diff --git a/.DS_Store b/.DS_Store new file mode 100644 index 0000000..7c7a649 Binary files /dev/null and b/.DS_Store differ diff --git a/README.md b/README.md index 9ae4578..663aa5d 100644 --- a/README.md +++ b/README.md @@ -1,152 +1,104 @@ -This template is designed to give a framework for public distributions of "science" projects. -It is a guideline, showing the minimum things recommended to include with your public repository, -to make your results easily replicable. -It is not exhaustive by any means, nor is everything here strictly required in all cases! -Please consider this as a loose list of things considered "nice to have", and as reference material above all. - -# DeepSkies Science Repo Template -Include status links to different outside resources, such as build info, paper info, license, etc. -You can select your license from the [choose a license page.](https://choosealicense.com/licenses/), and then change the name of the license in the badge and link included. -For workflows, change the name of the repo listed in the img.shields link to point to your repo and workflows. - -[![status](https://img.shields.io/badge/arXiv-000.000-red)](arxiv link if applicable) -[![status](https://img.shields.io/badge/PyPi-0.0.0.0-blue)](pypi link if applicable) -[![status](https://img.shields.io/badge/License-MIT-lightgrey)](MIT or Apache 2.0 or another requires link changed) -![GitHub Workflow Status](https://img.shields.io/github/workflow/status/owner/repo/build-repo) -![GitHub Workflow Status](https://img.shields.io/github/workflow/status/owner/repo/test-repo?label=test) - -Your overview should contain a brief summary of the project, and figures and examples showing input and output. - -## Installation -Information about install. -We recommend publishing to pypi using a poetry package management system (described below) but we also provide instructions for using python virtual environments and showyourwork with conda integration. +We present **DeepCosmoSLIDE**: a **S**trong **L**ens based **I**nference for **D**ark **E**nergy. We use Simulation Based Inference (SBI) with Neural Ratio Estimation (NRE) to constrain Dark Energy parameter from a population of strong galaxy-galaxy lenses. -Example of what your installation instructions should look like: - -To install with pip: -> pip install git+https://github.com/DeepSkies/science_template.git -> -This will set up a virtual environment, which can b e run with on mac or linux -> source venv/bin/activate -> -Or on windows with -> venv\Scripts\activate.bat +## Introduction -Verify installation is functional is all tests are passing -> pytest +Strong gravitational lensing offers crucial insights into cosmology, but traditional Monte Carlo methods for cosmological inference are computationally prohibitive and inadequate for processing the thousands of lenses anticipated from future cosmic surveys. New tools for inference, such as SBI using NRE, address this challenge effectively. NRE is a classifier neural network to differentiate between two probability distributions: -Additionally, include how to install from source (via git clone) and associated setup. +$(x,w) \sim\ p(x,w)$ with class label y=1 -### poetry -Poetry is our recommended method of handling a package environment as publishing and building is handled by a toml file that handles all possibly conflicting dependencies. -Full docs can be found [here](https://python-poetry.org/docs/basic-usage/). - -Install instructions: - -Add poetry to your python install -> pip install poetry - -Install the pyproject file -> poetry install - -To add another package to your environment -> poetry add (package name) - -To run within your environment ->poetry run (file).py - -If you wish to start from scratch: -> pip install poetry -> poetry init - -### virtual environment -At the bare minimum, project dependencies must be contained and strictly defined and shared for replication purposes. -The easiest way to do this is to use a python virtual environment. -Full instructions are found [here.](https://docs.python.org/3/library/venv.html) - -To initialize an environment: -> python3 -m venv /path/to/env -> -To activate it: -Linux and Mac: -> source venv/bin/activate -> -Windows: -> venv\Scripts\activate.bat - -And use pip as normal to install packages. - -In order to produce a file to share with your version of dependencies, produce a requirements.txt. -This can later be installed in full to a new system using `pip install -r requirements.txt`. -Note that this does not manage any versioning conflicts and can very quickly become depreciated. -> pip freeze >requirements.txt - -### show your work with conda -We also supply a ["show your work"](https://github.com/showyourwork/showyourwork) workflow to use with a conda venv which can compile the example tex file in `DeepTemplate-Science/src/tex/ms.tex` - -To execute this workflow: ->showyourwork build - -This will build your project and install the conda venv associated with the project (or just compile the document if you haven't been using it) and output the document as a pdf. -If you would like to integrate with overleaf to push your work remotely, you can do that by adding the following lines to your showyourwork.yml file: -> -> overleaf: -> -> id: URL identifying your project -> push: -> - src/tex/figures -> - src/tex/output -> pull: -> - src/tex/ms.tex -> - src/tex/bib.bib - -And adding the system variables `$OVERLEAF_EMAIL` and `$OVERLEAF_PASSWORD` with your credentials. -To do this, use a bash terminal to input the command `export OVERLEAF_EMAIL='youremail@server.org`, and do the same for your password. -To verify these are set correctly, run `echo $OVERLEAF_EMAIL`and `echo $OVERLEAF_PASSWORD`. -To complete this setup, run `showyourwork build` as if you were compiling a project. -The above snippet of the yaml file will then push anything in the `src/tex/figures` and `src/tex/output` folders to the remote, under the `images` folder. - -The existing yaml file is set up to modify the [template project](*https://www.overleaf.com/read/fsjwntpjmdzw). -The differences in the ID in the template and the url you'll see is due to the fact that only project owners have access to that ID (even if I want to share). -This limits the person who can build the project to the person that owns the overleaf page, at least until Latex sets up token authentication. -The workaround for this is account sharing, but this is not recommended. - -For more information please see the [showyourwork page on the topic](https://show-your.work/en/latest/overleaf/). +$(x,w) \sim\ p(x)p(w)$ with class label y=0 +where $x$ is the strong lens image and $w$ is the dark energy equation-of-state parameter that generated the image. + +By training a machine learning model on simulated data of strong lenses, we can learn the likelihood-to-evidence ratio $\frac{p(x|w)}{p(x)}$. This is used for robust inference of the posterior $p(w|\{x_{0}\})$ from a population of strong lens images $\{x_{0}\}$. + +$\textbf{Analysis Workflow}$ + +The following figure summarizes the workflow of the analysis. + +The strong lens images are genereated using a simulator where the parameters are sampled from a prior distribution. The training data for the NRE network (classifier) includes the image and the parameter of interest. The network outputs the likelihood-to-evidence ratio. The trained model is implemented on the observations to estimate the posterior. + +![Workflow](./figures/SBI_NRE_workflow.png) + +## Getting started + +### Data + +The data used for this analysis can be found on Zenodo (link will be provided shortly). + +The images are generated using [Deeplenstronomy](https://github.com/deepskies/deeplenstronomy) package. + +This data can be generated using the yaml files in `/deeplenstornomy_templates` as inputs to $\texttt{Deeplenstronomy}$. + +The simulation outputs the data into a folder which includes images (`/CONFIGURATION_1_images.npy`) and the metadata (`/CONFIGURATION_1_metadata.csv`) assocated with the image generation. + +$\textbf{Training data}$ +We train, validate, and test the NRE model on simulated data of 1M strong lens images. + +$\textbf{Test data for population-level analysis}$ +We generate three datasets of 3000 images each by fixing w = -1.2, -1.0, and -0.8 respectively. + + + + +### Notebooks for analysis + +The notebooks in `/notebooks` can be run to reproduce the results of the paper. The python scripts are currently being developed to be run from the terminal. + +$\textbf{Model Training}$ + +`train_model.ipynb` +This notebook includes reading in the data, preprocessing of the images, +and training the model. +Three models with random weight initializations (`seed` input to the model) are run in our analysis to check robustness. One of the models `working_model_1M-2-034_seed128_v2.keras` is available on Zonodo (link will be provided shortly). + +This trained model can be directly loaded (without having to re-train the model) using + +`model = tf.keras.models.load_model(model_name)` + +$\textbf{Model Evaluation}$ + +`compare_random_seeds.ipynb` code is for checking the performance of the model on test data of 2000 images. +The code includes plotting the Receiver Operating Curve (ROC) for the three models. It also includes calculating and plotting the analytical posteriors of a few randomly selected strong lenses. + +`plot_image_posterior.ipynb` code is for plotting the training data and show the correlation between the Einstein radius and $w$. This code also plots the image of strong lens from the training data and the corresponing analytical posterior. + +`plot_residuals` is to plot the predicted mean $w$ of the analytical posterior with 1 $\sigma$ error bar Vs the true $w$ of the 2000 test images. We also compute the posterior coverage plot to check the model uncertainity. + +$\textbf{Population-level Analysis}$ + +`NRE_varyastro_w12.ipynb`, `NRE_varyastro_w1.ipynb`, and `NRE_varyastro_w08.ipynb` include functions to compute the joint posterior from 3000 images with $w$ fixed to -1.2, -1.0, and -0.8 respectively. We show both MCMC and analytical methods to calculate the posterior. + +`Compare_mcmc_analytical.ipynb` compares the posteriors from the MCMC and analytical methods. + + +### Authors + +Sreevani Jarugula + +### References + +If you use this code, please cite our paper (Link to be posted shortly) + + + + + + + + + + + + + -## Quickstart -Description of the immediate steps to replicate your results, pointing to a script with cli execution. -You can also point to a notebook if your results are highly visual and showing plots in line with code is desired. - -Example: -To run full model training: -> python3 train.py --data /path/to/data/folder - -To evaluate a single ""data format of choice"" -> python3 eval.py --data /path/to/data - -## Documentation -Please include any further information needed to understand your work. -This can include an explanation of different notebooks, basic code diagrams, conceptual explanations, etc. -If you have a folder of documentation, summarize it here and point to it. -## Citation -Include a link to your bibtex citation for others to use. -``` -@article{key , - author = {You :D}, - title = {title}, - journal = {journal}, - volume = {v}, - year = {20XX}, - number = {X}, - pages = {XX--XX} -} - -``` - -## Acknowledgement -Include any acknowledgements for research groups, important collaborators not listed as a contributor, institutions, etc. diff --git a/deeplenstronomy_templates/.DS_Store b/deeplenstronomy_templates/.DS_Store new file mode 100644 index 0000000..5008ddf Binary files /dev/null and b/deeplenstronomy_templates/.DS_Store differ diff --git a/deeplenstronomy_templates/test_8_parameter_w0cosmo_fixzv_fixw0-08.yml b/deeplenstronomy_templates/test_8_parameter_w0cosmo_fixzv_fixw0-08.yml new file mode 100644 index 0000000..a9e3705 --- /dev/null +++ b/deeplenstronomy_templates/test_8_parameter_w0cosmo_fixzv_fixw0-08.yml @@ -0,0 +1,134 @@ +DATASET: + NAME: w0_8param_fixzv_test_fixw0-08_3000 + PARAMETERS: + SIZE: 3000 # number of images in the full datase. + OUTDIR: w0_8param_fixzv_test_fixw0-08_3000 # will be created on your system if your request to save images + SEED: 42 + +COSMOLOGY: + NAME: 'wCDM' + PARAMETERS: + H0: 70 + Om0: 0.3 + Ode0: 0.7 + w0: -0.8 +IMAGE: + PARAMETERS: + exposure_time: + DISTRIBUTION: + NAME: des_exposure_time + PARAMETERS: None + numPix: 32 + pixel_scale: 0.263 + psf_type: 'GAUSSIAN' + read_noise: 7 + ccd_gain: + DISTRIBUTION: + NAME: des_ccd_gain + PARAMETERS: None + +SURVEY: + PARAMETERS: + BANDS: g + seeing: 0.9 + magnitude_zero_point: 30.0 + sky_brightness: 30.0 + num_exposures: 10 + +SPECIES: + GALAXY_1: + NAME: LENS + LIGHT_PROFILE_1: + NAME: SERSIC_ELLIPSE + PARAMETERS: + magnitude: 100 + center_x: 0 + center_y: 0 + R_sersic: 1 + n_sersic: 4 + e1: 0 + e2: 0.5 + + MASS_PROFILE_1: + NAME: SIE + PARAMETERS: + sigma_v: 200 + e1: + DISTRIBUTION: + NAME: uniform + PARAMETERS: + minimum: -0.1 + maximum: 0.1 + e2: + DISTRIBUTION: + NAME: uniform + PARAMETERS: + minimum: -0.1 + maximum: 0.1 + center_x: 0.0 + center_y: 0.0 + + + GALAXY_2: + NAME: SOURCE + LIGHT_PROFILE_1: + NAME: SERSIC_ELLIPSE + PARAMETERS: + magnitude: + DISTRIBUTION: + NAME: uniform + PARAMETERS: + minimum: 19 + maximum: 24 + center_x: 0.0 + center_y: 0.0 + R_sersic: + DISTRIBUTION: + NAME: uniform + PARAMETERS: + minimum: 0.1 + maximum: 3 + n_sersic: + DISTRIBUTION: + NAME: uniform + PARAMETERS: + minimum: 0.5 + maximum: 8 + e1: + DISTRIBUTION: + NAME: uniform + PARAMETERS: + minimum: -0.1 + maximum: 0.1 + e2: + DISTRIBUTION: + NAME: uniform + PARAMETERS: + minimum: -0.1 + maximum: 0.1 + MASS_PROFILE_1: + NAME: SIE + PARAMETERS: + theta_E: 2.0 + e1: 0.1 + e2: -0.1 + center_x: 0.0 + center_y: 0.0 + + +GEOMETRY: + CONFIGURATION_1: + NAME: GALAXYGALAXY + FRACTION: 1 + PLANE_1: + OBJECT_1: LENS + PARAMETERS: + REDSHIFT: 0.1 + PLANE_2: + OBJECT_1: SOURCE + PARAMETERS: + REDSHIFT: 2.0 + + + + diff --git a/deeplenstronomy_templates/test_8_parameter_w0cosmo_fixzv_fixw0-1.yml b/deeplenstronomy_templates/test_8_parameter_w0cosmo_fixzv_fixw0-1.yml new file mode 100644 index 0000000..348b452 --- /dev/null +++ b/deeplenstronomy_templates/test_8_parameter_w0cosmo_fixzv_fixw0-1.yml @@ -0,0 +1,134 @@ +DATASET: + NAME: w0_8param_fixzv_test_fixw0-1_5000 + PARAMETERS: + SIZE: 5000 # number of images in the full datase. + OUTDIR: w0_8param_fixzv_test_fixw0-1_5000 # will be created on your system if your request to save images + SEED: 42 + +COSMOLOGY: + NAME: 'wCDM' + PARAMETERS: + H0: 70 + Om0: 0.3 + Ode0: 0.7 + w0: -1.0 +IMAGE: + PARAMETERS: + exposure_time: + DISTRIBUTION: + NAME: des_exposure_time + PARAMETERS: None + numPix: 32 + pixel_scale: 0.263 + psf_type: 'GAUSSIAN' + read_noise: 7 + ccd_gain: + DISTRIBUTION: + NAME: des_ccd_gain + PARAMETERS: None + +SURVEY: + PARAMETERS: + BANDS: g + seeing: 0.9 + magnitude_zero_point: 30.0 + sky_brightness: 30.0 + num_exposures: 10 + +SPECIES: + GALAXY_1: + NAME: LENS + LIGHT_PROFILE_1: + NAME: SERSIC_ELLIPSE + PARAMETERS: + magnitude: 100 + center_x: 0 + center_y: 0 + R_sersic: 1 + n_sersic: 4 + e1: 0 + e2: 0.5 + + MASS_PROFILE_1: + NAME: SIE + PARAMETERS: + sigma_v: 200 + e1: + DISTRIBUTION: + NAME: uniform + PARAMETERS: + minimum: -0.1 + maximum: 0.1 + e2: + DISTRIBUTION: + NAME: uniform + PARAMETERS: + minimum: -0.1 + maximum: 0.1 + center_x: 0.0 + center_y: 0.0 + + + GALAXY_2: + NAME: SOURCE + LIGHT_PROFILE_1: + NAME: SERSIC_ELLIPSE + PARAMETERS: + magnitude: + DISTRIBUTION: + NAME: uniform + PARAMETERS: + minimum: 19 + maximum: 24 + center_x: 0.0 + center_y: 0.0 + R_sersic: + DISTRIBUTION: + NAME: uniform + PARAMETERS: + minimum: 0.1 + maximum: 3 + n_sersic: + DISTRIBUTION: + NAME: uniform + PARAMETERS: + minimum: 0.5 + maximum: 8 + e1: + DISTRIBUTION: + NAME: uniform + PARAMETERS: + minimum: -0.1 + maximum: 0.1 + e2: + DISTRIBUTION: + NAME: uniform + PARAMETERS: + minimum: -0.1 + maximum: 0.1 + MASS_PROFILE_1: + NAME: SIE + PARAMETERS: + theta_E: 2.0 + e1: 0.1 + e2: -0.1 + center_x: 0.0 + center_y: 0.0 + + +GEOMETRY: + CONFIGURATION_1: + NAME: GALAXYGALAXY + FRACTION: 1 + PLANE_1: + OBJECT_1: LENS + PARAMETERS: + REDSHIFT: 0.1 + PLANE_2: + OBJECT_1: SOURCE + PARAMETERS: + REDSHIFT: 2.0 + + + + diff --git a/deeplenstronomy_templates/test_8_parameter_w0cosmo_fixzv_fixw0-12.yml b/deeplenstronomy_templates/test_8_parameter_w0cosmo_fixzv_fixw0-12.yml new file mode 100644 index 0000000..cd674af --- /dev/null +++ b/deeplenstronomy_templates/test_8_parameter_w0cosmo_fixzv_fixw0-12.yml @@ -0,0 +1,134 @@ +DATASET: + NAME: w0_8param_fixzv_test_fixw0-12_3000 + PARAMETERS: + SIZE: 3000 # number of images in the full datase. + OUTDIR: w0_8param_fixzv_test_fixw0-12_3000 # will be created on your system if your request to save images + SEED: 42 + +COSMOLOGY: + NAME: 'wCDM' + PARAMETERS: + H0: 70 + Om0: 0.3 + Ode0: 0.7 + w0: -1.2 +IMAGE: + PARAMETERS: + exposure_time: + DISTRIBUTION: + NAME: des_exposure_time + PARAMETERS: None + numPix: 32 + pixel_scale: 0.263 + psf_type: 'GAUSSIAN' + read_noise: 7 + ccd_gain: + DISTRIBUTION: + NAME: des_ccd_gain + PARAMETERS: None + +SURVEY: + PARAMETERS: + BANDS: g + seeing: 0.9 + magnitude_zero_point: 30.0 + sky_brightness: 30.0 + num_exposures: 10 + +SPECIES: + GALAXY_1: + NAME: LENS + LIGHT_PROFILE_1: + NAME: SERSIC_ELLIPSE + PARAMETERS: + magnitude: 100 + center_x: 0 + center_y: 0 + R_sersic: 1 + n_sersic: 4 + e1: 0 + e2: 0.5 + + MASS_PROFILE_1: + NAME: SIE + PARAMETERS: + sigma_v: 200 + e1: + DISTRIBUTION: + NAME: uniform + PARAMETERS: + minimum: -0.1 + maximum: 0.1 + e2: + DISTRIBUTION: + NAME: uniform + PARAMETERS: + minimum: -0.1 + maximum: 0.1 + center_x: 0.0 + center_y: 0.0 + + + GALAXY_2: + NAME: SOURCE + LIGHT_PROFILE_1: + NAME: SERSIC_ELLIPSE + PARAMETERS: + magnitude: + DISTRIBUTION: + NAME: uniform + PARAMETERS: + minimum: 19 + maximum: 24 + center_x: 0.0 + center_y: 0.0 + R_sersic: + DISTRIBUTION: + NAME: uniform + PARAMETERS: + minimum: 0.1 + maximum: 3 + n_sersic: + DISTRIBUTION: + NAME: uniform + PARAMETERS: + minimum: 0.5 + maximum: 8 + e1: + DISTRIBUTION: + NAME: uniform + PARAMETERS: + minimum: -0.1 + maximum: 0.1 + e2: + DISTRIBUTION: + NAME: uniform + PARAMETERS: + minimum: -0.1 + maximum: 0.1 + MASS_PROFILE_1: + NAME: SIE + PARAMETERS: + theta_E: 2.0 + e1: 0.1 + e2: -0.1 + center_x: 0.0 + center_y: 0.0 + + +GEOMETRY: + CONFIGURATION_1: + NAME: GALAXYGALAXY + FRACTION: 1 + PLANE_1: + OBJECT_1: LENS + PARAMETERS: + REDSHIFT: 0.1 + PLANE_2: + OBJECT_1: SOURCE + PARAMETERS: + REDSHIFT: 2.0 + + + + diff --git a/deeplenstronomy_templates/training_8_parameter_w0cosmo_fixzv.yml b/deeplenstronomy_templates/training_8_parameter_w0cosmo_fixzv.yml new file mode 100644 index 0000000..883677f --- /dev/null +++ b/deeplenstronomy_templates/training_8_parameter_w0cosmo_fixzv.yml @@ -0,0 +1,139 @@ +DATASET: + NAME: w0_8param_fixzv_train_1M + PARAMETERS: + SIZE: 1000000 + OUTDIR: w0_8param_fixzv_train_1M + SEED: 42 + +COSMOLOGY: + NAME: 'wCDM' + PARAMETERS: + H0: 70 + Om0: 0.3 + Ode0: 0.7 + w0: + DISTRIBUTION: + NAME: uniform + PARAMETERS: + minimum: -2.0 + maximum: -0.34 + +IMAGE: + PARAMETERS: + exposure_time: + DISTRIBUTION: + NAME: des_exposure_time + PARAMETERS: None + numPix: 32 + pixel_scale: 0.263 + psf_type: 'GAUSSIAN' + read_noise: 7 + ccd_gain: + DISTRIBUTION: + NAME: des_ccd_gain + PARAMETERS: None + +SURVEY: + PARAMETERS: + BANDS: g + seeing: 0.9 + magnitude_zero_point: 30.0 + sky_brightness: 30.0 + num_exposures: 10 + +SPECIES: + GALAXY_1: + NAME: LENS + LIGHT_PROFILE_1: + NAME: SERSIC_ELLIPSE + PARAMETERS: + magnitude: 100 + center_x: 0 + center_y: 0 + R_sersic: 1 + n_sersic: 4 + e1: 0 + e2: 0.5 + + MASS_PROFILE_1: + NAME: SIE + PARAMETERS: + sigma_v: 200 + e1: + DISTRIBUTION: + NAME: uniform + PARAMETERS: + minimum: -0.1 + maximum: 0.1 + e2: + DISTRIBUTION: + NAME: uniform + PARAMETERS: + minimum: -0.1 + maximum: 0.1 + center_x: 0 + center_y: 0 + + GALAXY_2: + NAME: SOURCE + LIGHT_PROFILE_1: + NAME: SERSIC_ELLIPSE + PARAMETERS: + magnitude: + DISTRIBUTION: + NAME: uniform + PARAMETERS: + minimum: 19 + maximum: 24 + center_x: 0 + center_y: 0 + R_sersic: + DISTRIBUTION: + NAME: uniform + PARAMETERS: + minimum: 0.1 + maximum: 3 + n_sersic: + DISTRIBUTION: + NAME: uniform + PARAMETERS: + minimum: 0.5 + maximum: 8 + e1: + DISTRIBUTION: + NAME: uniform + PARAMETERS: + minimum: -0.1 + maximum: 0.1 + e2: + DISTRIBUTION: + NAME: uniform + PARAMETERS: + minimum: -0.1 + maximum: 0.1 + MASS_PROFILE_1: + NAME: SIE + PARAMETERS: + theta_E: 2.0 + e1: 0.1 + e2: -0.1 + center_x: 0.0 + center_y: 0.0 + + +GEOMETRY: + CONFIGURATION_1: + NAME: GALAXYGALAXY + FRACTION: 1 + PLANE_1: + OBJECT_1: LENS + PARAMETERS: + REDSHIFT: 0.1 + PLANE_2: + OBJECT_1: SOURCE + PARAMETERS: + REDSHIFT: 2.0 + + + + diff --git a/notebooks/Compare_mcmc_analytical.ipynb b/notebooks/Compare_mcmc_analytical.ipynb new file mode 100644 index 0000000..ca36e69 --- /dev/null +++ b/notebooks/Compare_mcmc_analytical.ipynb @@ -0,0 +1,374 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "15c7f55b-0b30-4f7f-9eaa-379bf661c1db", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "5913a764-b31c-4829-b841-4e47aea9fa05", + "metadata": {}, + "outputs": [], + "source": [ + "best_style = {\n", + " \"font.family\": \"sans-serif\",\n", + " \"mathtext.fontset\": \"custom\",\n", + " \"mathtext.rm\": \"TeX Gyre Heros\",\n", + " \"mathtext.bf\": \"TeX Gyre Heros:bold\",\n", + " \"mathtext.sf\": \"TeX Gyre Heros\",\n", + " \"mathtext.it\": \"TeX Gyre Heros:italic\",\n", + " \"mathtext.tt\": \"TeX Gyre Heros\",\n", + " \"mathtext.cal\": \"TeX Gyre Heros\",\n", + " \"mathtext.default\": \"regular\",\n", + " \"figure.figsize\": (10.0, 10.0),\n", + " \"font.size\": 26,\n", + " \"axes.labelsize\": \"medium\",\n", + " \"axes.unicode_minus\": False,\n", + " \"xtick.labelsize\": \"small\",\n", + " \"ytick.labelsize\": \"small\",\n", + " \"legend.fontsize\": \"small\",\n", + " \"legend.handlelength\": 1.5,\n", + " \"legend.borderpad\": 0.5,\n", + " \"xtick.direction\": \"in\",\n", + " \"xtick.major.size\": 12,\n", + " \"xtick.minor.size\": 6,\n", + " \"xtick.major.pad\": 6,\n", + " \"xtick.top\": True,\n", + " \"xtick.major.top\": True,\n", + " \"xtick.major.bottom\": True,\n", + " \"xtick.minor.top\": True,\n", + " \"xtick.minor.bottom\": True,\n", + " \"xtick.minor.visible\": True,\n", + " \"ytick.direction\": \"in\",\n", + " \"ytick.major.size\": 12,\n", + " \"ytick.minor.size\": 6.0,\n", + " \"ytick.right\": True,\n", + " \"ytick.major.left\": True,\n", + " \"ytick.major.right\": True,\n", + " \"ytick.minor.left\": True,\n", + " \"ytick.minor.right\": True,\n", + " \"ytick.minor.visible\": True,\n", + " \"grid.alpha\": 0.8,\n", + " \"grid.linestyle\": \":\",\n", + " \"axes.linewidth\": 2,\n", + " \"savefig.transparent\": False,\n", + "}\n", + "plt.style.use(best_style)\n", + "cols = [\"#5790fc\", \"#f89c20\", \"#e42536\", \"#964a8b\", \"#9c9ca1\", \"#7a21dd\"]\n", + "plt.rcParams['axes.prop_cycle'] = plt.cycler(color=cols)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "c555cf11-fecb-492a-8079-b256800a9ce9", + "metadata": {}, + "outputs": [], + "source": [ + "# Functions to read MCMC and analytical posteriors\n", + "\n", + "def read_mcmc_samples(filename):\n", + " file = np.load(filename)\n", + " flat_samples = file['flat_samples']\n", + " mean_samples = np.mean(flat_samples, axis=0)\n", + " std_samples = np.std(flat_samples, axis=0)\n", + " return flat_samples, mean_samples, std_samples\n", + " \n", + "def read_analytic_samples(filename):\n", + " file = np.load(filename)\n", + " posterior_all_samples = file['samples']\n", + " posteriors_all = file['stats']\n", + " return posterior_all_samples, posteriors_all\n", + "\n", + "def read_logr(filename):\n", + " file = np.load(filename)\n", + " llr = file['logr']\n", + " return llr" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "6db19bcc-cf26-47b5-8132-69ed712959a3", + "metadata": {}, + "outputs": [], + "source": [ + "# w = -1\n", + "_, mean_samples11, std_samples11 = read_mcmc_samples('mcmc_posterior-1_'+str(5)+'_v2.npz')\n", + "_, mean_samples21, std_samples21 = read_mcmc_samples('mcmc_posterior-1_'+str(100)+'_v2.npz')\n", + "flat_samples31, mean_samples31, std_samples31 = read_mcmc_samples('mcmc_posterior-1_'+str(500)+'_v2.npz')\n", + "flat_samples41, mean_samples41, std_samples41 = read_mcmc_samples('mcmc_posterior-1_'+str(1000)+'_v2.npz')\n", + "flat_samples51, mean_samples51, std_samples51 = read_mcmc_samples('mcmc_posterior-1_'+str(2000)+'_v2.npz')\n", + "flat_samples61, mean_samples61, std_samples61 = read_mcmc_samples('mcmc_posterior-1_'+str(3000)+'_v2.npz')\n", + "\n", + "# w = -0.8\n", + "_,mean_samples12, std_samples12 = read_mcmc_samples('mcmc_posterior-08_'+str(5)+'.npz')\n", + "_, mean_samples22, std_samples22 = read_mcmc_samples('mcmc_posterior-08_'+str(100)+'.npz')\n", + "flat_samples32, mean_samples32, std_samples32 = read_mcmc_samples('mcmc_posterior-08_'+str(500)+'.npz')\n", + "flat_samples42, mean_samples42, std_samples42 = read_mcmc_samples('mcmc_posterior-08_'+str(1000)+'.npz')\n", + "flat_samples52, mean_samples52, std_samples52 = read_mcmc_samples('mcmc_posterior-08_'+str(2000)+'.npz')\n", + "flat_samples62, mean_samples62, std_samples62 = read_mcmc_samples('mcmc_posterior-08_'+str(3000)+'.npz')\n", + "\n", + "# w = -1.2\n", + "_, mean_samples13, std_samples13 = read_mcmc_samples('mcmc_posterior-12_'+str(5)+'_v2.npz')\n", + "_, mean_samples23, std_samples23 = read_mcmc_samples('mcmc_posterior-12_'+str(100)+'_v2.npz')\n", + "flat_samples33, mean_samples33, std_samples33 = read_mcmc_samples('mcmc_posterior-12_'+str(500)+'_v2.npz')\n", + "flat_samples43, mean_samples43, std_samples43 = read_mcmc_samples('mcmc_posterior-12_'+str(1000)+'_v2.npz')\n", + "flat_samples53, mean_samples53, std_samples53 = read_mcmc_samples('mcmc_posterior-12_'+str(2000)+'_v2.npz')\n", + "flat_samples63, mean_samples63, std_samples63 = read_mcmc_samples('mcmc_posterior-12_'+str(3000)+'_v2.npz')" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "a6d33212-acfb-4a7f-944b-a489a776eb14", + "metadata": {}, + "outputs": [], + "source": [ + "posterior_all_samples1, posteriors_all1 = read_analytic_samples('analytical_posteriors_w-1_v3.npz')\n", + "posterior_all_samples2, posteriors_all2 = read_analytic_samples('analytical_posteriors_w-08_v3.npz')\n", + "posterior_all_samples3, posteriors_all3 = read_analytic_samples('analytical_posteriors_w-12_v3.npz')" + ] + }, + { + "cell_type": "markdown", + "id": "09ec7175", + "metadata": {}, + "source": [ + "### Plot the mean and std of MCMC and Analytical posteriors " + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "2b5c9639-011e-488d-b2fc-01f6c4fa13bb", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "num_images_mcmc = [5, 100, 500, 1000, 2000, 3000]\n", + "num_images_analytic = np.array([0, 90, 490, 990, 1990, 2990])\n", + "\n", + "fig, (ax1, ax2, ax3) = plt.subplots(3, 1, sharex=True, figsize=(8, 12))\n", + "\n", + "# First panel\n", + "ax2.errorbar(num_images_mcmc, \n", + " [mean_samples11[0], mean_samples21[0], mean_samples31[0], \n", + " mean_samples41[0], mean_samples51[0], mean_samples61[0]], \n", + " yerr=[std_samples11[0], std_samples21[0], std_samples31[0], \n", + " std_samples41[0], std_samples51[0], std_samples61[0]],\n", + " markersize=4, fmt='o')\n", + "ax2.errorbar(num_images_analytic, posteriors_all1[:,0], \n", + " yerr=posteriors_all1[:,1], markersize=4, fmt='o')\n", + "ax2.plot([0, 3200], [-1.0, -1.0], ls='--', color='k', label=r'$w_{True}\\ = -1.0$')\n", + "\n", + "ax2.set_ylabel(r'$w_{Pred}$', fontsize='small')\n", + "# ax2.set_ylim([-1.015, -0.945])\n", + "ax2.set_ylim([-1.018, -0.976])\n", + "ax2.legend(loc='upper right', fontsize='x-small')\n", + "ax2.tick_params(axis='both', which='both', labelsize='xx-small')\n", + "# ax1.legend(loc='upper left', bbox_to_anchor=(0.6,0.8))\n", + "\n", + "# Second panel\n", + "ax1.errorbar(num_images_mcmc, \n", + " [mean_samples12[0], mean_samples22[0], mean_samples32[0], \n", + " mean_samples42[0], mean_samples52[0], mean_samples62[0]], \n", + " yerr=[std_samples12[0], std_samples22[0], std_samples32[0], \n", + " std_samples42[0], std_samples52[0], std_samples62[0]],\n", + " markersize=4, fmt='o', label='MCMC')\n", + "ax1.errorbar(num_images_analytic, posteriors_all2[:,0], \n", + " yerr=posteriors_all2[:,1],markersize=4, fmt='o',label='Analytical')\n", + "line1, = ax1.plot([0, 3200], [-0.8, -0.8], ls='--', color='k', label=r'$w_{True}\\ = -0.8$')\n", + "# ax1.set_ylim([-0.813, -0.781])\n", + "ax1.set_ylim([-0.82, -0.78])\n", + "ax1.set_ylabel(r'$w_{Pred}$', fontsize='small')\n", + "ax1.tick_params(axis='both', which='both', labelsize='xx-small')\n", + "\n", + "handles, labels = ax1.get_legend_handles_labels()\n", + "ax1.legend(handles=[handles[1], handles[2], line1], loc='upper right', fontsize='x-small')\n", + "\n", + "\n", + "# Third panel\n", + "ax3.errorbar(num_images_mcmc, \n", + " [mean_samples13[0], mean_samples23[0], mean_samples33[0], \n", + " mean_samples43[0], mean_samples53[0], mean_samples63[0]], \n", + " yerr=[std_samples13[0], std_samples23[0], std_samples33[0], \n", + " std_samples43[0], std_samples53[0], std_samples63[0]],\n", + " markersize=4, fmt='o')\n", + "ax3.errorbar(num_images_analytic, posteriors_all3[:,0], \n", + " yerr=posteriors_all3[:,1],markersize=4, fmt='o')\n", + "ax3.plot([0, 3200], [-1.2, -1.2], ls='--', color='k', label=r'$w_{True}\\ = -1.2$')\n", + "# ax3.set_ylim([-1.22, -1.17])\n", + "ax3.set_ylim([-1.22, -1.17])\n", + "ax3.set_xlabel('Number of images for inference', fontsize='small')\n", + "ax3.set_ylabel(r'$w_{Pred}$', fontsize='small')\n", + "ax3.legend(loc='upper right', fontsize='x-small')\n", + "ax3.tick_params(axis='both', which='both', labelsize='xx-small')\n", + "\n", + "plt.subplots_adjust(hspace=0) # No space between plots\n", + "plt.tight_layout()\n", + "plt.savefig(\"MCMC_analytical_posterior_all.pdf\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "1830875a", + "metadata": {}, + "source": [ + "### Plot the MCMC and analytical posteriors " + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "9038cd7f-45d2-496c-8d1a-b339baf2fc1c", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "mean_theta = 0.0\n", + "std_theta = 1.0\n", + "\n", + "sample_theta_unstd = np.linspace(-2.0, -0.4, 2000)\n", + "sample_theta = (sample_theta_unstd - mean_theta)/std_theta\n", + "\n", + "# Create a 2x3 subplot grid with shared X and Y axes\n", + "fig, axes = plt.subplots(2, 3, figsize=(20, 10), sharex='col', sharey='row')\n", + "\n", + "# MCMC posterior\n", + "# Plot histograms in the first row\n", + "axes[0, 0].hist(flat_samples32, bins=20, label='500 images', alpha=0.6, histtype='step', lw=1)\n", + "axes[0, 0].hist(flat_samples42, bins=20, label='1000 images', alpha=0.6, histtype='step', lw=1)\n", + "axes[0, 0].hist(flat_samples52, bins=20, label='2000 images', alpha=0.6, histtype='step', lw=1)\n", + "axes[0, 0].hist(flat_samples62, bins=20, label='3000 images', alpha=0.8, histtype='step', lw=2)\n", + "axes[0, 0].axvline(-0.8, linestyle='--', color='k', lw=3)\n", + "axes[0, 0].set_ylabel('Frequency', fontsize='small')\n", + "axes[0, 0].legend(fontsize='xx-small')\n", + "axes[0, 0].tick_params(axis='both', which='both', labelsize='xx-small')\n", + "\n", + "axes[0, 1].hist(flat_samples31, bins=20, label='500 images', alpha=0.6, histtype='step', lw=1)\n", + "axes[0, 1].hist(flat_samples41, bins=20, label='1000 images', alpha=0.6, histtype='step', lw=1)\n", + "axes[0, 1].hist(flat_samples51, bins=20, label='2000 images', alpha=0.6, histtype='step', lw=1)\n", + "axes[0, 1].hist(flat_samples61, bins=20, label='3000 images', alpha=0.8, histtype='step', lw=2)\n", + "axes[0, 1].axvline(-1, linestyle='--', color='k', lw=3)\n", + "axes[0, 1].tick_params(axis='both', which='both', labelsize='xx-small')\n", + "# axes[0, 1].set_ylabel('Frequency')\n", + "\n", + "axes[0, 2].hist(flat_samples33, bins=20, label='500 images', alpha=0.6, histtype='step', lw=1)\n", + "axes[0, 2].hist(flat_samples43, bins=20, label='1000 images', alpha=0.6, histtype='step', lw=1)\n", + "axes[0, 2].hist(flat_samples53, bins=20, label='2000 images', alpha=0.6, histtype='step', lw=1)\n", + "axes[0, 2].hist(flat_samples63, bins=20, label='3000 images', alpha=0.8, histtype='step', lw=2)\n", + "axes[0, 2].axvline(-1.2, linestyle='--', color='k', lw=3)\n", + "axes[0, 2].tick_params(axis='both', which='both', labelsize='xx-small')\n", + "# axes[0, 2].set_xlabel(r'$w$')\n", + "# axes[0, 2].set_ylabel('Frequency')\n", + "\n", + "# Analytical posterior\n", + "# Plot analytical posteriors in the second row\n", + "num_images = [5, 100, 500, 1000, 2000, 3000]\n", + "for i, pos in enumerate(posterior_all_samples2[2:]):\n", + " true_w = -0.8\n", + " if i != 3:\n", + " axes[1, 0].plot(sample_theta, pos / np.max(pos), label=str(num_images[i + 2]) + ' images', \n", + " drawstyle='steps-mid', alpha=0.6, lw=1)\n", + " if i == 3:\n", + " axes[1, 0].plot(sample_theta, pos / np.max(pos), label=str(num_images[i + 2]) + ' images', \n", + " drawstyle='steps-mid', alpha=0.8, lw=2)\n", + " \n", + "axes[1, 0].set_xlabel(r'$w$', fontsize='small')\n", + "axes[1, 0].set_ylabel(r'$p(w | x)$', fontsize='small')\n", + "axes[1, 0].set_xlim(-0.807, -0.793)\n", + "axes[1, 0].axvline(-0.8, linestyle='--', color='k', lw=3)\n", + "axes[1, 0].tick_params(axis='both', which='both', labelsize='xx-small')\n", + "\n", + "for i, pos in enumerate(posterior_all_samples1[2:]):\n", + " true_w = -1.0\n", + " if i != 3:\n", + " axes[1, 1].plot(sample_theta, pos / np.max(pos), label=str(num_images[i + 2]) + ' images', \n", + " drawstyle='steps-mid', alpha=0.6, lw=1)\n", + " if i == 3:\n", + " axes[1, 1].plot(sample_theta, pos / np.max(pos), label=str(num_images[i + 2]) + ' images', \n", + " drawstyle='steps-mid', alpha=0.8, lw=2)\n", + "axes[1, 1].set_xlabel(r'$w$', fontsize='small')\n", + "axes[1, 1].axvline(-1.0, linestyle='--', color='k', lw=3)\n", + "axes[1, 1].set_xlim(-0.991, -1.007)\n", + "axes[1,1].tick_params(axis='both', which='both', labelsize='xx-small')\n", + "\n", + "\n", + "for i, pos in enumerate(posterior_all_samples3[2:]):\n", + " true_w = -1.2\n", + " if i != 3:\n", + " axes[1, 2].plot(sample_theta, pos / np.max(pos), label=str(num_images[i + 2]) + ' images', \n", + " drawstyle='steps-mid', alpha=0.6, lw=1)\n", + " if i == 3:\n", + " axes[1, 2].plot(sample_theta, pos / np.max(pos), label=str(num_images[i + 2]) + ' images', \n", + " drawstyle='steps-mid', alpha=0.8, lw=2)\n", + "axes[1, 2].set_xlabel(r'$w$', fontsize='small')\n", + "axes[1, 2].set_xlim(-1.207, -1.19)\n", + "axes[1, 2].axvline(-1.2, linestyle='--', color='k', lw=3)\n", + "axes[1,2].tick_params(axis='both', which='both', labelsize='xx-small')\n", + "\n", + "for ax in axes.flatten():\n", + " ax.tick_params(axis='x', labelrotation=45)\n", + "\n", + "\n", + "plt.subplots_adjust(hspace=0.03, wspace=0.03)\n", + "plt.tight_layout()\n", + "plt.savefig('MCMC_analytical_posterior_probability.pdf')\n", + "plt.show()\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:.conda-bison]", + "language": "python", + "name": "conda-env-.conda-bison-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.4" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/NRE_varyastro_w08.ipynb b/notebooks/NRE_varyastro_w08.ipynb new file mode 100644 index 0000000..6c76117 --- /dev/null +++ b/notebooks/NRE_varyastro_w08.ipynb @@ -0,0 +1,647 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "6655e2be", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "!export LD_LIBRARY_PATH=/opt/conda/lib\n", + "!export XLA_FLAGS=--xla_gpu_cuda_data_dir=/home/jarugula/cuda_data_dir\n", + "!export PATH=/usr/local/cuda-11.7/bin:${PATH}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c2386057", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import logging\n", + "import warnings\n", + "import json\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from sklearn import metrics\n", + "from sklearn.metrics import confusion_matrix\n", + "from sklearn.metrics import auc, roc_curve\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib\n", + "import random\n", + "from tqdm import tqdm\n", + "import pandas as pd\n", + "from scipy.stats import uniform, norm\n", + "import emcee\n", + "from multiprocessing import Pool\n", + "import time\n", + "\n", + "import h5py\n", + "import os\n", + "from tqdm import tqdm\n", + "import tensorflow as tf\n", + "from tensorflow import keras\n", + "from tensorflow.keras import layers \n", + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import Reshape,Conv1D,Flatten,Dense, Lambda\n", + "from tensorflow.keras.models import Model\n", + "from sklearn.model_selection import train_test_split\n", + "from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau, TerminateOnNaN\n", + "physical_devices = tf.config.list_physical_devices('GPU')\n", + "try:\n", + " tf.config.experimental.set_memory_growth(physical_devices[0], True)\n", + "except:\n", + " pass\n", + "from sklearn.utils import shuffle\n", + "AUTOTUNE = tf.data.AUTOTUNE\n", + "from sklearn.preprocessing import StandardScaler\n", + "import tqdm\n", + "import gc\n", + "import wandb\n", + "os.environ['PATH']=os.environ['PATH']+':/usr/local/cuda/bin'\n", + "os.environ['XLA_FLAGS']='--xla_gpu_cuda_data_dir=/home/jarugula/cuda_data_dir'" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "e9bdd356", + "metadata": {}, + "outputs": [], + "source": [ + "best_style = {\n", + " \"font.family\": \"sans-serif\",\n", + " \"mathtext.fontset\": \"custom\",\n", + " \"mathtext.rm\": \"TeX Gyre Heros\",\n", + " \"mathtext.bf\": \"TeX Gyre Heros:bold\",\n", + " \"mathtext.sf\": \"TeX Gyre Heros\",\n", + " \"mathtext.it\": \"TeX Gyre Heros:italic\",\n", + " \"mathtext.tt\": \"TeX Gyre Heros\",\n", + " \"mathtext.cal\": \"TeX Gyre Heros\",\n", + " \"mathtext.default\": \"regular\",\n", + " \"figure.figsize\": (10.0, 10.0),\n", + " \"font.size\": 26,\n", + " \"axes.labelsize\": \"medium\",\n", + " \"axes.unicode_minus\": False,\n", + " \"xtick.labelsize\": \"small\",\n", + " \"ytick.labelsize\": \"small\",\n", + " \"legend.fontsize\": \"small\",\n", + " \"legend.handlelength\": 1.5,\n", + " \"legend.borderpad\": 0.5,\n", + " \"xtick.direction\": \"in\",\n", + " \"xtick.major.size\": 12,\n", + " \"xtick.minor.size\": 6,\n", + " \"xtick.major.pad\": 6,\n", + " \"xtick.top\": True,\n", + " \"xtick.major.top\": True,\n", + " \"xtick.major.bottom\": True,\n", + " \"xtick.minor.top\": True,\n", + " \"xtick.minor.bottom\": True,\n", + " \"xtick.minor.visible\": True,\n", + " \"ytick.direction\": \"in\",\n", + " \"ytick.major.size\": 12,\n", + " \"ytick.minor.size\": 6.0,\n", + " \"ytick.right\": True,\n", + " \"ytick.major.left\": True,\n", + " \"ytick.major.right\": True,\n", + " \"ytick.minor.left\": True,\n", + " \"ytick.minor.right\": True,\n", + " \"ytick.minor.visible\": True,\n", + " \"grid.alpha\": 0.8,\n", + " \"grid.linestyle\": \":\",\n", + " \"axes.linewidth\": 2,\n", + " \"savefig.transparent\": False,\n", + "}\n", + "plt.style.use(best_style)\n", + "cols = [\"#5790fc\", \"#f89c20\", \"#e42536\", \"#964a8b\", \"#9c9ca1\", \"#7a21dd\"]\n", + "#set cols as the matplotlib default color cycle\n", + "plt.rcParams['axes.prop_cycle'] = plt.cycler(color=cols)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "f585fd63-bc24-4dca-8935-597ae91af163", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# The test data for population analysis by fixing w=-0.8\n", + "\n", + "data_path = '/deepskieslab/stronglensing/hsbi/datasets/'\n", + "image_dir = 'w0_8param_fixzv_test_fixw0-08_3000'\n", + "column_name = \"w0-g\"\n", + "fig_title = 'w0'\n", + "\n", + "str_true_w = '-08' \n", + "true_w = -0.8 # Dark energy equation-of-state parameter \n", + "xlim_min = -0.9\n", + "xlim_max = -0.7" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3067c2a6", + "metadata": {}, + "outputs": [], + "source": [ + "# Load the trained model \n", + "\n", + "model = tf.keras.models.load_model(\"working_model_1M-2-034_seed38_v2.keras\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "d2e53fb0-4669-4f00-90a9-3664361c0ec9", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Read the images and metadata and pre-process the data\n", + "\n", + "images_test = np.load(data_path+image_dir+'/CONFIGURATION_1_images.npy', allow_pickle=True)\n", + "metadata_test = pd.read_csv(data_path+image_dir+'/CONFIGURATION_1_metadata.csv')\n", + "\n", + "\n", + "fixed_images_test = np.einsum('lkij->lijk',images_test)\n", + "fixed_true_theta_test = metadata_test[column_name].to_numpy()\n", + "\n", + "#normalize image each image by the sum of all pixels, make it such that the sum of all pixels is 1024\n", + "fixed_images_test = 1024*(fixed_images_test/np.sum(fixed_images_test, axis=(1,2), keepdims=True))\n", + "\n", + "#manually standardize the images and theta\n", + "means_image = 0.0\n", + "std_image = 1.0\n", + "\n", + "mean_theta = 0.0\n", + "std_theta = 1.0\n", + "\n", + "fixed_images_test = fixed_images_test.reshape(fixed_images_test.shape[0], -1)\n", + "fixed_images_test = (fixed_images_test - means_image) / std_image\n", + "fixed_images_test = fixed_images_test.reshape(fixed_images_test.shape[0], 32, 32, 1)\n", + "\n", + "fixed_theta_test = (fixed_true_theta_test - mean_theta)/std_theta" + ] + }, + { + "cell_type": "markdown", + "id": "cec58145", + "metadata": {}, + "source": [ + "### Calculate the Analytical Posterior \n", + "\n", + "The analytical equation to calculate the posterior is given by\n", + "\n", + "\\begin{equation}\n", + "\\begin{split}\n", + " p(w|\\{x\\}) &= \\frac{p(w)~\\prod_{i}r(x_i|w)}{\\int dw^{\\prime}~ p(w^{\\prime})~\\prod_{i}r(x_{i}|w^{\\prime})},\\\\\n", + " &= p(w)~\\left( \\int dw^{\\prime}~p(w^{\\prime})~\\prod_{i}\\frac{r(x_i|w^{\\prime})}{r(x_i|w)} \\right)^{-1}.\n", + "\\end{split}\n", + "\\end{equation}\n", + "\n", + "```likelihood_diff``` function calculates $log\\ r(x|w^{\\prime}) - log\\ r(x|w)$ for one image $x$ \n", + "\n", + "This is same as calculating $\\frac{r(x_i|w^{\\prime})}{r(x_i|w)}$ in the posterior equation\n", + "\n", + "```get_joint_likelihood``` function calculates $\\sum_{i} log\\ r(x_{i}|w^{\\prime}) - log\\ r(x_{i}|w)$ for a population of strong lens images $\\{x_{i}\\}$\n", + "\n", + "This is same as calculating $\\prod_{i}\\frac{r(x_i|w^{\\prime})}{r(x_i|w)}$ in the posterior equation\n", + "\n", + "```get_joint_posterior``` calculates the sum of posterior for all the theta ($w$) values and gives the inverse of the sum as shown in the equation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ad50c440-17d6-4b72-b252-5d0c4469844e", + "metadata": {}, + "outputs": [], + "source": [ + "import numba as nb\n", + "\n", + "@nb.jit\n", + "def get_logr_distribution(model, images, sample_theta):\n", + " '''\n", + " Function to predict the log likelihood-to-evidence ratio (logr) of the test data\n", + "\n", + " Input:\n", + " model: The trained model \n", + " images: test images\n", + " sample_theta: a list of theta values to compute logr for\n", + " '''\n", + " output_probs = []\n", + " for image in images:\n", + " test_image_array = np.concatenate([image[np.newaxis, :]]*len(sample_theta), axis=0)\n", + " output = model.predict([test_image_array, sample_theta], verbose=0).flatten()\n", + " output_probs.append(output)\n", + " return np.array(output_probs)\n", + "\n", + "class Posterior:\n", + " def __init__(self, lnr, thetas):\n", + " self.lnr = lnr\n", + " self.thetas = thetas\n", + "\n", + " def likelihood_diff(self, image_index):\n", + " # exp_diff_lnr = np.empty((len(self.thetas), len(self.thetas)))\n", + " diff_lnr_list = np.empty((len(self.thetas), len(self.thetas)))\n", + " for i in range(len(self.thetas)):\n", + " diff_lnr = self.lnr[image_index, i] - self.lnr[image_index]\n", + " # exp_diff_lnr[i] = np.exp(diff_lnr)\n", + " diff_lnr_list[i] = diff_lnr\n", + " # return exp_diff_lnr\n", + " return diff_lnr_list\n", + "\n", + " def get_joint_likelihood(self, n_images):\n", + " likelihood = np.empty((n_images, len(self.thetas), len(self.thetas)))\n", + " for i in range(n_images):\n", + " likelihood[i] = self.likelihood_diff(i)\n", + " # joint_likelihood = np.prod(likelihood, axis=0)\n", + " joint_likelihood = np.sum(likelihood, axis=0)\n", + " joint_likelihood = np.exp(joint_likelihood)\n", + " return joint_likelihood\n", + " \n", + " def get_joint_posterior(self, n_images):\n", + " joint_likelihood = self.get_joint_likelihood(n_images)\n", + " joint_posterior = 1. / np.sum(joint_likelihood, axis=0)\n", + " return joint_posterior\n", + " \n", + "def get_joint_posterior_probability(lnr, thetas, n_images):\n", + " '''\n", + " Function to sample from the posterior probability distribution.\n", + "\n", + " Output:\n", + " The posterior probability, mean and standard deviation\n", + " '''\n", + " posterior = Posterior(lnr, thetas)\n", + " joint_posterior = posterior.get_joint_posterior(n_images)\n", + " sampled_values = np.random.choice(thetas, size=1000, p=joint_posterior)\n", + " weighted_mean = np.mean(sampled_values)\n", + " weighted_std_dev = np.std(sampled_values)\n", + " # weighted_mean = np.sum(thetas * joint_posterior) / np.sum(joint_posterior)\n", + " # weighted_std_dev = np.sqrt(np.sum(joint_posterior * (thetas - weighted_mean)**2) / np.sum(joint_posterior))\n", + " return joint_posterior, weighted_mean, weighted_std_dev\n" + ] + }, + { + "cell_type": "markdown", + "id": "2a2fe76d", + "metadata": {}, + "source": [ + "### Calculate MCMC posterior" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "346d7c0b-d5d9-4f36-aa53-7c9a3ce98a06", + "metadata": {}, + "outputs": [], + "source": [ + "def get_logr_mcmc(model, images, sample_theta, mean_theta, std_theta):\n", + " ''''\n", + " Function to predict the log likelihood-to-evidence ratio (logr) of all the test data at a time\n", + "\n", + " Input:\n", + " model: The trained model \n", + " images: test images\n", + " sample_theta: a list of theta values to compute logr for\n", + "\n", + " Output:\n", + " log r \n", + " '''\n", + " sample_theta = (sample_theta - mean_theta)/std_theta\n", + " theta_array = np.array([sample_theta]*images.shape[0])\n", + " output = model.predict([images, theta_array], verbose=0).flatten()\n", + " return output\n", + "\n", + "def log_prior(theta, theta_low=-1.5, theta_high=-0.5):\n", + " \"\"\"\n", + " prior for w\n", + " \"\"\"\n", + " if theta_low < theta < theta_high:\n", + " return 0.0\n", + " return -np.inf\n", + "\n", + "def log_likelihood(theta_, data, theta_low, theta_high, model, mean_theta, std_theta):\n", + " \"\"\"\n", + " Calculate the log likelihood + log prior\n", + " \"\"\"\n", + " theta = theta_[0]\n", + " lp = log_prior(theta, theta_low, theta_high)\n", + " if not np.isfinite(lp):\n", + " return -np.inf\n", + " logr_array = get_logr_mcmc(model, data, theta, mean_theta, std_theta)\n", + " ll = np.sum(logr_array)\n", + " return ll+lp\n", + "\n", + "def get_posterior_mcmc(data, theta_low, theta_high, model, walkers=10, nsteps=10000, initial_w = -1.0, mean_theta=-1.0007, std_theta=0.288409, multithread=False):\n", + " \"\"\"\n", + " MCMC sampling\n", + "\n", + " Output:\n", + " Sampler and Samples\n", + " \"\"\"\n", + " pos = np.array([initial_w])+ np.array([initial_w])*1e-3* np.random.randn(walkers, 1)\n", + " nwalkers, ndim = pos.shape\n", + " sampler = emcee.EnsembleSampler(nwalkers, ndim, log_likelihood, args=(data, theta_low, theta_high, model, mean_theta, std_theta))\n", + " \n", + " if multithread:\n", + " with Pool(10) as pool:\n", + " sampler = emcee.EnsembleSampler(nwalkers, ndim, log_likelihood, args=(data, theta_low, theta_high, model, mean_theta, std_theta), pool=pool)\n", + " print(\"Running first burn-in...\")\n", + " pos, lp, _ = sampler.run_mcmc(pos, 100, progress=True)\n", + " print('Max lp @',pos[np.argmax(lp)])\n", + "\n", + " print(\"Running second burn-in...\")\n", + " pos =pos[np.argmax(lp)] + 1e-4 * np.random.randn(nwalkers, ndim)\n", + " sampler.reset()\n", + " pos, lp, _ = sampler.run_mcmc(pos, 500,progress=True)\n", + " print('Max lp @',pos[np.argmax(lp)])\n", + "\n", + " print(\"Running production...\")\n", + " pos = pos[np.argmax(lp)] + 1e-4 * np.random.randn(nwalkers, ndim)\n", + " sampler.reset()\n", + " pos, lp, _ = sampler.run_mcmc(pos, nsteps,progress=True)\n", + " print('Max lp @',pos[np.argmax(lp)]) \n", + "\n", + " else:\n", + " print(\"Running first burn-in...\")\n", + " pos, lp, _ = sampler.run_mcmc(pos, 100, progress=True)\n", + " print('Max lp @',pos[np.argmax(lp)])\n", + "\n", + " print(\"Running second burn-in...\")\n", + " pos =pos[np.argmax(lp)] + 1e-4 * np.random.randn(nwalkers, ndim)\n", + " sampler.reset()\n", + " pos, lp, _ = sampler.run_mcmc(pos, 500,progress=True)\n", + " print('Max lp @',pos[np.argmax(lp)])\n", + "\n", + " print(\"Running production...\")\n", + " pos = pos[np.argmax(lp)] + 1e-4 * np.random.randn(nwalkers, ndim)\n", + " sampler.reset()\n", + " pos, lp, _ = sampler.run_mcmc(pos, nsteps,progress=True)\n", + " print('Max lp @',pos[np.argmax(lp)])\n", + "\n", + "\n", + " samples = sampler.get_chain(discard=int(nsteps/4), flat=False)\n", + " print(samples.shape)\n", + "\n", + " flat_samples = sampler.get_chain(discard=int(nsteps/4), flat=True)\n", + " print(flat_samples.shape)\n", + "\n", + " return sampler, samples, flat_samples\n", + " return sampler, samples, flat_samples" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "88f0703a", + "metadata": {}, + "outputs": [], + "source": [ + "num_images = [5, 100, 500, 1000, 2000, 3000]\n", + "start_time=time.time()\n", + "for n in num_images:\n", + " sampler, samples, flat_samples = get_posterior_mcmc(fixed_images_test[0:n], -1.5, -0.5, model, walkers=5, nsteps=1000, \n", + " initial_w = -1.0, mean_theta=0.0, std_theta=1.0, multithread=False)\n", + " end_time=time.time()\n", + " print('Time taken for ', n, ' images: ', end_time-start_time)\n", + " start_time = end_time\n", + " #save the mcmc sampler\n", + " np.savez('mcmc_posterior'+str_true_w+'_'+str(n)+'.npz', sampler=sampler, samples=samples, flat_samples=flat_samples)\n", + " del sampler, samples, flat_samples" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "7957ed5f", + "metadata": {}, + "outputs": [], + "source": [ + "def read_mcmc_samples(filename):\n", + " file = np.load(filename)\n", + " # sampler = file['sampler']\n", + " samples = file['samples']\n", + " flat_samples = file['flat_samples']\n", + " mean_samples = np.mean(flat_samples, axis=0)\n", + " std_samples = np.std(flat_samples, axis=0)\n", + " return samples, flat_samples, mean_samples, std_samples" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "9a0e0709", + "metadata": {}, + "outputs": [], + "source": [ + "samples1, flat_samples1, mean_samples1, std_samples1 = read_mcmc_samples('mcmc_posterior'+str_true_w+'_'+str(500)+'.npz')\n", + "samples2, flat_samples2, mean_samples2, std_samples2 = read_mcmc_samples('mcmc_posterior'+str_true_w+'_'+str(1000)+'.npz')\n", + "samples3, flat_samples3, mean_samples3, std_samples3 = read_mcmc_samples('mcmc_posterior'+str_true_w+'_'+str(2000)+'.npz')\n", + "samples4, flat_samples4, mean_samples4, std_samples4 = read_mcmc_samples('mcmc_posterior'+str_true_w+'_'+str(3000)+'.npz')" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "e320f72b", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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SXV18cTsYVzUHDx5ERkYG3NzcsG/fPoMFelxcnMHYunX/7wvUR48eGX0ObOEZ18Lq1asHAFCpVLh37x6aNWtWxuwLtG3bFm3btgUA5Ofn48SJEwgNDcXJkycxc+ZMvPzyy1I7VQ3c2ImIiIiIarz79+8jKSkJANCkSRO9thYtWqBhw4YAgMOHDxuMz8rKku7bfPLe2x49ekgzk8bio6KicOvWLYPxVdnDhw8BFHxGxmaYDT2WBwD8/PykGdjiHnFkrK1bt25S/Pbt20uZcfGsra3x0ksv4cCBA7Czs4MoikbzJ/NhEUtEZIFcXV1x/PhxvR9XV1dzp0VEVCUZW45auH3mzJkACu6PHTRoUJE+uh1ut2/fjsjIyCLtYWFhUCqVkMlkGD16tF6bo6Mjhg8fDgBYu3Yt0tPTi8QvXrwYQME9qUOHDi3xPVUVuv/33L17F2q1ukj71atXsW3bNoOxtWrVwosvvggAWL58OXJzc4v0OXnypMFNnYCCmdwhQ4YAAJYuXYq7d+8Wm2tKSore68L38D7Jzs4OMpkMAKT/UtXBIpaIyALZ2tqid+/eej+2trbmTouIqEqKiopC586d8c033+Dff/+VilqtVovz58/jlVdewS+//AIAmDx5Mlq0aFFkjBkzZqBevXrIzs7GwIEDcfnyZQBAbm4u1q5di7lz5wIAAgMDDW4GFBoaCkdHRzx+/Bivvvoq/vnnHwAFM7ihoaFYt24dAGDOnDlwc3Mz/YdQQfr27QsrKyukpKRg9OjR0n29ubm52LFjB/r27VvsRlXz58+HIAi4ceMGBg8eLH0u+fn52L17N4YPH17s57F8+XK4u7sjIyMDPXr0wMaNG/W+JEhKSsLu3bsxbNgwvPnmm3qxjRo1wuzZs3H+/Hm9gvbevXsYPXo0srOzYWVlpfcIIqoaeE8sERERUQ2Wmg0Axc9UVpbUsu/NU2oXL17ExYsXARTMsjk7OyMzM1OveBk3bhxWr15tMN7V1RX79+9Hv379EBERgY4dO8LZ2RlqtVrasbdv375YuXKlwfgmTZpgx44dGDFiBE6dOoXmzZvD1dUVSqVS2qBo7Nix0oywpfD29sbMmTOxePFi7N69G7t374arqyuys7ORl5eHJk2a4PPPPy8yO63To0cPrFixAsHBwThy5AiaN28OhUIBlUqFnJwctG7dGhMmTEBwcDDkcnmR+KZNm+K3337DsGHDEBkZiQkTJmDixIlQKBTIy8uDUqmU+j65uVR8fDwWLVqERYsWwcrKCq6urlCpVNKMsiAIWL58udF7oMl8WMQSERER1UByW8BaBoTfrBoFrI61rCA3U/Lw8MBXX32Fc+fO4erVq0hMTERqairkcjmaNGmCbt26Yfz48ejevXux43To0AE3b97E4sWLsX//fjx8+BCOjo5o3bo1AgICMH78eFhZGV/oOGDAAFy/fh2LFy/Gb7/9hkePHkGhUKB9+/aYPHmytOTY0ixatAi+vr5Ys2YN/v77b+Tl5aFZs2Z47bXXMGvWLPz111/Fxk+fPh1+fn5YsmQJzp07B5VKhcaNG2PEiBH45JNPsH79egCAQqEwGO/n54eIiAhs3LgRe/bswbVr15CamgpbW1t4e3ujU6dOGDx4MAYMGKAXd/ToURw/fhynT59GdHS09JieZs2a4YUXXkBQUBA6dOjw9B8QmZwglnSTAFVZXl5eiI2NhaenZ5GHRxMRlURUxiL/ahis2wWZZXdic1+fqDpRq9V48OABmjRpYnC2yhilWoS66G2IZiW3BZzkxh+3QjXP6NGjsW3bNowfPx7fffedudOpFOX5M12TagPOxBIRERHVUE5yAU6lr3mJKt3du3exe/duAED//v3NnA1VFSxiiYgskFarRXJyst45d3f3YpexERERVUWfffYZ6tati8GDB8PLywtWVlbIysrC/v378eGHH0KtVqNly5YWtWszVSwWsUREFig5OVnvIfEAkJCQgDp16pgpIyIiovK5fv06fv31V0ybNg02NjZwdnZGWloatFotAMDT0xM///wzbGxszJwpVRU14iv7EydOYOrUqfD19YWbmxvs7e3RqFEjvPDCC/j0009x+vTpEsc4cuQIRo0ahcaNG0Mul6Nu3bro3r07Vq5ciaysrDLlc/78eUyYMAHNmjWDg4MD3N3d0bFjR3z++efSQ7aJiIiIiGqC4OBgTJkyBW3btoWbmxsyMjLg7OyMTp06Yf78+bh+/Tpat25t7jSpCqnWM7FxcXF499138euvvxZpi46ORnR0NE6fPo2DBw/i6tWrBsfIzc3F+PHjsXXrVr3ziYmJSExMxNmzZxEWFoZdu3ahbdu2xeYjiiJmzZqF5cuX6z10W6VSISUlBZcvX8aaNWuwbds2+Pv7l/0NExERERFZmF69eqFXr17mToMsSLUtYh8+fIiXXnpJemBy06ZNMXz4cLRq1Qr29vZ49OgRHjx4gMOHDxc7zrhx47Bt2zYABfebBQYGok2bNkhKSsKWLVtw4cIF3L9/H6+88gr+/PNPNGjQwOhYc+bMwbJlywAAjo6OmDBhAjp37gylUoldu3bht99+Q3x8PIYMGYKTJ0/Cz8/PRJ8GERERERFR9VAti9j8/Hy89tprUgEbEhKCOXPmwNra8Nt9+PChwfP79u2TCtiGDRvi1KlTaNiwodQeFBSEiRMnYtOmTXj8+DGCg4Oxc+dOg2Ndu3YNixYtAlDwsOyTJ0/iueeek9onT56MefPmYf78+VAqlQgMDMSFCxcgCNxinoiIiIiISKda3hO7YsUKXL58GQAwY8YMzJs3z2gBC8Do7Om8efOk47Vr1+oVsABgZWWFsLAw6fyuXbvw999/GxwrNDRUujn9iy++0CtgdUJCQtC5c2cAwKVLl3DgwAGjORMREREREdVE1a6Izc/Px6pVqwAULP8NDQ0t1zj37t3DlStXAADe3t4YMGCAwX729vaYNGmS9HrHjh1F+iiVShw8eBAA4OLigrFjxxocSxAETJs2TXr9008/lSt3IiIiIiKi6qraFbGHDh3C48ePAQCjR4+Gvb19ucYpfK9sv379iu1b+MHLhu6xDQ8Ph1qtBgD07NkTDg4ORscqfK2S7tclIiIiIiKqaapdEXvy5Enp2N/fH7m5ufjqq6/QtWtX1KpVCw4ODmjSpAlGjx6NY8eOGR3nxo0b0nGHDh2KvWa7du0gk8kAABEREXo7D5d1rDp16qBRo0YAgKSkJMTHxxfbn4iIiIiIqCapdhs7Xbp0STp2cXFBx44di9ynGhkZicjISGzbtg0jRozA5s2bi8yO3r17Vzpu3Lhxsde0traGp6cnoqOjkZ2djZiYGL37bMsyFgA0atQIUVFRUqyHh0ex/UVRREZGRonjGmNnZwc7O7tyxxMRERER0dPRarXSZJhGo4FWq4VSqURubm6p4p+cSKvOql0Rq1tKDACBgYG4d+8eFAoFJk6cCD8/P+Tl5eHkyZP44YcfkJeXh59//hk5OTnYs2eP3k7AaWlp0nHt2rVLvK67uzuio6Ol2MJFbHnGMhRrzKNHj+Dq6lpiP2NCQkL0NrEiIiIiIqLKFRcXh0ePHkmvk5KSMHDgQGlyi/5PtStiCxd99+7dQ7NmzXD8+HF4eXlJ5wMCAjB58mT06dMHGRkZ2Lt3L3766SeMGjVK6qNUKqVjuVxe4nUL33tbONbUYxlSv3593Lp1q8R+xnAWloiIiIjIvOrVqyetwFSr1YiMjMSlS5dga2tbqvhWrVrpFcHVWbUrYnWPsdHZvHmzXgGr07lzZyxYsEDaDfjLL7/UK2ILM+WzWiviua+CIMDFxcXk4xIRERERUeWwsvq/7YpkMhmsrKzg5ORUqkkwoGLqjKqq2m3s5OzsLB37+Pige/fuRvuOGzcONjY2AICLFy8iMzNTanNycpKOVSpVidct3KdwrKnHIiIiIiIiqsmq3UysQqGQjkvaCdjR0REtWrTAjRs3oNFoEBUVhdatWxcZJzk5ucTrFu5TONbUYxERERGZipiTBuRlmTsNfTaOEOwU5s6CiKqwalfEtmzZEleuXAGAUm12VLhPenq6dNy8eXMcP34cQMFuxr169TI6Rn5+PmJjYwEADg4ORZYvN2/eXDqOjIwsMafCN28XjiUi0nFxccGOHTuKnCMiKi0xJw35V1YCmjxzp6JPZgPr9sEsZCtRZGQkmjRpAgB48OBBqZ6mQWRO1a6Ife6557Bt2zYA+kWpMYX7FC5odTOyQMFjewICAoyOcfXqVWg0GgAFS5ifXI/+5FjFSUxMlIrY2rVrl/h4HSKqmezs7DBixAhzp0FEliwvC9DkQdZ8BASHuubOBgAgZidAc/fngtxMXMRmZ2fjxIkTuHz5Mq5cuYLLly9LT5Yoy5Ma4uPjsWTJEuzfvx/R0dGwt7eHr68vAgICMGHChBLvS7x//z6WLFmCo0eP4vHjx3BxcYGfnx8CAwMxfPjwEq9/5coVrFixAuHh4UhMTEStWrXQpUsXTJs2Df7+/qV6D0SWrtoVsQMGDMAnn3wCALh8+XKxfbOysnDnzh0AgI2NjfQNFAD069dPOj5y5Eix4xw+fNhgnE7v3r1hZ2eHnJwcnDx5EiqVSm8H4sIKX8vQWERERESmJDjUheDkae40KtyFCxcwYMCApxrj8uXL6Nevn3Trl5OTEzIzM3H69GmcPn0aP//8M/bu3Wv0yQ8HDx7EiBEjkJ2dDaBgBU1ycjKOHj2Ko0ePYty4cfjuu++MFsLffvstpkyZgvz8fAAFEzDx8fHYs2cP9uzZU+7HJtrY2KBFixbSMVFVV+02dmrTpo008xkREYEzZ84Y7btp0ybk5RUsoXnhhRfg6OgotXl7e8PPzw8A8M8//+DQoUMGx1Cr1diwYYP0euTIkUX6ODk5SX9pZmRkYPPmzQbHEkURa9asKXYsIiIiIiofNzc3vPTSS5g5cyZ+/PFH1KtXr9Sx6enpGDRoEJKTk9GyZUtpU9CsrCysWbMGNjY2OHr0KIKDgw3GP3jwACNHjkR2dja6d++OO3fuID09Henp6fjss88AFPzbdOnSpQbjz507h3fffRf5+fkYOnQoHj58iLS0NCQmJmLy5MkAgPnz5xe51aQ0PD09cfv2bdy+fRuentX/Cw2qBsRqaM+ePSIAEYDYrFkzMSYmpkifCxcuiC4uLlK/gwcPFjtOo0aNxKioKL12jUYjjh8/Xurz2muvGc3pypUroiAIIgDR1dVVvHbtWpE+8+bNk8Zq3769qNVqi32fnp6eIgDR09Oz2H5ERIZoM2PE3FOzRW1m0b8ja8L1iaoTlUolRkREiCqVqtQxVfHPYEXmlJ+fX+Rco0aNRABiSEhIifFz5swRAYj29vbiv//+W6T9iy++EAGIMplMvHPnTpH2t99+WwQg1qtXT0xNTS3SHhgYKAIQXVxcxJSUlCLtPXr0EAGIbdq0EXNzc4u09+vXT/o3q6H3SpalPH+ma1JtUO1mYgFgyJAheOeddwAA9+7dQ+vWrTFr1iz8+OOP+P777zFx4kR0794dGRkZAIBJkybhlVdeMTjOG2+8AaBgs6UOHTpgzpw52L59O8LCwtC1a1ds3LgRAODh4YGVK1cazcnPzw+zZs0CUPBNXrdu3TB9+nRs27YN69evR79+/aTlH46Ojli/fn2NetYTERERUUWSyWRPFf/9998DAEaNGqV3C5rOtGnT4OTkBI1Gg61bt+q1ZWVlYdeuXQCAKVOmGHz6xOzZswEUrNrbs2ePXtu///6L06dPAwBmzJhhcMmvLj4qKgonT54s03uLjIyEIAgQBKHIJqTh4eFSGwBcv34db775JurXrw97e3u0atUKy5Ytk5Y4A8CZM2cwdOhQPPPMM5DL5WjdujXCwsIgiqLB6yckJGDjxo0YNmwYWrVqBVdXV9jb26NZs2aYOHEibt68WeJ7+PXXX/HSSy9BoVDAyckJbdu2xZIlS5CXl4d58+ZBEAT07t3baHxcXBw++eQTtG3bFq6urpDL5WjatCkmTpyIiIgIo3ExMTEIDg6Gr68vHB0dYWdnh/r166NDhw4IDg7GxYsXS8ydysHcVXRFycvLEydOnCjNbBr6EQRBfP/994v9tkqtVoujRo0qdpwmTZqIV65cKTEnrVYrBgcHSzOyhn7q1KkjHj16tFTvsSZ920JEpmfuWRhzX5+oOuFMbPmUdib29u3b0r/VduzYYbTfK6+8IgIQu3Tponf+8OHDUvyFCxeMxrdq1UoEII4aNUrv/Lp166T4+Ph4g7H5+fmis7OzCED85JNPin0/T3rw4IE0/oMHD/Tajh8/rrdyUS6XSysLC/+bVpfzhg0bRJlMJgqCILq6uur9O/fjjz82eP2AgAC9fi4uLqK1tbX02s7OTty5c6fR/D/66CO9eIVCIcX37NlT/PTTT0UAYq9evQzG79u3T3RycpLibWxsREdHR+m1ra2t+L///a9I3NWrV0U3Nzepn0wmE93c3PQ+l4CAgFL9GjyJM7HFq5YzsQBgbW2NDRs24NSpUxg3bhyeffZZODg4wMHBAd7e3pg0aRIuXbqEL7/8sthv5uzs7PDjjz/i0KFDGDFiBBo0aAA7OzvUrl0bXbt2xbJly3D9+nXp/tniCIKAFStW4MyZMxg7diyaNm0KuVwOhUIBPz8/zJ8/Hzdv3kSfPn1M+VEQUTWUmJgofTOu+0lMTDR3WkRE1dKNGzek48JPnXhS4X1ZjMX7+vqWGP/kzKMuvm7duqhb1/BO0jKZDC1btjQYbypvvfUWhgwZgqioKKSlpSE9PV2aAd6+fTsWLVqEqVOnYurUqYiLi0NaWhpSUlIwduxYAMDSpUtx9+7dIuM2adIEc+bMwV9//QWlUon09HTk5OTgxo0bGD16NHJychAQEIBHjx4Vid2+fTuWL18u5RcTE4PU1FRkZmZi/fr1uHDhAtauXWv0PV24cAHDhw+HUqnE5MmTcevWLahUKiiVSkRFRWHq1KnIzc3FhAkTijxl5KOPPkJqairat2+Pc+fOIS8vDykpKVCr1bh79y6WLVtW7K83PQVzV9FUfjXp2xYi0peQkFBkJUdCQkKZxjD3LIy5r09UnXAmtnxKOxO7evVq6e/a9PR0o/1WrVol9cvMzJTOf/jhhyIA0c3NrdjrTJ8+XQQguru7650fNmyYCED08/MrNn7o0KEiALFDhw7F9ntSaWdi+/TpY3DPlhdeeEHqM3HixCLt+fn5YuPGjUUA4n//+98y5SaKojhw4ECDsVqtVvT29i42t02bNkm5GZqJ7dSpkwhAnDt3rtHrv//++yIAcciQIXrn7e3tRQDi2bNny/yeSsKZ2OJV25lYIiIiIiJTyMzMlI4dHByM9ivcVjhGd1xcbOH2wrGmiDeVjz/+2OCeLYUfC6mbmS1MJpPh5ZdfBlBwT21ZDRw4EACk+4J1rl69in/++QcA8OmnnxrMLSAgAA0bNjQ47rVr13Dx4kXY2Njgo48+Mnp93V47v//+OzQajXRed2/z48ePS/9myCSq3XNiiYiIiIjI9Dp37mzwvIeHBwCgVq1aaNq0abF9UlNTDbZfu3YN33zzDU6fPo3IyEgolcoiG0HFxMTovb5y5QqAgmfbduvWzeC4giCgV69e+OGHH4q06YpirVYrPSfXEF3hmpWVheTkZGlJ96BBg7BhwwYEBATgzJkzGDx4MDp16lTilw309FjEEhEREREVw9nZWTrOzs6Gi4uLwX7Z2dkGY3THhduLiy8ca4p4UzE2rrW1dYnX1fXJy8sr0rZmzRp88MEH0Gq1AAoKT1dXV9jZ2QEAVCoVMjIykJWVpRen2wvC3d0dtra2Rq9t7Nm3untsNRoN4uPjjcYXVvjXYMmSJbh37x6OHz+OFStWYMWKFZDJZGjXrh0GDhyIwMBAPne3gnA5MRERERFRMerXry8dx8bGGu2na3NxcYGTk1OR+NTU1GILUV184esVfl3ctYuLr8pu3bqF6dOnQ6vVYsSIEbhw4QLUajVSU1MRFxeHuLg4rFixAgCKzMzqXpf0WMon43R0M6wtW7aEKIql+mncuLEUr1Ao8Mcff+DUqVOYNWsWunfvDmtra1y+fBmhoaHw9vbGjz/+WN6PhorBIpaIiIiIqBiFdyQuvNPwk3RtPj4+RuOL2zlYF//kjra6+ISEBKM70Ws0Gty+fdtgfFW2c+dOaDQatGrVCtu3b0enTp2KzKrGxcUZjNUt601KSkJubq7Raxja1RgA6tWrB6DgObxPzvKWRY8ePbB48WKcPn0aaWlp+PXXX9GmTRuoVCqMHz++1LO8VHosYomIiIiIitGiRQtpc6DDhw8b7JOVlYVTp04BAPr27avX1qNHD9jb2xcbHxUVhVu3bhmML/z4RWPxZ86ckTZ0ejK+Knv48CEAoG3btrCyMlya/P777wbPt2/fHkDBEuWzZ88a7COKIk6ePGmwrXv37gCA3Nxc/PLLL2XK2xi5XI7Bgwdj9+7dAAC1Wl1kQyp6eixiiYiIiIhKoNuhdvv27YiMjCzSHhYWBqVSCZlMhtGjR+u1OTo6Yvjw4QCAtWvXIj09vUj84sWLARTcVzp06FC9tqZNm6JHjx4AgOXLlxu8r3TRokUAgEaNGqFnz55le3Nm5OrqCgD4+++/DS77PXToEMLDww3GtmvXDs2aNQNQ8P4NxW/ZsgVRUVEG4zt27Ag/Pz8AwH/+858Sn7eekpIiHefn50v38Bqi+9ICKNidmUyLRSwRERER1QipqalISkqSfnRFSHZ2tt55pVJZJHbGjBmoV68esrOzMXDgQFy+fBlAwSze2rVrMXfuXABAYGAgmjdvXiQ+NDQUjo6OePz4MV599VXp0TBZWVkIDQ3FunXrAABz5syBm5tbkfglS5ZAJpPh2rVrGDVqlHT/a0pKCqZOnYpDhw7p9bMU/fv3B1CwzDooKEgqFLOysvDNN9/g9ddfh7u7u8FYQRAwf/58AMCRI0cQEBAgLR1Wq9X47rvvMHnyZIOfpy5+3bp1sLOzQ3R0NJ5//nns3LlT777l2NhYbNmyBX369MHHH38snY+JiYG3tzc+//xz/PXXX8jPz5farl+/jrfffhtAwRcYlvSlgsWo9CfTksnUpAcaE5G+hIQE6eHtup+EhIQyjaHNjBFzT80WtZkxFZRl1b4+UXWiUqnEiIgIUaVSlTpG92dQE39F1GbGVIkfTfyVCv17oVGjRkX+7jT0ExAQYDD+0qVLoru7u9TP2dlZtLGxkV737dtXVKvVRq9/4MAB0cHBQerv6uoqymQy6fXYsWNFrVZrNH7Dhg2itbW11F+hUIiCIEivQ0JCyvW5PHjwQBrjwYMHem3Hjx+X2ozZtGmTCEBs1KiR0T4hISEiALFXr15F2kaNGqX3+SsUCulz6dChg/jVV18VO/706dOlWEEQRDc3N+nXxd/fX5w9e7YIQOzXr5/B+KNHj+r9uspkMtHd3V3v1wqAOHHiRIOfmS6mVq1aoq2trXTO1tZW/Pnnn41+JsUpz5/pmlQb8BE7RERERDWRjSMgs4Hm7s/mzkSfzKYgtyqoQ4cOuHnzJhYvXoz9+/fj4cOHcHR0ROvWrREQEIDx48cbva8TAAYMGIDr169j8eLF+O233/Do0SMoFAq0b98ekydPlpYcGzNx4kS0b98ey5cvx4kTJ5CYmIi6deuia9eumDZtGvz9/U39livF1q1b0aVLF2zcuBF37tyBRqNBmzZt8MYbbyA4OLjEHX5XrlyJnj17YvXq1bhy5QpycnLQqlUrjBkzBtOnT8dHH30EoGA3YUP69OmDe/fuYd26dThw4AAiIiKQlpYGe3t7+Pj4oGvXrhgyZIjevcmenp7Yu3cvjh8/jnPnziEmJgYJCQmwtrZGs2bN8OKLL+KDDz6At7e3yT4n+j+CKBrZc5qqPC8vL8TGxsLa2troH5CgoCAEBQVVcmZEVNF0/3ApLCEhAXXq1Cn1GKIyFvlXw2DdLgiCU+U/x87c1yeqTtRqNR48eIAmTZpALpeXOk7MSQPyyr8ra4WwcYRgpzB3FlSNdO/eHWfPnkVoaKi07LuqM/ZnOiwsDGFhYQZj/vnnH+Tn58PT0xMxMTGVlapZcCa2GvDw8EBERIS50yAiIiILI9gpABaMVI2dOHFC2rlYd/+tJStugko3wVUTcGMnIiIiIiKyWEFBQdi8eTPi4uKkHYrT0tLwzTffYMiQIQAAf39/dOrUyZxpkglxJpaIyAI5OTlhzZo1Rc4RERHVNGfOnMHXX38NALCzs4ODgwPS0tKkgtbHxwfff/+9OVMkE2MRS0Rkgezt7Xm/OxEREQoeX/TLL7/gwoULiI+PR3p6Otzc3ODr64thw4YhMDAQDg4O5k6TTIhFLBERERERWazBgwdj8ODB5k6DKhHviSUiIiIiIiKLwSKWiIiIiIiILAaLWCIiIiIiIrIYLGKJiIiIiIjIYnBjJyIiC5SUlIRWrVrpnbt16xZq165tpoyIiIiIKgeLWCIiCySKIpKSkoqcIyIiIqruuJyYiIiIiIiILAaLWCIiIiIiIrIYLGKJiIiIiIjIYrCIJSIiIiIiIovBIpaIiIiIiIgsBncnJiIiIqqhtGnp0GarzJ2GHisHe1gpXM2dRo0SGRmJJk2aAAAePHiAxo0bmzchohKwiCUiIiKqgbRp6chYvQFiXp65U9Ej2NjA5f1JJi9kk5OTsXfvXhw7dgxXrlxBVFQU8vPzUadOHXTs2BEBAQF47bXXShwnPj4eS5Yswf79+xEdHQ17e3v4+voiICAAEyZMgCAIxcbfv38fS5YswdGjR/H48WO4uLjAz88PgYGBGD58eInXv3LlClasWIHw8HAkJiaiVq1a6NKlC6ZNmwZ/f/9Sfx5EloxFLBEREVENpM1WQczLg+Prr8KqTm1zpwMA0CYmIWvnPmizVSYvYuvVq4f8/HzptVwuh42NDWJjYxEbG4tff/0Vr7zyCnbu3AkHBweDY1y+fBn9+vVDcnIyAMDJyQmZmZk4ffo0Tp8+jZ9//hl79+6FnZ2dwfiDBw9ixIgRyM7OBgC4uLggOTkZR48exdGjRzFu3Dh89913Rgvhb7/9FlOmTJHeh6urK+Lj47Fnzx7s2bMHISEhmDdvXpk/GxsbG7Ro0UI6JqrqeE8sERERUQ1mVac2rOvXqxI/FVlM5+fno3Pnzvj6669x//59qFQqKJVKPHjwABMmTAAAHDp0CJMnTzYYn56ejkGDBiE5ORktW7bExYsXkZmZiaysLKxZswY2NjY4evQogoODDcY/ePAAI0eORHZ2Nrp37447d+4gPT0d6enp+OyzzwAAmzZtwtKlSw3Gnzt3Du+++y7y8/MxdOhQPHz4EGlpaUhMTJRynj9/Pnbs2FHmz8bT0xO3b9/G7du34enpWeZ4osrGIpaIiIiIqr0//vgDf/75J6ZMmYKmTZtK5xs3boxvv/1WKgS3bNmChw8fFolftmwZ4uLiYG9vj4MHD6Jjx44AAFtbWwQFBWH+/PkAgPXr1+Pu3btF4j/77DNkZWWhXr162L9/P5o3bw6gYDZ3/vz5CAwMBAAsWLAAqampReJnzZoFjUaDNm3aYMeOHfDy8gIAuLu7Y926dejXr59eP6LqjEUsEREREVV7L774YrHtutlYALh06VKR9u+//x4AMGrUKGkTpMKmTZsGJycnaDQabN26Va8tKysLu3btAgBMmTIFCoWiSPzs2bMBABkZGdizZ49e27///ovTp08DAGbMmGFwya8uPioqCidPnjT2Ng2KjIyEIAgQBAGRkZF6beHh4VIbAFy/fh1vvvkm6tevD3t7e7Rq1QrLli3TW6p95swZDB06FM888wzkcjlat26NsLAwiKJo8PoJCQnYuHEjhg0bhlatWsHV1RX29vZo1qwZJk6ciJs3b5b4Hn799Ve89NJLUCgUcHJyQtu2bbFkyRLk5eVh3rx5EAQBvXv3NhofFxeHTz75BG3btoWrqyvkcjmaNm2KiRMnIiIiwmhcTEwMgoOD4evrC0dHR9jZ2aF+/fro0KEDgoODcfHixRJzp7JjEUtERERENZ5cLpeOn5zJvHPnDqKjowEAr7zyisF4JycnvPDCCwCAo0eP6rWdPn0aKpWq2PjGjRujVatWBuN/++036bh///4G43v06AFnZ2eD8aZy6NAhPP/889i+fTuys7ORk5OD27dvY+bMmRgzZgyAgvt2e/Xqhb1790KlUiEnJwc3b97Ee++9JxXaT5o1axYmTJiAX375Bbdv3wZQsPz7/v37+O6779ChQwfpSwBDZsyYgaFDh+KPP/5Aeno6bGxsEBERgY8//hgvv/wy8krYvGz//v3w9vbG4sWLcf36dahUKlhbW+PBgwf47rvv4OfnJ32JUdi1a9fw3HPPYdWqVYiIiEBOTg4cHR0RFxeHK1euYNWqVQgLCyvtx0tlwCK2GoiPj4ePj4/BH/7BISIiIipZeHi4dNymTRu9ths3bkjHrVu3NjqGru3JmbvC8b6+viXGPznzqIuvW7cu6tatazBWJpOhZcuWBuNN5a233sKQIUMQFRWFtLQ0pKenS4Xp9u3bsWjRIkydOhVTp05FXFwc0tLSkJKSgrFjxwIAli5danCpdZMmTTBnzhz89ddfUCqVSE9PR05ODm7cuIHRo0cjJycHAQEBePToUZHY7du3Y/ny5VJ+MTExSE1NRWZmJtavX48LFy5g7dq1Rt/ThQsXMHz4cCiVSkyePBm3bt2S7peOiorC1KlTkZubiwkTJhSZof/oo4+QmpqK9u3b49y5c8jLy0NKSgrUajXu3r2LZcuWFfvrXR5hYWFG/90fHx9v0mtVZdyduBrw8PAodpkDEVU/Dg4OCAkJKXKOiIjKLi0tDQsXLgQAvPDCC9JOvTqFi6fiNj7StWVkZECpVMLJyUkv3s3Nrdi/q3XxTxZrutclbbrk6emJixcvGiz2TKFTp0748ccfpaXFzs7O+OKLL3D69GmcOnUKs2fPxsSJE7F69Wopxs3NDd9++y3Cw8MRGRmJHTt2YM6cOXrjPvn/MwCwsrKCr68vtmzZgrS0NBw4cAAbN27UixVFUdoUq0+fPtiyZYuUm1wux6RJk2BjY4Nx48YZfU/vvfcecnNzMXfuXISGhuq1NWzYEGFhYbC2tsbq1avx+eef6y31Pnv2LABgzZo16NKli3Te1tYW3t7e+Oijj4r9PMsjKCgIQUFBBtu8vLwQGxtr8mtWRZyJJSKyQI6Ojpg3b57ej6Ojo7nTIiKyOFqtFmPGjMHjx49hZ2eHr776qkifzMxM6bi4IrRwW+EY3XFJXzbq2gvHmiLeVD7++GODj//RbSoFwOCSYZlMhpdffhlAwT21ZTVw4EAAkO4L1rl69Sr++ecfAMCnn35qMLeAgAA0bNjQ4LjXrl3DxYsXYWNjU2zB+c477wAAfv/9d72l5rp7mx8/flz6N0MmwZlYIiIiIqqxPvjgA+zfvx8A8PXXX6Nt27Zmzqjq6ty5s8HzHh4eAIBatWrp7fxsqI+hnZeBgoLym2++wenTpxEZGQmlUllkI6iYmBi911euXAFQ8Gzbbt26GRxXEAT06tULP/zwQ5E2XVGs1WqLzL4Xpitcs7KykJycLC3pHjRoEDZs2ICAgACcOXMGgwcPRqdOnbgyqhKwiCUiIiKiGmnGjBlYs2YNAGDlypUYP368wX66DZMAIDs7Gy4uLgb7ZWdnG4zRHRduLy6+cKwp4k3F2LjW1tYlXlfXx9AmS2vWrMEHH3wArVYLoKDwdHV1hZ2dHQBApVIhIyMDWVlZenGJiYkACh4zZGtra/TaxpZh65ZdazSaUt9PWvjXYMmSJbh37x6OHz+OFStWYMWKFZDJZGjXrh0GDhyIwMBAPne3gnA5MRERERHVOLNmzZI2BFq6dCmmT59utG/9+vWl4+LuOdS1ubi4SPfDFo5PTU0tthDVxRe+XuHXJd3vaCy+Krt16xamT58OrVaLESNG4MKFC1Cr1UhNTUVcXBzi4uKwYsUKACgyM6t7bWgZsaF+T9LNsLZs2RKiKJbqp3HjxlK8QqHAH3/8gVOnTmHWrFno3r07rK2tcfnyZYSGhsLb2xs//vhjeT8aKgaLWCIiIiKqUWbOnImlS5cCKJhNmzFjRrH9C+9IXHin4Sfp2nx8fIzGF7dzsC7+yR1tdfEJCQnS7OOTNBqN9HgaU++IW5F27twJjUaDVq1aYfv27ejUqVORWdW4uDiDsbplvUlJScjNzTV6DWMbXdWrVw9AwXN4n5zlLYsePXpg8eLFOH36NNLS0vDrr7+iTZs2UKlUGD9+fI3aNbiysIglIiIiohpjxowZWLZsGYCCAnbmzJklxrRo0ULaHOjw4cMG+2RlZeHUqVMAgL59++q19ejRA/b29sXGR0VF4datWwbj+/TpIx0biz9z5oy0odOT8VXZw4cPAQBt27aFlZXh0uT33383eL59+/YACpYo63YKfpIoijh58qTBtu7duwMAcnNz8csvv5Qpb2PkcjkGDx6M3bt3AwDUanWRDano6bGIJSKyQCkpKfD19dX7SUlJMXdaRERV2owZM6QlxMuWLStVAauj26F2+/btiIyMLNIeFhYGpVIJmUyG0aNH67U5Ojpi+PDhAIC1a9ciPT29SPzixYsBFNxXOnToUL22pk2bokePHgCA5cuXG7yvdNGiRQCARo0aoWfPnqV+X+bm6uoKAPj7778NLvs9dOiQ3jN8C2vXrh2aNWsGoOD9G4rfsmULoqKiDMZ37NgRfn5+AID//Oc/Rme5dQr/fzY/P1+6h9cQ3ZcWQMHuzGRaLGKJiCyQRqNBRESE3k/hbf+JiEjfxx9/LBWwK1asKPMzPGfMmIF69eohOzsbAwcOxOXLlwEUzOKtXbsWc+fOBQAEBgaiefPmReJDQ0Ph6OiIx48f49VXX5UeDZOVlYXQ0FCsW7cOADBnzhy4ubkViV+yZAlkMhmuXbuGUaNGSfe/pqSkYOrUqTh06JBeP0vRv39/AAXLrIOCgqRCMSsrC9988w1ef/11uLu7G4wVBAHz588HABw5cgQBAQHS0mG1Wo3vvvsOkydPNvh56uLXrVsHOzs7REdH4/nnn8fOnTv17luOjY3Fli1b0KdPH3z88cfS+ZiYGHh7e+Pzzz/HX3/9hfz8fKnt+vXrePvttwEUfIFhSV8qWAruTkxERERUg2kTk5BfcrdKoU1MqpBxo6OjsWTJEgCAlZUVFi9eLM18GjJjxowi98m6urpi//796NevHyIiItCxY0c4OztDrVZLM6N9+/bFypUrDY7ZpEkT7NixAyNGjMCpU6fQvHlzuLq6QqlUSl9Cjh071ujscNeuXbFu3TpMmTIFu3fvxu7du6FQKJCeni7NQIaEhGDkyJFl+3DM7KWXXsKoUaOwfft2rF27FmvXroVCoUBmZiY0Gg06dOiAsWPHYtq0aQbj33rrLVy8eBGrVq3CDz/8gC1btkChUECpVCIvLw/+/v54/vnnsXDhQsjl8iLxnTt3xr59+/Dmm2/iwYMHGDFiBGQyGRQKBVQqlV5BO3HiRL3Yf//9F3PnzsXcuXMhk8mkX0/d/bm2trbYvHkzatWqZcJPjAAWsUREREQ1kpWDPQQbG2Tt3GfuVPQINjawcrAvuWMZFF72qdVqS9xoR6lUGjzfoUMH3Lx5E4sXL8b+/fvx8OFDODo6onXr1ggICMD48eON3tcJAAMGDMD169exePFi/Pbbb3j06BEUCgXat2+PyZMnS0uOjZk4cSLat2+P5cuX48SJE0hMTETdunXRtWtXTJs2Df7+/sXGV1Vbt25Fly5dsHHjRty5cwcajQZt2rTBG2+8geDg4BJ3+F25ciV69uyJ1atX48qVK8jJyUGrVq0wZswYTJ8+XZp1VygUBuP79OmDe/fuYd26dThw4AAiIiKQlpYGe3t7+Pj4oGvXrhgyZIjevcmenp7Yu3cvjh8/jnPnziEmJgYJCQmwtrZGs2bN8OKLL+KDDz6At7e3yT4n+j+CaGzPaaryvLy8EBsbC09PzyIPfyai6k33D5fCEhISUKdOnVKPISpjkX81DNbtgiA4Vf5z7Mx9faLqRK1W48GDB2jSpInB2SZjtGnp0GarKjCzsrNysIeVwtXcaVA10r17d5w9exahoaHSsu+qrjx/pmtSbcCZWCIiIqIaykrhyoKRqrUTJ05IOxfr7r8ly8eNnYiIiIiIyGIFBQVh8+bNiIuLk+4PTktLwzfffIMhQ4YAAPz9/dGpUydzpkkmxJlYIiIiIiKyWGfOnMHXX38NALCzs4ODgwPS0tKkgtbHxwfff/+9OVMkE2MRS0REREREFis0NBS//PILLly4gPj4eKSnp8PNzQ2+vr4YNmwYAgMD4eDgYO40yYRYxBIRERERkcUaPHgwBg8ebO40qBLxnlgiIiIiIiKyGJyJJSIisxKzE8ybgI0jBDuFeXMgIiKiUmMRS0RE5mHjCMhsoLn7s3nzkNnAun0wC1kiIiILwSKWiIjMQrBTwLp9MJCXZbYcxOyEgiI6LwtgEUtERGQRWMQSEZHZCHYKFo9ERERUJixiq4H4+Hj4+PgYbAsKCkJQUFAlZ0REFU0ul2Pq1KlFzhEREVH1FRYWhrCwMINt8fHxlZyN+bCIrQY8PDwQERFh7jSIqBI5Ozsb/Z8YERERVU/FTVB5eXkhNja2kjMyDz5ih4iIiIiIiCwGi1giIiIiIiKyGCxiiYiIiIiIyGKwiCUiIiIiIiKLwY2diIiIiGqgY8sOQp2pMncaBsmd7fHSjAHmTqPGiIyMRJMmTQAADx48QOPGjc2bEFEJWMQSEVmgtLQ0DB06VO/cnj17oFAozJIPEVkedaYKqrRsc6dRaa5cuYJ9+/bh8uXLuHv3LhITE5GRkQEXFxe0bNkSAwYMwJQpU1CrVq1ix4mPj8eSJUuwf/9+REdHw97eHr6+vggICMCECRMgCEKx8ffv38eSJUtw9OhRPH78GC4uLvDz80NgYCCGDx9eqvexYsUKhIeHIzExEbVq1UKXLl0wbdo0+Pv7l+kzIbJULGKJiCxQXl4eTpw4UeQcEVFZCVYC5C725k4DAKDOUEHUihUy9saNG/UeTSaXy2Fvb4+UlBScPXsWZ8+exapVq7B371507drV4BiXL19Gv379kJycDABwcnJCZmYmTp8+jdOnT+Pnn3/G3r17YWdnZzD+4MGDGDFiBLKzC748cHFxQXJyMo4ePYqjR49i3Lhx+O6774wWwt9++y2mTJmC/Px8AICrqyvi4+OxZ88e7NmzByEhIZg3b16ZPxsbGxu0aNFCOiaq6ljEEhEREdVgchd7DJxf8gxgZTgQsqvCZoc7d+6Mxo0bo0ePHmjZsqW0ckWpVGLXrl2YOXMmEhMTMXToUNy9exeurq568enp6Rg0aBCSk5PRsmVL/PDDD+jYsSNyc3OxYcMGBAcH4+jRowgODsbXX39d5PoPHjzAyJEjkZ2dje7du2Pjxo1o3rw5lEolli5ditDQUGzatAktW7bErFmzisSfO3cO7777LjQaDYYOHYqvvvoKXl5eSE5Oxn/+8x988803mD9/Pnx8fDBy5MgyfTaenp64fft2mWKIzIkbOxERERFRtffOO+9gxowZ6NKli96tF05OTggICMCWLVsAAAkJCdi/f3+R+GXLliEuLg729vY4ePAgOnbsCACwtbVFUFAQ5s+fDwBYv3497t69WyT+s88+Q1ZWFurVq4f9+/ejefPm0vXnz5+PwMBAAMCCBQuQmppaJH7WrFnQaDRo06YNduzYAS8vLwCAu7s71q1bh379+un1I6rOWMQSERERUY3XpUsX6TgmJqZI+/fffw8AGDVqlLQJUmHTpk2Dk5MTNBoNtm7dqteWlZWFXbt2AQCmTJlicP+C2bNnAwAyMjKwZ88evbZ///0Xp0+fBgDMmDHD4JJfXXxUVBROnjxp7G0aFBkZCUEQIAgCIiMj9drCw8OlNgC4fv063nzzTdSvXx/29vZo1aoVli1bJi1xBoAzZ85g6NCheOaZZyCXy9G6dWuEhYVBFA0vFU9ISMDGjRsxbNgwtGrVCq6urrC3t0ezZs0wceJE3Lx5s8T38Ouvv+Kll16CQqGAk5MT2rZtiyVLliAvLw/z5s2DIAjo3bu30fi4uDh88sknaNu2LVxdXSGXy9G0aVNMnDgRERERRuNiYmIQHBwMX19fODo6ws7ODvXr10eHDh0QHByMixcvlpg7lR2LWCIiIiKq8U6dOiUdP/vss3ptd+7cQXR0NADglVdeMRjv5OSEF154AQBw9OhRvbbTp09DpVIVG9+4cWO0atXKYPxvv/0mHffv399gfI8ePeDs7Gww3lQOHTqE559/Htu3b0d2djZycnJw+/ZtzJw5E2PGjAFQcN9ur169sHfvXqhUKuTk5ODmzZt47733pEL7SbNmzcKECRPwyy+/SMua8/Pzcf/+fXz33Xfo0KGD9CWAITNmzMDQoUPxxx9/ID09HTY2NoiIiMDHH3+Ml19+ucQ9I/bv3w9vb28sXrwY169fh0qlgrW1NR48eIDvvvsOfn5+0pcYhV27dg3PPfccVq1ahYiICOTk5MDR0RFxcXG4cuUKVq1apXcfNpkOi1giIiIiqpFycnIQGRmJNWvWSEVYs2bN8Oqrr+r1u3HjhnTcunVro+Pp2p6cuSsc7+vrW2L8kzOPuvi6deuibt26BmNlMhlatmxpMN5U3nrrLQwZMgRRUVFIS0tDenq6VJhu374dixYtwtSpUzF16lTExcUhLS0NKSkpGDt2LABg6dKlBpdaN2nSBHPmzMFff/0FpVKJ9PR05OTk4MaNGxg9ejRycnIQEBCAR48eFYndvn07li9fLuUXExOD1NRUZGZmYv369bhw4QLWrl1r9D1duHABw4cPh1KpxOTJk3Hr1i2oVCoolUpERUVh6tSpyM3NxYQJE3Dp0iW92I8++gipqalo3749zp07h7y8PKSkpECtVuPu3btYtmxZsb/eVH7c2ImIiPQo1SLUuebOwjC5LeAkL/7xFUREJZHL5cjJySlyvnv37ti2bVuR3YULF0+enp5Gx9W1ZWRkQKlUwsnJSS/ezc0NDg4OJcY/WazpXhd3bV37xYsXDRZ7ptCpUyf8+OOP0tJiZ2dnfPHFFzh9+jROnTqF2bNnY+LEiVi9erUU4+bmhm+//Rbh4eGIjIzEjh07MGfOHL1xQ0JCilzLysoKvr6+2LJlC9LS0nDgwAFs3LhRL1YURXz22WcAgD59+mDLli1SbnK5HJMmTYKNjQ3GjRtn9D299957yM3Nxdy5cxEaGqrX1rBhQ4SFhcHa2hqrV6/G559/rrfU++zZswCANWvW6C1Ht7W1hbe3Nz766KNiP08qPxaxREQkUapF/HxeRH4V3RPEWgaM6MJCloieTr169aBWq6FUKpGVlQUAePHFF7FkyRI0bNiwSP/MzEzpuLgitHBbZmamVMTq4ouLLdxe+HqmiDeVjz/+2ODjf/r16yctxza0ZFgmk+Hll1/Gt99+i+vXr5f5ugMHDsSBAwek+4J1rl69in/++QcA8OmnnxrMLSAgACEhIdJy8MKuXbuGixcvwsbGptiC85133sHq1avx+++/Q6PRQCaTAQAUCgVUKhUeP35c5vdET4dFLBERSdS5QL4G6O0rwK34fytVutRsIPxmwSyxk9zc2RCRJSu8eVFCQgJ++OEHLFiwAJ07d8acOXOKzMhRgc6dOxs87+HhAQCoVasWmjZtWmwfQzsvAwUF5TfffIPTp08jMjISSqWyyEZQT264deXKFQAFz7bt1q2bwXEFQUCvXr3www8/FGnTFcVarVZ6Tq4hut2es7KykJycLC3pHjRoEDZs2ICAgACcOXMGgwcPRqdOnUr8soGeHotYIiIqws0BqO1S1WY7De9qSUT0NOrWrYuPPvoIL7zwArp27Yr//ve/6Ny5MwYNGiT10W2YBADZ2dlwcXExOFZ29v8947ZwjO64cHtx8YVjTRFvKsbGtba2LvG6uj6GNllas2YNPvjgA2i1WgAFhaerq6u0rFulUiEjI0OaNddJTEwEUPCYIVtbW6PXNrYMW7fsWqPRID4+3mh8YYV/DZYsWYJ79+7h+PHjWLFiBVasWAGZTIZ27dph4MCBCAwMLHEJOJUPN3YiIiIiohqvc+fO6NGjB4CCZ70WVr9+fek4NjbW6Bi6NhcXF2kpceH41NTUYgtRXXzh6xV+Xdy1i4uvym7duoXp06dDq9VixIgRuHDhAtRqNVJTUxEXF4e4uDisWLECAIrMzOpeG1pGbKjfk3QzrC1btoQoiqX6ady4sRSvUCjwxx9/4NSpU5g1axa6d+8Oa2trXL58GaGhofD29saPP/5Y3o+GisEiloiIiIgI/zdjd+/ePb3zhXckLrzT8JN0bT4+Pkbji9s5WBf/5I62uviEhARp9vFJGo1GejyNJe2Iu3PnTmg0GrRq1Qrbt29Hp06disyqxsXFGYzVLetNSkpCbq7xHQmNbXRVr149AAXP4X1ylrcsevTogcWLF+P06dNIS0vDr7/+ijZt2kClUmH8+PGlnuWl0mMRS0RERESEgmIGKLostkWLFtKGT4cPHzYYm5WVJW1u1LdvX722Hj16wN7evtj4qKgo3Lp1y2B8nz59pGNj8WfOnJE2dHoyvip7+PAhAKBt27awsjJcmvz+++8Gz7dv3x5AwRJl3U7BTxJFESdPnjTY1r17dwBAbm4ufvnllzLlbYxcLsfgwYOxe/duAIBarS6yIRU9PRaxRERERFStaTQao0tKdY4dO4YLFy4AAHr37l2k/Z133gFQ8FzSwhtD6YSFhUGpVEImk2H06NF6bY6Ojhg+fDgAYO3atUhPTy8Sv3jxYgAFBfTQoUP12po2bSotdV6+fLnB+0oXLVoEAGjUqBF69uxZzDutWlxdXQEAf//9t8Ffo0OHDiE8PNxgbLt27dCsWTMABe/fUPyWLVsQFRVlML5jx47w8/MDAPznP/8xOsutk5KSIh3n5+dL9/AaovvSAoC0mzGZDotYIiILZGtri9dff13vp7hNLYiIarKHDx/Cz88P33zzDf7991+9Yufhw4dYtGgRhgwZAlEUUatWLQQHBxcZY8aMGahXrx6ys7MxcOBAXL58GUDBLN7atWsxd+5cAEBgYCCaN29eJD40NBSOjo54/PgxXn31VenRMFlZWQgNDcW6desAAHPmzIGbm1uR+CVLlkAmk+HatWsYNWqUdP9rSkoKpk6dikOHDun1sxT9+/cHULDMOigoSCoUs7Ky8M033+D111+Hu7u7wVhBEDB//nwAwJEjRxAQECAtHVar1fjuu+8wefJkg5+nLn7dunWws7NDdHQ0nn/+eezcuVPvvuXY2Fhs2bIFffr0wccffyydj4mJgbe3Nz7//HP89ddfyM/Pl9quX7+Ot99+G0DBFxiW9KWCpeDuxEREFsjV1RU///yzudMgIrIY165dw7vvvgug4ItAFxcXqFQqvXshmzRpgl27dkn3Shbm6uqK/fv3o1+/foiIiEDHjh3h7OwMtVotzYz27dsXK1euNHj9Jk2aYMeOHRgxYgROnTqF5s2bw9XVFUqlUtpgaOzYsZg5c6bB+K5du2LdunWYMmUKdu/ejd27d0OhUCA9PV0qykNCQjBy5Mjyf0hm8NJLL2HUqFHYvn071q5di7Vr10KhUCAzMxMajQYdOnTA2LFjMW3aNIPxb731Fi5evIhVq1bhhx9+wJYtW6BQKKBUKpGXlwd/f388//zzWLhwIeTyos9n69y5M/bt24c333wTDx48wIgRIyCTyaRnwBYuaCdOnKgX+++//2Lu3LmYO3cuZDKZ9Oupuz/X1tYWmzdvRq1atUz4iRHAIrZaiI+PL7KBgE5QUBCCgoIqOSMiIiKyFOoMFQ6E7DJ3GgAKcqkI9evXx44dOxAeHo4///wTjx8/RlJSEmQyGRo2bIi2bdtiyJAheOutt/SWgT6pQ4cOuHnzJhYvXoz9+/fj4cOHcHR0ROvWrREQEIDx48cbva8TAAYMGIDr169j8eLF+O233/Do0SMoFAq0b98ekydPlpYcGzNx4kS0b98ey5cvx4kTJ5CYmIi6deuia9eumDZtGvz9/cv9GZnT1q1b0aVLF2zcuBF37tyBRqNBmzZt8MYbbyA4OLjEHX5XrlyJnj17YvXq1bhy5QpycnLQqlUrjBkzBtOnT8dHH30EoGA3YUP69OmDe/fuYd26dThw4AAiIiKQlpYGe3t7+Pj4oGvXrhgyZIjevcmenp7Yu3cvjh8/jnPnziEmJgYJCQmwtrZGs2bN8OKLL+KDDz6At7e3yT4noGDZelhYmMG2mrSBlCCWdIMAVVleXl6IjY2Fp6dnkYc/ExGVRFTGIv9qGKzbBUFwKtiRMylDxC8XRbzWSahyz4mtiNwMfQZElkitVuPBgwdo0qSJwdkmQw6E7IIqrfjnjpqLvcIBA+cXX9ARlVb37t1x9uxZhIaGSsu+q7ry/JmuSbUBZ2KJiIiIaiC5s/EZR3OryrmRZTlx4oS0c7Hu/luyfCxiiYiIiGqgl2YMMHcKRCYRFBSETp06oX///vDw8IAgCEhLS8NPP/0kbcbk7++PTp06mTlTMhUWsUREREREZLHOnDmDr7/+GgBgZ2cHBwcHpKWlSRte+fj44PvvvzdnimRiLGKJiCxQenp6kV0Sv/32W+l5e0RERDVFaGgofvnlF1y4cAHx8fFIT0+Hm5sbfH19MWzYMAQGBsLBwcHcaZIJsYglIrJAubm52Llzp9453bfQRERENcngwYMxePBgc6dBlYhFLBHVeMeWHYQ68+ke6yB3tuf9ZURERESVgEUsEdV46kxVlX3MBBERERHpYxFLRPT/CVYC5C5le6yDOkMFUcvHbRMRERFVFhaxRET/n9zFHgPnDy9TzIGQXVClZUOdocKBkF3luy6XIhMRERGVGotYIiITELUilyQTERERVQIWsURET0HuXLblx4VxKTIRERFR2bGIJSJ6Ck+zDHj/3O1Qp2VDzFdDVMaWKVZUJhs49xiifW7px8hOKNM1iajqE0V+MUZUHfDPcvGqZRHbu3dvnDhxolR9GzVqhMjIyBL7HTlyBJs2bcL58+cRFxcHFxcXeHt74/XXX0dgYCAcHR1Lnd/58+exYcMGnDhxAo8ePYK9vT2aNGmCoUOH4t1330Xt2rVLPRYRWSYxJw1ieiTEbECEFvlXw8oUn59adOly/o2NyHcr48PcZTaATen//iKiqsnKygoAoNFozJwJEZmC7s+y7s826auWRawp5ebmYvz48di6dave+cTERCQmJuLs2bMICwvDrl270LZt22LHEkURs2bNwvLly/W+XVGpVEhJScHly5exZs0abNu2Df7+/hXyfoioisjLAkQtIHeH4OIE63a9yxRunZgM4Ev9c63Hw7qOe9nysHGEYKcoWwwRVTk2NjawsbGBUqmEk5OTudMhoqeUmZkp/bmmoqp9EfvLL78U2+7gUPysxbhx47Bt2zYAgLu7OwIDA9GmTRskJSVhy5YtuHDhAu7fv49XXnkFf/75Jxo0aGB0rDlz5mDZsmUAAEdHR0yYMAGdO3eGUqnErl278NtvvyE+Ph5DhgzByZMn4efnV8Z3S0SWRrCygWAth+DkWbY4lW3Rc07PQHCqY6rUiMiCCIIAZ2dnpKWlwdXVFfb25b9fn4jMS6VSISMjAwqFAoIgmDudKqnaF7FDhw4td+y+ffukArZhw4Y4deoUGjZsKLUHBQVh4sSJ2LRpEx4/fozg4GDs3LnT4FjXrl3DokWLAACurq44efIknnvuOal98uTJmDdvHubPnw+lUonAwEBcuHCBv3GJiIioVGrXrg2VSoXo6Gi4uLjA2dkZMpmM/5YgsgCiKEKj0SAzMxMZGRmws7PjLYbFqPZF7NOYN2+edLx27Vq9AhYoWKMeFhaGY8eOITo6Grt27cLff/+NNm3aFBkrNDQUWq0WAPDFF1/oFbA6ISEhOHToEC5cuIBLly7hwIEDGDRokGnfFBEREVVLMpkMDRo0QFJSEjIzM5GWlmbulIiojGxsbKBQKFC7dm3IZDJzp1NlsYg14t69e7hy5QoAwNvbGwMGGN6B1N7eHpMmTcLcuXMBADt27ChSxCqVShw8eBAA4OLigrFjxxocSxAETJs2DWPGjAEA/PTTTyxiiYiIqNRkMhk8PDxQt25d5OXlSV+gE1HVZ2VlBRsbG66eKAUWsUYcPnxYOu7Xr1+xffv37y8VsYcPH8Z///tfvfbw8HCo1WoAQM+ePYu9D7fwtQrnQERUmI2NDXr16lXkHBERUPDFuK1t0XvniYiqg2pfxA4cOBBXrlxBcnIynJ2d0aBBA7zwwguYMGEC2rVrZzTuxo0b0nGHDh2KvUa7du0gk8mg0WgQEREBURT1vkEpy1h16tRBo0aNEBUVhaSkJMTHx8PDw6OEd0lENY1CoUB4eLi50yAiIiKqdNX+wUMHDx5EXFwc8vLykJKSgmvXrmHNmjXw8/PD+PHjoVKpDMbdvXtXOm7cuHGx17C2toanZ8HOotnZ2YiJiSn3WEDBs2sNxRIREREREdV01XYm1t3dHf369UOHDh1Qv359iKKIBw8eYN++fTh//jwAYNOmTYiOjsbhw4dhba3/URTeDKE0O4O5u7sjOjpaii38qJ3yjGUo1hhRFJGRkVFiP2Ps7OxgZ2dX7ngiIiIiIno6OTk5yMnJKXe8KIomzKZqq5ZF7MKFC9GxY0eD94d9+umn2LlzJ9555x2oVCocO3YMixYtwpw5c/T6KZVK6Vgul5d4zcLPYysca+qxDHn06BFcXV1L7GdMSEiI3k7MRERERERUuRYuXIj58+ebOw2LUC2L2K5duxbb/vrrr0OlUuGdd94BACxbtgwzZ840Ohtpyh3CKmK3sfr16+PWrVvljucsLBERERGRec2ePRsffvhhueNbtWqFR48emTCjqqtaFrGlMWbMGCxYsAB37txBeno6zpw5A39/f6ndyclJOjZ232xhhfsUjjX1WIYIggAXF5cS+xERERERUdX0tLf41aRH89TYIhYAevXqhTt37gAAbt++rVfEKhQK6Tg5ObnEsQr3KRxr6rGIiAAgMzMTn3zyid65RYsWwdnZ2UwZEREREVWOGl3EFt5k6ckNlJo3b47jx48DACIjI4s8j7Gw/Px8xMbGAgAcHBzg5eVVZCydyMjIEvOKiooyGEtEpKNWq/H111/rnZs3bx6LWCIiIqr2qv0jdopT3Ixn69atpeNLly4VO87Vq1eh0WgAAD4+PkWm8ssyVmJiolTE1q5dm8+IJSIiIiIiKqRGF7EnTpyQjlu0aKHX1q9fP+n4yJEjxY5z+PBhg3E6vXv3lta3nzx5stj7Ygtfy9BYRERERERENVmNLWJ//PFH3L59GwDg7OyMHj166LV7e3vDz88PAPDPP//g0KFDBsdRq9XYsGGD9HrkyJFF+jg5OWHAgAEAgIyMDGzevNngWKIoYs2aNcWORUREREREVJNVuyJ29erV+PPPP4vts2fPHkycOFF6PWPGDIM7gYWEhEjHU6ZMQXR0tF67VqtFUFCQdP61117Dc889Z/Cac+fOlZYZz549G9evXy/SJzQ0VMq9ffv2ePXVV4t9H0RERERERDVNtdvY6Y8//sAHH3yAFi1a4KWXXoKvry/c3d0hiiIiIyOxb98+nD17Vurv7+9fZIdPnSFDhuCNN97ATz/9hKioKHTo0AGTJ09G69atkZycjO+//x4XLlwAAHh4eGDlypVG8/Lz88OsWbOwePFipKeno1u3bpg4cSI6d+4MpVKJXbt24ejRowAAR0dHrF+/vkZtk01ERERERFQa1a6I1blz5470+BxDBEHApEmTsHLlStja2hrt97///Q+CIGD79u1ISkrCggULivRp0qQJdu3ahUaNGhWb08KFC5Gbm4tVq1YhKysLX375ZZE+derUwdatW9GhQ4dixyIiIiIiIqqJql0Ru3z5cgwaNAjnzp3D9evXkZCQgKSkJOTn58PNzQ3e3t544YUXMG7cOHh7e5c4np2dHX788UcEBARg48aNOH/+PBISEuDs7Axvb28MHz4ckydPhpOTU4ljCYKAFStWYMSIEVi/fj1OnjyJR48eQS6Xo0mTJhg6dCimTJmCOnXqmOKjICIiIiIiqnaqXRH77LPP4tlnn9W759UU+vfvj/79+5tkrK5du6Jr164mGYuIiIiIiKgmqXZFLBERVW+p2QAgmmQsIVuEbR6QqxQhap9uTLkt4CTnXgZEREQVjUUsERFZBLktYC0Dwm+apoAFAMd8oE2aiL9zgSzrpxvXWgaM6MJCloiIqKKxiCUiIovgJBcwogugzjXdmEI2YHtLQP1WgOhQ/uIzNbuguFbnAk5y0+VHRERERbGIJSIii+EkF0xaJIpWAvJtACcnAYLT08ygmm52mIiIiIpnZe4EiIiIiIiIiEqLM7FERBZIJpPBx8enyDkiIiKi6o5FLBGRBapVqxZu3rxp7jSIiIiIKh2XExMREREREZHFYBFLREREREREFoNFLBEREREREVkMFrFERERERERkMbixUzUQHx9fZJdSnaCgIAQFBVVyRkREREREZGphYWEICwsz2BYfH1/J2ZgPi9hqwMPDAxEREeZOg4gqUVZWFpYuXap3bubMmXB0dDRTRkRERFTRipug8vLyQmxsbCVnZB4sYomILFB2djbmz5+vdy4oKIhFLBEREVV7vCeWiIiIiIiILAaLWCIiIiIiIrIYLGKJiIiIiIjIYrCIJSIiIiIiIovBIpaIiIiIiIgsBotYIiIiIiIishh8xA4RURWkTUuHNltltD0/Oanoubh45OdpDPa3crCHlcLVZPkRERERmQuLWCKiKkablo6M1Rsg5uUZ7aPMUhY9t+lHyB2dDPYXbGzg8v4kFrJERERk8VjEEhFVMdpsFcS8PDi+/iqs6tQ22EednASs/ELvnNO4N+HsXrS/NjEJWTv3QZutYhFLREREFo9FLBFRFWVVpzas69cz2GZtIyt6rp4HrOvUKXI+3+SZEREREZkPN3YiIiIiIiIii8EiloiIiIiIiCwGi1giIiIiIiKyGLwnlojIAgmCgNq1axc5R0RERFTdsYglIrJAtWvXRmJiornTICIiIqp0XE5MREREREREFoNFLBEREREREVkMFrFERERERERkMVjEEhERERERkcVgEUtEREREREQWg7sTVwPx8fHw8fEx2BYUFISgoKBKzoiIKppKpcLGjRv1zo0fPx729vZmyoiIiIgqWlhYGMLCwgy2xcfHV3I25sMithrw8PBARESEudMgokqkVCrx3nvv6Z0bOXIki1giIqJqrLgJKi8vL8TGxlZyRubB5cRERERERERkMVjEEhERERERkcVgEUtEREREREQWg0UsERERERERWQwWsURERERERGQxWMQSERERERGRxWARS0RERERERBaDRSwRERERERFZDBaxREREREREZDFYxBIREREREZHFYBFLREREREREFoNFLBEREREREVkMFrFERERERERkMVjEEhERERERkcWwNncCRERUdnXq1IEoiuZOg4iIiKjScSaWiIiIiIiILAZnYomITESblg5ttqpUfcWsRIh5WojIg6jOQf6juP8bJzGpolIkIiIisngsYomITECblo6M1Rsg5uWVLiAvC5oELTRWqchPT0fm2k16zYKNDawc7CsgUyIiIiLLxiKWiMgEtNkqiHl5cHz9VVjVqV1ifzErDrLl+yGDG6xdneE8xV+v3crBHlYK14pKl4iIiMhisYglohpLzEkD8rIg5qsBbR7EfDVEZWz5xspKBPKyIDjkQ+aiKbm/tQjBxgoCbCDI7WBdv165rktERERU07CIJaIaScxJQ/6VlYAmD2KGE8RsK4jQIv9qWLnG0ySroU1+AE1ENhAvL12Q4ILy7q+Xk5ODvXv36p0bPHgw7OzsyjUeERERkaVgEUtENVNeFqDJg6z5CAguERCQA8HFDtbtepdvvMeJsDq3EzKf12H9TJ1ShQhHTgEZpbyH9gkZGRkYOXKk3rmEhATUqWP82trEJOSXMK6oFCFPESHGCchXCgb7cKkzERERmROL2GogPj4ePj4+BtuCgoIQFBRUyRkRWQ7BoS4E638BKy0EazkEJ8/yjeMoA2wcITjWg+BUuqXBgpUNgPIVsWVh5WAPwcYGWTv3ldg3Pxdoligi/5KATFvDfQQbG7i8P4mFLBERUSULCwtDWJjhVWPx8fGVnI35sIitBjw8PBAREWHuNIioirJSuMLl/UmlevxPvlLEvWsimrUV4OxUdCZWm5iErJ37oM1WsYglIiKqZMVNUHl5eSE2tnx7e1gaFrFERDWAlcK1VEWnkCFC/VCEUE+AtUvRIrak5chEREREFa18O4oQERERERERmQFnYomoRtOmZ0JU50DMy4OozkH+o7jyjZOYZOLMiIiIiMgQFrFEVGNplXnIWrcd+dEO0OQD+alA5tpN5R5PsLGBlYO9CTMkIiIioiexiCWiGkvM0UDMy4esXl3IcrSwdpbDeYp/ucfjo2eIiIiIKh6LWCKq8QRbWwhaDQS5Hazrl+7xOERERERkHixiiYiIagClWoQ619xZGCa3BZzkRXfDJiIiMoRFLBERUTWnVIv4+byIfI25MzHMWgaM6MJCloiISodFLBERkYmkZgOAaO40ikjNBvI1QG9fAW4O5s5GX2o2EH6zYJbYSW7ubIiIyBKwiCUiInpKctuC2cTwm1WvgNWxlgHPKKribGfV/cyIiKhqYhFLRET0lJzkAkZ0QZW95xTgfadERFR9sIglIiIyASe5wOWwRERElYBFLBGRBXJ3d0dCQkKRc0RERETVHYtYIiILZGVlhTp16pg7DSIiIqJKZ2XuBIiIiIiIiIhKi0UsERERERERWQwWsURERERERGQxeE8sEZGZqTNUOBCyq1yxcmd7vDRjgIkzIiIiIqq6WMQSEZmZqBWhSssuU0yeJh93H/8DWyc7yMIdAADdunWDra1tBWRIREREVHWwiCUiMhO5s325YzPi4jFv98KCF98X/CchIYE7FhMREVG1xyKWiMhMnmYZ8LYZG02YCREREZHl4MZOREREREREZDE4E1sNxMfHw8fHx2BbUFAQgoKCKjkjIiIiIiIytbCwMISFhRlsi4+Pr+RszIdFbDXg4eGBiIgIc6dBREREREQVqLgJKi8vL8TGxlZyRubB5cRERERERERkMVjEEhERERERkcVgEUtEREREREQWg0UsERERERERWQwWsURERERERGQxWMQSERERERGRxWARS0RERERERBaDRSwRERERERFZDBaxREREREREZDFYxBIREREREZHFsDZ3AkREVHZO9k5YMfoL2Dnbo9d7fQAAbm5uZs6KiIiIqOKxiCUiskAyKxkauHvBXuEAX19fc6dDREREVGlq3HLivn37QhAE6Wfz5s2lijty5AhGjRqFxo0bQy6Xo27duujevTtWrlyJrKysMuVw/vx5TJgwAc2aNYODgwPc3d3RsWNHfP7550hKSirHuyIiIiIiIqoZatRM7P/+9z/89ttvZYrJzc3F+PHjsXXrVr3ziYmJSExMxNmzZxEWFoZdu3ahbdu2xY4liiJmzZqF5cuXQxRF6bxKpUJKSgouX76MNWvWYNu2bfD39y9TnkRERERERDVBjZmJTUhIwIcffggAcHR0LHXcuHHjpALW3d0ds2fPxrZt27B69Wp07twZAHD//n288sorePjwYbFjzZkzB8uWLYMoinB0dMT777+PLVu2YN26dejTp+Cetvj4eAwZMgR//fVXed4mERERERFRtVZjZmKnTZuGlJQUtGvXDq1bt8aWLVtKjNm3bx+2bdsGAGjYsCFOnTqFhg0bSu1BQUGYOHEiNm3ahMePHyM4OBg7d+40ONa1a9ewaNEiAICrqytOnjyJ5557TmqfPHky5s2bh/nz50OpVCIwMBAXLlyAIAhP87aJiIiIiIiqlRoxE7t3717s2LEDVlZWWL9+PWQyWani5s2bJx2vXbtWr4AFACsrK4SFhUnnd+3ahb///tvgWKGhodBqtQCAL774Qq+A1QkJCZFmdy9duoQDBw6UKk8iqnk0Wg0eJscgKuEhbt68iZs3byI/P9/caRERERFVuGpfxGZkZGDq1KkAgPfeew+dOnUqVdy9e/dw5coVAIC3tzcGDBhgsJ+9vT0mTZokvd6xY0eRPkqlEgcPHgQAuLi4YOzYsQbHEgQB06ZNk17/9NNPpcqViGoepUqJD7d+iqB1wWjdujVat26N1NRUc6dFREREVOGq/XLiWbNmITY2Fl5eXvj8889LHXf48GHpuF+/fsX27d+/P+bOnSvF/fe//9VrDw8Ph1qtBgD07NkTDg4ORscqfK3CORARUcURsxPMd3EbRwh2CvNdn4iIyMJU6yL25MmTWL9+PQDgq6++grOzc6ljb9y4IR136NCh2L7t2rWDTCaDRqNBREQERFHUu5e1LGPVqVMHjRo1QlRUFJKSkhAfHw8PD49S501ERGVg4wjIbKC5+7P5cpDZwLp9MAtZIiKiUqq2RaxarcakSZMgiiJee+01DB06tEzxd+/elY4bN25cbF9ra2t4enoiOjoa2dnZiImJQYMGDco1FgCpiNXFllTEiqKIjIyMEsc1xs7ODnZ2duWOJyKyVIKdAtbtg4G8sj3v21TE7ISCAjovC2ARS0RUo+Xk5CAnJ6fc8YUf4VndVdsidv78+bh79y6cnZ3x1VdflTk+LS1NOq5du3aJ/d3d3REdHS3FFi5iyzOWoVhjHj16BFdX1xL7GRMSEqK3iRURUU0i2ClYQBIRkdktXLgQ8+fPN3caFqFaFrFXr17FsmXLAAALFiyAp6dnmcdQKpXSsVwuL7G/vb29wVhTj2VI/fr1cevWrRL7GcNZWCIiIiIi85o9ezY+/PDDcse3atUKjx49MmFGVVe1K2I1Gg0mTJiA/Px8dOrUCUFBQU89pimf1VoRz30VBAEuLi4mH5eIiIiIiCrH097iVxF1RlVl8kfsxMTEmHrIMlm+fDmuXLkCa2trrF+/HlZW5XuLTk5O0rFKpSqxf+E+hWNNPRYREREREVFNZvKZ2CZNmmDAgAF499138corr5h6+GLdu3dPurczODgY7dq1K/dYCoVCOk5OTi6xf+E+hWNNPRYRVQ9KtQh1bvnjc/OLnkvOFCHYPd2mDqnZTxVOREREVOFMXsRqNBrs378f+/fvR8OGDREYGIjx48dXymNitm7dCpVKBUEQYG1tbfS5sNevX5eO9+3bJ80e9+3bF507dwYANG/eHMePHwcAREZGolevXkavm5+fj9jYWACAg4MDvLy89NqbN28uHUdGRpb4PnQ7Ez8ZS0TVg1It4ufzIvI15R8jLaNosXrwLxHOiqffmdBaBshtn3oYIiIiogpRYffEiqKIqKgozJkzB/PmzcPQoUMxefJk+Pv7V9QlpW2lRVHEwoULSxWze/du7N69G0DB0l1dEdu6dWupz6VLlxAQEGB0jKtXr0KjKfjXqI+PT5H16E+OVZzExESpiK1duzafEUtUDalzgXwN0NtXgJtD+cbYv6/ofS8D/ATUrv3098PIbQEnec25r4aIiIgsi8mL2B07dmD9+vU4duwYRFGEKIrIy8vDzp07sXPnTjRr1gyTJ0/G2LFjUatWLVNf3mT69esnHR85cqTYvocPHzYYp9O7d2/Y2dkhJycHJ0+ehEql0tuBuLDC1zI0FhFVH24OQG2X8hWLtgb+9nZ3Fso9HhEREZGlMPnGTq+//jqOHj2Kf/75BzNmzECdOnUAQCpo7927h5kzZ8LLywvvvPMOzpw5Y7Jrz5s3T7pOcT+FZ1U3bdoknZ8+fbp03tvbG35+fgCAf/75B4cOHTJ4TbVajQ0bNkivR44cWaSPk5MTBgwYAADIyMjA5s2bDY4liiLWrFlT7FhEREREREQ1mcmLWJ2mTZtiyZIliImJwbZt26R7SnUFo1qtxtatW9GzZ0+0adMGX3/9NTIyMioqnXIJCQmRjqdMmYLo6Gi9dq1Wi6CgIOn8a6+9hueee87gWHPnzpWWGc+ePVvvvlyd0NBQ/PnnnwCA9u3b49VXXzXJ+yAiIiIiIqouKqyI1bGxscGoUaNw/Phx3L59G9OnT5eWEesK2oiICEybNg2enp6YNGlSifeNVpYhQ4bgjTfeAFCw2VKHDh0wZ84cbN++HWFhYejatSs2btwIAPDw8MDKlSuNjuXn54dZs2YBANLT09GtWzdMnz4d27Ztw/r169GvXz9pZ2VHR0esX7++Rj3riYiIiIiIqDQqbGMnQ5o3b44VK1Zg4cKF0r2zZ86ckTZkysrKwsaNG7Fx40b4+flhypQpePPNN+HgUM6dT0zgf//7HwRBwPbt25GUlIQFCxYU6dOkSRPs2rULjRo1KnashQsXIjc3F6tWrUJWVha+/PLLIn3q1KmDrVu3okOHDiZ7D0RU/TjIHTBv2GzYOtmh67iClS6urq5mzoqIiIio4lX4TKwhdnZ2GDNmDE6dOoW///4bQUFBUCgUEARBmp29cuUKAgMDUb9+fbz//vu4c+eOOVKFnZ0dfvzxRxw6dAgjRoxAgwYNYGdnh9q1a6Nr165YtmwZrl+/Lt0/WxxBELBixQqcOXMGY8eORdOmTSGXy6FQKODn54f58+fj5s2b6NOnTyW8MyKyZDYyG/h6tcJzjVujd+/e6N27N2xt+VwcIiIiqv4qdSbWEF9fXyxduhStWrXCzJkzoVar9dozMjIQFhaGsLAwDB8+HAsXLsSzzz77VNfcvHmz0c2VjOnfvz/69+//VNfV6dq1K7p27WqSsYiIiIiIiGoSs8zE6ty6dQvTp0+Hp6cnpk2bJhWwutlYKysrvde7du1C27ZtsXXrVnOmTURERERERGZS6UVsbm4utm3bhp49e6J169b46quvkJqaKhWqNjY2ePPNN3Hq1CkkJydjzZo18PX1leKzs7MREBCA8+fPV3bqREREREREZGaVVsTqnhvr6emJMWPGSBs66TZ1atiwIT7//HM8fPgQW7duRffu3eHi4oKpU6fi77//xu7du+Hl5SXdN7t06dLKSp2IiIiIiIiqiAq9JzY/Px+7d+/GN998g/DwcACQilagYKOjPn36YOrUqRg0aJC0fNiQoUOHwtfXF76+vtBoNDhz5kxFpk5EVKVpRS3SszOQa5OHxMREAIC7u3uxf48SERERVQcVUsQ+ePAA69evx6ZNm6R/XBUuXt3c3DB27FhMmTIFzZo1K/W43t7e6NixI86fP4/k5GST501EZCkyszMx8dv3Cl4sL/hPQkIC6tSpY76kiIiIiCqByYvYfv364dixY3pLhXU6duwoPftVLpeXa3wPDw8AgFarfepciYiIiIiIyLKYvIj97bffpPtWAUAul+ONN97A1KlT0alTJ1NfjoiIiIiIiGqQCllOLIoimjZtinfffRfjx49HrVq1TDb2+vXrsWrVKpONR0RERERERJbD5EXsoEGDMHXqVPTv39/UQwMA7/ciIiIiIiKqwUxexO7du9fUQxIREREREREBqOBH7FDliI+Ph4+Pj8G2oKAgBAUFVXJGRERERERkamFhYQgLCzPYFh8fX8nZmA+L2GrAw8MDERER5k6DiIiIiIgqUHETVF5eXoiNja3kjMzDqiIGHTZsGPz9/dG/f39kZ2eXKXb9+vXw9/eHv78/Dh48WBHpERERERERkYUy+UxseHg49uzZA0EQMGbMGDg4OJQp/uWXX8aUKVMAADY2NhgwYICpUyQiIiIiIiILZfKZ2MKzp2PGjClzfNOmTdGtWzeIoojw8HBkZWWZMj0iIiIiIiKyYCafiT1//jwAwM7ODr179y7XGH379sWZM2eQn5+PS5cuoVevXibMkIiqo2PLDkKdqSp1fzFfDW1yLWhTZcivnQNYc4sAIiIiIktg8n+13b17F4IgoEWLFpDJZOUa47nnntMbj0UsEZVEnamCKq0M9+Br86BVWUGrESATRQgVlxoRERERmZDJi9i0tDQAgLu7e7nHKBybmpr6tCkRUQ0iWAmQu9iX2E/MV0Obp4VWLcDaWQ5Bbge5c8lxRERERGReJi9ibWxskJeXB5Wq9Mv6nqRWq02YERHVJHIXewycP7zEfqIyFjnHLkN1zgHO7/vDun69SsiOiIiIiJ6WyYvY2rVrIyoqCpGRkeUe499//9Ubj4iI9DnYOeDDV96DraMt2o/sAgBwcXExc1ZEREREFc/kRWzz5s0RFRWFuLg4XL16Fe3atSvzGAcOHJCOmzZtasLsiIiqBxtrG3T17gx7hQMGjih55pmIiIioujD5I3Zeeukl6TgkJKTM8VeuXMH+/fsBAPb29ujWrZvJciMiIiIiIiLLZvIidtSoUbCxsQEA7N+/H5999lmpY6OjozFs2DCIoghBEDBixAjY2tqaOkUiIiIiIiKyUCYvYhs2bIgJEyZAFEUAwIIFCzBgwABcvHjRaIxSqcRXX32Fdu3a4eHDhwAAW1vbcs3kEhERERERUfVl8ntiAWDZsmU4e/Ysrl+/DkEQcOTIERw5cgQNGjRAp06dULduXdjZ2SE9PR137tzBlStXkJOTIxW+giBg/fr1aNy4cUWkR0RERERERBaqQopYBwcHHDp0CEOHDsXFixchCAJEUUR0dLQ001qYbvkwUPCInlWrVmHMmDEVkRoRERERERFZMJMvJ9Z55plncPr0aXz22WdQKBTSeVEUi/zozvv7++Ps2bOYMmVKRaVFRFQtpGelY8TqdzAo9HUIggBBEJCYmGjutIiIiIgqXIXMxOrY2Nhg3rx5mDFjBg4ePIgTJ04gIiICKSkpUKvVcHNzwzPPPINu3bqhf//+aNOmTUWmQ0RERERERBauQotYHScnJ4wcORIjR46sjMsRERERERFRNVVhy4mJiIiIiIiITI1FLBEREREREVkMFrFERERERERkMVjEEhERERERkcWo0I2dHj58iO+//x6nTp3CzZs3kZaWhuzs7FLHC4KA/Pz8CsyQiIiIiIiILEmFFLGiKGLu3LlYunSpVITqngdLphcfHw8fHx+DbUFBQQgKCqrkjIiIiIiIyNTCwsIQFhZmsC0+Pr6SszGfCili3333XXz77bcQRRGCILCArWAeHh6IiIgwdxpERERERFSBipug8vLyQmxsbCVnZB4mL2LDw8OxYcMGCIIAALC1tcWwYcPQq1cveHl5wdHR0dSXJCKiSqZNTIIpbvawcrCHlcLVBCMRERFRTWHyIvbbb7+Vjps3b44DBw7g2WefNfVliIjIDKwc7CHY2CBr5z6TjCfY2MDl/UksZImIiKjUTF7EnjlzRjrevn07C1giomrESuEKl/cnQZuteuqxtIlJyNq5D9psFYtYIiIiKjWTF7Hx8fEQBAHe3t5o166dqYcnIiIzs1K4mqTo5N7zREREVB4mf06svb09gIIbi4mIiIiIiIhMyeQzsQ0bNkRqaioyMjJMPTQREQBAm5ZeZDmrqM6BmJcHUZ2D/EdxJY4hZiVCm5YDwKGCsqxYcls5JvR6BzYONmg90A8A4OTkZOasiIiIiCqeyYvYAQMG4Nq1a7hx4wbUajXkcrmpL0FENZg2LR0ZqzdAzMvTO58fLYMmH8hPBTLXbip5oLwsaJMfQVavDqwc7Cso24pjZ2OH/m1fhr3CAQODhps7HSIiIqJKY/IiduLEiVixYgVycnKwYcMGTJs2zdSXIKIaTJutgpiXB8fXX4VVndrSeesv/4AsUw1rZzmcp/iXOI6YFQdNRDZsOo7ipkJEREREFsTkRWyTJk2wZMkSfPDBB5g9ezY6duyIrl27mvoyRFTDWdWpDev69aTXgtwOgloDQW6nd94YUakB4uWwcnWuyDSJiIiIyMRMvrETAEybNg0LFy6EWq3Giy++iM8++wyPHz+uiEsRERERERFRDWLymVh///9bxqdQKJCSkoIFCxZgwYIFaNy4MerVqwc7O7tSjSUIAo4dO2bqFImIiIiIiMhCmbyIDQ8PhyAI0mtBECCKIgDgwYMHiIyMLNU4oijqjUNERERERERk8iIWgFS0lrWNiIhKJz07A++u/wCClQDbr98FANy6dQu1a9cuIZKIiIjIspm8iA0JCTH1kERE9CRRRKY6s+A4W3eKXxISERFR9cciloiIiIiIiCxGhexOTERERERERFQRWMQSERERERGRxWARS0RERERERBajQnYnNkStVuPatWtITExEWloatFot3nnnncq6PBEREREREVUDFV7E7tmzB6tXr8bZs2eRl5en12aoiJ05cyaSk5MBACtWrIBCoajoFImIiIiIiMhCVFgRm5CQgLfffhvHjh0DUPTRD4IgGIyTy+XYvHkzBEFA586d8e6771ZUikRERERERGRhKuSe2OTkZPTs2RPHjh2DKIpSAatQKCCXy4uNDQwMlI537dpVEekRERERERGRhaqQmdh33nkHd+/ehSAIcHBwwCeffIKxY8fCy8sLr7zyCo4cOWI0tkGDBujYsSMuXbqEM2fOIDc3F7a2thWRZrURHx8PHx8fg21BQUEICgqq5IyIiIiIiMjUwsLCEBYWZrAtPj6+krMxH5MXsSdPnsShQ4cgCAJcXFwQHh6Otm3blmmMXr164dKlS8jJycHff/+NDh06mDrNasXDwwMRERHmToOIiIiIiCpQcRNUXl5eiI2NreSMzMPky4m3b98uHa9cubLMBSwAPPfcc9Lx3bt3TZIXERERERERWT6TF7EnTpwAADg5OWHMmDHlGsPDw0M6TkhIMEleREREREREZPlMXsQ+fvwYgiCgdevWkMlk5RrD0dFROs7KyjJVakRERERERGThTH5PbHZ2NgDA3t6+3GNkZmZKx05OTk+dExFRdWNnY4cRnYfCWm6D5i8WbOzm4OBg5qyIiIiIKp7Ji9jatWvj8ePHT7U71u3bt6Vjd3d3U6RFRFStyG3lGNllGOwVDhg4b7i50yEiIiKqNCZfTty4cWOIoojbt28jLS2tXGMcOnRIOm7Tpo2JMiMiIiIiIiJLZ/Ii9uWXXwYAaLVarFu3rszxly5dwm+//QagYFa38E7FREREREREVLOZfDnxG2+8gc8//xyiKOK///0v+vTpU+rnvD569AijRo0CAAiCgHHjxpk6PSKqIsScNCCv7Bu3iVmJQF4WxKw4iErN/53PVwPaPIj5aojKkp+RJmZz53MiIiIiS2TyIrZVq1Z4++238f3330OlUsHf3x9Lly7F+PHjYW1t+HKiKGLbtm2YNWsW4uLiAADOzs746KOPTJ0eEVUBYk4a8q+sBDR5ZY7VJKuhTX4ATUQ2EC//vzEznCBmW0GEFvlXw0o3mMwGsHEsuR8RERERVRkmL2IB4Msvv8SFCxdw+/ZtZGZmYsqUKZg9ezZ69uyJiIgIqd9nn32Gu3fv4tixY0hJSYEoigAAKysr/O9//0OdOnUqIj0iMre8LECTB1nzERAc6pYt9nEirM7thMzndVg/839/RwiHwiEgB4KLHazb9S7dWDaOEOwUZbs+EREREZlVhRSxrq6uOHLkCAYPHoxr164BAFJTU7F3714ABUuFAWDBggUACmZidedsbGwQFhaGIUOGVERqRFSFCA51ITh5li3GUVZQfDrWg+BU7//OW8sBKy0Ea3mZx7REmapMzNwyB1YyAU47PwMAnDp1CrVq1TJzZkREREQVy+QbO+k0aNAAf/75Jz7++GM4OzsDKChWdbOtT74WRRGdO3dGeHg4Jk6cWFFpERFVC1qtFjEpsYhOjEFERAQiIiKg0WhKDiQiIiKycBUyE6tja2uLhQsX4tNPP8Xu3btx4sQJXL9+HcnJycjKyoJCoUC9evXQrVs3DBgwAD179qzIdIiIiIiIiMjCVWgRq+Ps7IyAgAAEBARUxuWIiIiIiIiomqqw5cREREREREREpsYiloiIiIiIiCwGi1giIiIiIiKyGJVyTywRERFRcVKzAUAsqVulk9sCTnLB3GkQEVEhJi9imzZtarKxBEHA/fv3TTYeERERVS1yW8BaBoTfrHoFLFCQ24guLGSJiKoSkxexkZGREITS/0Vf+LmxOoIgQBTFMo1DRERElsdJLmBEF0Cda+5MikrNLiiu1bmAk9zc2RARkU6FLCc2VJiWpHDBWp54IiIiskxOcqGKFon89wgRUVVk8iJ206ZNpe6r0WiQmpqKv//+G4cOHUJiYiIEQcCYMWPg7+9v6tSqrfj4ePj4+BhsCwoKQlBQUCVnREREREREphYWFoawsDCDbfHx8ZWcjfmYvIgNCAgoV1xOTg6WLl2K0NBQbN++HS+++CLGjh1r2uSqKQ8PD0RERJg7DSIiIiIiqkDFTVB5eXkhNja2kjMyjyrziB07OzvMmTMH69atQ15eHiZPnowLFy6YOy0iIiIiIiKqQqpMEaszfvx49OzZE3l5eXjvvffMnQ4RERERERFVIVXyObGjRo3CyZMncfnyZURERBi935OIqKaysbZFvzYvwdrOGo06PwsAkMur5M44RERERCZVJYvY5s2bS8dXrlxhEUtE9AQHO3tMfDEA9goHDJw/3NzpEBEREVWaKrecGNB/3M6jR4/MmAkRERERERFVJVWyiL127Zp0zOVxREREREREpFPlitjMzEysXr1aev3ss8+aMRsiIiIiIiKqSqrUPbFnz57FtGnTEBkZCQCwt7fHiy++aN6kiKjSHFt2EOpMVbF9RHUO8qNlsP7yDwhyO+m8OqP4OCIiIiKqHkxexI4fP75M/fPy8pCSkoLr16/r3f8qCAI+/PBDODg4mDpFIqqi1JkqqNKyi+0j5uVBkw/IMtUQ1JpKyoyIiIiIqgqTF7GbN2/W25iptERRBPB/mzoNHjwYISEhJs2NiCyDYCVA7mJvsE1U5yA/FbB2luvNxOrInQ3HVTdKdRZCdy2ElbUVlp74CgCwZ88eKBQK8yZGREREVMEqZDmxriAtDy8vL8yaNQtTp04tVzFMRJZP7mJv9LEx+Y/ikLl2E5yn+MO6fr1Kzqzq0GjyERF7u+BFVMF/8vLyzJcQERERUSUxeREbEBBQpv62trZwcXFBo0aN0LFjRzz//PMsXomIiIiIiMggkxexmzZtMvWQRERERERERACq4CN2npZGo8HJkyexfPlyjBo1Cn5+fvDy8oK9vT0cHBzQoEEDDBgwAGFhYUhPTy/1uEeOHMGoUaPQuHFjyOVy1K1bF927d8fKlSuRlZVVphzPnz+PCRMmoFmzZnBwcIC7uzs6duyIzz//HElJSWV9y0RERERERDVGlXrEjikkJiaiV69eRttjYmIQExODQ4cOITQ0FN999x0GDRpktH9ubi7Gjx+PrVu3FrlOYmIizp49i7CwMOzatQtt27YtNjdRFDFr1iwsX75c775hlUqFlJQUXL58GWvWrMG2bdvg7+9fyndMRERERERUc1S7IlanYcOGeP7559G8eXM888wzqFu3LtRqNW7duoWff/4Z9+7dQ0JCAl577TUcOnQIL7/8ssFxxo0bh23btgEA3N3dERgYiDZt2iApKQlbtmzBhQsXcP/+fbzyyiv4888/0aBBA6M5zZkzB8uWLQMAODo6YsKECf+vvXuPj6K6/z/+nk022dwDSQhiDEgBS7jIRVGgFkQtiKVgK+Kl3gChGrXFr7Xa1gK23qqW0hqtWkVBVBS0FkHRIgitCiJFLrGCiiABNhfIPZtkd+f3R36MCdncN7vZ5PV8PPJgMnPmzGc3k5B35swZjRo1SqWlpVq1apXeffddOZ1OTZ06VZs2bdLw4cP9/8YAAAAAQAjrdCG2W7du+vzzzzVgwIAG29x777267bbb9MQTT8jtduu2225TdnZ2vXarV6+2Amx6ero2b96s9PR0a3tmZqZmz56tJUuW6MiRI5o3b55Wrlzp85iffvqpHnzwQUlSQkKCNm3apKFDh1rb586dqwULFmjhwoUqLS3VnDlztHXrVia5AgAAAIBaOt09sZGRkY0GWEkKDw/X4sWLlZSUJEn67LPP9NVXX9Vrt2DBAmv5iSeeqBNgJclmsykrK8tav2rVKu3atcvnMe+99155vV5J0v33318nwJ4wf/58jRo1SpK0bds2rVmzptHXAQAAAABdjd+vxIaFhfm7S58Mw5Db7W71/na7Xf3791dBQYEk6ejRo+rbt6+1/YsvvtD27dslSf3799fkyZN99hMVFaUbb7xR99xzjyTplVde0ZAhQ+q0KS0t1dq1ayVJ8fHxuv766xt8TbfeequuueYaSdKKFSsavV8XAGp796HVSohJaHZ7R1yULrjD9882AACAjsrvV2JPTFhkmma7f7SFx+PR/v37rc979uxZZ/vbb79tLU+cOLHRviZNmuRzvxM2btwol8slSfr+97+v6OjoBvuqfSxffQFAQyqKKlRRWN7sD1dJRbBLBgAAaLF2uSe2dsCsfU9nY8Gzue38wTRN/frXv5bT6ZQkDRs2rM5VWEnavXu3tTxy5MhG+xs2bJjCwsLk8XiUnZ0t0zTrvJ6W9JWSkqLevXvrwIEDys/Pl9PpVGpqarNfG4CuITI2qt66qIQoRcU0/EeyE1zFFTK97ftzFgAAoL34PcRu2LBBknT48GH94he/UH5+vkzTVEZGhiZPnqwhQ4YoKSlJkZGRKi4u1ldffaUtW7ZozZo1qqiokGEYuvrqqzV79my/1PPmm29aw47Ly8u1b9++OveuJiUl6Zlnnqm33969e63lPn36NHqM8PBwnXrqqTp48KDKy8t16NChOrMUt6QvSVaIPbFvUyHWNE0VFxc32W9DIiMjFRkZ2er9AQTe9zMvlBbWXXfRr6YoJSWlyX3XzF+lisLydqoMAAC0RmVlpSorK1u9f3tfCOxI/B5ix40bpz179mjGjBnKz89Xv3799Pjjj+uCCy5odL/i4mLNnz9fixcv1osvvqi4uDg9/vjjba7niiuuUFlZWb31kZGR+tGPfqQ//vGPPoNlYWGhtZycnNzkcZKSknTw4EFr39ohtjV9+dq3IYcPH1ZCQvPvgzvZ/Pnz60xiBQAAACCwHnjgAS1cuLDphvB/iK2oqNBll12m3NxcZWRk6P33368TyhoSHx+vRYsWqX///rrlllv05JNPauTIkZo1a5a/S5Qkffe739WFF17Y4FWL0tJSa9nhcDTZX1TUt0P7au/r77586dWrlz777LMm2zWEq7AAAABAcN199926/fbbW73/wIEDdfjwYT9W1HH5PcS++OKL+vzzz2UYhp588slmBdjabr75Zr3++utav369Fi5c2OYQeyIEnhhyu3v3br3wwgt6+umnNXfuXP31r3/VG2+8Ue+e2Nr8+azW9njuq2EYio+P93u/ADquiIgIXXbZZfXWAQCA0NTWW/zaI2d0VH4PsS+99JIkKT09XWPHjm1VH1dffbXWr1+vnJwcbd68Weedd16b6zIMQwkJCRo7dqzGjh2radOm6ZJLLtHu3bt14YUXateuXYqJibHax8bGWssVFU3P4Fm7Te19/d0XAEhSQkKCXn311WCXAQAAEHB+f8TOvn37ZBiGTj/99Fb3Ufuq6L59+/xRVj0TJ060nte6f/9+LV26tM72xMREa/nEs2QbU7tN7X393RcAAAAAdGV+D7G5ubmSmncvZ0NKSkqs5by8vDbX1JDaz3fduHFjnW0DBgywlr/++utG+3G73crJyZEkRUdHKy0trdV9SbJmJj55XwAAAADo6vweYrt37y7TNLVr165WP/Zl8+bN1nK3bt38VVo9cXFx1vLJswAPHjzYWt62bVuj/ezYsUMej0eSlJGRUW88ekv6ysvLs0JscnIyz4gFAAAAgFr8HmIHDRokSaqqqtL999/f4v0PHz6sp556yvq8dgD0t9pDlU9+9M3EiROt5XXr1jXaz9tvv+1zvxPGjx9v3aS9adOmRu+LrX0sX30BAAAAQFfm9xB7+eWXW8uPPPKIHn300Wbve+DAAU2cONG6KpqWlqYxY8b4u0RJksfj0TPPPGN9fvIkVP3799fw4cMl1YTdt956y2c/LpdLTz/9tPV57dd/QmxsrCZPniyp5nm4zz33nM++TNPUY4891mhfAAAAANCV+T3EXn/99dbVWK/XqzvvvFPnnHOOXnjhBeXn59dr7/F49Mknn+iOO+7QoEGDlJ2dLalmNuEHH3ywxcd/6KGHtH379kbbFBUV6eqrr9aOHTskSUlJSZoxY0a9dvPnz7eWb7rpJh08eLDOdq/Xq8zMTGv9pZdeqqFDh/o85j333GMNM7777ru1c+fOem3uvfdebdmyRZI0YsQITZkypdHXAaDrKioq0vTp0+t8FBUVBbssAACAduf3R+yEh4drxYoVmjBhgjUp07Zt23TddddJknr06KGkpCRFRESopKREhw4dUlVVlaSaK5Engt5NN92kK6+8ssXHf+utt3TXXXdp4MCBOv/88zVo0CAlJSXJMAzl5+frk08+0euvv67jx49Lkux2u5555hmfz7OdOnWqZsyYoRUrVujAgQMaOXKk5s6dq8GDB6ugoEBLly7V1q1bJUmpqalatGhRg3UNHz5cd955px566CEVFRVpzJgxmj17tkaNGqXS0lKtWrVK77zzjiQpJiZGTz31VJd61hOAlqmqqtLKlSvrrHv88ceDVA0AAEDg+D3ESjWTG73//vu68sortWPHDiuMmaYpp9NpzWBsmqa1z4k24eHh+s1vfqPf/e53barhs88+02effdZom379+umpp57S+eef32Cb559/XoZh6OWXX1Z+fr7uu+++em1OP/10rVq1Sr179270eA888ICqqqr05z//WWVlZVq8eHG9NikpKVq+fLlGjhzZaF8AAAAA0BW1S4iVpDPOOEMff/yxnnzyST3xxBPas2ePta12eD0hMjJSl112mX71q19Zw5Fb47XXXtPmzZu1ceNGbdu2TUeOHFFubq4qKioUFxen9PR0DR8+XFOnTtUll1wiu93eaH+RkZF66aWXdN111+nZZ5/VRx99pNzcXMXFxal///76yU9+orlz5yo2NrbJ2gzD0J/+9CdNnz5dTz31lDZt2qTDhw/L4XDo9NNP17Rp03TTTTcpJSWl1a8fAAAAADqzdguxkhQWFqabb75ZN998s/bu3astW7Zo7969On78uKqqqhQfH6/U1FSNHDlSo0aNalYQbEr37t01depUTZ061Q+v4FuTJk2q81zZthg9erRGjx7tl74AAAAAoCtp1xBb24ABAzRgwIBAHQ4AAAAA0An5fXZiAAAAAADaCyEWAAAAABAyAjac+Msvv9S2bduUl5enwsJCeb3eNs9ADAAAAADoWto1xFZWVuqJJ57QX/7yFx04cKDedl8h9pprrlFOTo4Mw9Dy5cvVs2fP9iwRAAAAABBC2m048f/+9z+dddZZ+r//+z8dOHBApmnW+WjIkCFDtHHjRm3cuFHLly9vr/IAAAAAACGoXULs/v37NX78eGVnZ1uhNSoqSsOGDVO3bt0a3feGG25QWFiYJOn1119vj/IAAAAAACGqXULsVVddpdzcXElSamqqli5dquPHj2v79u0aNWpUo/umpKRo7NixMk1TH3/8scrKytqjRAAAAABACPJ7iF29erW2bNkiwzDUq1cvffzxx/rpT3+qiIiIZvcxduxYSZLb7dauXbv8XSIAAAAAIET5PcSuWrXKWs7KylJaWlqL+xgyZIi1vG/fPr/UBQAAAAAIfX6fnfjDDz+UJHXv3l0/+tGPWtVHcnKytZyfn++Xujozp9OpjIwMn9syMzOVmZkZ4IoAtDe73a5x48bVWwcAADqvrKwsZWVl+dzmdDoDXE3w+D3EHj16VIZh6Lvf/W6r+4iKirKWKyoq/FFWp5aamqrs7OxglwFIktY/slauksa/b023S2ZxrIy3NsoId1jrXcV8vzdXYmKiNm7cGOwyAABAADV2gSotLU05OTkBrig4/B5iq6urJbXtikBhYaG1HB8f39aSAASQq6RCFYXljTfyVssst8lQpWTzBqYwAAAAdAp+D7EpKSn65ptv2vRXgN27d1vLtYcWAwgdhs2QIz7K5zbT7ZIpr4z4yDpXYk9wxPneDwAAAPB7iO3Xr5+++eYbffHFFzpy5IhOOeWUFvfxxhtvWMsjR470Z3kAAsQRH6VLFv7E5zazNEfuHVkKHzZeRuypAa4MAAAAoczvsxP/4Ac/sJYXLVrU4v3XrVunjz76SIZhKC0tTf379/dneQAAAACAEOb3EHvllVdaz4RdtGiR1q5d2+x9d+3apWuvvdb6/Gc/+5m/ywMAAAAAhDC/h9j09HTdfPPNMk1THo9Hl156qX79618rNze3wX2OHTum++67T2PHjlV+fr4Mw1DPnj116623+rs8AOgUSkpKrBkKT3yUlJQEuywAAIB25/d7YiXpwQcf1Mcff6z//Oc/crvdeuihh/Twww9r8ODBOnz4sNXu2muv1d69e7V9+3Z5PB6ZpilJioiI0KuvvqrY2Nj2KA8AQp7L5dLjjz9eZ92CBQsUFxcXpIoAAAACw+9XYqWaELp69WpNmjRJpmlaV2V37txpXWmVpOXLl+vjjz+W2+229o2Pj9frr7+uMWPGtEdpAAAAAIAQ1i4hVpISExO1du1aPfHEE+rbt68kWYH25I8Tpk+frk8++UQXX3xxe5UFAAAAAAhh7TKcuLa5c+fqxhtv1ObNm/X+++9r586dKigoUFlZmRITE9WzZ0+NGTNGkyZNUp8+fdq7HAAAAABACGv3ECtJNptN48aN07hx4wJxOAAAAABAJ+X34cTdu3dX9+7dlZycrK+++srf3QMAAAAAujC/h9iioiIVFhYqKSnJuhcWAAAAAAB/8Ptw4u7du+vYsWNKS0vzd9cAQpi3sEje8gpJklmWJ0+BSzqSJyMmrGX95OW3R3ldkqu4Qmvmr2rVvo64KF1wx2Q/VwQAANA0v4fYU045xZq4CQCkmgBb/JenZVZX16yoLpO3YL9sH66U7DEt7s+w22WLjvJzlV2P6TVVUVge7DIAAABaxO8hdty4cdq9e7f27Nmj6upq2e12fx8CQIjxllfIrK5WzGVTZEtJlll2VJ7scoVlXCYjpmeL+7NFR8mWmNAOlXYNjrjW/wHAVVwh02s23RAAAKCd+D3EXn311crKylJ5ebleeOEF3XDDDf4+BIAQZUtJVnivnjJLPZLTofBTUmTEtjzEom3aMgx4zfxVXL0FAABB5feJnc4991zNmjVLpmnql7/8pXbv3u3vQwAAAAAAuii/h1hJ+stf/qIf//jHOnbsmEaPHq0//vGPOnbsWHscCgAAAADQhfh9OPHMmTMlSXFxcYqPj1dxcbHuvvtu/fa3v9XAgQP1ne98R/Hx8bLZms7PhmHomWee8XeJAAAAAIAQ5fcQ+9xzz8kwDOtzwzBkmqbcbrd2797d4uHFhFgAqC8sLEwZGRn11gEAAHR2fg+xkmSavmeubGh9Q2qHYQDAt7p37649e/YEuwygSzheLkkdb1ZuR4QU6+B3JQBdj99D7HXXXefvLtEEp9NZ74rMCZmZmcrMzAxwRQAAhD5HhBQeJm3c0/ECrFRT2/RzCbJAV5KVlaWsrCyf25xOZ4CrCR6/h9glS5b4u0s0ITU1VdnZ2cEuAwCATiXWYWj6uZKrKtiV1He8vCZcu6qkWEewqwEQKI1doEpLS1NOTk6AKwqOVofYExM4DRkyRPPmzfNbQQAAAB1FrMPooCGxY14dBoBAaHWIPTGB08SJE5sMsffee68kqV+/frrqqqtae0gAAAAAQBfXLhM7nWzBggVW4CXEAgAAAABaKyAhFgDgX2VlZXr44YfrrPvlL3+pmJiYIFUEAAAQGIRYAAhB5eXlWrhwYZ11mZmZhFgAANDp2YJdAAAAAAAAzUWIBQAAAACEDIYTAwAQZGZ5bnALsMfIiEwMbg0AADQTIRYAgGCxx0hhdnn2vhrcOsLsCh8xjyALAAgJhFgAAILEiExU+Ih5UnVZ0Gowy3NrQnR1mUSIBQCEAEIsAABBZEQmEh4BAGgBJnYCAAAAAISMNl+JXbduncLCwppsZ5pms9ueYBiG3G53W8oDAAAAAHQifhlObJpmo9sNw2h2WwAAAAAAGtKmENvcQEpwBQAAAAD4Q6tD7Pz58/1ZBwAAAAAATSLEAgAAAABCBrMTAwAAAABCBs+JBdBplbpMuaqCXUV9x8vb3odhGEpOTq63DgAAoLMjxALolEpdpl79yJTbE+xKfAsPkxwRrd8/OTlZeXl5/isIAAAgRBBiAXRKrirJ7ZHGDzLULTrY1dTniJBiHVw5BQAAaClCbCfgdDqVkZHhc1tmZqYyMzMDXBHQcXSLlpLjCYsAACD0ZWVlKSsry+c2p9MZ4GqChxDbCaSmpio7OzvYZQAAAABoR41doEpLS1NOTk6AKwoOZicGAAAAAIQMQiwAAAAAIGQwnBgAQlBFRYWeffbZOutmzpypqKioIFUEAAAQGIRYAAhBpaWluuWWW+qsu/zyywmxAACg02M4MQAAAAAgZBBiAQAAAAAhgxALAAAAAAgZhFgAAAAAQMggxAIAAAAAQgYhFgAAAAAQMgixAAAAAICQQYgFAAAAAIQMQiwAAAAAIGQQYgEAAAAAIYMQCwAAAAAIGYRYAAAAAEDIIMQCAAAAAEJGeLALANBxeQuL5C2vaNE+pqtSZnW1TFel3IeP1vSTl98e5QEAAKALIsQC8MlbWKTivzwts7q6Rfu5D4bJ45bcx6WSJ5ZY6w27XbboKH+X2WWlpKTINM1glwEAABBwhFgAPnnLK2RWVyvmsimypSQ3e7/wxe8prMSl8DiH4m6aYK23RUfJlpjQHqUCAACgCyHEAmiULSVZ4b16Nru94YiU4fLIcES2aD8AAACgOZjYCQAAAAAQMrgS2wk4nU5lZGT43JaZmanMzMwAVwQAAADA37KyspSVleVzm9PpDHA1wUOI7QRSU1OVnZ0d7DIAAAAAtKPGLlClpaUpJycnwBUFByEWAEJQZWWl/vnPf9ZZ96Mf/UiRkZFBqggAACAwCLEAEIKKi4t1+eWX11mXm5urlJSUIFUEAAAQGEzsBAAAAAAIGYRYAAAAAEDIIMQCAAAAAEIGIRYAAAAAEDIIsQAAAACAkEGIBQAAAACEDEIsAAAAACBkEGIBAAAAACGDEAsAAAAACBmEWAAAAABAyOiUIba4uFivvvqqbr75Zp177rlKTk6W3W5XQkKCBg0apNmzZ+v9999vUZ/r1q3TFVdcoT59+sjhcKhHjx4aO3asFi1apLKyshb19dFHH2nWrFnq16+foqOjlZSUpLPOOkt/+MMflJ+f36K+AAAAAKArCQ92Af72xz/+Ub/73e9UWVlZb1txcbGys7OVnZ2tZ555RlOmTNFzzz2n7t27N9hfVVWVZs6cqeXLl9dZn5eXp7y8PH3wwQfKysrSqlWrdOaZZzZam2mauvPOO/Xoo4/KNE1rfUVFhY4dO6ZPPvlEjz32mF588UVNmDChha8cAAAAADq/Thdi9+7dawXY3r1768ILL9SIESOUnJyskpISbd68WS+//LIqKyu1evVqXXTRRfr3v/+tqKgon/3dcMMNevHFFyVJSUlJmjNnjoYMGaL8/Hy98MIL2rp1q7788ktdfPHF2rJli0477bQGa/vtb3+rRx55RJIUExOjWbNmadSoUSotLdWqVav07rvvyul0aurUqdq0aZOGDx/u53cHAAAAAEJbpwuxhmFo0qRJ+uUvf6nzzz9fhmHU2T5r1izdcccduvDCC+V0OrV9+3b98Y9/1Pz58+v1tXr1aivApqena/PmzUpPT7e2Z2Zmavbs2VqyZImOHDmiefPmaeXKlT7r+vTTT/Xggw9KkhISErRp0yYNHTrU2j537lwtWLBACxcuVGlpqebMmaOtW7fWqx8AAAAAurJOd0/sQw89pLfeeksTJkxoMAAOHjxYTz31lPX5kiVLfLZbsGCBtfzEE0/UCbCSZLPZlJWVZa1ftWqVdu3a5bOve++9V16vV5J0//331wmwJ8yfP1+jRo2SJG3btk1r1qxp4FUC6OqSkpKUm5tb5yMpKSnYZQEAALS7ThdiG7u/tbZLLrlEMTExkqQDBw6ouLi4zvYvvvhC27dvlyT1799fkydP9tlPVFSUbrzxRuvzV155pV6b0tJSrV27VpIUHx+v66+/3mdfhmHo1ltvtT5fsWJFs14LgK7HZrMpJSWlzofN1ul+pAMAANTTZX/jCQsLs0KsVDO5Um1vv/22tTxx4sRG+5o0aZLP/U7YuHGjXC6XJOn73/++oqOjG+yr9rF89QUAAAAAXVmXDbFOp1O5ubmSpOjoaKWkpNTZvnv3bmt55MiRjfY1bNgwhYWFSZKys7PrzDzc0r5SUlLUu3dvSVJ+fr6cTmcTrwQAAAAAuo4uG2L/9re/WcuTJk2qNwxv79691nKfPn0a7Ss8PFynnnqqJKm8vFyHDh1qdV+SrBB78r4AAAAA0NV1utmJm2Pv3r166KGHJNXch3rXXXfVa1NYWGgtJycnN9lnUlKSDh48aO1b+1E7renL174NMU2z3j29LREZGanIyMhW7w8AAACgbSorK61HhbbGyaNBO7MuF2JLSkp06aWXWvfA3nLLLTr77LPrtSstLbWWHQ5Hk/3Wfs5s7X393Zcvhw8fVkJCQpPtGjJ//vw6MzED6Piqqqr0wQcf1Fk3ZswYRUREBKkiAADQFg888IAWLlwY7DJCQpcKsdXV1br88suVnZ0tSTr77LP18MMPN7mfP5/V2h7Pfe3Vq5c+++yzVu/PVVgg9BQVFen888+vsy43N7fe/f0AACA03H333br99ttbvf/AgQN1+PBhP1bUcXWZEOvxeHTVVVdZM/4OGjRIa9eubTDAxcbGWssnz1zsS+02tff1d1++GIah+Pj4JtsBQEfkzcuX2w/92KKjZEts/agUAACCqa23+LXHxbKOqkuEWK/Xq2uvvVYrV66UJJ1xxhlav359o/enJiYmWssFBQVNHqN2m9r7+rsvAOgsbNFRMux2la1c7Zf+DLtd8bfdSJAFAKCT6/Qh1uv16vrrr9eLL74oSerXr5/ee+89paamNrrfgAEDtGHDBknS119/rXHjxjXY1u12KycnR1LN43rS0tLq9XXC119/3WTNBw4c8LkvAHQmtsQExd92o7zlTY9QaYo3L19lK1fLW15BiAUAoJPr1CHW6/Vq5syZWrZsmSSpb9++2rBhg3r16tXkvoMHD7aWt23bpuuuu67Btjt27JDH45EkZWRk1LuUf3JfjcnLy7NCbHJycpNhGwBCmS0xwS+h0x/DkQEAQGjotM+JNU1TN954o55//nlJNc9n3bBhQ72rpA2ZOHGitbxu3bpG2564z/bk/U4YP368Nb5906ZNjd4XW/tYvvoCAAAAgK6sU4ZY0zQ1d+5cPfvss5Kk3r17a+PGjUpPT292H/3799fw4cMlSfv27dNbb73ls53L5dLTTz9tfX755ZfXaxMbG6vJkydLkoqLi/Xcc881WPdjjz3WaF8AAAAA0JV1yhCbmZlpBcsTAbZ3794t7mf+/PnW8k033aSDBw/W2e71epWZmWmtv/TSSzV06FCffd1zzz3WMOO7775bO3furNfm3nvv1ZYtWyRJI0aM0JQpU1pcMwAAAAB0Zp3untjf/OY3euKJJyRJYWFh+vnPf64dO3Zox44dje73gx/8QNHR0XXWTZ06VTNmzNCKFSt04MABjRw5UnPnztXgwYNVUFCgpUuXauvWrZKk1NRULVq0qMH+hw8frjvvvFMPPfSQioqKNGbMGM2ePVujRo1SaWmpVq1apXfeeUeSFBMTo6eeeqpLTZMNAAAAAM3R6ULsf/7zH2vZ4/E0+4HB+/fvV58+feqtf/7552UYhl5++WXl5+frvvvuq9fm9NNP16pVq5q82vvAAw+oqqpKf/7zn1VWVqbFixfXa5OSkqLly5dr5MiRzaobAAAAALqSTjmc2J8iIyP10ksv6a233tL06dN12mmnKTIyUsnJyRo9erQeeeQR7dy507p/tjGGYehPf/qT/vOf/+j6669X37595XA4lJiYqOHDh2vhwoXas2ePLrroogC8MgAAAAAIPZ3uSuzGjRvbpd9JkyZp0qRJfulr9OjRGj16tF/6AgAAAICuhCuxAAAAAICQQYgFAAAAAIQMQiwAAAAAIGR0untiAaAr6Natm3bv3l1vHQAAQGdHiAWAEBQeHq5BgwYFuwwAAICAYzgxAAAAACBkEGIBAAAAACGDEAsAAAAACBncEwsAaDFXcYXWzF/Vqn0dcVG64I7Jfq4IAAB0FYRYAAhBbrdbn3/+eZ11Z5xxhsLDA/Nj3fSaqigsD8ixAAAAaiPEAkAIOn78uAYPHlxnXW5urlJSUtr1uI64qFbv6yqukOk1/VgNAADoigixnYDT6VRGRobPbZmZmcrMzAxwRQA6q7YMA14zfxVXbwEAaIOsrCxlZWX53OZ0OgNcTfAQYjuB1NRUZWdnB7sMAAAAAO2osQtUaWlpysnJCXBFwcHsxAAAAACAkEGIBQAAAACEDIYTA12UWVkoVZc1vL0sT6ouk1l2VGapp/n9ul2St1qm2yWz1PeQFrM8t6XlAgAAAJIIsUCXZFYWyr19keSpbrCNp8Alb8F+ebLLJaej+X0Xx8ost8mUV+4dvicekCSF2SV7TEvKBgAAAAixQJdUXSZ5qhU2YLqM6B6+2xzJk+3DlQrLuEzhpzT/sS3GWxtlqFJGfKTCh41vuKE9RkZkYovKBgAAAAixQBdmRPeQEXuq720xYTVBM6anjNieze8z3CHZvDLCHQ32DQAAALQWEzsBAAAAAEIGIRYAAAAAEDIIsQAAAACAkEGIBQAAAACEDCZ2AlDP+kfWqiKvUO6DYQpf/J4MR2Sz93UVV7RjZQAAAOjqCLEA6nGVVKiixCWPWworcclweYJdEgAAACCJEAugEYakqDhHi67EnuCIi/J/QbAkJCRow4YN9dYBAAB0doRYAA2KDJcm/XyCwns1/zmxCIyIiAiNHz8+2GUAAAAEHBM7AQAAAABCBiEWAAAAABAyCLEAAAAAgJBBiAUAAAAAhAwmdgKAEOT1elVQUFBnXVJSkmw2/jYJAAA6N0IsAISggoIC9ejRo8663NxcpaSkBKkiAACAwOBP9gAAAACAkMGV2E7A6XQqIyPD57bMzExlZmYGuCIAAAAA/paVlaWsrCyf25xOZ4CrCR5CbCeQmpqq7OzsYJcBAAAAoB01doEqLS1NOTk5Aa4oOBhODAAAAAAIGVyJBQAAMstzg3dwe4yMyMTgHR8AEFIIsQAAdGX2GCnMLs/eV4NXQ5hd4SPmEWQBAM1CiAUAoAszIhMVPmKeVF0WlOOb5bk1Abq6TCLEAgCagRALAEAXZ0QmEiABACGDiZ0AAAAAACGDEAsAAAAACBmEWAAAAABAyCDEAgAAAABCBhM7AQAAhKjj5ZJkBruMehwRUqzDCHYZADopQiwAAECIcURI4WHSxj0dL8BKNbVNP5cgC6B9EGIBIATFx8frlVdeqbcOQNcQ6zA0/VzJVRXsSuo7Xl4Trl1VUqwj2NUA6IwIsQAQgiIjIzV9+vRglwEgiGIdRgcNiR3z6jCAzoOJnQAAAAAAIYMQCwAAAAAIGYRYAAAAAEDI4J5YAG1S6jI77MQiAAAA6HwIsQBardRl6tWPTLk9wa7Et/CwmsdQAAAAoPMgxAJoNVeV5PZI4wcZ6hYd7Grqc0R03mcU5uXlqUePHnXW5ebmKiUlJUgVAQAABAYhFkCbdYuWkuM7Z1gEAABAx8LETgAAAACAkEGIBQAAAACEDIYTdwJOp1MZGRk+t2VmZiozMzPAFSGYvIVF8pZXNNrGLMuTp8AlHcmTERNWf7urUnK726tEAAAAtEJWVpaysrJ8bnM6nQGuJngIsZ1AamqqsrOzg10GOgBvYZGK//K0zOrqxhtWl8lbsF+2D1dK9ph6m90Hw+RxS3a7TbboqHaqFgAAAC3R2AWqtLQ05eTkBLii4CDEAp2It7xCZnW1Yi6bIltKcoPtzLKj8mSXKyzjMhkxPettD1/8nsJKXApPjJEtMaE9SwYAAABahBALdEK2lGSF96ofTk8wSz2S06HwU1JkxNZvZzgiZbg8Muz8iAAAAEDHwsROAAAAAICQQYgFAAAAAIQMQiwAAAAAIGQQYgEAAAAAIYMQCwAAAAAIGYRYAAAAAEDIIMQCAAAAAEIGD4EEgBAUGxurxx57rN46AACAzo4QCwAhKCoqSpmZmcEuAwAAIOAYTgwAAAAACBmEWAAAAABAyCDEAgAAAABCBiEWAAAAABAyCLEAAAAAgJDB7MQAEILy8/M1cODAOus+++wzJScnB6kiAACAwCDEAkAIMk1T+fn59dYBAAB0dgwnBgAAAACEDEIsAAAAACBkEGIBAAAAACGDEAsAAAAACBlM7NQJOJ1OZWRk+NyWmZmpzMzMAFcEAAAAwN+ysrKUlZXlc5vT6QxwNcFDiO0EUlNTlZ2dHewyAAAAALSjxi5QpaWlKScnJ8AVBQfDiQEAAAAAIYMQCwAAAAAIGYRYAAAAAEDI4J5YAECn4c3Ll9sP/diio2RLTPBDTwAAwN8IsQCAkGeLjpJht6ts5Wq/9GfY7Yq/7UaCLAAAHRAhFgAQ8myJCYq/7UZ5yyva3Jc3L19lK1fLW15BiAUAoAMixAIAOgVbYoJfQqc/hiMDAID2Q4gFgBAUHR2t+fPn11sHAADQ2RFiASAExcTEaMGCBcEuAwAAIOB4xA4AAAAAIGRwJRYIErOyUKou82+fZXlSdZnMsqMySz0NtyvP9etxgZZwFVdozfxVrdrXERelC+6Y7OeKAABAKCHEAkFgVhbKvX2R5Kn2a7+eApe8BfvlyS6XnI7GG4fZJXuMX48PNIfpNVVRWB7sMgAAQIjqlCHW4/Hos88+07Zt2/TJJ59o27Zt+vTTT1VRUfPoheuuu07PPfdci/pct26dlixZoo8++khHjx5VfHy8+vfvr8suu0xz5sxRTEzzw8BHH32kp59+Wu+//74OHz6sqKgonX766Zo2bZp+9rOfKTk5uUW1IQRVl0meaoUNmC4juof/+j2SJ9uHKxWWcZnCT0lpvK09RkZkov+ODTTBERfV6n1dxRUyvaYfqwEAAKGqU4bYyy+/XK+99ppf+qqqqtLMmTO1fPnyOuvz8vKUl5enDz74QFlZWVq1apXOPPPMRvsyTVN33nmnHn30UZnmt7+MVVRU6NixY/rkk0/02GOP6cUXX9SECRP8Uj86NiO6h4zYU/3XX0xYTTiN6Skjtqff+gX8oS3DgNfMX8XVWwAAIKmThliPp+69gN27d1dSUpL27dvX4r5uuOEGvfjii5KkpKQkzZkzR0OGDFF+fr5eeOEFbd26VV9++aUuvvhibdmyRaeddlqDff32t7/VI488IqlmZtFZs2Zp1KhRKi0t1apVq/Tuu+/K6XRq6tSp2rRpk4YPH97iegF0DceOHdN5551XZ93mzZvVvXv3IFUEAAAQGJ0yxI4aNUoDBw7UyJEjNXLkSJ1++ul67rnndMMNN7Son9WrV1sBNj09XZs3b1Z6erq1PTMzU7Nnz9aSJUt05MgRzZs3TytXrvTZ16effqoHH3xQkpSQkKBNmzZp6NCh1va5c+dqwYIFWrhwoUpLSzVnzhxt3bpVhmG09OUD6AI8Ho+ys7PrrQMAAOjsOmWI/fWvf+2Xfmo/g/GJJ56oE2AlyWazKSsrS+vXr9fBgwe1atUq7dq1S0OGDKnX17333iuv1ytJuv/+++sE2BPmz5+vt956S1u3btW2bdu0Zs0a/fCHP/TLa0HH5i0skre8ou395OX7oRoAAACg4+qUIdYfvvjiC23fvl2S1L9/f02e7PterqioKN1444265557JEmvvPJKvRBbWlqqtWvXSpLi4+N1/fXX++zLMAzdeuutuuaaayRJK1asIMR2Ad7CIhX/5WmZ1f6Zqdiw22WLjtL6R9bKVdK6YOwqbnugBgAAANoDIbYBb7/9trU8ceLERttOmjTJCrFvv/22fv/739fZvnHjRrlcLknS97//fUVHRzfYV+1j1a4BnZe3vEJmdbViLpsiW0rbZ6a2RUfJlpggV0kFE+EAAACg0yHENmD37t3W8siRIxttO2zYMIWFhVn3qJmmWede1pb0lZKSot69e+vAgQPKz8+X0+lUampqK18FQoktJVnhvfw/o7BhM+SIb92jTdrySBQAAACgPRBiG7B3715ruU+fPo22DQ8P16mnnqqDBw+qvLxchw4dqjNLcUv6kmSF2BP7NhViTdNUcXFxk/02JDIyUpGRka3eHx2bIz5Klyz8SbDLAAAAQCMqKytVWVnZ6v1rP8KzsyPENqCwsNBaTk5ueohnUlKSDh48aO1bO8S2pi9f+zbk8OHDSkhIaLJdQ+bPn19nEisAAAAAgfXAAw9o4cKFwS4jJBBiG1BaWmotOxyOJttHRX077LL2vv7uy5devXrps88+a7JdQ7gKCwAAAATX3Xffrdtvv73V+w8cOFCHDx/2Y0UdFyG2Gfz5rNb2eO6rYRiKj4/3e78AAAAAAqOtt/i1R87oqGzBLqCjio2NtZYrKpp+3EjtNrX39XdfAAAAANCVEWIbkJiYaC0XFBQ02b52m9r7+rsvAAAAAOjKCLENGDBggLX89ddfN9rW7XYrJydHkhQdHa20tLRW9yXJmpn45H0BAAAAoKsjxDZg8ODB1vK2bdsabbtjxw55PB5JUkZGRr3x6C3pKy8vzwqxycnJPCMWAAAAAGohxDZg4sSJ1vK6desabfv222/73O+E8ePHWzdpb9q0qdH7Ymsfy1dfAAAAANCVMTtxA/r376/hw4frv//9r/bt26e33npLF198cb12LpdLTz/9tPX55ZdfXq9NbGysJk+erNdff13FxcV67rnndNNNN9VrZ5qmHnvssUb7AgCp5nFdN998c711AAAAnR0hthHz58/XtGnTJEk33XSTNm3apPT0dGu71+tVZmamDh48KEm69NJLNXToUJ993XPPPfrHP/4h0zR19913a+zYsfXa3nvvvdqyZYskacSIEZoyZUo7vCoAnUFcXJyysrKCXQYAAEDAdcoQu3//fj3zzDN11u3cudNa/u9//6vf/va3dbb/5Cc/0fDhw+usmzp1qmbMmKEVK1bowIEDGjlypObOnavBgweroKBAS5cu1datWyVJqampWrRoUYM1DR8+XHfeeaceeughFRUVacyYMZo9e7ZGjRql0tJSrVq1Su+8844kKSYmRk899VSXetYTAAAAADRHpwyxBw4c0H333dfg9p07d9YJtZLUr1+/eiFWkp5//nkZhqGXX35Z+fn5Pvs9/fTTtWrVKvXu3bvRuh544AFVVVXpz3/+s8rKyrR48eJ6bVJSUrR8+XKNHDmy0b4AAAAAoCtiYqcmREZG6qWXXtJbb72l6dOn67TTTlNkZKSSk5M1evRoPfLII9q5c6fPAHwywzD0pz/9Sf/5z390/fXXq2/fvnI4HEpMTNTw4cO1cOFC7dmzRxdddFEAXhkAAAAAhJ5OeSV2/PjxMk3Tr31OmjRJkyZN8ktfo0eP1ujRo/3SFwAAAAB0JZ0yxAKdSanLlKuq5ftVuSW3p+bf/GL//lHnhOPl7dItAAAA0CBCLNCBlbpMvfqRKben5ft6Ck2ZFZJhmnr94/YJsZIUHiY5ItqtezSgsLDQmj39hH/84x9KTEwMSj0AAACBQogFOjBXVc3V1PGDDHWLbtm+m98zVGlIkQmGzju7/Wa6dkRIsQ5m0g606upqvf/++/XWAUBHUTNap/3+iNoW/N8FhDZCLBACukVLyfEt+882IlzyhNX829J9AQBoLUdEzSidjXs6ZoCVauqbfi5BFghVhFgAAAD4TazD0PRz1ar5HALheHlNwHZVSbGOYFcDoDUIsQAAAPCrWIfRgQNix71CDKB5eE4sAAAAACBkEGIBAAAAACGDEAsAAAAACBmEWAAAAABAyGBip07A6XQqIyPD57bMzExlZmYGuCIAAAAA/paVlaWsrCyf25xOZ4CrCR5CbCeQmpqq7OzsYJcBAAAAoB01doEqLS1NOTk5Aa4oOAixAAAg6Mzy3OAWYI+REZkY3BoAAM1CiAUAAMFjj5HC7PLsfTW4dYTZFT5iHkEWAEIAIRYAAASNEZmo8BHzpOqyoNVglufWhOjqMokQCwAdHiEWAAAElRGZSHgEADQbIRYAQlBERIQuu+yyeusAAAA6O0IsAISghIQEvfpqkO8hBAAACAJbsAsAAAAAAKC5CLEAAAAAgJBBiAUAAAAAhAxCLAAAAAAgZBBiAQAAAAAhg9mJASAEFRUVafbs2XXW/f3vf1dCQkKQKgIAAAgMQiwAhKCqqiqtXLmyzrrHH388SNUAAAAEDsOJAQAAAAAhgxALAAAAAAgZDCcGAMAHb16+3H7oxxYdJVsi9yoDAOAvhFgAAGqxRUfJsNtVtnK1X/oz7HbF33YjQRYAAD8hxAIAUIstMUHxt90ob3lFm/vy5uWrbOVqecsrCLEAAPgJIRYAgJPYEhP8Ejr9MRwZAADURYgFJJW6TLmqAnc8o9xURLVUVWrKW2rKXSW5S00ZxWaddsfLA1cTAAAAEAoIsejySl2mXv3IlNsTuGPGuKUhhaZ2VUmeYlP98kx98akp1zdmvbbhYZIjInC1AQAAAB0ZIRZdnqtKcnuk8YMMdYsOzDGNciniM0O9BkreYkPubYb6nWnI6GnUa+uIkGId9dcDAAAAXREhthNwOp3KyMjwuS0zM1OZmZkBrig0dYuWkuMDExZNmyG3XYqNNeTxGiqJkOJiDYUH6PgAAAAIPVlZWcrKyvK5zel0Bria4CHEdgKpqanKzs4OdhkAAAAA2lFjF6jS0tKUk5MT4IqCwxbsAgAAAAAAaC5CLAAAAAAgZDCcGABCkN1u17hx4+qtAwAA6OwIsQAQghITE7Vx48ZglwEAABBwDCcGAAAAAIQMQiwAAAAAIGQwnBjowNY/slaukopW7esqbt1+AAAAQEdGiAU6MFdJhSoKy4NdBgAAANBhEGKBEGDYDDnio1q1ryOudfsBANCZHS+XJDPYZdTjiJBiHUawywA6NEIsEAIc8VG6ZOFPgl0GOpCSkhLdddddddY9+OCDiouLC1JFABAaHBFSeJi0cU/HC7BSTW3TzyXIAo0hxAJACHK5XHr88cfrrFuwYAEhFgCaEOswNP1cyVUV7ErqO15eE65dVVKsI9jVAB0XIRYAAABdSqzD6KAhsWNeHQY6GkIsACBkuIortGb+qlbt64iL0gV3TPZzRQAAINAIsQCAkGF6TWbsBgCgiyPEAgA6vLbMsu0qrpDpZYgemmaW5wbv4PYYGZGJwTs+AIQQQizQSt7CInnLK1q1r1mWJ0+BSzqSJ7Ocb0OgKW0ZBrxm/iqu3qJx9hgpzC7P3leDV0OYXeEj5hFkAaAZ+O0ZaAVvYZGK//K0zOrq1nVQXSZvwX7ZPlxZ89d3u122aJ7nCgDBYEQmKnzEPKm6LCjHN8tzawJ0dZlEiAWAJhFigVbwllfIrK5WzGVTZEtJbvH+ZtlRebLLFZZxmYyYnrJFR8mWmNAOlQIAmsOITCRAAkCIIMQCbWBLSVZ4r54t3s8s9UhOh8JPSZER2/L9AQAAgK7KFuwCAAAAAABoLkIsAAAAACBkEGIBAAAAACGDe2I7AafTqYyMDJ/bMjMzlZmZGeCKAAAAAPhbVlaWsrKyfG5zOp0BriZ4CLGdQGpqqrKzs4NdBgAAAIB21NgFqrS0NOXk5AS4ouAgxAJACAoLC6s3AiMsLCxI1QAAAAQOIRYAQlD37t21Z8+eYJcBAAAQcEzsBAAAAAAIGYRYAAAAAEDIYDgxAKBLcBVXaM38Va3a1xEXpQvumOznigAAQGsQYgEAXYLpNVVRWB7sMgAAQBsRYgEAnZojLqrV+7qKK2R6TT9WAwAA2ooQCwAhqKysTA8//HCddb/85S8VExMTpIo6rrYMA14zfxVXbwEA6GAIsQAQgsrLy7Vw4cI66zIzMwmxAACg02N2YgAAAABAyOBKLAAAIcJbWCRveYVf+rJFR8mWmOCXvgAACCRCLLoks7JQqi6TJBnlpmLcklEumTajefuX5UnVZTLLjsos9bT8+OW5Ld4HQNfmLSxS8V+ellld7Zf+DLtd8bfdSJDtQIL+f4M9RkZkYnBrAIBmIMSiyzErC+Xevkjy1PwiGFEtDSk0FfGZIbe9eX14ClzyFuyXJ7tccjpaV0iYXbJz/yKA5vGWV8isrlbMZVNkS0luW195+SpbuVre8gpCbEdgj5HC7PLsfTW4dYTZFT5iHkEWQIdHiEXXU10meaoVNmC6jOgeqio1tatK6jVQio1t3pVYHcmT7cOVCsu4TOGnpLSuDv7iDaAVbCnJCu/Vs019uP1UC/zDiExU+Ih51gihYDDLc2tCdHWZxP9NADo4Qiy6FG9hkTx5efIUuKQiQ4Y7TN5SU55iU95iQx5vM4cTl4fXhNCYnjJi2/bLJAAARmQi4REAmokQiy7Dup+svFDegv2yfbhSssfIXSX1yzPl3maoJKL5/Rl2u2zRUe1XMAAAAIB6CLEImFKXKVdV8I5v5pbLXVat8IkTFHG8XNX9fiIzqqfKKqQv9prqd6ahuOYOJ1bzZ/Zc/8hauUpaN5uoq9g/s5ACAAAAnQUhFgFR6jL16kem3C2fyNdvHMdM9cszddjZTQNskdp1NEVl4amSpPAUKSrdULij+SG2uVwlFaooLPd7vwAAAEBXRIhFQLiqJLdHGj/IULfo4NRgHjXk3maof4YUecxQr4GSGV0TWh0RUmwjAdYfV1MNmyFHfOuGHzviGLYMAAAASIRYBFi3aCk53v9XO5vDXVpzz2tstCGV1MxEbDRz+LA/rqY64qN0ycKftKkPAAAAoKsjxAItwNVUAAAAILgIsZ2A0+lURkaGz22ZmZnKzMwMcEWdF1dTAQAAECxZWVnKysryuc3pdAa4muAhxHYCqampys7ODnYZAALIMAwlJyfXW4f24Squ0Jr5q1q8n+mqlO1wmH7QDjUBALqexi5QpaWlKScnJ8AVBQchFkFhVhZK1WWBPWZZXs0xKwoCelygPSQnJysvLy/YZXQZptds1X3xZnW17O52KAgAgC6MEIuAMysL5d6+SPJUB/S4ngKXvAX75dlfrrAecZI9JqDHBxB62nIvu6u4QqYfawEAADUIsQi86jLJU62wAdNlRPcI3HGP5Mn24UqFZVym8PQ+MiITA3dsACHpgjsmt3rfNfNXqTyvSpLkzctXWy/IevPy29gDAACdAyEWQWNE95ARe2rgjhcTJtljZMT0JMACCAybTTJsKlu52i/dGXa7bNHMdA50dsfLJXXAsRyOCCnWwfwLCD5CLAAA7cQIC1N4n9MUd9M4v/Rni46SLTHBL30B6HgcEVJ4mLRxT8cLsFJNbdPPJcgi+AixwEkqKyv1wAMP6O6771ZkZGSwywEaxfna8Rn2cIX36hnsMjoEzleEmkCfs7EOQ9PPlVxV7X6oFjteXhOuXVVSrCPY1aCrI8QCJ6msrNTChQt1++2380sWOqyKigo9++yzcrlcWrhwoeLi4nTzzTcrKoqhpui4+PmKUBOMczbWYXTQkNgxrw7jW6Zp1vm3MyPEdgJeU8ovrjlZuVcB6BpKS0t1yy23WJ/fcccduvbaawmxAACg0yPEdgIVVdLrH9eE2PAw6cIhUpQ9yEWd5HjLH69o8RYWyVte0eYamNkTAAAACH2E2E4gKkK69GxDFdXSv3aZentHxxxCEB5Wc6W4Jc+Z8BYWqfgvT8us9s8zZZnZEwC+5Y9H/0hMOAUACCxCbCdgM6Tk+JohxB11MgDp26HOZmnz9/GWV8isrlbMZVNkS0lucw38ogUANT8LDbvdr4/+ib/tRn6+AgACghDbyXTcyQDaxpaSzOyeAOAntsQExd92o99u1ShbuVre8gpCLAAgIAixXZBZWShVlwXv+OW5QTs2AKCGLTHBL6HTH8ORAQBoCUJsEJimqVdffVXLli3Tjh07lJubq6SkJGVkZGjGjBm6/vrrZbe3z8xMZmWh3NsXSR7/3GPaamF2yR4T3BoAAAAAhBxCbIAVFhZq+vTp+te//lVn/ZEjR3TkyBGtX79ef/vb3/T6668rPT3d/wVUl0meaoUNmC4juof/+2/E888v1XXXXVvziT1GRmRiQI/fWWVlZSkzMzPYZTQLtSKU3ldqRSi9r6FUqxR69YaKUHpfqRVtYZhd4Wm4HUR1dbV+8IMfaOPGjZKk0047TXPmzFG/fv106NAhPfvss/rss88kSRkZGfrggw+UkNDwUK+0tDTl5OTo1FNP1aFDh5pVg1maI/eOLIUPy5QRe2qbX1NLZGRkKDs7u0X7uA8fVckTSxR30w1tvid2/SNr5Spp+v4vt9utd955Rz/4wQ8UHl7zdx5XcYVMr6moxGhdsvAnbarD31rzvgYLtfpPXl6eevSo+4eo3NxcpaSkBKmi5uno72ttba11zfxVqigsD8jPjWC+ry35OV1cXKyEhAQVFRUpPj4+QBW2Xlc6XwP9+0GovLecs9/KLzb1+semxg8y1C267f1dcOEFWv+v9W3v6P87MYFoewiV8/XUU0/V4cOH1atXL+Xk5AS7nHbFldgA+tvf/mYF2BEjRuhf//qXunXrZm2/5ZZbNG3aNK1bt07Z2dn6/e9/r0ceeSRI1baNr2e7ppiG3IePtqwfPz7b1VVSoYrCph9Y6/F4FBcZK1dRhcLCwvx2fABdk6u4Qmvmr2rVvo64KF1wx2Q/VwQALeeIqHlc4sY9/rn+1WP4HL3+sf+upYWH1Tylo72CLDoWQmyAuN1u/eEPf5AkGYahpUuX1gmwkuRwOLR06VL17dtXZWVleuyxx3TXXXcpObntj5YJpIae7XqVN0IlTyxpcX/+frarYTPkiG+4P7fbrZLKUjkSoqwrsSc44njGLICWMb1ms/6ABgAdWazD8OujHB+7+yld+vAMv/R1vLwmXLuq1Cmf0oH6CLEBsnHjRuXm1szKe8EFF2jQoEE+2/Xo0UNXXHGFnnnmGVVWVuqNN97QrFmzAllqmzX0bNcXV76gWTfd0OL+/P1sV0d8VKND+4qLizXt/iv0wPpFITF0CEDH1JY/ep24hQEItEA9QaBnnFdmaQcc7sicHY3y56Mcq0uPKjneX1dN+XnZ1RBiA+Ttt9+2lidNmtRo20mTJumZZ56x9gu1EHvCyc92zTNMnvUKoMtoyzDgE/fTAgFjj5HC7PLsfTUgh5t1jlvuHVkBOVaLhNkVPmIeQRbo4AixAbJ7925reeTIkY22Peuss3zuBwAA0B6MyESFj5gXsOfIP/N/r+mq+zvWbK9mea48e1+VWbRfOvEEh/JSndY9TCo/ItNWEtwCm8EvV7i5Go0QwOzEAdK3b1/t379fkrR//3716dOnwbZut1sOh0Mej0fh4eGqrKyUzWar1y4iIkLV1dWy2Wzq2bOZVzhNT81/UPYYyaiZtMgw/HwDvMcrb1mZbDExUti3dTudTqWmpvr3WC1wYnheU/fEmqZpzezm9/emHQT7fW0JavUfr9erI0eO1Fl3yimn+PxZ0ZF09Pe1tmDW2tyfVycE9X1t4Ge+L/x8bT+hVKvUUes1paq6QdU0TZWXlys6OjokztkTtbZZRJyk9n29/jwHvKZUUSVFRUi2dig7kOdrW6LZ0aNH5fV6ZbfbVVXlp5uXOyhCbIB0795dx48flySVlJQoNja22e2Li4sVFxdXr01YWJi8Xq//iwUAAAAQkmw2mzweT7DLaFcMJw6Q0tJSa9nhaPqO+KioKCvElpaW+gyxDodDLpdLYWFhbXo2ZCj8ZREAAADo7NpyfTEvL08ej6dZWSPUEWJDWFlZYO5bAQAAAICOomPfPNWJ1B4+7HK5mmxfUVHhc18AAAAA6MoIsQGSmJhoLRcUFDTa1u12q7i4WJIUHh6umJiY9iwNAAAAAEIGITZABgwYYC1//fXXjbY9dOiQdTN2v379OvxsowAAAAAQKKSjABk8eLC1vG3btkbb1t5eez8AAAAA6OoIsQEyceJEa3ndunWNtn377bd97gcAAAAAXR3PiQ0Qt9utXr16KS8vT4ZhaNeuXRo0aFC9drm5uerbt6/KysoUERGhQ4cOtenxOQAAAADQmXAlNkDCw8P1m9/8RlLN85+uvfZa6zmwJ7hcLl133XXWo3MyMzMJsAAAAABQCyE2gG666Sadd955kqTt27frzDPP1H333acVK1bo0Ucf1YgRI6yhxN/97nf1u9/9LpjlBo3b7dbf//53XXjhherVq5ciIyN12mmnacqUKVqxYkWbHgLd0PFeeukl/fjHP1afPn0UHR2tiIgIpaamavz48brvvvt09OjRZvdXVlamRYsWaezYserRo4ccDof69OmjGTNm1Bkq3pi8vDwtW7ZMs2bN0siRI9WtWzfZ7XZ169ZNw4cP16233qr//ve/rX3J8CPO17q1BfK9QMsF6mvUp08fGYbR4o+NGzc22u+bb76pK664Qn369FFUVJQSEhI0aNAg3X777frss89aVOOmTZs0a9YsDRo0SPHx8bLb7erevbvOOuss/eIXv9DOnTvb8A7AXzhnffd5/fXX64wzzlB8fLxiYmLUt29fXXDBBfr973+vHTt2tKpftB3na8Oqqqo0ePDgFtXTKBMBdezYMXPChAmmpAY/hg0bZu7fvz/YpQbFgQMHzJEjRzb6/lx44YXm8ePH/XK8L7/80jzzzDMbPZ4kMy4uzly6dGmT/e3YscP8zne+02hfV111lVlZWdlgH7feeqsZFhbWZE2SzFmzZpkVFRV+eS/Qcpyv3wr0e4GWC+TXqHfv3s36GVb7wzCMBv/vczqdTf7fGRERYT788MNN1lZeXm5efvnlTdZjs9nMn//856bH42nz+4HW4Zyt6/PPPze/973vNVnX1KlT2/x+oOU4Xxu3YMGCen1u2LCh1f1xT2wQmKapV155RcuWLdN///tf5efnq1u3bsrIyNAVV1yhG264QXa7PdhlBlxRUZHGjBmj7OxsSdLAgQM1c+ZMpaWl6YsvvtBTTz2lb775RpI0fvx4vfPOO216n4qLizVkyBAdPHhQktStWzfdcMMNGjhwoBwOh7766istXbpUX375pSTJMAytXr1al1xyic/+Dh48qHPOOce6CjZq1Cj99Kc/VXJysnbt2qWnnnrKekbw1VdfrRdeeMFnPxdeeKHWr18vqeaK/IQJEzR06FB1795dx44d07vvvqvXX39dXq9XkjR58mS9+eabMgyj1e8FWo7zNXjvBVou0F+jd955R+Xl5U22e/3117V06VJJ0oQJE6yffbWVlZVp7Nix+vTTTyVJSUlJmjVrloYNGya3260tW7ZoyZIl1vEWL16s2267rcFjXnrppfrHP/4hqeZWnyuvvFKjRo1ScnKyvvnmG61Zs0bvv/++1f6Xv/yl/vjHPzb7tcM/OGfr2rlzpy666CLl5uZKkoYMGaJp06apX79+Cg8P16FDh/TFF19o7dq1Ouuss6xzHIHB+dq47OxsDR8+XFVVVYqJibFundywYYPGjx/for4srY6/gJ/dfvvt1l9mJk2aVO8KY0FBgTl8+HCrzV//+tc2He/ee++1+hoxYoTPv4xVV1ebM2fOtNqdeeaZDfb34x//2Go3c+bMen+9P3DggJmenm61Wb16tc9+fvCDH5hXXnmluWXLlgaPtWHDBjMmJsbq67nnnmvWa4b/cL5+K9DvBVquo36NzjnnHOuYL7zwgs82d955p9VmyJAhptPprNfm888/N0855RTrasGXX37ps69NmzZZfSUkJJg7d+702e7ZZ5+12oWHh5t5eXmtf5FoFc7ZbxUVFZmnnXaadT7+7W9/M71er8+2Xq/X/Oabb1r34tBqnK8N83g85rnnnmtKMqdMmWKOGzfOL1diCbHoEPLy8szIyEhTkhkTE+PzG8g0TXPXrl2mYRimJDM1NdWsrq5u9THHjh1rfROtXbu2wXaFhYWm3W632hYXF9drs3PnTmt7enp6g0N816xZY7UbOXKkzzYFBQXNqn/x4sVWX+PGjWvWPvAPztdvBeO9QMt01K9RdnZ2nUBZXl5er01VVZUZFxdnDYX79NNPG+zvH//4h9Xftdde67PNb37zG6vNnXfe2Wh9Z599ttX2n//8Z8teHNqEc7aum266yWr32GOP+eW1wH84Xxt34vfVmJgY88CBA34LsUzshA7hH//4hyorKyVJV155pXr06OGz3eDBgzVhwgRJktPprDPkq6VODMmRpH79+jXYLiEhQcnJydbnJ4ZA1LZixQprec6cOXI4HD77uvjii61jffLJJ9bQz9q6d+/edPGSLr/8cmuZCUgCi/P1W8F4L9AyHfVr9Oyzz1rLV1xxhaKiouq12bZtm0pKSiRJZ555poYOHdpgfz/60Y/UrVs3SdJrr70ml8tVr01zv49O3u7r+wjth3P2WwUFBVqyZIkkadCgQbr55pvb9Brgf5yvDTtw4ID1dJbf//73Sk9Pb9FraAwhFh1C7VlQJ02a1Gjb2ttbOntqbbV/yHzxxRcNtisqKlJ+fr4kKTk52ecPp+bWbxiGJk6c6HO/lkpISLCWKyoqWt0PWo7zteV9nby9Le8FWqYjfo3cbreWLVtmfT5z5kyf7Q4dOmQtn3HGGY32aRiG+vfvL0kqLS3Vpk2b6rVp7vfRydt9Pdcd7Ydz9lsvvviiFRZmzZrF/BcdEOdrw+bOnavS0lKNGDGixffRNoUQiw5h9+7d1vLIkSMbbXvWWWf53K+lpk6dai3/9re/VWFhYb02Ho9Ht99+u6qrqyVJv/jFL2Sz1f22MU3TmnY8PDxcZ555ZqPH9Vf9ta++9u7du9X9oOU4X+VzXaDeC7RMR/warV27Vk6nU1JNQBw1apTPdmYL556s3d7XCJXa30dPPvmkdu3a5bOfJUuW6OOPP5ZUM9nekCFDWlQH2oZz9lu1g8KECRNUXFys++67T8OHD1d8fLxiY2M1YMAAzZ49W9u2bWvRseEfnK++LVu2TOvWrVNYWJieeuophYWFtehYTQn3a29AK3i9XmuYYlhYmNLS0hptXzuw7d27t9XHvfXWW7Vy5Upt3bpV27dvV9++fTVz5kwNHDhQkZGR+uqrr7Rs2TLrr/G333677rrrrnr9fPPNN9aMbaeeeqrCwxv/tvJX/X/729+s5YZmoIX/cb5+K1jvBZqvo36Nag9za+gKgST17Nmz2fWYpllnyPvnn39er83ZZ5+tW2+9VX/9619VVFSkESNG6KqrrtKoUaOUlJSkQ4cOac2aNdazCy+44AK9/PLLzX1Z8APO2bpOBFPDMFRVVaWhQ4fqwIEDddrs27dP+/bt0zPPPKPbbrtNf/rTn/weGOAb56tveXl5mjdvniTptttuazLct0qr76YF/KSoqMi6wTspKalF7bt3796mY5eXl5tz5swxHQ6H1efJH9OmTWt0puBPP/20yclvGmo/YsSIVtX9/vvvW5MDOBwOZiIMIM5X368t0O8Fmqcjfo2cTqc1+Zjdbjdzc3MbbFteXm5NmGIYhrlr164G2/7zn/+s871w2WWXNdh20aJFZkpKSoPfR0OGDDFfe+01nhEbBJyzdZ3oKyoqyuzVq5cpyezVq5d5zz33mC+99JL597//3Zw+fbr1O4Ek89Zbb/XL60bTOF99u+KKK0xJ5mmnnWaWlJTU2cbETug0SktLreWGJpiprfaN6bX3bY2oqCgtXLhQt99+e71hlye8+eabuv/++31OanNyDYGoPycnRzNmzLCGdNx3331N/uUP/sP52j59oX10xK/RsmXLrCHvP/zhD5WSktJoPVdffbWkmqsA11xzjXXPd21ffPGFbrrppjrriouLG+x3zpw5euihh6xJSk62a9cuPfjgg/rXv/7V5OuBf3HOfquystKaMKiiokKHDx/WOeeco+zsbN1777264oorNGvWLL3yyiv65z//aY2s+etf/6qPPvqo9S8Yzcb5Wt+bb75pjWDJyspSbGxsq15HUwix6FCaM2GBPyc1eP7559WnTx/df//9mjZtmjZv3qySkhK5XC7t3r1bv/rVryRJb7zxhs4991xt3bo1YLX5UlxcrClTpujo0aOSau7vOjFcA4HH+dqyvpiQJLg6ytfoxEyrUuPD3E6477771KtXL0nSjh07NHDgQN111116+eWX9cILL+i2227TsGHDlJOToz59+lj7NfSHnm3btumMM87QzJkz1bt3b7322mvKy8tTVVWVDhw4oKysLKWkpGjr1q2aPHmynn766ba9YLRaVz9nPR5Pnc/tdrtefvnlOhM7nvDDH/5QP//5z63PFy9e3KzXBv/p6uerJJWUlFhh9yc/+YmmTJnSxlfTMO6JRbv53//+p//9738Nbh8xYoTS09Pr/IWmObPsnrifT1Kb/rrz9NNPa86cOZKkefPm6U9/+lOd7YMGDdKDDz6o0aNHa9q0acrPz9f06dO1d+9eRUZG+qyhOfXXbtOS+svKynTJJZfov//9ryTp/PPP10svvUQw8BPOV98aO1+D9V6g45+vDdm6dav27NkjSTrllFN08cUXN7lPz5499a9//UuXXnqpPv/8c+Xn5+uhhx6q1+7CCy/UT3/6U11//fWS5PMq686dO/X9739fFRUVGj16tN577706V0/S09N18803a9KkSRo1apQKCgp0880369xzz2VypzbinG35ORsdHa2wsDArzF500UV1QsTJ5syZo0cffVSS9N577zXn5aEBnK+t+xn7q1/9SocOHVJ8fLz+8pe/tO3FNKXVA5GBJsyfP7/B+40kmUuWLDFN0zQ9Ho8ZHh5uSjLDwsJMt9vdaL9fffWV1Uffvn1bVVtFRYWZlJRkSjK7devm8wHQtU2cONE65ssvv1xn24EDB6xtffr0afLY7733ntV+woQJzaq3vLzcPP/88639xo4da5aWljZrXzQP56tvjZ2vwXgvUKMjn6+N+dnPfmb1/6tf/apF+1ZWVppPP/20OWnSJDM1NdW02+1mcnKyOWHCBHPp0qWm1+s1H330Uav/efPm1etj8uTJ1vYPP/yw0eM98MADVtuf/exnLaoV9XHOtu6c7d69u7X9nnvuafKYsbGxVvuT70VE83G+tvx83bRpk3VvdlZWVoPH8dc9sVyJRdDZbDZ95zvf0eeffy6Px6NDhw41+siY2rPyDRgwoFXH/Oijj1RQUCBJGjNmjM8HQNd2wQUXaN26dZJq/so1Y8YMa1taWpqio6NVXl6uQ4cOye12Nzrja0vrr6io0JQpU7RhwwZJ0jnnnKO1a9cqJiamyX3hf5yv3wrGe4GW6UhfI5fLVWem3+YMc6stIiJCs2fP1uzZsxtsc+KxOFLNTMS1VVZWWve4xsbG6pxzzmn0eBdccIG13NTQfPgP52xd3/3ud/XBBx9Iks9hxCdLSEiw7rUsKipi1Es743z91rPPPivTNBUVFaX8/Hz94Q9/8NlH7fdg2bJl+ve//y1Juvzyy1v0nhBi0W4WLFigBQsWNKvt4MGDram6t23b1ugPgNrPQRs8eHCrajt8+LC13Jz/FBITE63lkpKSOttsNpsGDhyoTz75RG63W59++mmjU4m3pH6Xy6Vp06Zp/fr1kmp+YKxbt07x8fFN1oyW4Xz1ran6A/1eoEZHPl8b8tprr1nPNx47dqzff4Fzu93WEErDMDR27Ng62wsKClRVVSVJio+Pb/JWjMa+j9BynLP1NXXOStLQoUOtEFtUVNRkn7Un22nO/xfwjfO1vqbOV/P/TzhaUVGh+fPnN6vP2o8CGjx4cItqZmIndAgTJ060lk9cQWrI22+/7XO/loiLi7OWDx061GT7gwcPWstJSUn1tje3ftM062xvrP7KykpNmzZN77zzjqSa+y/eeecd/lPqADhfW96X5J/3Ai3XUb5GzX1uYWu9/vrrys3NlVRz72B6enqd7bW/j/Lz8+VyuRrtr6nvI7QfztlvTZ482Vr+5JNPGu1v79691h9cevXqxVXYAOF8DZJWD0QG/Cg3N9eMiIgwJZmxsbGm0+n02W737t3WePsePXqY1dXVrTpe7XsS7Ha7+fXXXzfY1u12m2eccYbVftWqVfXa7Nixw9qenp5uVlRU+OxrzZo1Vrvhw4c3eMzKyso6924NHz7cPHbsWMtfKNoF5+u3Av1eoOU6wtfo66+/tvqOjY31+716RUVF5umnn26dr++++67Pdunp6Vab5cuXN9rn7NmzrbY8dzOwOGe/5XK5rDkR7Ha7uX///gb7/L//+z+rv5kzZ/q1XjSM87Vl/HVPLCEWHcYvfvEL66S++OKL6/1ifezYMXP48OFWm8WLFzfYV+1vkBM335/snHPOsdqce+65ZkFBQb02bre7zk3yPXr0aHBCpWnTplntZs2aZXo8njrbDxw4UOcXqDfeeMNnP1VVVeaPfvQjq92wYcN81obg4nxtn/cC7SPQ5+vJFixYYO1zww03tLj+999/v8Fthw4dMseMGWP1f/311zfY9le/+pXVLjk52fz00099tlu2bJn1C6Ekc8uWLS2uGW3DOfutP//5z3V+/hcWFtZrs3r1amuCIZvNZu7evbvFNaP1OF+bz18h1jDN/z+AGQiywsJCjR492prSfODAgZo9e7ZOPfVUffHFF3ryySf1zTffSJLOO+88rV+/Xna73Wdf48eP1/vvvy+p5nlZJ6YDr23Lli0aP368NaQsJSVF1113nYYNGya73a4vvvhCy5cvV3Z2trXP8uXLddVVV/k85oEDB3TOOefI6XRKqpmA6ZprrlFSUpJ27dqlJ5980pqc54orrtBLL73ks5+rr75aL774oiQpJiZGixcvbtZQtmnTpjXZBv7D+do+7wXaR6DP19pM09R3vvMd7d+/X5K0efNmfe9732tR/bGxsUpNTdUll1yioUOHKjExUceOHdOHH36olStXWhPZnH/++Vq9enWDE98dP35cI0eOtGqJjIzUjBkzNG7cOMXHx+vIkSN68803rds4pJrHljz55JMtqhdtxzn7rerqal188cXW/Bi9evXS7NmzlZGRobKyMq1bt06vvvqqdU/ifffdp1//+tctqhdtw/nafLVf34YNGzR+/PjWddSmKA342f79++v8pcrXx4QJE5ocWtvcv2K98847Zs+ePRs9niQzJibGfPbZZ5usf/v27Wbfvn0b7WvGjBmmy+VqsI/evXs3WY+vDwQe56v/3wu0n0CfryesX7/eaj9gwIBW1R4TE9No3TabzZw7d26Tj58yzZrh+WeddVazfq7ecsstDH0PIs7ZbxUXF9cZpeXrIzw83HzggQdaVS/ajvO1eXjEDjqlPn36aMuWLXruuef08ssva8+ePTp+/LiSk5M1bNgwXXPNNZoxY0aTs0o210UXXaTPP/9cy5cv15tvvqmdO3cqPz9fHo9HiYmJysjI0EUXXaRZs2apZ8+eTfY3fPhw7dy5U08++aRWrlypffv2qaSkRD169NA555yjmTNnNuvB0wgNnK/fCvR7gZYL1tdoyZIl1vINN9zQqj5WrFihd999Vx988IFycnKUn5+v2NhYpaWl6aKLLtK1116roUOHNquv008/XR999JH++c9/6tVXX9W2bdt05MgRVVRUKC4uTn379tX3vvc9zZo1q9l9on1wzn4rLi5Ob7zxhtasWaNly5bpo48+0tGjRxUREaH09HRdeOGFuuWWW9SvX79W1Yu243wNLIYTAwAAAABCBo/YAQAAAACEDEIsAAAAACBkEGIBAAAAACGDEAsAAAAACBm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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the Posterior from MCMC sampling\n", + "\n", + "plt.hist(flat_samples1, bins=20, label='500 images', alpha=0.6, histtype='step', lw=1)\n", + "plt.hist(flat_samples2, bins=20, label='1000 images', alpha=0.6, histtype='step', lw=1)\n", + "plt.hist(flat_samples3, bins=20, label='2000 images', alpha=0.6, histtype='step', lw=1)\n", + "plt.hist(flat_samples4, bins=20, label='3000 images', alpha=0.8, histtype='step', lw=2)\n", + "plt.axvline(true_w, linestyle='--', color='k', lw=3)\n", + "# plt.xlim(xlim_min, xlim_max)\n", + "plt.xlim(-0.803, -0.794)\n", + "plt.xlabel(r'$w$')\n", + "plt.ylabel('Frequency')\n", + "plt.legend(fontsize=18)\n", + "plt.savefig(\"MCMC_posterior_w\"+str_true_w+'.pdf')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "203dac78", + "metadata": {}, + "source": [ + "### Analytical Sampling" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "98df7565", + "metadata": {}, + "outputs": [], + "source": [ + "sample_theta_unstd = np.linspace(-2.0, -0.4, 2000)\n", + "sample_theta = (sample_theta_unstd - mean_theta)/std_theta\n", + "\n", + "lnr_1 = get_logr_distribution(model, fixed_images_test, sample_theta)\n", + "np.savez('logr_w'+str_true_w+'_1000_v3.npz', logr=lnr_1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4558c63b", + "metadata": {}, + "outputs": [], + "source": [ + "import time\n", + "num_images = [5, 100, 500, 1000, 2000, 3000]\n", + "\n", + "posteriors_all_list = []\n", + "posterior_all_samples = []\n", + "start_time = time.time()\n", + "for n in num_images:\n", + " posterior, posterior_mean, posterior_std = get_joint_posterior_probability(lnr_1, sample_theta, n)\n", + " end_time = time.time()\n", + " print('Time taken for ', n, ' images: ', end_time-start_time)\n", + " start_time = end_time\n", + " posteriors_all_list.append((posterior_mean, posterior_std))\n", + " posterior_all_samples.append(posterior)\n", + "\n", + "posteriors_all = np.vstack(posteriors_all_list)\n", + "posterior_all_samples = np.array(posterior_all_samples)\n", + "print('shape of posteriors for ntrials:', np.shape(posterior_all_samples))\n", + "\n", + "np.savez('analytical_posteriors_w'+str_true_w+'_v3.npz', samples=posterior_all_samples, stats=posteriors_all)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "cdcfca53", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot the analytical posterior probability for different number of images\n", + "\n", + "for i, pos in enumerate(posterior_all_samples[2:]):\n", + " plt.plot(sample_theta, pos/np.max(pos), label=str(num_images[i+2])+' images')\n", + "plt.axvline(true_w, linestyle='--', color='k')\n", + "plt.xlabel(r'$w$')\n", + "plt.ylabel(r'$p(w | x)$')\n", + "plt.xlim(-0.85, -0.75)\n", + "plt.legend()\n", + "plt.savefig(\"Analytical_posterior_w\"+str_true_w+'.pdf')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "c1053648", + "metadata": {}, + "source": [ + "### Plot the mean and std of MCMC and Analytical posteriors " + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "aea92739", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1020/4203446349.py:1: UserWarning: marker is redundantly defined by the 'marker' keyword argument and the fmt string \"o\" (-> marker='o'). The keyword argument will take precedence.\n", + " plt.errorbar(num_images, np.array([mean_samples1[0], mean_samples2[0], mean_samples3[0], mean_samples4[0]]) ,\n", + "/tmp/ipykernel_1020/4203446349.py:3: UserWarning: marker is redundantly defined by the 'marker' keyword argument and the fmt string \"o\" (-> marker='o'). The keyword argument will take precedence.\n", + " plt.errorbar(np.array([3, 98, 498, 998]), posteriors_all[:,0] , yerr=posteriors_all[:,1],marker='o', fmt='o', label='Analytical')\n" + ] + }, + { + "data": { + "image/png": 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AAACgequ2YXXbtm3WcYcOHdy27dixY6HX2W3JkiVKSkqSJF111VXq3Llzoe1MD7ePydt+y5YtBc478VkAAAAAqN6qZVjNzs7W3r17JUne3t4KCwtz27558+bW8e7du8usrrxTgIsaVZWkSy65pMT1mKZpfa+StGvXrnznnfosAAAAAFRv1XLrmrNnz1qLAwUHB8vHx/1jqF+/vnV86tSpMqnp6NGjWrJkiSTJ19dXw4cPL7Jtp06d5O/vr/T0dG3atEnbtm1T69atC227ePFinThxwvr6wvrL+lmYpqkzZ84U264o/v7+8vf3L/X1AAAAAC5Odna2XC6XsrOzdfbsWWVkZHh0vaczQ3NV27Ca68JVbwuTd5GjvNfaafbs2crMzJQkDRo0SA0bNnRbzz333KNp06bJNE0NHz5cK1euVIMGDfK1i4uL05gxY/K9dmFwLOtnkZiYqDp16hTbrijjx4/XhAkTSn09AAAAgItz5MgRJSYmKjk5WQMHDtTBgwfL5b7VMqzmlbsVy8W2uVjTp0+3jt1NAc41ceJELVu2TImJidq0aZMiIiJ0//33q127dsrKytKGDRs0bdo0nTt3TuHh4Tpw4IAkycur6JnfZfEsQkNDtWPHDo+uyYtRVQAAAKBiXXLJJapTp44OHDig3377TX5+fh5dHxERocTERI/vW6XC6s6dO7Vz584iz7dv317NmjVTzZo1rddSU1OL7ff8+fPWcd5r7bJhwwZt375dktS4cWP179+/2GsuueQSrVq1SoMHD9auXbuUnJysN998s0C73r17a9iwYRo5cqQkWdvY5CrrZ2EYhmrXrl1sOwAAAADO5OXlJW9vb3l5ealmzZolmpGZV2kH/6pUWJ07d65effXVIs9Pnz5dI0eOVM2aNeXj46OsrCydOnVKLpdL3t7eRV53/Phx6zg4ONjOkq26co0YMcJtLXlFRERoy5YtmjVrlv79739r48aNOnHihOrUqaM2bdpo5MiRGjZsmCZPnmxdk3dxJkmOexYAAAAAIFWxsFpSXl5euuyyy7Rr1y65XC7Fx8fnW+X2QnnnZLds2dLWWtLS0jR37lzr65JMAc7Lz89PDzzwgB544IEi2/z666/WcadOnfKdc9KzAAAAAIBcVWrrmgkTJsg0zSL/5E6FlZRv9dzffvvNbb95zxe16m5pLViwwFpVt0uXLrYHwKysLH333XeScobfu3TpUqCNU54FAABAWUjNMNXrjXPq9cY5pWaUblVSAOWvSoVVT/Tt29c6Xr58udu2y5YtK/Q6O5R0b9XS+uabb3T06FFJUp8+fdSsWbMCbZzyLAAAAAAgV7UNq7fddpu1itWcOXOsQHeh7du3WyOTjRo1Uvfu3W2r4eDBg1bfNWvW1F133WVb31LONjXPPfec9fUzzzxTaDsnPAsAAAAAyKvahtWGDRtq7NixknL2Cx05cqTS0tLytTl58qSGDx9ubWL70ksvycen8I/5du/eXYZhyDAMzZgxo0Q1zJgxw+r7zjvv9Hil4XXr1hV5LiEhQf3799f+/fslSSNHjlTv3r0LbWv3swAAAABK68CBA9a/q/N+jK+iObWuouTNJ5VVtU4b48eP17Jly7Rz504tXbpU7du31wMPPKAmTZooLi5OH3/8sQ4fPixJ6tq1q8aMGWPbvU3T1MyZM62vSzMFeMCAAQoJCdHAgQPVpk0bBQcH68SJE/r55581f/58nT17VpLUo0cPffjhh277qshnAQAAgIsTHR2tf/3rX9bXO3bsUKtWrSqwosphwoQJkqTw8PBKEUCrm2odVoODg7V06VJFRUVp48aN2rFjh5566qkC7Xr27Kn58+fL19fXtnt///331qhny5YtdeONN5aqn3379umDDz4o9JyXl5cefPBBTZ48WYGBgW77qchnAQAAgNJLT0/XnDlz8r02ffp0vfnmmxVUUeWRu+1lt27dCKsOVG2nAecKDw/X+vXrNXXqVPXs2VMhISHy8/NTaGioBgwYoDlz5mjVqlWqW7eurffNu7fqfffdV6o+5s2bp8cee0ydOnVSaGio/Pz8VK9ePbVp00ZPPfWUNm7cqI8++qjYoJqrop5FeUnNMNVncrZG91itdC/PNjIGAABwqm+++UYnT57M99rs2bPlcrkqqKKqKTw83NplpKQf+8PFqdYjq7l8fX314IMP6sEHHyx1H2vWrPGo/ezZszV79uxS30+SBg4cqIEDB15UHxey41kAAACg/OQOgnh5eemee+7R7Nmz9eeff2rp0qUaNGhQBVcHlF61H1kFAAAAKqvDhw9r1apVkqTevXvrhRdesM7lnckHVEaEVQAAAKCSmjlzprKzsyVJw4cPV0REhDp06CBJ+vbbb5WcnOz2+pEjR1orxh44cECStHz5ct12220KCwuTv7+/QkNDddddd+nXX38ttp5du3bpn//8p2655Ra1aNFCQUFB8vf3V+PGjdWvXz/961//KrDrhCemTJli1Tt58uQSXXPnnXda1/z222+SVGCV3LVr11qv5f2Td/akp6sB//bbb3r00UfVtm1b1a9fX76+vqpXr56uvfZaPfXUU1q/fn2h1505c0Zffvml7r//fl1zzTUKDg62rm3fvr2eeuop7d27t0Tfe2VHWAUAAECV5so2reMth1z5vq7M8n52smbNmoqKipIkjRgxQpKUmZmpL774osT9ZWdna8yYMerXr5/+85//KCEhQRkZGfrzzz/19ddf67rrrnM7WvvFF1+oVatWevbZZ/Xtt99q//79Sk1NVUZGho4cOaLly5crOjparVu31o4dO0r1PQ8bNsza7vGTTz4ptv2xY8e0aNEiSVK7du3UsWPHUt3XE+fOndM999yjTp066YMPPtCWLVt04sQJZWVl6eTJk9qwYYPeeecdXXfddTp48GC+a10ul0JCQnTPPfdo2rRp2rRpk06fPm1du3HjRr3zzjtq1aqVYmJiyvx7qWh8ZhUAAABV1g87s/Thigzr6xe/SlfDWoai+/ipa6vK/U/hdevWWSNst99+u4KCgiRJf/3rX/X0008rMzNT06dP12OPPVai/l566SXNnTtXLVu21IgRI3T55ZcrJSVF//73v7Vs2TIrzHbp0kUtW7YscH1qaqoMw1D79u3VrVs3XXnllapbt67OnDmjgwcPat68edq9e7f27t2r/v37a9OmTQoODvboe65Vq5b++te/aurUqdqxY4d+/PFHdenSpcj2M2fOVEZGzn//hx56yHr9m2++kSQNHjxYknTVVVfp9ddfL3B969atPaovLS1NPXr0sEahAwICdNddd6lLly7Ws9i2bZuWLFmi3bt3yzTz/+LENE2lpaWpcePG6tOnj9q2bauQkBB5eXnp8OHD+umnn/Ttt98qKytL48aNU2hoqPU9VEWV+28oAAAAUIQfdmZpwoL0Aq8fSzE1YUG6JkSpUgfWadOmWcfDhw+3jhs0aKB+/frp22+/1ebNm/XHH3+offv2xfY3d+5cjRgxQp999pl8fP73XB544AGNGzdOMTExSk9P13vvvVfoqF6XLl20Z88eXXbZZYX2P2HCBL3zzjt65plndPDgQb333nsaP368J9+yJGnMmDGaOnWqpJzRVXdh9dNPP5UkBQUF6Z577rFev+222/K1a9CgQYHXSuPJJ5+0gmrbtm21aNEiNWvWrEC7yZMn67vvvisQ1r29vbVkyRL169cv3zTlvLZs2aK+ffvqyJEjevrpp3XrrbfKy6tqTpitmt8VAAAAqjVXtqmYlRlu28Ssyqi0U4JzRzwlKSwsTD169Mh3PncqsFTyhZZatWqlTz75JF9QzfXGG29Y2yEuX7680OsjIiKKDKpSzmrFTz/9tG666SZJKvXOGO3atVPnzp0lSV999ZVOnz5daLt169Zp165dkqQhQ4aodu3apbpfSR06dMiamly/fn0tXbq00KCaq2fPngXCqmEY6t+/f5FBVZLatGmjN954Q5K0b98+/fTTTxdfvEMRVgEAAFDlbD2crWMp7oPosTOmth7OLqeK7DVv3jydO3dOUs7nOC8cWbv55ptVt25dSdKXX36p9PSCI8wXGjt2rPz8/Ao9V7t2bevznvv27buoRZJuuOEGSdLevXuLXQCqKA8//LCknKnHRX0uN+9nWvNOAS4r8+bNU1ZWliTpkUceUePGjcvsXrnPUFKRCzVVBYRVAAAAVDknzpZsxLSk7Zwm72hp3inAufz9/XXXXXdJkk6cOKH//Oc/xfZ57bXXuj3fpEkTSTmfqzx16lSR7VatWqUHHnhAbdu2Vd26deXj45Nvhd1JkyZZbRMSEoqtqzBDhw61RiULW2jp5MmTmj9/vqScz51ed911pbqPJ/773/9ax7fccstF9XXgwAG9+uqr6tGjhxo3bqzAwMB8z7BVq1ZW2/j4+Iu6l5MRVgEAAFDl1KtZ9DTK0rRzkt27d1tTPzt06KDIyMhC23k6FbhBgwZuz/v7+1vHhY2snj59Wn379lWfPn302WefacuWLTp16pRcLleRfZ45c6bYugoTGBhofX+bNm2ytqTJ9fnnn1s1lseoqpQ/NEZERJS6n3fffVetWrXShAkTtGbNGh05csTtSHZpn2FlUHk/UQ4AAAAU4eqmXmpYy3A7FbhhbUNXN618Yzd5F1bKG0gvdMMNN+iKK67Qnj17tGLFCiUkJFijo4W52EV67rjjDq1atUpSzqq9N998s9q1a6fGjRsrKCjI6n/u3LmaN2+eJLkNssUZPXq03n//fUk5o6t5t6XJHW0NCAgodOS5LOSGRm9vbwUEBJSqjy+++EJPPPGE9XXXrl3VrVs3hYeHq1atWtY07aNHj2r06NGSLu4ZOh1hFQAAAFWOt1fO9jSFrQacK7q3n7y9KtfIqsvlyrcw0WOPPVairWmys7M1c+ZMvfjii2VS17p166yg2rZtW61cuVINGzYstO2PP/5oyz0jIyPVtWtX/fDDD5ozZ47efvtt1axZU+vXr9fWrVslSXfeeafH2+OUVu4CTi6XS2lpaaUKrK+88ookycfHR4sWLVL//v0Lbbd9+/bSF1qJVL5fJQEAAAAl0LWVjyZE+avBBVN9G9Y2NCHKv1JuW7Ns2TIlJiaW6toZM2bYW0weuUFVkiZOnFhkUJWkgwcP2nbf3IWWUlJSrNHa8l5YKVdYWJh1HBsb6/H1+/bt0759+yTlbK1TVFCV7H2GTlb5/oYCAAAAJdS1lY+uCffSre+kSpLeuMtfHVt4V7oR1VwXLqzUokWLYq+ZP3++tm/frj179ui///2vbrzxRtvrSkpKso7dbV+TkZGhNWvW2HbfO+64Q48//riOHTumTz75RHfddZfmzp0rKedzo8V9r4ZhyDRNmebFL7TVtWtXLVq0SJK0aNGiEu1tm1dJn6FU9PZBVQ1hFQAAAFVa3mDaplnlDarJycn69ttvJUk1a9bUlClTVKNGjWKva9q0qR544AFJOWG3LMJqUFCQdbx37958q9XmNWXKFB07dsy2+/r5+WnkyJH65z//qfXr1+uFF16wtvR58MEHi72+Zs2aSklJsa65GEOGDNGLL76ozMxMffDBBxo9erRH29dc+AyLEh8fX+K9cys7pgEDAAAAlcAXX3yhjIwMSVJUVFSJgqqUM/qY+/nJr776ypZgdqFOnTpZx6+99lqh+7ouXrxYzz//vO33Hj16tAwj5xcQMTExknJWLna3+FSuSy+9VJK0c+dOpaamXlQdTZs2tQLyiRMnNGDAAB06dKjI9mvXrs23BVBERIT13/Q///mPNmzYUOCao0eP6tZbb1VKSspF1VpZMLIKAAAAVALF7a1alDp16uiWW27RV199pbNnz+rrr7/WyJEjba1t8ODBatKkiRISErRhwwZFRkbq/vvvV4sWLXTq1CktWbJE3377rYKCghQVFaUFCxbYdu/LLrtMvXv31sqVK63Xbr/9dtWvX7/Ya3v16qUtW7bo3Llzuvnmm3XvvfeqQYMGVvjt3Lmz6tWrV+Ja3n77bf3666/69ddftWnTJl155ZUaMmSIbrjhBtWrV08pKSnavn27lixZoh07dmj//v3WAlB+fn4aPXq03nnnHWVmZuqmm27SqFGj1KlTJ/n6+uqPP/7QjBkzdPLkSY0YMUKzZs3y7EFVQoRVAAAAwOH++OMPbd68WZIUGhqqnj17enT98OHD9dVXX0nKCb12h9XAwEDNnz9fAwYM0MmTJ7Vv3z699NJL+doEBwfriy++0IYNG2wNq1LO6GresFqSKcCS9NRTT+nzzz/XsWPHtHr1aq1evTrf+e+//17du3cvcR0BAQH67rvvdN9992n+/PlKS0vTzJkzNXPmzELbX7hd0MSJE7Vx40Z9//33Sk9P15QpUzRlypR8bUaPHq1nn322WoRVpgEDAAAADpd3VPWee+7xeE/Ufv36WSv0rlu3TnFxcbbWJ0nXXXedNm/erHHjxumyyy6Tn5+f6tSpo9atW+u5557T5s2bNWDAANvvK0m9e/e2RkNbtmxZ4oDZpEkT/fHHH3rsscfUunVr1axZ0+qntGrWrKmvv/5aP/74ox566CFdeeWVqlWrlnx8fFS/fn1df/31evbZZ7Vx40Y1a9Ys37UBAQFasWKF/vWvf+n6669XrVq15O/vr+bNm+uuu+7S8uXL9dFHH130nriVhWHasfQVcIGwsDBr4+n4+PiKLkeSlJphatBb5yVJ768dqIiFS+UVGFjBVQEAULWkpaVp//79uvTSS0u1z2RZyPtvgMVPBynQr3IusISizZgxQ/fdd58k6Z///KeefvrpCq6o6rmYv9ulzQbVI5IDAAAAqLJyp8r6+/vr3nvvreBqYBc+s4oylZSUpMjIyELPRUdHKzo6upwrAgAAQFWyePFia+Xcu+++25rujPIVExNjrcZ8obx7yHqCsIoyFRISotjY2IouAwAAAFVEamqq1q5dq6ysLG3ZskX/+Mc/JEm+vr4FFnVC+XE3EJU7DdhThFUAAABUaYF+hla/WLI9SeF8SUlJ6t+/f4HXJ06cqMsvv7wCKkJZIawCAAAAqJSCg4MVERGhp556SrfffntFlwObEVYBAAAAVBrh4eFiQ5PqgdWAAQAAAACOQ1gFAAAAADgOYRUAAAAA4DiEVQAAAACA4xBWAQAAAACOQ1gFAAAAADgOYRUAAAAA4DiEVQAAANiOfTCBqqUi/k4TVgEAAGAbL6+cf15mZ2dXcCUA7ORyuST97+94eSCsAgAAwDa+vr7y9vbWuXPnKroUADY6f/68vL295evrW273JKwCAADANoZhqFatWjpz5gxTgYEqwjRNnTlzRrVq1ZJhGOV2X8IqAAAAbFWnTh1lZmYqMTGRwApUcqZpKjExUZmZmapTp0653tunXO8GAACAKi8oKEhhYWGKj49XamqqateuraCgIHl7e5frqAyA0jFNUy6XS+fPn9eZM2eUmZmpsLAwBQUFlWsdhFUAAADYrlatWmrevLlOnz6tU6dO6fjx4xVdEgAPeXt7q1atWqpTp065B1WJsAoAAIAyEhQUpKCgIF1yySXKzMxkhWCgEvHy8pKvr2+FzoYgrAIAAKBMGYYhPz+/ii4DQCXDAksAAAAAAMchrAIAAAAAHIewCgAAAABwHMIqAAAAAMBxCKsAAAAAAMchrAIAAAAAHIewCgAAAABwHMIqAAAAAMBxCKsAAAAAAMchrAIAAAAAHIewCgAAAABwHMIqAAAAAMBxCKsAAAAAAMfxqegCULUlJSUpMjKy0HPR0dGKjo4u54oAAAAA2C0mJkYxMTGFnktKSipVn4RVlKmQkBDFxsZWdBkAAAAAypC7gaiwsDAlJCR43CfTgAEAAAAAjkNYBQAAAAA4DmEVAAAAAOA4hFUAAAAAgOMQVgEAAAAAjkNYBQAAAAA4DmEVAAAAAOA4hFUAAAAAgOMQVgEAAAAAjkNYBQAAAAA4DmEVAAAAAOA4hFUAAAAAgOMQVgEAAAAAjkNYBQAAAAA4DmEVAAAAAOA4hFUAAAAAgOMQVgEAAAAAjkNYBQAAAAA4DmEVAAAAAOA4hFUAAAAAgOMQVgEAAAAAjkNYBQAAAAA4DmEVAAAAAOA4hFUAAAAAgOMQVgEAAAAAjkNYBQAAAAA4DmEVAAAAAOA4hFUAAAAAgOMQVgEAAAAAjkNYBQAAAAA4DmEVAAAAAOA4PhVdAKq2pKQkRUZGFnouOjpa0dHR5VwRAAAAALvFxMQoJiam0HNJSUml6pOwijIVEhKi2NjYii4DAAAAQBlyNxAVFhamhIQEj/tkGjAAAAAAwHEIqwAAAAAAxyGsAgAAAAAch7AKAAAAAHAcwioAAAAAwHEIqwAAAAAAxyGsAgAAAAAch7AKAAAAAHAcwioAAAAAwHEIqwAAAAAAxyGsAgAAAAAch7AKAAAAAHAcwioAAAAAwHEIqwAAAAAAxyGsAgAAAAAch7AKAAAAAHAcwioAAAAAwHEIqwAAAAAAxyGsAgAAAAAch7AKAAAAAHAcwioAAAAAwHEIqwAAAAAAxyGsAgAAAAAch7AKAAAAAHAcwioAAAAAwHEIqwAAAAAAxyGsAgAAAAAch7AKAAAAAHAcwioAAAAAwHEIqwAAAAAAxyGsAgAAAAAcx6eiC0DVlpSUpMjIyELPRUdHKzo6upwrAgAAAGC3mJgYxcTEFHouKSmpVH0SVlGmQkJCFBsbW9FlAAAAAChD7gaiwsLClJCQ4HGfTAMGAAAAADgOYRUAAAAA4DiEVQAAAACA4xBWAQAAAACOQ1gFAAAAADgOYRUAAAAA4DiEVQAAAACA4xBWAQAAAACOQ1gFAAAAADgOYRUAAAAA4DiEVQAAAACA4xBWAQAAAACOQ1gFAAAAADgOYRUAAAAA4DiEVQAAAACA4xBWAQAAAACOQ1gFAAAAADgOYRUAAAAA4DiEVQAAAACA4xBWAQAAAACOQ1gFAAAAADgOYRUAAAAA4DiEVUlZWVn69NNP1bt3b4WGhsrf319NmzbVzTffrHnz5sk0TVvuEx4eLsMwPP6zZs0at/0uXrxYQ4cOVXh4uAIDA1WnTh1dddVVevLJJ7Vjxw7ba+vevfvFPwwAAAAAcMOnoguoaIcOHVJUVJR+//33fK/Hx8crPj5eixcv1qeffqqvv/5awcHB5V6fYRgKDw8v9NzRo0d1991367vvvsv3elpammJjYxUbG6uYmBhNnDhRTz/9dDlUCwAAAAD2qNZh9fTp0+rfv79iY2MlSRERERo1apTCwsIUFxenqVOn6vDhw1q1apUGDx6sFStWyNfXt9T3mzp1qs6fP19su2+++UazZs2SJPXo0aPQsHru3Dn95S9/0ebNmyVJ9evX1/3336927dopKytL69ev1/Tp03X+/Hk988wz8vPz06OPPlrsvRs2bKipU6e6bdOgQYNi+wEAAACAi1Gtw+prr71mBdV+/frpm2++UUBAgHV+7Nix6t27tzZu3Kg1a9bo448/1rhx40p9v7/85S8lajdp0iTreNSoUUXWnhtUr776aq1atUqNGjWyzg8fPlyPPvqounfvrj///FPPPPOMBg0apBYtWri9d1BQkG677bYS1QkAAAAAZaXafmY1OTlZMTExkqQaNWpo5syZ+YKqJNWrV0+zZs2SYRiSpNdff11ZWVllWteOHTu0fv16SVKdOnUUFRVVoE1mZqamTJkiKWea8Oeff54vqOZq2bKl1S4jI0OvvvpqGVYOAAAAAPaptmF14cKFSk9PlyTdfffdhYY9SWrdurV69uwpSUpKStLatWvLtK5p06ZZx0OHDlVgYGCBNr/99ptSUlIkSW3btlWbNm2K7O+WW25R3bp1JUkLFixQWlqazRUDAAAAgP2qbVhdtmyZddyvXz+3bfOez3ud3bKysjR79mzr66KmAMfHx1vHV155pds+DcPQFVdcIUk6e/as1q1bZ0OlAAAAAFC2qm1Y3bZtm3XcoUMHt207duxY6HV2W7JkiZKSkiRJV111lTp37lxoO0+30snbfsuWLW7bHj9+XL1791ajRo3k5+enhg0bqnPnznruuee0d+9ej+4LAAAAAKVVLcNqdna2Fby8vb0VFhbmtn3z5s2t4927d5dZXXmnABc1qipJl1xySYnrMU0zX8jctWuX2/Znz57V6tWrdezYMWVmZio5OVm//vqr/vGPf+jKK6/UCy+8IJfLVdy3AgAAAAAXpVquBnz27FlroaTg4GD5+Lh/DPXr17eOT506VSY1HT16VEuWLJEk+fr6avjw4UW27dSpk/z9/ZWenq5NmzZp27Ztat26daFtFy9erBMnTlhfu6s/NDRU/fr1U7t27RQSEqKMjAzt2bNHCxYs0LZt2+RyuTRp0iQlJiZq5syZJfq+TNPUmTNnStS2MP7+/vL39y/19QAAAAAuTnp6urXeT2l4OjM0V7UNq7kuXAG4MHkXOcp7rZ1mz56tzMxMSdKgQYPUsGFDt/Xcc889mjZtmkzT1PDhw7Vy5coC+5/GxcVpzJgx+V4rKjh+/vnnuuGGG+TlVXCwfcKECYqJidFjjz2m7OxszZo1S3369NGwYcOK/b4SExNVp06dYtsVZfz48ZowYUKprwcAAABwcf7+979XyM4i1TKs5pW7Lc3FtrlY06dPt47dTQHONXHiRC1btkyJiYnatGmTIiIidP/996tdu3bKysrShg0bNG3aNJ07d07h4eE6cOCAJBUaRiXpxhtvLPJehmFo3LhxOnnypF555RVJOdv4lCSshoaGaseOHcW2KwqjqgAAAEDFeuGFF/Tkk0+W+vqIiAglJiZ6fF2VCqs7d+7Uzp07izzfvn17NWvWTDVr1rReS01NLbbf8+fPW8d5r7XLhg0btH37dklS48aN1b9//2KvueSSS7Rq1SoNHjxYu3btUnJyst58880C7Xr37q1hw4Zp5MiRkmRtY1MazzzzjN566y2dOXNGu3bt0r59+9SiRQu31xiGodq1a5f6ngAAAAAq1sV+NK+0g39VKqzOnTvX7fD09OnTNXLkSNWsWVM+Pj7KysrSqVOn5HK55O3tXeR1x48ft46Dg4PtLNmqK9eIESPc1pJXRESEtmzZolmzZunf//63Nm7cqBMnTqhOnTpq06aNRo4cqWHDhmny5MnWNXkXZ/JUQECArrvuOq1YsUJSzi8HigurAAAAAFAaVSqslpSXl5cuu+wy7dq1Sy6XS/Hx8flW/L3QwYMHreOWLVvaWktaWprmzp1rfV2SKcB5+fn56YEHHtADDzxQZJtff/3VOu7UqZPnReaR93OxZbXYFAAAAABUqa1rJkyYINM0i/yTOxVWUr7Vc3/77Te3/eY9X9Squ6W1YMECK/R16dLF9jCclZWl7777TlLO8HuXLl0uqr+yHmUGAAAAAKmKhVVP9O3b1zpevny527bLli0r9Do7lHRv1dL65ptvdPToUUlSnz591KxZs1L3lZ6erl9++cX6+sorr7zo+gAAAACgMNU2rN52223y8/OTJM2ZM8cKdBfavn27NTLZqFEjde/e3bYaDh48aPVds2ZN3XXXXbb1LeVsU/Pcc89ZXz/zzDMX1d8777yj06dPS5KuuOIKXXbZZRfVHwAAAAAUpdqG1YYNG2rs2LGScvZOHTlypNLS0vK1OXnypIYPH25tYvvSSy/Jx6fwj/l2795dhmHIMAzNmDGjRDXMmDHD6vvOO+/0eKXhdevWFXkuISFB/fv31/79+yVJI0eOVO/evQttO3HixGK3l/noo4/0t7/9zfr65Zdf9qhWAAAAAPBEtVxgKdf48eO1bNky7dy5U0uXLlX79u31wAMPqEmTJoqLi9PHH3+sw4cPS5K6du2qMWPG2HZv0zQ1c+ZM6+vSTAEeMGCAQkJCNHDgQLVp00bBwcE6ceKEfv75Z82fP19nz56VJPXo0UMffvhhkf18/fXXevnll3XNNdeoW7duioiIUN26dZWRkaG4uDgtWLBAW7ZssdoPGzZMI0aM8LheAAAAACipah1Wg4ODtXTpUkVFRWnjxo3asWOHnnrqqQLtevbsqfnz58vX19e2e3///ffWqGfLli114403lqqfffv26YMPPij0nJeXlx588EFNnjxZgYGBxfa1ceNGbdy4scjzPj4+ev755zV+/PhS1QoAAAAAJVWtw6okhYeHa/369ZoxY4bmzp2r7du36+TJk2rQoIHatWun4cOHa8iQIaXeyLYoefdWve+++0rVx7x587Ry5Ur99NNPSkhIUHJysmrWrKmwsDD16dNHI0aMUJs2bYrtZ/bs2Vq3bp1+/vlnbd++XceOHbNW/a1bt64iIyPVrVs3jRo1Sk2aNClVrQAAAADgCcPM/dAkYKOwsDAlJCSoSZMmio+Pr+hyJEmpGaYGvXVekvT+2oGKWLhUXiUYcQYAAABQeqXNBtV2gSUAAAAAgHMRVgEAAAAAjkNYBQAAAAA4DmEVAAAAAOA4hFUAAAAAgOMQVgEAAAAAjkNYBQAAAAA4DmEVAAAAAOA4hFUAAAAAgOMQVgEAAAAAjkNYBQAAAAA4DmEVAAAAAOA4hFUAAAAAgOMQVgEAAAAAjkNYBQAAAAA4DmEVAAAAAOA4hFUAAAAAgOMQVgEAAAAAjkNYBQAAAAA4DmEVAAAAAOA4PhVdAKq2pKQkRUZGFnouOjpa0dHR5VwRAAAAALvFxMQoJiam0HNJSUml6pOwijIVEhKi2NjYii4DAAAAQBlyNxAVFhamhIQEj/tkGjAAAAAAwHEIqwAAAAAAxyGsAgAAAAAch7AKAAAAAHAcwioAAAAAwHEIqwAAAAAAxyGsAgAAAAAch7AKAAAAAHAcwioAAAAAwHEIqwAAAAAAxyGsAgAAAAAch7AKAAAAAHAcwioAAAAAwHFsCavh4eG6//779fnnnysxMdGOLgEAAAAA1ZiPHZ0cOnRIM2bM0IwZMyRJV1xxhXr27KmePXuqR48eql+/vh23AQAAAABUE7aEVUkyTdM63rNnj/bs2aOPP/5YhmGodevWVnjt1q2batWqZddtAQAAAABVkC1h9YsvvtB3332n7777Tvv3788XXE3T1NatW7V161a999578vb2Vvv27a3weuONNyogIMCOMgAAAAAAVYQtYfXuu+/W3XffLUk6ePCgVq9ebYXXI0eO5AuvWVlZ+vXXX/Xrr7/qzTfflJ+fn6699lorvF533XXy8bFtwBcAAAAAUAkZZt4kWQZ27Nih7777TqtXr9batWt18uTJgkUYhnUcFBSklJSUsiwJ5SAsLEwJCQlq0qSJ4uPjK7ocSVJqhqlBb52XJL2/dqAiFi6VV2BgBVcFAAAAVG2lzQZlPoQZERGhiIgIRUdHyzRN/fHHH9ao63//+1+dO3cu38jr+fPny7okAAAAAIDDlet8W8Mw1KFDB3Xo0EF9+/bVsmXL9O677yopKUlS/kWaAAAAAADVV7mF1bi4OGs68Jo1a5ScnJzvPEEVAAAAAJCrzMJqYmJivoWW8s5NvjCYNm/e3FpgqWfPnmVVEgAAAACgkrAtrJ44cULff/+9FVD37NljnbswnDZq1Eg9evRQr1691LNnT7Vo0cKuMgAAAAAAVYAtYbV9+/basmWLFUovDKd16tRRt27drJHT1q1b23FbAAAAAEAVZUtY3bRpU76vAwMD1aVLF2vktEOHDvLy8rLjVgAAAACAasC2acC5e6UGBQVp1KhRGjBggLp27aqgoCC7bgEAAAAAqCZsG+40TVOmaer8+fP68MMPNWDAANWrV0833XSTXn31Vf3www/Kysqy63YAAAAAgCrMlrC6fPlyPffcc+rUqZO8vLys4JqRkaEff/xRr732mrp37666deuqf//++uc//6k//vjDjlsDAAAAAKogW6YB9+nTR3369JEknTlzRmvWrLH2VN2+fbvV7ty5c1qxYoVWrFghSQoODlb37t2tz7a2atXKjnIAAAAAAJWc7fus1q5dW7fccotuueUWSdKxY8es4Prdd99p3759VtuTJ09q4cKFWrhwoSTpkksuUa9evTRr1iy7ywIAAAAAVCJlvkRvw4YNNWTIEE2dOlVxcXE6cOCAPvvsM91zzz1q3LixNWXYNE39+eef+uKLL8q6JAAAAACAw9k+slqcZs2a6Z577lF4eLiaNWum6dOnKykpSVLB/VlR+SUlJSkyMrLQc9HR0YqOji7nigAAAADYLSYmRjExMYWey817niqXsJqdna0NGzZY04F//vlnpaenl8etUcFCQkIUGxtb0WUAAAAAKEPuBqLCwsKUkJDgcZ9lFla3bNlifU71hx9+UEpKinWusBHU2rVrq2vXrurVq1dZlQQAAAAAqCRsC6t79uyxRk7XrFmj48ePW+cKC6cBAQG64YYbrJWAc7e9AQAAAADAlrDarFmzfMO6hYVTHx8fde7cWT179lTPnj11ww03yM/Pz47bAwAAAACqGFvCanx8vAzDyBdSvby81LZtWyuc3nTTTapRo4YdtwMAAAAAVHG2TQM2TVOtWrWywmmPHj1Ut25du7oHAAAAAFQjtoTVzz//XD179tQll1xiR3cAAAAAgGrOlrD617/+1Y5uAAAAAACQJLH8LgAAAADAcQirAAAAAADHIawCAAAAAByHsAoAAAAAcBzCKgAAAADAcQirAAAAAADHIawCAAAAAByHsAoAAAAAcBzCKgAAAADAcQirAAAAAADHIawCAAAAAByHsAoAAAAAcBzCKgAAAADAcQirAAAAAADH8Slpw1GjRpVlHRbDMPTZZ5+Vy70AAAAAAM5U4rA6Y8YMGYZRlrVYCKsAAAAAUL2VOKxKkmmaJW6bN9gWdl3u+QvPlVcgBgAAAAA4V4nD6r333luidkuWLFFycrIVQhs0aKCrr75a9evXl7+/v86cOaN9+/Zp165dysrKssJp7969FRoaWopvAQAAAABQ1ZQ4rE6fPt3t+fT0dN1///06duyYDMPQPffco0cffVSdOnUqtP2ZM2c0d+5cTZw4UYcPH9amTZv03HPPqWfPnp59BwAAAACAKse21YAfffRRffnllwoICNCCBQs0e/bsIoOqJNWuXVsPPfSQYmNj1atXLx07dkyDBw/Wrl277CoJAAAAAFBJ2RJWf/jhB33yyScyDENvvPGGbr311hJfW6NGDS1YsEBhYWFKSUnRww8/bEdJAAAAAIBKzJawOm3aNElSzZo1NWbMGI+vr1WrlkaPHi1JWrdunfbv329HWQAAAACASsqWsPrLL7/IMAxdddVV8vf3L1UfeacMr1+/3o6yAAAAAACVlC1hNSEhQZLk5+dX6j58fX2t48TExIuuCQAAAABQeXm0z2pRvL29ZZrmRS2OtGPHjnz9oWpISkpSZGRkoeeio6MVHR1dzhUBAAAAsFtMTIxiYmIKPZeUlFSqPm0Jq82bN9eWLVt09OhRLVq0SLfccotH12dnZ+uzzz7L1x+qhpCQEMXGxlZ0GQAAAADKkLuBqLCwMGs2ridsmQY8YMAASZJpmhozZozi4uI8uv7ZZ5/Vxo0bJUn+/v7q1auXHWUBAAAAACopW8Lq6NGjFRQUJMMw9Oeff+q6667TlClTlJaW5va6bdu2aeDAgZo8ebIkyTAM3XfffapVq5YdZQEAAAAAKinbpgG/9dZbGjt2rAzD0IkTJzRu3Dg999xzuummm3T11Verfv368vPzU0pKivbv36/169cXmB562WWXadKkSXaUBAAAAACoxGwJq5L08MMPKy0tTU8//bT12tmzZ7V06VItXbq00GtM05RhGDJNUxEREVq5ciWjqgAAAAAAe6YB53r88cf1008/qUOHDjJNU1JOIM09zpX3taCgID3//PP6448/FBoaamc5AAAAAIBKyraR1VydO3fWhg0b9Ntvv2nBggVav369du/erZMnTyojI0O1a9dWSEiIOnTooG7duumuu+5iNBUAAAAAkI/tYTVXx44d1bFjx7LqHgAAAABQhdk6DRgAAAAAADsQVgEAAAAAjkNYBQAAAAA4Tpl9ZjVXUlKSjh07plOnTik7O1s33XRTWd8SAAAAAFDJlUlY3bRpk95//32tXLlSiYmJ1uuGYSgrK6tA+7ffflvnzp2TJD3zzDMKDAwsi7IAAAAAAJWErWE1NTVVjzzyiKZPn269duEeq4U5ePCgPvzwQxmGoRYtWmjYsGF2lgUAAAAAqGRs+8xqamqqevXqpenTp8s0TetPSURHR1vHX331lV0lAQAAAAAqKdvC6pgxY/TLL7/kdOrlpVGjRmnt2rU6deqU+vbt6/baK6+8UpGRkTJNU2vXrlV2drZdZQEAAAAAKiFbpgFv3LhRs2fPliT5+flp4cKFxQbUC/Xs2VOxsbE6e/astm3bpjZt2thRGgAAAACgErJlZPXzzz+XaZoyDEOvv/66x0FVktq1a2cd79q1y46yAAAAAACVlC1hdfXq1ZIkf3//fJ8/9URoaKh1fOTIETvKAgAAAABUUraE1YSEBBmGoauvvloBAQGl6qN27drW8dmzZ+0oCwAAAABQSdkSVlNSUiTlD5yeyhtQSxt4AQAAAABVgy1htV69epKk48ePl7qPffv2Wcf169e/6JoAAEDFyk5NVVyf6xTX5zplp6ZWdDkAgErGlrAaFhYm0zQVGxur1FL+z2jlypXWcUREhB1lAQAAAAAqKVvCao8ePSRJmZmZmjVrlsfXx8XFadGiRZJyphJ37NjRjrIAAAAAAJWULWH1jjvusI5ffPFFHThwoMTXnj17VkOHDpXL5ZJhGBo6dKgMw7CjLAAAAABAJWVLWO3UqZMGDRok0zR18uRJ3XjjjVqxYkWx1/3444+6/vrrtXHjRkmSr6+vnn/+eTtKAgAAAABUYj52dTRlyhT9/vvvOnLkiBITE9W/f39FRkaqd+/e2r9/v9Vu2rRp2r17t1asWKHNmzdLkkzTlGEYevfdd9W8eXO7SgIAAAAAVFK2hdUmTZpoyZIlGjRokBISEiRJsbGxio2NlSQZhiHTNPXggw9a15imaR2/9NJLevjhh+0qBwAAVKDUDFODJmdLPVbr/bUDK7ocAEAlZMs04Fxt27bV5s2bNWTIECuc5g2kuZ9Fzft606ZNNW/ePL322mt2lgIAAAAAqMRsDatSzp6rc+bM0e7duzV+/Hh1795ddevWtcKrj4+PwsLCdNddd2nGjBmKi4vTnXfeaXcZAAAAAIBKzLZpwBdq0aKFxo8fb31tmqZSU1MVFBRUVrcEimWYLl1xaqu8TJdSt25SUIfOMry9K7osAAAAABcos7B6IcMwCKqoUKk/rtEbP7+jeunHJEl/vvSEvBs0UsOxT6hm1x4VXB0AAACAvGwJq7NmzZKUs8hSr169StXHmjVrdOjQIUnSiBEj7CgLDpCUlKTIyMhCz0VHRys6Orpc6jj7w/c68cYLqnvB667kozry2gu65JW/E1gBAACAUoqJiVFMTEyh55KSkkrVp2HmXQGplLy8vGQYhvr27aslS5aUqo/Bgwdr0aJFMgxDWVlZF1sSKlhYWJgSEhLUpEkTxcfHV2gtpsulA8MGy5V8tMg2Pg1D1Hz2AqYEA4BNUjNMDXrrvCTp/bUDFbFwqbwCAyu4KgBARShtNrB9gaWLceHqwYAdUrdtchtUJSnrWJJSt20qn4IAAAAAFMtRYRUoC67jx21tBwAAAKDsOSasZmRkSJJ8fX0ruBJUNd7169vaDgAAAEDZc0xYPXDggCSpTp06FVsIqpzA1u3k3aCR2zY+DUMU2Lpd+RQEAAAAoFiOCKsrVqzQjh07ZBiGrrjiioouB1WM4e2thmOfkCQV9YnoBmMeZ3ElAAAAwEE83rpm1KhRRZ7bunWr2/N5maap1NRU7d69W1u2bLFe7969u6clAcWq2bWH6r34d8W9/b99VqWcEdUGYx5n2xoAAADAYTwOqzNmzJBhGAVeN01TiYmJmjlzpsdF5K4AXKNGDT300EMeXw+URGCX7nrxpw664tRWPbr5eTV7/R8K6tCZEVUAAADAgUo1DTh3i5kLt5q58PWS/pGkJk2aaOHChWrWrJk93xlQCNPw1u667ZRteCvw6nbVOqhmp6Yqrs91iutznbJTUyu6HAAAACAfj0dW77333gKvzZw5U4ZhKDQ0VL179y5RP15eXqpZs6YaN26sDh06qGfPnvKuxsEBAAAAAPA/HofV6dOnF3gtd+rv1VdfXeh5AAAAAAA8YdtqwHmnAwMAAAAAcDE8HlktTHZ2th3dAAAAAAAuQmqGqUFvnZckvb92oCIWLpVXYGAFV1U6jthnFQAAAACAvAirAAAAAADHsWUasCQ9+eSTOnXqlLy8vPT+++8rKCioxNfOmTNHK1eulCQ98MADuuGGG+wqC0AhUjNMDZqcLfVYrffXDqzocgAAAIACbAmrv/76q959910ZhqFbb73Vo6AqSVdddZXuueceGYahU6dOacGCBXaUBQAAAACopGyZBvztt99axyNHjvT4+jZt2qhdu3YyTVPLly9XRkaGHWUBAAAAACopW8LqTz/9JEny9vZW3759S9XHgAEDJElpaWn67bff7CgLAAAAAFBJ2RJWd+7cKcMwdOWVV8rf379UfVxzzTXW8a5du+woCwAAAABQSdkSVo8fPy5JatSoUan7aNiwYYH+AAAAAADVky1h1TAMSVJmZmap+8jKyir0GAAAAABQ/dgSVhs0aCDTNHX48OFS95H32vr169tRFgAAAACgkrIlrLZo0UKSdOjQIe3Zs6dUfaxYscI6btasmR1llVhWVpY+/fRT9e7dW6GhofL391fTpk118803a968eTJN0/b7zZkzR1FRUQoPD1dQUJD8/PwUEhKi7t27a+LEiTpy5EiJ+zt37pwmT56sLl26qFGjRgoICFB4eLiGDBmiZcuWeVxbeT4LAAAAACiMLfus9uzZU+vWrZMk/f3vf9e0adM8un7v3r2aP3++JMnX11ddu3a1o6wSOXTokKKiovT777/nez0+Pl7x8fFavHixPv30U3399dcKDg6+6Pvt27dPUVFR2rx5c4FzR48e1dGjR7V27Vq9+eabiomJ0fDhw932t3nzZt1+++3au3dvvtcPHjyogwcP6quvvtJf//pXTZ8+XX5+fm77Ku9nAQAAAABFsSWs3nnnnXrttddkmqZmzpypLl266P777y/RtWfOnNHtt9+uzMxMGYahAQMGKCgoyI6yinX69Gn1799fsbGxkqSIiAiNGjVKYWFhiouL09SpU3X48GGtWrVKgwcP1ooVK+Tr61vq+505c0Y9evTQoUOHJEl169bVfffdp4iICAUEBGjfvn2aNWuW9u7dq5SUFN17772qV6+eBg4cWGh/hw4dUr9+/axR2M6dO2vYsGFq0KCBtm7dqqlTp+r48eP68ssvZRiGPv/8c8c8CwAAAABwy7TJkCFDTMMwTMMwTC8vL/Phhx82ExIS3F6zaNEi89JLLzW9vLxMwzBMb29vc8uWLXaVVKwnn3zSlGRKMvv162empqbmO3/8+HHzmmuusdp88MEHF3W/1157zeqrffv25smTJwu0yczMNEeNGmW1a9u2bZH9RUVFWe1GjRplulyufOcPHjxoNmvWzGrz7bffFtmX3c+iSZMmpiSzSZMmbtuVp/Pp2WbPiWfNnhPPmtv+0s10nT9f0SVVGJ4FgLLG+wwAVAwnvv+WNhsYpmnPhxCTk5PVsWNHHT58WKZpyjAM+fj46Prrr1enTp3UqFEj+fv76/Tp09q1a5fWrVunhIQEq60kTZw4Uc8//7wd5ZSo3rCwMKWnp6tGjRrat29foVvvbNu2TW3atJFpmgoJCVF8fLx8fEo3IH3jjTfqxx9/lCQtWbJE/fv3L7Td6dOn1bBhQ2t15TNnzqhWrVr52mzdulVt2rSRlPMZ3127dikgIKBAX0uWLLFGZjt06KDffvutQJuyeBZhYWFKSEhQkyZNFB8fX9QjKVepGaYGvXVekvT+2oGKWLhUXoGBFVxVxeBZAChrvM8AQMVw4vtvabOBLdOApZwVgZcsWaJBgwbpwIEDknK2svnhhx/0ww8/FGifNyObpqlnn3223IKqJC1cuFDp6emSpLvvvrvIPWJbt26tnj17avXq1UpKStLatWvVq1evUt3z6NGj1vHll19eZLs6deqoQYMG+vPPPyXlLKB0YVidN2+edfzQQw8VGlQlqX///rr88ssVFxen33//XXv37tVll12Wr01FPAsAAAAAcMeW1YBzRUZG6o8//tCIESPk7e0tKSeIXhhM8359xRVXaOHChZo0aZKdpRQr7yq5/fr1c9s273lPV9fNK28IjIuLK7Ld6dOnlZycLCnnlwCFhceS1m8Yhvr27VvodZ72deH5i3kWAAAAAOCOrWFVkoKDgzVjxgzt2bNHkyZNUv/+/dW8eXPVrFlTPj4+atSokdq2basxY8boP//5j3bu3KlbbrnF7jKKtW3bNuu4Q4cObtt27Nix0Os8deutt1rHL7/8sk6dOlWgjcvl0pNPPmlNAX788cfl5ZX/P5NpmtqxY4ckycfHR23btnV73+Lqr4hnAQAAAADu2DYN+ELNmzfXs88+q2effbasblFq2dnZ1lYv3t7eCgsLc9u+efPm1vHu3btLfd9HHnlE8+fP14YNG/THH3+oRYsWGjVqlCIiIuTv7699+/Zp9uzZ1qjrk08+WejU6MOHD+v8+Zx56E2aNCn2M7Tu6q+oZ1ERzp07J1dGznM7n+XSuXPn5JWdLSnne887lfrcuXNF9uPl5aXAPPP+PWl7/vz5IveqNQwj30rYnrRNTU1V9v/7XgpTo0aNfG3PpbmKfBZ526alpcnlcpWo3+LaBgUFWZ9PT09PV1ZWli1tAwMDrV/oZGRkWL/oudi2AQEB1gwRT9pmZmYqIyOjyLb+/v7W31lP2mZlZVnT9Qvj5+dnrdDtSVuXy6W0tLQi2/r6+lrbXnnSNjs7W6mpqba09fHxkb+/v6ScX9blvv9dbFtP/t5Xt/eIkrZ19/c+NcOUZORra7rpl/eIHLxHeN6W94gcle09wtO2vEfkKMl7RGqGKVfGeRne/kX2U2lc7MpOldHp06etVW3r16/vUft69epd1L3Pnz9vPvTQQ2ZAQIDV54V/brvtNnP9+vVF9rF582arbYcOHYq9Z9727du3L/J7s/NZ5K74FRoaap4+fbrUf9LS0oqtqaSKet6SzAEDBuRrGxQUVGTbbt265WvboEGDItt27NgxX9vmzZsX2TYyMjJf28jIyCLbNm/ePF/bjh07Ftm2QYMG+dp269atyLZBQUH52g4YMMDtc8vrjjvucNv27NmzVtt7773XbdujR49abceOHeu27f79+622Tz/9tNu227Zts9qOHz/ebdsNGzZYbf/xj3+4bfv9999bbT/88EO3bRcvXmy1nT59utu2X331ldX2q6++ctt2+vTpVtvFixe7bfvhhx9abb///nu3bf/xj39YbTds2OC27fjx462227Ztc9v26aefttru37/fbduxY8dabY8ePeq27b333mu1PXv2rNu2d9xxR76fYXdteY/I+ePpe0Te1ShvHzzYbVveI3L+8B6R84f3iJw/Vf09Ii/+HZHDzveI1kNn2bYacFpa2kX9mz40NNSUPF8NuMxGVp3s7Nmz1nFRCxPllfc3WnmvLY3AwEC9+uqratCggSZNmlTob6YWL14s0zT19ttvF1gM6cIaLrb+sn4WiYmJqlOnTrHtijJ+/HhNmDCh1NfnY3gpOLyL/GtdovSUIzp14EfJLPo3gwCAi2OYLl1xaqu8TJdaZKXLSxLvugBQ+fz973/Xq6++Wu73tW3rmsokMTFRTZo0kZSzjPLhw4fdts/KyrKmw/j5+bmdOlOcmTNnavTo0UpPT1dUVJSeeOIJtWvXTr6+voqLi9Ps2bP19ttvKysrSw0aNND//d//qXPnzvn6+Omnn9SlSxdJOdvhFLbacl579uxRy5YtJUktW7bUrl27rHNl9Sxyl6cODQ21Pl9bGv7+/tZ0nYvxw84svb88TSfO/W9KWv0a2Xqwu5duuLz6Td85l+bSHe/lTH365w+3q9W8b6wlzZm+k4MpfjmY4ud526rwHmHHFL/TP6xT/If/Ur30Y9ZrXvUbqvYD0Qq8vmuB9rxH5OA9wvO2vEfkqGzvEZ625T0iR0mnAd/xXs404A//e6stW9ekp6dfVAaKiIiwckeFbF3jBDt37tTOnTuLPN++fXs1a9ZMNWvWtF5z9+aXK+8bWd5rPfXJJ5/ooYcekiQ98cQTeuedd/Kdv+qqqzRp0iRdf/31uu2225ScnKw777xTu3fvzhfYPK0/b5sL6y/rZ2EYhmrXrl1su7L0w84sTViQrryfnZKk4+e8NOn/pAlR/uraKv9fhbxvnsXxpG3e/zHY2TbQgzegwMBAyduUt1/O8wjy8VaNGjUKfRMryWh7adp68ksIT9r6+flZ/7ipqLa+vr7WP/LsbOvj41PiPZ49aevt7V3in2FP2np5eZVJW8MwyqStVHZ/7yvle0QJFfX3/uwP3+v826+q7gWvZx8/plNvTlDAK39Xza49iuyX9wjP2/IekYP3iNK1Le/3iItty3uE+7Zevv/7d55dLnYAKfeXB54qcVgdNWpUvpt99tlnhZ67WBf27Ym5c+e6HZ6ePn26Ro4caa1MnJWVpVOnTsnlclm/zSjM8ePHrePg4OBS1ZaWlqYXXnhBklS3bl1NnDixyLa33nqr+vbtq+XLl+vQoUNauHChhgwZUmgNeWsrirv6K+JZlCdXtqmYlUX/dkqSYlZl6IaW3vL2svcvNQBUR6bLpWP/mizpwl8R/k/ylHdV44abZLj5/w0AACUOqzNmzMiXiPMGygvPXazShtWS8vLy0mWXXaZdu3bJ5XIpPj4+3yq3Fzp48KB1nDud1lO//PKLFfRuuOGGYn8r1atXLy1fvlyStGHDhnxhNSwsTEFBQTp//rzi4+OVlZXl9jek7uqviGdRnrYeztaxFPcz3Y+dMbX1cLbaNecfTQBwsVK3bZIr+ajbNlnHkpS6bZOC2rrfLg0AUL15tM+qaZpFzn3PPXexfy7GhAkT3PY9cuRIq23r1q2t499++81tv3nP573OE4mJidZxSRYcyjtqmZKSku+cl5eXIiIiJOV87mTz5s1u+yqu/vJ+FuXpxNmS/UyVtB0AwD1XCWb8eNIOAFB9lXhk9d577y3VOafq27ev/v3vf0uSli9frttvv73ItsuWLct3XWnUqlXLOi7Jh4oPHTpkHdevX7/A+b59++r333+XlFN/hw6F/3baNE1rhDb3usL6Ks9nUZ7q1SzZiH9J2wEA3PMu5P9ZF9MOAFB9VcvVgCXp2LFjCgsLU0ZGhmrWrKm9e/eqUaNGBdpt375dV199tUzTVKNGjZSQkFDiRQny2r9/v1q0aCEp58PQe/bsKXK6rcvl0lVXXWWt2vvvf/9bUVFR+dps3rxZ7dq1kyQ1a9ZMu3btKvSD6UuWLNHAgQMlSddcc43++OOPAm3K4lnkrgbs6YpfdnNlm7onJtXtVOCGtQ19MTawWn1mNTXD1KC3chbLen/tQFtWiQMAKeczqweGDXY7FdinYYiaz17AZ1YBoAw48d95pc0GHk0DrkoaNmyosWPHSsrZL3TkyJEFlls/efKkhg8fbk1Pfumll4oMZ927d5dhGDIMQzNmzChw/tJLL9W1114rKWeZ6aFDh+rEiRMF2rlcLo0bN84Kqo0aNSp0BLNt27a67bbbJOWMwo4bN67AMuKHDh3SmDFjrK+L2q/U7mfhJN5ehqL7uF99Lbq3X7UKqgBQlgxvbzUc+4Sy5aWdwW21oVEP7Qpuq+w8/+RoMOZxgioAoFjOTxtlaPz48Vq2bJl27typpUuXqn379nrggQfUpEkTxcXF6eOPP7b2He3atWu+4Fca7733nrp37660tDT98ssvatWqle699958+6x+8cUXio2Nta6ZPHlykUuav/vuu/r555+VlJSkzz77TNu2bdPw4cNVv359bd26VR9//LG1qNPQoUN1yy23OOZZlKeurXw0IUr6cEWGkvN8NrVhTSn6LwW3rQEAXJyNDbvqg76LdTzjf9sc1E07qruPfqG+93Rxu20NAODiuLJNyXTpknOHFVfnKjXdskm1OnaulL8krLbTgHMdOHBAUVFR2rhxY5Ftevbsqfnz56tu3Qt3jPuf7t27a+3atZL+t0VOYVauXKkRI0boyJEjbuuqUaOGPvjgA913331u223cuFF33HGH9u3bV2SbIUOGaObMmcXujWTXs5CcMw04r7Np2br1nZy9ZB/Z9LwGTn9TvjVKvgdZVeLE6SEAqob/7W19IVOSUeje1gAAe/ywM0sfLD5T4JeFQ5M+V79hN1bYLwuZBlxK4eHhWr9+vaZOnaqePXsqJCREfn5+Cg0N1YABAzRnzhytWrWq2HBWUn369NGuXbv0r3/9SwMGDFBYWJgCAgLk6+urhg0bqlu3bnr99dcVFxdXbFCVcj6HumXLFr399tu6/vrr1aBBA/n7+6tp06a64447tGTJEs2dO7dEm/iW97Mob3mn+l5xeitTfwHAZu73ts55z41ZlZHzW38AgK1yflmYpuPp+T8Cd9K/gaY0e0JL/vV/OvvD9xVUXelU+5FVlA0njqwymvg/PAsAZWHTQZee+iKt2HZv3xPA3tYAYCNXtqm/xpxX8hlTMgoZkDGzVTc9Wf+Ie0KXzv53uU8JLm02KPE8nFGjRpWqME8ZhqHPPvusXO4FAADsw97WAFAxth7OVnKKCg+qkmR46WRAI+3IDNEl2zYpqG3h2146TYnD6owZM2QU9c3bjLAKAEDlw97WAFAxSvpLwNN+9eT6fwuwVgYerXDgyYzhvMG2sOtyz194rrwCMQAAsNfVTb3UsJZR7N7WVzet9ktmAICtSvpLwDoZJ+Rdv34ZV2OfEofVe++9t0TtlixZouTkZCuENmjQQFdffbXq168vf39/nTlzRvv27dOuXbuUlZVlhdPevXsrNDS0FN8CAABwgty9rQtfDTgHe1sDgP2ubuqlBrWk5DPZklHILwT/32dWI3yTFNi6XbnXV1olDqvTp093ez49PV3333+/jh07JsMwdM899+jRRx9Vp06dCm1/5swZzZ07VxMnTtThw4e1adMmPffcc+rZs6dn3wEAAHAM9rYGgPLn7WVoXB9/TViQJpkXBFYzW5Khu/bEqNHYxyrVfqu2zcN59NFH9eWXXyogIEALFizQ7NmziwyqklS7dm099NBDio2NVa9evXTs2DENHjxYu3btsqskAABQAbq28tFnDwVYXz+y6XnNvt8gqAJAGcr5ZWGA6vtn5nu9bnqyxh5+VwPGDqywfVZLy5aw+sMPP+iTTz6RYRh64403dOutt5b42ho1amjBggUKCwtTSkqKHn74YTtKAgAAFYi9rQGg/HVt5aNp44Il06VLzh7Qo5ue1bSBRxQV80KlC6qSTWF12rRpkqSaNWtqzJgxHl9fq1YtjR49WpK0bt067d+/346yAAAAAKBa8fYyJMNbR2qG6/LT21WzTbtKNfU3L1vC6i+//CLDMHTVVVfJ39+/VH3knTK8fv16O8oCAAAAAFRStoTVhIQESZKfn1+p+/D19bWOExMTL7omAAAAAEDlZUtY9fb2lmmaF7U40o4dO/L1BwAAAACovmwJq82bN5ckHT16VIsWLfL4+uzsbH322WcF+gMAAAAAVE+2hNUBAwZIkkzT1JgxYxQXF+fR9c8++6w2btwoSfL391evXr3sKAsAAAAAUEnZElZHjx6toKAgGYahP//8U9ddd52mTJmitLQ0t9dt27ZNAwcO1OTJkyVJhmHovvvuU61atewoCwAAAABQSdmyO3fz5s311ltvaezYsTIMQydOnNC4ceP03HPP6aabbtLVV1+t+vXry8/PTykpKdq/f7/Wr1+v2NjYfP1cdtllmjRpkh0lAQAAAAAqMVvCqiQ9/PDDSktL09NPP229dvbsWS1dulRLly4t9BrTNGUYhkzTVEREhFauXMmoKgAAAADAnmnAuR5//HH99NNP6tChg0zTlJQTSHOPc+V9LSgoSM8//7z++OMPhYaG2lkOAAAAAKCSsm1kNVfnzp21YcMG/fbbb1qwYIHWr1+v3bt36+TJk8rIyFDt2rUVEhKiDh06qFu3brrrrrsYTQUAAAAA5GN7WM3VsWNHdezYsay6BwAAAABUYbZOAwYAAAAAwA5lNrIKSFJSUpIiIyMLPRcdHa3o6OhyrggAAACA3WJiYhQTE1PouaSkpFL1WeZhNSkpSceOHdOpU6eUnZ2tm266qaxvCQcJCQkpsEURAAAAgKrF3UBUWFiYEhISPO6zTMLqpk2b9P7772vlypVKTEy0XjcMQ1lZWQXav/322zp37pwk6ZlnnlFgYGBZlAUAAAAAqCRsDaupqal65JFHNH36dOu1C7etKczBgwf14YcfyjAMtWjRQsOGDbOzLAAAAABAJWPbAkupqanq1auXpk+fbu2jWpKgKinfcPFXX31lV0kAAAAAgErKtrA6ZswY/fLLLzmdenlp1KhRWrt2rU6dOqW+ffu6vfbKK69UZGSkTNPU2rVrlZ2dbVdZAAAAAIBKyJZpwBs3btTs2bMlSX5+flq4cGGxAfVCPXv2VGxsrM6ePatt27apTZs2dpQGAAAAAKiEbBlZ/fzzz2WapgzD0Ouvv+5xUJWkdu3aWce7du2yoywAAAAAQCVlS1hdvXq1JMnf37/U+2aGhoZax0eOHLGjLAAAAABAJWVLWE1ISJBhGLr66qsVEBBQqj5q165tHZ89e9aOsgAAAAAAlZQtYTUlJUVS/sDpqbwBtbSBFwAAAABQNdgSVuvVqydJOn78eKn72Ldvn3Vcv379i64JAAAAAFB52RJWw8LCZJqmYmNjlZqaWqo+Vq5caR1HRETYURYAAAAAoJKyJaz26NFDkpSZmalZs2Z5fH1cXJwWLVokKWcqcceOHe0oCwAAAABQSdkSVu+44w7r+MUXX9SBAwdKfO3Zs2c1dOhQuVwuGYahoUOHyjAMO8oCAAAAAFRStoTVTp06adCgQTJNUydPntSNN96oFStWFHvdjz/+qOuvv14bN26UJPn6+ur555+3oyQAAAAAqHYC/QytfMJLH3/fS/7ZaRVdzkXxsaujKVOm6Pfff9eRI0eUmJio/v37KzIyUr1799b+/futdtOmTdPu3bu1YsUKbd68WZJkmqYMw9C7776r5s2b21USAAAAAFQ7XoGBunzlLxVdxkWzLaw2adJES5Ys0aBBg5SQkCBJio2NVWxsrCTJMAyZpqkHH3zQusY0Tev4pZde0sMPP2xXOQAAAACASsyWacC52rZtq82bN2vIkCFWOM0bSHM/i5r39aZNm2revHl67bXX7CwFAAAAAFCJ2RpWpZw9V+fMmaPdu3dr/Pjx6t69u+rWrWuFVx8fH4WFhemuu+7SjBkzFBcXpzvvvNPuMgAAAAAAlZht04Av1KJFC40fP9762jRNpaamKigoqKxuCQAAAACoImwfWS2KYRgEVQAAAABAidgystq+fXvreP78+WrRooUd3QIAAAAAqilbwuqmTZtkGIaaNWtGUAUAAPm0+PZ7efkZFV0GAKCSsWUacHBwsCQpPDzcju4AAAAAANWcLWH1kksukSSlp6fb0R0AAAAAoJqzJaxed911Mk1Tu3btUnZ2th1dAgAAAACqMVvC6tChQyVJp06d0qJFi+zoEgAAAABQjdkSVv/yl79o0KBBMk1Tjz32mBISEuzoFgAAAABQTdm2z+r06dN17bXX6vDhw7r22mv11VdfyeVy2dU9AAAAAKAasWXrmtdee02S1LNnT+3YsUOJiYm6++67Vb9+fV1//fW67LLLVLt2bXl5lSwbv/LKK3aUBQAAAACopGwJqxMmTJBh/G//NMMwZJqmkpOTtXjxYo/7I6wCAAAAQPVmS1iVJNM0PXq9KHlDLwAAqJwC/QytfrFGRZcBAKjEbAmrN910EyETAAAAAGAbW8LqmjVr7OgGVVBSUpIiIyMLPRcdHa3o6OhyrggAAACA3WJiYhQTE1PouaSkpFL1ads0YKAwISEhio2NregyAAAAAJQhdwNRYWFhpdre1LatawAAAAAAsMtFj6z+/vvv2rx5s5KTk+Xn56eQkBBdf/31Cg8Pt6E8AAAAAEB1VOqwOmXKFL3++us6cuRIoec7duyoN998U927dy/tLQAAAAAA1ZTH04BdLpeGDh2qcePG6c8//5Rpmvm2p8n9+tdff1WfPn300Ucf2VowAAAAAKDq8zis/n//3/+nr776SqZp5tuuJm9oNQxDhmHI5XLpkUce0c8//2xfxQAAAACAKs+jacDHjh3TpEmT8oXUW265RTfffLOaNm2qzMxM7dixQ19++aU2bdpkBdannnpKP/30k+3FAwAAAACqJo/C6uzZs5WRkSFJCgwM1DfffKO//OUv+doMHDhQTz/9tF555RW9/vrrkqT169drx44dioiIsKlsAAAAAEBV5tE04B9++EFSzjTfN954o0BQzeu1115T//79ra/XrVtXyhIBAAAAANWNR2F1y5YtkiR/f389/PDDxbZ/9NFHC1wLAAAAAEBxPAqrx48fl2EYat26tfz9/Ytt36lTJ+v4xIkTnlcHAAAAAKiWPAqrKSkpkqR69eqVqH3edrnXAgAAAABQHI/Cau7WNF5eHu94o+zsbI+vAQAAAABUT56nTgAAAAAAyhhhFQAAAADgOIRVAAAAAIDj+JTmouXLl8vb27vE7U3TLPE1hmEoKyurNGUB8IBhunTFqa3yMl1K3bpJQR06y/Dg7zUAAABQlkoVVqX/LbZUHMMwPL4GQNlK/XGN3vj5HdVLPyZJ+vOlJ+TdoJEajn1CNbv2qODqAAAAgFJMAzZN06PQmdueoAo4w9kfvteJN15Q3f8XVHO5ko/qyGsv6OwP31dQZQAAAMD/eDSyOn78+LKqA0A5MF0uHfvXZEmSUUSb5CnvqsYNNzElGAAAABWKsApUI6nbNsmVfNRtm6xjSUrdtklBbTuUU1UAAABAQawGDFQjruPHbW0HAAAAlBXCKlCNeNevb2s7AAAAoKwQVoFqJLB1O3k3aOS2jU/DEAW2blc+BQEAAABFIKwC1Yjh7a2GY5+QJBW1PneDMY+zuBIAAAAqXKn3WQVQOdXs2kN1Xvi7Vn+6TNmGl+pknNAVp7bKr2FDNRjzOPusAgAAwBEIq0A188POLH247Volt+5svVbfP0Pj+tdQeKRfBVYGAAAA/A/TgIFq5IedWZqwIF3JZ/NPAj6e7qdXF2bqh51ZFVQZAAAAkB9hFagmXNmmYlZmuG0TsypDruyiPs0KAAAAlB/CKlBNbD2crWMp7oPosTOmth7OLqeKAAAAgKIRVoFq4sTZko2YlrQdAAAAUJYIq0A1Ua+mYWs7AAAAoCyxGjDKVFJSkiIjIws9Fx0drejo6HKuqPq6uqmXGtYy3E4Fbljb0NVN+R0WAAAAPBMTE6OYmJhCzyUlJZWqT8IqylRISIhiY2MrugxI8vYyFN3HTxMWpBfZJrq3n7y9GFkFAACAZ9wNRIWFhSkhIcHjPhlCAaqRrq18NCHKXw0umOrbsKY0IcpfXVvx+ysAAAA4A2EVqGa6tvLRZw8FWF8/sul5zb7fIKgCAADAUQirQDWUd6rvFae3MvUXAAAAjkNYBQAAAAA4DmEVAAAAAOA4hFUAAAAAgOMQVgEAAAAAjkNYBQAAAAA4DmEVAAAAAOA4hFUAAAAAgOMQVgEAAAAAjkNYBQAAAAA4DmEVAAAAAOA4hFUAAAAAgOMQVgEAAAAAjkNYBQAAAAA4DmEVAAAAAOA4hFUAAAAAgOMQVgEAAAAAjkNYBQAAAAA4DmEVAAAAAOA4hFUAAAAAgOMQVgEAAAAAjkNYBQAAAAA4DmEVAAAAAOA4hFUAAAAAgOMQVgEAAAAAjkNYBQAAAAA4DmEVAAAAAOA4hFUAAAAAgOMQVgEAAAAAjuNT0QWgaktKSlJkZGSh56KjoxUdHV3OFQEAAACwW0xMjGJiYgo9l5SUVKo+CasoUyEhIYqNja3oMgAAAACUIXcDUWFhYUpISPC4T6YBAwAAAAAch7AKAAAAAHAcwioAAAAAwHEIqwAAAAAAxyGsAgAAAAAch7AKAAAAAHAcwioAAAAAwHEIqwAAAAAAxyGsAgAAAAAch7AKAAAAAHAcwioAAAAAwHEIqwAAAAAAxyGsAgAAAAAch7AKAAAAAHAcwioAAAAAwHEIqwAAAAAAxyGsAgAAAAAch7AKAAAAAHAcwioAAAAAwHEIqwAAAAAAxyGsAgAAAAAch7AKAAAAAHAcwqqkrKwsffrpp+rdu7dCQ0Pl7++vpk2b6uabb9a8efNkmqbt95szZ46ioqIUHh6uoKAg+fn5KSQkRN27d9fEiRN15MiREvd37tw5TZ48WV26dFGjRo0UEBCg8PBwDRkyRMuWLStRH+Hh4TIMo0R/unfvXsrvHAAAAABKxqeiC6hohw4dUlRUlH7//fd8r8fHxys+Pl6LFy/Wp59+qq+//lrBwcEXfb99+/YpKipKmzdvLnDu6NGjOnr0qNauXas333xTMTExGj58uNv+Nm/erNtvv1179+7N9/rBgwd18OBBffXVV/rrX/+q6dOny8/P76LrBwAAAIDyUK3D6unTp9W/f3/FxsZKkiIiIjRq1CiFhYUpLi5OU6dO1eHDh7Vq1SoNHjxYK1askK+vb6nvd+bMGfXo0UOHDh2SJNWtW1f33XefIiIiFBAQoH379mnWrFnau3evUlJSdO+996pevXoaOHBgof0dOnRI/fr1s0ZhO3furGHDhqlBgwbaunWrpk6dquPHj+vLL7+UYRj6/PPPi62xYcOGmjp1qts2DRo08PA7BwAAAADPVOuw+tprr1lBtV+/fvrmm28UEBBgnR87dqx69+6tjRs3as2aNfr44481bty4Ut/vvffes4Jq+/bttXr16gKjtS+++KJGjx6tadOmyTRNvfTSS0WG1SeeeMIKqqNGjdInn3wiL6+cmd133323Hn74YXXt2lWHDh3SF198oaFDh2rQoEFuawwKCtJtt91W6u8RAAAAAOxQbT+zmpycrJiYGElSjRo1NHPmzHxBVZLq1aunWbNmyTAMSdLrr7+urKysUt9z+fLl1vHrr79e6LRiHx8fvfPOO9YI7ubNm5WSklKg3datW7VgwQJJUrNmzRQTE2MF1VzNmjXTlClTrK8nTJhQ6toBAAAAoDxV27C6cOFCpaenS8oZhWzUqFGh7Vq3bq2ePXtKkpKSkrR27dpS3/Po0aPW8eWXX15kuzp16uSbanvu3LkCbebNm2cdP/TQQwWCdq7+/ftb9/r9998LfLYVAAAAAJyo2obVvKvk9uvXz23bvOdLurpuYfIG4ri4uCLbnT59WsnJyZJyPh9aWJAuaf2GYahv376FXgcAAAAATlVtw+q2bdus4w4dOrht27Fjx0Kv89Stt95qHb/88ss6depUgTYul0tPPvmkMjMzJUmPP/54gem9pmlqx44dknKmDbdt29btfT2p//jx4+rdu7caNWokPz8/NWzYUJ07d9Zzzz3HqCwAAACAclMtF1jKzs62gpe3t7fCwsLctm/evLl1vHv37lLf95FHHtH8+fO1YcMG/fHHH2rRooVGjRqliIgI+fv7a9++fZo9e7Y16vrkk0/q+eefL9DP4cOHdf78eUlSkyZN5OPj/j+jJ/WfPXtWq1evtr5OTk5WcnKyfv31V7399tt65pln9Prrr8vb27vE3zcAAAAAeKpahtWzZ89aCyUFBwcXG/bq169vHRc2GlpSAQEBWrNmjR5//HHNmjVLJ0+e1Ntvv12g3W233aYXXnhBnTt3LrSfvDWUZBuZktYfGhqqfv36qV27dgoJCVFGRob27NmjBQsWaNu2bXK5XJo0aZISExM1c+bMYu8r5YwCnzlzpkRtC+Pv7y9/f/9SXw8AAADg4qSnp1vr/ZSGaZqluq7ahtVcRS1MlFdgYGCh15ZGYGCgXn31VTVo0ECTJk1SdnZ2gTaLFy+WaZp6++23ddlllxU4Xxb1f/7557rhhhsKTDmWclYRjomJ0WOPPabs7GzNmjVLffr00bBhw4q9d2JiourUqVNsu6KMHz+eVYwBAACACvT3v/9dr776arnft1qG1bxyt6W52DYlNXPmTI0ePVrp6emKiorSE088oXbt2snX11dxcXGaPXu23n77bf3nP//Rjz/+qP/7v/8rcoTVztpuvPFGt/cYN26cTp48qVdeeUVSztY7JQmroaGh1udrS4NRVQAAAKBivfDCC3ryySdLfX1ERIQSExM9vq5KhdWdO3dq586dRZ5v3769mjVrppo1a1qvpaamFttv7udDJeW71lOffPKJHnroIUnSE088oXfeeSff+auuukqTJk3S9ddfr9tuu03Jycm68847tXv37nyhzdP687a5mPqfeeYZvfXWWzpz5ox27dqlffv2qUWLFm6vMQxDtWvXLvU9AQAAAFSsi/1oXmkH2KpUWJ07d67b4enp06dr5MiRqlmzpnx8fJSVlaVTp07J5XK5XTDo+PHj1nFwcHCpaktLS9MLL7wgSapbt64mTpxYZNtbb71Vffv21fLly3Xo0CEtXLhQQ4YMKbSGvLUVxY76pZwpx9ddd51WrFghKeeXA8WFVQAAAAAojWq5dY2Xl5f1WVCXy6X4+Hi37Q8ePGgdt2zZslT3/OWXX6zQeMMNN+T7HGlhevXqZR1v2LAh37mwsDAFBQVJkuLj463FoopiR/258i7odDGLTQEAAACAO1UqrE6YMEGmaRb5Z+TIkVbb1q1bW8e//fab237zns97nSfyztEuyYJDeUdAU1JS8p3z8vJSRESEJCkrK0ubN29225cd9eeya5QWAAAAANypUmHVE3379rWOly9f7rbtsmXLCr3OE7Vq1bKOixvJlaRDhw5Zx3m3nimsDnf1m6aZ73xp65dylqz+5ZdfrK+vvPLKUvcFAAAAAO5U27B62223yc/PT5I0Z84cHT16tNB227dv13fffSdJatSokbp3716q++Ud0fz555/zTc29kMvl0tdff2193alTpwJt7rrrLuv4448/VlpaWqF9LV26VHFxcZKka665RpdffrnHted65513dPr0aUnSFVdcUei2OgAAAABgh2obVhs2bKixY8dKytl7dOTIkQUC38mTJzV8+HBrE9uXXnpJPj6Fr0nVvXt3GYYhwzA0Y8aMAucvvfRSXXvttZKkzMxMDR06VCdOnCjQzuVyady4cdq1a5eknIBc2Gho27Ztddttt0nKGYUdN25cgT1bDx06pDFjxlhfF7Vf6cSJE4vdXuajjz7S3/72N+vrl19+2W17AAAAALgYVWo1YE+NHz9ey5Yt086dO7V06VK1b99eDzzwgJo0aaK4uDh9/PHHOnz4sCSpa9eu+YJfabz33nvq3r270tLS9Msvv6hVq1a699578+2z+sUXXyg2Nta6ZvLkyapRo0ah/b377rv6+eeflZSUpM8++0zbtm3T8OHDVb9+fW3dulUff/yx9RnToUOH6pZbbim0n6+//lovv/yyrrnmGnXr1k0RERGqW7euMjIyFBcXpwULFmjLli1W+2HDhmnEiBEX9SwAAAAAwJ1qHVaDg4O1dOlSRUVFaePGjdqxY4eeeuqpAu169uyp+fPny9fX96Lud+2112rRokUaMWKEjhw5omPHjumtt94qtG2NGjX0wQcf6K9//WuR/TVv3lxLly7VHXfcoX379mn9+vVav359gXZDhgwpdLT3Qhs3btTGjRuLPO/j46Pnn39e48ePL7YvAAAAALgY1TqsSlJ4eLjWr1+vGTNmaO7cudq+fbtOnjypBg0aqF27dho+fLiGDBlS6o1sL9SnTx/t2rVLX3zxhRYvXqwtW7YoOTlZLpdLwcHBioyMVJ8+fXT//ffrkksuKba/a665Rlu2bNHHH3+s+fPna8+ePUpJSVGjRo107bXXatSoUerfv7/bPmbPnq1169bp559/1vbt23Xs2DFrRLZu3bqKjIxUt27dNGrUKDVp0sSW5wAAAAAA7hhm7gcyARuFhYUpISFBTZo0KdHqx+UhNcPUoLfOS5LeXztQEQuXyquY/W6rKp4FAAAAyktps0G1XWAJAAAAAOBchFUAAAAAgOMQVgEAAAAAjkNYBQAAAAA4DmEVAAAAAOA4hFUAAAAAgOMQVgEAAAAAjkNYBQAAAAA4DmEVAAAAAOA4hFUAAAAAgOMQVgEAAAAAjkNYBQAAAAA4DmEVAAAAAOA4hFUAAAAAgOMQVgEAAAAAjkNYBQAAAAA4DmEVAAAAAOA4hFUAAAAAgOMQVgEAAAAAjkNYBQAAAAA4jk9FF4CqLSkpSZGRkYWei46OVnR0dDlXBAAAAMBuMTExiomJKfRcUlJSqfokrKJMhYSEKDY2tqLLAAAAAFCG3A1EhYWFKSEhweM+mQYMAAAAAHAcRlaBaq7Ft9/Ly8+o6DIAAACAfBhZBQAAAAA4DmEVAAAAAOA4hFUAAAAAgOMQVgEAAAAAjkNYBQAAAAA4DmEVAAAAAOA4hFUAAAAAgOMQVgEAAAAAjkNYBQAAAAA4DmEVAAAAAOA4PhVdAIDyF+hnaPWLNSq6DAAAAKBIjKwCAAAAAByHsAoAAAAAcBzCKgAAAADAcQirAAAAAADHIawCAAAAAByHsAoAAAAAcBzCKgAAAADAcQirAAAAAADHIawCAAAAAByHsAoAAAAAcBzCKgAAAADAcQirAAAAAADHIawCAAAAAByHsAoAAAAAcBzCKgAAAADAcQirAAAAAADHIawCAAAAAByHsAoAAAAAcByfii4AVVtSUpIiIyMLPRcdHa3o6OhyrggAAACA3WJiYhQTE1PouaSkpFL1SVhFmQoJCVFsbGxFlwEAAACgDLkbiAoLC1NCQoLHfTINGAAAAADgOIRVAAAAAIDjEFYBAAAAAI5DWAUAAAAAOA5hFQAAAADgOIRVAAAAAIDjEFYBAAAAAI5DWAUAAAAAOA5hFQAAAADgOIRVAAAAAIDjEFYBAAAAAI5DWAUAAAAAOA5hFQAAAADgOIRVAAAAAIDjEFYBAAAAAI5DWAUAAAAAOA5hFQAAAADgOIRVAAAAAIDjEFYBAAAAAI5DWAUAAAAAOA5hFQAAAADgOIRVAAAAAIDjEFYBAAAAAI5DWAUAAAAAOA5hFQAAAADgOIRVAAAAAIDjEFYBAAAAAI5DWAUAAAAAOA5hFQAAAADgOIRVAAAAAIDjEFYBAAAAAI5DWAUAAAAAOI5PRReAqi0pKUmRkZGFnouOjlZ0dHQ5VwQAAADAbjExMYqJiSn0XFJSUqn6JKyiTIWEhCg2NraiywAAAABQhtwNRIWFhSkhIcHjPpkGDAAAAABwHMIqAAAAAMBxCKsAAAAAAMchrAIAAAAAHIewCgAAAABwHMIqAAAAAMBxCKsAAAAAAMchrAIAAAAAHIewCgAAAABwHMIqAAAAAMBxCKsAAAAAAMchrAIAAAAAHIewCgAAAABwHMIqAAAAAMBxCKsAAAAAAMchrAIAAAAAHIewCgAAAABwHMIqAAAAAMBxCKsAAAAAAMchrAIAAAAAHIewCgAAAABwHMIqAAAAAMBxCKsAAAAAAMchrAIAAAAAHIewCgAAAABwHMIqAAAAAMBxCKsAAAAAAMchrAIAAAAAHIewCgAAAABwHMIqAAAAAMBxCKsAAAAAAMfxqegCULUlJSUpMjKy0HPR0dGKjo4u54oAAAAA2C0mJkYxMTGFnktKSipVn4RVlKmQkBDFxsZWdBkAAAAAypC7gaiwsDAlJCR43CfTgAEAAAAAjsPIKqqlFt9+Ly8/o6LLAAAAAFAERlYBAAAAAI5DWAUAAAAAOA5hFQAAAADgOIRVAAAAAIDjEFYBAAAAAI5DWAUAAAAAOA5hFQAAAADgOIRVAAAAAIDjEFYBAAAAAI5DWAUAAAAAOA5hFQAAAADgOIRVAAAAAIDjEFYBAAAAAI5DWAUAAAAAOA5hFQAAAADgOIRVAAAAAIDjEFYBAAAAAI5DWAUAAAAAOA5hFQAAAADgOIRVAAAAAIDjEFYlZWVl6dNPP1Xv3r0VGhoqf39/NW3aVDfffLPmzZsn0zRtv9+cOXMUFRWl8PBwBQUFyc/PTyEhIerevbsmTpyoI0eOFNvP+fPn9fPPP+uDDz7QyJEj1bp1a/n4+MgwDBmGoRkzZpSqtvJ8FgAAAABQGJ+KLqCiHTp0SFFRUfr999/zvR4fH6/4+HgtXrxYn376qb7++msFBwdf9P327dunqKgobd68ucC5o0eP6ujRo1q7dq3efPNNxcTEaPjw4UX21bRpU504ceKia8pV3s8CAAAAAIpSrcPq6dOn1b9/f8XGxkqSIiIiNGrUKIWFhSkuLk5Tp07V4cOHtWrVKg0ePFgrVqyQr69vqe935swZ9ejRQ4cOHZIk1a1bV/fdd58iIiIUEBCgffv2adasWdq7d69SUlJ07733ql69eho4cGCh/blcrnxfN2vWTBkZGSUalb1QeT8LAAAAAHCnWofV1157zQpn/fr10zfffKOAgADr/NixY9W7d29t3LhRa9as0ccff6xx48aV+n7vvfeeFVTbt2+v1atXFxihfPHFFzV69GhNmzZNpmnqpZdeKjKs3nrrrWrZsqU6duyoDh06qEGDBho5cqRmzpzpcW3l/SwAAAAAwB3DrKYfQkxOTlZYWJjS09NVo0YN7du3T40aNSrQbtu2bWrTpo1M01RISIji4+Pl41O6jH/jjTfqxx9/lCQtWbJE/fv3L7Td6dOn1bBhQ2VmZkrKGZGtVatWie6RN6xOnz5dI0eOLPaasngWYWFhSkhIUJMmTRQfH1+i2staaoapQW+dlyQtfjpIgX5GBVcEAAAAVH2lzQbVdoGlhQsXKj09Xfr/27vvqKav/3/gzxA2yJDlQAVFVBTrtm7qHrWi1j1wW2erddaeqq22tVZba7Wt1o+4W7Vabd17Fqniwq1VcKKgqMhO7u8Pfry/CWS8E0ECPB/ncM47yb03NzevhLze414Affr00ZmcAUCNGjXQsmVLAEBcXByOHDli9nM+fvxY2g4ICNBbztXVFZ6entLtV69emf2cchTEWBARERERERlSbJPV3bt3S9vt27c3WFbzcc16ptJMAm/evKm33PPnzxEfHw8A8PT01Js85pWCGAsiIiIiIiJDim2yGh0dLW3XrVvXYNl69erprGeqLl26SNuffvopEhMTc5VRqVSYOHGidArwRx99BCur/H2bCmIsiIiIiIiIDCmWEyyp1WrcunULAKBUKuHr62uwfIUKFaTt69evm/2848aNw+bNmxEZGYmoqChUrFgRQ4YMQbVq1WBnZ4f//vsPa9askY66Tpw4EdOmTTP7+eQoqLEgIiIiIiIypFgmq0lJScjMzAQAuLm5GZ0wycPDQ9rWdTRULnt7exw+fBgfffQRVq9ejWfPnmHBggW5yoWGhmL69Olo0KCB2c8lV36PhRACL168MLt/dnZ2sLOzM7s+ERERERG9nrS0NGmOG3OYO6dvsU1Ws2kuz6KPg4ODzrrmcHBwwOzZs+Hp6Ymvv/4aarU6V5m///4bQggsWLAAlSpVeq3nMya/x+LBgwdwdXU1r3MAZs6ciVmzZpldX5ODrQIHPnHKk7aIiIiIiIqLr776CrNnz37jz1ssk1VNCoXx5UvklJFr1apVGDlyJNLS0tCtWzdMmDABtWrVgo2NDW7evIk1a9ZgwYIF2LZtG06cOIEdO3a8kSOsQP6MRZkyZXDlyhVzu8SjqkREREREBWz69OmYOHGi2fWrVauGBw8emFyvSCWrV69exdWrV/U+XqdOHZQvXx7Ozs7SfSkpKUbbTU5OlrY165pq+fLlGDFiBABgwoQJWLhwodbj1atXx9dff41GjRohNDQU8fHx6NGjB65fv55vSVt+j4VCoYCLi4t5nSMiIiIiogL3upfmmXvwr0glq7/99pvBw9MrV67EoEGD4OzsDGtra2RmZiIxMREqlQpKpVJvvYSEBGnbzc3NrL6lpqZi+vTpAAB3d3fMnTtXb9kuXbqgXbt22LNnD2JjY/Hnn3+iV69eZj2vMQUxFkRERERERMYUy6VrrKyspGtBVSoV7t27Z7B8TEyMtB0YGGjWc0ZEREiJXuPGjbWu/dSlVatW0nZkZKRZzylHQYwFERERERGRMUUqWZ01axaEEHr/Bg0aJJWtUaOGtH369GmD7Wo+rlnPFJrnaMuZcEjzqOXLly/Nek653vRYEBERERERGVOkklVTtGvXTtres2ePwbK7d+/WWc8UJUqUkLaNHb0EgNjYWGlbc7mY/PCmx4KIiIiIiMiYYpushoaGwtbWFgCwYcMGPH78WGe5S5cu4eDBgwAAb29vhISEmPV8mkch//nnH63TaXNSqVTYtGmTdLt+/fpmPadcb3osiIiIiIiIjCm2yaqXlxdGjx4NIGu90EGDBiE1NVWrzLNnzzBgwABpEdsZM2bA2lr3nFQhISFQKBRQKBQIDw/P9bi/vz8aNmwIAMjIyEDv3r3x9OnTXOVUKhXGjh2La9euAchKCvP7CGZejwUREREREdHrKtbZxsyZM7F7925cvXoVu3btQp06dTBs2DCULVsWN2/exC+//IK7d+8CAJo1a4ZRo0a91vMtWrQIISEhSE1NRUREBKpWrYqwsDCtdVbXrVuHy5cvS3W+++47ODk56Wzv4MGD0pHObGfPnpW2t2zZgps3b2o9PmnSJJ2z+L7psSAiIiIiIjJEIbIPlRVTd+7cQbdu3bSSvJxatmyJzZs3w93dXW+ZkJAQHDlyBMD/LZGjy759+zBw4EA8evTIYL+cnJywePFiDB48WG+ZWbNmGVyqR5fbt2/Dz89P52N5NRYA4Ovri/v376Ns2bKyrtElIiIiIqKiydzcoFgfWQUAPz8/nDp1CuHh4fjtt99w6dIlPHv2DJ6enqhVqxYGDBiAXr16mb2QbU5t2rTBtWvXsG7dOvz999+4cOEC4uPjoVKp4ObmhqCgILRp0wZDhw5FqVKl8uQ55XrTY0FERERERKRPsT+ySvmDR1aJiIiIiAgwPzcothMsERERERERkeViskpEREREREQWh8kqERERERERWRwmq0RERERERGRxmKwSERERERGRxWGySsVCWloaZs2ahbS0tILuChUSjBkyBeOFTMWYIVMxZshURSFmuHQN5QtLW7rmxYsXcHV1xfPnz+Hi4lLQ3aFCgDFDpmC8kKkYM2QqxgyZypJihkvXEBERERERUZHBZJWIiIiIiIgsDpNVIiIiIiIisjhMVomIiIiIiMjiMFklIiIiIiIii8NklYiIiIiIiCwOk1UiIiIiIiKyOExWiQrAkiVLCroLEvalcLCksWFfCgdLGhv2pXCwpLFhXwoHSxob9qVoYrJKVAAs6UuMfSkcLGls2JfCwZLGhn0pHCxpbNiXwsGSxoZ9KZqYrBIREREREZHFYbJKREREREREFofJKhEREREREVkcJqtERERERERkcawLugNUtMXFxSEoKEjnY2PGjMGYMWPecI+IiIiIiCivLVmyRO/kUnFxcWa1qRBCiNfpFJEutra2yMjIgJWVFUqXLl3Q3YEQAg8ePECZMmWgUCgKujuIi4uDj49PQXcDAPuiD2NGP/YlN0uLF8ByxgZgX3RhzBjGvuTGmDGMfcnNkmLm4cOHUKvVsLGxQXp6uux6TFYpXyiVSqjV6oLuBhERERERWQgrKyuoVCrZ5XkaMOULe3t7pKamQqlUwtvbu6C7Q0REREREBeTx48dQqVSwt7c3qR6PrBIREREREZHF4WzAREREREREZHGYrBIREREREZHFYbJKREREREREFofJKhEREREREVkcJqtERERERERkcZisEhERERERkcVhskpEREREREQWh8kqFUlCCGzcuBGdO3dGuXLlYGdnhzJlyqB169ZYvnw5MjIyCrqL9BpevHiBTZs2YfTo0Xj77bfh6ekJGxsbuLq6onr16hg2bBiOHDliUpt79uxB79694efnB3t7e3h7e6NJkyb47rvv8OrVK5PaioiIwNChQxEQEABHR0d4eHigXr16mDNnDuLj401qi/Jf27ZtoVAopL/w8HBZ9RgzxcuRI0cwevRoVK9eHe7u7nBwcECFChXQrFkzfPLJJzh+/LjRNhgzxcO5c+cwbtw41K5dG25ubrC2toabmxtq1qyJESNGyIoVTYybwkelUiE6Ohrh4eEYN24cGjVqBEdHR+n/zKBBg0xu01Lj4NKlSxg/fjyqVq0KZ2dnKdanT5+OmJgYk9rSSRAVMc+ePROtW7cWAPT+1alTR8TExBR0V8kM8+bNE3Z2dgbf3+y/zp07i4SEBIPtpaWliX79+hlsp1KlSuLcuXNG+6ZWq8WkSZOEQqHQ25aPj484cOBAXg0Hvabw8PBc79HKlSsN1mHMFC8PHz4UXbp0Mfp989Zbb+ltgzFTPKhUKjF+/HiD7032X+/evUVKSorB9hg3hVe3bt0Mvm9hYWGy27LkOFi4cKGwtbXV21aJEiXE+vXrZb9WXZisUpGSnp4uQkJCpA9JuXLlxBdffCE2bNgg5s+fL6pVqyY9FhQUJBITEwu6y2SioUOHSu9hhQoVxNChQ8WSJUvE77//Ln799VcRFhamlczWqVNHJCcn622vb9++UlkPDw8xffp0sX79evHDDz+IBg0aSI+VLl1axMbGGuzbJ598IpV3cnIS48ePF2vXrhU///yzaNOmjfSYs7OziIqKyuuhIRPFxcWJkiVLSu+X3GSVMVN8xMbGisqVK0vvQ8WKFcXkyZPF//73P7FhwwaxYMECMXbsWBEQEGAwWWXMFA8ffvihNP4KhUKEhoaK7777TmzYsEEsXLhQvPfee1o/5Hv06GGwPcZN4ZVzB1fJkiW1vktMSVYtNQ6WLVsmlbexsRFDhgwRq1atEr/++qvo0aOHlBArlUqxa9cu2a83JyarVKT88MMPWknK06dPtR5PSUkR7dq1k8p8/PHHBdRTMtewYcNE+/btxYEDB4RardZZ5uLFi8LHx0d6n2fNmqWz3Pbt26Uy5cuXz3W0XaVSicGDB0tlunfvrrdf586dE1ZWVgKAcHV1FefPn89VZubMmVJb9erV09t/ejN69uwpAIhatWqJ/v37y0pWGTPFR0ZGhqhbt640/jNnzhQZGRl6y+v7kciYKR5u374tvTfW1tZ6j0zt3btXKJVK6T06e/asznKMm8Jt7ty5Ytq0aWLTpk3iv//+E0IIsXLlSpOTVUuNg4cPH0o7ea2trcW+fftyldF8vb6+vgYPHBjCZJWKjIyMDOHt7S3t0YyOjtZZLi4uTvqA2dnZiSdPnrzhntLrMHZab7Zt27ZpHYHVpU6dOlKZHTt26CyTnJwsypcvL5W7cOGCznKap/wsWbJEZxm1Wq21F/Svv/6S9Voo72XHh5WVlYiMjBRhYWGyklXGTPExb948adwnTZpkdjuMmeJh+fLl0pj37NnTYNkePXpIZX/44QedZRg3RY85yaqlxsHEiROlMpMnT9bbf81YX7x4sZFXqxsnWKIi4/Dhw3j8+DEAoFWrVqhevbrOct7e3ujduzcAIC0tDdu2bXtjfaTXV7JkSVnlOnXqBCcnJwBATEwMXrx4ofX4zZs3ERUVBQCoXLkyOnbsqLMdBwcHDB8+XLq9cePGXGWSkpKwc+dOAICLi4veiRMUCgXGjRsn3f79999lvRbKWy9evMDo0aMBAGPHjkX9+vVl1WPMFB+ZmZn4/vvvAQAeHh74/PPPzWqHMVN8ZP/+AICAgACDZTUf1zUxDuOGAMuNAyEENm3apLN8TuPHjzfYlhxMVqnI2L17t7Tdvn17g2U1H9esR0WHUqmUklUASElJ0Xpc831v166dwbaMxcvhw4eRmpoKAGjevDkcHR31tqX5XIy9gjFlyhTcv38fvr6+mDNnjux6jJniY9euXXj48CEAoF+/fnBwcDCrHcZM8eHt7S1t37x502BZzcd17Vhn3BBguXFw6dIl3L17F0BW/JYrV05vW40bN4aLiwsA4OTJk3j58qXB16ELk1UqMqKjo6XtunXrGixbr149nfWo6IiLi5P2dDs6OsLLy0vrcVPipVatWlAqlQCAy5cvQwhhdlteXl6oUKECACA+Ph5xcXFGXgnlpaNHj2LZsmUAgMWLF6NEiRKy6zJmio+jR49K2y1btkR6ejoWL16MRo0aoWTJknB0dIS/vz/69euHAwcO6G2HMVN8dOjQATY2NgCALVu24ODBgzrL7du3D1u2bAEAVKtWDR06dMhVhnFDgOXGgSltWVlZoXbt2gAAtVqNy5cvGyyvsw2TaxBZqOvXr0vbfn5+Bsv6+vpKH+qbN29CrVbnZ9eoAPz888/Sdvv27WFlpf11Z0q8WFtbo2zZsgCA5ORk3Lt3z+y2AEj/BHLWpfyVmpqK4cOHQwiBrl27IjQ01KT6jJni4/Tp09K2i4sL6tWrh/HjxyMiIgLPnj1DSkoK7ty5g/Xr16N169bo2bMnkpOTc7XDmCk+ypYti6+++gpA1mnkrVu3Rrdu3bBo0SL8/vvv+P777xEaGop27dpBpVKhVq1a2LlzJ6ytrXO1xbghwHLj4E3HFJNVKjISExOlbU9PT4Nlra2tpdMSMjMzTV5MmSzb9evXMW/ePABZ11NMmzYtVxlT4gXIum5NV928bovyz+zZs3H9+nWUKFECixcvNrk+Y6b4yD4FGABGjBiBixcvws3NDZMmTcK6desQHh6OIUOGSEfSNm3ahD59+uQ6msGYKV4+/vhjrF+/HuXLl4cQAlu3bsVHH32E3r17Y8KECdi2bRvKlSuHNWvWICIiQu8PfcYNAZYbB286pnLvziEqpJKSkqRte3t7o+UdHBzw7Nkzqa4ppwOS5Xr58iW6du0qXaOqbwIdc+JFV928bovyx7lz5/Dtt98CAObOnSvtgTYFY6b40PxBdfPmTQQEBODQoUPw9fWV7g8LC8PIkSPRpk0bvHjxAtu3b8fvv/8uTeAHMGaKo/fffx/29vb48MMPpev6NMXGxuKrr76CUqlEnz59dLbBuCHAcuPgTccUj6wSUZGRkZGBnj17StdE1K9fH/PnzzdaT6FQ5Fkf8rItyhsqlQpDhw5FZmYm6tevjzFjxrx2m4yZoi3npSHh4eFaiWq2Bg0aYO7cudLtRYsW6W2TMVP03bp1C7Vr10a3bt1gbW2N1atX4+HDh0hPT8fDhw+xevVqVKxYEZcvX0bfvn0xc+ZMo20ybgiw3Dh4EzHFZJWKDGdnZ2k7e8YzQzRnh9WsS4WTSqVC3759pZnrqlevjp07d8LOzk5nec33POdMwboYipe8bIvy3oIFCxAVFQVra2ssW7Ys1/XLcjFmig/NM22CgoLQpEkTvWUHDx4snQ7877//as12yZgpPh48eIC3334bly5dQkBAAE6fPo0BAwagVKlSsLGxQalSpTBgwAD8+++/qFSpEgDg888/1znbKuOGAMuNgzcdU0xWqchwc3OTthMSEgyWzczMlNbdtLa21lrihAoftVqNgQMHYvPmzQCAKlWq4MCBAwavpTAlXnKW0ayb121R3rp58yZmzZoFAJgwYQJq1apldluMmeJDc4yNzXbp5OSEKlWqAMjaaRYTE6OzHcZM0TZ37lzEx8cDAL744gu9a4KXLFlSa8ksXUfjGTcEWG4cvOmY4jWrVGQEBgbi9u3bAIA7d+5ozT6W071796BSqQBkLc5t7pEWKnhqtRqDBg3C+vXrAWS9nwcPHoSPj4/BeoGBgTh06BCArHhp0aKF3rKZmZm4f/8+gKxlcHKeDhgYGCht37lzx2ifNX/MatalvLdu3TqkpKRAoVDA2tpa77qqFy5ckLb/+usvaSbFtm3bokGDBgAYM8VJ1apVERUVBQBwdXU1Wl6zzPPnz6VtxkzxsWPHDmm7devWBsu2atVK2o6MjMz1OOOGAMuNgzcdU0xWqcioUaMG9uzZAyBr2QFDH2rNZQlq1KiR732j/KFWqzFkyBCsWbMGAFCxYkUcOnQIZcqUMVpX830/ffo0wsLC9JY9d+6ctHMjKCgo1zUaOdsy5MmTJ9IXt6enp9Gkml5P9uysQghpWQljtmzZIq2D6OzsLCWrjJnio2bNmtIOMM3kUx/NMpqJK2Om+Hjw4IG0bWwHh+bRJc3TxrMxbgiw3DgwpS21Wo2zZ88CyFpzNSgoyGB5XXg4iYqMdu3aSdvZSas+mteIaNajwkMIgeHDh2PVqlUAstb6yjlbpyF5GS8hISHStbFHjx41eA2H5nMx9goXxkzx0bFjR2n7zJkzBsu+evUK165dAwDY2NjA399feowxU3xoXuecc43LnGJjY6VtzWU9sjFuCLDcOKhevbr0W+vSpUsG4/3kyZPSZXeNGjUyb+UNQVREZGRkCC8vLwFAKBQKER0drbNcXFyccHJyEgCEra2tePz48RvuKb0utVothg8fLgAIAKJChQrizp07JrdTu3ZtqY2dO3fqLJOSkiLKly8vlTt//rzOcl27dpXKLF26VG+/GzZsKJXbtm2byX2m/BEWFia9LytXrtRbjjFTfNSoUUMa9+PHj+stt3jxYqlcy5Ytcz3OmCkemjdvLo353LlzDZadM2eOVLZz5846yzBuip6VK1dK4xsWFiarjqXGwYQJE6QyU6ZM0dv/Hj16SOUWLVpk5NXqxmSVipTvv/9e+lDUqVNHPH36VOvxlJQU0b59e6nMhAkTCqin9DpGjRqllajevn3brHb+/PNPrXZiYmK0HlepVGLIkCFSma5du+ptKyoqSigUCgFAuLq66vxnMWvWLK34VKvVZvWb8p7cZJUxU3xovtcBAQHi3r17ucpERkYKFxcXgz8mGTPFw08//SSNu4ODg9i/f7/Ocvv37xf29vZS2Y0bN+osx7gpesxJVi01Dh48eCAcHR0FAGFtba0z3jVfb9myZUVycrKs15yTQoj/f0EPURGQnp6O1q1b49ixYwCAcuXKYeTIkQgICMC9e/ewYsUKXLlyBUDWBBr//PMPZ7srZGbMmIEvv/wSAKBUKjF//nyt0+70adu2LRwdHXPd37t3b/z+++8Asq7NGDlyJGrUqIGEhASsXr1amvzCx8cHp06dMjhx17Rp0zBv3jwAWTOEDhs2DA0aNEBSUhL++OMP7N27V3rsyJEjRmcZpTdn0KBB0inlK1euxKBBg/SWZcwUH2FhYVi9ejWArOsMhw8fjtq1ayMjIwNHjx7F6tWrkZGRAQAYPnw4li1bprMdxkzRl5GRgSZNmuDff/8FkHV9XmhoKNq2bQsPDw8kJCRg7969+PPPP6V1fNu3b4+dO3fqXauScVN43b59GytWrNC678KFC/jrr78AZF0X37lzZ63Hu3fvjtq1a+dqy1Lj4Oeff8aoUaMAZF0CMXDgQLRo0QKZmZnYtWsXNm/eDCEElEoltm3bhk6dOhkcM73MSnGJLNjTp09Fy5Ytpb05uv5q1apl9tE4KlgtWrQw+N7q+9P3fqemporevXsbrOvv7y+ioqKM9k2tVosJEyZIey51/Xl5eYm9e/fm8ajQ65J7ZFUIxkxxkpGRIYYNG2bwvVYoFGL8+PEiMzNTbzuMmeIhPj5etGvXTtb/pB49eoiXL18abI9xU3gdOnTI5N8p+v73WHIczJ8/X9jY2Ohty8nJSaxdu9aUocuFySoVSWq1Wvz222+iU6dOokyZMsLW1lb4+PiId955R/zyyy8iPT29oLtIZsrrZDXbrl27RI8ePUS5cuWEnZ2d8PT0FI0aNRLffvut0R8UOZ08eVIMGjRIVKxYUdjb2ws3NzdRu3ZtMXv2bF4jbaFMSVazMWaKj2PHjonBgweLSpUqCUdHR+Ho6CgqV64shg8fLs6cOSO7HcZM8bB//34xePBgERQUJFxcXIRSqRQuLi6iRo0aYvjw4QavgdaFcVP45GWyms1S4+DixYtizJgxIjAwUDg5OYkSJUqI6tWriylTpuTJgSGeBkxEREREREQWh0vXEBERERERkcVhskpEREREREQWh8kqERERERERWRwmq0RERERERGRxmKwSERERERGRxWGySkRERERERBaHySoRERERERFZHCarREREREREZHGYrBIREREREZHFYbJKREREREREFofJKhEREREREVkcJqtERHkgJCQECoUCCoUCs2bNKujuFDvPnz/H/PnzERISAm9vb9ja2krvh5ubm9nt+vn5Se2Eh4fnWX+p+Lh16xYmTZqEunXrwt3dHUqlUoqp0NDQgu5enrlz5470uhQKBe7cuVOg/Xn06BFmzpyJxo0bw8PDAzY2NlLfatWqVaB9IyL5rAu6A0T05s2aNQuzZ8/Wum/q1Kn4+uuvZbehUCik7cWLF2Ps2LF51j8iU9y4cQOtWrXC3bt3C7orRFq2bt2Kfv36ISUlpaC7UqycPHkS7777Lp49e1bQXSGi18RklYgAAD/88APGjh0LX1/fgu4KkUn69u2rlahWqlQJfn5+sLbO+hfn7OxcUF2jYuzx48fo37+/lKhaWVmhZs2a8PLygpVV1oltderUKcguFklpaWno2bOnVqIaFBSEMmXKQKlUAsj6jiCiwoHJKhEBAFJSUjBz5kysWLGioLtCJFtUVBROnz4t3V61ahUGDhxYgD0iyrJ27VokJycDAJycnBAZGYmgoKAC7lXRt2PHDty/fx9A1g6C/fv345133ingXhGRuZisEpFk1apV+Pjjj/mDigqNyMhIabt8+fJ5nqgW9HV3VHhpxmb37t2L/Peqn58fhBAF3Q2tcW/atCkTVaJCjhMsERVzJUqUQKlSpQAAKpUK06dPL+AeEcmXkJAgbZcrV64Ae0KkjbFZMDjuREULk1WiYs7W1hYzZ86Ubm/fvh0nTpwowB4RyZeRkSFtZ1+jSmQJGJsFg+NOVLQwWSUiDBs2DIGBgdLtKVOm5Fnb5iz9MWjQIKnOoEGDTGpbpVJh8+bN6NKlCypWrAh7e3u4ubmhWbNmWL58OVQqVa52Xr58iQULFqBp06Zwd3eHra0typQpg+7du+PAgQNmvOr/s3PnTvTs2ROVK1eGo6MjPDw8UK9ePXzxxReIi4szq80DBw5g3LhxeOutt6RlWry9vdGgQQPMmDEDN27ckNWOvuV29uzZg8GDB6NatWpwd3fP86Ue/vnnH0yYMAG1atWCl5cXbG1tUapUKan/ly9fNlhfMz40Z7U+cuSI1tIZebGEhtz41Xy+w4cPAwBSU1MRHh6Otm3bonz58rCzs4OHhwfatm2LTZs26WznyZMnmDVrFurXrw8XFxfY2dmhQoUKGDBgAM6cOSO738nJydi2bRsmTpyIkJAQlC1bFg4ODrC3t0fp0qXRpEkTTJs2DdeuXTNlOCSnT5/GqFGjUKVKFTg7O8PNzQ3Vq1fH2LFjce7cOamcOUs6qdVqbNu2DcOGDUNQUBA8PDxga2uL0qVLo1mzZpgzZw4ePnwou69qtRrbt2/HwIEDUb16dbi5ucHa2hoODg5S3A0ePBjLly83+zOZTTNejhw5It0/e/bsXHHp5+ent5309HSsWrUKPXr0QKVKlVCiRAk4OjrCz88PnTt3xk8//YSkpCRZfdL3ffrPP/9g7NixCA4OhqenJ6ysrF5rmSe5S9ccPnxYq1y2xMRELF68GE2bNkXp0qVhZ2eH0qVLo0OHDli5cqXO7+5smnG2atUq6f5Vq1bp/E4w5PLly5g5cyaaNGmCsmXLws7ODu7u7ggKCsKoUaO03ldDZs2aJT1fSEiIdP/FixcxdepU1K5dGz4+PtJSRomJiTrbiY+Px6JFi9CxY0f4+/vD2dkZTk5OqFixInr27Il169YZHJts+THu+iQlJWH58uXo1asXAgMD4e7uDhsbG5QsWRJ169bF8OHDsXHjRtmzZMfExODrr79Gy5YtUb58eTg4OMDFxQWBgYEICwvD9u3bTe4jFSKCiIqdmTNnCgACgPDw8BBCCLFp0ybpPgDizz//NNiGZtnFixfrLVehQgWp3MqVK2X1LywsTKoTFhYmu+379++LFi1aaPUt51+rVq1EcnKy1MaRI0dE6dKlDdaZOHGi0T5rPu/MmTPFs2fPRJcuXQy26+7uLjZu3ChrTIQQIjo6WjRt2tRgmwCEtbW1+Pjjj0VmZqZJfX7y5Ino3Lmzzjbfeust2f3U58mTJyI0NNRo/5VKpRgxYoRISUnR2Y5mfMj5u337ttl9lhu/ms936NAhceXKFREcHGywX/379xcqlUpqY9OmTcLFxUVveSsrK/Hdd98Z7fMvv/wiHB0dZY2NlZWVGDFihEhNTZU1HpmZmWLs2LFCoVAYbPPTTz8VKpUqV4wZc+zYMaPjBkA4OjqK+fPnG23v5s2bom7durJjxcvLS9Y46KMZL8b+KlSooLONvXv3Cn9/f6P1fXx8xG+//Wa0Tzm/T1+9eiWGDRums01XV1ezX/vt27dlfe4OHTqkVS77vrJlyxp8vQ0aNBBPnjzR2aax7/2cf7o8e/ZMhIWFCSsrK6P1O3TooLcv2TT/z7Zo0UJkZmaKTz75RG/7z54906qvUqnE3LlzRYkSJYz2p2rVquL06dMG+5Mf467LokWLhIeHh6z3wc3NzWBbqampYsKECcLW1tZoWw0bNhS3bt2S3U8qPHh+BBEBAN5//300bNgQp06dAgBMnz4d7777rjTVv6VLSkpC27ZtcenSJQCAv78/KlSogOTkZJw7dw7p6ekAso5KDhgwAJs3b8axY8fQrl07pKamQqFQoHr16vD29sbjx48RHR0ttb1w4UJUrFgRY8aMkdUXlUqF0NBQaQ98yZIlUaVKFSgUCly5ckVaUuHZs2fo3bs3MjIy0LdvX4NtHjhwAN26dcOLFy+k++zt7REUFAQ3Nzc8ffoU0dHRyMzMRGZmJhYsWICbN29iy5Yt0jIZhqSnp+Pdd9+V3n83NzdUqVIFdnZ2eTLJ0L1799CqVStcv35dus/KygpBQUHw9PTEo0ePcPXqVQBZ47ds2TJcvnwZO3fuRIkSJbTaCg4ORrt27QAAN2/exK1btwAA7u7uaNCgQa7ndnBweO3+m+Lhw4fo06cPHj16BACoUqUKypQpg8TERJw/fx5qtRpA1myxPj4++Pbbb7Fx40b06dMHarUa1tbWCA4Ohru7O+7evSsdKVer1ZgwYQICAgLw7rvv6n3+69evS7PQAoCHhwf8/f3h4uKCjIwMxMbGIiYmRmpz2bJliI2Nxc6dOw0edRJCoH///vjtt9+07q9QoQL8/PyQnJyM6OhopKSkYM6cOSYfkVm3bh2GDBkifVaBrGvqq1WrBmdnZ8TFxeHy5csQQiA5ORmTJ09GTEwMFi9erLO9hIQENG/eHA8ePJDus7e3R5UqVeDh4QGVSoXExETcuHFDGq/s98ZcLVq0kI7ORkZGSp/1SpUqISAgQKusj49Prvrr169HWFgYMjMzpftcXV1RtWpV2NjY4Nq1a3jy5AkAIC4uDn369MH9+/cxceJEWf0TQmDAgAHYsmULAMDR0RHVq1eHk5MT7t27h/j4eNNf9GvK/h5OT0+HQqFAtWrV4OPjg8TERFy4cEGKo8jISISGhuLo0aO5vtMaNGgAe3t7AFlHLrPf8zJlyiA4ONhoH2JjY9G+fXtcuXJFus/KygpVq1aFj48PUlJSEB0dLR3N3rVrFxo3boyjR49Kcz4YM2nSJHz//fcAsi6/yT7Kr/ndly0tLQ19+/aV3qdsFSpUQPny5QFkrS+d/R1z9epVtGjRAjt27ECLFi1k9Scvxl1TRkYGwsLCsGHDBq37S5QogcDAQLi6uuLly5e4fv06nj9/DgB6jyYDWf8f33vvPRw/flzr/oCAAJQtWxYZGRm4evUqnj59CgA4deoUGjVqhEOHDhX5ycyKnQJOlomoAOg6siqEEIcPH9baU7l8+XK9bWiWs4Qjq9l7chs3biyioqK0ysXHx+c6yrl7927piOqQIUPEgwcPtOpcvHhRBAYGSuVdXFzEy5cv9fZFc8++l5eXtNc4PDxcpKenS+XS09NFeHi4cHNzk8o7ODiIGzdu6G37+vXrWnvXfX19xdq1a0VaWppWuadPn4opU6ZoHfWaPXu2rD5nH9Hz9fUVmzdvFhkZGVplDfXPGJVKJZo1a6Y1/oMGDco15jdu3BAdOnTQKjdw4ECDbec8epHXzDmymh2LnTt3zjVuMTExonHjxlJZW1tbcfjwYeHo6CgUCoWYOnVqriMsx44dE97e3lKdgIAAoVar9fZl0qRJIiQkRCxfvlzcvXtXZ5lbt26JESNGaPV70aJFBsdi6dKlWuVr1aolIiIitMokJSWJefPmCVtbW6FQKLSOsBg6snr8+HFhbW0tlQ0KChJ//fVXrrMD7t+/LwYPHqzVj1WrVulsc+LEiVKZEiVKiF9//VXn0XqVSiWioqLEZ599JoKDgw2OgSlMPap84cIFYWdnJ9VxdXUVK1as0Pqcq1QqsWXLFq2zQRQKhdi/f7/edjW/T7M/5+7u7mL58uW5jqi/zufc3COrnp6eAoAYOXJkru+Ehw8fik6dOmmVX7t2rcF+yP3/kS01NVXUqlVLqmNvby/mzJkjEhIStMqlp6eLFStWaJ390KpVK62zIzRpfjdlf3/b2dmJb775Jtf/kjt37mj9nxg5cqTWaw4LCxPXr1/P9RwHDx4U1apVk8qVKlVKxMXF6exPfo/7+PHjtcoHBQWJbdu2ab0uIYRQq9Xi7NmzYurUqcLd3V1nW2q1Wut/gZWVlZg4caK4d++eVjmVSiW2bt0qypQpo/W8mmdPUeHHZJWoGNKXrAohRMeOHaXHypYtq/dL39KS1exkRd/pjGlpaaJixYpaSQIAMXXqVL3tX758WSiVSqM/ioXIfRqavb19rh/ymiIiIoS9vb1U/t1339VbtlGjRlr/iI2dkvXLL79ovc6HDx/K6nPp0qVFTEyMwbbNsXz5cq3nmTRpkt6yKpVKvP/++1rlDx48qLe8JSarAETfvn31JpRPnjwRrq6uuWLx559/1tv+nj17tNo/cuSI3rKGdqrk9NVXX0ltli9fXu+p4y9fvtTqc3BwsHj+/Lnedjdu3JhrTPQlbGlpaVrjHBISYvTH5vTp06Xy3t7eOpPQSpUqSWWWLVtmsL1sxk6dN4WpyWqTJk2k8o6OjiIyMlJv2Rs3bkg7xQAIf39/vX3Pedq8o6OjOHfunLkvSy9zk1UAYt68eXrbTUtL00rIWrZsabAfpiar06ZNk8o7OzuLU6dOGSx/9uxZrdPs//jjD53lNL+bshOu3bt3G+3P7t27teotXbrUYPnExESt8Rk3bpzOcvk57gcPHtRqt3Xr1iIpKcnoa33x4oXO+3/++WepLWtra7F9+3aD7cTGxgofHx+pzoIFC4w+NxUeTFaJiiFDyeqFCxe0rqn56quvdLZhacmqUqkU//33n8F2586dq9XvwMDAXEcQc2rdurVUfvjw4XrL5Uz8Pv30U4PtCiHEjBkztH7I6Ppxp/kDQ6lUigsXLhhtVwghWrZsafSHcs4+r1+/XlbbptK8BjEoKMjomD99+lTriNx7772nt6wlJqtubm4GEzkhhBg+fLhWHWM/wIUQIiAgQCo/d+5cU1+KTiqVSvj6+krt6tvBorkDBIA4efKk0ba7desmK1lduXKlVMbFxUU8evTIaNuZmZmicuXKBt8bzevcrly5YrTNvGZKsnr69GmtsZLz/q5atUqrzpYtW3SWy5msfvnll+a8HKPMTVYbN25stG3N+HNwcDD4HWJKsvr8+XOtI6XGEsNsn3/+udHvnZzJ6ogRI2S1rRk3PXv2lFXn6NGjUh0nJyedO6zyc9xbtWollStVqlSuo9KmyMzM1NqxPGXKFFn1Vq9eLdXRdz04FU6cDZiItAQHB6N///7S7Xnz5knXhFiyNm3awN/f32CZt99+W+v2kCFDjC5toFnH2Cy12ZRKpazrW8eOHStdE6xWq7F169ZcZTRnoG3Xrp2s668AICwsTNret2+f0fIeHh54//33ZbVtihs3buDixYvS7fHjxxsdc3d3dwwZMkS6vWvXLtmzRlqC3r17w8XFxWCZnLE4YsQIo+2aE4vGWFlZoWHDhtLtyMhIneX+/vtvabtWrVpo1KiR0bZHjx4tqw+aMd6/f3+d13LmpFQqtb6ndMW45rXKZ8+eldWXgqJ5baK9vb2s749+/fqhdOnSOtvQR6lUYvjw4eZ1Mp/Iea2a12GmpKTg9u3befLcW7ZskeYBcHNzw9ChQ2XV05xV+eTJk1rXiOvzwQcfGC1z584drdmGJ02aJKs/zZo1k/7/vXr1Cv/884/ROnk17nFxcVqz5n/44YcoWbKknG7rdOzYMfz3338Asr6fJkyYIKtez549pc98TEyM7FnxyfIxWSWiXL744gvY2dkByJoA4csvvyzgHhkn58dzzokw5NTR/DGYPVmKMXXq1JE16UapUqVQu3Zt6Xb25EaaNH+4tG7dWtbzA8Bbb70lbctZ8qRRo0awsbGR3b5cOX80de7cWVa9Ll26SNsZGRk4ffp0nvYrP1lSLMbGxiI8PBwTJ05Ev3798N5776F9+/Zaf5rv0b1793S2oxmbrVq1kvXczZo1MxpTaWlpWm2bG+O64kMzCR87diw2bdpk1jIcb4Lme9CsWTO4uroaraNUKtGpUyfp9smTJ43WqVatGjw9Pc3rZD5p0qSJ0TK+vr5atw1NzGMKze/X5s2bw9bWVla9cuXKScv8ZGRk4Pz58wbLu7i4yFr+S7M/Li4uqF+/vqz+AMY/Dznl1bjnXMqnZ8+eRts1RLO9GjVqyJ7Ays7ODlWqVJFuF6b/GWQYZwMmolzKly+PMWPGYOHChQCAH3/8EePHj5dmIbREcv6hOTo6vlYdOXvPAcg++pldNvufquZMuUDW+q+aM/GuWrUKe/bskdWu5pHItLQ0vHjxwuDRvkqVKsnusyk09257enqiTJkysupp/vACsmb9bdasWZ72Lb9YQixGR0fj448/xr59+yCEMNp2Nl0/RtPS0vD48WPpdrVq1WS1ZWtri0qVKuWa6VTT9evXkZqaKt3+5ptv8Msvv8hqX/OMj+wZcjVNnjxZev1Pnz5Fz5494enpiXbt2qFZs2Zo3LgxatSoYXTdzTdB83OSM/YNqVmzprR9+/ZtqFQqgzO459fn/HXIiX0nJyet23K/i425cOGCtH327Fm0b99edl3NuNUVf5r8/f1lxZlmf1QqlUn90TyDxVh/gLwbd82zPEqWLImKFSsabdcQzTF48OCBSWOQPcs5IG8MqHBgskpEOs2YMQMrVqzA8+fPkZaWhs8++0zrdD1LI3eP+OvUkfuj38PDQ3abmmVzHi1LSEjQun3+/Hmje/D1SUxMNJisGjtt1Vyar8nLy0t2PWdnZ9jZ2SEtLS1XO5auoGNxx44d6N69uzR2ptBVJ+fYZx9RksNY2ZwxHhERIbttTbqS7NatW+Onn37C+PHjpeVw4uPjsW7dOqxbtw5A1o/rtm3bYsCAAejQoUOBJa7mfk40ywoh8Pz5c4OnYObX5/x1ZJ/FYwpTdsAYohl/d+/exd27d81qx9iRXrnjrtmfV69eyd45aWp/gLwbd82dRt7e3ia3mZPmGMTHx+frGFDhwNOAiUinkiVLYurUqdLtNWvWaK09SvqZknho/mDImSi8evUqz/pkbP1IOWuxmkPzNZmakGmOjeZRDNLv/v376NWrlzTujo6O+OCDD7B161ZcuXIFiYmJSEtLg8iaYBFCCK3rm+UwJaEzllTkVYzre56RI0fi6tWrGDt2rM4k8OnTp/jtt9/QqVMn1K9f3+BR4Pxk7uckZ8Jh7HOSX5/zwiqv4i+vvl/fVH/ykmbMmZMA51QYx4DyF7+1iEivjz76CGXLlgWQ9cU/bdq0N/K8lnpdmVwvX76UXTZ7cg8Aua5Ty3lUKvuURnP+/Pz8XuclmU3zNZgyLkIIJCUl6WyH9Pvuu++kH3uurq74999/8dNPPyE0NBRVq1aFq6trrmTI2Pvi7u6udduUo9zPnz83+HjO9/XGjRtmx7g+/v7+WLx4MeLi4nD+/HksXboUffr00boGGMi6trt58+ZmH117HeZ+TjS/P3K2Q8ZpjteMGTPMjj3NCZfyqj9NmjQxuz9v8iwoze8HY593OTTHoF+/fmaPwaxZs167L2QZmKwSkV4ODg5aX/g7duzINZmCMZo/jDMyMmTVKUynfOpiykyV2bMeAsg1C6qXl5fW9Wc5r2ktDDRPC7t79650OqYxt2/f1toznhenlxUHu3fvlrY//PBDBAUFGa1jLDmzs7PTGv8rV67I6kt6ejpu3bplsEzO6+byM8YVCgVq1qyJUaNGYf369bh//z5OnDihNUnRkydPMGfOnHzrgz6a42tszDRplnVycsp1LTQZphl/lvD9amn9kUNzp8+9e/de+3riwjgGlL+YrBKRQYMHD9aaUEXz1GA5NK/VkbsEjuZEEYVRZGSkrFOQ1Gq11oyFdevW1Xrc1tZW6769e/fmXSffEM3+mzKrb86ZTevVq5en/SqqNCcYadCggdHySUlJsq6D1mxLc5kKQ44dO2Z0B1WlSpW0fpy+yRhXKBRo3Lgxtm/fjubNm0v3ayb8b4rm50TOrL66yvIzYrrGjRtL2wcOHCjws3o0+/PkyROcO3eu4Dojk2afMzMzcfz48TxrLyoqCvHx8a/VHhV+TFaJyCClUqm1dM2pU6fwxx9/yK5foUIFaVtzlj99zpw5g9jYWNM6aWHi4uJw8OBBo+X279+PuLg46bbmmnbZOnbsKG3v3LlT60hsYdCwYUOt9S7XrFkjq96qVauk7QoVKhTYacyFjdyzF7KtWbNG1tFuzSWHzp07J2sipKVLl8rqg+Zsn6tXr86TUwlNYWVlhdDQUOn2o0eP3ujzA0BISIi0fePGDZ3LWOUUGxuLQ4cO6WyD5NH8fn369CnWr19fgL3JWj5HcwbexYsXF2Bv5Klbt67W9eA//fTTa7XXvn176bp4lUr12u1R4cdklYiMCg0N1drbOWPGDNl1Nff279y5U+s6xJyEEJg+fbp5nbQw06dPN7iXPjMzU2scS5UqpfXDKdvo0aOlHy8ZGRkYNmwYMjMz877D+cTJyQl9+/aVbq9YsQKXLl0yWGfXrl3Yv3+/dHvkyJH51r+iRnNpoKNHjxosGxcXh88++0xWu3379tU6S2LkyJG5rpfUtGnTJmzZskVW25MmTZJ+nD579gxjx46VVc8QU2eL1fxeMjSbbn7p1auX1jXrkydPNnp2hmYZpVKJoUOH5msfi6IWLVponTUwZcoUvWsNvwlOTk4YPXq0dNuU5coKilKpxLhx46Tbf/75p+zPvi7+/v54//33pdvz5s0zexZ8KhqYrBKRLPPmzZO2r127Jrue5j+dxMREfPTRRzrLZWRk4IMPPsC+ffvM7qMlOX36NAYPHqzzqFVaWhqGDBmidUrstGnTYG2dezUxLy8vzJw5U7p96NAhdOzYUdbRnzNnziAsLKzAjxZMmTJFOrqakZGBTp06aa0rqemff/5Bnz59pNulSpXCBx988Eb6WRS0bNlS2l6yZIne065jY2PRpk0b2afYOTs74+uvv5ZuX7hwASEhIYiMjNQq9+rVK8yfPx/9+/eHQqGQtYxT9erVMWrUKOn22rVrMWDAAKNHWNVqNQ4fPozQ0NBciXlMTAwaNWqEzZs3G13C59atW1iyZIl0+5133jHa57zm5OSESZMmSbePHTuGYcOG6fz+UKvVmD59OjZu3CjdN2zYMJQrV+6N9LWo+e6772BjYwMg66h6ixYt8O+//xqtFxsbi08//RQff/xxnvZn6tSp0nupUqnQvXt3WWekPH/+HD///DPatm2bp/2RY/z48Vpnv/Tt29donxMTE7XO2tL05ZdfSjvHXr16hTZt2sg6Pf/x48f45ptv0L9/f/mdJ4vHdVaJSJamTZuic+fO+Ouvv0yqV7lyZXTv3l06dXjFihW4du0ahg4diooVKyI5ORlRUVH43//+h1u3bqFMmTKoXr16oU5au3btil27dmHNmjWIjIzEiBEjULNmTQghcPHiRSxbtkwr4W/SpInWnumcJk+ejAsXLmDt2rUAsmYFzt77/M4776BcuXJwcHDA8+fPcffuXZw9exZ79+7FnTt3ABTMj29NgYGBWLhwoZSQxMTE4K233sLgwYPRunVreHh44NGjR/j777+xfv166Yi0UqlEeHh4rtloSb+PPvoI4eHhUKlUePXqFZo1a4Zhw4ahTZs2KFmyJB4/fowDBw4gPDwcycnJKFeuHIKDg7Fz506jbX/wwQc4cuQIfv/9dwDA2bNn0bBhQ/j5+cHPzw/JycmIjo6WJliZPn06Tp48KU3KZmhZi++//x6XL1/G4cOHAWQlrNu3b0fv3r3RrFkzlC5dGra2tkhMTMTt27cRFRWFPXv2SDttdO0Ei4iIQI8ePeDi4oJ27dqhfv36CAwMlOLp/v37OHLkCNauXSvNoGxra4tPPvlE3mDnsenTp2P37t04ceIEAGDlypU4ceIEhg0bhrfeegtKpRJXr17F//73P0RFRUn1qlSpggULFhRIn4uCxo0bY8mSJRg5ciSEEPjvv//QoEEDtGnTBh06dEDVqlVRokQJvHr1CnFxcbhw4QKOHDki7QgydeknYzw8PLBt2zY0b94cSUlJePXqFQYOHIhvvvkG3bt3R+3atVGyZEmkp6fj6dOnuHTpEiIiInDo0CGkp6drXXrzpri6umLjxo0ICQlBcnIy0tLSMHDgQPzwww94//33UaNGDbi6uuLFixe4du0ajh49it27dyM1NVXn5y0gIAAbNmxAaGgoMjIy8OTJE3To0AFvv/02unTpgho1asDNzQ0pKSmIj4/HxYsXceLECZw4cQIqlUrnJTVUeDFZJSLZvv76a+zcudPkSSh+/PFHnD9/Hjdv3gQAHD9+XOckDF5eXti+fXuhuE7HkJo1a6J79+4YNGgQrl27ZnDPe506dfDXX38ZXYdv1apV8PPzw9y5cyGEQGpqKtauXSslsJbugw8+QFpaGiZMmAAhBFJSUrB06VK91zXa29tjw4YNaNeu3RvuaeFWo0YNLFy4EB9++CGArDUQf/zxR/z444+5ynp5eWHr1q2yP28KhQLr1q2Dp6cnli5dKp1qe+fOHWnHSHa5Tz75BLNnz0aTJk2k+3MuzaTJxsYGu3fvxpgxY7BixQoAWcuyLFu2DMuWLZPVP31evHiBTZs2YdOmTQbLOTg4YP369ahRo8ZrPZ+5lEoldu7cidDQUOla1OvXr2PKlCl669SqVQu7du3Sus6RTDd8+HB4enoiLCxMWjpo3759BbbTtHbt2jh16hS6du0qzYgbHR1t0Wud169fH0ePHsV7772HBw8eAMg6w0jupHo5dezYEQcPHkSPHj2knVIRERGyrpenooWnARORbEFBQWbtRS5VqhSOHTuGnj17StemaVIqlQgNDcW5c+dyzYhbWPXr1w9HjhxB7dq1dT7u5OSEadOm4fjx47KOHFpZWeGLL77AuXPn0KtXL61Ji3QpWbIkevTogT/++EPrmtGC9OGHH+Lff/9F27Zt9Sbntra26NWrF6Kjo7UmvSH5xo8fj82bN+udlMrW1hY9evTAhQsXTP68KZVK/Pjjj9IZAwEBAXB0dISLiwuCgoIwevRonDlzBnPmzIFSqdSaQExzEhZd7Ozs8Ouvv+LYsWPo0KGDdGqmPqVLl8bAgQOxe/durdl8gaxloL799lu0bNnS6GfF0dER/fr1s4iYc3Fxwb59+/DLL78YnFTMx8cH33zzDSIiInIt/0Pm6dq1K27cuIHJkyfD09PTYFk7Ozu0bNkSS5YswcKFC/OlP0FBQTh//jx+/PFHBAYGGiyrUChQu3ZtzJw5U/Zs3fmhbt26uHLlCj777DOjn/fg4GB8++23Bss0bdoU165dw5w5c+Dr62uwrLW1NRo1aoRvvvmmwC99obylEKbOQkBE9BoePnyIQ4cO4f79+1AqlfD19ZVO8yuqoqOjcfbsWTx48AAODg7w9/dHq1atXmtNxLS0NERERODWrVtISEhARkYGnJ2d4evri6pVq6Jq1apGj9YWpISEBBw5cgQPHjzAixcv4O7ujvLly6NFixZwdnYu6O4VCSqVChERETh37hwSExPh7u6OsmXLokWLFnBzc8v353/8+LHW2sE3btxAQECA7PqvXr3CiRMnEBsbi4SEBKjVari4uKB8+fKoVq2a7LYyMjIQHR2NGzdu4MGDB0hKSoKNjQ3c3d1RtWpV1K1b12KPTF68eBFnz57F48ePoVar4eXlheDgYNStW1fnjj/KG0IInD9/HhcvXkRCQgKSkpLg5OQEb29vBAYGIjg4GPb29m+0TzExMYiMjERcXBwSExNhb28Pd3d3BAQEIDg4uEAmBjNECIGzZ8/i4sWLePLkCdLT01GiRAn4+/ujTp06WpPByXXt2jWcOXMG8fHxePHiBRwcHODp6YnKlSsjODgYJUqUyIdXQgWNySoRERHlublz5+LTTz8FkHV2xYMHD5hgERGRSSx3tzsRERFZFLn7t8+cOYO5c+dKt4cMGcJElYiITMYjq0RERCTL4MGD4ebmhh49eqBhw4ZQKpVajyckJODXX3/F559/Ls0K7OnpiejoaK1TgomIiORgskpERESyhIaGYtu2bQCyZs8NDAxEyZIloVKpEBcXh+vXr2sdfbW1tcXWrVvRsWPHguoyEREVYly6hoiIiGTRnLQrJSUF58+f11u2UqVKCA8PR9OmTd9E14iIqAjikVUiIiKSJTk5GXv27MGhQ4cQFRWFO3fuSLNRu7q6wtvbGw0bNkSHDh3QvXt3WFtznzgREZmPySoRERERERFZHM4GTERERERERBaHySoRERERERFZHCarREREREREZHGYrBIREREREZHFYbJKREREREREFofJKhEREREREVkcJqtERERERERkcZisEhERERERkcX5f38a3moq8uh2AAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "plt.errorbar(num_images, np.array([mean_samples1[0], mean_samples2[0], mean_samples3[0], mean_samples4[0]]) , \n", + " yerr=np.array([std_samples1[0], std_samples2[0], std_samples3[0], std_samples4[0]]),marker='o', fmt='o', label = 'MCMC')\n", + "plt.errorbar(np.array([3, 98, 498, 998]), posteriors_all[:,0] , yerr=posteriors_all[:,1],marker='o', fmt='o', label='Analytical')\n", + "plt.hlines(true_w, 0, 1000, ls='--', color='k')\n", + "plt.xlabel('Number of images for inference')\n", + "plt.ylabel(r'$Predicted\\ w$')\n", + "plt.legend()\n", + "plt.savefig(\"MCMC_analytical_posterior_w\"+str_true_w+\".pdf\")\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:.conda-bison]", + "language": "python", + "name": "conda-env-.conda-bison-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.4" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/NRE_varyastro_w1.ipynb b/notebooks/NRE_varyastro_w1.ipynb new file mode 100644 index 0000000..a648919 --- /dev/null +++ b/notebooks/NRE_varyastro_w1.ipynb @@ -0,0 +1,666 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "6655e2be", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "!export LD_LIBRARY_PATH=/opt/conda/lib\n", + "!export XLA_FLAGS=--xla_gpu_cuda_data_dir=/home/jarugula/cuda_data_dir\n", + "!export PATH=/usr/local/cuda-11.7/bin:${PATH}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c2386057", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# import the libraries\n", + "\n", + "import logging\n", + "import warnings\n", + "import json\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from sklearn import metrics\n", + "from sklearn.metrics import confusion_matrix\n", + "from sklearn.metrics import auc, roc_curve\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib\n", + "import random\n", + "from tqdm import tqdm\n", + "import pandas as pd\n", + "from scipy.stats import uniform, norm\n", + "import emcee\n", + "from multiprocessing import Pool\n", + "import time\n", + "\n", + "import h5py\n", + "import os\n", + "from tqdm import tqdm\n", + "import tensorflow as tf\n", + "from tensorflow import keras\n", + "from tensorflow.keras import layers \n", + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import Reshape,Conv1D,Flatten,Dense, Lambda\n", + "from tensorflow.keras.models import Model\n", + "from sklearn.model_selection import train_test_split\n", + "from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau, TerminateOnNaN\n", + "physical_devices = tf.config.list_physical_devices('GPU')\n", + "try:\n", + " tf.config.experimental.set_memory_growth(physical_devices[0], True)\n", + "except:\n", + " pass\n", + "from sklearn.utils import shuffle\n", + "AUTOTUNE = tf.data.AUTOTUNE\n", + "from sklearn.preprocessing import StandardScaler\n", + "import tqdm\n", + "import gc\n", + "import wandb\n", + "os.environ['PATH']=os.environ['PATH']+':/usr/local/cuda/bin'\n", + "os.environ['XLA_FLAGS']='--xla_gpu_cuda_data_dir=/home/jarugula/cuda_data_dir'" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "e9bdd356", + "metadata": {}, + "outputs": [], + "source": [ + "# define your plot style\n", + "\n", + "best_style = {\n", + " \"font.family\": \"sans-serif\",\n", + " \"mathtext.fontset\": \"custom\",\n", + " \"mathtext.rm\": \"TeX Gyre Heros\",\n", + " \"mathtext.bf\": \"TeX Gyre Heros:bold\",\n", + " \"mathtext.sf\": \"TeX Gyre Heros\",\n", + " \"mathtext.it\": \"TeX Gyre Heros:italic\",\n", + " \"mathtext.tt\": \"TeX Gyre Heros\",\n", + " \"mathtext.cal\": \"TeX Gyre Heros\",\n", + " \"mathtext.default\": \"regular\",\n", + " \"figure.figsize\": (10.0, 10.0),\n", + " \"font.size\": 26,\n", + " \"axes.labelsize\": \"medium\",\n", + " \"axes.unicode_minus\": False,\n", + " \"xtick.labelsize\": \"small\",\n", + " \"ytick.labelsize\": \"small\",\n", + " \"legend.fontsize\": \"small\",\n", + " \"legend.handlelength\": 1.5,\n", + " \"legend.borderpad\": 0.5,\n", + " \"xtick.direction\": \"in\",\n", + " \"xtick.major.size\": 12,\n", + " \"xtick.minor.size\": 6,\n", + " \"xtick.major.pad\": 6,\n", + " \"xtick.top\": True,\n", + " \"xtick.major.top\": True,\n", + " \"xtick.major.bottom\": True,\n", + " \"xtick.minor.top\": True,\n", + " \"xtick.minor.bottom\": True,\n", + " \"xtick.minor.visible\": True,\n", + " \"ytick.direction\": \"in\",\n", + " \"ytick.major.size\": 12,\n", + " \"ytick.minor.size\": 6.0,\n", + " \"ytick.right\": True,\n", + " \"ytick.major.left\": True,\n", + " \"ytick.major.right\": True,\n", + " \"ytick.minor.left\": True,\n", + " \"ytick.minor.right\": True,\n", + " \"ytick.minor.visible\": True,\n", + " \"grid.alpha\": 0.8,\n", + " \"grid.linestyle\": \":\",\n", + " \"axes.linewidth\": 2,\n", + " \"savefig.transparent\": False,\n", + "}\n", + "plt.style.use(best_style)\n", + "cols = [\"#5790fc\", \"#f89c20\", \"#e42536\", \"#964a8b\", \"#9c9ca1\", \"#7a21dd\"]\n", + "#set cols as the matplotlib default color cycle\n", + "plt.rcParams['axes.prop_cycle'] = plt.cycler(color=cols)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "f585fd63-bc24-4dca-8935-597ae91af163", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# The test data for population analysis by fixing w=-1.0\n", + "\n", + "data_path = '/deepskieslab/stronglensing/hsbi/datasets/'\n", + "image_dir = 'w0_8param_fixzv_test_fixw0-1_5000'\n", + "column_name = \"w0-g\"\n", + "fig_title = 'w0'\n", + "\n", + "str_true_w = '-1' # Dark energy equation-of-state parameter \n", + "true_w = -1.0\n", + "xlim_min = -1.1\n", + "xlim_max = -0.9" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3067c2a6", + "metadata": {}, + "outputs": [], + "source": [ + "# Load the trained model \n", + "\n", + "model = tf.keras.models.load_model(\"working_model_1M-2-034_seed38_v2.keras\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "d2e53fb0-4669-4f00-90a9-3664361c0ec9", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Read the images and metadata and pre-process the data\n", + "\n", + "images_test = np.load(data_path+image_dir+'/CONFIGURATION_1_images.npy', allow_pickle=True)\n", + "metadata_test = pd.read_csv(data_path+image_dir+'/CONFIGURATION_1_metadata.csv')\n", + "\n", + "\n", + "fixed_images_test = np.einsum('lkij->lijk',images_test)\n", + "fixed_true_theta_test = metadata_test[column_name].to_numpy()\n", + "\n", + "#normalize image each image by the sum of all pixels, make it such that the sum of all pixels is 1024\n", + "fixed_images_test = 1024*(fixed_images_test/np.sum(fixed_images_test, axis=(1,2), keepdims=True))\n", + "\n", + "#manually standardize the images and theta\n", + "means_image = 0.0\n", + "std_image = 1.0\n", + "\n", + "mean_theta = 0.0\n", + "std_theta = 1.0\n", + "\n", + "fixed_images_test = fixed_images_test.reshape(fixed_images_test.shape[0], -1)\n", + "fixed_images_test = (fixed_images_test - means_image) / std_image\n", + "fixed_images_test = fixed_images_test.reshape(fixed_images_test.shape[0], 32, 32, 1)\n", + "\n", + "fixed_theta_test = (fixed_true_theta_test - mean_theta)/std_theta" + ] + }, + { + "cell_type": "markdown", + "id": "b400de33", + "metadata": {}, + "source": [ + "### Calculate the Analytical Posterior \n", + "\n", + "The analytical equation to calculate the posterior is given by\n", + "\n", + "\\begin{equation}\n", + "\\begin{split}\n", + " p(w|\\{x\\}) &= \\frac{p(w)~\\prod_{i}r(x_i|w)}{\\int dw^{\\prime}~ p(w^{\\prime})~\\prod_{i}r(x_{i}|w^{\\prime})},\\\\\n", + " &= p(w)~\\left( \\int dw^{\\prime}~p(w^{\\prime})~\\prod_{i}\\frac{r(x_i|w^{\\prime})}{r(x_i|w)} \\right)^{-1}.\n", + "\\end{split}\n", + "\\end{equation}\n", + "\n", + "```likelihood_diff``` function calculates $log\\ r(x|w^{\\prime}) - log\\ r(x|w)$ for one image $x$ \n", + "\n", + "This is same as calculating $\\frac{r(x_i|w^{\\prime})}{r(x_i|w)}$ in the posterior equation\n", + "\n", + "```get_joint_likelihood``` function calculates $\\sum_{i} log\\ r(x_{i}|w^{\\prime}) - log\\ r(x_{i}|w)$ for a population of strong lens images $\\{x_{i}\\}$\n", + "\n", + "This is same as calculating $\\prod_{i}\\frac{r(x_i|w^{\\prime})}{r(x_i|w)}$ in the posterior equation\n", + "\n", + "```get_joint_posterior``` calculates the sum of posterior for all the theta ($w$) values and gives the inverse of the sum as shown in the equation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ad50c440-17d6-4b72-b252-5d0c4469844e", + "metadata": {}, + "outputs": [], + "source": [ + "import numba as nb\n", + "\n", + "@nb.jit\n", + "def get_logr_distribution(model, images, sample_theta):\n", + " '''\n", + " Function to predict the log likelihood-to-evidence ratio (logr) of the test data\n", + "\n", + " Input:\n", + " model: The trained model \n", + " images: test images\n", + " sample_theta: a list of theta values to compute logr for\n", + " '''\n", + " output_probs = []\n", + " for image in images:\n", + " test_image_array = np.concatenate([image[np.newaxis, :]]*len(sample_theta), axis=0)\n", + " output = model.predict([test_image_array, sample_theta], verbose=0).flatten()\n", + " output_probs.append(output)\n", + " return np.array(output_probs)\n", + "\n", + "class Posterior:\n", + " def __init__(self, lnr, thetas):\n", + " self.lnr = lnr\n", + " self.thetas = thetas\n", + "\n", + " def likelihood_diff(self, image_index):\n", + " # exp_diff_lnr = np.empty((len(self.thetas), len(self.thetas)))\n", + " diff_lnr_list = np.empty((len(self.thetas), len(self.thetas)))\n", + " for i in range(len(self.thetas)):\n", + " diff_lnr = self.lnr[image_index, i] - self.lnr[image_index]\n", + " # exp_diff_lnr[i] = np.exp(diff_lnr)\n", + " diff_lnr_list[i] = diff_lnr\n", + " # return exp_diff_lnr\n", + " return diff_lnr_list\n", + "\n", + " def get_joint_likelihood(self, n_images):\n", + " likelihood = np.empty((n_images, len(self.thetas), len(self.thetas)))\n", + " for i in range(n_images):\n", + " likelihood[i] = self.likelihood_diff(i)\n", + " # joint_likelihood = np.prod(likelihood, axis=0)\n", + " joint_likelihood = np.sum(likelihood, axis=0)\n", + " joint_likelihood = np.exp(joint_likelihood)\n", + " return joint_likelihood\n", + " \n", + " def get_joint_posterior(self, n_images):\n", + " joint_likelihood = self.get_joint_likelihood(n_images)\n", + " joint_posterior = 1. / np.sum(joint_likelihood, axis=0)\n", + " return joint_posterior\n", + " \n", + "def get_joint_posterior_probability(lnr, thetas, n_images):\n", + " '''\n", + " Function to sample from the posterior probability distribution.\n", + "\n", + " Output:\n", + " The posterior probability, mean and standard deviation\n", + " '''\n", + " posterior = Posterior(lnr, thetas)\n", + " joint_posterior = posterior.get_joint_posterior(n_images)\n", + " sampled_values = np.random.choice(thetas, size=1000, p=joint_posterior)\n", + " weighted_mean = np.mean(sampled_values)\n", + " weighted_std_dev = np.std(sampled_values)\n", + " # weighted_mean = np.sum(thetas * joint_posterior) / np.sum(joint_posterior)\n", + " # weighted_std_dev = np.sqrt(np.sum(joint_posterior * (thetas - weighted_mean)**2) / np.sum(joint_posterior))\n", + " return joint_posterior, weighted_mean, weighted_std_dev\n" + ] + }, + { + "cell_type": "markdown", + "id": "2317bc95", + "metadata": {}, + "source": [ + "### Calculate MCMC posterior" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "346d7c0b-d5d9-4f36-aa53-7c9a3ce98a06", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "def get_logr_mcmc(model, images, sample_theta, mean_theta, std_theta):\n", + " ''''\n", + " Function to predict the log likelihood-to-evidence ratio (logr) of all the test data at a time\n", + "\n", + " Input:\n", + " model: The trained model \n", + " images: test images\n", + " sample_theta: a list of theta values to compute logr for\n", + "\n", + " Output:\n", + " log r \n", + " '''\n", + " sample_theta = (sample_theta - mean_theta)/std_theta\n", + " theta_array = np.array([sample_theta]*images.shape[0])\n", + " output = model.predict([images, theta_array], verbose=0).flatten()\n", + " return output\n", + "\n", + "def log_prior(theta, theta_low=-1.5, theta_high=-0.5):\n", + " \"\"\"\n", + " prior for w\n", + " \"\"\"\n", + " if theta_low < theta < theta_high:\n", + " return 0.0\n", + " return -np.inf\n", + "\n", + "def log_likelihood(theta_, data, theta_low, theta_high, model, mean_theta, std_theta):\n", + " \"\"\"\n", + " Calculate the log likelihood + log prior\n", + " \"\"\"\n", + " theta = theta_[0]\n", + " lp = log_prior(theta, theta_low, theta_high)\n", + " if not np.isfinite(lp):\n", + " return -np.inf\n", + " logr_array = get_logr_mcmc(model, data, theta, mean_theta, std_theta)\n", + " ll = np.sum(logr_array)\n", + " return ll+lp\n", + "\n", + "def get_posterior_mcmc(data, theta_low, theta_high, model, walkers=10, nsteps=10000, initial_w = -1.0, mean_theta=-1.0007, std_theta=0.288409, multithread=False):\n", + " \"\"\"\n", + " MCMC sampling\n", + "\n", + " Output:\n", + " Sampler and Samples\n", + " \"\"\"\n", + " pos = np.array([initial_w])+ np.array([initial_w])*1e-3* np.random.randn(walkers, 1)\n", + " nwalkers, ndim = pos.shape\n", + " sampler = emcee.EnsembleSampler(nwalkers, ndim, log_likelihood, args=(data, theta_low, theta_high, model, mean_theta, std_theta))\n", + " \n", + " if multithread:\n", + " with Pool(10) as pool:\n", + " sampler = emcee.EnsembleSampler(nwalkers, ndim, log_likelihood, args=(data, theta_low, theta_high, model, mean_theta, std_theta), pool=pool)\n", + " print(\"Running first burn-in...\")\n", + " pos, lp, _ = sampler.run_mcmc(pos, 100, progress=True)\n", + " print('Max lp @',pos[np.argmax(lp)])\n", + "\n", + " print(\"Running second burn-in...\")\n", + " pos =pos[np.argmax(lp)] + 1e-4 * np.random.randn(nwalkers, ndim)\n", + " sampler.reset()\n", + " pos, lp, _ = sampler.run_mcmc(pos, 500,progress=True)\n", + " print('Max lp @',pos[np.argmax(lp)])\n", + "\n", + " print(\"Running production...\")\n", + " pos = pos[np.argmax(lp)] + 1e-4 * np.random.randn(nwalkers, ndim)\n", + " sampler.reset()\n", + " pos, lp, _ = sampler.run_mcmc(pos, nsteps,progress=True)\n", + " print('Max lp @',pos[np.argmax(lp)]) \n", + "\n", + " else:\n", + " print(\"Running first burn-in...\")\n", + " pos, lp, _ = sampler.run_mcmc(pos, 100, progress=True)\n", + " print('Max lp @',pos[np.argmax(lp)])\n", + "\n", + " print(\"Running second burn-in...\")\n", + " pos =pos[np.argmax(lp)] + 1e-4 * np.random.randn(nwalkers, ndim)\n", + " sampler.reset()\n", + " pos, lp, _ = sampler.run_mcmc(pos, 500,progress=True)\n", + " print('Max lp @',pos[np.argmax(lp)])\n", + "\n", + " print(\"Running production...\")\n", + " pos = pos[np.argmax(lp)] + 1e-4 * np.random.randn(nwalkers, ndim)\n", + " sampler.reset()\n", + " pos, lp, _ = sampler.run_mcmc(pos, nsteps,progress=True)\n", + " print('Max lp @',pos[np.argmax(lp)])\n", + "\n", + "\n", + " samples = sampler.get_chain(discard=int(nsteps/4), flat=False)\n", + " print(samples.shape)\n", + "\n", + " flat_samples = sampler.get_chain(discard=int(nsteps/4), flat=True)\n", + " print(flat_samples.shape)\n", + "\n", + " return sampler, samples, flat_samples" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "88f0703a", + "metadata": {}, + "outputs": [], + "source": [ + "num_images = [5, 100, 500, 1000, 2000, 3000]\n", + "start_time=time.time()\n", + "for n in num_images:\n", + " sampler, samples, flat_samples = get_posterior_mcmc(fixed_images_test[0:n], -1.5, -0.5, model, walkers=5, nsteps=1000, \n", + " initial_w = -1.0, mean_theta=0.0, std_theta=1.0, multithread=False)\n", + " end_time=time.time()\n", + " print('Time taken for ', n, ' images: ', end_time-start_time)\n", + " start_time = end_time\n", + " #save the mcmc sampler\n", + " np.savez('mcmc_posterior'+str_true_w+'_'+str(n)+'_v2.npz', sampler=sampler, samples=samples, flat_samples=flat_samples)\n", + " del sampler, samples, flat_samples" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "7957ed5f", + "metadata": {}, + "outputs": [], + "source": [ + "def read_mcmc_samples(filename):\n", + " file = np.load(filename)\n", + " # sampler = file['sampler']\n", + " samples = file['samples']\n", + " flat_samples = file['flat_samples']\n", + " mean_samples = np.mean(flat_samples, axis=0)\n", + " std_samples = np.std(flat_samples, axis=0)\n", + " return samples, flat_samples, mean_samples, std_samples" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "9a0e0709", + "metadata": {}, + "outputs": [], + "source": [ + "samples1, flat_samples1, mean_samples1, std_samples1 = read_mcmc_samples('mcmc_posterior'+str_true_w+'_'+str(500)+'_v2.npz')\n", + "samples2, flat_samples2, mean_samples2, std_samples2 = read_mcmc_samples('mcmc_posterior'+str_true_w+'_'+str(1000)+'_v2.npz')\n", + "samples3, flat_samples3, mean_samples3, std_samples3 = read_mcmc_samples('mcmc_posterior'+str_true_w+'_'+str(2000)+'_v2.npz')\n", + "samples4, flat_samples4, mean_samples4, std_samples4 = read_mcmc_samples('mcmc_posterior'+str_true_w+'_'+str(3000)+'_v2.npz')" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "e320f72b", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the Posterior from MCMC sampling\n", + "\n", + "plt.hist(flat_samples1, bins=20, label='500 images', alpha=0.6, histtype='step', lw=1)\n", + "plt.hist(flat_samples2, bins=20, label='1000 images', alpha=0.6, histtype='step', lw=1)\n", + "plt.hist(flat_samples3, bins=20, label='2000 images', alpha=0.6, histtype='step', lw=1)\n", + "plt.hist(flat_samples4, bins=20, label='3000 images', alpha=0.8, histtype='step', lw=2)\n", + "plt.axvline(true_w, linestyle='--', color='k', lw=3)\n", + "plt.xlim(-0.993, -1.004)\n", + "plt.xlabel(r'$w$')\n", + "plt.ylabel('Frequency')\n", + "plt.legend(fontsize=18)\n", + "plt.savefig(\"MCMC_posterior_w\"+str_true_w+'.pdf')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "203dac78", + "metadata": {}, + "source": [ + "### Analytical Sampling" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "98df7565", + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "sample_theta_unstd = np.linspace(-2.0, -0.4, 2000)\n", + "sample_theta = (sample_theta_unstd - mean_theta)/std_theta\n", + "\n", + "lnr_1 = get_logr_distribution(model, fixed_images_test[0:3000], sample_theta)\n", + "# np.savez('logr_w'+str_true_w+'_1000_v3.npz', logr=lnr_1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4558c63b", + "metadata": {}, + "outputs": [], + "source": [ + "import time\n", + "num_images = [5, 100, 500, 1000, 2000, 3000]\n", + "\n", + "posteriors_all_list = []\n", + "posterior_all_samples = []\n", + "start_time = time.time()\n", + "for n in num_images:\n", + " posterior, posterior_mean, posterior_std = get_joint_posterior_probability(lnr_1, sample_theta, n)\n", + " end_time = time.time()\n", + " print('Time taken for ', n, ' images: ', end_time-start_time)\n", + " start_time = end_time\n", + " posteriors_all_list.append((posterior_mean, posterior_std))\n", + " posterior_all_samples.append(posterior)\n", + "\n", + "posteriors_all = np.vstack(posteriors_all_list)\n", + "posterior_all_samples = np.array(posterior_all_samples)\n", + "print('shape of posteriors for ntrials:', np.shape(posterior_all_samples))\n", + "\n", + "# np.savez('analytical_posteriors_w'+str_true_w+'_v3.npz', samples=posterior_all_samples, stats=posteriors_all)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5682f8b9", + "metadata": {}, + "outputs": [], + "source": [ + "def read_analytic_samples(filename):\n", + " file = np.load(filename)\n", + " posterior_all_samples = file['samples']\n", + " posteriors_all = file['stats']\n", + " return posterior_all_samples, posteriors_all\n", + "\n", + "posterior_all_samples, posteriors_all = read_analytic_samples('analytical_posteriors_w-1_v3.npz')" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "cdcfca53", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot the analytical posterior probability for different number of images\n", + "\n", + "for i, pos in enumerate(posterior_all_samples[2:]):\n", + " plt.plot(sample_theta, pos/np.max(pos), label=str(num_images[i+2])+' images',drawstyle='steps-mid')\n", + "plt.axvline(true_w, linestyle='--', color='k')\n", + "plt.xlabel(r'$w$')\n", + "plt.ylabel(r'$p(w | x)$')\n", + "plt.xlim(-0.99, -1.01)\n", + "plt.legend()\n", + "# plt.savefig(\"Analytical_posterior_w\"+str_true_w+'.pdf')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "c1053648", + "metadata": {}, + "source": [ + "### Plot the mean and std of MCMC and Analytical posteriors " + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "aea92739", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1813566/815134857.py:1: UserWarning: marker is redundantly defined by the 'marker' keyword argument and the fmt string \"o\" (-> marker='o'). The keyword argument will take precedence.\n", + " plt.errorbar(num_images, np.array([mean_samples1[0], mean_samples2[0], mean_samples3[0], mean_samples4[0]]) ,\n", + "/tmp/ipykernel_1813566/815134857.py:3: UserWarning: marker is redundantly defined by the 'marker' keyword argument and the fmt string \"o\" (-> marker='o'). The keyword argument will take precedence.\n", + " plt.errorbar(np.array([3, 98, 498, 998]), posteriors_all[:,0] , yerr=posteriors_all[:,1],marker='o', fmt='o', label='Analytical')\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.errorbar(num_images, np.array([mean_samples1[0], mean_samples2[0], mean_samples3[0], mean_samples4[0]]) , \n", + " yerr=np.array([std_samples1[0], std_samples2[0], std_samples3[0], std_samples4[0]]),marker='o', fmt='o', label = 'MCMC')\n", + "plt.errorbar(np.array([3, 98, 498, 998]), posteriors_all[:,0] , yerr=posteriors_all[:,1],marker='o', fmt='o', label='Analytical')\n", + "plt.hlines(true_w, 0, 1000, ls='--', color='k')\n", + "plt.xlabel('Number of images for inference')\n", + "plt.ylabel(r'$Predicted\\ w$')\n", + "plt.legend()\n", + "plt.savefig(\"MCMC_analytical_posterior_w\"+str_true_w+\".pdf\")\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:.conda-bison]", + "language": "python", + "name": "conda-env-.conda-bison-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.4" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/NRE_varyastro_w12.ipynb b/notebooks/NRE_varyastro_w12.ipynb new file mode 100644 index 0000000..25a2660 --- /dev/null +++ b/notebooks/NRE_varyastro_w12.ipynb @@ -0,0 +1,643 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "id": "6655e2be", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "!export LD_LIBRARY_PATH=/opt/conda/lib\n", + "!export XLA_FLAGS=--xla_gpu_cuda_data_dir=/home/jarugula/cuda_data_dir\n", + "!export PATH=/usr/local/cuda-11.7/bin:${PATH}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c2386057", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import logging\n", + "import warnings\n", + "import json\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from sklearn import metrics\n", + "from sklearn.metrics import confusion_matrix\n", + "from sklearn.metrics import auc, roc_curve\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib\n", + "import random\n", + "from tqdm import tqdm\n", + "import pandas as pd\n", + "from scipy.stats import uniform, norm\n", + "import emcee\n", + "from multiprocessing import Pool\n", + "import time\n", + "\n", + "import h5py\n", + "import os\n", + "from tqdm import tqdm\n", + "import tensorflow as tf\n", + "from tensorflow import keras\n", + "from tensorflow.keras import layers \n", + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import Reshape,Conv1D,Flatten,Dense, Lambda\n", + "from tensorflow.keras.models import Model\n", + "from sklearn.model_selection import train_test_split\n", + "from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau, TerminateOnNaN\n", + "physical_devices = tf.config.list_physical_devices('GPU')\n", + "try:\n", + " tf.config.experimental.set_memory_growth(physical_devices[0], True)\n", + "except:\n", + " pass\n", + "from sklearn.utils import shuffle\n", + "AUTOTUNE = tf.data.AUTOTUNE\n", + "from sklearn.preprocessing import StandardScaler\n", + "import tqdm\n", + "import gc\n", + "import wandb\n", + "os.environ['PATH']=os.environ['PATH']+':/usr/local/cuda/bin'\n", + "os.environ['XLA_FLAGS']='--xla_gpu_cuda_data_dir=/home/jarugula/cuda_data_dir'" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "e9bdd356", + "metadata": {}, + "outputs": [], + "source": [ + "best_style = {\n", + " \"font.family\": \"sans-serif\",\n", + " \"mathtext.fontset\": \"custom\",\n", + " \"mathtext.rm\": \"TeX Gyre Heros\",\n", + " \"mathtext.bf\": \"TeX Gyre Heros:bold\",\n", + " \"mathtext.sf\": \"TeX Gyre Heros\",\n", + " \"mathtext.it\": \"TeX Gyre Heros:italic\",\n", + " \"mathtext.tt\": \"TeX Gyre Heros\",\n", + " \"mathtext.cal\": \"TeX Gyre Heros\",\n", + " \"mathtext.default\": \"regular\",\n", + " \"figure.figsize\": (10.0, 10.0),\n", + " \"font.size\": 26,\n", + " \"axes.labelsize\": \"medium\",\n", + " \"axes.unicode_minus\": False,\n", + " \"xtick.labelsize\": \"small\",\n", + " \"ytick.labelsize\": \"small\",\n", + " \"legend.fontsize\": \"small\",\n", + " \"legend.handlelength\": 1.5,\n", + " \"legend.borderpad\": 0.5,\n", + " \"xtick.direction\": \"in\",\n", + " \"xtick.major.size\": 12,\n", + " \"xtick.minor.size\": 6,\n", + " \"xtick.major.pad\": 6,\n", + " \"xtick.top\": True,\n", + " \"xtick.major.top\": True,\n", + " \"xtick.major.bottom\": True,\n", + " \"xtick.minor.top\": True,\n", + " \"xtick.minor.bottom\": True,\n", + " \"xtick.minor.visible\": True,\n", + " \"ytick.direction\": \"in\",\n", + " \"ytick.major.size\": 12,\n", + " \"ytick.minor.size\": 6.0,\n", + " \"ytick.right\": True,\n", + " \"ytick.major.left\": True,\n", + " \"ytick.major.right\": True,\n", + " \"ytick.minor.left\": True,\n", + " \"ytick.minor.right\": True,\n", + " \"ytick.minor.visible\": True,\n", + " \"grid.alpha\": 0.8,\n", + " \"grid.linestyle\": \":\",\n", + " \"axes.linewidth\": 2,\n", + " \"savefig.transparent\": False,\n", + "}\n", + "plt.style.use(best_style)\n", + "cols = [\"#5790fc\", \"#f89c20\", \"#e42536\", \"#964a8b\", \"#9c9ca1\", \"#7a21dd\"]\n", + "#set cols as the matplotlib default color cycle\n", + "plt.rcParams['axes.prop_cycle'] = plt.cycler(color=cols)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "f585fd63-bc24-4dca-8935-597ae91af163", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# The test data for population analysis by fixing w=-1.2\n", + "\n", + "data_path = '/deepskieslab/stronglensing/hsbi/datasets/'\n", + "image_dir = 'w0_8param_fixzv_test_fixw0-12_3000'\n", + "column_name = \"w0-g\"\n", + "fig_title = 'w0'\n", + "\n", + "str_true_w = '-12'\n", + "true_w = -1.2 # Dark energy equation-of-state parameter \n", + "xlim_min = -1.3\n", + "xlim_max = -1.1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3067c2a6", + "metadata": {}, + "outputs": [], + "source": [ + "# Load the trained model \n", + "\n", + "model = tf.keras.models.load_model(\"working_model_1M-2-034_seed38_v2.keras\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "d2e53fb0-4669-4f00-90a9-3664361c0ec9", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Read the images and metadata and pre-process the data\n", + "\n", + "images_test = np.load(data_path+image_dir+'/CONFIGURATION_1_images.npy', allow_pickle=True)\n", + "metadata_test = pd.read_csv(data_path+image_dir+'/CONFIGURATION_1_metadata.csv')\n", + "\n", + "\n", + "fixed_images_test = np.einsum('lkij->lijk',images_test)\n", + "fixed_true_theta_test = metadata_test[column_name].to_numpy()\n", + "\n", + "#normalize image each image by the sum of all pixels, make it such that the sum of all pixels is 1024\n", + "fixed_images_test = 1024*(fixed_images_test/np.sum(fixed_images_test, axis=(1,2), keepdims=True))\n", + "\n", + "#manually standardize the images and theta\n", + "means_image = 0.0\n", + "std_image = 1.0\n", + "\n", + "mean_theta = 0.0\n", + "std_theta = 1.0\n", + "\n", + "fixed_images_test = fixed_images_test.reshape(fixed_images_test.shape[0], -1)\n", + "fixed_images_test = (fixed_images_test - means_image) / std_image\n", + "fixed_images_test = fixed_images_test.reshape(fixed_images_test.shape[0], 32, 32, 1)\n", + "\n", + "fixed_theta_test = (fixed_true_theta_test - mean_theta)/std_theta" + ] + }, + { + "cell_type": "markdown", + "id": "3150d9da", + "metadata": {}, + "source": [ + "### Calculate the Analytical Posterior \n", + "\n", + "The analytical equation to calculate the posterior is given by\n", + "\n", + "\\begin{equation}\n", + "\\begin{split}\n", + " p(w|\\{x\\}) &= \\frac{p(w)~\\prod_{i}r(x_i|w)}{\\int dw^{\\prime}~ p(w^{\\prime})~\\prod_{i}r(x_{i}|w^{\\prime})},\\\\\n", + " &= p(w)~\\left( \\int dw^{\\prime}~p(w^{\\prime})~\\prod_{i}\\frac{r(x_i|w^{\\prime})}{r(x_i|w)} \\right)^{-1}.\n", + "\\end{split}\n", + "\\end{equation}\n", + "\n", + "```likelihood_diff``` function calculates $log\\ r(x|w^{\\prime}) - log\\ r(x|w)$ for one image $x$ \n", + "\n", + "This is same as calculating $\\frac{r(x_i|w^{\\prime})}{r(x_i|w)}$ in the posterior equation\n", + "\n", + "```get_joint_likelihood``` function calculates $\\sum_{i} log\\ r(x_{i}|w^{\\prime}) - log\\ r(x_{i}|w)$ for a population of strong lens images $\\{x_{i}\\}$\n", + "\n", + "This is same as calculating $\\prod_{i}\\frac{r(x_i|w^{\\prime})}{r(x_i|w)}$ in the posterior equation\n", + "\n", + "```get_joint_posterior``` calculates the sum of posterior for all the theta ($w$) values and gives the inverse of the sum as shown in the equation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ad50c440-17d6-4b72-b252-5d0c4469844e", + "metadata": {}, + "outputs": [], + "source": [ + "import numba as nb\n", + "\n", + "@nb.jit\n", + "def get_logr_distribution(model, images, sample_theta):\n", + " '''\n", + " Function to predict the log likelihood-to-evidence ratio (logr) of the test data\n", + "\n", + " Input:\n", + " model: The trained model \n", + " images: test images\n", + " sample_theta: a list of theta values to compute logr for\n", + " '''\n", + " output_probs = []\n", + " for image in images:\n", + " test_image_array = np.concatenate([image[np.newaxis, :]]*len(sample_theta), axis=0)\n", + " output = model.predict([test_image_array, sample_theta], verbose=0).flatten()\n", + " output_probs.append(output)\n", + " return np.array(output_probs)\n", + "\n", + "class Posterior:\n", + " def __init__(self, lnr, thetas):\n", + " self.lnr = lnr\n", + " self.thetas = thetas\n", + "\n", + " def likelihood_diff(self, image_index):\n", + " # exp_diff_lnr = np.empty((len(self.thetas), len(self.thetas)))\n", + " diff_lnr_list = np.empty((len(self.thetas), len(self.thetas)))\n", + " for i in range(len(self.thetas)):\n", + " diff_lnr = self.lnr[image_index, i] - self.lnr[image_index]\n", + " # exp_diff_lnr[i] = np.exp(diff_lnr)\n", + " diff_lnr_list[i] = diff_lnr\n", + " # return exp_diff_lnr\n", + " return diff_lnr_list\n", + "\n", + " def get_joint_likelihood(self, n_images):\n", + " likelihood = np.empty((n_images, len(self.thetas), len(self.thetas)))\n", + " for i in range(n_images):\n", + " likelihood[i] = self.likelihood_diff(i)\n", + " # joint_likelihood = np.prod(likelihood, axis=0)\n", + " joint_likelihood = np.sum(likelihood, axis=0)\n", + " joint_likelihood = np.exp(joint_likelihood)\n", + " return joint_likelihood\n", + " \n", + " def get_joint_posterior(self, n_images):\n", + " joint_likelihood = self.get_joint_likelihood(n_images)\n", + " joint_posterior = 1. / np.sum(joint_likelihood, axis=0)\n", + " return joint_posterior\n", + " \n", + "def get_joint_posterior_probability(lnr, thetas, n_images):\n", + " '''\n", + " Function to sample from the posterior probability distribution.\n", + "\n", + " Output:\n", + " The posterior probability, mean and standard deviation\n", + " '''\n", + " posterior = Posterior(lnr, thetas)\n", + " joint_posterior = posterior.get_joint_posterior(n_images)\n", + " sampled_values = np.random.choice(thetas, size=1000, p=joint_posterior)\n", + " weighted_mean = np.mean(sampled_values)\n", + " weighted_std_dev = np.std(sampled_values)\n", + " # weighted_mean = np.sum(thetas * joint_posterior) / np.sum(joint_posterior)\n", + " # weighted_std_dev = np.sqrt(np.sum(joint_posterior * (thetas - weighted_mean)**2) / np.sum(joint_posterior))\n", + " return joint_posterior, weighted_mean, weighted_std_dev" + ] + }, + { + "cell_type": "markdown", + "id": "ade99e8d", + "metadata": {}, + "source": [ + "### Calculate MCMC posterior" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "346d7c0b-d5d9-4f36-aa53-7c9a3ce98a06", + "metadata": {}, + "outputs": [], + "source": [ + "def get_logr_mcmc(model, images, sample_theta, mean_theta, std_theta):\n", + " ''''\n", + " Function to predict the log likelihood-to-evidence ratio (logr) of all the test data at a time\n", + "\n", + " Input:\n", + " model: The trained model \n", + " images: test images\n", + " sample_theta: a list of theta values to compute logr for\n", + "\n", + " Output:\n", + " log r \n", + " '''\n", + " sample_theta = (sample_theta - mean_theta)/std_theta\n", + " theta_array = np.array([sample_theta]*images.shape[0])\n", + " output = model.predict([images, theta_array], verbose=0).flatten()\n", + " return output\n", + "\n", + "def log_prior(theta, theta_low=-1.5, theta_high=-0.5):\n", + " \"\"\"\n", + " prior for w\n", + " \"\"\"\n", + " if theta_low < theta < theta_high:\n", + " return 0.0\n", + " return -np.inf\n", + "\n", + "def log_likelihood(theta_, data, theta_low, theta_high, model, mean_theta, std_theta):\n", + " \"\"\"\n", + " Calculate the log likelihood + log prior\n", + " \"\"\"\n", + " theta = theta_[0]\n", + " lp = log_prior(theta, theta_low, theta_high)\n", + " if not np.isfinite(lp):\n", + " return -np.inf\n", + " logr_array = get_logr_mcmc(model, data, theta, mean_theta, std_theta)\n", + " ll = np.sum(logr_array)\n", + " return ll+lp\n", + "\n", + "def get_posterior_mcmc(data, theta_low, theta_high, model, walkers=10, nsteps=10000, initial_w = -1.0, mean_theta=-1.0007, std_theta=0.288409, multithread=False):\n", + " \"\"\"\n", + " MCMC sampling\n", + "\n", + " Output:\n", + " Sampler and Samples\n", + " \"\"\"\n", + " pos = np.array([initial_w])+ np.array([initial_w])*1e-3* np.random.randn(walkers, 1)\n", + " nwalkers, ndim = pos.shape\n", + " sampler = emcee.EnsembleSampler(nwalkers, ndim, log_likelihood, args=(data, theta_low, theta_high, model, mean_theta, std_theta))\n", + " \n", + " if multithread:\n", + " with Pool(10) as pool:\n", + " sampler = emcee.EnsembleSampler(nwalkers, ndim, log_likelihood, args=(data, theta_low, theta_high, model, mean_theta, std_theta), pool=pool)\n", + " print(\"Running first burn-in...\")\n", + " pos, lp, _ = sampler.run_mcmc(pos, 100, progress=True)\n", + " print('Max lp @',pos[np.argmax(lp)])\n", + "\n", + " print(\"Running second burn-in...\")\n", + " pos =pos[np.argmax(lp)] + 1e-4 * np.random.randn(nwalkers, ndim)\n", + " sampler.reset()\n", + " pos, lp, _ = sampler.run_mcmc(pos, 500,progress=True)\n", + " print('Max lp @',pos[np.argmax(lp)])\n", + "\n", + " print(\"Running production...\")\n", + " pos = pos[np.argmax(lp)] + 1e-4 * np.random.randn(nwalkers, ndim)\n", + " sampler.reset()\n", + " pos, lp, _ = sampler.run_mcmc(pos, nsteps,progress=True)\n", + " print('Max lp @',pos[np.argmax(lp)]) \n", + "\n", + " else:\n", + " print(\"Running first burn-in...\")\n", + " pos, lp, _ = sampler.run_mcmc(pos, 100, progress=True)\n", + " print('Max lp @',pos[np.argmax(lp)])\n", + "\n", + " print(\"Running second burn-in...\")\n", + " pos =pos[np.argmax(lp)] + 1e-4 * np.random.randn(nwalkers, ndim)\n", + " sampler.reset()\n", + " pos, lp, _ = sampler.run_mcmc(pos, 500,progress=True)\n", + " print('Max lp @',pos[np.argmax(lp)])\n", + "\n", + " print(\"Running production...\")\n", + " pos = pos[np.argmax(lp)] + 1e-4 * np.random.randn(nwalkers, ndim)\n", + " sampler.reset()\n", + " pos, lp, _ = sampler.run_mcmc(pos, nsteps,progress=True)\n", + " print('Max lp @',pos[np.argmax(lp)])\n", + "\n", + "\n", + " samples = sampler.get_chain(discard=int(nsteps/4), flat=False)\n", + " print(samples.shape)\n", + "\n", + " flat_samples = sampler.get_chain(discard=int(nsteps/4), flat=True)\n", + " print(flat_samples.shape)\n", + "\n", + " return sampler, samples, flat_samples\n", + " return sampler, samples, flat_samples" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "88f0703a", + "metadata": {}, + "outputs": [], + "source": [ + "num_images = [5, 100, 500, 1000, 2000, 3000]\n", + "start_time=time.time()\n", + "for n in num_images:\n", + " sampler, samples, flat_samples = get_posterior_mcmc(fixed_images_test[0:n], -1.5, -0.5, model, walkers=5, nsteps=1000, \n", + " initial_w = -1.0, mean_theta=0.0, std_theta=1.0, multithread=False)\n", + " end_time=time.time()\n", + " print('Time taken for ', n, ' images: ', end_time-start_time)\n", + " start_time = end_time\n", + " #save the mcmc sampler\n", + " np.savez('mcmc_posterior'+str_true_w+'_'+str(n)+'_v2.npz', sampler=sampler, samples=samples, flat_samples=flat_samples)\n", + " del sampler, samples, flat_samples" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "7957ed5f", + "metadata": {}, + "outputs": [], + "source": [ + "def read_mcmc_samples(filename):\n", + " file = np.load(filename)\n", + " # sampler = file['sampler']\n", + " samples = file['samples']\n", + " flat_samples = file['flat_samples']\n", + " mean_samples = np.mean(flat_samples, axis=0)\n", + " std_samples = np.std(flat_samples, axis=0)\n", + " return samples, flat_samples, mean_samples, std_samples" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "9a0e0709", + "metadata": {}, + "outputs": [], + "source": [ + "samples1, flat_samples1, mean_samples1, std_samples1 = read_mcmc_samples('mcmc_posterior'+str_true_w+'_'+str(500)+'_v2.npz')\n", + "samples2, flat_samples2, mean_samples2, std_samples2 = read_mcmc_samples('mcmc_posterior'+str_true_w+'_'+str(1000)+'_v2.npz')\n", + "samples3, flat_samples3, mean_samples3, std_samples3 = read_mcmc_samples('mcmc_posterior'+str_true_w+'_'+str(2000)+'_v2.npz')\n", + "samples4, flat_samples4, mean_samples4, std_samples4 = read_mcmc_samples('mcmc_posterior'+str_true_w+'_'+str(3000)+'_v2.npz')" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "e320f72b", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the Posterior from MCMC sampling\n", + "\n", + "plt.hist(flat_samples1, bins=20, label='500 images', alpha=0.6, histtype='step', lw=1)\n", + "plt.hist(flat_samples2, bins=20, label='1000 images', alpha=0.6, histtype='step', lw=1)\n", + "plt.hist(flat_samples3, bins=20, label='2000 images', alpha=0.6, histtype='step', lw=1)\n", + "plt.hist(flat_samples4, bins=20, label='3000 images', alpha=0.8, histtype='step', lw=2)\n", + "plt.axvline(true_w, linestyle='--', color='k', lw=3)\n", + "# plt.xlim(xlim_min, xlim_max)\n", + "plt.xlim(-1.207, -1.19)\n", + "plt.xlabel(r'$w$')\n", + "plt.ylabel('Frequency')\n", + "plt.legend(fontsize=18)\n", + "plt.savefig(\"MCMC_posterior_w\"+str_true_w+'.pdf')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "203dac78", + "metadata": {}, + "source": [ + "### Analytical Sampling" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "98df7565", + "metadata": {}, + "outputs": [], + "source": [ + "sample_theta_unstd = np.linspace(-2.0, -0.4, 2000)\n", + "sample_theta = (sample_theta_unstd - mean_theta)/std_theta\n", + "\n", + "lnr_1 = get_logr_distribution(model, fixed_images_test, sample_theta)\n", + "np.savez('logr_w'+str_true_w+'_1000_v3.npz', logr=lnr_1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4558c63b", + "metadata": {}, + "outputs": [], + "source": [ + "import time\n", + "num_images = [5, 100, 500, 1000, 2000, 3000]\n", + "\n", + "posteriors_all_list = []\n", + "posterior_all_samples = []\n", + "start_time = time.time()\n", + "for n in num_images:\n", + " posterior, posterior_mean, posterior_std = get_joint_posterior_probability(lnr_1, sample_theta, n)\n", + " end_time = time.time()\n", + " print('Time taken for ', n, ' images: ', end_time-start_time)\n", + " start_time = end_time\n", + " posteriors_all_list.append((posterior_mean, posterior_std))\n", + " posterior_all_samples.append(posterior)\n", + "\n", + "posteriors_all = np.vstack(posteriors_all_list)\n", + "posterior_all_samples = np.array(posterior_all_samples)\n", + "print('shape of posteriors for ntrials:', np.shape(posterior_all_samples))\n", + "\n", + "np.savez('analytical_posteriors_w'+str_true_w+'_v3.npz', samples=posterior_all_samples, stats=posteriors_all)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cdcfca53", + "metadata": {}, + "outputs": [], + "source": [ + "# plot the analytical posterior probability for different number of images\n", + "\n", + "for i, pos in enumerate(posterior_all_samples[2:]):\n", + " plt.plot(sample_theta, pos/np.max(pos), label=str(num_images[i+2])+' images')\n", + "plt.axvline(true_w, linestyle='--', color='k')\n", + "plt.xlabel(r'$w$')\n", + "plt.ylabel(r'$p(w | x)$')\n", + "plt.xlim(-1.27, -1.13)\n", + "plt.legend()\n", + "plt.savefig(\"Analytical_posterior_w\"+str_true_w+'.pdf')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "c1053648", + "metadata": {}, + "source": [ + "### Plot the mean and std of MCMC and Analytical posteriors " + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "aea92739", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_941/815134857.py:1: UserWarning: marker is redundantly defined by the 'marker' keyword argument and the fmt string \"o\" (-> marker='o'). The keyword argument will take precedence.\n", + " plt.errorbar(num_images, np.array([mean_samples1[0], mean_samples2[0], mean_samples3[0], mean_samples4[0]]) ,\n", + "/tmp/ipykernel_941/815134857.py:3: UserWarning: marker is redundantly defined by the 'marker' keyword argument and the fmt string \"o\" (-> marker='o'). The keyword argument will take precedence.\n", + " plt.errorbar(np.array([3, 98, 498, 998]), posteriors_all[:,0] , yerr=posteriors_all[:,1],marker='o', fmt='o', label='Analytical')\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.errorbar(num_images, np.array([mean_samples1[0], mean_samples2[0], mean_samples3[0], mean_samples4[0]]) , \n", + " yerr=np.array([std_samples1[0], std_samples2[0], std_samples3[0], std_samples4[0]]),marker='o', fmt='o', label = 'MCMC')\n", + "plt.errorbar(np.array([3, 98, 498, 998]), posteriors_all[:,0] , yerr=posteriors_all[:,1],marker='o', fmt='o', label='Analytical')\n", + "plt.hlines(true_w, 0, 1000, ls='--', color='k')\n", + "plt.xlabel('Number of images for inference')\n", + "plt.ylabel(r'$Predicted\\ w$')\n", + "plt.legend()\n", + "plt.savefig(\"MCMC_analytical_posterior_w\"+str_true_w+\".pdf\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "345db83f-3fe4-4580-a7fb-5cc91daa5b6e", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:.conda-bison]", + "language": "python", + "name": "conda-env-.conda-bison-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.4" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/compare_random_seeds.ipynb b/notebooks/compare_random_seeds.ipynb new file mode 100644 index 0000000..4dcdc7c --- /dev/null +++ b/notebooks/compare_random_seeds.ipynb @@ -0,0 +1,723 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "c4831e8e-ecd9-44a7-a706-f60bf22cd881", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "!export LD_LIBRARY_PATH=/opt/conda/lib\n", + "!export XLA_FLAGS=--xla_gpu_cuda_data_dir=/home/jarugula/cuda_data_dir\n", + "!export PATH=/usr/local/cuda-11.7/bin:${PATH}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "de40a397-cc33-4d1e-9270-52a019925b1b", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# import the libraries\n", + "\n", + "import logging\n", + "import warnings\n", + "import json\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from sklearn import metrics\n", + "from sklearn.metrics import confusion_matrix\n", + "from sklearn.metrics import auc, roc_curve\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib.ticker import (MultipleLocator, AutoMinorLocator)\n", + "import matplotlib\n", + "import random\n", + "from tqdm import tqdm\n", + "import pandas as pd\n", + "\n", + "import h5py\n", + "import os\n", + "from tqdm import tqdm\n", + "import tensorflow as tf\n", + "from tensorflow import keras\n", + "from tensorflow.keras import layers \n", + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import Reshape,Conv1D,Flatten,Dense, Lambda\n", + "from tensorflow.keras.models import Model\n", + "from sklearn.model_selection import train_test_split\n", + "from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau, TerminateOnNaN\n", + "physical_devices = tf.config.list_physical_devices('GPU')\n", + "try:\n", + " tf.config.experimental.set_memory_growth(physical_devices[0], True)\n", + "except:\n", + " pass\n", + "from sklearn.utils import shuffle\n", + "AUTOTUNE = tf.data.AUTOTUNE\n", + "from sklearn.preprocessing import StandardScaler\n", + "import tqdm\n", + "import gc\n", + "\n", + "os.environ['PATH']=os.environ['PATH']+':/usr/local/cuda/bin'\n", + "os.environ['XLA_FLAGS']='--xla_gpu_cuda_data_dir=/home/jarugula/cuda_data_dir'" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "df1e061d", + "metadata": {}, + "outputs": [], + "source": [ + "# define your plot style\n", + "\n", + "best_style = {\n", + " \"font.family\": \"sans-serif\",\n", + " \"mathtext.fontset\": \"custom\",\n", + " \"mathtext.rm\": \"TeX Gyre Heros\",\n", + " \"mathtext.bf\": \"TeX Gyre Heros:bold\",\n", + " \"mathtext.sf\": \"TeX Gyre Heros\",\n", + " \"mathtext.it\": \"TeX Gyre Heros:italic\",\n", + " \"mathtext.tt\": \"TeX Gyre Heros\",\n", + " \"mathtext.cal\": \"TeX Gyre Heros\",\n", + " \"mathtext.default\": \"regular\",\n", + " \"figure.figsize\": (10.0, 10.0),\n", + " \"font.size\": 26,\n", + " \"axes.labelsize\": \"medium\",\n", + " \"axes.unicode_minus\": False,\n", + " \"xtick.labelsize\": \"small\",\n", + " \"ytick.labelsize\": \"small\",\n", + " \"legend.fontsize\": \"small\",\n", + " \"legend.handlelength\": 1.5,\n", + " \"legend.borderpad\": 0.5,\n", + " \"xtick.direction\": \"in\",\n", + " \"xtick.major.size\": 12,\n", + " \"xtick.minor.size\": 6,\n", + " \"xtick.major.pad\": 6,\n", + " \"xtick.top\": True,\n", + " \"xtick.major.top\": True,\n", + " \"xtick.major.bottom\": True,\n", + " \"xtick.minor.top\": True,\n", + " \"xtick.minor.bottom\": True,\n", + " \"xtick.minor.visible\": True,\n", + " \"ytick.direction\": \"in\",\n", + " \"ytick.major.size\": 12,\n", + " \"ytick.minor.size\": 6.0,\n", + " \"ytick.right\": True,\n", + " \"ytick.major.left\": True,\n", + " \"ytick.major.right\": True,\n", + " \"ytick.minor.left\": True,\n", + " \"ytick.minor.right\": True,\n", + " \"ytick.minor.visible\": True,\n", + " \"grid.alpha\": 0.8,\n", + " \"grid.linestyle\": \":\",\n", + " \"axes.linewidth\": 2,\n", + " \"savefig.transparent\": False,\n", + "}\n", + "plt.style.use(best_style)\n", + "cols = [\"#5790fc\", \"#f89c20\", \"#e42536\", \"#964a8b\", \"#9c9ca1\", \"#7a21dd\"]\n", + "plt.rcParams['axes.prop_cycle'] = plt.cycler(color=cols)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "a9d13ea4-ab34-4d6b-9d68-0f35108b4573", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# Trained Models with three different weight initializations\n", + "\n", + "model_name1 = 'working_model_1M-2-034_seed38_v2.keras'\n", + "model_name2 = 'working_model_1M-2-034_seed128_v2.keras'\n", + "model_name3 = 'working_model_1M-2-034_seed1024_v2.keras'" + ] + }, + { + "cell_type": "markdown", + "id": "f307b553", + "metadata": {}, + "source": [ + "### Data import and pre-processing" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "4a8d2e07-9624-4643-b498-99bf7d71532c", + "metadata": {}, + "outputs": [], + "source": [ + "# Read the training and test data\n", + "\n", + "data_path = '/deepskieslab/stronglensing/hsbi/datasets/'\n", + "image_dir = 'w0_8param_fixzv_train_1M'\n", + "column_name = \"w0-g\" # dark energy equation-of-state parameter" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "558426f2-6679-4fa3-bdd2-207ef45936ee", + "metadata": {}, + "outputs": [], + "source": [ + "# Read the images and the corresponding metadata\n", + "\n", + "if os.path.isdir(data_path+image_dir):\n", + " images = np.load(data_path+image_dir+'/CONFIGURATION_1_images.npy', allow_pickle=True)\n", + " metadata = pd.read_csv(data_path+image_dir+'/CONFIGURATION_1_metadata.csv')\n", + "else:\n", + " print(\"Data not found\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "e4c745ea-4f83-4634-871a-f50a668f5c9a", + "metadata": {}, + "outputs": [], + "source": [ + "images = np.einsum('lkij->lijk',images)\n", + "theta = metadata[column_name].to_numpy()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "9e62239f-1f92-4611-999c-c2e312c78e3d", + "metadata": {}, + "outputs": [], + "source": [ + "# normalize image each image by the sum of all pixels, make it such that the sum of all pixels is 1024 (32 X32)\n", + "images = 1024*(images/np.sum(images, axis=(1,2), keepdims=True))\n", + "\n", + "# manually standardies pixels across all images. \n", + "# In this analysis we do not standerdize the images and parameter. Hence we use mean=0 and std=1.0\n", + "\n", + "images = images.reshape(images.shape[0], -1)\n", + "# means_image = np.mean(images, axis=0)\n", + "# std_image = np.std(images, axis=0)\n", + "means_image = 0.0\n", + "std_image = 1.0\n", + "images = (images - means_image) / std_image\n", + "images = images.reshape(images.shape[0], 32, 32, 1)\n", + "\n", + "\n", + "#manually standardize the theta (w)\n", + "mean_theta = 0.0 \n", + "std_theta = 1.0 \n", + "theta = (theta - mean_theta)/std_theta" + ] + }, + { + "cell_type": "markdown", + "id": "f54411eb", + "metadata": {}, + "source": [ + "### Split the data into train and test" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "d367c37a-96ee-4c68-b64c-df13bf66231a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "53" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x_train , x_test, theta_train, theta_test, y_train, y_test = train_test_split(images, theta, np.ones_like(theta), test_size=0.2, random_state=0)\n", + "true_test_theta = np.copy(theta_test)\n", + "true_test_theta = true_test_theta*std_theta + mean_theta\n", + "\n", + "del images, theta\n", + "gc.collect()" + ] + }, + { + "cell_type": "markdown", + "id": "da292b9d", + "metadata": {}, + "source": [ + "### Load the saved trained models " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3b541bc5-baf5-4235-a319-367dcdb4edb0", + "metadata": {}, + "outputs": [], + "source": [ + "# load models with different seeds\n", + "model1 = tf.keras.models.load_model(model_name1)\n", + "model2 = tf.keras.models.load_model(model_name2)\n", + "model3 = tf.keras.models.load_model(model_name3)" + ] + }, + { + "cell_type": "markdown", + "id": "8c21ebeb", + "metadata": {}, + "source": [ + "### Pre-processing of the test data" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "3cf16aca", + "metadata": {}, + "outputs": [], + "source": [ + "# for the test set create shuffled dataset\n", + "y_test_noshuffle = np.ones(len(theta_test))\n", + "\n", + "# take a copy of image and theta dataset and shuffle just the theta values\n", + "images_shuffle = np.copy(x_test)\n", + "theta_test_shuffle = np.copy(theta_test)\n", + "theta_test_shuffle = shuffle(theta_test_shuffle, random_state=0)\n", + "y_test_shuffle = np.zeros(len(theta_test_shuffle))\n", + "\n", + "x_test_concat = np.concatenate((x_test, images_shuffle), axis=0)\n", + "theta_test_concat = np.concatenate((theta_test, theta_test_shuffle), axis=0)\n", + "y_test_concat = np.concatenate((y_test_noshuffle, y_test_shuffle), axis=0)" + ] + }, + { + "cell_type": "markdown", + "id": "bd004b5c", + "metadata": {}, + "source": [ + "### Plot the Receiver Operating Curve (ROC) " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "57bf0eec", + "metadata": {}, + "outputs": [], + "source": [ + "# make predictions\n", + "def roc_auc(x_test_concat, theta_test_concat, model):\n", + " '''\n", + " Inputs:\n", + " x_test_concat: List of strong lens images \n", + " theta_test_concat: List of thetas both shuffled and unshuffled\n", + " model: The model for testing\n", + "\n", + " Returns:\n", + " fpr: False positive Rate\n", + " tpr: true Positive Rate\n", + " thresholds: to calculate fpr and tpr\n", + " roc_auc: The AUC score\n", + " '''\n", + " y_pred = model.predict([x_test_concat, theta_test_concat])\n", + " y_pred_prob = tf.nn.sigmoid(y_pred) # probability that sample is from p(x,theta)\n", + " fpr, tpr, thresholds = roc_curve(y_test_concat, y_pred_prob)\n", + " roc_auc = auc(fpr, tpr)\n", + " return fpr, tpr, thresholds, roc_auc\n", + "\n", + "def save_roc_auc(fpr, tpr, thresholds, roc_auc, model_name, seed):\n", + " np.savez('aucroc_seed'+seed+'.npy', fpr=fpr, tpr=tpr, thresholds=thresholds, roc_auc=roc_auc)\n", + " return " + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "77a1d9b0", + "metadata": {}, + "outputs": [], + "source": [ + "fpr1, tpr1, thresholds1, roc_auc1 = roc_auc(x_test_concat, theta_test_concat, model1)\n", + "fpr2, tpr2, thresholds2, roc_auc2 = roc_auc(x_test_concat, theta_test_concat, model2)\n", + "fpr3, tpr3, thresholds3, roc_auc3 = roc_auc(x_test_concat, theta_test_concat, model3)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "b07b580b", + "metadata": {}, + "outputs": [], + "source": [ + "# # Save the roc outputs to a file\n", + "# _ = save_roc_auc(fpr1, tpr1, thresholds1, roc_auc1, model_name1, '38')\n", + "# _ = save_roc_auc(fpr2, tpr2, thresholds2, roc_auc2, model_name2, '128')\n", + "# _ = save_roc_auc(fpr3, tpr3, thresholds3, roc_auc3, model_name3, '1024')" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "bd524645-bb1e-4522-84ec-71b142c9f1b6", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# # Function to read the saved roc outputs\n", + "# def read_roc_file(filename):\n", + "# npy = np.load(filename)\n", + "# fpr = npy[\"fpr\"]\n", + "# tpr = npy[\"tpr\"]\n", + "# thresholds = npy[\"thresholds\"]\n", + "# roc_auc = npy[\"roc_auc\"]\n", + "# return fpr, tpr, thresholds, roc_auc" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "c0f7f786", + "metadata": {}, + "outputs": [], + "source": [ + "# # Read the saved outputs of the roc\n", + "# roc_name1 = \"aucroc_seed42.npz\"\n", + "# roc_name2 = \"aucroc_seed82.npz\"\n", + "# roc_name3 = \"aucroc_seed124.npz\"\n", + "# fpr1, tpr1, thresholds1, roc_auc1 = read_roc_file(roc_name1)\n", + "# fpr2, tpr2, thresholds2, roc_auc2 = read_roc_file(roc_name2)\n", + "# fpr3, tpr3, thresholds3, roc_auc3 = read_roc_file(roc_name3)\n", + "# fpr4, tpr4, thresholds4, roc_auc4 = read_roc_file(roc_name4)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "f945d964-69ab-4e9e-98de-94c7a4b9f0a2", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(6, 6))\n", + "lw = 1.2\n", + "plt.plot(fpr1, tpr1, lw=lw, label='Seed 1', ls='--')\n", + "plt.plot(fpr2, tpr2, lw=lw, label='Seed 2', ls='-')\n", + "plt.plot(fpr3, tpr3, lw=lw, label='Seed 3', ls=':')\n", + "\n", + "plt.plot([0, 1], [0, 1], color='k', lw=lw, linestyle='--')\n", + "plt.xlim([0.0, 1.0])\n", + "plt.ylim([0.0, 1.05])\n", + "\n", + "# plt.text(0.7, 0.05, 'AUC = {}'.format(np.round(roc_auc, 2)), fontsize = 16, \n", + "# bbox = dict(facecolor = 'grey', alpha = 0.2))\n", + "\n", + "plt.tick_params(axis='both', which='both', labelsize='xx-small')\n", + "plt.xlabel('False Positive Rate', fontsize='x-small')\n", + "plt.ylabel('True Positive Rate', fontsize='x-small')\n", + "plt.legend(fontsize='xx-small')\n", + "plt.savefig('roc_auc_random_seeds.pdf')\n", + "# plt.title('Receiver operating characteristic example')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "e38fb702", + "metadata": {}, + "source": [ + "### Calculate the Analytical Posterior \n", + "\n", + "The analytical equation to calculate the posterior is given by\n", + "\n", + "\\begin{equation}\n", + "\\begin{split}\n", + " p(w|\\{x\\}) &= \\frac{p(w)~\\prod_{i}r(x_i|w)}{\\int dw^{\\prime}~ p(w^{\\prime})~\\prod_{i}r(x_{i}|w^{\\prime})},\\\\\n", + " &= p(w)~\\left( \\int dw^{\\prime}~p(w^{\\prime})~\\prod_{i}\\frac{r(x_i|w^{\\prime})}{r(x_i|w)} \\right)^{-1}.\n", + "\\end{split}\n", + "\\end{equation}\n", + "\n", + "```likelihood_diff``` function calculates $log\\ r(x|w^{\\prime}) - log\\ r(x|w)$ for one image $x$ \n", + "\n", + "This is same as calculating $\\frac{r(x_i|w^{\\prime})}{r(x_i|w)}$ in the posterior equation\n", + "\n", + "```get_joint_likelihood``` function calculates $\\sum_{i} log\\ r(x_{i}|w^{\\prime}) - log\\ r(x_{i}|w)$ for a population of strong lens images $\\{x_{i}\\}$\n", + "\n", + "This is same as calculating $\\prod_{i}\\frac{r(x_i|w^{\\prime})}{r(x_i|w)}$ in the posterior equation\n", + "\n", + "```get_joint_posterior``` calculates the sum of posterior for all the theta ($w$) values and gives the inverse of the sum as shown in the equation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cb473e9f-2367-4e7b-8e07-875054ac01d1", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import numba as nb\n", + "\n", + "@nb.jit\n", + "def get_logr_distribution(model, images, sample_theta):\n", + " '''\n", + " Function to predict the log likelihood-to-evidence ratio (logr) of the test data\n", + "\n", + " Input:\n", + " model: The trained model \n", + " images: test images\n", + " sample_theta: a list of theta values to compute logr for\n", + " '''\n", + " output_probs = []\n", + " for image in images:\n", + " test_image_array = np.concatenate([image[np.newaxis, :]]*len(sample_theta), axis=0)\n", + " output = model.predict([test_image_array, sample_theta], verbose=0).flatten()\n", + " output_probs.append(output)\n", + " return np.array(output_probs)\n", + "\n", + "class Posterior:\n", + " def __init__(self, lnr, thetas):\n", + " self.lnr = lnr\n", + " self.thetas = thetas\n", + "\n", + " def likelihood_diff(self, image_index):\n", + " # exp_diff_lnr = np.empty((len(self.thetas), len(self.thetas)))\n", + " diff_lnr_list = np.empty((len(self.thetas), len(self.thetas)))\n", + " for i in range(len(self.thetas)):\n", + " diff_lnr = self.lnr[image_index, i] - self.lnr[image_index]\n", + " # exp_diff_lnr[i] = np.exp(diff_lnr)\n", + " diff_lnr_list[i] = diff_lnr\n", + " # return exp_diff_lnr\n", + " return diff_lnr_list\n", + "\n", + " def get_joint_likelihood(self, n_images):\n", + " likelihood = np.empty((n_images, len(self.thetas), len(self.thetas)))\n", + " for i in range(n_images):\n", + " likelihood[i] = self.likelihood_diff(i)\n", + " # joint_likelihood = np.prod(likelihood, axis=0)\n", + " joint_likelihood = np.sum(likelihood, axis=0)\n", + " joint_likelihood = np.exp(joint_likelihood)\n", + " return joint_likelihood\n", + " \n", + " def get_joint_posterior(self, n_images):\n", + " joint_likelihood = self.get_joint_likelihood(n_images)\n", + " joint_posterior = 1. / np.sum(joint_likelihood, axis=0)\n", + " return joint_posterior\n", + " \n", + "def get_joint_posterior_probability(lnr, thetas, n_images):\n", + " '''\n", + " Function to sample from the posterior probability distribution.\n", + "\n", + " Output:\n", + " The posterior probability, mean and standard deviation\n", + " '''\n", + " posterior = Posterior(lnr, thetas)\n", + " joint_posterior = posterior.get_joint_posterior(n_images)\n", + " sampled_values = np.random.choice(thetas, size=1000, p=joint_posterior)\n", + " weighted_mean = np.mean(sampled_values)\n", + " weighted_std_dev = np.std(sampled_values)\n", + " # weighted_mean = np.sum(thetas * joint_posterior) / np.sum(joint_posterior)\n", + " # weighted_std_dev = np.sqrt(np.sum(joint_posterior * (thetas - weighted_mean)**2) / np.sum(joint_posterior))\n", + " return joint_posterior, weighted_mean, weighted_std_dev\n" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "id": "5a696f3f", + "metadata": {}, + "outputs": [], + "source": [ + "# select the images where theta is between -1.5, -0.5\n", + "x_test_copy = np.copy(x_test[0:100])\n", + "inx = np.where((true_test_theta[0:100] > -1.5) & (true_test_theta[0:100] < -0.5))\n", + "x_test_copy = x_test_copy[inx]\n", + "theta_test_copy = true_test_theta[0:100][inx]\n", + "\n", + "# select 20 images\n", + "x_test_copy = x_test_copy[0:20]\n", + "theta_test_copy = theta_test_copy[0:20]" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "id": "7e7ed240", + "metadata": {}, + "outputs": [], + "source": [ + "sample_theta_unstd = np.linspace(-2.5, -0.15, 1000)\n", + "\n", + "sample_theta = (sample_theta_unstd - mean_theta)/std_theta\n", + "x_test_lnr_20_model1 = get_logr_distribution(model1, x_test_copy, sample_theta)\n", + "x_test_lnr_20_model2 = get_logr_distribution(model2, x_test_copy, sample_theta)\n", + "x_test_lnr_20_model3 = get_logr_distribution(model3, x_test_copy, sample_theta)" + ] + }, + { + "cell_type": "markdown", + "id": "c6c9933a", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": 72, + "id": "dfe3da0d", + "metadata": {}, + "outputs": [], + "source": [ + "def get_posterior_samples(lnr, thetas, n_images):\n", + " '''\n", + " Function to calculate the analytical posterior from individual strong lens image\n", + "\n", + " Outputs:\n", + " Posterior probability distribution\n", + " Samples, mean, and std obtained by sampling from the posterior\n", + " '''\n", + " posteriors_all_list = []\n", + " posterior_all_samples = []\n", + " for i in range(n_images):\n", + " posterior, posterior_mean, posterior_std = get_joint_posterior_probability(lnr[i:i+1], thetas, 1)\n", + " posteriors_all_list.append((posterior_mean, posterior_std))\n", + " posterior_all_samples.append(posterior)\n", + " posteriors_all = np.vstack(posteriors_all_list)\n", + " posterior_all_samples = np.array(posterior_all_samples)\n", + " return posteriors_all, posterior_all_samples" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "id": "31603227", + "metadata": {}, + "outputs": [], + "source": [ + "# calculate the analytical posterior for 20 strong lenses \n", + "posteriors_all1, posterior_all_samples1 = get_posterior_samples(x_test_lnr_20_model1, sample_theta, 20)\n", + "posteriors_all2, posterior_all_samples2 = get_posterior_samples(x_test_lnr_20_model2, sample_theta, 20)\n", + "posteriors_all3, posterior_all_samples3 = get_posterior_samples(x_test_lnr_20_model3, sample_theta, 20)" + ] + }, + { + "cell_type": "markdown", + "id": "2bd194a5", + "metadata": {}, + "source": [ + "### Plot the analytical posteriors for random test images for the three models" + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "id": "5bae87a6", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "num_rows= 4\n", + "num_cols = 5\n", + "hspace = 0.00\n", + "wspace = 0.00\n", + "fig, axes = plt.subplots(num_rows, num_cols, figsize=(14, 10), sharex=True, sharey=True)\n", + "for i in range(num_rows):\n", + " for j in range(num_cols):\n", + " index = i * num_cols + j\n", + " axes[i, j].plot(sample_theta_unstd, posterior_all_samples1[index]/np.max(posterior_all_samples1[index]), ls='--', label='Seed 1') \n", + " axes[i, j].plot(sample_theta_unstd, posterior_all_samples2[index]/np.max(posterior_all_samples2[index]), ls='-', label='Seed 2')\n", + " axes[i, j].plot(sample_theta_unstd, posterior_all_samples3[index]/np.max(posterior_all_samples3[index]), ls=':', label='Seed 3')\n", + " axes[i, j].axvline(theta_test_copy[index], color='k', ls='--', linewidth=2, label=r'$\\rm w_{True}$')\n", + " axes[i, j].set_ylim([0.0, 1.2])\n", + " axes[i, j].set_xlim([-2.2, 0.2])\n", + " axes[i, j].tick_params(axis='both', which='both', labelsize='xx-small', length=3)\n", + " axes[i, j].xaxis.set_major_locator(MultipleLocator(1.0))\n", + " axes[i, j].xaxis.set_major_formatter('{x:.0f}')\n", + "\n", + "axes[0, 4].legend(fontsize='12', loc='upper right')\n", + "axes[3, 0].set_xlabel(r'$w$', fontsize='xx-small')\n", + "axes[3, 0].set_ylabel(r'$p(w | x)$', fontsize='xx-small')\n", + "plt.subplots_adjust(hspace=hspace, wspace=wspace)\n", + "# plt.tight_layout()\n", + "plt.savefig('posterior_seeds.pdf')\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:.conda-bison]", + "language": "python", + "name": "conda-env-.conda-bison-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.4" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/plot_image_posterior.ipynb b/notebooks/plot_image_posterior.ipynb new file mode 100644 index 0000000..d211d20 --- /dev/null +++ b/notebooks/plot_image_posterior.ipynb @@ -0,0 +1,660 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "c4831e8e-ecd9-44a7-a706-f60bf22cd881", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "!export LD_LIBRARY_PATH=/opt/conda/lib\n", + "!export XLA_FLAGS=--xla_gpu_cuda_data_dir=/home/jarugula/cuda_data_dir\n", + "!export PATH=/usr/local/cuda-11.7/bin:${PATH}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "de40a397-cc33-4d1e-9270-52a019925b1b", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# import the libraries\n", + "\n", + "import logging\n", + "import warnings\n", + "import json\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from sklearn import metrics\n", + "from sklearn.metrics import confusion_matrix\n", + "from sklearn.metrics import auc, roc_curve\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib\n", + "from matplotlib.gridspec import GridSpec\n", + "import random\n", + "from tqdm import tqdm\n", + "import pandas as pd\n", + "\n", + "import h5py\n", + "import os\n", + "from tqdm import tqdm\n", + "import tensorflow as tf\n", + "from tensorflow import keras\n", + "from tensorflow.keras import layers \n", + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import Reshape,Conv1D,Flatten,Dense, Lambda\n", + "from tensorflow.keras.models import Model\n", + "from sklearn.model_selection import train_test_split\n", + "from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau, TerminateOnNaN\n", + "physical_devices = tf.config.list_physical_devices('GPU')\n", + "try:\n", + " tf.config.experimental.set_memory_growth(physical_devices[0], True)\n", + "except:\n", + " pass\n", + "from sklearn.utils import shuffle\n", + "AUTOTUNE = tf.data.AUTOTUNE\n", + "from sklearn.preprocessing import StandardScaler\n", + "import tqdm\n", + "import gc\n", + "\n", + "os.environ['PATH']=os.environ['PATH']+':/usr/local/cuda/bin'\n", + "os.environ['XLA_FLAGS']='--xla_gpu_cuda_data_dir=/home/jarugula/cuda_data_dir'" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "df1e061d", + "metadata": {}, + "outputs": [], + "source": [ + "# define your plot style\n", + "\n", + "best_style = {\n", + " # \"font.sans-serif\": [\"TeX Gyre Heros\", \"Helvetica\", \"Arial\"],\n", + " \"font.family\": \"sans-serif\",\n", + " \"mathtext.fontset\": \"custom\",\n", + " \"mathtext.rm\": \"TeX Gyre Heros\",\n", + " \"mathtext.bf\": \"TeX Gyre Heros:bold\",\n", + " \"mathtext.sf\": \"TeX Gyre Heros\",\n", + " \"mathtext.it\": \"TeX Gyre Heros:italic\",\n", + " \"mathtext.tt\": \"TeX Gyre Heros\",\n", + " \"mathtext.cal\": \"TeX Gyre Heros\",\n", + " \"mathtext.default\": \"regular\",\n", + " \"figure.figsize\": (10.0, 10.0),\n", + " \"font.size\": 26,\n", + " #\"text.usetex\": True,\n", + " \"axes.labelsize\": \"x-small\",\n", + " \"axes.unicode_minus\": False,\n", + " \"xtick.labelsize\": \"x-small\",\n", + " \"ytick.labelsize\": \"x-small\",\n", + " \"legend.fontsize\": \"x-small\",\n", + " \"legend.handlelength\": 1.0,\n", + " \"legend.borderpad\": 0.5,\n", + " \"xtick.direction\": \"in\",\n", + " # \"xtick.major.size\": 12,\n", + " # \"xtick.minor.size\": 6,\n", + " # \"xtick.major.pad\": 6,\n", + " \"xtick.major.size\": 12,\n", + " \"xtick.minor.size\": 6,\n", + " \"xtick.major.pad\": 6,\n", + " \"xtick.top\": True,\n", + " \"xtick.major.top\": True,\n", + " \"xtick.major.bottom\": True,\n", + " \"xtick.minor.top\": True,\n", + " \"xtick.minor.bottom\": True,\n", + " \"xtick.minor.visible\": True,\n", + " \"ytick.direction\": \"in\",\n", + " \"ytick.major.size\": 12,\n", + " \"ytick.minor.size\": 6.0,\n", + " # \"ytick.major.size\": 12,\n", + " # \"ytick.minor.size\": 6.0,\n", + " \"ytick.right\": True,\n", + " \"ytick.major.left\": True,\n", + " \"ytick.major.right\": True,\n", + " \"ytick.minor.left\": True,\n", + " \"ytick.minor.right\": True,\n", + " \"ytick.minor.visible\": True,\n", + "\n", + "}\n", + "plt.style.use(best_style)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "a9d13ea4-ab34-4d6b-9d68-0f35108b4573", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# Trained Models with three different weight initializations\n", + "\n", + "model_name1 = 'working_model_1M-2-034_seed38_v2.keras'\n", + "model_name2 = 'working_model_1M-2-034_seed128_v2.keras'\n", + "model_name3 = 'working_model_1M-2-034_seed1024_v2.keras'" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "4a8d2e07-9624-4643-b498-99bf7d71532c", + "metadata": {}, + "outputs": [], + "source": [ + "# data with single image for a particular w value. \n", + "# This data can be generated by modifying the deeplentronomy template to fix the w value.\n", + "\n", + "data_path = '/deepskieslab/stronglensing/hsbi/datasets/'\n", + "train_image_dir = 'w0_8param_fixzv_train_1M'\n", + "image_dir_2 = 'w0_8param_w0-2_1'\n", + "image_dir_18 = 'w0_8param_w0-18_1'\n", + "image_dir_16 = 'w0_8param_w0-16_1'\n", + "image_dir_14 = 'w0_8param_w0-14_1'\n", + "image_dir_12 = 'w0_8param_w0-12_1'\n", + "image_dir_1 = 'w0_8param_w0-1_1'\n", + "image_dir_08 = 'w0_8param_w0-08_1'\n", + "image_dir_06 = 'w0_8param_w0-06_1'\n", + "image_dir_04 = 'w0_8param_w0-04_1'\n", + "column_name = \"w0-g\" # dark energy equation-of-state parameter" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "558426f2-6679-4fa3-bdd2-207ef45936ee", + "metadata": {}, + "outputs": [], + "source": [ + "# Read the images and the corresponding metadata\n", + "\n", + "def read_image_files(image_dir):\n", + " images = np.load(data_path+image_dir+'/CONFIGURATION_1_images.npy', allow_pickle=True)\n", + " metadata = pd.read_csv(data_path+image_dir+'/CONFIGURATION_1_metadata.csv')\n", + " return images, metadata\n", + "\n", + "images_train, metadata_train = read_image_files(train_image_dir)\n", + "images_2, metadata_2 = read_image_files(image_dir_2)\n", + "images_18, metadata_18 = read_image_files(image_dir_18)\n", + "images_16, metadata_16 = read_image_files(image_dir_16)\n", + "images_14, metadata_14 = read_image_files(image_dir_14)\n", + "images_12, metadata_12 = read_image_files(image_dir_12)\n", + "images_1, metadata_1 = read_image_files(image_dir_1)\n", + "images_08, metadata_08 = read_image_files(image_dir_08)\n", + "images_06, metadata_06 = read_image_files(image_dir_06)\n", + "images_04, metadata_04, = read_image_files(image_dir_04)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "06b23ec0", + "metadata": {}, + "outputs": [], + "source": [ + "# List of w and einstein radius\n", + "w_all = metadata_train[\"w0-g\"].to_numpy()\n", + "einstein_radius_all = metadata_train[\"PLANE_1-OBJECT_1-MASS_PROFILE_1-theta_E-g\"].to_numpy()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "632b278e", + "metadata": {}, + "outputs": [], + "source": [ + "# sort according to the w value\n", + "sorted_inx = np.argsort(w_all)\n", + "w_all_sorted = w_all[sorted_inx]\n", + "einstein_radius_all_sorted = einstein_radius_all[sorted_inx]" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "85e2656b", + "metadata": {}, + "outputs": [], + "source": [ + "# combined all images to one array of shape (n_images, 1, 32, 32)\n", + "images = np.concatenate((images_2, images_18, images_16, images_14, images_12, images_1, images_08, images_06, images_04), axis=0)\n", + "theta = np.array([-2.0, -1.8, -1.6, -1.4, -1.2, -1.0, -0.8, -0.6, -0.4])" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "cdc6ca41", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1345780/1526992155.py:33: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n", + " plt.tight_layout()\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "num_rows = 3\n", + "num_cols = 3\n", + "\n", + "# Create a figure with GridSpec\n", + "fig = plt.figure(figsize=(12, 6))\n", + "# gs = GridSpec(1, 2, width_ratios=[0.4, 0.2], wspace=0.3)\n", + "gs = GridSpec(1, 2, figure=fig, width_ratios=[0.65, 0.45], wspace=0.35)\n", + "\n", + "# Left plot: 3x3 images with annotations\n", + "# left_gs = GridSpec(num_rows, num_cols, hspace=0.01, wspace=0.01)\n", + "left_gs = gs[0,0].subgridspec(num_rows, num_cols, hspace=0.01, wspace=0.01)\n", + "for i in range(num_rows):\n", + " for j in range(num_cols):\n", + " index = i * num_cols + j\n", + " ax = plt.subplot(left_gs[i, j])\n", + " ax.imshow(images[index][0], cmap='viridis', aspect='auto') # Assuming grayscale images\n", + " # ax.text(6, 4, r'$w_{\\mathrm{True}} = $' + str(theta[index]), color=\"k\", fontsize=12.5)\n", + " ax.text(12, 4, str(theta[index]), color=\"white\", fontsize=14)\n", + " ax.axis('off')\n", + "\n", + "# Right plot: w vs einstein radius\n", + "ax1 = plt.subplot(gs[0, 1])\n", + "lw = 1.2\n", + "ax1.plot(w_all_sorted, einstein_radius_all_sorted, color=\"#f89c20\", lw=3)\n", + "ax1.set_xlim(-2.2, -0.2)\n", + "ax1.set_xlabel(r'$w$', fontsize='x-small')\n", + "# ax1.set_ylabel(r'$Einstein\\ Radius\\ \\theta_{E}$', fontsize='small')\n", + "# ax1.set_ylabel(r'$\\theta_{E}$', fontsize='small')\n", + "ax1.set_ylabel(r'$\\theta_{E}$ (arcsec)', fontsize='x-small')\n", + "ax1.tick_params(axis='both', which='both', labelsize='x-small')\n", + "ax1.set_aspect('auto',adjustable='box')\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig('images_w_thetaE.pdf', dpi=400, bbox_inches='tight')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "e4c745ea-4f83-4634-871a-f50a668f5c9a", + "metadata": {}, + "outputs": [], + "source": [ + "images = np.einsum('lkij->lijk',images)\n", + "# theta = metadata[column_name].to_numpy()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "9e62239f-1f92-4611-999c-c2e312c78e3d", + "metadata": {}, + "outputs": [], + "source": [ + "# normalize image each image by the sum of all pixels, make it such that the sum of all pixels is 1024 (32 X32)\n", + "images = 1024*(images/np.sum(images, axis=(1,2), keepdims=True))\n", + "\n", + "# manually standardies pixels across all images. \n", + "# In this analysis we do not standerdize the images and parameter. Hence we use mean=0 and std=1.0\n", + "\n", + "images = images.reshape(images.shape[0], -1)\n", + "# means_image = np.mean(images, axis=0)\n", + "# std_image = np.std(images, axis=0)\n", + "means_image = 0.0\n", + "std_image = 1.0\n", + "images = (images - means_image) / std_image\n", + "images = images.reshape(images.shape[0], 32, 32, 1)\n", + "\n", + "\n", + "#manually standardize the theta (w)\n", + "mean_theta = 0.0 \n", + "std_theta = 1.0 \n", + "theta = (theta - mean_theta)/std_theta" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3b541bc5-baf5-4235-a319-367dcdb4edb0", + "metadata": {}, + "outputs": [], + "source": [ + "# load models with different seeds\n", + "\n", + "model1 = tf.keras.models.load_model(model_name1)\n", + "model2 = tf.keras.models.load_model(model_name2)\n", + "model3 = tf.keras.models.load_model(model_name3)" + ] + }, + { + "cell_type": "markdown", + "id": "ce5424f6", + "metadata": {}, + "source": [ + "### Calculate the Analytical Posterior \n", + "\n", + "The analytical equation to calculate the posterior is given by\n", + "\n", + "\\begin{equation}\n", + "\\begin{split}\n", + " p(w|\\{x\\}) &= \\frac{p(w)~\\prod_{i}r(x_i|w)}{\\int dw^{\\prime}~ p(w^{\\prime})~\\prod_{i}r(x_{i}|w^{\\prime})},\\\\\n", + " &= p(w)~\\left( \\int dw^{\\prime}~p(w^{\\prime})~\\prod_{i}\\frac{r(x_i|w^{\\prime})}{r(x_i|w)} \\right)^{-1}.\n", + "\\end{split}\n", + "\\end{equation}\n", + "\n", + "```likelihood_diff``` function calculates $log\\ r(x|w^{\\prime}) - log\\ r(x|w)$ for one image $x$ \n", + "\n", + "This is same as calculating $\\frac{r(x_i|w^{\\prime})}{r(x_i|w)}$ in the posterior equation\n", + "\n", + "```get_joint_likelihood``` function calculates $\\sum_{i} log\\ r(x_{i}|w^{\\prime}) - log\\ r(x_{i}|w)$ for a population of strong lens images $\\{x_{i}\\}$\n", + "\n", + "This is same as calculating $\\prod_{i}\\frac{r(x_i|w^{\\prime})}{r(x_i|w)}$ in the posterior equation\n", + "\n", + "```get_joint_posterior``` calculates the sum of posterior for all the theta ($w$) values and gives the inverse of the sum as shown in the equation" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "cb473e9f-2367-4e7b-8e07-875054ac01d1", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import numba as nb\n", + "\n", + "@nb.jit\n", + "def get_logr_distribution(model, images, sample_theta):\n", + " '''\n", + " Function to predict the log likelihood-to-evidence ratio (logr) of the test data\n", + "\n", + " Input:\n", + " model: The trained model \n", + " images: test images\n", + " sample_theta: a list of theta values to compute logr for\n", + " '''\n", + " output_probs = []\n", + " for image in images:\n", + " test_image_array = np.concatenate([image[np.newaxis, :]]*len(sample_theta), axis=0)\n", + " output = model.predict([test_image_array, sample_theta], verbose=0).flatten()\n", + " output_probs.append(output)\n", + " return np.array(output_probs)\n", + "\n", + "class Posterior:\n", + " def __init__(self, lnr, thetas):\n", + " self.lnr = lnr\n", + " self.thetas = thetas\n", + "\n", + " def likelihood_diff(self, image_index):\n", + " # exp_diff_lnr = np.empty((len(self.thetas), len(self.thetas)))\n", + " diff_lnr_list = np.empty((len(self.thetas), len(self.thetas)))\n", + " for i in range(len(self.thetas)):\n", + " diff_lnr = self.lnr[image_index, i] - self.lnr[image_index]\n", + " # exp_diff_lnr[i] = np.exp(diff_lnr)\n", + " diff_lnr_list[i] = diff_lnr\n", + " # return exp_diff_lnr\n", + " return diff_lnr_list\n", + "\n", + " def get_joint_likelihood(self, n_images):\n", + " likelihood = np.empty((n_images, len(self.thetas), len(self.thetas)))\n", + " for i in range(n_images):\n", + " likelihood[i] = self.likelihood_diff(i)\n", + " # joint_likelihood = np.prod(likelihood, axis=0)\n", + " joint_likelihood = np.sum(likelihood, axis=0)\n", + " joint_likelihood = np.exp(joint_likelihood)\n", + " return joint_likelihood\n", + " \n", + " def get_joint_posterior(self, n_images):\n", + " joint_likelihood = self.get_joint_likelihood(n_images)\n", + " joint_posterior = 1. / np.sum(joint_likelihood, axis=0)\n", + " return joint_posterior\n", + " \n", + "def get_joint_posterior_probability(lnr, thetas, n_images):\n", + " '''\n", + " Function to sample from the posterior probability distribution.\n", + "\n", + " Output:\n", + " The posterior probability, mean and standard deviation\n", + " '''\n", + " posterior = Posterior(lnr, thetas)\n", + " joint_posterior = posterior.get_joint_posterior(n_images)\n", + " sampled_values = np.random.choice(thetas, size=1000, p=joint_posterior)\n", + " weighted_mean = np.mean(sampled_values)\n", + " weighted_std_dev = np.std(sampled_values)\n", + " # weighted_mean = np.sum(thetas * joint_posterior) / np.sum(joint_posterior)\n", + " # weighted_std_dev = np.sqrt(np.sum(joint_posterior * (thetas - weighted_mean)**2) / np.sum(joint_posterior))\n", + " return joint_posterior, weighted_mean, weighted_std_dev" + ] + }, + { + "cell_type": "markdown", + "id": "4a86b559", + "metadata": {}, + "source": [ + "### Plot of the test image and the corresponding posterior for the three models with randome weight initialization" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "6cc24ee7", + "metadata": {}, + "outputs": [], + "source": [ + "# Plotting function to get the image-posterior figure \n", + "\n", + "from matplotlib import gridspec\n", + "\n", + "def grid2(nx=4, ny=2, height=6., large_margin=0.14, small_margin=0.03, sep=0.03, cbar_width=0.06):\n", + " # Geometry\n", + " left = large_margin\n", + " right = large_margin\n", + " top = small_margin\n", + " bottom = large_margin\n", + "\n", + " panel_size = (1. - top - bottom - (ny - 1)*sep)/ny\n", + " width = height*(left + nx*panel_size + cbar_width + nx*sep + right)\n", + "\n", + " # wspace and hspace are complicated beasts\n", + " avg_width_abs = (height*panel_size * nx * ny + ny * cbar_width * height) / (nx * ny + ny)\n", + " avg_height_abs = height*panel_size\n", + " wspace = sep * height / avg_width_abs\n", + " hspace = sep * height / avg_height_abs\n", + "\n", + " # Set up figure\n", + " fig = plt.figure(figsize=(width, height))\n", + " gs = gridspec.GridSpec(ny, nx + 1, width_ratios=[1.]*nx + [cbar_width], height_ratios=[1.] * ny)\n", + " plt.subplots_adjust(\n", + " left=left * height / width,\n", + " right=1. - right * height / width,\n", + " bottom=bottom,\n", + " top=1. - top,\n", + " wspace=wspace,\n", + " hspace=hspace,\n", + " )\n", + " return fig, gs\n", + "\n", + "\n", + "def grid2_width(nx=4, ny=2, width=7.1, large_margin=0.14, small_margin=0.03, sep=0.03, cbar_width=0.06):\n", + " left = large_margin\n", + " right = large_margin\n", + " top = small_margin\n", + " bottom = large_margin\n", + " panel_size = (1. - top - bottom - (ny - 1)*sep)/ny\n", + " height = width / (left + nx*panel_size + cbar_width + nx*sep + right)\n", + " return grid2(nx, ny, height, large_margin, small_margin, sep, cbar_width)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6e0f13f6", + "metadata": {}, + "outputs": [], + "source": [ + "# list of w values to calculate the logr for\n", + "sample_theta_unstd = np.linspace(-2.5, -0.15, 1000)\n", + "sample_theta = (sample_theta_unstd - mean_theta)/std_theta\n", + "\n", + "# Get logr for the w\n", + "x_test_lnr_model1 = get_logr_distribution(model1, images, sample_theta)\n", + "x_test_lnr_model2 = get_logr_distribution(model2, images, sample_theta)\n", + "x_test_lnr_model3 = get_logr_distribution(model3, images, sample_theta)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "909f734e", + "metadata": {}, + "outputs": [], + "source": [ + "x_test_copy = images[4:]\n", + "theta_test_copy = theta[4:]\n", + "x_test_lnr_model1 = x_test_lnr_model1[4:]\n", + "x_test_lnr_model2 = x_test_lnr_model2[4:]\n", + "x_test_lnr_model3 = x_test_lnr_model3[4:]" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "814186ce", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1328493/1466675953.py:62: UserWarning: FixedFormatter should only be used together with FixedLocator\n", + " ax.set_yticklabels(['' for item in ax.get_xticklabels()])\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cols = [\"#5790fc\", \"#f89c20\", \"#e42536\", \"#964a8b\", \"#9c9ca1\", \"#7a21dd\"]\n", + "ncols = 5\n", + "nrows = 2\n", + "\n", + "fig, gs = grid2_width(ncols, nrows, sep=0.03, width=14.0)\n", + "\n", + "#plot for w>-1.2\n", + "lw=2.4\n", + "\n", + "for i, image in enumerate(x_test_copy):\n", + " ax = plt.subplot(gs[i])\n", + " im = plt.imshow(\n", + " x_test_copy[i],\n", + " cmap='viridis',\n", + " origin=\"lower\",\n", + " alpha=1.