From db633de6e8dc39da336c8f8bc03c4615ed077e28 Mon Sep 17 00:00:00 2001 From: Jonas-Verhellen Date: Thu, 30 May 2024 18:57:49 +0200 Subject: [PATCH 1/4] Initial implementation of the Vietoris-Rips lifting (graph to complex) --- .../vietoris_rips_lifting.yaml | 7 + modules/transforms/data_transform.py | 4 + .../graph2simplicial/vietoris_rips_lifting.py | 57 ++ .../test_vietoris_rips_lifting.py | 87 +++ .../graph2simplicial/clique_lifting.ipynb | 75 +-- .../vietorisrips_lifting.ipynb | 520 ++++++++++++++++++ 6 files changed, 715 insertions(+), 35 deletions(-) create mode 100755 configs/transforms/liftings/graph2simplicial/vietoris_rips_lifting.yaml create mode 100755 modules/transforms/liftings/graph2simplicial/vietoris_rips_lifting.py create mode 100644 test/transforms/liftings/graph2simplicial/test_vietoris_rips_lifting.py create mode 100644 tutorials/graph2simplicial/vietorisrips_lifting.ipynb diff --git a/configs/transforms/liftings/graph2simplicial/vietoris_rips_lifting.yaml b/configs/transforms/liftings/graph2simplicial/vietoris_rips_lifting.yaml new file mode 100755 index 00000000..fec3beb9 --- /dev/null +++ b/configs/transforms/liftings/graph2simplicial/vietoris_rips_lifting.yaml @@ -0,0 +1,7 @@ +transform_type: 'lifting' +transform_name: "SimplicialVietorisRipsLifting" +complex_dim: 3 +preserve_edge_attr: False +signed: True +distance_threshold: 2.0 +feature_lifting: ProjectionSum diff --git a/modules/transforms/data_transform.py b/modules/transforms/data_transform.py index 59253ecf..9b29b280 100755 --- a/modules/transforms/data_transform.py +++ b/modules/transforms/data_transform.py @@ -15,12 +15,16 @@ from modules.transforms.liftings.graph2simplicial.clique_lifting import ( SimplicialCliqueLifting, ) +from modules.transforms.liftings.graph2simplicial.vietoris_rips_lifting import ( + SimplicialVietorisRipsLifting, +) TRANSFORMS = { # Graph -> Hypergraph "HypergraphKNNLifting": HypergraphKNNLifting, # Graph -> Simplicial Complex "SimplicialCliqueLifting": SimplicialCliqueLifting, + "SimplicialVietorisRipsLifting": SimplicialVietorisRipsLifting, # Graph -> Cell Complex "CellCycleLifting": CellCycleLifting, # Feature Liftings diff --git a/modules/transforms/liftings/graph2simplicial/vietoris_rips_lifting.py b/modules/transforms/liftings/graph2simplicial/vietoris_rips_lifting.py new file mode 100755 index 00000000..030e7e2e --- /dev/null +++ b/modules/transforms/liftings/graph2simplicial/vietoris_rips_lifting.py @@ -0,0 +1,57 @@ +from itertools import combinations + +import networkx as nx +import torch_geometric +from toponetx.classes import SimplicialComplex + +from modules.transforms.liftings.graph2simplicial.base import Graph2SimplicialLifting + + +class SimplicialVietorisRipsLifting(Graph2SimplicialLifting): + r"""Lifts graphs to simplicial complex domain using the Vietoris-Rips complex based on pairwise distances. + + Parameters + ---------- + distance_threshold : float + The maximum distance between vertices to form a simplex. + **kwargs : optional + Additional arguments for the class. + """ + + def __init__(self, distance_threshold=1.0, **kwargs): + super().__init__(**kwargs) + self.distance_threshold = distance_threshold + + def lift_topology(self, data: torch_geometric.data.Data) -> dict: + r"""Lifts the topology of a graph to a simplicial complex using the Vietoris-Rips complex. + + Parameters + ---------- + data : torch_geometric.data.Data + The input data to be lifted. + + Returns + ------- + dict + The lifted topology. + """ + graph = self._generate_graph_from_data(data) + simplicial_complex = SimplicialComplex(graph) + all_nodes = list(graph.nodes) + simplices = [set() for _ in range(2, self.complex_dim + 1)] + + # Calculate pairwise shortest path distances + path_lengths = dict(nx.all_pairs_shortest_path_length(graph)) + + for k in range(2, self.complex_dim + 1): + for combination in combinations(all_nodes, k + 1): + if all( + path_lengths[u][v] <= self.distance_threshold + for u, v in combinations(combination, 2) + ): + simplices[k - 2].add(tuple(sorted(combination))) + + for set_k_simplices in simplices: + simplicial_complex.add_simplices_from(list(set_k_simplices)) + + return self._get_lifted_topology(simplicial_complex, graph) diff --git a/test/transforms/liftings/graph2simplicial/test_vietoris_rips_lifting.py b/test/transforms/liftings/graph2simplicial/test_vietoris_rips_lifting.py new file mode 100644 index 00000000..208cc580 --- /dev/null +++ b/test/transforms/liftings/graph2simplicial/test_vietoris_rips_lifting.py @@ -0,0 +1,87 @@ +"""Test the message passing module.""" + +import torch + +from modules.data.utils.utils import load_manual_graph +from modules.transforms.liftings.graph2simplicial.clique_lifting import ( + SimplicialCliqueLifting, +) + + +class TestSimplicialCliqueLifting: + """Test the SimplicialCliqueLifting class.""" + + def setup_method(self): + # Load the graph + self.data = load_manual_graph() + + # Initialise the SimplicialCliqueLifting class + self.lifting_signed = SimplicialCliqueLifting(complex_dim=3, signed=True) + self.lifting_unsigned = SimplicialCliqueLifting(complex_dim=3, signed=False) + + def test_lift_topology(self): + """Test the lift_topology method.""" + + # Test the lift_topology method + lifted_data_signed = self.lifting_signed.forward(self.data.clone()) + lifted_data_unsigned = self.lifting_unsigned.forward(self.data.clone()) + + expected_incidence_1 = torch.tensor( + [ + [-1.0, -1.0, -1.0, -1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], + [1.0, 0.0, 0.0, 0.0, -1.0, -1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], + [0.0, 1.0, 0.0, 0.0, 1.0, 0.0, -1.0, -1.0, -1.0, -1.0, 0.0, 0.0, 0.0], + [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, -1.0, 0.0, 0.0], + [0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0], + [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, -1.0, -1.0], + [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0], + [0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0], + ] + ) + + assert ( + abs(expected_incidence_1) == lifted_data_unsigned.incidence_1.to_dense() + ).all(), "Something is wrong with unsigned incidence_1 (nodes to edges)." + assert ( + expected_incidence_1 == lifted_data_signed.incidence_1.to_dense() + ).all(), "Something is wrong with signed incidence_1 (nodes to edges)." + + expected_incidence_2 = torch.tensor( + [ + [1.0, 1.0, 0.0, 0.0, 0.0, 0.0], + [-1.0, 0.0, 1.0, 1.0, 0.0, 0.0], + [0.0, -1.0, -1.0, 0.0, 0.0, 0.0], + [0.0, 0.0, 0.0, -1.0, 0.0, 0.0], + [1.0, 0.0, 0.0, 0.0, 1.0, 0.0], + [0.0, 1.0, 0.0, 0.0, -1.0, 0.0], + [0.0, 0.0, 0.0, 0.0, 0.0, 0.0], + [0.0, 0.0, 1.0, 0.0, 1.0, 0.0], + [0.0, 0.0, 0.0, 0.0, 0.0, 1.0], + [0.0, 0.0, 0.0, 1.0, 0.0, -1.0], + [0.0, 0.0, 0.0, 0.0, 0.0, 0.0], + [0.0, 0.0, 0.0, 0.0, 0.0, 0.0], + [0.0, 0.0, 0.0, 0.0, 0.0, 1.0], + ] + ) + + assert ( + abs(expected_incidence_2) == lifted_data_unsigned.incidence_2.to_dense() + ).all(), "Something is wrong with unsigned incidence_2 (edges to triangles)." + assert ( + expected_incidence_2 == lifted_data_signed.incidence_2.to_dense() + ).all(), "Something is wrong with signed incidence_2 (edges to triangles)." + + expected_incidence_3 = torch.tensor( + [[-1.0], [1.0], [-1.0], [0.0], [1.0], [0.0]] + ) + + assert ( + abs(expected_incidence_3) == lifted_data_unsigned.incidence_3.to_dense() + ).all(), ( + "Something is wrong with unsigned incidence_3 (triangles to tetrahedrons)." + ) + assert ( + expected_incidence_3 == lifted_data_signed.incidence_3.to_dense() + ).all(), ( + "Something is wrong with signed incidence_3 (triangles to tetrahedrons)." + ) diff --git a/tutorials/graph2simplicial/clique_lifting.ipynb b/tutorials/graph2simplicial/clique_lifting.ipynb index 4d551516..5364e2f9 100644 --- a/tutorials/graph2simplicial/clique_lifting.ipynb +++ b/tutorials/graph2simplicial/clique_lifting.ipynb @@ -48,9 +48,18 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n" + ] + } + ], "source": [ "# With this cell any imported module is reloaded before each cell execution\n", "%load_ext autoreload\n", @@ -67,21 +76,9 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'data' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[14], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mdata\u001b[49m\n", - "\u001b[0;31mNameError\u001b[0m: name 'data' is not defined" - ] - } - ], + "outputs": [], "source": [] }, { @@ -100,7 +97,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -138,7 +135,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -149,17 +146,9 @@ "Dataset only contains 1 sample:\n" ] }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Processing...\n", - "Done!\n" - ] - }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -211,7 +200,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -252,21 +241,30 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Transform parameters are the same, using existing data_dir: /Users/leone/Desktop/PhD-S/projects/challenge-icml-2024/datasets/graph/toy_dataset/manual/lifting/2744620725\n", "\n", "Dataset only contains 1 sample:\n" ] }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Processing...\n", + "/home/jonasver/anaconda3/envs/topox/lib/python3.11/site-packages/scipy/sparse/_index.py:143: SparseEfficiencyWarning: Changing the sparsity structure of a csr_matrix is expensive. lil_matrix is more efficient.\n", + " self._set_arrayXarray(i, j, x)\n", + "Done!\n" + ] + }, { "data": { - "image/png": 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", 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" ] @@ -311,7 +309,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -343,7 +341,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -356,11 +354,18 @@ "source": [ "If everything is correct the cell above should execute without errors. " ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "venv_topox", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -378,5 +383,5 @@ } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/tutorials/graph2simplicial/vietorisrips_lifting.ipynb b/tutorials/graph2simplicial/vietorisrips_lifting.ipynb new file mode 100644 index 00000000..3fae3294 --- /dev/null +++ b/tutorials/graph2simplicial/vietorisrips_lifting.ipynb @@ -0,0 +1,520 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Graph-to-Simplicial Clique Lifting Tutorial" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "This notebook shows how to import a dataset, with the desired lifting, and how to run a neural network using the loaded data.\n", + "\n", + "The notebook is divided into sections:\n", + "\n", + "- [Loading the dataset](#loading-the-dataset) loads the config files for the data and the desired tranformation, createsa a dataset object and visualizes it.\n", + "- [Loading and applying the lifting](#loading-and-applying-the-lifting) defines a simple neural network to test that the lifting creates the expected incidence matrices.\n", + "- [Create and run a simplicial nn model](#create-and-run-a-simplicial-nn-model) simply runs a forward pass of the model to check that everything is working as expected.\n", + "\n", + "***\n", + "***\n", + "\n", + "Note that for simplicity the notebook is setup to use a simple graph. However, there is a set of available datasets that you can play with.\n", + "\n", + "To switch to one of the available datasets, simply change the *dataset_name* variable in [Dataset config](#dataset-config) to one of the following names:\n", + "\n", + "* cocitation_cora\n", + "* cocitation_citeseer\n", + "* cocitation_pubmed\n", + "* MUTAG\n", + "* NCI1\n", + "* NCI109\n", + "* PROTEINS_TU\n", + "* AQSOL\n", + "* ZINC\n", + "***" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Imports and utilities" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n" + ] + } + ], + "source": [ + "# With this cell any imported module is reloaded before each cell execution\n", + "%load_ext autoreload\n", + "%autoreload 2\n", + "from modules.data.load.loaders import GraphLoader\n", + "from modules.data.preprocess.preprocessor import PreProcessor\n", + "from modules.utils.utils import (\n", + " describe_data,\n", + " load_dataset_config,\n", + " load_model_config,\n", + " load_transform_config,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Loading the Dataset" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we just need to spicify the name of the available dataset that we want to load. First, the dataset config is read from the corresponding yaml file (located at `/configs/datasets/` directory), and then the data is loaded via the implemented `Loaders`.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Dataset configuration for manual_dataset:\n", + "\n", + "{'data_domain': 'graph',\n", + " 'data_type': 'toy_dataset',\n", + " 'data_name': 'manual',\n", + " 'data_dir': 'datasets/graph/toy_dataset',\n", + " 'num_features': 1,\n", + " 'num_classes': 2,\n", + " 'task': 'classification',\n", + " 'loss_type': 'cross_entropy',\n", + " 'monitor_metric': 'accuracy',\n", + " 'task_level': 'node'}\n" + ] + } + ], + "source": [ + "dataset_name = \"manual_dataset\"\n", + "dataset_config = load_dataset_config(dataset_name)\n", + "loader = GraphLoader(dataset_config)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can then access to the data through the `load()`method:" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Dataset only contains 1 sample:\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " - Graph with 8 vertices and 13 edges.\n", + " - Features dimensions: [1, 0]\n", + " - There are 0 isolated nodes.\n", + "\n" + ] + } + ], + "source": [ + "dataset = loader.load()\n", + "describe_data(dataset)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Loading and Applying the Lifting" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this section we will instantiate the lifting we want to apply to the data. For this example the clique lifting was chosen. For a clique of n nodes the algorithm for $m=3,...,max(n, complex\\_dim)$ will create simplicials for every possible combinations containing m nodes of the clique. $complex\\_dim$ is a parameter of the lifting. This is a deterministic lifting, based on connectivity, that does not modify the initial connectivity of the graph. The problem of extracting all the cliques in a graph is NP-hard, on in some formulaitons NP-complete (clique decision problem). The computational complexity of this algorithm is $O(n^k k^2)$[[1]](https://www.sciencedirect.com/science/article/pii/S0019995885800413), where $n$ is the number of nodes in the graph and $k$ is the highest clique dimension considered.\n", + "\n", + "***\n", + "[[1]](https://www.sciencedirect.com/science/article/pii/S0019995885800413) Cook, S. A. (1985). A taxonomy of problems with fast parallel algorithms. Information and control, 64(1-3), 2-22.\n", + "***\n", + "\n", + "For simplicial complexes creating a lifting involves creating a `SimplicialComplex` object from topomodelx and adding simplices to it using the method `add_simplices_from`. The `SimplicialComplex` class then takes care of creating all the needed matrices.\n", + "\n", + "Similarly to before, we can specify the transformation we want to apply through its type and id --the correxponding config files located at `/configs/transforms`. \n", + "\n", + "Note that the *tranform_config* dictionary generated below can contain a sequence of tranforms if it is needed.\n", + "\n", + "This can also be used to explore liftings from one topological domain to another, for example using two liftings it is possible to achieve a sequence such as: graph -> simplicial complex -> hypergraph. " + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Transform configuration for graph2simplicial/vietoris_rips_lifting:\n", + "\n", + "{'transform_type': 'lifting',\n", + " 'transform_name': 'SimplicialVietorisRipsLifting',\n", + " 'complex_dim': 3,\n", + " 'preserve_edge_attr': False,\n", + " 'signed': True,\n", + " 'distance_threshold': 2.0,\n", + " 'feature_lifting': 'ProjectionSum'}\n" + ] + } + ], + "source": [ + "# Define transformation type and id\n", + "transform_type = \"liftings\"\n", + "# If the transform is a topological lifting, it should include both the type of the lifting and the identifier\n", + "transform_id = \"graph2simplicial/vietoris_rips_lifting\"\n", + "\n", + "# Read yaml file\n", + "transform_config = {\n", + " \"lifting\": load_transform_config(transform_type, transform_id)\n", + " # other transforms (e.g. data manipulations, feature liftings) can be added here\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We than apply the transform via our `PreProcesor`:" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Processing...\n", + "Done!\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Dataset only contains 1 sample:\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " - The complex has 8 0-cells.\n", + " - The 0-cells have features dimension 1\n", + " - The complex has 25 1-cells.\n", + " - The 1-cells have features dimension 1\n", + " - The complex has 41 2-cells.\n", + " - The 2-cells have features dimension 1\n", + " - The complex has 39 3-cells.\n", + " - The 3-cells have features dimension 1\n", + "\n" + ] + } + ], + "source": [ + "lifted_dataset = PreProcessor(dataset, transform_config, loader.data_dir)\n", + "describe_data(lifted_dataset)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create and Run a Simplicial NN Model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this section a simple model is created to test that the used lifting works as intended. In this case the model uses the `up_laplacian_1` and the `down_laplacian_1` so the lifting should make sure to add them to the data." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Model configuration for simplicial SAN:\n", + "\n", + "{'in_channels': None,\n", + " 'hidden_channels': 32,\n", + " 'out_channels': None,\n", + " 'n_layers': 2,\n", + " 'n_filters': 2,\n", + " 'order_harmonic': 5,\n", + " 'epsilon_harmonic': 0.1}\n" + ] + } + ], + "source": [ + "from modules.models.simplicial.san import SANModel\n", + "\n", + "model_type = \"simplicial\"\n", + "model_id = \"san\"\n", + "model_config = load_model_config(model_type, model_id)\n", + "\n", + "model = SANModel(model_config, dataset_config)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "y_hat = model(lifted_dataset.get(0))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If everything is correct the cell above should execute without errors. " + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "from modules.transforms.liftings.graph2simplicial.vietoris_rips_lifting import (\n", + " SimplicialVietorisRipsLifting,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "from modules.data.utils.utils import load_manual_graph" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "data = load_manual_graph()\n", + "\n", + "lifting_signed = SimplicialVietorisRipsLifting(complex_dim=3, signed=True)\n", + "lifting_unsigned = SimplicialVietorisRipsLifting(complex_dim=3, signed=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "lifted_data_signed = lifting_signed.forward(data)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "lifted_data_signed_unsigned = lifting_unsigned.forward(data)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([[-1., -1., -1., -1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n", + " [ 1., 0., 0., 0., -1., -1., 0., 0., 0., 0., 0., 0., 0.],\n", + " [ 0., 1., 0., 0., 1., 0., -1., -1., -1., -1., 0., 0., 0.],\n", + " [ 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., -1., 0., 0.],\n", + " [ 0., 0., 1., 0., 0., 1., 0., 1., 0., 0., 0., 0., 0.],\n", + " [ 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., -1., -1.],\n", + " [ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 0.],\n", + " [ 0., 0., 0., 1., 0., 0., 0., 0., 0., 1., 0., 0., 1.]])" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lifted_data_signed.incidence_1.to_dense()" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([[ 1., 1., 0., 0., 0., 0.],\n", + " [-1., 0., 1., 1., 0., 0.],\n", + " [ 0., -1., -1., 0., 0., 0.],\n", + " [ 0., 0., 0., -1., 0., 0.],\n", + " [ 1., 0., 0., 0., 1., 0.],\n", + " [ 0., 1., 0., 0., -1., 0.],\n", + " [ 0., 0., 0., 0., 0., 0.],\n", + " [ 0., 0., 1., 0., 1., 0.],\n", + " [ 0., 0., 0., 0., 0., 1.],\n", + " [ 0., 0., 0., 1., 0., -1.],\n", + " [ 0., 0., 0., 0., 0., 0.],\n", + " [ 0., 0., 0., 0., 0., 0.],\n", + " [ 0., 0., 0., 0., 0., 1.]])" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lifted_data_signed.incidence_2.to_dense()" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([[-1.],\n", + " [ 1.],\n", + " [-1.],\n", + " [ 0.],\n", + " [ 1.],\n", + " [ 0.]])" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lifted_data_signed.incidence_3.to_dense()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.11.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From f42a978d2f8b2690cf658e85d8912d19673c4e62 Mon Sep 17 00:00:00 2001 From: Jonas-Verhellen Date: Thu, 30 May 2024 19:04:43 +0200 Subject: [PATCH 2/4] Clean-up Tutorial Notebook --- .../vietorisrips_lifting.ipynb | 141 ------------------ 1 file changed, 141 deletions(-) diff --git a/tutorials/graph2simplicial/vietorisrips_lifting.ipynb b/tutorials/graph2simplicial/vietorisrips_lifting.ipynb index 3fae3294..1460a783 100644 --- a/tutorials/graph2simplicial/vietorisrips_lifting.ipynb +++ b/tutorials/graph2simplicial/vietorisrips_lifting.ipynb @@ -353,147 +353,6 @@ "source": [ "If everything is correct the cell above should execute without errors. " ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "from modules.transforms.liftings.graph2simplicial.vietoris_rips_lifting import (\n", - " SimplicialVietorisRipsLifting,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "from modules.data.utils.utils import load_manual_graph" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [], - "source": [ - "data = load_manual_graph()\n", - "\n", - "lifting_signed = SimplicialVietorisRipsLifting(complex_dim=3, signed=True)\n", - "lifting_unsigned = SimplicialVietorisRipsLifting(complex_dim=3, signed=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [], - "source": [ - "lifted_data_signed = lifting_signed.forward(data)" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [], - "source": [ - "lifted_data_signed_unsigned = lifting_unsigned.forward(data)" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "tensor([[-1., -1., -1., -1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n", - " [ 1., 0., 0., 0., -1., -1., 0., 0., 0., 0., 0., 0., 0.],\n", - " [ 0., 1., 0., 0., 1., 0., -1., -1., -1., -1., 0., 0., 0.],\n", - " [ 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., -1., 0., 0.],\n", - " [ 0., 0., 1., 0., 0., 1., 0., 1., 0., 0., 0., 0., 0.],\n", - " [ 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., -1., -1.],\n", - " [ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 0.],\n", - " [ 0., 0., 0., 1., 0., 0., 0., 0., 0., 1., 0., 0., 1.]])" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "lifted_data_signed.incidence_1.to_dense()" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "tensor([[ 1., 1., 0., 0., 0., 0.],\n", - " [-1., 0., 1., 1., 0., 0.],\n", - " [ 0., -1., -1., 0., 0., 0.],\n", - " [ 0., 0., 0., -1., 0., 0.],\n", - " [ 1., 0., 0., 0., 1., 0.],\n", - " [ 0., 1., 0., 0., -1., 0.],\n", - " [ 0., 0., 0., 0., 0., 0.],\n", - " [ 0., 0., 1., 0., 1., 0.],\n", - " [ 0., 0., 0., 0., 0., 1.],\n", - " [ 0., 0., 0., 1., 0., -1.],\n", - " [ 0., 0., 0., 0., 0., 0.],\n", - " [ 0., 0., 0., 0., 0., 0.],\n", - " [ 0., 0., 0., 0., 0., 1.]])" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "lifted_data_signed.incidence_2.to_dense()" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "tensor([[-1.],\n", - " [ 1.],\n", - " [-1.],\n", - " [ 0.],\n", - " [ 1.],\n", - " [ 0.]])" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "lifted_data_signed.incidence_3.to_dense()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { From 37897eba36201ba1d5f7f775e551b742e098ce55 Mon Sep 17 00:00:00 2001 From: Jonas-Verhellen Date: Fri, 31 May 2024 00:41:44 +0200 Subject: [PATCH 3/4] Implementation of the graph induced lifting (graph to simplicial complex) --- ...ifting.yaml => graph_induced_lifting.yaml} | 3 +- modules/transforms/data_transform.py | 6 +- ...ps_lifting.py => graph_induced_lifting.py} | 20 +- .../test_graph_induced_lifting.py | 219 ++++++++++ .../test_vietoris_rips_lifting.py | 87 ---- .../graph_induced_lifting.ipynb | 356 ++++++++++++++++ .../vietorisrips_lifting.ipynb | 379 ------------------ 7 files changed, 585 insertions(+), 485 deletions(-) rename configs/transforms/liftings/graph2simplicial/{vietoris_rips_lifting.yaml => graph_induced_lifting.yaml} (60%) rename modules/transforms/liftings/graph2simplicial/{vietoris_rips_lifting.py => graph_induced_lifting.py} (64%) create mode 100644 test/transforms/liftings/graph2simplicial/test_graph_induced_lifting.py delete mode 100644 test/transforms/liftings/graph2simplicial/test_vietoris_rips_lifting.py create mode 100644 tutorials/graph2simplicial/graph_induced_lifting.ipynb delete mode 100644 tutorials/graph2simplicial/vietorisrips_lifting.ipynb diff --git a/configs/transforms/liftings/graph2simplicial/vietoris_rips_lifting.yaml b/configs/transforms/liftings/graph2simplicial/graph_induced_lifting.yaml similarity index 60% rename from configs/transforms/liftings/graph2simplicial/vietoris_rips_lifting.yaml rename to configs/transforms/liftings/graph2simplicial/graph_induced_lifting.yaml index fec3beb9..353ebccf 100755 --- a/configs/transforms/liftings/graph2simplicial/vietoris_rips_lifting.yaml +++ b/configs/transforms/liftings/graph2simplicial/graph_induced_lifting.yaml @@ -1,7 +1,6 @@ transform_type: 'lifting' -transform_name: "SimplicialVietorisRipsLifting" +transform_name: "SimplicialGraphInducedLifting" complex_dim: 3 preserve_edge_attr: False signed: True -distance_threshold: 2.0 feature_lifting: ProjectionSum diff --git a/modules/transforms/data_transform.py b/modules/transforms/data_transform.py index 9b29b280..faeda4bd 100755 --- a/modules/transforms/data_transform.py +++ b/modules/transforms/data_transform.py @@ -15,8 +15,8 @@ from modules.transforms.liftings.graph2simplicial.clique_lifting import ( SimplicialCliqueLifting, ) -from modules.transforms.liftings.graph2simplicial.vietoris_rips_lifting import ( - SimplicialVietorisRipsLifting, +from modules.transforms.liftings.graph2simplicial.graph_induced_lifting import ( + SimplicialGraphInducedLifting, ) TRANSFORMS = { @@ -24,7 +24,7 @@ "HypergraphKNNLifting": HypergraphKNNLifting, # Graph -> Simplicial Complex "SimplicialCliqueLifting": SimplicialCliqueLifting, - "SimplicialVietorisRipsLifting": SimplicialVietorisRipsLifting, + "SimplicialGraphInducedLifting": SimplicialGraphInducedLifting, # Graph -> Cell Complex "CellCycleLifting": CellCycleLifting, # Feature Liftings diff --git a/modules/transforms/liftings/graph2simplicial/vietoris_rips_lifting.py b/modules/transforms/liftings/graph2simplicial/graph_induced_lifting.py similarity index 64% rename from modules/transforms/liftings/graph2simplicial/vietoris_rips_lifting.py rename to modules/transforms/liftings/graph2simplicial/graph_induced_lifting.py index 030e7e2e..96a017b0 100755 --- a/modules/transforms/liftings/graph2simplicial/vietoris_rips_lifting.py +++ b/modules/transforms/liftings/graph2simplicial/graph_induced_lifting.py @@ -7,23 +7,20 @@ from modules.transforms.liftings.graph2simplicial.base import Graph2SimplicialLifting -class SimplicialVietorisRipsLifting(Graph2SimplicialLifting): - r"""Lifts graphs to simplicial complex domain using the Vietoris-Rips complex based on pairwise distances. +class SimplicialGraphInducedLifting(Graph2SimplicialLifting): + r"""Lifts graphs to simplicial complex domain by identifying connected subgraphs as simplices. Parameters ---------- - distance_threshold : float - The maximum distance between vertices to form a simplex. **kwargs : optional Additional arguments for the class. """ - def __init__(self, distance_threshold=1.0, **kwargs): + def __init__(self, **kwargs): super().__init__(**kwargs) - self.distance_threshold = distance_threshold def lift_topology(self, data: torch_geometric.data.Data) -> dict: - r"""Lifts the topology of a graph to a simplicial complex using the Vietoris-Rips complex. + r"""Lifts the topology of a graph to a simplicial complex by identifying connected subgraphs as simplices. Parameters ---------- @@ -40,15 +37,10 @@ def lift_topology(self, data: torch_geometric.data.Data) -> dict: all_nodes = list(graph.nodes) simplices = [set() for _ in range(2, self.complex_dim + 1)] - # Calculate pairwise shortest path distances - path_lengths = dict(nx.all_pairs_shortest_path_length(graph)) - for k in range(2, self.complex_dim + 1): for combination in combinations(all_nodes, k + 1): - if all( - path_lengths[u][v] <= self.distance_threshold - for u, v in combinations(combination, 2) - ): + subgraph = graph.subgraph(combination) + if nx.is_connected(subgraph): simplices[k - 2].add(tuple(sorted(combination))) for set_k_simplices in simplices: diff --git a/test/transforms/liftings/graph2simplicial/test_graph_induced_lifting.py b/test/transforms/liftings/graph2simplicial/test_graph_induced_lifting.py new file mode 100644 index 00000000..ea1c3ed3 --- /dev/null +++ b/test/transforms/liftings/graph2simplicial/test_graph_induced_lifting.py @@ -0,0 +1,219 @@ +"""Test the message passing module.""" + +import torch + +from modules.data.utils.utils import load_manual_graph +from modules.transforms.liftings.graph2simplicial.graph_induced_lifting import ( + SimplicialGraphInducedLifting, +) + + +class TestSimplicialCliqueLifting: + """Test the SimplicialCliqueLifting class.""" + + def setup_method(self): + # Load the graph + self.data = load_manual_graph() + + # Initialise the SimplicialCliqueLifting class + self.lifting_signed = SimplicialGraphInducedLifting(complex_dim=3, signed=True) + self.lifting_unsigned = SimplicialGraphInducedLifting( + complex_dim=3, signed=False + ) + + def test_lift_topology(self): + """Test the lift_topology method.""" + + # Test the lift_topology method + lifted_data_signed = self.lifting_signed.forward(self.data.clone()) + lifted_data_unsigned = self.lifting_unsigned.forward(self.data.clone()) + + expected_incidence_1_singular_values_unsigned = torch.tensor( + [3.7417, 2.4495, 2.4495, 2.4495, 2.4495, 2.4495, 2.4495, 2.4495] + ) + + expected_incidence_1_singular_values_signed = torch.tensor( + [ + 2.8284e00, + 2.8284e00, + 2.8284e00, + 2.8284e00, + 2.8284e00, + 2.8284e00, + 2.8284e00, + 6.8993e-08, + ] + ) + + U, S_unsigned, V = torch.svd(lifted_data_unsigned.incidence_1.to_dense()) + U, S_signed, V = torch.svd(lifted_data_signed.incidence_1.to_dense()) + + assert torch.allclose( + expected_incidence_1_singular_values_unsigned, S_unsigned, atol=1.0e-04 + ), "Something is wrong with unsigned incidence_1 (nodes to edges)." + assert torch.allclose( + expected_incidence_1_singular_values_signed, S_signed, atol=1.0e-04 + ), "Something is wrong with signed incidence_1 (nodes to edges)." + + expected_incidence_2_singular_values_unsigned = torch.tensor( + [ + 4.1190, + 3.1623, + 3.1623, + 3.1623, + 3.0961, + 3.0000, + 3.0000, + 2.7564, + 2.0000, + 2.0000, + 2.0000, + 2.0000, + 2.0000, + 2.0000, + 2.0000, + 2.0000, + 2.0000, + 2.0000, + 2.0000, + 2.0000, + 2.0000, + 2.0000, + 2.0000, + 1.7321, + 1.6350, + 1.4142, + 1.4142, + 1.0849, + ] + ) + + expected_incidence_2_singular_values_signed = torch.tensor( + [ + 2.8284e00, + 2.8284e00, + 2.8284e00, + 2.8284e00, + 2.8284e00, + 2.8284e00, + 2.8284e00, + 2.8284e00, + 2.8284e00, + 2.8284e00, + 2.8284e00, + 2.8284e00, + 2.8284e00, + 2.8284e00, + 2.8284e00, + 2.8284e00, + 2.6458e00, + 2.6458e00, + 2.2361e00, + 1.7321e00, + 1.7321e00, + 9.3758e-07, + 4.7145e-07, + 4.3417e-07, + 4.0241e-07, + 3.1333e-07, + 2.2512e-07, + 1.9160e-07, + ] + ) + + U, S_unsigned, V = torch.svd(lifted_data_unsigned.incidence_2.to_dense()) + U, S_signed, V = torch.svd(lifted_data_signed.incidence_2.to_dense()) + assert torch.allclose( + expected_incidence_2_singular_values_unsigned, S_unsigned, atol=1.0e-04 + ), "Something is wrong with unsigned incidence_2 (edges to triangles)." + assert torch.allclose( + expected_incidence_2_singular_values_signed, S_signed, atol=1.0e-04 + ), "Something is wrong with signed incidence_2 (edges to triangles)." + + expected_incidence_3_singular_values_unsigned = torch.tensor( + [ + 3.8466, + 3.1379, + 3.0614, + 2.8749, + 2.8392, + 2.8125, + 2.5726, + 2.3709, + 2.2858, + 2.2369, + 2.1823, + 2.0724, + 2.0000, + 2.0000, + 2.0000, + 1.8937, + 1.7814, + 1.7321, + 1.7256, + 1.5469, + 1.5340, + 1.4834, + 1.4519, + 1.4359, + 1.4142, + 1.0525, + 1.0000, + 1.0000, + 1.0000, + 1.0000, + 0.9837, + 0.9462, + 0.8853, + 0.7850, + ] + ) + + expected_incidence_3_singular_values_signed = torch.tensor( + [ + 2.8284e00, + 2.8284e00, + 2.8284e00, + 2.8284e00, + 2.8284e00, + 2.8284e00, + 2.8284e00, + 2.8284e00, + 2.8284e00, + 2.6933e00, + 2.6458e00, + 2.6458e00, + 2.6280e00, + 2.4495e00, + 2.3040e00, + 1.9475e00, + 1.7321e00, + 1.7321e00, + 1.7321e00, + 1.4823e00, + 1.0000e00, + 1.0000e00, + 1.0000e00, + 1.0000e00, + 1.0000e00, + 1.0000e00, + 1.0000e00, + 1.0000e00, + 1.0000e00, + 7.3584e-01, + 2.7959e-07, + 2.1776e-07, + 1.4498e-07, + 5.5373e-08, + ] + ) + + U, S_unsigned, V = torch.svd(lifted_data_unsigned.incidence_3.to_dense()) + U, S_signed, V = torch.svd(lifted_data_signed.incidence_3.to_dense()) + + assert torch.allclose( + expected_incidence_3_singular_values_unsigned, S_unsigned, atol=1.0e-04 + ), "Something is wrong with unsigned incidence_3 (triangles to tetrahedrons)." + assert torch.allclose( + expected_incidence_3_singular_values_signed, S_signed, atol=1.0e-04 + ), "Something is wrong with signed incidence_3 (triangles to tetrahedrons)." diff --git a/test/transforms/liftings/graph2simplicial/test_vietoris_rips_lifting.py b/test/transforms/liftings/graph2simplicial/test_vietoris_rips_lifting.py deleted file mode 100644 index 208cc580..00000000 --- a/test/transforms/liftings/graph2simplicial/test_vietoris_rips_lifting.py +++ /dev/null @@ -1,87 +0,0 @@ -"""Test the message passing module.""" - -import torch - -from modules.data.utils.utils import load_manual_graph -from modules.transforms.liftings.graph2simplicial.clique_lifting import ( - SimplicialCliqueLifting, -) - - -class TestSimplicialCliqueLifting: - """Test the SimplicialCliqueLifting class.""" - - def setup_method(self): - # Load the graph - self.data = load_manual_graph() - - # Initialise the SimplicialCliqueLifting class - self.lifting_signed = SimplicialCliqueLifting(complex_dim=3, signed=True) - self.lifting_unsigned = SimplicialCliqueLifting(complex_dim=3, signed=False) - - def test_lift_topology(self): - """Test the lift_topology method.""" - - # Test the lift_topology method - lifted_data_signed = self.lifting_signed.forward(self.data.clone()) - lifted_data_unsigned = self.lifting_unsigned.forward(self.data.clone()) - - expected_incidence_1 = torch.tensor( - [ - [-1.0, -1.0, -1.0, -1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], - [1.0, 0.0, 0.0, 0.0, -1.0, -1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], - [0.0, 1.0, 0.0, 0.0, 1.0, 0.0, -1.0, -1.0, -1.0, -1.0, 0.0, 0.0, 0.0], - [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, -1.0, 0.0, 0.0], - [0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0], - [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, -1.0, -1.0], - [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0], - [0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0], - ] - ) - - assert ( - abs(expected_incidence_1) == lifted_data_unsigned.incidence_1.to_dense() - ).all(), "Something is wrong with unsigned incidence_1 (nodes to edges)." - assert ( - expected_incidence_1 == lifted_data_signed.incidence_1.to_dense() - ).all(), "Something is wrong with signed incidence_1 (nodes to edges)." - - expected_incidence_2 = torch.tensor( - [ - [1.0, 1.0, 0.0, 0.0, 0.0, 0.0], - [-1.0, 0.0, 1.0, 1.0, 0.0, 0.0], - [0.0, -1.0, -1.0, 0.0, 0.0, 0.0], - [0.0, 0.0, 0.0, -1.0, 0.0, 0.0], - [1.0, 0.0, 0.0, 0.0, 1.0, 0.0], - [0.0, 1.0, 0.0, 0.0, -1.0, 0.0], - [0.0, 0.0, 0.0, 0.0, 0.0, 0.0], - [0.0, 0.0, 1.0, 0.0, 1.0, 0.0], - [0.0, 0.0, 0.0, 0.0, 0.0, 1.0], - [0.0, 0.0, 0.0, 1.0, 0.0, -1.0], - [0.0, 0.0, 0.0, 0.0, 0.0, 0.0], - [0.0, 0.0, 0.0, 0.0, 0.0, 0.0], - [0.0, 0.0, 0.0, 0.0, 0.0, 1.0], - ] - ) - - assert ( - abs(expected_incidence_2) == lifted_data_unsigned.incidence_2.to_dense() - ).all(), "Something is wrong with unsigned incidence_2 (edges to triangles)." - assert ( - expected_incidence_2 == lifted_data_signed.incidence_2.to_dense() - ).all(), "Something is wrong with signed incidence_2 (edges to triangles)." - - expected_incidence_3 = torch.tensor( - [[-1.0], [1.0], [-1.0], [0.0], [1.0], [0.0]] - ) - - assert ( - abs(expected_incidence_3) == lifted_data_unsigned.incidence_3.to_dense() - ).all(), ( - "Something is wrong with unsigned incidence_3 (triangles to tetrahedrons)." - ) - assert ( - expected_incidence_3 == lifted_data_signed.incidence_3.to_dense() - ).all(), ( - "Something is wrong with signed incidence_3 (triangles to tetrahedrons)." - ) diff --git a/tutorials/graph2simplicial/graph_induced_lifting.ipynb b/tutorials/graph2simplicial/graph_induced_lifting.ipynb new file mode 100644 index 00000000..fd92075c --- /dev/null +++ b/tutorials/graph2simplicial/graph_induced_lifting.ipynb @@ -0,0 +1,356 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Graph-to-Simplicial Graph Induced Lifting Tutorial" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "This notebook shows how to import a dataset, with the desired lifting, and how to run a neural network using the loaded data.\n", + "\n", + "The notebook is divided into sections:\n", + "\n", + "- [Loading the dataset](#loading-the-dataset) loads the config files for the data and the desired tranformation, createsa a dataset object and visualizes it.\n", + "- [Loading and applying the lifting](#loading-and-applying-the-lifting) defines a simple neural network to test that the lifting creates the expected incidence matrices.\n", + "- [Create and run a simplicial nn model](#create-and-run-a-simplicial-nn-model) simply runs a forward pass of the model to check that everything is working as expected.\n", + "\n", + "***\n", + "***\n", + "\n", + "Note that for simplicity the notebook is setup to use a simple graph. However, there is a set of available datasets that you can play with.\n", + "\n", + "To switch to one of the available datasets, simply change the *dataset_name* variable in [Dataset config](#dataset-config) to one of the following names:\n", + "\n", + "* cocitation_cora\n", + "* cocitation_citeseer\n", + "* cocitation_pubmed\n", + "* MUTAG\n", + "* NCI1\n", + "* NCI109\n", + "* PROTEINS_TU\n", + "* AQSOL\n", + "* ZINC\n", + "***" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Imports and utilities" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# With this cell any imported module is reloaded before each cell execution\n", + "%load_ext autoreload\n", + "%autoreload 2\n", + "from modules.data.load.loaders import GraphLoader\n", + "from modules.data.preprocess.preprocessor import PreProcessor\n", + "from modules.utils.utils import (\n", + " describe_data,\n", + " load_dataset_config,\n", + " load_model_config,\n", + " load_transform_config,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Loading the Dataset" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we just need to spicify the name of the available dataset that we want to load. First, the dataset config is read from the corresponding yaml file (located at `/configs/datasets/` directory), and then the data is loaded via the implemented `Loaders`.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Dataset configuration for manual_dataset:\n", + "\n", + "{'data_domain': 'graph',\n", + " 'data_type': 'toy_dataset',\n", + " 'data_name': 'manual',\n", + " 'data_dir': 'datasets/graph/toy_dataset',\n", + " 'num_features': 1,\n", + " 'num_classes': 2,\n", + " 'task': 'classification',\n", + " 'loss_type': 'cross_entropy',\n", + " 'monitor_metric': 'accuracy',\n", + " 'task_level': 'node'}\n" + ] + } + ], + "source": [ + "dataset_name = \"manual_dataset\"\n", + "dataset_config = load_dataset_config(dataset_name)\n", + "loader = GraphLoader(dataset_config)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can then access to the data through the `load()`method:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Dataset only contains 1 sample:\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " - Graph with 8 vertices and 13 edges.\n", + " - Features dimensions: [1, 0]\n", + " - There are 0 isolated nodes.\n", + "\n" + ] + } + ], + "source": [ + "dataset = loader.load()\n", + "describe_data(dataset)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Loading and Applying the Lifting" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For simplicial complexes creating a lifting involves creating a `SimplicialComplex` object from topomodelx and adding simplices to it using the method `add_simplices_from`. The `SimplicialComplex` class then takes care of creating all the needed matrices.\n", + "\n", + "Similarly to before, we can specify the transformation we want to apply through its type and id --the correxponding config files located at `/configs/transforms`. \n", + "\n", + "Note that the *tranform_config* dictionary generated below can contain a sequence of tranforms if it is needed.\n", + "\n", + "This can also be used to explore liftings from one topological domain to another, for example using two liftings it is possible to achieve a sequence such as: graph -> simplicial complex -> hypergraph. " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Transform configuration for graph2simplicial/graph_induced_lifting:\n", + "\n", + "{'transform_type': 'lifting',\n", + " 'transform_name': 'SimplicialGraphInducedLifting',\n", + " 'complex_dim': 3,\n", + " 'preserve_edge_attr': False,\n", + " 'signed': True,\n", + " 'feature_lifting': 'ProjectionSum'}\n" + ] + } + ], + "source": [ + "# Define transformation type and id\n", + "transform_type = \"liftings\"\n", + "# If the transform is a topological lifting, it should include both the type of the lifting and the identifier\n", + "transform_id = \"graph2simplicial/graph_induced_lifting\"\n", + "\n", + "# Read yaml file\n", + "transform_config = {\n", + " \"lifting\": load_transform_config(transform_type, transform_id)\n", + " # other transforms (e.g. data manipulations, feature liftings) can be added here\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We than apply the transform via our `PreProcesor`:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Processing...\n", + "Done!\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Dataset only contains 1 sample:\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " - The complex has 8 0-cells.\n", + " - The 0-cells have features dimension 1\n", + " - The complex has 28 1-cells.\n", + " - The 1-cells have features dimension 1\n", + " - The complex has 51 2-cells.\n", + " - The 2-cells have features dimension 1\n", + " - The complex has 34 3-cells.\n", + " - The 3-cells have features dimension 1\n", + "\n" + ] + } + ], + "source": [ + "lifted_dataset = PreProcessor(dataset, transform_config, loader.data_dir)\n", + "describe_data(lifted_dataset)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create and Run a Simplicial NN Model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this section a simple model is created to test that the used lifting works as intended. In this case the model uses the `up_laplacian_1` and the `down_laplacian_1` so the lifting should make sure to add them to the data." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Model configuration for simplicial SAN:\n", + "\n", + "{'in_channels': None,\n", + " 'hidden_channels': 32,\n", + " 'out_channels': None,\n", + " 'n_layers': 2,\n", + " 'n_filters': 2,\n", + " 'order_harmonic': 5,\n", + " 'epsilon_harmonic': 0.1}\n" + ] + } + ], + "source": [ + "from modules.models.simplicial.san import SANModel\n", + "\n", + "model_type = \"simplicial\"\n", + "model_id = \"san\"\n", + "model_config = load_model_config(model_type, model_id)\n", + "\n", + "model = SANModel(model_config, dataset_config)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "y_hat = model(lifted_dataset.get(0))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If everything is correct the cell above should execute without errors. " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.11.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/tutorials/graph2simplicial/vietorisrips_lifting.ipynb b/tutorials/graph2simplicial/vietorisrips_lifting.ipynb deleted file mode 100644 index 1460a783..00000000 --- a/tutorials/graph2simplicial/vietorisrips_lifting.ipynb +++ /dev/null @@ -1,379 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Graph-to-Simplicial Clique Lifting Tutorial" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "***\n", - "This notebook shows how to import a dataset, with the desired lifting, and how to run a neural network using the loaded data.\n", - "\n", - "The notebook is divided into sections:\n", - "\n", - "- [Loading the dataset](#loading-the-dataset) loads the config files for the data and the desired tranformation, createsa a dataset object and visualizes it.\n", - "- [Loading and applying the lifting](#loading-and-applying-the-lifting) defines a simple neural network to test that the lifting creates the expected incidence matrices.\n", - "- [Create and run a simplicial nn model](#create-and-run-a-simplicial-nn-model) simply runs a forward pass of the model to check that everything is working as expected.\n", - "\n", - "***\n", - "***\n", - "\n", - "Note that for simplicity the notebook is setup to use a simple graph. However, there is a set of available datasets that you can play with.\n", - "\n", - "To switch to one of the available datasets, simply change the *dataset_name* variable in [Dataset config](#dataset-config) to one of the following names:\n", - "\n", - "* cocitation_cora\n", - "* cocitation_citeseer\n", - "* cocitation_pubmed\n", - "* MUTAG\n", - "* NCI1\n", - "* NCI109\n", - "* PROTEINS_TU\n", - "* AQSOL\n", - "* ZINC\n", - "***" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Imports and utilities" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The autoreload extension is already loaded. To reload it, use:\n", - " %reload_ext autoreload\n" - ] - } - ], - "source": [ - "# With this cell any imported module is reloaded before each cell execution\n", - "%load_ext autoreload\n", - "%autoreload 2\n", - "from modules.data.load.loaders import GraphLoader\n", - "from modules.data.preprocess.preprocessor import PreProcessor\n", - "from modules.utils.utils import (\n", - " describe_data,\n", - " load_dataset_config,\n", - " load_model_config,\n", - " load_transform_config,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Loading the Dataset" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here we just need to spicify the name of the available dataset that we want to load. First, the dataset config is read from the corresponding yaml file (located at `/configs/datasets/` directory), and then the data is loaded via the implemented `Loaders`.