diff --git a/.github/workflows/testing.yml b/.github/workflows/testing.yml
index ffc74b0..614694e 100644
--- a/.github/workflows/testing.yml
+++ b/.github/workflows/testing.yml
@@ -29,8 +29,7 @@ jobs:
- name: Install dependencies
run: |
python -m pip install --upgrade pip
- pip install .
- pip install pytest omegaconf
+ pip install ".[dev]"
- name: Test with pytest
run: |
diff --git a/config_files/config_svd.yaml b/config_files/config_svd.yaml
index 4e3f9bd..d5e2da0 100644
--- a/config_files/config_svd.yaml
+++ b/config_files/config_svd.yaml
@@ -1,14 +1,21 @@
-path_to_volumes: /path/to/volumes
-box_size_ds: 32
-submission_list: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
-experiment_mode: "all_vs_ref" # options are "all_vs_all", "all_vs_ref"
-# optional unless experiment_mode is "all_vs_ref"
-path_to_reference: /path/to/reference/volumes.pt
-dtype: "float32" # options are "float32", "float64"
-output_options:
- # path will be created if it does not exist
- output_path: /path/to/output
- # whether or not to save the processed volumes (downsampled, normalized, etc.)
- save_volumes: True
- # whether or not to save the SVD matrices (U, S, V)
- save_svd_matrices: True
+path_to_submissions: path/to/preprocessed/submissions/ # where all the submission_i.pt files are
+#excluded_submissions: # you can exclude some submissions by filename, default = []
+# - "submission_0.pt"
+# - "submission_1.pt"
+voxel_size: 1.0 # voxel size of the input maps (will probably be removed soon)
+
+dtype: float32 # optional, default = float32
+svd_max_rank: 5 # optional, default = full rank svd
+normalize_params: # optional, if not given there will be no normalization
+ mask_path: path/to/mask.mrc # default = None, no masking applied
+ bfactor: 170 # default = None, no bfactor applied
+ box_size_ds: 16 # default = None, no downsampling applied
+
+gt_params: # optional, if provided there will be extra results
+ gt_vols_file: path/to/gt_volumes.npy # volumes must be in .npy format (memory stuff)
+ skip_vols: 1 # default = 1, no volumes skipped. Equivalent to volumes[::skip_vols]
+
+output_params:
+ output_file: path/to/output_file.pt # where the results will be saved
+ save_svd_data: True # optional, default = False
+ generate_plots: True # optional, default = False
diff --git a/docs/setup_zernike3d_distance.md b/docs/setup_zernike3d_distance.md
new file mode 100644
index 0000000..88f2852
--- /dev/null
+++ b/docs/setup_zernike3d_distance.md
@@ -0,0 +1,50 @@
+How to setup Zernike3D distance?
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Zernike3D distance relies on the external software **[Flexutils](https://github.com/I2PC/Flexutils-Toolkit)**. The following document includes the installation guide to setup this software in your machine, as well as some guidelines on the parameters and characteristics of the Zernike3D distance.
+
+# Flexutils installation
+**Flexutils** can be installed in your system with the following commands:
+
+```bash
+git clone https://github.com/I2PC/Flexutils-Toolkit.git
+cd Flexutils-Toolkit
+bash install.sh
+```
+
+Any errors raised during the installation of the software or the computation of the Zernike3D distance can be reported through Flexutils GitHub issue [webpage](https://github.com/I2PC/Flexutils-Toolkit/issues).
+
+# Defining the config file parameters
+Zernike3D distance relies on the approximation of a deformation field between two volumes to measure their similarity metric. A detailed explanation on the theory behind the computation of these deformation fields is provided in the following publications: [Zernike3D-IUCRJ](https://journals.iucr.org/m/issues/2021/06/00/eh5012/) and [Zernike3D-NatComm](https://www.nature.com/articles/s41467-023-35791-y).
+
+The software follows a neural network approximation, so the usage of a GPU is strongly recommended.
+
+The Zernike3D distance requires a set of additional execution parameters that need to be supplied through the `config_map_to_map.yaml` file passed to the distance compution step. These additional parameters are presented below:
+
+- **gpuID**: An integer larger than 0 determining the GPU to be used to train the Zernike3Deep neural network.
+- **tmpDir**: A path to a folder needed to store the intermediate files generated by the software. This folder is **NOT** emptied once the execution finishes.
+- **thr**: An integer larger than 0 determining the number of processes to use during the execution of the software.
+
+```yaml
+ metrics:
+ - zernike3d
+ zernike3d_extra_params:
+ gpuID: 0
+ tmpDir: where/to/save/intermediate/files/folder
+ thr: 20
+```
diff --git a/figure_utils.py b/figure_utils.py
new file mode 100644
index 0000000..79c6fc7
--- /dev/null
+++ b/figure_utils.py
@@ -0,0 +1,110 @@
+"""
+Some random code that I have found to be useful for plotting figures.
+This should become part of the main repo at some point, I will leave it out for now.
+
+- David
+"""
+
+from natsort import natsorted
+
+# Here is how I generate the general dictionary parameter for plots
+COLORS = {
+ "Coffee": "#97b4ff",
+ "Salted Caramel": "#97b4ff",
+ "Neapolitan": "#648fff",
+ "Peanut Butter": "#1858ff",
+ "Cherry": "#b3a4f7",
+ "Pina Colada": "#8c75f2",
+ "Chocolate": "#785ef0",
+ "Cookie Dough": "#512fec",
+ "Chocolate Chip": "#3d18e9",
+ "Vanilla": "#e35299",
+ "Mango": "#dc267f",
+ "Black Raspberry": "#ff8032",
+ "Rocky Road": "#fe6100",
+ "Ground Truth": "#ffb000",
+ "Mint Chocolate Chip": "#ffb000",
+ "Bubble Gum": "#ffb000",
+}
+
+PLOT_SETUP = {
+ "Salted Caramel": {"category": "1", "marker": "o"},
+ "Neapolitan": {"category": "1", "marker": "v"},
+ "Peanut Butter": {"category": "1", "marker": "^"},
+ "Coffee": {"category": "1", "marker": "<"},
+ "Cherry": {"category": "2", "marker": "o"},
+ "Pina Colada": {"category": "2", "marker": "v"},
+ "Cookie Dough": {"category": "2", "marker": "^"},
+ "Chocolate Chip": {"category": "2", "marker": "<"},
+ "Chocolate": {"category": "2", "marker": ">"},
+ "Vanilla": {"category": "3", "marker": "o"},
+ "Mango": {"category": "3", "marker": "v"},
+ "Rocky Road": {"category": "4", "marker": "o"},
+ "Black Raspberry": {"category": "4", "marker": "v"},
+ "Ground Truth": {"category": "5", "marker": "o"},
+ "Bubble Gum": {"category": "5", "marker": "v"},
+ "Mint Chocolate Chip": {"category": "5", "marker": "^"},
+}
+
+for key in list(PLOT_SETUP.keys()):
+ # PLOT_SETUP[key]["color"] = COLORS[PLOT_SETUP[key]["category"]]
+ PLOT_SETUP[key]["color"] = COLORS[key]
+
+
+# These two functions are useful when setting the order of how to plot figures
+def compare_strings(fixed_string, other_string):
+ return other_string.startswith(fixed_string)
+
+
+def sort_labels_category(labels, plot_setup):
+ labels_sorted = []
+ for i in range(5): # there are 5 categories
+ for label in labels:
+ if plot_setup[label]["category"] == str(i + 1):
+ labels_sorted.append(label)
+
+ return labels_sorted
+
+
+labels = ... # get labels from somwhere (pipeline results for example)
+
+# This is the particular plot_setup for your data
+plot_setup = {}
+for i, label in enumerate(labels):
+ for (
+ possible_label
+ ) in PLOT_SETUP.keys(): # generalized for labels like FLAVOR 1, FLAVOR 2, etc.
+ # print(label, possible_label)
+ if compare_strings(possible_label, label):
+ plot_setup[label] = PLOT_SETUP[possible_label]
+
+for label in labels:
+ if label not in plot_setup.keys():
+ raise ValueError(f"Label {label} not found in PLOT_SETUP")
+
+labels = sort_labels_category(natsorted(labels), plot_setup)
+
+
+# Then I do something like this, which let's me configure how the
+# labels will be displayed in the plot
+labels_for_plot = {
+ "Neapolitan": "Neapolitan R1",
+ "Neapolitan 2": "Neapolitan R2",
+ "Peanut Butter": "Peanut Butter R1",
+ "Peanut Butter 2": "Peanut Butter R2",
+ "Salted Caramel": "Salted Caramel R1",
+ "Salted Caramel 2": "Salted Caramel R2 1",
+ "Salted Caramel 3": "Salted Caramel R2 2",
+ "Chocolate": "Chocolate R1",
+ "Chocolate 2": "Chocolate R2",
+ "Chocolate Chip": "Chocolate Chip R1",
+ "Cookie Dough": "Cookie Dough R1",
+ "Cookie Dough 2": "Cookie Dough R2",
+ "Pina Colada 1": "Piña Colada R2",
+ "Mango": "Mango R1",
+ "Vanilla": "Vanilla R1",
+ "Vanilla 2": "Vanilla R2",
+ "Black Raspberry": "Black Raspberry R1",
+ "Black Raspberry 2": "Black Raspberry R2",
+ "Rocky Road": "Rocky Road R1",
+}
diff --git a/pyproject.toml b/pyproject.toml
index 597890e..45e044b 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -52,7 +52,9 @@ dependencies = [
"osfclient",
"seaborn",
"ipyfilechooser",
- "omegaconf"
+ "omegaconf",
+ "pydantic",
+ "ecos"
]
[project.optional-dependencies]
diff --git a/src/cryo_challenge/_commands/run_svd.py b/src/cryo_challenge/_commands/run_svd.py
index 791ab4c..e74df38 100644
--- a/src/cryo_challenge/_commands/run_svd.py
+++ b/src/cryo_challenge/_commands/run_svd.py
@@ -6,8 +6,8 @@
import os
import yaml
-from .._svd.svd_pipeline import run_all_vs_all_pipeline, run_all_vs_ref_pipeline
-from ..data._validation.config_validators import validate_config_svd
+from .._svd.svd_pipeline import run_svd_noref, run_svd_with_ref
+from ..data._validation.config_validators import SVDConfig
def add_args(parser):
@@ -35,15 +35,21 @@ def main(args):
with open(args.config, "r") as file:
config = yaml.safe_load(file)
- validate_config_svd(config)
- warnexists(config["output_options"]["output_path"])
- mkbasedir(config["output_options"]["output_path"])
+ config = SVDConfig(**config).model_dump()
- if config["experiment_mode"] == "all_vs_all":
- run_all_vs_all_pipeline(config)
+ warnexists(config["output_params"]["output_file"])
+ mkbasedir(os.path.dirname(config["output_params"]["output_file"]))
- elif config["experiment_mode"] == "all_vs_ref":
- run_all_vs_ref_pipeline(config)
+ output_path = os.path.dirname(config["output_params"]["output_file"])
+
+ with open(os.path.join(output_path, "config.yaml"), "w") as file:
+ yaml.dump(config, file)
+
+ if config["gt_params"] is None:
+ run_svd_noref(config)
+
+ else:
+ run_svd_with_ref(config)
return
diff --git a/src/cryo_challenge/_map_to_map/map_to_map_distance.py b/src/cryo_challenge/_map_to_map/map_to_map_distance.py
index 3021db5..91b4432 100644
--- a/src/cryo_challenge/_map_to_map/map_to_map_distance.py
+++ b/src/cryo_challenge/_map_to_map/map_to_map_distance.py
@@ -1,3 +1,5 @@
+import os
+import subprocess
import math
import torch
from typing import Optional, Sequence
@@ -55,6 +57,7 @@ def get_distance_matrix(self, maps1, maps2, global_store_of_running_results):
"""Compute the distance matrix between two sets of maps."""
if self.config["data"]["mask"]["do"]:
maps2 = maps2[:, self.mask]
+
else:
maps2 = maps2.reshape(len(maps2), -1)
@@ -87,6 +90,8 @@ def get_distance_matrix(self, maps1, maps2, global_store_of_running_results):
else:
maps1 = maps1.reshape(len(maps1), -1)
+ if self.config["data"]["mask"]["do"]:
+ maps1 = maps1.reshape(len(maps1), -1)[:, self.mask]
maps2 = maps2.reshape(len(maps2), -1)
distance_matrix = torch.vmap(
lambda maps1: torch.vmap(
@@ -398,3 +403,80 @@ def res_at_fsc_threshold(fscs, threshold=0.5):
res_fsc_half, fraction_nyquist = res_at_fsc_threshold(fsc_matrix)
self.stored_computed_assets = {"fraction_nyquist": fraction_nyquist}
return units_Angstroms[res_fsc_half]
+
+
+class Zernike3DDistance(MapToMapDistance):
+ """Zernike3D based distance.
+
+ Zernike3D distance relies on the estimation of the non-linear transformation needed to align two different maps.
+ The RMSD of the associated non-linear alignment represented as a deformation field is then used as the distance
+ between two maps
+ """
+
+ @override
+ def get_distance_matrix(self, maps1, maps2, global_store_of_running_results):
+ gpuID = self.config["analysis"]["zernike3d_extra_params"]["gpuID"]
+ outputPath = self.config["analysis"]["zernike3d_extra_params"]["tmpDir"]
+ thr = self.config["analysis"]["zernike3d_extra_params"]["thr"]
+ numProjections = self.config["analysis"]["zernike3d_extra_params"][
+ "numProjections"
+ ]
+
+ # Create output directory
+ if not os.path.isdir(outputPath):
+ os.mkdir(outputPath)
+
+ # Prepare data to call external
+ targets_paths = os.path.join(outputPath, "target_maps.npy")
+ references_path = os.path.join(outputPath, "reference_maps.npy")
+ if not os.path.isfile(targets_paths):
+ np.save(targets_paths, maps1)
+ if not os.path.isfile(references_path):
+ np.save(references_path, maps2)
+
+ # Check conda is in PATH (otherwise abort as external software is not installed)
+ try:
+ subprocess.check_call("conda", shell=True, stdout=subprocess.PIPE)
+ except FileNotFoundError:
+ raise Exception("Conda not found in PATH... Aborting")
+
+ # Check if conda env is installed
+ env_installed = subprocess.run(
+ r"conda env list | grep 'flexutils-tensorflow '",
+ shell=True,
+ check=False,
+ stdout=subprocess.PIPE,
+ ).stdout
+ env_installed = bool(
+ env_installed.decode("utf-8").replace("\n", "").replace("*", "")
+ )
+ if not env_installed:
+ raise Exception("External software not found... Aborting")
+
+ # Find conda executable (needed to activate conda envs in a subprocess)
+ condabin_path = subprocess.run(
+ r"which conda | sed 's: ::g'",
+ shell=True,
+ check=False,
+ stdout=subprocess.PIPE,
+ ).stdout
+ condabin_path = condabin_path.decode("utf-8").replace("\n", "").replace("*", "")
+
+ # Call external program
+ subprocess.check_call(
+ f'eval "$({condabin_path} shell.bash hook)" &&'
+ f" conda activate flexutils-tensorflow && "
+ f"compute_distance_matrix_zernike3deep.py --references_file {references_path} "
+ f"--targets_file {targets_paths} --out_path {outputPath} --gpu {gpuID} --num_projections {numProjections} "
+ f"--thr {thr}",
+ shell=True,
+ )
+
+ # Read distance matrix
+ dists = np.load(os.path.join(outputPath, "dist_mat.npy")).T
+ self.stored_computed_assets = {"zernike3d": dists}
+ return dists
+
+ @override
+ def get_computed_assets(self, maps1, maps2, global_store_of_running_results):
+ return self.stored_computed_assets # must run get_distance_matrix first
diff --git a/src/cryo_challenge/_map_to_map/map_to_map_pipeline.py b/src/cryo_challenge/_map_to_map/map_to_map_pipeline.py
index 06ce66f..903b424 100644
--- a/src/cryo_challenge/_map_to_map/map_to_map_pipeline.py
+++ b/src/cryo_challenge/_map_to_map/map_to_map_pipeline.py
@@ -9,6 +9,7 @@
L2DistanceNorm,
BioEM3dDistance,
FSCResDistance,
+ Zernike3DDistance,
)
@@ -18,6 +19,7 @@
"l2": L2DistanceNorm,
"bioem": BioEM3dDistance,
"res": FSCResDistance,
+ "zernike3d": Zernike3DDistance,
}
@@ -51,6 +53,7 @@ def run(config):
maps_user_flat = submission[submission_volume_key].reshape(
len(submission["volumes"]), -1
)
+
maps_gt_flat = torch.load(
config["data"]["ground_truth"]["volumes"], mmap=do_low_memory_mode
)
diff --git a/src/cryo_challenge/_svd/svd_pipeline.py b/src/cryo_challenge/_svd/svd_pipeline.py
index 08ffa59..5df13b9 100644
--- a/src/cryo_challenge/_svd/svd_pipeline.py
+++ b/src/cryo_challenge/_svd/svd_pipeline.py
@@ -1,240 +1,131 @@
import torch
-from typing import Tuple
-import yaml
-import argparse
-import os
-
-from .svd_utils import get_vols_svd, project_vols_to_svd
-from ..data._io.svd_io_utils import load_volumes, load_ref_vols
-from ..data._validation.config_validators import validate_config_svd
-
-
-def run_svd_with_ref(
- volumes: torch.tensor, ref_volumes: torch.tensor
-) -> Tuple[torch.tensor, torch.tensor, torch.tensor, torch.tensor]:
- """
- Compute the singular value decomposition of the reference volumes and project the input volumes onto the right singular vectors of the reference volumes.
-
- Parameters
- ----------
- volumes: torch.tensor
- Tensor of shape (n_volumes, n_x, n_y, n_z) containing the volumes to be projected.
- ref_volumes: torch.tensor
- Tensor of shape (n_volumes_ref, n_x, n_y, n_z) containing the reference volumes.
-
- Returns
- -------
- U: torch.tensor
- Left singular vectors of the reference volumes.
- S: torch.tensor
- Singular values of the reference volumes.
- V: torch.tensor
- Right singular vectors of the reference volumes.
- coeffs: torch.tensor
- Coefficients of the input volumes projected onto the right singular vectors of the reference volumes.
-
- Examples
- --------
- >>> volumes = torch.randn(10, 32, 32, 32)
- >>> ref_volumes = torch.randn(10, 32, 32, 32)
- >>> U, S, V, coeffs = run_svd_with_ref(volumes, ref_volumes)
- """ # noqa: E501
-
- assert volumes.ndim == 4, "Input volumes must have shape (n_volumes, n_x, n_y, n_z)"
- assert volumes.shape[0] > 0, "Input volumes must have at least one volume"
- assert (
- ref_volumes.ndim == 4
- ), "Reference volumes must have shape (n_volumes, n_x, n_y, n_z)"
- assert ref_volumes.shape[0] > 0, "Reference volumes must have at least one volume"
- assert (
- volumes.shape[1:] == ref_volumes.shape[1:]
- ), "Input volumes and reference volumes must have the same shape"
-
- U, S, V = get_vols_svd(ref_volumes)
- coeffs = project_vols_to_svd(volumes, V)
- coeffs_ref = U @ torch.diag(S)
-
- return U, S, V, coeffs, coeffs_ref
-
-
-def run_svd_all_vs_all(volumes: torch.tensor):
- """
- Compute the singular value decomposition of the input volumes.
-
- Parameters
- ----------
- volumes: torch.tensor
- Tensor of shape (n_volumes, n_x, n_y, n_z) containing the volumes to be decomposed.
-
- Returns
- -------
- U: torch.tensor
- Left singular vectors of the input volumes.
- S: torch.tensor
- Singular values of the input volumes.
- V: torch.tensor
- Right singular vectors of the input volumes.
- coeffs: torch.tensor
- Coefficients of the input volumes projected onto the right singular vectors.
-
- Examples
- --------
- >>> volumes = torch.randn(10, 32, 32, 32)
- >>> U, S, V, coeffs = run_svd_all_vs_all(volumes)
- """ # noqa: E501
- U, S, V = get_vols_svd(volumes)
- coeffs = U @ torch.diag(S)
- return U, S, V, coeffs
-
-
-def run_all_vs_all_pipeline(config: dict):
- """
- Run the all-vs-all SVD pipeline. Load the volumes, compute the SVD, and save the results.
-
- Parameters
- ----------
- config: dict
- Dictionary containing the configuration options for the pipeline.
-
- The results are saved in a dictionary with the following
- keys:
- - coeffs: Coefficients of the input volumes projected onto the right singular vectors.
- - metadata: Dictionary containing the populations and indices of each submission (see Tutorial for details).
- - vols_per_submission: Dictionary containing the number of volumes per submission.
-
- if save_volumes set to True
- - volumes: Tensor of shape (n_volumes, n_x, n_y, n_z) containing the volumes.
- - mean_volumes: Tensor of shape (n_submissions, n_x, n_y, n_z) containing the mean volume of each submission.
- * Note: the volumes will be downsampled, normalized and mean-removed
-
- if save_svd_matrices set to True
- - U: Left singular vectors of the input volumes.
- - S: Singular values of the input volumes.
- - V: Right singular vectors of the input volumes.
-
- """ # noqa: E501
-
- dtype = torch.float32 if config["dtype"] == "float32" else torch.float64
- volumes, mean_volumes, metadata = load_volumes(
- box_size_ds=config["box_size_ds"],
- submission_list=config["submission_list"],
- path_to_submissions=config["path_to_volumes"],
- dtype=dtype,
- )
-
- U, S, V, coeffs = run_svd_all_vs_all(volumes=volumes)
-
- output_dict = {
- "coeffs": coeffs,
- "metadata": metadata,
- "config": config,
- }
- if config["output_options"]["save_volumes"]:
- output_dict["volumes"] = volumes
- output_dict["mean_volumes"] = mean_volumes
-
- if config["output_options"]["save_svd_matrices"]:
- output_dict["U"] = U
- output_dict["S"] = S
- output_dict["V"] = V
-
- output_file = os.path.join(
- config["output_options"]["output_path"], "svd_results.pt"
- )
- torch.save(output_dict, output_file)
-
- return output_dict
-
-
-def run_all_vs_ref_pipeline(config: dict):
- """
- Run the all-vs-ref SVD pipeline. Load the volumes, compute the SVD, and save the results.
-
- Parameters
- ----------
- config: dict
- Dictionary containing the configuration options for the pipeline.
-
- The results are saved in a dictionary with the following
- keys:
- - coeffs: Coefficients of the input volumes projected onto the right singular vectors.
- - populations: Dictionary containing the populations of each submission.
- - vols_per_submission: Dictionary containing the number of volumes per submission.
-
- if save_volumes set to True
- - volumes: Tensor of shape (n_volumes, n_x, n_y, n_z) containing the volumes.
- - mean_volumes: Tensor of shape (n_submissions, n_x, n_y, n_z) containing the mean volume of each submission.
- - ref_volumes: Tensor of shape (n_volumes_ref, n_x, n_y, n_z) containing the reference volumes.
- - mean_ref_volumes: Tensor of shape (n_x, n_y, n_z) containing the mean reference volume.
- * Note: volumes and ref_volumes will be downsampled, normalized and mean-removed
-
- if save_svd_matrices set to True
- - U: Left singular vectors of the reference volumes.
- - S: Singular values of the reference volumes.
- - V: Right singular vectors of the reference volumes.
-
- """ # noqa: E501
-
- dtype = torch.float32 if config["dtype"] == "float32" else torch.float64
-
- ref_volumes, mean_volume = load_ref_vols(
- box_size_ds=config["box_size_ds"],
- path_to_volumes=config["path_to_reference"],
- dtype=dtype,
- )
-
- volumes, mean_volumes, metadata = load_volumes(
- box_size_ds=config["box_size_ds"],
- submission_list=config["submission_list"],
- path_to_submissions=config["path_to_volumes"],
- dtype=dtype,
- )
-
- U, S, V, coeffs, coeffs_ref = run_svd_with_ref(
- volumes=volumes, ref_volumes=ref_volumes
- )
-
- output_dict = {
- "coeffs": coeffs,
- "coeffs_ref": coeffs_ref,
- "metadata": metadata,
- "config": config,
- }
+from .svd_utils import (
+ compute_distance_matrix,
+ compute_common_embedding,
+ project_to_gt_embedding,
+)
+from ..data._io.svd_io_utils import load_submissions_svd, load_gt_svd
- if config["output_options"]["save_volumes"]:
- output_dict["volumes"] = volumes
- output_dict["mean_volumes"] = mean_volumes
- output_dict["ref_volumes"] = ref_volumes
- output_dict["mean_ref_volume"] = mean_volume
- if config["output_options"]["save_svd_matrices"]:
- output_dict["U"] = U
- output_dict["S"] = S
- output_dict["V"] = V
+def run_svd_with_ref(config: dict):
+ # outputs_path = os.path.dirname(config["output_params"]["output_file"])
- output_file = os.path.join(
- config["output_options"]["output_path"], "svd_results.pt"
- )
- torch.save(output_dict, output_file)
+ submissions_data = load_submissions_svd(config)
+ gt_data = load_gt_svd(config)
- return output_dict
+ dist_mtx_results = compute_distance_matrix(submissions_data, gt_data)
+ common_embedding_results = compute_common_embedding(submissions_data, gt_data)
+ gt_embedding_results = project_to_gt_embedding(submissions_data, gt_data)
+ results = {
+ "distance_matrix_results": dist_mtx_results,
+ "common_embedding_results": common_embedding_results,
+ "gt_embedding_results": gt_embedding_results,
+ }
-def run_svd_pipeline():
- parser = argparse.ArgumentParser(description="Run SVD on volumes")
- parser.add_argument(
- "--config", type=str, default=None, help="Path to the config (yaml) file"
- )
- args = parser.parse_args()
+ if config["output_params"]["save_svd_data"]:
+ results["submissions_data"] = submissions_data
+ results["gt_data"] = gt_data
+
+ torch.save(results, config["output_params"]["output_file"])
+
+ if config["output_params"]["generate_plots"]:
+ raise NotImplementedError(
+ "Plots are currently turned off due to incompatibilities. Your results were saved right before this error triggered."
+ )
+ # outputs_fname_nopath_noext = os.path.basename(
+ # config["output_params"]["output_file"]
+ # )
+ # outputs_fname_nopath_noext = os.path.splitext(outputs_fname_nopath_noext)[0]
+ # path_plots = os.path.join(outputs_path, f"plots_{outputs_fname_nopath_noext}")
+
+ # os.makedirs(path_plots, exist_ok=True)
+
+ # print("Plotting distance matrix")
+ # plot_distance_matrix(
+ # dist_mtx_results["dist_matrix"],
+ # dist_mtx_results["labels"],
+ # title="SVD Distance Matrix",
+ # save_path=os.path.join(path_plots, "svd_distance_matrix.png"),
+ # )
+
+ # print("Plotting common embedding")
+ # plot_common_embedding(
+ # submissions_data,
+ # common_embedding_results,
+ # title="Common Embedding between submissions",
+ # save_path=os.path.join(path_plots, "common_embedding.png"),
+ # )
+
+ # print("Plotting gt embedding")
+ # plot_gt_embedding(
+ # submissions_data,
+ # gt_embedding_results,
+ # title="",
+ # save_path=os.path.join(path_plots, "gt_embedding.png"),
+ # )
+
+ # print("Plotting common eigenvectors")
+ # plot_common_eigenvectors(
+ # common_embedding_results["common_eigenvectors"],
+ # title="Common Eigenvectors between submissions",
+ # save_path=os.path.join(path_plots, "common_eigenvectors.png"),
+ # )
- with open(args.config, "r") as file:
- config = yaml.safe_load(file)
+ return
- validate_config_svd(config)
- return
+def run_svd_noref(config: dict):
+ # outputs_path = os.path.dirname(config["output_params"]["output_file"])
+ submissions_data = load_submissions_svd(config)
+ dist_mtx_results = compute_distance_matrix(submissions_data)
+ common_embedding_results = compute_common_embedding(submissions_data)
-if __name__ == "__main__":
- run_svd_pipeline()
+ results = {
+ "distance_matrix_results": dist_mtx_results,
+ "common_embedding_results": common_embedding_results,
+ }
+
+ if config["output_params"]["save_svd_data"]:
+ results["submissions_data"] = submissions_data
+
+ torch.save(results, config["output_params"]["output_file"])
+
+ if config["output_params"]["generate_plots"]:
+ raise NotImplementedError(
+ "Plots are currently turned off due to incompatibilities. Your results were saved right before this error triggered."
+ )
+ # outputs_fname_nopath_noext = os.path.basename(
+ # config["output_params"]["output_file"]
+ # )
+ # outputs_fname_nopath_noext = os.path.splitext(outputs_fname_nopath_noext)[0]
+ # path_plots = os.path.join(outputs_path, f"plots_{outputs_fname_nopath_noext}")
+ # os.makedirs(path_plots, exist_ok=True)
+
+ # print("Plotting distance matrix")
+
+ # plot_distance_matrix(
+ # dist_mtx_results["dist_matrix"],
+ # dist_mtx_results["labels"],
+ # "SVD Distance Matrix",
+ # save_path=os.path.join(path_plots, "svd_distance_matrix.png"),
+ # )
+
+ # print("Plotting common embedding")
+ # plot_common_embedding(
+ # submissions_data,
+ # common_embedding_results,
+ # "Common Embedding between submissions",
+ # save_path=os.path.join(path_plots, "common_embedding.png"),
+ # )
+
+ # print("Plotting common eigenvectors")
+ # plot_common_eigenvectors(
+ # common_embedding_results["common_eigenvectors"],
+ # title="Common Eigenvectors between submissions",
+ # save_path=os.path.join(path_plots, "common_eigenvectors.png"),
+ # )
+
+ return
diff --git a/src/cryo_challenge/_svd/svd_plots.py b/src/cryo_challenge/_svd/svd_plots.py
new file mode 100644
index 0000000..1642204
--- /dev/null
+++ b/src/cryo_challenge/_svd/svd_plots.py
@@ -0,0 +1,628 @@
+import matplotlib.pyplot as plt
+import seaborn as sns
+import torch
+import numpy as np
+
+# PLOT_SETUP = {
+# "Ground Truth": {"color": "#e41a1c", "marker": "o"},
+# "Cookie Dough": {"color": "#377eb8", "marker": "v"},
+# "Mango": {"color": "#4daf4a", "marker": "^"},
+# "Vanilla": {"color": "#984ea3", "marker": "<"},
+# "Peanut Butter": {"color": "#ff7f00", "marker": ">"},
+# "Neapolitan": {"color": "#ffff33", "marker": "D"},
+# "Chocolate": {"color": "#a65628", "marker": "x"},
+# "Black Raspberry": {"color": "#f781bf", "marker": "*"},
+# "Cherry": {"color": "#999999", "marker": "s"},
+# "Salted Caramel": {"color": "#e41a1c", "marker": "p"},
+# "Chocolate Chip": {"color": "#377eb8", "marker": "P"},
+# "Rocky Road": {"color": "#4daf4a", "marker": "*"},
+# }
+
+
+# MARKERS = ["o", "v", "^", "<", ">", "D", "X", "*", "s", "p", "P", "*", "h", "H"]
+# LABELS = [
+# "Mango",
+# "Cookie Dough",
+# "Vanilla",
+# "Peanut Butter",
+# "Neapolitan",
+# "Chocolate",
+# "Black Raspberry",
+# "Cherry",
+# "Salted Caramel",
+# "Chocolate Chip",
+# "Rocky Road",
+# "Pina Colada",
+# "Ground Truth",
+# "Mint Chocolate Chip",
+# "Bubble Gum",
+# ]
+
+COLORS = {
+ "1": "#648fff",
+ "2": "#785ef0",
+ "3": "#dc267f",
+ "4": "#fe6100",
+ "5": "#ffb000",
+}
+
+PLOT_SETUP = {
+ "Salted Caramel": {"category": "1", "marker": "o"},
+ "Neapolitan": {"category": "1", "marker": "v"},
+ "Peanut Butter": {"category": "1", "marker": "^"},
+ "Coffee": {"category": "1", "marker": "<"},
+ "Cherry": {"category": "2", "marker": "o"},
+ "Pina Colada": {"category": "2", "marker": "v"},
+ "Cookie Dough": {"category": "2", "marker": "^"},
+ "Chocolate Chip": {"category": "2", "marker": "<"},
+ "Chocolate": {"category": "2", "marker": ">"},
+ "Vanilla": {"category": "3", "marker": "o"},
+ "Mango": {"category": "3", "marker": "v"},
+ "Rocky Road": {"category": "4", "marker": "o"},
+ "Black Raspberry": {"category": "4", "marker": "v"},
+ "Ground Truth": {"category": "5", "marker": "o"},
+ "Bubble Gum": {"category": "5", "marker": "v"},
+ "Mint Chocolate Chip": {"category": "5", "marker": "^"},
+}
+
+for key in list(PLOT_SETUP.keys()):
+ PLOT_SETUP[key]["color"] = COLORS[PLOT_SETUP[key]["category"]]
+
+
+def compare_strings(fixed_string, other_string):
+ return other_string.startswith(fixed_string)
+
+
+def sort_labels_category(labels, plot_setup):
+ labels_sorted = []
+ for i in range(5): # there are 5 categories
+ for label in labels:
+ if plot_setup[label]["category"] == str(i + 1):
+ labels_sorted.append(label)
+
+ return labels_sorted
+
+
+def plot_distance_matrix(dist_matrix, labels, title="", save_path=None):
+ fig, ax = plt.subplots()
+ cax = ax.matshow(dist_matrix, cmap="viridis")
+ fig.colorbar(cax)
+ ax.set_xticks(np.arange(len(labels)), labels, rotation=90)
+ ax.set_yticks(np.arange(len(labels)), labels)
+
+ ax.set_title(title)
+ if save_path is not None:
+ plt.savefig(save_path, bbox_inches="tight", pad_inches=0.1)
+
+ return
+
+
+# def plot_common_embedding(
+# submissions_data, embedding_results, title="", pcs=(0, 1), save_path=None
+# ):
+# all_embeddings = []
+# labels = []
+# pc1, pc2 = pcs
+
+# for label, embedding in embedding_results["common_embedding"].items():
+# all_embeddings.append(embedding)
+# labels.append(label)
+
+# plot_setup = {}
+# for i, label in enumerate(labels):
+# for possible_label in PLOT_SETUP.keys():
+# # print(label, possible_label)
+# if compare_strings(possible_label, label):
+# plot_setup[label] = PLOT_SETUP[possible_label]
+
+# for label in labels:
+# if label not in plot_setup.keys():
+# raise ValueError(f"Label {label} not found in PLOT_SETUP")
+
+# if "gt_embedding" in embedding_results:
+# plot_setup["Ground Truth"] = PLOT_SETUP["Ground Truth"]
+
+# labels = sort_labels_category(labels, plot_setup)
+# all_embeddings = torch.cat(all_embeddings, dim=0)
+
+# weights = []
+# for i in range(len(labels)):
+# weights += submissions_data[labels[i]]["populations"].numpy().tolist()
+
+# weights = torch.tensor(weights)
+# weights = weights / weights.sum()
+
+# if "gt_embedding" in embedding_results:
+# n_rows = np.sqrt(len(labels) + 1)
+# n_rows = np.ceil(n_rows).astype(int)
+# n_cols = np.ceil((len(labels) + 1) / n_rows).astype(int)
+
+# else:
+# n_rows = np.sqrt(len(labels))
+# n_rows = np.ceil(n_rows).astype(int)
+# n_cols = np.ceil(len(labels) / n_rows).astype(int)
+
+# fig, ax = plt.subplots(
+# n_rows, n_cols, figsize=(n_cols * 5, n_rows * 3), sharex=True, sharey=True
+# )
+# if n_rows == 1 and n_cols == 1:
+# ax = np.array([ax])
+
+# # for i in range(len(labels)):
+# # sns.kdeplot(
+# # x=all_embeddings[:, pc1],
+# # y=all_embeddings[:, pc2],
+# # cmap="gray",
+# # fill=False,
+# # cbar=False,
+# # ax=ax.flatten()[i],
+# # weights=weights,
+# # alpha=0.8,
+# # zorder=1,
+# # )
+
+# # if "gt_embedding" in embedding_results:
+# # sns.kdeplot(
+# # x=all_embeddings[:, pc1],
+# # y=all_embeddings[:, pc2],
+# # cmap="gray",
+# # fill=False,
+# # cbar=False,
+# # ax=ax.flatten()[len(labels)],
+# # weights=weights,
+# # # alpha=0.5,
+# # zorder=1,
+# # )
+
+# for i in range(len(labels)):
+# pops = submissions_data[labels[i]]["populations"].numpy()
+# pops = pops / pops.sum()
+
+# # put a value of i in the top left corner of each plot
+# # ax.flatten()[i].text(
+# # 0.05,
+# # 0.95,
+# # str(i + 1),
+# # fontsize=12,
+# # transform=ax.flatten()[i].transAxes,
+# # verticalalignment="top",
+# # bbox=dict(facecolor="white", alpha=0.5),
+# # )
+# ax.flatten()[i].scatter(
+# x=embedding_results["common_embedding"][labels[i]][:, pc1],
+# y=embedding_results["common_embedding"][labels[i]][:, pc2],
+# color=plot_setup[labels[i]]["color"],
+# s=pops / pops.max() * 200,
+# marker=plot_setup[labels[i]]["marker"],
+# linewidth=0.3,
+# edgecolor="black",
+# label=f"{labels[i]} - Group {plot_setup[labels[i]]['category']}.",
+# #label=labels[i],
+# zorder=2,
+# )
+
+# ax.flatten()[i].set_xticks([])
+# ax.flatten()[i].set_yticks([])
+
+# if i >= n_rows:
+# ax.flatten()[i].set_xlabel(f"Z{pc1 + 1}", fontsize=12)
+# if i % n_cols == 0:
+# ax.flatten()[i].set_ylabel(f"Z{pc2 + 1}", fontsize=12)
+
+# ax.flatten()[i].legend(loc="upper left", fontsize=12)
+
+# i_max = i
+
+# if "gt_embedding" in embedding_results:
+# i_max += 1
+# # ax.flatten()[i_max].text(
+# # 0.05,
+# # 0.95,
+# # str(i_max + 1),
+# # fontsize=12,
+# # transform=ax.flatten()[i_max].transAxes,
+# # verticalalignment="top",
+# # bbox=dict(facecolor="white", alpha=0.5),
+# # )
+# ax.flatten()[i_max].scatter(
+# x=embedding_results["gt_embedding"][:, pc1],
+# y=embedding_results["gt_embedding"][:, pc2],
+# color=plot_setup["Ground Truth"]["color"],
+# s=100,
+# marker=plot_setup["Ground Truth"]["marker"],
+# linewidth=0.3,
+# edgecolor="black",
+# #label=f"{i_max + 1}. Ground Truth",
+# label="Ground Truth - Group 5",
+# zorder=2,
+# )
+
+# ax.flatten()[i_max].set_xlabel(f"Z{pc1 + 1}", fontsize=12)
+# ax.flatten()[i_max].set_ylabel(f"Z{pc2 + 1}", fontsize=12)
+# ax.flatten()[i_max].set_xticks([])
+# ax.flatten()[i_max].set_yticks([])
+# ax.flatten()[i_max].legend(loc="upper left", fontsize=12)
+
+# if i_max < n_cols * n_rows:
+# for j in range(i_max + 1, n_cols * n_rows):
+# ax.flatten()[j].axis("off")
+
+# # adjust horizontal space
+# plt.subplots_adjust(wspace=0.0, hspace=0.0)
+
+# fig.suptitle(title, fontsize=16)
+# # lines_labels = [ax.get_legend_handles_labels() for ax in fig.axes]
+# # lines, labels = [sum(lol, []) for lol in zip(*lines_labels)]
+# # fig.legend(
+# # lines, labels, loc="center right", fontsize=12, bbox_to_anchor=(1.071, 0.5)
+# # )
+
+# # Generate a box of test on the left side with "category = definition"
+# group_definitions = {
+# "Group 1": "Physics-informed",
+# "Group 2": "Neural network (no physics)",
+# "Group 3": "Linear Method",
+# "Group 4": "Non-linear Method",
+# }
+
+# if "gt_embedding" in embedding_results:
+# group_definitions["Group 5"] = "Based on GT"
+
+# text = "\n".join([f"{group}: {definition}" for group, definition in group_definitions.items()])
+# fig.text(
+# 0.915, 0.825, text,
+# va='center', ha='left', fontsize=12,
+# bbox=dict(boxstyle="round,pad=0.5", facecolor='lightgrey', alpha=0.3, edgecolor='black'),
+# multialignment='left', linespacing=1.8 # Increased vertical spacing
+# )
+
+# if save_path is not None:
+# plt.savefig(save_path, bbox_inches="tight", pad_inches=0.1)
+
+# return
+
+
+def plot_common_embedding(
+ submissions_data, embedding_results, title="", pcs=(0, 1), save_path=None
+):
+ all_embeddings = []
+ labels = []
+ pc1, pc2 = pcs
+
+ for label, embedding in embedding_results["common_embedding"].items():
+ all_embeddings.append(embedding)
+ labels.append(label)
+
+ if "gt_embedding" in embedding_results:
+ labels.append("Ground Truth")
+
+ plot_setup = {}
+ for i, label in enumerate(labels):
+ for possible_label in PLOT_SETUP.keys():
+ # print(label, possible_label)
+ if compare_strings(possible_label, label):
+ plot_setup[label] = PLOT_SETUP[possible_label]
+
+ for label in labels:
+ if label not in plot_setup.keys():
+ raise ValueError(f"Label {label} not found in PLOT_SETUP")
+
+ labels = sort_labels_category(labels, plot_setup)
+ all_embeddings = torch.cat(all_embeddings, dim=0)
+
+ weights = []
+ for i in range(len(labels)):
+ if labels[i] != "Ground Truth":
+ weights += submissions_data[labels[i]]["populations"].numpy().tolist()
+
+ weights = torch.tensor(weights)
+ weights = weights / weights.sum()
+
+ n_cols = 3
+
+ if n_cols > len(labels):
+ n_cols = len(labels)
+ n_rows = 1
+ else:
+ n_rows = len(labels) // n_cols + 1
+
+ fig, ax = plt.subplots(
+ n_rows, n_cols, figsize=(n_cols * 5, n_rows * 3), sharex=True, sharey=True
+ )
+ if n_rows == 1 and n_cols == 1:
+ ax = np.array([ax])
+
+ for i in range(len(labels)):
+ sns.kdeplot(
+ x=all_embeddings[:, pc1],
+ y=all_embeddings[:, pc2],
+ cmap="gray",
+ fill=False,
+ cbar=False,
+ ax=ax.flatten()[i],
+ weights=weights,
+ alpha=0.8,
+ zorder=1,
+ )
+
+ for i in range(len(labels)):
+ label = labels[i]
+ if label != "Ground Truth":
+ pops = submissions_data[label]["populations"].numpy()
+ pops = pops / pops.sum()
+
+ ax.flatten()[i].scatter(
+ x=embedding_results["common_embedding"][label][:, pc1],
+ y=embedding_results["common_embedding"][label][:, pc2],
+ color=plot_setup[label]["color"],
+ s=pops / pops.max() * 200,
+ marker=plot_setup[label]["marker"],
+ linewidth=0.3,
+ edgecolor="black",
+ label=f"{label} - Group {plot_setup[label]['category']}.",
+ zorder=2,
+ )
+ else:
+ ax.flatten()[i].scatter(
+ x=embedding_results["gt_embedding"][:, pc1],
+ y=embedding_results["gt_embedding"][:, pc2],
+ color=plot_setup["Ground Truth"]["color"],
+ s=100,
+ marker=plot_setup["Ground Truth"]["marker"],
+ linewidth=0.3,
+ edgecolor="black",
+ # label=f"{i_max + 1}. Ground Truth",
+ label="Ground Truth - Group 5",
+ zorder=2,
+ )
+
+ ax.flatten()[i].set_xticks([])
+ ax.flatten()[i].set_yticks([])
+ ax.flatten()[i].legend(loc="upper left", fontsize=12)
+ if i >= n_rows:
+ ax.flatten()[i].set_xlabel(f"Z{pc1 + 1}", fontsize=12)
+ if i % n_cols == 0:
+ ax.flatten()[i].set_ylabel(f"Z{pc2 + 1}", fontsize=12)
+
+ for i in range(len(labels), n_cols * n_rows):
+ ax.flatten()[i].axis("off")
+
+ # adjust horizontal space
+ plt.subplots_adjust(wspace=0.0, hspace=0.0)
+
+ fig.suptitle(title, fontsize=16)
+ # lines_labels = [ax.get_legend_handles_labels() for ax in fig.axes]
+ # lines, labels = [sum(lol, []) for lol in zip(*lines_labels)]
+ # fig.legend(
+ # lines, labels, loc="center right", fontsize=12, bbox_to_anchor=(1.071, 0.5)
+ # )
+
+ # Generate a box of test on the left side with "category = definition"
+ group_definitions = {
+ "Group 1": "Physics-informed",
+ "Group 2": "Neural network (no physics)",
+ "Group 3": "Linear Method",
+ "Group 4": "Non-linear Method",
+ }
+
+ if "gt_embedding" in embedding_results:
+ group_definitions["Group 5"] = "Based on GT"
+
+ text = "\n".join(
+ [f"{group}: {definition}" for group, definition in group_definitions.items()]
+ )
+ fig.text(
+ 0.915,
+ 0.825,
+ text,
+ va="center",
+ ha="left",
+ fontsize=12,
+ bbox=dict(
+ boxstyle="round,pad=0.5",
+ facecolor="lightgrey",
+ alpha=0.3,
+ edgecolor="black",
+ ),
+ multialignment="left",
+ linespacing=1.8, # Increased vertical spacing
+ )
+
+ if save_path is not None:
+ plt.savefig(save_path, bbox_inches="tight", pad_inches=0.1)
+
+ return
+
+
+def plot_gt_embedding(submissions_data, gt_embedding_results, title="", save_path=None):
+ def gauss_pdf(x, mu, var):
+ return 1 / np.sqrt(2 * np.pi * var) * np.exp(-0.5 * (x - mu) ** 2 / var)
+
+ def compute_gt_dist(z):
+ gauss1 = gauss_pdf(z, 150, 750)
+ gauss2 = 0.5 * gauss_pdf(z, 0, 500)
+ gauss3 = gauss_pdf(z, -150, 750)
+ return gauss1 + gauss2 + gauss3
+
+ labels = list(submissions_data.keys())
+
+ plot_setup = {}
+ for i, label in enumerate(submissions_data.keys()):
+ for possible_label in PLOT_SETUP.keys():
+ if compare_strings(possible_label, label):
+ plot_setup[label] = PLOT_SETUP[possible_label]
+
+ for label in submissions_data.keys():
+ if label not in plot_setup.keys():
+ raise ValueError(f"Label {label} not found in PLOT_SETUP")
+
+ labels = sort_labels_category(labels, plot_setup)
+
+ # low_gt = -231.62100638454024
+ # high_gt = 243.32448171011487
+ # Z = np.linspace(low_gt, high_gt, gt_embedding_results["gt_embedding"].shape[0])
+ # x_axis = np.linspace(
+ # torch.min(gt_embedding_results["gt_embedding"][:, 0]),
+ # torch.max(gt_embedding_results["gt_embedding"][:, 0]),
+ # gt_embedding_results["gt_embedding"].shape[0],
+ # )
+
+ # gt_dist = compute_gt_dist(Z)
+ # gt_dist /= np.max(gt_dist)
+
+ # frq, edges = np.histogram(gt_embedding_results["gt_embedding"][:, 0], bins=20)
+ label_ref = "Mint Chocolate Chip 1"
+ populations_ref = submissions_data[label_ref]["populations"]
+ embedding_ref = gt_embedding_results["submission_embedding"][label_ref]
+
+ labels.pop(labels.index(label_ref))
+
+ n_cols = 3
+
+ if n_cols > len(labels):
+ n_cols = len(labels)
+ n_rows = 1
+ else:
+ n_rows = len(labels) // n_cols + 1
+ fig, ax = plt.subplots(
+ n_rows, n_cols, figsize=(n_cols * 4, n_rows * 3), sharex=True, sharey=True
+ )
+ if n_rows == 1 and n_cols == 1:
+ ax = np.array([ax])
+
+ for i in range(len(labels)):
+ label = labels[i]
+ embedding = gt_embedding_results["submission_embedding"][label]
+ # ax.flatten()[i].text(
+ # 0.05,
+ # 0.95,
+ # str(i + 1),
+ # fontsize=12,
+ # transform=ax.flatten()[i].transAxes,
+ # verticalalignment="top",
+ # bbox=dict(facecolor="white", alpha=0.5),
+ # )
+
+ # ax.flatten()[i].bar(
+ # edges[:-1],
+ # frq / frq.max(),
+ # width=np.diff(edges),
+ # # label="Ground Truth",
+ # alpha=0.8,
+ # color="#a1c9f4",
+ # )
+
+ # ax.flatten()[i].plot(x_axis, gt_dist) # , label="True Distribution")
+
+ populations = submissions_data[label]["populations"]
+ ax.flatten()[i].scatter(
+ x=embedding[:, 0],
+ y=populations / populations.max(),
+ color=plot_setup[label]["color"],
+ marker=plot_setup[label]["marker"],
+ s=100,
+ linewidth=0.3,
+ edgecolor="black",
+ label=f"{i+1}. {label}",
+ )
+
+ ax.flatten()[i].plot(
+ embedding_ref[:, 0],
+ populations_ref / populations_ref.max(),
+ # color=plot_setup[label]["color"],
+ color="black",
+ marker="o",
+ # marker=plot_setup[label]["marker"],
+ # s=100,
+ linewidth=0.3,
+ # edgecolor="black",
+ # label=f"{i+1}. {label}",
+ alpha=0.7,
+ )
+
+ # set x label only for the last row
+ if i >= n_rows:
+ ax.flatten()[i].set_xlabel("SVD 1", fontsize=12)
+ # set y label only for the first column
+ if i % n_cols == 0:
+ ax.flatten()[i].set_ylabel("Scaled probability", fontsize=12)
+
+ ax.flatten()[i].legend(loc="upper left", fontsize=12)
+ ax.flatten()[i].set_ylim(0.0, 1.25)
+ # ax.flatten()[i].set_xlim(x_axis[0] * 1.3, x_axis[-1] * 1.3)
+ # set ticks to be maximum 5 ticks
+ ax.flatten()[i].set_yticks(np.arange(0.25, 1.25, 0.25))
+ ax.flatten()[i].set_xticks([])
+
+ plt.subplots_adjust(wspace=0.0, hspace=0.0)
+
+ if i < n_cols * n_rows:
+ for j in range(i + 1, n_cols * n_rows):
+ ax.flatten()[j].axis("off")
+
+ fig.suptitle(title, fontsize=16)
+ # lines_labels = [ax.get_legend_handles_labels() for ax in fig.axes]
+ # lines, labels = [sum(lol, []) for lol in zip(*lines_labels)]
+ # fig.legend(
+ # lines, labels, loc="center right", fontsize=12, bbox_to_anchor=(1.071, 0.5)
+ # )
+
+ if save_path is not None:
+ plt.savefig(save_path, bbox_inches="tight", pad_inches=0.1)
+
+ return
+
+
+def plot_common_eigenvectors(
+ common_eigenvectors, n_eig_to_plot=None, title="", save_path=None
+):
+ n_eig_to_plot = min(10, len(common_eigenvectors))
+ n_cols = 5
+ n_rows = int(np.ceil(n_eig_to_plot / n_cols))
+
+ fig, ax = plt.subplots(n_rows, n_cols, figsize=(n_cols * 4, n_rows * 5))
+
+ box_size = int(round((common_eigenvectors[0].shape[-1]) ** (1 / 3)))
+ for i in range(n_eig_to_plot):
+ eigvol = common_eigenvectors[i].reshape(box_size, box_size, box_size)
+
+ mask_small = torch.where(torch.abs(eigvol) < 1e-3)
+ mask_pos = torch.where(eigvol > 0)
+ mask_neg = torch.where(eigvol < 0)
+
+ eigvol_pos = torch.zeros_like(eigvol)
+ eigvol_neg = torch.zeros_like(eigvol)
+
+ eigvol_pos[mask_pos] = 1.0
+ eigvol_neg[mask_neg] = -1.0
+
+ eigvol_for_img = eigvol_neg + eigvol_pos
+ eigvol_for_img[mask_small] = 0.0
+
+ ax.flatten()[i].imshow(
+ eigvol_for_img.sum(0), cmap="coolwarm", label=f"Eigenvector {i}"
+ )
+ ax.flatten()[i].set_title(f"Eigenvector {i}")
+ ax.flatten()[i].axis("off")
+ i_max = i
+
+ if i_max < n_cols * n_rows:
+ for j in range(i_max + 1, n_cols * n_rows):
+ ax.flatten()[j].axis("off")
+
+ plt.subplots_adjust(wspace=0.0)
+
+ # add a colorbar for the whole figure
+ fig.colorbar(
+ ax.flatten()[i].imshow(eigvol_for_img.sum(1), cmap="coolwarm"),
+ ax=ax,
+ orientation="horizontal",
+ label="Eigenvector value (neg or pos)",
+ )
+
+ fig.suptitle(title, fontsize=16)
+
+ if save_path is not None:
+ plt.savefig(save_path, bbox_inches="tight", pad_inches=0.1)
+
+ return
diff --git a/src/cryo_challenge/_svd/svd_utils.py b/src/cryo_challenge/_svd/svd_utils.py
index 8107506..79c9a39 100644
--- a/src/cryo_challenge/_svd/svd_utils.py
+++ b/src/cryo_challenge/_svd/svd_utils.py
@@ -1,75 +1,153 @@
import torch
-from typing import Tuple
-
-
-def get_vols_svd(
- volumes: torch.tensor,
-) -> Tuple[torch.tensor, torch.tensor, torch.tensor]:
- """
- Compute the singular value decomposition of the input volumes. The volumes are flattened so that each volume is a row in the input matrix.
-
- Parameters
- ----------
- volumes: torch.tensor
- Tensor of shape (n_volumes, n_x, n_y, n_z) containing the volumes to be decomposed.
-
- Returns
- -------
- U: torch.tensor
- Left singular vectors of the input volumes.
- S: torch.tensor
- Singular values of the input volumes.
- V: torch.tensor
- Right singular vectors of the input volumes.
-
- Examples
- --------
- >>> volumes = torch.randn(10, 32, 32, 32)
- >>> U, S, V = get_vols_svd(volumes)
- """ # noqa: E501
- assert volumes.ndim == 4, "Input volumes must have shape (n_volumes, n_x, n_y, n_z)"
- assert volumes.shape[0] > 0, "Input volumes must have at least one volume"
-
- U, S, V = torch.linalg.svd(
- volumes.reshape(volumes.shape[0], -1), full_matrices=False
+import numpy as np
+
+
+### Compare subspaces for each submission ###
+
+
+def captured_variance(V, U, S):
+ US = U @ torch.diag(S)
+ return torch.norm(torch.adjoint(V) @ US) / torch.norm(torch.adjoint(U) @ US)
+
+
+# V US
+
+
+def svd_metric(V1, V2, S1, S2):
+ return 0.5 * (captured_variance(V1, V2, S2) + captured_variance(V2, V1, S1))
+
+
+def sort_matrix_using_gt(dist_matrix: torch.Tensor, labels: np.ndarray):
+ sort_idx = torch.argsort(dist_matrix[:, -1])
+ dist_matrix = dist_matrix[:, sort_idx][sort_idx]
+ labels = labels[sort_idx.numpy()]
+
+ dist_matrix = torch.flip(dist_matrix, dims=(0,))
+ dist_matrix = torch.flip(dist_matrix, dims=(1,))
+ labels = np.flip(labels)
+
+ return dist_matrix, labels
+
+
+def sort_matrix(dist_matrix, labels):
+ dist_matrix = dist_matrix.clone()
+ labels = labels.copy()
+
+ # Sort by sum of rows
+ row_sum = torch.sum(dist_matrix, dim=0)
+ sort_idx = torch.argsort(row_sum, descending=True)
+ dist_matrix = dist_matrix[:, sort_idx][sort_idx]
+ labels = labels[sort_idx.numpy()]
+
+ # Sort the first row
+ sort_idx = torch.argsort(dist_matrix[:, 0], descending=True)
+ dist_matrix = dist_matrix[:, sort_idx][sort_idx]
+ labels = labels[sort_idx.numpy()]
+
+ return dist_matrix, labels
+
+
+def compute_distance_matrix(submissions_data, gt_data=None):
+ n_subs = len(list(submissions_data.keys()))
+ labels = list(submissions_data.keys())
+ dtype = submissions_data[labels[0]]["eigenvectors"].dtype
+
+ if gt_data is not None:
+ dist_matrix = torch.ones((n_subs + 1, n_subs + 1), dtype=dtype)
+ else:
+ dist_matrix = torch.ones((n_subs, n_subs), dtype=dtype)
+
+ for i, label1 in enumerate(labels):
+ for j, label2 in enumerate(labels[i:]):
+ dist_matrix[i, j + i] = svd_metric(
+ submissions_data[label1]["eigenvectors"],
+ submissions_data[label2]["eigenvectors"],
+ submissions_data[label1]["singular_values"],
+ submissions_data[label2]["singular_values"],
+ )
+ dist_matrix[j + i, i] = dist_matrix[i, j + i]
+
+ if gt_data is not None:
+ for i, label in enumerate(labels):
+ dist_matrix[i, n_subs] = svd_metric(
+ submissions_data[label]["eigenvectors"],
+ gt_data["eigenvectors"],
+ submissions_data[label]["singular_values"],
+ gt_data["singular_values"],
+ )
+ dist_matrix[n_subs, i] = dist_matrix[i, n_subs]
+
+ labels.append("Ground Truth")
+ labels = np.array(labels)
+
+ dist_matrix, labels = sort_matrix_using_gt(dist_matrix, labels)
+
+ else:
+ labels = np.array(labels)
+ dist_matrix, labels = sort_matrix(dist_matrix, labels)
+
+ results = {"dist_matrix": dist_matrix, "labels": labels}
+ return results
+
+
+### Compute common embedding ###
+
+
+def compute_common_embedding(submissions_data, gt_data=None):
+ labels = list(submissions_data.keys())
+ n_subs = len(labels)
+ shape_per_sub = submissions_data[labels[0]]["eigenvectors"].T.shape
+ dtype = submissions_data[labels[0]]["eigenvectors"].dtype
+ eigenvectors = torch.zeros(
+ (n_subs * shape_per_sub[0], shape_per_sub[1]), dtype=dtype
)
- return U, S, V
-
-
-def project_vols_to_svd(
- volumes: torch.tensor, V_reference: torch.tensor
-) -> torch.tensor:
- """
- Project the input volumes onto the right singular vectors.
-
- Parameters
- ----------
- volumes: torch.tensor
- Tensor of shape (n_volumes, n_x, n_y, n_z) containing the volumes to be projected.
- V_reference: torch.tensor
- Right singular vectors of the reference volumes.
-
- Returns
- -------
- coeffs: torch.tensor
- Coefficients of the input volumes projected onto the right singular vectors.
-
- Examples
- --------
- >>> volumes1 = torch.randn(10, 32, 32, 32)
- >>> volumes2 = torch.randn(10, 32, 32, 32)
- >>> U, S, V = get_vols_svd(volumes1)
- >>> coeffs = project_vols_to_svd(volumes2, V)
- """ # noqa: E501
- assert volumes.ndim == 4, "Input volumes must have shape (n_volumes, n_x, n_y, n_z)"
- assert volumes.shape[0] > 0, "Input volumes must have at least one volume"
- assert (
- V_reference.ndim == 2
- ), "Right singular vectors must have shape (n_features, n_components)"
- assert (
- volumes.shape[1] * volumes.shape[2] * volumes.shape[3] == V_reference.shape[1]
- ), "Number of features in volumes must match number of features in right singular vectors" # noqa: E501
-
- coeffs = volumes.reshape(volumes.shape[0], -1) @ V_reference.T
-
- return coeffs
+
+ for i, label in enumerate(labels):
+ eigenvectors[i * shape_per_sub[0] : (i + 1) * shape_per_sub[0], :] = (
+ submissions_data[label]["eigenvectors"].T
+ ) * submissions_data[label]["singular_values"][:, None]
+
+ U, S, V = torch.linalg.svd(eigenvectors, full_matrices=False)
+
+ Z_common = (U @ torch.diag(S)).reshape(n_subs, shape_per_sub[0], -1)
+ embeddings = {}
+
+ for i, label in enumerate(labels):
+ Z_i = submissions_data[label]["u_matrices"] # @ torch.diag(
+ # submissions_data[label]["singular_values"]
+ # )
+ Z_i_common = torch.einsum("ij, jk -> ik", Z_i, Z_common[i])
+ embeddings[labels[i]] = Z_i_common
+
+ results = {
+ "common_embedding": embeddings,
+ "singular_values": S,
+ "common_eigenvectors": V,
+ }
+
+ if gt_data is not None:
+ gt_proj = gt_data["u_matrices"] @ torch.diag(gt_data["singular_values"])
+ gt_proj = gt_proj @ (gt_data["eigenvectors"].T @ V.T)
+
+ results["gt_embedding"] = gt_proj
+
+ return results
+
+
+### Project to GT embedding ###
+def project_to_gt_embedding(submissions_data, gt_data):
+ embedding_in_gt = {}
+
+ for label, submission in submissions_data.items():
+ projection = (
+ submission["u_matrices"]
+ @ torch.diag(submission["singular_values"])
+ @ (submission["eigenvectors"].T @ gt_data["eigenvectors"])
+ )
+ embedding_in_gt[label] = projection
+
+ gt_coords = gt_data["u_matrices"] @ torch.diag(gt_data["singular_values"])
+
+ results = {"submission_embedding": embedding_in_gt, "gt_embedding": gt_coords}
+
+ return results
diff --git a/src/cryo_challenge/data/_io/svd_io_utils.py b/src/cryo_challenge/data/_io/svd_io_utils.py
index 2a4d954..610e3ef 100644
--- a/src/cryo_challenge/data/_io/svd_io_utils.py
+++ b/src/cryo_challenge/data/_io/svd_io_utils.py
@@ -1,130 +1,203 @@
import torch
+import numpy as np
from typing import Tuple
+import os
+from natsort import natsorted
+import mrcfile
-from ..._preprocessing.fourier_utils import downsample_volume
+from ..._preprocessing.fourier_utils import downsample_volume, downsample_submission
+from ..._preprocessing.bfactor_normalize import bfactor_normalize_volumes
-def load_volumes(
- box_size_ds: float,
- submission_list: list,
- path_to_submissions: str,
- dtype=torch.float32,
+def load_submissions_svd(
+ config: dict,
) -> Tuple[torch.tensor, dict]:
"""
Load the volumes and populations from the submissions specified in submission_list. Volumes are first downsampled, then normalized so that they sum to 1, and finally the mean volume is removed from each submission.
Parameters
----------
- box_size_ds: float
- Size of the downsampled box.
- submission_list: list
- List of submission indices to load.
- path_to_submissions: str
- Path to the directory containing the submissions.
- dtype: torch.dtype
- Data type of the volumes.
-
+ config: dict
+ Dictionary containing the configuration parameters.
Returns
-------
- volumes: torch.tensor
- Tensor of shape (n_volumes, n_x, n_y, n_z) containing the volumes.
- populations: dict
- Dictionary containing the populations of each submission.
- vols_per_submission: dict
- Dictionary containing the number of volumes per submission.
-
- Examples
- --------
- >>> box_size_ds = 64
- >>> submission_list = [0, 1, 2, 3, 4] # submission 5 is ignored
- >>> path_to_submissions = "/path/to/submissions" # under this folder submissions should be name submission_i.pt
- >>> volumes, populations = load_volumes(box_size_ds, submission_list, path_to_submissions)
+ submissions_data: dict
+ Dictionary containing the populations, left singular vectors, singular values, and right singular vectors of each submission.
""" # noqa: E501
- metadata = {}
- volumes = torch.empty((0, box_size_ds, box_size_ds, box_size_ds), dtype=dtype)
- mean_volumes = torch.empty(
- (len(submission_list), box_size_ds, box_size_ds, box_size_ds), dtype=dtype
- )
- counter = 0
+ path_to_submissions = config["path_to_submissions"]
+ excluded_submissions = config["excluded_submissions"]
+
+ submissions_data = {}
+
+ submission_files = []
+ for file in os.listdir(path_to_submissions):
+ if file.endswith(".pt") and "submission" in file:
+ if file in excluded_submissions:
+ continue
+ submission_files.append(file)
+ submission_files = natsorted(submission_files)
- for i, idx in enumerate(submission_list):
- submission = torch.load(f"{path_to_submissions}/submission_{idx}.pt")
- vols = submission["volumes"]
- pops = submission["populations"]
+ vols = torch.load(os.path.join(path_to_submissions, submission_files[0]))["volumes"]
+ box_size = vols.shape[-1]
- vols_tmp = torch.empty(
- (vols.shape[0], box_size_ds, box_size_ds, box_size_ds), dtype=dtype
+ if config["normalize_params"]["mask_path"] is not None:
+ mask = torch.tensor(
+ mrcfile.open(config["normalize_params"]["mask_path"], mode="r").data.copy()
)
- counter_start = counter
- for j in range(vols.shape[0]):
- vol_ds = downsample_volume(vols[j], box_size_ds)
- vols_tmp[j] = vol_ds / vol_ds.sum()
- counter += 1
-
- metadata[submission["id"]] = {
- "n_vols": vols.shape[0],
- "populations": pops / pops.sum(),
- "indices": (counter_start, counter),
+ try:
+ mask = mask.reshape(1, box_size, box_size, box_size)
+ except RuntimeError:
+ raise ValueError(
+ "Mask shape does not match the box size of the volumes in the submissions."
+ )
+
+ for file in submission_files:
+ sub_path = os.path.join(path_to_submissions, file)
+ submission = torch.load(sub_path)
+
+ label = submission["id"]
+ populations = submission["populations"]
+
+ if not isinstance(populations, torch.Tensor):
+ populations = torch.tensor(populations)
+
+ volumes = submission["volumes"]
+ if config["normalize_params"]["mask_path"] is not None:
+ volumes = volumes * mask
+
+ if config["normalize_params"]["bfactor"] is not None:
+ voxel_size = config["voxel_size"]
+ volumes = bfactor_normalize_volumes(
+ volumes,
+ config["normalize_params"]["bfactor"],
+ voxel_size,
+ in_place=True,
+ )
+
+ if config["normalize_params"]["box_size_ds"] is not None:
+ volumes = downsample_submission(
+ volumes, box_size_ds=config["normalize_params"]["box_size_ds"]
+ )
+ box_size = config["normalize_params"]["box_size_ds"]
+ else:
+ box_size = volumes.shape[-1]
+
+ volumes = volumes.reshape(-1, box_size * box_size * box_size)
+
+ if config["dtype"] == "float32":
+ volumes = volumes.float()
+ elif config["dtype"] == "float64":
+ volumes = volumes.double()
+
+ volumes /= torch.norm(volumes, dim=1, keepdim=True)
+
+ if config["svd_max_rank"] is None:
+ u_matrices, singular_values, eigenvectors = torch.linalg.svd(
+ volumes - volumes.mean(0, keepdim=True), full_matrices=False
+ )
+ eigenvectors = eigenvectors.T
+
+ else:
+ u_matrices, singular_values, eigenvectors = torch.svd_lowrank(
+ volumes - volumes.mean(0, keepdim=True), q=config["svd_max_rank"]
+ )
+
+ submissions_data[label] = {
+ "populations": populations / populations.sum(),
+ "u_matrices": u_matrices.clone(),
+ "singular_values": singular_values.clone(),
+ "eigenvectors": eigenvectors.clone(),
}
- mean_volumes[i] = vols_tmp.mean(dim=0)
- vols_tmp = vols_tmp - mean_volumes[i][None, :, :, :]
- volumes = torch.cat((volumes, vols_tmp), dim=0)
+ return submissions_data
- return volumes, mean_volumes, metadata
-
-def load_ref_vols(box_size_ds: int, path_to_volumes: str, dtype=torch.float32):
+def load_gt_svd(config: dict) -> dict:
"""
- Load the reference volumes, downsample them, normalize them, and remove the mean volume.
+ Load the ground truth volumes, downsample them, normalize them, and remove the mean volume. Then compute the SVD of the volumes.
Parameters
----------
- box_size_ds: int
- Size of the downsampled box.
- path_to_volumes: str
- Path to the file containing the reference volumes. Must be in PyTorch format.
- dtype: torch.dtype
- Data type of the volumes.
+ config: dict
+ Dictionary containing the configuration parameters.
Returns
-------
- volumes_ds: torch.tensor
- Tensor of shape (n_volumes, n_x, n_y, n_z) containing the downsampled, normalized, and mean-removed reference volumes.
-
- Examples
- --------
- >>> box_size_ds = 64
- >>> path_to_volumes = "/path/to/volumes.pt"
- >>> volumes_ds = load_ref_vols(box_size_ds, path_to_volumes)
- """ # noqa: E501
- try:
- volumes = torch.load(path_to_volumes)
- except (FileNotFoundError, EOFError):
- raise ValueError("Volumes not found or not in PyTorch format.")
-
- # Reshape volumes to correct size
- if volumes.dim() == 2:
- box_size = int(round((float(volumes.shape[-1]) ** (1.0 / 3.0))))
- volumes = torch.reshape(volumes, (-1, box_size, box_size, box_size))
- elif volumes.dim() == 4:
- pass
+ gt_data: dict
+ Dictionary containing the left singular vectors, singular values, and right singular vectors of the ground truth volumes.
+ """
+
+ vols_gt = np.load(config["gt_params"]["gt_vols_file"], mmap_mode="r")
+
+ if len(vols_gt.shape) == 2:
+ box_size_gt = int(round((float(vols_gt.shape[-1]) ** (1.0 / 3.0))))
+
+ elif len(vols_gt.shape) == 4:
+ box_size_gt = vols_gt.shape[-1]
+
+ if config["normalize_params"]["box_size_ds"] is not None:
+ box_size = config["normalize_params"]["box_size_ds"]
else:
- raise ValueError(
- f"The shape of the volumes stored in {path_to_volumes} have the unexpected shape "
- f"{torch.shape}. Please, review the file and regenerate it so that volumes stored hasve the "
- f"shape (num_vols, box_size ** 3) or (num_vols, box_size, box_size, box_size)."
+ box_size = box_size_gt
+
+ if config["normalize_params"]["mask_path"] is not None:
+ mask = torch.tensor(
+ mrcfile.open(config["normalize_params"]["mask_path"], mode="r").data.copy()
)
- volumes_ds = torch.empty(
- (volumes.shape[0], box_size_ds, box_size_ds, box_size_ds), dtype=dtype
+ try:
+ mask = mask.reshape(box_size_gt, box_size_gt, box_size_gt)
+ except RuntimeError:
+ raise ValueError(
+ "Mask shape does not match the box size of the volumes in the submissions."
+ )
+
+ skip_vols = config["gt_params"]["skip_vols"]
+ n_vols = vols_gt.shape[0] // skip_vols
+
+ if config["dtype"] == "float32":
+ dtype = torch.float32
+
+ else:
+ dtype = torch.float64
+
+ volumes_gt = torch.zeros((n_vols, box_size * box_size * box_size), dtype=dtype)
+
+ for i in range(n_vols):
+ vol_tmp = torch.from_numpy(
+ vols_gt[i * skip_vols].copy().reshape(box_size_gt, box_size_gt, box_size_gt)
+ )
+
+ if dtype == torch.float32:
+ vol_tmp = vol_tmp.float()
+ else:
+ vol_tmp = vol_tmp.double()
+
+ if config["normalize_params"]["mask_path"] is not None:
+ vol_tmp *= mask
+
+ if config["normalize_params"]["bfactor"] is not None:
+ bfactor = config["normalize_params"]["bfactor"]
+ voxel_size = config["voxel_size"]
+ vol_tmp = bfactor_normalize_volumes(
+ vol_tmp, bfactor, voxel_size, in_place=True
+ )
+
+ if config["normalize_params"]["box_size_ds"] is not None:
+ vol_tmp = downsample_volume(vol_tmp, box_size_ds=box_size)
+
+ volumes_gt[i] = vol_tmp.reshape(-1)
+ volumes_gt /= torch.norm(volumes_gt, dim=1, keepdim=True)
+
+ U, S, V = torch.svd_lowrank(
+ volumes_gt - volumes_gt.mean(0, keepdim=True), q=config["svd_max_rank"]
)
- for i, vol in enumerate(volumes):
- volumes_ds[i] = downsample_volume(vol, box_size_ds)
- volumes_ds[i] = volumes_ds[i] / volumes_ds[i].sum()
- mean_volume = volumes_ds.mean(dim=0)
- volumes_ds = volumes_ds - mean_volume[None, :, :, :]
+ gt_data = {
+ "u_matrices": U.clone(),
+ "singular_values": S.clone(),
+ "eigenvectors": V.clone(),
+ }
- return volumes_ds, mean_volume
+ return gt_data
diff --git a/src/cryo_challenge/data/_validation/config_validators.py b/src/cryo_challenge/data/_validation/config_validators.py
index 83083ed..50f1298 100644
--- a/src/cryo_challenge/data/_validation/config_validators.py
+++ b/src/cryo_challenge/data/_validation/config_validators.py
@@ -1,6 +1,9 @@
from numbers import Number
+import numpy as np
import pandas as pd
import os
+from pydantic import BaseModel, field_validator, model_validator
+from typing import Optional, List
def validate_generic_config(config: dict, reference: dict) -> None:
@@ -253,60 +256,122 @@ def validate_input_config_disttodist(config: dict) -> None:
# SVD
-def validate_config_svd_output(config_output: dict) -> None:
- """
- Validate the output part of the config dictionary for the SVD pipeline.
- """ # noqa: E501
- keys_and_types = {
- "output_path": str,
- "save_volumes": bool,
- "save_svd_matrices": bool,
- }
- validate_generic_config(config_output, keys_and_types)
- return
-
-
-def validate_config_svd(config: dict) -> None:
- """
- Validate the config dictionary for the SVD pipeline.
- """ # noqa: E501
- keys_and_types = {
- "path_to_volumes": str,
- "box_size_ds": Number,
- "submission_list": list,
- "experiment_mode": str,
- "dtype": str,
- "output_options": dict,
- }
-
- validate_generic_config(config, keys_and_types)
- validate_config_svd_output(config["output_options"])
-
- if config["experiment_mode"] == "all_vs_ref":
- if "path_to_reference" not in config.keys():
+class SVDNormalizeParams(BaseModel):
+ mask_path: Optional[str] = None
+ bfactor: float = None
+ box_size_ds: Optional[int] = None
+
+ @field_validator("mask_path")
+ def check_mask_path_exists(cls, value):
+ if value is not None:
+ if not os.path.exists(value):
+ raise ValueError(f"Mask file {value} does not exist.")
+ return value
+
+ @field_validator("bfactor")
+ def check_bfactor(cls, value):
+ if value is not None:
+ if value < 0:
+ raise ValueError("B-factor must be non-negative.")
+ return value
+
+ @field_validator("box_size_ds")
+ def check_box_size_ds(cls, value):
+ if value is not None:
+ if value < 0:
+ raise ValueError("Downsampled box size must be non-negative.")
+ return value
+
+
+class SVDGtParams(BaseModel):
+ gt_vols_file: str
+ skip_vols: int = 1
+
+ @field_validator("gt_vols_file")
+ def check_mask_path_exists(cls, value):
+ if not os.path.exists(value):
+ raise ValueError(f"Could not find file {value}.")
+
+ assert value.endswith(".npy"), "Ground truth volumes file must be a .npy file."
+
+ vols_gt = np.load(value, mmap_mode="r")
+
+ if len(vols_gt.shape) not in [2, 4]:
raise ValueError(
- "Reference path is required for experiment mode 'all_vs_ref'"
+ "Invalid number of dimensions for the ground truth volumes"
+ )
+ return value
+
+ @field_validator("skip_vols")
+ def check_skip_vols(cls, value):
+ if value is not None:
+ if value < 0:
+ raise ValueError("Number of volumes to skip must be non-negative.")
+ return value
+
+
+class SVDOutputParams(BaseModel):
+ output_file: str
+ save_svd_data: bool = False
+ generate_plots: bool = False
+
+
+class SVDConfig(BaseModel):
+ # Main configuration fields
+ path_to_submissions: str
+ voxel_size: float
+ excluded_submissions: List[str] = []
+ dtype: str = "float32"
+ svd_max_rank: Optional[int] = None
+
+ # Subdictionaries
+ normalize_params: SVDNormalizeParams = SVDNormalizeParams()
+ gt_params: Optional[SVDGtParams] = None
+ output_params: SVDOutputParams
+
+ @model_validator(mode="after")
+ def check_path_to_submissions(self):
+ path_to_submissions = self.path_to_submissions
+ excluded_submissions = self.excluded_submissions
+
+ if not os.path.exists(path_to_submissions):
+ raise ValueError(f"Could not find path {path_to_submissions}.")
+
+ submission_files = []
+ for file in os.listdir(path_to_submissions):
+ if file.endswith(".pt") and "submission" in file:
+ submission_files.append(file)
+ if len(submission_files) == 0:
+ raise ValueError(f"No submission files found in {path_to_submissions}.")
+
+ submission_files = []
+ for file in os.listdir(path_to_submissions):
+ if file.endswith(".pt") and "submission" in file:
+ if file in excluded_submissions:
+ continue
+ submission_files.append(file)
+
+ if len(submission_files) == 0:
+ raise ValueError(
+ f"No submission files found after excluding {excluded_submissions}."
)
- else:
- assert isinstance(config["path_to_reference"], str)
- os.path.exists(config["path_to_reference"])
- assert (
- "pt" in config["path_to_reference"]
- ), "Reference path point to a .pt file"
-
- os.path.exists(config["path_to_volumes"])
- for submission in config["submission_list"]:
- sub_path = os.path.join(
- config["path_to_volumes"] + f"submission_{submission}.pt"
- )
- os.path.exists(sub_path)
-
- assert config["dtype"] in [
- "float32",
- "float64",
- ], "dtype must be either 'float32' or 'float64'"
- assert config["box_size_ds"] > 0, "box_size_ds must be greater than 0"
- assert config["submission_list"] != [], "submission_list must not be empty"
-
- return
+ return self
+
+ @field_validator("dtype")
+ def check_dtype(cls, value):
+ if value not in ["float32", "float64"]:
+ raise ValueError(f"Invalid dtype {value}.")
+ return value
+
+ @field_validator("svd_max_rank")
+ def check_svd_max_rank(cls, value):
+ if value < 1 and value is not None:
+ raise ValueError("Max rank must be at least 1.")
+ return value
+
+ @field_validator("voxel_size")
+ def check_voxel_size(cls, value):
+ if value <= 0:
+ raise ValueError("Voxel size must be positive.")
+ return value
diff --git a/src/cryo_challenge/data/_validation/output_validators.py b/src/cryo_challenge/data/_validation/output_validators.py
index 9f76a6d..e81a363 100644
--- a/src/cryo_challenge/data/_validation/output_validators.py
+++ b/src/cryo_challenge/data/_validation/output_validators.py
@@ -31,6 +31,7 @@ class MapToMapResultsValidator:
bioem: Optional[dict] = None
fsc: Optional[dict] = None
res: Optional[dict] = None
+ zernike3d: Optional[dict] = None
def __post_init__(self):
validate_input_config_mtm(self.config)
@@ -151,6 +152,7 @@ class DistributionToDistributionResultsValidator:
res: Optional[dict] = None
l2: Optional[dict] = None
corr: Optional[dict] = None
+ zernike3d: Optional[dict] = None
def __post_init__(self):
validate_input_config_disttodist(self.config)
diff --git a/tests/config_files/test_config_map_to_map.yaml b/tests/config_files/test_config_map_to_map.yaml
index 2244e21..e1c724c 100644
--- a/tests/config_files/test_config_map_to_map.yaml
+++ b/tests/config_files/test_config_map_to_map.yaml
@@ -11,7 +11,7 @@ data:
metadata: tests/data/Ground_truth/test_metadata_10.csv
mask:
do: true
- volume: tests/data/Ground_truth/test_mask_dilated_wide.mrc
+ volume: tests/data/Ground_truth/test_mask_bool.mrc
analysis:
metrics:
- l2
diff --git a/tests/config_files/test_config_map_to_map_external.yaml b/tests/config_files/test_config_map_to_map_external.yaml
new file mode 100644
index 0000000..3a78da5
--- /dev/null
+++ b/tests/config_files/test_config_map_to_map_external.yaml
@@ -0,0 +1,31 @@
+data:
+ n_pix: 16
+ psize: 30.044
+ submission:
+ fname: tests/data/dataset_2_submissions/submission_1000.pt
+ volume_key: volumes
+ metadata_key: populations
+ label_key: id
+ ground_truth:
+ volumes: tests/data/Ground_truth/test_maps_gt_flat_10.pt
+ metadata: tests/data/Ground_truth/test_metadata_10.csv
+ mask:
+ do: false
+ volume: tests/data/Ground_truth/test_mask_bool.mrc
+analysis:
+ zernike3d_extra_params:
+ gpuID: 0
+ tmpDir: tmp_zernike
+ thr: 20
+ numProjections: 20 # projecions should be 20-100
+ metrics:
+ - zernike3d
+ chunk_size_submission: 4
+ chunk_size_gt: 5
+ low_memory:
+ do: false
+ chunk_size_low_memory: null
+ normalize:
+ do: true
+ method: median_zscore
+output: tests/results/test_map_to_map_distance_matrix_submission_0.pkl
diff --git a/tests/config_files/test_config_map_to_map_low_memory_subbatch.yaml b/tests/config_files/test_config_map_to_map_low_memory_subbatch.yaml
index 8bc02e7..1f9a6a1 100644
--- a/tests/config_files/test_config_map_to_map_low_memory_subbatch.yaml
+++ b/tests/config_files/test_config_map_to_map_low_memory_subbatch.yaml
@@ -11,7 +11,7 @@ data:
metadata: tests/data/Ground_truth/test_metadata_10.csv
mask:
do: true
- volume: tests/data/Ground_truth/test_mask_dilated_wide.mrc
+ volume: tests/data/Ground_truth/test_mask_bool.mrc
analysis:
metrics:
- l2
diff --git a/tests/config_files/test_config_map_to_map_nomask_nonormalize.yaml b/tests/config_files/test_config_map_to_map_nomask_nonormalize.yaml
index a8a4f09..29a3042 100644
--- a/tests/config_files/test_config_map_to_map_nomask_nonormalize.yaml
+++ b/tests/config_files/test_config_map_to_map_nomask_nonormalize.yaml
@@ -11,7 +11,7 @@ data:
metadata: tests/data/Ground_truth/test_metadata_10.csv
mask:
do: false
- volume: tests/data/Ground_truth/test_mask_dilated_wide.mrc
+ volume: tests/data/Ground_truth/test_mask_bool.mrc
analysis:
metrics:
- l2
diff --git a/tests/config_files/test_config_svd.yaml b/tests/config_files/test_config_svd.yaml
index acff44b..a11162d 100644
--- a/tests/config_files/test_config_svd.yaml
+++ b/tests/config_files/test_config_svd.yaml
@@ -1,14 +1,21 @@
-path_to_volumes: tests/data/dataset_2_submissions/
-box_size_ds: 16
-submission_list: [1000]
-experiment_mode: "all_vs_ref" # options are "all_vs_all", "all_vs_ref"
-# optional unless experiment_mode is "all_vs_ref"
-path_to_reference: tests/data/Ground_truth/test_maps_gt_flat_10.pt
-dtype: "float32" # options are "float32", "float64"
-output_options:
- # path will be created if it does not exist
- output_path: tests/results/svd
- # whether or not to save the processed volumes (downsampled, normalized, etc.)
- save_volumes: True
- # whether or not to save the SVD matrices (U, S, V)
- save_svd_matrices: True
+path_to_submissions: tests/data/dataset_2_submissions/
+#excluded_submissions: # you can exclude some submissions by filename
+# - "submission_0.pt"
+# - "submission_1.pt"
+
+dtype: float32
+svd_max_rank: 5
+voxel_size: 1.0 # voxel size of the input maps
+normalize_params: # optional, if not given there will be no normalization
+ mask_path: tests/data/Ground_truth/test_mask_dilated_wide.mrc
+ bfactor: 170
+ box_size_ds: 16
+
+gt_params: # optional, if provided there will be extra results
+ gt_vols_file: tests/data/Ground_truth/test_maps_gt_flat_10.npy
+ skip_vols: 1
+
+output_params:
+ output_file: tests/results/svd/svd_result.pt
+ save_svd_data: True
+ generate_plots: False
diff --git a/tests/data/Ground_truth/mask_dilated_wide_224x224.mrc b/tests/data/Ground_truth/mask_dilated_wide_224x224.mrc
deleted file mode 100644
index e32296e..0000000
Binary files a/tests/data/Ground_truth/mask_dilated_wide_224x224.mrc and /dev/null differ
diff --git a/tests/data/Ground_truth/test_mask_bool.mrc b/tests/data/Ground_truth/test_mask_bool.mrc
new file mode 100644
index 0000000..1e024df
Binary files /dev/null and b/tests/data/Ground_truth/test_mask_bool.mrc differ
diff --git a/tests/test_map_to_map.py b/tests/test_map_to_map.py
index 907e6d3..5e35f27 100644
--- a/tests/test_map_to_map.py
+++ b/tests/test_map_to_map.py
@@ -4,6 +4,18 @@
def test_run_map2map_pipeline():
+ try:
+ args = OmegaConf.create(
+ {"config": "tests/config_files/test_config_map_to_map_external.yaml"}
+ )
+ results_dict = run_map2map_pipeline.main(args)
+ assert "zernike3d" in results_dict.keys()
+ except Exception as e:
+ print(e)
+ print(
+ "External test failed. Skipping test. Fails when running in CI if external dependencies are not installed."
+ )
+
for config_fname, config_fname_low_memory in zip(
[
"tests/config_files/test_config_map_to_map.yaml",
diff --git a/tests/test_svd.py b/tests/test_svd.py
index ea166ea..50c9a9b 100644
--- a/tests/test_svd.py
+++ b/tests/test_svd.py
@@ -2,6 +2,6 @@
from cryo_challenge._commands import run_svd
-def test_run_preprocessing():
+def test_run_svd():
args = OmegaConf.create({"config": "tests/config_files/test_config_svd.yaml"})
run_svd.main(args)
diff --git a/tutorials/1_tutorial_preprocessing.ipynb b/tutorials/1_tutorial_preprocessing.ipynb
index 84db8c9..bac0593 100644
--- a/tutorials/1_tutorial_preprocessing.ipynb
+++ b/tutorials/1_tutorial_preprocessing.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2024-06-17T15:40:12.854854Z",
@@ -17,7 +17,7 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2024-06-17T15:40:20.557563Z",
@@ -30,7 +30,6 @@
"import os\n",
"import torch\n",
"import matplotlib.pyplot as plt\n",
- "import numpy as np\n",
"import yaml\n",
"from ipyfilechooser import FileChooser"
]
@@ -80,6 +79,17 @@
"display(submission1_path)"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Select path to populations (submission 1)\n",
+ "submission1_pop_path = FileChooser(path_to_sub_set.selected_path)\n",
+ "display(submission1_pop_path)"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -97,6 +107,26 @@
"display(submission2_path)"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Select path to populations (submission 2)\n",
+ "submission2_pop_path = FileChooser(path_to_sub_set.selected_path)\n",
+ "display(submission2_pop_path)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "submission2_pop_path.selected"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -116,7 +146,7 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2024-06-13T07:40:59.387306Z",
@@ -140,6 +170,7 @@
" \"box_size\": 144,\n",
" \"pixel_size\": 1.073 * 2,\n",
" \"path\": submission1_path.selected_path,\n",
+ " \"populations_file\": submission1_pop_path.selected,\n",
" },\n",
" 1: {\n",
" \"name\": \"submission2\",\n",
@@ -148,13 +179,14 @@
" \"box_size\": 288,\n",
" \"pixel_size\": 1.073,\n",
" \"path\": submission2_path.selected_path,\n",
+ " \"populations_file\": submission2_pop_path.selected,\n",
" },\n",
"}"
]
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2024-06-13T07:41:01.194466Z",
@@ -176,17 +208,22 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "After you create your submission_config, simply grab a copy of the file \"config_preproc.yaml\" from the provided config_files, and change the path for the \"submission_config_file\" to the file we created in the previous cell. Also change the path for the output. The rest of the parameters you can leave untouched. Please see the publication \"Singer, A., & Yang, R. (2024). Alignment of density maps in Wasserstein distance. Biological Imaging, 4, e5\" for more details. Then simply run\n",
+ "Lastly, to run the preprocessing pipeline follow these steps\n",
"\n",
- "```bash\n",
- "cryo_challenge run_preprocessing --config /path/to/config_preproc.yaml\n",
- "```\n",
+ "0. Make sure to activate your environment and have the package installed!\n",
"\n",
- "Note: make sure to activate your environment and have the package installed!\n",
+ "1. Grab a copy of the file `config_preproc.yaml`from our config file templates.\n",
"\n",
- "You can run the following cell to visualize your volumes (more precisely, a projection of them)\n",
+ "2. In the copied config file, update the value of `submission_config_file` to match the path to the file we created in the last cell.\n",
"\n",
- "IMPORTANT: The execution of the previous program relies on the existence of file to be saved at {{ submission1_path.selected_path }} with a specific formatting. The file must be named \"populations.txt\", and should be formatted as a single row/column CSV file containing the populations computed from your results. If the previous file is not included, the execution of the program will result in a runtime error."