\n", + " )\n", + " ax.text(6, 28, r'$w_{\\mathrm{True}} = $'+str(theta_test_copy[i]), color=\"white\", fontsize=18)\n", + " ax.get_xaxis().set_visible(False)\n", + " ax.get_yaxis().set_visible(False)\n", + " \n", + " if i == 0:\n", + " ax = plt.subplot(gs[ncols])\n", + " cbar = plt.colorbar(im, cax=ax, pad=0.01)\n", + " cbar.remove() \n", + " ax = plt.subplot(gs[ncols + 1 + i])\n", + " posterior1, posterior_mean1, posterior_std1 = get_joint_posterior_probability(x_test_lnr_model1[i:i+1], sample_theta, 1)\n", + " posterior2, posterior_mean2, posterior_std2 = get_joint_posterior_probability(x_test_lnr_model2[i:i+1], sample_theta, 1)\n", + " posterior3, posterior_mean3, posterior_std3 = get_joint_posterior_probability(x_test_lnr_model3[i:i+1], sample_theta, 1)\n", + " plt.plot(sample_theta_unstd, posterior1/np.max(posterior1), ls='-', lw=lw, color=\"#5790fc\", label='Seed 1')\n", + " plt.plot(sample_theta_unstd, posterior2/np.max(posterior2), ls='--', lw=lw, color = \"#f89c20\", label='Seed 2')\n", + " plt.plot(sample_theta_unstd, posterior3/np.max(posterior3), ls=':', lw=lw, color = \"#964a8b\",label='Seed 3')\n", + "\n", + " plt.xlabel(r'$w$', fontsize='x-small')\n", + " plt.tick_params(axis='both', which='both', labelsize='12')\n", + " plt.ylim(0.0, 1.1)\n", + " # set xlimits\n", + " if i == 0:\n", + " plt.vlines(theta_test_copy[i], 0, 1.1, color='k', ls='--', lw=2, label=r'$w_{\\mathrm{True}}$')\n", + " plt.xlim(-1.6, -0.8)\n", + " elif i == 1:\n", + " plt.vlines(theta_test_copy[i], 0, 1.1, color='k', ls='--', lw=2)\n", + " plt.xlim(-1.4, -0.6)\n", + " elif i == 2:\n", + " plt.vlines(theta_test_copy[i], 0, 1.1, color='k', ls='--', lw=2)\n", + " plt.xlim(-1.2, -0.4)\n", + " elif i == 3:\n", + " plt.vlines(theta_test_copy[i], 0, 1.1, color='k', ls='--', lw=2)\n", + " plt.xlim(-0.8, -0.4)\n", + " elif i == 4:\n", + " plt.vlines(theta_test_copy[i], 0, 1.1, color='k', ls='--', lw=2)\n", + " plt.xlim(-0.6, -0.2)\n", + "\n", + " if i == 0:\n", + " plt.tick_params(axis='y', which='both')\n", + " plt.tick_params(axis='x', which='both', rotation=60)\n", + " plt.legend(loc='upper left', fontsize=11)\n", + " plt.ylabel(r'$p(w|x)$', fontsize='x-small')\n", + " else: \n", + " plt.tick_params(axis='y', which='both', left=False, labelbottom=False) \n", + " plt.tick_params(axis='x', which='both', rotation=60)\n", + " ax.set_yticklabels(['' for item in ax.get_xticklabels()])\n", + " # ax.legend([r'$w_{True} = $'+str(theta_test_copy[i])], loc='upper left', fontsize=8.4)\n", + "\n", + " \n", + "# plt.tight_layout()\n", + "plt.savefig('image_posterior.pdf', bbox_inches='tight', dpi=400)\n", + "plt.show() " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:.conda-astro]", + "language": "python", + "name": "conda-env-.conda-astro-py" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/plot_residuals.ipynb b/notebooks/plot_residuals.ipynb new file mode 100644 index 0000000..30118b9 --- /dev/null +++ b/notebooks/plot_residuals.ipynb @@ -0,0 +1,724 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "!export LD_LIBRARY_PATH=/opt/conda/lib\n", + "!export XLA_FLAGS=--xla_gpu_cuda_data_dir=/home/jarugula/cuda_data_dir\n", + "!export PATH=/usr/local/cuda-11.7/bin:${PATH}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# import the libraries\n", + "\n", + "import logging\n", + "import warnings\n", + "import json\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from sklearn import metrics\n", + "from sklearn.metrics import confusion_matrix\n", + "from sklearn.metrics import auc, roc_curve\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib\n", + "from matplotlib.colors import ListedColormap\n", + "import random\n", + "from tqdm import tqdm\n", + "import pandas as pd\n", + "\n", + "import h5py\n", + "import os\n", + "from tqdm import tqdm\n", + "import tensorflow as tf\n", + "from tensorflow import keras\n", + "from tensorflow.keras import layers \n", + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import Reshape,Conv1D,Flatten,Dense, Lambda\n", + "from tensorflow.keras.models import Model\n", + "from sklearn.model_selection import train_test_split\n", + "from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau, TerminateOnNaN\n", + "physical_devices = tf.config.list_physical_devices('GPU')\n", + "try:\n", + " tf.config.experimental.set_memory_growth(physical_devices[0], True)\n", + "except:\n", + " pass\n", + "from sklearn.utils import shuffle\n", + "AUTOTUNE = tf.data.AUTOTUNE\n", + "from sklearn.preprocessing import StandardScaler\n", + "import tqdm\n", + "import gc\n", + "import seaborn as sns\n", + "from cycler import cycler\n", + "\n", + "os.environ['PATH']=os.environ['PATH']+':/usr/local/cuda/bin'\n", + "os.environ['XLA_FLAGS']='--xla_gpu_cuda_data_dir=/home/jarugula/cuda_data_dir'" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# define your plot style\n", + "\n", + "best_style = {\n", + " # \"font.sans-serif\": [\"TeX Gyre Heros\", \"Helvetica\", \"Arial\"],\n", + " \"font.family\": \"sans-serif\",\n", + " \"mathtext.fontset\": \"custom\",\n", + " \"mathtext.rm\": \"TeX Gyre Heros\",\n", + " \"mathtext.bf\": \"TeX Gyre Heros:bold\",\n", + " \"mathtext.sf\": \"TeX Gyre Heros\",\n", + " \"mathtext.it\": \"TeX Gyre Heros:italic\",\n", + " \"mathtext.tt\": \"TeX Gyre Heros\",\n", + " \"mathtext.cal\": \"TeX Gyre Heros\",\n", + " \"mathtext.default\": \"regular\",\n", + " \"figure.figsize\": (10.0, 10.0),\n", + " \"font.size\": 26,\n", + " #\"text.usetex\": True,\n", + " \"axes.labelsize\": \"medium\",\n", + " \"axes.unicode_minus\": False,\n", + " \"xtick.labelsize\": \"small\",\n", + " \"ytick.labelsize\": \"small\",\n", + " \"legend.fontsize\": \"small\",\n", + " \"legend.handlelength\": 1.5,\n", + " \"legend.borderpad\": 0.5,\n", + " \"xtick.direction\": \"in\",\n", + " \"xtick.major.size\": 12,\n", + " \"xtick.minor.size\": 6,\n", + " \"xtick.major.pad\": 6,\n", + " \"xtick.top\": True,\n", + " \"xtick.major.top\": True,\n", + " \"xtick.major.bottom\": True,\n", + " \"xtick.minor.top\": True,\n", + " \"xtick.minor.bottom\": True,\n", + " \"xtick.minor.visible\": True,\n", + " \"ytick.direction\": \"in\",\n", + " \"ytick.major.size\": 12,\n", + " \"ytick.minor.size\": 6.0,\n", + " \"ytick.right\": True,\n", + " \"ytick.major.left\": True,\n", + " \"ytick.major.right\": True,\n", + " \"ytick.minor.left\": True,\n", + " \"ytick.minor.right\": True,\n", + " \"ytick.minor.visible\": True,\n", + " \"grid.alpha\": 0.8,\n", + " \"grid.linestyle\": \":\",\n", + " \"axes.linewidth\": 2,\n", + " \"savefig.transparent\": False,\n", + "}\n", + "plt.style.use(best_style)\n", + "cols = [\"#5790fc\", \"#f89c20\", \"#e42536\", \"#964a8b\", \"#9c9ca1\", \"#7a21dd\"]\n", + "plt.rcParams['axes.prop_cycle'] = plt.cycler(color=cols)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# The training data of 1M strong lens images\n", + "\n", + "model_name = 'working_model_1M-2-034_seed38_v2.keras'\n", + "model = tf.keras.models.load_model(model_name)\n", + "\n", + "data_path = '/deepskieslab/stronglensing/hsbi/datasets/'\n", + "image_dir = 'w0_8param_fixzv_train_1M'\n", + "column_name = \"w0-g\" # Dark energy equation-of-state parameter " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# Read the images and metadata\n", + "\n", + "if os.path.isdir(data_path+image_dir):\n", + " images = np.load(data_path+image_dir+'/CONFIGURATION_1_images.npy', allow_pickle=True)\n", + " metadata = pd.read_csv(data_path+image_dir+'/CONFIGURATION_1_metadata.csv')\n", + "else:\n", + " print(\"Data not found\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "images = np.einsum('lkij->lijk',images)\n", + "theta = metadata[column_name].to_numpy()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# normalize image each image by the sum of all pixels, make it such that the sum of all pixels is 1024 (32 X32)\n", + "images = 1024*(images/np.sum(images, axis=(1,2), keepdims=True))\n", + "\n", + "# manually standardies pixels across all images. \n", + "# In this analysis we do not standerdize the images and parameter. Hence we use mean=0 and std=1.0\n", + "\n", + "images = images.reshape(images.shape[0], -1)\n", + "# means_image = np.mean(images, axis=0)\n", + "# std_image = np.std(images, axis=0)\n", + "means_image = 0.0\n", + "std_image = 1.0\n", + "images = (images - means_image) / std_image\n", + "images = images.reshape(images.shape[0], 32, 32, 1)\n", + "\n", + "\n", + "#manually standardize the theta (w)\n", + "mean_theta = 0.0 \n", + "std_theta = 1.0 \n", + "theta = (theta - mean_theta)/std_theta" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Split the data into train and test" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "x_train , x_test, theta_train, theta_test, y_train, y_test = train_test_split(images, theta, np.ones_like(theta), test_size=0.2, random_state=0)\n", + "true_test_theta = np.copy(theta_test)\n", + "true_test_theta = true_test_theta*std_theta + mean_theta\n", + "\n", + "del images, theta\n", + "gc.collect()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Calculate the Analytical Posterior \n", + "\n", + "The analytical equation to calculate the posterior is given by\n", + "\n", + "\\begin{equation}\n", + "\\begin{split}\n", + " p(w|\\{x\\}) &= \\frac{p(w)~\\prod_{i}r(x_i|w)}{\\int dw^{\\prime}~ p(w^{\\prime})~\\prod_{i}r(x_{i}|w^{\\prime})},\\\\\n", + " &= p(w)~\\left( \\int dw^{\\prime}~p(w^{\\prime})~\\prod_{i}\\frac{r(x_i|w^{\\prime})}{r(x_i|w)} \\right)^{-1}.\n", + "\\end{split}\n", + "\\end{equation}\n", + "\n", + "```likelihood_diff``` function calculates $log\\ r(x|w^{\\prime}) - log\\ r(x|w)$ for one image $x$ \n", + "\n", + "This is same as calculating $\\frac{r(x_i|w^{\\prime})}{r(x_i|w)}$ in the posterior equation\n", + "\n", + "```get_joint_likelihood``` function calculates $\\sum_{i} log\\ r(x_{i}|w^{\\prime}) - log\\ r(x_{i}|w)$ for a population of strong lens images $\\{x_{i}\\}$\n", + "\n", + "This is same as calculating $\\prod_{i}\\frac{r(x_i|w^{\\prime})}{r(x_i|w)}$ in the posterior equation\n", + "\n", + "```get_joint_posterior``` calculates the sum of posterior for all the theta ($w$) values and gives the inverse of the sum as shown in the equation" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "import numba as nb\n", + "\n", + "@nb.jit\n", + "def get_logr_distribution(model, images, sample_theta):\n", + " '''\n", + " Function to predict the log likelihood-to-evidence ratio (logr) of the test data\n", + "\n", + " Input:\n", + " model: The trained model \n", + " images: test images\n", + " sample_theta: a list of theta values to compute logr for\n", + " '''\n", + " output_probs = []\n", + " for image in images:\n", + " test_image_array = np.concatenate([image[np.newaxis, :]]*len(sample_theta), axis=0)\n", + " output = model.predict([test_image_array, sample_theta], verbose=0).flatten()\n", + " output_probs.append(output)\n", + " return np.array(output_probs)\n", + "\n", + "class Posterior:\n", + " def __init__(self, lnr, thetas):\n", + " self.lnr = lnr\n", + " self.thetas = thetas\n", + "\n", + " def likelihood_diff(self, image_index):\n", + " # exp_diff_lnr = np.empty((len(self.thetas), len(self.thetas)))\n", + " diff_lnr_list = np.empty((len(self.thetas), len(self.thetas)))\n", + " for i in range(len(self.thetas)):\n", + " diff_lnr = self.lnr[image_index, i] - self.lnr[image_index]\n", + " # exp_diff_lnr[i] = np.exp(diff_lnr)\n", + " diff_lnr_list[i] = diff_lnr\n", + " # return exp_diff_lnr\n", + " return diff_lnr_list\n", + "\n", + " def get_joint_likelihood(self, n_images):\n", + " likelihood = np.empty((n_images, len(self.thetas), len(self.thetas)))\n", + " for i in range(n_images):\n", + " likelihood[i] = self.likelihood_diff(i)\n", + " # joint_likelihood = np.prod(likelihood, axis=0)\n", + " joint_likelihood = np.sum(likelihood, axis=0)\n", + " joint_likelihood = np.exp(joint_likelihood)\n", + " return joint_likelihood\n", + " \n", + " def get_joint_posterior(self, n_images):\n", + " joint_likelihood = self.get_joint_likelihood(n_images)\n", + " joint_posterior = 1. / np.sum(joint_likelihood, axis=0)\n", + " return joint_posterior\n", + " \n", + "def get_joint_posterior_probability(lnr, thetas, n_images):\n", + " '''\n", + " Function to sample from the posterior probability distribution.\n", + "\n", + " Output:\n", + " The posterior probability, mean and standard deviation\n", + " '''\n", + " posterior = Posterior(lnr, thetas)\n", + " joint_posterior = posterior.get_joint_posterior(n_images)\n", + " sampled_values = np.random.choice(thetas, size=1000, p=joint_posterior)\n", + " weighted_mean = np.mean(sampled_values)\n", + " weighted_std_dev = np.std(sampled_values)\n", + " # weighted_mean = np.sum(thetas * joint_posterior) / np.sum(joint_posterior)\n", + " # weighted_std_dev = np.sqrt(np.sum(joint_posterior * (thetas - weighted_mean)**2) / np.sum(joint_posterior))\n", + " return joint_posterior, weighted_mean, weighted_std_dev" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "sample_theta_unstd = np.linspace(-2.8, -0.15, 1500)\n", + "\n", + "sample_theta = (sample_theta_unstd - mean_theta)/std_theta\n", + "x_test_lnr_2000 = get_logr_distribution(model, x_test[0:2000], sample_theta)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# # save the log r\n", + "# np.savez('x_test_lnr_2000.npy', x_test_lnr_2000 = x_test_lnr_2000)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# # calculate analytical posteriors\n", + "# posteriors_all_list = []\n", + "# posterior_all_samples = []\n", + "\n", + "# for i in range(2000):\n", + "# posterior, posterior_mean, posterior_std = get_joint_posterior_probability(x_test_lnr_2000[i:i+1], sample_theta, 1)\n", + "# posteriors_all_list.append((posterior_mean, posterior_std))\n", + "# posterior_all_samples.append(posterior)\n", + "\n", + "# posteriors_all = np.vstack(posteriors_all_list)\n", + "# posterior_all_samples = np.array(posterior_all_samples)\n", + "# print('shape of posteriors for ntrials:', np.shape(posterior_all_samples))\n", + "# print('shape of posteriors for ntrials:', np.shape(posteriors_all))\n", + "\n", + "# # save the posteriors\n", + "# np.savez('posteriors_xtest_2000.npz', posteriors_all=posteriors_all, posterior_all_samples=posterior_all_samples)\n", + "\n", + "\n", + "#read posterior\n", + "posteriors_all = np.load('posteriors_xtest_2000.npz')['posteriors_all']\n", + "posterior_all_samples = np.load('posteriors_xtest_2000.npz')['posterior_all_samples']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot the parity plot with the bias" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Create a figure and subplots\n", + "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(8, 9), sharex=True, sharey=False, height_ratios=[0.7,0.3], gridspec_kw={'hspace': 0.0, 'wspace': 0.0})\n", + "ax1.grid(False)\n", + "ax2.grid(False)\n", + "\n", + "# Plot for the first subplot\n", + "ax1.plot([-2.4, -0.1], [-2.4, -0.1], 'r--', zorder=3, lw=1.2)\n", + "ax1.set_xlim(-2.4, -0.1)\n", + "ax1.set_ylim(-2.4, -0.1)\n", + "\n", + "ax1.errorbar(true_test_theta[0:2000], posteriors_all[:,0], yerr=posteriors_all[:,1], fmt='o', zorder=2, markersize=0.5, linewidth=0.3, mec='k', mfc='k', ecolor='steelblue')\n", + "\n", + "ax1.set_ylabel(r'$ \\mathrm{w_{Pred}}$')\n", + "\n", + "# Plot for the second subplot\n", + "ax2.plot([-2.4, -0.1], [0,0], 'r--', zorder=3, lw=1.2)\n", + "ax2.errorbar(true_test_theta[0:2000], (posteriors_all[:,0]-true_test_theta[0:2000])/true_test_theta[0:2000], yerr=posteriors_all[:,1]/abs(true_test_theta[0:2000]), fmt='o', zorder=2, markersize=0.5, linewidth=0.3, mec='k', mfc='k', ecolor='steelblue')\n", + "ax2.set_ylabel(r'$ \\frac{\\mathrm{w_{Pred}} - \\mathrm{w_{True}}}{\\mathrm{w_{True}}}$')\n", + "ax2.set_xlabel(r'$ \\mathrm{w_{True}}$')\n", + "ax2.set_xlim(-2.4, -0.1)\n", + "\n", + "# Show the plot\n", + "plt.tight_layout()\n", + "plt.savefig('residual_plot_xtest_2000_v2.pdf', dpi=400, bbox_inches='tight')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Calculate the Posterior Coverage Plot" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [], + "source": [ + "def calculate_coverage_fraction_density_estimator(posterior_prob, thetas, truth_array, percentile_list):\n", + " '''\n", + " Function to calculate the posteiror coverage fraction\n", + "\n", + " Inputs:\n", + " posterior_prob: Posterior probability distribution\n", + " thetas: A list of w values in a range\n", + " truth_array: The list of true w values\n", + " Percentile_list: The percentile list from 0 to 100\n", + "\n", + " Outputs:\n", + " The fraction of lenses within the confidence interval\n", + " '''\n", + " # sample from posterior probability\n", + " count_array = []\n", + " for i in range(0, 2000):\n", + " sampled_values = np.random.choice(thetas, size=1000, p=posterior_prob[i])\n", + " count_vector = []\n", + " for ind, cov in enumerate(percentile_list):\n", + " percentile_l = 50.0 - cov/2\n", + " percentile_u = 50.0 + cov/2 \n", + " confidence_l = np.percentile(sampled_values,percentile_l,axis=0)\n", + " confidence_u = np.percentile(sampled_values,percentile_u,axis=0)\n", + " # print(confidence_l, confidence_u)\n", + " count = np.logical_and(confidence_u - truth_array[i] > 0, truth_array[i] - confidence_l > 0)\n", + " count_vector.append(count)\n", + " count_array.append(count_vector)\n", + " count_sum_array = np.sum(count_array, axis=0)\n", + " frac_lens_within_vol = np.array(count_sum_array)\n", + " return frac_lens_within_vol/2000\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [], + "source": [ + "# Calculate the posterior converage plot\n", + "percentile_array = np.linspace(0,100,21)\n", + "coverage_fraction = calculate_coverage_fraction_density_estimator(posterior_all_samples, sample_theta, true_test_theta[0:2000], percentile_array)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Shape of fraction array: (21,)\n" + ] + }, + { + "data": { + "image/png": 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d2suJ2IWIDXuiim05wv8GdE1LERGRZ93atcbKKOfPG21PzymEhva2vD9o0CC+++675yKEQxKCeK5cuQgODub69evx9t23bx+hoaHkz5/f2suJ2I2ogTpPnjzky5cv3n4QfVqLiIjIs8RshlGjoFGjiIcyIU8eHx4+7GPpM2TIEEaOHPnchHBIQhCvVKkSAIsWLYqz3+PHj/n+++8xmUy89dZb1l5OxG4MHTqUM2fOcObMGbZu3RprvypVqlj6nTlzhsKFC6dglSIiIikjIADatYMhQ4xADvDaa5O4evV9S59PP/2Ub7755rkK4ZCEIN6pUyccHR2ZNWsWK1eujLHPH3/8Qfv27Tl16hRubm60a9fO6kJFRERExL78/TdUrgxRx229vX/gyJEPLO3PPvuMr7/++rkL4ZCEIJ4vXz6++OILQkJC+PTTT6lcuTL3798HoHv37lSrVo327dtz4sQJTCYTX331FVmzZk22wkVEREQk7dq5E8qVg8OHjbaHB7z33nh8fftb+nzxxRd8+eWXz2UIhyTurNm6dWt8fHzIli0b//77LyEhIZjNZnbs2MGNGzcwm81ky5aNSZMm0bBhw+SqWURERETSsB9/hBo14J9/jHbhwtCr1zhmzfrQ0mfYsGGMGDHiuQ3hAE5J/YBatWpRtWpVduzYwaFDh/jnn38IDw8na9aslClThho1auDi4pIctYqIiIhIGhYSAgMGwOTJkcdq1YIqVb7niy8+shwbPnw4w4YNS4UK05YkB3EAZ2dnatasSc2aNZPj40RERETEzty6Ba1bR64PDsaumTlzjmHw4E8sx7788ks+//zzFK8vLUqWIC4iIiIiz6+jR6FJE7h0yWi7uMD06XDz5ncMHjzY0u+rr77is88+S6Uq054kzREXERERkefb8uXwxhuRITxnTti+HW7cGBUthH/zzTcK4f+R5BHxS5cusX79es6cOcP9+/cJCQmJta/JZGLu3LlJvaSIiIiIpLLwcBg+HL76KvJYuXKwciXMnfstQ4cOtRwfOXJktFAuhiQF8XHjxvHTTz8RHh6OOWKF9jg8z0/FioiIiDwrHj6Ed9+FX3+NPNahA8yYAWPHfh1tDvioUaMYNGhQyhdpB6wO4j///DMzZswAjG2833jjDbJkyYKTk6adi4iIiDyrLlww5oOfOGG0HRxgzBhjtZSvv/6KL774wtJ39OjRfPzxx6lUadpndWpetGgRJpOJZs2a8fXXX+PgoOnmIiIiIs+yzZuNlVHu3jXaGTMau2bWrQsjRoxg+PDhlr5jx45l4MCBqVKnvbA6PV96MiN/yJAhCuEiIiIizzCzGX74AerViwzhL78M+/cbIXz48OHRQvj333+vEJ4AVo+IZ8iQgeDgYDw9PZOzHhERERFJQ4KCoFcvmD078ljDhvDzz+DpaWbYsOF8+eWXlvfGjx9P//79U75QO2T1UHapUqXw9/fnzp07yVmPiIiIiKQR169DtWrRQ/iQIcZDmp6eZr744otoIXzChAkK4YlgdRDv0qULJpOJadOmJWc9kgISssKN2D/dZxERSYoDB4zlCPfuNdrp0hnzwb/9FhwczHz++ed8/fXXlv4TJ06kX79+qVStfbI6iL/++usMHz6chQsX8sUXX3D58uXkrEtsIGIuf3h4eCpXIikhLCwMAEdHx1SuRERE7M2CBfDWW3D1qtHOlw927YI2bYyBnqFDh/LNN99Y+vv4+PD++++nUrX2K0lrDbZq1Yq7d+8ybtw4li5dSoYMGfDw8Ii1v8lkYvPmzUm5pCSBs7Mzjo6OPHr0CHd399QuR2wsICAAR0dHLSkqIiIJFhYGgwfD2LGRxypXNnbPzJ7dCOFDhgzhu+++s7w/efJkevfunQrV2j+rf0KHhYXxySef4OvrCxg35v79+9y/fz/Wc7ShT+oymUx4enry4MEDsmXLpvvxDDObzTx48ABPT0/dZxERSZC7d6FtW9iwIfJY9+4waRK4uBg/WwYPHszo0aMt70+ZMoVevXqlQrXPBquD+Ny5c1m7di0AZcuWpXLlytrQxw54eXlx7949rl27Ru7cuRXSnkFms5lr164REhKCl5dXapcjIiJ24PRpaNwYzp0z2k5OMHGisVoKGD9bPvnkE8ZGGSqfNm0aPXr0SIVqnx1Wp+Zly5ZhMpno0aOHno61I+nTpydv3rxcuXKFwMBAMmTIQPr06XF0dIwxlJvNZoKCggD9RiMtM5vNhIWFERAQwIMHDwgJCSFv3rykT5/eMldcREQkJr6+xkj4gwdGO2tWWLYMqlY12mazmY8++ohx48ZZzpk+fTrdu3dPhWqfLVYH8atXr2IymXQT7JCnpycFChTg/v373Lt3j9u3b8fa12w2ExoaipOTk4K4HXB0dMTT0xMvLy/Sp0+f2uWIiEgaN3069O4NEes4vPqqsTRhwYJG22w2M3DgQMaPH28558cff6Rr164pXuuzKEkb+gQFBemHvZ1Knz496dOnJ2fOnISEhMS6kkpYWBgXL16kQIECWn0jjXNwcMDZ2Vn/YBIRkXiZzfD55xBl4RNatIC5cyFiPQez2cyAAQP44YcfAOM34zNnzuS9995LhYqfTVYH8XLlyrFu3Tpu3rxJjhw5krMmSUEmkwkXF5dY3w8LC8PBwQE3NzcFcRERkWdAcDB06wbz5kUe+/hjGDUKnqx0jNlspn///kycOBEw8sJPP/1E586dU6HiZ5fV64j36NEDFxeXaE/OioiIiEja9eCBsT19RAg3mYyHMkePjh7CP/jgg2ghfNasWQrhNmB1EC9WrBg+Pj78/vvvdO3alb179/Lo0aPkrE1EREREksm1a1ClCmzaZLTd3IyHMqPuw2M2m+nbty8+Pj6AEcJnz55Np06dUr7g54DVU1Nefvlly+tdu3axa9eueM8xmUycPHnS2kuKiIiIiBVOnoT69eHvv4125sywejW8+WZkn/DwcPr27cvUqVMBI7fNnTuXDh06pELFzwerg7jZbE7OOkRERETEBnbsgCZN4N49o12wIKxfD8WKRfYJDw+nT58+TJs2DTAWAJg7dy7vvPNOitf7PLE6iM+LOsNfRERERNKcpUvhnXeMBzQBypSBtWshZ87IPuHh4fTq1YsZM2YARgifP38+7dq1S4WKny9WB/Hy5csnZx0iIiIikozGj4cPP4xs16tnBHMPj8hj4eHh9OjRg5kzZwJGCF+wYAFt27ZN4WqfT1Y/rCkiIiIiaU94OAwYED2Ev/eeMSf8vyG8e/fulhDu6OjIwoULFcJTkNUj4iIiIiKStjx+DB06GKuhRBg+HL74wliqMEJ4eDhdu3Zl9uzZQGQIb926dcoW/JyzOogfOHDAqvPKlStn7SVFREREJBZ37hgPZe7cabQdHY0t7Lt0id4vLCyMrl27MmfOnCf9HPnll19o1apVyhYs1gfxDh06JHorbS1fKCIiIpL8Ll0ylic8dcpou7sb88Hr14/eLywsjC5dujB37lwAnJycWLRoES1atEjhigWSODUlsUsYaslDERERkeR1+DB4e8ONG0Y7Rw5jZZTXX4/eLywsjM6dOzN//nzACOGLFy+mefPmKVyxRLA6iJ8+fTrO9/39/Tl69CjTpk3j7NmzTJ48mbJly1p7ORERERH5j40boUUL8Pc32sWKwbp18MIL0fuFhYXRqVMnFixYABghfMmSJTRr1iyFK5aobLZqioeHB2+++Sbz5s2jTJky9OrVi8uXL9vqciIiIiLPlTlzoEGDyBD+xhuwa9fTITw0NJR3333XEsKdnZ1ZtmyZQngaYPPlC00mEx9//DEPHz5kypQptr6ciIiIyDPNbIavv4bOnSE01DjWrBls3gxZskTvGxHCFy5cCESG8CZNmqRw1RKTFFm+sFChQnh4eLB79+6UuJyIiIjIMyk0FHr3hh9/jDz2/vvG5j2Ojv/tG0qHDh1YtGgRYITw5cuX06hRoxSsWOKSIkE8JCSEx48fExQUlBKXExEREXnmPHoEbdoYD2JGGDMGBg6MvkY4GCG8ffv2LFmyBAAXFxeWL19Ow4YNU7BiiU+K7Ky5efNmQkNDyfLf35eIpFGTJk2iWLFidOjQIbVLsTt//PEHPXv2pFKlSrz88svR/h5XrFhBsWLFqFGjhlWfndTzRUTs1T//QLVqkSHc2RkWLoSPPno6hIeEhNCuXbtoIXzlypUK4WmQzUbEg4ODuXHjBhs3bmTatGmYTCaqVKliq8uJnahRowZXr16lWbNmjBo1Ks6+K1asYMiQIQBs2bKFvHnzpkSJkgRHjx7l3XffJSQkBJPJhJeXF05OTnh5eaV2aTZ15coVVq5cCcD777+fytWIyLPm3DmoVw/++stoe3nBypVQvfrTfUNCQmjbti3Lly8HwNXVlZUrV1L/vwuKS5pgdRB/+eWXE9zXbDaTI0cO+vTpY+3lRMQOzJkzh5CQEMqUKcOUKVPIlClTtPc9PT154YUXyJEjRypVaBtXr17Fx8cHUBAXkeS1dy80bAi3bxvtvHmN5QlfeeXpviEhIbz99tusWLECMEL4r7/+Sr169VKwYkkMq4N4QjfncXNzo06dOgwcOPCZ++ErItGdPXsWgAYNGjwVwgFq165N7dq1U7osERG7tGoVtG0LgYFGu2RJ8PU1wvh/BQcH8/bbb1t+O+fq6sqqVauoW7duClYsiWV1EJ83b16c7zs6OuLl5UXBggVxckqRZ0JFJJUFPvlpkT59+lSuRETEvk2dCn37Qni40a5RA1asMKal/FdwcDCtW7dm1apVgDEIumrVKurUqZOCFYs1rE7I5cuXT846RBJs0qRJ+Pj4UL58eebPn8+ePXuYPXs2x44d49GjR+TNm5cGDRrQrVs3XF1dY/2c7du3M2fOHP7880/CwsLIly8fjRo1olOnTgmq49atW8ydO5cdO3Zw9epVgoODyZ49OxUrVqRz584UKVLkqXP27dvHu+++C8CZM2c4ffo0P/30E/v37+fff/+lTJkylq2HwfjmumrVKtavX8+pU6d48OABGTNmJE+ePLz11ls0adKEfPnyPXWdM2fOMGfOHPbt28etW7dwcnKiUKFC1K5dmw4dOuDu7h5vbZcuXWLatGns3r2b27dvkzlzZqpUqcL777//1G+3ihUrFq09ZMgQy/x+iJzjHzHvP0+ePGzdujXGv9cjR44wY8YMDh06RGBgILly5aJevXp07949tlsRzcOHD1mwYAFbt27l4sWLBAYGkiVLFl5//XXeffddXnvttafOuXLlCjVr1rTU6ubmxrRp09i6dSu3bt3C09OTChUq0LdvXwoXLhzt3IjnHmL7u0jI8xAiIhHMZhg6FEaOjDzWvj3MmgUuLk/3Dw4OplWrVqxevRowQviaNWuoVatWClUsSaGharFrM2fOZOzYsYAx/zgkJIS//vqLSZMmsX//fmbPno3jfxdWJTLMR8iQIQMXLlxg7NixbN++nTJlysR5XT8/Pz788EMCAgIAY21WZ2dnrly5wrJly1i1ahVff/01TZs2jfUzNm3axIABAwgJCcHDw+OpOi9fvkzv3r0t0z1MJhMZMmTg4cOHHDlyhCNHjnD//n2GDh0a7bwFCxbwzTffEP5kGMXDw4OgoCCOHz/O8ePHWbJkCTNnzqRQoUKx1rZ371569epFQEAA7u7umM1mbt68ydKlS9m+fTvLli2LFsazZs0KwJ07dwgPD8fDwwM3NzfL+zHdg5gsW7aMzz//PFrtV69eZdq0aWzcuJE2bdrEef7Ro0fp3bs3//77r+W6bm5u3Lhxg7Vr1+Lr68uAAQPo0aNHrJ9x/vx5Pv30U27fvk26dOkAuH37Nr6+vuzYsYOff/6Zl156ydI/U6ZM+Pv7c//+/Wh/FxE8PDwS9GcXEQkLg169oq8RPngwfPMNOMSwzl1QUBCtWrVizZo1AKRLl441a9ZYBhYk7VMQF7t1+vRpDh48SNeuXXnvvffInDkz/v7+zJo1i8mTJ7Nv3z5WrlxJy5Yto523ZcsWSwivV68egwcPJleuXDx+/JgVK1bw7bffcubMmVive+zYMd5//31CQkJo06YNHTt2pGDBgjg6OnLt2jV+/PFHFi5cyNChQylcuDAlS5aM8XM++eQT3nzzTQYNGmQJxRcvXgTA39+frl27cvHiRTJkyMBHH32Et7c3np6eAPzvf/9jzZo1T00B2b59O1999RUAVapUYejQoeTLl49Tp05x5coVvvzyS65evUqvXr1YuXJlrFNIPvjgAypUqMDHH39M4cKFCQ4OZvPmzXz22Wf8888/fP/994wePdrSf9euXUDk6PDQoUNp3rx5rH+HMTlx4gTDhg0jPDyc8uXLM3z4cAoXLkxoaCgbN25k2LBhTJ48Odbzr1y5QteuXXnw4AF169alR48eFCtWDCcnJ27fvs2CBQuYMWMG48aNo3DhwrGOFn3yyScUKlSIadOmUapUKUJDQ9m/fz+ffPIJt27d4quvvuLnn3+29F++fHm03yZE/F2IiCRGcDB06ABPVhzEZAIfH2PznpgEBQXRokUL1j5ZzzBdunT89ttvWt7VziQoiB84cCDZLliuXLlk+yx5vj148ICePXsyYMAAyzEPDw8++OADjh49ys6dO1m7du1TQXzcuHGAMb1q/PjxODwZZnBzc6Ndu3Y4ODgwbNiwWK/75ZdfEhISQu/evenXr1+093Lnzs2wYcNwdHRk/vz5TJ06lSlTpsT4OUWKFGHKlCnRRosLFiwIGCP9Fy9exMXFhblz51K8ePFo577wwgt88MEHT33mmDFjAHj11VeZMmUKzs7OhIWF4ejoSO3atcmTJw9t2rTh4sWLLF68mM6dO8dY24svvsiUKVMsfzcuLi54e3tz5coVvv/+ezZs2MC3336brM9/TJgwgdDQUAoWLMiPP/5oGVF3cnKy/COka9eusZ4/evRoHjx4QJMmTaL9IwEgS5Ys9OvXDy8vL0aOHMmkSZNiDeKZMmVi9uzZltFwJycn3njjDYYMGcKHH37IwYMHuXHjBjlz5kymP7mIPO8CAqBFC1i/3mg7OcGCBcbmPTF5/PgxLVq0wNfXFzCey1m7di3VqlVLmYIl2STop2iHDh0w/Xe1eCuYTCZOnjyZ5M8RAWM6SLdu3WJ8r27duuzcufOpke3Tp09z/vx5AHr16mUJmlG1bt2aKVOmcPPmzafeO336NH/++SfOzs689957sdbWtGlTy/z1iCD8X126dIl1ykbE+q+tWrV6KoTH5vTp05w7dw6APn364Ozs/FSfkiVLUqtWLTZs2MCaNWtiDeKx/d3UqVOH77//nsePH3Pp0qWn5ktb68GDB+zcuROArl27RpvWEuGtt96idOnSHD58+Kn37t27x6ZNmwDinEvepEkTRo4cyenTp/n333+fmkYC8N5771lCeFS1a9fGZDJhNps5c+aMgriIJIt794zlCSN+mZYuHSxfDrEt+/348WOaN2/OunXrACOE+/r6UrVq1ZQpWJJVgoezErpcoa0/QyRCkSJFYp1/GzF/OWLeboTjx48Dxihn2bJlYzzXwcGB8uXLW+bcRXXo0CEAwsPD41yXNSwsDICAgADu3bsX466ysc1Dv3r1Kv/88w8A1WParSEWUf9sFStWjLVf5cqV2bBhA2fOnCEkJCTGwP7qq6/GeG7U8Hnv3r0E1xafEydOWOaFx1V7hQoVYgziR44csZzfsWPHBF3z2rVrMQbx2P7sLi4uZM6cmdu3bz/135WIiDX++Qfq1oUjR4x2hgzw22/w1lsx93/8+DHNmjVj/ZOhc3d3d3x9fbVhoh1LUBA/ffq0resQSbSI+dIxiRhRDQ0NjXb8zp07gDH9wCWmx8+fiG20MyIgh4WFWR4IjE/Ekn7/FVM4B6J9bu7cuRN0DYj+Z4trtZiIP1toaCj379+PMYzG9ncbdaT6v3+3SRFROxDnfgPx3RcgyfclrocrY/vvSkQksf7+G2rXhifP45MtmzE1Jba1AgIDA2natCkbN24EjBC+bt063oottYtd0MOakqIiAuLjx4/j7Rs1KMU0VSE1RIx0FypUyPJrQWslZCWR5JgSlpqfn1Ii7oubmxtHjx5N5WpEROJ25owRwi9fNtr58sGmTfCf1U8tAgMDadKkiWUKnoeHB+vWraNy5copVLHYSgyL4YjYTsRuizHNv/6viFFOBwcHvGLawcAKmTNnBuDu3bsEBwfH2i+2+iJGjy9fvmxZujC5ZcuWzfI66vrU8Yn6ZwsKCoq1340bNwBjCkuGDBmsrDJ5RdQOcf+3Edt7EX9nEXPXRUTSqj/+MKaeRITwokVh587YQ3hAQACNGzeOFsLXr1+vEP6MSNYgfvXqVY4dO8aff/6ZqAAhz48SJUoAcOrUqXiDbMR87GLFisU4j9kar7zyCmBMLYj4/P8KDw9n//79Mb4XMa87JCTE8k0xueXOndsyBWPbtm0JPi9imcSI5fZis3v3biB5/16TqkSJEpaHQ/fu3Rtrv9jeK126tGV0P2Ipr5QU9cFWPQsjIrH5/XeoXh1u3TLar71mHMufP+b+ESF88+bNgDFtcMOGDbz55pspU7DYXJKD+K1bt/jmm2944403qFWrFm3atKF169bUqlWLN954g2+//TbBczbl2eft7Q0Yv2aLuoPkfx06dIiDBw8CUD+2R8et8NJLL1lW+pg6darlAb+oli9fbhk1/q+SJUtaVjGZMGFCtLnNMbH2gcYWLVoAsHTp0gQ/o1GsWDGKFi0KwOTJk2Ocx3zixAnLPyAaNWpkVW22kCFDBssPllmzZsU4or979+4YH9QEY759xAYWP/30E//73//ivF5yPmgK0eeVP3jwIFk/W0SeDb6+UKcORHyLqFwZ/Pwge/aY+z969IiGDRuyZcsWIDKEv/HGGylUsaSEJAXxI0eO0LhxYxYsWMCdO3cwm83R/nfnzh3mz59PkyZNNG9TAGPksm7duoARZMeNG8f169ct7z98+JBly5bRq1cvzGYz+fLlo3379slaQ8S64/v27WPgwIGW0B0UFMSiRYv48ssvY52yYTKZGDFiBC4uLly7do1WrVqxfv36aPPZb968yapVq+jcubNl18/Eeu+99yhYsCBBQUF07NiRpUuX4u/vDxij8f/73//w8fHhp59+inbeRx99BMDhw4fp27evZZpGWFgYmzdvpnv37pa1uuPbpTKl9evXD0dHR/766y+6d+/OX3/9BRgj/OvWraN///5xTqUZPHgwGTNmxN/fn3bt2rFs2TIePnxoef/OnTts3LiRvn37MnDgwGStvWDBgpbfLixdulSj4iISzeLF0KQJRDweVa8ebNgAGTPG3D8ihPv5+QHGYMXGjRupVKlSyhQsKcbqhzXv3LlDz549uXfvHu7u7rRp04bKlStbfqV+48YNdu3axeLFi7l9+zY9e/bE19fXMkdYnl/ffvstwcHB+Pn5MX36dKZPn0769OlxdnbmwYMHlhBTsGBBpkyZkuxbhNeuXZuePXsybdo0fH198fX1xcvLi4CAAEJCQihbtiyvv/4606dPj/H8UqVKMW3aND788EOuXLliCZCenp4EBQVFC+WtWrWyqkYPDw9mzpxJz549OX/+PJ999hlffPEFnp6e+Pv7Wx5OjNjNMULVqlX57LPP+Pbbb/Hz88PPzw9PT08eP35MSEgIAHny5GHq1Kmx7qqZWkqWLMmwYcMYNmwYe/fupX79+pa/0+DgYAoVKkSbNm0YOXJkjOfny5eP2bNn07dvX8vunp999hkZMmQgJCQk2lSo5B5RSpcuHU2aNGHZsmWMGTMGHx8fMmXKhMlkom7dugwaNChZryci9mPGDOjZEyL+fd66NcyfD7Et3PXo0SMaNGjA9u3bAfDy8mLjxo2UL18+hSqWlGR1EJ8zZw737t2jQIECzJkzh1y5ckV7v1ChQrzxxht06NCBjh078vfffzNnzpxouyDK88nDw4OpU6eyY8cOVq1axdGjR7l9+zYBAQFkzZqVYsWKUbt2bZo1axbnMnxJMWDAAEqXLs3s2bM5fvw4wcHBvPDCCzRq1IjOnTszbdq0OM9/88032bRpE4sWLWLbtm1cuHCBhw8f4urqSpEiRXjttdeoWbNmkubx5cuXj5UrV7Js2TLWrVvH2bNnefToEVmyZCFPnjxUqVKFJk2aPHVehw4dKFeuHHPnzmXfvn38888/ODk5UbRoUWrXrk2HDh2S/R83yaVNmzYULVqU6dOnc/jwYQIDA8mdOzd169ale/fulmW7YlO8eHF8fX1Zvnw5mzdv5vTp0zx48ABnZ2cKFizIK6+8Qs2aNW2y5u6wYcPIlSsXGzZs4PLly1y7dg0wHp4VkefTd9/B4MGR7W7dYOpUiG3RLH9/fxo0aMCOHTsAI4Rv2rRJu5I/w0xmK3+H2rhxY86dO8ecOXOoUKFCnH337dtHx44defHFF2PcJEUS7tixY5ZNWEqVKmXz64WFhXH27FmKFi2aoOX2JO3RPbR/uof2TffP/iX2HprN8OmnMGpU5LFPPjHasa0a6+/vj7e3N7///jsAGTNmZNOmTbFuPieJk9JfhwnNa1aPiF+5cgU3N7d4QzgYu+GlS5dOK6mIiIjIMy0sDPr2hai/WB05MvrI+H89fPgQb29vdu7cCRhL/W7atInXX3/dxtVKarM6iIeHhyfqXxQODg6Wea0iIiIiz5qQEHj3XVi0yGibTDB5MvTqFfs5Dx48oH79+palZTNlysTmzZsty+XKs83qVVNy5sxJQEAAp06dirfvqVOnePToUazbU4uIiIjYs4AAaNo0MoQ7OcHPP8cfwuvVq2cJ4ZkzZ2bLli0K4c8Rq4P4G2+8gdlsZsSIEXFuzBIYGMiIESMwmUzaBUpERESeOffvG0sS+voabTc3+PVXaNs2rnPuU7duXfbs2QNEhvDSpUvbvmBJM6wO4u+99x6urq4cPXqUxo0bs3jxYi5fvkxISAghISFcvnyZJUuW0KRJE44cOYKrqyudO3dOztpFREREUtWtW8ZumU+escTTE9avhwYNYj8nIoRH7BacJUsWtm7dymuvvWb7giVNsXqOeN68eRkzZgwDBw7kypUrDB8+PMZ+ZrMZZ2dnRo8eTZ48eay9nIiIiEiacvky1K4NZ84Y7axZjRAe1zOW9+7do27duuzfv//JOVnZsmVLiqyEJmlPknbWrFOnDkuWLKFKlSqYTKandtY0mUxUq1aNpUuXUqdOneSqWURERCRVnT1rbFMfEcLz5jVGxeML4XXq1IkWwrdu3aoQ/hxL8Ih4/fr1admyJU2bNiVLliyW4y+//DIzZszg4cOHnDhxgjt37gDGXKcSJUrg6emZ/FWLiIiIpJIjR6BuXfjnH6NdpAhs3gwFCsR+zt27d6lTpw4HDx4EIFu2bGzdupVXXnnF9gVLmpXgIP6///2PsWPHMn78eKpVq0aLFi2oWrUqDg7GoLqnpycVK1a0WaEiIiIiqW3XLmP+9/37RvvVV2HDBsiRI/Zz7t69S+3atTl06BAA2bNnZ+vWrZQoUSIFKpa0LMFTU6pUqYKDgwOhoaFs2bKF3r17U7VqVcaPH8+lS5dsWaOIiIhIqlu/3pgTHhHC33gDtm2LO4TfuXOHWrVqWUJ4jhw58PPzUwgXIBFBfMaMGfj5+TFgwADy58+P2Wzm1q1bzJgxg3r16tGhQwd+/fVXHj9+bMt6RURERFLc+vWeNGvmQGCg0a5TBzZuhIwZYz8nIoT/8ccfQGQIL168uO0LFruQqIc1s2fPTo8ePdiwYQMLFiygWbNmuLm5YTabOXDgAEOGDKFy5coMGzaMY8eO2apmERERkRTz008mPvooDyEhJgBatoTVq8HdPfZzbt++Tc2aNTl8+DBgbIS4bds2Xn755ZQoWeyE1aumlC1blpEjR7Jr1y6++uorSpcujdlsxt/fnyVLltCmTRsaNWrEvHnzuHv3bnLWLCIiIpIiJk6EHj0cCA83QniXLsbuma6usZ/z77//UrNmTY4cOQJArly52LZtGy+99FIKVCz2JEnLFwKkT5+eVq1a8csvv7Bu3Tq6dOlC1qxZMZvNnDt3jpEjR1K1alUGDBjA7xGr3YuIiIikcWPGQL9+ke0BA8L58UdwdIz9nIgQfvToUcAI4X5+fhQrVszG1Yo9SnIQj+qFF17g448/Zvv27UydOpVatWrh6OhIcHAw69ato0ePHsl5ORERERGb+Ppr+OSTyHbv3rcYPdqMyRT7Obdu3aJGjRqW6bm5c+dm27ZtCuESq2QN4pYPdXCgevXqfPXVV3Tt2tWyxKHZbLbF5URERESShdkMX3wBn38eeeyrr8Lp2/ffOEP4P//8Q40aNfjzzz8ByJMnD9u2baNo0aI2rljsmdVb3MfGbDbz+++/s3z5cvz8/AgJCbEE8Fy5ciX35URERESShdkMgwfD6NGRx8aOhf79zZw9G/t5ESH8xIkTAOTNmxc/Pz+KFCli44rF3iVbEL906RIrVqzg119/5Z8nW02ZzWacnJyoUaMGLVu25K233kqWa5nNZtatW8fq1as5deoUt2/fJmPGjBQpUgRvb2+aNWuGs7NzslwrwrZt21izZg1//vknt27dIiQkBHd3d1544QXKly9Py5YtyZ8/f7JeU0RERFKG2QwDBsAPP0QemzgR3n8fwsJiP+/mzZvUqFGDkydPApAvXz78/PwoXLiwjSuWZ0GSgnhgYCDr1q1j+fLlljUyI0a/ixQpQsuWLWncuDGZM2dOeqVPPHjwgH79+rF79+5ox2/dusWtW7fYs2cPixYtwsfHh9y5cyf5enfu3KFfv37s37//qffu3bvH4cOHOXz4MLNmzaJfv35069YtydcUERGRlBMeDn37wtSpkcemT4fu3eM+78aNG9SoUYNTp04BkD9/fvz8/ChUqJANq5VniVVB/NChQyxfvpz169cTGBhoCd/u7u54e3vTsmVLXn311WQtFCAkJIQ+ffpYQnGuXLlo3bo1BQoU4MaNGyxfvpwLFy5w4sQJunXrxqJFi/D09LT6eqGhoXTr1o3jx48D4ObmRtOmTXnppZfw8PDg2rVrrFu3jlOnThESEsLYsWNJnz497du3T5Y/r4iIiNhWWBj06AE//WS0TSbjdefOcZ93/fp1atSowenTpwEjhG/bto0XXnjBxhXLsyTBQfyff/7h119/ZcWKFZYt7SMCeOnSpWnZsiXe3t6kS5fONpUCixYtsoTwEiVKMHv2bLy8vCzvv/POO/Tu3ZudO3dy/vx5pkyZwqBBg6y+3m+//WYJ4Xny5OHnn39+ap579+7d8fHxwcfHB4BJkybRpk0bnJySffq9iIiIJKPQUHjvPZg/32g7OMC8eRDfeNr169epXr06Z86cAaBAgQL4+fkphEuiJTgt1qhRg7CwMEv4zpIlC02aNKFly5Yp8iuY0NBQpj75nZHJZOK7776LFsIBXF1dGT16NLVq1SIgIIAFCxbQrVs3q6fG7Ny50/K6a9euMT5sajKZ6NOnD4sXL+bWrVvcvXuXCxcuaKkiERGRNCwkBDp0gMWLjbaTEyxcCK1axX3etWvXqF69OmefPL1ZsGBB/Pz8KFiwoG0LlmdSgpcvDA0NxcHBgWrVquHj48P27dv55JNPUmwe1P79+7l9+zYAlSpV4sUXX4yxX5YsWfD29gYgODiYLVu2WH3NO3fuWF4XKFAg1n4ODg7kzZvX0g4MDLT6miIiImJbwcHQpk1kCHd2hmXL4g/hV69epVq1atFC+LZt2xTCxWoJDuL9+/fHz8+PadOmUatWrRSfehF1V874Vl+J+n5SdvOMOpIeMR0nJuHh4Vy5cgUAR0dH/WpKREQkjXr8GFq0gJUrjbarK/z6KzRpEvd5N27coGbNmpw7dw4wNjHcvn17nAN1IvFJcBDv2bMn2bNnt2UtcTobZQHPEiVKxNn3lVdesbyO+IKxRs2aNS2vf/zxR65fvx5jv8mTJ3Pr1i0Amjdv/tSUGREREUl9gYFG4P7tN6OdLh2sWQNPfpEeq8uXL9OxY0fOnz8PQKFChdi+fbuWLZYks5snCi9evGh5nSdPnjj75syZE0dHR8LCwvj7778JDw+37O6ZGPXq1aNGjRps3bqVa9euUb9+/RhXTYlYO7Rhw4Z89tlnib6OtcLiWtg0Ga9hNptT5FpiG7qH9k/30L7p/qUNjx5B06YO+PkZ22O6u5tZtSqcatXiXif88uXL1KhRg8uXLwNQuHBhNm/eTO7cuXVP7Uha/To0me1k3/ny5ctz//59AP744w/c3d0T3P/QoUN4eHhYdd2wsDAmTpzIggUL8Pf3j7FPuXLl6NOnD5UqVbLqGolx7NgxQkJCMJlMNl2hJoLZbCYoKAhXV1dMce3tK2mW7qH90z20b7p/qe/RIwd69szHoUPpAXB3D2P69MuUKRP3M13Xrl2jc+fOlhCeP39+5syZQ86cOW1esySvlP46jFje29nZmVKlSsXaz25GxAMCAiyvXV1d4+0ftU9AQIDVQdzR0ZEuXbqQMWNGxo8fT1BQ0FN9Dh48yLRp0/D09Iw2LcaWnJycKFq0qM2vExYWxvnz5ylSpAiOjo42v54kP91D+6d7aN90/1LX/fvQoIEDhw4Z4cvLy4yvL1SokC/O8y5dukS3bt0sITxiiUJNR7FPKf11eOLECUJCQuLtZzdBPLXs2LGDgQMH8uDBA8qXL0+PHj149dVXcXV15fr162zYsIGpU6eyd+9e3nnnHSZMmEC1atVSpLaU+oZuMplwdHTUDxA7pnto/3QP7ZvuX+q4cwfq1oWDB4125sywcaOJ11+P+z5cvHiRmjVrWqbFFi1alOnTp5M/f37dQzuWFr8OEz9xOpWkT5/e8jqmUen/iton6rmJsWPHDnr06MGDBw+oW7cuc+fOpXLlynh6euLi4kKBAgXo3r07c+fOxcXFhcDAQAYOHMi///5r1fVEREQkefz7L9SsGRnCs2aFrVvh9dfjPu/ixYtUq1bNEsKLFSvG5s2byZEjh20LlueS3QTxqFvV37t3L86+oaGhlvncTk5OVgfx7777zvKg59ChQ2N94LNUqVI0a9YMAH9/f1asWGHV9URERCTpbt6EatXgyBGjnSMHbNsGr74a93n/+9//qFq1qmXJ4pdeegk/Pz9y585ty3LlOWY3QTzqYvlXr16Ns++NGzcsT8Xmz5/fqhVTrly5YlmmqHDhwvH+Szjqg5p//vlnoq8nIiIiSXftmhHCT5ww2rlzw/btEM/Kx/z1119UrVqVv//+G4gM4THtqi2SXKwO4i+99BLFixePc6Ob5BT1wcTjx4/H2Tfq+7HtwBmff/75x/I6IQ96ZsiQwfL60aNHVl1TRERErHf5MlStCqdPG+38+WHHDihWLO7zLly4QLVq1SwPZhYvXpxt27ZpdRSxOauDuJubG+nTp0+xHaUqV65seb1z5844+yZmF87YRF0e8ebNm/H2v3btmuV1xowZrbqmiIiIWOd//4MqVeDJL7N54QUjhBcuHPd558+ffyqEb926VXPCJUVYHcRz5sxJaGhoctYSpwoVKli2nN+9e3esO2bevn0bX19fAJydnaPtjpkYBQoUsCyBeO3aNf744484+0dcE6BkyZJWXVNEREQS7/x5YyQ8Yu+/F180Qnh8Y4Xnzp2jWrVqXLlyBTB27vbz81MIlxRjdRCvXr06QUFBHDhwIDnriZWTkxM9e/YEjEXZBw0aZNmwJ0JQUBCDBg2yrDnevn17S3j/r8GDB1OsWDGKFSvGpEmTnnrfzc0tWogfMmRItFHvqKZNm8bu3bsBcHFxoV69eon/A4qIiEiinT5tjIQ/GdDm5ZeNOeF588Z9XkQIj3jurGTJkvj5+ZE9e3YbVywSyep1xLt3786aNWsYNmwYc+bMSZH/cNu2bcvGjRs5ePAgJ06coEmTJrRp04YCBQpw48YNli1bxoULFwAoVKgQffr0SdL1PvzwQ3bv3s29e/e4ePEiDRs2pHHjxrz22muWdcTXr1/P0aNHLef07dtXD3aIiIikgOPHjSUKIx7rKlkSNm+G+CLJmTNnqF69OtevX39yXkm2bNlCtmzZbFyxSHRWB/Hz58/Tv39/vv32Wxo0aECTJk0oU6YMmTNnjnOh9HLlyll7SVxcXJgyZQoffPABe/fu5fr160yYMOGpfi+//DI+Pj7RHqC0Rr58+Zg1axYffvghFy9e5NGjR/zyyy/88ssvT/V1cnKib9++9OjRI0nXFBERkfgdOQK1asHt20a7dGnYtAmyZIn7vP+G8FKlSrFlyxayZs1q24JFYmB1EO/QoQMmk8nS/vnnn/n555/jPMdkMnHy5ElrLwmAl5cXc+bMYd26daxatYqTJ09y9+5dvLy8KFy4MA0aNKB58+Y4Ozsn6ToRSpQowerVq1m3bh1btmzhxIkT3Llzh5CQEDw8PChYsCDly5endevW5MsX93a5IiIiknQHD0KdOnD3rtEuVw42bIBMmeI+7/Tp01SvXp0bN24A8Nprr7F582ayxJfeRWwkSVvcm81mm/aPjclkwtvbG29vb6s/Y9SoUYwaNSpBfV1dXWnatClNmza1+noiIiKSdDt3QoMG8OCB0X7jDfD1BS+vuM87deoU1atXt6yEVrp0aTZt2qQQLqnK6iB+OmKRThEREZEUsHo1tGkDjx8b7SpV4LffIMrm2zE6efIk1atXt+wRUrp0aTZv3hzrgg4iKcVudtYUERGR59fMmdCsWWQIr1PHGAmPL4SfOHEiWgh//fXXFcIlzVAQFxERkTTLbIavv4Zu3SA83DjWvj2sWQNR9t6L0fHjx6OF8LJly7Jp0yaFcEkzkjRHPCp/f39OnjzJ7du3MZlMZM6cmeLFiydoe3gRERGR/woLg379YPLkyGMffghjxoBDPEOJf/75JzVq1ODff/8FjFXbNm7cqN2vJU1JchA/d+4c48ePZ/v27YRH/FP1CQcHB6pVq0b//v158cUXk3opEREReU48fgwdOsCyZZHHxoyBjz6K/9xjx45Rs2ZNSwgvX748GzZsUAiXNCdJU1O2bNlCy5Yt8fPzIywsDLPZHO1/YWFhbN26lZYtW7J169bkqllERESeYffvQ/36kSHcyQnmzUtYCD969Gi0kfAKFSpoJFzSLKtHxK9du8bAgQMJCgoid+7cdOnShcqVK1t2lbx+/Tq7du3ip59+4urVq3z44YesXbuWPHnyJFvxIiIi8my5ft0I4RGbVqdPD8uXQ7168Z975MgRatasyZ07dwCoWLEi69evxyu+tQ1FUonVI+KzZs3i8ePHlCxZktWrV9O+fXsKFCiAi4sLLi4uFChQgHbt2rF69WpKlixJUFAQs2fPTs7aRURE5Bly7pyxLnhECM+SBbZuTVgIP3z4cLQQXqlSJTZs2KAQLmma1UF8586dmEwmRowYEecDme7u7gwfPhyz2czOnTutvZyIiIg8ww4ehDffhIsXjXaBArBrF1SoEP+5f/zxR7QQ/sYbb7BhwwYyZMhgu4JFkoHVQfzGjRukT5+e4sWLx9u3RIkSuLu7W7aUFREREYmwcSNUqwa3bhntkiVh924oViz+cw8dOkTNmjW5+2S/+zfffJP169fjGd8C4yJpgNVB3MHB4alVUuISHh6OQ3xrDYmIiMhz5eefjS3rHz0y2lWqwI4dkDt3/OcePHiQWrVqce/ePQDeeust1q1bpxAudsPqZJwnTx4eP37MgQMH4u174MABAgMD9aCmiIiIWIwbB++8A6GhRrtZM9iwARKywMmBAweihfAqVarg6+urEC52xeogXrVqVcxmM1988YVlx6qY3Lp1i2HDhmEymahevbq1lxMREZFnRHg4fPIJDBwYeaxHD1i6FNzc4j9///791KpVi/v37wNGJlm7dq02ERS7Y/Xyhe+99x5Lly7l4sWLNGzYkLfffptKlSqRK1cuTCYT169fZ8+ePSxatIj79+/j5eVFp06dkrF0ERERsTchIdClC8yfH3lsxAj4/HMwmeI/f9++fdSpU4cHDx4AUK1aNX777Tfc49vvXiQNsjqIZ86cmcmTJ9OnTx/u37/Pjz/+yI8//vhUP7PZjJeXF1OmTCFz5sxJKlZERETs16NH0KoVrFtntB0cYMoUYzQ8Ifbs2UPdunV5+PAhANWrV+e3334jffr0NqpYxLaS9PRk2bJlWb16NW3btsXLy+upnTW9vLxo3749q1ev5vXXX0+umkVERMTO/Psv1KgRGcJdXY2dMxMawnfv3h0thNeoUUMhXOye1SPiEXLkyMGwYcMYNmwYly9ftqzhmTlzZvLly5fkAkVERMS+XbwIdevC2bNG28sLVq82VkhJiF27dlGvXj38/f0BqFWrFqtWrVIIF7uX5CAeVb58+RS+RURExOLYMWNnzOvXjXauXMbKKCVLJuz8nTt3Ur9+fUsIr127NqtWrSJdunQ2qlgk5WhhbxEREbGJ7duNUe+IEF60qLFRT0JD+O+//x5tJLxOnToK4fJMURAXERGRZLdihTEd5ckKg5Qvb2xZX7Bgws7fsWMH9evX59GTnX7q1aunEC7PnARNTXn55ZcBKFSoEGvXro12LDFMJhMnT55M9HkiIiJiP6ZNgz59jPXCwZiasmwZJHSFwe3bt+Pt7U1AQAAA9evXZ8WKFbglZJFxETuSoBHxqCuhxHQsMf8TERGRZ5PZbKwJ3qtXZAh/913jwcyEhvBt27ZFC+He3t4K4fLMStCI+Lx58wCifRFEHBMREREJCzNGwadPjzz2yScwalTCNuoB2Lp1Kw0bNiQwMBCABg0asHz5clxdXW1QsUjqS1AQL1++fIKOiYiIyPPn8WNo1w5Wrow8Nm4cDBiQ8M/YsmULjRo1soTwRo0asXTpUoVweaZZvXzhgQMHAChWrBgZMmRItoJERETEfty7B02awI4dRtvJCebMgfbtE/4ZmzdvplGjRjx+/BiAxo0bs2TJEoVweeZZHcQ7dOiAg4MDe/bsSc56RERExE7cvg21a8Phw0bb3d1YLaVOnYR/xqZNm2jcuLElhDdp0oQlS5bg4uJig4pF0harg7inpycODg54eXklZz0iIiJiB/75B2rVgj//NNpZs4KvL5Qrl/DP2LhxI40bNyYoKAiAZs2asWjRIoVweW5YvY54gQIFePToEcHBwclZj4iIiKRx169DtWqRITxXLmNqSmJC+IYNG6KF8ObNm7N48WKFcHmuWB3EGzZsSGhoKL6+vslZj4iIiKRhV65A1apw6pTRzpvX2EEzMduLrFu3jiZNmlhCeIsWLVi0aBHOzs42qFgk7bI6iHfo0IEyZcrw1VdfsX379uSsSURERNKgS5eMEH7unNEuUMAYCX/xxYR/hq+vL02bNrWE8FatWvHLL78ohMtzyeo54lOnTqVs2bKcPn2anj17UqRIEcqUKUOWLFlwcIg93/ft29faS4qIiEgquXABatSAv/822oULw9atkD9/wj9j7dq1NG/e3DKttXXr1ixYsEAhXJ5bVgdxHx8fTCaTZbfMc+fOcf78+XjPUxAXERGxL2fOQM2acPWq0S5a1AjhefIk/DPWrFlDixYtCAkJAeDtt99m/vz5ODlZHUVE7J7V//WXS8wTGSIiImKXTp40QviNG0a7eHHYsgVy5kz4Z6xevZqWLVtaQnjbtm2ZN2+eQrg896z+Cpg/f35y1iEiIiJpzLFjxhKFt24Z7VKlYPNmyJYt4Z+xatUqWrVqZQnh7dq1Y+7cuQrhIiThYU0RERF5dv3xB1SvHhnCy5QxpqMkJoSvXLky2kj4O++8o5FwkSgUxEVERCSa/fuN6Sh37hjtChWM6ShZsiT8M1asWEHr1q0JDQ0FjNXW5syZg6Ojow0qFrFPyfJP0tOnT7Nz506uXbvG48eP+fbbby3vhYSEcOfOHUwmE9mzZ0+Oy4mIiIiN7NoF9evDw4dGu3JlWLsWMmRI+GcsX76ct99+2xLC3333XWbNmqUQLvIfSQriDx8+5NNPP2Xz5s0AmM1mTCZTtCAeGhpKkyZNePDgARs2bCBfvnxJq1hERERsYts2aNgQHj0y2tWrw+rV4OGR8M9YunQpbdu2JSwsDIBOnToxc+ZMhXCRGFg9NSU0NJRu3bqxefNm3NzcqFq1Kq6urk/1S5cuHS1atCA8PNwS2EVERCRt2bwZvL0jQ3jt2vDbb4kL4UuWLIkWwjt37qwQLhIHq4P4smXLOHLkCPny5WP9+vVMmzYNT0/PGPvWrVsXgB07dlh7OREREbERX19jJDww0Gg3aGCMhKdPn/DPWLx4Me3atbOE8C5duiiEi8TD6iD+22+/YTKZGDJkCDly5Iiz78svv4yDg0OCNvwRERGRlLNqFTRtCk92nKdpU1ixAtzcEv4Zv/zyS7QQ3rVrV2bMmBHnTtsikoQgfvbsWUwmE2+++Wa8fZ2dnfH09OTevXvWXk5ERESS2dKl0LIlPFldkFatYMkScHFJ+GcsXLiQd955h/DwcAC6devG9OnTFcJFEsDqr5KAgADc3d1xSeBXa3BwsNYNFRERSSMWLoS334YnC5vQvr1xzNk54Z/x888/06FDB0sI79GjB9OmTVMIF0kgq79SMmXKhL+/PwEBAfH2vXjxIoGBgWRLzC4AIiIiYhNz58I778CT/EynTsaxxIyXzZ8/n3fffdcSwnv16sWUKVMUwkUSweqvlpIlSwKwbdu2ePvOmTMHgDJlylh7OREREUkGP/4InTuD2Wy0e/SAn36CxDxTOW/ePDp27GgJ4b1792by5MkK4SKJZPVXTPPmzTGbzUycOJFbEfvfxmDmzJksWrQIk8lEixYtrL2ciIiIJNHkydC9e2QIf/99mDoVEpOf586dS6dOnTA/+ZC+ffvi4+ODyWSyQcUizzarJ23XqlWLatWqsW3bNlq0aEHjxo0JevLI9bp16zh79izr16/n4sWLAHh7e1OuXLlkKVpEREQSZ9w4GDgwsv3RRzB6NCQmP8+ePZsuXbpYQvj777/PDz/8oBAuYqUkPT05btw4Pv74Y7Zs2cJPP/1kOf7hhx8CWL5Qa9euHW23TREREUk5I0fCp59GtocOha++SlwInzVrFl27drX8bO/Xrx/jx49XCBdJgiQF8fTp0zN58mS2bdvG0qVL+eOPP7h79y4Anp6elClThtatW1OzZs1kKVZEREQSzmyGL7+E4cMjj335JXz+eeI+Z+bMmXTr1s3S7t+/P+PGjVMIF0miZFlPsFq1alSrVg2A0NBQwsPDE7ysoYiIiCQ/s9kY+R45MvLYqFEwaFDiPmfGjBn06NHD0v7www8ZO3asQrhIMkj2hb21VriIiEjqMpuNOeDjxkUeGzcOBgxI3OdMnz6dnj17WtoDBw5kzJgxCuEiycTqVVNq1qxJ69atE9y/Xbt21KpVy9rLiYiISAKEh8MHH0QP4T4+iQ/h06ZNixbCP/74Y4VwkWRm9fD11atXLaukJMSNGze4fv26tZcTERGReJjN0LevsSQhGA9jTp8OUaZ3J8iUKVPo06ePpT1o0CBGjhypEC6SzFJsHklYWJgW+hcREbERsxkGD44M4Q4OMGsWdOyYuM+ZPHkyffv2tbQHDx7Mt99+qxAuYgMpkowfP37M7du3cXd3T4nLiYiIPHdGjjTWBQdjJHzu3MSH8EmTJkUL4Z9++qlCuIgNJXhE/Nq1a1y9ejXasZCQEA4ePGhZU/S/zGYzDx48YM2aNYSGhlK0aNGkVSsiIiJP8fExVkiJMGUKvPNO4j7jhx9+oH///pb2Z599xpdffqkQLmJDCQ7iK1asYPLkydGOPXjwgA4dOsR7rtlsxmQy0aZNm8RXKCIiIrGaN8/Yqj7CqFEQ5RnLBJkwYQIDojzN+fnnnzNixAiFcBEbS9Qc8agj3yaTKdaR8Kh9PDw8ePHFF3n77bdp1KiRdVWKiIjIU1auhPfei2wPGZL4dcLHjx9v2REbYNiwYQyPugOQiNhMgoN43759o80be+mll8iaNSs7d+60SWEiIiISu02b4O23ISzMaPfpA998k7jP+P777/noo48s7eHDhzNs2LBkrFJE4mL1qilNmzbF09MzOWsRERGRBNi9G5o2heBgo92hA0ycaDykmVBjxozhk08+sbRHjBjBF198kbyFikicrA7io0aNSs46REREJAGOHAFvbwgIMNpNmxrLFCZmheDRo0czKMoclq+++orPPvssWesUkfjZbB3xM2fOsGfPHhwcHHjzzTcpXLiwrS4lIiLyXDh7FurUgfv3jXatWrBoETgl4qf5qFGjGDJkiKX9zTff8OmnnyZzpSKSEFYH8T179jB16lRee+21aA95AMyePZsxY8ZYHuZ0cHBg8ODBCVphRURERJ72999G8L51y2hXqmQ8rOnqmvDP+PbbbxkaZZ3Db7/9NlooF5GUZfWGPps2beLAgQPkyZMn2vGLFy8yduxYwsPDcXZ2xs3NjbCwMEaOHMnJkyeTXLCIiMjz5uZNI4Rfvmy0X30V1q4FD4+Ef8Y333wTLYT/d2RcRFKe1UH8yJEjAFSpUiXa8SVLlhAWFka5cuXYu3cv+/fvp27duoSHh7Nw4cIkFSsiIvK8uXvXmI5y7pzRLloUNmyATJkS/hn/nQP+3zniIpI6rA7it27dwtHRkZw5c0Y7/vvvv2Mymejduzfp06fH2dmZgQMHAnDgwIGkVSsiIvIc8fc3Hsw8dsxo58tnLFuYI0fCP+O/q6GMGTOGjz/+OJkrFRFrWD1H/O7du7i7u0fbdcvf35/z58/j5uZG+fLlLcfz58+Pq6srN2/eTFq1IiIiz4nHj40VUfbuNdrZs8PmzZA/f8I/Y/jw4YwYMcLS/v777596rktEUo/VI+Kurq74+/tH213z6NGjmM1mXnvtNRwdHaP1d3Nzs75KERGR50hIiLFZz5YtRjtjRmMkvGjRhJ1vNpsZNmxYtBA+btw4hXCRNMbqIF6gQAHCw8PZv3+/5dimTZswmUy8/vrr0foGBwfz8OFDsmTJYn2lIiIiz4HwcGPb+lWrjLa7O/j6QqlSCTvfbDbzxRdf8OWXX1qOTZgwgQEDBtigWhFJCquDePXq1TGbzQwdOhRfX1/mzZvH8uXLAahdu3a0vqdOnSI8PJzcuXMnrVoREZFnmNkMffvCggVG28UFfv3VWKowYeeb+fzzz/n6668txyZOnEi/fv2Sv1gRSTKr54h36tSJlStXcuXKFcvDmGazGW9vb4oVKxat75YtWzCZTJQpUyZp1YqIiDzDhg6FqVON146OsGSJsWxhQkQMjo0cOdJybNKkSfTt29cGlYpIcrA6iHt6erJo0SImTpzIkSNH8PDwoHr16nTp0iVav+DgYJYvX47ZbKZixYpJLlhERORZ9N13ECVDM2cONGmSsHPNZjNDhgzhu+++sxzz8fGhT58+yVukiCSrJG1xnz179mi//oqJi4sLu3btSsplREREnmlTp8LgwZHtyZPhnXcSdq7ZbGbw4MGMHj3acmzKlCn06tUrmasUkeSWpCAuIiIiSbNgAUQduB45Enr3Tti5ZrOZTz75hLFjx1qOTZs2jR49eiRzlSJiC8kaxK9evcrt27cxmUxkzpyZPHnyJOfHi4iIPFNWr4ZOnYyHNAEGDYo+Mh4Xs9nMRx99xLhx4yzHpk+fTvfu3ZO/UBGxiSQH8Vu3bjFjxgzWrl3L3bt3o72XKVMmGjZsSPfu3cmaNWtSLyUiIvLM2LIFWreGsDCj3atX9DnicTGbzQwcOJDx48dbjv3444907drVBpWKiK1YvXwhwJEjR2jcuDELFizgzp07mM3maP+7c+cO8+fPp0mTJhw9ejS5ahYREbFre/caD2IGBRnt9u3BxweibFYdK7PZzIABAywh3GQyMXPmTIVwETtk9Yj4nTt36NmzJ/fu3cPd3Z02bdpQuXJlcubMCcCNGzfYtWsXixcv5vbt2/Ts2RNfX18yZcqUbMWLiIjYm2PHoH59ePTIaDdpArNng0MChsbMZjP9+/dn4sSJQGQIf++992xYsYjYitVBfM6cOdy7d48CBQowZ84ccuXKFe39QoUK8cYbb9ChQwc6duzI33//zZw5c7Szl4iIPLfOnYM6deDePaNdsyYsWgTOzvGfazab+eCDD/Dx8QGMED5r1iw6depks3pFxLasnpqybds2TCYTX3755VMhPKqcOXPy5ZdfYjab2bp1q7WXExERsWuXLxub89y8abQrVjR2zXRzi/9cs9lM3759o4Xw2bNnK4SL2Dmrg/iVK1dwc3OjQoUK8fatUKEC6dKl4+rVq9ZeTkRExG7dvu1I3boO/P230S5ZEnx9wcMj/nPDw8Pp06cPU6ZMAYwQPnfuXDp27GjDikUkJVgdxMPDw3FIyIS2iAs5OBAeHm7t5UREROzSvXvQrVt+zp41nsQsUgQ2boSEPDIVEcKnPtn33sHBgXnz5tGhQwcbViwiKcXqIJ4zZ04CAgI4depUvH1PnTrFo0ePLA9yioiIPA8ePoSGDR04fdqYf5IvH2zeDAn5cRgeHk6vXr2YNm0aYITw+fPn805Ct9wUkTTP6iD+xhtvYDabGTFiBAEBAbH2CwwMZMSIEZhMJipXrmzt5UREROzKw4dQrx7s3WuMhGfLZmbTJihQIP5zw8PD6dmzJzNmzACMEL5gwQLatWtny5JFJIVZHcTfe+89XF1dOXr0KI0bN2bx4sVcvnyZkJAQQkJCuHz5MkuWLKFJkyYcOXIEV1dXOnfunJy1i4iIpEkPHxpLFO7ebbS9vEJZvz6cYsXiPzc8PJzu3bvz448/AuDo6MjChQtp27atDSsWkdRg9fKFefPmZcyYMQwcOJArV64wfPjwGPuZzWacnZ0ZPXq0trwXEZFn3sOH4O0Nu3YZ7cyZzfz449+8+mr8Q+Hh4eF069aNWbNmAZEhvHXr1rYsWURSSZJ21qxTpw5LliyhSpUqmEymp3bWNJlMVKtWjaVLl1KnTp3kqllERCRN8vc3QvjOnUY7UybYsCGcl18OivfcsLAwunTpEi2E//LLLwrhIs8wq0fEI7z88svMmDGDhw8fcuLECe7cuQNA5syZKVGiBJ6enkkuUkREJK2LKYRv2QKlSsHZs3GfGxHC586dC4CTkxOLFi2iRYsWNq5aRFKTVUE8PDycv/76C39/f7y8vHjhhRfw9PSkYsWKyV1fjMxmM+vWrWP16tWcOnWK27dvkzFjRooUKYK3tzfNmjXDOSHblCVSaGgoGzZsYOPGjZw4cYJ///0XBwcHsmbNSoECBahQoQK1atWiYMGCyX5tERFJuyJC+O+/G+1MmYzVUUqXhrCwuM8NCwujc+fOzJ8/HzBC+OLFi2nevLmNqxaR1JaoIB4SEsKECRNYvHgxjx49shz38vKiY8eO9OzZE5PJlOxFRvXgwQP69evH7ognYJ64desWt27dYs+ePSxatAgfHx9y586dbNc9duwYQ4cO5WwMwxqPHj3i0qVL7Nixg5s3bzJ06NBku66IiKRt/v7QoEFkCM+Y0QjhZcrEf25YWBidOnViwYIFgBHClyxZQrNmzWxXsIikGYkK4n369OH333/HbDZHO37v3j0mTpzIpUuXGDVqVLIWGFVISAh9+vRh//79AOTKlYvWrVtToEABbty4wfLly7lw4QInTpygW7duLFq0KFmmxuzZs4devXoRGBgIQOnSpalatSp58uTBbDZz48YNLly4wPbt25N8LRERsR+PHhkhfMcOo52YEB4aGkrHjh1ZuHAhAM7OzixdupQmTZrYrmARSVMSHMTXrVvHjiffaQoUKEC9evXIkSMHV69e5bfffuPmzZusWrWK5s2bU758eZsUu2jRIksIL1GiBLNnz8bLy8vy/jvvvEPv3r3ZuXMn58+fZ8qUKQwaNChJ17x27Rp9+/YlMDCQ9OnTM2bMGGrVqhVj37CwMG7fvp2k64mIiH2ILYS//nr854aGhvLuu+/yyy+/AEYIX7ZsGY0bN7ZdwSKS5iR41ZTVq1cD8Oabb7JmzRoGDBhAu3bt+Pjjj1m7di3FixcHYM2aNTYpNDQ01LLFr8lk4rvvvosWwgFcXV0ZPXo06dOnB2DBggWWh0etNWzYMPz9/QEYN25crCEcjCfcs2fPnqTriYhI2vfoETRsCBG/CM2YETZtSngI79ChQ7QQvnz5coVwkedQgoP4yZMnMZlMfPrpp7i4uER7z8PDg48//hiz2czJkyeTvUiA/fv3W0abK1WqxIsvvhhjvyxZsuDt7Q1AcHAwW7Zssfqa586ds/wWoGrVqlSvXt3qzxIRkWdDQAA0agTbthltLy8jhJctG/+5oaGhtG/fnkWLFgHg4uLCihUraNSoke0KFpE0K8FB/O7du7i6ulK4cOEY33/llVcs/Wzh94inYIC33norzr5R3496XmItX77c8rpVq1ZWf46IiDwbAgKMkXA/P6OdmBAeEhJCu3btWLJkCRAZwhs2bGjDikUkLUvwHPHg4GCyZs0a6/sRD0UGBwcnvaoYRF2tpESJEnH2jfhHARij2tY6cOAAYEyFKV++PLdu3WL+/Pls2bKFq1ev4ujoSK5cuahYsSLt27fnhRdesPpaIiKStkWMhEeE8AwZYONGKFcu/nNDQkJ45513LAM8rq6urFy5kvr169uwYhFJ65K8oU9KuXjxouV1njx54uybM2dOHB0dCQsL4++//yY8PBwHh8RtIhoSEsKZM2cAyJ49OydPnqR///7cu3cvWr9z585x7tw5Fi5cSP/+/enevXuiriMiImlfQAA0bgxbtxrtDBmMkfCErE0QEhLCRx99xKZNmwAjhP/666/Uq1fPhhWLiD2wmyD+8OFDy+tMmTLF2dfJyQkPDw/u379PaGgoAQEBeHh4JOp69+7dIyQkBDBWQ+nTpw+PHj2iYMGCNG/enHz58nHv3j22bNnCzp07CQsL4/vvv8fZ2ZnOnTsn/g9ohbD4dolIpmuYzeYUuZbYhu6h/dM9TF0BAdC0qQNbtxr7ZGTIYGbdunBefz3+zXqCg4Np27ZttBC+YsUKateurftpR/Q1aP/S6j00mf+7KHgsXnrppSRv1mMymax+mPOVV16xBOMTJ07g5BT3vyHeeust/vnnH8CYJ57Y1UwuXLhgeegzQs2aNZkwYcJTD6v+8ssvDB8+HDCeft+4cWOybiYU1bFjxwgJCcFkMpEuXTqbXCMqs9lMUFAQrq6uNt+sSWxD99D+6R6mnsePTfTpk5c9e4zBHHf3MGbO/JtXX30c77nBwcEMHDjQsmiAq6srPj4+vPnmmzatWZKfvgbtX0rfw8DAQMxmM87OzpQqVSrWfokaEU9gZn8m/PfPmjFjRr777runQjhA27Zt2bNnDxs2bCAkJIRffvmFgQMH2rQ+JycnihYtatNrgPEvyPPnz1OkSBEcHR1tfj1JfrqH9k/3MHUEBhoj4Xv2GD+0PT3NrFsHFSvmj/fc4OBg2rRpEy2Er1y5kjp16ti0ZrENfQ3av5S+hydOnLAMIMclwUG8b9++SSooqdKnT8/9+/cBCAoKindEPCgoKNq5ieXu7h6t7e3tHecuna1bt2bDhg0A7N27N9HXs0ZKfTMwmUw4Ojrqm48d0z20f7qHKSswEJo3h4gVcD09YeNGExUrxv/3HxQURJs2bSz7aqRLlw4fHx/q1Kmj+2fH9DVo/9LiPbSbIO7p6WkJ4vfu3XsqKEcVGhpq2YTHycnJqiD+39Ad30otUd//+++/E309ERFJG4yRcONhTDBC+IYNULFi/OcGBQXRsmVLfvvtN8AI4atWrSJv3ry2K1hE7FbilhJJRQULFrS8vnr1apx9b9y4YZmMnz9//kSvmALGJkVR55XHNRr+3/cj/hEgIiL25fFjaNbMWJYQwMMD1q+HSpUScu5jmjdvHi2Er127lho1atiwYhGxZ3YTxKPOhz5+/HicfaO+H9sOnAlRrFgxy+uoq7bEJOr7iV2hRUREUt/jx8ZI+JNZhnh4GK/feCMh5xoh3NfXFzCmRPr6+mpHZhGJk90E8cqVK1te79y5M86+idmFMy5Vq1a1vD5x4kScfaO+r419RETsS8RIeNQQvn59wkN4s2bNWLduHWA8Y7Ru3TqqVatmu4JF5JlgN0G8QoUKZM6cGYDdu3fHumPm7du3LSMSzs7O1KxZ0+pr1q1bF2dnZwB8fX3jHBWP2LIYkhb+RUQkZT1+bDyYuX690XZ3h3XrICGrDAYGBtKkSRPWPzk5IoRXqVLFhhWLyLPCboK4k5MTPXv2BIylBQcNGmR5eDNCUFAQgwYNIiAgAID27dtbwvt/DR48mGLFilGsWDEmTZoUY5/s2bPTrl07wHhAdNCgQQQHBz/Vb9GiRZYVU9KnT0/btm2t+0OKiEiKigjhTwazcXc3AnmUX8LGKiKEb3wyodzDw4P169drMEZEEsxudtYEY73ujRs3cvDgQU6cOEGTJk1o06YNBQoU4MaNGyxbtowLFy4AUKhQIfr06ZPka37wwQfs2bOHs2fPsmXLFho1akSLFi3ImzcvDx48YPPmzdGmwowYMSLW8C8iImlHUBC0aBE9hK9bl7AQHhAQQJMmTdi8eTMQGcK1WY+IJIZdBXEXFxemTJnCBx98wN69e7l+/ToTJkx4qt/LL7+Mj48PGTJkSPI1PTw8mDlzJv369ePw4cNcvHiR77///ql+bm5ujBgxgsaNGyf5miIiYltBQcZI+JOZjJYQnpDB7ICAABo3bmzZrMfT05P169fzRkImlIuIRGFXQRzAy8uLOXPmsG7dOlatWsXJkye5e/cuXl5eFC5cmAYNGtC8eXPL3O7kkCNHDhYuXMjatWtZu3Ytp06d4vbt27i5uZE/f37eeust2rdvH225QxERSZsiRsKjhnBf34SH8EaNGrF161bACOEbNmygUkLWNxQR+Q+7C+Jg7Izk7e2Nt7e31Z8xatQoRo0aleD+Dg4ONGrUiEaNGll9TRERSV3BwdCyJaxda7TTpzdCeEKerXz06BGNGjXCz88PgAwZMrBhwwYqJmSnHxGRGNhlEBcREUms0FBo1w6e7LeT6BDeoEEDtm/fDhghfOPGjVSoUMGGFYvIs85uVk0RERGxVng4dO4My5cb7XTpjFHxKNtFxMrf3x9vb29LCPfy8mLTpk0K4SKSZEkeEd+3bx9r167lzJkz3Lt3j9DQ0Fj7mkwmyxPmIiIiKcFshp49YcECo+3iAr/+CgnZbycihEesjpUxY0Y2bdpE2bJlbVaviDw/rA7iZrOZTz/9lF9//dXSjo/JZLL2ciIiIolmNsOAAfDjj0bbyQmWLoU6deI/9+HDh3h7e1t2c86YMSObN2/m9ddft2HFIvI8sTqIz58/n5UrVwJQuHBhatWqRc6cOXFzc0u24kRERJLis8/ghx+M1w4Oxqh4QlaZffjwIfXr12fXrl0AZMqUic2bN1OmTBkbVisizxurg/iKFSswmUy0atWKL7/8MjlrEhERSbJvvoFvv41s//QTtGkT/3kPHjygfv367N69G4DMmTOzefNmSpcubaNKReR5ZXUQv3jxIgADBw5MrlpERESSxfjxxmh4hMmToVOn+M+7f/8+9erVY+/evYARwrds2cJrr71mkzpF5PlmdRB3dXXF1dUVLy+v5KxHREQkSaZPhw8/jGyPGQO9e8d/3v3796lbty779u0DIEuWLGzZsoVXX33VRpWKyPPO6uULixYtir+/P/7+/slZj4iIiNXmz4devSLbI0bARx/Ff969e/eoU6eOJYRnzZqVrVu3KoSLiE1ZHcTbt29PWFgYy5YtS856RERErLJ0qTH9JGIRr08+gc8/j/+8iBC+f/9+IDKElypVynbFioiQhCBer1492rdvz/fff8/yiB0SREREUsFvvxm7ZoaHG+2+fWHUKIhv1dy7d+9Su3ZtDhw4AEC2bNnw8/OjZMmSNq5YRCQJc8SHDBkCQPr06fnss8+YNGkSJUuWxMPDI9ZzTCYT30Z9hF1ERCSJNm+Gli2NLewB3nvPWLIwoSH80KFDAGTPnp2tW7dSokQJG1csImKwOoivXLkSk8lk2cjnxo0b3LhxI8a+Ef0UxEVEJDnt3AlNmkBQkNFu2xZmzDDWDI/LnTt3qF27Nn/88QdghHA/Pz+KFy9u44pFRCJZHcSbNm2qnTJFRCTVHDgA3t4QEGC0mzaFuXPB0THu8+7cuUOtWrU4fPgwADly5GDr1q0K4SKS4qwO4qNGjUrOOkRERBLs2DGoWxcePjTa9erBokXg7Bz3ebdv36ZWrVocOXIEgJw5c+Ln58dLL71k24JFRGJg9cOaIiIiqeH0aahVC+7eNdpVq8Ly5eDqGvd5//77LzVr1lQIF5E0w+oRcRERkZR24QLUrAm3bhntihVhzRpInz7u8yJC+LFjxwDIlSsXfn5+FCtWzMYVi4jETiPiIiJiFy5fNkL4tWtGu3RpWLcOPD3jPu/WrVvUqFHDEsJz587Ntm3bFMJFJNUlaET83XffBSBPnjyMHDky2rHEMJlMzJ07N9HniYjI8+3GDSOEX7pktIsXh40bIWPGuM/7559/qFmzJsePHweMn2N+fn68+OKLti1YRCQBEhTEI3YbK1So0FPHEkOrrIiISGL9+68xJ/zcOaNdpIixdnjWrHGf988//1CjRg1OnDgBQN68efHz86NIkSI2rlhEJGESFMT79u0LQKZMmZ46JiIiYiv37hmrozzJ0uTPD1u2QK5ccZ938+ZNatSowcmTJwHIly8ffn5+FC5c2LYFi4gkQqKCeHzHREREkou/v7FO+JM9d8iVC7ZuNcJ4XG7cuEGNGjU4deoUYITwbdu2RfutrohIWqCHNUVEJM0JDITGjWHPHqOdLZsxEh7fgPb169epXr26JYTnz59fIVxE0iwtXygiImlKUBA0bw5+fkY7Y0bYtAlefjnu8yJC+JkzZwAoUKAAfn5+vPDCC7YtWETEShoRFxGRNCM0FNq2hfXrjbanJ2zYAK++Gvd5165do1q1apYQXrBgQbZt26YQLiJpWpJGxM1mMytXrmTt2rWcOXOGe/fuERYWFmt/k8lkeXBGREQkqrAw6NgRVq402unSwdq1UL583OddvXqV6tWrc+7JsioRIbxAgQI2rlhEJGmsDuLBwcF0796dffv2YTabk7MmERF5zoSHQ8+esHCh0XZxgVWr4K234j7vypUrVK9enfPnzwPwwgsvsG3bNvLH90SniEgaYHUQnzFjBnv37gWgcuXK1K1blxw5cuDm5pZsxYmIyLPPbIb+/WHmTKPt5ATLlkHt2nGfd+XKFapVq8aFCxcAY6+Lbdu2kS9fPtsWLCKSTKwO4r6+vphMJvr06aOlDEVExCpmMwwZApMmGW0HB/j5Z2jUKO7zLl++TPXq1S0hvHDhwvj5+SmEi4hdsfphzStXrmAymejcuXNy1iMiIs+JiBD+3XeRx2bNgtat4z7v77//jjYSXqRIEY2Ei4hdsnpE3N3dnfDwcNzd3ZOzHhEReQ6Eh0O/fuDjE3lsyhTjYc24XLp0ierVq/O///0PgBdffBE/Pz/y5Mljw2pFRGzD6hHxkiVL8vDhQ+7evZuc9YiIyDMuLAy6do0ewqdOhV694j7v4sWLVKtWzRLCixYtqhAuInbN6iDeuXNnzGYzc+bMScZyRETkWRYSAu3bw+zZRtvBAebONVZMiUtECL948SIAxYoVUwgXEbtn9dSUSpUq8dFHHzFu3DgcHR3p2rUr6dOnT87aRETkGfL4sTH/e80ao+3sbCxX2LJl3Of973//o1q1avz9998AvPTSS2zdupVcuXLZuGIREdtKUBB/9913Y33P3d2dqVOnMnPmTIoUKYKHh0esfU0mE3Pnzk18lSIiYtcePYImTWDLFqPt6gorVoC3d9zn/fXXX1SvXj1aCPfz8yNnzpw2rlhExPYSFMT3798fb5/g4OB4d800mUwJq0pERJ4Z9+9Dgwawa5fRdnc3RsWrV4/7vAsXLlC9enUuX74MQPHixdm6dSs5cuSwccUiIikjQUFc64SLiIg1bt+GunXh0CGj7eUF69ZBpUpxn3f+/HmqV6/OlStXAIVwEXk2KYiLiIhN3Lhh7I55/LjRzpoVNm6E0qXjPu/8+fNUq1aNq1evAlCiRAm2bt1K9uzZbVyxiEjKsvphTRERkdj8/TfUrAnnzxvtXLlg82YoXjzu886dO0e1atW4du0aAK+88gpbt24lW7ZsNq5YRCTlWb184ZAhQxg5cmSC+48ePZpPP/3U2suJiIidOH8e3norMoQXKAC//x5/CD979ixVq1a1hPCSJUsqhIvIM83qIL5y5UrWrl2b4P7r169n5cqV1l5ORETswIkTRgh/ssgJL74IO3ZA4cJxn3fmzBmqVavG9evXAShVqpRCuIg886wO4iIiIlH98QdUrWrMDQd45RUjhOfPH/d5p0+fjhbCX331VbZu3UrWrFltXLGISOpKsSB+9+5d3NzcUupyIiKSgnbvhho1jFVSAF5/HbZtg/iW+z516hTVqlXjxpP0/tprr7FlyxayZMli24JFRNIAmwfxhw8fMmvWLAIDA8mXL5+tLyciIils61aoU8dYLxygcmVj4574svTJkyepXr06N2/eBKB06dIK4SLyXEnwqik+Pj5Mnjw52rHbt2/z8ssvJ+h8k8lE3bp1E1ediIikaWvXQosWEBRktGvVgl9/NTbticuJEyeoUaMG//zzDwBlypRh06ZNZM6c2bYFi4ikIYlavtBsNltem0ymaO24ODs706RJE7p375646kREJM1auhTatYPQUKPdqBEsWQLxzUI8fvw4NWrU4NatWwC8/vrrbNq0iUyZMtm4YhGRtCXBQbxZs2aUL18eMAJ5x44d8fLyYtKkSbGe4+DggIeHBwULFtT8cBGRZ8i8edC5M4SHG+02bWD+fHB2jvu8P//8k5o1a1pCeLly5di4cSMZM2a0bcEiImlQgoN4njx5yJMnj6WdO3dusmTJYgnnIiLyfJg6FXr3jmx37gw//giOjnGfd+zYMWrWrMm///4LQPny5dmwYYNCuIg8t6zeWXPr1q3JWYeIiNiBsWPh448j2++/DxMmgEM8j/4fPXqUmjVrcvvJsioVKlRgw4YNeHl52a5YEZE0TuuIi4hIvMxmGD48eggfPBh++CH+EH7kyJFoIbxixYoK4SIiJHBEPGK7YScnJ7Jnzx7tWGLlzp3bqvNERCR1mM1GAP/++8hjX38NQ4fGf+7hw4epVasWd+7cAaBSpUqsX7+eDBky2KhaERH7kaAgXrNmTQAKFSpk2dY+4lhimEwmTp48mejzREQkdYSHQ58+MG1a5LHx46F///jP/eOPP6hVqxZ3794F4I033mDdunUK4SIiTyQoiEcsUxh1ucKELl0Y0+eIiEjaFxoK771nrIYCYDLB9OnQrVv85x46dIjatWtbQvibb77JunXr8PT0tGHFIiL2JUFBfMuWLUZnJ6enjomIyLMnONhYI3z5cqPt6GgsWdiuXfznHjx4kNq1a3Pv3j0AKleujK+vr0K4iMh/JCiIR122MK5jIiJi/wIDoWVL8PU12i4usHgxNG0a/7kHDhygdu3a3H+y3/1bb72Fr68vHh4etitYRMROWb18oYiIPHv8/aFxY/DzM9pubsaW9XXrxn/u/v37qVOnjiWEV61ald9++00hXEQkFlYvX+jn54e/v39y1iIiIqnI3x+8vSNDuIcHrF+fsBC+b9++aCPh1apVY+3atQrhIiJxsHpEvFevXjg6OlK8eHEqVqxIhQoVKFu2rLayFxGxQ48eQcOG8PvvRjtjRtiwARKyefLevXupU6cODx8+BKB69eqsWbMGd3d32xUsIvIMsDqIZ8iQgQcPHvDnn39y/PhxZs6ciZOTE6VKlbIE89KlS+Ps7Jyc9YqISDILCDCmo2zfbrQzZoTNm+H11+M/d8+ePdStW9cSwmvUqMGaNWtInz697QoWEXlGWB3E9+3bx8mTJ9m7dy979+7l0KFDBAQEcOjQIf744w+mTJmCq6srpUuXpkKFClSsWJFSpUrhEN8WbCIikmICA6FJE9i61WhnyAAbNyYshO/atYt69epZpinWqlWLVatWKYSLiCSQ1UHcZDJRokQJSpQoQZcuXQgNDeXYsWPs3buXPXv2cPToUR4/fsyePXvYu3cvP/zwA+7u7hw8eDA56xcRESs9fgzNmhmj3wCenkYIL1cu/nN37txJ/fr1LSG8du3arFq1inTp0tmwYhGRZ0uyrZri5OREmTJlKFOmDL179yY4OJjdu3czdepUjh49CsCjR4+S63IiIpIEQUHQooUxDxwiH8ysUCH+c3///Xfq169v+Z5ep04dfv31V4VwEZFEStblC8PCwjh69KhlusrRo0cJDg62vK9tjUVEUl9wcPR1wt3dYd06eOON+M/dsWMH3t7elhBet25dfv31Vz2oLyJihSQH8RMnTkSbJx4YGGjZyj59+vS89dZbloc3ixcvnuSCRUTEesHB0Lo1/Pab0U6f3gjklSvHf+727dvx9vYmICAAgHr16rFy5UqFcBERK1kdxPv27cuBAwd48OCBJXi7ublRsWJFS/AuWbIkjo6OyVasiIhYLyQE3n4bVq0y2unSwdq1UKVK/Odu27aNBg0aWEK4t7c3y5cvVwgXEUkCq4P45s2bMZlMeHh48Pbbb1O1alVKlSqFi4tLctYnIiLJIDQU2rWDlSuNtpsbrFkD1arFf+7WrVtp2LAhgYGBADRo0IDly5fj6upqu4JFRJ4DSZqaYjabefjwIfPnz+f48eNUqlSJihUr8sorr2iZQhGRNCI0FN55B5YtM9qursaoeM2a8Z+7ZcsWGjVqZAnhDRs2ZNmyZQrhIiLJwOogvnTpUsvc8D/++IM9e/awZ88eTCYT7u7ulC1blooVK1KpUiWKFSuWnDWLiEgChYVBx46weLHRdnGBX3+FOnXiP3fz5s00atSIx48fA9C4cWOWLFmiEC4ikkysDuIlS5akZMmSdOvWjdDQUI4cORJttZRt27axbds2TCYTmTJlonz58lSqVIk2bdokZ/0iIhKLsDDo3BkWLjTazs6wYgXUqxf/uZs2baJx48aWEN6kSROWLFmi6YciIskoWZYvdHJyomzZspQtW5a+ffsSFBTEoUOH2Lt3Lzt27OD06dOsX7+ejRs3KoiLiKSA8HDo2hXmzzfazs6wfDk0aBD/uRs3bqRx48YEBQUB0LRpUxYvXqwQLiKSzJJ9Ind4eDinTp3i2LFjHDt2jIsXL2IymQAsq6uIiIjthIdD9+4wZ47RdnKCJUugUaP4z92wYUO0EN68eXONhIuI2EiyjIifOnXKMi3l4MGDluWtIoK3i4sLr732GhUrVkyOy4mISCzCw6FXL/jpJ6Pt6AiLFkHTpvGfu27dOpo1a2YJ4S1atOCXX37B2dnZdgWLiDzHrA7iP//8M/v27WPfvn08ePAAiAzeTk5OvPLKK5Y1xcuUKaPRFBERGzOboW9fmDHDaDs6wi+/GFvZx8fX15dmzZpZdkNu2bIlCxcuVAgXEbEhq4P4V199hclkwmw24+DgwEsvvWQJ3mXLliV9+vTJWaeIiMTBbIYPPoCpU422gwMsWACtWsV/7tq1a2nevLklhLdu3ZoFCxYohIuI2JjVQbxIkSLRdtH09PRMzrpERCSBzGYYMAB8fIy2yQTz5hm7aMZnzZo1tGjRgpCQEADatGnDggULcHJKlpmLIiISB6u/0/7222/JWYeIiFjBbIaPPoIffjDaJpPxkGb79vGfu3r1alq2bGkJ4W+//Tbz589XCBcRSSHa/lJExE6ZzTB4MIwbZ7RNJuMhzXffjf/cVatWRQvh7dq1UwgXEUlhCuIiInbIbIahQ2H06MhjM2YYG/jEZ+XKldFCePv27Zk3b55CuIhIClMQFxGxQ8OGwciRke1p04wNfOKzYsUKWrduTWhoKAAdOnRg7ty5ODo62qhSERGJjYK4iIidGTECvvoqsj15MvToEf95y5cvp02bNpYQ/u677zJ79myFcBGRVKIgLiJiR77+GoYPj2xPnAi9e8d/3tKlS6OF8E6dOjFr1iyFcBGRVKQgLiJiJ777zsTnn0e2x4+H99+P/7wlS5bQtm1bwsLCAOjcuTMzZ85UCBcRSWV6MkdExA789FNmvv8+cuxk7Fjo3z/+8xYvXkz79u0tIbxLly7MmDEDBweNw4iIpDZ9JxYRSeMmTDDx/fc5LO1Ro2DgwPjP++WXX2jXrp0lhHft2lUhXEQkDdF3YxGRNOyHH+CjjyK/VX/zDQwaFP95Cxcu5J133iE8PByAbt26MX36dIVwEZE0xCZTU8LCwvjll1/YtWsXjo6OVK1alZYtW2IymWxxORGRZ9LEidGnnwwbFs6nn8YfpH/++WfeffddSwjv0aMHU6ZMUQgXEUljrA7iK1asYOjQodSpU4cfIvZWfuLDDz9k48aNAJjNZrZs2cLu3bsZP3580qoVEXlOTJoE/fpFtnv1usXnn2eO97z58+fTqVMnSwjv1asXPj4+CuEiImmQ1d+Zd+3aBUCjRo2iHd+3bx8bNmzAbDZTunRp3njjDQDWr1/P5s2bk1CqiMjzwccHPvggsj10aDh9+/4b73nz5s2jY8eOlhDeu3dvJk+erBAuIpJGWf3d+fTp0wCUKVMm2vFff/0VgFatWrFw4UJmzZrF+++/j9lsZuXKldZXKiLyHJg8OfqShJ99BsOHm4lvZt/cuXPp1KkTZrMZgL59++Lj46MpgSIiaZjVU1Nu376Ni4sLmTNH/1Xpnj17MJlMvPPOO5Zj7du3Z+LEiRw/ftz6SqMwm82sW7eO1atXc+rUKW7fvk3GjBkpUqQI3t7eNGvWDGdn52S5Vmzu37+Pt7c3//4bOUq1ZcsW8ubNa9Prisiza/Jk6Ns3sj10KHz5JTwZ4I7V7Nmz6dKliyWEv//++/zwww8K4SIiaZzVQdzf35/06dNHO/bPP/9w48YNsmbNSrFixSzHvby88PDw4M6dO9ZX+sSDBw/o168fu3fvjnb81q1b3Lp1iz179rBo0SJ8fHzInTt3kq8Xm1GjRkUL4SIiSTFlSvQQ/umnxjb28WXpWbNm0bVrV0sI79evH+PHj1cIFxGxA1YHcQ8PD+7fv09gYCDp0qUD4ODBgwCULl06xnNcXV2tvRwAISEh9OnTh/379wOQK1cuWrduTYECBbhx4wbLly/nwoULnDhxgm7durFo0SI8PT2TdM2Y7Nq1ixUrVuDg4ICzszNBQUHJfg0ReX5MnQp9+kS2P/3U2Mo+viw9c+ZMunXrZmn379+fcePGKYSLiNgJq+eIFy1aFIB169ZZjq1evRqTyUS5cuWi9X348CH+/v5kzZrV2ssBsGjRIksIL1GiBKtWraJ37940aNCALl26sHLlSipXrgzA+fPnmTJlSpKuF5PAwEC++OILwJhyk9Q/k4g836ZNg969I9tDhiQshM+YMSNaCP/www8VwkVE7IzVQbxRo0aYzWa+/PJLhg0bxvvvv8+2bdtwdnamfv360foePnwYgIIFC1pdaGhoKFOnTgXAZDLx3Xff4eXlFa2Pq6sro0ePtkyZWbBgQbJMh4lqwoQJXLlyhRw5ctA/IftLi4jEYvp06NUrsj14sLFhT3xZevr06fTo0cPSHjhwIGPHjlUIFxGxM1YH8RYtWlC5cmUeP37MkiVL2LRpEyaTif79+5MtW7ZofdevXx/jSHli7N+/n9u3bwNQqVIlXnzxxRj7ZcmSBW9vbwCCg4PZsmWL1df8r2PHjjFv3jwAPv/8czw8PJLts0Xk+TJjBvTsGdkeNAi+/Tb+ED5t2jR6Rjnx448/ZsyYMQrhIiJ2yOo54g4ODsyYMYO1a9dy+PBhPD09qVKlCq+//nq0fsHBwdy6dYuyZctSpUoVqwv9/fffLa/feuutOPu+9dZbLFu2zHJeq1atrL5uhJCQEIYOHUp4eDg1a9akdu3aSf5MEXk+zZgBUQa0+eQTGDky/hA+depU3o+ytuGgQYMYOXKkQriIiJ1K0hb3Dg4ONGrU6KlNfaJycXHhxx9/TMplADh79qzldYkSJeLs+8orr1henzt3LsnXBmM+5tmzZ0mfPr1ljriISGL9+GP0EP7xxzBqVPwhfOHChXz99deW9uDBg/n2228VwkVE7JjdbLd28eJFy+s8efLE2Tdnzpw4OjoC8Pfff1t2mbPWhQsXmDZtGgADBgwgZ86cSfo8EXk+zZwJ3btHtj/6CL77Lv4Q7uPjEy2Ef/rppwrhIiLPgCSNiEe4c+cO+/bt49q1awQGBtI36mK4yeThw4eW15kyZYqzr5OTk2V5xdDQUAICAqyezx0eHs7QoUMJDg7mlVdeibZRUWoLCwtLkWuYzeYUuZbYhu5h2vDTTyZ69Igc+xgwIJyRI83xbtYzceJEPvzwQ0v7008/ZcSIEUkeYJCUo69B+6d7aP/S6j1MUhAPDQ1l7NixLFy4kJCQEMvxqEH8/v371K5dm8DAQLZs2UL27NmtulZAQIDldULWI4/aJylBfOHChRw+fBhHR0e++uorHBzSxi8RQkNDo03XsRWz2UxQUBDnz5/X6Jud0j1MfcuXe/H555EbjHXseJuuXf8hvplz8+bNY9SoUZZ2z549ad++fbJNuZOUoa9B+6d7aP9S+h6GhoYmqF+Sgni/fv3YunUrAEWKFOF///vfU//S8PLyomHDhixcuJAtW7bQtm3bpFwyRV27do3vv/8egI4dO1K8ePFUriiSk5OTZS13WwoLC+P8+fMUKVLEMt1H7IvuYeqaPdvEF19EftPv3z+cMWMyYjJljPO8CRMmRAvhvXv3Zvz48bqHdkhfg/ZP99D+pfQ9PHHiRLRB6thYHcR9fX3ZsmULWbNmZcaMGRQvXpzKlStblhiMql69eixcuJBt27ZZHcTTp0/P/fv3AQgKCsLJKe7So+52GbGueGINHz6cgIAA8uTJE22lgrQipb4ZmEwmHB0d9c3Hjukepo7Zs4054U92n2fAAPj+e4d454R///33fPTRR5b2F198wdtvv617aMf0NWj/dA/tX1q8h1bPs1i+fDkmk4mPP/443pHiUqVKYTKZkjSVIupW9ffu3Yuzb2hoKP7+/oAxcmxNEF+9ejXbt28HjB+C1oZ5EXk+zZkDXbpEhvD+/eH77+N/MHPMmDHRQviIESO0UpOIyDPK6hHxkydPAlC3bt14+7q5ueHp6ZmkXS4LFizIlStXALh69WqcK6fcuHHDMkUmf/78Vs3rXrp0KQDZsmXj5MmTlj/vf0V9iPTnn3+2/IOhdevWZM2aNdHXFRH7N3cuvPdeZAjv1w/GjYs/hI8ePZpBgwZZ2l999RWfffZZmnu4SEREkofVQfzhw4d4enri5uaWoP5JfcK/aNGi7Ny5E4Djx49Tvnz5WPseP37c8jq2HTgT6tatW/zwww8J6jtr1izL6+rVqyuIizyH5s6Fzp2jh/Dx4+MP4aNGjWLIkCGW9jfffMOnn35qw0pFRCS1WT01xcvLi4cPH0abix2bGzdu4O/vT5YsWay9HJUrV7a8jgjksUnMLpwiIsll3rzoIfyDDxIWwr/99ttoIfzbb79VCBcReQ5YPSL+0ksvsXv3bvbv3x9v2F20aBFgzBW3VoUKFcicOTN37txh9+7dnDt3LsbR7tu3b+Pr6wuAs7MzNWvWtOp68+fPT1C/GjVqcPXqVQC2bNlC3rx5rbqeiNi3+fOhU6fIEP7++zBhQvwh/JtvvuGzzz6ztEeNGhVteoqIiDy7rB4Rb9CgAWazmR9++IHAwMBY+/n6+jJz5kxMJhNNmjSx9nI4OTnRs2dPwFgLctCgQZZVVCIEBQUxaNAgy5rj7du3J3PmzDF+3uDBgylWrBjFihVj0qRJVtclIjJ/PnTsGBnC+/aFH36IP4RHzAGP8N854iIi8myzekS8adOmLFq0iD///JM2bdrQtm1by3qJf/75J2fOnMHX15c9e/ZgNpupVKkS1atXT1Kxbdu2ZePGjRw8eJATJ07QpEkT2rRpQ4ECBbhx4wbLli3jwoULABQqVIg+ffok6XoiIvFZsCB6CO/TByZOjD+EjxgxguHDh1va/10tRUREnn1WB3EHBwemTp1Kjx49OH78OF9++aXlvdatW1tem81mXn31VcaPH5+0SgEXFxemTJnCBx98wN69e7l+/ToTJkx4qt/LL7+Mj48PGTJkSPI1RURi8/PPT4fwSZPiD+HDhw9nxIgRlvb3338fbRt7ERF5PiRpZ80sWbLwyy+/sGTJEpYuXcrZs2ejrY5SpEgRWrduzdtvv42Li0uSiwXjIdE5c+awbt06Vq1axcmTJ7l79y5eXl4ULlyYBg0a0Lx5c5ydnZPleiIiMVm4EN59FyK+5fXuHX8IN5vNDB8+PNrAxbhx4xgwYICNqxURkbQoSUEcjAci27dvT/v27Xn06BH//vsvYWFhZM2a1WYj0iaTCW9vb7y9va3+jFGjRkXbPtpaW7duTfJniIh9WbgQOnSIDOG9eoGPT/wh/IsvvuDrr7+2HJswYQL9+vWzcbUiIpJWJTmIR+Xu7o67u3tyfqSISJqyaFH0EN6zZ8JC+Oeff84333xjOTZx4kTef/99G1crIiJpmdWrpiTE/fv3LVvNi4jYu+XL4Z13IkN4jx4weTLEtXmv2Wxm6NCh0UL4pEmTFMJFRMT6IH7z5k1+/fVXduzY8dR7586do3nz5lSsWJFy5crRtm1b/vrrryQVKiKSmtasgbffhojd5rt3hylT4g/hQ4YMYeTIkZZjPj4+9O3b18bVioiIPbA6iK9cuZIhQ4awf//+aMcfP35M9+7dOXXqFGazGbPZzOHDh3nvvfc0Oi4idmnjRmjZEkJDjXanTjB1avwhfPDgwXz33XeWY1OmTNGyqiIiYmF1EN+zZw/AUw9Mrly5kuvXr+Pl5cVXX33FmDFjyJkzJzdv3uTnn39OWrUiIils2zZo0gSCg41227Ywc2b8IfyTTz5h9OjRlmPTpk2jV69eti1WRETsitVBPGJb90KFCkU7vmnTJkwmE/3796dVq1Y0atSIr776CrPZrBVGRMSu7N4NDRvC48dGu3lzmDsXHB1jP8dsNvPRRx8xduxYy7Hp06fTo0cPG1crIiL2xupVU27fvo2Hhwdubm6WY+Hh4Rw+fBiTyUTdunUtx998800cHBz43//+l7RqRURSyIEDUL8+PHpktBs0gF9+gbi2KDCbzQwcODDaBmY//vgjXbt2tXG1IiJij6wO4mFhYdE27wE4e/YsgYGBFC1alEyZMlmOOzg4kCFDBs0RFxG7cPQo1K0LDx4Y7dq1YdkyiGtfMrPZzIABA/jhhx8AY7+DH3/8kS5duqRAxSIiYo+snpqSLVs2goODuXz5suXY7t27AShduvRT/QMCAsiYMaO1lxMRSREnT0KtWnD3/+3dd1hT1xvA8W/YIu6ttWpVcFuto9YFbnHvXbfWUbW2v2q1Qzu1rdVW62qtW3HhHqi4tYp7oLgniANlb7i/P9JciZCAIRDB9/M8PubmnpNzwslN3pyc8Vx73LgxbNoESX78S0ZRFMaNG6cXhP/9998ShAshhDDK5EC8Zs2agHYprsTERJ49e8aqVavQaDQ0bNhQL+39+/eJjY2lUKFC6autEEJkoGvXoGlTePpUe1yvnnbZQkdHw3kURWHMmDH88ccfgDYI/+effxg0aFAm1FgIIURWZnIg3r9/fzQaDVu2bKFWrVq4urry4MED3nrrLVxdXfXS6nrKK1WqlK7KCiFERrl1C5o0gcBA7XGtWrBzJ+TKZTiPoiiMHj2aOXPmANogfPHixQwYMCDjKyyEECLLMzkQr1KlCj/99BM5c+YkMjKS2NhY3nnnHWbPno2Njf7Q802bNgFQt27ddFVWCCEywv372p7w/xaDolo18PKCPHkM50lMTGTUqFHMnTsX0AbhS5cupX///plQYyGEENmByZM1ATp06ECrVq24du0auXLl4u2338bqpcV1Y2Nj6dGjB927d0/WUy6EEJb28KG2J/zOHe1xxYqwdy/kz284jy4Inz9/PqCdkL506VL69u2b8RUWQgiRbaQrEAewt7enatWqBs/b2dnRsWPH9BYjhBBm9/ixtif8xg3tcbly4O0NxqazJCYmMmLECBYuXAhog/Dly5fTu3fvTKixEEKI7CTdgbgQQmRFz55plyW8ckV7XLo07NsHxYoZzpOYmMjw4cP5+++/AW0QvmLFCnr16pXxFRZCCJHtmC0Qj4mJISQkhPj4eKPpihcvbq4ihRDCJCEh0KIFXLigPX7rLW0QXrKk4TyJiYkMGzaMRYsWAWBtbc3KlSvp0aNHJtRYCCFEdpSuQDwqKoq///6bbdu2ce/evVTTazQaLl++nJ4ihRAiXcLCtDtmnj6tPS5aVDscpUwZw3kSExMZMmQIixcvBrRB+KpVq+jevXsm1FgIIUR2ZXIgHhoaSp8+fbhx4waKoqQpT1rTCSFERoiMhLZt4d9/tccFC2qDcGdnw3kSEhIYMmQIS5YsAbRB+OrVq+nWrVvGV1gIIUS2ZnIgPnfuXK5fv46NjQ1dunShZcuWFClSBAdj288JIYSFREdDhw5w6JD2OF8+7eooxrY3SEhIYPDgwSxduhQAGxsbPDw86NKlSybUWAghRHZnciC+d+9eNBoNkyZNktUChBCvtdhY6NpVG3gD5M4Nu3dD9eqG8yQkJDBw4ECWL18OaIPwNWvW0Llz50yosRBCiDeByYH4o0ePsLKykp4hIcRrLS4OevaE7du1xzlzanfMrFXLcJ6EhAQGDBjAihUrAG0QvnbtWjp16pQJNRZCCPGmMDkQz5MnD7Gxsdjb25uzPkIIYTYJCfDhh7Bxo/Y4Rw5tQP7BB4bzxMfH079/f1atWgWAra0t69ato0OHDplQYyGEEG8Sk7e4f++99wgLCyMwMNCc9RFCCLNITITBg8HDQ3tsbw+bN0PjxobzxMfH8+GHH+oF4evXr5cgXAghRIYwORAfOnQo1tbWzJkzx5z1EUKIdFMUGDkS/ptjiY0NrF+v3cDHkPj4ePr168fq1asBbRC+YcMG2rdvnwk1FkII8SYyORCvUqUK06ZNY8uWLUyYMIG7d++as15CCGESRYFPPoEFC7TH1tbaXvG2bQ3niY+Pp0+fPnj8131uZ2eHp6cn7dq1y4QaCyGEeFOZPEa8adOmgHZN3S1btrBlyxZy586Nk5OTwTwajYa9umULhBDCzBQFvvgCfv9de6zRwPLlYGxOeVxcHH369GHdunWANgjfuHEj7u7umVBjIYQQbzKTA3F/f/9k94WEhBASEmIwj0ajMbU4IYRI1bffwvTpL47/+Qd69TKcPi4ujl69erFhwwYA7O3t2bhxI61bt87gmgohhBDpCMR/+uknc9ZDCCHSZdo0mDLlxfG8eTBggOH0cXFx9OzZE09PT0AbhG/atIlWrVplaD2FEEIIHZMDcVlPVwjxupg1SzskRWfmTPjoI8PpY2Nj6dmzJxv/W9fQ3t6ezZs307Jly4ytqBBCCJGEyYG4EEK8DubP107O1Jk2DcaNM5w+NjaWHj16sGnTJgAcHBzYvHkzLVq0yNB6CiGEEC8zedUUIYSwtBkzYMSIF8fffAMTJhhOHxsbS7du3fSC8K1bt0oQLoQQwiLS3SMeGBjI4sWLOXLkCAEBAcTExHD58mX1fEhICKtXr0aj0TB06FCsrCT2F0Kkj6LApEna3m+dCRO0gbghMTExdOvWja1btwKQI0cOtm7dqq4AJYQQQmS2dAXix44dY+zYsYSHh6MoCpB8ZZQ8efLg7e3NpUuXqF69Ou+//356ihRCvOESErS94H/99eK+b7+FL7/ULleYkpiYGLp27cq2bdsAbRC+bds2mjRpkgk1FkIIIVJmcvf0w4cPGTNmDGFhYbi5ufHHH3+QJ0+eFNN26dIFRVFkDXEhRLrExEDPni+CcI0G/vwTvvrKcBAeHR1N586d1SDc0dGRHTt2SBAuhBDC4kwOxBcvXkx4eDitW7dm7ty5tGjRAltb2xTTNmjQAIAzZ86YWpwQ4g0XHg7t2mm3qgfttvUrV2q3sjdEF4Tv2LEDeBGEu7q6ZnyFhRBCiFSYPDTlyJEjaDQaxo4dm2rat956Czs7Ox48eGBqcUKIN1hQELi7g4+P9jhHDvD0BGNLfkdHR9OpUyd27doFQM6cOdmxYweNGjXKhBoLIYQQqTM5EA8ICMDBwYHSpUunKb2joyPh4eGmFieEeEP5+0OLFqCbA543L2zfDh98YDhPVFQUHTt2ZPfu3YA2CN+5cycNGzbM+AoLIYQQaWRyIK7RaEhMTExT2ri4OMLDw8mZM6epxQkh3kDXrmmD8Lt3tcdFi4KXF1SrZjhPVFQUHTp0YM+ePQA4OTmxc+dOdYicEEII8boweYx4sWLFiI2N5eHDh6mmPXHiBPHx8bz99tumFieEeMOcOQMNGrwIwt95B44eNR6ER0ZG0r59e70gfNeuXRKECyGEeC2ZHIjXq1cPAA8PD6PpoqOjmTFjBhqNRn4WFkKkycGD4OoKT55oj6tV0wbh77xjOI8uCNetzpQrVy68vLyoX79+xldYCCGEMIHJgfiAAQOwtrbmn3/+YePGjSmmOXPmDH369OHKlSs4ODjQu3dvkysqhHgzbNkCLVtCWJj2uH59bWBetKjhPJGRkbRr1w5vb2/gRRD+gbGB5EIIIYSFmRyIlyxZkq+//pq4uDgmTZpEgwYNCAkJAWDYsGG4urrSp08ffH190Wg0fPfddxQsWNBsFRdCZD/LlkHnztr1wkG7Usru3doJmoZERETQtm1b9u3bB0Du3LnZvXu3+qudEEII8bpK137z3bt3Z86cORQqVIinT58SFxeHoigcOnSIwMBAFEWhUKFCzJ49m7Zt25qrzkKIbGjmTOjfX7tzJkDv3rBpEzg6Gs4TERFBmzZt2L9/P6DdyXfPnj2yg68QQogsIV1b3AM0a9aMxo0bc+jQIU6fPs3jx49JTEykYMGC1KxZkyZNmmBnZ2eOugohsiFF0e6M+cMPL+4bPRp+/x2sjHQVhIeH06ZNGw4dOgS8CMJr166dwTUWQgghzCPdgTiAra0tTZs2pWnTpimej4+P5+zZswDyISmEUCUkaIPu+fNf3DdlCnz9teEt60EbhLu7u3P48GEA8ubNy549e6hVq1bGVlgIIYQwI7ME4qkJCwujX79+WFlZcVm3K4cQ4o0WGwsffghr1ry4748/4OOPjecLCwvD3d2dI0eOAJAvXz727NnDe++9l4G1FUIIIcwvUwJxHUVRMrM4IcRrKiJCOynzv40vsbGBJUugTx/j+cLCwmjdujVHjx4FtEH43r17qVmzZsZWWAghhMgAmRqICyHEs2fQpg0cP649dnCA9eu19xkTGhpK69atOXbsGAD58+dn79691KhRI4NrLIQQQmQMCcSFEJnG31+7Rrivr/Y4Tx7Ytk27g6YxISEhtGrViuP/Re/58+fH29ubd999N2MrLIQQQmQgCcSFEJni+nVo0QLu3NEeFymiHZpibMt60AbhLVu25MSJEwAUKFAAb29vqlevnrEVFkIIITJYutYRF0KItDh3TtvrrQvCy5TRblmfWhAeHBxMixYt1CC8YMGC7Nu3T4JwIYQQ2YIE4kKIDHX4MDRuDI8fa4+rVIEjR6BsWeP5dEG4j48P8CIIr5Za9C6EEEJkERKICyEyzLZt2uEooaHa43r14NAhKF7ceL7nz5/TvHlzTp48CUChQoXYv38/VatWzeAaCyGEEJknzWPEN23aZHIhERERJucVQmRNK1bAgAEvtqxv1Uq7OkrOnMbz6YLw06dPA1C4cGH27dtH5cqVM7bCQgghRCZLcyA+ceJENMa2uhNCiP/88QeMHfviuGdPWLoU7OyM53v27BnNmzfnzJkzgDYI379/P5UqVcrA2gohhBCW8UpDUxRFMfmfECL7UxT45hv9IHzECG3veFqC8GbNmqlBeJEiRSQIF0IIka2luUfc29s7I+shhMgGvv8evv32xfFXX8HUqZDaj2lBQUE0a9aMc+fOAVC0aFH2799PhQoVMq6yQgghhIWlORAvUaJERtZDCJHFzZ8PX3/94njmTBg3LvV8T58+pVmzZpw/fx6QIFwIIcSbQzb0EUKk2/r1MHLki+Nff017EN60aVMuXLgAQLFixdi/fz8uLi4ZU1EhhBDiNSLLFwoh0sXbG/r00Y4PB/j8c/j009TzPXnyhCZNmqhBePHixTlw4IAE4UIIId4YEogLIUx2+jR07AixsdrjgQNh2rTU8z1+/JgmTZpw8eJFQDv07cCBAzg7O2dcZYUQQojXjAxNEUKY5No1aN0awsO1x+3bw8KFqU/M1AXhvr6+ALz11lvs37+fcuXKZXCNhRBCiNeL9IgLIV6Zv792x8wnT7THDRuChwfYpPLV/tGjR7i5ualBeMmSJTlw4IAE4UIIId5I0iMuhHglz55By5Zw9672uFo12LIFcuQwni8wMJAmTZpw5coV4EUQ/s4772RwjYUQQojXk/SICyHSLDIS2rWD/zq0KVMGdu2CvHmN53v48CFubm5qEP72229LEC6EEOKNJz3iQog0iYuDbt3g2DHtceHCsHs3FCtmPJ8uCL969SoApUqVYv/+/ZQpUyaDayyEEEK83tLUI960aVO6d++ud9/JkyfVXfCEENlbYiIMGgQ7dmiPc+XS9oSnNrQ7ICAAV1dXNQgvXbo0Bw4ckCBcCCGEII094v7+/sTExOjd169fPwoVKsThw4czpGJCiNeDosBnn8GKFdpje3vtmPAaNYzn8/f3x83NjevXrwMvgvBSpUplcI2FEEKIrCFNPeI2NjbExcUlu1/R7eAhhMi2fv5Zu109gJUVrF4Nrq7G8zx48ABXV1c1CC9TpgwHDx6UIFwIIYRIIk2BeL58+QgNDeXx48cZXR8hxGtk0SKYOPHF8YIF0KmT8Tz379/H1dWVGzduAPDOO+9w8OBB3n777QysqRBCCJH1pGloSq1atdi5cyf9+vWjadOmODo6AhAZGcmcOXNeqcDRo0e/ei2FEJlu0yYYNuzF8Y8/wpAhxvPogvBbt24BULZsWfbv30/JkiUzrqJCCCFEFpWmQHz06NEcOnSIu3fvsnjxYvX+qKgo/vzzz1cqUAJxIV5/Bw9Cz57aSZoA48bp94yn5N69e7i5ualBeLly5di/fz9vvfVWxlZWCCGEyKLSFIiXLVuWLVu24OHhwbVr14iKisLHxwcbGxvefffdDK6iECIznTun3a5eNz+7b1+YMcP41vV3797Fzc2N27dvA1C+fHn2799PiRIlMr7CQgghRBaV5nXES5QowaeffqoeV6hQgTx58rB8+fIMqZgQIvPdvAmtWkFoqPa4dWv45x/tJE1D7ty5g5ubG3fu3AHA2dmZffv2SRAuhBBCpMLkDX2KFy9OgQIFzFkXIYQFPXwILVrAo0fa43r1YN06sLU1nOfOnTu4urpy97/97l1cXNi3bx/FixfPhBoLIYQQWZvJgfi+ffvMWQ8hhAUFB2t7v/8b3k2lSrBtG+TMaTjP7du3cXV15d69e4D2V7J9+/ZRLLWtNoUQQggBmHmL+4SEBEJCQgDIkycP1tbW5nx4IUQGiIqCDh3g/Hnt8dtvg5cX5M9vOM+tW7dwdXXl/v37gDYI379/P0WLFs2EGgshhBDZQ7oD8ejoaDw8PNi2bRt+fn4kJCQAYG1tTYUKFWjXrh09evTAwcEh3ZUVQphXfDz06gWHDmmPCxaE3bvB2EInN2/exM3NTQ3CK1WqxL59+yhSpEgm1FgIIYTIPtIViN+5c4ePPvqIu3fvJttlMz4+nkuXLuHr64uHhwfz58+XXfWEeI0oCgwfDps3a4+dnGDnTnBxMZznxo0buLm58eDBA0CCcCGEECI9TA7EIyMjGTx4MP7+/lhbW9OsWTMaNGig/jQdGBjIsWPH2LNnD7dv32bw4MFs2bJF3QxICGFZX3yhXREFtBMyN26EWrUMp79+/Tpubm74+/sDULlyZfbt20fhwoUzobZCCCFE9mNyIL58+XL8/f0pWLAg8+fPp0qVKsnSdOvWDV9fX4YPH46/vz8rVqxgWNKt+oQQFjFjBkyfrr2t0cCKFdCsmeH0169fx9XVlYCAAACqVq2Kt7c3hQoVyoTaCiGEENmTkdWBjduzZw8ajYYpU6akGITrVK5cmSlTpqAoCrt37za1OCGEmSxbBp999uL4zz+he3fD6a9evUrjxo0lCBdCCCHMzORA/O7du9jZ2dGkSZNU0zZp0gQ7Ozt1ww8hhGVs3w6DBr04njoVRowwnP7q1au4ubnx8OFDAKpVq8a+ffskCBdCCCHMwORAPCYmBjs7O6yMbbmnK8TKCnt7e2JjY00tTgiRTkePQrdu8N/CRowaBV99ZTi9n58frq6uahD+7rvvsm/fPgoWLJgJtRVCCCGyP5MD8UKFChEeHq4uYWbMvXv3CAsLk140ISzk2jV7OnSwIipKe9yjB/zxh3Z8eEquXLmCq6srgYGBANSoUYO9e/fKbrpCCCGEGZkciNepUwdFUfjhhx/UtcNTkpiYyI8//ohGo6Fu3bqmFieEMNGdOzB0aEmCg7VRd/Pm2nHihn7Munz5Mq6urjz6b697CcKFEEKIjGFyID5w4ECsrKw4ePAgffr04ciRI3pDT2JjYzl69Ch9+/blwIEDWFlZMWDAAHPUWQiRRoGB0Lq1FU+e2AJQpw54eoKdXcrpfX19cXNz4/HjxwC899577N27l/zGttkUQgghhElMXr7Q2dmZSZMm8f3333P+/HmGDh2KtbU1+fLlQ6PR8OzZMxISEtSNfiZNmoSzs7PZKi6EMO7uXe2ShDduaHvCXVwUtm/X4OSUcvpLly7RpEkTnjx5AkCtWrXYvXs3+fLly6wqCyGEEG8Uk3vEAfr27cuCBQsoW7YsiqIQHx/PkydPePz4MfHx8SiKQvny5VmwYAF9+vQxV52FEKm4cgXq14cbN7THxYrFsXNnIobmWV68eBE3Nzc1CK9duzZ79uyRIFwIIYTIQOna4h6gcePGNG7cmKtXr3Lp0iWCgoIAKFCgAFWrVpVecCEy2enT0KoVPH2qPXZxUZg79w5vv/1OiukvXLhA06ZNefpfhjp16uDl5UXevHkzqcZCCCHEmyndgbiOi4sLLi4u5no4oxRFYefOnWzZsoUrV64QFBRE3rx5KVeuHO7u7nTq1AlbW1uzlPXw4UMOHz6Mj48PV69eJSAggOjoaJycnChVqhS1a9emW7dulC5d2izlCZEehw5B27YQFqY9rlEDtm9PJDg4PsX058+fp2nTpuoX6Lp16+Ll5UWePHkyq8pCCCHEG8tsgXhmCQ0NZezYsRw7dkzv/idPnvDkyRP+/fdfPDw8mDNnDsWLF09XWaNGjcLb21sd555UcHAwwcHBnD9/nsWLFzNkyBDGjRuXpnXVhcgI27dD164QHa09btAAtm0DJycIDk6e/ty5czRt2pRnz54B8P7777Nr1y4JwoUQQohMkqUC8bi4OEaNGoWPjw8AxYoVo3v37pQqVYrAwEA2bNjAzZs38fX1ZejQoXh4eJArVy6Ty7t27ZoahFeqVIm6detStmxZnJycePr0KQcOHODIkSMkJCSwYMECQkJCmDp1qlmeqxCvYvVq+PBDiP+v47t1a1i/HhwdX2zgk9TZs2dp1qyZGoTXq1ePXbt2kTt37kystRBCCPFmy1KBuIeHhxqEV65cmcWLF+v13vXt25eRI0dy5MgRbty4wdy5c5kwYYLJ5dnZ2dGzZ0/69etHuXLlkp3v168f27dv53//+x8JCQl4eHjQpk0b6tSpY3KZQryqefO0u2Tqfrjp0UO7TrihJQrPnDlDs2bNeP78OQAffPABu3btSteXViGEEEK8uiwzjiI+Pp558+YBoNFomD59erKf0O3t7fn5559xdHQEYMWKFWqPnylWrlzJ1KlTUwzCddq0aUO/fv3UY09PT5PLE+JVKAr89BOMHPkiCB82DFauNByEnz59mqZNm6pBeP369SUIF0IIISwkywTiPj4+6oSyevXqUb58+RTTFShQAHd3d0C7qZC3t7fJZaZ11YjWrVurt69evWpyeUKklaLAhAkwadKL+yZMgPnzwdo65TynTp2iWbNmBP83YLxhw4bs3LlTgnAhhBDCQrJMIH748GH1dsOGDY2mTXo+ab6MkjSQidbNlBMigyQkwPDh8MsvL+776SeYNg00mpTzXLx4kZYtW6pBeKNGjdixY4cE4UIIIYQFZZkx4teuXVNvV65c2WjaKlWqqLevX7+eYXXSSdoLnt6VWoQwJjYW+vWDtWu1xxoNzJ0LH31kOI+Pjw+DBw8mPDwc0K79v23bNpwMbbEphBBCiEyRZQLxO3fuqLdLlChhNG3RokWxtrYmISGBe/fukZiYmKHLCq5evVq97erqmmHlvCwhpeUwMqAMRVEypSxhXGQkdOtmhZeXttvbxkZh6VKFHj2UFFdGAThx4gStW7fWC8K3bNlCjhw5pE2zELkOszZpv6xP2jDre13bMMsE4mG6HUog1W23bWxscHJyIiQkhPj4eCIjIzOs92/Tpk3qSi6FChWiS5cuGVLOy+Lj4/V+JcgoiqIQExPDjRs30Bga9yAyXGioFSNHluTMGe1EZHv7RGbNekCNGhEYehmcO3eOoUOHEhERAWh3zPztt9/w9/fPrGoLM5HrMGuT9sv6pA2zvsxuw/j4lDfSe1mWCcQjIyPV2/b29qmmT5omowJxPz8/vXXDv/rqK3XFloxmY2ODs7NzhpeTkJDAjRs3KFeuHNaGZgGKDPX4MfTubcW5c9o3jty5FTZvVmjY0PAvQ8eOHWP48OFqEF63bl1ZHSULk+swa5P2y/qkDbO+zG5DX19f4uLiUk2XrkBcURQ2btzI9u3buXr1KsHBwUa7/DUaDZcvX05Pka+NR48eMWLECPULQr9+/WjZsmWm1iGz3gw0Gg3W1tby5mMB9+5B8+aovd4FC4KXl4aaNQ23xdGjR3F3d1eHozRt2pSff/6ZXLlySRtmYXIdZm3SflmftGHW9zq2ocmBeGxsLMOGDePEiRMpbgFvbo6OjoSEhAAQExODjY3xqsfExOjlNaegoCD69+9PQEAAAG3btmVS0nXkhDCDq1e1Qfj9+9rjt96CPXugQgXDeY4cOaI3Jrx58+Z4enpyX/cgQgghhHhtmByIL1y4kOPHjwPQoEEDWrZsSZEiRXBwcDBb5ZLKlSuXGogHBweTM2dOg2nj4+PVQMTGxsasgfizZ88YMGAAt2/fBqBly5ZMnz49QyeDijfPmTPQsiU8fao9Ll9eG4SXKmU4z+HDh2ndurU6HKVFixZs2rQJO0O7+wghhBDCokwOxHfs2IFGo2HUqFGMHj3anHVKUenSpXnw4AEA/v7+RldOCQwMVIfIvP3222YLkp8/f86AAQPUSZJNmzZlxowZqfbOC/EqDh+Gtm0hNFR7XL06eHlBkSKG8xw6dAh3d3c1CG/VqhUbN27EwcHhtZshLoQQQggtkyPUBw8eoNFoGDhwoDnrY1DSiYmXLl0ymjbpeUM7cL6q4OBgBg4cqK4Z7urqyqxZs7C1tTXL4wsBsGMHtGjxIgivXx8OHDAehB88eFCvJ7x169ZqEC6EEEKI15fJgXjOnDnJlSuX0SEi5tSgQQP19pEjR4ymfZVdONMiJCSEgQMHcuXKFUC7K+Hs2bPlJ39hVmvWQIcOoNuctWVLbU943ryG8xw4cAB3d3d10rC7uzuenp4ShAshhBBZgMmBeNWqVQkLC+P58+fmrI9BdevWJX/+/IB2aTZDO2YGBQWxY8cOAGxtbWnatGm6yg0LC2PQoEHqai8NGjTgzz//lCBcmNXChdCrF+iWHe3WDbZsAWPfc/ft26cXhLdp00aCcCGEECILMTkQHzhwIIqisGTJEjNWxzAbGxs++m8fb0VRmDBhgjp5UycmJoYJEyaogUmfPn3U4P1lEydOxMXFBRcXF2bPnp1imvDwcAYPHqwOdWnQoAFz586VIFyY1fTpMHw46BYfGjIEVq8GYy8zb29v2rZtS1RUFADt2rVjw4YNaVpjXwghhBCvB5NnGdarV4/PPvuM3377DWtra4YMGZLhm9n06tWL3bt3c+rUKXx9fenQoQM9evSgVKlSBAYGsn79em7evAnAO++8w6hRo9JV3pAhQzh//jwABQoUoHPnznrDXlLi4OCgN4xGCEMUBSZNgmnTXtz3v/9pA3Njm37t3buXdu3aEf3fGJb27duzdu1aCcKFEEKILMbkQPzDDz8EtGPF582bx99//025cuWM7mCp0WhYunSpqUViZ2fH3LlzGTNmDMePH+fhw4fMmjUrWbqKFSsyZ84ccufObXJZAGfPnlVvBwUFMX78+FTzlChRgn379qWrXJH9JSTA6NEwf/6L+378ESZONB6E79mzh/bt26tBeIcOHVi7dq38SiOEEEJkQSYH4j4+PnrHsbGxqe6aqTEWYaRRnjx5WLJkCTt37mTz5s1cvnyZ58+fkydPHsqWLUubNm3o3LmzrGYiXluxsdC/P3h4aI81GvjzTxgxwng+Ly8vOnTooG5W1alTJzw8PCQIF0IIIbIokwPxzFg73BCNRoO7uzvu7u4mP8a0adOYlnRMQAp0SxUKYS6RkdqJmP/NJ8baGpYtg969jefbtWsXHTt2VIPwzp074+HhIV84hRBCiCwsSwbiQmRFCQnQs+eLINzBAdat027eY8zOnTvp1KmTGoR36dKF1atXSxAuhBBCZHGyJaQQmWTSJNi6VXs7Vy7t7caNjefZsWMHnTp1IjY2FoBu3bqxcuVKCcKFEEKIbMA8e78LIYxauhR+/ll729oaPD1TD8K3bdumF4R3795dgnAhhBAiGzFLj/iVK1fYunUrFy9e5NmzZ2g0GvLly0fVqlVp164dFStWNEcxQmRJx47BsGEvjn//HZo1M55n69atdOnShbi4OAB69uzJ8uXLsbGRH7GEEEKI7CJdn+pRUVF89dVXbN++HdButJPUqVOnWLx4Me3atWPq1KnkyJEjPcUJkeXcvQsdO2pXSgHtyiipLW+/ZcsWunbtqgbhvXr1YtmyZRKECyGEENmMyZ/siqIwevRojh07hqIoFCxYkPfff59ixYoB8PDhQ06cOMGTJ0/YunUrz5494++//zZbxYV43YWHQ/v28OSJ9rhpU21vuDGbN2+mW7duahDeu3dvli5dKkG4EEIIkQ2Z/Om+ZcsWjh49io2NDf/73//o27cv1tbWemkSExNZuXIl06dP5+jRo2zZsoX27dunu9JCvO4SE6FvX7hwQXtcrhysXQvGhndv3LiR7t27Ex8fD0Dfvn1ZsmRJsutKCCGEENmDyZM1t2zZgkajYfz48fTv3z/FYMHKyop+/foxfvx4FEVh06ZN6amrEFnG5MmwebP2dp482hVS8uc3nN7T01MvCO/Xr58E4UIIIUQ2Z3IgfvnyZaysrOjZs2eqaXv27Im1tTVXrlwxtTghsozly0G3V5S1tbYnvEIFw+k3bNigF4R/+OGHLF68WIJwIYQQIpszORAPCwvDyckJR0fHVNM6Ojri5OREeHi4qcUJkSX8+y8MGfLieOZMaNHCcPp169bRo0cPEhISABgwYAD//POPBOFCCCHEG8DkQDxv3ryEhYUREhKSatqQkBDCwsLIkyePqcUJ8dq7d09/hZThw8HYBrRr166lV69eahA+cOBAFi1aJEG4EEII8YYwORCvUqUKiqKwaNGiVNP+888/JCYmUrVqVVOLE+K1plsh5fFj7bGbG8yeDRpNyunXrFlD79691SB88ODB/P3331hZyR5bQgghxJvC5E/97t27oygKf/31Fz///DNhYWHJ0oSHhzNjxgwWLFiARqOhe/fu6aqsEK+jxETo1w/On9cely0L69YZXiFl9erVekH4kCFDWLhwoQThQgghxBvG5OULmzRpQtu2bdm2bRuLFy9m5cqVVKlShaJFi6LRaAgMDOTSpUvExMQA0LZtW9zc3MxWcSFeF199BboFgXLn1q6QUqBAymlXrVpFv379SExMBGDYsGHMmzdPgnAhhBDiDZSuXUKmTZtGkSJFWLp0KTExMZw+fRrNf7/F63bZtLGxYcCAAYwbNy7dlRXidbNqFfz4o/a2lRWsWQMVK6acdsWKFfTv318NwocPH87cuXMlCBdCCCHeUOkKxHWb+fTv3x8vLy8uXbpEUFAQAAUKFKBq1aq0aNGCwoULm6WyQrxOTpyAQYNeHM+YAa1apZx2+fLlDBgwQA3CR4wYwZw5cyQIF0IIId5gZtk3u3DhwvTr188cDyVElnD/vnaFlP9GXjFkCIwdm3LapUuXMnDgQPVXopEjRzJnzhz11yMhhBBCvJmkO06IVxQRAR06QGCg9rhxY/jzz5RXSFmyZIleED569GgJwoUQQggBSCAuxCtJTIT+/eHsWe3xO+/A+vVgZ5c87eLFixk0aJAahI8ZM4Y//vhDgnAhhBBCAGkcmjJnzhwA8uXLR58+ffTue1Wjje1wIsRrbsoU2LBBeztXLtiyBQoWTJ5u0aJFDB06VA3Cx44dy8yZMyUIF0IIIYQqzYG4RqOhTJkyeoG4KUGFBOIiq/LwgO++0962stIeV66cPN3ff//N0KFD1eNx48bx22+/SRAuhBBCCD1pCsRr164NQPHixZPdJ8SbwMcHBg58cfzLL+DunjzdwoULGT58uHo8fvx4fv31VwnChRBCCJFMmgLx5cuXp+k+IbIjf3/tCinR0drjQYPgk0+Sp1uwYAEfffSRevzZZ5/x888/SxAuhBBCiBTJZE0hjIiM1K6Q8vCh9rhhQ5g3L/kKKfPmzdMLwv/3v/9JEC6EEEIIo0wOxE+ePMm5c+fSnP7ChQucPHnS1OKEyHSJiTBgAJw+rT0uXVo7UfPlFVLmzp3LyJEj1eMJEyYwffp0CcKFEEIIYZTJG/r069ePQoUKcfjw4TSlHzduHIGBgVy+fNnUIoXIVN9+C+vWaW87OcHWrVCokH6aOXPm8PHHH6vHX3zxBT/88IME4UIIIYRIVbqGpuiWZsuo9EJYytq1MHWq9rZGA6tXQ5Uq+mlmz56tF4RPmjRJgnAhhBBCpFmmjRGPiorCxsbkDnghMs2pU9pNe3R+/hnattVP8/vvvzNmzBj1+Msvv+T777+XIFwIIYQQaZYpgfitW7d4/vw5BQoUyIzihDCZv792cqZuhZSBA+HTT/XTzJw5k3HjxqnHX3/9Nd9++60E4UIIIYR4JWnuot67dy/e3t5694WHh/PFF18YzRcaGsqpU6fQaDS89957ptVSiEwQFaVdpjAgQHvcoEHyFVJ+++03Pk0SmX/zzTdMmTIlU+sphBBCiOwhzYG4n58fGzduRKPRqGO9o6Oj2bhxY5ry58+fX3bVFK8tRdGuD37qlPa4VCntCin29i/SzJgxg88++0w9njJlCt98800m11QIIYQQ2UWaA/EKFSrQqVMn9Xjjxo04ODjQunVrg3k0Gg1OTk6UL1+eli1bkjt37vTVVogM8v332i3r4cUKKYULvzj/yy+/8Pnnn6vH3377LV999VUm11IIIYQQ2UmaA/FmzZrRrFkz9Xjjxo04OTnx008/ZUjFhMgsGzbA119rb2s0sGoVVK364vz06dOZOHGievzdd9/x5ZdfZnIthRBCCJHdmLyMybJly7C1tTVnXYTIdGfOQL9+L46nTYN27ZIeT9ObB/HDDz8wadKkTKyhEEIIIbIrkwPxOnXqmLMeQmS6W7egTRvtJE2ADz+E//3vxfkff/yRyZMnq8c//fSTXs+4EEIIIUR6mByIP3jwgOXLl1OkSBEGDRpkNO0///zDo0ePGDBgAMWKFTO1SCHM5uFDaN4cAgO1xx98AAsXvlgh5fvvv9cbAz5t2jQmTJhggZoKIYQQIrsyeR3xzZs3s2zZMiIjI1NNGxUVxbJly9iyZYupxQlhNs+fQ8uW2h5xgMqVtZMzdSukfPfdd3pB+M8//yxBuBBCCCHMzuRA/PDhwwC0Szqg1oA2bdqgKAoHDhwwtTghzCIyUjsG/OJF7XGpUuDlBfnza4+nTp3K17qZm8Cvv/7K/5KOVxFCCCGEMJN0DU1xcHCgVKlSqaZ96623sLa2JkC3U4oQFhAXB127wtGj2uPChWHPHihRQns8ZcoUpk6dqqafMWMG48ePt0BNhRBCCPEmMLlHPCQkBDs7uzSltbGxwcnJiWfPnplanBDpkpgIAwbAzp3a49y5YdcuKF8eFEXhm2++0QvCZ86cKUG4EEIIITKUyYF47ty5CQsLIzw8PNW04eHhhIWF4eTkZGpxQphMUWDsWO364AAODtox4TVqaIPwr7/+mm+//VZNP2vWLMaNG2eZygohhBDijWFyIF6xYkUURWGnrovRiJ07d5KYmIizs7OpxQlhsm+/hTlztLetrWHtWmjUSBuEf/nll3z//fdq2j/++IOxY8daqKZCCCGEeJOYHIi3bNkSRVH49ddf8fPzM5ju2rVr/Prrr2g0Glq3bm1qcUKYZPZsmDLlxfE//2gnayqKwuTJk/nxxx/Vc3PmzOHjjz/O/EoKIYQQ4o1k8mTNjh07smzZMq5fv06PHj3o1q0bbm5uFC9eHI1Gg7+/P/v372f9+vVER0dTvnx5unbtas66C2HUypUwZsyL45kztZv2KIrCF198wfTp09Vzf/75JyNHjrRALYUQQgjxpjI5ELe1tWXu3LkMGTKEu3fvsnLlSlauXJksnaIolCpVinnz5mFjY3JxQrySHTu0kzN1vvwSxo3Tvh4nTJjAL7/8op6bO3cuI0aMyPQ6CiGEEOLNZvLQFICSJUuyYcMGPvroIwoVKoSiKHr/ihQpwqhRo/D09OStt94yV52FMOrIEejSBeLjtccffaQdJ64oCp9//rleED5//nwJwoUQQghhEenuonZycmLcuHGMGzeOgIAAnj59CkChQoVkO3uR6c6fh7ZtITpae9yjh26ipsJnn33Gb7/9pqZdsGABw4YNs0g9hRBCCCHMOlakePHiFC9e3JwPKUSa3byp3bo+JER73KIFLFsGVlYK48ePZ9asWWrav/76iyFDhlimokIIIYQQmDkQF8JSHj6E5s3h0SPt8fvvg6cn2NoqfPLJJ/z+++8AaDQa/v77bwYNGmTB2gohhBBCmDEQVxSFkJAQoqKiUBTFYDrpMRfm9vy5tvf79m3tceXKsH07ODoqjB07ltmzZwPaIHzRokUMHDjQgrUVQgghhNBKdyB+8OBBli1bxpkzZ4jWDcw1QKPRcPny5fQWKYQqIkI7JvzSJe1x6dLg5QX58imMGTOGOf/t5KPRaPjnn38YkHQpFSGEEEIIC0pXIP7bb7/x119/Ge0BTyqt6YRIi9hY6NoVjh3THhcuDLt3Q/HiCqNHj2bu3LmANghfsmQJH374oQVrK4QQQgihz+TlC//9918WLlyItbU1n332Gdu3bwcgf/787Nmzh5UrVzJy5Ejy5MlDvnz5WLBgAd7e3maruHizJSZC//6wa5f2OHdubU942bKJjBo1Si8IX7p0qQThQgiziImJYfbs2bRt25bq1avj4uKCi4sLJ06cAKBJkya4uLjg6elp0uOnN78QImsxuUd89erVaDQahg4dqrf6hJWVFSVLlqRkyZK89957dO3alb59+zJ58mS2bNlilkqLN5uiwMcfg4eH9tjBAbZuhWrVEhk5ciQLFiwAtK/FpUuX0rdvXwvWVgjTxMTEsHHjRg4cOMCVK1d49uwZdnZ2FC5cmFq1atGmTRvef/99S1fzjTN27Fj2798PgIODAwULFgS0m9xlZ56envj7+1OnTh3q1q1r6eoIkW2YHIifP38egJ49expNV7x4cb766itGjBjBX3/9xYQJE0wtUggApkyB/zq8sbaGtWuhQYNERowYwcKFCwFtEL58+XJ69+5tuYoKYaKjR48yadIkAgMD1fscHR2JjY3l1q1b3Lp1i7Vr19KoUSN+/vln8uXLZ8Havjlu3rypBuEzZ87E3d09WZqSJUtiZ2dHrly5Mrt6GWrjxo34+PgwevRoCcSFMCOTh6Y8e/YMe3t7ihQpot5nbW2d4oTNRo0aYWtrq76BCWGqP/7Q7pKps3gxtGmTyPDhw/WC8BUrVkgQLrKkHTt2MGzYMAIDAylSpAjff/89//77L6tWreLcuXPs2LGD/v37Y2Njw6FDh+jRowdBQUGWrvYb4dq1awDkzZs3xSAcYOnSpezatYvmzZtnZtWEEFmUyYF4zpw5sbLSz+7k5ERERESyYNza2hp7e3se6RZ5FsIEK1bA2LEvjmfNgj59Ehk2bBh///03oH2trVq1il69elmmkkKkw82bN5k8eTLx8fE4OzuzadMmunXrRp48edQ0ZcuWZdKkScydOxdbW1vu3r3Lp59+asFavzl0n205c+a0cE2EENmFyYF40aJFiYqKIiwsTL2vZMmSAFy4cEEv7cOHDwkPDze1KCHYvh2Srjz41Vfw8ceJDBkyhEWLFgEvgvAePXpYppJCpNOsWbOIjIzEzs6O33//nfz58xtM27hxY0aMGAFoJ88fOHAAgMWLF+Pi4sIHH3xAfHy80fKaNm2Ki4sLM2fOTPH83r17GTlyJA0aNKBKlSrUrl2bPn36sHr1auLi4lLM069fP1xcXJg9ezbx8fEsWbKELl26UKtWLb1JjTo3b95k6tSpuLu7U6NGDWrUqEHLli355JNP8PLyIjExMVkZsbGx6q9ederUoUqVKjRq1IhPPvmE06dPG3y+SeumKApr166lW7du1KxZkxo1atCjRw82b96cLN/s2bNxcXFh4sSJAPj7+6uTNJPeD6lPtoyOjmbu3Lm4u7tTrVo16tWrx9ChQ/n3338N1vtlJ06cYPz48bi6ulK1alV1PtZff/1FZGRkinkmTpyoV9ddu3bRr18/6tSpQ/Xq1enQoQNLly5N9vf29PTExcUFHx8fAObMmaP33F1cXHjw4EGa6y6E0GfyGPEKFSpw9epVbt26RfXq1QGoW7culy5dYs6cOSxcuBAHBwdiY2P54Ycf1DxCvKrDh7XLFCYkaI9HjICvv05g8OAhLFmyBNAG4atXr6Zbt26Wq6gQ6fD48WP27t0LQNu2bXnnnXdSzTNgwAAWLVpEREQEK1euxNXVlXbt2vHLL78QFBTEkSNHcHV1TTHvqVOn1ACqQ4cOeuciIiL49NNP9YYTOjk5ERYWxqlTpzh16hSbN29mwYIFer31ScXGxvLhhx9y+vRpbGxsUuxFXrhwITNnzlSDP3t7exwcHLh37x537txhx44dnDx5kty5c+v9nYYOHYqfnx8ANjY2ODo68ujRI3bs2MGOHTsYPnw448ePN/h3S0hIYNSoUXh7e2NjY4ODgwMRERGcO3eOc+fOcffuXcaMGaOmd3R0pGDBgkRHRxMeHo6VlZXelyQnJyeDZSUVHBzMkCFD1P00bGxsiI+P59ChQxw+fJivv/7aaP74+HimTJnCunXr9OoWFRXFxYsXuXjxIhs2bGDRokWUKFHC4ON8++23rFy5EisrK5ycnIiOjsbPz48ff/yRy5cvM336dDWtbkJqSEgIcXFxODo64ujoqPd41tbWaXr+QojkTO4Rb9SoEYqisHPnTvW+3r17Y29vz8mTJ2ncuDE9e/akUaNGeHt7o9Fo6Nevn1kqLd4c585pN+zRjXbq0QNmzUpgyJDBahBuY2PDmjVrJAgXWZqPj48akLZo0SJNeXLmzEmDBg0AbWAdHx9PwYIFqV+/PkCKvbs6ulWsqlevnizo//zzz9m/fz+lSpVixowZnD59mtOnT3P+/Hnmzp1LyZIlOXv2LJMmTTL4+CtWrODKlSv89NNPnD59Gh8fH44fP46LiwsAq1atYsaMGSQmJuLm5sbGjRu5cOECPj4+nDp1ij///BM3Nze9IZAJCQmMGTMGPz8/HB0d+fHHHzl79iwnT57k4MGDtG/fHoAFCxawZs0ag3VbtWoVx48f58cff1Sf28GDB3FzcwNg3rx53LlzR00/ePBgjh49yuTJkwEoVqwYR48eVf99+eWXBstK6uuvv+by5cvY2dkxdepUzpw5w8mTJ9m/fz/Nmzfnxx9/5NmzZwbzT58+nXXr1lGwYEG++eYbTpw4wdmzZzl//jzLli2jUqVK3L59m48//jjFXxIA9u3bx9q1a5kwYQI+Pj6cPHmS48ePq++fmzZt0uudd3d35+jRo9SoUQOAQYMG6T33o0ePUqxYsTQ9fyFEcukKxEePHk2pUqXU+0qUKMGMGTNwcnIiJCSEc+fOERwcjEajYfjw4QYntwiRkhs3oFUrCA3VHrdsCYsXJzBkyECWLl0KvAjCu3TpYsGaCpF+169fV29XqlQpzfl0vzRGRkYSEBAAQMeOHQHw9vbWGz6oExsbq3aivNwbfuDAAfbu3UuhQoVYvnw5bdu2VXt87e3tadq0KStWrMDR0ZG9e/dy5cqVFOsVGRnJjBkz6Ny5Mw4ODgDky5ePvHnzEhISwq+//gpoA7158+bpPeecOXPSrFkz5s+fr9fbvHv3bs6ePQvAjBkz6NKlC3Z2doB2uOQvv/xCs2bNAO0wn5iYmBTrFhISwpw5c+jSpYtat6JFi/Lbb7+RN29eEhMT9TqZzOHatWvqLx7ffPMNPXv2xN7eHtCuLjZr1iyqVatGVFSUwfzLly8nR44c/PPPP/Tu3Zu8efMC2qUT69aty/LlyylatCi+vr7s27fP4HP/5ptvGDRokLqyS758+fjuu+8oW7YsgLoviBAi45kciDs5OTF69Ohkk+KaNWvGnj17mD59Op988glff/01u3btYty4cemtq3iDBARA8+agm9/7/vuwdm0Cw4YNYPny5YA2CF+7di2dO3e2YE2FMI/g4GD1ti7ASoukSxfqHqNp06Y4OTkRExPDLt2uV0ns27eP0NBQbG1tadOmjd453bCH9u3b662KlVTRokXVJewOHz6cYpry5cvTpEmTFM95eXkRERGBra0tX3zxBRqNxviT/I8uQKxWrZrBx9Z91jx79oxjum13X1KjRg0++OCDZPc7OjqqvzBcvXo1TXVKqyNHjgDa3vSUOg6sra0ZOXKkwfzr169HURQaN26s/qrwMicnJ/WLiKF2KVKkSIrlazQadaUXcz93IYRh6dri3pC8efMm62URIq2ePdP2fut+Ga5SBTZvjmfEiP6sWrUK0PYArVu3Tl5n4o2nKEqy+xwcHGjZsiUbNmxgy5YtyYZt6YasuLq6Jgv6dZMd165da3Roi66nXdcL/7KaNWsazKvr1a5cuTKFCxc2mO5lly5dAkgxiNYpX748hQsX5vHjx1y8eFEdbpJUtWrVDOYvWrQooO05NqcbN24AUKdOHYNfPGrXrq2OG3+Zrl0OHTqkDj1KiW6ypqF2qVq1arIVz3R0X7zM/dyFEIaZHIg3adIEKysrFi1apDc8RYj0iIjQjgn/7/OW0qVh+/Z4xo37kNWrVwPaIHz9+vXqeFAhsoOkAXFwcLDB3uiXGepJ79ixIxs2bODkyZP4+/urk/eeP3+u9pbqhrDoxMXF8fz5c0AbaKc0rOVlKe0dARhd8eXJkyeAdkjGq9Ctl57amORixYrx+PFjg+urG9tsRzdUJbUVZ16VLrg11q729vbkzZuXp0+fJjv3+PFjQBtoG1oZJSlD7WKJ5y6EMMzkQPzJkyfY2tpKEC7MJi5OuzqKbp5QkSKwc2c8Eyb0w+O//extbW3ZsGED7dq1s2BNhTC/cuXKqbd9fX3THIjrxmg7OjrqBba1a9emRIkS+Pv7s2XLFnWpw+3btxMXF0fevHlp3Lix3mMlneBnaOfItErLShppHZJiaj5THz8jmVonXdt8+umnDBs2zJxVEkJYkMljxIsUKZLiT6JCmEJR4OOPQTecNU8ebU/4N9/0UYNwOzs7PD09JQgX2VLdunXVIQO7d+9OU56IiAiOHj0KQK1atbCxedG3otFo1Gsl6RAT3e02bdpga2ur93j29vZqj2lGjhMuVKgQoF2P+1UUKFAA0O5NYYzuvLFe+cymW+YxMDDQYJrY2Fi9XziSKliwIPBid08hRPZgciBev359oqOj1fVQhUiPmTNhwQLtbVtb2LAhjunTe7N27VpAG4Rv3LiRtm3bWrCWQmScwoUL07RpU0Dba33r1q1U8yxZsoSIiAhAu3zsy3RDT27fvs2FCxfU/5Oee5lubPeuXbsMLoGXXroyfH191WEqaVG1alUAo5vf3Lx5Ux3GoUv/OtD94nHy5EmDnVgnT540OCxE9zc7ePCg2uaZSdeTLx1wQpiXyYH40KFDyZkzJ999953B5ZaESIvNm+Gzz14cL1wYx7x5vdTVG+zt7dm0aZMsfymyvbFjx6oboY0dO9bomtIHDx5k3rx5gLY3PaWNe8qUKaNuuLZ582Y2bdqk3m9owmL37t0BuHPnDn///bfR+kZGRhIbG5va00qmVatWODk5ERcXx7Rp09KcT7fCy7lz5zh48GCKaWbNmgVoe8ONTerMbLrVWAICAti4cWOy84mJiWp7pqR79+5oNBpCQ0P5+eefjZYVFxdn9mBdt4xkqG49WSGEWZgciFtZWTF16lSuXr1K27ZtWbJkCefPn+f+/fsEBAQY/CdEUmfOQO/e2qEpAJMnx7F1a082bNgAvAjCW7dubcFaCpE5ypcvz/fff4+1tTXXrl2jU6dOrF+/Xi/4uX37Nj/99BMjR44kLi6OkiVLMmPGDINjj3UrC23fvp2tW7cChnvDQbsErW4ZuxkzZvDNN99w+/Zt9XxsbCznz5/nl19+wc3NzeiXBUNy5crF//73PwC2bdvGyJEj9dYjDwkJ4cCBA4wYMYLw8HD1/ubNm6sby3zyySds2rRJ/SLw6NEjJk6cqA7r+eSTT9R1ul8Hzs7O6gouU6ZMYe3ateo65w8fPmTcuHGcO3eOHDlypJi/YsWK9O/fHwAPDw/GjBnDlStX1B7qhIQE/Pz8+PPPP2nevLnB9d1NVb58eUC7assj3bqyQoh0M3mypu4nVICoqCi9LXEN0Wg0MpRFqB48gHbtQLcAQPfusVy+3FPtLXJwcGDz5s1p3mVQiOygXbt25MmTh8mTJxMYGMjkyZOZPHkyjo6OJCQk6G1S06BBA3755RejY6Hd3d356aefeP78Oc+fP0ej0aS64tAvv/zC5MmT2b59Ox4eHnh4eODo6IitrS1hYWF6Q1ZMnXzYs2dPQkJCmDVrFt7e3nh7e+Pg4IC1tbVeb27Ssqytrfnjjz8YMmQIV69eZcKECXz55Zc4OjrqLbk3bNgwtWf/dfLDDz8waNAg/Pz8+Oqrr/j222/Vums0Gr766isWLVpkcOz8559/jqIoLF26FC8vL7y8vLC3tydHjhyEh4frDWsx90TVTp06sXjxYu7evYurqyv58+dXv+isWrVKXfZRCPFqTA7ETRknJmPLhE54uHaZQt2PJO+/H0tkZHe2bdNOJHNwcGDLli1qz5wQb5JGjRqxZ88ePD092b9/P1evXuXZs2fY2dlRrFgxatWqRdu2balXr16qj5UvXz4aN26s7upYp06dVJcNzJEjB7/99hs9evRgw4YNnDlzhidPnhAZGUmBAgUoW7YsDRs2pHnz5mle3SUlw4cPx83NjeXLl3PixAkePXqEoiiUKVOGypUrq0NYkipcuDDr169nzZo17Nixgxs3bhAZGUnhwoV577336Nu3L7Vq1TK5Thkpb968eHh4sGjRIrZv386DBw+wsrKiYcOGDB48mHr16rFo0SKD+a2trZk0aRIdO3bEw8MDHx8fHj16RHh4OLlz56Z06dLUr1+fZs2aqTuumkvp0qVZtmwZCxYs4MKFCwQHB6uBvyx3KITpNIqJ0fGrznbX0a1lK0xz4cIF4uLisLW1NbophbkkJCRw7do1nJ2d07QcWdoeEzp2hG3btMelS8dSoUI3du3aAmiD8K1bt6o7xIn0yYg2FJlL2jBrk/bL+qQNs77MbsO0xmsm94hLQC1M9emnL4Lw3LljKFOmG7t2aceu5siRg61bt+oNfRJCCCGEyI7SPFmzQoUKNGzYMMVzN2/exM/Pz2yVEtnXn3/C779rb1tbx1CxYhf2738RhG/btk2CcCGEEEK8EV6pR9zQKJb+/fvz7NkzmYgpjNq1C8aM0R1FU6lSF06c2AFodwXcvn17ikuwCSGEEEJkRyYPTXmZTMQUxly8CN27g3YBhGjeeaczFy/uBLRB+I4dO5Jtty2EEEIIkZ2ZLRAXwpDAQGjTBsLCAKIpUqQTt25p97LPmTMnO3bsoFGjRhatoxBCCCFEZpNAXGSoyEho3x7u3weIInfujjx6pN1wI2fOnOzcudPg3AMhhBBCiOxMAnGRYRIT4cMP4eRJgCjs7TsQGroH0G6XvHPnTnXbZyGEEEKIN40E4iLDTJoE2p3qI7G27kBMjHZDEScnJ3bt2kX9+vUtWj8hhBBCCEuSQFxkiEWLYPp0gEigPQkJ3gDkypWLXbt28cEHH1iyekIIIYQQFvdKgXhQUBAVK1Y0eN7YOQCNRiNLHL4BvL3ho48AIoB2wH5AG4R7eXmlaVtuIYQQQojszizriAuhc+UKdOkC8fERQFvgAAC5c+fGy8uL999/35LVE0IIIYR4baQ5EB89enRG1kNkA0+eQNu2EBISAbQBDgKQJ08edu/eTZ06dSxaPyGEEEKI14kE4sIsoqOhY0e4dSscbRB+CNAG4Xv27KF27dqWrJ4QQgghxGtHJmuKdFMUGDQIjh0LB9yBwwDkzZuXPXv2UKtWLYvWTwghhBDidWRl6QqIrG/KFFi9OgxojS4Iz5cvH3v37pUgXAghhBDCAOkRF+myYgV8+20o2iD8GPAiCK9Zs6ZF6yaEEEII8TqTQFyY7PBhGDQoFGgF/AtA/vz52bt3LzVq1LBo3YQQQgghXndZMhBXFIWdO3eyZcsWrly5QlBQEHnz5qVcuXK4u7vTqVMnbG1tzVpmZGQka9euZdeuXdy9e5fw8HAKFSpEtWrV6Ny5M40aNTJrea+7GzegQ4cQ4uJaAccBbRDu7e3Nu+++a9G6CSGEEEJkBVkuEA8NDWXs2LEcO3ZM7/4nT57w5MkT/v33Xzw8PJgzZw7Fixc3S5l+fn58/PHH3Lt3T+9+f39//P392blzJ23btuWnn37Czs7OLGW+zp49g1atQnj+vCVwAoACBQrg7e1N9erVLVs5IYQQQogsIksF4nFxcYwaNQofHx8AihUrRvfu3SlVqhSBgYFs2LCBmzdv4uvry9ChQ/Hw8CBXrlzpKjMgIIAhQ4bw5MkTAKpVq0b79u3Jly8f165dY82aNQQHB7Nt2zY0Gg2//vprup/n6yw2Ftq3D+bmzZaAth0KFCjIvn3eVKtWzbKVE0IIIYTIQrJUIO7h4aEG4ZUrV2bx4sXkyZNHPd+3b19GjhzJkSNHuHHjBnPnzmXChAnpKvOnn35Sg/AuXbrw/fffY2X1YrGZnj170qdPHwICAti6dStt2rTBzc0tXWW+rhQFBgwI5ujRFsBJAPLnL8j+/fuoWrWqZSsnhBBCCJHFZJnlC+Pj45k3bx4AGo2G6dOn6wXhAPb29vz88884OjoCsGLFCp49e2ZymVevXmX37t0AFC9enG+++UYvCNfdP2XKFPV49uzZJpf3uvvmm+esXt0cXRCeL18hDh7cL0G4EEIIIYQJskwg7uPjQ1BQEAD16tWjfPnyKaYrUKAA7u7uAMTGxuLt7W1ymTt27FBvd+/eHXt7+xTTNWrUiFKlSgHg6+ubbCx5drBkyXO++645cAqAPHkKc/jwfqpUqWLZigkhhBBCZFFZJhA/fPiwerthw4ZG0yY9nzRfRpWp0Who0KCBWcp8HR09Gs3QoS2B0wA4ORXh2LH9VK5c2bIVE0IIIYTIwrLMGPFr166pt1MLAJP20l6/ft2k8hRF4datWwDY2NhQoUKFNJeZtK5Z3dmzQXz00UAU5TwADg5FOHFiP5UqVbRwzYQQQgghsrYsE4jfuXNHvV2iRAmjaYsWLYq1tTUJCQncu3ePxMTEZGO7U/Pw4UOioqIAKFKkCDY2xv9USZdKTFrXjJSQkJChjx8fn0ijRm1ISNAG4XZ2Rfn33724uDhneNnCfBISElAURdosC5M2zNqk/bI+acOs73VtwywTiIeFham38+XLZzStjY0NTk5OhISEEB8fT2RkJE5OTq9UXmhoqHo7b968qaZPmiZpXTNKfHx8hve8HzniT1SUdky4tXUxli5dhL29Vbbq8X8TKIpCTEwMN27cQKPRWLo6wgTShlmbtF/WJ22Y9WV2G8bHx6cpXZYJxCMjI9XbhiZNJpU0jSmB+KuW5+DgoN6OiIh4pbJMYWNjg7Ozc4aWUa5ceRYsGM+9e9dZtuxHmjeX4ShZUUJCAjdu3KBcuXJYW1tbujrCBNKGWZu0X9YnbZj1ZXYb+vr6EhcXl2q6LBOIW9Lr+u03o19I1tZw4sTPXLt2DWdnZ3nzycI0Gg3W1tbShlmYtGHWJu2X9UkbZn2vYxtmmVVTdGuDA8TExKSaPmmapHlNKS86OjrV9EnT5MyZ85XLE0IIIYQQb5YsE4gn3ao+ODjYaNr4+HjCw8MB7RAOUwLx3Llzp7m8l9MkrasQQgghhBApyTKBeOnSpdXb/v7+RtMGBgaqs2LffvvtV14xBbQrr+TIkQOAR48epTroPiAgIMW6CiGEEEIIkZIsE4gnnZh46dIlo2mTnje0A2dqrKyseOeddwBtD7ufn1+ay8zoSZRCCCGEECLryzKBeNKdK48cOWI07avswmlM0rzGylQURe980roKIYQQQgiRkiwTiNetW5f8+fMDcOzYMYM7ZgYFBbFjxw4AbG1tadq0qclltm7dWr29Zs0ag5NEDx06xN27dwGoVKkSpUqVMrlMIYQQQgjxZsgygbiNjQ0fffQRoO2BnjBhAiEhIXppYmJimDBhgroGeJ8+fdTg/WUTJ07ExcUFFxcXZs+enWKaChUq0KxZM0A7Bvzbb78lMTFRL01AQABTpkxRj0ePHm3S8xNCCCGEEG+WLLWOeK9evdi9ezenTp3C19eXDh060KNHD0qVKkVgYCDr16/n5s2bALzzzjuMGjUq3WVOmjSJc+fO8fTpU9avX8/169fp0KEDefPm5dq1a3h4eKgrprRp0yZdPfBCCCGEEOLNkaUCcTs7O+bOncuYMWM4fvw4Dx8+ZNasWcnSVaxYkTlz5ugtQWiqEiVK8NdffzFmzBju37/P+fPnOX/+fLJ07u7uTJs2Ld3lCSGEEEKIN0OWCsQB8uTJw5IlS9i5cyebN2/m8uXLPH/+nDx58lC2bFnatGlD586dsbW1NVuZlSpVYsuWLaxZswYvLy/u3LlDREQEBQoUoFq1anTp0oXGjRubrTwhhBBCCJH9ZblAHLRblLq7u+Pu7m7yY0ybNu2VerAdHR0ZOHAgAwcONLlMIYQQQgghdLLMZE0hhBBCCCGyEwnEhRBCCCGEsAAJxIUQQgghhLAACcSFEEIIIYSwAAnEhRBCCCGEsAAJxIUQQgghhLAACcSFEEIIIYSwAAnEhRBCCCGEsAAJxIUQQgghhLAACcSFEEIIIYSwAAnEhRBCCCGEsAAJxIUQQgghhLAACcSFEEIIIYSwAI2iKIqlKyHS7syZM+iazNbWNlPKjI+Px8bGJlPKEhlD2jDrkzbM2qT9sj5pw6wvM9swLi4OAI1GQ82aNQ2mk1dUFpP0e5OukTNDZpYlMoa0YdYnbZi1SftlfdKGWV9mt2Fq/d0SiGcxVlZWJCYmotFo5Ju5EEIIIcRrKD4+HkVRsLIyPgpchqYIIYQQQghhATJZUwghhBBCCAuQQFwIIYQQQggLkEBcCCGEEEIIC5BAXAghhBBCCAuQQFwIIYQQQggLkEBcCCGEEEIIC5BAXAghhBBCCAuQQFwIIYQQQggLkEBcCCGEEEIIC5BAXAghhBBCCAuQQFwIIYQQQggLkEBcCCGEEEIIC5BAXAghhBBCCAuQQFwIIYQQQggLkEBcCCGEEEIIC5BAXAghhBBCCAuQQFwIIYQQQggLkEBcCCGEEEIIC5BAXAghhBBCCAuQQFwIIYQQQggLkEBcCCGEEEIIC5BAXAghhBBCCAuQQFwIIYQQQggLkEBcCCGEEEIIC7CxdAVExlAUhZ07d7JlyxauXLlCUFAQefPmpVy5cri7u9OpUydsbW3NWmZkZCRr165l165d3L17l/DwcAoVKkS1atXo3LkzjRo1Mmt52V1mtuHDhw85fPgwPj4+XL16lYCAAKKjo3FycqJUqVLUrl2bbt26Ubp0abOU96awxHX4spCQENzd3Xn69Kl6n7e3N2+99VaGlpsdWKr94uPj8fLyYvfu3fj6+vL06VOsrKwoWLAgpUqVom7dujRr1kyuxzSwRBseOHCArVu3cvHiRZ48eUJcXBw5c+akTJky1KlTh65du/L222+btczsKCEhgZs3b3Lp0iV8fX25dOkSfn5+REdHA9CpUyemTZtm9nIzO5bRKIqimPURhcWFhoYyduxYjh07ZjBN5cqVmTNnDsWLFzdLmX5+fnz88cfcu3fPYJq2bdvy008/YWdnZ5Yys7PMbMNRo0bh7e1Nam8F1tbWDBkyhHHjxmFlJT+mpcYS12FKvvjiCzw9PfXuk0A8dZZqvwsXLjB58mSuXbtmNN2HH37I5MmTzVZudpTZbfjs2TPGjh2Lj4+P0XS2traMHTuWoUOHprvM7Ozjjz9m9+7dBs9nRCBuiVhGesSzmbi4OEaNGqW+ERQrVozu3btTqlQpAgMD2bBhAzdv3sTX15ehQ4fi4eFBrly50lVmQEAAQ4YM4cmTJwBUq1aN9u3bky9fPq5du8aaNWsIDg5m27ZtaDQafv3113Q/z+wss9vw2rVrahBeqVIl6tatS9myZXFycuLp06ccOHCAI0eOkJCQwIIFCwgJCWHq1Klmea7ZlSWuw5QcPXoUT09PrKyssLW1JSYmxuxlZEeWar9///2XESNGEBUVBUCNGjVo3LgxJUqUQFEUAgMDuXnzJgcPHkx3WdldZrdhfHw8Q4cO5dKlSwA4ODjQsWNHKlSogJOTEwEBAezcuZMrV64QFxfHr7/+iqOjI3369DHL882OEhIS9I7z5s1L3rx5uXPnToaUZ7FYRhHZyrJlyxRnZ2fF2dlZ6dSpkxIcHKx3Pjo6Whk0aJCaZtq0aekuc/To0erjffHFF0pCQoLeeX9/f8XV1VVNs2/fvnSXmZ1ldhu6u7srX3/9tXL9+nWDabZt26ZUrFhRLfPEiRPpKjO7s8R1+LLIyEilSZMmirOzs/Ldd98pbm5uann37983e3nZiSXaz9/fX6lZs6bi7OysvPvuu8qePXsMpo2Pj1cePXqU7jKzs8xuw40bN6qP5ebmpgQEBCRLk5iYqPzxxx9qurp16ypxcXHpKjc7mzdvnvLrr78qO3fuVO7du6coiqJs2LBB/ftNmDDBrOVZKpaRQDwbiYuLU+rVq6c4OzsrLi4uyrVr11JM9/TpU+Xdd99VnJ2dlSpVqihBQUEml+nn56e+KF1dXZXo6OgU0x04cEDvTVGkzBJt+Pz58zSl+/HHHzPsDTA7sUQbpkTXXg0bNlTCwsIkEE8jS7XfkCFDpLPCTCzRhp9++qnafitXrjSYLiEhQalfv76a1s/Pz+Qy30QZFYhbMpaRgZ7ZiI+PD0FBQQDUq1eP8uXLp5iuQIECuLu7AxAbG4u3t7fJZe7YsUO93b17d+zt7VNM16hRI0qVKgWAr6+v0fFXbzJLtGHevHnTlK5169bq7atXr5pcXnZniTZ82YULF1i2bBkAX331FU5OTmZ77OzOEu13/fp1Dh06BEDjxo1xc3Mz+bGEZdrw2bNn6m3dZ11KrKys9OZn6IYhCcuyZCwjgXg2cvjwYfV2w4YNjaZNej5pvowqU6PR0KBBA7OUmZ1Zog3TKun4Sd2sdZGcpdswLi6OyZMnk5iYSNOmTWnevLlZHvdNYYn227Bhg3q7W7duJj+O0LJEG+bPn1+9fffuXYPpEhMTefDgAaCdAF+mTBmTyxTmY8lYRgLxbCTpLPvKlSsbTVulShX19vXr100qT1EUbt26BYCNjQ0VKlRIc5mprQjwpsrsNnwVSXvBM3KVj6zO0m24cOFCrl27hqOjI19//bVZHvNNYon2O3nyJKD9kK9Tpw5Pnjzht99+o02bNrz77ru89957tG3blu+//57bt2+bXM6bwhJt2LRpU/X2X3/9xcOHD1NM9+eff6qTATt37kyePHlMLlOYh6VjGVk1JRtJOpO4RIkSRtMWLVoUa2trEhISuHfvHomJia+8JN3Dhw/Vn9WKFCmCjY3xl1PS4C2jZj1ndZndhq9i9erV6m1XV9cMKyers2Qb3rx5k/nz5wPwySefULRoUZMf602V2e0XFxenfsktXLgwly9fZty4cQQHB+ulu379OtevX2fVqlWMGzeOYcOGvVI5bxJLXIOtWrWiSZMm7Nu3j4CAAFq3bp3iqimXL18GtEvgffnll69cjjA/S8cyEohnI2FhYertfPnyGU1rY2ODk5MTISEhxMfHExkZ+crjSENDQ9XbaRlnnDRN0rqKFzK7DdNq06ZN6jJghQoVokuXLhlSTnZgqTZMTExk8uTJxMbGUqVKFfr27WvS47zpMrv9goODiYuLA7TLtY0aNYqIiAhKly5N586dKVmyJMHBwXh7e6vLiM6YMQNbW1sGDhz46k/wDWCJa1Cj0TBnzhz++OMPVqxYQXh4uF7nhU7t2rUZNWoU9erVe+UyRMawdCwjQ1OykcjISPW2oYkGSSVNkzRvRpXn4OCg3o6IiHjl8t4Emd2GaeHn56e3bvhXX32Fo6NjhpSVHViqDVetWsXZs2extrbmu+++k02XTJTZ7Zc0CHj69CkRERE0bdqUrVu3Mnz4cNzd3enduzeLFi1iypQpatoZM2YQEBDwyuW9CSx1DVpbWzN48GBGjx5tsNxTp04xf/58db1xYXmWjmXknVqYhUajsXQVRAZ49OgRI0aMUN+o+vXrR8uWLS1cK/GygIAAZsyYAUD//v2pVKmShWsk0kp5aUfbvHnzMn369BR37evVq5d6/cXFxaXY4yos59ChQzRt2pRp06ZRvXp1Fi1axKlTp7h48SK7d+/m008/JUeOHBw/fpy+ffty4MABS1dZvMQSsYwE4tlI0l7KtOyglzSNKT2cSfOkZRWNpGly5sz5yuW9CTK7DY0JCgqif//+aq9b27ZtmTRpklnLyI4s0YZTpkwhMjKSEiVK8PHHH5v0GEIrs9vv5fdCd3d3ozs8du/eXb19/PjxVy7vTWCJa/DQoUMMHz6c0NBQWrZsydKlS2nQoAG5cuXCzs6OUqVKMWzYMJYuXYqdnR1RUVF8+umnPH361KTyhPlYOpaRQDwbSfrm/fJEn5fFx8cTHh4OaMfImfLmkzt37jSX93KajNjOOzvI7DY05NmzZwwYMEBdoaFly5ZMnz5dhjukQWa34ZYtW9Qtz7/++msZNpROmd1+L78XprbKR9Lzsh9DyizxPjp9+nR1oufkyZMNvldWq1aNTp06ARAeHo6np6dJ5QnzsXQsI5M1s5HSpUur65P6+/sbnS0eGBhIQkICAG+//bZJAVbRokXJkSMHUVFRPHr0iPj4eKOzjZOOZyxduvQrl/cmyOw2TMnz588ZMGCAuixT06ZNmTFjRqozyYVWZrfhunXrAO0k2suXL6urMrws6aSilStXqh8g3bt3p2DBgq9cbnaV2e3n5ORE4cKFefz4MZD6B3vS87oAUujL7DZ88OABN27cAKBs2bIUKVLEaPp69eqxZs0aAC5evPjK5QnzsnQsI5+s2YizszNHjhwB4NKlS9SpU8dg2qQTRQztOpYaKysr3nnnHXx9fYmPj8fPz09vfU1jZTo7O5tUZnaX2W34suDgYAYOHKgup+bq6sqsWbOwtbU1y+O/CSzVhk+ePOH3339PU9p//vlHve3m5iaBeBKWaD8XFxc1EE9tFYak52XH1JRldhvq2g7S1iZJe2Bl4QLLs3QsI78zZyNJd3vSvQkZ8io7jxmTNK+xMhVF0TuftK7iBUu0oU5ISAgDBw7kypUrgHYr39mzZ6c4aUwYZsk2FOlnifZr3LixetvX19do2qTnZVfGlGV2GyYdJ/zo0aNU0yftUU3Lcnki41kylpFAPBupW7euus3usWPHDO4SFhQUxI4dOwCwtbXV2xHsVbVu3Vq9vWbNGoMTYw4dOqRu+1upUiVKlSplcpnZmSXaELS9bIMGDVKHNTRo0IA///xTgnATZHYbLl++nKtXr6b6L+nP897e3ur9FStWNKnc7MoS12DLli3VX5127NhhtFd87dq16m358payzG7DUqVKqcveBQQEcObMGaPpdWUCVK1a1aQyhXlZMpaRQDwbsbGx4aOPPgK039omTJhASEiIXpqYmBgmTJigLkfXp08f9Q3rZRMnTsTFxQUXFxdmz56dYpoKFSrQrFkzQPsG9O2335KYmKiXJiAgQG/929GjR5v0/N4ElmjD8PBwBg8erP7c1qBBA+bOnStBuIks0YbCfCzRfoULF6Z3796AdnjYhAkTiI2NTZbOw8MDLy8vQLvSQ69evUx7ktlcZrehg4ODXhD/xRdfGFzjff78+Rw7dgwAOzs7WrVq9epPULyS1z2WkTHi2UyvXr3YvXs3p06dwtfXlw4dOtCjRw9KlSpFYGAg69ev5+bNmwC88847jBo1Kt1lTpo0iXPnzvH06VPWr1/P9evX6dChA3nz5uXatWt4eHios4zbtGmT7t7b7C6z23DIkCGcP38egAIFCtC5c2e9n2tT4uDgIMOLjLDEdSjMxxLtN2bMGP7991+uXbuGt7c37dq1o0uXLrz11luEhoayd+9evety6tSpBgNHkfltOH78eI4dO0ZwcDB37tyhbdu2tG/fnnfffRd7e3sePnzIrl271Pda0AZyxYoVS1e52dn9+/dZv3693n26+UsAly9fZubMmXrnW7ZsafI+CpaKZSQQz2bs7OyYO3cuY8aM4fjx4zx8+JBZs2YlS1exYkXmzJmjN2nEVCVKlOCvv/5izJgx3L9/n/Pnz+u92ei4u7szbdq0dJeX3WV2G549e1a9HRQUxPjx41PNU6JECfbt25eucrMzS1yHwnws0X5OTk78/fffjB07lrNnz3Lnzh11k6akHBwcmDp1Ku3bt093mdlZZrdhyZIl+eeffxg/fjx37twhIiKC1atXp7jpko2NDaNHj2b48OHpKjO7CwgIYP78+QbP64bXJVWqVCmTA3FLxTISiGdDefLkYcmSJezcuZPNmzdz+fJlnj9/Tp48eShbtixt2rShc+fOZl0Jo1KlSmzZsoU1a9bg5eWlvhEVKFCAatWq0aVLF70JScI4S7ShMC9pw6zNEu1XpEgRVq1axfbt29m+fTtXrlwhKCgIBwcH3n77bRo2bEifPn0oXLiw2crMzjK7DStXrsyWLVvYuXMn3t7e+Pr68uzZM+Li4nBycqJ06dLUqVOH7t27U7JkSbOUKczLErGMRnl5f10hhBBCCCFEhpPJmkIIIYQQQliABOJCCCGEEEJYgATiQgghhBBCWIAE4kIIIYQQQliABOJCCCGEEEJYgATiQgghhBBCWIAE4kIIIYQQQliABOJCCCGEEEJYgATiQgghhBBCWIAE4kIIIYQQQliABOJCCCGEEEJYgATiQrwkJCSEH3/8kRYtWlC1alVcXFxwcXHhwYMHAOrxiRMnTHr89OYXwpxOnDihviYtIbXrzRw8PT1xcXGhSZMmZntMYXmzZ8/GxcWFfv36Wboqrx1LX9ci7WwsXQGRPSUmJrJ7927279/P+fPnCQoKIioqipw5c1K6dGlq1qxJ27ZtqVy5sqWrqichIYEBAwZw+fJlABwdHcmdOzcA1tbWlqzaG8nT05MvvvgCAG9vb9566y2zPfaDBw/YuHEjAB9//LHZHlekXXqvt9DQUJYuXQpA//791bxZTb9+/fDx8Ul2v6OjI4ULF6ZGjRr07NmTd999N/Mrl4RcM2k3cOBAjh07xjvvvMPOnTvTlCc2NpaGDRsSHBxM9+7d+e677zK4luJ1IIG4MLsLFy7w+eefc/v2bfU+W1tbcubMSWhoKOfOnePcuXP8888/NGrUiJkzZ+Lk5GTBGr9w9OhRLl++jK2tLUuWLKFWrVrJ0pQpUwaAHDlyZHb1hBn5+/szZ84cQIIKS0nL9WZMaGio2oadOnXKsoG4jq2tLXny5FGPnz9/zp07d7hz5w6bNm1i9OjRjB492mL1e92umXz58lGmTBmKFStm6aok061bN44dO8atW7c4c+YMNWvWTDXP3r17CQ4OBqBr164ZXEPxupBAXJjVvn37GDt2LLGxseTNm5dBgwbRokULNXhNTEzk2rVr7N27l+XLl3Po0CGCg4Nfm0D82rVrADg7OxsMCnbt2pWZVRIi20rL9fYmqVGjBsuXL1ePY2NjOXnyJFOnTuXu3bvMnj2bKlWq4OrqarlKvkb69u1L3759LV2NFDVr1oy8efMSHByMp6dnmgLxDRs2AFC+fHmqV6+e0VUUrwkZIy7M5s6dO3z22WfExsZSrlw5Nm/ezPDhw9UgHMDKyooKFSowevRovL29adeunQVrnFx0dDQAOXPmtHBNhMj+5Hozzs7Ojvr16zN37lxsbW0BWLFihYVrJdLCzs5O/XzbsWMHUVFRRtM/fPiQY8eOAdIb/qaRHnFhNrNmzSIiIgIHBwfmzJlD0aJFjaZ3cnLi119/JTExMdm58PBwli9fzt69e7l79y4xMTEUKlSIunXrMmDAAIMTUJo0aYK/vz8//fQTbdu2ZdmyZWzZsoV79+5hbW1N5cqVGTJkCI0aNdLLN3HiRHXsI4CPj49eGaNHj1Z/itXdv2zZMurWrZusDiEhIcyfP589e/bw6NEj8uTJQ82aNRk2bBhVqlQx+jfR2bt3L56enly4cIHg4GBy5MiBs7Mzbdu2pWvXruqHclK6caa6n6/XrVvHunXruHnzJoqi4OzsTO/evenQoYPRsm/evMmKFSs4ceIEDx8+BKBw4cJUqlSJVq1a0bx5c6yskn+HP3HiBGvWrOHMmTMEBQVhZ2dHmTJlaNmyJX369MHR0TFNzz2tHjx4QNOmTQHt+HEHBwfmz5/Pvn37ePLkCbly5aJu3bqMHj2asmXL6uXVvU50Xn49derUiWnTpundFxMTw5o1a9i9ezfXr18nIiKCPHnyUL16dXr27JnsNfXyYy9btozy5cvz119/sW/fPgIDA4mOjubSpUs0bNiQ58+fM3nyZD788EODz3njxo1MnDgRBwcHjh49qv6SFBYWxuHDhzl48CB+fn4EBgYSERFB/vz5qV69Or169eKDDz5I41/21QUFBbF48WIOHjzIgwcPSExMpGjRojRo0IBBgwZRokQJvfSvcr0Z8vK4at1rQadOnTp6vctJXbp0ib/++ovTp08THBxMkSJFaNasGSNHjtQbGvIyU18D6VWuXDmqVKnC2bNnuXjxYrLzp06dYsWKFZw5c4Znz57h6OhIuXLlcHd3p3v37tjZ2aX4uLdv32bx4sWcOHGCwMBAFEWhQIECFC1alDp16tC+fXv12nmdrpmrV68C2smac+bMMdrWV69eZcmSJZw4cYInT55gb29PqVKlaN68Of369UvxS+CJEyfU6/Dq1av4+fmxaNEifHx8ePr0KTVr1jRYXlJdu3Zl+fLlREREsGvXLjp16mQwraenJ4mJidja2tK+fXu9c6a2ryFp+bu9/Dcwlt/b25tly5bh5+dHXFwczs7ODBkyhGbNmql5Nm3ahIeHBzdv3iQ+Pp5KlSoxevRo6tWrZ7Sumf25YgkSiAuzePr0KV5eXgC0a9dOrxc8NS8HdTdv3mTIkCEEBAQA2p4FOzs7/P398fT0ZPPmzXz55Zf07t3b4GNGRkbSt29fzp8/j62tLba2toSHh3PixAl8fHz4/vvv9XodnJycKFiwIJGRkURGRiYbq5nWi/3Bgwd8+OGH6geWra0tUVFReHl5sW/fPn7//Xej+SMiIvj000/Zv3+/Xt3CwsI4deoUp06dYvPmzSxYsMBgwJCQkMCoUaPw9vbGxsYGBwcHIiIi1LH5d+/eZcyYMSnmXbhwITNnzlS/HNnb2+Pg4MC9e/e4c+cOO3bs4OTJk3pjcePj45kyZQrr1q3T+3tFRUVx8eJFLl68yIYNG1i0aFGygMxcbty4waRJkwgKClLH7gcFBbFjxw4OHTrEypUrqVChgpo+X758hIeHExISAkDBggX1Hu/loVJ37txh+PDh3LlzBwCNRoOTkxNPnz7F29sbb29vevXqxZQpUwzW8d69e3z66adqMGBjo337tbW1xd3dnZUrV7J582ajgfjmzZsB7c/eSeu4ZMkSdewuaP/+VlZWPHr0iN27d7N7926GDh3KZ599ZvCxTXXq1ClGjRqljm11cHDAyspKHdu8fv16ZsyYofehbI7rLU+ePOTLl4/nz58D2jZNOsHT0PWxdetWvvjiC+Li4siVKxcJCQk8ePCAJUuWcPToUdasWZNicGaO10B6FClSBNC+RyT1888/s2jRIvU4d+7cREREcPr0aU6fPs2GDRtYuHAhhQoV0sv377//Mnz4cGJiYgDt6zBHjhwEBAQQEBDAmTNnsLGxUb8QvU7XTFqtWLGCH374QX0/c3JyIiYmhkuXLnHp0iXWrl3L33//zTvvvGPwMfbs2cMnn3xCXFwcTk5OrzRpv0KFClSpUoVLly7h6elpMBBXFEX9YtqkSRPy58+vnjO1fTPLnDlzmD17NlZWVuTMmZOIiAjOnj3LqFGjmDJlCj179uSLL75g48aN2NjYYG9vT2RkJKdOnWLw4MHMnTs3xaFWr8PnSqZRhDCDbdu2Kc7Ozoqzs7Oyf/9+kx8nPDxcadasmeLs7Kx88MEHyp49e5T4+HhFURTl1q1byqBBgxRnZ2fFxcVFOXToULL8bm5uirOzs1K7dm2lfv36yp49e5TY2FhFURTl5s2bSvfu3RVnZ2fl3XffVUJDQ5Pl/+OPPxRnZ2elb9++Buuoe57Hjx/Xuz8+Pl7p3LmzWv6OHTuUuLg4RVEU5fr160rv3r2VWrVqGcyvKIoycuRIxdnZWWnevLmydetWJSwsTFEURYmOjlb27t2rNG3aVHF2dlZGjhyZLG/fvn3VsmvUqKGsX79eiYqKUhRFUR4+fKgMHz5ccXZ2VipUqKDcvn07Wf6VK1eqdRs+fLji6+urngsPD1f27NmjDB8+XK2Tzvfff6+218qVK5Xnz58riqIosbGxyvHjx5WOHTsqzs7OSqdOnZSEhASDf9eUbNiwQa3T/fv39c7dv39fPVe7dm2lR48eyvnz5xVFUZS4uDjl6NGjSv369RVnZ2eld+/eyR77+PHjan5jQkJC1NfVhx9+qJw8eVKJiYlRFEVRQkNDlcWLFyvvvvuu4uzsrCxZsiRZfl0Z7777rtKyZUvl33//Vf8Ot27dUhRFUc6fP6+mu3HjRor1CAwMVCpUqKA4OzsrBw8e1Du3cuVK5YcfflDOnDmjBAcHq/cHBAQov/32m1KpUiXF2dlZ2bdvn8l/B0N1qlOnjuLs7Ky0aNFC+ffff5XExERFURTl4sWL6vVQpUoVxc/PL1n+tFxvxiR9Dbz8+khK9zqqXr26UqVKFWXixImKv7+/oiiKEhkZqaxYsUKpXLmy4uzsrMyaNStZ/vS+BlKju3aN/R26dOmiXmc6Sa/Zzz//XAkMDFQURft+sW7dOrVOvXr1SnbttWjRQnF2dlYGDRqkXL16Vb0/OjpauXr1qjJ79mxl/fr1enlep2tGUYy/fg4cOKA+zpAhQ9T3vLi4OMXLy0upV6+e+rqNiIgw+DzfffddZdiwYcrNmzfV8ym9fxqyatUq9TPr7t27Kab5999/1fKSXtvpaV9jbZWW6y4t+d977z2lYsWKyty5c9XP08DAQGXw4MGKs7OzUqNGDeX3339XqlatqqxevVqJjIxUFEX799O9N7i6uqb4uZCRnyuvGwnEhVnMnDlTvWh1bxam+OuvvxRnZ2elUqVKyqVLl5Kdj42NVS/gtm3bJjuve/OvXLmycu3atWTnAwIC1A/czZs3JzufnkB8+/bt6rljx44lyxcZGal+yUgp//79+xVnZ2elfv36Bv+GDx8+VN98L1++rHdO92Hu7OysHD16NFneiIgINWiaO3eu3rng4GClRo0airOzszJu3Dg1mErN1atXFRcXF6V69eopBlqKoihhYWFKo0aNFGdnZ2XPnj1pelydtAbiLVq0UN/kk0r6BfHhw4d659IaVEybNk0NKHRfrF62e/duxdnZWalbt26yNLoyatasmawOSbVs2VJxdnZWZsyYkeL5BQsWqK8P3ZfTtNLlHTBgQLJz6QnEp0yZon4gp/TcQkNDFVdXV/XL3csyOxB3dnZWPv300xTTfPXVV+qX4Jel9zWQmtQC8fPnz6tfwkaMGKEoijYY013PH3/8cYr59uzZoz5vLy8v9f6goCD1/kePHqW5nq/bNWPs9dOmTRvF2dlZ6datm9oZk9SFCxeUihUrKs7Ozso///xj8Hl27dr1la+3pEJDQ5Vq1aopzs7Oym+//ZZims8++0xxdnZWGjVqpAaV6Wnfl5/Dy8wViKf0WaIo2vd83eeUoc/au3fvqudPnTqldy6jP1deNzJZU5iF7mdpgLx585r8ONu3bwegefPmKa4xbmtrq/5Ueu3aNXXVhZe1bNmS8uXLJ7u/WLFi6jjtl8e9pdeOHTsAqFmzZorj3nLkyMGQIUMM5tf9BNe+fXv1Z+iXFS1aVB2Xfvjw4RTT1KhRI8XxwI6OjjRo0ABI/ty9vLyIiIjA1taWL774Ao1GY7CeSa1fvx5FUWjcuLHBcftOTk7qsARDdU6vQYMGpbicZPPmzdXnYkp7K4qCp6enWoahn8Z1Q0WeP3+Or69vimk6dOhgdN6Ebuz+1q1bURQl2fktW7YA2qFfr7qmvW789NmzZ0lISHilvIYoiqK+5nv06JHic8uVK5f6mj948CBhYWFmKTs9Ro0aleL9LVu2BODu3bt6E+vM+Rp4VY8ePWLTpk2MHDmSxMRENBoN/fv3B+DIkSPq++7YsWMN1kn3frdt2zb1ft3QJdAO4TKnzLxmDPHz8+P69euAtr1TmlNTtWpV9X1p69atBh9r8ODB6dpDIleuXOpra9OmTcnmRIWHh7Nnzx4AOnfurLZLeto3s9jb26uvx6ScnJzUNe+LFSuW4qIMb7/9NqVKlQKSvze/Lp8rmUXGiAuzSBo4pDWIe1lsbKx6QdavX99guvfffx9ra2sSEhK4ePEizs7OydJUq1bNYH7dG7turKO5XLp0Sa2fIcbOnT59GoC1a9eqY4FTogtmdGPoX2bKcz979iwAlStXpnDhwgbzG6rzoUOHjLZZZGSk0Tqnl6Glvuzs7MifPz9BQUEmtfeNGzfUD8OJEyemOElVR/cc/f39U6xPasuXdejQgd9//52AgAB8fHz0JgJfvnxZDSwMTbZ98OABq1ev5vjx49y/f5/w8PBkQXdUVBQhISF6Y1BN9eDBA/VvY6ztdecSExPx9fU1eg1ktNy5cxucv5I04AsNDVW/2JnzNZCalyetJmVra8vEiRPV14Xu/aZIkSLJJiMn1aBBAy5duqQ3ydPBwYF69epx9OhRBg8eTO/evWnUqBEVKlR45Yl/L8vMa8YQ3d/GxsbG6OutQYMGeHl5cfXqVeLi4lIM2E2tQ1Jdu3Zl8+bNBAYGcvToURo2bKie27ZtG1FRUWg0Gjp37pzsOZjSvpmlbNmyBudzFChQAIAqVaoYjAkKFCjA3bt3k703vy6fK5lFAnFhFvny5VNvP3/+3GCPrjEhISFq4GCsF8TBwYF8+fLx9OlTg705uXLlMpoftJNBzElXF2PP3dDziouLUyedhYWFpannULf028tMee5PnjwBoHjx4qmWm9Tjx48B1El3qTFU5/Qytg59etpb9/wAnj17lqY8hp6j7oPJkOLFi1O7dm18fHzYvHmzXiCu+2JWoUIFvUmnOnv37mX8+PHqxDvQLgno4OCARqMhISFBfX2ltoxaWiW99oxdr0k3W0nr3zCjGLs27O3t1dtxcXHqbXO+BlKTdNKqRqPB3t5e3VmzW7duel8idH//1Daz0bXNy++V33//PSNGjMDPz4/Zs2cze/ZsbG1tqVq1Kk2bNqVr164m/bqZmdeMIbpy8+XLp9euL9P9beLj4wkJCUk2ATU9dUiqTp06lC5dmjt37rBhwwa9QFy3dnjdunUpWbKken962zczGHvf1f0SkpY0L783vy6fK5lFAnFhFkmHgVy5csWkQDyptPaqm9r7npGM1cnQuaQ/V86cORN3d3ez1ystXvXvqav3p59+yrBhwzKiShaVtEf56NGjKX5Qp5WxnkGdDh064OPjg5eXF19//TUODg4kJCSoQ7Y6duyYLM/z58+ZOHEiMTEx1K5dm1GjRlG9enW9nqp79+7RvHlzgBSHvaSXKa/5rMKcr4HUvLyhT1qY+l5ZvHhxNm7cyNGjRzl48CBnzpzBz8+PM2fOcObMGRYuXMjvv/+e6vJyL8vsa8ZcDP0d0zMsJakuXbowY8YMvL29CQkJIU+ePNy4cYMLFy4AhtcOz8qfhabK7p8rL5Mx4sIs6tatq75p7t2716THyJMnj/qmp1u/OiXR0dHqT5/m+IndXHQ9J4GBgQbTGDpnb2+v9taZe+x6WuiWvkq6TnBa6D5kDY3Vz+qSLgmWGc+xVatWODg4EB4ejre3N6AdK/rkyROsra1p27Ztsjy6sde5c+dm4cKF1KtXL9nPxUl7Kc0laU+hsdd80p+NX6frNa0y+zWQVrq/v7H3SnjRNin97a2srGjYsCFffvklnp6enDhxgp9//pnixYsTEhKibtD2Kl6Hv5fuuT5//lzvV6KX6f42NjY2ekuyZoSOHTtibW1NbGysOiZd1xueO3duWrRooZfeHO1riO5z1tjfxpLzObL758rLJBAXZlGwYEF1QsrWrVu5e/dumvPqvv3a2dmpP7v/+++/BtOfOHFC/SmratWqplbZ7HSTZk6cOGEwzfHjxw2e041F3LVrV4qbHGUkXdm+vr7qMJVXyXfw4MFk6xu/7pL2thnqJS5fvrz606quVzojJZ2ApBuOopukWb9+/RTXCtZ9EJcpU8bgeE3djn3m9NZbb6lDF4w9vu6clZVVihOw0yMtbZhemf0aSCvde19gYCA3b940mE7390/Le2WuXLno0KEDP/zwA6DdHyJpMPQ6XjMp0T3X+Ph4vU2fXqb727i4uKQ4PtycChcurG5gtGHDBuLj4/UmYL88hCYj2ldHN/zJWJCv66m3hKz8uWIKCcSF2YwbN46cOXMSHR3N6NGjefTokdH04eHhfPbZZ3o9Zm3atAFg9+7d+Pn5JcsTHx/Pn3/+CWjfPFOaqGkpuuEkp0+fTjEYj46O1tuY4WXdu3cHtBth/P3330bLioyMfOWeKmNatWqFk5MTcXFxyXbHM6Z79+5oNBpCQ0P5+eefjaaNi4t7rd5Uk45dDA0NTTGNjY0NXbp0AbQrHpw6dcroYyZdPchUusmYR48e5e7du+ovTCkNS4EX4551O9C+LCAgIEO2RddoNOr16uHhkeL1Hh4err6WGzdubHSMtimStmFG9eBZ4jWQFvXr11fn5syePTvFNPv27VMDqqS/pqT23pE0KEw6NON1vWZelvSz4c8//0xxfoivr6+6WklKq3pkBN3wk8uXLzN//nyePn2qd39S6Wnf1OgmBD9+/Jjz588nOx8UFMTatWvT/HjmlpU/V0whgbgwm9KlS/Prr79iZ2fHtWvX6NChAwsXLlR3VgNt77efnx9z5syhadOmyZaN6tmzJ2+//TZxcXEMGTIEb29vdczhnTt3GDFiBOfPn0ej0fC///0vM59eqlq0aKH2+I0ZM4Zdu3apHwA3b95k6NChRifUNGvWTB3HO2PGDL755htu376tno+NjeX8+fP88ssvuLm5mXXiW65cudS/57Zt2xg5ciRXrlxRz4eEhHDgwAFGjBhBeHi4en/FihXV5as8PDwYM2YMV65cUXvLEhIS8PPz488//6R58+Z6j2lppUuXVnvB1q1bZ7CHb+TIkbz99tvEx8czdOhQFi9erPe3DwsL49ChQ0yYMIE+ffqku166nu/4+HjGjx9PdHQ0Tk5OybZw12nQoAFWVlYEBwczfvx49YttfHw8+/fvp2/fvhk2fnT48OHkzZuX0NBQBgwYgI+Pj/p39PX1ZeDAgfj7+2NnZ8cnn3xi9vJz586tzkfx9PQ0+wRsncx+DaSFvb29ukPuzp07mTRpVdJHIAAABbJJREFUkvplKDY2Fk9PTz799FMAatWqpbez6dmzZ2nXrh1Llizh5s2b6i9wiqJw5swZdbfLokWL6s3/eV2vmZTodpE9e/Yso0ePVn+lTUhIYO/evQwbNoz4+HhKly5Njx49MqQOL3N1dVV/1dJ1KFWqVIlKlSolS5ue9k1NzZo11d0oJ0yYwMWLF1EUhcTERE6cOEG/fv0y7BemtMjKnyumkMmawqyaNGnCihUr+Pzzz7lz5w4zZsxgxowZ2NrakjNnTsLCwvQm8zRr1kxvxZWcOXMyf/58dYv7kSNHYmdnh729vdrjZWVlxZdffqk38/x1YGNjw++//06/fv14+PAhY8eO1au7ra0tv//+OyNHjjT4GL/88guTJ09m+/bteHh44OHhgaOjI7a2toSFhekNWTF3cNWzZ09CQkKYNWuWugW1g4MD1tbWej0OLw+b+fzzz1EUhaVLl+Ll5YWXlxf29vbkyJGD8PBwveDodZpQlCNHDjp06MD69ev55ZdfmDNnDvny5UOj0dCyZUsmTJgAaNfF/+effxg9ejR+fn5MmzaNadOmkTt3bhITE/W+mOjWxU0P3VjwxYsXq0uY6caOp6RUqVIMGzaM+fPns3fvXvbu3atu5R0XF0eBAgWYPn06w4cPT3fdXlakSBH+/PNPRo4cya1bt+jXrx85cuTAyspKfc3Y29vz22+/GVyWL7169uzJ77//zvLly1mzZg0FChTAysqK6tWrM3PmTLOUkdmvgbTq3bs3Dx48YNGiRWzYsAFPT09y585NZGSkuvJLxYoVmTVrVrKJj9euXeOnn37ip59+Ut+fk16vTk5OzJgxQ28d8Nf1mklJ48aN+fLLL/nxxx/Zv38/+/fvJ1euXMTExKi/CJQoUYJ58+YZHNJlbjY2NnTo0IG///5bfR81NEkT0te+xlhZWTF16lRGjBjB7du36dq1Kzly5CAxMZGYmBhKly7N119/zfjx49P3hNMhq36umEICcWF21atXZ+fOnXh5ebF//37Onz9PUFAQ4eHh5MqVizJlyvDee+/RoUOHFIeWlC1blq1bt7J8+XL27NnDnTt3iI6OpkSJEtStW5cBAwZk2Id6epUsWZJNmzYxf/589uzZw6NHj7C3t+eDDz5g2LBh6jhyQ3LkyMFvv/1Gjx492LBhA2fOnOHJkydERkZSoEABypYtS8OGDWnevHm6V6ZJyfDhw3Fzc2P58uWcOHGCR48eoSgKZcqUoXLlyuoQlqSsra2ZNGkSHTt2xMPDAx8fHx49ekR4eDi5c+emdOnS1K9fn2bNmqW49J4lffPNNxQrVgwvLy/u37+v9ibrlvrTKVmyJBs2bGDbtm3s2rWLS5cuERwcjJWVFW+99RYVK1bEzc0NNzc3s9SrY8eOLF68WO/YmE8++YTy5cuzYsUK/Pz8iI+Pp0SJEjRu3Jhhw4Zl6PJetWrVYufOnSxevJgDBw7g7++v9jTWr1+fgQMH6i3LZm4fffQRTk5ObN68mVu3bhEYGIiiKGqPn7lk9msgrT7//HPc3NxYuXIlp0+f5vnz5+TIkYOqVavi7u5Ojx49kq0NXrVqVWbNmsWJEye4cOECjx8/5vnz59jZ2VG+fHnq16/Phx9+mOJ7zOt6zaSkX79+1K5dm6VLl3LixAkeP36sPsfmzZvTr18/o8vrZYSuXbuqw7Xs7e1THVJiSvumRcOGDVm5ciXz5s3jzJkzREVFUbx4cVq0aMHw4cPNtimVqbLy58qr0iiW/P1BCCGEEEKIN5SMERdCCCGEEMICJBAXQgghhBDCAiQQF0IIIYQQwgIkEBdCCCGEEMICJBAXQgghhBDCAiQQF0IIIYQQwgIkEBdCCCGEEMICJBAXQgghhBDCAiQQF0IIIYQQwgIkEBdCCCGEEMICJBAXQgghhBDCAiQQF0IIIYQQwgIkEBdCCCGEEMIC/g8oFfxhFD1T7AAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the Posterior Coverage plot\n", + "\n", + "print('Shape of fraction array: ', np.shape(coverage_fraction))\n", + " \n", + "percentile_array_norm = np.array(percentile_array)/100\n", + "\n", + "default_cycler = (cycler(color='bgrcmyk') *\n", + " cycler(linestyle=['-', '-.']))\n", + "\n", + "plt.rc('lines', linewidth=2)\n", + "plt.rc('axes', prop_cycle=default_cycler)\n", + "sns.set_style(\"whitegrid\")\n", + "fig, ax = plt.subplots(1,1,figsize=(8, 8))\n", + "plt.plot(percentile_array_norm, coverage_fraction)\n", + "plt.legend('w', loc='upper left')\n", + "plt.plot([0,0.5,1],[0,0.5,1], 'k-', zorder=1000)\n", + "plt.xlim([-0.05,1.05])\n", + "plt.ylim([-0.05,1.05])\n", + "plt.text(0.03,0.85,'Underconfident',horizontalalignment='left', fontsize='x-small')\n", + "plt.text(0.7,0.05,'Overconfident',horizontalalignment='right', fontsize='x-small')\n", + "plt.xlabel('Confidence Interval of the Posterior Volume', fontsize='x-small')\n", + "plt.ylabel('Fraction of Lenses within Posterior Volume', fontsize='x-small')\n", + "\n", + "ax.tick_params(axis='both', which='both', labelsize='small')\n", + "# plt.tight_layout()\n", + "# plt.savefig(results_path+results_name+'_'+'posterior_coverage.pdf')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [], + "source": [ + "# select only theta values and corresponing predicted values for theta > -1.5\n", + "\n", + "inx = np.where(true_test_theta[0:2000] > -1.5)\n", + "theta_2 = true_test_theta[0:2000][inx]\n", + "posteriors_all_2 = posteriors_all[inx]" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [], + "source": [ + "# calculate a similar plot from the residual values i.e. from predicted - true/ error_true\n", + "\n", + "residuals = (posteriors_all_2[:,0] - theta_2)/posteriors_all_2[:,1]\n", + "sigma_intervals = np.linspace(0, 3, 21)\n", + "num_samples = []\n", + "for sigma in sigma_intervals:\n", + " num_samples.append(np.sum(np.abs(residuals) < sigma)/len(residuals))\n", + "num_samples = np.array(num_samples)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import scipy.stats as stats\n", + "\n", + "# Function to calculate the percentage of values within a given sigma range\n", + "def percentage_within_sigma(sigma):\n", + " lower_bound = -sigma\n", + " upper_bound = sigma\n", + " cdf_lower = stats.norm.cdf(lower_bound)\n", + " cdf_upper = stats.norm.cdf(upper_bound)\n", + " percentage = (cdf_upper - cdf_lower) * 100\n", + " return percentage\n", + "\n", + "# Calculate the percentages for each sigma value\n", + "percentages = [percentage_within_sigma(sigma) for sigma in sigma_intervals]\n", + "fraction_of_percentages = np.array(percentages)/100\n", + "\n", + "# Print the results\n", + "for sigma, percentage in zip(sigma_intervals, percentages):\n", + " print(f\"{sigma:.3f} sigma: {percentage:.2f}%\")" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 103, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(fraction_of_percentages, num_samples)\n", + "plt.plot([0,0.5,1],[0,0.5,1], 'k-', zorder=1000)" + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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kANOmwfDh4e3Zs80t7GPDMAwGDx7MN99889T5s+nWrVs8V5myKIiLiIiIvOAWLoSnFzP5+mvo2jV25xqGwaBBg5g0aZL12Jw5c+ga2w+QKCmIi4iIiLzAVq+GTp3C2599BoMGxe5cwzAYOHAgkydPth6bN28enTt3jucqUyYFcREREZEX1Nat0KoVhIaa7T594KkNMKNlGAb9+/dn6tSpAFgsFubPn8+HH36YQNWmPAriIiIiIi+gH3+Exo3NjXsAOnaEyZNjt2W9YRj069ePadOmAWYI//bbb+nYsWMCVpzyKIiLiIiIvGCOHIE6dcDf32w3bQrz5oGDQ8znGoZBnz598PT0BMwQ/t1339GhQ4eEKziFUhAXEREReYGcPg21asHDh2a7Vi1YutRcMzwmhmHQu3dvZs6cCZghfMGCBbRv3z4BK065FMRFREREXhB//gnVqsGdO2a7YkVYswZcXGI+NzQ0lN69ezNr1izADOGLFi2ibdu2CVhxyqYgLiIiIvICuHIFqlaFa9fM9ptvwubNkCZNzOeGhobSq1cvZs+eDYCDgwOLFi2iTZs2CVixKIiLiIiI2Llbt6B6dbh0yWwXLWqumJI+fcznhoaG0qNHD+b+u++9g4MDixcvplWrVglXsAAK4iIiIiJ27cEDqFkTzpwx2wUKwI4dkDVrzOeGhobSrVs35s+fD5ghfMmSJbSM7ZabEicK4iIiIiJ26vFjc3WUY8fMdu7csHOn+f8xCQ0NpWvXrnz77bcAODo6snTpUlq0aJGAFcvTFMRFRERE7FBAgLlO+E8/me2sWc0Q/sorMZ8bGhpK586dWbBgAWCG8GXLltG8efMErFj+S0FcRERExM4EB5s7Zm7fbrbTp4dt2+D112M+NyQkhM6dO7Nw4ULADOHLly+nWbNmCVewREpBXERERMSOBAVBhw6wdq3ZTp0atmyBMmViPjckJIROnTqxaNEiAJycnFixYgVNmjRJuIIlSgriIiIiInbi0SNzl8xt28y2iwusXw8VKsR8bkhICB07dmTx4sWAGcJ/+OEHGjdunHAFS7QUxEVERETswK1b5oOZv/xitl1dYeVKqFEj5nNDQkLo0KEDS5YsAcwQvnLlSho1apSAFUtMFMRFREREkrlLl8zA/ccfZjtDBti4Ed5/P+Zzg4ODad++PcuWLQPA2dmZVatW0aBBg4QrWGJFQVxEREQkGTtxAmrVCt8xM3duc7OeEiViPjc4OJh27dqxfPlywAzhq1evpn79+glYscSWQ1IXICIiIiKR27sXKlYMD+GFC8OBA7EP4W3bto0QwtesWaMQnowoiIuIiIgkQ2vXmjtm+vqa7XLlYP9+yJcv5nODg4Np3bo1K1asAMDFxYW1a9dSr169BKxYnpeCuEgkpk+fTuHChWnbtm1Sl2J3fv31V7p370758uV5/fXXI/w9rl27lsKFC+Pu7m7TZ8f1fBERezF7trk6SkCA2a5dG3x8YrdtfVBQEK1atWLlypWAGcLXrVtH3bp1E7BisYWCuCQqd3d3ChcuzCeffBJj37DQVbhwYS5fvpwI1Ulc/fbbb7Rr147du3dz79490qdPT9asWcmQIUNSl5agLl++zPTp05k+fXpSlyIids4wYORI6NHDfA3Qvj1s2ABp08Z8flBQEC1btmTVqlUAuLq6sn79ejw8PBKuaLGZHtYUkXizcOFCgoKCKFOmDDNnziRTpkwR3k+XLh2vvPIKOXLkSKIKE8aVK1fw9PQE4KOPPkriakTEXoWEQM+eMHdu+LEhQ2DcOLBYYj4/KCiIDz74gLX/7vQTFsJr1aqVQBVLXCmIi0i8OX/+PAB16tR5JoQDVK9enerVqyd2WSIiyd6TJ+aW9evWhR+bPBn69Yvd+YGBgXzwwQes+/cDXF1d2bBhAzVr1oz/YiXeKIiLSLzx9/cHIE2aNElciYiI/bh/H+rXhx9/NNvOzrBwoRnMYyMwMJDmzZuzYcMGAFKlSsWGDRuoEZudfiRJKYiL3Zk+fTqenp68/fbbLF68mIMHD7JgwQJOnDjB48ePeemll6hTpw5dunTB1dU1ys/Zu3cvCxcu5PfffyckJIS8efNSr149OnToEKs6bt26xaJFi9i3bx9XrlwhMDCQ7Nmz884779CxY0cKFSr0zDmHDx+mXbt2AJw7d46zZ8/y7bff8vPPP3P79m3KlClj3XoYzG+uGzZsYOvWrZw5cwZfX18yZsxInjx5qFixIg0aNCBv3rzPjHPu3DkWLlzI4cOHuXXrFk5OThQoUIDq1avTtm1b0kYy0fC/tf3111/Mnj2bAwcOcOfOHTJnzsz777/PRx999MzUksKFC0doDx06lKFDh1rbPj4+vPTSS6xdu5ahQ4eSJ08edu3aFenf6/Hjx5k7dy5Hjx7F39+fXLlyUatWLbp27RrVpYjg4cOHLFmyhF27dnHp0iX8/f3JkiULb775Ju3ataNUqVLPnHP58mWqVq1qrTVVqlTMnj2bXbt2cevWLdKlS0e5cuXo3bs3BQsWjHCuu7s7V65cifLvolGjRowfPz5WtYtIynP1qrlG+O+/m+20ac3VUmKboQMDA2nWrBkbN24EzBC+adMmqlWrlkAVS3xSEBe7Nn/+fL755hvAnH8cFBTEn3/+yfTp0/n5559ZsGABjo6Oz5wXFubDpE+fnosXL/LNN9+wd+9eypQpE+24u3fvZsCAAfj5+QHm2qzOzs5cvnyZ1atXs2HDBsaMGUPDhg2j/IwdO3bQv39/goKCcHNze6bOf/75h549e1qne1gsFtKnT8/Dhw85fvw4x48f58GDBwwbNizCeUuWLOHLL78kNDQUADc3NwICAjh58iQnT55k5cqVzJ8/nwIFCkRZ26FDh+jRowd+fn6kTZsWwzC4ceMGq1atYu/evaxevTpCGM/672P8d+/eJTQ0FDc3N1KlSmV9P7JrEJnVq1fz2WefRaj9ypUrzJ49m+3bt9OiRYtoz//tt9/o2bMnt2/fto6bKlUqrl+/zpYtW/Dy8qJ///5069Ytys+4cOECn376KXfu3CF16tQA3LlzBy8vL/bt28fSpUspUqSItX+mTJl49OgRDx48iPB3EcbNzS1Wf3YRSXnOnTOXJ/zrL7OdLRt4eUHZsrE7PyAggGbNmrFp0yYAUqdOzaZNm6w3FiT5UxAXu3X27FmOHDlC586d+fDDD8mcOTOPHj3iu+++Y8aMGRw+fJh169bRtGnTCOf5+PhYQ3itWrX45JNPyJUrF0+ePGHt2rWMHTuWc+fORTnuiRMn+OijjwgKCqJFixa0b9+e/Pnz4+joyNWrV5k3bx7Lli1j2LBhFCxYkBJR7LowePBg3nvvPYYMGWINxZcuXQLg0aNHdO7cmUuXLpE+fXoGDRqEh4cH6dKlA+B///sfmzZtemYKyN69exk9ejQA77//PsOGDSNv3rycOXOGy5cv88UXX3DlyhV69OjBunXropxC0qdPH8qVK8fHH39MwYIFCQwMZOfOnQwfPpybN28yceJEJkyYYO3/008/AeF3h4cNG0bjxo2j/DuMzKlTpxgxYgShoaG8/fbbjBw5koIFCxIcHMz27dsZMWIEM2bMiPL8y5cv07lzZ3x9falZsybdunWjcOHCODk5cefOHZYsWcLcuXOZNGkSBQsWjPJu0eDBgylQoACzZ8+mZMmSBAcH8/PPPzN48GBu3brF6NGjWbp0qbX/mjVrIvw2IezvQkQkOocPQ506cOeO2X7lFdi2DV59NXbnBwQE0KRJE7Zs2QKYIXzz5s1a3tXOaPlCsVu+vr507dqVQYMGkTlzZsC8+9inTx8qVKgAYP0G9bRJkyYB8PbbbzN58mRy5coFmL/Oa9WqFcOHD8c3bPeESHzxxRcEBQXRs2dPvvjiCwoWLGi945s7d25GjBhB27ZtCQ4OZtasWVF+TqFChZg5c2aEO9P58+cHzDv9ly5dwsXFhUWLFtGiRQtrCAd45ZVX6NOnD507d47wmV9//TUAb7zxBjNnzrR+nqOjI9WrV2fOnDk4Ojpy6dIlfvjhhyhre/XVV5k5c6Z1GoaLiwseHh50794dgG3bthEcHBzl+baYMmUKwcHB5M+fn3nz5lnHdnJywsPDg0mTJkV7XSZMmICvry8NGjRg2rRpFCtWDCcn815DlixZ6Nu3Lx9//DFAtMsMZsqUiQULFlCyZEnr+O+++651qs2RI0e4fv16vPyZRSRl8vYGd/fwEF6qlLlbZmxD+JMnT2jcuLH1Z1yaNGnw8vJSCLdDCuJit5ydnenSpUuk74U9Jf7fO9tnz57lwoULAPTo0QMHh2e/BJo3bx7l8npnz57l999/x9nZmQ8//DDK2sKmpBw8eJCQkJBI+3Tq1CnKKRtr1qwBoFmzZhQtWjTKcf5b2x9//AFAr169cHZ2fqZPiRIlrHeCw36VGZmo/m7CHvx58uQJf4X9LjUe+Pr6sn//fgA6d+4cYVpLmIoVK1K6dOlIz79//z47duwAiHYueYMGDQDz7yps+sp/ffjhh9YpKU+rXr06ln/XD4vuNyYiItH5/nuoVw/+ndlIlSrmNvY5c8bu/LAQ7uXlBYSH8MqVKydMwZKgNDVF7FahQoWinH8bFqTD5u2GOXnyJGDe5SwbxSQ8BwcH3n777UiD6tGjRwEIDQ2Ndl3WsPDt5+fH/fv3yZIlyzN9opqHfuXKFW7evAlAlSpVohzjv57+s73zzjtR9qtQoQLbtm3j3LlzBAUFRRrY33jjjUjPzfnUT4r79+/HuraYnDp1yjovPLray5Urx7Fjx545fvz4cev57du3j9WYV69efWY+N0T9Z3dxcSFz5szcuXPnmf+uRERiYhjwzTcweHD4sWbNYPFiiGZdgQiePHlCo0aN2Lp1KwBp06bFy8uL999/PwEqlsSgIC526+mpGv8Vdkf1v9Mn7t69C5jTD1xcXKI8P2cUtybCAnJISEiUd1T/K2xJv/+KLJwDET43d+7csRoDIv7ZolstJuzPFhwczIMHDyINo1H93T59pzo+p6aE1Q5Eu9lPTNcFiPN1ie7hyqj+uxIRiU5oKAwaZK4LHqZXL5g6FWL5LDv+/v40bNiQ7du3A2YI9/b2pmLFiglQsSQWBXFJVGEB8cmTJzH2fTooRTZVISmE3ekuUKAA3t7ecfqs2KwkYonNVmpxkNCfn1jCrkuqVKn47bffkrgaEZFwgYHQsSMsWxZ+bMwY+PTT2O2WCebPwwYNGlin4Lm5ueHt7W19Hkrsl+aIS6IK223xxo0bMfYNu8vp4OBAhgwZ4mX8sIc67927R2BgYJT9oqov7O7xP//8Y126ML5ly5bN+vrp9alj8vSfLSAgIMp+YQ8aOjk5kT59ehurjF9htUP0/21E9V7Y31l8z10XEYmLhw/N+eBhIdzBAebNg2HDYh/C/fz8qF+/foQQvnXrVoXwF4SCuCSqYsWKAXDmzJkYg2zYfOzChQtHOo/ZFsWLFwfMqQVhn/9foaGh/Pzzz5G+FzavOygoyPpNMb7lzp3bOgVjz549sT4vbJnEsOX2onLgwAEgfv9e46pYsWLWh0MPHToUZb+o3itdurT17n5kK+UktKcfbDUMI9HHF5Hk5+ZNc2WUf2eSkCqVuX39fxa7ilZYCN+5cydgThvctm0b7733XgJULElBQVwSlYeHB2D+mu3pHST/6+jRoxw5cgSA2rVrx9v4RYoUsS6LN2vWLOsDfk9bs2ZNlMvTlShRwrqKyZQpUyLMbY6MrQ80NmnSBIBVq1Zx9uzZWJ1TuHBhXnvtNQBmzJgR6TzmU6dOWf8BUa9ePZtqSwjp06e3/mD57rvvIr2jf+DAgUgf1ARzvn3YBhbffvst//vf/6IdLz4fNIWI88qjW2JRRFKGK1egQgX498cYmTLBzp3mNvax9fjxY+rWrYuPjw8QHsLffffdBKhYkoqCuCSq0qVLW5cWnDJlCpMmTeLatWvW9x8+fMjq1avp0aMHhmGQN29eWrduHa819O/fHzC3dB84cKA1dAcEBLBixQq++OKLKKdsWCwWRo0ahYuLC1evXqVZs2Zs3bo1wnz2GzdusGHDBjp27Gjd9fN5ffjhh+TPn5+AgADat2/PqlWrePToEWDejf/f//6Hp6cn3377bYTzBg0aBMCxY8fo3bu3dZpGSEgIO3fupGvXrta1umPapTKx9e3bF0dHR/7880+6du3Kn3/+CZh3+L29venXr1+0U2k++eQTMmbMyKNHj2jVqhWrV6/m4cOH1vfv3r3L9u3b6d27NwMHDozX2vPnz2/97cKqVat0V1wkBbt9G6pXh39Xk+Wll+DHH+F5bmKHhfDdu3cD5s2K7du3U758+QSoWJKSHtaURDd27FgCAwPZvXs3c+bMYc6cOaRJkwZnZ2d8fX2tISZ//vzMnDkz3rcIr169Ot27d2f27Nl4eXnh5eVFhgwZ8PPzIygoiLJly/Lmm28yZ86cSM8vWbIks2fPZsCAAVy+fNkaINOlS0dAQECEUN6sWTObanRzc2P+/Pl0796dCxcuMHz4cD7//HPSpUvHo0ePrA8nhu3mGKZSpUoMHz6csWPHsnv3bnbv3k26dOl48uQJQUFBAOTJk4dZs2ZFuatmUilRogQjRoxgxIgRHDp0iNq1a1v/TgMDAylQoAAtWrRg3LhxkZ6fN29eFixYQO/eva27ew4fPpz06dMTFBQUYSpUfN9RSp06NQ0aNGD16tV8/fXXeHp6kilTJiwWCzVr1mTIkCHxOp6IJE8PH0Lt2nDmjNkuUAD27IG8eWP/GY8fP6ZOnTrs3bsXgAwZMrB9+3befvvt+C9YkpyCuCQ6Nzc3Zs2axb59+9iwYQO//fYbd+7cwc/Pj6xZs1K4cGGqV69Oo0aNol2GLy769+9P6dKlWbBgASdPniQwMJBXXnmFevXq0bFjR2bPnh3t+e+99x47duxgxYoV7Nmzh4sXL/Lw4UNcXV0pVKgQpUqVomrVqnGax5c3b17WrVvH6tWr8fb25vz58zx+/JgsWbKQJ08e3n//fesGNU9r27Ytb731FosWLeLw4cPcvHkTJycnXnvtNapXr07btm3j/R838aVFixa89tprzJkzh2PHjuHv70/u3LmpWbMmXbt2tS7bFZWiRYvi5eXFmjVr2LlzJ2fPnsXX1xdnZ2fy589P8eLFqVq1aoKsuTtixAhy5crFtm3b+Oeff7h69SpgPjwrIi++J0+gQYPw6Si5cpnTUZ4nhD969Ig6deqwb98+wAzhO3bs4K233kqAiiU5sBj6HapdOXHihHUTlrAtuBNSSEgI58+f57XXXovVcnuS/Oga2j9dQ/um62f/YrqGwcHQtCls2GC2M2eGffvg3/UJYuXRo0d4eHjw448/ApAxY0Z27NgR5eZz8nwS++swtnlNc8RFREREbBQaaq6EEhbC06YFL6/nC+EPHz6kdu3a1hCeKVMmdu7cqRCeAmhqioiIiIgNDAMGDIBFi8y2iwusXw/lysX+M3x9faldu7Z1admwEB62XK682BTERURERGwwZoy5TT2Ym/UsXw7VqsX+fF9fX2rVqsXBgwcBc3OznTt3Urp06QSoVpIjBXERERGR5+TpCZ9/Ht6eNw8aN479+Q8ePKBWrVrWjcoyZ86Mj48PpUqVit9CJVlTEBcRERF5DkuXwkcfhbcnToQPP4z9+Q8ePKBmzZocPnwYMDcl8/Hx4Y033ojnSiW508OaIiIiIrG0aRO0bx/eHjbMnCceW/fv36dGjRrWEJ41a1Z27dqlEJ5C6Y64iIiISCzs3QvNm8O/e6rRoweMHh3788NC+C+//AKEh/ASJUokQLViD3RHXERERCQGp0+nomFDB548MdstW5rzxC2W2J1/7949qlevbg3h2bJlY/fu3QrhKZzuiIuIiIhE4+xZ6NIlLw8fmqnbw8NcstAhlrczw0L40aNHAciePTu7du2i2PMsNi4vJAVxERERkSj8/TfUru3AvXtmCK9QAVatAmfn2J1/9+5dqlevzq+//gpAjhw52LVrF0WLFk2oksWOKIiLiIiIROLmTaheHf75xwzhpUoZbNpkIU2a2J1/9+5dqlWrxrFjxwAzhO/evZvXX389oUoWO6M54iIiIiL/8eAB1KoF58+b7Xz5AtiyJZSMGWN3/p07d6hatao1hOfMmZM9e/YohEsECuIiIiIiT/H3h/r14d8MzUsvGcyf/zc5csTu/Nu3b1O1alWOHz8OQK5cudizZw9FihRJmILFbmlqioiIiMi/goKgWTPYt89sZ80KW7eG4uAQHKvzw0L4iRMnADOE7969m8KFCydUyWLHdEdcREREBAgNhQ4dYMsWs50uHWzdCrG9kX3r1i3c3d2tITx37tzs2bNHIVyipCAuIiIiKZ5hQJ8+sGyZ2XZ1hY0b4c03Y3f+zZs3cXd35/fffwcgT5487Nmzh9deey2BKpYXgaamiIiISIo3YgTMmGG+dnSElSuhcuXYnRsWwk+dOgXASy+9xO7duylUqFDCFCsvDLsM4oZh4O3tzcaNGzlz5gx37twhY8aMFCpUCA8PDxo1aoRzbBf4jKU9e/awadMmfv/9d27dukVQUBBp06bllVde4e2336Zp06a8/PLL8TqmiIiIJLzJkyNuVb9ggfmwZmzcuHEDd3d3Tp8+DUDevHnZvXs3BQsWTIBK5UVjd0Hc19eXvn37cuDAgQjHb926xa1btzh48CArVqzA09OT3Llzx3m8u3fv0rdvX37++edn3rt//z7Hjh3j2LFjfPfdd/Tt25cuXbrEeUwRERFJHAsXwoAB4e2pU6Ft29ide/36ddzd3Tlz5gwAL7/8Mrt376ZAgQLxX6i8kOwqiAcFBdGrVy9rKM6VKxfNmzcnX758XL9+nTVr1nDx4kVOnTpFly5dWLFiBenSpbN5vODgYLp06cLJkycBSJUqFQ0bNqRIkSK4ublx9epVvL29OXPmDEFBQXzzzTekSZOG1q1bx8ufV0RERBLO+vXQqVN4e+RIc554bFy7dg13d3fOnj0LmCF8z549vPLKK/Fep7y47CqIr1ixwhrCixUrxoIFC8iQIYP1/TZt2tCzZ0/279/PhQsXmDlzJkOGDLF5vM2bN1tDeJ48eVi6dCm5cuWK0Kdr1654enri6ekJwPTp02nRogVOTnb1VysiIpKi+PhAixbmSilgBvDPP4/dudeuXaNKlSqcO3cOgHz58rF7926FcHludrNqSnBwMLNmzQLAYrHw1VdfRQjhAK6urkyYMIE0/+49u2TJEu7evWvzmPv377e+7ty58zMhPKyWXr16kS1bNgDu3bvHxYsXbR5TREREEtbPP0ODBhAYaLbbtjXniVssMZ979epVKleubA3h+fPn151wsZndBPGff/6ZO3fuAFC+fHleffXVSPtlyZIFDw8PAAIDA/Hx8bF5zKdDfL58+aLs5+DgwEsvvWRt+/v72zymiIiIJJzTp6F2bXj82GzXrw/ffgsOsUhEV65coXLlypz/d9/7sBCeP3/+hCtYXmh2E8R//PFH6+uKFStG2/fp958+73llzpzZ+vqvv/6Ksl9oaCiXL18GwNHRUf8qFhERSYYuXYLq1SHsPlvlyvDDDxCbhdauX79O1apV+eOPPwB45ZVX2Lt3b7Q36kRiYjdBPOxfn2DOD49O8eLFra/DvmBsUbVqVevrefPmce3atUj7zZgxg1u3bgHQuHHjZ6bMiIiISNK6ft0M4Vevmu0334QNGyBVqpjP/eeff2jfvj0XLlwAoECBAuzdu1fLFkuc2c0ThZcuXbK+zpMnT7R9c+bMiaOjIyEhIfz999+EhobiEJvfOf1HrVq1cHd3Z9euXVy9epXatWtHumpK2NqhdevWZfjw4c89jq1CQkISZQzDMBJlLEkYuob2T9fQvun6Jb3796FmTQcuXDAngRcpYrB5cyhp00JMl+Wff/7B3d2df/75B4CCBQuyc+dOcufOrWtqR5Lr16HFMAwjqYuIjbfffpsHDx4A8Ouvv5I2bdpY9z969Chubm42jRsSEsK0adNYsmQJjx49irTPW2+9Ra9evShfvrxNYzyPEydOEBQUhMViIXXq1Ak+nmEYBAQE4OrqiiU2T7FIsqNraP90De2brl/S8vOz0KXLyxw7Zi7kkCtXEEuWXCJXruAYz7169SodO3a0hvCXX36ZhQsXkjNnzgStWeJfYn8d+vv7YxgGzs7OlCxZMsp+dnNH3M/Pz/ra1dU1xv5P9/Hz87M5iDs6OtKpUycyZszI5MmTCQgIeKbPkSNHmD17NunSpYswLSYhOTk58dprryX4OCEhIVy4cIFChQrh6OiY4ONJ/NM1tH+6hvZN1y/pBARA48YOHDtmBq9s2Qx8fBx47bWYN9z566+/6NKlizWEhy1RqOko9imxvw5PnTpFUFBQjP3sJognlX379jFw4EB8fX15++236datG2+88Qaurq5cu3aNbdu2MWvWLA4dOkSbNm2YMmUKlStXTpTaEusbusViwdHRUT9A7Jiuof3TNbRvun6JLzAQPvgAtm0z2+nTw7ZtFl5/PeZrcOnSJapWrWqdFvvaa68xZ84cXn75ZV1DO5Ycvw7t5mHNsLXBgUjvSv/X032ePvd57Nu3j27duuHr60vNmjVZtGgRFSpUIF26dLi4uJAvXz66du3KokWLcHFxwd/fn4EDB3L79m2bxhMREZG4CwyE5s1h0yaznTo1bN4MpUvHfO6lS5eoXLmyNYQXLlyYnTt3kiNHjoQrWFIsuwniT29Vf//+/Wj7BgcHW+dzOzk52RzEv/rqK+uDnsOGDYvygc+SJUvSqFEjAB49esTatWttGk9ERETiJijI3DFzwwazHRbCY1j5GID//e9/VKpUybpkcZEiRdi9eze5c+dOwIolJbObIP70YvlXrlyJtu/169etT8W+/PLLNq2YcvnyZesyRQULFozxX8JPP6j5+++/P/d4IiIiEjdBQeZ0lPXrzXaqVOZdcXf3mM/9888/qVSpEn///TcQHsIj21VbJL7YTRB/+sHEkydPRtv36fej2oEzJjdv3rS+js2DnunTp7e+fhy2XZeIiIgkiqAgaNkSwn4pHRbCn9oSJEoXL16kcuXK1gczixYtyp49e7Q6iiQ4uwniFSpUsL7ev39/tH2fZxfOqDy9POKNGzdi7H81bIcAIGPGjDaNKSIiIs8vOBhat4Y1a8y2q6s5NaVatZjPvXDhwjMhfNeuXZoTLonCboJ4uXLlrFvOHzhwIModM+/cuYOXlxcAzs7OEXbHfB758uWzLoF49epVfv3112j7h40JUKJECZvGFBERkecTHAxt2sCqVWY7LITXqBHzuX/88QeVK1fm8uXLgLlz9+7duxXCJdHYHMSvXr3K1atX8ff3j896ouTk5ET37t0Bc1H2IUOGWDfsCRMQEMCQIUOsa463bt3aGt7/65NPPqFw4cIULlyY6dOnP/N+qlSpIoT4oUOHRrjr/bTZs2dz4MABAFxcXKhVq9bz/wFFRETkuQQHQ9u28MMPZtvFBdatg5o1Yz43LISHPXdWokQJdu/eTfbs2ROwYpGIbF5H3N3dHYvFwrhx42jYsGE8lhS1li1bsn37do4cOcKpU6do0KABLVq0IF++fFy/fp3Vq1dz8eJFAAoUKECvXr3iNN6AAQM4cOAA9+/f59KlS9StW5f69etTqlQp6zriW7du5bfffrOe07t3bz3YISIiksBCQqB9e1ixwmyHhfDatWM+99y5c1SpUoVr164BZgj38fEhW7ZsCVixyLPiZUOfH3/8kXXr1vHbb79x+/ZtHBwcyJo1KyVLlqR+/fpUqlQpPobBxcWFmTNn0qdPHw4dOsS1a9eYMmXKM/1ef/11PD09IzxAaYu8efPy3XffMWDAAC5dusTjx49Zvnw5y5cvf6avk5MTvXv3plu3bnEaU0RERKIXEgIdO8KyZWbb2dmcH+7hEfO5/w3hJUuWxMfHh6xZsyZgxSKRi3MQ/+abb7hz5w5gThkJ888//3D58mW8vLwoX748EydOJFOmTHEdjgwZMrBw4UK8vb3ZsGEDp0+f5t69e2TIkIGCBQtSp04dGjdujLOzc5zHAnO+2MaNG/H29sbHx4dTp05x9+5dgoKCcHNzI3/+/Lz99ts0b96cvHnzxsuYIiIiErmQEPjwQ1i82GyHhfC6dWM+9+zZs1SpUoXr168DUKpUKXbu3EmWLFkSsGKRqMU5iIftIlmyZEnee+89cuXKRUhICFevXuWnn37i9OnTHDx4kG7durF8+fJ42VbUYrHg4eGBR2z+6RuF8ePHM378+Fj1dXV1pWHDhok2BUdERESeFRoKnTvD99+bbScn8yHNevViPvfMmTNUqVLFuhJa6dKl2bFjh0K4JKk4B/H06dPz9ddfRzr9ZODAgWzdupWhQ4fy+++/s2rVKj744IO4DikiIiIpTGgodOkCCxeabScnWLkSGjSI+dzTp09TpUoV6x4hpUuXZufOnVEu6CCSWOK8fOHIkSOjnQNeq1YtPv30UwzDYPPmzXEdTkRERFKY0FDo1g2++85sOzqaK6U0ahTzuadOnYoQwt98802FcEk24hTEnZycqBGLhTobNGiAk5NTlGt/i4iIiEQmNBS6d4f58822o6O5UkrjxjGfe/LkyQghvGzZsuzYsUMhXJINm4O4o6MjadKkwckp5tktLi4upE2b1rq+t4iIiEhMQkOhZ0+YN89sOzrC8uXQtGnM5/7+++9UqVKFW7duAfDWW2+xY8eOeFk4QiS+2BzE06dPj6+vr3XFlOjcuHGDBw8e6IEIERERiRXDgN69Yc4cs+3gAEuXQrNmMZ974sQJ3N3drQtKvP3222zfvp2MGTMmXMEiNrA5iL/22msAke5K+V+TJ0/GYrFQtmxZW4cTERGRFMIw4KOPYNYss+3gAEuWQIsWMZ/722+/RQjh5cqVUwiXZMvmIF6jRg0Mw+CHH35g0KBB/PXXX8/0uXDhAh999BHr168HoF27djYXKiIiIi8+w4C+fWHGDLPt4GCuGd6yZcznHj9+HHd3d+tv69955x22bdtGhgwZErBiEdvZvHxhs2bN2LRpE8ePH2fLli1s2bKFXLlykTNnTkJDQ7ly5Yr1X6MAffv2pWTJkvFStIiIiLx4DAP694ewX7ZbLLBoEbRqFfO5x44do1q1aty9exeA8uXLs3Xr1jjvsi2SkGwO4i4uLixYsIAJEyawevVqgoKCuHr1KlevXo3QL0eOHAwePJg6derEuVgRERF5MRkGDBgAU6eabYvFXDO8TZuYz/3111+pVq0a9+7dA+Ddd99l69atpEuXLuEKFokHcdrQJ3Xq1IwYMYKPPvqI/fv3c/78eXx9fXFyciJr1qyUKlWKcuXKxctumiIiIvJiMgwYNAimTDHbFgssWACxmdF69OhRqlWrxv379wF477338Pb2VggXuxDnnTUBMmfOTP369ePjo0RERCQFMQwYPBgmTTLbFgt8+y20bx/zuUeOHKF69erWEF6xYkW2bNmiEC52I847a4qIiIjYwjDgk0/gm2/Cj82bBx07xnzuL7/8EuFO+Pvvv4+Xl5dCuNgVBXERERFJdIYBn34KEyaEH5s7Fzp1ivncn3/+mWrVqvHgwQMAKlWqxJYtW3Bzc0ugakUShoK4iIiIJCrDgOHDYfz48GNz5kCXLjGfe/jwYapXr46vry8AlStXVggXu6UgLiIiIonGMODzz2Hs2PBjs2ZB164xn3vw4MEIIbxKlSps2bKFtGnTJlC1IglLQVxEREQSzahRMGZMeHvGDOjePebzDhw4QM2aNXn48CEA7u7ubN68mTRp0iRQpSIJT0FcREREEsWoUeb/wkyfDj17xnzeTz/9FCGEV6tWjU2bNimEi91TEBcREZEEN2YMjBwZ3p46FXr3jvm8/fv3U6tWLR49egRA9erV2bhxo0K4vBAUxEVERCRBjR0Ln30W3p48Gfr0ifm8H3/8MUIIr1GjBhs2bCB16tQJVKlI4lIQFxERkQQzfjwMGxbenjQJ+vWL+bx9+/ZRu3ZtHj9+DECtWrUUwuWFoyAuIiIiCWLCBBg6NLz9zTfQv3/M5+3duzdCCK9duzbr1q0jVapUCVSpSNKI8xb3f//9N97e3pw7d44HDx4QFBQUZV+LxcKiRYviOqSIiIgkc998A0OGhLcnTICBA2M+b8+ePdSpUwc/Pz8APDw8WLNmjUK4vJDiFMQ9PT2ZNWsWoaGhGIYRY3+LxRKX4URERMQOTJoEH38c3h4/PmI7Krt27aJu3br4+/sDUKdOHdasWYOrq2sCVSqStGwO4hs3bsTT0xOAjBkzUqFCBXLmzKl/sYqIiKRgU6ZEvPM9dmzEO+NR8fHxoV69etYQXq9ePVatWqUQLi80m4P4smXLAHNXq0mTJunhCRERkRRu4cKIc8DHjIk4RzwqO3fupF69ejx58gSA+vXrs3LlSoVweeHZ/LDm+fPnsVgsjBw5UiFcREQkhduyBTp3Dm+PGhVxtZSo7NixI0IIb9Cgge6ES4phcxC3WCy4ubmRI0eO+KxHRERE7MyhQ9CsGYSEmO0+fSKuGx6V7du3RwjhjRo1YuXKlbi4uCRgtSLJh81BvECBAvj7+xMYGBif9YiIiIgdOXsW6tSBf6d206KFuWFPTOszbNu2jfr16xMQEABA48aN+eGHHxTCJUWxOYg3a9aM4OBgvLy84rMeERERsRNXrkDNmnD3rtmuWhUWLQKHGNKFt7c3DRo0sIbwJk2asGLFCpydnRO4YpHkxeYg3rx5c9zd3RkzZgyHDh2Kz5pEREQkmbt/H2rVgr//NtulS8PatRDT1G4vLy8aNmxoDeHNmjVj+fLlCuGSItm8aoqnpydFihTh6NGjdOzYkVKlSvHGG2/g5uYW7Xm9e/e2dUgRERFJBp48gQYN4ORJs/3KK+DlBenTR3/eli1baNy4sXVaa/PmzVmyZIlCuKRYcQriYRv0GIbBsWPHOH78eIznKYiLiIjYr5AQaN0a9u0z29mywfbtkDNn9Odt2rSJJk2aWHfg/uCDD1i8eDFOTnHe5FvEbtn8X/9bb70Vn3WIiIhIMmcY0Lu3OQUFIG1a8054oULRn7dx40aaNm1qDeEtW7bk+++/VwiXFM/mr4DFixfHZx0iIiKSzI0eDbNnm6+dnMxAXrZs9Ods2LCBZs2aWUN4q1atWLRokUK4CHF4WFNERERSjrlzYcSI8PbChVCjRvTnrFu3LsKd8DZt2uhOuMhTFMRFREQkWuvXQ48e4e2JE8154tFZu3YtzZs3Jzg4GIC2bduycOFCHB0dE65QETujIC4iIiJR2r8fWraE0FCzPWgQDBgQ/Tlr1qyhRYsW1hDerl07FixYoBAu8h+x+t3Q0KFDAciePTv9+/ePcOx5WCwWxo4d+9zniYiISOI7eRLq1TOXKwRo0wa++ir6c1atWkXLli0J+Xe/+w4dOjB//nyFcJFIxCqIr1u3DovFwiuvvGIN4mHHDMOI8fywfgriIiIi9uHvv80Ne+7fN9s1a8J330W/a+bKlStp1aqVNYR37NiRefPmKYSLRCFWQbxhw4ZYLBayZcv2zDERERF5sdy9a4bwK1fM9ltvwerVEN2+Oz/88AOtW7e2hvBOnToxd+5cHGLa714kBYtVEB8/fnysjomIiIh98/ODunXhzBmz/eqrsGULRLdx9vLly2nTpg2h/04k79y5M3PmzFEIF4mBvkJEREQEgOBgaNECDh402zlzwrZt5u6ZUVm2bFmEEN6lSxeFcJFY0leJiIiIYBjQrRts3my206UDb2945ZWoz1m6dClt27a1hvBu3boxe/ZshXCRWNJXioiIiPDZZ+bDmAAuLrBhA5QqFXX/xYsX065dO2sI79GjBzNnzlQIF3kOcd7a6u+//8bb25tz587x4MED6+5ZkbFYLCxatCiuQ4qIiEg88vSEL780X1sssGQJVKkSdf/vv/+eDh06WFdO69mzJ56enlrEQeQ5xSmIe3p6MmvWLEJDQ2O9jKGIiIgkH6tWQZ8+4e1p06BZs6j7L1q0iI4dO1p/7vfu3Ztp06bpZ7yIDWwO4hs3bsTT0xOAjBkzUqFCBXLmzEmqVKnirTgRERFJOLt3m5v0hN1L+/RT6N076v4LFiygU6dO1hD+0UcfMXXqVIVwERvZHMSXLVsGQJUqVZg0aRKpU6eOt6JEREQkYf32GzRsCIGBZvvDD2HMmKj7f/fdd3Tu3Nkawvv27cvkyZMVwkXiwOYnKs6fP4/FYmHkyJEK4SIiInbkf/8zN+zx9TXbderAnDnm/PDIzJ8/P8Kd8H79+imEi8QDm4O4xWLBzc2NHDlyxGc9IiIikoBu3TK3q79+3Wy/8w6sXAlOUfyOfO7cuXTp0sXaHjBgAJMmTVIIF4kHNgfxAgUK4O/vT2DY77REREQkWXv0yLz7/ccfZrtIEXPd8DRpIu8/Z84cunXrZm0PHDiQb775RiFcJJ7YHMSbNWtGcHAwXl5e8VmPiIiIJICgIGjaFH75xWznzm3umpklS+T9Z8+eTffu3a3tjz/+mK+//lohXCQe2RzEmzdvjru7O2PGjOHQoUPxWZOIiIjEo9BQ6NTJDN4AGTLA1q3w8suR9585cyY9evSwtocMGcJXX32lEC4Sz2K1akrYMoX/VaRIEY4ePUrHjh0pVaoUb7zxBm5ubtF+Vu/o1kUSERGRePfJJ7B4sfna1RU2boQSJSLvO2PGjAg/qz/55BPGjh2rEC6SAGIdxKP7AjQMg2PHjnH8+PEYP0tBXEREJPFMngxff22+dnCA5cvh/fcj7zt9+nT6PLW7z6effsqYMWMUwkUSSKyC+FtvvZXQdYiIiEg8W7YMBgwIb8+cCY0aRd536tSp9OvXz9oePnw4X3zxhUK4SAKKVRBfHPb7LBEREbELO3ZAhw7h7REj4KkFUCKYMmUK/fv3t7Y/++wzRo0apRAuksBsflhTREREkqejR6FxY3OlFICuXc0gHpnJkydHCOEjRozQnXCRRGJzEL969So3btyIdf8bN25w9epVW4cTERGRWLhwAWrXNtcMB3Mb+5kzI981c+LEiQx4au7KyJEjGTlyZKLUKSKxnJoSGXd3d7Jly8aPP/4Yq/4tW7bk+vXrnD592tYhRUREJBrXr5u7Zt66ZbYrVjTniTs6Ptv366+/ZvDgwdb2qFGj+PzzzxOpUhGBOARxMFdLScj+IiIiEju+vuDhAX/+abaLF4cNGyB16mf7TpgwgSFDhljbo0ePZvjw4YlUqYiESbQ54oGBgThG9k9yERERiZOAAHNO+LFjZjtvXvD2hkyZnu07fvz4CCH8yy+/VAgXSSKJEsRv3LjB3bt3yZgxY2IMJyIikmKEhkL79uDjY7YzZzZ30HzppWf7jh07lqFDh0Zof/rpp4lUqYj8V6ynpvzyyy8cPnw4wjE/P78od90M4+vry969ezEMgzfeeMO2KkVEROQZhmGuE/7DD2Y7dWrYvBlef/3Zvv+98/3fO+MikvhiHcQPHz78zA6b/v7+zJgxI8ZzDcPA1dWVblEtYCoiIiLPbcIEmDrVfO3oCCtXQvnyz/YbPXp0hAcxJ0yYwMcff5xIVYpIVGIdxPPkyRNhh81ffvkFJycnSpUqFeU5Dg4OuLm58eqrr9KoUSPy5csXp2JFRETEtGgRfPJJeHvePKhb99l+o0aNirAk4ddff82gQYMSvkARiVGsg3ijRo1o9NS+uEWKFCFDhgzadVNERCSReXlBp07h7S+/hI4dn+03cuRIRo0aZW3/d91wEUlaNi9fOG7cOFxdXeOzFhEREYnB4cPQrBmEhJjt3r3hqecvAXNK6MiRI/niiy+sxyZNmhRhB00RSXo2B/Gn746LiIhIwjt3DurUAT8/s92sGUyZEnHXTMMw+PzzzxkzZoz12JQpU+jbt2/iFisiMYrThj4iIiKSOK5eNXfNvHPHbFepAosXR9w10zAMPvvsM7788kvrsWnTpvHRRx8lcrUiEhuxCuJha45mz57d+mutof/9PVgsWCwWxo4d+9zniYiIpGS3b0OtWvDXX2b7jTdg3Tp4eoaoYRgMGzaMcePGWY9Nnz6d3r17J3K1IhJbsQri69atw2Kx8Morr1iDeNix2GxbH9ZPQVxEROT53LkD1arB77+b7fz5zV0zM2QI72MYBkOHDuWrr76yHvP09KRXr16JW6yIPJdYBfGGDRtisVjIli3bM8dEREQkYdy9C9Wrw2+/me1cuWD7dvP/wxiGwSeffMKECROsx2bOnEmPHj0SuVoReV6xCuLjx4+P1TERERGJH/fumSH82DGznTMn7N4Nr74a3scwDAYPHsw333xjPTZ79mxtoCdiJ/SwpoiISDJz/74Zwn/91WznyGGG8MKFw/sYhsGgQYOYNGmS9dicOXPo2rVr4hYrIjZTEBcREUlGHjyAGjXg6FGznT27GcKLFAnvYxgGAwcOZPLkydZj8+bNo3PnzolcrYjEhYOtJ+7evZtHjx7FZy0iIiIpmq+vuUThL7+Y7WzZYNcueP318D6GYdC/f39rCLdYLMyfP18hXMQO2XxHvEePHjg6OlK0aFHeeecdypUrR9myZUmVKlV81iciIpIi+PqaSxQePmy2s2Y1Q3ixYuF9DMOgX79+TJs2DQgP4R9++GESVCwicWVzEE+fPj2+vr78/vvvnDx5kvnz5+Pk5ETJkiWtwbx06dI4OzvHZ70iIiIvnIcPoXZtOHjQbGfJYobw4sXD+xiGQZ8+ffD09ATMEP7dd9/RoUOHxC9YROKFzUH88OHDnD59mkOHDnHo0CGOHj2Kn58fR48e5ddff2XmzJm4urpSunRpypUrxzvvvEPJkiVxcLB5NoyIiMgL59Ej8PCAAwfMdubM4OMDJUqE9zEMg969ezNz5kzADOELFiygffv2SVCxiMQXm4O4xWKhWLFiFCtWjE6dOhEcHMyJEyc4dOgQBw8e5LfffuPJkyccPHiQQ4cOMXXqVNKmTcuRI0fis34RERG7FRbC9+8325kymSH8jTfC+4SGhtK7d29mzZoFmD9/Fy1aRNu2bZOgYhGJT/G2aoqTkxNlypShTJky9OzZk8DAQA4cOMCsWbP47d+dCB4/fhxfw4mIiNi1x4+hbl348UeznTEj7NwJpUqF9wkNDaVXr17Mnj0bAAcHBxYtWkSbNm0SvV4RiX/xunxhSEgIv/32m3W6ym+//UZgYKD1/fTp08fncCIiInbJzw/q1YO9e812WAgvUya8T2hoKD169GDu3LmAGcIXL15Mq1atEr9gEUkQcQ7ip06dijBP3N/fH8MwAEiTJg0VK1a0PrxZtGjROBcsIiJiz/z9oX59c21wgAwZzG3r33wzvE9oaCjdu3dn3rx5gBnClyxZQsuWLZOgYhFJKDYH8d69e/PLL7/g6+trDd6pUqXinXfesQbvEiVK4OjoGG/FioiI2DN/f2jQwJwHDpA+PWzbBm+9Fd4nNDSUrl278u233wLg6OjI0qVLadGiRRJULCIJyeYgvnPnTiwWC25ubnzwwQdUqlSJkiVL4uLiEp/1iYiIvBCePIHGjWHHDrOdLp0ZwsuVC+8TGhpKly5d+O677wAzhC9btozmzZsnQcUiktDiNDXFMAwePnzI4sWLOXnyJOXLl+edd96hePHiWqZQRETkXwEBFpo0cWD7drPt5gZbt8I774T3CQkJoXPnzixcuBAwQ/jy5ctp1qxZ4hcsIonC5iC+atUq69zwX3/9lYMHD3Lw4EEsFgtp06albNmyvPPOO5QvX57ChQvHZ80iIiJ2IyAA+vZ9iX37LACkTQve3vDuu+F9QkJC6NSpE4sWLQLMlchWrFhBkyZNkqJkEUkkNgfxEiVKUKJECbp06UJwcDDHjx+PsFrKnj172LNnDxaLhUyZMvH2229Tvnz5eJnjZhgG3t7ebNy4kTNnznDnzh0yZsxIoUKF8PDwoFGjRgmyo2dwcDDbtm1j+/btnDp1itu3b+Pg4EDWrFnJly8f5cqVo1q1auTPnz/exxYREfsTEADNmzuwb58bEB7CK1QI7xMSEkLHjh1ZvHgxYIbwH374gcaNGydFySKSiOJl+UInJyfKli1L2bJl6d27NwEBARw9epRDhw6xb98+zp49y9atW9m+fXucg7ivry99+/blQNgWZP+6desWt27d4uDBg6xYsQJPT09y584dp7GeduLECYYNG8b58+efee/x48f89ddf7Nu3jxs3bjBs2LB4G1dEROxTYCA0bw5btph3wtOkMdiyxULFiuF9QkJC6NChA0uWLAHMn6crV66kUaNGSVGyiCSyeF1HHMwHTc6cOcOJEyc4ceIEly5dwmKxYBiGdXUVWwUFBdGrVy9+/vlnAHLlykXz5s3Jly8f169fZ82aNVy8eJFTp07RpUsXVqxYQbp06eL8Zzp48CA9evTA398fgNKlS1OpUiXy5MmDYRhcv36dixcvsjdsQVgREUnRwkL4xo1mO1WqUDZuNKhUKXwlseDgYNq3b8+yZcsAcHZ2ZtWqVTRo0CApShaRJBAvQfzMmTPWaSlHjhzBz88PwBq8XVxcKFWqFO88/VSKDVasWGEN4cWKFWPBggVkyJDB+n6bNm3o2bMn+/fv58KFC8ycOZMhQ4bEacyrV6/Su3dv/P39SZMmDV9//TXVqlWLtG9ISAh37tyJ03giImLfgoLggw9gwwaznSqVwcyZ/1C58kvWPsHBwbRr147ly5cDZghfvXo19evXT4qSRSSJ2BzEly5dyuHDhzl8+DC+vr5AePB2cnKiePHi1jXFy5QpE+dlDYODg5k1axYAFouFr776KkIIB3B1dWXChAlUq1YNPz8/lixZQpcuXcicObPN444YMYJHjx4BMGnSJKpUqRJlX0dHR7Jnz27zWCIiYt+CgqBlS1i3zmynSgXr14fy8st+1j7BwcG0bduWFStWAGYIX7NmDfXq1UuKkkUkCdkcxEePHm2dcuLg4ECRIkWswbts2bKkSZMmPuvk559/tt5tLl++PK+++mqk/bJkyYKHhwerV68mMDAQHx8fm5d++uOPP9i3bx8AlSpVijaEi4hIyhYcDK1bw5o1ZtvV1bwrXrUqhD1eFBwcTOvWrVm5ciVg/sZ4zZo11K1bN4mqFpGkZHMQL1SoUIRdNONjLnZ0fvzxR+vrik8/6RKJihUrsnr1aut5tgbxNWHfTUHruIqISJSCg6FtW1i1ymyHhfAaNSAkxDwWFBREu3btWPVvJxcXF9auXUudOnWSqGoRSWo2B/HNmzfHZx0xenq1kmLFikXbt3jx4tbXf/zxh81j/vLLL4A5Febtt9/m1q1bLF68GB8fH65cuYKjoyO5cuXinXfeoXXr1rzyyis2jyUiIvYpJATat4d/Z5rg4mJOTalZM7xPUFAQbdq0sd7gcXV1Zd26ddSuXTsJKhaR5CLeV01JKJcuXbK+zpMnT7R9c+bMiaOjIyEhIfz999+EhoY+906fQUFBnDt3DoDs2bNz+vRp+vXrx/379yP0++OPP/jjjz9YtmwZ/fr1o2vXrs81joiI2K+QEOjQAf5d+ARnZ1i7Fp7O10FBQQwaNIgd/+5t7+rqyvr166lVq1biFywiyYrNQTwwMJDbt2/j7OxMtmzZIrz3+PFjPD09+emnn3B0dOT999+nR48epEqVyuZCHz58aH2dKVOmaPs6OTnh5ubGgwcPCA4Oxs/PDzc3t+ca7/79+wQFBQHmaii9evXi8ePH5M+fn8aNG5M3b17u37+Pj48P+/fvJyQkhIkTJ+Ls7EzHjh2f/w9og5Cw33cm8BiGYSTKWJIwdA3tn65h8hQSAp06WViyxLzR4+xssHJlKLVqhU9HCQwMpGXLlhFC+Nq1a6levbqupx3R16D9S67X0GLYuLj3smXLGD16NA0bNmTcuHER3mvTpg1Hjx61rqJisVgoW7Ys33//PRaLxaZCixcvbg3Gp06dwskp+n9DVKxYkZs3bwLmPPHnXc3k4sWLeHh4RDhWtWpVpkyZ8swKMMuXL2fkyJGA+fT79u3b43UzoaedOHGCoKAgLBYLqVOnTpAxnmYYBgEBAbi6utp87SRp6RraP13D5Cc0FIYPz8X69RkBcHIymDz5MlWrPrL2CQwMZODAgfj4+ABmCPf09OS9995LipIlDvQ1aP8S+xr6+/tjGAbOzs6ULFkyyn423xEP29nyv8st+fj4cOTIERwcHKhbty6pUqVi/fr1HDlyhA0bNtCwYUNbh0xU//33ScaMGfnqq68iXYaxZcuWHDx4kG3bthEUFMTy5csZOHBggtbn5OTEa6+9lqBjgPkvyAsXLlCoUCEcHR1jPkGSHV1D+6drmLyEhkK3bhbWrzfvhDs5GaxYEUrDhuE3YAIDA2nRokWEEL5u3Tpq1KiRJDVL3Ohr0P4l9jU8deqU9QZydGwO4hcuXACefXBy8+bNWCwWOnfuzIABAwAoWrQoo0aNYtOmTTYH8TRp0vDgwQMAAgICYrwjHhAQEOHc55U2bdoIbQ8Pj2hXhmnevDnbtm0D4NChQ889ni0S65uBxWLB0dFR33zsmK6h/dM1TB5CQ6FnT1iwwGw7OsKKFRaaNAm/LgEBAbRo0YJNmzYBkDp1ajw9PalRo4aunx3T16D9S47X8PmeYHzK3bt3SZUq1TOb6hw+fBiApk2bWo+Fbdcb9vCjLZ4Owf99YPK/goODrZvwODk52RTE/xu6Y1qp5en3//777+ceT0REkrfQUOjRA+bPN9uOjrB8OTRpEt4nICCApk2bRgjhGzZsoHz58klQsYgkdzYHcT8/v2dWIrl8+TJ3794lV65cvPzyy9bjadKkIX369DEG6Ojkz5/f+vrKlSvR9r1+/bp1Mv7LL7/83CumALi5uUWYVx7TOulPvx/2jwAREXkxGAb06gVz55ptBwdYuhSe3mLiyZMnNG7c2Lq8b+rUqdmyZQvu7u5JULGI2AObg3jGjBnx8/Ozbm8P5u6XAKVLl36mf3Bw8DPTPZ7H0/OhT548GW3fp9+PagfO2ChcuLD19dOrtkTm6fefd4UWERFJvgwDeveG2bPNtoMDLFkCLVqE9wkL4V5eXoB5A8rLy0s7MotItGwO4kWLFgWw7hBmGAZr1qzBYrFQrly5CH3v3r2Ln58fWbNmtbnQChUqWF/v378/2r7PswtndCpVqmR9ferUqWj7Pv2+NvYREXkxGAb07QszZ5ptBwf4/nto2TK8z5MnT2jUqBHe3t6A+YyRt7c3lStXTvyCRcSu2BzEGzdujGEYTJo0ic6dO9O0aVOOHj1KmjRpntmk4MiRIwAULFjQ5kLLlStH5syZAXPFlqh2zLxz5471joSzszNVq1a1ecyaNWvi7OwMgJeXV7R3xVeuXGl9HZfwLyIiyYNhQP/+MH262bZYYOFCaN06vI+/vz8NGjRg69atQHgIf//99xO/YBGxOzYH8Vq1atGsWTNCQkLYv38/p06dwtXVlVGjRpE+ffoIfb28vCK9U/48nJyc6N69O2DefR8yZIh1FZUwAQEBDBkyBD8/PwBat25tDe//9cknn1C4cGEKFy7M9LDvsv+RPXt2WrVqBZgPiA4ZMoTAwMBn+q1YscK6YkqaNGlo+fStEhERsTuGAQMHwtSpZttiMVdKads2vE9YCN++fTtgTkvcunWrbsaISKzFaYv70aNH06hRI44dO0a6dOkoX748efPmjdAnMDCQdOnS0bBhwzjfIWjZsiXbt2/nyJEjnDp1igYNGtCiRQvy5cvH9evXWb16NRcvXgSgQIEC9OrVK07jAfTp04eDBw9y/vx5fHx8qFevHk2aNOGll17C19eXnTt3RpgKM2rUqCjDv4iIJH+GAYMHw+TJZttigW+/hfbtw/v4+fnRoEEDdu7cCYSHcG3WIyLPI05BHKBMmTKUKVMmyvddXFwYPXp0XIexftbMmTPp06cPhw4d4tq1a0yZMuWZfq+//jqenp7P3Jm3hZubG/Pnz6dv374cO3aMS5cuMXHixGf6pUqVilGjRlG/fv04jykiIknDMOCTT+Cbb8KPzZsHHTuGt/38/Khfv751s5506dKxdetW3n333USuVkTsXZyDeBjDMLh37x5PnjxJsO3dATJkyMDChQvx9vZmw4YNnD59mnv37pEhQwYKFixInTp1aNy4sXVud3zIkSMHy5YtY8uWLWzZsoUzZ85w584dUqVKxcsvv0zFihVp3bp1hOUORUTEvhgGfPopTJgQfmzuXOjUKbzt5+dHvXr12LVrF2CG8G3btmmdcBGxSZyD+KlTp5g1axYHDhzA398fi8XC6dOnre8/ePDAegd5+PDhkW4R/7wsFgseHh54eHjY/Bnjx49n/Pjxse7v4OBAvXr1qFevns1jiohI8mQY8Nln8PSPhdmzoUuX8Pbjx4+pV68eu3fvBiB9+vRs27aNd955J5GrFZEXhc0PawKsX7+eFi1asHPnTvz8/DAMA8MwIvTJkCEDly9fZtWqVRw4cCBOxYqIiCSEkSPhyy/D2zNmQLdu4e3Hjx9Tp06dCCF8+/btCuEiEic2B/GLFy/y2WefERwcTNu2bVmzZg2ZMmWKtG+DBg0wDMM6n05ERCS5GDUKvvgivD19OvTsGd5+9OgRHh4e7N27FzBvMO3YsSNOK4GJiEAcpqYsWLCAoKAgWrduzbBhwwBwdHSMtG/YHYPjx4/bOpyIiEi8Gz3avBseZupUcxfNMGEhPGx1rIwZM7Jjxw7Kli2buIWKyAvJ5iB+6NAhLBYLXZ6eQBeFHDlykDp1aq5evWrrcCIiIvHqyy/h88/D25MnQ58+4e2HDx/i4eFh3c05Y8aM7Ny5kzfffDORKxWRF5XNU1Nu3rxJ6tSpyZkzZ6z6u7q6EhAQYOtwIiIi8Wb8eBg+PLw9cSL06xfefvjwIbVr17aG8EyZMuHj46MQLiLxyuYg7uLiQlBQ0DMPZ0bmyZMnPHz4EDc3N1uHExERiRcTJsDQoRHbAwaEt319falVqxY//fQTAJkzZ8bHxyfaPTNERGxhcxDPkycPwcHBXLp0Kca+e/fuJSQkhEKFCtk6nIiISJxNnAhDhoS3x4+Hjz8Obz948ICaNWtaV/kKC+GlS5dO5EpFJCWwOYhXrFgRwzD4/vvvo+137949vv76aywWC5UqVbJ1OBERkTiZMgUGDQpvjx0bMZSHhfBDhw4BkCVLFnbt2kWpUqUStU4RSTlsDuIdOnQgTZo0rFixAk9PTx49ehTh/SdPnrBp0yaaNGnC5cuXyZgxIy1btoxzwSIiIs9r7lzo3z+8PXp0xOkp9+/fp0aNGhw+fBiArFmzsmvXLt54441ErlREUhKbV03JmjUrEydOpE+fPsyYMYM5c+ZY54vXqVOHf/75xzqH3MXFhUmTJmmOuIiIJLply6B79/D2yJERH9QMC+G//PILEB7CS5QokbiFikiKE6edNatUqcLSpUspVqwYQUFBBAcHYxgGFy9eJDAwEMMwKFq0KEuWLKF8+fLxVbOIiEisbNwI7dqZW9iDOR/86SUL7927R/Xq1a0hPFu2bOzevVshXEQShc13xMOULFmS1atXc/bsWY4ePcrNmzcJDQ0la9aslClTRt/MREQkSfj4QPPmEBJitrt3h6++AovFbIeF8KNHjwKQPXt2du3aRbFixZKoYhFJaeIcxMMUKVKEIkWKxNfHiYiI2OzgQWjQAMK2r2jdGmbMCA/hd+/epXr16vz666+AGcJ3795N0aJFk6hiEUmJ4jQ1RUREJLk5fhw8PODxY7PdoAEsWAAO//7Eu3v3LtWqVbOG8Bw5ciiEi0iSsPmOeGBgILdv38bZ2Zls2bJFeO/x48d4enry008/4ejoyPvvv0+PHj1IlSpVnAsWERGJyrlzUKMG3L9vtqtWhRUrwNnZbN+5c4dq1apx/PhxAHLmzMnu3bv1G10RSRI23xFfvXo1VatWZdKkSc+8161bNxYuXMj58+c5c+YMc+fOpUuXLrHahVNERMQWf/0F1arBrVtmu3x5WL8ewu4B3b59m6pVqyqEi0iyYXMQD9t1rF69ehGO+/j4cOTIESwWC/Xq1aNZs2Y4OTlx5MgRNmzYELdqRUREInHtmnn3+/Jls12qFHh5QdiquWEh/LfffgMgV65c7NmzRyFcRJKUzVNTLly4APDM0+WbN2/GYrHQuXNnBgwYAEDRokUZNWoUmzZtomHDhrZXKyIi8h937pjTUS5eNNuFC8O2bZAxo9m+desWVatW5ffffwcgd+7c7N69m9deey1pChYR+ZfNd8Tv3r1LqlSpyJAhQ4TjYbuSNW3a1HqsQYMGAJw7d87W4URERJ7h6wu1a8PJk2Y7Xz7YuROyZzfbN2/exN3d3RrC8+TJw549exTCRSRZsDmI+/n54eAQ8fTLly9z9+5dcuXKxcsvv2w9niZNGtKnT8/9sKdnRERE4sjPD+rVg3/34iFnTjOEv/SS2Q4L4Sf/TekvvfQSe/bs4dVXX02iikVEIrI5iGfMmBE/Pz98fX2tx37++WcASpcu/Uz/4OBg0qZNa+twIiIiVoGB0LQp7NtntjNnhh07oFAhs33jxg2qVKnCqVOnAMibNy979uyhUFgHEZFkwOYgHrbe6qpVqwAwDIM1a9ZgsVgoV65chL53797Fz8+PrFmzxqFUERERc6fMNm3A29tsp0sHW7dC8eJm+/r161SpUoXTp08D4SG8YMGCSVSxiEjkbH5Ys3Hjxuzbt49JkyZx8OBB7t27x6lTp0ibNi21atWK0PfIkSMA+iYoIiJxEhoKXbrAv/eASJUKNm+Gt94y29euXcPd3Z2zZ88C8PLLL7N7924KFCiQRBWLiETN5jvitWrVolmzZoSEhLB//35OnTqFq6sro0aNIn369BH6enl5RXqnXEREJLYMA/r3N3fJBHOTnrVr4f33zfa1a9eoUqWKNYTny5ePPXv2KISLSLJl8x1xgNGjR9OoUSOOHTtGunTpKF++PHnz5o3QJzAwkHTp0tGwYUPeD/tuKSIi8pxGjIBp08zXDg6wdKm5YgrA1atXqVKlCufPnwcgf/787N69m/z58ydNsSIisRCnIA5QpkwZypQpE+X7Li4ujB49Oq7DiIhICvbNN/D0j5L586FZM/P1lStXqFKlCn/88QdghvA9e/aQL1++JKhURCT2bJ6aIiIikhjmzoWPPw5vT50KHTuary9fvkzlypWtIfyVV15h7969CuEiYhfifEdcREQkoSxbBt27h7fHjIE+fczXYSH84r9bahYoUIA9e/Y8M0VSRCS5inMQ//vvv/H29ubcuXM8ePCAoKCgKPtaLBYWLVoU1yFFRCQF2LAB2rUzH9IE8674p5+ar//55x+qVKliDeEFCxZk9+7dCuEiYlfiFMQ9PT2ZNWsWoaGhGGHfKaNhsVjiMpyIiKQQO3dC8+bmmuFg3hX/6iuwWMwbQFWqVOHPP/8EoFChQuzevZuXwrbUFBGxEzYH8Y0bN+Lp6QmYu2xWqFCBnDlzkipVqngrTkREUp6DB6FBA3P3TIDWrWHGDDOE//XXX1SpUoX//e9/ALz66qvs3r2bPHnyJGHFIiK2sTmIL1u2DIAqVaowadIkUqdOHW9FiYhIynT8OHh4gJ+f2W7QwFw33MEBLl26RJUqVbh06RIAr732Grt27VIIFxG7ZXMQP3/+PBaLhZEjRyqEi4hInJ07BzVqwP37ZrtaNVixwty459KlS1SuXJm//voLgMKFC7Nr1y5y586ddAWLiMSRzcsXWiwW3NzcyJEjR3zWIyIiKdClS2bwvnXLbJcvD+vXm1vY/+9//6NSpUrWEF6kSBF2796tEC4ids/mIF6gQAH8/f0JDJvEJyIiYoNr18wQfvmy2S5VCry8IG1a+PPPP6lcuTJ///03EB7Cc+XKlXQFi4jEE5uDeLNmzQgODsbLyys+6xERkRTkzh2oXh3+XYWQwoVh2zbImBEuXrwYIYQXLVqUPXv2kDNnzqQrWEQkHtkcxJs3b467uztjxozh0KFD8VmTiIikAL6+ULs2nDpltvPlM5ctzJ4dLly4QOXKlfnnn38AM4Tv2rVL0yFF5IVi88Oanp6eFClShKNHj9KxY0dKlSrFG2+8gZubW7Tn9e7d29YhRUTkBeHnB/XqwS+/mO1cucDHB156KTyEX7lyBYBixYqxa9cusmfPnoQVi4jEvzgF8bANegzD4NixYxw/fjzG8xTERURStsBAaNoU9u0z25kzw44dULAg/PHHH1SuXJmrV68CULx4cXbt2kW2bNmSsGIRkYRhcxB/66234rMOERFJAYKDzQ16vL3Ndrp0sHUrFCtmLotbuXJlrl27BkCJEiXw8fFRCBeRF5bNQXzx4sXxWYeIiLzgQkOhSxdYvdpsp0oFmzfDW2/BuXPnqFKlijWElyxZEh8fH7JmzZqEFYuIJCybH9YUERGJLcOA/v1h4UKz7ewM69bB++/D2bNnI9wJf+ONN9i1a5dCuIi88OItiBuGwd27d63z+kRERMKMGAHTppmvHRxg2TKoVQvOnDlD5cqVuX79OgClSpXCx8eHLFmyJGG1IiKJw+apKWFOnTrFrFmzOHDgAP7+/lgsFk6fPm19/8GDB0ycOBGA4cOH4+LiEtchRUTEjnz9NYweHd7+9lvzYc3Tp0/j7u7OjRs3AChdujQ7d+4kc+bMSVSpiEjiitMd8fXr19OiRQt27tyJn58fhmFgGEaEPhkyZODy5cusWrWKAwcOxKlYERGxL3PmwODB4e2pU6FDB/MmTpUqVawhvEyZMgrhIpLi2BzEL168yGeffUZwcDBt27ZlzZo1ZMqUKdK+DRo0wDAMfHx8bC5URETsy9Kl0KNHeHvMGOjTB06ePEmVKlW4efMmAG+++aZCuIikSDZPTVmwYAFBQUG0bt2aYcOGAeDo6Bhp33feeQcgVuuMi4iI/duwAdq3Nx/SBPOu+Kefwu+//07VqlW5desWYC6Fu337djJmzJh0xYqIJBGb74gfOnQIi8VCly5dYuybI0cOUqdOrQc5RURSgJ07oXlzCAkx2927w/jx8PvvJ3B3d7eG8LffflshXERSNJuD+M2bN0mdOjU5c+aMVX9XV1cCAgJsHU5EROzAgQPQoIG5eyaYm/fMmAEnTvyGu7s7t2/fBqBcuXIK4SKS4tkcxF1cXAgKCnrm4czIPHnyhIcPH+Lm5mbrcCIikswdPw4eHuDnZ7YbNIAFC+DEieNUrVqVO3fuAOZ0xW3btpEhQ4akK1ZEJBmwOYjnyZOH4OBgLl26FGPfvXv3EhISQqFChWwdTkREkrGzZ6FGDXjwwGxXqwYrVsDJk8cihPDy5csrhIuI/MvmIF6xYkUMw+D777+Ptt+9e/f4+uuvsVgsVKpUydbhREQkmbp0yQze/0795t13Yf16OH36V6pWrcrdu3f/Pf4uW7duJX369ElWq4hIcmJzEO/QoQNp0qRhxYoVeHp68ujRowjvP3nyhE2bNtGkSRMuX75MxowZadmyZZwLFhGR5OPaNTOEX7litkuVgi1b4OzZo1SrVo179+4B8N577ymEi4j8h83LF2bNmpWJEyfSp08fZsyYwZw5c6zzxevUqcM///xjnUPu4uLCpEmTNEdcROQFcucOVK8OFy+a7cKFYds2uHDhCNWrV+f+/fsAVKhQAS8vL9KlS5d0xYqIJENx2lmzSpUqLF26lGLFihEUFERwcDCGYXDx4kUCAwMxDIOiRYuyZMkSypcvH181i4hIEvP1hVq14NQps50vn7ls4V9//UK1atWsIbxixYp4e3srhIuIRMLmO+JhSpYsyerVqzl79ixHjx7l5s2bhIaGkjVrVsqUKUOJEiXio04REUkm/PygXj04csRs58oFPj5w9erP1KhRgwf/PrFZqVIlNm/erN+GiohEIc5BPEyRIkUoUqRIfH2ciIgkQ4GB0KQJ7NtntjNnhh074Pbtw9SoUQNfX18AKleuzObNm0mbNm0SVisikrzFaWqKiIikHMHB5gY9W7ea7XTpzDnhDx8eonr16tYQXqVKFYVwEZFYUBAXEZEYhYZCly6werXZTpUKNm+GoKCD1KhRg4cPHwLg7u6uEC4iEkuxmpoydOjQeBnMYrEwduzYePksERFJHIYB/fvDwoVm29kZ1q0DR8efqFGjlnX52mrVqrFhwwbSpEmTdMWKiNiRWAXxdevWYbFYYrWdfWTCzlUQFxGxP59/DtOmma8dHGDZMnBz20+tWrWtIbx69eps2LCB1KlTJ2GlIiL2JVZBvGHDhlgsloSuRUREkpmvv4YxY8Lb334LOXL8SK1atXn8+DEANWrUYP369QrhIiLPKVZBfPz48Qldh4iIJDNz5sDgweHtadOgQIF91K7tYQ3hNWvWZP369aRKlSqJqhQRsV96WFNERJ6xdCn06BHeHjMGSpbcS+3a4XfCa9WqpRAuIhIHCuIiIhLB+vXQvr35kCaYd8XffXcPHh4e+Pn5AeDh4cG6desUwkVE4kBBXERErNatg2bNICTEbHfvDjVq7KJOnfAQXqdOHdauXasQLiISRwriIiICwJo10Ly5uXEPQNu20LixD/Xq1cXf3x+AunXrsmbNGlxdXZOwUhGRF4OCuIiIsGoVtGgRHsLbtYPWrXdSv354CK9fvz6rV69WCBcRiSexWjVFREReXCtXQqtW4dNROnSAFi120LBhfZ48eQJAgwYNWLlyJS4uLklXqIjIC0Z3xEVEUrAVKyKG8A8/hBYtttOwYT1rCG/YsKFCuIhIAlAQFxFJoZYtg9atw0N4587QtOk2GjasT0BAAACNGzdWCBcRSSCxCuLr16/H29s7oWsREZFEsmSJ+TBmaKjZ7tIFGjb0plGjBtYQ3qRJE1asWIGzs3MSVioi8uKK1RzxTz75hGzZslG7dm3rsXbt2pExY0amTZuWYMWJiEj8+/57cx542Drh3bpB3bpeNG7ciMDAQACaNm3KsmXLFMJFRBJQrB/WNMK+Y//r559/JmvWrPFekIiIJJxFi6Bjx/AQ3qMH1K69hSZNGltDePPmzVmyZIlCuIhIAotVEE+dOjW+vr4JXYuIiCSgBQugU6fwEN67N1SvvokmTZoQFBQEQIsWLViyZAlOTlpUS0QkocVqjvgrr7xCUFAQCxYssK4nKyIi9uPbbyOG8D59oFq1jTRtGh7CP/jgA4VwEZFEFKvvtg0aNOD06dNMmDCBCRMmWI/fuXOH119/PdaDWSwWTp8+/fxVioiIzebNg65dw9t9+0Llyhto1qyZNYS3atWKRYsWKYSLiCSiWN0Rb9u2LW3atMHR0RHDMKzzxcNeP8//REQk8cyZEzGE9+8P77+/jmbNmlpDeOvWrfn+++8VwkVEElmsvus6ODgwfPhw+vfvz59//om/vz/t2rUjQ4YMTJ8+PaFrFBERG8yaBT17hrcHDoTy5dfSokULgv/dy75t27YsWLAAR0fHJKpSRCTleq7bH2nTpqVEiRLWtrOzM2+//Xa8FyUiInEzY4b5MGaYwYPhrbfW8MEHH1hDeLt27fjuu+8UwkVEkojNv4ccN24crq6u8VmLiIjEg+nTzYcxw3zyCZQuvYoPPmhJyL/baHbo0IH58+crhIuIJCGbg3ijRo3isw4REYkHU6dCv37h7U8/hZIlV9KqVStrCO/YsSPz5s1TCBcRSWKxelhTRESSv8mTI4bw4cOhRIkfaN06PIR36tRJd8JFRJKJeHlEfufOnWzatInff/+du3fvYrFYyJQpEyVKlKBevXpUq1YtPoYREZEoTJwIgwaFtz//HAoXXk7r1m0IDQ0FoHPnzsyZMwcHB92DERFJDuIUxO/cuUPfvn05evQoQITlCf39/bl27Rrbt2+nbNmyTJkyhSxZssStWhERecbXX5sPY4YZORJefXUZbdu2tYbwLl26MHv2bIVwEZFkxOYgHhQURJcuXThz5gyGYVC8eHHee+89cuXKBcC1a9c4ePAgJ06c4MiRI3Tp0oWVK1dqnVoRkXj01Vfmw5hhvvgCChRYStu27awhvFu3bsycOVMhXEQkmbE5Fa9cuZLTp0+TJk0aJkyYEOn0k/79+7Nr1y4GDRrEmTNn+OGHH2jdunWcChYREdO4cebDmGHGjIGXX15Mu3YdrCG8R48eeHp6KoSLiCRDNn9n9vLywmKxMHz48GjngLu7uzN8+HAMw8DLy8vW4URE5CljxkQM4WPHQt6839O+fXtrCO/ZsyczZsxQCBcRSaZs/u584cIFHB0dqVu3box969ati5OTExcuXLB1OBER+dcXX8Bnn4W3x4+H3LkX0aFDB+uzOr1798bT0xOLxZJEVYqISExsnpri5+dH2rRpcXFxibGvi4sLadOmxc/Pz9bhIjAMA29vbzZu3MiZM2e4c+cOGTNmpFChQnh4eNCoUSOcnZ3jZayoPHjwAA8PD27fvm095uPjw0svvZSg44pIyjZyJIwaFd6eMAGyZl1Ax46drCH8o48+YurUqQrhIiLJnM1BPHPmzNy8eZObN2+SPXv2aPvevHkTX1/fGPvFhq+vL3379uXAgQMRjt+6dYtbt25x8OBBVqxYgaenJ7lz547zeFEZP358hBAuIpKQDANGjrQwZkz4sW++gUyZvqNTp87WEN63b18mT56sEC4iYgdsnppSunRpACZPnhxj36lTp2IYBm+++aatwwHmSi29evWyhvBcuXLRt29fJk2axODBgylYsCAAp06dokuXLjx8+DBO40Xlp59+Yu3atTg4OODq6pogY4iIhDEMmD49G2PGhH/LnjQJMmSYT6dO4XfC+/XrpxAuImJHbA7ibdq0wTAM1q9fT79+/fjzzz+f6fO///2PAQMGsGbNGiwWC61atYpTsStWrODnn38GoFixYmzYsIGePXtSp04dOnXqxLp166hQoQJgzmGfOXNmnMaLjL+/P59//jkArVu3JmvWrPE+hohIGMOAzz6zMHt2+PeaqVMhbdq5dOnSxXpswIABTJo0SSFcRMSO2Dw1pWzZsnz44Yd89913bNu2jW3btpEjRw5y5syJxWLh+vXrXL9+3dq/U6dOlC1b1uZCg4ODmTVrFgAWi4WvvvqKDBkyROjj6upqXUrRz8+PJUuW0KVLFzJnzmzzuP81ZcoULl++TI4cOejXrx+7du2Kt88WEXmaYcDQofDVV+H3TKZPB2fnOXTr1t16bODAgXz99dcK4SIidiZOa1oNHjyYzz//nAwZMmAYBtevX+f48eMcO3aMa9euYRgGGTNmZMSIEQx6eu9lG/z888/cuXMHgPLly/Pqq69G2i9Llix4eHgAEBgYiI+PT5zGfdqJEyf4/vvvAfjss89wc3OLt88WEXmaYcCQIeaGPWGmTQvFyWk23buHh/CPP/5YIVxExE7FeZvLVq1a0bRpU/bv38/JkyetYTlLliyUKFGC9957L1Yrq8Tkxx9/tL6uWLFitH0rVqzI6tWrrec1a9YszuMHBQUxbNgwQkNDqVq1KtWrV4/zZ4qIRMYw4OOPYeLE8GOff34Ni2UNPXp8ZD02ZMgQxo0bpxAuImKn4mW/eRcXF9zd3XF3d4+Pj4vU+fPnra+LFSsWbd/ixYtbX//xxx/xMv7cuXM5f/48adKksc4RFxGJb4YBAwfC08/Bz5wZytWrM/noo/AlUz755BPGjh2rEC4iYsfsZru1S5cuWV/nyZMn2r45c+bE0dERgL///tu6y5ytLl68yOzZswHo378/OXPmjNPniYhExjCgf/+IIXzuXAgMnM6Yp9Yt/PTTTxXCRUReAPFyRzwxPL0UYaZMmaLt6+TkhJubGw8ePCA4OBg/Pz+b53OHhoYybNgwAgMDKV68OG3atLHpcxJCSEhIooxhGEaijCUJQ9fQPpgh3IKnp3l/xGIxmDvX4OHDqQwYMMDa79NPP2XUqFFxvsEgiUdfg/ZP19D+JddraDdB/OldOWOzdvfTfeISxJctW8axY8dwdHRk9OjRODgkj18iBAcHR5iuk1AMwyAgIIALFy7o7pud0jVM/gwDxozJwfLl5gpPFovB6NHXuHhxOuPHj7f26969O61bt463KXeSOPQ1aP90De1fYl/D4ODgWPWzmyCeFK5evcrEf5+Wat++PUWLFk3iisI5OTnx2muvJfg4ISEhXLhwgUKFClmn+4h90TVM3kJDoU8fC8uXh98J//Zbg7t3lzN8eHgI79mzJ5MnT9Y1tEP6GrR/uob2L7Gv4alTpwgKCoqxn90E8TRp0vDgwQMAAgICcHKKvvSAgIAI59pi5MiR+Pn5kSdPHj766KOYT0hkifXNwGKx4OjoqG8+dkzXMHkKDYWPPoI5c8y2xQKLFlm4eXNShCVfP//8cz744ANdQzumr0H7p2to/5LjNUwe8yxiIV26dNbX9+/fj7ZvcHAwjx49Asw7x7YE8Y0bN7J3717A/CFoa5gXEYlMaCh07x4ewh0c4Pvv4fr1ryOE8FGjRmmlJhGRF5Td3BHPnz8/ly9fBuDKlSvRrpxy/fp162T8l19+2aZ53atWrQIgW7ZsnD59mtOnT0fa7+mHSJcuXWr9B0Pz5s3JmjVrpOeISMoWGgpdu8K335ptBwdYvBguX57AkCFDrP1Gjx7N8OHDk93DRSIiEj/sJoi/9tpr7N+/H4CTJ0/y9ttvR9n35MmT1tdR7cAZW7du3WLq1Kmx6vvdd99ZX1epUkVBXESeERICXbrAggVm29ERli6F//1vPEOHDrX2+/LLL/n000+TqEoREUkMdjM1pUKFCtbXYYE8Ks+zC6eISGIJCYFOnSKG8GXL4OLFsRFC+NixYxXCRURSALu5I16uXDkyZ87M3bt3OXDgAH/88Uekd7vv3LmDl5cXAM7OzlStWtWm8RYvXhyrfu7u7ly5cgUAHx8fXnrpJZvGE5EXW0gIdOxoTkEBM4SvWAHnzn3J8OHDrf3Gjx8fYXqKiIi8uOIcxP/++2+8vb05d+4cDx48iHapFovFwqJFi2wax8nJie7duzN27FgMw2DIkCEsWLCADBkyWPsEBAQwZMgQ65rjrVu3JnPmzJF+3ieffMK6desA6N27d7JcFUVEXgwhIdC+vTkFBcDJyQzhp0+PjvAg5oQJE/j444+TqEoREUlscQrinp6ezJo1i9DQUAzDiLF/XBdQb9myJdu3b+fIkSOcOnWKBg0a0KJFC/Lly8f169dZvXo1Fy9eBKBAgQL06tUrTuOJiMRVcLAZwpctM9tOTrByJZw4MYqRI0da+339dcTVUkRE5MVncxDfuHEjnp6eAGTMmJEKFSqQM2dOUqVKFW/F/ZeLiwszZ86kT58+HDp0iGvXrjFlypRn+r3++ut4enqSPn36BKtFRCQmwcHQtq159xvA2RlWrYJjx0YyatQoa7+JEydG2MZeRERSBpuD+LJ/b+9UqVKFSZMmkTp16ngrKjoZMmRg4cKFeHt7s2HDBk6fPs29e/fIkCEDBQsWpE6dOjRu3BhnZ+dEqUdEJDJBQdC6tRm8wQzhq1cbHD06ki+++MLab9KkSfTv3z+JqhQRkaRkcxA/f/48FouFkSNHJloID2OxWPDw8MDDw8Pmzxg/fjzjx4+PuWMMdu3aFefPEJEXS1AQtGoFq1ebbRcXM4T//PPnjBkzxtpvypQp9O3bN4mqFBGRpGZzELdYLLi5uZEjR474rEdExK4FBcEHH8DatWbbxQXWrjU4ePAzvvzyS2u/adOm6SFxEZEUzuZ1xAsUKIC/vz+BgYHxWY+IiN0KDIQWLcJDuKsrrF9v8NNPwyKE8OnTpyuEi4iI7UG8WbNmBAcHW9fsFhFJyQIDoXlz+HdVVGsI37t3KOPGjbP28/T0pHfv3klUpYiIJCc2B/HmzZvj7u7OmDFjOHToUHzWJCJiVwICoGlT2LDBbKdKBRs3Guze/QlfffWVtd/MmTO1rKqIiFjZPEfc09OTIkWKcPToUTp27EipUqV44403cHNzi/Y83QkSkRdJQAA0aQJbtpjt1KnNEL5t22C++eYba7/Zs2fTrVu3JKpSRESSozgF8bANegzD4NixYxw/fjzG8xTEReRF8eSJGcLDZuilTg2bNhl4eQ1i0qRJ1n5z5syha9euSVSliIgkVzYH8bfeeis+6xARsStPnkCjRrB1q9lOkwY2bzbYtGkgkydPtvabN28enTt3TqIqRUQkObM5iC9evDg+6xARsRv+/tCwIWzfbrbTpIEtWwzWr+/P1KlTAXOJ13nz5tGpU6ekK1RERJI1m4O4iEhK5O8PDRrAjh1mO21aM4SvXduPadOmAWYInz9/Ph9++GESVioiIsmdgriISCz5+UH9+uDjY7bd3MDLy2Dlyj54enoCZgj/7rvv6NChQ9IVKiIidiFegvjOnTvZtGkTv//+O3fv3sVisZApUyZKlChBvXr1qFatWnwMIyKSZPz8oF492LXLbKdLZ4bw5ct7M3PmTMAM4QsWLKB9+/ZJWKmIiNiLOAXxO3fu0LdvX44ePQqYq6eE8ff359q1a2zfvp2yZcsyZcoUsmTJErdqRUSSQGCguTrK0yHc2zuUpUt7M2vWLMAM4YsWLaJt27ZJWKmIiNgTm4N4UFAQXbp04cyZMxiGQfHixXnvvffIlSsXANeuXePgwYOcOHGCI0eO0KVLF1auXImTk2bDiIj9CAmBNm3CV0dJlw62bg1l8eJezJ49GwAHBwcWLVpEmzZtkrBSERGxNzan4pUrV3L69GnSpEnDhAkTIp1+0r9/f3bt2sWgQYM4c+YMP/zwA61bt45TwSIiicUwoHt3WLXKbJs7ZoayaFEP5s6dC5ghfPHixbRq1SoJKxUREXtk8xb3Xl5eWCwWhg8fHu0ccHd3d4YPH45hGHiF7XohIpLMGQYMHgzz55ttJydYtSqUpUu7RQjhS5YsUQgXERGb2BzEL1y4gKOjI3Xr1o2xb926dXFycuLChQu2DicikqjGjYOwHeotFli0KJT167sy/99k7ujoyLJly2jZsmUSVikiIvbM5qkpfn5+pE2bFhcXlxj7uri4kDZtWvz8/GwdTkQk0cycCcOGPd0OZefOzixYsAAID+HNmzdPogpFRORFYPMd8cyZM+Pr68vNmzdj7Hvz5k18fX3JnDmzrcOJiCSKpUuhV6/w9tixIRw+3ClCCF++fLlCuIiIxJnNQbx06dIATJ48Oca+U6dOxTAM3nzzTVuHExFJcJs2wdNLgA8eHMK5c51YuHAhAE5OTvzwww80a9YsaQoUEZEXis1BvE2bNhiGwfr16+nXrx9//vnnM33+97//MWDAANasWYPFYtEDTSKSbO3ZA82amcsVAnTtGsK1ax1ZtGgREB7CmzRpknRFiojIC8XmOeJly5blww8/5LvvvmPbtm1s27aNHDlykDNnTiwWC9evX+f69evW/p06daJs2bLxUrSISHz65Rdz18yAALPdokUIjx93YOnSJYAZwleuXEmjRo2SsEoREXnRxGl3ncGDB/PSSy8xbdo07t+//0z4BsiUKRN9+vTRygIikiydPg21asGjR2a7du1gLJb2LF26DABnZ2dWrVpFgwYNkrBKERF5EcV5m8tWrVrRtGlT9u/fz8mTJ7lz5w4AWbJkoUSJErz33nuxWllFRCSx/e9/UL063L1rtitWDCZdunasWLEcMEP46tWrqV+/fhJWKSIiL6p42W/excUFd3d33N3d4+PjREQS3LVrUK0aXL1qtsuUCSZ79rasXLkCMEP4mjVrqFevXhJWKSIiL7J4CeIiIvbk7l2oUQPCnjEvXDiYvHlbs2bNSsC8ubBmzZpYbVgmIiJiKwVxEUlRHj0CDw84edJsv/xyEK++2poNG1YBZghft24dHh4eSViliIikBLEK4kOHDgUge/bs9O/fP8Kx52GxWBg7duxznyciEh+ePIGGDeHwYbOdPXsQr7/eks2b1wDg6urKunXrqF27dtIVKSIiKUasgvi6deuwWCy88sor1iAedswwjBjPD+unIC4iSSU4GFq2BB8fs50hQxAlSnzAtm1rATOEr1+/nlq1aiVhlSIikpLEKog3bNgQi8VCtmzZnjkmIpLchYZC586wfr3ZTp06kFKlPsDHZx1ghvANGzZQs2bNpCtSRERSnFgF8fHjx8fqmIhIcmMY0L8//LtBJs7OgZQu3YK9e9cDkCpVKjZs2ECNGjWSrkgREUmR9LCmiLzQRo2CadPM1xZLIKVKNePAgY2AGcI3bdpEtWrVkrBCERFJqRxsPfHq1avcuHEj1v1v3LjB1bAFe0VEEsGUKWYQNwXwxhtN+eUXM4SnTp2azZs3K4SLiEiSsfmOuLu7O9myZePHH3+MVf+WLVty/fp1Tp8+beuQIiKxtmCBOSXFFEDRok05fnwzEB7CtQmZiIgkpThNTYnNiilx6S8iYou1a82HM01PKFSoCadPewGQJk0atmzZQuXKlZOqPBERESAR54gHBgbi6OiYWMOJSAq1Y4e5TGFoKMAT8uVrzIUL3oAZwr28vKhUqVKS1igiIgJxmCP+PG7cuMHdu3fJmDFjYgwnIinUwYPmhj2BgQBPyJ27EX/9ZYbwtGnT4u3trRAuIiLJRqzviP/yyy8cDtuO7l9+fn54enpGe56vry979+7FMAzeeOMN26oUEYnBiRPm1vV+fgD+ZM/ekKtXtwPhIbxixYpJWqOIiMjTYh3EDx8+jKenZ4RNfPz9/ZkxY0aM5xqGgaurK926dbOtShGRaFy4ADVqwP37AP5kytSAmzd3AODm5oa3tzcVKlRIyhJFRESeEesgnidPHt566y1r+5dffsHJyYlSpUpFeY6DgwNubm68+uqrNGrUiHz58sWpWBGR/7p8GapVA3M1VT/Sp2/AvXs7ATOEb926lffeey9JaxQREYlMrIN4o0aNaNSokbVdpEgRMmTIwOLFixOkMBGRmNy+bd4J/+svAD/Spq2Pr68PAOnSpWPr1q28++67SVqjiIhIVGxeNWXcuHG4urrGZy0iIrHm6wu1asGZMwB+pEpVj8ePdwFmCN+2bRvly5dP0hpFRESiY/OqKXXq1KFAgQJcvHgxxr4XL17k7NmzBAUF2TqciIiVvz/UqwdHjwI8xsWlLk+emCE8ffr0bN++XSFcRESSPZuDuJeXF40aNWLmzJkx9p09ezaNGjVi27Zttg4nIgJAUBA0awb79gE8xsmpDoGBuwHIkCEDO3bs4J133knSGkVERGLD5iC+fbu5LFjTpk1j7Nu0aVMMw1AQF5E4CQmB9u1hyxaARzg4eBAcvBcID+Fvv/12ktYoIiISWzbPEf/jjz9wdHTkzTffjLFvsWLFADh//rytw4lICmcY0Ls3LF8O8AiLxYPQ0B8ByJgxIzt27KBs2bJJWqOIiMjzsPmO+M2bN3Fzc8PFxSXGvm5ubqRLl46bN2/aOpyIpHDDhsHs2QAPgdoYhhnCM2XKxM6dOxXCRUTE7tgcxJ2dnfEzt7CLkWEYPHnyJMJmQCIisTVhAowbB2EhHPYD4SE8Nr+ZExERSW5sDuIvvfQSQUFB/PrrrzH2PXbsGEFBQeTJk8fW4UQkhZo7F4YMAfAFagE/AZA5c2Z8fHwoU6ZMElYnIiJiO5uD+LvvvothGEycOJHg4OAo+wUHBzNp0iQsFot2txOR5/LDD9C9O8ADoCZwAAgP4aVLl07C6kREROLG5iDerl07XF1d+fXXX+nYsSOnT59+ps/p06f58MMPOXLkCC4uLrRr1y5OxYpIyuHlBW3agGGEhfBDAGTJkoVdu3ZRqlSppCxPREQkzmxeNSVnzpyMGjWKoUOHcuTIEZo0aULWrFmt00+uXLnC7du3MQwDi8XCF198Qe7cueOtcBF5cf34IzRpAsHB9zFD+M8AZM2aFR8fH0qWLJmU5YmIiMQLm4M4QMOGDcmUKRNffPEFV65c4datW9y6dStCn7x58/LZZ5/x/vvvx6lQEUkZfv0V6taFJ0/uAzWAXwAzhO/atYsSJUokZXkiIiLxJk5BHKBSpUps376dw4cP8+uvv3L79m0AsmXLRpkyZShXrhwODjbPgBGRFOTsWahZE3x972GG8COA+f1k165dFC9ePEnrExERiU9xDuIAjo6OvPvuu7z77rvx8XEikgL99RdUrw63b98DqgNHAciePTu7du2ybgwmIiLyooiXIC4iEhc3bpgh/PLlu5gh3FwWNXv27OzevZuiRYsmaX0iIiIJQXNGRCRJ3b9vTkf544+7QDXCQniOHDkUwkVE5IUWb3fE/9/encfHdDV+HP9kjwii9no0WktqqZZaqrWEUG2otWgtrV3tLe1D6aoLWlpPqaWrKmrfWmuF2ImdhghaSwUlJEhIZpL5/ZHf3CYkEZHkmuT7fr366nXvmXvOzDHjO3fOOddmsxEdHc2NGzew2WxpltPKKSJiFxMDzZvDgQORJIXw/UDSqkwbNmzg0UcfNbN5IiIi2eqeg/jGjRuZOXMme/fu5ebNm+mWdXJySnW9cRHJe+Ljk5Yo3LbtEkkh/ACgEC4iInnHPQXxL774gm+//TbdK+DJZbSciORuCQnQuTOsWXMJCAAOAlCqVCk2bNiAn5+fqe0TERHJCZkeI759+3a++eYbXFxcePPNN1mxYgWQdOvp33//ndmzZ9O/f38KFSpE4cKFmT59OkFBQVnWcBFxTDYb9O0LCxdeBBpjD+EPPvggwcHBCuEiIpJnZDqI//LLLzg5OdG7d2969epFuXLlkk7o7EyZMmV48sknGTx4MEuWLCFfvnyMGjWKfPnyZVnDRcTx2Gzw1lvw/ff/kBTCDwFQunRpgoODqVixoqntExERyUmZDuIHDiSN53zppZfSLffggw/y7rvvcunSJb799tvMViciucCnn8KECfYQ/gcA//nPfwgODqZChQqmtk1ERCSnZTqIX758GQ8PD0qUKGHsc3FxSXXCZoMGDXBzc2PDhg2ZrU5EHNzkyfDOOxeARkAoAGXKlCE4OJjy5cub2jYREREzZDqI58+f/7Zb13t7exMTE3NbGHdxccHDw4MLFy5ktjoRcWCzZsGgQedJCuFJKyfZQ7h9WJuIiEhek+kgXrJkSW7cuMG1a9eMfWXKlAHg4MGDKcqeO3eO69evZ7YqEXFgy5fDq6+eIymEHwHgoYceIjg4mEceecTUtomIiJgp00Hcvsbvn3/+aeyrU6cONpuNyZMnG1fF4+Pj+eSTT1I8RkTyhg0boH37cyQmNgLCAPD19VUIFxER4R6CeIMGDbDZbKxatcrY16lTJzw8PNi1axcNGzbkpZdeokGDBgQFBeHk5ETXrl2zpNEicv8LCYEWLSKIj/cHjgJQtmxZgoODefjhh01tm4iIyP3gnoL4wIED8fX1NfaVLl2aCRMm4O3tTXR0NPv37ycqKgonJyf69u1LYGBgljRaRO5voaHQrNlZYmP9gXDg3xBetmxZM5smIiJy38j0nTW9vb0ZOHDgbfubNGlCzZo12bhxI+fPn6dAgQLUq1ePhx566J4aKiKO4c8/oXHjv4mKagQcB6Bs2YfZuDFYnwMiIiLJZDqI79q1CwA/Pz8KFiyY4piPjw+tWrW6t5aJiMM5dw4aNTrDP/80Ak4AULbsI2zaFGxM5hYREZEkmQ7iXbt2xdnZme3bt2dle0TEQUVGgr//GU6f9geSJnGXLVuOTZs2KISLiIikItNBvECBAjg7O1OoUKGsbI+IOKBr1yAg4DTh4Y34N4SXZ/PmDfznP/8xt3EiIiL3qUxP1vT19SUmJob4+PisbI+IOJibN6FZs1McOODPvyG8Alu2BCuEi4iIpCPTQbxFixZYrVZWrlyZle0REQditULLlifZvt0f+AsAX9+KbNmygdKlS5vaNhERkftdpoN4165dqVGjBh999BEbN27MyjaJiANITIQOHU7y++/+wEkAHnrIj23bFMJFREQyItNjxKdOnUrNmjUJCwvjtddeo3z58tSoUYMiRYrg7Jx2vk9tyUMRcSw2G3Tv/hdLlvgDpwEoU+ZRduxYT6lSpUxtm4iIiKPIdBCfPHkyTk5O2Gw2AI4dO8bx48fv+DgFcRHHN2TIn8yc6Q+cAaB06UcJCdlAyZIlTW2XiIiII8l0EK9Vq1ZWtkNEHMSoUSeYNKkR9hD+4IOV2bNnPSVKlDC3YSIiIg4m00H8559/zsp2iIgDeP/943z6aSPgbwBKlqzM3r0K4SIiIpmR6SAuInnL228fY+zYRsBZAIoVq8KBA+spXry4uQ0TERFxUAriInJHb755jAkT/IEIAIoXf4xDh4IoXryYqe0SERFxZBlevvDRRx+lfv36qR47ceIEYWFhWdYoEbk/2GwwePBRJkxoiD2ElyjxGH/8oRAuIiJyr+7qirh9hZRbvfrqq1y+fJnDhw9nSaNExHw2G/Tvf5Rp0xoB5wAoWbIahw4FUbRoUXMbJyIikgtk2dCUtEK6iDgemw169Qrjhx8aAecBKF36CQ4cWEeRIkXMbZyIiEguoTHiIpKCzQavvnqEn39uBFwA4D//qc7+/b8rhIuIiGQhBXERMSQmQqdOh5k3rxHwDwBlylRn//51PPDAA+Y2TkREJJdREBcRICmEt28fyuLFjbGH8LJln2TPnrUK4SIiItlAQVxESEiANm3+4NdfGwMXAXj44Zrs2bOWwoULm9s4ERGRXEpBXCSPs1rhhRcOsXp1Y+ASAOXK1WL37rX4+PiY2jYREZHc7K6CeGRkJJUqVUrzeHrHAJycnLTEoch9xGKBwMCDrFsXgD2EV6hQm5CQNQrhIiIi2SxL1hHPaTabjVWrVrF8+XKOHDlCZGQkPj4+lC9fnsDAQNq0aYObm1uW1HXu3Dk2b95MSEgIR48eJSIigps3b+Lt7Y2vry+1atWiffv2lC1bNkvqE8kpcXHQrNkBNm4MACIB8POrw86dayhUqJC5jRMREckDMhzEBw4cmJ3tyLCrV68yZMgQtm3blmL/xYsXuXjxItu3b2fu3LlMnjyZBx988J7qGjBgAEFBQal+AYmKiiIqKooDBw7w448/0qtXL15//XWcnTN8s1IR09y8CU2a7Gfr1gDgMgCPPvoUO3asVggXERHJIQ4VxC0WCwMGDCAkJASAUqVK0aFDB3x9fTl//jyLFi3ixIkThIaG0rt3b+bOnUuBAgUyXV94eLgRwitXrkydOnUoV64c3t7eXLp0ieDgYLZs2UJCQgLTp08nOjqaDz/8MEueq0h2iY2FRo32ERLSBHsIr1y5Ltu3r6ZgwYLmNk5ERCQPcajJmnPnzjVCeJUqVfjxxx9TXL3r0qUL/fv3Z8uWLRw/fpwpU6YwfPjwTNfn7u7OSy+9RNeuXSlfvvxtx7t27cqKFSt46623SEhIYO7cuTRv3pzatWtnuk6R7HT9Ovj772XPnibAFQCqVn2abdtW39OXVhEREbl7DjOOwmq1MnXqVCBp0ue4ceNu+wndw8ODzz77DC8vLwBmzZrF5cuXM13n7Nmz+fDDD1MN4XbNmzena9euxp8XL16c6fpEstPVq1Cv3h727AnAHsKrVXtGIVxERMQkDhPEQ0JCiIxMmlBWt25dKlSokGq5IkWKEBgYCEB8fDxBQUGZrjOjq0Y8//zzxvbRo0czXZ9Idrl61Zl69fZy4EATIAqAJ56oz5YtqxTCRURETOIwQXzz5s3Gdv369dMtm/x48sdll+RB5ubNm9len8jduHwZOnX6h8OHm2IP4TVqNGDz5pUK4SIiIiZymCAeHh5ubFepUiXdslWrVjW2jx07lm1tskt+FfxeV2oRyUqXLsEzz+zmzz9bANEA1KzZkI0bV+Dt7W1u40RERPI4h5msefLkSWO7dOnS6ZYtWbIkLi4uJCQkcPr0aRITE7N1WcFffvnF2Pb398+2em6VkJCQI3XYbLYcqUuy1oULUK/eLv76qxlwFYBatRqybt1y8uXLpz51IHofOjb1n+NTHzq++7UPHSaIX7t2zdguXLhwumVdXV3x9vYmOjoaq9VKbGxstl39W7p0qbGSS7FixWjXrl221HMrq9Wa4leC7GKz2YiLi+P48eM4OTlle32SNf75x5VOnSKIiHgBSHrvVKv2FNOmfcHZs2fNbZzcNb0PHZv6z/GpDx1fTveh1WrNUDmHCeKxsbHGtoeHxx3LJy+TXUE8LCwsxbrh7777rrFiS3ZzdXWlYsWK2V5PQkICx48fp3z58ri4uGR7fXLvzpyBzp13pAjhjz/+FMHBmpjpqPQ+dGzqP8enPnR8Od2HoaGhWCyWO5ZzmCB+v7lw4QL9+vUzviB07dqVZs2a5WgbcurDwMnJCRcXF334OICTJ+Hpp7dy7txzwHUAnnkmgK+++owCBQqoDx2Y3oeOTf3n+NSHju9+7EOHmayZ/EpzXFzcHcsnL5PVV6kjIyN59dVXiYiIAKBFixaMHDkyS+sQuVsnTkCdOltShPD69ZuyevVS8uXLZ27jRERE5DYOE8ST/6QeFRWVblmr1cr160lBxNXVNUuD+OXLl+nWrRt//fUXAM2aNWPcuHHZOhlU5E7Cw+Gppzbzzz//hvCGDZ9lzZplCuEiIiL3KYdJj2XLljW27zTZ7Pz588as2IceeijLQvKVK1fo1q2bMUkyICCACRMm4OqqET5insOHoW7dTVy69DwQA0CjRs+xerVCuIiIyP3MYYJ48omJf/zxR7plkx9P6w6cdysqKoru3bsba4b7+/szceJE3NzcsuT8Iplx6BA8/fRGLl/+N4QHBDzPypVL8PT0NLdxIiIiki6HCeL16tUztrds2ZJu2bu5C2dGREdH0717d44cOQJAgwYNmDRpEu7u7vd8bpHM2rcP6tULJjo6EEiaNNy0aSC//bZYIVxERMQBOEwQr1OnDg888AAA27ZtS/OOmZGRkaxcuRIANzc3AgIC7qnea9eu0aNHDw4fPgwkfSH4+uuvFcLFVLt2Qf3667l69d8Q/uyzzfn1V4VwERERR+EwQdzV1ZXXXnsNSFqUffjw4URHR6coExcXx/Dhw40lBTt37myE91uNGDECPz8//Pz8mDRpUqplrl+/Ts+ePY2hLvXq1WPKlCkK4WKq7dvB3z+ImJgWwA0Ann/+BZYvX5ShNfZFRETk/uBQswxffvll1q5dy+7duwkNDaVVq1Z07NgRX19fzp8/z8KFCzlx4gQAjzzyCAMGDLin+nr16sWBAwcAKFKkCG3btk0x7CU1np6eKYbRiGSlzZvh2WfXcfPmC8BNAAIDW7J48XyFcBEREQfjUEHc3d2dKVOmMHjwYHbs2MG5c+eYOHHibeUqVarE5MmTKViw4D3Vt2/fPmM7MjKSoUOH3vExpUuXZv369fdUr0hq1q+HwMDfiYtriT2Et2jRikWL5utXGhEREQfkUEEcoFChQsyYMYNVq1axbNkyDh8+zJUrVyhUqBDlypWjefPmtG3bVquZSK6ydi20aLEGi6UVkHSzqpYt27BgwVyFcBEREQflcEEckm5RGhgYSGBgYKbPMXbsWMaOHZtuGftShSJmWrECWrdejdXaGnsIb926LfPnz9UXThEREQfmMJM1RfKipUuhVatVKUJ4mzbtFMJFRERyAQVxkfvUggXQrt1KEhJaYw/hL77YnnnzflEIFxERyQUUxEXuQ3PmQMeOv5GY2AaIB6B9+w7MmTNbIVxERCSXUBAXuc/89BN07vwrNltb7CG8Y8eXFMJFRERyGQVxkfvIt99Ct27LgXaABYCXXnqZWbN+xtXVIedWi4iISBoUxEXuE19/DX36LANexB7CO3XqxM8/z1QIFxERyYUUxEXuA19+CQMHLiF5CO/SpQszZyqEi4iI5FYK4iIm++wzGDp0MdABsALQtWtXZsyYgYuLi6ltExERkeyjIC5ioo8+guHDF5E8hL/yyiv8+OOPCuEiIiK5nIK4iAlsNnj3XXjvvQVARyABgG7duvHDDz8ohIuIiOQBGnwqksNsNnj7bRg3bj7QCXsI7969O9999x3Ozvp+LCIikhcoiIvkIJsNhg6FiRPnAZ2xh/CePXvyzTffKISLiIjkIQriIjkkMREGDYIpU34BugCJAPTq1Yvp06crhIuIiOQxCuIiOSAxEV57Db79dg7QFXsI79OnD1OnTlUIFxERyYMUxEWyWUIC9OwJP/00C3gVewjv27cvU6ZMUQgXERHJoxTERbKR1Qqvvgpz5vwMdMMewvv168fkyZMVwkVERPIwBXGRbGKxQOfOsGDBT0B3wAZA//79mTx5Mk5OTqa2T0RERMyly3Ei2SAuDjp0gAULZpA8hA8cOFAhXERERABdERfJcjdvwosvwooVPwI9sYfwwYMHM3HiRIVwERERARTERbLUjRvQujWsXfs90Bt7CB8yZAhffvmlQriIiIgYFMRFskhMDLzwAmzY8B1JITzJ66+/zhdffKEQLiIiIilojLhIFrh2DZ5/HjZs+IbkIXzo0KEK4SIiIpIqBXGRexQdDc2awebN04G+xv4333yT8ePHK4SLiIhIqjQ0ReQeXLkCzz4Lu3dPBfob+9966y3GjRunEC4iIiJp0hVxkUy6dAkaN4bdu6eQPIQPHz5cIVxERETuSEFcJBP++ScphO/fPxkYYOx/++23GTNmjEK4iIiI3JGCuMhdOncO/P3h0KFJwCBj/8iRI/nkk08UwkVERCRDFMRF7sLff0PDhnDkyP+Awcb+d955h48//lghXERERDJMQVwkg06dSgrhx459Cbxu7H/vvfcYPXq0QriIiIjcFa2aIpIBp04lDUc5efILYJix//333+eDDz4wq1kiIiLiwBTERe7gzJmkiZknT04A3jT2f/DBB7z//vvmNUxEREQcmoK4SDrOnk0K4X/++TnwX2P/6NGjeffdd81rmIiIiDg8BXGRNJw7lxTCjx8fB4ww9n/00Ue888475jVMREREcgUFcZFUXLgAAQEQHj4WeNvY/8knnzBy5EjzGiYiIiK5hoK4yC0uXkwK4UeOfAqMMvaPGTOGESNGpP1AERERkbugIC6SzKVLSSE8NPRj4N8x4GPHjmX48OHmNUxERERyHQVxkf93+TI0bQqHDn0EvGfs/+yzz3jrrbfMa5iIiIjkSgriIsCVK0khfP/+D4EPjP3jx49n2LBhaT5OREREJLMUxCXPi46GZs1g794PgA+N/RMmTGDo0KGmtUtERERyNwVxydOuXoVmzWzs2vUBMNrY/+WXX/L666+b1SwRERHJAxTEJc+6fh2ef97Gzp3vAR8b+ydOnMiQIUPMa5iIiIjkCQrikifFxEBgoI1t294BPjX2f/XVVwwaNMi8homIiEieoSAueU5sLLRoYWPz5lHAGGP/5MmTGTBggHkNExERkTxFQVzylBs3oGVLG8HBbwPjjP1ff/01/fv3N69hIiIikucoiEuecfMmtG5tIyhoOPC5sX/KlCn069fPvIaJiIhInqQgLnlCXBy0bWtj7dr/AuON/dOmTaNv377mNUxERETyLAVxyfXi46F9exurVr0JfGHsnz59On369DGvYSIiIpKnKYhLrmaxQMeONn79dSgw0dj/7bff0qtXL9PaJSIiIqIgLrmW1Qovv2xj6dI3gP8B4OTkxHfffUePHj3MbZyIiIjkeQrikitZrdCli41Fi4YAk4CkEP7999/TvXt3cxsnIiIigoK45EIJCfDqqzbmzRsMTAaSQvgPP/xAt27dTG2biIiIiJ2CuOQqiYnQo4eNOXMGAlOApBA+Y8YMXnnlFXMbJyIiIpKMs9kNEMkqiYnQu3ciM2cOIHkI/+mnnxTCRSRLxMXFMWnSJFq0aMHjjz+On58ffn5+7Ny5E4DGjRvj5+fH4sWLM3X+e328iDgWXRGXXCExEV57LZEffugPTAfA2dmZn376iS5dupjbOJFMiIuLY8mSJQQHB3PkyBEuX76Mu7s7xYsXp2bNmjRv3pynnnrK7GbmOUOGDGHDhg0AeHp6UrRoUQDc3NzMbFa2W7x4MWfPnqV27drUqVPH7OaI5BoK4uLwbDYYODCRb7/tB3wDgJOTMz///DOdOnUyt3EimbB161ZGjhzJ+fPnjX1eXl7Ex8fz559/8ueffzJ//nwaNGjAZ599RuHChU1sbd5x4sQJI4R/+eWXBAYG3lamTJkyuLu7U6BAgZxuXrZasmQJISEhDBw4UEFcJAspiItDs9lgyJBEpk7tC3wHJIXw2bNn8fLLL5vbOJFMWLlyJW+99RZWq5USJUowaNAgAgICuHDhAhUrVuTkyZPMmzeP2bNns2nTJjp27Mgvv/xCkSJFzG56rhceHg6Aj49PqiEc4KeffsrJJomIg9MYcXFYNhsMHZrIpEl9sIdwZ2cXfvlljkK4OKQTJ04watQorFYrFStWZOnSpbRv355ChQoZZcqVK8fIkSOZMmUKbm5unDp1imHDhpnY6rzj5s2bAOTPn9/klohIbqEgLg7JZoP//jeRiRN7Ad8D/4bwjh07mts4kUyaOHEisbGxuLu787///Y8HHnggzbINGzakX79+AGzfvp3g4GAAfvzxR/z8/Hj66aexWq3p1hcQEICfnx9ffvllqsfXrVtH//79qVevHlWrVqVWrVp07tyZX375BYvFkupjunbtip+fH5MmTcJqtTJjxgzatWtHzZo1U0xqtDtx4gQffvghgYGBVK9enerVq9OsWTPeeOMN1qxZQ2Ji4m11xMfHM2vWLDp16kTt2rWpWrUqDRo04I033mDPnj1pPt/kbbPZbMyfP5/27dtTo0YNqlevTseOHVm2bNltj5s0aRJ+fn6MGDECgLNnzxqTNJPvhztPtrx58yZTpkwhMDCQatWqUbduXXr37s327dvTbPetdu7cydChQ/H39+exxx7jySef5MUXX+Tbb78lNjY21ceMGDEiRVtXr15N165dqV27No8//jitWrXip59+uu31Xrx4MX5+foSEhAAwefLkFM/dz8+Pv//+O8NtF5GUNDRFHI7NBiNHJjB+fC9gBpAUwufO/YX27dub2jaRzPrnn39Yt24dAC1atOCRRx6542O6devG999/T0xMDLNnz8bf358XXniBzz//nMjISLZs2YK/v3+qj929e7cRoFq1apXiWExMDMOGDTPGQwN4e3tz7do1du/eze7du1m2bBnTp09PcbU+ufj4eF555RX27NmDq6trqleRv/nmG7788ksj/Hl4eODp6cnp06c5efIkK1euZNeuXRQsWDDF69S7d2/CwsIAcHV1xcvLiwsXLrBy5UpWrlxJ3759GTp0aJqvW0JCAgMGDCAoKAhXV1c8PT2JiYlh//797N+/n1OnTjF48GCjvJeXF0WLFuXmzZtcv34dZ2fnFF+SvL2906wruaioKHr16sXhw4eNtlutVjZt2sTmzZt577330n281Wrlgw8+YMGCBSnaduPGDQ4dOsShQ4dYtGgR33//PaVLl07zPKNHj2b27Nk4Ozvj7e3NzZs3CQsL49NPP+Xw4cOMGzfOKGufkBodHY3FYsHLywsvL68U53NxccnQ8xeR2+mKuDic999PYOzYnvwbwl2ZP3+eQrg4tJCQECOQPvvssxl6TP78+alXrx6QFKytVitFixblmWeeAUj16q7d8uXLAXj88cdvC/3//e9/2bBhA76+vkyYMIE9e/awZ88eDhw4wJQpUyhTpgz79u1j5MiRaZ5/1qxZHDlyhDFjxrBnzx5CQkLYsWMHfn5+AMyZM4cJEyaQmJhIo0aNWLJkCQcPHiQkJITdu3fz9ddf06hRI5yd//1nKiEhgcGDBxMWFoaXlxeffvop+/btY9euXWzcuJGWLVsCMH36dObNm5dm2+bMmcOOHTv49NNPjee2ceNGGjVqBMDUqVM5efKkUb5nz55s3bqVUaNGAVCqVCm2bt1q/PfOO++kWVdy7733HocPH8bd3Z0PP/yQvXv3smvXLjZs2EDTpk359NNPuXz5cpqPHzduHAsWLKBo0aK8//777Ny5k3379nHgwAFmzpxJ5cqV+euvvxg0aFCqvyQArF+/nvnz5zN8+HBCQkLYtWsXO3bsMD4/ly5dmuLqfGBgIFu3bqV69eoA9OjRI8Vz37p1K6VKlcrQ8xeR2ymIi0P58MMEPvqoO5A0IcrZ2ZUFC+bRrl07cxsmco+OHTtmbFeuXDnDj3v00UcBiI2NJSIiAoDWrVsDEBQUxLVr1257THx8PKtWrQJuvxoeHBzMunXrKFasGD///DMtWrQwrvh6eHgQEBDArFmz8PLyYt26dRw5ciTVdsXGxjJhwgTatm2Lp6cnAIULF8bHx4fo6GjGjx8PJAW9qVOnpnjO+fPnp0mTJkybNi3F1ea1a9eyb98+ACZMmEC7du1wd3cHoGTJknz++ec0adIESBrmExcXl2rboqOjmTx5Mu3atTPaVrJkSb744gt8fHxITEw0Xp+sEh4ebvzi8f777/PSSy/h4eEBwIMPPsjEiROpVq0aN27cSPPxP//8M/ny5eOHH36gU6dO+Pj4AElLJ9apU4eff/6ZkiVLEhoayvr169N87u+//z49evQwVnYpXLgwH330EeXKlQNgxYoVWfnURSQdCuLiMD75JIEPPugG/AwkhfCFC+fTtm1bU9slkhWioqKMbXvAyojkSxfazxEQEIC3tzdxcXGsXr36tsesX7+eq1ev4ubmRvPmzVMcsw97aNmyJSVKlEi1zpIlSxpL2G3evDnVMhUqVKBx48apHluzZg0xMTG4ubnx9ttv4+TklP6T/H/2gFitWrU0z/36668DcPnyZbZt25ZqmerVq/P000/ftt/Ly8v4heHo0aMZalNGbdmyBUi6mp7ahQMXFxf69++f5uMXLlyIzWajYcOGxq8Kt/L29ja+iKTVLyVKlEi1ficnJ5o2bQpk/XMXkbRpjLg4hLFjrbzzzqvAHABcXNxYtGjBbVfzRPIam8122z5PT0+aNWvGokWLWL58+W3DtuxDVvz9/W8L/fbJjvPnz093aIv9Srv9KvytatSokeZj7Ve1q1SpQvHixdMsd6s//vgDINUQbVehQgWKFy/OP//8w6FDh4zhJslVq1YtzceXLFkSSLpynJWOHz8OQO3atdP84lGrVi1j3Pit7P2yadMmY+hRauyTNdPql8ceeyzFcJ/k7F+8svq5i0jaFMTlvjd+vJW3334F+AVICuGLFy80xoOK5AbJA3FUVFSaV6NvldaV9NatW7No0SJ27drF2bNnjcl7V65cMa6W2oew2FksFq5cuQIkBe3UhrXcyr6k363SW/Hl4sWLQNKQjLsRGRkJcMcxyaVKleKff/4xyt8qvZvt2Ieq3GnFmbtlD7fp9auHhwc+Pj5cunTptmP//PMPkBS001oZJbm0+sWM5y4iaVMQl/vaxIlW3nqrKzAXSArhS5Ys4oUXXjC3YSJZrHz58sZ2aGhohoO4fYy2l5dXimBbq1YtSpcuzdmzZ1m+fLmx1OGKFSuwWCz4+PjQsGHDFOdKPsEvrTtHZlRGVtLI6JCUzD4us+fPTpltk71vhg0bRp8+fbKySSJiIo0Rl/vW5MlW3nijM/+GcHeWLl2sEC65Up06dYwhA2vXrs3QY2JiYti6dSsANWvWxNX132srTk5Oxnsl+RAT+3bz5s1xc3NLcT4PDw/jiml2jhMuVqwYkLQe992w3z303Llz6ZazH0/vqnxOsy/zeP78+TTLxMfHp/iFI7miRYsC/97dU0RyBwVxuS9NnWph0KBOwHwgKYQvX76EFi1amNswkWxSvHhxAgICgKSr1n/++ecdHzNjxgxiYmIA6NSp023H7UNP/vrrLw4ePGj8P/mxW9nHdq9evTrNJfDulb2O0NBQY5hKRjz22GMA6d785sSJE8YwDnv5+4H9F49du3alOq7ffiytYSH212zjxo1Gn+ck+5X8tNouIpmjIC73nW++sdC//8tA0uoNLi4eLF++9J5+JhdxBEOGDMHT05P4+HiGDBmS7prSGzduZOrUqUDS1fTUbtzz8MMP8/jjjwNJV8KXLl1q7E9rwmKHDh0AOHnyJN9991267Y2NjSU+Pv5OT+s2zz33HN7e3lgsFsaOHZvhx9lXeNm/fz8bN25MtczEiROBpKvh6U3qzGn21VgiIiJYsmTJbccTExON/kxNhw4dcHJy4urVq3z22Wfp1mWxWLI8rNuXkbx69WqWnlckr1MQl/vKDz9Y6Nv3JWARkBTCf/11KYGBz5vbMJEcUKFCBT7++GNcXFwIDw+nTZs2LFy4MEX4+euvvxgzZgz9+/fHYrFQpkwZJkyYkObYY/vKQitWrODXX38F0r4aDtCkSRNjGbsJEybw/vvv89dffxnH4+PjOXDgAJ9//jmNGjVK98tCWgoUKMBbb70FwG+//Ub//v1TrEceHR1NcHAw/fr14/r168b+pk2bGjeWeeONN1i6dKnxReDChQuMGDHCGNbzxhtvGOt03w8qVqxorODywQcfMH/+fGOd83PnzvH666+zf/9+8uXLl+rjK1WqxKuvvgrA3LlzGTx4MEeOHDGuUCckJBAWFsbXX39N06ZN01zfPbMqVKgAJK3acuHChSw9t0hepsmact/46ad4evZ8CUi6WuTi4smKFcto1ixjdxkUyQ1eeOEFChUqxKhRozh//jyjRo1i1KhReHl5kZCQkOImNfXq1ePzzz9Pdyx0YGAgY8aM4cqVK1y5cgUnJ6c7rjj0+eefM2rUKFasWMHcuXOZO3cuXl5euLm5ce3atRRDVjI7+fCll14iOjqaiRMnEhQURFBQEJ6enri4uKS4mpu8LhcXF7766it69erF0aNHGT58OO+88w5eXl4pltzr06ePcWX/fvLJJ5/Qo0cPwsLCePfddxk9erTRdicnJ959912+//77NMfO//e//8Vms/HTTz+xZs0a1qxZg4eHB/ny5eP69esphrVk9UTVNm3a8OOPP3Lq1Cn8/f154IEHjC86c+bMMZZ9FJG7oyAu94XZs+Pp1q0DkDSRzMXFk5Url/Pss03NbZiICRo0aMDvv//O4sWL2bBhA0ePHuXy5cu4u7tTqlQpatasSYsWLahbt+4dz1W4cGEaNmxo3NWxdu3ad1w2MF++fHzxxRd07NiRRYsWsXfvXi5evEhsbCxFihShXLly1K9fn6ZNm2Z4dZfU9O3bl0aNGvHzzz+zc+dOLly4gM1m4+GHH6ZKlSrGEJbkihcvzsKFC5k3bx4rV67k+PHjxMbGUrx4cZ588km6dOlCzZo1M92m7OTj48PcuXP5/vvvWbFiBX///TfOzs7Ur1+fnj17UrduXb7//vs0H+/i4sLIkSNp3bo1c+fOJSQkhAsXLnD9+nUKFixI2bJleeaZZ2jSpIlxx9WsUrZsWWbOnMn06dM5ePAgUVFRRvDXcocimedk08wLh3Lw4EEsFgtubm7p3pQiqyQkJBAeHk7FihUztBxZZvzySzydOrUHlgNJIXzVql9p2rRJttSX1+REH0r2Uh86NvWf41MfOr6c7sOM5jWNERdTLVgQR+fOL/JvCM/HqlW/KYSLiIhIrqehKWKaxYvj6NixHTbbCiAphK9e/RtNmjQ2uWUiIiIi2U9BXEyxdOlNXnyxHTbbSgBcXLxYs2YFAQH+5jZMREREJIcoiEuO+/XXm7Rr1xabbRWQFMLXrl1J48YN7/BIERERkdxDQVxy1MqVN2ndug2JiasBcHXNz9q1K2nUqIHJLRMRERHJWQrikmPWrLnBCy+0JjEx6YYbLi75Wbt2FY0a1Te5ZSIiIiI5T0FccsS6dTcIDGxFYuLvALi6evP776vw969ncstEREREzKEgLtkuKCiWZs1akZiYdEMRFxdvfv99Nf7+z5jcMhERERHzKIhLtgoOjqVZs5YkJgYB4OJSgHXrVuPv/7TJLRMRERExl4K4ZJtNm2Jo0uQFEhI2APYQvgZ//zvflltEREQkt1MQl2yxZUsMAQEtSEgIBsDFpSBBQWto2PApcxsmIiIicp9QEJcst317DI0aNcdq3QiAi0sh1q9fS4MGtU1umYiIiMj9Q0FcstTOnddp0KA5VusmICmEBwX9ToMGtUxumYiIiMj9RUFcssyuXdepXz8Qq3UzAC4uPgQF/U7DhjVNbpmIiIjI/cfZ7AZI7rB79zWeeeZ5LBZ7CC9MUNA6hXARERGRNOiKuNyzPXuu8vTTz2OxbAOSh/AaJrdMRERE5P6lIC73ZN++q9St+xwWy3YAXFwe+P8QXt3klomIiIjc3xwyiNtsNlatWsXy5cs5cuQIkZGR+Pj4UL58eQIDA2nTpg1ubm5ZWmdsbCzz589n9erVnDp1iuvXr1OsWDGqVatG27ZtadCgQZbW5wgOHIjmqaeew2LZASSF8HXrgmjY8AlzGyYiIiLiABwuiF+9epUhQ4awbdu2FPsvXrzIxYsX2b59O3PnzmXy5Mk8+OCDWVJnWFgYgwYN4vTp0yn2nz17lrNnz7Jq1SpatGjBmDFjcHd3z5I673cHD0ZTu3Yz4uN3AuDiUoR164Lw93/c5JaJiIiIOAaHCuIWi4UBAwYQEhICQKlSpejQoQO+vr6cP3+eRYsWceLECUJDQ+nduzdz586lQIEC91RnREQEvXr14uLFiwBUq1aNli1bUrhwYcLDw5k3bx5RUVH89ttvODk5MX78+Ht+nve7Q4ei/j+EJ/WDi0tRfv89CH//aia3TERERMRxOFQQnzt3rhHCq1Spwo8//kihQoWM4126dKF///5s2bKF48ePM2XKFIYPH35PdY4ZM8YI4e3atePjjz/G2fnfxWZeeuklOnfuTEREBL/++ivNmzenUaNG91Tn/Sw0NIpatZ4lLm4XYA/h62nU6DGTWyYiIiLiWBxm+UKr1crUqVMBcHJyYty4cSlCOICHhwefffYZXl5eAMyaNYvLly9nus6jR4+ydu1aAB588EHef//9FCHcvv+DDz4w/jxp0qRM13e/O3z4Ck8+2TRZCC/G2rUbFMJFREREMsFhgnhISAiRkZEA1K1blwoVKqRarkiRIgQGBgIQHx9PUFBQputcuXKlsd2hQwc8PDxSLdegQQN8fX0BCA0NvW0seW4QFnaFGjWaEhe3GwAXl+KsXbuBxo2rmtwyEREREcfkMEF88+bNxnb9+vXTLZv8ePLHZVedTk5O1KtXL0vqvB/99dc1atZsRlzcHgBcXEqwZs0GGjeuYnLLRERERByXw4wRDw8PN7arVEk/AFat+u9V2mPHjmWqPpvNxp9//gmAq6srjz76aIbrTN5WRxceHknbtn2IizsAgLNzUggPCKhkcstEREREHJvDBPGTJ08a26VLl063bMmSJXFxcSEhIYHTp0+TmJh429juOzl37hw3btwAoESJEri6pv9SJV8qMXlbs1NCQkK2nt9qTaRmzebJQnhJVqxYh79/xWyvW7JOQkICNptNfebA1IeOTf3n+NSHju9+7UOHCeLXrl0ztgsXLpxuWVdXV7y9vYmOjsZqtRIbG4u3t/dd1Xf16lVj28fH547lk5dJ3tbsYrVas/3K+5YtZ4mNTRoT7uxciunTZ+Dr65yrrvjnBTabjbi4OI4fP46Tk5PZzZFMUB86NvWf41MfOr6c7kOr1Zqhcg4TxGNjY43ttCZNJpe8TGaC+N3W5+npaWzHxMTcVV2Z4erqSsWKFbO1jvLlKzB9+lBOnz7Gjz9+ynPPaTiKI0pISOD48eOUL18eFxcXs5sjmaA+dGzqP8enPnR8Od2HoaGhWCyWO5ZzmCBupvv12292/0VycYGdOz8jPDycihUr6sPHgTk5OeHi4qI+dGDqQ8em/nN86kPHdz/2ocOsmmJfGxwgLi7ujuWTl0n+2MzUd/PmzTuWT14mf/78d12fiIiIiOQtDhPEk9+qPioqKt2yVquV69evA0lDODITxAsWLJjh+m4tk7ytIiIiIiKpcZggXrZsWWP77Nmz6ZY9f/68MSv2oYceuusVUyBp5ZV8+fIBcOHChTsOuo+IiEi1rSIiIiIiqXGYIJ58YuIff/yRbtnkx9O6A+edODs788gjjwBJV9jDwsIyXGd2T6IUEREREcfnMEE8+Z0rt2zZkm7Zu7kLZ3qSPza9Om02W4rjydsqIiIiIpIahwniderU4YEHHgBg27Ztad4xMzIykpUrVwLg5uZGQEBAput8/vnnje158+alOUl006ZNnDp1CoDKlSvj6+ub6TpFREREJG9wmCDu6urKa6+9BiRdgR4+fDjR0dEpysTFxTF8+HBjDfDOnTsb4f1WI0aMwM/PDz8/PyZNmpRqmUcffZQmTZoASWPAR48eTWJiYooyERERfPDBB8afBw4cmKnnJyIiIiJ5i0OtI/7yyy+zdu1adu/eTWhoKK1ataJjx474+vpy/vx5Fi5cyIkTJwB45JFHGDBgwD3XOXLkSPbv38+lS5dYuHAhx44do1WrVvj4+BAeHs7cuXONFVOaN29+T1fgRURERCTvcKgg7u7uzpQpUxg8eDA7duzg3LlzTJw48bZylSpVYvLkySmWIMys0qVL8+233zJ48GDOnDnDgQMHOHDgwG3lAgMDGTt27D3XJyIiIiJ5g0MFcYBChQoxY8YMVq1axbJlyzh8+DBXrlyhUKFClCtXjubNm9O2bVvc3NyyrM7KlSuzfPly5s2bx5o1azh58iQxMTEUKVKEatWq0a5dOxo2bJhl9YmIiIhI7udwQRySblEaGBhIYGBgps8xduzYu7qC7eXlRffu3enevXum6xQRERERsXOYyZoiIiIiIrmJgriIiIiIiAkUxEVERERETKAgLiIiIiJiAgVxERERERETKIiLiIiIiJhAQVxERERExAQK4iIiIiIiJlAQFxERERExgYK4iIiIiIgJFMRFREREREygIC4iIiIiYgIFcREREREREzjZbDab2Y2QjNu7dy/2LnNzc8uROq1WK66urjlSl2QP9aHjUx86NvWf41MfOr6c7EOLxQKAk5MTNWrUSLOc/kY5mOTfm+ydnBNysi7JHupDx6c+dGzqP8enPnR8Od2Hd7rerSDuYJydnUlMTMTJyUnfzEVERETuQ1arFZvNhrNz+qPANTRFRERERMQEmqwpIiIiImICBXERERERERMoiIuIiIiImEBBXERERETEBAriIiIiIiImUBAXERERETGBgriIiIiIiAkUxEVERERETKAgLiIiIiJiAgVxERERERETKIiLiIiIiJhAQVxERERExAQK4iIiIiIiJlAQFxERERExgYK4iIiIiIgJFMRFREREREygIC4iIiIiYgIFcREREREREyiIi4iIiIiYQEFcRERERMQECuIiIiIiIiZQEBcRERERMYGCuIiIiIiICRTERURERERM4Gp2AyR72Gw2Vq1axfLlyzly5AiRkZH4+PhQvnx5AgMDadOmDW5ubllaZ2xsLPPnz2f16tWcOnWK69evU6xYMapVq0bbtm1p0KBBltaX2+VkH547d47NmzcTEhLC0aNHiYiI4ObNm3h7e+Pr60utWrVo3749ZcuWzZL68goz3oe3io6OJjAwkEuXLhn7goKC+M9//pOt9eYGZvWf1WplzZo1rF27ltDQUC5duoSzszNFixbF19eXOnXq0KRJE70fM8CMPgwODubXX3/l0KFDXLx4EYvFQv78+Xn44YepXbs2L774Ig899FCW1pkbJSQkcOLECf744w9CQ0P5448/CAsL4+bNmwC0adOGsWPHZnm9OZ1lnGw2my1Lzyimu3r1KkOGDGHbtm1plqlSpQqTJ0/mwQcfzJI6w8LCGDRoEKdPn06zTIsWLRgzZgzu7u5ZUmdulpN9OGDAAIKCgrjTR4GLiwu9evXi9ddfx9lZP6bdiRnvw9S8/fbbLF68OMU+BfE7M6v/Dh48yKhRowgPD0+33CuvvMKoUaOyrN7cKKf78PLlywwZMoSQkJB0y7m5uTFkyBB69+59z3XmZoMGDWLt2rVpHs+OIG5GltEV8VzGYrEwYMAA44OgVKlSdOjQAV9fX86fP8+iRYs4ceIEoaGh9O7dm7lz51KgQIF7qjMiIoJevXpx8eJFAKpVq0bLli0pXLgw4eHhzJs3j6ioKH777TecnJwYP378PT/P3Cyn+zA8PNwI4ZUrV6ZOnTqUK1cOb29vLl26RHBwMFu2bCEhIYHp06cTHR3Nhx9+mCXPNbcy432Ymq1bt7J48WKcnZ1xc3MjLi4uy+vIjczqv+3bt9OvXz9u3LgBQPXq1WnYsCGlS5fGZrNx/vx5Tpw4wcaNG++5rtwup/vQarXSu3dv/vjjDwA8PT1p3bo1jz76KN7e3kRERLBq1SqOHDmCxWJh/PjxeHl50blz5yx5vrlRQkJCij/7+Pjg4+PDyZMns6U+07KMTXKVmTNn2ipWrGirWLGirU2bNraoqKgUx2/evGnr0aOHUWbs2LH3XOfAgQON87399tu2hISEFMfPnj1r8/f3N8qsX7/+nuvMzXK6DwMDA23vvfee7dixY2mW+e2332yVKlUy6ty5c+c91ZnbmfE+vFVsbKytcePGtooVK9o++ugjW6NGjYz6zpw5k+X15SZm9N/Zs2dtNWrUsFWsWNH2xBNP2H7//fc0y1qtVtuFCxfuuc7cLKf7cMmSJca5GjVqZIuIiLitTGJiou2rr74yytWpU8dmsVjuqd7cbOrUqbbx48fbVq1aZTt9+rTNZrPZFi1aZLx+w4cPz9L6zMoyCuK5iMVisdWtW9dWsWJFm5+fny08PDzVcpcuXbI98cQTtooVK9qqVq1qi4yMzHSdYWFhxl9Kf39/282bN1MtFxwcnOJDUVJnRh9euXIlQ+U+/fTTbPsAzE3M6MPU2Purfv36tmvXrimIZ5BZ/derVy9drMgiZvThsGHDjP6bPXt2muUSEhJszzzzjFE2LCws03XmRdkVxM3MMhromYuEhIQQGRkJQN26dalQoUKq5YoUKUJgYCAA8fHxBAUFZbrOlStXGtsdOnTAw8Mj1XINGjTA19cXgNDQ0HTHX+VlZvShj49Phso9//zzxvbRo0czXV9uZ0Yf3urgwYPMnDkTgHfffRdvb+8sO3duZ0b/HTt2jE2bNgHQsGFDGjVqlOlziTl9ePnyZWPb/m9dapydnVPMz7APQxJzmZllFMRzkc2bNxvb9evXT7ds8uPJH5dddTo5OVGvXr0sqTM3M6MPMyr5+En7rHW5ndl9aLFYGDVqFImJiQQEBNC0adMsOW9eYUb/LVq0yNhu3759ps8jSczowwceeMDYPnXqVJrlEhMT+fvvv4GkCfAPP/xwpuuUrGNmllEQz0WSz7KvUqVKumWrVq1qbB87dixT9dlsNv78808AXF1defTRRzNc551WBMircroP70byq+DZucqHozO7D7/55hvCw8Px8vLivffey5Jz5iVm9N+uXbuApH/ka9euzcWLF/niiy9o3rw5TzzxBE8++SQtWrTg448/5q+//sp0PXmFGX0YEBBgbH/77becO3cu1XJff/21MRmwbdu2FCpUKNN1StYwO8to1ZRcJPlM4tKlS6dbtmTJkri4uJCQkMDp06dJTEy86yXpzp07Z/ysVqJECVxd0//rlDy8ZdesZ0eX0314N3755Rdj29/fP9vqcXRm9uGJEyeYNm0aAG+88QYlS5bM9LnyqpzuP4vFYnzJLV68OIcPH+b1118nKioqRbljx45x7Ngx5syZw+uvv06fPn3uqp68xIz34HPPPUfjxo1Zv349ERERPP/886mumnL48GEgaQm8d955567rkaxndpZREM9Frl27ZmwXLlw43bKurq54e3sTHR2N1WolNjb2rseRXr161djOyDjj5GWSt1X+ldN9mFFLly41lgErVqwY7dq1y5Z6cgOz+jAxMZFRo0YRHx9P1apV6dKlS6bOk9fldP9FRUVhsViApOXaBgwYQExMDGXLlqVt27aUKVOGqKgogoKCjGVEJ0yYgJubG927d7/7J5gHmPEedHJyYvLkyXz11VfMmjWL69evp7h4YVerVi0GDBhA3bp177oOyR5mZxkNTclFYmNjje20Jhokl7xM8sdmV32enp7GdkxMzF3XlxfkdB9mRFhYWIp1w9999128vLyypa7cwKw+nDNnDvv27cPFxYWPPvpIN13KpJzuv+Qh4NKlS8TExBAQEMCvv/5K3759CQwMpFOnTnz//fd88MEHRtkJEyYQERFx1/XlBWa9B11cXOjZsycDBw5Ms97du3czbdo0Y71xMZ/ZWUaf1JIlnJyczG6CZIMLFy7Qr18/44Oqa9euNGvWzORWya0iIiKYMGECAK+++iqVK1c2uUWSUbZb7mjr4+PDuHHjUr1r38svv2y8/ywWS6pXXMU8mzZtIiAggLFjx/L444/z/fffs3v3bg4dOsTatWsZNmwY+fLlY8eOHXTp0oXg4GCzmyy3MCPLKIjnIsmvUmbkDnrJy2TmCmfyx2RkFY3kZfLnz3/X9eUFOd2H6YmMjOTVV181rrq1aNGCkSNHZmkduZEZffjBBx8QGxtL6dKlGTRoUKbOIUlyuv9u/SwMDAxM9w6PHTp0MLZ37Nhx1/XlBWa8Bzdt2kTfvn25evUqzZo146effqJevXoUKFAAd3d3fH196dOnDz/99BPu7u7cuHGDYcOGcenSpUzVJ1nH7CyjIJ6LJP/wvnWiz62sVivXr18HksbIZebDp2DBghmu79Yy2XE779wgp/swLZcvX6Zbt27GCg3NmjVj3LhxGu6QATndh8uXLzduef7ee+9p2NA9yun+u/Wz8E6rfCQ/rvsxpM6Mz9Fx48YZEz1HjRqV5mdltWrVaNOmDQDXr19n8eLFmapPso7ZWUaTNXORsmXLGuuTnj17Nt3Z4ufPnychIQGAhx56KFMBq2TJkuTLl48bN25w4cIFrFZrurONk49nLFu27F3XlxfkdB+m5sqVK3Tr1s1YlikgIIAJEybccSa5JMnpPlywYAGQNIn28OHDxqoMt0o+qWj27NnGPyAdOnSgaNGid11vbpXT/eft7U3x4sX5559/gDv/w578uD1ASko53Yd///03x48fB6BcuXKUKFEi3fJ169Zl3rx5ABw6dOiu65OsZXaW0b+suUjFihXZsmULAH/88Qe1a9dOs2zyiSJp3XXsTpydnXnkkUcIDQ3FarUSFhaWYn3N9OqsWLFipurM7XK6D28VFRVF9+7djeXU/P39mThxIm5ublly/rzArD68ePEi//vf/zJU9ocffjC2GzVqpCCejBn95+fnZwTxO63CkPy47piaupzuQ3vfQcb6JPkVWC1cYD6zs4x+Z85Fkt/tyf4hlJa7ufNYepI/Nr06bTZbiuPJ2yr/MqMP7aKjo+nevTtHjhwBkm7lO2nSpFQnjUnazOxDuXdm9F/Dhg2N7dDQ0HTLJj+uuzKmLqf7MPk44QsXLtyxfPIrqhlZLk+yn5lZRkE8F6lTp45xm91t27aleZewyMhIVq5cCYCbm1uKO4Ldreeff97YnjdvXpoTYzZt2mTc9rdy5cr4+vpmus7czIw+hKSrbD169DCGNdSrV4+vv/5aITwTcroPf/75Z44ePXrH/5L/PB8UFGTsr1SpUqbqza3MeA82a9bM+NVp5cqV6V4Vnz9/vrGtL2+py+k+9PX1NZa9i4iIYO/evemWt9cJ8Nhjj2WqTslaZmYZBfFcxNXVlddeew1I+tY2fPhwoqOjU5SJi4tj+PDhxnJ0nTt3Nj6wbjVixAj8/Pzw8/Nj0qRJqZZ59NFHadKkCZD0ATR69GgSExNTlImIiEix/u3AgQMz9fzyAjP68Pr16/Ts2dP4ua1evXpMmTJFITyTzOhDyTpm9F/x4sXp1KkTkDQ8bPjw4cTHx99Wbu7cuaxZswZIWunh5ZdfztyTzOVyug89PT1ThPi33347zTXep02bxrZt2wBwd3fnueeeu/snKHflfs8yGiOey7z88susXbuW3bt3ExoaSqtWrejYsSO+vr6cP3+ehQsXcuLECQAeeeQRBgwYcM91jhw5kv3793Pp0iUWLlzIsWPHaNWqFT4+PoSHhzN37lxjlnHz5s3v+eptbpfTfdirVy8OHDgAQJEiRWjbtm2Kn2tT4+npqeFF6TDjfShZx4z+Gzx4MNu3byc8PJygoCBeeOEF2rVrx3/+8x+uXr3KunXrUrwvP/zwwzSDo+R8Hw4dOpRt27YRFRXFyZMnadGiBS1btuSJJ57Aw8ODc+fOsXr1auOzFpKCXKlSpe6p3tzszJkzLFy4MMU++/wlgMOHD/Pll1+mON6sWbNM30fBrCyjIJ7LuLu7M2XKFAYPHsyOHTs4d+4cEydOvK1cpUqVmDx5copJI5lVunRpvv32WwYPHsyZM2c4cOBAig8bu8DAQMaOHXvP9eV2Od2H+/btM7YjIyMZOnToHR9TunRp1q9ff0/15mZmvA8l65jRf97e3nz33XcMGTKEffv2cfLkSeMmTcl5enry4Ycf0rJly3uuMzfL6T4sU6YMP/zwA0OHDuXkyZPExMTwyy+/pHrTJVdXVwYOHEjfvn3vqc7cLiIigmnTpqV53D68LjlfX99MB3GzsoyCeC5UqFAhZsyYwapVq1i2bBmHDx/mypUrFCpUiHLlytG8eXPatm2bpSthVK5cmeXLlzNv3jzWrFljfBAVKVKEatWq0a5duxQTkiR9ZvShZC31oWMzo/9KlCjBnDlzWLFiBStWrODIkSNERkbi6enJQw89RP369encuTPFixfPsjpzs5zuwypVqrB8+XJWrVpFUFAQoaGhXL58GYvFgre3N2XLlqV27dp06NCBMmXKZEmdkrXMyDJOtlvvrysiIiIiItlOkzVFREREREygIC4iIiIiYgIFcREREREREyiIi4iIiIiYQEFcRERERMQECuIiIiIiIiZQEBcRERERMYGCuIiIiIiICRTERURERERMoCAuIiIiImICBXERERERERMoiIuIiIiImEBBXERERETEBAriIiIi92DEiBH4+fkxYsSITJ+ja9eu+Pn5MWnSJNPaICI5z9XsBoiIpGXSpElMnjz5tv3u7u4ULlyYypUr07JlS55//nmcnJxMaKHjWbx4MWfPnqV27drUqVPH7Obkenq9RSQ9uiIuIg6haNGixn9OTk5cuHCBDRs28MYbb9CnTx/i4+PNbqJDWLJkCZMnTyYkJMTspuQaxYoV4+GHH6ZYsWK3Hcup1zu9NojI/UtXxEXEIWzdutXYTkxM5MSJE4wZM4atW7eyadMmvvzyS4YPH25iCyWvGjZsGMOGDcvzbRCRu6cr4iLicJydnalQoQJTp07F19cXgHnz5mG1Wk1umYiISMYpiIuIw/Lw8OC5554DICYmhj///PO2MhcvXmT8+PG0bNmSJ598kscee4yAgABGjRrF8ePHUz1v8olzVquVGTNm0K5dO2rWrImfnx87d+5MUf7cuXN89tlntGrViieffJJq1arRpEkT+vXrx9KlS4mLi0u1nnttm81mY/78+bRv354aNWpQvXp1OnbsyLJly2573OLFi/Hz8zOGSEyePBk/P78U//39999G+WvXrrFy5UqGDx9Oq1atqFOnDlWrVqVBgwYMGjSIbdu2pdq+5Gw2G0uWLOGll16ievXqPPnkk3To0IEFCxZgs9nuOMEwM6/PnbRo0QI/Pz9mzZp127GDBw8ar0X//v1vO56YmEjt2rXx8/MjKCjI2J/a87jb1zu5u+nX9Npgl9m/M3cjPj6e2bNn88orr/DUU09RuXLl255vRp67SF6joSki4tBKlChhbF+/fj3FsQ0bNjB06FBiY2MBcHNzw83Njb///puFCxeybNkyPv74Y1q3bp3quePj43nllVfYs2cPrq6u5M+f/7YyS5cu5b333jPCtpubG/nz5yciIoIzZ86wfv16/Pz8qFSpUpa2LSEhgQEDBhAUFISrqyuenp7ExMSwf/9+9u/fz6lTpxg8eLBR3tPTk6JFixIdHY3FYsHLywsvL68U53RxcTG2Z8yYkWKirJeXF87Ozly4cIG1a9eydu1aevfuzZtvvplq+xITE3nrrbf47bffAHBycqJgwYIcOnSIAwcOsGPHDtzc3FJ9bFa8Pml56qmnOHbsGDt27KBLly4pju3YscPY3rVrF4mJiTg7/3u96vDhw0RHR+Ps7EytWrXSreduX2+7u+3Xu5Fd546NjaVHjx7s27fP2FewYEFu3LiBxWLJVFtF8gpdERcRh3b27Flju1ChQsb2wYMHGTRoELGxsXTs2JGVK1dy4MAB9u3bx4YNG+jUqRMWi4VRo0Zx6NChVM89a9Ysjhw5wpgxY9izZw8hISHs2LEDPz8/ADZu3MiIESOIi4ujevXqzJ49m4MHD7Jz50727dvHjz/+SPPmzW8LnFnRtjlz5rBjxw4+/fRT9uzZw549e9i4cSONGjUCYOrUqZw8edIoHxgYyNatW6levToAPXr0YOvWrSn+K1WqlFG+SJEivPrqq8ydO5eQkBD27dvHwYMHCQ4O5rXXXsPV1ZVvv/2WDRs2pNq+H374wQjh3bt3Z9u2bYSEhLBr1y6GDRvGypUrWb9+faqPzYrXJy1PPfUUACEhISQmJqY4Zv+lw9vbm6tXrxIaGpriuD2oV65cmYIFC6Zbz92+3nZ32693I7vO/fnnnxshvGPHjmzZsoVdu3axd+9eJkyYQIECBYCkCddjxoxhzJgxFC5cOFPPQSS3URAXEYd1/fp1I+z5+Pjw8MMPG8dGjx6NxWKhf//+jB49mnLlyhlXIB988EHef/99unbtitVqZerUqamePzY2lgkTJtC2bVs8PT0BKFy4MD4+PlitVkaPHo3NZuPJJ59k5syZ1KxZ07iC6uHhwdNPP80XX3xB+fLlU5w3K9oWHR3N5MmTadeundG2kiVL8sUXX+Dj40NiYiKrVq3K7EtLp06dGDlyJNWrV0/xBadUqVK88cYbDBkyBICZM2fe9tgbN24wbdo0AF588UVGjBjBAw88ACSF3D59+jBo0CCio6NTrTsrXp+01K5dG2dnZ6Kjozly5Iix32KxsHfvXjw8PHjppZeAlFfIk//ZHuazQ3b2a3acOzIykgULFgDQvHlzRo8ebazc4u7uTosWLZgwYQIAly5dokiRIrRt2zbVX5dE8iIFcRFxOFevXmX79u288sorXLhwAUgaB2sPwWFhYRw6dAg3Nzd69OiR5nnswxq2b99OQkLCbccrVKhA48aNU33szp07jXGub7/9Nu7u7hlqe1a1rXr16jz99NO37ffy8qJevXoAHD16NENtyoyAgAAA9u3bd1v7tm7dyrVr1wB47bXXUn18t27dyJcv3237s+r1SUvBggWNYULJg/aBAweIjY2levXqNGzY8LbjFouFPXv2AGTreuDZ2a/Zce4tW7YYw08GDRqUapmGDRtSrVo1IGk5RxH5l8aIi4hDsA8HSU3Lli3p16+f8Wd7YEpMTDQmc6bGHuBiY2OJioqiSJEiKY7XqFEjzcfaf4ovVqwYjz322J2fQBa3zR5sUlOyZEmANK84Z9Tff//NL7/8wo4dOzhz5gzXr1+/LfTeuHGD6Oho44o3wB9//AEkXb0uU6ZMquf28vKiSpUq7N69O8X+rHp90lOnTh1CQ0PZsWMHPXv2BFJe7X7iiSfw9PRk7969WCwW3NzcOHjwILGxsbi5uVGzZs0M13W3srNfs+Pc4eHhQFJfJ/9F6lbPPPMMBw8e5ODBg3d1fpHcTkFcRBxC0aJFjW37nTUrVarECy+8cNtQgX/++QdICmuXLl3K0Plv3Lhx277k4fJWFy9eBJICyN3IqrbZx92mxj7s4F6Wc1y3bh1Dhw5NseJL/vz58fT0xMnJiYSEBK5cuZJq+y5fvgxA8eLF060jteNZ9fqk56mnnuKHH35g9+7dWK1WXF1djSBet25d3N3dqV69Otu3b+fAgQPUrFnTOP7YY4/dNukyK2Vnv2bHua9evQqknDSdGvt4ePv7RkSSKIiLiENIfkOfO7FfLX3kkUfuaZx0aqta3MrJyemuzplVbctOV65cMSah1qpViwEDBvD444+nCKCnT5+madOmQNJye6m502uTfEUSu5x4fWrWrImrqyuxsbEcPHiQypUrs3//fvLnz0/VqlWBpLC+fft2duzYkSKIZ+f4cEfk6poUI26d+Hore79m5D0lkpdojLiI5Dr2q+dnzpwxlr/LavYJaclXbcmInGjbvdq4cSPXrl2jYMGCfPPNN9StW/e2q8D2K9epsa+IkV4ZwBjfn1xOvD758+c3hhPt2LGDPXv2YLFYqFWrlhEs7ePAd+7cSVxcHPv370+xX5LY+ysiIiLdcvbjdzOESCQvUBAXkVzHPrbbYrHw+++/Z2sdFy9eNMZE3y9tS4/9KnVaV7EBzp8/D8DDDz+c5jCM9G7oY7+qfPbs2TRv3BIbG3vb8oCQc6+PPVDv2LEj1avd9iEo+/fvZ9u2bcTHx+Ph4ZHuvIHUZOT1dmRPPPEEkPQ+sI8XT439F630xqmL5EUK4iKS6zz22GNUrlwZgIkTJxpjltMSFRV113XUqVPHmIg4bty4DN+4JCfalh5vb2/g37G9qbGPJT516lSqdwWNiIhI9c6UdvXq1TPqmT59eqplZs6cmeoV75x6feyhe//+/WzatAlIGh9u5+rqSs2aNYmPjzeWSKxevXqGV8exy8jr7cjq1KljjA//3//+l2qZ1atXc/jwYSBpYrWI/EtBXERyHScnJz788EPc3d2JiIigffv2rF69OsWkvgsXLrBs2TK6d+/O+PHj77oOFxcX3n33XZycnAgJCaFbt27s2bPHGCt7/fp1du7cyZtvvpniduw50bb0VKhQAYBNmzalOjQEkoK0s7MzUVFRDB061BhWYLVa2bBhA126dEl3/He+fPno27cvAPPnz2fcuHHGxM6YmBi+++47vvrqqxTrk9vl1OtTo0YN3N3diYuLIywsjMKFC9+2Mo89rB84cADI3LCUjLzejszV1ZX//ve/QNIE3+HDhxvPMy4ujgULFjBixAgg6fX09/c3q6ki9yVN1hSRXKlatWpMmzaNoUOH8vfffzNkyBBcXFwoUKAAcXFxKYJd+/btM1VHw4YNGTt2LO+++y67d++mU6dOuLm54enpaayjDRhL5OVk29LSpk0bfvzxR06dOoW/vz8PPPAAHh4eQNKdF0uWLImvry99+vRh2rRprFu3jnXr1uHt7U1cXBwWi4UiRYowbtw4I2ynpmfPnhw5coSVK1fyww8/MGPGDAoWLMi1a9dISEigRYsWuLq6snTp0tuuMufE6+Ph4cETTzxBSEgIkHSjn1u/XNwavDMzUTMjr7eja9GiBWfOnGHixIksXbqUpUuXUqhQIWJjY41fiqpWrcqXX35515ObRXI7BXERybWeeeYZfv/9d+bOnUtwcDAnTpzg2rVreHh4UL58eZ544gkCAgJ45plnMl1H69atqVmzJjNnzmTr1q1ERERgtVp56KGHqFixIs8++yzlypUzpW2pKVu2LDNnzmT69OkcPHiQqKgoY8m65EvXvfHGG1SoUIFZs2YRFhaG1WqldOnSNGzYkD59+nDz5s1063FxceGLL76gXr16zJs3j2PHjmGxWKhSpQrt27enffv29O/fHyDV28XnxOvz1FNPGUE8tZBduXJlChUqRHR0NF5eXpka35zR19vR9evXj6effpqZM2eya9cuLl++TL58+ahWrRrNmzenQ4cOuLm5md1MkfuOky23ziAREZH7ls1mw9/fn/PnzzNu3DjjTpkiInmJxoiLiEiOW7ZsGefPn8fV1TXFJEkRkbxEQVxERLLF0KFDWb16dYqVTy5dusQ333zDO++8A0CrVq3ueFdGEZHcSkNTREQkW9SsWdOYtJovXz5cXV1TTGKtWbMm06dPN5b4ExHJaxTERUQkWyxdupRNmzZx+PBhLl++TGxsLAUKFKBSpUoEBgbSqlUrTeATkTxNQVxERERExAQaIy4iIiIiYgIFcREREREREyiIi4iIiIiYQEFcRERERMQECuIiIiIiIiZQEBcRERERMYGCuIiIiIiICRTERURERERMoCAuIiIiImICBXERERERERP8H827wrBx83DNAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# similar plot from all the residuals in the w range [-2.0, -0.34]\n", + "\n", + "residuals = (posteriors_all[:,0] - true_test_theta[0:2000])/posteriors_all[:,1]\n", + "sigma_intervals = np.linspace(0, 3, 21)\n", + "num_samples = []\n", + "for sigma in sigma_intervals:\n", + " num_samples.append(np.sum(np.abs(residuals) < sigma)/len(residuals))\n", + "num_samples = np.array(num_samples)\n", + "\n", + "# Calculate the percentages for each sigma value\n", + "percentages = [percentage_within_sigma(sigma) for sigma in sigma_intervals]\n", + "fraction_of_percentages = np.array(percentages)/100\n", + "\n", + "fig, ax = plt.subplots(1,1,figsize=(8, 8))\n", + "plt.plot(fraction_of_percentages, num_samples)\n", + "plt.plot([0,0.5,1],[0,0.5,1], 'k-', zorder=1000)\n", + "\n", + "plt.xlim([-0.05,1.05])\n", + "plt.ylim([-0.05,1.05])\n", + "plt.text(0.03,0.85,'Underconfident',horizontalalignment='left', fontsize='x-small')\n", + "plt.text(0.7,0.05,'Overconfident',horizontalalignment='right', fontsize='x-small')\n", + "plt.xlabel(r'$Percentage\\ within\\ \\sigma$', fontsize='x-small')\n", + "plt.ylabel(r'$Fraction\\ of\\ lenses within\\ \\sigma$', fontsize='x-small')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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cVq9ezauvvsqkSZP48Ic/fOgbO0IkuAghhBBCjJD9CSx9vAd/ylhg7OgQrg+T811aM/CBUp/gjuDSmNDcscagPmNwVplB1E/xXJ2mC5+4a5Jn+7THXLqzPjgJEi3LUFUnQLAQrX1A4YQrufj4S5hdPcDj4jEcWobLihUrAJgzZ86A5SeccMJux+3Nc889B8B5551HMpnkoYceYsmSJSilmD17Nh//+MfJz88/RC0/fCS4CCGEEEKMgP0NLf3LGkP/TveWAR+q9HY7riWlueBZn21JMA2fzfEAHyhQmMpjNDHCgQAnFWl+15nFbV+N27oKtEcGh/wJ55L1FGhNga2pjBh7NuQYDy3a94nFYgd03t5s29a7atuoUaMGLO9bUazvuL159913AQiHwxx//PFs2LBht/Kvf/3rPPbYY3zgAx/YZ11HEgkuQgghhBCH0aEILIPJeHDbMtieAB+N52q2d2dZZyimFyhSnk/az/DAO+201b6Gl+5Go1FAJhUjlUpQHIoQsqAyBOdUqt0vcAyHFr1jfFhTUyPjq0sOef3xeByASCQyYHlOTs5ux+1NZ2cnAD/60Y8oLCzkscce44Mf/CDNzc1861vf4sEHH+Tyyy/n3XffpbKy8hDdwfCT4CKEEEIIcRgMZ2Dpsy0BjSkIWxB3eh+0A3is6fRwXZic6/DHt5eTbNv5DbxCYRZNwiw7Hse0yXia0pBiQp4iZO25etixGFqgd56J1pqKikoWL1253+efNncmTU2Ng5b3BaPdVmwboHwo/B29O67rcv/993P++ecDkJ+fzwMPPMCGDRt46623WLhwIf/1X/815HpHmgQXIYQQQohhdDgCS5+KMIzLUbRlFNvjGtCksy6u0mxvrGXZW8tJZzL9xxuRYoyyuZjhIgylqI4otDKZmK+5eZqBbex4iD7GQ8uulGGQm5e37wMHOG9v+lYRSyQSA5Ynk0lgZ8/LUOqqqqrqDy27uv7663nrrbeGvLTykUKCixBCCCHEMDng0LKfgQV6N4yMmPCvM+Hna3wWeS6xrKZZQ7rpLZq6NxG1wDEUrrIJVMyBkhloZWDiE1YuJTkmswvgthmayvCOig8gtByNgWVXw7Gq2OjRowGoq6sbsLzv9b7j9mbMmDEAjB07dsDyvtebm5v3s5UjS4KLEEIIIcQhdrgCS0NS83Sdx4aY5s0Og7CpyLd91ndrHNenx4HyqEGqeBTdsU1o4NRxoxg96RQWd+fR7lqkfIUfCKHdBKFoiKsnpKgMu70XkNBy2MyePRuAt99+e8Dyvtf7jtubvpXJOjo6Bixvb28HhtZ7cySR4CKEEEIIcYgcrmFhrq/Z3AM/Xu2yvN1nXY/CVRqFwsVEa01uIACmS6dnEMwdRUH5VMqLSgmVVHPp2CCp7Zpl3ZoWz8a1bHKi+bjKo9s10dplc4+HqwNMvfHGIbfr2Akth77LZf78+eTn57Np0yaWLVu2x7LIjzzyCACXXHLJPus699xziUajbNq0ie3bt++xUtnzzz8PDL708pFq74PthBBCCCHEkOxPaPEe/OnuvSxDCC1xR9OY1Pi+5uvL4aMvwaPbTTZ1uGRdH9eDbDZNdvsrOFv+TswzMEyTggCELMWoMceTX1iNAiblaP5vrs+nJ1lMK4kwIc/AM2ym5zrMK8jydLPJv2ws47bkXB5+a2jDiY6d0EJvbtnfX/tg2zY333wzAAsWLNht9bDvf//7vPPOO8yfP58TTzyx//U777yTKVOmcNttt+1WVzgc5h/+4R9wHIebbrppt7r++te/8pvf/AalFDfddNMB3PzIkR6XI0hzczPTpk0bsGzBggUsWLDgMLdICCGEEPtyKIaFaa15vQ1a0nBqCZSFd19ZalPM5+tLHDqzUNKwlb+FJ5BVJqDACqPcDH7netzm5WjfAcDv2ECsYBwxIGwanFpiUxbwOKXMYEqBQik4v8xhecIn5io+XJbisrIM9QnNq91h4sXVWK7Pi+s7ufrkir3e09ESWhYuXMjChQsHLPP3sQ/LofD1r3+dRYsW8dprrzFx4kROP/10tm7dytKlSykuLuaee+7Z7fi2tjbWrVtHY+Oeq5X9x3/8By+99BJPPfUUEyZM4OSTT6alpYW33noLrTX//d//zcknnzzs93QoSXA5gpSXl7N69eqRboYQQggh9qIrnubxV9bjdTZy8QlVlOQGh3Te3uaxvNIC33/XJ+7A84XwvbkGAXNnePnPR9fzgjUKBejgKFxl9pf5mRg0vIpKtaB80CiUGQDPQXkOPooMJuuyOdxxUoaK4M4H8OPzHP5nciedjsGmlM03NhXgaUXe2LF0NCeI2AZzRufu9b6OltACe/+i2DTN/vAyDHPzAQiFQjz//PPccccdPPjggzzxxBMUFhby6U9/mv/8z/8cdHPKwep67rnn+P73v8/999/PokWLCIVCfPCDH+QrX/kKF1988TDdxfBRen8WhRbDoqamhvr6eqqrqwddSUIIIYQQR4Yf/uF1/vjscrTWXDi7ktuuGHi0RJ+hzGP53RafX6zXFAZ6f/78FIvJeVD/4F8A+F97Oi/bVWR9hQ9g2Gh8vJZleK3vYiooUg4xX0HBBMKVJ+AaIRQKB8gJBhhbns/tU9OcU5xhj61CnDTfrC1lcXACWhm0Zw1ygwaFYZM7rx7PmKI9w9nRFFiGoi+4VFZVs2zN5v0+f87U8TQ2yPPewZAeFyGEEEKIIdIN62lvqAfANAza49lBj92fiffzShXPNmraMpDWJv/8YhoTnzMDE8kNh8j1AxR6Ls3KRmkfL1aL27QE7SRQABoCdpj5ZVNorp6Fp+GMUkVWwdpEED+ST8YIsnBbgNZsgmuqkjsvvmPlsJM+dBbLX26gO60JBy3GlYVJOT6xzJ5DpI610CKODBJchBBCCCH2Ydd5LFfOG0VDRwrP13zstIH31Njf5Y3H5Sj+9ySDh3/zMndG5tJtRkgYAR5kCkHHoVQnOE71kCGHtkwSZ/uLGIACDKUoKplMQcVMnMpiPlXh0Jj0qUvCKcXwk3k+/1Pr8Vy7j6vh6fbwzuCyy3LHl2rNuJIwPRmPZzYmeG5NO/kBRSqdBcL9bZXQMrhf3/Uz7rnr5wOWtba8v/ZMORJJcBFCCCGE2ItdQ0va8TmuKo+ff653ZSfL3H2B1qEGlvq0wfa0yZSoS4GteeWux0kri4LS0RS5mu2+hdIa5bs4ysDBRKPIUT7JUB7JgvEYXZuoyingwpqJrLcq0VVFxF14ZJtie0JRGoTOLJxaqpmZ4/B6V5CsVpQHNb9vyWOiHWduzs49WpRSzKzp3dejJ+nwwjtZujKanzyznTs/ESU/bElo6TPIRIsbPvtFbvjsFwcsmzOtd6iYOHASXIQQQgghBvDe1cJWNWf41VuduBo+eXw+80b19kJordl6768IKE15gH2GlmeaDG5ZYZLyDabEO/hy9+vEyidyvz2ZToJMNNrJJDtY64YBhZXpoCSgON5O8K4TxUATKZtJKFqMVTCa1W6MT5xSxvNt0J31qYv5KKApBeVhiFpwaVGKsqBHfcbmTx35LGkwCfohpk2uYtJT6/jEmePJi9j9bUy5vU/m4YBJ2vHJOD5qgoSWPjJBfGRIcBFCCCGEALoSWZq7UozxGglae2519/SGOHUxF8tQPLE23h9cHr3zQR5qq8E2Db5U1cHp+am9Xufn6zV1nRks7bE6WEhb2Xg2GvlsVzmEvQyvZXMIOV2UeF10t63B6djAuWNrKI+Oo8DXmJ5HjxkilT8R7bmEiioosj3uPknzWgt8bzX4GkIWfHESTMkDpeDUgizvdmf5rVtA6Zhq3lpZS+uqJpZtNMmL2HzizPH9bTxrcgHv1PWwuSXF5efMpnzK+L3ckRCHhwQXIYQQQhzz6juS/NfDK2hqauP46ghfO7+awHvCS1nUwgA8X1ORa/YPC/t7bCwpbdGZVbzYFd1rcPnL3YvYFj4RjDxcw6RAZynRKbp0kJB2qE04ZLJpjO4mYs0rMN0kUeXzaH0dpYWnkAoXEPcMvHQCA026oJKevDA9dgJTJTi9DL6ioT4Jp5TCxF1XMnbSTAopTj9tKm/V9lAYtsgLarSGrOPt1s6CiM1/XDpehoaJI4oEFyGEEEIc8955exWbtrVQnmOxoiFFXVeW8SW7D/m6ckYuRRET55VFnLmxFQzADjE1mqU2HSBgaCaFMwPW/8pdjwPwx5w5JEMF2I5PBRn+2dhCCJ9pXgd5yRZ6Eh7ppqUQr8dCk6s8Ck3N9IoJbDBz0CjShk1QGRg5eQSLi6iMwpPdQc4tTlNme5w90F6ROybhhz/xRW71Ne0Jh/V1pfzulVqKc4NcfGLNHqdIaBmclsFiI+KYCC7ZbJY777yThx56iPXr1+N5HuPGjeMjH/kIX/3qV8nN3fvGSoO59957uf766/d53KJFizj33HMP6BpCCCGEGD5981jGFgUpzbFpiTtMLg9TlmvvcWzENrhw6W96F9iyQ/Q9Rn2uopPpkQwBQ3Nq3s5lhlvS8MhDr1HlxSkBKKumkyKSaQfX9UnhEDMVaaX4Y6aQV1rrSDctR/suBpCnPE4vyOHaMWPxggV8w7do1AHwPFIYlAcMxueBZfRuyuL58Gi9RW3C4KxSl7mFO5Yx3mXlMADDUJTmBiidWsbpU8v2uE8JLOJIddQHl87OTs455xyWLVsGQDAYxDRNVq1axapVq7jvvvt48cUXGTNmzAFfwzAMSktLBy0PBoe2o64QQgghDp9dJ99PqQjzHxdVU9eZZWZ1hJyguduxe1stLGxqzilMALCmy2dzD7T//SUeCx5HQ3AaZUH4nLmNYhzO8JtZ5pYTAbKe5u50GTmpJpY0vE0q0wNaAwrTCjF91GSuLbEoMVyyfpqudKZ3/WPfwzJgdjjOJWU2m9NBzi2Isy4Gv6oNkPZgWbfJj6Z1URzo7RnoCy37IqFliKTDZUQc9cHluuuuY9myZeTl5XHXXXdx1VVXYRgGixYt4tOf/jRbt27l0ksvZdmyZZimue8KBzBq1Chqa2sPbcOFEEIIMSzeu1pYn1EFARrak2xpjlMcKcAw1H5tIrmuW/Ot5R4bt3ZCYAapQC41Kk2rDtKgQ2zyI/zWKe/dMNJ3aFEhWrSB27EaL90NKFAKs3gKwfLZvGOHuNNPcLPaTKRtG+HwJJK2jaugOKj4j5kwu6ir//pPNlm4uncVsYzrk/UV5rUDL807EAktQ6M5sNwiWefg7blkxlHkmWee4amnngLgF7/4BVdffTWG0XvL5513Ho8++igAK1eu5N577x2pZgohhBDiMNAN6wcNLQALn6nlv/6ygW/9aQNPrGjZvZdlR2hZnwzwWixM0lN7nP+3R15j09YO/ECQOiuPLs9gkx+iWqUYpVLc61ZRR4SYEaSbYG/viu9hlsxEmSFUsIDA+AsJV5+MGYhg49OqAyzugk1GPv92apSZ+XBWheLv5yhmF+3ehjNKXM4ucSmxXT5elab6+puG/N5IaBHvB0d1j8t9990HwPjx4/nYxz62R/mpp57KWWedxQsvvMB9993HjTfeeLibKIQQQojDYG+BBXr3YlmxLUbQMojX17P2qQ1cPGn3Hpa3ekJ8f3sxMc/klNwkt9S08sR2n7efX8lJfiuOWUg8UkSHtrA9h2qVIoPFeJJsdwNsVzk4bgKd7MAKFO6oVaHMAMGx52DZUaLBIBPNOA06hGEYBDI9PG1UUVo1let8zaLzDCwDDLVncMq14F8nxNAarOtkaNiwkS6XEXNUB5dnnnkGgAsvvBA1wF9wgIsuuogXXniBV199lVQqRTgcPpxNFEIIIcQw2ldg6aOU4tz4Sh5piVIW0JxS4veXNaU0rg9rEgHaHIsS22VNMsi3f7uCRUYlrlXFxkAFbYF82v0wGWXga0WX9kn6Bg+ki/B0EZmOpbgtK0GZBMdfBGYQhaYiAMdFLIp0jIvsdrZUTeXF4mlkW5po3JIgFo4S64HXW+EzkwZ+ntl1Ar7WGq31oM8+/fcsoeUA6QNcVUySy8E6aoNLe3s7TU1NAMyYMWPQ4/rKfN9nzZo1nHDCCft9rdbWVubOncu6devwPI/KykpOO+00PvvZz3LWWWcNuR6tNbFYbL+v3ycYDMpCAEIIIQTg1a3jt6/W89zaduaMzuOLHxxD0B58hLz34E+5rhJOKdGETU11qDe4vNzss3CtxvU1Z1QnqAjkEnMNRm98G1drXDtIUPnEtMcWL0Rqxyh8BwMPyHg+OtFMT+NSfKen92LaI9LxDuGy45lgZOi2cmhUUcpMHzsc5sWqOXQZQfToHOLtKQJOiqynKI/sO7Ss3N7Nz57ZglLwpXPHM70mb8BTjsXQkslkyGQGXq76ULn3Vz/nvl/9YsCyttaWYb32seCoDS4NDQ39v6+urh70uF3LGhoaDii4JJNJ3n77bQoLC0kkEmzZsoUtW7bwwAMPcP311/PLX/4Sy9r3W93Q0EB+fv5+X7/PN77xDb75zW8e8PlCCCHE0UA3rGd9c4JHljTia81fV7Rw4rh85h9XtMexu06+V4EQEwO7b8T4XKOmLa0JGLC+I8uVax6gxwpTlRtke36QN7MuWQxmmgk2ZHPAUOB7ePg0uy5e0xL87i0oNAoDlKKoZBLVpRMJGw5xM8gmN0BI+aw0c3C7M9S2xoh1xrAsi7ywRX7ApiAAHx3rvbf5eyx1/MCrdayuj4FW/O61Ov7zo9N2O/xYDCx97rjjDr71rW8N6zU+89kv8JnPfmHAshOnT6CpsWHAMjE0R21w6enp6f99JBIZ9Lhdy3Y9Zyiqqqr4xje+wUc+8hEmT55MMBjE8zzeeOMNvvGNb/DMM89wzz33EI1G+clPfjKk+tasWbNfbdiV9LYIIY41iYxLcyxDZX6IcODAVoYU709aa+rb4oQCJiX5vf+X7zosLGwbBC2DtniWaNAiZO/5+djbEsd9jsvrHaLV0djB5Lpmytwe6vMquTdTQQMhwKNYZ1nnhkn7HoaXwtPgxzbjtSxHeVkieCQxUeFi8qpOoionF9wMzb5JnCAB5ZPSBka2h82+RbKhAQcTfI/Lx7qcUaoZHdFMzd+lx+U9gaVPXtjqXVEZTW5o98e8Yzm0ANx222388z//8wGfX1hYiO/7+z5QDJujNrgcDueffz7nn3/+bq+Zpslpp53G008/zUc+8hH+/Oc/89Of/pR/+Id/YNKkSXutTylFXt7AXbpCCCF215N2+ekLm6nrTDO6KMwXzxpHTlD+WztW/OH5NfzuuTWEAhb//MEKThxfuFv5mJIIN583ltc2djGtOoe5Y3f+/zqUwLKkTfNkvaYiDBdsewsFzCwOscUfz68yVTSoCJ5WhJUB2qPBj5DGxHeSuI2v4SfbADABzwxglp6AWTARx1CESLNVRXCVSQawtCakHS7MbKFh6onUN2WxfRgThZMK4YLK9zRukNACcONZYyiM2hhK8ZGTqvpfP9ZDC8iQ+qPB+/Zf+I985CMsXrx4j9dHjRrFW2+9RW5ubv9ryWRyj+MGKtv1nINlGAbf//73+fOf/4zv+zz++OMHlfKFEELsbktbgi3tSYoiNpvbEmxtTzK9Sr78ORZorXn8tY2ksy7tbZ28uMbcI7gAnDmlmDOnFPf/PNQ9WeKO5idrfVZv6URpn9E548kzPCw/wXOqjBbC/RtFOhh0qTAZ7eEB2gygs3Ggd6/IssIadOkJdJm9vUKVKs0lZgc/pQYfC1fZhHSGyrwA06adyI0lcHIJbE/B3EK4cNfR7jsCCwy+oWR5fogvnju+/2cJLMNDyzz7EfG+DS4dHR00Nzfv8Xoo1PsPUVXVzm8Z6uvrB61n17JdzzkUJk6cSElJCW1tbWzevPmQ1i2EEMe6yvwQpTlBGmNpqvJDlOfJN6nHCqUUkwvg+YY44YDB+LKcfZ4zlF6WPq/e+zeagrOwsOm0I7zrK3LweKNoBolgLtp1yTbVg+dg5BSSox2S8ThoH2UGMStOwmtZTkHVXE4tjLDBCxLwfCx8bg3VUWF5TLd9lrkBbB/8QATDdCkNedRE4caBBmjspZdl0PdJQsuwkdwyMt63weWFF17Ya3lxcTEVFRU0NTWxatWqQY/rKzMMg6lTpx7KJgohhBhGpblBvnTWOLa2JxlbEqEkR4LLsaBvHss/XjiBueMKiARN5k8uAcD1NVlXEwnsXD1sKIEl68PmOJSH4N37HicKXJyT4GW3gEKdplkH8MwgcTuC67roQAgjGMTv6iS59R3smhNRCvSOyfdG7igCuVUYgSCqMkq+GaW6vZ4LsluZYPauanV9+2Ji0ZPIFhajgGvHaD5QtrNNcc9gazZITSBLvp8A9i+0UDmJ51fU8c6WNmaPK+Gs2TVDP1eII9T7NrgMxTnnnMMDDzzA008/Peh65n/7298AOO200w75Hi6bNm2ira13jOu4ceMOad1CCCF6e10q8/f9Dbo4Ouw6+T43bHPR8RX9P9d1Oyx8rYPOlMfl03I5b+lvd564l9Di+PCd1QaLVrZT5Kf5VOk4ylWWE4hTY3m8ZZayIqVZ6Rkk29sgHMXUGqdhGU7jUgztket0Mrr6JGqzJpaCtDaJBkxCldVsyC+gSKcpsCuYWf8utPSO9Dj/U5fQs12xLelySonmwkofY8djSo9n8N+N1WxK2YyyM/zHl86hODq0R7a+Xpa12zv50Z9W0B5L88I79VQVRzmuZs/hdEK8nwy+oPlR4NOf/jTQGyAefvjhPcrfeOMNnn/+eQA+9alP7Vfdeh+DG7XW3HrrrUBvb84ll1yyX/ULIYQQopduWL/PjSRfrU2ytjVDT8bnT0+vIK2N3sDyntCS9TRpb+f/4X++51meXdlGSLs0BItY6+ewTudQr0M8GJzKX+2JvBGoocsI47gubvMmkq/+HL/+dUJ+llKVpaBnC/9srOPKYDtn2N2cnKupyI0SMcDC710IWfvQUs+7gTLWnX8lX3nb4HebXFa1ZSiwvP7QArAlE2RDKkCh6bIpfxzrWtIMxa5Dw+Jph1TGpTAnSCrj0pNyhlSHEEeyo7rH5bzzzuOCCy7g6aef5vOf/zxKKa688koMw+DZZ5/tDyvTp0/n+uuv3+P8e++9t//1559/frfNJLdu3crVV1/NjTfeyHnnnce4ceNQSuH7PkuWLOGb3/wmTz31FAA33XQTkydPHv4bFkIIIY4y+wosAK7nU9cWp3vrNrxwgLGhLLYd2OO4VZ2aH672yXiaD9UY5D67iO1mAU1WHillMkYleZtCGv0wJpAlSmcmS9ZVaDRuwxK81lUYvoOJxkRzVnEx146qIce2uJZ2ALroYKVXhNm9jQ7KyaSznNK8gu1WPs9MO4fGbQaNCZspdg+dWZ/auOa0sh3JxUlTrVyq7Qy1eWOpzg8wtmjPe9nVQHNZZo8r5qKTxvDGumY+MKEKX2uau5KUFwy+RYQQR7qjOrgAPPjgg3zwgx9kxYoVXH311YRCIQzD6F9NbPTo0Tz++OND2iDyvd566y3eeustoHeJvdzcXHp6enbblfX666/nxz/+8aG5GSGEEOIYMZTA0ueRtxp5+m9v0u3ZjCky+GJVB+YAm8w/UeezuUezrTXBy1tNpgamkwrnkqezGNpivJFgnc6j2TeIaQsr6ZB2PLx4PV7DG+hMDxgG2lcYgVzGV03nshKPqOnQu8ZYrwIcztDN4AJrlgAw/3OX8lqHRWydQUXQJ5ZVpHyDMVHN3OKdoQWg7FOf5z96HNa1pBlfHKQqf/DgMtgEfNsy+acrjieRyvKtB5fwb/e9SUVhmNs/OY8x5YduFdVjlawqNjKO+uBSVFTEm2++yY9//GMeeughNmzYgOd5TJ8+nY985CN89atfPaC9U8rLy/nJT37Ca6+9xvLly2ltbaWzs5NQKMS4ceM47bTTuOGGGzj99NOH4a6EEEKIo9P+BBbonXy/ZnMhSfLwwznUOv6gT5UlQehu6SRuRMkzNE1mHnHXIqsM8g2XYpWlzTXo1CZaa7x4jGzDG3jdm8Ewe39psEpmUlQ8gfPDHYSj8OzU+aQCYWZtXsqY1i07L7hjPsv8z10KwOx8l/lFDqtiJteOcrmsTFMWMigJqT1WDSvPtSnPtfd670NZNayxM8XqbR0U5gapbelh1dZ2CS6HxMCfsfvu/jm/ufuXA5a1trYMZ4OOCUrva7KGGHY1NTXU19dTXV1NXV3dSDdHCCGEGBH7E1p23ZPlxVgeX6sfTZcRodj2uKqok38qbdjt+Ffuepw0JosLJ/OcW0i9H6SdAApNDh4BpSEQpstVaK1xPQ/PzeLFtuE1LO6dp5JTgV0+l7xAhGsiHZQHDHrGT6Zp7DQszyUnFeOSNx/FaN4O7Awsu92jhrgHOSYoxSFZ5jiWzPBubQdVJVHGlO3+ZWwy4/C1X7/GqtoOygoi/OenTmZSdcGQryV2Mk0T3/epqKzijZUb9/v8eTMn0tTYIM97B+Go73ERQgghxJHtQHpZgP6J92cWZzk7neHtVICgoXH0znFiz931VzarXHJVhOrSQj5IN7OMODdnpuBpA18psr4mrSwyRgRfZbDwMfGx8cjmVkP+OFS0AjtvNMWmz3lWB28FRrHBDVCRLWWqNomaEHQzqL2EFugNK7l9T1+HILSksy7f+s3rrNjcSml+mG99+rTdVg+LBG2+9cl5rKptZ3RprvS2HCrytf+IkOAihBBCiBFxsIFlV58taeWBTjCU5qMFbWgNv/nTap4adS5b4y4RJ8nH3TamWinCSuMqo3ffFSCtLHQ2SabpbbSXRo/5IHgpDEyKzCyJMafjWSEilsGpRgcfJMmTrgXap35rI6NUgpOy25hYuwLF4KGln7NzlbChhpbBhoU1dSRYX99JSV6YhvYEa7Z17LHscWFOkDNmHNpNto91kltGhgQXIYQQQhx2hyq0tDoWPZ7B2GCaf6/YzvoY/Pn+11ElZSyeeiG1wWKM7k66Vi+n1g8xlRRRw2e0kWadn4sHBHq20b39dXwvDSi8cCVmThU+kMUiGAjgmDYukK+zlJKilAx1OojtZRm/8iVOzGzcd2CBQ9LLsqvK4ihTRxexfGMLNaU5TB9TPOR6hXi/keAihBBCiMPmUPayrEyG+WFTGXHP4MycbpzOVu5dFscITqG0eDSRcB7RbIqecIQCWzHW6A0NCW1wjtFBaypDS8Pb9MQbUHrHumCGBdoFy8bUPjk45PlJMqZmHEkupw5baf7VWMuziSBFXpqznO3D0ssC+56AH7Qt/uMTp7BmWwdVxTlUl+QMuW5x4KTHZWRIcBFCCCHEsDvgwAIDhhaAtxMR6rM2BZbHr9f4xBsStJk5RHGJtyeYWhSnMmAys2Mb88xtjDIdYtrkV6kylrdso7llDVr7Ox5CFWb+GMyqeahQPrgOtp9hqpHkSrMTG3Aw6FIBwqQpb93CtfQNC5u595s5xL0s75UTDnDS5IohHy/E+5UEFyGEEEIMm/0NLLD3XpZdjQlm8BubaDRsgpkEjqEx8YlhoZJJut9dyWXhNlqSHvf7lZS5WQpSdbxU+wrJTA8eChuNb0Uorj6RdMFEHGXhKQWBACW+wxftZqLK52kqeJEybHwu71rOHIZnLgvsX2gR4lgiweUI0tzczLRp0wYsW7BgAQsWLDjMLRJCCCEO3KEcFjYQ8/cPcU2kinhJNXY6xl+NAkKFxbRkDHzX5R0nxDfiIZRS5CuHbfEEHZuWk9EmHgbaMKF4BoVFE1BWkApSaK3o8myCeMynlajyAVhOITqbIaYsUqfMZ/7k7N4bN8y9LO/Vk8xw/99X0dyZ4PIzJnP8xPIBj1vX1MMzq1soiga47PhKokF5FNzVwoULWbhw4YBlvu/v/EHGio0I+bQeQcrLy1m9evVIN0MIIYQ4KMMxLGxXr9z1ONC7U/2kHAXpBjBgVL7m6dnzeW1zJ5vb4vh4dHkmGBYJpanIMRibX8qKrg7IqcAefw4qUkIqk8B0UuT7WW4MNNDm9T4enW739F9zYk8drwdrqKwqYFqe0/96xodXOwJoYH5RlqDBYQ8tAE++vok/PL8araG2qZtf/L+LCAZ2f8xzPJ/fLN7G9o4USkFe2ObiWTLEbFd7+6K4bx8XkNwyUiS4CCGEEOKgrG3sobY1wVSrndEFe9/t/b2G0suS8WFJl03I1CT/8EcUQFn1HseZwIbGLrp6kigUvpNAGxGU9sFQjDKydI7/IIFUEpU3CYUBKLRpYTqauLJZpYq4MNhCJTuGebXUA/DdGy9lSadJyMwwJ3/nN+/314V5uCmM1rC5p4fP1fSMyLAwz/PxtcY0DFzPH/DBWmtwfY1pKDxf4/ny+C3eXyS4CCGEEOKAbW6J86OnN9DW0U11nsW/nVVCccTc53nv7WVpiZaxoWgSITfF9NbVhNyd80N+uTXMgysSWNpncmQmlm0yyU1zstnDZiL8yatgtRfBTzjoZCMqYOFufZtM00qs0R8gUDwBy/Go9UwSNVMJGDaGp3FjXRS6ScJOjJRSqGCYt40gTUT5AhspbKkFds5lOa3Y2+M+1icsfA1K+6xP2iM2l+XCeRPY2txNY3ucqz84jVBgz0e8gGVwzck1PLmymaKozQeOKzlk1xe9fnP3z7n/nl8OWNba2nKYW3P0keAihBBCiAPWtHkTHZ3dlERMOpIerQl3r8FloGFhPopVZTPoCeailSLiJJnatrb/sL+/3YJlhOgO5PCyHkd+uoO1XgQroPkbVbzj5xJDASZ+Wz1e4xuobJwIHl7DYsitJItFo2dipbMYtsK0LQpNl/luEzcEa3lRlfALYwotyiDuKFq6Ylw6hH1ZziuMsyWZi6oexwVnjR7SezYck++L8sLc9onT93nc8aMLOH50wSG//rFGDzJY7JM33sQnb7xpwLLTZ0+iqbFhOJt11JPgIoQQQoj91jePZVpZgIIArG5KMqHIZlTezkcL1/N5Z3sP0aDJ5MqcvQ4LM7SPRoEGY8dDYd9cltkVJ/OSkU9Eu+ClccwAjdrgV06AFh3ExUU7GdzGN/G7awGNqRRpbFThZDxMVCgHMgmyrY0Ylg2WTT5ZtKFQCmpDFSR1BN/3cYMWUy/7ALBnD8tunDTnFsPMT14DQFXBvufnHExoaYqlaYplyAlajCuOYBrqgOsS4v1IgosQQgghcD2f1oRDQdgibO99qNeuk++TGY+OthgkPRoy8Pa2PM6YlA/Az5/byl9XtBBsqePLo7s5u4iBQwuamc0r2VQ0npCbpumHP6M1lQBgTclxvJMOEDG6udJo5QmznFVmPinHxXMctJtFd2/GbXsH7fVNmlfo3CqM8pMxQnk7JndkUaaFdjJoz0V5LhnLYLrR3XtP6RThwijKNIiaPhGzZ4929nvP5PuqIby/B9vLknI8ajtSuL5PT8YlN2RRnhs8qDrFQZDpQSNCgosQQghxjHM9n8dWNrOlI0VR2Oaq2RUURvacZN8XWLa1JVm+tYvxZVHyokF8rYlavRO/s17vE53Wmtc2dmK11NHhGqxIRjl74BV6AShOtVNc384rdz2OCf2T7xc5xTTrAL6nWKfDGJaFkUrh+Qo/3Ynb8Do61YapFB4KZdqYZSdgFx+Hb5jge4AirDSlTjedyiatFQEDQvgku7rAqef8QJLOidOpz8CHCzqZnT9Ib8sIrBgGvSuoKQW+ZseSAmKkaA4st0jWOXgSXIQQQohjXFvCYUtHiqBl0BDLUNuZ2i247NrD0hHP8o2H32VLS5zSvBC3f3Qa151Uxt/XdDG1MszpE/IA8B/6GSd35/EkORQFNDNznT2uu6u+YWFAf2hJaIOmrEGLVuSZkGu65HopkjoH0Hhtq9DpDlAKhSZSOA6/eBYBO4TneyjLBs8haMAky+EKq5USP8WjXgXLKcBzHV61K/n8x45nfhg+quvwAWugVHAAgQUO3XyWkG0yoThCczxLTsCkJCdwSOoVR550Os0dd9zBQw89xLZt2ygqKuLCCy/k9ttvp6amZsj1jB07lq1btw5avmbNGqZMmXIomnzYSHARQgghjnH5YYuiiE1jLENeyKI0uvOh+L17sjR3p2npTlOWH6I1lqG+M80Vc8q5Yk7vClXegz/tnxly07gsp5b0EDU1k6PuoNfvDy3vWeL4lWwuPb5GaYdWI8y96TKyGsDHwkeXz8WJ16OsCKUVszm/MMRa16dZ+XQqA8d30aZFieWSVSZgMMHKcEH3eppDU4j4LqGaCrqymsowGKq3N2MPI9TL8l4lOUFKcmR42JFA6+HpP0mn05xzzjksXryYsrIyLr74Ympra7nnnnt44okneO2115gwYcJ+1XndddcN+Hp+fv6haPJhJcFFCCGEeB/SWpN1fYL7mI8yFGHb5KOzK9jakaI4GqCmIDToJpLjy6LMHV/I8touptfkcfzYgv6y906+t4G5+Q6+hqQH0fc8dTx711950a4hVTiHk61uKnSGF5w83nByGGNmCGofE7BMi55MCj8Tw4juGG+mQdkh7DHnYAQKcHBo8ZIo06IoGCBFCOX5+MogjsFMuphMD7TUMwmLM6eXUtvjc0apZlLuIG/MIexl8X1NezxDQTSAbQ4Yj4Tg29/+NosXL2bevHksWrSI3NzeD+f3vvc9br31Vm644QZefPHF/arz/vvvH46mjggJLkIIIcT7TCrr8vNnNrGmIcYpk4r59BnjDnqFqYKwTUG1jW5Yj04OflzQNvn3j0yjvjNFWV6QSNDa62phHVn43mqD2oTi3HKfG8ZrYi6suO9xfhGcySvBUVi+ZoMT5aNGHc85eTha0epGuTjQxTQ/zusdm8k2rQPDxB5/KcqwAI3hO/jBQrT2iasAzwVKUYZBwPMoC2gSrsLwXYqMNNe0vUmBTuMpg81Xf4FYNpcppPhEYS2W8Z75LM7OPWQORS+L6/nc+fR6lm7qYExphP936TQKozLUS+zOcRx+8pOfALBw4cL+0AJwyy23cP/99/PSSy+xdOlS5s6dO1LNHFESXIQQQoj3meVbu3h1fRuRgMmzq5qZf1wpx1UO1m0wNIP1sAzEtgzGlkZ3GxY2UGgBeK1NsaRDkWNp/tpg8PJrG6g3cikpPJVVbhQPhesrlhOhNVtBzDfp0Raegr/HPFoal5JIdRPCIu0qvLZVWGWzwfdRkYLe6/pub9gwTQzfI2OYpBNd5Jk2NZaL40GTlcuHP3MeW9wwv+3KJ6B8Nvl5rHLyOcPs2NngYZjLUtua4PX1bQQsxTvbulhR28lZ0/eyUoE44g3HQLFXXnmFrq4uJkyYsEcwUUpx1VVXsXLlSh5//HEJLkIIIYR4f8gN2UQCJl1Jh/L8ELmhg/vvfH9CS5+99bLsqjAAERPq6rvJ9TM0mUVEAiYbtUUUlxgWWWUSNyJsNkN4aDzt421/jaXt6+l9RLQIKs0ZpeV0FY+hR2VwbRsVsrDJ0myGMfwsnu/jKRPTdykgS6frk1Imx48v4PLppwKaQsOh0HCp80IUGA6lZqa3oYe4l2VXRTkBiqIBtrUnKIwGKc2TeSrvd8MRXFasWAHAnDlzBiw/4YQTdjtuqL773e+yYcMGgsEg06dP58orr6S8/P0ZnCW4CCGEEO8z02vy+OzZ49nSmmDW6AIqC8NDPndDUw8Pv7GdoGVyzUSXytw9lz3em6EGlj6nFmvO2byYFiNCZV6Yx71y2rRNvnK50G6hzbN42yjibTeHhJPFjdfhtywDp3cfF4XCCuVTXjGbqsIoJxoOnW4PFabLEiNIkwozQSe41N5GpU7ytFfClpTG0JqZupN/OnccU/MylId6HzULDJcv521hVTaXUVaaKXZi2FcMK8oJ8v8um8qK2k7GlOYwfVTBfl1HHFm0r4n3xA7ovL3Ztm0bAKNGjRqwvG9Fsb7jhuprX/vabj9/9atf5Yc//CGf//zn96ueI4EEFyGEEOJ9RinFB6aW8YGp+3/u/a9uZeWmZrTWRLM53DSvaEjn9QcWGHJo6VstbC7sWDHMIReXjTrKKJVimhEHCyzP4K1klmzjW+hEI4ZSGIZB2FTklE4jr2giGTPECjPIdtPnY9Y2ZtDNFC/LOiOfCp1kuupCKZjctp5lRgnlH5zHGeUlVEf8Pdo1xkoxxkr1BpYdqzQP94phE8pzmVB+cMP5xMjqW0mspbmRuZOGsu3o/onH4wBEIpEBy3NycnY7bl8uu+wyzj77bObOnUtpaSmbN2/m17/+NT/60Y+46aabKC4u5sorrzw0jT9MJLgIIYQQxxAj2YXWGk3vhoZDsa9eltY0/G6biePD1aM9aiKDL3E8wUgygd7Z/z3aZLkT5c1sEAONTrWigIDSzCoo4P+NKqUgYLHOb+EBNQ7L8IgRoJEQM+hmFAlG+b09M7TU994f8I83nrrvmxrhfVnE+49SCq01ZeUVPPny0v0+/0NnzKWluWnQ8r5gpAb5i7m/SzD/+Mc/3u3n6dOn84Mf/IDJkydz0003cdttt0lwEQeuubmZadOmDVi2YMECFixYcJhbJIQQ4mjRN4/lE3MKiAZiBE3FFdPy9nrOYIFlY1zR4ypm5PnYBjyw1eQvDSZaw/KlW5idb5CdegGzujeT7w68RFnCN/jfVBXveBGSWGCDWXo8un01+aPnMb/IZrtO0OBpppkJ5qgYb6licvGYqHt2r2xHaCn52KX8vQG218IVoyA00ErRg8xlaejO0hp3mVwWImQb+BpasgYeivKAh10tgeVYsHDhQhYuXDhgme/39t4pwyAnd+9/dwaijL0vg923ilgikRiwPJns/bvU1/NyoD772c/y7//+72zYsIHa2lrGjh17UPUdThJcjiDl5eWsXr16pJshhBDiKPLeiffjiwL8vzNK9nrOYMPCWjOKJ5ss/lxvkPHhvDKPBRNctieho7kLG5/tORV0TJxOPOXwZDzMicktnG93kqs8EtqkG4uoE+c727tYWzSWtLnzUcQonkKgdCrpYJgH8Ym6CaJ+lg9ZbcwNdbMiOIosJu+4lYx1NqF2BJb5n7uUjAf/sESxLgYBAyKm5sPvnSowSC/LmqYU33mmge6Uy7wxOfzLuVVsy1hsTtlooDtcyvQhvt/i/W1vXxSbptkfXoZjcv7o0aMBqKurG7C87/W+4w6UYRhMmDCBlpYWGhsbJbgIIYQQYmQdyEphMHAvS8xVLGoNcP+2ACu6DVKuZk5uhjc6THxf88q6LpIqxEQrzeiiMFu1oqG5lUzSJ+nm002AKsthiZfH+rZ6Who24roZTPcdjMp5vRdRBsq08OwgSkEPFikzh27DZa1O45kWPSpIrs6y3MvjvNYWIvSGFgBXQ9yBsAkZHxLuLje1S2Cpjzls3ZrkuJIAJTt2xFzZmKQxlqUsx+adhiQdSZcEAXw7hGkoYuldKxNieILL7NmzAXj77bcHLO97ve+4g9HZ2QlANBo96LoOJwkuQgghxFHmUC5v7Pjw3xtyeLbNZltcg/ZwfIP1cZNz2lfylFFARCmyVojjVJzT4m38LaeAbZ5D0E2RVjZvqCjpZA+t218nE2tA75hgo2PbCJbORpsB0D6G72JYAUx6HwyD2sVVJmWGR7mfJUc7JF3NFKebc2+8kF333Ixa8Knxmt9tVVSF4dzKvhvYGVoaYg7ffbGdxh6XsYU2Xz+rhIKwyeSyMGU5Nl0pjxNHRSkMW6jCKrpa4vgaqvOHthiBOHbs53STIZk/fz75+fls2rSJZcuW7bEs8iOPPALAJZdcclDXWbVqFWvXriUSiTBlypSDqutwk+AihDiiaK3JehrTUFgHuRO4EINJZl1s08A29z7m/P3moAIL9IeWprTizs0h2rOKC8tdNiQtck2frA8KgzzLZ1RXA1en1vJg8TyW+3l0YfGqX4SZaueTW5/hVB3hT7qEBhWgtWkF8eZVOL7uf+Azc2rIqzyBUChEzPHwd4QZ28sy1nToIEie6VJMkg8a7RQ1boFIO1kMbrh6Nobac7WwC6rg/Erdu+jAjhXDdh0WtrXLpSnuUpFr0RBzqY+5FIRNZldH+NaHamjpcZg1dxZ2yKYUyAtZeBrC1tH1ORFHJtu2ufnmm/mv//ovFixYwN///vf++Szf//73eeedd5g/fz4nnnhi/zl33nknd955J1dccQV33HFH/+tPPPEElmVxwQUX7DbZf9myZVxzzTVA71yXQCBwmO7u0JDgIoQ4Ymitae3J0pP2MA1FRX6QkC0PDOLQemZ1M0+tbCY/bHPDGWOp2Y89UI5Uh3JYGMCTzTavdFgElOaxBotpUYeHOgOEcMik0nhemjmBJMGySj6u67DxWOwXkYPL2zqf+XRgK2jO9LBh69u4mR5MrYngkQ1EscpOIJBTyUfDnYwxu7jHK6HTN4gqn0rTJ4ziAloYYyQpJ0O0bTt/KJ9P15TZTIukCZmtg95Tf2hhz7ksx5XYjCu0qY+5TCoJMKZw5x42E0pCTJw1a7fjg9ZAs/uFgOEZLAZf//rXWbRoEa+99hoTJ07k9NNPZ+vWrSxdupTi4mLuueee3Y5va2tj3bp1NDY27vb6kiVL+Na3vkV1dTWzZ88mEomwefNmVqxYged5nHnmmbsFnfcLCS5CiCOG50M87QGQcX2SGU+CizikMq7H31a1EM+4tPRkeXNLBzWF1fs+8Qg2HLveh0wwAUf3DsG6ZXwPL7/dSQNBsiqEafis8KKcadgE8BhPnDXkEseimhQ5uPx623bWtjTjoVCAiebi8goqyyax1C+gO2CzJjKWAq+JD7gxGvwAF9idTLdSZDCIKq9/tbDINVdTt72cHO3zRk+Ed5MhTsxJ7dnwfSxxXByx+NezSmiIuYwqsMkJ9P77MtgSx8m0w7aWGDWlueSE31/fTIthpA9wqNgQzgmFQjz//PPccccdPPjggzzxxBMUFhby6U9/mv/8z/8cdHPK97rooouor6/njTfe4I033qC7u5u8vDzOOOMMrrvuOq6//npM8/0XzCW4CCGOGKYBAcsgmfWwDEXAkqFi4tCyDIOiqE1LT4agZZAf3r9d448kh2pY2EAurciS9qAzq7ik0mHlfU9wrVHI/0TmopVBzFes0xEe9wvZ7tlkUEywuhhrO0xTccLKZ2bY5lV8UpgEIiVML5/MhwtTlJtxomYBj1tFONrl99kyepwkGmglyO1WLdHWbXjAO2YJY88/lTztkGd6NDk2ZbZLkeXtfB80bO7p/Xl8CKzr9r4vS37IJH+XdZIHCy09ySz/cc9LrNvWwYTqQm6//gwKc2Wuixh+4XCY22+/ndtvv32fx37zm9/km9/85h6vz5s3j3nz5g1D60aWBBchxBFDqd7hYYkdwSUSkN4WcWiZhuLGM8byxuZO8sIWp08sHukm7behBJaM4+H5mkhw53/z++pl2VWuBTeMyfLKXY/TClBWzXFaMSaTIu5bJDDp9g0e9UqI4DLWSFPvGHwq0E7f9w0XlhayobsNO7ea8vxqptgZys3eberzdYYgHkllkXETeFqhFLRqi3hbK2EgdtGl/G09ZNbCqSUuX5nQwtp0mCnhDOND2f62PtFkcU9jMUbVGK6fW81lQ3wf97WR5Lrt7by7pY2S/DCra9tYvbWN02fUDLF2cTTTHNhAseEZXHZskeAihDiiWKYiPyz/NInhU54X4rLjK/d94BHC9zVr6zqxO7YxoTQ86K7afVbXxfjfJ9eTzHhcf9ZYPrjy0d6CIQSWPv273gOU9Q6lc1C4ysbB2DFORuMpgx5Xs6llDaNIYE4oo0eb/N0rIYHJ5RPC1Kg0tWQJ7vLYdrzfSrOfwxYznxojzstWPp4yyODw9skX8ulxPisbNAkXcm3YEodZ0Qwn5GZ2tmvHsLCXukpJlVSjsy4vruvgsuNL93l/+wotAKPK8qgszqG2qZua0lxGl+3/hoPi6KUHiSG/u/cufnfvXQOWtbe1DGeTjgnydCCEEEIcwR54fj2/X/QOlqn4wlnVXDB9771ETy1vZENTnEBHEw/Wb+TsmaACBxBaynbO/dEatuoIWSNAjXLY7PTO9/B6GvCb3iDtxIkbWTbEQ2wMj+dlvwiFpkPbTFApXqWYAD5XGI3MNmI0GLmsjI6m3Yyg3QBlPT24WpEI5bKoCTJa8akxLm+09+7Hckk17Law1y5zWWa/2sC7bzRiALNrcvd6b0MJLH3KC6PcfsMZrN3WznE1RYyS4CKG4OOf+Rwf/8znBiw7d+5UWpoaDnOLji4SXIQQQogjlG5Yz0tvrsXTmkTK47VN3VwwvZiM4/PQWy1saUtzwfRCTpuQ339OyerFWO1hslpRGTGGHFoG6mUByGrFH50Slns5tClFUgUIeHFSdW/g9WzHQhNSPgFD0ZRO4YcVPpDAZiuKLh3EVJoeLFbqPN4MjGGzVUi3mc+YVDOtgRBjqgy2xnwCHgRM8DXMKIAfnQgpD8r7bmFHYIGdE/CvO6WC48ojAJw0bvBwsT+hpc+Y8nzGlOfv+0Bx7JFxXyNCgosQQghxhNl1HsuJY3PZ+naGSMBkdk3vng7Pr+/iwTdbcH3NhpYUUyoiFEVtvAd/ypWVEA1apHzFeaXOPq81WGDps8UPscTJpcGM0qUt/M5NePWv43tZFGCgmZ2Xw+fHjqYkFKGdGG84BbQRwgaCaHwUUVyUabPOKCCUTuCaEazqKs6PZvhoQQtrujRvdxpYhuJTY3sn2+cHoD82DLJimGUanDqxYND7O5DAIsS+SG4ZGRJchBBCiCPEQBPvr59fxexRuVim4oTRvUOhPF/ja41tKlxf4zzyazy792E/HApxRdXeA4u7Y+/G1+9+HBfFtuJxRJVPObufl9AGz7iF1BGiM96J07wUne4AQCmDsGExoXIqV5WF2BLM45GyGTRnwexoodTJYitNic5yidFCUPkkuntYYxSRMALMK/X42pgGJoRdAoZmdj58fIz33qbuc4njvZHQIsTRRYKLEEIIcQQYbLUw2zSYN3734UpnHVfA+uYUG19byiVWjJIcb8iT75d2wO1P9u6PcrmRz4rcMSxJ5xBWPlcH2igxHNp8izFmlpVeDpv9MOFUEy2bX0QbZu8Oj0phlM2iMr+a08JpHsyEafTy6GlKAQpT5zCGLvLwON9sY4oRh5Z6Mhicf1yIpozJJ2q6mRp1B2/oAMPChkoCixhu0uMyMiS4CCGEECPoQPZjiQZN/qHlLzCBHYFl99DiazAGWHxMa7jjr9uoM/MxbJvng1NochW+hjo/wGInh0bPpkXb5CjNDDuFApKhMlSkDJ1uQ0VKscadgxHIx0g20KUd2rSNoX3SGYdwJITpOJxKOx8y27Fa6wCY/7lLuX+rxUvbwNMeT1sGpxQOcoPSyyKOcIOtKiaGlwQXIYQQYgS8N7B4vmbRmi7a4g5nTMpjTNHAPSh720TS03B3XS4vd4WYm5fhi6NiBHesxvXKXY/zglXNysh02ggQ9H3GqgTFZHnXL8AHVnhRkr6ixQXHCrMuG2G6mcRQAQLV83BjdRjVJ6KUiZnoRBuKVRTQZgYI+i7j7TRZx6XcizOrezOW3wP0hhZfw9qkTdpXRExNfXqAZCW9LEKIvZDgIoQQ4pimtWZDQwxPa6ZU5+9zn5RDcs0BelmeXdfFT15oIJX1WLwlxv9dNZ7gLmsAD2XX+zUJm8fbooDm/oYoK9uyVK58g7OdOgLA4pzxKB98FGkfXtG5jCVJruFSiEOnb9LWvpF46xoCoz5ANqeSjA9jrAzJYB6UzsB0HLQbx1aalAqg0JQZLq0qTK7pcqHezszOteTiMP9zl/ber4Y7G4pZ4kTpCmqKVCcfrc7u3njpZRHvJ9LhMiIkuBxBmpubmTZt2oBlCxYsYMGCBYe5RUIIcfT765Lt3PPsBnytue7MCVx12rhhu9behoV1JFzSjk9uyKQz4ZJy/P7gMtRd76OmJqh8lvfYdKZ8tvV4VAfGk5OXx0yjh2jWxUdhaI2jNY5vsIYoETwakj04jW9CpguFwm1aQmj8RcwIJDnX7uYuPZa33Vw0GsO2KFVZDF+TwSCuLGxD0eMpVjsBTtsltAC0OiaLYxHybY0TMbmqzOC88h0T8aWXRRxBFi5cyMKFCwcs832///eSW0aGBJcjSHl5OatXrx7pZgghxDHltXUtJDMuhqF4dW3LsASXocxjOWNiHq9v7qG5J8tls4ooCFsDBpYtboRV2RzGmklmBuO71TEu7HJdZQ8rt0PA1bjaJ2mHafFt/t2ZQL0fxMKnVMep94MowPWydLa+g9exAYVGoYgqn+q8Qq4I1HNWKEFAaS72m+k0THwUPdoEFF2Y5OEwxu2hM1iAUnDyvHHMH1e1W7vyLZ+qoMPqRIgi22NiZMfqZdLLIo4we/ui2DTN3cKLOPwkuAghhDimHT+uiJVbO9Fac8L4okNe/1An31cXBPn+leNIOT6Rx36Bt2FHwS6hpdUL8F8d41idDmF5WW7J2chlpan+8ifv+hvPmNUEgqMhkI+BwWQVJ4tBnR/EAdJY5Phpivw07bF6nJblaDfT21YUoXABXx1byQfybGBnMJqu4pxsdLNVh7lQxVjhRVlPlBw3RchPc9uZYQylOL+st650tAjfsgnHWggamttGtfJWT5hy2+WEYBc4EljE+5eWLpcRIcFFCCHEMe3KU8cxoTIf3/eZM774kNV7IKuFWaYi8vtf9P4wwLCwDj/AlkwAN5Mla9n8pSXEpSUp/tRg8vTz6/Gt0SwLlNOlTfxsmjLbYJyRZBxJLKXJaAOFpieborj5VXrinTiY9O4kaZNTOpOrywv4QKhtj2sH8JlmJqnWDjNUN/R00GKNwleKGTOquap65x4wPcWjaZlwMr5hk9e8gfItSyi1PT6Uu7PeXUOLrzUtcY/8kEHYNhiMhBZxpBgst/zhvrt4+L5fDVjW3toyfA06RkhwEUIIcUxas72T7kSWORNKOGGEAwsMbR7LOCtBjdfFapWHlexhghHjv36zgl9GjicTmUrQy1KS6ezf0b4Lkzf9PM6y2vmcVc+jTgktOkCqeRl18U5KVZaMH8LLG41dfiKmZfGG5zDdTTHTTPKu7t3wcrrqYQX5POSPogubMZl2/tF9k+lnzSblwkXVu7czmV+OG4hgOmkSRaPQW5agBhkW5mvNXW928sb2FGVRi6/ML6Y8d/fHk6EGlsauFM+920IkaHHBzHIiQXnMEYfX1Z/+HFd/+nMDln3o5Gm0NDUc5hYdXeRvtBBCiGPOSysb+L8/rSCRdvjg7Bpu/eicg15N7GACy8oemxfbc6jJMbikPIs1SKdDQGl+WFXLY402da++xZxsA7+PTCdt2oTwcU2L8bbDaD/DKqOALm2wSUf4n/QoylSWUuVgK7CqZ7BpfSP5gRDp0lOIRcfgGRaO9tE4POUUUUeEl3UJAGcZbSjVO9zMVSbZ3BoCZ5dxbfkAO90D4Vgr8ZKx+KZNtH3joKEFoKnH5fVtKQwFG9ozvN2Q4sLjctjSo+nMwsRJEygYwvuoteau57ewfGsXpqFwPZ+Pzhs1lD8CIQ6AjBUDuOGGG/b7HKUUd9999wFdT4KLEEKIY8672zpojaUpyQ2ybFMbWdcnaJv95Y0dCWKJLJOqCzAG2snxPQ4mtMRcxfdrC9iaMgi2aRSaSyscVncrDAVT8zS7ZqqigGbK3x/lOODNoql4fpAoLllMxpDgVDtOWHmsyhaRxUC7ad7JulTYIfJNTY5yKQyFmTJ+Nt2hKup0ATqcj/J9jGwKUITw2a4j/Y9m2xI+Z7rbsXJrsEMWEVvR7Qz+vuS21WKne/CBcFfTXueyFIRMSqImm9sd8kImFbkWm3s0T7SESDke72aauWZOFYHB0twuupIOAcsg6/nE0u6Q/gyEOBASW3rde++9e7zW9yWQfs9EIKUUWmsJLkIIIcT+mD2+hGeX1xFPu3xgZvluD8VLNzTzP39YSjztcPHJY/nipbMHrSe7bS1vbOrENhUnjS8cUsiB3YeFpX1Fygff17zdrfhim8XCXAgoH8uAz4zzubym9wHglbseB2BzyUTuzlSxPhMirDzGeu1cGOiijjCP6BpcHzLawO9cj9u4BGUGSI09B9u0mWH08NlAAy/mF/BnJ4dSQ7PdCpLjZzCVzVjf5SqzjSYVptkLQjbNCW4LN37mNDrX27zYBjVhn3nFu/e2uD4s6TKwFcyJJgl31g9p8n0kYPCV+cUsb0hTkWtxfGWIpX4FqWwb0YBJV8ol6Xj7DC5KKT46r4bfvbaNaNDi3OllQ/qzEEIcuPeustbS0sJFF13E+PHj+epXv8rUqVMBWL16Nd///vfZsmULf/vb3w74ehJchBBCHHW01nv0ouzqtKkVVNxwGt3JLDPGFO02TOzNdc3UtcUpyg3xwjv13HDhjD3q6ethufuFrTy2pAFTKa4/czRXn1Kz13YNtIlkaUBzdVWGr68Kksl6ZIE3uiwmhD0Kbc2b7VDy1J/6T4uXjuZH7jg2+BZaQ0IrtIIKUrxOMSY+Xek4Xdvewk20ABrLTWF2rcOrnMNGlcNyP5cS5RBRLr6GclJEbUWRrfmo185onWF0y2bGqc2ceO25jIr0Dhn76nFZrh2tKLQ10fc8Qdy31ebRegsTn0vKw+SfeR5lq1o4Z1rpPgNdVZ5NVZ7dP5dlQjLLqrwgsbTL5LIoeaGhPa6cOrGYk8YVYig15BApxP7Te/QmDPW8o91XvvIVKioqePjhh3d7/ZRTTuGRRx7h4osv5itf+Qr333//AdUvwUUIIcRRpSue4X8eXsrGhi4umDua6y+YPuD8lfGVeQOeP7Eyn8KcIIm0w9xJZXt807/rsLAV27owFGRcn1V1Ma7eS7sGm3yvFFxVlWVRnc/vkzYaUCgavRAdhkXBxs2cZQaJFPeGh1V+LrVeEO33ruKlNWQMxe+dMhKmwaam1SSaV6HR2Ao8DUUF1eQUH4eJx2YvzB+8Mm4N1HKt3UyHtqn2ttNBlDJSjG7eeX9XfPbc3dpqKKgJD/zwtaxDo7RPqriCexOa8PO1RAMmCsW5M0r38s7seB92mYBfFAlw7QlVJLIuBWEbYz/mH1nmvoeUCXEwNAcWQY7+2AJPPfUU//3f/z1o+cUXX8y///u/H3D9ElyEEGI/aa15akUjK7d1ccK4Is6dUX7QE7vFobN4dQOvrW4gErL5y2ubOeeE0YwpGzikDOTcE0ZTlBcilshy8pSKneO1B5jHcva0UhperSMaVJwxuWTA+oa66/03Z7rUpgw2JAwKggaJRJqUmcuy/En8tqKaazveBM9lkV/R+22vUr2pRWvi2uSlzgRe84tYTpwAPo4yIJhHbsVcxkQjNKsgnb7d2yPjK97xo3zAjvVff7xOQUs9wG673gN0ugZ3t5YAmhtK2imydxkesmPi/RnFQeoKx2Jpjac0eWGLWMqltSez1/sebMWwoGUQtAJ7PVcIcWRxXZctW7YMWr5582Ycxxm0fF8kuAghxH5aXR/jwVe3ksx6rKqLMaYkynGVuSPdLLFDXiRIKGDRncgwuiyPnJC9X+crpZg7qbz/571NvP/ovGpOGFuAZSrGlkZ3KxtoWNjeVIbh6TOzvHDXEzwXnchf8mYRMwMUmA71dgHbA4VUJNtJYFCIS6dShJRPwsuSqV+KH9sKgKsMlDJRRdNQY07Dt4Js81JMTTeQ1hadvkmOcomqXeaoDBJY+vzL9hqeabYBzfq4zS8n7VjSdZfVwq7RmhMaejANeGV9J0+uaGFUcZgzJg++1LTsyyLE0eWcc87hzjvvZP78+Vx22WW7lf35z39m4cKFXHjhhQdcvwQXIYTYT67n4/qagGXg+T6u5+/7JHHYnDatkn+4/Hhqm2OcNq2K4rzwAde1r9XClFJMrMjZ4/Wh9rL0OIpbVwVZ1w0frnaZ88IfsYCzomls6lhkR/FDEQq9BGVODyv8XHo8TUwbBHyXMpWmCU2qp66vRVg55UTKjkdHSkjbEQLaIRKwOdtIMcrrYYUbpdhwOd5M9AcWGDy0AGxOBUD7oBSb03Z/YIGdSxwrpZhS3duzNbEil8tPrCQatAacVC+BRbzfHdAUl2PAj370I+bPn88VV1zB+PHjmTx5MgDr1q1j8+bNVFdX88Mf/vCA65fgIoQQ+2l6TT6Xzqni7dpO5k0sZkrV0IchieFnGIoLTxp7UHUc9CaSsFtoeaVVsbpbMbNAc2qJJu1plrZr/twY5E/bfNysy4YWl5vNMqYUhghpTSKVYGx2A6MjJrOcNgJuhrf8UsJ4mMqmwPQwfc3xIc2r5bPItq4hUHUip+bm0YHNVs9DaR9Mi3IVZyJJys0so81sb6P20cuyqytLe/hZKge05qriLmDgPVn6KKUojA48zEtCixBHr9GjR/POO+/wne98h7/85S88++yzAIwbN45bbrmFf/mXf6GoqOiA61f6wJZFEIdQTU0N9fX1VFdXU1dXt+8ThBBCDIsDDSwweC/L2hj82zsmrRlFeUjzndkev17n8PstPglXkdYmpmnjAJbvkKeznGp0scEL9e6DoqCVIBllorRPe8sa7OIpFBuacSrBdYFWNJq3zULywhFOSNeT9uB3bgWrrFKUHWC8jvElfx1/dYrpSmY5261npt85pNDSZ1MCDDQTP3PjAb0/ewssjV0pEhmPcaVRTFkNTByhTNPE931KKyr5y+vvDnjMI7/5FY/+5lcDlm3fshnPc+V57yBIj4sQQogRt3p7F4vXtVBVFOGC46swjcO/MpRuWM/2jjRvbYkxqjjEiWNyh7Towr6GhcUcRdxVlAQ0CVfRlYWn631iSQcPRdD08c3epY2zmHQRYLmXSwQHC9imQ8S1gdNTT6ZxCbhJlO8wpmI8ZSrLk04BVXlh0lNPoDMQxujcxpkbX2a07bJVuUT8LHEMnnOLWZYOYhoBXp98KjecOsT3eMewsAmB3XtZGrodmnscJpeFiAT2scfKXkLL8q1d/OK5TSQdj3OmlfGp+WOH1i4hRtBg3/tf+ckbufKTA4f7D586g9amxuFs1lFPgosQQogR1ZNy+L8nVrO1NUE0aBENWpw5veKwXb+vlyWWcrn9L1vY0JKkMGLxb5eOY+6YPF7fmmBdS5oZFSHmjto5AX+ok+9nF2g+WO6zvFNxRplP7NEnKLJnstUoxlSKOUYMjDRLvDzSysTToCyTtBki6fqkE90kmpbjx3uHdikg3bGRDcUTWakKsPCpyammLFRAWbaHNXmjeVNNpTHtEVMePcEQQc/hWauGwNhCwtlubCPLkDo2dpl8v6tNbRm++1wTbQmXWVVh/vWcigOey/LO9i6aYxnyIzavb+zgmlNHY8uSxkK8b7W1tfE///M/PPPMM7S0tPC73/2O008/nba2Nv73f/+X6667junTpx9Q3RJcjiDNzc1MmzZtwLIFCxawYMGCw9wiIYQYfsmsSzLjkhu2SGU9elLuYbnue4eFdSQcmnuyFEdtOhIODV0Z8iJpFr7SQlvcpSzX5vYLLcYUBffay5LyexNB2Oj9RjZowi1TfDI+vPXrx1HArJDHescnhSKD4hS6yRqK9V6IjFY0mnmEcIm1rybbsAT83vdEobByKhlbfTzKMHF8jacUXk+MYCZFezCX5lia9U4U3zaxfI9c1yPX1DTkFpMXzSO3vJAFJZuw95YNBph8v6u1LWkauh3Kc23Wt2RoibvUFOw+p2Woc1nGlkbJC9uksh7Hjy7AkqFiYgQtXLiQhQsXDlj23l3ixZ7q6uo47bTTaGxsZNKkSTQ0NJDJ9C6JXlJSwmOPPUY8HufHP/7xAdUvweUIUl5ezurVq0e6GUIIcViV5YX48MmjeertemaOjnL6lH1vVniwBprLMqooxPxJ+Sze2M3Uqignjc2jLuYSz/gURy0SGY/OP/2OmlBiwMDS7cDDDUH+3JlPaV6IBRXtzI0kAHjs7mfYZuYyWoUpLinizWQeCSNA2rBZYeWSJs637PV8yxvNGnLJxJtJbH8Vne5E73hYUlaYcPnxjMqv4NZwHY9mS9iigtj4TEs007HRojOYR3csiaEUWQxs5WHnRmi1I7hGgFF5NtW5JmPyLWCQgDhIL8uujisNUpFr05Z0mVUZpjRn5+PE/k6+P+O4EoqiAXrSLsePLpA9kcSI2tsXxX1zXODY2EzyQNx2223E43GWLl1KVVUVZWVlu5VfdtllPPnkkwdcvwQXIYQQI0opxdWnjeWKk0djmWpYH1x1w3q01vzh9Tqee7eF48cU8Nmzx2FbBqah+Or5Y2iYl6EoahMJmBREbOaPy2HlktWcEYoxuSg5YGj52dYI99Wa1HZlMFQXBbkRxoTymRtJ8Jdf/Z17gjNoDhZQpjJc5zfSZkZI+wG0HSZrmtQrk6WqmHwDko3LcdtWowC9Y49us2ASobJZVFq9j0tt2uZLoUbimBQrl18aE2lMejjJbgzXoxhNJmxTFrKpLAjREiqmxwyTDNrMD22nO+1jBRR59i6PX0MILH0mlYb4pzPL6Ep5HF8dJrhjmNiBrBimlGJGTf5+nyeEOPI8/fTTfPnLX2bWrFm0t7fvUT5u3LiDWphAgosQQhwmfZM55RvlgdkDzJE4VHbtYdnYHOfBV7eRdX1WbouxrjHBWdNKufSECkxDUVO4M5iEbIN/bHiUdKVBKBBAqSAAW5MGW5IG03I91nTDD97VxDJpMq5PIGCTSGXpWbKBV5pfp6XkOJqdfAJ4NOoQ/+dPoNWIYCjw8TG1QVwrfuaPYoyKY9th+raGNIP52OUnYYULyVGaGIowHn92iik1XMaYvUMwQtpDu72T+U/O1PHVD09gfY+D48P2QIhnMyHKtGa83UVTczefaM7FR7FgbIrrajIod+ihBeCJNTH+tLqHaMCgLNdm8uypB/+HJMT7iPS4DCwWizF69OhBy7PZLI7jHHD9ElyEEOIw8HxNyvHRuvdh2DaP7vDS2JlkY2MPEytzqSyMjGhb3jsszDJ6e1cSGY/6rgwvr+tgbVOCwqjNmVNL+o/rm8eiFIQDO+dvbE0a/PuaCPVpg0k5HlNDSTzfwzBMQONlsgRcnymxDVBWTaGfJY5Fhw5gKfC1CQpsBYZ2SHm9u91nCdKNTW5pCDe2jVDeKKy8MUTR5KkMJ9pJnnaLcJRJt7ZI6p1B79K2peSFxjDp1Ol8tHo8JUGf8TvWEWhx23BiFkltMt9u4T83BajPWGQ8+FltiBNzE0z/3NACC4Dra55cGyeV9WlNuLzWlcPkA/hzEeL9TDYTGdjYsWNZtWrVoOWvvPJK/6aUB0KCixBCHAYZ18fze/+nyzg+tmmOcIuGT2t3mm/+bhlbWxKMLoty+zUnUFFw4LvXH6jB9mQZVxbli+dN4KllTTi6i8JogIzjE894/cfsbfL95qRJXdrA8+G1DouJVSZjwx5re8A0FGOyPRiYNOVXMYUOugmQb/iE/DTbdRitQGlNwHdINS7H1Rpz1OloeifaG0D5xPMwlWKU38MZRgdzrQS/96qxMUl5EDQcJpjp/k0ki4H/vW4SCd9jvZtL1s1QZfX2xpRZDv9YtB2AlAdFgVyycQgYGkuBd8HH++8t4/q8ujmO62tOH59DNLDn59RUUJ1v0Zy1CUcV5fmDr6gmhDi2fOxjH+MHP/gB1157LRMmTAB2jjK4++67eeyxx/je9753wPVLcBFCiMOgr39Fa1BH+Uqvta1x6tqTVBaG2dwU583N7Vw4q2rA5XKHw1A2kTxvZjnnzijjD2/U8/jbzUyqyOGMyUX73JMFYFqOS9DQvBO3CBqaNYkgZS2baAmUETeCdIQLmeK2UaXSvOAW0OnblJNmFbkE8MnVWbpjzaTqXyOVSaBRqIIJGDllmJ5Dl1aYgQglpoelPGr8FtLKJEe5FFmACScZKQKtvePE+zaRzGrFj2PjWJ6JEvEdbiuu5bhAcre2h034weR2vrs5j/riCVw2p4IZNbn95Y8s7+D3yzrQGta3pPnyB8r3uH+lFF+6/GQWb2gjJ2Qx/7iSPY4R4minZbDYgL72ta/xzDPPMH/+fObMmYNSin/913+lvb2dTZs2ceaZZ/KP//iPB1y/BBchhDgMgrYBygfNYXuAHymTKvOYVJnH6rpuCvNDaNNgfWucGZV5w37tfYWW5oTHw2sTpFzNpRMjfOyUGj56cjX6dz+DPz7be9BeQgtAeUjzkcoMnY4i1/RZuqENdISMMig0PSw0XiDMb90xbEqDo2GMkeYiu5nFToDWxndIdGzF7I0smGiKejYTCNi06gAYBp5SOJ7Has/mW/54SpTDRVY75xqtqJ4uznDq99j1vtULsjIVYm1zmpirsBOF/HxCcud+LTsm31eHYOF/XbvHfWV9zRv1GboymryAYnN7Zo9j+ibfFwGXzKna6/skxNFMhooNLBwO88ILL/DDH/6Q3/3ud4RCIVasWMHEiRO54447+MpXvoJlHXj8kOAihBCHgaEUYfvoHR62q4JogNuvmcPza1tRtkFeOEAi66G1HraFCYbSywLw/NY0K1qyGErxx/VJbjs1vze0QH9gcX14tTPAO90GM3MyzC/RvDdrfrjS4eU3trHByKPDt7BMC9PJMEY5NFk5ONpkI2GyOoGFT5Nn8VZnHVua1oGXJaggjUkot5Ka6jm0qRyyWmHige9huFkwNFnPR6FoxebdpMG/pN8G2CO0AJSaGaxMis6sRY7lsy2heKfHYkaOi+Xte/L9sg4IFeahGzNkleaiqbuv9LW/K4a5vs/6liSe1kwujR71gV0IgD/+9m7+eP/dA5Z1tLYe5tYcXtlsltdff53KykpuueUWbrnllkN+DQkuQgghDrm8SIAzp5axriWO52tGFYSHJbQMNbD0iaWytHYkycsJYm9ai7dlxR49LI81h/nuphwaUxD2A5xSkOELE1zOKNk5B2b9b/7C54DvlJ7DWiMHT4OJJkQrFppuZVOsXDqUTzbdRVfjEpqTbZhoQOEbNnblSeSNmkOTGcHtbMLWLjl4jDGzjKabCSrFI6qCVm3jaEWbCjLm2ksYFR34fQwozecKGmjqLiHhQkfK5VsrTU4o0Nz29ZsI7iM4J10YVZZDeUGIPEtz3pTeBQkOZIljgNe3dvP61i60hqaeDOfJkDJxlNB65yqR73X5J27g8k/cMGDZVafPoq25cTibNqJM0+Tcc8/lBz/4AZMmTRqWaxzVwcV1XV566SWWLVvW/2vdunV4nseZZ57JCy+8cEiu09PTww9+8AMeffRRtmzZgmmaHHfccXz84x/n5ptvJhAI7LsSIYQ4yhSEbebW5OMP0/C4/Q0t29rTPL+0ni3NKcq8Hr42rg6CIXo8g9+2FtLi2FxW2M3GpEW3o/B9j04HXu8wcbRidMRn+wN/6a2srBoFpK0IZqQQP5PCsIO861nYlknGV3zAaMDuXss9m9/F1b3znBwUVt5YqDgBP5hHPJXBzy9EmzbadRlvZtHhHLYpgxrdwsdia/i9PQFXmdilxTy6Ff5p2uD3eEZBiv8xW1jUrHi83iAQhDeDY1nf2MPM0QV7fX+m5UNLGpyAxbwdGeNAQwtASzyL52tMQ9Hcs+ewMyHez2Sk2J5M06S6uppsNjts1ziqg0tdXR3nnHPOsF5j69atnHXWWdTW1gIQiUTIZDIsWbKEJUuW8MADD/Dss89SWFg4rO0QQogjkWWOfGDps645QePGrUwPOHR6AdqyFiFT8Uo8j8c6i7C1R0PW5uMFLfy9JcBGBzQ+ZUFNxle8+rsXGA1QVt1f5/l+PVsCVfSESgi6aTwnSMgyKMKj3S8gHhyDH2xApzshkEeg8iTsnEocFHg+rh0i102QqzJMsFNUBuEdZRLAZ33a5qtuPfGZc3i7o/d6wSGMNpwd6iZUbLIkOIMtbSl03OOZ1e1MKM8lspcKysOKq8bo3qBZc/ALHE8vz6G5J4OvNbMOw/ymeMZlfVMPZbkhaop2rmKXdjza4llMpSjNC2IZR/dS5EKMpOuvv57f/va3fPnLXyYYDB7y+o/q4AKQk5PDrFmzmDNnDnPmzOHhhx/m6aefPiR1e57HpZdeSm1tLZWVlfzmN7/h3HPPxfd9Hn74YT73uc+xbNkyrrvuOp588slDck0hhDhWHWhggd7ljY/LWIwJldHgBCi0fL65qQArGqGwpoJ2K0VYu1TTxWmFWR6a08mvNxk8Wm/S1ZHktPRGaopzgVy0hre8PJ71i8koiw/plThWGNtLk1Umq8xR9GQdqp0YKBO7+hSIN2BWzMHWmrDO4moLG49c5TLWaaPKjHOx2UKjirIhE8BDcfmplcwfcynVcc0jWyFiwtVj93KTOybfA0z7/E18dUMbtz28lmTW54llzYwtiXDFiZV7fZ9MpbCqD7yXZVeTy6JU5gXRWpMftg+6Pq01nmbA4JF1fX6yaCPv1nVTGA3wD+dPYlJ5DlprtnWk6Um7/Sv7VRbI8s3i4Mnk/IHNnTuXxx57jFmzZvH5z3+eCRMmEInsuZfX+eeff0D1H9XBZfTo0cRisd3GVb/88suHrP577rmHlStXAvDoo49y6qmnAmAYBh/72MfwfZ9rr72Wp556imeffXbYe3+EEOJodbChBaAmx+I7k7tYG7f4t1Uh1sU04yYVUFZUyDTPoydrc120jpChCRmaF1Y0E1Y2FiZF0RCNnsM6L0SntnnOL6GRECHlk84YnJdp4JmWFrY2r2POxPnErXJqtcNUM0VFbjnN+TUELIuIzjI920GtykXZAapsl0/5Wxlr9/BmMsg7kSJmqDjXn1vNnAIXgHE5ilum7+MmnT0n308fVUBFQZjWniyu55N1/d1OSTs+z6ztJONqzplcQOHEvYxBO0B5oUPzmJF1fTa2Jkg5HiU5AUYX7j5nqj2eYXNLgoJIgJZYhk3NcSaV5wDger2rt2mtcX152hSHxnB+ktLpNHfccQcPPfQQ27Zto6ioiAsvvJDbb7+dmpqaA653w4YNzJo1i3Q6zTnnnMMzzzxzCFvd69JLdy4ccsstt+wxt7FvkRbP89576pAc1cHFMIZ3BZP77rsPgLPPPrs/tOzq4x//OF//+tfZsmUL9913nwQXIYTYT4cisHQ4Bi/GcomYmrOLsjzfCBt7fLK+oqsrge07HD+piGqngzM6uwFYet+ThCIn0BnMw9aaBs/mkUw1TZ6NrTTaMLBMTVYbdMa7+L+6VWRSnRholmxdTnDMefQomyA+X4408AdzHAkUp/gdXGdsJY3Jm6qUmLZRaJo64zxZfjJ2dSUdaByznaRyUWiiuIPf5C69LO9dMSwnZPG5s8bwhzfqqSkKc/7Mst3Kf7+khfvfbMH3YUMyzG0TD/itHnYdSYeulINlKFp6spTlBndbpa8kN8ik8hxW1nVTnh9iUkVvaFFKUVkQpLErg2UoSnJlzqk4svWFisWLF1NWVsbFF19MbW0t99xzD0888QSvvfZa/8aO++umm24ikxne+Wb33HPPsNZ/VAeX4ZRMJlm8eDEAF1100YDHKKW48MIL+dnPfsaiRYsOZ/OEEOJ97WACC7DbRpI/2hzl1Y4AtgEbEjZ/7CkkkG/gJhJY3e1c3LmUyVaI0dk2Xr/rzwCEgKujXbzla6ImdGVdunwTH3CBoHbJ9TKkWlaytW0jzo6vXzWKUKiAhNa4WGwgwNu+xWdYj4vJcbo3GKUxWWyW0+SaLPFy+LCVoHB0OUmv95vcLXYxL9g1KDSXuRuZqtv3vMkBelne6+xpJZw9beDVvOq7snhmiEDQYFtr/ADe5f2jtaY96ZBxfcpzAvs1/ylgKkxD4XiaSNDYY7iYbRp8+bwJbGyOU5obpLJg5xyXwohNQbj3cWe4luMW4lD59re/zeLFi5k3bx6LFi0iN7d3g9jvfe973Hrrrdxwww28+OKL+13v3XffzfPPP8/nP/95fvnLXx7qZvf79Kc/PWx1gwSXA7ZmzRp8v7fbfcaMGYMe11fW1NREe3s7xcXFh6V9QgjxfuB6PqahdnugHGpoWVffzdr6GNNH5TNxx+TvXQML9I5D35oyCZqapKd4pTsMVgCloTgvyv+N7+S83CSk4ZW7Hu89d8fk+yoyZAoqWGcVEk+lSbZ1k/VcQoDu2U5b8zJMJwHaRKFQwXwqRp/MVbk+T2UMmpSFqWCVKmIO3czVHf1t71BBmh1FNxZtOZW4swr5ZEE3L3VFmBHN0Jkzjm4C+CjeNCuZ6u4SXJw0CU/x/IyrMBSck/WIBPZ/j6CLzjqedbFVZF2Pj5w2fr/P31/bu9K8ua2LrOcztijMqWMKhxwkCiM244ojpB2fwoiNPUDoiQQsZo0qGPB8CSziUBuOUYeO4/CTn/wEgIULF/aHFugddnX//ffz0ksvsXTpUubOnTvkeltaWrjllls499xzueaaa4Y1uAw3CS4HqKGhof/31dXVgx63a1ljY+Neg4vWmlgsdsBtCgaDw7KCgxBCDEVbLM2zq5oJ2gbnz64kEtj7fzF/eXUDv39+NZXFOXz1Y/MoTzfs9fhdbWtN8M3fv0NDR5KakgjfzVtFZWjHk8Qu+7IoBR8uz/BgQ4jSgM/UfOiM++TZitmRLOeW+TsDC6DLqnk5OoFlkVFE3DSbjALaspombaOjFma8mfj2V0n21KGBsIKQAaHSaUSKpzLOypKvOznDjvE3IiSNEC0ebFFhphsGcTNMftNGanQT1bNOoKHTJKJ83uw0+PL4OB8u6e35+CMJanU+7VnFmq4MtSGDsXayv533VnyIPy/ailL8f/bOO86uss7/7+ecc3ud3ie9V5JAICQhBRCRKlZABVddFcFd17Kuuy5i4bfK7uoquKurgi6IFEVApIYACQkkpPc+Sab32+9pz++Pm5lkkplkJplJPe/XixeTOe2559655/k83+/386W2PcvnF1b1ep/iGYsdTWkqo25Kwj37sswAHvr85Zi2TTQw9M+OlmSWlGHhc6k0xrNYtkRT+ycohBAUh5znm8Opkc1mBy1Vqq8+LqfCsmXL6OjoYNSoUccIEyEEH/rQh9i4cSPPPffcgITLPffcQzqd5uc//zkHDx4c7GED8Lvf/Y7/9//+H7t376awsJBbb72V73//+7hcp27McSSOcDlJ4vF498+9uSX0tu3IY3qjrq6OSCRy3H2Ox7/+679y7733nvTxDg6nm6RusWxvO/GsyYyKMCML+v5bcjj7+e9XdrJyZwuKIkhnLT4+d3if+8ZTOo++uonORJYDtc28Vgi3Xt775Ls3attSNMcylOX5aNq5h4MTFPbqHmrTgkvybIYFJO+mQ7zW4WXLwRhk4lxZKbmpSlDdahO3VN6fH2f5//aMsryhlPI7qwp3SiEUKkTXbZptBcvKgITswbeR8ToUBG4k48JR7hhWTY1SwCu6TsyCV+wIf+ttwhBeXrMKEULhOddw3sqfQchKM1obxo+v8vOxugytWQ8CKHRLjpzDX2PtYUdblvXNKntaWskGVP5jImi35dLC9v5+G0IIbFuyvzXd6z1K6Rb3vbCfTXUpysIu/vW6aoZPntpjn+AguH31l5KQl71taQzLpjrPj+rYEjucZu6//36+853vnOlh9Mn69esBuOiii3rdPmPGjB779YcXXniBP/zhD9x3332MHj16SITLn/70p+4UsYKCAhoaGvj3f/93EokEDz300KBea1CFy29/+9tTOv6Tn/zkII3k3KS8vJytW7ee9PFOtMXhXGNzQ4ItjQkUIUhm26mKentNAXE4dSxbEs+Y2BKCHnVIGkI2x7K4VAXdtGlNHH9V060phP0e6upa8LgUQr6BPY4mVUWZkj3ArlaV6RGLtOLhP7e76DQErwZtPjVa4Z4dAerb0uiGi6qAm6cPpLmuwuTjxZ0s++Vz1HSd7JBoiUuV18wCklh0ColHy7Iws4/dRgUIF7jcqJWXo255HEXzcHPlcO4s8qIoAsvU6cpGkggkMJwkhYSoV8JIodHmCeMPhGkuzWdLup4by5NIoMMQvK/E5Mh5vA+LSGcDeqOGS0CnqaB8/Avd22+8qJj6jiyKgA9MK+r1HtV26OxoTFMY0KhJKGzT8xk+oLs8uFREvCweU0jWtCkMuJ30LYfTzje/+U2+8pWvnPTxeXl52LaNlJJk4viL0b1xoijN/v37Aaiq6n0Rp8tRrGu/E5FMJvniF7/IuHHj+MY3vjGAkQ6Mn/zkJxQVFfHSSy8xffp0Wltb+eAHP8hvfvMbHnjggeMu8A+UQRUud9xxx0l9EXVZow1EuHzwgx/sLo4/kqqqKlatWjXgMQyUI/MOU6lUn/sdue3IY3pDCEE4PPRNuhwczhYUkesmbkuJIgTONGboSGYtkrqNACwbCoNi0CeOH7q0mt+9tRe/W+XqqcfvF+Ju2cNXF5XwWrFCUdjD+6aWDOhagWd+yfcmQoPlo8wLz9Yr7EtpeFWozcC7HRrtKQu3201c8VAjBabwYdPMG798ju1aIWZ+MROI4eo2NhW4pEW+lSIlXEyKHcCOHUQES0BVAYEWKqaqahZTI2Fu97cjkGyzA6RQmBhQ2BiopNyOUZypo1i2k0l28lZkAp3BCBmPRsayaNcl/7E/ylV5GndUdtJXttS1BQl2xgJ0VoznzgXDUY5QNgvG5zOtKoSiCCJ9iL7yiJuRhV62tEkqC3yMLT/zz5foaYzwODgczamm1HcJj9amBm6eeXLOXscjkcilivY10Q8Ggz32OxH//M//TE1NDUuWLMHtHjpHvfXr1/PlL3+Z6dOnA7moy/3338/cuXPZunXrgNLaTsSgCpdvf/vbxzwI9+zZw+9+9zv8fj9XX301w4cPR0pJTU0Nr7zyCslkkk9+8pOMGDFiQNdqa2ujsbHxmN97vaensVR5eXn3z7W1tUydOrXX/Wpra7t/Lis7/oPcweFCY3JpkKRuEc+aTCsPD0mXdYfeGJoOBPMmFHPx6AJURfQZOTuy8H5sWYixZcdf0DmaI4vvvS4Yfui11Jk+Ymg0Z20K/Srvz0/wxwYPu5IqiqKg2QaxpMlLj77BLk81z0cmkUFjHk18jAMABDG5XtTzpplPe8d+Nta+R7MSwj9hCjHVg4JNCVnuKvExTmnHLSRvW1EeN8toFF48RZUUuwX1RFgnOpld8zZXA1++bgavxixazUbiuuSlFh8ZW/DnlhCzQhmmBY+KTh1yC6v2wX9+93bgcHG5YUmW1aSI6zZzqnzkHSdSFRwxgXs/M4otB9qpLgxSWRgY0L0+W7ClxLIAAZriFNo7nDmEyPUDyi8u5ZcvLB/w8Z+99nLamhr63N4ljPr6jA+krmb16tX89Kc/5ZOf/CQLFy4c2EAHSCwWO8aiecyYMd3bBpNBFS5H11fs3LmT2bNn8/GPf5z/+q//OqYwva2tjXvuuYdnn32WlStXDuhaS5cuPcXRnhoTJkxAURRs22bTpk19WiJv2rQJgNLSUsdRzMHhKLwulStG5Z/pYVwQBDwqlpTYNgS96pBN/ryuvt2tBmpxbNuSNftz6RjTlv/f4VQq17ELVBmhUZXnR2DjUi0erslydThGRSDK0k4X6BJ3qh2iBWxXKqhVQuiovISba+wG2myNZ41C0tkE22qX0RJvRkWi0Ym/YTWussswNQ/trhB/8k3mS5m15MsMB6SXRtyYCGzhIm7bKLaOEe9k7me7GrFZfDQ/5yi2KenhnQ43jaaLkCbxiKMmIiewOH5tT5L/WxdDtyQbG7L80xUFvb6XXQX40YCbOeMHFs06m5BSYlq5KGHXyzzOR8zB4bSgCEEgOLBFl67jjkdXZk4ymex1e1cWT1fkpS9M0+Szn/0skUiEBx54YMDjHChSSlS15x9mVy/FwTYxGNLi/G9+85uEw2EeeeQRNO3YS+Xn5/Pwww8zevRovvnNb/L0008P5XAGFb/fz2WXXcby5ct58cUX+drXvnbMPlJKXnrpJQCuvPLK0z1EBwcHh25URZDnPzNpOifbk+Xxdxr4v5X1yMaD3Foc5tYqnZgp+HNdgIwtuK4oRZkn1335AwVJdqbcJCxBR0JnlaFiKV50l5uoR6C7fYzNpijJ6BRgkMKFRBITHnYQ5j3dzZqmvbQ0bsWUEhWBicL4aISygmKWKwq6N4QubbYLFweVEPlWholKghetQmK2QGlvIBXOR7EMCmZNAI6t85nkz3JxseCv8SA+v8YOzWA8df3qyQLQmrTImJKAS9CYMDHtnhP5LsFyPiHJiRYpGdp25Q4O/URy6PN4Escdj+rqaoA+C+i7ft+1X18cPHiQdevWUVpayoc//OEe2zo6OgB47733WLBgATA4wYAlS5b0SGFLJpMIIXj++efZtWvXMft/7nOfO6nrDKlwWbp0KYsXL+5VtHQPQNOYPXs2r7322lAOZUj41Kc+xfLly3n99dd55513mD17do/tTz75JHv27AEc4wEHB4cLg2TGIJbSKc3zQ/3OUzrXe68sw0h4EQjWpALcis7j9UEebwxiS6hJa3xvTDsAk4M6D45rYndCcPtqPx2GijeToLjMT7Fi0JHKMtxOogAFMoMibSw9g27qPJ7SaaxdSXsmVywvUTBdQQIVlyBCEXZJFSObRvEYWLZFKpNhs/QwScAUJcFnXQf5XbaU9oxA83QyPARvNKncUQWuozLmhABvKMCEgA8FyVYzyA1K/0QLwGXVPtY3Zklkbd4/NojriAKZ81G0CCHQFIlp52riVCfa4nAeM23aNADWrFnT6/au33ftdyIaGhpoaOg9Na2jo+OkGln2xa9//Wt+/etfH/P7H//4x8f8TghxdgqXdDrdo99JX9TX15PJZIZkDJ2dnRiG0f3vLv9uwzBoaWnp/r3L5TrGivjhhx/mzjvvBOD111/vVqZd3HnnnfzkJz9h8+bN3HLLLTzyyCMsXrwY27Z5+umn+exnPwvA+973Pq666qqheHkODg4OZw176zv5/mPv0BrL8L6xPj63oPqkU9Ksxx7isnCQHZlcB/Q50dx3d9xSkBJciqTD7KkKPIpkdYeLTEcCU3EjpeSa2FaesUrJZkzeNT2kqWCPoaG4MmRTndiN77GhfTcuReAREi82gZJxuEtnYCku2rJpKsjQITWsZDOG4qKCFOsIM0MNMq5pO6ZHpWjEZPJt2JcSdJhwUdRC6+OlT1U72GcHkNJmutraL8EC0JLQeWl9E+WqzbWzixhbkrs3/RUsyayJYUmiZyjydrKoikARx8/9d3A43QxFA8q5c+cSiUTYvXs3a9euPcYW+amnngLguuuuO+55uurJe2Pp0qUsXLiQxYsX8+qrrw7KuF9//fVBOU9/GFLhMnXqVFasWMFf//rXPmtAXnzxRZYvX35MtGKwuPHGG3tVlG+//TZFRYctJK+44ooBh8o0TeO5555jwYIF7N+/nyuvvBK/349t291CbNq0aTz22GOn9BocHBwczgXe3lLHjr31RPwar2xK8uFLysgPDMzJprv4Hrip3GRCtA2kYFzAYEsHbG5MkdBdjAzB7eXHOuvse2MNvsB44v4iUorKetMinGxFQeOA9NJsF2BKE3c2QVJPYLTvRnNpaD4v4cJyvlKahzQ9vIyNTyZICJ1m28VYJcH7lVqeo4IEKi49jTtdj4kgNGcWmQO5wvHZ+Ra3VhksKLK6azJ0VN70jKBZCXCRUccivYYRWhsSGHPb7f2+N799u4E/r2sGoCNlcu8NI/stWrbWx3l4WQ1Zy+ami8pYMK53C+WzFUewOJx9DL5ycblc3H333Xzve9/jrrvu4uWXX+6uZ3nggQfYsGEDc+fOZdasWd3H/OxnP+NnP/sZN998M/fff/+gj6k/XHHFFaftWkNe43LTTTdx44038tGPfpSPfexjDBs2DICamhoef/xxnnjiie59z0VGjBjBxo0beeCBB/jjH//I3r17cblcTJw4kY997GPcc889Tn8VBweH8x5Zt4MyOgh5NWJpkymVIVQheHVzC363yqWjoj3sfHvjSMcwyFllV/lsTCkQAn67V1ATMwnSypSI5NIjguTLfplrJHmp0NidN5JlikLITvOWWkahqMOWFlJKyknRJFy040LxF6AWTMBtN1B9+U2Mnjyb+vrtvH/ba0yTHQgBrR6NPbaXUkWnStFRpMKOhM1oq53b77yCZ+s1/rJHwwbKPDY/nZal2NtzQrPJVcpKdzUSQYvwUZVpZMzHPzHge5zIdokhQVILDyg17O1drdR3ZvBoCq9sbjrnhIuDw4XCt771LV555RVWrFjB6NGjufzyy6mpqeG9996joKCA3/zmNz32b2lpYfv27dTX15+W8T3//PPMnz//jLXvGFLhcsMNN/DQQw/xla98hUcfffSYyIOUEo/Hw0MPPcT111/fx1lOjVMpOLrjjju44447TrhfOBzmvvvu47777jvpazk4ODicq3QV3y8Yn4+mCJrjOpeNjvLzJft5dXMLLk3wtwuruWlGaY/j2hM6hmVT8JdDedFHuYVtzgb4RUcFWany4VAjPrUNm1zzH+8RT68u0UJxBX7gBns/tVYem5U8hG3SZKt49CyybRPJorFUawbVVoo1Mg9ROoNhE4dTMmokaWmxOqFQavqZrqZYahewQuZRqWSYpNQjmmuZRS2zoNsxrDEjyFhQ6Ja4FMhzH7sKKwGkRGBjI1Bu+TSGZQ+42epHLy6mJaFjesJ8YtHA6llKwh5cqsCwbSryfAM61sHB4ViGyifC6/Xy+uuvc//99/PYY4/x/PPPk5eXx6c+9Sm++93v9tmc8nRxww03oKoqM2bMYNGiRSxcuJB58+bh852e7xUhB9unrBf279/Pr371K5YtW0ZdXR1SSioqKpg3bx533nlndxTmQqWyspLa2loqKir6dJJwcHA491m7r51Hl+/D79H4zIKRVA+wr4ZtS97Z3UoiazF7ZD7hM1irUNuZ4c1N+1CAK0oFJb6e0RQpJZ/8xXpakzqpjMV100v46rUju7e/u7OFHz+3lc49u7m2SvCp4RKfYvc4x3+3V/BqsgCPYlOq6XwluIMna0AV8NHhsON3z7FXBKkXPkbl+ylRTABe1yP83iqjQQSwVQ2zYz+idjnSTFE8dj4jR83i4lQNW9vSbLWDFHhV1LISvLZJe80BymSGqAbbRASvsNGlwocTm7jUqD/C4jjH9rjCAztcxE3BLeUmC4pMCjxwZHApa5i87htLy8Q5jPTb/PmdOhpjOh+eWcgN0wr7fc9PpfjesGxW7m4ja9rMHplHyHtu1bk4OJwNqKqKbdsUlJTx8JJ1Az7+jkXTaW2sP6fne/n5+d3OZF0pnC6Xi0suuaRbyMyZMweXa2i+Y4Y04tJFdXU13/nOd07HpRwcHBzOWn7/dg17m5KYtuQv6+r4wpVjBnT8q5saefjNfeimzZpRbXz9uvFnLPd/xZZ91KdzlqDvtUqurew5DiEE759SxBOr6glFNOaOzeux/aXf/Zld9RqzL59B6exxrNI7mbP/dbTM4f4FpZqOJiSGVKjUMlT44e8m5LYt++Vz1IgAj/gn0SpdkFGY48twBS0ssfLosDWkEUNvXIvduRcpQHV5aDqwFjt/IjWihHvc6/kEDdgIlu+rZamVT0JxkxAu/LZNq3CTb6fxywwXX3sxc4uPXecbF7L5r+lZGjPw850qf6hRmZUv+YfxNi4rV+vov/WLfODQ/o+928R7NXF8boXfr2pm4bgoIe+JH8Wn6hjmUhXmje2/SHJwcHDojdbWVtauXcuSJUtYsmQJy5YtI5FIsGzZMpYvX853v/tdvF4vc+bMYeHChSxatIhLLrmku6/LqXJahIuDg4ODAwS9GqYtEQIC7oF//da2p0npJkGvi33NKSxboqmnV7h0pYV51MN9DDx9PI8+flk5l43Jw+tSKIvm0sC66liG+V34gkGuuHQCQbdKoKCcztQICg5u6j7+/cEWQopFVgou93V0/74rNaw5WkEsq5IRGi22mxeNAG2qi6g0yLbtIN24HmwDFbCFQihajhx9NSmpEbUyvGqX8FVlB0LAVLfKMsuDgYekLYhkWygTWabIdq5bOIa5RX0nJ/hU2JcQvNcuyHdJlrco3NieYUL4WIvjkFfFpSkkszbVeRruE6SLnY8Wxw4O5wOyj2Sxvzz2a174/W963dbR0jyUQzotCCGYMWMGM2bM4Ktf/SqmafLuu++yZMkSXnvtNVauXEk6nea1115jyZIl/Mu//AuhUKg7SnOqnBbh0tbWxqOPPso777xDc3MzCxYs6C7G37RpE3v27OGqq646bflxDg4ODmeCTy8YSVm0Hq9L5YaZFQM+fvaofFbvbSOt21w5uQRtgDUSp8LRTSTnFQsCqkRVYEZ+7+JJCMGIIn/3v48svv/wCEiqGaxUkmVbW9ixp55rgu3cnie7u0t7hGRxoK37+CNrWQA0Q6Kj0Gpr6EKlzRSsTGQor1+KJ5lERaAJiVd1ERyzkGzZNJImaOkEQTtNFL3b+asND83Ciy4FFhKfsPj7K4q4oTIfcXR3+14o9kKeS9KckVT5bYo+9inU6LHPtPdNzEM3Ja0JnUXj8/Ac3ejlyPt3lGhJZU1W7GghljaoLPAzuSqC7yQEsIODw6nTV6HFtR//NNd+/NO9bvv04otobTw9RfSnC03TmDNnDnPmzOGf//mfyWazvPrqq3zve9/jnXfeASAejw/e9QbtTH3w5JNP8pnPfIZEIoGUEiEEJSUl3du3bNnCxz/+cR599FE+9rGPDfVwHBwcHM4Ylfl+Prto1EkfP7Eywg8+MpWUblIa8Z74gEHiaNECEHELFpb1L9pzpMVxVwG+W4HPDc/y7OpXeWOzjUeaPGWkmTkDJkWPPcfRokWXgpeMfBK2QlYo2IDZsJbGlk1IkSVPsQC4rKCIWyqrecSdx9JkBsOGoGUyQcS4RantPn8UHcUycKEQDXj45rwS3lfW/xLQif40Xx+jsiWmMOqG6ykK9/7+uDWFW2YcP2WrryjLL17byQtr62nozFAQ9HD1tFK+dt2E0ypgHRwcHI7Gsizeeeed7vSxlStXdvdNBMjLyzvO0QNjSIXLihUruPXWWwmFQvzoRz9i/vz5XHLJJT32ufHGGwkEAvzxj390hIuDg4PDCYj4XUROU1F+b4JloBxtcXw01bKTkA41SUiY8OtdcN90CBx6Oh0tWI4kjSApVaQApI0twQbSqIz0uLlt+AjGhXKWnUlboEgTDYEAhpEkXxxqTtxUSxUa0/whOkqrqfDZjAkNwLfGyNWyVHzsdn7zxHqee24La/e28uXrJg64Bul4qWG7GhIYlk3WsJFIttbGaEvqFPchkhwcHIYI2XfE5UTHnS+sWbOmR51LMpnsbnoZDAa761sWLlx4TCPNU2FIhcsPfvADFEXhxRdfPEawdOHxeBg3bhwbN24cyqGcEzQ2NjJx4sRet911113cddddp3lEDg4OtpTdHZJVcf42wjvYkuT/lu7EsuG2iQrD8k9tMtxblKU3puXBNRXw4HYo8Qk2dMC7LbCw9PiixS0k17ra2GuVkZYSy+1FrZqNnaxDRqu4qjTAGNfhBpXzRAv7pZ926WIYSaYosdyGplzU5X2ffT8zMrC502JYQDIi2I8XeUiwQK6W5Z2397G9LkZewM2bmxv56NwRlOX5j3OCw/SnluXqqWXUtaWJZUw8LpVxZeEBN/gcKFnTJqVb+N0qHs2J7Dic/zz44IM8+OCDvW6z7cPOh+eRBhkQH/zgB3njjTfo6OjoFio+n49FixZ1C5WLL74YVVWH5PpDKlzefvttLr744j5FSxdVVVW8+uqrQzmUc4KSkhK2bNlypofh4OBwCCklpgWWzDVDlAq4hua7+Izzu9d38vK6WtAz6B1h7r3u+Db1tjxci3I0R0ZZpIR2HcJarrP80QghuLka3moWNKTBp8GuF97CZbUfI1h0KXjOKGS37cHXuoXxaj23Fbh5RC+jQ9WwhYJr4oewLIPf2BnetbPcqh6kjAxzaGW8iJEUKgXo+JsPdJ+3y+K4yAsLvP2cjhwSLUcW31fk+wn7XbQndSZWRcgLnLj58ECK76+fWcGsUfm0xLOYlmR8eXhI08Syps32hgQpw8LvUhlfGsR9logXKXOl0YLzdzHB4cxwvIXiLjvkC5lnnnkGIQThcJjPf/7zXHvttcyePRu3e2gXUboYUuGSSqUoKjpxd94jw0sODg4OZxMSuBDmRVasFfQMiMMRpt6QUvLUxk5e351kTKGHz83OJ+DOTWaPTguzJPx0l5u321RGBGz+cWyWvF6ebWU++PvxkpUtsOmdnexTI4Tz86gg22O/5VaUx2Ma8boViEwrG1WDxcHhRFWTLBYZlO5V0DbcbJIenhOVfE7uAiBfGOQ37es+39E9WY5ke0JlWbuHYrfF+4uyh0XXUVGWI7l0XBH/dMtUGjrSXDKmEK/7+Cp3oI5hQgjK8/yU9zOKc6qkdIuUYeHRFNKGRUq3zgrhIqXEsHJpgQrgUqUjXhxOK5KTSxU7X2a6Uko6Ozv5r//6L1avXs3ixYtZvHgxM2fOHDTb474YUuFSUVFxwgiCbdts3ryZkSNHHnc/BweHM0fWtHmnpoN41mRaeZjK6IWRUy+EQFMk5qEFtvO1BlrW7eD22UXolo2U8MlLi/vc92CnwfPb4lg2LK9JMqPCx9yVjxzeweUlaQlaDJWUYfOnBjdJS2VPymZxkcnVJdYx50wako3PvElMDbLZM5KMy8dmK8PtrnpWaaW0G5Lpei3PHtxNe0uuaZuCIGYrvNieJZBvUWp0YGheLClplG5ARVdVEkc+5g6lhR1PsAAkTcGP9gTZldTwaxKfKrmyUO81ynIkQgguHXfixbpzxeI44Fbxu1XSuoXv0M9nA121TKLrZ5lL43RwOJ2cLyJkoHRZHy9ZsoTly5fz2muv8dprryGEIBQKMX/+fBYtWsTixYuZMmXKoF9/SIXLNddcw89//nMeeeQRPvWpT/W6zy9+8Qvq6ur63O7g4HDm2VAX56297di2pLYzw52XVOI6X2fxR6EqAuWQHe75tqp7ZPH9sHwv3zlBehiA16Xg0xQa4iZ+t4L3jWfAQ3eUpUlX+d6+Qg5mNSpcWeoMm5gp8CmSTlMBLAxb8nQN7E/C3CLJfS/WsddzEaoqiAsPwgJbuvltaArvJSTpll08WbsJr5lAQ8MGQv4oheMW0iQjGNLEkoKAEPilTkjYWJoLG4WANFmajeC1dWYKhYWf+QDthqBdFwzz271OeNM2xExBSLNJWQqdGeuEoqW/HClapJS8u6uVhvY0s0YXUJF/eiIpRyOPqONSjqjjcmsK40uC3TUuZ0O0BQ6NkZxgEeLCiIg6OJwtzJo1i1mzZvH1r38d0zRZuXJldw+Xd955h+eff57nn38eIQSFhYUsWLCAxYsX87nPfW5Qrj+kwuUf//Efeeyxx/jMZz7D+vXrufnmmwFIJBKsXr2aP/3pTzzwwAMUFxfzd3/3d0M5FAcHh1NAt2xsmWt2aFjHTyU6HznXBEtDR5olmxoIel1cPa0M71GFOSfjFial5KW1dby9rZHRkSDTGrcx3Mgww9fZo/h+bcLLlpSHIpfJtrSXQm+aPCsnEMoPtTV5vQEe2Q1ZG55a28YBVxFSUTFlzvlLBRp9xezN2mR2PIuM14KUmEIjqAouLR1B+ejZrPBUobV3kLDdlAgDt8vNDKOTA7ZJg5RkszZ7QgXsClSRXxplRGk7OxIJ7t/hp90QLCw0uGdkpnvim7IEuhQUuGxuKc3wbKOXif408/PSgypYunh3Vyv/8dwWYimdsZvC3H/bDPye09+XxbAORxU1Bdza4c+7W1POGsHShRACl5r7HlIEfdZaOTgMJX01oLyQ0DSNuXPnMnfuXL797W+TyWRYtmwZS5Ys4a9//Svr16/nySef5Omnnz43hEtlZSV/+ctfuOWWW/jxj3/MT37yE4QQ/PnPf+bPf/4zUkpKS0v585//3K9aGAcHhzPDlLIQdbEsHWmDOcPzHHehsxgpJT/963bW7G3DpQosy+bm2dWHt5+kxfHuhjj/89I2YikDT9t67p2YZWbEDfRMG6zwmBS4TJoNjZBqkkAlZim8vyjDzEjOfjhpQlt9Gwo2Kc2PIlRsKVGBQsUiofpo0+Pom55EGhlAIgBXuIrRVVP4jL+RN80sLXqGjOpF0QRIlTSChOZhqtHJbsODgSCWV8ywkEbWFjQbKqszGvvSSq7DfZvG7VWCArdke8rNf9QWkrAUPlLYyUeLmrmpALy3fgFFObWJcV+pYY0daWIpnWjATVNnhljKOCPC5ciFiHNlUUIRglN8WxwcTgmnNLsntm2zbt063n33Xd5991127NiBECJnpDGIN2vIvyEvu+wytm/fzq9+9SteeeUV9u3bh23bVFVVcfXVV/O5z32OcDg81MNwcHA4BcJejQ9PKz3Tw3DoB7aE5lgGn0shbdi0JXRg4ILFtOwejlWGZWMc3I9bggUYiofesrwnB7J8a1grNRkXf2nykHBr+FyCkEfFJST/vR2eeqcBzeWh0xVGmhYhYeITFhUeQUt+NSnpxqPbGJ4w0jRQXH485bNxRapQRQKPtAllO/GQj46GbdskhY1HU9lpeGi0gkStFCOr8jhoxHGLMFU+m4WRJM1pm6gm6TQE0yMWES33Gt7oDLAn7cav2jzTEuQD4Wb8tw9+lOVIZo0qYFxFhMaONPMmFFM8wKaiti2JZwzCPtcpRQVVBWwLELnUSAcHh5Pnxcd/w4t/+E2v2zpam0/zaIaWdevWdde7vPXWWyQSOQv6LqHi8Xi47LLLWLRo0aBdU0jHzuuMU1lZSW1tLRUVFRw8ePBMD8fBweEc59WN9Ty+bB8hn4t7rh3PcLOu38d2pAwefHkPmw50MG9cAZ9bPBJNVTAffYjHD2i82eZmZEjhw9U2w/3HtwV9YG+Yv7b4EMDNJSmiLz/LA56pGHmFFE0cTyAawt62mfr9jRSR4UD5FHyqglQ1spk08aa9uFu2MbJ4NDuIoFgmpSLNF10H0QT8uz2aGtuPkBa2UPCrglI7yeiqKF4zzbqUH+lyM7fI5h9HJqjwWkgJazpVmrMKF+eZFLhzj8BnWkP87GAeHZbGtPHl/PoTo0/JHae/BfiprEksZVAS9Q5IfKSyJv/10g521MeZPizK568cc9IpXbkV0dzP4izsVdQ1TRnKcaV1i5ZDIr8o5D4mvdLBAQ7bIecXl/HfL68Z8PGfv3oGbU315/R876GHHmLJkiUsXbqU9vZ24PDfqKZpXHzxxd09XebMmYPHc2Jb+IEwpBGXT3/60+Tn5/PAAw8M5WUcHBwcLkhq29O8s7edqN/FvDEF3YYJV04p4/JxxWiNu9AGIFo2NWS4/7VGNuzPUqQK/rK2gTl7ljAlbCIEfHyURlHExS/2+3l3G9xZmeL9RRksCVovK/WfqYxT6bXY0KLz2NIDGK4J7BVBXIZCYl8DEyb7UUaMJrlhJXsbNpEXLCGdP4xymeH9xl5meWporcxji9RpU0AWDydpS95ug6TiZoaRRGYtDlou8jEQwXxG5EW5a1QWVSj8zQYvli14t01hTaGLCq+FEDAzapGLGx3CyDDPr/NrJUK7P0Jr0mDNgSSzhoUG/J4M1DHM79FOKj1s44FOVu9pJ+zTWLGzlSunlDKxIjLg80BOEJxlWqWbrGmTyNgIIOgdunqbtqRBxsgJ8fakSVnUES4Ox+dCXfX/0pe+1J0CpigK06dP7xYq8+bNIxAIDOn1h1S4PProo3zgAx8Yyks4ODg4XJAYls2vltWwqymBR1NIpHQmlgYZWRpGNOzMVZ4M0CP2tV1J2jI2UlVpMl2Ma9tNsED2KL5f0uKhw1RQBDzX6OHNAylqU3DLMEECN48d9DAmYPOt8RmKPZL855/gce98WtV8bARIiVso6Jks8aZaUiufIHNwG0iN1KbnqZx6I1dpTSxI72O3CPGoMoqE0Gj35uHzh9Gk5C/Z8ZimhVfJcmNiNavIRysuZHgwywPTLAq98Kv9XhJmrtjekBBU+4gOHXILy1x3J96n9jBGCDrSJo1xo9d7vqUuSZ5fo7rAd8z202lznB90E/ZptMR1yvK85PlPT/O3001Kt7EOFd6kdXvIhEtXHZMEp3bGweE4TJw4kcWLF7No0SIWLFhAJHJyCyYny5AKl6qqKgzj2C9/BwcHB4dTw7BsYmkDv1ulLZHlf17ejqLrXD3KzV0LKgacVvOL12v432XN2B4/BWSoEO18YbTBiIirx35jgwarOl0kLEFaN9jRCj4VfrkTtpsekqbC5pjC+JCLKa89jYmLlOJCIugaUToeR0vsZMeaTRTonYSwEQKKVZNPJtdxkTsnJlrxkBIaYWmQkCaFMouhuGhCwW1nSSsezBmzudWXIo8MF+fbFB7SWAFVUuG1SJiCKp/NnKh+1A3s2Uiy0pZcMSbCsl0xJpf5uXhYsMfuUkr+8+UaXt/WTtir8vVrRzBzWK4+81QFS9qweHN3Gx0Zk1lVEUYVnNgWeUxpiC9dPZZdjXEmVUYoyztWSJ0NGJZN1pSoCng1ZcCfS0Ucdm86VZOE41EYdHU3GY36Xcff2cGBC7c4f9OmTWf0+kMqXG6++WZ++ctf0t7eTl5e3lBeysHBweGCwu/WeN/kYl7a3EQ6pVPb1EqBX+XNHWk+dVkpIW//v97bkwb//tc9dKRNFC3JuLwsD45tIKKax+x7U3GaFxtd1KRV9iVV2kwPLtOixG1j2jk7XdOGbW9vYQoQLi7mknSM5VYUgaQqvZd9dRvR9SQSm7hQ8bqDXF05jtvyVTRxWFBMkJ1UywSteJhj1FKQiqOnUtiimJZgCJ+isD5lsjPt5hOlcUYF4yxr1TiYVhgfNHlfqUXSkNxansZzZOZPLz1ZFEVw94IyPjarkIhPO8Y5L5G1WLU3hs+lUN+ZZf3+ODOHhQclyrKhLs7qAzEkuZSlT8wqpzGWxbBsyqNewt7eJ9IzRuQxY8TZ+2y1pSSWtjBtmeu94hV4XQMTHyGP2p2G6HP3HW0xLZt97WlM26Y66sPvHtj0xqUqFIUGNxff4fzmAtUtZ5whFS733nsvS5cu5ZprruFnP/sZF1988VBezsHBweG0YVo2rfEsBSFPD/etgSKlxLAkqiIG7Oh01cQS5o0pZPlbq/nxfoVExmLiiBCBAXY41xQQyRjSVLGyOpWRdK+iBaAuq5IwYbjHYEWrwK240VSbm4br5LdkefugjrBtmnx5rPJPZJWZT9gl+aTcx/q6rdS21yGkBxdgCwUKJxAomchmVWGt3cjFaqz7WvlkWaw0s0UrZHzLDqYe3M+8z17PnmyKnVnJowd9NGcFhhTszWi8067ywE4vHabAXVBMaZ6fQs0g4q8BzGOiLEdi25KnV9Xx3t4OLh2dz40zS3tEB4IelcmVQZbt6KAw6GbClPGI8rIB3ee+6EgbJLIGgUO1Lk3xLI3xLABZUzK1QjvrCub7g5S5/5RchiD2SSxRK4og4Dnx53lXa5LtTQlsCe1pg8uG5Z/MkB0cHM5yhlS4XH/99bhcLlauXMmll15KaWkp1dXV+Hy95AYLwWuvvTaUw3FwcHAYFFJZk39/djNbDnYyviLCV2+c1D3pHAhSSjpTJvoh4RLxaWgDqEuRdTvwAAvHRQn7NNqSBrNHhLtTalozNvtiNiV+hcpg3+LK/6df8q2RAX5RG6HIbfOVEQksCUuaNZp1hXkFBlW+3KSz0msz3GezsVNBE5JRAYluC4p8Cp/c+BxZ3xR2e4pYZ4d5lSJ0oeFTBcGaN/Ek67BQMAH8xZRWzCDhLaIFMKRJlsNjtIEmNcgftNG0KH42Fuax+KZLWJoJ8ny6hFhGUmskaNEF4/w6i/PTNKYUEpYg7FGoVYOUSouDppeNeogR5Nxv+mokuf5AjEfeOkDWtNlSl2BsWZCJFYeL84UQfO2a4Vw1MU7BiDGMr+oZ6ZBS0pHOmRhEff1PNdpwsJPXtjTRGM8yrNDPldPL0BSBhCOS685NFAFelyBjgqYypP2fsqaNLXN2zmnDQkp5Too9h3OICzVX7AwzpMJl6dKl3T9LKamvr6e+vr7XfZ0vGAcHh3OFbbWdvLe7jXDAxXt7WtlyoIOLRxcO+DyWDbolEQJMS2JYNpp64tXl3nqyGJZk6d4Ua5ssrp8YZliem0e2ZtgXsyj0KfzNRB8VR4kX67GHcj+4vNxabXFrdVv3tleaNH6y20vSEixvVfnPKWncCoRdku+MT7IlrrKs3c3adpUyl4H64l/xY5Lvd7HO0mjBjYkCQpCUCpHh82Hz46QVHyWl0/FER+ISIDBJoiGlJCE13iWfuDvEagrQbdhp+1FUjaw3wt5Mghcpo9Vysd1w4/dIomaCGwoTXBbJ0OQVvN5iUptWGelOows/UaFTTSeQEy22lDTGTfL8Kt4jJtK2LbFlLqWp6+ej8btVLp9/Sa/vybamBJsbEwgEF1WEGdmPOhWA9bUxOjMm+X43QsLoQj+GJdEPfR7KIwOzST6bEEIQ9Gr4Za6B6FC+jmF5ftrTBqYlGVcUPGfvmcO5gSS3uHIyxzmcGkMqXPbu3TuUp3dwcHA4IxSFvUSDbho60pREvBSFB9Y4sAtFAU0VGJZEUUS/oi29iZa398T5x2f2sa9dJxxwUxe3+OyleaypTZPImDR6NDYXKVQEc9HubsECPRzDjqRFV0hagpAmadEVUlYuCrA67sWrSIqKg/irxjJqbyPRHRtYMukDlGQ7uLJ9Hyv1IMIywBMG2wYF8gsquX7YCHb6x7FRKUQiMRQPCXcYTc+QtiX/bVajKm58WpQCMtS5w2TSWQII/JiUaBn8lkk9HlRyKXYRVVLqyU0hij2STw/T+e99XtR4HXP8cFEwy8RP3o4QAtOWPLi8mdUHUpSFXXxtQQlFwdxj8KJhEW69rJJVezq4bEwekyp6WiGfqJZlf3sG05JY0uZAR7rfwmVYvo/V+xV0y2Z0UQAhBG5NMLpoaC1FTyfKaRARUZ+L+SMLkNJpoungcD4zpMJl2LBhQ3l6B4cLBltKzENtJzT19EwEHPqmqjDA12+axLaDMcaWhxleHDzxQb2giFx6WK7Ghe4+LL3Rm2DpYl9rhnjWQlMVMlmLlG6yYV8HbR06wZCPVDJLJqUCvh5RluMxr8BgeatKi65wfalB1CX56cE8XmgNIi2TUePLkTEdEcxj17T3sW9fC/hsZjbUkNnxR1RXAeaoG5DSRrVNPB2dXFZYQic+VMuDR5p0BvMIRorIGCaxxgYMzQO+MFnVTdblR7fAJkvaMLm0MMukgEnE3s/ybD5JVZIiRbXH4Ipoqnvcf6j1sC2mgIAiLcvtd32ie1ttp8E7NSlcKmxryrC2NsXV43LOYIoiuH1uFbfPrTrmXvSnAL846KY9baApgqJg/62J544uID/gJmPYTCkfeN8Yh8MoQnCOZ9c5nDPIk0wVc2Iup8qQCheHgdHY2MjEiRN73XbXXXdx1113neYROZwtmFYurSiXUgQDNMxxGAImVkaZWBk94X62nUv58fTRiVtVBJsOdrKjLsb4ijBTqo495/FEC8DM6iATS31sacwQ9nsw0xn+5/UG6mMWoYiPKaU+prz7F6z1h+zpTyBaANp1gS0hT7OZFc0V6q9LeFGx2NKUIObpoGhEJaoiaEkatCWTdG5+gb0H1lBspQjrKSrb3qEhMoE8YeC2TfYQZKVaiqYqxFFJhosR/iCalGhZHSOtg6LkhI5UyOoGqqGjWDpNMYO9SRgfTjHKdUio9FJ/7cVEoiIKSvBNK+mxLd+vUhRU2dOqE/WplIWPX4syEMewqeVhioJuhBCUh/vvTqUIweTycL/3d3BwGFoefPBBHnzwwV632fbhBDFHgpwZBn36E4vF+MEPfsAzzzxDTU0NHo+HyZMn84UvfIHbbrttsC93XlFSUsKWLVvO9DAczkKcRcRzk/r2FD98ej0N7Sk+MnckN1824ph99jUn+K+/bqc5nqU47OVfb5lM1aE0oxMJli7Gl/r56UdHsb0pzS+WN7N6f5xkBqpCChGPxfet1yn1mMcVLLaEFS0QM2BOITyy3827bQqmhIL9Lr43KcuiaJKfvZdGz9hkdu1gTzpLGIOdm1eROrgKjFwqlxCS6aEg10QsXlXjdEiN+WoHAWGjIImjkRIaWiqO16PgFZLCqIvdmQy2kOS7oVxJsl31khY5S6r6tOT3+wTfmdr7dEHqGYSAz979cSIrDiKE4GNzKnvsE/KofG1BCWtq01RGXEwu7ft+DNTmWFUEldGzo5eKLSXxtIVu2fhcCgGP6tR8ODj0k+MtFKuq2kO89MYrTzzMK0883Ou2ztaWUx3eBc+gCpdkMsncuXPZvHkz8lAILZvN8vbbb7NixQo2b97MD37wg8G8pIPDBYGmHl7d0QbmdOtwBnl9fR1rdrfgd2s8/uZuFk+rwONSiaV0CsO5ouvWhE5n2qAw5CGWNmhL6lQV+I8rWuIZi7f3xPG6FC4fGUJTBQUBF0GPQWfGojziYVfWxm5t4IaiVjarxfzKKiPV1E6V3sLcfJORAZvatKDSL/Gp8FI9PLhDIWvBO0WSupRNXUrFlrCm1WZPAiLPP8mXXEF+H53Jek8Z6dqD7N/9Gtm2WoSUuLAJuDSuqxzJdYVhhBCMlbUYCLxCkpAKF1kt/EWtQjEN9FSCaMTDFSXwt0WNPBMStJs60/Ilv92n4feGMbMZUBTStoJf633C8Gqrh6fay6mYOom7XBp/f+2YPu9dZdRNZbTvVK7B6MtypskaNkndQghIZHPRvj4CfmcdhmWz6kAnsYzJ5NIQldGTqx9zcBhq+soUu/LDd3Dlh+/odds9186kvalh6AZ1GnjzzTcBmDp1KtFo9LRff1CFy7//+7+zadMmotEoX/nKV5g+fTqJRILnnnuOxx9/nB/+8IfccccdjB177j8YHBxOJ7mC3TM9CoeBEgm4cWsKiYxBdVGA9niGHz25hrq2JIunV/LF66cysSLMJaMK2FoX45LRBYylAVnXdNzz/veyBpbsiKEpgpZEEbdcVADAuGIvlwwLsrEuxdjEXm4ta6As6uPHxggOtGdYu9cmanlZ2myQr5ocTCuMC9l8e6LB/qQgbkqiLtgZh3HBDGuFjUeFlqTFnc+1EfBN55ZAJwnNT+zASrJ1axHSRhGgYePJG0O0eCwbXII5sp4CkXMMS6PSZtn8wqii2RTg08kvCLI/CXpLI/tTOqFiha+MF5AzSyaWtdnVrGDlFxIUGa4otrizrA7ITRj+0JrHkk4/Y91pNuRPIp5nsX9fgollMT4yo+Ck3q/zQbR0ITjUQ0U5tyK2WxqTrKjpwLKhLpbl9hnluIfQRtnB4WS5UFPFFixYgKIoNDc3n5HrD+pU6E9/+hMej4e33nqLSZMmdf/+Yx/7GCNHjuT73/8+zz77LF/96lcH87IODg4OZyVXX1SJYdo0d6ZZOLWc93Y1s2lfK5GAm1fWHODmy0dTXhDgqx8YTyxtEOrY2y/jhR1NGdyqIG3Y7GvLdv/eoyl8tekZWoVGXqWJ5vbSaEtUJClTYlmSkAua0lBvKVT5c/1YtsUUxodtKryCjA1Xl8FFUcGeWJb9exppDRRilg6jWTd5x2hkaypD5uB7AKhC4vNFKCmfge3Jp5A0ManQITXapYc/UEWHdFFrqnRIBbdXJeTWCCgWEWFSoOpYEqyjZgGfHmEyck4lW7IByiNepnnjFG/NCZe9SckzbWEs4A3/eDyGTdq0casK/uN0V++LlmA1G2raqZC5vjznOl6XQtCrYlgSn0sZUG+gM41h5fqxuJScxbfl9MpwcDiriEQiqKpKXl7eiXceAgZVuOzatYvZs2f3EC1d/O3f/i3f//732bVr12Be0sHBweGsxaUp3HTZ8O5/N7QnCfpcdCR1xlREiQRyKUuiYScRyLkv9IMPTIryh/daifo0rhhzuLDbeuwhFAFFPo2ur/cSJcNHXDU85wvSpqYxdINrS0y2xAS1KYXqgGRVq2Rpk4pPlXxhrM3cItBtqNy1EY/wwLDp1CVN3AEvO4Uf06WhFE3GatnKiNKx/E1xiEddw6m3NBqNGJfKZqqULC9SSpMhSAiFdl8hZOLoUiCihXxgvCTV3ER9J1xbKSj1Hfvap1l1lI6YDkD0wD5aDYX/3udnV8ZDrKgEVBW/pnDbzAIaYjpFIRdXjR+Y8EgVjOT+x9exvS5GQdDNN26azKReDBIGgpSSVfs72d2cZExxgJlVkZOuMYlnDJpiWSryfHj7me8lhCDkPTdDtBNKgtTFsrSlDGZVhfGdKzluDhceF6ioHjNmDOvXryebzeLx9N+IZLAY9BqX6urqXrdVVuaKJNPp9GBe0sHBweGc4fKJZXz9IzOpbU0yZ0Ip/ra9J5VucOPUfC4ZHsKrKeQHtBP2ZZmptPLYgU7cWQUhJFU+i49USXbEBdV+yXc2KjSkJRlbsDshmFcs+ckjq3jNPRJDcxPR67h04nT8Xjc7Nh9AsS3UYXPRymeR8np4Js9Pne7CTKbIuDQ+YmzB3XyQEreNzxdEjxai2B5sReDLi+IrzmelZXJRqY8HJ+zu83VGm3bjj+XSETzxJh5vCrOkI4BWXIZmmiwan8fEMj/XToyiDLB3R1daWHNzgtrWFIUhD82xDDUtyVMWLjVtaZ7f1EjKsNjWlKAk5KEqb+CF+y2JLA++vofGWJYxJUG+cMWIfouXc5WAW+WGScVnehgODifkAtUt3HrrraxevZonnniCT3ziEyc+YJA57Usy8kJ9px0cHC54hBDMn1IB9N8xrK/zlEdy0Rr90Yd4t8OFpbqZnWfRm8GvBDoMgUeV6LYgbgpGBW1GBSWmDU0Z2JlQcAnJiuU70FdnqQlVEI8laN67mn1bk4wxWpk9fDQjYnvYzRh0lxdNc2H4fXQWFBGwBYm6BpRkCldzLQB//8mZXNqq0K5nuG+Xh0aCXH3ZaIrCHpoO1hOzNKTsO9AkAE8mBkYGU4J33tUoSw5gWDbVBV6+vKAMj2vgqWFH1rJU5PmZPiKPNXvaGFEcYtqwU09/0C0b05Z4tVy6lm6dTI9t2NmUZH9bisKAh11NCWo7Mow6jxpTOjg4nHvcfffdPP3003zpS1+ioKCAa6+99rRef9CFS2NjY7fjwEC3z58/f7CH4+Dg4DAomJbN0g21JNIGV0ytIC94ciHyUxEsXWQNmxV7OhFL/sz+uIdH6wJYwAfLDT43wjhmf0XAHcMtfrNXJeKyubbs8ETakpDvthnmh8aWFGuLJ7MxHKFl++u01KxGWCYhYbJ26bN8dOp0RqsqJbKN36qj6XQFCURCRKxOatQoAUXno7E1XPHZ67rPf3lh7loz8zvYWDWB8snD0BU3u4tcjNj66vGz44wMWVvwUEMRa/MmM7Mty0cvLqElYXDdtMJTFi2QS+f7h+snUdOcoCjsJRrof/PIvhhVGGDOiDy2NiaYXBZieL7/pM5TGfVSEPTQktAZVuCnJHT60zIcHBx650Jdhv/+97/PvHnzWL9+Pddffz2TJk3i8ssvp7i4GFXtOyL87W9/e1CuL+QghkAURTnpPF4hBKZpDtZQzikqKyupra2loqKCgwcPnunhODg49MLTy3bzPy9swjAt5k0u575PXjrgcwyGaAF48Ee/55nWMIqi4LfTdKZ1XAKq/ZL7JukI2+KZ/WBK+GA1lBzKUrIkKByOcHToku2dsLRR8OS6NuotFekXJHYvRTGS+C2dPDPJsECQW4cNZ3gg2D0GAwVLKCwNjmJP3nBKEk3cN0cjpFp9jjs2fRF66QiwJVZbE+byFyl3m72LFyMDwKrLbufe52rwuhQMW3L/jcOZWhns5YDjc646hh1oS1HbkWFMcZCC4KmLKgcHh5Onq49LXnEp//H86gEf/5XrZtHe1HBOz/e65vpHyof+zP0tq+9nw0AY1IhLdXW10+TKwcHhvORgS5x01iDoc7O7vhPblv2uqxgswQK5AvzNqTKEomBI8LlUhCFJoHFQjfDlPQpqOs7BlnYE0K7DP03JHXukuVTckPzrWpstHZL2jgwpBWJ1q7GT9aCqaNj4FIUJYy5jfnEpw/T67mN3qHk8I6sAwfW1a/jaggghIVDE8R9Mvv1bsYJ5dFguHnmzls17ivlgUZzZ4QxPNYfxqpLb8hopcFmot34RgFBdEp9boS1pUhxyEfQOvMbjZEWLlJK0YaEqCp4zZMlble+n6iQjNg4ODkOE5OSKXM6DMM38+fPP6Fx/UIXLvn37BvN0Dg4ODmcNC6ZWsnpnE6mMyc1zRvZLtAy2YAHA5eXKwix19W5UAXdWphjlzfBMa4TnO1z4pMVOI4jH7kQTNs1ZyY+35upYJucJLFTGhyUBxWJ3XNLYprMnVo9R9x5CmggpCdgZLivIx5x8MwcCZfzBNnB1WkwxmqCplrcKR9HgjxApyWP3hFIiyon9/N9rhSWb6xm2/s+0qBHeaPQTUgUvtAZ5L+FjbcKHkDYuO48vfeUj3cdNLPNzz8IKttSnmFYZYGRh/4vcTzXKsrs1ya6WFKqAyWVhSkMeZ3HOwcHhgmbp0qVn9Prnpl+ig4ODw2lm2shCHrxrAemsSXH0xJPnwRItaVPy9O9fYWX7JCaqMW4vaOGGohTTQzqqkFR6c1GOqbrBG3GbuKVSLOLsM1UEKlIaPHdAokt4er+gJKRS6oV/mmDjPniQxsh4rJbtSNtEAH6PnzurqphdEOWnvgLaTEFWCfCnTB4HUnFs/1hGTRvO/s4gtoSAYvGvuyLUZTU+WprkyoLMMa+hQ4cfb4PaJLgbTOZWxLF1lXrDZkKxRdZSUbGwC0oxp0/scawQgoXjoiwcFx3QfTtV0SKlZF9bGt2y6EibtKVbGFUQ5KKKCOoAHcwcHBzOP/oKnrz25MMseeq3vW7rbGsZugFdIJyUcFm1ahUzZ85EUZxutg4ODhcOYb+bsP/4dQaDHWVZ6h7NzxpLaEuaLJNe8hWdDxd2MszXsybwikgSTUhaTY1nd2ewA2DYUJtRkFiYFmQsiGg2nYbKO8+s4EN2hl2yGqN4Osm23XjyR3FbZSnXKQ0IK0FZqokaVwXhdAs1lodk1cW4VcHsdIbPlJtIIG1IHq0P4FYkv6kNclk0S0Dt+Ug3bHAHQ9xw5UxUj5u2TetJ7G+nMQMvZzS+MsHCM34yfpfChy4qPKV7Npi1LGGvRntKx7Rt/IpGXSzDsDwfRSdpzODg4HD+0FeJ+KIPfYpFH/pUr9u+ev0ltDc3DOWwzntOSrjMnj2bcDjMvHnzWLhwIQsXLuSiiy4a7LE5ODg4nFMMRWpYXAlgWBJVgCkFKVNhdaeLuKlwSVTvFgmWhEotzYyAZFdYZW9CoTarksBNZSCLK21wUFd4c/c+CswUpSJJflE+l9httKpF5I+/jnwVplHDVrWAjRkPZa01jDKbUEpLMIsCSAWyFrza7AJhMjFo8nyjn5asIN8t8HksNHHsw7zIC7cuGEemqhqpKGw5GONgNo5QFFptjaXhcTx8y8hTvmeDKVqEEEwrj+B3q+xoTiAluFVx3vdRORuREiw7t8KtKf3u03peIiXYMvd/Rck5Bjo4nCmeeeYZHnvsMVatWkVTUxNCCIqKirj44ou57bbbuPHGGwf9miedKhaLxXjhhRd44YUXAIhGo8yfP79byEyZMmXQBung4OBwNjMktSwALi+X2bUsKB3Nuw2S8WoCw9C5Z1cQKQRXF7n5YnWSqMvm33a4WdOhUumT3FGd5cmDGm3Cj+Hx4XGb2O27iO16h2ysgSZFZeWUywiFC4kIH7M6YnQiGJ5uJmzU84viBXQE/BSXRbmrpI1SLYNXM/j1foW3Wl14NXir1cWrTRr5LhPVNpgYsPjSsCSePgLxU0IWbUHJQUOjvCQPt9tFxoJAKEBR5NimmQNlKFzDPJrC5NIwhQE37SmDgoCbkOfMZ1hLKdEtCRLcmjjv624sOyfMAUwbLmTtaMvcPQCQNogLXMidSS7ktoRNTU18+MMfZtmyZUDP6FNNTQ379+/n6aefZv78+fzhD3+guHjwmsqe1Dfwd77zHZYuXcqKFSvIZHL5zO3t7Tz77LM8++yzAOTn57NgwYJuITNhwoRBG7SDw/GwbMnmxgSxjMnoQj+l50nvAyklDfEsCd2i0O8mz99bq0GH081giJaMbrGtLkZkyRNUeUC4D0/ky2WC/+dfhz1CoCC5bkWQ3Z0WQsDDaQ+bkh6mBTKsbrfwa7A1rvBcnUqt5UNqLmIpnW371yJ3vo5hgkCApbM6mYQR87FMk1LZzO17XqLITjHi9pv4/c4iPLZAl4KIRzAnH8Dmomiab2wRrOp0YdiHXcpKtCwfLs4wMdi3pX1+/VYkklorjwbdy4xLJpOnmVwyPML04VH+uiPB2EI3o/IHZvl7OmyOS0NeSkOnLq4Gi7RhkzZyy+4eWyHoOb9n8hfw/PAYZB8/O5xfZDIZ7r//fn7/+9+zf/9+8vPzueaaa7jvvvuorKzs1zlM0+T+++/nnXfeYcuWLTQ3N2MYBlVVVVx11VV84xvfYNiwYQMem2EYvP/972fdunVIKZk1axZXX301VVVVABw4cIBXX32Vd999lzfffJNrr72WlStXommDs+hzSn1cdF1nxYoVvP7667z++uu888476Lp++ORHLAMUFxd3C5kFCxYwduy56ak/FHT1cdE0jTFjxvS6z1133cVdd911mkd2brKtKcmrO1vQTUlR0M1HppWeMSvTwaQlqbOxPoZu2QRcGhdXR/Bo5/eE5WymP4JFSskLa+tZsrmRyVVRPjFvGJra87NoWjbfuPdRXjUrUbw+biro5BvFdbh6SblKmnDDCj/rOzV0GzSXi4sLBYqqUGjHqUsLCtyS9xXp3LsrTKKzjsyu11DMBFF0XIrE7c9jvr+QqtJhvFk2C822GFkZ5oHS3fz+gMrBuI3q87PXCDAxkOWLlZ34j6hZ2ZdS+GO9B58iKfVYvNuuMTZocVuVjruvP7NDPVnUW7+ILSUb6jNIKZla5qM+bnL/m600JSxKgyrfWlBIaah/D7hztTfLqRLPWOimBAGqAlHfmY8CDSX2oVQxyL3eCzk9Sh6KuEhyCwfquf9oO+t48MEHefDBB3vdtnXrVgDyikr50bPvDvjcX7shV+NyvD4umUyGxYsX8/bbb1NcXMzcuXPZt28fa9asoaioiBUrVjBq1KgTXiuRSBAKhfD7/UyePJny8nJ0XWf9+vXU1tYSDod57bXXmDVr1oBew4MPPsjdd99NMBjkd7/7XZ/pYM899xy33XYbyWSSn/70p3zxi18c0HX6YlAbUGYyGZYtW9YtZFavXt2jqWSXkLmQm032htOAcnBZWxvjjd1tuFUFl6rw8YtKCZ4F6R2nSl1nhs0NMTRVAIKLq6Lnxes61xhIhGXtvnbueOhdklmTkrCXf7ttKnPHF/XY572f/4IPby7Hyi9DSJsRQcl/Vuxliid57LUl3LvFxa/2agihoGtegsEAhXl+rq+2mRjfx1i/SSFx3veHteyorUEoAiwLt5CMHTmVOdMvIc/r4oZyg7+kS2gy3dwSbuGVevi/+hBIm0lKK/87K0vZUeZplsuL7fagJTvp99zxCNHSG2vqMjzwVhtRn0Isa/P1eflMLT1xdON8Fi1tKQPdsikKuHt1MNNNm6RuIwGfJvC5nQWMC4muWZuTInb66WpAGS0q5Ud/PgnhcuMldJxAuHz729/mu9/9LrNnz+aVV14hFAoB8MMf/pBvfOMbzJ8/nzfeeOOE1zJNk+XLl3PZZZfhdrt7/P5b3/oWP/zhD5k5cyarVw+skeb8+fNZvnw5v/rVr7jjjjuOu+/DDz/Mpz/9aebOncubb745oOv0xaAKl6NJJpO89dZbPPXUU/z2t7/FsiyklAghBq2D5vmAI1wGl0TW5JUdrbSnDaaUhZhVGT4vcsB1y2ZLQ5x41qQk5GFMYeC8eF0nS9dX1+m8BwNNC/vhc9v5z+e3Y0mJ363yyBcv4YqJh3N9rcce4sUmN3ftKMHOLwVNY5js4NbAQa4pNqgOHHvOuAFfWyvYG5cIX4BYuASRTRIKB/nBiFYyB7fyoUeX0JExEQg8GgQCRVwyZTYbZQUhn4eICxYVGvzz2CRSQtoW3LShnO1xgSVUKmQnj8/oYMQRDer1UAEdk6/Adnvx1+0itPPd44uXEwiWLhK6zU/ebmNPm8GYQjf3XJaH39X3MvLpFiyWLREClNP0OatpT7N8bzuGbTOuKMClw/L6HBfkog8X8veAg8Pp5Ejh8sM/vzPg479+4+zjChfDMCguLqajo4PVq1czc+bM7m1SSqZNm8bGjRuP2TZQTNMkFAqRyWTo6OggEon0+9iCggISiQTxeLyHIOoNXdcJhUIEg0FaW1tPerxHMiTLtbFYjDfffLM78rJhw4Y+beMcHAaboEfjpsnFWLY8Ji3nXMatKkwrD2NJ0C7kXAlykzbj0NqHS5VD3lfjZOtYCoJuiqI+YimdEUUBLh1dAPRsJjk2qjArkGR7shmvBiLRwuOWyaoG+NFMiB71XAi54D9mSPYnIamafGZdB61Jg2xtJ39fpxK0imk3XNi2iepyUzluDhdXVXAgBREFPCpIRPdqrRDgVSTTwzq7Un4MUzIxbDH8KNGULazCDOah6GnSpSMI7FuPamSPfdHG4T4uJxItAEG3wtfmFtCatij0q7jUvt/LvkSLlJIVO1s40Jpixoh8xpSGTnjd/rCxtpMnV9eiqQq3XVLJqOLgiQ86RepiGZK6idelUNOe4ZJq2atocnrJODicfyxbtoyOjg5GjRp1jDARQvChD32IjRs38txzz52ScBFCoCgKQogTio+jSSaTBIPBfh3ndrsJBoMkk8dmEJwsgyJckslkd4rYkiVLWLt2LbadS0jtEiyqqjJjxozuYn0Hh6FECHEoper8QgiBdv69rAFR154inrEoDvvwuFRMa2jzvE+l+P7GWeW0JgzaUwYfu6wSj1vtIVoARgZs7p+UYV9Kpylp8stOKPBAUxZasscKFwC/BuMj0JzJUmmbNKQVsiZs6lSBMO6KSzBidYSqLuaa8X6+PlZnZxxcaoYXWxSEgE9UprvPpwi4KhLjrWYFS0iStkJdVqHCa3fvo6U6UYwM0uXB1VqHYurHDqyfUZajcWuCshPUtRwv0rJuXzsP/GUbbUmdynw///XJGeQPQq+VFzY2UteZwbYlr25rPi3CpSjgxudSMSyb4Xme0xbpcXBwGBhDsRy/fv16gD5bjMyYMaPHfieDbdt873vfI5VKsWjRIny+EzdUPpKioiLq6uqor6+nrKzsuPvW19fT0dFBeXn5SY/3aE5KuGQyGZYvX95rLUuXUFEUhWnTpnULlfnz53fn6Tk4OAwOXamXFwqt8Sw/+OMmplTnM2d8MVUF/iHL7x8Mt7DCkIevX5+bcFuPPURXgmyL7eWpnQLThpurJCMDNiMDNk0hWN4EtSm4tBCGB2BpMsrvE+Xkazpfi+4jXz38Xbtyz36a1q+FkmvBFcFAI6BauIrGMqxiNB8aZvPNCQYuRVDoBZDMyu995cstbDTbwIUkYwsyVs/PlbdxL8LUsT0BPC0HcmNAIJADjrIMhP6khh1oS9Mc13G7FOo7M+xuTg6KcIn4tFyqGBD1nR4Xv1EFfoIeDd20KY+cH46IDg7nG1JKUon4SR13PPbv3w/Q7dB1NF2OYl379ZevfvWrNDQ0EIvF2LhxI/v27WP8+PH87//+74DOAzBnzhyeeuopvvWtb/HrX//6uPv+y7/8C1JK5s6dO+Dr9MVJCZdoNIphGEDPPPMpU6b0ECrRaHTQBurg4HAYw7J5eXsL+9szTCwJMm9U3mlbmU0bFrtaUvhcCiPy/Vh27rqaOvRuP3Xtaerb0yhKB2nd5Jrp5YwsCaGbElUZnPSZwezJ0sXRUZbHtgn+dDA31uYsfGdq7nu02Av/NgNas1DmAx2FH8VG0ar4sQ3JxhqVi6wG3h9q4IFXV7Bs90EkEBQriYx5P+26SZmaZVwefHSEYEc2wmMNFjcXxwlrx39gKgJSlqBNVyj1mAz39axDFIC3tRYAs7CC9KRLc3a8a19HbasfdMEC/a9nmVwdpbIoQFssy4iyICWD0BcG4COzKikJe9EUwcJxRSc+YBAQQpw3Fu4ODucbXXPezpZG/v7qyYN+/kQiAYDf7+91ezAY7LFff3nmmWfYvXt397+nTJnCY489xogRIwY8xrvvvpsnn3ySRx55hGQyyX333ce4ceN67LNjxw7+9V//lT/84Q8IIQbVFfekhIuu692rvHPmzOHv/u7vWLRoEfn5+YM2MAcHh77Z3ZJiQ30cTVFYdbCTscV+ysJD32dCSslzm5vY1pTAoyrcNquSwkBukmVa4B5ik7NRJUEmVEbYURdDUwQlUS9dyUy2DUL0Xg/QX05FtKzen2RDXYqxxV4uHxHs/o48WrQAJC0AiSpyFsdH4tdy/wGkLcgKDSElugUHMiob1mzioX3LsQ6lagkgoplcW5KiPKAwM89iRFDho5vL2ZVy41NtOkzBl6s7jzv+PSmNkAYj/Sa2gFUJHxUekyqPccy+xuhp2MEoCIF546dxZ9uP2Wdv3OatBhufCosrVPI9A3tfBlKEP6LQzz/eMIEdTUlGFPipiB5OfZBSIsndpyOjkw3xLLGMSVXUi6+XjoZSSjoyJsVRL6UhD4HzvFeKg4PDiRFCIKUkUljCvY++NuDj771tMZ0tjX1uP5HpzMnWi+/atQuAlpYWVq9ezT/90z8xY8YMfv3rX3P77bcP6Fxz587lq1/9Kg888ABPPfUUTz31FBUVFVRVVSGE4MCBAz2MB772ta+d+YjLkaxYsYKtW7eyYMECFi1axIIFC5g0adJgjM3BwaEP3JqCpihkTIuwR8N9mkwIDFtS15nBqykksxbJjEmhPydcxGkYgt+j8U83T+ZAa4ryPB9+j5Yr0j/FQMupRln2t2f5yRsNNMZNCgIaBQGNsa/9JrfRdayg/FC1pCkjMGz4xIjDD6KEKXij1cWqJgO3NLmyXHKr/yBPZirI1u2hZunjJDsbc36oUqC6/YwYexnfuLSSK8tgQ9xFgVewL+uiNquhCUnKEuxKn7iIckrIpNRrETMVfH4fP2rIJ6ja/H1pI9MD6R77ilQcCiugsARhHLvyJ6Xk9TqbprTElpI8D1xZkXvc2LYka0l8fTiHnYxrmBCCaRURplX0dMaRUtIc10lmbXwuheKIG0UIDnRkeHl7CynDojLq5YaJxcdE61pTBqsPdJIxLQ52Zgh7NYoGIf3MwcHh3EcIgTcw8Jq3E6V2d5VU9FXMnkqlgMORl4FSWFjINddcw+zZs5kyZQqf+9znWLBgQb+bWnbxwx/+kBEjRvDtb3+b1tZWDh48eIxLWmFhIffddx+f//znT2qsfXFSwuUnP/kJb7zxBm+88Qatra20t7fzpz/9iWeeeQbIFe50NZtctGhRn00VHRxOJ1JKsqbEsiVuTTmue9HZzoh8H4vHFFAfyzC6MEBBYGCuICeLW1WYWBpiXW0nBQEXPrfSXRh/ugzc/B6NceVhIPeeqkquQV2uMd3A3tPeBIuUkpfW17NpfwczRxawcHLJCc8Ty1ikdJv8gEpSt2h/9ikI0KtoARgTgv+ceViwxE2ojQynY+xs3ll7kEfefRfLkPxfDXxmfAPhHa+wb/NatKxEBWwgUDaJkhEXc2OVyvvKDL69K4+tSRdFbou/rUowNmCyJeEiX7P5cPGJc7FnRAx+OD7OwazKQy1hJJL6rMobHV4m+dK4FLprWXzjx6FrftDjuI0+ambU3PuCOOyC154y+fHSBmrada4cG+a2WQU9HuSDYXVs25La1iRhvxu3SyWWthAC4lmTYFYl6NVoTugkdYuAW6UlaZDULcLeno9D05ZY0satKli2xLQdZ0wHB4ehpbq6GqDP1hhdv+/a72TJy8vjAx/4AL/4xS94+eWX+fSnPz3gc3zhC1/g05/+NC+//DKrV6+mqakJyDWcv/jii7nqqqvweAZ/seekhMvdd9/N3XffDcCGDRu6i/Tfeust2tvbaWpq4sknn+TJJ58EoLy8vLv2ZdGiRQwbNmzwXoGDQz8xbNCt3OQja9ioinLGHXtsKY9JYekPQghmVIaB8JCM63hcObaAqWUhfC6FyGkqWO4LIQRdWT5SygGZFfQVZdm4v4OHXtpJZ0pnxfYWqgv9jDqOvW7WsAgqNleMDrLi7a0MV7LsE1kSGS/zi+E4LUmAXKrYvVt9LPjIbCKBPAoLYvj8PhpadBIG/MszL0GmFY+qUenVKfXmEy+7AneomClRmzuGZ2kzVfZnVIrdJo1ZjbQl+MmYRtbFPexLqazs8BDRbKaFjnUCk8BaimgSfib6WrnMm+DNVJo3Ovw0tMZ4ps2gvV3lH0cn8aqHiu+ljdfoWwwJIbiqQuW9FguPKrikKHcTVu5LsPpAEr9b5YUtnSwaG6Y8khPdgyFapJT84sWtvLjmINGgm69+cBp+nwfDkmiK6I6qVEQ8RH0ukobFyHwfwV7SwIoDbsYVBTnQkaEi4qHkDERbLFsOWJRbtkQeOuZCMu5wcDjdDEWXj2nTpgGwZs2aXrd3/b5rv1OhsLAQgObm5gEd19VIcurUqUSjUa6//nquv/76Ux5PfznlVLGpU6cydepUvvzlLyOlZN26dT2ETCwWo7a2lkcffZRHH30UgOHDh/coEnJwOB2cbY/w5niW2o4MmqJQXeAj5FHPiYmGIgSl4bMrZca0JfG0hS0h4FHwHkctnCgtLJW1yOoWYZ+LjGmT0vtultuZ1PnuE+vYWdfJiNgBVF8JT9cb/C4ZIIzO3462+cak4z/d9qYUtsUV5utZXJrC1DHlVFcW0dwWQ/H6sYbNxd72DDoerrnsUkaUjuR3+93UpODNRsndaZW/GQ1Tgzrr425G+Q2mhnQqvRbbki5eaQtiSsG2pJsfjm2lwGX36Li9hXyeEmNICY11FPIlNnBPaRN5lp/fNkOpx2BVh8bu+bcydVj0uK/lSEp8gmurej5iIj4Vn0shlrYYXuAm4FYGtaFkLGWwZEMdINnbEOe9nc18aO4oUlkTr1vt/lyUhDzcPKWYpG5RGHAfIwrqOzPsaUlRne9jRmX/G7MNJpYtyRg5EaIo4HOdWIiYliRj2khyUS6v5ogXB4ehQg6BIfLcuXOJRCLs3r2btWvXHmOL/NRTTwFw3XXXnfK1li5dCsCoUaMGdNyCBQtQFGXAgmewGNTkDiEEF110EV/5yld47rnnaGtr45133uFv/uZvUFW1e0V03759g3lZB4d+oSngVgWaAh7XmY222FJS25EhY9h0pA0aOrNkDPvEBzr0SsawSek2dbEMG+ri7Gg+Nn1J1u3oVy3LjJF5fGBmOVG/mxtnVTCxsu+o1saaNtbuacXTuJ8V7SrvNhnEY0myhkmbLnjmoGBLQuOvrQFqs72vE1X6JNV+m8f/8i6rVqxj18q3meNuZdqIQkojXkqrxhMct5hJiz/HXbNG0W6qtOqChCmImwpvtyj80wYXl0Uy/HBcBz8c106lNye2srbAkgIVydq4i89vzufnB0JYRzxv47jJCJWg1EniIomGX5EsDnVQ5TFoyKoUjRlFSfTUzR8uHRbk85cXc+usAv5+QSnRURNP+ZxHEvBqVBUFiaVMwn43VUVBAh6VorCHkFfrMYkPeTRKQ55jmrm2JXV+sayG371zgP95q4b6zszRlzkt2DK3oitE7uf+ZKpZMid0BDnh43B+IqU8FI1z3uPzDZfL1Z3RdNddd/VwD3vggQfYsGEDc+fOZdasWd2//9nPfsb48eP55je/2eNczzzzDM8//3x3X8UuEokE3/jGN3j77bcpLS3lmmuuGdAYI5EI0WiUvLy8gb68QWFIPIBqamq6oy6vv/46tbU5C80uNwYHhzOBEAKv6+xYfRSAS1VI6rn8e03J9fRwODkUAUndxLAk8YzJewc7GF3o7xanAym+d2sq91w7vl9pZ8XL/kR+h5sGXaXclxOkjUKAPNTjRFH5zt4Cmg2N0X6DfxvVTJ6r5xsddUnum5DhD1vX86PvPgjuEBMnL+TrExSuLIVnWg3WlE7i/dE4w7wmN5UrPFOr0pAB7JxAydiwK6XxgZKegm1RfpqtCRerYh5UQ2IjeKnVx+KCNGMDJrFQCVVCY2yig0b8zKCJItJgZBgdgG9/+Tp21ieYOizSbTHckDB5bGOclGFzy4QQE4r6X1+lKIIrx+UiGIMZaelCUxW++eHprNzWSEHYy6Xjigd8juaETmtSpzDopi2l0xjPUtaLvXI8a2LZkshRgmiwUJVcpMWWoIr+WY2rikCI3DP2aEHmcH7QVavZVXPlcymDYgPvcBIM0XT2W9/6Fq+88gorVqxg9OjRXH755dTU1PDee+9RUFDAb37zmx77t7S0sH37durr63v8ft26dXznO9+htLSU6dOnEw6HaWhoYMOGDXR0dBCJRHjiiScGXOg/ZswY1q9fTzabHZIalhMxKMKlrq6uW6QsWbKEmpqa7m1HChUhBFOnTmXhwoWDcVkHhzNKVy65chJF4UIIRhb6aYhlsKUgz+86YS2EQ9/4XAqxrMHu1hQHOzLkBzQEp+YWdqLJqPXYQ4wKwj9PFeyISyZHbPZlk3xtvZuDSQW/IsHnoSGr4hEWq9o1Xmp08bHKbPc5DiShMZHhL2tW8+ymXcSTYMY72Ve/n/SYYdRbHv6YLqXe8tCRDDIzspeJYZtHL85w5yo32+MCIQRj81zMiBwbGch32fzLqA7Wxtx8Z3eUNlOh0mMR1WwaisdysHwKCMH7G7dRVPcePiOB4HATySnAlOpoj3O+vDvFmvosioA/bI5z74KCgd/bIRAtXRSGvVx3Sf/rKOtiGTrSJhVhDxGfi+p8H2OKAuxtTTGqMMCowsAxx+xuTfHqjlYsKbm0OjIk6WSKEPhcOeGiiP6lfGmKwOdSkOTEzulIE5Myl56mIHBr4pSumYsk5BYANEWgOBPyXukSLbbM/ewIlzPDUK3De71eXn/9de6//34ee+wxnn/+efLy8vjUpz7Fd7/73T6bUx7Nhz70IZLJJG+88QZr1qyhra0Nn8/H6NGjueaaa7j77rtP2Pm+N2699VZWr17NE088wSc+8YkBH3+qCHkSIZCmpqYeEZUuf2g41mN60qRJLFy4kAULFrBgwQKn10svVFZWUltbS0VFRZ9OEg5nF5adW/XqKoL1nOQDW0rZnRLiFNMexrJzE5iBNJVMGxZv720nbVjM8rRT4h8aJdjdlwWOcQ371s4of2n20ZhViMoMPnQCbo0degAJDFfjPDajk9EheKtR8k+v72LnrtUEFZ2wS7InDra3kLIRs/n1ogJq/eX8v8ZyPB4Xhmnxn+V7medpBaAhI9gaU7ARjAhKRvr7rsWREpa2e9mb0pgR1pke1tk+ah4dkTKEtAkkW5m0+a8AJ2wk+ftNcf6yI4kEJhe7+ebc/n+ni/KxAzJQGGrqY1le2ZGzRS4MuLluQhFel0rWtGmKZykKuvH20uPl+a3NbG1M4FIEUb+LO2ZV9PuaSd1kQ30cy5ZMKg2Rd4YNLk6VjpRBMpurm4r4tFPqd9Ndo3NoQcjvUs6az8rZgmXbpPTDi2ZeTUE7hx0yzzVUVcW2baJFpXzvqRUDPv6fP3QZHc0N5/R8z7IsFixYwIYNG/j973/Ptddee1qvf1IRlyMV2tFCZfz48d0OYgsWLOh2LXBwOJ84Ov+8q8HdQBFC4DxzemLZkrRuY5Nbafa7+1eP5HOpLBpTwLqVq9ir2+SNiOLWji9eJAJb0VBsE9GPuH9vzSS7MGxozioUuGwSuqQjmQXVRJEm4MUjJHWWn5WdWZRsA9/40wq21jehAHEFCn1uxoyZTmX5GDoMQcyAYj94vS46s5JQwMc73irmyFZUoNQrKfX2LVaORAhYmJ9h4REaI6+zlkSwECkleW01xxUssYyFRxN4NIX3j/ZjWpK0KblmdM/uznHdpi0jKQsouI/+YJeNYX1djO3NSYoCbuYMz8NzgvdnqOlIG6SMnC1yPGuS1C28LhWPplCV5+vzuKKAi52KwLQlZaGBWZFvaoizrSmXt542LK4aW3RKr+F00ZfDWda0D30PSrKmfUrCxebw96o8he/V8xnTOnRPDqUPni4beodjuVALH77//e8zb9481q9fz/XXX8+kSZO4/PLLKS4uRlX7/vv/9re/PSjXPynhcqRYGTNmTA+hUlJy4p4HDg7nOl05590pGWd6QOcRtsxNYLomL12pMidC1u3ghXUN/PyVPWRNm+suKuWea0b3vT+CtK8AW3Gh2AbeTBuK7LvQ6EjR8m6byqtNKsP8kg9XGrzcqPLEARWLNIUucHtt1sdt2kyVFIKoX5K2BVHFYMOWd/n55nW0pCUKuc/QsPKRTJk2j9aMQnPSIGYIfrVX5Sv+Zq4ZNoI1mSDx+gZe2BLjsnI3F4Ut1ll5uLGZrrajiYE/QovqtxCINSCFQvj6j/S535/WtfL0ujaifpV/WFzOiAIvn5h2rGFBU8ri4S1pWjM24/I0PjnB111nIcrH0pE2uiMNuzMpKiJexhYdm4Z1OqmIeCkKuIllTaqjXqL9jH7MqowQ8WqYtmRML6lkx6OraP5cKqA3bUnGsLu/73xHREK8LoVk1kZVxHHd/PpCSklLUseWUBBwdfdl0hThfK/2gRC5e9PfFEKHwUdycq5i58Zf/PG59957e9Ssb9q0ic2bN5/wuDMqXP7mb/6mW6ycTH6cQ+80NjYycWLvLjt33XUXd91112kekUNfKIrAO8D8c4f+kUsPA7srVewEt/bIOpZNB2LEMyZBr8aqPe3HPc5S3ViKGyFNLMWNrbjR00mSukl+wN39nh6dGtauw3/tdlOfEbgEhDTJ4wdUOg1IWZI7i2PU6B7ea3OjGya6pXJ9YZaQC/ydO3lu1Voas4KUKYiGQnx+3iW8p46j0xciKTNoZhJbdbEy7eFft5r8W2gLmxpL2bu3DcW2+H67ixumV7CWIlQkN7sOcJWroXuIKzq9PNscoMJjckdZjKDWy6PSyCCA8I0fO+49Shs2f9rQRkK3aIgbvLkrzoiC3t3FdnVY1CYs8r0KOztMmtM2ZQG1u57FpQpcqiClWwNqANv1cByKv7GwV+O6icUkdZOI19UjLdG0JIYlUQTH1G6oimB88cl1rp5UEiJl2Ji2zUXlZ8ZqeaDYh+r5eoswh70aPpdEiJzhyEDZ3Zpka1MCKWF0YYAJxUHkoWuaNqiKPCkHSFseitzKXCrviaKv5wouVXRPmM/lJspnMw8++CAPPvhgr9uOdujqjTf/9Fve+tP/9bot3nZmLIQHk/nz55/ROc9JCZdf/vKXgz0OB6CkpIQtW7ac6WE49JPzOc1LN21MS+LSxElNRvqiuTPDjroORpaGKcvz97qPIgR+l3JcUZjImqxcswVFwKXlHnxabp+LR+WxclcbWcNiwYTjp6kqdi6NKxdxMalpaOffntlCe0LnpovLuW3usF5Tw0wpMGxwK2BKaNNzobeEKfAqEHFLql3g8XnRFRupKDxZazIrkOYn04rZvaeaPdtrGT5yGnlVM5gxUhIdcyn5leU0t8V5469vsKMRFCTNlou2zgSzsgdYnXWRNGGP4mOFWURIk2RQOGgfvo8JU/DTAxEOZDTcClR4TG4uPsJpzMgV8J+ojqULtyooDrloiBn4XAqFgb4fGeVBhahHIeD3MTHiIegBUX64P0DArXH58Hz2tafI97kYdpxUrC72tqV4a087bk1h8egCioIDS8vqjaxpk9YtAh4Vl6rg0RQ8Ws/z2jIXYbDJTdAV5XCj01Ml4nNx5ZizN4W6q+7uyL89VREoIjddVo+KhAiRK8o/WZoTOpadE4jNiSwTS0LYtkS3cmNRBHj70cPmaHRTdjcctg1wqWdPbdWpoCgCr1OMP6Qcb6G4q8blUMilV+bf9Enm3/TJXrf9y4fm0NnS0Ou2c4Wu/i9niiGxQ3ZwcOid+liG7c0JvJrC1LIwfvfhP0FbSlbsbqM1qTOjOkplPyZ2Q4Fh2nSmLSw75+yTFxCD4lrTGs/wz/+3it0NMaqLgnz/Exf3KV5OJAr/+OZG3q7NIgS0ZWxuGZs7z8KJRQwr9JPKWkysyHW7r2lJkTFsxpYGekxcWhJZGprqqS7w49cs3trSyLa6GGGfi2ffq+e6PS8Q1DimnqXII/nUMINn6zV0G35R48E0JaP9Bh+ssrimxELKJH9tcfPStoNY4eGkbMnbbQr/tMXLPy6YTWuhIK4VEPXYVJUEcY+tpC4Jw6uK0aYPo2XFXloNjWq/TWMGVrVAxhKEQx7u+tClBMN+Nu6qI5Tq5GItV6y/N+vhzU4/azpdJEwFvybRuxYHjcOOY/0VLZCbpP7DonLe2BUjz6exeFzfEYLhYY07p+WRcoXxaSrJgJ+gLXs4Q1VFvVQNoB/M8r0dNCVyaURrajt537hTqwfJGBa7m1NkDRu/W2VUsb93YS4Pz0nOh9SO/mLZkpRuY0uJW82lfgmR+/v3uZVcQfggR5hLw17a0jpSQtmhxrbyiBpCyWFL6IHQY4jOPN9hCLiQvhvOJhzh4nDe0pLUWV8bw+tSmVUVOeOFwLaUbGmMk8hatEtJ2JNiYunhWoFV+9p59N2DZHSLdQc6+fr7xvTqaDTUWDI31lyuucQeJLvNmqYE+5rilOf5qWlKsLs+1qdw6YuutLDmlIUicrUwLemeofuRxYdrDpZubeFnL+dqXj5ySQWfmJezkayPmzywrI2GhMmofBdfn1tAccRDwKMRP1jL8IiFu1CA67BHfdyAd9tVIi7J+0tMLi+wuPHdMLvSGi4hCeo215bqxA34h9Up3lrzGkZbA4y6BhEdBVJSl1YYV+DnvpkKGzoNxoUsRqkJatJZiqN5JFIZWmNp/n1Cgo0JF8P9FiuaBa26wvCAja84wrwpVZSqaa6MRAmueps8YdBiavyoqYKNHYK4niLssnHZJtXu7ICjLEdTFnHzsZn9ixAU+F0oQkX1+Lob5CmnMGv0uhRsKREIPNqp/y2kdJuMYePRBGnDImvYvQoXIXLRJsOWqCLXtPZCwLByjQ0FoFsSj+vwnF8RQ1PMNzzPR9SnISXdDmvKoR42cgA9bI7GrQosTRxKFXPcyRwcBos333wTgKlTpxKNRk/79R3h4nBeIqXkxW0t7GtLd6/UXT7izHR57UIATZ1ZGuMZ3JrCsGjPiEpn2iRr5FJYYmmDjGEfI1ykzOXdS3IP5qF4GLtVgVsVmFYuN3ywrDZHloYYXRZhV10nI0vDjK2IIKVk1b4O9rYkGVcaZHpVtNdjZd0OklmLRNaiOORi0TAvHdkUihDMq/TQacC69pyQmRaF/EOZPyt2ttGa0PG5VZZsae4WLnvbDWpjJiVBlZoOk9qYyfumlqK++Rf2T61g1Ixp7PVC9d6VeNMxjIoxPNfs5+nN+zFTKe4ZrTMmJLEQSFuSkII6w82bjQm+8eI6ttdsRVVVfH4f3paVmNFyIl4XVxQaJAyYHrG5KHpIcBkZwhvf4C3XKLbsa+Hgzj3sD9rcMy6Dz+2iLmDjVSWqbXN5vs4oT+4zraUaEOEwyYIKWttStB5QCGkGmqoQsFNM9mcY6U4BJy9aBoq/pJJ0wsC0bQJu9ZQF76LRBeT5XLhUwaxB6JUS8Kj43Ep3xKWvYnIhBB6XwC0l1gXUNDkXTekSDKenOF4IQZ6vZ6qeIgRe7XDU5WS+54QQ+N2nf+HH4cLhAvpq6MGCBQtQFIXm5jNTr+MIF4fzEgmkdAuXkls1zRj9s40dShJZkxVbm/H5VNK6TbnfzYwjJuozqiOs299Bc0Jn/tgCIr5j/zwzhk3akEgkXk3pth49UtC4VHFSxaxdKIog6tcG3XggGvDwvdtnsachxrDiEIVhL7ubEjz27gE6Ugbv7m2nKOih4ogUua4Iy+7mNPf/dT8dKZPrpxbwqTmljMt3IQCvJnirGfYmc+O0JSwuyT1RJlSEWLajDd20uWj44YnvyDwXlRGNhrjFqAIXFRENHv85i4oUts2cRW1a5Zl9SYZnKrhxWCXZCbMZl9b4UGE5//PYEvYmFRYWGSwu0Nmb8BBVbVyx3XzpD2/REEshyXnda8FC/vmuz3JJ205qUiYvHLC5a5XgqjLJ50YfTmeZTR1V8Qbu2SvwCsmGDoWv7YgQiESY5EvyD2NaMW24PL8G3+pXkYoC7c20z3wfViBKJJsmvGM9a9oUAgGNxcEEnyuNUX7n57EkJEzwq/1buU7oNu/VZvC6BLPKvf0WH6J8LBpQHHYN2mcn3+9i8ZiBN7jsC4+mMLooQMaw8bsVtOPUbxmWzeqDHbSlDCoiXqaWhU/p7+pcwKUKhFCxpcSlDM3CSH8RQnCe326Hc5yTaIN4XhCJRFBVlby8M7MY7AgXh/MSRQjmjczjrT3t+N0q0yuOtW893bgUBUXAjgNxXJroUd8CUBj08JWrxxyKuvT+p2nah1Z5RE6QdaFbEt3M/duyZQ+70pNhsIwHugSVquTy5POCHmaOPlynkNQt0rpFxKcRzxjEM+bhY49wC1u2q5OdTWkiPo0XNrXxwRmFhLyH71FvQ9UtydwJxVREvSSzFq0pg4ffOsDCCQUMK/Tzj/MKqI0bjMhz4Xvqv3Pn0dyQSvKrt+Nsb0oRsLKMrihljKYRwCS/IEKVH6rzPGyx/dwzMs7e9gRvrHmbzvYacLmxhUDRNLTyi5l/00f5yNQI+eu3s7MTdicEERe81iC4pVpSeDgTjSK3TZlXYVOnwBYKzbaXfGGzPhXklvxOJvgybPZWsF4vo9iIcanLj3T7EZkUcU+ETttFtTuJS9oEp1/M2OuqyVqwok2hTYdij+TSfHnCtKffvNfJigNpXKrgtmlhrh59YrvfLucwOPtNK9ya0i+HqYZ4ltrODC5VYV97iuqoj3z/qZsDnM0I0WVCcBa/gQ69Yh2yrFYUgfckGyI7OPSHMWPGsH79erLZLB6P58QHDDKOcHE4b5lQEmRccQDB2WFX7HWrfGHxaN7Y2kRR2MMVE44tNNYUgdaHaIGcLatp5yIrniNmh0cu/Jwti0BSSjrTuTqCXBRHPaaeYHxpiFnDovx1UyO2ZfPsmjqqJ+eE15GUht0EPSqJrMWoIi++o1LopkYP1eOQSxVrzdj8fkeW9qxkaoEfkengF0v2kzEs3t3TwU9um0RhQCXvz/+TO8GhAnwBFOx+l4bmMpKGm07Lxa/faeTfRpZSobnxN67nM5M8PMsIkhmV9PoXeWfZy9iGSZ5HoxOBv3A4vrFX4vcEmK02E9m6CYAyHwQ16NRhYhRCR73NLgW+MdHmrWZBFpUlhkaz6aLak6XYZRBXvCwPjCGtuGlyRSiOJxjWsJem4jHs7NCx/F46OwIMKwkxozpn1but0+ave3LWxyPzPYwNCoqPUxsvpWR3u45HEyR1mz1tBvtiFqV+BW8fzlFHipaBksya6JZN3lkoCDyHLJuzpkXAreF2Ov05nKVIKUlkLQxLHopSqfhcZ/6Z53B+cuutt7J69WqeeOIJPvGJT5z26zvCxeG85mxL7RhXFmJcWeikj/doCpoiuhvBdeFSBbbM9VpwnyWFqJaErJl7kJp2LiJ0tNeAW1OYPTyPpZuaCGoGa3fWs7UknxnlPWfXV03Iw60ptCUN5o6OHFN3E3bB/OLD/15eb7E/bhF0Cda3WJRkTDKHIlmtCZ2saaM8kYuyHO0aVmwlGKWlOJh1EVIt2pImvrf+TLOp0hbTOegup026yRc6r+9voimVS0MM+j3ceN1HaQtPYNuBDkRHB0u3JLjE0plXCPOKQQhJUwYuK4QjG4zXpgV1acHEsM3Hh0lAZ3a6kb1ZD5N9aQo0iyQqKhJbKChSokmT8NZlrN9Ty3KGMWlSNdXDMnxivI9ZVTnTg7WNOs0JM2frK8E/4viOXkIIFo3w85cdSbwBjQMZwf9sSlMdVJiar7Ch2WBYWOXqEbnmkicSLc2dGX63dBeprMlH545gzBF9S3Y2JfjtigNkTItrJhWzeHzxcc409Fi2ZFtTnJqODKPy/Ywp9HNRRZSOtEFJ0EPwOAsKDg5nmlw9UK4poH2ONDY9t8mZj5zMcec6d999N08//TRf+tKXKCgo4Nprrz2t1z+vv4lN0+TNN99k7dq13f9t374dy7K44oorTtmL+uGHH+bOO+884X6vvPIKV1555Sldy8Ghi95qDlRFdEcpzgbRAjlh5VIha+YKfVXlsGuRporurur5qVrCSpampE2hX6XAf2xBraIIFo6L9vva+R6BXxMkDEllUOGqEXlsqmmnOabz4UvKSP7+lzxc60bHxS1VFpVHmJsJAXeWdpJo9GAgWBSJcyBu8p2tKtuzEUTEhadUpcb2UjTxKlp3bSC/ZDQ3XzyDb4xR2KXv5/s1BpbfJm4KmrOi+7zzepmbb48LvrfVTXNWcFHU4juTDASwpNXL6riX+gjcWaYTsHUWxrewxVtBgRVnTHwfApvJ5j52TLmU9qzk/cPDXFx+OHoR1CCoSSwpqPTLnL3zcTBsSUHUy/yxGiE3vFlnku/KNZfc2pxFN2w2t0BpQGXmjCknfB+efHsvL7x3ACEEnSmdH91xSfe2VfvaqevM4HMpLNnWwsJxRX0uNEgpWbGnjYbOLFMrw4w+yeaPx2N7c4KnNzYSz1qsdndy24wKRhX6qY6eGVtyB4f+IkTOrjqt2whF4OnDdOJ4SClJZq2cm5ym4HefHQtgDmcf3//+95k3bx7r16/n+uuvZ9KkSVx++eUUFxejqn0bYnz7298elOuf18Ll4MGDLF68eMivoygKRUV99xc4EzmADhceRz9k7CMayZ2JyJMQgqhPI2tJtEOF2mnDRpKLwPja9qFIi4qwiy9dmse2Zp0xBW6GRXOWqOvr0uxu1ZlU4mHc8fKbemF8nspHxnhozUjG56kU+RT+89aJpB/7H/w7JT+pDfBMbe4LtlUX3DfF7HH83HCCMrdB0laY6EvzYr3Cmr17yfojWLYbso0Uu0FLZ7ls/scpCbj4yHCTMDoXuXTeX+Ti5SaVKQGbOQXHN4bYFlM4mBaUeSXb4goNGUGN7uXhxjwkghrTRyqUxzCPwULZyHV6cw+L4yLgszLXZdx9VCRqUZUbm1xt1KLKE6djrWuxePWghSkFUbckzytozUiiXkGrkauPse1cNK0/2FJ2d1k3jzqoOOTFoylkTZvKPN9xP6PrD3by6MoDJLMWq2va+cdrxhI+ZJ07WLQkdTKGjVtVSBk2bWmdUQzMrtvB4Uzh0ZTuv/+TERy6JUlmc9/PhmXhUk+tsajD+cu9997bHd0D2LRpE5s3bz7hcY5w6SfBYJCpU6dy0UUXcdFFF/Hkk0/y0ksvDeo1qqqq2Ldv36Ce08HhVMh1/pbdDeM8rjMjXhRF4DsUWelyPROAzCSRQgGZm9RPLPbw/9l77zDJrvLO/3NurJw65+npyZokzSiNIooIBAiRgzHBxhiB7cXexWF3wTjgn43Xa4Nk1osBr0FYNhiERBCSEEJCWRrNSJqcp3OsXHXj+f1RPT3T6u6Znjwa3c/zzDPVdeveOnWr6tb5nvd9v++KxsMCf8+Yxd8/PspoyaUlrvGnNzXTnJj/RFUIwYrM9MubuOerRFRAD1HxamlbAijPoSt6QhYAvdk8//7o0wzsHKCqxWi68mMonoesVGnQPf58hUJP1ObQIqcQ8LEFDu9oc4hpcKzFz6UJn9aQZNQWrE15NJmSH2QjjPkGLoI8OgcbWulY0cYWa5T1236E8a7p9saKEMzm/JoJKbx78fxFn+WB4/tMFB3ySD6xNkrFg46YwpZRjSf7qixM6axZc8G8jveOyxeQLdqUbZcPXrNo2rZrl9YTNVWqjse6rtRRj5OvuFQdn3hYo2R5lGzvlAuXpQ1RNvYVGCnatCRMFmYC0RLw2iKIkJxZ5soU+9W9/8qv7v3WrNsKE6OncURnhquvvvqsftbOa+HS2dlJPp+fdoIfe+yxsziigIAzg39E52lfMhV5OZtoCmilcTzVQHMrKL4z52NHSh65ikdDVCNb9Rkte1PCxXI8Nu7PETU1VrbH53UBdb/zVTwjjCprYuWdHT6jtsD24UMLZlcujufxrWdf4etPbcL2fOoMGKiWkH2b6Vy4msvTDldnHJbFZ+4vBKTnWW++LC754iqb/orggoSPqUKD7tFgeNi+AFVj/bI26lIxEnoEe9UdGF5xfgc/TlZlVO7fbrOtt0RUh4d3ST51ac3y8ppOlWs6Q8dVjN+SjvDf372W8bLNjpESz/fmuKApRkhX0RTB5Qsz8zrO2o4kz7cn6JuocnlPmubEqY9iN8VD/NZlHYyWHOqj+llp/hpw7iEno5lQu4adr+LAUAURU8F2JaYm0M9la8BznCve9mtc8bbZi9b/7L1XkBsdOsMjOrWcbJnFyXJeCxdFCVxgAs4MUkryVq1vzLnQ9EwRtc7Tvl/7/2z/Bh2yNj609n+s4VzQZHJhW5hdYzYXd0RYXH9YBXzlob08sHmEsKHwqRsXcuPKudM0vbvvwtNM+ldcTzXZSHRigObdT7Eo7vOlte6s+xQc+NuNo3znyadxy+NkdB9FQCwcZeHiK1nS1oXjlfntDpuW8Nw5U9vyglEL1qaPXVvSGZF0Rg4f6/pUgW0Vk0Fbp6PD5MWRKrvKkgvqw0xUKqyv8wmfhnbuSVOwMCrZG4KwrrBvYrq4PBEHMSklG/tyjJZtkLUJ0qqW47MnT4R1fvf6Hmz3cFNWy/V5eMcoAwWLde3JU2J5HtJV2lNn//sbcO5QdXysSat5UxczHA3PF4QQ0yzmA+bBa7/O/jVJ8CkNCDgFPHcwzwt9eQxN4aYldbQlj68m41RzqPP0qW4iebwc2YsFwBYGrtAwfAsdj4IreKWg4khYGvVoNGu/BDFT5Q+vayRb8chE1Kk0t5Ll8dyeHCFdYaLksqUvP6dw8e6+q7ZPfRel+k4Uu0qhrpP00A7ChdnD9dlylQ9+/wUe2bEHDwVNUZEIblu9lLdedCFfOxBl3BFcnPZomBzrhA0vTCi0hCQrkrX7nh0X/M1WjawDl9ZJPr/SPS7x2Kh7/GnLfipofDVxFVpIcGC8zLbeLHHNZ0eLwcdWn7g73dG4vDPCKyM2tiu5fuHhdKmTsT32JQhqbngn2rRNEWJaFGTLYJHne/MoAn6+c4zuTJjkKU4fCwhw/cPzU9cDgo9YwCTydaJc8vk8iqIQi52YKcpTTz2FbdtcffXVp2Q8gXA5BYyMjLBu3bopx7KWlhY2bNjAb/zGb3Dttdee7eEFnGY8X7J5sIDjSwolm+3DJVoT5llPKTjbjQBfLVosoZNVEnhCRRdhMt44+yoKw7aCIiTbSyqN5uEoiKYI6qOHL1EP7S3zsz1VKkaYcjFHQ9zgogWpWZ/7kGhBD6FbZVTHwjUjGJUCql2ZdZ8XDg7yhz/8Ba+MW0g0BKDGmkmtuJpXwmGsfskVDR6Pjmgk9FqamQ/8xRadF7MKaUPyh8tc1mV8dhRqbmINps/2vCBrQ91RspssodFnZIj6Fk1Obqr4PvK+36R+u8PevIfjOPieh6or7J5w8aU8LXVLq5pM/vS6emxP0jCLw9vxIoRgdUuCrcNFTFXQU3/shpbzQVVqKXmeL8+aAcV88fyaLfi5PMaA2dFVgTcptoP0qYDXI6lUipaWFvr6+mZs+9jHPkY2m+V73/venPu//e1vZ2RkBNedPcvheAmEyymgXC7zwgsvkE6nKZVK7N27l7179/Ltb3+bj3zkI/zTP/0TmnbsUy2lJJ/Pn/A4TNMMHMzOAoqAhqjB7tEyIHBdyWDOpilpnLcTlZ39OV7cO8ailgQXLqyftu3VguUQHupkDxIPDwUfFXWyQN6XAk3MvXpVcXx+tqdKyfZJNtZz/fJ6rmw3eGDTIP/5dC/vubydyxbX1Z5nUrT4WoiHi2n6xlXW+S/QkokQzQ1iWKVZn6Mrk8TzfVI62EIltPAaFi+8ALtSoTnssC0v2FVWURSFB4YVViZ91iRc9pUEGUMyYgl2FQXrMrA6KWkNS7KO4Mp6n7RRW7n95wMhnpzQ2JB2+WhntebSBTycXMVes4mQV+Wysc20vO1W6iMqQgje3q2zNauwNwk/3OkxWJG0Jk/vsm8qNF2wnEy0BaApbtIUP7XXpuVNMcZLDsNFi9WtiXM2zeVgtsLLgwV0VXBRW5LMOdhsM2BuTE2gTVq8BrrltY9lWViWdUqOda40ez4TzBUp//GPf8zw8PAJ738inJtX+tcIra2tfO5zn+P2229n6dKlmKaJ53k8/fTTfO5zn+Ohhx7iG9/4BtFolC9/+cvHPF5/fz/JZPKYj5uLz33uc3z+858/4f0DTgwhBDcsrqM5blKqunSkQpQsj6rtEzGPvWK9d6TED1/oJ2qqvOPidtLRMz+x2T9S5OHNA2RiJrdc1IZ5lDzu4VyFP7tnIweGCzQkw/zZB9ezrD01p2A5hCltdOngoWFKCw2XnslMJFdCd9ifc9+xfBXFs6l4KiFdYXVHlK29I/x00yCKIhgtWCx4/B5eyGpEzAgb6n2eqST4xkQzJV/h2V0Wf9m0lbBaK6TPeSo/zadwfbglmSWjedRFw/z2VRfxYu8QH7vyYlKRCJY3zv94WedASdAVleR8yDmgCdCFpDEE6zI+j41oFLUo/zamIvUS72ku8ddrHMZtwdJELSKwMafxw0EDVUh+OGRwSdphbdLDEjqDWoKQV+WAY/KksYHIL8a4eVGUty6NkTQFlzXVOrcvziuEFEnEUMjakDkD6xQnK1pOF7qqcO2kWD2X2TZcpGTXImR7xsuBcHmNIYQgcAU+f/jiF7/In/7pn57tYQScBIFwOQluuukmbrrppmn3qarKhg0beOCBB7j99tu59957ueuuu/id3/kdFi9efNTjtba2snXr1hMeTxBtOXtEDJU1LXH6J6o4vkRXxYzu7rMhpeTrj+5lS18eBJiayq9d2XUGRnwY1/P5u/u28MqBCXRNQQh42yWdcz5+JFdlLF+lMRVmrGDRv3MHS5XUMZ9HxafOm8BDRcVDAKYCK2JH73PywMZ+vvbgTixPcsnaBVy1ookrOkJ8t08ghMD3JcrBnXxVN/nluIGhwG8uhGhKxfIVwtLhYNZmZ1RhfbL2XH/1UpHvPP1Lll31Pl4umVyuj7Aq4fGONUt559pl057/L1c57CkJFsUkW4uSh4ZVFkUlV9V7vKQ00Lq8jm4zz+5+wY4y/MtAjKvSVdoiHm1HFNyHVYmpSHKuIKlLDgU1QnaBzuowu7ovJddXIF8to4UUfrG3wi2LolPpKWkT6sIKZRdSpiB6jl29pZQULLdW5Bt0mZ8iYihMVCabBJ4GQ4WAgID580d/9Ed85jOfOeH90+k0vj/3IlvA6ec1++ty++2388QTT8y4v6Ojg2efffYsjGg6iqLwpS99iXvvvRff97nvvvuO+WURQpBInLwzTsDZwdQVmpImVccnZCgY85ykOJ6sTcClxDkLF0TXk2RLNhFTo2y75Mtz2xQDLGpJcOnSRjbuGWNNo8LajvkX7AlA4+hC5dX87MV+smUbKaEwMsY1XQsAeNOFzYwWLIaefIK3dlX5h91hTAVKLuwpCj7WkucZM8oPdpXwyyX+17jLp3sq/PiFZ7n7pV6KnsKeFx9ibPnNPFU06Yr4/PXKypRBwCEaQtAQqt13pelx5WRDyRHC/FDpIS8Mnrch65YRSDShElJnhsWXxzw+saDKi3mNC5MuS40SOLVzcsPNl7GyCs8LjR8UBFXXZ1m9wZEfoQUxwVu7VCYs6I4LzDOQt3I80ZZ9E2W2DtVsmlc2x+lMB31QAC5sTZEOl9EUQXfQGyYg4KxyqlLqT9Ro5HWUXXbaeM0Kl/HxcYaGZnphh0Jn183pSBYtWkR9fT2jo6Ps2bPnbA8n4AwQMdV5pYcdQgjBB6/o5HvP9BIxNd68puU0jm52QobK+67q5p7H97IknuC6Vc1Hfbypq/zxlVGG1+hkojrmaV5FXtyaYPP+CYSAxUfY6CbCOr899jNYAugh3lT2+PeDKikdrmr0SaqSX4/38oitUlEUntm1nd966jmE7xIWAksI3MIIanmCFtNnxBIMVBUazfkJKxcFF4GOR9xUyGguSMnaaJU6faYAFQJuanS4sdGh7HpIKdDe/9tT29si0Lw4QmNUoWRL1rea5Ks+//LsGEMFh7etSnFJZ5TueZqJSWoubh4KprRQT/NPZm+2iuPVOm/35qqBcJkkYqisaDo9DnABAQFnkUCFnBVes8LlbDfACTi/cD2/5sJ1Fro0rmxPsrL9xGubTpbxks36RQ1ct7IFVREoRzkHh+pYVEXQkjwzqYkfua6H7sYYD78ywjMHSoQeP8h79t9Xa6ip1xYqxh2FbXaccFzj7Y0lLs7UokZSVRkoVTiw5ef4pRHSpqAqNaQWpvWC61i86AIUu0LZElyY8lhyjLS1I2mmxHXyABtp4n2tFXZhk3cU2k3BPw1muDWdZ1CNMyJN1qhZmpUqvmPx49AytvVcRlNE4V2OJKofPt+qIrisPTz19/c2TfDgjjyKEIyVx1jdEiakTxeKji/Zl/eI6YKW6GHRbAmTrJJACoEhbTJe9pj9c47keGtbMhGd8bKNELXbAQEBAeczrxc75HON16xweS2we/duRkdr/SK6u7vP8mgC5mLbUJEfvjSIAN6+poVFDafGrvW1wDN7x/nO071ICe+6uI0rFs1e7Dy+bwe7ChBRYVnyzLrrmLpKfTrCSwM1G+Pv3Ps0qxbprMwcnqQ/PBbi0YkQuoDvDce4sd5C9W3+z2MbGdq8Hd33kLqKLSHTthKx6AYsI46DS3fc4DNLJ2gN+RjzDB69UDB5eCJKu1ngkw19jNa3sumC9fxke4kf7RonVCjzQjWG3tBAWWo8I1L8V/0lJtQYmzsvQ5WSXTmPHVmPCxvmvgwriphKI1RnGZuUku/urLJx2CGsCd61JIKrmxRdQdnxkGqFhWkdx1d5edTjwjqFhHHsN+9ECvKXNcZJhXUE4rR0tj8ae8fKjJVsFjdEg14uAQEBAecxgXA5QaSUR+3TIaXks5/9LFCrd7n11lvP1NACjpNHdo4ylLfwgcd2j72uhMvjO8fIlh0UAb/cMTpDuEgpcft28IshwWCl5qblShCKoOoJFsV8MmfAJEmdjIZV+nqJqqDqBhxRK2MoElWALQWmIlGR/Na//YQdwxPEVZUSCk2pNO/ccCXPq8sYsDUQPhLBBVGLBZHpqV0DVYWv90ap+oIPtpVZHHUZNDJIIFbO8g99GQ5aOoaQZHSP5uZ6qlqYqpPHVnVwNZ6vRknlVJbFLSYwqbz9N0hoCtEtVUaqENPFMUXEjUvijJUchgseb16RmBFtsTzYPuFiqoJxy2dnURCNqhzIWbw8VMVQy2yJh2hLGmwddJioqtzWfXom9qoiaEuGj/3AU8yukRLffq6PguXSnYnwmxs6511fFjA3Ukost+aId6rPpy8lhYqLJyFqqqc93TQg4JQj57ZDfvK+b/PUfd+edVtxYvbmxwHz57wXLrlcDsc5XGx8yL/bcZypaAiAruszrIi/+c1v8pGPfASARx55ZFozyf379/Pud7+bj33sY9x44410d3dPOhz5PPfcc3z+85/nJz/5CQC/9Vu/xdKlS0/XSww4SVIhDV9KEILE62y1tiMT5pW+PBLoqptek/D4L5/l678aIBXVWbZqAYaqYvvQWxUUvdpEI+cq3NDgc6LtanYNFshXXFob4vSWJK1RhdbYzBqhNZ0JPqJs5fmUwaUZybJXpXTdVFcl6ygM2iq31FeIaHDpspU80/8r6nTBf716FbdvuJhipI4bx0uoVoWqVPERbEiUZzzfPQMRHhoLIYCqL/jAugzPxmuugMuU3Vh+hZDi4/iCqi/IVMfpc9tYvyBOYTzPbjdEXPXIVVxGdcHt1yyhLqwghOBdiwx2Zn2aIoKFiaNP2GKmym9c1jDndlOFnqTKphGXlKHQGBaUgbzl4/qStO7Rmy2RK5dQhKDonn+pDYN5i3zVJRXWGS7WbtfHAsvhkyVXdinZPoqAVEQjbJx8M9JDlCyPQtVDAo7r05gwznrD3oCAU8Xlb/kAl7/lA7Nu++IHryY/NrM++1xnaGgIVZ37GnC0bcda6D9eznvh8ra3vY1HH310xv1PPPEEDQ2HJwTXXHPNcdfNPPvss1MOZqZpEo/HKRQK05obfeQjH+Ef/uEfTmzwAacEX0oqdq1oOKwrM+pY3ryyibqYiQAu706flTGeLd62tpW2VBhPSi6ZfO2yfwe+L/n6rwbYP2axc7hCc3OW1rY66kyoN6BQqTXedP1afeKJXJIe3zbC392/nULVJdZYx8IV3WRMwUdWhKbVahxqKHlbe4jbqM56LEN4vKshS8SoCc9HRnW+61+C0+rRuXQVb7jY5Im2dRT0KKlUnhuHniLizRQsMLmKNvmCmloydK9pZ39jDL/oIH2f8Wg9H2p6me+PxlkYtrk2VSaRLxK1S4wYSdZ1WNy5J0LO00g3JHjfJRluWxGdunB3xFQ6ZhFnJ4IQgvcsCXNRo0vcUGiLCfZXXJQ6FceqWUVf3qQwbtfSzTY0HfuSf672bZmLRQ0RWhIm2YrL8uYY6aC+5qSRUlJ1fATg+RLL8U+pcJHy2dhbogABAABJREFU8HXDlyd+DQkICDgznMoGkifLeS9cThdNTU18+ctf5sknn+TFF19kZGSEiYkJQqEQ3d3dbNiwgY9+9KNcccUVZ3uor3uKVY+SVVuhdz2F1KsmNomQzk3L5l7VPheRUlJxfEK6gvKqlYzBgsVQwaI9GaLuGM0sDU1hwxHpYYeK74WAWEjDlVXS8RCNqsO7u0Ag+dErEzx70GJhS5wrlsU4UT+DTfuyjOSrxJJR1FQKzdAZdzyGypKWyWy9Q6LlUBH+9HNQ+3/L4Ah/9eCThGL1rL/wWuoMydcPhNie94m3X0pWDXFA0SloEWJOiYIeJa/HiHjTuyePWvDlXToHK4IbmixublRp2rCYtkwEXzeJyirlfA7bl3gLl/HptiFWVgenJlwVT/ArvYtyncmK1gSlbJEFaYOLO6PYHpyu1iaGKrig7vBnemHEY2EELs2YWJ4kY4qpEtJXf1bOJlXXQxUCfbbineOgORHiE1d2ka04NMfNs2Kwcb4hhMDUFcq2j6rUbp9KoqaK40lcXxIPqefU5zIgYL6cQ3P508rnPve5sz2EaZz3wuVk3Mc+/OEP8+EPf3jWbeFwmE996lN86lOfOuHjB5wZPF9Orei5x9dC5JzE8yX3vTLEjuESbakQt69uJjzZ6X6kaPOfm4bIVhwaYgbvvbCFeOjYX/NXd70XQvC713fwUs4gGg2zIClQRIWNB0t888khClWPweE8ty9cAOETS8tZ2ZnkoZdM9EyKxqY0LoKwqdMeq02SjiZadhbgf73k8OL2FygOb0MV0FfNsVFbSTLTStWTNBo+445Cd8hiHeM8bXeQ1eM0VMfJWLkZx/z5sMrjoyqGKrm/X3DXugK74w5lVYJv0VbaQyU/wVORHnxVYUhL0uxkMXwHHItBM0pei5BobiIk4bcuinHv1iL/36+yNERUPrE+SWN07lXrrA1ZBxpMTklzyZguiE06ls13Wnimoi07R4psHy1hqIL17amT7iYfMzViZ7DppZQSX9aijudrilMtPez01LioiqAuFkTGAl7jvE6USyBcAgLOMBGjtroHEDVf+0Wg/bkqLw0U0BXBtqESu0bLrGqp9YkYK9nkqi6psEa26jBRcY4qXF4tWI6kIR1jUTyKjo+NQkU6lCyPquOTCKlUHZ+SfeJK8OrlDTQlQ7ySg17CWB60RwXpkJhTtBwoKwxWBf/4zC7uf/5ZXLtCWIU6E0LRDBlTQQrJ4phkwna4qk7y50vKpIAbhp4mr0dJ2kUM6VLxBc+XooQVnwsjZcJqzXyg6gkiIR9T+CzsfZG+xsWYdpkFQy+zlyRuWKAKiUSwTWvkWaUJQnDxRYtJlWqd7RfEoGx5PNtvIYDdEw6bhyxuWDh7b5OsDY8MC4quoM6UXNcoCZ26zJxzCl9Kdo2VcFyfku1zMFs5LuFyKGUhb7ls6s8jJaxqiZ+0+Jkvni8ZzFpUXUkspNAYPz/rM4QQhPTz73UFBAS8tgmES8B5j6kr1Gu11b3zISUhZmpEdJWxskPc1Igf0fCyPRWiPWUyUrTpTIVpis9uS3s0wXIIXXg10SJVDOGhC5+Lu2JcuyTJKwNl3rA4ycK642v4Ol60+D8P7KB/vMyitjQXdGW4qSfN86MeUlFZlRHwnX+cHMD0Y28tqPzxRocXX3qc/Oh+PM8HAaam8fvXrmE0dSEv5HXWpVx+s8si6wqaQ3LK4tj0HRqs7NTxvjbSyM8LCQwh+XD9CDc15Si6MFgV3NTkEVYhXBgiXagVUh60dO7qNyg05OipN3ijt4vNSgMlYUBDKwcLHm9foFJwoCUCnqdSF1bYm63VnzTMEm0pS52iNMn6LkXXJqZLco6g4J5a4VKVGuMyikCSESVMMVNwnqloiwCihkbRstAUQdSY/8/QeNnmwHgFRQjylsPBbBVBrUbi2p7ZrbxPNSXLo2R7KEJQqHgkwzKY4AcEvA55fcRbzj0C4RLwuuB8ECyHSEd03rGmmT1jFVoSJl3pwxa0MVPj3WtbmKg41EX0WesH5iNaAEKKT7teouJrRBQXU/hgqHz2xjY8X55QLcFPnu/jpxt7Gcnb3Pv8AIu6GulImxQrDg2je1jYUyAdnymGHM/jzie28OjTm9Bwa6l/qkqsfgG/f9MGPrxYQUoH23cwlFqNTsKY+2dFSnilEsYQPiVPZVc1xC3JHO/rnDuC9Fg+ymY3iVISHBzL8eHGfsauvZ6DQx4CaI4oZExB5pBWVASfWJ9k85BNU1RldeP0iIArFUZlDAcFoZu0xCQjZYcGU5KaI4vG8SWbR11sH1bXadOaVx6NCRmhOnm5z8kwjaKI7cPOsk7ZE3SGXJrndaSTR4haelhvroKuKnSm5mehLKXk4HiV8mSUzznCIe1MxlE1RaAIgedLdE2Ztb/OuYKUkrLtYbuSSGA7HBBwSnmdZIqdcwTCJSDgJCnbHgO5aq2PRSp00sXG82FBJsKCTC3tSEpJf65KwXKpixrURw2aZ4m0HEuwSCn5z6cO8NON/SxvT/KJm5cQMSGizJzMH020jBUsHts2QiZqcOWyBpQjHqupAgVBxfYABceVPLt7gkX+GFtdnSfzUd4Vt2cc85e7DvLYpmfRUHAlhMJxWtbeQnv7Ep7Dp+L3ElYk5jyjFELAG+J57s2madBdLokWj7lP1FBoWNJNKBbC8ySPdS/nnW06zZHa+31BZuaTt8Q0WmKzX2b9yX8KNavIC9PgxXzSBsxVC/2rfocHDzr4EvbnPd67ZH4RL0EttU0gUSbXCQctlUFLRQjJbpmmzvPPyGcXaumbSxpi8368lJKRgo2QAkNRcHyf1mSIkKnie5LmmMlowaIudvrTtsKGQlPSwHJ8oqZ6xs7ZiVCxfQayNq7vE9JVOjKhwLwgICDgNU0gXAICTgIpJbtHS2TLzlQB9IK62esYThcTZYc9Y2Vcz2e87BDV1RnWpfOJsvSOlfn2L/dStjz2DhdZ1ZXixjWtxzUW35f81Q+28tyeccK6yu+8yeONa1umtr9pXTtjeYsnd40xVJLEoiHSuV5yrkJMhybTn/W41y3p4vKuJp49OMy6tZex8OZf4+d7bMrlIgu1ErqYufR1wA3xrJWiXrG5KjQ+w/3s3ZlxLo6VCAmfpOKwr6zQFvIpeAr/ORLHRXBbfYFmwwOnytpElQcTOmUpSCQMfF2n6KusqRNH7WNjeZJdhVoh9+J4bcUeQMcnRYUCIUxc0oqNcgwdMlL1cXyJqQoGy7Ofq9moEyVU6aMgSYqapbQiACGRshZBOJeDkp4vKVU9woaC4kDa0OmsC9MD7BmpULZ9qo6Nps50DTzVCCGIhzRmCQyedYqWS8FySYd1QrqK7fl4vkRTBI7n43onFikNCAgIOFcIhEtAwEnierUVcykl3iyxYyklRcvD0JRTlqrheLUeC5qq4EmJLyWaquD7EveIMRQP7mR/VUdi0hVyiKlzT3Z1TUFXFSzHxtAVDO2w+LE8OFiWbOstUC1bXNIdpzMzc+ZWdTwOjJaImRq5ss3uoSK/2jGGZ5i0ZSIsiGt88k3L+CQwkK3if/9fcFfAE/kojSHJlRkXKSUv9g1zYXvT1HGFEPzxTRvY44XZfMkHKUqNCyMFFhT7uLW6D+1Vc7GqVLgr38VeJ0JY+GhCckVoYtpjhICFpsWwJfjslii9FYWLUi6xSIgfjcWRwJij8ietfQC0v/M9rNvhsjPn05UO05Qx2FISLAw7tJrunOf1yRF4OVer7cjZcKifpBCQElVSc/SmmY0L63X25X0cHy5v0al6kpIjSZviqOmQuvBpEKVp9zWbHlVfUI3W05YMoSmnN3IwXrZ5oTePqSqs70xOOeHNB1UR6JqCa3uEDZWmhIGuKtiujy8lqgK+Xyv8nw+eL7FcH0NV0NTzYyJfqLo8dWCCsu2RCmtc3pUhaqqEdAXHq9kOG6/+ogQEBJww51Jvk9cTgXAJCDgJhBB0ZcLsH6+gqYKWRAgpJTtGSuwdr9ASN3F8n63DJUKawrU9dTScZFfvFw/muOfZXsrlKh+6YgFruzI0xU1yFZemuEHMUKciLActg1FXm6oivCBqzXnc5lSYT795GY+8PMjiljhXTPa2kRKeGhM8tqfE/U8cRLguK1vC/K93LZzhWBYxNW65sJUfPtdLV0OUFw8W+X/PDOOpBm+4rIdblye5pkXFu/suGgFiNfFzWWsDr2SWcs/gQR7+9v/hpf0H+fI7b+SSrsMRn850gv2hHvpyDumETlNLitvcAVr2ODNeS8VXyfs6McWj5Ktk/blX4TfmNLYWVOpNn+eyGj1KLX1KkT55u3bi1Pd/EhW4fYHC9pzA0XUUBVwJBVfA7B4IQK0/jAJ4EsbmPv3zYlFK5Y5VYVwpsX34xlaLnC1ZkVG5tUs/rlouTcCiiItoSZzcoObJT7aOsnOkhCIErpS8YdH8i+mFEDQlDcqWh6YqRIyaaKk6HqmITqHiEjIVEqFjR1t8XzKQtajYPoYmaE2Z6OdB7Ueu6lCyXaKGSsHyKNoumYhBR10I15Poqjgv3c8CAs41nv7Rt3n6R9+ZdVsxO3qGR3P+EQiXc4ihoSFWrFgx67Y77riDO+644wyPKGA+ZKIG6Yg+NSkYK9k8cyBH1fXpy1WR1NKDxsoO+ycqJy1cfvzSII8+t5uBwSzPb9rHN3/vepa2pZByMvJzRFqYgKm21GIeHihXLm/kyuWN0+7zgXFHoWL7VGyfTMRgpGAzXnJntVr+9WsWcNOaZvJll8/++1YMw2AwbzEyUWJ/MTHD6lgCm+IL+dFDD/Lowz9Bt0qEgb95+Gm+8+tvQ5usIXB8+NFel19U9hOJhnjfyij12ZdmfR0pxeGG0Ag/r9bTaVS42MzO+Zo7wj51hs+IpbAo6vH+pgLfGpC4UvBrH7oBtb1Wi5G3ff51m0V/yWNxg8LihgghRVJvHD1l64IUFEZqEZblyaM+dF5EdAEInhpy6S9LYhpsmfC4slkjEzq+iemZchIDKNkumloraq+cgI22riokJ+uJXM9nMGdjez66KuisC2HOM4JjuT4Vp9ZYserUbp8PwiUd0UmYGiXbIxPRiU/2tVGECCItAQHHwZ133smdd9456zbfP+J6P8dP6qVv+gCXvukDs277m49cS35s6CRH+PomEC7nEE1NTWzZsuVsDyPgBDhyJVMyWXQtwHZ9NEVQtH1MXWHnUIGxfJUrFtWdcMM8U5EMDedQhCCbL/Ps9kEWtaVgYOeM62in6UyN6dDt40UVUB9SWdyWpqNxlGLJ4opFSdrTs4cZhBC0psM0xH0uaI/z6I4s6bhJa32Mxa88VHvQEVbHz+7v52/v/nP6x7OAxJCS1kSM37v24inRAnDANtidgw51gtFxjdadL6OnZk+zEgJujw1xc2QUczJVbC5WxD3+59IKe8sKF0bLtIc8Lv2T90+9lkP0Fn36ih7pkMKO4RKX1MHihEpYPbogXJ4UdEQkQkD0FE4g06YgokHRrfW/ma/D2Nni6p4Mj+wcx9QU1nWcnIKzPYkzKVpcT+L6Rw16TcPQFAy1Jlr0U5i+eTzYrs9EqfZ9zET1UyKcoobG5QsyFC2XZHh2R8GT4dDCyLmGlJKKU0udDenKOTnGgNcWR1soVlV1SrzIwBD5rBAIl4CAU0xdROeitjg7hstoQtCeCDFSshnIVXjw5REARgo2H7ys44SO/6ENC3jsuT3s65+gPROlsykxZ/F9WJUsjcx06TpeFsV8UA1+7y3L0VyLqxr9Yxb5DuUtutImt62u59q1LbQ+eS9Nbm5KtIyXKvz9o8/y06178YRCSDXQhODD65by8ctWEdJrl6eCp3B/NsVBS0cTknFb0qpXWWAczrsasVWeKYRpNlwuilWnCs2jsziizcbKcIkLQrC5FGLPRe9mveVNiyYNFxyGxiukDIWxik9zVKEzxjFFyyFiJygqpJQ8dMBi04jL4rTGm7vNqeL+JUmFdy40GLcki5MK5jleq7GsMcaS+ijiFHSbNzWFsK5QdWsLAuZcNmyzoCqC1rRJ1fZrdWfHse+pYqzoUKwe/mw2Jecru45OWFePq3ZoPkgpGS865CouYUOhMWGeUwX+Y0WbkUJNBDYlDTLRM9OINCAg4OwQCJeAgFOMEIJVLQmW1MfYO1LG8SRdqTD7R4soolYzMlo88WKHhkSIf/6dN/DMtkEa3FEuShWAUzORKLsSXQH9VROTJsNFE+BIqNePbocMtTqCL92/nY17s7VUnh2PcVFLFfQQvi/5zqZd/PPjz1Gya6JKlT6XNsb5wxsvp6c+Pe1Y/z6e4V9G6xlydGKKw1uTWW7LZFkZqUVbLF/wl/vreKlkYnlwbbLIx9ryLAh7SAkTnkZU8TCVOUSGUzvOExe/j7/92UGK9+9lw6Ikf/rWhSiKYCBn8z9/dIDeCZvuxjAf2tDKkjqdlHlqJrxZyydrSdqiCvqrxMfBgs/PD9p4PgyWbRalVC6oq9VxCCFYnDr6JLUqVVypEhYO6quiTmcyTewQyima8KqKoDlpYk/WbhzvRFpXFfTw2UsPk9SigpJzv4md7UomSg4SyFdcoqZGInzuTB3yVW/KlKFQcQPhEnDGOJ21+dVqlS9+8Yt85zvf4cCBA2QyGd74xjfyhS98gfb29nkdI5vN8uMf/5j77ruPTZs2sX//fhRFYcWKFbz//e/nk5/8JLp+el0YTwfnztUnIOA8w9QVWtMhilWXsKFy6cI0+8bKCOD65Q0ndey6RJg3tjvAKSiamOSFMZ8XJyCswg0t0HBEvYQioMGY2znr1Xi+ZDRvE5oYpOwJJrpMyvVNKK7D40M+f7V5grGSoEFVaIxofOqqdbxl5eJZJ7Z5TyXvqfiAK1XyUmNN9HCKWNZV6LV0bB/yiQY2tS3hz2WRv/Jf5Ke5JI+UUtSrDr9X30eLXhNKY9LgB1YLFXRuUQ6w5AMfZNfj/UyUHOpiOtsHy5TsWtRlx3CFfWMWbSmDA6MVIrhkQie/Ql70BAMln+/vrFCwJUvTGu9bMn0125OSsieouJKEDtpxRCrKUmPIj+FKhYhwaFEKMyyhX8soiiD0Gn1BdVF9qvYsHT23Jw6KAKEIXNdHUcQ59xmKmSpV2wcBkfk2cgoIOIepVqtcf/31PPHEEzQ2NvLmN7+Zffv28Y1vfIP777+fJ598kp6enmMe50tf+hJ/8Rd/gRCC1atXc8sttzAyMsJTTz3FM888w3e/+10eeOABIpEz28LhZAmES0DAaSQRPrw6mY6mWNYcByB0Eukc8+nJcrw4vmTzBNgeFGzYVYCGk+hToWsK73Vf4m4jzEJDcNXlq+jvWozwXF6o9tNzicnQff9IZ88y/uXmpWSic3dPf3Mqyy/zcXZaJvWaw7LQ9LqWBt3jimSZe0aTtHXWETYElpbmCdnOIyUNR8IOO8IzlThv08cAeNiq52nZjFLfRDm9mj8C1nXFeXDLOIWqxzVLUsQmJ0GLG8MsyJj0Zi0WN4bpykwXLXnLp+BImiPKvFf+B22V3RWd8apE6j4hv8rOrMemIZtFGZ2KL3hi2Gf3hIsvFKQiaYopdKUMRv0wCj5JUZ0RRTkSS2rYvoIufCyp4aGgUMvNPtFoS2+uSm+2QiZisLg+csbqCQ7VhCgCMjHjmOfZ9SWK4Lhc1s4ktUWNU5MedrrRNYWmhEHR8jA1heg5Jg4a4gZhQ0XAOTe2gIAT4S//8i954oknuPTSS3nwwQeJx2vzhr/+67/ms5/9LB/96Ed59NFHj3mcSCTCf/tv/4077riDzs7Oqfu3bdvGjTfeyOOPP86f//mf85d/+Zen7bWcDoQMjKjPOu3t7fT19dHW1kZvb+/ZHk7AOczpEC1Q63/xgwOS3nKtGP/qJsGq9IlP+g45h1WVEE/v7SW/9kZWXLAGTzfZtHuIe57twy1N8OstFm9PjMx9HFmzEnYkPFWMIQRcEi3NSPvyJDydD/O1yHqcUJR6zeNd3la+PxBhZ8UkKhzuyPSzwRgG4D+8hTxUtx6AxWmNz16eAmAgZ5EruyxqDE8zBhjK2+wbt1jSGCYdObze01vw+NetFfKOz9p6nXcvCc1rMv9K0WDYUbE9SW/eYXt/jmLFxa5YNEVVFrbEGXFV+gsOrudTHxK0RBTeuryRCgYgyYgyGaUy6/FzluTeg5JhS2F1Y4RVGYVmpThV+3M04SKlZLRgU6x6REIqjfFaN/qi5fLgzlFKloeuCq7oztCePDNdGA+MVShUXUBQH9OPWhNyIFth50gRQ1VY05qYl0VyQEBAwHw4VJwfr2vi9//558e9/99+7DoKY0Nzzvccx6GxsZFsNstzzz3HunXrprZJKVmzZg0vvfTSjG2zcTRDjbvvvpsPfOADLFiwgL179x736zibBBGXgIBzFM+XFC0XTVEIj+6e0dlcSsnLWcn2nKQtApc0KKgnuMKsCMENLbCzABEVlhwlA2285PD8vhzNSZNV7fEZ2/d8/f/y5b5mRisWzs6f8dyefTQ+sYs7//5OolS40t5FOJUlnPa4LFFmqGEZIAkP7uVgWaHFcMhoHk8UovzrSIaY6nNH8whXJ4pzjkkVsCFZoVO8yEa1hXppsbghzruWL2fzQJnu8e1cXq2JFvX9n+Smioe1q0LZ8bll4eFoT0vSpGWWSXFTwqApMT133vYk28YdBsseCUPh5XGXN9qSpHns9yCl+4y7KoomWJOGTkXjns0lorpgf9YhHLXRImGSIQXLlsR1wYbmWtREIJEIfOau0XhyDLblfBQh2TRY4Kr0YbelY0VbKo5PtlxLC8yVXWKGSjSk4foSz6/VlHiy5up1pnA9iZg09Pb8udfafCnZNVKk6voUbZfeXIUVgXAJOAW4viRfcVAVQSKkBe5lAaelQO3xxx8nm83S09MzQ5gIIXjnO9/JSy+9xH333XdM4XK0z+jatWsB6O/vP+kxn2kC4RIQcA4ipaRvokqu7KLYJdoMnaQ63c54zILHBn0qHhws1VK7Fp1EL8GEIVh3jJ6AtuvzZ/fuYtPBAqmIxh+/pYf1Cw6rHO/uu/j+aJqfvryNwR2P47o2rtTo3TfAF+/8V75+Yzuhwii31lqjsL/jYkYbF1G1ff59W4iDwwXaDIc/bh3g26MZ+mwDWwp+mk3wiaZRBhyDQddgiVlmq9rAC0ojHbLADf4BdHzaZYF2u8DzpQi/WnQb0VCIK5bHqe8fR7voDezJezzwcgVDEby5J0Jz5MQKtJ8cdHlmxMP3IawLio7P4pQ2b0viVsMlovh4CDKaRyVq8Pgeld0TDpmwyk1dJn22QBMaF9frJAxBRBOUZJkxP4KKT0LMbgUN4KIBPp4vEYrC8bjtKpOuX55Xq2kQk2lZyZDG8sYYu8fKNMSMMxZtAWhIGAzlbFTl6DUhNUtclaLtoQqBqQWpQwEnj5SSg+OV2vVYgbZUiLqT7McVEDAbmzZtAuDCCy+cdftFF1007XEnyp49ewBobm4+qeOcDQLhEhBwDuJLKOVyKBIcFCq+OkO4HHIkUo74+3iwfAUXhbBw511wO1Z02DdaIRPVGSnY7BkuTwkX7+672DI0xn/87Bf0DY8jJbgIfCNGdPH17I2s5mA8Sjnezf0bh5jIV3h7d5KyJdgz7vJKXqNL89hjmbxcDjHhKAzaGindI6F67LFDfHGkk343xJJQhWRzHRWhs0ckaZEl1srDKWffGa+nfbjIou4YY5ZP29r1QJWHDzocLPpICY8POLyz5/jrDEqO5Klhj6IrqHgKq5piLI65LMtoU1bFx0IISOuHIxZRQ+HTl6XZNmrTGtdYmJ59ch4VNhHFnjrGXKytE+Qck7Ljc2GdQJ+0hZ5PbYupKTQmDEqWS8RQCU/aBQshuKA5zoqm2BlfbY6HtKmao6M9txCCNa0JenNVDFWhIzV37VRAwHyRQMX2UJRa9K/qHH8D1YDzDN+nWp47C+Bo+x2NAwcOANDRMXu7hEOOYoced6L8/d//PQBvf/vbT+o4Z4NAuAQEnGWGixa9ExVCukpPfRRTUxADO4krYbKegSF8ospMR696EzY0KmzLSdqjsDA2/+cs+hr9ThQPQVKxadHKR50IH6IpYXBJd5Jf7ZpgQX2Yi7oSeHffRcl2+OrTW/jui9uxpCCtK7iKTnrJBobrr0BXdW5c185oSyPDtobX08CLv9xM5dlhVq5NULI8SuUqvejUGz6PVVL0FiWWdImpNm9N5Xi4