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Dataset configuration for manual_dataset:\n", - "\n", - "{'data_domain': 'graph',\n", - " 'data_type': 'toy_dataset',\n", - " 'data_name': 'manual',\n", - " 'data_dir': 'datasets/graph/toy_dataset',\n", - " 'num_features': 1,\n", - " 'num_classes': 2,\n", - " 'task': 'classification',\n", - " 'loss_type': 'cross_entropy',\n", - " 'monitor_metric': 'accuracy',\n", - " 'task_level': 'node'}\n" - ] - } - ], - "source": [ - "dataset_name = \"manual_dataset\"\n", - "dataset_config = load_dataset_config(dataset_name)\n", - "loader = GraphLoader(dataset_config)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can then access to the data through the `load()`method:" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Dataset only contains 1 sample:\n" - ] - }, - { - "data": { - "image/png": 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j+vTpUCgULb+IuA0KB4QQ0kH871frceD8TUxZ9gb0PAfGGDiOg/DIX+MS7uHjhuoK5O/Jwmer30KIj7KZKztP/dkNNHLQsdBm2oQQ0gEwxrAl7Rv0798frw0ORbmBR5neglI9jzqLAAtjkD04XTFMbT0rofJ2Bf73678hZ+RAzJ49W5S6ucaWYxYUABoNEBkJVFUBKSmi1EaaRiMHhBDSARQWFmLy5Mn49ttvMXHiRLtfN3/+fBgMBuzcudOJ1bWCVgskJwN79wK3bgHffQesXAlMmQIEBQFDhwKrV4tdpdejkQNCCOkAMjMz0alTJ4wbN65Vr1u2bBmWLVuGgoICDB061EnVtUJODtCjx8ORg5UrrY+/8QYwfbq4tdmJZwzlBh6legvK9Dx0FgE8Y5ByHHxlEnRWSxGmliFUJYW0g24SReGAEELcnMViwbZt2zBnzhzIZK37a3vSpEno2rUr1q9fj48//thJFbZCYCAwbBiQkGD9+tYta1goKrIGhiNHmhw54HkeUmlzB1o7l9bE41yFEfkVBujMDMKDpaGP93zUP+4r5xAdosLgECUCFOLV3RbUIUIIIW7uyJEjuH//PpKTk1v9WplMhiVLlmDbtm2oqqpyfHGtlZAAVFQA2dnWX0VF1sdXrrR+r2dP6+ONGDFiBD744APk5+fDlTPiRl7AoeJarLtYhWMldag1CZBygFLCQSHhoJJKbL8UEg5KCQcpB9SaBBwrqcO6i1U4VFwLIy+4rOb2onBACCFuLjMzEwMGDMDgwYPb9PpFixZBEARkZGQ4uLI2Wr3aOoUwfbo1EGg01l+AtSehCdOmTcOWLVvw/PPPY+zYsfjkk09w8+ZNp5ZaVGPGt5ercabcAMYABcdBKZVA2syx2hzHQfrgeQqOA2PAmXIDUi9Xo6jG7NR6HYUaEgkhxI1ptVpER0fjvffew8r6+fk2+NWvfoWTJ0/i6NGjog7NNyk72zrlUFDwsA+hERaLBRqNBtu2bcPu3buh0+kQGxuLOXPmYObMmQ/Om3CMxnaobCtn7VDpLBQOCCHEjaWnp+Pdd99FXl4ennrqqTZf58yZM5g2bRq++eYbTJo0yYEVikev12Pfvn3YunUrvv/+ezDGMHbsWMyZMwfPP/88/PwaP3vCHk2dbdEejjrbwhUoHBBCiBubM2cOlEolNm7c2O5rTZ06FcHBwUhLS3NAZe6lsrISu3btwtatW3Hy5EmoVCpMmTIFSUlJeO655yCX279ldEunYrZHW0/FdDUKB4QQ4qaKioowatQo/O1vf8PcuXPbfb3MzEy8+eabOHr0KHr16uWACt1TcXExtm/fjq1bt+LixYsIDg7GjBkzkJSUhOHDhze7W6ORF/Dt5WpUm3gomukraA/GGEzMetZFSlSgy7a0bg0KB4QQ4qY++eQT/N///R/y8/Mdcrqh0WhEbGws5s6diw8++MABFbq/CxcuYOvWrdi2bRvu3r2LiIgIzJ49G0lJSRgwYMATzz9UXIsz5YZ29xi0pL4H4ZlQFSZEtH36w1koHBBCiBtijCEuLg4jR47Ep59+6rDr/ulPf8I333yD06dPe9VxyoIg4OTJk9i6dSt27dqF6upqDBw4EHPnzsWsWbPQrVs3aE081l2sAmOAXOL8zYvMAgPHAcsGBLndPggUDgghxA3l5eVhxowZ2Lx5M+Lj4x123eLiYowaNQr/3//3/yHFS880MJvN+P7777F161bs3bsXRqMRo0aNwqTX3kHdU1FQOrjPoCn10wtjwn0w+in3CmruN9FBCCEEmZmZ6NKlC0aPHu3Q60ZERCAxMRHr16936UZC7kQul2Py5Mn4xz/+gYKCAvzlL3+BUq1GkUWJujodqqqrYDAawODc/z4cxwEMyK8wgHez3wsKB4QQ4mZMJhN27NiBuXPnOmVPgmXLluHChQs4ceKEw6/d0fj5+SE5ORl/+WoDOkdEQimTQhAEVFVVoay0FNXaahhNRqcFBZmEg85sPavBnVA4IIQQN3PgwAFUV1dj3rx5Trl+fHw8+vTpg6+//top1++ISvUWMHDwUakRGhKKTp06wcfXF2azGZWVlSgrK4O2RotLJzUoPLIHp7I3216b8cFvGnzdGhJYpxfK9BYH/SSOQeGAEELcTGZmJp5++mlERUU55focx2HZsmX45z//iZ9//tkp9+hoyvQ8JI8sXZRJZfDz9UOn0FCEhoZCrVKh5NY1GAUGVacu+D79C1h46xt6t/5DUH63qE335R7cs1RPIweEEEKaUFFRgYMHDzpt1KDevHnzoFQqkZqa6tT7dBQ6i9DgdMWHOMhlcvj7B4Az6tE/5llcP6VBxKBncP/+fZRXlKPPyASEdo1s870FxlBnca9DmSgcEEKIG9mxYwcAYNasWU69j7+/P5KTk5GamgqzuWMcBuRM9jQE9o0dA6VCiSs/HMbIF+YgKDAIPM+jtqYGPZ4eAQDQ12qRs3EtcjaubdX9LdSQSAghpClZWVkYP368Qw8Qasorr7yC0tJS/POf/3T6vdyd1M6li/paLe5eOY8+saNhMBogCAJKblxGaDfryMG1vOOo01a2+v4yFyydbA0KB4QQ4iauXr2KM2fOIDk52SX3i4qKQnx8PNatW+eS+7kzX5nErh0RK+8WI6RLd1RWVsJoNEKlUkEqlUIqsa4qGTJuSqunGCQcBx+Ze70du1c1hBDixbKyshAQEIDExESX3fOVV17BqVOncO7cOZfd0x11VkshMNbi3g9yHx9YLBZYLBYEBwfj4tGDGJTQ9t8v9uCeYWr32iGRwgEhhLgBQRCwZcsWzJw5E0ql0mX3nTx5Mrp06YL169e77J7uKEwtg4Tj0FxboIW3ACpf9H12HK4fP4TLx75HaGTvVp34+DgB1hULndWyNl/DGSgcEEKIG/jhhx9w584dl00p1JPJZFiyZAm2bt2Kqqoql97bnYSqpPCVc7AIjY8cmMwmVFRUgOM4zH7zfYya+SIGJkyCf+cukMna/sZuERh85RxCVTRyQAgh5DGZmZno0aMHhg8f7vJ7L1q0CDzPY+PGjS6/t7uQchyiQ1QAhyemFowmIyorKyGTyRASEmLrL7BYrPsctHXkgDEGcEB0iMruhkhXoXBACCEi0+v1yM7Oxrx581xy4M/jOnXqhJkzZ2LDhg0QBPdab+9Kg0OUkHIcLI9kA71Bj6rKKigVSgQHB0PCPXzbNJvNkEgkkEoePnY17/iDX8dQeGRvs/ezMGsoGRziumkke7nXJAchhHihPXv2QKfTOX3jo+YsXboUW7Zswffff4+JEyeKVoeYAhRSRIcocabcAIEx6PV61NTUwEftA/8Af3BoGNwsFsuDKYWHj/eNHY2+sS0fliUwBgEMz4So3O64ZoBGDgghRHSZmZkYMWIEevToIVoNzzzzDJ5++mmvb0yM6+KDQIUUOoMJNTU18PPzQ0AjwQCwjhy0ZUqBMQYzYwhSSBHXxb2Oaq5H4YAQQkRUUlKCnJwclzciPo7jOLzyyis4dOgQbt68KWotYuJ4Cwo2fw6jwQC/oBD4+vgCjQQDgQngeb7VzYiMMZgEBgnHITHCD0qpe74Nu2dVhBDiJbZt2waZTIYZM2aIXQpmzZqF4OBgbNiwQexSRFFTU4OUlBTsWP8P9OXvQymXwyQ0vveB5cGW060ZOXg0GEzs5otI/7YvgXQ2CgeEECKizMxMTJkyBYGBgWKXApVKhUWLFiEjIwN1dXVil+NSpaWlmDt3LvLz87Fx40akjB+JiRG+kEg4mBh74lAms8UCjuMgldrXLyAwBhNjkEg4TIzwRXSoyhk/hsNQOCCEEJGcP38eFy5cELUR8XEvv/wyampqsG3bNrFLcZmbN29i1qxZKCsrw7Zt2zBq1CgAQHSoCnN7BSBIIYWZMZgfGUWwmM2Qy+SN9iI8ij14XX2PwdxeAW4fDAAKB4QQIprMzEyEhITgueeeE7sUm+7duyMxMRHr1q1rcSthT/DTTz9hxowZkMlk2LVrFwYOHNjg+5H+cqREBeKZUBU4DjAxBiMvwMIYZPLG+w0YY+AfPM/EGDgOeCZUhZSoQLeeSngUhQNCCBGBxWLBtm3bMGfOnHZtv+sMy5Ytw4ULF3Dq1CmxS3GqnJwczJ07Fz169MCOHTsQERHR6POUUgkmRPhh2YAgjAn3gY+MAzgJpEo1TAKDgRdsv0yCta+AZ4CfQoIx4T5YNiAIE9y4+bAxtM8BIYSIQKPRoLS0VPRVCo2Jj49Hnz59sG7dOowcOVLscpxi27Zt+PWvf42xY8fi888/h49Py0sKAxRSjH7KB9Li83j13bfxP5+vgyL0KdRZHowkPDhdMUwtRWe1DKEqqdvtfGgvCgeEECKCzMxMREVFYejQoWKX8gSJRIJXXnkFH3zwAUpKShAeHi52SQ71xRdf4I9//CPmz5+P//7v/271yM25ggJo797EhIGRbjfq4ygdZ4yDEEI8RE1NDXbv3i3adsn2SE5OhkKhQGpqqtilOIwgCPjwww/xxz/+Ef/6r/+KTz75pE1v7gUFBRgwYIDHBgOAwgEhhLjcP//5T5hMJiQlJYldSpMCAgKQnJyMb7/9FuYHa/o7MrPZjF//+tf47LPP8B//8R/43e9+1+ZgVlBQgCFDhji4QvdC4YAQQlwsMzMTcXFx6Nq1q9ilNOuVV15BaWkpdu/eLXYp7aLT6fDKK69gx44d+Pvf/47ly5e3+VomkwmXL192y+kgR6JwQAghLlRcXIxjx465ZSPi4/r3748xY8Zg3bp1YpfSZhUVFZg/fz5OnjyJ1NRUzJo1q13Xu3TpEsxmM4UDQgghjrNlyxao1WpMnTpV7FLssnTpUpw8eRLnz58Xu5RWu337NmbOnInbt29j69atSEhIaPc1CwsLIZFIntgPwdNQOCCEEBdhjCEzMxNTp06Fr6+v2OXYZcqUKejSpUuHO63x/PnzmDFjBgRBwM6dOx32Sb+goAB9+/a1a+ljR0bhgBBCXOTs2bO4fv16h5hSqCeTyfDSSy9hy5YtqK6uFrscuxw7dgxz5sxBeHg4duzYgZ49ezrs2t7QjAhQOCCEEJfJyspCeHg44uLixC6lVRYvXgye57Fx40axS2lRdnY2Fi5ciGHDhmHLli3o3Lmzw67N8zzOnTvn8f0GAIUDQghxCbPZjG3btmHu3Ll2n+TnLjp37owZM2Zgw4YNEARB7HKa9PXXX+P111/HtGnTkJqaCj8/P4de/9q1azAYDDRyQAghxDEOHjyIqqoqtzqBsTWWLl2Kmzdv4vDhw2KX8gTGGD766CP87ne/w/Lly/G///u/TtmgqLCwEABo5IAQQohjZGZmYsiQIRgwYIDYpbRJTEwMhg4d6naNiRaLBe+++y4+/fRTrF69Gn/84x8hkTjnra2goACRkZEICAhwyvXdCYUDQghxsqqqKhw4cKBDNSI+juM4LFu2DIcOHcLNmzfFLgcAYDAYsHz5cmzatAl/+ctfsHLlSqduR11YWOgVowYAhQNCCHG6HTt2QBAEzJ49W+xS2mXWrFkIDAzEN998I3YpqKqqwoIFC6DRaLBhwwanBy/GGAoKCigcEEIIcYysrCyMHz/eoZ3zYlCpVFi0aBHS09Oh1+tFq+Pu3buYPXs2rl69iszMTEyYMMHp9ywqKoJWq/WKZkSAwgEhhDjV9evXkZeX12EbER/38ssvo6amBtu2bRPl/pcvX8aMGTNQV1eHnTt3IiYmxiX39aZmRIDCASGEOFVWVhb8/f0xefJksUtxiMjISEyaNAnr168HY8yl9/7xxx8xa9YsBAUFYdeuXejTp4/L7l1QUIDw8PAOP/pjLwoHhBDiJIIgYMuWLZgxYwZUKpXY5TjMsmXLcO7cOfz4448uu+e+ffuQnJyMgQMHYtu2bQgPD3fZvQHvakYEKBwQQojTnDx5Erdv3+7QqxQak5CQgF69ernstMaMjAwsW7YMEydOREZGhihLCb1l2+R6FA4IIcRJMjMzERkZiREjRohdikNJJBIsXboU3333HUpKSpx2H8YY/vKXv+Dtt9/GSy+9hM8//xxKpdJp92tKSUkJysrKaOSAEEJI+xgMBmRnZ2Pu3LlO25RHTPPnz4dCoUBaWppTrs/zPFavXo01a9bg3XffxZ/+9CfRtp0uKCgAABo5IIQQ0j779u1DTU2Nx6xSeFxAQADmzZuHb7/9Fmaz2aHXNhqN+MUvfoENGzbgv//7v/Gb3/zGqZsbtaSwsBCBgYGIiIgQrQZXo3BACCFOkJmZidjYWPTq1UvsUpzmlVdeQUlJCXbv3u2wa2q1WixevBj79+/HV199hcWLFzvs2m1Vv/mRmAHF1SgcEEKIg5WWluLw4cMe14j4uAEDBmDFihUOW7VQUlKCpKQkFBYWYtOmTXj++ecdct328qadEevJxC6AEEI8zfbt2yGRSDBz5kyxS3G6Dz74wCHXuX79OhYuXAiz2YwdO3agf//+Drlue1VVVaG4uNjrwgGNHBBCiINlZmYiMTERQUFBYpfSIZw9exYzZ86ESqXCrl273CYYAA93RvSmZkSAwgEhhDjUhQsXcO7cOY+fUnCU77//HnPnzkWvXr2wY8cOdOvWTeySGigoKICPj49H9440hsIBIYQ40JYtWxAcHOySw4A6uqysLCxZsgTx8fHYvHmzW460FBQUYNCgQaItoxQLhQNCCHEQnuexZcsWzJ49G3K5XOxy3NrGjRvxq1/9CvPmzcPatWuhVqvFLqlR3rZtcj0KB4QQ4iC5ubkoKSmhKQU7/P3vf8ebb76JP//5z5DJ3LM3XqfT4dq1a4iOjha7FJdzz98RQgjpoP72t7/h6aefFrsMt/fmm28iKSlJ7DKadf78eTDGvK4ZEaBwQAghDsEYw5gxYyCRSLxqsxwAQEEBoNEAkZFAVRWQktLiS9w9GADWKQW5XI6oqCixS3E5CgeEEOIAHMd5Z5+BVgu88w6wdy9w6xbw3XfWx7Ozrf88exZYvVq08tojPz8fAwYM8MrfV+o5IIQQ0nY5OUCPHtaRAwBYudIaDAIDgenTgZAQIDVV3BrbyFubEQEKB4QQQtojMBAYNgxISLCGhFu3rKEgIcH6/Zs3gQ7Yg2EymXDp0iWv7DcAaFqBEEJIeyQkAEeOPJxGCAy0hgTAOpoQHQ10wE/fly5dgsVi8dqRAwoHhBBC2qexnoKCAqC62tqcWFDQ4QJCQUEBJBIJBg0aJHYpoqBpBUIIIY516xbw2mvAt98CU6ZYVzB0MAUFBejbt6/bbs7kbDRyQAghrdWGpXtepUcP4PhxsatoF29uRgRo5IAQQlqnfuneypXWoXKt1vr4hx9a590/+0zc+ki78TyPc+fOeW0zIkDhgBBCWqeppXs9e1q79AMCHn7PCzDGwPM8LBYLBEFo8L36xxljIlXXNvfu3cOzzz6LhPoVF16IwgEhhLRGY0v3qqoejiAA1mkHL2A0GnH48GFkZ2ejvLwcEknDt5Ta2lrs2LEDd+7cEanCtomIiEBGRobXNiMCFA4IIaR1EhKAigrraEF2NlBUZO05qKiwjhjk54tdoUvcuXMHiYmJeOONN9CjRw+Eh4c/8ZzAwECkpaXhzTffFKFC0h4c62jjPYQQ4o60WuuUwmefAdOmPVzr74EuXryIRYsWQaFQICMjA7169WryuTt37sS//Mu/4ODBgxg4cKALqyTtQSMHhBDSXlqtdYvg7GzrCgYPDgYnTpzA7NmzERISgp07dzYbDADghRdeQHh4OL7++mvXFEgcgkYOCCGE2GXPnj34l3/5F4wYMQJr165FQECAXa/7+OOP8X//9384c+aM3a8h4qKRA0IIIS1KTU3F8uXLMWXKFKSlpbXqTT4lJQUWiwWbN292YoXEkSgcEEIIaRJjDB9//DHee+89LFmyBJ999hkUCkWrrhEWFoapU6di/fr1Tyx3JO6JwgEhhLTClStX8Oqrr+LSpUtil+ISVVVVSEtLw7/927/hww8/hFQqbdN1li1bhhs3bkDjjntAFBRYG0mzszvs8dKORj0HhBDSCu+++y4OHz6MEydOPLGu3xNZLBYUFxejZ8+e7boOYwyTJ09G165dsWHDBscU5whaLZCcDOzda92z4rvvrBtbpaY+3MfCC7fH9vw/2YQQ4iBGoxG7du3CvHnzvCIYAIBMJmt3MAAAjuOwbNkyHDhwAEVFRe0vzFEa2/Gy/t8TEoCgoIfHUXsR7/jTTQghDrBv3z5otVrMmzdP7FI6pNmzZyMgIADffPON2KU81NiOlwUFD5ejBgYCZ8+KWaEoKBwQQoidsrKyEBMTgz59+ohdSoekVquxcOFCpKenw2AwiF2OVWM7XgJAdbW4dYmMwgEhhNjh/v37OHToEI0atNPLL7+M6upq7NixQ+xSHlq92npo1vTp1rAwdKj1vAzAGhKGDROzOlFQOCCEEDts374dEokEs2bNEruUDq1nz56YMGEC1q1b576nNSYkWBsVNRrrSML06WJX5HK0WoEQQuzw/PPPo2vXrli3bp3YpbRPQYH1TS8y0vrpWIRO/EOHDiElJQW7du1CbGysy+9PWkYjB4QQ0oJLly4hPz8fycnJYpfSPlot8M471o78oUMbHjOt1QLvveeSMp577jn07NkT69evd8n9SOtROCCEkBZkZWUhKCgIEydOFLuU9mls2d6j36ufZ3cyiUSCV155Bbt27UJZWZlL7tkSnudhNpvdd6rDxSgcEEJIM3iex5YtWzBr1qxWbxvsdhpbtgdYu/RdPK++YMECSKVSpKWlufS+TTlx4gSOHDkCjuPELsUtUDgghJBmHDt2DD///LNnrFJobNleQYF1isHFAgMDMXfuXHzzzTewWCwuv//jVq1ahe+//17sMtwGhQNCCGlGVlYWevXqhZiYGLFLcYzHl+0B1oCQnf1wAyAXWbp0KX7++Wfs3bvXZfdsjE6nw/Xr1xEdHS1qHe6EwgEhhDRBp9Phu+++Q3JysucONw8dag0KVVUNGxRdYNCgQRg5cqTojYnnz58HYwxDRRhBcVcUDgghpAm7d+9GXV0d5s6dK3YpbWZ3g11KCnD8uMunGJYtW4Zjx47h4sWLLr3vowoKCiCXy9GvXz/RanA3FA4IIaQJmZmZGDVqFLp37y52KW1y7Ngx6PV68DwvdilNeuGFFxAeHi7qSY0FBQUYOHAg5HK5aDW4GwoHhBDSiJ9//hm5ubkddm+D7du3Y+HChVizZo1bT4nI5XKkpKQgMzMTWhdPa9QrKCjAkCFDRLm3u6JwQAghjdi6dSsUCgWmTZsmdimt9uWXX2LlypWYM2cOVq9e7fbHS6ekpMBkMiEzM9Pl9zaZTLh8+TL1GzzGvf/EEEKICBhj2Lx5M55//nkEBASIXY7dGGP48MMP8Yc//AG//OUv8cknn3SIofLw8HBMnToV69evhyAILr33xYsXYbFYaOTgMRQOCCHkMYWFhbh8+XKHmlIwm8349a9/jc8++wwffPAB3n//fbeeTnjcsmXLcP36deTm5rr0voWFhZBIJBg0aJBL7+vuKBwQQshjMjMz0blzZ4wdO1bsUuxSV1eHpUuXYvv27fjss8+wYsUKsUtqtREjRmDQoEEuP9iqoKAAffv2hVqtdul93R2FA0IIeYTZbMb27dsxZ84cyGQysctpUUVFBZKTk3HixAl8++23mD17ttgltQnHcVi6dCn279+P27dvu+y+BQUF1G/QCAoHhBDyiCNHjuD+/fsdYkrh9u3bmDVrFm7fvo0tW7Z0mJGOpsyZMwf+/v745ptvXHI/i8WC8+fPUzhoBIUDQgh5RFZWFgYOHOj2c9AXLlzAzJkzYbFYsHPnTo/Y+tfHxwcLFy5Eeno6DAaD0+937do1GAwGakZsBIUDQgh5QKvVYs+ePZg3b55bN/MdP34cc+bMQVhYGHbu3ImePXuKXZLDLFmyBJWVldixY4fT71VYWAgAFA4aQeGAEEIe2LVrFywWC5KSksQupUnfffcdFi5ciOjoaGzZsgWdO3cWuySH6tmzJyZMmID169fbv/VzGxUUFKBHjx4darmqq1A4IISQB7KyspCQkIDw8HCxS2nUhg0b8Nprr+GFF15Aamoq/Pz8xC7JKZYuXYr8/HycOXPGqfcpLCykfoMmUDgghBAAt27dwokTJ9yyEZExhv/+7//Gb3/7W7z66qv4v//7PygUCrHLcprx48ejR48eTj2tURAE2ja5GRQOCCEEwJYtW+Dr64vnn39e7FIasFgseO+99/DJJ5/g/fffxwcffOD22yG3l0QiwZIlS7Bz507cv3/fKfcoKipCTU0NjRw0wbP/hBFCiB0YY8jKysK0adPg4+Mjdjk2BoMBK1aswMaNG/Hpp5/il7/8pVs3SjrSiy++CKlUivT0dKdcn5oRm0fhgBDi9fLy8nDz5k3MmzdP7FJsqqur8eKLLyInJwdff/015s+fL3ZJLhUUFISkpCRs2LABFovF4dcvKChAeHi4xzV0OgqFA0KI18vKykLXrl0xZswYsUsBANy7dw+zZ8/GlStXkJmZiYkTJ4pdkiiWLl2Ke/fuYd++fQ6/dmFhoUfsDeEsFA4IIV7NZDJh+/btmDt3rlvM5V+5cgUzZsxAbW0tdu7ciZiYGLFLEs3gwYMxYsQIhzcmMsaoGbEF4v+fQAghItq/fz+0Wq1bTCn8+OOPmDlzJgIDA5GdnY0+ffqIXZLoli1bhqNHj+Ly5csOu2ZpaSnu379PzYjNoHBACPFqmZmZePrpp9GvXz9R69i/fz/mz5+PgQMHYtu2bW6714KrTZ06FWFhYfj6668dds38/HwA1IzYHAoHhBCvVV5ejkOHDom+t8HGjRuxbNkyTJgwARkZGbRj3yPkcjlSUlKQmZmJmpoah1yzsLAQQUFB6Natm0Ou54koHBBCvNbOnTsBALNmzRLl/owx/OUvf8Fbb72FxYsX4/PPP4dSqRSlFnf20ksvwWg0IisryyHXq+838JZloW1B4YAQ4rUyMzMxYcIEhIaGuvzePM9j9erVWLNmDd555x3813/9F6RSqcvr6AjCw8MxdepUrFu3ziHnLRQUFNBKhRZQOCCEeKUrV67g7NmzokwpmEwmrFy5Ehs2bMBHH32Et956iz7FtmDp0qW4du0acnNz23WdyspK3Llzh5oRW0DhgBDilbKyshAQEIDExESX3ler1WLx4sXYu3cvvvrqK6SkpLj0/h3VyJEjMXDgQKxbt65d16GdEe1D4YAQ4nUEQcCWLVswa9Yslx5gVFJSgqSkJBQUFGDz5s1ud46DO+M4DkuXLsX+/ftRXFzc5usUFBTA19cXvXr1cmB1nofCASHE6xw/fhx379516ZTCjRs3MHPmTFRUVGD79u0YOXKky+7tKZKSkuDn54dvvvmmzdcoKCjAoEGD3GLDK3dG/3UIIV4nKysLPXv2RGxsrEvud/bsWcyYMQNKpRK7du3CgAEDXHJfT+Pj44MFCxYgLS0NRqOxTdcoLCykfgM7UDgghHiVuro6ZGdnY968eS5pAjx8+DDmzZuHXr16YceOHbS2vp1eeeUVVFZW2pahtkZtbS2uX79O4cAOFA4IIV5lz5490Ol0mDt3rtPvtWXLFrz88suIi4vD5s2bERwc7PR7erpevXph/PjxbTpv4fz582CMUTiwA4UDQohXycrKwsiRI9GjRw+n3ucf//gH3njjDcydOxdr166FWq126v28ydKlS3H27FmcOXOmVa8rLCyEXC4XfavsjoDCASHEa5SUlCAnJ8epjYiCIOA//uM/8B//8R/41a9+hY8//hgymcxp9/NG48ePR2RkZKtHDwoKCjBw4EDI5XInVeY5KBwQQrzG1q1bIZPJMGPGDKdc32w241e/+hU+//xzfPjhh/i3f/s32tzICaRSKZYsWYIdO3agvLzc7tcVFBTQlIKdKBwQQrwCYwyZmZl4/vnnnXKwkU6nw8svv4xdu3bh73//O5YtW+bwe5CHFi5cCIlEgvT0dLuebzKZcPnyZdr8yE4UDgghXuH8+fO4ePGiUxoR79+/j3nz5iEvLw9paWmYOXOmw+9BGgoKCsKcOXOwYcMGWCyWFp9/8eJFWCwWGjmwE4UDQohXyMrKQmhoKJ577jmHXreoqAgzZ87E3bt3sXXrVsTHxzv0+qRpS5cuxd27d7F///4Wn1tQUACJRIKBAwe6oLKOj8IBIcTjWSwWbN26FXPmzHFoM9q5c+dsowQ7d+6kIWsXGzp0KIYPH25XY2JhYSH69etHq0bsRC20hBCPl5OTg7KyMoeuUsjNzcWyZcvQp08ffPvtt+jUqZPDrk3st2zZMqxcuRJXrlxB7759UW7gUaq3oEzPQ2cRwDMGKcehpssAjJjVC6V6C0JVUkipUbRZHHPE4diEEOLGfvGLX+DChQv4/vvvHbJ6YOfOnXjjjTcwZswYfPXVV/D19XVAlaQtzGYzEhKfxwvL3sBTw8dCZ2YQGIOE4yA88vZWp9NBoVRCIZfDV84hOkSFwSFKBCikIlbvvmhagRDi0bRaLfbs2YPk5GSHBIP169fjF7/4BWbMmIFvvvmGgoGIjLwATYkRsz/aAEm/GNSYBEg5QCnhoJBwUEklUEklkEGASa+DXAJIOaDWJOBYSR3WXazCoeJaGHlB7B/F7dC0AiHEo/3zn/+EyWRCUlJSu67DGMOaNWvw17/+Fa+//jp+//vf08l+IiqqMWNfcS2qTTzkCiV09+9DKZVAqvZ54rlmsxkAIJfJIOE4SKUcGGOwMOBMuQE3asxIjPBDpD9tjlSPphUIIR5t7ty5kMlk2LRpU5uvYbFY8N5772Hjxo34/e9/j1/84hcOrJC0Vn65AQfv6CAwBjnHQcJxqKqugsViQafQUAANR4hqamtgNBjQqVPnJ64lMAbzg2mIid18ER2qctFP4d4o9hJCPNbt27dx/PjxdjUi6vV6vPrqq8jKysJf//pXCgYiyy834GCxDoLAoHgQDADrcc4WiwUmk+mJ15jNZsiaWKUi4TgoOA6CwHCwWIf8coNT6+8oaFqBEOKxtmzZArVajRdeeKFNr6+srMTLL7+MCxcuYMOGDRg/fryDKyStUVRjto0YKCRcgx4ShVwOmUyGOr0eCoXS9jgDg8Vsga/vk9MN9TiOg0ICmASGg3d0CFJIvX6KgUYOCCEeiTGGrKwsTJs2rU1Ng3fv3sXs2bNx48YNZGZmUjAQmZEXsK+4ttFgYMXBx8cHRoMBvMDbHuV5HgITmhw5sL2aszYxCoxhPzUpUjgghHimM2fO4Pr1622aUrh06RKmT58Og8GAnTt34plnnnFChaQ1jt6rszYfco0FAyu1SgWOk6Curs72WP3WynI7TsbkOA5yjkOVicfRe3UtPt+TUTgghHikrKwsPPXUUxgzZkyrXnfy5EnMmjULISEh2LlzJ3r37u2kCom9tCYe+RVGSPCwx6AxHCeBSq2CXq8Hg7XX3mw2QyqVQiKxbz8DCcdBAg75FUZoTXzLL/BQFA4IIR7HZDJh+/btmDt3LqRS+ze52bt3LxYsWIAhQ4Zg69atCA8Pd2KVxF7nKozgGYPMjm0qfHx8IAgCDAZrY6HFYoHMjlGDR8k4gGcM5yqMbSnXI1A4IIR4nIMHD6Kqqgrz5s2z+zVpaWl49dVXkZiYiPT0dKcc60xaj2cM+RUGgMGuTaxkUhmUSuWDqQUGs9nc6vM0OI4DGJBfYQDvpav9KRwQQjxOVlYWhg4div79+7f4XMYYPv74Y7z77rtYsmQJ/v73v0OhULigSmKPcgMPnZlBJrF/d0sftQ/MZjOMJhMEQWj1yAEAyCQcdGaGcoN3Ti3QUkZCSIfBM9bkwTq+Mgk6q6VQmetw6PvDeP93v235ejyP1atXY8OGDVi1ahV+9atfOWSLZeI4pXqLbbOj5lzNOw5DrRb6mmoMn54MqVSKjD/+Bt0GP4MJC5e1+r4SABbGUKa3IEztfW+V3vcTE0I6HK2Jx7kKI/IrDE0erFP/NW8xY8FfN6PnoB7QmvgmD9YxGo345S9/iT179uDPf/4zFi5c6Kofh7RCmZ6HpJkVCgBQcfc2fPwDEdIlAuvfW44R0+fDx8cHnXr2g7bkLqRt2Oaa4zhwHFCq5zG4PT9AB0XhgBDitoy8gKP36pD/oCENzDrc+3A5W8M3DMaASr0e/p3D8VMNUHixCtEhSsR18YFS+vANQqvVYunSpTh9+jTWrVuHyZMnu/gnI/bSWYQHIbCpcMBQfq8IvYY9i5yNa9Ejejh0dTrwFgv6jEzAvQs/NfPa5gmMoc7infsdUDgghLilRw/WkcC6xS3XwrwzL/AwGQ0IUqug4LhGD9YpKSnBokWLcPfuXWzevBkjRoxw0U9EWkMQBJSXl6Oi0gBeUEJvNlo3NBIE6y+eB//g3/0j+uD+/fv46UA24hb/C3Q6nW20of+zCQCAwiN7AQD6mmoEd+mOvrGj7arD4qUNiRQOCCFup7GDdexhMBggkUigVCrBgYOcAwQGVJl4bLmhxVC5Dr9bMg88z2PHjh2Iiopy8k9CHmc2m3H//n2UlJSgtLS0wT/r//3nn3/G/fv3wfM8Jv3m/6Ff/GQYdTWQSCSQSiSQSKWQymSQSyQP9jCQwFSnw/1b1zBi0lRw4FCnr8P9m1fQb0YyKu7extW845j91h8BAOvefdXucCDz0h4UCgeEELdiO1inyW1yG8fAoNfroXoQDOpJOA4KAAYLj2M1Avo9Nw3/85vX0KVLFyf9BN7JaDSitLTU9ub++Bt+/T/Ly8vx6GHAEokEnTt3RlhYGMLDwzF48GBMmDAB4eHhCA8PR2VYPxRzKgT5+TT4fX1cxa3rCOna3fYcvV4PmVwOqUSKa6ePQ+XnZ3uu2i8AV/OOtxgQJBwHH5l3LuqjcEAIcRvNHazTErPZDJ7noVKrn/ieyWyCtqoKKl9/xC7+Jcx+gY4s26PpdLpGP+E//sZfXV3d4HVyudz2hh8WFoYRI0Y0+Pqpp55CWFgYQkNDm92oqrDCgOKiWqC5tgMAKj9/27+bLWac1+zHiOdnAwDK7xbBJyDY9n21fyAMtdpmf27GGBhjCFPbv4mWJ6FwQAhxCy0frNM8vV4PqVQKxWMb3ugNemirtVAoFfD3UcHMgP3FtUiJCmzQpOhNGGOoqalp9hN+/WM6na7Ba1Uqle1TfXh4OPr37297w3/0n8HBwQ5ZFhqmlllXogBo7m06pGt3DB6biFPZm8FkcoT36Q+lUtnk8/U11U1+DwAEWFcsdPbCZYwAhQNCiJuw52CdpjAwGA0G+Pj64uHHSwZdXR1qamqgVqsREBBg7UMAsx2sMyHCr7nLdgwFBYBGA0RGAlVVQEpKiy+ZOXMm8vLyGjzm7+9ve2MPDw/H008//cQbflhYGPz9/V26F0SoSgpfOYdakwCptPn7Pv/aO2BgKCsrg1qttk0xhHaNhL62xva8+qbE5lgEBj+FBKEqGjkghBBR2HuwTlOMBiMExqBSqQBYw0JtTQ10dXXw8/OD3yOhQcJxkDAgv8KI4WHqJvdBcCcWiwU6nQ6BgY9Nh2i1wDvvAHv3ArduAd99Z308Oxv429+sjzdi2bJleO211xq88asbmY5xB1KOQ3SICsdK6sAYazGYGI1GCILQ4OfpEzMae7/82PZ1xb3bzfYbMMYADogOUUHqpQ2JHGNeuk6DEOI2jv9ch2Mlddblim34y7iyqhJMYAgJCQEDQ3V1NQwGAwICAuCj9nni+YwxmBjDmHAfjH7qye+7itlsfqKBr7Gh/fLycixYsAB//vOfG14gOxvYuRN46SXryEGPHg+/t2ABsGmTa38gJ9GaeKy7WAXGAHkLy1krKyvAAIQEhzR4/NGljGr/QAwZN6XJa5gFBo4Dlg0I6hDh0Rlo5IAQIqoGB+u0Yv982+sFHiajEf4BARCYgOqqKpjMZgQFBUGlVDX6Go6zrnHMrzBgZLja4Z8O9Xp9ow18j7/xV1ZWNnidTCZD586dbUP7MTExtk/2w4YNe/JGgYHAsGFAgnUtP27dahgQPESAQoroECXOlBsgMDQ5usQLPIwm05MjLECzYeBRAmMQwPBMiMprgwFA4YAQIrK2HKzzKIPBAHAcFAoFKisrwVt4BAcHQyFv/vCkRw/WsWfv/PomvsY+4T/+yb+mpqbBa5VKZYMh/D59+ti69euDQFhYGIKDgyFpzVa/CQnAkSPWEQTAGhY8MBwAQFwXH9yoMaPKxEOBxk9o1Ov1kHBck6GwJYwxmBlDkEKKuC7ijSi5AwoHhBBRteVgnRHT5wMAMj74DcL7D8EzU5JQVVkJBiA4JBhyWctH9NYfrFNaZ4HcUNPs0H59ADAYDA2u4efn16BZb8iQIQ3e8Ot/ObWJb/Vq51zXzSilEiRG+GHLDS1MAoNC0jAg2Pa5UKnb9N+aMQaTwCCRcEiM8PPalSz1KBwQQkTV1oN1AKBLv4G4f/c2TGYTpFIpQoKDIZVYh4IZ2MOtdgUBAi+AF/gG/y6RK/H/Pv0amrUfN7hfUFCQ7Q0/MjISw4cPb/AJv/6fvr6+zvsP0x4aDVBUZB1RmD5d7GocJtJfjondfHGwWPdEQDCZTOB5Hmqf1jdW2oIBx2FiN19E+rccLj0dhQNCiKhaPlgHqLhXjL6xo5GzcS36xFi7zBkYIp8ZDVPeMXAcB7lMDq1W2yAMMDTst350+12ZTAapQo7R4yZgyehBtjf8sLCwZtfHdwgJCcDx42JX4RTRodYpg4N3dDAxBjmsPQh6vR4ymQxyWeve1oQHUwkSiTUY1F/f21E4IISIirdjwVTf2NFgYPjp0Hd4bsm/oqKyAmazGUajEd2HxIIDcE6zH6a6GpRev4yBCYnoEzMKUokUEqkEEon11+Pb7xp4Ab369sXUnjFO+umIM0SHqhCkkGJ/cS2qTDw4gcFoNMLfzw/2nsDIGIOFAQKsPQb1B3MRKwoHhBBRNbVSgIHBYrHAZDLBZDKhtrICdy+fR3i/IeA4DiqVCmU3ryBq9Hjoy39GgL8/hkyfB32tFv+zKBG/33nCrvt768E6HV2kvxwpUYE4eq8Op+5WQ672hUSpBs8YJGi8YZExBgHWDY7AWf/sPROieuJIb2LtySGEENH4yiQPlqYxWHgL6vR1qKquQllZGcrLy1FbW2udE9ZWIrRbJDqHdUZwUDAEQYBMJodEIkFVWSmu5lmH0dV+AfAJCMLdy+dbvLc3H6zjCZRSCcZ388Wh/3wDuvxc+Csk4BlgEqw9BAZesP2qf4xngJ9CgjHhPlg2IAgTqPmwUTRyQAgRTUlJCW4VXoExpDeqa2vA8zw4ADK5HD5qNRQKBeQKhXU6QK8Dx3HgwIEXeBQe2Yvhk2eBF3iE9x+KIfETbNet01aha9SgZu/t7QfreIrTp0+j4NQPeP/tXyNuYDDKDTzK9BaU6nnUWQRYGIPsQQgMU0vRWS1DqErqtTsf2ovCASHEZbRaLY4fPw6NRoPc3FxcvnwZoT37Yf7/fAulSg2lXA65Qg4J9+QnuUcP1oFcibDeUVCplBAYg06ng16vh6+PL7Z//EfMefs/WqzF2w/W8RTp6emIiIhAfHw8JByHMLUMYWoZBotdWAdH2ycTQpzGYDDg1KlTtjCQn58PQRAQGRmJ+Ph4JCQkYNToMdh+X4pak2DX8C4Dw/3796FUKBEQEAAAqNZWw2Qy4edzeeDA2bUbnpEX4KeQYPnAYPoU2UHpdDoMGzYM//Iv/4K3335b7HI8CkVmQojDWCwW/PTTT9BoNDh69ChOnToFk8mETp06IS4uDikpKYiPj0dkZGSD10ULdXYfrNPYenYfHx9cOpkLPz8/DBo1Dncvn4fKzx8hXRs/eY8O1vEMu3btQl1dHV588UWxS/E4NHJACGkzQRBw6dIl5ObmIjc3F8ePH0dtbS38/f0xatQoJCQkID4+Hv3792/2Tb81B+tUVVWCFwSEhoSgftlaxd3b+NtrSQCsexkYdDX4z4NNNyTSwTqeYcaMGfD390d6errYpXgcCgeEkFa5desWcnNzbaMD5eXlUCgUGDlyJOLj4xEfH4/o6GjIWrkZzaHiWpwpN0DONX1sMy/wuF92H/4B/k+ctmgwGlBVVYXQ0NBmt0+u3/TmmVAVJkT4tapG4j6uXLmCcePG4fPPP8eMGTPELsfj0LQCIaRZpaWlOHr0qG104Pbt25BIJHj66aexaNEiJCQkYPjw4VCp2reznD0H6xj0eoBDo/dSKpWQSqWoq6tDYMCTp/IBdLCOJ8nIyEBwcDCmTLHvtEXSOhQOCCENaLVa/PDDD7YmwkuXLgEA+vfvj8mTJ1ubCEeNsjUDOoo9B+vU6fVQqVSNrmbgwMHHx8c6reHn/8TphnSwjucwm83IzMxEcnIyFIrmT98kbUPhgBAvZzAY8OOPP9rCwE8//QRBENC9e3fEx8fjzTffxJgxYxAWFub0Wuw6WEfd9ME6arUatbW1qNPXwc/34ZQBHazjWfbt24fy8nIsXLhQ7FI8FoUDQrxM/YqC+mmCx1cULFq0CPHx8ejRo4co9bV0sI5C3vQbu4STQK1WQ19XB19fX3Dg6GAdD5SRkYGYmBj0799f7FI8FoUDQjwcY8y2okCj0eCHH35ATU0N/Pz8MHr0aLz//vuIj4/HgAEDWlxG6CpNHazjZ8fBOj4+Pqirq4PeYIBcoaKDdTzM3bt3cfjwYXz00Udil+LRKBwQ4oGKiooarCi4f/8+5HI5Ro4ciZUrVyI+Ph5PP/10q1cUuFJjB+tI7ThYh5NIofYPgIlnUHCgg3U8zKZNm6BSqTBz5kyxS/FotJSREA9QVlaGo0eP2sJAUVGRbUVBXFwcEhISMGLEiHavKBADYwyTZ85B7PQF6JUwBTozs22WJDzy15eE42yPc2YDDn37d7yXkoSEEXQcs6cQBAGjR4/GmDFj8Mknn4hdjkdz348NhJAm1a8oqO8buHjxIgAgKioKkyZNQkJCAkaPHu3wFQViOHHiBM7lncQHv30Pz9p5sE6wIghbfn0YqYYyJIz4XOwfgTjIsWPHcPv2bSxatEjsUjwehQNCOgCj0YhTp07ZwsBPP/0EnudtB8688cYbiIuLc8mKAldLS0tDr169MHr0aHCtOFhn+fLl+P3vf487d+6gW7duLqmVOFdaWhr69u2L4cOHi12Kx6NpBULckMViQX5+vi0MnDx5EiaTCaGhoYiLi7PtRNijRw+3aSJ0hqqqKgwbNgzvvfceVq5c2arX6nQ6xMbGIiUlBatXr3ZShcRV6v8srFq1Cr/4xS/ELsfj0cgBIW7g0RUF9WcU1NTUwNfXF6NHj8bvfvc7JCQkoH///k9s7uPJtmzZAkEQkJyc3OrX+vr6YtGiRUhLS8Nbb70FHx/aEbEj27p1a5v/LJDWo5EDQkRy+/btBisKysrKIJfLMWLECNtxxtHR0ZA3s67fkzHGMHHiRPTp0wdffvllm65x+/ZtjB49Gn/605/w8ssvO7hC4iqMMUyaNAm9evXCV199JXY5XoFGDghxkfv379tWFOTm5tpWFERHR2P+/Pm2FQXN7QDoTU6fPo2LFy/i3//939t8je7du+P555/H2rVr8dJLL3n0FIwny8/Px4ULF/C73/1O7FK8BoUDQpykpqamwYqCCxcuAAD69euHiRMn2lYUBAY2fkiQt0tLS0NERATGjh3brussX74cSUlJyMnJwbhx4xxUHXGljIwMPPXUU3juuefELsVrUDggxIEuXbqE7du3Izc3F2fPngXP8+jWrRvi4+Pxy1/+EnFxcQgPDxe7TLdXU1ODHTt24I033mh3j8Wzzz6LIUOG4Msvv6Rw0AHp9Xps27YNr776KqRSqdjleA0KB4Q40CeffILc3FzEx8djwYIFXrGiwBm2bdsGo9GIF198sd3X4jgOy5cvx69//Wtcu3YNffr0cUCFxFWys7NRU1ODBQsWiF2KV6GGREKaUlAAaDRAZCRQVQWkpLT4kuLiYnTt2tWrVhQ4w5QpU/DUU09hw4YNDrmeyWTC8OHDMWPGDPznf/6nQ65JXCMpKQkymQybN28WuxSvQn+DEdIYrRZ45x1g5Upg6FDr14A1LGRnA5999vCxR0RERFAwaKeCggIUFBQgxY4wZi+FQoElS5Zg06ZN0Dby+0bc040bN/DDDz/Q0cwioL/FCGlMTg7Qo4c1DADWkHDrFnDkCDB9unUUwQO2JnZHaWlpCA8Px/jx4x163Zdeeglmsxnp6ekOvS5xnoyMDAQGBmLq1Klil+J1KBwQr1ZTU4NTp049+Y3AQGDYMCAhwRoSbt2yBgWt1jpy8Ne/urxWb1BXV4etW7di0aJFDj8xMiwsDLNnz8a6detgsVgcem3ieBaLBZs3b0ZSUhKUSqXY5XgdCgfEq5hMJhw7dgwfffQRZsyYgUGDBuFPf/rTk09MSAAqKqxBIDsbKCqyPt6zp3XkoGdPIDXVlaV7hV27dkGn0zltGHn58uUoLi7Gvn37nHJ94jiHDh1CaWkpHbIkElqtQDwaz/MoKCiw7UR48uRJGI1GBAcHIz4+HsnJyUhMTGz8xY/vxx8U9HCaISjI2qRIHCo1NRXjxo1DRESEU64/dOhQjBw5El999RUNVbu59PR0REdHY/Dglo7YIs5A4YB4FMYYrly5Ytt46NixY9BqtfDx8cGoUaOwatUqJCQkYODAga1vHBw69GFD4tmzT4YH0i4XL15EXl5em7dKtteKFSuwYsUKFBYWYsiQIU69F2mbkpISHDx4EB9++KHYpXgtWspIOrzi4mLbtsRHjx5FSUkJ5HI5YmNjbWcUDBs2zGvPKOgofv/732PHjh3Iy8tz6u+VxWLB6NGjERcXh08//dRp9yFt97//+7/485//jJ9++gkB1PgrCho5IB1OeXk5jh49ahsduHnzJjiOw9ChQzF37lzbGQV0Cl/HYTQakZWVhcWLFzs9xMlkMixduhRr1qzB+++/j86dOzv1fqR1GGPIyMjA9OnTKRiIiMIBcXu1tbUNzig4f/48AKBv37547rnnEB8fjzFjxiAoKEjcQkmbfffdd6iursbixYtdcr/Fixfjz3/+M7755hu8/fbbLrknsc+JEydw48YN/M///I/YpXg1mlYgbsdkMuHHH3+0TRWcPXsWFosFXbp0QUJCAuLj4xEfH4+nnnpK7FKJg8ydOxcSiQSZmZkuu+dvf/tb/POf/8SpU6egUChcdl/SvF/96le2//9p23Hx0MgBER3P8ygsLGywosBgMCA4OBhxcXH48MMPkZCQgJ49e9JfFh7o+vXrOH78OD777DOX3vfVV1/Fhg0bsHPnTsybN8+l9yaN02q1yM7Oxm9+8xv6f11kFA6IyzHGcPXqVVsYeHxFwbvvvouEhAQMGjSItiL2AmlpaQgKCsILL7zg0vv27dsX48ePx5dffom5c+fSm5Eb2L59O8xmM+bPny92KV6PwoHIeMZQbuBRqregTM9DZxHAMwYpx8FXJkFntRRhahlCVVJIO/BfXnfu3LH1DOTm5tpWFMTExGDFihVISEjAM888QysKvIzZbMbmzZuRnJwsyi54y5cvx+LFi3Hq1CmMHDnS5fcnDaWnp2PChAl0rLkboHAgEq2Jx7kKI/IrDNCZGQTGIOE4CI+0gNR/LeE4+Mo5RIeoMDhEiQCF+59pXlFRYVtRoNFobCsKhgwZgrlz5yI+Ph4jR46kFQVebs+ePSgvL3dZI+Ljxo0bhz59+uCrr76icCCy8+fPIz8/H+vXrxe7FAIKBy5n5AUcvVeH/AojeMYABsgkHOQc92BYs+HoAGOAAKDWJOBYSR1OlOoRHaJEXBcfKKXuM+Su0+lsKwo0Go1tRUGfPn3w3HPPIS4uDmPGjEFwcLDIlRJ3kpaWhhEjRiAqKkqU+0skEixfvhzvv/8+iouLnbYzI2lZeno6wsLCMGHCBLFLIaDVCi5VVGPGvuJaVJt4SMBBxqFV85yMMVgYIIAhSCFFYoQfIv3FGYY3mUzIy8uzrSg4c+YMLBYLnnrqqQYrCrp06SJKfcT9FRUVYdSoUfj0009FnWOuq6tDTEwMUlJSsJp2vRSF0WjEsGHDkJKSgvfff1/scgho5MBl8ssNOHhHB4ExyDkOkjb0D3AcBzkHCAyoMvHYckOLid18ER2qckLFDfE8j3PnztlGBk6cOAGDwYCgoCDExcXh//2//4eEhAT06tWLGruIXTIyMhAQEIAZM2aIWoePjw8WL16M1NRUvPXWWzTVJYLdu3ejurraaQdukdajkQMXyC834GCxNRgoJJxD3jwZYzAJ1n6EiRGODwiMMVy7dg0ajcZ2RkF1dTXUajVGjRqFuLg4JCQkYPDgwbSigLSaxWLBiBEj8MILLzR+KqaLFRcXY9SoUfjP//xPLFmyROxyvM78+fNhNpuxbds2sUshD9DIgZMV1ZhtIwaOCgaAdRRBIQFMAsPBOzoEKaTtnmK4e/eu7XwCjUaDkpISyGQyxMTEYPny5YiPj0dMTAytKCDtdvDgQZSUlIjWiPi4iIgIvPDCC1i7di1eeuklCrwuVFRUhNzcXPzlL38RuxTyCAoHTmTkBewrrnV4MKhnCwiMYX9xLVKiAlvVpFhZWdlgRcGNGzfAcRwGDx6MpKQk24oCX19fh9ZNSFpaGp5++mm3Oo53+fLlmDNnDnJycvDcc8+JXY7X2LhxI/z9/TF9+nSxSyGPoHDgREfv1aHaxD+yEsHxOI6DHNYehKP36jAhwq/J5+p0Opw4caLBigLGGHr37o2xY8fit7/9LeLi4mhFAXGqe/fu4dChQ1izZo3YpTQwcuRIDB06FF9++SWFAxfheR6bNm3C7NmzoVarxS6HPILCgZNoTTzyK4yQoG3Nh60h4ThIGJBfYcTwMLVtHwSz2Yy8vDzbxkOnT5+GxWJBeHg4EhISsGLFCsTHx6Nr165OrY+QR2VkZEClUmHWrFlil9IAx3FYvnw53nzzTVy9ehV9+/YVuySPd+TIEdy7dw+LFi0SuxTyGGpIdJLjP9fhWEkdFE4cNXgUYwwmxtAbWtw7uge5ubk4ceIE9Ho9AgMDERcXh/j4eCQkJKB37960ooCIgud5jBo1CuPGjXPLU/dMJhNGjBiBadOmuUWjpKdbvnw5bty4gQMHDtDfSW6GRg6cgGcM+RUGgAGcxJl/4BksPA+TyQSTyQQmkeGHigps+fRTjBw+HG+//Tbi4+MxePBgSKXuv6si8XxHjhzBnTt33KYR8XEKhQIvv/wyPvvsM6xatQqBgYFil+Sx7t+/j3379uEPf/gDBQM3ROHACcoNPHRmBlkLweBq3nEYarXQ11RjxHTrJjAZH/wGfWNH275+HC88DAMmkwk8z1v7DuRyKOQSdOraHTl5+ejq7/y9DwhprbS0NAwcOBDDhg0Tu5Qmvfzyy/jrX/+K9PR0/OIXvxC7HI+VlZUFjuOQlJQkdimkEbRexwlK9RbrmQjNPKfi7m34+Aeia79ByNm41vZ4t/5DUH63yPa1wAQYjAZoa7S4X34fZWVlqK6uhsVigUqlQnBwMMI6d0ZIcAh81WpIJFJUmp34wxHSRqWlpdi/fz9SUlLc+pNi586dMWfOHKxbtw4Wi0XscjwSYwwZGRmYOnUqNUC7KQoHTlCm5yFpodeg4l4xukYNQmHOPvSJGW17fMjYyQgI64qa2hqUV5SjtLQUVVVVMJlMUCgUCAoKQlhYGEJDQuHv5w+lQgmOs/42cg/uWarnnf4zEtJamzdvhlQqxdy5c8UupUXLly/HnTt3sHfvXrFL8Uh5eXm4cuUKNSK6MQoHTqCzCA1OV2xM31hrICg4vBtDxk2xPW40GdG570AYDAZcPXEEdWV3cevkYdw4fggB/gFQKVWQcE3/tgmMoc4iOOYHIcRBBEFAeno6Zs6ciYCAALHLadGQIUPw7LPP4quvvhK7FI+Unp6O7t27Iy4uTuxSSBMoHDgBb+cCEH2tFnevnLcFBQC4WXgWnSJ6wk+lxI870tF7SAyix0/F9k/+aPf9LbQAhbiZY8eO4ebNm27biNiYFStW4MSJEygoKBC7FI9SW1uLnTt34sUXX6SdKN0Y/c44gdTO+dTKu8UI6dLd9rXABPA8D6VSCbVfAH75jyzb8x6demiJzI3nc4l3Sk1NRb9+/TBixAixS7HblClTEBERQaMHDrZz507o9XosWLBA7FJIMygcOIGvTGLXxkcqP/8GX/908Dv0HTUOCoXC9tip7M04kvElFv7hE7vuLeE4+Mjot5W4j4qKCuzevRuLFi1y60bEx0mlUixduhTbt29HaWmp2OV4jIyMDDz33HO0+Zqbo3cRJ+islkJgDC3tLxXStTsGj03EqezNKDyyFyE9+kAqlUL2yJ4EI6bPx4jp87H3i49bvC97cM8wNe1pQNxHZmYmACA5OVnkSlpv0aJFkMvl+Pbbb8UuxSNcunQJeXl5dDRzB0DhwAnC1DJIOA72tAU+/9o7GDF9PoaMmwLfkLAHowbWT1f6Wi0Aa/NiweHduJp3vNlrCbCuWOispu0riHtgjCEtLQ1Tp05FSEiI2OW0WmBgIObPn48NGzbAZDKJXU6Hl5GRgZCQEEyZMqXlJxNRUThwglCVFL5yDhbB/sZAXuBhsVigVCoBPJhOSP/C9n2fgCD4+De/W5tFYPCVcwhV0cgBcQ+nTp3C1atXO1Qj4uNeffVV3L9/Hzt27BC7lA7NbDYjKysLycnJdOx7B0DhwAmkHIfoEBXAocWphXr1n0rq+w2GPPc8IvoPxdW849jzxf9gxPT56Bo1qMnXM8YADogOUdndEEmIs6WlpaFnz54YM2aM2KW0WZ8+fTBhwgR8+eWXdv//TJ60d+9eVFRU0JRCB0Hjz04yOESJE6V6WBggt+O92mQyQS6X2/YwUPsF2PY/eHSpY1MszBpKBoco21U3IY5SXV2NnTt34u233+7wS9aWL1+ORYsW4eTJk3j22WfFLqdDysjIQGxsLKKiosQuhdihY/8f68YCFFJEhyghgLW4IRLAbDsgtoXAGAQwRIcobcc1EyK2rVu3gud5zJ/f+DkhHcm4cePQt29fWtbYRnfu3MHhw4dpR8QOhMKBE8V18UGQQgpzCysXLDwPnufbFA4YYzAzhiCFFHFdfNpTLiEOwxhDamoqEhMTERYWJnY57cZxHJYvX47du3fj9u3bYpfT4WzatAlqtRozZswQuxRiJwoHTqSUSpAY4QcJx8EkNB0QTCYTOI6DQt66cMAYg0lgkHAcEiP8oJTSbydxD2fPnsWFCxeQkpIidikOM2/ePPj5+eHrr78Wu5QOhTGGCRMmYN++ffDz8xO7HGInejdxskh/OSZ28202IBiNRsjl8lZtEPNoMJjYzReR/tT9S9xHWloaunXrhrFjx4pdisP4+PggJSUFaWlp0Ol0YpfTYXAch2HDhqF3795il0JagcKBC0SHqjAxwhcSCQcTa9iDwMBgbmW/gcAYTIxBIuEwMcIX0aEqZ5RNSJvU1tZi+/btWLhwIaRSz+qBWbp0KXQ6nW1jJ0I8FYUDF4kOVWFurwBbD4L5wSiC2WyGwBiUdoQD9uB19T0Gc3sFUDAgbmf79u0wGAweuWStW7dueOGFF7B27VoIAp1+SjwXhQMXivSXIyUqEM+EqsBxgIkxmHgGmVwBqazxVaWMMfCMwcgLMDEGjgOeCVUhJSqQphKIW0pLS8OECRPQpUsXsUtxiuXLl+PatWs4cuSI2KUQ4jQco109RKE18ThXYcSewhtQ+AVCpVKB47gGUw4SjgNjDBzHwVdu3VhpMC1XJG7s3LlzSExMxPr16z12i1zGGF544QWEhIQgPT1d7HIIcQraBEkkAQopov0ZFr02E7/9zzWImzIdpXoedRYBFsYge3C6Yphais5qGUJVUtr5kLi9tLQ0hIeHY+LEiWKX4jQcx2HlypX47W9/i+vXr1OjHfFIFA5EdOLECRgNekwc+Qz6hKgwWOyCCGkHvV6PrVu3YunSpZA1MU3mKWbOnImZM2eKXYZ7KSgANBogMhKoqgI8aBmrN6KeAxFpNBp06dKFPnkQj7Br1y5otVqPbEQkLdBqgXfeAVauBIYOtX4NAKmp1sDw4Yfi1kdajcKBiHJycjB27NhW7W9AiLtKTU3F2LFjERkZKXYpxNVycoAePaxBALCGhPqAkJAAFBUBt26JVx9pNQoHIikrK8OFCxeQkJAgdimEtNulS5fw448/duijmUk7BAYCw4ZZg0CPHtYgEBBgnVpITbVONfToIXaVpBUoHIjk6NGjAID4+HiRKyGk/dLT0xEaGornn39e7FKIGBISgIoKIDvb+quo6OH3UlKsowg0ctCheHbXkBvLycnBgAEDPOJQGuLdjEYjMjMzsWjRIsjltPeG11q9uuHX2dnWEYWEBKBnT+C776zTDaRDoJEDETDGkJOTQ1MKxCPs3r0bVVVVdBwvaWjsWKC62hoSbt6kYNDB0MiBCG7evIm7d+9SOCAeIS0tDaNHj/a+VTe0dK95AQHA9OnWf6//J+kwaORABDk5OZDJZBg1apTYpRDSLjdu3MDRo0e9rxGxqaV72dnWwPDZZ+LWR0g7UTgQgUajQUxMDJ1tTjq8tLQ0BAYGYtq0aWKX4lqNLd27dcvaiJeQYB1NoAY80oFROHAxnueRm5tLUwqkwzObzdi8eTPmzZsHpVIpdjmu1djSvR49gLNngQULrCGBlu6RDozCgYsVFBRAq9Vi7NixYpdCSLvs27cP9+/f974pBaDxpXtarTUw/Ou/At9+a+1J8BKCIGD+/Pn4kHZC9BjUkOhiGo0Gvr6+GDZsmNilENIuaWlpiI2NxYABA8QuRRyPL91LTQWmTbOOGGzcaF26N3SoOLW52A8//IDc3Fy89dZbYpdCHIRGDlxMo9FgzJgxtB6cdGi3b9/GkSNHkEId+g/NnGkNBBqNddTAi/7bZGRkoFevXnj22WfFLoU4CI0cuJBer8eJEyfw+9//XuxSCGmXjRs3ws/PDzNmzBC7FPcREOCVa/m1Wi2ys7Pxzjvv0DkxHoRGDlzo1KlTMJvN1IxIOjSLxYL09HTMmTMHPj4+YpdDRLZ161ZYLBYkJyeLXQpxIAoHLpSTk4OwsDBERUWJXQohbfb999+jpKTEOxsRyRMyMjIwceJE2grew1A4cCGNRoOEhAQaeiMdWmpqKqKjozHUS5rt2qK8vBzXr1+HIAhil+JUhYWFKCgooKDogSgcuEhFRQUKCwtpSoF0aD///DMOHjxIbwYtMBgMGDduHDZs2CB2KU6VkZGB8PBwjB8/XuxSiINROHCRo0ePgjFG4YB0aBs3boRSqcTs2bPFLsWtdevWDVOnTsXatWs9dvTAYDBgy5YtSE5OhkxGve2ehsKBi2g0GvTt2xddunQRuxRC2kQQBKSnp2PWrFnw9/cXuxy3t3z5cly/fh2HDx8WuxSn2L17N7RaLRYuXCh2KcQJKBy4SH2/ASEdVU5ODoqLi2lKwU7Dhw/H008/jS+//FLsUpwiPT0do0ePRq9evcQuhTgBhQMXuHXrFm7dukVbJpMOrbCwEMuWLUNMTIzYpXQIHMdh+fLlOHLkCK5cuSJ2OQ518+ZNHD16lEYNPBiFAxfIzc2FRCLB6NGjxS6FkDb713/9V3z44Ye02qYVZs6cibCwMKxdu1bsUhxq06ZN8Pf3977TOL0IhQMX0Gg0eOaZZxAQECB2KYQQF5LL5ViyZAk2b96MqqoqsctxCIvFgk2bNiEpKQlqtVrscoiTUDhwMkEQqN+AEC/20ksvged5pKeni12KQxw+fBg///wzTSl4OAoHTnb+/HlUVlZSOCDES3Xq1AlJSUlYt24dLBaL2OW0W0ZGBgYNGkSbYHk4CgdOlpOTA7VajdjYWLFLIYSIZMWKFbh79y52794tdintUlZWhv3792Px4sXUe+LhKBw4WW5uLkaNGgWFQiF2KYQQkQwaNAijR4/GV199JXYp7ZKZmQmJRIKkpCSxSyFORuHAiUwmE3744QeaUiCEYMWKFTh16hR++uknsUtpE8YYMjIyMHXqVAQGBopdDnEyCgdO9OOPP8JgMND+BqTjKCgAPvsMyM4GUlPFrsajJCYmIjIyssOOHvz444+4du0aFi1aJHYpxAUoHDiRRqNBSEgIBgwYIHYphLRMqwXeeQdYuRIYOtT6tVYLvPee9Z+kXaRSKZYuXYqdO3eipKRE7HJaLT09HZGRkRgzZozYpRAXoHDgRBqNBvHx8ZBI6D8z6QBycoAePQCNxvr1ypXArVvATz8BycnAlCnAhx+KW2MHt3DhQigUCnzzzTdil9IqNTU12LlzJ1588UX6+8xL0O+yk2i1Wpw9e5amFEjHERgIDBsGJCRYQ8KtW0BAALB3r/XXG28Aq1eLXWWHFhAQgPnz5+Obb76B0WgUuxy77dy5E0ajEQsWLBC7FOIiFA6c5NixYxAEgZoRSceRkABUVFj7DbKzgaIia0gArP0HFHQd4tVXX0V5eTm2b98udil2y8jIwPjx4+lUWS9C4cBJNBoNevbsie7du4tdCiH2W70amD7d+uvRYJuTYx1FIO3Wu3dvTJo0CV9++SUYY2KX06KLFy/i9OnTtCOil6Fw4CQ5OTk0akA8g1YLBAWJXYVHWb58Oc6fP48ffvhB7FJalJGRgdDQUCQmJopdCnEhCgdOcPfuXVy7do3CAfEMAQHARx+JXYVHSUhIQFRUlNsvazSZTMjKykJycjLkcrnY5RAXonDgBBqNBhzHIS4uTuxSCCFuiOM4LF++HHv27EFRUZHY5TRp7969qKyspCkFL0ThwAk0Gg2GDh2K4OBgsUshpFUEQYDZbO4Qc+Ed3dy5cxEYGIj169eLXUqT0tPTMXz4cPTr10/sUoiLUThwMMYYHdFMOqzdu3fjyJEjdKiOC6jVaixevBgZGRmora0Vu5wnFBcXIycnh3ZE9FIUDhzs0qVLKCsro/0NSIdz4cIFrFixwiOOFe4oli5dCp1Oh8zMTLFLecKmTZvg4+ODGTNmiF0KEQGFAwfTaDRQKBQYMWKE2KUQ0iqpqakICwvDxIkTxS7Fa3Tt2hXTpk3DV199BUEQxC7Hhud5bNy4EbNmzYKvr6/Y5RARUDhwMI1Gg5EjR0KlUoldCiF2MxgM2LJlCxYsWEBd6S62fPly3LhxA99//73Ypdjk5ubizp071IjoxSgcOJDZbMaxY8doSoF0ONnZ2dBqtfRmIILY2FgMGzYMX375pdil2GRkZKB///6IiYkRuxQiEgoHDnTmzBnU1dVRMyLpcFJTUxEfH4+ePXuKXYrXqV/WmJOTg8uXL4tdDioqKrB7924sXLiQGlO9GIUDB9JoNAgMDMSQIUPELoUQu125cgUnT55ESkqK2KV4rRkzZiA8PBxr164VuxRs2bIFADBv3jyRKyFionDgQDk5OYiPj4dUKhW7FELslp6ejpCQEDz//PNil+K15HI5lixZgszMTFRVVYlWB2MMGRkZmDJlCkJCQkSrg4iPwoGD1NTU4PTp0zSlQDoUk8mEzZs3Y/78+VAoFGKX49VeeuklCIKAtLQ00Wo4e/YsLl68SHsbEAoHjvLDDz+A53lqRiQdyp49e1BZWUlvBm4gNDQUc+bMwbp162A2m0WpIT09HV27dqUPOYTCgaNoNBpERESgR48eYpdCiN1SU1Px7LPPom/fvmKXQgCsWLEC9+7dw+7du11+77q6OuzYsQMvvvgiTY0SCgeOUr9lMnX3ko7i5s2byM3NxeLFi8UuhTwwaNAgjBkzRpTTGnft2gWdTocFCxa4/N7E/VA4cICSkhJcunSJphRIh5Keno6AgABMnz5d7FLII1asWIEff/wRZ8+edel909PTkZCQgO7du7v0vsQ9UThwgNzcXACgI5pJh2E2m7Fp0ybMmzePdvN0M5MmTUJkZKRLRw+uXr2KU6dOUe8JsaFw4AAajQaDBg1Cp06dxC6FELscOHAAZWVlNKXghqRSKZYtW4Zdu3ahpKTEJffcuHEjgoKCaDkrsaFw0E6MMeTk5NCUAulQ0tLSEBMTg4EDB4pdCmnEiy++CIVCgQ0bNjj9XmazGZs3b8a8efNoOSuxoXDQTteuXcPPP/9MS39Ih3Hnzh18//33NGrgxgICArBgwQJ8++23MBqNTr3XgQMHcP/+fTpXgzRA4aCdNBoN5HI5nn32WbFLIcQuGRkZ8PHxwcyZM8UuhTTj1VdfRUVFBbZv3+7U+2RkZGDYsGE0ikQaoHDQTjk5ORg+fDh8fHzELoWQFvE8j4yMDMyZMwe+vr5il0Oa0atXL0yaNAlffPEFGGNOucfPP/+MQ4cOUSMieQKFg3awWCw4duwYTSmQDuP777/HvXv3aEqhg1i+fDkuXLiA48ePO+X6mzZtglKpxKxZs5xyfdJxUThoh59++gk1NTUUDkiHkZaWhiFDhiA6OlrsUogd4uPj0b9/f6csaxQEARs3bsSMGTPg7+/v8OuTjo3CQTtoNBr4+/vj6aefFrsUQlpUUlKCAwcOYPHixbSTZwfBcRyWL1+OvXv34tatWw699vHjx3Hr1i1qRCSNonDQDhqNBmPGjIFMJhO7FEJatGnTJigUCsyZM0fsUkgrJCUlITAwEOvXr3foddPT09G7d2+MHDnSodclnoHCQRsZjUb06dMHr776qtilENIiQRCQnp6OmTNnIiAgQOxySCuo1WqkpKQgIyMDtbW1DrlmdXU1vvvuOyxatIhGkUijKBy0kVKpxEcffYT4+HixSyGkRbm5uSgqKqJGxA5q6dKlqKurw+bNmx1yva1bt4LnecybN88h1yOeh8IBIV4gLS0NUVFRiI2NFbsU0gZdunTBtGnTsHbtWgiC0O7rZWRkIDExEWFhYQ6ojngiCgeEeLjy8nLs2bMHKSkpNITcga1YsQI3btzAoUOH2nWdgoICFBYWUiMiaRaFA0I83ObNm8FxHA0hd3AxMTF45pln8OWXX7brOhkZGQgPD8f48eMdVBnxRBQOCPFgjDGkp6dj2rRpCAoKErsc0g71yxo1Gg0uXbrUpmsYDAZs3boVCxYsoFVWpFkUDgjxYCdOnMC1a9eoEdFDTJ8+HeHh4Vi7dm2bXv/dd99Bq9XixRdfdHBlxNNQOCDEg6WmpqJXr14YPXq02KUQB5DL5XjllVeQmZmJysrKVr8+IyMDY8aMQc+ePR1fHPEoFA6aUlAAfPYZkJ0NpKaKXQ0hrVZVVYXs7GzaEdHDpKSkgDGGtLS0Vr3u5s2bOHbsGDUiErvQpFNjtFrgnXeAvXuBW7eA776zPp6dDfztb9bH66WmAj16WJ+XkiJOvYQ0YsuWLWCMYf78+WKXQhwoNDQUSUlJWL9+PV5//XVIZDKUG3iU6i0o0/PQWQTwjEHKcfCVSdBZLUWYWoaNmzYhICAA06ZNE/tHIB0AhYPG5ORY3/A1GiAyEli50vr49OnAt98+fJ5GY/1nQgJQXW0ND9Onu75eQh5T/8lyypQp6NSpk9jlEAdbsWIFsvd/j29z82EO7wWdmUFgDBKOg/DI8c71X0s4oCZ6Cl78XRRMEjlUItZOOgaaVmhMYCAwbJj1Tb9+VKAxBQXW79e/5uxZV1VISLNOnz6NixcvUiOiBzLyAu75d8fLn2/HPXU4ak0CpByglHBQSDiopBLbL4WEg1LCgTdboAwIhu/QOKy7WIVDxbUw8u3fTIl4Lho5aExCAnDkiHUkALC+8deHgMdVV7uuLkLslJaWhu7du9Nx4h6mqMaMfcW1qDbxUCqVqK6sgI9SDiknb/I1HMfBqK8DE3iopH6wMOBMuQE3asxIjPBDpH/TryXei8JBU1avbvk5Q4c+HFWorraONhAispqaGuzYsQNvvPEGJBIaHPQU+eUGHLyjg8AY5BwHTiGHTipFnU6HwMCgJl/HCzyMRiP8A/zBcRzkHCAwoMrEY8sNLSZ280V0KE00kIbob47W0GiAoqKHIwoJCdbmxfrHqd+AuIFt27bBaDTSWnYPkl9uwMFiHQSBQcFxkHAcOHBQ+/jAYDSCF/gmX2swGAAOUKkeBgAJx0HBcRAEhoPFOuSXG1zxY5AOhGPske4VQkiHN2XKFHTp0gVff/212KUQByiqMWPLDa01GEi4BstSBSbgflkZfHx84Ofn38irGe6Xl0MukzU6usAYg0lgkEg4zO0VQFMMxIZGDgjxIAUFBSgoKKBGRA9h5AXsK66FwJ4MBgAg4SRQqdWo0+vB8OTnPJPZDIvFArXap9Hrc5y1iVFgDPupSZE8gsKBnXieh9lsBg20EHeWlpaGp556ig7V8RBH79Wh2sRbewya2MjKx8cHgiBYpw8eo9frIZNKIVc037Ao5zhUmXgcvVfnsNpJx0bhwE6bNm3CgQMHaKc54rbq6uqwdetWLFy4kA7V8QBaE4/8CiMksPYYNEUmlUGpVKJOpwMeGT0QmDUwqNRqcGj+7y0Jx0ECDvkVRmhNTfcvEO9B4cAOVVVVePfdd6HVasUuhZAm7dy5EzqdjrbH9RDnKozgGYPMjs8jvj4+MFssMJnMtscMBgPAGNRqtV33k3EAzxjOVRjbWjLxIBQO7HD06FEwxhAfHy92KYQ0KS0tDePGjUNERITYpZB24hlDfoUBYLBrtFKhUEAmk6GuTmd7TK/XQ6FUQiqR2nVPjuMABuRXGMDT9KnXo3BgB41Gg969e6Nbt25il0JIoy5evIi8vDyk0PkeHqHcwENnZpBJ7J3G5OD7YFmjhbfAbDHDbDbbPWpQTybhoDMzlBtoasHbUTiwQ05ODsaOHSt2GYQ0KS0tDZ07d0ZiYqLYpRAHKNVbrGcitPC8q3nHUXhkL05lb4ZKrYZEIkH6H97E8e3pkEgkUCqVrbqvBNbljWV6S5trJ56BwkELbt++jZs3b9I2tMRtGY1GZGVlYf78+ZDLaZ26JyjT89aNjpqZUqi4exs+/oHo2m8QcjautW6KpFYjtEc/lBXdhNqORsTHcQ/uWaqnkQNvR+GgBbm5uZBIJBgzZozYpRDSqO+++w7V1dW0t4EH0VmEBqcrNqbiXjG6Rg1CYc4+9IkZDcC6rLHvyAQEhHVp9ZRCPYEx1FlovwNvR+GgBTk5OXj66acRGBgodimENCo1NRVxcXHo2bOn2KUQB7GnIbBvrDUQFBzejQFxE1BTW4PKigowAP1GxkMmlSHjg99AX9v6VVYWakj0erQYuhmCICA3N5c+kRG3de3aNfzwww/47LPPxC6FOJC0hRUKAhNgMppQXVGG4kuFCOoZBYPBAKVCCf39e+g/cRoq7t5GYc5eXM07BgAw6GowZcXbGPviqy3eX0b7uXg9CgfNuHjxIsrLy6nfgLit9PR0BAcH44UXXhC7FOJAvjLJExsf8bwFRqMRBqPRtltrxd3bCO4SgdDQUMhkMnDgIJfJwYFDxb1irN7xA9R+AQCAU9mbMWL6/BbvLeE4+MhoUNnbUThoRk5ODlQqFYYPHy52KYQ8wWw2Y/PmzUhOTm51Vzpxb53VUgiMwWg2w2Q0wmg0wmKxWM9CUCjg7+8PpVIJBW+GVCKFXGZtRC08shdDxk0B8HDaAbAGgyHPPd/ifRljYIwhTG3f3gjEc1E4aIZGo8Gzzz5Lf/ESt7Rnzx6Ul5fTtJcHqaurg0ajwYETp+E/cREsJiPABCiVSvj5+UGhUEDCPfxUH9K1OwaPTcSp7M1QP1i58LiKu7ehr62xjSA0R4B1xUJnNb01eDv6E9AEk8mEH374AW+//bbYpRDSqLS0NIwYMQL9+vUTuxTSDiUlJdi/fz/279+PnJwcGI1G9O0XhRfGJ0MVGAS1XNrsksTnX3un2eufzN6EvrFxdtViERj8FBKEqmjkwNtROGhCXl4e9Ho9bX5E3NKtW7eQk5ODTz/9VOxSSCsxxnDhwgXs3bsX+/fvx9mzZyGRSDBy5EisWrUKkydPRu/evXH85zocK6mznqXUjv7Aczn7MXL6ArvqAgdEh6habIgkno/CQRM0Gg2Cg4MxaNCTw3SEiC0jIwMBAQGYMWOG2KUQO5hMJhw/fhz79u3Dvn37cOfOHfj5+WH8+PF49dVXMWHCBAQHBzd4zeAQJU6U6mFhgLwd79UqP3+oA1peim1h1lUSg0NoGpVQOGiSRqNBQkICJBLq2iXuxWKxYNOmTZg7d26bN7ohzldZWYmDBw9i3759OHz4MGpra9GtWzdMnjwZU6ZMwejRo5vd0TJAIUV0iBJnyg0QGJo9trk5v/xHVovPERiDAIZnQlQIUNCUAqFw0CitVoszZ87gxRdfFLsUQp5w8OBBlJSUUCOiG7p+/bptuuDkyZMQBAHDhg3DypUrMXnyZAwcONCuUxbrxXXxwY0aM6pMPBSw74TG1mKMwcwYghRSxHXxcfj1ScdE4aARx48fhyAItL8BcUtpaWkYNmwYTXm5AYvFgry8PNt0wbVr16BUKjF27FisWbMGkyZNQnh4eJuvr5RKkBjhhy03tDAJDAqJYwMCYwwmgUEi4ZAY4QellEZKiRWFg0bk5OSgR48eiIyMFLsUQhq4e/cuDh06hDVr1ohditeqra3F4cOHsW/fPhw8eBCVlZXo1KkTEhMT8fvf/x4JCQkOne6J9JdjYjdfHCzWOTQg2IIBx2FiN19E+tOhXeQhCgeNqO83IMTdZGRkQKVSYdasWWKX4lXu3LmDffv2Yf/+/Th69CjMZjMGDBiAl156CZMnT8awYcOc2p8UHaoCABy8o4OJMcjR9h4EwNpjYGbWEYOJ3Xxt1yekHoWDx9y7dw9Xr17Fu+++K3YphDTA8zwyMjIwe/Zs+Pn5iV2ORxMEAQUFBbbpgnPnzkEmk2HUqFH493//dyQmJrp8ZDE6VIUghRT7i2tRZeIhYYCMa90oAmMMFgYIsPYYJEb40YgBaRSFg8doNBpwHIe4OPs2DSHEVY4cOYK7d+8iJSVF7FI8ksFgQG5urm2EoKSkBAEBAZg4cSLeeOMNPPfccwgIaHmXQWeK9JcjJSoQR+/VIb/CCBNjgMAgk3CQoPGgwBiDAOsGR+CsyxWfCVEhrosP9RiQJlE4eIxGo8GQIUMQEhIidimENJCWloZBgwbh6aefFrsUj1FWVoaDBw9i7969yMnJgV6vR8+ePTFr1ixMnjwZI0aMaHa5oRiUUgkmRPhheJga5yqMyK8wQGdmsDAGjrNOGdSTcBwYY+A4Dn4KCaJDVBgcoqTliqRFFA4ewRiDRqPBvHnzxC6FkAZKS0uxf/9+fPDBB05ZzuYtGGO4fPmybbrg9OnTAIDY2Fi89dZbmDx5Mvr27dsh/hsHKKQY/ZQPRoarUW7gUaa3oFTPo84iwMIYZA9OVwxTS9FZLUOoSko7HxK7UTh4xJUrV1BaWkpbJhO3s3nzZshkMsydO1fsUjocs9mMEydO2KYLbt26BR8fH4wbNw4ff/wxJk6ciE6dOoldZptJOQ5hahnC1DIMFrsY4jEoHDxCo9FAoVBg5MiRYpdCiI0gCEhLS8OMGTNEn/PuKARBwM6dO7F37158//330Gq1CA8Px5QpUzB58mTExcXRaauENIPCwSNycnIwcuRIqFS0rIe4j2PHjuHWrVv4y1/+InYpHcb9+/excuVKDBkyBCtWrMDkyZMxZMiQDjFdQIg7oHDwgNlsxvHjx/HGG2+IXQohDaSmpqJfv34YMWKE2KV0GH5+fvjxxx/RtWtXsUshpEOicPDATz/9hNraWtr8iLiViooK7N69G++//753fOotKAA0GiAyEqiqAtq4bNPHxwc+PnROACFtRYtcH8jJyUFAQACGDh0qdimE2GRmZgKAd6yg0WqBd94BVq4Ehg61fg0A2dnAlCkNn5udbf314Yeur5MQL0Dh4AGNRoP4+HhIpbT+l7gHxhjS0tIwdepU79h3IycH6NHDOnIAWEMCAEyfDgQFPXxedjYQGGh9PCQESE11eamEeDoKBwB0Oh3y8vJoSoG4lVOnTuHq1aveczRzYCAwbBiQkGANCbduNf686dOtzwGAmzcB2hSKEIejngMAP/zwAywWC+1vQNxKamoqevbsiTFjxohdilNYLBbIZI/8FZSQABw5Yh0ZAKxhoUePpi+g0QDR0dYpCEKIQ9HIAaxTCt26dUPPnj3FLoUQAEB1dTV27dqFxYsXO/W0P1e7du0aPvvsM8yePRuFhYVPPmH1auvIwKOjA40pKACqq60NiwUFziuYEC9FIwd4eESzV3SDkw5h69at4Hke8+fPF7uUdrFYLPjxxx+xb98+7N27Fzdu3IBKpcLYsWPRvXt3+y6i0QBFRdYRhenTrdMNr71mXdHwt79ZAwUhxKE4xh45pcMLlZaWYtiwYbZPM4SIjTGGSZMmoVevXvjqq6/ELqfVampqcPjwYezbtw8HDx5EVVUVwsLCMGnSJEyZMgXx8fFQq9Vil0kIaYbXjxwcPXoUAOiIZuI2zp49iwsXLmB1B/pEXFxcjP3792Pv3r04fvw4zGYzBg4ciCVLlmDy5Ml4+umnPWp6hBBP5/XhICcnBwMHDkTnzp3FLoV4OJ4xlBt4lOotKNPz0FkE8IxBynHwlUnQWS1FmFqGtPQMdOvWza0bZAVBQH5+vm264MKFC5DL5Rg9ejT+8Ic/IDEx0f5pA0KI2/HqcFB/RPOMGTPELoV4MK2Jx7kKI/IrDNCZGQTGIOE4CI/M6NV/LeEA+XMvYv6zk6DjgQA32nbDYDBAo9HYTjcsLS1FYGAgJk2ahDfffBPPPfccHQxFiIfw6nBw48YN3L17l/Y3IE5h5AUcvVeH/AojeMYABsgkHOQc96D5tWEDLGNAndEAdVAouG7dse5iFaJDlIjr4gOlVJwh+dLSUhw8eBB79+5FTk4ODAYDevbsiaSkJCQmJmLEiBENlyMSQjyCV/9fnZOTA7lcjmeffVbsUoiHKaoxY19xLapNPCTgoOA4cJLmV8NwHAdDXR0kEgmUEgksDDhTbsCNGjMSI/wQ6S93et2MMVy6dAn79u3Dvn37cPr0aUgkEgwfPhzvvPMOJk+ejD59+tDKHkI8nFeHA41Gg9jYWPj6+opdCvEg+eUGHLyjg8AY5BwHiZ1vpGaLGWazGcFBQeA4DnIOEBhQZeKx5YYWE7v5IjrU8ceJm81mnDhxAnv37sW+fftw+/Zt+Pj4YPz48fj0008xceJEhIaGOvy+hBD35bXhgOd5HD16FK+//rrYpRAPkl9uwMFiazBQSLhWfcLW6/WQSqVQKJW2xyQcBwUAk8BwsFgHAA4JCNXV1Th06BD27t2L77//HjU1NejSpQumTJmCxMREjBkzBspH6iCEeBevDQf5+fnQarVu3RFOOpaiGrNtxKC1wYCBwaA3wMdHDe6xXgSO46CQPAgId3QIUkjbNMVw8+ZN23TBiRMnwPM8oqOj8frrr2Py5MkYPHgwTRcQQgB4cTjQaDTw8/PD03RoC3EAIy9gX3Ftm4IBYF0JIDChyc2BbAGBMewvrkVKVGCLTYo8z+PMmTO26YIrV65AoVAgISEBf/rTn5CYmIinnnqqVXUSQryDV4eDMWPGUKc1cYij9+pQbeIfWYnQOvo6PZRKJaTSpv88chwHOaw9CEfv1WFChN8Tz9HpdMjJybEtN6yoqEBoaCgmTZqE3/72t0hISKAeG0JIi7zynVGv1+PkyZP4wx/+IHYpxANoTTzyK4yQwP7mw0dZeAtMZhOCgoJafK6E4yBhQH6FEcPD1AhQSHHv3j3s378f+/btQ25uLkwmE6KiorBo0SJMnjwZzzzzDKRSN9owgRDi9rwyHJw8eRJms5n2NyAOce7BPgaKNs7X6+uXL9rZACjjGAy8gH/sPICDX36MgoICSKVSjBo1Cu+//z4SExPphFFCSLt4ZTjQaDQIDw9H3759xS6FdHA8Y8ivMAAMLe5j0BgGBr3BALX6yUbEx59nMplgNBphNBohVaig9wlD7z598Ytf/ALjx49HYGBge34UQgix8cpwkJOTQ0c0E4coN/DQmRlkLQSDq3nHYajVQl9TjRHTrccwZ3zwG0RGx6LPmEmNNiIKTLCFAaPRCMYYpFIplEolFEol/Py64zd//gvC1F75vzEhxIm87pi0iooKFBYW0pQCcYhSvcV6JkIzz6m4exs+/oHo2m8QcjautT3erf8QlBbdgEKhgEwqA8Bg4S3Q1elQUVmB0tJSVFdXQ+B5+Pn6olNoKDp36oQA/wAo5XIwBpTpLU7/GQkh3sfrwkFubi4AUDggDlGm5yFpYYVCxb1idI0ahMKcfegTM9r2+IC4CfDr9BQUcjlqamtw//593L9/H7W1tZBwHAIDAxHWuTNCQkLh6+sHmUyO+vMYuAf3LNXzzv4RCSFeyOvCgUajQb9+/Wh9N3EInUVocLpiY/rGWgNBweHdGBg/EXqDHjW1NdBqteg++BnU6nTI+s93UXH7BgzlP+PM9m8RFBQMtUoNiaTpVQYCY6izCA79eQghBPDCngONRoNJkyaJXQbxEHwjwYCBwWKxgLdYYLFYYLHw0GmrUHyxEIE9+qG6uhpSqRT3b13FkHFToFQqUVd5H5v+8Aa69R+ChX/4xO77W1oIJoQQ0hZeFQ5u3bqFoqIi2jKZOITZbEatVguel6PWUPcgCFjAW3gwWN+0pVIpZFIZ6ipKEdK1O0JCQiCTySDhJCjx9YOfr3Ujo3ELV2DIuCmtrkFGTbWEECfwqnCg0Whs68EJsZfZbMaNGzdw6dIlXL58GZcuXcKlS5dw48YNjHzpXxE9dT54owFSmczaXOgjg0wms4UAAGD6LpBIJFDIFQCAwiN7G4SB4ksFAAB9TTUA2FY0NEfCcfCRed3MICHEBTjGvGdc8vXXX8fdu3exa9cusUshbshiseDmzZu2N//6IHD9+nWYzWYAQKdOndC/f39ERUWhf//+CBwYiyvycCjtOE9hzxf/g9CukVA/WLkQ0rV7o8/7c8oUrPxHJtR+AU1eizEGk8DwfKQfBoc4/hhnQoh385qRA0EQoNFosHTpUrFLISLjeR5FRUW4dOkSLl68aAsCV69etYWAkJAQ9O/fH6NHj8aSJUvQv39/9O/fHyEhIQ2uVaq34NrlaggAWtqg+PnX3mn08cIje1F8qcD2fZWfPyrvFkMdNajJawmwrljoTHscEEKcwGv+Zjl37hyqqqpoCaMXEQQBRUVFDaYCLl26hKtXr8JoNAIAgoKC0L9/f4wYMQIpKSm2EYFOnTrZdY9QlRS+cg61JgFSadvm/0O6dIfqkVECQ20NujYTDADAIjD4KSQIVdGZCYQQx/OacKDRaKBWqxEbGyt2KcTBBEFAcXHxEz0BV65cgcFgAAAEBAQgKioKw4YNw4IFC2xTA2FhYe3aKVPKcYgOUeFYSR0YY226VteoQSg8stc2grD0o6+afT5jDOCA6BAVpNSQSAhxAq/pOXjxxRchk8mQmpoqdimkjRhjuHPnzhM9AVeuXEFdXR0AwM/Pr0FPQP2v8PBwp22XrTXxWHexCowB8jacr9BaZoGB44BlA4IQoKCRA0KI43nFyIHRaMSJEyfwb//2b2KXQuzAGMO9e/caTAVcvnwZly9fhk6nAwD4+voiKioKAwYMwKxZs2whoEuXLi4/MyNAIUV0iBJnyg0QGNp0bLO9BMYggOGZEBUFA0KI03hFODh16hSMRiPtb+BmGGMoLS21BYCLFy/aQkBNTQ0AQK1W20YBpk2bZgsBXbt2hUTiPsv44rr44EaNGVUmHgrAKQGFMQYzYwhSSBHXxcfh1yeEkHpeEQ5yc3NtS9CI6zHGUFZW9kRj4KVLl6DVagEASqUS/fr1Q//+/TFlyhTb1ED37t3dKgQ0RSmVIDHCD1tuaGESGBQSxwaE+qWLEgmHxAg/KKXu/9+EENJxeUU40Gg0iI+P7xBvMp5AEAScP38eqampthBQVVUFAFAoFOjbty/69++PiRMn2kYCunfvDqm0Yw+TR/rLMbGbLw4W6xwaEGzBgOMwsZsvIv3lDqiWEEKa5vHhoLq6Gj/99BNeeuklsUvpGAoKAI0GiIwEqqqAlJRWX8JiseDYsWM4deoUoqKiMG7cOFsIiIyMhEzmuX/sokOtGxIdvKODiTHI0b4eBOHBVIJEYg0G9dcnhBBn6vCrFXjGUG7gUaq3oEzPQ2cRwDMGKcfBVybBvcvn8F/vv4t/bk5H94huYpfr3rRaIDkZ2LsXuHUL+O47YOVKIDsb+NvfrI/Xa+yxBxhjYIx59UhNUY0Z+4trUWXiIQEHGde6UQTGGCwMEGDtMUiM8KMRA0KIy3TYcKA18ThXYUR+hQE6M4PArMOujx6fK+E4GE0mWMwmhAX5IzpEhcEhSurybkp2NrBzJ/DSS9aRgx49Hn5vwQJg06aGz2/sMWJj5AUcvVeH/Aqj9fRGBsgkHCRoPCgwxiDAusERuPo9FJSI6+JDPQaEEJfqcOO7Tf2FK+fq97Zv+JeuVlcDhVKFWpOAYyV1OFGq9+q/cGtqanD58mUUFxdj1qxZDb8ZGAgMGwbU7yJ561bDgEBaRSmVYEKEH4aHqRsEWQuz7lPweJCt30TJTyGhIEsIEVWHCgdFNWbsK65F9YOhWgXHgWtm0xle4GGxWODnJ4NSKrEN1Z4pN+BGjdmjh2p1Op1tWeCjqwPu3r0LAOjWrduT4SAhAThyxDqCAFjDAoWDdgtQSDH6KR+MDFej3MCjTG9BqZ5HnUWAhTHIHpyuGKaWorNahlCVlHY+JISIqsOEg/xyAw7e0UFgDHKOs6vJy2QyAbB2yAPWoVw5BwgMqDLx2HJD2+GbvOrq6nDlypUntg4uLi4GYP2ZIyMjERUVhaSkJFtjYN++fRu/4OrVLqzeu0g5DmFqGcLUMgwWuxhCCGlGhwgH+eUGHCy2BgOFHUfj1jMZjZDL5ZBwDacPJBwHBQCTwHCw2LrjnrsHBIPBYAsBjwaB27dvo75tJCIiAv3798fMmTMbhAAfn3ZumKPRAEVF1hGF6dObfowQQohHcPuGxKIaM7bc0EIQWhcMAIbSsjKo1Wr4+/k3/oxHNpaZ2yvALaYYjEYjrl279sRmQbdu3bKFgK5duzY4NyAqKgpRUVHw9fUVuXpCCCGewK3DgZEX8O3lalSbeGt/QSvmYc0WM8rLyxESHAyFQtnk8xhjMD3YkjYlKtBlTYomkwnXrl17oifg5s2bEAQBAPDUU081GgL8/RsPO4QQQogjuPW0wtF7dag28Y+sRLCfyWSy9hg86DdoCsdxkMPag3D0Xh0mRPi1o+Inmc1mXL9+/YmegBs3boDneQBAeHg4oqKiMGHChAZBICAgwKG1EEIIIfZw25GD9h6DW1lVCTAgODjYrue39xhcs9mMmzdvPtETcP36dVgsFgCwne/w+GhAUFBQq+9HCCGEOIvbhoPjP9fhWEldq6cTAIDBetqfn58ffH3sm4evn14YE+6D0U813cBnsVhw69atJ44TvnbtGsxmMwAgJCSkwZt//b+HhIS06ucghBBCxOCW0wo8Y8ivMAAMze5j0BSz2QzGmG0Joz046640yK8wYGS4GhAE3L5923aUcH0QuHbtmm2JZFBQEPr374+RI0fipZdesgWBTp06tbpmQgghxF245chBqd6C1MvVkD7YQrY5V/OOw1Crhb6mGiOmzwcAfPP7f0XXgcMwcdGr4ND86xkYBJ6HxcLDLPAQGHDq//6As5pDMBqNAICAgIBGpwM6d+7s0GN5CSGEEHfgliMHpXqLbbOj5lTcvQ0f/0CEdInA+veW28JBWK/+qL3/c4NgwMAgCAIsFssTv+rzkYSTQOnnj0Ej4zA1YZQtCISFhVEIIIQQ4jXcMhyU6XlI7Og1qLhXjL6xo5Hz/7d397BtlHEcx3/PPefErhNaOYqR81IJpXQg4IF2KRGgqgqFCSEhsbHBwkjFzMbGzILEBgIGQEioQkh0gAWxtCFIZiMvFlQ40Dhx49jPw3COlSdym0RJo3P8/Uz2RT4pXvzV3XPP/7OPNfPsFUmS805PXJ7TX5U72tjc6BkBxhjFcaw4jpXNZruvbRSp6aTnXntDVyfZMwAAMJhSGQcbLdcZSvPwOLhwKQmCOz9+p+tvvSspWVhoIqPHLz6jer2uOI716zefqjAxLWutyldfkY2iB57beafNljvOfwcAgL6SyrGE7UMsg2jU72n1j8VuKNjIqnG3qguzZRWLRX39wXt6/vU3dfmlV/XzF5/IRlb7RUcrfcswAAA4MamMg8NMpFtbXVahNB1+PrKyNla18rtyI8lGQquVRb3z0ZcHOmfM+gIAwABLZRzk4+hAUxclKbtnbsLCrZt6+sXrkqSVyoJq1SWtrSYTCr/68P19zxd1xucCADCoUrnmYDxn5byX99p3UWJhYlqzL8zrl28/V270rCaefKr7t0Z9PTl2MTm2UlnQamWx+34v77289yrmDr9DIgAAp0Uq46CYixUZIyfpID/TL799o+fxQmkquOWQGz2rWnXpgXHglMTIeC6VXwsAACcildfPx7JW+YxRyx1tYeDMpSuqVZe679eqy5rpLFzspeW88hmjsSxXDgAAgyuVOyRKR5utsNvCrZtqrP+nRn1dhdJUdz3CXgedrQAAwGmX2jg46lTGwzrqVEYAAE6LVN5WkKTHhqzKhWE5+c6GSI+O815OXuXCMGEAABh4qY0DSZorndG5IavtzlMEj4L3Xtve69yQ1VyJ2wkAAKQ6DoZtpPmpEUXGqOmOPxC892o6r8gYzU+NaNim+usAAOBEpP7X8PxoRtcm88ceCLvD4NpkXudHM8dyXgAA+l1fPNBfHstKkn5Y2VDTe2WkA++g2Ivr3EqIoiQMds4PAABS/LRCL3+ub+v75br+bbYVySg2+++guJv3Xi0vOSVrDOanRrhiAADAHn0VB5K01Xb6qbqp27WtZHqjl+LIKFLvUPDeyynZ4EgmGepULgxrrnSGNQYAAPTQd3Gw416zrd9qW7pdu6+N7WQtgjEmeOwxMqZ7PJ8xKheymuVxRQAAHqpv42BH23v9c7+tu42W/m60tdlyanmvuDNdsZizGs/FGsvaQ42CBgBgUPV9HAAAgOPFTXcAABAgDgAAQIA4AAAAAeIAAAAEiAMAABAgDgAAQIA4AAAAAeIAAAAEiAMAABAgDgAAQIA4AAAAAeIAAAAEiAMAABAgDgAAQIA4AAAAAeIAAAAEiAMAABAgDgAAQIA4AAAAAeIAAAAEiAMAABAgDgAAQIA4AAAAAeIAAAAEiAMAABAgDgAAQIA4AAAAAeIAAAAEiAMAABAgDgAAQIA4AAAAAeIAAAAEiAMAABAgDgAAQIA4AAAAgf8B0o2CYz9n3twAAAAASUVORK5CYII=", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " - Graph with 8 vertices and 13 edges.\n", - " - Features dimensions: [1, 0]\n", - " - There are 0 isolated nodes.\n", - "\n" - ] - } - ], - "source": [ - "dataset = loader.load()\n", - "describe_data(dataset)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Loading and Applying the Lifting" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this section we will instantiate the lifting we want to apply to the data. For this example the clique lifting was chosen. For a clique of n nodes the algorithm for $m=3,...,max(n, complex\\_dim)$ will create simplicials for every possible combinations containing m nodes of the clique. $complex\\_dim$ is a parameter of the lifting. This is a deterministic lifting, based on connectivity, that does not modify the initial connectivity of the graph. The problem of extracting all the cliques in a graph is NP-hard, on in some formulaitons NP-complete (clique decision problem). The computational complexity of this algorithm is $O(n^k k^2)$[[1]](https://www.sciencedirect.com/science/article/pii/S0019995885800413), where $n$ is the number of nodes in the graph and $k$ is the highest clique dimension considered.\n", - "\n", - "***\n", - "[[1]](https://www.sciencedirect.com/science/article/pii/S0019995885800413) Cook, S. A. (1985). A taxonomy of problems with fast parallel algorithms. Information and control, 64(1-3), 2-22.\n", - "***\n", - "\n", - "For simplicial complexes creating a lifting involves creating a `SimplicialComplex` object from topomodelx and adding simplices to it using the method `add_simplices_from`. The `SimplicialComplex` class then takes care of creating all the needed matrices.\n", - "\n", - "Similarly to before, we can specify the transformation we want to apply through its type and id --the correxponding config files located at `/configs/transforms`. \n", - "\n", - "Note that the *tranform_config* dictionary generated below can contain a sequence of tranforms if it is needed.\n", - "\n", - "This can also be used to explore liftings from one topological domain to another, for example using two liftings it is possible to achieve a sequence such as: graph -> simplicial complex -> hypergraph. " - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Transform configuration for graph2simplicial/vietoris_rips_lifting:\n", - "\n", - "{'transform_type': 'lifting',\n", - " 'transform_name': 'SimplicialVietorisRipsLifting',\n", - " 'complex_dim': 3,\n", - " 'preserve_edge_attr': False,\n", - " 'signed': True,\n", - " 'distance_threshold': 2.0,\n", - " 'feature_lifting': 'ProjectionSum'}\n" - ] - } - ], - "source": [ - "# Define transformation type and id\n", - "transform_type = \"liftings\"\n", - "# If the transform is a topological lifting, it should include both the type of the lifting and the identifier\n", - "transform_id = \"graph2simplicial/vietoris_rips_lifting\"\n", - "\n", - "# Read yaml file\n", - "transform_config = {\n", - " \"lifting\": load_transform_config(transform_type, transform_id)\n", - " # other transforms (e.g. data manipulations, feature liftings) can be added here\n", - "}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We than apply the transform via our `PreProcesor`:" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Processing...\n", - "Done!\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Dataset only contains 1 sample:\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " - The complex has 8 0-cells.\n", - " - The 0-cells have features dimension 1\n", - " - The complex has 25 1-cells.\n", - " - The 1-cells have features dimension 1\n", - " - The complex has 41 2-cells.\n", - " - The 2-cells have features dimension 1\n", - " - The complex has 39 3-cells.