+ "3. Optionally, change the other parameters. \n",
+ " * Most of the parameters (BOT_* and thresh_percentile) are for the alignment. For details on how they work, please see the publication \"Singer, A., & Yang, R. (2024). Alignment of density maps in Wasserstein distance. Biological Imaging, 4, e5\" for more details. \n",
+ "\n",
+ " * The other parameters are self explanatory, \"seed_flavor_assignment\" changes which submission gets assigned which ice cream flavor, keep this if you want to revert anonymity.\n",
+ "\n",
+ "4. Run the command: `cryo_challenge run_preprocessing --config /path/to/config_preproc.yaml`\n",
+ "\n",
+ "You can run the following cell to visualize your volumes (more precisely, a projection of them)\n"
]
},
{
@@ -209,7 +246,7 @@
},
{
"cell_type": "code",
- "execution_count": 25,
+ "execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2024-06-13T07:43:16.259106Z",
@@ -242,12 +279,10 @@
"source": [
"n_submissions = 2 # change this to however many submissions you preprocessed\n",
"\n",
- "fig, ax = plt.subplots(2, 6, figsize=(20, 8)) # change values here too\n",
+ "fig, ax = plt.subplots(1, 2, figsize=(10, 4)) # change values here too\n",
"\n",
"for i in range(n_submissions):\n",
- " idx = np.random.randint(\n",
- " 0, 20\n",
- " ) # doing random volumes to check that everything went fine\n",
+ " idx = 0\n",
"\n",
" submission = torch.load(os.path.join(full_output_path, f\"submission_{i}.pt\"))\n",
" print(submission[\"volumes\"].shape, submission[\"id\"])\n",
@@ -258,9 +293,9 @@
],
"metadata": {
"kernelspec": {
- "display_name": "Python 3 (ipykernel)",
+ "display_name": "cryo-challenge-kernel",
"language": "python",
- "name": "python3"
+ "name": "cryo-challenge-kernel"
},
"language_info": {
"codemirror_mode": {
@@ -272,7 +307,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.17"
+ "version": "3.10.10"
}
},
"nbformat": 4,
diff --git a/tutorials/2_tutorial_svd.ipynb b/tutorials/2_tutorial_svd.ipynb
index fe8f432..e2d7483 100644
--- a/tutorials/2_tutorial_svd.ipynb
+++ b/tutorials/2_tutorial_svd.ipynb
@@ -1,15 +1,5 @@
{
"cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "%load_ext autoreload\n",
- "%autoreload 2"
- ]
- },
{
"cell_type": "code",
"execution_count": 2,
@@ -25,6 +15,15 @@
"from ipyfilechooser import FileChooser"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from IPython.display import Image"
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
@@ -93,13 +92,21 @@
"Here is a brief explanation of each key\n",
"\n",
"* path_to_volumes (str): this is the path to your submissions (the result of running the preprocessing). They should be called submission_0.pt, submission_1.pt, ...\n",
+ "\n",
"* box_size_ds (int): you can choose to downsample the volumes to speed up the analysis, or to get rid of high frequency features.\n",
+ "\n",
"* submission_list (List): here you can choose which submissions are used for the analysis. If you want to use submissions 0, 3, 6; then this should be [0, 3, 6]\n",
+ "\n",
"* experiment_mode (str): the options are \"all_vs_all\", \"all_vs_ref\". If you are using ref, then SVD is computed from the refence volumes and the rest of the volumes are projected to it. Otherwise, all volumes are used to do the projection\n",
+ "\n",
+ "* power_spectrum_normalization (dict): here we set up the power spectrum normalization. We do this as the submissions have very different resolutions. There are two parameters that should be included here\n",
+ " * ref_vol_key (str): this is the ice cream flavour of the submission that should be used as a reference, e.g., \"Chocolate Chip\"\n",
+ " * ref_vol_index (int): this says which volume of that submission should be used, e.g., setting it up as `0` would use the first volume of that submission.\n",
+ "\n",
"* path_to_reference (str): path to the reference volumes (only needed if mode is \"all_vs_ref\")\n",
"* dtype (str): can be float32 or float64\n",
"* output_options (dict): dictionary with options to personalize the output\n",
- " * output_path (str): where the volumes will be saved\n",
+ " * output_file (str): path to where the results should be saved. If the path does not exist it will be created. Use filetype \".pt\" as this will be created as a pytorch file.\n",
" * save_volumes (bool): whether or not to save the volumes used (this will save the normalized, downsampled, and mean-removed volumes)\n",
" * save_svd_matrices (bool): whether or not to save the matrices computed from the SVD"
]
@@ -118,10 +125,8 @@
]
},
{
- "cell_type": "code",
- "execution_count": null,
+ "cell_type": "markdown",
"metadata": {},
- "outputs": [],
"source": [
"# Select path to SVD results\n",
"svd_results_path = FileChooser(os.path.expanduser(\"~\"))\n",
@@ -135,7 +140,182 @@
"metadata": {},
"outputs": [],
"source": [
- "data_svd_vs_gt = torch.load(svd_results_path.value)"
+ "def plot_all_vs_all(results, fig_title=\"\", fig_fname=None):\n",
+ " weights = []\n",
+ " for i, id in enumerate(results[\"metadata\"].keys()):\n",
+ " populations = results[\"metadata\"][id][\"populations\"]\n",
+ " weights += populations.numpy().tolist()\n",
+ "\n",
+ " weights = torch.tensor(weights)\n",
+ " weights = weights / weights.sum()\n",
+ "\n",
+ " fig, ax = plt.subplots(3, 4, figsize=(3 * 5, 2 * 5))\n",
+ "\n",
+ " for i, id in enumerate(results[\"metadata\"].keys()):\n",
+ " sns.kdeplot(\n",
+ " x=results[\"coeffs\"][:, 0],\n",
+ " y=results[\"coeffs\"][:, 1],\n",
+ " cmap=\"viridis\",\n",
+ " fill=True,\n",
+ " cbar=False,\n",
+ " ax=ax.flatten()[i],\n",
+ " weights=weights,\n",
+ " )\n",
+ "\n",
+ " idx_0, idx_1 = results[\"metadata\"][id][\"indices\"]\n",
+ " populations = results[\"metadata\"][id][\"populations\"]\n",
+ "\n",
+ " ax.flatten()[i].scatter(\n",
+ " x=results[\"coeffs\"][idx_0:idx_1, 0],\n",
+ " y=results[\"coeffs\"][idx_0:idx_1, 1],\n",
+ " color=\"red\",\n",
+ " s=populations / populations.max() * 200,\n",
+ " marker=\"o\",\n",
+ " linewidth=0.3,\n",
+ " edgecolor=\"white\",\n",
+ " label=id,\n",
+ " )\n",
+ "\n",
+ " ax.flatten()[i].set_xlabel(\"SVD 1\", fontsize=12)\n",
+ " ax.flatten()[i].set_ylabel(\"SVD 2\", fontsize=12)\n",
+ " ax.flatten()[i].legend(fontsize=12)\n",
+ "\n",
+ " # adjust horizontal space\n",
+ " plt.subplots_adjust(wspace=0.5, hspace=0.5)\n",
+ " fig.suptitle(fig_title, fontsize=16, y=0.95)\n",
+ "\n",
+ " if fig_fname is not None:\n",
+ " plt.savefig(fig_fname, dpi=100, bbox_inches=\"tight\")\n",
+ "\n",
+ " return\n",
+ "\n",
+ "\n",
+ "def plot_all_vs_ref_plot1(results, fig_title, fig_fname=None):\n",
+ " fig, ax = plt.subplots(3, 4, figsize=(15, 10), sharex=True, sharey=True)\n",
+ "\n",
+ " for i, id in enumerate(results[\"metadata\"].keys()):\n",
+ " sns.scatterplot(\n",
+ " x=results[\"coeffs_ref\"][:, 0],\n",
+ " y=results[\"coeffs_ref\"][:, 1],\n",
+ " edgecolors=None,\n",
+ " linewidth=0,\n",
+ " marker=\"X\",\n",
+ " label=\"Ground Truth\",\n",
+ " ax=ax.flatten()[i],\n",
+ " s=40,\n",
+ " alpha=0.8,\n",
+ " color=\"#a1c9f4\",\n",
+ " )\n",
+ "\n",
+ " idx_0, idx_1 = results[\"metadata\"][id][\"indices\"]\n",
+ " populations = results[\"metadata\"][id][\"populations\"]\n",
+ "\n",
+ " ax.flatten()[i].scatter(\n",
+ " x=results[\"coeffs\"][idx_0:idx_1, 0],\n",
+ " y=results[\"coeffs\"][idx_0:idx_1, 1],\n",
+ " color=\"red\",\n",
+ " s=populations / populations.max() * 200,\n",
+ " marker=\"o\",\n",
+ " linewidth=0.3,\n",
+ " edgecolor=\"white\",\n",
+ " label=id,\n",
+ " )\n",
+ "\n",
+ " ax.flatten()[i].set_xlabel(\"SVD 1\", fontsize=12)\n",
+ " ax.flatten()[i].set_ylabel(\"SVD 2\", fontsize=12)\n",
+ " ax.flatten()[i].legend(loc=\"upper left\", fontsize=12)\n",
+ "\n",
+ " # ax[0].set_title(\"Submission vs all submissions\")\n",
+ " ax[2, 3].axis(\"off\")\n",
+ "\n",
+ " # adjust horizontal space\n",
+ " plt.subplots_adjust(wspace=0.5, hspace=0.5)\n",
+ " fig.suptitle(fig_title, fontsize=16, y=0.95)\n",
+ "\n",
+ " if fig_fname is not None:\n",
+ " plt.savefig(fig_fname, dpi=100, bbox_inches=\"tight\")\n",
+ "\n",
+ " return\n",
+ "\n",
+ "\n",
+ "def plot_all_vs_ref_plot2(results, fig_title, fig_fname=None):\n",
+ " fig, ax = plt.subplots(3, 4, figsize=(15, 10), sharex=True, sharey=True)\n",
+ "\n",
+ " for i, id in enumerate(results[\"metadata\"].keys()):\n",
+ " frq, edges = np.histogram(results[\"coeffs_ref\"][:, 0], bins=30)\n",
+ " ax.flatten()[i].bar(\n",
+ " edges[:-1],\n",
+ " frq / frq.max(),\n",
+ " width=np.diff(edges),\n",
+ " label=\"Ground Truth\",\n",
+ " alpha=0.8,\n",
+ " color=\"#a1c9f4\",\n",
+ " )\n",
+ "\n",
+ " idx_0, idx_1 = results[\"metadata\"][id][\"indices\"]\n",
+ " populations = results[\"metadata\"][id][\"populations\"]\n",
+ "\n",
+ " ax.flatten()[i].scatter(\n",
+ " x=results[\"coeffs\"][idx_0:idx_1, 0],\n",
+ " y=populations / populations.max(),\n",
+ " color=\"red\",\n",
+ " marker=\"o\",\n",
+ " s=60,\n",
+ " linewidth=0.3,\n",
+ " edgecolor=\"white\",\n",
+ " label=id,\n",
+ " )\n",
+ "\n",
+ " ax.flatten()[i].set_xlabel(\"SVD 1\", fontsize=12)\n",
+ " ax.flatten()[i].set_ylabel(\"SVD 2\", fontsize=12)\n",
+ " ax.flatten()[i].legend(loc=\"upper left\", fontsize=12)\n",
+ "\n",
+ " # ax[0].set_title(\"Submission vs all submissions\")\n",
+ " ax[2, 3].axis(\"off\")\n",
+ "\n",
+ " # adjust horizontal space\n",
+ " plt.subplots_adjust(wspace=0.5, hspace=0.5)\n",
+ " fig.suptitle(fig_title, fontsize=16, y=0.95)\n",
+ "\n",
+ " if fig_fname is not None:\n",
+ " plt.savefig(fig_fname, dpi=100, bbox_inches=\"tight\")\n",
+ "\n",
+ " return"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Analysis for Set 2"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Submissions vs GT"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Select path to SVD results\n",
+ "results_svd_all_vs_ref_path = FileChooser(os.path.expanduser(\"~\"))\n",
+ "results_svd_all_vs_ref_path.filter_pattern = \"*.pt\"\n",
+ "display(results_svd_all_vs_ref_path)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "results_svd_all_vs_ref = torch.load(results_svd_all_vs_ref_path.value)"
]
},
{
@@ -151,7 +331,7 @@
"metadata": {},
"outputs": [],
"source": [
- "data_svd_vs_gt.keys()"
+ "results_svd_all_vs_ref.keys()"
]
},
{
@@ -160,7 +340,7 @@
"metadata": {},
"outputs": [],
"source": [
- "data_svd_vs_gt[\"metadata\"][\"Salted Caramel\"].keys()"
+ "results_svd_all_vs_ref[\"metadata\"][\"Salted Caramel\"].keys()"
]
},
{
@@ -180,6 +360,8 @@
"\n",
"* config: a copy of the config used to generate the data\n",
"\n",
+ "* sing_vals: singular values computed with SVD\n",
+ "\n",
"If you chose to save volumes. The volumes saved are downsampled, normalized and without mean. The means are provided in case they are necessary.\n",
"\n",
"* volumes: Submission volumes. They are useful for rerunning things or to create animations.\n",
@@ -198,7 +380,9 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Here is how to generate the plots we have showed "
+ "### Results\n",
+ "\n",
+ "First we will compare the first principal components found"
]
},
{
@@ -207,46 +391,38 @@
"metadata": {},
"outputs": [],
"source": [
- "fig, ax = plt.subplots(3, 4, figsize=(23, 15), sharex=True, sharey=True)\n",
- "\n",
- "for i, id in enumerate(data_svd_vs_gt[\"metadata\"].keys()):\n",
- " sns.scatterplot(\n",
- " x=data_svd_vs_gt[\"coeffs_ref\"][:, 0],\n",
- " y=data_svd_vs_gt[\"coeffs_ref\"][:, 1],\n",
- " edgecolors=None,\n",
- " linewidth=0,\n",
- " marker=\"X\",\n",
- " label=\"Ground Truth\",\n",
- " ax=ax.flatten()[i],\n",
- " s=40,\n",
- " alpha=0.8,\n",
- " color=\"#a1c9f4\",\n",
- " )\n",
- "\n",
- " idx_0, idx_1 = data_svd_vs_gt[\"metadata\"][id][\"indices\"]\n",
- " populations = data_svd_vs_gt[\"metadata\"][id][\"populations\"]\n",
- "\n",
- " ax.flatten()[i].scatter(\n",
- " x=data_svd_vs_gt[\"coeffs\"][idx_0:idx_1, 0],\n",
- " y=data_svd_vs_gt[\"coeffs\"][idx_0:idx_1, 1],\n",
- " color=\"red\",\n",
- " s=populations / populations.max() * 200,\n",
- " marker=\"o\",\n",
- " linewidth=0.3,\n",
- " edgecolor=\"white\",\n",
- " label=id,\n",
- " )\n",
- "\n",
- " ax.flatten()[i].set_xlabel(\"SVD 1\", fontsize=12)\n",
- " ax.flatten()[i].set_ylabel(\"SVD 2\", fontsize=12)\n",
- " ax.flatten()[i].legend(loc=\"upper left\", fontsize=12)\n",
- "\n",
- "# ax[0].set_title(\"Submission vs all submissions\")\n",
- "ax[2, 3].axis(\"off\")\n",
- "\n",
- "# adjust horizontal space\n",
- "plt.subplots_adjust(wspace=0.5, hspace=0.5)\n",
- "fig.suptitle(\"Set2: Projection of each submission onto GT's SVD\", fontsize=16, y=0.92)"
+ "title_fig = \"your title\"\n",
+ "fig_fname = \"your figfname\" # for saving a file (optional)\n",
+ "\n",
+ "plot_all_vs_ref_plot1(results_svd_all_vs_ref, title_fig, fig_fname=fig_fname)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Here is an example of how this figures looks"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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j6U+ePKFBgwYRAOrZs6fJvubMmSM9tP/8889GD8WxsbE0e/ZsiomJkaaJ10yhUFCdOnXo7t270rzff/9d+jxeuHAhR8ealpZGRYoUISsrK5OH9rS0NNq8eTPt2bPHaPrLVuBZWVlRx44dKT4+noj0n4958+ZJ806dOmW0nngtFQoFdezYUboHtVqt9HkMCgoif39/Gj58OKWkpBCR/vPavHlz6d7NzNxxrFu3Ttre7du3jZaPiYmhOXPmUEJCgjStWbNmBIA+//xzab+io0eP0qpVq4ymWcqFu3fvShVZQ4YMkbal1Wpp6tSp0vFHR0dbPDdt27Y1qlycP3++VOGemx92Nm/eLF2LlStXStOfPXtGrVq1kiqkDfs/I8o4nwqFgqZNmybd56mpqdSlSxeL92p2feDlNYOzYmdnZ/EHpLzITQWeOWJG9OjRg168eGE078KFC7RgwYIcb2vhwoVSWdq1a0e7d+82+WxaIvYdmjnfRGLF5+jRo03m5ebf7REjRhAAatSoUY7KxRhjTI8r8Bhj7C0hPuSJfwRBoDJlylDPnj1p9erVlJycbLJOTEwMqVQqcnBwMHlYJdI/PIaGhhJg3LH1y1TgxcfHS4NY9O7d2+wybdu2JS8vL7OtcbKS00Es9u3bJ80THzqbN29udpviw5C7u7tUIWVIrECoXbu20XRLD+piRUWrVq3M7i86OpoEQaASJUoYTS9fvrzUeiknXqYCT3zYztx6iEhf2SJWMmZu9SI+wAUFBZm0zkhNTSUPDw8CQCdOnMjRMeh0OvLx8THbCoyI6M6dO1KruH/++cdoXl4r8KKjo6XPtmFFgSghIYF8fHxIEASTwWKy4uPjQ2q1WqogJtJXkru4uBAAWrFiRY62I14zGxsbs/esWMH49ddf52h79+/flyrwc+plK/Dc3NzM3kti2bt37240XbyWnp6eRpVnRPqKU7GlYJUqVUxarl68eJEAfUvUnBzHtGnTCADNnTs3izOQoUyZMgTAZKAASyzlwtixYwkAVa5c2ex6TZo0IQDUrVs3o+niufHw8JAqRA0FBwcTAFq/fn2OykdEUsX4kCFDTOYlJCRILcUWL15sNC+rLH306JHUujAuLs5oXnYVeHnNYEuePn0q/VuQuUI0r162Aq9Ro0b5Vp60tDTp8yL+USqVFBISQkOGDMlysBBxAAxra2uTHwHv378vVZaeO3fOZN3cVOCJP1zkJncYY4zxIBaMMfbW+Pzzz7Fz5040adIESqUSRIRLly5h2bJl6NixI0qXLm3SJ9W2bduQkpKCRo0awdvb22SbMpkMzZo1AwCpn6KX1adPH1y5cgXe3t746quvzC7z22+/4c6dOxg+fHie9rF9+3aEh4cjPDwcISEhcHV1xaxZswAAw4YNk/rCM2SpQ+2//voLAPDRRx/B2traZP6QIUMA6PuLS0hIyLZs69evBwB8+OGHZudXqlQJ/v7+uHbtGu7cuQMAuHr1Ks6fPw+lUomhQ4dmu4+XJR7zJ598YjJPEAQMHjzYaLnMevfubdKnlEKhQFBQEICMPpqyc+HCBdy+fRvW1tb46KOPTOZ7eXmhTZs2WZYltzZs2AAAaN++Pezt7U3mq9VqvPfeeyAi7Nu3z2T+zp07MWzYMDRt2hR16tSRPofPnj1DYmKiUf+LBw4cQGxsLIoVK4YuXbrkqpzvv/++2Xs2NDQUQM7PcdGiRaFSqXD58mVER0fnqgx51adPH7P30oABAwAAf/75p9n1OnXqZNLHp6Ojo9Qnp9jfoaEyZcrAxsYGz58/R2xsbLZl8/HxAaDv/y0xMTHHy+e1f0eR+PkdNGiQ2flizlj6nHfq1Am2trYm03P7eYiPj8ehQ4cAmL//1Wq1dC9aKou5bHN1dZX638xpWUT5ncEvXryQ/t/cOQP091fm/l0zf7byk/g5WrduHYjopbZlZWWF33//HYsWLUJISAgEQUBqaiqOHTuGuXPnIjIyEuHh4Wb7MqxZsyZKlSqF5ORkrFu3zmjeL7/8Aq1Wi+DgYJQvX/6lyiied8NrwRhjLHtWBV0Axhhj+ScyMhKRkZFISkrCsWPH8O+//2Lbtm3YvXs3bt26hSZNmuDEiRMoW7YsAH2H7wBw+PBhs5VaAPDw4UMAwN27d1+6fKNHj8aaNWtgZ2eHjRs3ZjnQxcuIiYlBTEwMAEAul8PZ2Rnh4eH4+OOP0bJlS7PrlCtXzux0cZANSw8sAQEBUCqVSE1NxX///YdKlSplWTbxnI8fPx5ffvml2WXEAQ/u3r0Lb29vaRCB8uXLm61Uyk9Pnz7Fo0ePpP2ZU6FCBQCWByApWbKk2elubm4A9JUEOSFu39fX1+KDdnZlyS3x+mzYsAEHDx40u8zNmzcBGN8Tqamp6NChg8WBGESGg26I17VatWrZdqKfWX6dY7lcjsGDB+Orr75CcHAwwsLCEBkZidq1ayM8PNxshcnLsnSvidMfPnyI58+fm4yeaemYixYtigsXLmQ5/9atW4iPj4eLi0uWZWvZsiX8/f3x119/oVixYnj//fdRu3Zt1K1bV/qsGRo6dCj+/vtvfPTRR5g9ezYaNWqE8PBwREZGZrsvQ9nljLjv3J6b3H4erl69Cp1OJw1klFVZ8nL/X7p0KcdlEeV3BhtmqKUKv8DAQKmcqampOHr0aK7KnFsDBw7E8uXLMXnyZKxYsUL63EVGRqJYsWK53p5cLkefPn3Qp08fxMbG4vDhwzh48CA2bdqEc+fO4cCBA2jYsCFOnToFlUpltG7Xrl0RFRWFVatWoU+fPtJ0cfTZvAxekZl4bjN/jhljjGWNK/AYY+wtZGNjg9q1a6N27doYOXIk9u/fj/fffx8JCQmYPXs2Fi5cCADSqKW3b9+2OLKgKCkp6aXKNGvWLMyYMQMqlQqbNm1C1apVX2p7WenRo4fJCIbZsVRBJD5oiA/CmQmCgKJFi+Lu3bs5ak0gnvPjx49nu6x4zsVRT52cnLJd52UZPlxbOmZ3d3cAlltPWDqXYiVVTluYZHfuc1KW3BKvz9WrV3H16tUslzW8J6ZPn46NGzfCw8MDM2fORJ06deDh4SE9HIeHh+PAgQNGI5i+zHXNr3Mslt3Lywvff/899u3bJ7UsdHBwwIABAxAVFWXykP8yLF1Pw+kvXrwwebi3NMK22DIqu/k5OSe2trbYt28fxo8fj3Xr1mHNmjVYs2YNAH0F0owZM6RWyQDQtGlTbN26FVOnTsXhw4dx8eJFzJ07F1ZWVmjVqhW++eYbeHl5Zbvf7D7r4uccMH9u8vueK1q0qMUWZ6/r/s9cpvzKYEdHR9jZ2SE+Ph43btwwW+EnttgGgDt37kgt5F6VypUrY+/evZgwYQJ27tyJBQsWYMGCBRAEAQ0aNMCcOXMsVnxnx8XFBU2bNkXTpk0xZcoUzJ07F8OGDcPFixexbt06k9a/3bp1Q1RUFPbs2YM7d+5IPyKdPHkScrkcnTp1eunjvXXrFoCss50xxpgpfoWWMcbeAeHh4dLraUeOHJGm29nZAQDGjh0L0veLavFPbivEDC1cuBCffvoprKyssHbtWtSrV++ljud1Es+R2KIvMyKSWqzlpHWcuL0rV65ke87r1q1rtN2nT5++5NFkTywfYPmYxVaZr7o1YHbn/lWURdznwoULs70+UVFR0nqrVq0CACxbtgzdunWDn5+fUaWXuQry13ldsyKTyTBkyBBcvnwZ169fx/Lly9GxY0ckJydj+vTpGDFihNHy2VWIZfcao3i/ZDX9VX+2suLt7Y0lS5YgLi4Ohw8fxvTp0xESEoLz58+jZcuW+Pfff42Wb9KkCQ4cOIBHjx5h48aN+OSTT+Dk5ITffvsNzZs3N6q0tSS7z7r4OQde7bkRy/Ho0SOL1/d13f+Zy5RfGQwANWrUAACzr8EXlBo1auDPP//EkydP8Mcff2DUqFHw9vbGX3/9hQYNGuRLTgiCgKFDh0qvVht+HxCVKFECtWrVgk6nwy+//AIA+PnnnwEADRs2NKpMzqv9+/cD0Lc+ZowxlnNcgccYY+8I8XWo1NRUaZr4StLZs2dzta3c9AW0evVq9OvXDzKZDMuXL0eLFi1yta+CVrp0aQDA+fPnzc6/cuUKUlNTIZfLLb46Zigv51x8Ze38+fM5bmmW1/6anJycULRoUWl/5pw7dw5Axrl5VcTti68/vo6y5PWeuHHjBgCgVq1aJvNiY2PNvoIuXtejR49Cp9PlsqSvhr+/P7p3745ff/0Vv//+OwBgyZIlRuUTW1hZqoj777//styH+Oqwpenu7u6F4tU6KysrVK9eHaNGjcLRo0fRsWNHaLVaLFmyxOzyzs7O+OCDDzBv3jycPXsWjo6OOHnyJI4dO5btvrLLGfFz/qrPTalSpSCTyZCSkmKxr7r8vueyy6r8zmBA38cloK+Yykm/ea+TnZ0dGjVqhOnTp+PixYsoWbIk7t69i+3bt+fbPsx9HzAk9gm7atUqEJFUkZcfr8+eOHFCeiW5adOmL709xhh7l3AFHmOMvQUeP36c7WtJYn9eAQEB0rSmTZtCqVRi27ZtRp3rZ8fGxgZA9q/Vbtu2Dd27d4dOp8P8+fPRuXPnHO+jsGjUqBEAfYus5ORkk/nz5s0DAISFhVl8dcxQ69atpfVy+ipZyZIlERgYiNTUVGl/2cnpNTJHPOZvv/3WZB4RSdPF5V6VcuXKwdfXF8nJyVi0aJHJ/Hv37uF///tfvpalVatWAPT9PeVk0AOReL4NW0qJZs+eDa1WazI9LCwMrq6uuHv3Ln799dc8lvjVEVspJSUl4cmTJ9J08eHfXL9g//vf/4yWNWfx4sVISUkxmT5//nwA+lY+hZF4Pu7du5ftsu7u7tLgGjlZXvz8fvfdd2bni/f9q77n7OzspEpoc/d/UlKSdC/mV1myy6r8zmBAX0Hl7++Phw8fYsCAAYWmAj0ztVqNihUrAsjZ5wjIusUyAKSlpUn3ruH3AUPt27eHSqXC6dOnMX/+fNy8eRP29vb44IMPclF6U3FxcejRowcAoH79+twCjzHGcokr8Bhj7C2wcuVKVK5cGQsXLjSpdHj69CnGjx8vdUDdq1cvaV6xYsUwdOhQpKWloVGjRiaj1BIRjhw5gv79+xu1xihatCjs7e0RExNjsTXN/v370bZtW6SlpWHmzJno27dvjo+nY8eO8Pf3x5w5c3K8zqvSqVMn+Pr64uHDh+jZs6dRS7CVK1diwYIFAPQDdORE3759UaJECezatQtdunTB/fv3jebHx8dj7dq1JiPwTpkyBQAQFRWFefPmGb2Wl5iYiEWLFhldC7Hy4Pz58xZbSlkyYsQIWFlZYdOmTZg9e7b0cJuamoohQ4ZIrYv69++fq+3mliAI+PTTTwEAEyZMwD///CPNe/jwITp27IjU1FTUqFEDkZGR+bLPkJAQtG/fHrGxsWjQoAFOnjxpNF+r1WL37t3o0qWLUSWUOAjMiBEjpM8IEWHFihWYNWuW2cEgrK2tMW7cOAD6z8Wvv/5qVKn75MkTfPPNN7m+frlx/vx59O3bF0ePHjXad0pKCqZOnQoA8PPzMxqQoXHjxgCAmTNnGlX8Hz16FIMHD4ZCochyn7GxsejTp4/U8omIMH/+fKxfvx5yuTzPo0/nh2+++QZz5swxqYi9deuWVHEVHBwsTe/YsSO2bt1q0pJp3bp1OHPmDARBQJUqVbLdb//+/eHg4IBTp05h2LBh0vZ0Oh1mzpyJrVu3QqFQmLzO/CqMGjUKgL5CVWx5Bej7vOvevTsePXoEf39/dOzYMV/2J1YIWxrpPL8zGABUKhXWrFkDGxsbrFixAo0aNcLhw4dNflR58OABfvzxx9weUq71798fa9asMRn5eO/evVLuGX7ustK3b180b94cmzdvNqkU/e+//9ChQwdcu3YNarVaaomYWZEiRaTWcSNHjgQAtGnTxmI/k9l5/vw5li9fjuDgYJw9exYeHh4v1S0HY4y9s4gxxtgbb86cOQRA+lO8eHGqVq0aBQQEkFKplKaPHDnSZN20tDTq2rWrtIyHhwdVq1aNgoKCyN7eXpp+4cIFo/V69+5NAMja2ppCQkIoIiKCIiIipPmlS5cmAKRSqSgsLMzin23btpmUKSIiggDQhAkTcnUeevToQQCoR48eOV7Hz8+PAND169ctLnP48GFydHQkAGRra0shISHk4+MjnZsvvvjCZJ2lS5daLMuFCxeoePHiBIBkMhmVK1eOqlevTqVLlya5XE4AqHr16ibrTZs2jQRBIADk6OhIISEhFBAQQAqFggDQrl27jJavV68eASB7e3uqXr06RUREUIcOHaT5EyZMsHie58+fL+3L3d2dQkNDycnJSbqmW7ZsMVlHvG6ZyyESr8/SpUvNzjdHp9NR586dpXNdqlQpCg4Olj7Xvr6+9N9//+XLvkQvXrygBg0aSPv09fWl6tWrU8WKFcnGxkaanpSUJK1z7NgxUqlUBIAcHByoatWqVKxYMQJA3bp1s3hudDod9e/fX9qmq6srhYaGkr+/v/RZMPxsZnXNiLL+3Jlz8uRJad9OTk4UHBxMVapUkT7vSqXS5B5NSkqiChUqEACysrKiwMBA6X7v2LGjxWMVp0+aNImUSiXZ29tTSEiIdJ4A0MyZM03KmN21zO5zZ+keNzd9yJAhUln8/f2pWrVqVLZsWelaBAYG0tOnT6XlxfOkUqkoMDCQQkNDydPTU9rGuHHjjPaZ1fXZtGmT9LkuUqQIhYaGkpubm5QTCxYsyPW5ye7zYsno0aOlY/Dx8aGQkBCytbWVynbkyBGTdbLLUkvXae/evdK+SpcuTXXq1KGIiAjavn27tExeMjgnjhw5Qv7+/tJ2ihQpQpUrV6bg4GAqVqwYyWQyAkA2NjYm19LQ9evXpW1k9W+JJUFBQdL9VK5cOapWrZp0PgFQ165dc7ytli1bSuspFAppe76+vtLxWFtb02+//ZbldjZs2GD0veKff/7Jcnnx+vr4+Ej/vlerVo1KlSol7RcARUZG0q1bt3J8PIwxxjJwCzzGGHsLDBgwADt37sSnn36KWrVqQavV4tSpU7h79y78/PzQvXt37Nu3D1999ZXJulZWVvj555+xdetWtGzZEgBw8uRJ3L9/H6VLl8agQYOwe/duk/6O5s6diyFDhsDDwwPR0dHYs2ePUQsKsXVSSkoKDhw4YPGPuVcOC5vq1asjOjoaffv2haurK06fPo34+Hg0bNgQW7duxeTJk3O1vbJlyyI6OhrTp09HaGgo7t69i1OnTiE1NRURERGYNWsWVq9ebbLe6NGjcfDgQbRv3x5qtRrR0dF4/vw5QkND8dVXX5m00Pjll1/Qs2dPODg44Pjx49izZw8OHz6cozL2798f+/btQ8uWLaHT6XDq1Cmo1Wp07doVJ06ceG19FwmCgJUrV2LFihWoXbs2YmJicO7cOfj5+eHTTz/FiRMnpBY8+cXOzg5//PEHVq1ahUaNGiExMREnTpzA48ePUalSJYwaNQpHjhwxalVXtWpV7N27Fw0aNIBOp8PFixfh5uaGefPmYfny5Vke3/z587F161Y0a9YMgiAgOjoaaWlpiIiIwPz581GsWLF8PT5DAQEBWLhwIdq1a4eiRYvi8uXLuHLlCry8vNCvXz+cP39eanEnsra2xs6dO9GnTx84OzvjypUrkMlkmDVrljSYR1Zq166Nffv2ITw8HFevXsWTJ09Qo0YNrF+/XmpxWVD69euHqKgo1KlTB2lpaTh16hSePHmC0NBQfPvttzhy5AgcHR2l5ZcvX46PP/4YAQEBuHfvHk6fPg21Wo1WrVphz549mDRpUo733aJFCxw/fhxdunSBtbU1Tp06BSJCq1atsH//fnz88cev4pDNmjZtGjZv3owGDRogPj4ep0+fhqurK/r164fo6GhpEIT8ULt2bfzyyy+oVq0a7t69i71792LPnj148OCBtEx+Z7AoNDQUly5dwuLFi9GiRQvY2tri4sWLOH/+PORyOZo0aYK5c+fi1q1bubqWufXNN99gyJAhqFSpEh4/foxTp04B0L8+/Pvvv2PFihU53tby5cuxbt069OnTB4GBgYiLi8OJEyfw9OlTVKpUCSNGjMC5c+fQtm3bLLfTpEkTqeWtl5eXNKhSdm7fvi39+3727FkkJCQgLCwMI0aMwJEjR7Bz585XPqovY4y9rQSiXI7lzhhjjLFsLVmyBH369EGfPn3M9t/GGGOMMcYYYznFLfAYY4yxV+Dp06cA9H0JMcYYY4wxxtjL4Ao8xhhjLJ+lpaVhy5YtAICgoKACLg1jjDHGGGPsTcev0DLGGGP5qE2bNlLffh4eHrh8+TLs7e0LuliMMcYYY4yxNxi3wGOMMcby0YkTJ5CQkICGDRtix44dXHnHGGOMMcYYe2ncAo8xxhhjjDHGGGOMsUKMW+AxxhhjjDHGGGOMMVaIcQUeY4wxxhhjjDHGGGOFGFfgMcYYY4wxxhhjjDFWiHEFHmOMsXdGdHQ0mjVrBmdnZ8hkMgiCgN27dxd0sVghUrdu3Tx9LhITEzFy5EgUL14cCoUCgiCgZ8+er6SMjLG30+7duyEIAurWrWsyTxAECIJgMj2vmcUYY+zNwxV4jDHGCoT40GH4R6lUwtvbG61bt8bff/+dr/uLiYlBZGQktm7dCrVajZo1ayIsLAyOjo75uh/2bvroo48we/ZsxMTEoHLlyggLC0Pp0qULtEw9e/Y0uccUCgU8PT3RokULbN++vUDLV9isWbMGjRs3hru7O5RKJVxcXFC+fHm0a9cO33//Pe7fv1/QRcyVGzduICoqCsuWLSvoojDGGGMsH1gVdAEYY4y923x8fODr6wsASEhIwNWrV7FhwwZs2LABU6dOxeeff54v+1m9ejWePHmCDz74AOvXr4dMxr9hsfzx5MkTrF69Gmq1GhcvXoSPj09BF8mIm5sbAgICAADJycm4fPkyNm/ejM2bN2PMmDH48ssvC7iEBSstLQ3t27fHxo0bAQCOjo4oV64c5HI5rl27hgsXLmDdunUgIgwaNKhgC5sLN27cwMSJExEREcGtQd8QarUaZcqUkf5NZIwxxgzx0wtjjLEC1bt3b+zfvx/79+/HyZMnERMTIz0kjxs3DpcuXcqX/Vy8eBEA0KhRI668Y/nqypUr0Ol0CAwMLHSVdwDQuHFj6R47duwYHj9+jJEjRwIApk2bhkOHDhVwCQvWrFmzsHHjRtjY2GDVqlWIjY1FdHQ0Tpw4gSdPnuDo0aMYPHgwHBwcCrqo7C1XrVo1XLx4EStWrCjoojDGGCuE+AmGMcZYoWJjY4NvvvkG/v7+0Ol0UquYl5WUlCRtn7H89KZ9tpRKJWbOnInKlSsD0LdOfZctX74cADB27Fh07twZcrlcmicIAkJCQjB37lx07969oIrIGGOMMcYVeIwxxgofKysrVKlSBYD+NTBzjhw5go4dO8LLywtKpRLu7u5o164dTp48abRcVFQUBEGQ+oHq1auX1B9Y5o7CExMTMWPGDISEhMDBwQFqtRqVK1fGV199hZSUFJMyiNuOiorCo0ePMGjQIPj7+0OhUJi8svbnn3+iRYsWcHd3h0qlgre3N3r16oX//vvPZLs3btyAIAjw9/cHAKxcuRIhISFQq9VwdnZGu3btcO3aNYvnLzExEbNmzUKNGjXg5OQEtVqNgIAAdOvWDXv27DFZnoiwevVqNGjQAC4uLlCpVChRogQGDx6MBw8eWNyPOVl1wm7u2AydPXsWXbp0gY+PD5RKJZycnBAQEIDOnTvjjz/+MLu9ixcvonfv3vD394dKpYKLiwuaNm2KnTt3Wizj48ePMWDAAHh5ecHa2hplypTB5MmTkZaWlqtjFY9FPNY9e/YY9Tdn+NlNS0vDt99+i2rVqsHBwQG2trYICgrC1KlTkZiYmO15WrhwIUJDQ2Fvb2+2I/vcEgQB4eHhAPQtCA2dO3cO3bp1g7e3t3RvtWnTBocPHzbZzpAhQyAIAubNm2cyr2zZshAEAbVr1zaZZ3jvZJbba+rv7y+d7127dqFx48ZwdXXNccf+4r0kVmjm1LJly6TBSl68eIHhw4fD398f1tbWKFGiBMaOHWv22ori4uIwduxYBAYGwtbWFvb29qhRowYWLlwInU5ncb2jR4+ia9eu8PX1hUqlgru7O2rVqoWZM2fi2bNnAPR9jEZGRgIw/Vwa3ntiP4nLli3D9evX0bNnT3h5ecHKykq6Nlldq8znwdL0pKQkjBkzBiVKlICNjQ3KlCmDb7/9Vlo2NjYWQ4YMgZ+fH6ytrVGhQoV87btvy5YtEAQBgYGBFpfRarVwd3eHIAg4ffq0NP3s2bOYMGECatasCU9PTyiVSnh6eqJ169Y4ePCg2W0ZHntKSgqioqJQqlQpWFtbw8fHB8OHD0dCQoLJetnlZ248ffoUixcvxgcffIBSpUrBxsYGjo6OqF69OubNmweNRvPS+2CMMfaaEWOMMVYAIiIiCABNmDDB7PxGjRoRABo+fLjJvK+//poEQSAA5OzsTFWqVCEXFxcCQAqFgv73v/9Jyy5evJjCwsLIzc2NAFBAQACFhYVRWFgYDRo0SFruzp07VL58eQJAVlZWVKpUKSpXrhxZWVkRAAoPD6fExESjckyYMIEA0IABA8jX15fkcjlVqlSJKlWqRL1795aWGzJkCAEgAOTm5kZVqlQhBwcHAkAODg504MABo+1ev36dAJCfnx+NHj1a+v+goCBSqVQEgDw9PenRo0cm5+bmzZtUrlw5aX8BAQEUHBxMzs7OBIAiIiKMlk9NTaV27dpJyxcrVoyCgoJIrVZL+7l06ZLF65jZrl27zO7H3LEZ+vfff8nGxoYAkKOjIwUFBVFgYCA5OjoSAPrggw9MtrVmzRpSKpUEgOzt7aly5crk4eFBAEgQBJo3b57JOvfv36cSJUpI17ly5coUEBBAAKhZs2ZUp04dAkC7du3K9ljv379PYWFhFBgYKF1L8bMVFhZG9+/fJyKixMREqlevnnSOy5UrR5UqVSKZTEYAqHLlyvT48WOL56lfv34EgHx8fCgkJIScnJyyLRsRUY8ePQgA9ejRw+z8gQMHEgBq3LixNG3Tpk3SZ8zJyYlCQkKoaNGiBIBkMhn99NNPRttYt24dAaDWrVsbTX/48KF0vEql0uTeiYyMJAC0c+dOo+l5uaZ+fn4EgL788kuSyWRUpEgRCg0NJW9v7xxdRzE7xo8fn+2yhpYuXUoAqGPHjlSlShUSBIEqVKhAgYGBUj7VqFGDEhISTNY9e/YseXl5SeenfPnyVLJkSWm9tm3bkk6nM1lvxowZ0jIODg5UtWpVKlmyJCkUCqPP7aBBgyx+Ltu2bSttT/yMjB49mpycnEilUlFwcDCVLVuWoqKiiCgj5yxltXgeMn/OxOmdOnWimjVrSvno7+8vfTYmTpxIDx8+pICAAFIqlVSlShUqVqyYNH/JkiUm+xPvDQB0/fr1HF2r1NRU6TqfPn3a7DJ//vknAaDy5csbTa9fv750P5QrV46Cg4PJ1dWVAJBcLqdVq1ZZPCedO3emOnXqSJ+NMmXKSPd9gwYNTNbLKj/FY85M/Lc082f9559/lj5ffn5+FBoaSiVKlJD237RpU9JqtVmcNcYYY4UNV+AxxhgrEFlV4MXGxlKRIkUIAK1YscJo3vbt20kQBHJ1dTWqqCMiWrRoEVlZWZG9vT3du3fPaJ74oLp06VKT/Wm1WqpVq5b0MP7gwQNp3u3bt6l27doEgEaOHGm0nvhgK5fLqWbNmnT79m1pXlJSEhER/fjjjwSAihcvbvSApdFoaMqUKQSAvL29peWJMh5QraysyMHBgbZt2ybNu3//PlWqVIkA0KhRo4zKo9FoqGrVqgSAQkJC6Pz580bzT548SfPnzzeaJlYQVqlShU6ePClNT0xMpAEDBkjbyqm8VuA1a9aMANDnn39OKSkpRvOOHj1q8pAcHR1NKpWKrK2t6aeffjJ6EP3999/JwcGB5HI5nTp1ymi9Vq1aEQAKDg6mW7duSdP/+ecfsre3N6kIyY9jHjFihFQ5evz4cWn6lStXqGzZsgSA2rdvb7SOeJ7kcjnZ2trSpk2bpHmZK8MsyaoCT6fTUeXKlQkAffLJJ0REdPfuXalieciQIdJ10Gq1NHXqVKmCPDo6WtpOTEwMASBXV1ejCqe1a9cSAKmSyrCiLiUlhWxsbEwq9vJ6TcUKPLlcThMnTqS0tDTpGJOTk7M9T126dJGObfTo0XTmzBmzlWeZiZU0VlZW5OXlZVSuM2fOkI+Pj9nciI+Pp5IlSxIAGjx4MD179kyad+7cOapQoQIBoO+++85ovY0bN0rHOXv2bEpNTZXmJSQk0E8//WR0z2f3uSTK+IzI5XJq0aIFxcbGSvPETHrZCjyFQkEVK1aka9euSfN+/fVXAkA2NjbUsGFDioyMpIcPH0rzxc+bp6cnaTQao+3mpQKPiKhv374EgMaMGWN2fs+ePQkATZkyxWj6b7/9ZlLpp9PpaOPGjWRnZ0cODg70/Plzi8devnx5ox9BDh06JN1n27dvN1ovPyvwoqOjacuWLSb3wH///Sf9ULFs2TKz54IxxljhxBV4jDHGCoS5Crz4+Hg6ePAghYWFEQAqUaKEycNHcHAwATCq0DAkVpZMmjTJaHpWFXi///47AaDQ0FDp4d/QvXv3yM7Ojuzs7IwqHMQHW5VKRXfv3jVZLyUlhTw8PEgul9OJEyfMlrdNmzYmFZWGD6izZ8+2WN5KlSoZTRcrTdzc3ExadJkTExNDKpWKHBwcjCofRVqtlkJDQwkA7d27N9vtEeW9Aq9MmTIEwKgyIyutW7cmADR37lyz87/99lsCYNQS8sqVK1LrpbNnz5qs8/XXX0vnPb8q8J49eya1ZtywYYPJ/CNHjkity65evSpNz+4zkBOWKvBSUlJo5MiR0vb37dtHRERjx46VWgSa06RJEwJA3bp1M5outvg0PKdi6765c+ea3Of79u0jQN+q1VBerilRRgVe8+bNszwflty5c4eKFy8unQ9A3wq0Xr16FBUVRRcvXjS7nlhJA4DWr19vMl+8T21tbY0qeObNm0cAqFWrVma3Gx0dTYIgUIkSJYymiy2EM2ebJbmpwPPw8KD4+Hizy7xsBZ4gCGbzr2bNmlIlXub81Gg0UuVv5nVv375NXl5e5OXlZTa3LNmzZ4/0Y0pmycnJUmtfw/swO1988QUBMPmBwfDYjx49arLe8OHDpQpcQ/lZgZeVq1evWmwFyBhjrPDiPvAYY4wVqIkTJ0p9M9nZ2aFWrVo4cOAAGjZsiJ07d0KlUknL3rx5EydOnICbmxtatGhhdnvidHN9vVmyfv16APr+oKysrEzme3p6IjQ0FPHx8Th+/LjJ/Pfeew/FihUzmX7o0CE8ePAAwcHBUp9+uS1vnz59TKaFhoYCgEk/eJs2bQKgH9nXxcXF7PYMbdu2DSkpKWjUqBG8vb1N5stkMjRr1izL8uUXcfTWtWvXZrtsamoqtm3bBrlcbtLvlsjcef3rr79ARKhTpw4qVKhgss6HH34IpVKZh9Jbtn//fiQmJsLX1xcffPCByfzQ0FDUrFkTRIQdO3aY3cbLDp6wfft2hIeHIzw8HCEhIXB1dcWsWbMAAMOGDZP6wvvrr78AQBoFOrMhQ4YYLSeKiIgAAOzdu1eatnfvXhQpUgQffvghVCqV0XUQl6tTp440La/X1FBez5OXlxdOnjyJCRMmSP3DPXv2DDt37kRUVBTKlSuHjz76yGw/mOL65q5ts2bN4Ovri4SEBBw4cECaLubNhx9+aHZ7lSpVgr+/P65du4Y7d+4AAK5evYrz589DqVRi6NCheTrOrLRp0wa2trb5vl0AqFKlitn8E/scbNy4sUl+yuVyVKpUCYBpznl7e+POnTu4c+eO2dyypHbt2vDx8cH169dN+nPctm0bnj17hurVq6NkyZIm6966dQvTp09H+/btUa9ePel+WrNmDQAgOjra7D4rV66MkJAQk+mWMjy/paSk4JdffsFHH32ERo0aoXbt2ggPD0ePHj0AWC43Y4yxwsn0KYUxxhh7jXx8fODr6wtAP7jA5cuXIQgCKlasKE0XnTlzBgCQnJwsVTpklpycDAC4e/dujssgbveHH37AL7/8YnaZy5cvW9xuuXLlstzujRs3LJb36dOnFrfr6uoKR0dHk+lubm4AgPj4eKPpFy5cAADUqFHD7L4sle/w4cMWy/fw4UOL5ctPQ4cOxd9//42PPvoIs2fPRqNGjRAeHo7IyEiTysjLly8jOTkZSqUSTZo0Mbs9IjIpt3gNLV0ve3t7eHl54fr16/lxSEb7FAd0MKdChQo4dOiQtKwhV1dXuLq6vlQZYmJiEBMTA0BfMeLs7Izw8HB8/PHHaNmypUlZy5cvb7GcgP4z8fz5czg4OADQV8T9+OOP2LNnD/r374+4uDicPXsWLVq0gFqtRrVq1XD48GGkpKQYVeaJFX/ivvNyTQ1Zuq454ejoiKioKERFReH69es4cuQIdu3ahU2bNuHBgwdYtGgRtFotlixZYrJumTJlIJOZ/iYuCALKlCmDW7du4fLly3j//fcBZNx348ePx5dffmm2PI8fPwagP1Zvb2/p3i5fvjzs7e3zfJyWvMy5y465CjEAKFq0aI7mZ865vBIEAR07dsRXX32FX3/91Sgnf/31VwBAp06dTNZbvnw5+vXrJ/3bYk5cXJzZ6ZaOzVKG56dbt26hYcOGuHTpksVlLJWbMcZY4cQVeIwxxgpU7969jUY3PH/+PFq0aIHZs2fDyckJX3zxhTRPHGHx+fPnRi1azElKSspxGcTtnj17NttlzW3XUssVcbuPHj3Co0eP8m275ioLAP15AQAnJ6cs95W5fLdv38bt27dzXb781LRpU2zduhVTp07F4cOHcfHiRcydOxdWVlZo1aoVvvnmG3h5eRmVOzU1NdvPgeFDt/iwLFYMmOPu7p6vFXjiPsUHdkv7BIAXL16YzMuPVlE9evTI0Yie2ZVVLCegL6tYgSdWxIkVc3v37gURSdPr1KmDffv24ciRI6hZsyYOHjwIKysr1KpVS9peXq+pofxqQVa8eHEUL14cHTp0wNdff40PP/wQv/76K5YtW4aJEydKrUVFub224rGaa82bmXjf5fbezq1X1foOANRqtdnpYoV2dvPFitv80LlzZ3z11VdYu3Ytvv76a8jlcsTHx2PLli2QyWTo0KGD0fL//fcfPvroI6SlpWHEiBHo2rUrSpYsCTs7OwiCgEWLFknzzckuw/Pz2DLr2bMnLl26hOrVq2PixImoXLkynJ2doVAooNFopP8yxhh7c/ArtIwxxgqV8uXLY82aNZDJZJg0aZJRZYqdnR0AICwsDKTvx9Xinxs3buR4n+J2d+zYke12Lb3el9V2u3Tpku12d+/enePtWiK2zBFb9eW0fGPHjs22fDmpAAKyf+hOSEiwuG6TJk1w4MABPHr0CBs3bsQnn3wCJycn/Pbbb2jevLn0kCyW28vLK9tyG5ZDXC+rylSxpVp+EfeZ1XbFVo6vomVVbmRXVrGcgHFZixUrhpIlS+LBgwe4fPmySQs7wwq+48ePIz4+HlWrVpX2Z7jv3F7TV02tVmPBggWQyWQgIhw7dsxkmZx8ngzPl3isV65cyfY469ata7R+Tu/t/PQy93RhU7lyZZQrVw4PHjyQMnfjxo1ISkpCZGQkPDw8jJZfu3Yt0tLS0LFjR8yaNQuVK1eGvb29dE6y++GjoNy7dw+7du2CWq3Gtm3b0KhRI7i7u0OhUAAovOVmjDGWNa7AY4wxVuhUrVoV7du3R1paGiZOnChNF1/tu3DhAnQ6Xb7tT9xuTlrgFYbtWiK+4pi5fydLXkX5xBYnlio1rl69mu02nJ2d8cEHH2DevHk4e/YsHB0dcfLkSanyJCAgAAqFAvfv38/VK2ClS5cGAFy8eNHs/Pj4eKnPsfwi7vPChQsWK0DOnTtntGxBEfd//vx5s/PFcrq7u0ut70SG/eDt3bsXjo6OUh9ntWrVgkKhwO7du832fwfk/Zq+Dvb29lKrzdTUVJP5ly5dMptHRCS9vmh4bfNy34n39vnz58221DTH0ivbuZUf93RhIr4mK3aXIP63c+fOJsuKPwQZthY1VFj7kLt58yYA/av7zs7OJvMLa7kZY4xljSvwGGOMFUqjR48GAKxatUp6GAkICEBgYCDi4uKwYsWKfNtX69atAQALFizIsp+j3KpduzZcXV0RHR2dLy3ssiP2Z7ZkyZIcVYI0bdoUSqUS27Ztw5UrV/KlDCVKlACg75w9NjbWZP6iRYtytT13d3cUL14cgL5VCaBvFdWoUSPodDrMmzcvx9tq2LAhAH0lk7lKqkWLFpmtoHkZ4eHhUKvVuH37tjTIiKFjx47h0KFDEAQBDRo0yNd951ajRo0AAN99953Z+eK5FpczJFbI/f777zh16hTCw8Ol1wRtbW0RHByMQ4cOSQN1GPZ/B+T9muaH7FpdXr16VVomICDAZP6dO3ewefNmk+lbt27FzZs3YWtri7CwMGm6mDfz5s3LcWvCkiVLIjAwEKmpqTk+PzY2NgBe/vV38Z4+evSoybyEhASsXr36pbb/uokVdevXr8e9e/ewY8cOqFQq6boYEs+hYetT0cWLF81e98JALHdMTIzZz9jMmTNfd5EYY4zlA67AY4wxVigFBQWhUaNG0Gg0Rg8bM2bMgCAIGDhwIBYtWmTSh8+1a9cwdepUaaTHnGjVqhVq1KiBixcvonnz5iYtSlJSUrB161b07t07V8dgbW2NSZMmAQDatWuHDRs2mDxMnT17FqNGjcq236+caNmyJUJCQhATE4MmTZqYdF4eHR2NH374Qfp7sWLFMHToUKSlpaFRo0YmlYxEhCNHjqB///45Hi3R2dkZ1apVQ0pKCoYPHy699qrVajF9+nT8+eefZtfr2LEjtm7dalKBtm7dOpw5cwaCIBiNZDl58mSoVCpMmTIF06dPN6mkuH//PubOnYsff/xRmlaqVCl88MEHICL06NHDqLXd7t27ERUVJb1ill8cHBzQv39/APrRXU+ePCnN+++//6TRINu3b2+xw/vXpX///nBwcMCpU6cwbNgw6VrodDrMnDkTW7duhUKhwIgRI0zWFSvktmzZAp1OZ1JBFxERgcTERPz999+QyWRmB03JyzXND0FBQejfvz/+/fdfk5Z0e/fuRatWrUBECAoKMjuaqpWVFT755BNpcApA31JOHM23X79+Rq/Q9u3bFyVKlMCuXbvQpUsX3L9/32h78fHxWLt2LYYPH240fcqUKQCAqKgozJs3z6jftcTERCxatEga7AKAVPF9/vz5bPvgzEpkZCSsra1x7Ngx/PTTT9L0p0+fomfPnmYr6l+lO3fuwN/fH/7+/nlqMVuyZElUq1YNT58+RZ8+faDRaNC4cWOz/QuKn9P58+fj1KlT0vTLly+jXbt2+T5qdX6pUKECihQpgjt37mDq1KnSvzvJyckYMmSIUQ4xxhh7gxBjjDFWACIiIggATZgwweIy//zzDwEga2trun//vjT9u+++I7lcTgDI3t6eqlatSiEhIeTu7k4ACAD98MMPRtvq0aMHAaClS5ea3de9e/eoSpUq0vqlSpWi6tWrU/ny5UmpVBIAcnd3N1pnwoQJ2R4DEdHo0aOl7To7O1NoaCgFBweTs7OzNH379u3S8tevXycA5OfnZ3Gb4nqZ3bx5k8qUKSPNL126NFWtWpVcXFwIAEVERBgtn5aWRl27dpWW9/DwoGrVqlFQUBDZ29tL0y9cuJDlMRratWsXWVlZEQBycnKikJAQcnFxISsrK/r222/NHpujoyMBIJVKRYGBgRQaGkqenp7S/seNG2eyn/Xr15NarZY+I5UrV6Zq1aqRj4+PtN6oUaOM1rl79y75+/sTAFIoFFSlShUqXbo0AaCmTZtSnTp1CADt2rUrV8dr7tyKEhMTKTIyUipT+fLlKSgoSPoMBwUF0ePHj43WyclnIDviZ75Hjx45XmfTpk3S571IkSIUGhpKbm5uBIBkMhktWLDA4rq+vr7SMf77779G87Zu3SrNCw4OtriNvFxTPz8/AkDXr1/P8XEacnJykrZtb29PlSpVouDgYCpatKg03dvb2+QeWLp0KQGgjh07UpUqVUgQBAoMDKSKFSuSIAgEgEJDQyk+Pt5knxcuXKDixYtL57VcuXJUvXp1Kl26tPS5qF69usl606ZNk7bt6OhIISEhFBAQQAqFwuzntl69etJxVa9enSIiIqhDhw7S/OxyUTR58mTpXHh5eVHVqlXJxsaG3N3dKSoqyuznTDw/lj5/2eWnpbKJ98bLXPM5c+ZI2wBAa9asMbtcWloa1ahRgwCQXC6ncuXKUWBgIAmCQJ6enjRlypQ8HbulzMgqSyxlvvhvaeZr/9133xnlekhICDk4OJAgCLRw4UKL22OMMVZ4cQs8xhhjhVa9evUQEhKC5ORkfP3119L0gQMH4tSpU/jwww9RtGhRnDt3DleuXIGrqys6deqE3377Dd27d8/Vvjw9PXHo0CHMnz8fderUQWxsLE6ePIkXL16gWrVqmDhxInbt2pWn45g2bRoOHDiAzp07w9bWFtHR0bhx4wa8vb3Ru3dvbN26FfXr18/TtjPz9fXF8ePHMW3aNAQHB+PevXu4cOECnJ2d0aNHD0yePNloeSsrK/z888/YunWr9AruyZMncf/+fZQuXRqDBg3C7t27c9U/W926dfHnn38iPDwcqampuHz5MoKDg7F79240a9bM7DrLly/Hxx9/jICAANy7dw+nT5+GWq1Gq1atsGfPHqklo6FWrVrh/PnzGDJkCPz9/XHp0iWcP39eWm/58uXSq9iiYsWK4ciRI+jXrx9cXV1x/vx5EBEmTZqEDRs25Fu/YYZsbGzw559/Yu7cuQgJCcHNmzdx+fJllC9fHlOmTMHBgwfh4uKS7/vNixYtWuD48ePo0qULrK2tcerUKRARWrVqhf379+Pjjz+2uK74Gq2dnR2Cg4ON5oWHh0MulxstZ05erunLOnPmDBYsWIDWrVvD19cXt27dwunTp6HT6VC7dm3MnDkT58+fR9myZc2ur1KpsGfPHgwZMgTPnz/HpUuX4Ovri9GjR2PXrl1mRyItW7YsoqOjMX36dISGhuLu3bs4deoUUlNTERERgVmzZpl9NXX06NE4ePAg2rdvD7VajejoaDx//hyhoaH46quvTM77L7/8gp49e8LBwQHHjx/Hnj17ctxHpqEvvvgC33//PcqXL49Hjx7h9u3baNu2LY4dOwY/P79cb6+gdejQQfo82tnZoXnz5maXs7Kywp9//olPPvkE7u7uuHr1qtRy7/jx49LI2IXRwIEDsXLlSlSuXBlxcXG4evUqQkJCsG3bNnz44YcFXTzGGGN5IBC9xqG8GGOMMcYYewssW7YMvXr1Qo8ePXI8SjNjjDHGWF5xCzzGGGOMMcYYY4wxxgoxrsBjjDHGGGOMMcYYY6wQ4wo8xhhjjDHGGGOMMcYKMa7AY4wxxhhjjDHGGGOsEONBLBhjjDHGGGOMMcYYK8S4BR5jjDHGGGOMMcYYY4UYV+AxxhhjjDHGGGOMMVaIcQUeY4wxxhhjjDHGGGOFGFfgMcYYY4wxxhhjjDFWiHEFHmOMMcYYY4wxxhhjhRhX4DHGGGOMMcYYY4wxVohxBR5jjDHGGGOMMcYYY4UYV+AxxhhjjDHGGGOMMVaIcQUeY4wxxhhjjDHGGGOFGFfgMcYYY4wxxhhjjDFWiHEFHmOMMcYYY4wxxhhjhRhX4DHGGGOMMcYYY4wxVohxBR5jjDHGGGOMMcYYY4UYV+AxxhhjjDHGGGOMMVaIcQUeY4wxxhhjjDHGGGOFGFfgMcYYY4wxxhhjjDFWiHEFHmOMMcYYY4wxxhhjhRhX4DHGGGOMMcYYY4wxVohZFXQB3iU6nQ737t2Dvb09BEEo6OIw9sYiIrx48QLFihWDTMa/QxRWnHmM5Q/OvDcDZx5j+YMz783AmcdY/shN5nEF3mt07949+Pj4FHQxGHtr3L59G97e3gVdDGYBZx5j+Yszr3DjzGMsf3HmFW6ceYzlr5xkHlfgvUb29vYA9BfGwcGhgEvD2Jvr+fPn8PHxke4pVjhx5jGWPzjz3gyceYzlD868NwNnHmP5IzeZxxV4r5HYtNjBwYFDjrF8wM31CzfOPMbyF2de4caZx1j+4swr3DjzGMtfOck87lSAMcYYY4wxxhhjjLFCjCvw2DvpRbIOp++lQkcEANAR4fS9VLxI1hVwyRhjLP9x5jHG3iWceYyxdwln3ruDK/DYO+dFsg6Hb6TgdpwWJ2+nQasjnLydhttxWhy+kVIog45DmTGWV5x5jLF3CWceY+xdwpn3buEKPPbOuR6nQapG//8Pnmux83IyHjzXAgBSNfr5hcmbGMqMscKDM48x9i7hzGOMvUs4894tPIhFIaXVapGWllbQxXgrlSpCSEtOw6N4fThoUwF5+ryidjKUKgIkJycXXAEzuRaTCm2qDnIAj54Cu+OBNI2+zNpU4FpMGsq4KQu4lLljZWUFuVzOnRMzCWfeq8OZV7hxHr6biAharRYaTeF6sHobcOblHucQe9U4814dzrzce5MzjyvwChkiwoMHD/D06dOCLspbzYYANxBABhMFwDpZwM0bBVUq8xQAPAWCVvwxwqCOQy4DFAkCrl8viJK9HLlcDjc3Nzg6Or6R4cnyB2fe68GZV7hxHr47iAhPnz7Fo0ePoNVqC7o4by3OvNzjHGKvAmfe68GZl3tvauZxBV4hIz7Iurm5Qa1Wv1EfpjcFESEpjaAx0zrXSgbYKIRCd96JCAkpBMMiywDYqgpfWbNDRNBoNHj+/Dnu37+PpKQkeHp6FnSxWAHhzHv1OPMKL87Dd4+YeQ4ODnBwcICVldVb9ZkuDDjzcr9vziH2qnDmvXqcebnf95uceVyBV4hotVrpQdbFxaWgi/PWSkzVQQZAbJgrA4zCg+SAjbLwdA9JREhMJVipTOfpZIBaWfhCOSfs7e2hUqnw+PFjuLm5QS6XZ78Se6tw5r0enHmFH+fhu0Gr1eLZs2coWrQoXF1dC7o4by3OvLzhHGL5jTPv9eDMy5s3NfMKz5VkUv9ParW6gEvydlNZCdIHXyED7K0FKNInyNLnFyZJaYQ0gxQ2vGnTdPr5bypbW1sQEfd99o7izHs9OPPeDJyHb7+0tDQQEWxtbQu6KG81zry84xxi+Ykz7/XgzMu7NzHzuAKvEHrbWhYUNnKZAFuVAKU8o4ZfrdT/3VYlQC4rXOf/TQvl3ODPOgP4c/Cqcea9Gfg+eHfwtX61OPPyjj+b7FXgz9WrxZmXd2/iZ5NfoWXvJLlMH2wiMegKI30oAykakvowUCv1v06orApfKDPGCh/OPMbYu4QzjzH2LuHMe3dwCzwGANDqCImpOhDpm6zq303XQat7O19VetPoQ1km/UqgDzoZBxxjecSZV7hx5jGWvzjzCjfOPMbyF2de4caZl3dcgceg1elHgUnVAompJHUsmaoFElIo26DLTUCePn0affr0QcmSJWFjYwMbGxsEBASgb9++OHbs2Cs5vryWNbcEQUBUVJTF+XXr1oUgCNn+yWobOfEiPgFjx03Arl27ABgfY1RUFARBwOPHj19qH4y9yTjzOPMYe5e8zswDCjb3XvVDe2HIPa2O8PhpPCZMmIDdu3ebHCPnHnvXceZx5r3N+BVahhRNxhDOaTrgRXLG33Xp8y01wRUDUgd9YKiV+qBM0wEaLcFWBakmfcGCBRg0aBDKlCmDIUOGoEKFChAEARcuXMCvv/6K0NBQXL16FSVLlnwlx5mbsr4K8+fPx/Pnz6W/b926FVOmTMHSpUtRtmxZabq3t3ee96HVER4/ScCXUyaBSB+qhseoI/7ViTHOPM48xt4lryvzgILNvYLOPODV5554jM9eJGLSpEkAgNCadYyOkbF3HWceZ97bjCvwGGwUgn70lfRkMxx2WiHTz7ckpwF54MABDBgwAE2bNsW6deugVCqlbdSrVw8DBw7Eb7/9BhsbmyzLmpiYmOcRK18mzPND+fLljf5+8eJFAEBgYCBCQkIsrpebYzY8Rh2ZHqNWZ2FFxt4hnHmceYy9S15H5gEo8Nwr6MwDXn3uGR4jAKRqYHRdUzT8owVjnHmceW8zfoX2LfciWYfT91KlVgg6Ipy+l4oXyRm3gtjJZeYPgwwZI9lYYqPIGDUGsByQX375JeRyORYsWGAUbobatWuHYsWKSX/v2bMn7OzscObMGTRs2BD29vaoX78+ACAuLg4DBgyAl5cXlEolSpQogbFjxyIlJUVa/8aNGxAEAcuWLTMpaxG1HF9OmSgtO3PqRNiq5Dh37hw6deoER0dHuLu7o3fv3nj27JlROZ8/f46PPvoILi4usLOzw/vvv4/Lly9bPEe5ITYBPnHiBNq2bYsiRYpIv9jUrVsXdevWNVmnZ8+e8Pf3BwA8vHsTpXzdAQAzvpwER7UcRdRyDPi4FxQywCr9+B8+fJjtcTL2JuLM48zjzGPvmuxy73VkHlDwuZe5rI5qOaan555CBsyYOhGCILzRuWejEHDv1g0p96Z/OQlF0nNv0Me9jK4H5x57W3Hmcea9y5nHLfDeYi+SdTh8I0VfU60BKnsrcOpOGh481+Lhcy1q+Ktgby2T+gXI3FBBB30zXLXS8hDL4qgxhjX+gHFAarVa7Nq1CyEhIfD09MzVMaSmpqJFixbo27cvRo8eDY1Gg+TkZERGRuK///7DxIkTUalSJezbtw/Tpk3DqVOnsHXr1mzLajQdgHh4bdq0Qfv27bFq9W+4dOEsxn7+OQBgyZIlAPTNk1u2bImDBw9i/PjxCA0NxYEDB9C4ceNcHVd2WrdujY4dO6Jfv35ISEjI8XrFihXD9u3b0bhxY3Tr0RvdevUBABR1LWr0D1abNm3QoUMH9OnTB2fOnMGYMWOMjpOxNxFnnuWyGk0HZx5nHntb5CT37FTCK808AIUi9yyV1TDzAH0etG7bHmt/W4fz586a5EFhzj1BEFDCrxj+t2kb2nzQRMo9GQA/Lzej68i5x95GnHnZl5Uz7+3OPK7Ae4tdj9MgVaP//wfPtdh5WSv9PVWjn1+pmBJJaRlNjAF9QBk2x01Ks9z8NicPwo8fP0ZSUhL8/PxM1tdqtVKnmwAgl8uNbsS0tDSMHz8evXr1kqYtWLAAp0+fxtq1a9GuXTsAQIMGDWBnZ4dRo0Zhx44daNCgQc7KSgQN6d+hB4DevXtj4JCRSNMBdeq+h/+uXsXSpUuxePFiCIKAP//8E7t27cLcuXMxePBgad9KpRJjx441e47yokePHpg4cWL2C2aiVCpRrmIwAKCYlzdCq9WQ5omduAJAnz598OmnnwIA3nvvPVy9ehVLliyRjpOxNxFnXg7KypnHmcfeKjnJvVKuVq8088TOwws698yXlaDREbQ6IE2rn9KtR28MHDoSMpjPg8Kce0QEraBEUJWqAIxzTyGD0Xnm3GNvI8687MrKmQe83ZnHr9C+xQI9FfBwkEt/F8MNADwc5Aj0VAAAVFYZTYwVMsDeOqM5rix9viXmHoRFYkBmpWrVqlAoFNKf2bNnmyzTpk0bo7/v3LkTtra2aNu2rdH0nj17AgD++eefHJVVQEaYi6Ws36i50Xv15SpURHJyMmJiYgBAGuWwS5cuRtvu3LlzlseZW5mP2Rxx1CEREeF5MkkP5oDp9dCkL96iRQujbVWqVMnoOBl7E3HmZV1WzrwMnHnsbZGT3CvIzANeX+5lLiuQkXUEQJv+oNewSXMAGf0nZc6DwpJ7mUeXBIDnyYQ0g8wzvHKZrwfnHnsbceZZLivAmWfobc28N7ICb/78+ShevDisra1RtWpV7Nu3L8vl9+zZg6pVq8La2holSpTAjz/+aLLM//73P5QvXx4qlQrly5fHhg0bjOZPmzYNoaGhsLe3h5ubG1q2bIlLly7l63HlN5kgoLK3AspM7SyVVvrmxrL0mmi5TICtSoBSntE0WK3U/91WJWQ5ek1OAtLV1RU2Nja4efOmyfq//PILjh49it9//93s9tVqNRwcHIymxcbGwsPDw6Qm3c3NDVZWVoiNjc22rOJ5UMkFoyBwcnaR/l8hA+zU1gCApKQkad9WVlZwcXGBIQ8PD7P7zKvsmmIbDo8uPryKrU3EUcNlgun1kKf/f+byq1T6IXzE42TsTcSZZ7ms4nngzNPjzGNvi5zk3qvOPACFIvcylxUA5EJG5on/dU7PPbE/q8x5UBhyzzDzElP1IUeEjMxLf8BVWsFixQTnHnsbceZZLivAmWfobc28N64Cb82aNRg6dCjGjh2LkydPonbt2mjcuDFu3bpldvnr16+jSZMmqF27Nk6ePInPP/8cgwcPxv/+9z9pmUOHDqFDhw7o1q0boqOj0a1bN7Rv3x7//vuvtMyePXswcOBAHD58GDt27IBGo0HDhg1z1VfP66Yjwqk7aUa/TAD6XypO3UmTOv4E9A+0aqVMCg190MmyHXo6JwEpl8tRr149HDt2DPfv3zdav3z58ggJCUHFihXNbt9cc1cXFxc8fPjQqHYeAGJiYqDRaODq6goAsLbWP4iKHX+KZX3xNNbgGAVYKwRkPkxLnZy6uLhAo9GYhOiDBw+yOEu5Z+64ra2tpWMxHJHn0ePHIEofkUfIOBaFHCbXQ/YWNR9mLDPOPM48zjz2rslp7r3KzANQKHLPsKxJL+IA6HPBWiGkD2iTsZ+sOrMvDLmXeXTJBw8f6VvWpGee8XXMWcUEY28DzjzOvHc98964Cryvv/4affr0wYcffohy5cphzpw58PHxwQ8//GB2+R9//BG+vr6YM2cOypUrhw8//BC9e/fGrFmzpGXmzJmDBg0aYMyYMShbtizGjBmD+vXrY86cOdIyf/zxB3r27IkKFSogKCgIS5cuxa1bt3D8+PFXfch5dva+vkNPkeEvFQ+ea3H2flq+7CcnATlmzBhotVr069cPaWl5369WR6gdEYn4+Hhs3Lgx/d1/HbQ6wooVKwBAGsnH3d0d1tbWOH36tFFZd2zfbLRNfU4a3/xiPweZQzQyMhIAsGrVKqPpv/zyS56PKaf8/f1x+fJlpKSkSKMOxcXG4si/h2BYStv0FjTJyckAcv4PFmNvOs48zjyAM4+9W15H7uX0QTg/ck98hapevXqIj4/Hhg0bpMwDkG3uiWU1bPlCBFAOMw8oHLkn06VKrUziYmPx77+HpGUUcgGujhmtpTnz2LuEM48z713PvDdqEIvU1FQcP34co0ePNpresGFDHDx40Ow6hw4dQsOGDY2mNWrUCIsXL0ZaWhoUCgUOHTqEYcOGmSxjWIGXmTgksbOzs8VlUlJSjIZ9fv78ucVlX4XizlZ4+FzfsaeHg9xolB6llX7+6xIWFobvv/8en3zyCYKDg/Hxxx+jQoUKkMlkuH//vtQiMnNzYkNi09q2nbpjwY8/oEePHhg7Lgplygfi34MHMPuraWjSpAnee+89APqw7dq1K5YsWYKSJUsiKCgIR44cMQqj7Po5SNUah1zDhg1Rp04dfPbZZ0hISEBISAgOHDiAn3/++eVPUhbHnaIhdO3aFQsWLEDXrl3RrWcfPH8ah5kzv4K9fcY5kwFwd3GAn58fNm3ahPr168PZ2Rmurq7w9/d/ZWVkDODMM8SZl3eceexNUdCZB7xduSdmng5A247dMH/+fPTs2ROjv4hChQqBOH7kAKZPy13uaXQw6SNKZKk/q9ede2Lm2SgEdOvWDQsWLEDnLl3x0Ucf4f7Dx5jz9Swp96RWNCrOPfb6ceYZ48zLG868l/NGVeA9fvwYWq0W7u7uRtPd3d0tNvF88OCB2eU1Gg0eP34MT09Pi8tY2iYRYfjw4QgPD0dgYKDF8k6bNi1Po+rlF3trGWr4q3A9ToNAT32fAFV8FDh7Xx9u9tb53wDT8IYUBAFEhKQ0gspKQL9+/VCzZk3MnTsX33zzDe7duwdBEODt7Y1atWrhn3/+Qb169SxuW2xaa21tjd+3/4MpUV9gztez8PjxI3gW88KQYSMwZVKU0Tpip6EzZ85EfHw86tWrhy1btkg3u8pKMOoA3S69n4M0nT4wFJlq9mUy/S8cw4cPx8yZM5GamoqwsDBs27YNZcuWzYczaMww2KuE1sKyZcswbfoMdGjbCn7FS2DUmHHY8dd27N+7B0DGryuLFi3CZ599hhYtWiAlJQU9evTAsmXLpGblZPDfpDSCTmca5ozlFmceZ97L4sxjb5KCzjzg9edeVpknl71c7hm+QiVXWmPTtn8wKeoLfPuNPveKeXlh5MiRmDBhgtF6WeWeXJYxAqUYb1bZdGb/OnPPMPOICLVq1cJPi5Zi9qyZaN2qJfyLl8Bnn4/Djj/1uWc4EubixYvx6aefGuXe4iVLjTp8N7w+jL0szjzOvJfFmffyBDLXjrKQunfvHry8vHDw4EHUrFlTmj516lT8/PPPuHjxosk6pUuXRq9evTBmzBhp2oEDBxAeHo779+/Dw8MDSqUSy5cvR6dOnaRlVq1ahT59+kiv5BgaOHAgtm7div3798Pb29tiec39SuHj44Nnz56ZrYlPTk7G9evXpQE63jSGN6RCpq8tT0wl6cHwZd9TF4fKNvergrg/S0NEZxW+ALIM5oKUmKpDakYrcSmQdUQgAgRB35mr4fDoANL7bTD+B+xVX5+8yOtn/vnz53B0dLR4L7GCwZnHmfey3vbMy0pW9wNnXuHEmceZ97IKW+YVls8oZ17hxJnHmfeyOPPMy03mvVEt8FxdXSGXy01axsXExJi0oBN5eHiYXd5wpBVLy5jb5ieffILff/8de/fuzbLyDtCPfCKOfvK2MRca8Snp79ULAtJ0wIvkjF8VxGGr1cq8h4b+fXfj7QJZd8opltWwpl+thHRza7QEW5VgFAhi55iFgY1Cf27FYBePWwaABH0vVuYCy9yvDpk7B83v68MYZx5n3svizGNvEs48zryXxZnH3iSceZx5L4sz7+W9UYNYKJVKVK1aFTt27DCavmPHDtSqVcvsOjVr1jRZ/q+//kJISAgUCkWWyxhuk4gwaNAgrF+/Hjt37kTx4sXz45AKlTQN4VmSzuhVo2dJOqRpjBtpZh7mWfz1gIj0HYunr28YROKw1S9D3E/mHynEprWWGpOau7kNQyNFU3gboYqBm/lGlQkC7FUyKK2EHA+PLnYCL8rv68PYm4Yzr/DhzGPs1eHMK3w48xh7dTjzCh/OvJf3RlXgAcDw4cOxaNEiLFmyBBcuXMCwYcNw69Yt9OvXD4B+NJju3btLy/fr1w83b97E8OHDceHCBSxZsgSLFy/GyJEjpWWGDBmCv/76CzNmzMDFixcxY8YM/P333xg6dKi0zMCBA7Fy5Ur88ssvsLe3x4MHD/DgwQMkJSW9tmN/ldI0hBcpOmh0hGfJBB3p/6vR6acbBp3F0BAECAAyR0Z2vyLkVHadr5vrlBN4s2/urILd8FciIPuRFy0GJvLn+jD2JuHMK5w48xh7NTjzCifOPMZeDc68wokz7+W9cRV4HTp0wJw5czBp0iRUrlwZe/fuxbZt2+Dn5wcAuH//Pm7duiUtX7x4cWzbtg27d+9G5cqVMXnyZMybNw9t2rSRlqlVqxZWr16NpUuXolKlSli2bBnWrFmD6tWrS8v88MMPePbsGerWrQtPT0/pz5o1a17fwb9CiWkkhZNWR3iamDF8NaXPF1kMjfQmxplvlux+RcgplVXGDaqQAfbWGeWw1LQWeLNv7iyDXUt4nkxGvyoZDjueWV5/5WHsbcSZVzhx5jH2anDmFU6ceYy9Gpx5hRNn3st7o/rAEw0YMAADBgwwO2/ZsmUm0yIiInDixIkst9m2bVu0bdvW4vy39QMgcrAW8CwZRsEmkssEOFhnBIGl9/UJ+vfWRYadT4q/IrzMe+hymQBblXHtvFqJbDvlzO7mVithFHTZjTb0OokjRuqIIAgC7KwFJKUS0rQELQEyEBJTYabvA5iU1Vxg5uf1YexNwplnvG5hyT3OPMZeDc48zjzOPPYu4czjzHtbM++Na4HHXg1B0AdZ5o+4AH0AGoaApdAQkFHRmZtfEXJDLtM3pTVsWquyEpCisVxbn5vmyZb6QEjVAgkpZPEXgFdFLhP0nX1C/49IUirBRilI/wjpSP9rxYtkffDpSP9HPB+G5yGvv/Iw9jbizMtQmHKPM4+xV4MzLwNnHmNvP868DJx5bxeuwGMA9MHwPJmQ+fYlwKgpK5BFaKT3EyAgo/ludp1PvqycBFJubu7C2Clomo6kUE/TAfHJ+r+LpdZBP/S2ltL7WBUEWCsEk/Og/5VHSB+G+/VcH8YKK868DIUt9zjzGMt/nHkZOPMYe/tx5mXgzHu7cAUeA6APMsPad8OPu1anD0BRlqEhCLBT5bzzyZeVk0DKzc39KjoF1er0vxbk9H3+zCyVSSYTIBOMmwuL4i0Es7lfeV7l9WGssOLMy5DfuceZx1jhw5mXobB91+PMYyz/ceZl4Mx7u3AFHgMAqBUZtd5ymQAndcYHX0ifD4P5ea3tftkHu8xyGkg5vbnzu1PQ/GiybKlMRKS/ZoJ+nvSrhY7yJZgZe5tx5mXIz9zjzGOscOLMy1DYvutx5jGW/15X5gH5m3uceeDMywZX4DEAgMJKgL1KBiuZAEdrATJB/18rmX66IlNT3LzUdr+K9+/zO5DyezSb/GiybK5M+v4A9P0EgAgyQf+LBaBvGq5LL6el85DfX7AZe9Nw5mXIz9zjzGOscOLMy1DYvutx5jGW/15H5gH5n3uceeDMywZX4DGJwkqAo41xcDnamAZcXu0/eBidO7RGYGl/ODvYwN3DA5F1wvDF6JF5ev8+KioKgiAYBcCiBT/gl5+X5SqQDG94sQ8EHelfuje8Qeys5fhifFSOyvbw4UOMHj0a1asGwbuoAzyKqFG1Yhl8NnIo/rt6BUDOfz0w1y+DWC59twD6EXxgEGTifHPn4XV0ZNqzZ0/4+/u/9HYYe5VeZeYtW7YMVnIZHNVyFFHLYWctR1E3NzRqUA9/bNtiknmCICAqKirb7eb1S5ggCBg0aBAA0y85SWmEFKmjEdPOkP2LF9f3TZL+x9bWFsHBwfjuu+9M9pcfr2nkNvNu3bwBF1srfDtndoFlHmNvglf9PU98qDt75jQ+/rA3/IuXgIujGt5FHVC7ZgimTZ+JuLg4AIC/vz+aNWuW7Tbz48Ezt5mXlEbw9/dHz549sz9omObeVzOnYevvGwHkLPfexO95jL0JXnXmid/13IqocevWTZPKrCaN6qFixYq52mZBZZ7I3DNc5jx88ug+Zk6diDPRp6SyiTjzXj2rgi4Ae8UuXwYOHQL+/huIiQHc3ID33gNq1gRKl35txdi6dStatGiB2nXqYuLU6fDw8MSDB/dx6sRxrP9tDWbMnJXnprCGAbBk4Q9wdnFF5649kKIFhDR9Lb2lobLFG14HfWCqFAJSNDoQASTo103RZISMPAdV3keOHEGzZs1ARBg0aBBq1KgBDRS4fPkS1q7+BfXr1MCte7E5/hVFGm4b+lBUKwUkpgLQ6jtmtVUJSE4PXiG93wBBEKBN//UiI5hJGtUo868m4t/FSoW3ccht9g55+BC4cgVITgasrYGAAMDdvUCK8uNPi1GidFkQEWIePMDCBfPRqe0HWPu/jWjbqkWut2fuS48OAIjylHlqpf7XTiJAC0AlB2yV+h9G0nSQXmEICwvDrFmzAAD37t3D119/jU8++QTPnz/H559/Lm1f/8uxca6I5XxVmScXf6FN/zLHmcfeSY8fA1evAklJgI0NUKoU4Or6WotgoxCwZNFPGDZkEEqVLoNPho5E2XLlkJaWhtMnj2PJogU4fvQwNmzYkONtFkTm5Xbkwsy5981X09CiVRs0b9EyR7nH3/MYy4Nr14BLlzK+65UpA5QoUSBFSUlJwdSJ47Bg8Qqj7z4CgGwa8JkojJm3YcMGODg4SH+/f/8+pk2dBB9fP1QMqmxU1leVeTJB0LfSA5CmE5CURrBR6M8XEd6pzOMKvLfVgwfA4sXAl18CiYnG81auBNRq4PPPgQ8/zNcH25Q0HRLTCI42MulGe5akw/QZM1G8eHHs+OsPJGnk0k3Vpl1HTJ46I89NgoGMYFPI9CEpANCmV7TrA4ukkNJoCbYqSEGX+UuONkX/7r2Qvp1Urf6GF3+ZkGVTxufPn+ODDz6AtbU1Dh48CC8vL2nfYXXqolefj7Fxw/+gI8LzZMDeGkhOy/jClTmEAbFfBn1ZbRSC9EUx7nkSHO1sYCUTIFjpjw2CACuZPrxB+paEcpkAHQEa6fj1oW/Y1Fn0rvcpwN5wV6/qf6yYNg24dStjup8fMHq0/seLUqXyfbfmci8hRX9nBVeuiDKBVaX7rH7D91G8mAs2rFuDdq0/yPW+zH3pSUglpGj08y1lnlRWCw92ckGfBTIh42FU/GIIAE5OTqhRo4a0nffeew++vr5YsGCBUQWeyS/HlPH/L5t5SWmQljfMPMP1dbpXl3lpaWkQBAFWVqZfnRITE6FWq3O0Hcby1bVrwM6dwMyZ+h8uRAEBwGefAfXq5ftDraXveqePH8bQwQMRWe89rFy7ASqVPnxkAFo2bYgxo0bijz/+yNW+8pp5lr7n5STzctv5uVHuia1CCNAQITEVEIT8zTz+nsfeaadPA5s2AbNmAc+fZ0x3cABGjgRatgRy2eotO5YyL02rv7vef/99rFvzKwYNGYGKlYIA6HMvJw0/MiuMmVelShWjv0uvqWZM0L/+CiAxFdK2iQBrxctnnkKmn/4iRb9NAkFpJZPOgQCClUyA5h3JPH6F9m304AEwbBjwxRemlXeixET9/KFD9a1V8iBz89zkNB1epOqHfH6apINOR3iapIOWgMePY+Hs4opUndzopgIAyGRGTWHXrFmDhg0bwtPTEzY2NihXrhxGjx6NhIQEs+UQOx2tUKYEzp07h/379sDVzgqudlYoX7qE1JT5+fPnGDvmU5QsUQJKpRJeXl4Y8+kwpCQlSK/M6qBfbujAvijh4wY3Zwc0btwYd25czdE5WbhwIR48eICZM2fC29vb+FeU9HBr3rINtARodIRdB46ga5dOKF68OFwcbVGieHF07NgJN2/eNDq/P69YDluVHJu3/YlevXrBzc0Nrk520KSl4urVq/iwT29UrVQGxVzsULqEDzq0+QAXzp1J7/2ToNEB+/fuhqNajhUrV2HiuNEoW9wL3kUd0LFNC8Q8fIiEFy8wdFBfFC1aFK6urujVqxfi4+ONjo+IMH/+fFSuXBk2NjYoUqQI2rZti2vXruXo/DD2ykRHA40bA/37G1feAcDNm/rpjRvrl8sjc/1rvEjWId5M7om3vfgrqMja2hpKpRKCzCrL1yAePXqEAQMGoHz58rCzs4Obmxvq1auHgwf2m3S0nJaaglnTJ6NW1UC4Oqrh4uqK9xvWx7+HD5q8qmujEB/+dJg0/nO4OKiwfMlC6AAo5YL0JSe7vl8cHBxQunRpPMz078eW7X+hXZuWqFDKFx5F1AiuWAbDBvVHzKPH0KSfm1QN4drthxjQry/8fH2hUqlQtGhR1AoLw5btf4HSH0ibNKyHCoEVsXvPXtSsWRMujrbw9fHGuHHjANJJ50HqI0Wnw+yvvkRgaX+4OqoRGVYNu3b9I/3yKn7h+e/qFXzYswsC/Dzg7GCD8uXL4/vvvzc6jt27d0MQBPz8888YMWIEvLy8oFKpcPXqVfTs2RN2dnY4c+YMGjZsCHt7e9SvXx+TJ0+GlZUVbt++bXK+evfuDRcXFyQnJ1u85ozl2pkzQPPmwEcfGVfeAfq/f/SRfv6ZM3nafG4yT0vAl9OmQRAEfPP9AqnyDsh45UmhUKBFC+OWx3/88QeCg4NhY2ODsmXLYsmSJUbz5TIB16+cQ9f2LeHt4QIbGxvUqhaM1atWQC7oK63E73nPnj7F56NHolTJklCpVHBzc0Pbls3w35WL0ve82Lg4jBgyEOVK+cLDyQaBZUth7NixSE1NzTLzkpOTMWLECFSuXBmOjo5wdnZGzZo1sWnTpozvekQoYmuFhIQE/LpqBVxsrWBnLUfD+pF4npyefTfv4+OPP4a3tzeUSiX8/YsjKioKpNPCRqH/wVirI5MMNuxkXyYAhPT+oQy+54nnOnPmiV6mDy3GCoWDB4HISGD8eOPKO0D/9/Hjgbp19cvlQW4zT6xU+2ToSDi7uGDiF6OlbekAaDM99ObkOUouE3Bw79/o2r4lypT0hY2NDYLKl8aIwf3xNPaxUeZNnzIRjmo5/j12Aq1bt4aDgwM8ixZBv97dEBMTk/EjKhE0Oh2+nzMLwZXKQ6VSwd3dHf0+7In79+5me14MX6HdvXs3qlWrBgAY2LcPiqjlKGJrhelTJ4IAHD5yFG3bd0TZgBJwdbJF8Wyebf/YsRP9+vVD0aJF4eNZFO3atsG9e/dMBhZJ1Ro3sklMyXjGFvPwXck8boH3Nlq8GFi9OmfLrl4NBAYCY8fmahfmmucmpGY8JOoIeJKkk2rmQ6rXwMplizF82BC069AZQVWCoVIojH4hSErTf+G4cuUKmjRpgqFDh8LW1hYXL17EjBkzcOTIEezcudOkLPpORwVs2LABbdu2haOjI2bP+Q4aAlRKlf6LY2IimjWMxL17d/D5mDEICgrCuXPnMH78eJw6fRb/2/wnIAgQSIcuHVrj6L+H8NmYcahVIxSHDx1E48aNc3Re/vrrL8jlcjRv3hyA8a8oQnpnnFqdvnmwAODWjZsoWao0WrXpgCLORRDz8AGWLVqA0NBQ/Hv8LIq4uoIoo3Kz/8cfouH7TbBs+QokJyVCJrfCtZt34OzsjC+/nAZHZ1c8iYvDypUr0KBuLew6eAxlSpcxGjt94vgvULtOXXz/0xLcunkD4z//DB/27AIrKzkqVQrCL7/8glOnTuHzzz+Hvb095s2bJ63bt29fLFu2DIMHD8aMGTMQFxeHSZMmoVatWoiOjoZ7Ab2myN5xV68Cbdvq/5vdcu3aAdu25bolnrnM0/evQVLOZc49AEjVaKDRaEBEePzwIebOmYWEhAS0bt9JyjxzxH6iJkyYAA8PD8THx2PDhg2oW7cu/vnnH9StWxcAoNFo0OaDpti3bx/6DRqCOhGR0Gg0OHbkX9y5fQvhtWoZ/fKoIyAxKQX9PuqFHX9uw6+/bUS9Bu/rX63Q6efLc/A9R6PR4Pbt2yidqSuG2zeuoVr1mujWsw8cHR1x6+YNfD9vDpo2iMC+I6egUCigBdDvwx44feokvoiajLJlSiPxxTP8e/QEYh7HIjFVf361OuDhwwfo3KkTRo0ehUmTJmHLli2YMmUK4uLiMO/b74z2vXDBfPj4+uLLmV9DRzrM+3oW2rVsit27dyOoag3oAFy8cB6N6oXD28cXk6d9BS8PD+ze+RcGDx6Mx48fY8KECUbbHDNmDGrWrIkff/wRMpkMbm5u+uuamooWLVqgb9++GD16NDQaDSpXroypU6diwYIFmDJlitG1XL16NQYNGgRra+vsTy5jOXHtGtCxI3D+fNbLnT+vX27z5ly1xMtt5mm1WuzfswtBVYLh7e0DwODVLxh/zxNFR0djxIgRGD16NNzd3bFo0SL06dMHpUqVQp06dQAAly5dQu3wMLi5uWHevHlwcXHBypUrMahvbzx+FIMhwz+FDsCLFy/w/nt1cPvmDXz22WeoUaMG4uPjsWfPHty7dx/+JcsgMTkZzd+vjxvX/8NnYyegUsVKOPbvfsyYPh2nTp3C1q1bLZ6PlJQUxMXFYeTIkfDy8kJqair+/vtvtG7dGosXL0HrDt2gAfDHzv1o1bQBwurUxchR+u/W9vYO0BEQ8/ABGkTUhFwuwxdfjEMx3xI48u9hTJ8+FTdv3sR3Py4226JGqyOkaAjK9BbR1goBaQbXgcQvlchobZJVH1pqJd66B1r2Djh9Wv+DRPr3I4vi4vTL7d6dq5Z4efmeJ/7XxtYeI0eNxeiRQ7Fv907UrltPmm/Y/VpOn6NuXL+G8LBa6PvxR3B0dMT169cx++tv0LhBBA4cjYZCoTAqe+f2bdC+fXv069cPZ86exYTx43H+wgXs2H0QKqUCOgJGDB6IFUsXYuDAgWjevDlu3LiBcePGYffu3Thx4gRcc9jlQnBwMBYvXoI+fXpj5KixaNS4CbREKFbMGwTg1s2bKFGqNFq26QCnIvpn2+WLLT/bDuj3MRq93wQ/r1yFe3fv4NNPP0WXrl2x9Y+/YaMQoLLKeBPOSp7xXA0iqY88hQxGr9GK3trMI/baPHv2jADQs2fPzM5PSkqi8+fPU1JSUt53cukSkVqd/jtjDv+o1USXL+dqNwkpWnqSmPHnWaKWniRo6HG8hh6Z+XPl1kMKCwvXd9ABkEKhoOo1atHEyV/S7YdPKTZBQ08SNPQiWUs6nY6IiHQ6HcUnayg5JZX27NlDACg6Oloqw4QJEyjzR7hChQoUERFBOp1OX6b0P+MnfUkymYyOHDlCREQarY4SUrS0avVaAkCr12+mx/EaWrNhCwGgL2d+Q7HxGkpI0RIR0dSpUwkATZgwIcvzUrZsWfLw8DCaJu5LfzwZ58nwT2y8/vhjX6TSixcvyNbWlqZ/9Y10XuYvWEwAqGOXbvQkUUsJKVrSaDOO8UWylp4maik2XkMxz1Lo0bMkKlkqgPoNGkKP07e95Y9/CAA1adrM6NoNGDSEAFDfAZ9I2yYiatmyJTk7O0vHcejQIQJAs2fPNjq+27dvk42NDX322WfStB49epCfn1+2n6O8fuazu5dY4fBaMo+I6Icfcpd5P/yQ612Yzbwscm/ej4ulvDP8o1KpaNac7yguQUNJqVqKS9BQSpqWAND48eOlezszjUZDaWlpVL9+fWrVqpU0fcWKFQSAfvrpJ6PME8so5ikA6ttvAN2+/4hq1Awjz2JetPvQcZMcEu9/Q35+ftSkSRNKS0ujtLQ0unnzJn300UekUChoy5YtpmVNzzytVktP41Po9MVrBIB+XrNB2petnR31HziYnqaXMSFFfy7FzHuWqKWw2hEEgFat3WCUeT16fUgymYzOXLpOsfEaOnnuKgEgD89idOdxvJR5dx4+JWdnZ4qsV186J/Xfa0heXt5088ETaVpCipYGDRpE1tbWFBcXR0REu3btIgBUp04dk+Pr0aMHAaAlS5aYnefm5kYpKSnStBkzZpBMJqPr169n+RnL6n7gzHszvLbMIyJauDB3ubdoUa42n9vMO/ffXQJAbdp1oCcJGnqaqKWUNH3GPUnQmGSen58fWVtb08Ur16XMS0pKImdnZ+rbt69Ujo4dO5JKpaJbt24Zla9x48akVqvpxv04epKopc/HTSQA9NdffxFRRg4lpOi/Gz2O19CsufMJAC1esdoo82bMmGG0LpE+93r06GHx/IiZ3KdPH6pSpYq0v/hkLdna2lLHLt1N8rVnn4/Jzs6Obty4YZR5k7+cSQDo0PEzRrkkHof4HTsuQZNxTTJ9lxRzU8pTc9cu07bNydfP6EvgzHszvNbMmzQpd5k3aVKuNp/bzHsUr6Fv07/r7dp3mO4/SST/4iWoSnAIvUjS39thtetQ2XIVKCVNKz1HTZsxy+h7nrnnKEM6nY7S0tLoxo0b0ncisYyjPx9PAGjYsGFGmbdgyc8EgH5YvIIex2vo0PGzBIB6f9Tf6P7/999/CQB9/vnn0jRzz3CZ8/Do0aMEgH5cuFh6vo2NN/98m92zbZ+P+xvl0vT0PL547a7R821svIaeJurPa+bce9cyj1+hfdscOmT5tVlLEhP16+WC2ZEGBcFsyw0BQAmvoti/fx8OHTqC8ZO+ROOmLXD1ymVMGPc5wqpVxuNHj6EhfbP/Mxf/Q+fOneHh6Ql7GwWsVUpEREQAAC5cuJCzQ8r0y+Of27eiXIVAlC4fhJTUNDxLSENiigZ1IhtCEAQc2LcHBP1rpgDQpkNno+117mz899wwHJZcrdQPYy6ep/j4eEwcNxohlcrA1UEFF3sl7O3tkZCQgEuXLupbxVBGlyrNP2gt/boq9nGg0WgwffqXqFYlEB5FbODmqEJRRxv8d/UKrly6CEB/fcQfgJo1ayY1MVbIgEqB5QAADd9vYtSRably5RAXFye9RrtlyxYIgoCuXbtCk96qSKPRwMPDA0FBQdi9e3eezxFjefbwITB9eu7WmTFDP6hPLlgcXVXQ/zqYOfrEvy9Zuhz/7DuMf/YdxpoNW9Chc3d8OuwTLPzxe+mVjBcp+rszTQujEbN+/PFHBAcHw9raGlZWVlAoFPjnn3+McnD79u2wtrZGx669LP7yKI6+de36dUTWCcOLFy/wx64DqFAxyORXZEu2bdsGhUIBhUIBPz8/LFy4EN9++y2aNm1qtFxMTAwGDuiPMiX9oFAo4GSnQqWy+lY/Vy5llLtq1VD8smoFvpo+Bbv3HYIcGkAQpMzTpYeevb09PmjRwijz2nboBJ1Oh3379ko1owDQrEUrqYWbDoCtvT0aN2mGA/v3gbRaJCcnY8/unWjVqiUc7dTQaDTQaTSQQ4smTZogOTkZhw8fNjqeNm3aWDwn5uYNGTIEMTEx+O233/Tl0Onwww8/oGnTpjwyN8s/jx/r+7zLjRkz9OvlUF4zTy4T0ludEF6k6LsOEfslzpx5lYIqw93LV8o8a2trlC5dGjdv3pS2u3PnTtSvXx8+Pj7SNCJCxy7dkZiYiKP/6r+/7vjrD5QKKI1adeobjUiYqiEpJ/bt2QW1rS1atGpjlHniq2H//PNPlufkt99+Q1hYGOzs7KRMXrx4MS5cuCB91xNbGGY+PzJB/100vE5dOLl6wkrQQqPVIi1Ng3oN3gcAHNi3B4Bxn01i7umgb8mj1enPlZaMc1tcJjFV31LP8HuevXXGtczLIB2MFbhr1/R93uXG7NnA9es5XjwvmaeW3nAQoFQoMWbcJJw8cQxr1q4FDJ7fXqQQNv2+GYIgoE2HLniWkIaU1DSLz1ExMTHo168ffHx8pKwRv0NcTn+2AzIyoGOnzkaZ90HrdrCyssL+vbv12Zf+fNupa3ejY6hWrRrKlSuXbfZZorLKeL4V+/sTn21DK5WBm2P2z7aNmzY3yryy5fWtJm/fuolULUGjy8hwjS5jPQBG2SheiXch8/gV2rfN33/nfb3u3bNfLp3Y2eTzZJJGwQKR9CXNEAF4mqyDk40MlYKrIqBiMAj6DsEnjRuNH7+bi2/nfIUJU2bgRXw8GtWPgLW1NcaOn4SSAaWhtlHj4f3b6NS+LZKSkrItm45gMnrPo5iHuPbfVTjZqcyuExsbC0D/qpOVlRWcXVxAABTpry94eHjk6Lz4+vriypUrSEhIgK2trfF5EDs5Tu8LDwD69u6Kfbt3YsSosagSHAJ7BwfIZQLat26O5OSMYxUPx9PDU3qX30ah3+aoUSOwaMF8DB7+KWqF14GTUxEIMhmGDeyLpKQkaWAPkYNTEdiqBKnjULGfGjdXZ9iqMjoaVSqVAPT9vtjZ2eHhw4cgIouvyZYooJGf2DvuyhV9H3e5ceOGfoTu9Nchc8Js5kHfcW6KxrQCTPx7QJmyCKwcIr06X7/B+7h9+yaivhiNNh26wNHJyejVDEB/v38162uMGTUS/fr1w+TJk+Hq6gq5XI5x48YZVeA9evQInp7FoDXo+SPza2ua9IflE8ePIvbxY3w+YTKKeXmblNUw8zILDw/HN998A61WiytXrmDcuHEYNGgQKlSogPDwcH25dTo0bNgQ9+7dw7hx4xAYGAi5Uo1UjRYN6oYZ5ffC5b/i65lfYsWyJZg6aQLs7OzQtEVLjJ88He7uHlL5i7q5m2Sem7s+j5+m57bILT2bDDPP1d0DqampoLQExD99AY1Gg++++w7ffWf8+q3ocaYKDk9PT7PLqdVqoxHZRFWqVEHt2rXx/fffo0uXLtiyZQtu3LiBBQsWmN0OY3ly9appn3fZuXJFv14OX5PKbeY5u7pCrVbj+vXr+hFSDboLIcDo+6G+gg9wKuIMwLjfNpVKZZQVsbGxJvdhUhqhqEcxAEBcXCxkAGIfP4K3j6+UeQRI30vFXT+Ji9XnR/qxiJnn5uYGKysr6bugOevXr0f79u3Rrl07fPrpp/Dw8ICVlRV++OEHqd8+8XueuG1DWtJ/F/1j2xaL30XjYmNN+myyUaQPSKE1/jFWPLeAceal6QBBQ0bf8152kA7GCtylS6Z93mXn2TPg4kWgePEcLZ6X73mJaRn3OwFo1a4Dvp/3NaZMHIcmLVpJ6zvokhFz9y6ICKX9zX+vEJ+jMn+XqlixIuRKG6Sk6dCgbi0kJyUZfc8DAPsi7tJrpVoC5FZWKOLsgifS863+v+4enibf84oVK2b0o0luibkn9veX22dbF2cXo8xzsNX/EJucnHGc5n5kNsw9jU5fcScIeCcyjyvw3ja5bFUiefQoV4sTERJS9bXigP6XVsOWEEDGlzb9fOBZkr4SjwhI1ug7M/50zHj8+N1cXDh/DgCwf88uPLh/D1v+3Imw2vpWdwoZcHDvsxyXTRxu2nD0HldXV1jb2OD7HxbBWikgTWPcp4Gzi/4LrbOzMzQaDeJiY+Hi4oI0HUEBAQ8ePMjRvhs1aoS//voLmzdvRseOHY3miZ0ci0H0/Nkz/LV9Kz4dMw5DRoySlktNTcGTuDjpFwYBBp1yCoLRu/xqJbB29Sp07NwNEyZONfqCHBv7GA6OjrBXyZCiyWiFo5AJUr+BhqwVljtvBgBXV1cIgoB9+/YZdU4tMjeNsVcur4MC5HI9c5knkwlGPxYAxrkH6EeyVsj0E8UHyQqBFbHr77/w39XLCA6pZvKrrkKmv6/r1q2LH374wWjeixcvjP5etGhR7N+/H9DpAJlMyjxxZC4ZALVKv4eWrdujqLs7vpw4DjqdDiNGmfZ9KmZeZo6OjggJCQEAVK9eHdWrV0dQUBAGDBiAU6dOQSaT4ezZs4iOjsayZcvQo0cPJKbqkKoFrl41rWhwcXXF1JlfY+rMr3H3zi38sXULJo0fg0cxMfht4zYp8x7FPDTJvEcP9Xns7OJitE1xQA0x85I1hJiHD6BUKuHoYA9rlRJyuRzdunXDwIEDTcoEAMUzfdG31GdKVn2pDB48GO3atcOJEyfw3XffoXTp0mjQoIHF5RnLtRz8mGlWLnIvt5knl8tRu249/PPXH4i5dwdunl4mrcQM1zGU1SiBLi4uuH//vtE0lZWAh/fvAQDci7rC3lpA0aJFce/uHSnzUtJMH7qLOLvg+LEjICLpHk7TEZ7EPIJGo8myD6iVK1eiePHiWLNmjdH9n5KSIv2/0cBlZji7uKJCYEWMjZoMGQTpu7MA/cOoh6eXSZ9NYu69SAYAMqkIFWCceXKZvlVM5u95YisZxt5Ief2uZ3B/Zicv3/PE/1rJxP6DBYyf/CXaNn8fK5YshBV0kGk1UFz/D24KKwiCgN1//AnBzhHWCuPBFcTnqMzfpQB9y9vT5/XfpWSCvoVZokHf88+fPISvj5eUeRqNBk/iYlEk/XuSs7P+vw8e3EdJfx+j73n37t3Lcf935iSlEVLSn6nz8mxLMM08cZnMxMwTBMDBWobnyfomeXJBMBnt9m3OPH6F9m2TixYlRooWzdXiSWn6CjCRuS9o1gbNjR88uK//e/rNZCXT34CX01+p8vAsBhn0AQgAyvQQE3+J/Omnn3JULpVKheSkJJNRGps3a4ob1/6Dt6cralQLRXjNUARXDUHlYP0fXz9/AEBYnboAgHVrfjHa7i+//IKc6NOnDzw8PPDZZ5/h7l3jUX1U6a80bN20ATIBkMtlICJYq1T6v6f/krBy2WJotVppPf1w3xnbETuCFn/xEAQBSpVK+qJMAP78Y6s0qtDzFB3SDK6VztzFyoFmzZqBiHD37l2EhISY/KmYz0O2M5YjeR0UIJfrmcs8baabSQBMXrOwtjJ+dV4AcPZ0NADA1bUoBBgPGmHY+iJzpfjp06dxKFN3B40bN0ZycjLW/brcKPPUSn0G2qoEWKV/oREEYMSosZgy42vMmBKFyRM+z9U5MBQQEIDPPvsMZ86cwZo1a9K3r9+PWG4x85Yv1ue3TNB/uZILMMo8Xx9ffNhvACIi38PpUyf1I4mlb+vFixfY9PvvRpn325pfIZPJUCOsttEPR1t/34Ck5GQp8168eIE/tm1BeHhtyOVyqNVqREZG4uTJk6hUqZLZHHPJVCmYF61atYKvry9GjBiBv//+GwMGDHi7Ok9mBc/GJm/r5SL38pJ5Q0eMAhFhyKC+0KSlSfe4uKwmLQ1/bttsMfPMqV+/Pnbu3Il79+5J0+QyAb/9uhJqtRoR4TUhCAKaNH4fV69cxr8HdsEqvfIq8ybr1K2HhPh4bNu8yWj6ihUrpH1ZIgiCfgRxg40+ePAAmzZlbEvMPKVKheTkJH3eGWTe+42b4sL5cyhZvCSCgquicnAIqgSHoGrVEFStGgrPYvpWheL3PMD47Q3D73kigvH3PK1O/3sOY2+VvH7Xy0Xjgrxknvhfa0XG97y6ke+hbr33MHv6FCQ8eQJoNEBqKprVrAkiwoOLFxBeNQihoaFmn6Myf5cC9Jm3cpn+u5RCnlE5Jb62uubXX4wyb9P636DRaKTGMLUjIgEA61YbP88ePXoUFy5cyDL7zBHLlpSUpG/hlr5fQdC3GLa1sTb6LpzTZ1sARiMAG2aeYaUppTcM0pH+uVZlhbeulV1WuAXe2+a994CVK/O2Xi6orASkaQFt+k0mE4xfj5BB/yVCnj69wwdN4O3thQ9aNId/yTJISdPizJlozJ/3DWzt7PBx/0+gA1C1Wi04FSmC4Z8MwKjPx8FKocD/1vyC6OjoHJWrYsWKWL16Ndb9thYlSpSAtbU1KlasiGHDhmH9+vWIrBuBoUOHoky5ikjVaHH79m3s/GcHBgwehqqh1RFZvyFqhtXGpHGjkZKUgJrV9aPQ/vzzzznav6OjIzZt2oRmzZqhSpUqGDRoEGrWrAmlUokrV67g55UrcTo6Gp06tIHKygF16tTBd3Nno5hHUXh4+WH/vj1YuXwpHJ2cMjYqCFAZ/DItvssv/tLbqHFT/LpyOUqVLoPyFSoi+tQJfD9ntvSKnI6Mm1mnmnvPOQfCwsLw8ccfo1evXjh27Bjq1KkDW1tb3L9/H/v370fFihXRv3//PG2bsTwLCAD8/HL3Gq2/P5Bp9NTsZJd5gP4HCI0ORg+tly6cQ0qqBhod4XFsLLb8vhG7d/6Nps1bwte/uMmrZWLri6ZNm2LKlCmYMGECIiIicOnSJUyaNAnFixeHRqORlu/UqROWLl2KAQP648qVy4iMjIROp8O///6LcuXKoWPHjtKXIUCfH30HDoatnR1GfNIPCfEJmDZrDmSC/oeV3PQTMnLkSPz444+YOHEi2rdvj7Jly6JkyZIYPXo0iAjOzs74/fffsWOHvmsHuQxwtBbw4PEzNGlUH23adUKp0qVhb++AE8eOYufff6Jpi1aAIMDOWv8F2dnFBSOHDMTjB7fhXyIA27dvx/Kli9Drw37w9vE1epCVy+Vo27wR+n8yDDqdDt9+MxMvnj/HZ2MnSMvMnTsX4eHhqF27Nvr37w9/f3+8ePECV69exebNm82OdJ5bcrkcAwcOxKhRo2Brayv1r8VYvilVSp99uXmNNiAgV6Nv5zbztASE1aqF+fPnY+DAgahTKxS9PuyL0uXKQ6NJw5noU1ixdBHKlauA95s0l7aR3SiBEyZMwJYtWxAZGYnx48fD2dkZq1atwrZtWzFz5kw4pX9fGjZsGNauXYvWrVpi1KhRCKoSiheJSTiwbw8avN8U4RGRaN+5G5b89AMG9e2F27duoGJgII4fOYjp06ahSZMmeC+L78LNmjXD+vXrMWDAALRt2xa3b9/G5MmT4enpiSvp10EuE2CrAgIDK+Lgvj34e/tm+PkUg8LaDoHly+LzcROxa+ffaFS/Nj7uPwglSpVGSkoy7ty6hV1/b8c3c+fDrZi3UZ9N4nc98UE2cytvION7nnj2EtMIjm9Zn0/sHVemDODgkLvXaB0dgbJlc7x4XjJPbHiSnGZcyT5u8jS8F14Njx7FoEL6q7FhQUH4uFUr9Bo1CkevXkWdBg1gZ2dn8hxl7rvU5s2bsWPHDgAZOSkIAhTpNWfr168HCXLUqVsfFy6cx5eTJ6BCxSB80LodAKBU6TLo3usjLPzxO1grZWjapIk0Cq2Pjw+GDRuW8/MKoGTJkrCxscGqVatQrlw52Kht4VLUE8W8iqFOnTqY+80suLq6oJiPPw7k4tkW0HenAJiOKGtIrNATt5CsAVSKLFZ42+TXyBkse2/TKLREGSN8PU3UmIyIFWs4Ulailpb+/Ct16tSZSpUKIFs7O1IoFOTt40vtO3Wlg8fOGI3os/2ffVStek1Sq9XkWrQode/Zhw78e4wA0NKlS6X9mxuF9saNG9SwYUOyt7cnAEaj6MTHx9MXX3xBpUuXIaVSSQ6OjlS+QkXqN2gInfvvrrT//+7GUpfuvcjJyYnUajU1aNCALl68SED2o9CKHjx4QKNGjaIKFSqQWq0mlUpFpUqVor59+9KZM2ek5e7cuUNt2rShIkWKkL29PdVv0IgOHjtNvr5+1KlLd+k8/rhQP1LPvoP/SiMXiSOT3bgXS9179qaiRd1IrVZTjZphtPmv3VQrvA7VCq9Dj9KvycZtfxMAWrt2rVFZly5dSgDo6NGjRtPF8/vo0SOj6UuWLKHq1auTra0t2djYUMmSJal79+507NgxaRkehZYRvV2j0BLlLvO+X2A6Cq2DoyNVrFSZJk+fRXdiE4xyDwCN+nx8xjZeJNHIkSPJy8uLrK2tKTg4mDZu3Gj23kpKSqLx48dTQEAAKZVKcnFxoXr16tHBgweJSD+yGgD6sO8AijXY509LV5GVlRV16taTYp6n0oukjFFrDfn5+VHTpk3NnpPvv/+eANDy5cuJiOj8+fPUoEEDsre3pyJFilC7du3o1q1bRvmZnJxMH37UlypUrET2Dg5kY2NDAaXL0KjPx9HtR8+lEcMiIiKoXPkK9M/OXRQSEkIqlYo8PDxp+Kdj6MmLFIpLP/8n0kehHT95Go0aO4GKeXmTUqmkikFV6H+/bzc5puvXr1Pv3r3Jy8uLFAoFFS1alGrVqkVTpkyRlhFHof3tt99MjrlHjx5ka2ub5WdFHC2uX79+WS5niEehffO9TaPQEuUu854maqXl9x4+QR27dCdvH19SKpWktrWlikFVaOToL+jC9fv0KF5DPr5+1PD9JiajBEZERFBERIRROc6cOUPNmzcnR0dHUiqVFBQUZPR9UPTkyRMaMmQI+fj6pt/bbtTw/SZ06MQ5Kfcu34qhnn36kruHJ1lZWZGfnx+NGTOGkpOTjbZlbhTa6dOnk7+/P6lUKipXrhwtXLjQ7HfRU6dOUVhYGKnVagJAERER0iiJV289pL4DPiE//+KkUCioiLMzVa5SlT4b/Tm9ePHCZCRyw1FoDUd3zDwapjQqY6L5HM+pN3FERlZwCvUotJP/z951x1dRpt0zM7enFwgJCRAg9CogiCAKAoICCvbede2irmvZXXXddW2IXVexfaJir4iAFEUQEanSIRBKEkivt8zM+/1x7ty5NwVCCBBgzu8XSO6d8s6dzMlTz/Ovgz7FwXLetGlvCQBi3s9LQ8+lsf2lEycKAKJ7x45CbN4sxKpVQixbJt76+9/FwH799utHNcSWEsL01Rb9ukycNfYcER0dLaJjYsTECy4W67btieCIvWV+8djj/xWdOnUSdrtdJCcni8svv1zs3Lkz4jNoyBRaIYT48MMPRZcuXYTdbo9Y16H4tkIIMffHeQKA+Ob7uRGff11TgE9UzpOECJ/lYeFwoqysDHFxcSgtLa1T/Nrr9SI7OxuZmZmhSXqNwr//DTz8cMO3f/xx4KHaWkgHggiW9UdoA4RlH2QAssxSWiFYfeGxy6gM6CGhS0UGbDKFdo1IukOREO2M1HAKH65wqDCmk4VPtakJKfjlsnOq2OGGsaZw3b6GXL+mi5BIsaYD5T499NmHZ2sNJLjl0D1pDsKejf2dP9CzZKF54Ihx3pYtwJgx/P9AyMoCZs48qEoUA43lvOoABZGNTgwpWKFnZHapYcTMr8V55vWPGH4GCgoKsHbt2oj9jgXOe/HFF3HHHXdg7dq16N69e4P22d/zYHHesYEjxnkApzKOGwesW3fgbbt3B77+GjjIYVMW5zUdGmvnGfv6VAGHIqHcp0OSEGodM67FwKFyXpP+jh4CLM47NnBEOW/NGuD004GiogNvm5gILFgAHKS8T1Nxnhsq3JWlkAIBVg36/UB0NFt68/OhdcwCYmIOmfMeeeQRPProo8jL3wt3TNJxx3kuu4Ty4FARIBiZDbse4MTkPEsD73jE9dcDNQYo1IuLL+b2jUBNsV4JkdO+dPAB1IOvqTrbN2OcMpw26jLFuWREOSS47Wb7VrSztoZTUzpcbHEwjymFfckIM4SkIzd22lhTfRpW9V0/RYr5GHuDQVCj5Nu4D+ElQCVeHaqmo8wr4FdJrJouUOXXa+k8WLBwzKBjR+DTTw8clMvKAj75pFHBO6DxnBfllOC0SYgJ6tHFu8mBsU7qhsQ4JThsssV5xwHnrVixAp9//jkee+wxTJgwocHBOwsWDhrt2wMffQR067b/7bp353aNmBRvcV7TobGcZ+zrtkshzgOA8PILy86zcEKgZ0/gm28YnNsfEhO5XSO0uZuK89yqF5KuA7m5QGUlEAgAxcVAQQFERgYQHW1xXgM4r8pvTASODN4BJzbnWQG84xEpKcDUqays83jq3sbj4ftTp3L7RsAQ6wUYWbcrdT+I4ZkASeJDGe2UEeeWwyZsyYh3K4h2yhG9/R7H/iejNhZcA0lWAjUMEjw0MKWg2HDMYTr3/tbkcTTs+g1iMgpoq/zMVAhB7cH66EoXQKmXWWlmbwUqfAJ+DSHCs2DhmETv3sD33wOvvUZNvHC0bQu8+ior73r3bvQpDoXzPA4ZDht5T5bMnxM8ChxBERWL8459zjvvvPNw6aWXok+fPnjttdea9NgWLNSC4dC++SYTFOHIyuLrX38N9OjRqMNbnNf0azreOM+ChSOKwYNZWffYY9TEC0dcHF+fP5/bNQJNxXmSLAMFBbV3VFUOL1OaPgRjcd6Jw3nWEIvjFSkpbIu98EJgyRJg7lxg3z5Omz3zTOCUU2obe/tBeNuSMWHG+Dmg839dAAENofHb4ZAAOBVG05sLSHSATwUcNgnegOC4aT/fD+gCsk7CO1KtV3V9zjXPHV6SLASFnwO62YJcE8ZrNe9KzRJrHRQOPV5Hbls4AdCxI7/OPZfi7l4vJ5dlZR10osLivKPPeQsWLAhtc6xw3vbt25vsWBYsNAjt2/NrwgTKCBi817EjkJzc4MNYnHf0Oc+y8yxYaAB69uTX5ZcDGzYAPh9bU7t0ATIzG3yYw8p5NhvXVRf8/gav8UB45JFH8Mgjj4R+tjjvxOA8K4B3vCMri19XXtnoQ9T1UBk97GqwbFgKjs6OcgKVvkiiMx6ZOoaLHXUosgSnDaHr04WApptjwwOagCJLUEPXevjGVB/4c+a5faoITeYJ6EC5lzXFMkz9J1liRkKSqCnosUsoqaZuSoR+QNhNsctoVoa3BQuNRkpKoyuLgYPjvBh4US3Z4BVmNtXivIbB4jwLFpoQyckHFbALh2XnWZxnwcIxh8zMgwrYheOwcp7fD1RXU/OuvLz2+253o9bcUFicd/xzntVCa+GAqOuhMvQBjMi2Ab9af6mqT2XEvTlB01lmqwfLdf1qpOixoW0A1L5WY//wcl+Knzau576hn7PbLsEe9uTqACBJkIPl0bIc/KMjS7AF9QaqAyL0ejhH68F1yjD1CSxYONHRYM6rroa8bh2claX1HsfivPphcZ4FC80Dlp1ncZ4FCycSDhvnqSqQk8MhQzExrIZWFPP9qCgG9g4jLM47/jnvmAzgvfLKK6FJIf369cPPP/+83+0XLlyIfv36weVyoX379nXq0nz22Wfo1q0bnE4nunXrhi+++CLi/Z9++gnjxo1DWloaJEnCl19+2ZSXFIHmNhjYGDChBwdz6wD/FwK2GpHtmtNhjD58IFjW2owuzSA4TQgu1iAqRF6DHHzwa0bxjayCX2M2wZhc1Nie+zrJK4jwcxsioBJE6J5AhE0dCvuQdUSSpbGdAQHeVx3mNRxJNLffdQtHB83t96DBnGezQc9oA83JbKrFeRbnHQqa01osHF40t3tt2XkW54W2b0430MJxg+b2e3XYOK+qCigp4SANTWNrb1ISkJEBtGtHuQOn87Bdl8V5JwbnHXMBvBkzZuCuu+7CQw89hBUrVmDo0KEYM2YMcnJy6tw+OzsbY8eOxdChQ7FixQo8+OCDuOOOO/DZZ5+FtlmyZAkuuugiXHHFFVi1ahWuuOIKXHjhhVi6dGlom8rKSvTu3RsvvfTSYbs2m40dzaqqHrZzNAa6CIpHiuDIehGcvhN8PfxZdtmD4pkShUBjXVJIPNMmS3A1ozJWbyCYkRCAHryOmo+wHAzp1xXFP5jsTUNgkFfNh1JCZIm2EAKlXh1q2D0xvoTgNMbwT5mjzkXoHJJk/uGRYJJAQMcRz5wHAgEAgBKenbJwwuCY5zy7HSI5GV672+I8WJx3qLD48PiHcW+Ne91cYNl5FucZsHjIQlPihOM8VQXi46lFmp8PlJYCe/cCu3cDDsdhDd4BFucZxz7eOe+YC+BNmTIF1113Ha6//np07doVU6dORUZGBl599dU6t3/ttdfQpk0bTJ06FV27dsX111+Pa6+9Fs8880xom6lTp2LkyJF44IEH0KVLFzzwwAMYMWIEpk6dGtpmzJgxePzxxzFx4sTDdm2KokBRFJSVlR22czQGvrDSYQFEjNM2hCkNGBNwXDYpYmS0y8bXj+TkmwNBkiJFMMOzE8ZrRhluXVH8WlkFYWYNjKzCwZQcG1kOPfJFqLqANyBC5w8RXB1rD8UVFGY2NJ2MF+2QQ2O7ZZC8bbKEWJccmrAk48iNFuelCZSWlsLpdMJutx+x81poPrA478jC4ryjy3n7g8WHJwbsdjucTidKS0ubVdbf4rzmxXllXhExdfFIcZ7FQxaaGicc5zmd/KqqijyhrgPFxYf3omBx3onCecfUEAu/34/ly5fjb3/7W8Tro0aNwuLFi+vcZ8mSJRg1alTEa6NHj8a0adMQCARgt9uxZMkS3H333bW2CQ/gNQY+nw++sAk0B3JSJUlCy5YtkZubC6fTiaioqGbRt+3z6/CrkeWpBiQAkiZB1iNjwTIih+/IAAJ+oDnlX6RgSN9/gGwCR29L8APw+wC3w7xWWQioQZ2BoJwAZAlwuGQUlwuoOlAFwO04sFFb7dfNcmDwM1OFCFUN+/1AVbVUaxKShEhylgC4JRmVfh1a8HiKJsNuk6D6AUUIaKqA0y5BDUiQhYAICCg2CQG/dNjvkRACgUAApaWlqKioQOvWrQ/zGS0cKVicByAQABQFsixbnGdx3gFh8eGxjYPlPABITk7G7t27sWvXLsTFxcFutx913rPsvObDedXVErRgNZABRUaI2ww0JedZPGShobA4L/LnWpxnTFSoC2VlDOzJh69+yuK8E4PzjqkAXkFBATRNQ0qN6YIpKSnIy8urc5+8vLw6t1dVFQUFBUhNTa13m/qO2VA88cQTePTRRw9qn7i4OFRXV6OgoAD79u07pPM3FXTBTISuA5AAGRJ0MFUhy4xsy83A6T5YCASFPbXaBC4F/5GDBGdcu9Nm6gYIAAGVGQPj8wjtLwFSWLGvonBizv7Az5kHVmTArkjwa8xSQAT/JhgnDl9o5KnDXw59L8v1jDk/inA6nWjdujViY2OP9lIsNBFOeM7z+dgqERsLxMUd8XUfCMc159WwmZVmyHn7g8WHxyYaw3nGPS4oKMDu3bsPx7IOGpad13w4Twr77GteSzh2HwbOs3jIwoFgcV4DUFUFFBTUfj02Ftix45DWfSBYnHdwOFY575gK4BmoGbUXQuw3kl/X9jVfP9hjNgQPPPAAJk+eHPq5rKwMGRkZ+91HkiSkpqaiZcuWzUYvQBcCq3YHkF+msecffKh8mkBKtIIure21SK7Sp2NXqYqsFnxPFwKb9wWQHmdDlLN5dG5vyPcju0CDKkTIIQz1z0tAvFtG33QH3A6pzrVv3OtHbokOSICAQEAX8Gnm8e0yy6tbRMvISq39GdWFuj63TfkB7K3QoOsSBABvsOxbkQFVhOk31INYF5Aaq6Blgr3ZfPaKohxTpcoWGoYTnfOweDGi/nIjMHo09ClTsLlQszjvAGgM5wGA0CPFkAEzcWGTgYwEGW2THc3ms98fLD48dtEYzgPo0MbGxiIQCEDTtANuf7hh2XnNh/OAoBOu6dDFkeM8i4csNAQW5zWA84qLgTfeAGbNMl9LSgLefBPIzDys12VxXsNxLHPeMRXAS05OhqIotSrj9u7dW6uCzkCrVq3q3N5msyEpKWm/29R3zIbC6XTC2UixSkMbqjlg9R4/iryA3WFHQBdQBeCQAbtDQmG1wKIdwCntHIhx8QEq9+r4I9cHv2pHlSrD45AQ0ATyy+3IrxRI9EjonGIPbX+0EJAkeIUKHeyrj3dLKKkWCGgMisVGK0iKc0EXApJNgcNpgytsze1bOrC3yge/CrSKVdAzzYZv1nrhVQV77u0SFLuEPu1cDdaEcbmApLDCHV0IaLKCALSQYqXdCVRW6xAasyiGsKfxfTgkAFAk7KmQUOAFBgXvU7lXR3aRih6pJpmuzQ0gM9F21O+LhWMXJzrnOdqcjO7TPoC/RSvsKbAhv1y2OO8AaAzn6eD/QN2cJ8lATpmEsoBkcZ6Fw4pD4TyA+lDNwYGw7LzmxXkA4JcEfJqABHKeJNWdrLU4z8KRhMV59XNed6UI7gU/AnPmAEOHAqeeCmzdCrRuzZ+7dDns12Vx3onBecfU1TgcDvTr1w9z5syJeH3OnDkYPHhwnfuccsoptbafPXs2+vfvHyKQ+rap75gnGjITbXDYKBoZ0FkuK8kShOCD5g0I/Lrdh3Iv4+Qb8wMoqWZ57PYiFevyAti0T4Wq6yipFsgu0iK2P1ro0tKOeI8EhyKhfZINIzq7kR6nQIAPhqZL0HSBFTsD2FnHmmNcMga1cyIjUUHvdBvW7FHhtPF4UU4JiiTBrwIrdwUoANoIrM0NIK/MTH04bBQcBUzRVYBkVtORNbYpqhTQdR1+FcguUlHu1fHrdh92FmlYsTOw32u0YOFERGM4z1VZhg7bVyLfmYCf/CkW5x1GzhOC663LsGP7COCUhcV5Fo5tLF0KbN9+RE5l2XnNi/MEeC90YXJefekti/MsWDh4NDXnFZd4If/rX8A11wCaBqxeDaxcCYwfD9xwwxEJ3gHHF+fJ9Rze4rxjLIAHAJMnT8abb76Jt956C+vXr8fdd9+NnJwc3HzzzQBY2nvllVeGtr/55puxY8cOTJ48GevXr8dbb72FadOm4d577w1tc+edd2L27Nl48sknsWHDBjz55JOYO3cu7rrrrtA2FRUVWLlyJVauXAkAyM7OxsqVK5GTk3NErvtwoNyrY/Uef+gB1IXA6j3+Wr/kxsPsckhmmbHgaGa7Yj7MxgNUVKVBCIHK4NQZVedXmZcPndtubt+Y9TQVYlwyhrR3oXOKDSe1cUCRJUS5ZDhtEqJdEooqdczb5A2RTF1rjnHJ6JXmwLo8FXllGiQAHntkT35emYa1uY1rDTT+wADMhLSIkVFbFaB2iXHN98r93L9Hqh3ZRSr8qrm2A12jBQvHCw4X51X7VPSb8TzSzxmGrhePRK/KbIvzDjPnBerQwTMgAFQELM6z0IxRWLj/96uqgDvvBH7//ZBO09zsvINZU1PgWOS84Z1csMn8PA1IIOfVB4vzLFggjhbndSncBOebrwMPPADMnw+8+CIwZQowbhzUX5ZYnBeG+jivZiHK/ljqROe8Yy6Ad9FFF2Hq1Kl47LHH0KdPH/z000+YOXMm2rZtCwDIzc2NCKplZmZi5syZWLBgAfr06YN//etfeOGFFzBp0qTQNoMHD8ZHH32Et99+G7169cI777yDGTNmYODAgaFtfv/9d/Tt2xd9+/YFwEBi37598Y9//OMIXXnT4mAj1TEuGWd2diE1ljlACcxWKMHe9/AHKKBJ8NipPi70SCdLlrivsX1j19NUMEjK6OHvmWZH+yRb6Lr8Yc97zTWHoy4yahX8rBw2vt/Y9RmZkL4ZdnRIsiPeLUGWAKcCuOo5bM2i5igH9QIAoEeqHSkxMqoC1JVq6DVasHAs43BynifgheeLTwAA8u7diM7fGTqOxXkHv74DcV5dTRs1X/PYLc6z0IxRl8B5ODwe4PPPgbPOavQpmpud15g1NQWONc5TZAmDMx2wyYAiUfi9rqGVFudZsBCJo8l5SmUFMHo08NlnwJ495pt79kC+4TroBUUW54Wtry7Oc9slOBVyntKACNWJzHnHXAAPAG655RZs374dPp8Py5cvx2mnnRZ675133sGCBQsith82bBj++OMP+Hw+ZGdnh6r1wnH++edjw4YN8Pv9WL9+PSZOnBjx/umnnw4hRK2vd95553Bc4mFHYyLVsiShT7o99DAbcNiAPul2VPo4FrpltISqADMUNROGQpjbhwtfNnQ9TZW5re84lT5R6xo1neXUvVrbQj31Nc9ZFxn1zbAjI1HBoHbOJuu9j3HJGNjOiaQoCRkJCjwOGfVJEBiDgewyUO4DNuwN4I+dfgR1TaFqzCRpwT60uu6LBQvHCw4n50kxUSj5++NAcjJ8F12K3RldQ9tanHdoqIvzbPU4swbnSbA4z0IzR+fOB94mLQ2Ijm70KZqbndfQNZ3onAcA0S4Z7ZMVxLslxLkkyKAeVDgszrNgIRJHk/PK2naE6NULWLeu9jnWr0eLPVsOO+cBdfPe2twAOiQrzZ7zMhIUtE9SkNXCZnHeASAJYySrhcOOsrIyxMXFobS09KiPK9YFMwDhPegGWsXyQa35y76/fRKjZJR7NQQ0VkoUV+mhKTIGjGi60Zcffo6GrKfSRz0CQ1izT7odK3dxH4cNDSYTI0NT13HsikCMS0FRJQlM081y6Xi3jFFdnFi9Wz3oczYW+1urTdZR7pPgVUW9Ip92BdB0c9CFw8bScFWj5oMxwjvaIUGRpXrvfXNDc3qWLNSP5nSfDjfnVfk0ZGjFKJSjsE+jwLPFeQePhnCeTxN1Tt9WJPKZppuT1yzOs3AkcUTvU1UV8OefQEUF0LEjUGMSZHOz8xqypqwWCpbu8FucZ3GexXnHCJrTfTqanOdUJIzbuxjOMaMY0auBnfN+x+oW3Wqtqak4D6ifS3JLVfg0EWoNbs6cZ1cEopwycoo0aBbn1YtjsgLPwqHjQBmHun7J6xKdNLCrRENpUAegzKfXPVAhGERy2mr3zjdkPU3V376/45R6eS0GVGHqy5VU6/h27cGf81AyK/WtVQiBwiqgWhWQJJZv16zEkyQg1i3BJptjt/0aUOET8KkIpZA0na9pujgkTQMLFpozDjfnVakSNojEUPAOsDjvcHEeQH6z1WGLuewW51k4TlFVBXz6KTBrFrBhA3DRRcDJJwPDhwN9+/I9zXyWm5ud15A1bS/WLM6zOM+ChUbhaHKewwb8lnEytMsvr73NxRcjt3VWnWtqKs4D6ueSahXwqiAnoPlyHtcjsKNIC02hrcl5QlicB1gBvBMWuhBYuSsQ0SMOYL+TZfbXCx/nlpAep6A6IKDqpgaK8eVUAIfCgJIiSbV65xuynh6p9tD5jPcMHEx/+/6Okx6nIM5tah+M6+FCvJuPiQxmWjQhUBUQSImV0SPVvl/SOlS9l/rWWq0GdVHA7IRd5mfuCHuiZQnwBZjJcNqoE2DcVhHcTwhqq+igkXgomgYWLDRnWJx3fHGeLfhBh3OeJAESBGJcFudZOA6xbh1wwQXAI49QKP3bb833CgsZ0Pv119BLzY3zGrKmbq1sFudZnGfBQqNwNDkv0aYis2ALxPU3AHfdBTidgMMBceedyP3ro9jnj+SupuY8oH4uoX4wh1A0Z84DgCiHHKo2lkHuCuc8WJwHwArgnbDYX8ahrkh1uVdHdpGKk9s6QqOl/8wLIKuFgoxEBae0c+Lkdg7EOKnVAQlw2iQkR8tIcEuQJCDeLeG09k6kJyiI90T+6jVkPY3JrNSF/R3n5HYOnBLW729XWFrssXN8NgBU+gTUYDuDENgvaR1qBU19a412AjFOfq4yODVJ1fm/UYmn6tQH8Ad0KDLbacMhjC/BP0AJHvmwl01bsHC0YHGexXkW51k4ptG+PTB1KnDPPcCXX9Z+74ILgB07gPJyYN8+5O4oQOzeneim5qGr2If2oggpcjVk6ehwHnBg3luXp1qcF4TFeRYsHByOlp03UtmFQY/fivTT+8M27DTy8JIlwObN2HD/f7HCnlHvmpqK84D6ucTtkDCisxNtmjnnOWxAnwwbh/aIIOdpgAZzaI8uLM4DgOMrHGmhwchMtCG/TKu35z48Uh3eqx5QEbFtfpmGQe2ciHJKwSg8EOWU4FMBt11CQBMIBGuOY1wKopwyVE3D3jIdJVW+0EMVvp7EKBluBx/a/DIddhvgUwVKqzVs2afVm1lpaH97fRmaar/AnA1enNnFiV5pDuhCYNVuH6r81HMBEBIwBYDsQhXFVdRGMNaRXaSiV5ojdMweqXYEVEQQm4GGZFbqW6uu85yKLBDQTI0Ag7TCURkIGnuSWW5swNAPaJeowGk3yc34o9Yj1R4SN12bG0Bmou24I0ELJwYOF+c5hIZTy9bDnp+L6nYdsd7ZDj6L8yzOs2ChqZGYCNx5JzBzpvla27bAxRcD2dlA9+4M3r3/PlBRgbR16yAlJwO5ucAffwB9+kCkpcF30gCUtu+C6A7dAF0HAgFUwo6l233wHUY7DzB5uDog4FAknJ5F7aXcMg1+TSA5StpvBc2JyHl2BfCHaR1bnGfBQt04Gr5tS5eA49H/QPrgfcDtBm67Ddi9G7jxRmD4cHS46BLs9nRG1WHmPKBuLtF0gZJqYMteFSe1IW81V87zq8Ca3So8Dgll1cHW3NA/kTjROc8aYnEE0ZyEPoGG/yKv3uPHzqLIjEb4Q5eRyPB3XdtUBQT8GknLY5fq3LdXmgPlXh0b8wMQQqC4WodfBRRZQlKUDE0Hiqp0+DQBm8TX61tHOMHUh7qup9pvCnomeWSM7OLCyl0BbCtUoWoCUU6zJaS4SiBgTLcJXhdQv0CqpgvM2+SNWKvDBgzv5Apdy8Gs1TiOqumoUunI6jVGmofDOINBaDW3UwDEeciCThvQM9WONbmBJhFUPVxobs+ShbrR3O5TU3NeXqmGUxbNQMzN1/EhjI5G/kffYG7KAIvzLM5rUjS3Z8lC3Tjs96myEnjqKeDNN/nzZZcBL7/MyrzlywGHA/jpJ6BTJ6C6Gti8GTj7bA66WLQIGDIE6NOHEaBly4CSEqBHD+gOB9Q27eDNyMTG1r1RImxNbucB5OA1e/zILdPgVNjOZVOA3SUaNJ3aTBbnmdcsBHWOS71s4asPFudZOFpobvfpSPu2A8o3o90ZJ9Eoufde4LXXyLdBiJYtkfflHCx2dzisnFfXNcmyQEkVOc8uA51SbNA0yeK844DzmnSlgUAAOTk5TXlIC4cRMS4ZvdIcoYdSliT0SnPU+gVuiA5TfRoCbrsEl02CU6l/XyMLkl+uo7BKhMixqFrHtkIV+yr4kNtkhKb/1NQpOJj+9rrW6rBJ0MEHwq+JUDmwsW6fam6bmaSwLx8IvV9fqXNj9BgOtFbjmjWQYA2NgLoQ72a5t1RHhsKABhJ3dYBBhNW55noPVVDVgoXmhKbmvI6VuxAz+XYabgBQUYGkZx9Ha5dqcZ7FeRYsND22bQOefBK4/34G7559FvjnP9lSm5sL/PIL0KoVUFTEgRYDBgCvv87KkAkTgNatgeeeA775hhV9ug788APkkhI4fv8NsR+8g/7P3YcRHz2F09b9gAytuEnsPMCseNldqkMLUua2QhXr81SUefmkWpwXec1Ou4QW0QokmNMua8JlszjPggUDR9q3VQI+8mhiIrB3b0TwDgCkvXsR/fOPoarZw8V5QG0uSYu1hYJpmg7kleoW5+H44LwGB/Def/99dOrUCW63GwMGDMC34eK5Qfzxxx/IzMxs0gVaOPrYX696h2QFa3MDiHJKGNSOuic2RaDKL9A3w442iQpGdHbC7ZBq7VvXpEVVB3wa+94BQBP8GQBax9lwZidTt0SRJfTNsCMjUTlg5Dx8Wk6MS8bAtg7YbUDHFgoUWcKZXZxI8sihbISxHkWW0C7Rhk4tbeibYQ+RhU2REBUcTw3UT1oHq8dQc83ZRSoG1tBm6BjUZhiR5YBNoUGq1ZOlKPMCDkXAaWOGxbgLEoKjuIM/i+AxSKTOJhNUtWDhWERDOS/d7q1lrNm2b8OAlrrFeWHXbcDiPAsWDgGaRr6x24GXXmJQzm4HbDa21XbsyFbZ7t0BVeV7333HqpD58znF9p//BM46iwG+n38GunQBPB5gyhQe8913IX31FeSPPkTM5NvQ89KR6DPrXeSvzT4kOw8wdZPcdlZFlPsEAjq7o3SQ9yzOi+S8gW0dkKWgyHw9nOdTLc6zYOFg0VS+rS85BaJfPyA9nVIGAJCZSbmDW24B7rsPLpcdduXwch7ANt8Et4yWsTL6ZtjRs7Ud7ZMVOBRq3RkcYnHesc95DQrgzZ8/H1deeSU8Hg8uvfRSlJaWYsKECbj//vsP9/osNAPUqyUSEPhxkxc5hSpW7AzA45CgasCuYmZZK32cori1Hg2nX7b5MHtDNbqkKGgVq0AXAmVeHbIkQr+Ydhlw20xDMNatNCizEo66puVs3qchoAK/7eCEHZvMdgq3vbYBenI7B3q35jnX5gaQX67DYyfBHYi09pdl2F9mJXzNm/dp6N7KjlW7VOws0vDbDj8yE23YvE9DpU8PFf/UBV0AZT7AJgu47VJogpLDBrjsFPg0IAB0aKHAJstNJqhqwcKxiIZynpLVEer5F0ZsIyZPxiZflMV5sDjPgoUmgd8PzJ5Nbbvx4+kUXnop8NtvwBVXsPquWzdg7Vrg9NOBhQtZeff993Qin3sOOOcc4PnngVtvZbAvPh7o0IHtuAsXAllZ3Pbmm4HBg4GMDKB/f0hDh0LZtBEtnnoURb//CU91KZJLchGVuxO5G3ejusIXYedJEhDtlJDokdChOg95S9di58I/oG3ahB4xPrSMkVDm1WFXIh1CuwzEuSzOq8l5m/aqyC7SENDqPAQA8pjFeRYsHByawrd1qT60/r9XIZ19NlBayoTIqacCw4cD+/YBLhewbx9sK5ZjzKL/Q9/q7aGhC4eD81bsJHeVVOmo9AnIkoS+6Q7EByeDG7A4z8SxynkN0sAbOXIkXC4XvvrqK8iyDE3T8M9//hP/+c9/cPPNN+OVV14BACxduhSDBw+Gpu3nUz+B0dx0AhqK+nrV96d7oukCLoeElBgFu4o1CJAUY4IZAL+mo8IHQAISPTKGZznw5WpvqArFGGvNCVz778Nv7PoNZCQqaOGRsHi7nyW5kCAgUOYV8NglZCTYQucNFz1taA99Y0QzD7TmlBgZeytUFFWaE3f2B0UCYlwSvAEBn8rP1tAN0IKjuiFRF+bMLk6s2qVGZFcMNPYeNDWO1WfpRMOxep8OhvMySnch/aeZcKz+A97Tz0ThsNFY542xOM/ivCbFsfosnWg4LPfp55+Bq68GOndmUC4zE7jrLrbL9u8P/PWvwHnnAb/+Cpx/PrB+PVu5unXjvmecAcyZA7RrB0RFsRIvPR349FMgLQ246ioG/2bOZJWfgU6d2HLbrh1w8skQ+fnAe+9BqqpiFV98PLTUNGjde8DbIhWBLdlwlxbAVVECaetWSNOnR2pBDR6MkhtuQ06nAVjnTIWmAZLMZ9HgPYvzzPdTYmTsq9RQXEkNK/0ApGdxnoWjgWP1PjWFb9t39x9IO2sIq52vvpp8vHMnNUanTwe2b488qceDwjfex8Kuo+CwWZxncV4kDuZZalBj9Zo1azBt2jTIQdEZRVHw+OOPo127drj55psRCATwxhtvHPrKLTRL1DfVJ7dMgywxiwBEEpxPE/BVA9F2HXYFKK7SoQkEWxsU5BTzIVMAFFfq+GSlN+KcOsh/1aqA2wZIkhTMAqDBYp4GDjQtp4VHwrzNfmgC8KoCsS6gIiieWe4T2Fmswm5DKBsyqJ0zgrT6ZtixNhf1kpahx2DAyKzsD1E2oMynI9opQYYErypQWi1gk4GOLWywK0Cln2QMkMS0eohOkYBYJ2BXqB2lCcEKFon3wCYhJHCqagLzNvkQqCFKGq4b0Jh7YMHCsYSD4bytUenYctYNwBigOgC0lmTYFWFxnsV5Fiw0DVwuoLCQAbnvv2ebVn4+g3Dl5dxGCJa/6TrQoweHWowcyWmIrVpx+8suYzXeffex8q5zZ1bmPfmkqePpcgETJwKpqcC6dcAppzAgePvtkHr3ZqCwqIjBv/JyKGeeCSU/D47p01kVuHIl23XrgLR4MRIWL0ZCfDza/mcKFvcfj0LhggQ6zR5745+345XzNF2CkASEbnGeBQtNiabwbaPWr+abgQDwxhvAn38CZ57JIUN79vC9du2AMWMo3FtaiqRbrsGg//sCi1qfbHFekPNcNgnRTgk2hdO3FUVAVTlltr4g3onOeQ0K4FVVVSEqKqrW69dffz0URcENN9wATdNwww03NPkCLTQPJLhl2G1AzzT2iNsUICVWRoJbwu85AUTZBWRZhq7rKPPSjoQksKtUQ7STRocQQEkVUBXQ4LTxoQxodU6HJoLve4Jt6Qcr5mnA0DmYtymyldcom/1xkzdUzaHpQGlwdLUhjOlVRcR5G0NaB4Nt+wJYvD0AAa4lzgWUVNNt9WtASZWGYVku7CjSUB0wdAuAGBtQEhYTMDIPsU4gLd6O3uk2rMtTkeyWsHCbH6pOoVKPQ4ZPFdB0AUWR0OsAk3oacw8sWDjWcDCcVxriPFic1whYnGfBwn4wYADwww8klDPOALxBwpk2jUG99HQG2/r25fZ+P9CrF7BiBV9btgxITgYKCoBRo1hp16oV23Gffto8z+jRbAH7+GPq6/3979TM27ULuPtuHu/ZZ83t77yTDuuUKcDf/sYW3127aq//1FM5+RbgNWga4p/4J4ZevRu/jLsZRZIbugCqVQ6hsTjP5LxdJT6E90k5g05nuE8rweI8CxYag0P1be3+yEQsWrYkP+/Zw4ngd94JbN3K4J6qkocvuQQt1y9HVJv+qFKVE5vzvAJCMIEDCKiajJYxMsoLaCULwXbYgGZxXk00SAOvXbt2WLVqVZ3vXXPNNXjjjTfw3nvv4Y477mjSxVk4+giflqhqEoQAVuwMYFexhvwyDUuyA/Br7EXXdZ3/w3ReAzpQWh188IIOrhASPHYZHoeEuhq4E9zsZ5dl7tIiJkiwqfZGjXg+0LSc07McSPDI7KEPVs9Kwe/ddglndTuyo6VX7gmEiEoXQHG1iCCuPWUC8zd5IYMZBoONvSozEnLwS5J5DX4d6NXaBpsso0eqHTtK9Fqfu6YzKyOEjignMzGNEVS1YOFYh8V5FudZnGehWWHrVuCii1j91rEjMGkSMGQI4PMBJSXA9dcDmzYx+FZRwaEWPXsCy5dz+3XrGLj75htW561dC1x5JQNvBiZNYhXe888z6HfPPQzg7dwJPPAA8MEHwLx53HbAAOCVV4AlS4C5c4FrrwU++SQyeBcVxdbcyZNZnfLyy/x65RXg7beBQYMQU1WC07YugMOGoMi6xXkGDM4TuqAou7FuwTYx4zXj74bFeRYsNBxNZefJqa0iD9y7N6UNAMocvPkm8PnnDN4BTKK89hps336NfhVbLM4L46iqACvhiqs0CEFuAxh8tDivNhoUbjzttNPwwQcf4M4776zz/WuuuQaSJOH6669v0sVZOPw4UA97+LTEvDINczZoKPXqkCX2nIei+wIorpGI0AUfOKPaRAYn+8gyW8PKvJEGi4GSahESpGyTqECGhIAqsCY3gCinfNAPWV3TcsKvyW4DRnZ24vNV1RHtCTYZmNDTBYftyD7U5/Rw4tMVXqj1lOkoErMXAoCQAKfMUdp+TYRKjQ0dAIBO7o8bfRjV1YW1uQHsKqEmoSyRGMu9eugeVfo5IbNXmuOwZmIsWDhaaCrOkySgfWkOkrewhaKgY0+screFgMV5BwuL8yxY2A8qK1nRoeumplzLlnQOP/2UFR/p6QzMaRqr8qqrqXXn8fD7uDh6ngUFrL7btcvUuxs+nNvOmcOW2sREBuZ27QJuuw147z0gN5fbXnABj/Xrrxyi4Xbza+tWc72dO7MN93//Y+svALRoQX0+w5MUAvD54FmxDGNatMCazIEorCLnxSgaovJ2UhR+0ybq+ek64HSyHW3AAB4vDMcz5+lggDPEeaFJkhbnWbBQH/Zn6zWFb6soAIqLgEGDyIcAEBvLpEqHDsDGjUBxMbVEb7yRPOz1Mvrk8yF57w44ErtYdl4QikTd6KoAOc8I1gkgbHquxXkGGhTAu/baa+F2u1FQUIDk5OQ6t7n66qsRFRWFmTNnNukCLRw+hItWVvoE3A5A1YD8Mh15ZRoSPDI6t7CFeuw1IVBaKaAKQIJAtBMo99V/fAkABB9EOajdEdAEPDag3CsixkMbJb3BXaAJwC4BlT4dJRoNPr8KbC9W0TPFRoPWZqPheADUp3NglM22SZAxZ6OvlraIJoA5G30Y3dUJm3zkiM6hKBjZ2YHv1/trvafItHt1IGxCDwVJvQHzM7QFsxVVwQoVv2r+4cor01BSZe5XFaBBKANIjzv2RmlbsNBQNBXnyRIwPHcpWl1+HlBWBgBIiYlBy+lf4Me0QdD0oKYdmKntn27Dxnw1gmMaynmG0XEwsDjP4jwLxxF69uSkWE0z22QBBrPuvZcO45lnUuPutNM4mGLjRjqTpaX8XwgzcDdxIvDWW+Zx+vZla2y3bkBMDCfUfvABty8qYhUewP3y83neZ54xX/vsM/NYXbpwLU88wZ+7dQPGjmUQ7t13GSgEeOyJE4FAAFG/LMSA9WtR3bELpNISuL/eBuzMAb76yjx3ODIz2bI7ciS/B9AhyYZAlQ/VYDVLj7RIzmubIGPlljJkikposoJSexSKfLLFeRYsHIcwbL3qgMCeEg0jOjuxereK3DINW/apOKWdHQFVabxvC8AmSZBWriTHpaaSr8rLOdE7LY16pQ8/zCTE/fdHDggCYHO5MPSmW1A0diJ2xA1Aj9QT186LdUqo9IsQ5wkAEOxOCa9OtjjPRIOm0FpoGjS3ST3GNBhNCFT6+GugyBI8DgmVfh2aDiRHSTi5rRNLtvtRWMny1FAFCg48CbAuKMEWKF0PirrLFJn0hel6RNuB1kolsnx58JQWQqqshK20GPLefEg+H4WS//wTaN+eWd2sLO5YXMwsB0CyzMkBHA6orVJR3joTcf17Qna7oVdUYHd+FZKSPPhtn4LdpXrE+sIJr0W0jFFdgoHCvXt57qIilkS7XGxR6d6dU4iaAH5Nw5ervfAG6t/GLoNlwcGfE1xAqdes/EmLkRDrlrGlgEyYECVhSHsXYlwyyr06thWoyC9XEdAkCCFQrQLRTmBUFzcUo265GaO5PUsW6kZzu09NxXmdUISTLzoNco0JY6JtW1Q9PRU7WmVhpbtdKNPoslHXKLxarCbnGW0Odhnw2A9tOhmw/+zzku0+7KtoIOcdAVicd2A0t2fJQt04qvdp1y5g6VJW4kVHsyrk11+BwYPZxlpZya8BAzjkAqCmXkkJ9e3uvht49VXguuvY7nrLLQz0eb10Unv0YCDxzDM5YRFgq62hixcXB9x8M4diAMCIEZxi+9575hozM9muu2cPW8uEYHvwsGHAv//N9776ik7vgTB+PFt016zhsQoKIKKigEGDII0bB/2kk7C1woZ2u9Yh8MNseGZ8QBvR4YCa1Qkll16LPV37Y4MrDfFui/OaGyzOOzbQHO/T6j1+bC9UUeljUMhlk+C00c7za0CUHRjeydVo39ZQ8rhg8Vtw3n0Hq+zOOouJCSHIt0VFwIsvok7dlHA4nRAffghpwgRW5x0Ejnc7zxH82eK82ji2FPssNCmMCTbbCtXQwyGEQHVAQAsKrRdXCSza5kNJtag1CaahwbtwMky2q4hXK2GDjkpXFPIDDigykIhqdCpYj+S9O+AsK4Kk65CiooAdO7hjYSFbRqKjaYA5nZzqs3IlcM01bPm49FKWLW/fDnz7LY3JCROA0lLY/vY3JPh8bA+54ALIs2YhY+FCIDYWp40YAbXPSdDTWkMqLIAdOrSoWKiqCikpCWqJBLF0O6Tycog9eyAtX85WE4OUZRm4+GIanr16MaAXjupqikpHRbFq0ICq8lr8fgb/EhIAux3frvVFEFxdf0w0YY7Kbi+Xod3GlfDs2g5IQHVqG+zs0Af5gQTEuiSUeQXiXGZ5dpRTgqoDAU1CCirRsmA7lGpmxYuKopHUoz3k+PgG3l0LFo4d1MV5ui5Q5jWnVzWE81rmZ9cK3gGAtGMHohbORbf3rkTye59ibvqpUPWgVpvMTKAEivL6NVKHEEC8W0K5l4amqgMt1VK0370OScV7IFVVsVcjJYV8sXs3uSImhsLwXbvWNvp27EDM9u3oparArhiga1fIMTGhSr5eqXYs2MLMbLxbxsjOTszZ6ENJtQ5ZAqIcNATrajE+HDgYzjNQ7DUz4QCQXyFQFdAPyHkAJ/x67EwirdwVaHSQ1IKFZoX0dH5NnMjJsWVlbLHNyKD23OTJwKpVtDkMnHQSA3Bxcdx+xAhWjigK7RJvsH/srLM4wfaSS/g+wPeDFcgA2F776qv8fsgQVvKFB+/69QMGDmT1XnIy28okiU7v7bebWnp1cGsEZJnBxq1bGUw09KUQ5IRffwVefhny448ja9Ei4JtvUDO9asvJQfKPc5EcH4/MB/+J3edcBF24LM6zYOE4QI9UO/aUaCgPPkFeVQQHGvB9v4pD8m2NbdQ2beFMSGDCwUg6TJ1KfdIXXmjYYn0+SBdcQL/1rLMatk8Q+xs8cSzaeWoNzvPXaLe1OM+EFcA7gWFMsPFV+9Fq9wZEbduEqD05UIoKoXs88LZui8o27bGtdXcUCU+jzmEIjHdR89F603IkfPA27KtXApoGvUMH4LrrobZIge3tNyGvXcsgV1kZ2ybcblbXtWjBbIbPR12WkSOZRRaCU34uvJBT0156idPOsrJoKNrtnKCWkMD3VqygMfnddzQWn3wSqKiA/PbbcLz8Mg3Y++4DVq6EPGsW7JMm0Wl2ufh/RQWkd97h988+y/VNmwb8/jvbTT74gELRl14KtG1Lw/d//wM2bDA1aM48kxnv5GQGH9euZXByxw4e9+qrMbz7SVhsS0KZV4csA+O6u/D1Wi98QRvVLgPRDsAbAIblLELyg3dD3rA+9JknAGiVlYXiJ57D4g7D4FAkJFQUwP9HIRwxHmyOaYO43O3osPZ3xLz+ApRlv0XcM90QsB40iAFRCxaOExicV1SloahKMIgGs90VYACtLqMuHJrbQ8ezZmZVCqrrlpej5aXnYtCX87EovgdsQd2OBI+MUV2c+D0ngO2FfKDbJimwKxK8ARWn+LKR8udvUP5cC8nv5/FdLmDLFmDBAjrIAwawsjgvj5UnqsrXBw7kz99+y8mSxcWhZYnBgyHdcQcqh5yBX8pjMKitA6d3dGLVHj9SYmT4VGB0VyfmrPdBEzpKqgRW7AxEtGTkl2mHTei3T5odv2T7IQC47PVzXok3MksebgFqAvBpgFDZZlHh01Hu1RHjkg+oFbM2F8esDooFC7UgSayYmzSJgytWr6ZdVF1NfTpDl06WzQDdpEkM9k2aBPz4I3nGEGOPjaVdJgSTpUVFfD0+3uQZSTK3i47m/s89Z64pK8scfpGVxSDjlCms8nvySSZo8/IaFrx74AHaXnl5dW9js/GYjz9urrU+lJQg5q93o93qlch54N/I6JJmcZ4FC8c4ZEnCiM5OfLXai2pVQAagwXyWNHFgO29/cCr8v8QvI+qOO8hHxhCf7dsbFryTJCZiKyvZMXbZZfQLMzIatoiSEkBRUG6PqlWFt3ynH5IAhnVwYE2eitOzHNiQr+LU9nYs2RY4cnae30/ZBb8fZ3jsWO9ORLkPEDYZwzo48c2fZkWeBFbcaToDeaGPCZH3zeI8wgrgncDQhUDZL8vQ/38vw/Hh9Fr9+VEAkgCkDTwFRfc8hN8yB6NIr/+X3yh1TbT50XnfJrjKiqDYFMTGOuGZP4dk07E9UFYCLFkCee9eYMkSOKKjgcceY4BryxZqB4wbx7D555/z4b/nHrZVTJpEHZWrrwbef58BvmXLgLPPZmDul19Yned2s0Xj+usp9HzLLcz63nADg2UvvABcfjkJdMIEHnfmTOoVjB4NPPUUg3yaxmMnJlK7ZfJktqhMnkxj9dlnGUD87Teub/Zsas60bUsthHHj+OGsWcPM96pV3GfAAAb6WrakgRkby9aS6dMR99udGPOXv0BUe4HevSBHdcOoLu3w81YVyR729ftVYPj2BUi6aHyt+wYA8ubNSLpwPE5//2OIigrE/OvvkHbuBOx2dLrsMkitWvEadb32vitW8LOJjuZaL72U31uwcIzDmNpVHQhmYoWp0SkQjMcZwrmi/kzsjhYd0fHiSyB9+EHkGxdeyHb6Vq2AvDy0XvgdWl7WE36VBsmoLk54A4DHAQyIq0Tc7mzYirwQkHCS5IO8cSOkykoKzf/yC7BtG4/bsSPb2v78k4bizTfTCZ4/n21td98N3Hkn3+vYkc+srjPhsXkzpMWLgcWL4bzwIvS6/a/YUp2C9p1bITnKht0lGvZV+DConROtExTsDPq7eWUa5m3SUB0QwVZfKWQkNjZTu792j1MzHVi5J4BzejipldLFgR/W+5ESzayqXwWcioA3SHeyjAjjWwIQUAVtYqeEgGaKFh9IKyYz0TKFLByH6NULeOkliIkTId10E/DFF7SBjKBdXJwZzHO5aLs4nUyWJifTXgEYbPv0U34fbjMEAqZ0yPDhDPwBtMvCK++MYzz5JCsEx4/n93FxHMpRUsKpuEb1Xl3o2JHVKYMHU0+qvuDdkCG0Dx944MDBO0WhPl+bNnAJgfS5n2NfyVAUxWehKiBZnGfBwjEKXQj8nhOAP1jGGtJiD74vhNkBcaAYXngbpwQG3N02VnLlZ/ZE2v23QhoyhHafJJFD6/CtInD55SxO2beP/mX79vRNP/8cOOUU+o8LFpDDzjiDiZetW3nc1q3pa06bBuF2Q7rpVshDzsEKNQ190u34bbsf24sYuQroNozo5IoI0CXHyNgbLJw+HHYe/H4E/lgJff4COL76HJLbDfTrh4SCApyyeTOkqiqI1FRIF1yAcQMGYZbcBh2TFGwu1GGX2PaqBeVlQoMwgzfJ4jwTx9fVWGg4vF74P/gYCbfeZBpz9cC2dAlaXngOht9yJ/Iuvwn23TmwVZSjMiMTaxM6oUzlw+2UNIws+gPxa5dDyd7GgNdXX5FwrrqKxPbSS6zsmjyZ5cbffksDbvJkVo3YbDT8vv+eJDhxIoNbTz1Fo+2NN4C//IUBOKPdYuhQGoIuF69l3jxW3hlBwXHjmIV+4AGWNi9bRmPxvfcotPz++2wj6duXhGpMdXvlFQbxvvuO7/XpA/zwA0Wab7yR1XU338wgl8MBPPggg4VLl9K53riRgbu//pVBtgkTSMDffMM1/P47j19czM/kueeAmBhI11wDvPoqpEsuoVPeqhXib70V50RHQwQC8JdVwde6DeJ+/JbtwzYbK3Jmz45oJYGuI+b2m9g2bAhBBwKsIkxN5dpfeaX+G19RAdx0E7NKf/2rFcSzcMzDyND5VGb5DMNNF5HfOxTArkjwBaeR1TTw0tQSSP1OAlxOCrgLwSSAz0cj7NprgWnT4Jz2P5w58GQE8vfCnrcH8ncyPC1aoEdVFeSiIkjr15Mj/X5qqJxzDr/3eDj90esFFi1icuG555hcOO884LXXyKkdOrBVrnt34L//ZQJCkhj8e/ttcuP48dSt+v132D6egVZCR0phIcSo0ejVrx9SUtrjT71VyGgzBngAnAhmaMhEOyV00/eheHEeMiorALuCQKtk2Nu1NYcJCWFaxjUQPkAkoKLOrO/E3vbQtstzVMQ4ZVSrgNAFqgICvrBchaZHtpMJcEKtLoAyn0BWsoweqXboQiC7SEXPVDv2VuohR7pvhh1rc3FYW0YsWDjqGDwYpV/MROxtN0KeNIl8kZrK92y2SJsBYBAvLo42jdFq6/EwyAaYU2SNbRMT+X2XLtTNA4CkJDMwaLy3di2/v/pqat0BDPR9+inPpesMCNbEqFFMUlRUcN0//VT3YAuAtl5CAhMb+/bV/5nExNCBdjpp333zDT+O9HS0OuVnJJ0xApVZXbEnrROyUlpixc7GVadYnGfBwtGBMZW0pp1nQAerXPdn5xkI6EFtNsGODeMprAoIbLQlo/vTU+C85kr6vJdfTj9xf7jmGmDJEnZnAbQX336bCZOkJP789NN8T5Lol/7lL0zsXnEF992yJbSS6LtvR6dJP2Hl31/CV8XRUDURkojZVaKhwucNVaL5VU60bhWr1GnneRwSuqbYGs152LYN+ksvw/7cFHbU3XMP/fmgTqrBX9LKlcD338PtduOsu+7FtknXweFogapApDa0JizOqw/H19VYaBhUFXj7bbiuu+qAwbtweF55HpmP3o2MaVORes0F6Dh6IEbP+C+62kuR5SjH+B1zkbRkPpTt2czKKgorUq6/Hvi//6MTOm0aA1tTptAge/xx09m7/362gT34IA01nw/48EMaY3fdRQK45BIG8UaPZoVdZSUDfJ9/zmCfgc8/B+64gxmMwYP5WlUVfz7zTFa2nHsug3yTJ7NSpaqKjujYsXR4168HHn2UBLpiBfeRJBqtRUW8Nk1j2+2QIcCsWSTv9HSu+6qrGPx67jmea+9eku7DDwNt2tDRfeEFOt9vvw385z889r/+RcP2gw8YdMzNhXTLLZD//BPKzp1wlxQg/pd5rNTp2hX46CNg82ZWGd58c+QwjcJCZtJrIjeXa4+KOvCN/9e/KGRdR6WfBQvHEjITbXDYAEBADlba1anDoVEPtD6jrkXlPuAf/6Djd9555J5vv+Vzv2wZX7/4YqCiArbPP4W7cC9sc+dAfughKDfeCOXbb/n8/vYbpym2acOExpQpNP6io2n4/Pkn2ynuuYfc88MPdHQHDmQlsqGFt2IFNUFvv53OcI8e5MvBg8kjmZnkPQD49FNIffpA/tv9sE8Yj/Q1SzAybwl65P8JuaQEfSu3oVfZFrTVihHQAI+iYYR3I85c8SVsU59D0odvI+GhexEzfCjsZ47g5/D+++T32bOBRx6hcPPMmdTrCyK7SI1oZ5i3yRsyII1puwAd2SXbffCpNCxLqnQUV4sIo85ALc0onYadpgMFlQJlXh2z13uRU6hiTW4A7RIUrM0NoNyrh7RijjejzoKFmlD69sGa1z9DflYfaB06AL17829/SQk5JBxr1gD9+0e+Fx60C/9eiMjgPUBuWrw48phjxtCJ69cP+Plnc9uYGNo8PXsyoVkTt9zCSpTZs2nXZGeTY+vCNdeQN6OiaP/Vh9ataU+++y4Tulu2UJblnnvIl4sWwX7vZMRfMgldb78M6gcfwVOwB7IUyVMNgcV5FiwcHWQm2qAEf80l0NariQPZeQZkmQE/XTCgZJODVWI60FYvBUqKWdl8yy1MVuwveeB0kqOM4F1sLE9gVDtfcIGZCAHITZ99xuCdJDHAFwzehcPx2SdI37YK1QEBVQNsEmCXJbjtUoiDAFam9Uyzo0+6PWgLM2CmgwEhoQss2Oyrk6cOiFWrgFGjID83hdd53320Q5ctq3+f6mq4n/gXOj58C1qV51qcdxA4/q7IwoGxaBFw662N2lX64QegXTu2fmoa3E/9B/2/+h8GLZwBJT+fFWZvvQU8/zwd2oQEtlXcdhsNtddfZ/a1VSsG02bPJlllZtIx/eMP6rRcey2JCqBjO28enWSfj4ZlWhqNNEPjpV8/tqUaWLOGFS1OJzBjBp1KwGwDSU1loE3XaTjeeisDgykpdKYTE9nOUV3NCWkAg3q9etEhTk9nS0fr1swav/EGifedd7h2I3vdrRuNzt9/57nOPZeVfxdeyM8RoNM+aRJw770M6LlcJL2rruIa/vpXnm/KFGaefT7qzHg8vJ5nn6WB/Mkn/HroIV63Ab+fgb6a+PFHBh4bgsmTzQy6BQvHKGJcMga1cyIz0YbkKJbnOxRqaRgQoDHjUxGZ+guDqoMB/4ICOoLvvsvvARpvcXF0Urt2JUc8/DCrQx56iE7r3LnkpfPOY4D80kvNQPvmzcDXX7NKePp0Bv63bWNCAmDFnvHczpzJ855+OiuCx42jcZedTb4tLKQRtXw5A4GZmVxXWRkNx6oq4JprIOfmQi4qgpg1C/K0N5HxyVvo/vIjmDjnZYwrWIpWs7+E/fffWNn38st02idPZqvHM8/Qef7+expwnTvTkZ4xg2t6/32gvBw9Uu1oFauEPsOaRmWPVLtZsRJgi4RNNoXcGyJVE367Sr06Zq3zorBKZyVLQGBxNqcQ/7rdh3LvAVpcLFg4ThDjkpGS1Rq/dDsTPw6/FtvTukDceCNtF2PSnSwz6bpmDQN8a9aYNlW41ueWLbStDPz6K4N2BrKymHgIh6rS/hk2jHaUAaPCL1xLz8BNN9FG2biRrbMvv8y1lpZGbpeaSn5t356c43TSbqsLCQnsnnj8cTNh+/DDlGl59llqKefmhrhdmjcP7v/+G11nvIoRSz7E0LXfo8ea+UzuHmiyJGBxngULRwkxLhmD2zkQ5TDsPAnOGj2HB7LzDIhg4E6R+aXqEtLjFAz2Z+Pkuy+Gc/2f9NWmTavNYzXRoUOkL9W/v6k1CpCDq6rMn7OyzO0TEuqXDgDg2pkNPdjqa1M4HK06IKAFucphYxUwwKEOfpWfTTe5CAOqt6G3Lwdu3ReqSKwKCCRGscIthO3b6e9ecgn96j/+4OubNtGe3bqVP99yC4tXKiv3/3kE4Zw1E92nPYUkxX/AbS3OIw4qgLdv3z48/vjjGDVqFHr06IEePXpg1KhReOKJJ1AYXi5/mPHKK68gMzMTLpcL/fr1w88//7zf7RcuXIh+/frB5XKhffv2eO2112pt89lnn6Fbt25wOp3o1q0bvvjii0M+b7PEvn0MpjXA+KgXb77JSrMgpA8/hPTzzyz5bdeOztwFF9Ao+u9/6VxOm8YMxW+/0UC68Ubu/NNPNAgnTaKD+eGHzLSuXs11Gli9mu9/9hm3XbmSxGaz0Vlt25ZZ3zvvNPfZvJmEsnIlHer0dBqMRUUM0n3xBd+fNYuVIiNGsKpvzhxWwPl8NBaHDTPbRFatYnWL30+D8rHH+PqKFSTW889ne9v55zOQee+9fP/dd9mCu3Qpz/nZZ/wM3W4atj4fr/uTT9jWK0l0fNu35/VNmMBr9flIillZDJAOHco16Do/lw4dmFW+5Rbzc6io4LknT45sbQsEIqfi7g+axntlwcIxjhiXjJPaOBDrtsGmSIhyyoh2SqEgniIBMfZgZV6QJmv+oSyOawkRnjAIx8CB5AlVBU47jc+spjEI99prrPRwuVidm53NZ/aZZyhgbGDzZnKW3U6e6tCB22Zl8f2cHPLhypV8tjdtYsLD7aYT2qYNnd82bWhQ3XQTEw8TJnD/P/4wg/p+PxMPRUWQEhOZDNmxA9K8eZAKCyE/8wxfS0ggf/z1rzQwp0yhc33XXbzWl14C1q0jTxQV8VzTp4cqC+WKCvRJt6OzqwLd/HvQybsHcXIgZFTKkhSqWJEkiS1k/si/U+FZdAkIZdiNe5TgpgMsS2x7CQRtt4BO4WM1bALdwVTSWLBwrKNlrILUOBtK4cRaVwbyxpxPfsnNZRBs1iy28APkJoeDCU2ATqVhO/z4Y+SkxEWLaCPJMu2W6GjaHOFQVSYcy8vN18KHAPn9PJ+Bk06iFMHGjeZkW1mOdGwB8tuVVzIZ/O23tNP27Kn7Azj5ZOru/fvfPK/dzq6P556jfVkTY8bQZmrTBnj6aThuuA6x//gb5DfeYOLk+ef59frrtFvXrGGlyfffMwEzezbkDRvQp7UtVOliwOI8CxYOPwzOs9skeBwSXPaDs/MMiGA0XRJAepyCTi0VDHQVos3d18H2y8/07x56iDZZfckDAwUFpoQBwCSrwbNAbfkRVTULMioqaldMhyEQl8Cgv2B14b5KHX6N7bGaoHb6yl0BrNnjR365hh7FmzHkm1fQfeRJ6HRaL3Qb0g1D/3Y5Bq7+AXG+UqiaQLlXQ6Uv+AHt3k079cYb6Sd7vey2eP11dpBlZ3O7+HgmiWsmWw4A91v/Q7e96yM4zy4DnWxl6KPnoafYiwS7ZnFeEA0O4P3444/IysrCP/7xD8yfPx+FhYUoKCjA/Pnz8dBDD6FTp0746Qg4+DNmzMBdd92Fhx56CCtWrMDQoUMxZswY5OTk1Ll9dnY2xo4di6FDh2LFihV48MEHcccdd+Czzz4LbbNkyRJcdNFFuOKKK7Bq1SpcccUVuPDCC7F06dJGn7fZYtmy2tnRg4WqsgouPp4/79zJhzovj87bH3/w+3PP5fuvvcag1caNrACprGQga9gwvr9qFcngssv4wBcXM8Obk0PxTgPz5rHVIjbWrLAzNFP8fhpuaWlmUKqoiOcDzJZZgMap4cCmpZF0FIXVdZ9/TuM0KooG5qpVzLb+4x/c5scfGej77DMOt8jOZkDPOK4xoS0lhYbxrl2slqmsJPmlpLD65rLL2FZstP1On85W41mzWHFzzTVcc/v23O+jj9iS9+mnrLLZvJmG7bRpDJDa7STRXr34x6GoyKxg7NSJwc0ZM6ijYGDUKN6HBkL897/1G8cWLBxDWJsbQFGlDo9dggTAbZcQ7WS7gV0BAkJCjJNGQowDcNolJLkRarv17NoO6Y47GIgLR7t25Inycj7rGzcyY5mWRv466SQ6fUYC5NtvySOqSi4z2tEAVtqNH8/vly5lQmTsWP785Zems11aSgd27Fj+b7fT4mzZkvvs2UO+HjuWPGlohcbFmef68Uc6sYEAEyqtWlFr5c03mfh44QVy0XXXMdjYpQvbzdau5TUYCZl33yUHLl7MFpF//5tVeADEtGmQpzyLDp+/jcxP3kTWR6/i1OlPYsTiDyG+/BL6woXoIfYhLU6BENR/AsI0U0BKdij83mnjlEbDkMtMUjCmuwdZLWyQJUABtwVo6DhtUuhYRvWLgXKvjtV7/NCDlrwuBFbv8R/X2VsLJxZqct7Gtn1Q+sa7pvzIli3sKgCYNLj8clbXnXwy+WH0aL4XCJBHjKQmwLZYm422UUVFbb1cSSKnhNvLQpjtuLt3mx0JAG2aWbPozLrdtJ9qBgbdbgbvnnqKa1y61JyCWxMTJpCzFi0yq/4MLeWawUaA0inl5UxSzJvHjonHHmMiJC6O9uDddzN5MXcuOyzuuIPrGDuW5xs9GujbF9pdd6PX5sVI0s3zGI60LkSoSs/iPAsWmhaHaucZEOBzKElArEdC79ZOyEuXQvrtN25QXEx+6NqV/mB4MqIm9u5lwYkSfFBXrQJOPdV8Pzs7smPq229Z7QaY+sh1HF9PS0Nu1kn8HoBfY5DSFbx2p9DQqXoXPLk5gKpi2M7FaDNxBKIfuDei5dcxayZaXD4R/V97FBl6CUqqBbYVBv3s4DA0AKb++7595Nx33zUXY2ibAvRHb7qJXW4TJpjXXQ+Sf5kLpw2IcQgMLluPc+a+jkETTkavIZ3Rd3gPnPXUXzB2108YGFN+wnNegwJ4+/btw0UXXYS4uDh8/PHHKC0tRW5uLvLy8lBaWoqPPvoIUVFROP/88w97Jd6UKVNw3XXX4frrr0fXrl0xdepUZGRk4NV6ple99tpraNOmDaZOnYquXbvi+uuvx7XXXotnnnkmtM3UqVMxcuRIPPDAA+jSpQseeOABjBgxAlOnTm30eZst5s9vmuN8+61p0A0YQEPo/vtpyA0bxv8NkvJ6STjffEPjJjubVSP9+nH/efPoDK5bZxqQGzfSKLrsMrO1bMUKBvAAZjk8HnM9Rjb3p59Mp1eSGKzs0oWZW4eDzm1JCYNrvXqxOqR9e1aw7drFc8kyjbLhw3nO0lKS1Dnn8Bw+Hx3j+HgajZdeyvN98w0NzXPOIckNGsTtjPaSP/7ges48k47xqaeamZeSEhKdrpvBN1nm+nw+Gr+yzLXExtLA7tiR+yUlme3Eb77JgIBRqWhk0a+8kkayz0fDOyqK6zLa/hoAac8eBFZbbbQWjn2YWnj8Iz+8kwut42zw2CXEu2VEOwGbIiPOJUORJciSQIsYBQ6FFKBUVrDieMoUBq/OPZcO3IgRTFhkZTFQ9t13fMYSEvjs9u5NYy8ujvykaaa25MyZ1DsxsGMHHUKAfDpoEPlBlulEG85vXh6faU1jQK17dzqfCQmseD75ZPJlhw4MqrVrR07o3988lxDknR07yAsnncTg2223MdHQvz+TBrNnMwA5fTp5Pz6ejn+4Qz9jhnm83btZFf3ii5A2bYKUmgrpxx/Jq+XlkL78EvK2bbC98zbk1ash/+tf6DPvQ5xesgrtbRWIdYiQTqGRmA5ovHSPXYIQ/AycNhrfAV3DjiIVsgxEuyRE2SXIoEFb5WdbSHj1C2AKze8s0rBiZwCaLrBiZ+CEaMGwcOKgJucN7ejGzmHjUfrqW3x+27dnsrJvX/48dy5tuNGj+Xq4U/n223TKDCxZQltlxAhW/NaU7JBlSgfUbKcyEiDbtpltuQkJ5sCMkSNNQfiagcFJk9jKZdhkAI8fbhcCTFR6vQz4GYn72FhyYviUWofD1Fj+6CMG+wYOJH8VFZHH77uPnRVVVbRt77+fyeQpU5g8qQldh+PN/yFl/Jno88xf0SmQhyiHhDi3hAqfjnV5AciShD7pdviNShIJFudZsNAEOFQ7LxxC8LlUNUDXdXJgODSNPuATT5idDvXh7bfZum8UfyxcyA6G3r2Bn36CdscdEH378r3du8l9//kPq4G//x7i1VchMjLMU5/UD4UffYmEgp0YvvlHjKhaj9E7F2L8+4/inBfvwvlbvsWZ/70JWYO6oOszD6DnrPcRdeUlkOry/zIygOHD4f7+G/SePwN2WUIopWAMD8rMJO/pOm3FlSsjj+Hx0N497zz+/Xj3XXas/fknr7tly3o/muhXn8dpeg6GrZmJjmNOQcwD99A31zSgqgr2jz6APHYs0u+9CT38u09ozmtQAG/atGnQNA2//PILzj//fHjC/kB6PB5ceOGFWLRoEQKBAKZNm3bYFuv3+7F8+XKMGjUq4vVRo0ZhcU3R3CCWLFlSa/vRo0fj999/RyBYvVXfNsYxG3PeZonKSjqUTYG9e/kQyjLbxBYsMA2nxEQaWt9+a1aMlJaalXFRUdzPMLp0ndbJd9+xbUGW6cwOGcLsxIUXmuc1JqbFx9PIMywcIRh8W7PGzOTGxNChbduW73//PcnE4+Fn4fORGFq14vY//shAoqbRYO3Qges2xJ6N4KIBSSJxRUXxeo1jtm7NAJ7hIO/dyyBbXh6d6d27afylprIi0tiutJSvbd7MapixY0l4LVpwzV9/zeo7XTdJvWVLbuNwsFpRlpnlTkjg533ttTSAKyoYAJgxg5nje++l0R1ehdMAeIvryG5bsHCMwdDCy0hU0DfDDkXmtKr0BAUxThm6zj/6mi5Q6ReoCgCbCzRAYjZWi4rmM/r993yWV6/mROdp0+gwnn8+nTuAvFRczP/btOFrK1aYTq5hKebl8VkPh99vJjpUlU6k8cwaATyj/cwIBkoSeUhRWNkcH0+n2unk604nnXSDf8OxezfXs3Mn2/PfeYcJkd9/J7eeeip5yhi8c/753O/TT83vs7Np4G3bxu+TkugwezxMnCQlsSpwyRLy4euvM8kydy7QtSukKVMQPetb9F8zF2MWvI3h2+ajNcoAYU4L1gVQ7hco8wmoOnVsdpdo+HylD1UBVrhomoAvbBpbQBeoDoiI6heg4ULzFiwcy6iL87q19WDnqIkouuBKqA89THtr+HDaMMuXkwcqK1lptn27aQP5fHRWjcpbgDZbWRn3Ca8oAZg4LS+vXZm3ezfPBdAWS09nB8TXX/M1Y8o2QLsnvEK5VSsmSAGTQwsLI9vTnE7qEP/wAznSCCBOmmRWiADk5gcf5Po/+ojnPP102pKffcbg4gsvRHr1Tz9NDty4MfKaBgygTMLtt3Pi7qWXAv/+N1xDTkHH6S/jtMkX4NTLTseQOyeh28uPQV+4ENnbS2CTgg5Z8BSSBIvzLFg4BByqnVcTmgD2lGrYsaeCCYu6sHMng2D7Q34+K/ZSU1mZ1qoVOaNlSxTPnIdFp12OFe9+j+Lv50OfNYtc88ADtBt/+gnStddi95wlyPniR2z/9icUPf0iEu67E20nnIH0F/+D1v+bgpbnnw3P1KfhbJkM23XXQvnwQxbAaBrk9etrD9rweGizDhhA++yqqxCTGIMzd/6ETlIJtzGuKzaWSQ2j+CVc11SWyZf33087MzaWvjjAZO/jj7PDrD4UFaHl+uVIDAST5PV8ls5vv0anf9+LNF/RCct5DQrgzZ49G9deey3S09Pr3aZNmza45pprMGvWrCZbXE0UFBRA0zSkhPeLA0hJSUFePcKOeXl5dW6vqioKgtHn+rYxjtmY8wKAz+dDWVlZxNdRRUkJKyyaCrGxwN//zqovwNQ3ycvj4IetW82Hz+k0WxdatqTDa7R4ejymTlRSEsnAqDTRNBpeRvDPqLQ74wy2dxilxG43q15ycszJuobBFh3N9zdupLHYqhUd4UCAXx4Pz2W0BUsSnWW7nccw2nQ3bSIxGUacIQa/Y0dkq6+BmBg6sUbrcCBAAv3yS2Ymli3jdRsBvOJitrIYbcRZWQy8JSZyXdu3Rxqnxtrz8+n4l5Wx0u7LL1mF16kTP+ePP+Znf+ONvEdt2/KPh9cbOVGuAYi2HYJ2ooXjHs2O8/aDGJeMXmmOUIZOliRIElBUZWbiVMGsngROvPIGqSAntTO03r3pxH71FZ3Om25itUZBAbOwBtq2pUF0xhnkA4DGjxGIM5IQbndtjae8PFNqQAiTFwHz2U1IIF+oKnlSCP5vJEYCAVabOJ3kY6+X22dnR/KJ3c5ty8vpCHfpQm5r145V0hkZrJj56Se21xmDMADyVLhznZPDAIDDwTWcdx7327GDfDZqFAOCksR1rltHfi0sZBZ6/Xpg4ULYcncj9edZOPXVv6OdXgQljK7UsKHYugBKvQJa8NYJAFUqnVybxPdlAD5VwKfq2FaoYvkOH1bv8aNtgnJAoXkLFurDsc55OiQsSe2HBadcjPL/+whi1SoGnbp25TP7/fcMcPXrx9eNZ37tWlb43nefOc3+mWcoBVJWFqnVNHcu+cSQNDFg6BAb319+OW2d+jp5ysrIm3371j211rDbDI4891zqCgNmpTPAazDE5mNjqRn8+ONc448/mmudMYPSJeFTIWWZmnyvvcbkrIGBA/lZGEPIVq9mQLJ/f9q0N90E6dlnIX/3LaSlS6F8PxPyv/8N+fTT0e6q8Rj65w9o7fBBkc2WPQFEcF5AM9trLc6zcLRwrHNeQ+08A8FYOlSdFV4OlwO60RlWFxYvNiVO6oMQTLC+/DKTCT4fKk8bjrUtuqJCV5ArxWJx+kCs7XmGWZSSmAgkJUEXAjvtyfglfSDKElORdPVFsC1dwm2GDDEndXfuTDvLsCtHjGDgLjzgZuAvf2EV4Oefs3hk3jzgxhsR859H4XrtJWqrR0WxEGTtWvJdWRl9z927eYyOHcmjTz9NvdF//INcecMNZtWdpnF7w/+vCUmC5PUyWLl6NfC3v9GerllAA8A58xtkbFx2wnJegwJ469evx5AGTKscOnQo1hvZssMIqUbQQQhR67UDbV/z9YYc82DP+8QTTyAuLi70lXGgqPyRwEEGbPYLm41aIUYQMyaGxldpKb8PP5fbbbZY5ebSoTQcvvPOY7UewGDbmjX83giU/fADs8IAtaS++Yak0asXja2BA+kAGu0QkkRjc/lyGnulpTSqdJ1VKXFxPL8QXK/bzeCYLHMbwAwedu/OYwvBCsEzzzTFmmWZ6923j2Rks5nBPoDO8Q8/8PxxcTRoCwu5jyTROO7f39QEEILfR0WZ1XyxsSQ+o71EkmigArym0lLT8bbZ+Bn7/fw+Lo5/SITgV1UV12Q4261ama0qDYRkGOoWLNSBZsl5B0C4LkZmog12hVpEiR4Z43q4EO+WqX8Sts8OPQYldz/IH/bsYfXdyy+zvXT5cnNDQ8vTaBXzeskbSUl89ux2kyeNpISB1FQ6mfv2kQPsdjrSJSWscFm2jNtlZDBoaLPRgTWqbwMBM+DXogXPnZ7O5MqMGTS+jMxo69ZMBBjV0StWmPwbLsqs6zTsDOM1P98MRKphFtHOneY5t23j8Zct47YdO/KaWrQwK/e+/57V1zt3skrxq6+4lg4dgKIiOH+YiQFz/g+eMOkXpcY9ARBqP3MoNG7UoKixJPi9LoAKP+ALCGwv1rC9UMVvO/zo0ELZr9C8BQv14XjhvGKvwNq2