diff --git a/.gitignore b/.gitignore
index 50471bfbb9a60abf0cb0811b74d8d86243c9f448..51059296ac4198453a54eea30014b165ca80f890 100644
--- a/.gitignore
+++ b/.gitignore
@@ -6,4 +6,5 @@ runs/
experiments/debug
experiments/GM_MI_DEBUG
-dataset
\ No newline at end of file
+dataset
+cifar-100-python
\ No newline at end of file
diff --git a/GM_MI.py b/GM_MI.py
index 7ea050e61a9918aec0c8e944088eaedebc5c8a71..62d270d94110f0ee4e8ba918af73f787f450a326 100644
--- a/GM_MI.py
+++ b/GM_MI.py
@@ -1477,6 +1477,8 @@ if __name__ == "__main__":
parser.add_argument("--simsiam_weight", type=float, default=1.0)
parser.add_argument("--debug", action="store_true", default=False)
+
+ parser.add_argument("--new_ood", action="store_true", default=False, description="Use new GMM-based OOD detection")
args = parser.parse_args()
args.cuda = torch.cuda.is_available()
diff --git a/experiments/baseline_for_vis/cluster_1.pth.notneeded? b/experiments/baseline_for_vis/cluster_1.pth.notneeded?
deleted file mode 100644
index 9d5ae21d5de2dcab452c692ace5347d2bda5001a..0000000000000000000000000000000000000000
Binary files a/experiments/baseline_for_vis/cluster_1.pth.notneeded? and /dev/null differ
diff --git a/experiments/baseline_for_vis/cluster_2.pth.notneeded? b/experiments/baseline_for_vis/cluster_2.pth.notneeded?
deleted file mode 100644
index f480ad810c796e91033e79a1b9fcaf79822743d6..0000000000000000000000000000000000000000
Binary files a/experiments/baseline_for_vis/cluster_2.pth.notneeded? and /dev/null differ
diff --git a/experiments/baseline_for_vis/cluster_3.pth.notneeded? b/experiments/baseline_for_vis/cluster_3.pth.notneeded?
deleted file mode 100644
index 45100cdf0fc937f5aea0efe14e3e5e9adb2eb237..0000000000000000000000000000000000000000
Binary files a/experiments/baseline_for_vis/cluster_3.pth.notneeded? and /dev/null differ
diff --git a/experiments/thesh-exp.ipynb b/experiments/thesh-exp.ipynb
index 9347978a7483f90d29e14f89222bd131871c07c9..ca73f5b07e756f9a7a8b0605b2afef5dc21886bb 100644
--- a/experiments/thesh-exp.ipynb
+++ b/experiments/thesh-exp.ipynb
@@ -1,3 +1,6236 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:c5bdbc6bcab433b5768fc5469c439e73c15746952c520581a4294b1fefc2f371
-size 1072168
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# User Parameters:\n",
+ "GIT_ROOT = \"/cl/gm\" # Change this to the root of the git repo\n",
+ "SEED = 8008135\n",
+ "USE_SAVED_LOGITS = True # Set to True to use saved logits, False to recompute them\n",
+ "EXEMPLAR_SET_SIZE = 28 # per class exemplar set size"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# System Setup:\n",
+ "import os, sys\n",
+ "\n",
+ "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"\n",
+ "sys.path.append(os.path.join(GIT_ROOT))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Load and seed modules that need seeding\n",
+ "import random\n",
+ "import numpy as np\n",
+ "import torch\n",
+ "\n",
+ "random.seed(SEED)\n",
+ "os.environ[\"PYTHONHASHSEED\"] = str(SEED)\n",
+ "np.random.seed(SEED)\n",
+ "torch.manual_seed(SEED)\n",
+ "torch.cuda.manual_seed(SEED)\n",
+ "torch.cuda.manual_seed_all(SEED) # if you are using multi-GPU.\n",
+ "torch.backends.cudnn.benchmark = False\n",
+ "torch.backends.cudnn.deterministic = True"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# General Imports\n",
+ "import pickle\n",
+ "from sklearn.metrics.pairwise import cosine_distances\n",
+ "import matplotlib.pyplot as pltpipr\n",
+ "import pandas as pd\n",
+ "from scipy.special import softmax\n",
+ "from torchvision.datasets import CIFAR100\n",
+ "import torchvision\n",
+ "from tqdm.notebook import tqdm\n",
+ "import ipywidgets as widgets\n",
+ "from ipywidgets import HBox, VBox, interactive\n",
+ "import seaborn as sns\n",
+ "import ipywidgets as widgets\n",
+ "from ipywidgets import HBox, VBox\n",
+ "from IPython.display import clear_output\n",
+ "\n",
+ "from models.model_CI import model_CI\n",
+ "from data.cifarloader import (\n",
+ " StageCIFAR100Loader,\n",
+ " PARTITION_CONFIG_MIX,\n",
+ " CIFAR100Loader,\n",
+ " CIFAR100Data,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 CUDA devices available, using device cuda:0\n",
+ "Namespace(lr=0.1, ssl_lr=0.01, ce_lr=0.05, cluster_lr=0.05, inc_ssl_lr=0.05, together_lr=0.01, gamma=0.1, momentum=0.9, weight_decay=0.0001, ssl_epochs=100, ce_epochs=200, cluster_epochs=100, inc_ssl_epochs=100, together_cluster_ssl=False, together_epochs=100, update_exemplar_epochs=50, rampup_length=150, rampup_coefficient=50, increment_coefficient=0.05, step_size=70, batch_size=128, num_unlabeled_classes_per_stage=10, num_labeled_classes=70, dataset_root='./dataset', exp_root='/cl/gm/experiments/', topk=5, model_name='GM_MI_DEBUG', dataset_name='cifar100', seed=1, mode='train', feature_extractor='resnet18', workers=4, moco_dim=128, moco_k=4096, moco_m=0.99, moco_t=0.1, bn_splits=1, ssl_temperature=0.1, bce=True, mse=True, first_ssl=True, first_ce=True, skip_ssl=False, no_ce=True, no_fc=True, skip_first=True, skip_model_dir='baseline/stage_0.pth', budgets=2000, herding=True, dist='cosine', discovery_confidence=False, discovery_confidence_threshold=0.0, discovery_confidence_percent=1.0, confidence_type='all', geo=True, geo_dist='cosine', geo_k=15, geo_percent=0.5, geo_print_statistics=False, thres1_ratio=2.0, thres2_ratio=5.0, branch_feat='closest', ema_beta=0.99, norm_before_add=True, branch_depth=3, no_sync=False, all_branch=True, same_branch=True, pull_exemplar_features=True, feature_dist_weight=1.0, cluster_with_pef=True, cluster_with_pef_weight=5.0, pef_all=True, pef_type='cos', no_refuse=False, no_cache_means=True, ssl_exemplars=True, ssl_with_cluster=True, ssl_nce=False, ssl_nce_weight=1.0, reselect_exemplars=False, reselect_exemplars_interval=10, replace_wta=False, rw_feat=False, rw_label=False, rw_graph=False, rw_graph_k=10, replace_mse=False, mixup=False, mixup_n=512, mixup_alpha=0.5, mixup_beta=0.5, re_mixup=False, batch_mixup=False, bmix_diff_alpha=False, pseudo_softmax=False, drop_more=False, mixall=False, print_cls_statistics=False, save_exemplars=True, ema_branch=True, ema_branch_beta=0.99, sync_new_branch=False, sync_backbone=True, sync_backbone_beta=0.99, mix_cluster=False, mlp_ce=False, mlp_ce_simple=False, mlp_wta=False, ssl_with_ce=False, ssl_with_ce_weight=1.0, pure_ce=False, no_pll=False, cache_first_ssl=False, cache_first_ssl_dir='b4_r_2_5_ema99_nb_incssl5e2_clulr5e2_nofc_ab_bw3_cos_sb_pef1_bn1_save/ssl_1.pth', ema_fast_slow=False, ema_fs_beta=0.9, ema_epoch=False, pll_exem=True, update_exem=False, ood_thres=0.6, cw_ce=False, cw_ce_weight=1.0, cw_sim=False, cw_sim_weight=1.0, simsiam=False, simsiam_weight=1.0, debug=False, cuda=True, model_dir='/cl/gm/experiments/baseline_for_vis')\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Program Arguments\n",
+ "batch_size = 128\n",
+ "num_workers = 4\n",
+ "dataset_root = os.path.join(GIT_ROOT, \"data\")\n",
+ "cuda_device = 0\n",
+ "device = torch.device(f\"cuda:{cuda_device}\" if torch.cuda.is_available() else \"cpu\")\n",
+ "print(torch.cuda.device_count(), \"CUDA devices available, using device \", device)\n",
+ "\n",
+ "gm_args = pickle.load(open(os.path.join(GIT_ROOT, \"gm_args.pkl\"), \"rb\"))\n",
+ "gm_args.model_dir = os.path.join(\n",
+ " GIT_ROOT, \"experiments\", \"baseline_for_vis\"\n",
+ ") # G&M code saves some stuff and may overwrite other experiments\n",
+ "print(gm_args)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Threshold Experiment 2: Electric Boogaloo\n",
+ "Date: Monday, 30 September 2024\n",
+ "\n",
+ "- Reimplementation of the first logit-based threshold, expanding it to look at known-novel classes as well (from the model resulting from stages 1 and 2).\n",
+ "\n",
+ "- Also implemented is the per-logit based threshold, where a theshold is calculated for each logit index based on the exemplar selection.\n",
+ "\n",
+ "- Finally a threshold will be generated based on the entropy-density observed in the visualisations. Novel classes have very low entropy, whereas known and known-novel have higher entropy.\n",
+ "\n",
+ "From previous experiment (Logit-based threshold experiment):\n",
+ "The idea of this experiment is to create a threshold dynamically derived from the minmax of the exemplar set logits. A threshold is set to be a percentage of that minmax logit, any exposed sample with a max logit below the threshold is hypothesised to be a high-likelihood novel sample.\n",
+ "\n",
+ "We are using the Grow&Merge Stage 0 Model: trained on 87% of the training data available for classes 0-69 (inclusive).\n",
+ "\n",
+ "Exemplar selection will be done in a two ways:\n",
+ "- Random Initialisation\n",
+ " - Samples are chosen at random\n",
+ "- Centroid Initialisation\n",
+ " - Samples close to the cluster centroid are chosen\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Setup some stuff"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Create Master Dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Files already downloaded and verified\n",
+ "Files already downloaded and verified\n"
+ ]
+ }
+ ],
+ "source": [
+ "train_dataset = CIFAR100Data(\n",
+ " root=dataset_root, split=\"train\", aug=None, target_list=range(0, 100)\n",
+ ")\n",
+ "train_loader = torch.utils.data.DataLoader(\n",
+ " train_dataset,\n",
+ " batch_size=batch_size,\n",
+ " shuffle=False,\n",
+ " num_workers=num_workers,\n",
+ " pin_memory=True,\n",
+ ")\n",
+ "\n",
+ "test_dataset = CIFAR100Data(\n",
+ " root=dataset_root, split=\"test\", aug=None, target_list=range(0, 100)\n",
+ ")\n",
+ "test_loader = torch.utils.data.DataLoader(\n",
+ " test_dataset,\n",
+ " batch_size=batch_size,\n",
+ " shuffle=False,\n",
+ " num_workers=num_workers,\n",
+ " pin_memory=True,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Various Funcs"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def get_class_lists(stage_idx):\n",
+ " known_classes = list(range(0, 70))\n",
+ " novel_classes = list(range(70, 100))\n",
+ " known_novel_classes = list(range(70, 70 + 10 * stage_idx)) if stage_idx > 0 else [] # [] (stage 0), 70-80 (stage 1), 70-90 (stage 2)\n",
+ " novel_classes = list(set(novel_classes) - set(known_novel_classes))\n",
+ " return known_classes, novel_classes, known_novel_classes\n",
+ "\n",
+ "# Modified function to handle both row and int inputs\n",
+ "def find_type(input_data, stage_idx):\n",
+ " known_classes, novel_classes, known_novel_classes = get_class_lists(stage_idx)\n",
+ " \n",
+ " # If input_data is an integer, treat it as a class directly\n",
+ " if isinstance(input_data, int):\n",
+ " class_val = input_data\n",
+ " else:\n",
+ " # Assume input_data is a row and extract the \"class\" column\n",
+ " class_val = input_data[\"class\"]\n",
+ "\n",
+ " # Now proceed with the same classification logic\n",
+ " if class_val in known_classes:\n",
+ " return \"known\"\n",
+ " elif class_val in novel_classes:\n",
+ " return \"novel\"\n",
+ " elif class_val in known_novel_classes:\n",
+ " return \"known_novel\"\n",
+ " else:\n",
+ " return \"unknown\" # This should not happen unless an invalid class is passed\n",
+ "\n",
+ "out_df_template = pd.DataFrame(\n",
+ " columns=[\n",
+ " \"Sample Type\",\n",
+ " \"Correct\",\n",
+ " \"Incorrect\",\n",
+ " \"Total\",\n",
+ " \"Identification Rate (%)\",\n",
+ " \"Misidentification Rate (%)\",\n",
+ " ]\n",
+ ")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Data Gathering\n",
+ "Here is where the model is loaded and logits extracted. This code is based on the code developed in the visualisation notebook"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Training Dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Loaded logits & features from file\n"
+ ]
+ }
+ ],
+ "source": [
+ "if os.path.exists(os.path.join(GIT_ROOT, \"thesh_experiment_logits.pkl\")) and USE_SAVED_LOGITS:\n",
+ " master_dfs = pickle.load(\n",
+ " open(os.path.join(GIT_ROOT, \"thesh_experiment_logits.pkl\"), \"rb\")\n",
+ " )\n",
+ " print(\"Loaded logits & features from file\")\n",
+ "else:\n",
+ " print(\"Computing Logits & features\")\n",
+ " \n",
+ " # create empty master_df for each stage\n",
+ " master_dfs = [\n",
+ " pd.DataFrame(\n",
+ " columns=[\"class\"]\n",
+ " + [f\"logit_{logit_idx}\" for logit_idx in range(90)]\n",
+ " + [f\"feat_{feat_idx}\" for feat_idx in range(128)]\n",
+ " )\n",
+ " for _ in range(3)\n",
+ " ]\n",
+ " \n",
+ " # create & load pretrained model\n",
+ " model = model_CI(gm_args).to(device)\n",
+ " model.load_state_dict(\n",
+ " torch.load(os.path.join(gm_args.model_dir, \"stage_0.pth\"), weights_only=False)\n",
+ " )\n",
+ " model.cuda()\n",
+ " model.sync_new_branches() # changes some internal stuff, required after load\n",
+ " \n",
+ " for stage_idx in [0, 1, 2]:\n",
+ " \n",
+ " # load stage 1 or 2 model if we're past stage 0\n",
+ " if stage_idx > 0:\n",
+ " model.save_backbone(stage_idx) # changes model internals, required before loading >0 stages\n",
+ " model.load_state_dict(\n",
+ " torch.load(\n",
+ " os.path.join(gm_args.model_dir, f\"ssl_{stage_idx}.pth\"),\n",
+ " weights_only=False,\n",
+ " )\n",
+ " )\n",
+ " \n",
+ " model.to(device)\n",
+ " model.eval()\n",
+ " \n",
+ " # Compute logits and features for all samples\n",
+ " with torch.no_grad():\n",
+ " for img, label, _ in tqdm(\n",
+ " train_loader, desc=f\"Processing Stage {stage_idx}\", unit=\"batch\"\n",
+ " ):\n",
+ " img = img.to(device)\n",
+ " logits, _, feats = model.ce_stage(img, stage_idx)\n",
+ " logits = logits.cpu().numpy()\n",
+ " feats = feats.cpu().numpy()\n",
+ " label = label.cpu().numpy()\n",
+ " \n",
+ " # pad logits with nan to 90 classes\n",
+ " padded_logits = np.full((logits.shape[0], 90), np.nan)\n",
+ " padded_logits[:, : logits.shape[-1]] = logits\n",
+ "\n",
+ " logits_df = pd.DataFrame(\n",
+ " padded_logits, columns=[f\"logit_{logit_idx}\" for logit_idx in range(90)]\n",
+ " )\n",
+ " \n",
+ " feats_df = pd.DataFrame(\n",
+ " feats, columns=[f\"feat_{feat_idx}\" for feat_idx in range(128)]\n",
+ " )\n",
+ " \n",
+ " pred_class_df = pd.DataFrame(\n",
+ " np.nanargmax(logits, axis=1), columns=[\"pred_class\"]\n",
+ " )\n",
+ " \n",
+ " # combine logits and feature dataframes\n",
+ " batch_df = pd.concat([logits_df, feats_df, pred_class_df], axis=1)\n",
+ " batch_df[\"class\"] = label\n",
+ " batch_df[\"type\"] = batch_df.apply(find_type, axis=1, args=(stage_idx,))\n",
+ " \n",
+ " # append to master_df\n",
+ " master_dfs[stage_idx] = (\n",
+ " pd.concat([master_dfs[stage_idx], batch_df], ignore_index=True)\n",
+ " if not master_dfs[stage_idx].empty\n",
+ " else batch_df\n",
+ " )\n",
+ " \n",
+ " master_dfs[stage_idx] = master_dfs[stage_idx].reset_index(drop=True) # reset indexes for this stage's df\n",
+ " \n",
+ " if stage_idx > 0: # stages 1 and 2 require some additional steps\n",
+ " model.sync_ema()\n",
+ " model.ema_backbone(stage_idx)\n",
+ " else: # stage 0 requires some additional steps\n",
+ " model.fix_static(stage_idx)\n",
+ " \n",
+ " # increase classification head size for next stage\n",
+ " model.increment_classes(10, device)\n",
+ " \n",
+ " # save master_dfs to file\n",
+ " pickle.dump(\n",
+ " master_dfs,\n",
+ " open(os.path.join(GIT_ROOT, \"thesh_experiment_logits.pkl\"), \"wb\"),\n",
+ " )\n",
+ " print(\"Logits saved to file\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Testing Dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Loaded logits & features from file\n"
+ ]
+ }
+ ],
+ "source": [
+ "if os.path.exists(os.path.join(GIT_ROOT, \"thesh_experiment_logits_test.pkl\")) and USE_SAVED_LOGITS:\n",
+ " test_dfs = pickle.load(\n",
+ " open(os.path.join(GIT_ROOT, \"thesh_experiment_logits_test.pkl\"), \"rb\")\n",
+ " )\n",
+ " print(\"Loaded logits & features from file\")\n",
+ "else:\n",
+ " print(\"Computing Logits & features\")\n",
+ " \n",
+ " # create empty master_df for each stage\n",
+ " test_dfs = [\n",
+ " pd.DataFrame(\n",
+ " columns=[\"class\"]\n",
+ " + [f\"logit_{logit_idx}\" for logit_idx in range(90)]\n",
+ " + [f\"feat_{feat_idx}\" for feat_idx in range(128)]\n",
+ " )\n",
+ " for _ in range(3)\n",
+ " ]\n",
+ " \n",
+ " # create & load pretrained model\n",
+ " model = model_CI(gm_args).to(device)\n",
+ " model.load_state_dict(\n",
+ " torch.load(os.path.join(gm_args.model_dir, \"stage_0.pth\"), weights_only=False)\n",
+ " )\n",
+ " model.cuda()\n",
+ " model.sync_new_branches() # changes some internal stuff, required after load\n",
+ " \n",
+ " for stage_idx in [0, 1, 2]:\n",
+ " \n",
+ " # load stage 1 or 2 model if we're past stage 0\n",
+ " if stage_idx > 0:\n",
+ " model.save_backbone(stage_idx) # changes model internals, required before loading >0 stages\n",
+ " model.load_state_dict(\n",
+ " torch.load(\n",
+ " os.path.join(gm_args.model_dir, f\"ssl_{stage_idx}.pth\"),\n",
+ " weights_only=False,\n",
+ " )\n",
+ " )\n",
+ " \n",
+ " model.to(device)\n",
+ " model.eval()\n",
+ " \n",
+ " # Compute logits and features for all samples\n",
+ " with torch.no_grad():\n",
+ " for img, label, _ in tqdm(\n",
+ " test_loader, desc=f\"Processing Stage {stage_idx}\", unit=\"batch\"\n",
+ " ):\n",
+ " img = img.to(device)\n",
+ " logits, _, feats = model.ce_stage(img, stage_idx)\n",
+ " logits = logits.cpu().numpy()\n",
+ " feats = feats.cpu().numpy()\n",
+ " label = label.cpu().numpy()\n",
+ " \n",
+ " # pad logits with nan to 90 classes\n",
+ " padded_logits = np.full((logits.shape[0], 90), np.nan)\n",
+ " padded_logits[:, : logits.shape[-1]] = logits\n",
+ "\n",
+ " logits_df = pd.DataFrame(\n",
+ " padded_logits, columns=[f\"logit_{logit_idx}\" for logit_idx in range(90)]\n",
+ " )\n",
+ " \n",
+ " feats_df = pd.DataFrame(\n",
+ " feats, columns=[f\"feat_{feat_idx}\" for feat_idx in range(128)]\n",
+ " )\n",
+ " \n",
+ " pred_class_df = pd.DataFrame(\n",
+ " np.nanargmax(logits, axis=1), columns=[\"pred_class\"]\n",
+ " )\n",
+ " \n",
+ " # combine logits and feature dataframes\n",
+ " batch_df = pd.concat([logits_df, feats_df, pred_class_df], axis=1)\n",
+ " batch_df[\"class\"] = label\n",
+ " batch_df[\"type\"] = batch_df.apply(find_type, axis=1, args=(stage_idx,))\n",
+ " \n",
+ " # append to master_df\n",
+ " test_dfs[stage_idx] = (\n",
+ " pd.concat([test_dfs[stage_idx], batch_df], ignore_index=True)\n",
+ " if not test_dfs[stage_idx].empty\n",
+ " else batch_df\n",
+ " )\n",
+ " \n",
+ " test_dfs[stage_idx] = test_dfs[stage_idx].reset_index(drop=True) # reset indexes for this stage's df\n",
+ " \n",
+ " if stage_idx > 0: # stages 1 and 2 require some additional steps\n",
+ " model.sync_ema()\n",
+ " model.ema_backbone(stage_idx)\n",
+ " else: # stage 0 requires some additional steps\n",
+ " model.fix_static(stage_idx)\n",
+ " \n",
+ " # increase classification head size for next stage\n",
+ " model.increment_classes(10, device)\n",
+ " \n",
+ " # save test_dfs to file\n",
+ " pickle.dump(\n",
+ " test_dfs,\n",
+ " open(os.path.join(GIT_ROOT, \"thesh_experiment_logits_test.pkl\"), \"wb\"),\n",
+ " )\n",
+ " print(\"Logits saved to file\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Classes are the same across stages: True\n"
+ ]
+ }
+ ],
+ "source": [
+ "is_the_same = (master_dfs[0][\"class\"].equals(master_dfs[1][\"class\"]) and master_dfs[1][\"class\"].equals(master_dfs[2][\"class\"]))\n",
+ "print(f\"Classes are the same across stages: {is_the_same}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Model Accuracy\n",
+ "Compute the accuracy of the model for each stage on the training and testing data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Training Data Model Performance:\n",
+ "Stage 0\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\tKnown Classification Accuracy: 96.4171%\n",
+ "\t\t Correct/Total: 33746/35000\n",
+ "\tNovel Classification Accuracy: 0.0000%\n",
+ "\t\t Correct/Total: 0/15000\n",
+ "\tKnown Novel Classification Accuracy: 0.0000%\n",
+ "\t\t Correct/Total: 0/0\n",
+ "Stage 1\n",
+ "\tKnown Classification Accuracy: 96.4429%\n",
+ "\t\t Correct/Total: 33755/35000\n",
+ "\tNovel Classification Accuracy: 0.0000%\n",
+ "\t\t Correct/Total: 0/10000\n",
+ "\tKnown Novel Classification Accuracy: 0.0000%\n",
+ "\t\t Correct/Total: 0/5000\n",
+ "Stage 2\n",
+ "\tKnown Classification Accuracy: 96.4486%\n",
+ "\t\t Correct/Total: 33757/35000\n",
+ "\tNovel Classification Accuracy: 0.0000%\n",
+ "\t\t Correct/Total: 0/5000\n",
+ "\tKnown Novel Classification Accuracy: 0.0000%\n",
+ "\t\t Correct/Total: 0/10000\n",
+ "\n",
+ "\n",
+ "Test Data Model Performance:\n",
+ "Stage 0\n",
+ "\tKnown Classification Accuracy: 72.7429%\n",
+ "\t\t Correct/Total: 5092/7000\n",
+ "\tNovel Classification Accuracy: 0.0000%\n",
+ "\t\t Correct/Total: 0/3000\n",
+ "\tKnown Novel Classification Accuracy: 0.0000%\n",
+ "\t\t Correct/Total: 0/0\n",
+ "Stage 1\n",
+ "\tKnown Classification Accuracy: 72.2571%\n",
+ "\t\t Correct/Total: 5058/7000\n",
+ "\tNovel Classification Accuracy: 0.0000%\n",
+ "\t\t Correct/Total: 0/2000\n",
+ "\tKnown Novel Classification Accuracy: 0.0000%\n",
+ "\t\t Correct/Total: 0/1000\n",
+ "Stage 2\n",
+ "\tKnown Classification Accuracy: 72.2857%\n",
+ "\t\t Correct/Total: 5060/7000\n",
+ "\tNovel Classification Accuracy: 0.0000%\n",
+ "\t\t Correct/Total: 0/1000\n",
+ "\tKnown Novel Classification Accuracy: 0.0000%\n",
+ "\t\t Correct/Total: 0/2000\n"
+ ]
+ }
+ ],
+ "source": [
+ "def print_accs(df_set):\n",
+ " for stage_idx, df in enumerate(df_set):\n",
+ " print(f\"Stage {stage_idx}\")\n",
+ " for t, t_label in [(\"known\", \"Known\"), (\"novel\", \"Novel\"), (\"known_novel\", \"Known Novel\")]:\n",
+ " df_t = df[df[\"type\"] == t] # filter df by type\n",
+ " correct = (df_t[\"class\"] == df_t[\"pred_class\"]).sum() # count correct predictions\n",
+ " total = len(df_t)\n",
+ " acc = (correct / total) if total > 0 else 0\n",
+ " print(f\"\\t{t_label} Classification Accuracy: {acc*100:.4f}%\")\n",
+ " print(f\"\\t\\t Correct/Total: {correct}/{total}\")\n",
+ " \n",
+ "print(\"Training Data Model Performance:\")\n",
+ "print_accs(master_dfs)\n",
+ "print(\"\\n\\nTest Data Model Performance:\")\n",
+ "print_accs(test_dfs)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Compute Exemplar Sets"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Random Exemplar Sets\n",
+ "Randomly selected samples from each class used as exemplars. This is supervised, using samples we know are part of the class, not samples the model thinks are part of the class, since the model suck major cock."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "for df in master_dfs:\n",
+ " df[\"rand_set_member\"] = False\n",
+ "\n",
+ "for class_idx in range(100):\n",
+ " class_df = master_dfs[0][\n",
+ " master_dfs[0][\"class\"] == class_idx\n",
+ " ]\n",
+ " \n",
+ " sampled_rows = class_df.sample(EXEMPLAR_SET_SIZE, random_state=SEED)\n",
+ " for df in master_dfs:\n",
+ " df.loc[sampled_rows.index, \"rand_set_member\"] = True"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Centroid Distance Exemplar Sets\n",
+ "Selects exemplars based on their cosine distance to the mean features of their class, which acts as the centroid. The extracted features may change between stage, as the feature extractor was trained in all stages. exemplars are therefore based on their features from the stage\n",
+ "\n",
+ "Again, based on supervised knowledge of the class labels, as the classifier doesn't actually work."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "feat_columns = [f\"feat_{feat_idx}\" for feat_idx in range(128)]\n",
+ "for df in master_dfs:\n",
+ " df[\"cent_set_member\"] = False\n",
+ " \n",
+ "for stage_idx, classes_to_exemplar in enumerate([list(range(0, 70)), list(range(70, 80)), list(range(80, 90))]): # loop through stages and their corresponding learned classes\n",
+ " for class_idx in classes_to_exemplar:\n",
+ " class_df = master_dfs[stage_idx][\n",
+ " master_dfs[stage_idx][\"class\"] == class_idx\n",
+ " ]\n",
+ " \n",
+ " mean_feats = class_df[feat_columns].mean(axis=0) # get mean features for class\n",
+ " dists = cosine_distances(class_df[feat_columns], [mean_feats]) # compute cosine distances\n",
+ " \n",
+ " top_k_idxs = dists.flatten().argsort()[:EXEMPLAR_SET_SIZE] # get top k indices\n",
+ " for df in master_dfs:\n",
+ " df.loc[class_df.index[top_k_idxs], \"cent_set_member\"] = True"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Logit-Based Global Threshold\n",
+ "Visualises the Known-class threshold based on the minmax logit in the exemplar sets."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Precalculate Relevant Stats"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "9a215e3069fc4fc98bd29226b196b7f5",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Precomputing Stats: 0%| | 0/3 [00:00<?, ?stage/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "55ca8734a994448fa25e1641bedc4d2d",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Stage 0: Calculating MinMax of Exemplar Sets: 0%| | 0/70 [00:00<?, ?class/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "42f2a4d92ef44688b41d00c8785fea29",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Stage 0: Calculating Stats: 0%| | 0/100 [00:00<?, ?class/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "ec9d20f9f3d148e782e32b96366d0e89",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Stage 1: Calculating MinMax of Exemplar Sets: 0%| | 0/80 [00:00<?, ?class/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "1f2e4843b859497d9373a6bc465c9fa6",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Stage 1: Calculating Stats: 0%| | 0/100 [00:00<?, ?class/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "386ec798347d45cbad02d001e3d33693",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Stage 2: Calculating MinMax of Exemplar Sets: 0%| | 0/90 [00:00<?, ?class/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "961c024dd27c4071ba3dcca3ac18382f",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Stage 2: Calculating Stats: 0%| | 0/100 [00:00<?, ?class/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "logit_glob_thresh_data = [pd.DataFrame(columns=[\"class\", \"rand_minmax\", \"cent_minmax\", \"type\", \"mean\", \"maxmax\", \"minmax\", \"lowerq\", \"upperq\"]) for _ in range(3)]\n",
+ "logit_columns = [f\"logit_{logit_idx}\" for logit_idx in range(90)]\n",
+ "for stage_idx, df in enumerate(tqdm(master_dfs, desc=\"Precomputing Stats\", unit=\"stage\")):\n",
+ " rand_minmax = []\n",
+ " cent_minmax = []\n",
+ " mean = []\n",
+ " maxmax = []\n",
+ " minmax = []\n",
+ " lower_q = []\n",
+ " upper_q = []\n",
+ " \n",
+ " classes = range(0, 70+10*stage_idx) # 0-70 stage 1, 0-80 stage 2, 0-90 stage 3\n",
+ " for class_idx in tqdm(classes, desc=f\"Stage {stage_idx}: Calculating MinMax of Exemplar Sets\", unit=\"class\", leave=False):\n",
+ " \n",
+ " # calculate exemplar set points\n",
+ " rand_set_df = df[(df[\"class\"] == class_idx) & (df[\"rand_set_member\"])]\n",
+ " cent_set_df = df[(df[\"class\"] == class_idx) & (df[\"cent_set_member\"])]\n",
+ " \n",
+ " rand_minmax.append(rand_set_df[logit_columns].max(axis=1, skipna=True).min())\n",
+ " cent_minmax.append(cent_set_df[logit_columns].max(axis=1, skipna=True).min())\n",
+ " \n",
+ " for class_idx in tqdm(range(100), desc=f\"Stage {stage_idx}: Calculating Stats\", unit=\"class\", leave=False):\n",
+ " # calculate stats for all classes\n",
+ " \n",
+ " class_df = df[df[\"class\"] == class_idx]\n",
+ " # calculate mean logit for each logit column, store as np array in mean column\n",
+ " mean.append(class_df[logit_columns].mean(axis=0).values)\n",
+ " # calculate max of maxes for each row, store as np array in maxmax column\n",
+ " maxmax.append(class_df[logit_columns].max(axis=1).values)\n",
+ " # calculate min of maxes for each row, store as np array in minmax column\n",
+ " minmax.append(class_df[logit_columns].min(axis=1).values)\n",
+ " # calculate 25th percentile of maxes for each row, store as np array in lowerq column\n",
+ " lower_q.append(class_df[logit_columns].quantile(0.25, axis=1).values)\n",
+ " # calculate 75th percentile of maxes for each row, store as np array in upperq column\n",
+ " upper_q.append(class_df[logit_columns].quantile(0.75, axis=1).values)\n",
+ " \n",
+ " rand_minmax_padded = rand_minmax + [np.nan] * (100 - len(rand_minmax))\n",
+ " cent_minmax_padded = cent_minmax + [np.nan] * (100 - len(cent_minmax))\n",
+ " \n",
+ " logit_glob_thresh_data[stage_idx][\"class\"] = list(range(100))\n",
+ " logit_glob_thresh_data[stage_idx][\"rand_minmax\"] = rand_minmax_padded\n",
+ " logit_glob_thresh_data[stage_idx][\"cent_minmax\"] = cent_minmax_padded\n",
+ " logit_glob_thresh_data[stage_idx][\"mean\"] = mean\n",
+ " logit_glob_thresh_data[stage_idx][\"maxmax\"] = maxmax\n",
+ " logit_glob_thresh_data[stage_idx][\"minmax\"] = minmax\n",
+ " logit_glob_thresh_data[stage_idx][\"lowerq\"] = lower_q\n",
+ " logit_glob_thresh_data[stage_idx][\"upperq\"] = upper_q\n",
+ " logit_glob_thresh_data[stage_idx][\"type\"] = logit_glob_thresh_data[stage_idx].apply(find_type, axis=1, args=(stage_idx,))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Visualise"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "8411735e95d24891934f847ab5713890",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "VBox(children=(HBox(children=(VBox(children=(RadioButtons(description='Exemplar Set:', index=1, options=('Rand…"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import ipywidgets as widgets\n",
+ "from IPython.display import display, clear_output\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "# Initial settings\n",
+ "max_y = 30\n",
+ "min_y = 0\n",
+ "\n",
+ "stat_label_mapping = {\n",
+ " \"Mean\": \"mean\",\n",
+ " \"Max\": \"maxmax\",\n",
+ " \"MinMax\": \"minmax\",\n",
+ " \"Lower Quartile\": \"lowerq\",\n",
+ " \"Upper Quartile\": \"upperq\",\n",
+ "}\n",
+ "\n",
+ "line_style_stat_mapping = {\n",
+ " \"Mean\": \"-\",\n",
+ " \"Max\": \"--\",\n",
+ " \"MinMax\": \"-.\",\n",
+ " \"Lower Quartile\": \":\",\n",
+ " \"Upper Quartile\": (0, (3, 5, 1, 5)), # Custom dash pattern\n",
+ "}\n",
+ "\n",
+ "exemplar_choice_mapping = {\n",
+ " \"Random\": \"rand_minmax\",\n",
+ " \"Centroid-Distance\": \"cent_minmax\",\n",
+ "}\n",
+ "\n",
+ "# Global flag to detect changes\n",
+ "changed_flag = False\n",
+ "\n",
+ "# Define stage selection checkboxes\n",
+ "stage_checkboxes = {f\"Stage {i}\": widgets.Checkbox(description=f\"Stage {i}\", value=(i == 0)) for i in range(4)} # Stage 0 selected by default\n",
+ "\n",
+ "# Plot function\n",
+ "def plot(\n",
+ " exemplar_choice,\n",
+ " selected_classes,\n",
+ " stat_type_choices,\n",
+ " threshold_type,\n",
+ " threshold_manual_value,\n",
+ " scaling_factor=100,\n",
+ "):\n",
+ " plt.close('all')\n",
+ "\n",
+ " # Get selected stages\n",
+ " selected_stages = [i for i, checkbox in enumerate(stage_checkboxes.values()) if checkbox.value]\n",
+ " num_stages = len(selected_stages)\n",
+ "\n",
+ " # Adjust number of subplots based on selected stages\n",
+ " fig, axs = plt.subplots(num_stages, 1, figsize=(20, 10 * num_stages)) # Adjust figure size based on num of stages\n",
+ " if num_stages == 1:\n",
+ " axs = [axs]\n",
+ "\n",
+ " for idx, stage_idx in enumerate(selected_stages):\n",
+ " ax = axs[idx]\n",
+ " df = logit_glob_thresh_data[stage_idx]\n",
+ " \n",
+ " ax.set_title(f\"Stage {stage_idx}\")\n",
+ " ax.set_ylim([min_y, max_y])\n",
+ " ax.set_xlim([0, 90])\n",
+ " ax.set_xlabel(\"Class Index\")\n",
+ " ax.set_ylabel(\"Logit Value\")\n",
+ " \n",
+ " # plot selected exemplar choice\n",
+ " ax.plot(\n",
+ " df[\"class\"],\n",
+ " df[exemplar_choice_mapping[exemplar_choice]],\n",
+ " label=exemplar_choice,\n",
+ " color=\"black\",\n",
+ " marker=\"o\",\n",
+ " )\n",
+ "\n",
+ " scaling_factor_adj = 0.01 * scaling_factor # comes as a percentage\n",
+ " \n",
+ " # plot threshold\n",
+ " if threshold_type == \"Manual\":\n",
+ " ax.axhline(\n",
+ " y=threshold_manual_value,\n",
+ " color=\"red\",\n",
+ " linestyle=\"--\",\n",
+ " label=f\"Global Threshold {threshold_manual_value:.2f}\",\n",
+ " )\n",
+ " elif threshold_type == \"Per Logit\":\n",
+ " # plot a threshold line scaling_factor times the selected exemplar choice points\n",
+ " ax.plot(\n",
+ " df[\"class\"],\n",
+ " df[exemplar_choice_mapping[exemplar_choice]] * scaling_factor_adj,\n",
+ " label=f\"Per-Logit Threshold {scaling_factor:.2f}%\",\n",
+ " color=\"red\",\n",
+ " linestyle=\"--\",\n",
+ " )\n",
+ " elif threshold_type == \"Global\":\n",
+ " # plot a horizontal line at the min of the selected exemplar choice\n",
+ " ax.axhline(\n",
+ " y=df[exemplar_choice_mapping[exemplar_choice]].min() * scaling_factor_adj,\n",
+ " color=\"red\",\n",
+ " linestyle=\"--\",\n",
+ " label=f\"Global Threshold {df[exemplar_choice_mapping[exemplar_choice]].min():.2f} x {scaling_factor:.2f}%\",\n",
+ " )\n",
+ " \n",
+ " # plot selected stat types for selected classes\n",
+ " for class_idx in selected_classes:\n",
+ " for stat_type in stat_type_choices:\n",
+ " ax.plot(\n",
+ " list(range(90)),\n",
+ " df.loc[class_idx, stat_label_mapping[stat_type]],\n",
+ " label=f\"Class {class_idx} ({stat_type})\",\n",
+ " color=f\"C{class_idx}\",\n",
+ " linestyle=line_style_stat_mapping[stat_type],\n",
+ " )\n",
+ " ax.grid()\n",
+ " \n",
+ " # plot legend in subfigure 4:\n",
+ " handles, labels = ax.get_legend_handles_labels()\n",
+ " fig.legend(handles, labels, loc=\"center\", bbox_to_anchor=(0.5, -0.1), ncol=10)\n",
+ " plt.tight_layout()\n",
+ " plt.show()\n",
+ "\n",
+ "# Widgets\n",
+ "exemplar_choice_radio = widgets.RadioButtons(\n",
+ " options=[\"Random\", \"Centroid-Distance\"],\n",
+ " description=\"Exemplar Set:\",\n",
+ " disabled=False,\n",
+ " value=\"Centroid-Distance\",\n",
+ ")\n",
+ "\n",
+ "stat_type_label = widgets.Label(value=\"Stat Types:\")\n",
+ "\n",
+ "stat_type_checkboxes = {logit_type: widgets.Checkbox(description=logit_type, value=False if logit_type != \"Mean\" else True) for logit_type in stat_label_mapping.keys()}\n",
+ "\n",
+ "class_selector = widgets.SelectMultiple(\n",
+ " options=list(range(100)),\n",
+ " value=list(range(70, 100)),\n",
+ " description=\"Selected Classes:\",\n",
+ " disabled=False,\n",
+ " layout=widgets.Layout(height=\"300px\", width=\"90%\"),\n",
+ " style={'description_width': '125px'}\n",
+ " \n",
+ ")\n",
+ "\n",
+ "# Toggle buttons for class selection\n",
+ "toggle_all_button = widgets.Button(description=\"Toggle All\")\n",
+ "toggle_all_novel_button = widgets.Button(description=\"Toggle All Novel\")\n",
+ "toggle_all_known_button = widgets.Button(description=\"Toggle All Known\")\n",
+ "\n",
+ "def toggle_all_classes(b):\n",
+ " if len(class_selector.value) > 0:\n",
+ " class_selector.value = []\n",
+ " else:\n",
+ " class_selector.value = list(range(100))\n",
+ "\n",
+ "def toggle_all_novel_classes(b):\n",
+ " if len(class_selector.value) > 0:\n",
+ " class_selector.value = list(set(class_selector.value) - set(range(70, 100)))\n",
+ " else:\n",
+ " class_selector.value = list(set(range(70, 100)) + set(class_selector.value))\n",
+ "\n",
+ "def toggle_all_known_classes(b):\n",
+ " if len(class_selector.value) > 0:\n",
+ " class_selector.value = list(set(class_selector.value) - set(range(0, 70)))\n",
+ " else:\n",
+ " class_selector.value = list(set(range(0, 70)) + set(class_selector.value))\n",
+ "\n",
+ "toggle_all_button.on_click(toggle_all_classes)\n",
+ "toggle_all_novel_button.on_click(toggle_all_novel_classes)\n",
+ "toggle_all_known_button.on_click(toggle_all_known_classes)\n",
+ "\n",
+ "# Threshold and scaling sliders\n",
+ "threshold_type_radio = widgets.RadioButtons(\n",
+ " options=[\"Manual\", \"Per Logit\", \"Global\"],\n",
+ " description=\"Threshold Type:\",\n",
+ " disabled=False,\n",
+ " value=\"Per Logit\",\n",
+ " \n",
+ ")\n",
+ "\n",
+ "threshold_manual_slider = widgets.FloatSlider(\n",
+ " value=0.0,\n",
+ " min=min_y,\n",
+ " max=max_y,\n",
+ " step=0.05,\n",
+ " description=\"Manual Threshold:\",\n",
+ " disabled=True,\n",
+ " continuous_update=False,\n",
+ " orientation=\"horizontal\",\n",
+ " readout=True,\n",
+ " readout_format=\".2f\",\n",
+ " layout=widgets.Layout(width=\"90%\"),\n",
+ " style={'description_width': '125px'}\n",
+ ")\n",
+ "\n",
+ "scaling_factor_slider = widgets.FloatSlider(\n",
+ " value=100,\n",
+ " min=0,\n",
+ " max=200,\n",
+ " step=0.1,\n",
+ " description=\"Threshold Scaling %:\",\n",
+ " disabled=False,\n",
+ " continuous_update=False,\n",
+ " orientation=\"horizontal\",\n",
+ " readout=True,\n",
+ " readout_format=\".2f\",\n",
+ " layout=widgets.Layout(width=\"90%\"),\n",
+ " style={'description_width': '125px'}\n",
+ ")\n",
+ "\n",
+ "# Revisualize button (disabled initially)\n",
+ "revisualize_button = widgets.Button(description=\"Revisualize\", disabled=False)\n",
+ "\n",
+ "# Change detection logic\n",
+ "def on_change(*args):\n",
+ " global changed_flag\n",
+ " changed_flag = True\n",
+ " revisualize_button.disabled = False\n",
+ "\n",
+ "# Attach change detection to widgets\n",
+ "exemplar_choice_radio.observe(on_change)\n",
+ "threshold_type_radio.observe(on_change)\n",
+ "threshold_manual_slider.observe(on_change)\n",
+ "scaling_factor_slider.observe(on_change)\n",
+ "for cb in stat_type_checkboxes.values():\n",
+ " cb.observe(on_change)\n",
+ "for cb in stage_checkboxes.values():\n",
+ " cb.observe(on_change)\n",
+ "class_selector.observe(on_change)\n",
+ "\n",
+ "# Threshold type change logic for enabling/disabling sliders\n",
+ "def on_threshold_type_change(change):\n",
+ " if change['new'] == \"Manual\":\n",
+ " threshold_manual_slider.disabled = False\n",
+ " scaling_factor_slider.disabled = True\n",
+ " else:\n",
+ " threshold_manual_slider.disabled = True\n",
+ " scaling_factor_slider.disabled = False\n",
+ "\n",
+ "threshold_type_radio.observe(on_threshold_type_change, names=\"value\")\n",
+ "\n",
+ "# Redefine the plot update logic\n",
+ "def update_plot(*args):\n",
+ " global changed_flag\n",
+ " # Get the selected stat types from the checkboxes\n",
+ " stat_type_choices = [k for k, v in stat_type_checkboxes.items() if v.value]\n",
+ " \n",
+ " # Update the plot with dynamic stat_type_choices\n",
+ " plot(\n",
+ " exemplar_choice_radio.value,\n",
+ " class_selector.value,\n",
+ " stat_type_choices,\n",
+ " threshold_type_radio.value,\n",
+ " threshold_manual_slider.value,\n",
+ " scaling_factor_slider.value\n",
+ " )\n",
+ " \n",
+ " revisualize_button.disabled = True\n",
+ " changed_flag = False\n",
+ "\n",
+ "revisualize_button.on_click(update_plot)\n",
+ "\n",
+ "# Layout for interactive widgets\n",
+ "selectors = HBox(\n",
+ " [VBox([exemplar_choice_radio, threshold_type_radio, threshold_manual_slider, scaling_factor_slider], layout=widgets.Layout(width=\"25%\")), \n",
+ " VBox([stat_type_label, *stat_type_checkboxes.values()], layout=widgets.Layout(width=\"25%\")), \n",
+ " VBox([class_selector, HBox([toggle_all_button, toggle_all_novel_button, toggle_all_known_button])], layout=widgets.Layout(width=\"25%\")),\n",
+ " VBox([widgets.Label(\"Stages to Visualize:\"), *stage_checkboxes.values()], layout=widgets.Layout(width=\"25%\"))],\n",
+ " layout=widgets.Layout(width=\"100%\")\n",
+ ")\n",
+ "\n",
+ "# Display all elements\n",
+ "display(VBox([selectors, revisualize_button]))\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Calculate Logit-Based Threshold Accuracies"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# logic to get the correctness of a row based on the threshold\n",
+ "def get_correctness_logit(row, thresh):\n",
+ " row = row.dropna()\n",
+ " logit_columns = [col for col in row.index if col.startswith(\"logit_\")]\n",
+ " logits_only = row[logit_columns]\n",
+ " best_logit_idx = logits_only.idxmax(skipna=True)\n",
+ " best_logit_val = logits_only[best_logit_idx]\n",
+ " if type(thresh) == dict:\n",
+ " thresh = thresh[best_logit_idx]\n",
+ " if row[\"type\"] == \"known\" or row[\"type\"] == \"known_novel\":\n",
+ " return best_logit_val >= thresh # known classes are correct if their best logit is above the threshold\n",
+ " elif row[\"type\"] == \"novel\":\n",
+ " return best_logit_val < thresh # novel classes are correct if their best logit is below the threshold\n",
+ " else:\n",
+ " raise ValueError(f\"Invalid type {row['type']}\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "8659f4207fae47e4b44c1a5b83beecc2",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "VBox(children=(FloatText(value=1.0, description='Rand Logit Scaler:', step=0.01), FloatText(value=0.82, descri…"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Float input for the threshold scalers\n",
+ "rand_logit_thresh_scaler_input = widgets.FloatText(value=1.0, description='Rand Logit Scaler:', step=0.01)\n",
+ "cent_logit_thresh_scaler_input = widgets.FloatText(value=0.82, description='Cent Logit Scaler:', step=0.01)\n",
+ "\n",
+ "# Float input for the global thresholds\n",
+ "rand_glob_thresh_input = widgets.FloatText(value=6.62, description='Rand Global Thresh:', step=0.01)\n",
+ "cent_glob_thresh_input = widgets.FloatText(value=15.7, description='Cent Global Thresh:', step=0.01)\n",
+ "\n",
+ "# Dropdown for stage selection\n",
+ "stage_dropdown = widgets.Dropdown(\n",
+ " options=[0, 1, 2],\n",
+ " value=0,\n",
+ " description='Stage:'\n",
+ ")\n",
+ "\n",
+ "# Button to run the computation\n",
+ "run_button = widgets.Button(description=\"Run Code\")\n",
+ "\n",
+ "# Output area to display the results\n",
+ "output = widgets.Output()\n",
+ "\n",
+ "def run_code(b):\n",
+ " # Clear the previous output\n",
+ " output.clear_output()\n",
+ " \n",
+ " # Retrieve the user-input values\n",
+ " stage_idx = stage_dropdown.value\n",
+ " rand_logit_thresh_scaler = rand_logit_thresh_scaler_input.value\n",
+ " cent_logit_thresh_scaler = cent_logit_thresh_scaler_input.value\n",
+ " rand_glob_thesh = rand_glob_thresh_input.value\n",
+ " cent_glob_thesh = cent_glob_thresh_input.value\n",
+ "\n",
+ " # Begin the computation process and show progress\n",
+ " with output:\n",
+ "\n",
+ " rand_logit_thresh = logit_glob_thresh_data[0][\"rand_minmax\"].dropna(how=\"all\").to_dict()\n",
+ " rand_logit_thresh = {f\"logit_{logit_idx}\": val * rand_logit_thresh_scaler for logit_idx, val in rand_logit_thresh.items()}\n",
+ "\n",
+ " cent_logit_thresh = logit_glob_thresh_data[0][\"cent_minmax\"].dropna(how=\"all\").to_dict()\n",
+ " cent_logit_thresh = {f\"logit_{logit_idx}\": val * cent_logit_thresh_scaler for logit_idx, val in cent_logit_thresh.items()}\n",
+ "\n",
+ " threshes = {\n",
+ " \"Random Exemplar Set: Global Threshold\": rand_glob_thesh,\n",
+ " \"Centroid Exemplar Set: Global Threshold\": cent_glob_thesh,\n",
+ " \"Random Exemplar Set: Per-Logit Threshold\": rand_logit_thresh,\n",
+ " \"Centroid Exemplar Set: Per-Logit Threshold\": cent_logit_thresh,\n",
+ " }\n",
+ "\n",
+ " stage_results = master_dfs[stage_idx].copy().dropna(subset=[\"class\"])\n",
+ " for thresh_name, thresh in threshes.items():\n",
+ " tqdm.pandas(desc=f\"Calculating Correctness for {thresh_name}\", unit=\"row\")\n",
+ " stage_results[thresh_name] = stage_results.progress_apply(get_correctness_logit, axis=1, args=(thresh,))\n",
+ "\n",
+ " for_display = pd.DataFrame(columns=[\"Threshold Type\", \"Sample Type\", \"Correct\", \"Incorrect\", \"Total\", \"Identification Rate (%)\", \"Misidentification Rate (%)\"])\n",
+ " for thresh_name, thresh in threshes.items():\n",
+ " for sample_type in [\"known\", \"novel\", \"known_novel\"]:\n",
+ " sample_df = stage_results[stage_results[\"type\"] == sample_type]\n",
+ " if sample_df.empty:\n",
+ " continue\n",
+ " correct = sample_df[thresh_name].sum()\n",
+ " incorrect = len(sample_df) - correct\n",
+ " total = len(sample_df)\n",
+ " ident_rate = correct / total\n",
+ " misident_rate = incorrect / total\n",
+ " to_concat = pd.DataFrame({\n",
+ " \"Threshold Type\": thresh_name,\n",
+ " \"Sample Type\": sample_type.capitalize(),\n",
+ " \"Correct\": correct,\n",
+ " \"Incorrect\": incorrect,\n",
+ " \"Total\": total,\n",
+ " \"Identification Rate (%)\": ident_rate * 100,\n",
+ " \"Misidentification Rate (%)\": misident_rate\n",
+ " }, index=[0])\n",
+ " for_display = pd.concat([for_display, to_concat], ignore_index=True) if not for_display.empty else to_concat\n",
+ "\n",
+ " display(for_display)\n",
+ "\n",
+ "# Attach the click event to the run_button\n",
+ "run_button.on_click(run_code)\n",
+ "\n",
+ "# Layout the widgets\n",
+ "ui = widgets.VBox([rand_logit_thresh_scaler_input, cent_logit_thresh_scaler_input, \n",
+ " rand_glob_thresh_input, cent_glob_thresh_input, stage_dropdown, run_button, output])\n",
+ "\n",
+ "# Display the UI\n",
+ "display(ui)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Find Best Scaler for Centroid-Distance Logit Threshold"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Loaded results from file\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "cent_logit_thresh = logit_glob_thresh_data[0][\"cent_minmax\"].dropna(how=\"all\").to_dict()\n",
+ "cent_logit_thresh = {f\"logit_{logit_idx}\": val for logit_idx, val in cent_logit_thresh.items()}\n",
+ "if os.path.exists(os.path.join(GIT_ROOT, \"cent_thresh_scaling_results.pkl\")) and USE_SAVED_LOGITS:\n",
+ " stage_results = pd.read_pickle(\n",
+ " os.path.join(GIT_ROOT, \"cent_thresh_scaling_results.pkl\")\n",
+ " )\n",
+ " print(\"Loaded results from file\")\n",
+ "else:\n",
+ "\n",
+ " def calc_cent_thresh_acc(scaler, threshold_dict, df):\n",
+ " # scale the threshold dict\n",
+ " threshold_dict = {k: v * scaler for k, v in threshold_dict.items()}\n",
+ " tqdm.pandas(desc=f\"Calculating Correctness for Scaler {scaler}\", unit=\"row\", leave=False)\n",
+ " df[\"correct\"] = df.progress_apply(get_correctness_logit, axis=1, args=(threshold_dict,))\n",
+ "\n",
+ " # return scaler, knwon & correct / total known, novel correct / total novel\n",
+ " return (\n",
+ " scaler, # scaler value\n",
+ " df[df[\"type\"] == \"known\"][\"correct\"].sum() / len(df[df[\"type\"] == \"known\"]), # known accuracy (correct known/ total known)\n",
+ " df[df[\"type\"] == \"novel\"][\"correct\"].sum() / len(df[df[\"type\"] == \"novel\"]), # novel accuracy (correct novel/ total novel)\n",
+ " )\n",
+ "\n",
+ " scaler_range = np.around(np.arange(0.1, 2.1, 0.01), 2)\n",
+ " \n",
+ " data = master_dfs[0].copy()\n",
+ " \n",
+ " results = [\n",
+ " calc_cent_thresh_acc(scaler, cent_logit_thresh, data)\n",
+ " for scaler in tqdm(\n",
+ " scaler_range, desc=\"Finding Scaler-Threshold Accuracy\", unit=\"scaler\"\n",
+ " )\n",
+ " ]\n",
+ "\n",
+ " stage_results = pd.DataFrame(results, columns=[\"scaler\", \"known_acc\", \"novel_acc\"])\n",
+ " stage_results.to_pickle(os.path.join(GIT_ROOT, \"cent_thresh_scaling_results.pkl\"))\n",
+ "\n",
+ "known_acc = stage_results[\"known_acc\"]\n",
+ "novel_acc = stage_results[\"novel_acc\"]\n",
+ "scaler = stage_results[\"scaler\"]\n",
+ "\n",
+ "difference = known_acc - novel_acc\n",
+ "intersection_idx = np.argmin(np.abs(difference))\n",
+ "\n",
+ "intersection_x = scaler.iloc[intersection_idx]\n",
+ "intersection_y = known_acc.iloc[intersection_idx]\n",
+ "\n",
+ "plt.plot(scaler, known_acc, label=\"Known\")\n",
+ "plt.plot(scaler, novel_acc, label=\"Novel\")\n",
+ "\n",
+ "plt.axvline(\n",
+ " x=intersection_x,\n",
+ " color=\"red\",\n",
+ " linestyle=\"--\",\n",
+ " label=f\"Intersection: {intersection_x:.2f}\",\n",
+ ")\n",
+ "\n",
+ "plt.scatter([intersection_x], [intersection_y], color=\"red\", zorder=5)\n",
+ "plt.grid()\n",
+ "plt.xlabel(\"Scaling Factor\")\n",
+ "plt.ylabel(\"Accuracy\")\n",
+ "plt.title(\"Centroid-Distance Threshold Scaling Factor vs Accuracy\")\n",
+ "plt.legend()\n",
+ "\n",
+ "# Show the plot\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Entropy-Based Threshold\n",
+ "This threshold uses the exemplar set element's entropy to create a threshold. There is no point in using other stages, 'cause they suck. Stage 0 only."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Calculate Entropy"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Loaded results from file\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>class</th>\n",
+ " <th>type</th>\n",
+ " <th>entropy</th>\n",
+ " <th>rand_set_member</th>\n",
+ " <th>cent_set_member</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>19</td>\n",
+ " <td>known</td>\n",
+ " <td>0.000007</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>29</td>\n",
+ " <td>known</td>\n",
+ " <td>0.000005</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>0</td>\n",
+ " <td>known</td>\n",
+ " <td>0.000303</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>11</td>\n",
+ " <td>known</td>\n",
+ " <td>0.018936</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>1</td>\n",
+ " <td>known</td>\n",
+ " <td>0.000003</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " class type entropy rand_set_member cent_set_member\n",
+ "0 19 known 0.000007 False False\n",
+ "1 29 known 0.000005 False False\n",
+ "2 0 known 0.000303 False False\n",
+ "3 11 known 0.018936 False False\n",
+ "4 1 known 0.000003 False False"
+ ]
+ },
+ "execution_count": 19,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "def get_entropy(row):\n",
+ " logit_columns = [col for col in row.index if col.startswith(\"logit_\")]\n",
+ " logits_only = row[logit_columns]\n",
+ " logits_only = logits_only.dropna()\n",
+ " logits_only = logits_only.astype(float)\n",
+ " return -np.sum(softmax(logits_only) * np.log(softmax(logits_only)))\n",
+ "\n",
+ "# copy the old dataframe\n",
+ "entropy_df = master_dfs[0].copy()\n",
+ "\n",
+ "if os.path.exists(os.path.join(GIT_ROOT, \"entropy_results.pkl\")) and USE_SAVED_LOGITS:\n",
+ " entropy_df = pd.read_pickle(\n",
+ " os.path.join(GIT_ROOT, \"entropy_results.pkl\")\n",
+ " )\n",
+ " print(\"Loaded results from file\")\n",
+ "else:\n",
+ " # Calculate entropy for each row\n",
+ " tqdm.pandas(desc=\"Calculating Entropy\", unit=\"row\", leave=False)\n",
+ " entropy_df[\"entropy\"] = entropy_df.progress_apply(get_entropy, axis=1)\n",
+ "\n",
+ " # get rid of anything unneeded\n",
+ " entropy_df = entropy_df[[\"class\", \"type\", \"entropy\", \"rand_set_member\", \"cent_set_member\"]]\n",
+ " # set rand_set_member & cent_set_member to false for any novel samples\n",
+ " entropy_df.loc[entropy_df[\"type\"] == \"novel\", \"rand_set_member\"] = False\n",
+ " entropy_df.loc[entropy_df[\"type\"] == \"novel\", \"cent_set_member\"] = False\n",
+ " \n",
+ " pd.to_pickle(entropy_df, os.path.join(GIT_ROOT, \"entropy_results.pkl\"))\n",
+ " print(\"Saved results to file\")\n",
+ "entropy_df.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Visualise Entropy & Threshold"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "148b6c46b8964c76a7291ebfd85d1aeb",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "VBox(children=(HBox(children=(VBox(children=(Label(value='Random Exemplar Set:'), FloatSlider(value=0.25, desc…"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "def get_correctness_ent(row, thresh):\n",
+ " if row[\"type\"] == \"known\":\n",
+ " return (\n",
+ " row[\"entropy\"] < thresh\n",
+ " ) # known classes are correct if their entropy is below the threshold\n",
+ " elif row[\"type\"] == \"novel\":\n",
+ " return (\n",
+ " row[\"entropy\"] >= thresh\n",
+ " ) # novel classes are correct if their entropy is above the threshold\n",
+ " else:\n",
+ " raise ValueError(f\"Invalid type {row['type']}, index {row.name}\")\n",
+ " \n",
+ "centroid_checkbox = widgets.Checkbox(value=False, description=\"Centroid Exemplar Set\")\n",
+ "random_checkbox = widgets.Checkbox(value=False, description=\"Random Exemplar Set\")\n",
+ "all_checkbox = widgets.Checkbox(value=False, description=\"All Known Data\")\n",
+ "\n",
+ "rand_thresh_desc = widgets.Label(value=\"Random Exemplar Set:\")\n",
+ "rand_thresh_slider = widgets.FloatSlider(\n",
+ " value=0.25,\n",
+ " min=-1,\n",
+ " max=5,\n",
+ " step=0.01,\n",
+ " description=\"Entropy Threshold:\",\n",
+ " style={'description_width': '125px'},\n",
+ " layout=widgets.Layout(width=\"90%\")\n",
+ ")\n",
+ "rand_thresh_checkbox = widgets.Checkbox(value=True, description=\"Enabled\")\n",
+ "old_rand_thresh = 0 # store the old threshold, so we only recalculate when the slider changes\n",
+ "\n",
+ "cent_thresh_desc = widgets.Label(value=\"Centroid Exemplar Set:\")\n",
+ "cent_thresh_slider = widgets.FloatSlider(\n",
+ " value=0.25,\n",
+ " min=-1,\n",
+ " max=5,\n",
+ " step=0.01,\n",
+ " description=\"Entropy Threshold:\",\n",
+ " style={'description_width': '125px'},\n",
+ " layout=widgets.Layout(width=\"90%\")\n",
+ ")\n",
+ "cent_thresh_checkbox = widgets.Checkbox(value=True, description=\"Enabled\")\n",
+ "old_cent_thresh = 0 # store the old threshold, so we only recalculate when the slider changes\n",
+ "\n",
+ "# Handler function that updates the plot and table\n",
+ "def handler(rand_thresh, rand_thresh_enabled, cent_thresh, cent_thresh_enabled, centroid, random, all_known):\n",
+ " global old_rand_thresh, old_cent_thresh\n",
+ " \n",
+ " \n",
+ " # Apply correctness logic only if thresholds have changed\n",
+ " if rand_thresh != old_rand_thresh:\n",
+ " old_rand_thresh = rand_thresh\n",
+ " entropy_df[\"rand_correct\"] = entropy_df.apply(get_correctness_ent, axis=1, args=(rand_thresh,))\n",
+ " if cent_thresh != old_cent_thresh:\n",
+ " old_cent_thresh = cent_thresh\n",
+ " entropy_df[\"cent_correct\"] = entropy_df.apply(get_correctness_ent, axis=1, args=(cent_thresh,))\n",
+ " \n",
+ " # outputting\n",
+ " plt.close(\"all\")\n",
+ " fig, ax = plt.subplots(1, 1, figsize=(10, 8))\n",
+ " # plot the known entropy kde\n",
+ " if all_known:\n",
+ " sns.kdeplot(\n",
+ " entropy_df[entropy_df[\"type\"] == \"known\"][\"entropy\"],\n",
+ " label=\"Known\",\n",
+ " color=\"green\",\n",
+ " fill=True,\n",
+ " ax=ax,\n",
+ " )\n",
+ " \n",
+ " if centroid:\n",
+ " sns.kdeplot(\n",
+ " entropy_df[entropy_df[\"cent_set_member\"]][\"entropy\"],\n",
+ " label=\"Centroid Exemplar Set\",\n",
+ " color=\"purple\",\n",
+ " fill=True,\n",
+ " ax=ax,\n",
+ " )\n",
+ " \n",
+ " if random:\n",
+ " sns.kdeplot(\n",
+ " entropy_df[entropy_df[\"rand_set_member\"]][\"entropy\"],\n",
+ " label=\"Random Exemplar Set\",\n",
+ " color=\"blue\",\n",
+ " fill=True,\n",
+ " ax=ax,\n",
+ " )\n",
+ " \n",
+ " # plot the novel entropy kde\n",
+ " sns.kdeplot(\n",
+ " entropy_df[entropy_df[\"type\"] == \"novel\"][\"entropy\"],\n",
+ " label=\"Novel\",\n",
+ " color=\"orange\",\n",
+ " fill=True,\n",
+ " ax=ax,\n",
+ " )\n",
+ " \n",
+ " # plot the threshold lines\n",
+ " if rand_thresh_enabled:\n",
+ " ax.axvline(rand_thresh, color=\"red\", linestyle=\"--\", label=\"Random Threshold\")\n",
+ " if cent_thresh_enabled:\n",
+ " ax.axvline(cent_thresh, color=\"blue\", linestyle=\"--\", label=\"Centroid Threshold\")\n",
+ " \n",
+ " ax.set_xlim(-0.5, 5)\n",
+ " ax.grid()\n",
+ " ax.set_title(\"Entropy Distribution with Thresholds\")\n",
+ " ax.set_xlabel(\"Entropy\")\n",
+ " ax.set_ylabel(\"Density\")\n",
+ " ax.legend(loc=\"upper right\", bbox_to_anchor=(1, 1))\n",
+ " plt.show()\n",
+ " \n",
+ " \n",
+ " \n",
+ " # Create the output dataframe\n",
+ " out_df = out_df_template.copy()\n",
+ " for thresh_type, thresh_key in [\n",
+ " (\"Random Exemplar Set\", \"rand\"),\n",
+ " (\"Centroid Exemplar Set\", \"cent\"),\n",
+ " ]:\n",
+ " for sample_type in [\"known\", \"novel\"]:\n",
+ " filtered_df = entropy_df[entropy_df[\"type\"] == sample_type]\n",
+ " out_dict = {\n",
+ " \"Threshold Type\": thresh_type,\n",
+ " \"Sample Type\": sample_type.capitalize(),\n",
+ " \"Correct\": filtered_df[f\"{thresh_key}_correct\"].sum(),\n",
+ " \"Incorrect\": len(filtered_df)\n",
+ " - filtered_df[f\"{thresh_key}_correct\"].sum(),\n",
+ " \"Total\": len(filtered_df),\n",
+ " \"Identification Rate (%)\": (\n",
+ " filtered_df[f\"{thresh_key}_correct\"].sum() / len(filtered_df)\n",
+ " )\n",
+ " * 100,\n",
+ " \"Misidentification Rate (%)\": (\n",
+ " (len(filtered_df) - filtered_df[f\"{thresh_key}_correct\"].sum())\n",
+ " / len(filtered_df)\n",
+ " )\n",
+ " * 100,\n",
+ " }\n",
+ " out_df = pd.concat([out_df, pd.DataFrame(out_dict, index=[0])]) if not out_df.empty else pd.DataFrame(out_dict, index=[0])\n",
+ " \n",
+ " display(out_df)\n",
+ "\n",
+ "# make plot change when selectors change:\n",
+ "output = widgets.interactive_output(handler, {\"rand_thresh\": rand_thresh_slider, \"rand_thresh_enabled\":rand_thresh_checkbox, \"cent_thresh_enabled\": cent_thresh_checkbox, \"cent_thresh\": cent_thresh_slider, \"centroid\": centroid_checkbox, \"random\": random_checkbox, \"all_known\": all_checkbox})\n",
+ "\n",
+ "# Display selectors at the top\n",
+ "selectors = HBox(\n",
+ " [\n",
+ " VBox([rand_thresh_desc, rand_thresh_slider, rand_thresh_checkbox, cent_thresh_desc, cent_thresh_slider, cent_thresh_checkbox], layout=widgets.Layout(width=\"50%\")),\n",
+ " VBox([widgets.Label(\"Select Exemplar Sets:\"), centroid_checkbox, random_checkbox, all_checkbox], layout=widgets.Layout(width=\"25%\")),\n",
+ " ]\n",
+ ")\n",
+ "\n",
+ "# Combine selectors and output into a single display\n",
+ "display(VBox([selectors, output]))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Calculate Optimal Entropy Threshold"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "94dda752ed51426cb67d587501e72138",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Finding Entropy Threshold Accuracy: 0%| | 0/100 [00:00<?, ?threshold/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Intersection at 0.03 with accuracy 0.8765\n"
+ ]
+ }
+ ],
+ "source": [
+ "def score_ent_thresh(df, thresh):\n",
+ " df[\"correct\"] = df.apply(get_correctness_ent, axis=1, args=(thresh,))\n",
+ " return (\n",
+ " thresh,\n",
+ " df[df[\"type\"] == \"known\"][\"correct\"].sum() / len(df[df[\"type\"] == \"known\"]),\n",
+ " df[df[\"type\"] == \"novel\"][\"correct\"].sum() / len(df[df[\"type\"] == \"novel\"]),\n",
+ " )\n",
+ "\n",
+ "if os.path.exists(os.path.join(GIT_ROOT, \"entropy_threshold_results.pkl\")) and USE_SAVED_LOGITS:\n",
+ " results_df = pd.read_pickle(\n",
+ " os.path.join(GIT_ROOT, \"entropy_threshold_results.pkl\")\n",
+ " )\n",
+ " print(\"Loaded results from file\")\n",
+ "else:\n",
+ " thresh_range = np.around(np.arange(0, 0.5, 0.005), decimals = 3) # range of thresholds\n",
+ " just_entropy = entropy_df[[\"class\", \"type\", \"entropy\"]].copy()\n",
+ " results = [\n",
+ " score_ent_thresh(just_entropy, thresh)\n",
+ " for thresh in tqdm(thresh_range, desc=\"Finding Entropy Threshold Accuracy\", unit=\"threshold\")\n",
+ " ]\n",
+ "\n",
+ " results_df = pd.DataFrame(results, columns=[\"thresh\", \"known_acc\", \"novel_acc\"])\n",
+ " \n",
+ " \n",
+ "known_acc = results_df[\"known_acc\"]\n",
+ "novel_acc = results_df[\"novel_acc\"]\n",
+ "thresh = results_df[\"thresh\"]\n",
+ "\n",
+ "difference = known_acc - novel_acc\n",
+ "intersection_idx = np.argmin(np.abs(difference))\n",
+ "intersection_x = thresh.iloc[intersection_idx]\n",
+ "intersection_y = known_acc.iloc[intersection_idx]\n",
+ "\n",
+ "plt.plot(thresh, known_acc, label=\"Known\")\n",
+ "plt.plot(thresh, novel_acc, label=\"Novel\")\n",
+ "plt.axvline(\n",
+ " x=intersection_x,\n",
+ " color=\"red\",\n",
+ " linestyle=\"--\",\n",
+ " label=f\"Intersection: {intersection_x:.2f}\",\n",
+ ")\n",
+ "plt.scatter([intersection_x], [intersection_y], color=\"red\", zorder=5)\n",
+ "plt.grid()\n",
+ "plt.xlabel(\"Entropy Threshold\")\n",
+ "plt.ylabel(\"Accuracy\")\n",
+ "plt.title(\"Entropy Threshold vs Accuracy\")\n",
+ "plt.legend()\n",
+ "plt.show()\n",
+ "\n",
+ "print(f\"Intersection at {intersection_x:.2f} with accuracy {intersection_y:.4f}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## DEAN - Test Energy-based GMM OOD Detection"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Calculate Energy"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>logit_0</th>\n",
+ " <th>logit_1</th>\n",
+ " <th>logit_2</th>\n",
+ " <th>logit_3</th>\n",
+ " <th>logit_4</th>\n",
+ " <th>logit_5</th>\n",
+ " <th>logit_6</th>\n",
+ " <th>logit_7</th>\n",
+ " <th>logit_8</th>\n",
+ " <th>logit_9</th>\n",
+ " <th>...</th>\n",
+ " <th>logit_62</th>\n",
+ " <th>logit_63</th>\n",
+ " <th>logit_64</th>\n",
+ " <th>logit_65</th>\n",
+ " <th>logit_66</th>\n",
+ " <th>logit_67</th>\n",
+ " <th>logit_68</th>\n",
+ " <th>logit_69</th>\n",
+ " <th>class</th>\n",
+ " <th>type</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>-2.440972</td>\n",
+ " <td>-4.758019</td>\n",
+ " <td>6.184047</td>\n",
+ " <td>1.529722</td>\n",
+ " <td>-5.321949</td>\n",
+ " <td>-4.473522</td>\n",
+ " <td>0.304163</td>\n",
+ " <td>-5.695143</td>\n",
+ " <td>2.027558</td>\n",
+ " <td>2.920707</td>\n",
+ " <td>...</td>\n",
+ " <td>3.465998</td>\n",
+ " <td>-4.818081</td>\n",
+ " <td>1.917040</td>\n",
+ " <td>5.784508</td>\n",
+ " <td>0.776843</td>\n",
+ " <td>-5.423034</td>\n",
+ " <td>-0.688107</td>\n",
+ " <td>-4.291896</td>\n",
+ " <td>19</td>\n",
+ " <td>known</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>-1.794608</td>\n",
+ " <td>4.166612</td>\n",
+ " <td>-5.779216</td>\n",
+ " <td>-2.639845</td>\n",
+ " <td>1.387825</td>\n",
+ " <td>2.357318</td>\n",
+ " <td>1.094379</td>\n",
+ " <td>-0.949892</td>\n",
+ " <td>-4.485704</td>\n",
+ " <td>-3.085227</td>\n",
+ " <td>...</td>\n",
+ " <td>-4.224628</td>\n",
+ " <td>1.552391</td>\n",
+ " <td>3.057348</td>\n",
+ " <td>-0.010166</td>\n",
+ " <td>0.257023</td>\n",
+ " <td>4.879888</td>\n",
+ " <td>-1.197542</td>\n",
+ " <td>-3.567023</td>\n",
+ " <td>29</td>\n",
+ " <td>known</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>19.311203</td>\n",
+ " <td>-0.442169</td>\n",
+ " <td>-0.779103</td>\n",
+ " <td>-0.483327</td>\n",
+ " <td>1.395438</td>\n",
+ " <td>6.152684</td>\n",
+ " <td>-3.120951</td>\n",
+ " <td>3.421103</td>\n",
+ " <td>-6.234009</td>\n",
+ " <td>0.382977</td>\n",
+ " <td>...</td>\n",
+ " <td>1.119721</td>\n",
+ " <td>-1.134392</td>\n",
+ " <td>-3.988314</td>\n",
+ " <td>-1.300758</td>\n",
+ " <td>-3.329110</td>\n",
+ " <td>-3.342161</td>\n",
+ " <td>0.435587</td>\n",
+ " <td>0.628139</td>\n",
+ " <td>0</td>\n",
+ " <td>known</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>3.666947</td>\n",
+ " <td>-2.224134</td>\n",
+ " <td>11.881984</td>\n",
+ " <td>-1.441697</td>\n",
+ " <td>1.174212</td>\n",
+ " <td>-0.457986</td>\n",
+ " <td>0.906805</td>\n",
+ " <td>-2.365767</td>\n",
+ " <td>-1.361884</td>\n",
+ " <td>-6.902156</td>\n",
+ " <td>...</td>\n",
+ " <td>0.093747</td>\n",
+ " <td>2.505970</td>\n",
+ " <td>1.091138</td>\n",
+ " <td>-2.456901</td>\n",
+ " <td>1.649820</td>\n",
+ " <td>3.129997</td>\n",
+ " <td>-3.622472</td>\n",
+ " <td>-2.044070</td>\n",
+ " <td>11</td>\n",
+ " <td>known</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>0.650620</td>\n",
+ " <td>24.235085</td>\n",
+ " <td>-1.362441</td>\n",
+ " <td>-0.827639</td>\n",
+ " <td>-0.463138</td>\n",
+ " <td>2.897365</td>\n",
+ " <td>-2.437450</td>\n",
+ " <td>5.379138</td>\n",
+ " <td>-1.233105</td>\n",
+ " <td>-1.137817</td>\n",
+ " <td>...</td>\n",
+ " <td>1.109354</td>\n",
+ " <td>0.904165</td>\n",
+ " <td>2.969673</td>\n",
+ " <td>-3.313163</td>\n",
+ " <td>-0.012695</td>\n",
+ " <td>-2.557245</td>\n",
+ " <td>-4.001384</td>\n",
+ " <td>-1.676884</td>\n",
+ " <td>1</td>\n",
+ " <td>known</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>5 rows × 72 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " logit_0 logit_1 logit_2 logit_3 logit_4 logit_5 logit_6 \\\n",
+ "0 -2.440972 -4.758019 6.184047 1.529722 -5.321949 -4.473522 0.304163 \n",
+ "1 -1.794608 4.166612 -5.779216 -2.639845 1.387825 2.357318 1.094379 \n",
+ "2 19.311203 -0.442169 -0.779103 -0.483327 1.395438 6.152684 -3.120951 \n",
+ "3 3.666947 -2.224134 11.881984 -1.441697 1.174212 -0.457986 0.906805 \n",
+ "4 0.650620 24.235085 -1.362441 -0.827639 -0.463138 2.897365 -2.437450 \n",
+ "\n",
+ " logit_7 logit_8 logit_9 ... logit_62 logit_63 logit_64 logit_65 \\\n",
+ "0 -5.695143 2.027558 2.920707 ... 3.465998 -4.818081 1.917040 5.784508 \n",