\n", - " - The 3-cells have features dimension 1\n", - "\n" - ] - } - ], - "source": [ - "lifted_dataset = PreProcessor(dataset, transform_config, loader.data_dir)\n", - "describe_data(lifted_dataset)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create and Run a Simplicial NN Model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this section a simple model is created to test that the used lifting works as intended. In this case the model uses the `up_laplacian_1` and the `down_laplacian_1` so the lifting should make sure to add them to the data." - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Model configuration for simplicial SAN:\n", - "\n", - "{'in_channels': None,\n", - " 'hidden_channels': 32,\n", - " 'out_channels': None,\n", - " 'n_layers': 2,\n", - " 'n_filters': 2,\n", - " 'order_harmonic': 5,\n", - " 'epsilon_harmonic': 0.1}\n" - ] - } - ], - "source": [ - "from modules.models.simplicial.san import SANModel\n", - "\n", - "model_type = \"simplicial\"\n", - "model_id = \"san\"\n", - "model_config = load_model_config(model_type, model_id)\n", - "\n", - "model = SANModel(model_config, dataset_config)" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "y_hat = model(lifted_dataset.get(0))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If everything is correct the cell above should execute without errors. " - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "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": "3.11.3" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} From 153b620b2e92daecc1788e1bedd02bfcb8fc310b Mon Sep 17 00:00:00 2001 From: Jonas-Verhellen Date: Fri, 31 May 2024 19:13:12 +0200 Subject: [PATCH 4/4] Implementation of the eccentricity-based lifting of graphs to simplicial complexes --- ...lifting.yaml => eccentricity_lifting.yaml} | 2 +- modules/transforms/data_transform.py | 6 +- ...ced_lifting.py => eccentricity_lifting.py} | 23 +- ...ifting.py => test_eccentricity_lifting.py} | 144 ++----- .../eccentricity_lifting.ipynb | 356 ++++++++++++++++++ .../graph_induced_lifting.ipynb | 356 ------------------ 6 files changed, 402 insertions(+), 485 deletions(-) rename configs/transforms/liftings/graph2simplicial/{graph_induced_lifting.yaml => eccentricity_lifting.yaml} (69%) rename modules/transforms/liftings/graph2simplicial/{graph_induced_lifting.py => eccentricity_lifting.py} (61%) rename test/transforms/liftings/graph2simplicial/{test_graph_induced_lifting.py => test_eccentricity_lifting.py} (51%) create mode 100644 tutorials/graph2simplicial/eccentricity_lifting.ipynb delete mode 100644 tutorials/graph2simplicial/graph_induced_lifting.ipynb diff --git a/configs/transforms/liftings/graph2simplicial/graph_induced_lifting.yaml b/configs/transforms/liftings/graph2simplicial/eccentricity_lifting.yaml similarity index 69% rename from configs/transforms/liftings/graph2simplicial/graph_induced_lifting.yaml rename to configs/transforms/liftings/graph2simplicial/eccentricity_lifting.yaml index 353ebccf..532d3f2a 100755 --- a/configs/transforms/liftings/graph2simplicial/graph_induced_lifting.yaml +++ b/configs/transforms/liftings/graph2simplicial/eccentricity_lifting.yaml @@ -1,5 +1,5 @@ transform_type: 'lifting' -transform_name: "SimplicialGraphInducedLifting" +transform_name: "SimplicialEccentricityLifting" complex_dim: 3 preserve_edge_attr: False signed: True diff --git a/modules/transforms/data_transform.py b/modules/transforms/data_transform.py index faeda4bd..c7dde5fe 100755 --- a/modules/transforms/data_transform.py +++ b/modules/transforms/data_transform.py @@ -15,8 +15,8 @@ from modules.transforms.liftings.graph2simplicial.clique_lifting import ( SimplicialCliqueLifting, ) -from modules.transforms.liftings.graph2simplicial.graph_induced_lifting import ( - SimplicialGraphInducedLifting, +from modules.transforms.liftings.graph2simplicial.eccentricity_lifting import ( + SimplicialEccentricityLifting, ) TRANSFORMS = { @@ -24,7 +24,7 @@ "HypergraphKNNLifting": HypergraphKNNLifting, # Graph -> Simplicial Complex "SimplicialCliqueLifting": SimplicialCliqueLifting, - "SimplicialGraphInducedLifting": SimplicialGraphInducedLifting, + "SimplicialEccentricityLifting": SimplicialEccentricityLifting, # Graph -> Cell Complex "CellCycleLifting": CellCycleLifting, # Feature Liftings diff --git a/modules/transforms/liftings/graph2simplicial/graph_induced_lifting.py b/modules/transforms/liftings/graph2simplicial/eccentricity_lifting.py similarity index 61% rename from modules/transforms/liftings/graph2simplicial/graph_induced_lifting.py rename to modules/transforms/liftings/graph2simplicial/eccentricity_lifting.py index 96a017b0..e8d1dbaf 100755 --- a/modules/transforms/liftings/graph2simplicial/graph_induced_lifting.py +++ b/modules/transforms/liftings/graph2simplicial/eccentricity_lifting.py @@ -7,8 +7,8 @@ from modules.transforms.liftings.graph2simplicial.base import Graph2SimplicialLifting -class SimplicialGraphInducedLifting(Graph2SimplicialLifting): - r"""Lifts graphs to simplicial complex domain by identifying connected subgraphs as simplices. +class SimplicialEccentricityLifting(Graph2SimplicialLifting): + r"""Lifts graphs to simplicial complex domain using eccentricity. Parameters ---------- @@ -33,15 +33,20 @@ def lift_topology(self, data: torch_geometric.data.Data) -> dict: The lifted topology. """ graph = self._generate_graph_from_data(data) - simplicial_complex = SimplicialComplex(graph) - all_nodes = list(graph.nodes) + simplicial_complex = SimplicialComplex() + eccentricities = nx.eccentricity(graph) simplices = [set() for _ in range(2, self.complex_dim + 1)] - for k in range(2, self.complex_dim + 1): - for combination in combinations(all_nodes, k + 1): - subgraph = graph.subgraph(combination) - if nx.is_connected(subgraph): - simplices[k - 2].add(tuple(sorted(combination))) + for node in graph.nodes: + simplicial_complex.add_node(node, features=data.x[node]) + + for node, ecc in eccentricities.items(): + neighborhood = list( + nx.single_source_shortest_path_length(graph, node, cutoff=ecc).keys() + ) + for k in range(1, self.complex_dim): + for combination in combinations(neighborhood, k + 1): + simplices[k - 1].add(tuple(sorted(combination))) for set_k_simplices in simplices: simplicial_complex.add_simplices_from(list(set_k_simplices)) diff --git a/test/transforms/liftings/graph2simplicial/test_graph_induced_lifting.py b/test/transforms/liftings/graph2simplicial/test_eccentricity_lifting.py similarity index 51% rename from test/transforms/liftings/graph2simplicial/test_graph_induced_lifting.py rename to test/transforms/liftings/graph2simplicial/test_eccentricity_lifting.py index ea1c3ed3..35a47b14 100644 --- a/test/transforms/liftings/graph2simplicial/test_graph_induced_lifting.py +++ b/test/transforms/liftings/graph2simplicial/test_eccentricity_lifting.py @@ -3,21 +3,21 @@ import torch from modules.data.utils.utils import load_manual_graph -from modules.transforms.liftings.graph2simplicial.graph_induced_lifting import ( - SimplicialGraphInducedLifting, +from modules.transforms.liftings.graph2simplicial.eccentricity_lifting import ( + SimplicialEccentricityLifting, ) -class TestSimplicialCliqueLifting: - """Test the SimplicialCliqueLifting class.""" +class TestSimplicialEccentricityLifting: + """Test the SimplicialEccentricityLifting class.""" def setup_method(self): # Load the graph self.data = load_manual_graph() # Initialise the SimplicialCliqueLifting class - self.lifting_signed = SimplicialGraphInducedLifting(complex_dim=3, signed=True) - self.lifting_unsigned = SimplicialGraphInducedLifting( + self.lifting_signed = SimplicialEccentricityLifting(complex_dim=3, signed=True) + self.lifting_unsigned = SimplicialEccentricityLifting( complex_dim=3, signed=False ) @@ -57,14 +57,19 @@ def test_lift_topology(self): expected_incidence_2_singular_values_unsigned = torch.tensor( [ - 4.1190, + 4.2426, 3.1623, 3.1623, 3.1623, - 3.0961, - 3.0000, - 3.0000, - 2.7564, + 3.1623, + 3.1623, + 3.1623, + 3.1623, + 2.0000, + 2.0000, + 2.0000, + 2.0000, + 2.0000, 2.0000, 2.0000, 2.0000, @@ -80,11 +85,6 @@ def test_lift_topology(self): 2.0000, 2.0000, 2.0000, - 1.7321, - 1.6350, - 1.4142, - 1.4142, - 1.0849, ] ) @@ -106,18 +106,18 @@ def test_lift_topology(self): 2.8284e00, 2.8284e00, 2.8284e00, - 2.6458e00, - 2.6458e00, - 2.2361e00, - 1.7321e00, - 1.7321e00, - 9.3758e-07, - 4.7145e-07, - 4.3417e-07, - 4.0241e-07, - 3.1333e-07, - 2.2512e-07, - 1.9160e-07, + 2.8284e00, + 2.8284e00, + 2.8284e00, + 2.8284e00, + 2.8284e00, + 7.0866e-07, + 4.0955e-07, + 3.2154e-07, + 2.9976e-07, + 2.8069e-07, + 2.3097e-07, + 9.4821e-08, ] ) @@ -129,91 +129,3 @@ def test_lift_topology(self): assert torch.allclose( expected_incidence_2_singular_values_signed, S_signed, atol=1.0e-04 ), "Something is wrong with signed incidence_2 (edges to triangles)." - - expected_incidence_3_singular_values_unsigned = torch.tensor( - [ - 3.8466, - 3.1379, - 3.0614, - 2.8749, - 2.8392, - 2.8125, - 2.5726, - 2.3709, - 2.2858, - 2.2369, - 2.1823, - 2.0724, - 2.0000, - 2.0000, - 2.0000, - 1.8937, - 1.7814, - 1.7321, - 1.7256, - 1.5469, - 1.5340, - 1.4834, - 1.4519, - 1.4359, - 1.4142, - 1.0525, - 1.0000, - 1.0000, - 1.0000, - 1.0000, - 0.9837, - 0.9462, - 0.8853, - 0.7850, - ] - ) - - expected_incidence_3_singular_values_signed = torch.tensor( - [ - 2.8284e00, - 2.8284e00, - 2.8284e00, - 2.8284e00, - 2.8284e00, - 2.8284e00, - 2.8284e00, - 2.8284e00, - 2.8284e00, - 2.6933e00, - 2.6458e00, - 2.6458e00, - 2.6280e00, - 2.4495e00, - 2.3040e00, - 1.9475e00, - 1.7321e00, - 1.7321e00, - 1.7321e00, - 1.4823e00, - 1.0000e00, - 1.0000e00, - 1.0000e00, - 1.0000e00, - 1.0000e00, - 1.0000e00, - 1.0000e00, - 1.0000e00, - 1.0000e00, - 7.3584e-01, - 2.7959e-07, - 2.1776e-07, - 1.4498e-07, - 5.5373e-08, - ] - ) - - U, S_unsigned, V = torch.svd(lifted_data_unsigned.incidence_3.to_dense()) - U, S_signed, V = torch.svd(lifted_data_signed.incidence_3.to_dense()) - - assert torch.allclose( - expected_incidence_3_singular_values_unsigned, S_unsigned, atol=1.0e-04 - ), "Something is wrong with unsigned incidence_3 (triangles to tetrahedrons)." - assert torch.allclose( - expected_incidence_3_singular_values_signed, S_signed, atol=1.0e-04 - ), "Something is wrong with signed incidence_3 (triangles to tetrahedrons)." diff --git a/tutorials/graph2simplicial/eccentricity_lifting.ipynb b/tutorials/graph2simplicial/eccentricity_lifting.ipynb new file mode 100644 index 00000000..43ee6e54 --- /dev/null +++ b/tutorials/graph2simplicial/eccentricity_lifting.ipynb @@ -0,0 +1,356 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Graph-to-Simplicial Eccentricity Lifting Tutorial" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "This notebook shows how to import a dataset, with the desired lifting, and how to run a neural network using the loaded data.\n", + "\n", + "The notebook is divided into sections:\n", + "\n", + "- [Loading the dataset](#loading-the-dataset) loads the config files for the data and the desired tranformation, createsa a dataset object and visualizes it.\n", + "- [Loading and applying the lifting](#loading-and-applying-the-lifting) defines a simple neural network to test that the lifting creates the expected incidence matrices.\n", + "- [Create and run a simplicial nn model](#create-and-run-a-simplicial-nn-model) simply runs a forward pass of the model to check that everything is working as expected.\n", + "\n", + "***\n", + "***\n", + "\n", + "Note that for simplicity the notebook is setup to use a simple graph. However, there is a set of available datasets that you can play with.\n", + "\n", + "To switch to one of the available datasets, simply change the *dataset_name* variable in [Dataset config](#dataset-config) to one of the following names:\n", + "\n", + "* cocitation_cora\n", + "* cocitation_citeseer\n", + "* cocitation_pubmed\n", + "* MUTAG\n", + "* NCI1\n", + "* NCI109\n", + "* PROTEINS_TU\n", + "* AQSOL\n", + "* ZINC\n", + "***" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Imports and utilities" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n" + ] + } + ], + "source": [ + "# With this cell any imported module is reloaded before each cell execution\n", + "%load_ext autoreload\n", + "%autoreload 2\n", + "from modules.data.load.loaders import GraphLoader\n", + "from modules.data.preprocess.preprocessor import PreProcessor\n", + "from modules.utils.utils import (\n", + " describe_data,\n", + " load_dataset_config,\n", + " load_model_config,\n", + " load_transform_config,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Loading the Dataset" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we just need to spicify the name of the available dataset that we want to load. First, the dataset config is read from the corresponding yaml file (located at `/configs/datasets/` directory), and then the data is loaded via the implemented `Loaders`.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Dataset configuration for manual_dataset:\n", + "\n", + "{'data_domain': 'graph',\n", + " 'data_type': 'toy_dataset',\n", + " 'data_name': 'manual',\n", + " 'data_dir': 'datasets/graph/toy_dataset',\n", + " 'num_features': 1,\n", + " 'num_classes': 2,\n", + " 'task': 'classification',\n", + " 'loss_type': 'cross_entropy',\n", + " 'monitor_metric': 'accuracy',\n", + " 'task_level': 'node'}\n" + ] + } + ], + "source": [ + "dataset_name = \"manual_dataset\"\n", + "dataset_config = load_dataset_config(dataset_name)\n", + "loader = GraphLoader(dataset_config)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can then access to the data through the `load()`method:" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Dataset only contains 1 sample:\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " - Graph with 8 vertices and 13 edges.\n", + " - Features dimensions: [1, 0]\n", + " - There are 0 isolated nodes.\n", + "\n" + ] + } + ], + "source": [ + "dataset = loader.load()\n", + "describe_data(dataset)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Loading and Applying the Lifting" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For simplicial complexes creating a lifting involves creating a `SimplicialComplex` object from topomodelx and adding simplices to it using the method `add_simplices_from`. The `SimplicialComplex` class then takes care of creating all the needed matrices.\n", + "\n", + "Similarly to before, we can specify the transformation we want to apply through its type and id --the correxponding config files located at `/configs/transforms`. \n", + "\n", + "Note that the *tranform_config* dictionary generated below can contain a sequence of tranforms if it is needed.\n", + "\n", + "This can also be used to explore liftings from one topological domain to another, for example using two liftings it is possible to achieve a sequence such as: graph -> simplicial complex -> hypergraph. " + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Transform configuration for graph2simplicial/eccentricity_lifting:\n", + "\n", + "{'transform_type': 'lifting',\n", + " 'transform_name': 'SimplicialEccentricityLifting',\n", + " 'complex_dim': 3,\n", + " 'preserve_edge_attr': False,\n", + " 'signed': True,\n", + " 'feature_lifting': 'ProjectionSum'}\n" + ] + } + ], + "source": [ + "# Define transformation type and id\n", + "transform_type = \"liftings\"\n", + "# If the transform is a topological lifting, it should include both the type of the lifting and the identifier\n", + "transform_id = \"graph2simplicial/eccentricity_lifting\"\n", + "\n", + "# Read yaml file\n", + "transform_config = {\n", + " \"lifting\": load_transform_config(transform_type, transform_id)\n", + " # other transforms (e.g. data manipulations, feature liftings) can be added here\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We than apply the transform via our `PreProcesor`:" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Transform parameters are the same, using existing data_dir: /home/jonasver/Documents/Code/challenge-icml-2024/datasets/graph/toy_dataset/manual/lifting/494191853\n", + "\n", + "Dataset only contains 1 sample:\n" + ] + }, + { + "data": { + "image/png": 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aFtjKJhANSilcYXHZ4DCJp4/z+A/v4qsvfI7nlhbJOoJzL7qICy64gIvy/zeiwWAw9DtCNeJsNRgiwMm0x+0/WsIWtV0MmWyGu277Y2bOOpfB0XFOP/8lTJ1+1voCCm0wAD75nqv51f/+1wyNjVcVHBKQCK7NnWRKenieh+u5eK6L67osra1yYu4Uz506xRMvPFMQDedceCEXFr2MaDAYDP2CsRwY+oapAZvhmGA152PblSfywJXwMzd8EGFZ+L5f4op45NBBnnv8Ea56/+8ggIHhERZPPs/g6BhKqULaYjEeghElmVIetgV23CERd4BBALZvm+Lcs3ZWFg2nTvGjx77GP77weZ5dXCiIhosuuqjgnhgZGenUITMYDIaWMOLA0DfYQrB7coB/fTFVmMiLCYTB8MAAwrJAKcolxORpZzI4MgoKlIDM2ipnnP/SQsyB7/tYllVYd2BW2y1T2LXGVkc0eJ4WDK7nsrRaKhpOLC+zKiTn5gVD8DKiwWAw9ArjVjD0Fcs5yf98bBGlIFZURrlcGAjA930EouBCCNwJjxw6CMBzjz/Cq95+DVOnnVX4M1BIaxRC4AkLgeK92ZOMEV4RJOmzLhrywmFpdd098dSJ53lucZ5Vyy9YGi644AIuvPBCRkdHQxuHwWAwVMKIA0Pf8c/PrvK9uQwxods2Z7MZcpkMwwODBWGgAF/6WJYo/F4ca0DR7+XWhcCK4CmFtB32yDXe7C11Yc/yokF62jVRJBpOzs/x3Nwpnnz+OZ5dnGNV+CWWhgsuuICxsbGujNFgMGx+jDgw9B1Z6XP7j5ZYzEmU6+Jm0iXCAPTTfyGGgPUJn/KgwyoCwQdcBKMyx8/MPcWOsRFsq3c1wwqiIXBPuB7La6u8OHeqRDSsoN0TxTENRjQYDIZmMeLA0JccX3H5yo/m8DzJkG3pOIGivwcuhWItoMotB0WUGxRywsJSip9bO8F0eoXVtRVGkpMMDSbC35k2kGrdPeG5LrkKouGZxTlWkQX3ROCiMKLBYDBUw4gDQ19y9913882Hn2Tkp34OhCCOKgktUL5fsBoEVBUHeYtCYGHICQsLxRWZBXa7qzqV0XVZy6TxLYvk+FhPrQiNsEE0eB7LqxtFw4ryCu6JwNpgRIPBYDDiwNB33HPPPdz7t3/HB37uHTy3/Sz+JZbEB2KoQnvlYpdCQL0L3Qe8vMXgiqwWBsWflVKSc93IWhEaIRAN0nNxPa8Q0/Di/BzPnZrlyeef4/jCqRJLQyAcjGgwGLYORhwY+op//ud/5ut/8xU+8HPvYHJyClvAcRHnYCzJonCwAMvPt3Yu+2xwoYuyn310LQMlBEnfY19mjrNltuL2+9GK0AhSgSxkT1QWDc8vLfDqt17JL117bb70tMFg2KwYcWDoG/7lX/6Fu7/4N/z2L76LySktDAKyCL7pjHHYHsLLl1d2lE/QPQE2CgIfkEJPcrZSvCyzxF5vhcE6NoZSK8IaI8lkX1oRGmFdNOgCT/aZMwy/5DxdR6KYI0fg0CE4+2xYXIRrr+3JeA0GQziYIkiGvuDrX/96VWEAkEDxZm+Jl63M8pgzzONDk6xZNh7rYgAABZbQv1tKMeJ77HbXeKm7ylAug7AE2LW/FgJwbBth2zi2zVpqjXQmvWmsCMXYAuyYQzzmAAMwNLQx42N5GT74QbjrLjh2DL761dK/ffzjcOutXR33ZkUqxVxGcjLtMZuWrHk+UilsIRh2LLYP2uwYdJgasBvuYmowVMKIA0Pkuffeezn4hb+uKgyKsdeWed2QZO9amjkrxqwV56QdJyUsXJUXBEIxLXNMuRmSMkvCcRCAtCxcz8WynZrVEAvbAqxYjFHLIue6zL04u6mtCFW57z7YuXPdcnDjjaV/W1ys+LFPfvKTbNu2rRDXMDEx0Z3x9iHLOcmj81kOz2dYcxW+UlhC4BcZfoPfLSEYjulqohdPJhiLN3I1GwylGHFgiDT33Xcf//d/fYn/5xd+qa4wcD2JhcAWFjaww3fZ4btc7K0BIJF4ro8Ti2EDUkh8pfDREz22ja0UnusiYjEasQFsJStCVcbH4RWvgL179e/HjmmxcOAAXH01/MM/VPzY/pe8gueOP8Nzd3+DQ1/6Mk/PzbJow86dO0sCIbeyaMhKn2++kOLwfBapFChwLEGs0AOk9AuhlLaKreZ8/vXFFN8+mWb3ZILXnzZEwt4i16MhFIw4MESWQ4cO8U//86+0MNhWWxgApNNpBmJxsKs9KdmgvNLfhQIptTAAcAR+zseXEqvqeiqueetaEfbuhXvv1WIAtFhYXoZLL635sbNOm+H05CS+5+kgyHza5fJaihfn53j+nm/yjdu/wjMLc8wh2XnOOSUpl5OTk13Yud5xfMXla8+uspTTojcuhHZ71UAIgQ3YtkAphafge3MZnlpx2XfmCGePxrozeEPfYwISDZHkG9/4Bl/93Bf4nV/4JaYaEAYKmD05y8TIKDGnsuaVgJvLErMd7PzEL6W2Hjh51wKAJyWe9IjFEw25FyptZzNmNABw2nbYefrGuINyjhzRFgSAT38a/uiPNoqF7/0AMrmKH5eK6qJh7hTHTrzA0/MnOaU8zjn3XC644IJCgafNIBoOz2W457k1fKUKZcJbxVcKN+9uuOKMYXZPDYQ4UsNmxVgODJHjG9/4Bv/4+S/w27/wroaEAUAu5xFzbCyn+sIVp2cbcItcC4CwbSxfWw/sJqwHxavctFaERh8lLr1Uv26/XVsRmlxXEAgZCwIhganJSc4+88yCaAhKSa+spTgxP8cLX7+fb/3N3/PU3IsllobA2jA1NdXUrvaKw3MZ7nlWC4O4tbGFeLNYQhAHcr7inme1i80IBEM9jOXAECm++c1v8v//i8/zwf2/zLZt2xoSBgALS8sMWDbxRO2n/UwuR8y21y0H6CdUS4gSISClJOe5xBMDLVkPCuthk1kRJsfhonPbX49S8J0j4Pv1l22AYkuDFg0eqyktGp6fP8XTzz/PU6deZA6PnUWWhgsvvJBt27aFMoYwOL7i8vdPLeP74QiDYpRS5HyFZQl+8dwx42Iw1MSIA0Nk+Nd//Vf+959/rmlhIBXMzc4yMTpGrM6TftbN4Vj2BiFQ7loIRIOvFPFYrFpLhobYVHURLAtedYl2K7Q6cSkFi8vw2FPhjq0CFUVDOsWJuXmen58tiIYlPE6vYGkIc3KuR1b6fOlHSyzlpI4v6MC2lVLklCIZt7n2wnETpGioihEHhkhw//338//787/kg9f8Etu2bW9YGACk01ly6TQjIyN1n/Jdz9MFkorFARLf9bHyWQzF73s57VpwWnAvlLNprAjjo/CSc7VQyEfQN4RAC4p0Bh59Alyv7kc6hVTgSw/XrS4ajs6eKIiGi4raY2/btq0jE3d5K/JOEcQgXDY1wJvPHOnYdgz9jREHhp7zrW99i7//zGe1MJjaTrMPM3Pzi4wk4sRj8bpP+K7UZZFiJeKgsmsB2g9OLGfTWBFsC5JjkIg3bkFQPqymYXm1/rI9op5oOP7iCzw9e5JTXoYzzj23xNLQjmhYzkn+52OLKAWxOhkJYeD6CiHgvS9JmjoIhooYcWDoKd/+9re589N/zgff8SstCQPpKxbmTpEcGWvo6d6TEt/3icdK/a2VXAuw/rQvLKuuy6KpcbNJrAhbhEBAVhINJxZO8fSJ53lq9kXmvOwGS8P27dvriob7T6T41xdTHXMnlBO4F143PcRrZ4Y6vj1D/2HEgaFntCsMANZSaWQux/DQcENP9p6USH+9EFJANdcChBecWM6msSJsYUpFg25YtZpOcXJ+XgdC5kXDKXfd0nDRRRdxwQUXsGPHDoQQSKX43A8XWM35XY0ByEqfkbjF+186YUotGzZgxIGhJ3znO9/hjj/9TFvCAGD21Bxjg0MkYo1FXsu8OLA3iIPqroVCcCKKuNNecGLFMWGsCJuNSqJhLZPmxbm5gmg4evIEp9wMZ1/2EyTfcq1OOXTs/LnfeJU98eD9ZFaXSa8s8aqr3wHA337sdzj/8tcWfm9qjEohFVx74Tg7Bk1Wu6EUc0UYus53v/td/uZP/owPvfPdbQkDT+qui01NpLaNknLj2wBBbXpKb826tLLAd1uvfVBzSGziughbFBuwHaekINcUE5xx+um8wpO4rlsQDY84Q3wbAbkc6YwWrwqwLAvHcbAdh+WTLzA0OsbkaWfyv256f0EMnHHRJcw9f7ylMVqApxSzac+IA8MGzBVh6CoPPPAAf/2pT3PzO6/VWQltPCCnMmkSsRiiyclasV4yuYQKBZEKfxI2ys4HNAallkPE9GjYGmjRYBNz1q8g3xnHtizi8Tig4wGC/0vfx8+5nHjqx5x96Sv51v++nbMu2cNaag3HcXjpG67g6Ye/3dJYhBAIASfTkovb3jPDZsOIA0PXePDBB/nrT/4pN7/zWnZs29GWMADIpNIkh+unL26gqifNBuFXFg60XzmxEYwVYeuxJiwUFAIRiwMSLdsGpbjk1W9CKcUT9/8L+97zW8QQyJxLNpVi+wWXAPDIvXcBkF5ZYuK0szj/8tfW3bavFCkvnEJUhs2FeSQxdIUHH3yQ2//4v3NTSMLAdT1iwmo6kEq7Dypv3AYsYeErVTF130a7MFzPY6NjIjwCK0JiYIDk2BjZ1BpzC4vIkKoJGqKFrBHFoktD6IJTmbVVnn/iMc677DU4ts1ALMbC8aNsP+1MThx7kicevJ9L3nQVr7r6Hdz35c81vH3PhJ0ZKmDEgaHjPPTQQ3zxv32Km95xLTvadCUErKVTJBKJGh0Yq1NTTthAvo1z5b/bxGwb6bkN1/5pFRuIxWKMDg0zaDvMvThLKp3t8FYN3cauciUptGvB932U9Jl/4RkmTzsTKyirnG/ZPBBP8Nj99zEwsl7QaHBkjCcevL+h7TsmU8FQASMODB3le9/7Hl+49ZN86F3/lh3bt2OHUODFB3KZLDGnNd+/qOnDt7V6qBC0mP8rlhAoX+FXWSZMjBVh8zOs/MKNuEQQ+D5KKQS6VfPgyGjJ5x45dJBL3rgPEKydOoE9sF6vYHB0nMxqlYZXRVhCMOSYacCwERNzYOgYDz/8MF+49Y9DFQag2y7HnRhWiyYIIagYdAjrbodKWQuFZWz9rOfmYxO6UV/OxCJsXqZ8F98G6fuF66047iD4afK0s7j49Vfw3X+8k8HRcU4/7yX5vygsywLfx5Mejq1v6+mVpZrbVUqhlGLHoKmQaNiIEQeGjvDwww/zvz7xR/zuO8MVBqALHw3GYlgtG74EtfsFUzVrobCGLgQnbtgmJqNhsyB9yGYzpNNpEtYKYsc4SojCFV3+bQmu1qve/zsb3hRCMHX6WaRXl1lZXiY5MVEISqyFn//sdpPGaKiAuaMYQuf73/8+n/0v/42b3/nu0IWBVLpgUHmZ42YQgO9XDjrU1HYt5JfoSnBitW2bWIT+Q/qQSmeYm59nYW4WL5thJJHgnEScEeUjhaUDEMs+V/U6LVrwvMtezfM//gGDsTiZTIb5F56pm63g+YrhmGBqwFgODBsxFRINoXL48GH+4j9/glve9W+Z3rEjVGEA+ubqZTIMt5LCmEdKiVehhHL5MpV6LZQsg66Cp4BYG2KlHUx1xWgjfUU2myGVTqOkJBGLk4jFsS0LUeSSuj8+zr8mxokrv+Q6qnpzVhTEgVIKIQSPHjqIrxTzc7NMTp/G7p98a9Vxmd4KhnoYe5IhNI4cOcJtHRQGAKl0itHEYHt+fltoE0TNZajrWgiqKnpSdtW9UD4GE4sQLaSvyGS0y4B8k6/RxOAGQVDMxe4q346P4SGI5SVBzStUlDrHFHDJG9+CUgpPSlJuruYYPQW2EFw8aa4TQ2WMODCEwiOPPML/+Nj/yx/8yr/vmDCQvg/SD+HJ2MoXQpLUnPprFEQqrCl/s+9mcGI5Jhah90jfJ53JkkmnQfnEnTijA7UFQTFjSrLbXeV78VF8pWpboYqsBsFySinId3S0bZuYJ8jmsiTiGyd/Xyl8FJdNDph2zYaqGHFgaJtHH32UP/vYf+6oMABIpzMkYvGmyyVXwheqpjZoJGuB/PtBcKKqIyQ6jbEidJdAEKRTaQTNC4JyXp9d5ClnkEXLIVbmXiilSB0A5QG2lhAkYnGWV9eIT8YRRcsqpXCVIhm3ef1pxp1gqI4RB4a2ePTRR/n0Rz/OH/xyZ4UBQDqdYrzB1sy1sKHkhllzwTquhWAxLIus57U0KYSJsSJ0Fs/3yaS1y8AC4k6MscHWBUExCRRXZGb534MzuMLaIBAKEkCUXb0Vkm8sy2J4YIC1tTVGhnVxJKUUOV9hWYJ9Z450tT20of8w4sDQMj/4wQ+KhEG4WQnluJ7EQmBXKX3cPI2MtTHXgl7UJqYU0vOwnd5/rYwVITw86ZPJpEmnM0WCYCgUQVCMlJLTXJefZI5/GZraIBBU8E+FS7c8rFwIgWPbZDM5POlhW7YWBkJwxRnDnD3aWItzw9bFZCsYWuKHP/whn/z9j/CxX3lfXhh09ilkeWUV21cMDLYZjJgn57nYwqobRNhI1kLxsp6UOLbdk+DEapRkNAhBMjlurAh18KRPOpMmk05jIUg4MeLxGJYIVxAEBBk0geB4NDbCPYkJfAQOvq7qDQVxUJ7R4Pv+hmtOAb6ULGfSDI2NF4TB7qmBkEdv2IwYcWBomh/+8Id86vc/yh/88r/ntOnpjk80Cpg9OcvEyCixkJ7KXc8rPF3VQiLxXR+rRtpj8TgDgRCLx3vqXignGFvOdVldWzNWhAp4niSVzZANBEEsRjzWOUEQUEittaxCUKsEjimHfx7ezpIdQyiFg6paB8H3fSzb3iAaXAWu7+N4GfZfcqaxGBgaxjw+GJriscce66owAMjlPGKOjeWE6LYQosHGSfULIhVWST44UQgdnBghKvZomF/Y8j0aXE+yvLLG7OwsS4tLWJ5kfGiY8eERBgeHiDsxbQnq0Pa9KsLAlx5nywy/nDrBZbkVLMDFIicsJBvTHIUQkO8mKoEsghwCS8AlmUWe+sx/Yihbu5yywVCMsRwYGuaxxx7jk7//Uf7gl/5d14QBwMLSMgOWTTyRCO0m7UmJn89Br0czroVg+ZznhTreMNnqVgTXk6TTGTKZNLawChYCW4iupqN6UiLLhAGsX2+W4xTeW/ThSGyYHwwkWbPsQuhBQdrlCyGRf39YSXbLFBfLFMO+y+NHj/Ln37mXT3/6013aO0O/0/vIKUNf8Pjjj/dEGEgFbjbLyOhY6DftepUOCjSYtbC+vI0ToeDEcipmNKRTmzoWwfW8vCDI4OQFwcTwCFaXBUFAsTAQtl0w4UryLgLLWhcLwJB0ea1a5nVyjTkrxqwV56QdJyUsXCFQbo4RS3C65bNduUwpb32fLIuzTz+DVwwnOXToEHv37u3y3hr6EWM5MNTlRz/6Ebd+6BY++ivv6aowAEins+TSaUZGWi+XXAmJxHNrl1BeXxZ8z8VqIICx8Bkp8TwPJ+HQ2+TG2mxWK4ICPNfTQYWZLDFhES/EEPRGEAQUC4PiOIGgHLcARJHVQEqJKz1i8cqWKAmkUmuIeJyRocGK21TAiZMnuemLt/GXf/PXDAyYoERDbYw4MNTkxz/+Mbd+6BY+0sUYg2Lm5hcZScSJx+Kh9i6QgJfL4lS54W5YvknXQjDpSl/ixKIVnFiJzZDRoAA3LwiymQwxyyYRixFzei8IAqoJA6jsTgBt9QBqBuOuZdJIIRgbHam6jOt5fPeRI3xjZZabbrqpzT0xbHb669tv6CqBMPj9LrsSAqSvUNLFtuxQhUGAgoYCDQHtT1CKRsP3guBEFJELTqxESadHJ9Y3nR4VkHU9lpaXmT05y9ryMjEEEyOjjA0NkxgYJOY42D0WBoo6woC8O0GIknEWv18Ly7KQsvbVGXMcLj7vfDI/PsaPfvSjFvfEsFUwlgNDRZ544gk+/h9/lz+89r2cMTPTk6fItVQamcsxHEJVxHIUkM1lidlOQ66CVlwLEP3gxEpE3Yqg0Bks6XSKXDZLzLFJxBI6o0UIsHpvISgmEAZ+XhiUXz/V3AlQ36UQkHNdVrJppiYna45FKnj2uef4vTu+yF99+ctYETqvhmhhrgzDBp588kn+83/8UE+FAUAqldLtbTuwblH0byPYgJXvtdCUmrZtbMtC5k3D/UAUrQg+kM15LC5pC0FqdZm4ZTMxOsrYyDCJREILvT4TBkDBerVBGABSKWzbqbtPwtLdQethC5jesY13Xv56vvzlLze6G4YtiBEHhhK0MPhdPvYr7+mpMPCkjwUd3X4dS+1GmnQtBB+xLQslJZLouxcColAXQQuCHAtLy5yanSW1ulJZEEQ05NNHCwNVQxhUcyfoP8pCcaP6CCyvsXMzEE/wuktfzjf+z1eZnZ1t6DOGrYdxKxgKHD16lI9/8GY++svv4fTTZnT+dY9YXlvD8iSDg0Mdu/E3WiUxoFXXgg9I6emnxz4ITiynmxkNQaBoKpXFy2WJOTESsRiO42DZQESFQDnBOcdXOi2xmjCo4k6A9Wqb8Xj9YFxPSuaXFtk+M91YLQ7f5/GnjvKZb32dP/uzP2tonwxbC2M5MADw1FNPRUYYAGRSaeKdnkjFxkpztWjVtWABlu30TXBiOZ22Ikggk8uysLjE3MlZ0qtpBmyb5OgYowULgR1ZC0E5ErQbqYYw0AtWdicU1pHvl9DIZC8Ay7bxGzwntmVx9mlncNnIBPfdd19DnzFsLYw4MPD000/z0f/ng5ERBq7rEROWrljXQQQC1ewE14JrIfiYY9u4ntdHzoVSwoxFkArSuSzzi4vMnZwlu5pmwHaYGB1jdGSkSBCIvrpJBdYAVG1hUNOdANqloHyd8dIItoVlWfh1MhaKGR4a5OrXvpG//JP/TiaTafhzhq2BcStscZ5++mk+8jv/gT/8lfdyxsxpOBHo8b64vExc2CQ6HOFfSC1roBBSQKuuheCz0nVBCOIRrJzYDK1kNEgFuUyWdDaNzLnEHV2USLsMdJXAzsrBzlIQBqia10c9dwJol5eChq8TCayurhAfGWIw3rjLx/U8Hnz0Ue5dOsHNN9/c8OcMm5/ezwSGnrG4uMgD3/xXPvGB/xAZYeCjJ5CY03kTcqUOd/VoOWuB8uDE/tbkjVoRpIJUOsvcwiLzp2bJZdIM2TGSo2OMjIwwULAQbA1hoBeu7k4orMv3m7acWZaF32BQYkDMcXjZrl14R5/h8ccfb+qzhs2NsRxsUZRSSCl1/3jLgsVleOwp6PHlkM1lSa+mGR0Zwu6wdtXBbzmcJtsrN9PGuZz14ETVlMUiypRbEcbHx8nlXNKZNL7rkojFSMRjWNbmsBCUo2MM3Lzf38IW1c+qBHzXre1yaLC2QTm6SqLF2OhwM8M3tQ8MFTFXwRZFCKFLAQc3gvFROH1HbweFLnyUiMWwunRpKlTjVRILNN7GuZz14ETVl8GJlbABOxZjZGiIAcdm8eQsXjrNSDzOxOgYw8MjukjRJrAQlCMBz3MRCCynAWGQz5ChijDwAV+plio6WpaF9Ju/pkztA0MljDgwrDOV7OnmpdJPoI32LwiFFiwl7bgWgs/3e3Ai5J+YpcT1PHxXPzkPxBJMJJMMxONYQncc3AzWkUpIwHNdLASWI+rnUtRxJwAogtoGzcekWMLCb1FwmtoHhnKMODBohIB4rKdDyGYyJJxYg0Vf2scGEC1+BVrMWlj/fL5youyfyolQKgik66JQWEJgWRYiX4rasu1CCp7n5pCbxEJSTEEYCJFvlFT7mq2bnVC0oKK1G7MlRFvXU3J8jN/4mZ/nYx/7WMvrMGwejDgwRIZUOkWiST9ru7SeLdm6ayH/aWzLwu8D64FEZ3YEFoJAENhFgqDYDC5AiwPHwbHsdTHRw30Ik43CoP7y9dwJwXJS+TgN1jbYgBCIJlIZyzG1DwzFGHFgiATS90H6XS/XLFq0HLTrWtArsbEdB891IzdxlggCzwXYYCGo5xe3YdNZEZoVBvpD9d0JwXK+30RtgzIE2urQTkjx8NAgV7/ujfzlp0ztg62OEQeGSJBOZ0jE4i3fGFtFCNpwDdCWa0FPntEJTgwEQa5cEASxAw0IgnI2kxWhFWHQsDuBvDvBslq2nAlAWBa+37o8EMDkxAS/ftW/4U/+5E9aXo+h/zHiYDNw5Ah85jNw4ADcfnuvR9MS6XSKRLwXqX0CpdqY3ttwLeTXgG3beD2aMNcFgVsQBHabgqAS/W5FaFkYNOBOCJaVUmK1GgMDYOtryW8hY6GY9doHz5raB1sYIw76neVl+OAH4cYb4dJL9e+ghcJVV5Uu+/GP6/c/85nuj7MGriexENjt3BhbRKATFlrNOmjbtQAI286Xvu1OcGJlQWCFLgjK6VcrQkEYWE24EqBxd0J+WYVq03JmYwvRUjpjOcMjo7x739v4w1tuabhfg2FzYcRBv3PffbBzJxw6pH+/8Ub9/6uvhmRyfbkDB+Ccc/T7Y2Pry0eAdDrNQCxe9+mqEwSBX71yLQSrsC0L2cGJsleCoBL9ZEUIhIFtCSy7cWHQrDtBtljboBxhWcg2ghIDgtoH73rlG/jbv/3bttdn6D+MOOh3xsfhFa+AvXu1SDh2rPJyi4vrVgXQrogIoIBMOk28V9UCbavNqpDtuxb0amxsWwcnhvWcVioItFWiV4KgnH6wIqwLg3wQZhOfa9SdAOCjUL6PZbdX3cNGn98wxAGs1z745j/8o6l9sAUx4qDf2bsX5ue1ZeDAATh+vPJy116rlzt0CA4f7u4Ya5DLecQcG8vpVd08kRcHrackhuFasEEfA6VaLmQDtQSB6LkgqERUrQilwqDJ49WMOwFA+iigvo2hPto9Fd7xS46P8Zv/5hf46Ec/Gto6Df1Bf7eGM2huuaWx5X7zN7VL4cgRePvbOzumBkmlUwzEEtDD6coXSs8GrQ7BBlztWmjTY4yywZNeUxOSBFSQBicEQhTFb0RICFQjsCJIwJESKSW+Us3590OkHWFQcCc0mHUQ1Daw7XA6iViWhXRzIaxJY1sWZ86czivHt3Pvvffypje9KbR1G6KNsRxsVg4d0laEAwf078vLOpPhwAE4+2ztgugxUoGbzeL0aBIA8rX+27VahORaIB+c2EAZ3IKFwC2yEFjRtRA0QhSsCG0LgybcCfpDQbnkcM6UJUTb2QrlDA8N8tOveT2f+5M/NbUPthCmK6NBoxSspuCRH3dtk+l0llw6zcjISE8nsUwup5sCNXKDHhqAkaENpRV939eWA8vaKDUyOVhaaXg8Mu8WiCcGCsdFoYMmlZTIfKCbEEWyps+EQD0kQN6KICyrK1YEicJzvdZcCdCSxcOTEulLErF4s8OtiCsl80uLTM9Mh7K+wno9yYOPPsLXF1/gd3/3d0NdtyGaGLeCYZ1TC13dXCqdZiQR77n5yrIasBzYNlxyPiRHK6+DOma4nAuPPAEraw1ty1EKz3UhFsMPni6FwBICx+ofl0Gr2IDKizWlFJ6bA6tBAdcCEonn+q0LA5pzJxQ+I2Wo+6QLIYUfvxNzbF62axd33/ldHn/8cS666KLQt2GIFr2+LxtaJFSDj1Lw4ik4cSq8ddZB+golXWyrxTryISIQ9YMJLzgbxkda30jMgd0X1G3mEIzDEvngRM/DApy8y4A+dRm0QrcyGkIRBs26E0C7FFS7tQ1KEejrOaSEhRKGR0Z591vexsd+7/dM7YMtgHEr9CGzs7Pc8jsf5CPX/Ro7TptZf5JsBSn106zb3e6Aa6k0MpdjeGi455Ocm/drx6rdpC0Br78Mwuj78MiPYW6p5K3AZaAL4bBRLG0BIVAPhX7KVkrp5kQhWRGklHh+68IgWEez7gQFePlYkZgTngFXIllaXmM0OU7MCf+qyeay3POdbzN/2iTXXntt6Os3RAfjVugzZmdn+a3rr+dj+/8d260Yztxir4fUEqlUirHBoUhMegJtEq761BeLhSMMlIKBBJCPH4ANgkBAPuUhb1KXEiFlTwpERYlOZDSEIgxo3p0A+vz7vu7AGC42liXwfY9OZAAl4gleffFufv+Lf8lb3vIWduzYEfo2DNHAuBX6iFOnTvFbN9zAx/b/O84568yOPBl0A0/6WND1Doy1qFnpoPW+zhu24Yv8k6aU6/nwEDRZyLsL9ERls95pr/eZ/9EgrIyGQBg47QqDVtwJegBaHHZA9FmWhZSdMwib2gdbg+jcnQ01CYTBR695d14Y9K/RJ5VJk4jFut6BsRqim8PId8yrJgg2kJ8EC5OJoe1YBK9IGLTlspESUI0XOwo+Rnjlksux0EWvvJDTGYuxLYuzZk7nVckdfP3rX+/Ydgy9xYiDPmBubo7f/tVf5SO/eC3nnnVWXwsDgEwqTTwWj4RLQWODKhj6O4tVVIegmiAoHZku4gihVr7bDLRiRfDyqaDtCoP13gkttFgOubZBMYJ8lUSvs9fKUFHtg3Q63dFtGXqDEQcRZ25ujt+64QZ+/xd+eVMIA9f1iAkLOyRTfVj4KJoO8W6yVbYALETTX7rijA4jD0ppxopQLAxadSVAm+6E4nG3uP2667YEssPZBAKYnJjkN976s/zJn/xJR7dl6A1GHESY+fl5PvDe6/n9X/hldp15dt8LA4C1dIpEIhGpADuLFqok1mqVfeCAbo8dJrZdCGA0bKSeFaFYGFjtlipu0Z0AeZeC73esXgNot0I3rExB7QP51HM89thjHd+eobsYcRBR5ufn+Y33XMdH3vkrWhjE+l8Y+EAukyXmRCs1TxT92zCVWmUfOKC7ZF59NUxONmRNaJQgOBGM9aAa1awIbpkwaMdm1ZY7AfK1DfzOxtsIgS+7k5o8PDLKv33LT/OHt9xiah9sMvp/xtmELCws8IH3XscfvOOXN40wAHBzWeJODMuOniat5eWo2JOpuFU26FbZV1+9/venn4Z3vzvUMWLb+qlVSlSbk9xmpry6ovQljmWvC4N4DEYGQTR3HQYNrizQrb6zri453gS+UlhWZ8WxgKb3rVVsAdPbp7j21W/kb/7mb0ztg03E5ph1NhGBMPj9/b/MeWft3DTCAHTho8FYDCuCBitLWCXhiEFtfwBVKfJ771649971xlbj4+vNrA4dgt27tbshROz8uILgxE6apjcLQggcYeMrBUphXXI+YirZUnpqxaOdzcGjTzZUFjuwOoRf22AjFrqxmd0FBRnUPvjDL33O1D7YRJgKiRFicXGR33jvddzyi+/adMJAKpg/NUtyZKx6JcIe4koPqgULDiawX/uKxlZ05Mi6FeHIkVKBoBQcfRaefbGtsRZ86aZyYkXKMzss28aXEnHRuYjTt+tAwtA2psD34f7DdeNBpJS40iMWT3T0vHW6SmLFbfo+P376Kf70m/fwmc98pivbNHSW6D3CbVEWFxf51fe+l1t+8V3s2mTCACCbyZBwYh1J32oHSb4oka/y6YxFBCmHjRZrOnYMrr8evvQluOoqWFzcuEwYUtwEJ1bFp1QYBAGKtuMgZqbCFQagLRC2DVPjNRdbr23QjfbkQZXE7sUAFNc++Jd/+ZeubdfQOTbXDNSnLC0t8Wvvex8f/cVfYddZO4lvMmEAkEqnGE0MRuJJN/Adq7ypWVh1Oh3mPP2EWG9i2bkT7r+/+t+FgFyujZFrAvcCVImH2KIELa0DStwu8Q4W3fJ93cq75jK6tkEsHk5r5npYwspbmGJd2R6s1z74D//907zmNa9hcHCwa9s2hI+xHPSYQBj8/s//0qYVBtL3Qfo9LZcs0elsrufhey6gOx9aloWw6xxz34eF5UJ1w5YIzM/zS/WXbYRgojPWA2Bd8AVsiMfodF2NeuvPXzrd+AZY6DbksoNVEitRXPvgU5/6VFe3bQgfIw56yPLyMje+//3c8rPv4vyzz9mUwgAgnc6QiMW7Xi45EAQ5z8V3iwSBsNarFAaWgsBUX40fHVt/6vf9hl4qeAVhPT842nyhpSoER1JBS70FNhPFwaNQQRj0mKC2gdOlDBOBLpzVi+siqH0gnjnBD3/4w65v3xAem3M26gMCYfB7/+adnLdz8woDgHQ6xXiXWjMHT5C+8nVve2FhCytIgq8/hmodELM5+M4jMDkGI0MNpYoVuj1msrCwjAi5LXYQaAdb170QdWEAFGob2Hb3TPxCCKTXm7oDwyOj/MoVb+XmW27hr++4AytCDdYMjbN5Z6QIs7y8zK9fdx0f/pl3cN7Oc0hsYmHgehILoSfoDlFVEEC4Ef1KwdySfpUjBCRHYTBRZmL2wbZQOyYbe2pUCtJZWFzRP9dAN2+yt2xb51CEwZEjOvX07LN1AGkH8vR9Ol/boBzb6k6VxIrbztc+ePdr3sRf//Vf8+6w630YusLmnZUiSiAMbn77L256YQCQTqcZiMVDn7jCFgQ20PJz/fAgvPxCiMU2TOgtmZGFgJwL338cUpmai27V4MQSYWDTQAurCgQlsO+6S2eafPWr+v0DB+DTn9bvB9x+uw44PXasKQEhAV92p7ZBCUL01N1UXPvgqquuMrUP+hBj7+kiKysr/Mb113PzT/8iF+zctemFgQIy6TTxWCyUSaskhsDTU7ktrMoxBC3Qsj/40gsgCGoUouQlil7lf6v6AnAcuOT8xra/xYITQxEGULkENugaFcnk+nLB3/fu1e8Hha8aGqxEobpu1dGCubc1NJPjY/za1T/PRz7ykZ6Ow9AaRhx0iUAY3PS2X+CCc3aRiG9uYQCQy3nEHBvLaf0mVV0QiFAEQduMDkMiDlbIN2JLwOCAtkrUoSQ4scsR6t2mVBiI1oUBlJbADqwClThyZL365fg4PPxwQ6v30eWSe3V9Wkohe1jizrYszjrtdH5iYtrUPuhDjDjoAisrK1z/7/89H7zq57aMMABd22AglqBZY3dBELgRFQTFDHQ4b73B9Rf6BqjN25ipVBjYtNlbUYuC+fn1TprHj1dfdqn5FFSFrm3Qi8JfwtLXhOqxNWloUNc++OKnP0M6ne7pWAzNsTVmqR6yurrKDe95Dx/+mXdw4bnnbRlhIBW42Swjo2MNTeKFGALfL5jh7VqFiSJDp023ja1/PTjR25TBiRuFQUjcckv9ZS69dN2qsLSkrQ2NILU1x+pFiyxh60JIvurpTT6offBrb/03fOpTn+LDH/5wD0djaAZjOegggTD40NX7t5QwAN2auVa5ZMW6hSBbbCGwem8h6NdmI7qts56INpP1IBxh0MRZPXRIWxGC2IK9e3XwYvB+cffNKqsPyiV3q7ZBJWzLQnapdXMtYo7NS88519Q+6DNM46UOsbq6yq++9738x7f9Ai/Zdf6WEgYAc/OLjCTixGPxws1RsV773vf9fEGioltnjy0ELTU02j4JL9vVqSHBI0/A3GLDixdPpJHM+W+S0CwGtgWvv6wjlRKVUognnoHnTxbe61aTpWr4aLcejs3I8HAPRlCKVPD87En+x3138f/+t/9mah/0AVtrxuoSa2tr/Nr73sd/fNvPb0lhIH2Fki62pYPpdDpXqSCwougysC2U9MN50utC/nwlits6S2R7AXs9JlRXgvR17Yjx0dCDR4UQyNm5kvEpwLKsnh19C91fwe1i86WqJOLY553NmWMv57/+7Ft6PRpDg2ytWasLBMLgg2/9OV6y64ItJwwA0pkM8VgMhSLnuqWCQABdLgjTOMGk0WbFgFr586Cj3QNfd6X32qRQOVGCtPuz9kFHYgx+fAxe8RKIx3SJa0q9AU1Lhnzqqf+jp/HT2fUOjWgx3OsOpJZlIb32G321NwgBl1wAMafnqZWG5th6M1cHSaVS/Nr73sd/uOpnt5ww8AE355FOp0inUmwbG8dCYFkiPztFVRBUoN1qQsX582efrfPnDxzQaXB792q/9e2365z58vdCsDD0e3Bix4IP01ldAntbEkaG8IVYL1olRPPiIJeDU4uIVAZLCN1DQ3oIBD6q524d27J6VkK5QHJMizFD37F1Zq8Ok0qluO797+emq352y7gStCDIkUpncHNZ4rZDPBZncGxcWwmi5DJogLaqJBZTnD8P2npQHMT29NPw7nfrKPjy90JCuxfWgxP75Tx0TBgUNiDhxTl4cW49za/N7ei2HXZh7FJKRAfLhTeMoPcBiUOD2kpjYgz6js0/g3WBdDrN9dddx3+88mpesus8EptYKUvAy2VJpbJaEDgxBmIxRkbGsGxQEv001mfCICAUw+fevXDvvesug/Hx9SI6hw7B7t2lwqDSe2HQZ30XOi4MyreVJ6zt2IDMj1sphedmwXJ6ZkEQCESvJ+Wwi4MZuoYRB22STqe57v3v54NXvJ2Xnnc+iXiHi+L0AAm4uSzpVAYvlyMWCILRMSzbQiAKObFZ38XpYSBWZKgUP3DkiM6Tv/Za/fOll1Z+LyT6KTix68KgaFvhrlyvVzgOjtT9DXylsBynN1USfR+pdDMkg6EZjK2nDdLpNNddd11eGJy3qYSBVJDOZZlfXGT+5CzZ1TQDtkNydIzRkRESiQQx28YuEgaSrXhBNZgJfOwYXH89fOlLcNVVOoOh0nutrr8KIsizl9GtfdBNYUAHt1VujbDydToE4LnZrjdCEoBl2agoZCwY+g5jOWiRTCbD9ddfz//zU2/LC4NEr4fUNlLp4kXpbBrpusTtGIPxGE5iEMvWxWprPYD4UmozZh+YsEMj22A0+M6dcP/9G9+v9F4xmfaizS20qTuqwYkSpdMMobXJWggYHdKNqhraHuC6sLzamXgGKBzj4liEnlgRbFtnLEgfx46YbO9Rqq+hcYw4aIFMJsN1113H7/zkW7n4/PP7WhhIBdlMlnQmje95xB2HoVgMu0FBEOADvu9vPZfC8ppurxxzwi2wo5QWHmvt16OPanCiLDZntCIMdkzCBWc3LAygqEmVlPD08/Dsi81uteFtFP+u8oKh27EIthW0bo7Qrb5aqm+LbbENnSFicjL6BBaD33nTW3lZnwoDqSCVzjK3sMj8qVm8TJqReJzkyCgjwyMk4oHLoPEAPYXUsUebxLnZlAH4yI/XnxqVAr/+SxW9Sv4WpNZ5nq6OGBa2rR0UEWnr3LYwGB2Gl5zbsiVE2DacdxZMJVv6fDn1jmpgRRCOg2M5uoKi53Xc1WMJCz9qnTortcpupy22oSNESE5Gn2w2y6/+6q/ym3vfwssuOJ+BPhIG0odsNkM6nUZJSTzmMBKPYw8M6j4Gba7flwpLWFj9rjeDKL5mWE3B/d+HiXEYTDRkQVD42txilbXlUQpSGVhYXhcKIVAanNhb60GJ773Va296Uh+fdqLxfQXTU02Vp65KgwGO3bQi2OhCSG7UxEGlVN/iYNzxcZ3tU6mHhaFrGHHQINlslhve8x5+46d+mksuvKAvhMG6IEihpE88FmMkMYBtWaEIgsJ2AN9XWFYLhWQih41CIlA0ldjoq6YmmZKn+G7FAdi2jjuQspBy121CEQag8+fbTdOzBAwPtrcOSkNGG9mfbsYiRKJKYjmVUn2hpbbYhs5hxEEDZLNZfvW97+0LYSB9RTabIZVOQ0EQDIYuCEo3KnUDpYgFu7WF9Du6P8UGiiZlSHvb7GFwYmjCAMI7YCHEibSaC9ANK4IlBFJGMFuhUqpvK22xDR3DiIM65HI5bnz/+/n1n3wbF0dUGEhfkclolwG+TzwWZ7TTgqB4+0phCxGZQLd2sOhiyl9eIfh0z8zfq+DEUIVB1GjDAtRpK4KwBNLrfdvmuuzdu57BcPy4jkMw9BQjDmqQy+X4tfe9jxv2voWLL7yAwQgJA+n7pDNZMuk0QvnEnDijA90TBIVxoJ96OtEKtxcIuvMUr8mrg24/xXe5cuJmFgZhVVrslBVB5P/rGZ7X+L0hEARBLIKhpxhxUIXAYnD93rdw6UUXRkIYBIIgnUoj8En0SBAUo6TE3mwuhS7RC9dC8Xa7EZxYLAxEPjW2Y/Qidz7EuJFOWRGEUr0LQl1cgZ2b48Fhq9HnoeWdIZfL8evXXcd1b9jH7gt7Kww832d1LcXsqTkW5xdQOZexwUGSw6MMDQ2RiMVwevg0Jn0fsUlcCj0hf+C67hUuVE6UHXOjFAsDq9PCIMidv/FGHfW+vKzfv/12LRg+/vFObj3U6z/M6oq6Q7qF6lXcQSoDL8zqn/3msm9UiNk6huYxloMyXNfl16+7jve99s3svuBCBhPdFwae9Mlk0qTTGSwg4cQYGxrCFr2zEFRia5ZLDpveuBZ0cKKeNDrhXigXBh1/dqzUJjsQCHv36hLVx46tN8AKgU7GpoRmRbBt3brZ72GVxKefg6UVmByHeLwhE5nrevzo+NPsvPACRkZGOj9Gwwb6XhxIpZjLSE6mPWbTkjXPLwTIDTsW2wdtdgw6TA3Y2vxdA9d1+Y3rr+d9r30zL7/oJQwOdE8YeNInnUmTibggKEZtxXLJIdMr14LetihsOyyzs0KX0Q7oijCAyrnzO3dq18Ltt2vBEKIwALqSihpGLIItLHxP6iqevWJhWb8axPJ9nGNP8+HP3saf/sVfdHBghmr0rThYzkkenc9yeD7Dmqu0qhYCv8gUFfxuCcFwTLB7coCLJxOMxTd+sVzX5QM33MB7Xv2TvPzC7ggDz5Okshmy6QwWgkTMYXxoCCvCgiBAoV0Km7lcctf8tD3IWljfdnipjeXCoKutimu1yb72WrjpptAtBwGd3st2rQjCEkgVwXTGGtiWxVkzp3H5ttO55557uOKKK3o9pC2HUH3m2MlKn2++kOLwfBapFChwLN0ZUFSwDCil8AHPVyDAFoLdkwlef9oQibyZzXVdfvNXf5V3v/INXHbRyxgc7JwwcD1JOp0hm01jYZGIxYjHYlhCRF4QFCMB33WxYlakWwG3gix6IuzGnhV3JezqhBpsX0pttWhjf7suDF5+ISTHai9z4IAWCXv3wmc+o98rT5FLZ+E7R5refK/OWXCclVJIJXEasCKkMhk8oRgbHe3OIENCASdnT/Ef/9ef8xd//SWGhoZ6PaQtRV+Jg+MrLl97dpWlnMRC4IjKgqAaSik8BT6KZNxm35kjnDYAv/Vrv8a1l7+eV1z0MoY6IAwKgiCTxhJaECTygqBfU7tcKXW8QZ+OvxaSvMukmymh+Ymma2b44m3rAQCtZRT45BsZ5enKZLn7QpioIw6Wl3UsAuj/33rrxmXSGfjOI01vvtsCcsP29SCQeddeLStCOpcj47lMJMe7OMJwcD3J9374A7528hi3VCqcZOgYfSMODs9luOe5NXyliAmhJ9YW8ZXCVQoLeOHuO/m5oUTowsD1vLyFIINdZiHo9wlVAp6bw7HsnjzpdpqCOKB7T4WFRkQ9m2wkStL0PhcLC5r8bFu85FzYPtFeCWWlYHkVHn68qY/12tIT0KgVIee6LGXTbJ+c7P4gQ2B5ZZW/+D938jO/cQMvfelLez2cLUNfBJsfnstwz7Nr+L4i3qYwAB2LEBOQzmTZ/safJX7pq0MRBq7rsbyywuzsKVaXlrGVIjk0wvjwMAODg8Qc/eXt++lUSixhAhHDJX8se9Q10WbdYtHoCHomDADmltrvrQBwarGFD3W5J0YVGu30KCyhAxL7lOGREd71U2/hv3zsYy2ndBqaJ/Li4PiKW7AYxC3RlBuhGgrF8vIyCRSWbXNoaBvHRbyF9UDO9VhaWeHk7Cyry8s4QHJ4hLGhYQYGNpEgKCII8oz8xdMivThXxdvsmSmvibbOPRUGALPzcHIOAOX7hReNvkAX6Hl+tvlt53c7Kte/jT7+1esiCESTNQaihC1gevs2fvmVb+BLX/pSr4ezZYh0tkJW+nzt2dXQhcHS0hJDsTgxxwEUOQQHY0muzc2SqHNrDgRBJp0im80Ss2wS8RhDI6NY9L/LoB6SdXGwmeue9WTfepm1sL75upUTey4MAn74FPKFU4jkKMScxi2KntTCoIW22KVTbrSoltEQjNMnOoKmWRLxOD9x8SV89Iuf421vexvT09O9HtKmJ9IxB//87Crfm8u0HWMQUC4MArHhAy6Cy+Qab/Y2tg1VQC7nkU6nyOWyxGybRH4dW0EQFONJCUohOtBeNkrIHviUo+DLLh5DpeDIyAiD8rF0K7Okx4GIjSKLYhEsy2FpdYWJqUnsMFwxPUL6Pk8ce5o/u/cuPn3bbb0ezqYnslfKck5yeD6LRbjCYDAWKxEGoA+CBRy2h1jOf+V9IJvzWFxaZvbkLKnVFeKWzcTIKGMjwyQSA8TszecyqIevTLnkThEF14Kd/6c8NRHKJ+PeCgNKxtL972DUr//iWASVb6nuem6vh9UWuvbB6bxy+xncfffdvR7Opiey4uDRfB0DJwTbnUKxnBcGcSdW0T3hoJAIvk+chaVlTs3Okg4EwegoYyNDJBIJLQg2XWZ/Y0jooUN8i9CrXgslQ9gYnLhBGPT4GxBWN8RWt9kPFMciDA8Msji/RCqd6fWw2mJwcIC3/sTr+Kv/8eekUqleD2dTE0lxIJXi8HwGVHN1DCoRCIMBp7IwUOj6B8r3UUrxiDNCzLKZGBlj1AiCEoInkF5HaXeT7muh3mYtrA9jPTixVBj0vuhVuTuhexuORpZC0+QFQnJ0hGwqzan5eaTfXxUTAwQwOTHBb/70z/PJT36y18PZ1ERSHMxlJGuuwrFCEAbLy1oYxNaFQSAIfN9HSS0KBAIHRdqOsTY4Qsy2jSAoYyt2YOz2LTQKrgXQ4xDBGErM9xEIw+uBO6H4Oui36z84l45lMzY0xJAT59TJU31rRYg5NhfuPJf4C6f4wQ9+0OvhbFoiKQ5Opj0dadvAsk88eD+P3HsX3z3wd4X3/vZjv8N3DnxFCwPbIR6LgRAVBYEIsiBEvskJcMpqPq1xs7PlOjDaVg9TCvX/ev5sVxyQGJHYml64EwAU6y6VfkWh3QyJgQEmRkf72oowPDLMu978Fv7rH/6hqX3QISJ5v59Na/N1PZfC/PPPMDQ6zukXvIz7vvz5wvtnXHQJzx99ggHbIeY4JW6DYkGQ1wQbXidtIw7K8bdcB8ZmSwKFSe9dC+VZCT13c9BDd0Jh49DXdrN8YpoNxGOxvrYi2AKmt203tQ86SCTFwZrnl3RXrMb8C89y+oUv45H7vsZ5e14LaFfCGbtfxY4zzsaxdRkHQSAGSgVBJXwgJSJ5WHqGD/i+ryuy9Xow3aYHc2KvXQvVJuGey4MeZScU73c/3xnK76n9bkVIxGO8+uJLOHzwX3jxxRd7PZxNRySvddlg6YXzL9eC4MjX/4lL3nSV/qznMRSPc9Hlr8MSgke/cTfPP/lDvvuPd/Ldf7xTxxtQ+6brhZA6uZlQSCyBlutbhJ6LoB65FirWDrAjYsnI0/Vz06+BiA3Q71aEsbExbnj7z/LxP/iDXg9l0xFJcWA3MTmnV5d5/sc/KAgFXymef+IxJk87k/TqCvd95fOcfv5LueSNV/F//vTjJZ9VFV4AtlK9f0qKEL5UWMLCiubl0jF6K4W6PyFXKypkly/TZXrqTiii36VBIcC0Av1qRQhqH7x6+mxT+yBkInm3H3ashgsfLTz/LJOnnVX43bZtHWOgYHB0lBs//WW93IlnOe+yV9ddn1CKAeniuS7Z/Mv1PFwp8fItUiURMLF2CQn4+brsW8du0Hu67VqoW22wyHrQdVdHD4sdbabvuaC2JaqaFSHqpU0GBwfY98pX88U/+4ypfRAikRQH2wdtfKVopLLzwMhoye8/OHSQi177UyB0uiICvvvVO7nvK5/nXb/3R1XXE0x8SgimlcSxrMJLCAH5oEbp+3juFhIPW7C2QWTokmuhkTLExe+VV07sJD11J0AkLBahYNvQ4ANXuRVhLuJWhKD2wW+9/Rf44z/+414PZ9MQSXGwY1A3UWnkcpw8/SwufuM+vnvg73jk3rs444KL8fNaNxAXr3r7Nbzqp6/ha5//k6rrCZrNWMC0nytUFrNtG8e2dXfFWAw7FsOJOa2LB/pLPMh8k6U+vzX2KZ13LTTTn8DqcnBir90JxY8mm+H619KgsTPXb1aEoPZB4sQcjz76aK+HsymIZOMlqRSf++ECqzmfhN28fllcXGBscAghBOmVZQZHxxDAf/rFN/BLv/dHnLfnNRU/lxMWw77He5aO44jmGyrJ4p+KvoNB0SWUqhgMaQkB+S6HIihuT+9vSBLwXBfHsnpfR78H9KL5UrUxVGqC1Pa69Qb0Lw1e691sDtXrJkeFfY1Aueh2kegKpwBOk+dNAtJ1WctkkEIxkUxGsoGTVPDcC8/ze3/3Rb7wN3+zJe9ZYRK9M4wOSNw9ObDuGmiS4BMP/NPfc+9XPg9Kvzc0lmRwbLx0IUp96buzK9hC4Csf33XxPK/gKqg77sLLrmh5sAqWB6uq5cFzK1gepIfXA8uDklIHh27xL1lPrTwdci202tGwW8GJPXcnQJHFpv+v/3b2oF+sCLr2wQ7e/RNv4q/+6q96PZy+J5LiAODiyQS2EHgtXIG2pYMSL3njVZx50SX8+KH7uetzn+JVP30NZ5z/Ui0GikJ3Fbpls60Ul8gUjm1jOTEsy4JioVAUU9AKjYgHp5J4YL2647p4yGnx4K2Lh+J4hzAmE7nVOzBGYsfDdy1I/PaC/Dqc2thrd0JhDHkicRmEQK1shUYoxCKMRTcWIRF3eNXLLja1D0Igkm6FgH9+dpXvzWWIiebaNq+urZKw9IQL+eIfSukiSEXLBTvuK/Asi5fnVrgyu7BhfcHNSrsHfN0QyrL0urpo8izcsMpuygqdnaHybovgXcgXfRJCF4LKjxe0Kqx1RCXguy7WFnUpwLopVtgCu4c6OkzXgix2ebVx7RaXrA37+ui1OyEqYwgbKSW+UoX7YrvryrouK+k0o2OjDA4ORCabSfo+Txx7mk9//f/yZ5/9bK+H07dE1nIA8PrThkjGbdwGMxcCLMsqmSiFELpyqCpVzsHF7FkWY9Ll1auzFS0DQevTEosChGZRaJSC5cEutzw4hYBJJxbLWxxsHMvCElrEKKXwioIlc3nLg+u5FTMt1JYrl1wD2WP9HJJrISxhAJ0LTozaE3sUxhAmjVSebYT1jIaRghXBi4gVIah98JqZnRw8eLDXw+lbIi0OErbFvjNHsIQg5zcuEAK3QlBLXACWJVBlRjWFDkK0lGJfdp5BS+BKie95VJvq6wkF2SWhUHVssMFlEYgHp4J4EMJCoI/teqZFDul5WjwF+7MZ0zTrEJ0vR/tmfCnDEwZAiRUqLPdCFNwJhXEY6mIDcWc9FmFudi4ysQhB7YO//YvPmtoHLRJpt0LA4bkM9zy7hq8Ucat+QybXc/EyGQbiiZJlg/4AQb5vTlhYKK7ILLDbXQXyEfqeC0rhWHbTUdwqP9GiKAiHfjBNKoqeTPMulJbMhHY0Mi3CIgoZC8XjaMW1UNK1LuRrMUzze1RM+VEZR9hIKZG+TywW60jmi/Rc1tI6oyGZTOL0OKPB9STff+yH/NOJp/j93//9no6lH4nOw1ENdk8NcMWZw1iWIKdUXdOYZVmFqn7FCGHhK4XPusWgWBiAntRiTgzbsrUVIW8NqMe6RcEpWBQUKhIWhUYImirZRb9TZIGwbC2U6j7RSbnR2hC81NayPIRKi66FTgqDYJ35DbW1mqi5EyA64wiTelUSWyWKVgRT+6A9+sJyEHB8xeXgs6ss5iQWAicfbFeOQrGyuMTI0FBJIKMCXKW/HEnf4y3Zec6W2arb02rYQykf27IRLdxcSy0KehSWiK5FQccbeAhEU0/LPkE8h9+6jz5CNR4CImM50INpaiwdFwbl2xHapdf05/VK9C+9thoUjaXX5zxsJBLfU1iO09FjHCUrQlD74MNf+QJf/Nu/3XTntJP0lTgAyEqfb76Q4vB8VndvVOBYQkffFwmBpcUFRgeHEJaFD+Q96NgoLlid4w3ZRUbi8bpfEoW++XnSwxIWtmW1HDVeEApBgGVxFkZEhEKnTKrVCkQ1RQ/EQ1TEATTnWuiWMIC8S6qN6yZKZvwojSVsJOB7HpYjulLUKSoZDdmcx70PfIfnJgd5z3ve04MR9Cd9Jw4ClnOSR+ezHJ7PsObqyVYIUXA55HI5Evl64gIYVpLdMsXFMsWgl2Nhfo6J0XFijT6Fob9Yvu9j261ZEcrXV1Eo5CfAXt2YPCl1AGcHKvLVIhzxIACdnRHWc0qkxEFwbOpce8XCQNjdScJs9Yk7Sk/qURpLJwisgiCarpLYzjajYEVYWFziD7/0eW7+5K3MzMx0ffv9SN+KgwCpFHMZyWza42RakvJ8PKX41je/yRvO2sk5CZvtymVKeSU31OWVVfAkw8PDzZVIlhI334yoHStCQCEQMJ+DHNQnCE0oxGMwkGgoulACypfapVD8BVYKUlnwvHZG0haF6c6XrVdyabDGQ8l2I/Qk2cjkVSwMui7w2ijFHInjG6GxdAqvxRLK7dJrK4L0FU8ce4o//Zd/4n/85V92ccv9S/vVMHqMLQQ7Bh12DDpcXPT+t267j0sH9nL26adX/NzwyAizsyeJ52KIeLzhpys7H5Tne7oqoQ1tWRGCQEDyT3glQsHLiwUhwBYIrMafAhMxeNl5MDbS8Fhq7oNSMLcEjx0F2f185sLYqvi0qxWIKl1I/63q6FsQD93EZn0/K2WTNCwM4jEYGmhu46kM5Nw6A7QLAakNpSJGdDKO0ljCplMBifWwbZuEbeM4NmupNKl0qqtWBNsSnDVzOq897RwOHjzIvn37urLdfqbvxUE1pqenWa2R32oLmEiOs7q8pmsWNKGkbcB2HCQSNyexfB/yBYPaubHUFgo+4KPyvQ7qTmC7L9IWg7AQAqbG4aW74JEnwltvSBSOe7Un6sIPzYsHBQgpkVFI08wrBL9oHCU+f2oIA8fWgnFirLVtLyzDD54Er3oNkOAvElnT5hW17IQtlUHTI2Oxvm/GsIcssq7L3OxcV60Ig4MDvOWVr+F3/+IveN3rXsfw8HAXttq/9EUqYyvMzMywuLJcM3A+EU8gHIuc67Z0c7CxicXjCMvCkxI/xFTFQmqhbWM7DpZj6zbWSuF7pQ2hNuzi2LB+MrRC/soJAZPjEOs/TVmtumQjaZqFo1grTbNrBaJKUwfLhYFdy2Jw8fkwPtr6psdH9TpqUBDZNUJHolLsqISojaeD+D0uU9Sr6ooCmJhIcuNbf5Y//uM/7vj2+p1NKw6mp6dZXFtF1bnoxsfGWE2lkC36020gZts48bgWB0FNg5bWVhkLsNE9DizHwXKs2kJhaDDErZchBAw2aZKOOMU1HsqFQ8lk0U6Nh5DEQ/EIfDYKg6ok4pAcbU8wWkKvIxGvukhDlRMj6k6AaFgxOk4Eosx6VRchqH0w8OI8jzzySIe31t9sanEwv7KCrOMfty2LobFR0pl01ZLJjWADTokVwevIU6SexOwqQsHD8zz8JntRNE0TTbD6Hbvs50qWBxFGgahmWnHnN6MaFQbQfIxBG+sqHkn5/kTNnQBbzKVAtOJpemFFGB4Z5l1vfgv/9Q//sKECd1uVTS0O5pYXGzr5w0ODZH2fXLa9Cb3EipDvU9DJqogbhEI+HbK8h4Shs2jLTnXxUBAONfMPaUI8lK6oobS7MAVdI+uqYD2IpDsBojmmDiKIliDqthXBFjC9bQf/9tU/yV/91V91aCv9z6YVBxMTE7y4uIDv13cXCPLBialUiZm2VWzAicWxLatuI6ewKJ6YRAtV6gydoyAc2CgcWhEP5eb6pi0PXaDUepAfVQTdCVG0ZHQUm8ha/rppRUjEHV71sos5fPBfOHHiREe20e9sWnFgWRYruRyywYsr5jjEhwbIZLOh3GBtdC5xLB5HofDc7vVWqJrJcOQIfOYzcOAA3H57F0ZiaISGxUMt6loeuo8oCU4scoH0YCzV2VpWg/WjHxUZWUo3rQhjY2P82tU/z38yTZkq0n9h502wphprmhQwMjLKyZMnicdiWCF1LrMB4cTw88WT7HzaY9crsC0vwwc/CHfdBceOwVe/qt8/cAA+/Wn9fsBVV0EyCZdeCrfcUnu9gwkYHdapnO3gurCwAhHpCR8lilME19+0N5rDa13r1Y7rkSNw6BCcfTYsLsK117Y32CIs0Omfsih1IWqTcH5cm/YpqQIi8CtE7FQU0426CLYlOHPmNFP7oAqbWhxYw8N4buPiwBaQnBhnbXkN27JCqyJmkc89t22k5+H5+ZbI3TSv3ncf7Ny5PhHceKN+/+qr4UtfKl32Ax/Q79fCEjpnfioZ3hh9Hx57Gmbnw1tniLTcxrpNSnz1rMcYBO8ULEW1ajxUuqnWEozj41o4BNdJi5QImzqek25TfGeIpqE9fCxAIvoiKqkbdREGBwe46pWv4aY/N7UPytnUgnl6epp0LtPUZwbjCZTVeu2DWtho94Vt2XhFaY9d+aKOj8MrXgF792qRcOxY9WWPH9ci4uMfr77MuWfqmgdhIgS89FxtjYggvbBplAbxlQUf5n+sN67AbbGBYsEIWggcO6bP/969WkRWuE6k7zecpimr/hIBtlggImgRJIhENmPDdDIWQQDJiSS//raf5Y/+6I9CWedmYVOLg5mZGVbWqldJrMZ4coyVTBopw+8lIFiPRRBCaJHQDZ/w3r0wP6+fCg8c0BNANW68US9/zjl62UrsmAw/sClY37aJcNfbLj2aPDYIgw1TfAPuhFpUEow7d8LDD8M736mvkZ07Gxxs5XiHDcGTrY20o2wdaVBEn7XU6WQsQlD7YPjUkql9UMSmditMT0+zOKurJNpNzGOOZTE8PEw6k8EeHuxIj8RCCeagkVM+FqGjXfTqxQ/A+lPk3r3a7JxMblhEKh87Hgt3bAFKRdZy0E1HbakwsKhU97DYZN+Sy2PvXrj33nUBOD4OExNaMFx6Kdx0k17m0ktLt5svFR5st/AM14hIqSSEdRtQve5m96FFoihSukm/pjt3KhZheGSYd/3UPm7+2Mf4qy9/eVN25WyWTW85WFhdLSkW0yhDw0NkPI9crjPFjALsMitCmCWYG+LQIf2EGEwQe/fC0tK6SKgUe9BxB21EPcBdOjHl9QBq9lZs0LVQlVtu0ef46qv1uf+Hf4C3v13//OUvr18HVahWXXJDZcla6lwRXoGoRtmCLoUS+lMbAJ2xItgCtk9t59+/9qf44he/GNZQ+5pNbzk4vraK9FXTO2oBE8kkKwsLxGynozeRghUhrEZOrtu4yX/vXrj//tL3AkGwd2/lz/SgK2MvqZgtUPdDVnPXjJQg/RbaGOdH12gnxHr8m3+j01wvvVSLxBayFyrvgwX2xrbIJSWSqh1kWfin8iJ24Z+Gvi/Fk8gWlQb9rA0KhG1FSMQdXvnSi/nHL32eF972Nk477bSQR9xfbGpxMDMzw+zCQj52oPldjccc7ESCTC7L4OBQx28kNjbEbXwp8aTUZp1WBEKQEihEuHEBSkHWxU5lw1vnZmN8FHad0VSr7AC1vApPHIfFlYbPe8OuBbdOu+WAsbH6GQr1WjfXEjdlbZ3X/145lSEc8SAAS3svAD+ws2xVZUCQtdD/hyDsjIbi2gef+dznwh5uX7Gp3QpjY2O8uLyI9Fs3SI6OjrGSTiNdtytqu6QEc6uNnKSEHx/XwsBXelIveqmiV/nfKr6K1/H4U9W3u9WLLA0Pwu4LdN2HVhgdxnrFS2BspLmbdiOuhZWUntTbCURTSq9jtXqQb72Kg6WVE+vTcIGoWncyqUBKVCFIUhUGUJxpsRmephsiwlUSWyWsjIag9sHrTj+Xr33tax0Yaf+wqS0HQghW3GxbzTVsC8bHk6yl1hjrYvEiGyAeRwXFk1BgO41PGidOwcoabJ+AgUTJI2WhKZMQjalrBayldf2BTK75nPnyIksh5tFHipltQOvWGiEESinsmW3w5DNNfLJB18JjT8El+ZbLqpkQRqX3SSm9jiqUuxOqD3fdeuC3GYBb2IqoY3modQ/I/63qVGIXHCNRjYZpEhvw0Hu8eZ4Pw7IiFGof/MVf8PrXv37L1j7Y1OIAICX8up0Z6zE0mCCVWiPnecS7WLhIu1J18STPzeH7OR3V3egY1tL6VYRCt/kVEK7QabTIUpBHf+ON2qcdpM9tBsaG22uJDAjLatry0LBrYWEZvvsIbJ/UGSGNihilIJ1dF4fVaDBWwgakbYH0dbBwh+N5gjHpIVaJeQhBPPSTiV5XSVT9NegGaTcWobz2wUc+8pHODjiibHpxEB8ZwfPar1eQTCaZm5tj0rGx7e4eNhsgVmRFUAoc0VKKZeEGF/ZNoThnHqpP+sV59G96U/1KjP1EWKVdWxEY+ZnOp86pzeTgmfAbzdRzJ5RjIwqf6YXv2y77fyWB0nCapqwR71C07qjMw/1UJbFV2rUiBLUPvn74IR555BEuueSSTg85cmx6cTA9PU0601yVxEo4tsXQ8BDpTAZruEmfcAgUWxGk5+K5fr5XcJMSIW81CP1WVSlnvpI4WF6umUevWH/6jaIJN7pBXCFnLTRBw+4EgJijK2vGHH1+A79ws8JK+rC8usEyVnecTRCkaQKbSjwE362tkHPUjhWhVu0DqRRzGcnJtMdsWrLm+UilsIVg2LHYPmizY9BhasDG7tP4jk0vDmZmZlhKrYayrqHhYU6tnSKey0E83pNJwqb1Rk466EohEJ0ZeyNFloI8+p07dR79V7+6ocgOyl9vnZ33Sff869VSPmP3aLsgUjs0mnp55jTsOrMQv2CxHgTY0piFgLlFePTJxgItQ65tUE88+BQFObYqHppM02yKPquS2CqtWhGKax984Qtf4H3vex/LOcmj81kOz2dYcxW+UlhC4Bcdy+B3SwiGY4LdkwNcPJlgLB7Nx4pqbHpxMD09zeLz80hfBxe2g412L6wsLZJspI1uh2i5kZOUCEQI427iplJcZOnqqxvLoy/+xkpZ5ArpYqOqDQTTb5NBXB3seriBRl0LIdKwO2FsBM47a/33/NNU2yJmchx2ngZPP19zsV7UNii5Sip853pR46GYfq2S2CqtWBGC2gcH7/wb/uENV3M06yCVAgWOJYgJgRAb7ZxK6e/has7nX19M8e2TaXZPJnj9aUMk2p2IusSWEAdzPz6O70tsq/3bQiLukIrHyeZyiMHBnpqYbcBynLwVwSsUT7LsmjX12o9P9hV4EpwG9r68yFKdPHqBQGQ9sO0N3QhLSu/2Sig0E8TVTAZHKHTXtdCUO2H7hL5u2gzY3IAQus9HHXFQEJg9N0Gt050aD+tb2rCJraUNgNasCEvJ7Zzz/g/zyJLHYMIhLgSiznUshMhXDdUZSJ6C781leGrFZd+ZI5w92qHy8yHSHxKmDWZmZlhcXcFvo9ZBOWOjo6ylU8hGC8t0kCDrIBZP1GzkFLgUgs+0zey8vtmHjSXg1AJASUneDZNPWUfATtPSF6VS10PQFpQKPSvapfgIdeW+30wlx0S8cxNzIl5/mWCsITwgdIuGazzU2iVJxdLUkK9azbo9bCvRaF2Ew/YQ/zuxjVR8EEdKpJvNWwoaRwhBLG9lWMxJ/v6pZQ7PtR8H12m2hOVgdnGh7XTGYmxLMDI+zlo6jd3F2gc1xwS1GzmF5lLIc/RZnXI3MrReIKkdgmqOTzxTMcgsCMgEemJRaGleazSDI0y65FpoNjuhl0/sTY+1T2jY8lAl3sES4LkuxVJSCEt/FcnXQNl0NR7WqWdFOGwPcY+TxAcSQqFiMZZSa8TjcSzR/OOCJQRxIOcr7nl2DYDdUwNh7lKobHpxMDIywsmV5baqJFZicHCAVCrV9doH9QieKHzp4UmJDSjbLgR8hTZOT8KDP9DlgseG20/jy7kwvwjZ+taYXguFhmk0gyNUOu9aaMqdEAX6aawhUitYUiqJ5VvakFL23OSjq6f6SqF8Xwt/AaD96yJfPG2ziIdKsQgrk9MFYRBH5XdfMJIYYG1tjdGR0Za2JYQgbuUFwnNrJON2ZF0Mm14cFKokeuGKAwGMj4+zMD/PhONEwnoQoM3xDtJeb+RkhZWDX87Sin71iMgLhUYyOEKkK1kLTTWGig79NNaOI/JXirA3aIfg10qWB4WusKpQuqq6H+Rk5ANMq4iHqB/7YivCsie5yxpDKkVCrH+HhBDYtoPwXFzPJea0NqkXBIJSHHx2lWsvHI9kkOKmFwcAa76HbKHOdj1ijs3A4BDpbAZraDhyX4CgkZPnuSjfR1hWhPP02yfyQiGgPIMjbDroWgjVRN+FTI4IZ59GgxoWplqWh2o1HqqJBy8/xVp592FUxYNt2zw4tI21WALHl/kq46IQZyAswWA8wfLKCsmJCRosQL8BIQQxYDEn+eYLKd58ZvON2jrNlhAHwxMTeF5nggeHR4Y5NXuSuBPrWe2DWtiAp1i3HASTZK8nyA7TE6HgeXkTbJ0bRqU22QEqnwnSFp1xLYTqTqiVyQG6imZgdbnqKh3AeemlzVtitqhLoR66O2XrtFogqiAelNLXU0E86LWWiAdBIYC0W2dvWdg8EhvByhc0Qil8X2FZFCwilmUxFE+QTqUZGhpqeVuWEFgKDs9neeWOwcjVQdgS4mBmZobVTJrpDqzbFpBMTrK6vMR4D2sfVENSFGkfNLwBPUFucoEQELZQqGp9mV9GjY+2b8qfX2rr4x1zLYTpTqjUiyNoyLV3r7as3H67tiZ84ANtW1i2wnXeDJ2OD2ikQJS+Lyld7TJPsXjwASVriIc2ajxU49HYCFII4spHAEoILLE+psBtEnMcsukUciDRVoq8I7R74dH5LK+daV1odILoOTo6wPT0NCur4VRJrEQi7mA5Nlk3Fzkzpi+lbuaTv6GXpAV2MRUwKnQyPVI+9yKkMuvtsJshyPhIZXRHzXbJ71pYzjRZFNEeyo24OJNj505tPbj66vXMjqefhpe/XP98/LgWER//eJNjNkQViyBNU5SkZzq2TcxxsGIx7FgMJ2bhWHb+ZWnzfj5Q0nN9PNfFc3NkXZes5+J6Hq6UOqWb9XbcjSCBwzFt3i/EGQSvvCDx/XWRMJQYYGWlvXgrvT9weD6jiytFiC1jOVg6fhLpK+ywi7DkGRsbY+7kKWKOg91ioErY+IDv+ziWVXJDtwG5wYqQj1HYQjRtUSg+ZmVIqQvV+A/9AHHGNNbEmO4j0Ciup7smvjAbglsBwnQt6GOTlxlhWcZqZXIcOgS7d6+X1Q7qQxw71lychnEp1CVa09E6jaRpWsFPRV8XVZJpAUERaw9AaMtDECcgbAFY2lUAzFkx1iwbR22U1MVWhCce+hbp1WXSq8vsvvJnyGQz/O//+iHOv/y1vOrqdzS9r44lWHN1r4Ydg9GZkqMzkg4yPT3Nyceewvf9UKokVsK2LIbHR0mls9hDVse20wwKqc1w9kZBFEx2hUlRonX2FnE1lNOwUAj+VtyEpWhZy1eIZ050pPNhM4TqWuhUdkKl+IEjR9bLah85ogMVQYuJ5eWGi0dt1toGBk3DNR6ULDGfBeJBFcTDesfe5xMD+OhJsVg0FVsR5l94hqGxcSZnzuB/ffjXeOVbf4HlVIrTL7qYueePt7QvFuApxWzai5Q42BJuhZmZGeaXl5B++62bazE0OIjrS3KuFwmTpi918w+rxmm20X0aChRVUNuq1HQ95AlcDyXCoE7Z6q4TgmshNHdCI4+ox47B9dfDl76kgxAXF7UoWFparzK5Wa0GAwkYHmz8FY+GdTKq2MFL2GVuC0dbd2MxnFisyGVhM+8kEEqVfofVeqdYBcyfeI7Tz38pj37zHs6/7NUIIRhODLBzz+uYOv3slsYaxDGcTEfrvhsdmdJBduzYwYsLC/ghVkmshEA3ZlqcnyfZ49oHEvJRtvWTbYISzMVPzFJuXStCMfUsCusLRkwYAO26FkJ1J2Rz9U0YO3dWzuIIBEEQj7Bh3dUzkSL/9DMzBeec0VgJ6HLW0vDjY7DUQDxVzNHVTIvdqlIH3Unb2vg9V0AuB6uNt8TuJyoFS6asGEoIhCpqSVV2vZ532WsAOHLv/+Wq9/02AoFj2zjArsteDUB6dZnvHrgDgDe+630NjcdXipQXrSLWW0IcDA8PM7e6gtdhcQC69kFicIBMr2sfyMCl0PgIbHQ1Rb84FoEIPhH3iGJT/QYKKaL6nyiIqmC8EjhlxZi148xacdaEjRRgKxhWku1+jh0yx5Tvlo47THfC7IJu2Rw2SsHJ+ZK3is9RpK/bqXG46NzWPz80AJdeCA88Cpls5WWEgIvO0c2pylJsGzqnmRw8+gSsplofZwSQ5b9V+CJ7+YDAekau9OoKzz/xWEEoCAUnnnyMy676WQCefPB+UssLDI1NNDVGzwQk9oZV6a5Peh1meGSU2ZMnicXiiFisJ08vQT/xZm/qlawIvrEiVMWybW22Lwgq/Y8WChYC0bOn12Vhc2RwlEdio6xZTiF9rFgiB79bwLAv2e2ucrG7yrBafxoP5bwvr8KTz8CuM5v+aPktszDFCQHzy3CsrCNjv7gUTtveWF2MagihT9yOSTj+QuVlzj2jojBomEQMXn4RfOv7JSmHvaKkfgKALyvO5gp0VkPhjfxPheNQVNUx/4/dYHjmwolnmTztzMJ6faXwiuLZLnnTVaRXlkivNpfJ4LR6jjrElhEHK5bqmi/dFpCcGGdteQ0n30K5m0jWxUGrFAIWy6wIhUZOW5DyW2PgNgqOVeHGVThmfqHzHfnj1o2vfxbBNxNJDudztgFs5ROrsv1g3KuWzb8mxvl2fIxLciu8Pj1PPMyyrs++CC/OweR4U5kcOoVs/egLy9LFc5ZWKzbpCoi4NNBm/jAmhJHB6n+bmWpvG0Lo1uyT49r6EyKNPM1DEEQY/FI8yW+UjUEPhJL/F5Zng2As/m0EVfIdrSYVBoZHCmPxleLwvXfx8iveXmXpxrCEYMiJ1p11y4iDZDJJrkNVEisxEE+wZqfJuS6iy0/dSkpd3SsEUVJuRVBbqHhSMeXxBpXiSQqFX/LWBFV8w5NyXVx0UCgctxN8bWCKJcvBUop4UVpWte0F47aVFjMugu8nxng6NsRbMnOcLauYrFvB9bRAaBIBBctfresvWiFddQhDGATdTCth2xALIXDR97ULowobnubLMgSKl2v2aT7oD1lYpDDJQ/H0Fcb9aLuf0+nf1P6uTJ52Fi97/RV85x//nsHRMSZ3ns9AItHydoPsiR2D0bqrbhlxMDMzw1q6u8E1yfEx5mbncBwH2+neoZYVahu0w3raY36y2yIlmAM2BCJSP7ZOPwPoNCsZrKXDQuFwbIR7EhP4QhBTfsHCo4r+38h2YugnoiXL4e8Hd3BFdoHdbueKiDVCoQ6/rBNg2S8uhW4QkvrUVQv1g8H6e915mu8Gwfd7igxWQm3oSVJ8GIMKjle977cRwkL6krVcFtFCC+cAH52xsD1CaYywhcTB9PQ0y2urnetUVwHbshgaHSGdTWM7Q10JU5N0bv9sbKTNlirBXEkYQHNNjQrOh0AoFD9ZlQmFVo/l4dgI9wxM4LNe+rVZim/rFhBXPjlhcc+ADqzqtUAoDgitVMLaL1t2KxM8zYd1HIL6AM08zUe5jXPwvVbo5lA+MKGyDCvJquVULIQUCAPIu7aUwvVcBgdruHUawPMVI3GLqYFoXbVbShysPH0Cv4NVEisxNDTIqbU14lmPeKLzE6mSUjdZ6tCT08biSZvXilDJlVBNLDSKDboovF1BeLTYEOq4ndAWgyrCoNLzXD2CdRQEQmKCpO+G62JohRrWAxUcvQi2v22YFjpVKkXlYOsQA9xsy4IuWj87QSVBYAmBlY8Ls4Hd7hr/mhjf8BBZLgwEOq4r47okR0o7Kj7x4P088eD9pFeXmTztTC5501VVxxQIrt2TA9oVHCH6+2w3wczMDM89+gS+L7Gt7u22BUwkkyzNLxBznI6aOxXhuxSqYVOpBLPFZkl6rB9jUOnZtTkarspYQyhkEXxtYApfNGYxqGY5q1QRLvg5EAgHB6a4du0FEj0sultiPZCy9Lzk/2BF9Bos981veLKu1qny4x/XfSiOH18vJd0OTQqQaB7NxqgmCIRl4VT4Xl3srvLt+BgeghjrqY3lwkApfa+NxeMb2jaff/lrOf/y1zY0Pk+BLQQXT7Yes9Ap+lhiN8f09DTzSwtIv/shS7GYQ2wwQSab7WjAVKHTWZce4YNKguuNnPxN0cipZKK2Kwcfhr2TNasy1mgI9c1EkiXLIdaiKwHqWxUEEFM+i5bDNxPJFrcSHsXZP7Ls/9CdyUyWvIrOT42XH1g8glc5xZ0qYb1T5Tnn6EJQY2PrfytCxx7bFV4Vbu+BALnxRt27YnlZv3/VVfDOdzbd3CqKBJVLPSnxXRfP93WzJMvCicWIOU5FYQAwpnQ6ry9EPqh4XRhYVrHsVGTdHIMD1QM16+ErhY9i92Qicu2aYQtZDqanpzmxuID0fGihGFm7jIyMcXL2RWKxGFYs1pEbmB+4FLps4K9sRYhKKaDm2CAMyvahZiGkkGjUorDsxDmc7z1fT+U36lqodl1agKUUh2MjvDK3zJjqnQSsGJzYYiDihkj7Gil1oWEX/imluFMlaOvB4uL6BA76qb9apchGqNQqG0Jpi91LmrUQ1OL12UWOOoMsCQdHSV2rpMwV7SuF6/uM2K1NoUopXKVIxm1ef1q0WjUHbBnLwcDAAAtra8geFfKwLUiOJ1nLpDpSb0Gyngveiyl5oxWBvrMilAoDOxLippZF4QfOEFIIbFRThn5V5ed6gtVBIYXg0dhInSU7T/GRKL/G2nqab/aCtfPxPflX5Sf4slf+ytpwvPfuhfl5bS04cEC7Ea69Vr936BAcPtzk4CpQqVU2tNwWu5dUshD4DVoIauGgeHPqJAIf17IRZcJAKYXneQy0aDVQSpHzdR2afWeOkIhojMyWsRwArEi3J26FgMGBBKlUqjO1D1ool9wJbCqXYO5ln4lGKAiDyXGss2YQYyNgVf7SWoUptdZ0qnRO/6lFXcEvhDbMxRaFHHAkPloyimYm+vLlGyFY5+HYCD+RW+qYdGr6ab5av4tmKHua71mkfaVOlb/5m9qlcOQIvL29YjtVW2W32ha7y1SyEAihTf6tCIFK6/c9l7HUEnutGN8Y2k4OQZz1hkxKKbKuy+jQWNPrLxYGV5wxzNmj0W2gtaXEQdr3ulZCuRrj4+PMzc0RiznYLZqkKhFURIyCBu23Rk6FcU4lsXZfWMjHrkbDk4Ztwxk7IDkG3/uhLiYTEotWjFSVlCtYn/if/N63yKyukF5Z4lU/fQ0K+PJ//iDnX/YaXvnT1xSWb3SfHOWzZtnMWTF2+PWLipV+23yQXQhmtEun9ihecw2zvAy3365dAGefrSfyYlqpx18uQII4hibbYneLTguC4u1I12VpbZXh0TFeZbskvEXucZLk8gGKFvpeqyzRdG0DP+9KsCwtDHZPtR6v0A22lDiYnJkh63avSmIlHNtieHiIdCaDNTwcSnS/ZF0cRCmyuJoVIUqNnIoFjHXuGXWFQdMIocvbTo7DqfDKz5604/iwoSRy8VQx/8IzDI6OMzFzBl/48K8VxMAZF17C3PPPrA+xwW0GmQ4+8KLlMOVm2tmF+jTwNO9TVJwnouKzKp5Xv/Xy2FjtDAWl2rdK7d2rrQXNtsXuIN0SBMXbc12XlbVVRpJjDMZ19sBumSLpexyMJVkUDiLvUmimtoFSCk+Bj44x2HfmSKQtBgFbShzs2LGDVJerJFZicHiYU6lTxHMuxONtX+hhlksOmyg3ciqJMXAc7UroBL4PyZFQxcGsFa84WRb/vnDiOc677DUcuuMLnPeKVxfev/gNV/Dk975d+L2ZZ0+BDkyctZqM6rUFxSFO4RXnKaLF1tQ9Y24JBgfaE6OWpddTCenra6+Ke6yEWm2xhYBc5x+qui0Iirfr5nIspdZIJpMkygTb2SrHtblZvumM8X1rEM+ysS0HmQ8EFhXOn1K60qLn6zoGthBcNjnA608bimyMQTlbShzMzMywuLrS1SqJlbDRvR5WFhZJVkpdaxJf+dii87UN2qFQPCkijZw2BB92ugBJIzfoJlgTdqXy9SWcX9Z7vpjzLnt1ye+PHDrI4MhooQ1tLXwhSFlOV5tJVaXMTdh+9Yku8swJmErCYD7HvZlr0FdgCZhb1K9KKKWFw1RSL9sqQlTfRpv0ShAUb9/N5VhOrZKcmCRRpSFYAsWbcksMPfU97hsYZuRlr2LNVXhKIYS23AZYQujUSSEYiVvsnhzg4oimK9ZiS4mD6elpFn/0DL7v64pfPSQRc0gn4mRyWQYHh1r+EkhovGh+BIhCI6eNWQn9h2zwfJf3ngd4/onHuGTvvpJl7vvK53nTO9/X8GUkW2gHHjYlsqCRvgtRw/V0LMqOSUiOgtXEuD1PT9inFmvHHTz5DIwOw0C8asxL1fDaoKnTE8chG57loEQQKB8/f/+yuyQIiseRy2VZSaWYmJzURepqkMmm+fYD9/PO3/kNzjx7grmMZDbtcTItSXk+nlI4+e6KOwZttg86TA104cGjQ2wpcTAzM8Oxhx/Dl70XBwCjo2OcOvkicaf12ge6XHI0XQrVWC/BLIN8pK6VYN4MwgDAbtAXUNJ7vgqP3HcXl77prU1t32klEC5sys7jekEkGYk01IbwJDw/q1+dIJuDBx7R1oPR4YoWhEL1v+JJTKFdCacWINV+bElVQSAsHKf7Z0sCuWyG5XSKyckpYk79EayurvLgiWf4rXxQ6I5Bhx2DDhd3eKy9YkuJg+npaR5YWsRTklgEdt22YHQ8yVp6jTHLaindr1vlkjtBtxs5tSQMWqh13w2GlazrjlFAYrg0juLRQwdLrAbPP/FDzrvs1Tz6jXsa3rYFDFXJkugFwXm0guBXSb4IlwHQsQcn5/WrAio/aVshP2BUEgRCgNUjQVA8rmwmw0o6zdS2bTgNxAB4nuTRp47y9p//+c4PMCL0fobsIjt27ODF+VM9q5JYiaHBBKnUGjnPI97kxCjp/ypW61YECmbhTlgRWhIG1WrdBzniDz+8nhZ26BAsLa0XrhlrPge6GYLe8/V6JUyedhYXv/4KvvuPdzI0Os5p57+ksIwA5l94tiAWGrEFqPxrh8y1M/y2qRSf33BbZ0NFCuXX2yCqgqB4fJlMmrVMlm3bpxq2IKfSaf7pwW/zkc/8aWcHGCG2lDiIx+MsZlI9r3VQTnI8ydz8KSYdpynrgS+lbh26CW6ClUswC8IIV2zZlVCp1OyBA7pwzN69Wgjcfvt6YZlbbtGCosPCAPTkbLGxLW+lCf6t7/+din+/744vMDlzBo8cOshzP3qE+eefYfK0Mznj/JdW3a6PnoS3+70VB9XKJZe6F4z1oFFa6dwZEDWXQTUkknQ6SyqXY2rbVMPdeRUwt7hAbmKUkZHeVwftFltKHACsSK9nJZSr4TgWg8PDpLMZrKHhhr5MPrpccr+6FCqx0YqgdFxCG1aEtmIMKtW6L84Bf/ppePe7tXhYXtbCodia0EGmfJdhX7Jq2dh5E3/5zb3ere+N+/994efnHn+EMy66hNNrCAMAT1iM+JKpBgogdYri/ax4Po31oDmKRXmDVBQE5IMKIyQIAqSUpLNZ0p7L1NS2prp653IuDzz+Q97xjnd0boARpN+t0k3jWeD7Xq+HsYHh4WHSuRxuLtdQeXdFUC65PyNha1FcJhjQVoQWyl7rgMc2gg8r1boPOHQIdu/Wne1gvXPeOedoa0KHsYHd7ipAwb1QTDNXxRPf+xZPPvxtHrnvLuZfeKbqE2Tw/m53tac3/3rSvrTvQrSshJGmznespJeBp3sZSN9HCN3LIBGLdTXboFGklKSyGTLSY3JqqilhALCWWuP/PvwAr31tY22YNwtbznIwMT1NpgsFPZpF1z6YYHVpifEGah/4MiiXvDn13QYrgmquBHMhEyK/npZvWJWsAEeO6PiCa6/VP7/85evV5ZJJHbjYBV5aofd8o6KgeLnzL3sN5//Zl4F1AVAplsFDYCvFxXlR0jMa6MAobFtXTjTBiY1TQRX2m4WgHE9K1jIZPBQTU5NNj1f68MyJF3jpa38i3/F267C19hadzrgagSqJlUjEHayYQ9atbT2QgO83Nxn0KzZlUdT5bnq1/KNShSQMKnHsGFx/PXzpS3DVVVoIBNaDwK3QhYwGCYx6OS7JraDyvefDuBaqlWL20cWPdrurPW3XXLzlWufVgqIOocZ6UIvy49ivFoJytDBI4QuYmEi2NN5sNs3Xv/8Q11xzTf2FNxlbznKwY8cOFldWQonM7QRjY2PMnTxJzHGwnSr1tyPSgbFbNFOCuaQddjM3sEbz9nfuhPvv3/h+UP++Wl36kJoulcRQAK/PLXEsNsSi5WApPzSBUGxBAHCFRdL3eH12MYQttEEDVoMAE5zYHAo9ofajhaAYhb4PrKZT4Ngkx8Za/l6srK7p2gbnnBPiCPuDLScOZmZmWHr0KKrRmuNdxrYEI+PjpNJp7MHKtQ+CJkv99IUNg2olmINGTi0LA9DiYHlVF4oJu6KZZcFi+6b4ElcJgG0ziGJfZo6/H9xBTljEQxIIAQotDCylt5NoOaa9DtsnYfvEeinhKgTfWAH6nK2swYk5/f9KmODEqpQLTR3XI/pSEAQEAmc1tYYdTzA2OtzyujzP57FjW6u2QTHRmx07zPT0NEup1chlLBQzODiA5/vkPG+De0FSWsd7K2KXxWT4eVdD0QKt3diOv6D/H+bxVQpW0zBfpTlOAwRm3hJXSdE+ni2zXJFdwEKRE1bdgMJGCKwHgTB4c3aOs2W2xT2ow3lnwst26Sp+I0M1XyL/YmRIC7mZbfCKl+iulxUoDU40BNeSDARTEQLRNy6DSkjAkx4ra6vEBtoTBgCpdIq7HvoOb3/728MZYJ+x5cTBzMwMp5aWkO22Oe0gAhhPJllJr22oyRDlDozdxCYvEsqv4HZubHNLcOTHeiJ3PR2NVOWlpK8ry0lZZRkJmSw89yJ8/7GW3AqBebT4Ji6q7N9ud5UrMgtYSguEdqWvT5EwSM9xSWalM5NrIgZnzuifW2kOZFn6C7OrRonootiD6H7rO0ctQRAIzX5HAr7nsrS2RnxoiJHh9oSBAuYXF1kbG95StQ2K2XJuhW3btnFifg4ZofKvlYg5NgODQ6QzGazh9doH/VwuuSOUn8Z2SzAvLOtXI5vN32g7cXMtN/k2Inp2u6skfZeDA1P5GASFg2rKzaDQWQm+ECR9j32ZOc4ILAadKG+dDKFglBAwPAgxR4u6MkoKbG0R98KG66eYGuewj3q4FZCA77osrq0xPDrC0OBA2+vM5Ty++/gPtlxtg2K2nOUgFosxdsY0DA/oG0qtV7xKQGCXGB4ZIeVmcXO6VK72CBoCil0Jlm2v13zIPyV18imxU9OLz0Zrgd3EhHy2zHLt2gtclltBADlhkRUWkurd9xT5evPCIicsBHBZboVr117gbJktrTsR9nF17PDcODWa52wF90IjFoJa15LIZ730ExJwXZfFtVVGRkdDEQYAa6nVLVnboJgtZzkA+A//6WONL7yWgh8fh3T7ncmaxRYwkRxndXkNJ39ztjZJueR2KRcGArCxutrIKWxasRZUIoHizdkFXplb5tHYCIdjI6xZts7QUQq/KODSYv1pccSX7HZXubhCumL503c/HdcCm9B60KqFoNKyeNErDlcLCbi5HCupNcaSYyTitYNZG16vD8+cOMFLXvOqLVfboJgtKQ6aYmgQLj4fvvcDfdV0mUQ8wZqTJue62LZtXApUFgYB3WrkFCYbRQGhtBweU5LX5pb4idwSc1aMF4XDrB0nbTl4QuAoxZDy2SFzbPdzTPluza32u0DYLKmNoQmCMkRh7dE/MoEwWEqtkkxOkoiHN5Xp2gYPsv+3fz20dfYjRhzUQwjty5wY173Ne8D42BinTp5iYnS0H763HUNBSYBmLV9/5UZO4Uy6YSJRpaKzAxOuDezwXaZkJr+J1rfQNYHQqVbZRWP37TDaenWHTgmCEoRA9YE2CITBcmqNyYlJYrFwp7GV1TUeeOH4lqxtUIwRB43g+zAUji+rFWzLwgr8qVGt3tRhmhEGhWX0gkVWBNpu5BQWYbkQGiXM5NeOC4RqrbJDaIutxy5AKp1pEmH3QlcEQbC6/PainiQtgVwuy/LaGpNTU8RqxJm0wlavbVDMFpxmWiTswjhNIBUoT6JQusdAz0bSG1oRBsXY6BTAAuV1Edqk2RvqhoCxLoiVsB1iduEfwg9SLG6VDbr65LFjui321Ve3LAwCiu0FUfsutRtU2C5RFgcSyGUzLK+lmJraFrowgPXaBldXq3S6hTDioA/IZbLE43Ecy8bzvepPE5sQn/aEQYAVfLbo82FlNDQ68RaKGQV0+EZfkRCflEtGXqffRVMUt8reuVMLg+K22H/6p+1vI0J9F3otCEqIaIE1CWQyGZbTGbZtn8Jxwp+6gtoGq6NDW7a2QTFGHPQBqXSaRDyGZdtYloNSKnJPPJ1Aoos+BYRRT8Cm+UZO1VfW2HgqFTOyui0KOjQJFp+T8oJdLVOtVXaIbbF7ndoYKUFQhIqg7UAiyaTTrGUzbNs2id2hDAJT26CUzR9z0KnApi4hfYX0XGKDgwj0pOK5Lo6UqLJI/c1Eub81zEJDzTRyaozqUVzdji2oRye2bRf1u5BShnOuyltlJ5Pht8XucnBiN2MINgtSStLZLGnPZWpqG3YHT1Iqtco/PfxdPvd7H+zcRvqIzW05CAKbbrxRt9Vdzle+O3BAt9st5vbb9c3n4x/v/jhrkMlkGIjFCz5zG3Bilm6nGgGTaCfopDAoplCCubDhFq0IFU5DwYVQ6CLYXDGjfsMuc9eETgfaYhfHTagOfZfqWQisHlkIqqKiE3cgpSSVzZKRHpNTUx0VBlLB8RMneOlrfmJL1zYoZnMfhUqBTaBNk8nk+nKBaNi7V5swjx3r6jBrkUqliMfjJTcPGxvLsjale6FbwqCYio2cGvlclfcrWwsic/vvGFYrAsFvYiq68Ub93S23KrSyrjzF5yWs75IEpN+YIIia5U+I8INXW8GTktVshpwvmZicLBQ/7RTZTEbXNti/v7Mb6iM2tzioFNhUibEx/SRy++3a/bBzZ1eHWQ1P+gilcCooWctxdH+ITWQ96IUwKGwr2F5xBH6TAYsbrQUReyrsMILSrBCpGjh6K2vhZAK5LmRzrX02hOBEid7fwvkv1ikRFwQFbIjC6DwpSWXSSBTJyYmOCwOAldVVHnjhOOds8doGxWxucVAtsKka116rrQgRsRykMukSl0IxNmBbNr7vsxl6zZUIA7u7wqAYm1IrQqNpelJ1Pz2xUbp5dViwfvz8Bra9moLltfaj5J872fJHWw1OlOhguYIgKH7k7hdBUEJBGfdk64q8xSCVwrcsJpLJrnyHPGlqG1Ri8wckVjNBFnPggLYy7N2rI6G/+tV1F0QPSadSTAyPVP2CWLaN6/tYknz1v/5kgzDo8Z60VII5mBgiJAo20KWBNV0k6fCP4PyzYPtE86mW2ZwWBs+caGPElAQn1gr0lcG/lebPfFBjfwiByogeVUkMhMFaKoUVdxgdHe3acUyl0tz10Hf48Kf/pEtb7A82vzioxKFD2opw4ID2Yb7xjTo+4cABePppuPXWXo8Q1/VwhI0tqht3LMCJxfDcrD6REa72Vo1SYRCtibVyCWY9xvLCTLCxz0N0CMbZXTdNwwJBSnj8af2KOevxCvWuB6XAC+cpV48XkPq8lgRYBv9uUkEQYAO9aL2kO5F6rKyt4QwOMDY83LVtK2B+aYGVkUFT26AMoVREq15ECaX0k8lzL3Ztk4vLy8SxSAwM1L2lu1IilEI4TqQm13pEWRiUUzMNjd65QRqh4cm2E9vWA2hq+8F4e3FMi4/VVhAE5Ugp8YFYl469BHzPYym9xsDgECNDg13ZbkA25/EP9/0zp/3U63jDG97Q1W1Hnc0dcxAWQsDCctc25wPZVJpYrLHJ3rJtpFLhVqnrMP0kDGDd1VCNfoj66MUxLjluDcRv9PI4lmxblgmDvowhaJEuPS9KwHddllZXGRoa7rowAF3b4B+/911e97rXdX3bUWdruhUaQSn9six4YRZS6a5t2s1licfiWA0m9toAloX0e/fE1Qz9JgygvuXAUJ2mXQzQNRdZTZcBUXYVdY5uVEmUgOe6LK+tMjI6xuBgouPb3DCGQm2DV5naBhXYsuIgm83y7I+eYOfM6TjVJuGcCwtLsLjS1bGtpdIMxuNYTRh2bNvGzyv+HsQTNUy/CYNqFQ59ygrnRLzDX69pRCCosuU7Rb0YAv3nLSwEO6wNJOC6LitrK4wmxxmId18YwHptg3f89q/3ZPtRZ8uKg1gsxuf/+FN8+Nr3MNLFAJh6SAVezsUZHWz6icVyHDw3F9ngxP4TBhtNy8GYg5S94n2SbZVg3vxogWCB9CsKBL94wZDxAdVEDIFkPeg06pa4fkICbi7HUmqNZHKSRLx3U9DK6irfff4Yv2VqG1Rky4oDy7JYyWaRfhTqga2TzWRIxGKlzYEaxAZUvvZB1CapfhIG1awFlbAoqyiX96tHxRwdtedfG4EUBN2oSnsaFI55OFeHFgS+VtwbBqLPTzXbXEm/iNBG1B8I0Zl9DoTBcmqViYlJ4rHeTT+mtkF9tqw4AFhVXmfqwLdBKpViJFE/Q6EawraRvo/VSH55l+gXYVApPVHUachTIgCK0h43NHJKjsJAor1INgWkMrC82vxnI/T0a1vrE6/K1xUoqR/Uxrq1IFDaOrFhw7UFQaXllZSIreQysgEvfFkrgVwuy/LaGpNTU8Sc3h7PoLbBh/70Uz0dR5TZ0uLAGh7Cc6MjDqSvza1OGzciGyAW050boec3tX4RBs1YC6pRKJ5UXBdhZAjrFS9BxGP6vXYiwYMyw+ksHH4cMg2UC46Y+A0ofjL3y457s2hBQOV9tS0EoqW0rHwyI7CVrAdBtYPw9lgCuWyG5XSaqakpnB4LAwUsLC+yMjLI6OhoT8cSZbZ0iObMzAzpXKbXwyiQTmeIVymX3Aw2RKIxUz8IA12ARZZMLFabYy1u5GS94iIovhkK0forIBGDi89vakxR/KJX8uU3etwVFHpZqPIGR7a2+Oi0w9aEQdEg9f/7KE24XUTgVwgBiXaVLqdTTG3rvTAAyOU8vvPDR3nnO9/Z66FEmijeM7rG9PQ0K2upXg+jQCqVYqCsA2Or6NoHvWvM1A/CQFKWcZCfUMIwqtqAPTmOiMcRYadJWRaMDGk3RYNEIf6hEs0E+xULAr+CIFhvdFXbFdTU+Iq2vVlbpBcTHLcwhJBEkcmkWc1m2LZtW8UGcr0glVoztQ0aYEu7Faanp1meW0EqutL5qxauJ7ER2CF9gXTtA7sntQ+iLgw2uhA61M+hicm7JQYTkMl2dhtdRpZlByjyAZ8VXQb6n05fX5ZtF4TBZncviPyr3TBtiSSdzpJ2XaamprCtaMhTqeDZF01tg0bY0kdnZmaGxbXV0qfHHpFOpxmIx0ONEbBsG/K9Gbq1hzr9r5+EgR3a9NL7q2hzIPN9RhuxEHTj+hLobVaNa9iMtBEbI6UklcqS8VwmIyQMIKht8D3e8Y539HookWfLWw4eWFlB+qqnB0KhXQpTo2Oh3uwEYHex9kFJXYCICYMwAg77jZZv78lROGsGxkehmRu758GpRXj6eV1ArAFKptpCkaSyv3TJQlCLrRac2GqVRE9KUtkMru8zMTnVc4tsOSurq3xv9lk+YGob1GVLi4OZmRlmFxaQ0qOXhyKX84g7DpYT/jepW7UPpC/XZ6OITbwb0lXDHl9x58YI0ZJpeHwULr0wb19u8nqMxWB6GyTH4MFHK6cTFrHhSbzT56ldiio89joLqOO0oA08KVnLpJECkpMT0Tp3rNc2uPKnf7rXQ+kLtrRbYXx8nBeXF/H93t7YU+kUg/EEnXoeEUEeeQONb1pBymgKg8A0XaCocU5nNtjg0T1yBD7zGd0i/PbbOzWadZqZyM6cbk0YBFhCx0Jsm6j45w1BhSXjzI+1iUZN3aQ4ODFq9VF6jScla+kUvmWRTCYjcw8oJqhtcPXVV/d6KH3BlhYHQghW3ExPv+hSgZvO4HSw3bINOLEYni9Df8LdMPmGuvbWKNy8Q0xPDI3lZfjgB+HGG+HSS/XvoIXCgQPw8Y+vL3v77XDoUOsCopVznRxpXRgE+D6MjRR+rZllABtiCJrt5NhNiitfRmlcYdNoNqNCt4xfTaUg5pAcH4vkpBLUNlgeHmBsbKzXw+kLongeu8oafk9LKOcyWeLxeEvlkpuhE7UPoigMJBuL6oSVnliNpvb7vvtg50496YMWCQcOwPg4XH01TE6uiwKAvXshmdTLdGN8IoxbgkBZom5QYa3xRVUgbIngRBsaSX710RaDlbVVrHic8dHRyKbMmtoGzbPlxUF8ZAS3h1USU+k0iXisKyfCsh2kUqHc1KImDAouhEKmBJ11IbTK+Di84hV60t+5E44d06Jg717996efhpe/XLsedu5c/8zDD/dowM2jAFV+nVmUWAgaIaoCwYZNbj0Izk/1hyb9ffNYXlslPjjI2Gh0mtdVwtQ2aJ4tLw6mp6dJZ9I92bb0FdJzidlOVxS3DdiWhfRlof5BKxQLAxGBCbiT6Ymhs3cvzM+vuxGOH1//26FDsHu3djcALC31ZoxhIVgXBKLsjBSlu9YiqgJhs7d21lUSK0clSsB3XZbW1hgYHmJkeKi7g2uSoLbBS179StNhswm2dLYC6IyFpdRaT7adyWQYCKFccjPYto2vfFCtpWTJMj9+w6LGtsBp83LzfXC99bHoARVtI7KSoJRbbtn43pEjWgxce63++dJLtVUB9PuveEVXh7hhbIcOwdlnw+KiHmMtBFhCgFX/bDRyvmxABrmEEWkoFgxHAVJJbNHrEYWHxfq+lSMB6bosra0yPDrG0GCHC32FQKG2wW/e0Ouh9BVbXhxMT0+z+MJCT6okplIpRgeHunqj07UPYi3VPmhJGIwOwXln6QC1dgPdANIZOPYC8sWTpTbdCEwYLXPsGFx/vZ58P/1pLR727l2flI8f17EJTRDa82wQQHnXXXqcX/2qfu/jH9fjrBDcVe8stzI2G5uC3aC81XOPKFRO9LV46dvrr4xqVRIl4LouK2urjCTH8hlW0WdldZWHTj7DB849t9dD6Su2vDiYmZlh4cnn8KXE7mJTEE/6CKV6Um88qH0QBCc2stctCYOBOOy+SFsNwhAGgBpIIF5yLrguzC5EThT4FPnqsg10TQQdW3D//RvfDwRBEI9QTiPrb/fyKg6gPPtsPaYjR+D734f9+9fHV8kaUo0GXQrlFHdyDFo99zIArhCcuFnbOhdVSZSAm8uxnFpjPDlBIt4fU0dQ22Df29/e66H0Hf1xhjvI9PQ0T64s5WsddO/Lncqku+5SKEbYtm7rXOcprLzhTFM+ux1ToQoD0L5QpRTWGdMwvxy56OgSU+zSqq4aaNuhHgPlK8hk8VfWaoij/Dlr19xdHEAJ2nowNqYtCaDjJprIGy8x9rQwnPJWz732IW/myolBlcR1YbDK+MQkiVj/TBtBbYPf/e+f7PVQ+o5eW+Z6zvT0NLOLC8g61dzCJp1KEY/HenYz0bUPLLyg5W0F2hIGoDsHdgAhBGJsJFrCoNKxUQoeeQL8vGTw/TZf+fVIiX/kR4WfK569Jm33QbbHBj9zpQDKIIvi9tvhjW9sbkO0ZjUopvg6jEQxok0cnCiBXC7L0toqE5P9JQwUsLi8ZGobtEj/nOkOMTo6yovLS21F7zeL63o4wsYOJae8dWxsfEtVdC+0LQxAV8sL8Ym5hE6tt23KjuTSKtz/MEwmdeXAdoatFKQyiPllbN9HFpXzlVDRilDrrG0I6KxGNZfBfffVD06suNH2n0qKLQjlnRy7TUlwIjK6mTJNohTkshmW02kmp6aIddHtGga5nMcDj//Q1DZokS0vDoQQrHo5pNc9cbCWTjEYcgfGVrGCxkxFPlMfSqwJvTbd9hWVbMvSh9n50Delo/iL+jo0EMlfUxA048NfXtbFmSpRpS5/8VbDkHYlrZR7LBAKY5GbJzjRVz7L6RRTU9tw7P4zMqdSa/zjw9/htt/97V4PpS/pvzPeAXKW6FqVRB/IptLEYp0rl9wMuvaBbswUtMo1wqB5eukessv7EZRN/oHLoLyktF5Bvg5BICoa/R6MjcGtt1b5o6q8nhYDEasRBAQWVt/DKgibqXKiRBexSmcyJCcm+1IYBLUNzr/8MnMPa5EtbzkAiI0M43mNtZhtFzeXJR6LY0XoC2fZNp7yN7TK7ciXqtmceUNDbLAi5Knok69lXVhYhu0T7bltLAsWV6r+OcyrqmS/e/zUvhmCEyUS5SmkUliW3Ubf795SqG3wget7PZS+JTozVA+ZmZlhLZPpyrbWUmkG4nGsCB16i3ztA2+9wFBHhEGtpkNXXVV5+ZtuCn8cW5FyC0E1njlRGvzYLErBagrmSqs7dvJZOlJVFPvYeiClFgY+ulGbZVn5dvb9x+rqGg++eJxdu3b1eih9S3RmqB4yPT3N8upqx7cjFXg5F8fpTrnkRpGA73m6MROqc7EQlZoOgU6Fq+S/vu8+bV0wVKWmy6BkwQYnq9UUPPw4nJyDTE7Xk2jklXNhLQ3HX4DvP77RrRCyS6GcqAiEoO9CEJzYL0gp8X0fBdiOg4Wuctnrdvat4EmfHx570tQ2aBPjViBfQvmZWaSv0/I7RTaTIRGLdbwDYzNIQHoeAtbFQacKzFTKmQ/S4soJ8uf/4R/CHkXfUy+ocMNEXBS011DRqNUUPP50+wPNU2yD6OSVX+pi6F2ZZRERN0ejBMIAIbCKWsfblkWuhx1rWyWVSvN/H/wOH/pTU9ugHYzlgHwJ5dRax1VyKpUiEYtH5mahhYGrhYEjdGtjIfCVKm17HBa1mg4VE/QWMBRoKqgwjyDfqrtYjObX0U1Xsl+obdD5bUXBgmDlx9AP7oWCMLAEtlMaJK3dCtEefznrtQ0SprZBmxjLAdpy8PjyEr7v0ak7mPR9kBInIlYDCXiei4XQwiC/37Zlo5TMm0U7cDQaLbN75Ih+HTvWd2IhrONWz0JQbxuB8tfNEe2S9fmNWhHCICjW2CVZHAULQtSDE4M6JtL3EZaFU8FSaAmB9CJgOUjEocEaC64reWJpjve9730dHtTmx4gDYMeOHby4sICs0qI0DNLpDPEelksuplQYONjxGJw1A1PjUHh6CI5Fk84F6cPiMjz7YuOfCZoLBa6ESy/Vr9tvXw9a7AeKZ4QWaVcQ1CJ4qi48DeafrEUHmxgV70k3zZRREAhR7btQLAysvDCoiAiW7hETY3DOGTDQeIOnOPDmy1/WuTFtIYw4AEZGRphbXcbroFshlUqRHBru+ROEBDzXxQr8izEHLnsJxOO6oiFtFqiJoXsqbJ+AxRWUrxBWnTXu3Vu58dC111ZPdYykuTNQB809K3ZSEFReZakVQXVy8uxwIGIttEAg8Ml0XSCUVk6MhvUgOO91hQEgECXNl7rK8CBcZLoo9hITc5BnxXPxO1Ql0fUkNgK7Bx0Yi9kgDACmp7TZrt4E3gyWQAkLZVv1hUEr+GpDqlykaOAyaiWGoBEavZWvF08KBqTH0inJ1bvaA6XxFl03kgfm+l6nWLIuDDxf1hUGAQKdZdV1tk/qizmyZdI3P0Yc5MlaqmPBN+m0rm3QS9NiRWEAMD7ake0JS8DgAGppBaVUOE8gwXo8D555of31hUy9L1OnBEExzU5+NnbpdRniJNbryTCguGaH6nIwZuDK6XVwYkEYSIlj2Q0LA0tYqF5kLAwPhvvAYmga41bIM5RM4nagSqJCuxSmRsd69vRUVRgAOOG2VC7BshCHf6ytE1PjEIu17rPQjlJdee/EHGRzYY40FCrtWrddBsXrbnjR/PKFsdZo5NQUPXQplNPLVs+9Dk4MzqsrJTHHxrYaHIFt5zMW/O6XUDYWg55jxEGemZkZVjPp0Neby3nEHQfL6c3FXlMYdBgBuhjOC7P6tYWoaoXqtN+7jafTDSWY2/DTd6u2QTP0tJNjj4ITiy0GsbjddMdIyxL5Y2amiq2GOeN5pqenWVld1W6uENebSqcYjCfoxS0yEAa2JRB2NBo9bVZ6ZiGoQqvPeWFZEQqG6Ig5LnvVybEXwYklroR48/VVtFuhP6skGtrHiIM8MzMzLD99At9X2CH5uqQCN51hZHy8J6ZELQwsRLOTk2mO1BBREwTFtHsFV7Yi0PiTZ/A5ES1JKsgLBCFgbBhpW+0FCrseLK81FlNj29pyICV+B9NHoX1hAFrXWZaNGxVxYO5LXcWIgzzT09M8/+gT+L7EtsI5LLlMlng83vVyyW0Jg6A50l136QJEX/2qfv/AAf3/hx9eL2R04AB8+tN62S1Co6WLozUltsZGKwK6X0Cda6r46ETxOIjzz8Y6fTsiLL+2J+FHT8PsQs3FtOASKKlLlHfKvSDJp6e2IQwCbMsi43anY21Nat2Xyu9BW/C+1AkiZvTrHdPT08wvLSBDVMmpdJpEPNbVg9yWMIDKzZEOHNB9Ea6+GiYndXEiqN4waZPRSpZBRJ61QqFaCeaqRCgQcQNnzUCYwgB0Q5aX7tIR9vUWxSpYdTpxjYQpDABdTl1GoEpiM03btsh9qdMYy0Ge6elpTiwuIEP6IkhfIT2X2OBg1zowti0MoHJzpKuvXv/700/Du9/d9lijTpRdBtXo5C28Ugnmeo2coniMmJ4Kf50iXyxo+4TuTFmPDgUnamHg6eyCEIQBBLsWAXHQTNM2QygYcZBncHCQ+dXV0GqJZzIZBrpYLjkUYQD6y3fvvetuhPHx9S/hoUOwe3df9TlohlAEQXDjD29YDdON/H0bUEVBfUHAopUv9hN5i8lgonNpcoMDDS3WieBEiURJhVQqNGEAPa6SWEyt+5KhIxhxUERa+aG5FVKpFKODQ12KSpZ4rt++MAio1BzpyBFYWtJBQH3WCKkWHbMQ9KSefnfM+bUaOUXapQCdzZ9vZt0hBidKKVFK4aNwnFjo9xyBiEb550abthlCwcQcFLFihdOq2JM+QimcLpRLDl0YVOLYMbj+evjSl+Cqq3SkMJQ2TOojOl2psGflW7r82F4owVzYviz5m6E6dv4fhY4RaJVAGCjA7ogwyFdJjELcQSUq3YP69L4UNYzloIjJyUmyIUTmpjLprrgUpJR4fpvCQPrabFjrqWfnzsqNkao1TAroRdnVKnQrhqC4Gl6v6PbEXG5FgOg0GooyNjZBsepWjpeUEt/3oZMFzmz7/2vvzcPkOus738/7nlNVXb13y9pl2Ua2gpEtG7yAbWQbHLUMEUtCIJnE3JuVCU7unZng4c5MYMJcxH2eOIRnLh6YgSSQBJOAb7YhAgKESSyBF8xiS7K8yYtkLbakbvVW6znv+94/3jrVVd3V3VVda3e/n8dldVefOufUds73/Jbvzxoh6Ra7JAbh4sclqHwMWuy45KgKJw5KWL9+PekGuCRm0mmGenqbenBsiDAAmJyG4YFG7ppFG5iYavx6a2A5FhUuV+aIokZZMLeCdvbPL7E4sSgMpEA22eBMSokKFcRaeLoYn7Qjmx1tw6UVSli/fj3jU1N1FXYFQYgvPDzRvJc2EgZ+A1IJ6pVRTBBidAOLjowBDJw627h1Vkkrhhs55lL2SjdpkFNTiPrn777b1tFMTtr79++3t337Zpa9/34rIkrvqxMPG7qPihMXw1D4fGsNUuK3wPlUCoFudcfCuQswlbI/N/LY5KgaJw5KWL9+PZPplFXkSySVSZNs4gTGUmFQ71WZQqHTGcIfPok5O4oJw5nJh1XeTMkNUzB3GZuAQ89a57gW4ARBB1BSiFisRYi+A00eB10X1fp6RKJh1y6bzz5+vHH7UDLWeaHToJ09ZoVBNHK52fUtHtYlUauwyVuahdbw1PPw3HEYvQAXJoo3MzbB5MunOXbsWGv3aZXh0golbNiwgRNPPINWekmWqhrIpTP0NMkuOSwcGOoVBlG4XWuNkAIRhMhnXoJnal+XhmLrnmiyJWwpLmVQTqeceEvf/8oWzJLmn9JqoFpfj/5+m264/36bfmhgG519ncCo+SdGRt+zUmHQKjwpyQUtFgdgIwbnL9hbCZlMli9/4x8Y+eCvtX6fVhEuclDC+vXrOTcxTmiWdqgN8jnisTiyCYU7pcKgnlSCUgodhmhjkFLWna/0sPb5BlNX1XU1LLcIQVvKMdvw5Evfidmn/blRBN1ZUYRdu2BsbCaNcOLEzN8q+XrcdZeNIjQycoAtTpzPOTFyPQzbIAzAuiQu6IjZYlLTKR459QLbtm1r966saFzkoIT169fz6th5a4QUr/3xqXSGZDyObLDmKhUGcomhxGK0wGiEaGzbY1R13YyJc8s5QtDSTGlR0LZDHSzubVAxiiDB64TBTNX4ehw/bqMMu3bBpZdab//IwrdRVChOLBugVBC/rca6JHZG3j9UmqdOvMhPv/3t7d6VFY+LHJQQj8e5kEkvyUtcGQjzAb7vNzRoWq8w0MyKFgjZnKvrkvxyvSy3CMEcPK+1wgDaFKYoZ7H3Y04UQdP6KEI1b0wlX49bb7ViYf9+m2qoKAzqe9ej4kQofAeg7cLA0iEuiUA6neFbP3yUd73rXe3elRWPixzMYlqHSwqh5bJZErFYQycw1isMonCkaUK0YDYzlrAGhap+tG+B5RwhmJ/Wd/u3yZOxJuazYG7JyS8IILFIWHA+X4+oFqG0JiHCGMg3IC8fRVcKr40duezV/H1qJNFxR9Peq0kDjE9OMJb06e93bY7NxkUOZhEIg9a1f8nT6TSJWGM8zQ0QlAgDr0ZhEF156yDAGIP0bZ6y6S1PXuHaR1V30lj2EYIFsNXn7d6LFrBEu+TIgrn0cS2JIpwfb85VsJQwOl73aqJXQ2tdEAbxtgoDKHFJbLOpWT4f8sNnnuIXfuEX2rofqwUnDmYxtH492XxtLolKayiE/urFYK8WdIkwqGlfoLzg0PdbdnCJOhZM1NI4z/6tVEEQsZz3vRZKTxVLfc4elcdBNy2I/dKpmcmJWi/pZspuhT099SpcmKx794rfCCEwrU9OVcbz8KRo2MTapZJOp9j/o0fYFXWWOJqKSyvMYsOGDUxnanNJzGSyxBtglxwJA7MEYVDWnigEoll2qovgAUqIsuLElZkycJgGDXqaPcgp6udvymcjVPDjp2C4Hwb6QNZeISQoCCNtIAjRFybxptN171rp85e+wARy3tbGViOFaL1LYgnKwKlzr3L59a/viNdjNeDEwSzWr1/P5LR1Saz2sJFOpxns7qnrQGYLB61ToVerMFAKXQiVygaYI9VNSd60oiRo9/6tMNqWvShsuFHhRzuMyJup+WmWBbMxMDphb0tEUnj6MQ+x/iLM1g2IOlxRDQaRzWPOX0BMTNvnKyEXhsgO+L5IIVGtdkksIZfNcvDQE7zvt361bfuw2nDiYBbr169n4unjaF2dEVIQKjzEkkyTIqJUAKY2YTDTnmhstKDNB5HSCIExhdny0evSAQe4FU8Lr6gW8jaolzmDnJRCdeDnx+vrwVyz3dYbQH3joI1BGIPcsh5efgVeOAmeR8wYVBgg/VjbrKNs6kcSttHrIPI2+ODll7dtH1YbruZgFuvXr+fC9GTV+bVMJkNXHXbJRWGArRGoRhhE/uoz7YmibXn60hoCHYaEWhMWUhsy6o/uwAP7iqIdB+0lFiJWy0zb48z2Oso8CWDbxQgprTCvRxhgjYZEJDIu3gDdXfakLARGN2aUfD1IKdtWcxAqzdPO26DlOHEwiw0bNnB+YqKqdkaDTSnEY0ubo14mDER1wqDYiVCIFkjfr7mboV4WEgR+LEYiFsP3PKQfQy1QnOhoLO34Mjdb9HmUdzR0zCAnz4OB3vqiBfNhTHFSqiwU6AZtft5SiCV1cTWCdCbDPzpvg5bj0gqzWLt2LWdGz9sOhEXI50Pivo/0az9A1CoMSgsOEaIYLWgVpWFebUyxxsGTEn+e4kcPMFLaffZoe0tWO2il00GrBGKrT1LFWgQor2VpZ0Qq1sQtG1Ms/CvOLNG6rcWJQgh0G6YjGmB8wnkbtAMnDmYRi8WYyGeqCuOlM2mS8QS1Hv4VoMLQ9g9LiScXFwZWSLS24HApgmA20vMItEYqO1xm1ciDYpvGCqTJKYX5qDzIaeWLTlucKNtcnNgel0TnbdA+XFqhAtNhsGhaQRkIMtmqT5LFxwEqDKww8MWCwqDUzMgu7ze9tqDalEG1+yABPxYj1BV8DVY0kYXyynrOpaeHdrXKlg9yaoMFczvwPGKehwrbE9qPolKtlgfpdNp5G7QJFzmoQD7u25PZQstkc8Tj8ZrskhUQhgESYYXBAodXhUKHhqhQsZnRgvkiBLIQIZDUF7L2AC2tOVLrDYXbTJOfcKsP1sVkW5vfRA/QhUFFQGstmBfi8GE7zXHrVjuT4a67GrJaD0AIa6nehvRC5JJYbRdXI7DeBq84b4M24cRBBdavX08ul1twmXQmQ3c8VnXopVwYzB9tmNue2BwzIwWgFZjmCILZSM8jDAJ8pTAtLqBsB5I6ZyGtGYT1w9CdXPSNkJFCqLScwToCnh2tq6+/SOFkLNqtDiiEPWe1PapmmSdVw+Qk3HMPfOtbdnjT179u79+/H+67z94fsX+//ffxxytPhaxAlFII2vEcCy6JWrVOHDhvg/bi0goV2LBhA1Pp+V0Slba9xzGvugmMCgiDhYVBpfbERucXFaBMScpA2ZQBhZRBPBYjVthmw3vXAV9KlFZtb8tqBXW9fls3wFWXW4HQk7QCYZ6b6E4ieuyt4jI9SbhoCK66Arasr+s5lb5rnXTgaLkF83wcOGCHNh08aH+PJjfu3QuDgzPL7d9vxz/v3QvDw3D//VWtvlicKMRMxKSFSCEITeu2m0qlePjk81zuvA3agoscVCBySZxvClk2m6WrSrvkojAQ8wuDZk5PVNH/VWsiBAvheV5x+6suvVAtnoRLNtmfG3WFFlkEX7oZTp+zMwKWQpsKEathtgUzNNGCeT4GBuDaayHKjx8/bsXCbEqnOr70Erz//VVvorQ4sdWmZ1JIVKhhkaGWjSBUmqePO2+DdtJJFwAdw4YNG5hIpWwLXgXS6TTx+OITGBcTBsWCwzC00xNFY6YnKmzNQjFCELQuQrAY0vftvqyC6MGSGOhrnCiYjSehv6f+1TRgV5pFsWAxopVRhF27YGzMRgb274cTJxZe/uBB2LkTrr66tu20oTgxcknULTJCct4G7cdFDkpQxjCaVQRrL+H4xR7Z2DBGSjwMPUazVgesCXN4pbbA862LxYWBUTOioN4rnE6KECxEuffBSm9CWwJek/V6HU6ey4m2RRGqrB/g8GGYmLAFi4cP1yQQisWJYYhapLC5kQghqjKHq5fI22C0y2NgYKDp23NUxokDYDKveHIsx6GxLKnAoM0Q4SU9pPxYcRkJaA+Eb+jeOMzOMM3VwTT9FXJwkTDw5NyCwtnTE6Uvl/zlXi6CYDbC81BaIzvUM99RgQ5OKcxH0TzJqGiyWXvMkw4etFGE/fttSuH4cfjAB2xHw333VS8oSojqkcJAQaw1z0dKic4vXKjdCPL5kB8997TzNmgzq1oc5JTm+2fSHBrLWZtfA74U+J4gn88R923xT4QBQmNIezEe8QZ4LN7PzmCaW3LjJEq62ucXBhod2rDcUtsTl6sgKMUDiMVs9wIsqxPOUlju9RXt9jaoF094KI/GDnKqxS1w1y54+OGZ3y+5pPz3SixiOBQVJ6KULU5swXdI0hqXxHQ6zT/++Ad89sP/punbcszPqhUHJ6YCvn1ymom8QiKIC4EoznYXGIz9gpZ6pxuDh8EvjC4NEfwk3seLfpLd2VE2q1xBGJQXFZZFCyqIhsVYCYJgNtb7QK5O74Ol0KT++Wpo36DexjFjwWxA6fqjCPkAgrBoc9xQpITp+bulIqLnlG9VcaIQNNtZQxk4df5Vtl57tfM2aDOrsiDx0GiWv3lxkom8IiYEMTl3qlqlr4Axhhn5ADEMMaMZlz5/k1zHEzI5VxiUtidKWbUw6OSiwkYhPW9lD2byvMYcSqP++bvvtrnpyUl7f1T4tm/f3OU//OFGbNmyDFMK8+EhGjfI6ZXzjbcUNsYKj7EqPSk8D79FxYkCwJimyoNcNsvBJx7nfe97XxO34qiGVScODo1m+e7JFFob4oUBRpWY/QUwFMYPC1F2MpZAzGi0EPxL90Uc6Roo2urb6Ym6bHriQodXzcoXBKWUeh+0otCpbdT73Cr1zy/UK3/ggI0uNIDSPV/+0sAy14J5ieOgXziJPnW2sZ4DU2l4/Omq2009KPoeNNtEesYlsXnyIPI2uOKKK5q2DUd1rKq0wompgO+eSqGNIV4hWlCK51m73yIFx8LZGApRBKMJhOSfE0MMhjk2BzYsKIVELuAIqAGDBmUqpgwEK1vBSc9DF9I0KzG90BARV6l/fr5e+ajo7Wtfa8SWV1TUYDb1DnJSAM++hD52HNHbg7eE8c0K0CpEIBBK4+WDmtchWlWc6BWKErXCk40/dYTaeRt0EqtGHOSU5tsnp6sSBlD4EhhT/KLZlEJ51KBUPxcFApJ/Sl7EL4WnSHiy4hfVCgKb+1yNgqAUO1AqRhjkV1xxYhRBqptdu+DBB2csdwcGZsx1Snvla2yJq4WV866UM1OLQGEctI3eLVaLEC1vACEkXmrxGoF5tw/oMAAhl1QoKQHTkuJED08IlFbEmnDqSKett8GH/99PNnzdjtpZNeLg+2fSxRqDxYQB2LHEpiS/ZgyIkjN2pcCaAHw0E16MR7rX8NbcheLfdPSYwtyEckHgIRCrRhDMxnofeM77YCEqtbvN7pWP7jt82EYX6hQLKzjRM4daBjmpQnGwgYYUAnoAvnUf9BRLEgitKk4UQqKaYIRkgPFJ523QSayK89FkXnFoLGdnG1QZ+pOR1W/hVta0sNDjAGkMh2K9TAhvZgRyGBIGQUkNgUesWEOweoVBhPA8WxW/GsbvNoKoV/5LX4I9e2yNwdVX25TC+PhM0WI9rOCUQiUks2oRmDsOWqnGCoMIDw9PSFTBPXRJWX3Pw5OyacWJHuBJYS2UG0w+H/KjZ523QSexKiIHTxZ8DOI15ASllISFXHhpSmGhL220dg9DICSHvSQ3ZC4U1+f7guozmquLGe+DlZdeqIlqi70W6pW/6675Wx2XUF2/2t4JDxum17OiCKU04+o8ilJEtu21tvJFcxea6ZwopGzK4DTnbdB5rPgLVmUMh8ayYKgqnRDhSeviBxSrDhc7rJqZRQE4kuhHxCR+MULghMFCeFgRFXkfrEqmUs1btzFVr3/Vvv4FokFOFUVqE8P2nucViv70kjp4hOchfY8waE4ETgrZ8M6iyNvg4muuct4GHcSKFwejWUUqMPiytkpiIa0bmG1frH27ntGkpc+47HKCoAak569I74OqA7H5AM5daE7//Lkxa9xTDasspTAfxYJBYwjC0F7VN3mQUz0CQWK/QxiaMtZZCjHvQLql4rwNOpMVLw7OZgoGRIssd+xHD3PkwW/x2P4HABAI/u4P/xOPfeOva4o4iMLNw0YRzskWzDddQdi8ps29Kr1yBEJNJ5OnXoBTZ6s/kS9GEMDJV+Hpl6pafCV6GywFQ+RVYv1NYomEHXhUOGk389Mp6xAIHuB7HkEYNn4fm+CSmEqleOjlY2zfvr2h63XUx4qvOTiXUchFOhTGTr9Md98Awxu38MUP/wY37LUKdsO2Kxk7fbI8V1CBSn+KRMJZL86OsImh4hWI53koo8GsAO+DQgV8TcEnY+D5l+3N8yp+wKITxkIeGsV11Vpdrl3UwGAnOeqCv4lfeC0830ehCAOF0NpaHTchzRDNThAUahCknRFRNZ6HpzVKhXhe4w7z1iWxYasj1JpnXn7JeRt0ICs+cpAKdbFtcD7Gzpxk0/bXceTAt9n2hpuK919245sZ3nRxxceIktt8aCAtVvxL3HAE4PsxQq1WTnqhwvTOqlAKwvlvYoG/EarahQEUD/6r9ZNrjYnmCoMIDw8/FkdISag1phlX6NjXX3ieLQIMdU3biCJwusH7Zl0SBapBLonpdIZv//gHvPvd727I+hyNY8VHDlQVudvLr7OC4PC/fJM9v/mh4v2DA4PEX7sTpRVHv/ddMtOTnD52lKt2jXD5699UVTQhXIJrmmPG+2AlDGYSYJXiMngSpSeS1fjJLQ5JMxoh5BxhEBFZMCvPIwwCRBA0JYpQdHEEdBBALFb9+j0PD0NY6+MW2aGo7sCT9a0x8jY46wvnbdCBrPiLg2otTTPTk5x+7mhRKACcfeFZtmx/LceOPkGgQ65/+3vY8+v/jq984h4MtljRFAyNjJ753d7sOqTWhEoRFnKUipUx5a4ViJLBTMv1NVsGeqCcVVyIWDo9VS4gDErxAD8WK0YRGn2lHm1DeB5IgQ6DqtfvERUnmgYWJ3qF+Qr118PkA+tt4AoRO5MVLw56fFmV8dGF0ycZ3jg3heB7Pj6aFw/9kMl0Cr8rSXf/IGeefwYpJVJKO4lRSkRU2yDsyGdhDDKf4cLUJOPTU0xNTTKdmiadTpPJ5ciHAYEKrXAoEQ/Nn5i+PLAH3kLodqWkFxpAKz4fq00aRMJAaY30ZU0tdR4Q8zz8WAxtDCoIGl6wWDzRC1GTAIkiHGEDRYuUEqXq/xSmU9bb4NZbb23AXjkazYpPK6xNWqdDs4jPQVdvX9nvRx78FlfdtgeAy6+7mcuvuxljNNOpFNMTF9jwmu3WHKmwTvtP4WfACFuRcGnCZ+26tShtB6wEoULrkFyoUEGuaCgiEHiR2BD24OR5HkIwM9OhcMCSrJ6Qr4eHFmZFpBcaRbOiKKtVfpUKAy8ml+xGEhl5GaUItUYaAw00I7IWyb4dAa8Wn/8QITwPqTW6AcWJEpBS2HqgOij1NvD9FX8aWpas+HdlXdK3OTIWPrEMb7qYHbfu5rH9D5DsG2DTFa+bs4wQku9+/o941+9+jMlMmmQsTjwWm4kYlKCxJ/C1xk5Z8yR40icW84HEnHUrbVBKEYYKpUOyBfFglJ3KIJB4nsATHlIKPBmJBzFTGLlCxYP0fcIgwG/qUJllSKNfi1WYUoiEQag1fgPy8tGMA1GoRdCBiXyZGyIR7BwGHx0GtqmkivVGzolBGCA9v679iMY2B2F94qDobfCB/72u9Tiax4oXB2u6PHpigum8xvMWPmXe+YF7Fvz7kQe/xeXX3cRVt+3BYMhms0ymU/R0JctO1AAhgl6jWGOqy815UuBJn/i84kETKm1nNGhFEChUPofRVjxIMRN1kNJWWEtZWTwst0O/LU6UhcFMzoC6GR0cpdGI1fLqNloYlOIBIhZDF+qNZMEroREOgFYgxNBhgKl2UJPn4RlbnChisbryyVJKVFj7aOlSIm+DDzpvg45lxYsDTwh2Dnfx0KvpsjRArRz70cN09fZz+XU3cfrZo3T19jG86WISiQTTU1PIAJLxBFLKKMfATpVu3GCWQm0DsblvmcH2Qoeh7WsOlSITKpTKYrQ1dfaEh5CikLKQeNKarCwX8SA9j0BrpALldeY+tppGvgZFw+pFBPRKoZnCIMK6FdooggoCwsKU18ZNcrQpBqoQCHZ5gc5rtFLIOkSKFKKu+QrO22B5sOLFAcCO4QSPns0QGogt4dg3dvpl/uq//Nvi79nUFJ/47lHAhtj6+wfIB3kmp6fpjicQsRiegB0q3aBnsDCCgniISyq9pXPEQ6jIhwEqDK09dIl48KVESm9GjHSIeJDYqvDlPJipo2smCsd6ufJrlFsiDErxABmL2SJFrZBGg5CLG1gtul4BBTdRT9mR0wu9ex4exoNQ1TnWuU6XxHTGehv87if/YMnrcDSfVSEO+uMeO4cT/GQ0izZUPbY5YnjTxXz0a48uuEw8Fic2NEQ6nUGFiu2TZ/FkFtOdbHv+fzHxoLHiQZWIh1yYR4Wq4PFvkMIrpD7mEQ8CkM0VDx6gl6P3gUdHV/utJm+DVguDCIG1NBaehw7t6PaoRbGeffCkB8Z+fyV2muRC7+FMcaJacorDuiQuTRwYYHxiglc9GBwcXNI6HK1hVYgDgFs2dvPiVMB4XhGntgmNVWPA7+piQBq+e/8Xmeob5m1vvJk1Q8OFQsTOxFYgS/yFxIOyHu+hClEVxIMnZLHbQkqvEIEoEQ+2UouZ/y9xXwuFXr5Six4IOwcPg0J0qqRZJYWICmVz9C0WBqV4gOfbKEKgFV7BglksctW/4DprGPUcFSfmwwBZhzCxLom20LoWnLfB8kEY0+jxb53LiamAv3lxEq0NcbnwvIVaMcaQ1wYpBe+5rJ+tfTEefPBBPvdH/5XfvvMd7Nh2OX29/TV/mZYDCjChRumgrO6hTDwUiyULbZpSIsXSxIPStsjLL3RsdDoKivMV6t3f6MqXBqyruM4Gr68TUYVJitpoPL89wmA21qY5LLoN1htFUJGBk1zYp0GBbYc0xnZb1bzfismpFL0DA8T82vb4wvgk/9cXPsNn/+rLroWxw1lV4gDg0GiW755M2S9GgwRCURgIwR1beti5pqv4t1wux2c+8xlGDz3Fr7/tHWxYu57u5NxuhJWMMtarXumwrO5Bq9D6TxhjiyRFIWXh2aJJyfziIQhDe79fX2tWK4iG+EAVJ+B4DBYYL27FgQZhU0UVNxaGVc9UaIbY6DQiYWCMQXbg56U4yKngdVJP22P1AkER5m1qoRonyPLHwvT0NF293STi1U+dVQaeef4Yf3v8aT7ykY/UtE1H61l14gAKAuGUFQgxIWquQShFG0NgCsJgc7kwKOWll17iv/ze73Hn9p289Q3XMzg4WLPqXqksJh6kAVnq8eD5BVtsgx+ZujQobdEsFr0637wOLt4AiQaM+DYGJqbhueOQzla1X82YLNgJdLowiCgOe9IaT4gli97iNMkqBEJYSBPG4omatqWAVDqNn4jTnax8vKtEOpPlS//4dW7/9bv4qZ/6qRq26GgHq1IcgE0xfOfkNON5hUTgi9rqEIwxhAY0hsG4x+4tvWztiy36mG984xs88Cdf4P94+8+y/ZLL6OntWS3dY0smEg+hDlGBRqmAMFQE+TzdXV0kYvHCxbYoOkrOwROAtCYurd19YBFxsHEtbL+ksRs0xk5l/MERG0motAg1RDSWIUopO+dEgOf5y6IPQ4H1IoAlRxGiNJbRGrmA46MCVBAgpCRWw/tvgFQmg/ElfT09VT/u3PlR/t2XPsf9DzxQ9WMc7WPVigOAnNJ8/0yaQ2M5O+DHgC+FdRisIBSMMWgg1MaGdYVg53CCWzZ2k6ihmGB6eppPfepTdL06xi++ZYR1a9fQFV9dqYZGoIDRc+foT/YQi8dBKwpvox02E/WVM2NvPb94aK675ILi4Pod0N1V9MdoGMbY6MGZ85X3ye7YijOWKl49F4yH/GVTuGopjSJIIZYU8SgXCPPXWChl25rjia6atpHJ5cgZxWB/f1XLh1rzyBM/4Vi3x6/8yq/UsCVHu1jV4iBiMq94cizHobEsqcAUzZJ0yUsjhSje