A6B99jnyzzgb/tvvZEAqOpoC7R9/TK55/nmzUnjlSjpoN94IXHUVkwTPP8/KjKuuMk+4Zg3bxVyuSJF2v5/2SFYWv//hB9pcBmr2sRl80amTaScCpsg7YHIJQN4xnMvw5zjcTvvLX6ijl5hoBuTOOYfVdX37RnIywO6FnTsjK3BGj2ai5umnydv33MPPqKiIFTazZ9d7LyBJsK1Zg4RrLsHAj5/HOeleuO0MJDgUoE2CHKGJV9NpE7A4z8KRxfHCeQey8wzULFtYVyBQcfm19Z/s11/pY51+eoPXV33nZOReeB0Gd3SHAk0OG9uAa2JtbgBFVTqiHBJar/8NstHa2qYNq9wMnHEG+dCAz0dbMjc38oBGQqOykmtetIjXMGIEkJIC6V//YgvvggVM4vzzn/RVb7iB1zlkCHmxc2dWcV96qZmsCQTIixddZJ7P6MSoC2lptCUVhUVAH3/M78eP53lqIOHDt5Eg+09IzmtQAK+kpAQt99OzbKBly5YoOciAwMEgOTkZiqLUqnrbu3dvreo4A61atapze5vNhqTgL1B92xjHbMx5AeCBBx5AaWlp6Gun8ZAdLSQm0iFqKoRnSVNS6Jxdfz2NLL+fVRYLFzKbuXUrDa+0NLYkpKczaGbsa7RCGFV63brR8XM6uV3r1nzgk5OZjTDarwwB0HbtzJYIIVi1snAhg3xCsM3UZuO5DIPM4aAz2akTndKkJJKYy0UDdOdOBtj+/NOsFDGml40ezX1692aWwGZjK/Fvv5kGqhEQNAJy/fqxXezcc81scPgkNqeTxJWZyXYSSaIBWVbGqhiPh9e/ciXX5/HQcTdafR0OjiMfNIhEvnEjHXTjd/SPP2hMLl1KjYa6Jq/tDy4X9JoZdAsWwtDsOO8AqKmL4XFIiHHReCr36/AGgFFdnPDYpYg//ALA8m7Dof7jn/UfvG1bJie++IKO8HvvmZVxRpXZueeyYhYghxlTvABW437+OfnyzjtpPDmd5I5Bg/gcJyaaPOx0khdataKBZLPRiZRlOt66TgPr9dcZzDdaewEOxPnySzPoZrOZAUJDbD4lhXyjquRpt7u2FqmBQMCsvquuNjlR1/k1cyaTMuXl5iTuQIBOcV6eydtbtzIAOnEiHM88hVHFK5DgFCFfXK5hc8lSUJDaKUENs7p1RBrhhsizL1ix8mu2PyIjC6BWC4YFC3XheOO8akcUEkcNxdzTrsC6867HrmdeQ8UnX6LqL7ejwi+wV/bA9+770O69j/ZFRQUnuX77LavVLrsMKCyEfuqpEOHyJ999R04In2ALsE3/3HPJlytWmMF/gJqdffqY2xp2TmpqZLVyXh5tS4B2T+/etOfCNZ5qTncEyIvZ2eS88ePJtS1amIG8M86gnWfgssu4fbiW9KBBDHJ+8gkDd+vWsQKmbVsGM2vyh9PJSsOXXmLF4n33kX+vvBJOpw2eRQswavlnOHvJ+xix+EP0+O179NHzYFeCQvo1LkOyOM/CEcbxxnn12XkAhySEmxkxTglndHKi+uRTIfZXhffJJxAZGVD//Z/9SxWlpUF7623k3vkQ2mclh9p8MxIVDGrnRIyrNm8Z2n6KLCE6L+yzT0iI5LzwQUEAA2EVFWbi1kB4FV2vXvRjAXLvV19xv1tvJc/fcQfwr3/RhuvYkb78BRcwgf3dd0wOP/EEOc4oTjGSyQaM7o66cO21tHULCxmw0zRy6vLlkX8LgnB89w3aVJOvTzTOa1AAz+fzwW5U/ewHNpsNfiMAcxjgcDjQr18/zJkzJ+L1OXPmYPDgwXXuc8opp9Tafvbs2ejfv3/omurbxjhmY84LAE6nE7GxsRFfRxVu94HLehuKtDQzih8fDzzyCInhhx8YcLLZKG5ZUkIn1ii5feYZOn8DBtChveYaEgEAnHKKmS0YPZrbLVgQVA+VmdH94QcG1Xr3psE0YgSDYyUlZgtvSgoNQYCBrN69uV/v3nR4f/mF77lczEYExTFx1lk05H76iS2oP/zAzMSZZ5oVgq1bM5tqVOb16MFqkuJiBtuiouhY9+/PthGAjvmePSTNPXtowBmkGhXF4GXfvgzMSZJZZSMEP6fp0xmMvOgiGqULFtDgNSbIpqUxWPrttzRee/Tgdc6YQR2riRN57+fO5bETEuiE1/h9PhC8N92CDfHtD2ofCycWmh3nHQB16WIYU8sCKrCtUMXq3SocioQohwynzTTm8oUbqydcC/3ppyl+bjzXHTqYAy3mzqVo7//+ZzqfRvVdVhYNp5wc8p1hNAHkGVUlHxnBtvh4PvtdujDAD5jTvgw9qNGjeZ7yciYqrr6a76ensyXuzjvNZEnr1uSus88ml40cSUf07beZHDFa1JxOGmrx8TQODR2rQIA8YwQBbWGWrzGNO1xuwJgoWV3N6zeMWqMaxmj5NaaJ+3w8Rrdu3L+0FJ6VyzHyjYcwwJtNLcIwm8sI5mk6UFIlICM4xbGO+65IgF3mNDqbDKhhBwo34PPKNKzNDdRxBAsWiOOV84SQsC0mA79Ed8YXbc7Ep30vxhf9L8UPXc7Gx8mn4psr/gntjxXA9OnQb78DgWFnwF/tQ2VsEnafOhpLOgxF1ZPPQLvrbp7szz9Z2XHOObWnKD7zDANo48czwTBxIl+fP7+2PMlrr9HGCXeKjUm6Bl55hZp24UlmVTW7PsJbbI0EisNBrp00iYE8RYnUCTUqmVu3ZgIZIF+ddhq3v+suVousXcu/B889F7nudu3Iv1OmkB8ff5xVK++9x0Bijx7AmjWQL7oIMddfhaS7bkbS7Tcg/tJJ6Hn2yRi/7EOcVf0neul56IpCxNm1EL9ZnGfhSOJ45by67Dwj7CRJgNsOjOrqgk2W4ezSEXvf/zxyGnc4WraEOPscSO3aQZ87F1X/9yHEueeaAxyvuILcs3w5lGuuRvvMxIg2315pjjqDd+VeHdlFKga2dSAjUYE9OUyqYNu2yAFCc+ZQN92AEJRxmjyZVdNt2/L1nTtpjwJmoK1VKzOhfM45HGamquSxzp3Jt3/9K+3Hf/+b34fLJsyYYQ66BMxgXtu2tPnq0mBWFOgDBtAWPeUUcu6YMSxqWbWq7iQMAEUNnJCcV7s2sx5s3LgRNtv+N9+wYcMhL+hAmDx5Mq644gr0798fp5xyCv73v/8hJycHN998MwBmBnbv3o333nsPAHDzzTfjpZdewuTJk3HDDTdgyZIlmDZtGj40esQB3HnnnTjttNPw5JNPYsKECfjqq68wd+5cLFq0qMHnPWYwbBhLYQ8Vl11GJ+yee+hg5eUxKm9Ub/Tpw+qJc86h03nLLYysC8HgVk4O9dpWr2aWFWAl3TPPMGhWVUWSe/ppOo+FhQxm2Wx88JcsoSHVvz/f++gjHsNoiZ0/n+uqrqbDKoQ5pRUgyQUCdGyNzyOoL4C5cxlsHDuWwbpRo8zqvqQkBgx37DAd1qFDea12O7Oxzz7L3v/HHuP7mZl0qI2AnhG8c7l4bc8/z2mPzz7LazQGoyQlsbplyxaS6ZAhrOaJjua13XEHjb716xkkfeklEuigQTx3ZSUrcgwtviFDqGMwdqypC3MQKBt5DjISjj2dAAsW6kOPVDsCKiLEbDUh4FOBzCQFgEBumY7qgIDTBiS4Zeyt0NmVDmCdloR2cSlI/OMP8kh8PBMYW7fy+5gYygwAbE1YtozP7bp15JWXX+b/MTHmtMW0NAbewnX08vPJj//3f6xyefppPvs+HxMnV17J96ZOZQCsRQue6+qrTSPtuuvMNtc2bcjfd91F/li9mg5laSmTGj//zO3cbp7joouYof3mG1bv5ufzWLGx5PyYmMhW2x49WLly1ll0jFWVHJyayuRKdDTXHQ5ZZnBy9WoeT1HIqYZMAADk5sLltKHj7Vei6qUZWOtg1U2mXoy2BdsQ5RDYEN0OW23JkI3YYY17LoHBPo9DgtMGDGjjwLIcZmZbxSrok27Hyl0B5JVp9bawWLBwrOJQOU+SqDdkk2WsdGSgw7lt8XPPCdAhQYYECRy0IDRgTzGQdt1DyBwxDi3mfQf7qy8zYfrss+QeoxNBCODFF+kYXn01/ze6Ewzdu9JSc9tHHyW3vPMOX/P5yD/t29PWKSnhez17mhduBPmmTzd18hTF7Pow7DKbjbZTcnKkxt1ZZwGzZjExY2DECPL2uHG0OXfupN1WWBhZcTJ2LBPLUVG0cbOzycH33UeO3bqVgTwDKSnkWY+HHSJVVYj66H1ErViB5KoqICYGvpFnoWjipcjp2BebRTz1n5oL5xkDlWoGai1YOApoKs5z22Ws3q2iQ7KMHzf54E8+Cf2/mI+U5T8h5rUXIe3dy2d31CjA74f8yyLyWmIiKmZ8gxXPfoi+rWR4nIoZ0DoIGJWEfhUIqGz9LOpxMpIN+ZTyctpOHg996G3bWB3Xty87HnJz6UNWV/PZHD+eSeA5c7hvt27krrQ0c3I3QD951SpKKaxeTbtP103+3rsX+O9/2VL70kt8bds2s4U4I4Pcd9NN/DBff73O69Nvvx3e3H3w3HQ9r6FjRx5zyxZTo68m3G74nZ6jz3lHAQ1e8dVXX33AbQ6kCdcUuOiii1BYWIjHHnsMubm56NGjB2bOnIm2QSclNzcXOTk5oe0zMzMxc+ZM3H333Xj55ZeRlpaGF154AZMmTQptM3jwYHz00Ud4+OGH8fe//x0dOnTAjBkzMHDgwAaf95hB//6srgiv9jhYOJ3QT+oHedlvNK7uvdeswgCY+ayoYFDPcGLvuYfvZWby+yVLWHlnlF5PmMA12WyckrpzpxnwGjOGBOT10lD7+WcaTm3asDLt88/Nc19xBR1RgCW8u3fz/fh4GmqrVvE9Q7Pk669p7A0ZwuqSoiJOvf35Z76Wk8PMMUDnMz+flYCPP87/P/uMzm27dsAHH5Cw4uLoRAM0KHfsYEDQWJcQrGqZOJHXmZ5uVsV06kQ9lk6dWMFoTHZ0OkmAs2Yxs/t//8fXJ00iIRq6Wz16kIhzcugMDxvGyrwZM3ifkpLMCW8HAfWiixE98CR46sgIWbBwrEKWJPRJt2PeJi1k1FX6aAoIAbRNtGHLPh/8moAssWQ9vNJeBrC2zwgMGLIIboPj6kJGBg0pp5MJi1Wr+HxOnsxKt9mzafhNmkSj6IknTAdQUchVn37K9595hs/5GWfQOIyLozN6113kHVkG3n+fTnJ0NI8fbjB5PNQw+eknvt67NwOCZWXky+3bzereq64iR3XsyNe3buV17NnDKmSDG88/30wKeDzk6g4d6KiedRb3NfRDv/iCBq4RJHQ6+boxNMioru7bl5/JnDncHghVONqmTEG7Pxfjz77nI9amY8DrT8D1v1eAMWPQ5dZ7sLUFW1vqaooQMMSodQAythSoiHdRPK9vBrVQ+mbYsTaXRl1dWXALFo5VHCrnKWBVSpVfILdMw45iFdUBABAQQoSKbQHAqwLbAi7sTT8ZMTcOQqcLroF760bYhIqYd9+D7fbbIm3HjRv5NWCAOSn244850TU88bxzZ+3Kl2nTKHg+fTrf37gxUjMpP5+Oqc1GO2rsWHPgmXHx4f8b1cIGOnemzRoewOvdmzw7YgSTGwC5cPp0c5vx43mchAQGFffs4X7DhjHol5Vlis4PHMiEcF4ebeQrrmB3S3ggEQCKi+H8+EOkfvwhUjp2ROYzr2B++ikIaNLh5bxdu4DNm/k3Kj3dDLSG49dfeR/Kyvh3Zty4eitnLFg4EjgcnOcN5kKXujtAGtIBZ/Xqi6RpLzMA9tprLBB54AFuVFSEpPvvwM53v8eWkjj0Sjv44B1QVyWhBsR3wsnvfIC4qy9lEO+NN1gN9957DKItWEB/97//jTyY328Wplx5JW27Xr1oq/7zn/TD27Zl0C0ri8mGW28197fbuc1TT/HnmhV1p59OLjj9dAYPv/6ayWJjEnhNTJyIotHnwe0JhqWqqhgsvP32/X4mlTfdig3OVkAgkvmMDo0yr45Yl4StBRpObmPH0pwAOrZQQq3Kx7Kd16AA3ttvv32413FQuOWWW3DLLbfU+d47RkYuDMOGDcMff/yx32Oef/75ON8IljTivMcMEhMZABo2LFJ0/CBQ+uRz2NBpKLrmFiD2v4+FDDDRrh0qHnoUWkZbRM34P9g//9TMmsbHswIkJoZts4bhpCgMtOXn0xl8+GEG0TZvZqWGw0FHdedOklGbNiz9nTuXlWvh1xAby+OVlTFTUFxsTg+74Qa2LgB0nLt1YzvH/Pl0OM85h4G2tWtp5I0fz2q9K6/kNB2A61YU/tylC40yo7Xtq69YYfjkk2yTeO017nPeeTQEX3mFP7dowUDghx9yTdOnM2D4xBMkuq++Mtd7001mhqN7d4pFv/IKnVsjG2KI1RsO+qRJ/AwBConedRewezf0v96PitOGw/3B/8EeLnLaEAweDNt/n4AteT86DhYsHIPQhcDKXYGQUeRTzZaJ7EIVxVUa7DKgKxI0XaBSZ5uSBpbnxziB3EAclt34T/Tp2Yd8GK6V6nBAu+pqaOdNguOhvzEwn5hIJy0qikZUz578Ki1l+2q40wiQHwz+W7yY3ODzMXjncPAZX72aTmFVFfD++xCPPAKpqCiSawE6tJdcQqmCVq2AF14gH23dSv2SOXNMy/Xii8ltvXuTm196iZzidnPbG29kpW+nTuTFcD29zz5jlfby5XTwfvmFju327cwCp6fzmjp3pu7dmDHksZ9+YqAvKYnX6HabjnPPnuT2ZcuAwYOR8OE7SBl6AWz+ABwFedxn7VqUtMlCgiShpKp+TRMRvNeSpGNbATX14t0SKn0CMS4p1MJiwcLxhqbgvKoAJ/1pusDJbexYuUdFdUBADz5ymm4OmdEBlPsBXehYE9Me6EMZjrQ4Bb2XnwL5t99oyxltqQCf8ZEjGchbtowdFvfcQxvO4KfVq9npsXKlud+TT9Lh27OHtlT4ZGyA/HrffXRmJ00ydZ8ABvz69DGPbwwfM2A4qMb7Hg+36daNFdUGnE6zurh3b/6cn89Eyp49TPaedhq5969/NR3gK65gEvmZZ1htcsEFfP8AkLdsQcvzx2L0+5/gh44j4dPq3u6QOG/NGiaCn3uOsgvG9d98M+1UY8jajh20nY1tJk3i36yTTz7gdViwcLhwJDjv5/geOGvUGLieeoKFG1deSXvljjuAl1+G8sdytMvfhITu9ctuHQh1VRICEn7rezb6LvwNSX/+TrsvM5M2U34+fcU77tj/gd97j9vMm2dqqn/2GW3HK64gn40fz6SHIXkVCDAYpyimn2p0pbVoQfvwp59o6xkTZeuSWGvbFrjwQvgcLixP6gbYFIwYczZs33/XoM+kcPjZ8AbqtvV0wa8yrwCgYV+FBk0HftvhD+kLHst2XoMCeFeFT5OycOxj0CAaMldeWVtg9wDwXncjtgw7F3pcIpaMvhotTxmD+PwcyLKEsvT22CAS4VWBlNt7oftl1yOxugSFfhn2FklwbdmI2BnvQercmc5rnz5sI926lYbO2WfTqfvuO74G0Nh64glmNSorzUq1mnA6mQ14/nkaRD//bGZEDb24sjIG5/7xD1awff89z//44zzfypUMuBUV8f3rrjOzFjYbie3WW0mOI0bQoX3pJQbUxo5lgO2665hpKC1l5rFDBx7fmJA7aRKvIyaGBuHIkax+AehcP/44W4NzciKzrmefze1feIHfX3IJyXXdOjNI2bEjDa2KChpXRUXQBp6CqsuvxtJ2p2BPwImet7RH14AK1ycfNeyGT5zIDLMxxtyCheMIa3MDIWMIoENTur9+nwABAABJREFUXAUEdIGADpR6AY9dgkcGEqMUFFepKA5Ou7IpgCRLAAR2IA57Tr0SfWaOQbuctSjPL4Fms6M0szPWxHVEDPwY3iIF9hkzDn6RvXuTm846iw7eBx/wGY+LI+ft2wdcfjnE1q2obt0Ouf/7GLudycjM34S4KS8ixlcByelkAKxrVwb5Ro/mUBpVRfXiZfB8/TWk1asZzBsxgvojcXHk42eeIT9ecgm5afVqGmdTpjBjO2wYeQlgZaAs8+9KdTV5saCAjmtKCrn5rLPMQUITJwJvvcVM9fz5wODBdLyvuYZrefdd/r369lsmJioqWE08ciSkTZvQXitE9O6tQFU1xHXXQT9nHCoTWyG2UttvAM/QiQpogASBKIcEv8oM97Fs0FmwcCA0FecBdI42FagY1M6OBZsjnTNDbsjYuioA2BUBRaZQfK/WdshyJu2psWMZyDeqM6Kjac9cdx1w//2sFJkxg0nLBQuYfDX0RbdvZxcFQN554QXaK3/5Cx1JY6AZYPLngw/STjv7bHPB8+axItrQ5CwrMweAAWalmdF+m5hIe3TkSJP/gMik8pln0n6aPNlMIF94IXlw8GBzXZdfzuqU33+n3EybNqxiayhUFfFXXoQhn8/Bjy361blJozhv715+Tt98Y3ZuXHghq3QqKmiTfvghu1C6dKGdawTvUlJ4/5Yu5fapqQ2/HgsWmhBHgvPKNBuqW7eFq0cPcscjj5BLWrXicMfXX0eCXTukaac1KwnD3kDCoD6QBveN3MHtjtQG3R/efJNBu3POYUDO6aTvbSQjvv6aUk8RC5LNTpELL6R9eMcdtDGXLjVbXtPTaTPabOSI6mpygiQBO3ZATJ+O7R/MQr5wAwGg8K//QMvff4NkcEk9qHryWaxI7WNGY2vA4DwhgAofNQ8V+fix9RoUwDv33HNx/fXXY+zYsZCtUuhjH7LMP7hRUXSUjCq5/UGSUPXQI9h43nUocsVjeFsHVuwE1lelQLSmkSNpJDdZAvZpDixO6ImYVBmqLlBcLSC6d0Hys+filMLVSFg0zxwj3bIlK99efjlSF+nhh4EffzSnIdaH5GQG+oSgfsqTT5rixZddRoPip5/YknXffTSMcnKYJXn4YRqE7dpx26+/JrGMHUthzvBqlH//m1lRgNUvTz5Jx/q00+hoXnMNnc8NG+jsnnwyM6jGNckyjdVffuE6Pv+cxiRAI/P331lFeO21PJeBVq2gp7SCXF4G/P3vNFgXLKBRa0xiS0vj2p54AnA4oE//ANsze2GbnISigAI7uyXwpz0VpfdOQecLrkDi1x/D+eH7ZvYk7PdDvegSKJdfCmngwPrHfVuwcIwjM9GG/DItQhfjj51+bNlH68gZ7HRw2ICTg5xXUq1CltiSVOYVIc7TAKwRLbA9awTUDkHOEwBUoApOFN14B1Lm/HBwSZP+/enYzZ5tJgGSkshVqamhVizdbsf6Mge2FwWfZQ1YndwVGNoVrWKVUJtUaP8g1u7xY2fPs5Hwwkh0qcpBXF4O5B/nQrr/frbiR0UxAXLxxXRYFYXrWLqUlYDbtpnO60knMdj2zjvktwUL6KS+/TZ56aGHWEkcG8sWthtvZNXLk0/SCezenVx/6600eufPZ1Ljww+57+LFdOyNaeG9eyN93a9w3X8fKz9mfQ/RvgM2RZ8EXQhEO4DKAIIiNoBbAao1INoBBHQJDkXAr0lwKpzm1ipWQY9US+PTwvGNpuQ8SECFV2DNHhVxbsnkvDAYP8oS4NMAj2xO/gvxUkwMNY9rIjaWrbG3307b6plnmIi45x6guhpi1y7gyaeAf/4DUnjl8/bt5CWHg470okWmnbNjB23Nyy+nYxlaqKDjXVVFOy07mwFFQ6/TuLA5c5gA2biRFSWBQN2c3qIFbc/kZNMezcyknhPAJMmzzzIJUlFB+++KK+gQv/tuA+9mGPx+JH/wJhLu7YPSgBLiPL8AWkRJEKCmYIkXDeO80lImu6OizODd3XezAufjj+l8X3IJP+O33uJ96d+fbcBLl9LHePJJfu5RUbRrLVg4CjhSnOfY8GdtffG8PMDlgsjIwK6WHZAuRKODeDUrCQ3U4lMDS5dGDvLZH6qqyEEtWnDNPh9EdjaTv0b1cfgw04QEJigyMuhLDxlCTnzsMdqO55/P1tvNm9l6bwTzDK4PBIAvv4RITsbu6V/hN0/70B+L6kofpOuuq9//j4tD6X+ewe+DzoUu2yHXYee1SQCq/DJ8qh5h5wE4bmy9BgXwZs+ejW+++QYpKSm46qqrcM0116CTUTJt4diE3c5qs06daCy89FLt1i0AkCT4x5+Lkutvw+8ZA6DKCpQgWfTJsMOvCWwv0lhGHOQNHYBdAqKdEnyagF8F4pxAiRcoqBT43tMTgwbIaDnvW0S/PNWcHgsAsozABRfDe9kViDlzGFu2Fi2iIWBU5Rk4+WRWtMXG8kH/8ktmP10u7peWRoHhtm1pKK1cSaPt3HOZgbXbSUxDhrB9o6KC237yiZnRNc7Trx8DjV9+SXKbOpUGTnU11z9iBNtmO3TgMI/582nshOOKK2gQjh9Pw8fI9J50EvebNYvtt08/HSGAXPTUC9iT2h1t1n2J2OefjiTk6GgSZXQ08NRTUC+6GOIvt+Dn1P6o8gno4OQdvwbYZCDaKWOvPw77Op6O1n8/Df3uugvVK9dCrqoEBKC5Paju2AUpp/aGZAkQWzgBEO+R4VAkZCYqWLPHh30VOhQZcNok+DTAKQn4VanRnGfgl3anYsRzryDurr80bGFZWdSt/PVXZlHj4shBHTsymNa7d2jq19o9fuwsMgPxDhsitFLW5qLObKNh2BarDmS37Iw+J/VAflZPxIw+G7bKCthtEmRdJ79s3syKX5eLHDhrFg/StSur6nbuJHf+9a987+67WTVzySUM4p13Hts5tmxhpXRqKjl382aIYcMg4uOBM8+ErKpsbWvbllw3eDAd8tatGRy87Tb+f9NNcPy5Bvott0J+6EFAVaHHxcNhAyq8iOC8Kr+ALgEpMVKYmDEnkxmfV590+yFlxy1YOFZwpDjPgAQ+i3EuVoAB++elCMTGMkk6dCir00pLAV1HAZzIjm6DMjjQ/rNT0eKPnxE15UlIxuREgAG2Dz6gvXbnnaZdVVJCm3fSJNpehm350UeUJEhOJkd9+intq3feMQcyrFvHlv+lS8m/9XHGxInUIr3wQtOpHz/e7Jgw2snOPJOtqXFx5NaoKCZtGgHnR9PR49rb8WdKDzj0ADrv3YDkZQvhWfwTRGkptNYZqBx1Nvb1OBm74lrvn/N+/ZVJmPZsecYpp9CZjo9nRWFmJvW2jCQ0wGDkG2/Qnk5KMoMB9U3qtGDhCOFwc54sAcjNq/vk5eXQH3kUJdUCJbmBRld+1awkPKCdt337wZ1g61YmYYOTubXMDlA6dIC0ZQt89z+A8hFj4ElrA6fHAWXo0MghQQCrbhcuZEfFp5+yo8zrZdJX03jcjAwW0zz3HHwTJmLnnQ9jWWwnSBpjcGxltrP77bTTyDUVFRAlJRCt01He8yTkdxuALdHpKKkS0Oux8/qmO+FxSJi3yQtbWMDzeLL1GhTAy8/Px4cffoi33noLTz75JJ566ikMGTIE1157LS644AJ4PJ7DvU4Lhwvdu6Pisf+icOLVcGdvQsy+PXD4qlAFG6qSWqEiPRO70rog32+HFhAABKKcJAt9l0CFzxD+5cMXnojMamnD7zkBaDoiMgaqDiyK6w7nBd3RfewlSNq5EcmSD9WwoywlAztbd0GPtlGAS6Zz2LUrg26bNzMAJkk06rp0oaP3558s+x09Grqmw9+iJbwVPiiyhKhx41Fq86C6sAyOrn2ROG4c5DfeYEAwPp4Gk6aRYLZurZ1JHTGChuPf/862hnvvpWOdm0sDLDGRFXetWrFdYNUqZi1rHuess3isP/4wte1sNlbNtW7N7UePZuWdYWRKEkqnvoJfe4xCsWrDmjG3ocuI85GyYx0cviroDgfsrVrCV1QGKeCDNvFKRPfriaV7BHx+ln3LughJFOgC0HWB9skK7AqQmejE2n3dkDewc61fi1Z5QN+MxmeKLFho7gif6JUYJePX7T4UVArqN8mAHmBmVdVwyJwHABWaggWnXoyhb8Uh8d7bzIq6ujBhAjmqc2dWMhwAdWWYa07YKvfqyC5S0SOVxosuBLKLVPRMtWNvpR56vWWnDKyNaYXMRBuchrDv5s1sP2vbltXLp57KwJrLRWfzhx/IX+edx5/vvZdO8s03kyf/8hcG6845h8ZbbKy5eE3D4h0BbC/S4LEJjJrzJqK2bTSFzxMSaPjt3k2nMRBgBc4LL0C+6iqKFb/1NspccVjT7TTIOvbDeTZs3qc1PINtwcJxhCPNeQYE6OCe3M6Jrfu0xk3+Cw6wMeD06igJXktReke0GdQVOSPHw/XnatjLSxDrkmGLi4G3a09k2xPRpX17SLffzqphA199xYrf558PfkDllE/5xz+YtCgsZGAtOZkyAOnprCZZu5b2Z2pqbV0nl4v/2+20V91uswtD15lkdjpZ8RIXx6o/IRgoXLCg7krEhkLXkbhmGfq4XUib+jjkTz8J3SwJFOeP/+gDxMfFIfWfjyP7nIvQvmNybc7TNAbmOnQwBxUNHMjgpaHpfO21lFNo04YBTb+ftvCKFfwb4fGw9VbTyPkWLBwFHCnO0wVQ2akboupaRPfuUB58AJ2u3AT10X81+loaYucZ15xdpKKnpuGgrBmbjRyXmAjf+POwZcg4qCMuRLRWjZ2OFqgUNuCsXshIVOoPQvbtS23333/nwLJ33jH1jG+6CYiKgm/HLhTO+BYb2/dHvhQNXTcrtXUBbGnZCanX3wT3m6+ze87thvbV1/i57amo9AcHlO/Ht22fZEeUU8KKnQdRrXgMQhLi4ETQNmzYgLfeegvvv/8+8vLyEBMTg4svvhjXXnttxNRWC7VRVlaGuLg4lJaWIjbcgTnKWF2jesOvCVQFyFq64MPiUKgRUBXgpOF4t4R4t4zsQhU+te6x9W47YFcklPtMoc+6IAHITJJRFQBaRkvokOyA2wHM2+RDr1Q7WsUd3HjnuhzVtbkBTpqRVajzF0K8/Tbsn8yIqHSLQLdudD47dqTBFRdHfScjk7hmDcS990LKyWEVXkFB/dN1bryRVXwPP8xsZOvWJDJDyH3hQuqvhEEdNBjF9zyItd1Ow6mdovD1mmpUBfhZRTn4eXvDswqKFOzvZ8WdJHEaWYVPh6qHqovhcUgY18OFlbsDKCjXoQlToyagCSgyoARJbb8kfZTRXJ8lC5FozvcpnPeqAgKqJkKitwCNu1hn03OeXQa6BXKRuXU5ot58DcrqVcxSJiai+sprgLPGwD2gb20B9gNgf7wHIGTE1mX4GYK++4XXC33dOuhr/oRt+TJaTkLQUd29GyIrCxg0CFJSEh2/g9A8yitVsWCLD5oAOjoqMeCXzyAbA40A/hEaP96cVv7BB6yw7tYtNBRo46zfsNmdZnGehaOK5nyfjhbnGds2pZ0HHMDWc8kRzntqrILezhJULl4G94wPYF/8M222885ju2y4DdahA6VWbr6Z3PPQQwxaXXSR2Qp2001MLgQC7PAwMGYMK1+GD2e13a23mlV3xvdOJzsy/H62p5aUsNJk4UI60cYU8IOFzQb9448h3XU3pJwddW+TnMyEcmwsAr36IG/UucjIbBG5TVERq2s6d6YO3p9/svLZbme1YK9etImzsvgZfv45r+mxx3gdBi6/nNpaTmejLqc5P0sWTDTn+3QkOa+DVoh+z9wL1+dhbbSjRjGj+P33lDdauRJzpXZHhPNO//VjRN1wdcMP/vzzwI03Qi8pwQZfDLLLa9uEtSRZ6oLfT657881ab+l33YX51z+GvGoZ8W4ZIzs7Q/4tANgVAAJorZei05ZlcBTkozKrO7Zk9kVlAA3ybSUB2G0SdhVrEACqAwIxTglamLvfXG29g3mWDjqAZ0DTNMycORNvv/02vvvuO6iqii5duuC6667D5HACtxBCcyU5XQis2GmW5gpwXHZAF7BJtF88dgmSJCElRobdJqF9kg0b8vzYsFerc2w9wIcqzsWJZOV1DJ8Jhxw8j/FAz9noQ0m1DkUCTu/obBTR1YfVe/woKfWhfe56xP/2M5wrlkPOz4WIi4PaoRP8Z4xA7KkDgMTE/ZNlRRH0Zcugvf8B7B99EBkMjI1lK8bYsQwClpYyC+Fw8L0WLZh5/eMPajsZbR+ZmcCQISjq1Bu/lzgwvJMTNllGSZWKWet9cNoAt12Gqpv3SJFIXkqQ2GwypyVV+pllMu6PBCDGAciKxOxSkOTcdimU0fBpAnaFrzXIqT9KaK7PkoVINOf7FM574ZyHYNIixgHIsnxYOS/F4UeaqEDnRAl/ltqwWYuBqh0ezquvxRaINGYOZCBqukD2im1wlBRB9nuhO1zwJyQis0/7kMZIY5BXqmJ1biDEedVbslHy+1qklOVCzs5mNYdRDZKVRUfx0UcplzBlCnJm/YJFSb0tzrNwVNGc71Nz4LwjZecB9fNelE1HfKACLZwqWreKgS93L5S77oRt1kxz5xYtKAfwz38yWWHoLL/xhjlg7LbbGOR69lmzXU2WKeQeCDBYd8stDP4BkcG822/ncV95hUmIU07hUIyKCnZyHCxkGXj9dYipUyEZPGlg4EAOVUtIIId++CF1RN1uiHPPhXTVVZz4aySoi4sZpCsspLbzK6/QuX/lFepcpaaSd9euZfU1wOrsXr3M4XEAb/b69fyMGoHm/CxZMNGc79OR5rw2KMWgdfPg+v1XBq5XroxIDuya9QsWJvU+IpzXrWwbMs8cULc8Vk0oCop+XgZ7394hO2/eJm+EneiwAcM7uQ5s5y1fTj6pK7xks6Hq19+xKKpTLf8W4KAOj0NClZ8TfwEgyskp2TaZHXwVPh3+MOl2RWJi3GFDqNCldbwCb0CgpJqdg/FuCTEuBUWVesOT1kcBB/MsNfo3R1EUjBs3DuPGjUNBQQGefPJJTJkyBffdd58VwDvGUHOyjRHNrvBxUpUUjLQL6CiqAoZ3dqLaD+ytqJ/guD1Q6QdiXIBdBQJh8S0Z5uAYCXzOJQkoqdbx+arq0AQzTQCrcwNNSnI9Uu1YoQKrWvaEPK4nnOdJsCusPIQAhnVyArKMsmoNczf5YJOAgIqIqpX8Mg0D2yZgc/cRqHhoKNrdOBmOkiJIAT80lxtyejpSeneoUzg+hNhY4PTT+VUDiQBGtTJ/jvfYcHZ3GdlFKtomKPhthx+ABJssYVA7B7YWsC3FaQO6pdqwYJM/9BkaEADK/MZ3wfJxQX08h8zMhlMBHM3ckbVgoSlQk/c8DgnlXpZnxTilYKbv8HLe3oADe5GINfsQ8bweDs4LqAglaQyjTNMFHDYJ3VrxXAfmPAc279OQ50wDUtIizlF6iG0JreJsEdfs7pgJNb0tKmbPRuyiRdSKGjaMRnFODvWYLrkESEqC6NIFuUltodYoqLY4z4IFE82B846UnQfUzXuaLrCvWkJ0YjxS2zhQ5tUx15uGjo+9jI4T5sI95SlImzdzAMUjj7AKr7yclXeDB1Ob6Y47GGh76SUmESZNMqvwdJ0VbMaAjKVLzUm44dzocLDyGqCcS3Exv1q2bNzFXn89sG1bZPCue3cG7goKKBHz9tu0RS+8kDyqqrTvf/iBuqV33cVAXHw8KxNffJHVQ3FxrKrZEazqy81lC3F45WFhYeTUXoCBvri4ute7dSs/o0ZW51mw0BAcac7bhThkt++DrrfcWCtwJtq2RW5iBoAjw3lbE9sj5d774fnXIwfcL/CXW7E6LguB7b6Qndfo9tNdu+of1qaq8OzLxah+vUMvGf7tvuxcxK/8Dc6cbRAprVDebxC2R6eHgm4D2jrw40ZvRPAOYDWlV+MXQDsvv1xHtJOU67ZJCGgSPA4gyqmEktHHOg7pN0dVVXz99dd46623MHv2bAgh0NfSOjjmUHOyjZGl0ARLjj12thpV+AFAxw/rvUiNk1Fctf/jKpIxwYdZjlKvacwZmigCQKwDUBQJpV4+8OGObLxbxvBOTfsHviahVweod1AVEIh2yli1S0WfdDsWZ/tRHRCQAewuUVFUZRKaXwV+3eFHQAUABWuTusCRUqOq5RDESutCjEsOHW9QO2dElUzfDBlrc4G2CQqWZB8gDQ6SmiIBUQ45QiMhNc52XGgDWLBwIITznsF5Bj9VBQTs8vHLeQAd2aqAgCxLjeA8oqHDMhqLGJcMnNwHeOAe6i8ZsNlYDfLqqxBDhyL3fx9gi4jf77EszrNwouNE4jygNu9pOjsTjLUJgRDn/Sm1wI7Bl6L9qWPRcuMKeLZugFxcBHg88HftA3HZtcD27bCVFsM17W0o994DaWcOg3xvvUXtOyMg99575KfevVmNcs89DODZ7UEBJ8HhbOPG8We/nwG9zZuZqFiw4OAu1OGgFt3PP5uv9e3LqbCffAKccQaDd5dfzm2nTzd1+QCuYdQoVs9ddBGrD40A3htvsJ140SLqUa9YwX02boxcQ0UFdQMff5wBw6++Al59lfrQNbFjB9CnD/DttwxuWrBwmHA0OG9tTCZSn3sF8X+51gxkORzQ//YAEisLgOjEI8J5PhXYOP5adN22Da7p79W7j3/Cedhy5V2o1BVAbwI7Lzhc7WDejyndh5gHbwsN0ACA6C5dkPLll1ibmIm2CQq27NMi2mAN1AwVKhLgtkvQdcATHDbbKlZBz7Tjy85rVABvzZo1eOuttzB9+nQUFBQgISEBN910E6677jr06dOniZdo4XBjbW4Au0tU+DTAbWM2wa8JCAQn+SnUWzMekqIqgXKvhjqeowgYBlqUHfBqEqQaj5kAYJM42jktXkZFvhZh1CkSMLIzS2ybEnWN4vZpXG+VXyC3TEPRJg2qDthlCQFdwKcBStj2rWIVdGzBSrgDCYoeDoQH8wCSdq80B1bv8UPVBOwKoNeh3wDwc5UkwGGTIhzZ42k6jwULB8LynX5sL9TgtgHVKrUjdbBSRNPJfRbnmdsfNc5r1YrTbF9/nc5oVhadzYceAvbtQ+CCi7EyoTPsfmFxngUL+8GJxHlAbd7zaaajnV2ooaTaG8F5Xg3Ybk/A9i7DgS7DkRqnoENyGOdlnhTiPM8X/ZC6YTni3nkd0uTJHDp0112mw37nnRxKtmoVK9ZSU4E5c4CRI4HZs1mZd+GFHG72668MZAUCrOBzOGoPx9gfzjuPkiyzZ/Nnj4fneeopSg089xyDdxs2UFy+JoRgFd6vvzIQOH48224fe4wDPf7zHw6tGDrUDOCtWAGcdBJlYAzIMisSZRn48UcOO6oLrVvzfF26NPwaLVhoBI4G53lV4KcBE3HmFy3g+THYYi7LUB58AJmxcaj6cA4692l/RDhvi5KEijv+g65njEL86y/Atuw38zp69ULZrZOR0+8M7HJSB7NJ7Lw+fSgJsGRJ7ffGjKk9vRYAfvklIngHANiwAfKXX6LX/fdj9R4/8so0eOzk6roCeRJYfXei2HkN/u0pLS3FK6+8ggEDBqBPnz544YUX0KtXL0yfPh179uzBiy++aAXvjlG0jJJRFRDwqwJeVWBoezuUYJJQCKBXmg1ZyQqdIPCXRhPBsdlBOGSgvkejwg+0io50rGSJgzE0AZR6BTbvDUbWBe0XXSe5ztnog1rfoIlGoq5R3G4be+gDukB1QES0EruC74dv3yfdjji3gkHtnMhIpKinIkvom2FHRqLS5O1Y5V4dq/f4oQeNQ10IrN7jR7k38rPJTLTBaedAC7cdcNbgWaONRQjAH2AFjqEzYJRH6/WVPluwcJyg3KujoEJHQBMo8wmkxUp8VoLPRrQTyEyQLc4L2/6ocl6PHtCnTsXuRSugPfooHeN9+6gvOnYM7DaL8yxY2B9ONM4DavNenIucB5D3SqrFfjmvd+v6OS+Q2hrKpImQvvySgbPevVlxpijBEwSATZuoK/f552xx/fNP6sQZeO89JiMqKsyqlC++ACZOPLgLbdmSwyQMHps0iRV3MTE8dosWrP6rK3gXjtJSavPt3Mkg4N13A++/D2RkAK+9Zl6HJDFAN2YMg3gGjLZhSQKi6pzHSdhsbEc2NPcsWDgMOJqcV6kr0FetYRXriy9SQ7K4GPKO7UjZ+McR5bxcWzx+6H0ufnntG2z/fjFyv5yLXbN+wfJ3Z+GXUy4IBe+azM6LjWXCNZzrACYFnn2W3BKEYeeJmhW9Br76CgB9W4eNsidZyUqosi4chq13oth5DbK2L730UqSmpuK2225Dfn4+HnroIWzduhVz587FJZdcAqelYXBMY2+lDpeNOQSfBizcGoDHIUGWSEar96jolGJH11QFNgUhNjOeBadM0qvv0dAEsKNYRGQ1pLBjaALwqQhNBhLglyaAkiod8zb5mvR6DSIAmF0Y3smF1Dgbp7hKpgMowGEQTsXUAQQiycCohDMi+0YlXFM7sr9u92FnkYYVOwPQdIqy7izS8Ot2H8Xfw4J7cS4JXpXC7DVvigAz0LpgpaVXjSQ6lkcHmmztFiw0R2QXqQhoZitXXrmATeEUZ0iAT5PQo7XjkDgvpySS82QZcCp89izO2z/q5LzdGlZ64/Fz+iBULv0DJd/Pg75wIXydu1ucZ8HCAXCicR5Qm/dGdHYjM8kGGXR+nLYm4LyoKLaqDhsG3HADK8vOOIMH+b//o16ergPvvMNBGHPnshoOYHVecTEDZL//zuPs3k0Nuo4dG36huh6pOdWyJRMcgwdTQH/SJFYxNwQ5OayGAdgSe9llrKT58Ufg/vuBq69mq+706QwM/ve/HMS2YgX1Apcs4c9WQYeFo4yjyXmQALmosM795KrKo8J5e/QorG7dG8s7DMbSlD7Yq8QdPjuvZ09yxqxZ5MHZs4GZM9mGH0S4nVedXEerPYBA/wEh3zbeJcGmCKjBxE9NnGh2XoPuxGeffYazzz4bM2fOxI4dO/DYY4+hXbt2h3lpFo4UeqTa4QpO5QOCv/h+AUli+6yqCWwrCCC31By/rYdN+vPpkXomEiK1egFEEJzTBnRMViKmBQKmQVdzwE2v1DpC7YeAGJdcK7vQO90GmyKFprkC1MYzSEAIESJF4MiSQXaRGqE/MG+TN5RhqfYLLN7OqUNLt/uxJNuLLQUaqgNApV8goNV9TOOzdihAtFOGw8ZrPtytvxYsNAf0SLXDoUghvjE4z3gmdP3QOS/cn1JkYEJPJwJ65EYW59WN/XFemRf4Ae3wS/ogLPUlWZxnwUIDsD/OcyqAXRb4dbsPuaU6gj5PhI12rHEeUJv3pGArvU2REOUk7zUp58kyW2K/+YaacXffze///ncG6j74gO2pfr8ZxHv6aQb5fv0VGDuWr732GrcLc3b3Bz05mdNe+/Rh8C8vj2/ExgIlJay+qzqAsFc4vvsu8ucWLYDhw7mmU0/l16WXAh06MPh4yik8t9sN9OtH/T0LFo4yjibnBTSgvP/g2ouSZfiyOJX5uOC8/SE5GRg9mlw3cmStittwOy+3+yDoGRmR+7tcKDznwpBvm12kobBSYHuRZtl5aGAAb/fu3fjkk09w1llnRURrLRwfkCUJIzo74bKZ9zakERB8SLYWaCip1msl+gwYeyoS0DKaVWD1wSYDeysEYpwcCy1J5v5ykGycCsUnh3dyNPmUHgC1sgvr8lRoOqBIEjQh4NcEnDY+IAGNlYkD2jrQKpbtEUeSDHqk2kPnBSIHZThsEhwKr2FXCcnNuD+qzvuohN0KKfizBN6HKIeM1FgFZ3ZxHpY2OAsWmiMON+dJAETYc2dXgF+2BeCy8bmLXIvFeTVhcZ4FC02L+jjPqERTdaCwUqCokg+TrtduHTvWOA+I5L21uQHkl+nw2LnQw8Z5UVEMcj3xBDB/vlmZd8UVbKVTVVao/Pe/1JV7/HHggQeAzz6jdh4ATJnC9tQ77wTat6/7PL17Q3vyKZQOPoNDMs44g22zpaV8v6yMU2BVte7968PmzfVPkQzHOedYOnYWmi2ONuet7jQYlQ/+ky3jACta//Y3tPj0XYyL23t8cV4jEG7nbYhth13vf4Xq+x8CeveG77obkPfFbKxKPylk5wWCtrhh54XiBmHHPJHsvAbdpeTk5DpfLyoqwlNPPYW1a9eidevWuOOOO9C9e/cmXaCFww9dCKzercJpk+BXzXJgTQCSoC6IXzNLVhWJk3cqAnxNkc3qFLsM9G/rxE9hpcHGQ2aYA5V+QNN1eBwyYl1AmZcTbxWJG8sAEjwSRnR2Q6mZpj1ElHv1iOmtuhBYmxtAyygZ+WUaqgMCAS3YTiFz7HRVQMAmA8t2+HFyWwfsQYI7UmRQ1wRJgER7epYTq3eryCvT4LZLqBKsHopYmQQkeSS0ipWQXSDg0wRsEhDlpB5En3RW5DTl9EgLFpozDjvnSaDOSvBnbwAoFuQ8RRLQJRoissV5dcLiPAsWmhb1cZ4OoCoARNsBKEBlsD3J4LxSX3AQhcV5jUNMDL9SUtjSes01HGCxZQs/tNtu4/+7d0P87W8QW7ZC/tvfqJs1fTqd/3POASZMAHw+swTI5UJV5274ve8YlChRGH3RpXCWlbHaLi6O2yxZwqmyB1t4ERd38PtYsNDMcLQ5L1+Kxo8X3IOzumTB8eti6mK++iocxcVw9OkDdPpLk11rs+I8gBO5izjFG/HxdW5S085bk9gF9qseQsL1f0OfTA9271ah1rDzIPFeqBoLnuPdMs7IsuOrNT7oOv++nCh2XoMCePfeey8+/vhj5OTkhF6rrKzEgAEDsH37doj/Z+++w6OoFjaAvzNb00MCIQmEKlWqgAoWmiA2rIANRbGXK3axAeoVRT/LvV4Uryj32nvhigUFsVCsiChFNDQhtJCeLTNzvj/OzuxusikbUjbJ+3ueiNmd2Z2Z3Xlz5pwz5wRaal577TV8++236NWrV8NsLTWI9bv92F2kW9Nrqwi0Uijy1rGiQJlBBYBAwNnsKpJUA6U+wO2QlXI2AH4BfLrBC3/IPbMClVs1PJqsxFOVYJfl0PcuKBf4aYcPR3QK9hg5VOb99j4N8GuwZtb5q1DDln3A0V0cWL9bgwI5W5HTZkAXChIcCmyqAp8GbN4n/xgkuOQ2mSF5KKFXVfCarxlpBklA9kpZ95eGAR3s2Feio9grEO8Eij2yVcgsEztswAm9XPhllwYBHQ4VcDtkV2pzzIPBOS1vhh6iqjR05hnmhWpIJwaPBmi6Yd2Swcxj5hE1lqoyD5B5Va7Jng2hmefR5a1IAsy8esu8ww6D2qMHDCGwYY8fnVKDmffTDj/yh+joUbIdmcOOhuPPLVD/s0hOlBEgOnRAyU23Y8+gY/Brmx4oN1TAAArOOg/tLzgTuPNOOXEFIHvgJSdHv7Fnn12nfSSKJbGQebDZoP7nBTkeXAixdCmUq+unAi+mMs/nk+NuPvUUsGIFRPv2wM03Q5k4EUZ2do3lPL8O7NVtVZfzIOsTHDaznOfEL7s02FUFQhGtqpxXq09l5cqVOPfcc8Mee+qpp5Cbm4sZM2agoKAAK1euRGJiIh566KEG2VBqOF3T7PDpAn5DwKECSS45IxcQ6GkSGIBYCdzuWhaYitujKUh0yW6qTlugIi4wgGQou4Kwe+wBGXqakN13zRPSvM3JEPIkzs3X8csuX8TZVusi0rhKfxVqKPXKWRjX/aVhdA8XVFWBTxco88kWGLN1OC1excEyo8rJJOqyjTVNUFHsMbB+tx9/FWgo8wtougGvLmAYBsr8Ajvy/XhvXTnyywz4NIHCQG9GLTA9uk+Xx3LxLx78sV+DT5fTbztD7jFriYN7ElXnUDOvS5odiU6ZeYYhsyyUQwWSXeENF6oC6AiO0cHMY+YRNZbqMk9FoHItJPNK/YGLWxXMvAbKvK37K2degUfgZ2dHLOk+DkvGX4U1L36GP75chz+XfovNy3/Gd++swv9GXorVSb1Q6FetzFvX7SiU3nibvPW2Xz85/hQgx+BLSJC36taG0wkce2zU+1jJjz8CIRWPRI0tFjKvzCfgGX58pW0zBh2BP7YXtrzMe+MNOTv1hx8CJSVQ/vgDyjXXQFxzDbZt3M1yXj2qVQXen3/+iaFDh4Y9tnjxYrRr1w7z5s1DcnIyjj76aNx000344osvGmI7qQEluVWM6OJEnENBvENBVoodE/vHIS1eharIApcz5JuiGfK2V58mUOI1sLMgeJuTUSHgbGpgIoxA92RVQaVx7wA5HkHnNjY5e0+gS3KCUwbNoRScQkUaV8mryRYZ2SVX4IvfvRCB27EMyOcBWQEZ71Tgr2JgdZ8mQzQaxR4Dq7Z6rffYXaTjg1/K8VehFvaaGQkqyvwCXk2gzC9bHcp1OXNasU9229YCg63qRuXPwKfL7uJeTT6vqkBmior2yfJ1VTV8zANzWm9z5h9DiHorXBPFgkPJvDKfgXK/QGngTgqB8OGCFABxDjnDmTmTt00JXriabCozj5lH1Diqyrz0eDUwkY3MBJN5seTxA6XMPOu1GjvztvgTsdLVDd8k98Xq+O7YiDT49cqZt1tzY82Z16D4b7cAM2bIHwDYvFmOj3fBBfJ2throjz9e68kzqqWqwIEDwG+/HfprEdXBoWReud9Am3gFxYeYeYYAik49G/rAQcEHBwyAbftWZP9rHn7NLWo5mbd1qxwSIALlgw+Q9OPqsNfMSFDRRivG0VtXYvh372H4zm+RbJTVqZxn3uqcFq+0mnJerSrwCgoKkJWVZf2uaRq+++47jBo1CjZb8EszePBg7N69u/63khpcZood43q50SndjsE5DjhsKrJTVNhUBYlmq4UAROAkEpAnlU8PjGlXxXi3hoA1YKaqAKlxChIcgdl8EOidZ5PPOWyAy67A7VDgsAGqoiK/VJ5YdSk4VWTebx/aGzDOocBtl+GuKAq8foFirxwsUwHgtgMCAnuLDWw94Eeb+GA0h3b7zUy2oV8UMwpZXZ79crZfs6WmzC9Q6hXQhbBec2+pgXin/COhC6CwXIS9dy2GGg6j60DnVFvIisFXqE3vGKKWoK6Z59GA3/fplVpjTQKyF7LLJi9YnTaztTOYdSrkY8w8Zh5RY4mUeeP7uBHnlBPDuO2Bi1QRvFgSkBdIzLzYz7ydSjKWnX4dcl/+H8rik4ErrpBPrFgBLFwox9XLyIi8ssOBksefwg+jzkWxN9qEjWDQIGDCBDlTLVETqWvmlfuBFVv8lSqNTNFk3s723bF10bsw5twnZ5zu0gVYuBBx8x5Ezo7fWk7mrV8fnEAngsSvl8FuC2aekvsnht9+EXLOGoeMyy9E1qmjcfT916KPX9YjRZNCArIS72CZDp/1h6pll/NqNQZe+/btwyrmfvzxR/j9/kq98lRVhcvlqt8tpEZjzlxj6pbuwN5iA2VeA2V+AErtJqYKJYSs4Et0KijxCZR4BAwRmClQBeLt8jZarw7sKNCRnWIPFOYUawB5IPqCUySR7rdXIAuTXs2AywaU+IR1C7BmyB4fHk0Ge6EX8Oo6kt1q2LY5A4NlRnOfvdnlWVEUCEOgxCesSlADsrXBfM1+WQ74NWDnQT+KfYEu3wiM0RUo7CmQ/y9EcIyHKo8DgC+2+OC0yXA3DLk9A7KdEbpiB3tXmoXrljogKLU+DZF5gDxXZMFJyOnuBQBVFvZcNnnO+g1mHjOPqHFVzDxVUTC8iwtf/VGO/LLARBPm/WVRYOZV1hSZV2g48VXbQXBnDsKgY3ch5/jRcC38N9Tly4C1a2WlXqdOwJdfAjt2QCSnoPTsKcg/4hhsbtsTXkOBu74yr0OHQ38NokMUC5mXHueG+uwC4K+/wl4jrSAPWWbmaVpwxtooxEzmGdWXxBSvF4YhKxtVABn/eRrKZ5+GLeN65030GjAIf5x2AzxadOU8KEB+GVDkNVpFOa9WPfCGDBmCf//739ZkFS+//DIURcHYsWPDltu4cWNYTz1qvswBdw/PsqM8cJIbom4TUyU6FatXiybk+ABOm4IklwpNKPAHBhF12BTEOSuPl1eXglMk63f7rW7B5utaYxRocnBPvUJClPvDu+16NVRqnTQHyzSiuNI3uzwLIVtjQ3swOlQFDptivabZuqJDsXoummM3hK5n1BBwSsi/9pAzPzPZhs5tbFi3y4e+mXa5XZAzFJX7Rdhyh1q4JopV9Zl5SYGBgOMcijxXFXleux0qVFWBLph5zDyiphV6S1GZT5GNFUqw8igazLzKmjLzvBrwk5qNzweejm//+Sbyl34F/39eROnQ4TD69kXe4wuw5q1v8Nm/P8EXJ1yCX9J6wWsozDxq0Zoq88qS26D80ivCX6BtW8St+BzqG28AJ54IDBsmZ5t+4w1g27Zab0fMZF6vXkBcXJVPlxw3BroIvGZuLpSnn464XOI/H0NXPd/6vbaZZ85yHueQj7T0cl6tKvBuv/12LF++HL169cKIESPwz3/+E8ceeyyOOOKIsOUWL16MYcOGNciGUuMxu5pu3a9hVa4fboc8McwfNYqUsytyJp+0BBU2RUF6goq0OAXxTsVqIXDa5Mw32ck2aHp4912gbgWnSLqm2a1CY2ayDWN6umFXAX9gXACXDWibEB7ioe8otzcYDqEF0GgHyzQLaz4jfNIPmwLr2JivabauOBRRaZtCt7M2R0cJ/McTMv5B93Y2fLvNhx35On7eqaF/th1eTcCnB2/zqK/CNVEsqs/MkzkhmHkVMPOIYkdo5n22yQu7TchKo0DmRfO1Z+ZF1lSZZ72/Kt+j2BYH9Zjh+GLwRHwx9Cz8NGA80rpmYWepir0lBoo9tcg8wwA2bYri3YliS1NmntcP/DHxInjnPAAcdpi8vfyKK4B//xu4917A4ZA9ZD/4AJgyBRg6VPaSrYWYybxevYC//z3iU9oRQ7Gn/9FW5h0o8AJeb+TXKSmBQ/db21mXzGsN5bxaVeAdddRReP/995GdnY3i4mJcdtllePfdd8OWycvLw86dO3H66ac3yIZS4zG7mnp1wKMJlPuCz5ktgrVlUwG/riDeCeSk2XBsNxcm9I2DKxAQCmQX5DiHbI3cUxQs5RxKwSmSJLeKo7u4kJNmw+AcB2yqghFd5QCnCU4FHVLtOKF3PHpm2CqtqwBIS1CQGqdCQTAkzYFDnfbwwTJrYhbW7ErwJHSosreiOVaK+Zpm64pHV6yZ3KIR2porIP9QOQO76NOA1bm+4ADLhRoWry+HJzCGgDnYaX0VroliUX1mnqoARV5mXkXMPKLYwcxruZmHwL+6ISBQT5mnqnI2W6Jmqj4zT0H0mbfN3R5/nXYe0KOH7GH34IOA3w/88QcwaZKstDPt3w+cfDKwZk2N2xJTmTd9OspeeBGGOfZlXBzKbrwV2/75H+TGZVqZF9+9E/QTJ0R8Dc+5F2Crq12N79XgmRfjav2pnHLKKTjllFOqfD4zMxM///xzvWwUNS1zLI7dhRqEkBNV1PWrLQC0T5ZFl65pdiS4FPy0wx+x9bXMJ+CwA35Nhsigjg6s3SkLNdGGSFUqjoWQHGfDuF5u5OZr6JflgCEE9pXoldYTAArKBbq0UZHoVtE/W9bYD85xYP1uuW1J7lrVhwMIdnm2qQoSnHJ6cqcaHCvF6VBwdBcXktwquqbZsadIh6YLaIrspg0EZ92JRAmMGWCNH4Dg70D4HypNF7Db5Fg05Zr8vE1uuwKXXa4lC9doduMEENWkPjPPEECHFGZeRcw8otjBzGv+mWdT5a1xZuYZgZ1QEMzDcr9AvEOpn8zr2LHW+04Ua+o181C3zCtrmwXRoQOUjz4KLjRxoqzMO+MM4IcfggMvl5YCjz8OvPii7KFXjZjJvBI3dg49Gx3fHoPEwn3wueKxI7kDfHp45iW4VfjvuRe2VSuBoqLg9mRkYPeFV6HIH/k9Gz3zYljtP5UYcPDgQUydOhUpKSlISUnB1KlTUVBQUO06QgjMnj0b2dnZiIuLw6hRo/Drr7+GLeP1enH99dejbdu2SEhIwMSJE7Fz586wZf7+979jxIgRiI+PR2pqaj3vWWwxu/27AlNvA7BaBM1byWp7S5kA4PUL7Dwouy7/uN1X6V59U36ZgTbxalgrwuAcB3LSbFYhpyGYwacqCpZt9uJgefC50N3UDWDbQT0wqLB8RlUUDMh2Rr1toV2eO6TacXr/OGSlyAdcDjnAqvmaZutKnEtFvEt2y05xy5aVih+DCiDZFZgZSZHjPnVNs6FnOzviHApUVU59PrqHy2phcTkUHN3FCaddrmfuidsOnNbPjaw6tsQQNRf1mXk2VRYimHnhmHlEsYOZF3yuuWaeXEbO7JvoBNLiFLRNVAO37wHpCQo6pjLziIDaZ57Zs6s6bjtQWG5g2wE9qszr29ENZfJk4LrrgCOOAC69VPZs3bwZ2LixciX5G28A69bVaX+bKvMcdmCHrQ0OdumDHsMOQ1pC5MxzHDMcvhVfofTRJ+G98moUPv4vbH37M6xtezgzrxaaVQXe+eefj7Vr1+Ljjz/Gxx9/jLVr12Lq1KnVrjNv3jw89thjeOqpp/Ddd98hMzMT48aNQ3FxsbXMjBkz8O677+K1117D119/jZKSEpx66qnQ9eDJ6PP5MGnSJFx99dUNtn+xwuz27/XLgXdNZhdjFTLkUtwKbDWknF8Hcg/o0HQDPg0QSjDYInXV7Z3hsAIHqHuI1NWALAcClfKwqUCvDDvS4oODCie7lXprIa7Y5bm6QmySW8XwLi7EB/7wZCbbEFehQUaBbI3w6XIsg7R4FSnxCg7PduDILi4c0dEOmwKM6eFEarwN/TvY4NUFuqXZ8Md+OSOPbCmWhUeXTcEvuzQM7Ghv8MI1UVOqz8zTDGZeJMw8otjBzJP/35wzz20H4pwqOqXakN3GjpMOj0PvdnbYbUCPdjYM7+rGkE52Zh4Rap95yW7FqsgDIlfmeTVgf6lAiVeg3Ceiy7zffwe++QYYOxZ45x3gpZfkip06AXv2hL+REMCffx7yvsdq5jkHDYBx7XVYdfP/4ZsTLsGBTj3hZubViiJE87jxd8OGDejbty9Wr16No446CgCwevVqDB8+HBs3bkSvXr0qrSOEQHZ2NmbMmIHbb78dgOxt1759ezz88MO48sorUVhYiHbt2uHFF1/ElClTAAC7du1CTk4OlixZghNPPDHsNRctWoQZM2bU2PMvkqKiIqSkpKCwsBDJyclRr99Y1u2Sgz6W+eVgjyrkLFhCBForAsFmU2X3fr0W3yC7CqTFqzihtwvlPuDPAxoAgW7pDiS4FKzf7Y+6q25DySvU8ONfPmQn2zCggxOGEPh8kwdJLhX9G7GQGYk5U6YQAtsO6CjxisCsvoCuBz+LBJeCrCQb8ssMOZhnuh3fbvdBF0BqnIpxvVxYusmLgnJ5f0aCU47TAMg/NqHdwHPSbDHXtbi5nEutXXP5nJh5zDxmHtWH5vI5MfOYecw8qg/N5XNqiMxTICekOX2Au/aZ969/yR54V18N7NoF/PgjMG4ccOAA8P77ld/k1VeBc8895P1n5rWszGs2fQZXrVqFlJQUq/IOAI4++mikpKRg5cqVESvwcnNzkZeXh/Hjx1uPuVwujBw5EitXrsSVV16JH374AX6/P2yZ7Oxs9OvXDytXrqxUgRcNr9cLb8gsK0Uh93nHMnMsDt2QLaMuuxxw19ANlPgCLRWKvF3JPBdCB5KMSMhxh37eqWFQRwc0HcgrMrC32Iuju7hi6iTKTLHj5JTgqaEqCk7sE9+EWxRkdoku9hjYWywHgnbaFWQmqdi8T4cRSDmXTXbbBgKDdO7yWwFYUG7gnZ/Lrd8F5ICuDpvSYGPSUOvAzAvBzKsXzDyKZcy8EMy8esHMo1jGzAvnVKPMvIwM+e/TTwPduwODBwOLFwP79kV+g/bt67jH4WIq84qLwybGYeZFr9lsfV5eHjLML32IjIwM5OXlVbkOALSv8OVv3749tm3bZi3jdDrRpk2bSstU9bq1NXfuXMyZM+eQXqMpmF1gc/M1dG5jw7aDOvpm2vFbnoacFBVfbPHBpsqTX/gMq0a7qqBTIAfvLfEJ/LHfj70lGozACL0+Tc4MFEsFu+Yg9DPqlyX7G2vChz/3yw9DQLE+D7sKjO7qwMqtmmyVQHjLUpt4Fcd0c2D7QQP9sg5tAFNq3Zh5wceZefWLmUexiJkXfJyZV7+YeRSLWmPmAZFzTwAo9SO6zBs2DEhNBQoK5Ay0f/xR9UYPHCgr+JobIWQlndMJuN3BxzdvBt57D3juOTkxx1VXyUk8OncGwMyLRpNv/ezZs6EoSrU/33//PQA5c1NFQoiIj4eq+Hxt1qnNMjWZOXMmCgsLrZ8dO3Yc0us1JrM2PCVOdjG1qyr6ZTnw5wEDTpsCmyJPogSnClvgW1RVK4X5uE8HPBpQUCagB6YEzEy2WSdpQyn2GFi3y2dNE20IgXW7fCj2GA36vg0tdIBSVVFwWFs7DAH4DTnrkQDg1QQ0XeD7HRqO7uKoNJaNTQHG9XIhNc7epGPSUMvAzAt/3My8g6XMvPrAzKNYw8wLf5yZV7+YeRRrWlvm1dQLL+rM69IFuP/+2m3w7Nmysi+CmM28b78FbrkFGDQIOO444IUX5K3Cf/4JnHUWcPvtchzA334D/vY34JJLgN27rdWZebXT5D3wrrvuOpxbw73dXbp0wbp167Cn4uCOAPbt21eph50pMzMTgOxll5WVZT2+d+9ea53MzEz4fD4cPHgwrBfe3r17MWLEiKj3J5TL5YLL5Tqk14gl63f7K82yc7BMRD0Nty4Arw6kOoFBHR3WidUQij1yVjSfJqfwDu1Cu6dIb7aDV1ZkCIHVW31W64PfECj2CBiQMwwZQmD5Zm+lMR10ASzd5MWJfVywq83/OFDTYuZFpgmgXBNIi1eYefWEmUexgJkXGTOv/jHzKBa0psxTlUDlXS0CMKrMu+ACIDcXeOyxqpf5xz+ACRMiPhWzmff118D48UB5YMrb3Fw50+6kScCYMcCvv1ZeZ/lyYPVq4MwzKz3FzKtak+9V27Zt0bt372p/3G43hg8fjsLCQnz77bfWumvWrEFhYWGVFW1du3ZFZmYmli5daj3m8/mwYsUKa50hQ4bA4XCELbN7926sX7/+kCvwWpquafawWXbaJakA5OifigLYavk6qgLEBQaTXLvTb7UeNITcfM3qBp1XpGPZZo8V1GYX50hisWWjum36YYcPxV5hzTKkCPnHRARaLUr9ctYkU2hrRUG5gWWbvSCicPWVeQrkH1tmXnSYeUSNi5nHzCNqTarLPAEg2RV5JtqKosq8Nm2AWbPk2Hdnny3DFQDsduDKK4HPP5f/ht5+GiImM6+kBLjrrmDlXaiffwaWLKlyVf+33zPzotTkPfBqq0+fPpgwYQIuv/xyLFiwAABwxRVX4NRTTw2bwKJ3796YO3cuzjzzTCiKghkzZuDBBx9Ejx490KNHDzz44IOIj4/H+eefDwBISUnB9OnTcfPNNyM9PR1paWm45ZZb0L9/f5xwwgnW627fvh35+fnYvn07dF3H2rVrAQCHHXYYEhMTG+9ANKGK96aXegV2FWjILwPsChDvVuTMMUbVjRUKgPR4wK8DZX6Bvwo0OOxosLFR+mU54NcQFmymqro4x2LLRnXb9FeBBt2Qg0fbVMClysFYdU1Ove1QgGE5Dny/wx9xph6bIqcYJ6Jw9ZF5ABDvkLNpMfNqj5lH1PiYecw8otakpsxTVQVOm4BXr/51os685GTg1FNlj7WtWwGPB4iPB7p2BWzVN5XEZOZt2QJ8+WXk50pL5f5WwZ+Ygl92+DGQmVdrTd4DLxovv/wy+vfvj/Hjx2P8+PEYMGAAXnzxxbBlNm3ahMLCQuv32267DTNmzMA111yDoUOH4q+//sKnn36KpKQka5nHH38cZ5xxBiZPnoxjjjkG8fHxWLx4MWwhJ9C9996LwYMHY9asWSgpKcHgwYMxePBga3y+1iL03vQkt4qMRDtcdgU2m4JiryzOVVeoUxUgNd4GRQF8ukCZXyAjIfxrWJ8tBKoiuzI7K1RVO+1V376bm6+h3C+3bXegZWN3kZx6vNwvqmzZaEjVtbaUeIFyPxDvUqAZcjwGvyGPtaLIKdELPcDIw5xol6jixD4uOO3y33aJKkYd5kJmSrOpyydqVIeaeQCQnapCVRVmXhSYeURNg5nHzCNqTarLvEKPqHSLZiR1zjzDDvTsCQwYABx2WI2Vd0CMZp4RIbsTE2VPwrPOAqq5q7Fw8FHWNjHzakcRogH7tVOYoqIipKSkoLCwEMnV1EQ3J4YQ+GmHH38VaCj1CWg1tMoCcuYYR6Cfq8sOdEm3W60UoS0EkaZ9jraFwNy+0PENTJnJNgzOqRx0ReU6lm7ywKMBDlVBvFNBmU/Abwi47cC4Xm4kx9X2RpK6K/YYYTPx/Ljdh9x8HS4bYFOD29w+SQUUIPeADl/IXxkVMuBcNsBnKOiZYcPADi4YQmD9bn+znoWnJZ5LLVFL/JzqknmqIrMOUJh51WDmVa0lnkstUUv8nJh5DYeZV7WWeC61RC3xc2Lm1UFBgRzn7qef5O/JycBNNwGPPipvrx0yRE5ssXBhcB1VRfk/5mP1yPOw328Pyz1mXvXnUvPcQ4oZZitAnFNBglOxZu2pjiFkyMU7FHRIsYd19a3rff1ViTQ4qSmvSMf63f5K62w9qMNlU6DCHDDTgN8QUAG4bAq2HqyhH3U9MMN+R76On3b4IQJjMWi6QKkvONOR0w4MznFicEcnklyKdUI7VAXpiSo6pNhQ5pfrlfkA3ZChvyNfx+qt3qafrYiomalL5onAmB3MvKox84hiEzOvYTDziGITM68OUlOBhx4K9iC88EJg3jxZeQcAP/wgJ7m46SZojzyKwqf+jQMrvoVt+iXI12zwheQeM69mrMCjKuUVavh0Yzm0QLdYzTDw6cZy5BUGg8YQAt/86cW+EgOKAiS7VWuwSQCBYAj+rgBwqLKQ4tUFdMNAqTdYs94vy4HM5OAKtbmvvzoVBycd09Ntvb7TLp+vqG+mHYqqwDz9Q/9VVAV9M+u/S27FrtV/HvCjoFwGmRn2uQd0GIHtMMdi8GnAmlwvPt3ogWYACS4FTpvsXuzxCxR55dgBCS4F+aXGIf/RIGrJ6pp5jgp/SW2KzD4rChXZ65iZF8TMI2p6zLzAPob8y8wjarnqK/PM383MMyc7LdeM1pt5J5wALF0qb5l1OICysvDnN20CHnsMYu3P+OGEC7EqtS8Wb9Tg0YLb4tWZebXBCjyKKK9QwxdbZHh9ssELnyb/3Vdi4IstXivoVv/pxbZ8HZoBFHkEVEVAC+lnHFoIAWTFnU8Hij0CpV6BzXt1fPWHx6otr8t9/dUxByfNSZNdim2qgsE5DuSk2arssvxbngYhgidH6L9CyOfrU2gr7JpcH37e6YNPExACKPEKlPoM5JfJlhJA/tFICUxMpBsCf+br2F9qoMwnoCoKVEWOwVDqFfDpAsluFbbAcTvUPxpELdWhZJ6/QmOfLmT2WVEogFIfM8/EzCNqesw8Zh5Ra1JfmacA1u+hmefVgDIfsKm1Zp6qAqNHA6++CqNHz6oX270LfsNAqc+AJ6TSwG0H4uzMvNpgBR5FHFhz5VYf9EA4FZQbeOfnchSUywd0AawLdM8tDGlhMASQX1bzkIoisKyALPAVlAts3OvHul0+aIaBtTv98AYG2jRvIajVtNxVCB2cFJBBOiDbWeU98p3b2ODVhJzlRgWSXAocaqAyUhPo3KZ+x0Uxu1brhsDWfA2/5fmRe0BHnEMeJ68O6IacPlsF0DXdjrG94pCZbINXD4wHoMgu0V7NgMsGa3t1Azi6q7Pe/mgQtQTMvHDMPKKWjZkXjplH1PJVzL11u/2y4k0cWuZFSihdBB8XaOWZ53SiuOfhVT5dcuqZOFAqrMwDALddwWn94pCVYmfm1QIr8Fq5imNwmPeRO1Q5IKeZRqEz8KTGqRjT0wUAGNfbhdQ4BYoil482g1QF6JCi4mCZgR35Oj7b6MXuQg1lfmHN5GMGXVX39de3bQd1OFTAaVMQ71Dgcsh/nTYZdtvqeWwUs2u1V4d164TfAAo9AgKB1hEAdhtgt8ljrSjA4BwHembY0LWtHYlOuX0umwJFkdubHq9iTE8n/tinh7VOAIf2R4OoOWPmVcbMI2q5mHmVMfOIWrZIudcmTo6nZggAgpnXkJmXNHwIfFddU+lxvXcf7Bg62hrz026T2+SyK/hll4aBHe3MvFpgBV4rV9XAmqqiINFVuQbbpgDjerlgD9zsb1dVnNjHLccCCARdxeWroipAWoKC1Dgb/IFt8OkCJT7ZVdmhKkh0qXAGBtWr6r7++tY1zY44pwyKrBQ7xvR0IyvFjniHgjinUu/bYHatTnEHx1QwAoOhqgoARXYrjg+E7Z4iA+t3+6EqCgZ2cOHIzk5re5VAq4PLoWBcbze2HzSiHuiUqCVj5lXGzCNquZh5lTHziFq2SLm3t1gg2S3Pn9BRAOqSeVXFHjNPUhMTYbtvDgqffwnamLHAoEEonPt/2LjgTfzs7izHSVWB1LhArkF+Tr/lacy8WmAFXitX1cCahhAo1yrXYOsCWLrJGzb459JN3rBWDCXkX72KSvBAeQUlHsCnG2ifLL+KNkVObe1QZetjZrKKE3q70LGNijZxjfN1rcvYAofCEAJrd/rh12VQqQiemIYI71YMhIe9uW5VrRBd2tiiHuiUqCVj5lXGzCNquZh5lTHziFq2SLlnCIEijwys0DM82syrroOXEECJRzDzhMDa8mR8PewsrHz6A3z20pf4aPyVWBvXBQLyOIYOFQAE84qZVzNW4LVyVQ2sWeoTsuUgkFihLawF5QaWbfYCAJZt9lrjB5hEhX8jMcdF8RsCW/MN2YU2ZBt0Q04PLQSgQIGmK9hTbDTa9NDRji1wKMzpwIWQ3aoBBFt8FFm4M7sVVwzamqYS33pQb9TAJop1zLzImHlELRMzLzJmHlHLFSn3ir0CRkiv17pmXtiEPRUIyDEtmXnBzMsrEdhXrsoxV0N6L4YOFRCaV8y8mrX8PaRqVVXL7bIHwknIcQHOGhiH1DizJQEYEJjhZUCWwwrA1DgVE/q6wqbWDvQ+DmNTgHgH4FTl8wlOoNwXbBX2asExQnIPaPh8U3mLnh7anA68XAMABUluFQ5VgaoAdkV+Fma34opBW3Eq8WGdnLCp8rNz2uWgpbn5Grqm2RslsIliHTOv6THziBoPM6/pMfOIGlek3ItzmM8BqW6lzpkX76iceW474LIFJ19g5lWReZAVqImu8KECQvOKmVezlt/HkKoVqZbbpwFOmwo4DQgoOLGPKzAegAvLNnsxIMuBzEA3/8wUO0YdJmfuGdNTLndSX+DjDV64bIBNVeDRBFQF0IzgOAKd0uxw2WGND7CnONjykBqn4GCZbLWVg/zKsARa5vTQZrfmjXv9OFhmwK8B3draYLfJ8M8vNartEpwar8JpU9A1zYbV23wo8hhIcKoY1tmJLft05BXp2FOkt5pWCaLqMPOaHjOPqPEw85oeM4+ocUXuxaUi0WWg3A90SLXBaa9b5jntKhyqgTINgJATz7htAi6nitQ4BWqgeo+Zx8xrKIoQrWCqjhhRVFSElJQUFBYWIjk5uak3B0Bwlh6fJgNkUEcH1u6Uoee0o84nRjSvG2nZH3f4sGWfLPUlOBXYVAVOOzCmpxu2iqOJtiDFHgO5+Rr6ZclpsA0hsH63H13T7JU+h4rHza4Cm/Zq8BsCKoA28YHuygE5aTYMyHY27g41kFg8l6iyWPycmHmxhZlXO7F4LlFlsfg5MfNiCzOvdmLxXKLKYvVzaojcY+bVDTOvdqI5l1pflSWFaahBLaN53YrLKoH74+02xQo4oHVNDx3K5xfYuDe434YQWLfLh017ZNdw3RD484CGvCI/ADm2giaAwpDxFFpi6w5RXTDzYh8zj6j+MPNiHzOPqH41RO4x8+oPM+/QsAdeI4rVVopYs26XDzvyK9/uYWpJte2hIrXWfLvNh60H5M53SbPjyC5Oq7XHYRNwO1T8VaDDgJyOO84uu2ULADYVSHTK6cFbWusOz6XmgZ9T7TDzmHk14bnUPPBzqh1mHjOvJjyXmgd+TrXDzGPm1YQ98KhZqzh4ZWuZHjo3X7PCPK9Ix7LNHuwMBJgBYGehfMwc08GvKyjXgvXvfh0o9MqAU9XgTEittXWHqLlg5jHziFoTZh4zj6g1YeYx8+oTK/Ao5jTU7R6xrl+WwwpzQIZTnEOBQ1WsFojQ1pr2SSrS4hTYAofDgJxZSQAwDDkjUlzg70FekY71u/2Nti9EVHvMPImZR9Q6MPMkZh5R68DMk5h59aNlfluo2UtyqxiQ7WzS6aGLPQbW7fJVuj+/OOT++/qkKgoGdXRYLTSAHC8hNV5BapwCRQl2E3baAYdNwd5iAZcNEEJOWx7akTg7VUVWYEallty6Q9QSMPMkZh5R68DMk5h5RK0DM09i5h261rnXRDUIvWffryFspqGGmrbaEAJrd/rDWiIEgIIyAUAg3iFbIrwaIKCgzCcAGCj0mgOjyqDz6nK9nQcNjO/lgCMQcC21dYeIDh0zj4hak1jNPEVRoAuBgnIgwWkAioGiwC1kDmYeEdURM6/laJ17TVSDSPfsm/fn+zT5fH1bv9tvvQcgWxbK/QJ+Q8BvAGV+gTKfgE+X/x4oM1DokcsKyHECBABFkWHn04E12/2N3rpDRM0PM4+IWpNYzLxyTQ7SXuqVubezwEC5T4E5ypNPk40azDwiihYzr+VovXtOVI1I9+ybGmra6kgDnHZMtUGFPFETnAoSnXLMAL8hUOI1EGcHzAl4FEXOzOOyAVAAt13BUZ1b3oxGRFT/mHlE1JrEYuZ1TLEhM0W1xn/SDQGXXSB0okWbAmYeEUWNmddy8BZaogjMe/aXbdbDAs5pl12OVUWpeuU6Mgc4zc3X0C9LvsdRXZxwqIBQgMEdHfh5p4bdhRrKNRlmNlWBqhgo0+SFrKIoiHcAPgMY09OFlDhbzW9MRK0eM4+IWpNYzLwhOfLCVNOA3HwdLpvcziSngWIf4FCBeKcKCMHMI6KoMPNaDvbAI4og0j37QMNPWx1pgNOhnV0Y1skFu6piUEcHXA4F8Q4FNlWBEAKaAJJcqrWOoihw2RT8sU9vtdNrE1F0mHlE1JrEYuapigJVUTA4x4nUuGDmeXXZCyXBpUIBM4+IosfMazlYgUcUQaR79gFAFwJ/HtDwyy45bXVDz94TKlLwlmuARwPKfAIiZDuB1j29NhFFh5lHRK1JLGae+X6huVeuAX5DjgNV5hPMPCKqE2Zey8EKPKIIIt2zn5agotQroOkCZV4B3RD4aYcfO/J1rN7qbfCgixS8Lps8if2GgF0FxvR0W+MbtObptYkoOsw8ImpNYjHzgMq5l+gKvVgTaJekMvOIKGrMvJajWVXgHTx4EFOnTkVKSgpSUlIwdepUFBQUVLuOEAKzZ89GdnY24uLiMGrUKPz6669hy3i9Xlx//fVo27YtEhISMHHiROzcudN6fuvWrZg+fTq6du2KuLg4dO/eHbNmzYLP52uI3aRaKvYYWLfLZ3Wlrc8WA/Oe/Zw0GwbnOGBTFcQ7AbtNQYJTQX6Z0Siz94SKFLwdUu1IcCqIcygY0dUJm6pgcI4DOWm2BpkOnIiaDjOPmUfUmrS2zAMq59743nHokmaH06YgNU5B93QHM4+ohWLmMfNqo1lVYZ5//vnYuXMnPv74YwDAFVdcgalTp2Lx4sVVrjNv3jw89thjWLRoEXr27IkHHngA48aNw6ZNm5CUlAQAmDFjBhYvXozXXnsN6enpuPnmm3Hqqafihx9+gM1mw8aNG2EYBhYsWIDDDjsM69evx+WXX47S0lI8+uijjbLvFK7YY2D1Vi98GuDX5OCba3fKGvw9RXq9nODmPfum/tlOaLo/LNhMDTV7T8XtqTgQ6OAcBxyB1ghzf1VFCdtuImr+mHnMPKLWpDVmnrlNlQZ97+pEwm5/WO4x84haFmYeM6+2FCGax0iAGzZsQN++fbF69WocddRRAIDVq1dj+PDh2LhxI3r16lVpHSEEsrOzMWPGDNx+++0AZG+79u3b4+GHH8aVV16JwsJCtGvXDi+++CKmTJkCANi1axdycnKwZMkSnHjiiRG355FHHsHTTz+NP//8s9b7UFRUhJSUFBQWFiI5OTnaQ0Ah1u3yYUd++K1VoaGTk2ZrkBNdNwSWbfZUmr1nTE83bGr9z95DkfFcah74OdUfZl7rxnOpeeDnVH+Yea0bz6XmgZ9T/WHmtW7RnEvNpg/iqlWrkJKSYlXeAcDRRx+NlJQUrFy5MuI6ubm5yMvLw/jx463HXC4XRo4caa3zww8/wO/3hy2TnZ2Nfv36Vfm6AFBYWIi0tLRqt9nr9aKoqCjsh+pHvyyHdT880DgtBk01ew9Rc8HMazjMPKLYw8xrOMw8otjDzGs4zDyqrWZTgZeXl4eMjIxKj2dkZCAvL6/KdQCgffv2YY+3b9/eei4vLw9OpxNt2rSpcpmK/vjjD/zzn//EVVddVe02z5071xqvLyUlBTk5OdUuT7WnKgoGdXSEzUwDyBaDQR0d1lTV9amq2XsAzopDBDDzGhIzjyj2MPMaDjOPKPYw8xoOM49qq8kr8GbPng1FUar9+f777wEASoQvrhAi4uOhKj5fm3WqWmbXrl2YMGECJk2ahMsuu6za15g5cyYKCwutnx07dlS7PNVeU7QYRBpQnbPiEAUx8xoOM48o9jDzGg4zjyj2MPMaDjOPaqvJP5XrrrsO5557brXLdOnSBevWrcOePXsqPbdv375KPexMmZmZAGQvu6ysLOvxvXv3WutkZmbC5/Ph4MGDYb3w9u7dixEjRoS93q5duzB69GgMHz4czz77bI375nK54HK5alyOohepxcAMPNliUP+DXVY1oPr63eEDqjeFYo8Rtl2GEFhfYfBPoobGzGs4zLzKmHvU1Jh5DYeZVxkzj5oaM6/hMPMqY+ZF1uR73rZtW/Tu3bvaH7fbjeHDh6OwsBDffvutte6aNWtQWFhYqaLN1LVrV2RmZmLp0qXWYz6fDytWrLDWGTJkCBwOR9gyu3fvxvr168Ne96+//sKoUaNwxBFH4IUXXoCqNvmha9WaqsXAnL3H7MZszn7Y1JV3q7d6sSNfx087/NANgZ92+LEjX8fqrd56mXqciJoWMy8cc4+oZWPmhWPmEbVszLxwzLyqNZtaqD59+mDChAm4/PLLsXr1aqxevRqXX345Tj311LAZaHv37o13330XgLx1dsaMGXjwwQfx7rvvYv369Zg2bRri4+Nx/vnnAwBSUlIwffp03Hzzzfj888/x008/4cILL0T//v1xwgknAJA970aNGoWcnBw8+uij2LdvH/Ly8qocI48antlikJNmw+AcB2yqbDHISbMd0jTbxR4D63b5rG7KhhBYt8sX0yGRm6+FtdAs2+wJmw48N1+rZm0iag6YeeGYe0QtGzMvHDOPqGVj5oVj5lWtyW+hjcbLL7+Mv/3tb9aMsRMnTsRTTz0VtsymTZtQWFho/X7bbbehvLwc11xzDQ4ePIijjjoKn376KZKSkqxlHn/8cdjtdkyePBnl5eUYO3YsFi1aBJtN1np/+umn2LJlC7Zs2YKOHTuGvZ/g7CxNxmwxMJktBnVl1vT7NMCvyQFD1+6U3Zn3FOmHFJ4NqV+WA34NYaFmaqhZi4io8THzgph7RC0fMy+ImUfU8jHzgph5VVMEa6AaTVFREVJSUlBYWIjk5OSm3hyqYN0uH3bkRx57AABy0mz1PvZAfdENgWWbPWHb67QDY3q6YVPrf9aipsZzqXng5xTbmnPmAa0r93guNQ/8nGIbM6/54LnUPPBzim3MvOYjmnMpNqtciZpAvyyHNdYA0Hxq+iPNWqQbAgXlAj/tkF2mm0t3aSJqPM0184DKuacbAmV+Aa9fPq4ZBjOPiMIw84ioNWHmtUyswCMKUBUFgzo6rAFETU677HJsDu4ZayrOWqSqAqU+AZ8u8OcBDet2+cIG/cwr1JrlWAhEVL+aa+YB4blnFup8uvx3d6GGzzZ6mXlEFIaZx8wjak2YeS0z81iBRxQQqScbIFsr1u70W6EQayrOWpSdbLe6FesGkFdoWAFY7hdYudXHGX2IqNlmHhCee067gkSXCoeqwG8AJYEGDICZR0RBzDxmHlFrwsxrmZnHCjyigIo92UJbK/4q0PDZJk9M1uxXnLWofwcHurW1wWlTkOBSoIdsotOmwBk46zmjD1Hr1lwzDwjPvRN6u5CVbEO8U4HTpiDeqcAWaFVm5hGRiZnHzCNqTZh5LTPzWIFHFFCxJ9uYnm5kJtusbrsev4jZmn1z1iJVUaAqCgZ3dCI1LhhugAztsb1cyEoJpndzGguBiOpXc848IJh7dlXFoI4OuOxAvCOkUMfMI6IQzDxmHlFrwsxrmZnHCjyigIo92WyqgsE5DridwZr+5lCzX1136XV/aRjQwd4sx0IgovrFzGPmEbUmzDxmHlFrwsxrmZnHCjyiEKE92QA5+OcJvdzo0MQ1+8Ueo9aDc1bXXXp3kY7PN3mb5VgIRFT/mHnMPKLWhJnHzCNqTZh5LS/zWIFHVIOmnsGn2GNg9VZvrQfnrKq7NAD4dAGfFgyy0H3KK9Kxfre/QfeFiGIfM4+IWhNmHhG1Jsy85o0VeEQ1aOoZfHLzNeu9a9PNuaru0jlpNozo4kScU4ZyxQB02mVAElHrxswjotaEmUdErQkzr3lreXtEVM8iddsNDZ31u4EB2c4Ge/9+WQ74NYQFm6mqbs5md2mTqijW7wkuFbn5GvplyRaWwTkOrN8tAy7JzTp9otaOmUdErQkzj4haE2Ze89by9oionlXXbbcxavbNbs42FSjzC5htIk47YLMJlHojt5JUNbYAgEpjIQzIdrbIgCOi6DHziKg1YeYRUWvCzGve2AOPqAZmt93Grtkv9hjIzdfQN9OO77b5kV9mQBcyrBKcKg6WCRws07AjX4NdVTGujwt2VYVmGPh0oweGAShQ4NfkeAZrd8rWlj1FOo7u4mqxoUZEh4aZR0StCTOPiFqTWM+8vUU6DmvrwJYDGsb0lLlXUK7h49+8cNuZeYoQLXBqjhhVVFSElJQUFBYWIjk5uak3h2KYObinTwNsKlDsFfAEWigUyMcEACEAQwCKAqTFqxjXy4Wlm7zILzMAASS5FDhsSljXaADISbM1aNfohsZzqXng50S1xcyrHs+l5oGfE9UWM696PJeaB35OVFu1zTwAsCmAbsgnUuNk7r3/iwflfgEFMvcSXEqrzTz2wCOKQaGDe/o0Ad2QkaZAFuJMZvW7qgAF5Qbe+bkcupDLCchuySk2pVZjCxARNRVmHhG1Jsw8ImpNapt5KmRlnh7IPjP3NCM89xy24EqtLfNaRz9DomamX5bDGovApiqIdyhw22WLhNOmIMmlwGlTkBYPpMUHA8wMO0UB2sQraJsYfoo31vTgRETRYOYRUWvCzCOi1qS2mZcar2BifzdS44LZpgd6ISsK4FCBZHcw31pj5rECjygGmYN7mgOMqopscVAUIN6pQFVk8AEq2iWqsFXILBVARqIN/iaaHpyIKBrMPCJqTZh5RNSa1DbzDEPB+l06xvZ0huWeGWmJbgUKgk+0xsxjBR5RDDKEwNqdfqurcbkG+A3AAFDmE1b4GYaBLft0+I3w9TUB/L5fgzmvjzPkZnk5Pbi/4XeCiKiWmHlE1Jow84ioNalt5gHArgI//rfeY/U4BmRlnyGAIo+AgGjVmccKPKIYtH63nFXHlOgKPVkF2iWpyEy2odgnw8x8zmypUCAH/yzyiCaZHpyIKBrMPCJqTZh5RNSa1DbzAKDUD3hDehfbFFh97nQD8GqiVWceK/CIYlDXNLvVspCZbMP43nHokmaXYwPEKeie7sDgHAc6p9lgt8GapeesgXFIjVOtMQK6pNkwOMcBmyqnB89Js7WqabaJqHlg5hFRa8LMI6LWpLaZl5Nmw5GdnLAFIszMvTbxKlRFVuaN6OJs1ZmnCNGKbhhuYpxqm6JR7DGQm6+hX5YcmNMQAut3+9E1zR4WUnmFGtbt9mNMTxfsqgrNMLBssxcDshzITGmZrRE8l5oHfk4UDWZe1XguNQ/8nCgazLyq8VxqHvg5UTRqm3lA68u9aM4lVuA1IoYcUf3gudQ88HMiqh88l5oHfk5E9YPnUvPAz4mofkRzLrWevoZERERERERERETNECvwiIiIiIiIiIiIYljLu4E4hpl3KxcVFTXxlhA1b+Y5xBEAYhszj6h+MPOaB2YeUf1g5jUPzDyi+hFN5rECrxEVFxcDAHJycpp4S4hahuLiYqSkpDT1ZlAVmHlE9YuZF9uYeUT1i5kX25h5RPWrNpnHSSwakWEY2LVrF5KSkqAoSlTrFhUVIScnBzt27Gg2g4RymxtHa9xmIQSKi4uRnZ0NVeVIALGKmRf7uM2Ng5nXOjDzYh+3uXEw81oHZl7s4zY3jsbMPPbAa0SqqqJjx46H9BrJycnN5ots4jY3jta2zWyRjX3MvOaD29w4mHktGzOv+eA2Nw5mXsvGzGs+uM2NozEyj00aREREREREREREMYwVeERERERERERERDGMFXjNhMvlwqxZs+ByuZp6U2qN29w4uM3UEjXH7wi3uXFwm6klao7fEW5z4+A2U0vUHL8j3ObGwW2uHiexICIiIiIiIiIiimHsgUdERERERERERBTDWIFHREREREREREQUw1iBR0REREREREREFMNYgUdERERERERERBTDWIFHREREREREREQUw1iBR0REREREREREFMNYgUdERERERERERBTDWIFHREREREREREQUw1iBR0REREREREREFMNYgUdERERERERERBTDWIFHREREREREREQUw1iBR0REREREREREFMNYgUdERERERERERBTDWIFHREREREREREQUw1iBR0REREREREREFMNYgUdERERERERERBTDWIFHREREREREREQUw1iBR0REREREREREFMNYgUdERERERERERBTDWIFHREREREREREQUw1iBR0REREREREREFMNYgUdERERERERERBTDWIFHREREREREREQUw1iBR0REREREREREFMNYgUdERERERERERBTDWIFHREREREREREQUw1iBR0REREREREREFMNYgUdERERERERERBTDWIFHREREREREREQUw1iBR0REREREREREFMNYgUdERERERERERBTDWIFHREREREREREQUw1iBR0REREREREREFMNYgUdERERERERERBTDWIFHREREREREREQUw1iBR0REREREREREFMNYgUdERERERERERBTDWIFHREREREREREQUw1iBR0REREREREREFMNYgUdERERERERERBTDWIFHREREREREREQUw1iBR0REREREREREFMNYgUdERERERERERBTDWIFHREREREREREQUw1iBR0REREREREREFMNYgUdERERERERERBTDWIFHREREREREREQUw1iBR0REREREREREFMNYgUdERERERERERBTDWIFHREREREREREQUw1iBR0REREREREREFMNYgUdERERERERERBTDWIFHREREREREREQUw1iBR0REREREREREFMNYgUdERERERERERBTDWIFHREREREREREQUw1iBR0REREREREREFMNYgUdERERERERERBTDWIFHREREREREREQUw1iBR0REREREREREFMNYgUdERERERERERBTDWIFHREREREREREQUw1iBR0REREREREREFMNYgUdERERERERERBTDWIFHREREREREREQUw1iBR0REREREREREFMNYgUdERERERERERBTDWIFHREREREREREQUw1iBR0REREREREREFMNYgUdERERERERERBTDWIFHREREREREREQUw1iBR0REREREREREFMNYgUdERERERERERBTDWIFHREREREREREQUw1iBR0REREREREREFMNYgUdERERERERERBTDWIFHREREREREREQUw1iBR0REREREREREFMNYgUdERERERERERBTDWIFHREREREREREQUw1iBR0REREREREREFMNYgUdERERERERERBTDWIFHREREREREREQUw1iBR0REREREREREFMNYgUdERERERERERBTDWIFHREREREREREQUw1iBR0REREREREREFMNYgUdERERERERERBTD7E29Aa2JYRjYtWsXkpKSoChKU28OUbMlhEBxcTGys7OhqmyHiFXMPKL6wcxrHph5RPWDmdc8MPOI6kc0mccKvEa0a9cu5OTkNPVmELUYO3bsQMeOHZt6M6gKzDyi+sXMi23MPKL6xcyLbcw8ovpVm8xjBV4jSkpKAiA/mOTk5CbeGqLmq6ioCDk5OdY5RbGJmUdUP5h5zQMzj6h+MPOaB2YeUf2IJvNYgdeIzK7FycnJDDmiesDu+rGNmUdUv5h5sY2ZR1S/mHmxjZlHVL9qk3kcVICIiIiIiIiIiCiGsQKPiIiIiIiIiIgohrECj1qlYo+Bdbt8MIQAABhCYN0uH4o9RhNvGRFR/WPmEVFrwswjotaEmdd6sAKPWp1ij4HVW73Yka/jpx1+6IbATzv82JGvY/VWb0wGHUOZiOqKmUdErQkzj4haE2Ze68IKPGp1cvM1+DT5/3lFOpZt9iCvSAcA+DT5fCxpjqFMRLGDmUdErQkzj4haE2Ze68JZaGOUruvw+/1NvRkt0mFtBPweP/aVyHDQfYAt8Fy7RBWHtQE8Hk/TbWAFf+71QfcZsAHYVwB8UQL4NbnNug/4c68fvTKcTbqNDocDNput5gWJqsDMazjMvOjZ7XbYbDbOgEgNRggBXdehabF1YdUSMPPqF8t4VB+YeQ2Hmdd0miIfWYEXY4QQyMvLQ0FBQVNvSosWJ4AMCECEPKgAbo+CbVubaqsicwDIUgR0szEipI7DpgKOUgW5uU2xZeFSU1ORmZnJC16KCjOvcTDzomez2ZCRkYGUlBTmGtUbIQQKCgqwb98+6Lre1JvTYjHz6hfLeFRXzLzGwcxrOo2dj6zAizHmhWxGRgbi4+P5h7IBCCFQ7hfQIvTOtatAnEOJueMuhECpVyB0k1UACa6m31YhBMrKyrB3714AQFZWVpNuDzUvzLyGx8yL/r01TUNRURF2796N8vJy5hrVGzPzkpOTkZycDLvdHnPnX3PHzKs/LOPRoWLmNTxmXtNoqnxkBV4M0XXdupBNT09v6s1pscp8BlQAZsdcFQgLD2ED4pyxMzykEAJlPgG7q/JzhgrEO5s+6OLi4gAAe/fuRUZGBm+1oFph5jUOZl7dJCUlweVyYf/+/cw1qhe6rqOwsBDt2rVD27Ztm3pzWixmXv1iGY/qipnXOJh5Tacp8jF2Pkmyxn+Kj49v4i1p2Vx2xfriO1Qgya3AEXhADTwfS8r9Av6QFA49af2GfD4WmN9bjmNGtcXMaxzMvLpLSEiAEIK5RvXC7/dDCIGEhISm3pQWjZlX/1jGo7pg5jUOZl7Taux8ZAVeDGouNc7NlU1VkOBS4LQFa/jjnfL3BJcCmxpbx7+5hDK/t1RX/O40LGZe3fG7SQ2B36uGxcyrf/zO0qHg96dhMfOaVmN/v3kLLbVKNlUGm8kMulgkQxnwasIawyDeKVsnXPbYC2Uiij3MPCJqTZh5RNSaMPNaD/bAIwCAbgiU+QwIIbusynvTDehGzV1YD2Vdqh0ZyqpVwy+DTmXAEdURMy+2MfOI6hczL7Yx84jqFzMvtjHz6o4VeATdkLPA+HSgzCesgSV9OlDqFdWGVbTrrlu3DtOnT0f37t0RFxeHuLg49OjRA1deeSW+//77ht7VBg1kRVEwe/bsKp8fNWoUFEWp8ae616iN4pJS3HXPLCxfvhxA+D7Onj0biqJg//79h/QeRM0ZM4+ZR9SaNGbmAU2bew194R0LuacbAvsLSjBr1ix88cUXlfaRuUetHTOPmdeS8RZaglcLTuHsN4BiT/B3I/B8VV1wo1l3wYIFuO6669CrVy/ccMMNOPzww6EoCjZs2IBXX30Vw4YNw5YtW9C9e/cG2U8zkA3IcIt3ymD2G4CmCyS40KC1/vPnz0dRUZH1+4cffogHHngAL7zwAnr37m093rFjxzq/h24I7D9YigcfuA9CyFAN3UdDsOWIiJnHzCNqTRor84Cmzb2mzjyg4XPP3MfC4jLcd999AIBhw48P20ei1o6Zx8xryViBR4hzKHKWvUA6hU477VDl84e67jfffINrrrkGp5xyCt566y04nU5ruTFjxuDaa6/Fm2++aU3FXJWysrI6z1h5KGFeH/r27Rv2+8aNGwEA/fr1w9ChQ6tcL5p9Dt1HQ1TeR92oYkWiVoSZx8wjak0aI/OAps+9ps48oOFzL3QfAcCnIeyz8WpstCBi5jHzWjLeQtvCFXsMrNvls3ohGEJg3S4fij3BU8Ec5LLil0FFcCabqtR23QcffBA2mw0LFiwIC7dQkyZNQnZ2tvX7tGnTkJiYiF9++QXjx49HUlISxo4dCwDIz8/HNddcgw4dOsDpdKJbt26466674PV6rfW3bt0KRVGwaNEiADJwzRlu2sTb8OADc+QvQuChB+YgwWXDr7/+ivPOOw8pKSlo3749Lr30UhQWFoZtZ1FRES6//HKkp6cjMTEREyZMwObNm6s8RtEwuwD/+OOPOOecc9CmTRurxWbUqFEYNWpUpXWmTZuGLl26AAD2/LUNh3VqDwB4+MH7kBJvQ5t4G6654hI4VMAe2P89e/bUuJ9EzREzj5nHzKPWpqbca4zMA5o+90IzDwBS4m146IE5gBBQADz89zlQFAXr16/HpCnnNsvci3Mo2LV9q5V7Dz14H9oEcu+6Ky4Jq1xg7lFLxcxj5rXmzGMPvBas2GNg9VavrKnWgEEdHVi704+8Ih17inQc3cWFJLdq3dtfsaOCAdkNN95Z9fTItVnXMAwsX74cQ4cORVZWVlT74PP5MHHiRFx55ZW44447oGkaPB4PRo8ejT/++ANz5szBgAED8NVXX2Hu3LlYu3YtPvzww4ivZc5wU+wJqakXAvL2efnY2WefjTPPnoz/vPImNvy6HnPuvRMA8Pzzz1v7e8YZZ2DlypW49957MWzYMHzzzTc46aSTotqvmpx11lk499xzcdVVV6G0tLTW62VnZ+Ojjz7CSSedhKkXX4qpl0wHALRr2y7sj87ZZ5+NKVOmYPr06fjll18wc+bMsP0kao6YeeGYecw8avlqk3uJLqVBM09RFOi63uS5F5p5we2VmadCwK/LR846+xycefZk/PeVS7Fl03rcdWfzyT1FUdCtczbefn8Jzj79ZCv3VACdO2SEfY7MPWqJmHlBzLzWmXmswGvBcvM1+DT5/3lFOpZt1q3ffZp8fkC2E+X+YDdhQLYwhHbHLfdX3f22NusWH9yP8vJydO7cudL6uq5bg24CgM1mCzsR/X4/7r33XlxyySXWYwsWLMC6devwxhtvYNKkSQCAcePGITExEbfffjuWLl2KcePGVXqvSIFsQF7GmptwwUWX4vobbwEAjBpzArb+uQX//c8LWLhwIRRFwSeffILly5fjySefxN/+9jfrvZ1OJ+66666Ix6guLr74YsyZMyfq9ZxOJ/r0PwIAkN2hI4YdebT1nDkQKwBMnz4dt956KwDghBNOwJYtW/D8889b+0nUHDHzwjHzmHnU8tUm9w5ra2/QzIt3ysHDmzr3ImWeCPwYAlZvnQsvvhR/C+TeuHEn4M8//gjLg1jOPSEEdMWJgYOHAAjPPYeKsOPM3KOWiJkXxMxrnZnXLG+hnT9/Prp27Qq3240hQ4bgq6++qnb5FStWYMiQIXC73ejWrRueeeaZSsu8/fbb6Nu3L1wuF/r27Yt333037Pm5c+di2LBhSEpKQkZGBs444wxs2rSpXvervvXLciAz2Wb9boYbAGQm29AvywEAcNmD3YQdKpDkDnbHVQPPV+VQ1gWAIUOGwOFwWD//93//V2mZs88+O+z3ZcuWISEhAeecc07Y49OmTQMAfP755xHfq2IgKwBURYECsy8KMOGU06znHSoweNAAeDwe7N27FwCsWQ4vuOCCsNc+//zzq93PaFXc50jMWYdMQggUeQQ0PRhkoSe43wC0wOITJ04Me60BA8L3k6g5YuaFY+Yx86jlq03uNWXmAY2XexUzDwAUBDPPTIqTArlnjmdVMQ9iJfcqzi4JAEUeAX9I5oUefbNywcTco5aImRfEzGudmdfsKvBef/11zJgxA3fddRd++uknHHfccTjppJOwffv2iMvn5ubi5JNPxnHHHYeffvoJd955J/72t7/h7bfftpZZtWoVpkyZgqlTp+Lnn3/G1KlTMXnyZKxZs8ZaZsWKFbj22muxevVqLF26FJqmYfz48VHd6tPYVEXBoI4OOCv0s3TaZXdjNVATbVMVJLgUOG3Be/vjnfL3BJdS7ew1tVm3bdu2iIuLw7Zt2yqt/8orr+C7777DBx98EPH14+PjkZycHPbYgQMHkJmZWakmPSMjA3a7HQcOHIj4WqGBbB4HhwqoqmKFQVpaOoDgOAdutxsAUF5ebr233W5Henp62GtnZmZGfM+6qqkrdugU5+bFq9nbxJw1XFUq/9GxBf6/4va7XHIKH3M/iZojZl44Zh4zj1q+2uReQ2cegJjIvYoX3vL4BDMvNPdCx7OqmAexkHuhmVfmkyEnBIKZF7jANXMdqFy5wNyjloiZF8TMa52Z1+wq8B577DFMnz4dl112Gfr06YMnnngCOTk5ePrppyMu/8wzz6BTp0544okn0KdPH1x22WW49NJL8eijj1rLPPHEExg3bhxmzpyJ3r17Y+bMmRg7diyeeOIJa5mPP/4Y06ZNw+GHH46BAwfihRdewPbt2/HDDz809C7XmSEE1u70h7VMALKlYu1Ov9WtFpBhFe9UrdCQYaXWaurpmta12WwYM2YMvv/+e+zevTts3b59+2Lo0KHo379/xNeO1N01PT0de/bsCaudB4C9e/dC0zS0bdsWAKwLUXPgTzOQiwsOhGxnIOAqvI05zkHF90hPT4emaZVCNC8vL+L211Wk/Xa73da+hM7Is2//fggRmJFHUWB+ZA4bKv3RUSO8LlFLwcxj5jHzqLWpbe41ZOYBsZF7oRfe5cX5AAK5AARyIvg+VWWe+d5NnXsVZ5fM27NP9qYJZF74Z1G7igmiloCZx8xr7ZnXrCrwfD4ffvjhB4wfPz7s8fHjx2PlypUR11m1alWl5U888UR8//338Pv91S5T1WsCsGY0SUtLi3o/Gsv63XJAT1NoS0VekY71u/2Nti0zZ86Eruu46qqrrONeF7ohcNzI0SgpKcF7770XuPffgG4I/Pe//wUAayaf9u3bw+12Y926ddb6NlXB0o8WW7+X+0Wge7E8+SvefuXTw0Nu9OjRAICXX3457PFXXnmlzvtUW126dMHmzZvh9XqtWYfyDxzAt2tWIXQrE+JlsHs8HgDR/cEias6Yecw8gJlHrUtLyz3zFqoxY8agpKQE7777rpV5AGrMPfPC2+z5ohmwLgIrqnj7lSkWck81fFYvk/wDB7BmzSprGYdNQduUYG9pZh61Jsw8Zl5rz7xmNYnF/v37oes62rdvH/Z4+/btq6whzsvLi7i8pmnYv38/srKyqlymqtcUQuCmm27Csccei379+lW5vV6vN2za56Kiomr3r751TbNjT5Ec2DMz2RY2S4/TLp9vLMcccwz+9a9/4frrr8cRRxyBK664AocffjhUVcXu3butW5ordicOZXatPee8i7Dgmadx8cUX4657ZqNX335Ys/Ib/N8jc3HyySfjhBNOACAv4i688EI8//zz6N69OwYOHIhvv/02LIxcdiVs/KTEwO1XfkNe2DoqBMP48eNx/PHH47bbbkNpaSmGDh2Kb775Bi+++GI9Hq3K++3VBC688EIsWLAAF154IaZOm46ignzMm/cIkpKCx0wF0D49GZ07d8b777+PsWPHIi0tDW3btkWXLl0abBuJAGZeKGZe3THzqLlo6swDWlbumZlnADjn3KmYP38+pk2bhjvuno3DD++HH779Bg/NjS73bGpwEHoz3uw1jGfV2LlnZl6cQ8HUqVOxYMECnH/Bhbj88suxe89+PPHYo1buWbfBuZh71PiYeeGYeXXDzDs0zaoCz1Sx+6UQotqZRSItX/HxaF7zuuuuw7p16/D1119Xu51z586t06x69SXJreLoLi7k5mvolyXHBBic48D63TLcktz13wEz9IRUFAVCCJT7BVx2BVdddRWGDx+OJ598Eo8//jh27doFRVHQsWNHjBgxAp9//jnGjBlT5WubXWvdbjc++OhzPDD7bjzx2KPYv38fsrI74IYbb8YD980OW8ccNHTevHkoKSnBmDFj8L///c862WXX4+A4SWa3XHOb1QoXs6oqWzhuuukmzJs3Dz6fD8cccwyWLFmC3r1719NRDAoN9sHDRmDRokWY+9DDmHLOmejctRtun3kPln76Eb7+cgWAYPfo5557DrfddhsmTpwIr9eLiy++GIsWLbK6lYuQf8v9AoZRuTWGKFrMPGbeoWLmUXPS1JkHNH7uVZd5NvXQci/0Fiqb0433l3yO+2bfjX8+LnMvu0MH3HLLLZg1a1bYetXlnqrIW8y8moAjMO69efuVuc0VNWbuhWaeEAIjRozAs8+9gP97dB7OOvMMdOnaDbfdeQ+WfiJzz8y8eCewcOFC3HrrrWG5t/D5F8IGfA/9fIgOFTOPmXeomHmHThGRboSOUT6fD/Hx8XjzzTdx5plnWo/fcMMNWLt2LVasWFFpneOPPx6DBw/Gk08+aT327rvvYvLkySgrK4PD4UCnTp1w44034sYbb7SWefzxx/HEE09UGpjy+uuvx3vvvYcvv/wSXbt2rXZ7I7VS5OTkoLCwMGJNvMfjQW5urjXDbnMTekI6VBkWZT5h9ew41PvUzamyK862g5D3q6rStabwjVVlPgO+YC9xq0XFEAJCyN7RqqKETXEOIDDwavgfsIb+fBrz+1tUVISUlJQqzyVqGsw8Zt6hirXMi5XvKDMvNjHzmHmHKtYyryqN/d1l5sUmZh4z71A1l8yLRn18z6PJvGY1Bp7T6cSQIUOwdOnSsMeXLl2KESNGRFxn+PDhlZb/9NNPMXToUDgcjmqXCX1NIQSuu+46vPPOO1i2bFmNlXeAnPkkOTk57KelqDjNsxACJV5h9XbwG0CxJxhIBmQrw6Ewe4pU/NKGzqpT1baGzmpjhqVPB0q9whpjIBaZ4z6ZzCBTEZhlCLWf4rzi4KD1/fkQMfOYeYeKmUfNCTOPmXeomHnUnDDzmHmHipl36JpVBR4A3HTTTXjuuefw/PPPY8OGDbjxxhuxfft2XHXVVQDkYJIXXXSRtfxVV12Fbdu24aabbsKGDRvw/PPPY+HChbjlllusZW644QZ8+umnePjhh7Fx40Y8/PDD+OyzzzBjxgxrmWuvvRYvvfQSXnnlFSQlJSEvLw95eXktalpivyZQWB4eXIXlBvwVvvxVhYYQgcHRA+uH1po7VHnCHgrzfSo2UlQ3qw7QvE/uKoNdUZDkUuG0K7WeHr2qwATq5/Mham6YebGHmUfUcJh5sYeZR9RwmHmxh5l36JpdBd6UKVPwxBNP4L777sOgQYPw5ZdfYsmSJejcuTMAYPfu3di+fbu1fNeuXbFkyRJ88cUXGDRoEO6//3784x//wNlnn20tM2LECLz22mt44YUXMGDAACxatAivv/46jjrqKGuZp59+GoWFhRg1ahSysrKsn9dff73xdr4B+TWBYq8BzRAo9MjWhkKPgGbIx0ODrsrQUBQoACpGRk2tCLVV7g/vYlxx9sRIs+oAzfvkri7YQ7tNAzXPvFjXVh6iloiZF5uYeUQNg5kXm5h5RA2DmRebmHmHrllOYnHNNdfgmmuuifjcokWLKj02cuRI/Pjjj9W+5jnnnINzzjmnyueb0VCBdVLmF1Y46YZAQVnwdxF4PiXQdTXOIe+zD63plwsKK+hCma0I8c7Kk4VEw5w9sar73KsarFKe3DKMQ8OiOZzckYLd+gOjCxR5gGQ3ajX2QU2tPIf6+RA1J8y82MTMI2oYzLzYxMwjahjMvNjEzDt0za4HHjWMZHf4iRFaXWlTFSS7w2fsjVTbHal1wlRdK0JtydkTlcAglrXrWgtE3z050hgIZT6jScYTcNkDx1kIKAASzfEAhIAu5ICftR37oK6tPEQtETMvXKzkHjOPqGEw88Ix84haNmZeOGZey8EKPAIggyvZXbmFQYEMwNDa66pCQ0Gwp2JtB5+Mlk2VXWlDu9a67HKq7KoCKZqTO9YGBbWpimwVgvwjUu4TiHMq1h8UQ8jWimKPgF+X3cMNIazjEXocrMBEw30+RM0FMy8olnKPmUfUMJh5Qcw8opaPmRfEzGtZWIFHAGQwFHlEpZYGAcjHQ2ryqwyNQBdjBdG1IhyK2gRSNCd3LA4K6jeEFep+AyjxyN/NrTYgWyt0ERhjVVHgdiiVjkNdW3mIWiJmXlCs5R4zj6j+MfOCmHlELR8zL4iZ17KwAo8AyCALrX0P/brrhgxAU7WhoShIdNV+8MlDVZtAiubkbohBQQ+1y3KV022rijXddsUWo5IqgjlSK09Dfj5EsYqZF1TfucfMI4o9zLygWCvrMfOI6h8zL4iZ17KwAo8AAPGOYK23TVWQGh/84iuB5xHyfF1ru+v7/vvaBlJtT+76ns2mProsVzkuQ2DsACjyOavVwhD1EsxELRkzL6g+c4+ZRxSbmHlBsVbWY+YR1b/GyjygfnOPmQdmXg2a5Sy0VP8cdgVJUFHmF9a4ACluoMgjA85RoSuuDI3gY+aJWB3zZDcgT9B4J6yZdjRdIMGFqGvL63sWnvqezSZSK4r5u9l6UNNxi7RNhhByYiQAKgRURQEQ6GoceF4NhF+k46AbImyq7ppm+SFqNoQANm0Ctm4FvF7A5QK6dAF69QJCzgNmXlB95h4zjyg2MfOCYq2sx8wjqn+NkXlA/eceM4+ZVxP2wGvpCguBzz8H/vlP4JFH5L+ffy4fr8BhV5ASF16TnxKnVgq4uvJqAut+WYdrr7gUfXt1R1xcHNqnJ2Pk8KF4/LFHsHvvgahfs7az8EybNg2JiYkRXyO01WTkqNE4cdwYGELedH+os9k8/Pc5aBNvs34y0xNx+GGdcPbECXjumaegeUpqfI1I4zKY2yWHBVCQ6FbCKifM5yseB3N/q2o1uejiqo8TUUzz+YAvvwRuuQUYPBg46STgjDPkv4MHy8e/+kouF9CQmbdo0SLYbSpSAud+SoITWVnZmHrhefhjy+91HnOkpsx74YUXoCgKvv/++ypfo2JLcblfwGsNNFLzYMiKooT9JCcnY8SIEXj11Vfr5TaNumTeoD7dcc0Vl0SdeU0xORFRvdu/H/joI+Cxx4DZs+W/H30kHw/R0OW8555/wcq8RLcNDocDPbt1wrVXXIqdf/3VIJkXabbFiuqSeStXrsTs2bNRUFBQ4+vHORTMCynvpcTbkJZgR++uHTDlzFPw47crq12/usx7841X8fS//lEp8zxlZXjogTn48ssvmHnUOu3bB3zzjbyu/eYb+XsFDZ15QHTXt126dMGpp55a7es1VeaZ2zdt2rQaXz9SWe//5s3Fhx+8V6uyXrTlPMMw8MYrL+GMU8aja04GnE4nMjIycOqpp2Lx4sXwazpKvQLLln8BVVXx5ptv1ph506ZNQ5cuXWrc11jEHngtldcLfPwxMGsW8PPPlZ8fOBCYMweYMEH2UGkELy16Dtdeey0O69kL18+4Bb379IHf78dPP/6ARc8twNrvV+Pdd9+N6jUjBYABAELAqwOKH2EtAOaAl6G/h7aaPPWvf6HUa0AIQChyXa8m36Mus9mYfzDefn8JklJS4PP5kLd7F75cvgz33Hk7/vH4o1i8eDEGDhxY5Wu47Ao0XW6jQ5XbVOYDoMuBWRNcCjyB4FUC4wYoigI90FIRDGZhzWpUVasJi3TULBUVAc8+C9x6a+TnPR55QfvYY7Ih48orgaSkRtm0Z55diG49e8Pr8WD1qpV4bN6D+PrLL/Djz7+hQ/u0qF+vpszz6eG3b1RseYzUUmwEynQ6AJcNSHAqVutxVbl3zjnn4Oabb4YQArm5uXjwwQdx/vnnQwiB884775BajuuSeYFDAABRZV5tewUSxaSyMmDxYuCee4Dff6/8fI8ewP33A6edBsTHN/jmOG3yPPrXgoXo0as3POXlWPn1V3j80Yew8usv8csv6wBndI2EtS3nVdXboq6Zt3LlSsyZMwfTpk1DampqtduoKAocNvn/b72/BMkpKTAMA3/t2I6nnngUo0ePxpo1a3DEEUdEXL+6zHvnjVexacOvuOGGGwLvJbfRU16Ohx+8D8C9OPb4Ucw8aj22bAGWLQMeegjIzQ0+3rUrcMcdwNixQPfujbY59X1921SZF41IvQQff2QuTj/zbJw/+cway3rRlPO8Xg8umnIWln2+FGedMwWPPvEvtM/MRGH+Pixb+gkmTZqE/7z0Kk485fSIx7CqzLvnnnusXG1uWIHXEnm9wDPPADNmVL3Mzz/L3ilPPikvZutQiRepq2qJV8BvCKTGqVAVBYYQKCw3sO6H1bjmmmswbtw4/PfVd+AIeb+xY8fhrttvxieffBL1NkQKgFKfgFeTz8vAEtACF7Wl3vCuzBULOV2695HTVUN24fXp8oSvaxdcs+Vj4OAhSG/bFgi89tnnTMHlV12LU08cjdMmTsTGjZsAmzPsPUKPb4JLsVquDYHANsFaXrHLbtpQFNhVuS6E7G5sUxUYAtCsrtzyswoNNhO75FKz4/NVX3lX0a23yiug668HnM6o3qqq7vkKAI8mkBKSe6VeeWYdMag/evUbAgPAscePgqHrmPvAbHy65H1ceuml0e0ras48sxLL45ctjxVv36jqws6myCxQlWDBrLrca9++PY4++mgAwPDhw3HMMcegS5cuWLBgAU4/+9xgrojg+xV5gCS33DbzIrO+Ms9kGNFlXmsfR4WasbIy4IkngLvuqnqZ338Hzj0X+PvfZZkwykq8aDKvsNyAFujl0LdvPwwaMhQAcNzI0TB0HfMeegDvv/8+Lrjggqi2obblvKpuWauvzKuOEAJ+Xf7/oEFHoE3btvKi+6jhOHLYkejftwfeePNN9O43KOrMsweWNY9DMPOCjTVKYJ36yDy/3w9FUWC38xKRYtDPPwOTJwObN1d+LjdXXtP26gW88QYwYEDULx9t5jXE9W1zybywXoIimF3mLbnlfnlLrNtxaOW8e+64BZ9/9imef2ERzph8IQwh9yHJrWLi6Wfhqr/dAk95WVhZMLRDSlWZ170RK3nrG6/XW6KPP66+8i7UDTfI5aMUqXt+sUd2zzUEUFBmwDAECsoN6AJ4cO5cKIqCJ/75TFi4ATJMNDhw2mmnBR8zDMybNw+9e/eGy+VCRkYGLrroIuzcuTNsXZuq4I2XX8BxRw1Geko80tPTcf7ks7B50wbYFFmYidTyCADffPMNOmVn4NyzT0NxSQkgBE4+cQxOnTAGAGC3KYhzKPD7/Xhs3oM4vG8fuFwutGvXDpdccgn2ReiqXZEWWmoSItgCIoA+/QbgxltnYsf27fjPS6/Bpwmrm+9zzy3EoEGDkJYs9+mcs8/Chg0brK7AhgBOHj8GY8eMto6DOfjqVZdfgv69u0NVEBj9U2Db9p24+PxJ6JCRgvS0Nrji0qn46fvv0CbehldeXARAhoE90Hq+ZcsWnHzyyUhMTEROTg5uvvlmeL3emr8YRI1t9eraV96ZbrlFrheFqm5J8moCpX45RkdBeTD3zFPfbAU1DTpiCABg5649YbdBfPDBBxg+fDji4+ORlJSEcePGYdWqVZW24/fNm3DFJRegZ5cspCXHoXPnzrhi+sXw+byycBZ4SS3kwm3rjl0YMmQIevTogS0b16NLVhpmXHdVsHIt8O/uHduQFOfAI488EvUsXp07d0a7du2Ql7cn2HIsBLZv344rL70IPTpnISM1Dr379MVj//d/KCjTwjJv1uzZOOqoo9Ehsy1SUlIwbOgQLHp+oTy+gcyLd6owdA233XYbOmRnITM9ESefcDx+/P5bAIG4C2Re6P6bLa/1NXgzUUxYvLj6yrtQd90F/O9/Ub18tJmnCwQbEiq81pAjjwIAbN26VT4vBObPn49BgwYhLi4Obdq0wTnnnIM///wzbL2lS5firDPPQN8enZDZJh4DD++Jq666CvkH9kNRYJXz7rx7NhLdNmz47VdccvH5SGuTivbt2+PSSy+Fr6wIdlU2aG7fmouUeBte+e8iGJA9Bs0LO1VVMe/B+2BTFcyePRu3Bv6udO3a1Rou4Isvvoh4rMr9wspePaScJwTgSkgGABiK3cq855+XQx38tjnXOr6qAqz44gskuGz4eOlyq5y3ZMmH2LZtW9iQDDu2bUX3TpkAgHlz70dqvA1Jbps1jIBXE/hr2xZcPu0C9OicifapcThq8OFY+Mz8sMz74osvoCgKXnzxRdx8883o0KEDXC4XtmzZEtV3hahRbNlSdeVdqE2bgEmTgD/+iOrl65J50V7fmj7++GMcccQRiIuLQ+/evfH8889bz5nXdL9vXI/zJ5+JtLQ0tE2Jx6jhQ/DGy/+1rm3NstbBggLceNPN6NatG1wuF7p0zMSUM0/Bxo0brPLdwQMHcNOM69CvRyekJrrRrVs33H333bAJf7XlPI/Hg5tvvhmDBg1CSkoK0tLSMHz4cLz59nthZb02CXaUlpbitZf/i0S3DaqqYvzY0fBosnPPX7t244orrkCnnBykJrrRtWs33H/fHPj9Wti1bWi506YqKD64By8uWogTTzwRU86fCgWyDV4BUOaVx6D7YT1weP+BsmIysEl+vx/3z7obfbp1RFa7VIwbNw6bNm0K27dIt9AqioLrrrsOCxYsQM+ePeFyudC3b1+89tpr1X95Ghkr8FqawkJ522w05swBajHGR6jIU1wHi2wGgIPlBgwB6LqOr1csx8DBR6B9hxwAsvBmmLVZAPy6DEN/oHLt6quvxu23345x48bhgw8+wP3334+PP/4YI0aMwP6QMV3mzp2Lyy+/DP37HY533nkHTz75JH5d/wtOGnMs/vhji7UtJrMW/o033sDYsWMxadIkvPLGe4iPTwgUvmTPNSEA3QA03cDpp5+Ohx56COeffz4+/PBDPPTQQ1i6dClGjRqF8vLyao+TLeQMUxQFaiCUzCN14sky1L/55isYgW194EG5T7369MV/X30LDz7yONb+vA5jRh5TaewsAYSNcWC9V8j/l5SUYuJJY/HVl1/gvr8/hNdffx1pbTNwyUXnha1jQLZ0+P1+TJw4EWPHjsX778teQo8//jgefvjhaveVqNEJAbz/ft3Wff/9YHe1WoicecEKM0D+v5l7Ji3sPQS2Bm736Nq9B8r9sgX3vy++jNNPPx3Jycl49dVXsXDhQhw8eBCjRo3C119/ba39888/Y9iwYVizZjXuv+8+fPTRR5g7dy50vw+K7g8bH8m0ecN6jD5uOFwuF1atWoXD+/XHBRddgrdefwUFBYVhDQvPLngaTqezTj0DCwsLkZ+fj549e1oFiwP79+Okscfhi8+X4o575uClN97FyFFjcc+dt+HWG/9mZZ5XE/gzdyumTb8cL7z4Gv776ls45bQzcctNN+CRuQ+EZd5ll12ORx99FFOnTsU7776H0884C+dNOQeFBQeDRznkkJuZf6jjyRDFlP375W2z0bj7buBA7ccarkvmVT6TZAV9bqA8lprWFoXlBi6//ArMmDEDJ5xwAt577z3Mnz8fv/76K0aMGIE9e/ZYa//xxx8YPnw4nn76aXz66ae49957sWbNGowfczxUQ7Myz3zfi8+fhJ49euKtt97CHXfcgVdeeQU33ngjdEPmgrmd8tYyWc6LNBzcZZddhuuvvx4A8M4772DVqlVYtWpVtbfAmulr6Dp0XYPP58Off2zBbTddD5fLhdNOP9vKPLO8bL61eXz1kOPo1WQl5zHHHIPMzEws//IbfPXNSiz/8ht06ZSNt99fAgC48OJL8enyr/HpF9/g1jvuhkMFcn/fgCOPPBK//fYr7p/7CF57+wOMn3AybrvlBtx975xKmTdz5kxs374dzzzzDBYvXoyMjIyI+0nUpJYtq7nyzrR5sxwbLwrRZl5drm8BWZa7+eabceONN+L999/HgAEDMH36dHz55ZfWMlt+34yxI4/Fr7/+in/84x94++230adPH1xz5aV48rFHrO0sLi7GSSccj+efexaXXHIJFi9ejPnzn0a3w3oib/duGELebn/aSSfgjVdexDXX34gPFv8PF154IebNm4ezzjqr+mPi9SI/Px+33HIL3nvvPbz66qs49thjcd6Uc/D6y/8FIK9vP17+NeLi4nDCiSfh42Vf4+NlX2Pe408BAHbt3o2jjz4Kn3z6KW6deTfefO9DXHjxpXjooYdwzVVXyOOK8HGhzTH8VnyxHH6/HyefOhGA7Ghi3lJshOSYQ5WH3Hzk/ll3Y8eObXhy/rP4x7+ewe+//47TTjsNuq7X8C2QDer/+Mc/cN999+Gtt95C586dcd555+Gtt96qcd1GI6jRFBYWCgCisLAw4vPl5eXit99+E+Xl5XV/k88+C0y/EOXP559H9TaGYYgSjy4OloX8lGpif4km9lX4+fWPvwQAMWXKFFFYpov8wHL7SzRxoFSz1jN/fv7lVwFAXH311aLUqwtNN4QQQqxZs0YAEHfeeacQQoiDBw+KuLg4cfLJJ4dt2/bt24XL5RKTppxnbdt5F14kEhIShGEY4qGHHhI2m03c//eHRLFHD3vvEcceL0Yce7zcthJNLHrxZQFAvP3222Hv8d133wkAYv78+dUep1mzZgkAYttfe4LHLGR/d+4vEQDECeMniIOlmti664C1TyUe3To+v2zeKlwulzhnynmixKMLwzDE8SNHimOPGykOlumi2KOLgjJdHCjRxLkXXCRyOnUWBwLvM++xf8p9eO9DYRiGKPXKYzJt+hVyHxYsDDtOAMQbb7wRth8nn3yy6NWrV7X7Wi/f31qq6Vyi2NDgmbdhgxAuV90yz+2W69dSxMyrJvf+8cxCAUB89sU3Yl+hR+zYUyDefO9DkdE+Uww/5jixp9Aj8ks1sbfIJzKzskW/fv2FpmlW5hUXF4uMjAwxYsQIaxvGjBkjUlNTxd69eyNuX2GZLv61QL7vsq/WiPf+94lITk4W55xzjigpLROlXpkV3/+yWaiqKh54+P/CsigtLV1MvXhajccCgLjmmmuE3+8XPp9PbN68WUycOFEkJSWJ77//Xmi6zJnbb79dHoMVK8Ny9pLLrhKKoojv1v4mCspknpnH90CpJg4U+8S+Iq+48545Ii09XRSXa8IwDLH+19/ke18/IyzzFjz/ogAgzr3gIus9DpZqojDw2mbmmT+FFT7DUq9e5b42Zq5Vh5nXPDRKOU8IIZYsqVvuffRRrd8i2szbV6KJfwZyb2kg97blFYhX3npftG3bTiQmJYmNf/4lPl72tQAg5s171Do/Nd0QO3bsEHFxceK2226rcnv8fr/Ytm2bACDee+8961y+/c57BQBx398fFoYhy4yabogrrrxauN1usa/YL/aXaOLHX7cIAOKfzyy0ynnm+Q9AzJo1y3q/Rx55RAAQubm5tTpe99wrt6HiT1JysvjPK2+JA4FcKijTxfPPPy8AiF83/mGV8w6W6WLxx58LAGLJJ59b+3HyyaeITp06i4OlmsgPLHewTBdbtuUJAOLWmfdUyrwTxo0X2R06im15B8My7/KrrhVut1vszNsvhBBi+fLlAoA4/vjja9y/xs5CZl7z0GiZt3evEF26RJd3XbvK9Wop2swLvb4tKJN5Yl3fllT+3ec3ROfOnYXb7RZ/5m4NO0ZpaWniyiuvtB4799xzhcvlEtu3bw/bvnHjJ4j4+HixdXe+OFimi7vumSMzd+lSq+xV8dr20SfnCwBi4X9fC8u8hx9+WAAQn376qfUenTt3FhdffHGVx0jTNOH3+8X06dPF4MGDRalXt45bQkJCWDnM3O9Lpl8hEhMTxdatW8Oube+fKzN21Q+/WNe2QsjsNjNrzgNzBQDx5nsfigMlmjzOIdfRZu6Z5TwzQ8efeFLYZ/jiK6/J91q1ytqXiy++WHTu3Dls/wCIuLg4kZeXF7bPvXv3FocddliVx6U+vufRZB574LU0v/1Wt/V+/TWqxc2pteW4G8J8MOz+c2vZkHXiHHJsIrOG3LrNAMGfpZ8vBwCce8HFYbPHHHnkkejTpw8+D7SorFq1CuXl5ZVmy+nYsSNGjhqNFcuXhT0uhMD0y6/ErFmz8Nyil3DdjFvCerKFtkea///Rkg+RmpqK0047DZqmWT+DBg1CZmZmlbdTVBTvVK1jpioKAneqhrWCGgL4dvVKlJeX4+KLLw47Ph07dMRxI0fjy+XLrNsfQlsafLqAZojwfQj88s3XXyIxKQljxk9AmU/AaZetF+dMPheAHJvAnElIdk1WKnX3HjBgALZt21arfSVqNFu3yjE/68LjAaL4TkfMPACqosAd0vvCWj7w7wmjjkG7FDdy2qdi0hmnIDW1DV58/V2oNjt0IW+Jzdu9C5PPuxAeTbEyLy4+AWeffTZWr16NsrIylJWVYcWKFZg8eTLatWsX9l6i4lgkAF59+b+YdOapuPiS6Xj1tdehKy74NHkrSJeu3TD+pFPw/L+fgSFkbrz1xqvIzz+AK6++tlbHY/78+XA4HHA6nejZsyc++ugjvPrqqxgyZAhsqrz9dvny5ejbty+GDTvKyjwAOO/CiyCEwIovllvbvmzZMpx60nh0yUpDepIT7ZJdePD+Wcg/cAAlBfugKAo++1xm+qQp54dl3ulnTao0XpPZ0yU08wDZSpvkDmZeXQZvJmpyGzbUbb0oyoh1ybz4wO2o4wK51zkzFeefczoy2mfitXf+h/SM9vjkow+hKArOnHwBisr8KPNqKCz1o11GewwcODCsXLV3715cddVVyMnJgd1uh8PhQOfOnQEA69ZvqNSrdsIpp6HMJ6xb4Xof3h8ejwf79u4NK+dVLPPVBzXQG3Dp0qVY/tUafP7lKrzy1vsYOWosLp92PpYsfs8au8oX0tPELOeFdh12O4K3uZoZLXucyJ4puhHsrWcyMy+/qBxfLF+G0yaegfj4eCiGhji7DsXQMO7Ek+DxePDT92vC1j377LPr+WgQ1bPNm2WZLxq5uZEn96lCXct5gaunSteRBsKvb8sCY8L1HzAI6e1zrJlR3W43evbsGXadtWzZMowdOxY5OcGefWU+gfOmXoyysjJ8t0YOsfLppx+jR4+eGD1mrLz9N1DOQ8j7frViOeITEnDameHnuXn9/HkNPRXffPNNHHPMMUhMTLRyeOHChdiwYUPY9W1VPvnoQxw/chSys7OhaRr8fg1ev4YTxp0IAFj51YqwW/tDe0LqRvi+aEZYVFq5Z4jwu8/OPGNiWDnviEEDAaBW17Jjx45F+/btrd9tNhumTJmCLVu2VBrKq6lwhNKWxuNplPWEECj1CWvAYkPI8Tv8EUpEaW3bIj4+Hrm5ufAbAjYlWGmnILwQpQA4mC9v8UjPyJKvjeAYRtnZ2dbJdyBwK0hWVlbY+5X7BdpnZiM//zMAwfvEfT4f3n7rDfTuczjGjj+pxplXBYD9e/eioKAAzioGuw+9nbcm1kW2CI6VsnO73Jf2WdkQAA7k5wMAUtIzw7oSGwCysrLxxbLPrMFBVSUYYmZ34kj7UpB/ABkZMoj8BqBocoDj7Ez5mBm85X4Bu6ogPj4ebrc77DVcLhc8df1uETWUQx2XMYr1I2aeqsBvCPgqXkUieC7++/lF6N6jD0qKi/D2W2/iP88/iysuuQCvv/shACA/kHftMzMrzZiVnZ0NwzBw8OBBAPJ2jY4dO1Z6r4ozlgHAO2+9DndcHC64eDpKfcHCpLltV1x9Pc46dTy+WLYUo8eOx/PPPo1hRx2NI4cMqdXxmDx5Mm699Vb4/X788ssvmDlzJs4991z8+OOP6NGjBwCZ0TmduoRlHgBkZmVb+24IYPWaNTjxxBNxzHEj8fg/FyC7Qwc4XU58tPgD/N+8B5FfWIb27QWKCmQ+ZrTPDMs8u92OtLR06/XDxkEJybzQgakPZfBmoiZXVFS39YqLa71oXTKvLFAIfPq5RejVqzcUmx3tMtqjfWawnLZv314IIdCjS1blFwHQrVs3+X6GgfHjx2PXrl2455570L9/fyQkJMAwDBx99NEoLQsOYWKewWlp6XJQd6+s9HI45ZhU5Z7Iw50IAI56Pv979BmAlDbpVmP1CeNPwnFHDsItN16Pk047I1AJJ5f16eHlPJPHL2/tV5TAYO6oXM4LLe+FZt6efQegaRoWPP0UFjz9VMRtNMvZporlaKKYU9drkBqGOgoVbeaZ17d/5uZCANb1LVDVtaXMpTZpaZVmRnW5XGHDMh04cCDsvDTLeWb5qSBwDh/Yvw8dczqhxCsqlfNMB/MPIKN9puz8gWDmZWRkwG63W9fTkbzzzjuYPHkyJk2ahFtvvRWZmZmw2+14+umnrXH7zOvbyHsM7N27B0s+/F+V19IHDhywrm3Nzj5CyP3NyekEANgWofI2NPc0QzbQmh2J2rZtGzYJZXycvK6taegrAMjMzKzysQMHDkQshzc2VuC1NBUqXhpqvXK/CCt4CABa5HMXNpsNx40ag88//RgH9vyFtIwOgC6sSjyTAhl+bQIXYnvydqNDx45hs8fs2rULbdu2BQCkp8vldu/eHfZ+LruCvN27kJ7e1pq9R4UMx/999DnOPv0knHXqeLz57odITG1T7X62SU9Heno6Pq5ioo+kpKRq1w9lhm9oAeyjJYsBAMccN1K+X1qa3M/du61lzOOyO7BPfkO+ltvtRmFhoSzUKQpUyGN64ICsVDQrSDPatcWPP3wH3RCwqYDLLgcILczfa22b1erE61hqTuowe3Zd14+UeXqFXq+RdOvRG0cOGwq/IWdj1A0dLy1aiA/efRsTzzwb6YG8y8vLs9YxM2/Xrl1QVRVt2rSRF3I2W8TWv9AZy8yebs+/8CLumzMLp544Gh99/An6HD4Qfh0IjDGP40aNQZ++/bBwwXwkJCRi3dof8fTC/8JvCDgqtTNX1q5dOwwdKmeZHD58OPr06YORI0fixhtvxP8Cg+W3SUtHXt7uSo0Lebt3AQDS0ttCAHj7zdfhcDjwylsfwO12y8xTgY8WfwBA/m0p9wsr+/ftyUN2hw5W5mmahvz8A9Z7JLlUeDUBjxaeeaEtxDW1GBPFtOTkuq0XZZkl2swznzu8Tx/0HzwUEJXLeunp6VAUBUs+WwFXoILNrgZ7nbkCubx+/Xr8/PPPWLRokXVXAgBrggXz7HWogDPkakYFEO9S4PWLSgN9uwJlXV9I443fECg6kF/9wYiCVqGcp6oqevfpi/fffQv79u5Fu4wMOAPb4Qlsh1nOM8tveiDzzIxSFLOHX+Wed0B45qW2SYXNZsPUqVNx7bWRe1R37do17HdO5EMxr67Xt3FxtV402swLvb7du2snMrI7whbhHDXPbxFStqppNuj09PSw61uznGeWnzpmtYNDBdLbtsOuv3bKRkq/CCvnmdqkpeOH77+1GgXMct7evXuhaZpVtorkpZdeQteuXfH666+H5UToxIblfjmJZVXS0tvi8H79cdfs+62OJ6HHJDMr27q2NXvixTvlGITHHT8KDocDH/3vfVxy2ZXWta2iAMluFUUe2SXPpihwOxS4HcHUr2s5L7Q8XvExs+6hqfEW2pamb9+6rXf44VEt7rIrsCkKzIlObQoqXfI51OBjN9x8O4QQuOKKK2CDv1KBTvP78fGSxdAFcPxIObPqG6+9HDZL4HfffYcNGzZg7NixAORFY1xcHF566aWw99296y98uWI5Ro0ZY61rzq464qgjsGLFCuz6aydOnTAW+/fuRXVOOvkUHDhwALquY+jQoZV+evXqFdUxUyFPOlUBflu/Dk88+hA6de6Cs86eBJsCHHmk3Kc3X3s5bL2i/bvw1YrlOH70GOuWr86dO2Pz5s0o93qtQvKBAwesbtWAPMbDRhyH4uJifPbpR3LA5kDzTKzNqEMUtS5dDq3RosLsU9WJlHkVKQDsFbLQbTdvyZDn6Kz7H0JqmzZ4+IHZEIaBw3r2QlZ2B7z1+qsQQliZV1ZWhrffftuamTYuLg4jR47Em2++Wannb+gs1M7A7aBZ7dPx4cefok+fPhg3dgzW/bjGKviYLr/6Oiz9eAkemHUX2mW0x+lnnlPr41HRcccdh4suuggffvihNXvu2LFjsHHDb/jlpx+hKnI7baqCN159CYqiYOTIUbJniaLAbrfDZrNZx9ohvHjj1Zes4+qyKxg5UjZ0vPH6K2EVA++98yY0TRZZBYAirwF/oDAZmnlELUafPnVbL4oyYl0zDwhUxiG88s58nfETToEQArv/+guDhwzFkCFDcezwYRg2bBiGDh2K/v37y+WVYM+UUAsWLAAAOOyA0xY+k7TDBiS4FHk3gVOpdOGdkdEebrcbv67/Jezx9yNMhmS+b216bIQKLefZVAUQBjb8th4ulwupKclQAOs24N8C2+GyK0h2q/jkw8XWsTJv7Xe5XCgrL4cRknkiZPs8nvKwzHO54zFq1Gj89NNPGDBgQMSya6xchBLVWs+eQIWK5xp17QoE7giojbpknnl9e8N1V8Lv81bKvNDrW7P2SkF4bkUyduxYLFu2DLt2yQo7s5z3xisvIj4+XpYNnQomTJiALb9vxpdfLLcyr+KrHj9qDEpLSrBkcXjO/fe//7XeqyqKosDpdIZta15eXlhmyuMGOF0ueMrL4baH33I8/qRTsPG3X9G9a3cMPmIoBh8xFEcfOQwjjzkSRw4biqzs7LDhTELvWGvbPhMXXjwdyz77FK+98qJ8PnAoCwOTifzxxx/Y/Nu6erub4vPPPw+bTEnXdbz++uvo3r17TPS+A9gDr+UZOhQYOBD4+efarzN4MFDFzFpVsakKEl2AV5Mz7AgoVquDgAw3AQU2RT52zPARmD9/Pq699locccRQTLvsSvTq0xea5scvP6/Ff194Dn36HI4TTz4N3Xr0wsWXXo5nn34KqqpiwokTsPuvbbj33nuRk5ODG2+8EQCQmpqKe+65B3feeScuuuginHfeeThw4ADmzJkDt9uN++fMrhSONlVB79698clnK3DqyeNx2omj8fb/PkF2h8onpF0FLjz/PLz1+qs4+eSTccMNN+DII4+Ew+HAzp07sXz5cpx++uk488wzazxeP/zwA1JSUuDx+rB951/48otlePmll5CRkYG33n0f6UlulPoE2rRJxS133I37Z92Fay+fhrMmTUFxQT4eevB+uN1u3H33vUhwyYvgyeddiGeffRZXXjoVF06bjvwD+fjnE48iKSm8ZX7y+Rfh6aeexNWXXYyZ99yHHocdhi+XfYJPPvkEgGwdJmqWevUCrrkGeOyx6Ne99lpZIKyl6jLPWibQLGgLaWd12OQMqJohszG1TRvccPPtmHP3HXjrjVcx6dwLMOuBh3DVpVMx5azTMG36FdD9Xvzzif9DQUEBHnroIev1H3vsMRx77LE46qijcMcdd+Cwww7Dnj178MEHH2DBggVISkqyMk9RFLRPT8EnH3+Ms846C+PHj8crb7yLEcePtl7vnHMvwAOz7sKqb77CTbfdiXi385DGg7v//vvx+uuv45577sFnn32Gm2+6CS+9+CKmnDMRs2fPQXbHTvjskyVY+OzTuPKqq9CjZy8IITD+pFPwr38+gasuvRBTL7kMxQX5eOrJx+B2ywvUeKfMvC6H9cbk8y7A0089CZvdjpGjx2LDb7/iX08+hqSQHkmGkD1gzD0p8wukcJw7akmGDZMXplGM74SePeV6tRRt5ukCcAeuKjz+YOaFEgCOHH4MLrrkclx31XT89NMPGHHMcUhOTMDB/Xn45ptv0L9/f1x99dXo3bs3unfvjjvuuANCCKSlpWHx4sVYunQpAFnxH+8ML7/EO2VvW/MCsNKYVYqCc869AK+++AK6duuGgQMG4Je13+O1V1+ttP9mReKTTz6Jiy++GA6HA7169arxzosN639EfGIy7DYFu3bl4eUXF2Hzpo244YYZiIuLgxACRww9Ej169sKsu26Drmtol94GSxa/h9WrvgEgK0DNi9HeffvhnXfewb+ffQYDBx8BVVUx6IihSExKQk6nzvjof4tx3KgxaNMmDenpbdGpcxf8fd7jOOmE43Hcccfh6quvRpcuXVBcXIwtW7Zg8eLFWLZsWXW7QBR72rUD7rgDuPLK2q9zxx1yvVqqS+aNPi54fTtyxJHVXt+G3l4bestoJLNmzcL//vc/jB49Gvfeey/S0tLw8ssv4+OPlmDevHlISUkBANx684145603cPrpp+OmW27HoCOGorTcg5Vfr8D4Cafg2JGjMfn8qXj+2adx3ZWX4K8dWzFk0ACsWvkNHnzwQZx88sk44YQTqjwmp556Kt555x1cc801OOecc7Bjxw7cf//9yMrKwu+Bvz/yuKk4/PD++ObrFfj8k/8hKysLDnciuhzWC3fdPRsrln2GE8cehyuuvg7de/SE4fdi985t+Oijj/D4P+ajW5ccK/NC71gDgPsfehRbt/6J66+8FMs/+xQnn3YG2mVkIP/AAaxY9hlefWkRFv7nFQwdMqjWn3V12rZtizFjxuCee+5BQkIC5s+fj40bN8ZWx5c6T5VBUWu0mXreey+6WXree69Ob1PdTLT7SzRxsEyzZr8yZxlb9d2P4rwLLhIdczoJp9MpEhISxICBg8Utd9wtNuTuFvtKArNslfjF7AceEof16CkcDodo27atuPDCC8WOHTsqbcdzzz0nBgwYIJxOp0hJSRGnn366+PXXX8OWufjii0VCQoIQQlgz1fzy+zbRo2dv0alzF/HdL5vFvpBZaPeVyG03Zz179NFHxcCBA4Xb7RaJiYmid+/e4sorrxS///57tcfInIXW/HG5XCIrK0uMHz9ePPnkk6KoqChsm8yff85/VhzeX+5TckqKOPW0iZX2SdMN8cy/XxC9evcRbrdb9OrTVzz3n1etWWhDZ0r6eWOuOHXimSIxMVEkJSWJs88+WyxZskQAEO+//37E4xRpP6rDWWipokbJvC+/rNtsjCtWRP1W0WTeM/+WszF+tXKNOFhh9u2d+0tEx5xOotthPUReoVfsL9HEy6+/I4YOO0q43W6RkJAgRo0eK7755ptK2/Dbb7+JSZMmifT0dOF0OkWnTp3EtGnThMfjEUII8cILLwgA4rvvvrPWOVhcLiaecZZwu93i1bc/CMuGcy+8WNjtdrFu8zYr82oCQFx77bURn7v11lsFALEicHy3bdsmzj//fJGeni4cDofo1auXeOSRR0RxuT/sOP7rmedEj569hMvlEl26dhNzHnhQLFwoj6E5C6SmG2JfQbm47oabRLuMDOF2u8XQI48SHy/7WuR06iymXHCRtV/Bz6R2+1QVzkJL0Wi0cp4QQrz2WnSZ9/rrUb9FtOW8hQvl7KrLvloTlnnmbISh5+dTzzwnhg47SiQkJIi4uDjRrVt3cdFFF4nvv//eev/ffvtNjBs3TiQlJYk2bdqISZMmie3btwsgfMZYs4yyb98+IUSwTGXOivvDr1us9/5zV764cNp00S6jvUhISBCnnXaa2Lp1a6XXFEKImTNniuzsbKGqqgAgli9fXuWxqljeAyDS0tLEUUcdJZ5//vlKmffjuo1izAnjRFJysmjbrp24+prrxIcffljpffbtPyBOP/NskZKaKhRFEQCsGTHfXvyJ6D9wsHC5XAKQM3GbmZebmysuvfRS0aFDB+FwOES7du3EiBEjxAMPPGC9tjkL7Ztvvlnjd4Gz0FIkjZp5v/8uRM+etcu7Xr2E2LIl6reoy7VtqVcXX66u/vrWXDenU2cxfsLJ4mBgfdPIkSPFyJEjw7bll19+EaeddppISUkRTqdTDBw4ULzwwguVtvngwYPimuv+JjrmdAqc6xli3ISTxaoff7Vyb/P2vWLa9CtFZmaWsNvtonPnzmLmzJlW2dEUaRbahx56SHTp0kW4XC7Rp08f8e9//zvideHatWvFMcccI+Lj4wUAcdzxI4OzZm/fI6665nrRuUtX4XA4RJu0NDH4iCHirrvuEiUlJWGvY81CG5itdn+JJvYUesVTz74gjhs5WrRJSxN2u120bdtOnDB+gvj3opeEpmlCiKozLTc3VwAIO35VzUJ77bXXivnz54vu3bsLh8MhevfuLV5++eVKxz1UY89CqwQ2lhpBUVERUlJSUFhYiOQI45d4PB7k5uaia9eulSYRiIrXCyxYANxwQ83LPvmkbM2ow1hSZT4DPj34uwIhb1eyfpe18kZg/AC7CsQ7VJT7BfTA1y4lTkW5T445YAh5y4HTJm99KPPJGngVsHqd1QdzdrLQ2xEqMrtOO+2VW3gbgrVNgDVuX232XzeENTC7Xxco8QpAkeuEjklortkmToWAHDj1kYfnYs6se7Bt2zakt+9wyIO519v3txZqOpcoNjRK5vl8wD/+Adx6a+3XefRR4PrrgSoG1K3KoWSeOT6dIeTjfj3Yquu0KUh0NX7m+Xw+HNG3O44afgyef/G1Fp95QGAoA6X2E1g0Zq5Vh5nXPDRaOQ8AysqAJ54A7rqr5mX//ndgxgwgPj66t2hhmRequZTzzHW9moBdBUq8Aqoa6GncAJlXlcbOQmZe89ComQfIO8wmT5az0lald2/g9deBAQOifnlmXv2pj8xz2hQUew1rfxo68xRFwbXXXounnoo8AVBV6uN7Hk3m8RbalsjlkpVynTsDc+YAP/1UeZnBg4FZs4AJE+o8EHzowOkOVX7pNSM8MkKnf9YMOeOWnA0QYbMBKn45K47faPhZAuU4AkCJF1ARPjtixRmEDuV2srpsU7SzJJoDs+uGnDlJAFAEoFdY7t/P/AsA0LNXLxiahhVfLMOzTz+FyeddgDYZHeDTAU0XSHCBMzJS8+J0Bm+pqE0l3qOPAldcEXXlHXComRc8t4UQKG/CzNu7dx+2/L4Jr770H+zbuwc33Hybtc0tJfPMT6Wg3IBNVazPxa8L2FQFmsHMo2YuPl5Wyh12GHD33ZFvp+3RA3jgAeC006IazN3UUjKvOZfzzHVddqDIIy9kjSpuTwaYedTCDRwILFkCLFsGzJ0L5OYGn+vaFZg5ExgzBujevU4vz8yrP/WVeYZsp2XmhWAFXkvlcgGnnw6MHAn8+CPw669yCm63W05YccQRQGrqIb1FxRPTEPL/dRE+gKZ5ggWGDah2NsDQ2Q8bcpZAc5yDUq9sXVEUBUluBeU+ARhyAM3EwFgqjSWaWRJDe6EoioISbzCsI43XHhcfjwVPPYnt27fC5/WiY04nXH/jrbj59rvg0wVURak0pTlRs5GUBPztb8CRRwLvvw/Mny/zzuR2y7HyTj8dOProOlXeAS0n8z7/ZAmuvWo6srKy8PiTT+GII4a0uMwzGQBESElWMwBdMPOohYiPl71RxowBvv8e+O03oLhYZmLfvnJc5GpmGKxJS8m81lDOMzHzqMXr3l3+nHGGbLgoL5cNFD16RDXmXSTMvPrfJmZe/WMFXkuXmioLdmPGHNLLVDypZMuCrEE3u+HaFCDJraLUK8JaK8xTxmVXqp0yu7EFQ1p2J/YEpq8u8wkAssVENVAvtx7UVnXH2Xzv0C7JQshBUG1KcNrwiq0UqgJMvegSXHDRJfJ3hP/hUc3Z22qY0pwopjmdwPHHA8cdB1x+ObBtmxxOwOWSs8327ClLWbXUkjNv2iXTcMXll8AX2L+WmHkQ4be8MPOoRWvbVt5RMWFCnV+iJWdeayjnMfOo1WnX7pAq7Jh5zLyKmsvIcqzAoxpFOqnMe9grdk21KfK+f626qvIYYnbRNffPEHKsg9p2w61NKNVWbY+zVxNWYPkNoNgTHHfBbCFSFVhjCia5VagKUFBmQFHk82ZXajWwjSpqntKcqFlQFDn+Se/edX4JZh4zj6g1YeYx84haE2YeM685a/gRDBvA/PnzrUEChwwZgq+++qra5VesWIEhQ4bA7XajW7dueOaZZyot8/bbb6Nv375wuVzo27cv3n333bDnv/zyS5x22mnIzs6Goih477336nOXYlqkk8ofeMDsmmoq94uw3yu+Trk/tmq2dUMEuujK7fJpsoVFIHxsA6Dyvpqh5NNlGAkhUOaTv5d6hRWUtVXb4xznUOBQAQi53UZgziXz7VRFdlG2qTK0vH6Bcp+AqipQED4AqGEE983cB6LWjpnHzCNqTZh5zDyi1oSZx8xrzppdBd7rr7+OGTNm4K677sJPP/2E4447DieddBK2b98ecfnc3FycfPLJOO644/DTTz/hzjvvxN/+9je8/fbb1jKrVq3ClClTMHXqVPz888+YOnUqJk+ejDVr1ljLlJaWYuDAgVHPSlIXsfZFi3MosKsInkyAdYLZK3RNFSK8C6s9cGIB8vFY2jUz4HQRqNoPbJwZcED13XCjCf/asMILwdcwhb63oihwBcZlECI4E5n5bhXHafDqAt6QoA7dKgFYAe83cEh/hGLte0vNR6x9d5h5zDxr+Vj6AKnFiLXvFTOPmVeTWPvOUvMSa98fZh4zrz419ve72VXgPfbYY5g+fTouu+wy9OnTB0888QRycnLw9NNPR1z+mWeeQadOnfDEE0+gT58+uOyyy3DppZfi0UcftZZ54oknMG7cOMycORO9e/fGzJkzMXbsWDzxxBPWMieddBIeeOABnHXWWQ22bw6HAwBQVlbWYO9RF4YAdCN4Qpk14iLweGitt9uhyGBT5LgAyW4FLrv83a4qcMfQOAEef6BFQsgZvUKDwlRdN9zahlJtmQN7VjwpFYQP3SWEQLEnOKW2QOVgdthgbZtNAWyBF3DaFOsPj/wjJB8z9/FQZiYyv7fm95ioJsy8xsXMiz7zSktLoSgKc43qhcPhgKIoKC0tbepNCcPMY+bVhGU8qgtmXuNi5tXvtW1tNXY+Nqsx8Hw+H3744QfccccdYY+PHz8eK1eujLjOqlWrMH78+LDHTjzxRCxcuBB+vx8OhwOrVq3CjTfeWGmZ0Aq8uvB6vfB6vdbvRUVF1S5vs9mQmpqKvXv3AgDi4+Nj4r7tcp8BryYqBQAA+AEYfgVxzuCpaRcChiZnf/F6FahCQNEE7HYFfp8Cf6NtefV8PgP+KvbLpCAYdJof1ngAJlUIaN6QlhvIUHG5VHg8MkidtRwzwBxjIGyMBRGc+cgTGCi12GtAr2YcBrsKKKr8PLweA3F2BTabAp8mYHMoUARgBFot7IoC1QD8fgFbHT8fIQTKysqwd+9epKamwmazRfkK1FIw85h5LSHzhBDQNA1FRUUoKipirlGV6pJ5KSkp2LdvH7xeL5KTk2G325s895h5sZV5JRUGzK+oMct5LONRKGYeM6+lZ140miofm1UF3v79+6HrOtq3bx/2ePv27ZGXlxdxnby8vIjLa5qG/fv3Iysrq8plqnrN2po7dy7mzJkT1TqZmZkAYF3QxgKfJuCPVIUPAArgUBU4G6F2u74JBPZNjxwYSsj/qIHfbLZgrb4A4NfkwKACItiFOtAiA8jWDyiAyx7srlwVny6g6+EbYFTot60qCgxDhLVIQAnvvq0qMow9WmAcAAVwN8IsQ6mpqdb3l1onZl5sY+ZFx2azISsrCykpKYf0OtRy1TXz4uLisHfv3hovfhtLxcyzKQKqEDAUBToUZl4jZ54IGfsJCA7eHvp7Y5fzWMYjoOVmXhiW85h5UWrsfGxWFXimirX2Qohqa/IjLV/x8WhfszZmzpyJm266yfq9qKgIOTk51a6jKAqysrKQkZEBvz826vNLvQZ+2ulFQbmATZFdhT1+eX99apyCwR1dSHCpldbZWajh/9k77zg56vr/Pz8zs+V6crlc7i710gPplCT0IkWQKkVRkKqIioCI4lf9+vuqKNhQsSJFBUUEpCiEhC6E0EMK6b3c5S65XrbMzOf3x3tny93e5S65JJdkno/HJbe7s7MzuzevfX/eddzggCzAtGZ1bZxhRVanbfcXrtYs2xZnY72dEKqUsBkKTAMKw4q8kEljm0vAgulDg8njX1kTo6rBBQWRtAahkOqREE6If3m+wYTSYLfH0xp1WbQ1RtyGwfkGE8sCLK+Os7newdGa3IAiZkPc1ejE8ZqG/J+enaIAMwYOqRTlgQGD4yrDffXWdSIQCPhRWR9f83zN2y+aB2DFU6UiCijeQ82zLAvTNPd7loBP/2Z3NW/AgAEUFRXhOA62be/tw9wlnuYF2luZsGohBQ8/iLlmNc7IUbRffhXWSScSHjyw03N8zet7zTNQRBxNLJFFI83bO0/A7GvN6w7fxvPxONg0z7fzfM3bU/aHPh5QDrySkhJM0+yUGVdTU9Mpg86jrKws6/aWZTFo0KBut+lqnz0lFAoRCoV267mmafabL8tgSJOXa1IfczBMaHEgHATbhtwcgy0tBqNDFgVhufibIy7vV0WJ2QHabIPcoCLuaLY3B9jeqinOVUwYEkhuv7/4cGuM9Y2aOKZkdJASOaWgMNdgVmWIvJBiaVWcymIr45hHlwapaYsSs2HYIAMNrN/pEHc1BhAMKBylKCs0mTI8sMsoRTgMR4fCrK+zmVwu28+sDBMIxtnWZOO6CsuEWEwT0xoF2DrV2DMj1pJ2Y0AYSgpN4gT3+3vuc3Dja56veftC81zEwEsPMMfclA4GDCjINXzN89nr7InmKaWwLAvL2v+meDCkKclxGff3P5H3w7TsmlWryJs/j9iNNxH/3vfJG5gP+Jq3NzXPAawgtGtXMl2QRauveT79gYNJ83w7z9e8A5UD6oyDwSBHHHEE8+fPz7h//vz5HHPMMVmfM2fOnE7bz5s3jyOPPDLZaLCrbbra56HG0qo4da0uIVPGMduO1LPnWLClwWFVjc3CDVGaI3LVrdwep6FdvPYb6mw+qo6zqtbGdl0a2jXr65yM7fcXMSc1DjtgQmm+IpDwH7gaBuaqbkWhIGwwe1SI4cUmM0cEmTk8yMBcRdBU5IUUplIELZg+bNcCl77PqRXBjO1tF1xXbitINgN1dUrYVCKVuSNetGRrg854z5sjLou3xZJTelytWbwttt8/Ex+f/oCvednpN5qXMOw6ap5nqBoGbKr3Nc/Hp6csrYozeMNH5N3xf1kfD/7qblrefj+r5m1ttLE2rWfIO68ybdM7FESbfc3rhl1pHiR0L6Ak2zitk3vHajFf83x8dg/fzsuOr3kHBgeUAw/glltu4U9/+hP3338/y5cv5+abb2bTpk1cf/31gKT2XnHFFcntr7/+ejZu3Mgtt9zC8uXLuf/++7nvvvu49dZbk9t89atfZd68edx5552sWLGCO++8kxdeeIGbbropuU1LSwuLFi1i0aJFAKxfv55FixaxadOmfXLe+5PKYougBVEHTMO78BXNUamRD1kQs2F9nU1zxKWuzUFrTWtMGl/arvw0ReTCy0mURa2v278p1BNLAwxIiNLoQRanTshhWJEp6buA4yocV/PB5jibuxBmT5QAFm2JE7NFgLzJODFb7vfEpLcsrYpT3ZRqHhC05ItGq8yMO1dDNMvbqYG6Vo3ruhmf0cINUTbXOXywOb7Lc/TxOdTwNa//a57tdq15MRtChvY1z8enh1QWWxSsXJLZXLIDuR8t7qR5ubFWTn/1zxz+8aMZfvEZDDv7RI6++ixO3PEBtuNrXk/Ipnka0bf0HlCuzrzt4Wuej0/v8e28/q15niPP17zsHHAOvEsvvZS7776b//u//2P69Om89tprPPvss4wcORKAqqqqDKdaZWUlzz77LK+88grTp0/n+9//Pr/61a/45Cc/mdzmmGOO4ZFHHuGBBx5g6tSpPPjgg/zjH/9g1qxZyW3effddZsyYwYwZMwBxJM6YMYPvfve7++jM+56eeqo9b/z4UpPRJWYyI8JK88aXFZpMLg+wvs4m7ihyA7La8voWeRhKPOfe9rtzPH1FQdjguNFhJgyxmDkiiGko8sIGIUuRH1bUtbq8tCqSFJnuhDmbGHlUNzksrdq93l7eFwzIeza4wIBEU1Gvv92u4h8u0BSVvgeHlVmsr7OJxjVtcc3WBrvH5+jjc6Dja97BoXmQve+0hwaaY77m+fj0RvPyzO4XY5bSGZqXF1Acueh5Cm/+EqQ1pjc/eJ/Si8/miJY1nTSvN8fUFxyImnfK+DCWIZk0CpI/vub5+Oya/mbn9eaY+oIDWfPirsZUKc3rjkNd85TWu+k+9ek1TU1NFBUV0djYSGFh4X49Fs9THbPl4pk+LMCiLXKhBi2YPSqUNcXWcTUvrYoQS7sOghacMj6MaUhDz/c3xVi30ybuSjDX854rJKIxMNdIbt/b42mOuBl19K7WWWv4dxdXi8c+XbAcVxN1oDKRTgx0es3dfT97Qvo5t0Y1b66PsKNVYxryZdPQ1nlceMfpPZYBhWGD8kKTqUMt5q2I0tDuYgB5wdQUn7JCkxk96Gmwv+lP15JP1/Snz8nXvOwciJrX2K47RWV9zfPpD/Snz6nX1+jChTBnTpf7c196CePkk5Oap3fu5IhPnYSxejUEg3D++VBeDrEYKIWeOAl1zidg1KheHRNwSGueoRRN7Q7PfhTBcaUMzsv2ScfXPJ/+QH/6nPqbndfTYwJf85raHV5YFcVO9IbKpnkdOZQ174DLwPPpG9bX2Umhqm5yeuSpdrVOptKm46XSNrWLN94yZEKg62YKnGFA3IWGdt0p9bYnx9OX6bFdRUNao5rpwwLJyIDjSrq07YiTTGuyvmZ6z4AZwwOYhmLG8ADDi809EriOFIQNZo0KMShPMXyASXuMrGEKV0NiIjhmogloa8ylqsnhldVRtJaL30XSx4Fe9zTw8TmQ8DXv4NG8bBKVrnkKMfx8zfM5lOm15k2bhv7KV7LuK3rZ5awZMS1D8wpb6sR5d8YZcMMNsGAB/PKX8LvfwW9/i7rxKzBjBvzwh7B+fY+OaeX2+CGveQD5YYPRJSZFOaJNms6652uej08m/c3O68kx9aXmQXbdW1oVZ0yJ2e81b/hAk1EDDfJCKqvmQaon3qGueX4G3j6kP0UpsnnjPbryVC/eFmNzXWYqrSdKjtbYLgRNhdaa5ojGzpIhYSjIT6QmDy82kzX2PTmepVXxLl8fyNhfd3QXUQiYmoKwSV2riFdbXBNLRAMChmJgrtqt19xdujvWiO0m+wW4OnuJRdAUYfaiGEELDCWfUdyVKT4AYUsiFQdjlMJn/9GfPidf8w4ezfMMTq0zNU8h5RSOm2p07Guez76kP31Ou6N5m1ZVUfzcv8i764eobdtg8GBab/0W6069gPXBwRmaN6Ktmmk/+yY0N8Pcud0fzNFHw0MP4Y4d2+0xWaZmS31qweprnq95/eFa8uma/vQ59Tc7ryfH1FeaB11rSVWjTdTRBEw5xv6seVFHYypNW0yST3zNy46fgXeIYiiVEYH06M5Tna1mvaxQRtvYLlgK2uOapqh4k9Jr2C1DnEmFOakJNpXFqRfvyfFMLg8kXw8yBa6rvgPZ6C4a0hiR6UMeReGUEMRdTUN7SkZ6+pp70vugq2PVWmM7CcedkmPM1g8vJyDNWb2PM+bIZxS1ZVvXlftaojK1aE96Gvj49Gd8zZPfDwbNA3l/jQ6ap5Fys4CZitL6mudzqLI7mjdwxBDeOvMqlv3nLWreWYa7aBGrPn0Dm0KlnTRvc+4Q3KHDdu28A3j7bbjxRoympm6PaUpF0Nc8X/N8fHaL/mbn9eSY+krzoGstabchYqcGf/VXzQMw0bTFJYvO17yu8R14hyi7ShnONlmmu1TaU8eHGDrAImTJH5UGQpaiMEeRG4CCoPQHOGF0KGvqbU+OZ3eEORvdLYqHFaVKFsoKTU6dkEPlIAsDOa+QJRGZmKOZOtRK9iroSrT2tASuq2Ntt0EpRV4Q0JK+7WXhpb8LjREwlU5GiLyPVSNfTPHEy7tAu62zfvn4+BwM+Jp3cGlezJHobEfNa4+LYeprns+hzu5q3tEjQ7ilpZQcMQmjooJpw7Jr3vidazF+99ueH9DcubjLltH06pvMnvsAJ//rbk568pcc/9KDHLblQ1ZtbgXwNc/XPB+f3aK/2Xk9OSboG82DrrUkZEo2Wk5A9WvNAwgFDMIWSc3Ltrb1Nc8vod2n9Kc04+5ShqFz+mxPGql7TUA9L3hOQBGy4IjhAd7dHGf2yCCFOWbW5/bkeCaXB3qdGt0V3TUsbYvp5LkCvL85xvqdDqHEtd8alRHiA3IMTp8YYvFWu8uGnr19n3t6rIYhUxkb23XWFON0vHRjyN4QVCkR9wG5BnP6uKfB3qI/XUs+XdOfPidf83zNSz7ua57PXqI/fU59oXmrqqJUtmwlZEchLw9n+AheWhUhEtec8PJfKPrqF7O/eHk5XHghmCbYNuTlwfjx6AVvoh64v/P2ShE/7wKiV17DpmnHs76l82IrqXkNDbBsGWzfDpGIvMbQoTBuHAwZkmyc5Gte2uO+5vnsJfrT59Tf7LyeHNOwgSa2Q5/YeenH21H3jhoZZFO90681L2jBYeUW/10TI5bYvdfTriOHuuYdXO5Inx5TWWyxvcnpcrJMuqc6vVY9bpOx7fYmh9mjQuSFVDLCYCqJTICktL68OkrIVKyudZg+zOj03IKwQWmewZpam6ABg/JNwgFNdZMmbmvCQUXU1ry3OUZNU/Y+AZIeS49q9ruKhrTHNPNXRPjYxBBTK4K4WvPCSjFUcwMinjFHJ4Wkod3lmaURQokuwl6D1PRjmFweIG6nhLm3qdFdHavrymsahozcjtmpCEQ2nMRBewMt0jGBUcUmoUBK3Pb25Esfn32Nr3m+5nn4mudz0OI4sHw51NQwMb+ISNFYauPBHmneltVVlK1cTGtLHXm5QeKFRVS+9BKhX/xMnHA5OTjX38DkMy9gdeUR5L7+SvZj+MxnxKl2333iYPNQCnXGGXDrrfCLX8ixemhN4MknCDz5BCO/90Oaz7+WHWZBhubtaHGIvvI2Oa/Mhw8+gNdeg8bG1D4GDoRzz4UTT0TbNrH2GIdPmsH6isNp0KJRfa5527czefUKhjfH2Tl4BOsKh/e55lkm5Jia5ihde/DwNc/Hp7/ZeUDS1jOVJmQZnDA2yMurYkRtccy3RF0a2lIX7O7aeZBdSxxX09AOa2rs5LTZ/mrnxWxYstUmJ6hQcZ3MwOuKQ1nz+jQDLx6PU1VVxYgRI/pqlwcV/SlKAT3/Q+6Jpx3Iuo3XKDNoKnIDKutzK4stFm6I0h6XhZllaBoTNp9lKEryDJqjGsMA0Liu2qNx1tnOpz0mE3lcYFCuwWkTwyzaEmdrg01bXJMbVAwtsnZrTPWuxpP39li9/diOS1tiiIVlpHobpGMm8o69yK2BbJ9+0VtK+jeARJWmlAdYUhXv8guwrycP7Q797VryyU5/+5x8zUsdq695vub59D379XNatQp+9jO4/35xuAH63HNp/MZ3KJxzRNea19JC+xNPEf7ed1CJabEABAJw3nmS3farX6U85sEgTQ89St4jf8V84vHMY7jkEti4Ed56q+vjLC+Hz34WfvKTLjdpveMuNnz6i0wckceiLXFqmh1O2fgaoTvvEMfdrjjrLDj+ePjOd4hdfCmbb/4uy0LD+k7z2tvhySfhm9+EzZvlNQsKaLv1m6w9+7NsCg3uE83TWqMUNEXkuJ0smSYGoAxf83z2D/3tc+ovdt7UimDSSdga1bTbmvwAGKYhAzGimrAJ4aAU5+6pnZftnAxD09Am2hEwYPwQC8dR/drO8zWvj4dYPPTQQ4wfP56cnByOOuoo/v3vf3fa5v3336eysrL3R+yzXygIG0ytCCYvSkMpplYEO/0B96SReldNQHMCirClCJldP9drZimTcaCuTerX4y5Ebc32Frl6XRcG55l7PM4627EGLYWLXBAxRycbapqGiHM4IK8VMCW1OCegMgSuq14Fu9OPYVfH6n0WDnJsiuwLWRAxM0kNuOgocAC2hro2TWvMJWbD4qp4RvSnJ2PYfXwOBHzN8zUPfM3zOQjZuhUuvxz++Mek8w5APf00Ay44G+Ojj4AsmtfeDr/+NTmf+2ym8w4gHofHHoMnnoCbb07dH4tR+NlLMD52aub2OTkwbFj3zjuAqir48EOYMqXLTfK+dRuTNr2f1LxTt75B6LJLe+a8A3j2WfjpT+Geewj+8x+M/p8vM9re0Xea99e/Sqah57wDaG4m93//h3G//F8GuS19onmhgGJwvokm+0IWfM3z8Umnv9h5kBrW4PUpb4lJhltzRONqsLXqMzsPOmtJRaGV1C/HhepGt9/beb7m9Ywe/VW8/PLLXHHFFeTm5nLZZZfR2NjIeeedxze+8Y29fXw+/YDuGqmPKTFZWhUnL6SYPSrEsIEyErstppkxPMCIYpNTJ4TICapOz802aTEnoDI896YBOWkX+cwRwR4Jc0fSp+UUhA1mjQwSsGDsYBPTUHxsYohBuQZ5iTHg6aI0dIDFxyaEU009t9qEzMzj7Eq0llZl9q9Kfw93NRXHiyLNGhlkeLHJtGEWy6rjjB0sQn/quCCWqbLW/nu4iQEXliETezqO4k4n7kBxrsEp40N9NhHJx+dAxNc8X/M8fM3zOSBYuFAmvWajpqbrSbELFsC3vtX9vjdvlrLcww5L3ReLSabdgAFy+7zz4O674eGHe3a88+fDxz7W7SbG009DLIbx3HMEL7kos1y2J+zcCd/5Dvz1r6i6Og5b93bfaN6aNVIG3AXhvz5I5cbFwJ5r3qyRQQzV9UIWRON8zfPx6R17286DlJMwJ6AIGCpZpuplxOVYfWfnAeSFFANzDEoLDWYMDzBlaIDRJSZBU5EXUhk60l/tPF/zekaPHHh33HEHZ599Nu+//z733Xcfy5cv5/bbb+cnP/kJN9xww94+Rp/9TJf9k+KaF1dF2LTT5oPNcXKDCtuBLfWplOHJ5QHW7XAwlWJIrmaE2cpIu47BTgsrtkVYtLIO3dLC9KEWAVPTFHEJmDo5GSc3oFCJ0dy9ncbjkW1azupah7gNb2+UCTvtMSgrMshJ9APQSIq0aWS+bm9Fq7soQ3dTcdKPeXWtw+FlAT7cYrO5zuHtjTEqiy1W1zq0Rl3MHrwlliHThzxdtgxxFKRnORsKcoMKyzD6bCKSj8+ByJ5q3tpaJ2tk8o11UeataMfVmunDAr7mdXHMvub5+PSSF1/s/vH77pNsu3QcB/7xj57tf+5cOOOMjLvUn/4kJbPXXy8ZgEuXymCJnqB1RqZgVn7zG3H0/eQn3TvvxoyBr34VbrgBvvQl+X3CBHmsthb++U+YPRu1dCljC2J7rHlt73wALS3dHvqARW9hqD3XvFU1NuvrHJlEuwt8zfPx6Tl7286zXVcCthUmLdGUnQcpWy8UUH1q532wOc72ZpeGNpfWqKY1qrFMGJAjQYsDwc7zNa9n9GiIxZIlS7jvvvswpCEPpmnygx/8gFGjRnH99dcTj8e599579+qB+uw/sl3YMVtKmLwGk5KOmhKzlojL1lVbGbP2PaZv2Yyqq4NYDDVkCIRCEAqht25FFRbC8OG4O3dyiob2kjK2jDic5VYp7baWGv0AxGxpJNrbaTyQSmGGzscZs2Hl9jg1LQ4N7RrLgNygQVvMJepA3NG8vTHGrFESGelNg1RIjSdP78cwY3iApVV02zSzZ8cs22gkAhHvPMAIhYhXflBTmGOhtU1bDAxDpvZ46ccGInxxR2O7Lh9usbN+Me3uZ+DjcyCxu5o3f2WE0YNMqptc3ESPk6KwRD3b4pqdbXLNzV3WjmEo6lqlx0dzNNXTw9c8X/N8fHrNrpxhjiM1+els3QoPPtiz/WstJbXp7NxJ/JMXEfj6rbB4Mcya1ePDBTofT0ciEfjoI3jlleyPKwU33iiZgPfckxqKYRjw8Y/DOefAz38uJcC3346aN4/Q6RdSHx6zR5qXE4tkP540gk31DC82GTlwzzSvttUhbmtidK154Guej09v2et23kcRKopMVtXaxBzZp2fnuci2sPvX28Fq5/ma1zN65MBra2sjLy+v0/3XXnstpmly3XXX4TgO1113XZ8foM/+p6sLu6rJwVCSBgypdNRxsWrGbFmCsX07qrER6uokKjtggBhiTU1w5JEo04QhQ2DuXIzSUoxRoyhY+gGTXp7HhJkzaSguZ1H5dHba+eQGej+Nx2NX03Jc16WuTfoROC7Yriv19Fqm2myut8kLpdKZeytaXj8GDy81ujvyLGiKuuSHFAaKiK1pTIjw2MEWARNaYwqdSBzWWsQsfVqPF4EoDIHtGuQGobIkQCTmsmaHg5t4jqVS6cfbmhzqVrnEOzQl3d2JSD4+ByK91byY7dIaB6U0m+sdgibsaBVNaYpItDOWMEKUhrp26Nixw008ZjvQrvA1z9c8H5+ec/LJ0F0g/fLLoaMdH4t1dsp1R0eH24gRuO8vEucdyOTZnjB0KJSWwuDBu962ubnrx66/Hp56CjZs6Hyc//mPTKr9ylfgl7+UMuJVqxj07hvE5ozBjrgMCLgMdVswcYm25LKsSjGlB5pnbhsK4TDk54s9G4t1OjQ1cSKTy/dc8xxXoZXGdeX7QdG515PC1zwfn96yt+28+jZNXVumt8iz80ykBLTd9u28xnZN2FLkhxSWKdO3TVMTtX3N644eldCOGjWKDz/8MOtjV111Fffeey9/+ctfuPHGG/v04Hz6DwNzDIYNlJp6pWSs/ZBCg2lDLdriGjdh2M1pXcG4mpWYLS2otWthzRop7cjPl/KNZcvguOOkAfDmzfD00zB2rERaH31UDEzXxZg/n+KNqzj53u/w8aZFRG2NYegu03K7o7s+B9OHBQgGFFbiStCkbFTPD2+ZKuN1e9ogdXdZVxtnwYY4cQca2zWuTkwR0vLl0NDmcFhZgJCVEqf0L5t0lJIviCEFBlMqgkwuDxCJi5h7LQ3yw5JWHbWhNaoZO2j3UqN9fA4meqp5ruvSkrZ+a4pq6tols85Q0kjXdiEnIBNSs+WcJC9dLRHE0jxFW9zXPF/zfHx6yDHHwLhx2R8rKICzz+58f15eqoddTygtlcm0CaLf+wGhX/w09fjGjWLPdcXpp8PXvgazZ4sDbOlSuX3hhV0/pysH3qBB0NbW2XmXzrZtEjweOlSCx0cdRfieX3C8u5HTlv6H079xCcdecDSzzzmSEy4/mcOeuk+OyXW71rxtG2S/n/scnHoqXHEF3HKLOEhzc+V1w2H5PLqhp5qnVEq3vKBFR3zN8/HZPfamnZdtjIOR2B4FQVPKeA9pO69dNK8trmmJutgOlBYYSd2yXV/zuqJHR3vCCSfwt7/9ja9+9atZH7/qqqtQSnHttdf26cH57H+8enUvQtHU7rJwQyzRRFyzolryIWwXzreXkrd0EeqFF8TAmTZNppJNny5RyqeflnKHO+8Ug+f3v5fGx48/Dp/4hBif8+bB8cdDQwO8+CLqzDPJ/8LVnH/nnSyddDwQ7vU57GpazrRhAWI2rK6x8TJ1lRKhHZhrcMzovhOwnrBoWzwp/K6G+vbMr4FtTZqXV0UkNViBQ6LZp5IvDm+ktk6InuNCwFQYSrF4W4ztLa5krshTaIkkxvco6R3QEte7lRrt43Mw0BvNKwq5NEXl8tE6UR6RyArzjI6BYcWQApOaZpdwQBZpHQ070wDtJgZYBBWpoO3upfP7mudrns8hxsiRYkvdeqvYUR5TpsDvfgczZnR+Tnm5bP/tb+96/7m54jD74hdh0yaYO1cam9fUpLb597/hppvgZz/r/PzLL4d167I/NnkyfPnLUgabTmWl9Lf71Kfkdf7731TG4IUXwt//vuvj/te/4Jpr4Le/hTPOwJg7l5GPPSATatNQ1dWYN9wgDsqvf11s1SFDUhusXQsPPSTHn82pOHgwXHkl7Ngh5zppUreH1VPN065onEPKIWAZ8n3j3VbK1zwfn96wP+w8y5DMt6ABSilyAtAeh052XmOjrJltW/SooACKijqdw8Fm57XFSZbspldYONrXvGz0yIF39dVXk5OTw44dOygpKcm6zZVXXkleXh7PPvtsnx6gz97Fmwbj/TG7WrO0Kp78Y06vV9/aaLO6Np70iDtu6mI6xtlM3svzUR98IFfW8cdLc+SmJjH4nn5aepL84hfwhS/AXXeJgfSzn0kEdv58idy2tMjksMpK2LJFSh++9S2Mz3+eKb/9LfHtQ+D443p1jl31OQARC6tKfveEzUMpKMk39vlF/YnJIR77INLlpEVTSfRCIwvWkKEIWdAa0zgJ0SsISXQinthHW0yiHZXFFtVNDg1tAJrcgEpEmUQMhg+wkn8LvU2N9vE5EOgrzXM11HdoheRqMgYsmApOnxjGNBQvropQ29LZqAO5Tj1HlO1oTKTBsetKz5DeXnu+5vma53MIMmWK9HtbvFgyz4qKJIA6cGCXujfp9DMJ/OAHUgXRHZ/5DDzwgAyFOOEEWh57CnvVGnLSt3EcePddca498UTq/pkzxa57443s+166FIJBOOssePZZGZYxeTKsXw933AHRqDgbr79eVnGPPCLb72KQBCDP7djXqK2t6+3jcXnNNWvgV78SJ97y5XL+H3zQ9fNqa8VJePrpcPjhuzys3mieCwTNNM1LfB8V+Zrn49Ml3dl6+8POsxN2XtyFsKVBq6Sdt7nB5rDalbBqlayb8/Mlk9e2xYHX1CQtCpSS22PGsDxQdtDZee1xTVs84YRT4uz0SoB9zctEaa2z/Z357AWampooKiqisbGRwsLC/X04GRGI4jyDnKD0X9re5BKwxEM/YbBM/qtucmiLayLxlDCZSjzjYQsufuN+VMcS6m9/WwyhmTMlejtlCvzpT2IIPfEEfPKT8NhjkoX33HMStVRKIqZnnSURzzFjJBph25KVd/XVkgd8yinQhTO5u/PM1pyzKMdgw06HeKKZkkGqzC1gKCYMsZg2dN9e4LXNcZ5b3rmvipmIQqQ39gyaUBg2aGhzibkSeQgHFIUhaaaaY0HQMhhebDK1IkhzxGXdDpvtzTZxR6G1pt2G/BCcPjEnY4R4f6W/XUs+2elvn1NfaV5XKKQEFp3SkKIQlBWZrKpxMvq1ZevrETCgICRTaEH0anca6/qa52uez/6hP35Onh60xzVBU3HqhBCLt9pUNTm4WnPmhlcIXnyhOLuycfrpsph8+unUPu/4CU5eAQO+en3n7U86CY44Quy89eul4uLnP9/1gd5yi/STe/tt+clGKCRTZg0DfvzjXe8TZDLt734nVR8ffijZilu3wuuvw3vvdf28//f/4Npr4aKL4M035b4pU8QuLSoSB+LWrfDWW6m6LZCKkttv32UZra953dMfryWfzvTHz6k7zYs5mjmjAmxr1PvdzlPAqS2LCa5aiWprk9YAjiPOukBAMo/r6qSq7eijZY1cWwu2jQ6FcCuGsnbCbJoLSw5oO68wpGiNycTcuJN4zzSU5Bk0tvual40DK1/Qp0/xIhCO1mzYabO8ymbdDgcN1Le5rK6xWbghypgSE8OAmJ0ZVfAE7mh3K+qXv+z8Ak88IY62d9+V8o0nn5SpYI89JhHajv/X10tpQmurGGmTJqUceqecIk6+1atF0J57Tsopqqp2eZ5ec87hxbIYNg1Jmx1ebDJ7VCiRiisnEzBgUJ5K9layXc3WRltKRZCU5cXbYjRHdjE9bQ+IOQ4vr+kscCBRCFeLD1Nr+T1iQ9x2k5+HBgaGoTjPwjIUEZuMHgt5IYXtQtwRMVPKiwLJ1EvX9+n7HKT0leZ1hddnxGsUrDU0RGBlTeYYLdOAUFr+u0rc500m01rv0Wh7X/N8zfPx8VhfZ9Me17RGNTvbXJ5eEqGqyaE15tIY0Tw/6kRiL71C5OZbpe2Jx4wZ4lSDDOcdQMHP7yQwtCz7C77yilRPTJsmlRb5+T070FhMsvS6ct6BOBnvugtGj4Zhw3a9z8GDxa6cOFFKbn/zG7jtNjm+/HypADn11OzP/fGPxX794AO47DLZtrRUju/xx8UB6LpSNnzddTBwoDzv3/8We3fu3K5P1dc8H5+9Rnea1xrTvLk+tt/tPIXmzB1vEVz8Icob9FhTk3LSLVkCixZJwGDsWLj/fnjnHdHo0lJUQQFmUyPjFr/CzJWvYm7aeMDaeU1RqabwNM8rl21s9zWvKw6sjn0+fYo3wWbdTjvplXddTVNE0k5RMkXn9XXRZKPJbORFWyQ60JGqKinfAMmg27pVyhGamiSa29goQtTWJtGG+fMlYnn++dIrpbxcxOqMM2T/I0dKKcPs2fC3v0kmX0sLvPqqlFtMntzluXY3LWdiaYAdLQ4tUagsNpkxPMgHm2Os22nL5B4bPtgcz4hubG9ymD0qtFdSkP+9NEokbTBctgiOozPvq49kdlHY3qJpizvkBqEpAkXhVLr0rsrrDrRJPD4+PaWvNG9XGECGKadBGRBQcr3lBhR1iWmDyUyLxAF5k8mU2rPR9l1qXizGES1rOWJgSCK9O3ei2pxkmZk2TbSTx5LNE5jsa56PzwHP5PIA2xocmhNXUMTWRG0pxQRojcJHucMpP/F0gkfMwFjwhujBRx91nTm3Ywdu3JEskWyTbG1bnFggGXA9oaFBWqd4lJRIpUYoJPszDCmdnTtXnGm33gr/+7/d7/Pii+HPf4ZPf1rauqTz6qvy87GPSZ+9Rx7JfLy9HV1VhfrKV6T/XceAcUuLlCq//baUtV12mTg9P/pIjvfVV8XuPe44mDBBnI+LF8OyZbQ1RTlh4GC2jZ/BcrNUFq4dDt3XPB+f3WNXmhez2a92nu3CGbVvY6xcIe0FRo0Sp11Rkax3W1uln+YJJ0iw4ROfEA3bvl3KbG0bKiqgpARlmrKuXr8eo7mZqVOmAAfm2tbuoHmxDv5EX/NS+A68Qxhvgk1dm0NdmybmpFJsvQvIdtmlwG3OK6Pk7LNRjz2W+cDpp4sBYxjioMvLk0m02VBKRCwvDyxLHHi5uXJ7yBCJSpxyikQ8J0+WstyVKyXdePhwePhhOOooicqOH5+aBpZGx34Ije0Ob22MMXtkkONGh1m3Mw4o2mKamSOC1LW7RGIa01BUNzm8tMpJGye+e72pesL0igBvrI+hgXAAzjk8zNNLI0QTrx0wID8oER/vY1HeP4k7HA1RB7QtzT9boi7NEZms1tXodC/t+kCbxOPj01P6SvO6QiGlFBEHXFv2GTAgN6iIOWJEzRwRpCXi8sKqKJaCoKWwHU1bXCd74YVkMNYeGx0ZmrdxI9qyoKEBw7bFOHQcseLMxAsaBsq20bbN1Ma30dVBgoMnAZaveT4+ByiGkhKypxZHaLd1cuHpXUsz2tYx+rbPE3hroQxwyM2V7DbTlEqInBx4/314+eWM/TaZYazPXUPOn37f/QFYPby+BgyQSgyQxWo4LI6z1ta0kzEkqHvOObLYPfPMrjPdTjpJFrytrbJtVxNtX3hBnHg//SlUV6eOt6gIlZ8vmTC7qvZoboY//AGmTpVF969+lXosJ0d6QQ8bJo5HrRmQeKhsyBBG//gXhD95Lk+txtc8H58+YFea5+j9a+edqddibN4kejNihPTcrKiQSd6GIbp11FESQPniF2VNvGIFlJXJ9pGIBA0MQ/RNaym1rapCr1mDLizEqBjJcZWVrK93OKzMYll1nHGlVr9a2z6zLJJ06imk553jiiMv/f1O/9x8zRMOrrPx6RXeBJv2eCIqkajnN1Rq2g4qcVt39pR7fBQvYsYnPymR04UL5c7jjhODaetW6XH3/PMSSX3mGXG41dZmGnVai7HoJLznLS2SSztgQOp2cbGI3M6dMHSolFpMmyavW1EBjz4K554rxlZjo6QcFxZCZSUtA4ewaEuM1qgmbsOYwSYvrowSsTXzV0Y4dXwY2xExq2mWCMTHJoT5YHPKo++V3kVtqBxkMrk80KkBfm/orsHqsZVBFm2L84nJIYKmyWkTgzy/PMaQfEkRjtkQMjWRxNulVOfPJ25r6XcaUsQdlRRlr7zuYJnE4+PTU/pK8zzMxJSsxNMYmAsKg1xDJ0sXCsIGCsgNwozhQVqjmg31DqeOD7Gx3mHkAIMXV8cIJXocza4MsjbRg29PjA6vB8zgQBy18D10Tg7KdcXYi8VEX0EMwWhUgiWBAJimLBRdFwVM3rKIikEVrM8f5muej8+BRHU1NDfjVlTwbo1FLJHS5WmeAww3Wxjz3a9ivbVQ+r0NHSqB1w5TWpkzRzLpfvMbuW0YtA0cgnHp5eT8/a+ZTraOxGLixOoqgAtyQQcCElD41KdkwfrWW523c11pofLWW/DNb8Jhh8nP3LmS+QYwbhycfbbs4/HHxRnYoQS4Ey+8IPZkxym5liVVIffdJ0M8Xn+9+/0sXizHeOWV8OCDcl97uyzEKyulJDn9NbZvZ8BVlwEPcPoln2Gur3k+PnuMqzXvbopn1TxITDk19p+dN3DtRlRtrWTObd0qjrmNG6Wsf9kySYB55BH4/OelH/x778lwSNcVey03V/6PxSR4oLVkJ1dVobZvR9XWkhcOo8vKmHLYYdQdcSxxo5iGzTWcGIjRYJtECouJWCEKmnbiuppmAqiyIYwvD++RnQfZbb2WuOaIYQFW1NpJO+9jE4LMWx7j8CEmq3e6BJT0rbMTDkUDGVyW/iH5mif4DrxDGC/dNGp3nrqT/nvQlFHNsbjOTBdO28YZMQLr7LMlYqCU9Az54x+l6WZJiRiSJSXiuPvyl+Wxc84RQ2zwYHHQDRkiWSEFBfJj21Je6wmT48jv7e0iWMuWyeu1tsri8/DDJQJ6xRXy3D//GaZNQy9ZQl4sxrEVFeLsKyxCl5RwTnuUyKDBbBgyjldWKwKJsULpEYjpwwLJ6ISjpZ8CpGr0dzftOL3JfNwmawrzhdMCyW3f22RTEDJot0G7EsWJpn0Y2aJILoCW3gLjSgxGDjT5cGsM0IweFGByeSBDoA+m1GIfn2z0leZ5JKpuk89tjUFeQK7P5PUXcSkMK2K24q0NUaqbXSwj87q3DLBdxZzKIEU5JjOGG3tkdDRHXN7cEGWk0cL4qpWodGMvGhV99G7n5kqmS1tbymoyTenn5LoorSlurMEImGg90tc8H5/+TmurlIvefjvU1KBPOpmR3/oB6wZMy9A5gLGbFmO9+rLYY3V18Npr0ne4I2++KRpx7rnw9NNELr2M5YPGEVcmJ//tCYo/fUHX010fe0wWotl6JXt86Uvwn/9I4LegILvzLp26OrHxLrlEhk2cfrpk3CklC+Ff/1psxvHjxXm4evWu3jV46SU4+eTMTEPbluN/9ln4yU9kgd1NbztApurOnCk27fbtqfvXr5f9XHKJBJzTueEGmiYdTUFepa95Pj57yNKqOFsanE52noeLZM3tDzvvhNIIanGrrGMrK2HDBtE8EIfejBmwebMMzKmrk6SUY48VPcvPl1YDkYjcXrlSkmLy8qQNVSQiWjhlCkSjqEGDoLSUQQteovi551CHHw4TJzLEdSXgobXYgYnkGV1aCqNH0zpqLI0DJ7GwKdDrktrubL2gBadNCBM0jaSdlx8y2NSoCRrQFk9lIXvvfUcPq695gu/AO4Tx0k3b4jojA6VTTboDAVPjZitYT7A2dyjjp0xBbd4shtLUqTBrlhgyCxfK1LA775RGwk1NIhijR4uh+MUviiF25ZXw1FPi0Bs9OtXIc8wYEa3t22Vh6bry/Px8iVxYloiXYYixtXNnqjRs/XrUpk0wbBjqz3+Wfihr18Jf/4J5wQUEV69gSvwlpowYgTtiJO1DynEiNgVNQdx4DjurmxgXh5b8YlYyMNk3a/1Om/o2J9kss7dpx+kjzLtLYfYW4jFbxmvH7FTK966iRsntNGxvdqlvj9HQrgHN9iaHgrBJXau7V3se+Pj0J/pS8yBz8CCIkdeecA6iU02OmyKawrBiQ52Do8FSsLXRpq5NrntTKUwTNtY7TM0xMZSistjqMlutu2u1utFmwYYYQ/I146tXoQoKRHOVEt30AiOOI4671lb5cV0JlASD8nskIgZmOIwKBBiws5r3Q6VUN/ma5+PTr5k3D665JnnTfOVlKhafw7Sn3uCD4HDJ5EpcK/kbEo6tsWPl/7vv7nq/H34oAx/y89l+xRdpjJuELHhl5HEc/fg8yv/yW8xH/5GqpPBob5dAwQ9/KJl9XpksiO125ZWS+bdqlRx3R+dWVyxbJv3xXDe7U23sWHEcPvxw58cMQ8pmvfN2HHHgnXBCp1JhQDTz61+XTLqPPoJNm7o/tn/+E666Cn7728z7ly+Hs87qvH17OwM+fJv2o0b5mufjs4dUFlusqRUDQ0GG5nnsLzsv9P67kqzS3i7aNXaslOhblrSLqqwUx9zhh0vyy8iRkuQSDIpDz7IkALtsmQRaw2HRuKuvlhO1bdnf1KniHLzrLjj7bNTJJ8tjr7wiB5yXJwGFGTPkNQoLUUVFUFVF/s6dHDdoDbHR41hVOJ0pvXCC9cTWqyy2fDtvD+mVA6+2tpY//OEPvPbaa2xLDC2oqKjg5JNP5vOf/zyDBg3aKwfps3fw0k1Xbo+zs9Wmrk1sGkdLHyhITNtBPOId0/fTR1K/Ey9hnNZSouXlJbe3S/8UgO9/X7LlZs2SyOjll0vJgmWJARaNijidfbZMCrvuOnnBeFzKb7/8ZSlfOOwwcdrl58v+43F5vfZ2EbgjjhAHXXk5nHYafOUrYsDt3Cli9vvfSynviSdKNPWKK1CBAMyfj1lcTP6sWSKGO3ZAYyOlgwaBaaJffplJk6cQHT+R7QOHs6x4LC0xi7wgNEc0QwdISW2XRKPyk5MDAYkQiLg7aC1fJB5lhbKvZBQjrlGGwjKgPe0D2MV3TsbjjVGNEdXJz2tnq6Yx4pAbUHu154GPT3+iLzUPUqW3ppKsvRxLMSjPpDnqsqNVQr+WksebI3I7aEh/j6gt93t41z30LFstm1HSHHFZsCFGe1wzx90mpbCRiLQSqK8XvWxvl/tyc8WxV1srRmBrq+ikF93Ny0v1v2prQ+XmMqNhDc8a42iPa4btSvM64A0QSW9J0PHcfc3z8dlDtJYKh47U1VG2ZhHm4cOxTLlSIjbSdR3Eue89vzuiUaJ/fog3S6cRVinN+yg4k+hXv8uYYUPF1vGceIGAaM4f/yivcdFFqQoL05TF49tvy3Zf+pJkrV1zDSxYkGrJMmKENHH3hvns3CnB32hU7L28vMwSXqWkbDYYFKfb9ddLVQiI7fiZz4j+zZsnPyC26Mc/Ls3kzz5bsgE70tYmbWCuvRa++93u36f2dnn99NYwHq+/LgPbFizIuDu08iPCc6B9F43e0/E1z8enMwVhg2NGBVmwXgYnZGhegv1l56nlDaIJSomDrbJS7LNJk8T2isVS69q1ayWhpblZBuG8/75o56ZNkmU3eLCU2l52mWzv6c7kyRKQWLBAgi7BoDgHX35Z1sYPPigBjPPOE11TShJuBg4UbQyFUKZJsGoLk9uaobZEWhMEArKfeFz2OWRIqodygl3ZeiMHmr6d1wf02IH34osv8slPfpKmpiZM06SkpAStNStXruSFF17gpz/9Kf/617844YQT9ubxAvDb3/6Wn/zkJ1RVVXH44Ydz9913c/zxx3e5/auvvsott9zCsmXLqKio4LbbbuP666/P2Obxxx/nO9/5DmvXrmXMmDH88Ic/5IILLtij1z0QKAgbzBwR5K0N0BR1yAkkatWjGjvRUD0vAC0xsesUknZsuymBU4mf6sGVVGzZkmoWvHatCEN5Odx8s/QG+c1vxDhbvVqiubffDvfeK2UZ//mPlHEMGSLRg3BYhGvlShGNhgbJoIvFpMddTU1K5BxHHIHNzTLBx9vH5MkiTKtXiyhpLRGPxx4T8Vq4UG5XVqYMuqOPFoGsrRUD8Z13UJdcgrlxI7k330jlV7/KqNX3ok86KZHlt0Gix0MrxDjMyRFj0HXF2bhjh0RRqqulp19eHkZBAZPy8pgUj0MohM7JEQdkfj6UlmIsaySUm8+04nJ2OkFajVxWx3MyPjuVUDGdeP+NRPNPkC+TohxojKTKfXXal4hpKHICcke648DH52CnrzTPUKlIoKHg2FFBdrRrJpcHaI1q1u6Is6XeoTWuybUg4iiChpREhQxFOJC6IIOWOOm8abM9zVbryPo6m6AhUqhaW8UYq6/P7HnX1CQOvbY20aSBA6XkzLalX5TXRwpku89+VvS5vR2Vk0O7rbFdTXPEoTWqKQirTseRDW+ASPq5dDz39XVxYjYopaSELJZpxvma5+OzC7Tush9dUDsELJXQPLBdTdOYiQwEWYx5TrzuME3aNlejR8CxYzI1b0PdKNqNIDm/+knXz3/ggdSh3nYbavlyscd27Mjc7phj4Mc/lnNZsULsRG/abUWFONHa2lKTG+NxWdAedZTYYE89JT3wIOX4KyuTgO4vftF5oIVti3PumWfEqXjzzZKN2NGh+dxz4kz0As/dMW+eDF+bPz/z/rfekqB0ugNPKbQVoLWHmldsxRlmN2CFLJa7A4nZvub5+KRTWmhSXmSxpcHJ0Lz9bue96cqaVimxy9rb5bbWsg6tr5eWU8Fg4oJ3RNMWL5bBjSBBkNpa6QV/8cWyr+Zm0aXycglYzJ0rQYncXKlgW7RIBgD95CfSi/Pf/4YLL4T//leSWgoKZN0+Z444BhsbUV71xogR0l7h5ZfluIYOFc0tLJTf58yRHqJK7dLWW1bt23l9QY/yCmtra7n00kspKiri0UcfpbGxkaqqKqqrq2lsbOSRRx4hLy+Piy66iJ07d+7VA/7HP/7BTTfdxP/8z//wwQcfcPzxx/Pxj3+cTV2ks69fv56zzjqL448/ng8++IBvfetb3HjjjTz++OPJbd58800uvfRSLr/8cj788EMuv/xyLrnkEt5K68HR29c9kFhaFaeu1SU3oFBATkCRH5KLIGBCXCsKQmAaUBiSBXBxrsJQKU+4q+GV0FhiQ0ck1FCJE621VdJ1//UvKYW98Ua5vWCBNCD+xz/EqVVWBtOni+Ccd54YZIsXS7nBZz8Lf/mL7LOgQAQsL08O3nXFiPNKvrROZeXt2CGG2IcfygK1pARmz5bo5yc/KUMxXnxRXnfgQCl5iEZlUbtunezrww9lH/ffL697xRXwwx+ipk7F+PKXMbZvxygqxFj0Acbtt0sk+e23pV/M66+LM9HrKzVqlDgen3sOtm1DPfMMqr4etWkTxooVGFu2YGzejDF3LmzbRnDdGkqfeJhJ787jiGUv8am3HuTybfP5bO0rfHb7S3z6w0f41PaXOdddydRQIwNCqVTxgrDilAkhgonAiKkgLygXvIFMS1J0dhyARJAWb4vhJgxXV2sWb4tJZMnH5yCgrzRPk7DBgMVVqXLXgrDBmJIAQUuJfWZDjgURWxxwMUcnry+Q+xZtiSfvm1weoKzQzHjcozujZHJ5gPIiizPUBlRjoxiGXjZ0PC56HAzK77W1EjDYuVMMv+9/P9N5B+Ls++1vpfQNQGs+kbcNF5ngJpO7e4Y3QCT9XDqeu3feWkt/GUhMXiSlbUbijpAFYdPXPB+fDAwjo3w2SShE/djJnTRvw/ApxM6/ULIvhg7d9f4DAbShsmreqOIA28+/DGfipF3v5/zz0dXVspjs6LwDGVjW0gJ33CG2WTxNa7Ztk2Dwa6/JAvXTn4bPfU607c9/luySE06AG26QBatnO153HfzgB11Po/X44AOxWb/85c6Pua70sps+fdfnuG6d9KfKhmnKcX/ta3KcX/gCwcHFnF29gNNq3uakDf9ldvNKigJuhuaVBuKcsfFVTv+/a5h+ymQmn34EZz3+U45uWYVl+Jrn4+PRb+08Ejs0zVSAIB4XJ144LL+HQuLIKy2VNeT48ZKtt3GjtBoIBERbjjxSnHOLF6ecgFu2wD33yDCgDRvg3XdlnXzKKRIUuflmqXQ77zxpV3DyyWLnlZdLlZuXCLNmjQR18vLge9+TTL+xY+Gdd2TQUSAga9odOyTb7/HHYfnyXdp6h5VZvp3XB/TIgXfffffhOA5vvPEGF110Ebm5ucnHcnNzueSSS3j99deJx+Pcd999e+1gAX7+859zzTXXcO211zJp0iTuvvtuhg8fzu9+97us2//+979nxIgR3H333UyaNIlrr72Wq6++mp+mTdi6++67Oe2007j99tuZOHEit99+O6eeeip3p/UC6e3rHkhUFlsEE7mYZYUmp4wPM7TIIjegGJBjkB8CyzQoChsopYjYLiX5RjKtGOR/x4W5I0/GOexwMSJNUwzCE06QWv6HHxZH3KmnSmnrffdJZOCcc8RRprVk3DU0yILznHNEIAIBEYzLL5dIbHW1RAkuvFAeA3G8aS2RiiFDUlNsAwH5PScntd1rr4kofvCBHJfjSES0pASGDZPXz8+X4x8zRsRv504R0xdeEIfit78thuVtt4nA7dghhuT//I8c38aN0qtgzRpxVhqGRD9ycyVC/NBD0nPg4YdFWNvbRQgXLpRtn39empgWFMCOHag//xllWag1azD++U+MxkZMxyawdQsD5j7NtHee5eMrnuOMliUMM6WR9L+XRmmLS3lg0JRUbhf5aYtpNJ0dB14J2+Y6hw82x3FczQeb42yuc1i4IXrACp2PTzp9pXkKkR1LSeR2aVVqkblup01Du6T2x11ojmpiieiu48r16B0DSKad93wvgpn+OGQ3StIxlGLqUAszNyfVZqCsLLOtQSgkBqJnODY1SV+n7vjvf+HNN1EtLYQcL0vHy73uGd4AkfRz6Xju3nnHXHnPvInAXhTW1aJnhpJoeVs8s9TC1zwfH6QE9JvflEoAgLIynMf/xboh4+Vmmua1WLlsvv0O4nmJXplnn931fkePhq1baRo7uUvNezs4inV/eAT72OO63s+4cejjj8f4y1+63ubqq+FHP+pcfprO2rXSFmXNGsnQe/NNKSW74AJ44glZlG7aJMHTW28VZ6Hbw+t5wwbZ7+TJnR9raZEA8K6IRFK2ZzrTpomt+eqrMpH2t7+F3/8e8+u3UnDumZT//meMePsFxn98Nqf//EZO3vQ6A804BZbL0a/+gyGXnE3wqSckIFNTQ94Pv8fYT53JsU3Lfc3z8UnQX+286LCRYtCEQuKwC4XkNoizLhhMpZc5jqxjt2+XHvKDB8s6duVKqSwrKBBbb9w40Rvblv8nThQ9nDBBNKymRjKCL75YEmcuv1zW42eeKRnJt90mAYd162Sf//mPrN9tW4YElZWJw/APf5AWBEVFss9QSBJtvGEbL7+Mfvll2lpT2ckdz/+jatu38/qAHjnw5s2bx9VXX82wYcO63GbEiBFcddVVzN3VdKY9IBaL8d5773H66adn3H/66aezoEMvCY8333yz0/ZnnHEG7777LvFERK+rbbx97s7rAkSjUZqamjJ++iNeX6jhxSYzhgcwDRm9PGygSUHIwHVlkea4mtaYpi0Oq3c4GanBIN7xRtvgP5POxp5zjAhTRYWk2I4YIdlr554Lf/ublDdcdpmUPKxcKY+3t4uhlZMjJa2vvSZOvG98I9VL78gjxUH33HMpkRs8WAQuP1+ipiUlsu2AAVLCOnCgOMe8BWtbmzjnamrECItGZbvycnHGrV8vhu/OnSKaK1ZIz4AFCyRi4ThiONXVyWu8+KJELR59VB7/zW/EiReJyDHm5MhzhwyRpqOuK1PI/vQnKcX4+9/ldZqaJLrx4Ydi3P33vxIJaWuT6Wr33ivHOmeOGLauK47N8nJ49FGMZUspffU5Tvzw38xatwCdNnIp2XA1geNq2hORj3THQefSvUhGH4P1dR1CKj4+aRyKmgdiSJiGGIxelE9r6XCsdarJcfqz80NwyvhwMtMuaMnzoWfZatlwtebFlVHRBtMUHYvF5EC9hXA8LvdpLYGHHTt6tqj9wx9E04NBDGBAjmL0oJ630c1mTHc8d++8LZUwThKn6ZVUGMh76GqSvWw847qhXWf0jvI1z2df0C81r6REMs0WLRI74r33MM/+OEeOyK55NSWjeP/791J//MdwL7xQMjI6MnYsXHIJMReWD5nUrea9lTOGdb/8M3zrW9Lz7rDDZCF59tlSuvW5z6F+85uuj3/0aLG7unPeeXzwgQRaAS69VCog7r9f7CmQxeX//I/sKxLp3fs4d67Yfh1RSpx4Hjk5MiXyYx+T80y/f+bMlP0K8vuxx0pvvkQv8QxcVxbPf/873Hwzob8+SMVFZ3Li839iSsNqim75UtZDVTU1lP7xbvKN1Hvma57P3qBfal4W+qud58w8UtaOjpNq3xSPp9osFRSIjRYOp+y4trZUdduwYbLeDYdlHbtmjTyvsVHWjP/5j2j4xo2iMS0tkqzy3nviiBs3TtoWXHaZOO9uvFFKcWMx0dIHHhDdfv99aSkQi0l/+WeeEf3+zW/g+OPFQWgYckwLFshrL1uG+eyzHPfuk0yPbsxq640aaPp2Xh/QIwfe8uXLOe64bqJpCY4//niWL1++xwfVFTt27MBxHIYMGZJx/5AhQ6iurs76nOrq6qzb27bNjkTaflfbePvcndcF+NGPfkRRUVHyZ3hXqfT9AG/UspfZYSiFUlDXlroybJ0ap+240BLNXER6qcYNEZg37jQiV1yJO2sW2huPDSJUX/iCGDW1teJ8CoXEsz9rljxeXCzOsbPOkj4nEyfKWOySEok4LF8O558vzqz16xMNn5QYbAMGyOusWycOs+efl+EVO3akpvgMHiyCVFAgjjjLEgegZ9zZdqrcbOdOiULEYlL7X1wsRt2pp6aGazz/vDx38mRxWHpN4T/8UEStuloclNGoGKaLFkkm36WXSsrx6NGSoqy1lIp4gjhtmpSCrFghwnzSSeIs3LRJotO//rW85nPPSRQlMVnIePttSres5LTFT5NnOJgGlBXI52klBDJogtaamKOJ2C4tEZfmiLvbpXs+PnDoap7tgu24tEZTUb72uKIoTGLUrezEMuQazAvCsZWhpEE5vNjMGEzRk2y1bCytihOzE0ael33junLb67cCqcdzc2XB2xO2bBEtc10sU1EQMsgL9TwDrytjOv3cvfM2DUVeUGEaqfIV73/krcQ0pLwiPVLuGTQdNU8hJTJxRxO1XaKOZnCuYvG2GCMHmr7m+ew2/VbzTFMWbccdJ3YJ3WveDnJYMOIY1o8/CmfseCnt/NKX4ItflN9nziT++pus/PJ3qbWDu9S81UYJsWUrpKqgtFQWnatWSbZvTU2qP102Pv5x6c/UU9asETuqokKyQDpy/vni1OstWovdmG7Dgtii774r53XTTVKaO3u2BJgvv1wy6r73PXHmPfywfAazZsnPlVd2nkybjdxcsSWvvRaUovB/vk75Wy91PpY0Qo/+nfH1a33N89mr9FvNy0J/tPPycyxxvg0fLk6vlStl7drWJvdrLevOkpLUsAuvikIpWcemJ8iUlclatbhY1rJbt4puffzjYrOtXCmOvOOOk4DO4YkquXfekX1s2SLrzf/+V9aaU6fKWnTSJFnHfvKTkq137bWSxfzFL4qGXX65JOSMGyfHVlcnx9bejnrvPSoWvsiMncs6nf+GeqdbO89bApP4PRzw7bxs9MiB19DQQGlp6S63Ky0tpaGhYU+PaZeoDuVDWutO9+1q+47392SfvX3d22+/ncbGxuTP5s2bu9y2v5BeI15ZbBEwpUa9ONfgnMlhBuQYdHPKSRoi8Fp4HM9PPZ9tl16DPfsYdGmpOKpcVwybcFjE57TTUg0xc3Ol19xLL0nm3dVXSw+6nBwxZlxX/vey7o4+WkQrP18y4D79aXmNsWPFQAwGxRF3YaLHy6pV4hjcuFGMrSVLZN+zZ4tQhUJiaGotz7XtVKPiaFRUpbpaDLctW+T4XVfKa4cNE+fi2LESmaitFQfbqFFybq+/LuW5ritCGg5Lz6nJk6WUYutWOaZRo6Q0ZN062eeIEeKobG2V449E5Ke4WF7P6x14xhnS/2XAAFi/noFVGzh59YtUDjLY0aqxHYg58mXUFpfIRnNUE7NhY53D6+uitEb1bpXu+fjAoa15zVFYXJXKmtvZ6tIcVWjP9kIa7AYsRXmRRX7CWWcoxdSKYMZU2Z5kq2WjstgiJ6hS2Saum1meYaR95Wsti/yeNK73SGT25QYUdW1ul47ErshmTKefe2WxhUbTGHEpLzI487AwOQExqi0DAmnDzmy38/51ouRixMCU5sVdMDTUReQ5LTFwHc2CDTE27LR5e2OMMYNNX/N8douDTfNGzDmclZ//BrWTj8Jeux5ee434qrXUnH0x7971Zz4IjUjtpxvNa3BM6i+/VuykV16R4OPq1bKh3YNsh55s4+E1Xs/mvANZCG/Z0vP9pbNkiSxQPQxDAs+lpRKM9src/v536S/17W9LT7uHH5Z2K9OmiY35iU+Ik/HFF7t/vXPOEYfp+PGSMbNunfSsuuoqzDt+KJkxXeG6mC3Nvub57FUONs3bL3be8ceLbWbbkmxSWSm2WFtbqnVUS4us/xxH1rXeATY2pgY3GkZq0re3Vi0vl0QPrWWtXVAgbaKOPlqqvTZulGzhF16Q4MZ778n2n/ykJJUcfrg4Fr0e/8XFsn/bloSa11+XBJRf/UqCFw8+KOfz/vvymqNGwbhxqPx8jNpaWLUq4/y7s/OCJgwfYCQ9dnEHcjvolItv50EPHXjRaJRAYNfeScuyiPVmMdBLSkpKME2zU9ZbTU1Np+w4j7KysqzbW5bFoMQ0l6628fa5O68LEAqFKCwszPjpz3SsEc8NKgrCsmJqjrlE4nD6xBC5AUVBSCWbTEJmNyRDwahik1PGhygtMFkUGMa/Jl/Avy+8lfcuv5Wayz9P9IJP4l51FfrII0WYWlokgvDEE/Jz0knSs2TIEBEDw5DHt22TRpy5uWJAlZaKcXTffdKw07Jkm8pK+OMf4frrxbmVk5PKmjvsMBEprSUCsnatGHgjRogAFRfLQnXIEDHcPvpIhLK0VAQ2JyclsumlbN7U2bKylPMtHpdtX35Zegbk5oo42rYYcieeKOe1erWc58CB8vtHH6WyEj/6SH4CAentsmGDRLBPO02iJLGYiHl9vRiJq1YlswKL7/8tgzavJe7K4tYLEqXHl+KJfg0tUc26nfZule75+ICveaeMDyUdbe1xTdTRyeeELWlwnBtQ1LV27/zqSbZad89DKXHgeboJEpAwzdT/Sithxh0AAQAASURBVIkmpZd8dUcohB44MBkA686RuLu0Rl2aoxpHQ02zNJ8OJE417ogTL2gCSqQ30kGnDCVatrneTWqeqyGmM6W63ZbnRhM/C9fHfM3z2S0ORs0bN2Mkr884h1d/8U9e/NvrPPuDh5k78eOsMUt6pXmLxh1D031/FbvGo7RUyrG6o7fXnGeLdVVyuyeLs5YWCRB7nHeelJ9df73o65/+JCVlHcthV68WG/bvfxeb8vvfl3PvOJE2neuuk/387Gfw7LPSmP6llyRrcd48yYgsLOz6fEIhIkXFvub57FUORs3b53ZecbEkp4weLfpYWyvrw5YWyYqLx8VOGzhQ1qdlZeI4i0bl/kGD5L70QKxKGEaFheKA8wZlDB8uSSDRqFRrzZ+fqj4zDElqeeopWdvm58s69rDDxLGXkyOZdeedl1qbv/mmnMPHPiaZzZ/7nDz/pJPkHFatEidje7usV994Q7L7Eg7G7uw8CczKkBGvpHZne+Zbp3w7D4AeW98rV67EsrrffMWKFXt8QN0RDAY54ogjmD9/PhdccEHy/vnz53Peeedlfc6cOXN45plnMu6bN28eRx55ZNIpOWfOHObPn8/NN9+csc0xxxyz2697INK5RlxGQOcGFHFbmnXaDgTNRPZEGJojmrRWaxgKivMUs0ZJ2vCYEpNN9TYhC1qisELnscrI4/SJIVZtd6gpdDitYg05RUWokSNF0OrqUoMnmpulPGL1anHAlZZKqWxFhWThrVkjonb55WLENTfLPr75TTGGXnlFJrKtXCnRhC98QSIQF10k2Wo33CBZbC+9JJHP+fNFCNetExEsL5cMt4ULRcC2bhUH4ptvSjTDWxx704Ryc1M99rzU59ZWuW/gQLnPy9hraJBU5aoqORfbln14PQFtW36ef16MRa8M7t//FkOuuFgW47GYHGNNjTgN77tPzrO1FaZPZ8SSN3j7qMouP3dTQcBQVA4yAU11Uyq1JWiR8TextAqmVgT76k/Ox2e/src1z05kvBoGnDRONG9Lo0NRONXzbWlVnMpiq5NTzstWS72O6vbaa464rK+TCWkqEhGtGThQ2geEQqKPOTmprDzblt+POkoCGrvi6qtRgwahxoxheBfHvKcsrorL+6WgIaJ54sP2ZN8ZNLhakRdSuBEx3NIJGGLUoaUkBlLGd0fTzEhoXk5AYRlgO6lsel/zfA5meqN5ERtsFaQ5knkF9VTzjhyTx4qCi8l7dhaDt6ymON/CmHwYO3e0MDgvT2yUbLS2plqp9IQZM6Q/02c+I8HSnBzJJhkwQDTusMN69yZNmSKLUdcV26y2VhahoZAMZXvuOVn83nFH5nTcbFRXw913S0XJoEFdOydPPlkCte+9l/3xrVulN9UNN0hgedWqTpu0ff5LLMuvRNm+5vn4ePRbO+/ww0UPli0TbWlslOy49eslA7e5We6z7dTatrBQAq/ekAvXFaebt340Tdm2tDS71pimOP7WrhXn4bJlklUXDMp6dvhwWf9WVIgDzlvTlpSkerufdJKslc8+W4YxPvecJI9s3y4tombNEg2vrZXsP8tKRFylcmxt0cQu7TwNtMU1o0ss1tTaxDucQtK56tt5PcvAA7jyyis56qijuv256qqr9uaxAnDLLbfwpz/9ifvvv5/ly5dz8803s2nTJq6//npAUnuvuOKK5PbXX389Gzdu5JZbbmH58uXcf//93Hfffdx6663Jbb761a8yb9487rzzTlasWMGdd97JCy+8wE033dTj1z0YyNb/zEmMeR5SKCuoqiaHtrjG1pK26umDBkj4l1oi8MGWGA1tNi+uitAW10TiifHOCU/5S6ui1LXbxBz4cMAE3HPOZfuUWUSKS3CDQbRtixi0tckC8wtfkAy18nLJWvMGQhQXS3ns4MEiFK+/Lj2dPv95cQKefbYIydat0ndvxIhUk+BrrpGyitbWVDrx6aeL8XfUUSJy//ynPPbWWyJ28+aJwG3eLCUbDzwg5b9KiTPxyCOlVPfww8XpaFmp6K3XhFTrVD8Dr6eBh5c5Ew537ltl2+LsKypKORlPOUXeB8uSY4vFJDPPcaQst6iI8CMPMdHK3mTWE70BOYoZw4JUFltEHY3j6l6V7vn4HIjsD80DKAib5AZVn03B6jRh6/DJ6LIy0ZLcXDHOQqHUZFrTFAOvvV10LVuj9nSKimQAkWFkLfntC6obbbTWFIUTeqil5N97vwfkGpx1eJCoLRPf0qPkBjC6xGRAjkr2ssmzkrvJwNO83KD00Js1Uqah+ZrncyiwrzWvts3mo9BQ1h5xKur00/jALeWD0Ejav3xT1wf5+OPigOspXqCivR198cXSo2n+fLjnHunV9PzzskjdFeXlMvCivFye+5vfSC+7efOkjPV3v4PvfCc1rGxXzjuPhgYJAnc3RGPmTHESdse2baLZgwd3eih+5NFsufRa2rM473zN8zmU6dd23uTJ0sJp8GDRncmT5cWXLEll340dK9sed1yqnVJtbap6zDBkvRoMyhp4/HhZ65qmbBMMprKglRInoNczvr5e7i8oEMeflxTS2CjP8bbz1qlay2tu3y4ZddOni1353HOy3yOPTPUOjcdlHbxpk9if27bB+vUc8+FcCoIkhcpx5UdrGJBjcNK49MB15ts1psRgYG7P7DxvCR2yYPaoIEGLZNn0waB5PTriBx54YG8fR4+59NJL2blzJ//3f/9HVVUVkydP5tlnn2XkyJEAVFVVscmr2wYqKyt59tlnufnmm/nNb35DRUUFv/rVr/hkmnFwzDHH8Mgjj/Dtb3+b73znO4wZM4Z//OMfzJo1q8evezBgKMX0YYFkdMLRmtZEM0+tYWSxxZraKDFHY7uZ3l8FSP4WxF3N+p0Om+qdZIlTzNFEbdnQQNJa2+MQNDW1zTIRJmYOg/HDsCZCZYnJ+EGmGCte2WpOTuYBe9mQNTWSRbd9O+4116C2b0eFw7Lo3LQJPWAA6uqrxYhas0YyUb7wBYnuFhSIcNbUSAluY6OIUCQi2Xuf/aw46W69VcRq6FARyMmTZWFcXS3ZcOGwZPGNGyfHO2WKiNWKFeLMO/ZYMc5GjJDXHzRInJCbNsnUnwULUlGKgQPlWL033vtfKRFYr4lxLCbCunq1OAfz80XYR4yQ86yvhx07UBMmMGLTYpZXdB5EoxO7rm11eW9zDAMpU7NdGDvYTJbuLa1ir2Tc+PjsT/a75nnb2hIl3t0IYLYI86ntbaiWFtGzlhYx8kwzFVDIyRENaWmRBa83xbsjw4bJBNqBA1PTHvuY6kabV9ZEcbQ0ITYVxBJ2rqulQfGp44O8vCpKW1wnjTNTieEXMKG22WXYQIOAqQkqzdYm3cmog5TmNUY0RWFYsCGOqTS2Vr7m+Rz09BfN237OpYx89G+o9es7H2RLi9hDFRXZJ7WmoT/3OWwU7X98kKZpR1H26INYP/tJ5kb//rdMWuyuSqi0FK66Cn70o85TuVevhp/+VKo0jjlGbMENG7o9rk7Mny/BZsvq3N8vFBLbrgfo116j5tf3kfvkY+TN/w96wEBaLr6M2qmzeccty5p042uez6FMf9G8Lu28kSPlp7ZWKrC8Hnfr18tacfDgVKup0aNFL8rK5LljxkjSyvTpsg7euVOyeZWSwMO4cbI+nTlTbEDHSWXUNTXJvrROrS2XL5d17ptvwhVXSFZwfr6sJwsLUxl+RUXy+o4jwy7++U9JiBk8OFXKq5QcP4jmFRZKcOajj5j06MksjIQwkOm+ILbelHKT5dU263bY2Do1sMxxxd7bXO8ybrBBwFS7tPPsRJCiXmtW19rkhwwaIy7NMZe2mD7gNa9HDrzPfe5ze/s4esUNN9zADTfckPWxBx98sNN9J554Iu+//363+7zooou4qLvmsLt43YMBV+uM/mdRO1GSBKzfaVPf5kiZkqkImlqaRjrgKhgQgiGFJut3OonSJs208gCLttm0x9NSkXVinwmvesyRCwyk8SdASb7J2MEBufjTe490hdcLD0BrVm9oxNxRi+HYxEO5tBUMoKhlJ8VuOwXHHotRX58a193aKqJyxBEiSq4rpQmuK5l3Tz4pJQuuK6UNWotz7Je/lCy/o44SR9xLL4kj7+GHRTA3bZKhEi++KMI1fbpEWbx+d8cfL6UYv/iFlOJOmyb99yZPlsy+hx9OCW4oJCIZCIjAtrSkxNF15TFvAIhHQ4Ocn21DMEhhQw1UdP0WOi6sqrEJWYrcgMI0YWO9w9Qcc5elez4+Byr9RfP2dArW5PIAcZvk5NqYDa/lTuREaw2qvV2cdVqLhngTuwMBccq1tYmmXHcdXHyxDPPZuFH0Y9YsMRgDATEgQ6HdPsbuWFwVx0kkJNe3d37cduGpJZFkZFwDYVMGSzlKY2tx+I0pCTJ9mMG85e277GniuNDQrlFKEzRF93zN8znY2VPNmxRoonDR2+SsW4lbOADmzOblwFhaYr3TvJ3DJzDiyScluJqtbPRPf5I2J//v/8miNgvxG29i3bVfY2uwhPa4Zubmdzo770DspEhE9M7LOOnIZz8Ld97Z2XmXzjPPwA9+IAHX3WHpUnHiPfpo5v0FBbJo7wGqtpYtwUGsPf8mii69haEDTCZXBHlreTtuc/dZ3L7m+RyKHDB23uDBqezaxkZx4DmOBAxCIdzBg1EFBTB3LioQEMdcfb2sFWMxWS+fd54EPbzenaNGyT6mTpVtPAfd4MGSpDJ7dmpgxqBBorVnnSVr15wcWYOuWpVqPaWUJLFcdJHYg5GI3BeNyut58xISx5xMilm0CI49Fn3JJWgrwDsNITQp553Hq2viFOcqqb5I3BcOSJmyo71sPYPTJ4Z6ZOdpIOrAmlobK6F58TRH6oGseT1y4J1//vlce+21nHXWWRjGgeel9OkZS6viyQUgSFllfZtEHeIuNEakZ0CuAWWFFhVFitfXxYja0iwSBQVhRX27Ju7Aqh02s0cFeGV15mATnfxHcLRcYLnGnk+EMZRizMgiXoqGUs0qI1AbLueU8WGMjvm4HtXV6KVLReBGj0Zt3iyOudtuk6itbUvZrFJw112SsXLiiSKWzz0nIvjkk/L4RRfJInjpUpnw45X1zpkj0QvbFuGcNUscdkuWiCPQG+Jx2WXS7++NN+TxCy6QCPKnPy0p1jt3pvpZLV8uRqlSqaw9EGH3IrpKYXrTibpAIanKoUSW+YE6VtvHpzccDJoHnSPMAK1xWBGqYGJsA8owRHdMM2WUNTfL/8XFonutreIdO/ro1JTsQCDVBmDy5N0+vl1xyvgQzy+PsrO16wVoNHFenlYVhBTNUYnahiw4ZpQYYh9ujSV1rCtU2o+B6J6veT6HAnuieSXRBkb+6OsEHvl7aocFBZz57+d4PGd6xuv0SPNGTJXy1vfeg6eflkVeXp60PjnxRKleOPFEsZ8eeEBsqkBAymvPPhtj2nQ2bVbE4hrTgKIFr3R94vffD9/6ljjp2jtECYqKUlkvu2LxYslK2R02bZIMvscey3QUNjaKDveEoUOZ+uBPGPSJT7Fi8ERK8gxf83x8uuGAtPOKiiTxIw0DcQruHD6OwW++DKtXo4qLRTNNU9aNr78uAyOuu04SO1atkiDtCSeIBs6cKZnNWktrqVGjRHenTRNnXE5Oyvn3n//AJZdIwOE73xEb8YknpHVTQYG0dFq7VtayJ5wgGrZ5swyRfPppOYaSEjm2khJoacE96SQe3TE4MayiMxqoa0u9iYYCrRNDLVxNYdhg2ACzR5oXUPIZeO/4wba+7ZE3bt68eZx33nkMGzaM22+/nVVZmqf6HPhUFlvJEcteX4zKQSYGqS98SAlRbasGpPY/7sLaWofG9kRaMtAS0SzZZlOUozrVsacTMmVqD+z5RJiOkRaPXe63rIwlh53A/OOvYOVZn6Xx/IuxTQv94Yfojz5Ct7XJoIzp0+GDD2R89mWXSR+6gQOlxOzww6WJclWViODs2SKkw4ZJCa1hiCFaUSGOv3/8Q6KxO3aIYTd4sAjh3LmSFn3kkZJuXFEh6dVvvSWi6w36GDxYUqSnTJG+BXV1suiuq5P06tpaOTetccNhjESKtwKChqQjjxqkyE/0RckPKkxDHdBjtX18esPBoHnQte6ti+WypnQ8OhBILRiDQTHOystTEw0tS4yvIUMkCltcjM7PRxcUiH5NmZLcZ3PEZfG2WPJ4Xa1ZvC22Rz38LMPgtAkhrA4WiUJ0ysNIrEDDAYWjFblBRcBUjBpokRcyWLghyqoam+0tLvlBkppnKOmVYigYWQz5IUVBGIKWIi+oyAkqX/N8Dgn2RPNGr3o703kH0NyM9b/fZUxudPc0b9Ag6T18zz2y+Hz+eSl3nTYtVUZ7/vmycFy0SOyvH/4Qd84cFu20kpoXshSBFcu6PoBYTKa73nYbevTozMdOPVUCsT1h586eOfqy4TjSpuDrX8/sfdyxgqI7pk8n8NO7GPmZczi5aSkra+K7pXl5IV/zfA4NDjY77x23nGdnXcaKq75GtLQcd+s2tGWh8/Nl3fn5z8Mf/yiVaQMGyFqyoUECHxUVomGbN0vp6+bNUhV2yimy3rzwQgkwfPaz4vyLx8U598c/io148cViO772mlRnfPzjYlMefXSqFZRlSZLLW2/JGjkvT9a0l12GOWoUx43OzHrr6IhSKqFjStqj5AQUIUsxMNdgxtAAS6p2rXmVxYpJFRaD8lRS8w629W2PMvC2b9/O3//+d+6//37uvPNO7rrrLo477jiuvvpqLr74YnJ7+sXj02/xJhgePTLIxnqHkQMMXlwVIS8AlqkImhJJCClNzFYs2hJn2jCLlojLhjoHV0u6sVfeFDDEYIg6mpgNRYnoRUcMZOx2KKD6ZCJMx0hLbybNVBZbbG9yWGsX0TrmaKafeCwrqmPsaHUJB+Dwwji5eYnpjQsXohctkmhwRQXqm99MlbaOGiURj+ZmmDBB0pUNQ6LH06aJmN13nwjcSy9JOe2AASKeJ50k0Yrly8Vpd/jhYrDOmSNOvqeeSqUxb96c2SPvnHNESJ96SqbWDhsmr7twIU3nfxrLgPyQQVtMoxQUhyFgmIQsl/TCOO+LZsbwg0PkfHy6Y0CuQdBUVBabLNkWpbbFxTRkQXggaB50r3urGi3ai8czNX+bLGK9wRZNTRJFLSqScgcSwWPTRIdC1A8aQnBAcUZvEG9YRsyGuC2G0KIt8trbmxxmjwrtVi8R23WZvzKKTrw/3pAKL2PEVCJlrpZ+NCFLJaOqowdZjBts8uaGKLG4lonnESlhSdc8V0FpPgRNk6ApLZA9h6GveT6HErujeZvqHfJXLMm6P+uVlxjUUEVtYeXe0zzTTPX/pbPmKaVxysrpNq+iuRnuuIO6/76D3rSZAU/8DevVVyQY2pR90Fcntm9HV1aiuijr7ZayMln4VlVJRstzz8E778hjL78sg4Kefrrr5x9+uNiBgNq6ldCvfsHRYyfSMOck3hwyk9a4aFpByGB4WzVly94jp64GXTqEqnEzWBsuJ2jCYTtXMmjBi/D2AvRhk1Af/7jYmH6Flc9BysFo523R+awb+zECE+Q8yotMxhcr6bdpmtLq6cYbpaQ2P1+CJatXSzB31Cjpl/7rX0ubqLo6KXkdNEjWtZs3Sxupxx+XhJPzzxcn3jXXiAOvqEhKfKdMkd9tW3rmvfGG2JgtLdKLr6xMXmfWLDAMbNdlcZWdaecpMFy5HTBS5c0mkBs0UIjjddxgk4UbY7u084YUKKYNDbK61sHVity0L4WDydZTWvfOHbxixQruv/9+HnroIaqrqykoKOBTn/oUV199dcbQB5/ONDU1UVRURGNjI4WFhfv7cJKkL8rKCk3GlJiJCTuyeCoIKaJ2qp4/L6QwlaK00KC+1WVHqyvJHSrlTwqYcOSIAO9uiieny3SVn2EZcNQIi8XbHIKGJhw0OHpEgLc2xZlaHqCsqOfTYTqeS/oCM2iRXGB6DsvJ5XIRe2O+S/MMalrdTvdna3K5eFuMmiaH8VYzQ2rXE6iuQjU3SeZbfj4qFpOFcn29OPTy8iQa8dFHImxVVXJfQYEI7uzZMlG2pERSkO+/XwZrfPSRGFaHHQZ//asI6fjxMl3tqKNEmJ9/XlKdbVtKeWfOFIFWCr18OXO//xD1bpD8oKJigImrNTtbdDK9WANxR8pQzISoDS82+21/gP56Lflk0p8/p3StKM4zaI447GjV0izX8FL3Zdv+rHkdz6Ur3QPp+zF5sMKoqcHNy5ODDIdxHI0ZCmAEArvUvM112R2FsPuaMW9FO7UtqXfLG07hJoxmy4DCkKI5qhNGnmTfhSw4akSQdzZJuYtSoF1Na1wTc2Q/vub57Ev68+e0p5p39msPUvS1L3fecVERi55/n6VqyD7VvOUbmhi66SMKdlaTXxDEbW7BuuzT3T5Pf/azLPvh74loi5nlCqOpCbelBXXmmT12ykXvvY/Qddf06ngB+NrXJAvQ47jjJHPF6yM1frxUbTz2WOfnzpwplRy//nXqvlBIFtR/+AMN9/2V+VPOxXbh5MbFDLniIlTaABBdXk7tXx7DQVF+8VmZLVcMQ8rkejP5dx/Qn68lnxT9+XM61Ow8b33bsm4zZZuWo95+W9aa06dLNl1BgfSA37RJ1pqLFon+GIZk6v3ud+LQ+8MfZE2anw+PPCLPu+gicdbF4/KY60pZreNIdp7WkozS2CjbV1aKplmp89yVrWcmMu+8LQKGYmCu6pWdZ6AIWIot9Q4aaI9rCkIKJ+2D6q+2Xm+upV478Dwcx+HZZ5/lgQce4D//+Q+2bTNx4kSuueYabrnllt068IOd/ipyHRdlMUfGa6PlolIKgqb0CGiLa5RSDMhJNcB13OwjnHMCEDATi65d/JUpEvMaTDh5XIiFG+M0tLuYCk4aG+q1Ey+bc85bkPZUBHe1r7yQjAdPj4gYSkR7YmGcodWrMWprpfnntm1SKjtkiGTJxePSh6WpScStvl6ceUpJ9OPJJyWV+f33JTo8dqxEU266SbZbulT64l1zjYjmG29IGcjf/iYNSP/2N0mBrqmh7ePn8Pj4c9BAbhBOHR/i5dUxFBpXS2qyJ4hRRxMwFTkBtdvZNPuC/not+WTSnz+ndN1ri2tsR3TK0yoz4TQ6EDQPutcqYK9pnkdZobnbUc30KbQDcqSc9ukl7ckgUl5Q3u9ImrPQa8JuGiQ/u6gjv3sDMXzN89nX9OfPaU8175im5Yw594ROPeRaf3gX/znzhozrMxt9qnnLl6O/9S3Uk0+m7rv8cgmSvvFG9ucEAjT+ez6vVxzdacEb+NMfCH9l10PqnDnHUPWr+xh2xrGStdJTpkyRbJT587vf7qijcL/+dYwXXoCaGlkQFxdLVUa2Mt8bbpBgrmmy4T//ZWdROTMvPh61ZUunTfWpp+Lm5mE+kyXLr7hYqj1GjOj5Oe1l+vO15JOiP39Oh5Kdl3V9OyhO88oNBDZtwEQTzAthxOPo9nbU8uXSdum112Td6TjS6/iRRyRRZN066QF/xhmi+U8/Lc6/ggIpnz3zTOlReuqpqeEVu6A7Ww/EQZocCJIgaCoKQgrbkc/IdjUt3nufiMYGTDhuTJC3N8axDBicZ9IcdWlo14BmQI6iIGxS1+p2snn7E/vEgZfOjh07uPPOO/n5z38OiHPPpzP9VeRcrTMWZRpoi2nirsZKpLfmBhRKKYYUGAQsxZB8gzfWR2mNdb1fBRSFxbve3M12kEqjhYRHPu2vcnC+wekTc/boHNPpaRZJTxx9uUGVMSbc298p48PJyUOArCY3bpSf6mpx5rW2Suma44iRtnWrTAAaO1YcfJYlzysogAcfhC98QbZbsUKm1N58s7xpDz0EV10F774rzsKSEtlHURH6scd4749P8RElyfc2YEpfBwOZ7qNRBA2Z6Ki1JhhQzOmn4ubRX68ln0z68+eUrnvpmkciaFEQBMMwfM3bXc3rJdWNNour4pwyPoRlGDS02cxdHiVkQU7AwHZTn5GpIDco0XLPhmuMuFkNbl/zfPYl/flz2lPNCxhwctVblHz/dqx33oaCAlpuvZ1VZ3+WjWbxvtO8rVslULl4cefHPv95sbOefz7z/kGDaLz3z7w18RTiCSkcXmxSWWyxcEOUcTvWMOrcE7ueUpug4ZF/sXDKGRz7zpMUXHlZz47XsuB//xe++91UKo9HcbEMtggGYcUKHNPC/c53CXzucumv3NycGkiWjS99CX7zGwCav/3/aJs8kyGfOmeX22bluedkUd5P6M/Xkk+K/vw5HUp2HvTM1qsstvhgS5SJdg2DNq7EaGpErVkjFWDl5aJX8bjo6NChUtm1aZO0hBo0SBJRRo1KVYD1kq5sPZCBbLlBRVtMZ1T9GUraA0Rt3clx6rVaQaWGnOWFZPBFS1T6ECqlGDbQQCmVtbqkv9Cba6l3rt8O2LbN008/zf3338+8efPQWjNjxow92aXPfqDjBEOFLI5aopq8oDjuADQudW1wyoQQH1Xb7GIADBpojSGNcx2pVffwhExKdMF2JXsCMgVuQI7BKePTO7TtOZPLA8Rtkg5LT9wcVxO0FIeVyWWxbmechnZNyJRt0yc8xmx53HZUhjh6j3WqsVdKBG/UKLnd2iqOuPXrxZlXXS3G2qxZqTTn7dvFQB07Fr79bRHVxx4T4/XrX5f05SeflAjswoVy/6RJsv+CAnjmGbb8/N6k8w4SY7htef9dBbYr0SaP8iLroOgN4OOzKzrqXm5Q0RwRUSoIie75mrcHmtdLyoqsjGj0gFyLsw83WF9nM3KgydsbY0jfOsXsUUHW7nCobnIIWVAUNqhvdzs578DXPB8fjz3VvLgL84bMYsTvnqGsqYpoKI8V4XJcFwqsfah5Cxdmd96B9Gm65RaYNQsdDBIN5dFWOpTaw45gXU458bgm6kBlsUwiXLItRkO7ZmnRGPL+/iSDP3VeZnlpGq0/+SWLDz8Jx4UtR5zIxNtvR/34x52dcumEQvDNb0qGSjCY7DdKSQl87nPSUP7ll+X+GTNQl1+BwpWMvbff7v598BrSJyh44h/ow7uZFq51oploF0V/fg88n4OMQ8nOg+y2nuN21rztzZoGczDm0FLCo2DUpJ0MO2YD1trVGNu2glKoigp0Tg46HodZs7FLh9A6cixFE0btkb3Ula23oiZOfZtL3IbRJSaWCe0xkllzR40MMvejSKesR02HCcAKora0RvH635UVmkypOLjsvN1y4C1ZsoT777+fhx9+mB07djBw4EC+8IUvcM011zC9w9hjn/5PxwmGXpTC0ZJynBuQXkEtMQCX55dHOHV8iOXV8W73ayoRr6aIRDkcNyVgrpbUZYCAZXD6uABPL4lmCJypSEwm7FujoqOggwhcW1xjGIoPt9hMHxYg7oi90xrT5AUhZqcu/LJCkfjdHZhBXh4ccYT8gJTYrlwpTrv6einNKC+XoRft7WKUjholQy5yc6WcoqxMSmgfe0wm/IwYIdGQ4cPRzz/P1jt+xasFh3VOSSFRqZuY7pN+/AfLdB4fn12Rrnue5nnLmra4JmD4mtenmrcbFISN5P5mjwpllI7MGG6wtApGDjRZXWMTMMVo6wpf83wOdfpK8zY5eWzKGwuA6Yi27VPN++9/u3/8l7+E665Dbd1K4MmneC+RMew4mtaYvLhGtM7TvOao5q2KI5ny9MsMWvgK+Xf/ROyynByiV1xNw9kXsGL0UcTMANiQv30L6qGH4BvfkEyVRx/NnE6bkyNtUE44QfpMPfWUBF5fe036Gl9/Pfz4x5Ld4vHccxjPPYfx2c9K4/hdOfAuvhh+//vUba2xC4q63v7FF3EvvhjjH//o/NiQIVI+5+NzEHEo2XnQ2dZz3K41z7PzInHFCgaxYtAgyiqPJhwQp5nl2tiGhaM1rYm4AzYMr4r3ef+4grDBUSNCXZYIjxxosqbWISeQOP4u9mMZh46d1+MS2sbGRh5++GEeeOAB3n//fQBOPvlkrrnmGi688EJCob73JB9s9Nc048XbYmzYYRN1JNU07sqF7ZUm5QYVkbjGTqieUlJKEXe7Dzx6FIUg5ipicY1n3njprpaCsAmRhBHo1bO7SLnTgFyDMyb1rdB1LBkGEfKYozOao3uC3xxxscxU1oZXLtYW0z3uK9Vr6uqk/8C2bdJg1LLEkVdbKx+AZUm5w4QJEk0NhaQnwfbttA4oYfvM41kQrMzan8EASPQ1VEoRMkmWvu1JH6t9SX+9lnwy6c+f0zubomzY6ZBjQbsNcVv0SZGKonrXj695+0DzdhOvZERrTUMke08aX/N89hX9+XM6aDTvppvESdcVpgnXXYd2HJb87z1srk/1wIolVtJec/RsmmcoGOXWMyEvQtwI8HZsEE0xlaF5pSveZ/gZc+T1PvMZmRC7ZYu8ccOGSeP4f/wj1dbkooskC88rP7vyykznXUfuuAPWroX77sv++Cc+Ic+fOzd5V+ut32TVp25g2iePx9i4sdNT9LBhNP3lEQqv+FRmj7xgUByM/ah8Fvr3teSToj9/TgeN5vWQjrZeTzUPDgw7D6Al5mYN1ipSPfsPBTuvRw68yy67jCeffJJIJMKwYcO48sorufrqqxnllQP69Ij+KnLJppIuhCw4aWyQ+StjxF25IOZUBqhvdVlVKyO1jUTBefpEl6Aneln2byoYMUCxsT4V+TAVWGZqwQip2nVvGo13e3DB3u8HFY1LNkrcTTVH96I1oJM9AD08MWiN6m4bivYVzRGXxtpGKqrXYNTUoFtaIBaDxkZUSYlMuR00iKbJM3m5pTijmXTUyb5PM/E5mopEM3g5v/46nSed/not+WTSXz+n5ojL6+ui1LeJIo0caFDd5CYb6Q7IgeIcgw31rq95CfaH5vXkdaobbRasjyb7WoGveT77j/76OR1UmvfYY5J91hVnnQXr19P8/R/z2oTTkncHTE1Du2geiO7lBNRuad725ZsoP/FIaYECcMUVsGGDZNh112tu0CA45xzpa9wdZWUylAOkXPill8SrcNRR0jPv/fel9NZDKTY+/QqvlR7BSU1LGXrdpzA2bEg+7A4fTs0Dj/L+kGmMb9nI0CVvkrPqIxg+HI4/Hvph+6P+ei35ZNJfP6eDSvN6SEdbry81b7/beRtiBJRGGYrWqD7k7bweldA+/vjjnHvuuVxzzTWcccYZGR+2z4FPTatL2FK0xOSCeHVtXPoERDUKWLzN5tTxIUwLVm53Mib4AIQMcOg6pdXRZAgcpJp6pj/HS+9VKjVBEGBqec+m2/SUymKL7U1Op+hCVaNNS0wTSlwV7XFpoun1SeiqXCxdEAyl+lwgUo3lw2wfMo3pR8jx1rY45AYVE0stalpdRgwweWtjjIjtEnchP0iXjVgV8rlYCvJDBpYhX1pBi+TUSh+fg5X1dTbxRFTS0VDdrLFMhZmYYBp1FJOHBskJx3db8zY1ZGqeN4ksPXLoa1520odpxG0yIsDbmxymlAeoaXUZNdBkSVWcuIOveT4+3XBQad7s2ZLxtmxZ58csS5xRY8ZgHn88wToydO/9zTHW1MoBhazd17zyw0bCbbfJD8Bf/iLTGL/2Neln3BXHHgsvvrjrc6yuxq2owNi8WUpbTzpJnG2PPgo/+1mnzfXtt1NbMRZtwyuFk5n4yMsMW7OI4M4a4oMGUzdpJpvDpWgX1g8YyZDLx0M/beTu49MXHFSa10Oy2Xp9pXn7284zlaYlDjlWqiIwG4eKndejs9m6dSslJSW73tDngGRyeYBtDQ6tcZGViK0xSFRqGmA7mnU74lQ1pcZvey5cDcQTwpTIEE5GF9Jr/tOvNVPBmBKTtTtS7nOV9otSkmJsGpIN2Nsx27uiIGx06qc0bZiVcIhJ40sQobMdSUEePchkxvBgRhrxvhKD9XV2hrim97FqaHN5fX2MkKnYUu9gJ7qnSk+XtPc1De8+Q0F+SFFeaDJtmMVH1Xa/ns7j49NXeJqn0RgqpXkaSb933T3XvPTcdgWcMznIcx/F8cw3X/O6pjvNa49pFmwQzdvW4Guej09P6E7zQiYEDCmbst20RWyahvUrzRs2TBxZX/86PPts6v6hQ+H222HcODj6aHIHDGB2YSrDAxJ2rSktA0ylCFl69zXvoovgX/+CN9+U2y++KD/f+IaUy8az9NJKH2TRHcccA5s2wy9+nrrPMOD//T/Zx+OPS6nwGWfAlCmov/+dsunH8dHIE9DAcjWYFeNOQ42HwrBiaJHFqb7m+RxCHFSa10M62nrQx5rXh/TWzova0jGqOdq1U9Uroz0U7Lwe98DLRl1dHXfddRdLly5l6NCh3HjjjRx++OF9eXwHFf01zRgg7rg8vSRCxE79OSgALenAhpKyJNdNCZlSmZNfNJATgFPGhXhzQ4y6tux/WlZCTHICmob21P1e+jJA2IJzp+QQMPfNBZeeduxojeNCwJR03qitKcoxmDMqRF5I7ZU04u7I1r/KwzSkmar3BRO1tXyGOjEJKfGFoZQszqOJtG5XyxfYwFyDU8aHkynGBwr9+VrySdGfP6e9qXkq8Y/37WoqCAcUQVM0z3tF09e8rPia15n+fC35pOjPn1M2zTOA3GBiWqIt2Qrpg0o97fPoV5rX1iblpdu2yWCw6dOlx1wX7BXNW7UK/vpXuOee1PTaY44RJ+Kf/9x5+8mTpTz2hRe63+9tt8Fdd2V/7I47YNMm+ZBefVUGoAE7f/VH3jn1M+xoleoZF1/zfPY+/flzOug0r5ccdHYeKZvcTDhFi3PVIal5PfqUbr31VkaMGJFxX2trK0cddRQ/+clPePbZZ7n33ns55phjWJn4IvE5cHC1ZvFWm5ClMv4gvOaeOVai/CjtoikKyf9eNMG7RMKWRADi8cxFcfolZOvEVNuoZLtAYoGVeNwAQqZi8VYbd/f9y1lpjrgs3hZL7tfVmsXbYpTmGQQtEbi4owkYiYWgUhhKEUk09WyNaqZWBPepJ9+bKhTsEBQJWnDqhBDliemQClKfoRIRMxFHxNgSkxEDLayEmFmJL5qYDYu2xPv8ffbx6c/sdc1ToHTmfiNx0bx0LfQ1Lzu+5vn49C1daZ4LtMWl11PIFL1zXLmGikIpveqXmpebK+W0F14omWgJ590+1bxXX4Vf/QouuABuuEF+hg6FOXPk/44sXQpTp3a/z5kz0W+91fXjS5ZIBt4f/5h03gGEcoIMyjMJmirZSD8noNAaPvQ1z+cQ46DUvC44ZOw8xG9gqZSd11HzDhU7r0ef1IIFC/jUpz6Vcd8999zD+vXruemmm2hoaGDBggXk5+fz4x//eK8cqM/eY2lVnKomJzle2/ujMBLZJk1RyUQxEvcVBMG0DPJDInBhC3TiYmqKwmMftNOcVjmQ7fJxtEQt7LTsFiOxOBZx1VQ12iyt6n6cd2/w6u031zl8sDmO44r3f8NOaY55eLmViJ4o2m2wHTcxpVEm2cRsWFETzyqSzZFuCvJ7cFzd7dPVmrc3xGho13gJs1pLY9J3N8aZOtTCNKAt7tIWc5PvrdYSVTIUbKp32dboJKcsDcw1kmVz0vOg795nH5/+zp5qXm6QZElEYwQeX5SpecmpY2k4iQwxTyl8zfM1z8dnX9GV5oHoVbvdWfMijiyWAqaveVk1b/VquPlmaGqCBx6A3/5Wfv75T3Hk3X+/TJs1zdRzAgH56bCmSkePH4/atq3rk6ytheLizPsMgx2VhyU1L2TCMbH1nPjKXznx17cx4aG7aXptIThddH738TnI8DXPt/MOZjuvR4XO69at46abbsq475lnnmHw4MHcddddmKbJ7NmzueWWW7jnnnv2xnH67EUqiy3W1NrEXbmgcwOKqKOJ2LJY9SbyeNOuW20wHLdTSZmb+L8jlhL7Jb2pp6GkOSg6IXAGhC2FZUJrVBN3IGbCqIEmi7fF+iStN1u9fXtc0xoVcV+81ebUCSHmrYgSc1xsB/JCqf5QxbkG9W0u8S4abu7OaO1dNfGcPSrEipo4G+psXBLNOE15f2wN63bYbG6wsd2UU9QjlpbWHXM07XF538MBmD0qyNodzj7veeDj0x/YE80zDSgrMFgTdZMNid0OumcZMlChMZKSRM85SNptX/N8zfPx2Rd0p3leXyhXg0pczi1xuR0w5XryNS+L5i1bBq2t2Q/EdSVT7t57ca68CvOfj4pYmSY88ghMmgQ33ii/19TIcwIBIldczfarvsSQv/2J8OrV2fc9bhy8/nrGXdErrmJJ8QSaIhpTwSn1H1B26TnJst4QyBfaX/8qzkNjz95nH5/+jq95vp13MNt5PfpEGhoaKC8vT962bZt33nmHk046CTMtsjRjxgyqqqr6/ih99ioFYYNjRgXJCShyA4ryIotzp+RQnGtgKLlIgt5fioa4I864iC3/t8a6bihpKOknZbuJenVSdetebTtItPCkcQFsR6MR22LWyACrax021zks3BDdo0gASEPTssLU32sscfwu4rm3Hc0rq6NoVyejJZ7TMWhBblARzxDJSLJ2P2aLiPaG5ojLmxuiydeoanJ4ekk7WxvtjH16KdpexmLETmQuJpym3u2uPoSMYSJavnQ2NThMG2YRsGBKeSBDnHcVNfHxOdDZI82Lw9odbidjLh3LkAlnhkpFYD398zCUr3m+5vn47Bu60rxBuQa5QYWZyGAAIHHduVr0wNe83dQ816XZNlgyeBLOW29Ldt6vfw0bN8LcufCnP+F84Yu0/vt56p+cy+rn3+a5L/2U/wZHU3vOJRAKdd5ncbE4ASOR1Dl+7AyWX/N1GuJy3iNVM4Nv+WKqJ1/a8fC5z8GyZb7m+Rz07InmxWxYv9PXvAPFzvPKoNfU2oeMndcjB96QIUMyHHPvv/8+8XicI488MnNnhkEo2xeOT7+nrMjitAlhRgyymDE8QMA0qCgyMA1FfkgRtkg2Ce8NWksjcS/aEbIkYuGJnaUSUx81vL3RJmRKmm9eQPFRtb1HItKRbPX2OQFF2BJxV0oRjWuao5p4onlm2AKNpqbZZcPOOANzU9IcSzucskIzOfGnJySjE3GdzGJsi2naElETR+vkPmeOCDJqkIXXi9PRKT2zEv0MQO4LqE4Z3UDmNm0xGF5k8OEWm7gNS6riSQHrKhW7r75ofHz6C7ureS671sGcgAQlQHROShWkt56RaDeglK95vub5+Ow7smne6ZPC5ARFg8JWYvHka17yud1q3qRJkJPT5bHYU6excEOULU4eTf/z/zJLaQFiMdpmH0fOWadRdO7p7Bg1iXZHXvuN0ulUP/Ys9rHHJTd3zj6HyL33Y2/ZBlOnEr/oErY/9ATvfP9eFgeHAfIeD9u8DHPZ0i4OysZ59z1f83wOCXZX8xwN8V1cBoek5u2CfW3nQaISMBFo39lq887G+CFh5/Uot/CII47g3nvv5eKLL0YpxcMPP4xSilNPPTVjuxUrVmRk6vkcWBSEDaZWBJO3Rw8KUNPs0hZ1aYsDShqE98aJp5EMvfygSk6QaY2JyBWGDQrDiq2NjjSL15pB+SZ1rXIh7YmIZMPVmkVb4hn7VUhzzKjtEjKhJaaToi0TiiTd2tXQGIWo41AYNpIldiARjOnDAhiqK3npjJfyrJRCu5qWmE6+r146cfo+jx4ZpL7Npq4tc+KRMkDbKYdoV8GK9C8nR8Mra2LS9JPUF8jUimC3Y73Tt/PxORjYG5oHMua+KKzIDUp6v+1AwFIMH2iws1XTGEmUZfiaJ8eJr3k+PvuCjppnKMWcUSH+u7Y9ea2FLIg5nRe13XFIat6ECTIp9itf6Xwgn/oUm8fOINYumvfuuBOY8sTzDHz0z4TeeYvonOOov+RyBp9xQlbNi9kwv3QWw371L4Y3bsLBZF3RCOqcIIX/dxpBbdOGRXMk9SFpEv2hIm3dvkfxphZf83wOGXzNO3jtPE/zvN/r2qAp6pCbGGRxMNt5PXLgfeMb3+DYY49lwoQJlJSUsHDhQo4//nhmzpyZsd0zzzzDUUcdtVcO1Gff0hxxWV9nc3i5xWtrYriJlFaDzNLXnpAfVJiGIi+oaI2J+z8vZOBqaeiplCIvBForcoIQjGYK3O6ISDaWVmWOqw5akkbclhA2V5MhXgDt8cxzjdrQHNXkBjKjFYu2xJkxvOfHOLk8QNyGqkabtrjOcBAEDEXAVMl9guy/NaZQ6OT7b2sg7X1ydc8+F4VENzzKCk1GJvoxHFZmyXE1SQ8FR6f6JPTFF42PT3+lLzUvL3GZBE2DuONiI2UKtS2aoCWNg0OWr3kevub5+Ox7PM0bOdCkLaakkbiSTIbeKs8hq3lXXw0VFeLIe+cdmTz7jW/ABRcwsryEus1xqhpt6mPwYsVs8r8+iwFEaVFhtGFQvtXuVvM22zlszpsAgEq8X/XtGgktZVe/1vKRUn4bjWZ9PDhhLGWFpq95PoccvuYJB6Od5w23yAmktOxgtvN6VEI7a9YsnnrqKSoqKmhububaa6/lX//6V8Y21dXVbNmyhfPOO2+vHKjPvsNLNd2ww+bN9XHCgdTkl95EJ0A86eEAFOfJn9qAXIPKYisplDFbGouaSjGkwMB2MgXO26YvxkFXFlvJFOOyQpNTxoexDIgn+gKETCjJyxTx9FeUiEZKHNLTlXs77cZLeY65mWnappIvAJW2T0+cw2bX56/phYNBSW8B7xzGDDZ5e2OMzXUOH26xmVJhEbU1MSeV8txXXzQ+Pv2RvtQ8hWR8+ZqXia95Pj79h3TNe2FlFMtMLKASmtcb3TukNS83Fy68EObPh3Xr4IMP4EtfgoqKrJrXEldU2eK821uat6RwDK233p71MfvEk9hYOd3XPJ9DDl/zUhyUdh5gGPIah4Kd1+OxImeffTavvPIKS5Ys4Y9//CPFHUaYl5WV8eGHH3LRRRf1+UH67Fu8VNOoAxFb0x5LPdbbiynHgpijaIo4DCkwmDMqxNGjghkCAXKxBUzF9iY34z6PvhgHXRA2mD0qxPBikxnDA5iG4phKaXCaF1QMHWDxsYm5jC81Oz1XAcV5igE5YnR5Iuk1Du3ttBsv5dlSqYswYEi2otc3wNunJ84RR2Eleiz0Bq/BqofW0ncQ5Atk4fpYqtloo80zS9uT5c5es9O++qLx8emP9KXmGUjk1de8THzN8/HpP/ia18eaV1AAI0fCoEHJu/aX5rXb8O4519F8589h4EC5Mxgk8sUvs+HHv2dZrJBnlrYTczR5QYUyfM3zOfjpS81T+JqXjf1q5wGOK8NCDgU77+Cdr+uz26SnwGotfQG8P+3elpJFHAgFZYx0fbuL1ppFW+yskYi2mCZgybZlhWbG2Om+GgfdsRdCYY7JaRPCrK+zmVwewNWa2han0/M00NCuGTXQID9sMKVCPPYzhgdYWkWvR4F7kQcpLZaU4aCR6hsQDKiM0d2zR4VYvj3G5noH25YGpLYradEdPw/5YpHHFSKeGunPoBNqlx5psh2NZSocVwy/WNrphy1FyJInyRcNB1yfAB+fXdGXmocBlqmI29rXvDR8zfPx6T/4mtfPNE9rji3XrG42WLOjZ5oXCkAsUQoXMBJN4BOtHzarImo+9nkmnXgORY07iOfls6lkNK1xRXtcM7lpHUPfepHwe28RmzqTnSeczrKB433N8zlo6UvN08rXvGzsbTuvO83zsinb41IKfLDbeb30d+5f6uvrufzyyykqKqKoqIjLL7+cho5j0jugteZ73/seFRUV5OTkcNJJJ7Fs2bKMbaLRKF/5ylcoKSkhLy+Pc889ly1btmRs88Mf/pBjjjmG3NxcBgwY0Mdn1r/wUmBDidHbkLqQNKnFUrCDMz9bAqrjgm27KCUNJRdujHWq1feoa3MZmGtkRBFmDA8wvNjMuOD7Gk/4DKV4aVWU+vbs5+S4sLHeQSmS6baGUkytCPb62NJTnocOsDhvSg7lRXJHKCANVtP3WRA2CAcMAolJRrlBg5I8g2xZvwNyoCCkklN8ygtNzjwsTDAxbSlswcnjQpQWGDRGZMU7OxE5CpkpUQhbcM7kMOW7GYnx8TlQ6EvNc33Ny4qveT4+/Qdf87Kf0z7XPGIwdy589rPkzjmSw799A8dvXMBAy9ml5uUFDQrCCkPJ51RZbFGUIyVkAROKchSNg4fx3+IpfFQ0munDQ1Iy1raeCVedT+E3bib46CPkf/s2Rlx8BofXrfQ1z+egpaeaF0pMke12Xxpaoy6uxte8NPa2nbcrzRuUp6goOjTsvAPKgXfZZZexaNEi5s6dy9y5c1m0aBGXX355t8+56667+PnPf84999zDO++8Q1lZGaeddhrNzc3JbW666Sb+9a9/8cgjj/D666/T0tLCJz7xCRwndTHGYjEuvvhivvjFL+618+sveCmw0biMfu6ISoQqtJa6dgMRu2zRC41M6rEdl7JCk1kjUynG2VJ1J5YGkoIDuy8iu8vU8gAJpzymARNKLYpzRRwUUBhWfRYt6ZjyvCtBryy2yEl88ZQVGpIqnOVNb4oCaAKmImBCxNHUtbjJJqZBU3oRbG92cDQ0tWsWb5UJRt6wkaCpCJmKJdtspg2z9voXjY/P/sTXPPnd1zxf83wODXzNk9/3q+YFgQcfhI9/HP72N1i+HOvPD1B6wRkc/96TDB3Qvea5rovtQm4QtFKUFiiaI7K9ARwxIkh9m5uheXEHyt5+BWP9uoz9qe3bKfvvXF/zfA5aeqp5ritaFzASU1Cz7QspwWyJaopzDV/zEuxtO293NO9gtfOU1gdG4e/y5cs57LDDWLhwIbNmzQJg4cKFzJkzhxUrVjBhwoROz9FaU1FRwU033cQ3vvENQLLthgwZwp133skXvvAFGhsbGTx4MH/961+59NJLAdi2bRvDhw/n2Wef5YwzzsjY54MPPshNN920y8y/bDQ1NVFUVERjYyOFhYW9fv6+YvE2afrYFpdmjwaSpoqXpqpSo5295p+7+iMKmDCpzGJqRZDWqGbdThvQjB4UIC+kWFoV73Wq7t6iutHm/a0xKgpNpg4N4mrNiysjFIQMpuxDwc2GN0FJa83GnQ4tUY2DGNhuwtjWidsFIZn+CxB1NO1pX1imIjXaO2GgFyXOK2hlNlsdXmz2u9TiA+VaOtQ5UD4nX/N8zfM1z6cvOFA+J1/z+oHmLV0KM2dCPEsPrPx8al57h4XmiD7VvNJ8g+N/+lVC993b+TXPOw+efLLPT3N3OVCupUOdA+Vz2h3Ng+51z1AwuMDgtAlhX/P2AN/OE3pzLR0wOYNvvvkmRUVFSecdwOzZsykqKmLBggVZHXjr16+nurqa008/PXlfKBTixBNPZMGCBXzhC1/gvffeIx6PZ2xTUVHB5MmTWbBgQScHXm+IRqNE08a4NzU17fa+9iWVxRbbmxwcV6IEISvRfNLVRGxNzEmlGkcTPQQMRAC7wnVg1XabqgaHOaND2A5UN7nUNEeZPSrUry6isiKLs4pSl4ahFGdMyt2PR5TCS4lujrjUNMs7HrQUJ48P8sLKGPVtLgqJTngCV1ZoMmWoyfwVMRra5Tnpo72LwoqwpSQyu5f6M/gcGvial8LXvL7B1zyf/oyveSl8zeslS5Zkd94BtLRQvG45OYePBPpO82I2uFOnZ3/NOXP66MR8DmYONc3rzoGnNTS0ujz/UbuveXuAb+f1nv3vEu4h1dXVlJaWdrq/tLSU6urqLp8DMGTIkIz7hwwZknysurqaYDDIQG9SU5Ztdpcf/ehHyX59RUVFDB8+fI/2t6/wUmBHlVh8bEKIEcUmZ0ySlNiKIpOAqcgLSa160BTB686oA3CQdOMdbZq5H0XY2iBu8Jgtk4F8ekf6Z3T6pDAhy+T0iSFyAoqgCQEzFXEwTU00rjhtQgizQ18BU8GZk8LMqexdyrOPTzZ8zUvha17f4mueT3/E17wUvub1krQ2PdmwtNPnmjes2CR05sdgzJjMjSoq4BOf6Muz8zlI8TUvhUaGI/ia1zf4dl7P2e9H/73vfQ+lVLc/7777LiBTTDqitc56fzodH+/Jc3qyza64/fbbaWxsTP5s3rx5j/a3L/G84UU5kmJqGQZKKerbZLqLqRQhSxpK9nT0s07842iJboB4xSeXB/bWaQCSmrt4Wyw5JtrVmsXbYjRHdiXN/Zv0BqWu1ry7KU7M1sRdmXqkgfo2zartNm+sa+f55ZGM6ATIZzF/ZZScIPu1P4PPwYGveZl4mme7vub1Bb7m+fQ3fM3LxNe8XnDYYWB08cYGgzBp0t7RvPHj4dln4de/hssvh5/9DObPh8MP3+un7HPgc6hpntcnrit8zetbfDuvZ+z3/MEvf/nLfOpTn+p2m1GjRrF48WK2b9/e6bHa2tpOGXYeZWVlgGTZlZeXJ++vqalJPqesrIxYLEZ9fX1GFl5NTQ3HHHNMr88nnVAoRCgU2qN99Ce89ONYYhT21KEW81ZEJc1YpfoFdIdXw55jiQd9+rBA8sLaGzRHXBZuiBKzZYR3egrt9ibngPLCez0CJpcHksLm9VdYX2ezpcFJRoscV9Mc0cnbO1pBo5OTldL7BDS0u7y0KsrpE3P29Sn5HGT4mtcZb7pZ0NAELeVrXi/wNc+nv+NrXmd8zeshkyfDt74FP/hBp4f0D3+ImjRp72ne+PHy4+PTSw4lzbMMKZPt6CDqiK95u49v5+0e+/3TLSkpYeLEid3+hMNh5syZQ2NjI2+//XbyuW+99RaNjY1dOtoqKyspKytj/vz5yftisRivvvpq8jlHHHEEgUAgY5uqqiqWLl26xw68g42O02WWb7fRriZgiMhZPdQqU0lWZMyGRVviyejB3mB9nZ1sWlnd5PDSqkhy1Hd3Kc79LbLhifXmOocPNsdxXM0Hm+NsrnNYuCFKfkBhJj6DgAH5QflC8SbzlBcaBBJX+4Acgwun5TAgR+4wlUwo8vHxyaSvNE8BMdfXvN7ga56Pz77H17x9qHnBIHzta/DQQzBlCoRCOEccScOfH2HVBdfgoHzN8/HZy3SneaYBA3K6z8Dz8DWv9/h23u6z3zPwesqkSZM488wzue666/jDH/4AwOc//3k+8YlPZAywmDhxIj/60Y+44IILUEpx0003cccddzBu3DjGjRvHHXfcQW5uLpdddhkARUVFXHPNNXzta19j0KBBFBcXc+uttzJlyhQ+9rGPJfe7adMm6urq2LRpE47jsGjRIgDGjh1Lfn7+vnsj9jNeaitI1GJrg40TlchsfljJ5Bi3+6afSsko6Iij2NpgE7DYa40+J5cHiNtkCJtHVynO/TGy0VmsneTt9phm0bY4lgE5YQNDuTRGSI7hLgjDSePC1DY7LK6Kc8r4EJZhcMakEC+tijK1PEBZ0QEjBT4++5S+0DxDQcjQtMXxNa+H+Jrn47N/8DVvH2regAHwmc/AJz5B9dZ6Nrv51JAH7bBpVcTXPB+ffUB3mqdRBE2dLI/tCl/zeo9v5+0+B9SZPfzww9x4443JibHnnnsu99xzT8Y2K1eupLGxMXn7tttuo729nRtuuIH6+npmzZrFvHnzKCgoSG7zi1/8AsuyuOSSS2hvb+fUU0/lwQcfxDTN5Dbf/e53+fOf/5y8PWPGDABefvllTjrppL1xuv2egrDB4DyTloSqNUc1iu6NOlNByFJEbRnj7bhQmpcpGN2l0/ZWXAwlqczpogDdl++ur7Npj2uiNlQ1OdStcoja0B7XOFoe39eThboT66ClMBLBk+aEYe0CJEait8cVi7bEmTE8wOlpYmYZxkGbWuzjszfYHc0DyAtKo2Nf83qOr3k+PvsfX/P2EUVFlBYWsnVzHHzN8/HZb3TUvMaI7lEGnq95vce383af/V5C2xuKi4t56KGHaGpqoqmpiYceeogBAwZkbKO15sorr0zeVkrxve99j6qqKiKRCK+++iqTJ0/OeE44HObXv/41O3fupK2tjWeeeabTVJ0HH3wQrXWnn0PVeecxc0SQ0YMsQqb8MTm7yMJ1NLTGNAFLETQVuUFFTWvqSbtKp+1tmq+rNYu2xDNEAeg2xXnUQJO4Iw7GtpiIXVtMbscdzaiBZqfn7A3SU50NpZg61CLqaJy0JjRBC06dEKK80KQ9rom7qd4ABpJyHDQ063baLNkWA/Z/yrSPz4FMbzUPoCkKAdPXvF3ha56PT//D17y9h695Pj79D1/z9i6e7oE4HAOmpi2e0j1f83bNAeXA8+l/eFGAnKAiL6g6TSrLFrUwFFhKJcZCqwzR2N26/q5YWhVPPh9EFDyqmxyWVsU7PWdDvUPIVBhA3NU0R1zirsYAQqZiQ/0u8qj7gI5iH3dc5q2I0h7XtMZSIhezYfFWm6lDLQpCKnlBBwzFoHyDoUUmbXGwHU1bjD3+0vDxOdTZHc1TSGTW17yu8TXPx6d/4mve3sHXPB+f/omveXuPdN17f3OM9zfHaGgXR6Kne77m7RrfgefTJdWNNvNWtGO7ciHYrsu8Fe1UN6aExtWaN9ZFqW1xUQoKw0ZGk2PLgFAHp37cBdvVNEddorbLWxtTHvPJ5QHKClNP6Eldf3dUFltJYSsrNDllfDi5/6Alj3fksDILZaiktz/9f2UoDivr+8rzjo1F1+2M09AuQlbd5PDvpREa2t3kcdhpwZUt9XGeXtKO7UJeSBE0pc9gJK5pimpMQ+6va3X3+EvDx+dgZnc1L9Dhm9RUmQaeq0ULfc1L4Wuej8/+x9e8xPGm/e9rno/PwUtfaJ5KDFVIRyee1xTxNc8j29CMNzdEaY/J7fU7HdbU2sTT/G1er0Ff87rHd+D5ZKW60eaVNSJezy+PErPl/9oWl1fWRJNCt3BdlI11DrYLTRGNoXSG0RF36dT403Glp0DcgeYItMXc5MXmRT2CHXSku7r+7ug4Xcg0FDOGBxhebHbZsPOjajs54QYy/9daHu9L0qMRb62P8eGWGDFbozW0RDWtMRfQyeMYkGNwzmQRa8fVtMQgEpdUaEMpDKUkkhGViEZh2MBMvG97+qXh43Owsieal258aKRVQHoBg0Z6ePiaJ/ia5+Oz//E1z9c8H59DiT7TPE3Gbe++qE3iOb7mZdO89zfFiNkksu1crP/P3n3HV1XefwD/POfcmU1YSUiEgAiyZKgoLkTFvWetVMVZbR20tVV/rVtLtYrWRStiq1i1rroriqKCuBVQAdGwE1YgCUnuOuf5/fG9544sEsi4ST7v14tXSHLuvefe5H7ynGd8HyP+V8NtKOzZ24XiXGZec7ADjxrsIV+4KhRb87+91saL39TGRgYtDSyOTs+tCMbffLYGymtatm22DSBiK/TvYWLxhhAito2v14URDCevh9+dbbmd3YWcgDSUwqgCT6NFQ/v3MBGMyHp7twFkehXchpxrMKLRv5XrBDhTqy1bY1V5BN+VhVGy1YLfLa9p0JIQ87mBNLfC5KFeuE0DY4rc8HkU0jyyLDlsawQjNrwmYudr2cABxZ5W+6NB1BUw85Ix84i6NmZeMmYeUddXN/cWl4al403vXuY1lFB1BzCYefUz76etEUBr2FqWGzuZ5zEVeqQpjC3yYOweHmZeM7ADr5trrLCm25A3jpNIVkK25PgNTNrLCwA4aqgXOX4FpaLbbbcwgwwFjB/gxqerQ1hbbuGdZUGUVkRQE44W2kwIusbW9be21dssKZBpKqS5Fbxu+egxJexWt3KdAGdqddCSYLIhIzsVAQ0NeZM6DWCPqbB4fSRW8PjIIT7s0cONdI98z2sqKCXn2zPNwKS9PPhxc/IuRcDu/dEg6syYefUx84i6LmZefcw8oq6todzr4Zd6arYGoJl5HZF5lUGZzagQz7w0t4rlFQBmXjOwA6+ba6ywpqEUMrz1e7BNBRw1xAuXIb86LsPA0Xv7pBZANOjqHt8YQ8kIwNINEQSj5xCyNHaE5M3tNhQyvAY80aJ6ja3rb23FuS74PRIU+dkuTNrLh/xsF9LcCn6PavVzcKZWZ/viNRVsLX8wDAVAAT4X4I8+bGLYJxZaTXNLwAGA161w1FAf1myzW1zolKgrY+bVx8wj6rqYefUx84i6toZyb1OVRpZP3j+JK2B3JfMaiz1mnmgo84B45ymidQSzffHvOXnFzNs5duB1c40V1rS1Rm2kfg+2pYG5y4NJxT/nLg8mjWKohI9WI53gRjT8ghEgbGm4or+JppLtt92GFKvMyzJw5FAvCnsY6OFvn1/XXaktsDuc7cDDlgSVgfgb09aAz6Vw4gg/8rMloRLDfmdbiQ/oYba40ClRV8bMq4+ZR9R1MfPqY+YRdW0N5Z5sMiGBlfgOb2nmNTXBS2rhaWZeA5kHRF+/6Mfini4cMcRfL6+YeTvHDrxurrHCmtUhjXAEscRKHGHdXmtj3oogAGDeimCsfoBD1/lYVywEo2vbwzZwwIDkteyWLdtDaw0oKEQshY1VdrttD93S2gK7w9kOXGuZVg0gPuKjpHG3ZEME+xS66gXtzrYSX7XNatfAJkp1zLyGMfOIuiZmXsOYeURdV0O5VxWU+mvO+25XM89G/exTiM/UC1pg5jWUeQCgkv8+KIV6ecXM27mu/wypSY31cntd0XDSUhfgtH38yImOEpgKGBXd4WVUvjsWgDl+A8cM88amyirIL1jdacYeE/C7FdLd8vHA/m78uCW+lj0Yia+XL9kawbvLa7v09tDOduC1EQBQyPQZcBsKhgJcSn4WZZUWviuL1AvauluJ77eHB6YhPzuPS4qWlpRHUJzrapfAJkp1zLyOx8wjaj/MvI7HzCNqXw3lnt/tfA/I8aldzrw0d3LmGQBy/JJ7ppLHYeY1knmQjs4Mr8LGSju2ZDYxr5h5O9c9niU1qrFebo9pIN0D+D0KR+/thcdl4Oi9veidYWDinl7kRaf552W7MHFP+frRe3vRM92FY4d54TGBDA+Q5VPwuiTsvC5IAVFDoaiHieLebhw1xIfNNTrpHHL8Cu7oMIYU+Y2fb1fcHtqZ1jygp4mcNAUFYGAvE3vnuzCgpwumUk1OCc5JM1DYw8Tg3iY+XRNCZcCGArBffw9Wbrawttxqt9EdolTHzOt4zDyi9sPM63jMPKL21VDueUwDGV7ANIB+OeYuZ57fbSDLC7hN6bTLSTOgoJCTZmBwXxeOHupn5jHz2lTXXyRMTSrOdWFjpYwQ5GWZGF3oxtfrJPQyvPLmSyzqOXmov9595GW7YqEHADlpLhw/3MCiVUGEIsCgXq6k+83xA3v3dSf1tNc9hy/XhrBys4xGeKMlDLry9tCZPgP77eFFWUUEi0vDGF3ohsswpA7D90GkeRTSo8Wmba2xtDSMPukGlpSGURvS8LgUtK2xvUYjZAGhWhsLfgzCGSNyRndGFXg68FkSdTxmXmpg5hG1D2ZeamDmEbWfxnMPyPACg3pJh9muZl5hD3dS5nncKmn5JjOPmdeWlNbdYK/dFFFZWYns7GxUVFQgKyuro08npipgo6Q8ghH5EiDOm6g417VbU1Fbcr+JxwLAl2tCKCm34DUBM2H7n7wsWe/eFYPO2fI8Mew/XR3Cqq0S9gNyXdh/gCf2xyJoaShoBMIyJdtnAhEbCFkyzdhQQJZXwTRUl3vdUvW9RMlS9efEzEsNzLzmS9X3EiVL1Z8TMy81MPOaL1XfS5QslX9ObZF7zLyWYeY1X0veS+zAa0epHHKpZPGGENaWJ097TqxhUJRrdsne9oae9/ZajVB0GySPqZDjV7HXQmuNoAUEEnaRU5AiqQBgGPHbTNrLl/THorPje6lz4M+peZh5gpnXOL6XOgf+nJqHmSeYeY3je6lz4M+peZh5gpnXuJa8l1gDj1JO3eKV3WV76Ia2PPe7pWaC2wD8dcI+P9uFE0Z44XNeDp0ccIm3+XpdGDb76olSEjNPMPOIugdmnmDmEXUPzDzBzGsd7MCjlOMUvuxu20M3tOW5ApCTJiMNKmGKsMcFjOrnwtINFrym7OrjbGuuAWiNpNuUVVpYWhpux2dDRM3FzIt/jZlH1PUx8+JfY+YRdX3MvPjXmHm7r2v+tlCnl+kzMKrA06HbQ1cFbCzeEIr17ttaY/GGUJvteNPQlucawPYaje21GlprWFqjJqwRjADvLg9iQ0UYFQGNiC1bl6vYfQFBC+ibKa9XVx7dIeoKmHmCmUfUPTDzRN3MAwBLy+fvLA9iQ0UEO0IaYVsu2ph5RJ0TM08w83Zf93zWRDuRWHQzHEHSTkMbK602GS1paMvz7bUaYVvCrSasYWvA0jIK4XUBVUEJNEC2RXebQG3YeQ4aeZlSV2F3C1UTUdfGzCOi7iQVM682AmR6gcoaDRtARa2G1kA4em2tITkYiDjPgZlHRM3DzOs6uuezJtqJkvJIbLSgrNLCvBWBWAA521a3tobqIxTmmDAgb9R0j0KGR2oGhG2N2rCGmVC707KBbJ+Cy0iYmaLQ7qM7RNT5MPOIqDtJxcwrzDaRl23AjMaX1lL3yaG1DFww84iopZh5XQdn4BE1YES+G+EIkoLNkZdlxrYEb01OfYTE7cnHD/DAbQBaAWMK3fhmXQSlFRHURgCvKdtp7wjJLJVMr0LYUsjyyohGYbaJcUVdb0cjImp9zDwi6k5SMfOc/IpEgJJyK5Z5WstyMo8BeF0GvKZm5hFRizDzug524BE1wCm6OW+FlRRwHpdMOTYSim62Jqc+QuJ57NvfG/t8dKEb5TUWEh8+Nw2AUghHz1MphRw/sP8AT5udJxF1Lcw8IupOUjXzAGBMkQfbagOx88rwyMBFuteIzkBh5hFRyzDzuo7uO/eQqAkNFd0EOnbb6gYLgUaLfm6v0Ug8o+6+vTYRtQwzj4i6k1TMvIbOS0c38rE0UBOK515HnycRdS7MvK6DHXhEDWio6CYgu+T8tDWCJRukanpb796zs3OqjUihT6c+VOI23d15e20iahlmHhF1J6mYeQ2dV8iOF3QP2xphK37xyswjouZi5nUd7MAjakBDRTdz0w1UBzUilkZNUMOyNb5aG8bacguLVgXbPOgaLASanVAINEe+lpdlAuje22sTUcsw84ioO0nFzGv4vLzwuWTJmM8FHD7Yy8wjohZj5nUdnaoDb9u2bZgyZQqys7ORnZ2NKVOmYPv27U3eRmuNm2++GQUFBfD7/Zg4cSK+/fbbpGOCwSB+/etfo1evXkhPT8dJJ52EdevWxb6/atUqXHTRRSguLobf78egQYNw0003IRQKtcXTpGaqCthYvCEUm0rbmiMGTtHNolwTY4rcMA2FNA/gMhXSPQrlNXa77N6zs3MaX+zBnn1cGNzbhfEDPDANhTFFbhTlmm2yHTgRdRxmHjOPqDvpbpnX0Hnl+E0cOcSL3hkGjhriQ06aycwj6qKYecy85uhUr8C5556Lr7/+Gm+99RbeeustfP3115gyZUqTt/nLX/6Ce++9Fw8++CA+++wz5OXl4aijjkJVVVXsmGuuuQYvvfQSnnnmGXz00UfYsWMHTjjhBFiW/AIvW7YMtm1j5syZ+Pbbb3Hffffh0UcfxQ033NCmz5caVxWwsWhVEGvLLXy1NtwmIwZO0U2nWObIAg8G9nTBNOTz9ti9Z2fnZCiF/fbwYt/+3qSvdffttYm6GmYeM4+oO+mumdfQeWX7TUwe6keWX2ahMPOIuh5mHjOvuZTWnaMS4Pfff49hw4Zh0aJFGD9+PABg0aJFOPDAA7Fs2TIMGTKk3m201igoKMA111yD3//+9wBktl3fvn0xffp0XHbZZaioqEDv3r3x5JNP4uyzzwYAbNiwAUVFRXjjjTdw9NFHN3g+d999Nx555BH89NNPzX4OlZWVyM7ORkVFBbKyslr6ElCCxRtCWFuevI4/MXSKcs2kHW9ai2VrzFsRqLd7z6S9fLHwo7bH91LnwJ9T62HmdW98L3UO/Dm1HmZe98b3UufAn1PrYeZ1by15L3WabsyPP/4Y2dnZsc47ADjggAOQnZ2NhQsXNnibkpISlJWVYfLkybGveb1eHHbYYbHbfPHFFwiHw0nHFBQUYMSIEY3eLwBUVFQgNze3yXMOBoOorKxM+ketY0S+O7YeHmifEYNU3b2HKFUw89oOM48o9TDz2g4zjyj1MPPaDjOPmqvTdOCVlZWhT58+9b7ep08flJWVNXobAOjbt2/S1/v27Rv7XllZGTweD3r06NHoMXX9+OOP+Nvf/obLL7+8yXO+6667YvX6srOzUVRU1OTx1HyGUhhd6E7agRCQEYPRhe7YFNzW1NjuPQB3xSECmHltiZlHlHqYeW2HmUeUeph5bYeZR83V4R14N998M5RSTf77/PPPAQCqgV9crXWDX09U9/vNuU1jx2zYsAHHHHMMzjzzTFx88cVN3sf111+PioqK2L+1a9c2eTw1X0eMGDS0e0+q7IrTlkVPiZqLmdd2mHn1MfeoozHz2g4zrz5mHnU0Zl7bYebVx8xrWIfvw/urX/0K55xzTpPHDBgwAIsXL8bGjRvrfW/z5s31Ztg58vLyAMgsu/z8/NjXN23aFLtNXl4eQqEQtm3bljQLb9OmTZgwYULS/W3YsAGHH344DjzwQPz973/f6XPzer3wer07PY5arqERAyfwZMQArV4nwNklp6Q8ghH5MhIypsiNpaUScB1VWNMpehqKAOGIjNJ8vU5en42VFnfsoXbDzGs7zLxkzD1KBcy8tsPMS8bMo1TAzGs7zLxkzLzGdfiz7tWrF4YOHdrkP5/PhwMPPBAVFRX49NNPY7f95JNPUFFRUa+jzVFcXIy8vDzMnTs39rVQKIT58+fHbjNu3Di43e6kY0pLS7F06dKk+12/fj0mTpyIsWPHYvbs2TCMDn/purW2GjHYWU9/QzsidvSuOCXlkaSA74gtwImobTHzkjH3iLo2Zl4yZh5R18bMS8bMa1yHz8Brrr333hvHHHMMLrnkEsycORMAcOmll+KEE05I2oF26NChuOuuu3DqqadCKYVrrrkGd955JwYPHozBgwfjzjvvRFpaGs4991wAQHZ2Ni666CL85je/Qc+ePZGbm4vf/va3GDlyJI488kgAMvNu4sSJ2GOPPXDPPfdg8+bNscdzZvlR+2qLEYPO2tM/It+NcARJoeZozy3AiajtMPOSMfeIujZmXjJmHlHXxsxLxsxrXKfpwAOAOXPm4KqrrortGHvSSSfhwQcfTDpm+fLlqKioiH1+3XXXoba2FldccQW2bduG8ePH4+2330ZmZmbsmPvuuw8ulwtnnXUWamtrccQRR+CJJ56AaUqv99tvv42VK1di5cqVKCwsTHo8zd1ZOowzYuBwRgx2Vf2efiv2udPT3xbbd+8up+hp4vkCbVv0lIjaHzMvjrlH1PUx8+KYeURdHzMvjpnXOKXZA9VuKisrkZ2djYqKCmRlZXX06VAdttb4am1y/QFHXpaJMUWpGRYNnbdlawQtoDjXxNg9JJiXloY7vJ5Ba+F7qXPgzym1ddbMA+qfu5N5fheQn+3CPoUufFcWYeZRu+LPKbUx8zoPvpc6B/6cUhszr/NoyXup8z9bolbSEdt3t4a6RU8NQ6M6pBGyNH7aGsHiDSF8tTaMteUWFq0Koqwiwh19iKjTZh6QnHuWrVETlsyrCWuUVkTwzrIgM4+IkjDzmHlE3Qkzr2tmHjvwiKI6Yvvu1lC36GlBlgumIYFs2UBZhR0LwNqwxsJVIawtt/DV2jAsWyd17nXVoCOi+jpr5gHJuedxKWR4DbgNhbAN7IgOYADMPCKKY+Yx84i6E2Ze18w8duARRTW0fbdj/fYI3lkeSMmefafoaVGuTIUe2c+Ngb1MeEyFdK+ClXCKHlPBE33Xc0cfou6ts2YekJx7Rw71Ij/LRJpHwWMqpHkUzOioMjOPiBzMPGYeUXfCzOuamccOPKKoxrbvdqbtBsI6ZXv2E7cAN5TCmEIPcvzxcAMktI8Y4kV+djy9uaMPUffVmTMPiOeeyzAwutANrwtIcyc06ph5RJSAmcfMI+pOmHldM/PYgUcUVXcmm2nI9t0+T7ynvzP07Dc1XXrx+ghG9XN1yloIRNS6mHnMPKLuhJnHzCPqTph5XTPz2IFHlCBxJhsgxT+PHOJDv07Us9/UdOnSSgvvLg92yloIRNT6mHnMPKLuhJnHzCPqTph5XS/z2IFHtBOpsINPVcBu9u46jU2XBoCQpRGKxIMs8TmVVVpYWhpuuydBRJ0CM4+IuhNmHhF1J8y8zo0deEQ70dE7+FQFbCxaFWz27jqNTZcuyjUxYYAHfo+Ect0A9LgkIImoe2PmEVF3wswjou6Emde5Ka274LzCFFVZWYns7GxUVFQgKyuro0+HmmnxBtma2uFxJU81Lso1MarA02kevypgo6Q8ghH5MsJia42lpWEU57qQ6escffp8L3UO/Dl1Tsy81MP3UufAn1PnxMxLPXwvdQ78OXVOzLzU05L3Uud4RkQdqKlpu+3Rsz8i3x17PKD5dQoam5oMoF4thFEFnk4TcETUtph5RNSdMPOIqDth5nVuXfNZEbWipqbtHjDA2+bh4NQpMA2gJqzhTJn1uADT1Fi1JYK3l9UiYst044ht443vajB/ZaDZU5OJiBzMPCLqTph5RNSdpHrmVQc1yiqSc297bQSvf1uLnzZHun3mcQltO+I0Y2oJZzrwsDwXPlsdRsnWCCwNeEwg3WOgJqRh2RphGzAVkJNm4KghXsxdHkR5jQ1oINOr4DZVu0+Nbmt8L3UO/DlRSzDzGsf3UufAnxO1BDOvcXwvdQ78OVFLNCfzAA2/G9gRAmwN5Pgl9/67JIDasIaC5F66V3XbzOt6Vf2IugCnuGcoAmzYbqEqqGFJWw2hCGDZNjQAKzrYYAPYXmvjxW9qYWlAQY6tCWtkm6rZU5OJiDoCM4+IuhNmHhF1J83NPAAIBeTrhornXsROzj23Gd8pt7tlHjvwiFJQSXkk1hgLRWQEFpDgStzZ22UAhhFv4FnR5FMKyPEreFwK4YRGXXtuD05E1FzMPCLqTph5RNSdNDfzDAB75BrYHtDYXivHWDp+jEsBmb74Dbpj5rEGHlEKSizuaRoKaW4Fn0uWUHhMhUyvgsdU6JGmcPIIH8w6mWUA6JNhJjXqgPbbHpyIqCWYeUTUnTDziKg7aW7m5aQpHFDsw1FDknPPibQMn4JC/BvdMfPYgUeUgpzins4OQYaSKcNKAWkeBUNJ8EUiGq9+G4iNyDoiGvhhSwROWVBPwlzbskoLS0vD7fNEiIiagZlHRN0JM4+IupPmZp5tK3y5Joi5y5NzT0Nq4lUGNDR0t848duARpSBba3y9LhybalwbAcK21ECpCcVDqyoEBMISaABiIxUKstyiMqA7ZHtwIqKWYOYRUXfCzCOi7qS5mQcAP2yxUF4T770zFWJz7iwbCEZ0t848duARpaClpWGUVVqxzzO8iW9Wjd6ZBvKyTPhdEmgGZJee0/bxI8dvQCnAbQADOmh7cCKilmDmEVF3wswjou6kuZkHAFk+qf8JxHOvR5oBQ0ln3oQBnm6ded2nq5KoEynOdWFjpYVQRHbWGV3oxqerQlhXYSHbBwzq6Ua6V8HtAjLcCiu3RjBpLy9choGj9/Zi3oogRuW7kZcdf4sbSnXq7bWJqOti5hFRd8LMI6LupLmZt7QUKM71ojpoY3FpuMnc666Zp7TuRhX/OlhlZSWys7NRUVGBrKysjj4dSnFVARsl5RGMyJeddWytsbQ0jOJcV7caZWgI30udA39O1BLMvMbxvdQ58OdELcHMaxzfS50Df07UEsy8xrXkvcQZeEQpKtNnJI0qdNdRBiLqHph5RNSdMPOIqDth5rWO7t3VSURERERERERElOLYgUdERERERERERJTCuIS2HTnlBisrKzv4TIg6N+c9xBKeqY2ZR9Q6mHmdAzOPqHUw8zoHZh5R62hJ5rEDrx1VVVUBAIqKijr4TIi6hqqqKmRnZ3f0aVAjmHlErYuZl9qYeUSti5mX2ph5RK2rOZnHXWjbkW3b2LBhAzIzM6GUatFtKysrUVRUhLVr13aaXX54zu2jO56z1hpVVVUoKCiAYbASQKpi5qU+nnP7YOZ1D8y81Mdzbh/MvO6BmZf6eM7toz0zjzPw2pFhGCgsLNyt+8jKyuo0v8gOnnP76G7nzBHZ1MfM6zx4zu2Dmde1MfM6D55z+2DmdW3MvM6D59w+2iPzOKRBRERERERERESUwtiBR0RERERERERElMLYgddJeL1e3HTTTfB6vR19Ks3Gc24fPGfqijrj7wjPuX3wnKkr6oy/Izzn9sFzpq6oM/6O8JzbB8+5adzEgoiIiIiIiIiIKIVxBh4REREREREREVEKYwceERERERERERFRCmMHHhERERERERERUQpjBx4REREREREREVEKYwceERERERERERFRCmMHHhERERERERERUQpjBx4REREREREREVEKYwceERERERERERFRCmMHHhERERERERERUQpjBx4REREREREREVEKYwceERERERERERFRCmMHHhERERERERERUQpjBx4REREREREREVEKYwceERERERERERFRCmMHHhERERERERERUQpjBx4REREREREREVEKYwceERERERERERFRCmMHHhERERERERERUQpjBx4REREREREREVEKYwceERERERERERFRCmMHHhERERERERERUQpjBx4REREREREREVEKYwceERERERERERFRCmMHHhERERERERERUQpjBx4REREREREREVEKYwceERERERERERFRCmMHHhERERERERERUQpjBx4REREREREREVEKYwceERERERERERFRCmMHHhERERERERERUQpjBx4REREREREREVEKYwceERERERERERFRCmMHHhERERERERERUQpjBx4REREREREREVEKYwceERERERERERFRCmMHHhERERERERERUQpjBx4REREREREREVEKYwceERERERERERFRCmMHHhERERERERERUQpjBx4REREREREREVEKYwceERERERERERFRCmMHHhERERERERERUQpjBx4REREREREREVEKYwceERERERERERFRCmMHHhERERERERERUQpjBx4REREREREREVEKYwceERERERERERFRCmMHHhERERERERERUQpjBx4REREREREREVEKYwceERERERERERFRCmMHHhERERERERERUQpjBx4REREREREREVEKYwceERERERERERFRCmMHHhERERERERERUQpjBx4REREREREREVEKYwceERERERERERFRCmMHHhERERERERERUQpjBx4REREREREREVEKYwceERERERERERFRCmMHHhERERERERERUQpjBx4REREREREREVEKYwceERERERERERFRCmMHHhERERERERERUQpjBx4REREREREREVEKYwceERERERERERFRCmMHHhERERERERERUQpjBx4REREREREREVEKYwceERERERERERFRCmMHHhERERERERERUQpjBx4REREREREREVEKYwceERERERERERFRCmMHHhERERERERERUQpjBx4REREREREREVEKYwceERERERERERFRCmMHHhERERERERERUQpjBx4REREREREREVEKYwceERERERERERFRCmMHHhERERERERERUQpjBx4REREREREREVEKYwceERERERERERFRCmMHHhERERERERERUQpjBx4REREREREREVEKYwceERERERERERFRCmMHHhERERERERERUQpjBx4REREREREREVEKYwceERERERERERFRCmMHHhERERERERERUQpjBx4REREREREREVEKc3X0CXQntm1jw4YNyMzMhFKqo0+HqNPSWqOqqgoFBQUwDI5DpCpmHlHrYOZ1Dsw8otbBzCMiahg78NrRhg0bUFRU1NGnQdRlrF27FoWFhR19GtQIZh5R62LmpTZmHlHrYuYRESVjB147yszMBCB/jLKysjr4bIg6r8rKShQVFcXeU5SamHlErYOZ1zkw84haBzOPiKhh7MBrR85yiqysLDbsiFoBlyilNmYeUeti5qU2Zh5R62LmERElY1EBIiIiIiIiIiKiFMYOPCIiIiIiIiIiohTGDjzqlqoCNhZvCMHWGgBga43FG0KoCtgdfGZERK2PmUdE3Qkzj4iIuiJ24FG3UxWwsWhVEGvLLXy1NgzL1vhqbRhryy0sWhVMycYdG6JEtKuYeUTUnTDziIioq2IHHnU7JeURhCLy/7JKC/NWBFBWaQEAQhH5firpjA1RIkodzDwi6k6YeURE1FVxF9oUZVkWwuFwR59Gl7RnD41wIIzNO6RBZIUAM/q93hkG9uwBBAKBjjvBOn7aFIIVsmEC2LwdeH8HEI7IOVsh4KdNYQzp42nTc3C5XDBNk7uBUZth5rUdZl7LMfOorWmtYVkWIpHU6kzqCph5LcfMIyLqHNiBl2K01igrK8P27ds7+lS6NL8G+kADOuGLCvAFFFav6qizapgbQL7SsJwB2IQ+DtMA3NUKJSVtfx6maaJPnz7Izs5mA49aDTOvfTDzWo6ZR21Ba43t27dj8+bNsCyro0+ny2LmtRwzj4go9bEDL8U4F7J9+vRBWloa/4C2Aa01asMakQZWJLgMwO9WKfe6a61RHdRIPGUDQLq37c9Va41IJILKykqUlpaitrYW+fn5bfqY1H0w89oeM6/lj83Mo7biZF5WVhaysrLgcrlS7v3X2THzWv7YzDwios6BHXgpxLKs2IVsz549O/p0uqyakA0DgLMYwQCSGkzaBPye1CkPqbVGTUjD5a3/PdsA0jzt0xDNzMyE1+vFli1b0KdPH5imufMbETWBmdc+mHm7hplHrc2yLFRUVKB3797o1atXR59Ol8XM2zXMPCKi1Jc6f70oVv8pLS2tg8+ka/O6VOwX320AmT4Fd/QLRvT7qaQ2rBFOaHkmvmnDtny/vaSnp0NrzVpl1CqYee2DmbfrmHnUmsLhMLTWSE9P7+hT6dKYebuOmUdElNrYgZeCUm1af1djGgrpXgWPGR/VTPPI5+leBdNIrdc/lRqi/N2ktsDfq7bFzNt1/N2ktsDfq7bFzNt1/N0kIkptXEJL3ZJpSGPO4TTuUpE0RIFgRMfqtqR5ZETW60q9higRpR5mHhF1J8w8IiLqijgDjwAAlq1RE7KhtUzTl3ocNix759P2W3LbxYsX46KLLsKgQYPg9/vh9/sxePBgXHbZZfj8889b90nt5rm2lFIKN998c6PfnzhxIpRSO/3X0H1IQ9SIjYxK485osFFXU1ODm2++Ge+//3697918881QSmHLli27+jSJugRmHjOPqDtpr8wDOjb32jLzgLbLvZZkHsDcIyLqrjgDj2DZ8Z2vtNZI8wA1IanHEbE00r1otAHRktvOnDkTv/rVrzBkyBBcffXVGD58OJRS+P777/Hvf/8b++23H1auXIlBgwal3PNsDQ8//DAqKytjn7/++uu4/fbbMXv2bAwdOjT29cLCwt16nKod1bjlllugtcbEiRNju7GlWs0Xoo7CzGPmEXUn7ZV5QMfmXkdnHtA+uWfZGlu278Att9wCADjssMM4W4+IqJtgBx4hGIlvWx+2gapA/HM7+v3Glh0097YLFizAFVdcgeOPPx7PP/88PB5P7D4mTZqEK6+8Ev/5z3/g9/ubPNeamppdLni/O8+zNQwbNizp82XLlgEARowYgX333bfR27XkOVu2Rk1QRpnDVnxnM6fxauv2K4RMlKqYecw8ou6kPTIPQIfnXkdnHtD2ued0UoYt+bxu5qU3sJMtERF1HVxCS/C748VyAcQaO4AU0/W7G2/sNPe2d955J0zTxMyZM5MadInOPPNMFBQUxD6/4IILkJGRgSVLlmDy5MnIzMzEEUccAQAoLy/HFVdcgX79+sHj8WDgwIG48cYbEQwGY7dftWoVlFJ44okn6p1rjzQTd95+S+zYv9xxC9K9Jr799lv87Gc/Q3Z2Nvr27YupU6eioqIi6TwrKytxySWXoGfPnsjIyMAxxxyDFStWNPoatYSz7OHLL7/EGWecgR49esRGqSdOnIiJEyfWu80FF1yAAQMGAABWrCzBwD36AgDuuuNWGIaBDJ+JKy69EDYAK/oD2rhx406fJ1FXxcxj5jHzqDtpj8wDOj736p5rdpqJP0dzz20A0++4BUqpTp17wYjGqtWrsGc092699VZk+Ez0SDNx+aUXIhiJD1ow94iIuh7OwOviqgI2SsojGJHvhqEUbK2xtDSM4lwXMn3SynGK5SaOVALSu+vs3tWY5tzWsiy899572HfffZGfn9+i8w+FQjjppJNw2WWX4Q9/+AMikQgCgQAOP/xw/Pjjj7jlllswatQofPjhh7jrrrvw9ddf4/XXX9/puSZ9HYDzFE8//XScddZZmPPMf7D8+6W48YYbAACPP/44ABnpPOWUU7Bw4UL86U9/wn777YcFCxbg2GOPbdHz2pnTTjsN55xzDi6//HJUV1c3+3bFexTg5VffwCknHocp50/FlAsvAgD06tUbbgNwGfHnefbZZ+Oiiy7CkiVLcP311yc9T6LOipnX8LkmfR3MPGYedSU7y722zjwAKZF7jZ1rYuYBkgennXEWnvvP8/ju26X18iCVc8/vVigsyMfz/30DZ5ycnHt5vXsndagy94iIuh524HVhVQEbi1YFEYoA4QgwutCNr9eFUVZpYWOlhQMGeJHpM2LT7+06t7chtUPSPI1vK9+c227ZsgW1tbXo379/vdtblhUrNAwApmkmPVY4HMaf/vQnXHjhhbGvzZw5E4sXL8Zzzz2HM888EwBw1FFHISMjA7///e8xd+5cHHXUUc07V60R0bLsAACmTp2KK6/+LcI2cOjEI/HjypWYPXs2Zs2aBaUU/ve//+G9997D/fffj6uuuir22B6PBzfeeGODr9GuOP/882O1TVrC5/NhwnhZolHQrxD77X8AgPoN7Ysuugi/+93vAABHHnkkVq5ciccffzz2PIk6I2ZeM86VmcfMoy6lObmX4VVtmnnOhgkdnXsNn6tGxNawbMSWnU45fyquvOa3MNBwHqRy7iml0CPTh7FjxgGI554BINOX3BnL3CMi6nq4hLYLKymPIBSR/5dVWpi3IoCySmm9hCLyfUC2qQ8ntHYSfynCtny/MbtzWwAYN24c3G537N9f//rXesecfvrpSZ/PmzcP6enpOOOMM5K+fsEFFwAA3n333Wadq0J8KYhzlkccfWLsGBvA3sNHIhAIYNOmTQCA9957DwDw85//POm+zz333CafZ0vVfc7N5TRe63Ia2k4D+qSTTkr6/qhRo5KeJ1FnxMxr+lyZeXHMPOoqmpN7HZl5QPvlXt1zBeJZpwFY0TyYfNyJAOJ18ermQSrn3s46VBM7Spl7RERdT6fswHv44YdRXFwMn8+HcePG4cMPP2zy+Pnz52PcuHHw+XwYOHAgHn300XrHvPDCCxg2bBi8Xi+GDRuGl156Ken7d911F/bbbz9kZmaiT58+OOWUU7B8+fJWfV6tbUS+G3lZZuxzp4EHAHlZJkbkuwEAXpeK/SK4DRnBc2qIGNHvN6Y5t+3Vqxf8fj9Wr15d7/ZPP/00PvvsM7zyyisN3n9aWhqysrKSvrZ161bk5eXVGz3s06cPXC4Xtm7dutNzBQCPC/CaCon3kpPbM/Z/twFkpPkAALW1tbHHdrlc6NmzJxLl5eU1+Ji7qjnLTyxboyYUb8JprVEZ0LGZNUD9hnYkenjd8/d6peqx8zyJOiNmXuPnCjDzEjHzqKtoTu61deYBSIncq3uuAGCqeOY5H3OjuefU8KubB6mSe07mJXbKVQY0wgmZl/jq1O1QZe4REXU9na4D79lnn8U111yDG2+8EV999RUOOeQQHHvssVizZk2Dx5eUlOC4447DIYccgq+++go33HADrrrqKrzwwguxYz7++GOcffbZmDJlCr755htMmTIFZ511Fj755JPYMfPnz8eVV16JRYsWYe7cuYhEIpg8eXKLavW0N0MpjC50w1NnobTHJUssjGijyDQU0r0KHjO+5CjNI5+ne5vekr45tzVNE5MmTcLnn3+O0tLSpNsPGzYM++67L0aOHNng/Tc0xb9nz57YuHFjUoMGADZt2oRIJIJevXoBkKVVAGLFjp1zrdq+NXbfaR4Fn1uh7lNsrC5Mz549EYlE6jUcy8rKGn2NdkVDz9vn88Wei7MLWcgCNm7aDCA+8mpHXxZD1W9om53uHU/UfMw8Zh4zj7qb5uReW2cegJTIvcRzra0qBwC4TcDnVtF6mPHHaar+XyrkXmLm1YQ0tmzZAq0Rz7zo6+JxodmdsURE1Pl1uqbtvffei4suuggXX3wx9t57b8yYMQNFRUV45JFHGjz+0UcfxR577IEZM2Zg7733xsUXX4ypU6finnvuiR0zY8YMHHXUUbj++usxdOhQXH/99TjiiCMwY8aM2DFvvfUWLrjgAgwfPhz77LMPZs+ejTVr1uCLL75o66e8y2yt8fW6cNJoLCCjs1+vC8NOrEdiKKR5jFhjQhpoRpONupbc9vrrr4dlWbj88ssRDod363kdccQR2LFjB15++eWkr//rX/+KfR8A+vbtC5/Ph8WLFyed69w3X026nbwMyc+zoaUIAHD44YcDAObMmZP09aeffnoXn03zDRgwACtWrEAwGEQwIssnyrduxaJFH0NrGXmFUvD5ZIQ1Eqqt19A2GmgsEnUVzDxmHjOPupvm5l5bZx6QGrnnnGvibD+tAd3MzANSI/cqqwOxZbIbN2/FgoULZTmwkgEYb7TjMhAINLszloiIOr9OtYlFKBTCF198gT/84Q9JX588eTIWLlzY4G0+/vhjTJ48OelrRx99NGbNmoVwOAy3242PP/4Y1157bb1jEjvw6nK2Yc/NzW30mGAwmLTVfWVlZaPHtoWlpeFYHRRARukS66QsLQVGFXja5VwOOuggPPTQQ/j1r3+NsWPH4tJLL8Xw4cNhGAZKS0tjMyLrLqGoy7I1zjjnPDz00EM4//zzUVJSgr32HoFPFy3An++6C8cddxyOPPJIANLAPO+88/D4449j0KBB2GefffDpp58mNcB2VtslZCU37CZPnoxDDz0U1113Haqrq7HvvvtiwYIFePLJJ3fvBWqGKVOmYObMmTjvvPNw8cUXY8PGLbjvr/cgMzMLiWeZm5OF/v3745VXXsGRRx6J3Nxc9OrVCwMGDGjzc6TujZkXx8zbfcw8SnUdnXlA18s9y9YIRjSmTJkSy70VK0swep+R+HjhAtx5550tyr2IjXp18RyN1fBLhdy7dOovMOX8i7Bp61Y8cK/knsNtKvTrLbn33//+F0cccQRzj4iom+hUHXhbtmyBZVno27dv0tf79u3b6LT2srKyBo+PRCLYsmUL8vPzGz2msfvUWmPatGk4+OCDMWLEiEbP96677tqlXfVaS3GuCxsrLYQiUgclcWcyj0u+39qchpffLcsStNaoDWt4XQqXX345DjzwQNx///247777sGHDBiilUFhYiAkTJuDdd9/FpEmTmrzv6qCG4fbhtbfexR23/B/uvucebNm8GQUF/TDtN7/BLTffnHQbp1DyX/7yF+zYsQOTJk3Ca6+9FmvgeF0qqX5SRnT5VdiWC1t3nZFMw5BR3WnTpuEvf/kLQqEQDjroILzxxhsYOnRoq7yGDT3vYERjwoQJ+Oc//4k///nPOOWUUzBw4ED87vo/4u3/vYmPPpgv5wdZEjJr1iz87ne/w0knnYRgMIjzzz8fTzzxRJucH5GDmcfMaw3MPOosOjrzgPbPvaYyzzR2L/eczLMBuA0v5s2bh99ffyNm3HsPtmzZjH79+uG3v/0tbrrppqTbNZV7piE5YQOx0gGunSw5be/cS3xNDzroIDzxxBP485+n4+wzT8WA4oG47oY/Ym409xKX/jL3iIi6H6UbmjueojZs2IB+/fph4cKFOPDAA2Nfv+OOO/Dkk09i2bJl9W6z11574cILL8T1118f+9qCBQtw8MEHo7S0FHl5efB4PPjnP/+Jn/3sZ7Fj5syZg4suugiBQKDefV555ZV4/fXX8dFHH6GwsLDR821oZLaoqAgVFRUNjj4GAgGUlJTENuhoDVUBGyXlEYzIlzoottZYWhpGca4Lmb7WXUGd3PCSBkZNSMcuDHd3an9NyEYoPsgca5A5pD5Ly5/TzhqjHamp11Tr6IKQOkvEnOMaqq2yu8+1LX5Hd0VlZSWys7MbfS9Rx2DmMfN2FzOvYcy81JQKmQe0X+4x81ofM69hzDwiooZ1qhl4vXr1gmma9WbGbdq0qd4MOkdeXl6DxyfuLtXYMQ3d569//Wu88sor+OCDD5rsvANktydnx6eOkukzkpZOGEq1ylKKhhoIO4LRWiJKIWwDVYH4Nvc2gGBEI82z6w0lv1sex1kKkdioc3YSa8n5JjZoEs/LqaGUCpy6TwCSXlNba0RfahhIbuQ6S0LqPofERqLWGmkexBqJEUsj3YsOb8hS58bMY+btLmYedSapkHlA2+QeM699MPOIiKglOtUmFh6PB+PGjcPcuXOTvj537lxMmDChwdsceOCB9Y5/++23se+++8Ltdjd5TOJ9aq3xq1/9Ci+++CLmzZuH4uLi1nhKnVLdnbG01rGPGojtjNWShldzOA2uur+0Te0k1tT5hiygOqhh2ak7CdXvju+oCMRfUwOyFESWvNXfebGhJSENNRITG8nBSOq+DkQdiZnXfph5RB2Pmdd+mHlERNQSnaoDDwCmTZuGxx57DI8//ji+//57XHvttVizZg0uv/xyALID1i9+8YvY8ZdffjlWr16NadOm4fvvv8fjjz+OWbNm4be//W3smKuvvhpvv/02pk+fjmXLlmH69Ol45513cM0118SOufLKK/HUU0/h6aefRmZmJsrKylBWVoba2tp2e+5tLRzRqKi1YztyaS2fh+v8wW+0gaAUFIC6zYOdNbyay2mQ1a1F3NROYk2eL1K/QdNoY1YpZHoNeFwq9trubBeyxhqJQOs0vIk6G2Ze6mHmEbUdZl7qYeYREVFLdLoOvLPPPhszZszArbfeitGjR+ODDz7AG2+8gf79+wMASktLsWbNmtjxxcXFeOONN/D+++9j9OjRuO222/DAAw/g9NNPjx0zYcIEPPPMM5g9ezZGjRqFJ554As8++yzGjx8fO+aRRx5BRUUFJk6ciPz8/Ni/Z599tv2efBsKRzSqgjYitkZFQMPW8jFiy9cTG3eNNhCic/3rNuB21vBqrp3tntjQTmJNni9Sv0HTVGM20MIG6a6ObBN1Rcy81MTMI2obzLzUxMwjIqKW6FQ18BxXXHEFrrjiiga/19DuS4cddhi+/PLLJu/zjDPOwBlnnNHo9zvRXh+7pCasYyOqlq2xvSb+uY5+Pzs6XV8aCMm1T5zjEpsGzanX0RLO7omNFU9uaDlBU+fbWIMmlYodxxqzWs7dVICGArRGMCKvt9ZoVp2TnY1sp3nAxh11G8y8ZKmSe8w8orbBzEvGzCMios6o083Ao7aR5UtusCR2V5qGQpYv4XuNNBAU4h2dza3X0RKmoZDuVdFdyJq3nKCp821oxDjV6qh4XQoKGpYGoAGXIa+pFT0NDSBs6WYtF9nVkW2iroiZF5dKucfMI2obzLw4Zh4REXVW7MAjADIil+VTqNs0UpBGX+KIXaMNhGhtFIWWNbxaQnYSM2Lno5SC16UQjMQbaNIQs2MNsJY0aFKtjoppKLgMJbuQGbLrm2XLrmTOq2lH/0HLkhhnuUjd18Hrii+raIuGN1FnwsyLS6XcY+YRtQ1mXhwzj4iIOit24BEAaQxVBnS9wsQakK8njF422UBQChleldTwSvMYbbYcoTmjqC1p0LRFHRXLlgZWYw3PnUnzKHjN+OPakNfZMFRshzJoGb1NHGSu+zrs6sg2UVfEzItr7dxj5hGlHmZeXKq19Zh5RETUXOzAIwDSeEtsZCT+ibdsafQ5dqeBsLsXdnU1ZxS1Jefb2gWAW2OZRmPnpLWWn5NSSY3PYKTxpRYNjWy3ZcObKFUx8+JaM/eYeUSpiZkXl2ptPWYeERE1FzvwCACQ5o4vqzANhZy0+B97Ff1+ol1pILRFzZHmjqI293xbWkdlZ1pjmUZD52RrDVsDtgagNcyEpRYaQCThPFN9BzaijsDMi2vN3GPmEaUmZl5cqrX1mHlERNRc7MDrLmwbqK6Wjw1wuxQyvQZchkK2T8FQ8tFlyNfdrVA3oy1qjrTGKGriaLFTR8XWsk5hdwsAt8YyjYZquzh3qeHUtTGSlogYCR8b22m3NUfIiVJOJALs2CEfG8DMi7//a8MawYS1WbuTe8w8otTEzGubzAN2P/eYeURE1FzswOvKQiHgyy+Be+4BDjgAGDdOPt5zj3w9HE463O1SyPYnj15m+1unUXfqqaeiZ3Y6qiu3x75Wt4FzyYVT4Ha7sXHjxmbfb3NGUW+++eZ6DZuJEydi4sSJSaPFhmHgz3fcAq01tJZGU5pn9woAt0bDs8HaLqaCqQBDyfKQ+OMBpor+Bw2PJqfS7mtErSoUAr74Apg+HRg/HhgzRj5Ony5fD4WSDm/LzAOAZ+f8Ez3STPRIM/HRB+/XyzyfC9hzzz2hlMLEiRObdZ+7O3Okofe/Hb2OtTTg2s3C58w8og6yZQuwaBHw3nvyccuWeoe0deY5HVlLlyzGlZdOxci9ByGvRxoKe2dh4oR98bf77kZ5eTkAYMCAATjhhBN2ep+pnnnA7uceM4+IiJrL1dEnQG2kpAS4807g8cfrz7r77DPAMICpU4EbbwQGDGjz07nooovw8ssv4+Xn/40pU3+Z1BAzAIRrK/HSSy/hhBNOQN++fZt9vw2NWjo7dQUtQIWBiy++GEcffTRqQja8ruRaKImjxQAQjsgSBWf3r5ClkeZRqA3rerdtjp01PNM82GnDTmq7yLn63U5tF6A2jNg51YRshG0pegzIuVtaw1AqYTRZx3ZyqztC7nzujJCnebgUgzqZkhLgttuAJ55IrvINyICFUsAFFwB//CNQXNwup+S8tzMzM/HkPx/HwYdOBBC/qJs/fz5+/PFHZGZmNvs+m5N5aR4Vm1HcVOYlvv9N5RROR0LGtDz3mHlE7eynn4B584C//AX44Yf41wcPBq67Dpg0CRg4sF1ORSmFOf/8B6688krsudcQ/Pqa32Lo3nsjEg7ju8VfYObMmVi0aBFeeumlZt9nqmeenMru5R4zj4iImosdeF1RSQlw3nnAwoWNH2PbwGOPAd99B8yZs0udeJatkxobWmvsCGqEbY0cvwFDKdhao6LWxqQjj0ZBQQFmPT4bP5/6y+RTAfDPp55GbW0tLrroohadg9elELGkYeI2pBFXHdIIRlfN2Rro168fevQuQMgCIpZGujd+e79bzttpHGoAUDIS6ix7cEZWd0XdhqeC7CJmALGGZ2KDK7HhmPj6moaC3+00Lp1aL/FzSnwdXIbcFlqWApuGgq2BiO08/+TnvCtL3IhSSkkJcM45wKefNn6M1sDs2cC33wL//vcuXdA2lHm1YSkyHohoZNfJvbAl765TTz8L/3n2adx934PIysqKXdTNmjULBx54ICorK5t9Ds3JPOdiMmzHM8/JlbqZF3v/KwVvwvt/V3OPmUfUjpYskez77rv63/vhB+CSS4Bhw4BnngFGjmzx3bc08775YhGuuOIKTJx0JOY89xK83niD66ijjsJvfvMb/O9//2vROexO5lmWBcMOw2146r3/a2prkZ2RttuZB9TJPS07/aroY4UtoDIAZEQ76Jh5RES0O7iEtqsJhWTmXVOdd4kWLgTuuKPectqdaWh6flVAaorYGtheY8O2NbbX2rA0UGsZ+NnPf4GvvvwC3y5dAiD5l+/Jf/4Tefn52G+//XDFFVdg2LBhyMjIQJ8+fTBp0iR8+OGHSY+/atUqKKVw371/xd8fvg+j9x6Evj2zMGHCBHz+6aLYEoOIDdzwfzcjw2cCqF+DRSkFb/SCNkbL86ncvgVXXnnlTs+lKYnLIlwGACggumxDayAU0QhFNCoDNkIRHVva0NLlD4k7sBkK0FAwFKIVjzUidWrQtObua0QdKhSSmXdNdd4l+vRT4PbbWyXzakJy8VUdlk6q7bXJuReIXmCeftY5AIAXn/t37P62bKvACy+8gKlTp9Z7rFtuuQXjx49Hbm4usrKyMHbsWMyaNQtaJ++2OHzIQJx44ol45+23MOmg/VDYKwNjRw3DwzNn1as79dFHH+HAAw+E3+/HXgOLcPstf8S/Zj+GHmkm1qwqgdY6loV/+ctfMHToUHi9XvTp0we/+MUvsG7duma9Tsw8onby00+Nd94l+u47Oe6nn1p097uSeXfddReUUpjx0Ex4vd56teUs5cZJJ52U9DhvvfUWxo4dC7/fj6FDh+Lxxx9P+r5pKFRt24jf/PpyDBm0B7xeL0YMGYS777oV2oogEp1h9mPJKvRIM3HfvXfj1ttuR3FxMbxeL95//31Mv/MW9Egz8c2XX+D8c8/EgIKeGDt8MP7zzFMwDAMff/xxved/6623wu12Y8OGDTt9rWK5l9B552SeFe1gZOYREVFrYAdeV7N0qSybbYnHH5dR3BZouFBxvLFhA9hWa8P5kgZwzpQLZHnFv2bDlGs6uA1g2fff4YvPP8U5P5+C9ZvKobXGTTfdhNdeew2P/n0WiouLMXHiRLz//vv1zuOhhx7Cu++8g/vvn4E5c+aguroap518AgI7KmP1QRKbQHVHHiO2RlUgfp5AfNevtaVbYEfP5fXXX8fs2bMxcODARs+lIfUbXIhvIwZp2FnRx7MRb3g1txB03SLFDlfCOzvxW87zb83d14g61JIlsmy2JZ54Ali8uEU3aew9mXiNZevk3HNkZmbhlFNPx9NPPiHvL63x4nP/hmEYmHziGbGMcoqMl5SswmWXXYbnnnsOL774Ik477TT8+te/xm233QYgvtsiAHzzzTe48Q+/w6+vuhpznnsJw0eMxK9+eQkWfPQBAHnP//D9Ehx11FGoqanB47OfwD33P4Rvvv4Kf/3LXbHztjVQFbBx2eWX4/e//z2OOuoovPLKK7jtttvw1ltvYcKECdjSQE2tuph5RO1k3rydd945vvtOauO1QEszz7IsfDT/PewzZiwKC4ukzqcbscwzIO/TbTUWQtHepm+++QbTpv0GV199Df773/9i1KhRuOiii/DBBx/EHqOsrAwHHjAe777zNv70pz/hzTffxEUXTcWMe6bj6isvk/NIOO+/P/I3fDj/Pdxzzz148803sedeQ2L5MeXcM1E8cBAef/IZ3H3/wzj25DORl5eHhx56KOm5RyIRzJw5E6eeeioKCgp2+lo5uaeU/KubNbuTeUBC7kU/9yXsKAww84iIuhMuoe1q5s1rdKfZRtm23G7s2GbfpKFlWKaKjjRGj0lsHhgKGDtiLxxy6KF47pk5+NNtf4bb7YYG8O8nZwMAzjnvQvQfOBh/ue8hZHil8REIW5h4xNFYtXo1HnjggXqF3jMzM/Haa6/BNGWGXUFBAfbff3/Mf/ctHH/K2fVq7TkjjxrS4KkO2vUao86ngwYPwfR7H0K2X64MLcvC0UcfjVWrVjV4Lo1xlkHo6My+sCWvV+Lro6LnpxDvYNRaZjQaAOyE0VKnceaM3tqQJRSWjdiIrWlEH0MDFgATUifFqefXYD0ZxHdfY20U6jTeead+zbud0Voyb9y4Zt+ksaWnTeWe8y5yGcB551+I448+At99+y32Hj4cT/1rNk469QykZWTCtuWUnCVgDzwyC+leWVJl2zYmTpwIrTXuv/9+/PGPf0yaPbFlyxYsWLAARUVFqApoTDj4UMx/fx6ef/bfOOTgQ5HmUbjjjjtgmiZefXMusnv0RMQGjjz6eByy/+ikc16+bBke+8c/cMUVV+Bvf/tb7DHGjBmD8ePH47777sMdd9yx09eKmUfUxrZskZp3LTF9OnDyyUCvXs06vKWZt3XLFtTU1GDPgcVwG9Ke2hGMzkhTgKk0qoJyfFVQR5/GFrzx7ofoX7QH0r0Khx56KN599108/fTTOPTQQwEAN998M7Zt24Zvv/0We+yxBwDgiCOOgN/vx+9+9zv8+trfYujew+Ln7fPhf//7HwzThWBEI2zp2J+Is8+dgj/8381Jz/P8qZfivnv+jHvvvRd9+vQBALz44ovYsGEDfvWrXzXzxZXcy/JFc7yVMg+Iz4S0tXT6KaUQik7vs6IPwMwjIuo+OAOvK7Ft4Lnndu22//lPizr+nFohCnIhFf1i0iyI2LEAcnxSJ2XqhVOxdcsWvPn6qwCAYCiCZ56egwMmHIxBew6GBjBz5qMYPWYcemanoVemBz2zvJj37rv4/vvv69338ccfH+u8A4BRo0YBAFb+tLrRkUfL1rDt+AWglP2Ncz5XAObMnomxY8fC5/PB5XLB7Xbj3UbOpbmvmaGiyx4SGCq5jkvsXKKzVZzWZ2InZOLobcjSiNg6du4RO7lPwxn1rQlpeBra7Ww3dl8j6jCRyK5n3jPPAJbV7MMbzDxIQXGfK3k2BCD5kRa7AAP2n3AoigcOwpwnZ+PbJUvw5Ref42dTLogdb9nxC67335+HI488CtnZ2TBNE263G3/605+wdetWbNq0KelxRo8ejaKiothsC5/Phz333Atr16yOvefnz5+PQw87HFk5PWOZpwwDJ592BoB45n30wfsAgAsuuCDpMfbff3/svffeePfdd5v9eiW+Zsw8ola2cmXyhhXN8cMPcrtm2pXMc/5n2fIejc8ulhqYiZ1+tgZGjhqNoqI9YjPOfD4f9tprL6xevTp2b6+99hoOP/xwFBQUIBKJIBKJIBwOY+KRxwAAFnw4P+k8jj3+RBimS5anRqSd5zjhlNNimeG08676ldRl/sc//hE77sEHH8TIkSNjnYgtfc1aK/OA+ExIO/qaOctvEztRAWYeEVF3wQ68rqS2FmhBMfQklZVAINDsw7XWqA5FL6A0YEurJGnEL3YsonVStMYJp5yOrOxs/PupJ6ABzP3fm9i0aSN+fr7UgXrkb/fhd9dciXH77Y9/Pv0fzH1/IT5Y8AmOOeYY1NbW1rvvnj17Jn3uFEyurokfm9hMCdvxEWFo1OvkS/Tw3+7DlVdegfHjx+OFF17AokWL8NlnnzV6LjsT26VM63pL7JyGmK2BsBWtJ5iwfMJO+Ogsf/C7kxtkjTXHDBV/o4dtqUHlLHFzGolpHvncmfVD1CkEArueeVVVu595AMK2Rm1EQ9c9HkBNWMf+7zIUfnbe+Xj+macxe9bfMWjwXjjwoEPiN4i+7b747FOcduIxMA25oFywYAE+++wz3HjjjQBQL3t69uxZb7aF1+tFIPrcwjawdetW9O7TN7aEy9G7T/KO3+XlWwEA+fn59Z5/QUEBtm7duvMXKvE1YOYRtY1daIMAaNPMy+3VC2lpafippCS2KiN2X3WOdb7VIzcXQPKMM6/Xm5RzGzduxKuvvgq32x375/F4sN8Y2ZSjfOvWpIuZ3n3zsSM2Uzc58/r2rZ9tffv2xdlnn42ZM2fCsiwsXrwYH374YYtm38WeZytnHoBY7tXNPJ3wOTOPiKj74BLarsTvB7Kydu22WVmAz9fsw2vDWqbwR2kAkbottAQ2pBMvNysNZ551Dp54/DFsLCvF00/ORkZmJk469QwoAM8/8zQOPnQi7n3gYQDSIMn0KVRVVbXo6ThNE7cBeBJ+yw0AaV4ZOd7Zorvnnnkahx42EY888kjS11t6Lg7nIrvusopEGs4oa7xxrIBYzUAgcfmDgTSP1E2xlYKB+HIK574UgEyvgWBEIxDRMA3A6zLq7W62O7uvEXUYn2/XMy8zc7czz7LrX8Qmcr7nMgAohZ9POR9/vv1m/HPWTNxw0211OqDks5eefxZutxuvvfYafAnn9/LLLzf6OHV3aTQNxGadGJBOvi2bN9a73aaNZUmf98iVAZHS0lIUFhYmfW/Dhg3o1cyldw5mHlEb8ft37XZtmHmmaeKQiZPw7ttvYdOGdehTUAizznvUeccldu7tbGOFXr16YdSoUUnL9y1bozYk51LUrwCZPgVnUpmhpB5dMKwRrDNKW/cxNGSG29VXX40nn3wS//3vf/HWW28hJycHP//5zxt7aRrV+pnndL4BVQG5NTOPiKh74wy8rsQwgLPO2rXbnnmm3L6ZvC4FU0lHmNPwqNsscBvJX3Mb0hC59JKLYFkWHpxxD97535s49fSzkZ6WJgcpBa/HE7uNDeDTL75pcIewprhdSBp5BBAbeXQZCmZ0KLOppoyhFDwJ5wIAixcvbvG5OJxdygzIaKlpyCioqaKfq3iNFKPO7bJ8Rr3lD7GRXiBWCyVxaQiiHyuDNsLRFp9lt7xEIlHKcrl2PfPOOQdIWH6/Mw1lXl0KgKuBXJGC4xp98/vhymt+g6OPOwHn/PwXABIv8hJKEbhcMBLyuLa2Fk8++WSj55a4cYRzgWYa8cw77LDDMP/997C9fEvs3GzbxisvvZB0P4cedjgA4Kmnnkr6+meffYbvv/8eRxxxRKPn0BBmHlEb2XNPYPDglt1m8GC5XTPtSuZd/ZvfQ2uNq391GcKhYNIyTwUgHA7jrTdeTeqE2tnGCieccAKWLl2KQYMGYd9998W+++6L8fvvh0Mm7Ifx+++LQQP6QSkFfzT7PC6Z8ZzmkbZeU+0853vjxo3DhAkTMH36dMyZMwcXXHAB0tPTm/lKxbV25gHJs/oSM8/BzCMi6l7YgdfVTJrUoo44AHL8pEktuolpKGR4FXxupzNMJXXiGZAt7p2vmQqxouaDh4/F8BGjMPOhBxAOh3He+RfGGiSTjzkO896diz/fdjM+eH8eZv39EZx8wrEYMKC4ZU9JyS6NiaOtaR4ZkdQ6XhOlsYadAnD0scfh3Xfm4qabbsK8efPwyCOP4Oijj0ZxccvOxRG7yHYpZPoMeEwg2xf93CvnaihItWfn9Uw4wbrLHxKXzVmNDfVClmskzo50lvURdQlHHpn8RmkOpVo185IucBO+7o32DwbCWmq0AfjTrXfhX8+8iL55+ckzNKKfTD7mOOzYsQPn/OxczJ07F8888wwOOeSQWHmAps6vscy74YYbYFkWTjpuMv77wn/wvzdexc/PPBk1NdUAAMMwoAAMHToEl1xyCf72t7/h2muvxdtvv42///3vOOGEE1BUVIRrr722xa8ZM4+oDfTqBVx3Xctu8/vfN3sDC2DXMm/iwRPw8MMP45133sFhE/bHrL8/ggUfzsf8997B32bcgwn7jsTT/3qi3uw0Z8ZZQ2699Va43W5MmDABjzzyCObNm4c33ngDMx99BGeddhLWr18PID67zlDxzn6tm27nyWw1OeLqq6/Gp59+itraWlxxxRXNfp3qvmatmXlAfFaf03nX0PNh5hERdR/swOtqRowApk5t2W2mTgVGjmzxQxlKau5qpzmR0IjTiNZLiTZWFIDqYLxw789/cSG01hgydBj23398rEFy7XU34NdXT8NT/3ocZ592Ip584nHc98DDOPjgg1t8fo2pDcdr4DW1xOE3192AadOmYdasWTj++OPx2GOP4dFHH92tc3Eusl0JF9tpHiNe7NlpgEb/bygVa9g6xzqNOm9CkWKvGX+dG2rcxZatGApZPi6hoC5k5EigzqYLO3XBBUB0w5uWaCzzHE7NI43oTo3RzqaIjaT3Z0OzWUxD6hwdOnESHnr0MXz37VKceOKJuPHGG3HGGWfgD3/4Q4vP17HXsFF48bX/wef34YpLL8C0X/8SQ/cehgsvuRwAkJWVHduZ+5FHHsGf//xnvPHGGzjhhBNw4403YvLkyVi4cGG9mqPNwcwjaiOTJgHDhu38OAAYPhw4/PAWP0RLMy9sAT8//2LM++hTjB4zFn+7726cdfKxOP+c0/HSf57F6Weeg7/+7VGZhRadlQY0vbFCfn4+Pv/8c0yePBl33303jjnmGEyZMgWPP/44Ro8ejR49etS7TWwp607aeVrHz+GUU06B1+vF0UcfjcEtnd2YoDUzD4jnXuIgOTOPiKj7UrqxOevU6iorK5GdnY2KigpkNVC3KRAIoKSkBMXFxUm1j1ps1Srg5z8HFi7c+bETJgBz5gADBrT4YWpCNkIJmzgqxGeZyOfJTAW4TdmdzIr+2mX7DdSGNMKWLA3wmDLiq6IjqLVhLcs4WrHgrmVrVAeTdzCsy7ko9LmlMdXWnHNyalileRRqQtIANdB40WHLliLIfrdC2NLYEZThZgPJo7HOLXv4jVjNFyDaMFRo9uvcar+ju2ln7yVKDe2WeSUlsiT20093fuz++wP//jcwcGCLH2Z3Ms8p6m5rqYknM8mk0HmGR8HjMto988486RisWbMan3z9PTOvEcw8aol2yzzHkiWSfd991/gxw4fLrtsjRrT47rta5iWqm3mvvvoqTjrpJLz++us47rjjWu1c6p5TSzPPuW0wouEyZCM2w4hu0MHMIyLqdriJRVc0YIB0yt1xB/D44w0XwzAMmXl344271HkH1C+crpRqtMCxhkz/z3BLPZVgRHbWcorz1oZRr2HRVgV3ZYkDEAjLbmqWHV9KYRoq1uhJXFrR1pxzci5M469Lww2uxIvYNI+BiKVRFZTzVhqw6ty/8zPZXmvDNOTnBMhOaKahELGBiKWR7gV3J6POp7hYOuVuvx144gmZVlGXUjLz7v/+b5c674Ddzbz4ezt+0Wq0W+b96YZpGDFyNPL6FWLr1m14/tmn8f68d/DQo/9ImLnBzCPqVEaOBF59FXjvPWD6dOCHH+LfGzxYls0efni3zLzmtvN+XPE91q1dg9/85jcYPXo0jj322FY/n8Rz2pXMMw1ZWlsRkPa8bdfvlGTmERF1D+zA66oGDAAefhj45S+BefOA//wHqKyUHRvPPFOWXowcCbjdu/wQdRsjtpb/qzqtCqdmh6lka3vZSbDtG3A7P3dp4ATCGkpJhyKi5xqxgYzoaGhbjRA3dE7NeV0SR3G11rI7WdCOvc5N1S62AeiEIdtIdDakoRRsyM+Pu5RRpzRwIDBzJnDllZJ5zzwDVFXJbrPnnCOZN2pUt808bdu447abUVZWBqUUhu49DP/6179w3nnnoSakmXlEndXAgfLv5JOBlSuBQEB2m91zzxbVvGtIZ8685rbzfv2rK7FgwQKMHjMWTzzxRKO74bbWOTHziIhod7ADrytzu4GxY+XftGnxRl1LN7lA8khg3eWtznIrUwGZPgOVATtpAkysmaBUrPGUKhIbeLVhDY9Lln5oyDKFdI80dsLtNHLZ1OvsPG4womONt7ANVAV0bCmITvjoMKJfcG6T+H2pQyP36zaQcj8fohZxu4Fx4+RfYua1YLdZR1fLvPvvvx/3339/7HPn+VkazDyirqBXr93qsOtqmefYWTvvnXffi2WeAXkdmHlERJSq2IHXXRgGkJa2SzdtaCTQqdtR9wIvFNHSAGrojrRGbRgpNfLnjMxKnRSFsGXL7obRwseVAR3bMayhkcvmNMRaci7NeZ39bnkcZzdGG3J+JuRi3FTRTUSixZkzfQYMBWyvsWXjM8R3cDSi52hAfi5tOfJM1K5ME0hP36WbMvPAzCPqRph5YOYREVGn0Cl3oX344YdjxVXHjRuHDz/8sMnj58+fj3HjxsHn82HgwIF49NFH6x3zwgsvYNiwYfB6vRg2bBheeumlpO9/8MEHOPHEE1FQUAClFF5++eXWfEopraGRwMRGhVNLBJB6KYmjfqahYiOzGu1XX6k5LFsKoAciUh8FWj7qhFFMjfj/645cOg2xkCUNMK1ll92QJTvuWrZGSzT3dXaWXCho2FpHt4iTmjROu0wpaVgqpRAMa9SGNIzozyLxtOzoJzbiz4Gou2PmCWYeUffAzBPMPCIiSnWdrgPv2WefxTXXXIMbb7wRX331FQ455BAce+yxWLNmTYPHl5SU4LjjjsMhhxyCr776CjfccAOuuuoqvPDCC7FjPv74Y5x99tmYMmUKvvnmG0yZMgVnnXUWPvnkk9gx1dXV2GefffDggw+2+XNMtT+ufreCy0CsESEbiUmjwlWnsROMRKf5Kxn1MyAfnRHBxEZgR3NGZIF48eXooGyMs/SgoZHLljR4m8PvVnAnvCMTa5wkNiq11qiO1qzSOr4TmR1t41k6eWe4oKURtOINuMSzkhFcHXsOteGmzznVfjepa0i13ytmHjMvdnyK/W5S15Bqv1fMPGZe7PgU+90kIqJkna4D795778VFF12Eiy++GHvvvTdmzJiBoqIiPPLIIw0e/+ijj2KPPfbAjBkzsPfee+Piiy/G1KlTcc8998SOmTFjBo466ihcf/31GDp0KK6//nocccQRmDFjRuyYY489FrfffjtOO+20Nntu7mhx9ZqamjZ7jF1ha8RHLDWijbpoI8JOHumT7erln9sAMn3y0flaKo3MOo3NRHWbLc7Sg4ZGLus1xHR8pNRpiNWE7GaP0DojrnXflE5D2VEb1kmNRl3nHwC4TcTOzVSAGb0Dj6ngio7QKgAuQ74GSBjs7OdTXV0NpVTsd5VodzDz2hczj5lHHcvtdkMpherq6o4+lSTMPGaeg5lHRJTaOlUNvFAohC+++AJ/+MMfkr4+efJkLFy4sMHbfPzxx5g8eXLS144++mjMmjUL4XAYbrcbH3/8Ma699tp6xyR24O2KYDCIYDAY+7yysrLJ403TRE5ODjZt2gQASEtLS4laFbUhG8GIrtfoAYAwADus4PfEmyOm1rAiGoZbIRhUMLSGDmuYLoVwSCHcbmfeNBUdxgw1MYJqKMR+BiEAoSCSnquhNSJBadDF6o0owOMzsK1KRk9rAPg9O6+V4tRVidhJX4yNtAZcUhelNqQRij6YgpQ3tBJu4zEVlFMIOWDD71IwTYVQRMN0KygN2NHbu5SCYQPhJn4+WmtEIhFUVlaisrISOTk5MHdhUwDq+ph5zDxmHnUnu5J52dnZ2Lx5M4LBILKysuByuTo895h5KZZ54fg5S+Yp2Hb858PMIyLqvjpVB96WLVtgWRb69u2b9PW+ffuirKyswduUlZU1eHwkEsGWLVuQn5/f6DGN3Wdz3XXXXbjllltadJu8vDwAiF3QpoJQRCNs6/rDlgCgALeh4EmhEdfm0og+N6vhhp1S0SmqSsW2/fK64rt5aQDhaF0VG8mvj4z6xl8T04yPgDYmZGlYiVWhlbOcJf65oVSsnkniqSWueDAV4HMr1IQ1tJZjfLtQbLku0zSRn5+P7Ozs3bof6rqYeamtq2aeoWQmTCAcvxD2uZl51PZ2NfP8fj82bdq00w6/9sLMS63M01rabzp2jgo2dCz3mHlERN1Xp+rAc9QdqdRaNzl62dDxdb/e0vtsjuuvvx7Tpk2LfV5ZWYmioqImb6OUQn5+Pvr06YNwODXGMKuDNr5aF8T2Wg1TKfhijQaNHL/CmEIv0r1Gvdusq4hgcG+3XIBpjR82h1GY7ap3bEdZtjGEki0WInZ0qQicWiiylMJjAFl+A/sUuLG+0qp37ss3hVC63QYUoCGN32BCw8xtKPhcCr0zDAzOd8cahI2pDtr4en0I4QjQO8PA0Dw3vi8LY+02C5bWSHMrhCJA2I52zEEadk5dlERGMF5bxVBArtvAwcW+XX6tXC4XTNPs8FkClNqYecy89sg8jeQZeADgCsXrWwFAD5fCYcX+XX6tmHnUHLuaeTk5OcjOzoZlWYhEIm19mjvFzEudzDOgELBk1rFzb0ohedYemHlERN1Vp+rA69WrF0zTrDczbtOmTfVm0Dny8vIaPN7lcqFnz55NHtPYfTaX1+uF1+vdpduappky09c9Xo30NBPbQhYME9hhAT4PEIkAaX4D63YYGOh1IdMnjZ6qgI0vS4MIRdyoiRhI8yiELY2NVW5srNbITVMY0tcdO76jhJVCQEdgK6kR4jblojAkbTV4/Qr7D/Qh02egV0792w/s48GmmiBCESAvy8TIAhdeXRpAIKKlzohbwXQrjB7ga9aoqM8H7O/1oaQ8ghHRhuDYYh/cnjA2VEZg2wouEwiFEhp2sjqkSX4X0CvLRBieDn/NqWtj5jHz2iPzbMQHLxyhxFnIBgBTMfOoze1O5iml4HK54HJ1fFOcmZc6mWcBcHmA2qAN29kwQzPziIhIdKqU93g8GDduHObOnZv09blz52LChAkN3ubAAw+sd/zbb7+NfffdN1agtbFjGrvP7mZpaRjl1Ta8phT4jVhSw8PvAtZtt7BiUwSLVgVRFZCWxvKNYWyvlZ2/VpVH8F1ZGCs2RxCxbWyv1Sgpt5KO7yhD+7iRk6bgMRUG9XLhlFFpGNjTBbcBeFzAwcVeZPoM2Fpj8YZQvfPN9Bk4YIAXRbkm9il0YcmGCLwuub90r4KpZPbI1+vCsR3AdibTZ2BUgSdpFDdiA7YtnysgVgDZTui8cxmy7KMuQwFFOQbWb9dJr3lVwMbiDaHYeTX2HIm6I2Zeamee1oDXBHo0MNlEAcjyKYQjYOYRNRMzL3UyD4jmnlvBRjzzXNFOyLqYeURE3Uun6sADgGnTpuGxxx7D448/ju+//x7XXnst1qxZg8svvxyALGf4xS9+ETv+8ssvx+rVqzFt2jR8//33ePzxxzFr1iz89re/jR1z9dVX4+2338b06dOxbNkyTJ8+He+88w6uueaa2DE7duzA119/ja+//hoAUFJSgq+//hpr1qxpl+fdkYpzXfC4gKAFmIaKju4pVAWlLojXBYQiQEl5BFUBG+U1FrTWqA5p2JCGScQGKgMy1d8fXRZVUt6xy0YyfQYOHujDkL4ujN3DA9NQGNzHBZcpjbMfNluwbI2v1oaxtpHGqNMQ+64sgrJKK9bo8rvjDbGySgtLS3dtaeDS0jDKKuPrNTwuaVzbSC5VE7GBYAMvp62BH7fYsG076We0aFUQa8stfLU2vNPnSNTdMPNSP/MCEWBbbf3bawAVNbKTJDOPqHmYeamVeRqyE21in2DYrr+MFmDmERF1N52uA+/ss8/GjBkzcOutt2L06NH44IMP8MYbb6B///4AgNLS0qROteLiYrzxxht4//33MXr0aNx222144IEHcPrpp8eOmTBhAp555hnMnj0bo0aNwhNPPIFnn30W48ePjx3z+eefY8yYMRgzZgwA6UgcM2YM/vSnP7XTM299zR2dc0Yg9+pjYmAvMzYjwpUwApmXZWJEvhsl5RGELYU0txQq0nbyRZehZLTQOX5Xzqc11R0JXb3NgteU51RWaWHeikCsYdVUY9Rp/ALy3Cbt5UNeliwH9Ljk+7ui7v32zjQglVhEc0qVWBqoDMoSi2F5LpSURxAMa9SENdZvjzT7ORJ1dsy8rpN5Tc11sQFUBJh5RKmWeS05p9bS2TJv0l4+uAzpsAOitT/BzCMiIqG0buacb9ptlZWVyM7ORkVFBbKysjr0XJzROaeux+hCN75eJ6OAHhdwwABvg3U0LFtj3ooAQgl/+z0uYNJeUv/D1hpfrgnhp60RhG2Z9u/U7FCQUdweaUbs+JaeT1XATqodYmuNpaVhFOe6WlT3o7H7GdDDxA+braTRUCtatLg418TYPTwA0OBjtta5NXWu1UGNj0sC2F6rZfRXAdtrdL2GnctIHql1GUCWz0B+lolR/Vx4e1kQ22ttGADSPfGdy/KyTIwp2nkh5o6WSu8lalwq/ZyYeV0n8ypqdb2Ne9wJF7yAdCLk+Jl51L5S6eeUapnX3HMC0K0zz1AKlbUW5i4PIGLFN7CoO/uOmUdE1D11uhl41DpKyiOxxllzRyBtrfH1unBSo845/ut1YVTWyhIClyFFgm07uVFnRBsb22t1vXohzTmf1loS0NT9fLI6hEG9zNhoqGXLEpGIJZ1kWqPRx6w7ymsohVEFrVtQONNnYPwAL3LSDBxU7EYoUn8Wngl5/c3oKLjbkFl41SEbpZUW3v8hCK3lzW8DsR3VPC5gdGHqN+qIdgUzr+tmnoK87h4j/rlm5lE3l2qZ15xzWr4x3O0zDwAyfAb2yHVhQK4B01DQkA46BzOPiKj74gy8dpRKo0m2lsZM4giko7HRucUbQlhbnlynw2mIWVojYgMeU0FrjaqARqTOb5ah5F9GdDlGUa6JUQWeZp/P0tJwo48PIOn+mtLU89Baw2UqWNH2Wk1Ydj8EALeh0CNN7dJj7qrGRqtLKyKoCmnZoUwhViclcVaKoYDcNKAqAIQSGm+Gkp9R2JbOPQDwuWR0liOz1JpS6efEzOtamQcASiPpNTcV4HdLPVBntgozj9pTKv2cUi3zmnNOLlNj3bZ4hxkzj5mXCu8lIqJUwhl43ZShFEYXumMjkI6mRueaqv8RsWWHrNqwRmVQAypetwOQJZweE8jyS6Oubr2Q5pzPiHx37PGA5EZdY7VWGtLU/bhMlbRMIdsXb/yEbY3ttfGWU3Mfc3fqvTQ2Wl0bkZFv52w8Zrzh7NAAbK2glIrNWAlZ8jMKRqIjuLZ8bUdQw7L1bhViJkplzLyulXluqbGflHk2AE/0IpWZR91dqmVec85pZIGHmcfMIyKiJrADr5va2TKJussegHiR46JcGb0zDYUxRW4U5Zo4Yi8v+uW44HXJL5UG4HUpZPkVfC4ZJczxKxw60IvCHiZy0pJ/9ZpzPrvSGG1IU/dzQLEH3oTG6xFD/Cju6YIBeV5el4xChyyNUf1csRoojTXUdncJXGONUK8J+NwK6R7ElrJY0W0anVdBa6C8RsNUOta55/xYNaQx7tRPsQHURvRuFWImSmXMPGYeM4+6k1TLvOacEwBmHjOPiIiawA68bqqhbesdDY3OOaOL6V4VW0qweEMI1UGNUQUeZPtl2r/fLTuWeUyFNI+C11TI9isYhkKmz0S610DEAjZV2kkNm+acz640RhvS2P3UhjQWlYSw3x7SWN2n0IWlpSGELR3biQ0AqoOys9fby4IIW3aTDbVdqUGTqLFGqN+jcECxG5YGwpY00BSkgVb3VaiJ/igbavfq6D+3AbhNhZH57lav5UKUCph5zDyAmUfdR6plXnPOacmGMDMvysk8DYWIldwRx8wjIuq+mODdVFPLJOqOzjVndDGxsWQqhTS3ggJQFdSoqNXwu4DyarvRhk2fdANBS0Nrjdx0A70yFAxDigu7XUAwovHF2lCLGqONaagB6RQx3lpjY9GqMIbnufHNughWbLKwutyC1yXPy7Kl8QQA22ttvLq06Yba7i6Ba/ICfm04VtDduZBtjDNyazbQuDM0Yo3xJaVhVAXs3VoOQpSKmHnMPAczj7qDVMs8IJ57EduGaQCH7umBaQARWyNoaewI2sy8hPtZsj4iG5JFc6yp7ktmHhFR99CqHXjhcBhr1qxpzbukNtLUMokDBniTRueaM7rY2Kiq3y0VUmqjt2+oYVMVsLEkuquZUkBlbQQ/bLJQFbARsQGfqbCp0saWHTYMQ8du21RjtCkNNWo9LgUb8oYIWTr2HL3RNlkwWlz4hBE+5PjltTEg9WDqPp9Eu7sErqnR6ppQvClnNPJONpV8TyUUqqn7iHbCfbXmLnBEqYSZFz8HZh4zj7q+VMo8ALHcMyAzxsIRG+8sDyFiy2w3pTUqA5qZl3A/EUs2DtEAzEYyz9n9l5lHRNQ9NLsD76mnnsJee+0Fv9+P/fbbD6+99lq9Y7788ksUFxe36glS28n0GRhV4Ik1LgwlyybqTq1vzuhiYyO9ClITJcOb/NiJDRun4WgqhZowUF4jSwXCtsxC2bhDGhK2DfRON5vVGN3Z867bqD1yqBc90wykR3dOc56jaSgMyHVhrz4ujClyw20amDzUK0tIPFI4uO7zSbS7S+CaGkHP9iv0z5XX2GqkraUBmEiYsaLrj+BqAIEIUB2ykZdlwp2wW9uuLAchSlWZHmBUTgSGLW8YIxTCqL4mMsPVwNKlwLx5wNtvY+TCVzB52ZuYVLkYh239Cvuv+RQTqpdjBDZjZFYII/LdGNjThbToxoTMvDhmHlHqSJV2HhDvJAzb8r7cEZIZblUBDVsDEa2YeXUyz+tWmDTYA5+r8cwDmHlERN1Js4ay3nvvPfziF7/AqFGjcO655+LDDz/EySefjN/+9reYPn16W58jdTBndHHeCiupgeJxAYN6mVhaGsaIfDcOGODFT1sjADRqQhpjitxYsgGoCWqU1yS3PJyGzZgiN0bkuxGOSCPC71ao0YBtS/PDNAB/QsNmTFFy48lpjO5MVcBGSXkEI/Ll9ol1TjJ9Ci7DwFFDfZi3IlDvOe4/wBNrwNlaY/H6CLx11igkPp/E82toZDWx0bS0FI2ev3PO4/t7sGqbhWF5LnxbFsaevaXxNaCHieWbIk026mwNhHV8RDcYfWwFaezZCa28sAUM6m0iy2cgYoWTGnOOluwCR9RutAa+/x748UcgEgEqK4HsbJmWsHw5kJkJuN3A9u1AVRXgcgHp6UBODrB+PbBlCzBoEBAOA998A2WacOfmwr1iBTB0qHzvp5/QQ2ugb19orxfpPh8mWha02w2dkQGVno4xPXuiIrsPtqf3RJnlRXVQx95zzDxmHlGqaut2nrOrdjgClFZa0Fp2fAVkdpg7mnvMvPqZ98NmC4EmVg5rSKcoM4+IqHtoVgfenXfeieOPPx7//e9/YRgGLMvCTTfdhDvvvBNVVVV4+OGH2/o8qQM1Wgw4rPHuigC8pkI4IiOTEQsoq7SxqSqIAwZ4oRSSGnWJDZs128JYvS2Ck0b6MLrQjXeXR7ClWsPvjo80prkVlFIt3oEskVPbJRRB7Dy/XicNl42VFg4Y4MWOgIWFq0LwuhQUFDRkKUeaWyU12FraUCvOdWFjpTSI87LMpMduajlIc855WVkIP2yxoBJ2HWuMy1Bwm0DYkpFuRGuqGIg37hSARSUhTN7b12hDfld/BkRtpqQEePBB4OGHgUAA8PmAM8+UX/CqKuCgg4DPPgM+/RQ49VRgwwagrAzo2xdYvRoYPRro2RN47TUgFALGj5cOvuefBwYPluP/+U/gyiuB8nLgxReh+vcHPB6gf3+osjKgTx/5XkEBegQC6NG3Lwa43dAuF7RlQ/fogUCvfGyzC/BRMA8TBnmZebtwzsw8orbRHu08l2FgRIGJHzaHZafXaK05A5J7Xrdi5tU55+Ubw/hpa0Tqo1oN3k0MM4+IqHtQWu98S6e8vDzMmjULxx9/fNLXH3vsMVx++eW48MIL8Y9//AOffPIJJkyYAGtnf2W6qcrKSmRnZ6OiogJZWVkdfTrNtnhDCGvL6zdmasIaIUvLTmRuhTQ30EfVIi1cA+3zILdiIzKrK6DDYcDvB3x+GBXbgW3l0IYJ3asXVDgEOxhCtTsN5e4srE/vi5JIBgwAypBRWeeCtqGR2d05f0ePNIWSrRYsLbNfsnwKOwI6tsthpldhYG8XRhV4khpcDTXUGlriUXdU2NYaS0vDKM51NbocZP4PAazdbsnOblBwmcDmHTZcBpDhNZCXaWDVtghqQnKOLlNGVutSAAwF9EoHsvwubKqKoCoYrY/iFEXW8l+3Ka91Ua4r2kCvf4e7+jNobZ31vdTdtPnPaetW4LzzgLfeqv+9CROAjAzpjMvKkiuZd98Fzj4bWLtWOuaGDAE+/xw45hhg82agtBRYtw447DDJrFdflY6+wkLgzTeBadNklt/KlTKrz+eTTr733gNOOQV46ingZz8D3n8f2GcfmQEYCgG9egE//ACMGiUz9nr2gpWdjW3ZefiuzzCsCbiZecy8JjHzOofO+nNqbjsvMUtCERtayRLPz9aEEQhrhCzggAFurCm3sXpbBCFL6lJm+xUKsk2s2ByvtedkHhDPvfxsFzMvIfM2VVvYukMu0xrLPEBeY2YeEVH30KxiEjU1NUhPT6/39Ysvvhj/+Mc/MHv2bEydOhW2zaKnXVHibol5WSYmDvbCNACPCbgMYJDehqPXf4iJX76M4au/QPHKLzDw8/eQs3wpzE0b4aqtgWvNargWfAhj+TIYAMx1a+F65GGYn3wC97q1yFn9AwYueAMHf/5fTNk4D+duW4DTI99hrGcbsk1phbVkB7JEO6vtUhW0Y/VCLBuoqNWwoo0dqRuiYyOoLSkK7WhuDRrHT5vDWL3Ngq2B7bUattbYVGXD1kDIkkazywAi0XaXhvwcvGby/Tg7l2V5gYhtIM0DFPdyY9KeHhiGjMi6DSDHb8DnlnMzTYWQpVtlFziiNvf55w133gHAwoXAqFHAG28A/frFZ8nV1EiH2yGHAE8+CRx7rMzey8mR2XgrVwI7dgD/+Y909n38sSzH9fnk+GBQbvvxx0CPHsArrwDHHQc88ghw7rnAo48CBx8s9fRqauS+3n9fOgv/8Q+o6moYL78E9yeL0OfdV3HYrJtw3qrXcUb5x5holaDI2IFg9O3HzGPmEbWHnbXzQpaGbdsIRQDbtrGtxkZ1WGboLSgJY2yRG8GIRiCs8cHKELbWWIjY8UzZVqOxtDSSlEU25D2ptCwBrY0w8+pm3o6AHK8hnXB1d5h19q1g5hERdR/NWkI7YMAAfPPNN5g4cWK971144YUAgEsuuQRLlixp1ZOjjpe4W6LLVBjVz4XF6yOI2NKgGxdeg0FrlkJt2CB1otaskTpShiG1qHJzgWXLgG3bgOJiubq65x6ZpXLQQTLr5ZNP5OJ5yBCozZuB9euhamrgS0vDnjk5GJSZCV1QALtvHrTdF6jNldkxzdRUbZfRhW5ouPC/74PYVhNv4DkDjz6XwlFDkxthTkMt8f6bU5+lub7eEG84aQ1sq02eJFsdAjbtsGAomWmiIcXuEwdLDUCWT0AaxQN7GhgZPcdPVoXqLT+z7OhOZ9pGUbYb22vsFi8HIWp3333X9Pdra+VjMCiddocdJktjTzlFZuH16yedeunpgGkCr78OnHAC8MILwMUXA198AYwcCfz738CUKdI5d+qpsqT29NOBOXOAX/8amDkTOPFE4LnngP33B/77X8nDkhJ57FGj5DYXXgj89a8yk++pp4DTToNavx7qiSfgGzcO/bKyUGDb0IVFqM0vwoaioSjo2bPFLwszj5lH1Fw7a+dZlnQEVQaBLK+NyqB0vkHL+257rY25y4Kwoss2IxqoDGjk+BUqArrRmpUG4u9hjynLeA1j195vXTXzAHlNnPxyGYBtSQY6Ne6UYuYREXUnzZqBd+ihh+Lpp59u9PsXXnghHnvsMXz11VetdmLUPqoCNhZvCMV2ybK1xuINodgW8om7JVo28O7yYLSAMZCuIhi4eol03hUVyZK06mop1LFjh8xa+e47WZbWs6d8b+ZMYOpUKTg/f74UmB8+XC5yV66UZWZr1sjJBYMAAFVZCePjj+F643W477sXmD5dZsw8/TQwdy6walWTz3FnO4QZSuGoIV64DLk4dBp1LgM4eaQPOf72bcicMMILbxMP6THl3NPcCqYCvKaCYSiEbTlnlyFLRByWDbhNFavtsm67BQ15rpYtvwNhWxqE1SGFTdV2i0efiTqEayfvTSP6u6o1sHGjDChUVMjS2qVLZfnr559LBn35JeD1Av37S/a4XJIvRxwh2eX3y/19+60sq+3dW7IuFJKZdj17ymDFkCGSe4MHy5LdAw4A/v536TS87z4ZvHjwQZn59/bb0hPVp490/pWVQf34I4w5TyH9g3cx+IFbkfbcHOlAfPNNycedV71g5jHziMS33wLBIGrKq7CktOG2XlPtvLAlHXIagKWB7QH56HBqqyV+rYdfYXBvFxQUsnwKCf3sMS4D0Epmh7lNBb9bRWev7dqyza6ceQYAj6ngNhUi0SXATu45z4OZR0TUfTTrL9bUqVPh9/uxZcsW9OrVq8FjLrjgAqSnp+ONN95o1ROkttOcArqJuyVaWqOyRpYdhC2NwwM/wqiokAvbigqZ7bJtm9SGSksDVqyQYvG9e8vnDz8M/OY3wIcfysV0//5AXp7Mehk/XmaznHeeXFgPHCgnuWSJXHR/953MkBkyRGpU1dTITD/blvvp0UOO8/ul3lSfPkB+PmCaOy1IvHi9xvoKO6kBCkiDdO7yII7e2wuX0X6NGY9pYnAvF5aWRep9zzSAkA2osBQptjRgQkeXuAARWxps2V4ZkQ1a0sCrCckSjeJcF8oqLWyvAQAp3lwT1rBt6c0vzDZjNVzacvSZqFWMGdP0973e+Me99pLOftOU3DBN6YALh2WHWmcmXiT6vguHpbMsFJLPV62SvHr7beCXvwS+/lo6/l58ETj5ZMm1gw8GfvpJ8mvlSmDPPWWX2z595H6DQek8LC+XnXLLyoCJE4HHHpNZeXfdBfzhD7IZxyuvAMcfD/W3vwGTJ0uH4rPPygy/jAzJvYwMYO+9JV8TMPOYeUTYtg347W8RvP9BGBs3QucMxFfh3Hptvf37expt52X7gK018btMjAyn3qTzDWc32clDfVAKeHVpLWpDybdxRGxZDhq2AZ9LA1pq7Nm2DBy39L3HzGPmERF1F836azV27FjcfffdjXbeOc4880zMnj27VU6M2p4z6goA6ysieGVJLUqjDaBgBPh4VRDVQY3RhW6YBrAjKNPvbS0NiEwrILs8ZmbKBeeOHdKRVl0tF5vV1VI8vrAQuP9+WWr23nvS0sjOlmPnzZPOu9mzZefIxYuBggK5sP3qK7nQfeYZuWjdtEnut7JSOvlcLrkw3rBBLnaffVYurh9+GLj6auD884Fbb8Xwr99Ff1RAQZYKTNrLF6uV4nEBGyotbK+Nr/FIrDGyvdbGvBXB9vqRAABCloUVW+o36gAZSdVaGqah6LKWQASoDcV3sbQ1UBNOblhv3iE1TTJ9Bg4c4MWQPi7k+KVQfppbwWMq5KQp7D8gXsOFKOWNGQP86lcNf++ss4B33pGl++vWyUy4zz6TJbJvvy2ZsmSJzJRbvVo6/rdta/yxKisl67SWDFu7VgYJyspk8ODTT2Wp7GuvSYfb66/L5hgvviiz7158UZbfzp0LHHWU1NhzluGefjrw8svA4YdLzb7SUpn5d/fd8rVXXpHnMGAAcO+9MqAxc6bc14wZwKxZwIIFwKJFwNatGJjris3uYOYx86ib6tEDePFFrMgowvyeY7DFk4WqoI15KwIoq5QZWttrNX7YFGm0nbe9tvG714jOwNPyUUfrt735XS3+930AO4LJM/Pqvsus6BJcrynvS0DyakS+u8VPtTjXFavjxsxj5hERdWWcI92NOUV/La1RHdSoCWvUhHS0WK7G9hobC3+qxYIfgyivsaV2RsLtLZdbZn6EwzKTxeWSWXgZGXJRW10tM1u2bJFZeBUVsvTs+edlye3mzXJRPWMGcNFFUgsvN1dmxqxaJZ2D33wjheFnzQKOPFI6AgcMiM9kmT1bOvYKC2UGzL33yoyX4mJpvHo8MH9cieHzX8AxJe9gTMVymFYkaanA2H6eWGMux2/gtH38yPHLW8NQQLpHNbrEuC28tjRYbxnIzkR0/c99bgNeU0FHa9I4NU3SvQoRGwhb8qSdxp1tK3y9Lhx7rkQpLz0duOUW2UBi2DCZaTdsmMz03bJFcujnP5fZb19+KdkybJjsJJudDWzfLjPzfvxRdo1ds0Zm5wGAJzoTwVmmm5EhgxSAXF05M/eA+DompeSxlJJMtO1YKYBYKYElS2TWXG2tLMndskVqgC5ZEl/Ku88+shx3wAA5p61b5XyeeAI4+mjp/MvOlo06vv5azv+Xv5TbXHcdMv5yOyZ99yYOivyEMYWuesujmHnMPOom/H4Mz3djWGAdJt5yMQ676woM3v5TdCmljbClsWVHpPF23k7eGrqBYyoCQHmNjncuQWaVJS4ZVdGv2ZCdbrXWsXp1u9K5tLONJ5h5zDwioq6CHXjdmFP017KjBYkBhG2NqqCNqoBG2JalE+sqrAaLEP+UXgjds6dc4GotF5gej1xYVlfL/3NyZFbIMcdI/aZQSJabRSKyM2Npqezg+PjjwL77SkffCy/ILLxPP5WL73/8Q4q/33UXcO21MsPloIOA22+X+/riC6kptddesgxtyxbp/HO7pW7U999DrVwJ48EHYVx3HfRdd8F46CGM/OJ/wLffIsOtMXFPL3pnGDh6by88LvmY6zeQ6QW212h8tTYMy5aPa8stLFoVbLPG3egCd2yk2ucGzhztS2r4ug0g25f85jVVcuMYGrBtjTQP4HMBB/Z3x2qaNLTUxMHdx6jTyc0FLr9cOrXmzJEZdt99B4wdK18PhWQm70cfyfLU2bOBq66SnBkzRnIpJ0c68844Q76eni4dbH37SucZIEtWy8pkFl5trczYKy+X7zkXQ87HxA69xI9O52A4HP88sdPPWb67Y4fMEjz2WOCll2Tm3osvyhJdpWTWcSgks+4KC+W5H3448Mc/yozCDRtgnH8+cl55Aeo3v4F+5hkY06djxMevQi37DtlWDY7cy4u8TGYeM4+6OkMp9P3+cxg5OVAZGehXVYZ9v3sXh3/+Eg5d9SFyApVY30g7z+F3AfvYG7FfzY/Yv+ZHjLZKkeGWvDOVvOcSORssZHkV+mYq5PrjNSvrvnedHWidenW72rmU6TNQnOvC0lK5D0MpjMh3o6Q8gnSv0eXbeRP6u5G5aR0wfz4q5n6AjKrNOHDbUhz55Ys48tvXcfCWr7Df9u9RW1HNzCMi6sS4zVA35hT9dZsKLksjZEljIRJdCgEtjQilAKuB238bzsKo7ByYlRUy88WZhadUfKZKjx7ABx9IJ11JiVyE/uIXcqF69NFyMf3LX0rn23ffySyXU0+VTrvzzwceeEA2vfjLX+Tz556TOnl33glceqksO/vpJ1miFgrJBfxll8WLxQ8YILs/jh4NTJggReLnzAGOOQZq5Upk9usH/elHSK+txVG9eyNY0hc6vy/cxQMwoKcPP22Rp1FWaSXtbhaK7FqdluYY2Ftm9Xy9IYwTRnjhMU0cNdSD/30fQt8MGVUNRQBD6VgBaSgkNb5tLXVRDFuWpywtiyDDZ8YauBsrLe4+Rl3LsGHy7/jjZZOchQtlVu4PP8hy2TFjZEntRRdJDmVmykDAI48AN94o2XH88TIzeOpU6cg780yZ9ebzSb5Zlix3/e9/JYceeUQ69kpLpdMvWGcZ1s4uRKuq5HalpdJZ6Hz87juZpefM4HO5ZKZxfr6c52mnSafdz34mnx99tHQuApKJw4fLed56K9SNNwK33gpceimMRYuQcdNNwAUXwOd2Y1JWFkI/9YMeNBDufvkY1MuNHzbL3TDziLoGW2vovDzoN96AuvBCuC+eijxns7DevdHvkksxrrAQgfIqaNPEjuK9UDpkHLa4MtBL1yB/1bfImfsa0v7xiJRLAQC3G4N/MRXlx52GskGjUGpkIRTRsCDtxjSPgqWB40f44DYNVNZaeGdFEC4FeFwKEUtWfYSjtfC8sto12qGOXcqZxuo6r98ewcrNERy5lxeTh/pjM+yKc13o18PE2vL4Yzcr8yIRGcTx+eKzsFsoKfOGe+BZsRynLV+Myi1V8BgaEV8aqgfshcW5Q7AlHM2nJjKvv65A5uwXgNtvBsrL0eOww5AzZgzUrFnyd2bCBHh79QJeeQW5d01H4Jrf7tJ5ExFRx2OrtRtzZiXYWkujIFqE2EjosNMAfCZgGgqBsE5aWmFr4N2+++Mo4zMo7RRACcVnmwDyee/eMlMlMzO+LG3BArl4HjxY6t6NGSPHzpoFXHKJ/D8jQxpKoZBcOGdnS1H4558Hhg6VZbY//QQceqgct3Ch1Nm77TaZiffSS7K07LLLpKD8zJnAlVdKw2vLFrkYfuYZqF69gJEjgU8+gW/pUjmv/v0xtH9/DNm2DXbEQsDlQygjG9Ujx6A0rQ96uyPo3yMTttZYWhpGca6rxbt2Obu/OcWEE+9rYG93rIFXFbDxxZoIMr0GaiOAtqXhm7hspe7IuQYQjmjYkHqGmVrHHqukPIKR+W5sqrZjjz2myI2lpdil50GUUnw+yZMxY6TW3HffyU6zO3ZIFr3+usxsW79eOuL+7/9kpu7ZZwN//jOw335ykZqbKx9ra6Wz7sUX5f5zc+W+nGW0J58sHXnnniv3d/DBMnvY64134JnRq9O6S8Pcbskuy5JjnJqi69fLoEY4LLfZtEnyb80aObZPn3jn3+bN0un3wAMyqPHgg3IO27dLDdJ77pFMdXa+Pflk4He/g7rqKmDFCnj32Qd46AHglFMwuKICg3J6INyzN6p6F2JDwV6ocBmoDQN9Mg2MyHcz84g6maWlYQz69At4zzpLBkNrortSHHYYMGIE1Iz74A4E4D7mGODss5G1YyPyZ1wPNWSIrHhYuLD+ne61F/weA/3Wfo/8H7/BSF86qpQHkYxshPsVYVPeQJQamVi8PoI9e5tYvc3CEXt5sXqbhf45Bt79IQSvCWT5DBxQ7MGPm63d7lBPrOvsdMbVhjSqQ5ILC0tCOGqor9ENPADptLO0RjACFPc0Y5m3ZnU5Clcuhuu1/wLz58fzd7/9ZOb2vvvKrGxA6qN+8w1QUQErYqHG5UP68L1gjBgBO7qxWnGuCwPTQhi47TNg2ovA3/8OdzCIns6TyclBj7POQsHhhwPVNdAVFbA8PtTm9YPH60Zo81aEQhZstxeBPYegz2vPwX/Pn+W2PXoAI0dCzZgRf3FGjJCVLgDMl19ExsVTAV/Tdc2JiCg1sQOvG3NmJWyvjddl19FixA4rurTBZWgYqD8TryzowoK+Y3BQ5DOoQEBmini9UtvJWS6Wnh7v2OvZUz5u2yY70R58sMzCGzhQ6ukdfbQUbD/qKFlCdtppcuF84onAq6/KTLv//U+W0jqNk/32A/76V+C666RxOnWqXEj/+KNc0H74oSw5mzZNisL/8pdywf7mm7KU7sUXpYE6dapcNCsFLFkCddttUIYB46yzkLHffkDFRuTefweK8vLkYrt3b9hp6RhRUwPt98MqyIc5YIDcR51dIetqzg7AmT4DVQEbH0ePqw1rhCJOjcKGd3ZLZOl4kemgBRTlGHj7+wAilsbGSoXx/T1JF+LcfYy6nL32kn/77gssWwa9fj2wejXUhg0yeDB0qHTQ9e8PTJ8ueeP3y7L844+XmbwjR0pHW2mp5NB778kF28svS9bZtnS09eolAwP77y+1OH/2M8mhoUNloCEjI7501hnISEuTTsLevWWprlNnLyND7jcSkUytqJBlvlu2SAeiM9Pvxx9lCe0338jghlJyruvWSW28M86Q2cyRiHQKAjKz8LzzpMPvxhuBp5+W2Xp/+hPUBRfA/OEHmCtXwrfPPuilNbDnntBKwd5eAYweja1DR6M2nI6lQRsj8j3N7vxi5hF1jOJcF3RuD2BLabzzbswYYMgQ2fTrggukLvHQoTJbd9kyqLvvlvzbsCF+Rz4fcM01kpc1NbIJ2cyZMGwbHiDe+QQgf/QYDP7jrajuNwCbVtlId7uwY3smhhf1QGlJOUaEwwgoN4qLeyPTb2JMkbFbHepVARtaA30zDWyssqWOc63Uc5bdcWUm77wVgaQZdqu3WRhd6MaiEhs1IanDh5oADGXD78qErg0g8NpbKLr7Lpiff1b/gRcvloHngw6S1+ann6QdGi29YALIBADDgD7vPFSfewEK3F64q7dDv/Eq1KOPJt/f2LHApEnyGv/wA4wbbgDGjQP22QemacDzr8eA116Dv1cv+XvVqxeQoYB7/xK/j9NPB/71r+gPv1j+bqWlSTv5rbegzjxTNnQ69tgWv85ERNTx2IHXjTlFfz9eFcS2ap1UIDdW0gmyE5llQ4qaNHAF9VOtD2bBOBxgfAW1ebPcuLBQZo2Ypuze6PHI110uucB0u2WGSE6OjBaWl8sFa36+zJxzdoo84gjp6CsuluLua9bIDJWqKrm/Aw6Qzrf8fLlo9XrlpH74QRpUP/wghd3/9CdZdnvZZTKDb/t22an2rrvk8adNkx0dx44FHnpILoQvuURq8VmWNGSPPVY6I8vL5bxfeEHqkxx4oFxgfxKWzoJFi4C8PGnsFhbKzJn8/HgxfDQ8Ulx32UZxrksueMMaylDwmLITmdbyozBUcmdrIlXn/6YB/O/7IKzosmhARqMtG0kXz0RdUmEhUFgIBaB2czmMJYvh+fJzqG+/jefLr34ls3r795dOu/vukwGG4cNl9u7++8uMtkhE8mvtWqmx9+KLwMSJMqu4b1+5P0CyY8MGmdl3//1SOuD552XX7c+iF4LOzDy/XzrynF1tjz0WWLFCOuXCYbn4qq6WN79hxGfylZdLfq1ZIxfgK1bIx5dekrqh6elynLMD7ksvSS726SNfnz1bZuF89JF0aD7zjMxoHjAAePNNqH33BT76COqAA2CEw0BZKXpd/wf0vuIK6GAQOhQGCvLlcfbeW+7XsuRzJ4ujmHlEHSPTZyA0bjRw/1/jX7z0UuCKK4Df/14GRydNkq/17g08+6y0mZzOu4wM2RjI7ZZBg3XrgKeeqv9ASslmY8cdBzMcRua9f0bm+vXIO/dcydXNm6Gffx6FP/wgHYBZWbAOmwhMvRDG+PEozu3T6Azdeu/Vykpg+XKgogKBkIXtth9G4UCgR2+4TY0t1TqWGx4X4HcrKCCWOWmmjZHbViB3/kJYK3/CAYX9oGprod59B6q0FLBt2IcdBvTug/S77mz6BR4+XNqiZ58t7dj0dMn77Ox4ndOxY6EiEWQ+9TgyDzpIBqJffjn5fq64Qv6upKVJjedRo2TQeulS+bvwxz/Kfd9zj6wqWbtW/h5VVcUHzAHJ3spK+VszYoS0aZ1cvvxymbW9bRs78IiIOqkWdeBt3rwZM2fOxAcffIAN0T/sBQUFOPzww3HppZeiZ8+eO7kHSjXOdvMflwSwtVrDNBWUQtJyWTN60dRUOacfAmmoKTgQk4xPoNatk4vKHTvkwjQ7W2aNbNokHWfOLBGfT27s9caXjzm/Q1Z0rp8zYyUQAA45JD4L74UX5OsTJshslyuvlFHQn/1MLpIBabzce680Up59VupjLV8OrF4ty+amT5eOxcsuA/79b5n199BDci5/+IN0GFqW1Jj6/e+Bv/9dGmUvvSTnc/75UlPr+++lAZeRIY2ikhLpZOzdW0ZnnecbDsvXwmGMdLkwpKAQW/vtiVVp+agKxF/cvCxZtrG0NIxQRHYPc5aQJf4M6l7Mmip5NziPGZ2RooHacLzvNWzL7BQoxBq0bVXbiijV+HvnomrCofiyaF+MPmwFfD8sg1q8WDrIKiqks+3EE6VT/5tv5H1/+umSFT/+KJkzc6bUx/vuO+mY32cfmdH2f/8ns3wPPliW7aanS1bYtmRgebl0oN17r3TOrVol2eB0yDn1lPbcU2YIOxnZu7dkS26uDEg4QeB2y+zmQEBydOtWGVz46SfJsS1bJJecHXDXr5eOxffek07H99+XTsN//1su0D/4QDr6ZsyQz+++G/jd74C//U0uLufPhzrySOCXv4S67joo2wbeelPyPj9fzmmffYBvv5XHGzRIvpedjRHp6cjKLMSaoAdVIZW0AyMzj6gNffIJ3KtXx/Pl9NOl0/4Xv5BO+9/9Drj+enkPT5smubZ8uRw7dqy0dd5/X3Jq61ZZQppo1CjJB5dLsuf22yWzTjxR2kJr1kim1NQkdbSjogLmq68A782DPuAAeH59NXrl9sOWDR706pOBb42+0OXl2LF4JXy6Fm63IZm4apWUBPjkEwCAD0ARgH577IGq31yP8v0Pg5G9B2oiCsGwRj/sQM+aShmE8HvRY2spsv79BDxP/hOYOhVGIAD8Y6b8DQCAPfYALrwQRigE1O28y8yUmW/p6fJ65ORI1t56q7x+55wjnZMvvCD5e+yxMiD92msySHzTTfJ3pW7n3bXXSvv2Zz+T1+qII2R2+L/+JTMk//pXuZ9TTpFjR4yQxwuFZAAqkd8vAz2HHCKdf3/9q7RlV62SNu7tt8ug1qefyt+UPfds2e8TERF1qGZ34L377rs4/fTTUVlZCdM00atXL2itsXz5crzzzju455578NJLL+HQQw9ty/MFADz88MO4++67UVpaiuHDh2PGjBk45JBDGj1+/vz5mDZtGr799lsUFBTguuuuw+WXX550zAsvvIA//vGP+PHHHzFo0CDccccdOPXUU3frcTuLdK9Cps9ERcCC3y2jnmFLLoQ8JpDmAmrCQMiWTS5cBuB3A5XRVVxay4XVhhoD/8k9EKe7P4dRXS0deevXyzIuZ8mXxyMNn7w8aeABMlLo9SYXA667eyMgs18qK+MdZUD8fl0uuYjt0UM6CfPy4jNhhg2Ti+Fp0+TCeZ99ZJZcMCiz7O65R2rnTZ8ux199tdTJOvhgaezceKPMxvnlL6WgfXa2PKc77pBjv/hCntPzz0sDy++PX0wbhnTcmaaMlmZnA+PHQ23aBO+OHShY8CHye/cGevaEnZuLcF4B3H2HxnZPC0eA0ooIasIakQY2Q3MmRTo7vqnoEjJTAVl+BcsCqoJSO8pQ8j0DgNelYg1p5+KZqLtI9yrA5cZrrqEYsE9/DM3bA+k/fAfD7ZZlZG+/LTN7TzlFLk63bJHZZZWVckF15ZXSOb95s8y6+Otf5SL4iScke0aPlgvMG2+UDDn1VFlulpcX39X29NMle844Q8oGDB8unXNOh78zgw+QTIlEZBbLCy/EO/YKCuQ8iook99LT49nobCbkLMt1esLCYeCrr+Q5vP++XKwPGSIzPPLygDfekF3Dly+XWXlPPikXrA8+KDN03n5b6uhNny4zQnr0kA7CxYtlEOTmm+W4YFC+P3o0MHIkjKoqDMjMRH+fD7pPX9h+P+y0NFjKBW+vHBiBHhiRmwszNwtrt0VQGWLmEbUKnw9q9Wrp0Fm8WDqHbrlF6nauXy9tp40bpaN+5UrgrbfkduPHy7ELFgBLlsjniZ13kycDJ50kAxtvvSVvSGeW79Chkl+rVsntE6WnS/717SvZFQxCffghvKeejALLkk6ln/8cw3v1gpozB2rFCsnfX/5SBli2b6//HIuLYZx4IrJXLEX2qy9ggGlCT5wIbdswnnkGaulS6Xy75RbJ5ZoaGaR45RVZqeE48UQZfKipkQEMx/DhUuKlqkoyuLxccvq++2T29siRkn8zZsiAjcslfxOCQTl+/nwZ4Fm3TmY+J/rZz6Rzb9w4ydtRo6TD7a235Db33Sd/iw46SDrvqqrk/0ol74YOSIdiUZHcZ0aG5O+//iUD0tnZsszX5ZJa0UuXys/i73+X4+vWaCUiopTUrPUjmzdvxtlnn43s7Gw899xzqKioQGlpKcrKylBRUYFnnnkG6enpOOOMM7DVuThpI88++yyuueYa3Hjjjfjqq69wyCGH4Nhjj8UaZ0etOkpKSnDcccfhkEMOwVdffYUbbrgBV111FV5wZnAB+Pjjj3H22WdjypQp+OabbzBlyhScddZZ+CQ6urcrj9uZLC0No7zaRlp0iYHfrZDhVfC5FNI9CmEtH30uhTQ34HYpFOaYcnEUvXhyag+FLOCZ9H2xo1df6HHjpEEwYIDMjsvLkwbf2rXSQMnIkJkg48dLgykjI3l5GJA87c9ZitBQIyMcloaL02l34omybKyoSGbcZWfLxTcgDdJ3343PfCkslItYQGaM/PSTLCt75BG5n+eflwval1+Wxzn1VOkIvOoq6eibNCk+S+fLL+Vxqqulk9Dnkwv3rVvlPtLS5CJ93Tr5mJUFtWMH1IcfwvzwQ/gWfw3jpRehX30Vxrx5GLNtGXp5wghHO09V9J9hJK9m9rriu7gpBWT6FA7b0x27kDUVkO6R+7AB1IRkhqXHJbWojITXtCpgY/GGEOzoa+/s2FYVaOBqmqgTSsy8TToNiwrGYe6h52H5L65G4OxzYd9zD/Rtt8n71bJkRtmrr0qH1gUXSKf9uHFSW+/55+Uia84cyaDTTpNOu9/9TurLDR8ub9gVK2S271NPSWYsXCgXlPn5klGnnirLqk48UT46S14POURmxjkz+AYOlFm+QLxDcP/9ZVbHgAFy0Wyakp3OoEdDnGwtKZEM/OADqSe6bJnMyHj9ddk93OnYc+qYLlsm56C1zGyuqZHlYyUlcqE+dizw6KNykX3SSTLLY84c6SRYtAhq0SIYLzwP11tvwvP2/+Bf+AGMV14BnnoKxgMPYNic+zF5wVM47cc3cOb6d3Fc7VIcrNeiMEPDSGixMPOImiktTbLA6wX69ZP37vjxsoLgsstkgPOUU6Ttk5cnHTuFhZJvwSDw3HMy43jOHLk/paQjaN99JePmz5dBhpdekg6jgQNl1t3Gjcmddy6XdHZNmSIdiU5dtt/9TjrSLEtmlv3hD8A//gHjppuk8y47W87z9tvrd955vVL/eJ99pJ7fQw8BO3ZADRkC47bbYN54I9QPP0iN4/vuk/uuqZH7q9t5d+qp0nZ7+21ppzrOO0+y/777pKZoebk83wcflM/33FNKD9x7b7yd+vvfywDJtm3SBjzxRJnprHXybuUul3ROLl0qP5tVqyRPX3lFct3rldezRw9pp1ZVSfs5M1Me6777JJOHDo2f6+9/L/fl98vxX34p36uokDbswoXyeIA836lTYS9blpx5O3bI3xQiIko5zerAmzVrFizLwoIFC3DGGWcgLaFAf1paGs466yx89NFHCIfD/9/emcdHVd77//Occ2bLNiFkJ4GEHWQVlUUqi7JYEVtXXGhVtGLrgsvV2vZ39d77uhVtXVq11noVW69XWqsWtRQBF1zYhQjIvhOSkJBtss7MOef5/fGdM0s2AiRkJvm+X68QcuYsz5nJ+eT7fLcHr732WqcNFgCeffZZLFiwAHfccQeGDRuG559/Hrm5uXj55Zdb3P+Pf/wj+vbti+effx7Dhg3DHXfcgdtvvx2//e1vg/s8//zzmDFjBh577DEMHToUjz32GC699FI8H7aC0+leN5bIT9GocS8oM2H6YCf6uDW4bAJeHdAElTQ5Av2I6n0S+09SiWvTqlopaf81ccNROGoSGRWKQhM5q8TqX/+iyOuRI5SRN3QoGRGaRobF9Ok0Ke3TJ9RHyW4no8la5aspVimDlX1is1H5wZAhZChOm0bRTKeTjDcpKYtk2TKapC5fTsddeSVFQv1+MiYHDSLjKDeXMlJuvJEiwFdcESo3+93vyBhcvpzuKz6eHIQzZ5JBec01lAX40UehxTussuIvviBD6fzzyYjbvx/i0CGIrVupgfEXazB+xRu4cdtfcV3VBvRNNKEGUlDMgNNUE9Tjqd4f+Xn8Y5sPfpP2s6vUy9CajvpNGWgQDxQU+oMTV6vR/LEKA1uP+WGYEluP+XGswsD6w16e0DLdgpY0LzVewz4tHf9MHod/DpiFHROvhOfqG2Bc/D3IBx6giaeu06SoTx/KOklPp4njsmWUiTd8ODn6/v3fabJ23nm07yef0CTzmWdon4EDSesWLSJn1803U5ZGcjKVQ5WV0eTM66Uem5s3k468+25oQZ9Bg2jyKQRpTm0tHV9TQ/q0alUokwSI6MEJIDIjz2ajyV1SEm0zTdI/q4WB5eT7xz9Iy7dto4yTPXtIv5cuJS3cupWCIMePk84tXUrld3v2UMmey0Xj9HppW3ExOUO/+46CGp9/DrF7N5R334W2YgWcn65G6uqP0H/lO5i66jVcv3Up5h1ajqsr1mOiKEG8jTWPYU7JoEEwzhsBmZVFpa6qSotY7NpFfXsLCiho+Y9/kKMdIL2xMnTPO4+ebYuHHiK9+PLLkM329tukMZdfHsoiW706dEx8PGW+LV0a6m/8n/9JdiBAx/7yl6SjjzwSGgdAY3766VBrFQuXi0p/X3mFxq7r5PQaMIBaGtTXk8346KNkYz79NGlPRgb9HO68GzOGbNVPPw0FTwBgwQLSp3/8I6SZikJjPHCAtHDePHIcWtxyC93npEnk4ATICWf9fQjH0vMxY+g9HjmSzpmaSud97z3K7NO0UHuYKVPIHpaSAizLllHWc//+9Nk2NtK9ZmfTceGLqqWlRX6WAOD1oupQcaTmHTtGzsGGBjAMwzDRRbsceCtXrsTtt9+OnJycVvfp27cvbrvtNqywUu87AZ/Ph2+++QYzZ86M2D5z5kysbWmZe1B2XdP9Z82ahc2bN8MfmJy0to91zjO5LgB4vV54PJ6Ir2jEWswiN0XF2FwbVEVgbK4Ndo0cQ6pCmQq6pMmQBDnyjCbzGiufwZDUQDw5MwW1l18Ff3YfSGsSOmYMGUelpVQONn06RRw3bqSJaN++ZFi+/z7tt24dGUG1tdTvZM4cmkwOGUIXC5+UNjaGSsssEhLIUOvdmybFmZkUGQZC/zcM+rLbacJ61VVkNFkT5IsuomtrWqhUd+hQMqD27yeD9U9/onspKSFjeM4cKkdZsIDO+/nndO6EBLp2SQltu+wyureiIprwWo7H0lJ6T44fh9A0KA0NsO/ZiUs2vIt5ng0Y7vBAFYEeUIHPw/oMpASqGmTE5+MzAYemQFNocqsKymCRAA6W69hy1AegpUbzjSjxkNFs9Y1imNboLpqnKAJH4cbqxJFY2v9yLB32Q+yYNBd1P7wexkUXQX7/+6RTXi9NFvv2pclcnz6UQbJ3L3D99ZT9pmmkA7/+NTn1zz+fJne//CU5+GfNooDF4cNUkv+//0ulYn/6Ezn2Vq+miRlAE8+qKtKrH/yAsuRmzqQJ8eTJNKEGaFJeXh4qrbL60gEUFElKoow6IHIFXKsHVNOGp6WlNKE8dozGsHo1ZZwA5HjTdXLwAaTzF1xAE94f/pAmp1On0qSxXz8a63nnkb6pKt23EOQoyMsjh15SEmnvjh3k6Nu1C2L5cih79sD2xutI2LQWuX95EbNW/Qm37HkfV3u+wTCjDL2cAoE/V6x5zDkhVjRPnXwxfHOugn/gIMiMDNIsK6XVWkQnKYkCAE4nPdMzZlA28LRp9B0gp9Lq1aQXX35JttDu3fTanDmUlTtpUmTmnaoCDz9MJal+P53jmWdCOhMXR3rodFLwI5yBA0kbwrPWLB5+mM5TXU0/JyWRDlqZgomJ5Pz77/8mbbb6+l19daiPssX06aFtikLXu+IKOqapw+ummygge+QIvUfh/exSU+k+iopIYw2D3qM9e8ihdvBg5Ln696dtkydTRcpll1Hw5dprKXOuupq+NI0+G4D+5hw4QFprUVpK93X++dQe4ec/J8fe6NH0t+cXv6AMwawsmNdeFzmG9HSUZlAfvaDmDRsG/M//0O8GwzAME1W0y4G3a9cuTJ48+ZT7fe9738OuXbvOelCtcfLkSRiGgYyMjIjtGRkZKCkpafGYkpKSFvfXdR0nT55scx/rnGdyXQB48skn4Xa7g1+5ubntu9EuINGpYFS2PVhWpAiBiXkOuOz0c2aSiitHOJHsUoJlnE2z74DQqrW6YaLOa2LdER9Wpk/AgfMmw3/jTZCFhRSZ9XjIQaZpZJz84Adk6AwfThPCtDQy+jZvpgyUd94hQ6lXLzIkrdWz6urIaHM6aRJqfUZShlbATUqic6sqGYpWRoplPFpR3ZwcMop69SIH24wZNEGeNImyR+bMocnokCFkjM2dSwZSVhZNPD//nKKnpaU0gZ8wgYyfDz+k+7HOa7eTwXb55aFJrGHQ5HjvXjIKU1LIwejxUCR1zx4qt7XboRw6iDHfrMC8ok8wXKuGCDhMrcmp9Tkogr5cNoELcjXUNJowAqW48TbA55eo9Zrw6xKHK3WUVNPqb5lJavDzbKnRPMO0RnfVPMMEthqpeDfhfLyVPwfvjbke3835MaquvRn+yZfAvPlmyIsvJge81b/INEkPPvuMHFEPPED64vGQQ+/ll6mJ/MmT5Kz6t3+jzJV77qGJ7g03UIZaaSk5A5cvp0ndn/8cCm4kJVFQZO9emoBu3EgTr7176XgrY8NqGzBtGo3n2mspSAGQTm3ZQiXBVlmVqka+WapKE3qAnG3hDr5Dh2hyXF1NzsEvviDN9HppIrxtG00qAbr2tGmkm3PmkK5///s0Ab74Yrr/yy+ncY8eTX23GhpIG1NS6PjBg8nJmZAA8fbbUPbtRfzf3sLAj5di9saluHHfh7i65htcmlaP2oDmqQLIiAfqvBI1jSZ0Q6K0VsfmI17066Wy5jFnTCxpnmPoINiuvw7iqquoCmDiRNKewYPJ2T9yJNlUloPL0jEpA0tBC7J3hgwJ6cfll4cqGCxn1MSJ1ELE4tZbKUuuvp6cd7//feg1VSXtW7cuMsvNwloAoilTplA1h+XUAkjXXn899PPdd5OW9utHwVbrenZ7ZGZZairpsIVlEw4dSrZfUzIy6P379lt6zyzdtMbwf/9H76H1Hl16KY21afsXIULjsK7p9dJ2p5P+P348OfTCj7XZyOEaPv8wDHLK3ncfZQMuXkyfq2Vfrl9PjlAhYI4fD9+jvwAyMqDPmo3S/30fh5yZAJpoXtOsbYZhGCYqaJcDr6qqCunp6afcLz09HVUtNZftYESTP4JSymbbTrV/0+3tOefpXvexxx5DdXV18OtYeE+NKCW8F1CiU8H4fnbYNGBgmgqbqmDmUAfi7aEsh3DCza4aL7AtsKIgAOxucGFZwvn4fM5CNGT3hZwxgyangweTcbN+PUVL09PJ2bVgAWXh3XEHTRD79aPJ6rp11BOqsZGccX//OxlM33xDE2crWrhqFUVPv/2WMuhOnKDop+XQA0KGovUZWs3ewzGMUKZKnz5knM6YQec3Tbrm5s00CS4ro9ctg/OCC8gZN3EiGbiaRgbhF19QRsq2bWQMx8dTBPvkSXLaGQZFb199le77P/6DnIsff0zXPXkSorERyuHDGLN1BW6q+BITcZxKa8MwQQuOjM7WsPGIDr9JzlUBoLIRqNepZ6EQgFcH1h72oc4rMSbHFiwvtGipbxTDNKWnaF6dLvCNzMAHcaPwdvaleCt/Dj6f81OcuOUONFw8BfqcK2FmZlKmy/33k07U1VG2nNNJ2nPNNdTn6PzzKeu3tpZ04cMPaVGdvXtp8nXttVSmdsMN5My/7jrSv+++o8naCy9QtsWLL1KQ4JprSEtOnqTJbf/+pE1SkiZt3RpqN+B0htoFDB5Mk9whQ0jH7PZQhk7fvqTXLeH1kuOuqorOC4Qmo0ePhsrV7Ha69pgxdP7+/UlDGxpIyw8coMySzz4jB+CSJXQvH31EGr50Kf2t+P3vyQnw29+SxlsLC61cCfH551D370XCh+8h7U+/w/V7l+Fqz2ZMcFXiRD2tRus3Kcuy1gscqjCw8YgPA9JU1jzmjIhJzfNKHDtvPOS8eVTCP3s2GQJxcfScpqSQc8h6ji1bycr27dOHyuSBkJNv8OBQhlt4tpzV3qSkhPTEKve3uOkmyvQaOZLsuHDsdjq+aeksQFoWnoEGhBYxA8jeO3qU9GXyZApgAJQ5HdbfGkDzjDwhQr1HmzJ+PNmhbje9X00/b5uN9M7tDvVkljLkCA0nKSmUPRiuM1OnUpZjURFpb1VVZFDlxAl6DxMTQ9vq6uj81vkAer83baK/Eb170/v80Ueo9Uk0PP6f2LVqCzY+uxSbssZS2wHWPIZhmJigXQ48r9cLm+3UUWhN0+CznB2dQGpqKlRVbZb1Vlpa2iw7ziIzM7PF/TVNQ+/evdvcxzrnmVwXABwOB5KSkiK+opmWegHtKzPg14GNR3yobjCw7bgOuyqQ4GzZiQdQ1ldeiorpgx3BzIYGv4TXkCjyO/Fer4uwfNAsnJhzPYz0DMjBg6lUrLKSjJCRI8k4uekmmqTabDRxW7GCvl98MU3ybr01NGncvJmMnjVrqLRr/34qvSgvp+jqp59SqVpxMfWYAsggCl8Uo6aGDKKmjr2mxoxpkqPt0CGaaG7aRI7BOXNC/fP69ydD9eOP6bzXXEPOvM8+o54nHg8574qLKcLrdNLk9fBhmpx/8gk1bK6rIyfljh1UmpeZScccOADs3g2xfz+UffuQu28rflj2NUbYqyI+B8MENhzxw5ChEme9iQ1JE1oBuypwsFxHQaE/IgsFQLO+UQzTEj1V84QAHA4VvYf3x84hF+PjsdfgH7PuxarrHsbOubeh8sprUTvzCvjGT4T/shkw+/WDOWYM5COPUNDCNMnhlpxME8/ycsqoGzeOJmG6TtkTixbRhNFup+DG++9T9srLL1Pw4t57aRI8YABlt2VkkLPrzTdD5bj33huarN50E51j4MBQPyirJ+gPfhDqA5WRQWOaMoUmr2lpoYly796k3XFxoRJcC4+HNPXkSZpcA6GASF0dafu779K1PvqINHTDBgp61NXRPSkK/R047zz6GzB9OpXIzZ5NJchTplD2s8NBE/333wfcbtLGN9+Ec+2X6L/8bVy//e+YV/QJLtEPBSRdBPu8rj/kY81jzohY1bxtem+UTJ4Feckl9Oy4XJQht3lz6FltyvDhZJ9YjjpVDTnrRo4MOdTMsP4d06eHeuHNmhVa4dYiPZ00wXJ2Nb1eSwvFZWWFVui2mDAhsmw3vI9duNMwNZWymsPRtEjtkpKCBps2Nb/2hAkUUGlooIDstm2h16wey00JtyXDV+IxzcifLayWDGvX0v/j4+k648bR66WllJ08aVLomI0bKYvywgvp57g4CgDv3k1Zz1Y24PLlkNl9sO6QDwdFL1Sa9uApWPMYhmFig3Y58ABgz5492LJlS5tfu60+GJ2E3W7HuHHjsGrVqojtq1atwqTwP2RhTJw4sdn+K1euxAUXXBB0Sra2j3XOM7luLHKqXkAbjviCP2tCoHe8EjGhFSCnUUq8wPg8BzRFwYBUFT5DwqEFftkk7TMqx4avtDwszZ6Ofw2eBe+F4+G75RaY550HqaqQF15ImSCXX06TQJ+PjL+NG8nYvPlmMuCuvJImrw89RAZNRgZFToWgDJYRI8iou+CCkMNO18nQ+uwzyqazBwyYwkKayGpa6yvhAvSataqtaZIBevw4TR4nTSJn4ezZdL7vf58Mp7g4yjzZuZMy/fbuJQMwM5O2r1tHkdPsbJqMPvIITai//pqMsORkylK02eg+haASM7s92IxZKSjA2D1f4VplX3B1RqtnIdByyTNAWXpxdoGsJBWADH7GACKyUko8BnYU+0/9i8QwMUJnap6mAuW1JrbWxmNF3HmonDwdHw6Yhb+Nuh7vT70Tnvl3YOfl87Fz3s9wfMH98D38CDnbfvlLcmidfz7pjM1G2peWRsGByy6jDBjTpO9vvUVN0++/n7Sgvp4cduPH0wISTz1F3wsLQ33yjhyhQEdlJenOzTdTu4B+/cgxJyW1BTh+nDTUKhEbN47Kba+9NjQ5Hj2aJvS9e4cmydakNDGRtjUtuwVospmcTNmBycmRi2bU19N9/+tfdO8rV1L2z7ffUgaNla1XWkoaumkTTfbffpuy8l59lRwSOTm0ra4Oyrffwr5+LfLeeQ1Xr/szfnhkFUbXHkCCTUI3QmNjzWO6M+GatwWZOPTfL8G/bgOV3m/fTi+kpNB3yw6ynmevlzTHWnjC6QyVgCYnN18hFiDHnrWaqdXPzmLCBLJ9BgyI3G5x002kD02ZMoU0IZzwMlmA9MNyLlotVIBQSWlbWOWrLWFp1PHj5FwL73nYrx8FYZtiad/q1aH2LwAFd91u+n9REQV+bTb60nU6ztLqNWsoSA2QVv/gB6GxAuQ8bWigQMdvf0uLqyUm0vu7ZEnwnuWIETjWO581j2EYJoZpd4ODW2+99ZT7nKqktCN48MEHMX/+fFxwwQWYOHEi/vSnP+Ho0aNYuHAhACpnOH78OP7yl78AABYuXIgXX3wRDz74IO68806sW7cOr732Gt5+++3gOe+//35ccskleOqpp3DVVVdh2bJlWL16Nb4K631xqut2B0Zk2eDXETGBNaSEVwfye6sYnKZh/REfqhsk+ripOXhFWNDSSmarbQS2FvowMFXFJ3u9aNQpwyvOLlDnkzAl8NVBP1waYFcFdAh8IXPgi8sBBoyEcwgwUqtEekM5GUujRpFRVFVFjjErU+6KKyg6m5hIxsqiRZQVd+wYlZb97ndUWvb661SSu3w5laG99x5FZ99+myaFVtbeF1/QDXz9NUWN160jg8ly8Fk3KARNMDOpZwji40NR1969yZCyItBxcWRQffMNGWfXXEP3lJJCY7VWur3iCjK+/vxn4M476TW/n4w7v59Kzi66iMZ8xx00Kd66lYzR5cvJEE5Lg/B44CotxXVZ+/H1yFk4WqNAAnCpVDLb1ImnCFp0xKtLjOqjocEnceCkF5oA+iRrGJNjQ0GhHyUeA3aNVu9kmO5CV2meTQU2HbWyvpwAnMjtrWJUtr35IKWkAIbXSzpQWkpaWFFB2uL3U4baH/9IzrhZs4AHH6RJ8+HD1OhdCNKm996jCeE119CkfPVqChb89reUmXLzzcCTT5KWvvUW6cs119BqkVYZraJQEKW+PqR9Tmdo1ciRI0NN7fv2pclmWlpohXBrMmuzhSbD4W8oEHLq7dlDWixlaN8TJ2ise/ZQkGfdOnJWrlhBev7VV+Rk6N2bVlG87z5y6E2bRtnXFRUQy5fDNngwcvOPIkdZg7o+edg6eDLiElyseUy3pqnm7Yrvi/JHf4NBnqNw9+0LkZcXcl45HJEHWyteW2WjlgYAoUXErN6XFuFVOU0dZxddRCXxM2eSzRNOfDw5x8JXULWwWgCEE+5MtAKbFlYp6qFDdFxKSsgJ2RJ799L+bbF9O3DXXZHjU5SQ7Rf+HtTW0jX37CFb75//DL1m2Zcffkj9T48cIdsyNZX09tVXaRGOv/6VbD4rQ7pPH+or+PDDpNmGQXo+ahRtUxRyKC5aRAt9NDYCaWlofPy/kJvrxv79PmhCsp3HMAwTg7RLpZcsWdLZ42g3N9xwA8rLy/Gf//mfKC4uxogRI7B8+XL069cPAFBcXIyjYSn3+fn5WL58OR544AG89NJLyM7Oxu9//3tcc801wX0mTZqEpUuX4le/+hX+3//7fxgwYAD++te/Yvz48e2+bndAEQJjcmz4dK8RnMjWeWmyJSUQ71CQaFdQ3WCg2GPAMMkhJASgBr6bAPymxKFyA0crjeDqqD6DJsUQlIlnAqj1AZoiASkiypeS41Sk5mYBIju0UUoq4dq7lyZyjY0UoczLIyPQauCuKJQRIiXwq1/RxPSBByhb5PrryZDr148md7m5wK5dNJG86CJy4C1bRiUI/fqR0fPQQ5SxYpXtzpxJk+ikJDpeCDIy+/ena1qTUrs9tLJjnz6U/bJvH2XbWQtv9OpFpWX//Cf1wEpMpHvq25fu8513KLMGIIPu9ddpRTWPhybqa9bQBP666wIfggr07g3RuzeEy4WLSwtgyxyLPqk2fHVAbzEDzwxsrPdJrN7TiLR4FZpCvfIGpqnB1Tl3FJNRl+hsd9Iuw0Q90aJ5bS6WQPW59DVmTPPXa2pgVlSg8fqboXtqoNVWw17rgXLReCi7dlKmyHvvhfruJSVROdawYcCNN9Lk73vfo8y23/2OJn8ffECOsp//nByDiYmUxfff/02TTGtxjBtvpBLYa64JleVedhnw/POhTGZdp8m45YCznAIpKZH9mqw3HaBJv7Wf5dSzJv8HD1Jm3apVpI+vvUZOut//npx0L75I2Yiff0731dBA18nMpH2nTaNJ7ooVwLFjEOPHI2H5Mkze+DWMq34Ate9FrHlMt6UlzdvvT8J+1whc/aPb4HhnKdkrY8YABQX0/cAB0gspm/cRtjLAysroGbMWs7FoK6hvLYxTWxtyBFpcey3wl79QQKIpVulpeKludTU59urq6FzhDryPPwZ+8hPShq1bqf1K05Vl7faQs/GTT2j/U1FYSLaflflXXBxasOfoUco43L+f7M9588gZZ/X/tFaj/fZbslm//ZY0ctUqanMwezZlzlVWUlDZWojoyispM/Gdd+g+Xn2VsrZXrqQqDSGARx8lu3XtWuCVVyDvvRf+gYNRPnwc1iYMhuuIH+OM49ii9mE7j2EYJgZplwPvxz/+cWeP47T46U9/ip/+9KctvvbGG2802zZlyhRs2bKlzXNee+21uPbaa8/4ut0BU8qI/mdenSadAHCoXEdlvQG/IRBnEzAkICGhGIAUQLJTIDVBwaFyA34TACRGZ9lQUKSjwS+DjiLI0DmlpEUUDFMi0QGoimi9ia4QFJFMTW39Bn78Y2DXLpj79qEBKrSRo2E7fxzEju3A1KkQ9fXkdBs9mgy6W26h1cJmz6aI8nXXkVE0dy4ZlAMHksPv2DEymJ55hozJ55+nCaOqUtlHcjKdV1UpM6ZXLzIsrQlsfDz931rlzDRDX243lY+Ul5ORfPXVdE2HgzLzLKflxIlUBlxVReVs1dVU2mZFvSsqqIn0vn00WR81CkpCAsYP2YdDw8bDr+ad8vOvrJeo9RqIswmoKnCk0sAolwpFiJYzgxgmxolqzWsviYlQEhPhyO2Lr/c2Bu/FpgL5Q8djgKMByq23kqYdPEjN5IcOpazg4cOBn/2MMkN8PpoQvv46aeT115PzzumkyexvfkOrOn7xBU1cJ00iPUpNpX1KSylYYZXgzp1LjkCr6TtAk9BPP6X/2+203/jxoRI7q9dur17NM2zCe5UmJ4cWzwBCjgALw6D7e+ABKie+5hrqrZebS/2zPvuMSmz79gWeeAL4xS8gvvoKmscDnCiBkpaGUaNHA07WPaZ70Zbmrep3CWZOOAb7yRP0LD/3HD1Dv/sdZfU2NlIW28UXh57Zmhpy6K1ZQ9UDe/aQ83zKFNrWVj8165k+fJjOWVAQei0piZxgLfUULCujdinWQhoAPd9Tp1KVQk0NBU8tfD66ls1GDn2HI9IB+NFHFER95x36uaEh5JhsiqaFjn3hBdIPq5zXsv8ACszecw+9d9Z4hKBMu//3/yhw4vWSHj7wAL2ff/0rHVNWRu+/xVtvkVOuvJyOHzyYSmjj4ijD7n/+h96PBx6ge62pAa67Dr5FD6Kidx8cSeiDfSIFUgKqDnh1E/WKHXZVsJ3HMAwTg7QrzPKDH/wAH330EczwaBfT7dhR7I/of5bsErAFGj75TaC6MbRvH7eGi/NptUYpgVofrZKQ6BQQAvAbwN6TOibk2dB0WioRadMZEvAGLntWTXQ1DeaIEdh6wRx8PngmVmdMwL8GzcKKqx/Grrt+gYrL5sCcOpWinw8+SNlrjz5Kk73kZIowX3stRWmTk4Hbb6eJ3l13UWR1yBAyNAcPJgOvoIAcaZddRpPRsjLab+JEukGrtMLjIYMvvNQifLKekkJOPiuzz+ejyfDYseTgE4KiqQMG0ER12DCKHqenUyaJ1SD+D3+gayQnU2nF009DPPcc8j9bhu/5D4Yu3eRtUwR9CSDYO6/NjCCG6SbEvOYFaDopBwLjUdOxNa4/zEmTKPhw993A448DixcDf/sbzNtugzF+Aszvfz9U+vbjH5Pz7PPP6f/Tp1Ng4J57qCddQQEFPfLyKLtkxgzKAklJIc184w0qmc3Ppwn4rFmh5vJjxtBE1erpBNDE3er1aU3IU1MpgyYlJbLHFNDyauGWnlo2ivVeer2kj0lJVFo7cSK1OPD76f+ffEIOxLIy+nvw0kuUyb1tG71HK1aE+v8xTDegLc2r1lV8NvlG1A8eTk58t5tsnrFjKViZnk6ZueHlpX//OznILf2w2ch5bi2mYA9zCtXVUfWDhfWcWiWu4VjP8ldfkUaEs2oVLXgTTnExOecBspPCHXgAVVfceCP9f/lyKkW1OHq0+fWblvdbrFwZ6mNXV0f7We1UgJCDzzDIlnO5QsdddRVtf+45ym62ViTftYsyg8vLgX/8g/QqLS1yjE89Rdl9t99OOvnccxRMfu89cuZddhngdMLsk4PSCy7Bdxd9H+/1n42VCSOxBylQBfW6kyBbb5+WxnYewzBMjNIuB97KlStx1VVXIScnB4899hj27t3b2eNizjE1jSa8uoQtkJOZkaQgLVFBn2QFCugXxXLuWBkjZXVkCghBk90DZQaqGwLlZwBqGyW2HvfDrqHV1RsBwK4A7rBg59k00W1qnNo1KhM9pKViXZ+LsGPgBDKKpk2jfk8LF5IT7+aboQ8aDPOGGyCffx5yzRqanD75JJWGDRpEzr2vvyZjacUKMiqnTydn3mefUXR6wgQq71q9moxJIainidtN2R/hfVGkDH05HHS8lZVSWUkGrN9PDrtvvyWj7uBBmpBefTVNpA8eJOffjh00UX7tNYomW+VmO3ZA/PKX6LfxE6TYTSQ7AaeNPkubAkzKsyE9QcCuCcTbRcdkBDFMjJAer8BrSEiQ03rqIAcSHYIWp0BsaB7Qsu61em5FIc3o0wc78i7AiuFzsGrqbdh51y9ReMMCNMy8HMb/+3eY110f6nH15ZdUonrhhRT86NuX9C0/n7JQRo4kLV28mCaed99N2x95hJx7ADn9vvmGNPF736P/p6bSpBWgjL1ly+h4a5XIa66hCS0Qmgjn59MENi4u1POq6crhTX+29NDvJ8fh+PGUpbJnDzkVP/qIHBBTp1L/vu3baXL9l7+QA+GFF6gcjWFinFNpnlex48sxV+LoqIshX3yRsmgnTCBbQ9PoOd21i+wSgLLOUlLIdvngA3odoIy4uXOpP+fIkbTt3XfJjrLw+UIZdrt2UWawhfUMb9hA9lo4VsltUxtly5ZQCavHE1ogAiAnXU0Nac/eveSwT04Ovb5pU+R1rIVymrJ7d+jeAeC//iuy3Hb58tB78O67lJUI0PuQlER6U11NDrhHH6WFgf71L7IPJ04kTXrvPbIBb7459N4BlB394YfA/PmUeffjH5Mm6zpQUYFG1Y7PL7sN68fNwe64HNgVQBXAxH4aUhMUxNsE7CrZei67YDuPYRgmRmmXA+/EiRN4+eWX0bdvXzz11FMYNmwYpkyZgj//+c+ob2nJdCamqGk0sf6wF6UeE4l2BTnJKny6xL5SHcXVBtTAKqVeg/qlWBkjwzM19HErECBbywRllpiSjAaXTcDTIOEzaKXT1tBU4KI8BzKTaLZ8Nk1081O04OQ1M0nF9MHOts8rBJXKfu97aLj2Rnw64gp8MvNOHH/7I5i33AJfdQ30J/4DZloaZEIC9dX74AMyrFasIAMxMZF64w0fHiqrVVUyajdtokyV/HzKMDl2jCLQdjt9mSYZc0KQMen307FWZBYgo9k0aX9FoclrUhJFxRMT6bUvv6RosDUZboLy+99hYu1eqIoCl01Br3gFV5znRHUjgiWCasDj0BEZQQwT7dQ0mthe7IcmSJ/691awek8jPI0mTAk4NcSE5gFnoHtNjtNNoCElHVkTR2HnsEuwYtAsbJ1xM3x3/ITaB7zyCvDOO5Djx5NWffst9WdSVXLoZWfThPSGG0J98R57jMpXi4sp0JGURFl9990H/O//0nkWLqSytfBVaK1+ekKEsu0uuogm8gA5DktLKRDzySehjBeg+aqZTR154ZPVEydIixsbQz2szj+f9N1uD2X+HT1Kk/aPPqJ7Xrcusr8Ww8QI7dU8ryGx3dYH26ZcC/Ptt8kJP3kyBTBnzCDb58orQyd+7TXg3/6NgpV9+5IefPkl6YOq0jFAyPFmPZ9W9h5A5wzrTR1cNRYx32VeAABV80lEQVSgstim7Ws+/pgWhAjn888pMy8hgXTluusiX3//fdKPK68kvVq0KORA/OILstNGjaKfP/oo8h7DsRz/AAUR1q0LOe327qUqj6Qk0qm1a8nhBlBA4MILqf2AxwP8x3/QWB96iJxw/fvTImUnT1Jp7quvks151VWha5eVUWuD55+nqovAV11aNjZddRdqHIlQFQUOTSDBpeDKEU54vNRzVYiQrcd2HsMwTOzSLgdeYmIifvKTn2D9+vXYuXMnHnroIezbtw+33XYbsrKycNddd2GDZVwzMcehCj1YelVRb6LYY+BIBTVjb/DTH32vDugGNXg3pESJx8DWQj9qAw3fEZZMZq2WkBxHE10E+j61hm4CO4p8KK830CdZYEKeAy47sHJ3A0qq9dYPbIFEp4IJeQ7kpqgYm2sLNufNTVExIc8RbM5b02hiW5EvaLyYUuJQhY4RWTakJ6nI7pcCZdo0aPfdg10TrkDdgrshFi4kp9kzzwAZGZAzZ1L0OT+f+pWYJk0IR4ygLL1PPiHDtU8fmiyWl1NmR2IiTWp9PtqemUkT2O99j6KumhbK3rPbyQhMT6fzu1yhvk9S0uuNjdQfzypJa4nDh+E6dhCGKZGVpGJ8PzvWH/GhOJC1I0FN943A+3G2GUEME+1YuqcqAj5D4uPdXpysI+ebEEBDjGge0D7da03zRmbZWjxucJ4b9rGjKdP4iiuAuXNx8Lo7cGLebWh4/L+gP/sc5KDBkH4/6c9jj9HkXFUpWPFf/0Wadd99pFvLltEkf+VKCmQ88ghNaA0j1MdpyBDKhKuvp4n4qlV0g9/7HmXCjRgRKmnt14+ca3PnUr8pVY1sag+QloZjTValpIl3XFxk+W1jIzkR/X4aR1YWldSmpQF/+hNlPa9dSxl5q1dTP0GGiRFOV/MKqyUKJs5F2T9WwltTR7/v1dVUTv/llyEHWVUVPcuPPUYOpdtvp55s779Pz9qQIWTDAOQYu/nm0HFWzzjTpGzbO+6gn8vLQyWxBw6Q8/C++0LP63ffkQbk5UXe5G9+E1p52+mkcYTzt7/RfdxzD5UHP/hgqNz39dcpI272bNKlysqWs/A+/JCCEjk59POqVaQ/lhPv5ZdJ01SVSvb37iUHpKqSU653b7rumDFktz3zDFVxaBrZk3/5C+Szz0JWVAC33UZ6+MwzkIsWhd4vRYHMz0fNcy/iyKqN+PSqe3EUSaj3yVPaeaoicX7jYbbzGIZhYhQh5ZmFXwzDwPLly7FkyRL885//hK7rGDp0KBYsWIAHH3ywo8fZLfB4PHC73aiurkZSS415uwhTSmw9FirBqvdL+KhjO6SkUjBNBeJsAvV+CSEEkl0UyTtSGVqZMRwBIDVewO1ScLDcCDV0bwVFBJLQXApmDHFg1R4vqhpMqAKYOtCBTHfHLWtvZRz6dMpWGZNjQ0Eh3b9dQzNHn+XYU4SAKSV2FPuRn6Ih3mhAzYatUGpr4YAOTRUQJSUQNTVk+JWXk+G6axc1fh4+PJRlEh9PBmv4qotOJ/VgevZZ2v/dd6mR/Jo11I/G6qFnGDT5VFVy4Ok6RYSPHqXvrVD31/fwr8EzcelgBz7b54OAhCkFHBqVBEpTwmtI2FQBl01EvA/RRrQ+S0wk0fw5heteuOaZ1NoONhWIt7PmRWieQwTfMwFAVYActQ45RXuRWLAR4tsCiOJimqBmZVGgYssWKu83TWrS3rcvOQT/539oYrpoEWXDpKZS4OOZZ6iUbvJk2ueCCygIYjkAf/tbcuRlZ1MWzkMP0TFXXUXOvaoqcv7961903XffpVIza1L94ovkJCgvp0nzDTeQ7v7sZ6THf/gD/V/X6cvK/jl5MhR8GTGCso3GjKH7nD49srdXJxDNzxITIpo/p7PRvERFx/CTe5C6+QvEN9RALSmCOHaMMs2sxR9SUqic9OBBckRt2kSLNLjdwNNP0/PX0EA95EyTnt/hw+k5+r//o3NceCFl3L7+OjkCX3opdAN9+5LTsLycsvdqa8lZ989/kp1lYbeTg+7kSXLAPf9880VxAHLu/fCH5ARMSKAA6//9H5XITp5Mx/ftS063Eycij1UUcsItX05VFwD19rzwQsrI27ePdOOFF2iRsb596Vo+H2lSaSnpxtixtM3jgVQUyNxc1AwegaNDx8NlF0ior4Jm+OFVHShzZ8LeWA+XvwGGUFDnSMBRJMHTGFg0SQCQ9DlOHmDHxiP+Fu08vynRHx6UOpLZzmMYholBztiBF87Jkyfx1FNP4dlnnwVAzj2mOdH8x8gwJT4NrGAogWAUL84G+EwBl0aZeBmJCmyaQEaCgq8PeVHna/2cAkBebwXVDSYqTlFpLRCqqlAFlaVZpCUomDnUdZZ3GGJbkQ/HKiL7RYU3f89NUTEq296uSW+cXQTfNwCwq8DYhkPoffwARHk5OdSGDKESrJISmohu3UqTzW+/DTWJrqujieoHH4Qmuzt20LEA/fzpp8C8eWRIvvceRXsTEihq+3//R2W8f/xji/cshw3DwT8vwzqZTQuoBUr+nDZAQsCu0OcrpYTdJjAxio06ILqfJSZEtH9Olu55ddI8vykhQOVlcTbBmtcOzbPON32wk8rwDx6kktQDB6jcq6yMnHgDBlAWzubNlIF35ZU0cX3zTXLopaSQk23cOOq59dJLtGDQjBn0/2uuIefZhg20iuN//Af1h8rJofK7Rx6hHnwLF1KJ7vz5wJ//TMetWEFa6vORBn/7Lenn738P3HsvrRR5zz2ks3/4A028vV5yNqxeHerbtX49BV8mTaKAyeDBtODFtGm0j5Ux1AlE+7PEENH+OZ2t5qkKOfkyUYfhG1cg4aN3oQwZQs/Ytm20U79+9HxlZZEz7cABcsDfeWfIoXXFFeT8e/vt0GINy5fT97w86vM7cCCtrOr1Rt5E7970XDscFNAcOpTO+ec/U/DTIj2dgqAXXAD58ssQp6gUMq66CuJXv0KZmoh6rwmREIcsux+2okKI+++HsO4vnBtuIA369FOy7QDqsXnxxRQQGDSItv/lL3QfLhfdW0oKjV0ISJcL4nvfQ91Fk/DhcRccGuCyKdDN0GekCmplowoBGfi8vLpEjVdGBIvCO9pZQRa28xiGYboXZxXi13UdH3zwAV5//XWsXLkSUkqMHTu2o8bGnCOarmAoQIZCTaOE1wDibIE/+qBJ6fQhDuws0aG2eVZyBB6pMBFvAxJsQG0LmfoCQJIDUFWB6kayQsInsskuBdMHOzrgLkOMyLLBryOYcWjdt2FK2DWB4Zn0WBws96OqQcKh0r6f7jWC+/p0el03RMRE1mcAG+z5yBw9EGPTTSh791J0dtw4mrQWFlJkd8UKMnD37aPoc2UlRXtvvJGyUf7rv2ii+PHH5Jj75hs6zuMho/jaa8kY1DQyCK+5hkpb+vWjzJBwNA3Gzx/DBmTDBM1RBQADgG5ShN0iy61hbC43Nma6P+G6F6554UWYrHmn1jzrtYJCP2lH//7UywmgCey+feTQO3yYtGn8eHp97Vpy1N16K5XOpaRQRsvhw+SwmzqVHGQvvUQT3vp6cqA9+ig5+nr3JifAk09Sps5bb1EjeL+f9rXb6Xt6OjkRf/QjytR76CHSVa+XWhd4PJTNF16yZ7OFSnJrawOp6ArpdEYGbevdm37Oz6cy25SUUGkgw0QhHaF5hgl4GiU8iMP+0Vdj4Kip6HewAGnOOGhXXUXP7759wKefQqakQJ98CaqunQ/zJ4vgrffCPeESuDZ8Dfvvn6fn7qGHyPlWXk4Ovr/8hc7x7LOUtfbUU+TEC881KC+nkvZwrAU2fvhDsoni4iBdLtQn9UbpwPPhWfIRMg9uh/vjD+F87RXSBgCIj0f9HQtRPWMOjuWNQrXqIn0LLM5aHa9A798X9jc+QtbeLXAv/Qtsy94Pjeevf6XvN95I47T0QtNIX8aNo6DCAw9Q2e+hQ9TH2OmkoEZeHsSYMUBaGuIBXJFMGdD9eqnYeMQHQEBTBCbk2XHgpIESjwGHBlzYz44VOxubZXrLpv+XbOcxDMN0N84oA2/79u14/fXX8dZbb+HkyZPo1asXbrrpJixYsABjrMauTDOiNZrUUnZGZT1F/QDArgpoQqI2EIVNiRe4dLADy7ZTFLc1Atn80BTApgr4dQldhl6DADQBJDoEsnsp2HvCiJjIqgK4erQL9ra6wZ8h4RmH1s/1fokEh4KsQObJ1kIfDp40YJgyuEKrRWaSCk0FCitPndUCgDI/duygCWxZGU0O9++nUo3jx8ngrKoiwy85mZrG//u/02tffglMmUL9l5KTQ+VpBw7QpHLQIIrkbt9Ox9fX04S4vh6YOhXGDfPwxajv45gRH/HeKgqQ6FCCEduILJooJ1qfJSaSaP6cwnVPgrIZGvWQAKkC8Aceb9a8M9C8lmhoIB08cIACFvv2kXY5nZQV9/XXNAHPyqJMu8pKctJ99RXp26JFVFpnmlQG++STwGWXkZ6uXg38/OcU/LjvPup1NW4cZe0VFVG52ubNNGnu1Yt68f3gB5QB9KMfUbZ0ZSXtP2UKOReEIAfij35EDsEvvqAy2lGjIhfEWLoUuOUWygoMX/myA4nmZ4kJEc2fU2dqXopDojfq4TL9MJ1O+EyBculEgw74dRmheUIAQ30lyN69GXFHDiDOVwcxaFDAKEkkJ9dLL1E2b58+5OT/zW/IjjoVDz9M2bhpaQAidc8wJUxTIt9Xigy1EemJGop0B7YYaajzmu3SvFSbHznHdsJechxKYwPsLjsSM1KozUngmh1Fa+0M+vVSsb/MQGGVH57G5u0cLNjOYxiG6Z6024FXXV2Nt956C0uWLMGWLVsAANOmTcOCBQtw9dVXw+Ho2IyB7ki0/jGqaTTx1cFG1DSSkZXpJiPLasLeKw7wNFLjdYDmLTYF8JuRQdGmWIad2wGkJag4Xm2gIWAIagql9usmYBO0s98IHWSCVlhJjlMwa5gDmtJxE9qmPf+AUN8/myIQZxdwBCamMvD+aGoogmkZQPU+2e6+UhE0NJDzrqyMsuYMg95IXSenm7Xi7FdfUT8Vu51KU/r3p32tVWv79yfrzFrkIjERsqaGSnVTUmDEJ+BE1kCsd/ZHnREahwKrDAbwGgIOFUFjLjNJjYnIbLQ+S0wk0fw5lVTr+OqQF6ZJDjXdkPB4KRvOptCzz5rXQZrXEo2NFJQ4eJACFbW1pG8FBVRal5VFGSvHj1MZbV0dBSZmzqRstz/8gSb2RUXk+HvgAep1NW8eZffFxVEp3gcfUO+83/yGsvd+//tQT7yFC6mk76c/pay7Z56h/nfWYkPJyfThmyaVwU2fTv2r5swhXdY0+oV44w3qzfWf/0kOh04gmp8lJkQ0f07RqHkqgNREyjqO0LyTJ+mrsZECnKWlVKb67LOhFaItFIUy4G6+mRa8SUgA0EZ/ZwA2RaBXnDi3mtdBhDti/YaJGm/LPVk1tvMYhmG6Je1y4N100034xz/+gcbGRuTk5ODWW2/F7bffjrymqz8xbRKtf4xqGk2s2d+IqgYJuwr0SVZRXG2g3k9GXO84AbdD4HClCTOwqAUElVJY2C1Dr8m5BQCXDchJVrCvzAy+7nZQbz3dkPCboX0VQf3ZZNjPaYmd3w/K66dsFL9JGYdxNhHsBQjIYG8YC8sAqvPKVhu+n5ZR5/HQBPbECcoCqa6mCWVjI3D4MOSgQeT4Ky+HyMiAbGwEXHGAAGRGJmr6DkBiTjrKagx8dcCLRp0MZ5cGVHubX856byEoShtnC0WeT5lFEwVE67PERBKtnxMFLbyorCfx6ddLQYnHRH2g5DU5TiDFeXaa1z9Vxc5iI1ieluykVgTVjaGeRT1a85ri95PD7tgxcuoVFdH2oiJaxXHSJMrA+eADKr0dMIDKZseOpf8vW0aT+PfeI8ddSgo1t3/ggVCvu9deo8y9xYspS+fZZ8l5V1JC19yzh9oTpKZSie0f/gDcfTdl+D3zDDn8vF4ax6efUusCKanHaUoKlfXFx7d5m2dKtD5LTCTR+jl1G807eJB6alZWkmM9Pp4WwhgxgpzqYTTVPZsqUdUQsjntgQW7ukzzmtDWAkIAgq+Vegx8fcgLwwScNgFpSrbzGIZhehDtcuA5HA7MnTsXCxYswKxZsyL+wDHtJ1r/GG0r8mHPCT0YmVRAhpURMOLsqsDgdA2GNLHnBK2uaMpQVNahWP3UWr+GFaW1iLMDuoFglp+FEtjZMvZUAVw6+NysyFhcraPWJ4ONguv9ErpBWYlCiNMvF+vA8W4+6oUpBXrHKRjZx4btRX4UVRkQCpnApgmkxCuoaTRQVithSIq+tvWZALRPklOBppCh3lUR5dMlWp8lJpJo/Zw6W/Os5uHhr6cnUrZHVUPkn1zWvObUNpooPlqG9JPHEF9xAmpFOXDwIITXS860oiLKkNM0ytJJS6OMZr+fMve++Yaykvv1Az75hBYN+uADypyzVpx95RVy+DU2Um/SL74gZ5+VBf3ZZ5Ttp2nkKLz6ajrXtGk0huJi6oe3bBll340bF1rsohOI1meJiSRaP6eepnlAy7q35ZgP+8tI1OIdAl4dUaF5bS0gpLRg51U2SPgN+uzYzmMYhulZtOuv5fHjx5GamtrZY2G6iBFZNvh0iYMnDfjNUENjRdAf/gQ7MCRDwSd7Qkad5cKVAPxNXMBW1C+8t1P4LooAHJqAlICwLDgZOliIUJnn1IH2DjfqEp0KJuQ5IiKdo3M0lNUaiAvYafV+CYdGTsZ6v0SiA7iwnwMHyoxg6YQVFe1sDlXoqPcBgMTxagNldaHG8nVe2h5nEyisMqAH3nSBtg1tK/Kd4BDISlIxOkfDzhK9wyPKDBONdLbmWRPj4OsCEBDw6hJChCbFVl881rxIDlboOOZLwt6k84Ck82AfCNgmAJmmB8l1FUjwlMNZUghFmhClpRDHj1PW3bZt5MQbM4YccwCt6l1TQyvWlpdTr7q1a6lXVkMD9cIrL6fyWpuNyvXWrKFSXIBW+L7+eiqhvf56Ou++fXS+zz+nxvmZmXQdholS2tI8mwL0cSswTQPFnrBVTcM0LNY0D2iue9a4NFXApgBeHVGjeYcq9KBd13QBodoW7DwTbdt5FmznMQzDdD/OaBELi4qKCjz99NPYsWMH+vTpg/vuuw/nnXdeR46vWxHN0STDlPhkbyPKa82gYRfIvEeiQ8BrAI26hGmGnD9C0P/Df4MEgBlDbPhknz+i9CI8A08V1ODdZQMadAm/joiSDQBwasDckS7Y1I41MlorUfD5JU7UmDCkRJ2XRqoq1BeqzidhUwWSXQIX9bPjSKVxTg2glvpXWWQkKoAATnjMYPmb36pNCcOuAfm9BQ6XA15DQkogwS4Q7xAx09A4nGh+lpgQ0fw5dbbmKYFJq0Sk5nnCyslY81qmvZpn9elK1muQ7vfABT+cVeVwml4onmoojY3AiRMQHg9l0nk8EH37khPuvfdooYrx42m1S9OkbD5No+y+3bvpgr17k1MvKYkcgQ4H7VtaSscNGQKcdx5t70Si+VliQkTz59SS5ikAkl0CqgKcrJMwTGopZwZ2sLQv3KRgzet4zsjOQyADz7AC48Ds4XZ8uMMX/LvFdh7DMEz3o12hpYcffhh/+9vfcPTo0eC2uro6XHjhhTh8+DAsH+DSpUuxceNGDBkypHNGy3QKppTYWuhDVX0oKgsEoqmSDBsr9R4gwyzRDtT5yfFmItK4+3x/pFEHRBqARiBUa0ogTgN8gdesJu4KAIcqsO243qGNdsNLFPw6mpUoKIpAvZciwnE2Kier99HYHSqVVRypNDq8nKKtvieJTgWKEBiTY4uIyALklBubS2NZtbsRNV6JODtQ0whIETCUAdhUYM4IJ7YX6ZAwYFOob4oqqLyloNAfEw2NGaajOFeaZ22yNC98H9a8jtE8lw3Y743HPsST5rn7kuZNdeLbIh0lNSZsCulgqq8acfU1cNsMpFx3HZSsLMq6aw1dJ4cew8Q4rWmeCaCyQUIV9MzXmaR7luZZvdWaOvFY805/XB1u54HGbVMj7TxNEZBCsp3HMAzTTWlXaGnt2rWYZ5WTBHjxxRdx6NAhLFq0CFVVVVi7di0SEhKwePHiThko03nsKPbjULkRiuhZX4G/8z4DaNBD2xLtgFAoYqsqwNgcDa7AHEiIwCpjYSgAmth5MCSl/ld7Q+VpigiUBEgqZyiu1rG9yIdtRT7UNJ6iTqAdNC9RaESJx4AhJWq9QO94BYPTNeT3VtEQKK3QVIF4OzX+zUxS0a+Xim1FPpgBp7Up5VmNzzI2j1UY2HrMD8OkKOyxCgPrD3tR02jClBIbD/tQ1SCDznIpqRnzhkNebDhMjal9ukR1owy+t75Aj0G/AXy4vREHTlL/GyPQtN6ixGNgR7H/7N5chokhzlbzJubb4HaGzte0jEkN6+9kYcjQV3jzdta8ztO8/Sd1VDWYKK8z4dWBo3Bjd1wONtj6YYeW2bbzDmDnHdNtaE3zgNDqs001ryGQ2WVTWfNiRfPYzmMYhun+tMuBd/DgQVxwwQUR2z788EOkpaXh6aefRlJSEiZMmIAHH3wQn3/+eWeMk+lE8lM0JDgEFFA50sA0DQPTNNgCE1ar4kEo5KCr8wPVjdRA1zCBvaVG0GnXQvVmcAWscBSrFM36WaFoLEAbvTrgMygyGm7knA0jsmzITFKDP/t0BEsppJRQAvsAtDpuvY+MO1WhxsYD0lRsPOJr0wg7XVozNq3xHarQ8c0xHw5XkFFW65Pw6ibqfBI+Q+JguYGD5UbIUDabG9E+A6j303sKSQ6ITLcSfC+a9nmpaTQ71HhlmGjjbDTPlEB5rRnoSxnIKmnaHypQzhSOKppoo2DNY81jmHNDW5qngJ57RYQ0r8YXeH7AmseaxzAMw0QT7XLgVVVVISsrK/izruvYtGkTpk6dClUN/aEcO3YsiouLO36UTKeS6FQwub8Dg9I0DMnQMK6vHeP62jE4XYXbKZDoIGPAlIA0AX/AeLCMiVqfbLORbrydDEM10LTYpQYMxbDMOwVATrKAppAzTwQWuqiooxNbRs7ZYJUo2MOMTMtAjbMJnKgx8cmeBhwq12GCsgat1306sP6QL/hza0bY6VDTaEJKiYwkegwlaLU2I2BQZSapGJFlg7CaREuKstb76bsMlPJZTfbbwnJImJKaVY/KtmN0jgabBozMsgX7vLQnUswwsc7ZaJ5uAnvKDPjbeBRUBXBqpGcCgBbIYrGF/cVVBWseax7DnBta07whGRoGpGnITlKhiJDmWQtSsObFjuYBoVLnBAfQP0VjzWMYhumGtMuBl5GREeGY27JlC/x+f7OsPEVR4OjkRs5M55DoVHBBPwdG93FAEYK+At2GhRBkOMjmpbDtocGPQHkCre6oBwwRVaWfrZXMjlebyElWYVcFkhxhnY4RMnLOBlNKFBT6I/qLuGxUNlHvp+hsdSNFMg2Tru52AhISVQ0mqupNBK0sIOI8pzs+y4AqrDQhJWDTqDGxz6BIsapQ7xZFCJzf14683lrQMLOyHKWMzGwUaP2BFqGgN4o9JjwNJr4t1OHXge3F/qDB1p5IMcN0BzpT83Qj1BzdHohx+U1a4dahhsrNWPNY8xjmXNGS5o3uY4fTJnCyzmTNi3XNQ0j3ahqB9Ye92HTEz5rHMAzTzWiXA2/cuHF49dVXg30Z3nrrLQghcOmll0bst3v37ohMPSZ2qWk04dUlbBrg1SWVQYhwU6v9xAWMpzi7QINBzjq7KpDoUGBCRKxYFucQgRXRQleyayEj52zYURy5wpddo/uJswsYwf4vodXSDBNwuxTU+ajfiM8EDJ2MrnDOZHyWASWlxKFyHeW1ZrA3jQmgxktGqCklFEGrorlsofe/aSDW2t7aEMJLmxv8aNVga6n8xKIjjGuGiVY6UvPi7XSUTRXBLA+bIuC0KdBUEWwrwJrHmscwXYVVRtmvlwrdpL5plj12urDmNedca57l+DMlOShP1koUVrHmMQzDdDfa5cB79NFH8dlnn2HIkCGYNGkSXnjhBUyePBnnn39+xH4ffvghLrzwwk4ZKHPusKKGpR4TTlVQY3dJkUCrEXF7sVY8BQBVUKNgl42ceQJUPmFXaZn77CQVuhFpTAAIrqBl9eo4U/JTtGBZRWaSiumDnchIUlDvo/M6VMAbtgCHBPX384aNx+Mlo+tsx2cZUA16oDwvsJ3609B7ZDUdtiLKdpWi2+HGnVXmIkDGsdHOIRgyMuptNW0GECw/qfeHyjw6yrhmmGikIzXP6icFsOaFw5rHMNFDeBnlzmIdDi30e27I0II+7YE1r2W6UvOsAJQrYH+z5jEMw3Qf2uXAGz9+PJYtW4bs7GzU1NTgjjvuwPvvvx+xT0lJCQoLC3HVVVd1ykCZc4cVNTRMiePVBrxGZO+N0zGvJABFSKTE06+ayyZw6RBH0NgToAw9l03Apgqc8ISKN8J7mHTEClqJTgUT8hzITVExNtcGVRGwqRTNtFYg6x0X+VCE36vVpNkyiM5mfFaflgRH6Ho2BegdL5DfW4VAqOlweES5NdvRBJUmt4TVZ9BCDZS0WPcQ3rR5y1EfthzzobI+VOZhSNlhxjXDRCMdrXkNfta8prDmMUz0EK55hyt0VDdG/p63p9+aBWtey3Sl5gnQ/VrXYM1jGIbpPmin3oW44oorcMUVV7T6emZmJr799tsOGRTTtYzIssGvAwcDTX7P5m+5UwV8hoCn0UBGoopB6Rr2lxktRl/rfVS+5tcpWjgmx4aCQjJqmq6gdaYkOqmZuUX/3jaU1pjwBa/pxOajXuwpNSKOEwBS4gRsioDfOPvxWdFW0xSIt1NE2KUBfoMMrpxeKvr31pDoVJCfouGEx0BlPS0WoihU9tEeLKMuPItIEQj0+go1bdYDvWAOVRjQDRnRA8erA3E2y3hFxPvHMN2BjtQ8BdQLijUvEtY8hokeWPO6h+aFlzuHa54QpGOqnTWPYRimu9GuDDymZ2FFDd1OihYG+ho3i/C1h0aDDAm/DlQ2mNhXqjfrT2JRUW+iV5wSETkdm2tDboqKCXmO4ApaHUnTaK2ERFmt0Ww/CVo5LN4ukNPr7McXHm1VFeoHIwJlCydqTAiB4PmsMSY6lWDvE6dGn03Tj0MEXouz0WelBRYO0RRrEkvlF/6wGgzdkNACQ3eE2qLApgAD0zTk96aNHWVcM0y00ZGaBwXQVMGa1wTWPIaJHljzYl/zUuMV2FT6vKwF4dSwFX4NU6LBT7rHmscwDNN9iCkHXmVlJebPnw+32w2324358+ejqqqqzWOklHjiiSeQnZ0Nl8uFqVOn4rvvvovYx+v14t5770Vqairi4+Mxd+5cFBYWRuzz3//935g0aRLi4uKQnJzcwXcWXVhRQ78haAGKwHarQa4qAJcNSHREmhVaC79NhgnougkhBDXzFWjWn8RqpmvXgKHpNozKtgd7cChCYFS2vVOMOgsrWqsIgU/3elHZEHot/A4NEzhSaUAInPX4WurTEv4+NDWgEp0KJvd3oFecArsq0D9Vww9HOyMaLVtjjbMLZLtVuGxkyKUnapg93AmbSvfgUIFpgxxIT1RQ3Ugh2Ql5dtg1MjLj7dSvJtklcH6uHefn2jvVuGaYrqYjNc9kzWsR1jyGiR5Y80KvxarmpScoyEtRYdMAIQTyUzQkOQWkpM+pd7xAtps1j2EYprsRUyp90003oaCgACtWrMCKFStQUFCA+fPnt3nM008/jWeffRYvvvgiNm3ahMzMTMyYMQM1NTXBfRYtWoT3338fS5cuxVdffYXa2lrMmTMHhhGK0Pl8Plx33XW4++67O+3+ogUraiilRL1fBgyZ0OsmyGCToKifGnhdbyHdXwKo8QK6YSIzScW4XHuz/iSdHX09HUZl2WD1clYVYEi6hpQ4KncQAJKcosNKPE73fbCMuyEZGsbk2PD5Ph9MSZFWa28JoLpRoqLehFNToAiBGp+JilozWI5hV8n4O1FjwJCAp0Fi23F/sNxFVcig9xsCBYXU76WzjWuG6UpY8+j/rHmseUzPgDWP/h/LmldSY6CiXsKuCugSSE8UqGmUkJL2HdfXjsp6kzWPYRimmyGkjI1upbt27cLw4cOxfv16jB8/HgCwfv16TJw4Ebt378aQIUOaHSOlRHZ2NhYtWoRHH30UAGXbZWRk4KmnnsJdd92F6upqpKWl4c0338QNN9wAACgqKkJubi6WL1+OWbNmRZzzjTfewKJFi06Z+dcSHo8Hbrcb1dXVSEpKOu3jzxXW6mRVDWQIxNkF6n2SemYEDAkISr3XTYpYnuqXyKYCwzK1YATUlBI7iv3IT9GizmAoqdax5bgP2UkqRvWxw5QSn+xpRKJDwcgoMXC2Fflw+KSOOh/1MVFE4HMIrJqpCoqcqwGL3GuESimAyJXMrGi7O3Bfdi1yhbjcFDXq+qHEyrPU04mVz4k1jzWPNY/pCGLlc2LNY81jzWMYholNuv4vVDtZt24d3G530HkHABMmTIDb7cbatWtbPObQoUMoKSnBzJkzg9scDgemTJkSPOabb76B3++P2Cc7OxsjRoxo9bztxev1wuPxRHzFAlbUMK+3iuRAVLJ/qorh2RryU1XqKxTY1wgYEqf6RTINYO8JHR/vbEBVg4Gtx/w4VmFg/WEvahrb2Z38HJHp1vD94XEYk+OAIgQ0RcGsYXGY1N8ZFUYdQKUXLjuVQfSOU3D1aCd6xSnBCHKcnSKsEhRhnjbQhmRXaOxh7aDgdgqkBlaPa0+ZB8O0BmteCNa8joU1j4lGWPNCsOZ1LKx5DMMwTEtEx1+pdlBSUoL09PRm29PT01FSUtLqMQCQkZERsT0jIyP4WklJCex2O3r16tXqPmfKk08+GezX53a7kZube1bnO5ckOhVc2NeBiYH0//Nz7RiVbYdDE9BUgXiHQJxdgV0lQ+JUppkBWuXqZL3Eip2NOF5FoT+fDhyq0Ns+mGlG0PhO1TBzmBMOTcWkPFuwDMZvkFHn1SmivvmYjgl5togVywCKyM4e5sTE/Ogtd2FiB9a8EKx5HQtrHhONsOaFYM3rWFjzGIZhmJbocsV+4oknIIRo82vz5s0AEFy9KRwpZYvbw2n6enuOac8+p+Kxxx5DdXV18OvYsWNndb6uILzx745iP054TGp4LAQcGhBvVyIa7LaFDPxjSMAbaC+YmaRiRJats4YPgEpFthX5YAaqxU0psa3IF3UR4dMl/LMxpcSGI35YoVm/KVHTaKJRp/42DT4Tn+31RkRkAfosVu3xwmXHOW8qzXQ/WPMisTRPN1nzOgLWPCbaYM2LhDWvY2HNYxiGYZrS5TnT99xzD+bNm9fmPnl5edi2bRtOnDjR7LWysrJmGXYWmZmZACjLLisrK7i9tLQ0eExmZiZ8Ph8qKysjsvBKS0sxadKk076fcBwOBxwOx1mdI5rIT9FwwmPAp5NBNqqPhpW7vVRaIajHxqmw+na4NErbH5NjCxoTnYHV58WnA36drldQSM2bT3iMmIo81jSaOFShY0SWrVl/mUMVOnQTsCkCflNCQSha7jcBw08Nqa0lzMJ7o1Q1mPh0rxczh7q64K6Y7gRrXnMk6LGzKxJ2TbDmnQaseUy0w5rXHNa8M4c1j2EYhjkVXf4XLTU1FUOHDm3zy+l0YuLEiaiursbGjRuDx27YsAHV1dWtOtry8/ORmZmJVatWBbf5fD6sWbMmeMy4ceNgs9ki9ikuLsaOHTvO2oHX3Wi6otauEzqkKWFTaMl6rZ32mZXeX9UgsfFwKGLaGRyq0IONeks8Bj7d24gSD4WF2yrriLZormWgHqugvjKGKSP6yyTYBHyGhMsGODVaNU6Amh2rArgw1xaMoCe7FFw92hXslaIKWpWNYZhIOkrzBCgbhTWv/bDmMcy5hzWPNY9hGIaJbrrcgddehg0bhtmzZ+POO+/E+vXrsX79etx5552YM2dOxAq0Q4cOxfvvvw+ASmcXLVqEX//613j//fexY8cO3HrrrYiLi8NNN90EAHC73ViwYAEeeughfPLJJ9i6dStuueUWjBw5EpdddlnwvEePHkVBQQGOHj0KwzBQUFCAgoIC1NbWnts3oosJT+fPT9GgqrQqlgCQ4BTQlGDwr03q/RI+Q+JwhY5vjvk6bbwjsmzBRr1A5KpbrZV1nMqI6grjri0DtcEnUVDkh6pQLxS7AjToZNQBgEMFqhuBKQPtSEtQMGuYA3aNvqclKJg60IFMd5cn4zJMVNIRmmdNsljz2g9rHsN0Dax5rHkMwzBM9BJTav7WW2/hvvvuC64YO3fuXLz44osR++zZswfV1dXBnx955BE0NDTgpz/9KSorKzF+/HisXLkSiYmJwX2ee+45aJqG66+/Hg0NDbj00kvxxhtvQFVDBsG///u/489//nPw57FjxwIAPvvsM0ydOrUzbjfqSXQqSItXURtodFLjlWS0tXGMAKX6R2xrckBbJQSnWwahCCrf+HSvEWHUtVW+e6hCR4NfwqsDxR4DFXsNeHWgwS9hSHp9VLb9tMZxtozIssGvIyKqbGHXBBSTxuczAL9BZRWKoO+6FCjxGLBpakT5hKYoXE7BMKfBmWgeAEBErgjImndqWPMYputhzTt3sOYxDMMw7UFI2Yl57UwEHo8Hbrcb1dXVSEpK6urhdAimpKjl8SoddT4J3Ty1YacpQLxDwKcDOckqxueFmuqG9zLJTFIjepnYNZx2LxNrfJZBFE5mEpWINDXuPA0GVu1pRKNOvUbi7AL1Pgm/KeHUgBlDnEhyqc3O19E0NXD9hokPdzRCE4Cq0JjtGjB1kAPbjus4Xq2jzithIjRuIQBdN2FA4LLBDiS51LMylKOF7vgsdUe64+d0JpqnCCDRIeAzWPPagjWvdbrjs9Qd6Y6fE2te58Ga1zrd8VliGIbpCGJT1ZmowYp8uuwC8XbRbKWywGJZEVAfFYFkl4DLDtR5Q6bgmfYyaY0dxZFGnT0s57TEY2BHsb/ZMYcrDThUAQWhVb6shsEOVeBwZXMj8Wxp2ovFMi4Pn9Sx9ZgffsPEyt1eNPgl6nwSRqCTtE8Hth3XMaqPBpdNIN4hYFfJqHNowMQ8GwxQycu+MiMqykQYJpZpj+Y1RQDwGWDNC4M1j2FiA9a8joE1j2EYhukI2IHHtEpJtY6Vuxugm/THXzdNrNzdgJLqkHFlSomvD3pRVmtCCCDJqUQ0OdYUwNbkt8xvAropUVZrYs8JHesONQYNjDPpZdIW+Sla0JjLTFIxfbAzeH67Rq83ZXimBqGI4Ope4d+FIjA8s2Mrz1vqxbL2kC9oxB2v0vHRjkZUNZjBcehh4e/CSj8+2N4Arw6oQsBlE2jwSzT4JTYe8cOu0PaOMJQZpjtzpprXVOOa/mE1JWkhax7Bmscw0QFrXmC8Yd9Z8xiGYZhohh14TIsrca090IjP9pPB9vEuL3w6fS+rNfH5fm/QuFt/0IsjFQZ0E/A0SihCRhgdfhPwNQn+GSZQ3UhlGD6dVimzDAwr0mtvYju11cukLZquqKYqAmNzbchNUVst09hZokPK0MMR/l1Ker0jCY9GH6/SsWx7A/yGhE0RMCRQ65MAZHAcyS4FV44gA9UwJWp9QKMfqPdJSFCDY58hUeeV8BoSmhp6z87WUGaY7kBHa154X0+J0GQwfFuNlzXPgjWPYc4trHmRsOYxDMMwsQo78Ho4ra3EdbjSgG4AkEBVg4n3vm0IRgYNCWwLlCRUh5VFmBKoqD+9looSQLxdoF8vFduKfNBNEwWFfnj9EvX+yBKCgkJ/0Pg8HcJXVAPIeByVbW+1L0i/Xiq8utVjhPq42BQyUL26RL9eHdsXxYpGGyZFYq2oqssGqIKMyUY/4LQBcTaBmUMdsKkKxuba4LQLxNmp34zflPDqJhwqguM1TGBCvr3DDGWGiXVY85rDmscw3RfWvOaw5jEMwzCxCjvwejit9SJJdIjg6lZA5GpiyS4F0wc7AAAzhjqQ7KImuooILWnfHgQAlw2Y1N+OjUd8OFZhYPVuL4qrddT7KboYbty11sukozlSacCmgHqM2AQcNvpuV8nAO9LBvVGsaLQuQ++3P5ClaEoA1ipjJo1h23EdppRQhMBlQ5zo28uGeDu95lAFhKDx9o5TMH2wHQfKIldmA87OUGaYWIY1rzmseQzTfWHNaw5rHsMwDBOrsAOvh9NaLxJFCAxMU5v1OVEFMGOIA5pCL2iKglnDnLRfwLhrun9LCFB/FJdNwYbDfngD1/UZErU+Cb9B+yQ4BOyagJRUJpAe3/m/svkpGlx2Mo6y3BqmD3Yiy60hzibgsosW+6mcDaaUKCikHibW+23K0Bck4NQAV+Cy4QZueHPpOBsZdQDgsAnMGOrE0UrztJs7M0x3hjWvOax5DNN9Yc1rDmsewzAME6uwA6+H01ovEptKi0wYTQJ3hgRW7fFGNDxetccbsZ8I+970+OA+goyWep+E35DQAr+JqhBwagIBewZCCEwbbIem0gpb24v9nb6i1pn0UzkbrBXUrIiqKujBFKD3QFGAK0e4kOWmDym8KbNlFLYWec3rpZ52c2eG6c6w5jWHNY9hui+sec1hzWMYhmFiFXbg9XBaMwxO1klU1kuyLBAZYa1qMPHpXi8A4NO93mDPFAvZ5HtTgoZfoJ+H3wQm5IX6d/hNek0BIE2JL/b7YJhk9J2rFbVOt5/K2WCtoCalhFAEEhwKbCqVtigCiLMLbC/SMTpHa2ZcWkahRdPI6+FK45waqQwT7bDmtQxrHsN0T1jzWoY1j2EYholFWNV7OK0ZBq7AolUmqBfK1aNdSHbRr4sqgFGBVa1GZdmCRl+yS8Hs4Y5geYBAKMIYjl0FXDaBeBt9n9jPhgMnQ/07XDYBmyJgAqj3S3j9IROxO66oZUWC7TYBKen9irMLxNkFkpwCmhAo8RjYWaI3My4toxCg9+bCvnaoChnVdo0aNR+q0JGfop0TI5Vhoh3WvK6HNY9hzh2seV0Pax7DMAzTUbCy93CaGgZW6r1dVdArTiA1XsGsYQ7YNfqelqBg6kAHMgNp/pluDVMH0vZZwxzoHa/h8uEO2FUgwQ4kOQUcGhBnAxwa0MsloCgCub1U5KfZMGOIE2X1MsK4dGhk2CigKG1DwODrzitqJToVTMxzwBH4LLKSVMwd6UKfFsopmpIcpyCnl4pBaSo2HvXB02hCALiwnx37ywwcqzCw/rC300tSGCYWYM2LDljzGObcwJoXHbDmMQzDMB2BkJKXJzpXeDweuN1uVFdXIykpqauHE6Sm0cShCh0jsshoMqXEjmI/8lO0M47e1TSaWH/YC59OBuOYHBsKCikKbNcQkdbfdN9RfTSs3E0lGwqAeLuAGuianJlEJQLd0bgDgJJqHduK/Zg+mBpI66aJVbu8SHIJTMx3RHw+6fEKthf70eCTsGsCmYkK9pYZ8AUa0vRyCYTHxXNTVIzKtnfRnXUs0fosMZFE6+fEmhc9sOa1j2h9lphIovVzYs2LHljz2ke0PksMwzBdDTvwziE97Y/R6RiM4fvuKPbjaLmOBh1wqIDLLiJ6t3QnAyWclozhjUd8OFxON5+XouGiPHvQQPYaEgISjX4qgXGqgG4CPiPQFFkASQ4yirubQdzTnqVYpad9Tqx5pwdrXvvpac9SrNLTPifWvNODNa/99LRniWEYpr3w0kRMp2E1CLawenKcat/8FA0nPAaEaDmq211X1DpUoQcN2BKPgU/3GqhqkLAKIgqrDdTubQzuY1cAryFgBtpINxqRK8MJAXgNINnefUtSGCaaYM07PVjzGCa2Yc07PVjzGIZhmLOFe+AxUYfV7Lenrag1IsuGzCQ1+LNPDzV6timAS0NEhDrLrWHOCAeclp0rASNgBSoKIo4pKPTD5GRbholKWPMI1jyG6Rmw5hGseQzDMMzp0j3/QjIxjxWp7UkrailCYEyOLdhsGqAIa3KcQLJLQIRFVu0aMKqPhh1FBhwqNYI2QSUVEoCUiDimxGNgR7H/HN4NwzCnA2sewZrHMD0D1jyCNY9hGIY5HbrvX0mGOUtqGk1sK/IFI5qmlNhW5Ou0Vb5MKVFQ6I+IvkoAVfUSVQ0SUkoYUqLeL+HVgU/2eFFU7Ud1o4RuAqoIlVaYksoqMhLpEe/OJSkMw3QMrHkMw/QkolHzAMCQ9PPqPV4UVeuo9Un4TZq0seYxDMP0bFjpGaYFwhsN+3VE9Gc54TE6pcRjRzGd38KuAVUNEn6TDLp6v4QpAUNS5NWhATVeMuIAQFUAmwo0+K17kMhMpGbQZ7PSHMMw3R/WPIZhehLRqHkNOpDoADz11BevukFCSsAf8CdKkA426tY9sOYxDMP0NFjpGaYFmjcabgwaXT6dXu9o8lO0YFlFZpKK6YOdyElWoYAe1Hi7QIKd+qT4TYkGv4Qa1q/YMAG3U0BTwjJTBLp9SQrDMGcPax7DMD2JaNS8HLeKTLcCNSBfUlKvOwspKXDBmscwDNNz4Qw8hmmBEVk2+HVEGHMWmUkqRmTZOvyaVlPnQxU6RmTRamLj8+ywKYAUwNgcG74t1FFcraNBBxwqoAig1kdZKokOAb8hkOSgKG6OW8W43JZXg2MYhgmHNY9hmJ5ENGqepV+6DhyqMIKaJyWV0NoVwKEpcKiSNY9hGKaHwg48hmkBq9Hwp3uNCKPOrlGZhRLWaLgjsZo6h4/jgn6O4M9jcmyoqDcQfvmUOABCwB8YpxACyS7gojx7p42TYZjuBWsewzA9iWjVPAAYm2tHZUNjcFwJdgpcxDuUQNYdax7DMExPhfOtGaYFWmo0DAANfolVuxuhm2Zwv85seHyqMclAo+OqegkZtq9PBwoK/cHGzAzDMG3BmscwTE8iGjWvpXHJwEI+hgTqfSHdY81jGIbpmbADj2FaoKVGw4aUqPNKlNebWL3bC8OU2HrMj2MVBtYf9na6cdfSmBp0am5s9Yeyh+XUlngM7Cj2d+qYGIbpHrDmMQzTk4hGzWtpXD4ztIiF35TwGyGHHWsewzBMz4MdeEzMUtNoYluRLxh97MgoaUuNhu2qgAl6aHy6PCcNj081phx3WPPjZNqWmaQCIGM0P4Wr5Bmmu8Cax5rHMD2JnqZ5LY/LAadGZbJODZg2yMGaxzAM04OJKQdeZWUl5s+fD7fbDbfbjfnz56OqqqrNY6SUeOKJJ5CdnQ2Xy4WpU6fiu+++i9jH6/Xi3nvvRWpqKuLj4zF37lwUFhYGXz98+DAWLFiA/Px8uFwuDBgwAI8//jh8Pl9n3CbTDmoaTaw/7MWxCgNbj/k7PEpqNRrOTVExNtcGVRG4bKgDveMUxNsFVEWck4bHpxrT+Hw7BqZrGJSmYXyeHaoiMDbXhtwUFRPyHLwqGcN0E1jzWPMYpifREzWvpXElu1RcNsSBtAQFM4Y4kRynsuYxDMP0YGJK9W+66SYUFBRgxYoVWLFiBQoKCjB//vw2j3n66afx7LPP4sUXX8SmTZuQmZmJGTNmoKamJrjPokWL8P7772Pp0qX46quvUFtbizlz5sAwKOq2e/dumKaJV155Bd999x2ee+45/PGPf8QvfvGLTr1fpnUOVehBw6rEY3RKlNRqNGw1CNYUBTOGOuGyRzYM7uyGx22NSRECF/Z14IJ+johto7LtbNQxTDeCNY81j2F6Ej1V81oal9ulYuZQF5JclHnHmscwDNNzEVLGRvfTXbt2Yfjw4Vi/fj3Gjx8PAFi/fj0mTpyI3bt3Y8iQIc2OkVIiOzsbixYtwqOPPgqAsu0yMjLw1FNP4a677kJ1dTXS0tLw5ptv4oYbbgAAFBUVITc3F8uXL8esWbNaHM9vfvMbvPzyyzh48GC778Hj8cDtdqO6uhpJSUmn+xYwYZiSIrHhfUIsMpMoOtnRhlZXXJNpGX6WYgP+nDoO1ryeDT9LsQF/Th0Ha17Php8lhmGYlomZ0M26devgdruDzjsAmDBhAtxuN9auXdviMYcOHUJJSQlmzpwZ3OZwODBlypTgMd988w38fn/EPtnZ2RgxYkSr5wWA6upqpKSktDlmr9cLj8cT8cV0DIoQGJNji2hgDnRulLSlhscW3EiYYVjzOhPWPIaJPljzOg/WPIZhGIZpTsw48EpKSpCent5se3p6OkpKSlo9BgAyMjIitmdkZARfKykpgd1uR69evVrdpykHDhzACy+8gIULF7Y55ieffDLYr8/tdiM3N7fN/Zn2Y0qJgkJ/RH8SgMoqCgr9wYbHHUlLDY+5kTDDhGDN6zxY8xgm+mDN6zxY8xiGYRimOV3uwHviiScghGjza/PmzQAA0UK0TUrZ4vZwmr7enmNa26eoqAizZ8/GddddhzvuuKPNczz22GOorq4Ofh07dqzN/Zn20xVR0pYaHkdLI+HOXKmNYdoLa17nwZrXHNY9pqthzes8WPOaw5rHMAzDdHko6Z577sG8efPa3CcvLw/btm3DiRMnmr1WVlbWLMPOIjMzEwBl2WVlZQW3l5aWBo/JzMyEz+dDZWVlRBZeaWkpJk2aFHG+oqIiTJs2DRMnTsSf/vSnU96bw+GAw+E45X7M6ZOfouGEx4BPpyjpmBwbCgrJ2OvMKKnVWNjCaiTclVgrtfl0wK8j4r044TGiwuhkegaseZ0Ha14krHtMNMCa13mw5kXCmscwDMMAUZCBl5qaiqFDh7b55XQ6MXHiRFRXV2Pjxo3BYzds2IDq6upmjjaL/Px8ZGZmYtWqVcFtPp8Pa9asCR4zbtw42Gy2iH2Ki4uxY8eOiPMeP34cU6dOxfnnn48lS5ZAUbr8revRdFaUNBajm+dipTaGYboW1rxIWPcYpnvDmhcJax7DMAwDREEGXnsZNmwYZs+ejTvvvBOvvPIKAOAnP/kJ5syZE7EC7dChQ/Hkk0/ihz/8IYQQWLRoEX79619j0KBBGDRoEH79618jLi4ON910EwDA7XZjwYIFeOihh9C7d2+kpKTg4YcfxsiRI3HZZZcBoMy7qVOnom/fvvjtb3+LsrKy4PWsLD/m3NPRUdJYjW6OyLLBryPCkLPITFIxIsvWRSNjGKYjYc0LwbrHMN0f1rwQrHkMwzAMEEMOPAB46623cN999wVXjJ07dy5efPHFiH327NmD6urq4M+PPPIIGhoa8NOf/hSVlZUYP348Vq5cicTExOA+zz33HDRNw/XXX4+GhgZceumleOONN6Cq1Lh25cqV2L9/P/bv34+cnJyI68lOaKLLdA3No5tG8GcruhkNZRRNsVZqCx8v0LkrtTEME/vEquYBrHsMw5w+rHkMwzBMrCMke6DOGR6PB263G9XV1UhKSurq4TBNMKXE1mORTZMtMpOohCMaDaSWxm2YEl4DyE9RcX5fMkZ3FPuRn6JFbXT5dOBnKTbgzym6iVXNA5qP3dI8lwZkuTWMztGws0RnzWPOKfw5RTesebEDP0sMwzAtE/sKzzAdhBXdtDfJS4326GbTldoURaLOJ+EzJA6W69hW5MPWY34cqzCw/rAXJdV6TPZ/YRimY4lVzQMidc8wJer9pHn1foniah2rd3tZ8xiGiYA1jzWPYRgm1mEHHsMEMKVEQaE/ojQBoLKKgkJ/0BCKNvJTtKAxmpmkIjtJg6qQEWqYQEm1GTT6GvwSaw/7cKzCwNZjfhimjHDusXHHMD2HWNU8IFL37JpAgkOBTRHwm0BtIIABsOYxDBOCNY81j2EYJtZhBx7DBGiayRYeoT1epWP1nsaojGY2XaltZB8b+qeqsKsC8Q4BI2yIdlXAHnjqeRUzhunZxKrmAZG6d9lQB7KSVMTZBeyqQJxdQA1k0rDmMQxjwZrHmscwDBPrsAOPYQI0zWSbPtiJzCQ1WKrQ6JdRG820VmpThIAiBMbm2JHsChl0ABmqlw5xIMsdslh5FTOG6bnEsuYBId3TFAVjcmxwaECcLWwiy5rHMEwYrHmseQzDMLEOO/AYJkDTTDZVERiba4PTHopuxkI0s60SkW3HdYzqo8Vk/xeGYToW1jzWPIbpSbDmseYxDMPEOuzAY5gwwjPZAGp4fNkQJ/rEUDSzrRKRYo+BT/Z4Y7L/C8MwHQ9rHmsew/QkWPNY8xiGYWIZduAxzCmIhlXLahrNdq8o1lqJCAD4DAmfHjLewu+pxGNgR7G/826CYZiYgDWPYZieBGsewzAMEyuwA49hTkFXr1pW02hi/WFvu1cUa61EJDdFxaQ8O1x2MkSbGn12jYxChmF6Nqx5DMP0JFjzGIZhmFhBSMm51OcKj8cDt9uN6upqJCUldfVwmHayrciHYxWRpQrhRl5uiopR2faYuX5No4lDFTpGZFFU2ZQSO4r9yE/RkOiMDZ8+P0uxAX9OsQlrXvTBz1JswJ9TbMKaF33ws8QwDNMysaHiDNOFtFWqcC6imSOybLRKmqRV0rwBo04CUBUgr5fa4nGtlWMAaNb/ZVS2PWaMOoZhOhfWPIZhehKseQzDMEyswHnUDHMKrFKF8Gjm2FwbdhTjnEQzFSEwIFXFvjI/DAlICcTZBby6BFTgiwONUIWCGcMc0BQFumli5e5GmCYgIODXqYdLQSE1PT7hMTAhz8GGHMMwLcKaxzBMT4I1j2EYhokVWNkZph20tGpZZ0czrciqbppYf9gHQwKmBPyGRE2jiUZdosYrUVEPnKw38fEuL3w6fS+vk6isl/AbEiUeA5/ubQyuWObTgUMV+imuzjBMT4Y1j2GYngRrHsMwDBMLcAYew0QhVkNjnw4UVRnQTSqPkBIItjIWgD/wgyKAqgYT733bAEMCAlR6Ue+XcKsiopdKZpKKEVm2c3g3DMMwbcOaxzBMT4I1j2EYhjkT2IHHMFHIoQo9aIz5dOqJAhlImRWAEGS42VXAZQNqqeUJjMCSNEIAyS4Bu0alFRZ2jcosrAgzwzBMNMCaxzBMT4I1j2EYhjkTuISWYaIQq6ExAKiKgEsjYw0CsKsCiQ4BuyoQZxPITFKhNrHTFADpCWqEUQdQWUVBoT/Y8JhhGCYaYM1jGKYnwZrHMAzDnAnswGOYKEQRAmNybMFV0XymoNXIBDU2dtrIqBOQ2F9mBEssLHQJ7DupQ4IMOHtYrm2Jx8COYv+5uRGGYZh2wJrHMExPgjWPYRiGORPYgccwUYgpJQoK/cHyCodKD6sJitBOHeRAZpKKGh81PLYeZCtCKwAYJuBplMhMUjF9sDMY6bVrtKoawzBMtMCaxzBMT4I1j2EYhjkT2IHHMFHIjmJ/cDUxAHDZBeLtVE4hTYldJ3SMzbWhX4oKTQUggGSXgqtHu5DsUiAEYFOAvBQVY3NtUBWBsbk25KaomJDn6NRV1RiGYU4X1jyGYXoSrHkMwzDMmcDhGYaJQvJTNJzwGPDptJrYmBwbCgrJ2LMiq4oQuLi/EwOqdWwr9mP6YAc0RcGsYQ58uteLUVk2ZLpDj7giBEZl27vwrhiGYVqGNY9hmJ4Eax7DMAxzJggpucvpucLj8cDtdqO6uhpJSUldPRwmyqlpNHGoQseILFpNzJQSO4r9yE/RenxklZ+l2IA/J+Z0YM1rHX6WYgP+nJjTgTWvdfhZYhiGaRnOwGOYKCXRqUREUjmyyjBMd4Y1j2GYngRrHsMwDHO69OzwDsMwDMMwDMMwDMMwDMNEOezAYxiGYRiGYRiGYRiGYZgohktozyFWu0GPx9PFI2GY2MZ6hriFZ3TDmscwHQNrXmzAmscwHQNrHsMwTMuwA+8cUlNTAwDIzc3t4pEwTPegpqYGbre7q4fBtAJrHsN0LKx50Q1rHsN0LKx5DMMwkfAqtOcQ0zRRVFSExMRECCFO61iPx4Pc3FwcO3YsZlZj4jGfG3rimKWUqKmpQXZ2NhSFOwFEK6x50Q+P+dzAmtczYM2LfnjM5wbWPIZhmM6BM/DOIYqiICcn56zOkZSUFDN/vC14zOeGnjZmjshGP6x5sQOP+dzAmte9Yc2LHXjM5wbWPIZhmI6FQxoMwzAMwzAMwzAMwzAME8WwA49hGIZhGIZhGIZhGIZhohh24MUIDocDjz/+OBwOR1cPpd3wmM8NPGamOxKLvyM85nMDj5npjsTi7wiP+dzAY2YYhmEseBELhmEYhmEYhmEYhmEYholiOAOPYRiGYRiGYRiGYRiGYaIYduAxDMMwDMMwDMMwDMMwTBTDDjyGYRiGYRiGYRiGYRiGiWLYgccwDMMwDMMwDMMwDMMwUQw78LqAyspKzJ8/H263G263G/Pnz0dVVVWbx0gp8cQTTyA7OxsulwtTp07Fd999F7GP1+vFvffei9TUVMTHx2Pu3LkoLCxs8XxerxdjxoyBEAIFBQVRPe7Dhw9jwYIFyM/Ph8vlwoABA/D444/D5/M1u94f/vAH5Ofnw+l0Yty4cfjyyy/bHN+aNWswbtw4OJ1O9O/fH3/84x+b7fPuu+9i+PDhcDgcGD58ON5///2zvm5Xj/nJJ5/EhRdeiMTERKSnp+MHP/gB9uzZE9Vjbjp+IQQWLVrU7jEzXQdrHmteV4+ZNY8518Si7rHmseZ15Zibjp81j2EYpgUkc86ZPXu2HDFihFy7dq1cu3atHDFihJwzZ06bxyxevFgmJibKd999V27fvl3ecMMNMisrS3o8nuA+CxculH369JGrVq2SW7ZskdOmTZOjR4+Wuq43O999990nL7/8cglAbt26NarH/a9//Uveeuut8uOPP5YHDhyQy5Ytk+np6fKhhx6KuNbSpUulzWaTr776qty5c6e8//77ZXx8vDxy5EiLYzt48KCMi4uT999/v9y5c6d89dVXpc1mk3//+9+D+6xdu1aqqip//etfy127dslf//rXUtM0uX79+jO+bjSMedasWXLJkiVyx44dsqCgQF5xxRWyb9++sra2NmrHbLFx40aZl5cnR40aJe+///5TjpfpeljzWPO6esysecy5JhZ1jzWPNa8rx2zBmscwDNM67MA7x+zcuVMCiPiDtW7dOglA7t69u8VjTNOUmZmZcvHixcFtjY2N0u12yz/+8Y9SSimrqqqkzWaTS5cuDe5z/PhxqSiKXLFiRcT5li9fLocOHSq/++67dht10TDucJ5++mmZn58fse2iiy6SCxcujNg2dOhQ+fOf/7zFczzyyCNy6NChEdvuuusuOWHChODP119/vZw9e3bEPrNmzZLz5s074+tGw5ibUlpaKgHINWvWRPWYa2pq5KBBg+SqVavklClT2LCLAaJBO1jzCNa8EKx5TGcSDfpxuroXDWMOhzXv7MbcFNY8hmGY7gGX0J5j1q1bB7fbjfHjxwe3TZgwAW63G2vXrm3xmEOHDqGkpAQzZ84MbnM4HJgyZUrwmG+++QZ+vz9in+zsbIwYMSLivCdOnMCdd96JN998E3FxcTEz7qZUV1cjJSUl+LPP58M333wTcR4AmDlzZqvnWbduXbP9Z82ahc2bN8Pv97e5j3XOM7luV4+5JaqrqwEg4j2NxjH/7Gc/wxVXXIHLLruszXEy0UNXawdrXuQ9seYRrHlMZ9LV+nEmutfVY24Ka96Zj7klWPMYhmG6B+zAO8eUlJQgPT292fb09HSUlJS0egwAZGRkRGzPyMgIvlZSUgK73Y5evXq1uo+UErfeeisWLlyICy64IGbG3ZQDBw7ghRdewMKFC4PbTp48CcMw2rxWS+NraX9d13Hy5Mk297HOeSbX7eoxN0VKiQcffBCTJ0/GiBEjonbMS5cuxZYtW/Dkk0+2OUYmumDNY83r6jE3hTWP6WxiUfdY81o+J2veuRszax7DMMypYQdeB/HEE09ACNHm1+bNmwEAQohmx0spW9weTtPX23NM+D4vvPACPB4PHnvssYh9xo4dG9XjDqeoqAizZ8/GddddhzvuuOOsr9XS/k23t+ecZ3KPXT1mi3vuuQfbtm3D22+/3a7xdsWYjx07hvvvvx//+7//C6fT2e5xMp0Ha17njTsc1jzWPNa86CEWde/5558H0LbudfWYw2HNY81jzWMYhmkdrasH0F245557MG/evDb3ycvLw7Zt23DixIlmr5WVlTWLTFlkZmYCoOhVVlZWcHtpaWnwmMzMTPh8PlRWVkZEOUtLSzFp0iQAwKeffor169fD4XBEnF9RFFx55ZVYvHhxVI7boqioCNOmTcPEiRPxpz/9KeK11NRUqKraLDoYfq2WxtfS/pqmoXfv3m3uY53zTK7b1WMO595778UHH3yAL774Ajk5OW2OtyvH/M0336C0tBTjxo0Lvm4YBr744gu8+OKL8Hq9UFX1lONnOg7WvM4btwVrHmsea150EYu6ZzlS2tK9rh6zBWseax5rHsMwzCnowH56TDuwmgRv2LAhuG39+vXtahL81FNPBbd5vd4WmwT/9a9/De5TVFQU0ST4yJEjcvv27cGvjz/+WAKQf//73+WxY8eidtxSSllYWCgHDRok582b1+JKa1JS09277747YtuwYcPabLo7bNiwiG0LFy5s1nT38ssvj9hn9uzZzZobn851o2HMpmnKn/3sZzI7O1vu3bv3lOPs6jF7PJ6I393t27fLCy64QN5yyy1y+/btpzV+5tzCmseaFw1jZs1jziWxqHuseSFY81jzGIZhohV24HUBs2fPlqNGjZLr1q2T69atkyNHjpRz5syJ2GfIkCHyvffeC/68ePFi6Xa75XvvvSe3b98ub7zxRpmVlSU9Hk9wn4ULF8qcnBy5evVquWXLFjl9+nQ5evToVg2hQ4cOtXtFxq4c9/Hjx+XAgQPl9OnTZWFhoSwuLg5+hWMte//aa6/JnTt3ykWLFsn4+Hh5+PBhKaWUP//5z+X8+fOD+1vL3j/wwANy586d8rXXXmu27P3XX38tVVWVixcvlrt27ZKLFy9utuz9qa7bFl015rvvvlu63W75+eefR7yf9fX1UTvmpvDqZLEDax5rXlePmTWPOdfEou6x5rHmdeWYm8KaxzAM0xx24HUB5eXl8uabb5aJiYkyMTFR3nzzzbKysjJiHwByyZIlwZ9N05SPP/64zMzMlA6HQ15yySXNIlINDQ3ynnvukSkpKdLlcsk5c+bIo0ePtjqO053MdtW4lyxZIgG0+NWUl156Sfbr10/a7XZ5/vnnyzVr1gRf+/GPfyynTJkSsf/nn38ux44dK+12u8zLy5Mvv/xys3O+8847csiQIdJms8mhQ4fKd99997Sueyq6YsytvZ/hn120jbkpbNjFDqx5rHldPWbWPOZcE4u6x5rHmteVY24Kax7DMExzhJSB5hgMwzAMwzAMwzAMwzAMw0QdvAotwzAMwzAMwzAMwzAMw0Qx7MBjGIZhGIZhGIZhGIZhmCiGHXgMwzAMwzAMwzAMwzAME8WwA49hGIZhGIZhGIZhGIZhohh24DEMwzAMwzAMwzAMwzBMFMMOPIZhGIZhGIZhGIZhGIaJYtiBxzAMwzAMwzAMwzAMwzBRDDvwmJhkw4YN+OEPf4i+ffvC4XAgIyMDEydOxEMPPQQAKCsrg91ux7x581o9h8fjQVxcHObOnQsAeOONNyCECH45nU5kZmZi2rRpePLJJ1FaWtru8f3qV7/CnDlz0KdPHwghcOutt57V/TIM07NhzWMYpifBmscwDMMwzWEHHhNz/POf/8SkSZPg8Xjw9NNPY+XKlfjd736Hiy++GH/9618BAGlpaZg7dy7+8Y9/oLKyssXzLF26FA0NDViwYEHE9iVLlmDdunVYtWoVXnrpJYwZMwZPPfUUhg0bhtWrV7drjM899xzKy8sxd+5c2O32s7thhmF6NKx5DMP0JFjzGIZhGKYVJMPEGJdccokcMGCA9Pv9zV4zDCP4/+XLl0sA8oUXXmjxPOPHj5cZGRnB8yxZskQCkJs2bWq275EjR2Rubq5MTEyUJSUlpxxj+Dji4+Plj3/841MewzAM0xKseQzD9CRY8xiGYRimZTgDj4k5ysvLkZqaCk3Tmr2mKKFf6VmzZiEnJwdLlixptt+uXbuwYcMG/OhHP2rxPE3p27cvnnnmGdTU1OCVV1455f7h42AYhjkbWPMYhulJsOYxDMMwTMvwXx8m5pg4cSI2bNiA++67Dxs2bIDf729xP0VRcOutt2LLli349ttvI16zjL3bb7+93df9/ve/D1VV8cUXX5z54BmGYU4T1jyGYXoSrHkMwzAM0zLswGNijsWLF2Py5Ml44YUXMGHCBMTHx+Piiy/G4sWLUVtbG7Hv7bffDiEEXn/99eA2Xdfx5ptv4uKLL8bQoUPbfd34+HikpqaiqKiow+6FYRjmVLDmMQzTk2DNYxiGYZiWYQceE3P07t0bX375JTZt2oTFixfjqquuwt69e/HYY49h5MiROHnyZHDf/Px8TJs2DW+99RZ8Ph8A4F//+hdKSkpOKyprIaXssPtgGIZpD6x5DMP0JFjzGIZhGKZl2IHHxCwXXHABHn30UbzzzjsoKirCAw88gMOHD+Ppp5+O2G/BggUoLy/HBx98AIDKKhISEnD99def1vXq6upQXl6O7OzsDrsHhmGY9sKaxzBMT4I1j2EYhmEiYQce0y2w2Wx4/PHHAQA7duyIeO3qq69Gr1698Prrr6OsrAwfffQRbrjhBiQkJJzWNf75z3/CMAxMnTq1o4bNMAxzRrDmMQzTk2DNYxiGYRh24DExSHFxcYvbd+3aBQDNIqdOpxM33XQTVq5ciaeeegp+v/+0yyqOHj2Khx9+GG63G3fdddeZDZxhGOYMYM1jGKYnwZrHMAzDMC1z6nXVGSbKmDVrFnJycnDllVdi6NChME0TBQUFeOaZZ5CQkID777+/2TELFizASy+9hGeffRZDhw7FpEmTWj3/jh07oOs6dF1HaWkpvvzySyxZsgSqquL9999HWlraKce4Zs0alJWVAQAMw8CRI0fw97//HQAwZcqUdp2DYRgGYM1jGKZnwZrHMAzDMC0jJHdrZWKMv/3tb1i2bBk2bdqE4uJieL1eZGVlYcqUKXjssccwbNiwFo87//zzsXXrVjz99NP4t3/7t2avv/HGG7jtttuCP9vtdiQnJ2PYsGGYNWsW7rjjjnYbZFOnTsWaNWtafO2zzz7j8gyGYdoNax7DMD0J1jyGYRiGaRl24DEMwzAMwzAMwzAMwzBMFMM98BiGYRiGYRiGYRiGYRgmimEHHsMwDMMwDMMwDMMwDMNEMezAYxiGYRiGYRiGYRiGYZgohh14DMMwDMMwDMMwDMMwDBPFsAOPYRiGYRiGYRiGYRiGYaIYduAxDMMwDMMwDMMwDMMwTBTDDjyGYRiGYRiGYRiGYRiGiWLYgccwDMMwDMMwDMMwDMMwUQw78BiGYRiGYRiGYRiGYRgmimEHHsMwDMMwDMMwDMMwDMNEMezAYxiGYRiGYRiGYRiGYZgohh14DMMwDMMwDMMwDMMwDBPF/H/klhQpirZmJQAAAABJRU5ErkJggg==",
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "Image(filename=\"figures/example_all_vs_ref_plot1.png\")"
]
},
{
@@ -255,57 +431,37 @@
"metadata": {},
"outputs": [],
"source": [
- "fig, ax = plt.subplots(3, 4, figsize=(23, 15), sharex=True, sharey=True)\n",
- "\n",
- "for i, id in enumerate(data_svd_vs_gt[\"metadata\"].keys()):\n",
- " frq, edges = np.histogram(data_svd_vs_gt[\"coeffs_ref\"][:, 0], bins=30)\n",
- " ax.flatten()[i].bar(\n",
- " edges[:-1],\n",
- " frq / frq.max(),\n",
- " width=np.diff(edges),\n",
- " label=\"Ground Truth\",\n",
- " alpha=0.8,\n",
- " color=\"#a1c9f4\",\n",
- " )\n",
- "\n",
- " idx_0, idx_1 = data_svd_vs_gt[\"metadata\"][id][\"indices\"]\n",
- " populations = data_svd_vs_gt[\"metadata\"][id][\"populations\"]\n",
- "\n",
- " ax.flatten()[i].scatter(\n",
- " x=data_svd_vs_gt[\"coeffs\"][idx_0:idx_1, 0],\n",
- " y=populations / populations.max(),\n",
- " color=\"red\",\n",
- " marker=\"o\",\n",
- " s=60,\n",
- " linewidth=0.3,\n",
- " edgecolor=\"white\",\n",
- " label=id,\n",
- " )\n",
- "\n",
- " ax.flatten()[i].set_xlabel(\"SVD 1\", fontsize=12)\n",
- " ax.flatten()[i].set_ylabel(\"SVD 2\", fontsize=12)\n",
- " ax.flatten()[i].legend(loc=\"upper left\", fontsize=12)\n",
- "\n",
- "# ax[0].set_title(\"Submission vs all submissions\")\n",
- "ax[2, 3].axis(\"off\")\n",
- "\n",
- "# adjust horizontal space\n",
- "plt.subplots_adjust(wspace=0.5, hspace=0.5)\n",
- "fig.suptitle(\"Set2: Projection of each submission onto GT's SVD\", fontsize=16, y=0.92)"
+ "plot_all_vs_ref_plot2(results_svd_all_vs_ref, title_fig, fig_fname=fig_fname)"
]
},
{
- "cell_type": "markdown",
+ "cell_type": "code",
+ "execution_count": 7,
"metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
- "# Comparing submissions to each other (all vs all)"
+ "Image(filename=\"figures/svd_example_all_vs_ref_plot2.png\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "Follow the same instructions as before, but change the mode to all_vs_all. I will leave the code used to generate the images."
+ "## Submissions vs Submissions\n",
+ "\n",
+ "As before, let's load the results"
]
},
{
@@ -326,30 +482,61 @@
"metadata": {},
"outputs": [],
"source": [
- "data_svd_all_vs_all = torch.load(svd_all_vs_all_results_path.value)"
+ "results_svd_all_vs_all_set2 = torch.load(svd_all_vs_all_results_path.value)"
]
},
{
- "cell_type": "markdown",
+ "cell_type": "code",
+ "execution_count": null,
"metadata": {},
+ "outputs": [],
"source": [
- "The description for the keys is the same, except now we don't have the keys for the reference maps (as there is none)"
+ "title_fig = \"your title\"\n",
+ "fig_fname = \"your figfname\" # for saving a file (optional)\n",
+ "\n",
+ "plot_all_vs_ref_plot1(results_svd_all_vs_ref, title_fig, fig_fname=fig_fname)"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "Image(filename=\"figures/svd_example_all_vs_all_set2.png\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
"metadata": {},
- "outputs": [],
"source": [
- "data_svd_all_vs_all.keys()"
+ "# Analysis for Set 1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "We also need to compute the weights for each volume, as we will use this for the plot (we do weighted KDE)"
+ "In set 1 we only have the submissions vs submissions case"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's repeat the same process as before"
]
},
{
@@ -358,13 +545,10 @@
"metadata": {},
"outputs": [],
"source": [
- "weights = []\n",
- "for i, id in enumerate(data_svd_all_vs_all[\"metadata\"].keys()):\n",
- " populations = data_svd_vs_gt[\"metadata\"][id][\"populations\"]\n",
- " weights += populations.tolist()\n",
- "\n",
- "weights = np.array(weights)\n",
- "weights = weights / weights.sum()"
+ "# Select path to SVD results\n",
+ "results_svd_all_vs_all_set1_path = FileChooser(os.path.expanduser(\"~\"))\n",
+ "results_svd_all_vs_all_set1_path.filter_pattern = \"*.pt\"\n",
+ "display(results_svd_all_vs_all_set1_path)"
]
},
{
@@ -373,46 +557,7 @@
"metadata": {},
"outputs": [],
"source": [
- "fig, ax = plt.subplots(3, 4, figsize=(4 * 5, 3 * 5))\n",
- "# fig.suptitle(\"KDEPlots for UMAP Embedding of SVD Coefficients for Set 2 with GT as reference\")\n",
- "\n",
- "for i, id in enumerate(data_svd_all_vs_all[\"metadata\"].keys()):\n",
- " sns.kdeplot(\n",
- " x=data_svd_all_vs_all[\"coeffs\"][:, 0],\n",
- " y=data_svd_all_vs_all[\"coeffs\"][:, 1],\n",
- " cmap=\"viridis\",\n",
- " fill=True,\n",
- " cbar=False,\n",
- " ax=ax.flatten()[i],\n",
- " weights=weights,\n",
- " )\n",
- "\n",
- " idx_0, idx_1 = data_svd_vs_gt[\"metadata\"][id][\"indices\"]\n",
- " populations = data_svd_vs_gt[\"metadata\"][id][\"populations\"]\n",
- "\n",
- " ax.flatten()[i].scatter(\n",
- " x=data_svd_vs_gt[\"coeffs\"][idx_0:idx_1, 0],\n",
- " y=data_svd_vs_gt[\"coeffs\"][idx_0:idx_1, 1],\n",
- " color=\"red\",\n",
- " s=populations / populations.max() * 200,\n",
- " marker=\"o\",\n",
- " linewidth=0.3,\n",
- " edgecolor=\"white\",\n",
- " label=id,\n",
- " )\n",
- "\n",
- " ax.flatten()[i].set_xlabel(\"SVD 1\", fontsize=12)\n",
- " ax.flatten()[i].set_ylabel(\"SVD 2\", fontsize=12)\n",
- " ax.flatten()[i].legend(fontsize=12)\n",
- "\n",
- "\n",
- "# adjust horizontal space\n",
- "plt.subplots_adjust(wspace=0.5, hspace=0.5)\n",
- "fig.suptitle(\n",
- " \"Set2: 1st and 2nd SVD coefficient for each submission vs. distribution of all coefficients\",\n",
- " fontsize=16,\n",
- " y=0.92,\n",
- ")"
+ "results_svd_all_vs_all_set1 = torch.load(results_svd_all_vs_all_set1_path.value)"
]
},
{
@@ -420,14 +565,40 @@
"execution_count": null,
"metadata": {},
"outputs": [],
- "source": []
+ "source": [
+ "title_fig = \"your title\"\n",
+ "fig_fname = \"your figfname\" # for saving a file (optional)\n",
+ "\n",
+ "plot_all_vs_ref_plot1(results_svd_all_vs_ref, title_fig, fig_fname=fig_fname)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "Image(filename=\"figures/svd_example_all_vs_all_set1.png\")"
+ ]
}
],
"metadata": {
"kernelspec": {
- "display_name": "gpucryonerf",
+ "display_name": "cryo-challenge-kernel",
"language": "python",
- "name": "python3"
+ "name": "cryo-challenge-kernel"
},
"language_info": {
"codemirror_mode": {
@@ -439,7 +610,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.17"
+ "version": "3.10.10"
}
},
"nbformat": 4,
diff --git a/tutorials/figures/svd_example_all_vs_all_set1.png b/tutorials/figures/svd_example_all_vs_all_set1.png
new file mode 100644
index 0000000..c09b85e
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diff --git a/tutorials/figures/svd_example_all_vs_all_set2.png b/tutorials/figures/svd_example_all_vs_all_set2.png
new file mode 100644
index 0000000..811b3d9
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diff --git a/tutorials/figures/svd_example_all_vs_ref_plot1.png b/tutorials/figures/svd_example_all_vs_ref_plot1.png
new file mode 100644
index 0000000..e13f4f5
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diff --git a/tutorials/figures/svd_example_all_vs_ref_plot2.png b/tutorials/figures/svd_example_all_vs_ref_plot2.png
new file mode 100644
index 0000000..cf2d360
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