+ "1 -0.949892 -4.485704 -3.085227 ... -4.224628 1.552391 3.057348 -0.010166 \n",
+ "2 3.421103 -6.234009 0.382977 ... 1.119721 -1.134392 -3.988314 -1.300758 \n",
+ "3 -2.365767 -1.361884 -6.902156 ... 0.093747 2.505970 1.091138 -2.456901 \n",
+ "4 5.379138 -1.233105 -1.137817 ... 1.109354 0.904165 2.969673 -3.313163 \n",
+ "\n",
+ " logit_66 logit_67 logit_68 logit_69 class type \n",
+ "0 0.776843 -5.423034 -0.688107 -4.291896 19 known \n",
+ "1 0.257023 4.879888 -1.197542 -3.567023 29 known \n",
+ "2 -3.329110 -3.342161 0.435587 0.628139 0 known \n",
+ "3 1.649820 3.129997 -3.622472 -2.044070 11 known \n",
+ "4 -0.012695 -2.557245 -4.001384 -1.676884 1 known \n",
+ "\n",
+ "[5 rows x 72 columns]"
+ ]
+ },
+ "execution_count": 22,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# prep the data for the energy threshold\n",
+ "energy_df = master_dfs[0].copy()\n",
+ "energy_df = energy_df.drop(columns=[\"rand_set_member\", \"cent_set_member\", \"pred_class\"] + [col for col in energy_df.columns if col.startswith(\"feat_\")])\n",
+ "energy_df = energy_df.dropna(how=\"all\", axis=1)\n",
+ "energy_df.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "2ae4472a5ca84c3caa06f5b18dccc144",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Calculating Energy: 0%| | 0/50000 [00:00<?, ?row/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>class</th>\n",
+ " <th>type</th>\n",
+ " <th>energy</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>19</td>\n",
+ " <td>known</td>\n",
+ " <td>-25.081650</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>29</td>\n",
+ " <td>known</td>\n",
+ " <td>-24.432392</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>0</td>\n",
+ " <td>known</td>\n",
+ " <td>-19.311225</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>11</td>\n",
+ " <td>known</td>\n",
+ " <td>-18.396820</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>1</td>\n",
+ " <td>known</td>\n",
+ " <td>-24.235085</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " class type energy\n",
+ "0 19 known -25.081650\n",
+ "1 29 known -24.432392\n",
+ "2 0 known -19.311225\n",
+ "3 11 known -18.396820\n",
+ "4 1 known -24.235085"
+ ]
+ },
+ "execution_count": 23,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Calculate energy for each row\n",
+ "def get_energy(row):\n",
+ " logit_columns = [col for col in row.index if col.startswith(\"logit_\")]\n",
+ " logits_only = row[logit_columns]\n",
+ " logits_only = logits_only.dropna()\n",
+ " logits_only = logits_only.astype(float)#\n",
+ " \n",
+ " return -np.log(np.sum(np.exp(logits_only)))\n",
+ "\n",
+ "# Calculate energy for each row\n",
+ "tqdm.pandas(desc=\"Calculating Energy\", unit=\"row\", leave=False)\n",
+ "energy_df[\"energy\"] = energy_df.progress_apply(get_energy, axis=1)\n",
+ "\n",
+ "# get rid of logits now\n",
+ "energy_df = energy_df.drop(columns=[col for col in energy_df.columns if col.startswith(\"logit_\")])\n",
+ "energy_df.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Energy-based GMM Clustering"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>Sample Type</th>\n",
+ " <th>Correct</th>\n",
+ " <th>Incorrect</th>\n",
+ " <th>Total</th>\n",
+ " <th>Identification Rate (%)</th>\n",
+ " <th>Misidentification Rate (%)</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>Known</td>\n",
+ " <td>33915</td>\n",
+ " <td>1085</td>\n",
+ " <td>35000</td>\n",
+ " <td>96.9</td>\n",
+ " <td>3.1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>Novel</td>\n",
+ " <td>7680</td>\n",
+ " <td>7320</td>\n",
+ " <td>15000</td>\n",
+ " <td>51.2</td>\n",
+ " <td>48.8</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " Sample Type Correct Incorrect Total Identification Rate (%) \\\n",
+ "0 Known 33915 1085 35000 96.9 \n",
+ "0 Novel 7680 7320 15000 51.2 \n",
+ "\n",
+ " Misidentification Rate (%) \n",
+ "0 3.1 \n",
+ "0 48.8 "
+ ]
+ },
+ "execution_count": 24,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# create a GMM using the energy and the energy_df data\n",
+ "from sklearn.mixture import GaussianMixture\n",
+ "gmm = GaussianMixture(n_components=2, random_state=SEED, max_iter=1000, init_params=\"kmeans\", tol=1e-4)\n",
+ "gmm.fit(energy_df[[\"energy\"]])\n",
+ "energy_df[\"cluster\"] = gmm.predict(energy_df[[\"energy\"]])\n",
+ "means = gmm.means_.flatten()\n",
+ "# If the mean of cluster 0 is higher than cluster 1, swap the cluster labels\n",
+ "if means[0] > means[1]:\n",
+ " df['cluster'] = df['cluster'].apply(lambda x: 1 if x == 0 else 0)\n",
+ " \n",
+ "\n",
+ "# if a known sample has cluster 0 it is correct, if a novel sample has cluster 1 it is correct\n",
+ "def get_correctness_energy(row):\n",
+ " if row[\"type\"] == \"known\":\n",
+ " return row[\"cluster\"] == 0\n",
+ " elif row[\"type\"] == \"novel\":\n",
+ " return row[\"cluster\"] == 1\n",
+ " else:\n",
+ " raise ValueError(f\"Invalid type {row['type']}, index {row.name}\")\n",
+ "\n",
+ "# Apply correctness logic\n",
+ "energy_df[\"correct\"] = energy_df.apply(get_correctness_energy, axis=1)\n",
+ "\n",
+ "# Create the output dataframe\n",
+ "out_df = out_df_template.copy()\n",
+ "# Calculate the accuracy for known and novel samples\n",
+ "for sample_type in [\"known\", \"novel\"]:\n",
+ " filtered_df = energy_df[energy_df[\"type\"] == sample_type]\n",
+ " out_dict = {\n",
+ " \"Sample Type\": sample_type.capitalize(),\n",
+ " \"Correct\": filtered_df[\"correct\"].sum(),\n",
+ " \"Incorrect\": len(filtered_df) - filtered_df[\"correct\"].sum(),\n",
+ " \"Total\": len(filtered_df),\n",
+ " \"Identification Rate (%)\": (filtered_df[\"correct\"].sum() / len(filtered_df)) * 100,\n",
+ " \"Misidentification Rate (%)\": ((len(filtered_df) - filtered_df[\"correct\"].sum()) / len(filtered_df)) * 100,\n",
+ " }\n",
+ " out_df = pd.concat([out_df, pd.DataFrame(out_dict, index=[0])]) if not out_df.empty else pd.DataFrame(out_dict, index=[0])\n",
+ "\n",
+ "out_df\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Plot the Distributions of Energy for Each Type"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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+ "text/plain": [
+ "<Figure size 2000x800 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import seaborn as sns\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "from scipy.stats import norm\n",
+ "\n",
+ "# Assuming energy_df is already defined\n",
+ "grouped = energy_df.groupby(\"type\")\n",
+ "x_vals = np.linspace(energy_df[\"energy\"].min(), energy_df[\"energy\"].max(), 1000)\n",
+ "\n",
+ "# Create subplots\n",
+ "fig, axs = plt.subplots(1, 2, figsize=(20, 8))\n",
+ "\n",
+ "# Plot 1: Energy distribution by sample type using PDF\n",
+ "for name, group in grouped:\n",
+ " mean = group[\"energy\"].mean()\n",
+ " std = group[\"energy\"].std()\n",
+ " pdf_vals = norm.pdf(x_vals, mean, std)\n",
+ " \n",
+ " axs[0].plot(x_vals, pdf_vals, label=f\"{name.capitalize()}\", linewidth=2)\n",
+ " axs[0].fill_between(x_vals, pdf_vals, alpha=0.3)\n",
+ "\n",
+ "axs[0].set_xlabel(\"Energy\")\n",
+ "axs[0].set_ylabel(\"Density\")\n",
+ "axs[0].set_title(\"Energy: Normal Distribution\")\n",
+ "axs[0].legend()\n",
+ "axs[0].grid()\n",
+ "\n",
+ "# Plot 2: KDE plot of energy between known and novel samples\n",
+ "sns.kdeplot(\n",
+ " energy_df[energy_df[\"type\"] == \"known\"][\"energy\"],\n",
+ " label=\"Known\",\n",
+ " color=\"blue\",\n",
+ " fill=True,\n",
+ " ax=axs[1]\n",
+ ")\n",
+ "\n",
+ "sns.kdeplot(\n",
+ " energy_df[energy_df[\"type\"] == \"novel\"][\"energy\"],\n",
+ " label=\"Novel\",\n",
+ " color=\"orange\",\n",
+ " fill=True,\n",
+ " ax=axs[1]\n",
+ ")\n",
+ "\n",
+ "axs[1].set_xlabel(\"Energy\")\n",
+ "axs[1].set_ylabel(\"Density\")\n",
+ "axs[1].set_title(\"Energy: KDE Distribution\")\n",
+ "axs[1].legend()\n",
+ "axs[1].grid()\n",
+ "\n",
+ "# Adjust layout for better spacing\n",
+ "plt.tight_layout()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Test Entropy-based GMM OOD Detection"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Entropy-based GMM Clustering"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>Sample Type</th>\n",
+ " <th>Correct</th>\n",
+ " <th>Incorrect</th>\n",
+ " <th>Total</th>\n",
+ " <th>Identification Rate (%)</th>\n",
+ " <th>Misidentification Rate (%)</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>Known</td>\n",
+ " <td>28322</td>\n",
+ " <td>6678</td>\n",
+ " <td>35000</td>\n",
+ " <td>80.920000</td>\n",
+ " <td>19.080000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>Novel</td>\n",
+ " <td>13703</td>\n",
+ " <td>1297</td>\n",
+ " <td>15000</td>\n",
+ " <td>91.353333</td>\n",
+ " <td>8.646667</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " Sample Type Correct Incorrect Total Identification Rate (%) \\\n",
+ "0 Known 28322 6678 35000 80.920000 \n",
+ "0 Novel 13703 1297 15000 91.353333 \n",
+ "\n",
+ " Misidentification Rate (%) \n",
+ "0 19.080000 \n",
+ "0 8.646667 "
+ ]
+ },
+ "execution_count": 26,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from sklearn.mixture import GaussianMixture\n",
+ "\n",
+ "gmm_entropy = entropy_df[[\"class\", \"type\", \"entropy\"]].copy()\n",
+ "\n",
+ "# create a GMM using the entropy and the entropy_df data\n",
+ "gmm = GaussianMixture(n_components=2, random_state=SEED, max_iter=1000, init_params=\"kmeans\", tol=1e-4)\n",
+ "gmm.fit(gmm_entropy[[\"entropy\"]])\n",
+ "gmm_entropy[\"cluster\"] = gmm.predict(gmm_entropy[[\"entropy\"]])\n",
+ "means = gmm.means_.flatten()\n",
+ "# If the mean of cluster 0 is higher than cluster 1, swap the cluster labels\n",
+ "if means[0] > means[1]:\n",
+ " gmm_entropy['cluster'] = gmm_entropy['cluster'].apply(lambda x: 1 if x == 0 else 0)\n",
+ "\n",
+ "# if a known sample has cluster 0 it is correct, if a novel sample has cluster 1 it is correct\n",
+ "def get_correctness_entropy_cluster(row):\n",
+ " if row[\"type\"] == \"known\":\n",
+ " return row[\"cluster\"] == 0\n",
+ " elif row[\"type\"] == \"novel\":\n",
+ " return row[\"cluster\"] == 1\n",
+ " else:\n",
+ " raise ValueError(f\"Invalid type {row['type']}, index {row.name}\")\n",
+ " \n",
+ "# Apply correctness logic\n",
+ "gmm_entropy[\"correct\"] = gmm_entropy.apply(get_correctness_entropy_cluster, axis=1)\n",
+ "\n",
+ "# Create the output dataframe\n",
+ "out_df = out_df_template.copy()\n",
+ "# Calculate the accuracy for known and novel samples\n",
+ "\n",
+ "for sample_type in [\"known\", \"novel\"]:\n",
+ " filtered_df = gmm_entropy[gmm_entropy[\"type\"] == sample_type]\n",
+ " out_dict = {\n",
+ " \"Sample Type\": sample_type.capitalize(),\n",
+ " \"Correct\": filtered_df[\"correct\"].sum(),\n",
+ " \"Incorrect\": len(filtered_df) - filtered_df[\"correct\"].sum(),\n",
+ " \"Total\": len(filtered_df),\n",
+ " \"Identification Rate (%)\": (filtered_df[\"correct\"].sum() / len(filtered_df)) * 100,\n",
+ " \"Misidentification Rate (%)\": ((len(filtered_df) - filtered_df[\"correct\"].sum()) / len(filtered_df)) * 100,\n",
+ " }\n",
+ " out_df = pd.concat([out_df, pd.DataFrame(out_dict, index=[0])]) if not out_df.empty else pd.DataFrame(out_dict, index=[0])\n",
+ "\n",
+ "out_df\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Plot the Distributions of Entropy for Each Type"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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imUELJVNEapydEJFnQVyw2WwacnQnvbZsiyTptS83a9o5fU2uCgAAAMDhEhMTZbfbtW3bNmVnZysxMVG2Vl5+KVZ4vV7V1NTo4MGDsttD+xKyYRiqqanRrl27ZLfblZiY2EpVAm3DMA4F4zk5vt8lJdLpp5tXEwAAAGAGp9Mpm82mXbt2KTs727Jj7JaMec0Q6XE2wThCcmrPjvrH8q2q9Rr6x/Ituml07zabsQ4AAACgeex2u3r06KHt27dr27ZtZpdjKYZhqLq6WikpKWH/oSM1NVXdunWzxB8ZgCNxuw/dTkmRsrOZMQ4AAID45HA41LVrV23ZskUbNmwwu5ywRWLMa4ZIjbMJxhGS9slODSrI1LKN+7S7okaLV+7U2f3zzC4LAAAAwGESExPVrVs3eTwe1dbWml2OZbjdbi1dulRDhgyR0+kM+XiHw6GEhARL/YEBaIx/trhffj7BOAAAAOJXWlqajj76aLnrfoPUYlo65jVDJMfZBOMI2ZlHd9KyjfskSW8u30owDgAAAEQpm80mp9NpmcFuNHA4HPJ4PEpOTuZzQ9w7PBjPyyMYBwAAQHxzOBxyOBxmlxG2eB/z0tcNIeubm66MFN8/liWrd+lAtXW/GQMAAAAAABp2eDDepQvBOAAAAADrIhhHyOx2m04s7CBJqqn16v3vd5hcEQAAAAAAiDSXK/h+ly7Svn3S3r3m1AMAAAAALWFqML506VKNGzdO+fn5stlsWrBgwRH3nzRpkmw2W72ffv36Bfa544476j3ep0+fVn4n8efkHlmB2//6ZpuJlQAAAAAAgNbQ0IxxSSopaftaAAAAAKClTA3GKysrNWDAAM2ZM6dZ+z/88MPavn174Gfz5s3KysrSL37xi6D9+vXrF7TfJ5980hrlx7WendqpU1qiJOnT4t3aVe5q4ggAAAAAAGAlhwfj+fm+3wTjAAAAAKwowcwXHzNmjMaMGdPs/TMyMpSRkRG4v2DBAu3bt0+TJ08O2i8hIUG5ubkRqxP12Ww2ndwjS+9+u0NeQ3rnf9s06fQeZpcFAAAAAAAi5PBgPC1NysxknXEAAAAA1mRqMN5Szz77rEaMGKHCwsKg7WvXrlV+fr6Sk5N16qmnatasWerWrVujz+NyueSqs3BWWVmZJMntdsvtdrdO8RHgr83w1pry+id3y9C73/rWF1+4YqsuO7mrKXUgdP5rJ5qvb0Qnrh2Ei2sH4eLaQbi4dhCu1r52uCZhJYcH45KvnTrBOAAAAAArsmwwvm3bNr333nuaN29e0PbBgwdr7ty56t27t7Zv366ZM2fqzDPP1Hfffaf27ds3+FyzZs3SzJkz621ftGiRUlNTW6X+SKre8LUpr9tBUl6KQ9urbfp68wHN++e7ykwypRSEqaioyOwSYFFcOwgX1w7CxbWDcHHtIFytde1UVVW1yvMCrcHVwKppubnSunVtXwsAAAAAtJRlg/EXXnhBmZmZuuCCC4K2123Nfvzxx2vw4MEqLCzUa6+9pquuuqrB55o2bZqmTp0auF9WVqaCggKNGjVK6enprVJ/JLjdbhUVFSml+yDZ7A5TajixYof+9eOscSO/v84Z3PjMfEQP/7UzcuRIOZ1Os8uBhXDtIFxcOwgX1w7CxbWDcLX2tePvUAZYQUMzxlNSpH372r4WAAAAAGgpSwbjhmHoueee0xVXXKHExMQj7puZmaljjjlGxUfo85WUlKSkpPpTnZ1OpyX+iGazO2RzmHMqBxVmBYLxxat3a9IZR5lSB8JjlWsc0YdrB+Hi2kG4uHYQLq4dhKu1rh2uR1hJQ8G40ymxIgAAAAAAK7KbXUA4Pv74YxUXFzc6A7yuiooKlZSUKC8vrw0qiz8FHVLUsZ3vywn/XbdHZQcZHQMAAAAAEAsaCsYdDoJxAAAAANZkajBeUVGhFStWaMWKFZKk9evXa8WKFdq0aZMkX4vzK6+8st5xzz77rAYPHqz+/fvXe+ymm27Sxx9/rA0bNug///mPLrzwQjkcDk2YMKFV30u8stlsGliQKUly1xpasnqXuQUBAAAAAICIaGiN8YSEhgNzAAAAAIh2pgbjy5Yt06BBgzRo0CBJ0tSpUzVo0CBNnz5dkrR9+/ZASO534MAB/eMf/2h0tviWLVs0YcIE9e7dW+PHj1fHjh313//+V9nZ2a37ZuLYoG6ZgduLvt9hXiEAAAAAACBiaKUOAAAAIJaYusb4sGHDZBhGo4/PnTu33raMjAxVVVU1eswrr7wSidIQgqM7t1dqokNVNbX6ePUu1Xi8SkywZJd+AAAAAADwI1qpAwAAAIglpJdoMYfdpuO7ZkiSyl0e/XfdHpMrAgAAAAAALVVT42udXhet1AEAAABYFcE4ImJQQYfA7Q9W7jSxEgAAAAAAEAk1Nb7W6XXRSh0AAACAVRGMIyL65afLYbNJkpau2WVyNQAAAAAAoKVcrvozxh0OyeMxpx4AAAAAaAmCcUREstOhozq3kyRt2FOlDbsrTa4IAAAAAAC0REMzxhMSmDEOAAAAwJoIxhEx/fMzAreXrmXWOAAAAAAAVtZYMO7xSIZhTk0AAAAAEC6CcURMUDBOO3UAAAAAACytpqZ+K3X/fWaNAwAAALAagnFETNesFKUn+0bI/ynZoxqP1+SKAAAAAABNWbp0qcaNG6f8/HzZbDYtWLAg8Jjb7dYtt9yi4447Tu3atVN+fr6uvPJKbdu2zbyC0WYIxgEAAADEEoJxRIzdZlO/H2eNV9XUatnGvSZXBAAAAABoSmVlpQYMGKA5c+bUe6yqqkrLly/X7bffruXLl+vNN9/U6tWrdd5555lQKdqay9V4MF5T0/b1AAAAAEBLJDS9C9B8/fPT9dm6PZKkj9fs0mlHdTK5IgAAAADAkYwZM0Zjxoxp8LGMjAwVFRUFbXvsscd08skna9OmTerWrVtblAiTNLbGuMSMcQAAAADWQzCOiDo2P102SYakj1fv0rQxfc0uCQAAAAAQQQcOHJDNZlNmZmaj+7hcLrlcrsD9srIySb7W7G4SVcvweqV27XznyzB8vxMTpZQU6eBBwvFY5/+3yr/Z+MJ5j1+c+/jFuY9PnPf4FYvnPpT3QjCOiGqf7FRhx1Rt2FOlVTvKVVp+UJ3bJ5tdFgAAAAAgAg4ePKhbbrlFEyZMUHp6eqP7zZo1SzNnzqy3fdGiRUpNTW3NEhFBdTvml5f7Ogccc4w0f7709de+H8S+w7tGID5w3uMX5z5+ce7jE+c9fsXSua+qqmr2vgTjiLhj89K1YY/vIvysZI/OH9jF5IoAAAAAAC3ldrs1fvx4GYahJ5544oj7Tps2TVOnTg3cLysrU0FBgUaNGnXEQB3R5fLLpT173LruuiK1bz9SNptT334r3Xqr9NVXUq9eZleI1uR2u1VUVKSRI0fKeXhPfcQsznv84tzHL859fOK8x69YPPf+DmXNQTCOiOuTm653v9shiWAcAAAAAGKBPxTfuHGjPvzwwybD7aSkJCUlJdXb7nQ6Y+aPL/GgstLXTl2SbDanbDanHA6putq3nVMZH/h3G5847/GLcx+/OPfxifMev2Lp3IfyPgjGEXFHdW6nBLtNHq+hz9btMbscAAAAAEAL+EPxtWvX6qOPPlLHjh3NLgltxOWSEg77y5H/fk1N29cDAAAAAC1BMI6IS0pwqGd2O63ZWaGNe6q0ZV+VunZgDTkAAAAAiEYVFRUqLi4O3F+/fr1WrFihrKws5eXl6aKLLtLy5cv19ttvq7a2Vjt2+DqEZWVlKTEx0ayy0QZqaqS0tOBt/mDc7W77egAAAACgJexmF4DY1Cf3UFu9z0qYNQ4AAAAA0WrZsmUaNGiQBg0aJEmaOnWqBg0apOnTp2vr1q166623tGXLFg0cOFB5eXmBn//85z8mV47WVlPT+IxxgnEAAAAAVsOMcbSKPrnt9dY3vtufrdujX5xYYG5BAAAAAIAGDRs2TIZhNPr4kR5DbHO56q8jTit1AAAAAFbFjHG0ih6d2inR4bu8PivZwx9SAAAAAACwGGaMAwAAAIglBONoFU6HXb06+xYi237goDbsqTK5IgAAAAAAEAqCcQAAAACxhGAcraZPbvvAbdYZBwAAAADAWmpqaKUOAAAAIHYQjKPVBAXj6wjGAQAAAACwEpeLGeMAAAAAYgfBOFpNt46pSkzwXWJfrt/LOuMAAAAAAFjIkWaME4wDAAAAsBqCcbSaBLtdR3VqJ0naUXZQW/ZVm1wRAAAAAABoLrdbcjiCt9FKHQAAAIBVEYyjVR2dc6id+pcb9ppYCQAAAAAACEVDM8YdDslmY8Y4AAAAAOshGEerOrpzWuA2wTgAAAAAANZgGA0H45Jv1jjBOAAAAACrIRhHq+qZ3U4Om02S9MV6gnEAAAAAAKzA4/GF4/7W6XU5nbRSBwAAAGA9BONoVUkJDhV2TJUkleyq1J4Kl8kVAQAAAACApviD74aCcWaMAwAAALAignG0ul512qkv27jPxEoAAAAAAEBzEIwDAAAAiDUE42h1QeuM004dAAAAAICo5w/GExPrP5aQQCt1AAAAANZDMI5Wd3Tn9oHbX24gGAcAAAAAINq5flwJjRnjAAAAAGIFwThaXVpygvIzkiVJ320rU6XLY3JFAAAAAADgSGilDgAAACDWEIyjTfjXGa/1GvrflgMmVwMAAAAAAI6kqWCcVuoAAAAArIZgHG3iqOxD64x/vXmfiZUAAAAAAICm+INvp7P+Y8wYBwAAAGBFBONoEz2z2wVuL9+437xCAAAAAABAkwjGAQAAAMQagnG0iZz0ZKUmOiRJKzbvk2EYJlcEAAAAAAAa43L5fjsc9R9zOGilDgAAAMB6CMbRJuw2m3p28s0a311Roy37qk2uCAAAAAAANIYZ4wAAAABiDcE42kzPOuuML9/EOuMAAAAAAEQrfzCekFD/MYJxAAAAAFZEMI42458xLklfb9pvXiEAAAAAAOCI/MF4YmL9x2ilDgAAAMCKCMbRZnpm1w3GmTEOAAAAAEC0OtIa404nM8YBAAAAWA/BONpMamKC8jKSJUnfbyvTQXetyRUBAAAAAICGHGmNcWaMAwAAALAignG0KX87dY/X0HdbD5hcDQAAAAAAaIg/+G5oxnhCAsE4AAAAAOshGEeb6pmdFrjNOuMAAAAAAESnmhpfKN5YME4rdQAAAABWQzCONnVU3XXGN7POOAAAAAAA0aimpuE26hLBOAAAAABrIhhHm8rPSFFSgu+yW75xv7nFAAAAAACABrlcRw7GaaUOAAAAwGoIxtGm7Habevy4zviOsoPafqDa5IoAAAAAAMDhmDEOAAAAINYQjKPN9azbTp11xgEAAAAAiDo1Nb4AvCEE4wAAAACsiGAcbe6o7LTA7a83sc44AAAAAADRpqkZ47RSBwAAAGA1BONoc907Hpox/u3WAyZWAgAAAAAAGtLUGuPMGAcAAABgNQTjaHMZKU51SPWNrr/bWiav1zC5IgAAAAAAUBdrjAMAAACINQTjMEX3Tr5Z4xUuj9btrjS5GgAAAAAAUFdTa4zTSh0AAACA1RCMwxTB7dT3m1cIAAAAAACop6lg3ONp23oAAAAAoKUIxmGK7h1TA7e/2cw64wAAAAAARBNaqQMAAACINQTjMEVh0IxxgnEAAAAAAKKJy9X0jHHDaNuaAAAAAKAlCMZhirSkBGWnJUmSvt92QJ5ar8kVAQAAAAAAv6ZaqUvMGgcAAABgLQTjME3hj+3UD7q9Kt5VYXI1AAAAAADAz+U6cit1iWAcAAAAgLUQjMM0PTodaqf+P9YZBwAAAAAgajRnxnhNTdvVAwAAAAAtRTAO0/hnjEvS/7buN68QAAAAAAAQpKaGGeMAAAAAYgvBOExTmNVOth9vf7uFGeMAAAAAAEQLWqkDAAAAiDUE4zBNSqJDORnJkqSV28tV4/GaXBEAAAAAAJBopQ4AAAAg9hCMw1Tdf2ynXlPr1eod5SZXAwAAAAAAJFqpAwAAAIg9BOMwVfeO7QK3WWccAAAAAIDo0JwZ4wTjAAAAAKyEYBymqhuMs844AAAAAADRweWSEhMbfoxW6gAAAACsiGAcpirISpHN5rv9DcE4AAAAAABRgRnjAAAAAGINwThMlZTgUH5GiiRp7c5yuTy1JlcEAAAAAABYYxwAAABArCEYh+m6ZaVKkjxeQ2t2VJhcDQAAAAAAcLubnjFOK3UAAAAAVkIwDtMVZKUEbn+/jXbqAAAAAACY7Ugzxh0O329mjAMAAACwEoJxmM4/Y1ySvt9WZmIlAAAAAADA45G83saDcf92gnEAAAAAVkIwDtMFB+PMGAcAAAAAwEz+FulNtVInGAcAAABgJQTjMF1qYoKy05IkSSu3l6vWa5hcEQAAAAAA8csfjDfVSp01xgEAAABYCcE4ooJ/1ni1u1brd1eaXA0AAAAAAPGrqRnjtFIHAAAAYEUE44gKBVkpgdu0UwcAAAAAwDwul+93YzPGaaUOAAAAwIoIxhEVgtcZLzOxEgAAAAAA4ltTrdTt9uD9AAAAAMAKCMYRFYKDcWaMAwAAAABglqZaqdtsvtCcGeMAAAAArIRgHFEhMzVR6cm+Eff328pkGIbJFQEAAAAAEJ+amjEu+UJzgnEAAAAAVkIwjqjhnzW+v8qtbQcOmlwNAAAAAADxqakZ4/7HaKUOAAAAwEoIxhE1unWs0059K+3UAQAAAAAwg8vl+52Y2Pg+tFIHAAAAYDWmBuNLly7VuHHjlJ+fL5vNpgULFhxx/yVLlshms9X72bFjR9B+c+bMUffu3ZWcnKzBgwfriy++aMV3gUipu874d9vKTKwEAAAAAID41dwZ4wTjAAAAAKzE1GC8srJSAwYM0Jw5c0I6bvXq1dq+fXvgp3PnzoHHXn31VU2dOlUzZszQ8uXLNWDAAI0ePVqlpaWRLh8RVjcY/2EbM8YBAAAAADBDc9YYdzhopQ4AAADAWo7w3d/WN2bMGI0ZMybk4zp37qzMzMwGH3vwwQd19dVXa/LkyZKkJ598Uu+8846ee+453XrrrS0pF62sU1qSUpwOVbtr9T0zxgEAAAAAMEVzZozTSh0AAACA1ZgajIdr4MCBcrlc6t+/v+644w6dfvrpkqSamhp99dVXmjZtWmBfu92uESNG6LPPPmv0+Vwul1z+BbQklZX5Qlm32y13FI/y/LUZ3lqTK4kMm6SCDslaU1qp7QcOauf+SmW1O8KCZgib/9qJ5usb0YlrB+Hi2kG4uHYQLq4dhKu1rx2uSViB/08kR5oxTit1AAAAAFZjqWA8Ly9PTz75pE488US5XC4988wzGjZsmD7//HOdcMIJ2r17t2pra5WTkxN0XE5OjlatWtXo886aNUszZ86st33RokVKTU1t4IjoUr3ha7NLiJg8u11rfuzwP3fhYvXJNEyuKLYVFRWZXQIsimsH4eLaQbi4dhAurh2Eq7WunaqqqlZ5XiCSaKUOAAAAIBZZKhjv3bu3evfuHbh/2mmnqaSkRA899JD+/ve/h/2806ZN09SpUwP3y8rKVFBQoFGjRik9Pb1FNbcmt9utoqIipXQfJJvdYXY5EdHD2KOPd2yWJGUU9tU5p3c3t6AY5b92Ro4cKeeR/tIBHIZrB+Hi2kG4uHYQLq4dhKu1rx1/hzIgmtFKHQAAAEAsslQw3pCTTz5Zn3zyiSSpU6dOcjgc2rlzZ9A+O3fuVG5ubqPPkZSUpKSkpHrbnU6nJf6IZrM7ZHNY/lRKkgo6pgVurymttMTnb2VWucYRfbh2EC6uHYSLawfh4tpBuFrr2uF6hBXU1Eh2u29WuNFIIzeHg2AcAAAAgLXYzS6gpVasWKG8vDxJUmJion7yk59o8eLFgce9Xq8WL16sU0891awSEYL8zBTZbb7bK7eXm1sMAAAAAABxqKbmyG3UJd9sclqpAwAAALASU6cZV1RUqLi4OHB//fr1WrFihbKystStWzdNmzZNW7du1YsvvihJmj17tnr06KF+/frp4MGDeuaZZ/Thhx9q0aJFgeeYOnWqJk6cqBNPPFEnn3yyZs+ercrKSk2ePLnN3x9C53TYlZuerG0HDqq4tFzuWq+cDst/fwMAAAAAAMtwuZoXjDNjHAAAAICVmBqML1u2TMOHDw/c96/zPXHiRM2dO1fbt2/Xpk2bAo/X1NToD3/4g7Zu3arU1FQdf/zx+uCDD4Ke4+KLL9auXbs0ffp07dixQwMHDtS///1v5eTktN0bQ4t07ZCqbQcOyl1rqGRXhfrkRu867wAAAAAAxJrmzBinlToAAAAAqzE1GB82bJiMxharkjR37tyg+zfffLNuvvnmJp/32muv1bXXXtvS8mCSrh1S9MUG3+1V28sJxgEAAAAAaEO0UgcAAAAQi+hRjajTtUNK4PbKHWUmVgIAAAAAQPypqfEF30dCMA4AAADAagjGEXUKslIDt1duLzexEgAAAAAA4g9rjAMAAACIRQTjiDqZKU61S3RIklZtZ8Y4AAAAAABtiVbqAAAAAGIRwTiijs1mC8waLy13aU+Fy+SKAAAAAACIH81tpc6McQAAAABWQjCOqFR3nfFVO2inDgAAAABAW2nujHGCcQAAAABWQjCOqNS1Q911xmmnDgAAAABAW2nujHFaqQMAAACwEoJxRKW6M8ZXbmfGOAAAAAAAbcXlopU6AAAAgNhDMI6o1CUzRTab7zYzxgEAAAAAaDu0UgcAAAAQiwjGEZWcDrty05MlScWlFXLXek2uCAAAAACA+NDcGeO0UgcAAABgJQTjiFoFP64zXlPr1bpdlSZXAwAAAABAfGjuGuPMGAcAAABgJQTjiFp11xlftYN26gAAAAAAtAWXi1bqAAAAAGIPwTiiVt1gfOX2chMrAQAAAAAgfjRnjXGHg2AcAAAAgLUQjCNqdf2xlbokrdzOjHEAAAAAANpCc4Jxp5NgHAAAAIC1EIwjanVIdSo10SGJVuoAAAAAALSV5q4x7vFIhtE2NQEAAABASxGMI2rZbDYV/DhrfGeZS3sra0yuCAAAAACA2NfcVuoSs8YBAAAAWAfBOKJa3XXGV9FOHQAAAACAVudyNW/GuEQwDgAAAMA6CMYR1QrqrDP+A8E4AAAAAACtrrlrjEsE4wAAAACsg2AcUa1LnRnja3dWmFgJAAAAAADxIZRW6jWsegYAAADAIgjGEdXyM5IDt1fvLDexEgAAAAAA4oPbTSt1AAAAALGHYBxRLcnpUHZakiRp7c5yeb2GyRUBAAAAABDbaKUOAAAAIBYRjCPqdcn0tVOvrKnV1v3VJlcDAAAAAEDsqq31/TQ1Y5xW6gAAAACshmAcUS+/Q5126jtopw4AAAAAQGvxB91NzRinlToAAAAAqyEYR9TzzxiXWGccAAAAAIDW1NxgnFbqAAAAAKyGYBxRr24wvoZgHAAAAAAiaunSpRo3bpzy8/Nls9m0YMGCoMcNw9D06dOVl5enlJQUjRgxQmvXrjWnWLQ6fzBOK3UAAAAAsYZgHFEvNz1ZDptNEq3UAQAAACDSKisrNWDAAM2ZM6fBx++77z498sgjevLJJ/X555+rXbt2Gj16tA4ePNjGlaItuFy+37RSBwAAABBrmvj+L2C+BIddORlJ2rb/oNbtqpS71iung+90AAAAAEAkjBkzRmPGjGnwMcMwNHv2bP35z3/W+eefL0l68cUXlZOTowULFuiSSy5py1LRBmilDgAAACBWEYzDErpkpmjb/oOqqfVq455K9erc3uySAAAAACDmrV+/Xjt27NCIESMC2zIyMjR48GB99tlnjQbjLpdLLv/UY0llZWWSJLfbLTdJalSrrpZSUqTERMkwJMPwnS//bz+Hw7efy0U4Hqv8/1b5NxtfOO/xi3Mfvzj38YnzHr9i8dyH8l4IxmEJXTJT9KX2SZJW76ggGAcAAACANrBjxw5JUk5OTtD2nJycwGMNmTVrlmbOnFlv+6JFi5SamhrZIhFx8+f7fv/4fQZJUnl5UdA+KSm+/Q4elN59tw2LQ5srKipqeifEHM57/OLcxy/OfXzivMevWDr3VVVVzd6XYByW0CUzJXB79c5yjVWeidUAAAAAAI5k2rRpmjp1auB+WVmZCgoKNGrUKKWnp5tYGZry9dfSsGHSo49KPXv6ZoqXlxepffuRstkO9VcvK5MmTJDmzZPGjjWvXrQet9utoqIijRw5Us6meusjZnDe4xfnPn5x7uMT5z1+xeK5L6v7jd4mEIzDEvLrBONrdpSbWAkAAAAAxI/c3FxJ0s6dO5WXd+gLyjt37tTAgQMbPS4pKUlJSUn1tjudzpj540uscrt97dQdDslmO7TdZnMGBeMOh2+/mpqm1yOHtfHvNj5x3uMX5z5+ce7jE+c9fsXSuQ/lfdhbsQ4gYrLTkpTo8F2ua3YSjAMAAABAW+jRo4dyc3O1ePHiwLaysjJ9/vnnOvXUU02sDK2lpsb3u6m/LSX8ONUihpYmBAAAABDjmDEOS7DbbcrLTNbGPVXasKdSB921SnY6zC4LAAAAACyvoqJCxcXFgfvr16/XihUrlJWVpW7duumGG27QX/7yFx199NHq0aOHbr/9duXn5+uCCy4wr2i0muYG4/7HCcYBAAAAWAXBOCyjS2aKNu6pkteQiksr1L9LhtklAQAAAIDlLVu2TMOHDw/c968NPnHiRM2dO1c333yzKisr9etf/1r79+/XGWecoX//+99KTk42q2S0In8wntDEX4zs9uD9AQAAACDaEYzDMrrUWWd89Y5ygnEAAAAAiIBhw4bJMIxGH7fZbLrzzjt15513tmFVMIvL5fvd1Ixxm80XnjNjHAAAAIBVsMY4LKNuMM464wAAAAAARF5zZ4xLvvCcYBwAAACAVRCMwzLy684YJxgHAAAAACDi/MF4YmLT+yYk0EodAAAAgHUQjMMyOqQ6leJ0SJLW7CAYBwAAAAAg0mpqfG3S7c34ixGt1AEAAABYCcE4LMNmswXaqW87cFBlBxl9AwAAAAAQSTU1vhbpNlvT+9JKHQAAAICVEIzDUrp0ONROfS3t1AEAAAAAiCiXyxd4N4fDQSt1AAAAANZBMA5L6VJ3nfEdFSZWAgAAAABA7PHPGG8OWqkDAAAAsBKCcVhK3WB8DTPGAQAAAACIqJoaX+DdHLRSBwAAAGAlBOOwlPzM5MDt1TsIxgEAAAAAiCS3m1bqAAAAAGITwTgspX2yUxkpvhE6M8YBAAAAAIgsj8cXeDcHrdQBAAAAWAnBOCzHP2t8T2WNdle4TK4GAAAAAIDY4XYTjAMAAACITQTjsJygdcZppw4AAAAAQMQwYxwAAABArCIYh+XUDcZXEYwDAAAAABAxocwYZ41xAAAAAFZCMA7Lya8TjK8trTCxEgAAAAAAYgszxgEAAADEKoJxWE5eRnLgdnEpM8YBAAAAAIgUj8cXeDcHwTgAAAAAKyEYh+WkJiaoQ6pTkrRmZ4UMwzC5IgAAAAAAYkOordRdrtatBwAAAAAihWAclpSf4WunfqDarV0VjMIBAAAAAIiEUFqpO53MGAcAAABgHQTjsKS8zDrt1HeyzjgAAAAAAJHAGuMAAAAAYhXBOCzJP2NcktaWEowDAAAAABAJobZSr6lp3XoAAAAAIFIIxmFJ+ZmHgvE1O8tNrAQAAAAAgNjBjHEAAAAAsYpgHJaUl3GolTozxgEAAAAAiIyaGtYYBwAAABCbCMZhSe2SEpSR4pQkFROMAwAAAAAQEaHMGKeVOgAAAAArIRiHZeX/OGt8b2WN9lS4TK4GAAAAAADr83h8LdKbg1bqAAAAAKyEYByWFbzOOLPGAQAAAABoKbebVuoAAAAAYhPBOCyrbjBeXFpuYiUAAAAAAMQGWqkDAAAAiFUE47CsvB9bqUvSWtYZBwAAAACgxUIJxmmlDgAAAMBKCMZhWfkZh2aMr6WVOgAAAAAALRZKK/WEBF+QDgAAAABWQDAOy0pLTlB6coIkZowDAAAAABAJHo8v8G6OhARaqQMAAACwDoJxWJp/nfHdFS7tq2Q0DgAAAABAS4QzY9wwWrcmAAAAAIgEgnFYGuuMAwAAAAAQOaHOGPcfAwAAAADRjmAclha0znhpuYmVAAAAAABgfR5PaDPGJdqpAwAAALAGgnFYWl5mnRnjO5kxDgAAAABAS4TaSt1/DAAAAABEO4JxWFqXTGaMAwAAAAAQKeG0UicYBwAAAGAFBOOwtPbJTqUl+UbizBgHAAAAAKBlaKUOAAAAIFYRjMPy8n9sp15a7tKBKr6mDgAAAABAuDweyd7MvxYxYxwAAACAlRCMw/LyMg61Uy/eRTt1AAAAAADCRSt1AAAAALGKYByWV3ed8TW0UwcAAAAAICyG4Qu5aaUOAAAAIBYRjMPy8jKSA7dZZxwAAAAAgPB4vb7fzBgHAAAAEIsIxmF5+XVmjK8tpZU6AAAAAADh8Hh8v0OdMU4wDgAAAMAKCMZheenJCUpN9I3ai0uZMQ4AAAAAQDj8ATet1AEAAADEIoJxWJ7NZgusM779wEGVHeSr6gAAAAAAhMo/Y5xW6gAAAABiEcE4YkLddcaZNQ4AAAAAQOjCnTFOMA4AAADACgjGERPqrjNevJNgHAAAAACAUIW7xjit1AEAAABYganB+NKlSzVu3Djl5+fLZrNpwYIFR9z/zTff1MiRI5Wdna309HSdeuqpev/994P2ueOOO2Sz2YJ++vTp04rvAtGg7ozxtaXlJlYCAAAAAIA1hRuMM2McAAAAgBWYGoxXVlZqwIABmjNnTrP2X7p0qUaOHKl3331XX331lYYPH65x48bp66+/DtqvX79+2r59e+Dnk08+aY3yEUW61JkxvoYZ4wAAAAAAhIxW6gAAAABiWYKZLz5mzBiNGTOm2fvPnj076P4999yjhQsX6l//+pcGDRoU2J6QkKDc3NxIlQkLyEhxKsXpULW7ljXGAQAAAAAIg3/GeEIz/1pEK3UAAAAAVmJqMN5SXq9X5eXlysrKCtq+du1a5efnKzk5WaeeeqpmzZqlbt26Nfo8LpdLLpcrcL+srEyS5Ha75Y7irz37azO8tSZXEh3yM5JUsrtKW/dXa19FtdKSLH15tyr/tRPN1zeiE9cOwsW1g3Bx7SBcXDsIV2tfO1yTiGbMGAcAAAAQyyydHP6///f/VFFRofHjxwe2DR48WHPnzlXv3r21fft2zZw5U2eeeaa+++47tW/fvsHnmTVrlmbOnFlv+6JFi5Samtpq9UdK9Yavm94pDmTb7Sr5cXWAvy9cpMI0kwuygKKiIrNLgEVx7SBcXDsIF9cOwsW1g3C11rVTVVXVKs8LREKoa4z79yMYBwAAAGAFlg3G582bp5kzZ2rhwoXq3LlzYHvd1uzHH3+8Bg8erMLCQr322mu66qqrGnyuadOmaerUqYH7ZWVlKigo0KhRo5Sent56b6KF3G63ioqKlNJ9kGz2Zo5aY1g3V6n+W7pNktS51wCdc0IXkyuKXv5rZ+TIkXI6nWaXAwvh2kG4uHYQLq4dhItrB+Fq7WvH36EMiEahtlK32Xz70kodAAAAgBVYMhh/5ZVX9Ktf/Uqvv/66RowYccR9MzMzdcwxx6i4uLjRfZKSkpSUlFRvu9PptMQf0Wx2h2wOS57KiMrv0C5we/3eakucO7NZ5RpH9OHaQbi4dhAurh2Ei2sH4Wqta4frEdEs1Fbqki8YZ8Y4AAAAACuwm11AqObPn6/Jkydr/vz5Gjt2bJP7V1RUqKSkRHl5eW1QHcyUl5EcuF1SWmFiJQAAAAAAWE+oM8YlyekkGAcAAABgDaZOM66oqAiayb1+/XqtWLFCWVlZ6tatm6ZNm6atW7fqxRdflORrnz5x4kQ9/PDDGjx4sHbs2CFJSklJUUZGhiTppptu0rhx41RYWKht27ZpxowZcjgcmjBhQtu/QbSpDu0SlZhgV43Hq2KCcQAAAAAAQhLqGuMSrdQBAAAAWIepM8aXLVumQYMGadCgQZKkqVOnatCgQZo+fbokafv27dq0aVNg/7/97W/yeDy65pprlJeXF/j5/e9/H9hny5YtmjBhgnr37q3x48erY8eO+u9//6vs7Oy2fXNoc3abTbnpvlnjm/ZW6aC71uSKAAAAAACwDlqpAwAAAIhlps4YHzZsmAzDaPTxuXPnBt1fsmRJk8/5yiuvtLAqWFleRrI27a2S15A27KlUn9x0s0sCAAAAAMASwp0xTjAOAAAAwAost8Y4cCR11xmnnToAAAAAAM0X7oxxWqkDAAAAsAKCccSUvIyUwO21OwnGAQAAAABoLmaMAwAAAIhlBOOIKfmZdWaM7yIYBwAAAACgufzBeEIIC+8RjAMAAACwCoJxxJTs9kly2GySpBJaqQMAAAAA0Gy0UgcAAAAQywjGEVMS7HZ1Tk+SJK3bXalar2FyRQAAAAAAWAMzxgEAAADEMoJxxJy8DF879RqPV5v3VplcDQAAAAAA1uAPxu0h/LXI4SAYBwAAAGANBOOIOXkZKYHbxbRTBwAAAACgWdxuXygeSjBOK3UAAAAAVkEwjpjjnzEuScW7CMYBAAAAAGgOjye0NuoSrdQBAAAAWAfBOGJOUDDOjHEAAAAAAJrF7fa1Rg8FrdQBAAAAWAXBOGJObvqhYHwtwTgAAAAAAM3i8YQejCckSC5X69QDAAAAAJFEMI6Yk+R0qFNaoiSppLRChmGYXBEAAAAAANEvnFbqDofvOAAAAACIdgTjiEm5P7ZTr3B5tLOMr64DAAAAANCUcFqps8Y4AAAAAKsgGEdMystICdxmnXEAAAAAAJoWzoxxgnEAAAAAVkEwjpiUl3FonfHi0nITKwEAAAAAwBqYMQ4AAAAglhGMIyYFBeO7mDEOAAAAAEBTPJ7Qg3HWGAcAAABgFQTjiEm0UgcAAAAAIDThtFJ3OJgxDgAAAMAaCMYRk9KSEtQ+2TeaLy6tNLkaAAAAAACiH63UAQAAAMQygnHELH879d0VLu2vqjG5GgAAAAAAols4rdQJxgEAAABYBcE4YlY+7dQBAAAAAGg2gnEAAAAAsYxgHDHLP2NcIhgHAAAAAKAp4bRSdzh8gToAAAAARDuCccSsPGaMAwAAAADQbB6PbwZ4KBwOZowDAAAAsAaCccSsvMw6M8Z3EYwDAAAAAHAkbrdkD/EvRQkJzBgHAAAAYA0E44hZmSlOJTt9lzgzxgEAAAAAODLWGAcAAAAQywjGEbNsNlugnfrW/dWqrqk1uSIAAAAAAKIXrdQBAAAAxDKCccS0vAxfO3XDkEpopw4AAAAAQKPc7tBnjDscvjG319s6NQEAAABApBCMI6b5g3GJduoAAAAAABxJOMG4f4Y5s8YBAAAARDuCccQ0fyt1iWAcAAAAAIAjCXeNcYlgHAAAAED0IxhHTGPGOAAAAAAAzcOMcQAAAACxjGAcMS07LUkJdpskqZg1xgEAAAAAaJTHcyjobi5/kE4wDgAAACDaEYwjptntNuWk+2aNb9hdKXet1+SKAAAAAACITuHMGPfv7/FEvh4AAAAAiCSCccQ8fzt1j9fQxj1VJlcDAAAAAEB0CmfGOK3UAQAAAFgFwThiHuuMAwAAAADQNI+HNcYBAAAAxC6CccS8vIyUwO0S1hkHAAAAAKBB4bRSJxgHAAAAYBUE44h5eZmHZoyv3VluYiUAAAAAAESvcGaMs8Y4AAAAAKsgGEfMy01Pls3mu13MjHEAAAAAABoUzoxx//7MGAcAAAAQ7QjGEfOcDruy05IkSSWllfJ6DZMrAgAAAAAg+rDGOAAAAIBYRjCOuJCX4WunXu2u1bYD1SZXAwAAAABA9PF4DgXdzUUwDgAAAMAqCMYRF/IyUgK3i0tppw4AAAAAwOHCaaVOMA4AAADAKgjGERf8M8YlgnEAAAAAABpSWxv6jHF/kO7xRL4eAAAAAIgkgnHEhbrBeMkugnEAAAAAAA4Xzhrj/v2ZMQ4AAAAg2hGMIy7kMmMcAAAAAMJSW1ur22+/XT169FBKSoqOOuoo3XXXXTIMw+zSEGG0UgcAAAAQy0JskAVYU2pigjJTnNpf7dba0goZhiGbzWZ2WQAAAAAQ9e6991498cQTeuGFF9SvXz8tW7ZMkydPVkZGhq6//nqzy0OEeL2+H4JxAAAAALGKYBxxIy8jWfur3dpf5daeyhp1SksyuyQAAAAAiHr/+c9/dP7552vs2LGSpO7du2v+/Pn64osvTK4MkeRfI5xgHAAAAECsopU64kZeZkrgNu3UAQAAAKB5TjvtNC1evFhr1qyRJH3zzTf65JNPNGbMGJMrQySFG4z79/cfDwAAAADRihnjiBt5h60zfkrPjiZWAwAAAADWcOutt6qsrEx9+vSRw+FQbW2t7r77bl122WWNHuNyueRyuQL3y8rKJElut1tuphZHpYMHpZQUKTFRqrt8vGG4g34fzm73HefxMGs81vj/rfJvNr5w3uMX5z5+ce7jE+c9fsXiuQ/lvRCMI24cHowDAAAAAJr22muv6eWXX9a8efPUr18/rVixQjfccIPy8/M1ceLEBo+ZNWuWZs6cWW/7okWLlJqa2tolI0zz5/t+//g9hiDl5UVNHvfuu61QFExXVNT4uUfs4rzHL859/OLcxyfOe/yKpXNfVVXV7H0JxhE38jIOtVIv2UUwDgAAAADN8cc//lG33nqrLrnkEknScccdp40bN2rWrFmNBuPTpk3T1KlTA/fLyspUUFCgUaNGKT09vU3qRmhKS6Wjj5amT5cGDz603TDcKi8vUvv2I2WzORs89oILpHvukX7967apFW3D7XarqKhII0eOlNPZ8LlH7OG8xy/Offzi3Mcnznv8isVzX9bQN3sbQTCOuJGenKDURIeqamqZMQ4AAAAAzVRVVSW73R60zeFwyOv1NnpMUlKSkpKS6m13Op0x88eXWGMYUnW1ZLP5fg5nszkbDcbdbqmmRuLUxib+3cYnznv84tzHL859fOK8x69YOvehvA+CccQNm82mvIxkleyq1PYDB1V+0K32ybHxjx4AAAAAWsu4ceN09913q1u3burXr5++/vprPfjgg/rlL39pdmmIII/H99vhCP3YhIRDxwMAAABAtCIYR1zJy0hRya5KSVLJrkoNLMg0tyAAAAAAiHKPPvqobr/9dv3ud79TaWmp8vPzNWXKFE2fPt3s0hBB/mA7IYy/FDkcvlnjAAAAABDNCMYRV/IykgO3i0srCMYBAAAAoAnt27fX7NmzNXv2bLNLQSvyB9vhzhgnGAcAAAAQ7exN7wLEjvzMlMBt1hkHAAAAAMCnpa3UCcYBAAAARDuCccSVw2eMAwAAAAAAgnEAAAAAsY9gHHElq12iEhN8l33JLoJxAAAAAACklrVSdzgOBesAAAAAEK0IxhFX7DabctN9s8Y37qmUy1NrckUAAAAAAJjPH2wnJIR+rMPBjHEAAAAA0Y9gHHHH307da0gbdleZXA0AAAAAAOZryYxxWqkDAAAAsAKCccQd1hkHAAAAACAYa4wDAAAAiHUE44g7eRkpgdtrS8tNrAQAAAAAgOjQ0lbqrDEOAAAAINoRjCPuMGMcAAAAAIBgtFIHAAAAEOsIxhF3OrdPksNmk0QwDgAAAACA1LIZ43Y7wTgAAACA6EcwjriT4LArOz1JkrRud6VqvYbJFQEAAAAAYC7WGAcAAAAQ6wjGEZf87dRrPF5t2VdlcjUAAAAAAJirJa3UHQ6CcQAAAADRj2AccanuOuNrd9JOHQAAAAAQ35gxDgAAACDWEYwjLuVnpARuF+8iGAcAAAAAxLeWzBgnGAcAAABgBQTjiEt1Z4wXlxKMAwAAAADim3/GuD2MvxTRSh0AAACAFRCMIy7lZiTL9uPttQTjAAAAAIA45/H4Zn7bbE3veziCcQAAAABWQDCOuJSU4FDHtERJUklphQzDMLkiAAAAAADM43b7gvFw0EodAAAAgBUQjCNu5f24zniFy6MdZQdNrgYAAAAAAPP4Z4yHIyHhUCt2AAAAAIhWBOOIW/l11hlfu5N26gAAAACA+OV2+1qih4MZ4wAAAACsgGAcccs/Y1ySillnHAAAAAAQxzye8INx1hgHAAAAYAUE44hbeZl1ZowTjAMAAAAA4lhLWqkTjAMAAACwAoJxxK28Oq3USwjGAQAAAABxjFbqAAAAAGIdwTjiVmpigjJTnJKkNaXlMgzD5IoAAAAAADBHS1qpJyT4jgcAAACAaEYwjrjmb6e+v8qtPZU1JlcDAAAAAIA5WhqMM2McAAAAQLQjGEdcy8tICdwupp06AAAAACBOtaSVusPBjHEAAAAA0Y9gHHEtv84642sJxgEAAAAAccrj8c38DofDwYxxAAAAANHP1GB86dKlGjdunPLz82Wz2bRgwYImj1myZIlOOOEEJSUlqVevXpo7d269febMmaPu3bsrOTlZgwcP1hdffBH54hET6s4YLyEYBwAAAADEqZbMGKeVOgAAAAArMDUYr6ys1IABAzRnzpxm7b9+/XqNHTtWw4cP14oVK3TDDTfoV7/6ld5///3APq+++qqmTp2qGTNmaPny5RowYIBGjx6t0tLS1nobsLD8zLozxstNrAQAAAAAAPO0dI3x2lrJMCJbEwAAAABEUphNsiJjzJgxGjNmTLP3f/LJJ9WjRw898MADkqS+ffvqk08+0UMPPaTRo0dLkh588EFdffXVmjx5cuCYd955R88995xuvfXWyL8JWFr7ZKfSkhJU4fJo7U5mjAMAAAAA4lNLgnH/cR6P5HRGriYAAAAAiCRLrTH+2WefacSIEUHbRo8erc8++0ySVFNTo6+++ipoH7vdrhEjRgT2AQ6X9+M646XlLh2opvcbAAAAACD+tKSVuv842qkDAAAAiGamzhgP1Y4dO5STkxO0LScnR2VlZaqurta+fftUW1vb4D6rVq1q9HldLpdcLlfgfllZmSTJ7XbLHcWjOn9thrfW5EqsLS89SWt/XF989bb9GtQt09yC2oD/2onm6xvRiWsH4eLaQbi4dhAurh2Eq7WvHa5JRKuWtlKXCMYBAAAARDdLBeOtZdasWZo5c2a97YsWLVJqaqoJFYWmesPXZpdgaR09Nkm+0f8/P/xM2zvHz6JoRUVFZpcAi+LaQbi4dhAurh2Ei2sH4Wqta6eqqqpVnhdoqUgE4x5P5OoBAAAAgEizVDCem5urnTt3Bm3buXOn0tPTlZKSIofDIYfD0eA+ubm5jT7vtGnTNHXq1MD9srIyFRQUaNSoUUpPT4/sm4ggt9utoqIipXQfJJs9zNEr1D2lXNpQIklKze2pc87ubXJFrc9/7YwcOVJOFoBDCLh2EC6uHYSLawfh4tpBuFr72vF3KAOiTUtaqTNjHAAAAIAVWCoYP/XUU/Xuu+8GbSsqKtKpp54qSUpMTNRPfvITLV68WBdccIEkyev1avHixbr22msbfd6kpCQlJSXV2+50Oi3xRzSb3SGbw1KnMqrkZ7UL3C7ZXWWJcx4pVrnGEX24dhAurh2Ei2sH4eLaQbha69rhekS0crsPBdyhYo1xAAAAAFZgN/PFKyoqtGLFCq1YsUKStH79eq1YsUKbNm2S5JvJfeWVVwb2/81vfqN169bp5ptv1qpVq/T444/rtdde04033hjYZ+rUqXr66af1wgsvaOXKlfrtb3+ryspKTZ48uU3fG6wjM8WpZKfvn0Lxj2uNAwAAAAAQT1oyY5xgHAAAAIAVmDrNeNmyZRo+fHjgvr+d+cSJEzV37lxt3749EJJLUo8ePfTOO+/oxhtv1MMPP6yuXbvqmWee0ejRowP7XHzxxdq1a5emT5+uHTt2aODAgfr3v/+tnJyctntjsBSbzab8jBSt212pLfuqVVXjUWoiM/ABAAAAAPHD45FSUsI7llbqAAAAAKwgrPRv3bp16tmzZ4tffNiwYTIMo9HH586d2+AxX3/99RGf99prrz1i63TgcLkZyVq3u1KStG5Xpfp3yTC5IgAAAAA4skiNzQHJF4y3dI1xjydy9QAAAABApIXVSr1Xr14aPny4XnrpJR08eDDSNQFtLj/j0Nfi15aWm1gJAAAAADQPY3NEUktaqTNjHAAAAIAVhBWML1++XMcff7ymTp2q3NxcTZkyRV988UWkawPaTH5mcuD22p2sMw4AAAAg+jE2RyR5PIcC7lCxxjgAAAAAKwgrGB84cKAefvhhbdu2Tc8995y2b9+uM844Q/3799eDDz6oXbt2RbpOoFXl1ZkxXlxKMA4AAAAg+jE2RyS1ZMY4wTgAAAAAKwgrGPdLSEjQz372M73++uu69957VVxcrJtuukkFBQW68sortX379kjVCbSqju0Slejw/XMgGAcAAABgJYzNEQmRWGOcYBwAAABANGtRML5s2TL97ne/U15enh588EHddNNNKikpUVFRkbZt26bzzz8/UnUCrcputyknPUmStHFvlVyeWpMrAgAAAIDmYWyOSGhJK3X/cR5P5OoBAAAAgEgLa8jz4IMP6vnnn9fq1at1zjnn6MUXX9Q555wju92Xs/fo0UNz585V9+7dI1kr0KryM1O0eV+1ar2GNuyuUu/c9maXBAAAAACNYmyOSGpJK3VmjAMAAACwgrCC8SeeeEK//OUvNWnSJOXl5TW4T+fOnfXss8+2qDigLeVlJAdury0tJxgHAAAAENUYmyOSWjJjnDXGAQAAAFhBWEOeoqIidevWLfAtdD/DMLR582Z169ZNiYmJmjhxYkSKBNpCXkZK4DbrjAMAAACIdozNEUktWWOcYBwAAACAFYS1xvhRRx2l3bt319u+d+9e9ejRo8VFAWbIz6w7Y5xgHAAAAEB0Y2yOSKKVOgAAAIBYF1YwbhhGg9srKiqUnJzc4GNAtMtunySHzSZJKiEYBwAAABDlGJsjkmprWx6MezyRqwcAAAAAIi2kVupTp06VJNlsNk2fPl2pqamBx2pra/X5559r4MCBES0QaCsJdrty0pO07cBBrdtVKU+tVwmOsL47AgAAAACthrE5WgMzxgEAAADEupCC8a+//lqS71vp3377rRITEwOPJSYmasCAAbrpppsiWyHQhvIyU7TtwEHV1Hq1aW+VemanmV0SAAAAAARhbI7W0JI1xv3L3BOMAwAAAIhmIQXjH330kSRp8uTJevjhh5Went4qRQFmycs41G6wuLSCYBwAAABA1GFsjkgzDF8wnhDSX4kOsdl8xxKMAwAAAIhmYfWJfv755xl4IyblZ6QEbq9lnXEAAAAAUYyxOSKlttb3O9wZ4xLBOAAAAIDo1+zvAv/sZz/T3LlzlZ6erp/97GdH3PfNN99scWGAGerOGC8hGAcAAAAQZRibozV4PL7f4c4Y9x/rfx4AAAAAiEbNHvJkZGTIZrMFbgOxKCc9WTabr40cM8YBAAAARBvG5mgN/kC7JTPGHQ5mjAMAAACIbs0Oxp9//vkGbwOxJDHBruy0JJWWu1RcWiGv15DdbjO7LAAAAACQxNgcrcMfaNNKHQAAAEAsC2uN8erqalVVVQXub9y4UbNnz9aiRYsiVhhgFn879Wp3rbYdqDa5GgAAAABoGGNzRAozxgEAAADEg7CC8fPPP18vvviiJGn//v06+eST9cADD+j888/XE088EdECgbaWl5ESuE07dQAAAADRirE5IoUZ4wAAAADiQVjB+PLly3XmmWdKkt544w3l5uZq48aNevHFF/XII49EtECgreVlJgdulxCMAwAAAIhSjM0RKf4Z4wnNXnCvvoSEQ88DAAAAANEorGC8qqpK7du3lyQtWrRIP/vZz2S323XKKado48aNES0QaGv5dWeM7yQYBwAAABCdGJsjUiLRSp0Z4wAAAACiXVjBeK9evbRgwQJt3rxZ77//vkaNGiVJKi0tVXp6ekQLBNqaf41xSVpbWm5iJQAAAADQOMbmiJRItFJnjXEAAAAA0S6sYHz69Om66aab1L17dw0ePFinnnqqJN831AcNGhTRAoG2lux0KCs1UZJUXFohwzBMrggAAAAA6mNsjkiJRCt1gnEAAAAA0S6sIc9FF12kM844Q9u3b9eAAQMC28866yxdeOGFESsOMEteZrL2VtWo7KBHu8pd6pye3PRBAAAAANCGGJsjUiIxY5w1xgEAAABEu7C/C5ybm6vc3NygbSeffHKLCwKiQV5Gsr7fViZJWltaQTAOAAAAICoxNkckRGKNcWaMAwAAAIh2YQXjlZWV+utf/6rFixertLRUXq836PF169ZFpDjALPkZKYHbxaUVOr1XJxOrAQAAAID6GJsjUiLRSj0hgWAcAAAAQHQLa8jzq1/9Sh9//LGuuOIK5eXlyWazRbouwFR5GYdmiK8tLTexEgAAAABoGGNzREokWqnb7QTjAAAAAKJbWMH4e++9p3feeUenn356pOsBokJe5qEZ42t3VphYCQAAAAA0jLE5IiUSrdSZMQ4AAAAg2tnDOahDhw7KysqKdC1A1EhLSlBGilOSb41xAAAAAIg2jM0RKZEKxv3PAwAAAADRKKxg/K677tL06dNVVVUV6XqAqJH/Yzv1vZU12l3hMrkaAAAAAAjG2ByREolW6g6HVFMTmXoAAAAAoDWE1Ur9gQceUElJiXJyctS9e3c5nc6gx5cvXx6R4gAz5WemaOUO3/ria3aUq1OvJJMrAgAAAIBDGJsjUvwzvRPC+ivRoWMJxgEAAABEs7CGPBdccEGEywCiT5c664yv2Vmu03p1MrEaAAAAAAjG2ByREqkZ46wxDgAAACCahRWMz5gxI9J1AFEnv24wzjrjAAAAAKIMY3NESqTWGCcYBwAAABDNwlpjXJL279+vZ555RtOmTdPevXsl+dq0bd26NWLFAWbKz0wO3F67s9zESgAAAACgYYzNEQmRaKXucBx6HgAAAACIRmENef73v/9pxIgRysjI0IYNG3T11VcrKytLb775pjZt2qQXX3wx0nUCbS41MUEdUp3aV+XWmp0VMgxDNpvN7LIAAAAAQBJjc0ROJFqpM2McAAAAQLQLa8b41KlTNWnSJK1du1bJyYdm1Z5zzjlaunRpxIoDzJaf4WunfqDardJyl8nVAAAAAMAhjM0RKbRSBwAAABAPwgrGv/zyS02ZMqXe9i5dumjHjh0tLgqIFvkd6qwzTjt1AAAAAFGEsTkixe32heItaZLmcBCMAwAAAIhuYQXjSUlJKisrq7d9zZo1ys7ObnFRQLToklE3GK8wsRIAAAAACMbYHJHi8bRstrjEjHEAAAAA0S+sYPy8887TnXfeKfePIx6bzaZNmzbplltu0c9//vOIFgiYKT/zUDvCtcwYBwAAABBFGJsjUjweX7DdEg7HoZbsAAAAABCNwgrGH3jgAVVUVCg7O1vV1dUaOnSoevXqpfbt2+vuu++OdI2AafIzaaUOAAAAIDoxNkek+FuptwQzxgEAAABEu7C+D5yRkaGioiJ9+umn+uabb1RRUaETTjhBI0aMiHR9gKmSnQ51bJeoPZU1WruzQoZhyNaSRdcAAAAAIEIYmyNSIjFjnGAcAAAAQLQLedjj9Xo1d+5cvfnmm9qwYYNsNpt69Oih3NxcQkPEpPzMFO2prFG5y6PtBw4GzSIHAAAAADMwNkckRWKNcVqpAwAAAIh2IbVSNwxD5513nn71q19p69atOu6449SvXz9t3LhRkyZN0oUXXthadQKmqbvOOO3UAQAAAJiNsTkiLRKt1B0OZowDAAAAiG4hzRifO3euli5dqsWLF2v48OFBj3344Ye64IIL9OKLL+rKK6+MaJGAmerOEF+7s0LDenc2sRoAAAAA8Y6xOSItUq3UmTEOAAAAIJqFNGN8/vz5uu222+oNvCXppz/9qW699Va9/PLLESsOiAZdMg4F48wYBwAAAGA2xuaItEjMGE9IkAxDqq2NTE0AAAAAEGkhBeP/+9//dPbZZzf6+JgxY/TNN9+0uCggmuRlJMu/Ot+a0gpTawEAAAAAxuaItEisMe6fcU47dQAAAADRKqRgfO/evcrJyWn08ZycHO3bt6/FRQHRJMnpUKe0JEnS2p3l8noNkysCAAAAEM8YmyPSItFK3R+sE4wDAAAAiFYhBeO1tbVKOMJIyeFwyMOCUohB+ZnJkqSqmlpt3V9tcjUAAAAA4hljc0Sa2y3ZQ/oLUX3+YJxLDwAAAEC0Cun7wIZhaNKkSUpKSmrwcZfLFZGigGiTn5mib7YckCStLS1XQVaqyRUBAAAAiFeMzRFpkZgxTit1AAAAANEupGHPxIkTm9znyiuvDLsYIFp1yUwJ3F6zs0I/7dN420IAAAAAaE2MzRFprDEOAAAAIB6EFIw///zzrVUHENXyg4LxchMrAQAAABDvGJsj0txugnEAAAAAsa+FK0gB8SEvI1k2m+82wTgAAAAAIJZEYsa4/3iCcQAAAADRimAcaAanw67Oab71+4pLK+T1GiZXBAAAAABAZERixrj/eI+n5fUAAAAAQGsgGAeayd9O/aDbq837qkyuBgAAAACAyGCNcQAAAADxgGAcaKYuQeuMV5hYCQAAAAAAkUMwDgAAACAeEIwDzZQfFIyzzjgAAAAAIDZEopU6wTgAAACAaEcwDjRTfmZy4DbBOAAAAAAgVrjdh4LtcPmDdYJxAAAAANGKYBxoppz0ZNltvtu0UgcAAAAQT7Zu3arLL79cHTt2VEpKio477jgtW7bM7LIQIZGYMe4/3uNpeT0AAAAA0Bpa+H1gIH44HXblpCdr+4GDKtlVoVqvIYc/KQcAAACAGLVv3z6dfvrpGj58uN577z1lZ2dr7dq16tChg9mlIUJYYxwAAABAPCAYB0KQn5mi7QcOqsbj1cY9leqZnWZ2SQAAAADQqu69914VFBTo+eefD2zr0aOHiRUh0jyelrdSJxgHAAAAEO1opQ6EID+j7jrjtFMHAAAAEPveeustnXjiifrFL36hzp07a9CgQXr66afNLgsRFIlW6gTjAAAAAKIdM8aBEHTJTAncXruzXGf3zzWxGgAAAABofevWrdMTTzyhqVOn6rbbbtOXX36p66+/XomJiZo4cWKDx7hcLrlcrsD9srIySZLb7Zab5DTqOBxScrJkGPUfMwx30O/G2O1SSoovGOcUxwb/v1X+zcYXznv84tzHL859fOK8x69YPPehvBeCcSAE+XWC8VU7y02sBAAAAADahtfr1Yknnqh77rlHkjRo0CB99913evLJJxsNxmfNmqWZM2fW275o0SKlpqa2ar0Inf9U/fj9hQaVlxc1+Tzz5/t+v/tuBIpC1CgqavrcI/Zw3uMX5z5+ce7jE+c9fsXSua+qqmr2vgTjQAhy0pOVYLfJ4zW0egfBOAAAAIDYl5eXp2OPPTZoW9++ffWPf/yj0WOmTZumqVOnBu6XlZWpoKBAo0aNUnp6eqvVivD07y+dcoo0aVL9xwzDrfLyIrVvP1I2m7PR53C7pQsukJ58UpowodVKRRtyu90qKirSyJEj5XQ2fu4RWzjv8YtzH7849/GJ8x6/YvHclx3pG76HIRgHQuCw25SXkazN+6q1fnelXJ5aJSW0cCE2AAAAAIhip59+ulavXh20bc2aNSosLGz0mKSkJCUlJdXb7nQ6Y+aPL7GkstL322ZrfB+bzXnEYDwhQaqu9gXknOLYwr/b+MR5j1+c+/jFuY9PnPf4FUvnPpT3YW/FOoCY1LWDr+1frddQcWmFydUAAAAAQOu68cYb9d///lf33HOPiouLNW/ePP3tb3/TNddcY3ZpiBC327fOeEvY7b6fGFqqEAAAAECMIRgHQtSlzjrjtFMHAAAAEOtOOukk/fOf/9T8+fPVv39/3XXXXZo9e7Yuu+wys0tDhHg8LQ/GJd+scYJxAAAAANGKVupAiLp2IBgHAAAAEF/OPfdcnXvuuWaXgVbi8fhC7ZZKSPA9FwAAAABEI2aMAyHqUicYX0UwDgAAAACwOGaMAwAAAIgHBONAiDJTnEpN9P3FYNWOMpOrAQAAAACgZQjGAQAAAMQDgnEgRDabLdBOfWeZS/urakyuCAAAAACA8EUqGHc4CMYBAAAARC+CcSAMXTNTA7dppw4AAAAAsCqv1/cTqRnjrDEOAAAAIFoRjANhqLvO+GqCcQAAAACARfmD7ISElj8XM8YBAAAARDOCcSAMXesE48wYBwAAAABYlT/IZo1xAAAAALGOYBwIQ5fMujPGy0ysBAAAAACA8EVyxjjBOAAAAIBoRjAOhCHZ6VCntERJ0pqdFTIMw+SKAAAAAAAInT8Yj8SMcVqpAwAAAIhmURGMz5kzR927d1dycrIGDx6sL774otF9hw0bJpvNVu9n7NixgX0mTZpU7/Gzzz67Ld4K4oh/1niFy6Mt+6pNrgYAAAAAgNBFspW6w3EoaAcAAACAaGN6MP7qq69q6tSpmjFjhpYvX64BAwZo9OjRKi0tbXD/N998U9u3bw/8fPfdd3I4HPrFL34RtN/ZZ58dtN/8+fPb4u0gjnRhnXEAAAAAgMXRSh0AAABAvDA9GH/wwQd19dVXa/LkyTr22GP15JNPKjU1Vc8991yD+2dlZSk3NzfwU1RUpNTU1HrBeFJSUtB+HTp0aIu3gzjSNTM1cJt1xgEAAAAAVhTpGeME4wAAAP+fvfuOk6uu9z/+mpmd7SWb3gtJSAKkESD0lpAEUMB2wXJRbL+r5lq4VwWkXBQF0auocEEQFDtWVFRMCIYivRMgQBLSe9teZmfm98eZLakkm909s7uv5+NxHmfmO2fOfJY5Cdl5z+f7lZStQg3GGxsbefbZZ5k9e3bLWDQaZfbs2Tz++OMHdI4777yTiy66iKKiol3GFy9ezMCBA5kwYQKf+tSn2LZtW4fWLg23Y1ySJEmS1M115BrjdoxLkiRJymYdMFFW+23dupVkMsmgQYN2GR80aBBLly592+c/9dRTLFmyhDvvvHOX8Xnz5vHud7+bMWPGsHz5cq644grOPvtsHn/8cWJ7+U2voaGBhoaGlvuVlUH3byKRIJHFv9E115ZOJUOupHcaUJRDTjRCUyrN0g2VWX2t7K651u5Us7KD147ay2tH7eW1o/by2lF7dfa14zWpbNORwXg0ajAuSZIkKXuFGowfqjvvvJPJkydz3HHH7TJ+0UUXtdyePHkyU6ZMYezYsSxevJhZs2btcZ7rr7+ea6+9do/xBQsWUFhYuMd4tqlb+XzYJfRag/JjrKuNsGJLNX++72/khL44wcFZuHBh2CWom/LaUXt57ai9vHbUXl47aq/OunZqa2s75bxSe3XkVOo5Oa1BuyRJkiRlm1CD8f79+xOLxdi0adMu45s2bWLw4MH7fW5NTQ2//vWv+epXv/q2r3PYYYfRv39/li1bttdg/PLLL+fSSy9tuV9ZWcmIESOYM2cOpaWlB/jTdL1EIsHChQspGD2dSLQDfoPVQRuxYRXrVu4gRYTxM05h0pCSsEs6IM3XzllnnUU8Hg+7HHUjXjtqL68dtZfXjtrLa0ft1dnXTvMMZVK2aA6yczrgE6KcHGhsPPTzSJIkSVJnCDUYz83NZcaMGSxatIgLLrgAgFQqxaJFi5g/f/5+n/vb3/6WhoYGPvShD73t66xdu5Zt27YxZMiQvT6el5dHXl7eHuPxeLxbfIgWicaIxLp183+3NaxvIazcAcDybbVMGdk35IoOTne5xpV9vHbUXl47ai+vHbWX147aq7OuHa9HZZuOnEo9FnMqdUmSJEnZK/SJny+99FLuuOMO7r77bl577TU+9alPUVNTwyWXXALAxRdfzOWXX77H8+68804uuOAC+vXrt8t4dXU1X/ziF3niiSdYuXIlixYt4vzzz2fcuHHMnTu3S34m9R7Dy1un2l+6oSrESiRJkiRJOngdPZW6wbgkSZKkbBV6m/GFF17Ili1buPrqq9m4cSPTpk3j/vvvZ9CgQQCsXr2aaHTX/P7111/n0UcfZcGCBXucLxaL8dJLL3H33Xezc+dOhg4dypw5c/ja1762165w6VAMLy9oub10o8G4JEmSJKl76cip1O0YlyRJkpTNQg/GAebPn7/PqdMXL168x9iECRNIp9N7Pb6goIB//OMfHVmetE99CuIU5saobUzyusG4JEmSJKmb6eiO8eagXZIkSZKyTehTqUvdWSQSaeka31hZT0WtX42XJEmSJHUfHbnGuFOpS5IkScpmBuPSIRrWp+106pUhViJJkiRJ0sFxKnVJkiRJvYXBuHSIhpcXttx+fZPTqUuSJEmSuo+OnkrdYFySJElStjIYlw5R247x1zYYjEuSJEmSuo+OnErdjnFJkiRJ2cxgXDpEzWuMg1OpS5IkSZK6l45eY7z5fJIkSZKUbQzGpUOUH48xoCQPgKUbqkim0iFXJEmSJEnSgXEqdUmSJEm9hcG41AFGZLrG6xJJVm2rCbkaSZIkSZIOTHOHd07OoZ/LqdQlSZIkZTODcakDjCgvbLntOuOSJEmSpO4ikYBIBKId8AmRU6lLkiRJymYG41IHaLvO+GsbXGdckiRJktQ9NDV1TLc42DEuSZIkKbsZjEsdYGTf1o7xVw3GJUmSJEndRFNTx6wvDnaMS5IkScpuBuNSB+hblEthbvBJgh3jkiRJkqTuIpHo2I7xZBLS6Y45nyRJkiR1JINxqQNEIpGW6dQ3VNSzo6Yx5IokSZIkSXp7jY0Qj3fMuZoDdqdTlyRJkpSNDMalDjKivHU6dbvGJUmSJEndgcG4JEmSpN7CYFzqIG2DcdcZlyRJkiR1B42NHTuVOrjOuCRJkqTsZDAudZDhfQtabr+2oSrESiRJkiRJOjB2jEuSJEnqLQzGpQ4yrE8B0Uhw245xSZIkSVJ30Bkd4wbjkiRJkrKRwbjUQeKxKIPL8gFYtrmKxqZUyBVJkiRJkrR/HRmM2zEuSZIkKZsZjEsdqHmd8UQyzfIt1SFXI0mSJEnS/jmVuiRJkqTewmBc6kDDy1vXGX91vdOpS5IkSZKyW2dMpd7U1DHnkyRJkqSOZDAudaCRfQtbbr/mOuOSJEmSpCznVOqSJEmSeguDcakDDS9vE4xvNBiXJEmSJGW3zugYNxiXJEmSlI0MxqUOVFYQpzQ/+EThtQ1VpNPpkCuSJEmSJGnfXGNckiRJUm9hMC51sBGZ6dS31zSyqbIh5GokSZIkSdq3hganUpckSZLUOxiMSx1sRLnrjEuSJEmSuofOmEq9qaljzidJkiRJHclgXOpgI8oLWm6/ajAuSZIkScpiTqUuSZIkqbcwGJc62PC+rR3jBuOSJEmSpGzWGR3jBuOSJEmSspHBuNTBBpfmkxONAPDaeoNxSZIkSVL2smNckiRJUm9hMC51sFg0wvDMdOpvbauhpsHF1SRJkiRJ2akjO8YNxiVJkiRlM4NxqROMzEynnk7Da06nLkmSJEnKUh3ZMd48lXqT3w+XJEmSlIUMxqVOMKpfUcvtJesqQqxEkiRJkqR9s2NckiRJUm9hMC51guaOcYAlrjMuSZIkScpSndExbjAuSZIkKRt10HeCJbU1vLyAaARSaTvGQ5Oog+rNwVZfAY1V0FAFDdXQWB3cTjVBKgnpZOs+nYJYLuTkQ04exPKCfbwA8sugoC8UlLfZ+kCsgz5FkiRJkqQulkh0XMd4LAaRiMG4JEmSpOxkMC51gngsypCyAtbtrGPZ5mrqE0ny47Gwy+o5UimoWg/b34IdK1u3qg1QvSkIwxu6qlM/AsUDoXQolA6DsuGt+37jgi238O1PI0mSJEkh6Mip1CE4l8G4JEmSpGxkMC51klH9Clm3s46mVJo3NlUxZXifsEvqflIp2LkSNr0Km1+Dza8G2/YVkGwMu7qMdCaM3wTrn9/7IaXDof846DceBkyAwVNg8FGQW7T34yVJkiSpC6TTHR+Mx2LQ1NRx55MkSZKkjmIwLnWSkX0LeWz5NgCWrKs0GH876XTQ9b3u2WBb+wxsWgKJ2oM7T/OU5/l9MlsZ5BVDTgHE81v38QKI5kAkutsWgWRTMM16shGSieB2U31QS0NVZir2mmB69vpKqNsOdTuB9N5rqlwbbCsWtxmMQP/xQUg+ZAoMnQ7DZhiWS5IkSeoyyWTwq1hHrTEOdoxLkiRJyl4G41InGdm3dfrsJetdZ3wPyQSsfwFWPgyrnwzC8Nqtb/+8aAxKhkDxoMw2sHVf2D9YDzwMqSao2wG126F2WzCde9V6qNwAleshUbPbE9Kw9Y1gW/K7YCgSC0LyEcfDyMxWMrjLfxRJkiRJvUNzgO1U6pIkSZJ6A4NxqZO0DcZfWd9V611nsVSyNQhf+SisfiLovN6f4oHQZxSUjYA+I4J9yeCg0zvbRHOgaECw7S6dDtY8r1wPFWta10TfuToI1FuOSwbTsa9/Hp68NRjrexgcdgaMPQNGnwIFfbrgh5EkSZLUGzRmVqiyY1ySJElSb5CF6ZLUM+THYwwqzWNTZQOvbagkkUwRj0XDLqtr1WyD5YvgjX8E+7od+z42tyRYh7vvWOg3DvqNhbySrqu1M0Uimendy2DgpNbxZBNUroMdb8HWN2Hr61Cxdtfnbl8RbM/cGUz1PuyYICQfNzu4He1l15QkSZKkDtMcjHd0x7hrjEuSJEnKRgbjUica1beITZUNNDalWL6lmomDS8MuqXOl07BlKbx2H7y5ANY+zT7X3S4oh4FHBNugI6B4cBAg9yaxHCgfFWyHnR6MNVRnplh/PfhvuXVZ0EkOkE7B2qeC7aFvQtFAmDAPJpwLh50WrJsuSZIkSQeoMzrGYzE7xiVJkiRlJ4NxqRON6FvAUyuD26+sq+yZwXg6DZuWwKt/Cratb+z9uHgBDJ4MgybDoCODdcJ7WxB+IPKKYdjRwQaQqIfNr8LGl2Djy0GHebOazfDcT4MtXghjz4QjzocJZ/ecbntJkiRJnaazOsYNxiVJkiRlI4NxqRON6lvUcnvJ+greM2N4iNV0sE2vwMu/DcLw7Sv2fkzZcBgyHYZOgwETsnNt8GwXz981KK/dBhtehHXPBkF5MvNJVqIWlt4XbDn5cPhcOOo9MH6OneSSJEmS9sqOcUmSJEm9iSmV1IlG9i1suf3KusoQK+kgVRuDMPzFe2DTy3s5IAIDJsKI42DYDCge2OUl9niF/YLO8LFnQlNDEI6vfQbWPwcNmWusqb61gz+3BCaeC1P+DQ47wzXJJUmSJLWwY1ySJElSb2IwLnWi4vwc+hblsr2mkVfWV5BKpYlGu9n04Ym6YM3wF38FK/4ZrHPdViQSrBM+YiYMPw4K+oRSZq+UkwfDjwm2VCpYl3zVY7DmCWioCo5prIKXfh1sZSNg2geCrXx0qKVLkiRJCl9ndYw3NXXc+SRJkiSpoxiMS51sVN9Cttc0UtOYZNX2Wsb0L3r7J2WDLa/DMz8OAvH6nXs+3m8cjD4FRh4P+WVdXp52E43CwEnBNuMjwbrvqx6DtU8FX24AqFgDD30z2MacCtMvhknvcKp1SZIkqZeyY1ySJElSb2IwLnWykf0KeX7NTgCWrKvI7mA8UQ+v/TkIxFc/tufjRQNg9MlBIF46tOvr04GJxmDI1GBLfgzWPQdvPQQbXoB0OjjmrYeDLa8Mpn8Qjv049BsbatmSJEmSupZrjEuSJEnqTQzGpU7Wdp3xJesreOfULAyUd6yCp26HF34BdTt2fSwaD7rCx54JAyZAxDWqu5VYbvD+jTwearcHYfiKxVC9MXi8oQKe+L9gGzcbjvskjDvLtcglSZKkXqAzOsYNxiVJkiRlK4NxqZONahOMv7q+MsRKdpNOE1nzBDx9Oyy9b8+1w0uHwbhZMPpUyCsOp0Z1rMK+cOQFcMT5sGVpsGb86schmfnUatkDwVY+Ouggn/4hKCgPs2JJkiRJnagzOsbjcWho6LjzSZIkSVJHMRiXOllZQZzS/Bwq65tYsq6CdDpNJBIJr6CmRiJLfsepb9xIzgtv7fpYNA4jZwadw/0nQJh1qvNEIq3rkU+/OAjI31wANVuCx3eshAVXwj+/AdP/HU74DJSPCrVkSZIkSR2vMzrGDcYlSZIkZSuDcamTRSIRRvYrZMm6SnbUJli7o44RbbrIu0x9JTxzJzz5Q3KqNrBLH3BBOYyfA2NnQX5p19em8OQVw6R3woRzYcPz8MY/YONLwWOJWnjqh/D0j4JO8xM/CwOODLVcSZIkSR2nMzrGc3Ohvr7jzidJkiRJHcVgXOoCo/sVsWRdMI36y+squjYYr9kGT94KT94erCfdRrrPaCITz4GRJ0LMvw56tWgUhs0Itsr1QUC+YjEkGyCdhCW/hyW/JzbmNAZEj4P02WFXLEmSJOkQdVbHeE1Nx51PkiRJkjqKSZjUBUb3K2q5/eLanZwzeUjnv2jFOnj8Znj2J0Hnb4sIqWHH8FjhWcycdhTxnGjn16LupXQoHHMJTH5vMMX6G/dDQxUA0bce4kQeIn3n/XD6ZTDxXKfclyRJkrqpzgjGc3OdSl2SJElSdjIYl7rA6H6tHeIvr63Yz5EdYPtb8Oh34IVfQSrROh6NwehTYdI7SRYNYdubCQNN7V9eCRz1Hpj4TnhrMSz9K1RvAiCy6WW454MwaDKc9iWY+I6g61ySJElSt9HYGITiHflPeadSlyRJkpStDMalLtCnMJfywjg7ahO8vLaCVCpNNNrBofTO1fDwt+D5XwRTXzeL5QZrh088F4r6B2PJdMe+tnq2nNzMGvSzaVr9FNUv3EufupXBY5teht/8Oww8MgjIJ51nQC5JkiR1E83BeEeKx+0YlyRJkpSdDMalLjKqXxE7andS1dDEym01HDaguGNOXLkeHvlfePbuXTvE4wUwfi5MOAfySzvmtdS7RaOkR8zkobrpnFu8hJxXfw/bVwSPbX4FfvthGDAJzrg8CMidkUCSJEnKao2NQZDdkZxKXZIkSVK2MhiXusjofoW8sGYnAC+trTj0YLxqE/zrJnj6Tki2+dQhXgATzg0C8dzCfT5dardIhPTQo2H40bDhBVjye9i2LHhsy2vwm4th6NEw+xo47PQwK5UkSZK0H50RjMfjTqUuSZIkKTsZjEtdZHS/opbbL62t4ILpw9p3ovpKeOz78PgtkKhtHc/Jg8PPDtZ6zuugbnRpfyIRGDodhkyDjS/Bkt/B1jeDx9Y/Bz89PwjGZ10Nw2aEWakkSZKkveisqdQbGzv2nJIkSZLUEQzGpS6yazC+8+BP0NQIz/4EHvom1G5tHY9l1n+edJ5TpisckQgMmQqDpwQd5C/+KljzHmDF4mCbdB6ceRUMODzEQiVJkiS11VlTqdsxLkmSJCkbGYxLXaQ4P4cBxXlsqW5gyfoKmpIpcmLRt39iOg2v/gkWXdu6njNANAbjzoIjLoCCPp1VtnTgWjrIp8Kqx+Dl30D15uCx1/4MS++DaR+EM6+EksHh1ipJkiSp0zrGEwlIpSB6AL/ySpIkSVJXMRiXutCofoVsqW6gPpFi2ZZqJg5+mw7vVY/Bgqtg3TO7jo88AaZcaLio7BSJwuiTYcTxsOLBYA3y+gpIp+D5n8GSP8DJX4AT50O8IOxqJUmSpF6rszrGm8+dn9+x55YkSZKkQ+F3d6UuNKZ/m+nU11Ts+8DtK+DXH4Qfn71rKD5wEsz5Opz0OUNxZb9YTjDN/zu/F3yRozkET9TAP6+DHxwDL/02mBVBkiRJUpfrjI7x5mDc6dQlSZIkZRuDcakLjepX2HL7pXU79zygoQoe+B+4ZWYw7XSzsuFw6pfgzKuh39hOr1PqUDn5cOS7goB8/Jygoxygci384ePwo9mw5qlwa5QkSZJ6oc7oGG8+X0NDx55XkiRJkg6VU6lLXWh0vyIiQBp4aW2bjvFUCl66JwjFqze2juf3CTptx5zm4mzq/vJK4ZiPBuH48z+HDS8E4+uegTvPgqPeA7OvhT4jQi1TkiRJ6i3sGJckSZLUmxiMS10oPx5jcFk+GyrqeW1DJQ1NSfI2vgB//9KuU6ZHc2DCuXDkBa7BrJ6nbDicfhlseBGe+1nQOQ7BWuRL/wan/hec+FnIyQu3TkmSJKmH64xg3I5xSZIkSdnKYFzqYqP7FbGhop6y5E5q7vkkeW/+btcDhh0D0z/kGuLq+YZMhbOPguWL4OXfBksJNNXBg9fBC7+Es2+E8WeFXaUkSZLUY3Vmx7jBuCRJkqRsYzAudbExffMYt2oBX8z5DaVv1rY+UDocjr4YhkwJrzipq0VjwdTqo06CJb+DN/4B6RRsXwG/eG8wc8K866F8VNiVSpIkST1OZ3aMO5W6JEmSpGxjMC51oZLtL/P5VVcxMP5q62C8CKa8D8adFYSEUm+UWwRHfxjGnA7P/hi2LA3GX/9r0FF+SmZ69Xh+mFVKkiRJPUpDQ2uQ3VHsGJckSZKUraJhFyD1BjmNlUx47lqOe+C9DKxqDcX/ETsN3vFdOHyeobgEQWf4rGvghPmQ3ycYa6qHf34d/u94WPZAqOVJkiRJPUlnTqVux7gkSZKkbGPHuNSZ0mkGr/4L41+8gbz6rS3DKxjOlxo+ynNM5JWcCAUhlihlnUgERp8Mw46Gl38Hb9wfTK++4y34+Xtg8vtg7vVQPCDsSiVJkqRurbER+vTp2HM2d6DbMS5JkiQp29gxLnWSwsoVHP3Qhznqyf9uCcWT0TzWjX43N5b/D8+kJ5JKw5JtIRcqZat4IRx9Mcz7JgyY2Dr+8m/h5mPguZ9BOh1efZIkSVI3Z8e4JEmSpN7EYFzqYJFUgtGv3srxC95J381PtIzv7DeNpTOuYcvwOYztE2kZf2FLGFVK3UifETDrajjuk8Fa5AD1O+HP8+En74Ctb4ZaniRJktRdNTZ2/BrjdoxLkiRJylZOpS51oJLtL3PE01dQUvF6y1hDXj/Wjb2Iyr6TW8YOL0u23H5+SxqIIGk/IlEYeyYMmwHP/RRW/SsYX/Uo3HoinPLfcPLnIScv1DIlSZKk7qQzOsYNxiVJkiRlK4NxqQNEm+oYu+R7jHzzJ0TSKQDSRNk8bDYbR76DdCx3l+OHF6UoiKWpS0Z4fnMYFUvdVH4ZnPifMOZUePpOqNkMyUZY/A1Y8jt45/dh1AlhVylJkiR1C53RMR6LBWG7U6lLkiRJyjZOpS4dovJNj3P8P97BqDfuagnFa4tG8Ma0y9gw5t17hOIAsQiMz3SNb6iFDTWukywdlCFT4ZxvwaR3Bt3kAFvfgB+fDX/7EjTWhFufJEmS1A10Rsc4BGG7HeOSJEmSso3BuNROOY2VTHr6CmY89GEKa9YAkIrksH7UBbwx9TLqikfu9/mHl7ZOp+4641I75OTBtA/C3Ouh39jMYBqe+iH83wnw1sOhlidJkiRlu87oGAfIzbVjXJIkSVL2MRiX2mHA2gWccP85DHvrdy1j1aXjeH36lWweMQ+isbc9x57rjEtql/JRMPtrMP3fIZb5VG/nKrj7nXDfF6ChKtz6JEmSpCyVSHROx3hurh3jkiRJkrKPwbh0EHIadnLUE5cy9bH55NUHi4MnY/msGfsBlk2+lIbCwQd8rl2D8Q4vVepdolGYeC6cfSMMmNg6/sxdQff4skXh1SZJktTD3HDDDUQiET7/+c+HXYoOUWdNpW4wLkmSJCkbGYxLB6j/+n9ywj/OZfDq+1rGKsons/Toa9g25NTWdY4PUJ/cNIMKgjXJX9oKiZRd49IhKxkCs66GGR+BWF4wVrEGfv5u+NN8qK8ItTxJkqTu7umnn+aHP/whU6ZMCbsUHaJ0uvOmUo/HnUpdkiRJUvbJimD8lltuYfTo0eTn5zNz5kyeeuqpfR77k5/8hEgkssuWn5+/yzHpdJqrr76aIUOGUFBQwOzZs3nzzTc7+8dQDxVrrOKIpy5j2qP/j7z6oLW7KVbIqsMv4a0jPk0ir7zd556Q6RpvSMLS7R1SrqRIFA6fB+fcCAOPbB1//mdwy/HwxoLwapMkSerGqqur+eAHP8gdd9xBeXn7fw9SdmhqCvad0TEej9sxLkmSJCn7dMKvPwfnnnvu4dJLL+W2225j5syZ3HTTTcydO5fXX3+dgQMH7vU5paWlvP766y33I5HILo/feOONfP/73+fuu+9mzJgxXHXVVcydO5dXX311jxBd2p++Gx/liKevIL9uY8tYRflRrBn3IZry+hzy+Q8vbeLhjcHX85/fApP7H/IpJTUrHgRnfiWYRv2FX0BTPVSth1++D46+GOZ+A/JKwq5SkiSp2/jMZz7Dueeey+zZs7nuuuv2e2xDQwMNbZLRyspKABKJBIlEolPr1IGprYWCAsjLC7rH9yWdTuyyPxAlJUHw7lvdvTX/WfXPbO/i+957+d73Xr73vZPve+/VE9/7g/lZQg/Gv/Od7/CJT3yCSy65BIDbbruNv/71r9x1111cdtlle31OJBJh8OC9r+WcTqe56aabuPLKKzn//PMB+OlPf8qgQYO49957ueiiizrnB1GPEkvUMP6lGxm+/FctY8lYPuvGvI/tg06E3b6M0V4TdllnPM3FkzrmvJIyIlEYfxYMnQ5P3Q4bXwrGn/sprHgI3nUbjDox3BolSZK6gV//+tc899xzPP300wd0/PXXX8+11167x/iCBQsoLCzs6PLUTr/K/Mqb+d7CflVVLTzg8159dbD/29/aUZSyzsKFB/7eq+fwfe+9fO97L9/73sn3vffqSe99bW3tAR8bajDe2NjIs88+y+WXX94yFo1GmT17No8//vg+n1ddXc2oUaNIpVIcffTRfOMb3+DII4Ppct966y02btzI7NmzW44vKytj5syZPP7443sNxrvrN9mba0unkm9zpA5G+ZanOeKZr1BYu7ZlrLJsIqvGX0wir28w0EHLgY8sThGPpkmkIjy3GRLJrllnvPl1uur11HN022snvx+cchmRFQ8Se/FnRJoaYOcq0j8+h9QJ80mdehnk5IVdZY/WE7+JqK7htaP28tpRe3X2tdMdr8k1a9bwuc99joULFx7wLGyXX345l156acv9yspKRowYwZw5cygtLe2sUnUQNm+G8eODEHvmzH0fl04nqKpaSEnJWUQiB7Yg+dVXw4gRcPfdHVSsQpFIJFi4cCFnnXUW8c5YjF5Zyfe99/K9771873sn3/feqye+95UH8k3fjFCD8a1bt5JMJhk0aNAu44MGDWLp0qV7fc6ECRO46667mDJlChUVFXz729/mxBNP5JVXXmH48OFs3Lix5Ry7n7P5sd1192+y1618PuwSeoRoqpEj1v+WsVv+0TLWFM3llaHvZ2X/MyEZgQP/0skBG14Eb1XBqir4zasJirvw76GFK5q67sXUo3Tfa+dUCg+fwNGrbqdfzZtESBN7/AdUP38vz436f1QWjgy7wB6vJ30TUV3La0ft5bWj9uqsa+dgvsmeLZ599lk2b97M0Ucf3TKWTCZ5+OGHufnmm2loaCAWi+3ynLy8PPLy9vziYTwe7zEfvnR3qRTU1UE0emCTokUi8QMOxlMpqKoK1hpX9+ef297J97338r3vvXzveyff996rJ733B/NzhD6V+sE64YQTOOGEE1run3jiiUyaNIkf/vCHfO1rX2vXObvrN9mbv9VRMHo6kWjs7Z+gfSre+RqTn7qW4qrlLWNVpeNYNe7DJAsGMIK6Tnvto8rzeKsq+MBoQGkOZwzv/OnUE8k0C1c0cdZhOcRjTt+uA9czrp3hcMT/kHzjPqJLfksk1URZ/RpOf/NaUqddRur4+eDfqR2uJ34TUV3Da0ft5bWj9ursa+dgvsmeLWbNmsXLL7+8y9gll1zCxIkT+fKXv7xHKK7uobEx2HfGX5HxONTXd/x5JUmSJOlQhBqM9+/fn1gsxqZNm3YZ37Rp0z7XEN9dPB5n+vTpLFu2DKDleZs2bWLIkCG7nHPatGl7PUd3/yZ7JBojEut233HIDukUo16/i7FLvks0FUxpmIrG2TDqfLYMPRMiUaKdXMLEsiR/ydx+aSvMGdV1YWM8FunG4abC1O2vnVgMjjwfhk6DJ26BnauJpBLE/vk1YssWBGuP9z0s7Cp7pO7y/1ZlH68dtZfXjtqrs66d7ng9lpSUcNRRR+0yVlRURL9+/fYYV/fRHIzndMLHCbm5QTe6JEmSJGWTzs789is3N5cZM2awaNGilrFUKsWiRYt26Qrfn2Qyycsvv9wSgo8ZM4bBgwfvcs7KykqefPLJAz6neoe82g0cvfjDjH/pxpZQvLZoBK9Pu5wtw2ZDpGv+eBxe1rpG/PNbuuQlJTUrHwVzvg6Tzm+dP3LNk3DryfDMXZDuZmupS5IkSQeoebn7zviuRm4uNDR0/HklSZIk6VCE3mZ86aWX8uEPf5hjjjmG4447jptuuomamhouueQSAC6++GKGDRvG9ddfD8BXv/pVjj/+eMaNG8fOnTv51re+xapVq/j4xz8OQCQS4fOf/zzXXXcd48ePZ8yYMVx11VUMHTqUCy64IKwfU1lm0Or7mPjsNcQTVQCkibB5+Bw2jnwn6WjX/rHon5emb16K7Q1RXtwKyVSaWLQbd+JK3U0sDtPeD8OODrrHqzdDogbu+wK8uRDO+wEU9Q+7SkmSpKyyePHisEvQIersjnGDcUmSJEnZJvRg/MILL2TLli1cffXVbNy4kWnTpnH//fczaNAgAFavXk002tq5u2PHDj7xiU+wceNGysvLmTFjBo899hhHHHFEyzFf+tKXqKmp4ZOf/CQ7d+7k5JNP5v777yc/P7/Lfz5ll5zGSiY891WGrP5zy1hjXjmrDr+EmrLDQ6kpEoHDS5M8sSVKdQKWVcCE8lBKkXq3ARNg3o3wws9h2QPB2Ot/g1ufDaZWH3tmuPVJkiRJHcg1xiVJkiT1NqEH4wDz589n/vz5e31s92+hf/e73+W73/3ufs8XiUT46le/yle/+tWOKlE9QJ8tT3Pkk1+koHZ9y9j2Aceybuz7SeYUhlgZTChL8sSW4NOI5zYbjEuhiefDsR+HoUfDk7dBQyVUb4KfvQtOmA+zroacvLCrlCRJkg5ZZ3aMx+N2jEuSJEnKPqGuMS51hUiykbEvfZsZ//xQSyiejBWw8vCPsnrCx0IPxQEm9GldZ/zZza5pLIVu2NFw9o0wZGrr2OM3wx2zYPPS8OqSJEmSOkhndozn5toxLkmSJCn7GIyrRyuseotjH7yQMUtvJ0IQOFeXjmfp9CvZOfC4kKtrNa4kSU4kqO+ZTSEXIylQ0AdO+zIcfTFEM200m16G20+Dp38Eab/EIkmSpO7LjnFJkiRJvY3BuHqmdJohb/2B4xa+i9IdrwCQisRYP/pdLJv8BRL5/UIucFe5MRhXGnSNr6yCLXUGblJWiERhwjkw5+tQOjwYa6qHv/4X/PoDULM13PokSZKkdursjnGDcUmSJEnZxmBcPU4sUc2RT/43Rz59GTlNtQDUFwzizalfZvPwuUHQlYUmtZ1O3a5xKbuUj4K534Dxc1rHXv8b3HoiLH8wvLokSZKkdrJjXJIkSVJvk50JodROpdteZOaC8xmy+i8tY9sGncQb066grnhkiJW9vbbB+DOuMy5ln5xcOOajcOoXIa80GKveBD97F/zjK9DkJ3+SJEnqPjq7YzyVgqamjj+3JEmSJLWXwbh6hnSKUUvv4JgH309hzRoAkrF8Vk74OGvG/zupWF7IBb69iWWtwfjTdoxL2WvYDDj7RhgytXXs8ZvhzjmwbXl4dUmSJEkHoTkYj8U6/ty5ucG+vr7jzy1JkiRJ7WUwrm4vt24L0x/+OONf+hbRdPB19JqSMbw+/Up2Djgm5OoOXGlumuFFQTj+yjaoa7JrXMpaBX3gtC/D0RdDNDP35IYX4Ienwcu/C7MySZIk6YA0Ngbd4pFIx5+7uQvd6dQlSZIkZRODcXVrfTc+wswF59Fv06MApImwafg83pz83zTm9w+5uoM3KdM13pSGF7aEXIyk/YtEYcI5cNbXoGRwMNZYBb//GPzpM9BYE259kiRJ0n40B+OdwY5xSZIkSdnIYFzdUiTZyLgXv8nRD3+MvIZtACTipSw/6nNsGH0BRDthLrgusMs6406nLnUPfcfA3Oth9CmtY8//HG4/Aza9El5dkiRJ0n50ZjBux7gkSZKkbGQwrm6noGoVxzx4EaNfv7NlrKL8KJYefRXVfSaGWNmhm9SnqeX2M5udSl3qNuIFcMJn4PhPQ05eMLb1dbjjTHj6Tkj751mSJEnZpbERcnI659wG45IkSZKykcG4upXBq/7EzIUXULZjCQCpSIx1Y97HW0d8hmS8JOTqDt3ggjR9clMAPLcZkinDNKlbGXNq0D3eZ1Rwv6ke/nop/PbDULcz1NIkSZKktjozGHcqdUmSJEnZyGBc3UK0qZYjnrqMo578IjlNwbq99fkDeXPql9kybBZEIiFX2DEikdbp1KsS8PrOcOuR1A6lQ2HO12D83NaxV/8Et50Ca54Ory5JkiSpja5YY9yOcUmSJEnZxGBcWa+o4k2Oe+C9DF35h5ax7QOP543pV1BXPDLEyjrHpLLWdcafdZ1xqXuK5cIxl8Ap/wW5RcFYxWr48Tx49CZIpUItT5IkSbJjXJIkSVJvYzCurDbkrT9w3APvobhyGQDJaB6rDr+E1Yd/hFQsP+TqOkfbdcafdp1xqXsbfizM+yb0nxDcTzXBA9fAL94D1ZvDrU2SJEm9Wmd2jLvGuCRJkqRsZDCurBRMnf5ljnz6MmLJ4CvmdYXDeGPa5ewYODPk6jrXmJIUedEgEH96I6TThuNSt1bUH2ZdDUe+C8gs+7D8Qbj1JFixOMzKJEmS1It1Rce4wbgkSZKkbGIwrqzTOnX6H1vGtg4+hTemfpmGwsEhVtY1cqIwMbPO+IZaWFMdckGSDl00BlMuhDOugPw+wVjNZvjpBbD4Bkgl9/dsSZIkqcN1ZjDe3DHuVOqSJEmSsonBuLLKvqZOXzvug6RjuSFX13WObDOd+hMbQyxEUscaPBnm3QCDp2QG0rD4evjZu5xaXZIkSV2qK4JxO8YlSZIkZRODcWWF3jx1+t4cWd7aPfrEBqdSl3qUgj5w+mVBB3kkM7X6Ww/BbSfDW4+EWpokSZJ6DzvGJUmSJPU2BuMKXTB1+nt67dTpe3N4WZLczDrjT24KuRhJHS8SDdYcP/MqKCgPxqo3wU/Pg4e+BalUuPVJkiSpx2tsbA2wO1okEqwzbse4JEmSpGxiMK5QtU6dvhyAZCyPlYd/tNdNnb67eBQmlAVd4+uqYW21XeNSjzTwiMzU6pOD++kU/PM6+Pm7oXpLuLVJkiSpR2to6LyOcTAYlyRJkpR9DMYVin1OnT71cnYOPC7k6rLDkeWt64w/6TrjUs+VXwanXQ6T39c6tfqKf8IPT4GV/wq3NkmSJPVYnTmVOgTBuFOpS5IkScomBuPqck6dfmCO7NO6zviTG+0Yl3q0aBSOeg+ccWUQlANUbYC73wGP/K9Tq0uSJKnDdeZU6hCc245xSZIkSdnEYFxdyqnTD9zhZUlyIkEg/oQd41LvMOhImPfNYA/B1OqLvgq/fB/UbAu3NkmSJPUodoxLkiRJ6m0MxtUlnDr94OXFWtcZX10FG2rsGpd6hYI+cPpXgg5yMlOrL3sAbjsZVj8RZmWSJEnqQewYlyRJktTbGIyr0zl1evsdWd52OvUQC5HUtaLRYM3xM66AvOap1dfDj8+BR29yanVJkiQdMjvGJUmSJPU2BuPqVE6dfmiOLG9que0641IvNHgynH0DDDwiuJ9OwgPXwK8uhNrt4dYmSZKkbs2OcUmSJEm9jcG4OoVTp3eMiW3WGbdjXOqlCsrhjCvhyHfTMrX6mwsyU6s/GWppkiRJ6r46u2PcYFySJElStjEYV4crrFzGcQ+816nTO0BeDMaXBtOpr6iETbV2jUu9UjQKU/4NTr8M8kqDscp18JNz4F/fh7R/N0iSJOngdHbHuFOpS5IkSco2BuPqUINX/YmZC99DceUyAJJRp04/VEe1WWf88Q0hFiIpfEOmwrwbYMCk4H6qCRZeBb96v1OrS5Ik6aDYMS5JkiSptzEYV4eINtUz6emvcNSTXySWrAOgrnAob0xz6vRDNaVv6zrjj663K1Tq9Qr7wplXwhEXtI698Xf44amw5unQypIkSVL3kkh0/hrjdXWdd35JkiRJOlgG4zpkhVVvceyi9zHsrd+2jG0bdCJvTL3MqdM7wISyJLnRIBB/bAOknTJZUjQGUy+C0y6DvJJgrGIN/HgePH6LU6tLkiTpbXV2x3hurh3jkiRJkrKLwbgOyaDVf+W4he+mpOJ1AFLROKvGf5g14y926vQOkhuDSX2C6dTX18DKypALkpQ9hk4LplbvPyG4n2qCf1wBv/4g1O0ItTRJkiRlr3S6azrGDcYlSZIkZRODcbVLNNnAhGf/h8lPfIGcphoA6gsG88bUy9kx6ISQq+t5pradTt11xiW1VdgPZl0Fk85rHXv9r8HU6uueDa8uSZIkZa1EIth3dsd4fX3nnV+SJEmSDpbBuA5aQfVqjnnwIkYs/2XL2PYBM3lj2mXUFw0NsbKeq+0644+5zrik3UVzYNoH4NQvQW5xMLZzNdw5F564zanVJUmStIvGxmBvx7gkSZKk3sRgXAdlwNp/MHPhBZTueAUIpk5fPe5DrD78I6Ri+SFX13ONKUlRnNO6zngyZcglaS+GHZ2ZWn18cD+VgPu/DL/5d6jbGWppkiRJyh7Nwbgd45IkSZJ6E4NxHZBIspHDn/86Ux/7T3IS1QDU5w/kjalfZvvgkyESCbnCni0WgaPKg67xikZ4dXvIBUnKXkX9YdY1MPEdrWOv/QVuPw3WPx9eXZIkScoadoxLkiRJ6o0MxvW28mvWccw/P8DIN+9uGdvR/xjemHYF9UXDQ6ysd5nar8064+tDLERS9ovmwPQPwalfhNyiYGzHSrhzDjx1h1OrS5Ik9XJd1TFuMC5JkiQpmxiMa7/6r3+QmQsvoGz7SwCkIjmsGft+Vk34GKkcp07vSlPKky23H9tgqCXpAAybEUyt3m9scD/ZCH/7b/jtR6C+ItTSJEmSFB47xiVJkiT1Rgbj2qtIKsG4F29k2qP/QbwxCE8a8vvz5tQvsW3IaU6dHoKhhSn656UAeHoT1DcZjks6AEUDYNa1MOGc1rFX74UfngYbXgytLEmSJIWnqzrGGxudrEiSJElS9jAY1x7yajcy45//zujXf9QytrPfNN6YdgV1xSNDrKx3i0RgSt9gOvX6JDy3JeSCJHUfsRw4+mI45b8gXhiM7XgLfnQWPH2nn1ZKkiT1Ml3RMZ6bG+ztGpckSZKULQzGtYt+Gx5m5oLz6LPtOQBSkRhrx7yPlRP/H8mcwpCr05S+rdOp/2u9QZakgzT82GBq9b6HBfeTDfDXS+H3H4OGqnBrkyRJUpfpio7x5tDdYFySJElStjAYFwCRVBNjX/4O0x/5OLmNOwFozOvLsin/zdZhs5w6PUtMzXSMAzy8LsRCJHVfxQNh9rVw+LzWsSW/D6ZW3/hyeHVJkiSpy3TVGuNgMC5JkiQpexiMi9y6zRz90EcY89ptLWMVfafw+rSvUFsyJsTKtLvyvDSHlQRd4y9vg611do1LaodYHGZ8BE7+AsQLgrHty+FHs+HZnzi1uiRJUg/XVWuMA9TXd95rSJIkSdLBMBjv5co3PcbMBedTvuUpANJEWTf63bw16VMk40UhV6e9md7PrnFJHWTETJh7PZRnvgTVVA9/+Rz84ZPQUB1ubZIkSeo0doxLkiRJ6o0MxnurVJIxr/yAox+6hLyGbQA05vZh2ZRL2TJ8jlOnZ7Gj2wTjD62zq1PSISoZDGd9FcbPaR17+Tdw++mw6ZXQypIkSVLnsWNckiRJUm9kMN4L5dZtYfojH2PsKz8gQhCsVpYfyevTr6SmdFzI1entTChLUhAL3reH10EyZTgu6RDF4nDMR+Gkz0FOZmr1bW/CHbPguZ85tbokSVIPk0gE+87sGG8Oxu0YlyRJkpQtDMZ7mb6b/sXMhefTb9NjAKSJsH7UBaw44jMk48UhV6cDkROFqX2DrvEdDbBkW8gFSeo5Rp4A874BfUYH95vq4M/z4Y//AY01oZYmSZKkjtOVHeMG45IkSZKyhcF4LxFJNXHYyzcx/aGPkle/FYBEbhnLJn+BzSPmQcRLoTuZvst06iEWIqnnKRkCc74K42a3jr30a7j9DNj8Wnh1SZIkqcN05RrjTqUuSZIkKVuYhvYCebUbOXrxxRz22v/tOnX6tK9QU3Z4yNWpPY7u7zrjkjpRLBeO/Tic8J+Qkx+MbX09CMef/0W4tUmSJOmQNTZCJALRTvxUqDkYt2NckiRJUrYwGO/h+q3/JzMXnEf51mcASBNl/eh3seKIz9CUWxpydWqvAflpRhQlAXh+C1Q0GI5L6gSjT4K534A+I4P7TXXwp0/DvZ+Gxtpwa5MkSVK7NTYGwXUk0nmv0TyVuh3jkiRJkrKFwXgPFUk2Mu7FbzL90f9HbuNOABrz+vLmlP9i8/C5Tp3eAzRPp55Kw6PrQy5GUs9VOhTOug7Gntk69sIv4I4zYcvr4dUlSZKkdmsOxjuTHeOSJEmSso3paA+UX72GY/75AUa/fmfLWEXfqbw+7SvUlo4NsTJ1pKP7OZ26pC6SkwvHfRJOmA85ecHYltfg9tPhhV9C2r+DJEmSupOuCMbtGJckSZKUbQzGe5iBa//BzIUXULb9JQBSkRhrD/s33pr0HyTjRSFXp450RJ8kudEgjFq8FlIGU5I62+iTg6nVy0YE9xO1cO+n4A+fhIaqcGuTJEnSAWtshJyczn2NWCzY7BiXJEmSlC0MxnuIaLKBCc9dy5TH/pN4IggnGvL78+aUL7F16Jmdu3CYQpEbgyl9g67xzXWwZFvIBUnqHUqHwZzr4LAzWsde/g3cdgqsey68uiRJknTAuqJjHILXMBiXJEmSlC0MxnuAwqqVHLvo3xix7BctYzv6z+D1aV+hrmRUiJWpsx3bv3U69QfW2DEuqYvk5MHM/wcnfhZyCoKxHW/BnXPgsZshlQq3PkmSJO1XV3SMA+TlOZW6JEmSpOxhMN7NDdv+GDMffC8lO18DIBWNs2bcB1k14eOkmsMK9VjHDmgNxhetDrEQSb3TqBPh7Bug39jgfioBC74Cv7oQaraGW5skSZL2yY5xSZIkSb2RwXh31VhL7L7Pccyq28hpqgWgvmAwb0y9jG2DT3Hq9F6ib16acaVJAF7ZDuur7RqX1MWKB8Gsa2HSO1vH3lwAt54EKx4Kry5JkiTtU1d1jOfm2jEuSZIkKXsYjHdX9/4H0Rdbp07fNvAE3ph2OfVFw0IsSmE4tn+i5faitSEWIqn3iuXAtA/C6ZdDXlkwVr0Rfno+LPoaJJv2/3xJkiR1qa4Kxu0YlyRJkpRNDMa7q9MvJ51TQFM0j5XjP8Kawz9MKpYXdlUKQdvp1B9Ybce4pBANmQpnfxMGTc4MpOGRb8NPzoGKNaGWJkmSpFZdNZV6bq7BuCRJkqTsYTDeXQ2cRPL823howrVsH3h82NUoRGOKU/TPSwHw+AaoSRiOSwpRQR8443KY+n6IZP6ZseZJcn50OkN2Ph1qaZIkSQp0Zce4U6lLkiRJyhYG491YeuK5VOcPDbsMhSwSae0ab0zBI+tCLkiSIlE44nyYfS0UDQiG6is47q0fEP37FyFRF3KBkiRJvZtTqUuSJEnqjQzGpR6g7TrjD6yxY1xSlug/HubdACNaZzaJPfdjuGMWbF4aYmGSJEm9W1cF47m5doxLkiRJyh4G41IPcFR5kvxYEIj/cy0kU4bjkrJEbhGc9DmaZnycZCSzkOXmV+D20+CpOyDt31eSJEldraHBjnFJkiRJvY/BuNQD5MZgWt9gOvVt9fD8lpALkqS2IhHSY2fx0IRrSZeNCMaa6uFv/w2//Deo3hxufZIkSb1MY2MQWnc21xiXJEmSlE0MxqUeYmZmnXGA+1fZgSkp+1QVDKdp1nVw+LzWwTcXwK0nwhsLwitMkiSpl3EqdUmSJEm9kcG41EMcOyBBLBIE4n9fBWmnJ5aUjXJyYcZH4LQvQ15ZMFazBX75Pvjrf0OiLtTyJEmSeoOu7Bh3KnVJkiRJ2cJgXOohiuMwpW8SgHXV8PK2kAuSpP0ZOh3OuRGGHt069vQdcPvpsPHl0MqSJEnqDewYlyRJktQbGYxLPcgJAxMtt/+20o5xSVkuvwxO/SIc81GIZVqWtiyFO86Ex26GVCrc+iRJknooO8YlSZIk9UYG41IPMnNAE1GCQPz+lU6nLqkbiERg/ByYewP0GR2MJRthwVfg5++Cyg2hlidJktQTdVXHuMG4JEmSpGxiMC71IGW5aY4sD6ZTX1kFr+0IuSBJOlBlw2DO12DiO1rHViyGW0+A1/4SWlmSJEk9UVd1jDuVuiRJkqRsYjAu9TBtp1O/3+nUJXUnsThM/xCc8RUoKA/G6nbAPR+CP/8nNFSHW58kSVIP0ZVrjNsxLkmSJClbGIxLPczxA5uIZKZT/9vKcGuRpHYZPBnOvhGGH9c69txP4baTYNXj4dUlSZLUQ7jGuCRJkqTeyGBc6mH65qWZWBZMp76sAt7cade4pG4orwRO/gIc9/8gJy8Y27ESfnw2LLgKEs7JKUmS1F6JhB3jkiRJknofg3GpBzphYFPL7b+vDK8OSTokkQiMPQPmfRP6T8gMpuGx78MdZ8CGl0ItT5Ikqbvqyo7xZBKamt7+WEmSJEnqbAbjUg90wqDWdcb/vCJNOm3XuKRurGQwzLoGpn4AopnWps2vBuH4w9+CpJ+0SpIkHahUKgiqY7HOf63c3GBv17gkSZKkbGAwLvVAA/LTTCoLgqJlFfDajpALkqRDFY3CEefB3G9An1HBWKoJHrwO7poLW5eFW58kSVI3kch8j7qrOsbBYFySJElSdsiKYPyWW25h9OjR5OfnM3PmTJ566ql9HnvHHXdwyimnUF5eTnl5ObNnz97j+I985CNEIpFdtnnz5nX2jyFllVOHtHaN/2m5HeOSeog+I2HO1+GIC4Kp1gHWPQO3nQxP3h60QEmSJGmfGhuDfVetMQ5QX9/5ryVJkiRJbyf0YPyee+7h0ksv5ZprruG5555j6tSpzJ07l82bN+/1+MWLF/P+97+ff/7znzz++OOMGDGCOXPmsG7dul2OmzdvHhs2bGjZfvWrX3XFjyNljZMGNhGLBIH4X96ClNOpS+opYjkw9SKYfW0wzTpAUx38/Yvw83dBxdpw65MkScpizcG4HeOSJEmSepvQg/HvfOc7fOITn+CSSy7hiCOO4LbbbqOwsJC77rprr8f/4he/4NOf/jTTpk1j4sSJ/OhHPyKVSrFo0aJdjsvLy2Pw4MEtW3l5eVf8OFLWKM1NM61vMJ36+hp4elPIBUlSR+t/OMy7AcbPaR1bsRj+7wR47mfgF4IkSZL2YMe4JEmSpN6qC34N2rfGxkaeffZZLr/88paxaDTK7Nmzefzxxw/oHLW1tSQSCfr27bvL+OLFixk4cCDl5eWceeaZXHfddfTr12+v52hoaKChzdeXKysrAUgkEiQSib0+Jxs015byc3/tw8mDEzy7LfiK/h+Xpzl6QDCeSKZ32UsHymtH7dVp104kD6ZfQmTIDGJP/5BI3XZoqIQ/zye15A8kz/kOlA3v2NdUl2r+9042/5tM2clrR+3V2deO16TC1pUd483BuB3jkiRJkrJBqMH41q1bSSaTDBo0aJfxQYMGsXTp0gM6x5e//GWGDh3K7NmzW8bmzZvHu9/9bsaMGcPy5cu54oorOPvss3n88ceJxWJ7nOP666/n2muv3WN8wYIFFBYWHuRP1fXW1WV/jQrHsCLIjaZpTEX484o0x5Y3kdNmnoiFK5rCK07dmteO2qvzrp1JxMd9naPW/ZyR2/8FQHTFgyT/73heGfZ+VvU7vXVNcnVLCxcuDLsEdVNeO2qvzrp2amtrO+W80oHqyo5xp1KXJEmSlE1CDcYP1Q033MCvf/1rFi9eTH5+fsv4RRdd1HJ78uTJTJkyhbFjx7J48WJmzZq1x3kuv/xyLr300pb7lZWVLWuXl5aWdu4PcQgSiQQLFy5kWEEtUT/r1z7MHFDAI5vi1DZFKC7K4czhERLJNAtXNHHWYTnEY148OnBeO2qvrrl2+sCk+TStP5HYsz8iUreDeKqeaWt+zJTYcpLn3gR9RnbSa6uzNP9756yzziLeFa1t6jG8dtRenX3tNM9QJoUljI5xp1KXJEmSlA1CDcb79+9PLBZj06ZdFz/etGkTgwcP3u9zv/3tb3PDDTfwwAMPMGXKlP0ee9hhh9G/f3+WLVu212A8Ly+PvLy8Pcbj8Xi3+BAtGsFgXPt06uAEj2wKruP73oK5o1ovlngsYripdvHaUXt1ybUzYgYMmgjP/yxYcxyIrnyY6O2nwFnXwjEfg2h0/+dQ1uku/y5T9vHaUXt11rXj9aiw2TEuSZIkqbcK9VPh3NxcZsyYwaJFi1rGUqkUixYt4oQTTtjn82688Ua+9rWvcf/993PMMce87eusXbuWbdu2MWTIkA6pW+pOpvVroiSeAmDhaqhqdG1oSb1AbhHM/A84/XIo7BeMJWrgb/8Nd78Ttq8Itz5JkqSQ2DEuSZIkqbcKvV3q0ksv5Y477uDuu+/mtdde41Of+hQ1NTVccsklAFx88cVcfvnlLcd/85vf5KqrruKuu+5i9OjRbNy4kY0bN1JdXQ1AdXU1X/ziF3niiSdYuXIlixYt4vzzz2fcuHHMnTs3lJ9RClM8CicPCtb0rU8GXeOS1GsMmQrnfAvGtpkxZtWjcOtJ8MStkEqGV5skSVII7BiXJEmS1FuFHoxfeOGFfPvb3+bqq69m2rRpvPDCC9x///0MGjQIgNWrV7Nhw4aW42+99VYaGxt573vfy5AhQ1q2b3/72wDEYjFeeuklzjvvPA4//HA+9rGPMWPGDB555JG9Tpcu9Qazhja23P7Nm3aMS+pl4oVw3CfgjK9AUf9gLFEL918Gd82FTa+EW58kSVIXCqNj3GBckiRJUjYIdY3xZvPnz2f+/Pl7fWzx4sW73F+5cuV+z1VQUMA//vGPDqpM6hnGlqQYXZxkZXWM57fAsp2G45J6ocGT4exvwYu/gjcXBGNrn4YfngonfR5O/SLE80MtUZIkqbOF0THuVOqSJEmSskHoHeOSOl8kArOGJlru/355iMVIUpjiBXDMR2HWNVAyNBhLNcEj34ZbT4S3Hgm3PkmSpE7WlR3jkUjwOnaMS5IkScoGBuNSL3Ha4AQ5kaBT/N7lkEyFXJAkhWngJDj7Bjjy3RCNBWPbl8Pd74A/zYe6HeHWJ0mS1Em6smMcIC/PjnFJkiRJ2cFgXOolSnPTHDegCYCt9fDqzkjIFUlSyGK5MOXfYN4N0H986/jzP4Obj4Mlv4e0S09IkqSepSs7xptfx45xSZIkSdnAYFzqRdpOp/7kZoNxSQKgbATMvjaYYj2nIBir2Qy/+yj88kLYuSbc+iRJkjpQV3eMG4xLkiRJyhYG41IvMq1fE/3ygjnUX9kRYUudnZCSBEAkCuPnwLnfhmHHtI6/+Q+4ZSY89gNIJvb9fEmSerDrr7+eY489lpKSEgYOHMgFF1zA66+/HnZZaqe6uiAUj8W65vVyc51KXZIkSVJ2MBiXepFYBM4YEgQ7KSL8flnIBUlStinsB6f+N5x8KRSUB2OJGlhwJfzwVFj1eLj1SZIUgoceeojPfOYzPPHEEyxcuJBEIsGcOXOoqakJuzS1Q1UVFBV13evl5toxLkmSJCk7GIxLvczsoY1ECDrFf/UGJFN2jUvSHkYcB+d8G8bNBjJLT2x+FX48D+79NFRvCbU8SZK60v33389HPvIRjjzySKZOncpPfvITVq9ezbPPPht2aWqHqiooLOy617NjXJIkSVK26KIVpSRli8GFaab3a+K5bXHW18CDa+GskWFXJUlZKLcIjv04HHYGPHMnbF8RjL/wC1h6H8y6BmZ8BKJdNA+pJElZoqKiAoC+ffvu85iGhgYa2rQJV1ZWApBIJEgkXJ4kTLW10KcPpA/wO9LpdGKX/cEqLQ2Ccd/27qf5z6p/ZnsX3/fey/e+9/K9751833uvnvjeH8zPYjAu9UJnD0/w3LY4AD99Lc1ZIyMhVyRJWazfWDjrOlj+ALz4a0jUQn0F/PVSeP7n8I7vwNDpYVcpSVKXSKVSfP7zn+ekk07iqKOO2udx119/Pddee+0e4wsWLKCwK9uVtYfTTgu2zHcVDlhV1cJ2vd4VVwT7v/2tXU9XFli4sH3vvbo33/fey/e+9/K9751833uvnvTe19bWHvCxBuNSLzS9XxP98tJsa4jwyHpYUZHmsDLDcUnap2gUxs+B4ccFHeMrHwnG1z8Ht58RdJaf+ZXWdcklSeqhPvOZz7BkyRIeffTR/R53+eWXc+mll7bcr6ysZMSIEcyZM4fS0tLOLlP78eEPw6pVcN11B3Z8Op2gqmohJSVnEYnED/r1/vd/oaYGFiw46KcqZIlEgoULF3LWWWcRjx/8e6/uyfe99/K9771873sn3/feqye+95UH8a1fg3GpF4pG4KRBKf68Opj+9+dL01w902Bckt5WQR844TNw2OnwzI+hci2QhqfvgFf+AGdeBUdf7PTqkqQeaf78+dx33308/PDDDB8+fL/H5uXlkZeXt8d4PB7vMR++dFfbt0MsBpGD/BUwEom3KxjPz4e33gLf9u7LP7e9k+977+V733v53vdOvu+9V0967w/m54h2Yh2SstjxA9PkRoNF5X67DGoTB7jAnCQJBh0J826AqR+AWOZD/9ptcN/n4fbTYdVjYVYnSVKHSqfTzJ8/nz/+8Y88+OCDjBkzJuySdAgqK6ErZ7MvLoYdO7ru9SRJkiRpXwzGpV6qKA4nDUoAUNUIf34r5IIkqbuJ5cAR58G5/wsjT2gd3/gS/Phs+N1HoWJtePVJktRBPvOZz/Dzn/+cX/7yl5SUlLBx40Y2btxIXV1d2KWpHaqruz4Yr6jouteTJEmSpH0xGJd6sXnDG1tu//iVNOm0XeOSdNCK+sNJn4NZ10CfUa3jS34PPzgGHroREgYHkqTu69Zbb6WiooLTTz+dIUOGtGz33HNP2KWpHaqqoKCg616vuBhqa6Gx8e2PlSRJkqTOZDAu9WLjS1NMKGsC4PWd8NC6cOuRpG5t4CSYez0c+3HILQnGmurgn1+HW46DV/8EfgFJktQNpdPpvW4f+chHwi5N7VBV1bUd4yWZfxbt3Nl1rylJkiRJe2MwLvVyF4xq/dr+7UsMbCTpkESjMG42vPO7cPg8iGT+qbVzNfzmYrj7nbD++XBrlCRJvVoYU6mDwbgkSZKk8BmMS73ccQOaGFKQBOCxDbBkq+G4JB2y3GKY8RE4+5swaHLr+MpH4PbT4Q+fhJ1rwqpOkiT1UokENDR0/VTqADt2dN1rSpIkSdLeGIxLvVwsAue36Rr/oV3jktRxykbAGVfAKf8FxYNbx1+6B34wAx74H6ivCK08SZLUu1RVBfuuDMadSl2SJElStjAYl8QZQxKUxVMA/G0lrKkyHJekDhOJwPBj4Zxvw9EfDrrJAZIN8Oh34fvT4cnbIZkIt05JktTjNQfjYUylbse4JEmSpLAZjEsiLwbnjAi6xpNpuOtVg3FJ6nCxHJhwNrzzezDxnRDNCcZrt8Hfvwj/dzy8dh+k/TtYkiR1jjCC8YICiMXsGJckSZIUPoNxSQCcPTxBbjQIY+55A7bXG8xIUqfILYLpH4RzvwOjTmwd37YM7vkg/PgcWP1EePVJkqQeq7o62HdlMB6JBF3jdoxLkiRJCpvBuCQASnPTzB4aTONb2wQ/esVgXJI6VfFAOPGzMOc6GDCxdXz1Y3DXXPjF+2DDS+HVJ0mSepww1hiHYJ1xO8YlSZIkhc1gXFKLd49uICcSBOJ3v2rXuCR1iX7jYNY1cMp/QcmQ1vE3F8APT4HffRS2LQ+vPkmS1GOEMZU6BB3jBuOSJEmSwmYwLqlF//w0c4YFXeM1do1LUteJRGD4sXDOt+G4T0Jhv9bHlvwebj4W/vxZqFgXXo2SJKnbC6tjvKjIqdQlSZIkhc9gXNIu7BqXpBBFYzD2THjHd2H6xZBXEoynk/Dc3fD96fCPr0DNtnDrlCRJ3VJVFcTjwdaVXGNckiRJUjYwGJe0C7vGJSkLxHJh4jnwzu/D5PdBPNPWlWyAx2+G702FRV+F2u3h1ilJkrqVqqqun0YdDMYlSZIkZQeDcUl7sGtckrJEvACOek8QkE98B8Qy7V2NVfDI/8JNk+GBa+0glyRJBySsYLykxDXGJUmSJIXPYFzSHnbvGv+/lwzGJSlUeSUw/UPwju/BuNnBlOsAjdXw6HeCgHzhNVCzNdw6JUlSVrNjXJIkSVJvZjAuaa/eM7qB3GgQiP/0NVhTZTguSaEr7AvHfjwTkJ/VGpAnauBfN8FNU2Dh1QbkkiRpr6qrw+sYr6iAtL9WSpIkSQqRwbikveqXn+a8kY0ANKbgxmf9BEOSskZRfzj2Y0FAPn4ORHOC8UQN/Ot7QQf5giuhalO4dUqSpKxSVQUFBV3/usXFkExCTU3Xv7YkSZIkNTMYl7RP7x7dQFk8BcBf3oIXthiOS1JWKeoPx3wU3rl7QF4Lj/0gCMjv+wJsXxFunZIkKStUVoYXjIPTqUuSJEkKl8G4pH0qzIELD2touf+Np9OknftOkrJPYb9MQP59OHweROPBeLIBnrkLfjADfvdR2PBSuHVKkqRQhdkxDrBzZ9e/tiRJkiQ1MxiXtF9zhiUYWpgE4KlNsHB1yAVJkvatsC/M+EjQQT7xHZCTH4ynU7Dk9/DDU+Dn74GVj7rIpyRJvVDYwbgd45IkSZLCZDAuab9yonDxuF27xuubDFMkKasV9oXpH4Lzb4YpF0Jeaetjyx6An5wLd54Fr90HqVR4dUqSpC5VVQWFhV3/uiUlwd6OcUmSJElhMhiX9LZmDmjiyD5NAKysgtuXhFyQJOnA5BbDke+C824OplovGtD62Nqn4Z4Pwi3HwlN3QGNNeHVKkqQuEVYwbse4JEmSpGxgMC7pbUUi8MmJ9UQjQaf4zS+lWVVp17gkdRs5uTB+DrzjJjjhP6HPyNbHti2Dv/03fGcSLLwaKtaGVqYkSepc1dXhTKUej0N+vh3jkiRJksJlMC7pgIwqTnHeyEYAGpPwP0+mSbs+rSR1L9EYjD4J5n0TTvsyDDyy9bH6CvjX9+CmKfDbS2DtM+HVKUmSOlxjIyQS4XSMQzCdusG4JEmSpDAZjEs6YBeOaaBfXrAW7T/Xwj9Wh1yQJKl9IhEYOh1mXQXzboAxp0E0J3gsnYRX/gA/mgU/mg1L/gDJpnDrlSRJh6yqKtiHFYwXFzuVuiRJkqRwGYxLOmAFOfCxw+tb7l/7RJqqRrvGJalbKx8Nx38qWIf8qPdAXmnrY2ufht9dAt+bAg/dCFUbQytTkiQdmmwIxu0YlyRJkhQmg3FJB+WEgU1M7xd0Dm6ohW88bTAuST1CQR+Y/D44/2Y47v9BWZt1yCvXwT+/Dt89En5zMax4CFxOQ5KkbqU5GA9jjXGAoiI7xiVJkiSFy2Bc0kGJROA/JtaRHwsCkV+9AYvXGo5IUo8Ry4WxZ8DZ34QzvgLDZgCR4LFUE7z6J/jpeXDzsfDErVC3M8xqJUnSAQo7GC8pMRiXJEmSFC6DcUkHbVBBmo+Mb51S/bJ/paloMByXpB4lEoHBk+HUL8J534cjLoC8stbHt70J918G/zsR/vQZWPecXeSSJGWxbJhK3WBckiRJUpgMxiW1y9xhCab2DaZU31gLX33KMESSeqyiATD1Ijj/FjjxszBwUutjTXXw/M/hjjPgtlPgidugdnt4tUqSpL3KhmDcNcYlSZIkhclgXFK7RCIw/4g6CjNTqv9+GfxjleG4JPVosRwYdSLMugbO+RaMnwvxNvOxbnoZ7v8y/O8E+M2H4c0HIJUMr15JktSiujrYhxmMV1SE89qSJEmSBAbjkg7BgPw0H5vQOqX6Fx9Ns6bKcFySeoWyEXDMJXD+rXDsJ6DfuNbHko3w6r3wi/fATZPhwetg+4rQSpUkSUHHeF4exGLhvH5xcVBDU1M4ry9JkiRJBuOSDsmZQxKcODABQGUjzF+cpjFpOC5JvUY8H8bNgjnXBV3kE8+FvNLWxyvXwcPfgu9Ph5+8A577GdTbLiZJUlerqgqvWxygpCTY2zUuSZIkKSwG45IOSSQCnzmijkEFKQBe3Ao3PGMwLkm9UtkImP7vcMH/wSn/BcNmQKTNPzdXPgJ/ng/fGg+/uRheuw+aGsKrV5KkXiTsYLy4ONjv2BFeDZIkSZJ6t5ywC5DU/RXlwJcm1/Llp4toSke461U4bnCaeaMiYZcmSQpDNAeGHxtstduDQHzFYqjaEDyebIBX/xRs+WVwxAUw5d9g5IkQ9XubkiR1hrCD8eaO8Z07w6tBkiRJUu9mMC6pQ4wtTfHRw+u5/fUCAP77kTRjSmFCueG4JPVqhX3hiPNh0nmwbRms+hesegwaKoPH6yvgubuDrXQ4TH4PHPVeGDw5mJZEkiR1iKoqyM8P7/XtGJckSZIUNoNxSR3m7OEJXt2Zw6Ob4lQn4KMPpPnTO6B/gcGGJPV6kQj0Hx9s0z8EG1+Glf+CdU+3TqdeuRb+9b1g63tYEKgffi6kXaJDkqRDVVUFBQXhvX5zMG7HuCRJkqSwGIxL6jCRCPznEXVsqI2yvCrGumr4fw+m+cVcyM8xHJckZURzYOj0YGuqh7XPBJ3kG16EdCo4ZvsKePS7xB/9LrNzBxDNfxqOejcMO9pOckmS2qGyMtxgvLAw+F+4wbgkSZKksLiIo6QOlReDK6bW0jcvCDae3QyXP5YmbbefJGlvcvJh9Mlw2pfhgtvgmI/CoCN3Cb+LGrcQe+Jm+NGZcNNk+MdXYPUTkEqGWLgkSd1LdXW4a4xHo0HXuFOpS5IkSQqLHeOSOly//DRXTK3limeKaExF+ONyGFCQ5vJjIGKXnyRpX/JLYfycYKuvhLVPkVr9JGx6hSiZTvKKNfD4zcFW2B8OnwcTzoaxZ0BuUbj1S5KUxaqqYPjwcGsoKbFjXJIkSVJ4DMYldYpxpSm+cFQdN75UQJoIty+Bkjj857SwK5MkdQv5pTBuNskxs3jgte2clfciOeuehI1LIJ3pFK/dCi/8PNhieXDYaUFIfvjZUDok3PolScoyVVXhdoyDHeOSJEmSwmUwLqnTnDCwif+YWM+tS4OF7P73+TTFuXDJEXaNS5IOXGNOCenDzoDxZ0JDNax7BtY9BxtfhKaG4KBkA7y5INj4QrB++eFnw7jZMHQaRGNh/giSJIWuqircNcYhCMbtGJckSZIUFoNxSZ1q7vAEdckIP3kzH4Brn0yTF4MPTDAclyS1Q14xHHZ6sCUbYdMrsO7ZYKtr04K2/vlgW/wNKOgLY8+EcbNg7CwoGRRW9ZIkhSKdDn+NcYCiIjvGJUmSJIXHYFxSp7tgVCM1TRF++1YeAFc8lqY2AR8/ynBcknQIYrlBZ/jQ6XDMx2DHW60h+Y6VrcfVbYclvws2gEGTg5B83GwYMRNyckMpX5KkrtLQAE1N2dExvm1buDVIkiRJ6r0MxiV1iQ8c1kAiBfeuCsLx655OU52Az02DSMSAXJJ0iCIR6HtYsE1+H9RshQ0vBtumlyFR13rsppeD7V83QbwIRp0IY04NtsGTnXZdktTjVFUF+7A7xktKYPnycGuQJEmS1HsZjEvqEpEIfHhcAwWxNL9aEUyrftMLaSoa4cpjIRY1HJckdaCi/pmu8FmQaoJty1qD8u0rWo9L1MCyhcEGkF8Go08JtjGnwsBJwf/EJEnqxpqD8S7rGE+nidBILF1DLF1DItKPVLSQ4mKnUpckSZIUHoNxSV0mEoELD2ukIAZ3ZdYc//GrsLoqzfdOg+K4wYMkqRNEc2DAxGCbciHUV8DGl4OQfOPLUL+z9dj6Clh6X7ABFA2A0SfDqJNg5PEw8Ag7yiVJ3U51dbDvrI7xWKqSvg0L6Fd/H+UN/yCe2kqUppbH00Soi42ndNp0Bm+fRnrzyUQGnAiRaOcUJEmSJEl7YTAuqcudN6qRwpw0/7c0n1Q6wqI18N6/pvnRbBhebDguSepk+WVB2D36ZEinoXI9bH4FNi2BTa9BY1XrsTVb4JU/BhtAXikMPxZGnhAE5cNmQG7I89JKkvQ2Omsq9T4NixlZ9Q36NC4mSoK62Bh25p5OY2wwqUg+KfJJRfKJp7ZSkFzOkKLXuPyd9xF5oAaKRsNhH4ExF0PxmI4tTJIkSZL2wmBcUihmD0swoCDFjS8VUtMUYekOOP8vaW46FU4ZZjguSeoikQiUDQu28XMgnYKda2DTK0FYvvnVXdcnb6iE5YuCDYJu9CFTYcTxQVA+4jgoGRzOzyJJ0j50dDBe0PQGYyu+SP+GP1MTm8j6wv+gMvcEGmND9vu8V1fBHXek+OOPX6ZP7f3w6jfh5f+BQWfCUVfDoNM6pkBJkiRJ2guDcUmhmdo3yY3H1vD1FwtYXxtjWz1cvCDNp6ek+cL0CDmuOy5J6mqRKJSPCraJ50AqCTtWwtbXYUtmazv1eqoJ1j0bbE/cEoyVDoNhRwfd5MNmwJBpkF8awg8jSVKgo9YYz0ntZHTV/zC05hYS0f6sLL6KnblnBF80OwAFBZAmyg6m0ueIqXD4Z2HLw7DmD7Do9CAgn3IdDDjh0AqVJEmSpL0wGJcUqmFFKb55bA3fXVLIc9tySAO3vARPbkxz02lOrS5JClk0Bv3GBtuEc4Kp16s3wdY3YMvSICivXLfrcyrXBdtrf8kMRGDAhExQngnMBx4JObld/uNIknqnjgjGCxJLmbrjAnJTG9lYeAlb8t9DOpJ3cOfIvH7zmufkFMCQuTB4Dmx5FN76CSw8EYbMg+nfhj5Htr9gSZIkSdqNwbik0JXE4cpptfxpVS4/X55HMh3hmc0w549pLjsGPjQRogfYgSBJUqeKRIKp0ksGw5hTg7GGStjyRtBVvm05bF8BTfVtnpTOhOhL4YVfBEPROAyYCEOmwODJwTboKCjo09U/kSSpF6iqgvx8iMXaf46p22ZBtITXy26nMTasXecoyEzl3hKMN4tEYOApMOAk2LwYlv8Y/j4NJnwWJl8DcWdekSRJknToDMYlZYVoBN41upEjypN8++UCttRHqW2Cq59I85e34IaTYGyZ4bgkKQvllcLwY4INIJWCqvWwbVkmKF8OO1ZBOtn6nFQCNr0cbG31GZUJyjOB+ZApwdTsfkFMknQIqqraub54OsXIym+whGlUx6eyofhSUpH2t523dIxX7eOASDSYTn3AybD6t/Dm/8HKnwfd46M/5P8PJUmSJB0Sg3FJWWVCWZKbjq/m7jfzWbAumGL26U0w7940H5mUZv7UCGV5fhgiScpi0SiUDQ+2w04PxpKNQTi+fXkQmO9YCZXrIZ3a9bk7VwXb0vtax/LKYODEYDr2AZMytycFXesGBJKkA9CeYDySTjBpx4foU38fS4p+yZqiy4lGoodUR24c4jl76RjfXTQXRn8QBp8Fb94Gj18My+6A426HsomHVIMkSZKk3stgXFLWKcqBT0+q55RBCW5+rYBNdVESKbjjFfj9sjRfOBouOhziUcMASVI3EcuF/uODrVlTI1SsCULynata900Nuz63oQLWPBlsbeWXBQH5gAkwcFIwNXv/w6FkSBDOS5KUUV19cOuLR9JNTNrxQfrX/5EVRddkBjvm96/CwtY1z99W/kCYfDVsPxeWfhf+PhWOvAKOuAxiB7e+uSRJkiQZjEvKWpP7Jvne8dX8YWUe967KpTEVYXsDXPV4mh++DPOnwnvGGZBLkrqpnFzoNzbYmqVTULWxNSjfsQoq1kLt1j2fX18Ba54ItrbihdD3sOC8fcdCv3GZbSwU9rPLXJJ6oYPpGA9C8Q/Rv/6PrCq5hsr4SdBQ2WG1FBTCzoqDfFLfGTDzTlj5M1hyHaz8Fcy8HQae2mF1SZIkSer5DMYlZbX8GHxgbAOzhzbys2X5PLIpDsDaarjsX2lufhE+cSS8dzwUxf2gX5LUzUWiUDo02Eae0DqeqIWKdUFIXrEWKjP72m17niNRC5uWBNvu8suCkLxvJpAvHx2sa95npJ3mktSDVVUdWMd4JN3ExB0XM6D+d6wsvpqK3JMhne7QWgYOhFUr2/HEWB6M/TgMmgVLvwMPnAaHfRSm3wh5/Tq0RkmSJEk9k8G4pG5hYEGa/5pcxztHNvLrFXk8ty3462ttNVzzZJpvPw8XjU/z75MijCwxIJck9TDxwj2nYoc9A/PqjVC5Aao3QTq553nqK2Dds8G2u1gulI2A8lGtYXn5KOgzOtjbbS5J3dYBBePpFBN2fpSB9b9hZfFVVOR1Tjf20CHwxJNvf9w+FY+BGd+DdffB8jtg7Z/g6O/AmH/3/1OSJEmS9stgXFK3cnhZkqun17J0Z4xfr8jjhe3BX2NVjcEa5He8kmbm4DTvHRfhnNF2kUuSerh9BeapJNRsgaoNma05MN8ANduAvXT/JRth+/Jg29drlQ6F0mGZbSiUtbldOgwKyg0lJCkLVVYGndr7M6bqSgbV/ZxVxVdSkXdap9UyZCjs3Anbd0Df8naeJBKF4efBgJPhzVvgiQ/Dih/DMTdDnyM7slxJkiRJPYjBuKRuaWKfJP9zdC0rq6L8ZU0uD2+Mk0gFH8Q/uRGe3Jjm6idg7sg0c0dFOHWYIbkkqReJxqBkcLAxfdfHmhqDjvLqjVC9OQjQm/c1m6GpYe/nTNTCtmXBti85Ba1TwZcOg9IhUDQQigdC8aDMNjCY0t0AXZK6TFUVjBq178eH1NzBqOrrWVf4H+zMO7NTaxk6NNivWA59jznEk+X1haOugiFz4Y2b4e9TYfxnYMr/QG57U3dJkiRJPZXBuKRubXRJiv88op6LxzXwwPo4D66Ps642BkBdE9y7Au5dkSY3BicPSTN7ZIRThsIIp1uXJPVWObnQZ0Sw7S6dhoaq1pC8JTjfArVbgjXN9xWcAzTV7b/rvFksrzUk32M/EAr7B1O3F/aDgj5B0C9Jarf9TaVeXr+Awys+xda889iS/2+dXku/vpCbC8tXwDGHGoy3nPQ4mPkjWPN7WP4jWPkLmHY9HHYJRP3oS5IkSVLA3w4k9QhluWneM7qRd49q5I3KGP9cH+eRTXFqmoIAvDEJD66FB9cGU8eOKE5z4hA4YUiEowfCiGKI2LkmSertIhHILw22fmP3fDydhkQd1G0LpmSv2x6E5bVt9nXbgmP2J9kAFauD7e2LCqZoL+zbGpbvcrvNlt8n6EYv6ANED/7nl6QeqqoKCgv3HC9KvMSRO95LZfxY1hZ9tktm84hGYciQoGO8Y0+cC6PeD4PPgmV3wFOfhKXfganfgOEXOFOJJEmSJINxST1LJAITypJMKEvysQn1LNkR48ktcZ7aksP2htYPyNdUwz1vwj1vBkF5eR5M6Z9m6gCY2j/ChHIYWgRRPzyRJKlVJAK5hcFWtpeO82aJ2kxIvhPqd0J9RZvbO6GuItg3VB7Ai6aDAL5u+/6ncd9NTk4+c8kjZ/XXgmA9v6w1NG++nd/2dhnklUBuMeQVQ7woSG8kqZtLp6GmZs9gPDe5kSnbzqExOphVJVdBpOtm5xg6BJZ3dDDeLK8/HHk5jHgXLL8THnk39D0Wpt0Agzt3mnhJkiRJ2S0rgvFbbrmFb33rW2zcuJGpU6fygx/8gOOOO26fx//2t7/lqquuYuXKlYwfP55vfvObnHPOOS2Pp9NprrnmGu644w527tzJSSedxK233sr48eO74seRlCXiUZjeL8n0fkk+OQGWV0Z5YXsOL23PYWlFrGVNcoAdDfDQumCDICwvzIFxfdKM7wPjyiKMKg06y4cXQ588O8wlSdqneCGUFULZ8P0fl2qC+sogOK/fmQnPK6CxKpjSffctUXvAJUSa6smnHrZVtP/nyC2G3KLWsDy3JLNvHmsTpOcWt97OyQ/+G8QLMvs293Py7VqU1KXq6yGZ3HUq9Ui6gSO3v5sIDbxV8l1Skb20k3eiIUPhmWeCumKdlceXToTp34LtzwYB+YOzoP9JcORlMPQciPjlJ0mSJKm3CT0Yv+eee7j00ku57bbbmDlzJjfddBNz587l9ddfZ+DAgXsc/9hjj/H+97+f66+/nne84x388pe/5IILLuC5557jqKOOAuDGG2/k+9//PnfffTdjxozhqquuYu7cubz66qvk5+d39Y8oKQtEIzC+LMX4skbeN6aRhiS8XhHj1Z0xllXGeLMiRkVi1w9Gapvgpa3B1hyWNyuOw/DiNMOLYUABDCiEAQWR4HZm65cfhOsG6JIk7UM0JzMtet8DOz7ZBI3VmaC8ci/BeQ001kBjLenGGupqqylI1xFpepup3felsTrY2NS+5+9LTsFuoXlBm+B8t8dy8iGWCzl5e9nnBWvGN+9z8vcci+Xt+Vz/bSL1KlVVwb6lYzydZnzFf1KSeIZlpTeRiA3o8pqGDoFEE6xZA6NHd/KL9Z0B5UfD1n/Bql/DQ++E0iPgiC/D6PdDNN7JBUiSJEnKFqEH49/5znf4xCc+wSWXXALAbbfdxl//+lfuuusuLrvssj2O/973vse8efP44he/CMDXvvY1Fi5cyM0338xtt91GOp3mpptu4sorr+T8888H4Kc//SmDBg3i3nvv5aKLLuq6H05S1sqLwZS+Sab0TQLB9IKb6yMsq4yxvDLGmpooa2pibKqLkGbPD4+rE7B0R7C1Su9xXCwCpblpSnOhLA9Kc1u3kjgUxKEgFqEgBwpyID8HCmKZfZvb8SjkRIN929s50eA1DN8lSb1CLCeYCr2gz9se2pRMs/DNBOeMjxOPpIJu88aa1v3ebifqoKm+zb4emupa9+lUx/wcTXXBVre9Y853sGK5QRAUy8ns48GXFKI5mdvNj+32+EEfFw+mo4/EIBprs4/udr/teM5eHtvbOfY1Hg3Wmz+Aa0TqLXYPxofW3sbQ2jtYXfQlauNHhFLTkCHBfsWKLgjGIfhC0ICTg47xnS8FAfkTH4YXvgiHfQzGfRyKD+uCQiRJkiSFKdRgvLGxkWeffZbLL7+8ZSwajTJ79mwef/zxvT7n8ccf59JLL91lbO7cudx7770AvPXWW2zcuJHZs2e3PF5WVsbMmTN5/PHHDcYl7VUkAoMK0gwqaOKkQU0t4w1JWFcTZW1tlE11UTbXR9lcF9zeWh+hKb3/QDqZDqZp39EAVO3rqD0D9YMVj6bJaQ7MI7TcjkQgStAxH4203t9l32aLsOt+9+eQhq11Ue55K00kkt6l4eztovnds/vIPm6/3bEH8vj+jlU40mnYVB3lL2uDa0c6UF47aq9dr50IUJTZDlAss7U5YQ4J8tP15KfryE8F+7x0Q3A/3Xo/N91IbrqBOI0tt4N9m/tkjsmMxWnaVyUdL9kYbImue8mu9MaUL3L4u68Muwwpa7QNxssaHmZcxWfZkv8utuefHVpNRUVQ3geWr4Azu3LZ70gEyqcGW/VbsO4v8MYP4NXrYfBsGPtxGPYOyDmI/19IkiRJ6jZCDca3bt1KMplk0KBBu4wPGjSIpUuX7vU5Gzdu3OvxGzdubHm8eWxfx+yuoaGBhoaGlvuVlZUAJBIJEons/bSoubbcskHEoiY/OnDJVBpqq7x2DkAeUNoPJu3lsWQ6zY76NDvrYXt9mp0Nwf3mraIhTU0iTXUCahJpahqhg/rM9pBIBVvXiPLGISzXqt4syss73v4oaU9eO2qvjrx2IkBuZivtqJO2iJEkn0byaaQg0kg+DRTQSC4JciNN5JEIbtNELgnyIq23c2kiN5Igr+V+61gubZ7bcp4mYiTJIUmcJnIiSXJIkUNTZizzWCTZ4T9nV9hak2DMIfwe1/x7Vmf9LpjNv2OqZ2oOxvsXrObIHe+hJmcy6wo/HW5RBF3jK5aHWEDxGJjwWRj3Sdi8GNb9Ff51EcQKg3B85L/B0LMhp2vXX5ckSZLUeUKfSj0bXH/99Vx77bV7jC9YsIDCwuz/BWj5hn22oUr75bXTcfoAfeIwOg6U7P2YVBoak1CbhLomqEtCIhmhMRNqN2a2RAoak5GWsUQKmlLB85N7bJHW8dSuj6UyjZUpgq659N72ex3zyxKSJHW1JDFqKKCGgj0nkwltsoQ0MVKtATptAvRIa4Ce0zZkJ5kJ2oMtRoooKWKZreV2JEU0c/5o5jXaPr7rcc3PT+/5+F6O3bQthy1/+9sh//QLFy7sgP+Ge6qtre2U80r7UlUF+fE6zsy5gDQ5rCy5BiLhfxw0ZCi8/HLYVQCxfBgyL9jq1sOmxbD5IVj9G4gVwKDTYcjZweMl45ySSpIkSerGQv1NqH///sRiMTZt2rTL+KZNmxg8ePBenzN48OD9Ht+837RpE0OaF63K3J82bdpez3n55ZfvMj17ZWUlI0aMYM6cOZSWdnw3SEdJJBIsXLiQs846i3g8HnY56ka8drQ/6XSadBpS6TRpgoA9nU6TSqdJJJp48MEHOfPMM8nJaf1fyB6f3+/xAX56n4+/3XPT+3nuHs/f7UEnXc4eiUQTDz30EKeddhrxePgfxKr78NpRe3nt9F6FuTEKc9v/nnf2v5WbZyiTusr27Wl++LH/RymvsazkBySjZWGXBMDQIbBoEVRXQ3Fx2NVkFAyF0R8Ittq1sOUR2PYMPHcppD8LRaODoHzAycFWcrhBuSRJktSNhPoJUW5uLjNmzGDRokVccMEFAKRSKRYtWsT8+fP3+pwTTjiBRYsW8fnPf75lbOHChZxwwgkAjBkzhsGDB7No0aKWILyyspInn3yST33qU3s9Z15eHnl5eXuMx+PxbhEadpc6lX28dnSwEokEeTEoK8r32tFBSSQSlObCkPIirx0dFK8dtZfXjg5VZ/1b2etRXa188/c595SfsbL4SupyxoVdTouhQ4P9ihUwZUq4texV4XAY9f5ga6qDnS/Atqdhy2Ow4qdACnL7Qf/joHwG9D062ApHGpZLkiRJWSr01olLL72UD3/4wxxzzDEcd9xx3HTTTdTU1HDJJZcAcPHFFzNs2DCuv/56AD73uc9x2mmn8b//+7+ce+65/PrXv+aZZ57h9ttvByASifD5z3+e6667jvHjxzNmzBiuuuoqhg4d2hK+S5IkSZIk9Xib/sm8gf/Fgtf/jYEnzgq7ml0MHAixGCzP1mC8rZwC6H9CsAE01UDFq7DzZah6E978P2jcHjwWL4XSI6Ass5VODKZgLxoNsT2bMiRJkiR1ndCD8QsvvJAtW7Zw9dVXs3HjRqZNm8b999/PoEGDAFi9ejXRaLTl+BNPPJFf/vKXXHnllVxxxRWMHz+ee++9l6OOOqrlmC996UvU1NTwyU9+kp07d3LyySdz//33k5+f3+U/nyRJkiRJUperWQWPvI/XNk7jXxs/ybvCrmc3sRgMHgwrloddSTvkFEG/Y4OtWcM2qHoDqt+CmpWw5V+w6peQrM8cEAm60IvHQtEoKBoZdJcXjoDCoZA/BPL62W3eVrIRmqqgqTr4MkKiGpI10FQb/HdN1gVbqjGzJYItnQTSuy61Fc2BSAwiOcHtaF6wvnwsP7idUxi8rzlFkFMcbPHS4H4kus8SJUmS1L2EHowDzJ8/f59Tpy9evHiPsfe97328733v2+f5IpEIX/3qV/nqV7/aUSVKkiRJkiR1D4lqeOg80tFcrv7NVZw5NxZ2RXs1ZEjQMd4j5PWDvDZd5QDpFDRshbr1ULcOatcHt7c/A+v/Dg1bgDbhbSQO+YMy20DIHwB5A4Jz55a3bvE+EC8Jgtt4SRDiZkN4m04HQXVTTWarhkRVa7idqMrcr4REZmusyNyuaB1LVAbHpxoO7HUj8UzwnQm9I1Eg0mYDSEIqGYTm6aZMgN50ICfPhORlkNsn2OKZfW55m/vlu461fZ+y4b2RJEkSkCXBuCRJkiRJkjpAOgWPfQCqlrNx+A/YXl1GZlK+rDN0CCx8AFIpiPbE7DASzQTcA6F82p6Pp5qC4LxxW9Bx3rAtmJK9cUcQFG9fmwmMK4KgOJ3c92vFCjJbYdD93NwJHctr3bcEx83hcTTTod68pYPrh1SwT0WBi+DRf4N0XRBUpxqCTu5Uw65d28232wb9exPNDbqwY0WZOgtbu7Vz+2ZuF2Yez/w8sczPEyvI7DM/UzQPovH2d9mnU5ku8wZINuz5szTVtnaoNwf9TdXBe1S7OjNWlelkr93XDxx8gWGXQL1Pa9AeLw1ux8sgN7NvGcvsc4qcSUCSJKmDGIxLkiRJkiT1FC9eAev+ClO/zrLXxgBkbTA+ZCjU1cHGjTB0aNjVhCCaAwWDg+3tpNNB+JrIdF83d2Yna4PgtiXcrYdUfeu04s3TjCcagiA43dw1naJ1uvHmMDvSGpRHIpDOLEnYWAHRpiCEjpdCbjy4Hc3NhNS5reF7c4AdzUxT3hJ+Z0LuaLxz/lu2RySaqTkPDrWsVFNrUN7SKV/duiXa3K7bCFXLW6eIb9pfsA5BuF6ya2DeHK43j7WE6ruF7G2Ps3NdkiTJYFySJEmSJKlHWHE3vPpNGP9p6H88q1ZDYSGUlIRd2N41h+ErVvTSYPxgRCKta2B3laY0PFMJR10FOXYs71c0Jwijc8va9/x0EprqWkPylunoa/cyVhN8WaFuw57d7PucHj6SCdfLdutc75MJ0dvsY2VALmx7Bgr6tAbtsUI71yVJUrdnMC5JkiRJktTdbX4UnvwEDD0XRrwXgFWrgm7xbM2ySkuC0P6FF+Dkk8OuRgpRJAbx4mBrr3Q6mB2gbVDedr33lo72zFjDVqhZtedxFEDRr+DBWUDdrjXmlAQheU5pm6C9ea37ksxY5nZOceZ2ceZ2cevtnGKI5R7qfzVJkqSDZjAuSZIkSZLUnVW8Bg+fB32Oggmfa0nCV62EgQPDLW1/IhE44QT4y33wgQ9A375hVyR1Y5FI69Twee38w5ROQkMtvJCCGTdDujqzzvpua603T+XfVA31mzJrs9cGXe/N0/uTept6c1pnQWgJzIvahOlFu+33Eq7Hi1sD+eZjnTJekiTth8G4JEmSJElSd1WzBh48C3L7wuRrW9ZwTqVgzRqYdETI9b2NM06HRx+FX/wC/vM/w65G6uUisSBgphKKx7R/Cv10OrPufV0mRK+FZH3r/WTdPu7XQ6IyE7bXt9ky50jVv90PkAnSm7vXS3ddm71ljfbd12Tv07rP7QOx/Pb93JIkKesZjEuSJEmSJHVH9VuDUDydgmk3BEFQxpYtUFcPg7K4YxygoCAIx//8Z7jwwuzucJd0gCKRIFyO5QPlHXfedBKSDbt1qO/Wrd5yu7nLvRbq1kHVm5mAvs1a7aT3/jrRvExQXp4Jy8v3crs8szZ7+W5jJXatS5KUxQzGJUmSJEmSuptENSw+Bxo2w4zvQ17/XR5etTrYDxoUQm0H6ZRT4KGH4Wc/g//6r7CrkZS1IjHIKQw2+h3audKpTIieCckT1W3WYM+s0Z6oal2DvX5T5pjm46r3ceJopiu9TVie17dNiN6nTaie2dretltdkqROZTAuSZIkSZLUnSTr4ZF3QcWrcPR3oHD4HoesXgW5uVDegc2anSU/H2bNgvvug4veD8OGhl2RpB4vEm1dq7w9UklIVreG5YmqIDBvCdOrWsP26hW7he/VQff73rR0q7ed5r1P63Tvu0z93nY6+Da3o37kL0nSvvh/SUmSJEmSpO4iUQ0PnQdbH4ep34DSw/d62KpVwbTk0W4yo+9JJ8LixfDTu+Hyy8OuRpLeRjQG0UwQfbDS6cy66W1D9ZrW7vSWzvXMWM1KqKjd9fFU477PHytsXVM9d7cwfZcgvc+u+0hmOY5UEogf/M8lSVI3YDAuSZIkSZLUHTRWwOKzYceLwZri5VP3eejKVTBwQBfWdohyc2H2LPjjH+Gii2DMmLArkqROEom0mRJ+YPvOkWpsE563DdX3MVa9IrPmes1+wvUCKPoV/L4v5OS0Bur7nf599+nhy4NQPhJp538cSZI6l8G4JEmSJElStmvYBg/Ogao3Yfq3oWzSfg9ftRJOOrlrSusoxx8P//oXfOFS+MY34Ij9/4iS1HtFc4NvFOUewnoZLeF6JkBvqIXlwOGfhVTlrmuuN2yDmtW7drkna/dVXNCBnlsOuX133ef13XM8r2/r/ViBobokqVMZjEuSJEmSJGWz2rXwz3lQtz5YU7xk3H4P37kTKqtg0KCuKa+jxOMwfz7ceRd8/vNwxeVw+ulhVyVJPdTu4XpTGqiEIXMh5wDC6VSyNShvWV+9zdrqzeutJ6qCL3W1faypeh815e0amOf1y4TmbQP05tv9Wsdyig3UJUkHxGBckiRJkiQpW21+BB55D0SicPR3oWjU2z5l1apgP6idM/SGqbgYPv0p+PU98D/XwifWwQc+YN4hSVknGst0hrdjnfVUUyY0r2yzrwr2iTb36zZC1bIgXE9UBhupPc8XydmtA71fa6ie12+3IL3NPqfI/8FIUi9jMC5JkiRJkpRt0ml481Z49nNQdhRMvvqAp8xdtRpiURjQjdYYbysehw99MKj/jh/B00/Dpz4FEyaEXZkkqUNEc1rXKz8Y6VRmrfTmoLxqt1A9szVug5pVmfsVwePp5F7qyG0TnvfPbG1v72WzO12SujWDcUmSJEmSpGySbICnPw0r7oLh74bxnwpChAO0ehX07w853fhTn0gE5s2FUaPgz3+G//cfcOaZ8ImPw5AhYVcnSQpFJArx4mArOIj/GaRTwZrobcPzPbaKIEzfuSS4naiAZN2e54rmtQnPB0D+wMx+wJ738wdCvI9BuiRlkW78K5IkSZIkSVIPs/UJeOKjUL0cJn0Zhs476FOsXAUDu9n64vsyaSIcfnjQNX7//fDvF8NZZ8F73gPjxoZdnSSpW4hEg07vnGIoGHrgz0s2tIbmiQpozOwTO6FxZ3C7cik0Ptk6tvtU75GcTJA+APIHtQbobcP0vP6tt3P7BtPUS5I6hcG4JEmSJElS2Jpq4MUr4fXvQelEOPZWKD6sXadatQqmTung+kIUi8LxM+Ho6fDov+DRR+Dvf4dpU+G974Xjj+/e3fGSpCwVy4NYJsA+EOlUZnr3ndC4IxOk78yE6Jl95euQeKr1sT2meI8EU8zn9c+sh94f8vu3rpvevHZ6y3rq5cGWU2JnuiQdAH9tkCRJkiRJCks6Dev/Cs/Mh7pNMP4/YMR7INK+brG6OtiyBQYN7uA6s0BuLpx5Bpx2Grz8Ejz0MFx5FZSWwKmnwRlnBGF5zEY7SVIYIlHILQu2olFvf3w6BU3Vrd3nu3SlN9/fBjUrM/cz66rv3pUOwb8b4mVBqB7vk1nDvTwYi2dqipdm7pcGW05JsI8UBudINQHxDvqPIUnZyWBckiRJkiSpq6XTsOF+eOka2P409J0BU74OhcMO6bSrVwf7QT1kKvW9iUVh2rRgW7cOnn8enngc7rsPyspg+nSYPBkmHwVjxxqUS5KyVCTaGlIfqHQKmmqhqbI1KG+qgkR1sG+qztyuhto10PRaMCtNU20wtrd10ymAol/B7/tBNB2s4Z5TnAnOiyGnNLO2e0nrlPQHejtWaCe7pKxiMC5JkiRJktRVUknY8HdY8jXY9hSUHQXTvw3lR3fIB8cVlcG+uPiQT9UtDBsWbOeeC2vWBp3kK1bAo49CUxPk58GIkTAys40YDgMHwoAB0K+fU7BLkrqZSDQTUh/keunNUskgHE82h+U10FgPK4AJn4d0VfB4U13muNrgduN2SNa3jjUfk6p/mxeMQk5RJjAvCfbNXwZo27XeMl626+PxNp3usdx2/AeTpF35z39JkiRJkvS2brnlFr71rW+xceNGpk6dyg9+8AOOO+64sMvqPipehRV3w1s/hfqNQSA+7VtBp3gndFL1tt6sSARGjgg2gEQC1qyBVath8yb+f3t3HxxVfe9x/HP2MYE8EEwJoVDQgkh9AEWDwTtjYrFh4nVK73TU3l7lwcdKWrmZsY1Wy6U6l3ZuizBelLZUcOq02JbBOqJYiySdUhDLQ0HEjFVU7CU8iJKQhGyy+7t/nD2b3ZCEZCHZzZ73a+bMedjf2fzOnt+enO/5/s5ZfXBQ2rFDampKXKdghDTyAmnkyLihwB4XFHQuy8nhhjcAQAbweCVPNLHu6DDS+43S6BslXz//2Zlwl0R6q51wjyXVW+KWN3e+3npYOvV+9PWWzjvaTUcvdQ+emTAPjIibd5blJ87Hr+PLsT8DAK5FYhwAAAAAAPTq+eefV3V1tVatWqUZM2Zo+fLlqqioUH19vUaNGpXq6qWnSLt0/A2p4TX7N8RP7LQvyBbdIBVXSLkXk2kdQH6/dNFF9hCvtVX67DPps5P2+ORndrK8sUmqr5caG+35UKjL+/mkgmiyfMQI+5Ht+flSbo40bJiUnW0PwaD9t53B57Mf5e712tMeT3Tw2o+Etyx7Pn5sWfYNgZ7o2OuJW88zOJ8fAAB9Ynk7H51+royxz5/CzdHHv0fvag8320nzjrgEekeznVBvPSydei+ufHSQ6fnvxB4Tn9d5J3sgL/rI+Ny4x8HHT+fEPWLeGYbbg4c0GzCU8I0FAAAAAAC9WrZsme6++27Nnz9fkrRq1Spt3LhRzzzzjGpqalJcuzRgjNT6f9Kne+zhkzekI7X2b33686SCK6XL/0sqLJU8PAY0lZwEdnFxz2WMkdraOhPmTU1SU2N0ulFqbpGOH5Oam+1E++k2u/xgcBLo3rgEu8c6c9rqWqaXeaes5bxu2WX8AemaSunx/5YU6X79+AS+JSliJJlobsNIkYj91N6IsX8WtyuvN/H9nE4EXq8SH3tgpHBE6miX2jukcIf9mbeFpLbT9nQoZM+HokNHu/04/XDYHpy6RbrkSpw/0/Wz9Pvtjg6BgP1I/mHD7ScHDB8mDR8u5eZKObn2spwcaVh2ZyeJrCz78wvEdZCQZf8tp/NDJBJN28R9XiYS/dzCcfWOJI7D0c/UWddpF85n6fXaHTl8fnsc30GDvjgA0ppl2Y9L9wakQEHy72MinXeoxxLrzZ3J9PgEe+wO9iNS+IMz73Y/66PiZd/J7hsmeYfbYydh7nWmh9nT3uzOaV+2PX/GkBU3jhs8QckbtMcczIFzQmIcAAAAAAD0KBQKaefOnXrooYdiyzwej2bNmqVt27alsGaDqKNFajsmtR2XTh+TWj6Wmj+wh1MHpcZ6KfSJXdaXK+VOksbfIo282p62eGTnUGJZdmIzK8v+LfK+iETsx7eHQnbyst1JyHZEE5lOgjhizzsJUCdZ7Ewb2UlSdUmUxo+d94tPonYtY9S53EkqJ7yXMx1NXhvnvcNS2EiRkJ3wl6QTn0jtoTPr4iRn47fBSfxKiUlzjyU7MRx3Ld/Ev1fX7ekmie4krH3RBHB88jc7W8rLj0sE+6MJ9miyPXY3fvSO+64pBWc7nM8jYjqT8B3tduL9dJvUeNJ+NH/rabtTRGuLPT1U+Hz25xffASH+qQZenxTMkv51vrToP+3PxOkAYSk6tqJtVOpM6kcS23lHONopocMex8+HI53rGdltw+vr3J+BgJQd/f5lRTuyZAU754MBu9OBP1rW70vcntg+ju5k06XNR8LddDSIdFke/53t0sHDqbsjvmOJ0ykhfnv8vs76Ou3T6TTh8yV2oHA6MTjrOdvjifvuxDpGGLu+HR2dg7M9sU4V4cS2Hd8Rw5mP/153hO2/se2N6NOx4ztmdLP9VpcOLV23JxhMHAJBe//5fP3P6xnTeYwNhaRQe/Q7Gh064rY57HQciWOp86kbVpdOOL64+vui+88X7RzjTPf3aR3x34uEzz/KY3Uem3gSyACwPJ3J6XNlwnG/re48Jt6Zjx9HpyOnpXCbPR9pk1pPRqdD9nLn9Ui0TG+Pju+JJ2AnyD2BaLI8bj5hmTMfHVt+yRM3mCxJ/yK99Vi095vfvvvdio49fsny2eexli/6mjOOLrN89j9lyxu3zNvzEF9WnrjXnH82zoE8btqiZxfOLxLj3TDR/+6NjY0prknv2tvb1dLSosbGRvn9/lRXB0MIbQfJou0gWbQdJIu2g2TRdpCsgW47TpxpTNdLtunr+PH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+ "text/plain": [
+ "<Figure size 2000x800 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# kde and normal distribution plots for entropy\n",
+ "import seaborn as sns\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "from scipy.stats import norm\n",
+ "\n",
+ "# Assuming gmm_entropy is already defined\n",
+ "grouped = gmm_entropy.groupby(\"type\")\n",
+ "x_vals = np.linspace(gmm_entropy[\"entropy\"].min(), gmm_entropy[\"entropy\"].max(), 1000)\n",
+ "\n",
+ "# Create subplots\n",
+ "fig, axs = plt.subplots(1, 2, figsize=(20, 8))\n",
+ "\n",
+ "# Plot 1: Energy distribution by sample type using PDF\n",
+ "for name, group in grouped:\n",
+ " mean = group[\"entropy\"].mean()\n",
+ " std = group[\"entropy\"].std()\n",
+ " pdf_vals = norm.pdf(x_vals, mean, std)\n",
+ " \n",
+ " axs[0].plot(x_vals, pdf_vals, label=f\"{name.capitalize()}\", linewidth=2)\n",
+ " axs[0].fill_between(x_vals, pdf_vals, alpha=0.3)\n",
+ "\n",
+ "axs[0].set_xlabel(\"Entropy\")\n",
+ "axs[0].set_ylabel(\"Density\")\n",
+ "axs[0].set_title(\"Entropy: Normal Distribution\")\n",
+ "axs[0].legend()\n",
+ "axs[0].grid()\n",
+ "\n",
+ "# Plot 2: KDE plot of energy between known and novel samples\n",
+ "sns.kdeplot(\n",
+ " gmm_entropy[gmm_entropy[\"type\"] == \"known\"][\"entropy\"],\n",
+ " label=\"Known\",\n",
+ " color=\"blue\",\n",
+ " fill=True,\n",
+ " ax=axs[1]\n",
+ ")\n",
+ "\n",
+ "sns.kdeplot(\n",
+ " gmm_entropy[gmm_entropy[\"type\"] == \"novel\"][\"entropy\"],\n",
+ " label=\"Novel\",\n",
+ " color=\"orange\",\n",
+ " fill=True,\n",
+ " ax=axs[1]\n",
+ ")\n",
+ "\n",
+ "axs[1].set_xlabel(\"Entropy\")\n",
+ "axs[1].set_ylabel(\"Density\")\n",
+ "axs[1].set_title(\"Entropy: KDE Distribution\")\n",
+ "axs[1].legend()\n",
+ "axs[1].grid()\n",
+ "\n",
+ "# Adjust layout for better spacing\n",
+ "plt.tight_layout()\n",
+ "plt.show()\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Weighted Random Between Three Known Samples\n",
+ "The idea here is to create a point randomly between three Known samples. Doing this several times will hopefully get us the entropy of the novel area"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Prepare the data\n",
+ "Prepare the data and create a set of triplets"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>logit_0</th>\n",
+ " <th>logit_1</th>\n",
+ " <th>logit_2</th>\n",
+ " <th>logit_3</th>\n",
+ " <th>logit_4</th>\n",
+ " <th>logit_5</th>\n",
+ " <th>logit_6</th>\n",
+ " <th>logit_7</th>\n",
+ " <th>logit_8</th>\n",
+ " <th>logit_9</th>\n",
+ " <th>...</th>\n",
+ " <th>feat_120</th>\n",
+ " <th>feat_121</th>\n",
+ " <th>feat_122</th>\n",
+ " <th>feat_123</th>\n",
+ " <th>feat_124</th>\n",
+ " <th>feat_125</th>\n",
+ " <th>feat_126</th>\n",
+ " <th>feat_127</th>\n",
+ " <th>class</th>\n",
+ " <th>type</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>5325</th>\n",
+ " <td>-3.464009</td>\n",
+ " <td>-4.065380</td>\n",
+ " <td>4.670951</td>\n",
+ " <td>3.943103</td>\n",
+ " <td>6.382225</td>\n",
+ " <td>-4.207092</td>\n",
+ " <td>-1.838107</td>\n",
+ " <td>-2.900783</td>\n",
+ " <td>-6.593166</td>\n",
+ " <td>-3.448262</td>\n",
+ " <td>...</td>\n",
+ " <td>-0.160013</td>\n",
+ " <td>-0.421526</td>\n",
+ " <td>0.590221</td>\n",
+ " <td>-0.319883</td>\n",
+ " <td>-0.170347</td>\n",
+ " <td>-0.415997</td>\n",
+ " <td>-0.679634</td>\n",
+ " <td>-0.375659</td>\n",
+ " <td>63</td>\n",
+ " <td>known</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>5423</th>\n",
+ " <td>-0.591529</td>\n",
+ " <td>0.984362</td>\n",
+ " <td>-4.146834</td>\n",
+ " <td>-0.724267</td>\n",
+ " <td>5.269270</td>\n",
+ " <td>-5.308410</td>\n",
+ " <td>1.059127</td>\n",
+ " <td>-2.142913</td>\n",
+ " <td>1.400815</td>\n",
+ " <td>-4.242834</td>\n",
+ " <td>...</td>\n",
+ " <td>-0.344498</td>\n",
+ " <td>-0.266467</td>\n",
+ " <td>0.286554</td>\n",
+ " <td>-0.107283</td>\n",
+ " <td>-0.764430</td>\n",
+ " <td>0.404030</td>\n",
+ " <td>0.769236</td>\n",
+ " <td>0.314161</td>\n",
+ " <td>64</td>\n",
+ " <td>known</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>5660</th>\n",
+ " <td>-1.819984</td>\n",
+ " <td>-4.451072</td>\n",
+ " <td>10.189980</td>\n",
+ " <td>5.669862</td>\n",
+ " <td>2.677325</td>\n",
+ " <td>-1.342148</td>\n",
+ " <td>-3.418654</td>\n",
+ " <td>-4.652941</td>\n",
+ " <td>-3.702533</td>\n",
+ " <td>-3.823347</td>\n",
+ " <td>...</td>\n",
+ " <td>-0.033270</td>\n",
+ " <td>-0.169137</td>\n",
+ " <td>-0.337149</td>\n",
+ " <td>-0.643458</td>\n",
+ " <td>-0.303914</td>\n",
+ " <td>0.619518</td>\n",
+ " <td>0.009608</td>\n",
+ " <td>-0.007420</td>\n",
+ " <td>11</td>\n",
+ " <td>known</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>5772</th>\n",
+ " <td>-1.538368</td>\n",
+ " <td>0.092462</td>\n",
+ " <td>-5.099878</td>\n",
+ " <td>11.599087</td>\n",
+ " <td>1.775226</td>\n",
+ " <td>-5.060294</td>\n",
+ " <td>0.679947</td>\n",
+ " <td>-2.038918</td>\n",
+ " <td>-3.773811</td>\n",
+ " <td>0.310163</td>\n",
+ " <td>...</td>\n",
+ " <td>-0.472988</td>\n",
+ " <td>0.154486</td>\n",
+ " <td>0.210189</td>\n",
+ " <td>-0.215814</td>\n",
+ " <td>-1.212438</td>\n",
+ " <td>-0.466347</td>\n",
+ " <td>-0.757830</td>\n",
+ " <td>-0.180894</td>\n",
+ " <td>66</td>\n",
+ " <td>known</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>5880</th>\n",
+ " <td>-0.584508</td>\n",
+ " <td>2.844945</td>\n",
+ " <td>-4.595065</td>\n",
+ " <td>0.016701</td>\n",
+ " <td>12.958841</td>\n",
+ " <td>0.323624</td>\n",
+ " <td>-2.357301</td>\n",
+ " <td>-5.764180</td>\n",
+ " <td>-8.732758</td>\n",
+ " <td>-1.016143</td>\n",
+ " <td>...</td>\n",
+ " <td>0.498180</td>\n",
+ " <td>-0.307059</td>\n",
+ " <td>0.243496</td>\n",
+ " <td>-0.458031</td>\n",
+ " <td>-0.929303</td>\n",
+ " <td>-0.345206</td>\n",
+ " <td>-0.031860</td>\n",
+ " <td>-0.286380</td>\n",
+ " <td>4</td>\n",
+ " <td>known</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>...</th>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>48987</th>\n",
+ " <td>-6.145474</td>\n",
+ " <td>1.368910</td>\n",
+ " <td>-1.596712</td>\n",
+ " <td>9.581457</td>\n",
+ " <td>1.045110</td>\n",
+ " <td>0.345885</td>\n",
+ " <td>-2.054478</td>\n",
+ " <td>-5.097582</td>\n",
+ " <td>2.370237</td>\n",
+ " <td>-7.108289</td>\n",
+ " <td>...</td>\n",
+ " <td>0.322217</td>\n",
+ " <td>0.055496</td>\n",
+ " <td>0.026494</td>\n",
+ " <td>-0.470229</td>\n",
+ " <td>-0.443494</td>\n",
+ " <td>-0.650726</td>\n",
+ " <td>-0.187364</td>\n",
+ " <td>-0.268039</td>\n",
+ " <td>49</td>\n",
+ " <td>known</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>49110</th>\n",
+ " <td>-1.499055</td>\n",
+ " <td>6.969433</td>\n",
+ " <td>-0.866342</td>\n",
+ " <td>-2.406220</td>\n",
+ " <td>-1.540061</td>\n",
+ " <td>4.063763</td>\n",
+ " <td>1.224819</td>\n",
+ " <td>-2.317764</td>\n",
+ " <td>-2.424361</td>\n",
+ " <td>2.129540</td>\n",
+ " <td>...</td>\n",
+ " <td>0.511093</td>\n",
+ " <td>-0.687465</td>\n",
+ " <td>0.166090</td>\n",
+ " <td>-0.421519</td>\n",
+ " <td>0.059527</td>\n",
+ " <td>0.281480</td>\n",
+ " <td>0.464180</td>\n",
+ " <td>0.265019</td>\n",
+ " <td>5</td>\n",
+ " <td>known</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>49155</th>\n",
+ " <td>-3.855552</td>\n",
+ " <td>0.285982</td>\n",
+ " <td>2.038477</td>\n",
+ " <td>-3.479786</td>\n",
+ " <td>-7.978263</td>\n",
+ " <td>0.083437</td>\n",
+ " <td>-0.226207</td>\n",
+ " <td>4.537358</td>\n",
+ " <td>1.874517</td>\n",
+ " <td>8.535936</td>\n",
+ " <td>...</td>\n",
+ " <td>-0.514537</td>\n",
+ " <td>-0.929816</td>\n",
+ " <td>-0.456013</td>\n",
+ " <td>-0.023876</td>\n",
+ " <td>0.542752</td>\n",
+ " <td>-1.099769</td>\n",
+ " <td>-0.562894</td>\n",
+ " <td>-0.163419</td>\n",
+ " <td>39</td>\n",
+ " <td>known</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>49219</th>\n",
+ " <td>4.211473</td>\n",
+ " <td>8.188478</td>\n",
+ " <td>1.622436</td>\n",
+ " <td>2.261892</td>\n",
+ " <td>3.849376</td>\n",
+ " <td>-6.200826</td>\n",
+ " <td>-1.188734</td>\n",
+ " <td>-1.466675</td>\n",
+ " <td>-2.300565</td>\n",
+ " <td>-0.077358</td>\n",
+ " <td>...</td>\n",
+ " <td>-0.011279</td>\n",
+ " <td>0.159064</td>\n",
+ " <td>1.184794</td>\n",
+ " <td>-0.583924</td>\n",
+ " <td>-0.519038</td>\n",
+ " <td>-0.324757</td>\n",
+ " <td>0.447302</td>\n",
+ " <td>0.096710</td>\n",
+ " <td>50</td>\n",
+ " <td>known</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>49221</th>\n",
+ " <td>5.007287</td>\n",
+ " <td>-1.299575</td>\n",
+ " <td>1.836351</td>\n",
+ " <td>-0.877186</td>\n",
+ " <td>0.796354</td>\n",
+ " <td>-0.242792</td>\n",
+ " <td>-2.042872</td>\n",
+ " <td>4.873226</td>\n",
+ " <td>0.983138</td>\n",
+ " <td>-1.884890</td>\n",
+ " <td>...</td>\n",
+ " <td>-0.208503</td>\n",
+ " <td>-0.140618</td>\n",
+ " <td>-0.251960</td>\n",
+ " <td>-0.954479</td>\n",
+ " <td>-0.961375</td>\n",
+ " <td>-0.167995</td>\n",
+ " <td>-0.201557</td>\n",
+ " <td>0.140859</td>\n",
+ " <td>11</td>\n",
+ " <td>known</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>1960 rows × 200 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " logit_0 logit_1 logit_2 logit_3 logit_4 logit_5 \\\n",
+ "5325 -3.464009 -4.065380 4.670951 3.943103 6.382225 -4.207092 \n",
+ "5423 -0.591529 0.984362 -4.146834 -0.724267 5.269270 -5.308410 \n",
+ "5660 -1.819984 -4.451072 10.189980 5.669862 2.677325 -1.342148 \n",
+ "5772 -1.538368 0.092462 -5.099878 11.599087 1.775226 -5.060294 \n",
+ "5880 -0.584508 2.844945 -4.595065 0.016701 12.958841 0.323624 \n",
+ "... ... ... ... ... ... ... \n",
+ "48987 -6.145474 1.368910 -1.596712 9.581457 1.045110 0.345885 \n",
+ "49110 -1.499055 6.969433 -0.866342 -2.406220 -1.540061 4.063763 \n",
+ "49155 -3.855552 0.285982 2.038477 -3.479786 -7.978263 0.083437 \n",
+ "49219 4.211473 8.188478 1.622436 2.261892 3.849376 -6.200826 \n",
+ "49221 5.007287 -1.299575 1.836351 -0.877186 0.796354 -0.242792 \n",
+ "\n",
+ " logit_6 logit_7 logit_8 logit_9 ... feat_120 feat_121 \\\n",
+ "5325 -1.838107 -2.900783 -6.593166 -3.448262 ... -0.160013 -0.421526 \n",
+ "5423 1.059127 -2.142913 1.400815 -4.242834 ... -0.344498 -0.266467 \n",
+ "5660 -3.418654 -4.652941 -3.702533 -3.823347 ... -0.033270 -0.169137 \n",
+ "5772 0.679947 -2.038918 -3.773811 0.310163 ... -0.472988 0.154486 \n",
+ "5880 -2.357301 -5.764180 -8.732758 -1.016143 ... 0.498180 -0.307059 \n",
+ "... ... ... ... ... ... ... ... \n",
+ "48987 -2.054478 -5.097582 2.370237 -7.108289 ... 0.322217 0.055496 \n",
+ "49110 1.224819 -2.317764 -2.424361 2.129540 ... 0.511093 -0.687465 \n",
+ "49155 -0.226207 4.537358 1.874517 8.535936 ... -0.514537 -0.929816 \n",
+ "49219 -1.188734 -1.466675 -2.300565 -0.077358 ... -0.011279 0.159064 \n",
+ "49221 -2.042872 4.873226 0.983138 -1.884890 ... -0.208503 -0.140618 \n",
+ "\n",
+ " feat_122 feat_123 feat_124 feat_125 feat_126 feat_127 class \\\n",
+ "5325 0.590221 -0.319883 -0.170347 -0.415997 -0.679634 -0.375659 63 \n",
+ "5423 0.286554 -0.107283 -0.764430 0.404030 0.769236 0.314161 64 \n",
+ "5660 -0.337149 -0.643458 -0.303914 0.619518 0.009608 -0.007420 11 \n",
+ "5772 0.210189 -0.215814 -1.212438 -0.466347 -0.757830 -0.180894 66 \n",
+ "5880 0.243496 -0.458031 -0.929303 -0.345206 -0.031860 -0.286380 4 \n",
+ "... ... ... ... ... ... ... ... \n",
+ "48987 0.026494 -0.470229 -0.443494 -0.650726 -0.187364 -0.268039 49 \n",
+ "49110 0.166090 -0.421519 0.059527 0.281480 0.464180 0.265019 5 \n",
+ "49155 -0.456013 -0.023876 0.542752 -1.099769 -0.562894 -0.163419 39 \n",
+ "49219 1.184794 -0.583924 -0.519038 -0.324757 0.447302 0.096710 50 \n",
+ "49221 -0.251960 -0.954479 -0.961375 -0.167995 -0.201557 0.140859 11 \n",
+ "\n",
+ " type \n",
+ "5325 known \n",
+ "5423 known \n",
+ "5660 known \n",
+ "5772 known \n",
+ "5880 known \n",
+ "... ... \n",
+ "48987 known \n",
+ "49110 known \n",
+ "49155 known \n",
+ "49219 known \n",
+ "49221 known \n",
+ "\n",
+ "[1960 rows x 200 columns]"
+ ]
+ },
+ "execution_count": 28,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "budget = 1920\n",
+ "\n",
+ "# copy the master df\n",
+ "exemplar_df = master_dfs[0].copy()\n",
+ "# remove any rows that are not random exemplars\n",
+ "exemplar_df = exemplar_df[exemplar_df[\"rand_set_member\"]]\n",
+ "exemplar_df = exemplar_df[exemplar_df[\"type\"] == \"known\"]\n",
+ "exemplar_df = exemplar_df.dropna(how=\"all\", axis=1).drop(columns=[\"rand_set_member\", \"cent_set_member\", \"pred_class\"])\n",
+ "sample = exemplar_df.sample()\n",
+ "# create a lisst of 2000 triplets of the form (point A, point B, point C), where each point is a row idx from the exemplar_df and of different classes\n",
+ "triplets = []\n",
+ "for i in range(budget):\n",
+ " # get a random row\n",
+ " row_a = exemplar_df.sample()\n",
+ " # get a random row of a different class\n",
+ " row_b = exemplar_df[exemplar_df[\"class\"] != row_a[\"class\"].values[0]].sample()\n",
+ " # get a random row of a different class\n",
+ " row_c = exemplar_df[exemplar_df[\"class\"] != row_a[\"class\"].values[0]].sample()\n",
+ " triplets.append((row_a.index, row_b.index, row_c.index))\n",
+ "exemplar_df"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Create a Feature Map For Each Triplet\n",
+ "New Point is the weighted sum of each point in the triplet"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# for each triplet calculate 3 random distances such that the sum of the distances is 1\n",
+ "# then calculate a new point as the weighted sum of the features of the 3 points\n",
+ "feat_columns = [col for col in exemplar_df.columns if col.startswith(\"feat_\")]\n",
+ "points = []\n",
+ "for triplet in triplets:\n",
+ " dists = np.random.rand(3)\n",
+ " dists /= dists.sum()\n",
+ " \n",
+ " pointA = exemplar_df.loc[triplet[0]][feat_columns].values.flatten()\n",
+ " pointB = exemplar_df.loc[triplet[1]][feat_columns].values.flatten()\n",
+ " pointC = exemplar_df.loc[triplet[2]][feat_columns].values.flatten()\n",
+ " \n",
+ " new_point = dists[0] * pointA + dists[1] * pointB + dists[2] * pointC\n",
+ " points.append(new_point)\n",
+ "points = np.array(points)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Plot a T-SNE of the New & Existing Points"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Fitting T-SNE on exemplar features\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Fitting T-SNE on new points\n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "724b9fa3bf8945b89ea05671cbb8b366",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Checkbox(value=True, description='Show New Points')"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "5366de715d184247806cbaecdf8e847b",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Output()"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# create a t-sne plot of the existing exemplar features\n",
+ "from sklearn.manifold import TSNE\n",
+ "tsne = TSNE(n_components=2, perplexity=30, max_iter=1000, random_state=SEED)\n",
+ "print(\"Fitting T-SNE on exemplar features\")\n",
+ "exemplar_tsne = tsne.fit_transform(exemplar_df[feat_columns].values)\n",
+ "\n",
+ "# create a t-sne plot of the new points\n",
+ "print(\"Fitting T-SNE on new points\")\n",
+ "new_points_tsne = tsne.fit_transform(points)\n",
+ "\n",
+ "# plot the t-sne of the exemplar features\n",
+ "# Create a checkbox widget\n",
+ "\n",
+ "# Calculate y_lim\n",
+ "y_min = min(new_points_tsne[:, 1].min(), exemplar_tsne[:, 1].min()) - 5\n",
+ "y_max = max(new_points_tsne[:, 1].max(), exemplar_tsne[:, 1].max()) + 5\n",
+ "y_lim = (y_min, y_max)\n",
+ "\n",
+ "x_min = min(new_points_tsne[:, 0].min(), exemplar_tsne[:, 0].min()) -5\n",
+ "x_max = max(new_points_tsne[:, 0].max(), exemplar_tsne[:, 0].max()) + 5\n",
+ "x_lim = (x_min, x_max)\n",
+ "\n",
+ "\n",
+ "toggle_new_points = widgets.Checkbox(\n",
+ " value=True,\n",
+ " description='Show New Points',\n",
+ " disabled=False\n",
+ ")\n",
+ "\n",
+ "# Function to update the plot based on the checkbox state\n",
+ "def update_plot(show_new_points):\n",
+ " plt.close(\"all\")\n",
+ " plt.figure(figsize=(10, 8))\n",
+ " plt.scatter(exemplar_tsne[:, 0], exemplar_tsne[:, 1], c=exemplar_df[\"class\"], cmap=\"tab20\", label=\"Exemplar Features\")\n",
+ " if show_new_points:\n",
+ " plt.scatter(new_points_tsne[:, 0], new_points_tsne[:, 1], c=\"red\", label=\"New Points\", marker=\"x\")\n",
+ " plt.title(\"Exemplar Features T-SNE\")\n",
+ " plt.legend()\n",
+ " plt.grid()\n",
+ " plt.ylim(y_lim)\n",
+ " plt.xlim(x_lim)\n",
+ " \n",
+ " plt.show()\n",
+ "\n",
+ "# Create an interactive output\n",
+ "out = widgets.interactive_output(update_plot, {'show_new_points': toggle_new_points})\n",
+ "\n",
+ "# Display the checkbox and plot together\n",
+ "display(toggle_new_points, out)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Compute Logits For Each Psuedo-Novel Point"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "77228513fac3434e99a368111ffec5c2",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Calculating Logits for Psuedo-Novel Points: 0%| | 0/15 [00:00<?, ?batch/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "e84a5c9941524601b24526623f90ea99",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Calculating Entropy: 0%| | 0/18880 [00:00<?, ?row/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# plot normal and KDE plots of the entropy of the exemplars, new points and actual novel data\n",
+ "# create a dataframe of the random exemplar set\n",
+ "df = master_dfs[0].copy()\n",
+ "novel = df[df[\"type\"] == \"novel\"]\n",
+ "exemplar = df[df[\"rand_set_member\"]]\n",
+ "exemplar = exemplar[exemplar[\"type\"] == \"known\"]\n",
+ "\n",
+ "df = pd.concat([novel, exemplar], ignore_index=True)\n",
+ "# set exemplar to false for all novel rows\n",
+ "df.loc[df[\"type\"] == \"novel\", \"rand_set_member\"] = False\n",
+ "\n",
+ "#df = df[(df[\"rand_set_member\"]) | (df[\"type\"] == \"novel\")]\n",
+ "df = df.dropna(how=\"all\", axis=1).drop(columns=[\"pred_class\", \"cent_set_member\"] + [col for col in df.columns if col.startswith(\"feat_\")])\n",
+ "# set type to \"exemplar\" for all random exemplar set rows\n",
+ "df.loc[df[\"rand_set_member\"], \"type\"] = \"exemplar\"\n",
+ "df = df.drop(columns=[\"rand_set_member\"])\n",
+ "\n",
+ "# convert points to a torch tensor\n",
+ "points_tensor = torch.tensor(points, dtype=torch.float32) # [2560, 128]\n",
+ "batches = torch.split(points_tensor, batch_size)\n",
+ "psuedo_novel_logits = []\n",
+ "\n",
+ "model = model_CI(gm_args).to(device)\n",
+ "model.load_state_dict(\n",
+ " torch.load(os.path.join(gm_args.model_dir, \"stage_0.pth\"), weights_only=False)\n",
+ ")\n",
+ "model.cuda()\n",
+ "model.sync_new_branches() \n",
+ "\n",
+ "with torch.no_grad():\n",
+ " for batch in tqdm(batches, desc=\"Calculating Logits for Psuedo-Novel Points\", unit=\"batch\"):\n",
+ " out = model.batch_norm(batch.to(device))\n",
+ " out = torch.nn.functional.relu(out)\n",
+ " logits = model.classification_layer(out)\n",
+ " logits = logits.cpu().numpy()\n",
+ " psuedo_novel_logits.append(logits)\n",
+ "\n",
+ "psuedo_novel_logits = np.concatenate(psuedo_novel_logits, axis=0)\n",
+ "\n",
+ "# create a dataframe of the new points\n",
+ "psuedo_novel_df = pd.DataFrame(psuedo_novel_logits, columns=[f\"logit_{i}\" for i in range(psuedo_novel_logits.shape[1])])\n",
+ "psuedo_novel_df[\"type\"] = \"psuedo_novel\"\n",
+ "\n",
+ "# combine the dataframes\n",
+ "combined_df = pd.concat([df, psuedo_novel_df], ignore_index=True)\n",
+ "combined_df = combined_df.reset_index(drop=True)\n",
+ "\n",
+ "tqdm.pandas(desc=\"Calculating Entropy\", unit=\"row\")\n",
+ "combined_df[\"entropy\"] = combined_df.progress_apply(get_entropy, axis=1)\n",
+ "combined_df = combined_df.drop(columns=[col for col in combined_df.columns if col.startswith(\"logit_\")])\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Plot Distribution of Novel, Known and Psuedo-Novel Points"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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+ "text/plain": [
+ "<Figure size 2000x800 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# create a kde and normal distribution of the entropy of the exemplars, new points and actual novel data\n",
+ "grouped = combined_df.groupby(\"type\")\n",
+ "x_vals = np.linspace(combined_df[\"entropy\"].min(), combined_df[\"entropy\"].max(), 1000)\n",
+ "fig, axs = plt.subplots(1, 2, figsize=(20, 8))\n",
+ "\n",
+ "for name, group in grouped:\n",
+ " mean = group[\"entropy\"].mean()\n",
+ " std = group[\"entropy\"].std()\n",
+ " pdf_vals = norm.pdf(x_vals, mean, std)\n",
+ " if name == \"psuedo_novel\":\n",
+ " name = \"Pseudo Novel\"\n",
+ " axs[0].plot(x_vals, pdf_vals, label=f\"{name.capitalize()}\", linewidth=2)\n",
+ " axs[0].fill_between(x_vals, pdf_vals, alpha=0.3)\n",
+ "\n",
+ "axs[0].set_xlabel(\"Entropy\")\n",
+ "axs[0].set_ylabel(\"Density\")\n",
+ "axs[0].set_title(\"Entropy: Normal Distribution\")\n",
+ "axs[0].legend()\n",
+ "axs[0].grid()\n",
+ "\n",
+ "sns.kdeplot(\n",
+ " combined_df[combined_df[\"type\"] == \"exemplar\"][\"entropy\"],\n",
+ " label=\"Exemplar\",\n",
+ " color=\"blue\",\n",
+ " fill=True,\n",
+ " ax=axs[1]\n",
+ ")\n",
+ "\n",
+ "sns.kdeplot(\n",
+ " combined_df[combined_df[\"type\"] == \"psuedo_novel\"][\"entropy\"],\n",
+ " label=\"Psuedo Novel\",\n",
+ " color=\"orange\",\n",
+ " fill=True,\n",
+ " ax=axs[1]\n",
+ ")\n",
+ "\n",
+ "sns.kdeplot(\n",
+ " combined_df[combined_df[\"type\"] == \"novel\"][\"entropy\"],\n",
+ " label=\"Novel\",\n",
+ " color=\"green\",\n",
+ " fill=True,\n",
+ " ax=axs[1]\n",
+ ")\n",
+ "\n",
+ "axs[1].set_xlabel(\"Entropy\")\n",
+ "axs[1].set_ylabel(\"Density\")\n",
+ "axs[1].set_title(\"Entropy: KDE Distribution\")\n",
+ "axs[1].legend()#\n",
+ "axs[1].grid()\n",
+ "\n",
+ "plt.tight_layout()\n",
+ "plt.show()\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Cluster Boundry Masking\n",
+ "Uses KDE on exemplar sets to mask the known classes from the feature space, then samples the remaining feature space to retrieve a distribution of entropy hopefully similar to the novel curve."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Prepare Data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>logit_0</th>\n",
+ " <th>logit_1</th>\n",
+ " <th>logit_2</th>\n",
+ " <th>logit_3</th>\n",
+ " <th>logit_4</th>\n",
+ " <th>logit_5</th>\n",
+ " <th>logit_6</th>\n",
+ " <th>logit_7</th>\n",
+ " <th>logit_8</th>\n",
+ " <th>logit_9</th>\n",
+ " <th>...</th>\n",
+ " <th>feat_120</th>\n",
+ " <th>feat_121</th>\n",
+ " <th>feat_122</th>\n",
+ " <th>feat_123</th>\n",
+ " <th>feat_124</th>\n",
+ " <th>feat_125</th>\n",
+ " <th>feat_126</th>\n",
+ " <th>feat_127</th>\n",
+ " <th>class</th>\n",
+ " <th>type</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>5325</th>\n",
+ " <td>-3.464009</td>\n",
+ " <td>-4.065380</td>\n",
+ " <td>4.670951</td>\n",
+ " <td>3.943103</td>\n",
+ " <td>6.382225</td>\n",
+ " <td>-4.207092</td>\n",
+ " <td>-1.838107</td>\n",
+ " <td>-2.900783</td>\n",
+ " <td>-6.593166</td>\n",
+ " <td>-3.448262</td>\n",
+ " <td>...</td>\n",
+ " <td>-0.160013</td>\n",
+ " <td>-0.421526</td>\n",
+ " <td>0.590221</td>\n",
+ " <td>-0.319883</td>\n",
+ " <td>-0.170347</td>\n",
+ " <td>-0.415997</td>\n",
+ " <td>-0.679634</td>\n",
+ " <td>-0.375659</td>\n",
+ " <td>63</td>\n",
+ " <td>exemplar</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>5423</th>\n",
+ " <td>-0.591529</td>\n",
+ " <td>0.984362</td>\n",
+ " <td>-4.146834</td>\n",
+ " <td>-0.724267</td>\n",
+ " <td>5.269270</td>\n",
+ " <td>-5.308410</td>\n",
+ " <td>1.059127</td>\n",
+ " <td>-2.142913</td>\n",
+ " <td>1.400815</td>\n",
+ " <td>-4.242834</td>\n",
+ " <td>...</td>\n",
+ " <td>-0.344498</td>\n",
+ " <td>-0.266467</td>\n",
+ " <td>0.286554</td>\n",
+ " <td>-0.107283</td>\n",
+ " <td>-0.764430</td>\n",
+ " <td>0.404030</td>\n",
+ " <td>0.769236</td>\n",
+ " <td>0.314161</td>\n",
+ " <td>64</td>\n",
+ " <td>exemplar</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>5660</th>\n",
+ " <td>-1.819984</td>\n",
+ " <td>-4.451072</td>\n",
+ " <td>10.189980</td>\n",
+ " <td>5.669862</td>\n",
+ " <td>2.677325</td>\n",
+ " <td>-1.342148</td>\n",
+ " <td>-3.418654</td>\n",
+ " <td>-4.652941</td>\n",
+ " <td>-3.702533</td>\n",
+ " <td>-3.823347</td>\n",
+ " <td>...</td>\n",
+ " <td>-0.033270</td>\n",
+ " <td>-0.169137</td>\n",
+ " <td>-0.337149</td>\n",
+ " <td>-0.643458</td>\n",
+ " <td>-0.303914</td>\n",
+ " <td>0.619518</td>\n",
+ " <td>0.009608</td>\n",
+ " <td>-0.007420</td>\n",
+ " <td>11</td>\n",
+ " <td>exemplar</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>5772</th>\n",
+ " <td>-1.538368</td>\n",
+ " <td>0.092462</td>\n",
+ " <td>-5.099878</td>\n",
+ " <td>11.599087</td>\n",
+ " <td>1.775226</td>\n",
+ " <td>-5.060294</td>\n",
+ " <td>0.679947</td>\n",
+ " <td>-2.038918</td>\n",
+ " <td>-3.773811</td>\n",
+ " <td>0.310163</td>\n",
+ " <td>...</td>\n",
+ " <td>-0.472988</td>\n",
+ " <td>0.154486</td>\n",
+ " <td>0.210189</td>\n",
+ " <td>-0.215814</td>\n",
+ " <td>-1.212438</td>\n",
+ " <td>-0.466347</td>\n",
+ " <td>-0.757830</td>\n",
+ " <td>-0.180894</td>\n",
+ " <td>66</td>\n",
+ " <td>exemplar</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>5880</th>\n",
+ " <td>-0.584508</td>\n",
+ " <td>2.844945</td>\n",
+ " <td>-4.595065</td>\n",
+ " <td>0.016701</td>\n",
+ " <td>12.958841</td>\n",
+ " <td>0.323624</td>\n",
+ " <td>-2.357301</td>\n",
+ " <td>-5.764180</td>\n",
+ " <td>-8.732758</td>\n",
+ " <td>-1.016143</td>\n",
+ " <td>...</td>\n",
+ " <td>0.498180</td>\n",
+ " <td>-0.307059</td>\n",
+ " <td>0.243496</td>\n",
+ " <td>-0.458031</td>\n",
+ " <td>-0.929303</td>\n",
+ " <td>-0.345206</td>\n",
+ " <td>-0.031860</td>\n",
+ " <td>-0.286380</td>\n",
+ " <td>4</td>\n",
+ " <td>exemplar</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>5 rows × 200 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " logit_0 logit_1 logit_2 logit_3 logit_4 logit_5 logit_6 \\\n",
+ "5325 -3.464009 -4.065380 4.670951 3.943103 6.382225 -4.207092 -1.838107 \n",
+ "5423 -0.591529 0.984362 -4.146834 -0.724267 5.269270 -5.308410 1.059127 \n",
+ "5660 -1.819984 -4.451072 10.189980 5.669862 2.677325 -1.342148 -3.418654 \n",
+ "5772 -1.538368 0.092462 -5.099878 11.599087 1.775226 -5.060294 0.679947 \n",
+ "5880 -0.584508 2.844945 -4.595065 0.016701 12.958841 0.323624 -2.357301 \n",
+ "\n",
+ " logit_7 logit_8 logit_9 ... feat_120 feat_121 feat_122 \\\n",
+ "5325 -2.900783 -6.593166 -3.448262 ... -0.160013 -0.421526 0.590221 \n",
+ "5423 -2.142913 1.400815 -4.242834 ... -0.344498 -0.266467 0.286554 \n",
+ "5660 -4.652941 -3.702533 -3.823347 ... -0.033270 -0.169137 -0.337149 \n",
+ "5772 -2.038918 -3.773811 0.310163 ... -0.472988 0.154486 0.210189 \n",
+ "5880 -5.764180 -8.732758 -1.016143 ... 0.498180 -0.307059 0.243496 \n",
+ "\n",
+ " feat_123 feat_124 feat_125 feat_126 feat_127 class type \n",
+ "5325 -0.319883 -0.170347 -0.415997 -0.679634 -0.375659 63 exemplar \n",
+ "5423 -0.107283 -0.764430 0.404030 0.769236 0.314161 64 exemplar \n",
+ "5660 -0.643458 -0.303914 0.619518 0.009608 -0.007420 11 exemplar \n",
+ "5772 -0.215814 -1.212438 -0.466347 -0.757830 -0.180894 66 exemplar \n",
+ "5880 -0.458031 -0.929303 -0.345206 -0.031860 -0.286380 4 exemplar \n",
+ "\n",
+ "[5 rows x 200 columns]"
+ ]
+ },
+ "execution_count": 33,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# prep data for KDE by masking non-random-exemplar data and non-known data, and removing all but feature & class columns\n",
+ "\n",
+ "# copy the master df\n",
+ "exemplars_df = master_dfs[0].copy()\n",
+ "\n",
+ "# remove any rows that are not random exemplars\n",
+ "exemplars_df = exemplars_df[exemplars_df[\"rand_set_member\"]]\n",
+ "\n",
+ "# remove any rows that are not known\n",
+ "exemplars_df = exemplars_df[exemplars_df[\"type\"] == \"known\"]\n",
+ "\n",
+ "# drop unnused columns\n",
+ "exemplars_df = exemplars_df.dropna(how=\"all\", axis=1).drop(columns=[\"rand_set_member\", \"cent_set_member\", \"pred_class\"]) # feat cols, logit cols, class, type\n",
+ "#exemplars_df = exemplars_df.reset_index(drop=True)\n",
+ "\n",
+ "# set type to exemplar for all rows\n",
+ "exemplars_df[\"type\"] = \"exemplar\"\n",
+ "\n",
+ "exemplars_df.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Normalise the Known Data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# normalise the features using z score norm from sklearn\n",
+ "from sklearn.preprocessing import StandardScaler\n",
+ "feat_columns = [col for col in exemplars_df.columns if col.startswith(\"feat_\")]\n",
+ "\n",
+ "scaler = StandardScaler()\n",
+ "normed_df = exemplars_df.copy()\n",
+ "normed_df[feat_columns] = scaler.fit_transform(normed_df[feat_columns])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Create a KDE For Each Known Class"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "70"
+ ]
+ },
+ "execution_count": 35,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "bandwidth = 0.50 # controls the smoothness of the KDE\n",
+ "kde_models = {}\n",
+ "# generate a KDE model for each known class\n",
+ "from sklearn.neighbors import KernelDensity\n",
+ "for class_idx in normed_df[\"class\"].unique():\n",
+ " # get the data for the current class\n",
+ " class_feats = normed_df[normed_df[\"class\"] == class_idx][feat_columns]\n",
+ " # class data should have shape (n_samples, n_features)\n",
+ " kde = KernelDensity(bandwidth=bandwidth, kernel=\"gaussian\")\n",
+ " kde.fit(class_feats)\n",
+ " kde_models[class_idx] = kde\n",
+ "\n",
+ "len(kde_models)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Generate Psuedo-Novel Set\n",
+ "Generate a Random Point, and accept it if it DOES NOT lies in ANY of the KDE Boundries generated above."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "73b19f7115e1416884fb2e51ebbd4056",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Generating Pseudo-Novel Samples: 0%| | 0/1920 [00:00<?, ?it/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Generated 1920 valid samples.\n"
+ ]
+ }
+ ],
+ "source": [
+ "samples_to_generate = 1920\n",
+ "threshold = 0.01 # threshold for the KDE model to consider a point as a member of a known class\n",
+ "samples = []\n",
+ "\n",
+ "# Batch size to speed up the generation\n",
+ "batch_size = 1000 # Adjust this for more samples in a single batch\n",
+ "\n",
+ "# List of column names, same as in the original dataframe (feat_0 to feat_127)\n",
+ "column_names = [f\"feat_{i}\" for i in range(128)]\n",
+ "\n",
+ "def is_valid_sample_batch(samples, kde_models, threshold):\n",
+ " # Convert batch of samples to DataFrame\n",
+ " samples_df = pd.DataFrame(samples, columns=column_names)\n",
+ " \n",
+ " # Loop over each KDE model and check density for each sample in the batch\n",
+ " for kde in kde_models.values():\n",
+ " log_density = kde.score_samples(samples_df) # Calculate log densities for all samples in the batch\n",
+ " density = np.exp(log_density) # Convert to probability densities\n",
+ " valid_mask = density < threshold # Boolean mask for valid samples (density below threshold)\n",
+ " samples_df = samples_df[valid_mask] # Keep only the valid samples for the next KDE check\n",
+ " \n",
+ " # Return only the valid samples that are outside all clusters\n",
+ " return samples_df.values\n",
+ "\n",
+ "with tqdm(total=samples_to_generate, desc=\"Generating Pseudo-Novel Samples\") as pbar:\n",
+ " while len(samples) < samples_to_generate:\n",
+ " # Generate a batch of random samples in 128D space\n",
+ " batch_samples = np.random.randn(batch_size, 128)\n",
+ " \n",
+ " # Get valid samples from the batch\n",
+ " valid_samples_batch = is_valid_sample_batch(batch_samples, kde_models, threshold)\n",
+ " \n",
+ " # Append valid samples to the list\n",
+ " samples.extend(valid_samples_batch)\n",
+ " pbar.update(len(valid_samples_batch)) # Update the progress bar based on the number of valid samples found\n",
+ "\n",
+ " # Stop if we've generated enough samples\n",
+ " if len(samples) >= samples_to_generate:\n",
+ " samples = samples[:samples_to_generate] # Trim the list if we overshoot the target\n",
+ "\n",
+ "# Concatenate the valid samples into a single numpy array\n",
+ "valid_samples = np.array(samples)\n",
+ "\n",
+ "print(f\"Generated {valid_samples.shape[0]} valid samples.\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Unnormalise the Psuedo-Novel Points and Compute Logits"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "KeyboardInterrupt",
+ "evalue": "",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
+ "Cell \u001b[0;32mIn[37], line 11\u001b[0m\n\u001b[1;32m 8\u001b[0m batches \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39msplit(samples_tensor, batch_size)\n\u001b[1;32m 10\u001b[0m \u001b[38;5;66;03m# compute logits for each sample using the model\u001b[39;00m\n\u001b[0;32m---> 11\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[43mmodel_CI\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgm_args\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mto(device)\n\u001b[1;32m 12\u001b[0m model\u001b[38;5;241m.\u001b[39mload_state_dict(\n\u001b[1;32m 13\u001b[0m torch\u001b[38;5;241m.\u001b[39mload(os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(gm_args\u001b[38;5;241m.\u001b[39mmodel_dir, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstage_0.pth\u001b[39m\u001b[38;5;124m\"\u001b[39m), weights_only\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m 14\u001b[0m )\n\u001b[1;32m 15\u001b[0m model\u001b[38;5;241m.\u001b[39mcuda()\n",
+ "File \u001b[0;32m/cl/gm/models/model_CI.py:18\u001b[0m, in \u001b[0;36mmodel_CI.__init__\u001b[0;34m(self, args, fully)\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[38;5;28msuper\u001b[39m(model_CI, \u001b[38;5;28mself\u001b[39m)\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__init__\u001b[39m()\n\u001b[1;32m 17\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39margs \u001b[38;5;241m=\u001b[39m args\n\u001b[0;32m---> 18\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmoco \u001b[38;5;241m=\u001b[39m \u001b[43mModelMoCo\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 19\u001b[0m \u001b[43m \u001b[49m\u001b[43mdim\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmoco_dim\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 20\u001b[0m \u001b[43m \u001b[49m\u001b[43mK\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmoco_k\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 21\u001b[0m \u001b[43m \u001b[49m\u001b[43mm\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmoco_m\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 22\u001b[0m \u001b[43m \u001b[49m\u001b[43mT\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmoco_t\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 23\u001b[0m \u001b[43m \u001b[49m\u001b[43mbn_splits\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbn_splits\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 24\u001b[0m \u001b[43m \u001b[49m\u001b[43mmulti_branch\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 25\u001b[0m \u001b[43m \u001b[49m\u001b[43mbranch_depth\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbranch_depth\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 26\u001b[0m \u001b[43m \u001b[49m\u001b[43mall_branch\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mall_branch\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 27\u001b[0m \u001b[43m \u001b[49m\u001b[43msame_branch\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msame_branch\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 28\u001b[0m \u001b[43m \u001b[49m\u001b[43mfully\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfully\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 29\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 30\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfeature_dim \u001b[38;5;241m=\u001b[39m args\u001b[38;5;241m.\u001b[39mmoco_dim\n\u001b[1;32m 32\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcluster_head \u001b[38;5;241m=\u001b[39m nn\u001b[38;5;241m.\u001b[39mModuleList(\n\u001b[1;32m 33\u001b[0m [\n\u001b[1;32m 34\u001b[0m nn\u001b[38;5;241m.\u001b[39mLinear(\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 38\u001b[0m ]\n\u001b[1;32m 39\u001b[0m )\n",
+ "File \u001b[0;32m/cl/gm/models/moco.py:425\u001b[0m, in \u001b[0;36mModelMoCo.__init__\u001b[0;34m(self, dim, K, m, T, arch, bn_splits, symmetric, multi_branch, branch_depth, all_branch, same_branch, fully)\u001b[0m\n\u001b[1;32m 423\u001b[0m \u001b[38;5;66;03m# create the encoders\u001b[39;00m\n\u001b[1;32m 424\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m multi_branch:\n\u001b[0;32m--> 425\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mencoder_q \u001b[38;5;241m=\u001b[39m \u001b[43mMultiBranchModelBase\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfeature_dim\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdim\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43march\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43march\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbn_splits\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbn_splits\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbranch_depth\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbranch_depth\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msame_branch\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msame_branch\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfully\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfully\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 426\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mencoder_k \u001b[38;5;241m=\u001b[39m MultiBranchModelBase(feature_dim\u001b[38;5;241m=\u001b[39mdim, arch\u001b[38;5;241m=\u001b[39march, bn_splits\u001b[38;5;241m=\u001b[39mbn_splits, branch_depth\u001b[38;5;241m=\u001b[39mbranch_depth, same_branch\u001b[38;5;241m=\u001b[39msame_branch, fully\u001b[38;5;241m=\u001b[39mfully)\n\u001b[1;32m 427\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
+ "File \u001b[0;32m/cl/gm/models/moco.py:398\u001b[0m, in \u001b[0;36mMultiBranchModelBase.__init__\u001b[0;34m(self, feature_dim, arch, bn_splits, branch_depth, same_branch, fully)\u001b[0m\n\u001b[1;32m 396\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnet\u001b[38;5;241m.\u001b[39mload_state_dict(pretrained\u001b[38;5;241m.\u001b[39mstate_dict(), strict\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m 397\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 398\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnet \u001b[38;5;241m=\u001b[39m \u001b[43mMultiBranchResNet\u001b[49m\u001b[43m(\u001b[49m\u001b[43mBasicBlock\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_classes\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfeature_dim\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnorm_layer\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnorm_layer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbranch_depth\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbranch_depth\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msame_branch\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msame_branch\u001b[49m\u001b[43m)\u001b[49m\n",
+ "File \u001b[0;32m/cl/gm/models/moco.py:115\u001b[0m, in \u001b[0;36mMultiBranchResNet.__init__\u001b[0;34m(self, block, layers, num_classes, zero_init_residual, groups, width_per_group, replace_stride_with_dilation, norm_layer, branch_depth, same_branch_size, same_branch)\u001b[0m\n\u001b[1;32m 113\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodules():\n\u001b[1;32m 114\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(m, nn\u001b[38;5;241m.\u001b[39mConv2d):\n\u001b[0;32m--> 115\u001b[0m \u001b[43mnn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minit\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkaiming_normal_\u001b[49m\u001b[43m(\u001b[49m\u001b[43mm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mweight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mfan_out\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnonlinearity\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mrelu\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 116\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(m, (nn\u001b[38;5;241m.\u001b[39mBatchNorm2d, nn\u001b[38;5;241m.\u001b[39mGroupNorm)):\n\u001b[1;32m 117\u001b[0m nn\u001b[38;5;241m.\u001b[39minit\u001b[38;5;241m.\u001b[39mconstant_(m\u001b[38;5;241m.\u001b[39mweight, \u001b[38;5;241m1\u001b[39m)\n",
+ "File \u001b[0;32m~/miniconda3/envs/gm/lib/python3.9/site-packages/torch/nn/init.py:505\u001b[0m, in \u001b[0;36mkaiming_normal_\u001b[0;34m(tensor, a, mode, nonlinearity, generator)\u001b[0m\n\u001b[1;32m 503\u001b[0m std \u001b[38;5;241m=\u001b[39m gain \u001b[38;5;241m/\u001b[39m math\u001b[38;5;241m.\u001b[39msqrt(fan)\n\u001b[1;32m 504\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mno_grad():\n\u001b[0;32m--> 505\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mtensor\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnormal_\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstd\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgenerator\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgenerator\u001b[49m\u001b[43m)\u001b[49m\n",
+ "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+ ]
+ }
+ ],
+ "source": [
+ "# unnormalise the samples\n",
+ "unnormed_samples = scaler.inverse_transform(valid_samples)\n",
+ "\n",
+ "# create a torch tensor of the samples\n",
+ "samples_tensor = torch.tensor(unnormed_samples, dtype=torch.float32)\n",
+ "\n",
+ "# batch the torch tensor into batches of size batch_size\n",
+ "batches = torch.split(samples_tensor, batch_size)\n",
+ "\n",
+ "# compute logits for each sample using the model\n",
+ "model = model_CI(gm_args).to(device)\n",
+ "model.load_state_dict(\n",
+ " torch.load(os.path.join(gm_args.model_dir, \"stage_0.pth\"), weights_only=False)\n",
+ ")\n",
+ "model.cuda()\n",
+ "model.sync_new_branches() \n",
+ "\n",
+ "psuedo_novel_logits = []\n",
+ "\n",
+ "with torch.no_grad():\n",
+ " for batch in tqdm(batches, desc=\"Calculating Logits for Psuedo-Novel Points\", unit=\"batch\"):\n",
+ " out = model.batch_norm(batch.to(device))\n",
+ " out = torch.nn.functional.relu(out)\n",
+ " logits = model.classification_layer(out)\n",
+ " logits = logits.cpu().numpy()\n",
+ " psuedo_novel_logits.append(logits)\n",
+ "\n",
+ "psuedo_novel_logits = np.concatenate(psuedo_novel_logits, axis=0)\n",
+ "\n",
+ "# put the logits into a dataframe\n",
+ "psuedo_novel_df = pd.DataFrame(psuedo_novel_logits, columns=[f\"logit_{i}\" for i in range(psuedo_novel_logits.shape[1])])\n",
+ "\n",
+ "# put the feats into a dataframe\n",
+ "psuedo_novel_feats = pd.DataFrame(unnormed_samples, columns=[f\"feat_{i}\" for i in range(unnormed_samples.shape[1])])\n",
+ "\n",
+ "# combine the dataframes\n",
+ "psuedo_novel_df = pd.concat([psuedo_novel_feats, psuedo_novel_df], axis=1)\n",
+ "\n",
+ "psuedo_novel_df[\"type\"] = \"psuedo_novel\"\n",
+ "\n",
+ "psuedo_novel_df.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Combine Psuedo-Novel, Novel and Known Data into One DataFrame"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 97,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>logit_0</th>\n",
+ " <th>logit_1</th>\n",
+ " <th>logit_2</th>\n",
+ " <th>logit_3</th>\n",
+ " <th>logit_4</th>\n",
+ " <th>logit_5</th>\n",
+ " <th>logit_6</th>\n",
+ " <th>logit_7</th>\n",
+ " <th>logit_8</th>\n",
+ " <th>logit_9</th>\n",
+ " <th>...</th>\n",
+ " <th>feat_120</th>\n",
+ " <th>feat_121</th>\n",
+ " <th>feat_122</th>\n",
+ " <th>feat_123</th>\n",
+ " <th>feat_124</th>\n",
+ " <th>feat_125</th>\n",
+ " <th>feat_126</th>\n",
+ " <th>feat_127</th>\n",
+ " <th>class</th>\n",
+ " <th>type</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>5</th>\n",
+ " <td>-3.011598</td>\n",
+ " <td>0.605260</td>\n",
+ " <td>4.596222</td>\n",
+ " <td>-7.253775</td>\n",
+ " <td>-4.368778</td>\n",
+ " <td>3.772873</td>\n",
+ " <td>-1.831226</td>\n",
+ " <td>6.351457</td>\n",
+ " <td>3.711203</td>\n",
+ " <td>3.307867</td>\n",
+ " <td>...</td>\n",
+ " <td>-0.427210</td>\n",
+ " <td>0.068112</td>\n",
+ " <td>-0.774189</td>\n",
+ " <td>-0.340240</td>\n",
+ " <td>-0.611624</td>\n",
+ " <td>-0.942040</td>\n",
+ " <td>-0.420253</td>\n",
+ " <td>-0.179402</td>\n",
+ " <td>86</td>\n",
+ " <td>novel</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>6</th>\n",
+ " <td>-4.556176</td>\n",
+ " <td>7.404184</td>\n",
+ " <td>-3.665211</td>\n",
+ " <td>-0.945363</td>\n",
+ " <td>-1.968143</td>\n",
+ " <td>-1.370196</td>\n",
+ " <td>0.542963</td>\n",
+ " <td>-0.096654</td>\n",
+ " <td>-3.129990</td>\n",
+ " <td>4.099134</td>\n",
+ " <td>...</td>\n",
+ " <td>0.430055</td>\n",
+ " <td>0.017025</td>\n",
+ " <td>-0.158906</td>\n",
+ " <td>-1.218398</td>\n",
+ " <td>-0.006374</td>\n",
+ " <td>-0.657446</td>\n",
+ " <td>-0.211061</td>\n",
+ " <td>-0.352740</td>\n",
+ " <td>90</td>\n",
+ " <td>novel</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>11</th>\n",
+ " <td>-4.594503</td>\n",
+ " <td>-3.517605</td>\n",
+ " <td>-0.825194</td>\n",
+ " <td>-3.654088</td>\n",
+ " <td>0.456029</td>\n",
+ " <td>0.588375</td>\n",
+ " <td>-4.334945</td>\n",
+ " <td>-3.052101</td>\n",
+ " <td>-0.971867</td>\n",
+ " <td>-3.496614</td>\n",
+ " <td>...</td>\n",
+ " <td>0.394019</td>\n",
+ " <td>0.096376</td>\n",
+ " <td>0.076737</td>\n",
+ " <td>-0.682279</td>\n",
+ " <td>-0.516208</td>\n",
+ " <td>0.188914</td>\n",
+ " <td>-0.540421</td>\n",
+ " <td>-0.764605</td>\n",
+ " <td>96</td>\n",
+ " <td>novel</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>12</th>\n",
+ " <td>-0.044976</td>\n",
+ " <td>4.223177</td>\n",
+ " <td>0.277746</td>\n",
+ " <td>-0.857068</td>\n",
+ " <td>-5.425841</td>\n",
+ " <td>-2.687069</td>\n",
+ " <td>9.358603</td>\n",
+ " <td>3.762712</td>\n",
+ " <td>1.410808</td>\n",
+ " <td>-3.475824</td>\n",
+ " <td>...</td>\n",
+ " <td>-0.043276</td>\n",
+ " <td>-0.620282</td>\n",
+ " <td>-0.163436</td>\n",
+ " <td>-0.162118</td>\n",
+ " <td>0.866229</td>\n",
+ " <td>0.301806</td>\n",
+ " <td>-0.161099</td>\n",
+ " <td>-0.115262</td>\n",
+ " <td>82</td>\n",
+ " <td>novel</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>14</th>\n",
+ " <td>1.060570</td>\n",
+ " <td>-4.212956</td>\n",
+ " <td>-1.081154</td>\n",
+ " <td>-2.627720</td>\n",
+ " <td>-2.734410</td>\n",
+ " <td>8.807901</td>\n",
+ " <td>-4.418684</td>\n",
+ " <td>-5.643659</td>\n",
+ " <td>-0.699761</td>\n",
+ " <td>5.276971</td>\n",
+ " <td>...</td>\n",
+ " <td>0.652230</td>\n",
+ " <td>-0.542009</td>\n",
+ " <td>-0.072060</td>\n",
+ " <td>-0.289358</td>\n",
+ " <td>-0.243858</td>\n",
+ " <td>0.255705</td>\n",
+ " <td>-0.666718</td>\n",
+ " <td>-0.033301</td>\n",
+ " <td>71</td>\n",
+ " <td>novel</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>5 rows × 200 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " logit_0 logit_1 logit_2 logit_3 logit_4 logit_5 logit_6 \\\n",
+ "5 -3.011598 0.605260 4.596222 -7.253775 -4.368778 3.772873 -1.831226 \n",
+ "6 -4.556176 7.404184 -3.665211 -0.945363 -1.968143 -1.370196 0.542963 \n",
+ "11 -4.594503 -3.517605 -0.825194 -3.654088 0.456029 0.588375 -4.334945 \n",
+ "12 -0.044976 4.223177 0.277746 -0.857068 -5.425841 -2.687069 9.358603 \n",
+ "14 1.060570 -4.212956 -1.081154 -2.627720 -2.734410 8.807901 -4.418684 \n",
+ "\n",
+ " logit_7 logit_8 logit_9 ... feat_120 feat_121 feat_122 feat_123 \\\n",
+ "5 6.351457 3.711203 3.307867 ... -0.427210 0.068112 -0.774189 -0.340240 \n",
+ "6 -0.096654 -3.129990 4.099134 ... 0.430055 0.017025 -0.158906 -1.218398 \n",
+ "11 -3.052101 -0.971867 -3.496614 ... 0.394019 0.096376 0.076737 -0.682279 \n",
+ "12 3.762712 1.410808 -3.475824 ... -0.043276 -0.620282 -0.163436 -0.162118 \n",
+ "14 -5.643659 -0.699761 5.276971 ... 0.652230 -0.542009 -0.072060 -0.289358 \n",
+ "\n",
+ " feat_124 feat_125 feat_126 feat_127 class type \n",
+ "5 -0.611624 -0.942040 -0.420253 -0.179402 86 novel \n",
+ "6 -0.006374 -0.657446 -0.211061 -0.352740 90 novel \n",
+ "11 -0.516208 0.188914 -0.540421 -0.764605 96 novel \n",
+ "12 0.866229 0.301806 -0.161099 -0.115262 82 novel \n",
+ "14 -0.243858 0.255705 -0.666718 -0.033301 71 novel \n",
+ "\n",
+ "[5 rows x 200 columns]"
+ ]
+ },
+ "execution_count": 97,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# get all novel samples from master_df, drop unused columns\n",
+ "novel_df = master_dfs[0].copy()\n",
+ "novel_df = novel_df[novel_df[\"type\"] == \"novel\"]\n",
+ "novel_df = novel_df.dropna(how=\"all\", axis=1).drop(columns=[\"rand_set_member\", \"cent_set_member\", \"pred_class\"])\n",
+ "novel_df.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 98,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "507fa19f1639437db8d1d9d8019cb3d8",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Calculating Entropy: 0%| | 0/18880 [00:00<?, ?row/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>feat_0</th>\n",
+ " <th>feat_1</th>\n",
+ " <th>feat_2</th>\n",
+ " <th>feat_3</th>\n",
+ " <th>feat_4</th>\n",
+ " <th>feat_5</th>\n",
+ " <th>feat_6</th>\n",
+ " <th>feat_7</th>\n",
+ " <th>feat_8</th>\n",
+ " <th>feat_9</th>\n",
+ " <th>...</th>\n",
+ " <th>feat_121</th>\n",
+ " <th>feat_122</th>\n",
+ " <th>feat_123</th>\n",
+ " <th>feat_124</th>\n",
+ " <th>feat_125</th>\n",
+ " <th>feat_126</th>\n",
+ " <th>feat_127</th>\n",
+ " <th>class</th>\n",
+ " <th>type</th>\n",
+ " <th>entropy</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>-0.552313</td>\n",
+ " <td>-0.267745</td>\n",
+ " <td>0.569595</td>\n",
+ " <td>-0.635653</td>\n",
+ " <td>0.610174</td>\n",
+ " <td>-0.419553</td>\n",
+ " <td>-0.877058</td>\n",
+ " <td>-0.650940</td>\n",
+ " <td>0.124088</td>\n",
+ " <td>-0.950005</td>\n",
+ " <td>...</td>\n",
+ " <td>-0.421526</td>\n",
+ " <td>0.590221</td>\n",
+ " <td>-0.319883</td>\n",
+ " <td>-0.170347</td>\n",
+ " <td>-0.415997</td>\n",
+ " <td>-0.679634</td>\n",
+ " <td>-0.375659</td>\n",
+ " <td>63.0</td>\n",
+ " <td>exemplar</td>\n",
+ " <td>0.000170</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>-0.849918</td>\n",
+ " <td>0.071474</td>\n",
+ " <td>-0.929781</td>\n",
+ " <td>0.184598</td>\n",
+ " <td>-0.854087</td>\n",
+ " <td>-0.229025</td>\n",
+ " <td>0.048777</td>\n",
+ " <td>-1.337597</td>\n",
+ " <td>-0.131864</td>\n",
+ " <td>0.290226</td>\n",
+ " <td>...</td>\n",
+ " <td>-0.266467</td>\n",
+ " <td>0.286554</td>\n",
+ " <td>-0.107283</td>\n",
+ " <td>-0.764430</td>\n",
+ " <td>0.404030</td>\n",
+ " <td>0.769236</td>\n",
+ " <td>0.314161</td>\n",
+ " <td>64.0</td>\n",
+ " <td>exemplar</td>\n",
+ " <td>0.001400</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>-0.006410</td>\n",
+ " <td>-0.269704</td>\n",
+ " <td>0.319203</td>\n",
+ " <td>0.042950</td>\n",
+ " <td>-0.114409</td>\n",
+ " <td>-0.264539</td>\n",
+ " <td>-1.107052</td>\n",
+ " <td>-0.001195</td>\n",
+ " <td>0.259073</td>\n",
+ " <td>-0.349285</td>\n",
+ " <td>...</td>\n",
+ " <td>-0.169137</td>\n",
+ " <td>-0.337149</td>\n",
+ " <td>-0.643458</td>\n",
+ " <td>-0.303914</td>\n",
+ " <td>0.619518</td>\n",
+ " <td>0.009608</td>\n",
+ " <td>-0.007420</td>\n",
+ " <td>11.0</td>\n",
+ " <td>exemplar</td>\n",
+ " <td>0.033248</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>0.032932</td>\n",
+ " <td>-0.556999</td>\n",
+ " <td>-0.536357</td>\n",
+ " <td>-0.387840</td>\n",
+ " <td>0.115782</td>\n",
+ " <td>-1.339129</td>\n",
+ " <td>-0.735811</td>\n",
+ " <td>0.225993</td>\n",
+ " <td>1.039192</td>\n",
+ " <td>-0.356084</td>\n",
+ " <td>...</td>\n",
+ " <td>0.154486</td>\n",
+ " <td>0.210189</td>\n",
+ " <td>-0.215814</td>\n",
+ " <td>-1.212438</td>\n",
+ " <td>-0.466347</td>\n",
+ " <td>-0.757830</td>\n",
+ " <td>-0.180894</td>\n",
+ " <td>66.0</td>\n",
+ " <td>exemplar</td>\n",
+ " <td>0.003726</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>0.088527</td>\n",
+ " <td>-0.253070</td>\n",
+ " <td>-0.681181</td>\n",
+ " <td>-0.555435</td>\n",
+ " <td>-0.225142</td>\n",
+ " <td>-0.159338</td>\n",
+ " <td>-0.777661</td>\n",
+ " <td>-0.359541</td>\n",
+ " <td>-0.231611</td>\n",
+ " <td>0.416037</td>\n",
+ " <td>...</td>\n",
+ " <td>-0.307059</td>\n",
+ " <td>0.243496</td>\n",
+ " <td>-0.458031</td>\n",
+ " <td>-0.929303</td>\n",
+ " <td>-0.345206</td>\n",
+ " <td>-0.031860</td>\n",
+ " <td>-0.286380</td>\n",
+ " <td>4.0</td>\n",
+ " <td>exemplar</td>\n",
+ " <td>0.163136</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>...</th>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>18875</th>\n",
+ " <td>-0.445055</td>\n",
+ " <td>-0.408932</td>\n",
+ " <td>-0.312858</td>\n",
+ " <td>-0.207656</td>\n",
+ " <td>-0.005539</td>\n",
+ " <td>-0.290190</td>\n",
+ " <td>-1.014628</td>\n",
+ " <td>-0.565354</td>\n",
+ " <td>-0.504563</td>\n",
+ " <td>0.070074</td>\n",
+ " <td>...</td>\n",
+ " <td>0.356080</td>\n",
+ " <td>0.709726</td>\n",
+ " <td>-0.782525</td>\n",
+ " <td>-0.402792</td>\n",
+ " <td>-0.679988</td>\n",
+ " <td>-0.245395</td>\n",
+ " <td>-0.048797</td>\n",
+ " <td>NaN</td>\n",
+ " <td>psuedo_novel</td>\n",
+ " <td>2.185516</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>18876</th>\n",
+ " <td>0.226067</td>\n",
+ " <td>-0.151508</td>\n",
+ " <td>0.220270</td>\n",
+ " <td>-1.219691</td>\n",
+ " <td>0.047572</td>\n",
+ " <td>-0.248033</td>\n",
+ " <td>-0.529846</td>\n",
+ " <td>-0.242606</td>\n",
+ " <td>-1.212034</td>\n",
+ " <td>0.297707</td>\n",
+ " <td>...</td>\n",
+ " <td>-0.295557</td>\n",
+ " <td>-0.845323</td>\n",
+ " <td>-0.237601</td>\n",
+ " <td>-0.646139</td>\n",
+ " <td>-0.381547</td>\n",
+ " <td>-0.781996</td>\n",
+ " <td>-0.139858</td>\n",
+ " <td>NaN</td>\n",
+ " <td>psuedo_novel</td>\n",
+ " <td>0.427634</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>18877</th>\n",
+ " <td>0.070643</td>\n",
+ " <td>-0.407661</td>\n",
+ " <td>-0.509418</td>\n",
+ " <td>-0.200558</td>\n",
+ " <td>0.208355</td>\n",
+ " <td>0.765851</td>\n",
+ " <td>-0.631523</td>\n",
+ " <td>-0.291098</td>\n",
+ " <td>-0.999370</td>\n",
+ " <td>-1.307745</td>\n",
+ " <td>...</td>\n",
+ " <td>-0.363790</td>\n",
+ " <td>0.071932</td>\n",
+ " <td>-0.584621</td>\n",
+ " <td>-0.022359</td>\n",
+ " <td>0.386017</td>\n",
+ " <td>0.221505</td>\n",
+ " <td>-0.390052</td>\n",
+ " <td>NaN</td>\n",
+ " <td>psuedo_novel</td>\n",
+ " <td>2.413514</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>18878</th>\n",
+ " <td>-0.000663</td>\n",
+ " <td>-0.063954</td>\n",
+ " <td>-0.095632</td>\n",
+ " <td>-0.710932</td>\n",
+ " <td>-0.679945</td>\n",
+ " <td>-0.229031</td>\n",
+ " <td>-0.445830</td>\n",
+ " <td>-0.708703</td>\n",
+ " <td>0.026336</td>\n",
+ " <td>-0.112378</td>\n",
+ " <td>...</td>\n",
+ " <td>-0.042648</td>\n",
+ " <td>-0.622527</td>\n",
+ " <td>-0.562778</td>\n",
+ " <td>0.021045</td>\n",
+ " <td>-0.443204</td>\n",
+ " <td>-0.218977</td>\n",
+ " <td>-0.368054</td>\n",
+ " <td>NaN</td>\n",
+ " <td>psuedo_novel</td>\n",
+ " <td>0.876167</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>18879</th>\n",
+ " <td>-0.297152</td>\n",
+ " <td>-1.042590</td>\n",
+ " <td>0.624790</td>\n",
+ " <td>-0.717376</td>\n",
+ " <td>0.293244</td>\n",
+ " <td>0.115037</td>\n",
+ " <td>-0.335595</td>\n",
+ " <td>-0.764487</td>\n",
+ " <td>-0.937899</td>\n",
+ " <td>-0.450234</td>\n",
+ " <td>...</td>\n",
+ " <td>-0.238888</td>\n",
+ " <td>-0.158517</td>\n",
+ " <td>-0.589777</td>\n",
+ " <td>-0.141795</td>\n",
+ " <td>-0.889280</td>\n",
+ " <td>0.173252</td>\n",
+ " <td>-0.093108</td>\n",
+ " <td>NaN</td>\n",
+ " <td>psuedo_novel</td>\n",
+ " <td>2.316686</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>18880 rows × 131 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " feat_0 feat_1 feat_2 feat_3 feat_4 feat_5 feat_6 \\\n",
+ "0 -0.552313 -0.267745 0.569595 -0.635653 0.610174 -0.419553 -0.877058 \n",
+ "1 -0.849918 0.071474 -0.929781 0.184598 -0.854087 -0.229025 0.048777 \n",
+ "2 -0.006410 -0.269704 0.319203 0.042950 -0.114409 -0.264539 -1.107052 \n",
+ "3 0.032932 -0.556999 -0.536357 -0.387840 0.115782 -1.339129 -0.735811 \n",
+ "4 0.088527 -0.253070 -0.681181 -0.555435 -0.225142 -0.159338 -0.777661 \n",
+ "... ... ... ... ... ... ... ... \n",
+ "18875 -0.445055 -0.408932 -0.312858 -0.207656 -0.005539 -0.290190 -1.014628 \n",
+ "18876 0.226067 -0.151508 0.220270 -1.219691 0.047572 -0.248033 -0.529846 \n",
+ "18877 0.070643 -0.407661 -0.509418 -0.200558 0.208355 0.765851 -0.631523 \n",
+ "18878 -0.000663 -0.063954 -0.095632 -0.710932 -0.679945 -0.229031 -0.445830 \n",
+ "18879 -0.297152 -1.042590 0.624790 -0.717376 0.293244 0.115037 -0.335595 \n",
+ "\n",
+ " feat_7 feat_8 feat_9 ... feat_121 feat_122 feat_123 \\\n",
+ "0 -0.650940 0.124088 -0.950005 ... -0.421526 0.590221 -0.319883 \n",
+ "1 -1.337597 -0.131864 0.290226 ... -0.266467 0.286554 -0.107283 \n",
+ "2 -0.001195 0.259073 -0.349285 ... -0.169137 -0.337149 -0.643458 \n",
+ "3 0.225993 1.039192 -0.356084 ... 0.154486 0.210189 -0.215814 \n",
+ "4 -0.359541 -0.231611 0.416037 ... -0.307059 0.243496 -0.458031 \n",
+ "... ... ... ... ... ... ... ... \n",
+ "18875 -0.565354 -0.504563 0.070074 ... 0.356080 0.709726 -0.782525 \n",
+ "18876 -0.242606 -1.212034 0.297707 ... -0.295557 -0.845323 -0.237601 \n",
+ "18877 -0.291098 -0.999370 -1.307745 ... -0.363790 0.071932 -0.584621 \n",
+ "18878 -0.708703 0.026336 -0.112378 ... -0.042648 -0.622527 -0.562778 \n",
+ "18879 -0.764487 -0.937899 -0.450234 ... -0.238888 -0.158517 -0.589777 \n",
+ "\n",
+ " feat_124 feat_125 feat_126 feat_127 class type entropy \n",
+ "0 -0.170347 -0.415997 -0.679634 -0.375659 63.0 exemplar 0.000170 \n",
+ "1 -0.764430 0.404030 0.769236 0.314161 64.0 exemplar 0.001400 \n",
+ "2 -0.303914 0.619518 0.009608 -0.007420 11.0 exemplar 0.033248 \n",
+ "3 -1.212438 -0.466347 -0.757830 -0.180894 66.0 exemplar 0.003726 \n",
+ "4 -0.929303 -0.345206 -0.031860 -0.286380 4.0 exemplar 0.163136 \n",
+ "... ... ... ... ... ... ... ... \n",
+ "18875 -0.402792 -0.679988 -0.245395 -0.048797 NaN psuedo_novel 2.185516 \n",
+ "18876 -0.646139 -0.381547 -0.781996 -0.139858 NaN psuedo_novel 0.427634 \n",
+ "18877 -0.022359 0.386017 0.221505 -0.390052 NaN psuedo_novel 2.413514 \n",
+ "18878 0.021045 -0.443204 -0.218977 -0.368054 NaN psuedo_novel 0.876167 \n",
+ "18879 -0.141795 -0.889280 0.173252 -0.093108 NaN psuedo_novel 2.316686 \n",
+ "\n",
+ "[18880 rows x 131 columns]"
+ ]
+ },
+ "execution_count": 98,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# combine exemplar_df, novel_df and psuedo_novel_df into one and calculate entrropy\n",
+ "combined_df = pd.concat([exemplars_df, novel_df, psuedo_novel_df], ignore_index=True)\n",
+ "combined_df = combined_df.reset_index(drop=True)\n",
+ "\n",
+ "tqdm.pandas(desc=\"Calculating Entropy\", unit=\"row\")\n",
+ "combined_df[\"entropy\"] = combined_df.progress_apply(get_entropy, axis=1)\n",
+ "combined_df = combined_df.drop(columns=[col for col in combined_df.columns if col.startswith(\"logit_\")])\n",
+ "combined_df"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Plot the Distribution of Psuedo-Novel, Novel and Known Samples"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 99,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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+ "text/plain": [
+ "<Figure size 2000x800 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# plot the KDE and normal distribution of the entropy of the exemplars, novel data and psuedo-novel data\n",
+ "grouped = combined_df.groupby(\"type\")\n",
+ "\n",
+ "x_vals = np.linspace(combined_df[\"entropy\"].min(), combined_df[\"entropy\"].max(), 1000)\n",
+ "fig, axs = plt.subplots(1, 2, figsize=(20, 8))\n",
+ "\n",
+ "for name, group in grouped:\n",
+ " mean = group[\"entropy\"].mean()\n",
+ " std = group[\"entropy\"].std()\n",
+ " pdf_vals = norm.pdf(x_vals, mean, std)\n",
+ " if name == \"psuedo_novel\":\n",
+ " name = \"Pseudo Novel\"\n",
+ " axs[0].plot(x_vals, pdf_vals, label=f\"{name.capitalize()}\", linewidth=2)\n",
+ " axs[0].fill_between(x_vals, pdf_vals, alpha=0.3)\n",
+ "\n",
+ "axs[0].set_xlabel(\"Entropy\")\n",
+ "axs[0].set_ylabel(\"Density\")\n",
+ "axs[0].set_title(\"Entropy: Normal Distribution\")\n",
+ "axs[0].legend()\n",
+ "axs[0].grid()\n",
+ "\n",
+ "sns.kdeplot(\n",
+ " combined_df[combined_df[\"type\"] == \"exemplar\"][\"entropy\"],\n",
+ " label=\"Exemplar\",\n",
+ " color=\"blue\",\n",
+ " fill=True,\n",
+ " ax=axs[1]\n",
+ ")\n",
+ "\n",
+ "sns.kdeplot(\n",
+ " combined_df[combined_df[\"type\"] == \"psuedo_novel\"][\"entropy\"],\n",
+ " label=\"Psuedo Novel\",\n",
+ " color=\"orange\",\n",
+ " fill=True,\n",
+ " ax=axs[1]\n",
+ ")\n",
+ "\n",
+ "sns.kdeplot(\n",
+ " combined_df[combined_df[\"type\"] == \"novel\"][\"entropy\"],\n",
+ " label=\"Novel\",\n",
+ " color=\"green\",\n",
+ " fill=True,\n",
+ " ax=axs[1]\n",
+ ")\n",
+ "\n",
+ "axs[1].set_xlabel(\"Entropy\")\n",
+ "axs[1].set_ylabel(\"Density\")\n",
+ "axs[1].set_title(\"Entropy: KDE Distribution\")\n",
+ "axs[1].legend()\n",
+ "axs[1].grid()\n",
+ "\n",
+ "plt.tight_layout()\n",
+ "plt.show()\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Plot a T-SNE of Known, Novel and Pseudo-Novel Points"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 100,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "651b66d73c4646808de36bccc95af7f4",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Checkbox(value=True, description='Show Pseudo-Novel Data')"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "fc07be953096484c978d6511f7c1a4f4",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Checkbox(value=True, description='Show Novel Data')"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "c98e6f817e314764907d63c011209808",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Output()"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "\n",
+ "# tsne the exemplar features, novel data and psuedo-novel data\n",
+ "from sklearn.manifold import TSNE\n",
+ "tsne = TSNE(n_components=2, perplexity=30, max_iter=1000, random_state=SEED)\n",
+ "\n",
+ "# tsne the exemplar features\n",
+ "exemplar_tsne = tsne.fit_transform(exemplars_df[feat_columns].values)\n",
+ "\n",
+ "# tsne the novel data\n",
+ "#novel_tsne = tsne.fit_transform(novel_df[feat_columns].values)\n",
+ "\n",
+ "# tsne the psuedo-novel data\n",
+ "\n",
+ "psuedo_novel_tsne = tsne.fit_transform(psuedo_novel_df[feat_columns].values)\n",
+ "\n",
+ "# plot the tsne of the exemplar features, novel data and psuedo-novel data\n",
+ "# Create checkboxes for selecting/deselecting pseudo-novel data and novel data\n",
+ "toggle_pseudo_novel = widgets.Checkbox(\n",
+ " value=True,\n",
+ " description='Show Pseudo-Novel Data',\n",
+ " disabled=False\n",
+ ")\n",
+ "\n",
+ "toggle_novel = widgets.Checkbox(\n",
+ " value=True,\n",
+ " description='Show Novel Data',\n",
+ " disabled=False\n",
+ ")\n",
+ "\n",
+ "# Function to update the plot based on the checkbox states\n",
+ "def update_plot(show_pseudo_novel, show_novel):\n",
+ " plt.close(\"all\")\n",
+ " plt.figure(figsize=(10, 8))\n",
+ " plt.scatter(exemplar_tsne[:, 0], exemplar_tsne[:, 1], c=exemplars_df[\"class\"], cmap=\"tab20\", label=\"Exemplar Features\")\n",
+ " if show_novel:\n",
+ " pass\n",
+ " #plt.scatter(novel_tsne[:, 0], novel_tsne[:, 1], c=\"green\", label=\"Novel Data\")\n",
+ " if show_pseudo_novel:\n",
+ " plt.scatter(psuedo_novel_tsne[:, 0], psuedo_novel_tsne[:, 1], c=\"red\", label=\"Pseudo-Novel Data\", marker=\"x\")\n",
+ " plt.title(\"Exemplar Features, Novel Data and Pseudo-Novel Data T-SNE\")\n",
+ " plt.legend()\n",
+ " plt.grid()\n",
+ " plt.show()\n",
+ "\n",
+ "# Create an interactive output\n",
+ "out = widgets.interactive_output(update_plot, {'show_pseudo_novel': toggle_pseudo_novel, 'show_novel': toggle_novel})\n",
+ "\n",
+ "# Display the checkboxes and plot together\n",
+ "display(toggle_pseudo_novel, toggle_novel, out)\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Combine Energy & Entropy GMM OOD Detection\n",
+ "Energy is good at detecting Known Classes, and Entropy is good at detecting Novel Classes. What if we combine the two?\n",
+ "\n",
+ "NOTE: This is exemplar-agnostic. It does not need exemplars."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Prepare Data & Recreate GMMs"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 109,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "af6afbbc673c4d01bdfdaca3e04bfa22",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Calculating Energy: 0%| | 0/50000 [00:00<?, ?row/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "1e01df4c20ff4efc9d39f8627b1ba4a2",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Calculating Entropy: 0%| | 0/50000 [00:00<?, ?row/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# GMM Args\n",
+ "gmm_args = {\n",
+ " \"n_components\": 2,\n",
+ " \"random_state\": SEED,\n",
+ " \"max_iter\": 1000,\n",
+ " \"init_params\": \"kmeans\",\n",
+ " \"tol\": 1e-4,\n",
+ "}\n",
+ "\n",
+ "gmm_df = master_dfs[0].copy()\n",
+ "\n",
+ "# compute energy for each row\n",
+ "tqdm.pandas(desc=\"Calculating Energy\", unit=\"row\")\n",
+ "gmm_df[\"energy\"] = gmm_df.progress_apply(get_energy, axis=1)\n",
+ "\n",
+ "# compute entropy for each row\n",
+ "tqdm.pandas(desc=\"Calculating Entropy\", unit=\"row\")\n",
+ "gmm_df[\"entropy\"] = gmm_df.progress_apply(get_entropy, axis=1)\n",
+ "\n",
+ "# Energy GMM -------------------------------------------------------\n",
+ "# create a GMM using the energy and the gmm_df data\n",
+ "energy_gmm = GaussianMixture(**gmm_args)\n",
+ "energy_gmm.fit(gmm_df[[\"energy\"]])\n",
+ "\n",
+ "gmm_df[\"energy_cluster\"] = energy_gmm.predict(gmm_df[[\"energy\"]]) # get hard cluster assignments\n",
+ "\n",
+ "energy_cluster_probas = energy_gmm.predict_proba(gmm_df[[\"energy\"]]) # get cluster probabilities\n",
+ "\n",
+ "high_cluster_idx = np.argmax(energy_gmm.means_) # get the index of the cluster with the higher mean\n",
+ "low_cluster_idx = 1 - high_cluster_idx # get the index of the cluster with the lower mean\n",
+ "\n",
+ "gmm_df[\"energy_cluster\"] = gmm_df[\"energy_cluster\"].apply(lambda x: \"High\" if x == high_cluster_idx else \"Low\") # change cluster assignments to high and low energy\n",
+ "gmm_df[\"energy_high_cluster_prob\"] = energy_cluster_probas[:, high_cluster_idx] # add the probability of being in the high energy cluster\n",
+ "gmm_df[\"energy_low_cluster_prob\"] = energy_cluster_probas[:, low_cluster_idx] # add the probability of being in the low energy cluster\n",
+ "\n",
+ "# Entropy GMM -------------------------------------------------------\n",
+ "# create a GMM using the entropy and the gmm_df data\n",
+ "entropy_gmm = GaussianMixture(**gmm_args)\n",
+ "entropy_gmm.fit(gmm_df[[\"entropy\"]])\n",
+ "\n",
+ "gmm_df[\"entropy_cluster\"] = entropy_gmm.predict(gmm_df[[\"entropy\"]]) # get hard cluster assignments\n",
+ "\n",
+ "entropy_cluster_probas = entropy_gmm.predict_proba(gmm_df[[\"entropy\"]]) # get cluster probabilities\n",
+ "\n",
+ "high_cluster_idx = np.argmax(entropy_gmm.means_) # get the index of the cluster with the higher mean\n",
+ "low_cluster_idx = 1 - high_cluster_idx # get the index of the cluster with the lower mean\n",
+ "\n",
+ "gmm_df[\"entropy_cluster\"] = gmm_df[\"entropy_cluster\"].apply(lambda x: \"High\" if x == high_cluster_idx else \"Low\") # change cluster assignments to high and low entropy\n",
+ "gmm_df[\"entropy_high_cluster_prob\"] = entropy_cluster_probas[:, high_cluster_idx] # add the probability of being in the high entropy cluster\n",
+ "gmm_df[\"entropy_low_cluster_prob\"] = entropy_cluster_probas[:, low_cluster_idx] # add the probability of being in the low entropy cluster\n",
+ "\n",
+ "gmm_df.head()\n",
+ "\n",
+ "results_dfs = {}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Voting"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Voting - Energy Priority\n",
+ "If the two GMMs agree, use the agreed-upon predtype, otherwise, go with ENERGY"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 110,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "ce45566a11a54ede920ae64b2ed6fefb",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Calculating Correctness (Energy): 0%| | 0/50000 [00:00<?, ?row/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>Sample Type</th>\n",
+ " <th>Correct</th>\n",
+ " <th>Incorrect</th>\n",
+ " <th>Total</th>\n",
+ " <th>Identification Rate (%)</th>\n",
+ " <th>Misidentification Rate (%)</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>Known</td>\n",
+ " <td>33915</td>\n",
+ " <td>1085</td>\n",
+ " <td>35000</td>\n",
+ " <td>96.9</td>\n",
+ " <td>3.1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>Novel</td>\n",
+ " <td>7680</td>\n",
+ " <td>7320</td>\n",
+ " <td>15000</td>\n",
+ " <td>51.2</td>\n",
+ " <td>48.8</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " Sample Type Correct Incorrect Total Identification Rate (%) \\\n",
+ "0 Known 33915 1085 35000 96.9 \n",
+ "0 Novel 7680 7320 15000 51.2 \n",
+ "\n",
+ " Misidentification Rate (%) \n",
+ "0 3.1 \n",
+ "0 48.8 "
+ ]
+ },
+ "execution_count": 110,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "def vote_energy(row):\n",
+ " if row[\"energy_cluster\"] == row[\"entropy_cluster\"]: # High/High = Novel, Low/Low = Known\n",
+ " return \"novel\" if row[\"energy_cluster\"] == \"High\" else \"known\"\n",
+ " else:\n",
+ " return \"known\" if row[\"energy_cluster\"] == \"Low\" else \"novel\"\n",
+ "\n",
+ "tqdm.pandas(desc=\"Applying Simple Vote (Energy)\", unit=\"row\")\n",
+ "gmm_df[\"vote_energy_predtype\"] = gmm_df.apply(vote_energy, axis=1)\n",
+ "\n",
+ "# calculate the accuracy of the simple vote for energy\n",
+ "def get_correctness_vote_energy(row):\n",
+ " return row[\"type\"] == row[\"vote_energy_predtype\"]\n",
+ "\n",
+ "tqdm.pandas(desc=\"Calculating Correctness (Energy)\", unit=\"row\")\n",
+ "gmm_df[\"vote_energy_iscorrect\"] = gmm_df.progress_apply(get_correctness_vote_energy, axis=1)\n",
+ "\n",
+ "# create the output dataframe\n",
+ "out_df = out_df_template.copy()\n",
+ "\n",
+ "# calculate the accuracy for known and novel samples\n",
+ "for sample_type in [\"known\", \"novel\"]:\n",
+ " filtered_df = gmm_df[gmm_df[\"type\"] == sample_type]\n",
+ " out_dict = {\n",
+ " \"Sample Type\": sample_type.capitalize(),\n",
+ " \"Correct\": filtered_df[\"vote_energy_iscorrect\"].sum(),\n",
+ " \"Incorrect\": len(filtered_df) - filtered_df[\"vote_energy_iscorrect\"].sum(),\n",
+ " \"Total\": len(filtered_df),\n",
+ " \"Identification Rate (%)\": (filtered_df[\"vote_energy_iscorrect\"].sum() / len(filtered_df)) * 100,\n",
+ " \"Misidentification Rate (%)\": ((len(filtered_df) - filtered_df[\"vote_energy_iscorrect\"].sum()) / len(filtered_df)) * 100,\n",
+ " }\n",
+ " out_df = pd.concat([out_df, pd.DataFrame(out_dict, index=[0])]) if not out_df.empty else pd.DataFrame(out_dict, index=[0])\n",
+ "\n",
+ "results_dfs[\"Simple Vote (Energy)\"] = out_df\n",
+ "out_df"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Voting - Entropy Priority\n",
+ "If the two GMMs agree, use the agreed-upon predtype, otherwise, go with ENTROPY"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 111,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "ce1ffc2cc2e84d95b275bbd8dfb8ec4a",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Applying Simple Vote (Entropy): 0%| | 0/50000 [00:00<?, ?row/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "afc28c58c98f4d22827bdb89463ee360",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Calculating Correctness (Entropy): 0%| | 0/50000 [00:00<?, ?row/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>Sample Type</th>\n",
+ " <th>Correct</th>\n",
+ " <th>Incorrect</th>\n",
+ " <th>Total</th>\n",
+ " <th>Identification Rate (%)</th>\n",
+ " <th>Misidentification Rate (%)</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>Known</td>\n",
+ " <td>28322</td>\n",
+ " <td>6678</td>\n",
+ " <td>35000</td>\n",
+ " <td>80.920000</td>\n",
+ " <td>19.080000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>Novel</td>\n",
+ " <td>13703</td>\n",
+ " <td>1297</td>\n",
+ " <td>15000</td>\n",
+ " <td>91.353333</td>\n",
+ " <td>8.646667</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " Sample Type Correct Incorrect Total Identification Rate (%) \\\n",
+ "0 Known 28322 6678 35000 80.920000 \n",
+ "0 Novel 13703 1297 15000 91.353333 \n",
+ "\n",
+ " Misidentification Rate (%) \n",
+ "0 19.080000 \n",
+ "0 8.646667 "
+ ]
+ },
+ "execution_count": 111,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "def vote_entropy(row):\n",
+ " if row[\"energy_cluster\"] == row[\"entropy_cluster\"]: # High/High = Novel, Low/Low = Known\n",
+ " return \"novel\" if row[\"energy_cluster\"] == \"High\" else \"known\"\n",
+ " else:\n",
+ " return \"novel\" if row[\"entropy_cluster\"] == \"High\" else \"known\"\n",
+ "\n",
+ "tqdm.pandas(desc=\"Applying Simple Vote (Entropy)\", unit=\"row\")\n",
+ "gmm_df[\"vote_entropy_predtype\"] = gmm_df.progress_apply(vote_entropy, axis=1)\n",
+ "\n",
+ "# calculate the accuracy of the simple vote for entropy\n",
+ "def get_correctness_vote_entropy(row):\n",
+ " return row[\"type\"] == row[\"vote_entropy_predtype\"]\n",
+ "\n",
+ "tqdm.pandas(desc=\"Calculating Correctness (Entropy)\", unit=\"row\")\n",
+ "gmm_df[\"vote_entropy_iscorrect\"] = gmm_df.progress_apply(get_correctness_vote_entropy, axis=1)\n",
+ "\n",
+ "# create the output dataframe\n",
+ "out_df = out_df_template.copy()\n",
+ "\n",
+ "# calculate the accuracy for known and novel samples\n",
+ "for sample_type in [\"known\", \"novel\"]:\n",
+ " filtered_df = gmm_df[gmm_df[\"type\"] == sample_type]\n",
+ " out_dict = {\n",
+ " \"Sample Type\": sample_type.capitalize(),\n",
+ " \"Correct\": filtered_df[\"vote_entropy_iscorrect\"].sum(),\n",
+ " \"Incorrect\": len(filtered_df) - filtered_df[\"vote_entropy_iscorrect\"].sum(),\n",
+ " \"Total\": len(filtered_df),\n",
+ " \"Identification Rate (%)\": (filtered_df[\"vote_entropy_iscorrect\"].sum() / len(filtered_df)) * 100,\n",
+ " \"Misidentification Rate (%)\": ((len(filtered_df) - filtered_df[\"vote_entropy_iscorrect\"].sum()) / len(filtered_df)) * 100,\n",
+ " }\n",
+ " out_df = pd.concat([out_df, pd.DataFrame(out_dict, index=[0])]) if not out_df.empty else pd.DataFrame(out_dict, index=[0])\n",
+ " \n",
+ "results_dfs[\"Simple Vote (Entropy)\"] = out_df\n",
+ "out_df"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Best Proba\n",
+ "If the two GMMs agree on a HARD predtype, use that. Otherwise, use the cluster with the highest proba"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 112,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "b61409c382a64133a163aa249aba47a2",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Applying Best Probability: 0%| | 0/50000 [00:00<?, ?row/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "5f80973bdb9b484aade02d067f20bb89",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Calculating Correctness (Best Probability): 0%| | 0/50000 [00:00<?, ?row/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>Sample Type</th>\n",
+ " <th>Correct</th>\n",
+ " <th>Incorrect</th>\n",
+ " <th>Total</th>\n",
+ " <th>Identification Rate (%)</th>\n",
+ " <th>Misidentification Rate (%)</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>Known</td>\n",
+ " <td>29362</td>\n",
+ " <td>5638</td>\n",
+ " <td>35000</td>\n",
+ " <td>83.891429</td>\n",
+ " <td>16.108571</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>Novel</td>\n",
+ " <td>13517</td>\n",
+ " <td>1483</td>\n",
+ " <td>15000</td>\n",
+ " <td>90.113333</td>\n",
+ " <td>9.886667</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " Sample Type Correct Incorrect Total Identification Rate (%) \\\n",
+ "0 Known 29362 5638 35000 83.891429 \n",
+ "0 Novel 13517 1483 15000 90.113333 \n",
+ "\n",
+ " Misidentification Rate (%) \n",
+ "0 16.108571 \n",
+ "0 9.886667 "
+ ]
+ },
+ "execution_count": 112,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "def best_proba(row):\n",
+ " if row[\"energy_cluster\"] == row[\"entropy_cluster\"]:\n",
+ " return \"novel\" if row[\"energy_cluster\"] == \"High\" else \"known\"\n",
+ " else:\n",
+ " # return the predtype witn the highest cluster probability\n",
+ " if row[\"energy_cluster\"] == \"High\":\n",
+ " energy_pred = (\"novel\", row[\"energy_high_cluster_prob\"]) # novel was predicted, return the probability of being in the novel cluster\n",
+ " else:\n",
+ " energy_pred = (\"known\", row[\"energy_low_cluster_prob\"]) # known was predicted, return the probability of being in the known cluster\n",
+ " \n",
+ " if row[\"entropy_cluster\"] == \"High\":#\n",
+ " entropy_pred = (\"novel\", row[\"entropy_high_cluster_prob\"]) # novel was predicted, return the probability of being in the novel cluster\n",
+ " else:\n",
+ " entropy_pred = (\"known\", row[\"entropy_low_cluster_prob\"]) # known was predicted, return the probability of being in the known cluster\n",
+ " \n",
+ " if energy_pred[1] > entropy_pred[1]: # if the probability of the energy prediction is higher, return the energy prediction\n",
+ " return energy_pred[0]\n",
+ " else:\n",
+ " return entropy_pred[0] # if the probability of the entropy prediction is higher, return the entropy prediction\n",
+ " \n",
+ "tqdm.pandas(desc=\"Applying Best Probability\", unit=\"row\")\n",
+ "gmm_df[\"best_proba_predtype\"] = gmm_df.progress_apply(best_proba, axis=1)\n",
+ "\n",
+ "# calculate the accuracy of the best probability method\n",
+ "def get_correctness_best_proba(row):\n",
+ " return row[\"type\"] == row[\"best_proba_predtype\"]\n",
+ "\n",
+ "tqdm.pandas(desc=\"Calculating Correctness (Best Probability)\", unit=\"row\")\n",
+ "gmm_df[\"best_proba_iscorrect\"] = gmm_df.progress_apply(get_correctness_best_proba, axis=1)\n",
+ "\n",
+ "# create the output dataframe\n",
+ "out_df = out_df_template.copy()\n",
+ "\n",
+ "# calculate the accuracy for known and novel samples\n",
+ "for sample_type in [\"known\", \"novel\"]:\n",
+ " filtered_df = gmm_df[gmm_df[\"type\"] == sample_type]\n",
+ " out_dict = {\n",
+ " \"Sample Type\": sample_type.capitalize(),\n",
+ " \"Correct\": filtered_df[\"best_proba_iscorrect\"].sum(),\n",
+ " \"Incorrect\": len(filtered_df) - filtered_df[\"best_proba_iscorrect\"].sum(),\n",
+ " \"Total\": len(filtered_df),\n",
+ " \"Identification Rate (%)\": (filtered_df[\"best_proba_iscorrect\"].sum() / len(filtered_df)) * 100,\n",
+ " \"Misidentification Rate (%)\": ((len(filtered_df) - filtered_df[\"best_proba_iscorrect\"].sum()) / len(filtered_df)) * 100,\n",
+ " }\n",
+ " out_df = pd.concat([out_df, pd.DataFrame(out_dict, index=[0])]) if not out_df.empty else pd.DataFrame(out_dict, index=[0])\n",
+ "\n",
+ "results_dfs[\"Best Probability\"] = out_df\n",
+ "out_df"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### GMM using Energy & Entropy\n",
+ "Create a GMM using both Energy and Entropy"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 113,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 1000x800 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# create a gmm using energy and entropy\n",
+ "\n",
+ "both_gmm = GaussianMixture(**gmm_args)\n",
+ "both_gmm.fit(gmm_df[[\"energy\", \"entropy\"]])\n",
+ "gmm_df[\"both_cluster\"] = both_gmm.predict(gmm_df[[\"energy\", \"entropy\"]])\n",
+ "\n",
+ "high_energy_cluster_idx = np.argmax(both_gmm.means_[:, 0])\n",
+ "assert high_energy_cluster_idx == np.argmax(both_gmm.means_[:, 1])\n",
+ "low_energy_cluster_idx = 1 - high_energy_cluster_idx\n",
+ "\n",
+ "gmm_df[\"both_cluster\"] = gmm_df[\"both_cluster\"].apply(lambda x: \"High\" if x == high_energy_cluster_idx else \"Low\")\n",
+ "\n",
+ "# visualise the clusters\n",
+ "plt.close(\"all\")\n",
+ "plt.figure(figsize=(10, 8))\n",
+ "\n",
+ "# Plot the clusters\n",
+ "sns.scatterplot(\n",
+ " x=\"energy\",\n",
+ " y=\"entropy\",\n",
+ " hue=\"both_cluster\",\n",
+ " data=gmm_df,\n",
+ " palette=\"tab10\",\n",
+ " legend=\"full\",\n",
+ " alpha=0.8,\n",
+ ")\n",
+ "\n",
+ "plt.title(\"Energy vs Entropy Clusters\")\n",
+ "plt.grid()\n",
+ "plt.show()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 114,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "cbe5b7af0f6f4d8899120b54945bb3cd",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Applying Both Cluster Predtype: 0%| | 0/50000 [00:00<?, ?row/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "4fe86a50df1d4328b9bc54c208e7c6ac",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Calculating Correctness (Both Cluster): 0%| | 0/50000 [00:00<?, ?row/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>Sample Type</th>\n",
+ " <th>Correct</th>\n",
+ " <th>Incorrect</th>\n",
+ " <th>Total</th>\n",
+ " <th>Identification Rate (%)</th>\n",
+ " <th>Misidentification Rate (%)</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>Known</td>\n",
+ " <td>28220</td>\n",
+ " <td>6780</td>\n",
+ " <td>35000</td>\n",
+ " <td>80.628571</td>\n",
+ " <td>19.371429</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>Novel</td>\n",
+ " <td>13710</td>\n",
+ " <td>1290</td>\n",
+ " <td>15000</td>\n",
+ " <td>91.400000</td>\n",
+ " <td>8.600000</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " Sample Type Correct Incorrect Total Identification Rate (%) \\\n",
+ "0 Known 28220 6780 35000 80.628571 \n",
+ "0 Novel 13710 1290 15000 91.400000 \n",
+ "\n",
+ " Misidentification Rate (%) \n",
+ "0 19.371429 \n",
+ "0 8.600000 "
+ ]
+ },
+ "execution_count": 114,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# get the predtype for the both cluster\n",
+ "def both_cluster_predtype(row):\n",
+ " return \"novel\" if row[\"both_cluster\"] == \"High\" else \"known\"\n",
+ "\n",
+ "tqdm.pandas(desc=\"Applying Both Cluster Predtype\", unit=\"row\")\n",
+ "gmm_df[\"both_cluster_predtype\"] = gmm_df.progress_apply(both_cluster_predtype, axis=1)\n",
+ "\n",
+ "# calculate the accuracy of the both cluster method\n",
+ "def get_correctness_both_cluster(row):\n",
+ " return row[\"type\"] == row[\"both_cluster_predtype\"]\n",
+ "\n",
+ "tqdm.pandas(desc=\"Calculating Correctness (Both Cluster)\", unit=\"row\")\n",
+ "gmm_df[\"both_cluster_iscorrect\"] = gmm_df.progress_apply(get_correctness_both_cluster, axis=1)\n",
+ "\n",
+ "# create the output dataframe\n",
+ "out_df = out_df_template.copy()\n",
+ "\n",
+ "# calculate the accuracy for known and novel samples\n",
+ "for sample_type in [\"known\", \"novel\"]:\n",
+ " filtered_df = gmm_df[gmm_df[\"type\"] == sample_type]\n",
+ " out_dict = {\n",
+ " \"Sample Type\": sample_type.capitalize(),\n",
+ " \"Correct\": filtered_df[\"both_cluster_iscorrect\"].sum(),\n",
+ " \"Incorrect\": len(filtered_df) - filtered_df[\"both_cluster_iscorrect\"].sum(),\n",
+ " \"Total\": len(filtered_df),\n",
+ " \"Identification Rate (%)\": (filtered_df[\"both_cluster_iscorrect\"].sum() / len(filtered_df)) * 100,\n",
+ " \"Misidentification Rate (%)\": ((len(filtered_df) - filtered_df[\"both_cluster_iscorrect\"].sum()) / len(filtered_df)) * 100,\n",
+ " }\n",
+ " out_df = pd.concat([out_df, pd.DataFrame(out_dict, index=[0])]) if not out_df.empty else pd.DataFrame(out_dict, index=[0])\n",
+ "\n",
+ "results_dfs[\"Energy & Entropy Clustering\"] = out_df\n",
+ "\n",
+ "out_df"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### GMM using Energy & Entropy (But Normalised)\n",
+ "Create a GMM using both Energy and Entropy (but Normalised)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 117,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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/0v9TYOfOnWzevLnSfWrWrBnw9/8dAhmq26xZM4YNG0Z0dDQ333xz2VwhpVTNoyFIKXXGHF2xOVrpH6mlk64ffvjhKqsfs2fPrhRu/m33shtuuIG8vDymT5+OYRh8/fXXXHDBBWV/sJ7IVVddRW5ublnV67vvviMhIYF27dqVHdOiRYuy1r+9e/fmhx9+oHfv3mV/dJ4uf3dvT8bZ6gQXHBzMBRdcUBaCpk2bRnFx8Ul1wQsNDT2pYFdaDVq3bh0//fTTv75mm8123HbUx7v3f/eZOJ1OhgwZwm+//cZjjz3G9OnTmT17dtmk/+M1ITievn37snv3bv773//SunVrPvvsMzp27Mhnn31W4Xyn8u/rVGRlZREWFnbCYxISEgDKGkecquOFLKfTWWnblVdeyZ49e3j33XepX78+r732Gq1ataqySn0sl8tFmzZtjnufxowZU+H4qv7tREREsG7dOn7++Wcuuugi5s6dy7Bhw7jxxhtP8t0qpc4mbYyglKo2jRs3BqTxwPG6f/2d2NhYQP6v99HVKIfDwb59+2jbtm2F41u3bk2HDh2YMmUK0dHR7N+/n3ffffekXqtv375ERUUxdepUevfuzV9//VVle19fX19GjRrFqFGjKCkp4bLLLuPFF1/kiSeeOKttdP/JujmxsbFs37690vZt27aV7T/asUOxDMNg165dle77DTfcwMUXX8zKlSuZMmUKHTp0oFWrVn97PQkJCUyZMoXs7Oy/DarXXXcdL7zwAhMmTOCiiy6q9L5AmkGUDnEqtX379krv60zYuHEjO3bsYPLkydxwww1l2//N8KnSroU33XQTeXl59O3bl/Hjx3Prrbee0r+v2NjYKofVVfW7AJCUlERJSUmFRhtVufDCC3nppZf43//+V2H45skqrR5nZWVV2H5s5a5UVFQUY8aMYcyYMaSkpNCxY0defPFFhg0bBhz/30STJk1Yv349gwYN+lfrTXl6enLhhRdy4YUX4nK5GDNmDB9//DHPPPOMrqOmVA2jlSClVLWJiIigf//+fPzxxyQnJ1faX1Vb5WN17tyZ0NBQPv30UxwOR9n2KVOmHLeCcP311zNr1iwmTpxIaGho2R9If6d0fZRffvmFr776CofDUWEoHMgclqN5enrSsmVLDMPAbref1OucLqXr2hz7B+SJDB8+nBUrVrB06dKybfn5+XzyySfExcVVmgfx5ZdfVhjqNG3aNJKTkyvd02HDhhEWFsYrr7zC/PnzT3otpB49emAYBqtXr/7bY4+uBh3bArpz585ERETw0UcfVWh5PHPmTLZu3cqIESNO6nr+jdJK0dHVOsMw/nEL5WN/1/z8/GjatGnZ+zuVf1/Dhw9n2bJlrFixosL+o+fPHa308+jZs+cJr7FHjx6cf/75fPbZZ1XO1yopKanQXv5YAQEBhIWFsWDBggrbP/jggwqPnU5npeGEERER1K9fv8Ln7evrW+WwwyuvvJKkpCQ+/fTTSvsKCwvLOlGeyLGfh9lsLvufAce22VZKVT+tBCmlTtnMmTPLKgNH69mzZ9n/fT5Z77//Pr1796ZNmzbcdtttNG7cmCNHjrB06VIOHjzI+vXrT/h8T09Pxo8fzz333MPAgQO58sor2bdvH5MmTaJJkyZV/l/da665hkcffZT/+7//46677ipr130yRo0axbvvvsu4ceNo06ZNpf8TPnToUCIjI+nVqxf16tVj69atvPfee4wYMeKkJoevWbOG//3vf5W2N2nShB49epz0dYIsJGqxWHjllVfIzs7GZrMxcOBAIiIijvucxx9/nG+++YZhw4Zx7733EhISwuTJk9m7dy8//PBDpSFiISEh9O7dm5tuuokjR44wceJEmjZtym233VbhOKvVylVXXcV7772HxWLh6quvPqn30Lt3b0JDQ/nzzz8rVXCqcu211/L8889XWr/GarXyyiuvcNNNN9GvXz+uvvrqshbZcXFxPPDAAyd1Pf9GQkICTZo04eGHHyYpKYmAgAB++OGHU5rHdbSWLVvSv39/OnXqREhICKtWrSprEV3qZP99Pfroo3z11Vecf/753HfffWUtsmNjYyvMMyo1e/ZsGjZseML22KW+/PJLhg4dymWXXcaFF17IoEGD8PX1ZefOnXz77bckJyefcK2gW2+9lZdffplbb72Vzp07s2DBAnbs2FHhmNzcXKKjoxk5ciTt2rXDz8+PP//8k5UrV/LGG2+UHdepUyemTp3Kgw8+SJcuXfDz8+PCCy/k+uuv57vvvuPOO+9k7ty59OrVC6fTybZt2/juu+/K1tM6kVtvvZWMjAwGDhxIdHQ0iYmJvPvuu7Rv3/5vK2ZKqWpQDR3plFK11IlaZHNUG9uj2xcfi2Pa2hqGYezevdu44YYbjMjISMNqtRoNGjQwLrjgAmPatGmVXnvlypVVXts777xjxMbGGjabzejatauxePFio1OnTsb5559f5fHDhw83AGPJkiWndA9cLpcRExNjAMYLL7xQaf/HH39s9O3b1wgNDTVsNpvRpEkT45FHHjGys7NPeN6/a5F9dGvk2NjYKlsj9+vXr1Kr708//dRo3LixYbFYKrSkPt45DEM+j5EjRxpBQUGGl5eX0bVrV+PXX3+tcExpi+xvvvnGeOKJJ4yIiAjD29vbGDFiRIVWz0dbsWKFARhDhw494b041r333ms0bdq0wrYT/Y4d/Xta2iK71NSpU40OHToYNpvNCAkJMa699lrj4MGDFY658cYbDV9f3yqvBajUhvl411J6j77//vuybVu2bDEGDx5s+Pn5GWFhYcZtt91mrF+/vlIb6JNpkf3CCy8YXbt2NYKCggxvb28jISHBePHFF42SkpIKzzuZf1+GYRgbNmww+vXrZ3h5eRkNGjQwnn/+eePzzz+v1CLb6XQaUVFRxtNPP13lPapKQUGB8frrrxtdunQx/Pz8DE9PTyM+Pt645557jF27dp3wfRcUFBi33HKLERgYaPj7+xtXXnmlkZKSUuG/JcXFxcYjjzxitGvXzvD39zd8fX2Ndu3aGR988EGFc+Xl5RnXXHONERQUZAAV2mWXlJQYr7zyitGqVSvDZrMZwcHBRqdOnYwJEyZU+Pdb1e+AYRjGtGnTjKFDhxoRERGGp6en0bBhQ+OOO+4wkpOTT/o+KaXOHpNhnMIsWqWUqiVcLhfh4eFcdtllVQ5xufTSS9m4cSO7du2qhqur/ebNm8eAAQP4/vvvGTly5Ek9Z/369bRv354vv/zylBbP3LNnDwkJCcycOZNBgwb900tWp8n06dO55ppr2L17N1FRUdV9OUop9Y/onCClVK1XVFRUqSval19+SUZGBv379690fHJyMr/99ttZWcVelfv000/x8/PjsssuO6XnNW7cmFtuuYWXX375DF2ZOhWvvPIKY8eO1QCklKrVdE6QUqrWW7ZsGQ888ABXXHEFoaGhrFmzhs8//5zWrVtXWP9n7969LF68mM8++wyr1codd9xRjVddd/zyyy9s2bKFTz75hLFjx5Y1bDgVH3744Rm4MvVPHN00QymlaisNQUqpWi8uLo6YmBjeeecdMjIyCAkJ4YYbbuDll1/G09Oz7Lj58+dz00030bBhQyZPnkxkZGQ1XnXdcc8993DkyBGGDx/OhAkTqvtylFJKKXROkFJKKaWUUqpO0TlBSimllFJKqTpFQ5BSSimllFKqTqnVc4JcLheHDh3C39+/ygURlVJKKaWUUnWDYRjk5uZSv379Sot7H6tWh6BDhw4RExNT3ZehlFJKKaWUqiEOHDhAdHT0CY+p1SHI398fkDcaEBBQzVdT+9jtdmbNmsXQoUOxWq3VfTnqOPRzqj30s6od9HOqPfSzqh30c6o9zvXPKicnh5iYmLKMcCK1OgSVDoELCAjQEPQP2O12fHx8CAgIOCf/IZwr9HOqPfSzqh30c6o99LOqHfRzqj3qymd1MtNktDGCUkoppZRSqk7REKSUUkoppZSqUzQEKaWUUkoppeqUWj0n6GQYhoHD4cDpdFb3pdQ4drsdDw8PioqK6vT9sVgseHh4aJt1pZRSSqk64pwOQSUlJSQnJ1NQUFDdl1IjGYZBZGQkBw4cqPMBwMfHh6ioKDw9Pav7UpRSSiml1Bl2zoYgl8vF3r17sVgs1K9fH09Pzzr/h/6xXC4XeXl5+Pn5/e2CUucqwzAoKSkhNTWVvXv3Eh8fX2fvhVJKKaVUXXHOhqCSkhJcLhcxMTH4+PhU9+XUSC6Xi5KSEry8vOr0H/7e3t5YrVYSExPL7odSSimllDp3nfN/+dblP+7VydPfE6WUUkqpukP/8lNKKaWUUkrVKRqClFJKKaWUUnVKnQ1B/fv35/777z/t542Li2PixImn/by1zbx58zCZTGRlZVX3pSillFJKKVVBnQ1B/9akSZMICgqq7suoM8aPH0/79u2r+zKUUkoppdQ5QENQHVBSUlLdl1Bj6L1QSimllFJ1OgQ5HA7Gjh1LYGAgYWFhPPPMMxiGAUBmZiY33HADwcHB+Pj4MGzYMHbu3AnIUK+bbrqJ7OxsTCYTJpOJ8ePHl523oKCAm2++GX9/fxo2bMgnn3xyUtezb98+TCYTP/74IwMGDMDHx4d27dqxdOnSCsf98MMPtGrVCpvNRlxcHG+88UaF/XFxcTz//PPccMMNBAQEcPvtt5dVrn799VeaN2+Oj48PV1xxBQUFBUyePJm4uDiCg4O59957cTqdJ3W9xcXFPPbYY8TExGCz2WjatCmff/55lcdWVcmZOHEicXFxZY/nzZtH165d8fX1JSgoiF69epGYmMikSZOYMGEC69evL7vfkyZNAiArK4tbb72V8PBwAgICGDhwIOvXr6/0up999hmNGjXS9tdKKaWUUqpuh6DJkyfj4eHBihUrePvtt3nzzTf57LPPABg9ejSrVq3i559/ZunSpRiGwfDhw7Hb7fTs2ZOJEycSEBBAcnIyycnJPPzww2XnfeONN+jcuTNr165lzJgx3HXXXWzfvv2kr+upp57i4YcfZt26dTRr1oyrr74ah8MBwOrVq7nyyiu56qqr2LhxI+PHj+eZZ54pCwWlXn/9ddq1a8fatWt55plnAAln77zzDt9++y2///478+fP5/rrr2fmzJnMmDGDr776io8//php06ad1HXecMMNfPPNN7zzzjts3bqVjz/+GD8/v5N+n0dzOBxccskl9OvXjw0bNrB06VJuv/12TCYTo0aN4qGHHqJVq1Zl93vUqFEAXHHFFaSkpDBz5kxWr15Nx44dGTRoEBkZGWXn3rVrFz/88AM//vgj69at+0fXp5RSSimlzh3n7GKpJyMmJoa33noLk8lE8+bN2bhxI2+99Rb9+/fn559/ZvHixfTs2ROAKVOmEBMTw/Tp07niiisIDAzEZDIRGRlZ6bzDhw9nzJgxADz22GO89dZbzJ07l+bNm5/UdT388MOMGDECgAkTJtCqVSt27dpFQkICb775JoMGDSoLNs2aNWPLli289tprjB49uuwcAwcO5KGHHip7vHDhQux2Ox9++CFNmjQB4PLLL+d///sfycnJBAQE0LJlSwYMGMDcuXPLQsbx7Nixg++++47Zs2czePBgABo3bnxS768qOTk5ZGdnc8EFF5RdX4sWLcr2+/n54eHhUeF+L1q0iBUrVpCSkoLNZgMk/E2fPp1p06Zx++23AzIE7ssvvyQ8PPwfX59SSimllDp31OlKUPfu3TGZTGWPe/Towc6dO9myZQseHh5069atbF9oaCjNmzdn69atf3vetm3blv1cGpRSUlJO+rqOfn5UVBRA2fO3bt1Kr169Khzfq1cvdu7cWWEYW+fOnSud18fHpyxgANSrV4+GDRtWqN7Uq1fvpK513bp1WCwW+vXrd5Lv6sRCQkIYPXo05513HhdeeCFvv/02ycnJJ3zO+vXrycvLIzQ0FD8/v7KvvXv3snv37rLjYmNjNQAppZRSSqkydboSdKZYrdYKj00mEy6X6x89vzSkncrzAXx9fU/qujw8PCptO5nX8vb2PqXrMZvNZfOtStnt9gqPv/jiC+69915+//13pk6dytNPP83s2bPp3r17lefMy8sjKiqKefPmVdp3dOe+qu6FUkoppZSqu+p0JWj58uUVHi9btoz4+HhatmyJw+GosD89PZ3t27fTsmVLADw9PU+6gcDp1KJFCxYvXlxh2+LFi2nWrBkWi+WsXUebNm1wuVzMnz//pI4PDw/n8OHDFYJQVfNzOnTowBNPPMGSJUto3bo1X3/9NVD1/e7YsSOHDx/Gw8ODpk2bVvgKCwv7529OKaWUUkqd0+p0CNq/fz8PPvgg27dv55tvvuHdd9/lvvvuIz4+nosvvpjbbruNRYsWsX79eq677joaNGjAxRdfDEgHtry8PObMmUNaWhoFBQVn5Zofeugh5syZw/PPP8+OHTuYPHky7733XoXGDGdDXFwcN954IzfffDPTp09n7969zJs3j++++67K4/v3709qaiqvvvoqu3fv5v3332fmzJll+/fu3csTTzzB0qVLSUxMZNasWezcubNsXlBcXBx79+5l3bp1pKWlUVxczODBg+nRoweXXHIJs2bNYt++fSxZsoSnnnqKVatWnZX7oJRSSimlap86HYJuuOEGCgsL6dq1K3fffTf33Xdf2WT6L774gk6dOnHBBRfQo0cPDMNgxowZZUPKevbsyZ133smoUaMIDw/n1VdfPSvX3LFjR7777ju+/fZbWrduzbPPPstzzz1XoSnC2fLhhx8ycuRIxowZQ0JCArfddhv5+flVHtuiRQs++OAD3n//fdq1a8eKFSsqBDcfHx+2bdvG5ZdfTrNmzbj99tu5++67ueOOOwBp4nD++eczYMAAwsPD+eabbzCZTMyYMYO+ffty00030axZM6666ioSExOpV6/eWbkHSimllFJ1UnEepO+G1B1QkF7dV3PKTMaxEzVqkZycHAIDA8nOziYgIKDCvqKiIvbu3atrw5yAy+UiJyeHgIAAzOY6nYdr9O+L3W5nxowZDB8+vNK8LlWz6GdVO+jnVHvoZ1U76OdUe5y2zyplK6z4BJLWAgYExUK32yG6K3h4nrbrPVUnygbHqtt/+SqllFJKKaVOXsZemPkYJK0B3LWUrESY9Swc3lCtl3YqNASdRf/5z38qtHI++mvYsGHVfXkVLFy48LjX+k8XRFVKKaWUUrVc4mIoyq683XDC2v/JMLlaQFtkn0V33nknV155ZZX7TrXl9JnWuXPnKru3KaWUUkqpOspphwPLj78/dbsEJFvN/x/mGoLOopCQEEJCQqr7Mk6Kt7c3TZs2re7LUEoppZRSNYXZA3xCj7/f5g+W2jEvTIfDKaWUUkoppf6eyQQJFxx/f4sLwS/i7F3Pv6AhSCmllFJKKXVywptDxxsBU8Xt0V0gfki1XNI/ocPhlFJKKaWUUifH5g/tRkHDbnBwFTiKoEEnCGl04qFyNYyGIKWUUkoppdTJ8/SFeq3kq5bS4XBKKaWUUkqpOkVDkFJKKaWUUqpO0eFwNVR2oZ1DWYXkFNoJ8LZSP8ibQO8z13Jw9OjRZGVlMX369DP2GkoppZRSStUEGoJqoP3p+bwzZyfbj5SvuNs80p97BzalYahvNV6ZUkoppZRStZ8Oh6thsgvtlQIQwPbDubzz1y6yC+1n/Zrmz59P165dsdlsREVF8fjjj+NwOAD49ddfCQoKwul0ArBu3TpMJhOPP/542fNvvfVWrrvuurN+3UoppZRS6ihF2fL9wAo4sgWKcqr3eqqRhqAa5lBWYaUAVGr74VwOZRWe1etJSkpi+PDhdOnShfXr1/Phhx/y+eef88ILLwDQp08fcnNzWbt2LSCBKSwsjHnz5pWdY/78+fTv3/+sXrdSSimllDpKxl74c7z8/Od4mD4G/hwHWQeq86qqjYagGibnbyo9uUVntxL0wQcfEBMTw3vvvUdCQgKXXHIJEyZM4I033sDlchEYGEj79u3LQs+8efN44IEHWLt2LXl5eSQlJbFr1y769et3Vq9bKaWUUkq5FaTD3BchdftRGw1IWgOL34ai3Gq7tOqiIaiGCfib5gf+XmeuOUJVtm7dSo8ePTCZylcF7tWrF3l5eRw8eBCAfv36MW/ePAzDYOHChVx22WW0aNGCRYsWMX/+fOrXr098fPxZvW6llFJKKeWWdRDSdla97+AqyD54dq+nBtAQVMPUD/KmeaR/lfuaR/pTP8j7LF/R3+vfvz+LFi1i/fr1WK1WEhIS6N+/P/PmzWP+/PlaBVJKKaWUqk6FGSfYaUBJ3ZsbpCGohgn0tnLvwKaVglDzSH/uHdT0jLbJrkqLFi1YunQphmGUbVu8eDH+/v5ER0cD5fOC3nrrrbLAUxqC5s2bp/OBlFJKKaWqk3fwCXaawLPq/wF/LtMW2TVQw1BfnrmgJYeyCsktsuPvdebXCQLIzs5m3bp1FbbdfvvtTJw4kXvuuYexY8eyfft2xo0bx4MPPojZLBk6ODiYtm3bMmXKFN577z0A+vbty5VXXondbtdKkFJKKaVUdQqMgZDGkFnFsLf67SEw+qxfUnXTEFRDBXpbz3rVZ968eXTo0KHCtltuuYUZM2bwyCOP0K5dO0JCQrjlllt4+umnKxzXr18/1q1bV1b1CQkJoWXLlhw5coTmzZufrbeglFJKKaWO5RsKA5+Cua9W3B7ZBvo8AF4B//zcxXlgMoOnz7+7xrNMQ5ACYNKkSUyaNOm4+1esWHHC50+cOJGJEydW2HZsVUkppZRSSlWT0KYw9HmYuwwGPQO+IVIB8g76Z+fL2g/7FsHuv8DsAQkjILoL+Eee1ss+UzQEKaWUUkopVRd4h8j3hj3A+i9GHGUmwszHIDe5fFvKVqksDXy6VgQhbYyglFJKKaWUOjkuF2yfUTEAlTq8EQ6tOfvX9A9oCFJKKaWUUkqdnII0GQJ3PNtmgr347F3PP6QhSCmllFJKKXVyDEOqQcfd7wROsL+G0BCklFJKKaWUOjk+oRDX6/j744eC1fvsXc8/pCFIKaWUUkopdXIsHtDy4vImC0cLbiQd4moB7Q6nlFJKKaWUOnmhTWDEG7DtN0hcJC2y44dC00EQ2KC6r+6kaAhSSimllFJKnZrQxtDjbmh3FZhM4BtW3Vd0SjQEKaWUUkoppU6d2Qx+4dV9Ff+IzglSJ23SpEkEBQWd0nNGjx7NJZdcckauRymllFJKnSSnHXIPu392VO+11AAagmqqwixZcGrfYji8SR6fQccLK/PmzcNkMpGVlcWoUaPYsWPHGb0OpZRSSil1mqXtgPmvwi8PyuOtP0FmovzstEvb6zpGh8PVRBn7YP4rkLKlfFu9VtD3UQiJq66rwtvbG2/vmt/yUCmllFJKuaXthN8ehsg20PICOATsmQ8mA1K2wq7ZEFAf4s+DsGbg4VndV3xWVGslaPz48ZhMpgpfCQkJ1XlJ1a8wq3IAAjiyGRa8esYrQidS1XC4F154gYiICPz9/bn11lt5/PHHad++faXnvv7660RFRREaGsrdd9+N3W4/OxetlFJKKVVXuZyw9VcJQM4S+HOcbN+/HH66Wx7X7wBbfoGf74U9c2WoXNpOWPMlzB4Ha/8nj0+0QGotVO3D4Vq1akVycnLZ16JFi6r7kqpX9oHKAajUkc2yv4aYMmUKL774Iq+88gqrV6+mYcOGfPjhh5WOmzt3Lrt372bu3LlMnjyZSZMmMWnSpLN/wUoppZRSdUlBBhxYCWHNYe1XR21PBVwSbvYtkpBkOGHx25C8QQLSys9hzzxY8Sn8dA8krSx/vtMhw+lStsr3WjjHqNqHw3l4eBAZGVndl1FzFOWceH9x7hl76V9//RU/P78K25xO53GPf/fdd7nlllu46aabAHj22WeZNWsWeXl5FY4LDg7mvffew2KxkJCQwIgRI5gzZw633Xbb6X8TSimllFJKmM2yps+BFRW3u44akbNzNvR/DA5vcM9J3wCOkorHOwph3qtw8XtgtsCG72SNIHsBWL2h+XBoOwr8653xt3S6VHsI2rlzJ/Xr18fLy4sePXrw0ksv0bBhwyqPLS4upri4uOxxTo4EBrvdXml4ld1uxzAMXC4XrlpUvjPZAk58gKc/xml6P4Z7EpxhGBiGQf/+/fnggw8qHLN8+XJuuOGGCvex9Pv27du58847K9zfLl26MHfu3LJthmHQsmVLTCZT2bbIyEg2bdpUoz4Xl8uFYRjY7XYsFkt1X04Fpb/bOoSw5tPPqnbQz6n20M+qdtDPqQazBkDDXrBxGpi9sJttANjNXrLfbAWnC7CCyRMMB5QUg9lLKkNHK8yB3DTY9ivs/ku2mTzB4YTNv8j+HveAp8/Ze3/HOJXfwWoNQd26dWPSpEk0b96c5ORkJkyYQJ8+fdi0aRP+/v6Vjn/ppZeYMGFCpe2zZs3Cx6fiDS+tMOXl5VFSUlLpOTWVpy0cz7AEGfp2rHqtKLGFU5LzN9WiU5Sbm4vdbsdmsxEREVFhX3BwcNkxRUVFGIZRFj4Nw6CoqKjsMUBJSQlOp7NCQDWZTBWOsdvtlJSUVNhW3UpKSigsLGTBggU4HDWzpDt79uzqvgR1kvSzqh30c6o99LOqHfRzqsFsF0LLC8sezm79dsX9yUC9sfJzKhBxV9XnWbUHaAn1Wlbelw/8Oe/fX+u/UFBQcNLHVmsIGjZsWNnPbdu2pVu3bsTGxvLdd99xyy23VDr+iSee4MEHHyx7nJOTQ0xMDEOHDiUgoGIFpaioiAMHDuDn54eXl9eZexOnXQCmAY/D/Ncg5aggFNEK+j2CV1AEp+vdGIZBbm4u/v7+WK1WPDw8Kt3H0nDp7++Pl5cXJpOp7JiEhAQ2bdpU4TkbNmzAYrGUbavqvJ6enlW+VnUqKirC29ubvn371rjfF7vdzuzZsxkyZAhWq7W6L0edgH5WtYN+TrWHfla1g35OtUD6Hpj1NPa0ncxuMoEhe1/CWpAMFisMGge758o8oaZDIesAJK+tfA6zB/R9BOa9dPzXGfQsNOx+5t7H3ziV/8Fe7cPhjhYUFESzZs3YtWtXlfttNhs2m63SdqvVWukfndPpxGQyYTabMZurvf/DqQlpBOf/R5ogFOeCzR8CY8A7CNNpfJnS4WhHd+c79l6VPj76PpZ+v+eee7jtttvo0qULPXv2ZOrUqWzYsIHGjRuXHVPVeU0mU4Xz1ARmsxmTyVTl71JNUZOvTVWkn1XtoJ9T7aGfVe2gn1MNFtkcLnwD1n0LOWD18sFavz+0uwrshRDRBELPh6BYwAH754OHDY7+y7PVpeATCMYJRlh5+UI1/g6cyu9fjQpBeXl57N69m+uvv766L6X6eQfJVw127bXXsmfPHh5++GGKioq48sorGT16NCtWrPj7JyullFJKqbOjMBNSt5YvitrnQQhrDAtehyOboDgHSvLAFgDDXoUO17kbHxSCX4Q0PWgyEFwO8I+C3OTKr+EfCYENzu77+heqNQQ9/PDDXHjhhcTGxnLo0CHGjRuHxWLh6quvrs7LqpOO17K6f//+ZQ0URo8ezejRoyvsf+aZZ3jmmWfKHg8ZMoSmTZue8LwTJ078t5erlFJKKaVORkkBrPsGNkyFoMbglSBd3WY9LS2yzWYozpOmCIUZ8Mfj0PcxaHERNB4APqHgf9Sc8YFPwR9PQ1FW+TavQBjwJPhpd7iTcvDgQa6++mrS09MJDw+nd+/eLFu2jPDw8Oq8LHWSCgoK+OijjzjvvPOwWCx88803/PnnnzoxUimllFKqpsg5KN3hAhpAo77SBKEgA/b8BZjAYgO/cNlmOCE/DRxFsOkHqNca6rWQ89gLwWSRNYUufhdStkFWIgQ2hIgWEFx1d+eaqlpD0LfffludL6/+JZPJxIwZM3jxxRcpKiqiefPm/PDDDwwePLi6L00ppZRSSoEsZmo4ZTjboonQ5LnydYIMQypA+WkyFK44W7YXZcucIJcTMvfBvsWyqKqHF7S4EKLaQrOh1fWOTosaNSdI1S7e3t78+eef1X0ZSimllFLqeMwe8mU4y0OOdzBEd4GU7eAokMVQfcPA6QXBjSGqA1h9wTcUfnsY8lPLz3doDTTuDz3vlf21lIYgpZRSSimlzlWhTaH1ZTIcrssdsp5PynZZfiX+PBn6tvxjaZfd636pAi18A4LjIGULFKRDQBQU5UBJvpxzzzxodh749qy+9/UvnfMhqHRSv1Inor8nSimllDrnZB+CXXOk01t4S/AOke1rvwRPm3R784uEfo9J+Nm7AFK3SfBpe4Us1RIUDfnpEBsjw+G2/Sbd5nbMglgNQTVOaZ/wgoICvL29q/lqVE1XusKwrm+glFJKqXNC7hH46zlI2SqPo9rCjMeh7YfgEwb5SeDhLYEmPwWaDoFV/wV7vrTF9qsHc/8jQ+FMZrB4Spe4fo9Ltzl7PmTtB0wQUB/Mlmp9u6fqnA1BFouFoKAgUlJSAPDx8SlbpFMJl8tFSUkJRUVFNWrh0rPJMAwKCgpISUkhKCgIi6V2/QNWSimllKpSypbyABTaFA6tliFvIHODPP0lyFissH0mRLaVxwBtroC/npehchhguOR7XgoseQ+aDwefEJg+RponJFwAbS6XtYJqiXM2BAFERsoHURqEVEWGYVBYWIi3t3edD4hBQUFlvy9KKaWUUrXe3gXlP3vYZGhb6d97TodUeowgCTYhTWQR1AFPgaMYguJg4ZsQGC2LqBouqQRZfaQyFN5MOsoV58r5Nn4n1aS+j4DN72y/03/knA5BJpOJqKgoIiIisNvt1X05NY7dbmfBggX07du3Tg8Ds1qtWgFSSiml1LnF4ln+c3YSNOkPO/+Sxyaz+8sDej8g84BmPAKpW6RC1O126HkPrPwcAqLdC6rmygKpJrOsF2TzB6u3rB8EsGc+tLtK1gyqBc7pEFTKYrHoH7lVsFgsOBwOvLy86nQIUkoppZQ65zQdBDt+l5+LssDTVyo7ADZfGQbXZDDsnQ9pO8Bpl8YJBeky5ye2JzTsAWnbIS8VnCXyXLNJqj5rv4LOt8Kaye4XNCD3cK0JQXVzIohSSimllFLnsrB4iD9qQdNN/wfdx8jPfvUAE9RrKYupmq1QUiAVngB3UNozX9pgO0tkHSHDIcPimp0HiUshYw/kJMkwulKevmft7f1bdaISpJRSSimlVJ3iHQzd74JGfWDrL+41ftxzgi77DEpyIHMPeHiC2RM8faRlttMddqzecg4Pm3w3e0ioCoqFRW/KefYthIbdITdZqkilAaoW0BCklFJKKaXUucgnBBr1lWFtLhdghgMzZChb4gKZw5OZ6K4MISHIMMBll+FyfhHQ52GpBtn8wRYEKz+WeUGGU9piG4Y0TBjwJATWr853e0o0BCmllFJKKXUus1jBAuRlyuO9C8AokXATFCutsPNSpMmByQCfcNm+fabMK8o+KIHH5geDJ0BRDhRmQKvL5LjON0NwXHW+w1Omc4KUUkoppZSqC/Lcy8Z4h8j3XXPg/Jek85s9HzAAkzRQaHkJ7JknbbU9/eR7ST6snypD7Bp0gYQR0GwohDQqb79dS2glSCmllFJKqXOZo0QaGeycBcRCdGfwD4P9yyBrP/QYI+sD5aXIwqp+EfD742AvAv96MsytOFe+jmyC/o9BSGMIqD3D346lIUgppZRSSqlzlcMuQ9r+egEMMzR8Apa+Bz7+cN5/YMefsO5/4BUg4SdtBwTHynA33GsCma1SPfIKAkzSEa4WByDQ4XBKKaWUUkqdu1K2wK8PQvZ+qeyAzAXKS4GV/4XoTuAVKPN8MhPh8EaIbCPHmS3SFc7lkC+TCaLagW9E9b2f00RDkFJKKaWUUueiggxpY23Pk8clufLdbJXFUfcvk/k/Nn8JQiaTdIZL3Q7NhoFvGDiLIfuADJsrzocO14GXf/W9p9NEh8MppZRSSil1Lso94h7W5mY45btPKBj+0uraYoO+j8Cy92WNIJcD9i2G3g9Cy4thzWRZMyiynbTbXvk5+ARDWLPqeU+niYYgpZRSSimlzhUul3Rx8/CUBVGD4iofU5QJuCC8GaTvgqRV0OMeyEqUYNRkoASiBa9BdFeweEJOMiz/GDBg4w/Q92FpvV1LaQhSSimllFKqtnO5IH0n7PgDkjfIQqltLofcJGjYC/Yvrni8VwDEnwe758hQt8Mb5TlmqwSn/UukirRnHviEybC58ObyGvuXQn5qrW6OoCFIKaWUUkqpmqSkAIqypdLiG3Zyz0leD78/AY5CeZwOYAIMaD5M1vLZPlv2BTeGbrfAkY0SgEoVZJSfC0PmCrW6DArSZJtXAHQaDRn73OeuvTQEKaWUUkopVRM47JCyCdZ9A6nbZJHSVhdDo/6yXs/xFGbJnJ7SAFTqwHJodxUUpIO9ELqPgVSg76Ngz4LEpVWf78gm6HYHFOfAwjcgJ6l83+b/gz4Py7XVYtodTimllFJKqZrg0Gr47WEJL0XZEj6WfgCL3iqv0lQl7wik7axihwHrv4X67aX5QVxv2ezlL1WmrrdB0yHSCttwSutsp11+jmwDu+ZUDEAgw+U2/SAtt2sxrQQppZRSSilV3QoypPGAy1F53/6lkLFH5uxUxTBOcGIDcg9DeIJUhAD+HA+OfGl9Hd0F2l4F818Ge4E0RmgxVn5O3yULoxZmyXk8/cHmJ40SEpdCvVb/7j1XIw1BSimllFJKVbeCdAk6x5O0GqI7V73PNxwCYyTUOO2yto/TLh3iTB6yBtDuObDwHWg8QV7L5gMBDaQznF8EDH1Bwo5fuCyqemSTBDJbQPnQN9NRg8hK8k7bW68OOhxOKaWUUkqp6mYyc8JmAydqR+0bCj3GyM9h8dDsPGjcHxzFMqzNK0jaZhdlyTGFGfKz2UM6vyUulmYMyz6AeS9D2g6weJV3fzOZKwYgOH4gqyU0BCmllFJKKVXd/OpBVNvj74/ueuLnB8XCwGfAcMH238FkgWGvQftrYNOPEmp6PSDH9nlImiSUDpHLPiBNFQrSZA7Qyk9lXlKH6yuHH5DnhdbuxVI1BCmllFJKKVXdbH7Skc3mX3lf26sgOPbEzz+8AabdBMW50PMeKEyHv56H1V9A04Hy/GUfyLFzX5R9gfWhyQCZ6+O0Vzzf2q9kmN35L0FESwlVtgBoeyUMehb8I07P+64mOidIKaWUUkqpmqBeK7jwbdgzX+bqeAVDywslhFQVjkrlpUrACYiSUDPzUXDZwQCObJaFUFtfLoudlnIUwbIPYcCTUoXaPafiOR1F0hwhvAWc/zIUZ8vwOb9IMNf+OoqGIKWUUkoppWqK0Cby1f4aaUdtsfz9c/LTpANcq0sk2LiOqup4+ki77eUfw8DxcNi9vXSez5750O5qWDNZthsGuJzSia4oC6bfCW2ugITh4BN6Wt9qddIQpJRSSimlVE1j9Tr5Y00maVvtFQx5h4/aDpg9Zf0fe6FUcwiD2L5gFMmxuYch5yD4R0JxPmCAh7d0jMtMlAC18jNpnNDlFrCcG/Gh9teylFJKKaWUqsv86slQN4tV5u6UdZkzSaMED2+w+kJYgmwOioGodtDzPmhxIdiLwRYoXeZcDjlHv0dlSFxEKznP5h8h+2A1vcHT79yIckoppZRSStVVPsHQ425IWgt+UVCQWr7P5i/r/HQaDau/hICrYct0MOyAC4a+CI36QUQLSNkMIU3AOwg2TJVOcSFNoOttsGuOtNYmrjre4WmnIUgppZRSSqnaLqqDVHvMFpj9rHsNoFCpDtVrA9n7IW07BCD7nCVS/Vn2IUR3gRYXQL3WsPMPmPEoGA7AgOT1sO1XGPhs+aKp5wANQUoppZRSStVkBRmQvhv2zgOnQyo34c3AN6z8mNSt8MfTENsTLnpH1gpK2yGVoLYjYdYz5QuueniD1UMWU83Ogl2zYf9yaciw6r/gFSAd4JwOmRPkLJHt8UOq492fERqClFJKKaWUqqny02Hp+xVbWO/4HaI7Q6/7pMObywXzXpHhatt+hUb9ZeFV3zCwF0HWAVkHKC9dnp+XDK4iCUM+oZCxG9pdCweWQsYemVIU00POYfGEPfMkCBVm/v16RbWEhiCllFJKKaVqquR1ldfwMVwSTAIaQNIaaV+dvFYWM/WPgsb9YPoYcBaBhxc0HSIVoawk9wncjRMchRKcIttD6japDAXUl3B1YLlUk6ze0Ow8CIyW9tnnCA1BSimllFJK1UT2QtjyU+XtxbnS3nrLdIg/TxY1zU8Fq4+s6ZO0CvLLFgQCez60uRxS/lP5XL7hENIYDm8A72Do8yD89YJUfTBJXsrcC/U7yEKs5wgNQUoppZRSStVETgeU5Ffc5rJDQZr8XJIvw9XMVqn4NBkAyz+CFiPAKwiaDgLfCFnzx+oFw9+Ag8jjwjRo2F2qPDmHpGmCX6R0hCvMPOZCDFlPKGMvhDY9C2/8zNN1gpRSSimllKqJbH4Q063iNpdD5gBZfSG2twxRS1oDrS6FoFg4sknaWvd+UELLnvmw5B1prHBwhZyj+xi4crIcN3scBEXDzlnSWW7/UhlSZ/WWRgqefhAYI8PpdsyS1z4HaCVIKaWUUkqpmshkkmrOtl+lSxsAZlkXyHBBo94yl6fVJe6qUS7EdIXDG2Hd11CYJUEqbRvs+hN63AeFwJwJYDbB4PFSNbIXQ3AjCVRF2VCSJ5Ukswd4eIKnr+wzXFKF8g4BS+2OEVoJUkoppZRSqqYKbQLDXpXW1yb3/J7gRtDvcVj0Nix8E767AYoyIaQR9HkYNk+X8BIYDcU5YCCBZu3Xck6LJxhOGfY28FnwsEl1yOWExgMBMziKpPOcp7/MTco+KEPhpo+BRW9BZmI13pR/r3ZHOKWUUkoppc51EQkw6Fn3kLZVkLIFZj8jc4LMHlKhWf8N1O8IPmGQuQcwQVBD2WdCKjnOYjlfYAPAKd3ggmPlqyhXht7lp0oL7ZxD8hxHsTRiiGovz89PlcrU4fUSzgLqV999+Rc0BCmllFJKKVWTFGZB5j5IXi9Vm/rtIChOhr6t/kKGpDncgcZZIh3e9syDJgOlWmS2ytwhewGYPACHVHVKmT3BwwPCE8q3eflL9ejwRmh/nQSt/cskZHUaDeHNYe5L5cdnHYDkDRqClFJKKaWUUv9SfhosfQ92zz1qowm63i4/Gi6pABlO+fLwkkDkFwHBcRJ2AqKlhXZJAXgFQkG6HBsYJedoNwpKcsA3BNJ3lXd8S98N816S1wiOg9gekHsEVnwECRdJZSlzb/ll7V0Izc8/Czfl9NMQpJRSSimlVHUwDMhLkaqNVxDYfGHfomMCEIABKz6B8/8jx5YudmrykKpP68slBC37QOb1dLlZWmUX5cjxPiGyfchzsL1QKjo+ARK2QprAJe9Bvdawd74EIJBKVNpOyEqUALX1Z+h+V8UQ5OV/5u/RGaIhSCmllFJKqbMt5xBsnylfJfkQ0RI63wSbfqj6eJcdDq2D6K6wazYUZgAmiOsrTQxWfg4Fqe4mCFboeQ8c2QLpe6BBe2hxESyYCH6jpLtbUZoEpLRt8OdzcMkHkJ1U8TUtHtIauyhLqklWn4r7mw453XflrNHucEoppZRSSp1Neanw14uw5ktpNGAvgKRVsHuOVIaqUpwL236BRn2g130ShvzCodVFkLhEusC5nDInaP8SqfY4imDoc9BkCCStlvODdJJzlkhgAmlykJMEUe2OeVETeAfLvKSweBmqV6rNSNlWS2klSCmllFJKqdOlJF/m5Vi9j39M2nZZ1PRYOcngGwFZe8FkKd/uckg76/ihsG2mVGY63SjH5qfJuj6+4TIPyHBBeAto3F9aZOceBgz5ubQ5guECpx0wyYKoJotUlsKbSbtse0H561vcrbZ7PSDD45oPh/jBEBoPXgGn555VAw1BSimllFJK/VtZ+6Uis3sumC3Q4gKo3wn861U+dv/Sqs+RtFrCzfKPKm43XBJO6neQuUEAyz6S9Xu63iYVIJcT/KMgoAG0vARW/RdSNkv3NkcxXPw+BMTIc0srRoZLApbVG3JTYPF70glu3ddSofLwkkpQp9GyaGvrS0/Tzap+GoKUUkoppZT6NzL3wczHITe5fNuRzbK2zoAnKwchj2Pm1pRylsC+xdLAYOV/IfuAbK/XCuKHwMYfy481W6SRgm84+IRCfgrYgW53wdwXpHJksYLTIfN5Nk+H6E7uJxtIcwWTVJNCm0LaDmm9vXaKLMwaHCfD3QKiIbB2tsE+EQ1BSimllFJK/VMuF2z9tWIAKpW8Tubb+A+tuL1Rb9j4XdXn8wmFhj0hsi0UZcv5vYNk/Z68wxWPDYiWSk7bK2Hh6zIMLitRApDJ7B4ulyrHbvoBhr0Bh5DqjlECXsEQ0xnajpKmCE0GSYe4nbPkOd3uhJiu/+Lm1FwagpRSSimllPqn8lNlodLj2TYDGg+Qqkyp4MYSXDYcE4R8QmU4nNVLusftmiND5yye0HwYDB4H81+VYXAgc4Q2/Qjtr4Krv5PQtP036f7mFQD2IhlGVzr8be5L0PwVuOwzcBXKvJ/cZJj1jKwXFNNdhr6t/FzmGe36U147OA5Cm0gYO0doCFJKKaWUUuofMyRkHI/LIdWco/oc4OUPHa6XDm/bfpXwEtsTYrpBcKw0IJjxiFR0SqVukzlGF73rHurmKcHKJxjWfCVD8sKaQYsLwdMf1k+R5gc+IeATJg0bAqPlXAdXw9K3pKOcbxi0u1o6w22aJu+nQQdY/42Eop2z5LWbD5MFW31CzsA9PPs0BCmllFJKKfVPeYdCbA/Y9lvV++OHgNVWebtXAMR0gejO7pDkTknOEtgwtWIAKnVotVSIGveDkkJY9xX88ZQELcMFGbvh8AZpyhDZTqo7RTlS1TFboc1VUAyU5ED9drBnvnSXW/wWdB8rw+kOrJBqVH46JAyDBa/La2+fCQ17yGufA3SdIKWUUkoppf4pDyu0uhS8AivvC24EDTqf+PkmU3kAAllDKHHJ8Y/fPlOOSdshQ+OcdglAIMPbCtJh+wxoc7mEo5I82ddkoFSEAJZ/DM3Ol6FyZos8b/0UWax10LOwb6E8d/8y6HKrdJwD2PozOEpO7r7UcFoJUkoppZRS6t8Ii4cRb8DWX2QOj9lD5us0HQyBDU79fIZR9XZHkXRwW/4xRLaStX2OZS+Qdtd+9aDnvXJMQH1IWgurJ0Hj8YABB1dB/fZwaK1UiVwuCXK/PQxBMbI4656/wOoDg8bJ3KPCLGnQkLJZmjKExcuxtZCGIKWUUkoppf6tsHgJHR2uc3dmC/tn5/ENg4bdYfdfFbc7iqRldofrZT2hyDYSXDAkdJlMFY8FCUuGU4a8mYCAONluMkNBBnj6uR8DtgDIOggWDxlChyGNGgoyYOWnkHCBBKwl78j8I5Auc0Ofl2upZXQ4nFJKKaWUUqeDxQP8Iv55AAIZotbuKgklpQxDFjxtPFDWDAppJGsP+UVIADIM97A49/HeQfL8wAZQnC0NFCw2WRQVJDBFtYWco9p6128PRzaCXyTE9pJA1+1O6P8EWLxkKF1ofHkAApm39NcLkHvkn7/faqIhSCmllFJKqZokvDlc8Ca0vhwCoiRUtb4MotrAdzdCVpKEnAFPS5c4wwWYpOpj8YSBz8LKL6DNFbLdZJLQZPWS83uHSttrDAiMgXptocstcGg9dL9LhtD9OQ5mPQ1L3pa1giLbwOYfK19r7mHIOXj27s1posPhlFJKKaWUOltcLshJAkehzLfxrw/mKuoSYfEQ0lhaV++ZD9kHwcNLhtsFNYTJF8GlH8Lln8Pm6dIZLriRVJEAirOktfX5L8HGae4hbu7XuewjWXi1/TUShrKTJMy0GAHLP5JzGQAmKMyGNZNkjpFXsAyPO1bpukW1iIYgpZRSSimlzobcI7DpB1kbqCRfqjmtLoUWF4HLLqHlwHLZ3qiPhI4N30NcL8jeD0lroPudsPBNsPnDzj8gbTc0GQSN+0uwmfsCYIYGHSVg1WsNUe2RipAnrNghj63uxVuPbIZf7pMmDg17SFttTBWv2zccVn8hVaKM3ce8KZPMHaplNAQppZRSSil1phXnSqOC3XOO2pYDayZLxzfDJe2vS22YKouTdrxO1v6Jag9OF2QlSoMEr0Cp4Nj8YPbT7sYIJqkSOYqgKB6atoBD62TtIb96EHFMA4PcI7D9dzn3obUyNM4vEoqyZI6Rh02e5yiS+UQe3pXfV1xveV4to3OClFJKKaWUOtOykyp3fAPAgLVfSdXmaLG9JBzNmQC/PwHzXwabt1R2QNYDSt0hi5davaTjm18EBEbDsNcguhPsWyLD7vJS4PfHYN8iee6uvySUZeyRalLryyXwmExQlA2evjIPySdMKlZFWTIsLqypDMkDOT5hOPS4W4JYLaOVIKWUUkoppc60vCOUt287itMhgcZZXL4tKBZ8gmHuf2TekMUqoWXd17JWz5AX4MBSqeSs+QqGPA87/pBub60vl+CUlQgup7xmWDz0fQQWvAbNXpZhcwdXScBxFMP6b6HTzbLmjy1ArsXDS1pi2/PlmuJ6gYcPXPoJ2PPkugKj5dpqIa0EKaWUUkopdbrZiyBtl4SNI1ula1uVDBkKd3SYaNgd1nzp3u2UKg/I3Jvk9eDpI5WlqHbQ/lrIT5XubcNeluflHioPQCYzpO+Bbb/JeQE2/R/smi1D51wOCUxzJkgVaOBTUuXJPQwleXJ8RCtodh6k74CQ2PI23bU0AIFWgpRSSimllDq9clOko9qOWdLwAKD7GBk2VpxX8VizBcJbQEFm+TYPmwQbAKuv/OwTJnNz8lOlmUFRFqz6HHwj4OL34JcHZW6OyQxtr4LgWAlX+5fC7rmwfxkMnABpgM0HVv1X5gJ5+pU3O8hJgrVT5FqdJVIpCmooVajVkyUgnSM0BCmllFJKKXW6OO2wbopUXo628XvocL2ED8dRQ9+8Q6DfYzJUrZTJApik0uLpC/kpUkkqSJNhaMFx0G0MZO6G3fNkXk+TgdIxrs1IWD1JApLFCo36wdAXYN7LErhA5giBNF8YPAH2zIWSXGnDHdIY5r8q8398I2DfYgly3sEQ2vTM3bezTEOQUkoppZRSp0v2Qdgxs/L2/FQZhnbefyDnkAxBC2kiQ8v8I6Hfo7DiM0jdCum7ofEAqdA4S6QaU5gFAfVlnSGTGWY+IpWczjdJlafjDVKx+b87yofPOe2w609pvd37/vJrSdsp56zfUQKO1Ue2hTWFvqOkE9zO2TKkzuwhLbIHPStzgM4RGoKUUkoppZQ6XYpzKlZ6jpZ3GDL2QtsrKu8LbgwJIyC2hwSf6E6waKLM2TEMGbZWlAU974H1UyXoHF4HM1ZLowSXU+YDWTzl+WYPeS5IMLNYIaI1pKRCo77QqCds+E4aK5iQtYpMZvj1Iej7EMQPlTbcPqESfgKizsz9qiYagpRSSimllDpdrD4y7MzlrHq/X0TlbQWZMPd52PqLBBiQ4WddbpN5RNmHpFuchxds/A4OrS5/rsUDNv8fRLWVkBUYLZ3oSvLAbAWzWRZdTdkOnsGAD9TvAH88BYENoDBdqkErP5FFV6NaQ/pOWROo9eVy/nPQufmulFJKKaWUqg6B0bLGz94Flfd5B0Nok8rbsxJh78LyAARQmAkLXoWAGBj1FWyfAX+OA3shUropbbdtgiJ3U4XGA2QOkcUTtvwCuUlSRbLng5cf7PwTvC6S1xn+Kjjs0mkueT2s/0YWcj3/VZkjNPy1czYAgYYgpZRSSimlTh+rN3S9XULM4Y3l231CYdC4yvNqXE6pvES2kSYJRZnSjMBRKPtNJvcaQmbwDgXPYpn743KWL2pakAlZ++X1UrbJc/s+LHOM8pJlTk9cH/A/AClA4jLI3S9zhjCk1fbISTBttAydC28BAQ3Oyu2qLhqClFJKKaWUOp2CYmDIc5B9QBYw9QqUltUB9WW/wy7VnyMbpdGBdwhgwIFl4B8lASZ1B4Q1k4YKKz8HnxDoda+03T6yUSo8JpN0bOs3Eg6sAKsXtBsl4WjFZ9Dtdlj2IbS8BDZOA/9oIAKy9kFOorTQBgk+hgtGfiHtt2N7yrnPYRqClFJKKaWUOt18QuQrql3F7Y4SGXa24HXZH9sTZo+T+TwgXdoOb4CLP4A/noSCdKkiFaTJnKBOo6USlLxWFjANbwFz3IELA8wzpYrT+36weEsYW/s/9xC3t+EgkJssbbbDmkkThwPLpaNdl9ukaUJQzFm9VdXBXN0XoJRSSimlVJ2RuVfWBHI5ZIja8o+hMAP86kl3NrMFmg+X+UAuuwQls0WaHBgu2PA9dL8Dmg6BzrfAuq/dAcpdGTKZJBBt/gm8AmS9osAG0P6a8jk+A56S7m9F2XLu/o9Du6ulOmWxwaYfYMtPkL5HKlXnIK0EKaWUUkopdbbsXybzeQwneNikk5thyM8B9cFeAJFtpX11YLSEpZICd8CxSDDKPgjhzSF1u1SIDEPCC4a7pbYL9i+F/o9Jl7jkddB8BPhFApmwajLk7CkfDrfrT+kE5x8JB5fLELqCNAhtBsNegpge0mXuHHJuvRullFJKKaXOtMJsyNgja/6UFJzac/OOSPgoyJAubYYLcIegrEQozpMgY7bIvuyDkJ8i6w8VpLuHxXlDTDeZL+TpK8HIWSznMXtIIPLwlEpP0hrofhckrZRFWAFyD0rVyWQpv66ds6RRQsY+yjrPZe6F2ePlus4xGoKUUkoppZQ6GU4HHFwNMx+F72+CabfIfJ6UbSd/jsh2En4KM6EkX7q7QXkjApdd9oU1l8BUGkgMQ0KRyyXD2lK3ScMFk1nmDJnM7sVRDQlGUR3k+Es+hOWfSMvu4lw5l9VXjjWZABPY/MHTHw6tlaoRJglaZg8JXylbTsvtq0k0BCmllFJKKXUyUrfC749LAAEZ0nZwhYSi9D0nd47IVuAVBBiw5Wfodpe7ImOG4MbSnKBeK+h+5zELrppkeaDG/aRaVK81hMZDdBcJK371ICAaAhvK+ZoMgHkvSwUo190mu7Thm6e3NE/wqycNEiyeEqIK0qWCZPaQ6zNc0jgh++DpuX81iIYgpZRSSiml/o69CNZ/W3FB01JF2bB3/smdJzAahkyQeTaZe2HfQrhwIvR9BDpeD4dWw8zHpRIzeAJEtZemCH71oPOtEo4OroRfH4Tvr4eYrtDiQglWhrsK1OdBWWvI5ZRhe4UZMrwubYdcQ2G2DKvLT5Xt3sFQnC2hKvewDH8rypLKUe5hCUbZSafnPtYQ2hhBKaWUUkqpv1OYCYc3HX//geXQ7ipZLBVkrlDmXlm/x14A0Z0hMFZClKe/tM5ueTHgkjbX22fCpmmQkyRD1fKSYck70OpS6exWnAv2Ylj9lXR8868nc3jmTID6HWSB1uBY2P2XhLX0nRKMghtBtzukSmTyhFRk/lFRrlSAMAATxPYGR5G7yuXeZvGA4IZQUiiv2eXWc6ZBgoYgpZRSSiml/o7FKlWWoqyq93sFuoeRIcPVNk2DVZMAQ4KIVyAseANyD0H9zhDaCFZ8JE0MBo+TxUxzDwJmqfzsnidD31Z8LAEGA3rdB4snyhygknzAJNWfpDXS9GDYq7B6klSN/CNlyFxgQ9gwFdJ3gVc4tHhD1iBa9bFss3hJyGrUD7b+BLYAsOeDyQMa9oA2I2HdFFmjqOVFct5zgIYgpZRSSiml/o5vmISAZR9Wvb/FRRKUQCpA6XsgfogMO2vQBf54Qjq4+dYDDyuExMPQ/8hQNXsRGA73mjylbatnQ9fboMdYSFwi3eTMFvAJlkBiMoFHsczZ8fAEW6BUcvwiZd0gv0ipPs18WBofmD3cHeSQNYj63CeVoL3zJDTlHZZ5QQOfKV+vKGM3rJksrwHHzFGq3WpMCHr55Zd54oknuO+++5g4cWJ1X45SSimllFIVNe4PB1ZC0qqK21teAvVays8F6dJlLWmlzBVq2F1CSkAD6bQ24g0ZHpeXLHNxTCbwi4C+D8uQN7NFXmPbb7KQamg8nPcS2HxliJ3ZKnN5MIFPmAy/M9zhJKC+NEGwF0Cz82DVf6UqBdIAwe5wX6O7M13iH1LtcZXInJ+Y7rDozfIuckeLaicLt54jakQIWrlyJR9//DFt27at7ktRSimllFKqav6RMOAJGUaWuFTm1sT2hJDG4BUgDQeWfwIrP5MhZQCFWbDhexg8HkKawOK3JSCV5EvFJbqzdIhb9LYMtWt3FcQPlWYHBelSUUrZCnE9JAjVawmH1oHVS4bCleRJZSgwGlJ3wsCnYeWnEBAJmfukAmTzB0wSykBab5s9JOzMfVkCmeGCJgOh02hY82X5sSAVo3ZXl893OgdUewjKy8vj2muv5dNPP+WFF16o7stRSimllFLq+HzD5Kth98r7MvfCjt9l7lBpCDIMGaaWnw77FkkXudLKjXcwJG+QtYa63iZhZuUnsPwjaZ5g8YDGA6DdKFjzFWz8HgY9A1n7Zaia1Rc8faR61Os++OsFaY/d/jppthAUJ0PgCjLAz0NeD2SY3oFlkLRWFmI1XDKUL3UbrP1SmiwseVcaL9RvBx1vlLbd55BqD0F33303I0aMYPDgwX8bgoqLiykuLi57nJOTA4Ddbsdut5/R6zwXld4zvXc1m35OtYd+VrWDfk61h35WtYN+TkfZv0o6sNmCobhAurw5XeAdDiFNYeV/wWSVLwBrIBTlQ85h8I+GhW9A5iEIaAw+QVKBObwVVk6SeTm2AFj+BbS4wN0cIU8WVQ1pDJmJEN5GOsUVpkGJA5oMlW5xPlYw2bDnpwNgb9AdNn4N+ZlgtoHF2z3HyB+KCuX7JZ/KHCCfYAl1Tpd81WCn8jtoMgzDOIPXckLffvstL774IitXrsTLy4v+/fvTvn37484JGj9+PBMmTKi0/euvv8bHx+cMX61SSimllFKqpiooKOCaa64hOzubgICAEx5bbSHowIEDdO7cmdmzZ5fNBfq7EFRVJSgmJoa0tLS/faOqMrvdzuzZsxkyZAhWq7W6L0cdh35OtYd+VrWDfk61h35WtUOd/ZxcLmlCUDonJyAKUrfDzMfKjzFcUg0yDJkTNP3OinNt/OtLy2yTBa6YLGv7LHzd3f4aaYLgHwndx0hVZs54qNcG2l8FTickLpIhdt4h8lq5h6BhL2h9iSymavODLreAhw+kbMYeEMvswtYMaRWG9dd7ZO0jk9k99M4qc348PKVDXGzPs3gzT4+cnBzCwsJOKgRV23C41atXk5KSQseOHcu2OZ1OFixYwHvvvUdxcTEWi6XCc2w2GzabrdK5rFZr3fpHd5rp/asd9HOqPfSzqh30c6o99LOqHerU55SfBpunw+b/c4cgmzQVaHsVNBsM22eUH2tGQpJPADQbChunyoKpAI5cMBtQLwF8/OGnV6EoU/aZzBKgsvfC+v9B34cgIh7aXAzzXpA5RFt/lADlKJBFTb18IH0zpMZDUJQMkVv0hjRN8KsHabsgqjXWTd9ijWoNW3+W1ypKk/lCdk/pIhccA7XwszyV379qC0GDBg1i48aNFbbddNNNJCQk8Nhjj1UKQEoppZRSSlU7Zwms+xo2/VC+zVEM22dKOOr9ADToCJt+lJbYUe2kEcGid6DzTWDYYcsv0rCgOBdaXgyNB0ont5IC9wlN0uzA6Z7jkpUI9kJo1FfWKYruCnvmyRyhzreAb6h0fPOrJ1WnHTOlocKaL8Fll5baBeng6a6ObP4Z+t4rAW7PX2AgVaGGPaVVd+JSCI6VazhHVVsI8vf3p3Xr1hW2+fr6EhoaWmm7UkoppZRSNULWAdj6S9X7Dq6UMBE/RNbfcZZIUMncB3E9IXMPdLkN2l8LOYdkCJp/tCyempkIgQ0gLxUcheUByBYg7bc9vCCsmbx+w17SenvgM9KOO20HYEjba79ICTI5ydLswGIDq48ENUvpiCoD5r8GCRfAef+R1zK7u8elbJGucY16SxA6R1V7dzillFJKKaVqjcLM8uFsVclJgsjWMh8HJHykbpP1gQrSZFtQHAx9UebgHFgGOQekitPtDqkSZe2TOUcmkwx3K8yGjL0SUoJjZe5P2ytg/deQtt39wib5lndEWmx3u8Pditss6wR5BYJfAznGWQKuQtj8A2yZLtUigA7XQb3Wsl5Rcc7pvGs1To0KQfPmzavuS1BKKaWUUur4/m7BUJt/xcep22HhmzLHp1RJroSl5R/Kfpu/VHxK8qH3fbD5Jwk3JrO0p243SobEeQVJRWj/EugxRio2ZQwJTIZTKjslhVL58Q2TbVYv6HIr7HbJOYuKpJkCJnkdwyUByGKTEHQOLYxaFfPfH6KUUkoppZQCpKNbWLOq93kFQdBRQ8icDtjyk4QQrwDwDXcvZno9rP5C5urYAtyVl2zwDYFVk6TK42GD4EbQ+35Z+yc3GfzrQftrJLSk75bqkc0fmUPkIeEmOE4aKviFwYCnILoLtLwEzn+5fDicT7gMxQO5NpMFmg4C33qw60/pDBfQ4AzdwJqhRlWClFJKKaWUqtF8gqHPQzDraQkxpTz9YODT0pjgyGap2HiHyhwekKAR0EDaXQc1hGXvS3DxCZG21AAFmRJgfMKg/xPSuGDfYojuDNt+g5iusHsunP8SeHjL0DyrN4SESwttkAqT0y5NFrZMlyF0+5dII4UONwHBcNFEyD0Ih9ZC8npocSGENpHmDb4h0PX2c74SpCFIKaWUUkqpk1HkHsZmL4Ahz0nQOLweAupDSBM4uAr+HCfD2sweENcbWl8GC16XikuH66S7W7tRMufGZJZ1g3zDpKIT2BAa9wPvINmWuQ9yD0tr65huEqwOrITDm2Hws3K+jd9LUwXDKSHL5YIGneHIJji0TkIVhoS04DhIzoaf7gGjRCpMPe+VitKOmdDnQRkS5xdWrbf5bNAQpJRSSiml1N9J3w2LJ0LyRsAAiyckjJBOb15BsPJT2DC1/HiXQ6ov2Qcl2KRsk8fpu6RqY/MHRxFgkuDS5VY4sEICk3eQNFRofw20vVIqO0GxEoAGPiXnP7RWQpaHJ2ycBgUZEnTqtYbGfWHOc3INZot8dxRDQSrgCdn7wT9cgk/yOrjsU+g4WobbmUxn865WGw1BSimllFJKnUjuEanwlA5tA+mwtvn/pOKTcAFs+b/yfaXNCVxOCT4XvSPd2ea/Itu3/w4db4TlHwMuaHeVnCtptbSzLs6VCtHiidLlzV4sawRZbRJatvwk5179BfS4By77TKpTVm+ZX7RoIjhKkAWA3I0P/KOkwxzNy6/fJ1TmEW39BQY8WWcCEGgIUkoppZRS6sQy91QMQKWcxbBmMkS2KW9hbTJDXopUiopzpNqTuk0WOPUOAV8zZB+AiBaysOrmH2U43eGNUlEyW6TNdWnL6/VTodd9MhQuPw02T5dAZfOHhOESfr6+QuYWGSZoNlTmLOUdkX3JG2HrrxKm0vYBzaHNlXBknVy/2QMOrYHCDLDWP1t3tNppCFJKKaWUUupEMvdX3uYokqFuhhPsRTIPyGKTMGEYUmFxFMqxJgvsnAXhzWHfQgkf67+GwBhofYW0pw6MlnBkdz8HQ74VZUl1yGSCvYtk+JvNT6o+tkBY8p7sczqg70OwczZ8f6MsmpqfKkPmrpsmIS1rPgT2gdSt0PpyyDssx5d2ratDtEW2UkoppZRSJ+IbXvGx4YK8VAlAZg8wHFLpMVzu4GNQFmK8Q2ROzqYfoPNN7pbUHjIPqCBDKkEY8lzDJWHEZJaqUkADaZYQ1RY8faDgCHgFQ2EWRLWDrT+VP7fJQNjxhwy/w5DrCIiSKs/iN+HgSkjbIdeUcwhWfCJziAY8Jd3gfELO0s2sGTQEKaWUUkopdSJh8TIErZTLDo4C+blRP0hcBp1Hg1+EbDOckoG8gqHfo7Dmf1LNcThg6H8gpotUaiweUK8lRLSUOTomD1m/J6C+PM45JO20V02WpgjR3QGXVJt8wmTtoFINOsDuefKz0yEVJZNZrnv3XxDVurza4yiSIXvrp8qwvTVfSsvvxCVQmH1m72UNocPhlFJKKaWUOpGgGBg8Hv6cIMPT3EUeotpDs/Nh9SRpZHDef6SFduY+qeKENJamCiNelSFz/vXBOxD6Pipr/OQkwZ758MeT0PUOOU9xjlSI7AXS0rrVJfDX87ADGPGGtOEGyDkIwY1lnpDJIsHHcAA2pHudVdpr+9UDTFCcB57utX8cJeAfCbmH4PAG2LcIfENh/zJoMxI6jXYvwnru0hCklFJKKaXU32nQES55XwJOfqq0nM46IFWURv3AKwDmvQRHtsg8HE8/mW+z609Zzydjt3R/c5ZA++ukccHyj6Sltacf7JorQasgDZLWQnCsVGnmvSwBylkCqz6HYa/Asvfg4GppmJCxR5omVOjsZgKrLxQmuStMJpmjVJwnu70DJYS5nNKIwXBBQbrMM9o4DRr3l2YP5zANQUoppZRSSp2MwGj5AqmarPgUwppJ0Jj/soQWnzDYOx+2/SJr7/S8B2Y+Ch42CT5mD1j2vlR+hr8mc3iC46BBJ9i/XM6fuVcWOy3MhOJsqTzZAmTekQloNkwWU/UJgwvfhdQtkJUI0V0gZYu0vi5IBwy5tvAW7m5x7qYLFpu7kYMVPP3BWQQui4Qii0Xem4YgpZRSSimlVAX1O8GI16Ua9OsDEoC8gmSej6NIwk5BunSFM1wyzM1klp+73QXrv5F5OYYLSn6VgNL1dhmWlrQGMGRIncUTMMGAx2Vu0fbfZK6QswTiz4dON8jQtYY9oc0oWYto919SHfIJhtB46P8EzHpGOsuBvCYGdL9T2mebzFIRKq0mOUuq556eRRqClFJKKaWUOlUeVqjfwT3Xxz3vxmSBklz3EDQz1G8HqyaVhx+QeURHNsu6QDZ/+SrJB7NVFkFtdp5Ul3Lc7bd9IyCyrXR+K8yQhVR9I6DnWAk7P98HwTEyNyhhhCzC2us+SN8l5805JHON+jwAB9dBPhDTHfrcB9tnQNJKCUBeQXINADFdq+GGnl0agpRSSimllPqnPGzgEy7zgHxDwGmH7CQJNFZfsHoBhsz7MZyymOmyDyE8Qdpamy1wYIVUlPIOw7pvode9MGeCDIsLbgyN+kpTBr8wqS71ugfmPC+vbbZKAFr+EWyfCa0uhW2/QuvLYPvv0LC7hLW1UyCshVxz4wHgyIU9c2UInM1f5jSBBKSQJtV2O88WDUFKKaWUUkqVykuRpgeevie3dk5AA2g3SkLIkS2UDWPrdqcsjhrSBHbMlGFtTrvMwen9AGQmws4/pJLUsLuEloWvQ1EGrP8WEi6AkEYScoIaSgAyXBJgdsyS6ytIk/k/WftlLaIjm6DlxTKXaM5zcNlnsg7R4relrffOOdDuE1lfqNutcNnnsON3CVs+odDyImjUv06sGaQhSCmllFJKqfw02DUHNk2TFtXBjaHb7RIu0ndJMIhoLW2oXQ5ZA8g/QgLH0vchYy9lC5fmJku1p+/D0Px8OLAMSvKkEUFgA1j0NhzZiHRx84J9C+DwehjyPKRsl1BSv4NcV+IyWazV4ilVm6i2sO4bmWME0u7a5Sx/H/sWQmwvCV6bprnnFBnSSKHp+XKMhw32LoD8IzIMbsSbssaRb9jZu9/VTEOQUkoppZSq24pzpdPbjt/lsckMjfrArKdkrZ3AGOhwLfz+GKTvlvV2/KKg933SSc3sIdWaoiyZh2P1kaFvW36GsOYweAIcWuPu0pYC+SlgGBI87PmQny5tt5PXQ8tLYM1XsjaQyQMShsmaQU0Gwrqv5dq8g2TOkC0AHIWUL1zkXh+oJFeu6cgWaH2pzBPyi4Adf0K9ofJ+QxpJV7rERbI4q3dQNdz46qMhSCmllFJK1W3ZB6TxQKmodlK9Sd8NGDJMbOGbEjwwQVCszN/Z/rv7GKS64h3irsoYElxSNsuQuBmPQM97JZhsmS7Vo6CGkH0QnMWUNVUIi4efxkq1KfugVJ8Wvy1hZcgL0nUufTfED5G5QWarvJbTLl8YENsTVk+GohwIbiQLtGYmwqr/gtkL6iHVov2L4KK3odf9FQNQUa670hUIZvNZuPnV49x9Z0oppZRSSp2M0qFsAKFNodUl0jTAWQzeoVLdKcqWdXm8g2V7ca5ss1gkNIBUYXxCpMrj4SXVF0ehDDMLbCDd3ExmGYJmL5ChcxZPeV5cL9i3SDrAFWXLELiCDDlv5j44sBQKc6D91bJekIeXhLeCdAlfXgFSLco9LNfjckCTAbLo6qZp5dcHErg8fWRYnY97CFzuEWnm8NsD8Mu9spZR+q6zc/+rgVaClFJKKaVU3ebhLd8DGkh76MObZV4PSMe3nEMSZvJTZJvZQxoTJC6GbndA2g7K/qw2WyWQuJzQ4kLYNlNCT3GedIEb/KwMdSvKlWYFpSJawp75Eo4w5Dk+oRK2SvJk2F3HG+GPJ+VcPcbKNR5cJVWoNiOlw9zid8DLH+L6yJC3vBTpQIfJXS3CHdZCZfhd9n7ZP/9VSFpVfj1Z+2HnbFnQNbz5Gbv11UUrQUoppZRSqm4LaSRBoskAWDwRbD5w/ssyl6f/k7KvOBdwLyaKSR7nHYYjW6HTTVJdcTklKOUckjlF9TtKA4PsA7KIqm+IdIeL7FC+MGkpT18JL067VJKKcuRnn1AZ1hbSRKo/aTtl/tCKTyQY+YTJ3KD130O9ltD7Xuk+F9IY0vbI61h95Jxmi7yWb7j7bZglOB3eWDEAlSrKlmqRo/gM3fjqo5UgpZRSSilVtwVGS3DIPSyNCZI3wq7ZUg05/2WZA2T2BMMORml4MWSYnM1X1tZpPAAOLpf5N8FxkLIVFk2EdlfJnJuwZtDiYlj0prS/bn4ebJgqQadhdwk6RdkSRjwDpflC3hFpg41J5hQd3gD+kdLJzuIh3wsz3NUjk3xt+UXmDfnXgy63yDkNJEzZguXS846AT4CEJpufNIQwXBKKjrV/qYSuwOgz/jGcTVoJUkoppZRSdZvFKvNpwppLEFgzGUoK3I8tMkytzwNg8ZI5PIZLnhfZBuKHSkUotClk7oeDq2HFZxIsDq2WtX8WvQVL3pMFVe0FMO8/0kmuYQ+Zs7NrDvx6n/wc2VaCSc4hWUMIILaHVKNs/uB0SDe3olxppZ1zCDBLIMs+KPOHmgyAPg9K6+7tf8g6RnmHyys6LocMAew+RsJafirkJMk5St9bGYNzkVaClFJKKaWU8rCBTzBsng6N+kKz8yQIBcXIGj6OIhgyQZoo2AshooVUYQoyZGhcziEJE4UZ4CyRuTilgcJRDBl7ZFhay0ukU9uSd6HL7bIeUHGuzEey+sKgcXLspmky9yj+PKkibftVQpTJQ0Jb7mE5t70QHMnQ+nJo0FnWJjq8SbrZFaTD/sXg5Qfn/QcObZbntLsG2l8Ja7+GPX9B9ztlXSF7gQyV8w6mbOhfw57lzRPOIRqClFJKKaWUAsAkFSG/MJg9XoaQJQyXykzSahmOZvWWrm8HV0rlplE/aVCw8C1o0Ek6vDlLJPBYPMs7yTU7X0JN/fbSHjs/VTqwBcdCeEsJYCYzzJkAzYbJPB+/SNk29Rqp3OQkQ7dbJeD41ZMg5CyBkDiI7iSLrq7/uryCFNBAAtiBlRAQA437w37kuX9OgOCGUjEyDBnSd2CZDLHz9AWLTdpkt79GFnQ9x2gIUkoppZRSCqTC0vx8SFoDg8dJIDi4StboydgjxZGCTJnL4+ElQ+GWvCvPCW/mnjtkgcwk93pBSKc4/wbQdpTM73E5oc9DsOtPSFoLHp4yvC1+CGz9yd122yrD6QY+A/NfkTBiALv/hMJ02Z6bLIHM6iVD4+Y8D/5R0P1u2Dsfts+U1/LwgmZDIKot5KQAMbDpB8jcIfOPts2AAU9Bi4tkjaFdsyWANR4g7yu0aTV+IGeOhiCllFJKKaXSdkj1Z/9SKMqUuUCRbaHfY/Drg+AokDk5PqEwZDyk7oRFb0PWPqmgjPwCfhojw9EOrJDhZY4SqQ71eUQWKC3Olq5uWKQVd/wg8I2QdtYbp0FIU/DdINUj/ygJQ84S9zwkh1SgspOkxbZPKAx9AY5slnlHIMPZ8lOhXhs5v9Mh13xonexb+g40f83dBMEi5zYcsPq/0OoyqW417A0tRkhnu3N4sVQNQUoppZRSqm7LT4e/XpRW1oYhAcFkkeFmfz0nlZT130qoyD4IP94h84OKs6XyY/GEvQsgtJms0xPaFDrfLEPZUrfDkU3gFw7zXwKrnwyxq99Rwo6jSKoxRRkSnPo8JJ3fzvuPvL5vuDQyKMmDwkwZUucVKNtyk2XRU1sA4F6gNbqLhLHCLAlAnr5QUggBUTJ/CNxVKkO+XE65Rt9QCVD7F0On68/pAAQagpRSSimlVF2XfUC6qpnMMsTMUSTD2IqyIPcQtL9WAkZJnlRR7AWyPlBoU8g5KHNwEpdI5ShjJ6TvgPRd0iQhKFaqOiYT1Gsrw+wyE2U9Ie8gOLQefMKh+Qio11qqNq5iaYgQ1U4CVtb+8mt12WVYnU8YBMZIBavJIAhrKmsJZeyRZg0+oXLNRTnyvhxFUvkBafXtG16+ICyGBDzDBV1ulflD5zgNQUoppZRSqm4ryS//2StA5uWYPdwtpU3lC5iarbLN7AEZu2SR1YMrZEibT4i0oc7aL8+xesl2v0ipyJgs0Ote+L87JQCBBKP48yCwgQSVDd9D8lqp8qz9H4ycJI0X0ndRqVV1m8tlIVbfCOhxj1Slfrkfet8n15qfAl5BMo+pKEuaJJQu0GrygKJ02VacI4HI6i3VpwYdwcN6Zu93DXBu17mUUkoppZT6O0e3hPbwklACMifHZJKA4B0oXxarHBsQLWHJJ0wCTPPhMuzN4umey+MeOpe2AyLbyYKof71QHoBAwsqO3+U8OUnyhTtsOR3SjtvqJfOMghvJucOaSSMDnzAJVoENZF7S5h/h4neky9zw16QZQ0GGdKkzDOla1+6a8tctyZe23iazLMRq9ZGKlKfv2bvv1UgrQUoppZRSqm4LjJYW0wdXyWOrj7SINlskdGQlShCy2MCzQAJE/Q7w++MSGpoPlwrSsFdg8duQsk3ClMUTOo0Gwwmp22Q4XSUm2PknhCe4KzUmGfJmMsvzVn0hi7ImXCjr/RTlyfyinXNg3TcyT6jpIFlQde0UwAXpe6D/Y7Dkfekm52GVuT9NBsMuu7yWUSRrHbW/Ts7hE3JOtsI+Hg1BSimllFKqbvMKgN4PwNIPYf8SmRtjuKBRfwkYv9wLLhf415MKzJDnZB5Ry4ulwuMdBJjgyBZZtNTllHlDPqGyvk92kszj8fCWoXSljQlMZglLUe0gtAn0uFuqM4lL5DlpO6XRwcGVkHdEwlGPe2DBG/I4MFqC0/6l0lRh8AQ5ZsvP0jL7kvehOE/OeWAZLHgZ6j8ECcOg9YVScVr2gcwVajpEKkElBdJs4RynIUgppZRSSqnAaBj4lAxhK0iDzP2yOOr+5TJXpiAd/OvL/Jp9i+Q5RdkSaLIOwKr/yuOweGhzBeyeB4fXw5VTZFvSKhkm5+kP9nzAkPP1vAd2z4XZz0oXt4bdoO9DUp3ZuxDajJSfcw+5W2QfkMe+EdIBzlkCnn6AGXbPgVaXy5yiwgzY/gdEtYFZT8r1trtevoc0hl2zYMO3sjBrVBsJXn8+K4vFJoyQUHYO0xCklFJKKaXqDnuRBInSjmvBcRKAPGxSAQlvBpu3wNJ3yytCO/+Qzm3+B2DDd9KVzewh1ZN9i6R6VJQtc4vyUmUB1UHjpI32lp+kMtN5tHRqs3iANQzykqHnWJkn5BMi83eKcyB9pyyk2uNueR3/KAlhyWulkcHm/5PrtOfL8Dyrt8wl8guH5I3Q8UbA5O5ily9zl4a9Jp3s0vdBMlI5Co+HHmMh/wjMeqZsShQHV8K23+CCN6Feq+r4hM4KDUFKKaWUUurc4HRIxcZkkmFrx651U5QrDQTWfCnVEpAhYV1uhhYXShe3nMOwZ760sXbZZc6P1VdaT/vXk+5whss9jK0tbJom+72C5Hx5KeAqgaXvS5Vnw1QJTBunwYAnpSIUngCR7WHXH+AbBsW5EoDMVsCQoW5ZByBjt1RtfnsIWl4izRGKcyRolbJ4gH+krPFTv6OseeQbJgHJLwLWTIb4IfDzPWD2hrinYeMPkLUHrpwkC606S+Q8pfOQCjNg2UcSvrz8z/jHVh00BCmllFJKqdovbRds+T9IXCoNDZoMgubDIDi2/JjD62XYGsgwNpcdjAKp3AQ3kqrJ4rdk2FrpkLXCYgkpIMPaDCQsdL0DDq2RqpLJ7K4aGRJICtIlSOSnyra8w7LfZIKghlKFsvlJ2DIMqSKZzO4wYpXrT1otTRn2LZQ1gGK6wqxnocM1sGceUroxpH13cY5UhFqMgO0zpGtccQ6ExkvY2/CdrAkUECfvw1EMQTFyHps/lOTKeSye5c0ZDm+UMHaOhiBtka2UUkoppWqvgnRZYPTnsbB5ujzOS4H138AfT0pFBaSj28bv5WdHkSxympXoHhqXKOEo77CEj3qtJZSUctml0cG+JdDnAekMd2iNu8EBEmRcDqmi5CaDdwhgkgpQYbpUqPo+IiGnJB9SNst3m7989w6WkGS2yLmcDvfirPkSkEKbQupWsOfJ+Zud774wd2ApyYeYbtKKe99CaZt94btSxWrYE5oOhmGvSpMFAA932HGWyJfZEzDk/ZitUhXCkPdzjtJKkFJKKaWUqn1cTgks22dK6EleJ5UM3wh3dzOTNDnYv1SqHvZCOc5ZItuP/gPfcEHqdunIFhgNO2dBv8fdQ8WKpbJi9pBqS9MhUjVZ/w00GypVluIcCUql5zKc0nI7fae8bqP+UvmZ9wrkuENZXop0nkvdKmHI7EHZXB6TCZoOhOUfQ8cbpGmBxVOOWfVf2TbkOZm/4yyGBp0huitYvOGa75AhdYdh4UR3RcsM7UZJ04VkoOP18jzvANj6a/kiqhgyhM5slflIPqFn45OsFloJUkoppZRStUtBBiStgSObpbqRc0i2O0ukSYCjqPzYXXOkUuLpJ00Q7IVVVzhCGsHu2dCwh6zps2s2DB4PfR+T7wOflnk/v9wPxdmy6Om6b6DDddLxza9e+bl8QqDzLRKaANpfA5t+kCYEAJggfbfMWwpyD9ez+pYHqMb9JeTZ/CT8JK+TNX0Ks+Q5qyfBgtdkuF1xLuycLW2+D6+D+a/Ia+WmyntyFEOPMRKofn9cXmvB69IZLqghJFwg4co7RIKP1VdCX7fbZW7ROUorQUoppZRSqvZIXg+LJkp1pDgbQppC60tloc89c5F5PFng70XZkDSTRfa3uUK6tR3L7CHNAxa9KVUVD09Z8PTwBOj3BGz+SVpUu5zQ92H44ylplQ1Sder/mFxXSb6s+RMcV77I6aWfyuNdf8lQN5BrBAkjPcZAYEM4tFbeT/0OkHsEUrZIV7df7pfzFmRAaGPI3CfhxVEkP3t4w3kvwPzXYPef8hopW2Q+T+ebpaI190WZo1T6p79PmFSKln0Eg56V4XNbf5b7VL+DBLvINmfm86shtBKklFJKKaVqh/Td8PuTMszMUSjbsg/IH/OxPWVuDUgb7NL5Oi0ukAAE0vJ50LPgG15+Tr96MOApaXXtEwZWHxkOFlDfPRzMJUPWDKQatOMPqb5YvaWqtOkHmPmoDLHrcqt0XpvxsISKFZ/AknekW5ynL7JAqqn8tZ3FEugy98q6PN3uAq9Aaa5QUijNCXreA/XawKpJEk7aXwfeYVJ9CoqFfo/AwVUyR8k3TBorOEvk/GsmSzUp95DMcbJY3S9skseZe6WKtnMODHgarpgEQ1+A6M7SMvwcppUgpZRSSilV8+UdgR2/S6tqq7dULUCChM1fQkf8UHdLaov8kR/VTiobpTxsENNd1scxe0hfAXsR7F0gYaDdNdBkgJx/x+8y/M0wZH2e+CFy3j1zZe2e7IPl83c8vCBtB6z5CgIiwXBA+2tleJmzWMJSXC+Zv+QygWEvvyZPX/dQt0xphZ2+U+Y1FWdLZefiD6D3/VJNcrkg/nwJdklrIGOvnHv5h+6mBh7gHVQeAD28IfewXJ9hlG93OeR9lyTJ6w56RtZH8os44x9jTaEhSCmllFJK1WwZe2Ddt5C8BooypRGBVyCU5EkwKciQdXHiz5NqkV8ktBkpAci/XsVzBcVAeAuY95J0fAOIai+hxWSW6ku9VjIvx2yVxgq75khVKD9VglFhloQKD2+Z/+NyAoYMo7PYoMstMm8neYNcn6cvjHgTUrZKtzd7AWCSeUoNOoFPOCz/FNK2yblykiTY+YZLkHI5ZAhetzth3wJZ6+jIZjnH4PEShCye0rDBwB3wzNIUwTfU3cLbIaENoDhLfrZYpaqVtl2aJtQhGoKUUkoppVTNZS+EFZ9K0PF1VyoMpwSD0hbTJndFJvuAzINp1E/++K+K2QJxveHSj6SSYrFK44GFr5VN1cEwZOhZQCRghl73yjV4h0jjhB0zpLriHSQVJJNZhqJFtJAW3cs+kmBhtkq1qShbFmjt8xBsmyFd3bwCodl50Poy+PpqyEuW17dY5fXtBXI+V4k0akjbLu85tKlUswKj3Yu67pXQlrJVwo+jUIKUf5SENg9vCXkHlrtbd7vvqcsur713gYS0dlcfNVzu3KdzgpRSSimlVM2Vc0j+gE/bIfN+cM+pKc6R8BHYQComnW+GJgNl2NrRASgnWbqn/fE0/PUfOLBCnhscC036ly8mWhqA7IVSeZr/MjidsgbR4Y1Sadm/WMKCdyj4BMtQs9L1dNpeKdUZ7yAJLBare+0gQ8JQ6jb49X5oPVJaXDfuJ0GlpAAadJA5PiaTrBFUul5Pz3shL1W60HW/Syo7G6ZKZcpphyET5P21v6Z8kVOLTYJeUY4sGOtXT9ppt7/Wvf4P4BkAba+C0GaQsVueW1olqiO0EqSUUkoppWoup718LsuBFdDnQVjyrnvh0Tz5o7/tKBn+duyclqwDMPtZCTWldv4hbaG73ipBY8N35ftcdihIkyBTlA3rv5a5OAGRENNVgsz6b+Di9yWY7fkLbAHuoXPuBUgLMiWcuVzIAqQOGdJWkidVq8zd0qGuxQVQv6MsnNpmpFSnts+USlJUO3lPhiGv6RsBhdlSYXI55Fp374O986Whwf6VcN5LMi8qL03mPsUPlmF/u/6UKlVYMwhpBhlAz7FwaAWs/VKaSTTqJw0U6hANQUoppZRSqubyCpSubQVp0obaUQxDX5R1euxF0sigXmtZJ+doLpcsBHp0ACq17VcJHaFNIT+tfLvTLsPccpMl3DiL5bgDKyUE+dWTTnEZu2U+T2CMDFtb/LY0GLjwPVmA1Oot12b1kZBRVrWKkaFrA5+C/Axpiz3rCZkX1P46iGwLAVFgC4KvR0nlp1E/6P0A/HSPvMecQxKKHEUSBFd8IpWgxe9IZzdPP5lbdGSzDL1rfSksegv6PQZzX4b4l2Hef8BVBCGN3SGo7xn9CGsiHQ6nlFJKKaVqroAo6Dy6/HHqNlj5may7U5AmQebYAASQlwI7Zx3/vNt+A5uvDIsrZRgyjMyvngyLswXIXKCtv0izgmEvQ4sLYfN0aaGdtAaSN8pcnOBGsH+JtK1uPkKqLx426WpXlCPNFMJbwNr/wcZpENxQXt/lgvAEWPq+dHlb+qHMA6rfHs57UYb3ZSYCLgmAnn5QmC6NGvzqSYUpNB4ueEOqXKnb5L0XZUG3OyAkHiLbSee63g/J+/QJliGETQbJMfbCf/sp1ToagpRSSimlVM3WuD/0e1S6voGEi9ieUiE5XltnwynVklIuhwSGgnQJCNkHYNc8CTXlHREkUOUmSze1Rn1l2FtcL6nkHFoni7MOeQ6i2kJ4cxlG1my4tN0uyZO5RLE9YcCTMjfH5ZBhcnF9pGq1Y7YEnJmPSXiKaCkVmaIseS+GUzq2Ne4Pf06AeS/LOj95Ke4GDOHuFtq57qAWCH7h8Mcz0skuNxkadoeON0pg2jtfrrPdVfJeADrcKGsSuVyQuFQCWHHeaf/YajIdDqeUUkoppWo2mz8kjIAGXWRomcUqFSKL5/Gf4x0MkW3kD3+XQ5oYmD1kCJnZDK0vh+xEyPWA7mOke5ujUEJG2yshuhscXAH9n4S986QZgdkiwcrTXxZd7XanLFRamAF/PCFrF/mEQtJamUt02ecyNM3kIUPo5r8q6/H4R0Kn0RJCeo6FnMMQ1lzaX9sCJDQteVfCjskkFR+TWQJOUKwEJbOVsrbc+5eDpw90uE1acwfGwIZvpEW3d5Dcv8IsaHmZ3JvVk8CRCx1ukA5yKVvl/tiantnPsQbREKSUUkoppWoH/wj5OhmePtLm+tBaCU6OIplf5DRg0DiZJ5O8VsJEWDx0ugkadJb21Ru/l1DU7Hw5V8Y+qbw4CiXoFKTD9Dvhyi+hUR/4aawEFYsV8tNlLk9RNqybAkENYflHEsq63Q6xvWHR65C4TMJZ/FDoeY8Mjds7D5oOkuF3pfOITBbY8jN0vR02fOte38cTTPlQUihhat030G4ULJooISx9p8yfwiThx2SWMLT+W2g3UO6Fyyktuv98jrIOd3WIhiCllFJKKXVuqtcKhr0CC16TRgUul1Rw1k+F1O3SJjqqnTRE8AmTALTiY1lwFWR9HUehzJtxFMrwsl2zpeW2s0SGqCUugax9gFkWZi3Odi9O6pQwM+p/0vjAXijD3bb8DOn7ZM6Q4YKkVfDLfXJdPe+TbQdWSKCy+UuFxtNbqlpBDWH7b7JmUkx3aHM5LHhDKlQg85baXiVVIEyUDfMrzJIAaHavAxQ/FKLbQdZBaZvtHynvvw7REKSUUkoppWq34nxpkmAvlOFdRVnSLCAoRr7H9pLua7lJ0lI7aTUMekZCSvZBaR+dcwiy9kOfR2DNJPAKkhAy90Wp6pQGjdaXQ5dbpfqSmyzr/GBy73evYVS65o5PiISYXx+QUBRQX6pOXW+Bea9AYH15nfw0aWUd2VY63TmK4MAy6TjXuB+0uQJ2/CHD2yISZF5SYYase9TyImmtnZMkne1s/nK9ZfOccP/sHlYHMtdpyVuyDpHLJRWq4y0ue47SEKSUUkoppWonhx1SNsHqydIUwFEsQ7zCm8Oit6HlxTLvxdMHNv8oDRXCmkkF5cAKCTR758t8n9DGEm4KMiC6KwRGy7o+/lHyWm2ugJA4Wcw0LF62mcwyx8fqC56+Ur3x9JMGCV6Bco6ULTKvx+T+szvnEDhK4JrvZDheQAMJQvYCCTGpW2U+UXCcNE04slmG7hkGNB0oQWrlFxL0rN7yuNudgAE7ZskwuKj2kLi4/D6ZrRKs8jLk8YpP5XqtXjD8Faky1TEagpRSSimlVO10eB3MfFzmz2QfkG1rJktL6JYXwaZpENEcYrpJ6+lut0uYqN9eKiB/jpOg4x0sC6j++hA4CspDidMOQ58HvyhYOxlWfiqv4ekHHW+A6C5S9YnpCnsXSCjyCXF3eTOg881SLRrwBOxfBfEDpQnBnnnS5jqqg7TL3r8Ezn9Z1htK3ykBxcNL5vz0f1TabOelyBC+eq1kcdWFr8v1pe2UClhYvDR+SNkK3W6Tpg4up2zz9JV7VBroghrCgEcl+HnWrUVSS2mLbKWUUkopVfsUZsOKz8Bll2FjLpeEAqddmiFYrLJY6YbvIG039HkA0ndJAAiKk4BUki/nans1rPwc8pJlKJnJLE0LrD6w7EMZYucVJPOLortI6Ng5S6o0BVnSDnvgMxDRQjrH+TeAXvdJaJrzHKz5H3QZLe2zV38hz8MMBanQ+jK49BPYPQ+S18lwtuJ8mcdTkCqLoLa/TsIMBhzeIIu4BsVKuCnJhW2/SLWo51gJO9tmwPmvSKMHTz/wDoRGA6Cve52g4a9Ki+86GoBAK0FKKaWUUqo2KsyQhUGdDmlS4BXgbpltSCBK3iBDxXJTIHMPbPlJ5txkJ0H9dtLGunQOT2hjWP5B+bnthRJ6svZLACpIhfXfwHoT9H9MQlJwI6nKrPwcdv4uLa473yyNFnJTpDW2vUSObXkxLHhdhqV5BUL/J6RT27JV8noRraBBB+h8i8xH8o+SeUJWb+lEF1BfrtVkkurOvoWyFtHG7yUMZe6T11nxqVSovALlfgx4UkJV1n4ZHpedBCRIG/A6TitBSimllFKq9jGZy9f98QqSCkhOksy5KUiT7mshjSXg5KfJsLXkdbD2S3fA8JHOaB42qR4dzWKVcxguwCUVI4tVqk4L34DWI6H9NTDtZtjxuzz/yGb4/Qn4+kqZg7R6MrS6FK76RubcpG6Ta+51n1R3ktchIcwk83vWfyvnj+kux3n6ul/fkPfX4XoJQRgy1M5shV73ylwnkGPzU2HpuzDrSQk86btg9jgJUyFNoMO1Z+vTqfE0BCmllFJKqdrHr54EBpdd5sw4isv3uRyymKpXkPzh7yyp+NwDy6HZUOkUZzLJ4qlWb/dOkwSeknwJSVhkno+9UHY77TKHaPtMqUZ5+kH8EKn2hDSSys22X6DtKHCVwPc3Qd5hCTrFefL8/CPyOiazvDYmGUa3cZqcKydJ5gAV50q4K0yX711uk1bWLS+RYXR75kmDh9Lrtvq45wFZ5f5s+Vm2leTLPKjsJPd7cJyJT6RW0RCklFJKKaVqH6sXdLxewoPTLsGldHhbu6vhwCpZfNS/gVRtLJ7lz83YKwupBsdJWDiwSkILJrAFSJAwDAkL8YPh0HoJFmarVFw8bJC4FDrdDL3ukW5v2Qcgrjec96I0J2jYXZoX2POhKBcCY2Q9ntxkuV6XXdpmu5xy2R429+KoZjmfyR2OGg+Ag6tg0w/SSa5hDwk2+5dIU4RStgB5jm+YBCV7gbTn7ngdLHgVfrgN/nhCjt04VeYc1WEagpRSSimlVO0U2kSGl/W8Bxr2hBYXwrDXpDpzYBlkJkrQiGwDF7whxwTGyGKhi96U57W+Qo4Jaw6DJ0hQcRSXV5Fie8K+xRJUXA4JQT6hkDBMqj5znpPOcEmrYeVnsGiizO3J2O0eZmfA1p+l/ba9QAKK4aIssNkCZCHXgnTwjXBXaQypTLUZKSFo26/SLe7IZuj9IFgsYJig1/3uRgz1wcNTKl7ZByFhOGyeDj3vhjkTJPRZPKHI3Qhi7RTYt+jsf141iDZGUEoppZRStZPFCmnbZV5OcCMZMjb3RemeZrJItzarlyxkGtkWBsdDTjLMfQmaj4CsRMAli6mGNJbhbR1vkGDiGyad3PYvk/P6hMo5mwyU75FtYd5LlIWZUvmpUoGKal++LSdJ5gQNeU6GuNkCwVkkQctklooNQFwvWcdo0Dh5nLwR5r0sQcdwyrA2ewHMeVHWIGp1KVzwJqyfKh3y/OtBj3uk2tSojwzhaz1SKkdF2eAVDunI4x2/SxMHs0Xer61udYrTEKSUUkoppapfXopUMjz9wDvo5J/X4iLY+qtUYkryZJunv8zjaXdNxU5oVm+p5rQYDrPHS3tpr0AoypFgM3gcRCRIZcZslZCx/pvyDnTNh0u1adZTEjK8AiVcHB2EbAGQuETm7Vg8ZZ9XgFReIttIp7aL3pYAlblfAhuGBLHYnrBvASx9T6615aXQ/3HZf2gtNB0MS96BAY9D+l7YPUfO2es+qST5RUqwM3tA+m4JhkvelRBmsQIe0PZj6DEW0jbDzEekHXdII2h/rYQiq9e/+xxrCQ1BSimllFKq+uSlwu4/YdP/STUjOFY6oUV3lg5pfyesmbScXvKuzL8BMHtC8/OhcT9pl51zSPa5HBJaln4AhkMWOW3YEzBkuFlhpuyf9RR4h0Bgfeg4WlpYF6bD6i8lbOGSttTeQdIkoThPzmHzlzk+GXugpAAiW0PGPglD+ckSgA6sgKQ10ObK8tf0DZehbmk7pO12WAJ0vRW2/iJtsA0XNBkg7bBbXgK5hyB7vwSukKbyvrIPwOYf5H4G1Jd5UOu/lfdusZYvnAqw7AMIbwxZB2Rb8nppKT7wKWnM8P/snXeYVeW5xX+nzTnTKzPDMEPvvfeOCFjQaNSosWsSa2KMLbEmdo2Jxh67ib2LgiAivfdeBwaY3vvp94+1hwOKUUe8iny/55nLzCl7f3ufk/t8y/W+6z0GMCLIYDAYDAaDwfDj0FgNS5+EHbMjj5Vuh1m3weg/yXX5JlweOTSZPeV+BBohtSMk5ej3ZU9b5WJl+nvgJdButByTmDRFXjdWyvmJz1BP0dTHoWybenpsdshfCdhg2BUqR1vzKrQapF4fhweSsqG+whrW6tPxS7fBuFtVdpY7D+LSJX6GXw2f3aGyvfgsiTNvHcSnw4ALJdBOeADev0Kpd3annKSK3fD2JTDmT7D0GRhzgxycxkqVvQUalf6W1llldgXrYNAl0HGChqdumcYBxypvMfQ4SYELBwjD0qflLMVnHpGP96eMEUEGg8FgMBgMhh+HyrxDBVATCa2gbJd+3PGRyOjoZImbL2/SnS6FJKR2iDzmb4AlT8Oyp9TPE7BK3Na+rsS3rifCWxdaEdlOICzXZNbtcmEW/EMlcja7xNigy4AQOKN1nmCjxNf+lerVqStG6XKJKi3b+B4sfER9PpPu1jm2zZC7M+YGWPumBqo6oxX13X2qjtXnVwo18NVYQs6r5LjKPDlCe5fBlPs172jvckjIVABEWheYcJuOn9VXwQxzH1BpX4dxMOhimH6L7o3dYYUzfIm6Ev0YEWQwGAwGg8FgMPxAlG0/9G+7UyKgOh82va/N+Lo39LfTrdfEZcJxt8ntqdqvGTw2h0rAElpZc3dQSdr6t8CTbMVOe8FXIdGz6UMFF8SmayiqzW5FVTvkzBRvVglc6VZFWW96HzpPksDIWwInPqxyuZb9oO1o2PSeYq2z+mn+0IJ/QPEmwAb7lktE1ZfDjlkw+DcSOx0nQN9fQVJrnX/LJ5C3VCVyxRt1fG9NRAQ2zToq3gxZ/SF3vsISAj6V29UWwUdXwylPwLRrdV2OKPUtzf+77teke2BTldyrpvv5ZWyOI/85/wQxIshgMBgMBoPB8OPgjD707+6nah5OwRr9vu51lW7ZHEpDc3okembdpub+OfdIHMRlQpcpSnQLh9Q7U1+itDdXtFyaQKPOYXfpeHWleq0rRmVsdoc1WLRepXIJLaGuDNoMk2DY8bmESbepWt/6tyDQABP/BoN+o/S3/Stgxs2A5S6FgurxCQV1jsG/VUlcRnc9VlemeUTr31S0dWO1eoSwqe8noZVEzoFBsDYJt6IN6gOyO7SG6CQJQpdHyXB2l87vb9Q1JrRUD1H+SqAjdBwHRZu++nmktDsmXCAwIshgMBgMBoPB8GOR3g16naENffkuuTQFa/RcVj8luNmQ41FfDvEt9ZryXL0+FNRjnSfBvPs1ADSptYag9r9AZWpRcQeJCKwhpSFFQjdUQFwLhQnEpilAoGo/JGRDy96QMwR2zVE6XJvhGoZatV/DRxsq1atTtEFpcH1+BYUbLcfGJhcmta2EjytGgQ81RSprW/e61pDaCQZeJHcnKlY/u+ZB/3M158hbKxF3MF1P1BBWwkTEVkh/e5J0b/y1VmodEkm+eqXl7ZqrIIX+F8Bntx56XFe0+pViUo7sZ/wTxYggg8FgMBgMBsP/L+GwnJO1r2lj7q2CrifLhXDFRFwbWzjyHp+V7ma3SxA1VMih6TxJIQNN8diNVSpTq8yzZv9UIMEAlqLShj82XYLKGa3QApDYcbihx6kSGnsWcSBMoGSzXJw+ZymCu9cvIS5Drkv2YAU8DP2dBFtdCSS2kQCJS4etn0KHMSpLK9uh8jdskNZRJXbhkASd063Sv8YaDXTdPV9DUKPidH3tRssRa3KLbDadn7D+dXok5poE0MHUlUJWvH7P7K3hsZunqYwus5fit1M7HrnP+CeOEUEGg8FgMBgMhv9fijfDx9cpaS0clmuy6QOVvo34A6x9VeLE7pJzA5FRPOGQHotN0/MNFZGIaogECWx4F4ZfpbS1+lIOCCBPIgy9Qmvo+UsJndpiCZOMHgotKN4MexYfdFIUR121F/KWwbgbYf7DULhez6V3h4EXy8XZu1hCKiZVc4f2roTWQyRy+p4jIbVvmUQJlhiqL4fkBN2L+Ezdix6nwcBLIdgAobCcq33LYelTcoM2vCMhFw5LGIYCSreLSVUoxCGEJc7aj4MiICoGWg1QX1TQr/tl+9LQ1585RgQZDAaDwWAwGI4c3loJE7vDckq+1Gjvb1RCm79efze5Mc5ovS/kV3R00UbNrNn6iV4XFQcOp0RQZi8JB3cs1BRaJWiWCHLH6xgJmZrrM/lehRlU7laZW3QSrP6P0tOGXiHHpXKP1tmyn5yoefdzoNzM4ZLg8tVqjUXrVUJXsi1yTcWb4LM74bR/Q6cJVp9SS/Uq1RXD7Dvl8PhqFYgw/BpY/hwUrlMZ3d4lOk7VPokVT5LK51b/R+WBvhqlvw24EDJ6SfREp8CW6eBwyHlqMxxG/REqclUSWJmnY9pdOmaLrhJ5RcWRddsdX/18jhGMCDIYDAaDwWAwfH+CfiheD6teloBxeiRievxCUc9N1JdZA0cPg9MD+atVxrZ3KfT7tZyOXXO06ceu+Od+52ugqb8BsgcBVtxzSkcYdiWUbIUuJ8LMW2Hho9D7TNizEHYvUrBCYrY1KHWYnJVdczVnyFsDcVlyphJa6dzhkMSZvxHcYZ0z5OdACVqTU+WrhTX/kfBZ/xb8+h0JkvevkMsUkyqXasvHcpr6nguLH9M1teoP1QUa4BoMqKxu0SM6vytGQrFoA3xwJUz8q47T+wy9LxxWz48nXsda+m/1IVXu0TXb7NBuLCTmHLOC53AYEWQwGAwGg8Fg+P4UbYSZN6ksC+SGbHhH5V+T71VCGWhTbv+aLajdqaCDXmdqo793qQTMiD9IbETFSpyASsvm3hdJgwsFJR5m3iqXKaOnBq6ufQ1WPA89T9fx7U6VfxVtgHd/A+3HwpT7JGiWPAkjf6/yti3TrHXaIkLH7oBW/TSUNehXv07YmiUUl67howMvgZP/oeCEpU9pfeGQ3muz6/fyXbpPidmw+QMYdZ2cos0fyvFyRev5ulJdb3V+5N6t+S+0HwPTrlPcdu482LNAYim9O/Q/X/OJUjtCm5G61j0Loe0o8HmBNj/Ix3+0YUSQwWAwGAwGg+H7s+Y/EQF0MBW5mnvTJILi0jW8c+N7hz9Ot6mQM0jOTCioXpjD0W6sxE9dMZz8qAIHljypOT82mxr+V74okZOQJdekoVK9PDUF0Fip9e6YBbUFMO4WmHCryuVqCjUnqGx7ZD4PWEl0k+HD3+t67C6rpMypMIeUDtBlstysuJZQuo0DfUV1Zer3qSlUeVrRRkhup+P7G2D920rLS+sC+1Yqsc4dZ/UOha17a1PpXZ9fqSyvvlSDX7d8JFFWtF6vGXUdbP1YDlpijuLE89dA9T6MCBJGBBkMBoPBYDAYvj/FW77+udwFSh8DiYYep8LeZRoCejDtxkgIwFejoZtoqFS/S9l2CYTC9bBztlUm1qB47KAf0jqBPQp2fSERdu7bsOQJ2LdUJWtOjzVs1Au7F0DuXCjcJKcnJll9OwWrYdunEiCdJ0HHiSo/i4oFwhJb3hrLscmCDuPh7YtVGjfsSqXIOcol5prK6uIzdYzUDtDhOMVsf/QHpeUVb4QW3STYQn65O42FB5WxhRXQ0NRPlb8G2oyAtM4SXDabxE/eAh17zE0qPcydp2vtfDJs/XJowrGJEUEGg8FgMBgMhu+PwwWhxsM/92U3J7ktTLlf5WO7PlfgQNcT1bgfm/b156jYDXMf0Psq8yROcgbDsKs1g6d6P2QPUQldxW4FDXQ/BXr/UqVklXkalGqzWYlwDiWluRMkKNzxCiHY+C6MvhE6TZKTFApA+R4Jjap9cmKWPBV5b4eJ0Hmieon6ngNbZ8C2mSpbW/cGhK3EOle0BJq/Xset3i8Xp8NYDTK1O9XPNOACq3QubM0BCujYQT90PQG2z1TZXGIWrHwB+pwtx233Ql1bVn8l0k37Awy+DLZOh+P/pvu7dYVcsvpiuVCxLaxBtO7v/x04ijAiyGAwGAwGg8Hw/Wk3CrZ9cvjnOoz/6mNJOfrpdhJgV8rZ4aivUJN/QyUs/7dS3QINqjKzOxSrDYqobjkAukyBz+6Q0AgFJCJaD4d242Do5RIWhGHH51C0TvOHsKKpW/WHgE9BAxU7Yfnzmge09nUYfKmEj78eMrrDhFsivUebPoRPrlfoQ3wW9P0VBPwSItX7JGwaquQE2Rww9iYNOA0H1DPVqh90PxV2zlHAQe48GHuzzutJsOb+2DSstc1I2LtCTlZye9jxb73u5EckZgJenW/OPbpnxVs1+LXVQOtagR2fASHNIarcK2eu79kKbzhGMCLIYDAYDAaDwfD96flLKFwTaeI/+PH/NYTT4fr652oKYN7Dmqsz6FKVvWHX8WwOJb05oiKBBKOvU9hByGcFMLggZ4iclrcvVr9QfRm4YmHA+eDyaFgpaHDpipckdkZcI9elpkCuyYRbYfHjck681VC4Udc59EqYeYte21gJhKGxHNa+oWNsmwXDf6+Y6yanqWUfiZxN78Oke+QcTbsWTvi7XKv9K3VNye1g3F9UWle1F7L6SeDkLdUaY1L1ugEXqU+obCcseVyP2x0R9yjkh37nSSyueBHSr5JIdDoVEe5OUJpdTKqE0DGCEUEGg8FgMBgMhu9PUms44UFt9nPnycHoPFm9OZ6E7368UAg2fiABhE0uRteTFYldvU9CxpOgFLf9K9Wb46uF6ETr9zoNLe11Oky/UQ5QchuJmEA9LPqX+ohy56nfJ3815K+A8gmab5TVXwEDq/8jJ8tfr5/YFjqWt0YOTel2lfBFxeqcMWlQkw+LHlWZ3upXVI7WaqBCEVa+qOuz2WHLJxJ0Ay6Cmv0w/Tod3+ZSOET7MQpcSOukuUjbZmi9UXGKEe/1S0Vuj/wDvHsZEmGV+iwaKuWWZfXT6+c9BN6D+oF8tTDvQZjygJLy1r+lwIr4zO/3PThKMCLIYDAYDAaDwXBkSMyG2AxoNUA9Oo1VSjlzuOW6HI6mWTxfnmFTUyiBoxdBeldFZs+4WU3+dgfYnDDscgkQV7T6gKLi5C65YrWOHbMVTIA14yc2XS6Jr1ahCac8pqS6De/IOclbrFlE22dCv3PhlMehoUyiDFReV1OgMrgya2Cq3QFRKRpyGg5JCNWWKEFu6dMST7lzlYwXDlqXFNS5QkGJnZUvwMjrFMqQ1hV2z4O5D0os1RbpHP3Phy4nwPo3JbTiWuhcy5+TUzbvQa3T36DH0zpB66Hwxf1K0QvbIp9Tq77qHyreqOGz+av1vmME+4+9AIPBYDAYDAbDzwRfg9LJ3roApv0ePrsN3vstrHnV6ms5iPoK2LNYc30+vk5CpHJv5PmgT25LVBwMuFjlXnsWQXy6xITdCYRgydPQ51wJobajrDK5UgkXp1tBBja7RIQjCgjLDQqH9dz+1RJK8ZkqCastlDAJNKg36MOrYPM0aDNca2moQM5UrQSfO14ir6ZQ4qhqr0ruEloq3c3l1rlCAZWzOaIi12izq1xt30qVrO2yXJ7yXepHCvo0i8jlkbha9oyiw5tCDLbNUN/R9plK1mvZV8LGnSAHbMoDCksotHqfXFZARftxcuz2rdB97HQ8xLc6psIRflQR9OSTT9K7d28SEhJISEhg2LBhTJ8+/cdcksFgMBgMBoOhuZRugQX/1Ga/iVAAVr2kAIAmGiq1oZ9xk5rz81frfdOvh/JcvcYdC6mdoN+vNcdnyRPQUC6x0VCukrX4TJXE7ZytMjCnG3r/CoJeq6enULOEbDaITlFpWTgsoRFogJgUldaFg9BYrbk72YM0c6jjRFjymMTH/pUKJUjrDDEtoP95MPp6OTjxmXJqmhwebBJRWf313m5TJdriWypNLjYdsgYoMrzPryTsMrordKH3LyF/Lax+WSKroVyJdlFxEYGy83PFYoOcG5sNCEnQdTsZJv4Njr8HvPUq1avYrfK4kB8GXKj3LXtGJXAlm2HVKxo6O+xyrfEY4UcVQdnZ2dx3332sXLmSFStWMH78eE455RQ2btz4Yy7LYDAYDAaDwfBdCfrVw0P48M+vewMaa/R76TbYepgkueoCWPemktViWyjeecWzEhN1JRJUNod+CGs+T2OVhELBOnjnUsVxj79FwmHfcvX7xGWoXK6hUmVkdqdCE3qcpn6dQKOEW3SKktTKdkkg+RsjMdVrX4Pj/wqj/gieRLlWnmSFC9hsWiM2vbZlb8geACtfgvbjVSZXkw9RCQpMyOyla6jYDcOuUPleh3Eqa3N5IgNSm45ZW6xzglympt+T28qZckbr9cGgBNemD2D/MpXA7V8pcZSYo/eCjklY/7oTdPyiDd/7K3A08aP2BJ188smH/H333Xfz5JNPsmTJEnr06PEjrcpgMBgMBoPB8J0J+uSqfB21xeqN8cTD9llf/7pdX8j9SbTKs+pKlcSW2hHyV0kYuJO1oQ8HVVLWogtU7Zcj89kd8Kv/wvCr9B5XLIy5AWbdJvcHwFuruTmpnWH8rRIxTeVtgXrN4vE3KIwgOlGio+uJ8IaVKFe5V8LGWy0BNeE2XV9jtRViUApz7oMWXZU+132qZvm44+Hzv0ooRcXKwYpOgeNuV59ORa5+WnRRaR0gsRKWu+T0aJhsmeWW9b9A4u3s13U/avZDySb1QuUMkhPmr9cg29E3SmSl9bFEYLRKC50xEmG586HXGf97TtPPiJ9MMEIwGOStt96irq6OYcOGHfY1Xq8Xrzdir1ZXVwPg9/vx+/3/L+v8OdF0z8y9+2ljPqejB/NZHR2Yz+nowXxWRwcHPieckNIZyvYc/oUJbcHuBr8ffI1g+1JvTHoPxVTbHBAI6nXeer2vvhb6nAclu/R6V4JcJbtHoiq9N+ycp+MThrzlkNpdm/vyvbD2VRhytUQEYYmT/Sthz38lEsp2qiStX3/45EYJoRG/h0AAGmqh/yWw8ElwxKpPyBlrCbFqCaFdi1S+F/DBhg8hOgk8qXJ+6sph83QY8ltY/gLgBG8jOOMhuZOuZ8mzMPJaXU/uEhh3M+StkPOFDZwusLkhKglaDobSPDj+AZXi1ZXA/EehcK2cqaAPds6FDscpfjt3EexfAwnt8Ee30GcVnw0Ou4TYARy63qP4f2/f5f9X2MLh8Nd4lv8/rF+/nmHDhtHY2EhcXByvvvoqJ5xwwmFfe8cdd3DnnXd+5fFXX32VmJiYH3qpBoPBYDAYDAaD4SdKfX0955xzDlVVVSQk/O9Y9h9dBPl8PvLy8qiqquLtt9/m2WefZe7cuXTv3v0rrz2cE5STk0Npaek3Xqjhq/j9fmbNmsXEiRNxuf7HoDLDj4r5nI4ezGd1dGA+p6MH81kdHRzyOdmAfUvV5N9QrhdExakhv/1YBQOA4rNn36memP7na6ZOXQlgg4RW6t9xOFXu9fnd4LN6iXKGyVHx1anfpaYANn6o/hunB8q2Q8FamHgn2KPUH+OKhvev0OsTczQb6P0rNDi1CUeUgg52fK6enbKdkNxa5/HWqQRu3kMqfXNEqSSvJl//dpoI2UNg3gNyYdpPgPajI0EG4ZCCH9K6wJy71cfTWKmSPLDac8Iw9TGV/s2+Qw5QQg70+gVEp0F0gvqkaosVBBEKqseqzXAoXK8ywZAVzOB0qwcqFIKETM04Kt8F/X6NPxBk1sZSJhY8huvgZIDYFgpUSG59pL8e/69UV1eTlpb2rUTQj14OFxUVRceOmiI8YMAAli9fziOPPMLTTz/9lde63W7c7q9G97lcLvP/HL8H5v4dHZjP6ejBfFZHB+ZzOnown9XRwYHPqdM4yOiqfpRwUBvyhFZgP2jXnZINE29TMlrhOqjdrzjp6BRwOiBQB6V7YO8iaDtUM3a8NbBnDvgrYeBlEB0PexfCoPNh+6cSUWmd4ZRHVSa25SNY9x/FXk+8VT0+vnpY+HfwV0fWEg4pAGHRwzDyj+CvgTUvQX0JZPSCHr+AuFSoypVgScxR/1HIp+vbOQv6nAFnPCsxY7NB0SbY8iHsX6Gkuo4ToMfJ4ImBYB3UFXJIgITDDb4KSMyA056EFc9H5iR1HA91Tg1ttdkgOhVOfhgW/wMay/W+YIPubyhotRD5oCoP7CFIygKnE5Y/DXHZwEBc427GtfYVrb/9WK0vuc3/x9fkB+W7/P+JH10EfZlQKHSI22MwGAwGg8FgOMpIaKmf/0VSDrgna6Of1NYafmoJpaBXG/S9S5TgVrJVAikcssIKkiSaWnSFL+6VQLI75aQUb5HztP4tOU4Ol+bpDLhQcdWLCxUX3YTNrqS4hkqtwe7Q8w2V0Ok4RUj3Ol1ioWCNhFTQp/eFgxJOtcWw8J8SIDUFEhQDL1UQQ+E6OUzpPWDiXTD9T3wlQa/XLxUW4atTUEO/c+VAlW5Vil5diVLcQgH1K5XtAuzqA8oZIjfI5pRjZI+yBqOG5G6tf1thE+GQepl6DtQg1kl3ySWLTrZito8tflQRdPPNNzNlyhRat25NTU0Nr776Kl988QWffvrpj7ksg8FgMBgMBsP3IRiAxgqJi+ikr3+dwykx47D+C34oIAES9MtVcUbL3bDZIuVlQZ817ycI8/8BMclq8A8FJWCq9sHyZ6D7qbD0aQ7EQa98SbN5COtYTbOMwiFrLS6JAm8ttB6qeO5w2Jrh8yT0PlODSte/rQGmUfHQ91xI7wpvXywR5orRWivzYPZfYdLfYOYmCaYN78JZr8BxdyrFzpOkMrvoZIU0rHkVWg/TepLbwTuXaI02u45dna/7ldAK9iyEnMGw+QMY+jul4AW9ElGJ2RoAW7he4nDHZxwQXUFL/G2ZBu2GKT78GOVHFUHFxcWcf/75FBQUkJiYSO/evfn000+ZOHHij7ksg8FgMBgMBkNzCIc1A2jTh9rYu6Kh20nQZiTEZ3z19VGx0P1kmLtFKW01RUAYepyqHpqELG3uGwYrhS0uQwIIm0rSgo0SRbXFel9SG/X6lO2EARdJPAR9HJiJE/SrPK5lH0VKF66TcLDZFaWd3BbevFDnik5SYpvTI3G18iXFag+8WDHVe5fpGGte1bGdnkhkd9CvaZw756hvZ+fncoj8DTr/xvc0kDUU1DX2PVvHzB4kxyt3npyoxiodr0kIgWK47VHQdZzuta8B1vwXqvdLwDmi5Fqd+DB8fG3kfYQjjk9DhURZu9H6jI5BflQR9Nxzz/2YpzcYDAaDwWAwHEmKN8En10tYNLHwUdi9EMbeDHEtvvqeVoM0PHTDu3I6Rt+oHqCNHyhIwd8gF6bz8XJ+YltARk8gDPFZh1nEQaVmTWVvrmg5N64Yla9tmQYJHuh+iiKsN74rV2T92xJACa10bleMghZ6nQFLnoDy3bDyea03s5eET+k2nScc1DwhrxXigE1CJ2eI/kzrpOOVbIX249S/tPUTCbHPtsHk+zRktbFS5+x4nJyeUPDQ8r2gD3qcAoseg8n3wAdXSSxFxUFMmgTb3iUqlfM3HPReG7isrX84qBK7YACO0Xa7n1xPkMFgMBgMBoPhKMTXILfkYAHUxP6V6m85nAiKz9CmPr27nKSds6Fks4Z2NlRowOiK51XmltFdrsquz+G0f0sQRMUoCKGmQOVjzhiITYXaEs0cis9Q8EHPX0r8bJshZ6WuBDa8I0Fy4kOQtxiKNsKU+zUM1V8vUdPv13KFMnrBmldUJvf5XRpqOvoGOTBBn87lcEkY+RskNBKyIilwI/6gFLf1b6kcLjEHBl0KJVtg+0wo3qwkOJdLoQhdJkkMlW0/9H71PB3qymDY5bB7ga45JlVldKGAri0qHjZPg3YjoXwnKglE97KJVv3AHXcEPvijE/s3v8RgMBgMBoPBYPgG6sskdr6OXV8c/vFQCDZ/DFs+hsQsbfwbqyVqomLAVyuBseNzyOpvRWkj96bP2TqvKwZSOsgFSWglobLpfQmg+jIgDCnt9J7q/XosqTWktFd8dNlOxW+3HwMzb4Elj0l4vfc7laXtXaGwgh6/gKp8OPVJxWKvfUMlZdjkuNQUSvi447XmTpPk7Ex9XCJl1m1QZQms0m0w9wH18GQP1XXv+gw+/AMMvAA+v0cCbPQNirnueiIcf5f+/egaRWZj0zVX77d+8iWwHE6Jn26nSBDZ7RCToh4tkIPWadIxGYjQhBFBBoPBYDAYDIbvj+3A/1EfSqsB0GUKdBgnR8XmOPz77HalvAUa5fb46+SiOD368dbq76BXQiMqTufZ+blKzKY8pH4aT4LOedxt0HqkBE18FrQeIRGSO1fOks2mMIKqfXJzKnfD8n/DvmUSL9EpcqQI67zlO7XGvKXqOXJGwbbpYAvLrUnrrNQ5e5Tea7PJmZpwG2QPlJBxx8Nnd1rpd2HdC7tDr131MnQYq2vz1ijZbdd8GHcjLH4ctn1qrccSPAv+qXX56xWI0DRfye6y4r7rdW2xaRKTpz4OHY+HhipoKNM9P+EBXcsxjCmHMxgMBoPBYDB8f2JaQJthUF0AHcdp875ngUq1upwAbUcd+vqGSqWoFW2QKJn4V5WVNfX5hINWWVnIiq5G5WZ2l0rJbCh4oKFcgQYZPSBrINQVwcJ/QYsuSltrqFDJmb8BsCmVraFSf9udip5uP049PqVb4cS/S/g0CaH6SkjrqHN8eKXVctTUd/QKdDsVhvxG5WVlOyVwUtorAvz9K3SewZeBtwpi06HGuqYwEle+OiAEXU/SLKBwEFY+Byf/Cwb/RgIp4Nd11lrzhUJBlejVlUJaV5UPNsV7g9be5URY9gwUrJMgHHcThMJQAZRuh/Yjf5jvwVFCs0RQWVkZt912G3PmzKG4uJhQKHTI8+Xl5UdkcQaDwWAwGAyGowSXGwZcoBCE6TdFGvIr87Tp9nshtYNckdpiWPAIbJ+hGUHdT4Ev7lNpmc2uDb8rRlHWngQ1/rfso9jnxkqVyLnjJBA+u0PipbYExt4EG9+HmvxI2VljpQRO29Gwb7mO21AB2OQc9TgFtkyHdW/osf7nQVw6fH631tVpoo5RVwqT7oGt05XeBlYS2yiYdq3K0KKT5eYEvRDfCsbcqHW06KqZPTvngK9GIowwBK3474RsCZhAoxUL7tf9bNVfYtJXr98Ts2Ho5TquzQblO2DIb/Xe1S+rpM7uhr5n6T77apWgt32mBsraPdDjUSjcEIkUP0Zplgg677zz2LFjB5dccgkZGRnYjuF6QoPBYDAYDAaDhSdJ4QPu+Ej0dNNg0y3ToOsUOTbbZihkwOnWY9Nv0GDR4k0qI5v7gMq84jJ0zNh06HMOzL5TG/dwEDJ7y2XyJMptSWmnNdQW6ryhgPVcvZygfufLQWqo0OtiUhXPPfM2la/56iQ+1r4mV+vUJxRi8PZFmtsTaJQLNegSlb/Ne0jlbttmSLS5E6Bit4RRXAb0/zXkfqGUO08CYIdep8mh2jYjMqcouZ3WueIFpdCte10hDnVl8OE1eq6uBJY8rvCI4+6A9e/CvqU6XyigxLzR18GQK3T9eUvBkatrB6tK0a70uCY2vqfPIrVjZE7TMUSzRND8+fNZsGABffr0OdLrMRgMBoPBYDAcrdSVyOWJbaFGfGxWHwxAWE6OJwmWP6/St64nKZY60KCX1JcqUrv/+RokGhWvHpy4TMVFx7eU2OgwVk7Gxvflouxbqn6chgqJFU+yzhf0SpT4amHB3+EUS9hseEdCZtOHEkD15Xp9OKQSOW+VnJ2t0zXLKBxSmELQL+fppIflOoWDMOcea46PXWt1xctNWveWyuocUZbrgq5t2JUShqXbNZuo52kSVC06SzwmtVUQwoe/t1yzosiQ07KdcnUqdshxik3TPa8vlZCcfL8crNg0GHSxri862Uqtc0OjldyX1QcWPyE3afxfoN1Yrf8YolkiqGvXrjQ0NBzptRgMBoPBYDAYjiT+RpVjVe1Tr0pSjkSD8//hv/w3BSGkdoLWQ7Xhjk1XaVl1vvpWWnSRKDmY2kJY9rSEy8DLFFywbSZk91dfkSMKNrwH1fug48QDWQxU7VW5mCdR11dtRWbHpql0LhTUeXudCV1Plsja/IHCA5qGqbpi5Kx0PVkixhUtwVK9P9IjBBIiQT8MuVzXV1+mtbTorFK3lPZQ95zERyigaw/6tL6t02H41VCwRmVyK16UA9Wyt+YKTX0MtnwEhHTOoK/phspRWv+2hNT+hyCxtRUeUa2X5C1WiV/70VbM9hQNUo1O0nfAaUViF6yXgAsD8x/WNSTlHOlvwE+aZomgJ554gptuuonbbruNnj174nId+j+khISEI7I4g8FgMBgMBkMzaaiUwFj7ujbiIDdg6BWKR46KPvLnjEtX6ljlHv3dfqyclXkPaKOemA29zlIZ2Y7PJCScnoM2+kh4BLwSLcFGBRKEAyrxyluq593xuqbsAXKVHG69N7m9XKjaIitwICxnyu6Uu1SxG+beD1Me0DkcHojz6L3hsESQr0Zzhnw1KmurK9a5HG7FeYfD6g9K6aAeo25TIeSD7bNgxb/1ns6TVc6WmKP+pKBXDlZNoeKr7S7IWwJrXpVQ6nUGlO6AhJbq8ynerDUFvUjlWQLM5gBvmT7HUEhCxhElYRv0aW0DL9JQ2cYyOPlRRWp7a6zEvXjYXCMB1oS3RiLvGBNBzfK9kpKSqK6uZvz48aSnp5OcnExycjJJSUkkJycf6TUaDAaDwWAwGL4r+1cqbaxJAIE2/gv+CaVbfphzxqTC8Ku0SY9LlwOx5HEJoKZUtvVvSxzFtlDAQIfxkZI5m0OiqKmfKKMX5AyNNPA3OSvhkIIC7C5pBLtDM4SKN8HAS/R70zDSMTfC5PskThb8Q/egfLfW2HaERFJdiURE0CuBU7JVsdu++sgA0nBQMdh2J2T0VAld7lzI7Kkyv7zFEia+Ol13OCRXKz5T1+WKVrlcMACNFXKgPEnqP+o4Ub1RPX4JM29VgITNJoFzIIkOrS+jp+Xs2awyvVKJPm+NygWXP6/PN6M3VOyBD66EGTfDO5doThFINB3MwSL0GKFZTtC5556Ly+Xi1VdfNcEIBoPBYDAYDD81GqvlAB2WsHpFMnqD4wdIB2s1AE7+p3pe5j0kd8WTZG20bRCoV3T2hFth2b/lVBStl0sSnay+noSWCgdIaQ8rX5Bzs/5t2L9KJWEdxkOvX8r5GPwbHT+5HXxyndylUx6H8lxY/ybsWaTjdpkCvc+SWEltB2+cr5lC+5ZB1X6JiaZQg/iWSqPLna81251avy0scddtqoadehL0+N5lGtLqrdGaYjPU7+Ovk4iKilV4Q1yGAh1qivR466HQdiR8coMEzfg/qyfI5pB4aqhUr5PdKRHmq4dhV8Hsv+maQ0HrIw3J2UtuCyueU9hB2+Ew/++6rpg0CbSqPMgGOh0HK57Re+1OXe8xRrNE0IYNG1i9ejVdunQ50usxGAwGg8FgMHxf/PVyNL6Oqv0qNXPEHvlz2x1KMXNGA2GJg1BQfS8N5XIzqvMhOlVJa7HpcMLDKsnyVmtOUPV+bexDfh1v1h3Q6XgYNVab9rwlMkiqCyQ8ynbKren3a8Chnp3lz6qvyO6SM1O2E/qdC2Nu0oDSYKNK44ZeIUGyf6VK1rqforK7TR/C6D+pN6honc6bMxQGXwqf3a6StbgWMPKPWmvQJ6cq4IXN02DsjTDnbokyV4xEkCdRTlXJVpWt7Vup13iSdO8WPgrH/03XNfl+rXPrdAnD1PYw8RqJq6G/U99QdT5gg4zuOu6ix3UtbUbA1oMS6Hx16o3yWX/nr1UfUNl2BTMkHlulcNBMETRw4ED27t1rRJDBYDAYDAbDT5GoOPXfNHzN7Ma0jpZI+QFxuiV0GisBv+b4BK1NeNiaj7PmVQmAYVepT2nta/DpX9RHQ0hr7H+BytoW/B2wSWCN+pNEib9O/UExqZC/BhKyoPVA+O8ZSpxzuOSA1JVKCG2fBR2Pg11fSCzUl8mtSmkrx6W+HFb9R8l2eYtVKjfi92hoqhWD/fGfdE2xLcDXoBlG3ho9Z0Nu0p750FAK4/4sEVa0UQNLfbVQuhlm/kWvc0ZLIDmsIAd/nUrr1r8tEVVTpPJCh1trXfas+qBaDYQRf9D7/fUScPMf0uvtTkhuI6Hos9LgAo1WQITl/JXvgrZDoPtUOVEuzw/7XfgJ0iwRdPXVV/P73/+e66+/nl69en0lGKF3795HZHEGg8FgMBgMhmbgjoO+58CM9RzSUwLaJHc98YePRI5vqUGkK1+KOEEH1uBSMtzaV/X3iN9D8UbY8Lb6ZprWHGiAVS+qp+eEhySsYlqAJx4+uhbKtklMjPojJGWrXM7ptlLdEiUKQG6Sr05ipbFa5WJ1pRJiAa8eL96sMrSmIIHsAepPWvI4DLsaUtrA7Lt035LaSPzY7bBjFnQ7CXbM1loaK+QMFW2CZc/IoSndqrK7yfdBTQFWI5OOHxUjgdUUE+5vkDuz9BnIX6khp033w2aDgtWQ1lnix+nR71n9JbDqS/V39iAJvpoCvcYdp39tUTpOq34SljHHbi9/s0TQWWedBcDFF1984DGbzUY4HMZmsxEMBo/M6gwGg8FgMBgMzaNlHxh5LSz/tzb1IMdkxB+0Uf6hsdkkKCr2wOaPrL6asATK6Os0qwc008cZpRAH0POeJLktNrucks3TYMhv9bq6cph+j1LUAIZfA9s+hfzVEN9KAqC+TO5KfKZ6jWwOibCgXwKlZV9Y/bLe7/SoTK0pHrvHaXp/wWoJp37nyRmq3KOkOF+9NEkooHOU7oDWwzXzKHeulToXpd6f/ufB0qegMg8CPti7VH07iTlah81uibBqCUNCkNRaay3ZIrepuiByT8OWGGqoAGeMyto8iRp8mr9GfUE7P4eUjnJ56oojyXkH0+WEY1oAQTNFUG5u7pFeh8FgMBgMBoPhSBIVA91OVopabVGk2T7h/7EJPi4DRl2nTffu+RIcjijY8K7iqhNzrNQ0jzb2INcmvqUEW8CrwaVVeVC4TkIlKlZui92lwAObTQIIoKFMYssdL+EXaNTxm8rwElpCwVrI6AGZfaBwrV5Tk6+ZO4N/owGnX9yn10cnKxwhLl1hC54kiZ+AV+V1oSB4axVCMfle6H0G7FulAIXqAiW9eaut9DubRNjqVxXSsOFduTnRKZYgcqhUr3Cd5gf5alXClpClssaAV9fmSVLpXVQ8bHoP0jrpZ8M7uhfDroQ1/9Esos5T5AjZrfM7rOqt1I7/X9+AnyzfWQT5/X7Gjx/PtGnT6Nat2w+xJoPBYDAYDAbDkcBu1/yXH3MGjCdBm/T1b0LhRgmR5DZam68O2oxUeEJGDyjZrEQ0sFyfEgmElA4SI7XF0GGshFI4JNdn94LIuUIBhSKM+D18ca8VG50pseKKhpHXKYjAV6sQhd5nQsE6SGylYaVOjxLVUtppPf562LNUPTS7F2hY6/Jn1dPUUKFjxreUyxOXDt46WPy4HJi49IgDZ3dA2Ka17JmvQIVxN8OSp+X6xLSAridI9Cx/HsZ017n2r5TY8iQCViR2wKdY7pm3qnTOHa9SwIEXKZmvsUr/lu/SdY/4vYRYXDqkdIElm4/JHqAv851FkMvlorGx8YdYi8FgMBgMBoPh50hMivpqCtaoZMxXr5jp+Exo2U/lYtkDJSCCjYp0ri3STB1XjBySmbco8MFpbeCdVplXOIR6bCyq9qk/Z/J9EjiNlRJi2YPVXzP4Mgmhz/8G7kRFdefOVzlZUjb0/IVcqH3LVUo2+joJnnkPwi+fUzR27lyrvM8vh2bKfbD6vxI74/8igRIOS9Q0VmuNnScqbCEckvuUv0ZCyO6A+gpY8phS42IzdDldpkgE1RTJ1XNGy8kZ9Sfwe5Xs5kmSSHInSvgUrLOElLVOT6JCIKJidS0T/vr/9pH/1GlWOdyVV17J/fffz7PPPovT2axDGAwGg8FgMBiOFby1GmS683Nt7IM+laUN+S2sfF7DQVe8JAGx6mWJjmBA4Qn9L4Bd86H/+SoNa9ENGmthx2fqN+p0nHptbHaJF4dTQmjx43J6YluoXG73wwoK2PIxjLgGPrsTCMuN2vyBSvZyhuj8e5daC7cporrdKBhwAZRsk6CbdI8cF1e0JWr8cmTi0uW+TH1UvUB1peoxyh4ol2rBP9VjFJehBLg2I+Cj3yvYIb27+ou6TlEfl80Fk+5W2EPxJl1H05DZBQ9B77MhvavcoB2f6brXvS3RlrdE52uoUIgDgK1eaXwM+1G+Aj81mqVgli9fzuzZs5k5cya9evUiNvbQjPl33333iCzOYDAYDAaDwXAUE/RraOqGdzUgNT5Twmf/KgmYxkrY9IHmBZVshIWPyAHpdLycjMo82DUH2o/T0NTSbXI0uk+FkdcoeS46SUEHVfutoax+OSY2m2YQzf6rkteikzVjp65EYigxWyV5Zdv1eGpHlb/tW37QBYTlyuTOg3ajtd5dczWA1emRgzP4UomN7Z9qbk+LrgprmPKgdX3vS1g53ZrvEwpKiJ3yGEz7ozWv6EElzE24VaEVWz8BbIoLb9FNpXk1BTDrdvV52V1K3qvcp3vmq9M9zB4ocRkORu5/Q7kEksMtJy7diCBopghKSkri9NNPP9JrMRgMBoPBYDD8f1G5Vz045bslCDK6K7nsSJK3RI5L1V4FHBSgJLeR12pzv/Ft9c2ErE17Qzms+a/EzPo3VTY3+joNS20KNwj5ddyizXDC/RJTU+5XSdumD1Rq1uNUXcuyZ/R30C/HJDpZLlThekhpL/HS/TT91BbJqep+qmYL7V8B1fuUBGeza4Bqclv1KvkaJDQ6jJXw2fCOyt9iUiWo6ssU5jDrdrlSQa/OXbwFOh8PE25X6Vt9mcSU0wObP1RE+ITbJNSi4uTm7FumiO1QQD9ZfeUI1RYDYfUCrX1TIqcpPS4cinwG3lqVxTncEoYGoJki6IUXXjjS6zAYDAaDwWAw/H9RuEE9Nk2JbKCUssn3QEK2yrwc37PlobpALkXIL8EQDlub9LBK1X7xFDRUyX2JSVGPTSig9256Hwb/TmVgG9+POBugcrCm+Oydn0tcvH2PHm81QD02MSlKeLPZJExq8vV70K90utROGl7q9EBjuXppOh6nfqCijZC3TKV0jihY8byipxurJNKq9qn0LSoZ2o2BBQ9bfT1l0OdsCY7aQlj1ksIJXNHgTlVUt8OpKPBWA3Wf+50Le5cruKChErCpb6jdGAmmxirdr1BA6+97ts4z61b1DoWDuqYxN6j0bu8SGPcXRWbb7FpLVCwHkunaDgLT2g80UwQ1UVJSwtatWwHo0qULLVq0OCKLMhgMBoPBYDD8QNSVSiAcLICyB6vk6ot7ARukd1PZVVrnSKzyd6W2SI4GKNygsVKuSjgoV6g6X4lsdSWwbyX0PVfCAaB6v4aQjrgGPv0ztBokIVBfJpfDESVBVbpdc4ZsNihar3hphxNSO0OXybDpQwmd1A5yd+wOCZS0jnLAolM0L6dwA2x8V69JbAW9zlC4QFQcjLoWFjwi98UVDSkrVcIWm64hpIQVjjDsSt27JY/D6Bs0fNXulJAJ+SXWGir0WPkuyOip8rUWXdQjVF0AM26CnXPgtH8rpa4m37p/0VpLi24w9z65OkGfzu2thmXPwtDL5TiV74TOk3VNDeVy2mwOiM+ADsfBxgoMzRRBdXV1XH311bz88suEQrLbHA4H559/Pv/617+IiYk5oos0GAwGg8FgMBwhqverPK2J9O7aoH96k/5OzNEMn+2z4Lg7FArQHEJBS/DUWE6OTWLA7gTCEgTtxkosFKyBjhNg/G0qC6sthqS2mocz4CIlqbmi1W+zc47ETjigpv+qvZZjggRQOCwHqftUaNkL9q8Bn3W++CydozwXcgZr1s5bF2pdoUBkbfMehNHXS5S0HaHSuXajYf7DcmOS2ykEoaZQx80ZDpm94N3L1J8TDlkuTUhr8tVDQpJESVPk9fQb5dw4XDpvrzPgnDdUqrd/NfQ6XX1CBetVhtd2hMRrKCTR5K+X+ErMVslf0Ub1Rq16Cc58RQJx+ywJo+xBEl2LH4eEc5r3ef7MaJYI+uMf/8jcuXP56KOPGDFiBAALFizgmmuu4brrruPJJ588oos0GAwGg8FgMBwhAl+qh2o9DGbdEvm7qZ8kFIBFj2rGT3zmdz9PbJo27NUFEgRxmRJE3mqFBCS00r9T7oeVLyrW2p0A3X8BbYYqEnr6DRI0tUU6ps0GQy6XaCnZCp0mwvx/RM4Z9EcGjy74B5z4MHQ7FUq3qCeoRVdIbi8x5ElWqVvQd9A1ByVsElopxS6rv3qDRl6rxDZnlEIdsMEHV2gQrN2p+OvynRI7dpcESFY/ibdQUA5UKKB1uRMk6PJXRUoAQwH1QiW1BleC+qGwQcfx6hFqrLRS6LxyrsIhlbmFAiqjc3oksMbcBCtfhrKdsOY1CSdXjEIj8paALQoSgNoSSM767p/pz4hmiaB33nmHt99+m7Fjxx547IQTTiA6OpozzzzTiCCDwWAwGAyGnyrRB/XfON0qTQv6Is/bD9oe1hZLFDRHBAH0PgPm3CsRVJOvwIOk1oqJTumgWT1T7leSWm2hyuXi0sFXo96Zqr0SDlGxlsMShqVPwuT7VfIWaJRbUrZVv9vsOm9TQEBjFZTt0Kye/BUSW64YOP8j2L9cAstm03n1Rv3jq1XgQss+clg2fag+pQl3SBS+fZHu2bq34bjbob5cgQgOl4TY5g81A6h0u45ld+o6PEnQ9UQNXg0HIz1QTax6Bc76j1LrQl4JyV1fKPAhe4B6k/x1VghC0BJGDXpviy7gitNw1LpCRYoXb4At08HllgPVhK+2eZ/nz4hmiaD6+noyMjK+8nh6ejr19fXfe1EGg8FgMBgMhh+IxBzFUG/+CLBHktlA5WGOqC+9IUyzqN6vBLop9ykRrq4MYpKg02Rt4OuK5UI5ohR97a2Wo7FztpyT5c+D0yUhFttCAiLQCNgUF01IoicuHXqdCSuek9sTDqtcbPjVsHmaysoWPw7+RsVgD/4NVOySyHHH6xgOKzigiaBPYrGmEFr1VwBDKCgxU1OgGUbYJL6q9kNKRwmq9G6KqG6shKVPSyDtW6H5P6ntFb7gq9cMoSYBZLNrOKzdofdV74fMbrp35bv1eKeJupYuJ0o4xmWopM4dL/Hlq1N5X0MZ5C2CbTPVP9VuJIz/s8SVvzYyYPYrn/GxR7NE0LBhw7j99tt5+eWX8Xg0tbehoYE777yTYcNM9rjBYDAYDAbDTxaXRwNI3QlyGGJStSl2xarUqslNAfUKxX71P3x/K+wuiZvWw2Dwb2HfUvUazb1fAiA6GXr+Ug7Qkqegpkib9gX/gIl/tTbtyRI1tcVyWRxRkdlDIStxriZfaXBnvKTkNsJySOrL5UQtfRoqd0vUDLsKPr9L7lHVfoUnFK6TuLE7LUEY1nk6jld/Tb9zILE1eOJVSuetirgwNpvK5tqNVkrc4Est96xA4QfTb4DWo2DinQpR2L04sr4m4jLVVxRoUJmeIwrev1KCLtAoYRTfEsbepFK8oVfIJXO4JMaSW8OYGxXU8PldCpyIitXn2FCpmUQDL4KFjx50zmZ+pj8jmiWCHnnkESZNmkR2djZ9+vQBYO3atXg8Hj799NMjukCDwWAwGAwGwxEmrgUMulSOUKARRt8I699Ak0GbsMGQ30FiM3tHErKg5xmwe6FiqDdPk3hoKj9zumHz+5DSDoo2aLbPuteBsErYUjuphyguQ+VhTWurL9NMo6JNEgwLH9HGv+ME6PFLq/zMAW1HwY7PoGSL0uB6nCZBUFeqXp2ep0s0dT1JjzuiwG7TGgddLBE36V7YMk0laf4Gvab7KXDuO1CySSLEFS0HrfNkpesN+Z3EWeUerT1nCKx+FbJ6w/Kn4eRHJcjqivXeQGOkpG3QpepTqtoX+ZwaqySqFj4q8bZ7sXqUUtpJCJXtVGng5g80XDbohz7nSGTmLbbudwgGXgxFSnX+3vHnPwOadQd69uzJ9u3b+e9//8uWLVsAOPvsszn33HOJjo4+ogs0GAwGg8FgMPwA2O2QlKPfY9NVyrX+TYmE5LbQ51dKFDsYXx0QVlzzNxGdArEpkNoWls2WCxOfqbQyV4yEyP4VKjVL66zXV1uR0Fumwfi/wLx/yAlpCnOwO5WAltwBOk5UypsjSvN5HC746KpIeV9sBoy5Hs59U4NUE1rB0mcguY2cl7Idcmc8STD5Xjk3To/EVDCo8ra1r8G2GXJUwlbS29rXJM58NQofyOgpZ6vzJGg7UmIkb4mCEZLawIoX5IBldtf69yyF056BGTfquPVluoZeZyphbs5dlhtnU+lcVIzue2We7l/hGshfKXeprhTWv6O5Qhvfl2Aa+Qcl6O1boXsSDuse9z4DBl8Jy7Y09xvzs6LZMjAmJobLLrvsSK7FYDAYDAaDwfBjEJ2o8q/sQXI83HHafDdRtR/2LlVvD6hHpfUwzdQ5HKGQXIg9ixQvPeJq9eVU7AZscic6TFB/Tu48pa7569Xj0pQg50mCIb9Ralr5TgULtB+rhn9HFKx7EypyodtUuT7v/zYigFwx0P/XEif7VsjVSWilv6v2qi9q7WuajdRtqjVIdJTeX7lXwqT1MJj/UGRgKej52mIFHxx3h0oIo2Jg0WOad5TSDloPVTnfhvfgszsl+oZdqYGs4ZDu9fbPYPztKk0s2SZxtf1TKNkcCa2wOSLR4k0likG/XCdPAgR8KgNs2Qf2LZf7ldhagmnfcuv1Pl07YQUktB0HHFTueAzTbBG0fft25syZQ3Fx8YFZQU3cdttt33thBoPBYDAYDIb/Zzzx+jmYqv0w81YJkSZKtqi8bdJdEHOY/pLqferFyRms3qMv7rEEECoBi05SCVxKe/W4rHtLpWtdT4S1r0uArH9bwqHLZA1SdXokBrbOgE3vKUygoUI9OZs/Ujz0zL/oHONvgdX/laiIipMoqi+F9mMkYrpMkYAJNEiQbZ1ulbPdA0OvkttUW3SoAAKrtCxghTWkS8QsfUplcTab1rP5IyXKZfVTktuAi1V69/6VClrwxEPZNohPVw/Q1ulQvBHaj5Mj1rK/kuvCQXAnQn2JhEw4qHtns0mAle2AtK4SRps+1PtrCnUfIwuW8HInShxtnQGuE77vt+RnQbNE0L///W8uv/xy0tLSyMzMxGaL1I/abDYjggwGg8FgMBh+LuxZeKgAaqIiF3bNhZ5nfvW5mgI5Lh3GKfgg6AdscphcMdqsh0Nq8E/tqHafrAEQmypHJj5TPT+1hXpNfEtpkRFXwbo3rJ6WsBLSAo0SXTs+U1mYzSF3qWy7SuycbrlM22ZobpDdBeU7IoEK9eXQZqTOkZCtXp4uJ+jch2DTTxggpLjq1f+RO9Tk1AS8crBqCuV0dZqkNWCHUX+UKMpfK4emeLNCEcbeICG3baZCIbL6QK/TYNlzYAtDVButMWcQ2Jwq8fviAegySbOB0jrB2jckjlLaKV3vAGE5VS6P3LWgD1zf8/vwM6FZIuiuu+7i7rvv5sYbbzzS6zEYDAaDwWAw/FRorI6UwB2O7TOh4/FffTyMBppu+Vhldo1VKkez2aEqD7BF3A2bHTa+p/k2G97TPJxOk8D+oUIOwmG5OCkdIG+pNvJOt5ycdmPU4xNogNwFKtMjrDK0mBS5Hw0VesxtDRidfqNS65oS2lI7qlytplCuVVSMrrntcM00qtzDIYERNpuGrvrq1dPT5BTZXerBCQX0WOl29esMOA/SuqinZ9GjEoGhgEoFg35IaAkbP1AZIGgdzITR18HcB1Qe136MnKo1r2nwaygIix+TqEtuJ8dt3oMw8W65XGtf07GiYnW+gFcpgG1GQFGzvw0/K5olgioqKjjjjDOO9FoMBoPBYDAYDD8lwqFD45wP+/xh5gjFZ2rzXbxRm3d3vNwbb40GfYJ0hStGIigxG/JXw9R/ydWpL5Wg2TVHzf9Br4Icgj4gDK1HQOfjlLq24llwxkD70SonK92mIaODLtNsHqdHJ7PbYc49cojsDgjbdCybXWIppa16l9zxEAhA5T4YfT3M/quS3JpIbA3DrwFvra6h6TihgJyp6nwd3+GEtA4qmwt6YckTVk9RkdXfNA56nQEf/9ESdR65Wv469f3s+BxOf1bzjRb/C977rdwqX4NcqNgWOlbAp+S7rR/DzJvghIf1fm+1hFoopB6i+JYSjUWbjtCX4+imWSLojDPOYObMmfzud7870usxGAwGg8FgMBwJwmFtqh1R2pQ3h+gkhRGU79LxQgFt/O1WTVX7MSr/+jKJ2XJXPvqDNvU2u9yggNeasROWa9LlBJWCNVZKvAS92tzbHTruyGthzr3Q4JOz0nWq+m06T4TpN0HIFznnri8kQMbcqJ4ZX62cl33LARuc+ZJ6mWzWvQiH1IOU1hkW/lP3yeZQSt7Qyy3h5YRR16mcrqZAAshm1xq3faogib3LdD9iW+g8UXESNcntlBy3dQb0OAXSu+v8dSXqF+p2kgab2p2KxA75ZSrZnRJ+/kYNV43PlBgMhxRa4YyC6gJdQ9U+2DFLc4rG/VkhEBvfgQm3KhFu71JorNFxBl0CiW0BI4KgmSKoY8eO3HrrrSxZsoRevXrhch1aXHjNNdcckcUZDAaDwWAwGL4joZAExfZZinlOyNKGO62zyqO+K+3Hwqb3oXC9xAA2lY216KKY6oN6ww9gd0DnKXBaLGx8Vxv0Va+ot6jBp41+lylKZ5tzL7jc0GqQoqVn3y4xEp0i5+KXz2kWjr9O5+x/Aax+JSKAbA45LJ5ECRVvtR7/9C8SQgCEVWZ2sABKzIHM3jDnbj2WmC2Bt/NzqN4vV2r7LAm9grVWz1Am1FcoyCFnkEIb5tyj1zedN6YFHP838FVL7LXqr5CGqBiV/K17Hfr+Wi6YM1rrCvrlVNnskRLBuiKFJ2z5GNqOVp9T0HuQMxeWyFr4T/VfOd26TxndNZvJFQdDr9R5Oh2n74Hf/90//58pzRJBzzzzDHFxccydO5e5c+ce8pzNZjMiyGAwGAwGg+HHYt8ymHWbXBeQENoxG4ZfBd1O1mb5YEJB9b0UbZQDkdZJM3YCjZEel55naHhp7lwgrE1566H/e+imJx7ajlBvTvEWGHyZnKWynUo5K94M8x+W8HC41ej/8XVWzHVQvTxbP5abMeQ3Ghaa2gkm3hFxbkJBla+54+WwYNNMoPiWkes/cJ1WT5C3SiKj6wmK327CESUnKeSXqCnbCftWSsSU74KKPREHKLUj7J4PS59U8psrWmlt7cbpembdqvXXFuo9w67UumbcDBNu0xymty+Bkx+RsGn6HAhZrp1NYsbfoJLC1sMVUOGIUvmbzQadJstZqi+FXfNg8j1aW02hPmOnGwrWwNibJIAMh9AsEZSbm3uk12EwGAwGg8FgaA715dqw712iBvwlT8ixsR1cAhdWZHVWP0jtEHk4FNLm+vO7JXrCQTX7R6fA0N9BwQY5EkUbVc7V4zTAJqdp0b8kkjr9j8jl6CRoM0wOjr/ecj/csOARBQX4apRoNugyRW43hQNg01qCYYmSmiK9Ly5d4gkkrsJIEHir5aa4otX/UrRej9sPqlbKXyXnKRzUPYvLjMR2exIlmkIBndvpUQla54nwwVVKdmsaPprcWsIsJhWG/BZ2L9L96XoCOBzw7m8kfJr6iOpK5HZNukfHWP0f9Ro5XJaYOV4BE02fE6g/qO85GgYb8Okelm6VwAn5VUZos8HyF+Q2udz6e/ytiuj2N6jHqc/ZGsBq+ArNnhP0bUhISGDNmjW0b9/+hzyNwWAwGAwGw7FJbbFilfcs0t8DLpQY8iSqLOvgXqCgT47GwSKocg98fo8EECgNrr5UPxs/gD5nyr3wN8g9+XJU9s450H6SfvfVQcipGOwvE5MCpOj3lI7Q79cSGhW5cneq8zWA9ABhK3DBCl6oL4P246FFJ/XcdJ6sBDS7Q8eOTlFQQdCvErq9S/U+mzUnJxSADe/CpLslxPJXS4REJ6M+nliVlNmdElLhkF7nSZKY2b0ABl4EH12jVDqnW6Jk52wYdS10twaubp8ltyYcjpSt2R067tbp0PM0K+yhRBHdhetUkteyt1ycxirI6KVSwSVPwYDzoVU/WPkKDLsC9iyWeNz4nlw7m03rbKjUOcty5fzEpSscwd2M8sdjhB90ZGz4cGkhBoPBYDAYDIYjw56FEQEEkY13Y5Vio79M0Hfo3yVbIq8L+eWQHDj2AokAp1vPNQmlg3FFQ2OFfv/0LzDtD4pxrtx7+PU2VMLc+yXcCtfD5o8lzCp3K9ENOBBHbbOrRCy+pUrOopNgy3RY/xZ0P0UDVaOTJdxqiyA+Q85MTLKCAEJ+az4RKrdLai1nZdDFEgqJ2SrRc7isRDeXBrA2HTOts9bliJJQW/G83KKGcomyuHS5WOvfVX9R0KuSOKcbnC71+8Rn6ZhR0SqP6zBegqjp+ja9r8vd8J5K3nqcpnv6xX2aF1RbpGtNyoadc2HD2+qHKt2m1zVUSgSmd1e5XkOZ1hKfaQTQN/CDOkEGg8FgMBgMhh+IhgrY+P6hjwX92nTXl+p5V2wkuMBm16ydg6kvi/weCkI4EPk7HFSwQIcJauYPNKr35mA6TYS5DwJjrNk7VorblmnqUUlqc+jrK/fKhQGJoGFXwo6ZinLu+UtY+lRkrYTlFg26GGbcKNcqOkUlfwv+Cf3OlYOz4W05XzlDdH2f/U1DVbuerHU4omCkNah0/2pFcKd1kcBLyIbMnlC4UcEFDZXQUAej/6RAh56nSzgmZCmRzWnFWAd9KtFLbgv41B9Usk2BEfVlcmdiUuX2hEOATfdizyIYdpXWa3dC5X5I7wkj28CKF+W0pbSHsTdqFlF5rsrxnG4JpEn3whf3QNFmy+myK4K7/3lyu3qfKRFo+EaMCDIYDAaDwWA4Ggn6VYJ2MLsXwKBL5baEQlhNM3qux6lyPw4mtVPkd5sdFQlZblJ0spyIVgMUM32gX8eiw3g5LEXrIWPMoc9V7VNp2MBLDk2Pqy2SGOg4QeVcFbt0HE8i7F6iHpaN70uMBQMw5HJdU20xDP+93KDSbSqJ89ZIaHSbCmvegOr39fj+5TDrTgmZvmfrvIsfV3IcQHGNenNSOyo9bcBF6p/av0LCIjZNpXOhgJyxoVfKXQk0RsSNr1YCMxTQNflqlSA38CKVyIHK6MJN9z8MXU9SZHfb0Uq4S2wNvc+AOXdJOHWeoh4kfwMs+zccd6eutc/Z+mzWvgbL/63+qQEX65gBr8RfXAbEtfgWXxpDE0YEGQwGg8FgMByNeJLU9L7z88hjVXu1iZ90jxLhvNVyT3qfqTS3L/frpLSXGCjbobIwT7xK6QB6/wr2LFVS2sCLdb49C1Tm1fUE9fbMvvOr6woFVYq25RP18YQCEmvuODkv7UbpfU63Hrc7JcaO/yvUdofOk6TdXB65OG9cABNugXVvWC6SJSzsDs0DSmoNOQOVaLfhbYUS+Ovgvcu0xi5TYP8qCQmHyyrhq1TfUKuBSn/bMk0hCXaHYqf7nadgAX8DLHtO99TpkbPTgNwXV4zcslBQIqmhTPdn1HUq2QsHdT7scq0qcvWeXXNUyjf4MmvmjzVcdfcC2BWA2gKJpxXPQfYQcEVpJlIT8x4Ad6ICFTodp3lEpvTtO/ODiiDb4XLjDQaDwWAwGAzfH2cU9Pql+oIOjoPOXy0n5oQHJFicnq8vkYprAcfdpo3+noUQnSo3pfspckfKd2jjntYJ2oyAHr+Qs2Ozyf04MLPGItAoZyTolyO18B+Kb3bHyTUa9UdY9ozW6/RIcCW0ktM09wGV8pVtB5tTpWjpXaH9aAUINJXRNTkr8S1VflaRp5CCxkqJvV1fqDwt4IWWfWDbdPU9RcVqvQ0Vcp5A7k9Ke8joqmv21ajnZuvHEiKtB0O3E5XoNuQ38MX9EnWNlTpe0A+9TleoATat0xWjsrXiTRJeUXGa8VOwVmvyVmvG0dbp6t1xenQ94aCVKGfd37wl6nH69C8SU039Ta4YJeDt/EzOkxFAzeIHFUEmGMFgMBgMBoPhB6RFN5h8n8qnijcBNqWJDbpUm/tvQ1IbDTOtzpeIcboVy1yyWe5RageVbtm/lKcVFaeyttJd+jtozdcJBVSilrdI6XGBRpW3pXTQvKC6MrlKTUELdSVyiPyN0GmCBENyW83JccdLDH1y3aHnjm0BUx6Az+5QmZvdqddu+kBiJmcIbP1Er23aj7rjVYIXkyaRUV+mn9pCSGorYfP+b2U0Od0SWVs+USLboEth+2yY+qjK/Mq2K2K7z1lQtAl2zY3MBaopVp/RvL/LTSrezIHo68QchSUEfXKQnB6JorgMvT8UkOAJY4k2dJ+SWuv1NruEnN0hIVe5Bxjx7T5nwyE0SwTNmTOHcePGfePrpk+fTqtWrZpzCoPBYDAYDAbDN2G3q5xryv0SEza73J2ow8RU/y9cHkj9kmjK6PbN72szHDZP1++BRmvODir3WvAPwC7norFam/uGMgjUQ2yqhIjDpbKudqMl4vathIl3qnfJFSMBkj1IzlBCy0iL0+T7NaeodKscK4dTqW2JOUqnO+52OUAFa7TGqr3qkfIkycFqWmvroQpA6DRJJYE2h0IQup0EyW3kVG2bofe0G63Sw4BX1+erg2XPyq3Z+IGcnLQuVtpcAWT1VZy1K0YzkkAi8bg75X61HwfZAySg6kutHi6b7lnYrx6iqFj1AAX9Ek675qhPyOmW8PMk6H1fFqiGb6RZImjy5MlkZ2dz0UUXccEFF5CTk3PY140cOfJ7Lc5gMBgMBoPB8C3wJOjn/5vEbJhwGyxcJzfDHoI2o1TmFQ5JGISxXJgS9fuAnouKUxhAXAZ8ca/6fxqrVC434g/QdpSipku3wcQ7lJ62f4VS08IhzUMCyx1BEdc1hdB2hETG6c/J6cnoASVbFQfujJLjEwrKAcuxBrk6oyU4+v5aCW0rnpPL4k7UYNKWfSWotn2qFLm8RRI7zmiY9xCc+rjWmbdI4iR7EHQ/VYNr969S1HZ0ssr14rNgzX+VEmdDYmfFs1p/U3x221GaKbTgn7BrttLz4lvC4EsVzJC/2hKcdvj4jxJ6OYPloIXDuo82+4/znThKaJYI2r9/P6+88govvfQSd955J+PHj+eSSy7h1FNPJSoq6kiv0WAwGAwGg8HwUyUxG1inNLa9iyGxlaKmq/dFXhMVJ8cnpSNkD5ZYcXmUDDfjZm3W68skbsbcIAdoxfOR4aU2G/Q9R0EQ7UZHNvmgf21I2Iy8Vi7PvAdVdpfSWi5S33OgdAfkLdQQ0TbDFP2dOx9Wv6TwiJg0uTtrXpXbY3PKAVrzHwU9JOZofVX7dV67U0IktgXsX2mJk7VygGqL1NPTZYriub3VEkNbP4HMvgo0mH07dDweev4COh8vgRVoVO+VI0oR4tX7tK5gocr+Pr9L/UZlOyWoNrwjByt/tcIdRl+v8+bOl0jreoLmDyW0/H/9ShwNNMs7S0tL49prr2XNmjUsXbqUzp07c8UVV5CVlcU111zD2rVrj/Q6DQaDwWAwGAw/ZeJbQv4qNfcXrIH0Hno8rQsMu0Lpbm9fpH6h1E5ygPLXaNint1piJqO3BFLuPD3mdFtBBNWw/m2FJOxfqfK25LYR5yQUlNDZvwqWPCnXqWoP7F4En94kQeRvhMG/0WyjxhoNaV31ksrJvNVyXla/ovV74nXeUEAOzf6VEhK+Gitlzjqv3aFStso8PT7pbjkxix6FpByV9828BVa9rN6qlPYq25t1OxRu0ONlO2DnF5qT1P0UuWZOj5ylfuerlC4hS9frToTtM2H8LbrWsh3WzbdBp8nw7m90zMo9em7ho/D533RPDYfwvYMR+vfvT2ZmJqmpqdx33308//zzPPHEEwwbNoynnnqKHj16HIl1GgwGg8FgMBzdhMNySGx2iE78sVfTPCr3anNetVdhCS06Q0yGnktqrc35p39Wf8/Ym1QONvBiBRgEGuXqLHsGMntDl8lyMKJiI0NbO0+0HCBHJNAg4NX7qvMlFpweK33uEtizBOIzFHiQ2VspbtHJirYOhyLpdevekEvidCs6vN0YuSihgM7lq1PvTkMFYNMMIle0nKCYND1WtQ/iWkbmDYUCHAg8iE7WfKD2Y5WyN/wamHOPJe4cKgvcNUfx2XVlGuTqrYbk9uBrgLSO8O5lOrbNIaeq77lycxKyJSrj0iUYq/bqO+SO5UCTVEZ3nbdit8SS46DKrKKNEptdJv8w34mjlGaLIL/fzwcffMDzzz/PrFmzGDhwII899hhnn302JSUl3HLLLZxxxhls2rTpSK7XYDAYDAaD4eijPBd2fKYyJbtDfSZtR+i/8H+ZoF8/TWVgPxUK1sHMW9VT00RMKoy3ZgXZbHI/PIn6WfUyjLxOjoQ7Qc5P0KsSr6o8zdQZfb029O3GKCSg9XCo3KeI63Aospm32RRzXV8K2DUEtb5Mj+/6QrN9fHUqWXN6LIfEZgVFZCiQoKYA9i5TiZjdpZK1cMjqJ3JaPy4gJHcoOknX4a0Gn0Nlf/4GIHzoINroZD3XsrdE1MCLVVLXWGmV6tkgbNO9ckZbJXXWPKdOE9Tv8+5lkR4q/Lqm8lyYch9k9Zc71Vila47Pgty5ULpdQ1fXvqa+puXPak2HS2feNkNJfoYDNEsEXX311bz22muEw2HOO+88HnjgAXr27Hng+djYWB566CGysg7zP2yDwWAwGAyGY4myXfDJ9dYG3mLxY3Ikjrs90q/RWKW45c0fajOdPVAJYqkdfpx1H0xNofpRDhZAoE35gofBc6r+dieoZ6cyT38XrldEdNAXmdHTVEoWDkJaV/XxbHhXomntG4r9HvkHlXJV5kkohsNySFLaK7q7YI2Ehq9OorJ8l5yocNiK3raEQFwL3fegT309Wz+BXmcooCGrH+xeKCfK7oCiDRIy+1eCxyM3qLEKsEm8JLeVgFr/jhLusGk9Q36rGUDFm7W2E/9uXb9NP6GAhNKAC1Ua544Fby3sq5Z4yVsqweutPujGhhWmULIFcgbBhjflSmGD7lP13SneqOvqeFzkbQ53pETQ8D9p1l3atGkT//rXvzjttNNwu92HfU1aWhpz5sz5XoszGAwGg8FgOKoJBjS75mAB1ETJZpWDJbTUhnv1f2Ddm5HnizfDxvcVf51xBNoLGmvkwjRUgCtWAQaxad/uvVX75Jx83XMe63ebTY7D5mkSTI3Vur6SLeCtktjAJoHX7RQdc9GjVihCWGLLX6+UtbE3STTUlajfKDpJ64224re9tRrC6rIES2K2NfOntSUEbBJegUbFiBdv1jHWv6lBpSc8oCADp0uiZftMhRhU7FFfTvlOXZPDCUMvh/py3cNJd0mQuGLUe7P4cRh2Jax8QSKwfJfcqKZrDfmVFLfyJb3P6Yo4TLEtYO9SpekdIoIs8tdoRpHXEkAdxkFsupwkbOoL6n+BHK62o3SscFD9T9hVTmd3QufJ+j3k/3af9zFAs0TQ7Nmzv/nATidjxoxpzuENBoPBYDAYfh7Ul6q06+vYNl3/Jb9iz6ECqAlvDSx9Gibdow1/c6naL7GRt5QDLklyOw1JbdH5m9/fNOfm25DSHk58UPN68hZDv/P0r79ebsqAC+TAxKSoRLB0e2RYqM2hHqCmdLSsfnJZUtpD77PU5D/4N5YoCOv+2F3gilMq2pDfSTjGZ0n8+OqtaOqz1Ytkd1lBC5UKWhh7Ayx8RMKisQo2vQ+nPqm+p9KtSohr0VmvdTih968kagvW6vzxmRJIy57RuqOd0FAldymUrF4lgJR2sPplq8TP6mmyOzSU1p1g/e2MzFkCwKbje5JgyG/UP1SyRT1TnkS5R41Vuq92F/Q/T1HgxZsin7EjSm5iVp9v//kdIzTbL9u6dSv/+te/2Lx5MwDdunXj6quvpkuXLkdscQaDwWAwGAzHBHuXff1zBevkmDRXBPkbJKTylhz6eEWuAgtO+ofCBf4XsS1Qeddh+k3sjq8+ltZZTk59udwhd4Jm3vQ9WyVvqR0lTvavipSqNYUZNPpV1lW2UwEDNQVy1Byug2K3D9rkxyTLZdr8odylk/8pp8dv9QjFZ0qQEdZabQ6dp2ANtBsFAy5Sv058SwmLT66Xk9N+rATQ3mUaJNvhOM0WalOmOPCynZpbNO9BXSdhuTn7lkH/82HxE3rMFR0RX+449TY5PRJq+6wepe2zrP6vg3rAbDYl2XmrYPNH+g7EZui6XDHqN0rM1gyjfufJyRp4kdyjvUt0vzoep5lJAd///nyPQZoVkf3OO+/Qs2dPVq5cSZ8+fejTpw+rVq2iZ8+evPPOO0d6jQaDwWAwGAxHJzFpKlP6OjpP0QDP/1mmFI6knDWHqr0KITgc1ftV0vW/CIUkJtqOPPzz7b6m8qfGmpWze4FcmVP+pX6YridCejcJg/gMOSABn5LbwCpha5CzsukDzREqt8IVpjyoHqGk1hItsS20voZKldNt/1S9PcUbdfxWA/T+wnVag80mIRLyS+SsewNm/1WhAnUlEmS9z5TwWvB3eP93Wr8rRnOG3rlE5Xe1JXKK1r8tB8vu0Kyh6GTYtwKq8nW9XU6Qy+SrkVhxxUQCHbBBwXqtsc1wuWFxGdYQVo/KIMv36NqS2kaO01ile+X0KKEvtZMeW/kSLPs31Ffoe9V+nAT0ihd0TwyH0Cwn6IYbbuDmm2/mr3/96yGP33777dxwww2cfvrpR2RxBoPBYDAYDEc1Dqdmv+xe8NW+oBbdVO4F2givefXwx0jtIKeiuXhrNUfn6/i6Xp/KPM252bMAPMnQ5ywJkZ2fSaDYnRqA2vlEWL7z0PeWbofpN0HrobB/uUTO2BtVHla1V06N0wMjrtE5/PW6Rn+D3m93QMfx6pOKitWsoXWvq7yNsMre5twjdyUhS30w4SAMvwq2fCKHZf8qxWZ3PUlpa2HUcxQVqxS69uN0jcOu0oDT2DQ5Oi37wYRbJRwCXkjvLmEz/wEJr6p9Om9yO4mRqDityd+gniSbXc6RM0albH1+JSer969g5p/VbxTw6t/GagnFzpMgZ7AirlM76thbpkHrYRI/I6+F+X9Xz4/NrnO16Kr+pLg0CbTGSn0+CZn6vCvzIvOBSndgOJRmiaCCggLOP//8rzz+61//mgcffPB7L8pgMBgMBoPhZ0NqeyWG7fwMds2TeOh6gv7rf1MyXEp7bczzFh36XrtTPTAxKc0/vzs+MqvmcMQdphSubCfMuAlqiyOP7fgMRlwNPU7R5rpqr3qMpl0LGVcp5CFnoGKwVzwv1yImRYNPpz4uV2PLx+oPaqJqr8RO/pqIoKkvlygMBeXajPwDbHxPAmHAhbD833JLJt4pp6M8V65O5ykqHcseqN6YQL36g8b9GTpNgt3zNOcoZ5CEy5Zpcrhc0RKpu+cpUGDKA/DFvRDTQmuoyJPY8STJ9fHV6nOp2KN+oEWPKaUuFFAZX99zILOXnKjMnirFK9mi0rTTn4NNH6qMLr4l9DpTAnDGTXINPYkqvxt7s9yudW9Aj1/oHB3GSVB5aySSsgdGvj9Ot4atElZZnL8OWvbXd2fjezqW4RCaJYLGjh3L/Pnz6dix4yGPL1iwgFGj/oflazAYDAaDwXAsktIWki+BnqdLkHgSDn0+JkWb/d39tXH31sqB6HPW90+GS8zWTKLceV99LiFLfS4HE/DC6lcOFUAAhGHZs5rtM/uvHOjLsVmzfD67E6bcJeGQmAPY1aSf0FplcJX7vtqXtOAfMOgyGDFaUeLeGpXdueMi4mbeQ3JMknLkjDSVBvrq5ea06KpBpdP+IJGS3k0OyeLHdN9DAUVl979QZW87Ple5WzgkMdXvXFjxnFyrzD66rugUpdxFJ2sIaTgk16qhSr/HtpAIWfqMep/CQLBRgrO6CNa/pRCIlS9B5+NVYtdYDctf0DEn/k3Cb+2rKudrCjioccnp2btEUdwDL5J75q+HVS9JzLgTJNSaBBCozK5sm87bRPFm2PoxHHeHSvEMh9AsETR16lRuvPFGVq5cydChQwFYsmQJb731FnfeeScffvjhIa81GAwGg8FgOOaxWYM1v474DM2waT9evSnRidrcfl9c0SofC3itAAZLvCS1gfG3fDUUobYIcr+mhyhnMCx/LnKMoM+ay4MGlK6yxFN0AvQ6S45MfEtd95ZPrAS0g/qfQkFt7jtNhjE3qoTr/d9BQ3nkNTa7yuNSO+m4O79QQEJKW0jIhg3vgN2uoIHUTiqDa6yCHqdJ/Ey/QeEIo66Dje/q/Emt5WYN+R1UF8Apj0N1odLh0jrLiVn8uASQza7r3TYdcoaqV2feg3Km+pwjd6lki87RFESwb4XOMeU+3Z+GStj0ERSuUe9ShzGw+QPdP5sT4jJVLhnwyrGrzJMAKt0JrQdBz9MgLl1BEHEZkHaoEUH5Ll13VLwcuCa8NUri63ryt/66HCs0SwRdccUVADzxxBM88cQTh30OwGazEQz+jxpUg8FgMBgMBsOhxH6P/p+vI7EVjL9V5WcNFXJaErIPf65Q6OuDGmJbqJ/HFS13ojofup6i54ZeATGJioPe9CG8c6lCBCrz4Jy31fMTDlrzakJyVPr9WqJj7zKY/5DcmOPu0Hwgbw0QlghxJ6oUrr4SavZJWHSYoMS5QZfq+sp26hx9fqXzt+gqRysqWtfqrYHTnlES277lKmVL7aTeH5sNklvLjcMuIVe4TmsMB7UGp1sCcdsMleH56mDJEzD0dxIx+asVQ15TpDK4tqMkovJXSyCNvR62TJeYSWglwVNTpOPGpkmUBrxyrtqMsHqFJuv5Vf+B0i1yqJLbqGyuzfBImESuVWYZnwnBFEVu2+wSTUUbJbDcsUf4S3V00ywRFAp9j4QSg8FgMBgMBsP/P5548HQ//HPeWqWjgfplsvqqXOvL+OpUllZTqJ8BF0BjrZ774h5IyJAo2TZDQstbK4dm1Usw5Lew8NHIBr3feXJsPrsdWg1Ukpq/AZzR8Ot34dNbINhgPTdFbkrQr419kzs0+jr1vCxdDdis4aAuPV5fDtGpCiRY9xq8f7lCBpLbwAkPys3JXwudjlOIg7dGa+75S5XiRcVZoQehyLFDfpUrdj0RVr6oPp2SrbDmvxJLCdlaV9lOlef1+iXsmKX1rn8bzngRdn6ueUc9TpXbFPAqbCEhW6LR5tTv7Ubrvn78R4VIpHQCf63mJ826TdHm2QOtD8bam9vseq2zaXotVvngYaLNj3GaPSfIYDAYDAaDwfATpaFKDkzxRjXrZ/ZQnLLLc+jrwmE5BSueg/2r9VjLPnJTXHFQV6QAgKA1Z6a+HPqcDXPvl/vicMO6F6H3BD3fcaJKyupK1IvUWCn3aNt0yBkCx/9VPT6hoGbwfHanxFBsCw0crS/TcXr8AibcIoeopkACJHeu3B1fg/5ObK2el8L1ckGaCPrgiwfgnNehRRd4/7eKjbY7IRzQ2vatkPgY8Cv45AZFctscEnk2m5yToE9ul68eCMl1ictU6EDQJ9clZxB8fg9gk3hxREFGL92bQKOcG1eMjhPy6Vq2fQpVebof/c6HDW/ptfVlEjWDL9NrPclyf4Zfba3LrlK4fcv0Wa35rxIG3bHqo9o87fDfhVb95DQZDqHZImju3Lk89NBDB4aldu/eneuvv94EIxgMBoPBYDD80IRCSmFzRH11WGltCSx5UmEBTdgcMPhSxXVHHVQWVbpdPTO+uqYXqo+mdJs216FklV156yQ8up0kp6ZwPRCWu2GzBnzGtpBQqC+zBESdytPqSiR6Zt8JQ69USVrpdihYq7KvmBRY+Ig2+SG/nIsNb0swZPZS+d2OWdD9F3J9TrhfzklSjtLpwlZpnc2htTTNHC3ZopjqulKVpiVkSdB0Pl6io+dpSn6LSYa6YCRGPH+NBMrqV9TL1HRvwlZpXvYglcr56+VMNVbohG2GKe1t84ewabl6oTJ7axjrkifkKlXnQ3SShrT6GyUix99m3fuwwhSm/VGR2QMugNp8DV31N0hQhsPQ+ywNUd31hWY8JbdVWV/OUAUqHIwrWr1FUc0ctPszplki6D//+Q8XXXQRp512Gtdccw0ACxcuZMKECbz44oucc845R3SRBoPBYDAYDD8r/A1QtV8OhDtBrsmXxczhCAYkULbN0L+JORImaV0iLk/uF4cKIFCp1tKn5aS06q/HQkHY+slBAggdq2iDktU8iSof271QG/zRN0RCFPqdr0Gra9+UOwIqW8vqo3S2+lK5L3UlsG+pyssIQ2UuLFql5Dt/PbQfA0ue0vrs9kjyWzikMrPOkyRYqku12U9oqRKy2mKJHkJKZPPW6v1BnxyfuBbq22nZB375Aqx6GfYuhwzrvLnzJdYcUTpWfKbK7QJeXX/fcxSJ3RSKgE1CwuFSad47l0pghMM6Rss+Krub96CuJRSUUJr3gFLmOk6APYs0F8ru1Ofjr4esAfrc5j0kgTT5Phh+Dez6XG7PjD9LGDYFItSVKFFu1HVa3+aPJLY6jIP+v4aWvfTdCPp1rh6nQmrn7/rtPCZolgi6++67eeCBB7j22msPPHbNNdfw8MMP87e//c2IIIPBYDAYDIavoyIPlj6l/2ofCsqZ6fELpZl9UyhC3mKVkDUFFxRvVqP/6OvURN9QCRve+/r3b/1Em2ObTT0w+1dFnnMnyMnZNkN/+xogxoqR3rdCjfo9rBCExFZWo34SeK0Bp9GJKg1rOxJa9obGXIm9jhPV9L/0KT0342Y5Qf3PU+9KXbFVqmb13hDWPQk0qKQvKlaiKj4T3v2NZgElZus5R5Tem9xWosJm0z2tL9d1Brzw0R8UX22zQ02+yvWGXqHeIU8ihD5Tf1NchgSRO14BB8fdoc9o43uAHbqdLLFTWwxdJitBb89iGHSJEv0++ZOCJ0DXFZuue7f6PzDpHq1x0/uw6QOtMRyCok0qFRx/m1y16GTY8pHu264vLBFmk7irKdAx60tU+jbyD7DiBd0buw1WvqzPo9UAXXvO4P+dRniMY2/Om3bt2sXJJ381am/q1Knk5uZ+70UZDAaDwWAw/CypLVFZ2J6FkfIrX502yhvekdPzdVTnKw3tK8ltYVj0uJyloE9hA19HTZHSx0AbdPdBZVKZvSSomrDbrU24xZZpUFcmB6ixUuVXQy9X2RbAhneVsBaTDJ/+ReVkuV/A2tdV7nbCQ7DhfcAmR6NpXpLdfuAyAOvxJN2X6GQJgowesH+lBJDNrmvY/IH6k+rLJAT8jbpHtUVKYEvKgWVPQUOZXBxXrERRTKrS47pMUl9SVFxElNidEhJ2p8RXu7Fw5ivwq//I+fn0FoUSRKfA6c+oVLDryVC+U6VpTQS8Eith67gNVUrB2zVXQiYUsEr3bFrvpvdh2BXgrdbxMnvrmsJBOYR2l1wnrLI/f70ez7fiwHfNhfQeEmhbp8OceyTQDF9Ls0RQTk4Os2fP/srjn332GTk5Od97UQaDwWAwGAw/Syp3a57O4dj4nubffB01RdoYH45Ag1wITyKktP/6Y2T1lfgBbfK7nxJ5zuk+dMbMgXIw1LfT8zTYu1hzgj65Xm5JendoN0avCfqVmrb48UistK9eTfkOFyx4GJKyJeJcsTp/bDp0PjESbOBJVO9OYxXEpKl8L6sftB0NBWv0GrsTsEn0Fa7XfCF3IjijtOauJ8Fxd0oQ1RRaF2NT2ZknUQ5YTb4EQ1QcnPSwZvoQlnDKHqzSv3Wv6xrnP6SABptdjlLQp9+3z4adc/S5eRJ1/+xOCRaHNUDWV6frSG2n8r6oWPVKpbaXkIpJheR2ElAOJ2z8APYsgG5TYcwNurY2I7Q2mw2wq/wto6cCFkJBiUB/PUR9aabU2tfUD2U4LM0qh7vuuuu45pprWLNmDcOHDwfUE/Tiiy/yyCOPHNEFGgwGg8FgMPxsKN/99c/566Gx+uufD3/D7MVQQM5Ov3NhuhVccDBRsRHB0kSrAdBhvGKbq/ZBy34qsYuKPzRAoe85crHm3CW3ookVL8JJ/wTsSmurzLMcC5TEZgvLCXG6dfxhV8oR8STKOap+RoLFV6Pho063REZCKxj9J5X/xaZJgC19WkEChOWG2J1yPcp2wPF3ab21hVCwXq5Vj9OsWOuAQh7qyjQjSYvTdcy5D467FQZeAuldJW4q8xRL7a8HbAp8KM+FvufCuL9IODndEmqth0oYlu2CyfcrwW7Lx1ZfUEgiMKm1XJpd8yQA3Qn6aFzROkZdqdywwo0qI+w4AVa9KOfL3yAROOlu+OI+Cb2gX2tZ9C9dRzgoUVS6/dDPtqZQ7zcclmaJoMsvv5zMzEz+/ve/8+abbwLQrVs33njjDU455ZRveLfBYDAYDAbDMUrM/+j5sdm/GmF9MLHp2oD7ar/6nN2pPhlQ38r4W2DZ03I7QMJj2JWQ2uFLx0xTI37XE1Wil95DqW0BbySoIbmdhM2ypw8VQCBn5PN7oO0t4HQqxKApSAD0eyiolLPoJKjcJ4fFV2+tLax/x/1F5WN7lkhgRCfJyWgzXMJw2h9h3M2wa45VSmZXSEJNoe5L+Q6FEvgbLBcmDCkddN21xVqDr07XFAroNe54IARrXoXOU6Bwg9yUNf+1BJC1/vpSibKt02Hqv+DDqyUc/Q0KXKgvlTCJTpZr0+dXetzmkAgbeJF6r7qdpHsc9Co2u65YJXyEoc1oCb6CtXKKGquhcq+cqZ2fywUb+UcJxx6nql+oZW8dc+nTuk/bPj30s4lLl9AyHJbvLIICgQD33HMPF198MQsWLPgh1mQwGAwGg8Hw8yS149cLmVYDIb7V1783MVuDSBf+86vP9f5VRAS5ojUANLMXNJRrMx6XLmFxOGKSIWZgZPBmQks13O9fKfHTZoTcnPKdh3mzLVKiV1Os6wMOCCFXtNwPp0eDQ7MHSZBs+UgpdR0maPMf9GoT74pW2Vj1fp1zx2cw4Talpe2cA6P+pPjv+hIl48VlwIALYfZflVJXV2IJODtsfFf9SiteVF9QyK9SNTsw+DdyZsJBOSjjb5NAcUVL3LhiJJZi07R2f6P6oKr3w8n/lPs04yY9HpMM1QUqV8tbDKP+qGGprhiFP2yZDoRg4MUSlLvmWEEODbpHniSJ0C/ul6hc/7ZE4bbpOl98S4msA8f7RCVzgUaV7v3iqch8pS9/J8x8oK/lO4sgp9PJAw88wPnnn/9DrMdgMBgMBoPh50tSjjb1s/96qBBKbqvGeHfs174Vu13zYeJawOpX1T8Ulw69z9Rm+OD/6h/wy/mo2qfNdtCr5vxAo8SAzS53Izrxq+fJ6AET77Tm+4RUgrZ7XiS9rWkuEPrzgGMUkwrFW1Vyt3u+xEN8lv5Oaa/HZtyokr3+56k8beYtEgYpHTSfJz5TKWjuBJXApXaQcJl8Pyx9ErpP1f2rL5MrFZcRCUYIh1QSF5eha/TVwo7ZKiVb/5ZCBOIyofvJisve8ZnWHQ5D4VoNlu11BgR8upeeRJWrBXy6Rr9PoqS+XELNV6dzJrfXfKCgXz1KWz9VX1LQD7nzJMYm3QX/OQNGXWuV0C3V55nVXzOJFj6ihLeGequfyhp21BSUEQ6p9yghS4NWo1P0ePlO2L9CgtDmsIIUnBKcbUd+p6/msUazyuEmTJjA3Llzadu27RFejsFgMBgMBsPPGJtN0cWnPqE5P3WlkNJWIiAu/Zvf747T5jazjzb5To9Ktyp2Q3WUXIOoGG3wFz4iVyTQqJk9bYZBy74SDKkdNIdm5B++WiIHVn9NiebZbJ8Jg3+rMANfDYTtkUQ3AHeS/u1zFthCGkba6Xht2t2JcqCW/1ub85BfQuLj62DY1Qo+SGqjwaZBqzQsJlX9QCtfhBXP69jJHaD9KIkyp0dCJ28xLHtO83G81RItrhgOlNiFgkpe6zxZ4ikqRqEIi5/SvKImcgZrps+2mTDkcpXKNZRHyuYaK61ytxSJuY3vWUNeU1VKWLbTir0OSNB0nqTPc9MHKotrNVDx4oEGzQrqdab6fHbPV/T4pr/ovTEpEqc2BwcCEJrEkLdGwszulLANeK10ObuEVvdfwNRHJdriMuQK/q/SSkPzRNCUKVO46aabWL9+PQMGDCA29tD/ajF16tQjsjiDwWAwGAyGnx02GyS30U9z8cQDYfWprH5Fm2RQOdqQ38AX98pFCAclOgI+RUonZqsca+8y9ZZ8djuc+E+5PsUbtYlO7y5BNut22Gm5JW1HwpDfwtz7wOaEkHUdrhiJHj/qiXG5tInfNRfajoJ2o+DTP2sNQb8VVBBUmda6N2D4VRIV6d3lyNidMOR3MPcBrQl0vECdrrNFVyjLhZwBEif1xXJ34lrK7arerzS2QKN6fgZcpGsO+fS6nXMAm8RLbAs5PGld5My54yFvEYy9Ceber+MfWINd84A++xt0O0Eu1vhbYcnjULQx8rlU7YP81Up2S8zWueIzJZRadNNQ04+uhtHXq+fJ7tJ9CwUk7mJSFdCQv0qOGOhxd7zcnVm3WX1XliMX8uv+eGsgs2fzv0/HIM0SQVdccQUADz/88Fees9lsBIPfkF5iMBgMBoPBYPh+7F4AS5449LFwSKVyjVUqgwt4I65B2CZ3oudpsOwZxWN3/4Xcnpm3yFFyeuRIJLfVkNPcL7RB/+I+mPg3OPlRWPmSnKekNtDrNMgZDStz5R554hWRvWeBHIqsPnJ5DqwvzAGnJqGVjl2+W/N+PEkSAaHAodHOnkSVnsVmSDAN+S34veon2rtMIqT/rxWMYHfpHkSnKkhh2wxFegca1CMz+X5orFD0dW2J3BVHlPp1CtZqkG3OUAUgrHgOKnK1zjYjNFuoaC1U5+k+lO2Qm2ezRRLxbNZg0y0fyw1qO0KJdAktlby39GmJmNWvqHfoi/utHianBGVGD5XkTbs2cjxCVl9WWMKttkiirOmcbYbr+IbvRLNEUCgUOtLrMBgMBoPBYDB8W2pLtJE+BCuMoCpPzo/To/6VcChSOlVbJFEB2vyHQypNC1u9J/46qKpTw78zWkJo33I5JgseliDodx6ktJOj0xRLDRJlOQOgaL3Wkd0fvAcHQNgiA0mDPomR2BaKfd72qXp91r4GFXsgoxe0HqKSroo8lY0F6pWSFgYyusP2TzWsta5E4ufEv2s2UHyW3JQ590ggBRp1znWv65gLH9H1Bb0qewuH5bCc8KBK3ja+r9fVFKskrbYYZt6q14NEjd2p/h+7U/fJEWX9HqXSw7xFKnlLzJb7NPJauTZNA1UrdksQjbhG96W+TKERrljIXymBlL9GwqhVf6XgfX4X9DsHijZE+n/ajdP707vqmkwJ3LemWSLo5Zdf5qyzzsLtdh/yuM/n4/XXXzehCQaDwWAwGAw/JP46bfgPJuiXM9F2hCKZnW7LpSAiPtI66X2OKEVLF66RIGnq8WlyaupLtRkffo3CBTpOUEO/r16v9STBhrdg8zSor4bu/4DWwxST7Y5XeVZ8lo7tTrDK9ZrmB4UkWtyJKgUbdJnWtn0WjPiD3BmnW2V7QZ/Ewcg/wIJ/IJG3V/HS7gRoN0DCY9dcuT/5a3Sc3mcp3c6TqHvgq1NJ3+6Fuv6Y1EgJYZM42/oJ9P01xKdDQ6XE5J49Eo+hg6qc7E7dP6dbgQSN1erPstn0nCcBsCsFMBSQgNy/UglwBzs4FXvkmkVZUd05wyRwVr+ie5PURsJp+bNyi1zRClJoO1rv73ScRGRSa9j8ia6n5y8gvVskrMLwtdi/+SVf5aKLLqKqquorj9fU1HDRRRd970UZDAaDwWAwGP4H9qiIo9NEKKB5OcltJS4CXmsWjkU4rKb8bZ/qNbUFciCa0t7CIR0jHJb4CIflogy5TOVkM26E2Xeqf2batZrB4/REHJLP71T6XdeTdJz6Cg1eHXZF5BwOlzbzUXFyO6r2aw3uOImsmGRY/JiEQPkuDS7d8A7MvVfJal1OVOjDujehdIcEWVMp37wHYO3r+r1yr66nvlznSmoNqV1UmhcKSMC4EyQWHC6JovpypcTlDIW0jqifp6UEj83aMrtiJEQ8idDnHLliI66G7qdK2FXvlyDqdboiynFAZm/1asVmWDHomQovSMiScK3cI5EWk6z1BHzgrYJ9SyWeqvbJwavap9eNuxmm3A8xLWDrDHjjPJUfpnVQj1fB2h/sa/dzollOUDgcxnZwPKLFvn37SEw8TNSiwWAwGAwGg+HIEZ8J3aYeWhLXtDdb+TKc8AB8frc29r46iaFBl6ppv74UupwgkdSia6SnpenHhjb9cekSCDWFCmAACQJHFJRsgvkPqz9nybN6rr4C1r6hnqPiLUpv6zwRNn8Mk+/TMNLq/XIv2o+1ku0adJ5l/5Z48dXCnsUSCNX5EmOg68hfrbjtae/Jren3a/U2lW5XAlzxZpWm5QyFlr10HpvNGk4aLdEXnwGlW63jhuUege6RzQZdToLpN6jcrPeZEmSeRGsAK3KExt6otLf1b+jetB+jEIjuUyVAds1VQlzBWpXbBerl+Kx+BUZdB7P/plLAoCU4o5Nh4KUq02s7Uveg3ivhVlOo9dvs0GGizps7V8l6M/8sQQcSQeU7YdhVco5S77McKcPX8Z1EUL9+/bDZbNhsNiZMmIDTGXl7MBgkNzeXyZMnH/FFGgwGg8FgMBgOwm6HbifLRcidZz0W5b227wABAABJREFUpTK1pgGdXU9Sw3xMqqKl9y6VGPrFMyq1Cge1qU5pLyHRJDjCdvWWDLhIYmveQxIB7niJjNoilWkFvJHfQWJh9zydb/BlcoACfhh9nURM1xNUsrVtJnx2B2CD4+7UMNO6Iuh4vOb3EFIfTlIbCYAw4HDKxckZKleqZR+td/NHkNVXAigqBsberPK92iKJqoZKrdtjk4gafpX6i8LIIToQMhBSKt3ql1UiGA5DbAocf5dETU2+zt1pokrWFj+max53C6x/E6b9UZ9JmxEShiXbVBq3d4nVQ+SS47XmVfUubfpA5XaeRAU27F6gHqfK3bqGJU/q/sZbA2CTWqs/a9VLKr8rWA0n/VOicuccnaemQOeqLtS5jAj6n3wnEXTqqacCsGbNGiZNmkRcXNyB56Kiomjbti2nn376EV2gwWAwGAwGwzFHKKSNd40VOhCXKXfk4Pk88RmKWu59lkrHomLlfix/Vi5E/ioJgK4nQcve0GWyRFJsCw1c9dZC/joYeiVseFtiKhTQxnvE71WqVZ0P2FQ65qtTbHQ4HBEO1fmRwZ2uWAmLqjyFEJz+HBQtUemav1albzFp6u/J6qeY7PpSGHypeo1S28P6d6zjWPN+fA36N+yGRitkYeyNitSuyFX5WG2RStS6n6JUtrzFULZdYmL+QwqR8CTI1fLW6fFFj2u2UGwLCYZBl6pEbcM7gF0CKG8J7Pxcc4QSWuna3AmR2UXDroQ5d0mkuGLU2xSXoXLD9K6Q2hnSeygkIeSXKMpbrPvXcaLislt0hRl/VhljQitweBSmMPFOfaa+WkV4x2fC539TqVygUWLRV6vr7TZVvVuL/gXFmyCx1aEDbQ2H5TuJoNtvvx2Atm3bctZZZ+HxmAQKg8FgMBgMhiOKvxF2faESKm+1HnPHKwmtw4RDE8A8CZoPc/CMmNHXQ9EmBQikd1Wq29rXIs8ntYYJt6vvpddpSn9rPQJ6ni4BkpSjUrouk6CmRI5RQ4Xe2zTM0x2ntSW1htwF1rrroNu5cjtGX6/46tUvy40J+SWa6opg6ZMw6k/w6V/AVy3hMPI6beI7HgdbP1YZmd2p8IaaAq1h+NVQuBFsYWio1oygnCEKUEhuo83/mle1lqp9sOgx6H+hBFP1fonE/FWQu1t9PI4olbc1DSQt3iwR6PJY6XWVOtbuhVapnFf301srt6uhQgLIZoNxf1Y/UvFmICzBFBUPfX+lte9ZaKX0OTQ8tmQLNJSpJHDvYp3f7pT4/Pg63UtXrHq3lj2jkrkxNyohzlttOURlcrvWva6o8/bjJEgTsnTfDP+TZvUEXXDBBYDS4IqLi78Smd26devvvzKDwWAwGAyGY5HizRrWGT5of+WtgbkPavPbasD/fn9Sjn5qSxRgUFd26POVeRp6OuVBNeN7a2HfMrkqZZYjkdIets2CTsdLHC1/Fg3odEgkRMVr8x8VB41WylpWf4UbtB2l4IE598pNIiyxYbcEQG2xREGLLoq87vdr+Pyvcpo6TlAaXN5iCZGGcjkj8ZnQfrxEyoe/h9ROcpV6nS4Rt3UG9D5Da2yKCq8pULBA5R71NLnjJVpCQZXtJeTApLsV8R2Tpn9zhsiBqS1WCRthq+/GpnXVFkmQxKVLyNhdKpPbv1JlcnanXhv0gi1BiXaT79X12KzEtvbjNHfpxL9bIQ0xEomt+utz8NZYiXa1Ol/AJ0ewZKs+m8YaicXZf4t8plumqbQwOlnlgVExzfzyHTs0SwRt376diy++mEWLFh3yeFNgghmWajAYDAaDwdAMAl7Y+M6hAugAYVj/tkqsvs08mOp9coMOR+l2Pe+rVY/N7vmRdLiyHdrktx+tkrPup8g12fyhnCB/o0TJ6D+pJ6edFdk85HfaoC9+VOJp71K5IDFp4PTKjbG75F5U7ZUzE5MGDrdK0kDlcF2n6Ji752s93U/VGt+6QGV9E2+H5HYSHKXb4YwXVaIW9EkkhIMR16XjBJj7gIRKSjsJl30rJFJqC+UYBXwQbSXhDbsKPv1zJPHOZtea07vqs8ldAF2mKIK8abBr+9FWfDeW0AnrX7tD/xZvluAr2QIDL1GIQYvOSp7zJKh/avmzEpE7ZlmfQ1Dpb01R1039RVl9odtJkGfdW3tU5HoTs5VIl9LuW33VjnWaJYIuvPBCnE4n06ZNo2XLlodNivs23Hvvvbz77rts2bKF6Ohohg8fzv3330+XLl2adTyDwWAwGAyGowp/g0ID7A6VMPnqND/m66iw4pS/jQg6MAfna/DVQXWBAgO6nwqb3lPfTDisxv3dC+Gs/0LROuh+mhLlvLUST/XlMOMvSmnDBbGor+ejyyNx0/EZElQhK3DBERWZkZOYHSkdCzRG1hQOKCUtIVupa75alYx9erOet7u17hXPK7AgrZMCIoZdKRGU3EYDUkMBia24dJWR7Zqj3pzEHJh4B2x4T6KisUJle11OgDYjVUJ3/N8UTLB/hVLlOh0P2QNh4WNa3+g/ySnK7KkSvMxeKoeLitVad3wBe+bLDUtuozCJgZdKrOTO12fX9UT1YfU6UyVvI6+1PtcYK5XPiuV2uCC1gxy1xNbQaZKS9Mp3yu1runeuGJ3LCKBvTbNE0Jo1a1i5ciVdu3b9XiefO3cuV155JYMGDSIQCPDnP/+Z448/nk2bNhEbG/u9jm0wGAwGg8HwkyUU1GZ9zWuaTeP0KFa528lyCCrzDv++hJbfvtSpKbAgHJRACPi0sXa65b5EJ0HBOgUAdD8FRlwrIVRbrHK4UX9SHPOm9y1nKqwSss7Hw7YZULUbKnZAzgidZ+UL6leJTtbg0V5nqK/JV6ef2BZKeXNEyfXYOkNDQeMyLPGFnJOoOG3um87bVP4Xn6l0tuk3KbjAEQXFG+WwdJ+q/p/R18OsWyWwsgbKYfrsdq09GIDC9UphG/cX9d2kd4fV/4WOXpXolW2X29ZhHLQeonu2e6HCDmwOlanVlkBWH0hoDcffDfP+LlfJVyvBM+S36rVa+hQUrpOLFJehMrv0rhJCG97TY91PgcVPSMAMu0qzl2bcCL5CK8EuRm5ZOKR+sIWPQNF63Q+bQ2LS6ZEYTDTtKN+FZomg7t27U1pa+r1PPmPGjEP+fvHFF0lPT2flypWMHj36ex/fYDAYDAaD4SdJ4Xr45PpILLWvTk39BWuh7zkqJTscvc6QO/FN+OrkLg2/WiVjm97XRh0khHqeAfFZchNAzk9cJvT8pQZ7OqIkCOpKYNAlinxe+5p6U3IGQZvhWks4pIQ5kKCKsoaz2h1yc0b+AVa/KqfCW61ytRMehI0fSFDUFcktyh6saw965SQFGlV+1vss2D4TsEGP02Hli2r7aayxhFMQPMkKkugwDpa9CJPulRNks8OMm+RyZfXTYw6Xrs1bB2P/rNCGIb+Dyn1KqmuoUIR3y74qedv1hZWEF4SMXjDwIoUr5C2FXr+ABf9UqVptkfVZVir+e+JfJRbDYd2zuQ/oXix6zErgS9dcJZsdjrtd17R/pf49+Z8SXitfkMAJhzRkNTpZg1LXvw0b39WaaksgewCMvkH9XYZvTbNE0P33388NN9zAPffcQ69evXC5XIc8n5DQvFzyqqoqAFJSUg77vNfrxev1Hvi7ulqJKX6/H7/f36xzHss03TNz737amM/p6MF8VkcH5nM6evjZfla+Olj5HwgBtqhDnyverjk9Q66CVS/KiQBwRkG/8yG1K3zT/SjbCRvfl1iKilEJ2YDL1GOzZ6EGjrbsB5X5kNIZ3ClyMerKwdsIuYs1+yYuIyKcOk+CXmerhCsUhjVvagMP+J2x0OFE/OndoHQLuOKh/wWw6EmJnoG/UeN/XLocKH+DHJNACBwxsHU2jL1BwnDrdAmRxBzocSZgg/yPIb4VJLWHypd0f5yxEhh+L9gaoGV/qKtS1LUtSoIyvTt0mgJV+TDnfq3Xk6Ro7r7nQNEWOV52NyR3gOyhEJUoAbrqVYUPdJmqFDd3osoVFz4CGd0gawDkrwdcStDDJYctFADCsPEjyB4Cw34PH1yh0sSts6HXWbBrvgIfhl4FRVvlZK15LdLbE5umz+jkJ2DnbMjooe/M9JtVFjfoYpj6tEIl4lpC2xGQ1O6bvxf8jP83ZfFdrssWDjcVZ3577Adl1B/cD/R9ghFCoRBTp06lsrKSBQsWHPY1d9xxB3feeedXHn/11VeJiTEpGAaDwWAwGAwGw7FKfX0955xzDlVVVd9oyjRLBM2dO/d/Pj9mzJjvekguv/xypk+fzoIFC8jOzj7saw7nBOXk5FBaWtps9+lYxu/3M2vWLCZOnPgVN8/w08F8TkcP5rM6OjCf09HDz/azqs6HD65Uudfh6DRRjfLNIW+J+lfW/EdOSVPKWRhFUXc9SbNmyrYpMjqtGwz9rQZyNlZpJk3Bmkhjvrcm4galdVFPy+aPVcK1/Fnofgp+HMyqbMvEhB24CtdCSltY+jQH4qrjW2nIa32ZStd6nApr/gtDr1AowCfXg7fq0OsY/Fs5MZs+kIPlcKnsq2SbkuWc0Zqp01ABexbBvpW6b65ordsdp5K9969UGV6M5XYFvCpfi0mF/ufrer016qcJNKgMb+jleryxRv048ZnQ5yyVtbnjdU3pPZTktnWGoribsDvl5qR2gA7jNQx17oMQqJN7NPg3cnPqy9QTVpOv44RCERcJwJMoNyxnqKKx2w5TIEJDpZ6PS4eBF0NjtVyt5Dbf6uvxs/3flEV1dTVpaWnfSgQ1qxxuzJgxzJ8/n6effpqdO3fy9ttv06pVK1555RXatfvuqRRXXXUV06ZNY968eV8rgADcbjdut/srj7tcrp/lB/n/hbl/Rwfmczp6MJ/V0YH5nI4efnafVVJLbWq3zzz8853Gw3e53oYqqNilMjinB9b+B4L1gE1pZqEgEIads6BlTyheqw23Oxryl8C7y+DMFyGjtzbrDjsQ0H48swe0vkgbd5dHooAgRLlh5DXaxIdUIeRa/C9c425Qj487Rj0/rhhwhDXgNMoD4UboMBpatNfGf+7dEB0LtmAkzc4dB1veh+RWEB0Hk+5U0IHNDt1Phu2fKZVt2zSVkmX1h9Megz2LoXiThFXbIRJW9hCEGnV+nyW03MnQUALVe8ATC43l4KtUUpwrVkEVw67UINdWA1XmVp4LG99WGVs4BK44OPFByP1c5whbs4nCXt2rHicr7jsuBap26rncz6BsM0x5ANweraGuAPw1Ek82Ij1iASc0lOredJ0Is26ToG3yLhqKYfEj6h9a9QIMu1whCd+Sn93/piy+yzXZv/klX+Wdd95h0qRJREdHs3r16gPuTFVVFffcc8+3Pk44HOaqq67ivffe4/PPP2+WgDIYDAaDwWA4qnBEQZ+z9V/zv0zXkxRM8GVCIYmcdW+qyX7Du9qYN1TCsqdhyyfqWQkHISo6kgwHCilwxcrp8dZIFDncmn8TCijCevlzckyy+ljvcaoHKbs/rHgBFv1L5135ooIGvLWaYxMmEtQQDshBcUSp78ffIOclbA0cdUYpIc5bo8CFFl3k5vQ6Q65NTIoEkK9OA0691eptqtitOOuAF3bOUardsmfkEDVUwO4FEhxthkHOYCXole6EpNbgSZE4aBpianNErjs+MzKY1GbX9QQade7MnhJzn/5Fa134T2sAqg3cCUAI1r8FI/6ggIlwSNdvc0K/87T2DmM1xFQ3B6JTFSO+8kUd2xmlfp9wKCJ+7JY/4fTIbbI5IHee9Zwr8hMVJ7dozasST9tmRASS4VvRLCforrvu4qmnnuL888/n9ddfP/D4iBEjuOuuu771ca688kpeffVVPvjgA+Lj4ykslN2amJhIdPS3SD4xGAwGg8FgOBpJbQ8n/l2DO3fN1ea/64kKEqgrlRCIb6kyMoB9y2DW7dqkh0PajEfFw5jrleqWtwjevhjG3qRyO7tDjzdWSTw4PRBs1OMOl2b4eGsj6ynZojK4zpNh51yVztWXwsxbgLDeH5ehyOdProeT/g5r/6sSsx5n6TXYNVw1ubU28zlDNLcmHNamv3iT5vGsfR3aDNXw1V1zrRk4rZSoVl8hUeaMlkgZfpVERk2x7k1aZ3jvNxJ54bDK0/qcrbW/8WsFH9SVSChNfRw6T4SVL+l6PYkSZkGvju2K1b0mrPtSUyBh2mY4LHgEdn8BHY9XeWHFbp3L7oSqPIm5nZ/rmKc8oZS76nw5X2U7lCQX9GqYbGy6osF9dTpn7lxo2UeJdf56a65SmYSZ3SnnKToZBl8Cu+ZB1X5dazjMgXK5qDgJ3rJdihDf9qni1eMy/p++wEc/zRJBW7duPWyEdWJiIpWVld/6OE8++SQAY8eOPeTxF154gQsvvLA5SzMYDAaDwWA4OkhqrZ9uU8FXIzfni/uhsVJuQ49TtbENBuCL+ywnp0obebtTG/4N72nDn7fYmmFTCElt5GbUFGizX1ci1yE6SSltUXGaBeRJhKYwrZg0a9hoe5hwq1LaNr9vCS673KPKvXJVGivUF1SdD+U7lGjX5i/agFfWaE3nvCHXascsOUPtxkD/8+Rmpdhhy8eKmg745NyU1ct1mXSPBMHyf0scBX1a28ALoWofxKZaDppd15PWVfHU22cBNgk8m029Mu/+Bi74UAJv11zFgdcUqi/phAd0PrtTx/HXS+S4E6DjRF1nco7udeVuIByJ+G6w6Z4mtFLPUp+zJZrajtA9DXjhi3s1RLXdWNj2idYV9MnZcbh0beveht6/lPhb8199hk6P4rm7T4VNH8olq8qT+AyHJLLiM3T9jVXQrp+u1d9olT0avi3NEkGZmZns2LGDtm3bHvL4ggULaN++/bc+TjMyGQwGg8FgMBh+XoQCsOpllbg14a3WY7XFmn9TvV8/4ZCet7vkwrToLGfFZoe6Ym2mh18Dc+4Df62Mg/gsbfBHXadQhIYysB8UzR0VBwMu1GZ6/ZsSEdGJEh0Oq8ciHJIz1X4MOGNUghWXIXcnaPXajLsZtn6k9c6+Qw6G3SlRsH2mQhv6nC2nI2+x1uxwyZFyuNRvE/LDgn/IhbI7tfa6Yph1B5z0sJwbu7UmZ4yEx6JHrQuxnBK7A7Dp+vcshIw+KjP0JElkBOpVYjbyD7D2DQUthPw6V5+zNKy0bJfK6/qcLcdn73Id298gQRnw6ie9m+b7lO/S+ivzdI7oZDldk+6BDW/qWhxuXXOHsZqptG8xlG/XbKahV2o4bVQM7JgjB6nT8fpMep4Oe5fp/IFGqNgjMRSdCH3OUXldVr9DSyAN30izRNBll13G73//e55//nlsNhv5+fksXryYP/3pT9x6661Heo0Gg8FgMBgMP1+q9+m/+oM2z65ouSEBL+z4TOVOdSURAeSKlZCoL5Oj0FBhPWeTO7P835q7U7FbG/PYDDlK+1dCxwmwPFeiyN+o+TfdTlY5We489SOFgyo7i8tQ35HdIWFls0vMBHzQbhR0mSI3x1undS18FI6/EwpWSwDVlegYdqdci6BPG/eiDRIKoWCkX8gRBdmDdL12p35CAZWU2Z3gSdDfia0kDEN+3Sd3QiQxzREl1yxk9eY4ouTi5K8D9yQo3CBBVV+u49WVqodo4IXST/lrYPV/dc3hoFLnfDUaRLrxHfVlHTQaBsLq/1n4iOWq1VvpcAH9uBM1ADatC5Ru02eb0k7zg+b/HbBrDcue1jDbtM4qQ2w9RIETpdtg6yc69sALdX+91da5QwpvKNkssdR6qIIrDN+aZomgm266iVAoxIQJE6ivr2f06NG43W7+9Kc/cfXVVx/pNRoMBoPBYDD8fKkrVQlbk+PTUKl4ZX8jbPlIwuHgxnmn24plDksoxaVHNvbeGpWcffpnCZoRv4f172qzv/4tCapTn9Tragq14V77hgSS0621JLdRIEHP07RZH3Qp7FuuIAKQE+Ovk1sx/haYcYseb6jU4xvejURFQ0Q41BZLRCW0kngJh1Ra54jS9SVkWwIkFBFGNkvYjfszzLlb6+97tlyyxirdr6Qcq1Suhe5DGLDbVEYXmw59zpQLkztfx+w4QWtZ9owExoRbYd7fJUbjW1rlalYIwv7VOuaYmxQO4YrW3w6X4q6j4iV+omJ0LU0x17XFOndsC/UYRadAj1M0pLZgjeV+OdX75IqW0Ox2Mqx+FTodp5CGmBRY/YrWVLVfn6UjSscPh6C2BPJX6zNo0VU9RzYnuL6apGz4Ks0SQTabjb/85S9cf/317Nixg9raWrp3705cXNyRXp/BYDAYDAbDzxt3POQMglm3qtypiRZdYeClck26nChBFBWnTTgo+MDpkROw4B9W4EGW5SI1SiQ0VAJhbZBTO0Kr/tqUBxqhqgBWv6x0NV8tRMVCdYF6kda+JoHV9zxIyIKlT+mcjij1GYWRSMhbDCc/CruAlr0VCtAk1Gx2ra+2WEIBJGi6nQJLnoTYNIkAT4LKyOpLIaW9RFwoCLGJem9yWyvFLqS1djoeJv5NYQCF62HAxbqO+nJdqydBosOTCG1Hw4yb5ErV5EvA7fgM2o2Gob/T7J2A13KPrJQ4T5LWGwrqWmoKVNp2wkMSHzWFENdC0eQFqyG9uwSiO/6gsAmbXKek1oruzuqnuUNvXaiSwrNfVaJbTT6kdtbnZkOhElumWZ9VPwVJ5K9R2WHh2oO+NDbo9UsFatQUqgwyd74EVfepCl6ITftBvq4/F5oVkd1EVFQU3bt3Z/DgwUYAGQwGg8FgMDQHmx3Wvmn9V/6DKNkCuz6Xa9JmBAy8xAow8Cp9rP8F2tzXV0DfcyUSavK1+W3ZBybcDjtmw4hrFGt90j9UbuVJhoX/gtwvNF/IW6Xyr4BPJVXbZ2qQ6bJ/a+hnZZ6ET0IrSG6n14WDKsXb/pn6U0COUdYA6DpFAiK+pV7nrVb5WjikxLh9y6HPryRWuk2FjJ4ScKXb1f/iq5UgsNn1nsG/gbylEiO1xXJH5t6nVLXkdnJh+p0fSYCzOSRI+p8v4dhutI5ndwKWO5U7T6/1JFux045IxHRdiURJdJI+k8Qc6Ptr2L8KljyhNdSX694ltoIuk7VOp1vuDgBhaDtK17R3me7f4n9JGG2fBflr1Xfk90pghbyw7g25cJ0nQ0Z3hVu0HSXR2uSqHUz2YKjIk3u34nko3SqXafZfYdFjEsOGr6VZTpDBYDAYDAaD4TsSCql8y1en/2Kf0ErJZsWbVJYWk6pN9KpXJGZsdj1nc0BaR7kgk++NDD+ddbs24p5EDTqdcLtEhydBjlHufEVmp3dX83/ZTh0/OkXldrvnaxZN8WatL9goAbVrrjbdpz2tTXr5Ljk6To8cJJdH5WuhAAQbVEZGiq7pkz/ClPskWkq3HhrDnd5Na137Bhz/N8jsreCC6GQ47d8SIeteV2DB0md0/U2ODqFDhYCvTul1rQbJNWk9RINd/Y1ycWpL9JqELEjpqHK4DW/JibKh+7RrrkQZ6B7VlUR6lKr3Sdxl9tLzNUUqu+t2D1Tl6/mKPUqHS2wNx98FuxfKnQs0yu2xR8H6N2DCLbpmb63EmcsDcWm6PxW5Emsl29TXM+9Brdvh0kDWYVeoLHHrJ9bMJasvqfNkqC+ReGqKTT+YXXMkzlqn/hDf5J8FRgQZDAaDwWAw/NDUlsDG97Rp9tVqg91xvEIL5j4ADaXagHuSJFzWWDN4HG4INKiZ3pOonpmVL8qlKFynY/tq5c7sW6r+IJsDTntGQsfugH0r1avTWKHggNz5arofeCEE/HKLQKlnYPWg7NVgz00fyLXZ9bncj8Yqq1/GobX2/oWuAb+ExbiboHw3TLgN9i5VyVd9udyY+Ey5R5Pvhc9uU3lYUwjEzjkw8CJI6aBrOfu1SODD5ml6/845QNhyiMKQ1gm6niAXpL5Ex3G6YeR1OteiR+S2uGKg43Ew+k8qwyvbCWG71tJxgsRnj1PlqJRu1b2tL1MpW59fwfQb1EOV1U+u3Ge3K3ACm+5VTYF6sLqcCEN+q3XbnRJjE+7Q8TuMk6Ozez4Ub5TDFp0EVU4JsHBQkd2g44bDSrGb/w844wUYf6sEoisGOk+SoN46HYZeJScJ9Fmn99D3pL5cn2vroT/0N/uoxYggg8FgMBgMhh8SfyOsfEGzcZrwVqtkqf0YpayteUX/hd9bpcjnQZdqzk5MqtwDp0sb7jn3aiPs9MjRaKK+VKVh4TA4nerv8STA/jXqlylcrw0+QGYfbe4/uUlDT094UD1FhWu1eW89HPqeo3k/7nidt9VAuRFhq08GgJA29g4H4FevTfsxWt8nN0qg9D8PNn+i8rO+ZystbccsuU82h0RQbAs5X/MfhjNekpDbsxhqC7TWgjUKCeh0nErJbA65Nf3Pk4CMTlF0NUB0qlyUOXdbQQF2iba8RZC/EoZcLjHaaSJk9de5q/dDeR4cdwcQtoatpkgszf6rPqvWw1RyWL1fx3e4ddyafEjtpPfYHSohzFskgTjuL7D1Y/X01BaplHHQxVap4XMw7Gr49GYl6K14PvJZOpwSq3YH2MIKoDjxIYmfuhIo2qx5SZ0nwef3qDyvZR+l6+2ao9lNye0gq6+EXlPMueEQjAgyGAwGg8Fg+CGp3C1H5GBCfm2it8+CKffD+qhIAlxNgWKw49LV9J+Qpcdri7QJtzu0iV//9qEJbP4GvabjcSqZq9oHxRtUbtZutJyW7bPl6mx8HzofD3Pvl3Bp2Rt6n6mSq5QO6kdqO0LlY42VGuCZ0VMCxlenXpV2o+VCZPQBspSeFpMEK55TL43DAXMfgt5nQGo7Jc0NvUK9L9i02ffVAWGVkYUCEmrFmxRNvehRzc7pOAFW/UfC7Pi/ad6QJ1nlZVHxcqecHt3P3mdKPDZWSbwFfXJkgr5IzHifX6ncbsM7ihFvPVy9SO9fobWMuR6WPy8Rd9I/dc/3r4TFjymoIDFbYrOuWILMW6PSvp6na+1dpqhXqLFGrlZNgYSjzQbzHtK8pvSu6mU67nYJsZyhSnoLePXZ2WwH9SiVquQtpZ1+cgbrcV89JGapvC65rRwrrPfkr9aao+Kh/egj/pX+OWBEkMFgMBgMBsMPQfku2DVP/yW+Mk9lSq5obZwP9HCEtZlNyNam2lcnJ8UdBx0myA3JX6uG+ab3hIJKYetzlgalhgLaMIdD6unpPBl2L1Jy2aqXdY5QQK7IwEu0kd/yscTAqpcVmrD030o8qy2CX74AuXPlHjlcOubsOzTHptMkyB6gHpaVL2itcZlQgpLewpb4iklTWVxMisTaqD+qhMvu0vXbbRG3I+Czhom65Lpk9oK5D8rN2PIxnPgg7PpCrkpUrNwhTxIUbQRCKl1LbCVREZOi2Tn/x95ZR0lWXV18l7e797i7uyvM4B5CSEgIJIQIFkIIbgECIYRAjAgx3B3GGHd3755pd60ur++P36upHhggCQTIl7vXmjXTJfddeT3r7LfP2UeCULRXSxk9IHION2YDjaX08Wmrg4yWroR8TbgS5Wf9H0klXHAr9tk2J9bceYNJh5v0fWqEtj4F8eo9Rxp6Ptfd+TJqnoQz3+ivM6/Dy1mjOxVSuuVJafVjpDo6EyGYM38iLbqDFEWHi3PzpEAgY+YTneFOkkZfgmHDwtt0jADJOgu7U1r5MPVkMSJtcAyGBBkYGBgYGBgY/DPoaKauxuag5uSj0oxq9vBk3teMAhNo409CBqTC5pRVoW+5ilnpbpGg5GtB1dn6FGNt/AtB+ahLSJ1qqya9rNcMam8OL4c89D8VonV4KaTg4OJOioINkrT+cenke7CXjtlWu5Mp+rc7MFio3omDW1IW/WkcLsZpPEwdzru3oI7IBukp2yL1vhNy5bMIgL+Z1Kwx36R2qXYPdTTNZZCpur18N2rtQTSC6pSQjuVzw2GMGqb9iPWNvoRGptU7IDGDzkI9ObAAchMJs79BHyl8IZ/Vs8dOfVNKHmYRDheqUMjPH7ubebRWklKYWsD6R38dEnFgMcrL6l9B2hRFOZp5I8TFlQhp89ZJ793P+AEv1/Y1QUJOuhujiI4Gady3eS3k58ySsll74yFc+YacL+14nr31pDLnIedxj0icWXO5ZWmeiqV4S3m8wazNzh56UvnZWw9hMyToAzAkyMDAwMDAwMDgoxAKUJey/o9S7V4MBHpOl0Z+hTSk9yPgpcYjRgi8jQSrDYcIjD2pBO6eNJ7mx8iIzU6gXTAMEtAZBxdT5D7hO9Kiuwjc971NPUiXMdKgMyV/O6loA04jkI71vWER8bFKVhDYOz0oHPX743U+3SfiZJacS5CuKKlV3SZClg4vsxQpi7zYnTqmQBQOlxx2ax1B9m39H1BiBpwGESlbhxvauzfF5xNTxgqG47ymKP1yooI47H6V8fqfgnrlcKOE5Q+0yKSlcvnbUIz6zYcI+lvjzUujEchgZk9p1+tWH508SFP5OkhJjKC1Vll9inKksg2k48XWKxspb+v/xNkdWEhd1NrfoNql5KP0BL1cX1HITddxnElSttR0lLGjEc4vvZh9qDsIuTuwgOtHo9Koi7lfJOYVI1M2O+qi00PtT2qB5RqoTjVbFqLhE9/X/+MwJMjAwMDAwMDAIIaQnxoMiaDd4aKY/+0fx4PMkJ/AtnonpgKxIDaGtmqC5xgOLZEmfBdTg/Ya3MNkk9IKUTq2PMnnHC7qbPKHSJv//sG5bf6HdNpDFMlve5b0q4QM6nN8LRgXSKgTviaUAlcSznB2p0UIxPW7TabHzNDzIE7dJqFopBYQ2Ic6mKPNjjvbmY/hZlayAmvv/EFSU5m0/TmpqZxxa3ZATApHoNg4nATsQS+KSsFQivoPL5fm3U9NTtV2XNj6zIY8HFws5fRHiWmpwBHN14K6seVJuEgkwtkk57P/yx5A+XE4pepdEEVvvXRwkeLuETZp8lWsedp1nE/McGHMpShfnjTqqUpXxxu+puRa5yWrzsnFnu57m2sXjZBK10Cggj6azabkWqqMJNlJC8zpBxn01lo22ZZFeiQECWypYPzETNLtJN47sBg1KNghla6CbLXXsp7i0dRJRcLxFMP3w53CXhl8AIYEGRgYGBgYGBhIqDxbn8baWTapxxQK7Tf8KU6AOqOlnCD+/SRI0eOfvvuaIQsTryRNzN9GepIrieL16dfjNuZwS7telva8Tk+dYAfqUUxp8bcQGBeP4v2OJkjJ5r9DemJorYJItVRAalorUSUcLghE1wlYNrfXMsbwL6M8Lf0ZfXNS8lmbJ5WmoWO+AbFb9jPrOnZpYxt9aObcTv2RX6hYe96gr03eQPYyMQPFIylLGv1VafmDNFg9sorapTGXSrn9pM1PUuCfmIEylTcQw4bVj/H9QDvqUFsdaXKBNup7ikdjluBKhhTandh6Fw6X+p6EC50nBSe1qJ2Uw1d/YDWItWqsDi7Gja/7FBqxzvgRPYSCPl7P6EqdlLeeVDp/GwTLmSAtuU/KH0CvoLp9qDdtNdREJeWwb9k94+U6Gd3Zi47GeDPTjO6cccRKy1twi+S06pu89ah8dfuo+7E74vdY+Qb24OR72fPtz3/wHh17GfVmBh+AIUEGBgYGBgYGBnUHpDd+aKWAWdj3NgFw1bYPphjFULICMtAZidlS7gDqYGJorZQW34lyc9LdOKS11fBe0xHp1J+jdhSNImCu3kEQPPF7vH90Dal3njS+40rkT/dJKAQVm+LXqj8gTfqBtPAOyFhqIYQmGrEapc6kNqixVBp7OYH6zpdQuEqWUzPUdFTqPQNSYndKb11PsB5ojzdA3fYcpgitVZJbkJnanVwjoyvNW10JUm5/0tZe/h6qz4wfW25uAWnjn6QRF2Pn/faNlnGCnbS72beijDUc4noBr6QIhCcShNy8eZ3UcFCacwfEYuvTECRPCs1G04uthqcOKdCMqjT9R5CYg0viZHX366SsVWyh18/aP2A8kduPc1z/RwioM4F9n/5jyOLMH7O/CemkK9qdUiQgHVpO3VNGN+qi2utIW0zJldK7Qkptdkm2eC1P75kQ8dQizsJbh8FBRjdpxcNxcwtbJ8WnvZb0vH7zuOe2PUNPqvRiLL2LRkh2uww+CEOCDAwMDAwMDP63EQ5Rd9KZAMXgb+MJ/YeRoMSME7yWLo27nKA+ZnsdsOp+cgdAPmIESEJ1aash2F92f7wuxmajR8yE70iDzkDd8TaQqpaUzXfTi7FZbjpKzxpPGgQkOVeae4e09rekatkcqBUjLkJdaavh5/QiFIZomN4zA06n783c20lbO7iYYN+ZSFpb0KdjtTES8+szn5KjbhOl4pHxPXClSIEW1utJxTq64SCEs2AY6teQc2kc2lqJO1vlVvYqJQe1qPdsq5aqGRLgb2X8hHTW6Uml+WpLldRtPCRh/7soQM5WrL97zZQ2/ZmxGw6xlj6zUJBWPYq6EmhjXpkW0Tv7MalkpdXj6S9Wupn4O6MHqtj4K/g7FIRs9JkD8fSkkHoXDnEdu4txq3eQmjjxu9Kmv5C6F/ZDZIach8K36lHWGfJJkSifDbRLzUd0nPtbZ9TukQafg216l3GckztFSkg98ecNJBkSZGBgYGBgYPC/jo5GKwXuBKjahtpStr6TyUAn9Jl94u8VjkDd2faMVLUDU4Gh55GetvWZ+OecidLIr0EmjqwmULe7IDNt1QTES+8n0H/6ImyUS1ZJQ88ldc2TAplwevheJBSvM+k5lbSy9jrStIKWYUNbDUHy+G/x2ek3QARS8kmt2/sG6sOe16kVailjfF+L1SzVZRkNhEnXSs6WmoUL2YqfWfUxHZCbSVdRuxMJSvmDWXOwg/qdii1S4VBspcs3Mqexl0s1O6Wj66XUfAwlJl8lbfgzLm+yUV8z9pvSgSWQh6odcXIZ9JK+l9MfF7WWCmn7syhLrkT2IewnZc7bAHHY8iRrCbbT2ycpR6reTjrdml9DZMMh9suTSi1Ybj9UvcHn8pmDi1HuBp5KH6P1f+T8Vz1GHc95f0ZtaimXHAmM3f9UaoDaqplvRwP3Wv1+iOigM0gddKeQOulI0AmJUFoXjBYkUh6D1hplSNBHwZAgAwMDAwMDg/8OtFRgPV27BzJROJwUsWN1Ev8m7A6IxIlQvYseN+111F90xoiLpew+J/6ew4kKkdMPktXRIC2+h2C5M/rMkrb8nT4zR1bxWkIaKWbRKHVC4SB9eVxJ0rrfo9YsulOacg2W1XvekHa9Fk97ciVKk68mvSo5Jx4gS5CZsg0Ey9GwtPhu0u1m3SS9dAVpdBlWDYm3HgKU0w8VIxqJ/3G4JdnpnRNzwStbz9kcM2BooOlpUwmkJtCKWhKKSm9cCzFY8QvGTiuWJp4vvXIl600vpg6mdJV06kPStOtpvtpSSbrf0p+h5PhbqQXa9hzr7z2ThqLrHqfeKDlb2vYUeyIbf4cDENqS5dLA0yBoNifEI30n+1+xBTJ3ZLUU6JDm3YOC1nA47jjnSZdm3YIKVLFZ2vcu1tkVm9mjkhVS/5Okid+J24q3Wwrghsdxp+s+CaLYeJh7rMcUHOwqNrPGYV/ivHpNp7Gut575HrvPEqTBZ5IiWL5J2vIUZ+BJ5fWeMyCTBh+AIUEGBgYGBgYGX3zU7ZfeuYmn5jE43NKsmwkcPwkRSsoiVWvtb0/wZpQ6kPn3U3tRupo0o14zUCk8H/O03ZUguQpRggqHW/11OiG10EqNi8atp6OdjBWiUUkR0rWcHohBSi4GBWt/h3HDiodQfopHkT6WkA6xaTxCQN9eQ9DvTCRdKxzAIe3Fb3HtopGQjezepPe9eT11O/4WalQGnMr47mSpwx+feyQqDT5L2vqcVDAGohYJsQaHU5p6nbT0Pkn2uHJ0dB11PtN+BGlqr2Nug89CUQl2kHrobyMlLOiVXr5COv0RCFDjYVzwsnqjlr12VVz5CvmkvW+hDM25gzmEfIwZs6xOthrC2q1+Qb4WCOGA00lLm32L9Or3Sc8bdBb1O0V5pEs2WrbldidrsUl676fsZcWWuKNeSwXXrd4hNZbwWltVvNePbJYJQhXKX1MpjVkjIdY98DQUsg0b+bi3DhIU7KD2KxSACDmTOKf8IZgkvHtT3MDD3yKt/jXzmnY997jBcTAkyMDAwMDAwOCLjUC7tOpXxxMgiSB2yb3S2b+Vsnp8smv0mk5vmZrdx7/eZaxUNJxUsbTCD09/+zgkpEnjLkM12PEi6kneIBStxAzSogpHkPLlcEsJmZbCYqU/5fYnPU2iaL6tmlSunS9ClIacBaHa9BcC+IQMAu+C4ZgF9JpODU3PKTiqxQhINELqW/1+0vWW3INa0tHENQMd0ro/EOjve4e0skgYQ4RRF0NI2huYV49J0qF3rH9Pkw4s4hoJ6ZJskEm7nX0ecg4238F2Xk/Oo+4lRjB8zexL2A9R8dZjuJAzAHe7jiZSDaNR1pqUzZ+eU0lncydLB99DBWsug6wm53EvpRRAKu12TAjmP4CiVbJM2vsOZDcaxSxi8lWcwaI72SubHbLicDNmSyVkJjXf6o3UAQGS2O/qXayxtQrSfKzfkPh+RxOEtcmqm7LZSc/rM4e1lK6ktivRIuqNJRBTTwr3T3o36q7W/ObEDoalq/iOIUEfgCFBBgYGBgYGBl9stFRIldtO/F6ogwD+k5KgtCLsnqu2k9ZkdxB05g1Cefk0kJSNVXP3KQTKnlQUrlFfp16k30k0QrU7USdSCwj+i0ZCekI+ev+ELLOFxGxezxtM3ciyB6wCficpeOv/JA05m0A+5CVdatnPpdMepH7GZuM6HQ1WMN0mdR1LHVBCujTzZumV70kDJqNijP0m6k5rNQYDu15GkZl2nlQu7KsnXwWJyh2ESiPFVZdIEItuuwvTgbyB7LcUJxgp+ZC5kA+XM08ahMPfIq38Jecx4ssQpMYj0pSrSBP0NkjpXVj7+j9CfHpMlkZ/g7TEyq1cx+GOG0gUjoAIHdlITVYkZNU7WaYUvmZqwvqfahGMKPtrs3OWkXD8c+5UiGaMqLqSSCPc8GcavNpsqFF2ZzydzZOKEhdTiPyt3Ie+FungQmnez+gxlZQrpRVY91AWil9nNNZ/MFWzM8o3fPA7BoYEGRgYGBgYGHzBEQ7oQ52xJJ6mfxpILeBP79kErTEr4kAbhfGetH/fbjgUpDamYjNpV/mDKYZPyqZIv3Q5gf7cO1BIqndJngh9XlLyITiSNPIr8aaoDjcBcu+ZEARJ7JOVWme3owLl9pdW/oraptN+AXFKK9IxVeLoBun0X0Ckyjdix5xeJA04Q7rkVWnrk9LCOy31I0XKGyKNuJDUslBHnJR1HSvtfIaUtynXSu4k0gFtjjhZUBQSEPbjJtdYCqFyuKTZt0PIavei2hQMwentwALIQ8NByFHlFhqVjv8W+9JSjlLSVgX5mHEjZgtH1jD2yfdIb1yH4pLWJV5rNflq6bXvY0O+7Smrxqwna8npLyVlUo/TfSrvxdLoohHJ10hNUDQq5Q5kjcm5fD69C053G/7EehsO4dpWtx9yHY25DUZRfDo31o3d5tEoKY+9ZpLOGCNBJ4LdruMUpvfjw+rd/sdhSJCBgYGBgYHBFxsJGQTogbYTv5/d+9O9XozotNcRoO56hTSqLmNQh3L6/mvjhYLS4SXSe/fHTQMkguXpNxBcu5Io1K/ZTc+XkV9DOcjtL731Y1Lxhl0Ikajda6XnFTOfaKSTvbdF3MJWTxtvPWpDoFV650bpjEchQK0VUkoe6XfRMCRk6f2objHikt0LwrX5H3GL8PY63ONKV0rnW8YGjWVS95uk16+S3AkQhaNr6c9zdC3kMeKwyKylPg04DfKaki/lD+Kzb92IK5sU750z5SrstA8u0rFAv6MRO+vyDSg+SVmQJ4m6maX30aPo3Zupoaragdpms3ryxPrt2O3SpKulnD6Q0dp90vjv0i+odi/kqt9JkjuR+a79LSpNjJSnJJCy1nUchK7hAKl1rZWkp7WUs4/7F0ijv04K4p7XWYfNiUNcdj/qimJr69wDKCUfhcvh+uj7Kzmf/as6kVpq4741+AAMCTIwMDAwMDD4YiO1kJSi9X/44HsFQ+nB8mnD20Ad0qH34q81lVIXc8rP4nbP/wyaSqX3fnY8AZII4Df8Ueo+EVIRDpJiVbKcWo5QB8H8SXcRpFfvJAg/+S5Ioa8VdzpPqnThkyhIe98kwE8rlhSl5iWnnzT8ImnPa5CFguG8d8Yj0vbnmdfhZSglgXbem/wDnND2L6BeqbWaWiRPqqR2aeRF1MnU7pFGXCIFxPv+JupwSlbglFa2DuLhslvKkQ9TifpD0o7nIHC9Z1HbFWyTMrtRYxT0Sk63tPlJ7MGXPYCyFPKjJLkT2Q9XopVuF9QxAhj0QbA8aZgKVG2jj1LNTmne/aTLTb8ecndggTTqEmqwMntJ4Q5UtZYK65CiUsKz0jm/xwlu+c+pXfKkQ3wKhljjl2GEkD8IYmhz6JhBhL8FB79RX6MeKxLEzS2rF8Qz5GeP3MmchSeF9MSkbPZxzKUffX8lpEoTrpDeuiHeRymGERfR5NbgAzAkyMDAwMDAwOCLDbtdGng6ysaWJwnknR6C55EXxxuHfpqoP3A8AYoh0CZteIK0NXfSPzdW1fbjbY1jCAdJ2Rr3bUiPM4HXo2FqZ5wJNA5d9ztSsnpNJ03Lk0IdVNgnrfqlVL0b44WRF7NPi+8h3aytBlWnpYIUtTMfJS1u3R/hC74W+g0NPld69quW0majHigpB8tof6ukKFbkh5ZIFVuZV1IO1tQSRKtelpOdUGpS8qWVj5Ca1nAYhcvupPdNJCKteiRuIJA7QNr0N8451vMm2AHBaSnHEa1gKJ+PRiBTUSvlLyWPf9udVgqgg787Gpinw2X1/dlFDVLZehqQbnzCIlHJqH2jL4EALrwdUpJWFG90606W3rtPGnQmNVZJOYy1+e/SvgWYXVTvRJFsqaAuasuT7ENygZTTW5r6QwhjNAJByu1HquKIL0OSgx0QTH8ryufYy0inm3s7jWc/DvmDpdN/KR1aikKWmMl8cwdwvxh8AIYEGRgYGBgYGHzxkZghDTufYnd/KyQorfjjU4X+XRxe/uHvlW+gTsPd458bK5aqFUMsfc1bTxButxM8p+RictBjKnbMIT+E4JQHIFKHFkNMkjIJsBfdGe9ZE2yXlvyUwHfY+SgMDjfB99Ynpf6nSG9cb5ExW3we3gbGdibQfLXnNKloFArElidRdkJ+0rSGXUj9Ue0+q+mnh7NwxMJJqx4pZpPdWkWz0Jk3EYxX70C1WXQLNTy+ZsYO+S3iF433IfKkWJbhoubIlYDLWSRsGQnYLGOJvSg+SZmsJRyEEGV042fZICyNJdKE70j+ZlIFDyyEvNnspOC5EmmyGmhnjwJtkOvELF6r3SMlXIxi13cuBCWjG0rc1qfYs64TOLdISDrzMcwSgu2cc9kmacUDkuyobL4WrLCby/hsYwn/jtWlRaPUFeX9C4pjdm/+jPiKZa7xCftn/T+HIUEGBgYGBgYGH49IhMaZTUd5Ip/ehaf2nuTPdh5pRZ/RhT7CiOH9aK8jUHa4CWA713VIOJp1hr81bvftSqIYv2CI5EolXerdmxmj+2QUnxW/YOy2GmnwOQT1u17kHOyueJqd3Ula2+CzCfgze5IeFwpgNNBYQvPQqOVy5kokVWrnS9JJd9JwdMOfpLF2af3jKELJeQTn0SjB/uxbpZJVKD0JlnNbQ4mkzg05rfXHFAx/i/TW9ZCaOXfE9yu1AAIUaIXstVahvsT6BLmTSIXM6olyE+qwjiYCERpyDqpXWw2fcyVBXvIHcY2s7tKs2yBL2X347KivooolZvPZoBdiVboCZdGVKCUV81okzFm5Ekg5Sy0kdbG9lvv/wEKp/iCqS6/pfG77c6hxgXZqiCq3oaJ1GSuddLckhxQNkcJZuop9LVkBoUorkg4ukdqrpbHfgjj+O/2vXMYI4Z+BIUEGBgYGBgYGH41wiCfgS3/WyZzAJvWfR9pOcs7nOr3/CHpMxTb5RCgahSubr4XgedPf4w5lg8/EUrmzrXZ2b8wU6vZDWLz18ffGf5sgfsj5pLBFI9Kk71Ov0muatPinqAnuFILspEwC9aqdEKVoGCISU1AiYdzTvI0YOrRW08OmqYRrB7wE8/4WztLhorh/ze+o3+k/X9r3FuSkpZzgPLWAOUbD1AgNPJVi+1W/QuUqXS4Vj+c+aKuAdAS9kKBRXyfVa8o1jOdOggBEQhCAjgaIw6ivcX9FI5g2pHWBgPSYCpnrNR21LKsXRGTjn6Uu46VZN1k9pKpQdoZfSM1R4xHptF9KO5637LFtUpfRWHo3l1NT1NxAqls0AqF0etgfdwrmBuEAe+3Ml2RZj299CtMEScfMDAqHQYTSiqWTfood+RvXo8QkZbHu/U38PfB0addbPEzoMZk0vUCHVL4ZVat8I/dRahHXGnPpf6bmzcCQIAMDAwMDA4OPQcMh+tccV9cSlfa+xRPxkRd/blP7jyG7j9R9Ek/rO8OVRNqUK1Ha9rS09nfx9zoa6AvTeESacjVKiUTdyuxbpY1/gUREghCR8d+BOLzzE5QDVyIKRu5Aaeo12EeHOmjuGWsC6vQQuCsqVAWrQabNcrRzOHmrtRKyZXdgFJCUa32nU7+ajgbqdXpMpr4nKYdAPta7x+FG9UjIgAhJKE8DTpWWP0wD1aUPQhwkaeIPpI4aHOcSs6Sh50tHVkub/4rKkz9E6jdfmvETqXw9jU8rNklb/oFicupD0ua/sQfhkDTsAq71zNdQd7J68vrbN0KiKrdRBzP66xAXCZK08lFpwrelN3/IdaNh3t/7Jm5xk34gbX8G5SXkg4wlZkjeJmnM17HXtlkKTDiIYcTMm6UtT0tDzkWlKl2FCtp1ktR9grR/IamNpSt4r6WM7zvdmBxEw5hqdJsIGT6ySlr7G2nuXVLfWdwbPadKM36MqYO3lnNe+QhEzzQ7/dRhSJCBgYGBgYHBR6N0xYkL+yVpx4v0OokFyf9fkJyNetF9kmWR7ZWKR0oDz8Btrfkoas2JULkVAhIjQRKKyvQbCKLL1qNyeFIgQL4mCEc4iLpTuws3sd6zpInfp2bowELITNEI9nvw2dKuVyXZrO9ZJCghg2B669ME95EwZMcm1ApXEqpOYoY0+lL63xxZDTGKRiENsRqaWINQVyKBv8ONQcHKR6ijCbZLX/qL1FAmlYm5e9LYq5QCnNeWPci9k5CBgvLcJdK0H2HmsOmvrGXqD63aIjfKUUoeak3eQP721uGYZ3dLfedQk9NSAVkLtErLH7SyF6PSvPtQZg69Rw1SrK7IWw8Rcrj57sybpFeuJFUwJU+adBVrzuxOL6b970otVZzhoLMgMcPO47yGnCuN+xZEKa0QU4S0YubZbSL22LF0ypCfve1oYh/K1lkqlA9is/hubMCHns97z33dImQN9F0afyW1U4YEfeowJMjAwMDAwMDgo9F45MPf89bHXb6+qPA2oGhI1LgkZf5z30vJI32p53SIRkJ63ASgvf6DfYs8qZATb6O08mFqQXrPJh3OnYwqkNOXNLua3aR4hQOS7Nb4yfExS1Zg47z47rhJg91BylTJSiybK7Zi1xwJWgF5kTT0PAhHv7koFzGlaNPfpNMelhbewTX7zafh57rfSYUjUVhCTagl8++X9r0LCQqHrLqdNlSg4tFS8SjISFZvqb2RWqKsb0hrfivZLWOEvnOZe2IG5MCdFLedXv84KX+tlVLFFtIHHS56AVXvREXqexLpY0lZ7FvX8ahCR9ex1knfx5ghGpHm/0za9YZUtRXnuOIx0pNfihOgWNpayAcBPPQetVCzb+Mci0fSw8nXzJyyeksD5mNC4fTgDOetky56TjrlQdS/2n1S3gDplSu4REY3VLXBZ0szbsCkQrLIqY0zitUyhYPsUTgIyUrvxtyqLLc9mwOiGOqAUGV0oRYsf/D/z9TTzwmGBBkYGBgYGBh8NHL7Y498IqQWkgb0RUQkLFVsllY/RkqfBCGZ8F2paGS8KerHISENIuVrIkC32z/oSmd30ZNl2YPUtKQWEeDvfBnVYPBZECGHCwc3TyoBdyQEKQsHed/fgnKQlAmZKtsYbxwaDXN9RSE15/9JOrKWhqTJOVKfuRCWmp3S0Auo7yldhfqRnItyNOLLkKTMHtJbP+LzwwdAjBxO1JztL0hzbqPupq2WVL30rqQBbnlKmvRdrL1TC+EXVVulLEmhdpqlOtyQBl8z5CAaOb5/TUej5ExkHTY75G7lIyhWAS+KUdV2iFCPqdTZtFVKb/8YB7bes6Q3rqPWKCVf8rdLE6+UZlzPesJWHyF/pzOKRlC6gl6u2VJOml63iaTI1e9HKYqEcHhb/Rh7MucO7iNPOmva+RLmE3PvYD6xsw/64o1R04pI/avdyxoV4W+7i3qyPW9B7rx1klfcL3veoj5saS225VuflGp2sTdpxdxL3SdJU649vt7M75W8NShaSdkomAb/FAwJMjAwMDAwMPhodB1H6lLQ+8H3Rlx0fFD2n4CvRao9SrAfjaAKZPVCZfgo1O6hfiTW70XCzeudG6klyRv48dduLsOxa/8CAuTuk1EJ0opQE5pK+VyX0Sg8rRUE2c5ODl3rHod05Q+S6g5I79xEADzyYvY02EGqWiQEQUnKoh/QgYWs0dfMZ1ILIEltrdLuV1AGDi6mZqRuv/Tit2g4Gg4ROPechinClichB4kZUsVGae7dFOArCqEr34Sysva3kmyQqIaDnG3xaPag4SAGBO111Nv0nw9By+wWd6cL+iRZRM3u5rVIiFqj1sp4nY3dARm0OaSek6iHaatm7ETLkS4aoeaqaCQpeGsek9xpKCLv3GSJO5F4qtjKX7DnW5+GaAw+V1r/e/ZNssiW1Uuozxxp6X3Mt3gUKlDYsuGWNa6ikLnyjexz2M/nd74idRsPSYuR09j6bYLsHVzKPBffyYtRi+wNOcdSutLZ02EXSEWjUbr6zeW1qdeg/NVYqlAkDAlNyITQ9pkj9Zlt/U7slg6+h4qU3YfUwe4T+P3ofP8ZnBCGBBkYGBgYGBh8NLJ6SSffIy17IJ7S5EyUhn+JQPs/jfV/kPa/Gf9569NWvcx3Pzw9KBxEhelMgGII+VFFsvpIzo/oM9RSIb17S1xFkqTtz0oHF0qn/BxjgHduIvDNHUCBv2zUwzjcnQaKSmVr2cfNf4MASQS3eYMIqDsa+E6gDTWmx2QIU7tVIJ+cy+dDAcsFLigpQgpWxWZstAOtFsFwo1qVrpRGfpW0vrYaSMaIL9MItqOBID4cQuUrGimdfK/Vy8dSH0Id0vOXQiTSivh+1/EoJrtfJWVuwncwx5BQt3x17K/DSf2RxFxdSfF+QykFjNVUiivc7lchRDZZjnVu1ml3WX2AUjjHXjOoX1LUKrmxGru2WE5uO1+kj9GuV1GGCodLR9fH66Uk1LLUQoiLK9FqumoZRviaIY3t1vnIxl5n95a6jIN4hK2eRm3VlnrmhqgEOyBpHfVS/T6uPfF7OPWldZH6zZP2vkENXVoRBgilq6XlP2c9/hZ6NZ36cykU6nTv2CyiFeRau1+TCkdIa35NWl40FP/cyIsh7E4P52TwkfgndWADAwMDAwOD/1nYbDwxP/0R6YxHCNTO/i0B9sepMZ8GDiz84GsHF0tlGz78O74WnrrHEA4Q5LbX8nfl5g/W9LwfZeuOJ0AxeBswS8gbzH7EyFhCOulfnpQPfsffBhEpWRl/bdfLNLbsOV2SnfmkFZEu522UcvtB5jqamH9HvY41JO0xFXe0nD6QnUjIssoW74f8mBwcWS3Nu5+AfObN7MnSe1HBgh2oEJ40VLNVv4JUFQxDWYpZokfDkKqMbqRsLbgVMtdwQNr0F2nYl1iPzcEeSNLu16VZNzOnjiZqgiIhyZEgjb8c44EYQU1Ij6uMUXE+rkQ+HwpA/prLIQnNR+OkxumBPEQjvNZWg5IWDpCWOOobGCAUjyKVbPqPuGfdSVzHWwcB7TUN4hfwco3UAs4zKRvLbXcq/ZMaDkin/AxCl9tfx0iSxP4kZfGdnH4Qnn1vS92nUB+1712+k90H9cfbiBFD1CJQnnSu/e7NUt+Z1g1i43fPmch9IHGudfuwFY92JktRziStiFowX8tH39sGRgkyMDAwMDAw+CeRkvufT33rjMAJ0u86Y8eLpKclpH7wPafHUgnKCbBbKuImARKpbG3VH26SEPRJ+xZ8+LVLVpAultWLP+11pGE1ncBEIhrlPUXiqVMSCtKSezBPOPkeFJ+0LtKSuyF4Yy9D2Ti0RMcCbpsDu+sBp0tL70cxaqngvcRMCEDIZwXBUYhB5WZp3e/5d8iHeYIricL/0pV815VI8F+zSwrOQ/0YfwVGCR2NkKYZP5H2vs1nYkSkeoc0+YdSowjAa7czTvdJkJeZN2Jm0FhKnVJOH+qZWquk7H6YEGT2iKcRynK0c7j4d/4gXrfZSBfL7GH1/RFzDrSR0he1UYfVWoVZR3oXTB/GXiaNvARlKhwkrbFsjXT6w/Th2fs26YQVWyx78QjzdiZI3SZABt+7l9ePruWMJn4XYpScA2FzWHVN/hbMGmbcILXW0t+odLW06hH2PSlbGnAais3L30EJi0bj94LdybxbKtnL1iorBbKVtUoor2UbsAV3OLnv9r4FOZRYX3Yf5tLZndDgAzAkyMDAwMDAwOCLicgJUtk6I9BGepJOQII8KVgZL9wa7xXTGf1OIr1v3r2ki30AtuPTqI7NyXL1CnWgJLgSCTaTc6Sxl6OSdLZH9rcQlLbVQsryBkIiYggHaE56dK004Uo+X7mFMTb9BSLSdRxBekI6TUq7TqDPTWImRDF3AM0322utIv4UgmibTcroSoF+LIUuqxdE4uXvYAFePJIgOuClP9GYb+AgV7OLefc/heA9qyfpaRv/zLyTc5hraoFF/LrhfNdzEqRp69MWsXFKX3uFNK0Bp/HZwWdT91O6XFp0J+pS+WaajNpdcTv2EV+BPNQfJOjfv5AUzD2vWw5rVqPYQDvnMugMiE1SFoRi/Lel+kOkL8oGMSseIR1dBVEKtFGDY3PgMHfgXeqccgdJvWdwfpv+wr53NPJ5d5K08Qlp3LdRlrY/T8PT1HzmMvgciNCy+zkPm4MztttRxLY8iXLWcChOgDrfW75mrlM4QrJtRUFUFFKYWgSZ3vBHlCBfI2R++JcZb/erpDkmZrLvBh8Js0MGBgYGBgYGX0y4TpBW1hmFQ+PpVydC8Sip38lW35ZOGHIuSkn9AZSiE5GgUAcuXUXDCcwrt6GatFZCvHpMllb8EsIx5RrIRtdxkKoNf4bIBNpIhSoaKa1+lEB2ytUWKbHUjqQcSVEpbwgGAOv/IPU9mcaaOf3pKeTwYFAw6CxSrBbeZrmc2Qic+82TDi2NW5V3NKJkpeRBOGK22JGQ1H+etO1ZSTYMCXL6YXKQ3Qczg5DfajwaJbBur5Fm3EgtyoiLcbqz2yEXQR9kJKOL1CAIQcQXd7JzuBhv+7MQtfZa6YVvsmeTvsc8vPUoNvPulfa/gyFAzJq8owlSkpAKMRn+JVSQWbdwpjGjAzloclq7J05SR30NQ4qKzXECXLcXl7tZt0irf42itO9tKRJhrL4n4RbXeyYpaC9ejllCQppFfH3sbTgEybQ76XEUbIfspHeVqrbRA2n8Fah8UavXUiQsKUzfp44GSGBrhY7Zd0uM52+BqGb3Q+k5uh51qN/JnOXqX2OKEWznO02lmDxMugoSmzcQMpqa/9G/OwaGBBkYGBgYGBh8QRHryeNKkALvU4WcHkjB+62qOyMpi7SxzF4QD5udILF6B8FvrHfM+1G3T1r6oFSzg9SkaBgVY9AZpDaldYGUrHucz6/6lTTnVshD94lSRg8azHrrUHA2WOpJJChtfZaGnpv/Jh1aTPDcZzYGCU1HUAk8qdQFVW1HgUnKYuy0IqliEwF5NCzZPRCGhHRp8g+YW8OheG3O+CtQwYpGSuVW/VRKPulzqYXMz9+KCcLOl7heehfS83pOYX1z7kCdGHUJRHDvm9S/dJsgzb5F2vx3UsPUTkpY1EppszsgC540SGPhKGnn81JqHq/veUuafSu1Ow2HpIW3o86Nv0JKL8Yyu2w9RE9inBEXkUoWjUoXPgmBdSZwhuv+SA1XQjo1VokZkq/1eAXQ38ZaKjbzPUUk2SB1YT9721IOWes5jXU4EyByCamS0uKmDza7VLJGSstnvmXrUc9S8plzlwn0Idr5EmQ7NZ9GuzYHfbeGnteJnFvGDNEwBhgp+Shzwy6URn4Ny+6OBgh4+Xrc9wLtkKZYeuXmv1H3ldOXPkmxfTP4UBgSZGBgYGBgYPDFxty7pI1/kKp28HPeQGn8t1BKPg7uZGn3G9LAU4g122tQP4Id0p43pKBfaiiRsnrw+ZZK6Z1bpLYqFKC0QgLOPW9IQ86TZt8puTz0y4nh6DrqVXIH8HOgFeXnRGg4CBlKyiT9LRyg587u1wjuz/0jZOjoeimzq7Ts58zFmQAx6jWTHjSREAFz5Rbp9aspuh9ynuWWZzmdHV5GKlv/eTTk3P4MpCe7D4G5J00a9VWK8Y+RQRuGDf3nS6c+IO14mc/abBDBIedJyx9kP0pWSifdhToiN8F5ewWqR8SqX0rMsJzVGiGEYcvyuXwD+zT665ZiFMB6urVaKl3DPtkdjJXdWxr7TZqy7ngBYjj6UhSlthrqm4ZfgOV0YjrnUbpaGnMJKtriu9gPpxvyUroaEley3FLTLNUqZj7gSYEYxdzsYvVKUYvc2d0YNfSbjStfrIeWzQ4Jm/ETSHBiFgS05zRIzJanUHQyu+MgN/KrOBj6GkX90xDc9iq3ohAuewBy5kkhFS4cQn1zJUBc22ribnXhAMQ8f6iUnPXxvxcGhgQZGBgYGBgYfMGRPxj75vYafk7O/eeLvtO6SCMuJNBvr4M8RENSzxmYEex8kUaZp/ycIvz6A5COGOwuHMfsLunAImpy1v72fdco4sn9jhchMOnFBPe7X4OEdB1rpW5FpY5mitcPLbXGt6ykM7oRyPpbUQ+6jsP4IGbkGw5APOoPSP1PlXY8T0reofcI8Ov2o2B1NJKW1dGAelM8Goc4m0M65QGCdFeStOSnKC/bnibYlyTZUDlSi5j34p/yWqCVgLt2L+pbzNbamUDAnlwsqbd08k8lBaTlD6E2uRKomSoYCtEb/VVSxRIzRM3TX3FdC3qlXrOkLmOpj+o1i9SvzU9KtjC1PYt/Sj1Oj2moMkvuQbWJRiG6pzzA/Ha9CKnxNUPmsntLF/xVWvlL5hryQ3h6Tsegwd/MGpMtG3FJKhwm7XiJde58Ma42RaOQuGFfQg3b/HcUoNg5Slx30Z3SzJ+gtuX2p97L4aLJ7P4F/LtyK9ecfj2k2O7k/II+yN7gM6X2atL9vPXM0ZWIQ6CziDVndI0rQXYXCpIhQP80DAkyMDAwMDAwAOGQ1HxEqtnD0+mcfjhcfVgvns8SCakndoH7ODQfIbUqEuapfjQMIajaRn1JYwnpUcsfghTFmp++Hzab1F5FrVBnpBRQ9/PGtfFC90iI1KszfikdWMzYvibqTKbfgHrjTqGWx52EaUJyNgFvxSYK+Lf8w3KDkyQbKkW4ifS2zO7Uf1TvQhmq3sVnk6w0qbYarJ1diaSZdTQRaG97hpqXaT9ELckbyGuymnmmFEDSek6jD03QjxtgwKo/iYYhFqf+nIae7dWoTXPukZo7pLeuYx3DLoAU1uyRBp9B2t32Z1lnSznXS8zClW3LU6R5DbtAaqtENQq0SUPPZx5Bn3R0jZWyFpRy+9Jbx231DrI5II4dTaTbhQK4/sXqpaq2Q1pHfIXmou319PDZ8ndUrGUPSmEf37eJlLLyjRCSnL6YH2z4MyQzuzfkNq2Y/d76JOTI7uDn2PkH2jjvPa+jtKUWQnqqdkCaSlZKZ/8GQrvyYch54XDI1ZHVUuNBUiIjYcaMhKx+San0EAoHIGZ2Z5x85Q2CjBv80zAkyMDAwMDAwAACVLIcO+BjyoAIlGfexFPnzwuBDskWJZ3pn4W/VTqylvqf2l0QkORcgsaglwL0zX+Tpl5HLcn+t1GMUgtOPJ7DbdW+vK/Wos9sae1vSLuKucnZnagau16N19BIEKiyDTzF7z+PGp7GEkthCEo9pvAnIQNFKZaCFXNLkzibfe9IY78hrX8CMlEwhN5FzUf52ZkA2Vl6P2l6ko7VPx1YiOJVNIr0qxRrvTZhFhH0Mr/WKsty23+8S140zJydHoJ0dyqESGMgUN5aaon6zIUAtdXTM6huP3NILaTmxt9KLdX4KyCMgXZp0z/oxWOzc+YDT2GcA4shKcMukHa+IrmSJX879Us2J4Soaht71V5tpaw5UbPsTnojjb1cmnwNylNqIXOMhCGqG5/g/LqMhQCVrOK8Dy6Ues+hJ9Osm6T2Bmndb6XafdK8n1oNXmMEKKjjTA689aRSttfyp6UcBe60X2B8ILtUvRcLb08q573lH7j8dZ1oEdvY2Vtj7ngRUrbu9+xRrCFvYhY1YcYS+1+CIUEGBgYGBgYGBLZLfhpvYBlDzW7IwrQfxoOuz2xOVs+dd26U3B4KywuHEtx+HA4vk7Y+E3d+C3VYvVgcxzfnjJkvREJScwWpawnppDV1ht0pjfgyZCMGmwN1yub8oJ12jym4uCWm8zm7A1LReFia8WP2urGEz0Yjkqw+NIPPhhDZXRCOzgTI4eZnT7K09nFUiZYymqE2lmJ1HWzHQKBqGylpsd47UVn9dsTnhl0obXiCVLXDS+MW2gmZ8QagriRUiqRsSEo0zBiuRIJ8d4pUNEI6slIqGGMV6luNVXe/CkEsGCKt/AXErnAY72d0haTFmqIWjkAdK7cIojNB8jfiNudKkoacQ+pZZk8IYI+ppALW72e8rhNRUCJB1ulOghC0VaMMKSrV7OTePv2XGBgM/xL1RSMvxolu5S9xq3N64rVDfU/iu+EghK10VVx56WjknknJI60tEkRpitUVZfVErXvxckhKJEQ655FV2Gj3myf1ncU1N/0NghrrTTThO9LTF1upbp1stGt2opDNvlVqLYcIFgyRCoZLmd0+/nfC4DgYEmRgYGBgYGBAus77CVAMB5dIwy+Kmwd8EoRDpJxVbuMJeW5/0u7SCo//XP0B6Z2bpLSLSE2KBnhK32c2KUtJ76t9CPowO3AnEaBufIKfu4yOf8YWK3a3nuB7UvmMRAF7UynB9dw7pEV3WbUYFopH0xvGlUj6W6ANwtB8tJNDXdQyBLDS7sJ+iugzuwsXMsvNK+hFabG+omiU+biSrPE8BOB73zh+jUnZEIzMHpCCMZdKnnQUPE+aNORsiJa/HYOHsFXYb3dZakWEIL2xlJqjPa9DBio2Ma47BQLQXC51nwJhc7iZY0K65Uhm2Xp3NBKw95snpXWnWWp6V6l+lxSxS4nZnFd6V0wWMntAcpKy2cva3aQpRiMQpJk3Sxe/KL32fZShqKVcbfqbdMETOKm110HwAm00Oe0+0WqY2iL1nCodXITK43BZzUOjkNyc/jjQ7XuHNex8GbOBWbdCkso3StOux7hhxwucV/EY7sloiHtu/zvUEQV9NJ8tWSnNupl7pGyjZLeRgli1jfTK3IHYncfIojOBv9trpdWPcd8nZuJe2P8UK/XRjmpVshwVbcs/jj//7pNZ26pHqM2aeeM/9ztncEIYEmRgYGBgYGBAEOZwn5gIhQNWU9JPiFjK3fsVp/QuOMBl9+LnkJ+i+I4G6f0ZPgcWEXgnjePnQDupQzteQPnI7Cb1mx+vw3Al87S+rQZCYLMTXDtcOJ3tX8C6h38Z4wGHB0J05q9ITfK1EAynd7UK+nU8AbPFGqN2kLIV8kF2bDYdMxqwWyQpRkTaagjCPSlWwB+2ajwc0uZ/UMhfONwii1t4LzGLsadcg0Iy5VqIUMkyamqiEYLs6T+igWbXcTTUjAkJsWtLUsFg/j3lGsmdaNlNV9DIs+c0TBqKR1BP5EpCPfKkQoRGfRVCMf9n1P+8+j3J2ywN/7008FSpaTBmDeEAhCu9GDvyDX9i71MLaGhaPIqeSR3N7O+BdyBJU66FIC69l+/b7JgRjLiIe2fmT1C/WqulVY9CetO74MiW0w8F0J0cNwywRXFhW/0or21/HuVn9aNSdl+UsMYS7qvs3tKMGyRHAilnFZuloV+S+s7GZKJiE+YD4y+X0rpiTlF/gFS/tmrS1UZfIp39B2rqsnoyJ18L+91eYznMuXC/q9tPbZaiEOrRl9Ck9bVrIJjz7mcO4SCpetl9pPd+GjvQf+MX0KAzDAkyMDAwMDD4X0V7HYHY4WUoIsMv5En1XquHTgxJWQTBnxRNR6g5ej/Rai6jeH3O7ZZRQA0B74fhwCKC/HBQ2vcuqUyx+TaVUkMy/EuQkt2vSdN/TFBbuwfyk5Am9T4HUpOUI827FDLU9+R4cXlaMX8+DmldCVC3/CM+h3CAJ/pdJ2AKEI2wvx0NvOdwEww3H427xrXX8t2UAj4XCZO6VX8AdUE2yNyO5/lO81Hp8HtW3ZClIvhbcE077WGC++xeELmYChUOsP4xl/Gd1Y9a7mYO3MimXsc+Lb2fNLdJP8BYoLEEk4CC4exJoBUlZNfLVtNOq05q1WPS+MuoNypbjwJTvz/uoJaQzmtDzoUkFo9FLVn3e/bOnYK1dHIe/YnW/BqDBX8zZ3XGI5zvmt+x/pQ8am1ayrnGwNMhXXvfZvyCYdQR7XqVdbfXSuFaiEzX8ZDdV74LkbbZIHd73uQ+nHKt1FRCHdmKh1CJHC7LfKJNGv0Ny7q6UOo1gzPd8TyW4uldIfGRgDTmG1LldunAAuH0Z/UCaqlgDd76OPnf+iT336k/l1b/inmnFWIdnpiJiULE6nvUZ87H35sGHwlDggwMDAwMDP4X0VqNy9bRtfwcCRIw95pBU9Bdr8Q/O+Irn47zVM2u400XOqNsPYFhTh9RwxKW5DjxZ2OqVHO5tPZ3+sBTcbuTNKpx38YFbPM/SDka/Q3UgKKR1P80HabOZPM/6N8y6AxcyP4VBDtQbSTqYPwtEKukHEjVmkdJP2urZpquRCmrN0/1Gw8zH7sjTmZy+1tqlZfg/NB71JEEvCg+spGGtfNFamoS0nWs2abdRTDeWg1hOeke1KIDC0nT6zIeopveRXr7xxCsSJii/v3vQnyOrsMNsGYP7+f05/OZvXGS89ai2m3+C1baCelSq0XgFCWQn3KNVDySe2bDW5xHdm9p4nepq9n6JClwacWkOtqd3H+REPOp2gZJcaeg1ux7G/OFaBQy1FJOWl0kzBwUQf156wYUo5jLX/0B+iwFrIawSTmk+wW8pLat+hVzbq+Lq1Qt5aRSzr1TOvge5KilIl7/40qBbL1wOQQwYhkiZPaUJn0P9ezgElzu9rwqLbxTmnMbRLKtivs/GsaUYverjB8OQbDsLlSyMx7Fsr21gpq8I2sgipEQ1xp7qWWuYPBJYEiQgYGBgYHB/yKOrI4TIIkALL2YILXLGCu4Fmlin9ZT547GD38vGomTm6Rs1ITyrSf+bK+Z/N1a8UHLaonA0uEi1Uviyf2eNyAbU68jAM3sIbV0JygddxnF+v+O8UN7NWpG7gDGtju53oHFBO+nPsQ+NxyGXLiTUBy6jkVAObSUObgSpV7TpR7TSclLzyAwH3s58936FGdUNAJTgZWPkCLX0SjJDjmwOzF/aDiIs1lWbwjsgFOkUJAUrbyBEChvA+peUjZErmgkaliMkAXaqF1qqaBeKXcbBfs7XkS5S7Dm5/Rg0OCX1H2qVLGW6/papK3/QOmIrWPFw6SyRcKoZDtf4gxT8riO00Nfp74nM7cp16HatVbR16lohLT+j8zZlcha26riBDISxI0vbyAEP1bvFQlZ1tlV9DLa8ZI06HSIaVqRqOUKodqlFvHvml0YOaz6pSQbJCoSkqZcRU3PsXorq6Fry1HIXf9TUFfdSahGIR+pgANO5XvuFD7jbUDJjIS5fsxwoa2GfU/Np6Fuci7qYHoXlKduEyFc7qR//V41OA6GBBkYGBgYGPyvoaP5eKUnBoeHoLB6B0+i3YkEfzbbBz/77yCn34e/l5AeJ17uZOojqn/Cz4nZkrdKUhRCkDeQ16PREw4lCUKT2YMn+lXbCUi7jKFexumWnFkfNFf4JKjdE3dki5E5u4uA1pOKi1c0TK+YnD6krfWZy/wiYd47uk4q3yTl9qMWxG4n4C0cCknxN9MQtG6/lDcA97xoCHIV8luKho2ano1/oZZo/v2c657XpOR8CMyeN1CVbHauHdv71kqu2VIRn/fA0+lBY7Pz9zCX5GsguB9/BWrVvkUYCSRl4FzWVmOlejUwT7uDOTcdsUwkwhCeoA8iEOyAHEy+mvH2vsFrvWdjVFG6iv5A794MIfDW8T1PCqltbdXcG3YX4+96RZr2I9LHYo1GHS7S8II+xopGeS0StlISI3FFJtBuNWFNwKo7oxtkMn8Q6puvmXvI12L16rGszCu2kEIo0Y8otx8kdetTpMhl97GMJLqgTkUjcQUoRsA9aceT8ZQ8/vSc8undqwaSDAkyMDAwMDD430MkGH9K/n7Y7KgGKXnYO3+ayOpFbUnd/g++N+Ki42twsnqRRrS5HAUgtzdqR/7guO11WhGBfsj3wfHcKQSvGd1QWD4tRCKoT44Emqf623Bpi0axnm48zOfsLor4N/0V0waHVZNTNBKL5OIx1GLV7YcwRCNS0Whp/Lekl6+kcajNAQGc8B32QHaUgNRClJD37pUiyZZldQPBeEohgXQkRGrX6kelPidjRBCNSiUrUBGiER2rFfI1U7vTZy5NTcNBFIjpP6KZ6ZYnuabzcWnQmaSqjb0cx7Sj6yRXqlQsyEfJe9IpD0jbn5Gy+1kkyE3D05iNuMMtVe/GhKFyM2llE77H3EqstDdPirTvLWqtzvuz9OoPJH+TdPK90q6XGMffBsFzp5DON/ab1IBV72DtsR48CZmQlu3PSUvuhqjZ7ah39Vb/omOIMk93IoYbwy/EFXHnS9KGP0qDz4J0pXeBLEVCnG3UJk2/nhq1w0shguEApHvK1fzdbx49qdrfxJo7MZvrHHMXFIpRsIN7yZWMU1161389TdPgY2FIkIGBgYGBwf8aEjIIive8ceL3u43/dIwQ3o+UXALQjU9Ih5ZBxhKzpJFfIeUupjj5Wwk6Nz4p5X2HRqb7ggSIRSPj46V1IdBd/ej7LmRDpUjr8vFz6mgiYFWUwD/YgQtY5TbIVo8p8eaW9fto/Cm7lD9QWvNbaoCC7TTSHHoujnM7npcGnIaCUbnVUgpEP5wh5+Ho1meu1P9U9mD5z1EkRl8iLbiNIDvW9DQSwjTipLukwytQZlyJUu9Z2Fpv+LPlOhaWCkdSL7L611YPoihpeV3H0Tg2tYAAfYCVshUNMzeHixS97N4oLS0V0oQrpWUPWgF/Nwr8Ha64gtJrDoRv8tWSr12KyFJFXNLGv0qFo0ip2/IUZLZ+n+RK4H1XIqYBef0J8IMdUlqBtGqpjjnqJWZSo2Z3sP5+J1GTVLMHArHpr5yfr9Gy7G7ivk0rxgGuahs9eIacK9UfRl3yJPN+3mBUtCHnSovuZBy7ZZsuG7VL+xdCsMZ9U3rqK1JKDvdJejHkpqUCgt1ajWrWa6ZUf5BmuK1VccLXVIpxx9RrpXW/g0Q7PezjxO9SC1e2kbS/PnMg9i9cpmPEzJUEGe0x5XiyZPCJYUiQgYGBgYHB/xocTp7oH1wSbxoagyeVPjj/qSfPGd2k6TdgWxwO8BQ//X0ubLV7qf2wWWlBdiv43vcO6XCDzuB1p4uAPqMrT/lbKnlCP+wCAt2PWkMkggqx+tcU0EvMo/8pBMAxRWfLkxgROBOkXa9hLx0JoVoMPEVqq6WAXjZUlCnXoLwMOVt65fsE/KMvRQlwuEjpCvv52WYjnevkn1IAX78fQpCcw7l4G/i+xDwnXokK9eIV0qkPQFymXkdKXEcTxGbxPRaZtILoaJjgPkZ0fE2kG3abhCoRCcfTClurpNm3QI7DAdLrCkdCOlZZzmyy0cNo5FeloB+lJqlQ6jIVVzNfI9fvOg5VpLFUKttMbVndAYL69C6od6seI32so4k0QZud+yExE1KiKHOt2g4BLl0lHVrMPTDvPuzDA17eazgkvXsbPaTm3o5C1VYLcWo+aqWYRXHyayzhHLqOxUQhpuDlD0H5SS6w6pQqpW3PYbvua+Hc6/aztqPrmXfMOrz/fKuXUQ2fs9lZQ8yVr7Vaciby7xjBba2EeI68mH0NeaVXvx9PT7TZuQ8W3yOdZfUWCvqPbwprFKJ/G4YEGRgYGBgY/C8ipx9pSxv+jPIhG0Hh6K9Tu/CfhNNDD5UTIRSA0HgbSDHKF4FjLNbb/jxPxWP1PO5keqoUDic9yZ0cJw4fhbq90ts3xt3qohFcuEpWSvPuxewgEib9LBqBmOX05qm+vyXeSLXrBGnEl6VNT6Ac7HuXPWwuQ73I6I56lDdAWngHBCfQxncD7dg2l28iiF73ewLbWK+htCI+oyjkx+6SZCe1a8cLOMeVb5SGfQlXsmjUaj6aFk8T7DYBhav3LMZunkmaWdcJzK/cqj3qNYPvyAZB9DZABEZeLC28LZ5ymNGNMV+8DEXR1ywFAlIX4eDmTsQVLbVIGny2dPZvSG2r3Yei0XQE0pncCyWmbq808hLmlJyLCth0BCKU0Z29iorrt5RBJCq3YoOd15+aqZo90prHmFskSD1UNIIiF7Pv9qRynsMuIN1vyDm4s9XskiZfG6/36Wjifju0HPWnbJ3kbeT7dpe09Rl6CaV14SFCzHY8vSvkNjGDfYxGIDIhHwpSayVKXNDLeDaRutdcBnmadx8k35UAkbI7SGvk5sTNsG4fKYe+Fmql8gax/uzen17d3v8QDAkyMDAwMDD4X4TNRrH+3DspNJcIQj9P16lIhHqOqu1YMdsTeL25TErNIbj0NZ/YZtuV+M+RH4kC+N2vxceJBAmMA20oFUfXSRN/gMISaLNSxfqQHuatt+y7RaB7dA1P6HMHSYH1GCT4mqw6DheBdlYfgthAGwQqJR9lwuFGldv3rtRvLnU3TUcsG+UIdS0xpHclCM7uDWHa/67laNYAcRh8DjU6vmZrHckQhsk/kJY+ANHN7oOKM/lqVB5fs0VQ9rL3K36JmtN3DkpXQqZUsxMi5nBJsqEgbvgT8+tohAglZDPHHc9LJ98hbbEMA1b8XJp1K0QvrVha/zj/DrRB2BIyUMHKN0IyYhbgEp+xifUNOI05RsX+RcOQyartpAdu/jt7Mf47UtVW9mb4hdRk7V9gWWJPsxqXNkuL7sZc49QHOaMdL0mb/yZldJFGfJWxcvtyzZQC5uBwct2gF8LZbRIpinYH5xUJsh9BL+dqc6BqOd0oOjH3wXBImvUT+gjZYv2b/KR0jrmU/Y5Guc+y+kB2uk+CKHc0sH9H15FSmTsQ2/dQB32hDP4lGA3NwMDAwMDgfxkeq/g6s/vnb7vbfFRa/yeCu86IhknVioQJ5I89If83EWgj/UqinqfpKGloSTm8VrKcgNPuQClbdJf02lUoM9OuhyjJZtkyh6hfKh6FG920H6JgpBSgvnSdQICf1YM6JWdiPKh2J6MeJWexth6TrZobN8TD7or/GXIOKYI1uwjooxG+M+pigvD0LtRbzbtfKhiJmcLFL5DCV7GJtY2/AqXrma9IS++lCWhyrjTzJpSXcZfz2pJ7qXEpGGKlklkNXW12yEdLBT9HQqytc0plw2EUlJYKSMCOF7DM3vIPUv5iltl2J5bVS+6RFIE8DjmPf0soKt4GUsd6z8TZLxboB7zM++SfSjYnc513P3PL7kv9zdH1pCmO+ArpjHX7pDevhaiEOiDU5Zuk5y/FOa9+P+5ui+5gPeEAaW+Tvsf1ohHIq83G2ktXcEarH2NOG/4MIZMs5dLB+75WVMq8QdKk70unPSTlD2VvZcPuOyGDvXQlSkm5nOvgs6XuE3j/0FII5v5F/G5c+A9UtNrd2KHveInrGPxLMEqQgYGBgYGBwRcDjYel6u3SuG9Ju1+WQuH4e2E/ysyIL0PcPgkcbitVyY9CowivNZdxnaLRpJS9/F1IUkImKUg7noeEjPkmzmw2BwSto1FKzpMGnI7yk90HorThT5ALbx0qTWoBJOm9+0j7i1jrSyvGLS4cQF1456Z4/xinRxr5NYiaolLpaqyue8+WkrMZK5aa50ywbLdvIwVvyU9JTzv5HsmTDpnL7oV6EA2jdGx5ku/lDpBW/kLyt+MKuPKXpI6Vb0S9iPWycbhJnwuHIM022/HKnMNN3YrEvBwexivfaNUnudhLm50/Ycvae+dLqFlz75YOLuZafQowk9j0N5zg+s8j9S/sRxk7uMSq3bJxdokZ1A0NPgdi1HIEkhJopx+RhNKW2x+1650bIaIJGZCcaJi1rPmtNPA06c3rGeuMR6x0w4jVnDUqjfoaDw4mX4W5Qmu1NPVqVKv9Czj3lnLuhTm3s8973qRmKiFd6j4F5WrxnZC3tlrmOfprpMu1VXGPbHuWNbZWoTZVbJC2PS2d/kt6abVUYNzRXisl/AfMTP4fw5AgAwMDAwMDgy8GAu38vecNafbt0qa/x99L7ybNvPnTSftxJ6GsHFoiKRLv5xO1VIhRXyUQjgT5OeQjcPU3Q4KGfYm0tMZSKRzhqb7DhXlAQiZ21m9cB6kJ+SA/kTCWy3mD+NNRHx970JkQpaw+UnGydPrDfNffSo3K/gWkebkSSdtrKSNF6p2bUUtiSMmjXueZi1FQWitRvfa9S7+guXfQTPTgYlSaAaegKBxaxnj5gy2FzI5ysvMlVJX9C5in0w0ZyxnAPiSko5LYHVzf7kSR6mjk3yE/vXaiEchSJMJ+O5yQqKjVo6ejAcVj75vcA92tOqvlD5HaJrGWxkOWaUQHStfh9yAMwy6AFC25G2J16D1p+JekFc9C8Nwp1gbZIKmnPiw17Oc9VxJEwu5AjZFIZ8wfxM/bn6FP06yf8P2oqNtpKIFojv4GPYl2vYzyNPR8asIaSzhDb4PUUCpt+btFIq0+RDU7uOb0G6QVv2CPEjMwQZh3H7U/A+ZLzUdYe2xuEjbzIR+1UVU7uFZGVxqsuj/hA4L/IZh0OAMDAwMDA4MvBjK683dLOeYIvWfz8+zbsGguGoEy8mmgcARP7SXG9LdKikIKbDaC1JhRQNhP4G63nh1XbyfdLTUfR7k5dxLAe9IwQDiyJn6daJiA1d9CAF+yHEOCmBPaqK9JpSulF75FitXKX0hvXCu9ciUBezQkla8jEG6tkoqGQzKay1GpUvKp/3El4di27ve8rwgKR2IWpG/d77C73vkiQXNWL4hHcxl230EvRGj4RZZxQxSi01IJ4XC4IAtbn4QkphVZRgxha59E8L/ndchjJMi+9j0ZE4vkPObo8FgNSq3Cf5sD8hUOon4EWiFdW/4hpRVa5CRMcB8JM5+EDO6NaTdIX32JPfS3kpoXDmC20FpFjU1KAWtJ70YK3civYuCQ0YP1t1kmBLG+SbKx/pDfSlmTtONF6cg63lv3O2qRek6Vuoyjlifm8pc7kF5CT32ZtMJ3b+Ze3vtqp4asAQhNOCDtfgXS2P8UiHDpKq7dXisVDsOmPfi+HljZvdnTl78HQfTWcU+98xMeHoSCn87vx/8AjBJkYGBgYGBg8MVARjec30pWUMC+f4GU359UotFfxTjg00JSJoFswRBSpMIBUp0qN0MwIhGC5JiTW0cT1/c2SO5UyVZLAD/u2wTndfsIpp0JBKbRCIFvJIRyESMRSVn0g8kfbKU7PQMJOuku7JHtVq2Qrxk14Og6afI10oJbGb/bJOnoBghQWzXEwpXAHFxJkKWkHMvooTXe+LTFSqeyuzANiISkt37E2tKK6btTPJqAvHiMlJqHkUCgjZSu+ffH1Q1XMile5ZullqOS3aolay6nAWwMPafRC0lRacpV1BrZ7KSKSexJ/mCIgNuqs4pZex9aKn3p79LKR6Sja/lOsIMzmH0rDVo9KaQlBlohRrNvRW0LeqnDSc6jJqjpCPbeKfnsR2Mpnw8F4oYP0Uj82jGDij5zITzhALbsC++AqGX2pEastZLxMrpQm9Ncxvml5JEW6E6FVD/9FcaNKY2SjhlANJdJfWZBkhffyzptdtQim536pRg5U5T0vLW/kRRmPIcLsmlzSOse5+yye30KvyD//2FIkIGBgYGBgcEXAwlpFI9ndMO9LWDVlkz8rtRnRjzt6tNCdl/ple8RhPeeKb19g+V61oxldOkqFI+ORqtmJMLPQ88jSE3Ktvr77GVuvjbSl7qOt9zbwpAOh5v3EzLoU9NaA6kZ/XVMC7qOw7Cgo8kKahOo+/G3oGg0H8E0oNt4aetTloIVQVWJhi23NSuIT8zkZ1+z1a/GRhAfbGc+iZmoQIvusDbBqunxpErlGyAuk76HirH5H4yZ3hUFot/J9PbpM0vqPpm6m2C75PdKR8W6c3qiyhQMi1tCL7pDyh+GorTtGVLqMrpzrYGnSztfkSZdJTUcYD8TMlD91v6eMQeexr64ktnHpjKInd2B+YTNRj+fd36CcUU4JC3+qTTtOun1n7OHKXkoLCkF0lm/pkZpxEXS8getxqYWKbE7qEnb9gznk1pI7c36P+ISOPxCafNfISdBL+e74S9cq72W3kXNFaQa5g6AqEbD8fovyTKT6LAIbxLpif42xmxvpZaqvVbqN8/q5dRs9cqKoJ6113GOkRBjxJS4cIB7xZCgfwqGBBkYGBgYGBh8cZBaII29nEDf55XW7KIZpsv16V8rq5c0+2asr9trpZ7TqSep2kEvmPoDqDoZ3Uj9aq3iyf7q32Bl7G+Rpv5QqjsIiVr1KwLjAaehrjQfjTuoBdpIbes3X2ouJaANBfh83kDqS2K9XiJhCvndKQThDYel3nN40m93UP8hUde082UpEsB1zpUEgfDWowzY7JYZgV2SHdLWZZx0cFF8DxzWvnrrIS87X6TmRJZRQFIOhKpiC9c493HIyfJf4AI35Voc6NynW/1tolLRSNSJQDvW03X72LvUQmnI+VYtUSsk1NfIeio3Q3wze6KO7HgR1ezIKtSNoedLb/6QhqbDzoe8tlWzppBfGvtNbK1X/xpC0me2tPEJrqso55vRlXNcfKc07Muc0cn3ksoYe7/PXNzWnIlWTZVXKhomvfkPzqfXTPbZ10StldPFZxfdAbGbeROqUEpR/BwLhlm9uMS94E7mvsofgipVfwiS5klFWdr9mjTrFuzXe06jNisStMwkbJBfd4okW5wAGfzLMCTIwMDAwMDA4IuH9GIpKShp1ycbJxqlLsPbgCKTWohbmUSdT4+pkKHmcqnnDOpIDiyiHubMR1BtyjcShGZ0x5GsfCPkJejFne3ku3E86z2Lp/pbn6bgfdcrUvVOyERmT2nClRCDA4ulSVb/nkiYwNeZELeOjtU9BVr5k9kdhSTkizcTXf8HacIVpPDte8vqZ1QqDf8yKWKRoCQbZCjkk8Z9k2acmT2k0qNcx2aHpDk9qFASxGLfu9KwC5mbvxUFxJXMuqsnYSmtKOYCu15GxXBL2ve2FPZSs9JwQOoxDVe8aIR1e+sgjk1HGXvGDdKRtaS1jbiI4L9iE3uRVsDcnR7qwZb8FKIw7AJpwe1SoIX5Br3UbK38hTTnDkwegh2YGWz5O3sbS3PztVrpjTYIlq8ZB7+CoaTPtddhTBDy0T+rdo/0pb+RYjfmUgjJkdUYaiRlS1OuIT1u61OM31gCmdv2LLbcax9HUTzpLkhj7W5IVXstNtljL5XevU0adBok02azbL778HNKAaS1zxyatAa9qJa5/SGtsbTCGBxuVDuDfwqGBBkYGBgYGBh8/vA2EnTue4uAudcMKW/YJxvTbxXZb/wLT+4lUpQm/4BaFInAM70Lf2LoMwfyZLf8o3L6Sq9fRy8YRVFHvHX8O9jO0/yq7QTSfecR5NpsBOVNJSgHiZkU4afkSzN+LD33dcnXAAkpXUWq2fo/EFy7EuJpbVHR4HT3qyhLQ8+HpIV80spHUclOf4T1JeeShjf3Towlmo5A+oaeS9H+tucgH7n9paptkt2GKpHVm8J8mx0CUrUDBabHVGnvWxA4uwOFJhrBsCDo47WaXdLAc6QGQW6cTqsx6h8gQVFZ5M4upXWF7LiTUcFy+qB+HFgs7X5TOvu3NGyt2sYeZvelt1HDIc4yqyckKdTR6YxbMH/wNUvbn+fsbJbT2zECZNXTxNSUQDvq1MYnsLje/qy06yV69GT3Jh3QmSDVHSC1ruEgytKKX6LstJRzrx5dI426BNK35zXIYEZ39jMlHwOJhbcz/pmPYXXtb+Vsm8uwLE8rgFztX8C9Oegs7sXnvmbdr4OkXlNJCY31app6rbT84WMZfMcw6mvsvbcBkpvwCftp/T+HIUEGBgYGBgYGH4+2WquBqJMn1J+0V09neBtx3dr7Vvy1sg1Seg8p8Zx/f9zSVfS76YzaPdLbN0pn/AqF5USw2eKpaZJUf1Cq3PS+D1nqQjSCglAwjOC72epD020i62qtRg059B6KkMMjjb5UOusxLKAbDsVNF7pPRomxOSjqtzkgViE/StXIi1F+Nv2FQLfPHJSdlQ9DwiJhPrP5b4zVcxrEa9erku9v0ik/o89Q4XDmk5AujbsM0rP7VUjDoDOlfnOllb+CdATbrZqhNKuRq4t7wZkAGWkuh7S4u0Nuuo/DyMBuKVAZ3TAlyOorjbkEO+i2aj6bWgS5GHwW+9hYgn143zmWJbQt3tTUZieobzik44hNzH47GuK9vifxuWjMiruTEhQzEWitgsgG2qWFd9J/aMDpXKPbBMnulg4vkfIHQGwGnka6YlZ3aeCpUnt93BVu8z9Qeo6ukQadDQnJ6CYte0iafy/mDhWbmVtGV5oBl29AtcnsibJ1eDkEpmgUtt/dxkvFY0kvjAQgiQcWs4ZZN5FSmdENUttYgkvh0AtIJV33OHNxeJh390m8bvABGBJkYGBgYGBg8OEI+kgBWvc4T8Btdql4FMXjuf0/nWvU7T2eAMXQUiEliqfx/2pNUHsdjmcngq+ZwPrDSND7EQnxZL+tOv6zM4EgP60YB7LWClKUXAmkjjWUEnBvf4Y0shiCXlK3vLXUEzUfgWTYHNLE76Ns7H+Xfe49i2DX1wQJObycYLijCQUjp2/cmnvyVTjJOT18t3oHCkbQZ6VZWcYAZev5zCkPMu6qX0LynImkiG19GqI762ZUi7ZaywY7kVqfuv30Cwq2W31vPOy1W9I5j6MEHXqPwH/78xgJvHevNPYbpLEF2yBA7mTmt2Yfcz+wENIy9nLpzRtIccvsDlkZcg7jeRtI94pY/YUiQVS2kA+ek5KPwrftaWpyhpyPwUHMRS3W56fbRJSgSIgGo0fX4tAXCbPfpashUqkFUvVW0hqbjrIP25+Tuk3Gtn3Z/ZxdWy2NT9uqpdevgRAPOUt6/WoUr67jSeWz2UihU5Q6r9KV0uK7mcesmzBfaKvCzvuMX1JjVr6J+SfnYtTQbRL3WPFoCGOgnf5RLZVc29ccv9dW/hKXvZk34fZncBwMCTIwMDAwMDD4cFRsxhq4s/JRtoFi7tN/gRLxSbHv7Y9+v71WSuj2r40ZaCPl6MNQtU0acvY/N1ZaEf1zVv6Cn/1tpK2110ln/UZa+2vqbUJ+SVHSyIZfKOX0R2WJxvYubP07SqA+5FxpzW8I5Kf/SCpdjmqT0Y0gv3on9TINh1HK2mskVwpNT3e+LK35Net0JFCXMu7bkJ3pN6AMdZsEkWg4LHWfiApTsRmClNMfIhvsIP0qFEDpi4QgUeUbIXgxW+fiURhHLLgVshTwQhrGXy7V7OczL31LCvuo4Rl2AQpV3T5p/gOk5gXbGDs5F0IdawC67VlSx5Y/SE3V0HPZz0C7ZUHdg1qZmt048x1eZrnieaXkbKmtjkajE7/H2EPOo6amyyjIx543LFWqO9fxpGG8EA5COhMymFdaPu81lbL+yVej3CTndLLQtvNQIH8w6WstFVJ6kZQ3mLql9C6sf9GdELb+80kt3PsW++ZJhSxn9oLUTbyS+qA9r0tHV6PO7XoZ17w5d1qNUoOk0L1f0XEl8ifkp/6pMwGKoXILKYuGBH0AhgQZGBgYGBgYnBgdzdRNxAjQce81SGUbPx0SFPB+zAdOcP2Pg8NNQOtvOfH7acX//FiZPQiux19JqllHveSzSWc8Km15CuUgMVPHguSqbRgazLyZ71bvtKyqrT49EsSnpZwn/3PuYC9X/ALy03AQ968Bp0hvXo9yM+A0GqHmdSPAbykjBSqvP4qNKwnCs/UZlCRnImqLK0ka+RVp9WOknuUPkg4tR0k4tARS1lYVrzeJteqp2Q2RKxxhNSwNxdPzmkp5bfDZqD71pVLPucwj1A4h6z0Tp7VDS6nZifVhsjtQp8Kdmnq2VqD4yMY+nPYQKWv1B1DOGkukk+6WavdCdqZdz/2XWoiaUroa4tReyxzWWy56258lxWzy1VJuP8jQ2t9BLIpHk8a29Wmpfj9EcuTF0js3svaeU6VDi3GvC/sgIM1HJVlW1/veIi2t4o8YGaz4BemDnlTmZLNxfu/eBNGM4chqTDE8GdL4bzGng4sh2rNulna8AHlvrYLQ5vT5+Puzvfb4Br3vx/53pd4zPn6c/zEYEmRgYGBgYGBwYvibCUQ/DEfXEnx+UvSewVgfhoTMD3/vw5BSIA08gyfk74fNQVNWf6vUUMKT+mAHaUvZfaSU3PeNlUd9TckKafr1vJaYRU3H4rsJyMOBTvPNQCHoaEQN6Gjk6X84SMpXUhakw9dsNWXNpZFoz5lSweC4GrP9ecZtKUdR6Hcy6sOWJymO3/q0tPFPfLZoNASnahvEJxqxnNCiKCzn/pHgvnCE1HUCqWMb/oSak1qA6hIJ8V1PKileNXt47dBiUuYSsyA+XUZDbnL6oXjEiJ3NQapcRwN9j066W1rxMM55OX1IAbQ5IGedkVYcV6GC7dxzxSMhQA2HpbwB0t636amz/g/sSTTC3o25hBS6w8u49sJbpMRsq/7KzdyLR0ob/kh9VMx9b+9bEIdZt2BekD+YVL+mI9b5Zkvt1VZtU4B9cibE1T5fM2lpw7/Ee/vfQZUZ9Q3m3f8U9jfoO36tkRDnevrD0js38TsWiZD2tutlacaNGDJ466WyNZyrJ+VjbvZYM9UPe/sj3vsfhv3znoCBgYGBgYHBFxR2FwHxhyHlU0qxKRxBqtL74XTztzvxXx/TbpcGnU5KWGc43BCZlHyK2l/9HqRi50vS2z8mqG+pPP47kQhP6/e9bRlENJPm5mtCkelMgCReDwcJhj1pBM4J6aRKedIIpnP6oxDl9OWzU69hnVufpAjfnUKzzICXua56FBLSY4rU/1RqgWr3oIak5MeL99tqIBlJWRgXRCPUKO19E4Vm3e8hfDW7qI2JBcjRKH+8DSgLg8+EACy5R6reTU1M5VbS4dY9LnWfonjgbf3tSoAQRCIQH0mafYs08iKu5fCwVzEL8BiGXYDjncR90FIhLf0Za+p7kmWs0FPa8g9ISvNRlJK2Kshj2TpqeNpr4wqTvw3Fp8s41t9Ywrm0V1NjltUT8nh0nTTzxzj2VW6R3KmcU1MpfXwilvFCoB21xpPKOrqMw1o7KdtyA3Rwplk9JE86Kk7NLsVVTFv8/rM7UNAcLtbqrbNcAZM552EXQFgbSjjjj0NyLvv7Yeh70seP8T8IQ4IMDAwMDAwMToyUfCyYPwx9537yawS8pG6ddBe1HAnpkIRuE6ST7vlkY6cW0IvmjF9Jk75PoHv272g8Wn8g3t+lM6q2keoU7ZSC560lpai1kr/3vI4qcazG5wToaELh6DOLVDl/ixWE1xBEj7sM8jfuW6R4bX+RtQ84jSf/m/4q7V8onfM71KS0ItSG/QukrmNRMRLSID2tlQTqHfUQLodbko3zkwiyvY1SwXBp8DkoEO/dB5nI6QuhcKdQNxMJkpo26Cxp6f3MqbWSGD4ShGAdXk6A315DEN9SwXUSsyz3wHwC/fZa6eXvSK9dAwk58zFIW9gyNHB6SEGz2bG+diVhMrH3TT7nbcSOu2a3VLoCUuxJhWAkZXPNaJg9aamMn4XNyumz2aVxl6OwdDSwN8EO1tNWzb5X74DA7HuH6wXbGKOpFOOPlAIdyxEMhyCViZlYlS+6G2WuoUQa9iVp5o1co/csCI7N0emGsBztkrJZfyQEIYzZebdWoCAmZnCm0bAkO8Q2HP7o+9zplkZcyPffj5iBgsEHYNLhDAwMDAwMDE4Mu52gvGoHT8mPwUY9Q/Y/Ua/wYfA2MuaOF0gtyh9C0fuQcyVFCKhtLkml/9q4/jarxiVCMJ+URWBZ2KnnUDQq7X3jw8fY8yZqS6pFIiKR42tYJIscVEldxlqpfO8jQ5ndUX2ay6Qx35QS0wjmk7IgSG/+kIB52o8gMzl9rUahbhSQjB6k61XvxELa4cHG+uBi6lUSswicoxFJNhSSnL4YVkSC2Dy7kqSOckhB/iAUjndvseYaxWxh3LdpEnt0DZ/JG8jaAu0QBrvTIkBhSIUnlfS85iOkKabkSR2d6q4yuhPEpxZAlPwtkJ0VD1ELc+ZjkBBPKvPevxBFrnAkhKV2tzT2MtLBbHastVPzcVprLmMespFWGHPsa6/FpKL3TMiHv5Xxs/uQ4mZ3KF7sJO7raIRzcPrYs8PvSf1OonYqEmZ/lz0gTbsOs4rGUs6mYAiEp2ytNOc2DA7SiyGar/6A+WR0J90tf7ClBtkkRdivcADSn9NPWvsb3oulEwbarLNzSKnFnG9bNTbZjo9RQ3P6UUu1fwHW8E6PNOgMFKv3p3caSDIkyMDAwMDAwOCjkFYozb6VPicVm1ECikcT6P07aWqS5GvFcGHXy/HXmo5IBxdJJ/9U6jKG14LBE30b9ai5DGXFnUQTTkUJvjc+QRCoqJTVC6WleIzk7GSxHbEC4BMiimrQfBSDgrQigu0uY7BxjqFmL/U7Q84h7ax6p0UUbJghTPsh62w4zHtTr8UJruEQJGzkxcwvGsG2Oq8/pLBsAz1+9i+QSpajziRl4zg3+uuQMU96pzoP6+9970BoFt7G+trKrPqedAJ6dwpEwdfEehwuUtbW/Jr6l+JRNDJ97z6UjvzB7EVMrVCE8WJGE84E7L+nXCMts1zzgj7JEZVcHmnsNxkr6COVLiWf9Z/5KCSrbCMkMq8/5CU5B8e85FzMBMKWIjXgVNaf1cPqEWS5tIVDEISkbAL9ntMJ9qfdgCGCzUHfnp0v0XfoyBrrfBzsv8dKnxv1NalkGcrgsAuwAS/fKCWESX1ccCv21+OvsJq+uri/XMlSZm+p8aB0cAlkcfoNpLmVbcBVbuaN9KQKdsTT4EJ+yH7ZeouTReIOfKEO3P8kegptfwa11ZnwIffq+5Ddmzq14Rcyn4+tJfrfhiFBBgYGBgYGBh+N5Gz+dB376YzXVHo8AYoh5CdQPuVBKelDzBBaKnH4OrwMhWfIudLmJwmQA+2YLAw5V9r5Iq+9e7N06oMEtzE4HKTbVWw+fuxoBJKQ3RcL6pLlFLtPuJLA8shqriFJ0SBOac9fJg2YLw3/MsQqMcOqq6nDEMHpoUmpJE38Ls1Fs3ujfhxYyGe99Xxu6nWoNdGItOsl3NSSs6VxV9Cr5vlLCY4nX01t0M4X+WxUBPhHVlODs/0FAvyAl1SoaT+Ulj1IE87UQuaWmE5gHuqgTubgQqnXDGqcxn8b8lI8VqrZKXl9EACHB9c7hwei1HgE0jnpKqlG2E+nF0otR6Wl96GOSHw30GrVwJRJr1/Lul0JzKe5HHOIUx+Qnr4w7lTnSiDdbNEd0rz7pEPLSB2zOyGBrmT2q+9J0mtX8V7hMMwFoiK1LdDGGnvNII0vds42O+fcbRKE3mbHznvgmZhQVO+wzDLGca6L7qLx6IDTUGv6nSy9eyOqWNMRvh8Jk3ZpszHuykelKdeiOlbvguCN+Qbj2WIVKZ0UKofbus/Wcm5BHw1x/xVjA7ude9DgY2FIkIGBgYGBgcFni/eTj86oP0CtyYlIUNCH49Yh68n70PMo3PfW84TfkyaVr0c1GXAa6WORkLTrNSmlELtjm52akm4TaKTpbeg0vpcAdfDZ0mbLVS7YIS1/iFSjU3+OBXV7HQH0e/cRuC+4DZtlVzKpToPPpBao8Qh1OO4UVIqkbKm9Xtr2lGUz7eSadgeKz9L7pS/9XXrnJ9a12+kftOhOyJfdTqrXzhex6/Y1Y9Zgd1FD0laJ4tL/FGyx7Q5SGdf+lkA/px+kIGw1+Ewr5vr+FuaemC7Nvp2+Pa5kaeJ3CP7tTklRXNIcHtLWdr5MyltbNeP3tPoHRQKYFUjsdVIO5Cfkt0wakkiha69lTa1VjJPeFTXLkcC1knMsRz3LmW3HCyiS6x6XkrOoWQoHOUubTTrlAfr01B9gzvN+CgntNV3a8rQ08quQmMPLGXPoeVJWb85575sQm+6TpaIR0pZXpJk/wTBj019RtfrMwhhiR4fUY1q8Tim9G+sMByTZpFWPSBc+CSlrqyY1M7MbRM3fSqplSgFzm3sXqlVjCWcx6XtS1U6pehsq4cn3cJ8Z/EdgSJCBgYGBgYHBZ4x/o++PRArcwcX8u3gUwau3nuFCHdYTcBsKTp85EJL0LgShb1wTL+DP7k19yrz7pA1PUN8RDhKMT/weaXneOsvS2oVKs+sV+rjM+LHUeFh65XsQrLW/R5lxJzEHmwPS8NaN8XV2nyRN/SGqjq8ZFcvuJLh3J0thq44lEuJvfyvfyx2IItHRyDxk9RqqP0i6Xkq+dPK91L/Y7JDLpQ9KCkPm/K2k5oUD0phLGaPrBAwW7E5qTpyJVg+kb1NHtPWP/Jw/GHI35puMk5SNMpOUBXGxORAxOhqlsi3M15kgtZWz581llrlBfdxWumCYdGAxpgKysbZAG6pSZg9UooxukDR/K6TWk8JcY31wJv0AFW71Y5AoZwJqzJhLaTy6+lGut/Z3fL/bBOaz7Gdcp3gMtT3pXaW3fsRexpShw8ul8s3SSXdiU51aIE34jrTyEQiNzUF64+GlmFqMusRytbNMD2Q57FVuJeWvrYa9L9tAmlys1ui0h6VXv0+9UNFISGvuAEh79Q7IXvfJ7LXBfwyGBBkYGBgYGHxRECv6tv8/N2/tnJr2fmT3Jgg/EQIWUZBQNbY/z79tspqRWv11omGC5tz+pDMtuJWA1m6FPfUHpbdukE5/RJpzi9RagyJyYCGOcTW7UQZisLv4TtAnJaSiBMXm0V4jrXmMQLq1iqB79Nep+XG6pdp9pKk1HCCo9TbEyVignYDf4bZ65TRKrdUQkJKVpERVbI7POxKBkPnbpI1/thSZl6TB50qlS1FLJD6fkG4RnSrIR89pzLvbREwbdrwAQUnJIwWrZo9UuY3v9JhCXU40SjPPPrOklb+wPp+PsjX0fMhia5U08GypWZaZwP2k3618BLUn5GdOaV2oz1p8F0Rw9i241Enc93V7mVv9Ics9LYwKt+9trMIPLYZURAJYmUejpCpGo+z9hj9Lpz6EMlS+EUI6/MvSjudJ1zu6mqaqvmaUvvJN7KMrkc/GjC8CbaSudRnD3ux8Kd5fKCkHIihZPaN+BKlzJkrhmDmEzUr1S+RPOBAnvLKstn3NuBam5HPm/hb2t+EwZ9p1giFAnwEMCTIwMDAwMPi80VJBsHtgEU/r+59CIJyc83nP7D+DjO7xvjad4XBThP5hAaAr2SpstwLKWEG5FFdgYq9FoxClfW+httjfF/KEfNRqTL6Kont/O05itXuOJ0BS3Bo65JOUal3/fYhGUA3KN0hvXEcalM1O6tToS6SjGwiWq3dA8io28T27k2Dc1gRRSC8maO9zEqYUbZXCCCDI/njSJH85hGbzk9Kc26UD76JM7HhRioakUEhq81uW1Q5ITDiIhfKO5yGMYy7l56CXQL52F/UrNhs24NEoKkv/eZAVm0iF87dRa1W9U5p/n7Til6SN9bwNNWf85ahrpz4I0WitxFzC4Ua9cbghBm01cXc3m4397z+f34HmI3FScmChNOMmiFvTEVS+aAT1K2ZuIBtnsuN5Gt4eWSUldmeuCek0lG06SqpbYhZOb+Xr+a4nld+zoDde17P3TdZSNNJKvXRAiHzNqFUODy56yXmoNo0lpAe2VbJn3a2ePUE/94Hdad1/Nu7TYLu06S/S3LuZXzSKG2Cwg/8HQu9rsGrwH4EhQQYGBgYGBp8nmsuwLW44FH/tyBpSqKZc+//T3jYhjTSrwpGdLLIHYwWdO+DDv5feheL+0lUUsXefjEIgobo43TqWgtZtPCpE05HjG75GggSddodUtZ0n8wlpkieZ6+94/oPXtTmwpW4p5zzSiq2n+yFUosweWCeXbaCuJaXA+p6T4LutFge0PW9i29xvnlSx0ZpPKN5TZvIPaN55aInV78clTb4GMhFoJ2D2NRP8253cI+010qa/QSr7nUwPI0nHXO56zZT6zGVMm0OaeRNWzlufZqywH4UpfyBGD0GfVPkcQ/SaASGy2ZhnUhrXi0aliF/a9y5K2yHLcKDxMPVYWb1wntvwJ4jd0bUQ/YAXJSutiN43nhTGHXsZa93+AkRy6z+kuoNcK7c/aYTZ/Xg4sPJhvh9oh2BFI/GaKG89joVpxdLQC2g8m5jFnh9aittfRnepaFS8Yau/BYLXXh+/d7J7Q4iLRljNbd2YN8TI8ZivQ+aWPWi9ZqPGyN8iHXyPz9btk8Z9U9rxktXw1EqXG3wmY/SbjypWvV1ypUoDTuE8a3f/a0YIBv82DAkyMDAwMDD4vBCNku7TmQDFULqK4LXPrM9+Xp8FkrKwLu42gUDck2bVvXwE3Ek4tQXaIQZjv4miEmiPqw0Z3XlKn9Wb1yq3EfxGggThuQNQFcIBntrH4s1wkIB9yrXS+sfjVtBJ2VyzZCX2wxL1JMMupIlqTl/MB3zNpJVNuIJ0Kn8bNUot5dKe16TSlShOOX2l+n3SlOukzX8j0I+EpDm3UjdSu4f9aK2GnFVtx8Y5GkVBigQtq+tm+tpUbCHtauNfcGebeyckOuTDoazbBAwX6vZiDpHdRzrzV5ahRIM06Bypz2yudWQNdtPl61FL0rtQJxOxmsLaOqtvVm+iouGkdEnM/c2rccHrMYX5tpSxB7JBPANBUs6KhqOu5fWXtlhGEeGgtP9tadCZ0vCLLKe8KKYTb1yFaUNSNoTKnRJvBhszT0jJI50uf7CU3QslcP87WF/veJ45RKPs6fAL6Y2UkIkjYOc6tT5zcWjrPhHCdHiJjllZ953Lerc9R82TotQvLXuA+2TGDdhiDzyDIWf8mBTLg0tQuvIGUYu1w3LwszlI8dv9CgrY2MtRmAz+4zAkyMDAwMDA4PNCe520b8GHv7/7NRSIjyMH/wlEwhABu+s/22/EkyLpXxg/oyuBfnMZROBL/0DlKN8EqRl0BrUlsUanwy7ANSx7gDRgHgYHe99CVZlyDTU7ZespUk/Jx4xg/s84G5uNfTi8jOvF0vRcCdhir3xEevNHzMPpgcz0PYmaoCX3QmB9TaQ4ZXRnjmnFkI2EvdLob6BA2ZyoRwcWQtxaKi2yUysdWUmK4LZnSaWLhkn5Gv5letEMOoP7I6MrgbndiU10ci7fD3pJsUsvhhxU76DmrN98CNLmv0tvXQ9BTO/C9cddDnmMhKmlajwsRSySYHNYDT1dkA5vffxsbE5IRt0+vjvsAuqxJEnRuL11dh9IXt+5UtVW9r61Quo9B/IUCbEXBcNRkXpNR1la/nMI3JE1kMe2ais1Lo2zm3AlKWgly6QXr5BOvpuz2/2GNPNm7Lwlass6GqWBZ/HZSKf0xqJRkBtfE/s38iJc4CKWNXqvmazJZfXICnq5dtCL22BOPxqsbnuOlLfELO6D85+AWCZl0Q8pHOD7dleciFdtZyyn+5//fTD4t2FIkIGBgYGBweeFaDgeDJ0IYX88XeqzRO0+6iLKN6G+DDqLBqmfRmpeRxMmA6UrCWC7Tyb96F8pBE/MOL4XStEIxrU7sXnujMJhPJ2v2UPw3GMKZKlmN0Tn7Z9gx+1wM59NfyMlq3I7ZgYxdJ+MAhRD1XZ6HdlsVnpXkOvX7CI4n/hd6eVvS8O+hClB0xHOMzGbdLjELBSS8g2SbLzmcBGgx9KhHG6I0uFl1Ob0mYOqdXiZ9PJ3qEep24shhCuB2pZomGt1n4Qq8toPCMz7z8fEIDkXsmOzU+vTVk0tSjggNR7Cnvvs39OzaMuTkICS5azNmcAcY6YQfedIi++O97zJGyAN/xJ7teA26aS7pLN+S1peyEfz2NYKSNz6P1LztOs11jnvfsjiyochZF3G8v6Ii1CJEtKpuWmpZG/XPAbxkFNyp0ojvkytTt1BCGtiOuS25zTu52hYOus3uOo5LGJvc2KAULKCs+86Lt5vqWiElNVHUkQ66W6c28qtOq6EDEivtx4y5GuyyKCNe+mlb7OnNofkb5b2vk3q4NzbIZStVag97hS+G43wb09q3DTD4D8OQ4IMDAwMDAw+LyRlEYTtf/fE7/eaEX/i/Fmheqf05vXxpqAShKHndGnK1Z/MtcpbL6357fHr3fkSxGTyVSgL/w7sDupGPnC9BlKRtjxlBfteXh90Fg5mi+6Q2qsIiB1uq4dQNqllU66GBNld1POMuTSuiAXaLCc2SylqqbCK6i1ysOMFaeQl0kn3SMsfxEJ79WP0qgn76QVTtw/1ZvhFEC5PCsSuozHeLHT4hZx/3V6c8KJBqdtkbLzX/0Gq3Su1N0AKVv7SqnNxomQ4PVh/95iKNfS7t1oKjlPK6knNTOFwzCGSsgnafc1ce82vMWYYeTEk8uSf0sTW34pS4W2Uxl0GGesxTeozX6oS5MPhstLfxH6s6mQvnZwLWTm6Gke2pGzu8aye0s5XSBuM2VUfWSlVbZPO/DUEYeRXraajOzGAGHUxZCSW5rj1aWqyhl8IoSxdBTEdcRH7tPs16q3cKdxnPaZIqx+Whp1POlooyHydHprU5g7gAUDTUb4fDUPwknMhdN569tKTavWasnGGgXbqqmLyjs0Oqe1okMo2QgCDHVJCmO+6UyRZ5x0JogJVbec6qQX/3u+DwT8FQ4IMDAwMDAw+LzjcFFSXriKw7ozUAtKVPkv42ylm70yAYji8VBp4+icjQRVbTkz4SlYQHA46g59DAZ7YN1fyc0uVlNXlXy8Yr9wi7XpVUhSFJRaY7nyBupNYsB5spz5FgvQkONn/Mx4lEE4rjlsjSwTXvhbG9TVbJgmdHOUaSyAva36LLbTNTi2IbNKCWyRPOuM1llCvM/G7pPBteTLuwOb0YHTw0pWd5m5D3ajagcX0zpelyd+Xtj1PimBHAzbLPadB5MIhHM4W3m4pHw5IRkczpGzKNSh+HQ3YbOf0kWSHLNgkLbpdmnaDNOBU3Odq93AG2X1R1pKyqWdb/3up64+xrk5Ko3Hp5KtQmpqOxPemtUJ6Z5t06i+lrhNJddv/LkQytx/NQZc/hKOaBKHa+AQqUL+TIS9732C8Vb+CTMQMKhTFca7XdFz48gdBBhffY9lQN2PY4K1D6Rl4Or973gap78mk6C39GUqbzU5qm2zs85BzpHc30jh1yrWQSlcS94bNzvwCbdSMNZfpWH2RzQ7Raa+DRFVsoias20TSEm0dltLrtuql2pnP8odY68TvQnqdn0M67P8A/p83IjAwMDAwMPiCI6cfdsK9Z/JUOCEDpWLe/TSO/CzhrY2n/JwIJcv//bEDXoL2D0PpGlzU2uukrc9IL10hLbyD916/BqLUuXbjY6/XjjOXRPAfc+eKBajtdaRRyc6faJSUsHCAf8smFQ4lVa8zAQr6JFcKzVo7muJ9iTqjcIRUuYn0waCPmpV+86Qtf4fkuJOt1DI3ZGnXK6gsYy/DAW3id6VZt0j7F6JS2C3yEiNbgVapcjMpb+EQttRpRQTyI78KEQq2Sb1nUZCfnEuwHWhjfa4E7rV9b5OuN/dOqalEWvqAtOIXBPdFI6T0bnz+4GLpmYtRiJY/JD37NVQyhxszB18z604thJgsupvxm8s+aPMelWSLomxtfAIy2XCYmqflD6HASYztTISwdBlDHU7vWbjEeVKt9EMHluCxM3UnQbpcVpPc9GLSzxTlIz2moOzYbIzbUiZ1HQup2/sW9UkdjShXoQ72cNEdrGP8FdKsWzHcmHM7+9lcxn3kcEGAJn2fGqPYPedJ5dxiJNCTigI45SrOsvkopKyljBS5k+9BLZIgdIvuxC3O4D+Cz1UJWrZsmR544AFt3LhRlZWVeumll3TWWWd9nlMyMDAwMDD4bGGz4WY240arGN/OU2C74/Oe2QcRjX78Zz4MsSD8/fCk8kS+6SgNTKNhAvCBZ0i73uAz/mYCwjMfxTL5w+BvI+gtWcketlbEa6o8qXHHN8mqz8kkRc6ViN1yoI01elJJTWsuJ7C326ljOrSUGp6ELGnI2dK63xPEd1aBPKm4iu16neaatXsgs65EFINwgHk4PJx1WzXn3l5rKSy7IXwDTuXfSZbDmsMyHYj1OyrfJM04mTqaaJjvSFxjzGUE60nZ7EdM8ZJNkg+iklqI8tf3JOn5S1HDbA72I9YE9dQHmedTFxKs+5ohFonpVt3PrcxNViF/e63ksZqPVm9HQfPW8be/BYUvdwBEpaUcwhEOkAIWdGJ2ULlV6jlDqj/A+8k5KFQTvwtZKhoJ0YiG4051Njtr6zsPUlY0ClOG167hPojapGnfRkVrKpGKxzBOOGg1gC1AYfK3xs/R7pDsbu6fsvUoOC0V9J3y1tMotf4Ar2X3YR8jEcwdavdCfMOBTjU+NhSnvEGQxGEXWESonDqipBz6PvWazt5JkP4dL7BnxizhU8fnSoLa29s1fPhwXXrppTrnnHM+z6kYGBgYGBh8vnB6CDA/TyTlEByWbzzx+z2n/vtjuy315Dg7cBsuZ8t/jnKTnMPT8f3vkC7Vf75kuS8rHCDA/TAS5G+NO3JJUv5QFJDyjdR4pOQT9IaDkqJScwVNTFf/mgDd32L1vwmhOKz7PYGtO03KKMb22N9q1YGkSSX5qHW7XiatzdeM+jD4HGnt79mrugMU2ydmQTLaqgnYY2YYTqunTWslCuAb1+CaZrORMhUJEugnZVvpdxFrjkGuFQ7w3eod0pBz4z/7mqQBp0Ei6vZ+cK/sDshJf6t2JtjOWdhsnJPTgxnB0fXU6ySkQ+6CHfTTye7L5xsOWeTMIoGRoNTRgYFEcxkpbtVBiFVCJtftNgGS6m+htibkxwAhGoWsHllD6lvZWkwkekyhH8/RdVL3KdLE70j9TiEFLxLE3CB/MIpWTj8IRVZPyMfwCznrrN44sw04lRqsXS+Rquet576Yc4dVx9MJlhgoiX30pGNx3l5NHVM4ALn1t7OHBxdLZ/4G1arLeJS6SJjzjYS4dmohxLzhEHtts7PmQ0vidWUDTz1+Hr5WrLO9jYyTks/ZG1L0ifG5kqD58+dr/vz5n+cUDAwMDAwMDGLwpGAAULM7biIQQ48pPPH+d2G3kxK29+24IpQ/GJe41ipMAqIRi6SI4vnec48fo25//N/+dgLKkJ8gvb0uToAkiMH4b9G81NeIG1dGd6v4P0IaVEq+lNGDFLBgBz8POYcx1/2O+qCiUQTNwQ6rJioHdcXpIhjtNRPCkJJPStrq37Ke3AH0ARp2gWUW0AR58jXF5xjqYJ9z+0OQWqv4TEcjPWx6z8YtLbOH1Si1BSLhTMCG2+lB1Rh4GmYWbVbPoVhT1sk/kHrNlg4sYs2uRKsQX5C5QWdKC26HZEWjpMk5PATdKQXsYa+Z0phv8F1vI4YM3SdBrlLzrSajnRAJQXAKR0prf8d1QwFIrt3J2SekWaROkqKWUUAan7HZIBhJOawzfzBNT2023Oue+4b0tVekgsEQj14zIaGb/oICUzCUP84kbL4bS6SG/awvMZNmsfWHULCCHSh+lZvpHeVrgri4kuKGJKEOziezh9Rkw7Rg8995LyWfFMaORvobNZdyf8+4gc+VbyL1Mb0LCuJ790rz75eGnYfBQ2IWJNnu4p5yuLn/+8+HuNtsPBjZ9rx0YEG8fmj4lyG973dCNPiX8F9ljOD3++X3+4/93NLCL1AwGFQwGPywrxl8CGJ7Zvbuiw1zTv89MGf13wFzTh+DrH7S/J9TL1K5lWBwwKk84XalSp9k39J7SCfdhw115RYpZ5C0+R9SRi+uEwlxDUspCZauk5LPVtDmis8tGJQaSgjGK7eQFlUwnHoQOaz6Hwt73pVm30GRu7+dsfOHowDlDWZ9G/8mFY6S+pxsucm9gDKSVCg1HpWGXyyt/SMKxI4XcCxzeHCXe+N6KdARt4B2uKQZN0mJ38CtbeQ3pbY66dByqbVOmvgDadnPj0+fC0elqT+Wlj4oOZKlpDwpMVdqa5ScyVLf+ahZiVlSpIXgfuZN0uZnCbSHniMt/yVEI7kIUwG7RzqyThrVIh1eIc24DTOI1gqpvZHgfdQ3pJJ1rO+9n1JbFGqlWD+5iLqniE1a9RjOaopS9zLjJta8+2Vp8AXShj8raIWTQbtVO+VMYX8nfA+VL2pjrOQ8yZkm9RohHV4t2S2pxd+B4UZqDgYNiVnSnBmWccRDki2BtLagT7InSKt+g+Oe3y/tfhOFMBKWPJlSR5v08g+kefdyr7RUsa/RiOTOklb8ShrxFYwdgkEpYpd2v81rdYetNDZ/XBnMGywVjZPaGthnb6uU0Rty5GuVlv5cmvETqeaA5M6QMntLB1dIngypeq80/QZpwx+Y45w7pCX3c24SpHDnq/SB6unnfszqL+18kPssMQfFrvskacTXcDqMhKXNT0kpRTS5/Rfx//3/v39lXbZo9JMk+H56sNlsH1sTdPvtt+uOO+74wOtPPvmkkpKS/oOzMzAwMDAwMDAwMDD4IsPr9eqiiy5Sc3Oz0tLSPvKz/1Uk6ERKUNeuXVVXV/exCzX4IILBoBYsWKC5c+fK5TL2i19UmHP674E5q/8OmHP6AiEYkFY9zFNyyTINaOVJvLdRwcnXaEFrP81t+LNcU68ldejoWhp0BtpIH7M7pD5zqeM5sJDUtEhI8YIOkc40907qWmKo3CotuU/qOUVa8bBVfxMmNSzQQa3LhCupK7I7pYW3WSldDmna9dLiu1AKnIlWvZGf950enN1KVlJsXzSK1LtNfyVtKrUQ++3ELJ7y213UD1VswZ450IGaY7Nz3WgEVaNwuFS9C7Xp3ZusGqux0pivM/bhpVafG8seeuJ3pYrtUrhDUgRFJ9CuY05qybkoWtEIaXMLbqGw3+4kBSt3ACl4W5+iVsvmIJUwEqL2xdcoLXtACvoUTMjRgp43am4vl1zNpTjquRNphHpkFWlbvWdLmb1oJrv1KWnU17h2yXLUjQGnSal50js3W/VBDlLmcgegRC693zo4G7Vlc+9CoVr7mxPcWDbS+U66W3r1e5aJQpQ1t1ZIZz6GE2H5Bs4zfwjuhYXDuC+Sc9iLii1S/kCpYivne3AxKWqxflCJGfE6svP+LL18BecZ7OB8597BnpUsl/qfKi25h3S3kE8K+1hfrC/S5KupA3v9Gs7AlUB6Y6gjvqY5d5BiKfH+GY9RT/Wv/Mr9P///r6WlRTk5Of8UCfqvSofzeDzyeDwfeN3lcv2/PMjPCmb//jtgzum/B+as/jtgzulzRNNRHLcOLaFO6PAi6nDaarB/diVJPSdKxSOlPe1yDT5TrtzekidBatwvhdul1jLLEvpi3Mw2Pi7JJg2YD0Hav4Br2RzSmK9JKe/rb+RyS75aHLJ7TJAOLbZqLhxcp998qXgY7m7RiDTpO7ifbfq7pDDkwu6UElOklsOd6plsUs0OqftYqd9sqX4faWTTr5PW/E4KtEjeaqnlCJ+dd69Usloa/00c8Or3EphH/FI4Qh1SSrb06pW83neuFGqXpnxPCoel1b+k1096MaSo+wRIRXOp5LJJB5ZQGxX1S/aI1QcpHcc9u4N9D7ZKM39MfVZCmjTkPAwfNv+ZObZXS4qSKpbZQwp7pSV3Up8SapFacFVzLbpVrjN+Drl66wbc3hSV/PXSmp2QjSlXSzuekpbeTT1TzOgiMVlaeq/kb+D7nkwp4pOqNktdRkhZ3XFjS8iQhpwluTxSQjKfeT9sdkkBySHJ5ZDam5hHOFkqGCj56qVFt1j1R0FpxzP0NardgbtdNCApIhUOoC7JX2+ZETj5TigMYe+owwQitUAKtUkzfiRtf4F6I3tE2vgHXB9DrZLTIQUaGa+9AjKblK5jZD3klep3Sw6b5LbSCu163/oC1txEymJiivRv/h/2//X/v39lTf9VJMjAwMDAwMDgvxwNh6XXr4Mo+Fuxdp7wXcjCkbUoMn3mYmKw4iEp59s0Ec3qTqCfnEf9SmI6pgPv/ISn9DYHweiqRyBWPaagFI29DBXl/UgtknpOR13pOQ13saYjGDQkZTHejhchWA4X7l25A6TTfwnh6TdfKtuAuhONSLJBCqJRVKkNf6Awv7UC0pHRQ5ryA2n36ygKBUOlERfREymvv+RIkGbdjIlBxSbG6jUd5WjvW9THpBWhZoy9DDvlkRczVnIOaw75aIxqd9AI1eFG7WicJ8mOehS1mopGI6wz0s73Nj/Jno25FNe0HS+yTzarv1IkhHHAkHOlbU/xb7uLXktOS2GLBCEq3gZ67hxrGmrD6rupFLOAKddiDFC/D7OLKVdLO1+BWKQVW65xAfatowmVa/hXMNdIyoF8hv3Uyjg8cbc9iWsmZuEI50pgvqGAZcBRLU36gbToLohSrMmqzUENlDOBXkXBNu6x1EJqv4ZdKCWkQgwjEYiixD6Fg6hci++iVm38t6TgYEiTw4XhhTPBMj5IQBnK6MbvQcDL3tgdUmZXSKaz08P+hJiRhmVVZ+8U4I+46JM1Ljb4fElQW1ubDhw4cOznw4cPa8uWLcrKylK3bp9xgzgDAwMDAwODTw/BDgJJT4oVSItgdfPfpaotcfe51kpSsfrM4al51XaKwg8ulqw2MPK3QgI6GlETnB6ay258gmBYIuXJ345BwtH10tm/IRCOhklxqtxKOlzRKBy76vdbxfJJ9IDxtaIgLbpLUoSAuftEyMTa35HO1GUMgbC3ATex2TejDFVuiZsdDPuSdGQFxhKJmZYZgAflZcXDNMTsOo4mmCsfJkVt/1vxdLOja7Apt1sNTUN+AvYpVxP8h7yk7MWsxkM+HNQSM7CSlqSwjTUeXg6RKt9I49eSFVwnZs/sSbXUrEysmYtGQpq6TZQGnw35lE3H1IqsPpCtt99jXUEvBgJBi4QEvRA/f5OOESDZIF7tNagmR9dZBHQK17DZpLJNBPshvxRugfyE/JCHzB5Wc9rRpCS2lHM/pHeTRl0infEr6Y1rud8cbvbBk4Z7XqBdOrxMkp2+U31Pgtw5nBDHcBCr6cQsrLVXPmzZhdsZq72WBrGxe2DCd3ASbKtGrUzKkXpMJo1u6b2sc/cb0hmPoHQ2lkk7nse1buxl0uxbpRcug3xl9WKccABFLCVfyh0ICazZxda5kyFibdVS1/FxB7nhX5a6Tvhkv58Gny8J2rBhg2bOnHns52uvvVaSdMkll+iJJ574nGZlYGBgYGDwBUWwg+DQlYxF8xcR7XXUUux6hfl2G48ykt1baq0hTe399tuKxtUPCcITaKdZpRTvX7PkPmnc5dRG+FtxDLPZsXI+tIyg2VtHEFy3H+VkyX2WKmFh9DfoQ9RcxndDPmnny9KIL0OKAm1WqlKmVLoa9WLsFVJHvVU3EyKIr9rGNcdfLnlrCZj7z5cGnyW98j1S0mI9gRwuydfO9fe9TQ1U0xEC9vZanvBve1o65UFpy99p+jrmUiyVXUnxhptZPaXpPyYYTs5l32x2FJGdLx+/pbtfh8wkZ0s7XkI1a6tmXxwegmtPmnTqz6XEPEjBy1fyenKuNPZySMLu1yGSA06HBNbtJWWxtYozScmXOqwmo65k1ImWsuPPVpaa5GuBFPc7GXc/mw1FIzWX1Mb97/AVXzMkwdvA2sddLq1+DLKZnMe5+pulJXdJpz4kXfgkZKe5DNKU05dxXvk+JNfpgYxUbYd4ZfWUIgF6DDUcRJHZ+ZKl8qTHa5/sLu7DPW8y3oFF0pzbmVewAyWx/iBjRMLSkPOx7l5wK/dwQhpKVOEIafWjpD6e8jPIe7ADNbDvydKAUyCiW/7B/dNWzT576+iVNOBU1NKgVxr5NdMn6FPC50qCZsyYoS+IL4OBgYGBgcEXF75mnrDveIEALG+gNOgMKac/T7U/K/jbaGbqbSC4TOsipeTG3/c2SKsepdYnhvr9FLCf8gBF+4H2E48dMyOIhFF3gl5qPiTSkXyNUu8ZBKI9JmM6sPlJFJiu46SZP6EnUFIW10nOgRh0JkBZVmH+oSWkJDkTCfSTc6R1j/OkPuQnzSkaIb2uuULK7ik9eztjxIrWnW6C5F2vSGf/njOq2GClg9XxWYebWhEJsmWzE+AmZkIIOhrjn0vO5ZrFo6Wh50t73oAAxXrDRCOkUG36KylXYT+pZT2mWMSj4vj9rNhELVJKLvbPdoc082bm0FIhamSC0tbnqKNKyKKHTVKutOYxqalcmnadNPxLqEfeemnRHVJGT1SVdb+HMIYta24J5aJgKMTuOFixXjRM0P/GdZDKpCzUocV3S33nQG5aKlhv0Mu9kpQN2Vj/VfYgGmbfvPUoO9EI90zv2aSseRuoOVt4G3se9OpYylvIR4PY9X/gvpT4XZp9u/TWjxg7MYt1eBv429/KnPIGYuhQvtFS+HIhQL1mom4Vj5LSClASnQkQ3+Zy1L+h5zG/LU9jgtF9MuvN6IZZhN3Ofb39Oe6pvidh4x70odblDZSKTpDSafCJYGqCDAwMDAwMvsgIeKkb2fKP+GtNpaSLnXSX1O0EaTEtFSgNvmae7Kd3Rdn4JGip5Gl2yUodC2rTu1DHkjeQn+v2H0+AYvA10w9oytWkXO0t/+BnQj4CxZZKlKSUfGnw+VK9CPybD0M8Tv+V9OZ1kAinW7InsR/rHpfm3MbfMVe1WKpcDMVj2EdPGk/io1GCZLsLMlW6UioaIZWuIiBuq5LSu6N6KMqYDk9ckXO6ufbRtdLCW0lZmnIdqVre+rgaFI1AZtwpUsEwqdcMCEjAa9XUOEmZCrTh6rZ/IfMaej4pV9ueIX3O7uRas25hDrtfZc2yk55Xs5t1Oj0oJjteoMnmyT9FvQp2SKt/BVEL+QjW3cnS7ldQODoapPV/RM1rq5IW3wkZmHINgX44RCPVXtMI1PcviJtBSNL4b5Omlz8IRaaxJL73Njv7k9kDErf3bchsoB1C0lolTb6GM6jeyf71noVCd2BR3C3P4SGF8uR7IamVW6WyddSQeVLZ30NLLAe2QPxeddiZ5+7XIcMxElSzW9rzGirX0fWsW0IRcieT7pdWyDhZPePNfL0N7GlSJoRzwnfptySr6Ww4GL/+jheleT+V3vsZr639NTVtm5/CTCGtUNr2LCmYgVbOWuJck3MgunkDmY/BpwZDggwMDAwMDD4vxIrUIyGeise61MfgbyU4XvMb4kynx0oZsxGEL/85QbDdjWmAOxk730V3xtUICWOAaddT9O+3rKVjT707KzknQqz4e9/bxxMgifSjd2+hBiKtCGIWQyTI92JE4OBiacw3CYCPrOJ1h4uUKIebwDQpm8D8tF9AMF6/QSq6BjKTkg95qdzME3hPKqleLVUEnQ0HSTNKzoEoLLgVVaLPHFSVAadKOb1Zj6+FvbI7uV44wJ4G2kjpiqVwRcXZxIrobTbOyeHi59ZqzsXh5ntH10mrfoWCsvRnnFFSFnvhSqTeI6O79PrVpMKlFKJMNNQRtB9eBuFLyaeG5vAyiObkqzF8aKmAUPmaOM+Ft0sL7+D9qT/kLOx2jAC8tVbtSx7XXPUrgunELPakciuk1ddM2ta+d6Sxl0rv3cc+95wG6TmyCqVt1SPxIHzz3zCGOO0U7oGUYqlKNIYtGCAtuVca9y3eO7KW/eo9i+u/cS37O+Yy3q/Zyf52NJCSNuEKyNTRdah2G56g5ikS4qyC7ZBDT6r08nfYY4eL36WMLpDQ4RdCZjc8wV75W1ljRyPXSkjn+35LpUspxHhj16uWs1yU82yvQy3sNYN9ton9cyVxnoXD2NeWSiyv/W2WUpjItTqTcX+blNvPqidKZoyaHZzhyfeiGgVa3/fLF+E+aKthnYYEfaqwf94TMDAwMDAw+J9EcxlpOS9fKb34LdJoqrZDDiQCsI1PSPvfldoqefrdWEoAHwkSTJWskI5ukF76NsSndg9pQJ0JkETAu+5xqf4QdS0vXMY1X/0+5CTY8YHpqblc2vQ36dUfEGyWb6I2Iyn7+M+110JKJMslTQT0HU0E7S3lBNohP0+4975FXUSv6QSuCZmkFPWcLj3zVengewSPVTvjqXOxwL/HJObhbSC9q/EIxDCmzJStgwQ1l3Hdo+sgeQXDUWFcyRgqpBXyBD/kZ7/9bXHThLp9FkFzxw0E0oota+lMFJpIGIUgGpESc/i3MwFlom4fdRwDzyBtqqkUMlg0CmKw/nH20NcsNZXQO8bhlrqMZb9j5y8R+Dcd5R7I7ksQnNlDUhRiM+9eUt3qDzDGpO+jlDicpGvlD8EgoOEw9UqBdvaleieB/eTvc432OqufTxZK1NRrqLfK7k1PnRgR9LUQlDeW0p/njR8yt+ajzDfcIR1cwjW3v0AKZ24/KacfxGPv25gZyC69/WP2KxplzSn51Ou88n1p67Okhu14gbX0nMp6YzVjQ8/j+yFfnGhEgqxz69PU+sgmnXw3xD85h7OIhCBiR9ZRg1QwjL89KZhITL6KfUvKgVgpSopkySpqiII+HiD4W1lPz2nsic3GNe0OSGtHU3xOsXvIZqOe5/AKafqPMFCwu7hXOxohcB+GjG7H97gy+FRglCADAwMDA4PPGq1VPAGu2x9/rXQlzlynPIByc2ipdGAxaTrHECVdJ6WAwFtRHUvTqtgila4hoLU7PnjNQ0ukvAGQiGPzqJQW3inNu4c6hRhaKiFTdfupK8npz2dXP0agve73ccIT+7wEkdnzBsTI28DcHC4C2bQiAuZD7/F3wRAc3EIBguq9b6IoVWzmqXtWD9QBKW6M4G3gb7sDlSISpA6kqYxruVMgRJGQFBLfr94tjf0GwfkL35KmXUMdj81uNbsMQ2wKhrLWlvK48uNOo04m5Keh6IpfxF3Lgj6C8vHfIkh2JVpBr530qN6zUSO89Xw+fwh23jGCmtNfqtkjyYbVtt3B9+3O+DUiYdZ18D1p+vXs28DTqZ/a+rTUdQzXkqTXfgDZmHsnc2i3lKBtL0jjLqPZaHMZZDIS4l7LHyxNvVZa+kBckTu6Fmczm4M57X8XV7RhX7Lc4qKS3WY1IA1z9nkDpFKRtjfqIoj1tOs412iU3kjrH4cs5w4gxW3T36QjayB/k6/m+st+htKz/x2sqaNhzCeWP8w8N/yJebVUoNrZHfFePxLzbyzl3Nf8BlLXeya/Q752KbOY+23OzeyztwFFpmILe53dG4Ut2MHYaYWsr88s1Mb2OuaU0x8Sv+whUg3tDojT7Nsgj84E5uKtZy8PLrIayKZxjlueZk+cVj+gxhLul33vdGqOasGZyBpsJ/idNvhEMCTIwMDAwMDgs0b1zuMJUAwhv7Txr9K0a3Gz6mi0nvxb7loSQaWvGQUktYCnzhIqQfMRUrvsiR8c299M4P4BRKUNf5HyLJtlCSLibUD5qdpBwOzwSEPO4el7wTCcumJIswrjc/tDJrbvir+XmEngOeRs6d3bsHi2Oxnn6DrUkeU/x/Ft69PSgYXMo/f0uBLkSJCiQalsozTxSmp2ImHGsTkJTCXqVA4uZo+iUWpuMrpL5ZulZQ/wuY1PYFO99SmC3/SupE8VDocAzbk9boGcWsh+PncJ6sP0G6gfiURJ3xt4BkFu+XpSnqKCHNqdFP4v+xmBf2ZPrpFSQG+f1ipI7JBzUFHKNnIdVxIBtbceBcPuJNiPBCBZE66E4BxYIOX2QQkcfDYBciQICQwHSbfrOo49sDuZQ2sl+9rREL+fqneSppbRDXK16xXUFMXUCzv32aK7pa88y31RaRlNxN6bdp1Us1fScM6p53TGXHgHhL2lgvs6Gmbc+gPskb+FueQPoY4oOQeilVrAnDN7YFbQeJizbi2Xhl4gFY8gDdHhYcxIyLrRbKT+tZajVnnr2MdgO2vvd7L0zk1cPxKUZEex6j2bVMmGg9Lr18RTGP2tnMmgM0n96zqevkZ2F25vnhTqeQ4tpYZr9Nd4AFC1nbUlpEtjvsFDgDMfQwXL7kO6Yc+ppBy2VHCvpRbgQDfndmnTE/HarryB0qivW/e6Sd76tGFIkIGBgYGBwWeNw0s//L3KzfSsiSkpVduwN972jBXcRwh0XcnU2Ox9m+/5WwnaI0FJJyBBkYjVmNHZKXC00HCA7ydmMPbBRdRdLLmbQFLi9SMrsUoeen6cBKXkSdm9+LfNDllIyZf2vw2JGXCqlDsIl6xAC0FhYibBf8QKYotHs7b0Yp6shwOoFDn9GDfQilOczU4QO+IrVuPKsNXrJxmFITmH92MNPqNhqetY6a0b4g1N6w/RELPbBKn/KVLeIJ72t5RLW6wmoF3GUiOSNwjyJEnbn5cSF0l9ZmOwEApKS+/jHIJe5i/xc3IODmjTf4TN9aTvxR3D3r6Rp/0OF/PvOgFnO18zpg02B+l3EkpGci6pcGlFlspWB2HJG0RfmUiI8ZqPQmQX3wnh7TObHjeDzkEF7Ghi7KQcSEIMh97jjLpPYs6xBp3uFMmdyJ4l51ATNvMmSEnNLtKz0rtBMu0JUoekWTdhntFjKgpVzEWttZL7T1a9Td1+3kvKZu5tVaQnHl1P4N9rOqpTJEy9TjSCqrX5r/SZOvNRztzXHF9HbN4J6dyrIV88VbJ4NGmHHY1xI4zUPGqbXAns7cpHGSMU4LuuJO7D7H4YQOx6mfsqEmbu/eZDhJqPSrNvIrU0ErLGd/D7tPIRlLkVD3PPuhJQOw8u4X4rGgExyu4t9T9Zeu9+3PNGXsKa2qpQh06668P/vzD4t2FIkIGBgYGBwWcN5wlISgx2F8FSRleCxSNreIo940acrXyN1Jf0nI5rXEcd3wkHeIqd2cMiUO/D0PMhSUPPJ3irP0QAHA3jUGaPhQQ2yMfuV+MESLLIk6R9bzGGw80T7Jk/QQHY/gKF/JndUUt6TCXQ73+a9Pdz4mPbHNZcA1aQnycVjpQS06TafRgDRCPMdf4vpP0BK5i1LKYzulGb02cWalliNoSmcmucsMTUocLhVmpYjeKGDlHI2fbn+DPqa9Q/tVZKIy6UEjL4XrBd2vPW8amFHY2QoaPrpfFXoGJU74iTSneyNP2HuIHZnXx+4ndJ45pzh/T6dXHHunAQNWPgqaSq9Z4NoajazvfSCq2+R/Wc/apHSO2bc5u08S/SiodQExLTOb9JP6ChZ7BDmvETTCxaKkkF2/cW1/Q1QXTTiq06sCikqNtE6skiYYhFRnfeCwdQNXzNGHS011lOgHZ6DyWkSnPvltb8Tup+o/TKd6XEFOpuIkEC/cQsiIm/hSNwenBZ86ShGm7+G4rM1qeloedCfjK7YavusqzOvfVWryWrHs5bR/+p0lWofdEIn22rkcb+gP23Odjf9C703Oloip9Tcg4EIxzis/N/Rt1drDapo517LDELkla6kuvHapBcidL2Z6WZN/Iw4Ohafh9iCq3E3kVCfK77FO6Nsg2cm8PFnlRsRiUsHMHvwMTvkjbYZpHU3AFcI7v3h/9/YfBvw5AgAwMDAwODzxp95/BkPQa7kz4juQMI/hNzaCL59k/IXNr9KsX73Sfz1Lr7ZOn5b1g1MRmoMbHakpPuwbEq1nXebhWWpxbxtNpmIzgsGklq1qa/SgNPI/iTeOJeNJLeLZ1hs1nmAOkEfmc+arm5JaI47HkdwtFnNkHr1icJMvvPR1XwNTKOMwES4kmN10043dLq30hlawloC4ax/pIVksbhfJbXj/Stw0tJRWutlPqdAsE5uBji0msmKW6eNOooCofFexp5HRCjkJ95x0ovkvNIl6vdC+nsOp7amYR0lIHeswhWY/Cksxcrf8keNJaQSuVJY51b/s5YacVSe4NUNIyzK10lJWeRyhVL3yseI5VvYc6tlTimFY1AefA2Sl3GSz2nYJ88/MsE6duew1HNnUowHgpKdh8qybx7CdobDkvRbdwnrVUQ0rp91vl62IPETIhY8Wj2udc0qftEGsQG2jjrjgaC+WgE4r3zBSv4vwm788yeKB5zb5f2WS5trVWk9/WaRW2PrwlSFQ5Y1uQ+KTmfeqjEDGngWbjCFY9CJbPZULps4rvhgOWWV8t52F0Q3gnfYQ3lmy0b6yJp5FfZ38rN3FueNBSd5jLGjSrecylWR9RSFicudif3aWI2hGfYBaSp2Wx8x5PGfRFT0g69h2p1dANnkZDB/tsc/DvQjnnH4Dzukc1/hRhFw5I3hEoWaKXGbu4dUr+TuGc7mqx6J7txhPsPwpAgAwMDAwODzxrZfUnF2vsm9QdDL6D5544XUVcKX5eGnEfgu/73kr+dp+61u3nKHvDyNL1uH0/HEzMJuqdfT4+WeffxFD6W+rPhjzytj4biT8NDPiymh5wrDTjt+JqDhHSCxUjw+Hk7XMzX7qL+RyKY3fMG/45GIAzDLsTZy9ckyS6NuphaHE+aZQAQ4hq9pkPc+s+TWisIHnP7svYFt0pRuzR8HA1YbRGelB9eRu1I5TYCY5ud+W97GnIz5lJJUYLtcIjanz4nUWsRtcXXkphFMJqcSy2VJ0XyZKAMrf4Vez7yYlSmmCLnsgwRAl7LQjqA+mSzYVYQsFzmUqy6lh6TqO+IpYR5GwjWw0H2tv88UqXcyZCTTU+gwo3+BkG100Ogve9t1IgzHiEAT+9K+p07mTm01eAiF/RSY1Kxmb0Ycg6qUlZPCEs4AOlMSKceJbUAgrH0Pql0BftbslLHaoaOpY4VQCR9zZDpg0tweXvqQr4/9gpJ3djTYCvK09w7CPzrD6A6JWYx96k/lA4ukCp3sKcn30MD1m3PcZY5fbje/Ae4f0J+5tRaCTlwuNj79X+SBp/Ln9i5HFwCCXWn8GCgtRqFp8c05tFaZTWw9bNGmw3ltKOZBxAxNcyVyFyjUWrYYnbqMXc9iX3xt5HmlzcQAn+sEbCNvUrOYfz2Osb3NbGvzgR+F2JW7WXrmFt2b+6fuv08nGg4xFkNOoP0u9T8T/b/jsFxMCTIwMDAwMDgs0ZiBoYAPacS2C28jcAsJY8AeedLPEU/7RGcsyIhyEN2H+oMnB5pyrUEf00VUnZPgqy0Qqm1xkrtCRGgVu3g6b1Nkhw0jYxGeOJ9ZK007ttWM8hOSC+WBpyCbbCvmWDQkxJXb3pOj3+2sVTH9Q46uJi6oRk/pmjcJgLwk+7Gpru5DKVn4GmSzUV/laajKAqeVGonllpNJe2We5bNLjUepB5k5Fepk0orpHA+GkU9m3AldszeeoLY1GLWmDsAtaO9jkDf4Yk3kZ19C8pH8Wj2tmgU+zXuW9K25wli97xJjVCPKRg5uJOlPnNJ2XrtWmnQWTzJt9kJ3gNeyFlqIaSucAQkJrOHRViq4+YJijLXQJtlpR0g8D30Ht8vHA6Bmntn3Mrb30LdUTSCihAV84xGUaAW3c51PWnSrpeknpYidtpDpCweXYfy1msGwfuqX6Fa1O3nfObfzz5XbuGse81E3XvvXgiVKwny3V7DfRj2W711RIpZLNVs1WOYYbiTIZQSjn8Nh6W0rqRQ7noVlbPvyag3PaehXr17M2tsq+KemHAl5OfAQqn3DAhca4X07k8wq8joDokaeq50ZAXqi6+Ve7bhsDTzZhquelL5fXN64sYPg8+hjmn016XF91gpbw6IbHMZBKpkGb+XvubjnQmLR1N/1WU8dvc2S22M/T6015Iuuus1CL8nDRtuh5v7MbXAUveetYiZ+J177774GN56acOfMZ+YcUPcvMTgE8OQIAMDAwMDg88DSZnUYmz4I4GwK4nAtq1ax+pWdr1EMFiyjCfh464gVchvWQ4HOxij+wQIz5G11By0VnENVxJko+/JkCqbdKwuR6K2IpaaJRHcBVohCmMvl2r3x/uTxALd4lFSTt/4d2KpRJ2x5zUC6y6jeWLuSZXeuVnqNxf1o2g09Sr1e3miP+c2gr20YitlzUopitUphYPsSfU27JXDQatuIkowWjhMajwkvXtTvHg9u5eU1RvFLRKANI75Bk53dheBaPlm6lKiYWp7Nv2VIDcxWzr15xSl73+HvXclsvaoDWJStUNKyeF6o75K/5eWMuZWMEwa+01s0MddzlqcHowEWsohqJEQcW52b8vtrpNZRcjHfTDgNCvgr6JGzJ1ELVWgHbXD4YaEhHwQG0V5PxKEMKUXS1VbSHNb81vWMf16lKLDy6jDsTtYTzSCWlK+UZr4fcat3c09teY38cainjTIZcxivKUi3mdq+g2S2wNZ2vsmrn+JWdJFz2L6EC6mPqelEhJ70h2Qk9QC7pf+p0jv3Mi++Ft5KBAKSCt/JZ3/J8hpRyNkZPI1nHlCurTpz+xt8xHqoer2UafjTuVBQ3sdNuGb/mr9XiRKskPS2mul6u3SmkZIhrcB8pNeTBqh3YmxRIy0hoNWWmgq1uDhEGudet3/sXfW4XJVZxdfo3euu8Vu3N0NEoghwSlSpS3UoJTSFqvg0mKFUopX0RYrFognxN3drrvL+Hx//PbJ3ECgtB/Fetbz5CF37sw5++y9J7xrr/ddr5mnJkNyXSiaLh97VjHpxGtwDGytiq91Tn8OOpwu1nndEzrmUMFC8UpSLxNHvv93Nv4j2CTIhg0bNmzY+LQQbCMlSCZNKxohoLVQvomUOHciQVhKLmrGnrfi72mrJSCfdbO08OZOKTkiIFv1e2nCd7FytvruWHB5IUrRKMHpzn9Sd+NLl0Z9WTr9N53UA5805GyUgZTc+DUyi/hd53FLpP5U7yY1btk91BrtmYfakZgpnf0wClgsSmCb3p30q46GuLWz1Tg01EbQHYua5prmJF6mvqloMj14JAJUyTiEBSmM3/o8dS8JqQS+6T2YlxEXSW8as4LkXMYVi0IOF91G4Jw3SNr4Z+a5ercUWCd5vFKPyczrW9eSMjfndkiE042qsuYRxrHrdU7/1z6GS9yu16TDy5n7pCwsmIvXSI6IafIpiFVqF8az8GaC8qQsFI1hX6L+ypNo6rvcEJSsfqRzSaxHYhbKVo+J1NIMO5+UxLp9EO9wMF7rEg5AQCNB9kHtXpSRd+/n2g4nhMTl5fm6jeM6bbWkU1r7asldUtRvlLnvsa8GnwnJTOsivfnT+NqWb2Q/TL+W655o+iDFYqxrNEw6m9vHe3a8TK1URwPP3nMK5KH+kDToHOm1K+IqWUoBBKXraCmzt/TSpdRyDT2fdFGHScms3Qex7Debe+95izS/vrPYH8vvw/xi5o3SjlcYl78Bs4nBZ2PiMPMmad2TzKHV2ygagSAWr4ZcV+9ADQ35jyVAcqCk7nwZw49oGFL2QajeQc2YjY8FNgmyYcOGDRs2Pi043QRtFhxGpbEakXqSjIuYx3S2T5cOGHtth9M4bxlr4PVPmtqE9Z2uZ9SUnf+U+s2CDHTG8AsgH+UbIRFWSo4krW5BiQh1kA4VCRIkVm6ncaWVQpfeXZr6Y1LYOqtK7kTe501BXWoph7A4XQS3jcWSYjzz5md578qHCKBjUX7nSeJagRZJDn7XdTT1MakF0tzfmtqPakhIzV5IR7AVFWDI2dJLl3HPjiaUkGX3YH4w6quoT9GwUWp8BKDBNn6u2k7NxvJ7MAJ451dSNCDJyXz1Ponml6fdQ2H8uic5rU9IQ/VpqWTOmkuld9tIvfM3cd9p1xqyF5U2/FE65Q6ah1ZsIsDvfwrW2SVrIQgp+TzTmz+RLn4O0rH3LRzMElKlXmNwy3v7BuYzOQ91qnaPtOhWQ6oKMakoOkFK64aqsPV5gnKHqY9prWR8+xZQnzb9BsiA08l6dh9P6lnRZJrudhmNkrH0XmnEBMYZ9TPWVQ9JZz9C2lxHg2mw+x6FIxaR1jzOmLpPxHnP6dbRPkVON86ItfuwqE7Og7REwzj0zbuWMe54SZpzV9wFsLkcdWb369LFzzOnsTB7celvUBH9DZDMaJS5GnYB++qta9jPJau4VvUO6Y2f0p9qxEXGZjybsU29mgODhBQUxgML2R+OmFHHHOzLfe9w74Q0PutvgiglpPJz6Ya489yHwfo+2PhYYJMgGzZs2LBh49OCx8dJudVzx+kmqGqv5ef+s+PNKfOGEJin5JrTdBcBsiQpRlrR1B+/hwQ5jb10G7UxFpwe0uqGX8Tp+arfH0uAJIjPWz8lsLU620sQia0voGo4XSgRfU4iratsA0YAWX2oofEkEux1HUNg2tHI2JNzIDa+NCmagmK19DeYGmT2pKC/fLPkNKl2qYWSQigvK34n7X6Vmp9oiH5CPacS7OcPk066DjOEnAEEwv5m7mchGmFOB5/L5yPBeH2IlerkcMZdvDoaSInrNYXC+6QsnM8iQWnxHZLTIQ2/WGotI30s0MK6JmYSlBdNkQacboLlNNK0dr3GfBVNgbg1/B5lY8jZkJFDy6h3mXGzSUOLxRW+5fdBDLqP5+cuo1Ecitcwfl8GvZHK1pPOJwepdB2N1P/U7oc47ntHmnKltO5P1GVFI1KWqYvyJNMPyOWRznmUPlKhDkhw6VqatQ49F/vmt66Lq2/+Rik1F1IQDVPf0nt6/FrWXpUgxElZ7Pn6/cxDdm/2QqCVve1wkuq49Df8PdDCfgsHmf+avfzpNR13wfGXSq//iD00+hLMO9wJRh3Kp9lp1Xb+HvJLoXLI3YrfYvW9dx4E0+mOK1YON8Rr1e9pelqzm/0V8kOsRl+CPflb1/I98TfFn7FwOGpZOMC87pvPeqR1Ndd2QLwjQX5OLaT/k+Xs2BlOF6qUjY8NNgmyYcOGDRtfLMRiJohym9z/zzgsa+eDi/nZl0bAmTeYk++WBZIcnOKXrDmWkByFw/Szcbz/V55EesFk9JRO+bWx1U4nJWzffKlwGIHdUUIlguSGw5xOB9vff8+98wiCM7pj0dxwmFP05goCUpcbh7X6Q6hXA2bHFSmXGyIVbKVgPjGDANmTiJoVaJZm3CjlDJQOreR+bp807eeSItQ/FU2UitfiqHdoOcpGl9EQp3cfILjvaGCcHt/7CV6wnfS4QafHG8hGQvFUO4k0On8Lfz+0nHStqp2oSJI07zpITVs1NT0n3WDcxPz0HUrvJs26FROHNY8YM4om5i27L+55qV0go7tfYy0ib6IOtFVLKV2EGpYQr/FSTOqow3Vty/OoCP3nYL4w7HzGqQjpWlZ6oCeJuhWrsei+d2hIu38hznTTrpFWPYz72IA52FRX7eBeXccxvtYKadPTpv4owHwVr5HO+C0Ew3IRTM7F4CEc4PkbD6O4+Jv5k9413h/JYRzUQu18Xzf8GQK7Zx4uaJEQypTDxXvSukEYkrINuQ5JTi/rVX9Q6jEeJa77JJzaNv6ZVD1/C+6EbbXU0UVNuprTKK6xCPcqWU0tV8jPoUFCKp/P6E46Yko+6uKW59i/3mTI34rfkkJ32m9I2dvzJt+v/qcYI5MH+D71OkHa8JQxVjB7zd9ovo9ZpPD50jhcmHf9sY1g5cC5L6PHcb77Nv5T2CTIhg0bNmx8cVB/UNq/SCpeRaA0+Cx63nRWAj5rSM4haB94GoFpLIJCkJTNCf/YbxHUZhQRmB1advzr9JmJCmOl1XRufjrmEuo42qoo/D6wSFps3L6mX0/gmt49TnYS0t5Tu/AehNoJdCMh6dBiOt1Hw5xw+9KpDUnvyvU6GkhBG3A6qteaRyVFMRKYcxtOYME2Phfym8A0CjE67TfS6l2kktXvpWFoey2Bd+9p0sxfSYvuJPCc8F0UnrZqAkhXAs8U9lOrI5k6I0Ny3F5eH3w2aWeh9rgLXyyCicKu1wl0nV7UAKcL0rH8vriTX2KWaSa6F0Xq4GLmuM9JuLwVr+KEv/EI993zNmmIZz8irXoQ97bs/jRJtRqsZvZkzVY+iFJhpQdKqAH1xpHP6eQZwgEC/DMfoAFsOMjbE1Ih0k3FhjRY9VJByGZyDvtj0hUoUwtuoR4otz8NPruO5T6LbuVe0Sjvj0b4efXD0oTvSysfju+LxIy4kubySnLFa4+CbTxjayXE3N8ImYhFJV8KJHbSFdLWZ02z0Qj3S+8OcU3O5bMxM45YBNOLSIDap+X3cKDQVgUh3/hn1FFfBgRitSFrgRYIqHUthxOylJInTfo+Ss/470B41vyBe6XkM54TrpL2LpBaysy4M0jH6zeLBrojv0IK3Z55uCN2GysNOUeq2Mb3unIbcx9sYV5S8iFbWT3N+g6RzniA737ZRtJO+86CUB3PhMTGfwybBNmwYcOGjS8GavZKb/7M9OIwqNyGnfOUH9ET57OKpCwpaXw8xclC4fBjf84bxKm/lT4nxeuH+s5EIdj5ctwe25sCEew5lXt01EuvXclJtoX6A6hCLRWcejtcnLj3ns7vvcdR05KyCV6bSiA4YT9/uowkMI4EqPvJ6Mb1PIkoH+c+BlFyODiZP7xCmnGTdHCR1FwpdR1FStvh5dTrDL2A+9Xuk1beH2+4GgnQm6ipTBp7CfU4iZmc1MtYZnuTUV0iIQLvzX+T6g9DBNwJBMt735ZGfUUqGIH9cvUO0g4HnsbPjcUoGr2nYzxw+n0Ezq1VBPwZRQSnUaO0DDkL44JAEwX5FZtoturymP48GWbuY1KwGQI1/xc48Y2/jHS6hDQ++/qPmN+UAsiMvwlSWTQF04aUAsYy4DSK68N+UrLOeYTxpXWBOIT9KEESRMjlNYYYhlyVb2S93WdCIGfcyNocXMzzDzqT7057gzFi8Bg1JWoMGzLj+yLQKgUbuXZyviEEuzHPOPkXONQ1FfM8QVPnNe7b0s5X2SflGyGQFnFprUIB6jXNpDY2GhLrgIQlGRK38kFqf3wZpF3KKHodbcznjpfZs13HssYSak9KHqprNMw65fSDRKZ35/mrtrFfYhFDUOulpfdg1LDiAcZnWWbvny/JJQ0+AzW0aArXDQdRWvctoK7Ik8RBjTeFPTvoDJSkzqp1Vi/+jLz4/d89Gx8bbBJkw4YNGzY+/wj56W3SmQBZOLRUGniqlDzpP7t2RyNB+N55pHD1ngYRSevy/xjwf4iUPFKGDq9ApQh3ELTn9OPU25sqnfxLE/iGSMHJGYCKIVEP0ZkASZCJST+QFt7KZ9wuAm5fOk1dfRmQgJQ8grq6A6SepRVCIoJtFJoHWwjkWsolOZircIDA3emBhFXtwAjA30zqTyzK/Ud8GRvvnH7SxqeZ7z4nc2+JADMhlWsqJsnJPSq2xM0dgu307hn7bYro6w7w3pYKgv7p10nrn6In0fALzZzEcA5LysaswN/E5zb91TT4zCBtqcso6aXvoJAMOQtVYcTF1LrUH5BGfpm5feOncbvzrN6c4C+5U+pmCO7qhyEu0Sh9bfIGUk8Said9raWCOek6lvSnLc8RQKd2oc5q1FekHf/kPS3lzE9WH9bBMnZoOMxzeVMI0t3eeINahwuXso56gvP2eowNqvdA0ib+QNr0F5MOJ1IZe03lO5CcbUwUXCb10g3BSEgjkG8T+1ExvhuTr6SRbNV2PjPoTGnWTdhUN1fwnj4no5SVbzTNYX3SoSWs/9QfYZedmM0hRjREvysp7p43+QpS+iTSDoeeh8LpNEYXkQDP39HAvwMn/ZzrO5zsIatZae4AqWCo9PzXpBm/YM3f+blRomKoip5Ek8LWwL8zVkqbVUeWlC11n4waW7ef6yZl8/vK7aRwVmyGYE69kvs7nChAvvSP598HG/8WbBJkw4YNGzY+/2irIZj6IOxfRD+dfxcdjdSp7Pxn/LXi1QTBs2/jhPuTRmoh9R99Z0jBDmnrcybFLCapiiJzpydunFA4Mv7Zo7UlndBeS0A++zYIQMNhCEdmkXTOY7ibrX2cQM7lpiYmpy+fDbaZuiFTO+NwEvBavXpCHQTaiZkEkb50FAxvCu/3+Ai8S9ZKS3/N+wqGE6ymFRoHOZG+5HTxjJGwqeMx6VmNR3D32v06aWX73uFzsQjvTesCSV75EM/oTaFXz7u/pbDdcqNbfJc08fucwJdtkJJyIWWFI+ntEmzhup4kUtUW3MS4iiaR9rblOe7ldJPudGARBGniD0jjeuun8WC5tZryrQOLUJIKhpN2FYvxnpZy3OL6nISaklkEwV3xgNR4CMLXYyKOZuufYt7DAeaobj/PM/tWlNG2Gua1vR5HvfGXQsyCrcxFTn/IV1Zv9nbNnvjeCDSRjudwMpeWYUAkBIm0aneaSiX3UJzavD7G+87PGUtaV5531z8xVjj9XlLuUvJwYgs0QzLCAdIcHU6pywjS+OSAnK17kjqmrmOMoUY6a7j8HkjYiddA9l0eHA0bD7MvkjKlpnJSzI6skLb/gzqtrS9AmpweCP6ky7GQDzRjdnHmg6x/NBw3MLBSNKNhqbWG70h7HWN0J6D+vP5jTA1cbva/J5l0097TpRX30wy2tRqjh1TjriiHdM4fUHltfKKwSZANGzZs2PgCIBZPCzserH4z/y6aKzjZtXrUWGgsxkJ48g8JeD4NJGZCMg4s0vuaK0ZD/Kncyom/x9QSZPaMv6ezXXH1LlKKvvoytRiH3yX9qbUa96+wn6Dd4SbF6+0bKIrP6nOs+tZez2v1B+NpV+EAAWtqIcGo0xV//8ivEcg2lZDa1FyOwrLrVeY4a4CkqZC9YBsEqr0O5cThgQhl90ctaatF4Vn9sCm+N/e3rIgtdaDvTNP80iVFY5IzKnW0Mp6EZGn3m7ynYChOdAcWMX6Xl7kJ+UmZCrUz731nkhKoWLzhq7+JwLnhMEShZB1zFzUEzpsUN2vY/SYEPbWQ/ZacA8lpLocsJOdCepvKcU8b922C/Ord0ju/ZJ2TcgjEnWmody9dJp33hHTGgxCZxmIIT3udtOw+0tZS8ugttftN02w18VhnQUmSg1SyKVfhLJeQznP70iG1E3/Az8Wrpd6nMA/DzpK2PMMeSMqBOFgucC6PtP3vUiggFQwm1bBuH7dy+yCuvnTIwpZnUedW/QGFqWJT3O0v1A5hGXwO5G3rc5Keh4AXTZbGfBNil9NfeuNqUg2TsqTyLVJbnTTwDAivwykl5UHAT/o56Y2xsNRgSHSgWUeVx8QMrtFawyGI1c/H6WIe971jUu0cvF8RiPPi26TT7kahfeMnkNA1j7GXXF6uf2SFTYI+BdgkyIYNGzZsfP6RlMMpccma4/++z0n/3vWaSlE+dr1GWsvYb0EoStbG31OyRmo8k1SbjkaC14zunPR/UnAbK+ZAy/F/n1LQyZpYpI11n0TD1WhYR8mTwymN/jp/f/sGapEaDpPuFWzmdadHckQJ1J0eahyGfYmmofvnE+QeXk6NzsJbUC7aaggeE9OlEV9hrLNvk5bcAZmo3EywmpJPetyQ83BKyx1EILx3gZQ7lUC3zg+58CRyHQkVoMsoguQTf0qNTWMJvw+2xQma082Ym0p41l7TCcD3L+Q5U/IgEUvvoa4pOTfey8jtjZNghxPSUX8AstNeFyd6Lk+cWMZiBLlyMF9hvzEmcKNepRYau2cveyfYJo29lOA+JRcS11YLAWsqRWFa8wdqtxqKUTh2Gae9SIBanoRUUgoT0qQv/525Wn4Pdt4Z3UmdS+tCTyJ3AkTLkyK110CskrJ51uRcyK8vDdWrdh+vnXY36YdNpaxnt3HUeQ05L17PEgsbpasKQtFeKzU38zzWfg0HSAPc9hL3bTiMiutOpJFpvzk44LncpEpOv8ak8DVKU36MK19yntTvFEjh4jviqWeNZs9WbkcRWvkgKs+791PrtGce656cw9gVg7QPnIsKOux83rv1OVLr1j3OVyStQEcPWnpMxJyi93TumT+Y79WiO8x3yRg2WIcmoXZcEjN70oPJlcC6h9rjvZDa6z/yV97GxwebBNmwYcOGjc8/vEnSmG9ghBBqP/Z3XUYd2yPnX6H+EESguRwFINhKU8lx3yHwK11HwDn4bAKy1ur4Z3MGYJVsOT39t5GUJQ09X3r3vuP80iENmotaYiG9K3bB6V2oKQo0EayO/IpUOBqi115H0FmzO96jSDIqhjnhjgSZh5Ffwc0rJR+b6IqtpAB96c/0k6nezc99TuLEvK2W+ewyFmvuVQ8TYIcDKCVJmVzL34idsDtNypXUZ5pUt5N5by6XovUE6JOvxFGs4TAKQc9upgdMG4Gl020C0ogUlelJc0jK6WMabq7F1Wvz0/x8wlXMwe7XCZSnXsU87HyVWhC3l3EG2yEVSdkE75ZZgEPxIFiSFIOUpHczcxhBlWsshjikdzepbilSdqq07F7UhFiU302+gvlb9zhEItAsFa+AfI76GqSzciuHAGMuwQSj7gBznJxHM8+a3dTc7HkbQhuN8P7JV0BKhl8kbX4G8lg4ErOJzJ7cq7WaZ9n1T0hfenfUtpI1kLgek3ie0d+QagTpTutGTdPh5ShOVlpmLAoRTMzC7n3bP7AOn/ADCG/5BpSo57/MvA0+hzqqVQ/znVpyB7VV5zxKnVGonWvPvBGyVLqO/elyk5I28mLUzJYq6dTfQD6Gnsvv1j7GegdbpANLmP9T72JNT/4l1utOjzTpSgwyWiuN49/JqL+hDshfsFla/Sg1dYrxnXO44g6EFgLNXMOTBGGLhqWOdlMv5+bfKBufOGwSZMOGDRs2vhjIG0yK1o5XqOlw+7Bk7jmV0/WPgnDAFG6X83NCiinGl7TuMenUuwnU+s02galptGmhdo/07r3S7Ns5Sf8k0HMKrmJ75umosuN0QxBSCiAJkSBkIyGVAL5wFKfZDmMwkJxL0Gr1e4mGCPR8aVKzdaNO6XPRMIGjyw2xSkin6L3+kFGlnDTerN1H8PfalaZJai71DztelpqOEBy2VXPplFwaefaYJM27lnHJnKYvu1sa9w1Sl9qquFZaF2nDnzi93/sOY6najlX2wpsYr8Nh0tAiUnYvFIo9b0iOuagsjUekI6voh5OaLy3/LdbHkSAB/45XSPnqO0PqaKJ5bWYRylSwDaXj0DKpzwwaqlr7wZsMKUvKRgVKLUCd8jczN24ffxoOk5YVbKV+x5cOAbHsutc9QdBdtdP0Mgry7Bv+RDH/DPOcnkRIzIGF7OH1f+bZ+82iD9W+d5hOTxLr2lFHfdGc26VXfsTP3lRpxq+MmhVF4bSC+cyePPPSu6RQK2Rh0e3sk7QuUnpPKetS5qG1ErKx+3XGIAdqlcQzFwyDRH3pT9wj2Ca98n2evaXC3D+G7fS0a1FPavZIvkxUmJe/a9LmMlClUgtQARuOQOCtNMOa3dhn75+PcUMkiOq18qG4S56VNulvoKaraAqKW69pzJOVMlixhTq4+kPSMxdyrcwi+lxN+gGqZno31tEyU+iMrqNJ68vpy79T7bWocdY+zh/yH3zxbfx/YZMgGzZs2LDxxYDDQWA49WqUBKf7WPvej4KWStNw0sCTFK8FkQiGMnsSsAVajt+MtWIbwZlvcPy1SJBaAgki4Pb+e+P6MCTnSBMvJ9Cr2cN484YQ2L5xddwNzp2AeuVvRDErHMUpfixGDVA0jKW1JMlJUfvQ8zEQCLcfW1fl8kpDzjZqRjdp/Lekg8t4va1WKlsHSUotpI7DctBqq6Eo/KTrpQU3koZUtd00xqxBbWgsNmUYLh1t/hpso94kuy/kbs0jECxfulEZIlJTFXUoo78hjbtU2vwsr3tTqBMZfhHOYdl9IIbjL6PhZ/VOaeZNFPJ31MUJYzRCELzmEencx3H2mn8jFt7951Ajk5KLcjPmEhqK1h3g894UgtsTf0avmMMrIRh73+beCakoI9Ovg1yVLuBzHfWQplgMlc7RBQLTY4LpfZUEcYhFsYT3JhOYt1Wz/yNh1uGs38UJU2slqtG2v/NZh6iFioSo5ek7U9o3jzGvfRR3tPKtqGl5gyAbiZnSigcZ2775KDJur+TNJt3PnSplSRpxobTzJebtoqexJHfE6HdTvpneS5v+hsJTsxsb7artrKO/KV5rE/Kz/sVrUHq2/YP9tuIBCISEAhcNodau/oM05FxpzcNmz8TMNZt55rCf72TdgbjFdjRiUkVjENeaPRxu+JtQ/lLy4jU721+W+s1AlXIa44/2Br57m/4mnf0HXlv5IPexxiChirkTpYaDOO1Vbed37iSpz3Rp/Pc+HadJGzYJsmHDho3/OQTbTD1AEoXgXzS4PR9d+XkvYpG4GiIRuKd1JZAKtHBC33U0QaFV32Cd2rt9uJ05PXH1SCJY3/oCxc+xGG5iwy/Cbvnjgi9V8g2JnyjXH5Tm/4q1thAOELANPZefGw6hIpVtIlDO6c8JeMU2gutBZxLYFU2Wyjfxd6tXzaAzj+1hlJDGKfyi24wJgRuHs65jpGnX8brVQLNsA9cPtfPeguFS2XqC36SceEDf2YzC4eK9bbWMwd8IwT3xGtQSh5O0v4hIw2urk8562NTL1DD3b/3UjHskzT935ZMytfIhHPDC/njRfyRkeuckGjOJKCmRiZm4s2X2oFfM7nnUGa1/ksJ6lxdS40vjWjv/yVzM/CXXzOpNT6BgC4R66/PUwNTt1dGak1A78xBqpX6o4TDr6jFr4k1mDgpHkKa29gk+a6WanXo3NTGb/4aa0VptmoBeDjGs3W1qamZBcoaczT13v8Y8732Hxra7XoF4BluYh5EXo6bteVsq8rDmbdXYdiflS62SXr9KGnUxZG/HK6yJw0XD0HMfgwy1VaEeLv+tdOZvUeTyh+I6GA1Blso28F1sreD5c/pD6jrbu4faIG5hvyG/pqZNMdLWiqbGCVwsCmGxzDIiAaNSie9kQhrf3Ywe/LGa9rbXc41BcyGRTjfXj4S4f8DLwcmGp0jDHPttVM42YyXe8wRp4OnSgpuZw6zefH9m3UoKXMHwT04xtvE+2CTIhg0bNv5XEGynfmDLc8atq4BgvHAEaV82TN+V3gRVFlxegsjELDq/F02G2ARb440vLTg98SJ0ieu8+TNIlIX9C0mpm3sfysZ/A6XrjyVAR5/FgxvYmG+SAvTWtfH6lQOL6D8080aKw6t2QFoGnwVZqt3LyX2/GRgXJGbEr1u7n7oSl5uTcou8lG0g/Sl/GIqJlX4XaCYFbf0fCaIHno46Vb0DZWLNowS9Vj2Tw0FqWXoPAujZt6EkbPobSkjRVNLoqrajPE39Cb2Cdrx0bHrSpmcIuCf+gNS42v2oEpFgPI0qMZN0SKexQ84fAol1ODnld3koqk/Jk065i9faaiEjzgRpwGzIQ9MRU6PiZRxTfsQwDi0luN76PEF/0RTSFhuOMM5YjIBcTlSPXidKOQOlmeMJsGv2oBzkDTZBeZQ5dbqZ99YKFB3FjGGDm0B9+b3U09TuI/WvvV5a/yfUpyFnkza68a/SiIukJXexdg6jaIT9KEEn/lTqNxNi4vJKRSdACFY8II14DAWyYjPzk9kTQjhoLkYIu1/nQOHsRyGdgUaeccqV1GSteIBn6DeTGqDVf4BEWz2VRn/NGEuYGi9/M4SlvY4xWKl7478DOXrxUvZ4Wjdsz6dfT2PZ1HyueWgZez4hxdRqJWEvP+YSaoYcHnojNZeZZrXNfLdDAQ4NYtG4uUhLFcpQoEWac5vpUeVgP87/FUS5/xzI6c5XpRN+giGDTYA+VTj/9Vts2LBhw8bnHtEIp7NvXcepfms1Rexv30BwEg7962v8LyApi1Qqx3v/9+ggkCsYxkm8N4WUqeNZU+cNNj1GotLe+ccSIAv+Jmn3W7znv4HOvV7ei7AfV6137+9UwO/gJDvsl5bfR/Dp8RH8HVombXmeMfeYANnoTIAk6mFcRjWLhI9NE9zzJjU1lsqW1QuC5vJwIr/nLe7jTeb1aIggMSkbFzKJtLq2OmnAKdLC2wjqN/4VA4dZN6FotlRAaEZfQrB6aCmn8dYaDT1fOuFqeh5tfhr1KbUAEhLqgOSmdmH8kWBcEUzKJlXv6FxJR5uwVu8gXXDjnwiW+8/Aka2jDqLiSSJYdvsgBn2ms+65A+LNS0vXQwKdrngj0pCf+ybn0Qw2swckof4g6xJqlxb8ip8HnxOvvek3E/IX6qAWyt/McyVl80xbnqOn0d+/iRo25CxI0srfUTsz+xbjAFfCM0aChkQaZW7Lc8ZcYBfP1W9GvIGphHHIrtd4Xn+TNOn7OOnNu5799u790gtf57MDzyBFc8md3DvQxJ+tL2D0MeIi1KH98yGWsQjfuZQ89kNmT8aU3hUylN1POu0e/r7G2GpnFDE3FZulv18St4zfv5B5nvxDSGCojZTBFQ+yrp5kCPicO1HzMntJ065h7w89hx5M3SeyX50extJwhNTKeTdwuBELsw59Z0oXPUvKar/Z0jdewxUvrVA2Pl3YSpANGzZs/C+gqZTTzfcG7RLNFrtPIDi1QerOnDtQKWr3EtD3m4UjV2oB76nbT/rN1hdIv7LqCnqfTFDdUSs5HVLxyg++T8lqadSX/zuW2h/WxDWjB8QsOYcgzekiVcdtHLxqdtHLJBbFccsicQ1HIEPdJxx7gh2LxXumuH0EqdEQapi/ydha+/i9K0Ga8D3Jm44pQbCdFKLVD6N6FJ1ADY4v3dTTmPqsvrOloXMJOmfeBDmafJXUXEKAbTVoPfyulF5EMB8Nm5P7dlLgUgukd34hKYbpQaBFWnw7dTm9TyJwbSqFQG56miDa4SAVq6kY0wtfunEfNKlUxWsgHgcWQSCdnvh8hTqY60gA1STYhmrVVIwSljdQ8rcQdNcdwGlvy/OkNrbXEfSP/Taq1LLfMMfWuGXqf3a/Lp36a2nvPNYvo6d06F2e0eUh0PYkQYb8zaRApndnfIFWGr7OvFF651eMoakE0tJejzoVaOJ90QgpY05XvO+TL9MQRmP9LcXVkeQco24Voy5a8xUNQzgW34lBxoY/mV5KXdgnoQ6eu7mc5ylZi2FHU7E06CzS9w6/G69Ps2q3Bp4KYfMkSRv+zNgcDtRafxPjCrTyvd3+Ek6S21+EeI/7DoYFu9+ClJWsk2b8EsWsdD2ucYvvQDlsqWCveXykeYb9EJ/svjxLNAypstJGT7+X97s69cay8ZmBTYJs2LBh438BLZXvt462EA6YjvE2CZJEIFQ0iYDL30Tgl1JwbFPU9jqCnp5TORmPBPlc9W4Uiu7jTXPIDzFAcBm75f8GekxiHJahQ2cMOYeg0JPEHwuxKKTE3wThObAAlcHfzGm8ZIrt36NeORz0CurcaLOlAhXD45Mye0MuekyGODYcpq7qhJ+gSFp9jkLtcbVl+4solz2n83PBUNzIStdCGvvORCFZ+UCnOXZIyVlSuA0lxpNMkB5oZT4W3y4pZvodOaTGUoL1xXdSL7PmEZS5giH00tnygtR/FuSg7yz6IqXkQy78zVIkAgn62osEzM1lxyqI3mTjxGZIkTcFUjbzJuZ99h3UElVulzb9RRr1deniZ9hDHfWobXuMahRsx70tHOR66d1QKqIuCENGdwhOqJ0xtpQTtAfb+DnQzLjTu5O653TFG+rWHZBy+tGHp3wL6knEIjrpRrFwQkraapjn4ReSouZwojQlm8OBrN7043G4MN1YdJu1ucwSOSHNsQj7pbUaItFabRTCdt4bjZiePj3496nuAN+n3tOpl3I42UOxKD8nZZKS5vJIVdv4fWoXyJxlXy5hupGSx7yPu4zv8LTrMOnw+DB5iISoXVrzCKmgTaV83z2JqHutNYxz0a3S6feRTrfwlnhqnNvHZ5JzmUP739XPLGwSZMOGDRv/C7CKgD/4DZ/IMD5XSMx4f9qXBatQ+8Ci9//Ol86fhBQc25bfe/xrDDojnu71cSOrN25kS39jut6LwLD/HFPTUWxsi53xZqqBJoJRbyrF8NW7+DPq6xRxl2+iPsV3nDF3Gws5aa/jPg4nSo83hZSjSJBgfMFN9AwqXk0wemg5KltSprHjLmNeDiwigG88IiVLeuMqSGhKHkH83rfivXcyehDkhv0QhEiA1LnUfOqaUgogPpEg/3W6eH9aoVEKmiXF4s1Lq3cx/uHnQ5DaqrB+nn4dKkJLJddOzMaOu3gtKVLN5aYnUA8TEEchlA4HgXVyNqRs0W38fsAcmvC21RCs5w8hqF9+NyqaNxnFwrLZdjjZU2ndIBJJOayX08PzOlw4yY24mOC+rVZHFcpQu+T0sn5vXx+vn3E4eV9mL6l9PXOXkseesEw/rEa86d2k4RdzzW5jIYbJubw+4mtStaR/XiGFW5nHid+HcK9/is87PSKtzoEi2HgEEioH1wgH4gpYJMghhEOMxenmvRVbqWPc8Q+U7UiI92f2lk66jmv1mSXV7eN1qz+RhbRCiHRLBaSmrQYF0t8oeYyNee+TSHuddQvjeuNn7JlICEKdkmeex8X+aSrh0MOqIxw4F8OKyT+UXv0hTn02EfpMwiZBNmzYsPG/gNRC6i6sgKYzPEm2Reu/i6xemAPU7Hr/70Z+mdoYidSxrqM5We6MguGoE/8tOF2k5Z39B4K0UAe1E75MiEfdfim7P31lfOnGea2Oz479Fg5gFrY8Sz+ZxmJqe45HqDOLMAlY9wQn/FZ9zcgvU29Suo73OZxSl9FYDp9yp3Tab1AsnF7JGYvXz3QZRcA74HRcx6yif0+ylJFMsBpsM/VZSXHFKzGLZ9/zBkrTaz/CKMDhMGTPadL1whCh9nqhUsS4jieZe3cby3fi9HtJxWouo+Zk5k2kSLq8Rj1pILD2JmMKoBjkc+HNmAJYqVDJOZCTqh0U/TscNCVtOCK9fZ2UN1RSFAMJdyL3nHMHBgUD51JzUrOb4L1zc9bETNL83F7+RMPUpA04VdrwR2MYYFITJ34fkuRJ4vWkHMZRMAxC0Gc6ts3rn5JO+iUpeFZD0sQsFKkuI6S3roFkDz4HxerkX0jv3CR1vw7CnZyJqrTuCRSjwhGGjOaZxsIOVNP8oZgMVG4zZgdRXne5IUn9ZmM57UunRqqjkXXe9BfqlmJR5iMcYB0X3koKZdEkFLzafdL6J4zaGjP/znVj3iXmKiVfpsOtqR9zSt3HsW+CbaQaug2BbqniM7EI8xFskxpKpC7DUCYLR3GfQDN1V+11Uvl6xmGToM8kbBJkw4YNG/8LSOsqTbpCWvrr96QzOQiOrFP1LwLCIdynOhoI7lK7fPyKS3KOdPLPaax6cAlBUlI2dR19Z8aJQmo+rlRVOzEPiMUI7gqGxk+U/1twOun7Y/X+iUZwcFv3hEhpuoDgbOcrBJlZfejz0lQWb4gZaGHMidk4sgVb6U+T1uX9joK5/SEJrVW8r3oX1sAtJuhMSJem/JD7Od2YUCRlQSj6nkyxuoQD2sivSj2PQN5bRdCZkESQLBlzh75GzUomaHYlEPC3VDHu9gZp5s2k36V34fmiIQLvlnKurRjr5m/mczN+heJVvcvYfFegdvQ/hWcZ/12C8bxBjHvJXew1y1giOQ8SMv0G6sGaSlHD+pxE4L53HmNzOCAhwy6QUgqZ70gYkjLjl9L+RcxHWy11Pyf+jHv5m6RwE/Pvr6Fnki8NkpqQhhX4u/fz91m3EKh7kiFRq37PGFJyIW0WoXIn4GCW2YseQe4E0hGnXWNS+ay6qBJpya+Zu4bDzNHUq42zniGh3mRISWoBz7h/EfuspRIC5HByf19G3N3uyLtGUY3Fnfz6zmTMiVmQivTufGdyB9BYVTGUt0iIfehOlI6sxI1t2ws8W4/J0pSrsEBPzpWm/hhi2GWU1HUUhK/XNNZTMSlnAOu0522In9PJuKPRuFOgJxGltKmEe/Y7Wdr2ojT/JqMCdoW09j4Z0ipxANB/9sf0pbbxccImQTZs2LDxvwCnk//BpxZg0dpwmMBiyNkEdM4vSOFuWx3WwztfJVCWUFzGfZsg2e2REj4mI4KM7tK0n2HxHA6iphzP8Skljz+9p/Hzv0xN/C+hqRQSJEmKMU8ZRdLYSwkwi1cReLfXMVcpeZCUPifTD2btowThctAjaPIVBMed4U2CWMVi2E9PvSquJoSDEJy6fdIpvzEkRASxE77HKf3OV7jHwcWkEzkSpIrDjCXWycEwEsSooO8sDCacJqXP6Yk3KbX6FfWdATlxuFAL2kvNRUxa1qTLcYvrMZF6nMLh1OJs/It5m4OgdvYtUqBNKlnDfAy/AKvtd25AYes3k8De4eFZx3xLUlTa9Fdp9aOSvx6CYKkzIT//zSiSit9F8UjvStA97lvSP74dTz3c/jIEs3Qdz53Zm/205y1Ixeo/oOCtewIlraUKdzy3D6I4/EK+/42HmQd/HSYKE74rbfwbhySxCM1AR35FevOn0vIjEMfp16GMtJRDkFLyIblOF+tVNAXiKzHf4VZUQ08i6aR5A/mMlZKY3Zf+TvsW4lI46Aypy0jm1ZOEetpUIr10GZbcO17hO/bmNdIJP2bvttezxyTIR7Mh2u11xhY8Aee+XidKc39Ljdi+hTg/lm+B6PubabCakEodmMsnvXa56Q2UKaV1Zz+MuIgeQS5D3JvLudf063CTO7SU5w40c5/Dy6XznkTFkviMjc8kbBJkw4YNG/8rcCcQbOQPIT3K4/vwwv3PG6JRyM/W5+OvFU2G7L30HQKw5Dxp4Jmivf3HAJfXNNP8CPi0yI+Fjvr3m2M0HuGP0ykdWPweIwUHAXpiFvUXR/dKDEVh3g3SGb89vorocJBS9tY1pLX5m7AMdrppDtp72rFGEyl50thvYoEdapcaSwh+s/pJyjKF9sZhLdRO0L3xr9KX/oQqd/hdSG/uQGn0N0jBshzpLPSZgaK17jFS1XL6QG4CraTo5Q3G6KCsBVcyp5vxFE3m9RUPSP1Po4Zp+4sYGpx0g3TB33iWA0ul6r2kjDUeJhWqYBh24yXrGF8sSlDscKCMZPSApO0ZCgGo3U9B/pQrqdGSSeOrP2AUnhQ+k1mEAulJxECguUzqaCbgbyqFXO36J2NM68paBDvon5RawPvbakl9ayqNq1OxKHN92r0oZM0lkMzmMtY9IZX/uhJM3VcxZNDa2oFWKWb6LXmTmefmMtLzknONUjZQOrJaqtoi5fTG8CI5D6IUCbKu4XbmIzmPOX79J+zR5nLIStgfH0/nflhWXZrTJTVWQ0Bn3Igd+vAvSUvvjJOxUAfK3LRrIYQ7X8WtL7O3NPqrptaqln8v8wZD0sIdzHv+UOb18Ls6ah8e6mAvuhNRn7qMQjXtMfEjfT1tfPKwSZANGzZs/K/B5YkXw3+R0FxGU0oLGT2pmVh0Cz+ndYEIbH5Gyr+Ck2DPf8Ge+tNGJAxJsAJubzKvfxDhTe+OwjHrVqyAA03x3xVNlna/cfz90lZDmt8HpVJmdJdOuxtCsWceQeyA0ygit6zGO8PhQLlY+3jccKKjWXLOoo9O1M/nDr1L76ETfozxQ0cdZKbvTALgzF7vJ0ASr+X0w3Z629+lyh3YIrdV0wy2YJgkBymjVtPO5FzInuUqV7GN2psTfyYtuAXL7WHnowic8xj2z3vnob50G4c6sOMVGn2+fYNx4GvjOXtMRulZ+XsMK9IKef/AMyCBvaaTmhcNo7A5nNTWVG6DWJVtQEFbcBMq0eEV0pZn4iYKg88mrTC7D6mBiVnUIDWXo/T4MlhXd4JJbXSQjtZcilHAoWV8btj5zF9yHoF+yCismWOkwWdQ35ZhLNkT01GZYjH23cC5xr0th9qYkjWQjcLhkJ72Oshgex1/2qq5TiTMfqg/yGstZZCO4jVSz8nUTIUDPKfl/JbZM24GYb3WcBjC3usE9lVbPc/aUsn3ITkHS+25v4XI9p4mDT0PJU2K93qq2IL7XiyKq1xaF/aOosyhw2maBAvFrKWMHkxzbifNzsZnEjYJsmHDhg0bXwwEWo81fug5OZ6SIhE0eVPjPzeVSqmfIAkKtpPm01RGIJ3e3VgdfwypiMFWiEywlSDzwCKet3AEttT5Q6iNyu5jutmLIHjY+QSnh5cRuE36PnPUWgkRyOpprJA/QMWq2v7h9Q7p3fgz4FRJTmyTPwzVOzldL5pCWlSgTSoRqWmRdtLMBsyRLvobTV0rNvG5jX+iEWa38ShFYy45dsz1hyFjBxejShVNgYiUriNlq3oPhCO9OypKLIId81vX6mjhvMTv2ltpoNl9nLRvPjU/w75Ebcu6J+I1d3vfIc1wyHnM+YC50u7XjKX4JFKzDi5jn25+GpONSJj0w4nfg4AcXMKaOhyQGF86hhbhQFwNGf0N1qhqO/dOSGVM2b1J7XI4IA+V2wjeR36F3jjrHid1sbk8njqa1g3C4kmCgIQDrJ87gSkI+dkT4y7jmfa+RXreiT+VDor1SsrlWkk5kLXELOmdn0OsnG5MOQ4soDlp17Fxe+6QHyOMbmPjVuPdxrFG028wxi6NEF5/K0pXNAK5Te/KmBbfxv61yFTeQJ6v62gMLcZcQp1eLMb4y9Zhi11/EKWy3xyUsHWP87lT7kTNDLYaNTJR2r8AlWfC96RdL7OvHWKO3F6e15skFU2lTs7byYbexmcKNgmyYcOGDRtfDFjpfVZKl9PD6bIFq27EQsNBqduIT2ZsbbUE8rtej/fC8SZTWN5rGrVK/wmCbQS3m58l0NvwJ4JNbzJBW+k6TrFPvwdCdMJPaC4abKPWYemd9D1J70YguPXvpIBl9iJonHIl6kvdPlNr4SEQdBkyczxF53jwJH6EZ+mgF8+YS0wqXC9p/s3S0N8bV64ojUR3vU6g2XltJVLGBp4OCex/Srw+q3af9MZPUFEcDtZizxuoFOMuJdUvGobYzLkdApGaj9I16AxIVfFa0wvKR0B8cLF00s8xMkjtghLy5k8gVC5P3MmsfBMKU+0+6od6TmYMZZukFb9FlfrnlViER8MoSEdWEICP+qo05zbpwBJS67wpEIeUfD4TapNaayG2656IW5OH/RCFkvVSnxNphGrZpFfvZD8MPI06Iav/k4Uuo3jusB9r8aZS+jVNvFxa8zDpZmO/jUGCQ6ipR1ZKJZukbj/DWKK1jLTbIytRAMMd3D/UYYwRCpjTVQ+hCPU5GTVm1k2ofIvvIB0uKZcxJudCLva8BdHzpklTfwQZbi6nxqh4FdbjngTm12mc3kZfYuy+gxiUbPqLVLWL70b3sTzLuscZT+UOyOmrl0MW+8ygJqjhEOO2HB89Sbg9+pt4ltZKKeZAVXIl8Nm+M6T8wXEjDwniFWiBCNrE6DMB56c9ABs2bNiwYeNjQXpXTu8tdFZYHM73B+KujxCYf1w4uJTg3CJAEkRkyZ1S/f7/7JqxGHU8b11HelBbDWpCS4UpHDeKRCRI8XuwHUXozN+Z9KllUjRGGlHYT6qgoqSapXclZWjez7G5bqng961V1IGE2rl+Rg/pnV9Ky+8nsA92/PvP4W/BtnvTnzFaWPprerdUbI73s2mtjKsDkQAF+X1nHnud5FzUQIuASCgrW54ndSzcgSmB1bhz9xsEtlm9UTfqTUPOCd/HSTEW47ljMdLfRlzEZ5Ny4ulWEy+HdG78M3PTWoXS50mEtEgQpa5jISDzrsd8YMfLqFabn5YcMcblSYKoe1N4b6BJevcB5n3A6VLPE0w64DJsqU+7BwJh2TZLrEk0IvWYQHrX1heMOmqpWTH2yaFlEEVfJp/zJJE+1+sEiFx7A+pWNCwdWsIemXUrZhhbnpcSktm/FjmtM3vY5YVYVe+WtjxHfdSBhZClxCxTjxOIk7IDiyAOZz4A8d03j73oy4RA7HiJ8TqcNCV2+1jHf3yTPeNOMArgOJQ5p9vYbw+jP0/OANL4EjNIG6zcbprMBlEcF92KgpTVG4OFw8agIhaVcvry7LEoz9lWw78pSVnstd1vcIjhcEFMQx0crMREf6TO6ad1ByGqr10lvfUziGVr7b//XbHxscJWgmzYsGHDxhcDLi/NJdtqOIFvqaCguWYPqsB7a2Ky+3wy42qtoQblGJjAN+ynhiRnAMHZv4PmMlQMxah/Kl0f/11HA0GddRJduYVg2ptEUb3DKdXsI6CLhIwVcieUrOV9++bzuz4zSWGSIAUuL+RowS2oM71OJB3M30Bw/1777A9COICNsr9B8qYbi+sKglKrLuPoMzVSZyQxp4mZ8d95U2jkeXgZ77HqoJpKmN9QW5xgHEWMMecN5tlyB/L+XidIb/4McmM5gR1cgp11j8kQsJ5TmZe8gdL+xQTmDleceLbVoggEWyELmT0gwk431+03mz8WKQm2siYdjfHhlW9GXTi0jOayeQN53953UD4mXSG98j3c7ZKyWd/ULtSiZPfhz8rfm+awYcXdC2Lsu5LVEAVr31Ruk9Y9aXoHjUApi4R43r1v0+y151RMH7wp8RQ6KU5Q6/ZDPFsqULccTu4dNsTHkxR3cpNQaFqrpaQTUd2GnSdV7mRs7fU80/qn6CcVi0pTfkQj39p9kPqtz0FkE9IYW/9TIIrRKCrW6ofp39NRz1xattrWOnU0QPa7jjM25pv5TlhphQ533B47EiRlL9QRd8fL7iNd8Je4q2LuAAjXotvo85TTD3MEaz9ZqNxOSuYJV5uGsTY+DdgkyIYNGzZsfHGQ3g03qMYjBNE9p2KLfEzAZhQiK73lv42w3xRsG0RCBEyBZkkxCMfeeRgG/DsBUWu1sawW9QzHKF0xTtwtEmS5eVmIhgjk/A3mdN3PvFh1NJEgfw/7GV/fmfxpPIz648uQVvxOGnQ6/VCW3AURcCdJk35Amllqgan1aKdXzfF6NTWXSc3FOJ/lDjRF5A6MAfrNlA6+a97oQO1wmH483jQC7ZwBKBbuBMY84HQCUWseIyHu7zBEwOmCiFj9aMJ+QxYTSaXzJKOexCLMZ0IqQa9lKX7KndKu17BcT+uGktd4mOC7eDVzHIvpaNNMbwo1LqmFJnWugBTD0vUoXq3VuM2lFkjv/pb3OZyM0ZeOkYHLS1CeUogK5/LipLf018yfv4nfDz4HQrf5aSy1XV5qkmbdIi285djvgBwE9w2HjTOccXqr20sq5KEl1Iu112NVHosR8DccYk3diceSPmtvWcpQejcIRs0u6oLqDnYiY7H4MIaeSwrcGz+R6g/x3N0nYve98Ob4mNvrUNBO/BnrlVqI0pWYBYlpOIQKF2iWzngApe3gEj7bZwapdLEI42qt5rpON/u47oC082X2jr+RdexogNj0mMC/JS4v4w+1mX5UYRziltyF8jf1RxwarPydacAraofSu7EenQmQhSMrpEFzbRL0KcJOh7Nhw4YNG59dRI2bViT0r99rISmTmoQBcyBB5zzCaWRy200AAQAASURBVHnRVFKaTrub9/2ndTj/LrzJqAISgVhrlXFgM8FgRk9ShzY/8x6L6n+BzqSmZg8OZ8e+If7XvjMgDxLNZJvKCCT9TeZkXNzbCmq7jiWlyWoCu/r3BOnlm+gd8+rlFN6XrpO2/wMCJFHLselpTuBL1uJC9tJ3OAk/uITUt85oriCQDLUbq+MoQWFHPSlYVp8cKwiVk9emX0vQOulyFIxQB4F6cznF+s0VfC4hBYe7pGyIRkyQX4scdhsHkRh/Gc+cmg/JaSql51RK/rGKU+1e6ayHIQI1u81atqLApHUhsHY6mftImEamEy+HMCTnSSf+BCe50vXco6NOWv8EczjuWwTfVnF94QjuETMpge11UnoP1JRoFAVJittA5/aHONTtM32I2gjENz9NQ2SHiz3j9pHONfgs0r82/429l9FdmnMn+2HU16jZ2fQXLKxjEWnedVLOQKlwJGqMLw1SarmjSVzDSu1rb5D2zqeuJzXfKJ8BM0dulLWaPdTkhAOSYlyv1NiRT/hufN6dLsja5r9BwlvKzfw1mKa7uaxpLMb+Kd/McxZN4U9Wz7jDYVoXQ6ST+Yp4DaFrLObZ/A08b/lG09Q4l5+Tc/m+OBzsm6IppMwNOp31difGCZBECmZLJbVRHwSrQbCNTwU2CbJhw4YNG589RKOkkax+WHr9avL5S9Ye6/72UZHVi14mp9xOMJg38GMf7ociKYtmj5JJqenU18STRKDVeCQefH9UpBYQmEkEkS1V0qCzzC+dBNMSRftDz43XSDUVU9g+7HzuH2o3ZCNG4N5lFMGev4lT9y4j6aOTkgsxioYhPd3GoW68F7Ewz1Kzh7Sl9lqpdC3EaetzBIYWDi+P17PU7qVmqaUCFWvbP+gRI6HceFMZ56QrMJNI68Jzx6LS8nup79j5MvNdvApHuPk30qcl2AYxaq8hUE7MJIWvh1EdohGK7ss2EuBHgqgK9QdIIcwoihstbPwz946GCKZbq9inU69GLXN6CfJ7n4gKs+TX1K3M/BUubdGwcd9z8TxuH0TO5eHnYDvkLhbF7CMaRYUZcjbObhO/x7pYRh8NhxnDnnnsiaSceE1SUjb1Rb4MXnMnQn6nXCm9cTXEtK2W8Wx/ieeYcwe1Rk1lfL7LKMhSWx37dMJ3IWmJWShlkSBzIdHnaPatfHbOHdiY5w2W5t7PHnL7UPzGXYoV9ZrHzJ6JQHStXkWNR1BcElIx/KjawbpWbmfNrV5L1lx6fHxu+EXGBryUvVQwDBfBbuPZV/UHIcpON/PjcFFrlZgOGVr3pDTtOuZLwoJ98pWoYTl9pdxBrM3wL0kvfI15CTRDPP2NPFdyLvPhSxcKZiflS+LnSDD+x8anBjsdzoYNGzZsfPZQsZm+KiGjMNSIgGPMNwlArJqPzwt6TJImfEda/Yf4a+ndcQzb8xY/R4KcJGf1/mjXTC2gRmLBzQRd+96R+s6SZt8OWXE4pD7TOd22lCjJ1FO0SLvflGbfRipe/SHeUzSFtJ7Fd0oZ3Uj/2v0maU35Q01jTBeKQCxCIGyRrWA7JNWTbFKOTO1Ec4UkozAtuZMAvts4lKSWKh0t2FeMuZh6tbT2ScbTXicpQ5p7n+RLIdDtOZk6pPKNEJLK7TqqqjUewV1s9h04jh1YxFycche1LSVrGffwCwjGXT72WP4QUqoiUeardF38mk2lBLYOF2vjTYOg7XwFknNoOc8+/1eoSSf+BIKSN5iUp8GnQz5S8gnaHU5D3iKoSCn5zEn1LmnqjzGHaK1EIZn6YymrL8rH+j9KLb9mLL2nSec9Lu18DeXLk4RduNNDkO1OkDqqWZ/MXsxj/1MJ2AecBkGt3ArJyegRrxlyOHndl4VlekqBUeYaUFb3vAmJOukGSJPbRz3NgLlShbBjd0QxCNjxonETTKb+afQ3mNeCIcxZ7V6pzax/oJXvQ0unvdJwGOI08HSUoUAL699WLZ38K8hNUzHv9Tdjwz7qy9Kmv7H3B54mLbsPW+2iSZCdw8vjfZpcHlSqnAHMQbWpRfLXQ3Q8idwvEpJaqqXJP4a4Vm0n7e2kn0O2Di7lu+tvJjWz+ziswvOHMI7u4+JqUDjAWgRb2V/ZfTAUKRh2bPNgG58I7Bm3YcOGDRufLbQ3kIpjEaDO2PhnAprcz1kDQl+aNOxCyEP5RoLVQAt2z+2d6oU+ipV0Z/SYKJ35IMFpzV6C2a5jUAyO1zBUkiKmzqK5lJNvS+lJ60KwHg2TMuh0kwLXWEyKUOVW7nPek5z2R4IElNb4vam4yrm8BPhOz7FBrYTqFGghvW7WLagMBxbG+7oUr2KOzv0DKYK7XpcKfwShqjyMtbX3JnoItVaRnhczaVQOJ7zF4aRpaP9TTJpdi7T0LtSjqT8mEO1zMulkb/wYIuZLJYid+H1p/Hd41nCAZ/Glsy4FI0hHy+4nvfxd5ufAYumk66St/yDwP7CQGqHx30UZ2P06ZgHuRMhlwXBSMpffT4Bt2VoH21CjGktMOluYe219gUaqKx6UFIsrfztekUo3sM5dRphURyfXSMyElEVCzJHDgaIy7nTSFdvrpU0LIR2hDsiGHBBpp4uA/YSfYgLQYnoIjf4GSuCRFRg1NBxGXXS4IDSrH5aKfoF6uPhm0hldXubU3yRteZb5aCqV3r1HGnQmpCWzF/vI5YEEpHU1amk76YBZfVFjnC7GkdsfJXH/QhQpdwKpiWkmHXHr80YBGg65cLpYx3fvw3p9wKk0+HUnQMw8PunFSyEv2b2Z45q9EOnOtUtOjzT8fNSpNY9Sk7X5GYhcNMT81e2XytbTP6h5E2vx1nXStJ9JFVuZk+bSuONh13GM/c2fSXPv5XltfKKwSZANGzZs2PhsobUq3tDzvYhFSR36vJEgiSAvNZ/A/niF0jn9/32zBpeHfiS5Awlo3Qn/+kQ5u5+Oqi/RMPUplkNYYibkpHKb9PqPzD0SqGexiv53vIwi11RKAN5QTIpfsJ1Tbst2urUqXmPUGU4nAXxzmWkUWmiC2QaUh7Qu0vNfRXEIh6VC0etmyJk854a/MKa6/TyHQyblKBo3QGipRC1MzOA+kSC1MWldsfHuNk56+XsQJIeTYNjfRFrd6fdJp/6aRqY1O1CHBpxCilTtHgJsj6kn2fsWwfmsm3m2SJDnCHVIb/88Tu58Jm3s4GJc1/qfAuELtUMWknOwVW6rhXiseACCM+cOGrYmZlLfFIvhYCcH6lw0KC35jTTnVq57eJnZAx7WNxZlDrqNk166jNfOfQwFqLmM9XJ54jV30SjExeUlXdCXHnfN2/RXSF4kyP33vY2ZR88TIW9FotatfAP8IbUA5SkljxS0g4ulCT/AQnvnq+Ye07im0819Ay0mzS6dMS2+jTH70pmD8d+lz8+or5La5kkk/a92L6pQ7T6uMehM9rRFOGJRXOa8KZA/xag5cydAXnf9E7UwtdAQd+loTZ3DIXUbA8kvXY8imd6dFFdL2dv7NipTcwXfnYYS5rylHJV17v3UV+19h/TKfrMY99bnIUubnuZ7afcP+kRh1wTZsGHDho3PFo4XOHfGv2OS8FlDejdpxi/fY9UsTvhP/CkB638Cp5PeLR8lpSajCHXheLBcz6p3x1+LBAh8u4zGRrjniZCBdU9IQ7+ko8F2WqEkB8924s/oo/JeJOVgi6yYFApgsnDKXQTaaV1QSTb+ifkI++PuYNEwRfHdxkC4StbEjR6sPjmWjbHDaXruuA0Re09NRs8pKBqeREiRz1g5O4wD3YY/QZR6nSj1nc0zl22k5mjTX7E7H34+Af6w82n4+erl0ms/lP5+CeTAk2hS+YwK40mCNLq8EIDuYyE1qQWYI2T2Iq1vy3NS3hB69uxfyGFAcxmpiKO+zvz0nRkv8m8qkQqHoU5MvgKDhqbSOPFzOKVJP0TtaioWtWKJrH9Hg7mOwzQY9bCP+pzMNWbfzt93vIyCUbyatD1JRnKTSkzaYEoBLzdXcI9YhOsnZsYd4ZrKSLEcdCb9vApHokKmd+M5O+ohmi63NPNG9tOIizDAmHyVdO4TEJz6fRDDVQ9Jy+6R5v8SK+qGwxwIbHmW9/We1un5XKaRciiuzDWVxR3uqnei6p1+n6kHMocETiepctN/Dqn1N0qTf0Sd1/wbcZ5cchfrN/bbkKr986X8AZApTxLppqmFEM9hXyJltWIrSpLVb6pqx/tt6m3812ErQTZs2LBh47OF5Jz3nMi+BwXDPtnxfNzoOkY6+/fUgDSXo5rkDji2bue/gbZaY0oQlUZeTOC47R+m/04PafTXGJs3iZ4wDpepn7kQsrLvHQL7PidLC2/n742HKAaXSIvrMprPZfSMF8tbcHowNajaTqF6cg7Bd9dRnJS3VKAGbH5acli9WExKX8xYWjdX4PaWnI8KktYVtSXUzvscTpSEnlNNj5qo5DLnvcm5FOP3PAEy4zLqg2IQyKRsUuQajuBy9urlpKkl5xDIpxWi8JRvQsHI6AWhXH6vcAfzofQ1luAMlpwLYUvOg0TEYsxZkqnTSe3CNfMGEYiv+j33e+eX0knXE6zHwtS/1OymxinYTN3RzBsxFUjMkjqaCPqLV9FrZ/CZKCO+dNZ45z8xU/AmS0PPIdjvPhbiWbWDQN+dAEHIHUja28FFpEBu+IvU+yTp0FLmOL0r69LZbvvAIiyiK8RenvEr9treeYw/FDBrGIWo+DJQdCo2k3I35Uf0nKrZDWnqMpo0ze6TIEGt1TxPRwP79MhK1szh5JreFFLmpl/H+iXnMpczb2bu22rjNU+xKH+Gncf90rsxn2XrpdW/kxrPlC5+ju9mwyHWJnegtPRuqWydNOJCaeWDJq3N7O9gmPTLyT/EhKW5jHu4E9i3ljrbVktd4/HgSYS02/hEYc+4DRs2bNj4bCElj9qMBTfHncMs9D+FQOjzDIeDVJzMnp/M/aIR0nhWPhh3n0vOpT5m7v3MsTflWBWq14mk6nSfSAC58GaCzqHnS2sf5TopBVJzpbT4dgLbpGz+ONwE42c9TA1XWx3KQ0aRqd1xUHex6zXuk9UbopGcgxri8qKedDQeU5YhKW573HhE2vx3yNzaxxl7TJKiqE0jLiI9b8jZmC0kZxKcb3oa1cHthRD6Mgg+A638N60LtTiWcYHV8LS9jjmLhCAckSDpa+se1TGphU43yllaISl2vnRSomJR3hYNozJFQtLor9D7p60aZSqjO+5qbdUEyxO+C3Hc8EeUL6eHwPvAQgwe5t4PsTm4hGL+SIh+S94UUiuD7dKR1QTsoXZpxs3M9cY/QXRGfpVnLF6BOtfnZD635hECf6ebvkE9JjI/0SAkNK2rMb1og/AmpEmZvaWKRmnZ3VLFesjH0HNIW9z6HGva8wTITPfx1Ept/hvXTUiXBp4K4Vh2D6liKQWQ1+e+CkFoq4E85g6Szn2c1Lba/Traj6mpFNJYuVWafoO061Vp058xyOioh0h1NEjFK6XeJ5Ou1mU05itTr4Lw+Jsxc9j1T+nsP7DXQkHGfHg5z+1vIr3Nk9zJqdKs/bZ/cGDQWiVVbKO2LRzgu+BJ5L8fRIIGnMa/ezY+UdgkyIYNGzZsfPbQY6J0+j3S5mcpFPdlUIhdNBmTARsfHbX7SBkKB+KvtdVI7/xCOu0eUszei/QenMTnDZTmXStS3iIE0bte4z0ddQT5HXWoBsE23L9m3ALJ6DqawLF8I654g86gwWewjTSw0nWkf037mdRnJn2bUvL5TFMxBORoXyGHlNoNBWbPm5yq9zkZBe3MYZCCplJ+tgryvcn0RDr5FyhPC26kjibYKs25nWC3ox7iFgnxuY5G6aRf8nc5DNnLi18zrZB7J+cwN6seJK3NchuzSGZCalxJs/ovOb1SQhLpVLGYtOoPpvmpB3Vz7KUoVG01WHMPv9AYT9ShJkQjENHkXMa28xXS5PrNNr13XPyZ+H3MAdqqeZ6s3riQNZbQg6dgGM/sdENQohE+9+593GvqTyjSr9pG2ljVdowo/M0Qu2AbaqE3leca+RVT++SF4Ljd7IlYjDqXiVdgDd57qlSyXqrYAjmOBCF2oVZS2PrNJtWvdh+poa9eiYKSkMJ3ftTXuPfeeTz34LNQhfa8yf6sOyBN+TGkOHcQaYAb/sjrsTCq2dhvsWf3L8T8IBJC+Tvr99zX6Wb+a/eR5vjPK6WZN9F7LLWraU7bCEnzJHUybzG1Whk9WJ/sPhCe7hMw/5AglkPOIb2wM/KHUHNmNSq28YnBJkE2bNiwYeOzB3cCQXTuQE56XQkEIjb+PUSjBImdCZCFWNTUoAx6f0F2a6VUMJhUK6sJpS+DYDkh1dTrBI5tJBoNS+lFKBoW/I04ofWcirIUaCHY8zcZ566g9PYvpfMLSBHzJKGAvPkzTtR9OVwnrbs05mKsiSu2SOO+SbC69YU4iRh4ulS8Vuo1mWC+YhvPVr453i/H3yTJgb3ztOu4XkdjnOiM/REEJNAEeajYDPGw+rnEoqQGpnVDGUnMZBz+ZohfUg7/3f4iBhMrH0SFSMrmWXtNJ0XtjasZh8vDNYtXS9U7qKVa8xiNfZNzITrBNlLnJOautYb537+QQH3JXdSZDDmbZqcOSa9dyfzJwXN1n0DK2aanJU+CVDAK8ntwCQqJVYfnTuA93cZT7zPgFEjvSdejhjSV6ag8506kTsmXLi29V8q/AtOPWb+ERGz+G9fqO0MacYFRm6ZLC24xJNPsGYfpX7V/PuOPGZc8TwLjbzjCXK57grWRINGttdKk70OeSlbz70XJWuytoyFp3vWkmwZaGGN7A6mGc25j3hIzIC2eRGnTM9Lu19iTfU6CTO16nZqllDyp3xx+l5TDfy3ynJBqFKEY1yoYhlrWeJj00rzBqG4S9xv7LWqVDi2DQBVNgbjbKtCnApsE2bBhw4aNzy68SbZj0v8HoXaC/w9C/QGCOG8S1snNldT2lKzGAKDvLCkxm8Av0EJq2YBTcbqSCwUkMYtA1pOIWpdgejjFYtQRRQKk/q15mCDem8J/6w+hBDhEul79EWnfPE7np15FvUrVXq415zZp27PSgfnSmG9BGg4sRMXwpaOoHFlB/cy+hVJqHvd0+UjF2/EyY1UT1ytZC7me8iMC8vSuEI1dr2H3nN2HHk6r2lARUrswzoQ0ado11JO4E0jP3P0G8+NvZu6iEYiROwGr8fZ61sDhhLyseoj6J7fPqF4lkJDUQn6e8SvIUfkmrtFey5z4MiBdEb+pNUmW5KCupWanFD6VNZj/K4hX7iBp0FzGHAmQ1tVrGilfU34MQYtGuK9lMx4NS60VEOch51Ib1m82RPHUuyGd5RtQgQafgdLz9s8xXsgXhGfto/G0xmAr67LjJQhOYgZj8aUzRxb5cnq4d6hd6jOD2iGHi/f0OoGapLp98ZRAcgullQ9Jp/6G1LiUfJzZdr8pRQPMQVs1r7u9Ukcb99vwZxqgbn5WqnmS1L4TfoISWbFVqjsoLbpNmn499wgHqPWpP0jPHysVrqWCZ/QmM9YBp/PzgFPjaZ/vhS8NZchSh2x8qrBJkA0bNmzYsPGvYJ2+e5M4Af68wJ1AEFi77/i/T8oiGG8sRlHoPgF3tK6jCfq6jiZVqaPeKCIhajZaKgjSHcZtzJdB75Sk3Pi1w356siTlENyf/Mt4YfrmZziZj0W5fzggLfil+VxA2jMPw4QhF0rlwlp45yuQg/SuNBJ1uCjql7hGc7m08S/S6K/yPKXrzcm8m/Q1p9uQLifEqPt4SEA0guXxW9dRUyKhPkRD0qxf8Qz1B7HGTsmT1j2FdbbDRV1V3mBO9ZPamOdYhIavC29Byew5VVp2L6Rr8g8hdxJBfzjAdXqfgDLz9s8JpBuPQLxm3SyVbaYRaqDFEDmDvjOxi7bmbPfrqEHttSgOnkTSwdpqIZ6D5kojvixte57AvaUS0hFqkyLGwc1hiG3RJFzs9r5NWmJzOc9xwk+pn6k/CPmd/6tjg/2MImn9Y/w9vSvP7EnCvtudwHdn6lWQjYNLSZuLhk26oFvK7islZKCCrTakuedU0iil+H6JxYyzWow5n3Il6mWfGdKaP7DOQaMAxaKM30r7K12L6jb4LGllMUrY0ruxRbfc7lorqaUqHEEqaXMFfZ66jaNR7IoHmL9IUPKHcbzLKEJZS8zk2W185mGTIBs2bNiwYeOD0F5HsLb1BU6l07qQVlQ0mQDrsw6XB7vlDyrIHnEx71n/R4hNSyXq0LDzJMUIOHtOoQ5j+vWoN69dhUrQ/1RO0dO7kSbVeEQ6tBilomAoNTD5Q6XuidKyX1MbJAfK0rhvMqZDyyFilqW0wwXRcLoodF/7qNTtZwTLDifqRuX2eM8ixQjwg62kJh1cIvWaSv1P1zEEra2VBLBho0iNvwylY/6vCJAzenK/AacyRpdbGnI+gfrS31D/0W08is07P2fdU7tAfhwuCEfxSsjGmEsY/5K7GFfhSILj8x7DBMDhJJUr1E7wH2giPa7/HOntX0iJ6bwejVAX9c4vpFm3Sq98n7H6G1F2vKmsy6qHmXdPEtfuaJAKR0MeVj0UX+dgK4X74SBjbKmiFqV0HfdyuflvLIKpxJbnIIQutyE5Md679lEIxs6XpRk3Qi5S8nW0p07dPh1NlwsHUOImfBdHujd/Ctlsq4HYTL0q7ngXM+lkSdnUj43+enxfubzGVl2MxZfOelluirEw6Y1V27C47mjk2WIxlKemUkOyXEb5yuc9m5/F4nzV7yVFpSOrIOs1u0zKWyNqY6CNfdRnOk53Of1RHAMt1DN5kpirlFxpxbM8g8vL+z9PByb/g7BJkA0bNmzYsHE8hNqljX8jlceCpZiM/TZk6KP05fmkEWyjHiQahWDkDSLwX/9UPH0sOY/C+2hEWv8EysCILxMARoIEt4POjPc4Oe9JXMNK16P87H6NWpvh59HItLlTrUjuQBpy5g4gfejpL/F6ci6KWluVtOROGoGWbcQ1rGyjOdmPmPFX8hl/m9RN9JXZ/TJBuifRuNA5dLTmxeGEpDgcnMSP/ga/2/IMKW8SdS1tdQTedXvjzmYut7TgJmyez30UgpA3iJoSlxc1I72rtOK3pEJNupygvHyjtOcN1AJvKvUv2/9BzdCMGyEKy++GDLXXUbPU8wTmfcUDPKcc1Kdsf4mxJ6RCEhxO5rO5nNTEM3+HEhbuoD4lFkP1aioxaWQdmIkk50gDZkvL73v/vnA4Ue8m/gDSO/0a5t0iME7Teyh/KOlnvnT2hSzi4aFOZs5t0va/x8lRoEVKMj2brFofbkhD0V2vSUfejTeyTczEVjsWgyjsnQeRGP0NGti2VNBP5/T72LNttfT/qd3H88kJQQm2cv/UQkiWO4Gx9JoudRvN4UUk0Mlh0ox30Bn0cqrbj9FFLIKjYUs5ZLl2D4pnaoE0+Bxp5NcgO+v/yJr7MjkkGHCqtOUFKbMHa75nHnMS7uC70lQiTfgOzyaxv+v2SweWsP+y+1F/lNXns/nvyP8A7Fm3YcOGDRs2jofGUlKCjofNz0i9TyTVq6mEviIOB+pAejcTPH4KqNmLO1bZek6/M3sSiA06S+oxGXLRUEzNzNJ7UCIiIVSHhHTptN9wWn9kFc0yDyyQdrxCwFazh+e1VIOhZ2NjHgmSDtdeS2BbuY3T9Rk3UgDuSyOQdWVyWt7RCMHc/Sbvye4jPfOleENLq6eLjNIjcd/cQahI/WbTSygS1NHmrIkZEL6Zt6JkHV7OWIomS3KSgpbZEwVn019I2/OY1LKmEgLhdU8aY4JHIURW8OxvRC0LtjLerc+hiCXnQFL2L0TxGXcpFtGNJdLBxVL1HowYPMk877a/8/OQc3G22/Mm5CK9O0F8Yib3cSfxXE4zz2WbTBpYIkQnFJQ2PAmJCgd4X+4gFCWniyA92PqejeEwjU5jBPttVVJTBURj+b0QL186z5GcA2EKtKAMOhwE8snJ1NZEgtyzehfvr94Zt4vOHcg6WoQuq5e09hHWIiElntKX1gVFb+TF3C+jJ79rrYQM7V8g7VvA82b0YF0W3c57Ag2Mx9eFlLywHzUsowgCUrYOY4Ixl0BcjiLGvPsy2MsSe8MyZsgdwL73N9KwNtSOY97wL3Hw4faxf1oqpLWPkRLYZwbz11FHqmDNbqzJJfozDThNyu3P8xevin9fJA4Utr9IDVjPqbY73KcAmwTZsGHDhg0bx0Nz+fv7FFkId1AIv/M1AhkrWHc4UVRGXPjJWXlHIpLLJdUflt66lvodCw2HcV879dcEdYtvR1XZ8BQpPO5E0oMSs3iG5ffTZyXULi34Fal0nkRc0lxe5qO1DsLUZuqkohFqczrP1YEl0vCLUJZ8mRCBSBAClFrI59obUDCKV0k5Awjk0wqpwSlZhdriTuR6qx7CIayjESOCAaeiMDhcRrkqo3B/yzNczyq4rz+I2jH3fohLSyUBvCMGUW2rNT18nASvsShkLrWQVCzL2authqahLeUE0L500tssVG3H9Su9G6Ry+4vsg+odxgHNpO/tX4DZRHZf+t3UH4AE5Q/l7x2NkIWInzlyJ5Ba6HCTcjdoLsqBv45UwrRuqG3950BK8gYRdCekoWBJPG9iljFmCKJaDThNyh/Ee85/kjS6xmKUio5GbKUTMyBmHQ2MpbkcBdHtk+SAuE2/FhXNSlfbv1Aa/x2MKForWd9YDBUpKYt5jIakpjr2W2s16W8JqTjizb4DcrfwZsjx/vm8v3AkKXLb/2HsuB0oPmO+Ib14qenp08EaHl7GvvG3SOc+gtIUasftr6E43tw2dwDj8fgkudh/655gDiddzmHCoDOlTX/j2cJ+9kZSFmYXVTsgQaF2UgK9KTSqtVJPYxHmLLc/NUXL7okToKPf3SCvZ/W264g+BdgkyIYNGzZs2Dge3Ca1JzkXRcGyg67eiRtV4xGCss6IRbEGzh9MzcZ/C0dTaxZJ1btJqXK4OJHWe06UYxFp/ZOcYocD1D1UbuX93hRS1ELtkmIE8J4E+q90H4960mOy5EvlPf5mLu9JIsXL6vXicHa6oVNSlOA5MVty7CeYt4ZlqQ7eJGpIUvIxDNj4N6l6O6fws2+T1v2JviqNIpXMm0JQ2VIuFY5Cldk3H0La80TmvnJbnABJjK2tBkWqagdkoc0Esg6nqdHpgnISDvD32bdCmFLyeEZfOqlOJ9+AIuBwxtUdy93O4SSFrNt46chyyFC3sVLaLSgLe9/G2jsaYbyb/ipd+DTKUGohz7ngRq5Xf8jUr4R5b+5AAvVeJ0AWVtwruRJJx0zvDtmORiBtWb1JeSuajBucBAlpryc1LGcA65qUKb10Ga+ffg+GDL50UijHfANVqWY3KWHWWJxuiIM3hYA92CateBCVpqVW6hDOgEWTMKTYO4/vTnJunDgl56KURUPxvdnzROq4ileTipZRhDqy8c/M86AzsUxPSCOVbeRXUIQOLpEW3Sr1PZn17TuLGh6JfVmzA4OLWIz5b65kHpNzqfMZcwnqXFZfzA7CfumCv0KOX72cfZSSG+/9JPGepBzSMf1NpOj1P4V9FY1QN9VSEX+/9W9Icxnfh+PB38i+tknQJw6bBNmwYcOGDRvHQ3o3gsmMHtRfNBhL597TpMlXfHCqnIQS0HVMPOXq40bJWoJmq/+P0w0pC7SYtKf3oHyTNGYkf4+YADQx0/SSiRni4IAwVGyFVC28ld4+Zz7Me9c9yeesAvzMXvzs8R1LghIzUI+ay5irkrXcw+VlbB11Uks7tTpp3aRDSyAXLi+ko+ltSNiZD0oBPyRo3VPShicIaIdfwKn/u/cbg4rukLLtLxKwW2O00rK8KdTczLoFx7CElHiti9uH8uHxSb1OpLh+69/p33Lqb6gLikUgXq21qFpphgC4vFK0kes4nJKcPPuor+G2tvAWiEhiBk5kvadDGlwJBNNlG5jvd++TzngABWXpryVFISPeFBSJ8o0Qh1k385nEbGnbixBwbxrkrGILKYJDz0ERGv11acmvSXN0OHlmTx4Ofs2l0lvXsA+6T0C5iIaZ+/RuGChM+gEpX43FkCOHk9qn8d9lr0z8vjFlKCblbO7vpK3VkJa/f5N16DoapSklDwISa2VfWAQof4jk8ECw37rOENJ8xpFZBMkNdTCHTkOsd78OoY4E48rdhO9JPadJtbuljX+V0rtApH2p0p7XUeTSuvDZ9jopf5g09DxSDyddAVFqr6N5qr9ROushfo6ZPetKgECyscyeSuZZuoyQcgahvlbt6JRmJ0MWu/F365k/CJF/8Xsb/xXYJMiGDRs2bNg4HtIMCXrpu5KMuhANSfsXoRwMOZ/g83horyPQdXmNSUGYWgRf6v9/XC2VpPR0boAaCeAs1lpFYH9MgboI2MJ+/u40hgBhv6RYvGGlFaj50iEhPSaiQjSXojrNvInT7w1/puaivU4qGBnvjeN0QxKcLgrKj6yUhpxFQL3mMa7vS+N+Q84hwG2thGBadSgy9SfREMSt5oDknUvgGWonqF72G2ni5QTwe95kzKMvIaiVCJKtYNWy7E5IQ8FrLJHO+gPzFzRBeSSEGtVzKu5fo79OTdW256VZN3Hd+kME6P1mYt0cbCM1zGVqZGIRqccE7t1SSU1QazVj6GjAdnzgXFSMlnL66ex4hb5KDUdQ8wqGQ3T8zcylyysdXCaVreW18s3SvOtInZvxS5zr3Amka425BFXn4GKMFzY/TWpWNMT1U7tI3cdKdYdRTaygOzkXI4hoBELRWgXhXXIX18nsyXuCrTxnUwmpXb5U6ZxHeLbOCkdWH4jvwSVG2dkrnXYva3z4Xclr0huzejNmh/idK4G96HDy95Yq0v8iEeYw0Ew9T/eJxs3OQ1rctn8YspFJmuQ5D6OWVWxDkckfxp4tHMn7/M3MWfUOSF7YL034AWmYLeWmT1I16YOtVezBnlOZM0lH014DLewdSfrHN0wT3d7sna0v8PsTf8K/IRL7y9or74XLy96y8YnDJkE2bNiwYcPG8eBvIKDJ6E5QEw6gGHhTpbZ6AszOtRedkTeQAGvrCwTqgVZqA8Zcwom6O+GjjSESJHBvr+feaV1ME8iaY99XvQsFYvcbfOa9JGjoeRR9t9fT8HTYBaTISaYmxigoPaeSUla+iUB74a0QoNRCgsm0QupADi1DFZt1M71oqndKyoPADD2XYLLhEEF03mBUsdq9jDu9BzU0tXtJnZp0BdbLPgdkKNTBWJJzpS3/kHrOJbDtdxKpSLV7qDmZfAXX8DdLpWtQBFoqjVmAEzLoMLVKg8/AVS0pC7Vi7v2GyDUwpx31pMlt+BPjVoxge+c/IQNTr0YJG3oOJDDQSpCcksfeSMwi0E3rQo2HwxnvfSMxH/veli5+nvqm3tOlna9LA+Yw9+31BNXzbuC/sSjz63Qx73LEa66qtksrf0cKoTsBEtJWQ51PSj732fcOqVmJWTxz7X7c7S74s7TzJR01noiGUStiEV4LB9g7TWX0Yuo+gboXfwNzvfcdxhQJkc54yq9RplqrGdu6J6Vuw0gpjEaNYpIsTb+OtWkq5XNNZdKBpUZFdMXnKhLiM7EYatfE7/PztGulkjXSvGskOeMW2Sf+jBqw2n18t+oPSm/8JO6853RL2b2lWbcZK+vtuD1aCqg3hT24fxFz11YDee8zHYK163WTHnmA+jq3jznzJHK/3W8ZpS2VlLZ3fyudcidqYXafeB+r9O4omFZ9UWcMOz+uGNn4RGGTIBs2bNiwYeN48DeT7uP2Hduk0kLDEYKb6h3Hvu7y0kNn0e3x5psS6TJvXUtQ1XPqv75/Wx2NSne+Gj9BzuxJYJiYdawBQjRMkDrmW9Q/dEbhSBqcbn1Baq+RDtdQYzPuMhSD9joKyfudQRH+4ttJe9r4V4I/idqGQLNU20Ka1/TrSUdb9zjXGXExilE4SFBat0/qfbIkBwFnZi9JTnqz7H+Hazq9EMSsPtKkH1ID0lLBs1r9XSwyN/BULKfb6zAVmHwFJCkhFeVg7KUYFxx+l3nrMZ6mm7GIIW/d+Gx7LWlzTWVcZ+j5UloBBfO735Lq9xOAuxMgOMEO6fAK5qtiI4HtlCtZ14NLeKaRX2Z+a/YSsPsbUa2SsnhGl5trOpy83n2CtPhOSLQrAaVj8zMU8TvdhgTGpNQsU19iamliURSdWIS1T+9Gul57bVyt6ToG97m0rjx3ayXjcThJ62w4Qs+b5jL2dNUuafLlEINIEIUl5IeMttcR6K99nOvufZvfdyYsy++Vzn7YCKU10oxfSA17MVZI78YYgm2Mx5PIPkhIk9Y+KSVn8yxOF8+YVih1NFFXFAnwPE4XxKyxBNMFSVJEkovv5ppHIRHdxvL9Wvu4cWczBWjRkNRUzp4deBrz2n8OBhWKSTkDpWiM5xl4JumStftRAAMtOPOteIBeUK4E9nJyLmrZhj9Lpavj3/n0HjxjqJ1DkM5we7H9Ti3kO91iCPSIi/m34KMeitj4WGGTIBs2bNiwYeN4cLoJFK00svcirRC3rmX3xIuh07txQh+NHkuALMSipIblDfrwRoqxGL14tv392NcbDhPQDb+QHiqdcWQFKWznPsq9Qx1S17HGZWuRNOH7BLvFK6lDmX49Ab11Yr7nDQrNYzGpxyRO9WOxeJPJlHxO/FvKOTHvaCDAz+4Diao/BCkccDpBnTeJk+/qXdgHB5pw7rIQDUnuNKl0rbEINil5DgdkKimHuhIJ1zqFJUWl8mbmL2cA/ZqSc/g5vTt1P+Ubsaqu2QtJyh9KQX/FVmndY5z+W3O581XmoOs4nOy8qRgDJOdysp+azx4o3yyN+64UasPmOHcgyotipH2te4I5uuiZTg5vYYhCSxPvk4P5S+9O8NtaQeAeC0stZQT7E7+PUhSLxY0kXB5p3LcwjmgpRyHK6M6YLFMKhwOy0VwOyesy0rgWGiSk8yxHVqLUrXuSwN3pYo/NvZ9xO80aOL2YJyTnUh+1/UXuE40akwSXJCfEaevfpYrtUv4V1KX1nmHMHRyQhbr9NEp1uqQzH5Lm/xJrbEvxqtjEHDs9UjQo+aMoKIUjISR5AyGcKfmQqmiENfSlQbYjIa7lcEAIna6445/DyZp11JPmufxerN9L1vK5E642xgSlZr0LpAmXsS6DzmJ/NRxhTtpqORgZOFd6cqaOpsZJjKmlHFOHqu0YOLwXiRn8e9FjIt9NT6Lpe2Tj04JNgmzYsGHDho3jIaUg3s/lfXAQuOcPoYDfSk9LySewWfX7D75u4xHSsD6MBDWXU8x/PATbTP1NukmV6oT07hSFdxsbf62jkWdoLsMZbOBcSVHSifa9g9W0Vd8Qi5GuFzPBriQl5hor7FDcFSwSosg+pQDzhP6nSF1GE8A3lxHQBpqkgWdIA06B/NQfRMFqKY+PLRZjHva9g/vZ9n/wWjhA8Ov0SVVMtyImOJ92DarAlmdRxlxeFJiTbmDu599IsJqQgqpRvBLVYNLlzLt135YKSTFI7MXP8VyzbkHdqt2no+liCWnSnNtJ58rty/yUrOa6MiQxJhzVQu00z/S3MIdtJk3M6aaGKNAi7XpDOuEq0xzWgZOaZaM97ALUks1PQ6Iy+6BgHFwi1e+jrsxSlJJzIJtTf8y+CwdYxwOLUW52vMy9HU7mp6kUg4Zdr5NGtvV5PpOcS4C/7UUp2AwBTUiWhl8sZfam1qylIp6iZzmeRU2dXONhY7EuqXitlJItbfiLVLOTNSscQbNXX6a06neQjnfvx/lu+g0Qz7p9KFXeFMaS1hWFZ+ldENymErPOKTyz080ah/yGWFrpZLG4KUbUkGbLLdDa07tfh4xk9IT4zrverE8OBwnb/iFNvQqSXL5ZGnImaZENR9h71btMGmyn755lgZ4zgIOH9nqUwOPBJj6fGdgkyIYNGzZs2Dge3B5OrGv3kN7TGeMvI4it3EaKS/6QY39vkYrjweGKu5h9EIJt7yc4ndFeJ530c2ppmsoJrEZcyKm99wPu7fJCruoOENQ6XVhRDzgNxSYhFYe0jgbSjxKNyUFCKsGjv5ETbLcP9WfN41LVFk7Lk7KwWW6rlhSVUrsSJO98metk9oQ4eJMgPR2NvB5o4b+eJE7RM3tRQ1Q0WdrygtRljBm8qa3oPp4+QqXrjDIRkVyK95aZ8mPTjLXt2GePBFBsuo7B+lmxuPIUizDuoedDHGr38QyWeYC/UXr3AWnS9zEMmH07alzJGsbgdJMKNf4yzAqmXYfiULvPWnCC5qk/5v25/aW3bzDmCrn0YqrcgkHA8AvZT3Pvg6AEmqUtz9M3aNatKBrRCOTEl4aKteIBCKbDiWo0cC6pfg6XjlErEjNJwdr6Aul7k3/IPdK7YkHtbzSmFGGI6qqHWDdfJoS0aqdRiTyoGi4v9+s+UTpkeuOM+5a040Uppy/vDbYwtn9eKZ33BOmKydnsX8UgOSf9AkJZup4xdx9HeuTu1yE60YhJM+tgjKmFjDvUzj1T8piLUAefD3cca9vucNJnyZUAuYrFSAlsq2OdomFjhFDNvVILpC3PSTNvZF843ZiCON1ce8WDkMlNf9VRhS+9GyS7ZB0HIN4Ueln1OYnDBhufSdgkyIYNGzZs2PggZPSQ5txJvU35JoKbzCJp95u4S0moL9N+xom3he4TOA3vHIQe/d04Tu4/DB4fKVeFw+OBaekGTq4lxtBjAmlewVbS0ZI/QFlKzEC1qdzKc2T04CT80HKC+5NukCZ8l3oMfyPP4UkmLWz9U6QZuTyMI9ACAao/CAHKHwoBbCimeN563lC7ccPyk4I340aUov0LcGBLLaAuJxbmZL/PyabR5w0QtJUPQmZyBkjK59lCXgJLy3jAYRzgLATbMWhI6yrV7e30/FkMq2ob422tIkBPyUNtcbohh72mSit/q6OmCFY6lTuB4LejkUC5bB3F75XbuVZCMn1nFt9hAuUAilLxKlS/jJ6kooVDNIE9uIT1CnWgUJVvlk66njFs/LM06qsYT+x5nbF3GUUq46uXQx5cCQTdJ/+cgLu+E0GPBEnxyxtMoL79Rcbfayopfxv+wt4r2wDxHXBqvBGqYsxhelfG5k2SNv1FmmVqxF67EgLsS4eMxaJYdKfkSyO/KhWL1Le2SojGoDMYz5ZnUdFK1vAsFZvj4w20kB7XfTy1cuEQ1333PnoFSczjwNP5zuX2l3IGS14fcxRsZ84rt0M2hpzN/ax6K8UkX5bUbwY1TW01pFiu/5M04gKUwZR89nZHAwSrvQY1rP4gKum2v6OcdR3L85WshfD0mm4as/ow91h8BwcGniTJUcO/D4eWU1+UWvDh33cbnwpsEmTDhg0b/yuIRjnhDXXwP+rUwrh7kY0PRmo+f3qdgLPawluO/X1TCSrImb+TskzvnMyeFJVv+OOx703KlsZdSoD5YfAkQy7W/IH0oIRUTvhHfZVg0FJIEjP482GoO4ibWOnauMGC0y2deA1BvcfUY+x8mUL69iYIzQk/lSZfKW15BsKSmIVq1OdkUq5O+TVqxsElcVtqy3XM4ZRinZSUsB+Vad98iE9HIwRJDgJsl1t6/SrSiLL7YCVdvTNuSBFo5XOKQUgcDkiWwwkJDLTw3tZq+sTU7YvPdyzCZxOzMDpwiM+11lBbk5QNOarbTx1SsNUU1yveDyZgrJXzhpKKFwpivlC8Wto/H5I46EzqPZbcRTB+YBEKUN0hiN1pd0vzfwFBSEiPp8opQpH9mG9Qq7Pq96Rj7XwZNbBsA1bsU34krXkEkuDyQmgDzaxlNByfd1865HXWzZBoxaT0IubX4aTuaP0fUcc8iVLNPvaXw4H6FQ5C7BuLUbjaayCFp9+HWlW5lZ9z+mMYsOmvWFkX/hhVSiHWonQtytuor/Keis2kkJZt0FEFReJ5Dr/L/l79B+zTS9dJL3xTOut35vuQKPWdjXPfwWXsnYFzpb4zmbtu41Buiyajim77OypSTn/2UsMh7MMziqRRXyf90nLOa6uVkl2k+QVbeX6J8TncqEtbnmVtvalcb/m9GGIMPpP1rNyCGtXRBOFzebh21TajFtsk6LMImwTZsGHDxv8CWqtJ9dn5T/5Hn5BGgfTAM6QUO0f9I6GlMt4w9L0ItHCib5Egb7I07DyaKe59m9SboomcJmcWffh9wiFO8Lc+R82Ddf0dr0gFI+i7s+cNoxKMRGlwe45/rVAHNS5tNSgkgZa4pff6p6QL/wIZ3vAX0pqSsk1D0TRUh7zB0rjvQFLkIJXu0DKCaY+PYNPlwUjBIROQByHZ7aZOKikX5cKTRAPSPW9Q7+RJgojV7YdYhAMQlobDnKpPuRJSIkGiIh3MR1IOp/bJuexrq0+NO4H6mgMLeVYrXSrkZ7yjvkafnXBQUswU07twt1vzKApBsJ058CRCLAKt5vpG4Rp8JsYLQ8/BPtzhgNTGIigtLZXSyb/gc8MvoqeRw0maW8nqTkYToU6LFINIe5JQWKw0vFAHalV7HesW6jApYc3MaUsla5mQxuccTuMiZ+qdYhGuFQ3z+/whkDZPkklHE4F7t7FSRx/IQ6AFkhMJsmbeFGqHtjyL8+Dor5maNBfpfit+C6H3m7nOHSAdWcpeSS3EXa3PDNYsuw8EestzUnMJ44yGee/Q81BY8ofGU099qZC+GTeS3rjvHdSkqHGU2/kqtu+zbpKe/TLvKRjGvM/9LetYvgXL8G5jWRdvCipTNAwxOfGnkFaJebSss636o1CrqSWKSJufgzT3OoG9uvVZUgxP/CnfiabSeG2XxLV8QWnvPObAPnD6zMEmQTZs2LDxRUewDevYfe/EXws00xOlo0Ga+AMCWhsfjmD7sf15XF7TZDLCzzW7j31/QirpP11G/Xv3aS7FIMDpIZCMBFHxJl3OmoU74gqJ000Q1mdGvGD9mGuVEzha403KjitHDhfuWwXDpYnfownorn8S2Mdi0uhvkA5Utf3Ya0ZDPHeoXep5AqYLGT0MyWrlPpajnsuHErDjFeo+2mqoAZnxS1SO9G7SgpskxSB1aV35nDtBOrxc6nGipDZSo8rWEliO+xZ1MqF2qaNOnNg7CbSTsnAVK1kDEQ37CUyHXwhBmHs/KWft9VL+YKnbeEjc9r9LTUcoxt/5qmkAa4wh5IBANJdJjWtILVzzGEpVazVpkm010sm/lBbeJGX3Q9XJG0Ca2L63USQaDsfn3fGegDhmapQihhQ43cyJ1TNJQvlJ7y75d/BcVn2LtaaxGN/rxEyc3YLtjDMxneass24hBa6jEUKQkML1nC5p9aPSkXdJKRvzDYr/97xF7dCqh8xec5KK2FTCvZsrSFPMGAKBbhR7JsGHCtZaSarZgUVct+sYaedr0hn3s36V2xh335OlgEllHP0NCNepv8FhzpdOup/TjTJmmTE4TV1dw2H2aN8ZpF2WbeCPO1E6/ylIWiyK+rXuSdSgaIhmvp5k5uzMh3j2Q8vjLo9jLmFfp3VFXQoHef6yDUY5yiONNKu3IY+tcTUuFhHhtQMVVDo2bdPGZwY2CbJhw4aNLzqaSk1fjONg9xuk8eT0/WTH9HmEO4E0psLh1K+01xkDBAeB3r9SeD4q2us4aZZMTYqPAPLwMql2N8TIIkHRMHbQWX2ol3gvIkHjQiaCv0jYKDYe/mv1GsroDjlpLkUNSEjFiezAovi1YlF+ZxEEp5dgsWgyBHDadbh/dTQQhCdlE0QnpECkLMeudU9Q43FomdR7mo6qLB31UizDnMhXs28bDkvKlbL6onZVbmXso75KcO5w8f4uo1HIGkuodxr55XjNTu4ASNHfv06907ALCd63vSwtuEWaeiX3L1knDb0AVWLzM7h/ORxSr2mkXS2+i3qZuv2YZRSOMP11Aia11MVeqNsnLb2bnj69plDPkzuAcVsF/tbaxqLUfg27APKYPwQy22Q57LXwGaeHwLulisDb6SLQT8yCgPkb2ROWEtR9onRgCYS5vk5avQ9XujMeQLlZdBv7ou/J0orfQRjb6yBaB5di4jDnDvZcLMpz+Zu4flMJByvZfVHr2uullQ9LA34jvfMLqctQ1JtFt/K5aEQa/XUId/fx0uonpJFfggC3VpNylpACgd32dwhN3X7SPjOLIJW7XuN5LXIdi5l6Lhe/m3mTsQR3cK2+s3B6U8xYaR9g7w09X+o6CgK28GbWLjGLe0z8vrRvgVQ4TKreg2oVaKEmyeGURlzEocLqR+mt9Pb10mn3cN1eJ8Td+KzxOUxqaO+TbBL0GYVNgmzYsGHji47WqrhN7HsRDRPUyiZB/xKphdIJP6YuYv2TOhq8+zIonO4+8eO5jzvB9GeZzKm+RFrUP3/E39+rIkQjUvmG45MgXwbBZcNhgmVL3XB5CdILO6lUCckE6xIEpGY3KUnNZZzIy8HpvoXs3vxu1+u4YDWXUW/hTSJgzeop1eyRVj8Sfy5XAkTLmwRx8yTpaG2IO4GA3zqNj4biqWHrnpBSMo1hwJv0OJp7P7/zG/Vj24uoR8EWKRKl8D1vMGlrET/1HKXraZZZu1cqWSmFWo5thLvvbRzqJl/B6y4vtSxrHpFS8zjlb62WKrbRGHTffIhGoJnAN7M3qk9yDvN3cKk0qghyFuqgrmfpXRDF7D6k6DWXo1C0Vku9p2OJ/eY19KEK+U0/n3YsuDf8MZ5St+tVVJXXfxKvu4qEpJ4nMrebbzU1YGafNpeini28BRIz+mvSygcgexFjVhEzSsbOVyBMxatN49dsSNWEy1jnadcw7weXoJBMvVqqEURp3zs8y5hvoShN+SEEpXg1ysrgs0h9C7RCVmt2G2WrkD1ZvYvnOPXXKHQlq1CvPEmQ85ZKHa0pcrjY/wlpqE2uBKnnFAjaigfZv31nM35fGs/dUsW6uTzc199In6bl91HX988rpYnfZd/W7uVebh9EKru3NPor1Bad9Qez7lGIcsla5ljxLa0eUzrZd9v4rMEmQTZs2LDxRYfnXxThdw4CbXwwHA7qIKwTZguBFuprep348dwnOQ/XrnVPUNDt9GCjPP1a0pFcx0l7a6s7/rXSCqXhF0iv/7jzg+Dc1WMS9S21e01tUQ8UgcYj0rwbSHca/21O2ptK2ScJ6QTNTmPF3FbLKfi7v4UgxUT9Rc8ppm4oCWUjHCClKa2QAHnlQ9LFz8b73YRNrU9LhVGZ3KgidfskdZEOLZVOu5O0utQCPudvlF79IXU6gWbqiiQIpNtNr53CEVL/WZCRFpOeVbGVOfb4GG/FZqzFj6ykmL/XCdhBZxYx7hbTqKilirnc+5YUDVArM+sW/luxjQB84vfoM9NWhcKaWiC98RPW7ISfMF+n3s09e00nvbGl3JAdN8rboWXS+O9I795L0O/24R63b0G8hsaXKfU/FWI18XumFitCkH5wCemvihHoW3AnQbiaiiEF7gSzbxw8Z1MJRL+1irVrq+WavnQK/hOSee95T0hvXUO6XUsFxGnnPGnYQ1K3MdKRZTzfyC/jknhoOdfI7iMdWQ3pKZoi5Q/j88vu5r+5g0hbtCyv26ogNwcWk6Z4cBH7KSXP1O7IqFkzIcEzb0RBO7iMtfdlSIk51OnNuw7iOfXH0voneO7UAghMqIN0t8YSSPTJxrK7do8xmsjge9FcwR7vP4fXF93G2k25Sto7nwOS6j2QoYQUFC13UryuzcZnDjYJsmHDho0vOtK6EvS0HydQTi20+1h8VLSU07skJZ+gKxI0vVm8BKGl6zit//+iZhdBbFMJP0dDpDMWr0ZJ2Pr8+z9TMPT41/K3SG310rRrpU1Pc1I94fuc5i+5iwA2OUeSQxr1FWnYl6Qdr0J6JGnfIlKN1jxGKp43GaVk3LchaJEwqW4t5QSo068lCFzzCL8bdAZ1HnveMg0vTa8WbypObTteRUVbchfzaaU7JWZIYy+Vlt4r9ZuG8lF/wDQI9WIwseBG5r37ePruWGivY4wtFaQyDT4LFcPhRGlyJfD5rc8TbG9/iRolbxJkJL0HRGLf2xCqAacZZ8B5FN6nFkCIO5ogY6O+Ik0vkjY8BZFyeVAcBoYhC0nZBO1L7qBOpf6QlD0A97XGwwTgwVbjKJYLoWurQVWLRUkZPLwCRWHOHTxja7W0+W+QgFFfpY6mrY6UrrKNKDuKxU3YYiIwb61iXfKG8N1PLYAA+Zs6qcUO05TUSxqbYpCRii0Ycyy+C8fB5Jy4UucyHx10FgqbHMxzSwXGAP5GTA9m3YzBwJEVjHXDX1mXXtOx8V5yO+NJSON6+UOYX6eHFNTq3RAqt4/xJuVCkF65Qpp4GSptw2HG7EvHGnv1H1jbjnrmoK3WzGGVSS1NMPsuBkHrMQminNaV+Q0HmFunmwct3SjlNJt/Tx2k8E38njT/FlLtiiazPw6tQAWz/339zMImQTZs2LDxRUdaIUW8839FcGYhIZW0opS8T29snydEwqQrJWYRgIX9WPs2l/F7q/D9/4P2OmndUwRmGT3MKbWfoC8SgCj40o9tpJrezfTTOQ6CLaSPJaSQepVayGsb/xRvfGqpWpv+RjrcoeXxz9fvxy576HkQiKQsSQ7Sm9K6Yhu86iHI1OQf4rLWeR4OLCLVa+aN0toncfAKdUj95lCP1mMCdTUnXU9tScVmUpnSuxubY/Oc0Qj7NSGNeiI5OGkfcq6U2UOa8D3ITFsNwWrEONT1m00NzQlXG6OEEOlrngSazW57AQVg6T3S8C9h4fz2L5gTbzI22hv+LA04hQamldukzc8aMhFAmckZKJ32GxQLfwP3zxtC8NzRQJAfCUp5w7GmzuxFQL/xL1xfxiXNl4EKpxjGGAPn8kz752O6UH8IFS4x29RMlXFdl5fUtJRcgvf+p0JCHW4pOQtSFWhl3fMGQVQrt6JqRoLMR2o+15AghgmpNOFd9wTPEIswXxE/ZD8aMvvG7B2LDDWXQlyCbfxu8W3GbjyGEle8GvVs3eO4JrrdNJnNKJJe+k78YMHfBHkoWc33oLlaGn0JvXtKVkGSu0+gxqdyi9RaIS24GWtxfzPzWjCEWp9tf48fVoT91F8VDo83YS3fjPrmSWI/J2aSIuxwSkGTKmilIPrM4UfdAUirywOp2vwsfY7aqlmb7L5837L72q5wn2HYJMiGDRs2/hfQdYx01u85zW08QmBYMIx+Njb+NaJRyM7OV0mTkVCExl1KMFmxhXSe/y/8zSYQFkGbyyspnZ9jUQKsvEEEk043Qdu4SyG6x4MrAeLSWAzZ6D+Hk3nLPtrlUbyAQVio5/bv1MNGELPdrxH45w8liG+rIdAdfAYBcWoBaYGdCZBlhJCQgitY3kDSzEZ93ahPMQLEyT+krqhgGMSqeLXU9A+CVMstLjGTXjfTruFedQcgFC0VWFOnFRCkr35UqtnB2kz5EbUtb/4sfq3hX4JUHFkhZfdHjVjxAKlx/ebQuLPNIiYmSHZ5CMaD38bwoLXCmBJkYgPub+Qa/WYScHtTUQQye+O8WLKWQn9vCu9NzZMOLGANIkHjdOY2rnshVA8J5cLpREl746cQoPY63t99Egcbi+8wtUtu1iMlFxJy4s8gnE2lKFEujwnO++ACWLGVNLEJ30Ohai5HAYuGIJqDz2Kvt1RCitw+5vvUX8eb1IY6IIqdD1YiEYweAs2oasE2yIT1TJGgtPGvpMMF21HcNj/DOPrNZG+6EtinRZPN591Szwxp8e2QinGXYYThcJOS5kuVzn0Msh1owSmxqZhrJ6SxB2NR5iGjJ2rSghtZX28qqacTvsvaZfaSdvyTa+xfEK9j8zfzNXF5UF0PLjU26hHmJ9ROneDor+PWaONzAZsE2bBhw8b/AhwOgh6rj83/CiJhAhS37/gW0h8Vdfs4abZ60kiQgsV3kKLUUvHvW2EfD043Y7XSwjrD4YS0Dj2H9ByXl9PyD7M3T84mxW35vfzsSTr2GXzpx77f30zq1+F333tzFLDe01E8JFOX04L7laKmCeZ7xutOIFhuqSDQ7VUVL1hvqYDwdZ9oCuT38vcdr8T7EFlk7KQbePZAM+l3y+6DhEy+gkC9qQyV4JTbMJAY+RVp6Z0oIMm5kI/WSmyYZ92E2lW1XVp+D01M2xv4jtTt49Tfst0OthkFKYwq1lyCKhJoJuBO78bnStZKJ13HHEz6obRvHj2COhpQrIaei6lB75PYk6XrSRWs2My9rHmShKPYdOpTxn6TVMG2aoidROBdsibeeLd8MwSn32zu01CMytZ4GIXG4YQIT7teqtzBe50u5uvQcumUuzAKaKmCYBdNJn2w/iCBvrVfUrtIDUcwfqjbC4lJyWM8ViZd0SSpdDXX8aVDCrY8F1eKFJMqNuG0lpwrrXuMZ9/4F9TAg8ukk2+AjL91PT2n3AmkRo7+BnM64iLp1SsgfhMvxzShrYa6rMlXoExWbGZ/5g9lDcMB04z2cYihJ5n1iwRQ3WJRmv+u/6O06xUOjLL7sm/8TRDoyi00vk0pYO+EO1g7vwui7060VfXPGWwSZMOGDRs2vngIh6hh2flPTuFT8gkQ8wZLzoR/71qxGKfCYb85WY6YwDAqKYZxwOzbcUP7qIhGqaNpLidATMknoE4pIGje/cZxPuQgXSs5lz8fFUVTcGnb/QaEIXew1LbMGAi8h0B1GYG6tPnpY3vUSASFA07D+KBmL7baGUUE2JufFo1iHCb9x0mQGosaUtfBcx5ZQZ+f2r2okxndObE/tJzgtHgVqWtWjVXuYKlBBOhNh1EIVj0EiRrzTU70Q+0oDdEwys/sW1Hm5CSlzOnm5D4SMumBb1Fns+8dFKWqnTxP0WRjJ+6QFIXsOD0E88EWky7mMMTCxdtayjv16XGgLM3/Zdx6OdAibX0BojLkPOOA5+N5fBnsx5rdZt5MYU1WX1LAWmvYJ23VfE4ydSmSYmGe6Ssv8KyFQ7Gg9jdhIHBkhVGVrH41TaSmTfoh5DMxA4Wj8TA9ewachq15lzHS61dDVBRjT1q9lpxOUivHfRuzh1iE/ZucI3mNi2HJGmnPa+yd9npSF8ddSl3O0W3sMMYMPsmbhmKVkEaq4ZQrUYpqdutoQVMshktcrxMxjKg7CEnc8iw21TNuZNxpXTG2KF1PuqMnCQVy3KU4v2X1kVY9zP0TMyHVkSBjKl3HNXP6SEPOZo42/BX1zW/29eAzpZN/zgGBL13qMHvF4YgfWuQP++jfSxufOmwSZMOGDRs2vngoXkUfEMsSuv4gr034njTgzH/vWuGAVLWDvztdqCu+tLhTV6iDhpQfFRYZWHYvioJEsDr0fE65R1wMaanb3+lDDk65M/8DJS85m8LtQacTQPafI7WUEmAHWuIkIiWfNKCsPtLp9xIcl21Acep7Mm5nmUXUi0RC1FEcWUWK1YTvcTp+0KhECal8rr2OQLrvLAL3UIf0xtXxZ44JK2unV+oznXvGYqTvDTtfWvcXKaUPJKu1FPexmj3S2G+hCkQNIYlFuF5HA6rCxO8TaB95F+Vl9FcJVlc8SD3MsC/Fmwd3NEiKoRR1GU3zUwtOtwmWQ6SQRkOQB8WMZXMKz+DyxOujwn7jrlaDehLuIKiPRaTyLazF4LNR56ZeJQXapOIVkKLcAdRDrXmc+zaX6agbnMfHvEYjqFMJKRCg5fdwr7TuuNftfsME9w6TbuchkG92G2Ul0ZgDOFjTnlNxvGurJZ1x0g9I76vaBnlKSEVNCXVQU9ZWT43X9hdxQ3O6cXVrEs54CjM+1XNAMO0aXPpaq9hvvaZBLjb/DTvwna/EeyYlGWtxh5M5dhrCFOqgj9OcO6R/fAuzjnbjXrfqYenk6xmXJ5G1PrSMNNXWcvbSKXeZGjhvPM3U34iK5PJw72AbStC5j0n//CFr7kvDVCEcQC1LzGR9xnxTWvtoPK00pz+pj+5/84DFxqcKmwTZsGHDho0vFloqpZUPxglQzgCp2zjJ5SIobKn8967n8pgUGEOE5OhUr6PjKyofhroD0sLbTHG5QTQsbX0OwjD4DIK9un0E5AlpFIFnFpnmrP8BElJRHfIGo5LNvAWS2HCI3xcMJ3WpsYSUyfwhpKB1NBI8+tIgU80VpABWbo1fe0utVDRVmnQ5zVN3vIzbWTQMsUrKJVWrdo+08+Vjn9nhkrb+ndP8vIEUsu94mflcfr9UvVcafCEBbyQk+Vu5pjfJ1O6Yk3g5CGSdhozseJkaDwv75/NM064hPetow1IHZCO1gJqunH703WmvwbFs4OlS9Q6Cf1eCdNGzkL0jK1EumsrYH8O+xHp1NElTf4p6s3+hUJSi7J/afdKw8wjE+89Gzdr4NApM19HcL9giLfkNJCHUivFDQpokQwytfWY5PaYVEqB7k1mjQEt833e2cbc+Ew2zF0Lt2JinFeKsFw2hbmx6Wko0as+qRubdkyylpnK5oedST3RoGQ57wy7ATW2nUS69yZJTED5vMnVNu19nbuv2USfVb7Y0/xfYlGf2hpQ5XZhvVG439VFhxp+UAyFyOCGE9QeN216l2T8OyIzTDQE/sJC/95vN3K19DPJSvlmafh2kVKJmKBqN3ysWZR3zh6BmWXV5To8hQikQruJV0sxTmfPzn4orua3VzF2Pif/Ot9LGpwybBNmwYcOGjS8WWipNgCzSofyN0vK7UV2cbmnoRZJO+OjXc7qkQXNxOntvYCmhWCRlffTrHVh8LAHqjK3PUVeRms+fnlM/+nU/KqIBxjDgdBysHE7Sf7Y8R3Cc1RMy0FTCSX7xGgLEAaeSplWz+9jr+TKYmy6jCIqLphKMBlqwr05Io4h933wzfZZnswiWY1HSDZMycUAr34SSs/0fUpKpsYjFCHjXPkp6k9XLxuHUUWMHhwOy4nBCUiTjNubiT9UOao5GfBmjh94n8Se1kPQup9M0wfwd5Ka5DMI35uuQwRe/SbF/WjdIy4TvSMt/S/pUdl9UupEXYuzgTZJOv5sgedk97MFgK+Rs8NnSi5ehyE36Qdz9L60L7z3hKmpvQu30b8rqTT8nhxPyFjNpmP1mY6/ty0Th6WhA9fKmQqZindzbLKR3Z27XPCINOUd66zrmzZXA55pLSeXb9Rqq5LrH48rfuEtwLszpCzmMOXBgq9uPspk3wqSFheL1YGld+WxOf5TO3tMgh6EOCH9rRdw+2+FmDtK6GvfFDkhlJGCUInHNoeeQlpeciyKTko+K1WMie2jDn1AOcwdQG7bhT+zlhFTjrNgoyYnKZH0Pu47hcCIWjdvDWwQ70MKP3mRDPmM4bYbb2e+RIClyGUXMoY3PDWwSZMOGDRs2vmAwgV/uIE6iN/wx/qtomIClxwkmVavgo10ydyAB65pHO520O0gxK5rybwwt9p40t/egtfr9tTgfNxqOoCglpFK3cWARPXhS8jj5rtxG0Pn61fF0PQlXsYQ0gv6tL8Rfd7ohbJXbqClKK4R8KkZw3FTG/EXDkCmHqfOQdNSFLRaW6g8zBsttzGEsia33SShQLRUoV06PSfuKmeL/PMjJkRWodAmprHEsCpF1JzHGERejdu1+A4Vo7zxp/KXUBh1cLL12lXTGA9K795Me52+mGW4swhjaa1ELIn7pnEchZkPPlRbeglJgvW/VQ9LkK+kVs/w+SEtrlVEuTH3NoaWQyMyejDcpC5UwKZOAP2+INPJi5q56J8/iScJUIbMnhDGnLyTSk8yYBp8BoXU6jbWzU4o6mBuXF5I27RqCe08ixCAaQr1yeiB6ZRtR9s57ktfb60mZqz/Eft/1ljTlCshmw6G44UprFbbXniRqwloqTa1VmDqwfQtw7dv9BvtoywsoNIUjUdwcDj5bf4DUs2gIFcaXDlFJLYQkNZZwQLDqYdbD5YEE9jxROvlX0sKbSJt0OJjThFSsxcd8Q1r/JylWHO+LlN2XNML1f2Iv9Z+jo8YYEnusvZ718KaihM25nXRBTzIqYvkmTCySs/8/30wbnzBsEmTDhg0bNr5YSM6jKL37OGnJne/5pSOextZ45KOTIG8StsFdRhPMRYOk2aV3I8D6qHA4sKAuW3/836cWfPSUt8ZiqXoXpCajB45cmUUf/H5/MypO7b54f5ykLAjLoLn08UlMI0jf89axBEiSFCPw6z+HwN3f2Om5jB12wQhO8Ff+jmtIBKihdlKRUgtJZbJ6x7i8UloeBghdRkrzroW4WupAq1H0rIC03ymoVG6vdPbDEMrdb0B2/E0EwlafIDlYH8vKORLm9x31EDxfGp8LtkJeRn+d2pVgBylcTpfUa4q09DfGFtoNYfM3Eajvek3qeQJzf3CJIV9uKWKUGom0zAv/ZtS9LqgibbXSqXej1nhT6XNUvpXP95gIwVn5O4hAoEla8RDrM/YSCIqiuLctvIX0w0lXQE4aDppGrP2wId/+d6McxehzNOLLELO986SyddLYbzPGjgbT/DfEuJ1uI64ZA4FXL6ceKeSH7BYMZh+8cTXr1GuaNOLr0s4mrhdsM7VLUVLdekyU3ro2nh7pctMryOHiO7r9JeyvZ/wKJWjCd1HgYlHIlb8F1afPSdKbP2XNpl8PKQy2YMzQWMy9j7zLug48E7WvYiv1apk9UduaylA/fSkog8l5qMaL7qSub/BZrElyDutsGSdY6zn8AmnjnyFlBcNQbif8QJpzJ2TUxucKNgmyYcOGDRtfLKR3pfi8qZQAtzMSs0xvHGElXDTho1/XnUCg8/8NdnpPp4D8aIDVCSMuNj10FO+R4ulUbB1slZqrOJ1fdItRTUw6WEIazmhdRr7/usEO0pBqdpEe1F5PkNlWS4F6j0lS/1kE8zkDpGV3v/8aTnPCf2QFRKR03bG/7znZOOktosmmZRzhTUY5Gfst6chyxpmYFU/XcvswRziyQhr/Xa7l8pD2tMiQWHeCNOc2iMH8Gw0RMZbEY74BaUhIISXP7YXsddRLATc1JB0NKB+jL6EGyd8IwUjOMcXtYWnTX7FJ3ve2qUEyqXRH91AndcDlJshvKpP6nyK99TNej4RMob3iitDet3GGazaKWGsFhKz+sOnZ1EENTmsV10ntJp1xn7TgFiyte52AWrfrVZ47GtFR57S8QTikzbkNMuxNZv83l0snXsNchIOsXUsVCk9CGnOfks+9IwGC+rQucXUkszfjbTjCXLnzITZjvyUtuAnFsqOB9LftL0rlO6Wi6yEnTkEa22oxzVj3R+bR5WStu44hra54BfcIB3i9tRqiWLySlMT6w4wnGpQaS6V3f8s4HU7WasL3pb3vQKhTC1mzcADDixk3oVINOZvnWfs49Ww9JzEnpZulvH6MveEIfZ3GX4ZStfkZmtOuf8q4DMaMHfuP2AsZPekbNeJCnmvDU3ynvSPe/52x8ZmGTYJs2LBhw8YXD72mkfrkSSHIc7oJZLxJ8UA2MfPDr/HfQnZf3K2W3wMZkQjyh19EqlH9YenwUunwSoLDwWcahaJNWvkQ6saWZzj99qUT5DvdKDeL75DOfBBFqTOaSzjx3vgXAunkHGMQESMgL17NKXj1Lq5nBcNHYU7CEzONq9t7ak0Ss0hFqtjCPToX54faCZg3Py1NuRoHuErjwJY7kLqOjkZp4a1x4iBJvU+WTr9fKpF0xu+kxv3Smoe5V6gdEhNsk1Y/Ip39CCSiuZxmlmld6f0TCRCoBlqYq8FnSE9/Ke4I1l7H6221/NxcBjHoOhrCcEwPphjjc3l5fpeH/eR0Mf6jdU4yz2Fqn6Jh9tzax3i9/iCf730SdUULbmYvzL4Ne+fmYmqDzvgtz+dvokdRU7GOKjUSJgsVmyEPgRbWM7uPtO1F6n0CzXGXupQ86e/fjDd/DbVD9oZ/Sdr0N8iDvzHeI2natfH+OFOughDGxJ7saDBqaqc9kG3S4fa+LTUfJvWw30yu1dgp9WzEhVyrtYrUu6Rs7hkOojStexJCd3AxilbYL+1abIw4jJGBN4Wx7Z2HGrN/AddOLWAuIkHeO/wC9vXmZyCtDhfzmJInnfATac0f+L6NvoRxNJei8lZvlxbslkZeRL1fLMb3xZNs9l+mNOOXUkphvJ5qx8uYlyQky8bnBzYJsmHDhg0bnwzaGyR/A8FISv6HN/n8/8Ljk/IHEdiXrO5URN8JWb3/e/f/MDhdOHNl9SJoj4YJzNK6S01HpDevibt/SQS6RZMhT6VrcbCy3KuslLSUPEkOgsumsveToJq93Le9XhCTDt7TXm+IgoN0uRN/RiDe6wRp1+vUZITaTY8cQRhGfUU6ss7MqXmWUV9FhShdCzk55nlN7U7ldtLwJn6XQNrhQJ3Y/qI0+msE3E2lkqIEnH1noA45Z0Ec1j8uJefH+9/EImZ8HdT2bHkOEpPeDfe7qh3SjheJ1fvPwaa7odik7kUYfzgoJXp0TEDfWi2l92CPVu3ABa18Q5wgJOcw9uy+pAU2HMEA4uCS+Lhi0fj8DD0f9STYFifk0TDNUL2JpHntX0D6YM+ppOIdWYXqVb6Fov4ZN0uVm5nDUIfUYwKEdfMzEB6Hg347ldukwuHsn/k3Sif+hFS0WTeb9kchyFf38TxzaiGW6IdXkFqY01fqfyrEpcdE1MOSNRCEk34ulazie5yYHndMSy3AfbFRrFN7Jdfd8xYK1vhLqSEbfj7pcTv/aRqdfo9UuOLVjCV/kNlD63HNi/i5fq9pkMVFt0uOepQsTyJqaN4QM58hyF5yDg1ynW7I8Du/ID3Tm41KF2qX6ltpyDvlKlLfVj0EeZ/2M/bxkrvon7TjFerPek3DSvyNn+HYJwdkb/r1EPXavUaF6rBJ0OcMNgmyYcOGDRv/XYQDnKqvexwHJqeHYG/016Xs/yIR8aZIky+X5lfHawakeM1NWtf/3r0/CtK68MdCJEgg35kAWdi/ANLm9r3fWc7fFG9+KcUbN3ZGzKr1cJDGZbmV+TIkVzZzkpRD0J5WSHrQoeWcwHe+XsFwHLsmXCqN/xaBfnIupLNmL8XoBcMJ+Id9ifqMsEm3OrCU9x1YSK2GBU8iFtK+DJ4v2EZq0sa/SE2V0pBZKGWNR0SdT3eet6WCU/m2KoLv5FxIUCwqvXQpQe4Ff4VYbX5GWno3AXlGEYqAwxmfE8nUqEwgtcmTLJ12Nw1Iu42VFlcxz0k5hnxFUEtS8rhOTl+C+VjYWC8bpSm7D8/TXMb4WprMWpjf73uH1Kv9C3i+frPNeoWltjrSDosmE2T7m6nfSsmRKneS7jXgdOatZC0ObLV7URJzB0hnPiCVbuDvxaulgWdAWHe/iT16LIYSN/47mH7Um5qiRbdQb/X61Yw7Jc+QvcOG3DlM6loBqWiDz0bdK7pB9E9Khsz4myDe+UMgUM2lKJqrH2GcBxZJp9zJHhv5FZSshFRp6a9RsdK788w7X4aUjL2ENLq2Gprs5g8lTS4aNvMZMXbl5zHv+xfxeYeTNM6AcRSMBKV986jTaq9n3pOyWadNz6BW+dLZa2ld+Ldj/i9Jy2NxMFlY8SCqVVMp301vyod92218BvGZIEG///3vdffdd6uyslIjRozQ7373O40fP/7THpYNGzZs2Pg4ULFZevvn8VSnaIh0l+pd0tx7Obn/byGrt3TaPZyq1x+kZ01WX2nldlyzPktorca57niIhAh6c/pDFKwGoZKOOqy5ZEhJzvs/n9ufOqTCUVLpGl6Lhgmu3T7J1wul4KVLEUUmfJdGnvsX0KTS7aWRqtNDylJCKml6neH2QnInXU6wu+xuTuslmqH2P4WT9vm/OvZzA+eyTsm5jKfbBGNl3ES6mQRhS84hba2jHkOHZENAIiEIbel6rhFsI1jf/CxqTVYv1Ax3AiYKYy+BBERCBOweH58fd6lUvRtXuFgE04Nh5xGMn/c4BKF2LwGzwyWt/D29fk7+uVSxTfrqixTrH35XOrgUNW38pdKhdyF6rZWSLFtmBwF757qwtK5xa/f0big7+UOlBb+Sep3IOs//JcSky2hp3GWQpMPvEqivfwrSEYtBxMZfJg06i7Vf+6h04rUYCzQcYvxON717Vj1EembpesYz61bUFIuUhNoZ6775rGHdAYhxSwVrkNYFBVNiPhPTmH+3D5Vs9xvMW2sVc37CT5kDy4Djyy9gMuFNljb8mVQ7p6nbS0hBWSzfRGqa02NUnzpp9Dekd6x/V1zshTHfZN1aylG7yjdyX4uYRgI8S9SoeqkFkpyQqnZjmrHpaZ7b6cas4d374w2FXR7SMSXGUXeQdRt2/n9X2bbxX8GnToKef/55XX311XrkkUc0YcIE/fa3v9WcOXO0Z88e5eXlfdrDs2HDho0vPqx6iMTMuGnAxwV/M13YO9d6WGitpInhf5MESfGeO0WT+TkUkrT9479PRyMBl1VDcDwy8mGIyRS9HwdWyo/DQbPOAafhfmXBSvXrczJ9bN6L9O4EfKO/hotYa7WOWkundSHFbfMzplbDzen4rn8SvHcdTQB4aBlpbBKBbZ+TCVItpHaRep9IIL3+KWP17SSoTc7BUax4tTT1ashV1XaUj+EXoDac+hsaqjaXo5B0NMT7BJVtjDe/DHWYk/82SR72bXZfSI4VJHtTCdS9yZzaT/4hvXECLdL2l2lGe3Apa+b2QiiKV2GwYM3LwcW4qE27lmdYdDvPkTeInkAtpVKLi/TDQJP02o8Ya9Fk6cK/oHa9/QvqT6zmuq4E03cmZuyuU+L1MgNOk5YZJ7rR3+BZmoqlrH7cY8eL8caeZeshjJN/iJq640XS96JhnikWpual21iI2civQX7yBpOGFhOHAOEQDmcrHsBZcN87HFoUjkJF3fAn1tDhQqFMzkUlPLiEazSXcc8ks9ddHqmjjrXoaICUJedwCGE1vF32a9IuGw5DHqMR1qv7JFO/44UgRYKQ39RC/o1qLGWM4YA07lsQnSKTihmNcEBQvVN6/UfskZR8yGNTGc+YnCuFvVL+YKnHZIh5sBWVyZcGoXR5TXrfWKPyObhG+UbWw+HguULtxuo9ggV63uCP9BW38dnCp06C7rvvPl122WX65je/KUl65JFH9MYbb+ipp57Sdddd9ymPzoYNGza+wGgqlw4u4jQ2HOB//EPOIZiwUoT+v/A3kiL1QTiyCvvfzzNiMQLHVX8giJdMU8rvoWq4P4RYhtoJ0lqrCSAnfl/a8qwhKZ3g8tKEdPfrnNIPv4DAbeerBMW+DJqZDpxr1JfcYwmKN0kaeh41JTNvhuQ0HKKXUloXiumLV5n0MCfBa6AJZaJyGyflzk7PEQm+3zzB7SV43/s2KoInicAzFuF5LAOCrknSwNOkqT8mWG6r4RTe4yNYTsolgPWlx6/dcFgqGAQR2vc2QW57PW5wo74G0W4uZw4T0iQZIpqQSi+enIEYRuyZR+Bef4hrVWylgH/vPBztOtcGRcPco3KrlNmHZ6ndTe3MyT+H4E+9CjUiEiIQ9yTRiPbISupwxl3KvPWbBSFLKeD7dngZ1x9+FkRgylWoVRk9pOEXSv5WKdbKWhQOobYlGjVW3wFJDgrz03tg4d1czpidHnrVBNqwj97zJilrXceicuX2p+bF38yzpuRjIb33bVwFDyziOkfelfrfQt1LczmEOBKEFE25Shp8jrTjJd6bWij1m8PUhf382+F0GfXETWra6ofj89rRKCzkYuwxlxcHxKYSqbWWeqO0rsxPsB11KaM7NT7dxrFPavezNqse4pkHnQEB2vOWaRobxPBj7eN8V7qOhfQNvwgyX7ML4tznZOnLf2fc0TDkdsCp9Bar2cUcF46QpvxYWvU77plawFpI3LfHxA/+ftv4TONTJUHBYFAbNmzQ9ddff/Q1p9OpmTNnatWqVe97fyAQUCAQOPpzczOFmqFQSKFQ6H3vt/HhsObMnrvPNux1+vzgc7VWrVXS4rviQbsk7V0oHVpNnnvugI/nPhFJ3oz3W1VbSMw2yswnh499nRqOSGXbpG4TpS4mBadun7TkPgJhp4uALCnzWMOC9jpOvve9E1eAPMmkYO2bT7qRhS7DpW6TpX2LJEdA2vYyazTjNk6wHQ5O51/6Ae8vGAYhyRsYJ7TuZOyXw2GpoURK6yUlF0qv/RRC5UoiIHU4pOq9UvfJqCXOBCnmlBze+Hh6TpOcvmPXLuiXyrZQzB+OSOE2yZ0itVRDsAfMRbHYu1AKt1MzNPgsAl9vEgHmnnew8x7zDWn/UoXkYq16nyzFHFLOEGnQedQBOZw854IbpWqzjyMBKVjLKX7Pk6XmGmnpvdSYOD301TnhWmnn69Lqx5mfkvVS6Uae0ZWI6mGl2TkklW+XMvoyd/WH+d3h1VL/06SOFuqWMoxC0d7EGKIRac980sWW3I3q0FEnKQb5cyVhZT3IpBQmpPIdiUYlRUlNO7QcFc6XKY2+lJTBUBvqUVMJhObwSqmtUXIm6iiBa2tElXN6pbZmKb1Qqt4HAXvrfmPjncQ6tzdKS+/BWr2jQZJHkldyOaSGMik1jzWcdJW08zUIZN0hUiQHnwWJkhQafK60o1ahhEwpkmhc05KlyVdIB5azVyw43axx0C8NPJu9tvbPUu9pUuFoqWwD1/WmSKkuanHaGqW2BmnJtfF1n3iF5DZ9loZeIL3xU/6t8WVzYJBeyJQUr5XGfx+L7KX3kaYnh3R4lbT2j9KJP2UeiyZI438gvfwdozb6IKPVe6Xag9LIS6QV90sxN4YW3SdIGX0+8X+//r/4XP1/6j/Av/NcjljsvT6XnxzKy8vVtWtXrVy5UpMmTTr6+jXXXKOlS5dqzZo1x7z/pptu0s033/y+6zzzzDNKSkr6r4/Xhg0bNmzYsGHDhg0bn020t7fry1/+spqampSWlvah7/3U0+H+HVx//fW6+uqrj/7c3Nys7t27a/bs2f/yQW28H6FQSPPnz9esWbPk8XzMdQA2PjbY6/T5wedqrebfhJ3x8eBOkM78vZTe5fi//3fRVC4tvZOC6s4Y+WWaGX7Crkof2zq110mLbqW+JRw49ndzH8Dlqq2a1Dhnp//dTrsWFee1H7/fTtrC+Mso8HY4KMT/IIRD0urfoyZJOmp/3V5His/gcyiu73UC6si8G1CpnG6pYAQOWVufl2r34d5lGS64vNRqzb5D2vkK6WhOD80mB86VMntyu5YqajMCraQptdWSHjj/RupSIiFpwClYTVduI0UtHCRVKhYjdWvqTxnr+idJSWqp4PVpNyjUcETz2wZr1t4b5PH4SAfzJJI2GGzDelrCoSyrl3EKc9PItXAENThuL8pFwTD6vARapMQc7I77zpS6juNa658kNc/hINXP+jP1xzQPnXedaawak064Wmqpo6dP/SHWOdTBZx0uUyMTwzxi56tcP9TOvCZmUZuVko/SNO/n8TlPyiZtKxKmlsiXhmPg/oXH9h7ypbMG3SeS1jX/V8bFz9QzWes3+hJp1YPSGQ/xHWyuYKyxKOOU+L4XTTa1OM1Sj/E0Jk3M5JqhDt436EzG7UzA6rznFPZ4/UGF0npqfq8bNGv/r+RJzsZ8oqlM2vQX1LVYlPv4G3Hf6zZBSs6iN1BqAY51u17h+c7+A5bkrTXMUWIGv6/Zyb29iaToZfWhyWyog3227XnS21Y9rGP6KcVi0tz7McPwNzHHrVWdvkQOadYtKHErTI+tphLmIxww1/KQJjf4bL67Gd0/+Dv5Gcfn6v9T/wGsLLGPgk+VBOXk5MjlcqmqquqY16uqqlRQUPC+9yckJCghIeF9r3s8ni/kQn5SsOfv8wF7nT4/+FyslcshxYIf8Eu35HFLH9cz5BRJs2+WanZDvLypWGRn9jy2buUTxv97nVrLpeptkiJStJONdGoXqXqLFGqSYiFJoWPrZzb/RTrpeslf88HXbjwoZVzwr8cQbKLxqLWWoQ4COCs1qnKDlJIlLfg5xgNus+6RoHRogbTrH5Cy2p1SYiopabGw5IhKoy6SdrxASpMvg+sl59LjJRplLZfcSc2XZUU87DwC87m/llY+iANgdi9p058JqmMhKWDMFdwJksslJadLr99EOpY3SfImQKyW3CpNulJqkzwenzzRDqnJBPD73pCGXSg5YxCvTU/FG7Q63QSqA+dQO1Kzm7qzV77H2kT8UqiRILd4uZTXj149VZuoF7F6L8kh9ZoqdR0pLb9X8iVKjjDXUFiqWC9NvdIQkFYz5w6z5kEC8iPLeN6UfKk1QB+ZlhJSBcvX8cxBMx9Rr9TQQF1QW620+2UMGyo3ScmZpKJZJCfql+r2SCMvkDb9UTr5Wpqe1uzG+axwKKYJK39HTVqoiVobl5PvfqAZcuBLo+7G7YIY9DlZWv8Ee8QpanWGn0/dWDggeX3S1n9I/U6CTNftoQ4qTODpaauUp61MWnGvdNIN0uivQOBaKiU52VM5A9lju18l7TMSxFDjpGulPW+zF9f+AWe72l3Srlf/j73vDrOrLLdfu53epveWyUx6b6T3AqGKgCLSVFBBKWIBaYJiQVCwgRXFgvQOIZWQhPReZzKTmUzvZ04vu/z+WPvMmUS81/vTq+I97/Pkmcw5+3z7a/vMu771vus1ay4JDDO01wDl0xlKq1iAE69SCW7chdxzepTroMfM8E6JayCC65YMnvm8AtzvggHIMqCFAauDBwkCuN/0KCDqgK8QyPtflPX/J9qH4u/U/4f9T8b0LwVBFosF06ZNw/r163HxxRcDAHRdx/r163HzzTf/K7uWsYxlLGP/2Va7ksUaP8iqFpjFN/+BllJoG7HwH9vuv9IGmgAIBAjDc568pUxCB+j4p07cU+ZvplNnz6Yk7wdZdvXf1gfJkpaRNnSzxtCwKHerm05muI8FIcdfRnEEgI5wIkRlsBmfJrvQYzq1BWP5uZk3MJfEkXXmffsbgXfuoTRzis3SVWDvb3nS3icxgV5XOf7sKoofhLvNoqMArLncZ6e3pwFMIgK4PYCSZN9OvAVkf4p9jfWl85uOvwVMuJxKeNGBNMgccwEVwzoOcJzuQjIP7ft430gf2SRBJHBToxQDKJnOWjWjVrHmDgygcgH7feRlrrViB6Zdz/b6GoApV5LxGXUuBQO0GIaYmuxqijYcfJb3i/SZjKfA+fWW8rXeOnNCTQdflAnCXHncQ2qC/2SLmRMUMQUHLJTnTkQJNLc+Doy7iLW3NJXzuvNXFDmZ/TkyLmMvArqPUOY60gvsfZrgxJnP1167lcxVIsI1L54ElEwD9v+B47V6yGjVrmAh3aQp3GCxp4vppvahv4Xr3b6frGZWFQHm8de5Jw88ky74K0pk07b9iLlJkoVre2ozBTR0zQRlCoumugop3KCaDFX+OM7zkrtN0QVgCJAKAoG7KJnPoXEmKwtwfUSJaoujzuX3ohYn8EuE08/R5E9wLXSN12fsQ2//8nC422+/Hddccw2mT5+OmTNn4oc//CHC4fCQWlzGMpaxjGXsf8Hyx9BZbN565uuOXKop/aOlsv8TzW4CA8XGU+loH3+PB5goLyp0sM42q4efnfBRSj6fbYqDzucHmWGQEQj30pnzFDFEZ/PDdM5SoUspG30B++ArJ8uTM5KSwvt+nwYjkV6GbM28gc5d9WIWmuyrB3Y8SXWvmTcyST+1L05vY1hZfHjoiSmlfOgFYOFXqCjWeYSFOIunURTBMJkMRw5BlruI7ZTNooR26Cx2rL8RyAavEQSTBVEB6AztmvpJ9jHSC1QtJLP49tfMUDMpLaM8/TpgB8hm2LPYlpbgHIb7qCy25i4mzReMA2Q7Q+BCPbz35CvpAHcfo5MuK1SBm3AZAc2FP6ZamiDygMGeTUbOmUcAM9AEWL0MacsewfCtUDffTwEnQ6M4QyLM5zB/lCkwoLAtQeQ6xoOAGiT7UTKVNX5EiWIKde/woCGvFhh7PgvX9tRRXQ4C5y3YRUGIBV8CNj9C4NR/iop3RZN4mWzlZ9c/kF6LZJRhcP0NQPlsqgsCbPeM9HKD8yeIlPI2NGD+lylT7cjlPh1oopS51UNw6D/N9dj2Y6BoIsMnp1/PazzFHP/cLwJNWyk8kirS27aXhytjL+Q6phgrPclQSKsJPE9tYejc+m+QyRTENHCefj3QsIlAefbNBOon1/P5tXk47tGrWcOo5zgw7lJg5BLOkbeM12TsQ2n/chB0xRVXoKenB/feey86OzsxefJkvP322ygoKPhXdy1jGctYxv5zzZnLXIfqxSxUmIwxb6RyPpBd+a/u3YfDUlXiEyEW77S6eEof7mNYUddh0zk8S39ozAV0BmtXEcwcezXNhLjyGZ6WXfWX94uHgJNrKQedCDPky1vGE/6qBUDDeuaTDMn3XkRG4rWbzcKjbkoPj1wGrH6UOTChLoIExcnaPtOuZcFUXSNAEUSyAGvuAs5/lE4yQMZIV88cmyBQ3rloEt8rngrkjmLI06wbCd4SZh6OI5tgr2gi0LyDJ+8TP8bx7/0dc3OceXRq0zdI32fsJZxvxQGs+g5zRJw5wOu3EYBqceYWyXb+7G+izPHuXxPoBDvonDsLCYp6601J5hClqi0uAqhzHwYOmPWTdA2YehUZitPvkwGsXsq8mHm3ExD5Wwkcwj3A+I8SPB15keyFGuczNvYivnZqE7Dwa5TwHjIDEGSu17iLOL7KecDhF/h2LMDXZn6a6/Ly5zgOQQJGLmc+zc6fA698Abj4p5TzfvMOM5dJYH5NViUZk54TDJHc+jjloIunMFStcALzpPY+nZ5zUUyD0O4jXMPcUQSEEMhKDd8HtSsoZR4Pch/0HAMGW5mf1r4vnTtl9XAtHDlk2iK9lNv2lJIp8rcCS+7h5yULP2txcs2iAwQ6XYe5hwdbCEj6GglQ4oO8Vo0S5M7+HLD0PsqhO3K5D8acD/Q2UOK+fDaBdcVc5r31N3JuPSVU6us8QFC85ftA92H2UYtzX/2j8icz9k+1fzkIAoCbb745E/6WsYxlLGP/bHPlsX5J5XzG6P+TBQo+dKbG6cilavp4SoGl9wDrvkEnU7bR6R57AUHM/NsxdMp/6AWenJfN4vuiSKd91o08ZQ538/Oe4jNltIdb624mrFfMAfJGsxZNxwG2NfVqtnNyPdmZ/LFkKJ6/3qwrA+aEwCCQyq6k6MGBPwD16xhSN+4SnvIno2kAlDJdZQhTbi2d1dwatjPc5t5CeeN37jGdWhtDoubdyoKU4y8l+Nn2IzqsFifB1/TryLAcfoHO6aKvkd0xNJ6694Cn92qCmMtbBky6gqFku39FieuqeYDFwzWyugkSsyrMUMAehnSd+zCT+Xf83GRWTJZlzAWsAZNy+C1OAo3eOoo5lJ0D7HyS6yLbCYAAjiXQTmCx72nmee18gu24CoGW7QQtk67kesX8dN5fuRlYei/Zjo6DDEXc8xu2aehsr2Y585kUJ8PJkhEWqI32A5M+wVC59n0EGVY3x924EQh1kHGqXcVror0mADIt6idItnspeZ0zkmsz8TKCmle/SIn8RJDFjEXJzMXRuV8VB+cz2s89tvQeYPP3AavJigoS90bZLNZP8lVyzI3vsk/BTrJZusb9IYBsZjxE0OLM5x5t2wOUz+KerH+b83lyHfeVoXOOZTsQ6WFdqYYNrNcz7Vpg47e5/pKcDoObfRPFFbIqGNYmWbiPQh18v3IuwzI3PMDDjeUP8vvwve9xHt2FPLCACQTr1gDnPcw6RLt/xeKvyjAZ8Ix9KOzfAgRlLGMZy1jG/oWW+eP931tkgCpqh55nqA3AkKfFd/HEvecE2YKiycDh54A/fsxMPtcIbFZ9hw6hK//M/BrFBuSO5L//yqJ+Km2VzaQT9vZX+bpsp/O37/dkM9xFrD9j8wCnd6QBkC3LrI9i2uEXeJo/7iMMe2raQue2dRdzgz4o56H7OBkKxc7T8r1P8yReizMvI9RF1seVz7FLCh3Rdx8GLv8tHd1XbjbzobIIKHWVSnKrvsMwtmg/sOtXwKSPc+5KpwM9rWSXEgGgdCb73XGQCfOtu+nE6joQ9/PzKdW5YAfzW2AwTyZiAoJ5txL0DbawUOjJtenrIHD8gTZz3XsBXwnZEVc+azGlQqnUhKlIpzGE7siLBEWilAaQgkTWILuawCcyAJz/CPOVJl5Bpz7cA6z8Nu8lOxiqevwNMmLzbgPe+hrV1qZezTCy7BHACzcAepwhW8koQbSrADi5ETjvuwQ07z1CgMGOmD8Ngg1R4iFI8VRg5UOsV3XiLTJTVhc/5yogmBAlAsNEhPMrylz39d8iA7TwK0BfM5AEmahgK4ujTriCeUWdh8mmlE5l7k7ZTO4/2c65i/QyPBCgWuG73yGrtf8ZFvPd9lOqSAZaea2hcc58ZdzXiRDZLXs2Qcmy+/n+QBPH4CkmuPKWsrhquI/7Y+A0UD6DIYXvPcqcLmcec8Hq3+H1XUfN9QbXfCifyDBDNEXuiclXMmw0Yx8qy4CgjGUsYxnL2P9NC/dQEcru/essWCLEE+DBNjrNokj1LTVOpuC5a4GLfkwQM3IZQ5H2/o4MBMBT9GAH8PLngU88Czj+xiK0Q7k/fXRCZRtDg6qXMLwpZed8jnkaPScILNzFZCAsTuY32LLo1MrWMwUa1Dj7aADY8QQdxtKZnIewmQfjLj4zN8zqSv+ePYKywhseJKMxcinBSypPyuYjY2Z109kO9zDkSEuYEtRaOgRQjTI0rGYl0LaLQKViNuWZU4IToy4A/CeZuzJwiv1o2ckTfUNn0d8Jl5ERSATJpiQipsOucG0C7cDgaX5m6rXM64n6AW85QVfdW5xzdZhymKcY2P4zvu+r4FzbvFwPLU7WwzA438FOE/wIHJs9i30Lm05+0xaCCqsHOPwiPy9ZGB7nzCOT1XPMDEdUyehkV3EO9vyG7NjCr5AhivZybS0OzjNAMCTJnPP+Js5VRaoG41khmVqcfQ73kH0beyGQewew4Zucp/JzGG7Zvp/jiAe5DyWLyZTZqEwYH+T8uIqA3FkUFZj4UYoU2HwEp93Hufb9DQx5zB5Btu/w8xRQSIVv5o0iID/0HPfqgi8BA43MYWt+nyF6fY3pwsKJCMdhdZM1qlrIfdhxkM/NmAvIADa/Dyz6KrD7twSeNg/b7z/F/KLdvzNFQoJcp4KxDLXLrjJFEyzEkKKcFuYAuAdSMurJ8H/zQGfs39EyIChjGctYxjL272dq3EySN5i/pNj/cW2naoS8cQelc/NHA5Ov4mm/bOF7hkHnfvevyDb4T5OVmHYdsN9kXVKOZd07DMuZfRPZohQAGm7JMJOvc2rPzJ/4IEuEGda261dpueapVxNodRxMX+cq4Dx1H2UeSSJsJoSb9U1adjJnpetQ+jMF45j7EAtwXDYvT7wHW9nO9OvpWGsqw6SG29iL0zWLRIlheZf+ygQlVjOPqJA/owMEJP5m5tYkwnRgHTkck5FS7xLZVvcxApW+OhNIGIAtGzi1FUAx59eZyz4qdrJKksJ2dJXgxebhWIKd6XwlUSbAGLmM/ap7i6yZaAFqljEccaCZDMOkjzH87P2fcIyVCwguBYGsSm4NMP4jBGlFk8noCRJDwk5uJFvVtofXW9wEOwNN7EfBOILmA88w32TEIjJHNpkgYuU3gc2PAjFT6U5LEJQ2biLQffc7bK9hI4UmPCV02DU1LUoQD5IRScaAps1AyRS+llM9rEaXGZ5pcZNt0ZNAVwND3w6/yD5KCpmQVd+mqEL/KbJgwU7O/bxbGUrnP821yqoCFPMQQbISIIX6CG42fDP9nAgCAfyy+xgeuPI7nIPYIHPD1ASVCpc9wHycHU9yH41eDUz+OEFHw/o0S6fFOQdqggD1T5czjK3rEAG17ADm3s77vv8T4PjLwInXuU8KJxL49dYBeoJ9zq4icNr7O4LRubcBOTXmOCWk2bTUs2eCZcnC+czYh84yIChjGctYxjL272U9dQzPOb2NDl7pDKqA5Y0+8yT2/8cifUwEx1yeghsJKkx1HABWfJOOPUDn6M0v0+HVVTqlzVuZI7LgK/xMygZP09EebE07aB9k/Q081f/vQFDrHjrdw+3kerJAKalngI7c6fcBCGSocJYIQ/teOtwpEFQ6gyFDa+4iGLG4eBI/9gKGxdm8ZAGiAxzvmAsJUI6+SuBUNf/MPgkCQ+88xZyb+nc4PwBzOGZ8iv1rWM9cEl3libu7kGFckgk4YZCNSEZN5bMEWa9gB0//e7vpbPecIGuSM5L91tQz5Y63/YgMw6ktTJYHyGiMvZgs1is3mQVNRTr/dW/T2Z5yFWv9tO2mKt/Cr5KRyh/PmjmhboZPdR0hwwGBYCbYyRpCU69lfSSri+plKelm/2n+lBTO5foHOda6NQQ9Jz0M9yqdSYC46iGCRsnCUMaqecyvmn0T5yzYyX2na2SJeo9zPZ25BPaGznDIwTbmeV3wGPDWV4E5NzG8rmkLr3HmM3+r+yh/76lj//c+TVZl3MV0/rUExTB6jnNvGwZDx/xNVA6UrOzX3Fs55wDQuAHwFfFA4ZXPpZ8HyUqAKqusLbX0PpOZayN7tf0Jhtot+wbFKwaaCPSiAzyIsGez7tHCrzI36NS7ZMKKJ5tFWx/mfvaWkbl6+fMEbuMuJkCSJOD8H/HZk2SCJ6sbmP4ZwFfK/K6oH3j1C0DYPCTZ9zsKXrz3CAGYpJiKdm4Wsj36Kq+rWcHnIGMfOsuAoIxlLGMZy9i/j/U3EnwMFawEHeyOA6z6nld75vWGQUcJSEsf/1fW10ggUTD3zNd1jSfPeaPoYB57NZ1QLoh0tvUkHaW23Tz179jP973ldPZiQYKh4TWDhtsHKb6dbancn7Mt0Mawssq5wIE/ATDZglTfDINAKBnj/2UrHbfKeQxRCvXQMX37zrSSnWGwncMvAFf8HlhzJ8OIrG7O485fUHXugsfpwA7PZRpsJ6CRrQQub99JUJkCYZFeYNO3gBXf4km/u4hJ7i3b6fhmj2CoUXSAn6leBLz/U/Z5wkeYp3PiLcCaA1R/g2zbzGvp7J94i2CocHy6HhPAtjY8RCGBadcQtOSOYghbIpSWDxdEMxzPYKjawCle13OcynDjLiVQ2PEEAY3NRzA46eNkz469YtbFMYBAJ8c+7hICt9k3sRZOxyHeI38c6wkdeYV1dQB+TpKB+bcR8EZ66UQLEnPOYn5gytXMR9ESBJI1KwlsBJA1m/kpFgYN9zL0z1PCPi9/gOzLiEVA2z6qAO79LUHpyocIJrKrgF2/Jiu25G6yO1Y3QcPcLwKHXyIgMnTukxmfBkadRxDWcRDwFBJEyDZev+4+QFWB8au5B7b/lAcWkz4BbHnULDaa5No589hnNU6Rje0/4x5MhCm0EGwnABIlzp8j2xRiaOB3QH8T9/msz5OZ3fEE1fgUJ9mZlh0E8zaThdv6Q4LUWJDiL+//hPsiGSYwn3Ydi/lanAS6532X8y5ZCH6b3gMu/BH3Tu8JPmtWL3DMFKkYuYwHNEOAPmMfJsuAoIxlLGMZy9i/hxkGHb3hAChliRCBSfatdCABnrSfXM9/AB3pkcupAPXXrG3PX39voIlMkQE6kCkTZTpjqTC69n1M7u7YT0ewcBxZq4Z1VKdad99ftm3zAYWTzLow/4XFQ2kGYbj5ysmEeEpYh6Z9H53u8Rfz3oJMJ1bXCKSsbmD8IkoSX/B4+kQ9q8LM6xHoUFvdDJc6/CKZFV1N39+RQ2Yn2s/PdR3l2vScYN6Gq4Ay0B37KC1scTCPKGzmqAgyAcGCr1ApbPr1PEE/9CJzSWQr71G7iiFXkkxGomIO8Nz17ItqzrmrgPLXy79BlrD3BMUE1j+QBqNWD4Fe9ggCi62PAYvuJAA78QYdc11lQryhkkGZ/AleXziJjncyDsCgdLLVA2x5DIh387PjL6UgRTKaBpmxQe7NfU8DC+4gc3PxT+gUdx9lqN3WHxFMyQ7ACHNNRAuw6buscRPppfN+8BnW09n6Q7Ily+4jMLBnUaGucCJBUcsuoGkTWROLm4xfzkhKXIf7CMhOvAG07jDn9KMEx3qC99v5CzI77kKOcetj/HnOZwkSAh1n7sf3HuE67f0tUDGPNbFyawisGjZw/KIpumHPAYLNDCcrnU6AFhlgPxJhgpNEiPtx/x8IVvc8xTEWTSFbBYPPiSCa82xl6FnzFqB8LtC+G7A6gY3f4jhEhaBs0hVk2YomsyaWbjKFh58HLv0l8MKn+Wx4igiCkhFg+094r1APULMEiPjN8NZeilnMuJ41rhw+AOdzLIFWoGAM++wt+8eG6mbsn2oZEJSxjGUsYxn797B4MC0//EHWups5E848Oupv30knKGV7nyaIOve7fx0IKdb/ogNm7RJJ4SnxcLO4AXuSzpXVTafSmcdQoDpTKnqwFVh0F8HSrl+lldmyqhhWUzAuDeD+mslWntYHO858vdyUadYNSm97S4Fjr9MhL5/N0KJ4iMndgsgQterFdKyTUX4+0veXJ9buImDa9XR0tQRPxCULndR4gM7i0Zc53y27CTI69/EEPG8MWZl+M1E/boa4+cy5V+NkfboOE/Ssu4/heRf9iJ8TROZjCaJZFyiXYVuSlaAx0ptWWSscCzS+Axx8DhixmAp84T6qymlxMmW6xtC/t74CVCwALv+dKQ4wB8gqB9oPMPdj5igyGkUT6YAHuoA5N7OfLTtMUYNshn5d9Wfuy8gAQ9VSJsoEzY5cU2BCN0OmZEoxV84xC9LqzDOxuDieQ88DU68hIJjycYoclEzjPOx5Ctj1C+bAHHoeaNpGYBULshZRpIdy29OvI/ht2so1yBvNvdt9jCCieSvBWaSPYWO5tdx/kkLQksrBGXUucPR1ynFDYA5WoJ2qbdA5boD7/dDzQO15ZJ/O/R4B2mAr8OZXCEAk83kRRe43m888LDiHeU1qDIBhSmPb2ZfWXTxcWPkt4MhLlG0faGJ/nflsJ2o+b1a3mdu1lHluVrPGlCufeVC5tayfNfnjwIE/c98KZv5TImwq25mMcQoc6Sqf992/BM77PvDGl8nkqVE+B6FOYONDDMEbfR4/68g6kxHN2IfaMiAoYxnLWMYy9u9hovxfn6rKtnQOSOO7ZwKglAU76OhNu/aDQ+NKZwJ7/vTB7RdNoFOkxoDJHzNzOFJ9k+jwWj3MdUnlR9S/wzAhgJLAvgpgzhfp7A80pwGJr5yn8P+dufKYfJ9Kzk+ZYmd4jq+cznJODVkVSQEKJhB4HXmRjmPlfJ527/8Tnf6T6ymS4MgB9KM8OQfo7E69mnM22JqW9JZt5ml5jEIRySgBnNVNxzEWJMNz9GVgxg10/Ft3gapow8KeAm1pdbNgFz97xJTmrl8DJKJppzkZobMqiDxpt/uAcGeaYUgl9PeeYJ6HplIy++2vAaWzAFlOF69d+RAZlJ46sifv/8gMCTRZJUEALn8aePsuhl9N+jjZlOOv831vWZqp6GugwlrHIWDWDeyHKBNIGDqBXkoRz1MC1J7L/WfPAi76CUHd8Tc5L+XnAB//E4Hu89ebanJme6JMAH34eSBvLIFtKker5wSlsvc9DRx9hQyOmuQe8JVx3TsPUdjhwJ84h+4Cfj42SODefZQMT+suzvlAI/dz6TTm4AS7GEJ37vfo/Bs6gUzju0DnQYYMTv0kxxEPcP/n1Jp5PlbAFHiDluTYRZn7LdTDPWr3MQxOlMj+HH+dc1m3hqB32nWcj3m3ELj2nSRr5MghAIv0EfyEOoCTmwhSK84x63Z18vpp13AsUb/5zCp8bmzZ/N3i5B4zDDOM1HwOAm3sY6jDlMGW0gp0AA9XCsYTyMcC7JenNAOG/gMsA4IylrGMZSxj/x5mcTCBvOvIB78/5nyzUvwgHc2/Zg0bmTPiyP7L93wVdBY7h72WqvtTMQ946UY6bJM+zhP33b/he7mjWL/FVwaUzyFj0PgqHTtPCeuEVM6j7LLsI0Aqmfr/MQkARiyh8123BmllLQlwlwBWHzD+Ejq3dW+RsSqbScc9t5an2C07mN/iyAHm38HP9pygElrDxrTzV70IOPgnskGV81iU0hDpyEYHCPjCfXTIN36bwgjr7qPzrNjpPL7xJYZ/HX6BoXKCZOozaHQsx14EnNpMAJRKSE+pjMWDQCJphjxlmzLDBpmzjkMMt4Ip692+nz8dOQRnrnwyXxAAd64ZauXjZ7c+Bgw8wtyNyjkEgBYXYI3xHtkjyJZAZ3vFU4G3vmxOvkAn2uYheB04xTAxRw5ZLquH4X7JGK+1usyxWch8dB6geptoATZ9DxhsBqCxf0de5LjDPZxfSUkrwWkJYNNDwEd+YUqDB8mCeIqYp7bnN9xj736Xjv65D3N/99YTqFTOBZq3c+8KEvelINLxt3rItEz/FEPkQr3M8ek8xJwdd6EZStfD3wXRzDGTyVqlgLSrgEDYW0oGaNW3+UycOAXA3FOizLkYbAVKppuFVpNkeGpW8Bk+/gYLk6YsHqRggSAQ4Ne9TXAJg0B98Z1kuZx5wKbvUOCjdCqw/Ulg8deA+k4Ke7x9JwG9zct+2rxsu3A8nxdR4bMqiAAG2b6hESSlxEaSpqx6CgRJFjJzGx6kWErK8kZThONvyfPL2L+tZUBQxjKWsYxl7N/HSqYxfKl525mvF01mgU7AZHj+O5W4v/K+1cWcic7NdOrDJrviKaS8tRqjk77lByykeeGPqRZ16j2qUokyT6rHXUzRgFSCtTP37xv3cHPlUglr7EU84VbszMHoOkKHbscvmHORVcl+GKYzVzEX2PhNOvGiZEpPH6XzWDCWDuXS+4BdPycrkD+OeUJt+xje1LSZJ92Skq5nU36OWQR2EhPe7dkMPxNNyWAtxrCvJV8H1tzN9wyBQGj0+cyD6WswxRt0OqB9DXTCN38fgHkqb89myKHiIBPVdZjveUZwTpJROrHjPkKwEe6lU77kHu6V09sJcNfcZdZEEhnCtOc36dpHzlxA9xIEDZxKM12psEVR5ufUKCDnp+Wt+xqYh1Q8FZh/K6Wsa1akFdsaNlDGes9vyD71NQEX/5gASEvSAU/luWWPILOhOExGRU+vu2wlsNz3tFlgtIhjdeYxJKu/gc53bJDvdR4Cdv2Sezg6wHFqcVO0ARyLFuf13jKuq/8057F0GouwAuxLVgWFKXQNEDSTEY0CO5+g7LTs4BpYPGYx1UIClQmXsY8Rc3yCwL5UL+G9dv2C/x99Hj870MJQyKEx27imqkog3bbXZCzVdG2iHT8HLnyMCn5anKyexUkRg4YNFL7obwBWPMgaS1E/52ygmSFz5ecQVBoaQxLdxWzfMPg1MeUqoMlUWXTmmQDMtKoFbD/UzfVJWc9xHgyc+50PPmzJ2IfCMiAoYxnLWMYy9u9jrjyGeY2+gKDDUBkGkz+Gp/8AT+lrlgM7Gj64jZrl/3WoitWsabLwa8BgE7D+G0wkP9v2/xE472Fg5y/PzNE5/jpzL85/hAnp/xtmdRG4FIylU+tvYRL/gT8zlKl0Ose58xcEBIZGJ33W55jY3X8yfZo+YiGdS08xc64KJxEIwqCDLEjAnt8RCDW+S+ZEcZINK5rA0K2FX6HD7ykBAr0ELalip40bKW6w4kGGLWlJhrwdeg7Y9lg6D8kw+DmveRo/7mKG1Bk6WY+S6axBs+MJMm+l04CqxUATqKTWso2OaM9RgtXZN7E47UATx251k4WBwDbt2Zw7NUEGMRk1i4PmMExOjZugzRhWSFYnSEiEOD+CRJAw0Ejlwqr5zMna8xugfi3nY/4dlIwOdHCPlkwjcxIPERTEg2mwY+hkGzwlHHNK2Q9Is2uCSHYuVZg0OsBnYcon2bfC8QRAFhfDGVt2EYjMuZls03BLKQDmjExLipfOYI6Tt4Tvl58DnHibQM1dxPCy4YcITZup9BZopxpcsIt7cdO3OYdTrgIC3UAcpircR8nWHjBZxs6DZCIHmoBl95OxSxXdnXot2atoL+dMUsxQxxDnPRUSWL+O6zf2IoL/E28D79zNcS1/ANj3R87r3NsJEHWVYzj9PsUzfJW893s/JMCXLGSoxn6EeUZv38VQxu5jZ85fViWZN2/pXz6jvSfIeGVA0IfWMiAoYxnLWMYy9u9lzlz+q5zz16+pms9wsYFTZ77uK2Nuw9mma3RYon5AMB0wQaCD9kG5RQCd6uZtzBtJOYUpxz/mZ8HIuV80WZH/JQt2Ae//mM5q9RLAnQcsvothXy/dQCc0VbW+r54CB8u/wZo5wQ6zSGc9AcWBPwFVCwkInHl0rketZrhbqIuOce0qqnRFBwieWnfxeslKJ12L04EUJTqGiRCBRqSHgExU6JhHB+nkp0Ky4kHOYcUcAAaw4QGOZ+l9aUnvYCcV76oWkblw5wN16wFMppJa1WygcAaBkd1HoJZbC0y8nG2svY/5RADgKmIY1YTLgQN/SKv76SoBw4xPUdrcYueeqV7CtU5GqAQWHTBDEAvJtBgGgVZfA3DiTbPekEBlupb3mYNWNIlrAIGgVJTSoZapRHw1ZkqQi2k5dUMn66apHHe4jyFoA80meDKojFg6ncIUFhcBRTJCFi/QCiz6CouT5o0i4ADMsC+BLM6oVVyvmTcyDLLhXYK2VN5csNOUgB9gGKIom4yfwhywRBgonsQ8s0ArwUnrTgKaXb8CcsYA7ulkTnb/nAymliAo9hSlGdfcUcDSe8kAOfO4L/b+lmIXwQ722eYhyyeIJqhOcH/Vnss9tfbedKHcSC/FHxZ9jZ9R7ASrukoQmj+Ge76vnrWdZt1g1shycgxqgszuim+w7tCuX2Eo/wxgO678tEDH2fZBSpYZ+9BYBgRlLGMZy1jGPnzmLWXByeatpjqbQfahch6dWjXOcJzOw3R2eo4CR16lYydYgYKb6EimKr278ikoYHXRQZLtdJA6D9AxtzjpeEX6TKfVTedrylXMZQq0U4HK5uEJ+/+kbogap8PbeZBOYcF45ho4coBjr7Fv7fuBN+9gocqaFTwZt7gI7lLhcKlws5PryTSM+whFE/wtlKsesZjJ/sEOhpH5yunsvv1Vzp8BOpHzbuPYT2/j+6kisFmmyIEgsg1NZTiZt4xJ+lYP81Zig0DDWrIHlfPTBV3Lz+F1f7yM427cxDBDPWmqgYUptFA00az58w0g0ANM/Bmd66wSClFklZM1smelZb7fvjPttOtJshlv3wlc/luGWRlamh3IqebP+rcpmS7bGPIV7ec6pmpCOQsYlrj1R3SwJ15OMJZiUIomkZWRZAKKubcx56a3gaCq6T2uj64SEEkKFf1m3gjs+BkBkafEzC8yGPLZdZTXBtrY55QZOj+/7j7O8foHuA7VS02Vt1cYajflk2Qz6t/hvUtnENi+/wQw9UqCN38rkDvS3DOg6l12JfcWNK6fYudPGMw38payNlTdW8wt2/o4xQGSEe4FQwTcMHO+uvj8pMB5LMA1mnIV9/ihZ9M1uCxOSqg7crimnQf4DMhW7kFdZR/yx/Ga12/h53TNFGTQuSePvkLm77VbOEYtwfWZchVQuwLY005A37aHIXznfg8I9wOnNpI9i5vM08hllGlvfJeFWLMquD/+mtky4ggfZsuAoIxlLGMZy9iH07ylwMQrKN0LgwAEoBN04m2qgvkqCYp2PknmwF2QBijrvgEsvYugo3gSw17GXEBHrnETHbSC8WRetv2IJ+DOAp5KR/t5Ch3pAd77LUPIDJ2OWe0qYPJVZDL+O0vG6LBu/eGZilSFExga1r6fOUIn3mBokeyg41z3djo8LdhOB9lq1mAZbAfm3kxmZ6CZYhPhbkCNMHelcRMd6K4jrL1icZoiAwad020/ZvHUtfcBZdMZ3nXoObI2G79FRiXFMsQTTLhXHMDMG4BDL/BeS+5msrtRzJAnCHRiT67lz3Av75eSKdY1YOzFdOg3fZvhaoF2wGo6md1Hgfd/wHacucC+PxAsjVjMHJpIH8P9YoPpxHbdzDO58HGyYKFuAAlgwZeBzY8AwTaTcZCYWzPho1SVi/RyPqL9wJ7fct5ig9w/C25PM1e2LK77/t+TkSqbRRnummV04MtmmXVvkAZg4R4KZpzzeeYGRQfosI8+n/d//nrOz/C8FIBt9Z4086sUMjiGzjC5+CABRPFU4PnrmBs28wbOQ/s+YO09vKe/jYxS3VsEfeM+QgDQ+C5DGRs28jPuAlP22sJxjl5NAQxJYRilZCEDFO5m3xWHKWoBAt6xF3K/pCTnEyGupz2LrKYa4/qnQhFfuYlFdefewhA3fxOBk2In0Ld5ee/+xnTooKHzvdgAf+88DAyeNvtiiht07Oe+mPEp1tbqOmyC18ls68AzLHxr9bINNcb5iPkJzGxu9qNoEgu1nm25owiIM/ahtQwIyljGMpaxjH24zeY+8/e+BiqEwQDKZxFgAHTUooOAy8nfQ110dideDrx4I/NO6t+hmplmqpYFOujYL72HylnJmMkSRYHKBcy/Cfelcz7UOE+lDZ1S2fIHMEKJiJlrYmWuyZYfnJkgDzDn48jLDPvb9JDZdoJhYLFBskODreyHZEnXqdGSgLeY9V/q3wY+8nOGGx16jiFNo1Yxn8NdxMKe4V6CJ6+Xp+qiyP41bQaMJNW6Vj6UBk7zbueJeu8JU1VuPtC4ATjwR+Ccm4DuIyzi6S6ifHVKpcxVwDC9rkNkK957hGMSZa7TuIv5/6bNZA1Ob+OYsip53al3+TOVYwMwHHLCZWS61CgZCV855yeVWN9bRzW/KZ+kypu/leIF/SfpWAsS5yU2yHUomkTltfceIUBMRtlO0USu5TsPp2v+aAkCjgVfpiDF2vuBFQ/QeX/lJu6r+XeQrRw4Rec7bxT3oy2bohsDjXS0j7/J9RyxmMVnRSW9JxQnFQ3XP8C50hJmuFqSv7fspDw7BIYi1r3DEEg1ToCkOIEpV3OOfGZuSzLCuV1yN3ObTu9k/tuBZxhOp9jIzk24lKyPniTImPEpPjO5NTwUkG1UC9TNvtavBS59gm0feYnzlDeawg7r7ufzE+03hQmQ7kv3ETJSKx4E3rjdXGOR4G3sxRTRGHUuhsJSU6A/NkhQHOxieynRDl3ntYMtnEdnPn+vmk8wbGjcU4JshtQK3PuShftq0scA2zgerMz/Eu/feTD9fKbU4TL5QB9qy4CgjGUsYxnL2H+WNWzEkIclKekiiQBPeR156d/D3XTCFpoSyS07GFYjSmnVLUHkCXpuLR0hq5vOds4IJuaPu9TMBRlmdWt4ap5TnX7N38KT8oYN7NeEKxj+czYASln9O8Diu00GAwRxkpWsyPiPshDphI8yt0UQyBQcfZmswYYHgFHnM5wu0s+wrqwRAHSySEWTTTlhHx3lVO0XUeL4u48z6dzfAmz7CUOD4gHWLyqcyDCwYBdFDLQknU5PCbD6EYbgvfhpDBXHjPURcG54iPkogy0UWtj7NB1ptykDXb+WjrtuymvbvMzpGG6CZIZZGWy/16wFlGKVdJUgNSU+UDyVQOy1L5BtsGebynMCnfdgBwGF1U2Q099EJ33hV4FdOcDe37DtKVfTETZ0OtBagv1p3kqmsWwOQcK2H7NgZ2wQOPoqWbaalaYi4QGKSEy4jIxToJ2ha/ZsqutZ3exz5RzmpsQGObbaFcDB5021NJkgLMWIeEvJVlocHOeEK/j7qU1A1zHO95Sr2JdDz3F+RREYcxFD9tY/wDwtxQYEOikeEOkjENYSXLe23VRUM8w5DndToKB9H8cX6QMcBexPVgXX5OirXFPrdD5vqZw8UeJ+EURARDrHzt/MulHhHmDG9UCW+dzUvUXGcsKl3L8WJ0Gm4iDAGh7iONjCObJ6uFdTuXqBdsrfL72H4zn0LNm6eNAERObzJygcc6CVTFHBuPSYVnzTbN8M7cvUCfqPsAwIyljGMpaxjP1nWap4KQBA4OluymnVtTPDztzFwM6f0aFPJfDHg2bidj4dS3chT9bnmSfUk6+kM7nzl2np57NNSxBwJSIAzATut+5krsfQNWauSKqC/dmmxtmn7Co6ZWqMp/r+VjqBH/k5X0+FDtk8wEU/AmQXcOGPeI0gUc730HN04HSNNWEKJ/A0v/MgwQAMzpViJ/vlLWZukZYAoJEJyqtlHxo3sB/JcFpVTUuSLVDjzHcZbOFrit0smtnFRPpZNxAEDjQDEy9jiJSvggxEv6l6V7+GhS/7m8wcn7PmxepmiJVkY56SzUtnOaXmJspMug/1MA/p5Dq2s/YeYOV3yd70neSaSIopEmGGN3pLeb1kIZOTXcUxJMJcz9S1Q+ppBlmcBV82QwV7gNmf4zxF+jgf2x7jOhdPJXNWOAnY9jjQusd05DXeZ/r1nGerE5h7K2Wf+xrSBWEr5hLgtuzg2s67haIFrTu5VmMv4lpnV1Pc4uRartO2n3DNRIUASEvyPTXKHLqjr5BZnPtFMlq5owjSfOVkjoqmENhULyUw9pSQTfvor4HtPyMYspshZZM/AWz+NvfioYP8eemvCTa9ZQSKQ2IRSvoZ9ZbxgCDcC+x+iuzLuw8D0DmP9mzu92nXkdmULNz3gkhQuOTrBDRzvshDkK5DaYDlLuKYt/yQz8D067lPtASZIdkBdB9Of28YOp/N4Wb3pseYsf8Yy4CgjGUsYxnL2H+WlUxlKBPAsLKRy6joZZjyx6k6KrpOx7Z+LZ2+4abGGApldZvhVRoT4D0lFFzwt9DJGrmSjufZllKnev02MjWiQCGGVBhP+TkMGdLirHFj+YA/x+5iOvhzb2MxT9UMJSqaQhbqtVuZD5QKRapdScCRVcF8noIJQF4N66w4c6iMl4yYOT0ynfxT75qFO610Cq0ugqKsSgIFIF1PxZnP/ifNcD6rmTOhJ4Hc0XRUBYkn9ymmJpUAb/PSmd3yKHDe9wl4jrxsgoMpZAD6G9i+rhF0lM0E+hqBrPnpOZGtBKiRPkp4H/gTQeqMT9E5NmDKUEeBc7/LMKvy2bwOoJN/0Y/J+qgx5vWEutO1gsZfStYv6ue9Og9xXrIq03LTklmHCAYA0WRXZFP5zUOnHDDlrU2FvCnXMNemdRdZGm95Wmo71MXxrLuPfX73e2RPbF7ec9V3eU2gnYzfxCs43re+SlZLsgK+bqrblc8iUNeTBK0WF2v9WNzsnwE+AxYX8+ZWPkTwMXIZoLiorHjkZeZS+copknHRj7imVg/luKEzFLR1F0FU5TwgEQOCIKgtmpyWsy6ZzuercAL3544nMKS+pqvsi2Qha7TvDxR3WHwXixTHB3nPVIHT6ADvVTod2PEkAVblPKBkBoFe6w4yNNOuYYhcw3qOs2gisObrBECl0xnyKFkYqnjyHa7R6NVUxdv+0/TBRDLOkNWM/cdaBgRlLGMZy1jGPjw22M7E6XAvE+F9FawtNNxKpzHMK+ansMC0a+hAtuygc5kI8boFt9PhVZxkRJZ9I+0Atewg+MmqInipXsIQn55jdKa85TwVLxifTn5PmZZk3xo3EiDljQb2/Y5haaPPB0qmAAefJWOy+C4yDXrSPBkXAD1BUYMJlwHPXk0HbcFXgMPPExzMuhF4/UtUQEuFKAHA8Tc4bkOnA101j7LVqbwFVwFBSSJEkDjuYrJbe35Dx1mycLwrHuT9lz/IPKTGDRyTr4K5NZsfTufleIoJXJbdD7z7XbJL7gKgdxgwTEbSzvH06yhJ7C1jjkf7PgIOTzHQBmDXryltveUxMhPWbOAUyMAYyXQhVwgET1sfZx5R2SxgxUOmZLhKp1m20HkvnUWGomIuHerGTXSA3/qayYyYOTbjL6Xz3t9IQHf0NeaJpZi2Y6+RhXMXYog5A8iipOajdgXQupvFYNv3k1EYfykQ7QP2/Jrz29/Ia+rXMYdo7+8YKiZZmF+2+C4yab4KhiFufIhMSmyQ61A6k4pt0QECVpvXDDED0L6XIXEbv8l9MfMz/JzFxb2djHBsspX9duUBFzxOcNN5gPslFUraHQZev5Vqdr31ZMiWP8ACuW9+icCtaBLHevhVoGgM0LSV+TQDTQQw7fsIrGN+Mi2jL2CuULiL97F7gXl3ACc3mKGdJcCm7/B+jhwCVGcesOROhmhG+/g8FYyjYuCBPxEwuQrSRXE3fZc1ryK96XDSkmnMlxIkwJ5DMY2T6zivusYcNE8pGT1BIhuaqmWUsf9Yy4CgjGUsYxnL2IfDOg4yuTrSl34tu5oJ95JCR9Dq4an9ed+jHG5fA0+1Ryxhcc2+kwAUoA9Ax2GzcKLCwqmxgMlsqHT8EhHmz9h8rKez42d0kgsn8kS5YDwdUXvWmXlH7gJg/EcoKgDw9D5VbyRvFAUJYBDAbPkhE6y7jpO1SckK54xkaFVONQFU3lg6x/YcOtGhrmEAyHRaRZmhTWUz6EBnVxNACSJzLk5uJNhy5dMZPrWZ413+oFl3x8GmNj5EFqt4cjqpPdBOJa0JVwCfeI4OvL+ZgKB8NkOrIr1AW4A1c5q2ABgWdli7inkn239GR9+eRXA2/hIgHmZNopNrmY+x92mu32ArmRxMAM5/DNjxIzrZrnwCAU+puRcEOtv9p+gEG2bI46I7CYJadpD16q0noPOUENyteCAtAa44GKZ3+EUz78YJJEPcH2vuAq5+meMwNLOWUA7vBYFOf/0a7ouiyaxjc85NaValci5ZHk9JWswAAlXn9v6OioSKg2tp8wKihSpzaoKAQRC5HlqSfSufSRBt85Fd8ZSwX52HTdbjLo5XUsgASRZ+zpENaObvgsB2HXkEwSVTge0/NvNoDAJyq3nt8deZU5Y3Gjj4DNkniwlsGjaSgVr1MEFs7gjuA0+x+Xz0m0yZROGJwgnAhY8RZMcGqe6272mgYjZVDF+9lfM16rz0vtY1SruPOtcEJiIPEwKtBDKCGeLnLuBHdJW5Whc8BrTuA3wlrDEU6uT3AQy258ghwALMXDSV+Xa2LD43ovj/9z2VsQ+NZUBQxjKWsYxl7N/fAu2UtB4OgAQRqDgH2PsU8yviQeZWjL8UaD9IhsBVQDlim5dOuyADTe8DrjLWDqpdSgdw58/pDNtN1qR9H0NtltzNsJzDLzIMK2ekmdg+mtf5yoCLfsJQrnAv8w966+jcpmSO+xuYmO0rIzBKAR1dJZhp2Mj39z7NPBtDB9QkpYYLJ1C+O9xLAPixPxG8iJLpI0rp9gAgYTI6o1aTeWrYYCp0jQGW3ctxtO5iqJMzl/PTsZ/1a+Z+EXjn68xZmXOzyU4oHI+vnCzQ9p8SiNSuZG6FzUvwoTjprMb7geb3KdG88xcMM/OWM9dm5y8IhESJACCUZF7V8geAUweBld8hYB17AdXV1BjgKAIqJ1A2e9nXTcU9K0GBxUEQJggYUgLTEtwXksK1rFhA5uv128HwvBivH72awhHtu4GN3+F7VhcdcVchAdCJt+ioT/woQyDn3kLw0bqLACF/LMGu4iTTJEj8zMpvmSITMuvRnHiTIYuDLeyvZCUYFSTuJ11j+GLtSqB6MUPwSqYRaMcGyZRVzCHY8Dfzc75y3nvv7yiuABCAjVzKELbdv6Ss86wbyfb0nCBrpNi5rp4i/t5Xnw5jTEbShVut7rSMdQrs5dUwnNEwwZogAdA5b6n8nk3fBZzZXGebj8zfjp8Dk67gAULPCeDPVxFg155LMDX5SgJb/2kgESDQ0pJkTMtmU+xj8pUc8+bvE4S5CrmGyx80wyDN5ykZMYvYHuR3wuu3ck4nXE5wWDbLVKczgbKhE8CLCp+9wy+xtlTeqH/4V1jG/v0sA4IylrGMZez/msUCDFdp2UHHoXSmWZzzXyz3qqk83Y36CTg8xenaPwPNdOaHW+1K5jWc3kbmQ7Yyv+H4GwQvFidPdnf/moDi1BaGaU25DmgHi44WjOX7/Y1gHZdukwlw89RY14D9z9CRzqpk7Z6zHSRvSbpeSNTPfIVkNP2+btBJjpvhTPEQGabBFjql1YuZ3+HINZ3PHjq/239C9mfHk0yqr1qYZodsPp5sA3T6BfME3+ol4Dj0Ak/w9STbCr/HeVp6H+9rdTHRfaAR6NzP0/cTbwKeMtb9SYS5Pxo3cY/4KhhaVbWA85Rbw5ykQBvXqXAiFd82fgto2Q5I84FLf8k1y65mnksiyD5b3IDk5lw7Cwhw80dzTy7+OvDe981cEYN5WABDEN+5B1j0Vdb0qZxH1qNyHiWhbW4zJEzgXBROoAx24Vh+zpFNcJgIE1wdfZX7fuRyYO/vmaOVV8u2Og9QHGDltyjO4CwwC5Cu5T3HXsh9oSWAziN0qqsWcL/5SoAN3yaIsriB0ecRlCs2jj8eNkUbugji8kdTCW7yldxjb95hKtzZuIezKjgvgy1kjKwuoO0Ac4JevcV04CWCrt7jHOvKb5EVAoADf6ZowPs/YTtZVey3q4BM2dFXCLBCXemcOYuTa5ISmkgVez3xNtmTSK8pp26uzeRPAO89ClTezdcUOw8k/KcJfGtWsAhs+SzWJHLmMhywdTfbzxsFzPocBR5C3WZ4q0DRB08RGVxHDrD+m5xrUeZ+OPIy52fsRXyGFXs6b85dyP0lWxl6GeoGVn6b0u6RPj5nVleagVNjvKfFyefH4vj/+w7L2IfKMiAoYxnLWMb+L1l0gDH0R19Jv3bgGapWzb3lL/Nr/lkW6aMM8JGX6NhBAArHUSUrt8ZU5BpmoszT4NPb+Luh07mL9AMwyLjMu50AoGkzHd3KOUDpVODIa0DWJ1kAdfaNDLmyZ9FxMww6oBbToWpYR9lnQ6Nj5cj5r8dh91GWePcp/u7IAWqW0kFt2kZQ4cjiKb4oU2WreRvBiq7S2ZUsBBbBTq5TzQqG71Qt5Cl8IsRciniATr1hygRLCk/edYMgK+bnCbuW5Am5rtNZXHw382V2/YIhg1kVwNRrCJp8FTz93/5TMjypkKTBFmD9g1ToGnM+Q75Uk3VJRjiuQBtBVjzA0LNDzzKBXjDD1bS4yTiEzZo1ZXR0UzWAuo4wtKttD5kGqyudvxUPAP0dZDGql7LQ6dHXyE7Zs1hMVjJzqqoW0DEPdTP8DSYrE+4183cCBGOH/gyUzyWTd+xVhgEaOgFOqJsAesZn2K6hE4D11lHIomETZcKLJhDUVMwl+DvyCiArgL2E63nqPYKBGZ82GQ6V+9SZC1QtIrBSHEBWOQGkrnEM828HDj5HlTNXPsF1604yevNuZ7ihoZK1KZnCOeo7yc+eeBMoncH+xPwc1/RPcU5hpCWetzwKLLqLcu55o9LS8JHUsyYwJCylxjdU2EcgOE3JbbsKeHgBUDxDT5qMrUHQMfUars+Mz/D1YCfXQxAZYjfvVh7MHPwzX0u9P+ZC3kONkTGMDRDM2kyVNlHiGo29iMypofP5i5q5dyfXc22KJxNo2rP4PVe9mHOVUjYUZT7zAIUc/lXfgRn7p1sGBGUsYxnL2P8l6zh4JgBKWdN7DL8Zd/E//p7xIJmctj0AdKB4Gh3vlDOj6zyZP/DHYR8yeJr9zt3A+T9kmNlwc+YNq81jnv4P5VuADq/FAbzyeVPJCybLNEjJ3uMROkv+03TALC46QqkaNLpO4BNoZ/2dUasIIIPdJqvwX/z5HLEYOP4WHe1R51HMYe3dZG90jSFN7/+Y4UBVC4HDz5rgy0oWKBkhSHDm8f4jl3FYfQ3MM4n0Modp24+Y2K0lCZzGXkTWISXUIMp0KB05dHANMJQv0ss8EFEhcIn0cp4XfZ1rEmwnazDk9Jpt6SrDmZq28HOyjc581E8Hs/soHdaDz/N9SSbAm/Fphn11HzXlkQVAltnvSC/7d3oHi1ZqCTrcKWU1w3RU4yGGnYkKsOlb6dytQ88BMz8NXPJzOrY2L/uz/adUl4sN0uEXhDTQFCSubX8TMPYSApuT69kPwyCjIIgcw/4/AkvvJkBJRNhe1QLgY7/n4UHdOwSslfMYSmXoBHtawiyIaycgnHgFpzPUlZZEz64kE1c5j2BIM6WjfZUcb9chOvqxAOdDjVOYwzBzV+Z8kQCleSv31dRrCNiOvMyaVm172J9EmDLdzgLOiWQDsnvIgvXWMdxs6jVUptv0bQIVUTZ1HwRg9s3Ajl8C064m2HUXcr/5yvg8qfG0Il4qT83QzD2jcT5ifu7XiZdRTCE2aObk5HMNd/0CQ6pxAGs91SwH1twDzLiOz7qk8PVQF6+xOAl6+k6yL6FOzlXtqjSrveQeYN/vTQXAJAVQJl4B5I/7S1VH2UoRldRYMvYfbxkQlLGMZSxj/1csGf9gAJSyo68wQd2e9Y+7Z9RPVubIS8NefIqhbDNvpHRzoI25IB9kwU46agXjeVrdc4Kva0mGGAHsr2wBksMS8S0uyliLkhn7byaju3JZEwhuhqx1HGRux8l1ZH9EBZAkAjc1zhN+LQ7s+jnQuJkOaMkMOmgpZan8sQRp0X468LKN7NGJN8hivXIzHWI1ZoZCJQAtyto24z7CXIf+JjIUAPupxum4Z1XR6Zt6DUOn3voa0H2IfVj4VTpuwQ4zGfx91pQZsYhhhJLC8LkRizgOxU727LVbqLIlWXiKXzaTuRLHX6cCmyMPWPUd5pt0HU6DBkOj0z3QxG6qMYJNRw7n3+ajYIKkcB5sJlt17FUKUwQ7GKIEE2hEBygCkD+GTJcxzAGO9JmqeaYCm64C7lz2IdrPdapdyXFJFvYpp4bJ98df42dEkevSvpcAIAWGBQEwBIIQ2cqcrWgfwWcKvHhKzD72c/9F/dxvhsFn5NUvmPWnksy9GTjFkEpnLvsaGzSZL5H7MxHmeu19im0VjCejlWIBw71pZqJsFvO+BJF7QRB4L4A//S0UPjj2+rD8MIOS0IWTWIvp+Bvsh6Twp9XF+Sibxf5ICsM74yGClV2/ALJHAuc/yvBPfzPfL5lOIBgf5F4YcyGZIy3I50dUuIdc+UNbl5Lqovns6RyXZCWw3/x9hqU1bSYwbt4OTLqMc9G0lXNROYfFZUNdQMIEWYrDlFtPAXODwEp3MdyyYo4Z7lfJ56njEAucrvk6MNjMOYgOAOd8luu+9F6Oo+4t5okVTzbB0VlS+Rn7j7YMCMpYxjKWsf8rpiXSNXI+yOJBhpX8I63jwFkAyLS6NcxzGbUqXaD0r1l/A0/fF9xB56bzEPNVCsbzlF+NEtgoNubVxAPMCek4mD5dTxU1TYTT4GnerXQOc6qZ7B5s5+v2bIZ6ZY3gqfXbdwEw6OjHgmx316+oPhVs47wefgGof4dOnGyjpPaUqxi+Fe4xmSPDFAooSTMZ/lPA5KsoEZyI0Hk0dAACnbMpV/F+VifD00atYnhdoJ25M6NWAwf/ZCbHCwQe0z8FJKJ0lCvnMJdj2+MARCb5B1rpuMaDQHYRQ5Le/irfz6rgHO3/PU/RdzxhnqInODeGTnAAId1XxZYu/Gpzk93RTEUwCOxb7Somsu99Kg1IZTuw5F6zSOoMOven3wcmf5xhewBBD0Dnv2wm95NkJaBo2wtseID9sHpZ86a3jjkdyRjgbwPm3MK1HGylI59itCQLMO5SrpUjxwxDE82fJnvhNJP1IaTD6UpmkA0ZbCGgjPaZIggKf6bC6SxO83Om4IDdR+d7xg1mfSIvhS5Gn8tiq6mcKD1JIJFizHSV4FK2Mm9PlEz57HL2AwAMMV2AtGMf+2kYXGOA6xUbZHu+MsBVRAAR6iZbKslAcJCs1J6nCQATYZO93ZtWxDvwZ+Da19nXujXcPyVTCapya82+wAT7JkM46lw+r7EApd9zqpmHp1h53bSrgTV3AyOXkHEDKNcd8/OZ0HWgZSfl3uvXmcp/w76jkjE+w55iyqu78lj8tGZJOt/IkcW1LD8HsPr4OW8JQ0fHnM85tmenvxcy9n/GMiAoYxnLWMb+U2ywjSEePcfpoBVP5smoaJ4wW5x0WobCyM6ygvH/2KroySjVyFJm6OnTeIB1byrm0EmTLGlH+2xz5Jjhcs+YifYjWAdETTD8a9vjAAw6uRCY1D7tWuD5680GBJ5UGzpPe09tBoSlZkV6lQ7cRT9mzsapzUykLhjPJPiuo0Cog45eSo5ajTPcpn4N+96+l32TLWaBU1AlLNzLnBWLk3kwAJ3lUA+dNS3JE+7BduDch4H19/P0W9PJuMy9lcxJ+SwCpAN/AnY+yX7kjQamXcf8j3GXsIZK+WyGM/YcAy55gj+D3WS5BlsIOiQr5yHYQZZkzAUULYCQVtiyeQjcdjwJjL+M0skAHfdEhHOeCNJxDHUCp3eyPdlGhbLm7WTidN38TJhhY6u/b4YHmn3JrqQz3XWEcz72EoJJZy5FAQ78Cehv4b1n3QgUjQdeuoFg6tRm1mESFTrG3hIWvgz3cL4r5nJvHXmBTn7VPCDnWmD7E2Q8pl/Lsb7zdYoxuAo4hlQRVNEErZ5irlMiRKe+eBLzywA68alin73HgXEXMawuNpgGw1YPFQnDvcDWxxj6ZnERaPSfYk5YzQru8dY9ZJmKJvO1LT9kvppi5xwmwgR4VQvNmlAm05ZSBgS4T4+9zpyiUBdBo7eU961eAhx9HZh6FcUfmreRwZp9M/DeD4CACUIXfoU5ZXqCz7CkEPDMuy0NPMvP4TNQt4YgbsbngV7w+0MWCa5Gn88cs92/Ng8oShlutvZ+spkWN+XhL/oxGd7WPYCnkHNQv5Z9tDh5CLDgtjQrnDJBoIph81bu62gfMOcmwJ7Lfr5+HnDlnwmAT22mSMPyb5z5+RSDlbH/k5YBQRnLWMYy9mG1qJ9/4KP9zJfY+K20hHQqFGbp/WRRRJH/apbTgUolnKdMsrBYoWz9x/VPTdBJU+MEKKnwGbuPDmIq5MxTTMfu5NqzGjDoQFqclJGuWcbPHH+DzuPOJxnec+73mFMS6WURxfwxdPDtWXw/u4qn3mqUJ/xHXgLGL2Uf1CAdzWevBS58nMnqyShDZ7JGUGrY5qNTnFLDkhS+X/8OcN4jwIuf4euCTOaoeBodyPYDvLb8HCb/p1S2LA7m98g2vv/GLfzM+T80k9tBQJhTDWz9EbD4a+xfbCCdUN7XQCZh+f1sN3ck52frY3T0jr3CYp3JCEGD1UPmSLYSgKSkhHWVAC3VbioEy1NCp9OVByguwGHjPBZNYrHaQ89TfS2rknVyBttYvLRlB9uLBwjwLC6Cm6wK5pv0NxEsTrqS49n+BOfKMAiKF32NggcVc4Bzv09G63iE+9xqp+NfMYcqaqlQMU3lvtLBdRm5jHkra0wGL7Xn3MVUTmvdTWYi0k+mwp4FTPkEQ/ImXEGhAUFK5zx1H2NYY7iXQEQU2WYK5GgJsg/lM3mtqJDZsLpYE2n0amDP77h39zxFgGJxmEVpTdYmtxb4yJPAhm8SCObWsHbOpI9x3uw+PqOOXL4X7GAbjmzuV0Pn+2qMwLRjPzDp42RPbV6GtBk65+OtrxKIls1gSGr9WoZvhrq4/4IdwEVmvlnLLoJiVwHB3Z5fce80bEg/pu5iYMdPgeoHgfN/wBC2YGc6pHHatdxPshVY/wD7IFnJ/qVktt2FzEk8/EL6OVXs/IxkA5IJHgp0H+HBhM3LZ/v0NopPVM4DJn4MCPWSWUpG+F0X6KD8PR/QjOhBxs6wDAjKWMYylrEPo/lbgPceoerWiCVA226e+nqKTJnYAYblvPYF4PLfMdZdttKBOvc7rN3ReQiAQeZk1g0ED/9Is7oY91+3BvRQASBGFsGRxzwAm5dO8fRrCVQ6D5jXGQBEYOon6dQE2znO6sVU6UoVjmzZQWc/t4Zj7zzEROvVj7D+TONGJojbfWQT1t6XrgXkLgRUJ8GkKNIxjPgBNUy2qesQndjBVjrbnhIyDfastMyuoRFQ5o/lGI6/AWx9lE5x7Uo62/Nup1MY7mGfQ6c57pUP0XGdfwfzefb9nqpxiZDpXCcJck6uI2AATBlj80+3rvIUftq1FDzY8SSd3vcepTPuKWa+VdcRsoKzbyJLsPDLdMYlE/Cm2rNn02kURDIr7iKOecanGUZl6MDxVxkWZfdx/mIBYPdTDC3SzNCtqJ/3jvmpCGZ1Md+jZhXnUbGRuYr0c872/paOvK7RYb34ZyyqOmIxsO2nQM23uc6FozlfyRidZ1FKs3OhbkpXH36W4OGNO8y8FJFbKcXm7H2aKoi6SlYh2EEmccxFwIU/pZrZhm9xvryl3FsWF0MWx1wMHH2JhwoH/mQWWj3NtSoYC7zyRbIa0X4CuliA4CYeBObfZoaFDfI5TEaAdQ+Q6ZMUzmXhJDO3pZlzWL+WjN/MGylcMmIh60klImT83v8p5wLAUMieYTD0sHIBwVNeLZmvqVcTgPQ1EFwkI2y/4xBw8U/ZlhYjOzP5Skqs59bwfq5c4LlPMZQyaSo3DleKU+zpx3v/n4DjLzHXzZnLe4oSgdSszxFMhbrICGlJAlVJ4UGHKJuFdrcCObXAgi+bQDyfe/W9R/iMZlVwTrf9iGs65SoCMU8JsPHb3GMF4yg8sufX6e+jijmUgM9YxkzLgKCMZSxjGfuwWSLCE/T2ffw9uxLY+QSdtVAXHRXBTCgP91DNKxYERizga4UTgFXfNmuDGHRW7L5/fD9jfgIrxWYmNQ+zeBAYs5qOUH8j/025CjA+zn4pdrIL+//I0/Kl9zKk6q2v0qnOqiTIiPopWdxz3MxdiDLZXrYB79xLhzTmp9M7cjnBi+RkH4IdgMPHe2kJgsjRF1DiuGkrhSImX8mE8HGXUjDhxFv8rKFTcCBuKndNv5Y1WlQzFyYRpnM/eJpAYPUjZKuatzJMqGQaHf6WnQRTI5eTgbA4qdYlSAxvy6qiSl4qaV4Q04wSdOZLGQbZnfyxPKGP+gEY6bA5TzGdb8VBsGL1APO/RCEEVwHBj8VJh9LQOL9qnCIOgsCwrcIJfO/wi2w33GsKLeRzTrf+iDkdpdPTIVkF45nH07iRBTQ3PphmA3NrWWxVsbPvtiyT1UmS5SucSAU90czTyKpi+OH8LxFEpPKNRInhT7EB7qGJVzC3JrX/U3Nm87KfLTu5djufZB6YxU3GwJUP9NVxf025koqAu37Jz4sK1y1rBAGGpFDxLaUu5qsgcBl3ERm4Y6+a66QxzOvYawQaO57guix7AFh3n8ng2rgGukawPmIRVf6OvcY92XWEct+jz2dYZriHIMCZT4AX6uVet3kJBL1lXIPBVh4WdB7mPLXu4kGJbqq2pcQRBInS1FYX0HKYwLTjAIU4GjawNlRfA6XA+08xzHB4jp9s5ZiCpmJb12H2ceeTBCETLgMOP8e+NWzg90EiSMGFU+8SbLYf4J4MtBG4rfoO6wkdf5PruucpAuj5t/PZqH+H+8ji4h5y5pFBG3cxcN73zXnpArb/nPtYspAdnHo1c9YyljHTMiAoYxnLWMY+bBZoY+x+ynQNTNy3AoEegoDhTmBskInueaMBtxkDb3Xx3/+mBbsYNrXsG8C+pylPC9ARmnI1AJHg7ehrDIlKST0vvpvSu3t/S8d19k1krvrq6fBrCTpNe37LopGiRDZpxGICCncJQ5uSIfNaNx3FRMhkb0xAkcqBmXwlHVFfJa8TRcBdwHyD0hnsb/1aQAPHotiZQ5FbSwA2/XozjCfOdlOCAaJIxiMZpVOWN4oMSzwAvPRZk8WAyRw8S+ZpyT1c2xSgCXcTxHiKeCqeKgYKgeF3eaPIvrjyub6bH2abvgrgnM9R5av7GEFjVhXDp35/KcdQtYjO6biL6VzKVrZt8wLhfgoUHH8TEAXg0DMEiJ7idCFYPUnmSItjSNHu9A5gxvXAyKWc+4EWMpP7/oAzJLe7jwCbv0dxCcNgm7UrycBlj6Bj6ykBJn+ShW2tLqBkDoGVrgFl53BsgsT5FBXOw5gLybi4Cs3XZYL8ZIz91RJkgEqnEyyeepe5Ux//M9kdSWbIWcOT6b7qSfav/yRrQPkqmY/Vc8zMuSkDckYQnLxxuymzjjRghUFANfo89u3gM2YInYVgJNRtzqlBlbbpn2Jo6rbHzdDHOqooDjQzp2/dN3ioMO925rqc2kSVt0lXMD+sbT/v0XuC8zFiIXDoNROAymmlNJuPwLZpK7DqW9xL7/2A4Gj/H7j/+xsZyrfiATKMYy9gvlA8SFDtLmS/nEWcq+Gy03VvU23uwDP8PdRNFs5XQRYnOsDxTLyCSoQ2H5/3pi1pcQxfBfdWw3qyWpVzOd96kt8Xp94jkBqxkEBv2rVmbSyVMtjJMPvpLUkr7GUsY6ZlQFDGMpaxjH3YLBFOq2ZpJniwZ5vOKHCGswkwrKdhAxDpSYOgf4YZOgHbwT8zAX/W5+jA9jfQeREECh4kw3TwU6Dg3e/wFFlXGZIliARAgmjKG+t0uLUET90nfZzO3ZYfsJ1V32FInMVJh0iN0wGsW8O8gb1/5H1GLAFGzqeD3nOcDld2FXM4on6eHm95lKpUKWbl4DPAqPOBCx9jXkrXEeCy35LVSbETukZA4Sni/4++xPyoZJQ5L5u/b66fuU6pXJxgJ/s86eN06GQ7QRhAxzCrio6jGmV4kKsAmPV5hhCeWANYbLx+8ifoyK69N504r6ss8DnnC2Rn2vdRSODkWt5vxg0MOXPkEoRUzCbzVv8O+ykIQPkcOt/JGNk1QRqm1GVw/EUTGAa240mG0FXONeXPz9qTAJ3icC/7PHIJWaZjrxJcjruY4gjP3wCM/r5ZcPRa4OCzadZw+8+4v9wlVBnb/Ru2cc5nyUY4c7kescF0EV1fOQHY9p8xdDDUSVZHjZE1KZ3B0LOzLWnmAh15ibk8e5+mslvtKq6x1Z1mgASJ9zIM/pRtZFNGrwZspxliGe5hXwZb09LgEPh6oJWgpngK50jXgC2PkRnsPEQxA10l4C2cCJxzk3noIRDg7f4FwamuEewdf4MAIMVOSRL3RMxPUJtSYOutZ+jj/t9zPK4Css7L7yO4q5jHPXnpr8iSntrM32tXARULgC6YQNrHz4d72A+bm99ZMz5NYNO2h+uuqxxLwybmvb1zL9dMjQO9DSxseuRlc+8IzEd76SYg7j9zjk+/zzUJdqbXS5LJkGcsY/+FZUBQxjKWsYz9oyxq5m00bARc2QQf7sJ//H1SDE64l47M4ecZg3/oWdNfGMYClUynGpmupUOq/lnmzOXpcsVcOrHN25gLNGolUDSFoUNanHLPwxWuAm1MYFZjlMDuOZFO4gcIAGQrxx4P0RHsOgwsuouhNhYXx5uIEMykQEmkj6Bg8pVAEEDVfODtO9P5PtF+oP4knfgl99Dh7DzMU2QtmQYgx1+j2tisz/KarkMEUMkInTJJMcUgwgQGjlw6u/0n2UZ/A5ms2pXMuzBMJ7RmBXOWek+w9sm4S8igufLJrBx5mc5lyhZ+hXkSgTbeM6KzP1lVBAmRXl6XqtNicwPvfhdYeg/vl3IuDz5DEH3ZUxxnuIvhk2MvojN5/E1emoxyDRw5nIehfBRw/pz5HO+JtxheWTKNJ/ILvsI1PPw810sAwa2oEJxMuwZ4/TaujyOH+3r/HwnCJlwOJEEp7be+QtbB0MgqTruG9/WWUVkvbIKqSB8V3wYaef3p99MM2vRPpZmJfX8gE9V1mO1UziUb0b73L/eyr5J5JlqCYxm1gmCl/xSBkxYz5afzWY8rEWRNKUHimEKdZuHTIJmLwRbukdS+T4H7vFHs86l3KQSw8SEyc0UTOPdHXuTzIsoEoGqc45MsZC/HXsI1TYFTXSNbOfcWghZDTd8zBb6mXEWw0riRBwOGYda8ChNUOrIIME69y9pXBqhYOP06rmNvPfObJvyY7FTMXANfOQHtiVL2x11EUQo9adYvMvP6Yn7umeqlXFdHNvd00UTmefUcAwonMzdOsQIJ89BguFT24RcJ8B25//33UsYyZloGBGUsYxnL2D/C/KeBjd8DxOU8oTUSDMlZdi+dp3+kOfMpo3voz/y96xATs+fdyhPjmN88oV1JZbK9T1NpLFU3JGVRP50VQeR7Fsff1y/DzA2IBwg6HLmMw3/jDjo9OdVA4XjmOwgify+eQgnm4ae4iRCdR1cRT+CtlWbIn2l2H/N57NlpCfB40Az7qgAKJgDjP8oQr2TMDGkbTVBq6EDhVOBQL9kWpylVrWsABAKqCR8FmrcwlM/fzHvafKaQQpzO4Yk36ZgrTrJrgQ7mrCTC7JtonroLAh3G6Ci+33uCeR+V83nivu9pgoGZn6FyWqSHp/PTrgE2PGgyGGDI0HxTYMGZy3627yODlQyn6/T4KnhN9xEKXvTWmcphCu+jxbjmdh8BmaRw/NWL6QRveYzXiDLbGnM+cNWLdEr7m4C5tzPZ3FNCQODM53hGLiVr4avgvjq5lvLTmsr75Y0isNz0XSbJh7r4eV8pJbq1OPeDoaed82g/+3U8TDYxHiTIiAfIMmx+2Fzrj3DetQRFFSx2AoncWub7TL2a4Va1K8nW9Dewn/EgQYyhM19GVNjGnC8AOyxkhOxZBBCxABXXHHlkdtxF3OsjFpKB7DzIMM5wb5ql01Xu8+gAkD8eCHSactXzCQg11ZSFFwhidJXhaxse5L6VLBTzaNnB/rZsJsCHyTBBMMMlSzgG2cxtE2X2L6VkFw9yvquXcA8C6TA9bznBW9lMoGEd0LaLSoP9p7jnbV4qxulJ7kXFzkK/nYe4/3zlnGNvKdvd/H0g3k+wBYHs0awb2O9jr3Cvugp4CDDYahbhNch0Lvwq53HMhQxhe/YqoGw2mV3JQtbY6uFhQzzE9mH+CLSzWK4o/n3fYRn7P2UZEJSxjGXsX2/JGMMimrfxD23JVOZheIr/1T372ywRArb9mCfEBcvTr4c6KWN8weMMjfpHWaiLoSHRfsbKGzqdxHm382Q70MbrOvZTuUuxm6ekWXxdTfK0e9cveSI95kI6e/EAQUFuDRkY2UqHT0uY9VD+CyYpMgh07gMOvQj0HuPnZ32OzsrcLzJZ3ZFjChAc5Cm9q4AO0OK7gA0PAdCpZJVy0ObdSnYrq/JMBTMtka4iP+1a4OXPs/1UyJWrkHLD2SMooysKDOva93uztsszQNlX6MAd6eD1apzMyczP0ClPOdYAHdlEOF07RkswH0eUGerWcYjqXx37WOskFZokypzbU+8xx+eCx4Cm98navfRZOoCiQnanaiGr20cHmO+SErNIWf1aviZZGVbkbyVosbrM4qUG1zU6QJDR/D4BcLDDVOAzE9hFCxkd2ZrOXbJ5+LzteSqd35PKGzn4LB3/o68yRG7EAkorN2zgeiajnNsTb5GJmHoNne76tbwvwBC/7mMsMDv7JuYCqXE6xhYXHV9BSMuQh3tNBi2HoVMYzVCyvGoCnFiAOSuBdgLC1t3s8+ofAi3vc1/rKtuU7cCcL5JVeeXzHJOWIHhw5hN4vXITn9XskQRsde9QldCRQxD23qPpfCxnHhXy5t7OkM0NDwLdR81aSG/xc7t+Rcfc6jHr7FjItuz9HZmNdfcTQB/8sxl6Z5ihjTcCjZu5LtkjCNwb1pHZO/EGFSEXfY3znaq5lVIntLhYg+nISya4MNc1GSeruusXrPNTMZvMUDJCtbTCCcALn+GzsOQe5vEIAp+9lu3Mq3MVEPBoSbOGlJnD5MzjelXMBUQn0Ad+h+gqX/eW8Lk8uZ57MRHm90GqkKsjx5QIH7ZWK7/J8eWP5f366oA1m6nI58ojm2r18vAhHkh/JxRP4f7LWMb+B5YBQRnLWMb+tZaM0YHa+hiGnNjjr/NkccW3Phxx3YPtDC8SlL98L9RNNuEfCYIivcyzKJtJxiMlOxwPUtK3bCaryWsq64yMXEqp6pR1H6Y0rSAypGXHkwSfsYF0Ac8VDwB5Y5jo7j/NNj2lBEJdR3ianl1NRyq7GmjZBrz0OZ7EKg465DAYptRTR0epaCIZiMV38fQ8Fa519FXgY38go9W2l85jbi3Q1wgUT2V4zPk/BDZ9h59JCRBM/SQd3lBXOuEbBlmCd79HwYK6t+k4ndoMQKCjlFKHc+QCi74O7H2KKluKk85juIchPiVTWUAylfsS7TfrpQQIGpq2ELgrTjpmU64GSmcSYCpO1i5RExR9UGx01mZcT2ABwwznEcjw9NZRBlxLUuDg0J9NZ1LhtYZGpy8eJPiZfTPZp1QooSCRBQPYpuIgCHFkMyxQVwl83AWc26jfZNvygemfobOsxgh6FbvplJr/b9nOnA3Zxrlv3QVAYhjZoef4DKfCE9fdD4y9GKheBvSdSAOtVI6Izc3fHdnAwq9xvJOvpOPesoOOtaEBmsZ1TdWtev12wGIhwM8ZwRC79Q9wPrKrTensoKnAZs6BAY7pvUdYPDa7mk61bCfYqVlhqg/2E+ilmDG7l477kruB7T9l2JvVw/2SjHCtd/yU+6O/kWskiPzMmAuYn9K4ifNbNJl75tirprKaGc62+9dUuhu92gRlvQzp6q3jek+4HNjxC7Y5/w6+n4yS+Rt/6ZkKbbpGZrB4CnPT9CRBZKSPhwjzvsSwun1/JNubM5K1n1wFVMNLtaurnJOEydbsupPzOuNTfI4Ak6UzQX6om+xN+35+v/WBcyd4Tbavm20df4PAvmwm/6/G2cdEkOsNgWAzu5LgfbCF9ZtGncf7puoxTbyCADUZ4pxb3GmBl6lX/e8oXGbsP9oyIChjGcvYv9YGmqiCdHbi9GArT6YXfY0O5N9hSVVHmz+KU71hxFUNlTlOlGY54LL9g74CEyF8YOI3AGRVokv3It4fhk2WkO/hWBKahp5gAoZhINtpgcPCviRUDZ2BGFTNgKYbsCkS8txW2BSyMMFYEm1GBTpq7sTkEi9cfXshH30JQssOGPljgClXQXDmAUvvo/Mt287MEUqEEerrRM/M+2DxFiB330+ARBySmoSsJaHbfEhYc5Dc/jvEVv8YveUfQ2XBCYj1a9Btr0WkrQlWw4Gs3g7oh9bAav8txGX340AgF/J5ryMvxwcLNGQrcShv3AYMNMFYeh9Pj4+9DiHSR+dx/peQPPwKjPZ9UHrrgM6DiJ7YgP7SZdBkO7yNe2EdtQThgV5Y27bC3b0bmP8lBJQ8dEsFUGU7smwiCurWQPVVIWmmOUjQIRkapMFWQI1hoOYjCHafhlR+EfKbXoXSthPGzJuAXsCoexuG0wdj+vXQdB1G0zYoKRait44MQuO7dO4BE2wIrGpfuwLq63cAkCHPupFhRL31wGALdHcJdAMQuusgZZUxHDHcw5Pv4sl0lDsO0KH3VUI1BITypiF67m+gRQfgdbkgC04MeEfDYsSQfep16D11MCBA9NmgOgrRZ61C/sSPQ970TQIVRw6BjRqDISnA9Ouh6gBUHUoyDE12QhMAcdRqGKWzIc24EWLZTCQhQuhvgNR7DMY5N0OLh2EcegGSJCOh6VBVHdZ4DLLihLjlUTICFgeMVd+Dsf8PEFMhf7oGQ5RhiDKEvU9DWPRVGKe3QlBjaZDmyIZq8WLgk+8iS+uD3HuMjE6wg6GGyx9gkvxgCwBAzx6FgfLlwMBJHD//RRRE65DTvRNaoB1K224MXPR79IXjELOrUKS1w7LjcahZowHFATnWBynhp2OvJYDDL0Cb/mmIWx6B0HWIgL52FfDcNVxvQYRWMAGtU7+K1rgN3T09KAgXo6r8QhQF24GB06bjHiLoM3TmyNg8QG8H4MiBEQtAb3ofQutuiPljgemfxIC9AoOCB2r+SvjkOHz9jYCqQYr0QVj/AEHxzp8DgQ5o7mKonnIY1UuRLJkPh6RAqnuLYCanBojuYh2eCR9lgeCWHQR5NVTVMzY/DGP+l2CsewCiGoEgWRjK1riJhwydh8isOcww0u0/4/csmC8l9J1kaONgK5mj2CCvPb2dLNWuXxHsSQrndfylBFQtO0w2biZBk2KyjYlQOo9PT/AwZcRCHhyk8hND3TwcGHUuQxjjIeCczxNcZlWQndISfLYsbta42vIDtm91EehPuQoonfX3fINn7P+oCYZh/JW/3P/+FggE4PV6MTg4CI/H8y/rR1LT0TkYQ1zV4LTK8NkV9IcTCMZU6IYBt01BkdcGWfr7Y1V7gzEMRlVIooBCrw2hmIrBaBKSKKDAa4Ndkc64NhhTIQhAUjMgAFBkEYIZSxyLJ3Fs17tYsmwFbDYrOgZjSKg6XFYJhV77/3cfA9EkekM8qc1xWeG1K4gnVTT2RBBJqrDKErIcCqJJDfluGzx2BZGEiu5AHJpuwOdQkOOyoicQQ1eQ7eS6LCj02hGKqegcjCKa1OC2ypAlAf2RJHTTWbTJIvI8VvQE4whEVSiyiFyngsGYimBUZQ1AuwJNMyCKAjTdgMsqIZLUEYolYRiAwyrDJonQAeS5LegPJzBg3iPbaUFZtgOCIGAgnIA/koBuGIgmNBgA3DYZhm4gktRhkQUYhoBoUmW7FmnITXYoIgIxDXFVg8Miw2OXEIlriCQ0aAZgU0RYJRGKJGAgHMepfVswYuo8uGwW2BURobiOhKYjqekQBAF2iwhDByIJDZIkwG1RkNAZWx9XNdgVGarO/wsAbBYJum5AN4Ach4KecAIJVYfdIkMSAU0HrLKAWFJHLKnBIouwKxIUScRgLImEqsNjkyGLIgIxJqfaZAmSABgCkFB1qLoBRRRhkQUkdQMiAFkSEYpzPlxWCbrBGnuabiCp6VAkARZRhGj2IaHpkEURCVWDTZYgSgJUjW0bugGbRUJM1aFqBnw2GboBdIXiMAzAZ1fgUESIggDNMBBOaEhqOmyKBKskQBK5xv5IArIowGNXYBicE66DAVkUoEgiBAGQRAHhGF/3WGWohoHuQByqrsPnsMBnlxGOJdCwdwuqpsxFlsuGUFyDrgNWWURS1xFNalBEEU6LCAFA0hAQiqmAYMBnV5BQdYTiGiySCLddRiCahNsqQ5FFGLqOYEJHIqnB61QQjWuwKdJQXxWR/ZRFAbIkQtV15jcnVWTZrfBHE9B0Aw6LDNFco0IpAPsrn4LoK0Nv+SpEJQ+sehS5bWuBjkMQltyFrvx5CMa5B3SNSMNlkxFPGggnVOhg362yiM5AHFZJhFWRoOlcu1hSg2FocFkkBBOcX4+Nz3xS1+GyKtANHbGkjq5AHG8c7ECh14Yp5T7kua3w2GT4o0nYFQm6ucfddhkOWURU1SEKgKrr0JJJWC0WAAISGvdnU18Erx9oRZlbQIHXiWy3A7PzYhDXfwPr3JdhScsPIINOml40GYOz7oBfzIJu6BAgwmmREIvHEI8E4VD9sDuciFjzkNAFBGMqkpqOLIcFFlmEP5KEyyrBgMBnRhLgtslIJFXAMKBDQFzVIUFDod6JkLUQ/QkZFkmEz6lgMJocSoUJx1Uc7Qgg12VFrssKWRIgCgJ0Q8dgOIEctw0QgDJxAK5N90AZOAkxwBAowzBg5NRAn3INhIEGaJ4yqAdfQFx0IFCxAoetE3FKy8eqcUWorz+Gd+v7ocgCVte4UOHSELTkIW7JhjUxAEfvAeQ0voL4yNU4nr0YMHTkSWFk7XkM7vKJwJ6noIo2DD8AsAkqMNAEYem90Lb9FKpIWWJRtiCUFBBf/hDqpZGY7F8Hy75fQ9QTkKFBCLYDEGEsvQeJw69C81Xh8Kgv4De7+3Fedg9+1ezDivHFWFCbB5dFQmNvGL99vxnNvSHUFLhx58I8oPMg2lUv+iIqijwKyhKn4D36B0jJIBR3Ho5MexC7GzqxYIQLI/QWiLnVwK9XArm1OD3zXpyyjcW3365Huz8Ct1VGllNBTqIdX1tcjDFbbyVDZarMGYIMTL8OetteiB37AWc++lULwrEERFFEttOG0CW/xbvdNry0pwX1fXHU5NpxabWBKcJxOPf+HD6bCKsaBqZehWjhdHQF4+hVnVjXImB/ewSXTC/HudpmePY8TuZv7f0MHVMczNdx5vLngjug7/0dOo7tgOqtRO60CyEHWiAF2yBllUOonAejfj2EcRcyJ8vfREBi6Om1c+QCC78KwV1INq2/kSyb3UfmqmIuwUaom3PgLSH7XTwFWHs3kuc+ijfbvDiv8X4ogkZ2KthBgGlxMlR355PApCv5WvcxIDJAgZERi8imB9vZXjIKbP0hGUdBJKO64MtUuosMMHQxbLLInmJGDaQYw4z9t5ZMJvHmm2/ivPPOg6J8QPTCh9z+J9ggwwT9ndY5GMWzu1uwua4Xsijg4zPLEVM1NPSE8dr+doQSKkp9dlw4qRgfmVaKAs//34l2JKFi56l+/H57M7oCcQgCMLbIg7kjc/CH7acRU3VMK/fhqtmVKPBYsaOxH+uPdWJCqQ/P7m5FU28YLpuMimwHrp5diWd2nkZzXxA3jwS2NfbhYHsIOxr7EVd1+OwKLp1WisWj8uF1/O0PiK4bONoRwFNbT+FEVwgAUJ3nxMdnluNEZwCPrT+J/nACbpuMy6aXYfWEQvzqvUZcMaMcL+5rw4EWP3QDmF+biwnFXjy1rQmH2gYhCgJWjS/E5dNL8eddLXj3RA9sFgkj85w4b2Ixnt11GgdaBzGmyI2PzShHIJpErtuKR9+pw/LReVgwugA/e7cBe5oHAAM4Z0Q2bl48Em8c7MAnZ1egsSeGZ3e3YktDDxRRhCQKuGhyCWQR6AzEsWhUHh54/Sh6gwmMyHXglmU1yHZasf5YF6pyXfjDztNo7gujwG3DzUtG4nDbIGryXchyWvD77c3YfqofMIBJZT5cM7sCNkWEP6Lil1saIQnAHStHoSsg4o87TmPLyV6EExomFHtw1+ox2NHYj7VH2nFDFXDvK0dwweQyTC33oaEnhD/vbsXupn44rTKmlPmwYlwh1h7pRGNvGPNqcnHR5BK8sq8VK8YVYiASxot727C9sQ9WWcSkMi8umFiMXJcVT29vxvbGPsSTOmRJxMdm8B77Wwfx+/eb0DYYg8+h4LxxRbhkagnue/UIshwWLBqVh3eOdqGlL4L+SALzqnPx6QVVeGbXabxX14tAXMXkUi8un16GylwHAlENv3u/CTtO9QEQMLHEi+vmVWEgHMdPNjYgktCQ7VSwoDYP500owhsHO1CR68RbhzowuzoH44u9ONkdwmA0ibXHOnHtnCq8e6IHB1v9cFhlFHqsWD62EJvrerDlZC8W1uThhoUjYJFFPL+nFa/ub0c0qaHQa8PV51RiZmUWfrTxJOq7QxAATCr14lPzR+DV/W3YXN+Ldn8MdouEy6aVYsnofDy+oR57mgegSALmVOdi8ah8vLivDfVdQXxmwQj0hxJ4r64Tn68GPv+HfbhgShkW1eZhMJpEfziBp7c3o64rCLsiYWFtHi6fUYY/bG/GhuM9WDomH7NH5OD1Q+041hFEQtWwaFQ+PjajHI+urcMFE4thVSQ8vr4OX189Fi/ta8OqCUX45ZYm7G3uR08oAZ9dwRUzyqAbBsqznXAoIt463Inr51Xhh+vrsamuG1ZJhEUWccmUUnQFokjGY/j04h9jR3073tjYgnAsAKsiY/HoC7F83FwILS04EvNDkQT8dFMDrpxVjgklXrR0BPHSvnZsPNEN3TAwutCNK2eWozecwFNbT2FqRRZuXFCNV/e3oH0wimvnVOKH6xtxrD2Azywcgf0tfvN5NyBLIs4bX4TBaALv1ffiswur0R2M4b5XDuNT80bgZHcI0yqz8PrBDuw7PQCPXUGJ147PL65Guz+Kpr4IXt7XhnBCQ67LgjnVuRhT5Mb+Fj8Wj87H+LJs/GnHaZzq7URNgRO1NQeQ5W8D3EB3KAGXrKM/nIDQvw16zk58aX8hLpxcjAOn/VBkEXNH5uLxDY2YUu7D4lo7BCGEx9bXo90fg9smw2uXMabIi0unlmJXUz/+tPM0OgNx2BUR544vwidmleN4ZxC/3tqE030RFHltqM53YdW4OJ7f2wB/JInZI3KwZEw+frm5ARdOLkU0qeJkTwiqZuBHG04iGFMxEEmgItuBK2eV47ENJ1GZ60JVjgMrJn8VY/vWwjjxJlQd0CoXQHMVIb7rjwiFglAueATvj54G2WKB1enDn955H+fNL8OXnz+ISHAAUrQHxdletAg1ePz9HrQOdKAv1ISqHAeumzMLoxYsxov7OvH8m3uR1AwUeuz42qo7MdfWgL4wEI6HoRsG7IqELKcFcQjwWd3QJSvaIiLiagKCpECWNeQVleNgPAdGohXR7b9CIJlAOK5BkYB8Rwksegzy3t/DWHg3tiZr8fWX6hFPqjgvGzjRFcSRzno09ITx8Rll+Ozv90LVDTgsIm6fVIK6gIHfb9PR01c3BCbL87y4Y+6XUbDjIdiKpuNEv4GXD/fj7WN9uGtxPiYnIkDWCDTPvB9re3x4va4BzX3829UfYb8FVcIj73bgoRk3I//9BwFBhKob0HQdUuFk6Kd3QdUMSNFBWOyFGFB5AJaoOQ+bumy4/7Wj0A0DSV3AnmgSff1JzC8pwMdHXojwkRdR4nGgVyjCnS+ehl/MAiIdMBw5ECQL/rBmG5yrZuP8czTmAy3/BsNgY4MMjbRnAfNuR8LiQ8uBLYQz/c3oWPMoZG8RJEc2PL3b4PWWIHxiHayuQig5VUDTZk6QIGKovpgjG9DiMF6+CcKSr7M47LFX0mqEkkxp762PEdToGsHM6NXAqu8RmAGmlLhB2W3ZwnDHiVcw5HmwjZLz06+jjHvTe2SSnrue7efWMAQu2EVlxKlXs4huThXzogDmNaZyGzOWsb/TMiDo77CBSAKPra/H4TYm5104vRTv1vVAlgS8tK8NkihAgICmvgie3dOCvnACtyyrgdv2P0fee5oH8Mg7dUO/B2NJvLK/HbuaBnDNnAo8t7sVO5sGMBBJYvXEIjy+vh5Xz67EN984imhCQ0LTEYqrEADc+ud9+Oq5o/Ho2zxJeXp7Mw60hlCcZYdFEuGPJvGrLacQVzVcNq0Moij8lV6daY29ITzw2lFEk2kVpxNdQdz+7H7cuLCaJ50AAjEVv9pyCt2BGD41rwpffGY/rLIImyLBbZNR7LHjC8/sQ77bCgFkCcYVe/D5P+yF0yrDoogwDGBbYz+2NfThrtVjcbg9gIOtAbT76/D5RSPxu23N+OzCalTkOnDj03sQT+qQRAGqYeD9xn4c69iL31w3E839EfzivVPY3+JHnsuKzgAlX594twHXz61Cc18YD77ei6tmVeKRtSfQ6o+hP5zEI+/U4XOLRuKelw8jrvHEfenMfDy17RSqcpyYWObDva8cQU8wPnRGuqd5AE09Ydx9/hgE40mouoEbFlZDkUQ88NpRNPVFoOq8ujzHiV9tOYVjHUE4ze0SSxBEdAwWoN0fxc5T/RAARBMatjb04VRvGNfPq0LDe43YdKIHR9sDuPPc0djb4sfv329G+yDHpiY0bG/sR9dgHNfMqcTao12wKxJ6TPZu7bEueOwKfri2DjFVhwDAH07iuT2tqOsO4s5zR+NwewDfevMYDAMo8dmhSCKOdQZwx3MHcNU5FdhS3wcBwO5mP6rzXHBYZXzz9aPoCyeg6gZEAdhzegDNr4Zxx4rRCMZUMkQwsOF4D451BPCxmeX4/tsnUJrtwKgCN55+vxnV+S48ta0Jty2vxZPvNqLNH4UiCUhoBkIxFTubjuErK0fheFcQx7uC2Fzfgw5/DK/sbx9ah3Z/DI+uq8ONC0bArohQRAFJzYDHruDHG06ioSeE0/0RGAYQUzW8cagDvaEET+J1wOVQ8M6RLmxv7Mety2pQnefE3uYBrDvWjWIPF8sfSeDJdxsQjCRx7sQifOFP+xA359LulLD3tB+b63tx+/Ja1HeFMKMyC/e/dgQAUOi1oWNQxTtHunCqN4zPL6rGQ28ex6VTS3DHilF4+O0TuHV5DR58/Sj8kSS6AjHoBvfBD9fV45ZlNXhxXyvOG1+Ea+ZU4t5XDuNIexD5HrK9iiTg8fX1uGHBCOiygD8cCmF7fQgI8b1oIonX9p9GT00hrpg+Dnc+cxhfXz0Gs6pykOeyYf/pQTy7pwXHOpi0LwA41BbAD9fV46bFI1GZ40Cey4ofbziJpr4wvnHhOHz1hUPoC8dxzexKvLyvDQdaB5HnsiKSVBFJaHhycwOumlWBqlwn7nnlMD6/eCQ+NrMcrx5sx6JR+fjJxgZ0DEZhGEBCTeCiScV47WA7shwW/HpLEwCeZ3cMxrDuWBea+yIYV+xGMKriF5tPod0fBQCsrFSQrHsHrYEoUAY4LQq6/YMQBAGSKMBy8k2MyvssHnnnBL5x4Xh847Wj2NXUjxsXVCOh6egOxvHrLafgj5L99EeTCMVVJHU+Bwdb/Wjzcx4jCQ1vH+7EzKps/OK9U2joCcNjk9EXTqC1rgf7Tg/gjpWj8Ni6k3j7SCfqu0O4Zk4lOvwxPLm5ATcsqMY33ziK3GHfS019EXz7reP4+uox+ObrR3HNnEr4YzpOH96K3Mp5CMSSCB/fg2ytFx2DUUiiALFuC37dOg91XX3IsXfivvNmYUdLGMfbB1DoscBiCDh39kR8a00DAjEN+W4rRBho7A3j8Y2N+OLy0dhYPwDdEAAY6AvH0Z8QsDPmQYlgg24QNESTGhKDMRR6bVALxiOqWxFXyRzqEKG5i1E34Q70GD5MxUkkEgn4o0kIAHRDQEtAhSRa4NYS0Dxj8MAfTqIvog59/wkAJEHAhuNdWDwqD4b5RE8s9SGh6nhi6ymoSesZUamnewbx870WfLF8OcIVy1GEPCCyBTEAv9gBfPvcCjgXfx1rTujwFnpxorPDvJMBCCL80QQ8nlx0Dzahwz4K+Sajp+k6UDQRof5OWCZfDUn/DbTeekiiCFEU4Kmdi4aR1+InrzaQ7TZEGIaBpKbBr1mxrTWEc5fMg+fQcwhIuTiGSvjlADRRATxlMJIxIB6B5MjB89uOYubcHOSF91PIYck9EPQkQymjfmDz93Bw/N3Iyx2BZG/jkGCC6m+H6m/HoDMLVlc5+kIJ2FuPIWv2tZAGWiC07ea1ih2GbIOw8E4I+/9IZcANDwCrvstwQTVGQYKDz1JeHQYw0E2Bjnm3UWa8eQtQMhvwfZIKfEdeYNvZI/i7GmOenD2bSnI5tQwP9ZYSVIkiw9oWfpV5QdmVQMnkdH5iRvEtY/9LltlZf4e19keHAJDHJiOS0DCmyINXD7RD0w0MDzTsCyWwraEXbeYf4/+J9YcS+OOO00O/q7qOvlACANDujyKS0OAxcxuq8+gkji32YHNdD2JJHSnCW9MNxJI64qqOXaf6MbmMyj2HWgah6gzpGm4v7WtD++Df1l9NN7D2aPcZAAhgyE37YAxb6nsxsyr7jPfePNSBmKqjKxCDP5KEYRiYXpGFF/a2QtMN+CNJOK0yRua70NgTQl8ogWiCoUKariOh6khoBtYc6cTCWhaA7A8n8ObhDkyrzEJ5jh0v7W1DLKn/RbaGP6ri7UMdSKg69jQPwGuT4Y8kht7XDeDFvW1YWJuPjsEYokkNeW4rFo3Kw+sHOzAy34V3jnQirumAwXCnXJcVR9uDWFCbhwMtfvSHEzjbBqIJvHO0Cx3+GD4+owyyJGDNkS4MRpNDAAgApldmYf3xbgRjdLIAQBAYgvPq/nZMLudJmCQK5qmkgUhCw9qjXVgwKg/+SBK5LgvWH+9GJKENAaDh4wvEVbywrxXnTyxG0LyHx6ZgVlU2fr65AR47vY9UrxKajua+CAYiSWw83jW0v3tDceS4LAjFVbT7YwjFybakhjO9MhuHWgfRF2Y4liQwDFEUBPSGElhztBNLxxQwrcAAQnEVVbkuvLS3DYG4igsnFWN/ix8zqrLx4t5W5Dgt0HR96FkyDB4KWGSC4xf2tuGCCUWIJFSU+hzYcrIXinzmV52uG/jD9masnlgMzQBEUcCoQg+2n+pDIKYOjdttlRGMqXjtQDsW1OZBkhjSZAAYjCZxsjuEeSNzsfFENwCGggKAqhuQRAHtgSjWHE5LP4sCw+vCcRWBaBL7W/y4fHoZXtnfbjpLQDCqwmXl83yyO4SBSBJWWcQL+9rgtikIxpMIxlQ09oQRT2pD85xap2d2nsbsETk41OpHZyCGg20BuO0y/JEkRIEHtAaA5/e04pyaArxysBNRTYIgKdANs8yNIGDHqQF0iblIaDp+934zlozJh24Y0A1jCAANv29/JIE/7z6Nj82oQE2BG1saelFb4MbJ7hDa/FFkOSzIc1txoHUQokC/JpLQIAkCdAN4aV8rZlXlAABe3NOKmnw3mnojsMkSTvdHuGdEAaIA5LitqMp14bndrZBEYagPus51OdUbxpgiL9461Im4qkGReI0iAtA1qBo/EU6oMEDxOE03YGgqFNGAAQHrjnVhdnUOugJxZDkt2HC8GwlNR+/w59pgqDEMAy/ta8X82ryh9RcEAYtG5eK5Pa1DYcZOcz9puoHBqIqTXWGU+GzwR5LY2tALt01Gx2AMI/Pd2HiiG5LI0LqUqTpDQd9v6MOU8iy8faQTDlcWwj3NCB5Zg9CRNbBFO+E3D5wEQYChM3zaMAB/JImkoOD9xj4AQDRpYHR5MXY2BxBO6DBgIBRXYbMoECQZKiS8eagLNQVuuOwMN5o9Mg/rj3fj8T1RCPO/lFbtA6AZBuJQ0DX5FrwUGoPoqh9AX/4gIisexo6x9+BTbwVxtDOILLtyxnOm6YAoiEioOvrDcQzExaHn20z1giwKUHUdFknEia4g8t3sT0WOA5EEw8I7IwLgLoQgpsPCj7YPwD/6Y9gfzUcgAXhcdhiCgJaufnRIhRhw1WB7rxUJ3fyOEECGQ5SgqUn0RQ0Y3nKE4AA85dAcecC0a5EYexn63n8aHRt+jsHSJdAufgJ9Cx6E9NFf4uSEL2F/t4aW/ghBk5GCbAJ6wkkERC+OJAqAlQ8hNv9uHI94ENdFnOqLobEvil7VhrDoQlJ2oUdzIZg/FUb1UqjdJ5B49TZo/c0wXv8SRUEkK9bXDSI88xaIDp/J0CjM8VFsSMz7MvrDcQgVc3Cs/EqcPN2CvrzpiC++D8kp1yAx+RpEV/8EfVmTWLB22f2st6UlKKKw8+fA8bcYklY0kWzN6AuYv9W+jyIdpTOABV/i/CXjwPL72c7Uq5n/VjEXuOgnwHnfIxCSbUD+OOYA+crJDH3k5xRBSZlsZc5PBgBl7H/RMrvr77Dm/vDQ/wu8NhzvCMCAMeQkDbekmeTceZYz+rdYMJ5E60AajOi6wT+8pp3oCKDAyzC7bJcFJzqDKPDYcKwjMHQ9wNzVaFI1T+2DqMknzZ1qKZpQz7hvOK5hMJrE32LheBKH2wb/4nXVBIPHOgOoynWe8Z4BoHUgCrsiIaYyr6HAY8PRDrYTS2qQJQHl2XYc7QjCABBXdegGEEtlQQM43hFAZY7DPFEkOC302CBAwP6WgfS8GWQgAP6dq+sODuUuWWTmlgy3QCwJReYHTnQGUJ7tQGWOE8c6AijPduKoOb8AkOe2oqU/wpwMScTxjgD0oT98w8ZsACc6g7BbJJRlO+GwSDjaPojosPFIooBwXCMTMcwBMgzuoVBChaad3TJPYk/1hFGW5URS0zEiz4WBcAJ1ncG/uFYAQyxb+iMoz3YgluD9FElElsOC0/1RiMOS6VN3i6s66ruDGD5VcVWHJAiIJpkXVdcVRKHHClFgbpMoAEfaB4fGP9x0AzjZFUJVLvOsFElENKGhIseB451BaDoZmuOdQVgkEYGYirJsB050hv5iXpOaDlkUcLI7hNJsB2yKhO4gAazlrHw83TB4Ci3QsRIFIBhTYZFERBIqBHPsFpk5LQlNN4Eb75Oy4x1BghfzpaT5rDHrTkCxz4FD7X5YTBCmyASAMVUzPx9Aabb9jL0UU9P91Q3gQIsf5dkOBKMMiZpU6sOR9gD7dtaeBRjC6bLKcNlkHGlnu1aJ4xAEAalvp75wAjFVQ1QFopoIQbFBN5WfBJsbMXshjnZGYZVFNPSE4LLKaB2Ioqkv/Bf3BPgdNxDmwcVgNAkYwKhCN8NQAWQ7LOgwv/8UiQ6vYZg1LAEE4xokkWup6gZaBqIo8NjQbN4vBfZ8DgWt/dGh/TBc80E3c7oEgeC8fTCKWFKHRZYgAHi/w4BRNmtYn8nOCYIA3TAQK1+I3a0xCACa+yLI91hhmO0GYsmh728DZ8pgRBMaYqoOxXTYDINjqshx4nhHAAmVuWiGAbM99vFY5yAqchyIqXz+AlEVJ7qCKM2yo6EnBMlkKdPjY7snu0MoybKjJxhHf1KGpWQiEqoOxQx3HP69ES46Bye7w+Y8GeiPqJxLQYQkAoXZbpzoCg89mwb4niCIiCf5vBf6bJBNefSyHCfqOoM42hHGI/UFCK34AeRJl8NZfQ6k6deiZ/GjeK6jAL/b04s7XmvC7ZtUfHqdhse2++GP8O+E5quCfkbh3vR3pewugCEpkAQC11TasgACOc0A7IqEhLn3k6qOiPn9FUroaIkoiDhKoHlKoXlKEbIV43C/iPaghlBcg8tu5rtaHNANAcLRlwBdg2KxQVKsBHWCRGVFc1+r8TByfR7ol/4csWk3oqulHl0bfgJDjUNPhNF/dCN2BbJxw0YJLzdZUD8I8ztEPGOMKVW1pMZ1uG/TIB7fMYhclxWBBKBDgCEqCMQ1dIV1hBMalIQf8fbDiA92Qa9fh7AmQdNUCI4cIB6EERtElVvDw5u70D7nQRjzvwR57PnAnJvRt/xH+HNnEWLZo/GU/Wr8YHcCgjMPPTtfROOan6BnYBCtWefguzuTFBYpnUaJ940PUVHv2KvAym+zDlHLDmD0eQRIs29mmNzIpcDqRwiI1t7PYbbsZLmADQ/yd3s2GZ22fRRcmHwlMOFSMj3L7if4mXsr60hlAE/G/smWCYf7O8wzLKwtltCQ77FC/isPMctGCHBa/+dTLosibIqYdvwFgcWlh5wCC7rMcAlV0+G2yognNZ7kD0R5b/NaSRQRV1V4bAoCcRUYFpknndV3AWQ4/hZTJBHuD1DaSjnSHpuCcPxMlsgAGbS4xtM9AUzE99os6AnHh04vIwkNXrtstoehJPWUee0KwqYwAQDYLCIdVYFzg76oOZ60A8jrpKH1SAGkYWQMRBGQBHHoHkOsm11BOKHCY7MMMSyp13XdgCQAHrsCQRAgfAAQ8thlqJqOeFKDVWZC/vDxUBGM9z1jTVJjFwTI0l+GKEqiAJdNQjRBxzCcUCFLwhCjc/bcy6IIuywhltSGQgUN8KT/bNAw/B5em4LheiqiqUSbcti8dgWd/qgJTHh677OnY1qEs7ruscsImyykYZBBiSY1rmuc4iIemwJJ4r4Px1V4zx6TADNx3IDTIiGukvF0WTm36geARlEQIIvC0OdsigjNvH9S5fW6kd5rsihA0wzYrGnnzW2XIYvCUASNclboaCShwmuzDIUM6fqZ+9djiiF47Qp6TXZXEgUTjLDZbKcFzX0RQACsioSBSBKjizzQzb6ebRZznpKqgSKvxRyHMfQ8pb4ORIEgT9UBQxARt+RCtmZBF0UkIaGlPwarIiGh6XCaQhoOqwiv3fKXG8PsqyyydfswJbscF6+PqtrQd8TwvqeAEMcumkIuOrx2GcFYks8whkR5EU1q8NhlhhufNfzh33VumwxFFsx9ySd/5+kAeieugrvxfa6pJCB1zGPJKsVx+wS0+vsBMCxxIJyAiR2Q1HQUmodNZ8+6JImwiwKS+pmgdPj3QiKpI6Vbk/q8x2ZBOE42TDUo7pLrsiAQTSLHaUXXYBxumwTzrGboczkuC4IxFZU5DhzpiiB7wrUo2nYvopEIdIPrkNAMSBXnYLvfh4TaC36VMx9xYmkWTvdH4FBERCMR+OzOoY3hsEhDoEISBXjtCiIJfeiZD8dVeB0KuoNxHOqI4hN1MYwtmo8shwWdJ2OoCQjw2VVIigWCbEEyFoTbm4u4+Uz5HBY0qVlwT7sKyV1PnTmXooTkzM9CFRRMLvNh72k/BPMKA9y8qqZjfIkXfWGu3IbjPVg5vnBoLyRUHW1Bc9EgwKEAMVWHRRJQ4LZgIBwD7NnIzS9CTtdWZOuDWDS+Ajsae7GgtgDrj3dxr8kWlu+xWzCtOh8+rR/hvWvQW3UBhLwOSJEAYKgQqhagp2w17l4/iInl2ejp7UYoCeT4vBhX4kFdZ2jobwufOwFWRUKRz4E+1YalNSNRkudDWBOhCTJgUOFQM0TIFiuWjp8EyRVAv7MKculhBE7tgaN+PYw5N8PY+F3og22YMVHHi/vj+O47jcgqLINHKUKwJY6OmB83LhuPa589CVU3MLMqGy+0AGPnPAK3VcSWQR0bt0UxrSoPuQ4JOPoeCztP+CjrDjmyqcoWHQB8VVSoi/lZpDbUxdo/NSsZ2rb8fuBgN+XdXVnAxMuBkmlUz8uuBMZfgr8w+YO/SzKWsX+WZWD332FVuc6hP/atA1GMK/aiZSCC2gIXROHMP9Bum4xcpwUlvv+56lqBh2FYKVNEYciZEARgbLFniCk60jaI8yYW4VBbAMvGsnBYCojoOuCyykioOhaPysO2kwyJcJpO3dkgZkyxB4Wev62/douM8yf+ZR0URRJgU0QsHZOPrSd7z3ivyGNj2JROx1kSBRxq82PFuAIIANw2BZGEhv0tfsypzmWdOiv/SNgUaeiP59IxBdhc1wOAf8Bnj8jBtoZexBIaLplSOnQ/UcQZp50XTCyBXZHgMkNUzgYLk0p9ONoxCEEAxpd4h3JMlo8twN5m9jNl/eEEspwW2C0SGnrCmDsydwgcDDeLLGLx6HwYAF490IZgTMWqcYVDa5AyfzSJfLcVnpSTDYI4myJhTFF6vYezWy6rjDnVudhysgcuq4yt9X2YVpGNccWeM/Zi6r9eu4I5I3Ow8XgX89TMULQDLX6sGl+AyDBmMPUZh0XC6CIPRgxj9VJgxWNTIArA+GIPGnro9Gu6gZb+CObW5A2FWemGCZzME90FNXnYXNcD3SCL6rXL2Fbfi+VjC+CwyNjd1I95Nbk43DaIKWVZONkTwpgi99CYBIEgXDTbXjamABuPdUM2neQir+0vwjQlUcC0iiyc6AzyOgiIJFQ4LDKy7BZo5kYJxVS4rTKq85xoHYhCMwxYZLJbAoC51bloG4gOheakAIJkAqsOfxSrJxYhYh4AJDQdAgS4rdxrC2rz8Mahdiwfm95LHpuCoBkq5LLJmFKehYaeEKaV++CzKdjd3I8JJR4kNf0DD1Xm1eThYOsgXDYZE0t9sMoignEVHpsMzWAYogDgnBE5aPfHMDLfBZsi4fRgAkFNRn8M6A+rZogjFctWji/Eic4gKnNcyPdYP/BwxGWTMb0yGztO9SGuavA5FGyp78WiUfkQBYYEOy0ynBYJSc2AIomQTcCn6QamlWfhVG8IhgFMKPHBqkgIxJIoMO+XAocM/1VQ1xnEtPIsaMNOLkRBgM1U3hNFAfNH5sFhkYZCfXXdwPf3aBiYdy/77MuD7MmHMPFytJ9zH+7bNDj0XC0fW4CtJ/tMdlfApFIfZFFAjtNC9mrYM2VXJJw7vhDvnugZ2l+GAWyu68XKcYVQZBHBmIqEqg+NRdUNzB2Zg4OtgxR78NnR0h/B6glF2NrQi6Vj8pHQ9KH9lmpXMwwsGpWPHY19uGhyCdYc7sQjBywQLngMes1yJOy5yC4fA8y7DZsLrsaLx6OUVxcEJA0JXoeCKeU+lGU5AVHGofYgFtXmQBIAiyRBkSREkzo0neNeNjYf+1v8CCd1QOSzuXJcIVxWmeDSAI60B7DlZC9OdocwsyoblbkOaBCRdJdAdxXBIrEtWRRxzogcPLO/F5uti6AvewD28imw5pRBqZ6PxMqH8eNThdjfMojPLBiBIq8NkjnRmmHAaZHxxaU16ApEMaeaoZP+aBID4STGFXvO+h4VIAkClo8rQG+QipFxVUPYXgLBkYtr5lTBN2Iajoz8NKaUetAfTmB0kRurxhdDFEXEVMCiyFgwqgCfmFmG2Dv3Q2jehkFVxhPqBdg37dvYN+Vb+Fl0Ke7fEkMwCSwaVYD9Axa8drQfXruCq8+phNsqsQ6s+Z2R57biwsklWHOsDwWFpRhXno+fbGzEF5bUwqFI5ngF5HusGFvkhWFx4KIXA1j1+w5sL78RRau/DlFWEOlrQ+Ijv0Z84idQ0LMVd15yDkpGjEF33ILDfgUduhcXzarF/tYgwgkNJT47Fo/Kxx93tuC7WwfxyPYgXj8egizJ+MgYF5SDfwTKZ0OoXcHaUckIFeTyxwGzP08QY88GCsYDqx8FLv8di/Z6illTyGqqcJ33MHD+o6z7Y/vXqfZmLGN/i2VA0N9hJT47bl9RC6sswgCwp2kARR4qTxV6rEMnWFZZRLHPji+tqEWR93+uDidLVHOqzqPjKQgCshyUSv7swmrsbR4YOtmdW5OHy6aXItdlgaYbWDY2n06iKCLXTQftkikl6AnGEUvS0frcwpEo9tmGQnYAIM9lxY0LRvyP6qiMK/Fi5TBgANDpv3JmOQzdGEq8BwCvTca3LhmP7Q29uHVZDVzmH6+GnjDGFHkwpzpnKOQhltRxrCOAzy+shlWmcIMkAvluK+ZU59CB649AkQTMqMzC2CIPxhZ58cLeVpRm2XHBxCI6W8MOaa+dUwFFFFCe48CXV42CAQM2WRpiYMqy7bh8ehkOnPbjswtGYP2xbhgG0NIXwYhcJyaX++C0SFhcmzfEbqw70okvLKnB+uNdKPLa8IlZFVAkYQhASKKA5WMLUJ7lQKHHhmMdQdR3h1Ga7cCyMQXIdaVPxV7Y04qvnTsaHpsMp1k/RtV1lPjs+Mz8EUOOUYqpyHFaMLbYgxynBR2DcXjtMq6bWwmnVUIomsQN80cM/RE2AGQ7FIwpcuOiySU40hmEVRHhsskIxzX0RxK46pwKFA8H7AJQ7LXhswursetUH0bmuzG+2AO7IsHnUDAQYb2b25bXYn/LwFDieMr5Lcuy41PzqkwgShZAFIEVYwtQ6LPhRGdwiPWwyCIumFyC0iw7xha7sf5YN7x2BcU+Gy6cXIwijw3vHOnCZxdUQxYJDgs8ViQ1HeOKPRhb7MHO5n7IggBFFHD7slFwDpOOl0QBZdkOfHFpDV7d3w6Ap/yb63tx57mj4bCK8JjMY0LTkeVUcNuyWrx9pAMABVEKPDZcOascLQMR/HFnM764tAYV2XYmTIMn8VkOBdfPq0KO04LFo/OHXvdHErDKIq6eXYE2fwSb63pRnefC1HIfvHZlKH/PbpFw8+KR2HCsCxNLvTh/YjF+s+0U7j1/LNYd7cL9F4xDQtWQ7UwzJSPynFg2Jh+SKKAqz4n9p/vxzYvHQ9cJFFMy7ZW5TqwcV4CfbDqJL68cBYskmiBWgN08YPj8opF4fncrJpZ68NGppfjpppPY2diHQo8Nty2vPYMttMoixhV7cMGkYrywtw2bjnfj3vPHQpIEHO0I4I6VoxCKJ7H+WCe+uLQGNkVENKkNqWWWZdtx8ZRi7GkewORyH5aPzcdvt53CrUtr8er+Nty2rBZ2izh0r3frejCpzIvLppeiyGtn0rwoQJEEFLit+NS8KvzyvUbMrs7BnOoceOwK8lwWFPvs6ByMYV+Y+Yn7J96H/mU/wD2d83DPuwy5VSQBn1tYjcNtPAD59PwReHlfG0YVuLH/tB9fWlGLEp+d4leSgCKPDTMqs3DR5BIEYyrznUy2rzcUx/KxBZhY4oWqGxg0DzcUWcDV51SgoScEqyKi0GPFbctr8IN1dQhEk7hlaQ16gnFcOrUE/kgChV4b7ApB45Uzy9HUG8LVsytwtD2AnmAc180fgSePyngodAHeqLobddO/gV/3T8Qv9zEMzmNXkOO04palNRDNiIRbltVgZFEWIpZchJLAdXNHoCTLga5gHKIgwICBudU5mFKWBV03EIxpgChDFxV4bQounlICt00eOggUBOCK6aXQdQNzR+Zi9ogchJMCorCizc89/7VzR6O5L4yNx7vh8mbhwcO5eMrzWbw76h48IX0Ct7xrYE5tMf60swXP7WrBD66YjM8tGgEAuHRqKZ64aioAA4+tO4nqPBfuXj0GH5tRhoFIArcvr8XFk0uQ7bRAkQS4rBIunFyM2SNyMLMqG2OLPXh6eytqS/Jw5wUTMKjKeLnNjcd3hnDjn4/i0hkVcFkVZDkU3HfBODxx1VQ8/rEpWDa2AIGkCOtHf44T834A1VmEeePK8dThJL6/O4nd3SKy3DY8fNkUBOM63A47plVkQ9MN5Los+P7lk/C5RdVYNa4Q182txD2rx0KRBJw3sRg3LanG+qOd+OScKkiigAcuHo9vXDQeD148EQ9cOB5Xza7A1HIfHrpkIh67cjpG1dQgNmIZepb+AKfH3YTjrhnonnwLtpXfiLeaBUyuKsKXVo3Ddz46Bd/96BRIIpXsvr56LO5aPRbVeU589/JpmD0yH5UF2Th/ejW+fsF45GR5kZh7O4S8MYCzkCAobxQLsGaXE+C4CoCiSUBeLVAwBigcz3pf/6+9ew+OqrzfAP6c3T17y16SQO43QpA7EiEQG0BgCobOVMt0lBnr1KQDTG0jLaViqdOWEgOMtdM6KqP9oyQtRUrHjAVrtSpjYAa08OMmIAkmAQK5k5DdzW72/v7+2LBlJdyVk815PjOZyTl7zu53/ZolT877vidnVuT7lPGRDwNbeuS+VURxgPcJukvhsMD5Xg8+b3Og0+nFtGw7rAYZvR4/LvR60OXyIX9UAqZl25E7eH+ZO3XJ5cXZSx6caO2DzaTH5AwrgiGB/zvfC5tJj8IcO3KSzDDIWlxyedF8yY3Lbj/0shZNXf3QAChItSDBoIXTG0RzpwMpfacxefZ8aLQ6nO5wossZmZR7X5oFGXdwryCnN4CLvR4caYksfzsjNwnZiSa0OQbwebsLZzpdyBtlxpRMGzodA8hINCMz0YjL7gCOtFyGxx/C/Vl2pNqMaOlx49D5y9DrNCjKS0JmohGOgchS4S5vEDNyE2Ex6vBJUw+8gRCmZNphkrWwm3Vw+8LY39iNpAQ95haMRne/D58090LWSnhwcIEGpzeIdJseJr0OPf1+nGxzwu0LYmyKBVZD5N4kY1MSEAqHcfh8HxyeAKZk2TAl0waLQcb5Xg+6nF5IEvBFpwuAhOKxyYNhyY2sJBN8IYFjLX0IhsMozE2KDiuyGHRo6OxHY5cLs/OTkW4zom9worzHH8LUTBsyE42QJAknWnph7joBZD+AgjQbzPrIkKhejx/17S5oNBKmZ9uh1Uj4v3O9sBplTM2yQ6sBOp0+mPRaJOgjE+MbOp0QInK1xh8S0GqA0RYjTrT2odPpw6QMKxL0ushVN70OLb0efNHVjzSbAdOy7LAYdKg7cwmOAT/mj0/BQCCEzy44oNNqMDXTBrtJRr8/iM8uOOAPhjAxI7IvEIzc08YbDOPo4P8bD+QmwazXos/th9sfwvleN0ZbDBifZoFR1qDD4YM/FJnn0NLjwdQs++DckSB6+v0IQyA70YQvuvrR5/FjRl4SbEYZB5p6oNdq8EBeIpKMMtyBIHyByPLtHY4BTEi3ISfZBKNWA4cviIPNvdDrNJg1JnL+5cGJ9Z1OL9JsRmTYjUg26+DyhXGk5TLCYYEZeUlIMsvodUcW9DDIGsgaDZq7nNC3H8dA2v2YmJkIXzCMnn4fUmwGXHYHcKLVAYtBh5l5kffe4/bjVKtj8PWT0eP242hLH2xGHaZm2XHxsgd2k4zsZDMCwRCauj0IhILIH22Fc8APi0HG6Q4nHAMB5CabYTfpYdJrYNBFPgMMsg46LWDQ6XCs5TJc3iAmpFthkrVodw4gw2aCc8CPjCQT2vu8uHA5Mp9utEWPhg4n8kZbkGo14ORFR2R+jADsJi1sJj163AHUdzjh8YUwJcuGrEQT9jZ0Q9ZpMD7NAqteC384MrcmOUEHu0mPIy19MOu1mJCthBluAAAQ/ElEQVQeWTChy+XDxHQrzHotznS6kJucgJwkE7zBEA6evQyjTouJGVZ0u7wwG2T09Pvg9gVRkGpBslmPnv7I6otdLh/aHQNItxsx2mJAt9OHMaMT0NrnwagEA/RaDU60OSCEwJRMOxJkoOX4AfSnTIPQaFGQkgCPLxS92nym04lQGJiSaYv8EabDiVEJeoxLteBSvx+BUGS1yUjY1eNSvw8XLw9gZl4SWvsGcLrdiVSrEZMybdBpgEsuP/yhMM509cOk02BGXhKCIYHT7U5k2I3ISjJjwB9Cp8uLLzpdKMpLjiyS4ByAUadDp9M7OD/SPHgfKAk9/X5IUmRUQFgI2Awy2p1eHLvQB6tBhwfHjoLHHxyckyVhWpYN6XYjAiGBU4OfdYU59shcHxGGRqOB2x/EqTYnXN4g7ku1IMVqQJJJRt9AAGc6I8vT35dmgc2og1HWwuUN4vM2JyQNMDHdhgS9Fia9FgO+IAyyFr2eAD5vc8KgkzApw440qx5ufxhdLi/cviDS7Sb0DfjR4fBilMWAFIsB3S4vJElCqjXSN40I4fND+5D/wFx09geQlWiCyxtEfYcLCXotpmRFXtftH7xvli8Ely8AvTay4qhGAxgHF03xBQV0WsAiy+gdiNyXziBr4QtE5vwlW2QEQwJuX2QeoF6rQb8/CIjIfcckSYI3GIJJp4Fe1sIxuLqdxaCDQdYgFBocRq0BPL4wnN5IHaMtekCKjOJw+8IQkkAoJBBGZN6kLxCZlxgSgCxJsBh18AZC8IXC0EqRK8xuXwBhSAgGw9DLkWGsA4EQbAYdrEYtulx+aDUSks2RRWqCYQGbSRe5OqrTRO4VKEWukIbDkT+qWY3yXf1OcrWRfu+ZkWSk9+p2sgFDkIqN9B+EkYJ9ih/sVXxgn+IHexUf2Kf4MdJ7dTvZgMPhiIiIiIhIVRiCiIiIiIhIVRiCiIiIiIhIVRiCiIiIiIhIVRiCiIiIiIhIVRiCiIiIiIhIVRiCiIiIiIhIVRiCiIiIiIhIVRiCiIiIiIhIVRiCiIiIiIhIVRiCiIiIiIhIVRiCiIiIiIhIVRiCiIiIiIhIVRiCiIiIiIhIVRiCiIiIiIhIVRiCiIiIiIhIVRiCiIiIiIhIVRiCiIiIiIhIVRiCiIiIiIhIVRiCiIiIiIhIVRiCiIiIiIhIVRiCiIiIiIhIVXRKF3A3hBAAAKfTqXAl8SkQCMDj8cDpdEKWZaXLoetgn+IHexUf2Kf4wV7FB/Ypfoz0Xl3JBFcywo3EdQhyuVwAgJycHIUrISIiIiKi4cDlcsFut9/wGEncSlQapsLhMNra2mC1WiFJktLlxB2n04mcnBxcuHABNptN6XLoOtin+MFexQf2KX6wV/GBfYofI71XQgi4XC5kZmZCo7nxrJ+4vhKk0WiQnZ2tdBlxz2azjcgfhJGGfYof7FV8YJ/iB3sVH9in+DGSe3WzK0BXcGEEIiIiIiJSFYYgIiIiIiJSFYYgFTMYDFi/fj0MBoPSpdANsE/xg72KD+xT/GCv4gP7FD/Yq/+J64URiIiIiIiIbhevBBERERERkaowBBERERERkaowBBERERERkaowBBERERERkaowBFEMn8+HwsJCSJKEY8eOKV0Ofcmjjz6K3NxcGI1GZGRk4Pvf/z7a2tqULou+5Ny5c1i+fDny8/NhMplQUFCA9evXw+/3K10aDWHjxo0oKSmB2WxGYmKi0uXQoC1btmDMmDEwGo0oLi7GwYMHlS6JvmTfvn145JFHkJmZCUmS8M9//lPpkug6Nm/ejFmzZsFqtSI1NRVLly5FQ0OD0mUpiiGIYjz33HPIzMxUugy6joULF+If//gHGhoaUFtbi6amJjz22GNKl0VfUl9fj3A4jD/96U84deoU/vjHP+KNN97A888/r3RpNAS/34/HH38cP/rRj5QuhQbt3LkTa9aswfr163HkyBFMnz4dpaWl6OrqUro0uorb7cb06dOxZcsWpUuhm9i7dy8qKirw6aef4sMPP0QgEMDDDz8Mt9utdGmK4RLZFPXee+9hzZo1qK2txZQpU3D06FEUFhYqXRbdwO7du7F06VL4fD7Isqx0OXQDL730El5//XU0NzcrXQpdR01NDVavXo2+vj6lS1G94uJizJo1C6+99hoAIBwOIycnB6tWrcK6desUro6GIkkS3n77bSxdulTpUugWdHd3IzU1FXv37sVDDz2kdDmK4JUgAgB0dnZi5cqV2LZtG8xms9Ll0C3o7e3F9u3bUVJSwgAUBxwOB5KTk5Uug2jY8/v9OHz4MBYtWhTdp9FosGjRInzyyScKVkY0cjgcDgBQ9b9LDEEEIQTKy8vx9NNPo6ioSOly6CZ+8YtfICEhAaNGjUJLSwt27dqldEl0E42NjXj11Vfxwx/+UOlSiIa9S5cuIRQKIS0tLWZ/WloaOjo6FKqKaOQIh8NYvXo15syZg6lTpypdjmIYgkawdevWQZKkG37V19fj1Vdfhcvlwi9/+UulS1alW+3TFWvXrsXRo0fxwQcfQKvV4qmnngJHtd4bt9srAGhtbcWSJUvw+OOPY+XKlQpVrj530isiIjWoqKjAyZMn8fe//13pUhTFOUEjWHd3N3p6em54zNixY7Fs2TK88847kCQpuj8UCkGr1eLJJ5/EX/7yl6+7VFW71T7p9fpr9l+8eBE5OTk4cOAAvvGNb3xdJdKg2+1VW1sbFixYgAcffBA1NTXQaPh3p3vlTn6uOCdoePD7/TCbzXjrrbdi5peUlZWhr6+PV7+HKc4Jig/PPPMMdu3ahX379iE/P1/pchSlU7oA+vqkpKQgJSXlpse98sorqKqqim63tbWhtLQUO3fuRHFx8ddZIuHW+zSUcDgMILK0OX39bqdXra2tWLhwIWbOnInq6moGoHvsbn6uSFl6vR4zZ87Enj17or9Qh8Nh7NmzB88884yyxRHFKSEEVq1ahbfffht1dXWqD0AAQxAByM3Njdm2WCwAgIKCAmRnZytREg3hv//9Lw4dOoS5c+ciKSkJTU1N+PWvf42CggJeBRpmWltbsWDBAuTl5eH3v/89uru7o4+lp6crWBkNpaWlBb29vWhpaUEoFIreI23cuHHRz0O6t9asWYOysjIUFRVh9uzZePnll+F2u/GDH/xA6dLoKv39/WhsbIxunz17FseOHUNycvI1v1uQsioqKvDmm29i165dsFqt0fl1drsdJpNJ4eqUweFwdI1z584hPz+fS2QPMydOnMBPf/pTHD9+HG63GxkZGViyZAl+9atfISsrS+ny6Co1NTXX/WWNH7nDT3l5+ZDDfj/++GMsWLDg3hdEAIDXXnsNL730Ejo6OlBYWIhXXnmFoxOGmbq6OixcuPCa/WVlZaipqbn3BdF1XT3l4WrV1dUoLy+/t8UMEwxBRERERESkKhykTkREREREqsIQREREREREqsIQREREREREqsIQREREREREqsIQREREREREqsIQREREREREqsIQREREREREqsIQREREREREqsIQREREREREqsIQREREREREqsIQREREw0YgEFC6hDsWz7UTEakNQxAREQ0pHA5j8+bNyM/Ph8lkwvTp0/HWW28BAOrq6iBJEvbs2YOioiKYzWaUlJSgoaEh5jl27dqFGTNmwGg0YuzYsdiwYQOCwWD0cUmS8Prrr+PRRx9FQkICNm7cCACoqqpCamoqrFYrVqxYgXXr1qGwsBAAsG/fPsiyjI6OjpjXWr16NebNm3fT91VTU4PExET85z//waRJk2CxWLBkyRK0t7fHvPfKykpkZ2fDYDCgsLAQ77//fvTxc+fOQZIk7Ny5E/Pnz4fRaMT27dtRXl6OpUuXYtOmTUhLS0NiYiIqKysRDAaxdu1aJCcnIzs7G9XV1bfXDCIi+moJIiKiIVRVVYmJEyeK999/XzQ1NYnq6mphMBhEXV2d+PjjjwUAUVxcLOrq6sSpU6fEvHnzRElJSfT8ffv2CZvNJmpqakRTU5P44IMPxJgxY8Rvf/vb6DEARGpqqti6datoamoS58+fF3/729+E0WgUW7duFQ0NDWLDhg3CZrOJ6dOnR88bP368+N3vfhfd9vv9YvTo0WLr1q03fV/V1dVClmWxaNEicejQIXH48GExadIk8b3vfS96zB/+8Adhs9nEjh07RH19vXjuueeELMvizJkzQgghzp49KwCIMWPGiNraWtHc3Cza2tpEWVmZsFqtoqKiQtTX14s///nPAoAoLS0VGzduFGfOnBEvvPCCkGVZXLhw4W7aQ0REd4EhiIiIruH1eoXZbBYHDhyI2b98+XLxxBNPREPQRx99FH3s3XffFQDEwMCAEEKIb37zm2LTpk0x52/btk1kZGREtwGI1atXxxxTXFwsKioqYvbNmTMnJgS9+OKLYtKkSdHt2tpaYbFYRH9//03fW3V1tQAgGhsbo/u2bNki0tLSotuZmZli48aNMefNmjVL/PjHPxZC/C8EvfzyyzHHlJWViby8PBEKhaL7JkyYIObNmxfdDgaDIiEhQezYseOmtRIR0deDw+GIiOgajY2N8Hg8WLx4MSwWS/Trr3/9K5qamqLH3X///dHvMzIyAABdXV0AgOPHj6OysjLm/JUrV6K9vR0ejyd6XlFRUcxrNzQ0YPbs2TH7vrxdXl6OxsZGfPrppwAiQ9yWLVuGhISEW3p/ZrMZBQUFMbVfqdvpdKKtrQ1z5syJOWfOnDk4ffp0zL4v1w4AU6ZMgUbzv39e09LSMG3atOi2VqvFqFGjoq9HRET3nk7pAoiIaPjp7+8HALz77rvIysqKecxgMESDkCzL0f2SJAGIzKe58hwbNmzAd7/73Wue32g0Rr+/1eBytdTUVDzyyCOorq5Gfn4+3nvvPdTV1d3y+VfXfaV2IcRt1zFU7UM991D7rvx3IiKie48hiIiIrjF58mQYDAa0tLRg/vz51zx+9dWg65kxYwYaGhowbty423rtCRMm4NChQ3jqqaei+w4dOnTNcStWrMATTzyB7OxsFBQUXHPl5k7ZbDZkZmZi//79Me99//7911yRIiKi+MQQRERE17BarXj22Wfxs5/9DOFwGHPnzoXD4cD+/fths9mQl5d30+f4zW9+g29/+9vIzc3FY489Bo1Gg+PHj+PkyZOoqqq67nmrVq3CypUrUVRUhJKSEuzcuROfffYZxo4dG3NcaWkpbDYbqqqqUFlZedfv+Wpr167F+vXrUVBQgMLCQlRXV+PYsWPYvn37V/o6RESkDIYgIiIa0gsvvICUlBRs3rwZzc3NSExMxIwZM/D888/f0lCu0tJS/Otf/0JlZSVefPFFyLKMiRMnYsWKFTc878knn0RzczOeffZZeL1eLFu2DOXl5Th48GDMcRqNBuXl5di0aVPMVaOvwk9+8hM4HA78/Oc/R1dXFyZPnozdu3fjvvvu+0pfh4iIlCGJOxkETUREdA8tXrwY6enp2LZtW8z+5cuXo7u7G7t371aoMiIiike8EkRERMOKx+PBG2+8gdLSUmi1WuzYsQMfffQRPvzww+gxDocDJ06cwJtvvskAREREt41LZBMR0bAiSRL+/e9/46GHHsLMmTPxzjvvoLa2FosWLYoe853vfAcPP/wwnn76aSxevDjm/G9961sxy3Jf/bVp06Z7/XaIiGgY4nA4IiIaUVpbWzEwMDDkY8nJyUhOTr7HFRER0XDDEERERERERKrC4XBERERERKQqDEFERERERKQqDEFERERERKQqDEFERERERKQqDEFERERERKQqDEFERERERKQqDEFERERERKQq/w81QvnJASnbeAAAAABJRU5ErkJggg==",
+ "text/plain": [
+ "<Figure size 1000x800 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# use z score normalisation to normalise the energy and entropy columns\n",
+ "scaler = StandardScaler()\n",
+ "gmm_df[\"energy_norm\"] = scaler.fit_transform(gmm_df[[\"energy\"]])\n",
+ "gmm_df[\"entropy_norm\"] = scaler.fit_transform(gmm_df[[\"entropy\"]])\n",
+ "\n",
+ "# create a gmm using energy and entropy\n",
+ "both_norm_gmm = GaussianMixture(**gmm_args)\n",
+ "both_norm_gmm.fit(gmm_df[[\"energy_norm\", \"entropy_norm\"]])\n",
+ "gmm_df[\"both_norm_cluster\"] = both_norm_gmm.predict(gmm_df[[\"energy_norm\", \"entropy_norm\"]])\n",
+ "\n",
+ "high_energy_cluster_idx = np.argmax(both_norm_gmm.means_[:, 0])\n",
+ "assert high_energy_cluster_idx == np.argmax(both_norm_gmm.means_[:, 1])\n",
+ "low_energy_cluster_idx = 1 - high_energy_cluster_idx\n",
+ "\n",
+ "gmm_df[\"both_norm_cluster\"] = gmm_df[\"both_norm_cluster\"].apply(lambda x: \"High\" if x == high_energy_cluster_idx else \"Low\")\n",
+ "\n",
+ "# visualise the clusters\n",
+ "plt.close(\"all\")\n",
+ "plt.figure(figsize=(10, 8))\n",
+ "\n",
+ "# Plot the clusters\n",
+ "sns.scatterplot(\n",
+ " x=\"energy_norm\",\n",
+ " y=\"entropy_norm\",\n",
+ " hue=\"both_norm_cluster\",\n",
+ " data=gmm_df,\n",
+ " palette=\"tab10\",\n",
+ " legend=\"full\",\n",
+ " alpha=0.8,\n",
+ ")\n",
+ "\n",
+ "plt.title(\"Energy vs Entropy (Normalised) Clusters\")\n",
+ "plt.grid()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 119,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "ed896800e034487683a5f83ec4b67b69",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Applying Both Normed Cluster Predtype: 0%| | 0/50000 [00:00<?, ?row/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "2f844ac80bce468c88015ae65e50b699",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Calculating Correctness (Both Normed Cluster): 0%| | 0/50000 [00:00<?, ?row/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>Sample Type</th>\n",
+ " <th>Correct</th>\n",
+ " <th>Incorrect</th>\n",
+ " <th>Total</th>\n",
+ " <th>Identification Rate (%)</th>\n",
+ " <th>Misidentification Rate (%)</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>Known</td>\n",
+ " <td>27332</td>\n",
+ " <td>7668</td>\n",
+ " <td>35000</td>\n",
+ " <td>78.091429</td>\n",
+ " <td>21.908571</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>Novel</td>\n",
+ " <td>13851</td>\n",
+ " <td>1149</td>\n",
+ " <td>15000</td>\n",
+ " <td>92.340000</td>\n",
+ " <td>7.660000</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " Sample Type Correct Incorrect Total Identification Rate (%) \\\n",
+ "0 Known 27332 7668 35000 78.091429 \n",
+ "0 Novel 13851 1149 15000 92.340000 \n",
+ "\n",
+ " Misidentification Rate (%) \n",
+ "0 21.908571 \n",
+ "0 7.660000 "
+ ]
+ },
+ "execution_count": 119,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# get the predtype for the both cluster\n",
+ "def both_norm_cluster_predtype(row):\n",
+ " return \"novel\" if row[\"both_norm_cluster\"] == \"High\" else \"known\"\n",
+ "\n",
+ "tqdm.pandas(desc=\"Applying Both Normed Cluster Predtype\", unit=\"row\")\n",
+ "gmm_df[\"both_norm_cluster_predtype\"] = gmm_df.progress_apply(both_norm_cluster_predtype, axis=1)\n",
+ "\n",
+ "# calculate the accuracy of the both cluster method\n",
+ "def get_correctness_both_norm_cluster(row):\n",
+ " return row[\"type\"] == row[\"both_norm_cluster_predtype\"]\n",
+ "\n",
+ "tqdm.pandas(desc=\"Calculating Correctness (Both Normed Cluster)\", unit=\"row\")\n",
+ "gmm_df[\"both_norm_cluster_iscorrect\"] = gmm_df.progress_apply(get_correctness_both_norm_cluster, axis=1)\n",
+ "\n",
+ "# create the output dataframe\n",
+ "out_df = out_df_template.copy()\n",
+ "\n",
+ "# calculate the accuracy for known and novel samples\n",
+ "for sample_type in [\"known\", \"novel\"]:\n",
+ " filtered_df = gmm_df[gmm_df[\"type\"] == sample_type]\n",
+ " out_dict = {\n",
+ " \"Sample Type\": sample_type.capitalize(),\n",
+ " \"Correct\": filtered_df[\"both_norm_cluster_iscorrect\"].sum(),\n",
+ " \"Incorrect\": len(filtered_df) - filtered_df[\"both_norm_cluster_iscorrect\"].sum(),\n",
+ " \"Total\": len(filtered_df),\n",
+ " \"Identification Rate (%)\": (filtered_df[\"both_norm_cluster_iscorrect\"].sum() / len(filtered_df)) * 100,\n",
+ " \"Misidentification Rate (%)\": ((len(filtered_df) - filtered_df[\"both_norm_cluster_iscorrect\"].sum()) / len(filtered_df)) * 100,\n",
+ " }\n",
+ " out_df = pd.concat([out_df, pd.DataFrame(out_dict, index=[0])]) if not out_df.empty else pd.DataFrame(out_dict, index=[0])\n",
+ "\n",
+ "results_dfs[\"Energy & Entropy (Normalised) Clustering\"] = out_df\n",
+ "\n",
+ "out_df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 120,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/markdown": [
+ "## Simple Vote (Energy)"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>Sample Type</th>\n",
+ " <th>Correct</th>\n",
+ " <th>Incorrect</th>\n",
+ " <th>Total</th>\n",
+ " <th>Identification Rate (%)</th>\n",
+ " <th>Misidentification Rate (%)</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>Known</td>\n",
+ " <td>33915</td>\n",
+ " <td>1085</td>\n",
+ " <td>35000</td>\n",
+ " <td>96.9</td>\n",
+ " <td>3.1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>Novel</td>\n",
+ " <td>7680</td>\n",
+ " <td>7320</td>\n",
+ " <td>15000</td>\n",
+ " <td>51.2</td>\n",
+ " <td>48.8</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " Sample Type Correct Incorrect Total Identification Rate (%) \\\n",
+ "0 Known 33915 1085 35000 96.9 \n",
+ "0 Novel 7680 7320 15000 51.2 \n",
+ "\n",
+ " Misidentification Rate (%) \n",
+ "0 3.1 \n",
+ "0 48.8 "
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "## Simple Vote (Entropy)"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>Sample Type</th>\n",
+ " <th>Correct</th>\n",
+ " <th>Incorrect</th>\n",
+ " <th>Total</th>\n",
+ " <th>Identification Rate (%)</th>\n",
+ " <th>Misidentification Rate (%)</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>Known</td>\n",
+ " <td>28322</td>\n",
+ " <td>6678</td>\n",
+ " <td>35000</td>\n",
+ " <td>80.920000</td>\n",
+ " <td>19.080000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>Novel</td>\n",
+ " <td>13703</td>\n",
+ " <td>1297</td>\n",
+ " <td>15000</td>\n",
+ " <td>91.353333</td>\n",
+ " <td>8.646667</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " Sample Type Correct Incorrect Total Identification Rate (%) \\\n",
+ "0 Known 28322 6678 35000 80.920000 \n",
+ "0 Novel 13703 1297 15000 91.353333 \n",
+ "\n",
+ " Misidentification Rate (%) \n",
+ "0 19.080000 \n",
+ "0 8.646667 "
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "## Best Probability"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>Sample Type</th>\n",
+ " <th>Correct</th>\n",
+ " <th>Incorrect</th>\n",
+ " <th>Total</th>\n",
+ " <th>Identification Rate (%)</th>\n",
+ " <th>Misidentification Rate (%)</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>Known</td>\n",
+ " <td>29362</td>\n",
+ " <td>5638</td>\n",
+ " <td>35000</td>\n",
+ " <td>83.891429</td>\n",
+ " <td>16.108571</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>Novel</td>\n",
+ " <td>13517</td>\n",
+ " <td>1483</td>\n",
+ " <td>15000</td>\n",
+ " <td>90.113333</td>\n",
+ " <td>9.886667</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " Sample Type Correct Incorrect Total Identification Rate (%) \\\n",
+ "0 Known 29362 5638 35000 83.891429 \n",
+ "0 Novel 13517 1483 15000 90.113333 \n",
+ "\n",
+ " Misidentification Rate (%) \n",
+ "0 16.108571 \n",
+ "0 9.886667 "
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "## Energy & Entropy Clustering"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>Sample Type</th>\n",
+ " <th>Correct</th>\n",
+ " <th>Incorrect</th>\n",
+ " <th>Total</th>\n",
+ " <th>Identification Rate (%)</th>\n",
+ " <th>Misidentification Rate (%)</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>Known</td>\n",
+ " <td>28220</td>\n",
+ " <td>6780</td>\n",
+ " <td>35000</td>\n",
+ " <td>80.628571</td>\n",
+ " <td>19.371429</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>Novel</td>\n",
+ " <td>13710</td>\n",
+ " <td>1290</td>\n",
+ " <td>15000</td>\n",
+ " <td>91.400000</td>\n",
+ " <td>8.600000</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " Sample Type Correct Incorrect Total Identification Rate (%) \\\n",
+ "0 Known 28220 6780 35000 80.628571 \n",
+ "0 Novel 13710 1290 15000 91.400000 \n",
+ "\n",
+ " Misidentification Rate (%) \n",
+ "0 19.371429 \n",
+ "0 8.600000 "
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "## Energy & Entropy (Normalised) Clustering"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>Sample Type</th>\n",
+ " <th>Correct</th>\n",
+ " <th>Incorrect</th>\n",
+ " <th>Total</th>\n",
+ " <th>Identification Rate (%)</th>\n",
+ " <th>Misidentification Rate (%)</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>Known</td>\n",
+ " <td>27332</td>\n",
+ " <td>7668</td>\n",
+ " <td>35000</td>\n",
+ " <td>78.091429</td>\n",
+ " <td>21.908571</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>Novel</td>\n",
+ " <td>13851</td>\n",
+ " <td>1149</td>\n",
+ " <td>15000</td>\n",
+ " <td>92.340000</td>\n",
+ " <td>7.660000</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " Sample Type Correct Incorrect Total Identification Rate (%) \\\n",
+ "0 Known 27332 7668 35000 78.091429 \n",
+ "0 Novel 13851 1149 15000 92.340000 \n",
+ "\n",
+ " Misidentification Rate (%) \n",
+ "0 21.908571 \n",
+ "0 7.660000 "
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from IPython.display import display, Markdown\n",
+ "for title, df in results_dfs.items():\n",
+ " display(Markdown(f\"## {title}\"))\n",
+ " display(df)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Testing DBSCAN for OOD Detection\n",
+ "Similar to the above, but using DBSCAN"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Testing Spectral Clustering for OOD Detection\n",
+ "Similar to the above, but using Spectral Clustering"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Prepare Data & Apply Algorithm"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 121,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\n",
+ "# spec_args = {\"n_clusters\": 2, \"random_state\": SEED, \"affinity\": \"rbf\", \"gamma\": 1.0}\n",
+ "# from sklearn.cluster import SpectralClustering\n",
+ "# import time\n",
+ "\n",
+ "# spec_df = gmm_df.copy() # copy the gmm_df\n",
+ "# # drop columns not appearing in the master df\n",
+ "# to_remove = (set(spec_df.columns) - set(master_dfs[0].columns)).union(\n",
+ "# {\"pred_class\", \"cent_set_member\", \"rand_set_member\"}\n",
+ "# ) - {\"energy\", \"entropy\"}\n",
+ "# spec_df = spec_df.drop(columns=to_remove)\n",
+ "\n",
+ "# def do_spec(df, args):\n",
+ "# start_t = time.time()\n",
+ "# energy_spec = SpectralClustering(**args)\n",
+ "# df[\"energy_cluster\"] = energy_spec.fit_predict(df[[\"energy\"]])\n",
+ "# elapsed_time = time.time() - start_t\n",
+ "# print(f\"Energy-based Clustering Took: {time.strftime('%H:%M:%S', time.gmtime(elapsed_time))}\")\n",
+ " \n",
+ "# start_t = time.time()\n",
+ "# entropy_spec = SpectralClustering(**args)\n",
+ "# df[\"entropy_cluster\"] = entropy_spec.fit_predict(df[[\"entropy\"]])\n",
+ "# elapsed_time = time.time() - start_t\n",
+ "# print(f\"Entropy-based Clustering Took: {time.strftime('%H:%M:%S', time.gmtime(elapsed_time))}\")\n",
+ " \n",
+ "# start_t = time.time()\n",
+ "# both_spec = SpectralClustering(**args)\n",
+ "# df[\"both_cluster\"] = both_spec.fit_predict(df[[\"energy\", \"entropy\"]])\n",
+ "# elapsed_time = time.time() - start_t\n",
+ "# print(f\"Both-based Clustering Took: {time.strftime('%H:%M:%S', time.gmtime(elapsed_time))}\")\n",
+ " \n",
+ "# pickle.dump(df, args, open(\"spec.pkl\", \"wb\"))\n",
+ "# print(\"Saved to spec.pkl\")\n",
+ "# return df\n",
+ "\n",
+ "# if os.path.exists(\"spec.pkl\"):\n",
+ "# spec_df, loaded_args = pickle.load(open(\"spec.pkl\", \"rb\"))\n",
+ "# if loaded_args != spec_args:\n",
+ "# print(\"Loaded args do not match current args, re-running\")\n",
+ "# spec_df = do_spec(spec_df, spec_args)\n",
+ "# else:\n",
+ "# print(\"Loaded cached spec_df from spec.pkl\")\n",
+ "# else:\n",
+ "# print(\"No cached spec_df found, running\")\n",
+ "# spec_df = do_spec(spec_df, spec_args)\n",
+ " \n",
+ "# spec_df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 122,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # visualise the both clusters\n",
+ "# plt.close(\"all\")\n",
+ "# plt.figure(figsize=(10, 8))\n",
+ "# plt.scatter(\n",
+ "# spec_df[\"energy\"],\n",
+ "# spec_df[\"entropy\"],\n",
+ "# c=spec_df[\"both_cluster\"],\n",
+ "# cmap=\"tab10\",\n",
+ "# alpha=0.8,\n",
+ "# )\n",
+ "# plt.title(\"Energy vs Entropy Clusters\")\n",
+ "# plt.grid()\n",
+ "# plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Self Supervised Clustering\n",
+ "Use the exemplar set to seed the GMM and then cluster using the remaining data"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Data Preparation"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 123,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "k_df = gmm_df.copy() # copy the gmm_df\n",
+ "# drop columns not appearing in the master df\n",
+ "to_remove = (set(k_df.columns) - set(master_dfs[0].columns)).union(\n",
+ " {\"pred_class\", \"cent_set_member\", \"rand_set_member\"}\n",
+ ") - {\"energy\", \"entropy\"}\n",
+ "k_df = k_df.drop(columns=to_remove)\n",
+ "\n",
+ "k_args = {\n",
+ " \"n_clusters\": 2,\n",
+ " \"random_state\": SEED,\n",
+ " \"n_init\": 100,\n",
+ " \"max_iter\": 1000,\n",
+ " \"tol\": 1e-4,\n",
+ "}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Unsupervised K-Means"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 124,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create a kmeans model\n",
+ "from sklearn.cluster import KMeans\n",
+ "kmeans = KMeans(**k_args)\n",
+ "k_df[\"entropy_cluster\"] = kmeans.fit_predict(k_df[[\"entropy\"]])\n",
+ "\n",
+ "# get the cluster idx with the higher mean\n",
+ "high_entropy_cluster_idx = np.argmax(kmeans.cluster_centers_)\n",
+ "low_entropy_cluster_idx = 1 - high_entropy_cluster_idx\n",
+ "\n",
+ "k_df[\"entropy_cluster\"] = k_df[\"entropy_cluster\"].apply(lambda x: \"novel\" if x == high_entropy_cluster_idx else \"known\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 125,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "16da0853de734014bf199900dc8e4e38",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Calculating Correctness (KMeans): 0%| | 0/50000 [00:00<?, ?row/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>Sample Type</th>\n",
+ " <th>Correct</th>\n",
+ " <th>Incorrect</th>\n",
+ " <th>Total</th>\n",
+ " <th>Identification Rate (%)</th>\n",
+ " <th>Misidentification Rate (%)</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>Known</td>\n",
+ " <td>33987</td>\n",
+ " <td>1013</td>\n",
+ " <td>35000</td>\n",
+ " <td>97.105714</td>\n",
+ " <td>2.894286</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>Novel</td>\n",
+ " <td>7496</td>\n",
+ " <td>7504</td>\n",
+ " <td>15000</td>\n",
+ " <td>49.973333</td>\n",
+ " <td>50.026667</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " Sample Type Correct Incorrect Total Identification Rate (%) \\\n",
+ "0 Known 33987 1013 35000 97.105714 \n",
+ "0 Novel 7496 7504 15000 49.973333 \n",
+ "\n",
+ " Misidentification Rate (%) \n",
+ "0 2.894286 \n",
+ "0 50.026667 "
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "def get_correctness_kmeans(row):\n",
+ " return row[\"type\"] == row[\"entropy_cluster\"]\n",
+ "\n",
+ "tqdm.pandas(desc=\"Calculating Correctness (KMeans)\", unit=\"row\")\n",
+ "\n",
+ "k_df[\"kmeans_iscorrect\"] = k_df.progress_apply(get_correctness_kmeans, axis=1)\n",
+ "\n",
+ "out_df = out_df_template.copy()\n",
+ "\n",
+ "for sample_type in [\"known\", \"novel\"]:\n",
+ " filtered_df = k_df[k_df[\"type\"] == sample_type]\n",
+ " out_dict = {\n",
+ " \"Sample Type\": sample_type.capitalize(),\n",
+ " \"Correct\": filtered_df[\"kmeans_iscorrect\"].sum(),\n",
+ " \"Incorrect\": len(filtered_df) - filtered_df[\"kmeans_iscorrect\"].sum(),\n",
+ " \"Total\": len(filtered_df),\n",
+ " \"Identification Rate (%)\": (filtered_df[\"kmeans_iscorrect\"].sum() / len(filtered_df)) * 100,\n",
+ " \"Misidentification Rate (%)\": ((len(filtered_df) - filtered_df[\"kmeans_iscorrect\"].sum()) / len(filtered_df)) * 100,\n",
+ " }\n",
+ " out_df = pd.concat([out_df, pd.DataFrame(out_dict, index=[0])]) if not out_df.empty else pd.DataFrame(out_dict, index=[0])\n",
+ "\n",
+ "display(out_df)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Semi-Supervised K-Means"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# from k_means_constrained import KMeansConstrained\n",
+ "\n",
+ "# kconst = KMeansConstrained(**k_args, n_jobs=-1)\n",
+ "# k_df[\"entropy_cluster_const\"] = kconst.fit_predict(k_df[[\"entropy\"]])#\n",
+ "\n",
+ "# # get the cluster idx with the higher mean\n",
+ "# high_entropy_cluster_idx = np.argmax(kconst.cluster_centers_)\n",
+ "# low_entropy_cluster_idx = 1 - high_entropy_cluster_idx\n",
+ "\n",
+ "# k_df[\"entropy_cluster_const\"] = k_df[\"entropy_cluster_const\"].apply(lambda x: \"novel\" if x == high_entropy_cluster_idx else \"known\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# def get_correctness_kmeans_const(row):\n",
+ "# return row[\"type\"] == row[\"entropy_cluster_const\"]\n",
+ "\n",
+ "# tqdm.pandas(desc=\"Calculating Correctness (KMeans Constrained)\", unit=\"row\")\n",
+ "# k_df[\"kmeans_const_iscorrect\"] = k_df.progress_apply(get_correctness_kmeans_const, axis=1)\n",
+ "\n",
+ "# out_df = out_df_template.copy()\n",
+ "\n",
+ "# for sample_type in [\"known\", \"novel\"]:\n",
+ "# filtered_df = k_df[k_df[\"type\"] == sample_type]\n",
+ "# out_dict = {\n",
+ "# \"Sample Type\": sample_type.capitalize(),\n",
+ "# \"Correct\": filtered_df[\"kmeans_const_iscorrect\"].sum(),\n",
+ "# \"Incorrect\": len(filtered_df) - filtered_df[\"kmeans_const_iscorrect\"].sum(),\n",
+ "# \"Total\": len(filtered_df),\n",
+ "# \"Identification Rate (%)\": (filtered_df[\"kmeans_const_iscorrect\"].sum() / len(filtered_df)) * 100,\n",
+ "# \"Misidentification Rate (%)\": ((len(filtered_df) - filtered_df[\"kmeans_const_iscorrect\"].sum()) / len(filtered_df)) * 100,\n",
+ "# }\n",
+ "# out_df = pd.concat([out_df, pd.DataFrame(out_dict, index=[0])]) if not out_df.empty else pd.DataFrame(out_dict, index=[0])\n",
+ "# display(out_df)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Creating a Threshold Using A GMM\n",
+ "If we manually assign the random exemplar set as one cluster, and cluster the rest of the data, what will the assignments look like? Can we create a threshold using that?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "gm",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.19"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}