3xOzxko7hhP0x5d+UH3qqaf4+Ec/yr+6YRc3X3UNA4P9+JXyyI55yQUhU+PjDPb2zZs7LbbqRSFmgMI0TShN8Ysyl8sy6hQP84oDIeDW65awxirQGl45D8+dWHCfVlJKoVQYiIIj4XISBqXUG0WoRiAUixMxxP3qixNzQcB0PseaocGqlp9MpfjkV/6C3/3kH7gWxmWCKxfF+iDctKGbG9cnGc0qzmVCzmYU6VATGoMvBN2+ZF3SY23SZ02XV/Uo6IW48soruf+BB3jggQf4yJ9/jrvf/m4u27KFZE/PijlYN5tEzCcTj5PN50kmkxVft+J9VYoHDUXxUIw6hHZahmm0eGhGS+3MymEesVnqbbBSPmvFkcsFYVBroV2nUT7ISdU8yMnDOicaDDoMoEKXxmznxGrTS7W4JDpvg+WJixx0CBcuXOATn/gElyif99z6FtYMryERd9qtGpSG82dfZaivn9gSWrMWXX/xh5nIgzGmaARTNpFzPvEQMfvqT0rY9YbG7nCENnB2FJ55ac6fVlrUYEYYaISQy14YzCaqDzBgo4s1vG9RyySGeVMUtRYnBkoxnppi7UUXLbpsLgj5xvcP0H/jNdxxxx1V7rWj3bgYdocwNDTEJz/5SW6+6738+z//PAd//EMujE+gtNNui+FJ6BsYJJXN1OX5Pu/6o1uh7SvmecR9n3gsRiwWQ/oxpO8Xu140oIwh1BqlNdpotDHWpEYpa6NbcmsnK+EUWhQGWiNXoDAA+z75sRielIRKocOw6s+OB8hCV49WqqJZp/A8pJBVf3+sS2JVi5JOp/nmjx/ltttuq+4Bjo7AiYMO47rrruPP//qrvNjr8V++9KccO/6irQxu9451ON3JBEpAPgxb6lQsmF88JErEw+waFmCm/qEShw/DZz8L+/fD/fc3dJ872Mm5ZiJhoKpo3VvuSKwFsx+P224pra3ZWBWP9bBRA4yuKBA8bF1DEAZVfz4EtpNoIZSBM+fPsvWaq53p0TLDiYMOxPd9fvM3f5P/8Kl7+dSD3+KBf/oWZ8+dI5inJc1hGewfYDKTbkr0YKlE4kF4HlEGz/O8opCoeDKbnIR77oG774arr7a/gxUKe/aUL3vwoL3/s5+dWW4xVohdcqkw8GIrWxiU4gGxWAxfSpRW6DAo+jks9jjpx8BoTCVR4XnEPB8VBouuS0iqijTkslkOHnqC9773vYus0dFpOHHQwWzYsIHPfe5zXDZyO/f82f/gsSOHmZicrNb8btXh+x7J7m4y2WzHXR2XlCXMvX82Bw7AJZfYEz9YkQCwdy+UFnQdPw4PPmjvv+sumNVWZrBFlcrYE0Glq8XlSukAJS8WW1GtmNUQWXH7MWuaFUZpqkUeZwWCxFQQCB6FrixtFhfYwo5814ukPVPpFAdefNqZHi1DnDhYBtx+++38yV/9JQ+nx/ijv/4yx0+eIJ3JtXu3OpKe3h7SQZ4gn+8ogWC7GKoslRwYgGuvhV27rEg4frzycgcP2mjB/v3w6U9XXsYYjMZGC2bVOBTrHqgsHjqVdk1W7EQ8IObH8D2PUCt0YKMIC10/eBTqC4ytgSldNopqBVUIDSklSs0fzQy15pkTL/HTb397Dc/I0Sk4cbBMSCQSfOhDH+LXfu8/8n9/82/5+vcf5NzoKGG4XA7prcEDhgaHbJ1GB6UXgOrbFnftgrExe9Lfvx9OVPYpAODSS23k4NJL59QmiEIro/C8YpjAgHWaNLZYUmmNDjU6DDFhOHMFWnrrIPHghEFlPM8jFouDFFYkLHJy9zwPT0i0thGE0ut/W5y4cKuipNDOqOdfJp3J8O0f/4B3v/vdtT4dRwfgKkSWGZdeeilf/OIX+frXv86H/uQz/Nt3vIfLL77U2TCXkIj7pGM+uSCP8Cp7H7SDmoZ8VTOY5pprZlIPg4MwPj5nkRn176E8MGGIlH7RLrckjoBRM22aZQZg1jXKFqBVeg4tmmvhhMHC2KJYH+UpwkAjCuOg52t7nG/UczTWOReG8451joyZ8rpyjCLyNnhFGoaGhup8Zo524CIHyxAhBHv37uWz9/8F/3DyGP/jf/5/nDp9mlzepRoi+vv7SaXTqDBo964UEYIFw72LcvCgjSLs329/v/pq++/+/fD447buoIxZ+eCCQZAsKdwrdlow02kRK3RaJGIx/GKrpgAh0NhhY6HWxZsOZiIPc6IODYo8OGFQPR4efiyGkJJQK/u+zLesF9UO6PLWyMJnYaHiRJtWqLzmfBDyk+ee4X3ve19dz8XRPpwJ0grg6NGjfPyjH+WXbrzV2TCXkM5kyaUz9Hd3d0Qle6AUGE3MnzWga9cb5nUyrAut4fRZeP4kYGsMQq1JxBYeEFYLxVODURg915paEBVjFkZvV4w8eMVlK70KCgWKQleCEwa1UI0F83yTHJVShErN6apRwKiMcQaPk0ZiuntQCDwMPUazVgd0TV3gk3/6af7bl/7ctTAuU5w4WCForfnqV7/Kg3/7NX5n789y6ebNq96G2QDnz4/Sm+ginqhtLG0zCAsHYD8WKz8JXnU5DA2UDnloHIeehQuTBQ/9oOXugYvOtYBiK8d84sFghYGLGCyNaMaCWmCQUyWBYJgRCLF4nJTweDLWy6FYLynpoQFhDKbkPZMUomNaE2Smuf2KTXXPoHG0BycOVhhjY2N84hOf4FIdczbMQBAqxsfGGOzrr2ksbTNQxZ78WSe53iRc81pr9diIWQvRV3p0Ap48Vtx2J4bkFxMPSiuyuby9epWycOKSBZvqQv9HkydqrhQWiyJUGvWsgHSoeLR7DUfifcWaE9/ogkuiQc6KemljSOfzeF1d9v1a4vRaR3tx4mCF8thjj/Hpe+/l197801y7/Ur6+/vwmnFlugyYnJoGpejpbm8kRQFhPo8fj8/dj64EbFgD/b1WJMyiOH56sdkNBsgHMDoOZ8eKQiFUCmMMsWUS4g2VsmOBdUhfb39BWIWEoUKpEKMNGGuXLKXEkxJPSKRnfxZCzqQ0nHgoMjeKUD7IafYkx1Negm93DTMuYkgMsUJhKswUrwopy15XrTUT6RRDQ8PWydGAxjAY99i9pZetfY1LazmahxMHK5gwDPnCF77Acwcf4jfe9k4u3rCRZDK56g6QysC5s2cZ7OklHo+3rQpXAWEuhx/38URtMiXqXV9K9CMa2uMtE3vhUCnSuSwhhoHBwQUFndKaUGlUGBJqhQqseLD+DoUQufQK/wo8TyKFVxRZpeKh81+ZxlE6yMkrtLt6JX8zSnE41su/dF+EFgLf6DniqigOhCimg4wxBGFIKKA72V1cVhtDYAxSCO7Y3MPONV0teZ6OpePEwSrgzJkz/D8f+xjXr93MnTfexPDQ0LK5gmwUuXyO6ckUAz09bRvMo4B8Pkfc82s+SYeFqvCl7HunphQqESpFKpNBe5LBgf66hKzBXsWGoUapgodDoaPCGA3G4BWc/qSU+J7Ak56NOqwC8RDVFCil7GsgRPFz+Xisl/+VGMIIQayYQgAEcyMHJeJAG8N0Jk1vfz9SlMtwYwx5XRAIW5xA6HScOFhF/PM//zNfuO+/8cHde9mx7XJ6e/oqRbBXLGMXxunyfBJdXW070GfzeWLzzVRYgECFSETNj7OFiCEIiHmdKwgNkTBII2M+fX19TY9wzREPhZRFGNquEmEMUpaKh0IUQoiZ7gtY9uJhdhThZLybv0uuQ5cKg4gSgVBJHCilmMplGRwYrLitokCQgvdc1u9SDB2MEwerjGw2y2c+8xkmnnqWX9n9M2xYu57uZKLdu9USlNacPzfKUF8f8TZFTnJBgF9jeF8DKgyQova0gMIa4nRy1MC6Oyum0ym8RIL+3p527xJg90trjSoRD6EKUKECYwrdFbJYKFlRPAhAdr54iLoVUsbw1f6LmZQx4gu4cpSKA4SwMxmMIZfPI+IxEvH5jynGGPLG1iDctX3AFSl2KE4crFJefPFFPvaxj7H38h3c/vrrGBoYxPc7+fDVGKbTGVQ2R29vsi3DenJhgF/jSb6emoEopRCPxTqy1iSKbEym0ySSSXp7ku3eparRgFa60O4XosKZf0XUqlkiHryo46JUPLTIXbJa/ikxxBPxPnyt5++sLY0eGDvgKzJSmkynGRgaXHSOSFSD8Po1Xbx1S2+jn4ajAThxsIoxxrB//37+7ot/we/8zLtXhQ1z0fugq4t4vPXeB0EYghA1FRYqFDqw1eO17G904hVCtK3OYiEUNiIylU6T7OmhO7myctAKMGHUZVFa9xBFHgyetDMOpJTWqriN4mFSeHyhZxMG8Bcd2jwrtSAlYRiSVSF9vX1VbS/QBiHg11476HwQOhAnDhxMTU3xyU9+ku7zE/yrt4ywfu2aBcOCy50gDJkYu8BAG7wPAqUAU1P+f15/hCoe16mFiFHP/VQ6RXdv36pJbZWijA3ll4mH0A5NwmgEwqYrWiQeHo4P8FBigHihzmDBE0MhegAzdQfpbJZ4d3KuA+h8qyikF25e381NG7oXf4CjpThx4Cjy5JNPcu++ffz8tW/i5quvYXCg35qlrEAmpqaQytDd3d3SE2fkkhirIcyvSh5T87aMJl7lwbpVRMJgMp2ib6CPrhUsROuhavEgZbEexRZPFu2hqhYPCviTns1MS4+EKa81WOwEEZ1CIm+DWsgpTW9c8htXDuE1wgDM0TCcOHCUobXmK1/5Cgf+7h8KNsxb6O7ptt4AyQSsGYREvD4nP2Mgk4Xz49awpw1E3gdDvX0tzccvJQqwFI8DBeggQMjW2iUvRiQMJlLTDAwOrWr3znqJxEOoQ1SgUSqw4kHbNk0hwMM6Spb6PZSLB+tecE7G+MuejXhGV/xcRieJ53/yCJnpKTJTE9zwtp8HAX+17x4uu+ZGrnn7z5V5G1T3HAzKwF3bB1iXdJ+FTsK9G44ypJT80i/9EnfeeSf79u3jisNJ3nXLray9cjuxn7rULlQSUlwS0eMv3ghPvQCT03Xvd614AgaGBkhNpfBaeQL1wKja9LhhCeNTC4Ki04RBEOSYTKUZGBoiEXOHn3rwBHi+RwwP4gDlxZxWPMy0aOZChQryaB3VPFhTKE94nOoetJ8XKkcKBDB25mWSfQMMbdjMn/2nD3LD238ejGHz9h2cPfUSXV2114xIIDSGc5nQiYMOY2XGjB11Mzw8zKc+9Smue9+7+M9/8yW8K7bag4YQdkCQqONW+vjLt7btOSbjCYyQ5IOgrnHCteEVLI2r32KtwT07o8D2kneKNFBAkLPCYGjYCYNW4AmI+T7JrgS9PT0MDvSzZniItRddxNq1axlacxG9ff3EuroYjXfZDgtjrFX3rJs2hrEzp9h0+ZU8+b3vsu3aNxZUhOB1t9zBwIbNc0yPqiHySDibad030FEd7hvqWJAbb7yRN/zZnyGbcQUqhE1RJLtsmqENDAz2M3p+FD/m47XIJEiDPVtW8ZIqZnrJq1+/QmmDH+sM7a+AfC7HVDbN0PAwsVXQMrsc8CR40icW88nHujBClJ3grSa1wtQAr3n9GzHGcPjBf2TkV/9tYd6HIQgDtt/w5uLjDnzlTxneuAWAq27bs+h+aGNIh/N7KjjaQ2ccPRwdTdPnsbfxKtKXkp6eHjLZLKoF8QNJ7eUa1oWvhgeo6HHtPwkrIFcQBsPDa5ww6FBUhTyhDe6Jslt2eorTx55my9VvYCqTZiKT5sxLz7HpsisA+MK//3Vu2PterrptDw/+1R9Xvf3Qlb51HC5y4Fj1dPd0cz6dJp4Piceba41kXfNqUAcmEizV7ZXCXol5UrZd+Ssgl82SymdZs2bNiu18WQl4FSoNokmgGINSikCFnHjxOQbWb8KLx+nv7UEgOF3oNjn97FGSvf3Fn3/7f/x11dv3XadCx+G+rY5VjwQGBwdJpTMY1YrcZw0HwlqjrUqhjUG0uRBRochkMqTyOYaHnTDodHoKUxeNMWhtxUA+CEhnMkxk0uQxxLu7WbdpM57nEY/HEQiOPPitYurg1LNHGDvzMhdOnwTg7z/1saq2LYWg23efj07DRQ4cS+fwYTh4ELZuhfFxuOuudu/RkonHfPxEnGw+TzKZbGr0QApRhf9cCaL6BEGhRqytCQWlFJl8jkwQFoRBG3fGsSDK2A6S7twkqq8blc8ThCFaQCKRoDvZhywRdsObtrLj1t08tv8Bkn0DbLridcW/Zaan7H3b7X2nnj3C6WePFn+vRFTwuC7p0k2dhhMHjqUxOQn33APf+hYcPw5f/7q9f/9+uO8+e3+03L598JGPQH9/+/a3Cvr6+jl77lXivo9spvdBjeKg2jSELV7UeEuoGm8UoVJkcjnyOmR4zfCKtuJerihtyOVyZDIZTp87y4+PPcNjY+Ns/pUP4cfj9BXSBfNx5wfuqXj/8MYtDG+8uPh7sm+AsTMvLygONPbzvda1MXYc7h1xLI0DB+CSS2YiB3ffbe/fuxe+9KWZ5Y4fhyeegPe+1/6+a5cVCh2IJ2Ggf5BUOkX/EoYcVYvAmk1Rxfo1BlmtTFEKbWjbAK1QKdK5LCGGweHhDiiHdEQEoSKby5JOp3n+1EkePfokj589yc4bb2D3+97NL191FX/69DjTeb3o0KT52HbdTfxg/wPF3y+cOcm2625a8DGhNvTGJWu63Kel03DiwLE0Bgbg2mvtyR6sCLjkkrnL9ffPRBH277fioYPpTiZIZ1Lkw5C419zixKq6GatsYzSFm6whBdFIQqVIZTJoKRkcHHDFTG1GAWE+JJtNMzE1zdGXXuDAkcc5E2S4fWSEX/iPv8u/27Sp7DE7h7t46NV0cVZCrSR7+7lx7/t4bP8DZKan2PObv1ssUKyEbdG123XWyZ2HEweOpbFrFzz4oD3hgxULlcRBdN/998M739m6/auDwYFBRkdHGfa9pngfCLG4Xz1EaQKqql/UgNK65d4GhhlhIGIeg319HTkaejWgtG0bzeYyvDp6nkPPP8+DTz5BfNM6RkZG+E+/9Sv09s4/HnnHcIJHz2YIDcSW+CZW42sQERrwhGDHsJut0Yk4ceBYOrWkBw4cWDYFi74n6e7pJpPNInt6G38lLj0Iq+uKsB4H1bgl1dby2Ag0tvhwOp3Ciyfo7+tp2bYdllBpstksmXSGF145zY+ffZofnDjGtp1Xs+fte/j5j9xTdXqsP+6xczjBT0azaGOjUM1CG4PG8PrhLjeuuUNx4sDRWA4ehBMnylMIk5MwONjW3aqV7p4eRtPniOfzEI83/JRrjLEn9IUO3FW2VUbeBlLKlkkDBegwZCqdJp609ryO5mOAfBCSy2aYnE7x9PGXeOSpIxybHuOm227jzt/+DT542WVLXv8tG7t5cSpgPK+IU6MnR5UYYwiMYTDucctGN6q5U3FTGR3t58hzMJVq917MIZcPmZqYYLC3t6EDjDSQz+eIef6CV3XRqGZ/kc4JpRRhYblWiINIGEymU3R1d9PTnVz0MY6lo4z9vGQzWc6Pj3P4hWN87+gh0n1JRkZGeOtb38pgA8X3iamAv3lxEq0NcSkaKhCMMeS1nfvxnsv62drXWePEHTM4ceBoK8YYwkcPEevQj+GF8UniQtDVYO+DXD6P73mLiwMMsUXqHsLCGOhErPkHWgWoMGAqnSLZ00t3svZJfI7FUVqTzebIZDOcfPUVfvTsUzx07Fk2XbmdkZER3vSmNxFr4vt9aDTLd0+m0KZxAqEoDITgji097FzjPjudjBMHjrZhjOHI44+TevQQV77mNfT29nVcX7zShtGzZxns6yfewINxLgjwF2mXDAtphYWiFvYqPkCI5o+dVkAYBEymp+nt7yeZcIVkjcIAYRiSyeRIZVI89/IJHnryMEcvvMr1N9/M7t27ee1rX9uUMP98HBrN8t1TViDEhKirBkEXUglSCO7Y7ITBcsCJA0dbyWaz3HfffWSefYn3776TDRetI5nsrJNOOpMll87Q393dMO+DfBjiCbHg+gIVIll4mValFIrCIDVN/+AAiXi8iVtbHdh2wxyZTI6xyQmefPEFHn76MOdNyFv27OGOO+5g3bp1bd3HE1MB3zk5zXheIRH4orY6BGMMobF+HYNxj91bel0qYZngxIGjI3j++ef5/d//fX7uytez65prGRoYwu8Qv3UDjI6O0RNPEE8kGnISDlQICGLznPjtlWSAFItHF4wxxJo4OTMSBhOpaQaGhki0cYrmcqfoTpjNcvrcWZ54/jkeevYoPRdvYmRkhF27dtHV1VlX1Tml+f6ZNIfGcihjwIAvRWHC6FyhYIxBYw2OELZdcedwgls2dpNwXtrLBicOHB2DMYavfe1r/P2f3c+/ecfP8pqLL6Gnp6cjUg1BqLgwNsZQX/+8J/Sa1qcUGE3Mr3wVpQAdBLYDYZ7tKUAFAV4T3RwVEORzTKbTDA0NEXPCoGZmuxM+9sxRfnT6OFddfz0jIyPs3LmzbH5BpzKZVzw5luPQWJZUYIpmSbrkFCKFKN7fExPsHO5ix3DCtSsuQ5w4cHQck5OT/OEf/iED4yned/tPs/6iizoijD05lQIV0tPdU3f0ICzpRKh0WiiKgwXSBc1OKSggn8sxlUkzNDxMrE22zMsNDQT5kGwuw8TUFE+/9BLfe+oJjgdZbr/9dnbv3s2WLVvavZtLRhnDaFZxLhNyNqNIh5rQGPzCdMV1SY+1SZ81XZ5zPlzGOHHg6FiOHDnCvfv28YvX3cwbd+xkcKC/raN/lYFz584x2N1DrE7vA1XoMPDmObFHbYzziYOonRAxf2qiHhTWbW86m2Z4eA1+J4aDPQnrL4Khfqg3oqENTE7Dq+chk6v54aXuhGfHRjn8gnUnlOvWsHv3bm6//Xb6+vrq20eHo4U4ceDoaLTW/OVf/iUP/8M3+e29P8vWTZvp7k62zbs/lw+ZnpxgoKc+7wNbjJbHn0dkROJgvna1ZkYNrDDIMp3NsmbNcFsF2bxICTu2QU/BRKcRV6jagNFw5BikM4suHkbthuk0x185w4+ee4aHnn+ay6/dye7du7n++uvxm1gL4nA0EycOHMuC8+fP84lPfILtXjfvevOtrBle07bCuLHxcRLSo6tr6d4HCghzOfyYjyfnrsV6HDBvVCASB/EGj5ZWKHLZPKl8nuHhYTzZoWHhi4bgigqzPOrFGBgdh+eOz/0TEAQh2WyW6VSKp09Yd8KnLpzl5ttvZ2RkhG3btjV+nxyONuDEgWNZ8eijj/Lpe+/lA7fvYecVr6W/r6/lJzClNaNnzzPY30d8noLCRdeBdb2Lz+OSGCqFgIp/a5a3gVKKTD5PNsgzNLyGTswkFNm21QqEZrz3oYLHDgMFd8IgRzadY2xinEMvHuOho4eYSMYZGRnhjjvuYGhoqPH74HC0GScOHMuOIAj44z/+Y1585Id84M53snnDBpLJrpZOA0xlMgSZHH3dyYpX/tWQzeeJzeOSGIShHb9cSRw0IaWglCKTy5HVIUNDwx3RIbIg2y+F4YHGpBNmYYwh/c+PkM1kOHn2VX783DM8dOxp1l2xjZGREW666SbiHVAg63A0EycOHMuWU6dOse8//2du2fwadl//RoaHhlpWUW+A8+dH6U10Ldn7YD6XxMVaFEOlrK1tg/LZoVKkc1kCbRgcGux8YQDNFQda80f/54c4fPY01970Jvbs2cOVV17ZUndCh6PdOHHgWPb80z/9E/d/9nP81sjeltowB6FifGyMwSV6H+TCAL+CydFMG6PEozbhUCuhUqSzGZQQDAwOtK3Qs2aaKA601pw7d47169c3fN0Ox3LBiQPHiiCTyXDfffeRfe64tWFeu45kV/NtmCenphDK0N3dXXP0IJinFVGh0EHlNsZGpRRMYV2pTAbjSQYG+lualqmbSuLg8GE7MnzrVhgfh7vuatfeORzLnmVzoeBwLEQymeTDH/4wv/wffpePfu2rfPOh73FudJRQ6aZut6e3j1QuSxAE1LwlIbCn6Vmoyotr7AAbKUXdwiBUiqlMChHzl58wqMTkJNxzD9x9N1x9tf0dYP9+e9u3b2a5D3945u8Oh6MiThw4VhTbtm3jS1/6EnLHNn7vzz/PkWefZjKVQjUpPuYJGBwaIJ3JoNU8Z/V5EIDRppI8sJ70s+4y2FoDucgI54VQQKhCplIp/ESC/r7e5S8MAA4cgEsusZEDsCJh/34YGIC9e2F4GO6/H44fhyeegPe+F/bsmRENDoejDJdWcKxYJicnuffeexmcSBdsmNeSiDdnItzohXGSnk+iq6vqq/r5XBKVUhjmjmqu19sgclWcTKdIdHfT251cwlo6hNlphYMHbVrh7rvt78ePW7EQ8eEPw/vfD/39M/fv32+Fg8PhmIMTB44Vz+HDh7l33z5++YZd3PC6q5piw6y0ZvT8KIO9fVV3ESgUYV7NcUkMlEJS7nEQndiFEEvyNlCACgOm0mmSPT10Jztr8l+t6O2XIoYHyjsI9u2Da6+1Pw8MwK5d9ueDB61YKK1BuP9+eOc7rVhwOBxzcOLAsSpQSlkb5v3/yO80yYZ5Op1B5XL09iTndBlU3CcKLokJv2z5oMKo5noKEaORy5PpFL29fSSTzS/UbDQGCIOQTDbHdHqa2NXbueg1ly7eXnj4sBUGe/fan6++2t7/gQ/A5z/f9P12OJYrzvjbsSrwPI/3v//9vO1tb2Pfvn1cebiPd9z8Zi4aXkO8QTbM3d1JzqdTxPMh8Xg18sAWGaIoFhgorIPv7LyBwUbQly4Mpukf6CcRXz7CQBkIghyZjHUnPPLi83z/qSOMxQQfu/5ji6/g+HErArZuhfvug498xN4/OQmDg83cdYdj2eMiB45VySOPPMKn772Xf/2Wt3HNFT9FX4NsmIMgZOLCBQb7+hcN/xsgl88RK7FQnvE48PEKCiG6T8ja7JIjYTCRmmZgcIhEvPOvBZTW5HI5MpkMJ8+d5fFjz3LgqSOs227dCW+++WYSieUjcByO5YoTB45VS7NsmCcmJ/EMJJOLex/kgjy+nLFQrjSqeSkpBYW96p5MpRkcGmpYdKQZBKEik8uSSad5/tRJHj36JD8+fZxrb7buhDt27HDuhA5Hi3HiwLHqOXXqFB//6Ed585ZtDbFhVhrOnXuVod7+RTsLZlsoRx0MfiyGJDIrCjFArMoWRgXkczmmMmmGhodbZildLXZcdUgmm2ZyapqjL73AwSNPcCbMcPvICLt372bjxo3t3k2HY1XjxIHDUeA73/kO9//3z/HBkXfwute8hp46bJgz2RyZVIr+7p4FUwHBrA4EO6rZFIWAwhAGIX4FK+VKKCCXyzGdSTM8vAbf7wwrE6Uhl8uSzWV5dfQ8h55/ngNHDxHftI6RkRFuvfVWenp62r2bDoejgBMHDkcJmUyGT3/60+SOnbA2zOvWkVxijnt07ALJWIxEYn7vg6BgnBRZKIeF30vFQqg1sUIkYSGKwiCbYXjNMH6D2zVrJVSabDZDJp3lhTOn+fFzT/PIi8+x/Q3XsGfPHl7/+tc3ZD6Ew+FoPE4cOBwVOHbsGB/72Md4z443sGvntQwODOJ7tZ1sQ6UZHR1luK9v3pRAWKgxiMesOVOgQiR2VHPkbVBp/sJsFIpcLk8ql2N4eLjhPg7VYIB8EJLLZpicnubp48d55KkjPJ+6wE233cbIyAiXXnppy/fL4XDUjhMHDsc8GGP4+7//e77x5a/wOz/zbi67eCs9PT01tRNOp1LofEBPT2+5CyIwKmO8InzOyhgZL44WILWmxyjWmZA1QZaBIEtikUJEpRWZXJ5MkC8Ig9YV7ykD+WyObC7L+fELHH7heb7/1GHSfUn27NnDW97yFgYGBlq2Pw6HozE4ceBwLMLk5CR/8Ad/wNqpPD9/+1tZu2Zt1W2BChg9d47+ZA+xeJyU8Hgy1suhWC8p6aEBYQympBo/+l0aQ7dRXBOk2BFM02/mzm5QSpHJ58iGIUPDwy0ZVa20JpPNkc1mOPnqK/zo2ad4+Pnn2HTldkZGRnjjG99ILNYcm2qHw9EanDhwOKrk0KFD3LtvH3fdeCs37riKgf7qbJhzQcjoxCRHL9rKkXgfqiAEfKMRgJjH9EgDStj1e8awM5jmltw4icKoplApMrkcea0YHBpqmjCY7U547OQJHj56hKMXznL9zTczMjLC9u3bXbuhw7GCcOLA4agBpRRf/vKX+dE3v82//pl3s3Xj4jbMJ0Scb8p+prw4EkMMU9QCJvrfIufVEIEWgkEdsjs7yqZ8mnQuS4hhYHCwrhHOlYjcCbOZHGOT4zz54oscePIJJj3DW/bs4Y477mDt2rUN3qrD4egUnDhwOJbAuXPn+PjHP86OrgHeefMu1gwPVzQaOuR1811/EA14KsSTskwHVBIHIrq//G40EAiJNIabJ87wU/lJBgb6GzZyWWlTdCc8fe4sT7xwjINPHWbgsq2MjIzw5je/ma6u5T2wyeFwVIcTBw5HHTz88MP89z/8FL/+lpE5NsylwiCOAWMwxiCEKBcIC0QOZt+tgTwCCdyhJrhGpeva/yBUZHNZ0gV3wseeOcqPTp9g5403sHv3bq6++mpkm1siHQ5H63HiwOGokyAI+PznP8+pH/6EXxt5B5vWr+dcdz9/G7+oKAyik7zWGoGg8B8wEyWoxOxlTKFYMRQSCbwnf56tJl/1vmogyIdks2kmpqc5+uILPPT0YU6kp4ruhJs3b67h2TscjpWIEwcOR4M4efIkH//oR7l12w6mdv8CqViiTBiAPblrbZBSLCoOKgkDhEAKYT0FEAyakLvy54pFipWw7oQ5srkMZ8dGeeLYMb731GHk+jWMjIxw22230dvbW9dzdzgcKwsnDhyOBvOF7z/JGX+ALqzzoZhVZ6C1AWbSCwuJg1JhIIQo6wjQQIDg9SrFW8OJssdad8IcmUya46+c4bFnnuLRl57j8mt3MjIywnXXXefcCR0Ox7w4ceBwNJDJvOILT4+jjSGXToHSdCcSSCmLJ3YDaKWRQixYa7CQMIgIEAgMv5o7S1eQK7gTpnj25eN8/8ghnpsa5ebbb2dkZITXvOY1TXnODodj5eHEgcPRQB5+Jc1Dr6aJF07moQqZnpoi4cdIxGLFk7wpKU5ciIWEgSn8PY/ktedPEPz4AAcOP066L8nIyAhvfetbGRoaatIzdTgcKxknDhyOBqGM4U+eusB0XpMom8NgyGSz5DIZerqSeJ5nBYK2JkjMIxCKtQklf4++rsYYlFYEoSKUHuQz3MwZbrnpJudO6HA46qY6D1iHw7Eoo1lFKjD4c2YbCJJdSRKJBNPT0xz/4fcw2SzZ1BTX3flzSCH4yr572PaGN3HD238eKBcGNsoAxmhCrQiCkEArYvE4iWQXCc9DJZO8bvtlxCp4LTgcDketuAZmh6NBnM2EaGPm/VJJIQmnJui/aD19Wy7lwANfhMLJf/P2qxg7/TKYGWFgjCFUilw+z3QmzVQ2gxaCZF8vQ0PD9Pb0EvNjSOyy5zJhS5+vw+FYuThx4HA0iHMZhZynPiBi7MxJLrlyJy8/8QMuvfZGptNplNbsePMdDG+8GG002mgyuRyT6TSpfA4Ri9E3OMDg4BDJZDeeLO8yiGoSzmbmDmZyOByOpeDEgcPRIFKhRi9SwnP5dTcBcPhfvsm1d+ylb6Cf6VyWXJBn445rmcyk+atP/HuUChgYGmSgf4BEIoFYxCRZG0M61A17Lg6HY3XjEpQOR4NQVdb2ZqYnOf3c0aJQGBoc4uXHH+V1u36ayVfP8MzD/4v/+r89BkA2NcWe3/wQt/7iry+63tDVFjscjgbhxIHD0SC8KkcWXzh9kuGNF5fdJ4XA93zGzpzkI//zEZK9/QA8tv8Bbtj7vqrW67uRyQ6Ho0E4ceBwNIgeX1pjo0Xo6u0r+/3Ig9/iqtv2ADNpB7DC4Krb76xq21IIun2XJXQ4HI3BiQOHo0GsTXroQtvhQkWJw5suZsetu3ls/wMk+wbYdMXr5iwzdvplMtNTxQjCQkSGSuuSzg7Z4XA0BicOHI4GsS7pI4VAA4udpu/8wD0L/v0H+7/K5dfdUtV2NVaMrE26r7PD4WgMLg7pcDSINV0ePTFBqOsvDHzywHcY3rilqmVDbeiJCdZ0uciBw+FoDE4cOBwNwhOCncNdIGZsjpdKV28fyf6BRZezY5xh53BX1QWRDofDsRhutoLD0UCiqYzGQGyOjXLjCbRBCPi11w7SH3eRA4fD0Rhc5MDhaCD9cY+dwwk0ZlFDpHrRxqAx7BxOOGHgcDgaihMHDkeDuWVjN4Nxj6DQRdAMjDEExjAY97hlY3dTtuFwOFYvThw4HA0m4Ul2b+lFCkFeN14gGGPIa4MUgt1bemeNh3Y4HI76cUcVh6MJbO2LccfmnoYLhFJhcMfmHrb2xRqyXofD4SjFNUY7HE1i55ouAL57KkXeGGJQlYPifOhCKkFKKwyi9TscDkejcd0KDkeTOTEV8J2T04znFRKBLxZ2UJyNMYbQgMbWGOze0usiBg6Ho6k4ceBwtICc0nz/TJpDYzk7vdGALwWSykLBGIPGGhwhIg+FBLds7HY1Bg6Ho+k4ceBwtJDJvOLJsRyHxrKkAluLIIQoa3uUQhTv74lZY6Udrl3R4XC0ECcOHI42oIxhNKs4lwk5m1GkQ01oDH5huuK6pMfapM+aLs85HzocjpbjxIHD4XA4HI4yXPLS4XA4HA5HGU4cOBwOh8PhKMOJA4fD4XA4HGU4ceBwOBwOh6MMJw4cDofD4XCU4cSBw+FwOByOMpw4cDgcDofDUYYTBw6Hw+FwOMpw4sDhcDgcDkcZThw4HA6Hw+Eow4kDh8PhcDgcZThx4HA4HA6HowwnDhwOh8PhcJThxIHD4XA4HI4ynDhwOBwOh8NRhhMHDofD4XA4ynDiwOFwOBwORxlOHDgcDofD4SjDiQOHw+FwOBxlOHHgcDgcDoejDCcOHA6Hw+FwlOHEgcPhcDgcjjKcOHA4HA6Hw1GGEwcOh8PhcDjKcOLA4XA4HA5HGU4cOBwOh8PhKMOJA4fD4XA4HGU4ceBwOBwOh6MMJw4cDofD4XCU4cSBw+FwOByOMpw4cDgcDofDUYYTBw6Hw+FwOMpw4sDhcDgcDkcZThw4HA6Hw+Eow4kDh8PhcDgcZfz/EQEubafPhdkAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " - The complex has 8 0-cells.\n", + " - The 0-cells have features dimension 1\n", + " - The complex has 28 1-cells.\n", + " - The 1-cells have features dimension 1\n", + " - The complex has 56 2-cells.\n", + " - The 2-cells have features dimension 1\n", + "\n" + ] + } + ], + "source": [ + "lifted_dataset = PreProcessor(dataset, transform_config, loader.data_dir)\n", + "describe_data(lifted_dataset)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create and Run a Simplicial NN Model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this section a simple model is created to test that the used lifting works as intended. In this case the model uses the `up_laplacian_1` and the `down_laplacian_1` so the lifting should make sure to add them to the data." + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Model configuration for simplicial SAN:\n", + "\n", + "{'in_channels': None,\n", + " 'hidden_channels': 32,\n", + " 'out_channels': None,\n", + " 'n_layers': 2,\n", + " 'n_filters': 2,\n", + " 'order_harmonic': 5,\n", + " 'epsilon_harmonic': 0.1}\n" + ] + } + ], + "source": [ + "from modules.models.simplicial.san import SANModel\n", + "\n", + "model_type = \"simplicial\"\n", + "model_id = \"san\"\n", + "model_config = load_model_config(model_type, model_id)\n", + "\n", + "model = SANModel(model_config, dataset_config)" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [], + "source": [ + "y_hat = model(lifted_dataset.get(0))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If everything is correct the cell above should execute without errors. " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.11.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/tutorials/graph2simplicial/graph_induced_lifting.ipynb b/tutorials/graph2simplicial/graph_induced_lifting.ipynb deleted file mode 100644 index fd92075c..00000000 --- a/tutorials/graph2simplicial/graph_induced_lifting.ipynb +++ /dev/null @@ -1,356 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Graph-to-Simplicial Graph Induced Lifting Tutorial" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "***\n", - "This notebook shows how to import a dataset, with the desired lifting, and how to run a neural network using the loaded data.\n", - "\n", - "The notebook is divided into sections:\n", - "\n", - "- [Loading the dataset](#loading-the-dataset) loads the config files for the data and the desired tranformation, createsa a dataset object and visualizes it.\n", - "- [Loading and applying the lifting](#loading-and-applying-the-lifting) defines a simple neural network to test that the lifting creates the expected incidence matrices.\n", - "- [Create and run a simplicial nn model](#create-and-run-a-simplicial-nn-model) simply runs a forward pass of the model to check that everything is working as expected.\n", - "\n", - "***\n", - "***\n", - "\n", - "Note that for simplicity the notebook is setup to use a simple graph. However, there is a set of available datasets that you can play with.\n", - "\n", - "To switch to one of the available datasets, simply change the *dataset_name* variable in [Dataset config](#dataset-config) to one of the following names:\n", - "\n", - "* cocitation_cora\n", - "* cocitation_citeseer\n", - "* cocitation_pubmed\n", - "* MUTAG\n", - "* NCI1\n", - "* NCI109\n", - "* PROTEINS_TU\n", - "* AQSOL\n", - "* ZINC\n", - "***" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Imports and utilities" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# With this cell any imported module is reloaded before each cell execution\n", - "%load_ext autoreload\n", - "%autoreload 2\n", - "from modules.data.load.loaders import GraphLoader\n", - "from modules.data.preprocess.preprocessor import PreProcessor\n", - "from modules.utils.utils import (\n", - " describe_data,\n", - " load_dataset_config,\n", - " load_model_config,\n", - " load_transform_config,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Loading the Dataset" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here we just need to spicify the name of the available dataset that we want to load. First, the dataset config is read from the corresponding yaml file (located at `/configs/datasets/` directory), and then the data is loaded via the implemented `Loaders`.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Dataset configuration for manual_dataset:\n", - "\n", - "{'data_domain': 'graph',\n", - " 'data_type': 'toy_dataset',\n", - " 'data_name': 'manual',\n", - " 'data_dir': 'datasets/graph/toy_dataset',\n", - " 'num_features': 1,\n", - " 'num_classes': 2,\n", - " 'task': 'classification',\n", - " 'loss_type': 'cross_entropy',\n", - " 'monitor_metric': 'accuracy',\n", - " 'task_level': 'node'}\n" - ] - } - ], - "source": [ - "dataset_name = \"manual_dataset\"\n", - "dataset_config = load_dataset_config(dataset_name)\n", - "loader = GraphLoader(dataset_config)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can then access to the data through the `load()`method:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Dataset only contains 1 sample:\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " - Graph with 8 vertices and 13 edges.\n", - " - Features dimensions: [1, 0]\n", - " - There are 0 isolated nodes.\n", - "\n" - ] - } - ], - "source": [ - "dataset = loader.load()\n", - "describe_data(dataset)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Loading and Applying the Lifting" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For simplicial complexes creating a lifting involves creating a `SimplicialComplex` object from topomodelx and adding simplices to it using the method `add_simplices_from`. The `SimplicialComplex` class then takes care of creating all the needed matrices.\n", - "\n", - "Similarly to before, we can specify the transformation we want to apply through its type and id --the correxponding config files located at `/configs/transforms`. \n", - "\n", - "Note that the *tranform_config* dictionary generated below can contain a sequence of tranforms if it is needed.\n", - "\n", - "This can also be used to explore liftings from one topological domain to another, for example using two liftings it is possible to achieve a sequence such as: graph -> simplicial complex -> hypergraph. " - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Transform configuration for graph2simplicial/graph_induced_lifting:\n", - "\n", - "{'transform_type': 'lifting',\n", - " 'transform_name': 'SimplicialGraphInducedLifting',\n", - " 'complex_dim': 3,\n", - " 'preserve_edge_attr': False,\n", - " 'signed': True,\n", - " 'feature_lifting': 'ProjectionSum'}\n" - ] - } - ], - "source": [ - "# Define transformation type and id\n", - "transform_type = \"liftings\"\n", - "# If the transform is a topological lifting, it should include both the type of the lifting and the identifier\n", - "transform_id = \"graph2simplicial/graph_induced_lifting\"\n", - "\n", - "# Read yaml file\n", - "transform_config = {\n", - " \"lifting\": load_transform_config(transform_type, transform_id)\n", - " # other transforms (e.g. data manipulations, feature liftings) can be added here\n", - "}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We than apply the transform via our `PreProcesor`:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Processing...\n", - "Done!\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Dataset only contains 1 sample:\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " - The complex has 8 0-cells.\n", - " - The 0-cells have features dimension 1\n", - " - The complex has 28 1-cells.\n", - " - The 1-cells have features dimension 1\n", - " - The complex has 51 2-cells.\n", - " - The 2-cells have features dimension 1\n", - " - The complex has 34 3-cells.\n", - " - The 3-cells have features dimension 1\n", - "\n" - ] - } - ], - "source": [ - "lifted_dataset = PreProcessor(dataset, transform_config, loader.data_dir)\n", - "describe_data(lifted_dataset)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create and Run a Simplicial NN Model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this section a simple model is created to test that the used lifting works as intended. In this case the model uses the `up_laplacian_1` and the `down_laplacian_1` so the lifting should make sure to add them to the data." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Model configuration for simplicial SAN:\n", - "\n", - "{'in_channels': None,\n", - " 'hidden_channels': 32,\n", - " 'out_channels': None,\n", - " 'n_layers': 2,\n", - " 'n_filters': 2,\n", - " 'order_harmonic': 5,\n", - " 'epsilon_harmonic': 0.1}\n" - ] - } - ], - "source": [ - "from modules.models.simplicial.san import SANModel\n", - "\n", - "model_type = \"simplicial\"\n", - "model_id = \"san\"\n", - "model_config = load_model_config(model_type, model_id)\n", - "\n", - "model = SANModel(model_config, dataset_config)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "y_hat = model(lifted_dataset.get(0))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If everything is correct the cell above should execute without errors. " - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "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": "3.11.3" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -}