zzjtq2bas9e/aoatWqunbtmn788UfZbDYrawQAAAAAIFfK8PTyP/74Q5GRkZKkypUry8vLS0OGDCFwAwAAAACQjgyH7uTkZHl6ejpe58uXT/nz57ekKAAAAAAA8oIMTy83xqhHjx7y8vKSJF2+fFlPPPGE/Pz8nNZbtmxZ1lYIAAAAAEAuleHQHR0d7fT6sccey/JiAAAAAADISzIcuhcsWGBlHQAAAAAA5DkZvqYbAAAAAABkDqEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIu4PHTPnDlTpUuXlre3t+rUqaPvvvsuQ9t98MEHstlsat++vVP76dOn1aNHD5UoUUK+vr568MEHFRsba0HlAAAAAADcnEtD94cffqihQ4dqzJgx2rlzp6pVq6aoqCjFxcXddLsjR45o2LBhatCggVO7MUbt27fXb7/9puXLl2vXrl0KCwtT06ZNdfHiRSsPBQAAAACAVFwauqdNm6a+ffuqZ8+eqlixombPni1fX1/Nnz8/3W2Sk5P16KOPaty4cSpTpozTstjYWG3btk2zZs3SPffco7vvvluzZs3S33//rffff9/qwwEAAAAAwEk+V73x1atX9cMPP2jkyJGONjc3NzVt2lTffvttutu98MILCgoKUu/evbV582anZVeuXJEkeXt7O+3Ty8tLW7ZsUZ8+fdLc55UrVxzbSlJCQoIkKSkpSUlJSZk/OOQ5KeOA8YC8iPGNvIzxjbyM8Y28LDeM74zW5rLQffbsWSUnJ6tYsWJO7cWKFdP+/fvT3GbLli166623tHv37jSXly9fXqVKldLIkSM1Z84c+fn5afr06frjjz908uTJdGuJiYnRuHHjUrWvWbNGvr6+GT8o5Hlr1651dQmAZRjfyMsY38jLGN/Iy3Ly+L506VKG1nNZ6M6sxMREPf7445o3b56KFi2a5joeHh5atmyZevfurcKFC8vd3V1NmzZVixYtZIxJd98jR47U0KFDHa8TEhIUGhqq5s2by9/fP8uPBblPUlKS1q5dq2bNmsnDw8PV5QBZivGNvIzxjbyM8Y28LDeM75QZ0rfistBdtGhRubu76/Tp007tp0+fVnBwcKr1Dx06pCNHjqhNmzaONrvdLknKly+fDhw4oIiICEVGRmr37t2Kj4/X1atXFRgYqDp16qhWrVrp1uLl5SUvL69U7R4eHjn2A4ZrMCaQlzG+kZcxvpGXMb6Rl+Xk8Z3Rulx2IzVPT09FRkZq/fr1jja73a7169frvvvuS7V++fLl9fPPP2v37t2On7Zt26pJkybavXu3QkNDndYPCAhQYGCgYmNj9f3336tdu3aWHxMAAAAAAP/k0unlQ4cOVXR0tGrVqqXatWtrxowZunjxonr27ClJ6t69u0qWLKmYmBh5e3urcuXKTtsXLFhQkpzaP/74YwUGBqpUqVL6+eef9dRTT6l9+/Zq3rx5th0XAAAAAACSi0P3I488ojNnzmj06NE6deqUqlevrlWrVjlurnb06FG5uWXuZPzJkyc1dOhQnT59WsWLF1f37t01atQoK8oHAAAAAOCmXH4jtYEDB2rgwIFpLtu0adNNt124cGGqtkGDBmnQoEFZUBkAAAAAAHfGZdd0AwAAAACQ1xG6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALCIy0P3zJkzVbp0aXl7e6tOnTr67rvvMrTdBx98IJvNpvbt2zu1X7hwQQMHDlRISIh8fHxUsWJFzZ4924LKAQAAAAC4OZeG7g8//FBDhw7VmDFjtHPnTlWrVk1RUVGKi4u76XZHjhzRsGHD1KBBg1TLhg4dqlWrVundd9/Vvn37NHjwYA0cOFArVqyw6jAAAAAAAEiTS0P3tGnT1LdvX/Xs2dNxRtrX11fz589Pd5vk5GQ9+uijGjdunMqUKZNq+datWxUdHa3GjRurdOnS6tevn6pVq5bhM+gAAAAAAGQVl4Xuq1ev6ocfflDTpk3/rxg3NzVt2lTffvttutu98MILCgoKUu/evdNcXrduXa1YsULHjx+XMUYbN27Ur7/+qubNm2f5MQAAAAAAcDP5XPXGZ8+eVXJysooVK+bUXqxYMe3fvz/NbbZs2aK33npLu3fvTne/r732mvr166eQkBDly5dPbm5umjdvnho2bJjuNleuXNGVK1ccrxMSEiRJSUlJSkpKysRRIa9KGQeMB+RFjG/kZYxv5GWMb+RluWF8Z7Q2l4XuzEpMTNTjjz+uefPmqWjRoumu99prr2nbtm1asWKFwsLC9PXXX2vAgAEqUaKE01n1f4qJidG4ceNSta9Zs0a+vr5ZdgzI/dauXevqEgDLML6RlzG+kZcxvpGX5eTxfenSpQytZzPGGItrSdPVq1fl6+urJUuWON2BPDo6WufPn9fy5cud1t+9e7dq1Kghd3d3R5vdbpd0fVr6gQMHVKJECQUEBOiTTz5Rq1atHOv16dNHf/zxh1atWpVmLWmd6Q4NDdXZs2fl7++fFYeLXC4pKUlr165Vs2bN5OHh4epygCzF+EZexvhGXsb4Rl6WG8Z3QkKCihYtqvj4+JvmRped6fb09FRkZKTWr1/vCN12u13r16/XwIEDU61fvnx5/fzzz05t//vf/5SYmKhXXnlFoaGhunz5spKSkuTm5nypuru7uyOgp8XLy0teXl6p2j08PHLsBwzXYEwgL2N8Iy9jfCMvY3wjL8vJ4zujdbl0evnQoUMVHR2tWrVqqXbt2poxY4YuXryonj17SpK6d++ukiVLKiYmRt7e3qpcubLT9gULFpQkR7unp6caNWqk4cOHy8fHR2FhYfrqq6/0zjvvaNq0adl6bAAAAAAAuDR0P/LIIzpz5oxGjx6tU6dOqXr16lq1apXj5mpHjx5Nddb6Vj744AONHDlSjz76qP766y+FhYVp4sSJeuKJJ6w4BAAAAAAA0uXyG6kNHDgwzenkkrRp06abbrtw4cJUbcHBwVqwYEEWVAYAAAAAwJ1x2XO6AQAAAADI6wjdAAAAAABYhNANAAAAAIBFCN0AAAAAAFiE0A0AAAAAgEUI3QAAAAAAWITQDQAAAACARQjdAAAAAABYhNANAAAAAIBFCN0AAAAAAFiE0A0AAAAAgEUI3QAAAAAAWITQDQAAAACARQjdAAAAAABYhNANAAAAAIBFCN0AAAAAAFiE0A0AAAAAgEUI3QAAAAAAWITQDQAAAACARQjdAAAAAABYhNANAAAAAIBFCN0AAAAAAFiE0A0AAAAAgEUI3QAAAAAAWITQDQAAAACARQjdAAAAAABYhNANAAAAAIBFCN0AAAAAAFiE0A0AAAAAgEUI3QAAAAAAWITQDQAAAACARQjdAAAAAABYhNANAAAAAIBFCN0AAAAAAFiE0A0AAAAAgEUI3QAAAAAAWITQDQAAAACARQjdAAAAAABYhNANAAAAAIBFCN0AAAAAAFiE0A0AAAAAgEUI3QAAAAAAWITQDQAAAACARQjdAAAAAABYhNANAAAAAIBFCN0AAAAAAFiE0A0AAAAAgEUI3QAAAAAAWITQDQAAAACARQjdAAAAAABYhNANAAAAAIBFCN0AAAAAAFiE0A0AAAAAgEUI3QAAAAAAWITQDQAAAACARQjdAAAAAABYhNANAAAAAIBFCN0AAAAAAFiE0A0AAAAAgEUI3QAAAAAAWITQDQAAAACARQjdAAAAAABYhNANAAAAAIBF8rm6gJzIGCNJSkhIcHElyCmSkpJ06dIlJSQkyMPDw9XlAFmK8Y28jPGNvIzxjbwsN4zvlLyYkh/TQ+hOQ2JioiQpNDTUxZUAAAAAAHKyxMREBQQEpLvcZm4Vy/+F7Ha7Tpw4oQIFCshms7m6HOQACQkJCg0N1bFjx+Tv7+/qcoAsxfhGXsb4Rl7G+EZelhvGtzFGiYmJKlGihNzc0r9ymzPdaXBzc1NISIiry0AO5O/vn2O/9MCdYnwjL2N8Iy9jfCMvy+nj+2ZnuFNwIzUAAAAAACxC6AYAAAAAwCKEbiADvLy8NGbMGHl5ebm6FCDLMb6RlzG+kZcxvpGX5aXxzY3UAAAAAACwCGe6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRu4iZiYGN1zzz0qUKCAgoKC1L59ex04cMDVZQFZ7sUXX5TNZtPgwYNdXQqQZY4fP67HHntMRYoUkY+Pj6pUqaLvv//e1WUBdyQ5OVmjRo1SeHi4fHx8FBERofHjx4vbNCE3+vrrr9WmTRuVKFFCNptNn376qdNyY4xGjx6t4sWLy8fHR02bNlVsbKxrir0DhG7gJr766isNGDBA27Zt09q1a5WUlKTmzZvr4sWLri4NyDI7duzQnDlzVLVqVVeXAmSZc+fOqV69evLw8NCXX36pX375RS+//LIKFSrk6tKAOzJ58mTNmjVLr7/+uvbt26fJkyfrpZde0muvvebq0oBMu3jxoqpVq6aZM2emufyll17Sq6++qtmzZ2v79u3y8/NTVFSULl++nM2V3hnuXg5kwpkzZxQUFKSvvvpKDRs2dHU5wB27cOGCatasqTfeeEMTJkxQ9erVNWPGDFeXBdyxESNG6JtvvtHmzZtdXQqQpVq3bq1ixYrprbfecrR16NBBPj4+evfdd11YGXBnbDabPvnkE7Vv317S9bPcJUqU0NNPP61hw4ZJkuLj41WsWDEtXLhQXbp0cWG1mcOZbiAT4uPjJUmFCxd2cSVA1hgwYIBatWqlpk2buroUIEutWLFCtWrVUqdOnRQUFKQaNWpo3rx5ri4LuGN169bV+vXr9euvv0qSfvzxR23ZskUtWrRwcWVA1jp8+LBOnTrl9G+UgIAA1alTR99++60LK8u8fK4uAMgt7Ha7Bg8erHr16qly5cquLge4Yx988IF27typHTt2uLoUIMv99ttvmjVrloYOHarnnntOO3bs0KBBg+Tp6ano6GhXlwfcthEjRighIUHly5eXu7u7kpOTNXHiRD366KOuLg3IUqdOnZIkFStWzKm9WLFijmW5BaEbyKABAwZoz5492rJli6tLAe7YsWPH9NRTT2nt2rXy9vZ2dTlAlrPb7apVq5YmTZokSapRo4b27Nmj2bNnE7qRq3300UdavHix3nvvPVWqVEm7d+/W4MGDVaJECcY2kEMxvRzIgIEDB2rlypXauHGjQkJCXF0OcMd++OEHxcXFqWbNmsqXL5/y5cunr776Sq+++qry5cun5ORkV5cI3JHixYurYsWKTm0VKlTQ0aNHXVQRkDWGDx+uESNGqEuXLqpSpYoef/xxDRkyRDExMa4uDchSwcHBkqTTp087tZ8+fdqxLLcgdAM3YYzRwIED9cknn2jDhg0KDw93dUlAlnjggQf0888/a/fu3Y6fWrVq6dFHH9Xu3bvl7u7u6hKBO1KvXr1Uj3j89ddfFRYW5qKKgKxx6dIlubk5/xPe3d1ddrvdRRUB1ggPD1dwcLDWr1/vaEtISND27dt13333ubCyzGN6OXATAwYM0Hvvvafly5erQIECjutHAgIC5OPj4+LqgNtXoECBVPcm8PPzU5EiRbhnAfKEIUOGqG7dupo0aZI6d+6s7777TnPnztXcuXNdXRpwR9q0aaOJEyeqVKlSqlSpknbt2qVp06apV69eri4NyLQLFy7o4MGDjteHDx/W7t27VbhwYZUqVUqDBw/WhAkTVK5cOYWHh2vUqFEqUaKE4w7nuQWPDANuwmazpdm+YMEC9ejRI3uLASzWuHFjHhmGPGXlypUaOXKkYmNjFR4erqFDh6pv376uLgu4I4mJiRo1apQ++eQTxcXFqUSJEuratatGjx4tT09PV5cHZMqmTZvUpEmTVO3R0dFauHChjDEaM2aM5s6dq/Pnz6t+/fp64403dNddd7mg2ttH6AYAAAAAwCJc0w0AAAAAgEUI3QAAAAAAWITQDQAAAACARQjdAAAAAABYhNANAAAAAIBFCN0AAAAAAFiE0A0AAAAAgEUI3QAAAAAAWITQDQAAAACARQjdAAD8S/Xo0UM2m002m00eHh4KDw/XM888o8uXL7u6NAAA8ox8ri4AAAC4zoMPPqgFCxYoKSlJP/zwg6Kjo2Wz2TR58mRXlwYAQJ7AmW4AAP7FvLy8FBwcrNDQULVv315NmzbV2rVrJUmlS5fWjBkznNavXr26xo4d63hts9n05ptv6qGHHpKvr6/KlSunFStWZOMRAACQsxG6AQCAJGnPnj3aunWrPD09M7XduHHj1LlzZ/30009q2bKlHn30Uf31118WVQkAQO5C6AYA4F9s5cqVyp8/v7y9vVWlShXFxcVp+PDhmdpHjx491LVrV5UtW1aTJk3ShQsX9N1331lUMQAAuQvXdAMA8C/WpEkTzZo1SxcvXtT06dOVL18+dejQIVP7qFq1quP//fz85O/vr7i4uKwuFQCAXIkz3QAA/Iv5+fmpbNmyqlatmubPn6/t27frrbfekiS5ubnJGOO0flJSUqp9eHh4OL222Wyy2+3WFQ0AQC5C6AYAAJKuh+znnntO//vf//T3338rMDBQJ0+edCxPSEjQ4cOHXVghAAC5D6EbAAA4dOrUSe7u7po5c6buv/9+LVq0SJs3b9bPP/+s6Ohoubu7u7pEAAByFa7pBgAADvny5dPAgQP10ksvKTY2VocPH1br1q0VEBCg8ePHc6YbAIBMspkbL9YCAAAAAABZgunlAAAAAABYhNANAAAAAIBFCN0AAAAAAFiE0A0AAAAAgEUI3QAAAAAAWITQDQAAAACARQjdAAAAAABYhNANAAAAAIBFCN0AAAAAAFiE0A0AAAAAgEUI3QAAAAAAWITQDQAAAACARQjdAAAAAABYhNANAAAAAIBFCN0AAAAAAFiE0A0AAAAAgEUI3QAAAAAAWITQDQBIZdOmTbLZbFqyZImrS8mQ06dPq2PHjipSpIhsNptmzJjh6pJwE2PHjpXNZnN1GbeldOnS6tGjh6vLAADkIoRuAHCRhQsXymazydvbW8ePH0+1vHHjxqpcubILKst9hgwZotWrV2vkyJFatGiRHnzwwXTXtdlsaf4EBwdbUtulS5c0duxYbdq0yZL9Z4cjR46oZ8+eioiIkLe3t4KDg9WwYUONGTPG1aWl6YsvvtDYsWNdXYYk6fz58/L29pbNZtO+fftcXY4lUn6Xpfzky5dPJUuWVI8ePdL83QYA/zb5XF0AAPzbXblyRS+++KJee+01V5eSa23YsEHt2rXTsGHDMrR+s2bN1L17d6c2Hx8fK0rTpUuXNG7cOEnX/5CS2xw8eFD33HOPfHx81KtXL5UuXVonT57Uzp07NXnyZMex5SRffPGFZs6cmSOC98cff+z4o87ixYs1YcIEV5dkmRdeeEHh4eG6fPmytm3bpoULF2rLli3as2ePvL29XV0eALgMoRsAXKx69eqaN2+eRo4cqRIlSri6nGx18eJF+fn53fF+4uLiVLBgwQyvf9ddd+mxxx674/d1pWvXrslut8vT09PS95k+fbouXLig3bt3KywszGlZXFycpe+dF7z77rtq2bKlwsLC9N5772VZ6DbG6PLly5b9seh2tGjRQrVq1ZIk9enTR0WLFtXkyZO1YsUKde7c2cXVAYDrML0cAFzsueeeU3Jysl588cWbrnfkyBHZbDYtXLgw1TKbzeZ0Vi/lmtlff/1Vjz32mAICAhQYGKhRo0bJGKNjx46pXbt28vf3V3BwsF5++eU03zM5OVnPPfecgoOD5efnp7Zt2+rYsWOp1tu+fbsefPBBBQQEyNfXV40aNdI333zjtE5KTb/88ou6deumQoUKqX79+jc95t9++02dOnVS4cKF5evrq3vvvVeff/65Y3nKtFZjjGbOnOmY3nqnjh8/rl69eqlYsWLy8vJSpUqVNH/+fKd1rl69qtGjRysyMlIBAQHy8/NTgwYNtHHjRsc6R44cUWBgoCRp3LhxjvpSPqvGjRunefa7R48eKl26tNN+bDabpk6dqhkzZigiIkJeXl765ZdfJEn79+9Xx44dVbhwYXl7e6tWrVpasWKF0z6TkpI0btw4lStXTt7e3ipSpIjq16+vtWvX3rQvDh06pJCQkFSBW5KCgoJStX355Zdq0KCB/Pz8VKBAAbVq1Up79+696XukePfddxUZGSkfHx8VLlxYXbp0SXe8tWzZUoUKFZKfn5+qVq2qV155RdL1vps5c6Yk50sJUtjtds2YMUOVKlWSt7e3ihUrpv79++vcuXNO72GM0YQJExQSEiJfX181adIkw8eR4ujRo9q8ebO6dOmiLl266PDhw9q6dWu6x167dm35+vqqUKFCatiwodasWeNYXrp0abVu3VqrV69WrVq15OPjozlz5ki69fckxWuvvaZKlSo53qNWrVp67733HMsTExM1ePBglS5dWl5eXgoKClKzZs20c+fOTB13igYNGki6PoZS3M6Ynzt3rmPM33PPPdqxY4fTtqdOnVLPnj0VEhIiLy8vFS9eXO3atdORI0duq24AyGqc6QYAFwsPD1f37t01b948jRgxIkvPdj/yyCOqUKGCXnzxRX3++eeaMGGCChcurDlz5uj+++/X5MmTtXjxYg0bNkz33HOPGjZs6LT9xIkTZbPZ9OyzzyouLk4zZsxQ06ZNtXv3bscZtg0bNqhFixaKjIzUmDFj5ObmpgULFuj+++/X5s2bVbt2bad9durUSeXKldOkSZNkjEm39tOnT6tu3bq6dOmSBg0apCJFiujtt99W27ZttWTJEj300ENq2LChFi1apMcffzzNKePpuXz5ss6ePevUVqBAAXl5een06dO69957ZbPZNHDgQAUGBurLL79U7969lZCQoMGDB0uSEhIS9Oabb6pr167q27evEhMT9dZbbykqKkrfffedqlevrsDAQM2aNUv/+c9/9NBDD+nhhx+WJFWtWjVDdd5owYIFunz5svr16ycvLy8VLlxYe/fuVb169VSyZEmNGDFCfn5++uijj9S+fXstXbpUDz30kKTrf/SIiYlRnz59VLt2bSUkJOj777/Xzp071axZs3TfMywsTOvWrdOGDRt0//3337S+RYsWKTo6WlFRUZo8ebIuXbqkWbNmqX79+tq1a5dTqLrRxIkTNWrUKHXu3Fl9+vTRmTNn9Nprr6lhw4batWuXYybD2rVr1bp1axUvXlxPPfWUgoODtW/fPq1cuVJPPfWU+vfvrxMnTmjt2rVatGhRqvfp37+/Fi5cqJ49e2rQoEE6fPiwXn/9de3atUvffPONPDw8JEmjR4/WhAkT1LJlS7Vs2VI7d+5U8+bNdfXq1Vt8Sv/n/fffl5+fn1q3bi0fHx9FRERo8eLFqlu3rtN648aN09ixY1W3bl298MIL8vT01Pbt27VhwwY1b97csd6BAwfUtWtX9e/fX3379tXdd9+doe+JJM2bN0+DBg1Sx44d9dRTT+ny5cv66aeftH37dnXr1k2S9MQTT2jJkiUaOHCgKlasqD///FNbtmzRvn37VLNmzQwfd4qU0FuoUKFMb5vivffeU2Jiovr37y+bzaaXXnpJDz/8sH777TfHZ9WhQwft3btXTz75pEqXLq24uDitXbtWR48evemYA4BsYwAALrFgwQIjyezYscMcOnTI5MuXzwwaNMixvFGjRqZSpUqO14cPHzaSzIIFC1LtS5IZM2aM4/WYMWOMJNOvXz9H27Vr10xISIix2WzmxRdfdLSfO3fO+Pj4mOjoaEfbxo0bjSRTsmRJk5CQ4Gj/6KOPjCTzyiuvGGOMsdvtply5ciYqKsrY7XbHepcuXTLh4eGmWbNmqWrq2rVrhvpn8ODBRpLZvHmzoy0xMdGEh4eb0qVLm+TkZKfjHzBgQIb2KynNn5R+7d27tylevLg5e/as03ZdunQxAQEB5tKlS8aY6/155coVp3XOnTtnihUrZnr16uVoO3PmTKrPJ0WjRo1Mo0aNUrVHR0ebsLAwx+uUz97f39/ExcU5rfvAAw+YKlWqmMuXLzva7Ha7qVu3rilXrpyjrVq1aqZVq1Y37Zu07Nmzx/j4+BhJpnr16uapp54yn376qbl48aLTeomJiaZgwYKmb9++Tu2nTp0yAQEBTu0pYyHFkSNHjLu7u5k4caLTtj///LPJly+fo/3atWsmPDzchIWFmXPnzjmt+8/xN2DAAJPWP3E2b95sJJnFixc7ta9atcqpPS4uznh6eppWrVo57fe5554zkpy+KzdTpUoV8+ijjzptX7RoUZOUlORoi42NNW5ubuahhx5yGtM3HlNYWJiRZFatWuW0Tka/J+3atXP6fZKWgICADH+P/inld9m6devMmTNnzLFjx8ySJUtMYGCg8fLyMseOHXOsm9kxX6RIEfPXX3852pcvX24kmc8++8wYc/07J8lMmTIl03UDQHZhejkA5ABlypTR448/rrlz5+rkyZNZtt8+ffo4/t/d3V21atWSMUa9e/d2tBcsWFB33323fvvtt1Tbd+/eXQUKFHC87tixo4oXL64vvvhCkrR7927FxsaqW7du+vPPP3X27FmdPXtWFy9e1AMPPKCvv/5adrvdaZ9PPPFEhmr/4osvVLt2bacp6Pnz51e/fv105MgRx9Tq29GuXTutXbvW6ScqKkrGGC1dulRt2rSRMcZxPGfPnlVUVJTi4+MdU23d3d0d11Pb7Xb99ddfunbtmmrVqnXb03FvpUOHDo7p6pL0119/acOGDercubMSExMdtf7555+KiopSbGys4+7RBQsW1N69exUbG5up96xUqZJ2796txx57TEeOHNErr7yi9u3bq1ixYpo3b55jvbVr1+r8+fPq2rWrU7+5u7urTp06TtPub7Rs2TLZ7XZ17tzZadvg4GCVK1fOse2uXbt0+PBhDR48ONU1/Bm5rODjjz9WQECAmjVr5vQ+kZGRyp8/v+N91q1bp6tXr+rJJ5902m/KLIeM+Omnn/Tzzz+ra9eujraUvlm9erWj7dNPP5Xdbtfo0aPl5ub8z7Ibjyk8PFxRUVFObRn9nhQsWFB//PFHqqnZ/1SwYEFt375dJ06cyPBx/lPTpk0VGBio0NBQdezYUX5+flqxYoVCQkJua3/S9dk6/zxTnjJlPeX3lY+Pjzw9PbVp06ZUlwgAQE7B9HIAyCH+97//adGiRXrxxRcd16feqVKlSjm9DggIkLe3t4oWLZqq/c8//0y1fbly5Zxe22w2lS1b1jFtNCXARUdHp1tDfHy80z+aw8PDM1T777//rjp16qRqr1ChgmP57T5SLSQkRE2bNk3VHhcXp/Pnz2vu3LmaO3dumtv+8+Zhb7/9tl5++WXt379fSUlJjvaMHmNm3bjfgwcPyhijUaNGadSoUenWW7JkSb3wwgtq166d7rrrLlWuXFkPPvigHn/88QxNdb/rrru0aNEiJScn65dfftHKlSv10ksvqV+/fgoPD1fTpk0dYyG9Kej+/v7p7j82NlbGmFTjLUXKNOKUa4Nv93OPjY1VfHx8mteiS//32f7++++SUo//wMDADE+Vfvfdd+Xn56cyZcro4MGDkiRvb2+VLl1aixcvVqtWrSRdPyY3NzdVrFjxlvtMa1xl9Hvy7LPPat26dapdu7bKli2r5s2bq1u3bqpXr55jm5deeknR0dEKDQ1VZGSkWrZsqe7du6tMmTIZOuaZM2fqrrvuUnx8vObPn6+vv/5aXl5eGdo2PTf+Dkvp/5SA7eXlpcmTJ+vpp59WsWLFdO+996p169bq3r27ZY8BBIDMInQDQA5RpkwZPfbYY5o7d65GjBiRanl6Z/KSk5PT3ae7u3uG2iTd9Prq9KScxZ4yZYqqV6+e5jr58+d3ep2T7rZ8o5Tjeeyxx9L9Q0JKSH333XfVo0cPtW/fXsOHD1dQUJDc3d0VExPjdOOom0m5CdyN0vtMb+y7lHqHDRuW6gxoirJly0qSGjZsqEOHDmn58uVas2aN3nzzTU2fPl2zZ892mhFxM+7u7qpSpYqqVKmi++67T02aNNHixYvVtGlTRy2LFi1KM+zky5f+PznsdrtsNpu+/PLLNMfnjWPodtntdgUFBWnx4sVpLv/nLII7YYzR+++/r4sXL6YZpuPi4nThwoVMH9edfHcqVKigAwcOaOXKlVq1apWWLl2qN954Q6NHj3Y89q1z585q0KCBPvnkE61Zs0ZTpkzR5MmTtWzZMrVo0eKW71G7dm3H3cvbt2+v+vXrq1u3bjpw4IDjWDM75jPy+2rw4MFq06aNPv30U61evVqjRo1STEyMNmzYoBo1atyybgCwGqEbAHKQ//3vf3r33Xc1efLkVMtSzvCcP3/eqT3lrJwVbpyKbIzRwYMHHcEzIiJC0vWzmGmdOb4TYWFhOnDgQKr2/fv3O5ZntcDAQBUoUEDJycm3PJ4lS5aoTJkyWrZsmdMfRMaMGeO03s2mPRcqVCjNaf0Z/UxTzkB6eHhkqP8LFy6snj17qmfPnrpw4YIaNmyosWPHZjh0/1NKuEq5HCJlLAQFBWV6LERERMgYo/DwcN111103XU+S9uzZc9P3SK/PIyIitG7dOtWrV++mATZlbMXGxjqd5T1z5kyGpjB/9dVX+uOPP/TCCy84zjinOHfunPr166dPP/1Ujz32mCIiImS32/XLL7+k+4erm8nM98TPz0+PPPKIHnnkEV29elUPP/ywJk6cqJEjRzqeo128eHH997//1X//+1/FxcWpZs2amjhxYoZC9z+l/AGqSZMmev311x1/SLzTMZ+eiIgIPf3003r66acVGxur6tWr6+WXX9a77757R/sFgKzANd0AkINEREToscce05w5c3Tq1CmnZf7+/ipatKi+/vprp/Y33njDsnreeecdJSYmOl4vWbJEJ0+edPwDPDIyUhEREZo6daouXLiQavszZ87c9nu3bNlS3333nb799ltH28WLFzV37lyVLl06Q9NxM8vd3V0dOnTQ0qVLtWfPnlTL/3k8KWfg/nnGbfv27U71SpKvr6+k1H8ska5/3vv373fa748//pjqcWvpCQoKUuPGjTVnzpw07wXwz/3eePlA/vz5VbZsWV25cuWm77F582anqfMpUq7rv/vuuyVJUVFR8vf316RJk9Jc/2Zj4eGHH5a7u7vGjRuX6iyoMcZRe82aNRUeHq4ZM2ak6s9/bpfy7Pcb1+ncubOSk5M1fvz4VDVcu3bNsX7Tpk3l4eGh1157zWm/M2bMSPcY/illavnw4cPVsWNHp5++ffuqXLlyjrPt7du3l5ubm1544YVU9z/IyOyTjH5Pbvz8PT09VbFiRRljlJSUpOTkZMXHxzutExQUpBIlStxyjKSncePGql27tmbMmKHLly9LuvMxf6NLly459p0iIiJCBQoUuO26ASCrcaYbAHKY559/XosWLdKBAwdUqVIlp2V9+vTRiy++qD59+qhWrVr6+uuv9euvv1pWS+HChVW/fn317NlTp0+f1owZM1S2bFn17dtXkuTm5qY333xTLVq0UKVKldSzZ0+VLFlSx48f18aNG+Xv76/PPvvstt57xIgRev/999WiRQsNGjRIhQsX1ttvv63Dhw9r6dKlqW46lVVefPFFbdy4UXXq1FHfvn1VsWJF/fXXX9q5c6fWrVunv/76S5LUunVrLVu2TA899JBatWqlw4cPa/bs2apYsaLTHyB8fHxUsWJFffjhh7rrrrtUuHBhVa5cWZUrV1avXr00bdo0RUVFqXfv3oqLi9Ps2bNVqVIlJSQkZKjemTNnqn79+qpSpYr69u2rMmXK6PTp0/r222/1xx9/6Mcff5QkVaxYUY0bN1ZkZKQKFy6s77//3vF4qJuZPHmyfvjhBz388MOOGQ47d+7UO++8o8KFCztuLubv769Zs2bp8ccfV82aNdWlSxcFBgbq6NGj+vzzz1WvXj29/vrrab5HRESEJkyYoJEjR+rIkSNq3769ChQooMOHD+uTTz5Rv379NGzYMLm5uWnWrFlq06aNqlevrp49e6p48eLav3+/9u7d67hBWWRkpCRp0KBBioqKkru7u7p06aJGjRqpf//+iomJ0e7du9W8eXN5eHgoNjZWH3/8sV555RV17NhRgYGBGjZsmGJiYtS6dWu1bNlSu3bt0pdffpnqfgg3unLlipYuXapmzZo5zh7fqG3btnrllVcUFxensmXL6vnnn9f48ePVoEEDPfzww/Ly8tKOHTtUokQJxcTE3PT9Mvo9ad68uYKDg1WvXj0VK1ZM+/bt0+uvv65WrVqpQIECOn/+vEJCQtSxY0dVq1ZN+fPn17p167Rjxw69/PLLN63hZoYPH65OnTpp4cKFeuKJJ7JkzP/Tr7/+qgceeECdO3dWxYoVlS9fPn3yySc6ffq0unTpctt1A0CWyua7pQMA/r9/PjLsRtHR0UZSqkf8XLp0yfTu3dsEBASYAgUKmM6dO5u4uLh0Hxl25syZVPv18/NL9X43Pp4s5ZFh77//vhk5cqQJCgoyPj4+plWrVub3339Ptf2uXbvMww8/bIoUKWK8vLxMWFiY6dy5s1m/fv0ta7qZQ4cOmY4dO5qCBQsab29vU7t2bbNy5cpU6ymTjwy71bqnT582AwYMMKGhocbDw8MEBwebBx54wMydO9exjt1uN5MmTTJhYWHGy8vL1KhRw6xcuTLVo4+MMWbr1q0mMjLSeHp6pvqs3n33XVOmTBnj6elpqlevblavXp3u45PSeyzSoUOHTPfu3U1wcLDx8PAwJUuWNK1btzZLlixxrDNhwgRTu3ZtU7BgQePj42PKly9vJk6caK5evXrTvvjmm2/MgAEDTOXKlU1AQIDx8PAwpUqVMj169DCHDh1Ktf7GjRtNVFSUCQgIMN7e3iYiIsL06NHDfP/99451bnxkWIqlS5ea+vXrGz8/P+Pn52fKly9vBgwYYA4cOOC03pYtW0yzZs1MgQIFjJ+fn6latap57bXXHMuvXbtmnnzySRMYGGhsNluq95o7d66JjIw0Pj4+pkCBAqZKlSrmmWeeMSdOnHCsk5ycbMaNG2eKFy9ufHx8TOPGjc2ePXtMWFjYTR8ZtnTpUiPJvPXWW+mus2nTJqdH7xljzPz5802NGjWMl5eXKVSokGnUqJFZu3atY3lYWFi6j3zLyPdkzpw5pmHDho7vaEREhBk+fLiJj483xhhz5coVM3z4cFOtWjVHv1arVs288cYb6R5Hipv9LktOTjYREREmIiLCXLt2zRhz52P+n9+hs2fPmgEDBpjy5csbPz8/ExAQYOrUqWM++uijW9YNANnFZsxt3DkHAAAAAADcEtd0AwAAAABgEUI3AAAAAAAWIXQDAAAAAGARQjcAAAAAABYhdAMAAAAAYBFCNwAAAAAAFsnn6gJyIrvdrhMnTqhAgQKy2WyuLgcAAAAAkMMYY5SYmKgSJUrIzS3989mE7jScOHFCoaGhri4DAAAAAJDDHTt2TCEhIekuJ3SnoUCBApKud56/v7+Lq0FOkJSUpDVr1qh58+by8PBwdTlAlmJ8Iy9jfCMvY3wjL8sN4zshIUGhoaGO/JgeQncaUqaU+/v7E7oh6fqX3tfXV/7+/jn2Sw/cLsY38jLGN/Iyxjfystw0vm91STI3UgMAAAAAwCKEbgAAAAAALELoBgAAAADAIoRuAAAAAAAsQugGAAAAAMAihG4AAAAAACxC6AYAAAAAwCKEbgAAAAAALELoBgAAAADAIoRuAAAAAAAsQugGAAAAAMAihG4AAAAAACxC6AYAAAAAwCKEbgAAAAAALELoBgAAAADAIoRuAAAAAAAskuND9/Hjx/XYY4+pSJEi8vHxUZUqVfT999+nu/6WLVtUr149x/rly5fX9OnTs7FiAAAAAACuy+fqAm7m3Llzqlevnpo0aaIvv/xSgYGBio2NVaFChdLdxs/PTwMHDlTVqlXl5+enLVu2qH///vLz81O/fv2ysXoAAAAAwL9djg7dkydPVmhoqBYsWOBoCw8Pv+k2NWrUUI0aNRyvS5curWXLlmnz5s2EbgAAAABAtsrR08tXrFihWrVqqVOnTgoKClKNGjU0b968TO1j165d2rp1qxo1amRRlQAAAAAApC1Hn+n+7bffNGvWLA0dOlTPPfecduzYoUGDBsnT01PR0dE33TYkJERnzpzRtWvXNHbsWPXp0yfdda9cuaIrV644XickJEiSkpKSlJSUlDUHg1wtZRwwHpAXMb6RlzG+kZcxvpGX5YbxndHabMYYY3Ett83T01O1atXS1q1bHW2DBg3Sjh079O23395028OHD+vChQvatm2bRowYoddff11du3ZNc92xY8dq3Lhxqdrfe+89+fr63tlBAAAAAADynEuXLqlbt26Kj4+Xv79/uuvl6NAdFhamZs2a6c0333S0zZo1SxMmTNDx48czvJ8JEyZo0aJFOnDgQJrL0zrTHRoaqrNnz9608/DvkZSUpLVr16pZs2by8PBwdTlAlmJ8Iy9jfCMvY3wjL8sN4zshIUFFixa9ZejO0dPL69Wrlyoo//rrrwoLC8vUfux2u1OovpGXl5e8vLxStXt4eOTYDxiuwZhAXsb4Rl7G+EZexvhGXpaTx3dG68rRoXvIkCGqW7euJk2apM6dO+u7777T3LlzNXfuXMc6I0eO1PHjx/XOO+9IkmbOnKlSpUqpfPnykqSvv/5aU6dO1aBBg1xyDAAAAACAf68cHbrvueceffLJJxo5cqReeOEFhYeHa8aMGXr00Ucd65w8eVJHjx51vLbb7Ro5cqQOHz6sfPnyKSIiQpMnT1b//v1dcQgAAAAAgH+xHB26Jal169Zq3bp1ussXLlzo9PrJJ5/Uk08+aXFVAAAAAADcWo5+TjcAAAAAALkZoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALBIjg/dx48f12OPPaYiRYrIx8dHVapU0ffff5/u+suWLVOzZs0UGBgof39/3XfffVq9enU2VgwAAAAAwHU5OnSfO3dO9erVk4eHh7788kv98ssvevnll1WoUKF0t/n666/VrFkzffHFF/rhhx/UpEkTtWnTRrt27crGygEAAAAAkPK5uoCbmTx5skJDQ7VgwQJHW3h4+E23mTFjhtPrSZMmafny5frss89Uo0YNK8oEAAAAACBNOfpM94oVK1SrVi116tRJQUFBqlGjhubNm5epfdjtdiUmJqpw4cIWVQkAAAAAQNpy9Jnu3377TbNmzdLQoUP13HPPaceOHRo0aJA8PT0VHR2doX1MnTpVFy5cUOfOndNd58qVK7py5YrjdUJCgiQpKSlJSUlJd3YQyBNSxgHjAXkR4xt5GeMbeRnjG3lZbhjfGa3NZowxFtdy2zw9PVWrVi1t3brV0TZo0CDt2LFD33777S23f++999S3b18tX75cTZs2TXe9sWPHaty4cWlu7+vre3vFAwAAAADyrEuXLqlbt26Kj4+Xv79/uuvl6DPdxYsXV8WKFZ3aKlSooKVLl95y2w8++EB9+vTRxx9/fNPALUkjR47U0KFDHa8TEhIUGhqq5s2b37Tz8O+RlJSktWvXqlmzZvLw8HB1OUCWYnwjL2N8Iy9jfCMvyw3jO2WG9K3k6NBdr149HThwwKnt119/VVhY2E23e//999WrVy998MEHatWq1S3fx8vLS15eXqnaPTw8cuwHDNdgTCAvY3wjL2N8Iy9jfCMvy8njO6N15egbqQ0ZMkTbtm3TpEmTdPDgQb333nuaO3euBgwY4Fhn5MiR6t69u+P1e++9p+7du+vll19WnTp1dOrUKZ06dUrx8fGuOAQAAAAAwL9Yjg7d99xzjz755BO9//77qly5ssaPH68ZM2bo0Ucfdaxz8uRJHT161PF67ty5unbtmgYMGKDixYs7fp566ilXHAIAAAAA4F8sR08vl6TWrVurdevW6S5fuHCh0+tNmzZZWxAAAAAAABmUo890AwAAAACQmxG6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAADA/2PvvsOjKPf3j9+bXiDUQAiE0HsLICqggBQFRIqIgPSifi00QUEFgaMinAOiHMuhC4iIgChKC9ItICX0XqT3EpKQPr8/+GUlhhJgZ2ezeb+uK5fuzOw8n508bPbeeeYZACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJnH50H3y5El16tRJ+fLlk7+/vypXrqxNmzbddvvTp0+rY8eOKlOmjDw8PNSvXz/nFQsAAAAAwE1cOnRfvnxZderUkbe3t5YsWaLdu3dr7NixypMnz22fk5CQoODgYL377ruqWrWqE6sFAAAAACA9L6sLuJPRo0crLCxM06ZNsy8rXrz4HZ9TrFgxffLJJ5KkqVOnmlofAAAAAAB34tJnun/88UfVrFlTzz33nAoUKKCIiAhNmjTJ6rIAAAAAAMgUlz7TffjwYX3xxRcaMGCA3n77bf3555/q06ePfHx81LVrV4e1k5CQoISEBPvj6OhoSVJSUpKSkpIc1g6yrrR+QH+AO6J/w53Rv+HO6N9wZ1mhf2e2NpthGIbJtdw3Hx8f1axZU7/99pt9WZ8+ffTnn3/q999/v+vz69evr2rVqmn8+PF33G748OEaMWJEhuWzZ89WQEDAPdcNAAAAAHBvcXFx6tixo65evaqgoKDbbufSZ7oLFSqkChUqpFtWvnx5zZ8/36HtDBkyRAMGDLA/jo6OVlhYmJo0aXLHg4fsIykpSZGRkWrcuLG8vb2tLgdwKPo33Bn9G+6M/g13lhX6d9oI6btx6dBdp04d7du3L92y/fv3Kzw83KHt+Pr6ytfXN8Nyb29vl/0Fwxr0Cbgz+jfcGf0b7oz+DXfmyv07s3W5dOju37+/ateurQ8//FDt2rXTxo0bNXHiRE2cONG+zZAhQ3Ty5EnNmDHDviwqKkqSFBMTo/PnzysqKko+Pj4ZzpoDAAAAAGAmlw7dDz30kL7//nsNGTJEI0eOVPHixTV+/Hi98MIL9m1Onz6tY8eOpXteRESE/f83b96s2bNnKzw8XEePHnVW6QAAAAAAuHbolqSnn35aTz/99G3XT58+PcMyF54bDgAAAACQjbj0fboBAAAAAMjKCN0AAAAAAJiE0A0AAAAAgEmcFrqvXLnirKYAAAAAAHAJpoTu0aNH69tvv7U/bteunfLly6fChQtr27ZtZjQJAAAAAIDLMSV0f/nllwoLC5MkRUZGKjIyUkuWLFHTpk01aNAgM5oEAAAAAMDlmHLLsDNnzthD908//aR27dqpSZMmKlasmB5++GEzmgQAAAAAwOWYcqY7T548On78uCRp6dKlatSokaQb989OSUkxo0kAAAAAAFyOKWe627Rpo44dO6p06dK6ePGimjZtKknaunWrSpUqZUaTAAAAAAC4HFNC98cff6xixYrp+PHjGjNmjHLkyCFJOn36tF555RUzmgQAAAAAwOWYErq9vb01cODADMv79+9vRnMAAAAAALgk0+7TPXPmTNWtW1ehoaH666+/JEnjx4/XDz/8YFaTAAAAAAC4FFNC9xdffKEBAwaoadOmunLlin3ytNy5c2v8+PFmNAkAAAAAgMsxJXRPmDBBkyZN0jvvvCNPT0/78po1a2rHjh1mNAkAAAAAgMsxJXQfOXJEERERGZb7+voqNjbWjCYBAAAAAHA5poTu4sWLKyoqKsPypUuXqnz58mY0CQAAAACAyzFl9vIBAwbo1VdfVXx8vAzD0MaNG/XNN99o1KhRmjx5shlNAgAAAADgckwJ3b169ZK/v7/effddxcXFqWPHjgoNDdUnn3yi9u3bm9EkAAAAAAAux+GhOzk5WbNnz9aTTz6pF154QXFxcYqJiVGBAgUc3RQAAAAAAC7N4dd0e3l56eWXX1Z8fLwkKSAggMANAAAAAMiWTJlIrVatWtq6dasZuwYAAAAAIMsw5ZruV155RW+88YZOnDihGjVqKDAwMN36KlWqmNEsAAAAAAAuxZTQnTZZWp8+fezLbDabDMOQzWZTSkqKGc0CAAAAAOBSTAndR44cMWO3AAAAAABkKaaE7vDwcDN2CwAAAABAlmJK6J4xY8Yd13fp0sWMZgEAAAAAcCmmhO6+ffume5yUlKS4uDj5+PgoICCA0A0AAAAAyBZMuWXY5cuX0/3ExMRo3759qlu3rr755hszmgQAAAAAwOWYErpvpXTp0vroo48ynAUHAAAAAMBdOS10S5KXl5dOnTrlzCYBAAAAALCMKdd0//jjj+keG4ah06dP67///a/q1KljRpMAAAAAALgcU0J3q1at0j222WwKDg7WE088obFjx5rRJAAAAAAALseU0J2ammrGbgEAAAAAyFJMuaZ75MiRiouLy7D8+vXrGjlypBlNAgAAAADgckwJ3SNGjFBMTEyG5XFxcRoxYoQZTQIAAAAA4HJMCd2GYchms2VYvm3bNuXNm9eMJgEAAAAAcDkOvaY7T548stlsstlsKlOmTLrgnZKSopiYGL388suObBIAAAAAAJfl0NA9fvx4GYahHj16aMSIEcqVK5d9nY+Pj4oVK6ZHH33UkU0CAAAAAOCyHBq6u3btKkkqXry4ateuLW9vb0fuHgAAAACALMWUW4bVq1fP/v/x8fFKTExMtz4oKMiMZgEAAAAAcCmmTKQWFxen1157TQUKFFBgYKDy5MmT7gcAAAAAgOzAlNA9aNAgrVy5Ul988YV8fX01efJkjRgxQqGhoZoxY4YZTQIAAAAA4HJMGV6+aNEizZgxQ/Xr11f37t312GOPqVSpUgoPD9fXX3+tF154wYxmAQAAAABwKaac6b506ZJKlCgh6cb125cuXZIk1a1bV2vXrjWjSQAAAAAAXI4pobtEiRI6cuSIJKlcuXKaO3eupBtnwHPnzm1GkwAAAAAAuBxTQnf37t21bds2SdLgwYP12Wefyc/PT/3799egQYPMaBIAAAAAAJdjyjXd/fv3t/9/o0aNtHfvXm3evFmlSpVSlSpVzGgSAAAAAACXY0rovll8fLzCw8MVHh5udlMAAAAAALgUU4aXp6Sk6F//+pcKFy6sHDly6PDhw5KkoUOHasqUKWY0CQAAAACAyzEldH/wwQeaPn26xowZIx8fH/vySpUqafLkyWY0CQAAAACAyzEldM+YMUMTJ07UCy+8IE9PT/vyqlWrau/evWY0CQAAAACAyzEldJ88eVKlSpXKsDw1NVVJSUlmNAkAAAAAgMsxJXRXqFBB69aty7B83rx5ioiIMKNJAAAAAABcjimzlw8bNkxdu3bVyZMnlZqaqgULFmjfvn2aMWOGfvrpJzOaBAAAAADA5Zhyprtly5ZatGiRVqxYocDAQA0bNkx79uzRokWL1LhxYzOaBAAAAADA5Tj0TPfhw4dVvHhx2Ww2PfbYY4qMjHTk7gEAAAAAyFIceqa7dOnSOn/+vP3x888/r7NnzzqyCQAAAAAAsgyHhm7DMNI9Xrx4sWJjYx3ZBAAAAAAAWYYp13QDAAAAAAAHh26bzSabzZZhGQAAAAAA2ZFDJ1IzDEPdunWTr6+vJCk+Pl4vv/yyAgMD0223YMECRzYLAAAAAIBLcmjo7tq1a7rHnTp1cuTuAQAAAADIUhwauqdNm+bI3QEAAAAAkKUxkRoAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEoeF7urVq+vy5cuSpJEjRyouLs4h+z158qQ6deqkfPnyyd/fX5UrV9amTZvu+JzVq1erevXq8vX1ValSpTR9+nSH1OIyUlKk1aulb7658d+UFKsrcm8pKbKtWaPCa9fKtmYNx9ts9G/non87F/3buejfzkX/di76t3PRv53L3fq34SB+fn7G8ePHDcMwDA8PD+Ps2bMPvM9Lly4Z4eHhRrdu3YwNGzYYhw8fNpYtW2YcPHjwts85fPiwERAQYAwYMMDYvXu3MWHCBMPT09NYunRpptu9evWqIcm4evXqA78Gh5s/3zCKFDEM6e+fIkVuLIfjcbydi+PtXBxv5+J4OxfH27k43s7F8XYujrdzZaHjndncaDMMw3BEeH/00UeVI0cO1a1bVyNGjNDAgQOVI0eOW247bNiwTO1z8ODB+vXXX7Vu3bpM1/HWW2/p559/1s6dO+3L2rdvrytXrmjp0qWZ2kd0dLRy5cqlq1evKigoKNNtm27BAqlt2xtd72Y2243/zpsntWnj/LrcFcfbuTjezsXxdi6Ot3NxvJ2L4+1cHG/n4ng7VxY73pnNjQ4L3fv27dN7772nQ4cOacuWLapQoYK8vDLeBtxms2nLli2Z2meFChX05JNP6sSJE1qzZo0KFy6sV155Rb17977tcx5//HFVr15d48ePty+bNm2a+vXrp6tXr2aqXZcM3SkpUrFi0okTt15vs0mFC0u7dkmenk4tzS2lpEgVKkgnT956PcfbsTjezsXxdi6Ot3NxvJ2L4+1cHG/n4ng7V2aOd5Ei0pEjLnO8nR66b+bh4aEzZ86oQIECD7QfPz8/SdKAAQP03HPP6c8//1Tfvn315ZdfqmvXrrd8TpkyZdS9e3cNGTLEvmzx4sVq3ry54uLi5O/vn+E5CQkJSkhIsD+Ojo5WWFiYLly44DKh27ZmjbwaN7a6DAAAAACwTHJkpIx69awuQ9KN3Jg/f/67hu6Mp6IdIDU11WH7qVmzpj788ENJUkREhHbu3HnH0H0/Ro0apREjRmRYvnz5cgUEBDisnQdReO1a1bS6CAAAAACwUNSSJToZG2t1GZKU6cnDTQndknTo0CGNHz9ee/bskXRjqHjfvn1VsmTJTO+jUKFCqlChQrpl5cuX1/z582/7nJCQEJ09ezbdsrNnzyooKOiWZ7klaciQIRowYID9cdqZ7iZNmrjOme7AQGncuLtul7xokYy6dZ1QkXuzrV8vrxYt7rodx9sxON7OxfF2Lo63c3G8nYvj7Vwcb+fieDtXZo93taZNVdWFznRnhinDy5ctW6ZnnnlG1apVU506dSRJv/76q7Zt26ZFixapcSaHSXfs2FHHjx9PN5Fa//79tWHDBv3222+3fM5bb72lxYsXa8eOHen2c+nSpaw9kVraNd0nT2acWEByyWscsjSOt3NxvJ2L4+1cHG/n4ng7F8fbuTjezsXxdq4seLwznRvNmDq9WrVqxltvvZVh+VtvvWVERERkej8bN240vLy8jA8++MA4cOCA8fXXXxsBAQHGrFmz7NsMHjzY6Ny5s/1x2i3DBg0aZOzZs8f47LPP3OeWYfPnG4bNduPn5in005a54DT6WRrH27k43s7F8XYujrdzcbydi+PtXBxv5+J4O1cWO96ZzY2mhG5fX19j//79GZbv27fP8PX1vad9LVq0yKhUqZLh6+trlCtXzpg4cWK69V27djXq1auXbtmqVauMatWqGT4+PkaJEiWMadOm3VObLhu6DePW960LC3O5Dug2ON7OxfF2Lo63c3G8nYvj7Vwcb+fieDsXx9u5stDxdvp9um8WFhamcePG6bnnnku3fO7cuRo4cKCOHTvm6CYdyiWHl98sJUVat046fVoqVEh67DGXGWLhllJSlLxqlaKWLFG1pk3l1aABx9tM9G/non87F/3buejfzkX/di76t3PRv50ri/TvzOZGUyZS6927t1588UUdPnxYtWvXlnTjmu7Ro0enm7AM98nTU6pf3+oqsg9PTxn16ulkbOyNSRtc8B+8W6F/Oxf927no385F/3Yu+rdz0b+di/7tXG7Wv00J3UOHDlXOnDk1duxY+/2yQ0NDNXz4cPXp08eMJgEAAAAAcDmmhG6bzab+/furf//+unbtmiQpZ86cZjQFAAAAAIDLMu0+3WkI2wAAAACA7MrD6gIAAAAAAHBXhG4AAAAAAExC6AYAAAAAwCQOD91JSUlq2LChDhw44OhdAwAAAACQpTg8dHt7e2v79u2O3i0AAAAAAFmOKcPLO3XqpClTppixawAAAAAAsgxTbhmWnJysqVOnasWKFapRo4YCAwPTrR83bpwZzQIAAAAA4FJMCd07d+5U9erVJUn79+9Pt85ms5nRJAAAAAAALseU0L1q1SozdgsAAAAAQJZi6i3DDh48qGXLlun69euSJMMwzGwOAAAAAACXYkrovnjxoho2bKgyZcqoWbNmOn36tCSpZ8+eeuONN8xoEgAAAAAAl2NK6O7fv7+8vb117NgxBQQE2Jc///zzWrp0qRlNAgAAAADgcky5pnv58uVatmyZihQpkm556dKl9ddff5nRJAAAAAAALseUM92xsbHpznCnuXTpknx9fc1oEgAAAAAAl2NK6H7sscc0Y8YM+2ObzabU1FSNGTNGDRo0MKNJAAAAAABcjinDy8eMGaOGDRtq06ZNSkxM1Jtvvqldu3bp0qVL+vXXX81oEgAAAAAAl2PKme5KlSpp//79qlu3rlq2bKnY2Fi1adNGW7duVcmSJc1oEgAAAAAAl2PKmW5JypUrl9555x2zdg8AAAAAgMszLXRfvnxZU6ZM0Z49eyRJFSpUUPfu3ZU3b16zmgQAAAAAwKWYMrx87dq1KlasmD799FNdvnxZly9f1qeffqrixYtr7dq1ZjQJAAAAAIDLMeVM96uvvqrnn39eX3zxhTw9PSVJKSkpeuWVV/Tqq69qx44dZjQLAAAAAIBLMeVM98GDB/XGG2/YA7ckeXp6asCAATp48KAZTQIAAAAA4HJMCd3Vq1e3X8t9sz179qhq1apmNAkAAAAAgMtx2PDy7du32/+/T58+6tu3rw4ePKhHHnlEkvTHH3/os88+00cffeSoJgEAAAAAcGkOC93VqlWTzWaTYRj2ZW+++WaG7Tp27Kjnn3/eUc0CAAAAAOCyHBa6jxw54qhdAQAAAADgFhwWusPDwx21KwAAAAAA3IIptwyTpFOnTmn9+vU6d+6cUlNT063r06ePWc0CAAAAAOAyTAnd06dP10svvSQfHx/ly5dPNpvNvs5msxG6AQAAAADZgimhe+jQoRo2bJiGDBkiDw9T7koGAAAAAIDLMyURx8XFqX379gRuAAAAAEC2Zkoq7tmzp7777jszdg0AAAAAQJZhyvDyUaNG6emnn9bSpUtVuXJleXt7p1s/btw4M5oFAAAAAMClmBa6ly1bprJly0pShonUAAAAAADIDkwJ3WPHjtXUqVPVrVs3M3YPAAAAAECWYMo13b6+vqpTp44ZuwYAAAAAIMswJXT37dtXEyZMMGPXAAAAAABkGaYML9+4caNWrlypn376SRUrVswwkdqCBQvMaBYAAAAAAJdiSujOnTu32rRpY8auAQAAAADIMkwJ3dOmTTNjtwAAAAAAZCmmXNMNAAAAAABMOtNdvHjxO96P+/Dhw2Y0CwAAAACASzEldPfr1y/d46SkJG3dulVLly7VoEGDzGgSAAAAAACXY0ro7tu37y2Xf/bZZ9q0aZMZTQIAAAAA4HKcek1306ZNNX/+fGc2CQAAAACAZZwauufNm6e8efM6s0kAAAAAACxjyvDyiIiIdBOpGYahM2fO6Pz58/r888/NaBIAAAAAAJdjSuhu1apVusceHh4KDg5W/fr1Va5cOTOaBAAAAADA5ZgSut977z0zdgsAAAAAQJbi1Gu6AQAAAADIThx6ptvDwyPdtdy3YrPZlJyc7MhmAQAAAABwSQ4N3d9///1t1/3+++/69NNPlZqa6sgmAQAAAABwWQ4N3S1btsywbN++fRo8eLAWLVqkF154QSNHjnRkkwAAAAAAuCzTruk+deqUevfurcqVKys5OVlRUVH66quvFB4eblaTAAAAAAC4FIeH7qtXr+qtt95SqVKltGvXLv3yyy9atGiRKlWq5OimAAAAAABwaQ4dXj5mzBiNHj1aISEh+uabb2453BwAAAAAgOzCoaF78ODB8vf3V6lSpfTVV1/pq6++uuV2CxYscGSzAAAAAAC4JIeG7i5dutz1lmEAAAAAAGQXDg3d06dPd+TuAAAAAADI0kybvRwAAAAAgOyO0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASVw6dA8fPlw2my3dT7ly5W67fVJSkkaOHKmSJUvKz89PVatW1dKlS51YMQAAAAAAf3Po7OVmqFixolasWGF/7OV1+5LfffddzZo1S5MmTVK5cuW0bNkytW7dWr/99psiIiKcUS4AAAAAAHYuH7q9vLwUEhKSqW1nzpypd955R82aNZMk/d///Z9WrFihsWPHatasWWaWCQAAAABABi4fug8cOKDQ0FD5+fnp0Ucf1ahRo1S0aNFbbpuQkCA/P790y/z9/bV+/fo7tpGQkKCEhAT74+joaEk3hqsnJSU94CuAO0jrB/QHuCP6N9wZ/RvujP4Nd5YV+ndma7MZhmGYXMt9W7JkiWJiYlS2bFmdPn1aI0aM0MmTJ7Vz507lzJkzw/YdO3bUtm3btHDhQpUsWVK//PKLWrZsqZSUlHSh+p+GDx+uESNGZFg+e/ZsBQQEOPQ1AQAAAACyvri4OHXs2FFXr15VUFDQbbdz6dD9T1euXFF4eLjGjRunnj17Zlh//vx59e7dW4sWLZLNZlPJkiXVqFEjTZ06VdevX7/tfm91pjssLEwXLly448FD9pGUlKTIyEg1btxY3t7eVpcDOBT9G+6M/g13Rv+GO8sK/Ts6Olr58+e/a+h2+eHlN8udO7fKlCmjgwcP3nJ9cHCwFi5cqPj4eF28eFGhoaEaPHiwSpQoccf9+vr6ytfXN8Nyb29vl/0Fwxr0Cbgz+jfcGf0b7oz+DXfmyv07s3W59C3D/ikmJkaHDh1SoUKF7ridn5+fChcurOTkZM2fP18tW7Z0UoUAAAAAAPzNpUP3wIEDtWbNGh09elS//fabWrduLU9PT3Xo0EGS1KVLFw0ZMsS+/YYNG7RgwQIdPnxY69at01NPPaXU1FS9+eabVr0EAAAAAEA25tLDy0+cOKEOHTro4sWLCg4OVt26dfXHH38oODhYknTs2DF5ePz9vUF8fLzeffddHT58WDly5FCzZs00c+ZM5c6d26JXAAAAAADIzlw6dM+ZM+eO61evXp3ucb169bR7924TKwIAAAAAIPNceng5AAAAAABZGaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwiUuH7uHDh8tms6X7KVeu3B2fM378eJUtW1b+/v4KCwtT//79FR8f76SKAQAAAAD4m5fVBdxNxYoVtWLFCvtjL6/blzx79mwNHjxYU6dOVe3atbV//35169ZNNptN48aNc0a5AAAAAADYuXzo9vLyUkhISKa2/e2331SnTh117NhRklSsWDF16NBBGzZsMLNEAAAAAABuyeVD94EDBxQaGio/Pz89+uijGjVqlIoWLXrLbWvXrq1Zs2Zp48aNqlWrlg4fPqzFixerc+fOd2wjISFBCQkJ9sfR0dGSpKSkJCUlJTnuxSDLSusH9Ae4I/o33Bn9G+6M/g13lhX6d2ZrsxmGYZhcy31bsmSJYmJiVLZsWZ0+fVojRozQyZMntXPnTuXMmfOWz/n00081cOBAGYah5ORkvfzyy/riiy/u2M7w4cM1YsSIDMtnz56tgIAAh7wWAAAAAID7iIuLU8eOHXX16lUFBQXddjuXDt3/dOXKFYWHh2vcuHHq2bNnhvWrV69W+/bt9f777+vhhx/WwYMH1bdvX/Xu3VtDhw697X5vdaY7LCxMFy5cuOPBQ/aRlJSkyMhINW7cWN7e3laXAzgU/RvujP4Nd0b/hjvLCv07Ojpa+fPnv2vodvnh5TfLnTu3ypQpo4MHD95y/dChQ9W5c2f16tVLklS5cmXFxsbqxRdf1DvvvCMPj1tP1u7r6ytfX98My729vV32Fwxr0CfgzujfcGf0b7gz+jfcmSv378zW5dK3DPunmJgYHTp0SIUKFbrl+ri4uAzB2tPTU5KUhU7oAwAAAADchEuH7oEDB2rNmjU6evSofvvtN7Vu3Vqenp7q0KGDJKlLly4aMmSIffsWLVroiy++0Jw5c3TkyBFFRkZq6NChatGihT18AwAAAADgLC49vPzEiRPq0KGDLl68qODgYNWtW1d//PGHgoODJUnHjh1Ld2b73Xfflc1m07vvvquTJ08qODhYLVq00AcffGDVSwAAAAAAZGMuHbrnzJlzx/WrV69O99jLy0vvvfee3nvvPROrAgAAAAAgc1x6eDkAAAAAAFkZoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADCJS4fu4cOHy2azpfspV67cbbevX79+hu1tNpuaN2/uxKoBAAAAALjBy+oC7qZixYpasWKF/bGX1+1LXrBggRITE+2PL168qKpVq+q5554ztUYAAAAAAG7F5UO3l5eXQkJCMrVt3rx50z2eM2eOAgICCN0AAAAAAEu4fOg+cOCAQkND5efnp0cffVSjRo1S0aJFM/XcKVOmqH379goMDLzjdgkJCUpISLA/jo6OliQlJSUpKSnp/ouH20jrB/QHuCP6N9wZ/RvujP4Nd5YV+ndma7MZhmGYXMt9W7JkiWJiYlS2bFmdPn1aI0aM0MmTJ7Vz507lzJnzjs/duHGjHn74YW3YsEG1atW647bDhw/XiBEjMiyfPXu2AgICHug1AAAAAADcT1xcnDp27KirV68qKCjottu5dOj+pytXrig8PFzjxo1Tz54977jtSy+9pN9//13bt2+/635vdaY7LCxMFy5cuOPBQ/aRlJSkyMhINW7cWN7e3laXAzgU/RvujP4Nd0b/hjvLCv07Ojpa+fPnv2vodvnh5TfLnTu3ypQpo4MHD95xu9jYWM2ZM0cjR47M1H59fX3l6+ubYbm3t7fL/oJhDfoE3Bn9G+6M/g13Rv+GO3Pl/p3Zulz6lmH/FBMTo0OHDqlQoUJ33O67775TQkKCOnXq5KTKAAAAAADIyKVD98CBA7VmzRodPXpUv/32m1q3bi1PT0916NBBktSlSxcNGTIkw/OmTJmiVq1aKV++fM4uGQAAAAAAO5ceXn7ixAl16NBBFy9eVHBwsOrWras//vhDwcHBkqRjx47JwyP99wb79u3T+vXrtXz5citKBgAAAADAzqVD95w5c+64fvXq1RmWlS1bVllobjgAAAAAgBtz6eHlAAAAAABkZYRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAEziZXUBrsgwDElSdHS0xZXAVSQlJSkuLk7R0dHy9va2uhzAoejfcGf0b7gz+jfcWVbo32l5MS0/3g6h+xauXbsmSQoLC7O4EgAAAACAK7t27Zpy5cp12/U2426xPBtKTU3VqVOnlDNnTtlsNqvLgQuIjo5WWFiYjh8/rqCgIKvLARyK/g13Rv+GO6N/w51lhf5tGIauXbum0NBQeXjc/sptznTfgoeHh4oUKWJ1GXBBQUFBLvuPHnhQ9G+4M/o33Bn9G+7M1fv3nc5wp2EiNQAAAAAATELoBgAAAADAJIRuIBN8fX313nvvydfX1+pSAIejf8Od0b/hzujfcGfu1L+ZSA0AAAAAAJNwphsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6gTsYNWqUHnroIeXMmVMFChRQq1attG/fPqvLAhzuo48+ks1mU79+/awuBXCYkydPqlOnTsqXL5/8/f1VuXJlbdq0yeqygAeSkpKioUOHqnjx4vL391fJkiX1r3/9S0zThKxo7dq1atGihUJDQ2Wz2bRw4cJ06w3D0LBhw1SoUCH5+/urUaNGOnDggDXFPgBCN3AHa9as0auvvqo//vhDkZGRSkpKUpMmTRQbG2t1aYDD/Pnnn/rf//6nKlWqWF0K4DCXL19WnTp15O3trSVLlmj37t0aO3as8uTJY3VpwAMZPXq0vvjiC/33v//Vnj17NHr0aI0ZM0YTJkywujTgnsXGxqpq1ar67LPPbrl+zJgx+vTTT/Xll19qw4YNCgwM1JNPPqn4+HgnV/pgmL0cuAfnz59XgQIFtGbNGj3++ONWlwM8sJiYGFWvXl2ff/653n//fVWrVk3jx4+3uizggQ0ePFi//vqr1q1bZ3UpgEM9/fTTKliwoKZMmWJf9uyzz8rf31+zZs2ysDLgwdhsNn3//fdq1aqVpBtnuUNDQ/XGG29o4MCBkqSrV6+qYMGCmj59utq3b29htfeGM93APbh69aokKW/evBZXAjjGq6++qubNm6tRo0ZWlwI41I8//qiaNWvqueeeU4ECBRQREaFJkyZZXRbwwGrXrq1ffvlF+/fvlyRt27ZN69evV9OmTS2uDHCsI0eO6MyZM+k+o+TKlUsPP/ywfv/9dwsru3deVhcAZBWpqanq16+f6tSpo0qVKlldDvDA5syZoy1btujPP/+0uhTA4Q4fPqwvvvhCAwYM0Ntvv60///xTffr0kY+Pj7p27Wp1ecB9Gzx4sKKjo1WuXDl5enoqJSVFH3zwgV544QWrSwMc6syZM5KkggULpltesGBB+7qsgtANZNKrr76qnTt3av369VaXAjyw48ePq2/fvoqMjJSfn5/V5QAOl5qaqpo1a+rDDz+UJEVERGjnzp368ssvCd3I0ubOnauvv/5as2fPVsWKFRUVFaV+/fopNDSUvg24KIaXA5nw2muv6aefftKqVatUpEgRq8sBHtjmzZt17tw5Va9eXV5eXvLy8tKaNWv06aefysvLSykpKVaXCDyQQoUKqUKFCumWlS9fXseOHbOoIsAxBg0apMGDB6t9+/aqXLmyOnfurP79+2vUqFFWlwY4VEhIiCTp7Nmz6ZafPXvWvi6rIHQDd2AYhl577TV9//33WrlypYoXL251SYBDNGzYUDt27FBUVJT9p2bNmnrhhRcUFRUlT09Pq0sEHkidOnUy3OJx//79Cg8Pt6giwDHi4uLk4ZH+I7ynp6dSU1MtqggwR/HixRUSEqJffvnFviw6OlobNmzQo48+amFl947h5cAdvPrqq5o9e7Z++OEH5cyZ0379SK5cueTv729xdcD9y5kzZ4a5CQIDA5UvXz7mLIBb6N+/v2rXrq0PP/xQ7dq108aNGzVx4kRNnDjR6tKAB9KiRQt98MEHKlq0qCpWrKitW7dq3Lhx6tGjh9WlAfcsJiZGBw8etD8+cuSIoqKilDdvXhUtWlT9+vXT+++/r9KlS6t48eIaOnSoQkND7TOcZxXcMgy4A5vNdsvl06ZNU7du3ZxbDGCy+vXrc8swuJWffvpJQ4YM0YEDB1S8eHENGDBAvXv3tros4IFcu3ZNQ4cO1ffff69z584pNDRUHTp00LBhw+Tj42N1ecA9Wb16tRo0aJBhedeuXTV9+nQZhqH33ntPEydO1JUrV1S3bl19/vnnKlOmjAXV3j9CNwAAAAAAJuGabgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGACCb6tatm2w2m2w2m7y9vVW8eHG9+eabio+Pt7o0AADchpfVBQAAAOs89dRTmjZtmpKSkrR582Z17dpVNptNo0ePtro0AADcAme6AQDIxnx9fRUSEqKwsDC1atVKjRo1UmRkpCSpWLFiGj9+fLrtq1WrpuHDh9sf22w2TZ48Wa1bt1ZAQIBKly6tH3/80YmvAAAA10boBgAAkqSdO3fqt99+k4+Pzz09b8SIEWrXrp22b9+uZs2a6YUXXtClS5dMqhIAgKyF0A0AQDb2008/KUeOHPLz81PlypV17tw5DRo06J720a1bN3Xo0EGlSpXShx9+qJiYGG3cuNGkigEAyFq4phsAgGysQYMG+uKLLxQbG6uPP/5YXl5eevbZZ+9pH1WqVLH/f2BgoIKCgnTu3DlHlwoAQJbEmW4AALKxwMBAlSpVSlWrVtXUqVO1YcMGTZkyRZLk4eEhwzDSbZ+UlJRhH97e3uke22w2paammlc0AABZCKEbAABIuhGy3377bb377ru6fv26goODdfr0afv66OhoHTlyxMIKAQDIegjdAADA7rnnnpOnp6c+++wzPfHEE5o5c6bWrVunHTt2qGvXrvL09LS6RAAAshSu6QYAAHZeXl567bXXNGbMGB04cEBHjhzR008/rVy5culf//oXZ7oBALhHNuOfF2sBAAAAAACHYHg5AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwDu2erVq2Wz2TRv3jyrS8mUs2fPqm3btsqXL59sNpvGjx//QPtLe/2rV692SH23U6xYMXXr1s3p7WZn/zzmAAA8KEI3ALio6dOny2azyc/PTydPnsywvn79+qpUqZIFlWU9/fv317JlyzRkyBDNnDlTTz311G23tdls9h8PDw+FhoaqSZMmBN07GD58eLrjdruf+vXrO6S9xYsXa/jw4Q7Z14O6cuWK/Pz8ZLPZtGfPHqvLMUXae1Haj5eXlwoXLqxu3brd8r0JAJCel9UFAADuLCEhQR999JEmTJhgdSlZ1sqVK9WyZUsNHDgwU9s3btxYXbp0kWEYOnLkiD7//HM98cQT+vnnn9W0aVM9/vjjun79unx8fEyuPD2r2r2bNm3aqFSpUvbHMTEx+r//+z+1bt1abdq0sS8vWLCgQ9pbvHixPvvsM5cI3t99951sNptCQkL09ddf6/3337e6JNOMHDlSxYsXV3x8vP744w9Nnz5d69ev186dO+Xn52d1eQDgsgjdAODiqlWrpkmTJmnIkCEKDQ21uhynio2NVWBg4APv59y5c8qdO3emty9Tpow6depkf9y6dWtVqVJF48ePV9OmTeXh4WFJyLCq3bupUqWKqlSpYn984cIF/d///Z+qVKmS7ji6o1mzZqlZs2YKDw/X7NmzHRa6DcNQfHy8/P39HbI/R2jatKlq1qwpSerVq5fy58+v0aNH68cff1S7du0srg4AXBfDywHAxb399ttKSUnRRx99dMftjh49KpvNpunTp2dYZ7PZ0p0VTBsOvH//fnXq1Em5cuVScHCwhg4dKsMwdPz4cbVs2VJBQUEKCQnR2LFjb9lmSkqK3n77bYWEhCgwMFDPPPOMjh8/nmG7DRs26KmnnlKuXLkUEBCgevXq6ddff023TVpNu3fvVseOHZUnTx7VrVv3jq/58OHDeu6555Q3b14FBATokUce0c8//2xfnzYs1jAMffbZZ/bhsfeqcuXKyp8/v44cOSLp1tdWpw3337x5s2rXri1/f38VL15cX375ZYb9JSQk6L333lOpUqXk6+ursLAwvfnmm0pISLhjHXdqd/fu3WrQoIECAgJUuHBhjRkz5r7bjYyMVN26dZU7d27lyJFDZcuW1dtvv30PR+zW9u7dq7Zt2ypv3rzy8/NTzZo19eOPP6bbJikpSSNGjFDp0qXl5+enfPnyqW7duoqMjJQkdevWTZ999pmk9JcCpElNTdX48eNVsWJF+fn5qWDBgnrppZd0+fLldO0YhqH3339fRYoUUUBAgBo0aKBdu3bd0+s5duyY1q1bp/bt26t9+/Y6cuSIfvvtt1tuO2vWLNWqVUsBAQHKkyePHn/8cS1fvty+vlixYnr66ae1bNky1axZU/7+/vrf//4n6e79PM2ECRNUsWJFexs1a9bU7Nmz7euvXbumfv36qVixYvL19VWBAgXUuHFjbdmy5Z5ed5rHHntMknTo0CH7svr169/yMoJu3bqpWLFi9sdp71f/+c9/NHHiRJUsWVK+vr566KGH9Oeff6Z77pkzZ9S9e3cVKVJEvr6+KlSokFq2bKmjR4/eV90A4Gyc6QYAF1e8eHF16dJFkyZN0uDBgx16tvv5559X+fLl9dFHH+nnn3/W+++/r7x58+p///ufnnjiCY0ePVpff/21Bg4cqIceekiPP/54uud/8MEHstlseuutt3Tu3DmNHz9ejRo1UlRUlP0M3cqVK9W0aVPVqFFD7733njw8PDRt2jQ98cQTWrdunWrVqpVun88995xKly6tDz/8UIZh3Lb2s2fPqnbt2oqLi1OfPn2UL18+ffXVV3rmmWc0b948tW7dWo8//rhmzpypzp0724eM34/Lly/r8uXL6YZQ3267Zs2aqV27durQoYPmzp2r//u//5OPj4969Ogh6UYofOaZZ7R+/Xq9+OKLKl++vHbs2KGPP/5Y+/fv18KFC++rvqeeekpt2rRRu3btNG/ePL311luqXLmymjZtek/t7tq1S08//bSqVKmikSNHytfXVwcPHszwJcm92rVrl+rUqaPChQtr8ODBCgwM1Ny5c9WqVSvNnz9frVu3lnTjy5dRo0apV69eqlWrlqKjo7Vp0yZt2bJFjRs31ksvvaRTp04pMjJSM2fOzNDOSy+9pOnTp6t79+7q06ePjhw5ov/+97/aunWrfv31V3l7e0uShg0bpvfff1/NmjVTs2bNtGXLFjVp0kSJiYmZfk3ffPONAgMD9fTTT8vf318lS5bU119/rdq1a6fbbsSIERo+fLhq166tkSNHysfHRxs2bNDKlSvVpEkT+3b79u1Thw4d9NJLL6l3794qW7Zspvq5JE2aNEl9+vRR27Zt1bdvX8XHx2v79u3asGGDOnbsKEl6+eWXNW/ePL322muqUKGCLl68qPXr12vPnj2qXr36vf1CJXvozZMnzz0/N83s2bN17do1vfTSS7LZbBozZozatGmjw4cP239Xzz77rHbt2qXXX39dxYoV07lz5xQZGaljx46lC/IA4LIMAIBLmjZtmiHJ+PPPP41Dhw4ZXl5eRp8+fezr69WrZ1SsWNH++MiRI4YkY9q0aRn2Jcl477337I/fe+89Q5Lx4osv2pclJycbRYoUMWw2m/HRRx/Zl1++fNnw9/c3unbtal+2atUqQ5JRuHBhIzo62r587ty5hiTjk08+MQzDMFJTU43SpUsbTz75pJGammrfLi4uzihevLjRuHHjDDV16NAhU8enX79+hiRj3bp19mXXrl0zihcvbhQrVsxISUlJ9/pfffXVTO1XktGzZ0/j/Pnzxrlz54wNGzYYDRs2NCQZY8eOTff6V61aZX9evXr10m1jGIaRkJBgVKtWzShQoICRmJhoGIZhzJw50/Dw8EhXt2EYxpdffmlIMn799Vf7svDw8Fse91u1O2PGjHTthoSEGM8++6x9WWbb/fjjjw1Jxvnz5zN1vG7l/PnzGfpcw4YNjcqVKxvx8fH2ZampqUbt2rWN0qVL25dVrVrVaN68+R33/+qrrxq3+gizbt06Q5Lx9ddfp1u+dOnSdMvPnTtn+Pj4GM2bN0/XL99++21DUrpjfieVK1c2XnjhhXTPz58/v5GUlGRfduDAAcPDw8No3bp1uj5pGEa6tsPDww1JxtKlS9Ntk9l+3rJly3TvB7eSK1euTP87uFnae9GKFSuM8+fPG8ePHzfmzZtnBAcHG76+vsbx48ft29arV8+oV69ehn107drVCA8Ptz9Oe7/Kly+fcenSJfvyH374wZBkLFq0yDCMG+8/kox///vf91w3ALgKhpcDQBZQokQJde7cWRMnTtTp06cdtt9evXrZ/9/T01M1a9aUYRjq2bOnfXnu3LlVtmxZHT58OMPzu3Tpopw5c9oft23bVoUKFdLixYslSVFRUTpw4IA6duyoixcv6sKFC7pw4YJiY2PVsGFDrV27Vqmpqen2+fLLL2eq9sWLF6tWrVrphqDnyJFDL774oo4ePardu3dn7iDcwpQpUxQcHKwCBQro4Ycf1q+//qoBAwaoX79+d3yel5eXXnrpJftjHx8fvfTSSzp37pw2b94s6cbEW+XLl1e5cuXsx+PChQt64oknJEmrVq2653pz5MiR7tppHx8f1apVK93vLLPtpl37/sMPP2T43dyvS5cuaeXKlWrXrp2uXbtmb/vixYt68skndeDAAfss2Llz59auXbt04MCBe27nu+++U65cudS4ceN0r7FGjRrKkSOH/TWuWLFCiYmJev3119MNTb/b7/dm27dv144dO9ShQwf7sg4dOujChQtatmyZfdnChQuVmpqqYcOGycMj/ceuf17qULx4cT355JPplmW2n+fOnVsnTpzIMDT7Zrlz59aGDRt06tSpTL/OmzVq1EjBwcEKCwtT27ZtFRgYqB9//FFFihS5r/1JN0bb3HymPG3Ielrf9ff3l4+Pj1avXp3hEgEAyCoI3QCQRbz77rtKTk6+67Xd96Jo0aLpHufKlUt+fn7Knz9/huW3+sBbunTpdI9tNptKlSplH3aaFpy6du2q4ODgdD+TJ09WQkKCrl69mm4fxYsXz1Ttf/31l8qWLZthefny5e3r71fLli0VGRmpFStWaMOGDbpw4YLGjh2bITT9U2hoaIaJ38qUKSNJ6Y7Jrl27MhyPtO3OnTt3z/UWKVIkQ4DLkydPut9ZZtt9/vnnVadOHfXq1UsFCxZU+/btNXfu3AcK4AcPHpRhGBo6dGiG9t9777107Y8cOVJXrlxRmTJlVLlyZQ0aNEjbt2/PVDsHDhzQ1atXVaBAgQztxMTE2NtI6xv/7L/BwcGZHio9a9YsBQYGqkSJEjp48KAOHjwoPz8/FStWTF9//bV9u0OHDsnDw0MVKlS46z5v1fcz28/feust5ciRQ7Vq1VLp0qX16quvZrgkYMyYMdq5c6fCwsJUq1YtDR8+/JZfpt3OZ599psjISM2bN0/NmjXThQsX5Ovrm+nn38o/34PSjn9a3/X19dXo0aO1ZMkSFSxYUI8//rjGjBmjM2fOPFC7AOBMXNMNAFlEiRIl1KlTJ02cOFGDBw/OsP52E4SlpKTcdp+enp6ZWibpjtdX305aUPv3v/+tatWq3XKbHDlypHvsCrM1FylSRI0aNTJl36mpqapcubLGjRt3y/VhYWH3vM/M/M4y266/v7/Wrl2rVatW6eeff9bSpUv17bff6oknntDy5ctv29adpPWDgQMHZjiTmybtevnHH39chw4d0g8//KDly5dr8uTJ+vjjj/Xll1+mG5lxu3YKFCiQLvTeLDg4+J5rvxXDMPTNN98oNjb2lmH63LlziomJydC37+ZB+n758uW1b98+/fTTT1q6dKnmz5+vzz//XMOGDdOIESMkSe3atdNjjz2m77//XsuXL9e///1vjR49WgsWLLBf+38ntWrVss9e3qpVK9WtW1cdO3bUvn377K81beLCf7rd+1Bm+m6/fv3UokULLVy4UMuWLdPQoUM1atQorVy5UhEREXetGwCsRugGgCzk3Xff1axZszR69OgM69LOEF25ciXd8gc543s3/xwCbBiGDh48aL99VMmSJSVJQUFBDg+x4eHh2rdvX4ble/futa93tlOnTmW4zdn+/fslyT7hU8mSJbVt2zY1bNjwvmZSv1/30q6Hh4caNmyohg0baty4cfrwww/1zjvvaNWqVff1eyxRooQkydvbO1PPz5s3r7p3767u3bsrJiZGjz/+uIYPH24P3berv2TJklqxYoXq1KlzxwCb1jcOHDhgr02Szp8/n6khzGvWrNGJEyc0cuRI+xnnNJcvX9aLL76ohQsXqlOnTipZsqRSU1O1e/fu237xdCf30s8DAwP1/PPP6/nnn1diYqLatGmjDz74QEOGDLHfaq5QoUJ65ZVX9Morr+jcuXOqXr26Pvjgg0yF7pt5enpq1KhRatCggf773//avwjMkyfPLc+eP+j7UMmSJfXGG2/ojTfe0IEDB1StWjWNHTtWs2bNeqD9AoAzMLwcALKQkiVLqlOnTvrf//6XYXhlUFCQ8ufPr7Vr16Zb/vnnn5tWz4wZM3Tt2jX743nz5un06dP2D/A1atRQyZIl9Z///EcxMTEZnn/+/Pn7brtZs2bauHGjfv/9d/uy2NhYTZw4UcWKFcvUcF5HS05Ott/mSZISExP1v//9T8HBwapRo4akG2cbT548qUmTJmV4/vXr1xUbG2tKbZlt99KlSxnWp4XFu93S7HYKFCig+vXr63//+98t5yS4uR9cvHgx3bocOXKoVKlS6dpO+1Ljn18wtWvXTikpKfrXv/6VoY3k5GT79o0aNZK3t7cmTJiQ7ozq+PHjM/V60oaWDxo0SG3btk3307t3b5UuXdp+tr1Vq1by8PDQyJEjMwzRz8zokcz2838eNx8fH1WoUEGGYSgpKUkpKSkZLuUoUKCAQkND7/v3Wr9+fdWqVUvjx49XfHy8pBvvUXv37k33O922bdt9z34fFxdn33eakiVLKmfOnPddNwA4G2e6ASCLeeeddzRz5kzt27dPFStWTLeuV69e+uijj9SrVy/VrFlTa9eutZ9pNUPevHlVt25dde/eXWfPntX48eNVqlQp9e7dW9KNM6aTJ09W06ZNVbFiRXXv3l2FCxfWyZMntWrVKgUFBWnRokX31fbgwYP1zTffqGnTpurTp4/y5s2rr776SkeOHNH8+fPvev21GUJDQzV69GgdPXpUZcqU0bfffquoqChNnDjRfvujzp07a+7cuXr55Ze1atUq1alTRykpKdq7d6/mzp1rv0+zo2W23ZEjR2rt2rVq3ry5wsPDde7cOX3++ecqUqTIXe+bfiefffaZ6tatq8qVK6t3794qUaKEzp49q99//10nTpzQtm3bJEkVKlRQ/fr1VaNGDeXNm1ebNm2y3+YqTdoXGH369NGTTz4pT09PtW/fXvXq1dNLL72kUaNGKSoqSk2aNJG3t7cOHDig7777Tp988onatm2r4OBgDRw4UKNGjdLTTz+tZs2aaevWrVqyZEmG+Qz+KSEhQfPnz1fjxo3tZ4//6ZlnntEnn3yic+fOqVSpUnrnnXf0r3/9S4899pjatGkjX19f/fnnnwoNDdWoUaPu2F5m+3mTJk0UEhKiOnXqqGDBgtqzZ4/++9//qnnz5sqZM6euXLmiIkWKqG3btqpatapy5MihFStW6M8//9TYsWMz/Xv8p0GDBum5557T9OnT9fLLL6tHjx4aN26cnnzySfXs2VPnzp3Tl19+qYoVKyo6Ovqe979//341bNhQ7dq1U4UKFeTl5aXvv/9eZ8+eVfv27e+7bgBwKotmTQcA3MXNtwz7p65duxqSMtwiKC4uzujZs6eRK1cuI2fOnEa7du2Mc+fO3faWYf+8LVTXrl2NwMDADO398/Zkabeu+uabb4whQ4YYBQoUMPz9/Y3mzZsbf/31V4bnb9261WjTpo2RL18+w9fX1wgPDzfatWtn/PLLL3et6U4OHTpktG3b1sidO7fh5+dn1KpVy/jpp58ybKd7vGXY3ba93a27KlasaGzatMl49NFHDT8/PyM8PNz473//m+H5iYmJxujRo42KFSsavr6+Rp48eYwaNWoYI0aMMK5evWrfLrO3DLvVraL+eYumzLb7yy+/GC1btjRCQ0MNHx8fIzQ01OjQoYOxf//+Ox6Tm93qlmGGceP31aVLFyMkJMTw9vY2ChcubDz99NPGvHnz7Nu8//77Rq1atYzcuXMb/v7+Rrly5YwPPvjAfss1w7hxe7vXX3/dCA4ONmw2W4bbh02cONGoUaOG4e/vb+TMmdOoXLmy8eabbxqnTp2yb5OSkmKMGDHCKFSokOHv72/Ur1/f2LlzZ4Zj/k/z5883JBlTpky57TarV69Od+s8wzCMqVOnGhEREfbjXq9ePSMyMtK+Pjw8/La3SstMP//f//5nPP744/Z/YyVLljQGDRpk/70mJCQYgwYNMqpWrWrkzJnTCAwMNKpWrWp8/vnnt30dae70XpSSkmKULFnSKFmypJGcnGwYhmHMmjXLKFGihOHj42NUq1bNWLZs2W1vGXarW4Hd3HcuXLhgvPrqq0a5cuWMwMBAI1euXMbDDz9szJ079651A4CrsBnGfcyMAwAA0qlfv74uXLignTt3Wl0KAABwIVzTDQAAAACASQjdAAAAAACYhNANAAAAAIBJuKYbAAAAAACTcKYbAAAAAACTELoBAAAAADCJl9UFuKLU1FSdOnVKOXPmlM1ms7ocAAAAAICLMQxD165dU2hoqDw8bn8+m9B9C6dOnVJYWJjVZQAAAAAAXNzx48dVpEiR264ndN9Czpw5Jd04eEFBQRZXA1eQlJSk5cuXq0mTJvL29ra6HMCh6N9wZ/RvuDP6N9xZVujf0dHRCgsLs+fH2yF030LakPKgoCBCNyTd+EcfEBCgoKAgl/1HD9wv+jfcGf0b7oz+DXeWlfr33S5JZiI1AAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCQuFbrXrl2rFi1aKDQ0VDabTQsXLky3fsGCBWrSpIny5csnm82mqKioDPs4dOiQWrdureDgYAUFBaldu3Y6e/asc14AgAeWkpqi1UdX65sd32j10dVKSU2xuiQAQCbw/g0At+ZSoTs2NlZVq1bVZ599dtv1devW1ejRo2+7vkmTJrLZbFq5cqV+/fVXJSYmqkWLFkpNTTWzdAAOsGDPAhX7pJgafNVAHRd0VIOvGqjYJ8W0YM8Cq0sDANwB798AcHteVhdws6ZNm6pp06a3Xd+5c2dJ0tGjR2+5/tdff9XRo0e1detWBQUFSZK++uor5cmTRytXrlSjRo0cXjMAx1iwZ4Hazm0rQ0a65SejT6rt3Laa126e2pRvY1F1AIDb4f0bAO7Mpc50P6iEhATZbDb5+vral/n5+cnDw0Pr16+3sDIAd5KSmqK+S/tm+MAmyb6s39J+DFUEABfD+zcA3J1Lnel+UI888ogCAwP11ltv6cMPP5RhGBo8eLBSUlJ0+vTp2z4vISFBCQkJ9sfR0dGSpKSkJCUlJZleN1xfWj+gP5hjzV9rdCL6xG3XGzJ0PPq4Vh1epXrh9ZxYWfZA/4Y7o3+bi/dva9G/4c6yQv/ObG1uFbqDg4P13Xff6f/+7//06aefysPDQx06dFD16tXl4XH7k/qjRo3SiBEjMixfvny5AgICzCwZWUxkZKTVJbiltZfXZmq7JeuXKHZXrMnVZF/0b7gz+rc5eP92DfRvuDNX7t9xcXGZ2s6tQrckNWnSRIcOHdKFCxfk5eWl3LlzKyQkRCVKlLjtc4YMGaIBAwbYH0dHRyssLExNmjSxXxuO7C0pKUmRkZFq3LixvL29rS7H7QT+Fahxf42763ZN6zblTIkJ6N9wZ/Rvc/H+bS36N9xZVujfaSOk78btQnea/PnzS5JWrlypc+fO6Zlnnrnttr6+vumuA0/j7e3tsr9gWIM+YY4GJRqoSFARnYw+ecvrAm2yqUhQETUo0UCeHp4WVJg90L/hzujf5mhQooFCc4bq1LVTt1zP+7dz0L/hzly5f2e2LpcK3TExMTp48KD98ZEjRxQVFaW8efOqaNGiunTpko4dO6ZTp268se/bt0+SFBISopCQEEnStGnTVL58eQUHB+v3339X37591b9/f5UtW9b5LwhApnh6eOqTpz7Rs3OfzbDOJpskafxT4/nABgAuxtPDU9VDqt82dEu8fwOAS81evmnTJkVERCgiIkKSNGDAAEVERGjYsGGSpB9//FERERFq3ry5JKl9+/aKiIjQl19+ad/Hvn371KpVK5UvX14jR47UO++8o//85z/OfzEA7kmb8m1UOm/pDMuDA4O53QwAuKhd53ZpycElkqT8AfnTrfO0eWpu27m8fwPI9lzqTHf9+vVlGBmHlqbp1q2bunXrdsd9fPTRR/roo48cXBkAs+04u0MHLh248SHtubn619p/KepMlN557B0+sAGACzIMQ32X9lWKkaJW5Vpp3nPztO7YOv119S+99vNrikmKUaBPoNVlAoDlXOpMN4Dsa/KWyZKkVuVaqU35NmpRpoUkKepMlIVVAQBuZ8GeBfrlyC/y8/LTuCbj5OnhqfrF6qtr1a7qEdFDkjR562SLqwQA6xG6AVguPjleM7fPlCT1qt5LklS9UHVJ0pbTWyyrCwBwa3FJcRqw/MadX96s/aaK5ymebn3ae/mP+37U2ZizTq8PAFwJoRuA5RbsWaDL8ZdVNFdRNS7RWNLfoXvX+V2KT463sjwAwD+MXj9ax64eU9FcRfVW3bcyrK9csLIeLvywklOT9dW2ryyoEABcB6EbgOUmbZkkSeoZ0dM+w21YUJjyB+RXcmqydpzdYWV5AICbHLl8RKN/HS1JGtdknAK8A265Xe/qvSXduHzoTnP2AIC7I3QDsNSBiwe0+uhqedg81L1ad/tym83GEHMAcEEDlg9QQkqCGhZveMeJLp+v9Lxy+OTQgUsHtOavNU6sEABcC6EbgKWmbJ0iSXqq1FMKyxWWbl31kBuhe/PpzU6vCwCQ0fJDy7Vw70J5eXjp06afymaz3XbbHD451KFSB0l/T5YJANkRoRuAZZJSkjQ9arqkv4ch3qxGaA1JnOkGAFeQmJKoPkv6SJJer/W6KgRXuOtz0t7b5+2ep0vXL5laHwC4KkI3AMss2r9IZ2PPqmBgQTUv3TzD+rTh5TvO7VBiSqKzywMA3OTTDZ9q38V9KhhYUO/Vey9Tz6kZWlNVClZRQkqCZm2fZXKFAOCaCN0ALJM23LB7te7y9vTOsL547uLK7ZdbiSmJ2nVul7PLAwD8f6evndaINSMkSR81+ki5/HJl6nk2m81+tnvSlklMqAYgWyJ0A7DEsavHtPTgUklSz+o9b7kNk6kBgGt4a8VbikmM0cOFH1aXql3u6bkvVH5Bfl5+2nlupzae3GhShQDgugjdACwxdetUGTLUoFgDlcpb6rbbpU2mRugGAGv8dvw3zdw+UzbZNKHpBHnY7u3jYx7/PGpboa2kv28RCQDZCaEbgNOlpKZo6tapkm49gdrN0s50M4M5ADhfSmqKXlv8miSpR0QPPVT4ofvaT9p7/Zydc3Qt4ZrD6gOArIDQDcDplh9aruPRx5XXP69al299x23TQve2s9uUnJrsjPIAAP/f5C2TtfXMVuXyzaUPG3543/t5rOhjKpOvjGKTYjVn5xwHVggAro/QDcDpJm+9MYFa5yqd5efld8dtS+crrRw+ORSfHK+9F/Y6ozwAgKRL1y/pnZXvSJJGNhipAoEF7ntfNptNvSJ6Sfr7bwAAZBeEbgBOdTbmrH7c96MkqVf1Xnfd3sPmoYiQCEnS5lMMMQcAZxm6cqguXr+oSgUq6ZWHXnng/XWt1lVeHl7aeHKjtp/d7oAKASBrIHQDcKrpUdOVnJqsR4o8okoFKmXqOcxgDgDOte3MNn25+UtJ0oSmE+Tl4fXA+ywQWEAty7aUJE3azIRqALIPQjcApzEMwz6s8G4TqN2sRqEakqQtZwjdAGA2wzD0+pLXlWqkql3FdqpfrL7D9p323j9rxyxdT7rusP0CgCsjdANwmjV/rdHBSweV0yen2lVsl+nnpZ3p3np6q1JSU8wqDwAg6Zud32jdsXUK8A7Qfxr/x6H7blyyscJzhetK/BXN3zPfofsGAFdF6AbgNGn3Z+1QqYNy+OTI9PPK5i8rfy9/xSbF6sClA2aVBwDZXkxijAZFDpIkvV33bYXlCnPo/j1sHuoR0UMS9+wGkH0QugE4xaXrlzR/942zGr1rZH5ouSR5eXipWkg1SVzXDQBmen/t+zp17ZRK5CmhN2q/YUobPSJ6yMPmobV/rdX+i/tNaQMAXAmhG4BTzNo+SwkpCapasKr9Gu17kTbEnBnMAcAc+y/u17jfx0mSxj85/q63dLxfRYKKqGmpppJu3AccANwdoRuA6QzDsA8j7F29t2w22z3vwz6DOZOpAYDDGYahfkv7KSk1SU1LNdXTZZ42tb20W0ZOj5quxJREU9sCAKsRugGYbuPJjdp5bqf8vPz0QpUX7msf9hnMT29RqpHqyPIAINv7+cDPWnJwibw9vDX+qfH39eXovWheurlCcoTofNx5Ldq3yNS2AMBqhG4Apks7y/1cheeU2y/3fe2jQnAF+Xj6KDohWkcuH3FgdQCQvcUnx6vf0n6SpAGPDlCZfGVMb9Pb01vdq3WXxIRqANwfoRuAqa4lXNOcnXMk3du9uf/J29NbVQpWkSRtPs113QDgKON+H6dDlw8pNGeo3n38Xae12zOipyRp+aHlOnrlqNPaBQBnI3QDMNWcnXMUmxSrsvnKqm7Rug+0r5uHmAMAHtzxq8f1wboPJEn/bvzve7qd44Mqmbeknij+hAwZmrZ1mtPaBQBnI3QDMFXasMFe1Xs98DWC9snUCN0A4BCDIgcpLilOjxV9TB0qdXB6+70ibkyoNjVqqlJSU5zePgA4A6EbgGm2ndmmP0/9KW8Pb3Wt2vWB92e/bdjpzTIM44H3BwDZ2eqjq/Xtrm/lYfPQp00/NX3ytFtpXb618vrn1YnoE1p2aJnT2wcAZyB0AzBN2v1XW5VrpeDA4AfeX+UCleXl4aVL1y/p2NVjD7w/AMiuklOT1WdJH0nSSzVeUrWQapbU4eflpy5VukhiQjUA7ovQDcAU15Oua9aOWZL+vh/rg/L18lWlApUkMcQcAB7EF39+oR3ndiivf179q8G/LK0l7W/Eon2LdPraaUtrAQAzELoBmGL+nvm6En9F4bnC1ahEI4ftt3rI30PMAQD37nzseQ1bPUyS9METHyhfQD5L66lYoKIeLfKoUowUfbXtK0trAQAzELoBmCJtmGDPiJ7ysDnuraZGKDOYA8CDePuXt3Ul/ooiQiIe6FaOjpRWx+Qtk5VqpFpcDQA4FqEbgMPtu7BPa/9aKw+bh7pHdHfovplMDQDu36ZTmzRl6xRJ0oSmE+Tp4WlxRTe0q9hOOX1y6tDlQ1p9dLXV5QCAQxG6AThc2ge6ZqWbqUhQEYfuu0rBKvKweehc7DmdunbKofsGAHeWaqTqtcWvyZChTlU6qU7ROlaXZBfoE6iOlTtK+nsSTgBwF4RuAA6VmJKo6VHTJf19/1VHCvAOUIXgCpIYYg4A92LGthnacHKDcvjk0JhGY6wuJ4O0Iebz98zXxbiLFlcDAI5D6AbgUIv2LdL5uPMqlKOQmpdpbkobaUPMCd0AkDlX46/qrRVvSZKGPT5MhXIWsriijKoXqq5qIdWUmJKomdtnWl0OADgMoRuAQ6VNoNa9Wnd5eXiZ0gYzmAPAvRmxZoTOxZ5T2Xxl1feRvlaXc0s2my3dhGrM2wHAXRC6ATjM0StHtfzQcklSj4geprXDmW4AyLzd53drwsYJkqRPnvpEPp4+Fld0ex0rd5S/l792nd+lP078YXU5AOAQhG4ADjNt6zQZMtSweEOVzFvStHaqhVSTTTadvHZSZ2POmtYOAGR1hmGoz5I+Sk5NVsuyLfVkqSetLumOcvvl1nMVn5PEhGoA3AehG4BDpKSmaGrUVEky/b6vOX1zqky+MpI42w0Ad7JgzwL9cuQX+Xr6atyT46wuJ1PS/obM2TVH0QnRFlcDAA+O0A3AIZYeXKoT0SeUzz+fWpVrZXp7DDEHgDuLS4rTgOUDJElv1nlTJfKUsLiizKkTVkfl8pdTXFKcvtnxjdXlAMADI3QDcIjJW28MA+xStYt8vXxNb69GoRqSpC1nCN0AcCuj14/WsavHVDRXUQ2uO9jqcjLNZrPZbzmZ9rcFALIyQjeAB3b62mkt2rdIktSruuPvzX0raWe6N59iBnMA+Kcjl49o9K+jJUljm4xVgHeAxRXdmy5Vu8jbw1ubTm1S1Jkoq8sBgAdC6AbwwKZHTVeKkaLaYbVVIbiCU9qMKBQhSfrr6l+6GHfRKW0CQFYxYPkAJaQk6IniT+jZ8s9aXc49Cw4Mtl+qNGnzJGuLAYAHROgG8EBSjVRN2TpFkvkTqN0st19ulcxzY4b0rWe2Oq1dAHB1yw8t18K9C+Vp89SnT30qm81mdUn3Je1vytc7vlZcUpzF1QDA/SN0A3ggq4+u1qHLhxTkG6TnKjzn1LYZYg4A6SWmJKrv0r6SpNdrva6KBSpaXNH9a1iioYrlLqarCVc1b/c8q8sBgPtG6AbwQNLuo9qxUkcF+gQ6tW37DOZMpgYAkqQJGyZo74W9KhBYQMPrD7e6nAfiYfNQz4iekqRJWxhiDiDrInQDuG8X4y5q/p75kqTeNZw3tDyNfQZzbhsGADp97bRGrBkhSfqo4UfK5ZfL4ooeXPdq3eVh89D6Y+u198Jeq8sBgPtC6AZw32Zun6nElERFhETYzzo7U9pkagcvHdTV+KtObx8AXMngXwbrWuI11SpcS12rdbW6HIcoHFRYzUo3k/T3yCoAyGoI3QDui2EY9g9AzpxA7Wb5A/KraK6ikphMDUD29tvx3zRj2wxJ0oSmE+Rhc5+PeGl/Y77a9pUSUxItrgYA7p37vCMDcKo/TvyhXed3yd/LXx0rd7SsDoaYA8juUlJT9PqS1yVJPar1UK3CtSyuyLGalW6mQjkK6ULcBf2w9werywGAe0boBnBf0ia1aVexnaXXDdonUyN0A8impmydoi2ntyiXby6NajTK6nIczsvDS92rdZfEhGoAsiZCN4B7Fp0QrW93fSvJuqHlaey3DTvNbcMAZD+Xrl/S27+8LUkaUX+ECgQWsLgic/SsfmMW88jDkTpy+YjF1QDAvSF0A7hn3+z4RnFJcSqfv7xqh9W2tJa04eX7LuxTTGKMpbUAgLMNWzVMF69fVMXginrloVesLsc0JfKUUKMSjSRJU7dOtbgaALg3hG4A9yxteF+v6r1ks9ksraVgjoIKzRkqQ4a2ndlmaS0A4EzbzmzTF5u+kHRj8jRvT2+LKzJXr4hekqSpUVOVnJpscTUAkHmEbgD3ZOvprdp8erO8PbzVpWoXq8uRxBBzANmPYRh6fcnrSjVS9VyF59SgeAOrSzJdq3KtlM8/n05dO6WlB5daXQ4AZBqhG8A9SbtNWOvyrZU/IL/F1dzADOYAsps5O+do3bF18vfy13+a/MfqcpzC18tXXaveuP84E6oByEoI3QAyLS4pTl/v+FqS9ROo3YwZzAFkJzGJMRoYOVCS9PZjb6torqIWV+Q8varfGGL+8/6fderaKYurAYDMIXQDyLR5u+fpasJVFc9dXE8Uf8LqcuzSQvfu87t1Pem6xdUAgLk+WPuBTl07pRJ5Smhg7YFWl+NU5YPLq05YHaUYKZoeNd3qcgAgUwjdADItbThfz4ie8rC5zttH4ZyFVSCwgFKMFG0/u93qcgDANAcuHtDY38dKkj5+8mP5eflZXJHzpY20mrxlslKNVIurAYC7c51PzQBc2t4Le7X+2Hp52DzUPaK71eWkY7PZGGIOIFvot6yfklKT9FSpp9SiTAury7FE2wptFeQbpCNXjmjVkVVWlwMAd0XoBpApaROoNS/dXKE5Qy2uJqPqIcxgDsC9/bT/Jy0+sFjeHt765KlPLL9lo1UCfQL1QuUXJDGhGoCsgdAN4K4SkhP01bavJLnWBGo340w3AHcWnxyvfkv7SZL6P9JfZfKVsbYgi6VNqPb93u91Ie6CxdUAwJ0RugHc1Y/7ftSFuAsKzRmqpqWbWl3OLdUIvXHbsJ3ndiohOcHiagDAscb9Pk6HLh9SoRyF9O7j71pdjuWqF6qu6oWqKzElUTO3zbS6HAC4I0I3gLtKG77XvVp3eXl4WVzNrYXnClcevzxKSk3SznM7rS4HABzm+NXj+mDdB5Kkfzf+t3L65rS4IteQNvJq0pZJMgzD4moA4PYI3QDu6MjlI4o8HCnpxqzlrorJ1AC4q0GRgxSXFKe6ReuqY+WOVpfjMjpU6qAA7wDtubBHvx3/zepyAOC2CN0A7mjq1qmSpMYlGqt4nuIWV3NnNQrdGGJO6AbgLlYfXa1vd30rD5uHJjSdkG0nT7uVXH651K5iO0nS5K2TLa4GAG7PpUL32rVr1aJFC4WGhspms2nhwoXp1i9YsEBNmjRRvnz5ZLPZFBUVlWEfZ86cUefOnRUSEqLAwEBVr15d8+fPd84LANxMcmqypkbdCN1pk9a4srQz3cxgDsAdJKcmq8+SPpKkF6u/qGoh1awtyAWlDTH/due3uhp/1eJqAODWXCp0x8bGqmrVqvrss89uu75u3boaPXr0bffRpUsX7du3Tz/++KN27NihNm3aqF27dtq6datZZQNua+nBpTp17ZTyB+RXy7ItrS7nrtJC9/az25WUkmRxNQDwYL7c9KV2nNuhvP559f4T71tdjkt6tMijKp+/vK4nX9fsHbOtLgcAbsmlQnfTpk31/vvvq3Xr1rdc37lzZw0bNkyNGjW67T5+++03vf7666pVq5ZKlCihd999V7lz59bmzZz5Au5V2gRqXat2la+Xr8XV3F3JvCUV5BukhJQE7bmwx+pyAOC+nY89r6GrhkqS3m/wvvIF5LO4Itdks9nsZ7sZYg7AVblU6HaE2rVr69tvv9WlS5eUmpqqOXPmKD4+XvXr17e6NCBLOXXtlH7e/7Mk155A7WYeNg9FhERIkjaf4os2AFnXOyvf0ZX4K6oWUk0v1njR6nJcWueqneXj6aMtp7cwpwcAl+Sa9/55AHPnztXzzz+vfPnyycvLSwEBAfr+++9VqlSp2z4nISFBCQl/39c3OjpakpSUlKSkJIaoQvZ+kJ36w5TNU5RipKhOkToqlbtUlnnt1QpW05q/1mjTyU3qVKmT1eVkCdmxfyP7yIr9e/PpzZq85cZZ248bf6zUlFSlpqRaXJXryuWdSy3LtNR3e77TxE0TNeGpCVaX5DRZsX8DmZUV+ndma3O70D106FBduXJFK1asUP78+bVw4UK1a9dO69atU+XKlW/5nFGjRmnEiBEZli9fvlwBAQFml4wsJDIy0uoSnCLVSNVne27MrVDDo4YWL15scUWZ53HpxgCelXtXanFK1qnbFWSX/o3sKav071QjVUMODJEhQ/Xy1NPVHVe1eAfvZXdTKbGSvtN3mhk1Uw2SGsjP08/qkpwqq/Rv4H64cv+Oi4vL1HY2wzAMk2u5LzabTd9//71atWqVYd3Ro0dVvHhxbd26VdWqVbMvP3TokEqVKqWdO3eqYsWK9uWNGjVSqVKl9OWXX96yrVud6Q4LC9OFCxcUFBTksNeErCspKUmRkZFq3LixvL29rS7HdL8c+UVNv2mqXL659FefvxTgnXW+fNpzYY+qTqyqAO8AXXzjojw9PK0uyeVlt/6N7CWr9e8Z22eo10+9lMMnh3a+tFOhOUOtLilLSDVSVf6L8jpy5YgmPz1ZXap0sbokp8hq/Ru4F1mhf0dHRyt//vy6evXqHXOjW53pTvumwcMj/aXqnp6eSk29/bAsX19f+fpmnCTK29vbZX/BsEZ26RPTt0+XJL1Q+QXlCshlbTH3qGLBigr0DlRsUqwORx9WheAKVpeUZWSX/o3sKSv076vxV/XOqnckSUMfH6rwvOEWV5S19KreS++sfEfTtk1TzxpZYy4SR8kK/Ru4X67cvzNbl0tNpBYTE6OoqCj7/bePHDmiqKgoHTt2TJJ06dIlRUVFaffu3ZKkffv2KSoqSmfOnJEklStXTqVKldJLL72kjRs36tChQxo7dqwiIyNvecYcQEYX4i7o+73fS8oa9+b+J08PT/u9bJlQB0BWMnLNSJ2NPasy+cqo3yP9rC4ny+lWrZs8bZ769fiv2n1+t9XlAICdS4XuTZs2KSIiQhERN2YfHjBggCIiIjRs2DBJ0o8//qiIiAg1b95cktS+fXtFRETYh417e3tr8eLFCg4OVosWLVSlShXNmDFDX331lZo1a2bNiwKymBnbZigxJVE1CtVQRKEIq8u5L2n36yZ0A8gq9pzfo083fipJ+uSpT+Tj6WNxRVlPaM5QNS9z4zNi2kR0AOAKXGp4ef369XWnS8y7deumbt263XEfpUuX1vz58x1cGZA9GIZh/6CSdt/TrCgtdG8+zW3DALg+wzDUZ2kfJacm65myz+ipUk9ZXVKW1bt6b/2470fN2DZDoxqOkq9XxssHAcDZXOpMNwBr/Xb8N+25sEcB3gHqULmD1eXctxqFakiStp7eqlSD2+wAcG3f7/1eKw6vkK+nrz5+8mOry8nSnir1lArnLKyL1y9q4d6FVpcDAJII3QBuMnnrjbPcz1d8XkG+WXfm/vLB5eXn5adridd06NIhq8sBgNuKS4rTgGUDJEmDag9SiTwlLK4oa/Py8FL3at0lSZO2TLK4GgC4gdANQNKNWXO/3fmtpKw9tFy68aGrSsEqkhhiDsC1jfl1jP66+pfCgsI05LEhVpfjFnpW7ymbbPrlyC86fPmw1eUAAKEbwA2zd8zW9eTrqhBcQY8UecTqch5Y2hBzJlMD4KqOXjmq0b+OliSNbTJWAd4BFlfkHorlLqbGJRtLkqZsmWJxNQBA6Abw/6UNLe9dvbdsNpvF1Tw4ZjAH4OoGLBug+OR4NSjWQG0rtLW6HLfSK+LGLS+nRU1TcmqyxdUAyO4I3QC05fQWbTm9RT6ePupUpZPV5TjEzaH7TndFAAArRB6K1Pd7v5enzVMTmk5wiy87XUnLci2VPyC/Tsec1uIDi60uB0A2R+gGoEmbb0w206Z8G+UPyG9xNY5RqUAleXt463L8ZR29ctTqcgDALjElUX2W9pEkvVbrNVUsUNHiityPj6ePulbtKokJ1QBYj9ANZHOxibGavXO2pKw/gdrNfDx9VLlgZUkMMQfgWiZsmKC9F/YqOCBYw+sPt7oct9Wr+o0h5osPLNbJ6JMWVwMgOyN0A9ncd7u/U3RCtErkKaH6xepbXY5DVQ+5McScGcwBuIrT105rxJoRkqSPGn2k3H65rS3IjZXLX06PFX1MqUaqpkVNs7ocANkYoRvI5tKG3fWK6CUPm3u9JTCZGgBXM/iXwbqWeE0PhT6kbtW6WV2O20s72z1l6xSlGqkWVwMgu3KvT9gA7snu87v12/Hf5GnzdMsPfzVC/75tGJOpAbDab8d/04xtMyRJE5pOcLsvOl1R2wptlcs3l45eOapfDv9idTkAsine7YFsbPKWG7cJe7rM0yqUs5DF1The5QKV5Wnz1Pm48zoRfcLqcgBkYympKXp9yeuSpO7VuuvhIg9bXFH2EOAdYL8rBxOqAbAKoRvIphKSE+xnXNxpArWb+Xv7q0JwBUkMMQdgralbp2rL6S0K8g3SqIajrC4nW0kbYr5w70Kdjz1vcTUAsiNCN5BNLdy7UBevX1ThnIX1VKmnrC7HNDcPMQcAK1y+fllvr3xbkjSi/ggVzFHQ4oqyl2oh1VQztKaSUpPsXzYDgDMRuoFsKm2YXY+IHvL08LS4GvMwgzkAqw1bNUwX4i6oQnAFvfrQq1aXky2ljeiatGUSc3wAcDpCN5ANHb58WL8c+UU22dQjoofV5ZiKGcwBWGn72e36fNPnkm5Mnubt6W1xRdlT+0rtFeAdoH0X92n9sfVWlwMgmyF0A9nQlC1TJEmNSzZWsdzFrC3GZNVCqskmm07HnNbpa6etLgdANmIYhl5f8rpSjVS1rdBWTxR/wuqSsq0g3yC1r9hekjR562SLqwGQ3RC6gWwmOTVZ06KmSXLfCdRuFugTqHL5y0nibDcA5/p217da+9da+Xv5a2yTsVaXk+31rnHjb953u77Tlfgr1hYDIFshdAPZzOIDi3U65rSCA4L1TNlnrC7HKRhiDsDZYhJjNHD5QEnSkLpDVDRXUYsrwsOFH1bF4Iq6nnxds3fMtrocANkIoRvIZtImUOtatat8PH0srsY5ahT6/zOYnyF0A3COD9d9qJPXTqp47uIaVGeQ1eVAks1mY0I1AJYgdAPZyInoE1p8YLGkv+9bmh1wphuAMx28dFBjf78xnPzjJz+Wn5efxRUhTacqneTj6aOoM1Hc1QKA0xC6gWxketR0pRqpejz8cZXNX9bqcpymWkg1SdKxq8d0Ie6CtcUAcHv9lvZTYkqiniz5ZLa5jCeryBeQT8+Wf1aSNHkLE6oBcA5CN5BNpBqpmrL1xqzlvSKyz1luScrll0ul85aWxNluAOb6ef/P+vnAz/L28NYnT30im81mdUn4h7Qh5rN3zFZMYozF1QDIDgjdQDbxy+FfdPTKUeX2y622FdpaXY7TMcQcgNkSkhPUb1k/SVK/R/plqxFFWUm9YvVUMk9JXUu8prm75lpdDoBsgNANZBNpE6h1qtxJ/t7+FlfjfGmhm2v4AJhl3O/jdPDSQRXKUUhDHx9qdTm4DQ+bh31eE4aYA3AGQjeQDZyPPa+FexdKyl4TqN3MPoM5Z7oBmOBE9Am9v+59SdKYxmOU0zenxRXhTrpV6yZPm6d+P/G7dp3bZXU5ANwcoRvIBmZsm6Gk1CQ9FPqQqoZUtbocS0QUipAkHb58WJevX7a4GgDuZlDkIMUlxalOWB29UPkFq8vBXYTkCFGLsi0k/T0SDADMQugG3JxhGPYPFNn1LLck5fXPq2K5i0mStp7Zam0xANzKmqNrNGfnHNlk04SmE5g8LYtIm1Bt5vaZik+Ot7gaAO6M0A24ufXH1mvfxX0K9A5Uh0odrC7HUgwxB+BoyanJen3J65KkF2u8aB9VA9f3ZMknVSSoiC5dv6Tv93xvdTkA3BihG3Bzk7femCSmfaX22f4aQ2YwB+BoX276UjvO7VAevzz64IkPrC4H98DTw1M9qvWQ9PffSgAwA6EbcGNX4q/ou13fScreQ8vTMIM5AEc6H3teQ1fdmKX8/SfeV76AfBZXhHvVI6KHbLJp5ZGVOnTpkNXlAHBThG7AjX29/WtdT76uSgUq6eHCD1tdjuXSQvf+i/sVnRBtcTUAsrp3Vr6jK/FXVLVgVb1U4yWry8F9CM8driYlm0ji9mEAzEPoBtzUzROo9a7em4l9JBUILKAiQUUkSdvObLO4GgBZ2aZTm+whbULTCfL08LS4ItyvtAnVpm+brqSUJIurAeCOCN2Am9p8erO2nd0mX09fdarSyepyXAZDzAE8qFQjVa8veV2GDHWs3FGPhT9mdUl4AC3KtlCBwAI6E3NGPx/42epyALghQjfgptLOwDxb4Vnl9c9rcTWuo3oIk6kBeDCzts/SHyf+UKB3oMY0GmN1OXhAPp4+6lq1qyTu2Q3AHIRuwA3FJMZo9o7Zkv4eNocbaoRy2zAA9y86IVpvRr4pSRr6+FAVDipscUVwhLTJRpceXKrjV49bXA0Ad0PoBtzQ3F1zdS3xmkrlLaV64fWsLselpA0v33Nhj2ITYy2uBkBWM3LNSJ2NPavSeUur3yP9rC4HDlImXxnVC6+nVCNV06KmWV0OADdD6AbcUNrQ8l4RvZhA7R8K5SikgoEFlWqkavvZ7VaXAyAL2XN+jz7Z8Ikk6ZOnPpGvl6/FFcGR0s52T9k6RSmpKRZXA8CdELoBN7Pr3C79fuJ3eXl4qWu1rlaX43JsNhtDzAHcM8Mw1HdpXyWnJqtFmRZqWrqp1SXBwZ4t/6xy++XWsavHtOLwCqvLAeBGCN2Am0mbBKZFmRYKyRFicTWuKW0yNWYwB5BZC/cuVOThSPl6+urjJz+2uhyYwN/bX52rdJbEhGoAHIvQDbiR+OR4zdw+UxITqN1J2nXdnOkGkBnXk65rwPIBkqSBtQeqZN6SFlcEs6QNMf9h3w86F3vO4moAuAtCN+BGvt/zvS5dv6SwoDA1KdnE6nJcVtrw8l3ndyk+Od7iagC4ujG/jtHRK0cVFhSmIXWHWF0OTFSlYBXVKlxLyanJ+irqK6vLAeAmCN2AG0kbDtcjooc8PTwtrsZ1hQWFKZ9/PiWnJmvH2R1WlwPAhR29clQf/fqRJOk/Tf6jQJ9AiyuC2dJGik3eOlmGYVhcDQB3QOgG3MTBSwe16ugq2WRTj4geVpfj0mw2G0PMAWTKG8vfUHxyvBoUa6DnKjxndTlwgucrPq9A70Dtv7hf646ts7ocAG6A0A24iSlbpkiSniz1pIrmKmpxNa6vRiFmMAdwZysOr9CCPQvkafPUp00/5RaM2URO35zqUKmDJCZUA+AYhG7ADSSlJGn6tumSmEAts+xnus8QugFklJSSpD5L+kiSXn3oVVUqUMniiuBMaROqzds9T5evX7a4GgBZHaEbcAM/H/hZZ2LOqEBgAT1d5mmry8kS0kL39rPblZiSaHE1AFzNhI0TtOfCHgUHBGtEgxFWlwMnq1W4lioXqKz45Hh9veNrq8sBkMURugE3kDb8rVvVbvLx9LG4mqyhRJ4SyuWbS4kpidp9frfV5QBwIWdizmj46uGSpFENRym3X25L64Hz2Ww2+8ixSVsmMaEagAdC6AayuONXj2vpwaWS/h4Oh7tjMjUAtzN4xWBdS7ymmqE11T2iu9XlwCIvVHlBvp6+2n52u/489afV5QDIwgjdQBY3LWqaUo1U1Quvp9L5SltdTpaSFro3n9pscSUAXMXvx3/XV9tu3J/5v03/Kw8bH5Wyq7z+edW2QltJ0uQtky2uBkBWxl8SIAtLSU3RlK03Zi1nArV7Z5/BnMnUAOjGe+rrS16XJHWr1k0PF3nY4opgtbS/rd/s/EYxiTEWVwMgqyJ0A1nYisMrdOzqMeXxy6NnKzxrdTlZTtqZ7m1ntik5NdniagBYberWqdp8erOCfIP0UcOPrC4HLuDx8MdVOm9pxSTG6Nud31pdDoAsitANZGFpE6h1qtJJfl5+FleT9ZTOV1o5fHLoevJ17b2w1+pyAFjo8vXLenvl25Kk4fWGq2COghZXBFdgs9ns86Vwz24A94vQDWRRZ2PO6od9P0hiaPn98rB5KCIkQhKTqQHZ3bBVw3Qh7oIqBFfQa7Ves7ocuJCuVbvKy8NLG05u0I6zO6wuB0AWROgGsqgZ22YoOTVZDxd+WJULVra6nCyLGcwBbD+7XZ9v+lyS9OlTn8rb09viiuBKCuYoqGfKPiOJCdUA3B9CN5AFGYahyVtv/OHnNmEPxj6D+WlmMAeyI8Mw9PqS15VqpOrZ8s+qYYmGVpcEF5Q2omzm9pmKT463uBoAWQ2hG8iC1h1bp/0X9yuHTw61r9Te6nKytLQZzLee3qpUI9XiagA427e7vtXav9bK38tfY5uMtbocuKjGJRqraK6iuhx/WfN3z7e6HABZDKEbyILSJnNpX7G9cvjksLiarK1s/rLy9/JXbFKsDlw8YHU5AJwoNjFWA5cPlCQNrjtY4bnDLa4IrsrTw1M9qvWQJPtIMwDILEI3kMVcvn5Z83bPkyT1rsEEag/Ky8NLVUOqSmKIOZDdfLjuQ528dlLFchfToNqDrC4HLq5HRA/ZZNPqo6v5khbAPSF0A1nM1zu+VnxyvKoUrKKHQh+yuhy3UD2EydSA7ObgpYP6z+//kSR9/OTH8vf2t7giuLqwXGF6qtRTkphQDcC9IXQDWYhhGPah5b0ieslms1lckXuoEXrjum5CN5B99F/WX4kpiWpSsolalm1pdTnIItImVJu+bbqSUpIsrgZAVkHoBrKQP0/9qe1nt8vPy0+dqnSyuhy3cfNtwwzDsLgaAGZbfGCxftr/k7w8vPTJU5/wBSYy7ekyT6tgYEGdiz2nRfsXWV0OgCyC0A1kIWnD2dpWaKs8/nksrsZ9VAiuIB9PH11NuKrDlw9bXQ4AEyUkJ6jv0r6SpH4P91O5/OUsrghZibent7pV6ybp70lNAeBuCN1AFhGTGKNvdn4j6cbQcjiOj6ePqhSsIokh5oC7+/iPj3Xw0kGF5AjR0HpDrS4HWVCv6jf+Bi87uEzHrh6zuBoAWQGhG8gi5uyco5jEGJXJV0aPhz9udTluJ20yNWYwB9zXyeiTen/t+5KkMY3GKMg3yOKKkBWVyltKDYo1kCFDU7dOtbocAFkAoRvIItKGljOBmjluvq4bgHsaFDlIsUmxqh1Wm3kx8EDSznZP3TpVKakpFlcDwNURuoEsYMfZHdpwcoO8PLzUpWoXq8txSzfPYM5kaoD7WXdsnb7Z+Y1ssmlC0wl8eYkH0qZ8G+Xxy6Pj0ce1/NByq8sB4OJcKnSvXbtWLVq0UGhoqGw2mxYuXJhu/YIFC9SkSRPly5dPNptNUVFR6dYfPXpUNpvtlj/fffed816IyVJSU7T66Gp9s+MbrT66mm9Ys4G0s9wty7ZUwRwFLa7GPVUqUEleHl66eP0i1+jBNLx/O1dKaorW/LVGqy+tVq+fbpyZ7F29t31kC3C//Lz81LlKZ0lMqAbg7rysLuBmsbGxqlq1qnr06KE2bdrccn3dunXVrl079e7dO8P6sLAwnT59Ot2yiRMn6t///reaNm1qWt3OtGDPAvVd2lcnok/YlxUJKqJPnvpEbcpnPGbI+uKT4zVz+0xJfw9ng+P5efmpYnBFbTu7TVtOb1F47nCrS4Kb4f3buW51vG2y6dGwRy2sCu6kd43e+nTjp1q0f5HOxJxRSI4Qq0sC4KJcKnQ3bdr0juG4c+cb3ygePXr0lus9PT0VEpL+De/7779Xu3btlCNHDofVaZUFexao7dy2MpR+6OvJ6JNqO7et5rWbxwc3NzR/93xdjr+sormKqnGJxlaX49ZqFKphD92ty7e2uhy4Ed6/net2x9uQoR4/9FCQbxDHGw+sUoFKeqTII/rjxB/6KuorvVX3LatLAuCiXCp0O9rmzZsVFRWlzz77zOpSHlhKaor6Lu2b4QOEdONDhE029VvaTy3LtpSnh6cFFcIsk7feGFreM6Inv1uTVS9UXVOjpmrLGSZTg+Pc7f1bknr+0FMHLx2Uh82lrvrKklKNVH247sNbHu80/L2Eo/SK6KU/TvyhyVsn6806bzJXAIBbcuvQPWXKFJUvX161a9e+43YJCQlKSEiwP46OjpYkJSUlKSkpydQaM2vNX2vSDZH7J0OGjkcf16rDq1QvvJ4TK8se0vqBs/vDgUsHtProannYPNSpUieX6Y/uqkrwjXt1bz61WYmJidnmw5NV/Tu7uNv7tyRdSbiit1ZwlswZ+HsJR2pTto36Leung5cO6pdDvzi9T/H+DXeWFfp3Zmtz29B9/fp1zZ49W0OHDr3rtqNGjdKIESMyLF++fLkCAgLMKO+erb28NlPbLVm/RLG7Yk2uJvuKjIx0antfnfpKkhSRM0I71u/QDu1wavvZTUJqgjzkobOxZ/X1j18rr3deq0tyKmf37+wis+/f5QPLq6APEyU+qLOJZ7Unds9dt+PvJRylds7aWn5xuf61+F+KDbemT/H+DXfmyv07Li4uU9u5beieN2+e4uLi1KXL3W+vNGTIEA0YMMD+ODo6WmFhYWrSpImCgoLMLDPTAv8K1Li/xt11u6Z1m/LNvQmSkpIUGRmpxo0by9vb2zltpiTpxQkvSpIGNx6sZmWbOaXd7K7cqXLafWG38lTIo2als8cxt6J/ZyeZff/+tNWnvH87wJq/1qjx13ef/4K/l3CUAqcKaPn05doQvUGPNHhEef2d94Ut799wZ1mhf6eNkL4btw3dU6ZM0TPPPKPg4OC7buvr6ytfX98My729vV3mF9ygRAMVCSqik9En73idWtS5KDUs2TDbDIt1Nmf2iUUHF+lc3DkVDCyoluVbytvTNfqiu6sRWkO7L+zWtnPb1KpCK6vLcSpXes9zJ3d7/7bJpiJBRdSgRAOuMXYAjjec7ZGij6hqwaradnabvt3zrfo83MfpNfD+DXfmyv07s3W51IwtMTExioqKst9/+8iRI4qKitKxYzfumXvp0iVFRUVp9+7dkqR9+/YpKipKZ86cSbefgwcPau3aterVy31ur+Tp4alPnvpE0o0PDDe7+fGgyEF6+aeXlZTiutc+IHPS7vvZvVp3ArcT1ShUQ5KYTA0Oc/P79z+lvX+Pf2o8AdBBMvP3kuMNR7LZbOpd/catbCdtmSTDuP3JEQDZk0uF7k2bNikiIkIRERGSpAEDBigiIkLDhg2TJP3444+KiIhQ8+bNJUnt27dXRESEvvzyy3T7mTp1qooUKaImTZo49wWYrE35NprXbp4KBxVOt7xIUBHNe26exjYZK5tsmrhloprNbqYr8VesKRQP7NjVY1p2cJkkqWf1nhZXk71UL1RdkrTlNKEbjtOmfBu1r9Q+w/IiQUW4XZgJ7vj3kuMNE3Ss3FF+Xn7aeW6nNp7caHU5AFyMSw0vr1+//h2/HezWrZu6det21/18+OGH+vDDDx1YmetoU76NWpZtqXXH1un0tdMqlLOQHiv6mP0b+1J5S6nj/I5acXiFHp3yqH7q8JNK5i1pcdW4V1O3TpUhQw2KNVCpvKWsLidbqRZSTTbZdCL6hM7FnlOBwAJWlwQ3ceTKEUnSgEcGqGZozQzv33CstL+Xqw6v0pL1S9S0blOGlMM0efzz6LkKz2nm9pmatGWSHi7ysNUlAXAhLnWmG5nj6eGp+sXqq0PlDqpfrH66DxDPlH1G63usV+GchbX3wl49PPlhrT+23sJqca9SUlM0detUSbIPV4Pz5PTNqTL5ykjibDcc53zseW04sUGS1P/R/rd8/4bjeXp4ql54PT2e53HVC6/H8Yap0v5mz9k5R9cSrllcDQBXQuh2Q9VCqmlj742qUaiGLl6/qIYzGmrW9llWl4VMWn5ouY5HH1de/7xqXb611eVkSwwxh6MtObhEhgxVC6mmIkFFrC4HgAnqFq2rsvnKKjYpVnN2zrG6HAAuhNDtpkJzhmpNtzVqU76NElMS1fn7zhq6cqhSjVSrS8NdpE2g1rlKZ/l5+VlcTfaUFro3n95scSVwFz8f+FmS1Lx0c4sr+X/t3Xd4lGX69//PTDohCS2QhBog9JZgRaSDhCJ2EQsgYd3vz30suLviPpZ115XFr7puYXWXakFdRVCkRxQQFFCS0CGVmoQaSIPU+f2RZ6IsgpCZyTXl/TqOHIeZSe75gDeZ+8x9XucFwFUsFouSEmqG+NrfywFAckLRvWrVKm3c+EP78qxZs9SnTx9NmDBBBQUFjh4eDggNDNXHd3+sp296WpL00tcv6b5P7tO5inOGk+FS8ovz9Xn655JU+8aN+lc7wZw73XCCiqoKrcpcJUka02mM4TQAXOmh3g8pwBqg73K/0/b87abjAHATDhfdv/nNb2o3Bd+5c6eeeuopjRo1Sjk5OZo2bZrDAeEYq8WqPw/7s+bdOk/+Vn99tPsjDX57sI4VHzMdDT/h7bS3VVldqRta3aAezXuYjuOz4qNrdlA4cOaATp87bTgNPN2mw5tUWFaoZg2a6dqYa03HAeBCzUOba1yXcZKkOSlzDKcB4C4cLrpzcnLUrVs3SdInn3yiMWPG6OWXX9asWbO0cuVKhwPCOSbHT1byg8lqHNxYW45u0XVzrtPOYztNx8KP2Gw2zUmteYNmgJpZjYIbqX3j9pK42w3HLUtfJkkaFTeKQV6AD7C/h7+38z26CwFIckLRHRgYqNLSUknSF198Ubs3dpMmTWrvgMM9DGo3SJuTNiuuSZwOnT2km+bdpJUZ/GLEXaw/uF6ZpzMVFhime7rfYzqOz2OYGpyF9dyAbxnWfpjaRrTVmfNntGjPItNxALgBh4vu/v37a9q0afrjH/+orVu3avTomouK9PR0tWrFhFZ306lpJ21O2qyBbQeqqLxIYz4Yo79v+bvpWNAPQ1fu63GfGgY2NJwGrOuGM2SdztK+k/vkZ/HTiA4jTMcBUA+sFqumxE+RpNoONgC+zeGi+x//+If8/f21aNEivfnmm2rZsqUkaeXKlRo5cqTDAeF8TUKaaM2DazSpzyRV26r12KrH9H9W/B9VVleajuazTp87rU/2fCJJmtqX1nJ3wARzOIP9LvfNbW9Wo+BGZsMAqDeT4yfLarFqw8EN2n9yv+k4AAzzd/QAbdq00bJlyy56/C9/+Yujh4YLBfoFat6t89S5aWc9s/YZ/eO7fyirIEsf3vWhwoPCTcfzOe/teE9lVWXqE9Wn9g4rzLIX3ZmnM3X2/FlFBEcYTgRPZF/PTWs54FtahbdSYsdELc9Yrrmpc/XK8FdMRwJgkFP26c7KytKzzz6r++67T8ePH5dUc6d79+7dzjg8XMRisWh6/+ladPcihfiHaGXmSt007yYdPHPQdDSfYrPZalvLk+KTZLFYDCeCJDVr0ExtItpIktLy08yGgUcqKivS+oPrJbFVGOCL7APVFqQtUHlVueE0AExyuOhev369evbsqS1btmjx4sUqLi6WJG3fvl0vvPCCwwHhend2u1MbJm9QVMMo7Tq+S9fNuU6bj2w2HctnbD26VbuO71Kwf7Du73W/6Tj4EVrM4Ygvsr9QeVW52jdur85NO5uOA6Ceje40WtENo3Wi9ISW7l9qOg4AgxwuuqdPn66XXnpJycnJCgwMrH18yJAh2ryZws1TXBNzjbYmbVXvFr11vOS4Bi0YpP/s+o/pWD7Bfpf77m53s+bTzSREMcEcdWdfzz0mbgwdLIAP8rf6a1KfSZLYsxvwdQ4X3Tt37tTtt99+0ePNmzfXyZMnHT086lHriNba+PBGjek0RmVVZRr/yXj9cf0fZbPZTEfzWkVlRfpw14eS2JvbHfWNYYI56qbaVv3DVmGdWM8N+Cr7FPM1WWt04MwBs2EAGONw0d2oUSPl5eVd9HhqamrtJHN4joaBDfXpvZ/qyRuelCQ9v+55PfTpQyqrLDOczDt9uOtDlVSUqHPTzurfpr/pOPgv9vbyfSf3qbi82HAaeJLUvFTlF+crNCBUA9sONB0HgCEdmnTQkNghssmmeanzTMcBYIjDRff48eP19NNPKz8/XxaLRdXV1dq0aZN+/etf66GHHnJGRtQzP6ufXr/ldb01+i35Wfz03o73NPSdoTpRcsJ0NK9TO0AtgQFq7iiqYZSiG0bLJpu25283HQcexD61fHiH4QryDzKcBoBJ9k62eanzVFVdZTgNABMcLrpffvlldenSRa1bt1ZxcbG6deumAQMGqF+/fnr22WedkRGGPHLNI1p5/0pFBEVo0+FNumHuDdp7Yq/pWF5je/52fZf7nQKsAZrYe6LpOLgEWsxRFz9ezw3At93e5XY1CWmio0VHtSpzlek4AAxwuOgODAzU7NmzlZWVpWXLlum9997Tvn379O6778rPz88ZGWHQ8A7D9e2UbxXbKFbZBdm6ce6N+iL7C9OxvIJ9qMptXW5TZGik4TS4lNphavkU3bgy+cX5+i73O0nSqLhRhtMAMC3IP0gP9arp/rR3uAHwLU7Zp1uS2rRpo1GjRumee+5RXFycsw4LN9A1squ2JG3RTa1v0tmysxr53kj96/t/mY7l0c5VnNN7O9+TVNNaDvdVu21YLtuG4cqszFgpSeob3VfRYdGG0wBwB1P71rSYL0tfpryii2chAfBuDhfdVVVVmjt3riZMmKBhw4ZpyJAhF3zAO0SGRuqLh77Q/T3vV5WtSr9c/ks9tfop1ibV0aI9i3Tm/Bm1a9ROw9oPMx0Hl2FvL99zYo/OVZwznAaeoHZqeRxTywHU6BbZTf1a91OVrUoL0haYjgOgnjlcdD/++ON6/PHHVVVVpR49eqh3794XfMB7BPsH693b39UfBv1BkvT65td1x0d3MNW5Duak1rSWT4mfIqvFaQ0ncIGWYS0V2SBSVbYq7Ty+03QcuLnyqnKtyVojSRrTifXcAH6QFF/T2TY3da6qbdWG0wCoT/6OHuDDDz/URx99pFGjWLfmCywWi54b+JzimsZp0qeTtHT/Ut08/2Z9ft/nahXeynQ8j7D/5H5tOLhBVotVk/pMMh0HP8NisSghOkGrs1ZrW+42XdfyOtOR4Ma+Pvi1isqL1CK0RW2XBABI0j3d79ETq59QVkGW1h1YpyGxdIQCvsIpg9Q6duzojCzwION7jNe6SevUPLS50vLTdN3s61jzeoXmps6VVDNgiV9UeIa+0Uwwx5WxbxU2Km4UXSwALhAaGKoJPSZIYqAa4GscviJ46qmn9Ne//lU2m80ZeeBBbmh1g7YkbVH3yO7KK87TzfNv1uK9i03HcmvlVeW1a7nsbWZwf/Zhakwwx89hPTeAy7EPT128d7FOlZ4ynAZAfXG46N64caMWLlyoDh06aOzYsbrjjjsu+IB3a9eonb6Z8o1Gdhypc5XndOdHd2rmxpn8EuYSlu5fqhOlJxTdMFqjO3FR7insRffOYztVVllmOA3cVfqpdGWczlCANUDDOww3HQeAG+ob01fxUfEqryrXuzveNR0HQD1xuOhu1KiRbr/9dg0cOFDNmjVTRETEBR/wfuFB4fr8vs/1q2t/JUmavna6kpYmqbyq3HAy92Pfm3tyn8nytzo8UgH1pF2jdmoc3FgV1RXafWK36ThwU8vTa+5yD2g7QOFB4YbTAHBX9rvds1Nmc5MC8BEOX/XPnz/fGTng4fyt/vr7qL+rU9NOemL1E5qXNk/ZZ7L1yT2fqElIE9Px3MKBMwdqpxo/HP+w4TS4GvZhamtz1iolL6X2zjfwY8syatZzM7UcwOXc3/N+/XrNr7XnxB5tPrJZN7a+0XQkAC7GlBc41f+5/v/o8/s+V1hgmNYdWKcb5tygjFMZpmO5hXmp82STTUNjh6pDkw6m4+Aq2QttBgbipxSWFWrDwQ2SWM8N4PIigiN0T/d7JDFQDfAVdSq6ExISVFBQIEmKj49XQkLCJT/ge0bFjdKmhzepTUQbZZzO0PVzrte6A+tMxzKqqrpK81LnSZKmJkw1nAZ1UTvBnGFq+AlrstaosrpSnZp2UlzTONNxALg5e4v5f3b/R4VlhYbTAHC1OrWXjxs3TkFBQZKk2267zZl54CV6tuipLUlbNO7Dcdp6dKtGvDtC/xrzL02On2w6mhGrMlfpaNFRNQ1pqtu63GY6DurAfqd7e/52VVRVKMAvwHAiuBOmlgO4Gje1vkldm3XV3pN79cHOD/TINY+YjgTAhepUdL/wwgs/+d/Aj0U1jNK6ies06bNJ+mj3R3p46cNKP5WuPw39k8/tX2tvH3uo90MK8g8ynAZ10aFJB4UFhqmovEh7T+5Vrxa9TEeCm6i2VWtFxgpJrOcGcGUsFouSEpL01JqnNDtlNkU34OV8q/JBvQsJCNEHd36gZ29+VpL0501/1j0f36PSilLDyepPXlGelqXXDFiyt5PB81gtVsVHx0uSUvJoMccPvs/9XsdLjissMEz92/Q3HQeAh3iw14MKsAZoW942pealmo4DwIXqVHQ3btxYTZo0uaIPwGqx6o9D/qh3bntHgX6B+mTvJxq4YKDyivJMR6sXC9IWqMpWpX6t+6lbZDfTceCA2nXdFN34Efsv1W7peIsC/QINpwHgKSJDI3V719sl/bClKADvVKf28jfeeMPJMeALHuz9oNo1aqfb/3O7vs/9XtfNuU7L7lum3lG9TUdzmWpbteamzpXEADVvUDvBPI8J5vgB67kB1NXUhKn6aPdHWrhzof53xP+qQUAD05EAuECdiu6JEyc6Owd8xM1tb9aWpC0a88EY7Tu5TzfNu0kf3PmBxnYeazqaS6w7sE5ZBVkKDwrX3d3uNh0HDrIX3Wn5aaqqrpKf1c9wIpiWW5Rb2/mQ2DHRcBoAnmZI7BDFNopVzpkcLdqzVO/QSQAAVixJREFUSA/1fsh0JAAu4JQ13VlZWXr22Wd133336fjx45KklStXavfu3c44PLxMhyYd9M3D32ho7FCVVJRo3Ifj9Jdv/yKbzWY6mtPZB6hN6DFBoYGhhtPAUZ2bdlaDgAYqrShV+ql003HgBuwD1K5reZ1aNGxhOA0AT2O1WDUlfook9uwGvJnDRff69evVs2dPbdmyRYsXL1ZxcbEkafv27Uw2xyU1Dmmslfev1C8SfiGbbJq2Zpr+Z/n/qKKqwnQ0pzlVekqL9y6WJE3tS2u5N/Cz+qlPVB9JtJijhn09N63lAOpqcvxkWS1WbTy0UXtP7DUdB4ALOFx0T58+XS+99JKSk5MVGPjDAJkhQ4Zo8+bNjh4eXizAL0BvjXlLr414TRZZ9K9t/9Lo90frzPkzpqM5xbs73lV5Vbnio+Jr25Lh+RKiav5fMkwN5yvP64vsLySxVRiAuosJi6n9xZ19DgwA7+Jw0b1z507dfvvtFz3evHlznTx50tHDw8tZLBZNu3GaPh3/qUIDQpWcnax+c/spuyDbdDSH2Gy22jYxBqh5l74xTDBHjfUH1qukokTRDaMVHxVvOg4AD2a/Vnh7+9sqqywznAaAszlcdDdq1Eh5eRdv/ZSamqqWLVs6enj4iFs736qvJ3+tlmEttffkXl0/53ptOrTJdKw623xks/ac2KMQ/xBN6DnBdBw4kb1rISUvRdW2asNpYNKPp5ZbLBbDaQB4ssS4RMWExehk6Ul9tv8z03EAOJnDRff48eP19NNPKz8/XxaLRdXV1dq0aZN+/etf66GHmMCIKxcfHa+tU7cqITpBJ0tPasg7Q7Rwx0LTserEfpf7nu73KCI4wnAaOFPXZl0V5BekovIiZZ3OMh0Hhthsth/Wc3diPTcAx/hb/TW5z2RJ7NkNeCOHi+6XX35ZXbp0UevWrVVcXKxu3bppwIAB6tevn5599llnZIQPiQmL0YZJG3Rbl9tUXlWuB5Y8oBe+esGjJpsXlhXqP7v/I4nWcm8U4BdQu7c8Lea+a9/Jfco5k6NAv0ANaz/MdBwAXsA+xTw5O1k5BTmG0wBwJoeL7sDAQM2ePVtZWVlatmyZ3nvvPe3bt0/vvvuu/PzYwxZXLzQwVJ/c84l+2++3kqQ/bPiDJiyeoPOV5w0nuzIf7PxApRWl6tqsq/q17mc6DlyAYWqwt5YPajdIDQMbGk4DwBvENo6t/SUeA9UA7+KUfbolqU2bNkpMTNTdd9+tuLg4Zx0WPspqsWrm8JmaM3aO/K3++nDXhxr89mAdKz5mOtrPsreWJyUksc7TS9nXdbNtmO+yt5aPiWNqOQDnsXfIzU+br8rqSsNpADiLU4ruuXPnqkePHgoODlZwcLB69OihOXNYjwLHTUmYojUPrFHj4MbafGSzrp9zvXYd32U61iWl5qVqW942BVgD9FBvZhp4qx9PMPekpQ9wjjPnz2jjoY2SWM8NwLnGdR6nZg2aKbcoVyszVpqOA8BJHC66n3/+eT3++OMaO3asPv74Y3388ccaO3asnnzyST3//PPOyAgfNzh2sDYnbVbHJh118OxB9ZvbT6syV5mO9ZPsw09u73q7mjVoZjgNXKV7ZHcFWANUcL5AB88eNB0H9Wx15mpV2arUtVlXtW/c3nQcAF4kyD9ID/Wq+aX9nFRuYAHewuGi+80339Ts2bM1Y8YM3Xrrrbr11ls1Y8YM/fvf/9Y///lPZ2QE1KlpJ22eslkD2g5QUXmRRr8/WrO2zjId6wKlFaVauLNm2joD1LxbkH+QejTvIUnalkuLua/58VZhAOBsSQlJkqTl6cuVW5RrOA0AZ3C46K6oqNA111xz0eN9+/ZVZSVrUeA8TRs0VfKDyZrUZ5KqbdX61cpf6bGVj7nNmqdFexbpbNlZxTaK1ZDYIabjwMX6Rv/QYg7fUVVdpRUZKyRJYzqxnhuA83WN7Kr+bfqrylal+anzTccB4AQOF90PPvig3nzzzYse//e//63777/f0cMDFwj0C9S8W+dpxtAZkqS/b/27bv3gVhWWFRpO9sMAtSnxU2S1OG1GIdyUfZhaSj5Fty/ZenSrTp07pYigCHYnAOAySfE1d7vnps5Vta3acBoAjnLqILWkpCQlJSWpZ8+emj17tqxWq6ZNm1b7ATiDxWLR9P7TtejuRQrxD9HKzJW6ad5NOnjG3NravSf2auOhjfKz+Gly/GRjOVB/aieY525jmJoPsU8tH9lxpAL8AgynAeCt7u5+t8KDwpVzJkdf5nxpOg4ABzlcdO/atUsJCQmKjIxUVlaWsrKy1KxZMyUkJGjXrl1KTU1Vamqq0tLSnBAX+MGd3e7U+knrFdUwSruO79L1c67XliNbjGSx76c5utNoxYTFGMmA+tWrRS/5Wfx0ovSEjhYdNR0H9YT13ADqQ4OABrq/Z03HqL2TDoDn8nf0AF999ZUzcgB1cm3La7U1aavGfDBGO47t0KC3B+nt297WPd3vqbcMZZVlenv725J+aAeD9wsJCFG3yG7aeXynUvJS1Cq8lelIcLHDZw9r+7HtssiixLhE03EAeLmpCVP15vdvasneJTpZepJdUQAPxsJTeLzWEa21cfJGjY4brfOV53Xvonv10oaX6q3l97P9n+lk6UnFhMVwIe5jftxiDu9nH6B2Q6sbuPgF4HLx0fHqG91XFdUVemf7O6bjAHBAne5033HHHVqwYIHCw8N1xx13XPZrFy9eXKdgwNUICwrTZ+M/06/X/FpvbHlDz331nNJPpWv22NkK8g9y6Wvb9+ae3Gey/K0ON4/Ag/SN7qu3t7/NMDUfsSyjZj03U8sB1JekhCRtW75Ns1Nm68kbnpTFYjEdCUAd1OlOd0RERO0/+vDwcEVERFzyA6gvflY//WXkX/Tm6DflZ/HTuzve1fB3h+tk6UmXvWZOQY6Ss5Ml1Uwth2+pnWDOtmFe71zFOa3NXiuJ9dwA6s+EnhPUIKCB9p3cp28Of2M6DoA6qtNtufnzf9gzcMGCBc7KAjjFL6/5pdo3bq+7P75bXx/6WjfMuUHLJixTl2ZdnP5a81LnSZKGtx+u2MaxTj8+3FvvqN6yyKLcolzlF+crqmGU6Uhwka8OfKVzlefUKryVerXoZToOAB8RHhSue7vfq/lp8zU7ZbZuanOT6UgA6qDOa7qrq6s1c+ZM3XTTTbr22ms1ffp0nTt3zpnZgDob0WGEvp3yrWIbxSqrIEs3zr2x9i6Vs1RWV2peWk3RnZTAADVf1DCwoTo36yyJu93ebnn6D1PLae8EUJ/s1xgf7f5IZ8+fNZwGQF3Uuej+05/+pN/97ndq2LChWrZsqb/+9a969NFHnZkNcEi3yG7akrRF/Vr305nzZzRy4UjN3ua8bTdWZqxUblGumjVopnGdxzntuPAsfaP7SqLo9mY2m4313ACMubHVjeoW2U3nKs/p/Z3vm44DoA7qXHS/8847+uc//6nVq1fr008/1eeff66FCxequrramfkAh0SGRmrtQ2s1oecEVVZX6hfLfqFfr/m1qqqrHD72nNSaAWoTe090+bA2uK/aCeZ5TDD3VrtP7Nahs4cU7B+sIbFDTMcB4GMsFoumJkyVxJ7dgKeqc9F96NAhjRo1qvbzYcOGyWKxKDc31ynBAGcJ9g/We7e/pxcHvShJeu3b13THR3eouLy4zsfMLcqtbTdlgJpvY5ia91uWXnOXe0jsEDUIaGA4DQBf9ECvBxToF6jU/FTebwAPVOeiu7KyUsHBwRc8FhAQoIqKCodDAc5msVj0/MDn9cGdHyjIL0hL9y/VzfNv1pHCI3U63vzU+aqyVal/m/7qGtnVyWnhSeKj4iVJh84ecumkfJizPOOH9dwAYEKzBs10R9eabXqduVQOQP2o86bCNptNkyZNUlDQD22158+f1y9/+UuFhobWPsY+3XAn43uMV9uIthr34Til5afp+jnXa+n4peob0/eKj1Ftq9bc1LmSVNvuBd8VERyhjk06KvN0plLyUjSiwwjTkeBEp8+drt2mh6IbgElTE6bqw10fauHOhXp1xKsKDQz9+W8C4BbqfKd74sSJat68+QV7cj/wwAOKiYlhn264tRtb36gtSVvULbKbcotyNWDBAC3Zu+SKv//LnC+VcyZHEUERuqvbXS5MCk9Bi7n3WpW5StW2avVo3kNtG7U1HQeADxvUbpDaN26vovIifbznY9NxAFyFOt/p/vFe3YCniW0cq28e/kb3LLpHa7LW6M6P7tSfh/1Zv+n3m5/dDsg+xOT+nvezvhOSaiaYf7T7I4puL0RrOQB3YbVYlRSfpN99+TvNTpmtSX0mmY4E4ArV+U434OkigiO0fMJy/X/X/H+yyaanv3haUz+fqvKq8kt+z8nSk7V3xdmbG3ZMMPdOldWVWpmxUhJbhQFwD5P6TJKfxU/fHP5Gu4/vNh0HwBWi6IZP87f66x+j/qG/jvyrrBar5qbO1cj3RqrgXMFPfv17O99TRXWF+kb3VXx0fD2nhbuyD1PLLsi+5LkDz7P5yGYVnC9Q4+DGuqHVDabjAICiw6Jrfwlony8DwP1RdMPnWSwWPXb9Y1o6fqkaBjbUVwe+0g1zb1Dm6cwLvs5ms2le2jxJDFDDhZo2aKp2jdpJktLy04xmgfPYtwpLjEuUv7XOq7EAwKns1yDvbH9HZZVlhtMAuBJuVXRv2LBBY8eOVUxMjCwWiz799NMLnl+8eLFGjBihpk2bymKxKC0t7SeP8+2332rIkCEKDQ1VeHi4BgwYoHPnzrn+DwCPNrrTaG16eJNah7dW+ql0XT/nem04uEFV1VVaf3C9Psz/UPtO7VOIf4ju63mf6bhwMwxT8z6s5wbgjm7peItahrXUqXOnNGPTDG0o2KD1B9erqrrKdDSvVlVdpXUH1umDnR9o3YF1/H3jqtSp6E5ISFBBQU0L5R/+8AeVlpY6JUxJSYl69+6tWbNmXfL5/v37a+bMmZc8xrfffquRI0dqxIgR2rp1q7777jv96le/ktXqVr9fgJvq1aKXtk7dqutaXqfT505ryNtDFPm/kRq+cLj+c+w/kmrujH+R/YXhpHA3CVGs6/YmB88c1K7ju2S1WDWy40jTcQCglr/VXze2vlGS9PKml/X6wdc1fOFwtftrOy3ey1a9rrB472K1+2s7DX57sCYsnqDBbw/m7xtXpU79cnv37lVJSYkaN26sF198Ub/85S/VoIHjU5wTExOVmJh4yecffPBBSdKBAwcu+TVPPvmkHnvsMU2fPr32sc6dOzucDb4jqmGU1k1cp6HvDNW3R75VwfkL1+iWVpTqro/u0qJ7FumOrncYSgl3Y9/rnTvd3sF+l7tf635qEtLEcBoA+MHivYv1yZ5PLnr8aOFRrk9cYPHexbrro7tkk+2Cx/n7xtWoU9Hdp08fTZ48Wf3795fNZtOrr76qhg0b/uTXPv/88w4FvBrHjx/Xli1bdP/996tfv37KyspSly5d9Kc//Un9+/evtxzwfIF+gTpcePiyX/PEqic0rvM4+Vn96ikV3Jl9mFr6qXQVlRUpLCjMcCI4wr6ee0wcU8sBuI+q6io9vurxiwpASbWPTf5ssr7P/V5WC12ejqq2VesfW/9xyb9viyxcD+KK1KnoXrBggV544QUtW7ZMFotFK1eulL//xYeyWCz1WnRnZ2dLkn7/+9/r1VdfVZ8+ffTOO+9o6NCh2rVrl+Li4n7y+8rKylRW9sMgisLCQklSRUWFKioqXB8cbmf9wfU6Unjkks/bZNPhwsP6KvsrDWw7sB6TwV01CWqilmEtdbToqL4/8r36t/GcX/TZf87x865GSXmJvsz5UpI0InYEfy8ejvMb3uTnrk8kqbCsUDM2zqinRL6N60HX8oSf31earU5Fd+fOnfXhhx9KkqxWq9auXavmzZvX5VBOVV1dLUl65JFHNHnyZElSfHy81q5dq3nz5mnGjJ/+ATRjxgy9+OKLFz2+Zs0ap7TNw/NsKNhwRV+3cuNKlewucXEaeIqWlpY6qqNa+NVCFUYWmo5z1ZKTk01HcAtbz25VWVWZIgMidfC7gzpkOWQ6EpyA8xve4EqvT/qE9VHLoJYuTuP9jpYdVVpR2s9+HdeDruXOP7+vdLaZw3ug2AtddxAdHS1J6tat2wWPd+3aVYcOXfqi6ZlnntG0adNqPy8sLFTr1q01YsQIhYeHuyYs3FrowVC9fvD1n/26xP6J/GYTtbZ9vU1bv96qsiZlGjVqlOk4V6yiokLJyckaPny4AgICTMcxbvnKmvXcd/a8U6NHMrnc03F+w5tc6fXJ/976v1yfOMH6g+s1fOHwn/06rgddwxN+fts7pH+OUzYezcrK0htvvKG9e/dKqil6H3/8cXXo0MEZh79i7dq1U0xMjPbv33/B4+np6Zcd0BYUFKSgoKCLHg8ICHDb/8FwrcHtB6tVeCsdLTz6k+t4LLKoVXgrDW4/mDU8qHVty2slSan5qR75s4OfeZLNZtPKrJWSpFu73Orzfx/ehPMb3oDrk/rF37d7cOef31eay+EJC6tXr1a3bt20detW9erVS7169dKWLVvUvXv3q24FKC4uVlpaWu3+2zk5OUpLS6u9S3369GmlpaVpz549kqT9+/crLS1N+fn5kmrWkP/mN7/R3/72Ny1atEiZmZl67rnntG/fPk2ZMsXRPyp8iJ/VT38d+VdJNT9Qf8z++Rsj3+AHLC5gn2C+9+RelVY4ZytF1K8dx3boSOERhfiHaFC7QabjAMAFuD6pX5f7+7bj7xtXwuGie/r06XryySe1ZcsWvf7663r99de1ZcsWPfHEE3r66aev6ljff/+94uPjFR9fMwV42rRpio+Prx3GtnTpUsXHx2v06Jp2v/Hjxys+Pl5vvfVW7TGeeOIJPfPMM3ryySfVu3dvrV27VsnJyfV+1x2e746ud2jRPYvUMvzCNVGtwluxPQR+UnTDaLUIbaFqW7V2HNthOg7qwD61fFj7YQoJCDGcBgAuxvVJ/brU33eANYC/b1wxi81mu7hX4ioEBwdr586dF00GT09PV69evXT+/HmHAppQWFioiIgInT17ljXdUFV1lb7K/korN65UYv9EWohwWaMWjtLKzJX6R+I/9Oh1j5qOc0UqKiq0YsUKjRo1ym3bt+pLv7n99O2Rb/XW6Lf0yDWPmI4DJ+D8hrfi+qR+VVVX6etDX2v/yf16dMWjqrJVKfWRVPWJ6mM6mtfyhJ/fV1o3OnynOzIysrYd/MfS0tLcYqI54Cg/q58Gth2oAY0HaGDbgbyh4bL6Rte0mKfkpRhOgqt1ouSENh/ZLEka3YkBagDcG9cn9cvP6qdB7QbpkWse0Z3d7pQkzd4223AqeAqHB6lNnTpVv/jFL5Sdna1+/fpJkjZt2qSZM2deMBEcAHxBQnSCJCkln6Lb06zKXCWbbOrdordahbcyHQcA4KamJkzVR7s/0sKdC/W/I/5XDQLYYhiX53DR/dxzzyksLEyvvfaannnmGUlSTEyMfv/73+uxxx5zOCAAeBJ70b3r+C6drzyvYP9gw4lwpZZl1KznHtNpjOEkAAB3NiR2iGIbxSrnTI4W7Vmkh3o/ZDoS3JzD7eUWi0VPPvmkjhw5orNnz+rs2bM6cuSIHn/8cVksPz3lDwC8VZuINmoS0kSV1ZXadXyX6Ti4QhVVFVqduVqSNDqO1nIAwKVZLVZNia/ZGWl2Ci3m+HkOF90/FhYWprCwMGceEgA8isViYV23B9p0eJPOlp1VswbNdF3L60zHAQC4ucnxk2W1WLXx0EbtO7nPdBy4OacW3QCAH1rMt+VuM5wEV2p5+nJJUmLHRIYRAQB+VkxYTG1n1JyUOYbTwN1RdAOAkzFMzfMsz6gpulnPDQC4UlMTpkqS3t7+tsqryg2ngTuj6AYAJ7O3l+84tkMVVRWG0+DnZBdka+/JvfKz+GlEhxGm4wAAPERiXKJiwmJ0svSkPtv3mek4cGMOFd0VFRUaOnSoMjIynJUHADxe+8btFREUofKqcu0+sdt0HPwMe2v5zW1vVqPgRmbDAAA8hr/VX5P7TJbEQDVcnkNFd0BAgHbs2OGsLADgFSwWi+Kj4yUxTM0T2LcKY2o5AOBq2aeYJ2cnK6cgx3AauCuH28sfeOABzZ071xlZAMBrMMHcMxSXF2vdgXWSKLoBAFcvtnGshrUfJkmalzrPcBq4K39HD1BZWal58+bpiy++UN++fRUaGnrB86+//rqjLwEAHqd2gnkeE8zd2RfZX6i8qlztG7dXl2ZdTMcBAHigqQlT9UX2F5qXNk8vDHpB/laHSyx4GYfPiF27dikhoebiMj09/YLnLBaLo4cHAI9kL7q3529XZXUlb8Buyr6ee3TcaN6zAAB1Mq7zODVr0Ey5RblalbmKnTBwEYevAr/66itn5AAAr9KpaSc1DGyo4vJi7T+5X92bdzcdCf/FZrOxVRgAwGFB/kF6qNdDen3z65qdMpv3FFzEaVuGZWZmavXq1Tp37pykmosZAPBVVotVfaL6SGJdt7tKzU9VXnGeQgNCNbDtQNNxAAAeLCkhSVJNB1VuUa7hNHA3Dhfdp06d0tChQ9WpUyeNGjVKeXl5kqQpU6boqaeecjggAHiqhCjWdbuzZek1U8uHdxiuIP8gw2kAAJ6sa2RX9W/TX1W2Ki1IW2A6DtyMw0X3k08+qYCAAB06dEgNGjSoffzee+/VqlWrHD08AHisvjFMMHdn9tZyppYDAJwhKb7mbveclDmqtlUbTgN34nDRvWbNGs2cOVOtWrW64PG4uDgdPHjQ0cMDgMeyD1NLzU/lzdfNHCs+pq1Ht0qSRsWNMpwGAOAN7u5+t8KDwpVzJkdf5nxpOg7ciMNFd0lJyQV3uO1Onz6toCDa9QD4ri7NuijEP0TF5cXKOJVhOg5+ZGXmSkk1vxiJCYsxnAYA4A0aBDTQ/T3vl1Rztxuwc7jovvnmm/XOO+/Ufm6xWFRdXa1XXnlFgwcPdvTwAOCx/K3+6h3VWxIt5u7Gvp57TBwTZgEAzjM1Yaokacm+JTpZetJwGrgLh4vuV155Rf/+97+VmJio8vJy/fa3v1WPHj20YcMGzZw50xkZAcBj2YepUXS7j/Kqcq3JWiNJGt2J9dwAAOeJj45X3+i+Kq8q17vb3zUdB27C4aK7R48eSk9PV//+/TVu3DiVlJTojjvuUGpqqjp06OCMjADgsezruplg7j6+Pvi1isqL1Dy0ua6JucZ0HACAl7FvHzY7ZTbbKEOS5O+Mg0REROj//t//64xDAYBX+fEEc5vNJovFYjgR7FPLR8WNktXi8O+eAQC4wISeE/TUmqe09+RefXP4G93U5ibTkWCYU642CgoK9Oqrr2rKlCmaMmWKXnvtNZ0+fdoZhwYAj9YtspsC/QJ1tuyscs7kmI4DsZ4bAOBa4UHhurf7vZKkOakMVIMTiu4NGzaoXbt2+tvf/qaCggIVFBTob3/7m2JjY7VhwwZnZAQAjxXoF6iezXtKkrbl0mJuWvqpdGWczlCANUDDOww3HQcA4KXsLeb/2fUfnT1/1nAamOZw0f3oo4/q3nvvVU5OjhYvXqzFixcrOztb48eP16OPPuqMjADg0fpG/9BiDrOWp9e0lg9oO0DhQeGG0wAAvNWNrW5Ut8huOld5Tu/vfN90HBjmcNGdmZmpp556Sn5+frWP+fn5adq0acrMzHT08ADg8ezD1FLyKbpNs6/nHh3H1HIAgOtYLJba7cNoMYfDRXdCQoL27t170eN79+5V7969HT08AHi82gnmuduYYmpQYVmhNhysWfY0phPruQEArvVArwcU6BeolLwUut18XJ2ml+/YsaP2vx977DE9/vjjyszM1A033CBJ2rx5s2bNmqU///nPzkkJAB6sZ4ue8rf669S5UzpceFhtItqYjuSTkrOSVVFdobgmcYprGmc6DgDAyzVr0Ex3dL1DH+76ULO3zdabY940HQmG1Kno7tOnjywWywV3bH77299e9HUTJkzQvffeW/d0AOAFgv2D1T2yu7Yf266UvBSKbkPsreXc5QYA1JepCVP14a4P9f6u9/XqiFcVGhhqOhIMqFPRnZPDtjcAcDUSohO0/dh2bcvdptu63GY6js+ptlWznhsAUO8GtRuk9o3bK7sgWx/v+ViT+kwyHQkG1Knobtu2rbNzAIBXS4hO0Py0+QxTM2Rb7jYdLzmusMAw3dz2ZtNxAAA+wmqxKik+Sb/78neakzKHottH1ano/m+5ubnauHGjjh8/rurq6guee+yxx5zxEgDg0dg2zKxl6cskSSM6jFCgX6DhNAAAXzKpzyQ999Vz2nR4k/ac2KNukd1MR0I9c7joXrBggR555BEFBgaqadOmslgstc9ZLBaKbgCQ1KtFL1ktVuUX5yu3KFcxYTGmI/kU1nMDAEyJDovWmE5j9Nn+zzQnZY5ev+V105FQzxzeMuy5557T888/r7Nnz+rAgQPKycmp/cjOznZGRgDweKGBoerSrIsk7nbXt7yiPG3L2yZJSuyYaDgNAMAX2ffsfmf7OyqrLDOcBvXN4aK7tLRU48ePl9Xq8KEAwKvRYm7GiowVkqRrY65Vi4YtDKcBAPiiWzreopZhLXXq3Cl9uu9T03FQzxyulKdMmaKPP/7YGVkAwKslRCdIUu1dV9SPZRk167lpLQcAmOJv9dfD8Q9LkmanzDacBvXN4TXdM2bM0JgxY7Rq1Sr17NlTAQEBFzz/+uusWQAA6Yeimzvd9aesskzJWcmS2CoMAGDWw/EP66UNL2ltzlplF2SrfeP2piOhnjil6F69erU6d+4sSRcNUgMA1IiPipckHSk8ouMlx9U8tLnhRN5v/cH1KqkoUXTDaMVHx5uOAwDwYe0atdPwDsO1JmuN5qbM1Z+G/sl0JNQTh4vu1157TfPmzdOkSZOcEAcAvFdYUJg6Ne2k9FPpSs1L1S0dbzEdyestT6+ZWj4qbpSsFmaPAADMmpowVWuy1mh+2ny9OPhF+VudsoMz3JzDVyBBQUG66aabnJEFALwe67rrj81mYz03AMCt3Nr5VkU2iFRecV7toE94P4eL7scff1x///vfnZEFALweE8zrz/5T+5VdkK1Av0ANaz/MdBwAABToF6iJvSdKYqCaL3G4n2Hr1q368ssvtWzZMnXv3v2iQWqLFy929CUAwGswTK3+LEuvucs9qN0gNQxsaDgNAAA1khKS9Oq3r2pFxgodLTyqluEtTUeCizl8p7tRo0a64447NHDgQDVr1kwREREXfAAAfmAfppZzJkenz502nMa7Lc+oWc/N1HIAgDvp3Kyzbm5zs6pt1ZqfNt90HNQDh+90z5/PiQIAV6pxSGO1b9xe2QXZSs1L1dD2Q01H8kpnzp/R1we/lkTRDQBwP1MTpurrQ19rbupc/e7m3zHs08vxfxcA6hkt5q63JmuNqmxV6tKsizo06WA6DgAAF7ir212KCIrQgTMHtDZ7rek4cDGHi+7Y2Fi1b9/+kh8AgAslRDHB3NXs67nHxDG1HADgfkICQvRArwckMVDNFzjcXv7EE09c8HlFRYVSU1O1atUq/eY3v3H08ADgdfrGMMHclaqqq7Qyc6UkaXQnWssBAO5pasJUzfpulj7d96lOlJxQZGik6UhwEYeL7scff/wnH581a5a+//57Rw8PAF7HPkwt43SGCssKFR4UbjiRd/ku9zudLD2piKAI3dT6JtNxAAD4Sb2jeuvamGv1Xe53emf7O3qq31OmI8FFXLamOzExUZ988omrDg8AHisyNFKtw1tLklLzUg2n8T721vJbOt6iAL+An/lqAADMSUpIklTTYm6z2Qyngau4rOhetGiRmjRp4qrDA4BHo8XcdexbhbGeGwDg7u7rcZ9CA0K1/9R+bTy00XQcuIjD7eXx8fGyWCy1n9tsNuXn5+vEiRP65z//6ejhAcArJUQl6NN9nyoln6LbmY4UHlFafpossmhkx5Gm4wAAcFlhQWEa32O85qbO1ZzUObq57c2mI8EFHC66b7vttgs+t1qtioyM1KBBg9SlSxdHDw8AXsm+bdi2XCaYO9OKjBWSpBta3cBAGgCAR0hKSNLc1Ln6ePfH+uvIv6pRcCPTkeBkDhfdL7zwgjNyAIBPsRfd+07uU0l5iUIDQw0n8g729dyj45haDgDwDNe3vF49mvfQruO7tHDHQj163aOmI8HJXLamGwBwadFh0YpuGC2bbNp+bLvpOF7hXMU5rc1ZK0ka04n13AAAz2CxWDQ1YaokBqp5qzoX3VarVX5+fpf98Pd3+EY6AHgtWsyda92BdSqtKFWr8Fbq1aKX6TgAAFyxB3o9oCC/IG0/tl3b8rgu8DZ1roqXLFlyyee+/fZb/e1vf1N1dXVdDw8AXi8hOkHLM5YzTM1J7FPLR8eNvmDAJwAA7q5JSBPd2e1Ovb/zfc1JmaNrYq4xHQlOVOeie9y4cRc9tn//fk2fPl2ff/657r//fv3hD39wKBwAeLO+0Wwb5iw2m4313AAAj5YUn6T3d76v93e+r1dHvKqGgQ1NR4KTOGVNd25urqZOnaqePXuqsrJSaWlpevvtt9W2bVtnHB4AvJK9vXz38d06V3HOcBrPtufEHh08e1DB/sEa2n6o6TgAAFy1Qe0GqWOTjioqL9JHuz8yHQdO5FDRffbsWT399NPq2LGjdu/erbVr1+rzzz9Xjx49nJUPALxWq/BWatagmapsVdp5fKfpOB7Nfpd7cLvBahDQwHAaAACunsViUVJ8kiRpTsocw2ngTHUuul955RW1b99ey5Yt0wcffKBvvvlGN9/MZu4AcKUsFgst5k5iX8/N1HIAgCeb2Gei/K3++vbIt9p9fLfpOHCSOq/pnj59ukJCQtSxY0e9/fbbevvtt3/y6xYvXlzncADg7RKiE7Q6azUTzB1w+txpbTq8SRLruQEAni2qYZTGdhqrJfuWaHbKbL0x8g3TkeAEdS66H3roIabDAoCD7Ou6mWBed6szV6vaVq3ukd3VthGzRAAAnm1qwlQt2bdE7+54V38e9mcF+webjgQH1bnoXrBggRNjAIBvsreX7zy2U+VV5Qr0CzScyPMsy6hZz01rOQDAG4zoMEKtw1vrcOFhLdm7RPf1vM90JDjIKdPLAQB1065ROzUKbqSK6grWbtVBZXWlVmWukkRrOQDAO/hZ/fRw/MOSpNkpsw2ngTNQdAOAQRaLpbbFfFse67qv1uYjm3X63Gk1Dm6sG1vfaDoOAABO8XD8w7LIoq8OfKXM05mm48BBFN0AYBgTzOtueXrN1PKRHUfK31rnFVMAALiVNhFtdEvHWyRJc1PmGk4DR7lV0b1hwwaNHTtWMTExslgs+vTTTy94fvHixRoxYoSaNm0qi8WitLS0i44xaNAgWSyWCz5++ctf1s8fAADqoHaYGkX3VWM9NwDAW01NmCpJWrB9gSqqKgyngSPcquguKSlR7969NWvWrEs+379/f82cOfOyx5k6dary8vJqP1555RVXxAUAp7AX3duPbVdldaXhNJ7j0NlD2nV8l6wWq0Z2HGk6DgAATjWm0xg1D22u/OJ8Lc9YbjoOHOBWvXiJiYlKTEy85PMPPvigJOnAgQOXPU6DBg0UFRXlzGgA4DIdm3RUWGCYisqLtPfEXvVs0dN0JI9gby3v17qfmoQ0MZwGAADnCvQL1KTek/TKN69odsps3dblNtORUEduVXQ7y8KFC/Xee+8pKipKY8eO1XPPPacGDRpc8uvLyspUVlZW+3lhYaEkqaKiQhUVtHJAtecB5wNcpU+LPvr68NfaemSrujTpUq+v7ann9+f7P5ckjWw/0uOyo/546vkNXAnOb+83sddEvfLNK1qVuUrZp7LVOry16Uj1xhPO7yvN5nVF94QJE9S2bVvFxMRox44devrpp7V//34tXrz4kt8zY8YMvfjiixc9vmbNmssW6/A9ycnJpiPASzU630iStGTzEjU70sxIBk86v8uqy7Q2e60kKSwvTCtWrDCcCO7Ok85v4Gpxfnu37qHdtbtkt55d9KzujbrXdJx6587nd2lp6RV9ndcV3b/4xS9q/7tnz56Kjo7W0KFDlZWVpQ4dOvzk9zzzzDOaNm1a7eeFhYVq3bq1RowYofDwcJdnhvurqKhQcnKyhg8froCAANNx4IUKdhXo86WfqyC4QKNGjarX1/bE83t5xnKV7yhXm/A2+uUdv5TFYjEdCW7KE89v4EpxfvuGM7vOaNLSSdpUuknzRs6Tn9XPdKR64Qnnt71D+ud4XdH9366//npJUmZm5iWL7qCgIAUFBV30eEBAgNv+D4YZnBNwletaXSepZpia1c9q5A3Vk87v1dmrJdUMmQkMDDScBp7Ak85v4Gpxfnu3e3veqyfXPKlDhYe0/vD62q3EfIU7n99Xmsutppe7gn1bsejoaLNBAOAyOjftrAYBDVRSUaL0U+mm47g1m81Wu1XY6E6jDacBAMC1gv2D9UCvByRJs1NmG06DunCroru4uFhpaWm1hXJOTo7S0tJ06NAhSdLp06eVlpamPXv2SJL279+vtLQ05efnS5KysrL0xz/+Udu2bdOBAwe0dOlSPfTQQxowYIB69epl5M8EAFfCz+qnPlF9JLFf98/ZeXynjhQeUYh/iAa3G2w6DgAALmffs/uz/Z/pWPExw2lwtdyq6P7+++8VHx+v+Ph4SdK0adMUHx+v559/XpK0dOlSxcfHa/Tomjsb48ePV3x8vN566y1JUmBgoL744guNGDFCXbp00VNPPaU777xTn3/+uZk/EABchYSomv26Kbovb1l6zV3uoe2HKiQgxHAaAABcr2eLnrq+5fWqrK7UO9vfMR0HV8mt1nQPGjRINpvtks9PmjRJkyZNuuTzrVu31vr1612QDABcLyG6pujelrfNcBL3tjyjZn/uMXFjDCcBAKD+JCUkacvRLZqTOke/7vdrhoh6ELe60w0AvsxedKfmp6raVm04jXs6WXpS3x7+VpI0Kq5+p7wDAGDS+B7j1TCwodJPpevrQ1+bjoOrQNENAG6iW2Q3BfkFqbCsUNkF2abjuKVVmatkk029W/RW64jWpuMAAFBvGgY21H097pPEQDVPQ9ENAG4iwC9AvVrUDH3clkuL+U+xr+ceHcfUcgCA70lKSJIkLdqzSAXnCgynwZWi6AYAN2JvMWeY2sUqqiq0OuuH/bkBAPA118Zcq14teul85Xkt3LnQdBxcIYpuAHAjfaP7SpJS8im6/9s3h7/RmfNn1KxBM13X8jrTcQAAqHcWi0VJ8TV3u2enzL7sEGq4D4puAHAjtRPMc7fxRvpf7FPLEzsmys/qZzgNAABmPNDrAQX7B2vHsR36Lvc703FwBSi6AcCN9GjeQwHWABWcL9DBswdNx3ErrOcGAEBqHNJYd3W7S5I0J2WO4TS4EhTdAOBGgvyD1KN5D0ms6/6x7IJs7T25V34WP93S8RbTcQAAMMreYv7Brg9UXF5sOA1+DkU3ALiZH7eYo8by9JrW8v5t+qtRcCOzYQAAMGxA2wHq1LSTisuL9eGuD03Hwc+g6AYAN1M7wZxharXs67mZWg4AwIUD1Wgxd38U3QDgZuwTzBmmVqO4vFhfHfhKEuu5AQCwe6j3Q/K3+mvL0S3aeWyn6Ti4DIpuAHAzvVr0kp/FTydKT+ho0VHTcYxbm71W5VXlim0Uqy7NupiOAwCAW2jRsIXGdR4nibvd7o6iGwDcTEhAiLpGdpXEMDXpwtZyi8ViOA0AAO5jasJUSdK7O97V+crzhtPgUii6AcAN2VvMfb3ottlstUU3reUAAFxoWPthahPRRgXnC/TJnk9Mx8ElUHQDgBuqHabm40V3Wn6acotyFRoQqoHtBpqOAwCAW/Gz+mlK/BRJ0pxUWszdFUU3ALih2m3D8nx727Bl6csk1fwmP9g/2HAaAADcz+Q+k2W1WLXuwDplnMowHQc/gaIbANxQn6g+ssii3KJc5Rfnm45jDFuFAQBwea0jWmtkx5GSGKjmrii6AcANNQxsqM7NOkuSUvNSDacx43jJcW09ulWSNCpulOE0AAC4L/tAtQXbF6iiqsJwGvw3im4AcFO+3mK+MmOlbLIpITpBMWExpuMAAOC2RseNVovQFjpeclyfp39uOg7+C0U3ALgpX59gviyjZj03U8sBALi8AL8ATe4zWZI0O2W24TT4bxTdAOCmfHmCeXlVudZkrZHEem4AAK7ElISaKearM1fr0NlDhtPgxyi6AcBNxUfFS5IOnj2oU6WnDKepXxsPbVRhWaGahzbXNTHXmI4DAIDb69ikowa3GyybbJqXOs90HPwIRTcAuKmI4Ah1bNJRku/d7V6eXjO1fFTcKFktvFUBAHAl7APV5qXOU1V1leE0sONKBgDcmK+2mLOeGwCAq3d719vVJKSJDhcerl2mBfMougHAjSVE+d4E84xTGUo/lS5/q79GdBhhOg4AAB4j2D9YD/Z6UBID1dwJRTcAuDFfvNO9PKOmtXxA2wEKDwo3nAYAAM9ibzH/PP1z5RfnG04DiaIbANyavejOKsjSmfNnzIapJ/aie0wcU8sBALha3Zt3142tblRldaXeTnvbdByIohsA3FrTBk3VNqKtJCk1L9VwGtcrKivS+gPrJUmjO7GeGwCAukhKSJIkzUmdI5vNZjgNKLoBwM35Uot5cnayKqorFNckTp2adjIdBwAAj3RP93sUFhimzNOZWn9wvek4Po+iGwDcXN/ovpKklHzvL7qXpTO1HAAARzUMbKj7etwniYFq7oCiGwDcnP1O97Zc755gXm2r1oqMFZKkMZ1Yzw0AgCOm9q0ZqPbJnk90+txpw2l8G0U3ALg5e9GdfipdRWVFhtO4zrbcbTpWckxhgWG6ue3NpuMAAODR+kb3VZ+oPiqrKtN7O94zHcenUXQDgJtr0bCFWoa1lE02bT+23XQcl7FPLR/RYYQC/QINpwEAwLNZLBYlxdcMVJudMpuBagZRdAOAB/CFFnN70c16bgAAnOP+XvcrxD9Eu47v0pajW0zH8VkU3QDgAWonmHvpMLW8ojx9n/u9JGlU3CjDaQAA8A6Nghvp7u53S5LmpMwxnMZ3UXQDgAeonWDupduGrcxcKUm6NuZatWjYwnAaAAC8h73F/MNdH3r1bBh3RtENAB7Afqd7z4k9Kq0oNZzG+exbhTG1HAAA5+rfpr+6NOuikooSfbjrQ9NxfBJFNwB4gJiwGDUPba5qW7V2HNthOo5TlVWWKTk7WRLruQEAcLb/HqiG+kfRDQAewGKxeG2L+YaDG1RcXqyohlGKj443HQcAAK/zUO+HFGAN0He532l7vvfuhOKuKLoBwEPUDlPzsqL7x1PLrRbelgAAcLbI0Ejd1uU2SQxUM4GrGwDwELXbhuV5z7ZhNputdj03reUAALhOUkJNi/l7O9/TuYpzhtP4FopuAPAQ9vbyXcd3qayyzHAa50g/la6sgiwF+gVqWPthpuMAAOC1hrUfpnaN2unM+TNatGeR6Tg+haIbADxEm4g2ahLSRJXVldp1fJfpOE5hv8s9sO1AhQWFGU4DAID3slqsmhI/RZI0J5UW8/pE0Q0AHsJisXhdi7l9PTdbhQEA4HqT+kyS1WLVhoMbtP/kftNxfAZFNwB4EG+aYH72/Fl9fehrSaznBgCgPrQKb6VRcaMkMVCtPlF0A4AH8aYJ5muy1qiyulJdmnVRhyYdTMcBAMAnTE2YKkl6e/vbKq8qN5zGN1B0A4AHsRfdO47tUEVVheE0jlmWwdRyAADq26i4UYpuGK0TpSe0dP9S03F8AkU3AHiQDo07KCIoQmVVZdpzYo/pOHVWVV2llRkrJbGeGwCA+uRv9dfkPpMl0WJeXyi6AcCDWCwWxUfHS/LsFvPvcr/TidITigiK0E2tbzIdBwAAnzIloWaK+ZqsNTpw5oDZMD6AohsAPExClOdPMF+eXjO1/JaOtyjAL8BwGgAAfEv7xu01NHaobLJpXuo803G8HkU3AHgYbximxnpuAADMsg9Um5c6T1XVVYbTeDeKbgDwMH1jarYNS8tP88g3yaOFR5WWnyaLLErsmGg6DgAAPum2LrepaUhTHS06qlWZq0zH8WoU3QDgYeKaxCk0IFTnKs9p38l9puNcteUZNa3l17e6XpGhkYbTAADgm4L8g/RQ74ckSbNTZhtO490ougHAw/hZ/dQnqo8kz2wxtxfdY+KYWg4AgElJCUmSpGXpy5RXlGc4jfei6AYAD9Q3uqbF3NOK7vOV5/VF9heSpNGdWM8NAIBJ3SK7qV/rfqqyVWlB2gLTcbwWRTcAeCD7MDVPm2C+7sA6lVaUqmVYS/Vu0dt0HAAAfJ59oNqc1DmqtlUbTuOdKLoBwAPZi+7U/FSPeoO0bxU2Om60LBaL4TQAAODubncrPChc2QXZWndgnek4XomiGwA8UNfIrgr2D1ZxebEyT2eajnNFbDZb7VZhYzqxnhsAAHcQGhiqCT0mSGKgmqtQdAOAB/K3+te2Z2/L9YwW870n9+rAmQMK8gvSkNghpuMAAID/Z2rfmhbzxXsX61TpKcNpvA9FNwB4KHuLuacMU1uWXnOXe0jsEIUGhhpOAwAA7BKiE5QQnaDyqnK9u+Nd03G8DkU3AHio2gnm+Z5RdNu3Chsdx9RyAADcTVJ8zfZhs1Nmy2azGU7jXSi6AcBD/fhOt7u/ORacK9CmQ5sksVUYAADuaELPCWoQ0EB7TuzR5iObTcfxKhTdAOChujfvrkC/QJ05f0Y5Z3JMx7ms1VmrVWWrUvfI7mrXqJ3pOAAA4L9EBEfonu73SGKgmrNRdAOAhwr0C1TP5j0luf+6bvt6bqaWAwDgvuwt5v/Z/R8VlhUaTuM9KLoBwIN5wjC1quoqrcxcKYn13AAAuLN+rfupa7OuKq0o1Qc7PzAdx2tQdAOAB7MX3dvy3HfbsM1HNuv0udNqHNxYN7a+0XQcAABwCRaLRUkJPwxUg3NQdAOAB6udYO7Gw9TsU8tHdhwpf6u/4TQAAOByHur9kAL9ArUtb5tS81JNx/EKblV0b9iwQWPHjlVMTIwsFos+/fTTC55fvHixRowYoaZNm8pisSgtLe2Sx7LZbEpMTPzJ4wCAt+jZoqf8LH46WXpSRwqPmI7zk+zruWktBwDA/TVr0Ey3d7ldkjQnZY7hNN7BrYrukpIS9e7dW7Nmzbrk8/3799fMmTN/9lhvvPGGLBaLsyMCgFsJ9g9W9+bdJblni/mhs4e08/hOWS1Wjew40nQcAABwBewt5gt3LlRpRanhNJ7Prfr8EhMTlZiYeMnnH3zwQUnSgQMHLnuctLQ0vfbaa/r+++8VHR3tzIgA4Hb6RvfVjmM7lJKXotu63GY6zgWWp9e0lt/Y6kY1bdDUcBoAAHAlhsQOUWyjWOWcydHHuz/WxD4TTUfyaG5VdDtDaWmpJkyYoFmzZikqKuqKvqesrExlZWW1nxcW1ozHr6ioUEVFhUtywrPYzwPOB7ij3s17S5K25W6r0znqyvPb3lqe2CGRfz8wgp/f8Gac33Clyb0n6/n1z2v2ttma0H1Cvb++J5zfV5rN64ruJ598Uv369dO4ceOu+HtmzJihF1988aLH16xZowYNGjgzHjxccnKy6QjARc6XnJckfXvgW61YsaLOx3H2+V1WXaYvsr6QJDXMa+hQNsBR/PyGN+P8hiu0qmglq6zadGST/rX4X2od3NpIDnc+v0tLr6z13quK7qVLl+rLL79UaurVTdl75plnNG3atNrPCwsL1bp1a40YMULh4eHOjgkPVFFRoeTkZA0fPlwBAQGm4wAXGFg+UL977XcqqCxQ/IB4RTe8umU1rjq/V2SuUPmOcrUJb6P/ueN/mLMBI/j5DW/G+Q1XW1y+WMsylik7IluPDH2kXl/bE85ve4f0z/GqovvLL79UVlaWGjVqdMHjd955p26++WatW7fuJ78vKChIQUFBFz0eEBDgtv+DYQbnBNxRo4BG6tKsi/ac2KOdJ3aqTeM2dTqOs8/vVVmrJEmjO41WYGCg044L1AU/v+HNOL/hKo9c84iWZSzTuzvf1YxhMxTkf3HN5GrufH5faS63ml7uqOnTp2vHjh1KS0ur/ZCkv/zlL5o/f77ZcADgQgnRCZLcZ4K5zWar3Z97TKcxhtMAAIC6GNlxpGLCYnSy9KQ+2/+Z6Tgey63udBcXFyszM7P285ycHKWlpalJkyZq06aNTp8+rUOHDik3N1eStH//fklSVFTUBR//rU2bNoqNja2fPwQAGJAQlaD3drynlLwU01EkSTuP79ThwsMK8Q/R4HaDTccBAAB14G/118N9HtZLX7+kOSlzdE/3e0xH8khudaf7+++/V3x8vOLj4yVJ06ZNU3x8vJ5//nlJNWu24+PjNXr0aEnS+PHjFR8fr7feestYZgBwB31j+kqS2xTd9q3ChrYfqpCAEMNpAABAXU1JmCKLLErOTlZOQY7pOB7Jre50Dxo0SDab7ZLPT5o0SZMmTbqqY17ueADgLfpE9ZEkHS48rBMlJxQZGmk0j721fHTcaKM5AACAY9o1aqdh7YcpOTtZc1Pn6qUhL5mO5HHc6k43AKBuwoPCFdckTpL5u92nSk/p2yPfSqLoBgDAG0xNmCpJmp82X5XVlYbTeB6KbgDwEu7SYr4qc5WqbdXq1aKXWkeY2dMTAAA4z62db1WzBs2UW5SrlRkrTcfxOBTdAOAlEqLcY4L5soxlkqQxcUwtBwDAGwT5B2li74mSpNkpsw2n8TwU3QDgJezbhpm8011ZXalVmT/szw0AALxDUkKSpJq5LUcLjxpO41kougHAS9iL7pwzOSo4V2AkwzeHv9GZ82fUNKSprm95vZEMAADA+bo066L+bfqr2latBWkLTMfxKBTdAOAlGoc0VmyjWEnm7nbbtwpLjEuUn9XPSAYAAOAa9oFqc1PnqtpWbTiN56DoBgAvYrrFnPXcAAB4r7u63aWIoAjlnMnRlzlfmo7jMSi6AcCL9I3+fxPM8+u/6M4pyNGeE3vkZ/HTLR1vqffXBwAArtUgoIHu73m/JAaqXQ2KbgDwIvY73dty63+C+fKMmtby/m36q1Fwo3p/fQAA4HpT+9a0mC/Zu0QnS08aTuMZKLoBwIvYi+6M0xkqLCus19e2F92j45haDgCAt+oT1UfXxFyjiuoKvbP9HdNxPAJFNwB4kcjQSLUOby1JSstPq7fXLSkv0Vc5X0mSxnRiPTcAAN4sKb5m+7DZKbNls9kMp3F/FN0A4GVMDFNbm7NWZVVlim0Uqy7NutTb6wIAgPp3X8/71CCggfad3KdvDn9jOo7bo+gGAC9Tu647r/7WdS9L/39TyzuNkcViqbfXBQAA9S88KFzju4+XxEC1K0HRDQBepnaCeT3d6bbZbKznBgDAxyQl1LSYf7T7I505f8ZsGDdH0Q0AXsZ+p3vfyX0qKS9x+eul5acptyhXDQIaaGC7gS5/PQAAYN4NrW5Q98juOld5Th/s/MB0HLdG0Q0AXiY6LFpRDaNUbavW9mPbXf569rvcw9sPV7B/sMtfDwAAmGexWGrvdtNifnkU3QDgheqzxdy+npvWcgAAfMuDvR5UoF+gUvNT63WAq6eh6AYAL1RfE8yPlxzX1qNbJUmj4ka59LUAAIB7adqgqe7seqckafY27nZfCkU3AHih+ppgvjJjpWyyKT4qXi3DW7r0tQAAgPuxt5gv3LmwXmbJeCKKbgDwQvb28t3Hd+t85XmXvY59PfeYTmNc9hoAAMB9DWo3SB0ad1BReZE+3vOx6ThuiaIbALxQq/BWatagmapsVdp5bKdLXqOiqkKrs1ZLYj03AAC+ymqxMlDtZ1B0A4AXslgsLm8x33hoowrLChXZIFLXtrzWJa8BAADc38TeE+Vn8dM3h7/R7uO7TcdxOxTdAOClEqJcO0zN3lo+Km6UrBbeTgAA8FXRYdEa23msJGlu6lzDadwPV0kA4KX6xrh22zD7VmGs5wYAAFMTpkqS3tn+jsoqywyncS8U3QDgpezt5TuP71R5VblTj515OlP7T+2Xv9Vfw9sPd+qxAQCA57mlwy1qFd5Kp86d0pJ9S0zHcSsU3QDgpWIbxapRcCOVV5U7fX3V8vSa1vIBbQcoIjjCqccGAACex8/qp4f7PCxJmpMyx3Aa90LRDQBe6sfD1JzdYm5fz83UcgAAYPdw/MOyyKK1OWuVdTrLdBy3QdENAF7MPkzNmRPMi8qKtO7AOkms5wYAAD9o26itRnQYIUmalzrPcBr3QdENAF7MFXe6v8j+QhXVFerYpKM6Ne3ktOMCAADPZx+oNj9tviqrKw2ncQ8U3QDgxewTzLcf2+60N77aqeVx3OUGAAAXGtt5rCIbRCqvOK92Boyvo+gGAC/WsUlHNQxsqPOV57X3xF6Hj1dtq9aKzBWSpNGdWM8NAAAuFOgXqEl9JkmS5qQyUE2i6AYAr2a1WBUfFS/JOS3mKXkpyi/OV8PAhhrQdoDDxwMAAN4nKSFJkrQiY4WOFB4xnMY8im4A8HJ9o2tazJ1RdNvbxEZ0GKFAv0CHjwcAALxPp6adNKDtAFXbqjU/db7pOMZRdAOAl7MPU3PGBPNlGaznBgAAP88+UG1u6lxV26oNpzGLohsAvJy96E7LT1NVdVWdj5NfnK/vc7+XJCXGJTolGwAA8E53dr1TjYIb6eDZg/oi+wvTcYyi6AYAL9elWReF+IeopKJEGacz6nycFRk1A9SujblWUQ2jnBUPAAB4oZCAED3Q8wFJ0uyU2YbTmEXRDQBezs/qpz5RfSQ5tq57eUbNeu7RcUwtBwAAP29q35oW88/2fabjJccNpzGHohsAfEDtuu7cuq3rLqss05qsNZKkMZ1Yzw0AAH5erxa9dF3L61RRXaF3tr9jOo4xFN0A4ANqJ5jn1+1O99eHvlZxebGiGkYpPjremdEAAIAXS4qv2T5sTsoc2Ww2w2nMoOgGAB9gv9OdkpdSpwmiy9JrppaPjhstq4W3DgAAcGXG9xiv0IBQ7T+1XxsPbTQdxwiunADAB3SL7KYgvyAVlhUquyD7qr+f9dwAAKAuwoLCNL7HeEm+O1CNohsAfECAX4B6tegl6eqHqaWfSlfm6UwFWAM0rP0wV8QDAABezL5n98d7PtaZ82fMhjGAohsAfMSPW8yvhr21fFC7QQoLCnN6LgAA4N2ua3mdejbvqfOV57Vwx0LTceodRTcA+IjaCeZ5VzfBnNZyAADgCIvFoqSEmoFqs1Nm+9xANYpuAPARtRPM81Ku+M3u7Pmz2nBwgyS2CgMAAHX3QK8HFOQXpO3Htl/1DQBPR9ENAD6iR/Me8rf66/S50zp09tAVfU9ydrIqqyvVuWlndWjSwcUJAQCAt2oS0kR3dbtLkjR7m28NVKPoBgAfEeQfpB7Ne0i68hZz+3pu7nIDAABH2VvM39/1vorLiw2nqT8U3QDgQxKirnyYWrWtWisyVkhiPTcAAHDcwLYDFdckTsXlxfpo90em49Qbim4A8CF9Y35Y1/1zvjv6nU6UnlB4ULj6t+nv6mgAAMDL/fdANV9B0Q0APuTHE8x/bpiafWr5LR1uUYBfgMuzAQAA7zex90T5W/21+chm7Tq+y3ScekHRDQA+pFeLXrJarDpecly5RbmX/VrWcwMAAGdr0bCFbu18qyRpTsocw2nqB0U3APiQBgEN1C2ym6TLt5gfLTyq1PxUWWRRYsfE+ooHAAB8QFJ8TYv5uzve1fnK84bTuB5FNwD4mB+3mF+KfYDa9a2uV2RoZL3kAgAAvmFEhxFqE9FGp8+d1pK9S0zHcTmKbgDwMVcywdy+npup5QAAwNn8rH56uM/DknxjoBpFNwD4mJ+bYH6+8rySs5MlsZ4bAAC4xuT4ybLIoq8OfKXM05mm47gURTcA+JjeLXrLIouOFh3VseJjFz2//sB6lVaUqmVYS/Vu0dtAQgAA4O3aRLTRyI4jJUlzU+YaTuNaFN0A4GPCgsLUqWknST99t9s+tXx03GhZLJZ6zQYAAHzH1ISpkqT5afNVUVVhOI3rUHQDgA+6VIu5zWb7YT13J9ZzAwAA1xnTaYxahLbQsZJjtb/090YU3QDgg+zD1P57gvnek3uVcyZHQX5BGho71EQ0AADgIwL8AjSpzyRJ0pxU792zm6IbAHyQfduw/77TvTy95i734NjBCg0MrfdcAADAt0yJnyJJWpW5SofPHjacxjUougHAB8VHx0uSDp49qFOlp2ofX5ZR09o1Jo6p5QAAwPXimsZpULtBqrZVa17qPNNxXIKiGwB8UKPgRurQuIMkKe1YmiSp4FyBNh3aJIn13AAAoP7YB6rNS5unquoqw2mcj6IbAHxUbYt5fk2LeXJOsqpsVeoe2V3tGrUzmAwAAPiSO7reocbBjXXo7CElZyebjuN0FN0A4KP6RtdMME/NT5UkrchcIalmqzAAAID6EuwfrAd7PShJmpPifQPVKLoBwEfZ73Sn5aepylal1VmrJdVs3wEAAFCfkhKSJEmf7f9Mx4qPGU7jXBTdAOCj7EV3ZkGm0orSdOrcKTUObqwbW99oOBkAAPA1PVv01PUtr1dldaXe3v626ThORdENAD6qaYOmahPeRpI09+hcSdKIDiPkb/U3GQsAAPgo+0C12dtma92BddpQsEHrD673+OFqblV0b9iwQWPHjlVMTIwsFos+/fTTC55fvHixRowYoaZNm8pisSgtLe2iYzzyyCPq0KGDQkJCFBkZqXHjxmnfvn318wcAAA+yeO9iHS89LknKLcuVJCVnJWvx3sUmYwEAAB91b497FewfrMyCTI14f4ReP/i6hi8crnZ/befR1yduVXSXlJSod+/emjVr1iWf79+/v2bOnHnJY/Tt21fz58/X3r17tXr1atlsNo0YMUJVVZ792xEAcKbFexfrro/u0vnK8xc8XnC+QHd9dJdHv7EBAADPtCZrzUXXJpJ0tPCoR1+fuFUPYWJiohITEy/5/IMP1ky0O3DgwCW/5he/+EXtf7dr104vvfSSevfurQMHDqhDhw5OywoAnqqqukqPr3pcNtkues4mmyyy6IlVT2hc53Hys/oZSAgAAHyN/frkp3j69YlbFd3OVlJSovnz5ys2NlatW7e+5NeVlZWprKys9vPCwkJJUkVFhSoqKlyeE+7Pfh5wPsAbrD+4XkcKj1zyeZtsOlx4WF9lf6WBbQfWYzLA+fj5DW/G+Q1v4onXJ1f6b88ri+5//vOf+u1vf6uSkhJ17txZycnJCgwMvOTXz5gxQy+++OJFj69Zs0YNGjRwZVR4mOTkZNMRAIdtKNhwRV+3cuNKlewucXEaoH7w8xvejPMb3sATr09KS0uv6OssNpvt4v5CN2CxWLRkyRLddtttFz134MABxcbGKjU1VX369Lno+bNnz+r48ePKy8vTq6++qqNHj2rTpk0KDg7+ydf6qTvdrVu31smTJxUeHu6sPxI8WEVFhZKTkzV8+HAFBASYjgM4ZP3B9Rq+cPjPfl3y/clu85tkoK74+Q1vxvkNb+KJ1yeFhYVq1qyZzp49e9m60SvvdEdERCgiIkJxcXG64YYb1LhxYy1ZskT33XffT359UFCQgoKCLno8ICCAH2C4AOcEvMHg9oPVKryVjhYe/cl13RZZ1Cq8lQa3H+xxa6aAS+HnN7wZ5ze8gSden1zpvzu3ml7uCjabTTab7YI72QDgy/ysfvrryL9KqnkD+zH752+MfMNt3tAAAID38+brE7cquouLi5WWlla7/3ZOTo7S0tJ06NAhSdLp06eVlpamPXv2SJL279+vtLQ05efnS5Kys7M1Y8YMbdu2TYcOHdI333yju+++WyEhIRo1apSRPxMAuKM7ut6hRfcsUsvwlhc83iq8lRbds0h3dL3DUDIAAOCrvPX6xK3ay7///nsNHjy49vNp06ZJkiZOnKgFCxZo6dKlmjx5cu3z48ePlyS98MIL+v3vf6/g4GB9/fXXeuONN1RQUKAWLVpowIAB+uabb9S8efP6/cMAgJu7o+sdGtd5nL7K/korN65UYv9Et2rZAgAAvscbr0/cqugeNGiQLjfXbdKkSZo0adIln4+JidGKFStckAwAvJOf1U8D2w5Uye4SDWw70KPf0AAAgHfwtusTt2ovBwAAAADAm1B0AwAAAADgIhTdAAAAAAC4CEU3AAAAAAAuQtENAAAAAICLUHQDAAAAAOAiFN0AAAAAALgIRTcAAAAAAC5C0Q0AAAAAgItQdAMAAAAA4CIU3QAAAAAAuAhFNwAAAAAALkLRDQAAAACAi1B0AwAAAADgIhTdAAAAAAC4CEU3AAAAAAAu4m86gDuy2WySpMLCQsNJ4C4qKipUWlqqwsJCBQQEmI4DOBXnN7wZ5ze8Gec3vJknnN/2etFeP14KRfdPKCoqkiS1bt3acBIAAAAAgDsrKipSRETEJZ+32H6uLPdB1dXVys3NVVhYmCwWi+k4cAOFhYVq3bq1Dh8+rPDwcNNxAKfi/IY34/yGN+P8hjfzhPPbZrOpqKhIMTExslovvXKbO90/wWq1qlWrVqZjwA2Fh4e77T96wFGc3/BmnN/wZpzf8Gbufn5f7g63HYPUAAAAAABwEYpuAAAAAABchKIbuAJBQUF64YUXFBQUZDoK4HSc3/BmnN/wZpzf8GbedH4zSA0AAAAAABfhTjcAAAAAAC5C0Q0AAAAAgItQdAMAAAAA4CIU3cBlzJgxQ9dee63CwsLUvHlz3Xbbbdq/f7/pWIDT/fnPf5bFYtETTzxhOgrgNEePHtUDDzygpk2bKiQkRD179tT3339vOhbgkKqqKj333HOKjY1VSEiIOnTooD/+8Y9iTBM80YYNGzR27FjFxMTIYrHo008/veB5m82m559/XtHR0QoJCdGwYcOUkZFhJqwDKLqBy1i/fr0effRRbd68WcnJyaqoqNCIESNUUlJiOhrgNN99953+9a9/qVevXqajAE5TUFCgm266SQEBAVq5cqX27Nmj1157TY0bNzYdDXDIzJkz9eabb+of//iH9u7dq5kzZ+qVV17R3//+d9PRgKtWUlKi3r17a9asWT/5/CuvvKK//e1veuutt7RlyxaFhobqlltu0fnz5+s5qWOYXg5chRMnTqh58+Zav369BgwYYDoO4LDi4mIlJCTon//8p1566SX16dNHb7zxhulYgMOmT5+uTZs26euvvzYdBXCqMWPGqEWLFpo7d27tY3feeadCQkL03nvvGUwGOMZisWjJkiW67bbbJNXc5Y6JidFTTz2lX//615Kks2fPqkWLFlqwYIHGjx9vMO3V4U43cBXOnj0rSWrSpInhJIBzPProoxo9erSGDRtmOgrgVEuXLtU111yju+++W82bN1d8fLxmz55tOhbgsH79+mnt2rVKT0+XJG3fvl0bN25UYmKi4WSAc+Xk5Cg/P/+Ca5SIiAhdf/31+vbbbw0mu3r+pgMAnqK6ulpPPPGEbrrpJvXo0cN0HMBhH374oVJSUvTdd9+ZjgI4XXZ2tt58801NmzZNv/vd7/Tdd9/pscceU2BgoCZOnGg6HlBn06dPV2Fhobp06SI/Pz9VVVXpT3/6k+6//37T0QCnys/PlyS1aNHigsdbtGhR+5ynoOgGrtCjjz6qXbt2aePGjaajAA47fPiwHn/8cSUnJys4ONh0HMDpqqurdc011+jll1+WJMXHx2vXrl166623KLrh0T766CMtXLhQ77//vrp37660tDQ98cQTiomJ4dwG3BTt5cAV+NWvfqVly5bpq6++UqtWrUzHARy2bds2HT9+XAkJCfL395e/v7/Wr1+vv/3tb/L391dVVZXpiIBDoqOj1a1btwse69q1qw4dOmQoEeAcv/nNbzR9+nSNHz9ePXv21IMPPqgnn3xSM2bMMB0NcKqoqChJ0rFjxy54/NixY7XPeQqKbuAybDabfvWrX2nJkiX68ssvFRsbazoS4BRDhw7Vzp07lZaWVvtxzTXX6P7771daWpr8/PxMRwQcctNNN120xWN6erratm1rKBHgHKWlpbJaL7yE9/PzU3V1taFEgGvExsYqKipKa9eurX2ssLBQW7Zs0Y033mgw2dWjvRy4jEcffVTvv/++PvvsM4WFhdWuH4mIiFBISIjhdEDdhYWFXTSbIDQ0VE2bNmVmAbzCk08+qX79+unll1/WPffco61bt+rf//63/v3vf5uOBjhk7Nix+tOf/qQ2bdqoe/fuSk1N1euvv66HH37YdDTgqhUXFyszM7P285ycHKWlpalJkyZq06aNnnjiCb300kuKi4tTbGysnnvuOcXExNROOPcUbBkGXIbFYvnJx+fPn69JkybVbxjAxQYNGsSWYfAqy5Yt0zPPPKOMjAzFxsZq2rRpmjp1qulYgEOKior03HPPacmSJTp+/LhiYmJ033336fnnn1dgYKDpeMBVWbdunQYPHnzR4xMnTtSCBQtks9n0wgsv6N///rfOnDmj/v3765///Kc6depkIG3dUXQDAAAAAOAirOkGAAAAAMBFKLoBAAAAAHARim4AAAAAAFyEohsAAAAAABeh6AYAAAAAwEUougEAAAAAcBGKbgAAAAAAXISiGwAAAAAAF6HoBgAAAADARSi6AQDwUZMmTZLFYpHFYlFAQIBiY2P129/+VufPnzcdDQAAr+FvOgAAADBn5MiRmj9/vioqKrRt2zZNnDhRFotFM2fONB0NAACvwJ1uAAB8WFBQkKKiotS6dWvddtttGjZsmJKTkyVJ7dq10xtvvHHB1/fp00e///3vaz+3WCyaM2eObr/9djVo0EBxcXFaunRpPf4JAABwbxTdAABAkrRr1y598803CgwMvKrve/HFF3XPPfdox44dGjVqlO6//36dPn3aRSkBAPAsFN0AAPiwZcuWqWHDhgoODlbPnj11/Phx/eY3v7mqY0yaNEn33XefOnbsqJdfflnFxcXaunWrixIDAOBZWNMNAIAPGzx4sN58802VlJToL3/5i/z9/XXnnXde1TF69epV+9+hoaEKDw/X8ePHnR0VAACPxJ1uAAB8WGhoqDp27KjevXtr3rx52rJli+bOnStJslqtstlsF3x9RUXFRccICAi44HOLxaLq6mrXhQYAwINQdAMAAEk1Rfbvfvc7Pfvsszp37pwiIyOVl5dX+3xhYaFycnIMJgQAwPNQdAMAgFp33323/Pz8NGvWLA0ZMkTvvvuuvv76a+3cuVMTJ06Un5+f6YgAAHgU1nQDAIBa/v7++tWvfqVXXnlFGRkZysnJ0ZgxYxQREaE//vGP3OkGAOAqWWz/vVgLAAAAAAA4Be3lAAAAAAC4CEU3AAAAAAAuQtENAAAAAICLUHQDAAAAAOAiFN0AAAAAALgIRTcAAAAAAC5C0Q0AAAAAgItQdAMAAAAA4CIU3QAAAAAAuAhFNwAAAAAALkLRDQAAAACAi1B0AwAAAADgIv8/fCIADzuH13UAAAAASUVORK5CYII=\n" 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\n" }, "metadata": {} } ] }, - { - "cell_type": "code", - "source": [ - "from sklearn.metrics import confusion_matrix, classification_report\n", - "\n", - "# Assuming you have access to the best model and test data\n", - "best_model = best_tpot.fitted_pipeline_\n", - "y_pred = best_model.predict(X_test)\n", - "\n", - "# Print confusion matrix\n", - "print(\"Confusion Matrix:\")\n", - "print(confusion_matrix(y_test, y_pred))\n", - "\n", - "# Print classification report\n", - "print(\"\\nClassification Report:\")\n", - "print(classification_report(y_test, y_pred))\n", - "\n", - "# Check unique predictions\n", - "unique_predictions = np.unique(y_pred)\n", - "print(\"\\nUnique Predictions:\", unique_predictions)\n", - "\n", - "# Check class distribution in test set\n", - "unique_classes, class_counts = np.unique(y_test, return_counts=True)\n", - "print(\"\\nClass Distribution in Test Set:\")\n", - "for cls, count in zip(unique_classes, class_counts):\n", - " print(f\"Class {cls}: {count}\")" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 218 - }, - "id": "8KwJfQvG0ZvJ", - "outputId": "13426649-eaaf-4fc9-8392-f648a79da6fe" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "error", - "ename": "NameError", - "evalue": "name 'best_tpot' is not defined", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;31m# Assuming you have access to the best model and test data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mbest_model\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbest_tpot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfitted_pipeline_\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbest_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mNameError\u001b[0m: name 'best_tpot' is not defined" - ] - } - ] - }, { "cell_type": "markdown", "source": [ @@ -9465,7 +6506,7 @@ "\n", "\n", "# Save the model using joblib\n", - "model_filename = 'manual_model_123.joblib'\n", + "model_filename = 'manual_model_1.joblib'\n", "joblib.dump(pipeline, model_filename)\n", "\n", "print(f\"\\nModel exported as joblib: {model_filename}\")\n", @@ -10846,23 +7887,41 @@ "height": 218 }, "id": "9qalTrUsqpVD", - "outputId": "38d1ca00-fa5d-4cc8-bbf1-a5386be19c6b" + "outputId": "5b8e6c0c-5c68-4be8-9741-3acdb5dfe53e" }, - "execution_count": null, + "execution_count": 22, "outputs": [ { "output_type": "error", - "ename": "NameError", - "evalue": "name 'results_df' is not defined", + "ename": "TypeError", + "evalue": "'int' object is not iterable", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[0;31m# Iterate through all runs and all evaluated pipelines\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrow\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mresults_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miterrows\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 17\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mpipeline_str\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrow\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'pipelines_tested'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[0msimple_pipeline\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msimplify_pipeline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpipeline_str\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mNameError\u001b[0m: name 'results_df' is not defined" + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[0;31m# Iterate through all runs and all evaluated pipelines\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrow\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mresults_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miterrows\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mpipeline_str\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrow\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'pipelines_tested'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 18\u001b[0m \u001b[0msimple_pipeline\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msimplify_pipeline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpipeline_str\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 19\u001b[0m \u001b[0mall_pipeline_stats\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0msimple_pipeline\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mTypeError\u001b[0m: 'int' object is not iterable" ] } ] }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "aXVq0kzkrdu8" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "# Building a NN classifier with Tpot for an imbalanced dataset" + ], + "metadata": { + "id": "PEWJBJqf15m-" + } + }, { "cell_type": "code", "source": [ @@ -10952,166 +8011,481 @@ "import numpy as np\n", "import pandas as pd\n", "from sklearn.model_selection import train_test_split\n", - "from sklearn.preprocessing import QuantileTransformer, LabelEncoder\n", + "from sklearn.preprocessing import StandardScaler, LabelEncoder\n", + "from sklearn.impute import SimpleImputer\n", + "from sklearn.metrics import make_scorer, confusion_matrix, recall_score\n", + "from sklearn.decomposition import TruncatedSVD\n", + "from imblearn.over_sampling import SMOTE\n", + "from imblearn.under_sampling import RandomUnderSampler\n", "from tpot import TPOTClassifier\n", - "from sklearn.metrics import f1_score\n", "from collections import defaultdict\n", "import time\n", "import re\n", - "from tabulate import tabulate\n", - "import warnings\n", - "warnings.filterwarnings('ignore')\n", + "import gc\n", "\n", - "# Assuming X and y are already loaded\n", + "def print_step_info(X, y, step_name):\n", + " print(f\"\\n--- {step_name} ---\")\n", + " print(f\"X shape: {X.shape}\")\n", + " print(f\"y shape: {y.shape}\")\n", + " print(f\"Class distribution: {np.bincount(y)}\")\n", + "\n", + "def custom_scorer(y_true, y_pred):\n", + " cm = confusion_matrix(y_true, y_pred)\n", + " tn, fp, fn, tp = cm.ravel()\n", + " if (tp + fn) == 0 or (tn + fp) == 0:\n", + " return 0.0\n", + " recall = tp / (tp + fn + 1e-8)\n", + " specificity = tn / (tn + fp + 1e-8)\n", + " recall_weight = 1.0\n", + " specificity_weight = 2.0\n", + " score = (recall_weight * recall + specificity_weight * specificity) / (recall_weight + specificity_weight)\n", + " return score\n", + "\n", + "custom_scorer_obj = make_scorer(custom_scorer, greater_is_better=True)\n", + "\n", + "# Assuming df is already loaded and contains 'message_vector' and 'label' columns\n", "\n", "# Encode labels\n", "le = LabelEncoder()\n", - "y_encoded = le.fit_transform(y)\n", + "df['label_encoded'] = le.fit_transform(df['label'])\n", "\n", - "# Function to perform PCA\n", - "def perform_pca(X, transformer, n_components=0.95):\n", - " X_transformed = transformer.fit_transform(X)\n", - " from sklearn.decomposition import PCA\n", - " pca = PCA(n_components=n_components)\n", - " X_pca = pca.fit_transform(X_transformed)\n", - " n_components_selected = X_pca.shape[1]\n", - " return X_pca, n_components_selected\n", + "# Split data\n", + "X = np.array(df['message_vector'].tolist())\n", + "y = df['label_encoded'].values\n", "\n", - "# Perform PCA with QuantileTransformer\n", - "transformer = QuantileTransformer(n_quantiles=1000, output_distribution='normal', random_state=42)\n", - "X_pca, n_components = perform_pca(X, transformer)\n", + "print_step_info(X, y, \"Original Data\")\n", "\n", - "print(f\"Number of components selected to explain 95% of variance: {n_components}\")\n", + "# Preprocessing\n", + "imputer = SimpleImputer(strategy='mean')\n", + "scaler = StandardScaler()\n", + "pca = TruncatedSVD(n_components=100, random_state=42) # Reduce to 100 components\n", + "\n", + "X = imputer.fit_transform(X)\n", + "X = scaler.fit_transform(X)\n", + "X = pca.fit_transform(X)\n", + "\n", + "print_step_info(X, y, \"After Preprocessing\")\n", + "\n", + "# Split the data\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)\n", + "print_step_info(X_train, y_train, \"Training Data\")\n", + "\n", + "# Resampling\n", + "smote = SMOTE(sampling_strategy=0.5, random_state=42)\n", + "rus = RandomUnderSampler(sampling_strategy=0.5, random_state=42)\n", + "X_resampled, y_resampled = rus.fit_resample(*smote.fit_resample(X_train, y_train))\n", + "print_step_info(X_resampled, y_resampled, \"Resampled Data\")\n", + "\n", + "# TPOT configuration for neural networks\n", + "tpot_config = {\n", + " 'sklearn.neural_network.MLPClassifier': {\n", + " 'hidden_layer_sizes': [(50,), (100,), (50, 50), (100, 50)],\n", + " 'activation': ['relu', 'tanh'],\n", + " 'alpha': [0.0001, 0.001, 0.01],\n", + " 'learning_rate': ['constant', 'adaptive'],\n", + " }\n", + "}\n", "\n", "# Initialize results storage\n", "results = defaultdict(list)\n", "\n", "# Number of runs\n", - "n_runs = 2\n", + "n_runs = 3\n", "\n", - "# Function to extract number of features selected\n", - "def get_n_features(pipeline_str):\n", - " match = re.search(r'SelectPercentile\\(score_func=f_classif, percentile=(\\d+)\\)', pipeline_str)\n", - " if match:\n", - " percentile = int(match.group(1))\n", - " return int(X_pca.shape[1] * percentile / 100)\n", - " return X_pca.shape[1] # If no feature selection, return all features\n", + "# Initialize best_tpot and best_recall\n", + "best_tpot = None\n", + "best_recall = 0\n", "\n", "for run in range(n_runs):\n", " print(f\"\\nStarting run {run + 1}/{n_runs}\")\n", " start_time = time.time()\n", "\n", - " # Stratified split\n", - " X_train, X_test, y_train, y_test = train_test_split(X_pca, y_encoded, test_size=0.2, random_state=42 + run, stratify=y_encoded)\n", - "\n", - " # TPOT classifier with Neural Network configuration\n", + " # Initialize TPOT\n", " tpot = TPOTClassifier(\n", - " config_dict='TPOT NN', # Use TPOT's built-in neural network configuration\n", - " scoring='f1_weighted',\n", - " verbosity=2,\n", + " config_dict=tpot_config,\n", " generations=5,\n", " population_size=20,\n", + " verbosity=2,\n", + " scoring=custom_scorer_obj,\n", " random_state=42 + run,\n", - " max_time_mins=10, # Increased runtime to 10 minutes per run\n", - " n_jobs=-1 # Use all available CPU cores\n", + " n_jobs=-1,\n", + " max_time_mins=120,\n", + " max_eval_time_mins=20\n", " )\n", "\n", " try:\n", - " # Fit\n", - " tpot.fit(X_train, y_train)\n", + " # Fit TPOT\n", + " tpot.fit(X_resampled, y_resampled)\n", "\n", - " # Predict and calculate F1 score\n", + " # Make predictions\n", " y_pred = tpot.predict(X_test)\n", - " f1 = f1_score(y_test, y_pred, average='weighted')\n", "\n", - " # Get pipeline string and extract number of features\n", + " # Calculate recall score\n", + " recall = recall_score(y_test, y_pred, average='macro')\n", + "\n", + " # Update best_tpot if this run has better recall score\n", + " if recall > best_recall:\n", + " best_recall = recall\n", + " best_tpot = tpot\n", + "\n", + " # Get pipeline string\n", " pipeline_str = str(tpot.fitted_pipeline_)\n", - " n_features = get_n_features(pipeline_str)\n", "\n", " # Store results\n", " results['run'].append(run + 1)\n", - " results['f1_score'].append(f1)\n", + " results['recall_score'].append(recall)\n", " results['best_pipeline'].append(pipeline_str)\n", - " results['n_features'].append(n_features)\n", " results['runtime'].append(time.time() - start_time)\n", " results['pipelines_tested'].append(len(tpot.evaluated_individuals_))\n", "\n", - " print(f\"Run {run + 1} completed. F1 Score: {f1:.4f}, Features selected: {n_features}, Pipelines tested: {len(tpot.evaluated_individuals_)}\")\n", + " print(f\"Run {run + 1} completed. Recall Score: {recall:.4f}, Pipelines tested: {len(tpot.evaluated_individuals_)}\")\n", "\n", " except Exception as e:\n", - " print(f\"Error in run {run + 1}: {str(e)}\")\n", - " results['run'].append(run + 1)\n", - " results['f1_score'].append(None)\n", - " results['best_pipeline'].append(None)\n", - " results['n_features'].append(None)\n", - " results['runtime'].append(time.time() - start_time)\n", - " results['pipelines_tested'].append(None)\n", + " print(f\"An error occurred during TPOT optimization: {str(e)}\")\n", + "\n", + " # Clear memory\n", + " gc.collect()\n", "\n", "# Convert results to DataFrame\n", "results_df = pd.DataFrame(results)\n", "\n", - "# Print results table\n", - "print(\"\\nResults Summary:\")\n", - "print(tabulate(results_df, headers='keys', tablefmt='grid'))\n", - "\n", "# Save results to CSV\n", - "results_df.to_csv('tpot_nn_results.csv', index=False)\n", - "print(\"\\nResults saved to 'tpot_nn_results.csv'\")" + "results_df.to_csv('tpot_nn_recall_results.csv', index=False)\n", + "\n", + "print(\"\\nResults saved to 'tpot_nn_recall_results.csv'\")\n", + "\n", + "# If best_tpot exists, export the best pipeline\n", + "if best_tpot:\n", + " best_tpot.export('tpot_nn_best_pipeline.py')\n", + " print(\"Best pipeline exported to 'tpot_nn_best_pipeline.py'\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", - "height": 870, + "height": 1000, "referenced_widgets": [ - "2380e728c4b94d4a82d99c5df53bcfb8", - "60e0fda3b0b64788b12de0492e48a8eb", - "b4aab6b20b704970a6d88b4203aafe33", - "94853d6fcd9e485ab00941270501bdc2", - "85171f4f5f6f4cf798c8f0afc85c0f45", - "188af8de0a894c4b862c04ae22f6e2ab", - "cca3270cffa747b0819b1a50fff1ef02", - "2f02cde9cf5f45fc8ce05a545baad40c", - "aa35eac1e7964054bf8ab8903cd3e182", - "bf41ac8c2a6f47e6b6956e4389485e3e", - "6f7d22c33bbd434e83d5764c7ebed942", - "612cffdae98544d480553f9a40e6bd01", - "9eb0c8bff0b6480caac00ebc3381b0d7", - 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"4749064df71f48f4b947ba208acbac26", + "14db8797959a4253a36dd334f4826a00", + "94e3a14430f64fa9ab318f0e6c37a883", + "9b974c27ffe7444ab7f9683167a6988c", + "e13925b2a40c4dcf993c62f64d332272", + "d143776d4b464a648b17ae8e280ecc39", + "16a6911e48b142cf83401adb3788792f", + "68f7ddcbed594c8c8a511c20be8d4acd", + "4c3ff39abc4b4f0baeee0538ce75d76f", + "1f8d747cea354729af6b532fb2a23a97", + "f634d0b3e4c34f058ab52077ccb97407", + "5df15aaaa49b425e827b57a427479f89", + "11ac4cb2fd7641e98779b027d47f2696", + "d7a0b39588cb421492dde89492430a84", + "fe991ac34e224d55b9c27ca1bad2808b", + "ba7425e6aedf46f5adce1a7e8e2a7c4d" ] }, "id": "iyExRJRjsDBO", - "outputId": "dfaff146-db7c-47d5-bd93-535282346bea" + "outputId": "48d4a650-08a3-4766-c47b-058b16b49222" }, - "execution_count": null, + "execution_count": 24, + "outputs": [ + { + "metadata": { + "tags": null + }, + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Original Data ---\n", + "X shape: (13455, 30000)\n", + "y shape: (13455,)\n", + "Class distribution: [ 114 13341]\n", + "\n", + "--- After Preprocessing ---\n", + "X shape: (13455, 100)\n", + "y shape: (13455,)\n", + "Class distribution: [ 114 13341]\n", + "\n", + "--- Training Data ---\n", + "X shape: (10764, 100)\n", + "y shape: (10764,)\n", + "Class distribution: [ 91 10673]\n", + "\n", + "--- Resampled Data ---\n", + "X shape: (16008, 100)\n", + "y shape: (16008,)\n", + "Class distribution: [ 5336 10672]\n", + "\n", + "Starting run 1/3\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "873451c433c1441796e0f2c582b54edc", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Optimization Progress: 0%| | 0/20 [00:00 0.05 else 'Recall scores are not normally distributed'}\")\n", + "\n", + "# Calculate effect size (Cohen's d) for recall scores compared to baseline\n", + "effect_size = (recall_mean - baseline_recall) / recall_std\n", + "print(f\"\\nEffect size (Cohen's d) compared to baseline: {effect_size:.4f}\")\n", + "\n", + "# Plot recall scores\n", + "plt.figure(figsize=(10, 6))\n", + "plt.plot(results_df['run'], results_df['recall_score'], 'bo-')\n", + "plt.title('Recall Scores Across Runs')\n", + "plt.xlabel('Run')\n", + "plt.ylabel('Recall Score')\n", + "plt.grid(True)\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# Runtime analysis\n", + "print(\"\\nRuntime Analysis:\")\n", + "print(f\" Total Runtime: {results_df['runtime'].sum():.2f} seconds\")\n", + "print(f\" Average Runtime per Run: {results_df['runtime'].mean():.2f} seconds\")\n", + "\n", + "# Pipelines tested analysis\n", + "print(\"\\nPipelines Tested Analysis:\")\n", + "print(f\" Total Pipelines Tested: {results_df['pipelines_tested'].sum()}\")\n", + "print(f\" Average Pipelines per Run: {results_df['pipelines_tested'].mean():.2f}\")\n", + "\n", + "# Best pipeline\n", + "best_run = results_df.loc[results_df['recall_score'].idxmax()]\n", + "print(f\"\\nBest Run (Run {best_run['run']}):\")\n", + "print(f\" Recall Score: {best_run['recall_score']:.4f}\")\n", + "print(f\" Runtime: {best_run['runtime']:.2f} seconds\")\n", + "print(f\" Pipelines Tested: {best_run['pipelines_tested']}\")\n", + "print(f\" Best Pipeline:\\n{best_run['best_pipeline']}\")\n", + "\n", + "# Plot runtime vs recall score\n", + "plt.figure(figsize=(10, 6))\n", + "plt.scatter(results_df['runtime'], results_df['recall_score'])\n", + "plt.title('Runtime vs Recall Score')\n", + "plt.xlabel('Runtime (seconds)')\n", + "plt.ylabel('Recall Score')\n", + "plt.grid(True)\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# Analyze the relationship between runtime and recall score\n", + "runtime_recall_corr, _ = stats.pearsonr(results_df['runtime'], results_df['recall_score'])\n", + "print(f\"\\nCorrelation between runtime and recall score: {runtime_recall_corr:.4f}\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "s7pRao7WxkB8", + "outputId": "eadc42dd-2673-4d07-f979-cb49327f30ed" + }, + "execution_count": 32, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ - "Number of components selected to explain 95% of variance: 5\n", "\n", - "Starting run 1/2\n" + "Summary Statistics:\n", + " run recall_score runtime pipelines_tested\n", + "count 3.0 3.000000 3.000000 3.000000\n", + "mean 2.0 0.846452 482.763048 93.000000\n", + "std 1.0 0.057801 26.729168 3.464102\n", + "min 1.0 0.802474 459.580693 89.000000\n", + "25% 1.5 0.813718 468.144446 92.000000\n", + "50% 2.0 0.824963 476.708200 95.000000\n", + "75% 2.5 0.868441 494.354225 95.000000\n", + "max 3.0 0.911919 512.000251 95.000000\n", + "\n", + "Recall Score Analysis:\n", + " Mean: 0.8465\n", + " Standard Deviation: 0.0578\n", + " 95.0% Confidence Interval: (0.7029, 0.9900)\n", + "\n", + "One-sample t-test (comparing to baseline recall of 0.5):\n", + " t-statistic: 10.3818\n", + " p-value: 0.0092\n", + "\n", + "Shapiro-Wilk test for normality of recall scores:\n", + " p-value: 0.3739\n", + " Recall scores are normally distributed\n", + "\n", + "Effect size (Cohen's d) compared to baseline: 5.9939\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ - "Optimization Progress: 0%| | 0/20 [00:00" ], - "application/vnd.jupyter.widget-view+json": { - "version_major": 2, - "version_minor": 0, - "model_id": "2380e728c4b94d4a82d99c5df53bcfb8" - } + "image/png": 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\n" }, "metadata": {} }, @@ -11120,33 +8494,38 @@ "name": "stdout", "text": [ "\n", - "Generation 1 - Current best internal CV score: 0.9873368055887921\n", + "Runtime Analysis:\n", + " Total Runtime: 1448.29 seconds\n", + " Average Runtime per Run: 482.76 seconds\n", "\n", - "Generation 2 - Current best internal CV score: 0.9873368055887921\n", + "Pipelines Tested Analysis:\n", + " Total Pipelines Tested: 279\n", + " Average Pipelines per Run: 93.00\n", "\n", - "Generation 3 - Current best internal CV score: 0.9873368055887921\n", - "\n", - "Generation 4 - Current best internal CV score: 0.9873368055887921\n", - "\n", - "Generation 5 - Current best internal CV score: 0.9873368055887921\n", - "\n", - "Best pipeline: GaussianNB(input_matrix)\n", - "Run 1 completed. F1 Score: 0.9872, Features selected: 5, Pipelines tested: 115\n", - "\n", - "Starting run 2/2\n" + "Best Run (Run 2):\n", + " Recall Score: 0.9119\n", + " Runtime: 512.00 seconds\n", + " Pipelines Tested: 95\n", + " Best Pipeline:\n", + "Pipeline(steps=[('stackingestimator',\n", + " StackingEstimator(estimator=MLPClassifier(activation='tanh',\n", + " alpha=0.01,\n", + " hidden_layer_sizes=(100,\n", + " 50),\n", + " learning_rate='adaptive',\n", + " random_state=43))),\n", + " ('mlpclassifier',\n", + " MLPClassifier(alpha=0.01, hidden_layer_sizes=(100, 50),\n", + " learning_rate='adaptive', random_state=43))])\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ - "Optimization Progress: 0%| | 0/20 [00:00" ], - "application/vnd.jupyter.widget-view+json": { - "version_major": 2, - "version_minor": 0, - "model_id": "612cffdae98544d480553f9a40e6bd01" - } + "image/png": 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\n" }, "metadata": {} }, @@ -11155,37 +8534,257 @@ "name": "stdout", "text": [ "\n", - "Generation 1 - Current best internal CV score: 0.9873368055887921\n", - "\n", - "Generation 2 - Current best internal CV score: 0.9873368055887921\n", - "\n", - "Generation 3 - Current best internal CV score: 0.9873368055887921\n", - "\n", - "Generation 4 - Current best internal CV score: 0.9873368055887921\n", - "\n", - "Generation 5 - Current best internal CV score: 0.9873368055887921\n", - "\n", - "Best pipeline: ExtraTreesClassifier(input_matrix, bootstrap=False, criterion=entropy, max_features=0.8500000000000001, min_samples_leaf=20, min_samples_split=19, n_estimators=100)\n", - "Run 2 completed. F1 Score: 0.9872, Features selected: 5, Pipelines tested: 114\n", - "\n", - "Results Summary:\n", - "+----+-------+------------+----------------------------------------------------------------------------------+--------------+-----------+--------------------+\n", - "| | run | f1_score | best_pipeline | n_features | runtime | pipelines_tested |\n", - "+====+=======+============+==================================================================================+==============+===========+====================+\n", - "| 0 | 1 | 0.987198 | Pipeline(steps=[('gaussiannb', GaussianNB())]) | 5 | 109.885 | 115 |\n", - "+----+-------+------------+----------------------------------------------------------------------------------+--------------+-----------+--------------------+\n", - "| 1 | 2 | 0.987198 | Pipeline(steps=[('extratreesclassifier', | 5 | 108.381 | 114 |\n", - "| | | | ExtraTreesClassifier(criterion='entropy', | | | |\n", - "| | | | max_features=0.8500000000000001, | | | |\n", - "| | | | min_samples_leaf=20, min_samples_split=19, | | | |\n", - "| | | | random_state=43))]) | | | |\n", - "+----+-------+------------+----------------------------------------------------------------------------------+--------------+-----------+--------------------+\n", - "\n", - "Results saved to 'tpot_nn_results.csv'\n" + "Correlation between runtime and recall score: 0.8669\n" ] } ] }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "K-pwEYJV-uZn" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Make predictions\n", + "y_pred = best_tpot.predict(X_test)\n", + "\n", + "# Create the confusion matrix\n", + "cm = confusion_matrix(y_test, y_pred)\n", + "\n", + "# Create a ConfusionMatrixDisplay object\n", + "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=['negative', 'positive'])\n", + "\n", + "# Plot the confusion matrix\n", + "plt.figure(figsize=(10, 7))\n", + "disp.plot(cmap='Blues', values_format='d')\n", + "plt.title('Confusion Matrix')\n", + "plt.show()\n", + "\n", + "# Print the confusion matrix\n", + "print(\"Confusion Matrix:\")\n", + "print(cm)\n", + "\n", + "# Calculate and print additional metrics\n", + "tn, fp, fn, tp = cm.ravel()\n", + "precision = tp / (tp + fp) if (tp + fp) > 0 else 0\n", + "recall = tp / (tp + fn) if (tp + fn) > 0 else 0\n", + "f1_score = 2 * (precision * recall) / (precision + recall) if (precision + recall) > 0 else 0\n", + "\n", + "print(f\"\\nPrecision: {precision:.4f}\")\n", + "print(f\"Recall: {recall:.4f}\")\n", + "print(f\"F1-score: {f1_score:.4f}\")\n", + "\n", + "# Additional metrics from the custom scorer\n", + "specificity = tn / (tn + fp) if (tn + fp) > 0 else 0\n", + "custom_score = (recall + 2 * specificity) / 3 # As per your custom scorer\n", + "\n", + "print(f\"Specificity: {specificity:.4f}\")\n", + "print(f\"Custom Score: {custom_score:.4f}\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 653 + }, + "id": "cmtpHeIzxk7f", + "outputId": "3ef42227-4089-4d84-a3fc-de50ab86cc99" + }, + "execution_count": 33, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Confusion Matrix:\n", + "[[ 19 4]\n", + " [ 6 2662]]\n", + "\n", + "Precision: 0.9985\n", + "Recall: 0.9978\n", + "F1-score: 0.9981\n", + "Specificity: 0.8261\n", + "Custom Score: 0.8833\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import re\n", + "\n", + "# Assuming results_df is already loaded from 'tpot_nn_recall_results.csv'\n", + "# If not, uncomment the following line:\n", + "# results_df = pd.read_csv('tpot_nn_recall_results.csv')\n", + "\n", + "def extract_pipeline_components(pipeline_str):\n", + " # Extract all components from the pipeline string, excluding 'Pipeline'\n", + " components = re.findall(r'(\\w+)\\(', pipeline_str)\n", + " return ', '.join([comp for comp in components if comp != 'Pipeline'])\n", + "\n", + "# Extract pipeline components\n", + "results_df['pipeline_components'] = results_df['best_pipeline'].apply(extract_pipeline_components)\n", + "\n", + "# Create a table of pipeline components and their frequencies\n", + "component_counts = results_df['pipeline_components'].value_counts().reset_index()\n", + "component_counts.columns = ['Pipeline Components', 'Frequency']\n", + "component_counts['Percentage'] = component_counts['Frequency'] / len(results_df) * 100\n", + "\n", + "print(\"Pipeline Components Frequency Table:\")\n", + "print(component_counts.to_string(index=False))\n", + "\n", + "# Visualize pipeline components frequency\n", + "plt.figure(figsize=(12, 6))\n", + "component_counts.plot(kind='bar', x='Pipeline Components', y='Frequency')\n", + "plt.title('Frequency of Pipeline Components in Best Pipelines')\n", + "plt.xlabel('Pipeline Components')\n", + "plt.ylabel('Frequency')\n", + "plt.xticks(rotation=45, ha='right')\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# Find the best performing pipeline components\n", + "best_components = results_df.loc[results_df['recall_score'].idxmax(), 'pipeline_components']\n", + "print(f\"\\nComponents of the best performing pipeline: {best_components}\")\n", + "\n", + "# Analyze individual components\n", + "all_components = [comp.strip() for comps in results_df['pipeline_components'].str.split(',') for comp in comps]\n", + "component_freq = pd.Series(all_components).value_counts()\n", + "\n", + "print(\"\\nIndividual Component Frequency:\")\n", + "print(component_freq)\n", + "\n", + "# Visualize individual component frequency\n", + "plt.figure(figsize=(10, 6))\n", + "component_freq.plot(kind='bar')\n", + "plt.title('Frequency of Individual Components in Best Pipelines')\n", + "plt.xlabel('Component')\n", + "plt.ylabel('Frequency')\n", + "plt.xticks(rotation=45, ha='right')\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# Additional analysis: Component combination performance\n", + "results_df['mean_recall'] = results_df.groupby('pipeline_components')['recall_score'].transform('mean')\n", + "best_combo = results_df.loc[results_df['mean_recall'].idxmax(), 'pipeline_components']\n", + "best_combo_recall = results_df.loc[results_df['mean_recall'].idxmax(), 'mean_recall']\n", + "\n", + "print(f\"\\nBest performing component combination: {best_combo}\")\n", + "print(f\"Mean recall score: {best_combo_recall:.4f}\")\n", + "\n", + "# [Rest of the previous analysis code remains the same]\n", + "\n", + "# ... [Keep all the previous analysis code] ..." + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "lUqkJTy-0SCE", + "outputId": "d343d5d2-741a-4c32-f904-7f4281bf258b" + }, + "execution_count": 36, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Pipeline Components Frequency Table:\n", + " Pipeline Components Frequency Percentage\n", + "StackingEstimator, MLPClassifier, MLPClassifier 3 100.0\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "Components of the best performing pipeline: StackingEstimator, MLPClassifier, MLPClassifier\n", + "\n", + "Individual Component Frequency:\n", + "MLPClassifier 6\n", + "StackingEstimator 3\n", + "Name: count, dtype: int64\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "Best performing component combination: StackingEstimator, MLPClassifier, MLPClassifier\n", + "Mean recall score: 0.8465\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "iaMlGpRsxk_F" + }, + "execution_count": null, + "outputs": [] + }, { "cell_type": "code", "source": [ @@ -11262,263 +8861,10 @@ } ] }, - { - "cell_type": "code", - "source": [ - "import pandas as pd\n", - "from collections import defaultdict\n", - "from tabulate import tabulate\n", - "\n", - "# Assuming results_df is already loaded or created from the previous step\n", - "\n", - "# Define the classifiers available in TPOT NN configuration\n", - "tpot_nn_classifiers = [\n", - " 'MLPClassifier',\n", - " 'KNeighborsClassifier',\n", - " 'XGBClassifier',\n", - " 'LinearSVC',\n", - " 'SGDClassifier'\n", - "]\n", - "\n", - "# Initialize a dictionary to store algorithm statistics\n", - "algorithm_stats = defaultdict(int)\n", - "\n", - "# Total number of pipelines tested\n", - "total_pipelines = 0\n", - "\n", - "# Iterate through all runs\n", - "for _, row in results_df.iterrows():\n", - " pipelines_tested = row['pipelines_tested']\n", - " total_pipelines += pipelines_tested\n", - "\n", - " # Distribute the pipelines tested equally among all classifiers\n", - " # This is an approximation, as we don't have the exact breakdown\n", - " for classifier in tpot_nn_classifiers:\n", - " algorithm_stats[classifier] += pipelines_tested // len(tpot_nn_classifiers)\n", - "\n", - " # Add the remainder to 'Other' category\n", - " algorithm_stats['Other'] += pipelines_tested % len(tpot_nn_classifiers)\n", - "\n", - "# Add the best pipelines to their respective categories\n", - "for _, row in results_df.iterrows():\n", - " best_pipeline = row['best_pipeline']\n", - " for classifier in tpot_nn_classifiers:\n", - " if classifier in best_pipeline:\n", - " algorithm_stats[classifier] += 1\n", - " break\n", - " else:\n", - " algorithm_stats['Other'] += 1\n", - "\n", - "# Prepare the summary table\n", - "summary_table = [[algorithm, count] for algorithm, count in algorithm_stats.items() if count > 0]\n", - "\n", - "# Sort by count\n", - "summary_table.sort(key=lambda x: -x[1])\n", - "\n", - "# Print the summary table\n", - "print(\"\\nEstimated Algorithms Used Across All Runs:\")\n", - "print(tabulate(summary_table, headers=['Algorithm', 'Estimated Count'], tablefmt='grid'))\n", - "\n", - "# Calculate and print additional statistics\n", - "total_runs = len(results_df)\n", - "successful_runs = results_df['f1_score'].notna().sum()\n", - "average_f1 = results_df['f1_score'].mean()\n", - "average_features = results_df['n_features'].mean() if 'n_features' in results_df.columns else None\n", - "average_runtime = results_df['runtime'].mean()\n", - "\n", - "print(f\"\\nTotal Runs: {total_runs}\")\n", - "print(f\"Successful Runs: {successful_runs}\")\n", - "print(f\"Average F1 Score: {average_f1:.4f}\")\n", - "if average_features is not None:\n", - " print(f\"Average Number of Features: {average_features:.2f}\")\n", - "print(f\"Average Runtime: {average_runtime:.2f} seconds\")\n", - "print(f\"Total Pipelines Tested: {total_pipelines}\")\n", - "\n", - "# Print full best pipelines for each run\n", - "print(\"\\nBest Pipelines for Each Run:\")\n", - "for index, row in results_df.iterrows():\n", - " print(f\"\\nRun {index + 1}:\")\n", - " print(f\"F1 Score: {row['f1_score']:.4f}\")\n", - " print(f\"Pipeline: {row['best_pipeline']}\")\n", - " print(f\"Pipelines tested in this run: {row['pipelines_tested']}\")\n", - "\n", - "# Optional: Add visualization\n", - "import matplotlib.pyplot as plt\n", - "\n", - "# Visualize F1 scores across runs\n", - "plt.figure(figsize=(10, 6))\n", - "plt.plot(results_df['run'], results_df['f1_score'], marker='o')\n", - "plt.title('F1 Score Across Runs')\n", - "plt.xlabel('Run')\n", - "plt.ylabel('F1 Score')\n", - "plt.grid(True)\n", - "plt.savefig('f1_scores_across_runs.png')\n", - "plt.close()\n", - "\n", - "# Visualize pipeline counts\n", - "algorithms, counts = zip(*summary_table)\n", - "plt.figure(figsize=(12, 6))\n", - "plt.bar(algorithms, counts)\n", - "plt.title('Estimated Algorithm Usage')\n", - "plt.xlabel('Algorithm')\n", - "plt.ylabel('Estimated Count')\n", - "plt.xticks(rotation=45, ha='right')\n", - "plt.tight_layout()\n", - "plt.savefig('estimated_algorithm_usage.png')\n", - "plt.close()\n", - "\n", - "print(\"\\nVisualization plots saved as 'f1_scores_across_runs.png' and 'estimated_algorithm_usage.png'\")" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "5m0mtcVZduol", - "outputId": "1d8b8679-4cbc-4574-dc35-6e7ab8b3d1cd" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "\n", - "Estimated Algorithms Used Across All Runs:\n", - "+----------------------+-------------------+\n", - "| Algorithm | Estimated Count |\n", - "+======================+===================+\n", - "| MLPClassifier | 45 |\n", - "+----------------------+-------------------+\n", - "| KNeighborsClassifier | 45 |\n", - "+----------------------+-------------------+\n", - "| XGBClassifier | 45 |\n", - "+----------------------+-------------------+\n", - "| LinearSVC | 45 |\n", - "+----------------------+-------------------+\n", - "| SGDClassifier | 45 |\n", - "+----------------------+-------------------+\n", - "| Other | 6 |\n", - "+----------------------+-------------------+\n", - "\n", - "Total Runs: 2\n", - "Successful Runs: 2\n", - "Average F1 Score: 0.9872\n", - "Average Number of Features: 5.00\n", - "Average Runtime: 109.13 seconds\n", - "Total Pipelines Tested: 229\n", - "\n", - "Best Pipelines for Each Run:\n", - "\n", - "Run 1:\n", - "F1 Score: 0.9872\n", - "Pipeline: Pipeline(steps=[('gaussiannb', GaussianNB())])\n", - "Pipelines tested in this run: 115\n", - "\n", - "Run 2:\n", - "F1 Score: 0.9872\n", - "Pipeline: Pipeline(steps=[('extratreesclassifier',\n", - " ExtraTreesClassifier(criterion='entropy',\n", - " max_features=0.8500000000000001,\n", - " min_samples_leaf=20, min_samples_split=19,\n", - " random_state=43))])\n", - "Pipelines tested in this run: 114\n", - "\n", - "Visualization plots saved as 'f1_scores_across_runs.png' and 'estimated_algorithm_usage.png'\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "import matplotlib.pyplot as plt\n", - "import pandas as pd\n", - "\n", - "# Assuming results_df is your DataFrame with the results\n", - "\n", - "fig, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=(10, 18))\n", - "\n", - "ax1.plot(results_df['run'], results_df['f1_score'], 'bo-')\n", - "ax1.set_title('TPOT-NN Performance (F1 Score) Across Runs')\n", - "ax1.set_xlabel('Run')\n", - "ax1.set_ylabel('F1 Score')\n", - "ax1.grid(True)\n", - "\n", - "ax2.plot(results_df['run'], results_df['n_features'], 'ro-')\n", - "ax2.set_title('Number of Features Selected Across Runs')\n", - "ax2.set_xlabel('Run')\n", - "ax2.set_ylabel('Number of Features')\n", - "ax2.grid(True)\n", - "\n", - "ax3.plot(results_df['run'], results_df['pipelines_tested'], 'go-')\n", - "ax3.set_title('Number of Pipelines Tested Across Runs')\n", - "ax3.set_xlabel('Run')\n", - "ax3.set_ylabel('Number of Pipelines')\n", - "ax3.grid(True)\n", - "\n", - "plt.tight_layout()\n", - "plt.savefig('tpot_nn_performance_features_pipelines.png')\n", - "plt.show()\n", - "\n", - "# Print summary statistics\n", - "print(\"\\nSummary Statistics:\")\n", - "print(results_df.describe())\n", - "\n", - "# Optional: If you want to see the correlation between different metrics\n", - "correlation_matrix = results_df[['f1_score', 'n_features', 'pipelines_tested', 'runtime']].corr()\n", - "print(\"\\nCorrelation Matrix:\")\n", - "print(correlation_matrix)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "id": "3lBpziEae4sf", - "outputId": "08837957-1ab6-4acb-ab65-b0f0fca2b56c" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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\n" - }, - "metadata": {} - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "\n", - "Summary Statistics:\n", - " run f1_score n_features runtime pipelines_tested\n", - "count 2.000000 2.000000 2.0 2.000000 2.000000\n", - "mean 1.500000 0.987198 5.0 109.132951 114.500000\n", - "std 0.707107 0.000000 0.0 1.063263 0.707107\n", - "min 1.000000 0.987198 5.0 108.381110 114.000000\n", - "25% 1.250000 0.987198 5.0 108.757030 114.250000\n", - "50% 1.500000 0.987198 5.0 109.132951 114.500000\n", - "75% 1.750000 0.987198 5.0 109.508871 114.750000\n", - "max 2.000000 0.987198 5.0 109.884791 115.000000\n", - "\n", - "Correlation Matrix:\n", - " f1_score n_features pipelines_tested runtime\n", - "f1_score NaN NaN NaN NaN\n", - "n_features NaN NaN NaN NaN\n", - "pipelines_tested NaN NaN 1.0 1.0\n", - "runtime NaN NaN 1.0 1.0\n" - ] - } - ] - }, { "cell_type": "markdown", "source": [ - "## Different Algorithm set" + "## Using cuML" ], "metadata": { "id": "QJQXi06FdUDN" @@ -11529,156 +8875,208 @@ "source": [ "import numpy as np\n", "import pandas as pd\n", - "from sklearn.decomposition import PCA\n", - "from sklearn.preprocessing import QuantileTransformer, LabelEncoder\n", "from sklearn.model_selection import train_test_split\n", + "from sklearn.preprocessing import StandardScaler, LabelEncoder\n", + "from sklearn.impute import SimpleImputer\n", + "from sklearn.metrics import make_scorer, confusion_matrix, recall_score, accuracy_score\n", + "from sklearn.decomposition import TruncatedSVD\n", + "from imblearn.over_sampling import SMOTE\n", + "from imblearn.under_sampling import RandomUnderSampler\n", "from tpot import TPOTClassifier\n", - "from sklearn.metrics import f1_score\n", "from collections import defaultdict\n", "import time\n", - "import re\n", - "from tabulate import tabulate\n", + "import gc\n", "\n", + "def print_step_info(X, y, step_name):\n", + " print(f\"\\n--- {step_name} ---\")\n", + " print(f\"X shape: {X.shape}\")\n", + " print(f\"y shape: {y.shape}\")\n", + " print(f\"Class distribution: {np.bincount(y)}\")\n", "\n", + "def custom_scorer(y_true, y_pred):\n", + " cm = confusion_matrix(y_true, y_pred)\n", + " tn, fp, fn, tp = cm.ravel()\n", + " if (tp + fn) == 0 or (tn + fp) == 0:\n", + " return 0.0\n", + " recall = tp / (tp + fn + 1e-8)\n", + " specificity = tn / (tn + fp + 1e-8)\n", + " recall_weight = 1.0\n", + " specificity_weight = 2.0\n", + " score = (recall_weight * recall + specificity_weight * specificity) / (recall_weight + specificity_weight)\n", + " return score\n", + "\n", + "custom_scorer_obj = make_scorer(custom_scorer, greater_is_better=True)\n", + "\n", + "# Assuming df is already loaded and contains 'message_vector' and 'label' columns\n", "\n", "# Encode labels\n", "le = LabelEncoder()\n", - "y_encoded = le.fit_transform(y)\n", + "df['label_encoded'] = le.fit_transform(df['label'])\n", "\n", - "# Function to perform PCA\n", - "def perform_pca(X, transformer, n_components=0.95):\n", - " X_transformed = transformer.fit_transform(X)\n", - " pca = PCA(n_components=n_components)\n", - " X_pca = pca.fit_transform(X_transformed)\n", - " n_components_selected = X_pca.shape[1]\n", - " return X_pca, n_components_selected\n", + "# Split data\n", + "X = np.array(df['message_vector'].tolist(), dtype='float32')\n", + "y = df['label_encoded'].values\n", "\n", - "# Perform PCA with QuantileTransformer\n", - "transformer = QuantileTransformer(n_quantiles=1000, output_distribution='normal', random_state=42)\n", - "X_pca, n_components = perform_pca(X, transformer)\n", + "print_step_info(X, y, \"Original Data\")\n", "\n", - "print(f\"Number of components selected to explain 95% of variance: {n_components}\")\n", + "# Preprocessing\n", + "imputer = SimpleImputer(strategy='mean')\n", + "scaler = StandardScaler()\n", + "pca = TruncatedSVD(n_components=100, random_state=42) # Reduce to 100 components\n", + "\n", + "X = imputer.fit_transform(X)\n", + "X = scaler.fit_transform(X)\n", + "X = pca.fit_transform(X)\n", + "\n", + "print_step_info(X, y, \"After Preprocessing\")\n", + "\n", + "# Split the data\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)\n", + "print_step_info(X_train, y_train, \"Training Data\")\n", + "\n", + "# Resampling\n", + "smote = SMOTE(sampling_strategy=0.5, random_state=42)\n", + "rus = RandomUnderSampler(sampling_strategy=0.5, random_state=42)\n", + "X_resampled, y_resampled = rus.fit_resample(*smote.fit_resample(X_train, y_train))\n", + "print_step_info(X_resampled, y_resampled, \"Resampled Data\")\n", "\n", "# Initialize results storage\n", "results = defaultdict(list)\n", "\n", "# Number of runs\n", - "n_runs = 2\n", + "n_runs = 1\n", "\n", - "# Function to extract number of features selected\n", - "def get_n_features(pipeline_str):\n", - " match = re.search(r'SelectPercentile\\(score_func=f_classif, percentile=(\\d+)\\)', pipeline_str)\n", - " if match:\n", - " percentile = int(match.group(1))\n", - " return int(X_pca.shape[1] * percentile / 100)\n", - " return X_pca.shape[1] # If no feature selection, return all features\n", + "# Initialize best_tpot and best_recall\n", + "best_tpot = None\n", + "best_recall = 0\n", "\n", "for run in range(n_runs):\n", " print(f\"\\nStarting run {run + 1}/{n_runs}\")\n", " start_time = time.time()\n", "\n", - " # Stratified split\n", - " X_train, X_test, y_train, y_test = train_test_split(X_pca, y_encoded, test_size=0.2, random_state=42 + run, stratify=y_encoded)\n", - "\n", - " # TPOT classifier with reduced complexity and CPU usage\n", + " # Initialize TPOT\n", " tpot = TPOTClassifier(\n", - " config_dict='TPOT light', # Use a lighter configuration\n", - " scoring='f1_weighted',\n", + " generations=5,\n", + " population_size=20,\n", " verbosity=2,\n", - " generations=3,\n", - " population_size=10,\n", - " n_jobs=-1,\n", - " random_state=42 + run\n", + " scoring=custom_scorer_obj,\n", + " random_state=42 + run,\n", + " config_dict=\"TPOT cuML\",\n", + " n_jobs=1, # cuML requires n_jobs=1\n", + " cv=5,\n", + " max_time_mins=120,\n", + " max_eval_time_mins=20\n", " )\n", "\n", " try:\n", - " # Fit\n", - " tpot.fit(X_train, y_train)\n", + " # Fit TPOT\n", + " tpot.fit(X_resampled, y_resampled)\n", "\n", - " # Predict and calculate F1 score\n", + " # Make predictions\n", " y_pred = tpot.predict(X_test)\n", - " f1 = f1_score(y_test, y_pred, average='weighted')\n", "\n", - " # Get pipeline string and extract number of features\n", + " # Calculate recall and accuracy scores\n", + " recall = recall_score(y_test, y_pred, average='macro')\n", + " accuracy = accuracy_score(y_test, y_pred)\n", + "\n", + " # Update best_tpot if this run has better recall score\n", + " if recall > best_recall:\n", + " best_recall = recall\n", + " best_tpot = tpot\n", + "\n", + " # Get pipeline string\n", " pipeline_str = str(tpot.fitted_pipeline_)\n", - " n_features = get_n_features(pipeline_str)\n", "\n", " # Store results\n", " results['run'].append(run + 1)\n", - " results['f1_score'].append(f1)\n", + " results['recall_score'].append(recall)\n", + " results['accuracy_score'].append(accuracy)\n", " results['best_pipeline'].append(pipeline_str)\n", - " results['n_features'].append(n_features)\n", " results['runtime'].append(time.time() - start_time)\n", " results['pipelines_tested'].append(len(tpot.evaluated_individuals_))\n", "\n", - " print(f\"Run {run + 1} completed. F1 Score: {f1:.4f}, Features selected: {n_features}, Pipelines tested: {len(tpot.evaluated_individuals_)}\")\n", + " print(f\"Run {run + 1} completed. Recall Score: {recall:.4f}, Accuracy: {accuracy:.4f}, Pipelines tested: {len(tpot.evaluated_individuals_)}\")\n", "\n", " except Exception as e:\n", - " print(f\"Error in run {run + 1}: {str(e)}\")\n", - " results['run'].append(run + 1)\n", - " results['f1_score'].append(None)\n", - " results['best_pipeline'].append(None)\n", - " results['n_features'].append(None)\n", - " results['runtime'].append(time.time() - start_time)\n", - " results['pipelines_tested'].append(None)\n", + " print(f\"An error occurred during TPOT optimization: {str(e)}\")\n", "\n", - "# Print results table\n", - "print(\"\\nResults Summary:\")\n", - "print(tabulate(results, headers='keys', tablefmt='grid'))" + " # Clear memory\n", + " gc.collect()\n", + "\n", + "# Convert results to DataFrame\n", + "results_df = pd.DataFrame(results)\n", + "\n", + "# Save results to CSV\n", + "results_df.to_csv('tpot_cuml_recall_results.csv', index=False)\n", + "\n", + "print(\"\\nResults saved to 'tpot_cuml_recall_results.csv'\")\n", + "\n", + "# If best_tpot exists, export the best pipeline\n", + "if best_tpot:\n", + " best_tpot.export('tpot_cuml_best_pipeline.py')\n", + " print(\"Best pipeline exported to 'tpot_cuml_best_pipeline.py'\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", - "height": 669, + "height": 1000, "referenced_widgets": [ - "e329ae9f00ae426caba593bf82062720", - "6b18e9941ad8489a856d8951086dc0ec", - "bd1ef0b1216f44e981e06617353af203", - "cb16a7ccc2d048699ec81bd9cc08782b", - "2057161353254330b31e15c030f5c77e", - "2768fc55816a4ddabd3389b3e6c9a0df", - "12c31e7d69934b1bb13b7e4de18f9aee", - "d3f693b6862747fbab9f786e7ef0a9cf", - "f3a099a3356044e7a641d590c6381041", - "aab64f46dfdf4948bec48ef02618cf75", - "b83434e8778040d4975f5fab89a976ec", - "3ae62907413b4d969f53c4a3092fb46d", - "5af08e85e25040b19544493df13617d5", - "439b9e942cec4fe699f3e561082585b5", - "b4b43f188f454890a04bc1869680c46d", - "4de8382a838648b6bf2d639b5ae65c7b", - "32aa5ea1c8354c0eb3538819a48e81ea", - "ed0c20dcb4764e629766a2e31d019edf", - "90db1881f3254f71adf5d809c0d83bae", - "87b95caad65649a9b85bdc015c665838", - "8151a7980fc346debb0caf1c20af5429", - "2f6c4ac937de426ab94fdde3fc4fc95a" + "92ce081546c945089cdd8b00604f9190", + "cfb57c0da6cf422f8e56a68fd2fa8620", + "fa57f776a9044f928368ff7299e01bd9", + "2f1b25096e404d069ef24ee45f236a64", + "08bb4312d1a948e986a03788d286dda1", + "17cc427fd9e9467e999b9164774b6eb5", + "db4793323d154fdba9d4d88d5789d8dd", + "cd4d7a2f6adc4f69b07b1b1230153d44", + "18ff42e892cf489fb6fa9003fbdecc6c", + "fcf7b83de17d491b8729ce64d8f3cede", + "695f315cc88c477084e7737b132ed93a" ] }, - "id": "pDB9efndcv9g", - "outputId": "922183ba-2287-40a7-8d73-32a476c0557f" + "id": "BDkHYUyp1vWI", + "outputId": "c6c5991b-5978-400e-c571-20290e98ecfc" }, - "execution_count": null, + "execution_count": 7, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ - "Number of components selected to explain 95% of variance: 5\n", "\n", - "Starting run 1/2\n" + "--- Original Data ---\n", + "X shape: (13455, 30000)\n", + "y shape: (13455,)\n", + "Class distribution: [ 114 13341]\n", + "\n", + "--- After Preprocessing ---\n", + "X shape: (13455, 100)\n", + "y shape: (13455,)\n", + "Class distribution: [ 114 13341]\n", + "\n", + "--- Training Data ---\n", + "X shape: (10764, 100)\n", + "y shape: (10764,)\n", + "Class distribution: [ 91 10673]\n", + "\n", + "--- Resampled Data ---\n", + "X shape: (16008, 100)\n", + "y shape: (16008,)\n", + "Class distribution: [ 5336 10672]\n", + "\n", + "Starting run 1/1\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ - "Optimization Progress: 0%| | 0/40 [00:00 0]\n", - "\n", - "# Sort by count\n", - "summary_table.sort(key=lambda x: -x[1])\n", - "\n", - "# Print the summary table\n", - "print(\"\\nEstimated Algorithms Used Across All Runs:\")\n", - "print(tabulate(summary_table, headers=['Algorithm', 'Estimated Count'], tablefmt='grid'))\n", - "\n", - "# Calculate and print additional statistics\n", - "total_runs = len(results_df)\n", - "successful_runs = results_df['f1_score'].notna().sum()\n", - "average_f1 = results_df['f1_score'].mean()\n", - "average_features = results_df['n_features'].mean()\n", - "average_runtime = results_df['runtime'].mean()\n", - "\n", - "print(f\"\\nTotal Runs: {total_runs}\")\n", - "print(f\"Successful Runs: {successful_runs}\")\n", - "print(f\"Average F1 Score: {average_f1:.4f}\")\n", - "print(f\"Average Number of Features: {average_features:.2f}\")\n", - "print(f\"Average Runtime: {average_runtime:.2f} seconds\")\n", - "print(f\"Total Pipelines Tested: {total_pipelines}\")\n", - "\n", - "# Print full best pipelines for each run\n", - "print(\"\\nBest Pipelines for Each Run:\")\n", - "for index, row in results_df.iterrows():\n", - " print(f\"\\nRun {index + 1}:\")\n", - " print(f\"F1 Score: {row['f1_score']:.4f}\")\n", - " print(f\"Pipeline: {row['best_pipeline']}\")\n", - " print(f\"Pipelines tested in this run: {row['pipelines_tested']}\")" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "VgKt6AU28-6f", - "outputId": "ab7671a3-1229-42f5-807a-eec12327733a" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "\n", - "Estimated Algorithms Used Across All Runs:\n", - "+----------------------------+-------------------+\n", - "| Algorithm | Estimated Count |\n", - "+============================+===================+\n", - "| LogisticRegression | 38 |\n", - "+----------------------------+-------------------+\n", - "| KNeighborsClassifier | 38 |\n", - "+----------------------------+-------------------+\n", - "| DecisionTreeClassifier | 38 |\n", - "+----------------------------+-------------------+\n", - "| RandomForestClassifier | 38 |\n", - "+----------------------------+-------------------+\n", - "| GradientBoostingClassifier | 38 |\n", - "+----------------------------+-------------------+\n", - "| MLPClassifier | 38 |\n", - "+----------------------------+-------------------+\n", - "| Other | 3 |\n", - "+----------------------------+-------------------+\n", - "\n", - "Total Runs: 2\n", - "Successful Runs: 2\n", - "Average F1 Score: 0.9872\n", - "Average Number of Features: 5.00\n", - "Average Runtime: 109.13 seconds\n", - "Total Pipelines Tested: 229\n", - "\n", - "Best Pipelines for Each Run:\n", - "\n", - "Run 1:\n", - "F1 Score: 0.9872\n", - "Pipeline: Pipeline(steps=[('gaussiannb', GaussianNB())])\n", - "Pipelines tested in this run: 115\n", - "\n", - "Run 2:\n", - "F1 Score: 0.9872\n", - "Pipeline: Pipeline(steps=[('extratreesclassifier',\n", - " ExtraTreesClassifier(criterion='entropy',\n", - " max_features=0.8500000000000001,\n", - " min_samples_leaf=20, min_samples_split=19,\n", - " random_state=43))])\n", - "Pipelines tested in this run: 114\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ + "import numpy as np\n", + "from scipy import stats\n", "import matplotlib.pyplot as plt\n", - "import pandas as pd\n", "\n", - "# Assuming results_df is your DataFrame with the results\n", - "\n", - "fig, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=(10, 18))\n", - "\n", - "ax1.plot(results_df['run'], results_df['f1_score'], 'bo-')\n", - "ax1.set_title('TPOT-NN Performance (F1 Score) Across Runs')\n", - "ax1.set_xlabel('Run')\n", - "ax1.set_ylabel('F1 Score')\n", - "ax1.grid(True)\n", - "\n", - "ax2.plot(results_df['run'], results_df['n_features'], 'ro-')\n", - "ax2.set_title('Number of Features Selected Across Runs')\n", - "ax2.set_xlabel('Run')\n", - "ax2.set_ylabel('Number of Features')\n", - "ax2.grid(True)\n", - "\n", - "ax3.plot(results_df['run'], results_df['pipelines_tested'], 'go-')\n", - "ax3.set_title('Number of Pipelines Tested Across Runs')\n", - "ax3.set_xlabel('Run')\n", - "ax3.set_ylabel('Number of Pipelines')\n", - "ax3.grid(True)\n", - "\n", - "plt.tight_layout()\n", - "plt.savefig('tpot_nn_performance_features_pipelines.png')\n", - "plt.show()\n", + "# Load the results\n", + "results_df = pd.read_csv('tpot_cuml_recall_results.csv')\n", "\n", "# Print summary statistics\n", "print(\"\\nSummary Statistics:\")\n", "print(results_df.describe())\n", "\n", - "# Optional: If you want to see the correlation between different metrics\n", - "correlation_matrix = results_df[['f1_score', 'n_features', 'pipelines_tested', 'runtime']].corr()\n", - "print(\"\\nCorrelation Matrix:\")\n", - "print(correlation_matrix)" + "# Calculate statistics for recall and accuracy scores\n", + "for metric in ['recall_score', 'accuracy_score']:\n", + " scores = results_df[metric]\n", + " mean = np.mean(scores)\n", + " std = np.std(scores, ddof=1) if len(scores) > 1 else np.nan\n", + "\n", + " print(f\"\\n{metric.capitalize()} Analysis:\")\n", + " print(f\" Mean: {mean:.4f}\")\n", + " print(f\" Standard Deviation: {std:.4f}\")\n", + "\n", + " if len(scores) > 1:\n", + " confidence_level = 0.95\n", + " se = std / np.sqrt(len(scores))\n", + " ci = stats.t.interval(confidence_level, len(scores)-1, loc=mean, scale=se)\n", + " print(f\" {confidence_level*100}% Confidence Interval: ({ci[0]:.4f}, {ci[1]:.4f})\")\n", + "\n", + " # Perform t-test to check if scores are significantly different from a baseline\n", + " baseline = 0.5 # You can adjust this value based on your expectations\n", + " t_stat, p_value = stats.ttest_1samp(scores, baseline)\n", + " print(f\"\\nOne-sample t-test (comparing to baseline of {baseline}):\")\n", + " print(f\" t-statistic: {t_stat:.4f}\")\n", + " print(f\" p-value: {p_value:.4f}\")\n", + "\n", + " if len(scores) >= 3:\n", + " # Check for normality of scores\n", + " _, normality_p_value = stats.shapiro(scores)\n", + " print(f\"\\nShapiro-Wilk test for normality of {metric}:\")\n", + " print(f\" p-value: {normality_p_value:.4f}\")\n", + " print(f\" {'Scores are normally distributed' if normality_p_value > 0.05 else 'Scores are not normally distributed'}\")\n", + "\n", + " # Calculate effect size (Cohen's d) for scores compared to baseline\n", + " effect_size = (mean - baseline) / std\n", + " print(f\"\\nEffect size (Cohen's d) compared to baseline: {effect_size:.4f}\")\n", + "\n", + " # Plot scores if there's more than one run\n", + " if len(scores) > 1:\n", + " plt.figure(figsize=(10, 6))\n", + " plt.plot(results_df['run'], scores, 'bo-')\n", + " plt.title(f'{metric.capitalize()} Across Runs')\n", + " plt.xlabel('Run')\n", + " plt.ylabel(metric.capitalize())\n", + " plt.grid(True)\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + "# Runtime analysis\n", + "print(\"\\nRuntime Analysis:\")\n", + "print(f\" Total Runtime: {results_df['runtime'].sum():.2f} seconds\")\n", + "print(f\" Average Runtime per Run: {results_df['runtime'].mean():.2f} seconds\")\n", + "\n", + "# Pipelines tested analysis\n", + "print(\"\\nPipelines Tested Analysis:\")\n", + "print(f\" Total Pipelines Tested: {results_df['pipelines_tested'].sum()}\")\n", + "print(f\" Average Pipelines per Run: {results_df['pipelines_tested'].mean():.2f}\")\n", + "\n", + "# Best pipeline (in this case, the only run)\n", + "best_run = results_df.iloc[0]\n", + "print(f\"\\nBest Run (Run {best_run['run']}):\")\n", + "print(f\" Recall Score: {best_run['recall_score']:.4f}\")\n", + "print(f\" Accuracy Score: {best_run['accuracy_score']:.4f}\")\n", + "print(f\" Runtime: {best_run['runtime']:.2f} seconds\")\n", + "print(f\" Pipelines Tested: {best_run['pipelines_tested']}\")\n", + "print(f\" Best Pipeline:\\n{best_run['best_pipeline']}\")\n", + "\n", + "# Plot runtime vs recall and accuracy scores only if there's more than one run\n", + "if len(results_df) > 1:\n", + " fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 6))\n", + " ax1.scatter(results_df['runtime'], results_df['recall_score'])\n", + " ax1.set_title('Runtime vs Recall Score')\n", + " ax1.set_xlabel('Runtime (seconds)')\n", + " ax1.set_ylabel('Recall Score')\n", + " ax1.grid(True)\n", + "\n", + " ax2.scatter(results_df['runtime'], results_df['accuracy_score'])\n", + " ax2.set_title('Runtime vs Accuracy Score')\n", + " ax2.set_xlabel('Runtime (seconds)')\n", + " ax2.set_ylabel('Accuracy Score')\n", + " ax2.grid(True)\n", + "\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + " # Analyze the relationship between runtime and scores\n", + " for metric in ['recall_score', 'accuracy_score']:\n", + " corr, _ = stats.pearsonr(results_df['runtime'], results_df[metric])\n", + " print(f\"\\nCorrelation between runtime and {metric}: {corr:.4f}\")\n", + "else:\n", + " print(\"\\nInsufficient data for runtime vs score analysis and correlation.\")" ], "metadata": { "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 + "base_uri": "https://localhost:8080/" }, - "id": "SYRHdR4_YKXA", - "outputId": "a25b02c4-ae41-4ef9-c701-a77d227b7ab9" + "id": "vD-eHcZD-wdY", + "outputId": "14fc9a53-c9b7-47f0-fe50-492797eed905" }, - "execution_count": null, + "execution_count": 10, "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" - }, - "metadata": {} - }, { "output_type": "stream", "name": "stdout", "text": [ "\n", "Summary Statistics:\n", - " run f1_score n_features runtime pipelines_tested\n", - "count 2.000000 2.000000 2.0 2.000000 2.000000\n", - "mean 1.500000 0.987198 5.0 109.132951 114.500000\n", - "std 0.707107 0.000000 0.0 1.063263 0.707107\n", - "min 1.000000 0.987198 5.0 108.381110 114.000000\n", - "25% 1.250000 0.987198 5.0 108.757030 114.250000\n", - "50% 1.500000 0.987198 5.0 109.132951 114.500000\n", - "75% 1.750000 0.987198 5.0 109.508871 114.750000\n", - "max 2.000000 0.987198 5.0 109.884791 115.000000\n", + " run recall_score accuracy_score runtime pipelines_tested\n", + "count 1.0 1.000000 1.000000 1.000000 1.0\n", + "mean 1.0 0.889055 0.993683 194.357202 115.0\n", + "std NaN NaN NaN NaN NaN\n", + "min 1.0 0.889055 0.993683 194.357202 115.0\n", + "25% 1.0 0.889055 0.993683 194.357202 115.0\n", + "50% 1.0 0.889055 0.993683 194.357202 115.0\n", + "75% 1.0 0.889055 0.993683 194.357202 115.0\n", + "max 1.0 0.889055 0.993683 194.357202 115.0\n", "\n", - "Correlation Matrix:\n", - " f1_score n_features pipelines_tested runtime\n", - "f1_score NaN NaN NaN NaN\n", - "n_features NaN NaN NaN NaN\n", - "pipelines_tested NaN NaN 1.0 1.0\n", - "runtime NaN NaN 1.0 1.0\n" + "Recall_score Analysis:\n", + " Mean: 0.8891\n", + " Standard Deviation: nan\n", + "\n", + "Accuracy_score Analysis:\n", + " Mean: 0.9937\n", + " Standard Deviation: nan\n", + "\n", + "Runtime Analysis:\n", + " Total Runtime: 194.36 seconds\n", + " Average Runtime per Run: 194.36 seconds\n", + "\n", + "Pipelines Tested Analysis:\n", + " Total Pipelines Tested: 115\n", + " Average Pipelines per Run: 115.00\n", + "\n", + "Best Run (Run 1):\n", + " Recall Score: 0.8891\n", + " Accuracy Score: 0.9937\n", + " Runtime: 194.36 seconds\n", + " Pipelines Tested: 115\n", + " Best Pipeline:\n", + "Pipeline(steps=[('variancethreshold', VarianceThreshold(threshold=0.05)),\n", + " ('xgbclassifier',\n", + " XGBClassifier(alpha=1, base_score=None, booster=None,\n", + " callbacks=None, colsample_bylevel=None,\n", + " colsample_bynode=None, colsample_bytree=None,\n", + " device=None, early_stopping_rounds=None,\n", + " enable_categorical=False, eval_metric=None,\n", + " feature_types=None, gamma=None, grow_policy=None,\n", + " importance_type=None,\n", + " interaction_constraints=None, learning_rate=1.0,\n", + " max_bin=None, max_cat_threshold=None,\n", + " max_cat_to_onehot=None, max_delta_step=None,\n", + " max_depth=9, max_leaves=None,\n", + " min_child_weight=10, missing=nan,\n", + " monotone_constraints=None, multi_strategy=None,\n", + " n_estimators=100, n_jobs=1,\n", + " num_parallel_tree=None, ...))])\n", + "\n", + "Insufficient data for runtime vs score analysis and correlation.\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import re\n", + "\n", + "# Assuming results_df is already loaded from 'tpot_nn_recall_results.csv'\n", + "# If not, uncomment the following line:\n", + "# results_df = pd.read_csv('tpot_nn_recall_results.csv')\n", + "\n", + "def extract_pipeline_components(pipeline_str):\n", + " # Extract all components from the pipeline string, excluding 'Pipeline'\n", + " components = re.findall(r'(\\w+)\\(', pipeline_str)\n", + " return ', '.join([comp for comp in components if comp != 'Pipeline'])\n", + "\n", + "# Extract pipeline components\n", + "results_df['pipeline_components'] = results_df['best_pipeline'].apply(extract_pipeline_components)\n", + "\n", + "# Create a table of pipeline components and their frequencies\n", + "component_counts = results_df['pipeline_components'].value_counts().reset_index()\n", + "component_counts.columns = ['Pipeline Components', 'Frequency']\n", + "component_counts['Percentage'] = component_counts['Frequency'] / len(results_df) * 100\n", + "\n", + "print(\"Pipeline Components Frequency Table:\")\n", + "print(component_counts.to_string(index=False))\n", + "\n", + "# Visualize pipeline components frequency\n", + "plt.figure(figsize=(12, 6))\n", + "component_counts.plot(kind='bar', x='Pipeline Components', y='Frequency')\n", + "plt.title('Frequency of Pipeline Components in Best Pipelines')\n", + "plt.xlabel('Pipeline Components')\n", + "plt.ylabel('Frequency')\n", + "plt.xticks(rotation=45, ha='right')\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# Find the best performing pipeline components\n", + "best_components = results_df.loc[results_df['recall_score'].idxmax(), 'pipeline_components']\n", + "print(f\"\\nComponents of the best performing pipeline: {best_components}\")\n", + "\n", + "# Analyze individual components\n", + "all_components = [comp.strip() for comps in results_df['pipeline_components'].str.split(',') for comp in comps]\n", + "component_freq = pd.Series(all_components).value_counts()\n", + "\n", + "print(\"\\nIndividual Component Frequency:\")\n", + "print(component_freq)\n", + "\n", + "# Visualize individual component frequency\n", + "plt.figure(figsize=(10, 6))\n", + "component_freq.plot(kind='bar')\n", + "plt.title('Frequency of Individual Components in Best Pipelines')\n", + "plt.xlabel('Component')\n", + "plt.ylabel('Frequency')\n", + "plt.xticks(rotation=45, ha='right')\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# Additional analysis: Component combination performance\n", + "results_df['mean_recall'] = results_df.groupby('pipeline_components')['recall_score'].transform('mean')\n", + "best_combo = results_df.loc[results_df['mean_recall'].idxmax(), 'pipeline_components']\n", + "best_combo_recall = results_df.loc[results_df['mean_recall'].idxmax(), 'mean_recall']\n", + "\n", + "print(f\"\\nBest performing component combination: {best_combo}\")\n", + "print(f\"Mean recall score: {best_combo_recall:.4f}\")\n", + "\n", + "# [Rest of the previous analysis code remains the same]\n", + "\n", + "# ... [Keep all the previous analysis code] ..." + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "tmGKt3iB_ze9", + "outputId": "2009081b-49e0-421e-d1a2-ece6eb3526ab" + }, + "execution_count": 11, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Pipeline Components Frequency Table:\n", + " Pipeline Components Frequency Percentage\n", + "VarianceThreshold, XGBClassifier 1 100.0\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "Components of the best performing pipeline: VarianceThreshold, XGBClassifier\n", + "\n", + "Individual Component Frequency:\n", + "VarianceThreshold 1\n", + "XGBClassifier 1\n", + "Name: count, dtype: int64\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "Best performing component combination: VarianceThreshold, XGBClassifier\n", + "Mean recall score: 0.8891\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Make predictions\n", + "y_pred = best_tpot.predict(X_test)\n", + "\n", + "# Create the confusion matrix\n", + "cm = confusion_matrix(y_test, y_pred)\n", + "\n", + "# Create a ConfusionMatrixDisplay object\n", + "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=['negative', 'positive'])\n", + "\n", + "# Plot the confusion matrix\n", + "plt.figure(figsize=(10, 7))\n", + "disp.plot(cmap='Blues', values_format='d')\n", + "plt.title('Confusion Matrix')\n", + "plt.show()\n", + "\n", + "# Print the confusion matrix\n", + "print(\"Confusion Matrix:\")\n", + "print(cm)\n", + "\n", + "# Calculate and print additional metrics\n", + "tn, fp, fn, tp = cm.ravel()\n", + "precision = tp / (tp + fp) if (tp + fp) > 0 else 0\n", + "recall = tp / (tp + fn) if (tp + fn) > 0 else 0\n", + "f1_score = 2 * (precision * recall) / (precision + recall) if (precision + recall) > 0 else 0\n", + "\n", + "print(f\"\\nPrecision: {precision:.4f}\")\n", + "print(f\"Recall: {recall:.4f}\")\n", + "print(f\"F1-score: {f1_score:.4f}\")\n", + "\n", + "# Additional metrics from the custom scorer\n", + "specificity = tn / (tn + fp) if (tn + fp) > 0 else 0\n", + "custom_score = (recall + 2 * specificity) / 3 # As per your custom scorer\n", + "\n", + "print(f\"Specificity: {specificity:.4f}\")\n", + "print(f\"Custom Score: {custom_score:.4f}\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 653 + }, + "id": "lVua-bgO_9T8", + "outputId": "9442618d-abb0-4f6c-940f-2cf17041cd3b" + }, + "execution_count": 13, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Confusion Matrix:\n", + "[[ 18 5]\n", + " [ 12 2656]]\n", + "\n", + "Precision: 0.9981\n", + "Recall: 0.9955\n", + "F1-score: 0.9968\n", + "Specificity: 0.7826\n", + "Custom Score: 0.8536\n" ] } ]