nGGTncAXCiMVk0ssQXQOM5S4sPjlszvprwgu64oQl4Kf7rPZMu5g+4KEIThRN2NdAVNVGKwKNEWQjkVZ1WijC5+ip2IqHvpRiubnoj6icmXnsSfa83mf2gyXt7Sr+FKQUOcvWmrHF9NMJWbbfiZwPZ+85U71Jpnv80YNjaUNx/FlCAg4BooQpCM6o0UbU1dIhIP0w9crh8rCCxMj/NUHLj3lxy8Wa2JoLqevWCw27XEnwle/+lUeeughMpkMf/iHf3jCxzlbBMIlIOAs4vqSfWNlLNenaHnEKqN0mA5CQKteIaXZqEhCysxVGiEEF9YJLjxGetdslH0dW6oo0qW3BC5VOjPHTv1RFMEf3NLN7SPN1MV00vd9jRd6h/mfDzzFSKmCo5k4epilPW1ceP37eWo8hlv06DJt3lhfraUh4WMYGj4w2D/Mnv2DhFRJk+/zji7Ykuzm2RGfIUp0q0WiuJiKT1LzapNXKdFVWCOHkTJChyxwgay5hR3qyfKbmQF+uvgSTE1y00KdHTmPR/sdVAApWV2ncW3b0VM9slWPXx2oYqqCq7rCmJNd7w1V8MYuk6fHNKKGwsK0iSsdBtwoFV/DFB6dRglDnLkakFcjBFOC5bWI50s29ecZL9uEdJW1bUniZvBzFXD2aE6apCI6qiIwjif3MuC8QgiBlJJYuoE7vnz/ce9/56dvpTgxtyHNIWE010LNyfppPfroo/zu7/4uiqLw9a9/PUgVCwgIOIzrS3aNlFAELGqIzmrNKgBVCHwpUZwq6hGd2IWAqKhNPssuaAoYp+j3MixcNDx2j1Z5adcgm1/ZzefesYylLbVi+6O5keiawtKW6JRzWDyRZmfepeKqxENR3vOOd3H9zbfy3edHsEeKtMQ1Gv0SjcM7qMZM2oXG0MHd3FxX4uZ0iR8Omewua7ytpcKiuhibtQgXpIuM5nQMV+EdDUV0BS4N57ghlWe7HaVLr/IWrZdm74hGnpOiRX3/J+kAfvOIMQ9U3Klz3RAWXNumEzeOvoL/r5vyPN1bRRGCvO1x+/LDRgTdUZ9ESKUodWKiigCqvoYufKpSpeKrGKp/1PN4prB8oKmHM1eRcvKUHY9s1cHQFEq2R7binDPCpWi5ZCsOqbBO7BwZ06nA9SRjJRtBzXJ6vmmPrxeEEISNoGYqoIYQCkY4ekL7HY1D1selUmnW7eVyGTgceTkeNm/ezG233YZt23zlK1/hbW9723Ef41zg/LnqBgScYzy8Y5TH94yjCLh+cT1XL5oZGlEVweLGKAO9fZimpHGWJo8vZ2FTtiZarmmExlMwA42rDkZulB//ci+5XJ6+iQpbegu01cd4sN9nwoL19YLVGQXH9dk/VqExYU6lDx0SLeghXvYy6F1XYE30s/DK23jDjVdhKHCVPkSv71KtwkQmwx9kW+h+coA/T+9gXdSHyWv+hQkbX9aKwnP4dMgCQ7EIv77M5p3OIM16rbYnLHz+OLGLvV6E+pCgUbrgMyVYoCZaZmNxWuWGDoO9eY9Vddq8ivIHix6aIrA9yUhpevRCCKjXKtRTsyN2pcAUHhWpYgofVbr8x44K2yZc1tZrvGlh6IQd304EKWtjHLIEL+VV/NIYyxvjdKZfGzUfYV0hbmpMlB0iukIidG78VJVsl6f2j1OwXOIhjQ1dGSLGuTG2k6U/W2W0WKufslyfjsxr47MSEHDWOA2uYof6rczVGuPQ/Uf2ZZkPu3fv5uabbyabzfLnf/7n3HHHHSc30LPI+XHFDQg4A0gpyVVdqo5PMqwRPkYTyT1jZQST6WDjFa6e7Zj9O4gD8TlqNTxZEy6OB0UHdhdOjXABaE+oRBWbHbkqrekwy1rjbM1JduUlmoAnh6HO8PjKA3t4+UCO1lSIdy33uO/v/pK/eNMVhCO1FR/HFzQuvIQWAQmgc/9zxBWX9X4fl3So3OMu4GEtiSYkO9UWNlf72BAtTBvL0/kQPx6N0hFyeX/zy+xTklR9gapOFxgRxSO9cAkTmS6K1SLbnt3ISzmVS954JTcumWnNfAhVCK7rOD4XoDctjvIfWwqYquDaBUfvLKwJSadRpOJrhITHrgmbJwdsdAUe67e5oF5nYXLm5faQwDhVVDx4pahR8QULwx79VUFZDSNcn73j5deMcNEUhbWtSSYqDlFDPWciGwXLpWh7RA2NouVRsNxzQrj4UrJzpEjeculIhWmOH/9FwnJ9ajFgie2evTTHgIDXM2vWrAHghRdemHX7ofsPPW4+9Pf3c8MNNzA4OMgf/MEf8Cd/8icnP9CzyNm/4gYEvEbIV112DpcoVl3qojorWhNHTadY3RKvFXMKWNE8M6w7n2aSCpAy4GC5VhSeeFVNqC8lfUWfkCZoCB9fHpmhKfyP25bxSm+elnSIrvoIm8Z9FFFr+OhI+P4ui4d35kkKi5/9+//j58ObSGrwR7/ax+KV19Bh2rwhXWJPRaPf0nhnU4mu3NDUc4xYgiHLh7jEQRDGp0Wf7o6WdxXu6k0xYGs8nZfEQgrbYm0MyjBtoszvGNtIi9pKsB2Kk6tbAL7PaLSRp2LL2eIJtj47RnfGYHHDqUuIuqIzzNpmE1WB0Dxy2nUh0Sed30xVYCiCkuuTMBQMRXCg6LNpzCNpCNbVq7yQVRi1oCcGq5LylAiYfktl2FZQBOwsqzRoHkLWcrITofmnueSrLq4vSYfPXr8KQ1Noip/57vJHyzFPh3VSYZ181SUTqd0+F+jNVXllqIjnS0ZKNm/o0Y+5sPJqGuIGtlsFIag/C+c9IOC1hUSeUMjl6PtceeWVJJNJdu/ezcaNG2fYIn/3u98F4NZbb53Xs42NjXHjjTeyb98+Pv7xj/M3f/M3JzDmc4tAuAQEzJNs2eHejf0M5Kp0ZiJ0ZSIkI3NPXDZ0p+mui6AIQVN8+mr/q0VL3oEXxmtOiWszkJmcNwgBVzXC3mItVaznVUGFB/bbPDXkYiqCt/cYLM8c31c6Hta4bHGGPWM2j+8rsaTB5OI6wUBFomkKOws+hQOb2PbMD9HsPK0mjPg69+wr09LdSWOuQLh5kD/szk0d88msyQ9HosRUj005hWGnSKhpkCV1Om8z++jWp/cfkYBHTaT5CMYJMyJDNIoqwzLEoB8mrdaEi+ZY6NUilVgdxNMUzSyGVsT34Xj6Ie4v+vSXJO1RQUdsblESPcGiooVJldsWhdibc1ma0WmMKHx9u0N/ycfyoLcs8FQFTcDWPHREIH0K2kJoQtbOowRNwJJFC4kXqkgJrfPsu7JnrMzPd43j+pKLOxKsaz9KN9LzjHzVYftwEV9KFjfEyESmvymmprKhK1NLFTO1c6ZI2/MlvpSoipi6fbykIjrxyZQ8NahvCQg4JidZJz8ruq7z6U9/eiqd62c/+9lUPcuXvvQlNm/ezJVXXsn69eun9vnKV77CV77yFd7+9rfzxS9+cer+YrHIm970JrZs2cL73/9+/vEf//HUD/gsEAiXc4ihoSFWrFgx67Y77rjjNZ2TeD4wXLDYO1rGVBX2jJToy1aOKlyEELNOFmeLtGwch2252iTe8uGWtsPbIqqkkCszWnRJdEZojtee05eSzWMeApiwfXZMeFPCxZdQ9QWmIlGPMQfZMWLx14+OMF52WVRv0tOZZrAKz298mV/9x/+lMrgTU5G0mh6JkE796rdSXHA1jjAo+Db77BA5v0pScSl5gq/2JumzVCqewPM82kyHwtAA74wXuDpmz3j+pObzidYc947G6Ag53J4sU/Qy9MkI3UqRTmWySNGpojpVOrb/gtKtH0WTFpe0qGz0DC5fEGVJw/xWiYcrkh/u88g5kpQheG+PoD50aidqQgg6kgbjvkHWh7wjsTxJ1vIpOvDyuMuCtIapQkyrRdNOBe0hH8f3qPqC9s4ODE2hK330NLdXs2OkTL7qoqsKrwyWTolw8Xyf8bJDxfGojxqnJL2q6njsm6gggAWZ8ClpNrl/okK24iCAfWPlGcIFapGgOu1VCxFSYnsSXREoZ2HS35YIMZIKM1FxWJiJEDnOaMshAsESEAB33nknd95556zb/GP0YTkV/Mmf/AkPPvggTz75JIsWLeKKK65g//79PP/889TV1fGNb3xj2uNHR0fZvn07AwMD0+7/7//9v/PMM8+gqiqe5/GhD31o1uf71re+ddpey+kgEC7nEE1NTWzZsuVsDyMAKNseQjAt3aIhbtIQNxgr2TTGataYx8tsosXxwfVrokVQqzc/kif2l/nK4yOMVXw6thX42ze3kAqpKEKwMKmycdglqgna4gr5qoeqCnZVTbKOwBA+a+IukaN80/dO2IyWXBpjGnvGHWS0wsaH7+Wpn/wndrWKLiTC97hhSRefue4Svu6v5ldlhazjkTIVHpMtbJuo547EfpqU6lQgPKRCd9ih6EquTDusS9TSqCRwUK/DUjS6rFEMPK5OV7g6XUFK2OvHuFYdwPBdXhwXfNOLcUtinIWhWvF9FIhScxP7tfV1/Nr6WV/WnORtSdmV6EiGy5Kxik/9UdKofAkFFyLq/AWGL+HpURiza6tyMU1wRbPKrpxPwgB8n4zq0p7Q6IhIYqfoSqwKWBSd7NtygmlMDTEdbVTB8yXNibnDQI7nM1y0UYDGuDnnpDdfddjUl2es7KApUBc1WN+RQldPTq1tHSqwf6ICAiqux9rWkxdYuiImqzxAP5bin8SXkoPjVYpVl5Cu0FkXPunXdrwYmsIlnekz+pwBAecrR1soVlX1tIuXUCjEI488whe/+EXuvvtu7r//ftLpNL/+67/On/3Zn83ZnPLVZLNZADzP45577pnzcYFwCQh4DVC2XF7cN0EqarC8LTEtn/25/Vl+8soQdTGDW1c1TxU0L6iL8LEru9gzUmZxY5TWWbpy7xkpMVK0WdoUmyZs5qpn2VcWbCsqSAXaIx6KgItfZT42VHAYKXtUpcL2MZf79jl8cKmCEIK3dRssT6uENMHW/Vn+/ifjtDbEuPqiTgpuzR2sv6RwU5NPfI557IpGk46UzmjJY1WTzo//7xfYs3cPivRQBSihJO+47FK+dGUdQsA7Kr30FSQTUsXSI5jC5aVSiN8fb+PiSIH3NRf4xXiInDSoKlFWmDa/15ojqtUkzSuhNn4RXY6rqLQWB0iN9dOhVlhmlNjoZfiWs4CK1FAmxtgxbOEBO4oq//DHbz2Od3huOmKCmCrZNOLgOS4/3yVYsj4xa02D7Um+scNloCqoM+HDi9Rj2igfQojJbGZRc0xbX68xUZU8P+oRVgWX1As646ch14D5N5ucjbWtCRKmhuNLFtXNHa3ZM1ZmMG/VrKBdj+662a1B+3JVCpaL5/sIFCqOh+X6Jz25r7r+1Dm2TlEn8+66CKoikBI65mlkULV98hUXVUDR8ihWPdLRcyOFLCAg4PRxOlLFDhEOh/nCF77AF77whWM+9vOf/zyf//znZ9z/zW9+k29+85unfnBnmUC4BLzukFLy5Z9u56kdo0RDGp+8aQkbljYAUKg6/O2Pt9M7ViYa0kiEND54yeHVjVVtSVa1zb6yu2OoyNd+tZ9c1WFRQ5TfuXYhpq7OKVp8CTuKCpYncCUsjiusTh5eySk6PntzHgvqQzQkDA7kPBY1RylKFVeCLmrNEFfWaTiez19smiBf9cj3F1mxxKYkdBQhydnQV4FlRwiXoYLD3RsnKNs+716T5k9vbGKo6NKV0mkavoq/+4eXKOkGCy+8mg+tXc776nJM9n7krv4MD2UNpFBY3pOiLCyGR4skUfhlPsaCBpvPdOf5zN42Kp7g6VKUXxXK3JKuOYkNaQmqio5t2Xwj14wvG0iWCnxWbmWfGiUrDVI47HBMXGkTam6ikDBrlskn++ZTK5xvVBxKuTLpkGDHOGwds3mi1yZpKrypJzxV27J5zOVAufbCB6uCZ0c9rms99mVTEbChAV6aqEWeLpj8yFzfrrG6TiWiiXkLoOPlZEQLTFp0NxwWIVuHijx7MEcmrHPd4joik70syrY3FSEs23OvQBqqgq4q2J6PEJKGmDl1jJNhUX2UqusBgp764+9pMBumprK44fiOpWsCXRVYjo+mBs0JAwICAk4ngXAJeN1RslxeOZgjbGqMF212DxWmhMtwzqJYcYiFNApVl0p1Zl+VuejPVclVHFIRncGcRbbi0jiye87HC2rpRwWvNtENHzGXsz3Jv26tsDvnkTQVfvuqJp4b8dF0lZVpBf1VaTmaImhK6PTlbHRfErMKhBJpSq4kos5MRfrByzl+vrMI0qdQtvirt3bRl7X5r9/fj7dP5/rFXXzq8gvobshgiMOF966EUUdDCLhxfReXL2+mSbV46KkdvLy/Zv88EG/m7vAisloJu2pRVkweVdpZRD+LydFtj7LbbGKTZeL4tfSrnBFjjxNhqV6gngplqXJjusTujm7KlseHLmtCe1XqjpSSh3aXeWxfmaX1Bu9alcCYZ3rPkjqd+rCg5EhWt5p8d1uZA/naRDxqCN7UU4s0xDQB0kcKBXxJdJ7HB2gKQVPL9PsUIWiKnL46gpMVLa+m6ng8smucfNWlL1ulKW5ycWdNhbUmQ+x1a+95S2Lu+qKudBhNFTiuTyZqkArrszZjPV4aYibX9NQDnJLjnSi6qtBVF6ZoeYR1hagZNCkMCHhdcDpDLgFzEgiXgNcVUkoOZCs01UV4ad8EjQmT1ZO54Zbj0ZYOcUl3hhf2T9DeEudNK5vmfezOdJjmpEmu7LKyLUEmv6+mSOZACLgw6XGwqmAISVfk8EVwwvLZlfMpOIIxy+eSJsnHLzCpelA3yxxRCMF/ua6VR3fmiJkqNy6LUfE9+iqCuCZpmyXrpTTez+5f3kO1pwfe+nv827cfZEs5hSDJFRfeztLGIWwJD3stFKTGpcooLUqFjzSOMz6gs3ZhHXWqS9Zy0eNRFtQVWaYXKabbQCh0d5mURiaY0KOUkzrfE2F+X25koT3C2yeeZY+3GkhQQSclS6w2Cyz1x/k9Lc/oDe/nuTFJW8WjJ6FyVc/MWou+vMs9L+Up2z5bhi2e6beoj6q8d2WcxXWz12bsLsCmCUjoJp+6JI3t+XSnNP6/p3IoohYFc7zD78PStMobGn0eOlBhtODwQBm6ogmaT1VRyjmOEAJF1L43QgiUI4IJTXGTdFhHCI6a9qWpx28QMF/OpmA5krChBl3VAwJeZ8wlW55/4N94/oF/m3VbKTt2+gb0OuH18esbEDBJxfEZLTm8+eJ2LuhKsbI1wequFPf8ah8PbOqnoz7KJ25cTKHaQVMydFTXsCN5fPcYv9g1Riyk89ZVLaxWh47a4+UQUQ2WxWam2aRMge1Dyaml7kxYkqgG+wo+2yYkS1PKjL4tTXGdd19UP/W3rkJCn35pfXr3BN96bB87Nz7Crhd+ie/7DFv9bPnfJcrRqxh34ggkfqgWaXrcb+R7bhcugp0ywWfEK9ycKdKQMtgqK1SMOn64LU/eCqOE6nljuMBO4ZMXOk0J6DF8nvZNFHx8BHKy9NnzfDzfZ4lWZMQzeFf4AEvlOAAdH/gw46MuB4sOCV1hV95nqCxpi00/n4pgyvp3wvJxsg6jFY//3Frks1dmZpxT26sVzJdcGK5CvamxthZo4/alUX66u0zKVLiq47ATnBCCS5s0frQ1jy4lu8Ylz/VbNCV8ftVv0xZTuXVhiLB2eGxSSvbkPMquZElawzyOKM3JcKqjLQCmpnDz0no29uVJh3VWNk/3436tpkWVbY9942WEAt3pCKETdOEKCAgIeDXrbn4v625+76zbvvLbN1AYHz7DIzq/CIRLwOsKU1MI6yr5qsPCpjjdDTH6J8p875kD+J7k+d1jvLConjdf1Hbsg01iuz6P7h6nZHuMOQ7D/QfRuk6ugZuhCJakNHbnPXRFUB9W2DLh84O9NlUXXh4XfHSZOe/UqEPj/Pw3HuC5X3wfq5ilPm5Ql9Dp8CvsrwiKyQxtQF0ixPqMByUoSh0XQQifvDTwUFDxeU5tYffBURaaMWzPR1FVKlqYvqrK8mo/D6gL0T2fBe4YCI8JYXKt7MWY9ExLY9FKmd0iwVI1x3o5gPr+T06NNRNSiOuCrO1jqoJ9BZ+6sCB0xOttTeh8YG2CJw9UGCp7DJZ8pISQNvs5EQI0BTxZS9M7MkiwptFgTePsUZqQJkiHFXaNu4S02mfoR3stCrZkf96jLaayofXwvhtHXP5zdxXLhQsbNN63NDRV+C8ljDkK+wsuiuexNK0R0Y/9Hkop2ZGHcUuyMC5oCk/f53hES8l22TlaxtQUltRHj2mB210XofsoRfqvBaSstYo7FKHZNlJgIF8FBJ4nWdmSOKvjCwgIeA0SZIqdFQLhEvC6QlUEK1vijJdtwrpKwtTY2Jul4vqM5i0a4yaJ8PF9LTRVkArrjJVsDK9Kwjj5rtNC1BpKPjngEtHh8madTWMeVa8WpcnbUPFgvtkpI2Pj/PVdX+O5Bx7BcmriQdM0Prqoifetu4D/PtjDiwdLeLrJsiboIQ/AJeoIO2SCnDR4o9qHIWr71lFluy9BFVzQlmDfSImwXWKJn2eP04x0KrhInqOR3xEvz7jAm8Lno8o2drlhGijT/f7p/vIdMYV39ug80u+yJ+fzUK/LuCV5y4LpEbBruqNc0x1lrOzxox0lPCm5ZfHs7la6UmvmuTULMR2WHjFX9SXYfi2C01cVqALawxJF1Gxxf/OiBM/3W6TDKhc06Dw57OJJiTKZSnUkBwseRVsS1wV78h7eZDNIgCFb4ecD8Ni+Cp7nc2G9wsdXRWaIB8urWRsPVmF7DraP2TzfXyVkqKxrDfG+hepUlOd4RIuUkkd2j7NvvIKqCGzXZ03rsSftvpQ8sW+CfeMVljREubgjOasL27lIyXZ55mCWkuWxrDHGovoojicRCCTg+sHsIyAg4PiRgXI5KwTCJeB1R1hXaUvWij7yVYey6/PWyzp5eX+WC7tSXLG08biOpwjBey5s5cVXdhDVQ6xvPDVfq/aYyrsWH1YmS1MKL40JcjasqVNIzCOLzfd9/uO+n3LXv3ybcqVCY8IgW3Lp7lnCXZc0c0FDnJyIMOZqtMgiY7bDgvERlqcOAtAsqnxGewUPgS4OX6RvkfvBjHKgUCCtCKq6w1J7F1fHshRkEoFEImikMvvAnCpJqqyjMC3SciTdCZVnhz1URRA2NUZchZIniKozfyzqIiofWnvsCXhLuPbvSCoevJjXKPm1iXzOrjXtXJ2AZYnac7XENG5dcvh9vX1RiKcHHZojChc1Tn8jlqQ1No26VF3JqjptWspg0RMMlxzKjk/MEPQWffK2JH1EA8xtOdicE2hCUnRgvOrzxL4yFcdHq7ikwyoVTyWsHX96mASyFQddFdiuJG/Nz3xi33iFx/dk8XyfgbxFWzJEWzJE2fYoWA5xUz8lTmGng/0TFQbzFqoi2DJUoDMVZlF9FNeXKEB35rUdTQoICAh4PREIl4DXNRFDJRnScZJhbl4X49Ku1Lw7X/fnqjy0YxSB4IZ0ges75m7WdyqoCyl8dLlJxYW4zrxWvH/88KN86atfm/q7tT7NX3z2Q9wwuLW2Yq+HSOLz1kye+8YTdOsFbguPTLMdrtWSTBcLEVzW+COM2624QmVlpMJVqQL/pqykiM6V7ggNfolLmCWX16kCzClYjmR1vcqQrRCLhkiZCi8XYV3CZo5ssBNi2FYYcwSGAhOugiY8PB+yztz7rKzXWVk/u3JcntH4+MoIZVdSF4J7d5Rwfbi0PcT+kkDTRM1uWfosTqkkzMMvxpfwcl5geZD1wXYnz7843PyyKwqGUrO5Tk4Wzc+GlJKi5eH5kkRYm4wOCVY3x3mhL0/cVFhcP3t0auaxaquLQogpI52K47GpP0fJ8oiaKmvbktMatp4rGKqCIsDxJQlVQVEEdRGDDV01U47XSuQoICDgHCNwFTsrBMIl4HWNpihc1pVitGQTMzSSYZ2Jks13n+ujaLm8eXUzS15VkHyIR3eNsWu0jLCrGFWFd8/ifHWq0RWBPsfTjJQ9io6kM65OpR7dct3VfOcH97Njz17edvMNXHnDW/n63b/ie7KBT3RbXJauzc7f3ZDjlkyBsOKjTfZrKfrK1N+z0V0ZJK+GGdcT9FQG2Czq2aLWoyKRQvAueyczdj0O0QKwLKUiVIU9VZWwKql64PigncL5cUiR6ApYviChSRwXNK2WKiZlrTZmNqSU2D4YyvTJ71AVnhxX8CSM7C3yXF/tNffaGnVJnaSpcPWCCOsTHp0JFfWIfQUQ1yDvgKkIFiYlBUfhugURssUqHXGN9W0G3zsArhFhvV5gbdvskabhgs2u0RK+lLQmQ/RMipTVrQl66qPoyvx7jnRnwlzZnWbPWJmljVFaEyZjZZuyXbMALtseJds9J4VLdyaC50vKjlezZp78bgSCJSAg4ESRnFiJSyB1Tp5AuAS87jG1WuqY5Xp887F9PPTKENmqSypSEzF/fOsyTE2ZMdHRVAXsCpLDq+EAnpQ8OwoDFVicqDUfdDyJpoh5R3OOl23jDt/aUmag9wBvvKiHdy4OI4RAVVX+x3/5JJWqRe8z2/iT72xlohSnQbP4zwExJVwA4mqtfkVK+H+jGX6eS9BqOHyqaZgfTiTZXIlwTbzAe+omEAJUJBcV90ztv12LTDYkFBjyVZ3Mj1OwHElHRJL1a6KlyfQJnWIjq0ZDsjruUfKgQZcoSCZs2JQVvJiFizOSpsNGY0gp2V/weGoEKqgsisMVDbV6F1/CK1kYtQAku4oCzTTAc7EmbZaFgIypsCg987MgBFxRL9lTAlOBRfFarQuYk//g5wM+WRnC8CSbBwqsaY3POgkfr9jYno+mCMZK9pRwAYgeZ1qXogiuWpjhqoWH3doSpk4ipFG0XBIhjYQ5Pwe+M42qCJY2npoGlQEBAQEBZ5dAuAQETPLM7nF+smmAibJNwfaJGCr9eYsfvjJIRzrCpR3T08jekMhh1Ksg4KqWw1+l/UV4caJ2e9yGXb15/vO5QTIRnf9yQytdmdCrn/qkeXLnAPf98z8zsnszxh2f4209a6YK95ct6mHnt/+VHxoXE2qski+OEPI8mkxr1mMNORo/yyZwpWBzOcR3Cs38tJJBEzCSNVgXK7M4NHPfK91ePBSqQmeD23s42nISogUgokrWJWwcH0JKbXJfdCS/HPKpuHBxg0Jn9MQFoRDQYk63pH5uQkyKD3g5J2gKHV4ne+SAxfd2VRm3BCuaQzi+QU8M+m2VCUfgSB/wKbkQjZig+Qjpc0WjgjQknoQLknOvu8V0WJ2ae7zpphb0/VlyFZey7fLIrnGu7E7PiJ6kJw0jfB8y87T1Ph4MTWFtW5Ki5REz1aP2cgkICAg47whSxc4KgXAJCJhEEQKBIGZoqKqkuyFKfSaMLwV7xsr01EVojNVWvWX/DupCCm/tnp63JSUUPQVX+ijUVufv3TTKaNHh4ITNQ1uzfOyK5lM2Ztd1ufv79/MP3/w3hvMVfAmb7v8m6jv/FqgpF+/uu3gycSnojXRkIGzqXFbcycdaZy+cj6k+Sc1jT9Ukpvpore04+6DkeOghgSlm9p0BiOHwZnf39DuPQ7RUXckPd5UZLPlc1W5yYdPhc6uJ6elhL477vJKtdYUpuT4f7FGO2YxwpOzx8AELRcANXSEyRwndmAowKb1MZfqP01MDNp4PtufTm3XoTBoUPIX+qoKmSKSisDrt018Cz1NoiSigCLKqhgGsSvjUn6DxnGhdwppJF6zH905gago7R0u0JUMsbZxer9IUNwnrKp6UpMKnJxqiqwrpSCBYAs4OUtZSAHVVwQiEc8CZJMgVO2sEwiUgYJJLejK8fX0be4aLXLWsgcUtcR7ZPUbZ9kiENEKTM2fZv2POYxy0dLKoZCIu0nNZm4SXdcE+R6KrguRxWi0fjc1btvHFL/8fdu3bjwY0hFViiSSf/sA72Jb16UoohP/jH/GBkhmnlQoTismaJp87MuPM1QImpvr8fssQTxdjZDJRsks6uDRmMzJW5Ipwls7q9Kp1CWzRmxhS43S5Wbr8LNIq46AQn0Ow2J7Ef1XPlWcGLB45YIGEoZLHorRG3Jh9MiImn1iKuWtQXs29u6u8MGwDAseHDyyf201qbUpOpaQtTUz/pVmY1OgvWtSZsLZB5YaWWk8YRUhKbq1GZXUKLkjAA34t6pYMq9hS4Liwp6zQHPJmPukxOOQgpiqCJQ1RtgyVKFRddFWgz/JmCiFInibBEhBwtpFSsmu0zGjRQlcVljbGiIeCKU1AwPlO8C0PeF3y3O4x9g0XuaAzxf6cxVDe4pIFKd5zWcfUY6SUXN6VZrxs0xw3SYS0o4oWqLlSCRQ64gZJTeWCmMVnbmjjoW1ZEiGVW1fN7Oh+vOQLRb7yjW/xg58+iJSH6iYEH7ztjdx423v5zwMKDz1fIjl6gN9zTdrCgrXWQZ4NddMiS9zMrjlFyyF6QjY9oXFeaWgi7/tc1BFBtIe5YecLMx67X03z0/Ay8moExQyxKOpycCiHrG/mmj6bq9qmR6VeGvP46QEHXYXr23RW1b2q3mIeQmRNRqHk+pQ9WF83PdpScSXDFUl9SBA9ormjfUTvDsebuexVcmHUEiR1ScqAizKzL429fUmYRWkNXam5i6mKwPd9CkWbgyXQ8Vgb1WmOqtzWVevJsrMEeycDXIfsnKWU/LLPYcu4x+K0wnXtxpxRo1fbHsdMjasX1orlMxGdBZnwrPsFBJyvOJ5krGSDgLLtMlFxAuEScEaZK3jyws/uYeOD98y6rZwbO30Dep0QfMsDzntGCxb3bxrA8yVvXtPCWL7K/7pvC+NFi2hIp74lgaqq7Bkt8wc39Ez9+Akh6EqH6UrXJoXHEi0AdZpHyast1We02qp6Z8bkoxuaTvp1SCl54BeP8Xf/9E3Gs9mp+5cs7OaPf+cTtC5cwvf3++wYy5HNFhkgzV25hfxFeC8XV/ez1B5Clx4R6eAj2JxeQl+kifbSIKuyO2dYHgPErQJhp4zr2TQXB8lUxmc8pqLoWEJDUxWMVIIJXYGOeqxSmcf6bS5r1qciAvsLHt/YZjFe9YkbgrAqpoTLxS0mg0WPgZLPVR3mnNEWgJguuKltZoF5xZXcs9uhvyxpCAne06OTMCYd1rpDOH4VdTJV7EgsDx4dEQxWBVENbmqSpOdwbzNVwfpmg6oreWG0ljbXEYVtI1WQUHAkWycUmqM1x7CIBsvjPjGt9kPXGa7tc6Dg89BBB9eX9JY8OuMqS1IzL8lz9WrpTIfpTJ96wWK7PrbnEzXU88p5y5eSveNlipZLezJMXfT0uwAGnD40VRAx1Mm+RAqRc9DRLuD8Rs5R43Lhje/mwhvfPeu2f/z0GylOzNImIGDeBMLlHGJoaIgVK1bMuu2OO+7gjjvuOMMjOj/4/gv9/GLbMFJCyfJY1RIjV7api5sM5SyiVZdMQsf1fbw5LkTzES0AbaZLQqtNTA+5dJ0sZQ/GHUFM8bn7P++bEi3hUIhPfOh9vOetb0JVVfZXFGIhFcWqgqohykVGK/6UpW/Sr7LHMtlaSZFOhNifXIhAsiXVQ3N1jKbqzJWgjnwvhmdjqybNxcEZwRAJxBSfBX6WXqWJeMRASElISCxVI216HJl6vjvnY3u1jvQlB+KTomLbiMVDe8qga7yhO8zKzIlNQoYqkv6yJKHDQFnSV/JJTLoUdCc1fvei2d2lCq7kQEmh7EpGqvCdvM01zQoX1M19ifx5v8tzIzVxur5epT6ksDfvEdEFDeHpZ8pQoCc6/fMgJvuzSGpBptkkwvE2mDxZRoo2D+4co+J4rGmJs74jecqfQx6l98zppDdbZXN/HteXDBUsru2pn7cddMC5hyIEyxpjjJdtTE0ldQrTcANe39x5553ceeeds27z/SOv40HBytkg+KafQzQ1NbFly5azPYzzDsv1kAiEArbrcWF3hjVdafaPlLhhVRONTQmGCjbXLK6btYh5vqIFapPRQ8LllIzdh2eyKnlXIaKqfPqTn+BTf/BZrrr0Yv7rb3+Mpob6qcdmdJ+W0X1c1aDz860ThCaGeXfd+FQNyJCj8VcDrfQ7Oqm8YEOjQyIewvAdFDn7mAXQXJp7deiVxEI2JXoAuHFxE4Mu5FwF6fksD6tc2qijCIHjS6ouNIUFzRHBaLX2/61dGlXX55+eyzIkDVQdei2H9y8RrK5Tmaj67Mq6NEYUuhLHvlzVhQR1IcFQudb8sSk8v4lpVAXXrzl+5Soue3MWI0VBfThMU2R2ETVWrZkDSGDClnxwWZitEy4ZU2Fp+tjCqyOmcFOnzpYJj8VJlZ7k9H3OtGgB2D1WZrhoEdJUXhossrolfsom976UbOzNs3+iQkvC5OLO1FRPlTOB4/n4EnRV4HgS15cEMZfXNoam0Jw49S6NAa9vjrZQrKrqq8RLwJkmEC4B5z1vWdtCyXJxfcltF7WRihr86XvWki3b1MXMo/ZWOVK0SCnZnvUpu5LlabXWef408vzml7G1MKX6pUQUSdkTLO9exHfu+jsWdnXMeHzk3+9kXTjFCiPCr6Vy+AlJ7Iioz6BjMOZqNGkOo5ZGeWAQ7BSXeP3UWxO8ojWxXWug3cuy3umlrEXwhSDulOYsOzlo1uMJFRpbGbXhhiaPUVsQ0yQpvXZ5maj6/Psui/GqZHWdyvsXGxRdWJRUiWiCou1juRLNUPD9WlPHCVtScSXfeKXMvrxH0hR89III3cmjX7LiuuA9C3U2jftUPMFgFdLm9BV+KSXbcnCwJOmICpYlIaTC0pjHM8Me/RNVMjpYnsRy536u9Q0qE1ZN7DSFFRwp2NAy/6mwEIIrWg2uaD1iXFmfoYpkQfcCFsz7SKeOuKlhqAq269MYM9COVQx1HIyWbLaP1D5Lu8bKtKdCdKTOXG1OWzLEYNEiX3XpqYsQ1oNoS0BAwEkQBFzOCoFwCTjv6aqL8tk3LZt2n64pNBxjpe6QaBkquDy6t8RQRTLs6/jAiozG+5cYpyXlJZvL87+/9i/86KFHWNTdzR1f+FvyvkZS80nrksgRosXxJI//v/+gKlUuj0aJu1VMt1oLlbxq0X9xqMKacJlt1RD1EcFBJYaXg2esZtxshER9ipa0yQ6tnt5wE9lMGzEcLhrfwqL8/pkDdaq0lwcZ71qJANoiNQHQHp5+Nd864bGv4BPRBC+OeVzeorMweXjSGDMU3rMqwX/sshC6TndSYWlSYaLqM1T2SZmCCcunv+TTPUfmUs6B7QWBpkBHSLK3JMjasK8kCWsCjZogqDdhvOLx5LhC1RfsKUjSpkJzWHBdMyyKqTxiaAwUPdbUa7THp09uSy7sKwsMBRYnVdqjgnv3uzw36rM1J7mtS6UjdmIT4t6S5IFej6oa4pUtw7zvotbT0n/laCxviqIqgorjsag+ckyL6eNBVxU0VVC2PULambevDekqG7rSSDilrysgIOD1SdDG5ewQCJeAgKMgpeQfnxpn00CVvC1pqI+wojVKX8nHk7X+ItMfD44EfdKm1/clYyWXdEQ75uq1lJL7Hvw5f/9//4V8sQjArr17Gd/4C6685g1ENTnZX+Qw93/zB/xHZSGeUNnuT/Dp5L45jx9RJH/Y0s+wq/O81syDikbOUdnnhBjJWaglm7eGFXzdZHNiAaYWJiddkvHO6cLliN4sayQ01f6keQ4dmDQFIVVQciStMYXI5EnzpESdnEBevSDClZ0h9uZqRfuNEQXXlyxJq2ybcGmNqixKzZ1+9cKEwkAVQFBywPY9wmot1e4XAx7bJzykhELZJjteQjN1LulJ4kiBPRmUUhVBd0LQvSKML+WMya2U8FxWYaBa8yYbqUKjKRip1iJMWbtWX9Nxgk3aC4lWtOg4GVXgepKC5Z5x4VKrG4ge+4EnQDqsc2lnioF8lYaoQWPszCdqCSHmY1oXEBAQEHCOEgiXgIBJyrbH8715qq7HGm2MhlDNOnes7GJqAsOTaFIiBKytV2fk57s+vFTUmHAU6g2fJWGbf3ikn+cPlFhYb/KHN7XP2cdlz/6DfPEr/4cXXz5c4xSLRvnURz7IrddfjaLMXNrx7r6LAW8hNhohPA64x871NhRJu2EDY2wnwyaZoFyoYAiJ73kYTpV6ygxZJpGwiYNC1MofPsCrGkoKAS3HyPZZkVa5vcdgvCpZllaRwHd2WPSVfC5qUHlDm44Qgp/sLHPf9iIhTeHj65OsaDT5teURBkoemZAy5TJmeTBUhbjOlPOXI0EBfMBUYXUatuUgosHurE/RAYmk5AqihsJowcKxPS5q0mmbbOeSdyTDFagPQcqYfXpbdmtidbQK41VBUhekQgrDZY+MIWiPzm9avD3n88yIJGnAdS0K8a6lxLMVEqZK1fVJhDXCZ6Bw3J9cMjxTEYgjXfoCAgICXtsEIZezQSBcAgIm2dSf54W+PNKuko3AOzprq+7vWJnk31/K0ZnS+eBFCRoTBk3hmRO9MUdhyFbQBRwowXcfG+C+F0foyoR44WCJF3tLXLN4eq6TZdn883f+g3/97r243uGCipuuuYrPfPzD1GXSs47Vu/supITLIwW2+SksqXBzZHTer7WdEr/LZg4oIf7I7mRA0bkiUuB/yH1MOGG+NeFTsCv0kOPS4ku1nV4lWuaLEIJVR7hzvTDi8vK4i6kInhh0WZnRyIQk9+0oUbR9xsseT/dWWNFoYqhiWlG+68PDg9BXqRXU39ACjSFYnfR5KaegK5JlcUlCV7i4XrIjKxkoCkKqxPIEmu+SKzosq9P5zWUasckQVtGR3H9QMmrVxNBbOyD5KvEiBCxPSF7Jgy8hokoqniAaMlgWFSyI+rRGDtcUVT3YVhBYPiyJHbZXtn3JY0OSvCMZrEBTSxOXUYtItCVDFG2PhKkSO809KfqyVX60ZRjHl9y0tJ7FDacn0hIQEBBwPhKkip0dAuESEDCJ6wN2FUVM3p7k2p4ol3eFURUxLcoyXHS4/+UsVR+a6qMkowaaqWL5Cv0TVZ7cnWWi7FJxKqxpj9IQm572s+mVbXzuS39P3+DQ1H3tLc189o6Pc9m6tXOO07v7Lg5Udf73QBM5V+U9zQe4OGWRVp0595kNE5/FWpnvdG0j52lkVJdyKIaihfl4+SWqBZUM1VpqzQmKltkIqVC2PHblHerDKlk7xEOjGkVhMFAuU28KWuKzp0gVXRiuAlKyJ+/zhCp4W4dCcwiaQ9OdXlQhWJyEy5oU+sqwIKoQkWGGijqrm01i5uHUswm79i+lQ9aBUQuSs2QyLYhI2kOS53XYURD4CHRNJR1SKEiwpEVI1H7NdhYFWwsKIMm7ghsa/JoFMqAp4ElQzPBUCmHc1FjfniJbdUiHjaP2pdg+XOKXe8aJGSo3L2s4oZSyp/ZnOZitfd4f3TUWCJeAgICAgHOeQLgEBEyyUhkmF4eKB5fUM1V4/2JfmR0jFiuaQqw8Ii/qm0+P8vMdeUQiRmpcoS3pckO3YHHa4KWBUVzbpSWu0ZoK8V9vaGNFSwQpJf0lH1MVhEMmA8MjAGiqxofedRsffe87Mc25c/+9u+8C4Me5DBsLIXQh+deBKJ6iEFNNNsSKHK8RlC6gXnMZi9azuX09tmbSlOtjTe9zp1y0AHTEBJWyjW15FKTPM+OQlYK1PWnqEiYrk3BDz8x0opIjUSQkNHj4QIWhvM3OIUFbKMrFjbNfyjRFcHWzymGnAhUwZzyuIQRNoZooajBrt+dCU+DiDHREJHlPMGALbCmIKz7aEakD3uRNIcCVR+4vuLFV4SUnQzyksro5PrUtGdZJzmLJfSS+L/nFrjHGyg4DvqSpL891i+tqz+lLtg0XyFZc2pImXenInAYS0cn+Nr4viZrBT0FAQEDA8SCDVLGzQvBrFfC6xJeSbUNFKo7HBc1xzJHdpAzBm9unP273mMX//uUII0WXprjGn93SQtvkUnyh6qEIgVQVpO/j+mDbDoujKu9YmWRkvIQv4TeubGFFS62Q4qGDNo/22eiK4Paedt77tlvZums3f/Spj9PdOdPi+EgOiRb0EDHVR0HiSMFB2+SuoTiGCjlvhFtTuRM6JxPRespGhLBdYTzWgK2amNXasU6VaAHwpSBmKCDBkRI5aWbg6SrLWqNc01iLTLwy5uD5sKJOY+u4x4/22wjgwkadXMnGELWGotvHnDmFy3wJqYJbO2qRlozBlIHA9HFLJqxa3UxYE7SGoUX6JHWXii9o0D2OLEtZFJPkXYnlw8qEP9VPR7QuoR1on/EM80SAqSt4fq2R55F9VoYKFgezFQSwe9SjPmoSm0OUXN2TwdQErs9paTQZEBAQcF4zh2558aF/58Wff3fWbaXczEbPAcdHIFwCXpc8sXeCf3n6IPmKS53us6FRsLLRZE3T4dV4KSWjRYdc1aM+qpK3PMbLHm2Tc7x3X5ghV/WoCJ+6tEFzTGV9Y221fFFjmC+9s2fqWDt27+Xb378P86oP4fsKOUfyypjLpz7yATRNO6at8pGiRQJrGxVuxiPsVniymsYRkpKv0Gsfjta8IBrYRpqF5LlUzux6/2pS5XHCTgVbM2gsDGBUc9MEy+5xm6cOVKiPqlzfE51X88Cs5bNxxMVUBesbNQxVkDQV3rYkwsN7K7TGVa5rhv1Vn4IDi6I+C2Pw84MWP9ln4Uu4pt1gqAI5y0cCvXmHxUmN7eMOGVNhyVHcxo6HkCpoj8y+TUrJzwckO/KSmAa3tCs0hETNnMD0Zt0npsHV9dPT105FU0lFCN64rIEXenNEDZV17YkjtoFA4EuJUI5edB8xVN6wuH7O7QEnhi8lB7MVLNenPRkmYpyaz2dAQMBrg7U3vJu1N7x71m3/9F/eTHFi7qbOAccmEC4Br0teHigwXnKQEvaUXQYnHJ4bsPlvG1RaYhrDZZ+7t5UZLHr0tEQYmahyeVeUJQ2Hhc2q1gh/f3snAI4PqqhZ6h5JuVLhq//v37jnhz/C933W643ELnozpgodcRVdP3ZtwtC3v45NhBbdRwBPiRbuN7rx2hU2yH7aRif4Vq6JmCFYk7AB6CPKvaKHAjqvUEeDrBXaH4364jDr9z1BVQ+TyfaiHSFaKo7PV5/JsnfCIawJQprCNd1zzPKP4Ad7bLaMu1Rc+Ml+m/VNOjd36FzVEeKqjsP5WG3RQ0tXtfO3N+/h+DUHr11Zj56Uzp587fU3RVTetTjEyyM2E46Co6j0lSRtczh65SebWTaGxQn33Sm4sKtQi3CMWrC/KGkIHd+xjke02K7P9pESAEsbojO61zfHTd60vHHGfo1xk0X1UfKWQ0s8FEyazwL7xsts6s/j+ZLBgsVV3ZnT0u8pICDgLBNU558VAuES8LpkeWOMR3eOUbEcPMcjGVGoOJKyXbsQPXKwys/3VXA8SV1Y56/elKE1oc9YwT40IZltfviLJ57mb/7xawyPjuH5MJS3+ekDj/ChJVfynkuaWJY59tfv+W/fwz3+WhxF40avn5u1fvaIJFU0QtJlO2ne2lRiXXsznqrzikixvPACtlRxUAjh4qJgc0Q6kWvwb4VmKlLlHbEhFhvlqW2Jao5EYWhGapjlSYq2T0wXVCZvHwspJeNVH4VaA0lPwgsjHm1RhfXHSO1aU6+zL+fhSVjXqHFpi0FTREEIuLBBw1QFrUmTjb0+pYLkgf1VMprP6nqNN3Qcbgy6N+/x/b0Olie5uEHjho4T64sSVmspZL3lydvm6RMtAE8fyLF5oADARMXh6oWZee2nCEFPfVBkfzYp2x6uL9FVQcny8CXHXXcWEBBw7hPIlrNDIFwCXpdcs7gOf7yfLaMwmIOS47GuxWRBqvaVyFV9qo5EUwUl26fqi3n3uhgcHuGv7/oajz397NR9lq+QvuAmFq+7jpcPFvnIJfUo4uiTaO/uu3hRLmVChAlJj6f9Bm6mn2VynB0ihSsU1vgjTGgRtJBJ0VN5nkZy4XW8u/oSV8k+nlVbqdNdTBlCVmvxjPtKDTxWSSOAqq/w+frdh5/Uqc5az5IKqbxlaZSf7iyxJKZxWcexe3EIIbimTecn+x0idq2uRQh4dcP0sgsjkzbEiclTcnGzQUdcxfWhLaYghODyFn3GfpYHnueze9wha0oGyx5dCZWeyfdxR9ZjrCqJarB53OPaNm0qxW2oIik40BEF8xgzS10R3NKusK9YG2PnPLXB8QqWiuOxsS/Ppv48rpToimCicnxucSfKaMlm12iZREhjWWM06C5/grSnwgwWLCzPp6c+MiMKGxAQEBBw4gTCJeB1iezfwbUdJtd2mEgpcWVtcnqI6ztNnum3yFV9Fmc0GiPHTrnxPI/v/OB+/ulb91CpVqfuv2zdhdz0lvfxzy865Cse7RmVuHn04x2qaelULTb6Ho5UiHtVHrIbUUIG19NLu5dnAXlG7RibjVb2U0/cs5gQYfaodVzlD1JsXEhOj/GklyQ8YtNijaEcsU40bW56hGhxfUnFkcSMw+lVb1oa58ZFMTQFbB9eHPVASsKapC6kUh+e2TBxXaPOBRmN3XmPl8d9GsOClZnDr93y4MFBGKnWBMFNLZCaLNNpjk66XknJzpyP7UNPXDBmC6q+oDksWRiHHRMQ1UGfpV9jY1ghrHlYHnQnlKmV7/1Fyc/6JRUPFsTg1vZjN2GM64JVs7fVmRXRugTL9XmxL4/l+qxsiR/TtvjRXeM8ezCH5XoYmkJXJsLKI1zHThe26/OTbaMMFSxMTUEVgqWNQeTmREiFda7tqcP1JaGjWFoHBAS8lpEnmCoWxGlOlkC4BLyuGSo4/HhrHl2FW1ekSIVrE42etM7/vCJJX8FjUVojYR69i7nneXzgd/6QLTt2YU46UtWl03zmtz7CjVdfAUAokePAhMWK5jAV18f3Jcosq7He3XeBXqv/uFYOUq9YbHeiPGJl+BlLMTyNi9IebxU7UNw8jV6R9+efhdBqepUEYenQ4JeoqiaWaqJ6DiNqnF4tSYs1xluiI1SkiiUVbotNFgkeIVpGyh5fe7HAWNnjio4Qty05bKmrT878f3bQ4fkRj5GKj/B9WiLwa8vDdMZnTtRCmuCCjMYFs2Q7ZR0YtyCu1W5vG3epli0W1Rm0J2uT/E3jkl8O+bgS2qMqQlNwfWgJqdzaDqVm+OFunYNFj0uaNBYmD49hbb1KRBeUXcnSlDr1OoaqtZ4wMQ2GKlByJZqo9Zg5FfUIhyItzx3M8cvd4/gSenNV3ndhy1GPn6s6ICCkq3SkQrx7TTPhMzD5tTyfsu0R1hSqrqRou8feKWBONFVBCzRLQEBAwCknEC4Brztk/46p2998doxf7ikhBJQcn9+6rGFqW2dCozMx91ekUPX41vNjDBdc1raF6VM76MttJawrvPctN/M/7/gQifjh1fIbl6fYNVLh/3uwj2zZ5YZlKT5+RdO0ieyRogVqLlGrxQR9Uifr6yi6wHIkeV9jTBwujo9Jm/dXNrFLqyMuLRZ6E0hP0FAa4fH4Uqqe4N+UZbSJYVq1InekDh5+Ia9KD9s4aLNz3CGqC37Z59KR8VlTp0yLSPWVfKSUlF2fmAajFZ+9OW9W4XI0ooqkWHbp9wQZE+5+foy+CYu2hM6fvKGBprjGaFVie2CqMGpDYlJg5FzwEfzsoMvLEz7JsEZ9VJ92PoUQLJ3Fdaw9UovslF1YEJX89IBPX1nSkxDc0qHNyzFtNkp1CxkrO6SrLomQRtmu1ThoSq3eQcJR3d0u6UwxUXHwJVzRnT4jogUgZqisaonx8kCR+pjGovpjGy+cLJbrs3WoiASWN0aD6ERAQEBAwDEJhMs5xNDQECtWrJh12x133MEdd9xxhkd0/nGkaAHIV31UpRbxLVSPXXA+dRwp+dn2LD/akkcIePpAiciyG4ge7EVdcDV76pbw/7N33/FxlVfi/z+3TC/qvdmWe69gsDGY3kuAQEgB0tiEQBqEsOnZfEOWhZDm5LcpC+wSUigJgYQAoWOaC7j3Jqt3jabf9vtjbNnGcpPVLJ/360Viz517n2fkkXTPPM85540ai4unHHjeOzujbG9NkuPTeWVzF9fNzifbn/k2/GDQsr8pejcVWgLDduHxuqlUEkwwWw94ThCDmWbjnvnB6nSQtU1JGq0kHitFWvOyRi2g1IqSRkXFQTcSB42V7VXx6Qop1Y3b5+GNZoWo5TAly+Gt+hQeTWFKjk4k7RByqTi2RY5XpSJ0+FWp3ry9O847m7pQdB23YxOJGxQHdZqjJvXdBkUhnXFZKjtjNgnTJksx0FBRURjtd+hOWfx1Y4yulIXPpTExK8CMvCPfAJf6Fa6qhJgJXSmHv+y08WkK6zocpuc6VIWOPXBJ5Ffz2rY2OpMGWR6dRdV5TC8NUdeVImlYnD4654jb0Ubn+fnUqZl+Pq4PJgMNIEVROH1UDrPKwrg1dVDyMt7e1cnK2ggO0BZLc+54Kc0shBh6S5YsYcmSJb0es+199wlSVGxoSOAyjBQVFbF+/fqhnsaI5dRvJmFmet3ubTD44Zk5xJbZuDWFK6YcXRO+2vpGfrTk1zjZoyD7dBTA51bRvT5C8z6GriqkTJt3d0W5eEr2AeeWZ7sJezUiSYsZZQGCe3JdDhe0AFTqCe4ObabddhPQIZQy8JNJ2t4Q99Bq6swMJAhpmR+qa9NBftFZQRsePL40+N34zAQTrDbW6MW86hmD10pxsb2Wius+esBYc0vc2E6Qt1oVDFVHU6ExAZuaE6xrM1EVOLfS4VOTPKQtaEta5HlVSgLH/ol50nAwTAe3Y6K5FKpz3dRFDMYXeBibl0l2GRVUuH6Uwv9uNHmz2cSrG3xorIfpWTqrmi0M08KtKSQNi17SbA4p261k8mkcBZ+mEDEccjwKQVffKoZFIkm6UyYht0YkZdKVMCgOe5lXGSZh2JRleY5wpYzBDFg+aP8VnoRhsa4xiqYoTC0J9vu8OuJGzwpU+yAVIBBCiCM53AfFmqb1BC+ORC5DQgIXcVJo6kry9/ci7EqqlOR6WVysMT5LYUapjwcuLwOOnNtgGAb/98RT/O7Rx0kbaTRtHZd+ehZpTx7nTwwTMRVeXq+zoSGOR1eY2Us3w0Vjw3h0ldaYwfxRIXRNOWLQsle2apKtHph78G63jwfqC4hYGvOCcb5V0YSmQJPlJmK5yFeTxOtrWZQf43SliSolwn97JhPBTZvuZfX8D1PxgXFURWFivhuPF5a1ZXrUjAsrvNLuoKsKpuMQMyDPm7mRLQkc+Ya2NWGzrMnAqyvML3bh2xM4nl7lY31LioZuk0snBDmtwkd9t0lpSMfvVnGcTO6L4yhs7LRIWApdaZu1bRYLi3XKQhrjc3RqIhZl2TqnFh/7j7SygMqlVTr1MZtRIZW8Y+jPsn/VsBy/m1y/i46EQa7fTW7AzZaWGCtrMxXCmqMpLpxQ0Gte03D04uY2Vjd0o5DJvTlrbF6/Xn96aYi2PcHLrNLwEZ8vhBBCSOAiRrykYfFff36bt2pTKJrGqRNyyfX4GL9fEnfEAL/uHJDHsb+Va9bxo5//Nzt21/Y8lp+TxaVjdcZPKOH/NiWpjdqUV+Zx2bRssj0a00oPLhmsKArzR+/Le9lbPexo1BlutqR8VLsTVLgzjSa3JT20mTp5usmWpIduSyVbt5nh7macO0aT6WGhu5UblNqeilrZZpRmlx+9oLin/PD+3moweL4mhaJAUciHpirsjMIZ5R5e3p3CqyksLMushli2Q7fhEHYfvlz0UztSbGy3UBUF04bzKjPn5/o0vnZGHo7j9ASOAZ/O8jabHI9DwlbZEFFwKRD26EQNE59LIXdPEYUCv8Ztc8Ls7japCuvkePuWJzE+S2V81tGvKPRW5tijq5w5Jo/OpEHYq+PVNVKm3VPWOGHY2I6Detgsl+GjLZZGVcCyoT3e/ysiY/L8lIY9ODBouTxCCNF/ZMVlKEjgIkakNQ3drKztoiDgpiDWwBu7EpiOgmWadCfMngaCjuPwQp3Fug6bLLfClaP0A5oLdnZF+Nn//C9PP/9Sz2OqqnL9FZdyy8evw+/zsa3LYnc0c35jwiFc7qUyrB8xEbsnaDmK1ZYmw8U9zeXsNjyU62k+nddIk+Ul6LYocxt0WSpnhGJk7dkqVqAbfCt3Ox22TpGWuQEFwEhyobWO8kWz8WgwO//gG8a3GtLETIc4Ltq6bLI9Cg4aE7Jc3DlHRwFMB57eZbK00cSybGbkKFw33ttTdWxjp83aTodCL8wvUOhOg65myixHjYN/2O8NWhKmw1O7TFoSDh5NIbxn9aPbgXmlHhpjLvy6wpml+4KMwoBGYR+2qfXV4XqzuHWVwuC+LWGj83w0dKdIpC2mloTQh3Ab2LGaW5nFy1vaUVWYMUArIpKQL4Q4ETmHqYa86uXHWf3yE70ei3W1D+CsTg4SuIgRJ5oyeXptE+1xA01V8KQtPF4XqWgar0fnotFeFhZmboi70rCuw8ZxoD5us7XL5pRCDcdxePqFl/jZb/+Xru7unmtPHj+Wy665ESdYTENModoHBT6FAp/Kzi4DXVV4o8nm9WaLsWGF88szPTE+aG/Q0qGFWJsKk6+mmKhHOdSiRb3pptFwU6AZ1BpuftJeSdTRydMMPlNaR46SZqw3dcD5XtWmRE0fdK2862/mzMN8/UqDGrvj4Oyp3hUzwLAdgvq+XifbIzYr2hwiBmiKyup2iwVRm9FZGt2GwyuNDjHDoSYKeR6FcytcPFfj4NOUA7ZzJU2H2phNjkchz6uSsCBhgk9XSFoObXGTpriNS1OYlevi4jINlUwQNJAcx2FVu8P2aKZB5ZQs8FRMOKpqYzvb4+zqSJAXcDOlKMiFE/Kx9lQWG2iZ3KpOEqbNrLIwBUF3n681pTjE6Fw/iiIrIkIIcbRmLL6GGYuv6fXY7+68nFhnyyDPaGSRwEWMOK2xNLs7k8TTFi5Nwa/pVFfl0NISZVxY5eLRHtx7VgZ8OoTdCg0xG6+ukO3OPP6XZ1/ghz/7/0jbmSaNXq+fhVd+lKrpC/nD+41Ek81U5Xq494pKsv06F1bo/LI1RW3SoSZicWq5n80RlbkpKPjAgsreoCWt+/hVdBQbjRAh1eSWwE6muyK9vqZCNUXEUNicDFDqMkijkK8ZtFkuTEVjkj/V63kdjptNdhZFSoLRVusBZY9hX3Lh/vk9l4/xkO8zWdkBKRsiKZuWiMFbdQoV4zxoqoJbzZQnzmz9cgh6HLJ6VrHYc819C+lTcjWaEi7WdTqsabcp9qvYDvxpW5rtEYssl8L14zyU+BVm5qmsabfJ9yg0xm2y3ZC2bNKGjVs9/ojFcRw2dNq0JB2qQyrlwYOv2ZSEt1oy+T0buxz+1eQmr3Y3V04voih06CT7SNLk7ZpO4mmLXR1Jwl6dymwf+iDtDnt3Vycvb2vHth0aupJ8Yl7ZcfWl8bslYBFCCDF8SOAiRhzbgbBXx+WYpFDI86kk0wqd3Uneazd4fqOHS6dmA+DRFK6s0tkascl2K4zLytzkXXLOWfz093+lraGJaacsYPE1NxHOyWXdri6aYxblIZ2OuEV73CTbrxNJObSlHUxFIWU5bGlPM6/US/AD32H7bw/bnPSyOR0grBp02m7qLB+WafNKLJtyV4orwq24lcyt/6akD91MM1oz0CyLcbrDbsvPRE+cqZ5or1+HhKPxa3M8O+wg2U6Sz6hp9t/kVNdt8ocNcRKmw5XjfEwryHw679czKyTzihxeqTN5o97A64ZNHQ5r2nTKgyqjggoXlWusaQOX4nBakY/cPcn6YbfComKFNR1Q5IXxWQobOm3+Uedg2LAz6lDstyn1K9RGbUIuhdakQ03UpjSgs7BYY2aeRlB3eHSryY5IZhvesSTNH86Oboe/15jEDIc1XoUbx7sJuXu/tuP20ZVM4nNsdncmWNPQfdjAxbIdLBt0VcV2HExrcPdAJ8xMHo1LU4gb9hG3KwohhOgjqSo2JCRwESNOZY6P06qy2bS7CZ+ukLAcmlqitLTESJk2S15vYnaFn9KszI16nlch1dlKaXZRzzU8HjeX3HQry3Z2ksydwHttGtO9FpWFATqL/CQSKRZWB6nMydzEjs7W9pTkdfDrCqUBlcsrtZ7qWXBg0LIq5uX++gJ2u8Dn9TLdF6NSibGkvYRG041LccjXDBYHOwHI0U1Cuk2XqVHutrk1txYLjVzNxKv23n+mw3HT5PjIcZJ04KFx0TUHBC6v7E6xud1AUxSe2ZroCVz2ynIrTM1VWd2q0JWyUVWFv+5M49cVrhrtZmaexsy83ldAJmerTM7e9/euNFgOaIqDhUK34ZDtUSkLqGyPWOR5FSqCKs1Jh5cbMys9c3IVrhztYlOHRdCtMDG7f/aHRQ2HpOkQdCnETWiI2ziohPcLXoq8MH98CVta4xiWRVfcwKWphDyH/5GZ7dOZXhJka2ucopCHyuwj5y/1p1llYRoiSeJpmzPH5h6xb4wQQojhJ5lMcs899/CHP/yBmpoacnNzufDCC/n+979PeXn5UV/n1Vdf5ZVXXuHdd9/l3XffpbW1laqqKnbu3Dlwkx9gEriIEUdXFS7P68LI8eJSoSNpsXpDirSV2Q6mqZAwMjf7be0d/PjXD/Hy0rf5w69+TFV5Wc918otH09TQRTqSoi2axq3B52aF+fT4chJpi/yA3rMNJ9+ncc14Ly/uTuPXVS6sdJHf2wqBy0uHqfNuNEBj2kWR04Ft6dyetxuPYmM4Km7FwXQUjP0+K5/pT3BrUQs1aTezA3GKXBZgHXR5C4VGPYuAnaLQSTLJaWM9OVSMKWNC7oElxPy6ggJYjkPA3XtQUJ2l8dHxHpriFi/Vm+BAe9JhW5fFuF460h/KtFyFt1sUGhJQ4lWYk6fiUhWuG+umJmqT68nkCb3R7NCSApcK73XApCw4pah/f0xVh1Wqs1Sa4g7ZHoV/1Fpois35ZRoTslWU0vEowGxgdnkW7fE0axqi+F0qs8oP3+tHURSmlYSZVnJgMvvO9jivbG3Hq6tcMLGAHH8v5dz6QUHQzSfmluHAcQUtXUmTd2o6sWyHueVZx5UrI4QQI9FALbgkk0nOOecc3nzzTQoLC7nkkkvYuXMnDz74IM888wxvvfUW1dXVR3WtL37xi6xatWpgJjpERnTgEo1G+fvf/84LL7zAsmXL2L59O6lUitzcXGbOnMn111/Pxz72MXT9+L4M3d3d3H///TzxxBPs2LEDTdMYP348119/Pbfddhtut/zSH0xO/WYURWHv9vxcn84PLyrhxy8rtETSXD41m1E5Lh5/5p8seej3RGMxAH70i1/zy3u+i6IorG9O8fLmLlKmvW+vjeMwNRs8mkqwlxv9i6o8zMh3oSpQEtCwHYeXtsfZ1mYwY92zjNLdvJfM5s+d+XSZGi7dJGppnOaPUqKnURS4LLuDP8TLKXHbTAzsK0GrKLAo3PuWsJ7XDTwXmMImdxF+2+CSruXcpGyg8bLPkOdTCe2Z8/Img9frDbI9CueO8uIAZ1b0vjLQlbJ5vykT9JX6dbZ324TcCmV78kJsxyFtgfcISRxBl8qtkxS6DMh201N22qcrTNgvAAq6QFPAtCHLNTDbnAIuheurXaQs+MM2k5TpYDoO64xsJpYWH/T8XL+bM6tz+zye4zg8t6GF3Z1JHDLbGC+eXHgcr+DwFEU57q/bytouNrXEUADDsrl8StERzxFCiJOJM0DlkH/4wx/y5ptvcuqpp/LCCy8QCmVaKNx7773cddddfPKTn+TVV189qmudf/75XHvttcybN4+SkhKmT58+IHMeTCM6cJk1axZbt27t+bvb7cbn89HU1MRzzz3Hc889xy9/+UueeeYZCgv7diOxa9cuzjrrrJ5lN7/fTyqVYvny5Sxfvpzf//73vPjii+Tk5PTHSxLHqK7L4NlN3fhcCt+7qIwsr8bmbTv49FfvY+2mzT3PCweDXHT2IiCTp/BmbSa5P9ujErMUCsIuLh/jxaMd+pZQURTKgvtuwtc0pnjk/S666xt5wp5MgZKiPqkSVgxsYFrI5LJgKzMD8UwiuwPrfOUkXblE3bDUa1OZXI2haOiOdcSb0bjiZocrD7dj0aF6qXHlUXXt9Yza7zlRw+G5mjTRtENdDK4Y7eaMskMH1v/cnuClmhQODtMLHK4f68evw6iQSlfK5s9bU7TEbeYW6ZxX4T5sIrhbUyg4wiLN1CxwKZmtYuNCR24K2leqouDTocSv0JRw0L1+ikID9wGDqio9v+JOtO1bsotbCCEGh2EY/PznPwdgyZIlPUELwJ133skjjzzCa6+9xooVK5gzZ84Rr3fvvff2/Lm2tvYwzzxxnDhNBfrAMAymTp3KAw88wMaNG0mlUnR1ddHQ0MBdd92FpmksW7aMq6++uqe60rGwLIvLLruMnTt3UlJSwgsvvEAsFiMej/PHP/6RUCjEe++9x0c/+tEBeHWiN0795gP+/j/LO3hmQzePr4nw55XN/OQ3D/Hx2+88IGi59LyzeeK3v+Dy88/BcuDZeof1UR2CAXLzgkws8TM6qPHKtm4eXduNaR/deyVhZlYjVBzabQ82CglHI2Jp6IrDTF+MBeEYAS1zvRrbz8p4gJgBdTHYbXh4PzyWJ0vO4p+F84lph8+XcNkmSjpNM14CVpLSs8/JfE0ch1UtBi/VpOhMWrjUTLUsTaGn78rhXgM4aHuKDkzJ1Rgd1lAUhXXtFls6LFIWvNNo0pHKvA7LduhIOViH+Dptakvz7NY4W9sN0pbD9ohNazKzdU9XFaZkK1T6oS6eaW45kM6ZM5FLZ1Zx2ZRC5o8amA8XFEXhokmFTCsJcUplNqePHv4fYswuDzO+IMCYPD+nVQ3/+QohxKDb28zlWP47gjfeeIPOzk6qq6sPCkwUReGaazJllp9++ukBeUknghG94vLwww+zaNGigz61LS4u5kc/+hHZ2dncfffdvPHGGyxdupSFCxce0/UffPBB1qxZA8ATTzzBaaedBmQaFF533XXYts0NN9zAs88+y4svvsg555zTPy9MHLVo2kZToXnban722BN4zX3lhsvKyrjwhs9QPWEyzp6O6y1JWNdisrUxSnfaoTDPj6JprGlMUBpQeG5bgnE5LuaVHTnpelaJh0Vdq3hfzcbQFBKWymifwQRXN9Pd3VwY7Djg+SoOuU6cpJPpQj/VamZtcAJx1UWX189OfwlTuncccrzn0sWsjhk43ihjp5ZRHc58LrG61eSR9XHipkNpUGVanos8j0NVWGNOgc6GlhSv7oxTGNC5ZHwAz35NUs6p8tKetElbDpdW+w4YL+jKbMeLGTalAQ2vnglunq6xqI9DqR8uq9QOWKXaHTH57xUR2hI2BX6VSaVBmtIKAR2uGu2iMqjSmnT4yy6brrRDkU/hmlHqAUUOYobDU9uS1Mcszihzc2rx4VdKOlM2r9SbGBacUapT7Fd7mkh6gOmlocOe3x/Ks71cP7t0wMfpL1leFxeMzx/qaQghxEllbz7KrFmzej0+e/bsA553MhrRgcuZZx6uzR586lOf4u677wZg2bJlxxy4PPzwwwAsXry4J2jZ3/XXX883vvENduzYwcMPPyyBywD74GoLwPUzsvi/Za2sX/EU7mQX6Apul5tPfuRqiuZfwpqIzvIWm5gBV47SCblgU22EutZEZmXFcQiXhdBVaE46OKrCf6+N43JpzCw8fIK1R1f5dGAHiewgDWkX76SyeD1dwHY1FwMfZxHBg9nz/BzV5FRvhJ1GilPUVhbQwe/UWURdAVy2iXG4lR4jyVbTi51fjKpAYyTNW3Ua/9qeIGlDJA1Bt8I7uxO8W5Mgz6dwzhk5mLbD71Z0sTuSaZ4Z9qicWx0AoCvtYCsKV00IUB5Ue/JS9pqap5G2PLQnHSbnafh1he3dNjujENBhZzSzajImtO+8trhFJG2T51dpT9ps6zIpDLnoSDnsjtpU7qks1pl2yHZDW8qhI53pt7PXOw1pXq03UFBoT6aYlKMT9hx68fj1BpMVzZlCBgnL4cbF0w7779YXlu3QlTQJezR07dgXsnd3JGiJpRmT5yfbd+j3leM4LNvdxeqGKKVhD+eMyzsg0BRCCDEIHId0Itan8w6npqYGgIqKil6P760otvd5J6MRHbgcide771Nzyzq4QtPhxONx3nzzTQAuuuiiXp+jKAoXXnghv/rVr3jhhRf6PlFxRL0FLQCzSn3MvLycj1Tdzufv/i6nzJrB12/9LBVlJfxtlwk4KAok9/zzB10KowIOO90KMTOTLH5OpYt4foB/bE+StBTqohaPbkoyPlfHryt0p2y2dxoUB3SK9stxsWyHfwamssVbQokVYUznbp4zXBQqKXZbPmosH9PUbgBs4An3RLZ68/BiUmE0E1U8GLEYfleaBBpquvcmkxhJAOafcyo162NYjsLUAjd/2RinK2WTMG2y/TppwyGatMGx6Ugo/H5NN7fPyyJlOng0FcNySO3pO7K+0+GftSab29IENDilUGNeoYamKFTu2SqmKgpzizI32W1Jhx3dDioOuuLQmXLI8ahkuQ4MdsbnuZha4GZ7h8msIje52S52RiHbo1AWyNyAl/kVCn0KHSmHMr9C/gfapmzvtknaGg4OmgmqcvhfBB1Jh860jcvtwfIFD/vcvjAsmydXNbKjPUFx2MOHZ5YcU+PGXR0J/riynkjSoDzbx02nlB+yU3173ODtXV0YtkN73KAqx8eU4v5/TUIIIQ62N60g1tXKr798Qb9fPxrNFOHx+/29Hg8Ggwc872R0Ugcu+1dlmDbt2D6F3bBhA7ad2Zc/derUQz5v77HGxkba2trIy8vrw0zFsVi/eSsBv6+ntLGiKMybOZ3f3f9Dpk2a0LN18NRCjahhYTqwoGjfp9Yfn5nFrqhNawLKS0Js7FK4ptLLX9d1s7sjjc+tkch1Ux+1aIub/HNbgtqIRbZb4dZ5YUZlZ27mGxIO6zxlaMAWvYBKbwsV8QS1tpcKLUGrofN/8RImuGNM98VoVIK4HZMGJcDrlHCDtZECo4sd6RBGIoVppg/MStsTsGg3fB6AmYbDqk6FthR0OgqaksKwHTyawhXVPsbm6tz+zxQdKYW0ZbOyMc3KFpNrp4b4x+YYJSGNBZWZ7WDrOh1a4jYxw0FX4K0Ggzd2xXGpcOV4P4v2q0JWG3N4ZrdNxHBIGJnAJdercEmlelDTSL9L5QvzsuhM2mR7VTqTNs/uSGR6uvgzW76y3ArXVKl0pKHAm0nq319xQCPL42DaChUhlYDr0CsOccOhCT+mEsc2bUbn9f7L4HjUdyXZ3BLD61LZ3hpnR1ucKSVHv/2suTtFV9Ig2+uiLZamK2EeMnBxaSq6qhBPW7g05Yg5SkIIIfqPoig4jkMgK58bvv3IMZ//6Pc/Rqyr9ZDH9wZGhypM05d87JHmpA1cTNPkm9/8JgBjxozh7LPPPqbz6+vre/5cVlZ2yOftf6yhoeGwgYvjOEQikUMePxKPx4PHc+iu3iPV3tWWaCzGLx9+lMef+Sczp07mv//z+wd880+fPPGA84p8CjeMPfhbYHSum4/MLeCVBpu45dCdsvjLliS6baErEEua7GyI8TssmpMOTTETj2Wxqj1FZ3eK/3d+IYVBHd/TD+PLmk+7FiJopyghzpU5FmuUAnJTcR5tLabNdJOnp/m6tpOpejPPKGPosDTeT4UoVIq5pHs1P+yeQKPh4iGlhOxwgjFOe89ctRs+z5qmFO/UJVFdGi0pnVTa4t1Gh1ML3YwxbIoCGgsrvHh0hdvmhrnv7U5cKBSG3axoMvjKnCCLRh14Q1/ozQQZHi3zQzpt2mA7mDa835Q+IHCpjztEjEzH+E4Dyn2ZpH7fIW6qdVUh35+5MX9sfZR361NoKqiOwyXjMtvUAi6FwCF2TJ1apNOWygQlc/NVTBt6u89XSseTiqWxt+ymPNtLyrQJHqaB5JF+YRxKls9FyKPTGksT9uq99mhJmTabm2O4NIXxBQHU/bbdjcnzUxr20hE3GFsQIP8wPVPCXp3zJ+SzqTlGYdDF2Pz+D8T6i+M4JE0bj66ecJXUhBAjUyqVIpU6xO6FY6EouH2BPp13OHuriMVivW9Di8fjwL6Vl5PRSRu43H777axatQpFUViyZAku17E1hOvu7u7586GW9D54bP9zelNfX09W1uEb3B3Od77zHb773e/2+fwTkVO/GcdxePH1t7j/v39Ha3sm4f29Net48fW3OHfR6X267ikFCmvbbGq6LWzLIjeoEXSpGKkUXl0h1p1gk0ejIqzTYDnsbE3h1qAxYvDmrjhXTgmTd/3NXPbnP7LTV0yR1U3Ccvg9EzBdLjx6Nu12lGwtTdzWiNoaExP1/H/xEqIYxBSFRq+PJApxw6FYSdBiuahJaYxx71tl6UxaPPh+hOaoiculkZXtpzlmkUxZ1NdF+dXlJRQE9723z60OYKgqz+9KYdlQ4FWwHeegG8vTCxUKvTotCYVsl8OWtjQv77JQFJiYe+CPjVK/guo47O62sIB2RWFslkroKL6l6rpNXBqkTIem2NFt1yzwqXx8vIuH34/w8Iokz4V0Pjc3m7w9wdDexHuAHL+LUyqzWFkXoTLHx6Si3n/Y72yL89fVjTg4XDGtmDH5R/8LKdvn4sOzStjZnqAk7KE06+DCDc9tbOH9ugiaonDO+LwDKpjlBdzcfEo5nUmTwqAbXT38L7ax+f5hHbAAmJbN3ze0sL0tQUW2h8umFEkujhBiyN1zzz1873vfG+ppHFJlZSVw6NLFex/f+7yT0UkZuNx777386le/AuBb3/oWF1544RDPKKO0tJQNGzb0+fyTcbWlvrGJHy35DW8tX9nzmNfj4TMfvY6zTj/liOc7jkNdzEbb0zRyr7BL4ebxOq/V2+DoLCx1M79Q5yevtVDTZZDSdJq7DToTFuPzXXgMnfpuk5gJnv0qYFWa7VQm4xiOwrfjU1lj6bhUi8qwB1cYahM2i9ytFOhpvtkxkbo0qKqN6lUYp3RRYncxUe1ivRWmqrqcyadPRwvt+7ZNWw6G5eB1KaQtiwIlzbqWOG2RFKZh8fiaCIsnZlEXtanO0hgV1ji/ykvQpfLXTTGW1yexbJuPTwmi7XfD7FIVJmXDpOzMWJNzdSbkulAVhYl5B/7YKA8oWLEY0S4Ly3YoznPzodGhnmpilu3wXotByoQZhTrB/bZ2nTfGz1ObYuR6FU4rP/iG/1CrIA3dFu/WJVEV2NSaZnVTisWj/QcELUnD4tWt7URSJldOLaI633/I1ZSXt7SyqyOOYTk8/n4DXzxrzDHdaJdmeXsNWPba3ZFAVxVSpk1j5OBP+wIencBhVoNONLVdKdY3RvHoKhubY0wrSTChsA+fTgohRD+6++67+cpXvtLn83NycnrSBHDsfprVPjNmzABg5cqVvR7f+/je552MTtjflB/60Id6kuP3V1FRwbJlyw553s9+9jPuuusuAL761a/2OfLevynQ3qW73ux/bP9zeqMoCuFwuE/zOdmYpsn//fw+fvvon0ml0z2PLzxlLl/7/KcpKTq6hqIv7U7zQk0at65w+WhPT7I5QJZH5bLR+25G55T5+M9LSvj6883sijqkbcCBMWEdK6kTN0FTwdxvC6p2w+exHv0l3babqOGQqyXpsD3Yls3oAhfVjkOJHaC+20enpRNSTWK2zhgnwny7Frdq88VPLmZXl0mBXyPHd+CeqAK/xmXjA7y4I87oHBdXTAjw+uZObNMm26dR223wx81JImmHbA9cUOmhOktna4fBunYLXVep2ZpiXpGbKYWHDnx1NZPwfyhZbuiMGdiOQ36Jiyz3vgDhtboUT29PYTmwpdPFTVP2rRacWeVjRpEbl6oQcB8YKOyIWDy1LXOTf/kYD2Oy9gssPSrZXpWaLpOwRyVcVkU8P8T+t8bLarp4ZWsbtpPJQ7n1jFHoh1jM8Ls1EoZNd8rCsDKrLx+eVXLEbWOO47CuMUpdV5IxeX7GFfR+cz6zLMzSHR14vToTD7HqM5IEPRo+t0Zn3CDs1Ql5jr5YgRBCDJThvqV+4cKFZGVlsW3bNt57772DyiI//vjjAFx66aVDMb1h4YQNXNrb22lqajro8f0rhX3QL37xC774xS8C8KUvfYn77ruvz+OXlu7ryVBXV8f06dN7fV5dXV3Pn0tKSvo8nthn3bp1fO/rd7C9ZnfPY4X5edzxb5/irNNPPeochYRh86eNCdpTmX34q0LqAYFLbwJuldyAi5poGtN2MGxoS0HIo5Hrz1Qoc3+gHK52w+fJdxzmPvgc71gKY5R2JmGymQpsRSWOCiqM0yI4ZohRZdl8eVEl+UVzM+cD4/MyQUN32mZ3t01RQCXPq6IoCuePDXD+2H03zN84u5D/XdGJrsH0Uj/P7EiSNkx2aTptSYdCn8rGNpNAwIPHrWLbDksbzcMGLoezptWk0dBQXBo+VUFxHfhjpSmW6QPj1RVqoxaO4xzwb5Tt7f2m9tXaNLVRG0WBV2rTjMna10cm5FH5/LxsVqdzQVF4c1cnr+/s5JzxeUwvzQT/hmXjOKCrYFgOtu3AIbZhXTipkN2dSba0xCgJe9nZHidl2ngPkSS/V01HkmfWNdOdMllT381Np5T3mqNy+ugcxhUEcGlqrzkwI01+wM0VUwvZ1Z6g7AirUUIIITJcLhe33XYbP/jBD7j11lt5/vnne/JZ7rvvPlavXs3ChQuZO3duzzm/+MUv+MUvfsFVV13FPffcM1RTHzQnbODyyiuvHNPzf/7zn3P77bcD8MUvfpEHHnjguMafNGkSqqpi2zZr1649ZEnktWvXApmml1JRrH+kUqmeoEVVVT582cV87saP4Pf5jnDmgbZ2mERTFoqikDBsPEcoqwuQ5dX4xIwwsRVd1MUcSsIuQj6dyyb6eGlHnMKAxhmjDp6HoijcdPMFXNCdyRN5akea5rokigpqbgEvlI3nyhKVgOIwKsd10MoKZBov/m5dgt3dNgU+hU9N9VPgU9nVZbC7y2RcrouioM4Zo4NML/GhKbC6KU19ayeRpI2mq0zMd9GWdLAAt67uubFXcJQjfyKeMm3+uDlJfczmjDI3C0pctCQc/rYjze4YeHweCn0KcevA4GB2kZstnRYpy2FhqfuwgWVn0mZ3t0l5KNMXxiGzqhH4QFllpXQ8laVQCTz2fgNt8Uwfmnd2dfYELnMqsmiIpOiIG5w1Lg/3YbZ+ZflcXD61mCdXN5AwbMYVBI5qq1jcsEiaNiGPTsq0SRi95+koikJhaPh+yjcQRuf6GZ07vHNxhBCib5wj9mQ55HlH8I1vfIMXXniBt956i7Fjx7JgwQJ27drFihUryMvL48EHHzzg+a2trWzatImGhoaDrvXb3/6W3/72twCk9+xOaWhoYP78+T3P+eUvf9nT2PJEcMIGLsdi/6Dl9ttv5yc/+clxX9Pv93PaaaexdOlS/vnPf3LnnXce9BzHcXjuuecAOPfcc497TJExqzjIZeefzbYdNdx9+y1MHFvd6/PSlkNnyibPqx6QvwFgOw7vNybpjqdRNZXykM7iin2flDuOQ0O3ha5CYTDzbZIwbJpiFpMK3HzrzDz+sDFBwnQYFVR4YmOMMTk6H5oSwneIG15VUSgL67xRn+b5TREa2lMYDswe5UCWj5SjsqBUpz1h05WyyfpAU8WmuEV9zCbXq9AUt6mNWqQMm5+900VLzKIiS+fO07PJ9mpk7VnFSJs2OW6FPK9OW9KmM2lTnqUzp0DnX7UmtqpS6FM4s+LIqwCPb03y9I4Ulu2wtctidFjDAQzbIc+byd/ojJkUuy22d7gYk5O55vgcnS/PDmDYmX4th9KVslmyMkJtt0VpUOMTU4ME92w5W1CSudb+OSx75fldqApYDhTst9qR5XPx0bmHrvj3QeMLA3zy1AqiaYvKHN9RrdyNyfMztTjIro4k00sDsrIghBDiuHi9Xl5++WXuueceHn30UZ555hlycnK48cYb+Y//+I9DNqfsTW1tLe+8884Bj6XT6QMeO55qtkNhxAcuH1xp6Y+gZa8bb7yRpUuX8vLLL/POO+9w6qmnHnD8scceY/v27QB84hOf6LdxTya7du3iiSee4Etf+hKqqvaUPr7z3z6Nx+NGVXsPErpSNg+ui9MYsxmfozEmoNCWsJhV4mFMjpv3m9K8WGsS8Lux0yZXj/dRtl8ZrOe3xnh8TTeaCjfOzmZqsYdfrexmd8SkKkvnc7NC3DE3SGPM5Janm+lI2LyuK2R5Na6aePgcBq8G7d0GKIBt0xE1mVSsMC5L5dWaJM9siaNrCh+dEqQkpPP7TQm2dFpUhjJ9UVoSDsUBlYqgxra2NK1xi4KASnPMojlmHbD1qjrfg+bWaew2mVXm49MzAxT7dfJ9KldV2zQnHcJuldzDBBR7dSQdbAc0VSFhZnJ8qkIqC0tdrGoxCasW29sstrRZ/N/aKN9YkN1TISu4X/5K2nL4544kOyMmU/JcLK70oCoKtd0mdVGLAp9KfdSiK2Vz8ajMKkVvActeC6tzCftcWLbDtGPon9KbwpCHo8uOyvDoKlfPKMa0HXRVOeZSykIIIU5QA9hTxefz8f3vf5/vf//7R3zud7/73UNWlD3csRPViA5clixZ0hO0fOlLXzrm7WEPPfQQN998MwAvv/wyZ5111gHHb775Zn7605+ybt06rr76ah5++GHOOeccbNvmiSee4DOf+QwAF1xwAeedd97xv6CTSDqd5sEHH+Shhx7CMAxGjx7NVVdd1XPc5zv8J9tbOky2dVpkeRTeqk/zdGcS07R5s8bF987OZ02HjaHpOIqDqqooe/qV7L3xfGV7nLhhY9jw5q44Aa/G9k6DXK/Gtg6TnV0mUwrcNEctutMOuqaQMh3qI+YB81jXnGJXp8HkQk9PY8pZBS7OGu3n7V0xcrwurp8S4OxqL2G3wv+8nyRmOqSTNu/WJwn43bzTaJCyoHPPjfzlY1wU+1VyvCpqXmZlo67bZEKei4rwgd/Su6M2xQU+qktUFAXyvRr5vkwQ4XepjDqGdIsLqtxs6jTpTDmcXuKiMpTpz3FuhZtzK9z8fWucHW1pdE0haWb6unxw8cl2HH67NsbzO1JYjsPbDQY13RY+t0pHMrPK1Jq0KQ1qVIwbjxI4dEGAvdyaytyKvpcRP16KIo0ghRDi5CPNIIfCiA5cbrvttp4//+EPf+APf/jDIZ973XXX8dOf/vSYrq/rOk8//TRnnXUWNTU1nHvuufj9fmzbJpnMdDWfMWMGjz76aN9ewEnq3Xff5Uc/+hE1NTU9jz3xxBNcccrko/5EO9+vEnYrtCdt4oZNc8xEVxR2dJlE03amKpVHpTFmAgov1poUBUzSaYv6iElhUGdHh4GuwphcF8VBjXyfRlPMoiSoUbSndHJ1rouJuTqb2w2KAxrnV+/Lb9ncluYXb3fQnrCoyHLxjTPzyPFpaKrCt87MZUNzAJ9LpTrX1fO6KsMadd2ZruilQY01nSYJA2wccBTCboVJ+/VRyfVpfPW0bJpjFh0Ji5d2JpiU7+7ZppXrVQm6VKKGQ1lQI+Tuey+NcTku7jsjjGlzUM4JwGllHja2pdnRZXFKmRd3L2kzXSmHbZ0WiuKQMsFI2rxYa+B3qQTdCkUBFx9dVE1Fjp/skyCJXQghxMiy5rW/sub1p3o9Fo90DPJsRp4RHbg4+y3j9VaBbH9dXV19GmP06NGsWbOG++67jyeffJIdO3bgcrmYPHky119/PbfffvuwLr03nLS3t/PAAw/w7LPP9jymaRof//jH+dTFZx7TNhyPrpITcGFrNl4NOoMeDAdy9gQdcwoUXq5Jkkxb5PtUImmbt3cn+euaTtriFqNzXHxyThYhj8a8ci+6qnDrnBA7u0xGZek9nd/DHo0fnlvA1rY05Vk6ZeF9N9stUZPOpE2eX6M9YdGesHqS7nVVYVrxwatG100OMjYnjVuDgoDG87UxvFpmW9a8Ip2zyg5egQi6VRqjJg+v7qYjYVMc1Pj6ghzy/BoTcjRumOijOW4zKVfvNeA4Fh5N4VCVbYNuFY/XjSdt8X67zaQO66BmlSG3wthsjZZ4Jlk/16fi1hQsBxS3D3fIy9TSsGy5EkIIMXxlKsf0emjaGVcw7Ywrej320LeuI9bVOoATG/lOmsClL2666SZuuummIz4vHA4f9V5EcTDbtvnrX//Kz3/+c7q7u3senzFjBv/+7/9OdXV1T27L0VrdbtGaAtOGNBqhoAfHgbywRtyESMpGdRxyvQpdKZsCn8r6hjg1nQaaqrC5NU2WV+XUin0rKCVBnZLgwd8yeX6NPL8Pw3LY1mWS5VbJ96lMKfIwudBNbZfJ7BIvxUGdpTUJAOaVeXFrCqbt0J3ObJFSFYWAS2VRZSag2dZpoikKJX4FB4cLqjwH5Irsry1hE0nZ5PpUIimbjqRFnl9DURSm5LmYcoSCdrbj0J60CbszgURfRA2HloRNyK0QSTk0JWwmfuA5uqrwickBTi/1sCtisa7dwhfOIuTRcXA4e3y+BC1CCCFOALJVbCiM6MBFDH+GYfBv//ZvrFq1quexcDjM7bffzuWXX35AQv6xSBk2uzrTpE0Ht+LgxiE36CLLo2A7DgoO7XGTuAE5HpWuzji72tKYNmgKBD0qq5rTtKccTi/3srYphQLMK/cdkM9gWA4J06YxbvPU9jQ1UYtcj8qVYzzs6DSYX+lncp6L4pDOY+siPL0ljuLArs4AV0wM8ttVUWoiJpPyXNw4LXjAtUdnaZxT6WZ1i8nkPJ1x2Yf+dp1c4GZqoZuaLpPZJR6qso9+m5VlOzy6Ic7qljRFfo1PTQ+S4z32LWXZHoUpuTpr2kxKAioTsntfmgm4FGYUuJg5YwoXWzaaqqBKsCKEEOJEMoDJ+eLQJHARQ8rlcjFmzJiewOXiiy/mS1/6Erm5uQB9CloAinwKfg18ikNTt0kilsJIanx8YR6dSZt/bI7R0pnCBOy4A5bN2DwXpu1Qle0m6NN4vSaJqqR4YXM39Z0mKHD5pCA3zMwGYEVTit+ujtGZdHDrKl2Wgq5Ac9zm/hUGMcNBx+GyUSZFfo2/b0vSZmZWVlY2pZlQYLCuJU3Yo/J+U5ozKzO9WPZSFYWLRvu4aPS+17Wr06AzYTGxwI0DvFOX6Sx/apmHL52aTSRlk+1VDxsIpCwHBXpWVhpjFu83pdHUzCrP+laDBeXHvr1RVRSuHuvhjDIXIbdC0HVw8PPB6mAure85N0IIIYQ4uUjgIgbdBzun33bbbezcuZPPfvazzJs3r1/GGJ+tMyVXY3WzhWFaaLpKR8rBsmz+Z02CNc0GMUfFMS1cmsOYsE5bwmZhlZ+7zszn/rc6iXYamLZDTaeJhgMObG3NNHBKWQ4PrUuwrcMkbTloqkLQ58JSFRQUYqaDioOJwr92JrHSJs2GiqMouHXwe3VyfSohj0p70qYooB1xlWN1Y5Jfvt1Bd9rm1HIfBWEXL+3KBC4NUYvrpwTJ7aVx5f5WNqZ4YmMcXYOPTgkyPtdF2KOS5VGoi9qE3Qp5vr4HE5qqUBI4eA6HK2cshBBCnHBkxWVISOAiBk1zczP33Xcfp5xyCtdcc03P4+FwmN/85jcHPb+vqy0AOV6Vz80I8OJ2+NmyJKbt4HaprGs16Eo7hDwqrYlMN/nsgMa/nZaNbTuMznETcKssrPSyrcPA51I5tyrI6ztiACwaEwDAtDNdcw3LwbQcbNsB0yJp2fg0yAu56Uip+BSHbBXa0+BTHVIoeBSH8pDG6GwXn5wepCZiMiHX1ZPwfyibWtO0xDK5K+uaU1Q5Cnv32NZHe+/Y/kF/3RxnWyTz3Bd3Jhif6yLkVvn0jCDrW01KgioTcvvnx4IEK0IIIUYuCVyGggQuYsDZts2f/vQnfvWrXxGPx3n33XdZvHgxeXlHyBg/Tj5d4eJxAZY3GWxpN8j2qozN0XFpKm/V25lSwZrK6GydUdmunsR3x3FY3WricqtomsKofC/njQ2gKFCyp0llwKVy7QQfOzoMTA2y3CqFPmiI2HgVhWwsPjYjQFVYoz1m8ciqCO6UTafhoDiwoTlFc8zPlAI3UwrcmLbDs1vjNMYsTi3zMDn/4Oph4/Pc5Ac0ommbeeU+Fozy8diGOGnboSJLpzOZ2SZ2KJbt0JyCqJUJvHZH7Z7Vr9KgTmkvhQf6QgIWIYQQQgwECVzEgFq/fj0//OEP2bhxY89jbrebmpqawwYux7Pasj9VUfjSvCzea0oR9qjMKnKzALik2sfuLoPmuM2UfNcB1boMG5riFn6XSsxwaEtYnFl5cOniReVetPnw7LYEYY9KkU/hX3ELcPDrsKg80xF+dJaLifluVjSkeGhVBF1VSJmZ/jKQWWV5tz7FXzfHSVsOm9sNvrEg+6AKYjNKvPz7Wfl0JCwmFbjx6Cr5fp2H1sV5rc5ge8TmczMChD29By+G7ZDnU6mPmhiGTU3EZHljmnklfS/XvaHNoClmMXnyeIrCh28KKoQQQowYslVsSEjgMow0NTUxefLkXo/deuut3HrrrYM8o76LxWL88pe/5LHHHsO27Z7HP/ShD/GFL3yBcDh8yHP7K2jZK9ursrjKd8BjOV6FHG/vN+xuTeHsSi//2pkkz6cwu3jf8yzb6WlgqSgKC8q9nFrqQVUgmnZwHNjRYXDBWN8BCfIBt8qsEjd/WKewrcNkdLZ+wOpIynSwHAevrpC2HJKmg67aeD/Qer4q23VAxbCudKaMcb5PpSFm0RCzew1c2hIWD63upiliYBg2Od5MIYFdXWafA5dN7QYPboHupE1Fxy4+v2g0r2xqobY9wYJxecyuyunTdYUQQoihsmTJEpYsWdLrsX33Mw592yomwc7xksBlGCkqKmL9+vVDPY3j4jgOL730Evfddx8tLS09j1dXV/ONb3yD6dOnD9nc0pbD2lYDFegybKJpmFPkoriXZPLzRvs4pdSDR1Pw6pkApKHbYMmbbTREDE6p8PPpU3LRVAVdzRz3u6AzYbE7YvDYOot8v874vH1bvtoTNgoKE/NcdKcd1rcYnF6RGXteqYfNHQa1EYtZRW7+v5XdRFI2F1Z7OXuU/5CvqTKsURrUaIxZVIY1yoK9r7asbEyzodXAqytkexRyfBoht8qEvGPvTr93K1hrvI3uZC1ZPhftMYPXt7Tx9PuNWI7NjtY4YwqCZPsPf/1I0iCWsigKew4I9BzH4YWNLSyv6WJsfoArZxTj1qUCmRBCiIF1uA+KNU074MNYMfgkcBH96vHHH+c///M/e/7u8Xi45ZZbuOGGG9D1I7/d+nu1ZX9/3ZLg9doU3Wkbm0yvlo3tBrdM9+PSVDx7ygN3pWxerklhOg5nVXjw6hqb2g3+a2k7K3dGKQvpvL4jxnnjgozJ27da0ZGw2NKeJuRWaY5ZbG03Dghcwh6VLK9KfbdJlkcjd7/qXUG3yi2zMqtQf90UY2uHgV9XeG57kgXlPjx67+WNc70qn58ZoCFmUR7UDtmgMuRWcGsKMcNhbLaeqUDmVXttqNmb3vJWJpeEGJ3vp6U7xfTyMCFvpomkrqpYtoN1hB/uNe0Jfr+ijljKZG5FFldML+6pNlfXmeRfG1sxLZvGSJJxhQFmVWQd1VyFEEKIASeLJ0NCAhfRry688EJ++9vf0tbWxsKFC/na175GaWnpUE+L1rjF+80pFBxSlkPahoKAyo5Ok/9Y2oVXU7hhSpAJeS7+uSPJq7VpwKEj6fCZ6QGWNaSJmqCqCvXdJuVhL1leDcdxaIlb+HSV9rhJd8KkNuYQ9GTyY/aX49X47Oww61vSlIX0Q652hD2ZICphOlRmqRxpoSHLo5J1iLyWveaVeDDszNdhTrGHyqy+Byx75QbcfHHxGLoSJvlBNynDYmNjNw0dCSaVhVlV3834QpvybF+v529sjtIUSRH2aqyq7+a8iQUEPJl56VpmJSuWdvBoCm7p9yKEEGI4cXr/cG7NG8+w5s1nej0W7+4cwAmdHCRwEcelo6ODnJx9uQyhUIhvfOMbmKbJ4sWLD+jXciQDtdqyoc3g/9bG2NJhEknaZHsUqnNd+FwK6bRNVwrabJvXdyeZkOciYWbOUxWFuJkJPgr9KvlBF3ZxgAIP3HZKNgGPxlMbozy/LY7fpRKNp2nqtkg6CkFF4dltcRqjJjOKPJxR6UVRFEZnu6gI6yyvS/L6rgRzy7z4P9CocVGlF0WB7pTDqaUeNPX4u8prqsIZFUeXPH8sVcG8Lg2vK7PdbfnuLpoTJrZb493dXaR3dFIU9nDbmaPJ6WXLWFHIQ9CjEUvbTCjy9VwHoDjs5epZJayui1CV62NSSfCo53SyS5s2O9vj5PhcFIT6XnhBCCHEsZu28FKmLby012MPff8TxLraBnlGI4sELqJPIpEIP//5z3n++ed57LHHKCws7Dm2aNGiIZzZwda3pmmMWcTTNrYDLhwurvIwv9zHI2ujvFOfQlMgf8/WrXOqPLQnbUzH4aLRmZv9s6u8hNwKHSkfU/N0/ro5zq5VMWo60mR7VXZHTNIpG7cOipFZQa7ttkgZKTa0GhQENCbtKXH8zy1R/rwmiuk4nNni45Z5Byax66pyUDGB/tAYzURkxb1sDzveEsa247B0RztJ06ItliaRsqnK9RJJGHQmjF4Dl+mlIbx6OZGkycTi4EEB2pzKbOZUZh/XvE42lu3wf+/WsrYhQtirc9P8SkbnHTpHSgghhDiRSOAijonjODz77LM88MADdHR0AHDfffdx7733Ht91BzC3pSKsE3Qp1Dvg18GlgkMm6f7DkwJUhHV0FRaUZ4KUipDGl+dkGk3uXTHSFGiJ2yytS/H8dpuOuE3YDTHDQcEm5FGYXRFgY1OCQkclO+Ciodsiy6f2VAnbq6bTJG05eHSFrW1Gv75Wx3GIph18rn1FAwBe3RnnsfVRFOD6qSEWVGYCo/7quaIqCqVhL22xbsJeF/kBFdN2mFoapjy795UeRVGYUCQrKf2pK2GwpSWK363R0p1mW0tMAhchhBgIUg55SEjgIo5aTU0N99xzD8uWLet5zO/3M3v27J5GhsPRvGI3YbfKO3VJ3qpNYDuQNDLNF8MelQvGHLy68cHXEkk7LK1LkbagNW5nyiIbCpMKPZxZ6aEkqDOn1INCDoqiYDsOS5Z38ebuFBPzXUzO37fiML/Cx/rmNIbtsHh0/91UOo7DYxtivFWbpDig8ZnZYXJ9me1Xb9QkiKVtHODNdi8L5/d/k8irZ5YwtiCAz6UxuThINGWR43f1y1Y3cXTCXp2qHD8bmrrJCbioyu3/lTshhBBiqEjgIo4onU7z0EMP8eCDD2IY+1YIzj77bO64444DtokNR4qSKUHs0WBFQ4q4YfPMtgRjcl1MyDu4Q31vfLpCjjeTzJ/rU5lT5CboVplW6GZ87oHboBzH4fVdSd5vNHDrCq1Jh3fqU7QnHfy6wqJKL987J5+t7WnqoxbL6pPMLfEcd+DXHLd4oyaJjcPGNoP3G9OcPTpz41o9uoyt0SYUBaoLA4e8RmNXksauJNWFAULeYyuVHPToLBiT2/P3/XNW9kqbNo8vr2VrU4yF4/NYPLFg2Aa8JyJdU7lxfgVbW2Lk+l2U50jgIoQQA0JWXIaEBC7isJYtW8Y999xDTU1Nz2MlJSXcddddLFy4sF/GGMhtYvszbbCcTINJ23Ewj7IUu+M4PL0+Qk1TnNyAi8smBplfdmDfkf29XpPkf96PsLPTJMun4tEVntmaoC1hoypgOQ6nlXn4y6Y49d0mQbeKV1eYVnh8idRBl0rYo7A7YhFwqeSUV6GUZgNwdaHN6PwAiqIwuyq71/N3t8f52YvbaI2mGVcY5Mvnj8XXS/BxPFbu6uC5NZkAqrEryaSSECWHqDom+sbv1phedugGr0IIIfqBBC5DQgIXcUjpdJrvfOc7NDc3A5nGSx/96Ef5zGc+g8934t1sVufoXDjGx3uNaaYWuo66+eL2doOn1kcwbYfWqIE63n/IoAWgpsvAtB3y/ZmSyKeUuNneZePWFFKWQ1fKJpp26E5nutzH0g6dyUwUtbPTYE1TitKQzuxjXIUJuFU+OzvMqlQuRWEPsyr39T1x6yrzq/MOe/6O1jhNXSkKQx5q2uM0R1JU9XN+hKooma10diaIk9UWIYQQQhwtCVzEIbndbu644w6+9rWvMX36dP793/+dsWPHDvW0+kxVFC4bH+Cy8YfeKtUbj67g0hS6UzZ+V2YF5Z3aBC9sjVMS0rhmSohtHQYKMK3Iw4wiDysbUiRNWDzKy7VTQrxTl+KPG6LgOOR7FEqCKqeXe1len2JKoc6MIg+RlM2vlndRFzEJuVU+Py+LqYVuVjenaYrZTC1wURrq/Vt2b5J9VSlU9fHrU10QoDTbS0s0zfiiIEXh/i+lO6sqmytmlbCtOcZpY/Mozjq6Es1CCCHE8CIrLkNBAhfRY9u2bQSDQYqKinoeW7x4MT/96U857bTTUNUTtwlg2nL409oIG1vTLKjwcdG4zLYpx3FY1ZiiPW4xq9RLju/grVHlWS4+OTeHZbUJxue7qc5z8+0X22hLWGxshd0Rk41tBroC104JcsXEEHcvzCVu2lSEM99iUwpcaKtNWjpM/rTapDCgccPUIJdPCODXFTRVoTaS6TOT5VHpTtu0J23WtRr8blWUaMqmKlvnjvlZBPb0femvimB7leX4+OoF42iOpBiV7+81R+V4uTSVq+aU9ft1hRBCiEHj0LetYhLrHDcJXATJZJLf/OY3PPLIIyxYsIAf//jHPccURWHBggVDOLv+8X5Dkn9ti6Op8LdNUaYWeqjMdvH27iS/Wd5JLG0ztcjD3WfmHVBGGDJBz7QSLwtGZVZqYmkbRcn0LlGAFfVJ2pI2mpKp3nXFxBAFAQ3Yd+PfmbDoSNjk+lU6kzZNUYvpxQoh976xSkMa8yu8rKhPMqnAzYwiN+81pYmmM8FMe8KmO3sUwQFcpSgIeaRpoRBCCCGGJQlcTnJLly7lP//zP6mvrwfgtddeY+nSpSMiWNmfrmVWNdJWZruXS8sEDPXdmVWObJ9KXcQknrYJe/cFHPXdJr96p4P2pM25Y/xcNTlEwK3yiZlhXtoepyCg8Y+tMSJpB8N2CLkPXJUyLIeaLoMsj8op5V6W1SUZn+dmRvHBwYGqKHxieogrJgQIulV0VWFagZuxOS5abC9zK7MHZPuWEEIIIY5V78sna978B2veerbXY/Fo5wDO5+QggctJqqWlhfvuu48XX3yx5zGXy8VNN93EvHnzhnBmx68rafHazgS6CmeO9uN3qcws9nDN5CBb2w3mlXkp2ZMrMrPEwxu7dCJJm/kVPkKeAwOPd2sTbG43CLoU/rU9zrnVAUIelVklXmaVeHEch7hh88quJD4dLhi7L3/Gsh3+v2WdrKxP4tIUrp0S5NIJQXL9Gk1Ri5d2xBmX66Iia1+RAEVRyN4TOCml4ykA7hhl0Z0wyQu6+y2ZvSth0J00KcnySp8VIYQQ4pg4h9wqNu20i5h22kW9Hnvoh58mFmkfyImNeBK4nGRs2+axxx5jyZIlxOPxnsfnzJnD3XffzahRo4Zucv3k96sivLYzgaJAR9LmhulhVEXh4vEHd2mvznXzvbPzWdecojlusbIhdUA1r1yfhk9XiKYdSsOZssX7UxSFm2dlc2p5iqBbZex+PV1aYharG1NEUzY7Ow0auk0+PjPMgkofP3mrg+a4SWlI5+tn5JHv3xesfJBH1/CE+i/fZGdbnN+9sYvupMkpo7P5yLxyqe4lhBBCiGFPApeTyKZNm/h//+//sX79+p7HsrOz+fKXv8zFF188Ym5em6IWqgq2Dc1R86DjnUmLzoRNeZaOrirYwF82xaiLmIQ9Kl84JZspe3qqLKjMlH1uT1icWu7r2WK2P4+uMKvk4LyTXJ9GeVhnc2saRVHwuRTea0gxJsdNa8KiwK/RGrNp9pVRUDp4fTfW1Ueo70qS5dN5b3cXl04vJnyMzSaFEEKIk5r0cRkSJ26ZqBGoqamJyZMn9/rfkiVLjvv6jY2NBwQtV1xxBU888QSXXHLJiAlaAC6eECDPq9Bev40zK90HHNvRkeb7L7fy/Zdb+Z8VnTiOQ1fSzuS5eFWiaZv2xL7OlJqqsGiUnysnhXq2lx0tt65w+2k5XDMlyNhcFwGXyqwSD+PyXEyoLCCu+pg0upDRBf3bK+VISrK8hLw60aRJaZYXv1s+vwBIpVJ897vfJZVKDfVUxAlC3jPiWMl7ZvhbsmTJIe/FbHv/ztVOH/4Tx0txHAkZh1p5eTl1dXWUlZVRW1s7oGN99atfZffu3dx9993MmjVrQMc6Fk795n69XlN7N6UzT6Nj3duEQ0Ecx8G04R+bozzyfoRcnwqKwj3nF5DlUfnfVRFWNqQoC+l8bl4WWd7+25rlOA5b2g3M3EomloTQVIVE2qK5O0Vx2INnAMoOH2k+GxqjdMYNppaFZLVlj0gkQlZWFl1dXYTD0nleHJm8Z8SxkvfMiU3TNGzbJhDO5aa7f33M5z90z2eJRdoH5X5vpJKPWkeohoYGnn32WW6++eYDVlO+853v4PP5cLlG9s2qz5V5ze/WpXG5o7y2LcruzjTjCr1keRW6UjZTCj2E3CqaqnDTzDBXTbJ7qnn1F6V0PAow4QOtS3xurd+70h/1nBSFySWhIRlbCCGEGBHkc/8hIYHLCGOaJo8++ii//vWvSSaTVFVVcc455/QcH66f8Cil4/t91SU881J+vTJKJGkRT5qUhV2srU/wqfn5OChML/b05KzsX83rePV3Y0ghhBBCCCGBy4iyZs0afvjDH7Jly5aexx5++GHOPvvsEyKHpb+DF0/BGHQVbEfBsKE7ZVGZ7WZmifeAXi39QYIVIYQQQoiBJYHLCNDd3c0vfvELnnzySfamLCmKwrXXXsvnP//5EyJo2as/g5f4zuW41IsYnetmSkGQkEfh1MpAvwUtEqwIIYQQJynHPvJzRL+TwOUE5jgOzz//PPfffz/t7fsaGk2YMIFvfOMbTJ48eQhn13f9FbxEN77K1xbcS244eMwVwQ5FghUhhBBCSJGwoSGBywkqlUrx1a9+lbfffrvnMZ/Px+c+9zmuu+46NG1wK1X1t/4KXirCOuHjCFqON1BxHIe2aJqQVx/06mFCCCGEECOJBC4nKI/HQyAQ6Pn7WWedxZ133klRUdEQzqp/DUTC/tGM2V9s2+HXL23l7S1tlOb4+MolE8kPefrt+kIIIYQYKr0vuax5+znWvPN8r8fi0c4BnM/JQRpQnsC++tWvUl1dzY9//GPuu+++Ywpa+qOh5bHqy5hK6fgB3Z619/r9Mc4HX199Z4Klm1txcFhf18WKHe2HOLN/xhtoJ8p75kQy0v8NR/p4Q2Gkf01H+nhDYaR/TYfs39Bxev1v2qnnc8Pt9/X6nz+YPTRzHUEkcDkBdHZ28t3vfpfnnz8wgi8sLOSPf/wjixYtOuZrnmg3of0RXHwwSOnvgOiDry/L5yI34KYjZhD0uigM9+9qy8nwy2Gk31SM9H/DkT7eUBjpX9ORPt5QGOlf05Ph31DsI1vFhjHHcXj66af5yU9+QiQS4c033+S0004jFNrXPPBEqhjWX44m4FAikcz/l4xFGaLeNSGfi69cMpGVO9opy/UzsypnSOYhhBBCiP4T7+7gof+6tU/nieMjgcswtX37du655x7ee++9nsfS6TRbtmxh9uzZQzgzcSyq8gNU5QeO/EQhhBBCnBAcxyEW6d/t3+LoSOAyzKRSKX73u9/xv//7v5im2fP4BRdcwFe+8hXy8vKGcHZCCCGEECcnTdOwbRtVVSkpKenzdYqLi/txVicXCVyGkVQqxYc//GHq6up6HisvL+frX/868+fPH8KZCSGEEEKc3AoLC6mrq6OkpITa2tqhns5JSQKXYaSrq6snaNF1nZtuuombb74Zj0dK6AohhBBCiJOb4jiO9P4cYh9MsFcUZcCT7vcudQ6mwR5TxjuxxxuKMWU8GW+4jynjndjjDcWYMl7/jrWX3D4PDQlchoGTsTKYEEIIIcSJSm6fh4ZsFRtGjjfZ61g0NTUdU8PKE21Mx3Gor6+ntLR00ALDwf6ajvTxBntMec/IeMdK3jMy3rGS98yJPV5DQ8MBqy5i8MmKyzBQXl5OXV0dZWVlg5bsNXnyZNavXz8oYw3FmJFIhKysLLq6uggPUh+Xwf6ajvTxBntMec/IeMdK3jMy3rGS98yJPd5Q3K+JAw3uxk4hhBBCCCGE6AMJXIQQQgghhBDDngQuQgghhBBCiGFPAhchhBBCCCHEsCeBixBCCCGEEGLYk8DlJHXrrbeeFGMOpsF+fSN9vKEaczCN9H/DkT7eUBjpX9ORPt5QGOlf05Ph31DsI+WQhwEpr9f/hqLkpDixyXtGHCt5z4hjJe+ZE5vcrw09WXERQgghhBBCDHsSuAghhBBCCCGGPQlchBBCCCGEEMOeBC5CCCGEEEKIYU8CFyGEEEIIIcSwJ4GLEEL0QTxlsrWxm0TaHOqpCCGEECcFfagnIIQQJ5quuMF/Pr2enS0xqouC3HXZZIJe+XEqhBBCDCT5TTuMNDU1MXny5F6P3XrrrdJk6Rh4PB6+853v4PF4hnoq4gRxLO+ZzY0RNtV3kx/2sLG+m21N3cyoyhmEWYrhRH7OiGMl75nhb8mSJSxZsqTXY01NTYM8G/FB0oByGJCGRkKcWOo7EvzHX9bS0JGkPM/Ptz80hcKwd6inJYQQYgDJ/drQkxUXIYQ4RqU5Pr5++WS2NUUZXxKSoEUIIYQYBBK4CCFEH1TlB6jKDwz1NIQQQoiThlQVE0IIIYQQQgx7ErgIIYQQQgghhj0JXIQQQgghhBDDngQuQgghhBBCiGFPAhchhBBCCCHEsCeBixBCCCGEEGLYk8BFCCGEEEIIMexJ4CKEEEIIIYQY9iRwEUIIIYQQQgx7ErgIIYQQQgghhj0JXIQQQgghhBDDngQuQgghhBBCiGFPAhchhBBCCCHEsCeBixBCCCGEEGLYk8BFCCGEEEIIMexJ4CKEEEIIIYQY9iRwEUIIIYQQQgx7ErgIIYQQQgghhj0JXIQQQgghhBDDngQuQgghhBBCiGFPAhchhBBCCCHEsCeBixBCCCGEEGLY04d6AmKfpqYmJk+e3OuxW2+9lVtvvXWQZySEEEIIcfJYsmQJS5Ys6fVYU1PTIM9GfJDiOI4z1JM42ZWXl1NXV0dZWRm1tbVDPR0hhBBCCPEBcr829GSrmBBCCCGEEGLYk8BFCCGEEEIIMexJ4CKEEEIIIYQY9iRwEUIIIYQQQgx7ErgIIYQQQgghhj0JXIQQQgghhBDDngQuQgghhBBCiGFPAhchhBBCCCHEsCeBixBCCCGEEGLY04d6AkIIIUR/SqZNfv2PtWzc3c7Fp4zm0lNHD/WUhBBC9ANZcRFCCDGivL6mjqfe3MbWuk4e/Oc66lqjQz0lIYQQ/UACFyFED8uyqW3uIpEyhnoqQvSZpqmoqoJp2aiqgqYpQz0lIYQQ/UC2igkhADBNi//6/Ru8vXY35YVZfPtTiynKDQ71tIQ4ZmdMLaX2nElsruvgvNmVFOcEhnpKQggh+oEELkIIAGqaunhzdQ1ul8a67U0s31DHJQsmDPW0hDhmLl3jE+dNGuppCCGE6GeyVUwIAUBelp/CnACtXXFyQj7KCsJDPSUhhBBCiB6y4iKEACAr6OXbnz6b5RvqqCrOZub4kqGekhBCCCFEDwlchBA9qoqzqSrOHuppCCGEEEIcRLaKCSGEEEIIIYY9CVyEEEIIIYQQw54ELkIIIYQQQohhTwIXIYQQQgghxLAngYsQQgghhBBi2JPARQghhBBCCDHsSeAihBBCCCGEGPYkcBFCCHHCao2mWFnTSVs0PdRTEUIIMcCkAeUw0tTUxOTJk3s9duutt3LrrbcO8oyEEGL4ao+l+cXLO6jvSlKW7eW2xWPIDbiHelpCiBPYkiVLWLJkSa/HmpqaBnk24oMkcBlGioqKWL9+/VBPQwghTgi1HQkaIkkKgm4au5LUdyYlcBFCHJfDfVBcXl5OXV3dIM9I7E+2igkxiBzHob4jQTRpDvVUhDjhVeX5qczx0RE3qMz1U5HrG+opCSGEGECy4iLEIHEch4de38lrG1vI8rv48oXjqcoPDPW0xFFq7ozzu+fW0xVLc8Pi8UwfnT/UUzrhbaxp44XlOyjMCXDlgnF43Mf2KynL5+K2s8dQ35mkLNtHyCu/0oQQYiSTn/JCDJKuuMHSza04DuxqibN8R0efA5dIPMV7W5opzPEzqTKvn2cqevPXN7fz7LJdqIpCe3eS/759MYqiDPW0TliptMl9f3qHTTXteFwaPrfO5QvGHfN1wl4X4WLXAMxQCCHEcCOBixCDJODVKcnysr4+QsirU5rt7dN1TMvmB79/h+Wbm8gOeLj7I6cwb0JxP89WfNDeIMXBQZWA5bilTZvueAq/RydpmMQSJ1dVsJ11bfzyT6+TTBl8+urTmT6+bKinJIQQw54ELkIMEpem8sULx7NiRwd5QTdzRuf06Trt3Um21nWSE/TS0hlna12nBC79yHEcXly+nXU7WpgzoYSFM6oAuGrBGDqiKbpiKa4/c7ysthynkN/NJ86fxh9fWk9JXpCz54wa1PFt2+HtTU1EYmlOm1RM1iAn9f/puZW8vXoHqqLwP395i5/cdc2gji+OXmd3gr+8tBrDMLn8rGkU54eHekpCnLQkcBFiEOWHPFww/fiCjPywjznji3hjbR3lBSFmVBf00+wEwNrtzfz88XdojyR47f2dlBWEGV2aQ37Yx9eunT3U0xsyjuPw5Mtree29ncyeWMrHLpyFph1ffZdLThvLRadWoygMeiD4/Mrd/Oxvq0mkLRZMLuY/Pn7KoM7B685sb7MdB69HtroNZ4/8fTmPPf9eprhKS4Tvfu6ioZ6SECctCVyEOMGoqsKd187l8tOqyc/yUZIrCf79KZpIk0ga5IS8JFIG3fHUUE9pWNhc08rDz6wkmTbZtKuFiVWFnDq14rivq6pDs3K1s7mb7oRBdsDN1vou0qaNx6UN2vg3XDIXRcnk+nz4gpM3ID4RdHUnANA1jbau+BDPRoiTmwQuQpyA3C6NaVLVakDMHl/C+aeOZcWmehZOr2Ty6MKhntKwsHdVxHEcFFSGw065WCJNXUuE8sIwfu+xbfU6fVIxr6+tJ5o0OG9W+aAGLQAFOUFu/+hZgzqm6Jurz51BbVMnacPk45fOHerpCHFSUxzHcYZ6Eie7vQ2NysrKqK2tHerpCCEAy7KPeyvUSOI4Dn97bQNLV+1k5vhSrj9/Oqo6dF+fzu4E3/71i2ytbWdiVT7f++w5hPyeY7pGayRJLGFQWRiUnCVxWI7j4DjOkL7nxdCT+7WhJysuQgjRCwlaDqQoClecOZkrzpw81FMBYP3OFtbtaKYoJ8CabU1s3NnCvMnlx3SN/LCX/HDv1f06okliSZOyvIAENQJFUeR9IMQwIIGLEEKIE05FYRbFeUFqmyJUFmdRXpjVb9des6OV//zTcqJJg6sWVHPjecMjWBNCiJOdBC5CCCFOOBVFWfzglnPZuKuVyaMKKMkP9du1l66rZ1dzhCy/h2ff3cn1Z00Y9BwYIYQQB5PARQgxoti2g+046LLVa8QbXZrL6NLcfr9uRUGIoM9NLGUwfUw+bl3eS0IIMRxI4CKEGDG21nXwwGPvEk2k+cwlM1k4/fjL9YqTz0XzRhH2u+mMpVg4tXTAchscx5G8CSGEOAYSuAghRoy/Ld3Cmu0tuDSVh55bI4GLOKSa5girtzYxtiyHiVUHlhZXVYUzppUN6PhL39/OQ399m6yQjy9+9CwqinMGdDzR//YWZZXgU4jBI4GLEGLEyA54UBVImxa5od6rRQnRFknwnd+9yvaGTopy/PzwM4sZW97/W84OxbZtfvfkW+yqb8MwbZ5+dS2fv+6MQRtfHL8NO1v4xZPLsCyHz101lxlji4Z6SkKcFCRwOU5tbW387W9/48UXX2TlypXs2rUL0zQpKChg7ty53HjjjVx11VVDPU0hTgofPnsSHrdGLGlwyWljh3o64iiYpkXKsAj4jq2B5PFoaIvS2B6lODdAU0eMmubIoAYuiqIQ9HtImzaaquD3ugZtbNE//vTiOtZtbwYFHnluNTPGnjfUUxLipCCBy3EqLi7GNM2ev3u9XlwuF3V1ddTV1fHUU09x0UUX8fjjj+P3+4dwpkKMfEGfm4+eN3Wop3FYacMiEk+TF/aO6C0mhmnTHElSGPbiOkRye0NrhB899Ao76tuYOa6UWz98OkV5/Vcd7FDGluUwa1wxq7c1M2VUAdOrC/vluh2ROP/z13do6Yzy4fNmMXtS731lFEXhyx9bzFOvrCYc8HL1uTP7Zfy9ItEkybRBQc7IbqzZ0hnnXyt2EvC6uGDeaDzuwbulCQU82A7gQDhwbI1PhRB9J4HLcTJNk9NOO42bb76Z8847j1GjRgGwc+dOfvCDH/C73/2OZ599ls9+9rM88sgjQztZIcSQamiP8f/+sIyGthjnzKrgc5dOG5E3lrGkyb1/WcP62k4mlmVx14emEexlVeG193bw7roaGpo7Wb62hl317fz4jsvJywoM6Py8bp3v3HQGtS3dFOcG+m2155nX1vG3V9eiKArNbVF+/a0Po+u9l1EeXZ7Hlz62uF/G3d+aLXX814MvEE+kue7COVx7wZx+H2O4+Mnj7/LGmlp0TSWaSHPDuVMGbewbL5pB0OfGth2uOnPioI0rxMlOajwep5dffpk333yTz3zmMz1BC8CoUaP47W9/yy233ALA73//e2pqaoZolkIMD8m0yfbGCPGUeeQnj0BvbWhk7Y42TMvmhZU1NLTHh3pKA2JDbScrtrfhdWms3N7GuprOXp+XG/bjOA6xRJpQwENdcyc76toHZY4et051WU6/blHbk6vN3lDU6bcrH71Xlm1hR20bsYTBX19ajWXZQzCLgec4Drubu/G6NAzTpqk9Nqjj52f5ueWKOXzuqrkU5wYHdWwhTmYSuByns84667DHP/WpT/X8ecWKFQM8GyGGr1jS4DuPruBLv3mLb/zfMjpjqaGe0qCob4+zZmc7hmlTmO0j6HPREU2RF/aRFRi8vI7BVJzjIy/kobEzQV7IQ0mOr9fnnT23mi9ev5Cp1cUU5gQYP6qA6vK8QZ5t/7l00RQuXTSFU6ZVcev1C3EdYrVlIJUVZuPzukmk0owqy0VVR96KHmS2211/9mSyQ16qS3M4f97ooZ6SEGIQyFaxAeb17qtsZFnWEM5EiKG1qa6LVTvayQ26WbOrnXU1HSyYVDzU0xpQq3e2cc+f36crnmbR1BK+9qHp3HntHOrbosyfVExghCZll+cFuPvq6Wyo7WRCaRaVBb1/Iq1pKh+5cBbnnzaeHXXtVJfnkRMemlzAtGnhOOBx9T3YyM3yc+eNZ/fjrI7d5WdNIxzw0h1PcubccSNyK+JeF8+v5ozpFbh0Fe8g5rcIIYaOfKcPsFdffbXnz9OmTRvCmQgxtEpy/RRm+9jdEqUk10953sDmMQyGtGHy30++zfL1dZwxazSfvHwuqrpvIfv97W3UtcUoyPKxfEsLXXGDM6cPbH+QoRRLGjz8/DrqWqNcfno1V51adVTn5WUFBjyv5XDe39HGkn9swLIdPnfhROaNKxiyuRwvXdc497STJ+ci5B+Zq5ZCiN5J4DKAurq6uOeeewA444wzmDBhwmGf7zgOkUikz+N5PB48Hqlu4jgOtV0pIkmTqhwvQY+8zYeDkhw/37thDutq2plQlk1V4cBXjxpoKzbW8fRrG3C5NP7y8lrmT6tkavW+VaQJZdnkh710xlLMn1hE2OeiJZLCrStkjcAbrueW7+Tx17bg4LCrqZupX84/5KpSR3eCJ15aSzJlcuVZkykvzBrk2e7zl7d3sbM5iqLA42/uOKbAxXEcdjR0oesqlYXhAZylEOJ4pVIpUqm+b1Pe23RUDB25oxsgtm3zsY99jPr6ejweDz//+c+PeE59fT1ZWX3/5f2d73yH7373u30+f6TY1ZHkuU2tJEyb0rCHD00tRNcknWs4GF0UYnTRiR+w7OXzuHC7daLxFLlhPz7PgTfpp04o5Acfn0drJMms6nyeX9PI4+/W4tJUbjmnmlmjRla3dNtxcMjkH9iOg20f+pf8o8+t4s//WoPjQF1rN/d8/vzBm+gHFIS9OI6D40BBVu/5OIfyxGubeOT5dWiawueumM25c0YNzCSHmaVrdvPWulomVORxyWnjTphcGsdxWLOlgbRpMnN82SGrvomR6Z577uF73/veUE9DHIdhF7homjYickFuu+02nnnmGQB++ctfMmPGjCOeU1payoYNG/o8pqy2ZLTFDeKGRcCt0Rk3iBs2YQlcxACYPraYz109nzVbGzllSkWvieWTK/cFJ8+tbiSWMkkaFm9sahlxgcv5c6qoaeqmpqWba84Yd9htPNF4GodMnkt3fGgLNXxi8VgKsrxYtsNFs3vvvXIo/1qxk4RhYsQtXlu9+6QIXGpbIvzksXdoao8T9O+iKDfIKZNKh3paR+XpV9fxmyfexLBsrj5nOp+5+vShnlIPw7R4+tV1NLVFOPuU8UwY1T/9hcQ+d999N1/5ylf6fP6kSZOor6/vxxmJYzXsApeRsAz31a9+lV/+8pcA/OxnP+OTn/zkUZ2nKArhsGw1OF5VOV7yA26iKZOx+X6CHvlETfQv07J58KWtvL2llXlj8/jqx848ZJPF/Y0uDLC7LY5bVynPPbZP9k8EYb+Hr1xzdH1Drl48hbqWCMm0yU2XzB7gmR1e2O/muoVj+nTu9DGFbKvvxO3SmFx19BXRaps6efHtjYSDXi4+Y+qgNk88XomUSSptEfK7SaZN4sn0UE/pqL23cTexZBqv28U7a3b1e+DiOA5vrtrB9t2tzJlcyeTqoy9A8q+3N/Orx94gkTJZtm43S+6+Bt8AFfCIxJJ4XPoJ9b7rD8e7pX4kF7s4UQy7d+yJ/qb42te+xo9//GMAHnjgAW677bYhntHJJz/g5urpRcTTFjk+F+oJ/p4Sw8/62i6efa8eXVN47v16Thmbz+wxR75p/dRZ1UwqzcLrUjltfP4gzHT4GluRx0+/cglwYv/c/9TF05k6Oh9NU5l/lKsOjuNw/0P/YsX6Gly6hmnZXHv+0AZvx6K6NIcPnTmRfy3fwfTqQk6dfOIUnJg3pYqVG2oxTJuFs/sWrB7O6s31/NeD/6KtK07l0g088LUPUZh7dNtjI7EkhmET8rvpjidJpo0BCVwe/9dq/vzCKrJDPr5+02LGnMAlyMXJZ9gFLieyO++8k/vuuw+AH//4x3zpS18a2gmdxPwuDf9xlDUV4nB8bg2vW6U9miYn4MZ/lAUggl6d86eP7BLQx2L/gCWWMtlY20Vxto+yvKEpidwXHrfOohmVx3SOado0t3fj87qIJdK0dw1u88TjpaoKN100gxsvnH7CBZ0XLZzEmPI80obF1LHH/73Y0hHlvx9bSltnlI9cPJfuWJJINEl+doDO7gQdkfhRBy6L541j+foaapu6uObcGQNSGjyZMnj8xdVEEyka2rp5ZcV2CVzECaVfAxdVVY/6h9hIyGPZ3x133MH9998PwP3338+Xv/zlIZ6REGKgjC0Occt5E1i9q52plTlMKD30Fs8tu9tYsbmRMaXZnDLpxPlk+mg5joNp2cfVbNEwbe7961re295OXsjN16+axoSyoasyNtBcLo0bLpnH/z39DmPK8jlv/qShnlKfnGhBC2TmPHF0Ub9d7+lX1vD8mxvQdZWO7gT333Elc6dUsr22lcXzxlFdfvQrq4W5Qe790uXH/f10OG6XRlFeiIbWbnxuF/nZJ35ZenFy6dfA5de//nXPD7JIJML3v/99Fi1axKJFi3Ach9dff53XXnuNb3/72/057JDbP2j58Y9/LEGLOGaW7aAqJ+aNwMlIURTOnFLEmVMOfwPU2hXn+w+9xq6mLvLDPr73qcVMqx45CbfN7VH+6+EXez4hvvrcIxch6U1LJMnG2i5yAi4aOhJsqOsa0YELwCWLprJ43njcLk0qW/UTx3HYWdeOaVmMrSwYlJ+nLl1DURQsy8GlaWQH/fzw9svojqfIDvmOeQ6KogxY0AKZD5jvunExLy/fSk7Yz/mnjhuwsYQYCP0auHz605/u+fONN97InXfeyb//+7/3PHbHHXfwox/9iBUrVvTnsEPqrrvu6glaHnjgAdkeJo7Zu9vaefStGvxujc+ePYZR+fIJ2EjR1hWnPZKgIMtPe3eSpo4o0xg5gcvLy7awbO1ufF6dPz63krNPGden7S0FYS8TyrJ4b0cbxTk+Jo7woGUvv2/k9fIZSs+/uZH/77E3sCybj106jw9fMPB5Q5edNY2OSIL2SIyrz52JtqeC5UBs8+ovpQVhPnrRiZNTJcT+BizH5amnnmL58uUHPX7NNdf0NGU80dXU1HDvvfcCmU8xfvSjH/GjH/3okM+/4447uOOOOwZreuIE4DgOf35nNw2dSQzL5oU1TXxmcf8njIqhMaY0h4XTK3l3Qx0zxxYxZ0LJUE+pX4UDXlwule54mpL8LLzuviUSu3SVr105hQ17clwqJHgXffD6yq10RRO4dY2Xl20ZlMAlO+Tj9o+eOeDjCCEyBixw0TSNVatWMXbs2AMeX7VqFbo+MmoC2LZ9wJ+bmpoO+/xoNDrQUxInGEVRyPK72N4SQ1UUwr6BKX0phoZL17jrowto6YyRE/LhHmEFI86dP55EyqCpvZuz5407rgpIQa+LeWNP7kpr4vhMG1fK8vW7sW2HmRNGXj6ZEGIAA5cbbriBz372szQ0NLBgwQIAli5dyve+9z1uuOGGgRp2UI0aNWpE9J0RQ+vTZ43mxXXNADTF0nz7bxs4b1IBZ4wbXjdxXQmDWNqiKORBO0G6ZA8HqqpQlBsc6mkMCJeu8aFzpg/1NIQA4NrzZ1FVmodp2Zw6reqAY47jSA6hECPAgAUuP/7xj3G73dx5552k02kcx8Hj8fC5z33usNuphDjZlGT7+NiCKl7c0MIry2pxaQp/W9XIjPKsYbMCU9OR4G/rmkkYNlOLg1w4MV9uAsSg29XQwaPPvoemq3zsotmUFhy5Ya9pWjz75iZaOmKcNWeMlH4dwVRVZf70UQc9/q+3N/LIM8soyQ/zpY+fTVHe0ZUnFkIMPwMWuLhcLu6//35+8IMfsHXrVhzHYezYsfj9wzdhTYw8J1K1Lo+uoqkKKdMmN6Cia0fuxD5YdrQnaI8bBD06m1tinDU2F18/bHsyLZvVjVHiKZOJRUHyA5KsLA7tt399h1dXbAcUbNvm6zedfcRz/vXuVn7+p6UkUwZvr9nFz7925QHdwqOJNOt2tlKaH6KiQG5oR5pEMs3//OVtWtq72V7byuTqEj5+2SlDPS0hRB8NeLJJIpEgHo8zc+ZMPB7PQA8nRI/1jVFe396O361x0aSCYX9TfMroHCJJg+buNKeNycHvHj75EEVBNwG3RsKwGJfvx6UqNHclyA16jivAWtcU5Y3tHZi2w+6uFB+eUSzb0MQhGaaNoig4TqaJ49HoiMRJpU2Cfg8d3QkSKaMncEmlTb738FLe39pMQbaf7920kHHlOQP5EvpFTWMn//vMchwHPnHpHKpKhv+ch4quawR8bmrTJrquDUgneiHE4BmwwCWRSHDLLbfwyCOPoCgKW7ZsYcyYMdxyyy1UVFTwzW9+c6CGFgLbcXhjRwedCZPWmMGqugjnjB9eOSMf5NZVLp42PLuqTygM4HNrRFMWFVke/uvJ1Szf2sLYkjD/fu0ssvoYFCYMG9NxcGsqCcPCsh0JXIaYbTs8+dpG3tvaxOlTyrl4fvWwWbG8+bK52LaDpqlHXc71zDnVvLO2hoa2bq4+exrZIV/Pscb2GJt2t5MX9lHXGmXdztYTInB5+JnlvPDOFtjT/PN7/3b+UE9p2HLpGl+98RyefX0d+TlBLjljylBPSQhxHAYscPnOd77D+++/z4svvshll13W8/iFF17ID37wAwlcxIBSAL9boyWaRlXol21NJzNFUajKydzwravp4I31jfg8Gsu3trJyeyuLp5X26boTCwPUdiWJpizmVoRx68Nne9zJ6r0tjfzPP1aRMkzWbm9mbFkOEyqHR17IhFGF3PvFS47pnNKCMD/+ymWkDAuf58BP24tzA0yoyOW9rc2U5gWZXDU8XueROLYDDqCA7RzdytPJbOLoIiaOPnyz2N7Ut3Sx9P0dFOWGOGP2mGETwAtxMhuwwOXxxx/nwQcf5Mwzzzzgm33y5Mls27ZtoIYVAsjcaF80MZ/V9d14XRqzy4+cxHsya+xM8Nq6RnKCHhZPLUbXVGJJg7c2NhHwujh1fCHqnpWQ/LCXnKCH3a1R8sNeirJ9R7j6oWX7XFw9rQjbQVZahgnLdrDtTBdwy3aw7OF9Y7xifQ3ba1uZM6mSMRW9r6qqqorPc3BQ7HHrfPsTC1i3s5WygqHPcWnuiPI/f1tONJHmhgtmMnl0781KP3HpHAzLwrbh5svmDfIsTw6ptMkPfvM867c1EfC5sR2Hs+aOPfKJQogBNWCBS0NDA6NGjTrocdu2MQxjoIYVokdewM3icSfGJ6hDybYd7v/bWlbv7MCta6RNm0vmlPOTp9bw0up63LrK5y+ZwmWnZMqLFmX7+OZ1s3hvextjS8JMrji+rTWKoqBJzHJcLMvm5TX1tEdTnDG5hJLcvhdBmT2+mGsXT2LFpkbOmF7OpKrhu8Vy7ZZ6/t9/P0tbZ4xRZXn8+GvXkJd9bM0rQ3438yf3bcWwvz3+rzX8483NKEBnd4Jf3HlFr88bVZrLf3zuwsGd3EmmO56iqa2bcNBDVzRJQ2tkqKd0EMdxeO7dbbz2/i6mVxdx7eLJaMOoqIsQA2HAApfq6mpWrFhBVdWBtdT/+c9/Mnny5IEaVoij0hVP8+7WNrIDbuaOyT2ptwCkTZumziQBj0530qCtO4ltO6yv6cDr0uhOGGytP/CX9sTybCaWZw/YnGzbQTlBqsENBy+8X8dPnlpDIm2ydH0T939qfp+LJuiayqcumcmnjm1HVq9s22FLXQc+j4vKwv5fzWho6aIjEqcgN0RrR5TWjugxBy79JRpPEUsaFOYE+vy+dfb8j6KCfZwtwmzbwXEcuZHto7wsP+fPn8Bzb21kQlUhp88Y3XNsR20r67bWM3F0MWOrel8VGww7Gzr576dW0BVN8da6OsoLwyycXjlk8xFiMAxY4PKFL3yBL37xi+h6Zoi1a9fy5JNP8r3vfY9f/vKXAzWsEEdk2w4//ccmVmxvw+fW+bfzxnHWlGPf/zxSeN0aV8yr4C/v1lCa6+eMSUWoqsL5syt4Yul2SnL8nD5p8L4+7dE0XQkLXVMoDrtx7Zf3YtsOj729i6WbWphZlcPHF4054PjJqqkzQTxlEPa5aWiPkUxbBH1D/3X533+t54nXtuBxa3zpQ7NZOLV/u5nPnlzJ9PFl7Gpo5/RZYxhTPjSrQ5t2tfCjh16mK5rkqsVT+PjFc/p0nWvOmUZXNEkknuLjF83q83y21rbzwGPvEk2k+cyls1g4vaLP1zpZKYrCZ685nSvPnk4o4MHvzRQgaWjp4ts//xs1De2UFmbzn1/9EJUluUMyR8t2MEyb3S0RYgmDXz/9PrPGFRPwDe8KmkIcjwELXP7t3/6N1tZWbrjhBuLxOFdeeSU+n49vfetbfPzjHx+oYYU4oqRhsbMlSsCj0xk32N0WG+opDbmrTxvFudNL8bo1PHsKGXzi7HGcMaUYv0enOGdw+i+ZtkNnwsK2HQzLpjtlkbtfYLKlsZu/vLsby3KobYsztTKbU8YOr61MLR0xnn1zIy5d49KFkwgFjr4MfNowefjp5WzY2cx5p47nogUTD3pOIm2RNm2y/PsSzc+YUszr6xrojKa49JQqgsOgcanjOPxrZQ2249DSmeDtDfX9HrjkZQe496sfoq0rRlFuqNfVBcdxWLpqJzUNHcybUsG4yoJ+nQPA6+/tYOvuNrKCHp55fSPXnDP9oEIAR6MoN8i/37z4uOfz9JtbWLO9GZeu8tA/V7NgWrmsXvaBoigHNausa+6kqTVCaWEWzW0Raps6hixwqS7LYeH0StbtbKG8MExdW5S1O1s5ddLw2PooxEAYkMDFsiy2bdvGl7/8Ze644w7WrVuHbdtMmTJFGlCKIef36CyaVMgLqxupyPMzt1ryYICDShorisKY4sEtaqAqoKlgWpmtYvqehH3DtIBMk05dVYkl0/jcOm59+FWLW/LYm7y0bCuqqtDeFaeivIDuWJpz546iKOfw25iWvr+TP7+wCsdx2FXfzrRxJZQXZvUc39LYzZIXthJPmVwxp4xLZmVuUMYUh/n5LQuIpUzyQsOjX5aiKMyqLuDZZTsJ+lxMKB+YmzuPW6e0IOuQx1dsqOXeh16iszvBc2/m8pM7ryQn7GdbbRt/eH41XrfOxy+eRVFusM9zKMkPEfS7icbTTBxV2BP872XbNn94fhXvrqtlwYwqrj1n2oAGElkBD6qqkDZtckPefhvr9ZXb+f2zKyjICfKF6xaeEB3oI/EUW+u6qCwMkZ/V90Iie00cXcz0ieWs3VLPqPJ8JlQN3Wq9oih85LypvL+thfq2KHlhH8VH+BkjxIluwFZcpk6dyrp16xg3bhxz5vRt2VyIgfKJRWM4Z2oxAa+Oqqr8fV0TKrCgOpewNCgbMqqiUBz2EE2Z6KpCyKuxfEMdv3jsbVRV4QvXzudT51SzbGsbk8uzmFGVPdRTPkhzexSXrmFaFkvX1dP8zi4M0+b9bU3ce8viI99EKuxJdjjY0k2t7GiJEfDo/P39Bi6YXtyTy+Lz6Pg8A95T+JjcesVM5k0oxu91MWfcwOcCtEcSNHdEGVOai3tP8NDcHqU7liIvy09HJEFHJEFO2M+vnnyXd9fXAuB2adx+3el9Hvei0yfg97ppj8RZNGsMqnrgys/7Wxr5/T9XYVk22+ramTy6iKnVR3/D6zgOW+u7cOsaVUVHDhY+fPZkPG6dWDLNxfP7pxJW2jD59ZNv0dASYf32JsZW5HPjMK9oFk2k+fZDb7J+VzsVhUF+cPNCSnKP78Y+6PfwuevP5Js/+xuNrd385oml3HnzeUOWS1RVlMW3PrGAtTtbmVyVR1XxoYN4IUaCAfktp2kao0aNoru7eyAuL8RxU1WFivzML7BHltWyvKYTBYimLa6dJcvsQ8njUvG49q3+/PnFNexq6AAFHn9pLT/83PksnjI8G3UC3HDhLP77ybfxuDSKivPYvaYOv8dFQ1t0T/NEhW21bSx57E3ShsVnrzqV6eNKAFgwcxTXnTeDjTubOW/+hANWWwAKsrx4XRpJw6I0OzzsS0h73TqLppcPyli7Gjv57u9epqk9ximTy/jmTWeiayqnTqtk5oRSaho6WDSvuqfLvGFaKCg4TiZPYH+27fSU/z4aqqqyeG71IY8re/7HdhwU4FCxa9oweehvy4kl09x46VxyszI7FP702lYefWULuqby+Uumcu6sw39Ngz43Hz1v6lHP/2ioioLX7cIwbTRVwe0aXkFyb3Y0Rti4u4OCLB/b6rvYsKvtuAMXgDVb6mloiZCXHeCtVdupbe6kaoi2iwFMGV3AlNH9vwVSiOFowH7y3HXXXXzzm9/kj3/8I+Gw9NAQw1csZaIqCg4OsbQ11NMRH5CfHchUWHIgP2v4bzVdOHMU8yaXo6oKOxq6aO7KJFt/5JwpPZ/K/un5VSxfX4uqKDz49HIe+EqmSa/bpfPpq+Yf8trnTyvC79aIJk1OH58neQv7Wbmpge31HRTmBFi5qZ66lghVxdnkZQW490uX0RVNkpfl7/maffbKeTz09/fwujU+fO40ILOy8YdXtvD3ZbuYUJbNl6+aQch//InOM8YVc+PFs1m2oZbTplYesj/L3T//B48+vwbbcXhlxQ7+ev8nUBSFF9+vwzBtuhNp3ljXcMTA5VjtqG3ln0vXkx0OcOXiafi8B79mXdf4ysfP5G+vrCMv289li4Z/ddCKghCjisNs2t1BRUGI6rLs/rlucQ45YT+tnTEmjSkif4gq2e3PMC0SKZPwMeTUCXEiGrDA5aGHHmLVqlWUlZUxfvx4AoEDv7Ffe+21gRpaiGNyzoQComkLVYGzxkq+y3DzmSvmUpIXREHh0oUTjuncpo4Y//PsGuJJg4+eO5mJA9wBPm2YvLhsG4mUwdlzqxlfkcsvbj8Pw7IJ7lfpx+d1gaJgOw6BY9ia6NJUFk8+8par7bVt/PrJt7Bsh09feSoTRg1dydbBUl2WQ0GWn5bOOFPHFFK4315/l64ddHM5ZUwR/3Xbgb1QaltjPPb6NlKmxStr6pg3oZCL5h5Y0r8vVFXluvOmc9150w/7vDVbGzFtB12FrbVtJFIGfq+bmWPy2dncjdelM3XU8fVN+iDLsvmvh15kzZZ6XLqGpip8+ILZvT530ugiJvWhA/1QyQ56+MFNC9hQ08ao4qwDGoy+9O5m/vz8+5Tkh/nCR84gL+vog485kyv51ucupraxg7lTqwj4BidYME2L59/cQFtnjEXzxvWs8uxu7uKeh1+jpSPGFYsm8bELZwzKfIQYCgMWuIwdO5axY6XL7LFoamo6ZI+bW2+9lVtvvXWQZ3RyGFsQ4MuLx+zZwiGfYA83OSEfH71gJm+sb+SVtU2cPrmIwqNMsv3Tyxt59p3tqIpCVyzFz247d0Dn+sRLa/nNX5fR0hnj//7xHj+74zIqirL54G3Nxy6ajUvXSBsm1557+JvZvnjkHyt4c/UuFEDTVH50Wz80ZRnmpo8t5j8+ew61zRFmTSg5bFWvlbs6+ceaRvICbq4/tZycPasqPo+G163RFkng97pwayppwxy0bVGXLZrE5t3tWLbD2fPG9JTg/cyFk5gxJg+XrjKvn3OFLNumK5rA53GRSBlE46l+vf5Qy8/ycca0A1eoYok0v/3L2zS3R9m4o5nxowr5yIW9B2uHMntSBbMnDW6Z6Rfe2shPH3mZRMrgrVU7+OnXr8Xl0nht5U7Wbm8m5HPz1GsbuHTBBLJD3kGd20iyZMkSlixZ0uuxpqamQZ6N+KAB+2n84IMPDtSlR6yioiLWr18/1NM4KXXG0qyp6aQ428eEUtnaONy8uKqOn/xtDfGUxavrGvivm089ugaLx9nE71jVNnfR1B6lpSNKe1ec+x95jQe+ctlBAXFBToAvfLjvyeBH4tqTmO444DkBchH6y6RRBUwadfi9/mnT5s/LamnqTrG5KUppjpcrZmby2vLDPu64ehavra3DNk0e/ts7/N/TmaIQcyf1bynn3nzhuoUsmDGaaDLNwhmjeh53uzQWTikZkDHdLp2brpjP//7tXfJzApx/+qQBGWc42ZujY5gWmqr2FHIY7to6YyRSBiG/h9bOKImUgculkZ/tx+9x0R1PU1mchV8KzByXw31QXF5eTl1d3SDPSOxvQH6jRaNR3n77bdLpNKeccgr5+cOrz4IQ+0sZFv/11DrW7O4kJ+Dha1dMZlpl/27HEMenvj1ONGGQ5fdQ1xojkbYIHUWDxWvPmkAkkSKeNPnYuQO/J//cU8bx5Mtrae9SKc4N0NoZx7Js9EEu23zjpfPQNQ3LsrjhoqOr6tjQHuf3r27Fdhw+sqiaivy+lwcezhQFXLqaScBXMtvv9jdnXAFzxhVw1y+eY3djJwCPvbh2UAIXRVGYPWnf6kBbVxxVVcgJHX8Z38M577SJnH3KeFRVOSFWnXc3drBpZzMTRhVSUXzsP6u9Hhdf+dhZ/P319RTlBblowYkRrJ05bxxvrtpOa0eUK8+eQTiYWVU5d141DtDaGeeMmVUnTCAmRF/0e+Cyfv16LrzwQurq6nAch3A4zJNPPsnZZ5/d30MJ0S/aulPsao2RF/TQ2p1ie1P0hAhcIkmTtnia4pAH3wj/RXX6pCJe+f/Ze+/wOM7zXvuesr0veu8gSIAkwN5FUo1qVrFkKbLkHsu2HFt27JPEcYmTnBOf75w4OXHsOI67ZbnKlizRkqxOFRaxN4AA0XvbxfY6M98fS4KkUIjGImXv69IlcHdn5t0yM+/zPs/z+x3rwxeKc+OKQmwzNFjMy7Dy5QcuXWbj7TQsyue/vvRevvmL14jFkjx4c8NlD1oA8rPsfPEDW2e1zU9ebuG5gz0IpEwuv3bf7Epn5oOmaTzz5ile3HeauoocHrg5VUp3KdBJIg+uL+blpiGcZh3XVE++sJbjPiMKcebvy82Lb53mP3+/D1EQ+PQ969lUX3pJj3el5HxnS/+Iny9/62k6+z2U5Ln5xuduJy9z9lnyZdX5LKu+cgqSqqpyonUAvU6muiRrRgFjUa6L//dX9xCNJ7BZzpWCSZLIjnVVMzrm6wdbCYRjbGqowHGJA+I0aS4FCx64fOlLXyIrK4vHHnsMk8nEV7/6VT7zmc9w/PjxhT5UmjQLQrbDyPISN/tbRynKtLC85OoPWryRBL8+3I83nKDAYeSe5bkY38XBS1W+g397aCOBSII819V9s62rzOX7f/tekor6jpCMPUsyqaZkejUmyANfaroGxvj+7/fiD8U4drqfyqIMNjeUX7LjVedYqc6ZPqP0kdtWjptS3rJxdqIQs6V32MfprhGqS7LGJ+F/eK0Rjy+Coqr88c1TlzxwudSMBSIMe0OU5rvmFZR29nnoHfJRkO2gd8hHV79nToHLleanT7/Fr/90GL0s8Yl7NrJjhlkfnU4aLwWdLU+/epzv/HIXsUSSvUfb+frDt74jMmxp0pzPgt9V9+7dy+OPP86GDalVzh/96Efk5eURDocxm69+KdM0//2QJZHP3bqYtsEAWXbjVeM8Ph19vijDwTgus44+f4zhUIIi57s3cAGwmXQzzrRcaURRRC++M1awz3L/NRWE40kUVePBbRdfvb0UXE1zKKfNxPt31F/y4/SP+PnSvz9DV/8Y5YVuvvEXN5PlslKe7+bY6UFEUaA07+pfTJmO7kEfX/+vF+gfCbCmtpC//fC2OWcia8pyWFyew+muYRaX57Co9J2jcnY+rx9sB8AfirLveNeMA5f50NnvIRSJYbcYaekaviJlrGnSzJcFD1yGhoaoqDhnxJWdnY3JZGJoaIjS0tKFPlyaNAuCXhapKXjnOA7n2Ay4zXq8kQR5NgOZlnfGhD7N1Ut5rp3/9eD8ndA1LVVfNZuV3CynmWtXV3C8bYhViwtZt3T+EsTvFNp6PXQP+ijIttM1MEZHv5csl5WP3b6a8gI3oihw7TTmlu8EDp3q5XT3CFkuKwcae1MeO3MMxpw2E//rM7fR1e+lOM91QcnUO4k1dcX87qWjmE0G6mvm1j/V1DXK7hO9FGfb2b6i5KLn3OYVlew50k44GueWLbXpoCXNO5JLUscgvm2lURTF8ZtZmjRp5k+mRc+99bmMhBLkO979PS5p3hnsbx3lZ6+1Y9ZLfOL6akqyLt4bEosn+cfvPcfBxm5yMuxct6bykvW3XI0sKsmiujiT9l4Pi0qyqCpK9dxYTHpu2/zOaBq/GKV5LjKcFobHQiwpy563YaPNYqS28tKorF0uPnLHWuprCjHoJZZVzb7XZiwY5Z9+vpv2fh92ix6zUcf62ukDoPqaQr71pfcRjiYoyH7nLNSlSXM+Cx64aJpGQ0PDBcFLKBRi06ZNSNK5m1FXV9dCHzpNmv9WZFj0ZFjm7+qd5p1NUlF542gXiqqxYWkRRv2V66v5+WsddAwFSSoaOw/28qkbqy+6Te/QGEdb+nBYTXT0jnK0uZeSfPclHefhlgF++cJxMhwmPnrrCtz2K9c3lem08E+fvpnOfi9lBW4c1ndmBmE6llXl8fcPXU/3oI+GRflYTOnrlixLrKkrnvP2vlCMsWAUp9VAIBLHE4jOaDu3w4J7hjFLIBTlQGMPGQ4zdZV56X6YNFcFC36H+9rXvrbQu0yTJk2aNFPwixeO8+hzR1FUjTs2L+LT710z530NecPYzXqMhrndGmxmmaSiIQhgMc5sH7mZdsoK3Bxv6Scn00ZVycIaLGqadsGES1FU/v3xfZzu8aBqGllOCx+6uX5exwiEYrT3jVKc68I5B6Uml92E6woGT5eD2vIcasvn1o+iqiqqqqVLm86jKMvOzWsreOFAB4uKc1m3ZGEV0lRV5Z9++AJ7jnZitxr5wge2sekSCmakSTNT0oFLmjRvo2M4xLFuH0UZZupLnFd6OGnSTEtz9yiJpIJOljjZOQKAqmrsbxkkFldYU5OLYQalhF/8z1d5/LVm7GYD333kujlNhD5+XRXPuHsx6WXes7rw4hsAZqOev/vkLRxr6aMo10lF0fQGkjMlHE3wb7/Zw7G2QW5cU8mDO5aPBzDnly7Pt4zZH4ry5W8/Q3PHEKUFbv7x4ZvnVAo1FoxytHUITVNp7/Xidpi4aV3Vf6uyuclo7xnhn3/8Ar5ghA/fuYHtay+twtuh00P85E8nsJn0fOK25RRcIj8jrz/M7sNtuBxm1i0rm3U2QxQFPn5bPfdfV4vFqEMUFzYb4g/FaO4cxmkzMuwN0do9kg5c0lwVzCtwOXToEPX19en0YZp3Dd5QnH95ppmu0RAui4Ev3LKIJQXvPKnNK4mqarT1ebFbDGS7LpzAeQMR/uuJfQx6Qty9vY7FZdmEownyMqxXxXUkEI4RjCTIdVuuivHMhO0rSmnsGEFVVW5ck2ri/sOeNr638xiJpMqt68r47J0N0+5jeCzM4681E4kn8Yfi/ODZ43MKXArdZv78upkrkp3NhmQ4LWxdvbBKZntOdPP8W60Y9TK/ffkkG5cVU1HgRpJEPv3eNfzqxRO47Sbes2l+E+GWrhGa2gfJcllp7hymqX1w1hO8WDzJ3//0DQ40D9A/6MVu0uG2GxEQuG2e43un88fXTnCkuRejTubHT+xm6+qqCX20C4Wmafzn00do6vKgqhrZLhN/ccfC+xmpqsr//v5z7D3Wgdmo53Mf2M5162ffzyQIAjbzpSm7c1iNrFtawktvtVCY46ChZmYLEWnSXGrmFbisXLkSp9PJ5s2b2bp1K1u3bk0HMmne0XiCcTzBOBkWA95wnCFfNB24zAJN0/juE/t5Zs9pbGY9f/3gJpZVnCsPeeaNU+x8/RSSJNLcNYrVaiKWUHjv1iW8/4alV3Dk0NLj4X/97A18wSi3bqjiI7fUX7Zj7znZy3P72ijJcfBn1y7BMIs+lW0rylhUnElSUSnKTv1WT3aOEo4lMRtkDp0eBiCRVPjliydo6fFw3coyttSfU+6ymnTYzAb84TiSJJLjurTS9V5/mH/9+S5ae0a5c1sdd127bMHvGyaDDr1OIhCOkeu2YTKcU95rqM6joXp2zd2apnGwsZtILMHq2pLx76gkz0VxrovT3SMU5TopLZh9f86wL0Jrr5cMm5Hm9hg2k45oXMEbiEw7nlcah2gdDLK8xMXq8kvbFzRXuocDPLOvHatRx+0bK7EYZ6eA6LAakSWRaDxJhvPSLyjoZSllPCqk/r4URGIJ2npGsJkNeP1huvq9l+Q4iaRCZ5+HbLcN+yx7pwRB4JH3X8PNm5fgspvfkV45ad6dzCtwue2223j99dd56qmneOqppxAEAYfDkQ5k0lzVKKrKE/u6aer1sakmh2tqz02sSzLNrKlwc7DDy5JCB8vTpWKzIhJL8sqhDgQBugZ97DvZe0HgIogCgiCgaiqj/giecAKnxcDTb5zifduXzKgsZvfJPn7y3HGcVgOfvmMFhVm2BRn760e7Od3rxWk18Oy+Vu7Zthib+dJ7+owFo3zth6/SOxzAbjGQ67awY+3s5G/zMqwcbB2lsbeX1dVZbFiSz8GWIWJJlWsbigB4/WgXP332CMmkSlPnCIuKM8YNFk0GHd995Dp+9Oxxsl1m/ua+NYSjcZ585TihcIwdGxdTmOOc0/t7q6mPPcd7KM93cdO6SkRR4NUDbew62IbRIPOLZw+xeUUF2e6FLclZu6SQT965mtPdHtYvLSI/c36/k527TvDdX79GLJ7k5s21fO4D24FUc/0/PHwTTe1DVBZnUpjtnPW+c90W6itz2NvYR3VxJjaDTGmek20ry6bc5ni3jx+80kYgkmRPyygF9ywl/yozZ9U0jX95/AD7mwYQJYGkovKBG2pntY+7rmtAkkQCoSg7NtVe0vmEIAj8xR0N/GZXMxajzN1bLi4uMRfMRj3Xrath52vHKS3IYN3yqb/nuZJIKvzP7/+J/Se6ycuy87WHdsz6HJZliSXluQs+tjRp5sO8Apcnn3wSTdM4cuQIr7zyCi+//PKUgcy2bdt45JFHFmjYadLMnbdOj/LorjbiSZXGHh8VuVYKM1IlTbIk8vD1lYwEYjgsOgz/zevLZ4tRL1NV5Gb3sR6cNiNlec4Lnr9l4yKGvSGGvEEynFae29dGOJagvjoXWbp4+YeqavzXziN0DPhJKirF2XY+dfv0ZVAzpSDLhs2sJxCJU1Xoxmy4PN44x9qGaOoYIRxLMhaIMuILz3ofbzQO8n9/f5RAJMGKikw+f8dSrltTgU4Sed81lQAkkqkGZ71OIqmoJBT1gn2sX5LP8vJMIrEkFpOenz39Fj98Yi9JRaGpfZD/8/nbZz1pHPAE+edfvEnvSACb2UCGw8S62kIsJh06WSQUjpPhsMxZDGA6RFHgPZtqFmx/jW0DBEIxrGYDBxq7L2j6z8u0z2tFWpZE/ub962nu9pDtsmA369HrpGnPiWAsSTSuYjfJRBMKwWhyzsefLdF4kid2NeIPxbhxbSUluc4pX+vxR9HrJWIJBV8oPqP9n+4a4q3jnRTnudlQX86f3Tx/f6GZUlXo4kv3r72kxxAEgY/dvZEbNy3BZjHidsxPHnoyuge8HDjZg8moo7lzmMOneue8+JAmzdXEvO8WgiBQX19PfX09jzzyyAWBzCuvvMKf/vQnnn76aXbu3JkOXNJcFSQVDUXV0MsiiqKRUC5szhVFgWzHu0+S9HIgigJ/9f5N7D3Zg9NqZFXNhX0SdouRz9y7AUipOzVU5+MPx9i8vHhGk+KUWpWOpKIiCGCeZdnJdFy3sgyjXmbUH2HT0iKkGQRSC0E8oeC2m9BH4uh1EotLMma9j86hIGOhOG6bgY7BAN98+iQne/zIkkiG08wdq4vYvLyYxs4RGjtHuHldJQVvy0A0dQzxv3/yCoFwjPuuX86YP0JSUTHqdYyMhSaoc82EcDRBOJrEYTEQiSUJRlIT122rKvEFo3QPjrFtVSX2d4CJ4Nqlpew92kE0nmD7muoFWflv7xnlP379GvGkwp/ftYFls/AmaShxcc3iLE70+NhQnUllzsSMlaKotPd5cNnNZDgWpvyvZ3CMf/rxy7x+tBur1cSx1kH+3yM3T9ocLggCH76xlh88exy7Wc8t6y6eWRgLhPnH/3yWtp4RXHYTX/nEzayqnbkhqaqqPLu7mc5+L+uXllC/aGHVthYKURQpyZ/9uT5TslxWCrIdNJ7pv5qr4WeaNFcbC77M1d/fz4kTJzh+/DjHjh0jGk1pi5/v4ZImzZVkdWUGt64s5ET3GNvqcimdgUnexWgdCbO7cwyzXmJbpRuX6b+vk73dYuD61RcvdZIkka0rSme1b0EQ+OxdK3nijRbsZgN3bVq4hm5RFNiyfO6+CnNlRXUe21eU0tY3xpol+ReU1s2UVVWZPH/EhjcYY1NtLif7Axh1EuFYkhF/6hpsMuj47D1TryS/cqCN090jWM0GHn/5OF/782tpbB/EH4rygdtWz6khuizPyZ1bFvGnt9pYV1vI+tpUg68sS9x93fJZ7+9KsmVVJSX5biKxBNUlUyufNbYPsetQO/lZdm7esGjaAPiXzx5gz7EOREHgh0/s5p+/cNeMx2PUS/zFjdUoqoY0SdCgaRr/9qs3eOmt0zhtJv72I9upKZ2f1LSqqvzfn7zEi3tOMeqLUFiQhScQQVFVRHHye/w1y4vYWFeAKAiTBjfJpEJnv5cslxW71YjXF8bjC5HhtDDmjzA0GpjVGN882sm3fvk6gXCM1w+18/++ePu8DS8B+oZ9jI6FqCnLeUcovdksRr72iR0cauqhJM9NbcXEki9FUfGHYzitRgRBYHQsRHPnEOWFmeRkLEwJbpo0C828A5fBwcHxMrGXX36Z06dPo2kakiTR0NDA3XffzbZt29i8efNCjDdNmnlj0El8/PqFq11WNY3XO8bwhhMowTiZ/QG2XKJG2YSiEk2qWPQS4n/T3rHKAhdfeN/cvUquNlw2I9/4xHY8gShZDvOMZE1DkTid/V4KcxzYLUYWFTj554+sxReKU5Jt5fmjA/zqjQ4K3CauWZIKhPY39vKzZw7jshn5xF1ryHCYGfKGyHFZkWWR3IxUA3sklmBpZS6LSrL59795L4qqzXmiJggCH76lgQ/sWD6nDNZYMMbOfR0A3LymFJd17j1Hw2NhDDoJu2Xu+yjIdvDq/hZaOoe4ZlXVhIbnQDjG//7pK5zu8WA16bEY9WyfJog/2+CvqhrGOZYmTha0AIwForxxuANJEuns9/LWiZ55By5JRWXIGyQ3w0Y4msCoE/ngTfUX/X1MVfKWTCr87x+/xO6jHeRm2PjaJ3ZQnOdm6+pqXj/UyvJFBaxZWjqrMQZCMaJxBafNRDASJxSJzztwOdbSx//8/vP4glG2rKzgrz987WXt3dU0jeMtfSSSCssXFc74XMrLtJO3acmkz/lDMf7pZ6/R0u1hXV0hH9yxjK/8+05Odw1RlOvif332PemG/DRXJfMKXGpra2lqagLOlYx97nOfY9u2bWzZsgWbLR2xp3n3IwAGSUBRU8Z7hktUYhRJKBzt8xNOKLjNOpbm2f/bBi/vNnSyRI5rZpOrYDjGV7/7HI1tg5Tmu/iHT91EptNChs1Ihi01kd5Rn8/GRVnoZRGDTkJVVf7z9ynTxaSi4rab6BsN0tQxQm15Nn/7oWu4dVMNJoOMPxRj68oKBCElpLAQyrNzLbv74XMn+cOedjRgwBviL987N2na37/WzM9fbMRokLl1fWVqYa0ii8r8GVqIn93Pi4f5r8ffJJFUONTYzVc/efMFz4ejCQKhOA6LgVA0wVhwalUwgAduTWWykkmFe26cfa9WKJrgj2+eIhwKs2F5GVXF5zJBNrOesgI3+xt7cNpMlBXMv1RIr5P5sx0r+PkfD1BTms1ffmDbBcecLb3DfvYe68Sok2jpGubAyW6Kti3lcx/YzgO3rcFlM6ObgQfR+WysL2XT0U5auoa5cf0iiqfpv4GUFLUkCtOaWx4+1UvvsI9sp5X9J7rw+iO4F6j0biY8/eoxvveb10kmVe6+oYGPvnfjpK/zBSKcbOunMMdFUe703/eBU33sPdmL02rkpQPtlGVbaesZIS/TTkefh9au4XTgkuaqZF6BS2NjI4IgUFdXx1e+8hVuvfVWjMarv1Y5TZqFRBAErq3K4Gh/AKNOZHn+pQnYPeE4vmgCoyziCScIxRRsM3QnX0g0TcMXTWKQRUyznFSkmT+nu0c40TpAht1MU8cQJ9sG2LJi4qq+7bxyRUEQkCURVVURBRjyhtjf2IfbbuKtk72cbB9i1eICblx/dXmGDPkiCEJqcWBobPogYCo0TeN3r7cQjMTpGPRzovMtbDYLpTk2vvnxjWTOwrG+s99LNJbAYtLT0jU0oe8n22Xhru21PPVaE7XluWxpmL6nI9tt45EHto7/W1U1Xjzax7AvyubaHIqmMT/sGQ3xme+8yr7GPswkWL+vhX/7q7twWFPvR5YlvvTh7ew70UWm08KKmoILth87I7XstM1Oiey2a+q4dm01elm6YLL/4pFeXjzax6ICB/dvqUQnXzxYzXJZKMhx0Ng2SIbDPN6HIQgC2e65XUftFiN//4kbSCrqRTNBL+9r5vu/243FrOcvP7CdRaWTl2lWl2ST5bTiDURYt6wExyylhefL/hOdhMIxjAYde462Txq4hCNxvvbtpzje3EdOpp1/+IvbKJ/GzDXDYcZuNjAyFqYw205dZR4VhZm0dA1TXpBB5TwC0jRpLiXzmvU8+OCD7Nq1i2PHjnHvvfei0+lYu3Yt27ZtY+vWraxfvx6D4dLJiYqiOON0raIol2wcadJkWfVcWzV9o2XbcIhf7+8B4N7VhZRlzq58waKXMcgS0aSK3Shh1F2e5vG3s6vVw+52LzaTzB1LcylICxlcVgpznBRkO2jtGaUw20FJ3sXLEgVB4LP3buC3Lx3HYTWypaGU5m4PA6NBCrJs5GRcGnfw+XL35koGvGHQ4J4tc+tnEgSB8jwHPcOpXomkqpHtNDHijzI8Fp1V4LJtTTWHGruJxOIUFmTzf3+1l3VLCti8rGj8WO/f0cDd25ei10kX3J/OlvcV5TixTVGu9tLRPv71D8cJx5K80TjAV+5ZRiyeoDjXNeFet+/UEK0DAVRFJSLrGfBG8AWj44ELgMtumjQYffVAG9/57W5EUeDhezawqb50xp+BLxjliV2NxBIKt21aRF6GjT5PiO/88STt/T6e2w9ZNgM3r754Q73ZqOfvHtrBgcZuCrId2M0GXtnXTG1VHlmu2QUu7f0+vvv0EaIJhY/dtJSlZZnTvl7TNH729FsMeQLEBpL88bUTUwYua5eW8I+fvpkRb4iGmoLLJtxxltV1pRw62U1SUdlQP7m5ae/QGC2dw2Rn2OgZ9NLUPjht4LK0PJsvvn8jrT0eVizKY1FJFv/zM7fS0jVMWUHGnAPHNGkuNfMKXH7yk58A0N7ePt7jsmvXLv7+7/+ef/iHf0Cv17Nu3brxQGbLli0LMuizfO973xu/mPv9fv7+7/+eLVu2sGXLFjRN47XXXmPXrl189atfXdDjpkkzF3YeHeDUQBAVeOrIAJ+5dnZeHU6TjuX5dkLxJC6THt1lvnkCeIIx/s/vjzLoCZOXbaM6y5IOXC4zmU4L//DJHZxsG6SqOHPGakE1pVl8+SPbxv/91Y9spbFzhLrybIqyZ1cydblYVZXN9z6TGrN+Htm9R967ivrKHFRN49XjA3QNBdmwOJeKvOlLYTRN4/m32mjuHmXlonzW1xXz7397L/sae/l/vztIqNnD3pN9FOfYKck59xm+3UDUF4zylf94jpauYSqLMvn7T9yIa5KAadgfJRxLYjPp6Bjw8dn/73dEIzFu2byEj9+z6YLX5rvN5Gfa8AWj6FG4aUMVhTP8Hp949cS4UtzTr52cMnA53ePBH46ztDxrPHvxi+eP8csXjqFqGl2DPv7x49eiqtA96KNvxA/Ak7saZxS4AORk2Lh50xLauof50r8+Sf+Ij8Xlufx/f3kXtlmozf3q1VPsbuxHFAR+8Mwx/vVT26Z9vSAI5GTYON01jCSJ6GSZF/adpijHwaJJxBeupJ/JLVvqKC/MJJ5Isqy6YMLzx1r6+NlT+wiGYwTDMYrz3Cy+yHgFQWDj0iI2Li0af8ztsLB26cJLM6dJs5AsSJ1JWVkZZWVlfOQjHwGgtbWVV155hZdeeonf//7348FMMrmwOvMf+9jHxv/+4Ac/yBe/+EW+9KUvjT/2hS98gW984xscOHBgQY+bJs1cMOokIkmVWEJlIBDFE47jNutntQ+nSYfzCiqWHW4fpW8oiAJ09fkIhWfmy5BmajRN44+H+zja5WNFqYsbluVeNJNckO2gYJ7BRl1FDnVzUDC73MwnYDmLy2bkzjMKdLeuq2DUHyXHabroyvnhlgG+9dt9jAUjvHqok3/+ixsoznFgNhmJJxSsJj3RuEI0Nv297VTn8BlZWguN7YM0dQ6xfunEif3mJbm80TjIsC+Kw6TS2OHFZTPxwt5m7r9lNdbzDFHXLsrma3+2ku7hIEuKHCwpyZhxBUJpnosjzf0IgjBl4Pv6sW6++au9hKMJblhdzufvTSnSjYyF6B3w4guEGfMG+PNbGyjOc1GVZWR41I9ZBlGZ/XWhpWuY/mEfeVkOOvs89A6OUTOLYMGgk0ADFQ2jfmZTm8+8fys1ZTkIosCbx/v47aunyHSa+bs/v25Bzo3+ET/xeJLivIkZs4vhD0b5zq9fp6N3lDuvXcaNGxZP+jpN0/j2L3bR1D5AUtW4ZmUln37/VvKzHBxo7OGlfS0U5zq569pl7wg1tDRpLsaCF8g3NTXx8ssvj/u4nJVDvtQ8+eST7N+/f8Ljd999N//0T/90WcaQJs103F6fS48/QlLRqMm30eeLzjpwudK4LAaKXCYGfBFyXWbWlLwzvAFiiSRPvNHKqC/C9StLqCq8esZ9stfPY292EY0rnOzxUZptYdFFMgFp5o5BJ5GfMbNV5bFglBFfCLvFQDiWIHgmUF9dk8fN6yo52DLAtQ0lVBVOX65XnOskP8tOR5+X4lznlA3jLouOOxqyMOh1RCNR/rWtg0A4Rk1ZzgTPIkEQWJRvIx4KYpKZ8cS4s2+UggwL91xbS2Gui2tXV076uqOtQwyPhcmwG9nb2Es8oaDXSRRkWvH5Q0RiCfqGvPzjfz3H9756H4/cWY/wq90oqsq919bNaCznU1uZR3lRJn1DPpZWF1CSPztlxvdfuxhJFIjGFe7dOrNerbxMOx98z1o6+rz85uUmst0WeoZD/OlgN8X5buzzWCR6/XA7//bYLuIJhftvWsH7bqif1fYvvdXCc282opMkfvD7PayuLbmoIIAkipQWZJCf5WAsEOFfHn2V7sExDHoZt8PC9esWTk0zTZorxbwDl+bm5gsClaGhISC1CmAymdi+fTtbt25l27bp07bzRZIkjhw5QmXlhRfhI0eOIMuXv4E5TZq3k2UzsGNpDoOBGJIgYNZL+CIJZEnEon9nrITVl7l55LZa2gcDrKjIIN99+ZR1JqNvJIAvFKOq0D2ty/gz+zr43s6jxBMKh1uH+NdPbWN/Yw+JpMrGZcVzlqJdCJKqhqpq6GQRRdNQ3maIOlc0TeO3b7bz4pE+lpa6+dj1i1Kr0nPYz+WUfr1aUFWVXQdb8fnDDHmCfGBHPYuKU31sRr3M59+3ZsafTW6GjX/45A5OdQxRXZJFQdbEbJmmafzfn77CqwfbsJr0PPL+zfztx2/E6wuzfnnpBB+dSDTB17/7LEda+slyWfj6J25icfn0WYIhT4CvfuspOvo85GU5+KdHbsdsnHzxpLY0k0yHiVA0wcalRePN9qV5TjIdJoY8SfSSSOiMqejqJYX851/fjqpp2Myz720tzHHxf77wXnqHxqgsysI0xbimIttp5jN3zl51LhJNsOtAC0o8Rrc/SkC28cdjwwxGjvB399VjmmH25u28drCVQU8As1HPM280zjpw0ckioigSTyjIkjTl9U0QBB7+sy38fOdbOG0m7rg25Y8USySJxhOYDDpiCYVILDGn97H/ZDdHm/uoKcthw/LSOe0jTZqFZF4z+oKCAgYGBoBzgcrZfpatW7eydu1adLrLMyG4//77+fjHP05/fz8bN6YUN9544w2+/vWvc//991+WMaS5uojH46iqeqWHcQGLM4y4DCKyAIl4kiZfDFGEYqcexxVQCJsL66tcrK9KZSwuV0Z1Mo62DvF/f7WPQCTO9oYSPn3niiknkV5/iFg8iUkvMRaI8LM/HuA3L51AVTVu2VjNw++9cr4wlZkGbl6WzaGuMVaVuih16xfkc20fDPLoyy3EkwptAz5q8qxsXDxzH49RX4Rv/f4AvcNB3ntNNTvWTN4UfD69w346+rwsKslcENO/K8mwN8TBxl6qC50Me0OU5lhJJOIk5jb/I9tpJLs+ZXA62fcbiSU4droPs0Fm1BeisW2Aj96+evz5t2/T1e+lpWuYDIeJ/iEfJ073UnYReeeO7iH6hsfIdlvpHx6jtWuQvCmUy9bW5PB3H9pIIBxnaXkmsVgMgFU1eXzw1gZ+9+JxMuxGPn7XuvHnJCH131x/vxajRHVxBqBetmvLEy8d44dP7CGeUDDYnegddvJcJk71jtHeP0Z5ztyEK0pyHRh0ErFYgkXFmbN+P5uWl9A3tJyeQR/Xr6tGL0/9uVYVufm7T9w4/u9oNIrdJPO+65bxh9dOUlGQwdraglmPoavfy//6wfMMjgbJcln435+9jUXz9AJKk2a+zGum5PV6r1ig8na++c1votfr+eIXv0g8HkfTNAwGA5/85Cf5xje+cUXGNFsGBwdZsmRys6iHH36Yhx9++DKP6J1LPB6nqalp/IZ6FknWIcs64rEImjb7lW1F1QgmNKw6YUrjt5mgIRA0ZAICqiDS7B3CmJydQ/R/d55+q5fT3UPYzTqe29PEmmIRs2HyS1quIU5ZhsRoIM6GCgf7j7Xg8weRJYHdh5rZUj07SdiFpsYMNTUAHk6c8CzIPgf9CSKhIP6ogk4S6Opsx54cnPH2zx8e4IV9Pehkke88PopT8GGZJrge9Eb4jz+cYGgsSnG2hYdvr8V2phRSVTX6R0PYzLp5GUBeTpKKisOo0tg1hs2sg6iHY8eOXbLjaZpGWZaePSc9WAwydjky7fESSYVsu8SprlHcdgM6xXfR8cXiSewmkUOn+nDbjYR9Qxw7Fp52GwPQ3DR8wWPbax1sW7IBAEHwz/pzOdnppaXHR1G2lYbKmffmXApOtbThDwTRyyJCLIhN56ZzwENFpgFPXyuhobmJoJRnaNyzpYR4QmFZhXVOv536YgP1xdmgjHHs2Nisty9zw2dvT5XNdbW3zHr7U91eBoY8GPQSgyNjDI36/1sELt/+9rf59re/Pelzg4Mzv4amuTTMK3Dx+XxXLFB5Ozqdjn/+53/mH//xHzl9+jSaplFZWYnZfGVLWWZDTk4OJ0+evNLDeFegqiqxWAxZlsdLBWWdHrs7C1GSiUcj+D1DF2wTS6o8fTJI11iCVYVGNpWZL7ihRpMaT58K0xdQKLJLvGeRGb00txuuBiQFlZigR9Y0TDIY5LQ612woz3PitI4SjinUF9px2ixTus4XGo188Q4bqpZyGrcZZLqGwiiqxpblhe84/6nhQII3WnwYdCLXVDswGyaWgJUYjdy3TuNQV5CyTCMryl2zMiy1WYzoZImEomIx6rGYTRjPlDTGk6lMpv48r44hn49hX4wct5mBsSjekEKW24imafz8uZPsO9mH3aLnY++ppzTv6lQxezufvH05Jzs9ZNiNVMzSrHIu3H9DLZuWF2M16ch2TX/vigajZNiNLCp2cdP6CsoLp5f/BTAYNHQmM2ZLBEWUONQ2RtkMtjufph4ffZ4INYX2OZWKDnrCPPZSKyO+CE6rgSyXlapC56z3s1Bcs6KUQy0jNHV5KMzR8/6N2cRUkdIMA5ZJzquLMeqL0DXooyTXwbq6ootvMC5GA/YAAQAASURBVAsGPSF+9UIj4WiC92ypYknp1N/d8FiYpi4P+ZlWKvKdczrekrJs1tbmc6JthOoKF3WVV7+Yx0Iw3UJxYWEhvb29l3lEac5nXoHL1RK0nE8kEiEcDlNfX39JPWTSvDOQZRm9PrXqazBZ0OkMKEoSg9GE0WRGVc6pAR0bDHOgN4okirzaHmVpgZVs27nfeIc/TrtPwaoTaRtTGYoIlLvn3lyv1xLEURHQ0AsC8M5q1L/SrFuci8tuwh9KUFfqwjhFtmUyNi0rpLrYTVLRyHOb33E9HI8fHORwVxBRAE2QuGPF5H4NG2sy2Vgzu4npWbYuKyCuwIgvyqbaXOxn/EFOdPt49NVOVFXj/i0lLC91ArCoJJOSXDv9oyEq8p2U5bvR63X4QzGOnB5CliX6R0O8cayPjsEgJbn28Z6RhWbYF+FQ6ygZNgMrKjPn/P269Xo2Lbt8JW96oKb04kG0pmn8n5/vYs/xHvSyhKrB8ur8GW2nagIWkz7VW6UJ49fHmdDc6+P7f2rDG4pRnGXlr+5eit08u3lAJBEimlBx2Y2Eo0miCW1WY5gpp3u89I0EWFyaSZZz6gArO0NG1utx2i10DYc41THCnVvm1sQ+6gvz77/ZT+9wgKIcO59//3pcszT4nI5XDp3icMsQkiTw+1dbqJ/iOw9G4vznH47R3u/DbTPyF/espLJg9oIkej08dMcKxgJhRC2J6Qr2AqZJc5Z3RlH9DIhEIjz00EM8+uijCIJAS0sL5eXlPPTQQxQVFfHlL3/5Sg8xzRUmmUigKElEUSKZiF4QtADoJAFJEIgnVSx6Cflt2RSHUcSqE/HFVFxGEYdxfg31ggAGZmaMKggCRrMFQRCIhkML1rvjiyq82RlBQ2N9sRmXaer3lFQ0XjoxQq83xupyB3VFV9agTBAEauaxUps9zWTmascXTqKTIKFCILqwMvNn0eskbl1TPOHx5w8P0DsaRhQEnj3UPx64ZDpM/OW9K+kfDVGcY8NyRpHJbNRRkGXjRPswRr3Mm8d6iSa6cNuMPHLvasrmuBo8FfGEwnd3NtLS68NskPnojYtYvWh25S2aptHUGyCeVFlSZL8inknTMTQWprnbg6ZpRGJJuodmVmYqCALv27aI37/Wgs2k59qVE7/f6RgcizIWjuO2GhgNxPAGY+OBSyKpIInihKxnKBJHlsRxb5vyfAcb6/I4fHqY2ppMlpQufPB6usfLt36zH28wSlmek//x/nXjv8fJEEUBBECbuTrbZHQN+OgbDpDtNtM75Kdn0L+ggYtOTpluq6qGYRpBl1FfhCFvmEyHkRFflL7h4JwCF0h9Hhajjmg0beKd5urgXRO4fO1rX+Pw4cO8+OKL3HbbbeOP79ixg3/8x39MBy5pUJJxgmMjSJJMMhGb8HxtrpEdi+30+RIsLzDhNl94euRYZd5ba6PXn6TIIZNhvnxKYGarHYvVBgjIsg7/2ML0QbzcGmZ/bwQNCMU17lk6tQzvoQ4fv9k3QDShcqInyJfvrMA5y9XWNAvD1honP3i1B4MssLp0eulkRVXZc7yPsWCM1YvzLlqCdDEybKlMtqpp43+fxWUz4rJdmDGQJZGH7lzB4ZZBQpE4v3+1hQy7CW8gypA3vOCBSyiWZHgsgtWkIxBOMDQ2+ybvl48P8+s3u0kqGtcuy+bPNk0+wQ9Fk4RiSbLshsuatbMYdZTkOgmEYkiiwLWrLy6ccJalFVksrZjaUX06aoudVOXbGfBEqC9zkZ+R+i29criHp/d04LYZ+MhNS8h1p7JUrx7s5MldzZiNMh+5rZ7yAheyJPKBG5fwvm0KRr2Eomq8frSXYCTOmsW5uCcx5pwtvcMBvMEobpuJQW+IUX9kysBFlkQeuGEJz+5tx2k1sn3F7IK58ynNd1KUa6d3KEBJnpOiHDuvHe3lWNswNcUZbGsonNfv5Kb1FSiKRjia4Mb1U3/n+ZlWassyOdY2jGgw8dopLyaLidVVc8u+pklzNfGuCVx++9vf8qMf/YhrrrnmggvDkiVLaG1tvYIjS3OlSaoaJwdjqJrGkhwjemnyFWpJFNhWNX0WocSpo8R5+SfrkiQBApqmIUkLd9pGkyqCAAIC0eT0WZxIQiWpaJh1EvEzRpoLReNglHZPnGKXnrrcd1a/yeVG0zTeah4mFomhyiIH2rzU5E9dzvTG0R5+9uxxonGFwy2D/NUD66aVjr4Yd64rxG3To2mwZcnFJ8DeYJyn3mjnYGMPOS4zlUVuugZ8LC7NYPEcVtvjSZU3T3mIKyrrqlwTvDacFj0bluTwxslBSnOs1FfO/hhNvX4icQWjTuR4l2/S13QOBfnPZ04RCCfYsjSHuzeWzmtSGoklONo6gtWkY0np9A3rVpOeR+5bw6HmQXLcFjbUTXRTvxS4bQa+eFcdY6E4mXYDsiQSjSd5anc7vlCcQU+Y3ScGuHNzBaqq8cc3T+MPxxjyhnj9SDflZ1b9BUHAdKa0c9fhbn7+QiOJpMKxtmG+cN/qeQeBi0szKc11MOQNU1eeRV7G9MpgNSUZ1JTMP/Pjspn4/P0b6BnyU5zrwBOI8quXThGIxDnaOkJ+hoWaktn505yPw2rk/Tsu7pGjkyU+cUc9O/d18/t9vTT3Bxl5tYOKXBtuW7qEPs07m3dN4NLf309paemEx1VVJTFX/co07wpeaQ2yqz2MBvT5E9xc884z94uEg8iyHkEQCAX9DASTyKJA5jyzPptLzQQTKqoKW8qmr+VfWWansddJ21CEbUvcZNsXpi59wJ/gN0d8jIWT2E0SDqObIufc9n1WKe5Sr373jAQ5dHqUbKeJNYuyLutq++/f6OD5fe1EEhrZ2U68oemvbx5/lEgsidWkZ2QsfMYXYu6Bi9Uoc8vKi/dTAPjDCb75xEl27WtGRCXbEeSe7Yv54E11uO0m9HPwlXnm0CBPvDWAqmm0DYT4xA1lFzwvCAL3XlPBtQ0FWI268QnybFhe6uRkd4CkqrK6YvKJ5qFWD13DKaW0N04Oc9PKQqxzNCzUNI0f7jzO/lODGPUSD9645KLBSHm+k/IFzlbNBKNeIld/LisiSyJ2s55BTxidThpXkhMEyMmw0DcSRCeLZDomz6QM+8JE40ksRh0Do2GSijpvh/dsl5n/8cA6Rn0R8jNt4x40l4KmjhGeer0Fm8XAPdtryHCYcZ7JOg54wiQUFaNOIqloJJTLJ8+vkyUynWZEQUiVl2kairowHlFp0lxJ3jWBS0VFBQcOHKCkpOSCx5999tkpJYbT/Pegz59E1UAUoMd3afoBLjWJeBzPyCCCAG92hXmjK4IowPUVFpbmzH0FTVASFAhhcjKMFDumvxxYDDIPXVu84IaEwbhKNKFiN0lEExqh+Nxu7odOD/O7Xa1YjDIP3lBDwRT+FPMlHE3y3Z2NdA4GsRhlZElgZdXcSm9mSySW5LXj/bitOrpHwhgEheuWTn/sVTW5HDw1gC8UZ/vK0gkO7JeSPk+YPm+qTMfrC6GoemwmHbkXWQGfjkFfDEXV0MkCvd7Jy8AEQSBrionyTNiwKIPCDBOJpEZ5zuQBfbbTiMUoE44mKSmyjCuuzYVEUqW1bwyTQcIfitM1GGDD7M3nZ82pzhGefKURs1HH+65fSrZ76sWLN5tGePnYIIWZZt63oWg8IJQlkVvWFLPbqqOi0M2WZamgVhAEPnTzchYV92Ay6Ni0fHKFrTWL8zhyephQNMH1q0vmHbScxWrSYzVdWsETTdN47PkTdPT7UFUNt83I+647N9+oLHCyY00pB04NsrQ8k8XzyLacz9GWATr7x1hSnk1F4dT7XFWZQcdgkM7hEJtrs8lypLPZad75vGsCl09/+tN89rOfHZe+PX78OL/73e/4+te/zne+850rPLo0V5L6fBN9gSSaprGi4OIXbm9E4dlTAUIJja3lZioz5pdaHwsl2N/ux2qUWF3umIf/i4amwfGhGAlFI6lC03B8zoFLJJbkP/54io6hIDajjo/vqKa+/OI31oXOLpS49NQXGGkejrM83zAnpTZN0/jdrla6hwMkFZWXDvbw4A01CzrOswSjCcaCcWxmHcFIAm8wfkmOMxkGnUSe24wnEGVRvo33X1tGTf70QUBhtp2//eAGYgnlsnuoFGaaKcuxEo26cdjM3La2iGtWlFx8w2nYVJPB6YEQCUXjhmWXxlNCEARKsqbPQK5blIVeFhkLxVlRkTFtFiupqNM+r9dJrKrJZdfhHrKcZurKZ9eLEE8ofG/nCU52etlWX8B7N1dMKQ1+bkwav3j2KG29HlRNw6CXuXZNBYXZ9gmZsLFQnN+80YUvHKelP0BxppltS1PSuIdO9fOznYeIxBI4jSJ6Xen4dm6HiVs2Vk07jrI8B1/54HpiiSRO6ztvYi0JApqWyjAJb/vMRVHgzs2V3Lm5csGO19w1wn/9fj/eQJS8TBt//aHNZDgm71sz6iUe2Dbz/qc0ad4JvGsCl0984hOMjIxw//33Ew6HueOOOzCZTHzlK1/hwQcfvNLDS3MFWZZnpMihSzUTWy7+k9/TFeboQAxJgGcTKg+v1086WdcbTEhyyhNGUSbP5Giaxg939XKsO4heFogmVLYunt+qW4lDx1BQQRYFCi+SJZkOfzg16XaYdfjDSUb8l8ep+nySqsbL7WFG4rC61MJ1FWYkcW5lHRajTFJREQUB8zRGifMly2FkU10uu08OsqjQQUPFpZH0nQxRFPjYTYs52DKMw6KnotDN7q4INoPIkmw9oiBwaiBM82CE0gwDSwtTQY1BL4+rOk1G91CAN4/34bAY2L6i6KIlXE3dPo53jeH1R+gc9FOcZeX+7VUTSqWsRh2fubWGrpEQ+W4TbquBwdEgB3u9FOc6KMyefdnmkkIbf3fPIpKKNmsp3rmiqBrBmILNKI174YiiwKqLNDsnkgq//NNxDjcPUFuexQM3LZ/ys71vew3ra/OxGC/u4fJ2du7r5De7WtFU6BoKsqIqa1rfmeN9EZ464ePkYAwSKiIarxzsZH9TP7Vl2Xzy7lUXZD5EQUAUBRRVQxQvNN9t6hhmxBfCaTVy6FQ/9924dNZZE5NBnrakLxpPcrB5EL0sUV+VPa9Sx4VEEATev6OOZ3a3YTPpuH512cU3miceXwR/OI7bbsIfiuENRKcMXNKkeTeyIHf3jo4Odu3aRX9//wSn8rMIgsBXvvKVhTjcBBRFobW1lc997nN84Qtf4MSJE6iqSm1t7TvKgDLNpcM1i14QWUwpY6oayFOsWuoNRqwOF4IoYjCa8HmGx/srziehaPT74pj0IsGYwpB//qvz28vNGPUyozEVp0U359KtLIeRtdWZ7GseoTLPxvKyhSljmA0d3gRv9UaRRDg0EGNxtoFih4iiajxzYoxTg1GWFZq5bpF92vcoCAIP3lDDSwd7MBtlblw9v1X96RAEgXu3lHPTqiLMRvmyS+W6bQauW1GIomr85JCPTm8CvSyQVK3kWUR+9PoAI8EELrPMw9tlSjOnX8VOJFV+sPM4p3vH0MkisiRy3aqplZWGfVH+60/N9I6EGfQEyHMa6RkOUVngYHv9xL4Mu1lHRbaFP7zeQt+wn9YeL75QlFy3lc/dv478zNnLapvn0LcyV6IJlZ/uHqJtJEpVtpEH1mVjmGHPxOluD7sOdaKTJd440s2qxQUsq0plKjRNo6lzlGAkQV15FiaDTNkcjTn7RkMkk6mgPRxLkrxIL8ULzX6GAglkh5ukkqDQpadvJITZoONkxzBtvV4sRh25mbYzPSw6HrimlF0nhsh3m1hbfS5YLy9w47SaiMQS1FfnXZKg4lcvNvHqoW5ESeCuLdXcPI2i1uUgHI3z1oleTvf6yHRZed+1i8mZpsxuIamtyGZpRTZdAz5WLc6nJHfuxqjn3ztiCYWjrcMYdBJ1ZZkXzdilSXOlmNfVP5FI8NBDD/HTn/4UTdMmnbid5VIGLgB1dXWcOHGCqqoqVq5cecmOk+bdz/piC7EkhBMqG0smNycUJRlBENEUFVGUEUQRTTmnc69qEFHAKIlsq3Hx/PFRXBYdq8omrjALoggIaOrMdPJDCY3uiEYoLuAfTGLXCxRYZ18XLooCD2wr5+ZVhdhM8pwapeeLThKQRYgmNSx6Ed2Zm+WpwSgvNwcQBBhq8lGVZaT0IiV7BZnWS1Ye9nYEQcBhmX1Jm85oRkAgEYugafNr1I0pGt6IilEnEE5ojEVUzKJKKK7gMssEYwpjkYv3dCUVlUA4jlEvEY0rBKPTN/sHIglC0SQOi47+EVLqW3oZ/TST+VcPdfHHN1oIRRN4/BHqK7MZ8oboHQrMKXCZikhcQVE1rAuYcTs9FOFYXwirQeJob5j2kSg1uTNbEDObdJj0MqP+CC6b6QJJ3t0n+nj02eNE4gprl+Tx0O31cy7D3FibyxvH+vEEYiyvyKCqwAmkMj4gTGhOd5hkfJ4hBrp6sIgqSYcet93IqD9CXoaVnzx1CG8gwvLqXD52xypkSaSh3EVD+UQvkDW1BWQ4TATCcZaUXRqhitO9Y0iSSCyh0DHgX/D9z5af7TzCM3vbGAkkcNtNHGsb5hN3reDNFh86SeCaGjdmw6W5ntrMBh65fwOBUAyH1XjRAKOpc4Tjp4cozLGztrYAQRCIxJM8+mILLb1+Ni7J4T3rS3j0+UZeP9qHTha5b/sitq+YvCcpTZorzbyu7l//+tf58Y9/jN1u54EHHqCmpgab7fKb0kmSRGlpKYHAzEy40qSZDqtB5NbF0/+O47EIBqMJUZKJRcKo5wUtigZHveCNC1hl2F6XydoKJ0adiOVtppWy3ojR6gAEYmE/iWj4ouOLq5BUwSCn/h+fhy+YIAhk2K+cPGaxQ+b6CgudvgQVLj25ZwIwSUyJKSRUDZ0kcpVUhswLg9mKwWxHEEDS6YkEvPPan1knsiLPwP6+KPk2icXZetwmkZUlNg53+pGTMY6f6qXMXYpjmt4Wk0HmPRsreHp3Gxl2I+tr86Y9bnGWhY2LsznY6mHz0lxyHEZKsq2srcmZcpuEoqBqGmajjlgiiT8Uo7zARWWRC03T2H2ij64BP7XlmSwtn5vQQWNvkJ/s6iGaUCl368hzSGxYkkNBxvxWwp1mGatBwhtKkmHV4TTN/LZZkuvkg7fW09QxQmWRe1wOGKCz34cvFMdh0dPS7SGppAQH5kJ9RRb/8OG1eIMxlhS7EEWBQy1D/PKlU+jO+KZUF5079q2LrRzaP4YWjWBxGgmE4/z5HStQFJWRsRCPv3gCl83I0eZBBj1BCrKmLukTBIHKoktbLrl5WQF/eKMVi1HH6sW5C7ZfTdMY8aU8f2bqCq9pGq09HgQEFEVFVTU8/ig/f72Pg11BRAGCUYV71i7cON+OLIm47CZ8wRhHW4dwWo3UlWdOCBpHfWG+/8RB+keD2C0GrCY9dRXZHGkb5c2Tg+h1Es8d7KGhMoNTXV70OpFwNMGpbg+ZdgO5GRayXZcnk5QmzUyZV+Dy85//HKvVyv79+6msXLjms7nwV3/1V3z5y1/ml7/8JXb7O0/uNs2lQ5BkRGPqN6FGg2jK/Mu1VEXB5xlBFEXUM5kSDVBFI56ExHAsil5U8cThdFDAG9djisMSWeN8X0udwYQoSoCG3miZUeCSYRRY4pZo86nk2QUKbe/cWb0gCKzIN7Ii/8JypqpsI7fUOWgZjlGXZ6JwjvLIl4sBT5h9p4ZSHiK1uZOWy6SydKmJjyQvTG/G1nIzKwqMGGUBgyzijyrcUOdmuKefw/3DPNcPaBoPXD99Juqa+kI2Ls1HEoWLrpjLksgD28p578YSjDpp0hVfVdWIJVWMupTT9+blxXQP+hnyhsiw55FUVLauKMVlM3GyY5RHnzuJLxRj78l+/vrBteTMYrJ0Vt75tSYPvd4owUiCI20RCswqjd1j/O19DfMQxIBCl4EPrs+mYyRGeZaRXMfkv0VN0+gaDpFQVCpybeOf48rF+axcPFE+urYsk70n+4nGkqysyZu3ZO/be1qefrON7qEAAvDCga4LApfDzQOMDI8RisRpDccozrGT47ZQlu+isX0Yh9WINxClvMCNexbO7wutOHiW61eXUleehU4WyXIuTPm3pmn86qUm3jjag9Nq5KHb6ynMvvjCqyAIbFlRisffSDSpkeO2sGNtKXv6VGRRIKlqjIUvvQVDUlH5zycPcaJtBItRx4dvXcbqxRcuOgTDcULRBA6rkXA0gT+cKuU36mR0cipIybQbMeplNtTm8ae3OnFaDbR0jvDWsW6yXGY+877VZDnS3i9prh7mFbj09/ezbdu2Kx60APz4xz/myJEjFBQUUF1djcVy4Y1v165dV2hkaa40os6CKOkBDQxWlPDCuM6DNh60AGiCTBATSSHlQxBJaBhFjcGIgAIEk9AXgcrz7o1KMoFObwJBQJmh35AoCKzO0bEq+9JMEq4GREFga7WDrdVXeiQXJ6GofG/nCVr6fOh1EhpwzbKJE9VENIKsM4AgEI8EF+TYgiDgOJPF6/TEeeygl1BcZWAwhCSkShbDsZlJgL892EoqKic6vYiiQO2ZVfzzjztVn4k/nOCHr/bSNRpldYWde9fm4bQZ+dR7V/HmsR5++IfDRONJBkZDVBW7CUXiROJJHBYDkViSUCQBEyuSJuWlt9rZ+WYLGXYT+aVFSKJIXFGRBQ2jXsIfTqAoKpI4v7KdmlzzRcvDXjsxxK9fayepatyyqpDb1k5farOsMpu/fmAtoWiS0ryFXWw73uXnQFeEwZEYBlEdv070DPlp7vJwuttLQlEx6FMTWKNBR3ufj7J8F4vLsnj4nrX0jwaoq8jBNAP57HhS5Q8n/JweiVFfYOKGRbZxEYOFQBAE8hdY3twfirP7WC+KotLRP8ahlsEZBS4At2yqZkVNqp/HYjJgMenIaPfxm32D6GWBbUsuvWBHOJqgdyiA1azHF4zSOxyYELgU5ThYuTif14/1UVmUwbKKVFZ0WZmb+66poHMwQH1lJtlOE7dvqqChKlXC+f0/HMJq1nGifZjfvtTIh2++DNrcadLMkHkFLgUFBVeNuWNlZeVVEUCluRo5r5dAm0dd1TRoGvSFFGKSQlKBXLMBnaqgF5J0BEXiioAogO5t9/J4JHimt0UgEY/M6pjv1qBlKtoHg7QPBqnMs1F8Eanay0kioeANxjAbUp4e3uDkAiXJRIygdwgEAU1deCO6U0NRBgMJ7AYJW3YO5vAoZr3EjdM02k/HE2928uyBHkRB4I4NJdy8+uI172+cHOSHL3XQ7dcozbbyetMYm6rdFGWkMmqRaIKEomA26ojEksTiSerKM1ldk8vpnjFW1eRSkjuzSXwkluDpN5oJhOIMeUJUlWTy4KZ8hnxRGtuGicTi3Lau5JL3boWjCfadGuKPB/oIRBLodRL7T49cNHABLupn0+eJkEiqFGdN3ms3Gf3eKN/5UzvDUQHBYscgJijIceLxR/j3xw/SOxzAYtRRlG0jEomhlwTyMq1UnOmLAagpy6KmbOYle83DMfZ3h9FJAm+0h1iSY6TYdXVnSc1GHVkuM81dHswXUXIb9oZ4dncLADvWV5HlspCfZScWT3K0ZQCDXqahIoelRTYEgcsi2GEz61m1OI/dx3opzLKxrHKiNHg0nqTPrzAWUXn9xADSbw/w8HtXYLcYxoU0NE1jz7Fu+oYDLKvKoa48k5JcB68e7CQaV9h9vJeCTAs3zOD8T5PmcjCvwOX+++/n3/7t3xgcHCQnZ+r65svBj370oyt6/DRXL0osNC4coc4yOJgJEVViTDUwlkzQOxog126kdyxMljFBo0dBEAVKnXryLSKFk9wbE7Hpx6RqGm91BPGGFZYXmsmbolTlLJqmoWggCe+e4KbPE+Hfd55iyBcj12Xki3cuIfsqMVMzG3Vcv6KQX+7qIC7IxEU9qqZNuuKsaVoqyp0lA2Mx9rT6sBgkrqlxTdoIn2PXYTVIhBIqi0tcfGRNxYxKv6aisXsMSGWUTvWMXRC4hOMqXd4YWVbduMR4NK7w+zc7UxmUmECfJ8KSIjvW8/q6Vi/J53jbMD1DfravKsVtNyEIAp+8o55EUp1VkCFLIg6LkSFPCL1OIsNmZHtdSp5YXV+AommXZQL5i1dO89qxPsbCSVRBxm0zUFvsnPd+32oZ5dFXO0gkNXasyOU9awonfV04mmDP8V4EQWD90gL8kSTxpIrNJOOPQFaGidIcG8NjYUZ9ETIdJjz+KB+6eSmVhevoGQqQm2GZlymoQRbRSSmRCKdJwjjHXp2FJpFU6Bn047Kbxt3sz6KTRR66vZ5DzYO47SZWLpp6DvO7l07y+uFOAMKRBA+9dzUAv3j2KLsOdSDLEvdeX8e1ayoWbOyapuENxLCYdBgmOS8EQeD9N9SytaEYu8WAwzqxnGtwLCVXHgpFCEcS7DvZT0N1LzvWnVNlO3Z6kB8/dYhgOMbuY9387Ueu4S/uWU3vkB9fKLUI47kCUvlp0kzFvAKXr3zlK7zxxhvccccd/PjHP2bRokULNa5ZEQwG2bNnD/F4nDVr1pCZOTvzrjTvcjQVNbYwpTkTdq3BmKonoQmYDHpULcYLjf1UuXV0JyWiioaa1AhGElTkzq1O+EBXiF8fGCWS1DjSE+Jz1+ZNqeCUUDX2DaoMRTUKLQIrs8QFLdm4UgyORfAE42Q7DHgCcYZ80cseuAyNRdjbNITNpGNjXe4Fk+K6ylxcrXFiSXijPcKykgg1eXOrxY/Gkzz+ciMd/T621BezcVkRP36tj1MD4XF57uvrJpaiLMszYpRd+KMqNTmGecvSrqrKpN8TRhQEVlSeu6bGkio/2jNM22gct1niI+syyXPokaSUf46khck2SdSX27h3UyEuy7lSI7vFwGfetxpNY0LpmV4noaoae072M+qPsqI6m4JpyoN0ssRHb29gz7EeHDYjmxvOZZZEUUDk4r97TdNQNZBEgb6xGP5IkvIs07QKaW+nayiAJInEBR2KqMdosdJQNTeBgfM51ObFG4pj0knsOTU6ZeDy6xdO8sqhThAEhsdC3Lm1hvXVbg7IIma9wP0bC1la6iSeUFhSmkFT5yjVxW4Wl2bisBrIXICekcpMPbfVOujyxqnJNpBtuzz+OtOhqho//MNBDjX147QZ+eTdayjJc17wmiynmRvWXNx7JRJLgCCQSCq8dbIXu9XITRuqaOocQSdLhGMJ2no9XEsqcOkc8PHcvg7MBpn3bKqctfGrpmk89lILe08OkOkw8dBtteSclxHSNI1TnSMEI3HqKnIwTuHRlOc2U5lnp61rGJ0sYjJOVI/0BaNEYgnsVgOhcJxgOEZhjoMHb1rGb19qxKiX2Fw/+W8vTZorwbwClx07dpBIJNi7dy91dXUUFxdTVFSEOImBnCAIvPjii/M53KScPHmSHTt20Nvbi6Zp2O12fve737F9+/YFP1aadx+KqnFgIIYnorIkU0/xHAwdRTQ0TUAnwco8AysyrWSaJHb3JxgOq4gCZJrmHjz4I8oZuWABf1QhklCnnFgNhDU6gyo6UaDNr1Fqg6yZ99ZetVTl21hUYKdzKMTiQjsVOVaCcRUBsOgv/aq6qmr84NlGTnaNoZdTPjPXNpzzLEmqGqIoopNTfSVJdfZZlbPsb+znhX3tCIKQKoEqcuOLJDHoRGIJlYGxGN99rhVfOMEtK/OoK041ZQuCwKLshQvmdqwqZFGRA0kQKM4+F0AMB5N0exM4jBKDgQSd3jh5Dj06SeSj11fz2okBnFY91zcUTLlSfDaWVlWN4bEwdosek0HHvqYBfvzsCSLRJPubBvjSg2sn3cdZCrPt3H3tkjm9v/6xGI/uHsAfUagvsrKnzU8oprCqzMaHN+VNm6lSVY03jvUw5A1TW+xi1B8nIQjkuMwEEwLHuoNU5V4YdA14QpzuHaM0105h1rleCn84wWOvdzPki7GjPoc1VSk/pbIcC/tbPSQUjZrCqcvnekcCCKKIqqr0jwTRSSIf2VbMfRsKMBuk8fcRiSeJKSqCJFGW78Q+BznvqRAEgTXFZtYUXzrftBNdYzy+uxuzXuL+a8rId09/YRv1hTl2OqWc1Tvs52T78ITAZaa855oawtEkR1oGGAvF+eObLSiqyoZlRTz7Zgtum4mGRam+NlVV+f7Tx2jrHUMUBQx6mXu2zW5Rd9QfZc+JAVRN43TvGIdbRy4o+dx9tJtHnzlCJJZkXV0hH79r1aS/V6Ne5pH31lNfnkFz1wgluQ42Lr3Qa2l5dR7LKnPoHvSzuraA/DMKcuvqCqivzkESU1L90Wg665Lm6mBegcsrr7wy/reiKLS3t9Pe3j7pay9VycqXvvQlsrKyeOyxxzCZTHz1q1/lM5/5DMePH78kx0vz7qJpNMGurhgJVaPLr/BgnQWTbuYTYUEAtxQjiIoggE1MIJ1Z/VqbqyPLLCICZY6519kvLzRzpDeEJ5RkY4UNh0mX8o2ZxPfFIAnoRYFIEqw6uERWAjPmeNcYe5pGyHebuL4hbzxLoaoaTb3+1GQ733ZRLwKrUcfn3lPDsC9GttNAi1fh5Y4QogDXl5upcl/aFd6kojLii2LSS4Qm6WMpyzRy01I3h7qCLM4zU5NnZvfJfvafGqIy38GNq0tmbOgmninxU9VUuZlelrilPounDw9jN8ok4nFebxpBEgX84QR//2d181LNOp9wXGVPR4ikqrGm2Ez5JP0mmRaZAqeO9tFUqdj5vQwlOVZKcs71GsaTKgfbxhBFaChzXpClUlWNnz17nLca+3HbjXzqrhV4/FEiMQWrWYc3GCMcTUwbuMwVTdN4cv8Ah9q82CxG/nh0BBVwW2Qa+0KE4yqWaU6e/U39/PTZE4SjCUpy7XzxngYefXOQ5sEIJp1I7ttUmDz+KP/v8UP0DofIcZn5/PtWkHdGpvm1plHePDWKJAr8encPeXYdrx/pRBIFPrS1BJ1eZnnp1GoF164qY8R3EkkU2HIm6yQIApa3edkcahmiscOL2Sjz5vE+tq8sIXeGpomKqvLEq6c40jJIfXUut2+pRppkgfJS8ts3u2gbCKKoGpkOAx/aPn1ZltNmpDDbTmP7ME6ricLsuQsglBe4+dJHtvC/frSLpo5RNE0jEkvy/h3LWF6dh0EvkZuRCkaf2t3BvuZR4okkmTY9ieTs+9ksRh1uu5G2fh8Wk45M+4ULEh39Y/hDMewWA81doySVlCDMZJgMMjetK+OmdZNnluwWA597/0YisQRmo+6CudrZTE58Ppr7adIsMPMKXKYKUi4ne/fu5fHHH2fDhg1AqtclLy+PcDiM2XzpVn/SvDuIKRqKpmGQBBKqRkKFeAJagwICUGnVsOkgoWiIApNOEHWChkueKLGskwQWueZvhJdp1fGZrXnEkip2qxmDxZFSpgoHSMYulE/OMsLqbJHRqEauWcCuv3JlYv5wgh+/2MbgWAS9LKWkghenSmh2Hujjqf19ANyxpoCbV05U4Xo7Bp1EYWbqnD7YHyaUUFE1ODIYu+SBi14nceOqIp55q5tct5m1NRc2wgqCwA21bm6odROMJvnXJ07yp71tmA0iR1tHyMuwUF85s/KhVYvz6RsJ0j3oZ+OyQtwOE5scJtaU25FEgT+81YsAqGrq97gQ33AsoXCyY5RDA0mODSuoGvT5Enxo7cSSNKNO5MPrMun0xMmyytOWBT2+t5fnjwwjCHDrilzuXHvuex7xRXirsR+Ajn4fR04PUVPkIhmP0zbiY+uKEpyT1O3Pl0FfnP/viUb2N/aTVFSyslzUlGWTSKqEYgoNJTbMF8ni+UNx4vEkNrOeQDiO2SDz8I2lHOrw4zDJNJReOEke8IQY8kbIdpkY9oXpGw2OBy46SUAUBJQznkWPv3ySA42pc+OGtUkevHn5tGNZV1fAkrKUh4fNfGEWRdM0BjwhdJKIw2LAoJcIhOMUZtswz0At7CzNXR6e29MGwLO7W6kpyaB3OMjR1iFqyzK5YU35JXdaN+olFFVDEMAwxST9fHSyxCfvXsOJ1iEyXWaqi+dfQn7n1sU89nwjY5IVOTsfT1i5IIsTisR54UA3ep1EMJLAbTdz3aqSWR/HZJD5xG21vHK4h1Ptgxw43kFhhpmcM31IteXZ7DvRk5LSXpw/adAy6Amy87VTaBrcunnR+LaTIYoCFtPVLaaQJs1Z5jWrKimZ/Qm50AwNDVFRcW7lJTs7G5PJxNDQEKWlpVduYGneEdRk6OjyJRkOq6zI1WM3iBz2CnhigAC6sEDMF+TZRj9mvci9K1zkT9Icr6gaQ2EVoyzgMi78SqQsCciShGwwIYgyGhqy0TwhcBEEgRKbQMnl94GdQFJRSSRVjDqJRFIlmji3ane8y4eipMqpjnX5ZhS4nE+WWaI3kDxThnfupu2Pa8QVjQzj3JvSp+KGlUVsWJKLXhanbSLf1zLKgVYv0YRCPKlgN+lJKDNfddXrJO7evnji42fKA69dmkMgksQXTnBjfe68J4yapvGTZ06w52QfEXMGFncGdoseX2RqGWWrQaI2z0QwHOdAYx+ZLjMluc4Jr2sbDCGQOj/ah0MXPGe36MlymmjtHcNq1pPjttDWN4ZOStXmD4768QVjE5qq58srTR6OtXtIJpWUnLSc5LPXFxJPavgiSapzL67gtWJRLvtPDTDoCbFleSGZjpTIwGQyuImkSkGWlapCJ239firynVTmO8ef37w4E18owbA/xra6LJ544SiimMq4haIzU+2cqofi+X3tPPXGaWRJ5P4bavngjloGPCEaqrKxm2c+UdVJIrKU8v0wG3UMjAZ57PkTxOIKzV1eSvOc1JRcWgngB64p4zl7Pya9xC2rCqZ83WgoSSCmUOTU47Aa2bB8bqp6k7G4LIu6VTLPNPp48XSEsdgon9uWauoPhuP84A8HOXaqm1BcQ2cw4rDqp1Urm468DAve0TGa2wc5ckrFG4zxVx/YDMDy6lz++kOb6ej3oWoCg54QOW/Lnv3+pZO8fqQTAYFYPMmn3rd2xsdu6/dzutdHZYGD8gWW6k6TZr7Mfzn4KuDtPTWiKI6rSKVJMx1mncgdiywXGKdJAnB2HVtTea7JjzecZDCgsa8zxB3LJq5qvtYbo3FUwSDDdcUGiu2X5tRSlSSgpVbck1eHFPlUuKx6bltTwEtHBynOMrOm+tzEpr7MSedwGEFIlRCFoqkejpk2lG8rNZFjlRAFWJKZ+j66Ayq7B5MkVahxiazIWpjvQG8wYLbaUVUVURpDVc4FYKFogqd2dzAWiLF9RSHVhU4MsojJqMPusKDEE1xTX4DVKPOn/Z0sKnTNWO53KuxmHR/YWjrPd3WORFKlqWsUk14mHBzDkOEgy2rm2uqpx3mqy8NbTf3sO96Lzx/CZTPxybtXsajkwlXtNdWZdHgGkAQmZJyMeplP3rWSo6eHyHKZWVaRxWuHu5ElkURSQSdLyPM0ZQQIxZL84s1+OkYiXFubgVEWMRj0BAIhjJLATStyyXNePLPjD8X4055W4kmFa1eV8T/uX0sskZzWcf3I6SF++WITsiTyvm2LsJj05LjNGHQSh1qGUFWNZRVZ3LPhXPOzumURkRcSyJLIjevmp1K160gP0bhCLB7jQFM/n7hzxZz2U1Ho4p5rF3O8bZilFdn0D/tp7/WiapDpNKOoGtF4kt++2kLnYIBt9YVsqJvdYsTFKMgw85Hrpv88Woaj/OKAl0hCZVWxmbuWORd8AaN5MIw3lEDT4HBXgHgyC70scrh5gEOn+jHrJaLRKNkO/biYRu+wn8On+sl2W1l1xoy0zxNmNBAnllCpyrfhnKTnKJpIMuiLE0movHliiNM9XioLU2WDkiTxxGun6RsJkp9p5Yv3ryUaT/Kbl5pIqiqRcJSz+dh4UsEfirHrYAeapnHNilLsViNJRaWpy4NelqgqdJJUVA41D/Hoi814g3GynSa+8L4GMq4CsYU0ac7yjg9cNE2joaHhguAlFAqxadMmJOncqmhXV9eVGF6aqxC/IuFTZPSCSqacmCAbXGHVkMVU6FJi1nAYJQb8CSRRwDpJ3XtChTafgiSAP6bRE1AuWeCSiATRFAUEgeR50s6iKGGyORBFkUgoQDI+uZfI5UQQBK5bnsd1y/MmPLejIY/KXBugcbzDy5d/epBMh4GP71hE1gzUwgyyQMPbVNp6QwrBhIZBFGjzqTRkLoxBp8XuRKfTAQJKMkko4Bt/7pUjvTyztxNV0+gZCfLgTXWcHo6xrMyNeVEG6xdl4jRJfPPXBxnxRch1W/gff7aKLOfCKyZomkYwmsRskGfV86KTReors3n9WC85VpkPrHawtCIbvTT5PvyhGD/YeYzOAR/9I0HKcqyM+sJ09I1NCFyWV+dhy8pCFCHPYUDTopz/lWS7zFy3unT83+vqCvAGogx4gmxcVoR1juUr8YRCc7cHh8VAmyfJy8cGCfiDtLQP8dX7lpJt0ogaJNx6hcqcmfV57Hy9mWfePI2qaQyOBvnc/eunDVoAdr7ZRv9Iqi/jtSM9fOquBgCeerONJ19vRdM0blxTyvu2nXNarS3P4ut/vhWYf29oZYGT3uEAep1Eca7joq/XNI2IkuqNk847tiAIbFtZyraVpQD868/fwG6SicQVnGaZxSUZvH68jxcOdCMKMDwWoabYjdt+eZX/modijISS2I0Sx/ui3LJEw/h286x5Uu6UOdSlIgoCVklFOSPEYTHp0OtkTHoJh9VAYZaVm9eXEYkm+O5v9tHRP4bFlApm+gMKj7/eSZc3hsNqYFmJky/cUTPB1HXLinJeONiH2SRgMMi09fvGA5fe4QD9niAOq4FBT4je4QC7j/dyoGkABCjLc7CsKgdNgzu2Lub3L5/kxX2pcr8hb4iP3r6Sx19t5oX9XciSwHuvqaKpc5TdJ/roHw1TU5qNNxhj2BdJBy5prirmPbsKh8N885vf5Mknn6S5uZlgcHLZWUEQSCZn5uA8G772ta8t+D7TvHtJagKjSR2KJhBFxChqGIUk/oSIUdKwyRpmGRbbz2bsBN63wsVbXWFMOpENZRMnOToR8swSrWNJzDqBLHMquNGAuGAgiR6JJAYtsiD9CMlJvGgMZgt6owk0MFtF/J6hBTjSzNDrz7jBx2auOiMIAlX5NrzBGK8cGyCWVDnV42d/ywg3rZqb9GaGUcQgaSRVjRKzOK9J32ggzuGOMVwWPdszshnPwHFhJjeRVMfldH3hJF/9TTN9o2FMeonP31LO0hInB5oH8QZjZDpMeINRRv2RBQ9cFFXj5y+28FbLCPmZFj51Sw2OGUiwNnWMcLxtmOpCB2sW52KzGKaVIAaIxBUisSQOqxFvIEoomqAsx0b1JKVCkgiZZ96rJChc7CvR6yRu31I9/YsugqZp/PCpw+xv7Mdi0rG2oYKhwVGCwTB6WWT38R7MCT81LhgLpswrZ7LPsWAUVVORxVSfyExw2owkz/RlOM4reWvp8aKoKpIo0NztnbDdQmUJ7r1uCdXFbnSyREP19F5rqqaxd1ChK6jh1MPmfBnzFH4si0qzONk+jKJq3LiuIiU/LaSEJVQtlRG+EirshU4ddoNEOK6yNN+EYZZ+Mp39YzR2jFCU46C2fPKetJuXuRn1RxkKxLl5eRYmfep6v7wqlwduWkbfsJ+a0iyqijKwmHQMeoJ4AxHsFj2BUJxRX4TXTnoIRJOEYgpWk0qvN8KQL0Zp9oVTsuVVOVy/towTbSO4bEaqC8+JNGQ5zQQjSTr6A+RnWMnPtCJJIhoaaAJuu4lH7l09/vonXmkEIfW9eHype8jR1hFEEcKxlIpf54APt83IoCfMqD/CmiV5VOY7ePt1L02aK8m8Ahe/38+mTZs4ceIEkiSh1+vRNI28vDwGBgbGy7UuZS9MOnBJMztSN1WN1AVc1TRawxIhRUQnaJRbFOzyhRfpLKuOm5ekVis1LSV3e/6CtiAIXFtioMotY5IF8i1nlLOQiGNCA1RBQtaSyFyi8i4NwjEVbzjBqC+MWUmQYZ37KplktCLKetR4FCUenvJ1ZosNiy312YSDfkJB/6yOY9LL2M06OgaDGA0STuvcG0QrHSIWnUBc0Siwzr3EKKGofPf5Npr7gpj0Eqogsb2hBFVVCYcuXJi5ZlkBfSMhRv1RKkqy+P6r/ehlkUhc4XCHjx31OVQXuqgpdtE9FGBpWSal8ywVm4ye4SAvHx9kMCpzYjSE0dLN526pnHab4bEw33vyEEOeEA6rgb+4Z/VFgxaAbKeJ61eV8OrhHm5cU8bW+kKKc+xkuSYG9U4pkSprBBzSwi9cTUYomqCxYwSzUWbEF0FW4+Q5dAxrOkw6EaNOor46l/2NfRRlO1g6ieP4+SSSCj/941EONw+gqpCfa+XOrTUzGst919aQl2FBEgW2rzzXa7G6Jpf2fh+qCmuX5M76PZ7u83GweZgcl4nNS/On7HMyGWQ2LJ3ZQoAvDh2BlMR4fxj6QiqVU6gh3ri+isIcO0lFo64i9fmtqcmlfzRE91CQzcvycS1wb9JUJJIKR1sG0TSNrGwXTj1YZIFtlVYEQUDTNKKxJEaDPCEgHPKGeWF/J3pZZHVNDt/57X76RgIoqobTYWFJaSYfuKnugh4il0XHZ26cOKcRRYEtDSUcbOrn5f3tNHUMc/s1NWS7LKxfVszeY91UFWeyvDqXzrEkXcMhjDoRQRSoyrWR55r4eelkkU/cXk9b3xhZTvMF/TI9w0FMBh1leXqi8SQdAz7es6kKAEVRuXXjhef/9esqGTwTpN+4vhJ/OE5DVRZ/eqsTq1FHtsvM3uO9BKMJKvKdfPDmZayuyUEnS8TjMwvU06S5HMwrcPnGN77B8ePH+djHPsa3vvUtPvrRj/KLX/yC3t5ewuEwjz32GH/zN3/Dxo0befTRRxdqzO9aBgcHWbJkck+Chx9+mIcffvgyj+jdhyxAlhzHr8roUdGjEFVldKJGXBWIKsKEwKV9JEqXJ05pphGrw0ESEZOQxCbECCRSr7XrBSqdU51OZ0OlS7dqFQwF2NXuQVHhtRN91BcYuXPl2UzB7I4r6gzIBgsaIBll1GQcTZ180qk3GFOTAzT0BuOsAxejXuKhmxax//QomTYDa6vnbtwnCAL5lvkv80ZiCkO+OBajTCCcpH80iH/MM+lrXTYDn7p9KQBj4SQvNvlp7Q/gMMusr075cdjMej5/zwo8gSgZdhO6BejbeDs2s56koCMY19DJAo0DMQKRJDbTeb9JUUIy2kEQUWMhAiEv4WgCh9VAOJoYd8m+GIIg8J5Nldy0rhxZmlwEIXlGjECWRNzy5e3Fshh11JRksr+pj0yHicUlmZQVZvC7Xa1YTTrqqvM4OKpSZsnmumoHRTnTK1mc7vaw53gPBp2M0aByz7W1LK2cmL0Y8IQZ9kWpzLdjOlPy47YbuXNL1YTXbl5WQHmeA1XTKMyanWN9MJLg+ztP0j0cxGyQMRtkVtdMn02ZCUYJjJLAWEzDKDNltgVSGb66ipwLvnu9TuKerfPLls2Fp3Y1sfP1ZgCc5dVExFSG7+VTY7x/dSY/2XmUk+3DLCrN5MO3LMNwnlnjYy80sr9pAAHoGfLjDUawmvQcah1FGgnT1OlF0zSqCx2U5jnxBmM4LAZqSjMn/d0HwjEee/YYw2Mhjp4WyHFb2baqjPt3LOOmjdVYTXr0Oon7t1WwuNhJPKGR7TJRlmOdIPvd0eclFE1QXZxBbdlERbQspwm7WU9jxwiaqvLLZ4/wVx/czMdum1yFrrY8m7//xHYSSZVHX2njBy91UpZj5VN3NmA1yvzkj0cwGSQ0TaWuLGPBe5TeSXz729/m29/+9qTPDQ4OXubRpHk78wpcnnjiCbKzs/nWt76FwWC44EQ2m8187GMfo6GhgbVr17Jq1SoeeeSReQ320KFD1NfXXzJPmCtNTk4OJ0+evNLDeNdjkVQsUmoFSdXAqdMYSwiYJRW7fKH6U78vzo/eHGIkmCTDpue+LTYyrBIRTUefL8Kh4dSK8qpsmUrnhTceEQWjFiYh6JC0JBKXbsVZ0FTeONZN+0iqXGt1iQWLzYXOYCARixEKTCxHmZIzcY5AKiA5S1xNCRec3/oQi0bQ6fWAQCw6sYRtJhRmWijMnFmfwUISiCmMBBXy7DLG87x7bCaZddUu3mgapTjLRH3pxXsDAJxmmX95cAmH2n3kuwxU5Z2bkOp10kU9M070R+jwxCjPMLA4d3alZG6bgdvXFfGrvUMIokBJpomW7lGGPCGWVmRRkGVD1JkQZAOgIRosFOc62LC0kIOnBmhYlEHdFKUxU6GTRYIJDUHQsJw3yW3s9PDo840APHD9YhaXuGe13/kiCAIfuW05G5cX4rAaKTnT27GiKgtREHjmdJjeQBTJYObgiMqKt4lO9Y+GONw6QqbDxKrqLOwWA2ajjpGxMG67CYc1tTKuaRqvHe2lvd9PptPMy8cG8ARi1Ja4uHdbNXqdSLbtwgziWDDKH15vJRxNcNO6svGxzYZwLEkomsBm0hGJJwlEFiYwNMkCW/IlekMaTr1AnnnygPSXLzVzsGWYxcUuPnDj4kviszMbWro8qT4TTcPrD2M8rwyzpdvD3pO9mPQyb53sZcPSApadF3QGw3GkMwpuBr1MQ3UeB5r6QBRBg0gswXN7W9l3RCAYSWCxGLFbjXzw5uWsrZuYyUpl5FVEMZXpaeocIRyN07Aon/zzTEdNepkNi6cONt860ctPdx4iEk+yub6ED97aMOE15flObl1fxsCgl9wMG8PeEB39Y5NmPs+ikyVaB4K81TyKySBxpN3LlrpcKgqc6CQJUUxJaudnXgWylFeQ6RaKCwsL6e3tvcwjSnM+8wpcOjo6uOaaazAYUmnUsw3yyWQSWU7teuXKlWzcuJEf/ehH8w5cVq5cidPpZPPmzWzdupWtW7e+qwOZNJceUYBSk0LUAHoB3r4YPhxIMBZWyLDI+CJJxkIxXFYDMgqtviSxZGpq3+ZTJgQuAqAjjk67tGl2TYOIJnPvljL2t4wio3D90mwMJjOapmEwmYnHwiRm2LCvJmMko0FEWY+SiKKpSdoD0B5MNe0uc8HZXs1IOEgiEUcAEokL32e/J8zrJwaxmXRsW553xSc45+MJJ/npfi/DQYVil44PrnKNBy+CIHDfhkK212VjNUgTjPymw26SuWYSSdyL0TMW55cHPXjDSdxmmU9syppUdvvttA2GGA3EqCmwc9eafAoyLYyFktjkJD98+ii+UJSiI3b+5sH1OPRns34CaCqyJPLgTUu5Z/viSctoLkZ7QOPkWOrv5W6NwjPZrmf2dtA9HBz/+3IELuerAgIY9PIFE1Rg3DDRrBMQhFTWwPo2v5ZILMl//OE47QN+LEYdorCEldXZfOw9DZzqHKW8wEV5QarP4GSnh8deaCIYSaAKIoogUZBh4UCrh85IB2aTgTsaMlhXfq408Nk97Tz/VgeQckf/0oNrZ/25ZzmMXL+yiFeP9FJV6GBF1dyzlG8nwyiSMU2FV2ufj11H+5Blgd0nB1hVk0PDDP2JFhpN0xj1RSgqyODlYwMkVZUti3W4sg2Y9TI31bpIRKNYjDpGfBEy7KYJfV93bK7iVy81oddJ3Ly+nMJsGwMj1XzjFwdo7hpFEAR0YhKbxUB73xgIIoqi0T3kZzJxYbvFwL3X1/HS/nYUReVAYy9vHulk7/FevvSRLeOGjhfjVNcIXn8Uu9XA0ZZBkoo6qeLipmWFvHW8i/ZeL9luC0XZFw+EnRY9NrPM0FgUt9WAy6pHEAQevGkpf9rXhtWk5/o1k5tVpklzNTCvwEWv12OxnIvurdbUCuPQ0BD5+efSjNnZ2Rw4cGA+hwLgtttu4/XXX+epp57iqaeeSmnwOxzpQCbNnFFVldfaw4yEFOoLTVS4L5wsVmQZqc4x0uWNszjHyJJMCVmIYRASZJsEhiMpwckM0+X/zWmaRotPoy2YmqgVOcxsqjeRJ0cx6EQ0TUWUZFQliarOzr1ZiYVQYql66KQKHUFIahCOw5GBGN0dg2Q7DFxTmwVMDMxUVeOHzzfT2OVDlgQ0mHPT/aWgy5ugz5fAbZHoGkvQH0hSdt53LwgCOY6FMT+MJhQ8wQTZdgPyFEpdgahCJK7iNMqE4yrB2MW/rxPdfr73fDtj4QR1RXYeubWS9ZVOAF491EUgHMNpNTIWijMWiGEzh8++OdREZPx9mmZhRHg+HUGIK6n+la4QFJ65FTiserQzSkuOefQsnWUskiShaGRaJgZXmqbx/N5WXtzXRnGugw/cWo/NbLjg+RFfBINOGu9TWFtgRCcKxBRtgjJdMJLAG4hhN+sJRBKM+FMZzKWVORPKwyKxJPGEgtWsIxJTsFv0jIXiqKLMkDdEqGcUJRpmbdmS8XEnldRChygIJJMzdyM/3ePhNy82IgoC912/hPdsKGPH6mJ08vxEKGaL1aTDpJcY8UdxWg3E4wr7Tw1QnufAbV94pbzp2PlGC3/a04onomLNzUfW63n5lB95dxNLSl0EepwUZNl48KaldPT7qCh0XWAWCVBXnklt2UbgnCBCQbadv3lgDQebhzHqRfYeauVwcz+iKDI8FiLTaaGufOq+qLV1haytK+TZN1to7hzBZjYwFogQjsRnHLgsKs5kz7FuorEka2oLp5SJt1uN3LSphh/uPMpwUOHXLzfx5+9ZPq3aXa7LxMdvXERTr4/yHCtluansSlm+k4fumJtcdpo0l5N5BS4FBQX09PSM/7uqKlXLu2fPHu666y4gdeM4evQoDsfsU+Jv58knn0TTNI4cOcIrr7zCyy+/PGUgs23btnlneNK8u4kkVB59a5TnTo6harCvy8L/2J5DxhlVMEWDnrjMiro8NqGwxCWglxUgNeHI0SvsHQ0gAHnzMOnSNFDOFGZNMa+dFF8cToylenPisSR2vUimWUbRBJRkgpDfi6wzkEzEUObh+SIKYJQhHANR03j12AAnmwcw6iVMeokNNRPrrxVNYywYx6ATiSUUApGrq7kzzy6TYZEZCScptOvIslyabJA3lOA7z3fS64mypNDKx7cXjxtJnk9FpoHlBSZOj8RoyDVTmnHxCX/ncBhPMI7bqqNzOIwvlCDTnpqIL63IorLIjVe2kZufzYmInmxFxRC/uILWTHHpwRsDEXCdN0+6e0sVGWcmsdvq5xesNg5E+M1hLwlF47pqG9dUXXieefwRdr7eTDiaYNATZHFZFttXl48//+y+Dp7Z04FBL/GRm2tZXJKBSSeysXjySXamw8i62lz2nhygMt8xbTZhaXkmG5cW0Njp4doVuayvK2DUH2VXi4/HX2tDUVTaSdI6EKDyzPXh+jUlePwRwrEkt2+qnHHQ8eSuZk52jICm8dQbOj599+oLTFA1TaOt14ssiRTnOi5ZMFOQaeVDNy2hsdOD22bgd7tOMeAJU5Jj5/P3rsRpXbiG/GA4ji8UJS/DNkF8IJFUePlAB7GEgj8QRhSMeKIa0XAEMRpjz/E+ugf9OCxGHrxp6aSGrmeZ7LPKz7CQv97CWDBKfUUmjz17lL0nuhEQyHJZWDQDs82Gmjz2nehh1Bdh4/JiXGfOibPBtCyJUwoYrK4tINNpJhSNT5AYP5/OQT/fe/oYjR0eTHqJZFJlfV0BqxdPlKA/n5oiBzVF08/J4gmFE+0j9I+GsJslFhWkjSjTXB3MK3BZv349v/71rwmHw5jNZm655Rb+8i//kkceeQSTyURhYSH/8R//wenTp7nzzjsXZMCCIFBfX099fT2PPPLIBYHMK6+8wp/+9Ceefvppdu7cmQ5c0lzA+eUk4YTKYydD7OoIEUmo6CSBQV+McFwdD1w8ceiPpGQ+/chENY3zp5N/POLhaFcQNA0jST66afqbxVSMqTo8CZnhcBKzkKTCpmGZwZkpCKlJI5qGhIYogEVMohdSq/XxWHRWEsVTIQqp8rCBCMTjCs8NeLAYJGIJlWBs8lVjnSRy+7oSntzTid2sZ+OS+TcPN4/EebM7gtMosqPaQabLAQJEggEUZXb9Qzk2HR9e46bfn6DYpZ/Un2chaOwN0twfwmXRcbQrQPdolIqcC520NU3jlaN9dLYOsyjPzt0NuehnYMRZU2Aj12nEF07QUOrEfV52w2038bG7VvOHtjh6WaQzoNIdUCeUM86UeEKhuWcMp1VP4Zla/ToXuFNK2OSf95YcVgN3bJqfceJZDvWEGQ2mzEnf7AhNCFx0soReJ+HxhdEEgcbOUQpzHFQXZ6CqGi8d7CaaSOIJRNnXOMji8yac/nCC7pEQRZnmcRd5QRC4f3sVN60uxmLSTVveaNBJfPSWOlRVG59YF2ZaQBB444gOURCwGESSyXPZsxyXhc/cs3LWn4NBL4GWytYYdBMvDjtfb2bn681IksC91y9lc8OlU/JsqMyioTKLA6cGGfSEyXaZ6R0O0jscXLDApWfQx388/hZef4Q1tYV88NYLKylkSaQw287e4z34AhEID6FIehKROKKqIYsCBp2EqqrjQhGnuz3sPt5DltPCtavLLiqS8eaxXn7zUhOiKLBpaR49Q348gSgVRZmM+qNkOqbPMI2OhVlUkklOhpUtDSXj43/5YBe/39WMJIl88KY6GqomvzaWFbgmffx8hrxhwtEEJr1EJK4gyxJO6/wzxZqm8bPnjrPzzTZG/REyHSaubShk1cr6ee87TZr5Mq/A5c4772Tnzp08//zz3H777VRVVfHII4/wL//yL9x6661A6gSw2Wx84xvfWJABv53+/n5OnDjB8ePHOXbsGNFoaqJ2vvlkmjSvnfbz6ukg+Q4d9zS4OTKSoM2nkpVlQxVEwoEoi3OM5J9nHCkJIAoaSQ0MIhOyIZp2VlpZmLNgmKJBSNXRH0owFFaQSZVmrchIBVf9QRVBEhhLyOhFjSp7qs8EwKEXWJ4h0hPScBs0qmwxZEG4wD9B1SCspd6TWUgyC1/CC7DIUGEDTZPYsSKfjoiMw2pgUfHUE5VNtTmsXZSFJApTyrXOlLii8dzpEJ6IQocGm2vMFJqtZ/wiRAJjo1Nu6/FHONY2QrbTTE2Je3wCkW2VybbOz8pK0zTCMQWjXprU9DHXacBp1jEajFPoNk0qUd01HOLJ3V1E40naB4MsLnTQUDn1KutZynMs/NUd1XiCcUqzLRM+Y6tBwmqQ8MdTKlGyoPHcW50cOj1KXambm9eWTNgmFIkzMhYmL9OGXiehaRqJpMoPnznB/qZBLCYdH7uljqXlmciiQPHsBLFmTb5Dh14WUt48rolZKLvFwEfes4Jnd5/mpYNd/HF3GyfbR/jL+9dRXuCiJMfO/qZBjHqZ/EwLmqbhDURRNYHv7GyiczhEYaaZR25fguvMhE8QhFkZJ779M6wtdvLe9cUcbvPQUOGmqmDyle239+VMxz3bF2M16REFge2rSvn1nh5O9IYI+30sztDR2j1KUlGJxBQOn+q/IHAJR+PsPtqNIAisX1Z0UdPMmVKWZ6c4x07vcHD87/lysKmPJ19pxOOPjJdlHWjs5bYti8hwnIuOBUHgw7fW81bjAMFwHEUSMBrAZTeQ53JyzbJceoeCZDhM2G0mOgd8/OCpw3QP+jHoJcwmHVvqi6cZCbx8sBNfKIqianQNhXjglga+++QR9jUPMxI4xBf/bDUW0+Sf5eBokO8/cYBBTwiXzUh+lo3q4tQ5/ehzx2jp9SOJApn21ikDl5mwqNjNsvIsGiURq0nPB25cPG5QOR+SisaJthEC4RihSAJZFGjp9Y1bXKRJcyWZ1137lltuob+//4LH/vmf/5nVq1fzxBNP4PV6WbRoEZ/5zGeorJzeU2CmDA4OjpeJvfzyy5w+fRpN05AkiYaGBu6++262bdvG5s2bF+R4ad75+CJJnj/lJxJXGQ4mqMwyEhJTK6J6vUxRro0dK51sqrBeMPl06aDSquFPCGQYNMySRk9AJapoFNsk3lOfiSikJuW3LLuwdKBjOMIv9/SjKBrvW5dLVe7kSi8iIAsqSZVx07aECglF45UeBb3BiCRLhOIJJEHAIKWCl7OU2gRKbVNPfnyqDr+amvDFBJEMeX4lW4IgUFudhz0iIgrgRaNIU6YMiAwmC6LBjKYkUaL+VF3cXI5LKvNzdvOzjdYaIEkyJouVeCyK8jaT21hC4du/O0xLjxe7Rc9Dty9n6SzVs6ZC0zR+/UYnu5tGyHeb+fiNlTgt5ybXoWgSp0nik9cX0eOJUZNvxWmZONHRGa1IOj1aEiRUhFkEeZl2w3h52Nux6UW2FuroDih0jyX48Zv9HD1wGoce2vp9lOXZqS091zjv8YX591/vpW84wKLSTO69vo5HnztJ32iQIX+cDIeJEV+UYx2jVBa6xo33LiWbK2w4zTLxpMay/MlXuBeXZbFzXxfeYAJREmjt8zHgCVFe4OKDO5ZQW5aB2SDTUJXNz/90gr0n+5H1OkYiIrluM51DITqGguOBy3yRJZF7Npdxz+bJG5z94Tg/e+k03cMhrqvPZ21NNic6vbhtBqqnCHJy3FY+dEtK5vbf/3CcV/s0YqqAqBno7++nMsuApmno9TKLSi8Meh9/8SQv7msFAQZGA9y/Y3K53Nnitpv4/L0r6RsJUZRtw2aeXz9TUlH59fPHGRgN4AvGEBDwh2JUF2dc0Ld0FqfNSCAUSWVbBRlFkSjLc/DA9TVsWVZANJ7kX397iO89dRyHRU84FMNkkIknFCLRi5fOFmTZaOnxIooi+ZlWxgIxgpE4GXYTA54Qg94QfS1BguE4a5bkXxDsBsIxgtEELpuRUDSBL5gSRjnU1EdLt5dQTEESRQbH5qbEeBa7Wc/n7121IFLr8UQqe67XSehkkZJcO3tP9gEakXiSomxLun84zVXB/JYbp+C+++7jvvvuW/D91tbW0tTUBJwrGfvc5z7Htm3b2LJlCzbbf28JvzSTo5NEDLKIN5xEJ4kYdQKZVok2n0Q4obE4Q8/WChOalqrZT2qQYUhNlPNNkG9KzZabvQqv9SZIKBoVTonrivU8tHVyrfudh4c42RMkmlDp8kT50JYC1lVMrD2PqZBIJMgxCYhnSr4q7eCLq2Q77dgNOjRBYCgkEIxNDDp8MZUmTxKDJFCbIaN7W1oooUmcTQclWBj/EOmMQ3aqyficp/wEBBHJaAVBQpB0aEoCdRIzy5gqoGpgFLXxbJHJYkc2GElEI0TDAXSSwM3VVt7qiWA3ili0EPGYHkmS0OlkZL2LWCRB64CXbCmGSdI4OJigfShE+1AQp9XAWDBK30howQKXgbEou04Mo2kaJ7rGONzuZWtdavW0pc/PD54/TSSmcPOqAm5cMfnvRBBlSgpc3HdtLfsa+6nKNbG0dPYqXJFYEoNOmrD6X2CVkNB4rTVGXNEIxVX0osAk1Uac6hrldI+HTIeZxvZhnt3dyrG2YQw6iXAkjl4nIUgSLzUFOD7cyoe2FFCdd2llrCVRoL7AfNHXqYiYzUbCkRg2q5HFpZkpRT29zNb6QgRBYNATYs+JfjQ0BkcCmOwOhsei5GeYKLqMctz7Tg2z79QIelnkD3u72NM0REufH5tJx8d2LKK+fPr+ifbBAIJoR40lEGQRRIkNS4vIvqYGWRJZUpaNqqbOJUEQ6B3yp4TkNOgfCU6779nitBoXrDxMFASMehlF0bCZ9KxfVkxNWRbLqnIu6Oc5i6Zp5LrMeMZCJJQES8uy+fQdy1l65vMb9IbpGPDjshkYGguzqiqL3sEx8jJsrKktuOh47r22hpJcO5IosnZJHmOhOCU5dobHIiwpzaCl28OvXzpFIqlwvG2Yv/yzNePX99J8FxuWFnG4uZ8VNXnjzfytvV4MskhCTX03i4pcPPbMEcaCEa5bU0n1NP0sUzETqfWLcbJ9mJ/98SiqpvFnN9RRX53LjWvL2H+yl0RSRRJh3eLpjVrTpLlcXJLA5VLR2NiIIAjU1dXxla98hVtvvRWj8fK486Z552LWi9y7ws2B7jBZFokVhRYkEex6kdiZ7AlAbxiOeVPlW6XWVA3/+YxEVKJJDZMMg2EVVZtYPgapUi9PQsIb0/CHkwQSKo/tHsBp1rE4/9wNRtOgMyLhT4oIAlQ7BHINZ4IMVcSUFIipGjoR7DpwShoFpjNytqRu3C93x+kOKIiCgKJprMy5cNXTIiaIK6nVSqs4Nx+Zc0dMkWNQSWqQ0CDXoDL1Ipz2tj8nZlvGkiJ9MRkVyJIVcgwKst6IwZyqQRLNVhKJGEoiTplLR9l4F7iC3zuC0fT/s/efAXae5bU3/nvq7m329D6a0Yx6725y7x0wxgZTYkIJCWkkkHP+STjvyZtwkpxDThJIAgRjTDO2MbZx77bcZPVepveye33a/f+wpZFGM6NiCWPyan3SaD/lfvq17mtd6/IRjJSRLDhoqk5dtIxkJkt/PMa7oyaWo+GOllGIxWioDLJozpkHBrPB51IJeFT6x3P43Soh77FsylsHJugbz+Fzqzy3fYgrltXMKCUTwkFCcOnKFi5d3oyZS+BYp2dbXVpf8MgrB3l1Rz81UT/33rhkWsGvW5XQVQnN66F1bh0hO8uGjgrmN069wWvLA0RDXsYSORqrQ1RFA2iqTMGwWNAY4fZL53H/GyNkTJn+WIFX98d+7cTldHHd2iYm0kUKRZNPXNmBz6PznRf72DuQYWlTkI9dUEvAqxMJuOkeThLy6dxx2RwCPg+NFT7Kz0AadrZw6yqqIlEwLHwelcGJHEGPRjxj0DeWnUZcDMsp9ZHyq7hUmcsWVfHg1gkkl4uQYnHJ8mouWt40KQHbcXicn750EJemcM9V87h8zRzG4lkkCS5bdW5tbi3bYfvBYRxHsKy9Gk1971k4WZa454blvPBOJ0G/i+suaMfnmTmL0zOa4bmtA1TXVqKoMj6XxqduWs6i1mPPd1XES3NVkAP9CaojPm67pIO6cv9py1Y9Lo1LVxyT3FXpKl/52Bomknlqy/088soBioaF160yFMti2aXmr1DKut1zwzI+UlyEx6USSxeZSBfpaIzSXhdkMJanrb6MyqDG46/sRQgYHE3xl797OVs6E4ynDZa1hKkre3+c2p59q5OBsRSyLPHUG4dY1l5NR2OUj16xgM37huhojDDvNAwJzuM83g+cE+ISi8V44IEHeOuttxgbG2Pjxo189atfBWDnzp10dXVx5ZVX4vGc3UP48Y9/nFdeeYWdO3dyxx13oGkaa9eu5dJLL2Xjxo2sX79+sqfMeZzHUZi2oD9pomkKcyo8k5a09YGpH9nxIhQc0OVSIfrCMFOC8oaAwuGEjeFAa2jmmgaAkQK0tVUykbPZ15egLqxTtByyMxSyG6JU/G+LkkTsKFQJhpNZvB4PRcvB5eTI2BLdKVgYkZgTlBFA1hRokoQpBDlzOjHwyTYuKX9km2cm0xJAUfJgSRqqMHGJPBKlXjdN3tOwVxYCK59C1j1gWzhGHllRUVQNyywiHIeUJWMeIYBxW6YKu1SELASSLJfY3UnkZSUDgiKK7MK0BbbjEPK6mYiBEBKqJLFq9TwuiFrURjxnLWc5HkGvxmevmsu2rjjVETfLWo4RgaqwG7euUDRs6upDs9cWCQczl0TWXAjbwjIKHBhKo6sy9WUeCoaF36MxVgDLEVT7ZJTjbsqxRJ4Xt/RiHtGkb9k/wuWrphZmh90KN7b7ORQzqFrQypIqfUbJR1NNmN+/Yx19o0k6GsspC3nRNIV4Ks+6RXU0VYd4s6fAO51JNEWiIvDBedcuai7j//nUGqCUYd3Wk+LNQwkkSeLJnRM0VQfY2B7ic7csY/uhUarKfCyfW8lzb3fy6PM7mNtQxkeuWDilq/rpQgjBu91pBuJFFtX5ppkvnIi18ypI5QxG4nnWzavgtd0jbD44Tk2ZlwWN4SnLFkyH770+zOGxAg0RF5+5sJrr1rWwbG4lqiJRNUOzwV+83snAWAbbETyzuZd7b1jEvJYKJCT85/D+B/jV6wf55av7EQKuWjuHO65cdFbba64N86mbVpyUXAghuP/5Q+zrTyABly1v45b1zZOuXUfh1lV+77ZlHB4sEZeqslNn7k6FgFeffIesnl/D9kOjZAsmV61unibTkiQJr1tjV/cE//nkXgqGzRUrG/iLz1xCOlOktaGMJ14rnTtFlrBsh82H43z/pR5yBYu3Dk7wtdvn434f+l9FQx4kSo04o2Hv5Pivv7Cd6y9sxzCMyfrh8ziP3zTOmrg8+OCD/M7v/A6ZTGay2LCq6lix2d69e7nzzjt54IEHzlo+dt999wHQ1dU1WePyyiuv8PWvf53/8T/+B7qus27dukkic/HFF5/V/s7jvwa2DuR5+kAGyxF0xQy+uCE6pVP6UZS7YChXIhFVHhjP2YwdscsNumQaAgo3t7oo2FB+kr4tmgwhn87G1Y1EQy4SE2nmVXtYVD81yJAkqHXZDBQVPJIgqh8jAwIYyxRJxvJYDlT4ZExJRZNhTxyaAgJFklhVpfH2sIlbkZgfnblQ9HQJS9IQ9BxpMtkaAGQdUyoFp4bkomiaaFh4z+CtIawi9pEMgqJq+EJRZEXBNg0yiXG8ikPSknEE+JXS8VtmkXw2habpmMbJrZwdxyYRG6MouUmaJYsrxSnSGlYYySqM5x0WVeh0nKGUIpGz+OW2cTIFm6sWldFWOfOky5xqP3Oqp1eoX7q4Cp9LJVe0WDU3elJtuLBNbLt0jL94e4Cntg4hbBvdSKNKgrlttfha5mADC6Mq62uOBZ9et4rfo9M3msbrVgnOUqcxp0xjTtmpi7Jb6iJT3IyuOqER3d0X1tJW7UNXJS5oP/si4HMJ7TgnNr9bQVVkOieKKC6dH7wbZzhj86GlEa5bX5IUH+qP86NndiMh6BtJMrcxyroZuqGfCnsHs/zgtUHSBZs3D7n4yvVNRGaoZTp+nNetbpj8u60uxGXLagn7dKInZH56JgrsG84TcCkcHM1zeCzPsgY/deWzuyIEvTqW4yBJEv4jxeMz1YicLoQQbNk/zHAsy9K2SuorjxXZHR6IYdkCRZY42Bd7z/sAGJzI8oNnD5DMGdx2QQurO2aXJhUMC0WWsB2B26VPIy1H4fdoLG399TTInFMb5r9/8gKKpnVSudzWg+OMxPP4PRqv7Rripg3N1FWUzuElK1oYHEsRT+a5/qJ5jOQscgUTRdiMxnNkC9Y5IS6btvfy7FuHqK0IcufVSyYJrBCCzXuHUFWFjauaKQ95uWRF0ym2dh7n8ZvFWRGXN954g4997GMEAgH+1//6X1x88cWsWbNmyjI333wzPp+Phx9++JzVvbS0tNDS0sKnP/1pAA4fPsxLL73ECy+8wCOPPDJJZizrvUljzuO/FgqWg+OAUbToyhXpjflon2FWtM5bcs+yBBSLFv/2bopU0aE5rHLP0iA+XSbokjmVd06FCxaHIWFIbLiwmjK9EkWWZgxeo7ogopXu0+MnGWVJYlW1yjvDFh4V6oMqh9IlGZrbxWS1SkeZSmtYQZKYMhN/pnCEYNsETBilbSsStARLhEcgkbcEPWkJ25HpCDiUvYeJW0XTUTQXjhAomhtZUYmqJrpkYgtwU5LfyZKEkc9i5E+z54gQvLq9hzcOZ/C4VNKZLH9+SzuXNr73YO3FfXFePZBEkkoNEL96XeMZFaaqisyG+WceMG3pjCEBI+NJ8skkbbVBXt/Rx6poDZGgh56Uw/rjXLf9Hp3P3ryULQdGqAx7Wdk+1aEomTUYihdoKPfic599gj3k1bh6ybmT2/260Frp5aY1Nfx8e4yMIZMuOLx8MEV7hYsVDT6yBYv7XuikazSLLBwaK/1TiM+ZIJmzyBkOIY9KpmiRLtgnJS4nQlNkWmfpAxX1a5R5VYZSBhV+jcrA1Acvb9jIsoTruNn+j13RTnWZF12TcWsy937jaTwujc/fsoz5TWdeP7Xz8BjffWwb6ZzBpp39fO0TGyYlXGsW1NE1kMARgvWLz65nzys7h9jVE0NXZR5+vYuVcytmzLxIksRHLp7DI5u6CXg0Lls6c/3YuYJhORweyRLyatRGphIUj0vF4zr5c9VQ4cfrVikYNkvmRCeNRQAiQQ9f+PC6yb9HkwV+/EyGnv4xKsJuhseaiQbOvLYkni6w7eAI4YCbOdVBHnxuF8lMga6BOC21Ea5YW7Ir33V4jO8dubZ1FQG+es88gj7XpHvY+WL88/gg4qy+ZH/zN3+DLMs89dRT0wjLUbhcLjo6Oti5c+fZ7GpW7Nu3jxdffHGyj8v5dOZ5nIhltR629GR5pTeDS4YHNo3wR1c3EDohdSBJEDkS6z7SXWQwY6MqEofjFrG8jU8/dWDjCMg4KiE31HmP2g+ffL3ZVBFtYZU5IQWJUhYooAtylqD6hGyPepZWw1BSYxlOSaJmHZGtyVi4RQ4TlZGsjWnbmA7EDYky/czdwWynRJAkqTRTajulc+6THV7pN+hK2lT5ZK5odOFSz+yYTNvh4EAMVZaI+HQc5+xtO49+s09lhJbMGhwcTFFT5qUuOp0QCyF4bucY7xxOMK/Wz02rqmfthL2kKcyz20fweXRctk46V6SiLEjQq+MIaApMX6+lJkRLzXQ3qrFkgb97aA8TGYO5tQH+4IaOc0JeThdCCPb2TJDKFlk0pwL/kWB3LFngqS0DSMC1K+uJzuKKdjaQJInL54XpjJts6s6hSKU6n6PoG88ylDRoa6mhbyjG0oVNVNZXYdgC/Uy6wAKL6v0srvfTFyuwpilIXeTcHY9bFlTaMXKZNC3lFWzfP4iYU05dhZ+3OlP8Yss4bk3irnVVtB2ZjKkMe/noZe2MJ/N88R+fo280DRJ886Et/OuXLz9ja/LxZI5M3iTkd5NIF0hljUnicsHSRubURXAcQW3F2ZnjeFwqsixhWA5+jzatds5xBC9s7uLwQJxl7dX85V3npst7z3CKH71wANNyuOPSuXQ0HMskOo7guy/28m5nkoBb4bNXNDG/7vSOUwjBaDzP8rZyQn6dfNFiaet00i+EoHc4ia4pRENeIpqFVu0nVzDYeWiEhXPOjLhYtsO/PbqNPV3jeN0ad1xeMm2wbAdFlqe8exKZArmiScjvIp0zSOcMYobEoztKTZlvWRKmtfyDIwk9j/OAsyQumzZtYvXq1bOSlqNoaGjgueeeO5tdTeLAgQNTiMro6ChQevg9Hg+XXXYZGzdu5NJLLz0n+zuP30681pXgpT6FGr/DpY0aCyp13j4AZX6NeM4injOnEZfj4dMldKXUP8LtUoh6Ti9dH7N10k5pttUUZ28/LB/5eqsSVHsEL/Rb7B4XtIYU1lWr52xGTJEl5oUF+5PgUaDRVyrI14SBKgw0IeEg41LAr743UuBYJtl8AUVWsCwDRKnmZyzvcCBeInldCZveiM3cyJm9mi7oiNIzlmM4WeSapVX43CqG5fDsviT9cYM1zX6WN5y+XGzjvDDJvE2mYHP1osis5zlftPi/j+3l0FCa8oCLL900n6bKqTKe3vE8v3h7mIJl0zmSo6XKx/LmmW1vb1tXz4KGEJoiEYunGE/kWTa3Ej3gxbShxn96WQHHEXz7V/t4a+8wfo+K7Ti8snOQA10jlAU93HpJ+ySR+HXh9Z0DfPsX2xmI5amMePnrT61nXmOEhzb18PqeUQSljMG9V7efdDtdg3G27B+mMuLjgiUNSFLJ5tqlKbNeF8cR/Oi5/ezZN0LA4ycYCSOyeXq6DRbXtFJb5qW2zEv3qMOqVfNoXN7OWxMSlVnBBVVnlr0MeFS+dGUDOcPB55LP+pnM5k0efvUQQxNZLMti94FBiqbF1j39VFVHeW3XEF/92Cqe2RUjmbcYSzu8fjA1SVyOPwdHCbwEWJaDIwTy7D6AM2JpWyXtjWWMxrKsXlBH1Qmyy5ry6YG8ECVZbtESzK1wndbkylUrShmbdM7k0qW1087j7s4xfvbCXgpFi92d49RXBqk7DbIkjjTulGe5Lk+81c2e7hiSBI++3smf3rECyxFoikymYLFvII1XlxlLG+wfzJ6UuCQzBfpGUjRWh3h+Sx/Pb+nD51a594bFLG+bOQP72Cv7eGrTQTRV4a5rlzCnLsK7+4YIeF3UV568u/1MyBcthiYy+L0aqaxBMmvwiRuW8cqWHqrL/axfckymuHRuFYvmVNI/lmL1/FrqKgJ8/+0JeuOl79bz+9Pnict5fOBwVsQll8tRUXFqOUQ2mz0njYvq6uoYHh4GjhGVo/UsGzduZO3atWjauWmudR6/veiO5fj5zlEyRRjIO7hVk82DBXKORCpmsGZOkNrwyV/GK2tcjOQFibzNRQ1uvCfJthi2oDtp4VYlNLeLSfthcW7sh4+iJ+0wlnfwKBKHkzaLoyr+cxh7Nvgk6r3TJQKSBE1+CbfpQpIkXLIJnDkhE7aBnUviyGrJOUuUalo8qoRLkUgUHXyahPcMsy1QKpT/3FVzpvzf9oEcz+1LYTuCgYRBc9RF5DQLdCJejU9eUH3K5UYSBfrGs0SDLkYTBXpGs9OIS8mWtpS5keWT2EdTkpgtajwSrNS994Z+46kCvaNpXJpMMmtQExU8//ZhRidKdrhlATfXX3BuemvNhHi6wGObDtM9kgZJYiiW5el3epjXGCFftEECCYncDIYVxyObN/iPX2yhZziJz60hBGw7NErXYILV82v56BXzZ8wgTKQKbDkwiqpIJMYnyIyOosqCxwcUaqM+1i6o5Q9unMfh4TT4AnQZCppts2cwz+C+EUJlYXYPFWgpdxPxKhwYzlEf0mgIQHtDGR731O+MLEv43e+tFiGVLXJ4IEFN1E911MdruwZ55p1uhpMGuaKFJgmCisAwbYJejXi6QDJbpCKg0TtRQJUlymZopFoZ8XLPtQt54Nm96JrC7968ZNZM38kQDXn5s7vXk84ZRALu0yJm7/TleXx3EsOG9U1ebl4cwnYcDNOZVVrlcanctK6ZojOzW6Nh2ViWg1tXMW2HomHhOOKkGaRUweaXezNM5GwuaPawqm56LYz3yHgcAYoi8+1f7ePQQIo18yq4fUMzrVU+tnWniPo1WqtL5LBgOuiqNIUMJdIF/veP3qB/NEVtRZBEsZT96B/LsvXgKG114RnH+MaOPmzHIZM22LZ/mE/ftJyV80sueItaz1wm5vdorF1Qy+s7+qmvCLCsrZLmmhCL26a/z4I+F3/0sbXkCtaRLJeETy/VHAL4Xef2G3Ye53EucFbEpa6ujj179px0Gcdx2L17N3PmzDnpcqeDeDx+nqicxymRKVhYTsn9yhHQnbIpoNBWF2I4Y+GvDHMgPd3u+HhEPQp3LfDiiJNLsYQQvNBbYH/MQpMl1tVLVIRLgWtAOXWTszNBQC8F+HlbUOmROYW0+j1htqCk6KjIsoyCICdUgsKY3SnrJBBWEcFUu1+fJjG/wsVoTtDkh9rTzCqcCo4oXR9ZOvrv6cu4fUF0twezWCCfSZ7xPmrKPLTVBNnfn6Q26qWtdvpsbEPUw4fW1bK5M0F7jZ/FjWdGSAYSRbb2Zol4VdbOCZzW7HXQq1Nf7mM4lsOyoNIjYRYcZFnCEQLnHHfA7hlO8aPn9mE7go9c2s4LW3o5OJDEtJ2S1bXfPdng8ca1DWSLFhJw45qT10XkCibpnEHA6yJfNNl5eJQt+4fxujVe3d7LBUtKbmc7uyb45RvdhHw6H93YRtCnU13m5eBAAp9bwyo6qIqMbTuYVokwR4MuokEXE0XB2Kjg8HCOwb4RenYfRorWEo342DuUw7Ftstki3b0jUMzTUePl23942axd088EuYLJP/18C9sPjVIsmly2vJ6m+nKKpkPesPHoJflUbYWLObqGjcKK9gpqynx8dK2b5nI3uipzQVuI/pEkW/cPUVnmZ83COiRJ4oYNrdywofWsx6mpCmWzFMDPhK6JIjlT4NYk9o0VuShV4N+f2MVILMfFS+q45cI5M75rhooyo4aMIkGzxyZwXHZ3cWsll65sZl/3OB1N5fz0hf2kcga3XTyX1fNLhV/5osWr23sxTZsLljSwa8Jh33gRXZF4qTPHgkoX3hOMWW66YA6aqmA7DtGQl+8/dxC3JvPidpM17RXce3kT+wYzlPk0Gso9PLJ1nHe60tRFdD6xvprAEcLaPZSgdyRJRcRL30iCmppyBidKluhVkdkdzeY1V/Dyli7cukZLfQSfR2fDcVmRM4UkSdx5xXwuXdGI36Of0klRkeUpy1wzP4TfpSAQXNgyuwnEeZzHbwpnFfpcc801fOtb3+K+++7jnnvumXGZf//3f2dwcHDW388EyWTyPFE5j1NiXnWApbV+dvQncOkKtiwTjQbIjmQoD6lUBtyMnUYplCxJpwzOHQGDGRtdLhWwpwomS9WS/fAZSuVPieaAjFSnkTYEjQEZ7SSDc0TJalmTxIzjcJAoSj4cSUYXBfL5PPGcRU1IQz9uVlYIwXM7RnhxT4zqyjDXrW8lpIszFJtw0mLPngwMF2RsGWLW9L4x7xXL6r30x4P0xYusbwlQ5pv6ulM1HZfHDwhcHh+mUcAyZu+hksoZTKQK1EV9kw3xXJrC790wj+7RDFVhD2XHWQTvHcqybyhHQ5mbSxZE2bjwzIvai5bDD94YpWeiiEuTUWSJdXNOLY9x6wr3XN7GwZ5xPLLEwGicCxfXk0jlKAt5uGR54xmP5WR44s0u9vRMICHx6OuHMU2HoFdHqQwS9OncdGEbly4vkZS2mgD/7Y4lk+tKkoyqu3Bsa5qDXHnYy2Wrmnllay9z6sKs6KhmV9c4yUyR6qgPv6dUz/STlw4xMFZyDqwMe/jIJW18/ubF7OycIBp00z+S5M3dg7TUhFjZMXXmOeqSuLga9rzTy/C+XhAStuNgOwIQCFEySzANE8kRHBjK8PTbXSTTOfwejWvXz33PNsPDsSw9wylS6QLpvMFLW3v4RHWIy1Y08PS7A0iKTFNlgC/ftpiaiJdUziAaLGU9wl6VaxaXemvkCybffngzPUMloqYqMivn/3qL1k+Gjko3e0eLWLZgSY2bzQdG2dMdw+tWeXFbPxctqaU8NJUIOQLGzdJsv+FIJExpCnHRNYWPX7uYzoEYP3hqN7u6YgR9Og+9fJAVHVUosswTmw7y+GuHcISgZzjJmrULUWUJwxKUeaRJ0i+E4LXtfRzqj7FoTiV3XdEBwNf/83V6BuMISWJOTQifW8WjK5PSzqGkwesHU6WmswM5dg5k2dBamohorApRWx6gfzSFS1OYXx9k/cIa6ioCLG+bPXPy0asXs6C1EpemsKi1atblTkQslefFrX1oisylKxoJeHWS2SK/fKObfNHi6lWNs5IW03LYdmAYy3FY0V49xQY85FG4fuGZS9TO4zzeL5wVcfnzP/9zfvSjH/E7v/M7bN++nVtvvRWATCbD5s2beeSRR/j7v/97Kisr+fKXv3zWg/0gk5Z0Os0//MM/8NBDD9HV1YWiKLS3t/PRj36UL33pS+j6r1dPfh7HoMoSv7O6hjfUCd6Il9Lfil9lTjiEgYyqyjT4zl1tSHtEY+e4SdAlURV002+oBBVBWLFP0pzxzCFJEs3BU8tRbAFDps5oAdKGQ6VuM9cvOL7NgCW5sGQdhCBlufnuaz2MZyw6qtzcs6Z8stfNcKLAL98ZIm/YDEzkWFTr4ZJ5ZWd0XOMFwbtjNo6A5eUy1d6pM56mAJtSHY/pcMRZ7PS3Pxtcqszty2d3USqRKYEsyziOM0muHAEmGhICDQtJKnXh/udHdzGaLLCwKcLnb1w46ULlcanMbwhP2fZY2uC+14bY2xvHNkw+sqGOj198Zs5kAIYlSBdsvLpC3rQZSxVJ5z0EPKd+dVdFvNSVuRkcL1nHttaFueLmpWe0/9OFW1dKLXcQeHSFK1c1kXq+iKrIfOLqBbQ3zJ7e9AYjJeLiOORTcSzzGHmUJIlbN87nmnVtuHS1dN9JEv2jaRa3VhANeXCO1CPYotQt/mg/jWjQw4q5FXzn8R30jaZZv7CWD1/aMeM1CGgSN6yoITURR5UlliyuYjALzVEdwxJ8Z2SctLARjoOmyby9q4/+0SQSEqoic+vG+ZPbyhQsXtgTw7IcNi4oo+wkes6aqJ/mmiCH+ktuWh5Nxq0rfO6m+dx6URv7+xM0VPhpqiyR1ROD/aNI5w0S6TwBr04mZxBL5U96vd4rekYzvLlvjGjQxSWLq2d1YltW56HCr2JYgsaIxub9OVy6SiZvUVfuIpMzKAu4p8i8JMAjC5KOjCoJZlLejUxk+JefvcX+vgSpgoMsB5hTq01KtmLJPLbjoKkKI/EcS2vcFCyHRMFhaY170njhQG+MHz+zm3SuyNb9I1RH/TRUBdm2pxepWERIMoqhURmeer69uozXJTOcNPDpCn7XsUGWhTz80V3r+cVLe3llaw/PvXmIqvIAH7piEaZlz9ojyKWrrF5QdyaXAYCfPLePTbsHkZBI503uunI+T73TyzPv9oEomWB87c4VM97vT75xiEdfPYAQcNnKRu6+ZskMeziP8/hg4qyIS319PU888QS33347/+f//B+++c1vIkkSjz76KI8++ihCCKqrq3n00UdPqxbmtxU9PT1s3LiR7u5uALxeL8Vikc2bN7N582YeeOABnn/+eSKRD1bfg//KkCQJvwq1XsHuMQOfLnFRk2vSKip0DjnwhjqduREVR1aICxcZC3K2wO128Jxh08dzgaIjk7YkYgUby4FBWyakOdR6ThiLAJBIFUyGkiZBt8zB0QK7x018Ho1ab6nmQpElTMtBVWUCmkA5w2PaH3cYL5QCk90xZxpxafDBeAEKNrQHz41L2okoURQJiWPZItsyyaUTqLoL2zSwTQMhIIOHIjoSAj953Jjs60vQM5ohGnSxpzfOcCxHQ8XsMop0wWYknieVLGUBntkywIUd0Rl7vpwMAbfC4gY/LxzOgQxPbRnkpW2DfPTCela3ndza1uNSueeahby0tY/KiIcLFp95cHQq2CW2wi0XtuHSFCzb4Zq1LVRFvCxsLgOkaY35jockK8iqhmPbyIqKrGpgTs96HV9Tsn7R1OOQZYmPX9HOs+/2EfDqXL78mPxs28FRth8aw+vSeHlbHxcuqad2lh4o85vL+frvXFQa1wnB3uoGN//759uIpYvceXk7L799aNKpqWBMtd3/5ZZRnto+hgCGkkW+eOXsfTE8LpUvf3gVi5qj7D40TENVkIuXNwMl4nkyidHxKA95WNpew/YDw3Q0lbO8o2bWZR1H8Or2PvpH0yxvr2JBSzmZvMlbe4bQVJl1C2owLYcfPrObrqEUl61o4MrVzRiWw3eePkDXSAaXqqCrMhcvmr0OrO64l+zqjqqSrfBAgp2Hx/hfP3mX1fOq+MTVCybJi3REHpawBKokCJ9gAmLZDj94Zm/JcMKrE3TLrOqo4GNXLpq8XpesaKJrKIlp2Vy7rhVNkbiwebopR75oUTQt/B6dommRL5YyfRVhL6OxLBLQVjP9Pgl5VO7ZUMWugRzVQY3FdaXrM54usrc/Q03ETcjvxrZL9Tyb9w4yljTYuKKRT14/MznY2z3OcCzL/KYo1dHTfz/EM0XUI71skpnSM2PapQyhLMHB3nH+78NbuW5dC211U2OP7qEkRcNmIpHjx8/upTLi58o1Leftj8/jtwJnrZJfv349+/fv57vf/S7PPvss3d3dOI5DQ0MDV111FZ/97GcJBt97kekHHbZtc+ONN9Ld3U1NTQ0/+MEPuOKKK3AchwcffJB7772XrVu3ctddd/GrX/3qNz3c/09BkmBiLMP+rhy6ItHui7CqcfpH7EDCZm/cocwlsbpKwX2GGi9ZkqjyKaRtmXgRJKkkdjrHZQSnDU0WaJI4EqKXajxODB1zuQxP7RvFsATzq10E3DKJvE1FUGdfRoKMQ68HLqnRueviRt4+GKO50svylvAZj0dXAASOANcMM7Q+VeKCypM7/5wIxxHs6Z7AtB0WtZSfNDgWSORlHzYKqjBxi9wkeTGLeczi1NlpExVwcJAxUXBjUhf1EQm4mEgVaa0JEg3M3nAOoDHqZlG9j56BCQIuGd8Rzfh7QdKSKQ+5GI4XyFoqLjPHczvGJolLZ8JkIG1TF1CYE57KyBc0R1nQHH1P+z0VBrMObwyZCCFwWyb9RKiPagR8pXOjqQpCCFLZIh6XNuM1Eo6NZRTRXG5s28I+gbR0juZ4btcEQY/KDcsr8M9i59xaG6K1drq8JeBz4dZVUrkideV+fO6Tz1jMFrjVVwT4h89fNPl3ZUDnkZf2EfTpXLZyapPOZK4UBKuyRCJ76jo3n0fj5ovbufnik7urzQYhBA++dIAdXXH8AT93X7+M8vDshGf7oREeeGY3ubzJ1gMjfO2e9Tz0yiFe2zmIIkvE0kUifp03dg2iqQqPb+pk2dwqPG6NTN7C61LJGzbp/OnX8MmyxEWLaykWLV54t5eQX+edfSNcu7aZXMHg6TcO4/dq3HTxPCp8Mxun7O1L8MLWXoqGTb6QY35zlC/etnLSmhmgozHKX33mIibyNmNFme6kRVg2+d7jO9jTl2TFvBo+dc18FrSUc/GyJnZ1jrJqfi2t9aXA/o/u3sD3f7kVt0vld25dNeM4Wis8tFYcy8RkCxb//FQXnSM5In6N21dEaa4Js6tzDJ/XjUtX2dM9Mdmge3vnBAcHU8yrD/HOrl4eeGonjijVutRE3FiWze2XL2TJ3JObg9x4QSs/fs5AVWQ6GiP84rXDVIfdrGqvYOehEfYPpjkwmOalHUP84M+vnOIiuHZhLW/tHiBTMKkq8/HE64dYOa+aaOj0iPJ5nMdvEuekvDcQCPDlL3/5nMjBftvwn//5n5M9ah566CHWr18PgCzL3HHHHTiOw8c+9jGefPJJnn/+eS6//PLf5HD/P4cdQ3kUSSJbdNg7UphGXHKmYPuEg2ELkoag0iPREXlv7kB+2SGqWmRsmaBi45F/M8xFkwQNLhMNmURREFIFle5jY3EcwfN7k7x6IIlAIp318Jn1FYymLYSu0Zkr2R6nTTBsWN1WdsrZ/eORypk8sXWMvGlz1eJyFpW50WVwgPbQzARDkqTTqmvJFiySOYMdh0Z4+OWD2JbD5aua+NiV82ddx5JULDQkHCxJxxFFFGZ2s5IkcAmTAjoyDi4skCQWtNXxJx/10zs0wbz6IN5T9ENRZYk/uLqZ5rDKgYEUK9vKmFM1dTZVCMHhgQQFw6KjMTor+VKV0rnRVZmiVJpRrTnSK2Q0a/NUZ4FU0SHokvlQh0yl7+w7bZ8K8azJzzbHyKISCXsYjpkUchYjaZPWChfrmv0IIfjJc/t4eXsfNREvn7t1+aSVbs4UbB83sRxYFJ2g3O/GcWyEc+y6OI7g/teH6BrLIwFel8JNK87MZWlZWwX3XLOQwfEMy+ZWEvKfG2vXZe3VLGufObC8clE5Q4lSfcf1y09/vLZTMg5wzyIpmg3JbJFNOwewHUH/eIZnNvdy+comWqoDU4hY0bDYemCEPd3jFE0bv1cnXzTJFy36xzKoikw6k+epTQeY3xTFdgT5bJGgT0fXZAIejWtX1fH0lkHmVAdY23FqFYUQgs0HxhiO51jSEqUy4iHg1UlkisypCRHw6vzbw+9wsDcGErh1jQ9dvoBDQ2l6x7LMqw9RW1YiCZoiY1s2mquUDZ3bVD6FtExCUXi2J09v3MCjQXk+xmNv91GwJQ6P5misCnLD2ibuuX7JJJk4io6mCv7fL10147HkDZvndk2QyltcPK+MhmiJoMeyJsOJImV+jVjGRKg6f/GZS3hr9wCPvHwQw7JZv6hklvDG7iH++j9fJ1e0qK+O4OQyFIoWsiyxv2ec0TEVv0fjwed2sbit6qQZkCWtFSxojjIwluK//8drJLJF6ivD/MGHV1AoGOzpS+HWZOIZk8HxLO0NxxOXOu66Js+Pn9mNEODSFTT11//eOI/zOBd4/7qR/RfFfffdB8Cll146SVqOx0c/+lH+4i/+gq6uLu67777zxOV9Rkelmzd78nh0mdbo9KBFkUu1FbkjFpwnmbg/JSQJqnSbqlmC4vcTLlnQ5LFpOkESv2nfGI+9M0jc1rBVHZemULAcmspcNJW5SBQFY6ZDxoSWI4nSvA1umdOua/nVtjF+ta3UX2k0VeTPb2pjafnZfxQHYzm+9cQ+YmkD0yhSKJq4dZW93RMnXc+xbQxhoaoqquQgnSLz4aOACxMZgSI56G4/Ll+Qhe0hOpqriE+M8kJnlnTRYUWtm4ZZdIeqInP7htmL4F/fOcCPn91D0bS5dEUTd129EE13I4TDaCLH1uECLkXmyjYf77hlJNy4LR2XIrG+vUQkc6agYAkCukwiazCcVKj0lWZNDUvw7KEMg2mLFbVuVs5gBQulnihPbB5kMJ7n4gWVNFX5easrg65KrGsJ4D7Bhcm0Hb79XA9vd6WxZZnlC2uQZYWi6aCr8qS5w9bDE3zvxW5MR9AXS7Bm/zDXri85XG0bM9kxYYMQpA3BVY3w5BuddA8lWLOgliVtlXz/5V7e2D+BrimEfdoRGYxg+6ExUjmDZXMrCZ6iKF6SJDb8GiRyM0GIkmSnvszFX942FyGYrBU7FUYTeb7zxB7GU3muWtnANWtml5edCJ9bozzk5WB/nGzB4vntQ2zrTnLHJa1cvPiYZOxnz+/lhXe7EUIQ9rtRVYULlzRQW+7nipWNPPjSAQaGctiGTCyRJW/aSJJCfYWPsL8UpLfVhXm3O4OiQM5wTjm2nV0xvvf0XtI5k027h/nzjy7nd29azEg8x5I55XhcKqZ1xO3OEUykcry8vZ+H3hoinjVpiHr5yu0LCPt0OupD3HpxO0+/cYiwX+eWWTJU6YLNvoEkmZyBqmtk83kcUZKqOkKQP07adybSqBf3xHj4nWEsR9A1mudrN89BliWqwy4W1gfY3Z+mqcLDvFo/HpfGxhXNtDeWUzQsmqpL2cCXt/aSyhRQFZmhkQQdtX5GYhlsW1AR9qJiY9kOHl1lJJahLOidNAI5Hvt7Jnh37yDV5X7e3TdEZ+8YklR618XTBa5c1ciru4bJFiwWNkVoqp6uerlqzRwUSWI4lmXtglqCs2S6zuM8Pmg4I+LS29t7VjtrbDy3Tja/aeRyOTZt2gTAtddeO+MykiRNuq89++yz7+fwzgO4bkGA+TU+dFWibYZGWi5FYl21QlfKIahBc/DMmEu6YJMt2lQGtdOWOf06kDUdXusrkjYcVte4aApNf7SLps3Db/YTzxjkzDy1tVHqy11cv/iY/tmrAo5DwRT0JiUMNFRFosHjUHdijcwsKFoOAoEiS+RPI7gBMByYMCRUCaK6mLE4f2dXnO7RLGGfRtYQ+F06sgRrFsyu5zcsh+++3E/WUmitCXBpux/5iMprOGngCEFNSJ/Wt0Y7jnxKcqmpoBAOkiyzZbDIq905bAGDaYt7V4VnrMtJ5Uw8ujJrJmVfzwSpnIHPrbH90AifuW0DLrcXIQT7ew1e7ckjSxKXNHv40OKjMqipcqi6oMK8qMamfWMcPjjA6F6FOy9pYW1HBXvGirzTX0CSIJ7P0VKmU8gbbO2MEQ24WDM3iixLvLl/gsc2D2Db0D2apbWlml1DBWQJMkWHGxZHGM4J9iTApUCjbjIYL1LpV+lLGIhCkRsWRzkYgJqgxtIjBGlvfwrDkSjaEiYqjnLsnjTF0XMrYTiw9cAIv3j1AHlL4o2ePFUVMd7tTpE3SgYX9eEgG+dHeGPXIPc9tZuiYbG0rZIvf2TlB0aX/9ir+3n+7U6CPhefvW0VDVWn78z09t4RdndPYDuCh1/rZMmcKKbtUFfuR1Vk4uk8ezrHqCrzEwn5SjP25V4kSUJTFT53y1K2HBjlF5u6sZDIFix2dMWmEJfD/bHJerXW2jBf+NAqVEXGcQRFS9BcE6Z/cKLUUyRXQFUVGqsD9A0lKRoWLl3lF28Psqc/BYDPNcQXrjm51XIsXSBXsAj5dZJZg0zeYknr1EzNHVcu4pev7KNQtNh+YJgX3+0lZSl0tNUxni4ynioS9pWe0c/fsoy7rpyPLEmzOrm5JJtiLI7iC2BkMjSVqVyyqIqdvSnmNUW5ZPF7c1vLFUs1gy5VIV20sI8089QUmc9d2cxArEDQq2CbFpZdst8+sZ6qrS6E36OSL9rUV/r5ysc38PbuATRV5oJljby8uZMDA0n6xjP8t2+/RFt9hC98aPUUUpHKFvneY1vpH03hdWm4dZmgr2TKEPJqLGopx+/R+O6fXEb/eJaFTWW4ZiA/qiJz9bqzt8o+j/N4v3FGxKW5ufk9fyQkScKyrFMveBJ8+tOfpqysjL//+78/q+2cK+zduxfHKQVmixYtmnW5o78NDw8zMTFBNPrr0Zyfx3SossTCmpP3H6jxytR4zzzV0jNR4L43RkkXbNa3Brh1WfSUz0fREhxOWGgKzAmpKKdRiB4zZRKWhE8WVOrOjJmPHaMG20YMJEmQNhw+scg/bduKLOFzqQzHC7gUiava/Vy9fGrQP1EQTBQEuiTYPVpg88ExKrwKq+ZGqTsNJyuAKxeXM5YskjNsbltT2r4taRiyFwkH3c4ic4zQCAGHszIxo2Q/bQmHGvd0klQV8RD0aKRyJh31Ye7a2ISEREvN7AFif6zI7oEMXpfC/oE4tb461rWGeKsrzcNbxhECrlscYWNHeNZtmIUciqojyTKdowneGjJIFAVhl4xhi8lmbVCyGX1xSw8vbh8kY8k0VYeo8kmMxnOsW1TLhgXVk/fIgpZyth0cxTBtVnRUo+k6jnCQZRmvW0eTJUwHBpImr3fnqA+pNEWmBmuaLHHtHDfbtyRxCYtk1ublncOs7ago2WBLpQJ6WQLTsvn2UwfpGknjdZUcuta2l2M5DkKU7o9M3mQkZaLKYDkQz1kIIdgVh6RZclzzqxormoM8tn0cSVHI2jLxosPHVk19r9WV+/D7PRhZE7fXTW/u2NgXR1UyhsBwBKurVPp6SvbDSTWIYenERosYNpMNBnO5ApoMg+MZMnmDoNdF72iKgmHP2tDwdFA07ZIMb4bA7nj0jGb4+aZeFFniQxuaqC+fWguQL5i8uLmLomHRlcyxec/AKYmLEIKxRA6fW8OtK4wl82RyBh6Xwv+47y0EsLKjkruumMe//OwdDvZNIGsu1EAITVO4YmkNt20oZWYiQQ9xWydpKxTyRSqDLhY0hqfs74KlDfzylQP4PBrrFtVNNqPc3jnOT186xFgqz1jWQbFN/G6NsoBO10CcSMDN/p5xlsyd6iJ2srqyo1jaWs78xgiDE1lWL6ikpmx6DcWi1koWtVbyk2d28virBwh4dVITeXJFi0VNYRorpq5zqsxAwOvigjqNl3d0EnErXHH5cuqvXjBNFjYTTMvmxc1dxFN51i1uoKkmPPnbhR0RDo/mSOQsbllZOe1cVIU0/v3hzRzsm2BOXYTP375mWqPS6ze04XWpJDJFLl/VRFWZnznHFc63z6nlV1uGOdQ1SsDromha7OseZ83CY1nDgmGRK5j4PToFw2L5vGpUWcIR8IkbluL3aNiOYNP+CbZ1xembKHDz2oaTNuo8EQXTpns0R0VQJxo4n4k5jw8WJHEGLe3lIzOPtbW1qOqZfyy6urpOuYyiKNj2zFIbl8vF9ddfz8MPP3zG+/514LHHHuOmm24CYPv27SxZMrNryKOPPsott9wCwM6dO6eRnPr6egYGBqitrWXv3r1AqXbmG9/4xinH0NrayuOPPw6Uzo/L5eKuu+7i5ZdfPuW69957L3/5l385bSyngx/+8Ids3Lhx8u+XXnqJu++++7TW7e/vn/L3X//1X/Mf//Efp1zvkksu4YEHHpjyf5dddhkHDhyYtqwQYpIoS5LEF77wBe64447J30dHR/nwhz98yn36IhX873/8BzrmHJNuPPbYYyXy3LAOmi4AMwe2BdvuB+tYg5jy8nIeeuihKdv7ynceJ6aWIWyb8c1Pktjz+oz7vf766/nKV75C3pY4nFexBOzYvp2n7/sX+g/smrZ8aPGlhJdfBY5gXlMVf3ZF6yRx2bVrF1/84hcBsF1lmME5SHYBPb4PSUydTPj5o0/wekxn92iBA10T9HYOI2yLgDVC8uX/O2sdyoIFC/jWt76FI6AgSkHxH3/xswyPjpPLZvjcH///qK5rQFFUXn7+KR5/8U2wTcT4IT75yU+y5NZPYzoSloByqcDv3nrFtH0IwPZUIzQ/Sm4I2cryL//yL1OepxdffJG/+qu/OraO6sVsvxnhiSKZGdRDTyDnxlAW38789dehyBINZS7+9Op6vvGNb/DEE0/gKG5Mf6ngWkt3IjvF0sy2N8icW36PcHUDkq+SsC5xQ7uPRVUubr/9dsbHx5GrlyLXr6FoatRVhPnUbet45a39/OqVnUjC5O5LavjjT99cGpsQvPrObr72F/8d2Yhzy20f4tobb6Vgwg8efopuJ1jK9Ghe0LysWjiXe9dXUXmkS/pPf/pT/vVf/xWAYtkyzEgHCNCS+3FNbANZwdN+IdHGeXztd26nxgt/9eOdmLbDnv2HMLpfQYvvxQh2UKxci3AFkK0MumzhCvqRrAJXtkh87fc+ycvDMHbEGe7xf/8GnZuexHfRveiRalRVodC3g8w7U+/1v/27v+P54XJ29mcJB72UqRk2/duXZ7x/hCRjBObDvKuRZYmmljmMxzIkswYhl4Q7P0jire8hFA+F8FKQNNRcH670vinbWb169bSJrXvuuWfS8fF4WO4qisEFSDi4Erv40u/cMes7Il9zCbavHiRQU924R6Y+t9/5znd58LVB9nWN4dJVOsqLPPbjf5m2T0fxYWllyHYWS48gh+bQ3rEARZHpmTAJBz0MDI1SLORRjARIEq6JdygGFoNwsIP1SLobyUxRXVHGo3/7MVRFZtP+cf7qe5sYHxtDUhRco2+gJvbhuMsRqhclNwyOgVB8gMPX//ufcemllwKwac8w33xwMwe6+hCaH7kwgeQYKOlBTHcNkmOiWBk88bcQup+P/dHf43a5uGFVDRVBF//5n//J97///RmvK4BARigaC9tb+fa3vzXlt89//vOTTawtVxVF/zyErKIUx9ALPUhGGqGFuOKaG/mjz99N0Ocimzd46Pld/Mu3v4OW70OxUjPsE4TsQhIW//rP/3TSd8TxMF21FP3tgIJOjpd/+j+nkNq/+7tv8MSvfjWj3NTSysiHliIUN6onzM0bF/Gnd62btEE++o6YNlbFTbFyJageyMcom7MO03AoFA0Wt5Rz16WN/MWf/t6UYzO9zZieemQrjTu1Z9Ix8ec/+xGVlZXs7Uvyv3+5l5GREfr7+/EMvoySH5ntEtHc3Dwpezcth//1i7088fJ2CukJ3IPPoxRiAKiqOo38/bbFERdeeCH//u//PuX/brjhBg4fPnzKdb/yla/wjW98g8HBQerq6qaN4TzeH5wx+zgaEH7oQx/i4x//OCtWrPh1jGtGNDQ0YJrnthv52SCdTk/+2+ud3Y3j+N+OX+dEDA4OEgqdWeOn49f5y7/8S/7qr/6K8fFxBgYGTrluMjm9U/jprAdQLBan/X266840jtNZd6aX/sjIyGmtm8vlpvztOA4jI7O/yAEWXHQNy668lS6pgrICBLRSHUyhUGBkZARd6cRbvhhJdWFOdJOLJZB0P3ZqCMTMEqmiHqSYySLrbkzNP+sYUqnSx1hwrCGjaVkkU6kZ15lIPUGNUND9EVoalqDIbZO/maZ53DojwN5Zj9mjwmX1GqmswSHLxDINHDNPT28vxlgMnJmfv+rqUqFy0nGRFRoScOXt96DrbtKpBIZh4DgC2y6ScdfgtF2FcCwyBZtMJk2NSzBQkNBlQVg2T3JtRqitb+Tqm26lkMvhOFMDiGKxOG1dKfFDlEgTTnoEJ1n60ASjB1CU6xFC0FZZ0o6ljpxbpeEiFF87eMI4rjqsPT8D20T1pKnJ5wgLh5YKDxfX6cyPll6h4+PjZHDRcfHNuMvqKeRNBvd188S2EW5YPY/HX9oKksxQYqq+vibiYryvdD2+/73v8cLuDEXhZXh0FGvobTwBP9FLP4uRSjGSNJjIWWzdP8xYIs/QeOHYsY49j1xWkvJmYwfBObKfoZ+htrWx8C8+hhCCixZW8NqeMVQjxkjXVhA2WmUDFNLIWgCzkMMEUnsewhnfj/zxu5AkiWVRweEU6DJ8/ekHMXIZfO/8gorVN5N3LMbeeYr8CefdMk0+eUkDP98ygWkLGp0xHjnZMzc8hJabwNW6kWUbl3HDDY28tmuYsUSBsX3beHqw58gx9SApOqI4/f0Vj8en/d/ExMSM95PashTJ0ZAUjUJkDS8PB1g0nGNBlYeBsQybtg0ympER2SGUYAbZZSIhUcikSE7bnuBj1y7j5a09NFcFGD709vR9Kjpa/RIkVwhhGaBo6IbN4f4Y5SEvChKSY+MiRzafxJJkRHaETP9BlOoQsq8GMmPgCSNJGsl4ln3d4yxqrWRgJEk+mwXANookJ0ZxLA9q7TrQPAilG+vAExzxQJ/y/l7eVs7Cejd7do6BmsK2DJzxvQgjj1oRLAXKxTzpkRFgiI9fVIfff0wClclkTvkuBYhXTG8HMB5LMFYobcsZ3YPkHkJSXDjZYRAWkr8WtXoemw4Z+B95ly/fuY4XNnfzzFtd5NQKhKpgDZxcgn1izDDTO+Io5EgY1WUjRBEbB8O0S3LKnX3kCiYTiTSjI8Mz70jPobnnIQcrEAj29sTY1TnOynmld+P4+PiM+1WaLkIJdpTe9Y6LDp+EVllBdcjFx6+aj2LGZ1hvBHgbEBxP244qQFRFQpElDMvBKBTIjY8iMrNfo0DgWFPb/okcL799iEK2gCm8FKjEHt0367q/bXHE5s2bzzjOOor/LxpQfRBxRsSlu7ub+++/nwceeIBvfvOb/NM//RPz5s3j7rvv5q677vq117Dceuut/Md//AfxePy/ZE+Uc5FxgdJMf13dqQtSZ3p4T2e94/d1/N+nu+5M4ziddcvLp3cer6qqmvHFeWLG5URiKcsyVVVV+CrqkBSFzPD0+q1ll96ALxDCklT2pyUsp1TIr4UqqKqqAnsUup8D3Y9uZPFf9kXQvIixvXDouRnH60n2ompVOIU0YryrtJ0ZcNRC3CMLKjWbpCVzeMvrZMcGZl3H2PUczcvXUl93DQVHwn3E1UzTtFnXOR7R5g7eTbnw6BIXNniIJ8KM9A8xPjqMN9tNpGJ2Z7GystJvBgoy4CAxb/Fyhvq6qaiq5s2Xn2V0eIBoVS0EKoj4bbImaPXt+P1+ql2CqMtGAQo556Tj/fwffpXW+UvQJGdacz+XyzXzumY/uEH21+NYJp5CF5+5sBrHESw60oshGAxSVVVFPhjFDNcidD+KL4xfXIFrbAsAsc1P0VDmZ2nFXNqOc58rLy8nVNaCPxgkn8/g8oWpb6qmYDrs6+xDkSUkp0BLxVTpiKIoVFdXc/OH7mT1hks4NFzkez9+Ct3lwx+tRlEc0hmBUIMMDibZ36Xy2OudFE0bJ++loqYRyTERqhfbHUI2kigVUyVbR6WpkiTxoQ2NXLWshr/e91OssBshu8hpKo6m4WCjuQPIxThut4NSEZ0MaMK6xMojt3OwsgWjYjmSY5J95yEkI4lPD+JrmIdsxCezcrquUx9x8eXLSzUFb73Vf+r70BpGHHiQP73mc8iSxML60nPwz//8CtumrTvdknqm70I0Gj02YeQKQOMGUFwUU2lMdBw9SHVHK1Z9Gz/ZneFGQ/CLJ7fSORDD13oZ7vgWpPxBjLwLhINudCGfMJZ0UfDLd3sYjOUZL6q06J5px2rpUfKBKiSniNB8yI6BrLvQNQUB1Ec9XHdhO28/t41N3dtxVC9KYRS5MopwenDy45ApUijfgKMG0D0+Xtraw6LWSjbMr+SX1QH2HIijmkn8Wha7vI2iL4xs5aCsAU9tI7Jdyggf//726Cq3ra3muR88D8gga0iiiNA1itYwyC40qw+lqlSb0jue55mXhlAVmdvW1uL3+0/r/XL0HXE8DF8bnobSM6HlanFlDwAm+Er3rOFtpOgJ4HUp9I4kyeYNDNPCEQJdVZG8frxV1ZMZECFrWP4mQKCme5CENa1x9azvCMCRCxRJ46ge/M4Yfq/OM28e4qdP78SybYxcGZVVVbNmnk2jk4IdxuMJEvDpU9wHZ/oeABRDIQxdQxIC2ePmxiVuLr50AxUhD7Is0dmZOq3zC6XvGkBbTYAPX9DEg08NkEzvQfUJJN+J92M5lqscxUpTFj12jjRZIAu71GPJNvB4XLgqS+54M2VcftviiFWrVrFly5Yp//deMi7n8ZvDGUnFjsfmzZu5//77+elPf8ro6CiyLHPhhRdy99138+EPf/g9M9qTScWy2SwbN25ElmX++Z//mdWrV7+nfZwrnGup2PnU47lDoVBg586duN1udH3mIs7DSYetEw4IWBCRWFA2Vee+JykxlC/1QinapdlmS0CzTzDvhNv7yV1xfrkjTsijoEjw1Wvq8c/Q+lkIQaIo0GTw62dhYTYD0rbMoKFRtMBxLBYELPQz0DW/OiI4OJLHAdY2eFgULnUBzxs2FUHX5AerL2GwYzBPxKuyttE7pZYmbWtkhA4S+DWF0aTJ4291g21w0/pGNI+PRN5m70CS3V1j3LG6ispo6WQGpAK6dHJHtqQheGNcJW9BW5mbRWUSkpE55bFZDuxPScRNKHdBe2BmAwCAfb0x/uQHu8iZgrKgh2uXV/GZy45JBYUQbD4Uo3ssx/z6IIsaS+OPFRye6CwQy9uMjGfJxXO01/i4Z20FfWMpgj6d2hk0/sgqsjeCEILxZIGf/eotRkcn+NxtK+maMPnWc724dQWE4MI5Xp59tweXplA0bSI+DY+ukCtYxNNFyoIuvnzbUpqrAxwYSDGRLrKoKXxS961d3THe2jeKW1epivporvTTVhOYdfn/+fA+9g9kEAIuX1JBjVfwyGulj/4tF7ZyzZrmU18P2+HQQAK/W6O+cvZ9nUvsG8ry47fH6IoZ+F0KjRGNRSGTp7cMYlbXo+o6QZ/OvBBsemkruqqQyBS598YlXLT05NKXN/aP862nDlIRdJHOW3zl1vlTzmEqa/CNB7exeWc3VrHI2gU1fObmFXQNJkikC+QKBrXlATauakGRT/5e+I9fbOW17b1IksR1F7TxkcsXADAwlqZnOEFbfRmVER+HBtN8+6kDpPMWq+dG+cyVrbPWePQMJdjXO0FzdZiOppPXYP79Lw+wvTuJAC5ZUM69V7ScdPmT4Rs/eJ09XSUHwkWtVfzJ3Rum/H54IM63H95MOltkzYI6PnXjMiaSeX741A4S6QI3XdTBinnH6vTuf+Ewz28fBklwzYo6PnrxmY/NtGySGYNoqESMH3hyO09tOojXrRHyu/nb37/qpNdoy/5hdh4eY05dmAuX1J+yriaWMfjZm8Mk8yY3LK9kYf2v/3kYjef4f3+wibFEDr9H54u3r2TxEdMExxH86yNb2LRriMqwhz/92BoqQi4KhQKLFy/G7T55D6v/6jgfr/3m8Z6rGletWsWqVav4x3/8R55++ml++MMf8stf/pJXXnmFL33pS1x33XV88pOf5MYbbzxng73xxhvRNI0333yTdevWUV1dTWNjIx7P9OJrSZJ4/vnnz9m+Z0Jt7TF3koGBgVmJy/Hpy5qa2R2QzuP9RV/WwbRLXYb7soIFZaWgtDsDAzmI6ALJNHilJ4+DTHO5m4aQq+S8dRyEENiaRjDqp1AwaQ8reGYhJZIkEXH/elyQTCGRMQTv9qfJGg6DIZmrm3UUWSJlCA4lnVIjzpCEPoNN6/bDMV7bU5LjKYVyFq+JEvBoBDzHZuMKlsPPtiUYSJqU+XTKggHmlGloFJEAv2zixkZCIDvw5Jt9PLN1CMsW1FZHWLfQT9ijsKo5yGWNMrInSJESwcsJHV3KTxvX8ejLCCZyBrqqcjiWZ663ZNV8KsQNGC5KqJKgJ+2gGAZtUX3GoGJeYxl/8ZFFPPTWIG5N4ZIFU2dKDw5l+P6L3SRyBq/tcXHNsgrKAy6Wt5Vza5ubZFHgW+AhlbOoDulYlsP2rhiJTJHLltYyt+4E1iscEA6SpFIR8nDXVQtwyTYuXUVzmUTKvAzEisyrD3DlikrGExkGJ3KMJXJk8gYD4wZFw6atJshEushQLEcyb/Kdpw6QzpssbArzh7cumlJMfDwWNZexqPn0+/QoskzBtMjkDA71TTCAiWmVJCrbDo2dFnH58bN7eXVHPx6XyievXcTy9tlnlE3LYUfXBJois6i5bMYi41TOYHgiS32FH+8MTSZHUgbfe2WQQ+NFCjaosgtNVbhhQyOL26r4/uZxCppGVcjFknoXhfYqdhwao6MxQktNkOc3d7OzO0Yk6GVNRyUuVaK2IjDZc6WuzEN5wM1YskBjhY/K0NTgbjCWZTieZ1FHPYPjaa6/ZCFz6sqYU3f65/0o7rhyAQ1VQRRF4sKlx1QOdRUB6iqOBb1ttQH+/EMLSWRNmip9swbQE8kc//zzzQxPZCgLefijj66dUpQ+5TwmiySy1mSh+xnMi8yIS1c1MzyRQQI2rmye9ntrXYSv3nMhyUyBhqoQkiRRHvby5Y+um3F7AxM5JKlkmDEwkZtxmZMhnTf4t8d30zuaobnSRz6VYmQig8+j49ZVrlzbekpiuaKjmhUdJ28eeTzK/Dqfu+L9dVzNF00Khk3Aq1MwbLIFY/I3WZb43C3LufnCuYT8LoI+F4ZhnGRr53Ee7y/Ouo+Loihcd911XHfddWQyGR566CG+853v8Mgjj/Daa6+dlvb1dPHSSy9N/lsIwdDQEENDQzMu+35YZM6fPx9ZlnEch127ds1qibxrV6mYurq6+ryj2AcIVR6J0XypoV+1t3S/pEzYkwDTgZ6kxev7E2SKDi5dRlfgigaNeu/Ue2ss7zBiyFRHPdiOh8tbXTO6hRUMm3jOoiKgn3Z/hzNBQLbJ5CwSeZuAJtGXsogVVMo9Mm+P2AzlBTIghMzi6PRsUCqeQaWUiRgbzwDT71XTFuRMgUeTWdVaid8fICNg854R0pkca9si1EePTST0T2RJ5CyEgJd2DLJhfgWyLFPmsvDKKgnnWC2QfBqd5X1qqcGmYRpks3m2GA5LGwL4XNOPx3EEqYJF0K2iySXSMpG1GY7lePbgEDcujXLJ/JkDx/XtZaxoCSHL0rSAP5UzyRk2Ya9O92iaHzwbx+uSuXl9Czetb+aoei18xIXtua0D/OrtPhwh6BvL8pd3r5i6TeHgFFJIqgtsi6Bb4uirOSsUVixtYJllo+sqisfNf/vURkDiPx7dzJNvHcbrUqmOeCmYDm11IeY3Rnh97wiJXJGIz0X/eJZM3iRyQvPFgunw/P4kyZzF+tYALdHTm0n9yPpatu7tx8rk6D4cZ3Fb5aRD1eKWU7/fLCEzECv1sphIFtjbM3FS4vLgq4d5fssAiiJx6wUtXLt6apAXTxf45s+30jeaxhGwoLmMq1c3s6T1GOFM5S2yRZvaoMZQ2qKpTOfW5VGGxjNgW/zFNY0MZhwUWaLWJ7PkhiWMJ/OE/C7+87Ft/OqtHiZyNmG/i4df2k+lT6KjsZwvfmQ1HpdGY4WPP7ihnb6JHO21AYLeqeSpocJPS3WAzqEUrbUROhpOX+ocTxfoH03RVB0i6CsFktesn25jW7QEL/fkmMg5rKhx0VGuUx50Ux48+XUdT+RJpItEQ17i6Tyj8dyMxOXQSJZvP9/HaNJA0zTWtoW4cdV7sxY+itUL6uhoKl2n2dzCyoIeyoInd4Y8ikuXVDOSLFl5b1x8+uThKHZ3x9nZFSPo03lpcycuLPwejapogK984kLKjnSWj6cLWLZDRfhYBnUka1OwBXV+ZUZ79NOFaTsMTuSI+F3T7qNzhYbKIJetbOLN3QMsaatkadvURqmqItNQNb33y3mcxwcB57QB5TvvvMMrr7wy2UlePsXMxJnidFzJ3k94vV7Wr1/P66+/zlNPPcWf/umfTltGCMHTTz8NwBVXTHdKOo/fHOaFZcKuqcTlKDJFm92DOdJFB1mCouGgIajzMm2W8Wi47dGVUhG96ZDMm4SOy1RMZEy+/UI/I8kiixv8fOqiunNOXjQZ2vwOB/VSX5dyj4T/SPPAgg22DWnToTfpsKhMnkbuVzT4GIyVNPBLG3wz7iPgUtg4x8eTe5N4dYVc0WRfb4rHtwwTS2TZ0ZPiv93WPmmVuqIlxPbeNJIEMg4hkcGrKJPnMCAVUY6oVb3SqWf1mgMSkiSzuTPFpp0jbLIcljak+cLlUyUZecPmOy/2cXA4R0eNj09vrGdeUOHhw3EO9cVJpgq8cSg+K3EBZux9ALCwMcSqOREODWcI6BIuScK2HbqGSrVWI4k8hwdTNFb6qS/3YVgOjhCoR4plhSPgxE3bJsKebnzgUSU8mkxeknCpEqGAH93tAQGfuG45Xr1Uo3DBwioSGYNo0M3Orgmef7eHeDyDCly4qIawb7pU7I3ONE/vTmALQW/c4E+uqD31Pam6aKh2Ma9cZcC2S5me2hDXbmhHlUvZqpNB1j143X4+fduFPPriDrbt7aG17uRB/P6+BLIsUTQdDg1Od5A6NJCgaziJ4wi6hlJk8hZjySJ/XR+etEpuKXezpjXErr4Ml3eE+NRFNew6NMIPn9qJaTlcs76Vmy461tBQkqAyUuqpc3ggPtkgsWDYWKbFnIoA+3vH6RtJ0d4YJZUtIguLde3RSRJ3PHxujT+4ZTG9oxlqo17C/pmD9BMRS+X53z95i4GxDE1VQf7wzrWzBvi7Rou8M1Aqdp7I2TSEVLzaqb/BzTUhlsytZF/3OPObK2aVih0YyjGaMogGXNiO4MMb6on6T94A9HRwLhsfru2oYH5DCJBOGvRv7c2w6XCK2rDOdYvLcB15X0UCLgJejUS6iM+jgyEwLYdwwE3kCHnaemCE+5/ahe0Ibrl4LpeuaOJg3OLF3iKmI5gfVbms8b3JqSzb4TvPHGJ7V5xowMUXr2+fWV56lpBliQ9fNo9bL2mf8X49j/P4IOOsicuuXbu4//77+fGPf8zAwABCCFauXMndd9/NnXfeeS7GOImmptPvJvx+4Z577uH111/nxRdf5K233mLt2rVTfn/wwQfp7OwE4BOf+MRvYojnMQskSaLmBMIS1GB+GH4xamAf6Xxt2QKfS+bGNg/KDJm8So/MqiqVzpRDMp7l/zwbxxJwy8pKbl4QQJIk9gxk6BzNEfZp7OjNMJgo0niaM9xngrqAwjUtLmIFh/qAgkcrjXduEPaOGuSKNuOjReYFg7Se0JDzqsVRWipKH+e51aWPZd50eLU3T8ZwWH2kS/zKBg+/eneYt/cOcMnyJvwhD5eubmHzrl5S2SJFy5kkLtcuLSddtBlKGmycV4b/hMyIIgkC0lRnmZNBkiSaAxJbrNKxBD0KPRMFCqbDzq4Y+/oStNeH8HjcbOtJE3ArbO1JcWgkx+KGANWqybvJPAKBKsu8eSjJ8qYArtMI8I7Coyt84do2CqbD67sGeXRTN7IssXZ+NYmMwT89upu+sSyVYQ9/eOtCLl5UTf94lolUgRvWNJ6yZ8jxKHdLrK6Q2TxiYhsOsWyRqkgQgcA0LW5Y2zQZePg9OrmCyQPP72fnwWEKBYOQDjevbZgxA23YAlsINFmiYJbIFSeUHb/bnWZzd4rGqJurltWiuYO4JInfvfNS/s9/PkXQ5yLnCfNEl01TRKO1XuA6yWyzrLqQJIW6ygB3XLWUSxeX01oXPuk5WDuvisff6sGjK6yceyyLki5YPLkzxsBEDp/HRf9oClWR0XX5yLEcg6rIfGJDNTnDwaPLyJLE1v3DxNMFPC6VN3f1TyEuRyFJEhcvaySROYgtJCoifoRVYCKRp7E6RFWZj/6RJP/687eJpwqsnF/Lp29aMUXOZtkO2w+OYtkOy9urzuj6dw8l6RtJU1nmo3ckRe9IikVzKmZcVpzw79OtXnXpKp+/bSUTyRyRgGfW8bVUeoj6NZJ5i5aIRjyZp8ynnVTd4CAjkJCxp9xZb+4c4P4ntxH0uvjyneuoivqxHYdU1iDkc512zxEhBE+9eZjXtvfTWhfmzqsWnbSeCyCZt3jw3TESOYs9Q1mqgjobWkvZhfa6EJ+5ZgF9Yxnqyz3sPTSMEILLVs+ZPM5NuwYYT+bRNZmXtvZy6YomBjMWWVPg1SS6k3apJ1GmwNt7hvCf0DfnZBhLFtnZHcelKfSlLB7ZneKidoXFlTPLWk8XPcMpuoeTtNWFp8gJz5OW8/htxHsiLoODg/zoRz/ihz/8ITt37kQIQXNzM1/72te4++676ejoONfj/MDiU5/6FN/85jfZvXs3t99+O/fddx+XX345juPw0EMPce+99wJw9dVXc+WVV/6GR3sep4IkScwJgFuBgE9H1WQsw6Y5qhGcpQGjJEksrdRZWgn/7dER4jkLCXjxQJLL5gYI6lAVchHyasSzJo1RN2W+c5rsnIL6gEJ9YGrwEVAF6VgaVYFMURDL29RYkLYgoIJXBVmS6KiZmmnZMlTkrf4CApjIOXx6eRBHgC0cekYSjKUqKPdrVEW8NFSFaAkI/Mc56QTcKretqeHJzjy7swJloMi62rP7CAMsqvPz9uEUOcNmZXOAkViO+58/SDxj8Pb+MT52+VwCHpWJtEF5QKfMV5p9vWFFJQ1RD5sOJtg5kOXgyAA9C8q4Y93MspKso5C0NRQcoqqJKpWiQUmS8OgKly+vZ0FTGYosURXxsq8/wUiiQGXYw1iywOBEjtXtFfzejQve87Em8jbdE0VsIUjunqDKp/H4poO8sLmbioiP3711NeUBjWwyzg+f2cue7nGyuSIel0Yub9A/lqIiMn3Wdk2zn95YkYmsxZXzQ+gnNBQcz5j87J1R4jmLHf1ZFjRV0uKVEcKmrirCn3zycrrH8zy+P4UiOewYKrC4xs3ikzR8FbYJmgtZkijzyQTrT55tEcDlq+cwt6kCr+pQFz12f768P8Gzu0v9Jcpraqj0a7y1dwijUOS2C5dMa0wpSdIUSWFLXZjN+4YwLYeOxpkdnwBuuHAuK+fX4FIVykIeekeS9A2nmNtYRsjv5o0dffSNpAgH3Gw7MMREMkdF5Ng4n36zk0deOYDjCC5f3cxdVy2c/G1zZ5LNXUlaKjxcsah8msS0sSpIbYWfofEM9ZUB9naO8suX97GsvYpr1s+dEuAvrnQxlDIYiBfZ0OjBdwbmH6oiU1XmP+ky82v9/OE1zby6a4hN23r4h58N0FEXoqM+xPKOaqqjAXKGw2udaYqWYH1rGV5/EIGE6hRwiVL9mmHa/OOPXqd/NIUQgs7hFFesbWMimWdwPEN7Q4R7b1o6a8bzeAxNZHji9UMYlsPgeJp5zeUsaa3kYN8EVWU+aiumy53EEY95SQIhJI73J5IkieVt5dRGvfzbL3cwniywYVENpmXzyAu7KY/4qCv3o6oyli1ori7Vq9X5VQ7EbAxH0B5RkSX47mPb2Xl4DF1TGIwXWbmwnubozDJigHzR4sm3uhgaSYCmUdk+hyFD5dmuPD5dpjXy3mRjQxMZ/u9DWxiJZ6mN+vmTO1dPkbjNBMO0eerNQ4zGslywpIHWE+vyzuM8foM4o+jpvvvu4/777+fll1/Gtm0ikQj33nsvd999NxdeeOGva4zTUCwWefDBB3nllVcYGBiYbIp58cUX8+EPf3iaxd6vE6qq8thjj7Fx40Z6e3u54oor8Hq9OI5DoXBEdrN0KT/60Y/etzGdRwmxgiCbt2iMqATO0MGrIaSRi9u4NR09CDVeCLskLEfw3O4YvbEiq5oDrGgqzV6ZtuDpzhyDOUHBdHCpMmU+naOxYHu1l89urGMoUWR+rW9KcP9+oMqvsrjGzd6RIi1lGs1RFzuTEllbwqcIloYFM5igYYtS8Bj0aKiaXOqc7lK4bVUVz++OoWHh93rRZfjo2krCJ0x25g2bXaMGg2kbtyqxY9RkaaWGVzs74tJe7eVPr2siW7SpL3Oxvy9JwbQJejUKpk3ArfDZyxo4NJJjbpWXurJSdktTZFbNCfHG4WQpeJElOsdmNgRwBMQsHUtICGRcjiCsTJVzSZJE7XHBdFOFn/a6IIcGU8yp9tNaM7NOfCSe4/BQiqaqwJRg/EQIITg4nGVsosDK1nIqQ24GhsZ49q3DmI5gf3+SLQfHuGbdXA4ODbJp1wBhj0YuI+PVFToaIzTO0sE94lW598LZa0tsR2A7Ak0pdeVOpLMIOwSSzMH+GP/+q32ksgbhsB/T7SHgVk4ZLJvFPJbtIEsOwjx1pq2Am4LsprzaTzGX4Zl9MRRZYn2zD8sWCECRJUwHdh4aJ2tL5NImA2Oz98s6istXtVBV5qNo2ixtm/08SJJEbfmxWeqm6jBN1eHJv+sqg4T8LlKZAnMbo4T8UzOpnYMJzCNZyIN9scn/H00WeeD1QeJZky3dKSqCLlY0T71fysNe/vjOtfSOpLBsh+/+YgumbdMznGBOXRnzmo8Rrly+wNbXtzE4liLbGWXuh1bhcZ3bGomGqJt4PEUqZ2CZFo91j/NuSOHtXX38+Scv5uVDWZ7el0IICASCrPLLSEJgSzpC5JEoEYaCYYEQmLZgYCzN45s6iSUyBL0ao7EMG5c3smjO7GTyKHRVQVMVUjkDl1aSoP7zz95kf/c4ZSEPX7pjHc21U8lx2Ktyy/JyXjuUpC6s016u0zkQo64iONksctvB0ZLrnUfnle39vLujk6HxNLqqcOHyFq5d20JlxMeaBSWznbaIStAlUbBKNS6WLRiOZXHrKmOpIo9uG2Vz0sX6Zj+3Lp2ZrL+9b4RXdgzg1yUsbCqDOhGfimEL8uZ7Mn8FYHgiy0QyT2XYx3gyz0gsN4W4DE9k+PkLezEsm7n1Zby7f5hcwWBovPQMHeid4Kuf2MCvoSzzPM7jPeGMoqdPfepTpRmJ5cu5++67ueGGGyatZnt7p/fBOBHnos/L66+/zp133jkpSzse3/3ud/na177GT37yEzZs2DDLFs49Wlpa2LlzJ3//93/Pww8/TFdXF5qmsWDBAj760Y/y+7//++8rmToP6Ivn+dlhh6xl0BRyuHOBD48685vXtAWSxJSCykXlKo6sYNiCWi8sKZPxaxJbetI8tq3UUO/waJ7GMjflAY3OhMnbQybRqiCSrlEfVPn02nK8x+1zXq2PebWzB6m/TiiyxO1LQiTzNn6XQtKSyNsSHkWQtyWGMybdg2kiPpUlDf7JjMjyahdpoaK6XYR0iZglqFIc1rWGWNkU4Ke7s3Qm45jFIte2eQhHj93newcz3P/aIHlJo6w6DJJCxC2jnaMvYNSvEfWXArO2uiCXLK5h6+EJ1nZEmNcQRtHctLXUIwmBY2WQxTGr5bWtIXrGCzgCNswNz7h9CZAQiCNdImJFQVpAjQfUWeJzj0vlSzctYGAiR3XYg98zPXCMpYt885Gd9I9nqYp4+OPbl1I9i479wEie1/eOkyjAVkvwp7csxC9UQj6d7tEMXo+HSMADCBRZQpYlvC6VFR3VfHhjOwtbyomGZs+A2KJ0bEczScejMqBxw9Iomw6naCl3M69KR+TiIEns74sxEstRFnCTzeTY2FFGa4WHlrLZZToWGgXZB46EJvKczhvRko59on6+eYQd3RN4fR6yhsPF7WFG0yaJnMWyGp1t7wocp3S9RhKFU25bliWWzEJYTNvhQH/Jrrmp6uT2tItaK/nih9cyFs+yqG26FGzNgloO9cexHcGGxcdslQ1bYNkCl1aavTftmZvWRkNeoiEv+3tKjn9CgITEiUnLAz0THOqLUR72sK9nnN7h1Cmtjc8Elu3wwye28cbWHmJ5B11T0RRBwOtiIpknmzfIGg5ClN6lw4kCknBAkpCFNSkV01SFj1+7lB8+uZ1UziQcDpA3DIpFk5hlYVo2gdMsSi8Pe/n4tYvZvHeQxuoQNVE/PUMJykJuRmNZOgfiNNdGcBxBLGMQ9GroqsyalgBrWgKMxjL8049eYySWYX5LJV/8yDp0TaEs6MHr1kjnDWqjXrLxLD63TudAnEdf3U9DdYTP3LhsCjGs9B677ooscdWaFn71Ric+R8EViqBIEjsH89y8JIx85OJl8wbPvnmQomHj8XuRJQkhQ3XAzfpGH91ZaAmrtJW994mu1voIcxsipfuhsYw5tVMnMh577SBv7R7AceDFd3soD3mYSOYRwqEq4sO0HCzbQXl/59rO4zxmxXu6Fbdu3crWrVv54z/+49NeR5KkyYaA7xWHDh3i2muvJZPJsHz5cj7+8Y/T0lLyae/q6uIHP/gB27Zt47rrrmPz5s20tbWdYovnDsFgkK9//et8/etff9/2eR6z4+XOBKMF0JWS5jiWt6kLTL/dDycsXhsookgSlza4qDsisWr2Q0iTiBfFESmVIGMICqaN5Qh0RcJ2BOYRVyxNlnAkCUmRqazws7haoyF4+lr294pE3ubZ/SVpxiVtPhpOTHkcB1mSiBzxcg5KENAEGUvCrzg8tGmQPQMZPLrMPRfWsnpOaebXkRXKwi7sI4FSynaowqEnafJkl0HWUUs9RRSdQ+MGrVEXliNIFgWv7I8zEC+iqwZuTebKNdW0R0oOX2cDGxkTFRkHjVJApCkyd1/Wxsc2tk7KZwzVi5AUhAQobmQrO7mNNXNCtJR7KJoOL27t4882HWR1ewW3bWieXF+SoFw1SNoq4wVBZ7Lkjpbyw8Ijk6a2I0gbApcqMWEqpToIF7TNkmkBGIplGUnkS3KyRIGBieysxCWes8gbDhUBDwXDwjAsvAGdz16/gHcPjlMZDbJqbjlOMUt7rZcbN7QyMJ7hgsV1LJ9bOeM2jyLvqCQcFwIIyUV88tT3syRJbJwXYeO842eIBQhBU9RFWcBNOm+wvDnMrYsjp6xLsCQNgYyEwJJc6KIwayO/o9CFgSMp9A0neP6lrSQzRUJlIRIN8ynza3z2kpKrleMIXltUw5aD44R9OhcsPj23q3TO4MWtfTiO4JJl9UQCboQQPPDcAV7bPYTXpXLPVfNYObeCTMHirUMJYlmTudU+ljUFJ8/T/JYK5rfMXHuydmEtzTUhRhJ5gj4XjiOQZYm6iIvrllWw6WCCudVeljVOvWfyxVIQf7R4vb0xyi0bO9hxaJTFrZW0N04lJbUVAcrDHsYSORoqg1Se46Lu7sE4m3b0EPbImKbJxlXNDI+W+tBsWNpIJOhhQ4vKQNKkYDosrJBwiwxCyOw+0EtXf4x5zRXMa6ngw1cs5Or1bfQMp3jizW56hmK4ZHCEoLEqSE305LK143G8/bBh2rTWlbG3a4zqqJ/6qhDpnMFPN/WzvTtJTcTN569uJXLEVGBX5yi7+1KE/W72do4xOJaiuTbCqo5KYCGjiTzL2srZvKuP5946jKapVJcHSWSK9AwnWX4S2+Or185h7cJadg8XeWJvGsMSrGhwT5IWIQQPvbCLZzYdAmDx3GquXdNE/1iGi5bUsaojhOUIFOnsXFKDXp0//MgqRuM5qqO+aRK8SemaJI7UdDoEvTqVZT48LpWr1s4h5HdNKkjO4zx+0zgj4tLY2Pi+2AzPhv/5P/8nmUyGv/3bv+UrX/nKtN+//OUv83d/93d89atf5W/+5m/43ve+9xsY5Xl8EOB3qbiO6JAD7lLzvrGcTblnqpvWu8MGyaLAEYIdYyZ1AYXhlMmTe5OMZh00nwu3rmDkDfKmg+bS8Id9WAWDaxaHqQ6WPoDNYZUNtRrbxywCLplVVWfvtnMUGcPBcgQh13QnsFcOZ3ijJ4eEIFW0+cIF5WSLNi/sjVM0HS5qD1MVmj4WXYYlIUHWEjiWxY/jBfxumXTeZiR1TMKTsiQcStkHS4BfKRVx/2RHmrSQKQt6yBs2Hk2mKqhgOoIX+kyGcw4FbwBdT2OaNk0BmXW1x+bYHVHqtTCTbWh30mIkZ1PvVyeJ5FEIIIMX6whx8ZND41jALcslzfpzWwfY2X+ARW21XLysCWmGSuWKoM7WwxO8tHMITZF5busAy+ZEaas9FkC6ZQe3bHA4DoWixZZtnbyQzvKxdbWsbK/kme4CPSmbmpCbqrCGLEHOUZjns6fNiB9Fc1WAttoQXcMp5tQEZpWTASyo8TKv2stgymLVnHJqgyp2MU1DhZ+GiiPBXbHUDFDXFG69eO6s2zoRWaGWOu4Iwcu7RhgbHGF+Uxlr5p/aRnZRnYcvXddKIlNkfp3/tIqp5SP9fQSgChMJyBo2e4byBFwKHVXuafe3CwNVWPR09yJbRdwuhWImw8LyqfeFLEt89c5V7OmNE/LptNWenib/0dcO8/Q7PUBJ2vO5m5di2aW+MS5NYSJV4EB/gvqKAN947BBbujOoikRHjZ8/ur6FOZWnJgeZnMF9T+3htYMJQgEPN65p4CMXNiFJEtcuq+DaZccIj2k5DE1kSGaK/Pi5PeQLFtdvaOWK1c1IksQ16+dyzfqZr3FDVYjf/8haekeSzG0oO5KJO3cI+Fz4PTqjsQyVIQ9XrGyiqWYp2bxBJOgpSepCOr9/cQWOOBIQC4vD/TG+88hm4qk8lWV+/uyei6iK+gn6XCxurWBxawUHesb59sPvYtoOn7lp+RkZGBwPXVP4wkfWcLg/znjG4jvPHiZbtEkUJcoCLvYNpNnVl+Si+aVzvmsgx4TlZmTCpqPKTXm4lBGXJInV8449B3UbF3DF2lZ+9Mwe3tk7RFWZj/ktp5ayhf1uNrS6qI24S3blFcdkhI+/dpBHXtxHPJmjpsJPNm/w4Y1Tr+3Z2CofD49Lpal65vfMDRfOxbIcDNtmcWsl+7sniATd3HDBXDxHeiKd7+NyHh8knBFx6e7u/jUN4/Tw3HPPsXDhwhlJy1H82Z/9Gffffz/PPvvs+ziy8/igYX1TiAMDo+RsmSqPxGOdpWB8fa3O0kodwxYcSNjkLI7Makn49dJH4um9Kbb15YkVHAJ+i7oKL4Mxg5BXI12wiJT5aYrorJxzLNCSJYnrWr1c2liSnblnkaWdKQ7HTJ48mMUSggsa3Kyu85C3BNtGDExHkDWPVJoiYdgCIQTP7o7xxPYJHCHojxf58lUNM25bkyGsg9AU1rQGeeNgkroyF0sajkljfIrAhYOFRFAVVGkOQ0mToWQRSdfwum3CHoWl5QpLq1VG8w6DWQe3IuH2urhmZS0ByWJdW3hym/GiYPOYQ9GBRRGZ5sCxczWctXmmu0DGEITdJre1ewm7jumykgWHhHBwqRa6pmIjc7yoxBGCtzsT/OzVbizb4UBfjMYyjY7qmR3c3FpJI58rmIT9JZI6Exp98OauEfbu68ejwE9eyhGJBulK2mgyJA1B2AavKmGfQo7uc2t8+dbFDIyXMi0zycmOIuhR+dIV9WQKNiGPgp2LYTilayef5SSShkNBQM9AnF88t5NcPs/m/cNURbyzBjnHY06lBypPPzjWRBFZOAhJQhUGjhD86J0Jdg3lcWsSH1keZWXjdCmlgkNTpY/aMi/JbJH5jWVT7tGj8LhUVs6dOesxGxLZIiWZnUw8XXpHqEcaXW7aM0xZwE1bbYjd/Wl6x0pudJYtiGdNEtnp9tUz4eUt3by4fYiEJZMrWLyxb4zb1jdMc3QyLYd/f2w7OzvHyWQNDNMi5Nd5+u0uLl3ZeMrGhwDNtWGaa8NndA5OF1Vlfu69bTV7OkdpqokwtzGKJEmTdSFHIUnSlHqIRLpAOlciN5lckUSmQNVxGRXHEfzkmZ2MTqRQVZnh8TS0z06ehRBs3jNIz3CC+S0VLJwzNbPodessbqvi//nJdnb0JDFtgctdcioLeTXKA8cmUCayNg3VQXIFk3mtFfhP4kjm87j41A1LuXRlE0Gfi8rI6cl+JUmi5YiENl80eez1bsYTOTbv7ifo95BI51FlhVsvPT0DD9NySOcNIn7XOZlIrgh7Wb6okYGJPPU1YS5Z/sFzbz2P8zgev1WqxdHRUS644IJTLrdw4UIeffTR92FE5/FBRVVAZ2XEJuXI7I3ZxAsOloDtoyZLK3V2jFtsHbMxbfBrCsurVBaXlwJISQKBQFdKPVt0tdQPJVEs6bUTeZtKnzMjOfGcZdH5idg9WiRWsNEViS1DRVbXeXhnqMjmYRMHqPEoLKj20JWyCYR97Io5ZIvWZN+QVP5Yl+vZIEkSH15dxcZ5EXwuFb9bQQiIm6Wi7Lk+BxsIqaXmlD5dJiRZjGVsxgsGFy8OElFgNFvq8xLQJGJFQVCX2NgYJOqZGnB1pwXjRVAl2JNwaPIfyyRlDUHeEvh1ibxZ+rtYNHhky3jJyjldZF5zFe11ERrKNMKuY9kWIQRvDFm8MWAymncI6xLCtpCsAtIsFRXzGkLcuXEOB/qTLGkpo7585mCk3gcLw7BNE7jcOq5gAE2R8GsSEwUHWTEJKC5cikSt25k123IUHpdK22k69aiyRNirIoRgZ1zQkxF4ZPBh4lcl5lfq72lmNigbqJLDoJXDNg0CHp2iYZMvnp6k17QcQKCpCgOxAj/aNEjRdLh9TTXz66ZLfUptNc1J396CJehLGHg1mVTBZig1OxFY1FLOH9y+nLFknoXNZWjquZFhXrWqiZFYDst2uH59SXYsSRJ3X97O6o5K/B6Nluog+wbTVIV1UgUbSZZZ3Rqa8RhngiMELkWg2AJLCFprAjM6Sw3HsuzqHMetKQzmDNw+N3JVLf4yH2lDED737ulnjPktlcxvObkE8UQsaKlgeUc1nf1xVi+sY84JfXtsxyGdLSJJEtm8yVgix9t7h6kIe2mZIRt5oHeC7z++lUS6wKbtvfzZPRfRNRjnyU0HqCrzc9e1Swn53cRyNulC6V6OlunceVEj9VEP8+qOkd4N8yqYSBWJBtxcvLjmlMeiKjJt9SfvVXQUmYLJts44AY/GkuYwkiTx0tZ+Hn31EJbjkM0W8bugtaGCu69bwvJ5p5Y3xjNFvvX4HoZiOZa3Rrnnyo5ZXcpORDJnMpwo0Bj14jnOXW9XT4LvPXeYdK5Eqr/64UUz9n06j/P4oOC3irhEIpHJnignQ3d3N+Fw+Nc/oPP4wGIwWeSZPoesZWI4kLEAJGIFQdESZMxSD4tkwcGwHPyYLC5XAYmr5x+p7xCChfU+oj6NVE7nFwdyFJ1SILm0XJ212P9cotynoMkSlgPVfpX+tM32MYuUIQjoEpKssKHdh2fcRpclDqUE69oi9McNcobNTcvLT2tWTpYlqkIuCo5ExoZYAfoLpV72tW6HFt+xNEI8a+KTHITkcPm8CIfiFt2dBQIumesXhFhb5yJVsKj0yJOk5cBIjs6xAq0VHty6C0UqOZZ5T4g/6wMKzUGF0ZxDW0Shyifz47fG2dKToWjZxNMGtjPM1oMjfGhVBXVzjwX/RRv6Mg7VFUFa22rITSRoqQ6SRcMRYsYMhSRJXLK4hktOI2i5eFEVo+kiY2qIYMjH7qybC5slUrkiUY9MQ6BUmPxeJkFt22FLV4KAV6ejxjfjNcta0JUuSewOpm3iySKOYZE2PGxoPPN6BkkCn2SxvDnIJUvr2dE5zvK5lbTVh0+57t6eCe5/eg9CCO68Yj6b+4rs6E0jy/DQO8P8t7pT1xe6VYlldV7e7slQFdSYX3XyyLytPnxaYzsTtDdE+MtPrgOm9rTQNYXFLcdqSObVBvj9a1sZiuVpqvTRGPXMKo9zhDjinlX6/ZIVzQxPZDg4lGF5Ry23XNAy4/WNBt1URXx0DiVoqg7SuLiDnFoyeHhn2OTK5vdG1npGsxweydBa7aep4v03B/G4Nb50x3oyeYOAd7oVuqYqLJhTybYDw8iyzAvbh3jkzX4UReYz1y3kmtVTTX1S2WIpQxpwky2YjCWy/Py5XcRSeboG4jTVhLn+wg6WdVTTOVGSOC2cU861y6un7fuq5TUsbAyjKhLVkZmzh0dVprbjsL1zgkLRYu38KuKpPCOxDHPqInjd+gnrCL77zCG2dsZxawofv6yFC+ZXUjQtbCFwaQrlNWEuX1FHecjL2kV1p3Uud3bF2N+fIOjVeefAGFeuqKe+3Me73WlGUgZLGvw0lE1/jsZSRf7piUMMJwq0Vvv5g+vaJslLImuSK1qEfBrpvEk6b54nLufxgcZvFXG54IIL+MUvfsFPf/pT7rjjjhmXeeihh3jnnXe47bbb3ufRnccHCcPpImkTwm6J8bxAk0CRShIbwxHML1PpStoUTAefZHM4ZtOftGiL6lQFND6x5gQ3nrCK16Wwa8yg3Kuwru79cYlbU+cm6JIxbcHcMo1f9Zg4lDIhikSpJ4pcIlOGXSIzTVEXX7m2EQE4QiJWBJ8KRyfZUrnSzLZLVykeWUeSJA6nBMOWiqZIyIAjSpmDpClxdJrccQQ/fH2IrrE8SDCc9DGcVwi6JMbzDnvigo5qF0GfSrluYTuCV3pyPL4jTjpdoNyj8IXL61gaVTFtaA5IU4IJlypxY1tJDudVS78pcikDZtgCXZVJ5CwqAzo1YZ3OhEXYJVPmkXEpEHVL9GdgxaJGhsfKGIoVeWhHElmSWFZ3dpp/j65yycoW3hhxUBSFuAFJR6e5TCWils7pmZKWktBP4X88vIcXtg2hqQqfu3YuH143PZDR5dI1TBilbIckBKYjGMvO7EY1E4qmzTsHJ5AkWD03esROVuYT1yyYLBo/HTzzdjf9Y2lkSeLJt7poaK5HksBxwH2azTwlSeKWpRHWNPvx6fKkccSp4AjBM3sS7BrMsajWy1ULwmclmzvdJnztNX7aa06eZXl5f4Ln9sapCurctbaSiE8j6HPxqZtWsGvCJlkUxEyZmV4fXrfGF29bxp7uCWqifvYV3XQlbeDU8sPZMBTP83+fOMBIskB12M2f3DKfqt9A6kaWpUmTgZlQNG2qywMoikzXSBpJVikYFg+8cIi186uI+I+tu2hOJSvn13K4b4Kl7dWkciaWU+pwryjSZH3MDcsqsB1BruhwzZLojGRRkiTqy4+zBo5l+eEze8nkTa5f30JDbRlFVwABbN3RybceeJVi0WDhnAp8mkQslaejqZw/uHP9FMmcYTl0j2bxuBRSOZOBiZLl+oVL6ukaSjKRLHDjBa2sXXDqCZPjEQ268bs1EhmDunIvIZ/Ojr4M9702SKZo89bhJF+5vnlao9+DQxl6xnNUBl10jmQZiOVpO3IvL22JsKgxTP9EjtVt5dQdMXUYjWfZsm+YcMDNsrZT1/Ocx3m8X/itIi5f/epXeeyxx7jrrrv4+c9/PukqJkkSXV1dPPDAAzz00EOoqsqf//mf/6aHex6/QbSUeahwQ9oSRF0SXXETGwnVBgUPVV6FaxsU7ovniBcEIV0m7D55ANMe1WmPvr8zUaossaiy9NG2nFL04lJlagISa6tV5oTVI7bgMilD4JJLjl5Rj4ztwLa4RNwEnwLLIoJ9PTF+9EoXlqxS11qH16OzvEpnYYXOnqSExy2RNR1CbgVFBklAhX4sanJEycZVkUs9bVQEjSGVwzGTkEdBVhWGczYZVaJCFuyLm7w+YJBXNTxhhVQqSzJnUh+WMCSBTz1W32E5gljGJORV8R0X/F6zqIw9gzlig1mCXpWFdX7uWFvFq0MWg5k8AV3ihlYPFV6Fi+s0BjIOju3wQI+JT5fJGjbJwjEr5LOBVxGoMuQsQcCl4NNk7CNTso4QdGcgZ0H9kb4/J4MAbC1AUaisXjSHLZ1JRibSPLV9dGbiokisrZAZzAkGJNiZcfB6FBbNYgQxljLonchTE/WiuXQCquCRN3p5ZusQkgT94znuuKgZKF3TzBE50umQgLKgG5BwHEFZwM11RwrMi/9/9v46ypL8vvJFP7/Aw5SZJxkqs5i5q6qhGtQkbLUY2rZkS9ZYGs/Ynjtoj+HOu++t9ea9e9/c8dgzMoxJBpEtcgtazVCN1cWYzJmHOej3/ogsyMosVLfAk3utXn0qT5yIOHECfvv3/e69bY8Ht14a5DQsh++/PESmWOfObV0X24TyNRfL8WiJaHRdwwlvOZydrfGtozkUYCpvMdASYM1NaG3eLpTqDo8fy1JpeMwWK7zSWuaBBfu5wYLH4TkXT0rm6h7vWWVgLmML3pIIcXC7P3CMV1w8aaEIwZ62W7vvzOTrZMoN0nGTTMlitlD/iRCX66G/M8nrpyaxHZd0MsRktk4ooKNpCpW6s4i4BAM6n//QXvKlOn/492/wxW+8ia4KNq9uY3V3kju2+xqNVFjnU3feWCXjAn7w2iiHz81hux6vnJ6jq7uZ23b2c/uOVdTVAJVKDVVVOXF+htZEkK7WGIMTWaYzZXrbExfXY+oq+9Y18/SxGTpSQbb2+e81x4P82od33/Jx2tiT4JceWs/fP3OG2bkMX/rHN+lf20fV8ogFVLIVm6fenCQeEMzMFVEUwb27V9HbHKI1HmCmUGdVOkw6cel4xkI6v/7IRqoNh0hAQwjh663+/g3OjGYwDY1PPrCRHWveOmvtFazgR8HPFHHZvXs3f/Znf8ZnPvMZvvrVr/K1r31t0ftSSkKhEH/0R3/E7t23fnNYwc8+kiGdd/UqNITJSN5laKZC1FDwbKjaEl2VPD9SY74BQlExAip5C64icbhllG3JXE2SMAVJU2A5Hj84niFbsdm/OsHADbgSXYCmCA606xyZd4gYgg1NFzQ5gt4oPDHmMFL0gx7v7tKJGCp5G0wBJRvyNjx5dIa5QgMtEWSsYDNg6ByZtelN6NQtD9PwEIrAxGF11APhk56L+6AKPrQnzbfenCcR1LhzXZK6orO21aZsQ06olBoexbrk7HSDsOKiqQphQ6XekGzuCNNoOPzvXx/FcjzevSPNA1tbsF2PP31umlNTVToSBp+5q4P4wgx8KqzTmTCYL1loiqBmSxRNZbZqETEE+YZkpuLRElIxVUF/XEVKhb09IV4Zq9GXMth0FXH+zcByJUdyvrtayhSsbtIJa5Ko6vfRj1fgSBZsD6arcLBdXtv6WahIxUD1XHpbE6zpTDKXr9BzjdyQ5MJ5tDFhsrddR1fEsqGP8yWL/+8/DmGjsWVtG/1dJhFdMDRTAQSelIzNVwEoNDy+da5GoeHRn9C4vy9AseZwZqpMRzJAd9NSUvDInWtIRgN4Eg5u6yIe0vn4gYUefUUDoYJr8eybY3zlqVM4jse58Qy/9ak7OZ+1+drRArYjuXt1mLtXXzsn5XJIKXl+qMpEwUYg6YwbP7JJwVsFQ1UI6irzZQdDE4Qu+10sVzKTq6KpgqAWwPWA63R+tYZVHlnzoxGy1e1R1nXGGJmtsL4zRsVTefJsic3tQVoiP/nH/7npMtmSxb6tvaRTYWzbo7sjwR988wTzJYuDW9rpWMbWWQhBuWYzMlMkFQ0wm6+yd0s39+zsRUrJyFQeVRF0XSV8FXwr82zVJRZQMBeCmQK6P3AvVmzqjkfDcjj05hi3beklGdLQNRXHcelMR2lJBMkWaqzpaSKdWvrg+NDtvdy+IU0ooJKKvDUVeiEEqbDG7EwGieTQsTFW96ZZ1xZipmjhlMt8+YcTzGdyaNLPw8mVavzie3fya+9ezXimxuq2CLErDEFURRC97G8N22E+XyUU0CnXLDKF5UN6V7CCnwR+8neum8THPvYx7rrrLr74xS/y7LPPMjk5CUBHRwd33XUXv/RLv0Rn583NsqzgnyaCmiAZUIgFdE7M1JmvuGzrCNAUUjk93+DFSYs6CorwqNuCQuPGW24AqrbHdNmlKaQSN5cOHuuO5MkJh0xdEtUF93ZpHB7M8/XX5nBcydnpGv/+vasuPjRvBF1Rla7o0hFPw4XpioepCkqWx0zVAwnzJRdVU2kNa0Q16GgKcmwkj3QcApqg7kJbRNAWEvRFPMYqdZpDKuvD4qrOaFt7omzt8QebZ0swUhaASd1zQJHIBf1Kse5hKx7dMY1EQGFjKsTt3UH+59PjzBQaBHSVH57I8MDWFsayDY5NVAhoCmdnapyeqbJ31SVh7t5VMUYydVwJ+1fHSJgKbWGFibJLKqDQFll8DIUQPLQ+yh2rwgT0tyb0smRD0YKYLig7HgGvTrsmuMBNGq5PagwFGh44C+5fV4X0ENJBVXXSCZPOzgTvaQnzzw5eX6QrhCARWHwevHY+x6GzWfrSIVoTIeZKLg3HIffGOEG1h66uCHs2tDKTryEQ3LHRF1kPFxymKi4RXXA+77CpaPOnTwxzfqZKU9TgVx/qo69l8eAxGjJ494GBpTum6BCIXSQutYaD63qYhkq17uB6HkenamQrLgFd8PJojYMDkWvqsOSCdbaqCCqWx2TJoT1hkqnYrG0L0d9s8tKZDE8fm6WrOcQH9nVd1R0OfK3CG2fnqVsuu9a2EDTfmsegqSs8dqCVl4eKNEd09vVfOn9Pj+Y5cS6HK6F9axMhPcBktsYbg1laYiZ71izfynQrkNJPWjc1QSSg8WvvWcdcocFURfK1I3lqtsfhiSqfvzONscx1UXHA9SCq35pe60ZxZCTPF78/RKlms7knzs/d5TutpWIBfuex3ViOdzFzpFq3OTk0RyIaYGBBHJ9OhhjoSHBqJEtHU4Q1XX516x9fOMu3njmNqgo++sAWbt++1CXL8SR/dzjP6+fmiWiSL9zfR3PU4MG9vRe1LKPzNTzHpacpSMirc/bMOD1dLfSmw3z6nZsJmDrT8yUGupsWBVFegKIsbkN7qxAM6ARMjblchUjQoDUR4Nd3tFCoOvw//uIVXFOl3rAxFYkiDIpl3y2vIxWkI3VjRDgSNDi4o4cnXxthVXuCHeuWD2pdwQp+EviZIy4AnZ2d/M7v/M5PejdW8DOCRFDlF/emqFoe0YUslHxdYi/MPEt817C++I2LX2uOx1dOlpkquSSDCh/cECEVXPz5oi0pWJKoDiVbkmtI6raH6/lp2TXb9ZOz34Kr0FShPaIwXHSJGgphDb475Fs6BzSFPakgUV3lkdu6aUsEcT1JczqOg8LalI6qKOxpVdh1FRH71VCw/ByQki0p1CQNu0Y0qFOsORTrFr0tBg/1B3E8X78C0JE0MTUF2/XobgqRr7skQxrJkMZkvkEipJEIacxVHBIB1Q/r60jyqYeSBDyLngVdzLv6g8xUXeKmsixxFEL4GT5vEWIGJE3INCCuQyQYZM7VOT5bQpMW65Ia6YCgYkN/FK63aYFEtUpIVadFdfnCXa2oCqhXHP98w2Oo6BHUBGsSCqoQWLaLJ7k4QD8/X+cvX5omk6vw+lCeT9zZg7ugWak1HAYn8kS0Ont7ouzs2YoQkFqwhU0GFEK6oGxLWkMqtbrDZK5BU9Svco3O15YQl0rD4fnTeSSS29cmiQT8lkUPFVWo4LmgaBzY2sPZsQyzuSrvun0NQVOnPaZjanVsV9KT1K85YC9Wbf7nE+cZnatwcHMrD+/qIB3VKVkeLYkAd62Nk6/Y/O1zo+QrFqcminQ3Bblr09Wdr374xgR/99Q5bNfjxEgbn333pmv/UDeBVc0BVjUvre6dnamSMAW2KymV69Qslz98/AxDM2XCpoaiCHav/tFbcaSUfOdkiTcma6TDGh/dkSAWUOlqDnFoPEeu5t+vLgSbGlfcs0ZLLieLChLBQBTWXGbq9f1Xhvj7Z8/S1hLnV9+/nfh1qghDUwWqdZu13Sn0ZSZnRuaq5CoWqbDB4cEsg0OTmJrCx+5bx94NbRdJi+dJ/sfXX+PwmWmiIYNfemQn29a0YRoaX/jgLr776jiT+QZnZ6p0NEc5dHQM23Go1j1ePzW5hLjYjse33pjm269MMz80jGU7KOUsv/XYbURDBh+5dx0fOOhx6NQs5brD3nUtvHB8lFdOTmEEA5QdhZZUlEhQp705iuV4i5wbHdfDtt2LGShvNdLJMJ96zw6OnJuhpy3O5oE0QgiaogZ3bGnjidfH6e9qolapYBoqd+3su6XtPHJwHQd39hIK6Ch4KwGUK/ipwVtKXL785S+Ty+X47Gc/+1au9iI+/elPk0ql+M//+T+/LetfwT9NlBseZzIWIUNhrakggLaoRktYpWJLwrrgkXUhmoLXHmlWLY+Zkk06qpOte8yUXWKGYLbi8uZ0g90dAaKXDaAThiAdFMxWJU0B/3VyIM7ZmSqzRYuHtjQTNpdus+ZIpISQLijZMFuHoCrJFhrk6i7rW8wlbR6KEBzs1FmfVAlrgrLl+ZoF039dsjw8qRA0VO7ZcvXZs5ttu2kP+q1wMwWbXKVBreGwu8ViIKkjkwG2thqoii+wv4D7t7SQjBjMVBwmbZ0/P1pmV5vJZw52cHqqSktM59C0w1SpTldM4761cfJSQ6gSWwvi0kDDJ0I9sR/f3IuuCPanfXF8yNBRzBDPDOcZztsIPDzpcLBTx71KsOYF1BzJTNUjYQoSJgjXnxFdbvbblZJnJxymq56/TqlBpcSf/+A0tuPx4YMDdHQ08YMxByWVIm4EKM9mMHWFh7Y18/1jWQKawujQMGffyPJic5hf/fBeWhKXiEhPTONd/UGOT1UZm8pxrKbRnw5xdrpCd1NwWUH611+Z4QfHMgBM5y0e3JLiT75/FlXT+PS7d9OSCIPboClm8usf27doYHegL0wiqNJwJJuv08L3+vksL52ex5XwDy+Ps3dtEx/dnuDkTINYQGF92ryYp3Kjjm5jsyXqlkPQ1Dg/WbiuXfiPgueOz/DS6TkcYQAK1YbHmekaX351jglLR08kqJTKZEpvTcDffMXl1fEanpScyzQ4MVNnX2+Y10YrHBouMzkxz9n5Wda1R2nsT0DQb3GSUvLVF8f4zpE5QtEw9+4bYFzRLxKXbLHGF791lPlinZOjOeLJGL/67g1X3Y9DJ6b4i++doGG53LWtk8ceXEoON3TFSEUMZosWnmVTkzZV4NmjE4tCUMs1i6GJHNGwQa5YZ2gyz7Y1/vuVhsvTJzPMFRocHs6TjBhsWNXC+GwRQ1dZ3b2UDH7v9Qn+4YVRpjMVqqUqsWiQyakM1bpNreGQigXRVIXbN13ah5CpoakKdcslnQiiawpSSr5+aILnTmboaQny6ftWUS7X+aO/f5V8qc5DB9bwwL7ru+tdgOdJzo5lUBXBQFfqqueklLB+oJ2CBZNzZU6P51nf7Veb3n9HP3vXt/LqyUm++expLMfj+SNjtDVFSMWCS6qLjuvx/NFJcuUG63tThIImXSnfMU8IQSrmV2hWAihX8NOEt/Sp/x//43/kzJkzbxtx+au/+ive9a53vS3rXsE/TXhS8o3TZc7nbAxV8NDqMGubdBxPcmd3gEzdY3VSp3uZ9qvLUWm4/OmhLGMFi/aYzsd3JmkJKUyWHCqW5JnhKidnG3x4c5TmsH9ZGargnk6NXMNvFQtqAnSdX72/56qDpdGSx0vTDhLY3qIyb2sUbWjYkjNjNbLFOm9O6nzmttSSFjNdEXRF/O8R0QWrEhpjRQdFVTiWl8xbLre3qcsOkC9H1fZ4crTBfNVle6sf2Hk1dAQl3xjN8NTZErqu0NYcolZ12bfx6gGGmirYtzrBD4aqzIw1COuCN2ct9nZE6UqanJ63GMmXCOmCNydrxE2V/h4TEOTKFtPTFdqjGgNNb7+zm+dJvv/aKCeGs2xf08Ld2zpJBwWugIaUuNI/lgK/NQwhuJZLtuVKvjtiMVP1iBmCB3oNmhZMIaSUPP7SeV4+McnGvmYeObgeKQQVx88UslxJ1ZEcenOC8bkKqiJ4/JUxHr4vSUMKWmMGWQW2Nyvs7E+wT1XY0RtlbKbIX3/7PKlYgJHpIoMTuUXEBaAzovKnJ2YZmquiKoJHd7XyyJ420jGDRHjpzPFs0br4vWeLDV44OcuJ0TwBQ+X3v/IS//6j2zGUS62Xl5/rqiLY1Bq4IQczR8JM0aZmOcTCfo5Ka0Lltt5L+5+MGHz8zh6ePj5HV3OQ29Zeu3Kxe12aY8NZLNvj4LaOt4W0SCl55dQ0f/L4KTxFQxEKB7Z18+p4nYbt8cxIHSUSpWG5JIImO/qTSz7/9NEpzk8V2bqqiT1rbyxYM2QohA3BdNElZCjEFiZGTs3UqVsujcwM2DbFXJ7nj07y/rv8pPbJbI0nj0zj2pKxiSxD4yn6t1yqWmmKwPJAeh4IZSH49uo4NZolX6oTCxscPjfHJ+5f6ljXFDUJRsIoDQXV0LFLDQwBva2L7x3RkMGmgRZePTFJcyJEMGAyna3SlgpRtzws2yMS1GjYLtWGywfu28Ta3mY/SHRg6STNbKGO47ikAgLP0GgOCrYONPP7f3eIkekC7R1p7tk7QEzzENKjvSXK7ZvaqFkO2WKd/Rv9atBktsYTR2dxPcnhoTyHh/JkZ+c5O5ohGNB5/IWz3Lm994YrL9985hTfef4MqqLwofs3cc/u/iXLSAkZ12Cm4jHjBPnh0fO8cmaOf/+xnaSifiBlV0uEH75Sp2G5BE2NJ18b4eTwPO1NUT7/wV00xS9dO88dneAvv3eSQsWm6gi6utu4a1MzH7+j+20j8ytYwY+Kn6lWse7ubmz7xtKKV7ACANuFuaovWK9aHnMVh2PzFuNFh4Sp8uiGMC1Xhoksg/GCzUjeIhFUGc/bnJ23kEJBUVVUHOKmymzFZbTgXCQu4JOJdHB5G06ATNXh1GyDZFBlQ9rkbN6l4kgU4EzOIxL0NRNlT+JIX4hdqLtU7Wu3mOmq4F39Ac4XXF6e8zAUwWRFMluTdEWu/UA6k3M4nbXRFDg0aTGQ0IgsIwAHmCs7jM5WUaVHqewQViQbN1zfOtP1JOdzDrmaR6EBO1pVLvCwVFAhaigcm65RrTt8fa7Izwc12lJBnjoxS6ZsEwuofGJHgt7kzbktuVIyWfZQBbSHles+nE+P5fjas+exbJdzkwV6W6P0t8dRpIPhVdjeooErUFDYmA5TEUFUHIZHJ3jpxAytyRD37+pCXyg5FRqSbN0jqgsKDcl8zbtIXIanCnzz2bPYjsvodJHV3Sl2rG1jc0rlWNYlbggqDsy6Og0PTCTNsQDpkELUEGiKwZaOEA/2XRKsr20L0RZVeaYlwvBUgbZUmO7WpaTS9SQ1y0VXfettV8La9qs7Vdy3uYnpQgMp4R2bm5nNli7OSAs8FOlQtgWDRYmuwOq48POIXI8//u4pvvfSIG2pEP/hE3voaFkqzs+ULY6OlanY0JSKouCh6AblxvID5j1rmthzheuR4/q5OhfalPzWsBymrvIfPr6LhuPRvozw+63Ac4dH+Z/fPszgZJFoIka6tYlyqUy54mAaOiKkkwgb6DHBumaDdHxx5enEaJ6/eeo8lbrDkcEMHU0hOpuu7xwSNhQ+uiPJyZk6LWGNDa0+uR9oMTkyWUVVNTTVRlWVRbPv4YBGKKCRr9ZIBjW2pjU2Jy6tNxYJ8LGHtvGNF4eIxcO8b3/fNfdjXXeSV05OU7cd9m9qWZakTuUbFGoufS1hZksWd24YYE06wO4r9BRCCD71nh3ctb2Pb706xldeHOO7h6f57Ds3sK4rzgM72nnu5Cw7+pPsWJVE11R2XiPM8cCGNKfHCzTsAB+8vZdN3XFKFYs//NoryGCMU0MNnhk7g1MuoJYzbOhO8s8/chvv3Lu45SxkqoRMlclsfSG4V8MJmxiGRq1u09UaR9dvvFX19VOTSCmpNiyOnZvhnt39FKsWozMlOpsjJKMmNoKq1HCcOol4mNW9LQyPzFCt2xdbPwH2bOzg6PlZssU6ivCt789N5DgxnOHObZfO+XzZom652B4UqxYCj1fO5Xjfng4igZ+p4eEK/hfCz9SZ+f73v58vfvGL5HI5ksnk9T+wgv/lYWqCra0Gr081SIc1msMab87ViBkKmZrLVMm5IeLSGtVoCWtMlWxaIzpncg6vTzVQFV+Um6+5JIIKbdHFl1Sh5jJXceiI6YQMhaGCy2TFozWk0B0R/M3hPGN5m6Cu8MGtcRKmxljZt8pNmdASkkzWBOmAYE73yFmwrSN4Xetm8Ge2W8Mq4ZyvQQlrgkyxwcnROr1NJmtbQ1Qsj++cLjJbdtnfE2JnZxBdEajC78eP6FwzmTliKsSCGqmGRyqo8oHtSbZ2Xz9RPF/3qFoe7VGVcsOvel0gES1hjQO9QV4+n6dRt6lYLqdHs6yKN1NpuMQDKsWGS6bq0nuTt4FXZ2yOzjkoiqAv6DE2XSagCx7enCIWXHo7dDyJ50k0VcFdeA0LKfDSojMAnX0aLioVEcYDSnXBH3/3LDOZMoauEAvp3LHZz2uIm4J0UGG66pEM+K/Bt5merCuUXQXHsgmbKpriv7e5WWNNUmWs7PHyrEfvmh4UzWBjAu7b1k4iovKu/gAV26MzoiIWcn4u/GyxsMm/+Mhezo/n6G6N0da09PcxdYVH97TyzTfmaArrHFibuOZx3NYTY22bP5AOGiqNzjCeJylWbW7fmEZTFV6edpgsS4QAx1PY0qQyOF3iH54+Q7FYZmo2zxe/dYTf/tTti9ZtOR7/44fjnJmpEtAV+jvilBseXSmTgRu0PT43VeTPfnAWy/b4yF2r2Lm6mS8/c54fvjGBpil85OBq7tl2fROEW8Wx8zM4tkM6EaDhWmzujnHixBBlS8eORHnPpiYaqoEE7ulfSkjqloPleIQDGg3bo2HduJ13V1ynK754lv+23jAtEZ2RAZPR0TlScZOD27svvp+KBfnC+7bz2rk5WiMq+9clUa64xXzk9l4e2tWFqoirTmRc3N7GdtLJENWGw7ru5ZPme5oC9LUEGcnUWdUc5JF93RedBMGvML6Z8ShYkjVxhfZ0jNG5KkFTZTZfW2iRSvC+fd28Z2/XDWcQreuK89sf347tesRC/sTH4ESWeMTkXMnD0xRcx8UWBsL2GJ0tcnxwljU9i4lxImzwS+/o5/BgjvZUkO19CdyeGEIICuU6ezd13XBGEMC2tW1MzZcIGDob+9MUqxb/15cPMzJbpKMpwr/84DYS0SCa8IhGAhSrFpZlcc/2DjqvsMPc0NfMb336TuZyVf7kW4cZnSmSjAZpu8L9bO/6Vo6cm2VwuowWDDGVt4A6f/XUII/d00/oLTKuWMEK3kr8TJ2Vv/M7v8NTTz3FQw89xH/9r/+VPXv2/KR3aQU/A7i7L8TW1gABTaApgmNzFhMlh1RQoT16Y5dAIqjx6X1NjOUsOuM6f/hqnqrlIoH+kMqjGyI0h1TSl2lP5isO//PlLPMVl76Uzvu2JnhhyqFiw2DBxUur5GvuQtaIR67qsq/XJGb4A89VMQVdgb6wRFME+9NJqpZHPHD9SsEFxA3B7e0qczWJcBy+9Pw0cyWHVFjj8/d2MFZ0OTxZRxWC758ts7bZZH1Ko2TpjOVttqYNv8XtKggZKj9/WzPHpmqkIxrbukKcn6nwwukcyYjOA1tbMJYR5kZMhaawymTRpTWi0bWgVbFdyWuTdSYrkq6mIMMTNoGQxpauKG1RjXXNBmfmG/QmDFbfQqbOaNEFAQ3H4xtvzFOrNACBrio8unNppWhDT5KHb+vl6FCG3WvTDHRczV5VIpB4KNiOi2U7BAyVfM3hjYkq6U6HVFCjyRTc32swV/NIGAoRwz+2WQucSJz9BzYyODjF1u4o2QYMzpRRDJMnBit4qooRCGDoGps3dPFgj0po4bdJhxRAwfIEow0NWyqkNIeU7g94k7EQu7akAIl0GwiWVi5uG0iwtz9+w+dW8DLnLlNXeXh316WjISU1x3dW8iRciNEJmRpiwXVOVQTL7Ablusts0SIaUCnWXB7aEmdbb5R0zCB0Dbewy/HDw5MMz5TRFME3Xx5jx+oWjg/nUFWFWsPh9Fj+bSUuG1elOXJuBkN3ecdtq0mnIhw+6tAZUsDNc+9AkI7mq9tAb+lLcdfmNk6M5tm3IU3fNSyyrwW5kDEkhGCg2WSgOQ2blxoXhCJxdrWE2Lmhh0atQrmQW3Z98cscJ3LFGqoiiEWW6pSEEPR3JK65b5GAxhfe0c1krkF7wiB6xQz/aFlyruABgrLtcX+nxkBHjJfPZDBNg57LKnU3SlouIGhqXE6B+ztT/PKje3jq+DxvzLhMzFfwygWEgOZYkM708q2v6zqirOu4fD9U7tm9CvCP/fX0U0dHchwbztGbjvDeg+tZ19uCpims7WnixEiWkdkSTbEAY7MlhqdL7IoFaVEb1BWV1nad29634WLg5pWIhU1iYZMvfHAPJ4fn6UxHWXMFiexojvDvPnkbDdvhtcE8v/+tk2hIXjg1z8aeBHduvLrJxQpW8JPCzxRxec973oOu67z00kvs27ePtrY2enp6CAaXzsIJIXjiiSd+Anu5gp8m1GyP754pkKu53L4qwkCzyaPrw0wt2OgmryPIvxy5ukfBhtJ8g7rloSu+jmVzi87G9FK9xWjOYrrk0BRWGc3bTBYcHA8Cmp/1YeqCbR1BDk/W6IrrrGsx0RTBmsTifdIU3+nLUMUSF6AbQTqokA7C6ekGxbpLKqxSrDtkKza6qiAQ2J5EV7kooj85mOPN0TInIzqfOdhOe/zqepKOhEHHQohgw/b4o6cnyKkBtLxLmRwf3blUc2CqgkfWhhkp+ASyc4FAvjZZ53vnKrgS2lJBNndFaAoqNCcMyg2XUsWiUbPpbl9shHA92K7k6XGLyZKLy4IoXkpsDzQhca4STa6pCo/euZpH7/RFtnVHkq37587ldtEqHgFZxRE6bWGP9+7r4asvjqJpQXLBBE9OSjqiko0JwUBM0BlWaUiFmgcB4SHwjRE2behl04Yennn+FI8fGyQUNNmwoZOiLfCw6W9RaEsF6I8LQpqfC3R8tEDQUFnXGaXoalQ9FQVJ1tHQpcOROQtbBNjSYRIzfb2F6lSX/b5vVV+7EIJNKYUj8x66CgMx/7fqag7zufdt46tPniIe1kkmo/zy/+8ZNM/m4LZO3nfHapJhjZ2rYrx8vkBPU4Bdq2J0pW4uhyceXmiXE4L9O9ZxImtyx75tfOOJ14iGFLb2L18F+FFxgaDdtbOX9pYIlu2yvq+FUtViQ98EU5kyezZ20Ja6dlXS0FV+4YF1P5JxwMnhef72BycxdJVPPLiJ3rarZ5oIxXdYrFsOxXID9Rrbfen4JN9+/izD4xnSyRCffHgbO9bfXAL8BURMlbVty7frXdj8hStT11QObu/hzSkbx5W8MVZh+8DVRezXg2W7fOXJ05wZy3Bgcyf3713Fpv4049k6U9kqxUKRaq3B6s4U6/tuLjl+fKbAn3/nTao1iw++YxPb1y49PrOFOn/yvXPMFepEQhrxsMGmgUtEoaslQjoZYnyuRHdLhJ60T5Ck4/CVJ09xbDjL7rUtfPiugWtWxdubI7Q3++eb43q8MZSnZnnsHkgQMjV0TUHXDFS3QXZyirrl0NScwNQUXhoscn6uzppmg/UtN36/XcEK3k78TBGXp5566uJrKSVTU1NMTU0tu+zPorBsZmaGjRs3Lvve5z//eT7/+c//mPfoZx8vj9Z48mwZT8J00ebX7m4lpCsMJBUmiw5jhTp9CZ3Ydbxrp8sO3zpTIVd1mC5YmCoIKdjQbHLvwPK9551xneawRqbq0JPQWdusYyseIyWP/rhCZ0SlZ32UA70hwoaCKwUnMg4hDXpj/v68Me8yWPBIBwX72rTlnac8yUimTlBXaE9cnWD0NQfY3BHm7GyNdW0h+poDBHWVQt1lvuKyqzNIUFeYKVocHaugawpjmTonJ6vXJC6Xw3Y96qqOETLxXI+hqsDx5CKXLSl9rY3rwaYWfZGTWcX2cD2fEKqex+bWEG/Oe8xMulCrcXyqjqkJnh8ssbc3TEv0xoSvoyWXUxkHUwXbk7QFFM5HAsyWHVpiOnevu/qg7gKqtuRvTlYYLfp6k1/YGiZ52XmjY6NLX4P3jh1dhJtSPH6mQiIWxvEktieZawgGgJKnkXF1JBBXHJKGTU9I+gSlVmNwskhZmkznXcRUlXQqhKIImgMqd/a3AB6eXeYvnzrNcyfnMXWVTxzsZfOaNgTgIjCEx6vTFkfmbKSQlGzBw+tSfsbKjwF9UYXusECIxW5179m/ivfsX8XhwSz/59ePMTY+g2U5ZPIVWpMhDu7o4RP723nHpiaiAfWWeu3fvbeb6bJH3lZpak5Sqjt0tiZ4x/717O7W6U3fWgXjWpiuC0aqvmthf9hjbc+lwW4yGuBffXI/lZpFLGze0PNJSr+Oh5S8cmKS2WyZaNjENDS2DKQJB69dcfz6M2cYmsrjefDdQ4N89n07rrpsrVzifD7PxFyJx595k4PbuziwtXvJciPTRf7qeyc4dnYSx3FoWDbPvDF808TFdlwyhSqpWOiqFYPeiKBkKeQtyZ6uGO3NcZ48Xcb2BPGQzpujRSoN95a1GG+em+X7rwyiCIVv5M+xcVUzXWmfJPtE+dbJ7ROvDHJicBZNVfjaD0+weaCV546MkynU2LOhnZ7WGJW6bzoRDWnULY9K3Vm0jvPTFSpSQw+GuGdXHy0Jf4L2+GiOZ49NY+oqT745xa7VLaztuv79C+CJo7P83QsTuJ7k1EQTn71/1cX3BseyxAIKQcMgqrpEI0H++IVZKg2XN8cUfnFfnC23fER+tvD7v//7/P7v//6y783MzPyY92YFV+JnirgMDQ39pHfhbUVraysnTpz4Se/GPym4nh9epyj+ALpYdwnoCvMVh789VqJieXTFdB7bESNwjSDIsuVRczxCuq/9SJgqqiLY3m5edO65Eq1RnV+8LcV0yaY3aRA1Vfa1q+xt8zNdvjti4Xqwt00noQq+O9hgrOSiK4KDXQYtIYVTOV9gPFTy6Ip69MeWbusfDmd45kzez0DYm2Z7z/Kzuaam8It3tvHGdINzJfjhhMvtHQr3XZFcnghqtMZ0zs/VSUYNmqI33pIVCWjsXhXjtTkXTdeJGAozhQadyUsz5mcLHq/M+DkkG1IKu9OXbkNbW02GczYVS3Jnb5BswxfUqwqUXb/qVLE82mI6gWumOy6GoQp0FWoOxAyFUt3FDOhsW50ibCikY9f/jlNlh3M5B9v1KFuCZ0dqvHfd1WfO1zWbHJuxqNg2iVCQgCZoWygOVz0FTwoUIal6Kk2aTW9YAhIrYtCaCjM6ViNqqmBZrEnFSEcN9gw043gCVdVwhMGJsSKmrlCq2ZyfLnNgvYs0wJGCqOpyZkGTo+DRcBzwXIT748tjuNZMcEBX0TUFx5UL9qv+9Qr+9XotEn49eEKhoEZQdYVizaY5alKtO8SC6ttCWsAnLo709UqzDUGzubiKp6kK8UiAWsPm3Ljv7Lac3gjAkgo5L4BEMD0+xZ984w1msmXKNZv25gi7N3Twzz+895oEKGTquJ5EIJYNSLwcttXg//3HP6BctSjXLNKJwLLEpW45NByPaMgkm7dBiKu2UV0NDcvhD7/6MmdGMvS0xfmVD99GNLT0t9YUwfZm/36XaoohhEJXSxxV05jKNzi4IXXDrYPLYXS2xMhMGc+T9LXHbliPcnY0w5tnp+loibJ/y/LuW6GAr9lzXI9IyOSl45P85XePU7ccDp+d5Td/fj+9LRHu3tLGK2fm2dQbIVeo8OWncuxal6a/Pc6Tx2YpVF2EovH6UJ77d/itjSFTI2CoFKsWqYhJ6CayqsYzNSzHI2goDM5WFr3X0RIlGvTPk53rW7FcieV4hAwVy/WoX8dJ7p8SrjVR3NXVxcTExI95j1ZwOX6miMvv/u7vruS4rOCmsLs7yHxNkq24dDebfOlEBdeTFOseE3mbdERlvupQqHsErkhfz1V9EXc8oNIT19nYbHB6tk5HWEFTBaubzevmULRENFoiGoWay5HJGu0xjZaIzptzNqMlv0Xo1RmbB3oNsnWJqQhqjiTf8OiI+E5bZdv/u7nMINCTktdHSgghyFYcXh6pUNGDhHXYlFLQr/iMImC0Kmh4knJdcjbn0hJc/L1NXeEzBzt4ZbJBQTEZ8zTiJUlf9MaqmB/ZnmLtTIPHj+U4fjrHxIjgswc76U8H8aTkyLzDfM0jZiiMlz12tUDdAwVfmP+pnXFcz3dGO19wmal6eBLuWhWi1KQwW7LZ2hm6qXDJrojCXV2+jWhvXCVXdRktOjiuZE3qxqo2yYCCgkQKgZDyurbSqZDKL+yMU7E8HKGgCMEFE7SQ4lGTEgmElMXCa0NT+GcP9NP4/hiluseqlMmjm2JUHMnXjszS8ODO/hSbmwV71qT44dFZmqIGm3viKAKS2qX17UzrlBoSR8JtaVCt/LL6lh8Xqg2XXNWhLW6wrivGY/eu5gcxjVy+Qiwa5MhEHVebYfuqBM1R45Yr55oiMDTBTNHiudPTbEiHiOouB1Zd35nrVhHRoOz4LU4RdfljbDsef/D1Nzg2OE8yavL5D+xapAU5dC7P6yNFdqxrp6sthAAIRqg1HITww0RNQ2NoMk+94VzTavdj92+kORFC1wQP3rbUWvdyCCHYsa6NJ18bJh4x2bBq+dao1V0J3rGrh5dOaMTMNu7a3sPt25YSnGthaCLHsXOzREI6J4fmOD08z+6Nndf8jOs4uKpKdzrG1lUtHBma4x1b0zetbbkcI3M1QpEgVsMhkYhelURejnypzv/4+qtMzZeIBA1CAX3ZNrCHD6xFEYKa5XDfnn5ePzNLw3YJB3SK1QZ1y8U0ND561yo+cKCXV05N86ePn6TacHjj3By/9dhesBvksjlCwSAdqUtOa2s743zintWcmyyyqTdJV/P19/sC9q5OcWK8hOV43L3x0m9crdu8dmIcx3GpOx4nxgoUf3iMbf09jOZstnSE6Em+PYGaK1jBzeJniris5Lis4GYRC6h8cncTFcvjL46WqFou+Yak7kgMTZCpeqzrDlwMnxzLWcxVHPI1l2fPV1AUeP+WOJvbgzw4EOLMaIFcvkEiqHGgO+7PiF+G5XrSK5bHn7w0z1jepims8ov7mi9a/0ok2oJWZm1S5WTG13z0xlSihuBAm8Z42aUpoNARXrzesUyNasNlTTrIy0MlwqZCVTUYKkkUIQlqgnUJv3e9bEkihkARgqghyDUkqhCE9eUf/KmITjSuMV+CuQa8PA9toeunwYM/aOwJKWTmSsRNwVzJZmi+Rn86yJmcy1TZpdSQNFzJurjCV9/M4AVCpOMBtjQpNBmgLGxnIK7SHBC4EpKmQCxjnXsjEEKwoUmnJQyDJcl4XZBOhYlocGevhiv9KoUh5FVDDFNBlXf2GTwxWCUdUdnfdX3dha4KEsvokqKKgyE8PCAovCXv9zQF+NcP9zKRb9DfEiAa0PiL52d5Y9YhYmrU6xZbDsT54IEedg2kCBoqnU2LtQISSEVCvG9DBMVroOKxrBr+x4Rjo0X+9NAciVSE/tYwH1gf5uCWNg5uaeP4WIH/8u1znD2b41tvztOXDvPhfR08sG15cbDteIxnqsRDOroqiAR16p6gYAsimiSiK3xoW4LXxqo0hTXuGgijX4doXglNN3BUk+myjXQt0oZvo301rAp7RDW/ctRsLH+cM4Uq58dzJMIms7kq58ZzF4nLZK7Ol16colB1aAiTdyZjhEyNVMRgoCvJ8GQeTfMrvbvWtxO4juNTayrCJx/eCt6NOZJ9+B2b2L62jYChseoqwnpVUfjwvev58L3rb2idyyEaMSk2PM5PZ+lsiZBOXZ9MlgpZMnWVr786w+h8kVDwRx9EtySCpBL+/WTLwI2J0Cs162K7X7VuU6w0ll0uEjL4wH2XQjf3aipvnptlPl/lvt19xCOXKky6plCuOzRsl0hQp1yzOTE0x+DQJNTKmLrLPZsukQwhBLdvalsUkHk9ZApVXjo+STRo8G/fvxbPg/Rl7b9DkznOjGVJxYK8eW6GgBngbCPLbRvb+PR7erFtm3r9x1epXcEKroWfKeKykuOyglvBWMHhW2crjBVsPMBQFTRDEAipNAUUPrgpiqYKhjIWf/FajmLdpVh3iQUUpAevjdfY3B4kX3OZK/uOXNmKw0TBumjPWqo7/OO5GhlLsKFF50CHcXFGfq5kM110SIVUZksOx6Zq7OqJoAC2lHRFVAoNj/3tOhtSGqYqCC0Qiq6IQldkaQvDq4N5/vyZCeq2y53rUnz2YDuaKjhTN6i5fruKJ31L0W+cKjNasOmO67x3XYQD7TrpkIuhCNYkrt4eEdTA8sADMoU6/3jS4vae4A21VTVFNPpbgpycrNAS1RGGwaEpm6rjoQloDiqoQvLSiTnemHEJBWrsGkjSFAxzpVlY/CZE+MuhbnsLg1bBibzv4FVyFYI6NDyP2QZYjo7lCeKaR6fhLEteLNvlxVfPMzyaoxg1KfRtJh648dnOyyGEL8q/FtriBm3xSwfj/EwVx1XJOS4GNgpxhCJY3b48mfMUA0eLIIXA80wUu/ATq7bULJf/8zuDjOZtghNFFLWbkWKQdSmf1NmOb5BQsz1qlkfd9nji2Dz3b21ZMhFgux7//bvneP18hvlMkY6kycHtXazesoaKKwiqgs1xb8FF69bazYSiEIwmOJZpkLdUpGsgpUVH4Oq/mSqgNXDt49sUD9HfmeD44DwtiRADnYnLvpd/DExNcHJ4nns3pgi5Kt9/6k2iIZ3Pf3g33a0JKjWLnrbruL8JBTWUAEVDOg28WuG631lTFTauurGgy8n5EufHc/S1x+luvTF9xQUMZ2zURJq4XsVIRgmGFpvreJ7ktTOz5CsWu9a0kIoF8DyPqOYS1WzChsr+gdiyon7b8fj6C0Ocmyyyf0Mrd29tv+pxev/tq0jFAiAld265MY1Oe3OUu3b2cejYOKu7U8tWW5ZDSyLEv/nkbUzMlrBsB9tx0bVLExq716V58/wc05kq9+zowrJdvy2wNUnRFgzPVem6xUkbKSV//M03OXZ+Fl1X+cg7NvLA3sUVuLamCC2JEGMzRUJBA0dKIqZGMnJjeqwVrODHiZ8p4rKS47KCW8GZrMV81SURULE8yUMDIVRFUHcl61KXtBJTRZts1SGkK3iexHYkpqbQvmDV2xLRWZsOcHK6RnvcYM0CabEcj//XM1lG8jae45KtxuiOaQzE/QdTW0ynJ6UzOG9RdSQ/HKxwOOPS32yCUBgq2Jiq4J4unY7IjbU/nRgvk61YxAIah0eLfPyOTlRFEC55nMp5RHWFVVHBWMHmTNYipAnOZCzGijarUwbbmq9/6W+Iw1RNMJGr88rRSTzb5sh5k395/+K8heWgqX672dB8nZInOJIDq2wTNQTNQUHdFfRHBV867mDbHiUJU7ka1XaTQkMlbl77YVlxJG9m/KrNhoRCW2jp8lJKvn+qwHPnSjRHdD66pxkPDVUIVCGpuxDXQSgqVU+gAwVHoUUXmGLpAHSuUOfcZIF42GCmUOfV8QqBaOhiFstbBU/6uiztijaYjU06z41UUBSFXX3RGxhQKEhASG/Boknwk6q4ZEoWVcshYKrUGw7lcp144NL+b+qJcf+2NN87MoumuqgK9DQHl/2Os/k6x8eK1OsWc7kqpgpPHR4n1tNNUzxIvu7yjZMjJDWP+3b3Lhog3iiEUBBCwXY8hICqK5nw/JpVh+lxqx1Kuqbwz96/k3PjOZoTIToua/PpaQrwzu3NHDpXYF17mPXNKt985iRPvXoeEORLdX77M/eQTl6/QjFTcolrAlNz0TQTFA0857qfuxFkClX+y98eYnKuRGsqwm98Yv8NtVldgBAQCIUwgkGiocXmHAAvHJ/mz79/irrl8trpWf63j+5AVRQ0RfChva18cE+afLnOm2em6UrHaL1s24fPz/PdV8cBmMpWWdcVp2OZ4M6q5ZFrSN6xswvzGtrGK6Eogo8+sIX33bUe09BuqlXt/HiW//7VVylVG+zZ1MkvvW/XxfM7FQ3wGx/aieN6GLpKsdJgdW8LL08LtGCA75yqMtBp0R6/eQt4x5XM5ioETJ1K3SZbqAFweqrCyckyvU1BtvdG+dWP7ufceIaAqTOVrdGSCLJr3Y1XdVawgh8XfqaIy0qOywpuBU1BlYDmi+rXpnR2tZt4El4eqXBoqMHWjgDZqse5uTrZisOkI0kEFcJBjYipsLvLn9nTVMHP708znrdoCmtkGvDV01VmSg4FTyWR0PA8yWzZuSgyBgjoCp+6rZnnhso8ca5CIKAzWweZd6m7Lp1RlaIlmah4tIUVSg2PiKFcU9i8ui3MoXN56rbLnoHExWX7ogp90UsP4pipEF4IyIwHVGI3Ub3IW9Aa8Mh7DTzbpj1qMF+ymStZ1yUuF773TLbCC6NViCdojRp+0nqPQUBT0AW81BIkM1zCsz1CAY0TeY+xiuT2NpX28NX39XxRMlqWKAKsrEdrcGm2TbHu8szZEg3H48xsnWMTFTb1xBkqQ4sJcV3QFtLQNCg1wJYQUiQqEikv2bHO1GG2DkE9SG9rlFNjebrXrWJWjfLEmMNdHRqdy1TFbgWzZYevHStSbrjctybCjo5Ls9Ef39/GurYiuirYuSrGyEyJMxMFVrVFWb1MvoziWSiegRQqqlfHr51dG1JKpqqSqi3pjCjXzPC5GbQmTPavTvL82RzJuMm7NsZpvYzw6arCR+/o4YP7u3nlfI6a7bGnP7HsulIRg/ZkgJlsGV0TzM5mKGUVzh4fJLJ3AyMjs7z03ClwXUxD5Z6dvcuu51rwXAerXqEnZnAmW8OTElWRzFkKCU0S0W6dAIYCOltXL21NEkLwru1p3rX90nu+s5hfzbn8ngKg6n41ybUXtyudGivwjVenefgOjXQiRGtMw3HsJUT4RiGl71Kn4rdRTmcqzOWqpJNh5vJVJudKVyUueUvy5rzfErklpdAcEOweSDKZrTEyX+OO9U1LzD9m81WqdYdIUGcmX6VhuYQCClJCXWqUGi5/9s2jHD87SWtTmF/72GXESQgQftVGsLy7aKHu8j8PZZkp+6YpP78ndVNGH8A1tUVXw4nBOWayZZKxIMfOzVKqNBZl4CiKwFjokY2FTR59x1YmfzhBKqyTqzrMFG+NuOiawoO39fPtF86RToa4bVMnc0WLP3pqnJlCg0RY558HeljTFqXjFqs6K1jBjxNvKXHZuXMnzc0353d+M1jJcVnBrWBrqx+iWLUla5t8t5fXRsp89XCOhuPx0pCGIwWzZYe6LelL6YyVXWolF7Xi8ZXjJT6716/wGZpCf3OAhiN58myFfMNjquQHUaqKQAhB2BCUrcWDxJChsL0zxBuTNaYqDmZYxVBACkHDhZAuSJqCrx4tci5j0R7T+PDWOKGrPFD3r/HFy1XLZWPn1Wc7W8Iaj6yPMF506IpppMM3dsnP1yUvzrrUHFADAVa3BJktWqxtC9F9g5kaY/NVvvrCGDVHEmkXxM0k27sDJMxLJOMX9jWzuzfMVENlXmpUHb9lZqYmab/GxLKCP0aRcNXZb1NTiARUclmHgK4QC6i0BqE1CH71wf+glJIe06bhCTxPcrSooAoYCPvmCScL/m+kKioffXAz0zN5TtkhArpG1ZHM1yVX+wkyVYdzGYtUUGV10/XF5q9P1BjKNjBUhR+eq7C1LXCRlAYNlTvW+efhbL7Gf/3GMaazNVoSAX7t0S2cnHN4fbTCmnSQ921LoakeulMExLItYlJCxVNwpCCseuhCMlLyeG7SoeFCZ0TwQM/iGfGpbBXb9ehuDi/7XU4NzzM0mWddbxP9nZeq4rqq8LkHVvGuXW00Rw3ioeUHfpoq2L/22ja0QVPjC+9cw7EfaAkQAADAEklEQVSxNM+8cp5DR0Zoigc5d3qY/eubeenp13Fsv7pQrd16a3G9UsIAejXBeVXFlQJVkbdcbbkV3Lt7FZl8lWKlwbvuWHtxhl8LhDFCUUDg1CtY1eLFz4zMlTk6OIsiIBIOUi2XmJov8s69vTcduulJyHlBbKmiC5ekUqOvPc6anhRDE3lWdyVZ3X313+tkzmOiKheuNI97OlQMTeGD+7uu+plda9O8emaOUtXizi0dhBZIQh2VnBeg4jms3biafLFMoWQxOl24SFy2DzTx7tt6GZwqcNv6VtpT/qTT62fnePnUDD2tUdo6mhnN+9fkUMZiomDfckvhzaCvI0HQ1JjLVti7uZNw6NokpL8lwMaOMIPzdfpbggy03FyW0eW4f28/t23qxNRVTEPj3EyVct0hGdYp113y1bemGreCFfw48JYSl7/6q796K1e3BP/Uc1xW8PZAEYJ1zYsfEqWGh+1KQoafY4JQSIZUSpZHueGhCIEi/PMsX7+6uNXzJNLz+9IdJAFN0BpRl7h5AaQjGj0xlfNTBRQkgXiYB3sDGJpCSBM0LIcTsw1MTXAuY3E+Y7HlKq5lQgjWXmtkfxl6Ezq9iZubIaw6fitVSANHUfm5g51I23eDMjWF2arH63MOmhDsaVWX1aHIhf/wPCqTM+zbFmN36+LfoSmsccdAlB9OSapFl5LlETQEzYFrX7+r4wLLg4YLa+OC8/MNjs+7hE2FO3oCBDRBQFf45J5m3pyokAxp7Ohe/ngJAVFVElUlbxYUqq4/zJ+qK7SavqOZKvxBXMDQuG1NE/akw3BJEjUE7cu0qQHUHY+/O1pkouAQNgQf3BxjzXUGSGHDb4mxXUnUVK46SJ4r1MiVGjTHA2RLDY6Olnhy2MJ1JVMFizXpAKs7E0gpOTU8T75is70vvmh2u+ypzNi6bxDgeXTpFrmGpOZIQrogW5c0XF/rBHDo9BxfevI8jufx7r3dPLx7sZvUyFSeP/jaq2QKVdqbovzrxw7Qcllbk6EpDLT6/7Zsl5G5Ck1Rk1TUPyZSSo6N5JnK1djSm6A9FaJue4zM12iK6JRKVU6PZulrj7O+t4m7NqYJSJuRiXmqdZuAofKtp0+TLdap1m32b2pn/5bFblWeJxmbKxENGr6+4QYQ0STdAY+yK4hqHqGrOIa9HWhKhPmVjxzAdRYTMFXTYSHj5ULl5QLWd8XpSIU4M54lqAsy+QpBQ+ObLw1z27o0octyT6SUvHpqmvl8lR1rW5dUTixULKmh4GFJDQt/4Pvp9+ygXG3Q3hS9ZvXBth3qdZdAwLhYNRoueGi6QltQsJyTfG9rlN/8xC6qDYemy34jVyp4CAKaoDkV5T337GJ1bwvtqSBOrYiUEl1VeP+BvkXryxTr/MUPTpMp1Xn17BwfukcnFVLJVFzaYhrNkR9P44nn+bk8iqKQTkVRlWtXeYKGymcPtjNbtGmOaAR/BOtn8Ks4F9DbHGDvQIIjYyW29UbZ1BnBdjxqDZtSw+PLL01hux7v39PO6ra3z4lvBSu4FfxMtYr9U89xWcHbi7G8zYnZBsmQypaOICema0yVXZqiJjXHQ3E87l8boSNh8ux4namCTcRQuH/10hu3qQkOdJh882wFgYfqejQFFNakg3THVGq2x7fO11mdUFnfdOnBPjJfJ2KoWOUaWk1hfdOlDIScohIxFKZLDvGAQuIm7H4vYLTkka37LWdtoRtrfzg3W2OyYLE2HbwoBk8HBW1BQd6SdIYFnVEFVVy6Xbw66zBR9haOBdzZsXRbPc0hHt3XxaEzGdZ3xdjee6mdyfb8dqSoIVCFoDMksKVKylHYlGBZQ4LLoQnB6rggpPphdl8bsTBCJoWG4MXxBvf0+QOerqRBV/LG2ys0fyx48XVEg1URyWRNkNQlLaZPGg+0a6xNSkKa79K2HMoNj3zNJWIolC2PbO367k639fgzxFXLY2fX8hoPgP72GJv6UpydKLCxJ8nazjjPjMxRcz0MTSEQDlOUAQ6fn+crT45hWTYvnc3yr9+3Bn0hr8KWAlcKNCGxPYELdIYVzpqCugMDcWWRi9yrZ+fIVywMTeHFk3NLiMtcvkqh3KA5HiJfrpMp1hYRlwtwXI8//MczHB3OkYoa/Mq71tObjnBirMB//8fTFKo2fekw/+rRLfzFC1McG68Q0qGRmaFQKNEUC/IvP7ybVR0J+rubGRjoplqtc/umVr769FkGOpPM5irct6ePVOxSNV5Kyd/88DQvnZwhnYrwwTv7Wd9zfa2kENBkeDRdd8mbx4mxAi+enGFobJ6WmMkjd62ht9W/JyiKSiTRhKpq2FaDciHLBY2SazUWCIvAsWqL1tmbjvBvPriZbKnB4FSBv/zBGUbGZtAVyTOvp3lw/8DF8+q109P88TffpFK3OXRikn/32H5M49J1riJRhIcrFVThUavW+aNvvMH4XJnd61r5+P2Jq363U6NZvvGd48xXXe7Yu5bt+7r485emefp0nmjY4EMHurizU1vWCCMU0AkFdF47M8sb5+bpb49xx9YuTKHhaApbe2K0JjrRVIFp6BTcBlZjederhu1Qt1xUTcN2JRFD4VN7m5go2PSlDOK3cJ+9FRw9N0PdcolFAxw+M83HH9xyXY2MqSl0pxYTUykls7kqQVMjFjZpWA5jM0WaEiGS0Rsj47qq8At3dlBuuIQNlbl8lf/+94fJFGuo0SSzVRCKX4H+N+9bc8vfeQUreDvwM0Vcentvvld5BSsAqNkeXz9RYqbsYGqCRzZG+ZU70/zZ4QLjJQdTUdncFeLRDRG+ea5GLKQTCWh0xbRFOoPLUbE8PM+3XrUVuK8vyL7eMMMFh28P1nElTFVcWkIqTUGFV8drTJZd5ks26YhGf/Pih0wioCA8l/FsnTlNMFuO0H0TlZKZqsfzUw5VRxIreDzUo191UH0BQ/N1/ujZaXJVh66kwb98RyfRgEZAFdzVrlB3/KrLlQNo5SqvL4cQgvu2tnLf1tZFfy9bHj8Ytcg3JD0xha1pA10RbIgJWgOCkA75is1XDk1RrDk8vD3Nhst6sSarcKKo4AHNJnRqFh4L0nMpqf0IQWn9YY8pVUFF0hH0e/pXRXzycjlURdAaEkgpGSs6FC1JX0wlbFw6GqmQyoa0ycnZBp1xjdVX2qUtA0MV3HkDWSNBQ+ML79vMfKFOU9TE0FUe3elxZLzKQItJSzxAHZgv1Kg0XJIhlZmCRaXukljQDoUVl5Lit0DFVBcVSIcU3tVnMFVROTlV5dm6y84ug7AuGGiLcXgwi+V42LrJV48WONAXoj2qk6/YmMEQ6/dupWBDq1ulv+MSKSjagomGgo5kcirPk8MWQoQpZis8eXiC3atTTBctilWbRNggU2owNFfhzHSVSEBlPFPFLTt0xoPkSw3mCzVWdST4mxenOD4PggBr6hqrOxOcGcvRnY6ytmtxC1Pdcjk2nOWBg9uJRIKUkHhc/fx9OzFTaPDGcIFvvDjK2eEpapUabckgrif59Y/sBkAzTDTNwHNddMNE1TRcx8b1JIeHskhRZF1rgJC6lBCnFipZvekIx85N88TMLKlwgO+8cIZdG9ovEsr5fI1q3SYaMsgUapRr9iLioguPpFLHkiqGcHj5/CzHhjJEgjovHJvi4PZuelqXD6B89sgEM5kKZsBk6NwUh+JBvvvqKEowzFSmwrGxIrd3prgabZjOVvnz750mW6rzyqlZWuJBNq9qwkOgKypBQ/WPieviuctPCnie5OlXB8nN56g5cPDARjau6SRpuHTEf7y5JKs6k0RCBpblsmFD8y1n0HzrxSG+98owAUPj5x/YyBOvDnJ8cI7mRJDPf3APnVfoVCzbZXAiRzIaWGRkIIQgulB9e+30NGfHs4QDOrOTecxYHG3hnv/DY3M0LJvt3cs/B1ewgh83fqaIywpWcKtouH52S1AXNBx/cKsqgqCuLMywC8K6r70Iab7+QSjXtuIVAhxPMl9xcF2P750p0xnXcfHXeaG9yJWSiuXxxLky8YiJJxQOrApzcO3iB36m4nBkvIrjeNQbku+eLrOra6nl59VQc3yXrZAmqLv+v69HXGaLFvnaJYvnTMUhGtAoNjxUBcJX0djsadNwphwsD/piNzdjOV72mK56BDUYLkpcVfgZKookX7OxbZdzYzmePplFCMiWbX73Q2sXnODgRAHKnk9UMhb0BA3ajTLTNYGpCrZ13Xq/elD1yctcocFfvZjFdj0e2JCkt2X532Go4PL4UI26I+mOaTy6JnhRk+J58J71Ee7oDRExFQI34V50IxBAc8xEURQmSg5r2sLs7fMHLQ1pY3sKG7oTHD4TYCpfZ2NfDHGZw5apSLqMBh4CjUv5NZqm8eVDk5ybLqEoCi/3JdjeHeK+7R20JoM8P1xmrK7z6kSdfN3j7h6D//H9IV9H1ZVi3/ZuylWLqiswdL+CNVJTyNQ88hWbJw/nkKpG3XKwMXjilfM8//ogB7Z2sbo9ykyhzs6BJtZ1ROlpynNqqkJHMkgykWBmNsfGVc2s7/XrH7mKjaoIXE9StTz++Qd2MDJdJJ0ML2kFCxgqm9d00JSKUanWSaWTuNgo3FjOyc3A8yTPHh5hNldlx7o2Vl9GouaKFv/X4yMMzlbIFCxMTaPiSRqOR64BFVsS1gWuY+N5Loqm4tj2xcH5y6NVvnmiiO1KNqRNPrU3hRCCsxMF/u7ZIVRF8LG7B+hNR9A1hX0b2jh+ZhLLdjF0bREx2bG2lUPHJ5gv1Ni/uWvZ9jlDuBjC33YiahIO6BTKDTpbosTCl8h4rtzgySPTKIrg3q1tpBMhNE3FcSUzmSJ/9fhh5ubL6LEkTa1N9CW0Rbk4luMxXrBJBVUSIY2G7frJ7aaG7brUbRch/CqQ5zkU8xl0w8S2LBxneS3TxFyRZ94YIRo20eo263ua0A0DqC27/NuJO7f30JwIUavbbL7B3Jgr4XoeTx8ex3I8cqUKT705xqnhecJBnbGZEqdHMouIi+dJ/vgbb/D66SmiIZPPPbqLtT1La4fJaICAoVGp2axpDdE/0OS3tSH5i6dHcT2P4dUx9u2+5a+/ghW8ZfipJi47d+7k3e9+N7/3e7+35L3f+73fY+vWrTzyyCNL3nvsscf467/+axxnRXC2Ah9xU+FAT4CXx+t0RBXWt/gP3PsHwkQNBV0VHFiYUdrfZWKq4ErY0Xb1QfDmFoPzmQbTBZuELqhYHtmqy+Y2nW1pnYmSy9qkRktQoeH6SeuOB8mIQX9LYIkNaNhUiQV9zY2mCFqiS8Mtq5ZHyFjqoAXQHvYzXzJ1SX9E0BTwKwJV29feLOdStrY1yKrmALNFiw3tIToTBkfnbA5NWagK3NNt0hdf7jYhsIVK3ZMczkiSAblA+JaiaksKDY+pqiRmCCI6BBbMElJBX09iqlBsSE7MNyhULey6WNiKH9J58Rhc3Lr/WhcQ1uG9W1PULBdDu7Yb27XgSai5YCrwvbNl6kF/APCdU0X+2RXERUpJtmIzXfao2pKwBkeGcshcnr39cU7PWbw62aC9NcbmtiBbltHsVByoLlgyGzfJac5P5Pmzx49TtxwGNqzCiiYxNcFDqwL0xDSqDZsXJqpUHcnu7T3MVB0iQYPX513u6by0MXVhIHg5HNslW25g6hrFmk2hanMm57ChWWfHQBPjls7ESNV3dHMlR0cKTGTroGsMT+TZuamDcEBfpM8RSOZLDeqWR6XuYOoKiXAIrVHCqXmAxumJIr/52G1UajYt8QCKIvjcvV2cnq6Sjhq0JwyyxTrJaAB9gQS+e2eav31xCl0V3Le5maCpXyQ1V0IIwbtv62amYdDRHPY1RCwfIPij4tWTk/zl40f9RPJTk/zmp+4isiDGHs/WmSk26GkKUqk7JMJxYmGdaFOclrWreH7a5R1dKjg25UIGVdOxrQZS+q2ZmYpD3fbvA9MlB1f6bY1//+IIZ8YLbF3TxlDGoa9NQXoeezd1UijXmZwrcdvmrkVah9ZUmH/72H7KNZtULHBdbeimviY+9c7NTMyV2TrQTOIyV6yvPDfMM8dnAciXLT5+cBXxiIkjDB4/NESpWqc5rNHTZvC+Ozs4sPrSANtxJX/1ao5TM3USIZWf39tETzrCQ3t6eOH4FBt6k2ztX/y72paFbVnX3N9w0CASNJiYKxMMB4mHdEyWfiZXqvPaqSliYZPd69t9c7K3WCcrhLjhnJyrQRGCntYor5+ZJWhqrO5Mki+UOTOapSkepKdt8WRYoVLn5NAcIVNnNlfm9EhmWeJy28YOpJRkCjV2rW+/aNP9X75zDk9KFCGYyr8918oKVnCz+KkjLlJeeogePnyYzZs3L7vc7/zO7/DJT35yWeJy5XpWsAIhBHu7Q0yUHEbyNv94pswjG6M0h1TevW6xIDWsK9zZs7gs7rcEudiepDeuoSkCUxNETRVDExRsyUBcZ6DJQFUEd14x6x/QBI9sivHGZI2mkMq29qVl96Cu8Ot3t/L46RLxoMr7Nl16CFmOx5demefsbI3VLQE+sbcF44oZfFMV3Nel0XB9IiCB7w9WOTFn0RxSeWR9hIihMJa3+fbZCniS964P86v3dpCrOqSjBpoqODpfp+FKbBtOZZ1liUvZviDi9jMuqgstZVfi+LzNoWmb2aokbAiihsLBLp37ug1yDY/OsMp0A+YaoEmPYtUmpAs8xeDAhhS1hVaxi85aKqyPwVDZw1AF62OSC8H0l4tXpZRUXQgo3BCRcSWcKSnkbUFQlQjTRDg+qTJCS8+Fv35xikPnCsRjJu09zUzNVzh1ZoYhIXnpTBYlGKanL01dqgyXJYmAYG3MJ0d5R6VkC8arHrYHMR22xj1upiDz5BujDE0VUBSF0VeHOHhfkkLDY7To0hPTOJNzmKr4BLjmQCRooCg+cb4ewgbcsy7G907kCYU0jKCGqYmLxHR/T4h8zaNme9y3OkI2J4gFNXI1l/bWMImQTqvpEVvI4vGkR7vmcihbYjZXxSnk2diZZN+6ZmTZ4B+emSdrNqGEE/ztmwU+sTN5sY0mEtDY1XfpOkgnFxPIHX1xNndHEQi0a8XaLyAWUAkFPBxsNNwlpO16mMlW8KSkLbW8q9oFlKoNLNslGjKo1GxqDfsicVmVDtLXHGQ8W2f/mhS/eHcnM47G4YxHQBUULYnt+dewY1s49uKB9pb2ICdnG1QtlyavyHdfKLBjXTumrrJrQycfvHcLLfEQ4ZBgbGyMSNDg4QNX1ymYhl+FqTVsVEXB0K9eQRVCsGd9G7vXSY6dn+UHLw+yZSBNa1OEfMW+aGhSqFgYusrd27vwdTgNvv3iebqaw/zcPf1s6V9s352tOpybbxAyBDNFm/PzDTriOu+7fRWP3LkGLejfo51aCendeIUsFQvyS4/s4ui5GdpbYuxfHV1SYfM8yRf/4Q2OD86h6QrffqWFmi3ZvbaFD9zRf8sTIW8HhBB86uFNbB1oJhzQ2bk2zW0b2zg9kqG9KUxve2LR8rGwyarOJEfOzpCKBVnVkVh2vYoiOLBlqdPb3ZtaGJ2v4bgu92y8ttvfClbw48JPHXFZwQreLowXbM5mbIK64EzGZiRvs+4GbTCPzFo8MVTD8WBHm8H9/SFsV3I2a9MR0yjUPTZ3BIksZ5OzgFUpg1WpS60VxZpDpeHSGjcuVl/6mkw+d2DpPg3ONzg8XiGoKxwerxIN5shUXPqaTN6xLnbx4SqE4IJp0KnZOi+O1gjqCsN5h7MZmy2tBn99ssp02UMi+ZsjRX79zuZFg/6UKTg2bVG1JLoQ5DokSVPQcCXncg66Ap0RlXRAMFHxaNgur005HOw2CVxWdfE8j8NzDjXHb9PTVbA9vwKzqUmje6GilAqALSWlOszmVIoNj81tJvf3J5cMDh0JnhC0hwVtAY/lDIFcT/J6BubqENVhd7O8bh5J1YGc7QvVS45gdTrImXkLCWxILm6fyZRtXjqXx5MwMl1mz6oY7c0qQwqEAzqVuksyJLFcD0PxqxoXtp53VWZtnYLlUXQgoXlUHL/ScyF+R0rJqakKY5ZGLKizOaUQ0S/tf7HhEYiEUITAdT1fz+JCRBe0LuhXApqCKgSOJ2kLKcRMBYlge/ONtfW9c3OCOwYiTFQ88hZ0RhSSAX/dzWGNx3YmLi7bn9IJmirZksVAd4JYECILJGJ8rsyfPH6SYs1m99pWjJrNvtUJPnigm+aYiZRx1FCIb5yq0Ro3ODtnMZq3Wdty/evy2VNZnjieobspwMf2d2C7kqePTFOzHO7Y1EZLfHmhsoaLdgvtYS8cneBvf3ACT0oevXvdNfNhdq3v4I3T00zOlzi4o4/mxCXClQjp/NpDfUzlG3QkTSIBDbMhGS1D3ZW06jZff3YEy/F4YGcXbanFZK03ZfD525s5dHycv3n8FM/VbA4dH+cXH9lNtqHTmgoTNVUmM0X+jz99lmQ0wOce3UMqfnWNwgtHRvnaD09gGhqfes8OVndf24rg2PlZ/vBrr1GqNujrSPDvfu4O3rmni3zFQhHw4K7L3dwkD+7uYvtAClNXlxWQJ4IqHXGNc3MWiZBK52XaPi0YRdUDF8usdjV/zX27Eut6m1nX68c0ZIs18qU63a0xRqbyTGfKrOpIMpUpEwzozBbqzJ3P0tEc4YnDk+wYaGZN59KMpJ8koiGDu7dfMsZIRgPs29y57LKqovDL79/FyeG5BeJyc8HdW3vj/O6HN9CwLDRWOlhW8NOBt524ZLNZzp49y/bt2zHNt98rfQUruBpiAYWwoZCrusSDyk25yYwVHeqO33I1mPdv4JoCPXGNE3MW8YBKV+z6l5Mn/UyDobk6f/LsJOWGyx1rE3xgV8uyM7hSSkaKLqfzLlIIclWHkKHy0nAFT8K5OX9mckvH4sHNeN7ib1/PMVmX6JpKb0InukA+Gq5ESokQgrq3dMY5aUC57mJ7MJJr8MqswcaEwpFZi9NZG0UR3NFpsKdFYzDboGZLTjWgPaKwtcXwB955yWBJUnUFngch3Z9Bbg8r9F6hiRECDAFNIZVPbIlSczzi5vLtcDN1wUjVT4QvOQrb4otTzF1P8sykw2BFwVSg4SnM1aHnOsHepgoBRVJxBYYC62MqG5uCSMkSchQJqKQiOkNzNcIBlY6YTldzkBfOF5kt1Nm1JslDm1IcmWpghGBVUqFnQW/vSr+KE1T99jDLg4S+uFr1rRcGeSUnUEJRoiEdiLCv1V9gsODw7LiNlWxn/+0Gw6eHaU8Z7E44rGqLXQzt3NjkH+O667E2oV0kHTeDWFAjdgN6XCEEm7svb1G5dE49f3yaMxMFAobKa2dm+D9+cd9FV7MLn93am+DlaY9sxaU5opIKXTo/cg3JybyH67jsTGsXNVe5is1XX56mVHcYnquyqiVEuVTma88P43iS85NFfuMDW65aFfEknC/57XrdYWi6yqOp7kLZ8c+BQ8cnyJfrlBse/+Obx0BRuXtb57LbSEQD/MYn9mPZ7iJNyQVEgxrR4KW/J03Bg90qlgdff2aI7706hgQyhTq/9oGtF5c7O57jb354BkUR9DQFqNUdomGDXLFOJKDR3xkjHFKwbI/vP3+Cat1mJlvh8Nlp7t29avkvCXzn+bNkizUs2+Xp14evS1xmshVK1QbxSIBMvkah0mBTT4Lf/cR2hFjaliqEoC11ddMJQ1P4ub1NnJ9r0BzR6EosY2TxIzZRvHx8nP/7717Gdjy2rU4zNVckX64z0JVi/6ZOnjsyRntThIqnUak7JCMGgR/RgvinAaGAzq71N5ffcznCAQ1d8ajXV4jLCn468LYRl1qtxi//8i/zl3/5l75w8OxZ+vv7+eVf/mW6u7v5zd/8zbdr0ytYwbJoDmk8ujHKaN6mK67RdhP+/f0JncGcg+1J1qX82UAhBA+vDrOuySCkC3qW1YJcwnDe4anRGooQiFqNyXyDWFDj5fNF3rm1iZChkq26zJYduuI6EVPh6LzDN8/VKDuScFOUmG2jKApTmToR3c9CX4Z7MJqzyFcd0kGNsu1yoDvKQNLf770dBs+PNxCe5J1rlo7oBX7bmRB+7/nxGYvBrCBbtQnrAseVzFU9NjQJdAFV6YdAqgsDuIoDpwsSV0LI0OiNKnSFBT0xlaAuLi53OZ45meW7R+boTAX45B3LDwbBb+m6EDrpyKVjmfm6ZLwi8aSkaINt26gpFa7qXeTDJyseBVsQViWxaxgOBXSVz93bw+GRIi0xg519MbK2yofuX0e1alFoSL5zbIaHNyZZe4VGKq66VF2BIwRtcYkuPJAeMxVJOuRnuDx/dJx6qp2h0TlAYpeS3PaOdoQQDOZdyraHrghymEzMV5iYq1BvONz+8T0Xt6Mrgm0tN3d7b9guX3p6mNOTRQ6sb+E9e67+O9wo4mG//bBuuSQj5rLp7a1Rncd2JRnN2/QmdZovC0l9ZqzBqbkGrpQcHbH49IEedAVUxUFT/XNRVfw2sXzFwnElhq6QKV27H3+8AqcK4HiQqcOdbUt1Rg0XjhUUKi6EVVjV3cQrp2eYKzaIhODvnjxLZ3OYtV3Lz2ILIZYlLVeDoQoM1Xc/A3/wX2ksHix+4/lBzo7nkUg0pYnV3Unm8zUObOsmFQviOjal7BzVustrxwYplhtEQgZN16i2gN+CNzKVR1MVUrHrG4JsWd3Kqo4k8/kqu9a3k15wKdPUWzegiJoq25cxI3HqpYVXEqdevvj3fKnGEy+fR0rJvXtXL7K+vhK1us0ffPVVzo1l/ba4uk3QUIhHgkzMlvjUe3dy+9Yu5gtVJnINhmfL7Bxoprtl6f2x1nBQFXHNlroVrGAFbx/eNuLy27/92xw+fJgnnniC97znPRf//tBDD/Gf/tN/WiEuK/iJ4FbCGGdKNq8PlzAdyV2rImy9bDBqaoINLTeWE/LiRJ3ZiosnIa6qBA2NfM1lc2eAgK4wV3b4yzfyZGsu3XGdx3YmGCk61FwQEtA0dFOnKSBoeIIwDts7gmxqW/rA7ksZtET89a9vNmkJaxyesehP6DywKsjtXQFMVaAvowvY3GoylLcZKrikwgY1zw8h1BSBLQVBTbAmqRHSFe7uCXB83iYVUFib9G8nmgBdgZq9cHyadLojVx8AF2sOX391mlLNt4Je3RbmgS1+a8dMxWUw75AK+utPm1BaCMfsCno8dSrPqZkaWzvD3LE6iqn6oXZTuTq2A8OZIinLoHN783V/n5DKDYcLtiVMHkpcEtoGVElAVclKldl8mRNjJepVi3/z7kuz3K7n8dLRSbKFGjvXt5JOx5irunx3uEHFlvTEVB7oM1nTleQfT85SIUQyYjIzV6FqeYRNlZaQwvmCwJUS3Wn47nWKH14KPpmbtTRsKUjpLlH1BkQtC3hzKMczJ2YxVIXHX59k+6okPS0/WvjcPdt98lOp2xzY2HZVItSTNOi5Im+nXHd4YyiHq/m6sdF8jbGcS09rjHBY8sk7OnnudJaORIB9qxP0JnXOTxWp1h029cT53T97mUhI57F3rKf1inYrR/qEX1Muvb4SFdc3Twgq/v/37+jHdVy+/PR5oiEDx/Ww7Bs/vpejVG3w7efOUK5a3H/bwCJtwkO7u8kU6jRslw/csbhKEjS1i1Xb5niQxz60k0rdIhG5JKyXUhI0Ff7ZB3bz5pkZ2pojbF292JL8Sjz2zu30d6YwdJW7dvZdd/9bU2H+7c/dzmSmREsifMvWvjcC6TrYldySv//9kyd44uXzAEzOl+jrakERgoM7eogu6ImklHzvtXEOnZwmU6wTMDQatkMqliBbqjM5mqUrHSNo6nzp+yc4en6OnCVoSkb91sqB5kVVlxdOzPC154cwdYVPPbCO1R0/XW1kK1jB/wp424jLV77yFf70T/+UgwcPLnpYbdy4kfPnz79dm13BCt5yPH6iyOvjVQCCmmB7+/VDvqq2x+GpBgLY3m4S1BVCuoIrQSDoSRrMNIVwig5CN6hZHhNFm/mqSyqoMlVymK+49MY0TmUcCg3fkctcsDruaQnyntUBuqPLX8IdcYPP3d7CfNnBRvDd4To126MjavPRjWEihkK+7vLk2RqWK7m9O3ix1S2kK3xks9/6M1NxeXzYomx5hAwVoQgMVeAtqDZWxTVWXVFpCmiC3S0K4xVJTBd0XmfsqykCXVVoOBa6qmAu6FGqtse3z9eYr7oEdIGhhFiV0NgU85ASTs/U+PaxHI4rGck0SMc0upIBDrRpnB3NM5+vUy03yKeWzox60ic/AZWL+qKGK5mu+a5ircEbcxWSEvKuhiUF7QGPs0N5Xj8+u2A9u3j2+dCJKf7i8ePULIfXz87wH35uPxNlj1zdI2IIJsoeZUvy8fs3ooXHeOpsGVUTrGoxCS6UA7Y0a8QMX2RvtCVxyn00UHhws0+i8o5K1vG/ryUF4YDFjY4pDV1FVxWqlksqamAu7P9MtsJsrsrqrgRBU8d2PI6N5jE0hY3d8WseJ1NXefCKoMobRabYYHJ4mkAqgWW5hByLeMQABAiFrT1RBlrDBA2/UtXbGuW3PrYD1/X4vb94hYn5Mo4r6Wwa42P3rbu43vmyzcRMnYARQFV1BqKwXNdoRPP/KzsQ1iBhCt5/5xqEovD62Tm29jffUIDlcvjBofN8+7kzSCmZnC/xW794EMt2mc5WSCdD/NuP7lj2cx+8ew3RkG+08PDePgxdxdAXT1xUGg5//cIU49k6921qZvf664uqk7Eg775z3XWXuxxvDmb4/e8P05AKj+xu5+N33NrvfKsolP37K0Lw2skpXj2TASBXrPPYw76pz7nJIl99bpBMrkzdFbSkIqQTId59cD1//cOztLcr1Cx449wcp0YySEVlOl/DME1eO5fh9o15tl/mZvadV0bJlhrYjsvTR6dWiMsKVvATwNtGXKampujr61vyd8/zsO3lPdeXw8zMDM8888xNvTczM3PD61/BCq6HCzOcnoT5qstM2aH1Om1mT5yv8OpEHceTnJyt8+EtMe7uDRA3BaqAiCIpNSTdCYPJos1IzqIrrpMOa2RqDt1xneawSpcmaAoEKVmSjgWr45GCQ2tYpSty7VaFprBGU1jjlUlfhxLWFQoNj5ot0U3B06N1TuQkSEnVrvKp7UuD5FrDKu/uN8nVXV6YcvCAqg2ZuscafE1J2fbzYi63d24NClqDiwe0rpTUFtzHLl82ZKp86mAXz53O0ho32b/GHwzWHEnR8rARFKqSQ7MuJU8lZgi6Qv62o4kIqqlRKNT46zdLhIwKD62L8vBAiH84XKW5yWR1d4JX51y6wgptIYHtSV6Zk2QafoDl7ha/SvRGBmbqvph+a3J5XYyU/ky8ApRsyDkqaDqqAF1I7uoPUy5FydQ8bt/QhOX5LUh1F0pGhN0HdzI3NU9mfJpaw6YlqBPRFaq2pDMiCOsCTdH4ubv72LO2QrHmsqkrfPF4KUJcJIpDxRBta1dhuZIJFCx3cdlA4pshZGzfO6vZcNGRV50d39qb4EO39zA8W2HnQIrWRJCR6QL/91deI1eq09/XxoHb1nHs/BxvnJjA0BQ+fGcf925tv+Z5eKtoTwXZ2Brk5bOTBHTBr7x/B4mQgfAsXLvOkRnQ9BCVWp1tLS6RgIqm+nbYQUPDdf1smsBl7VpVy+WPn59lNNcgHlD51IFWVl0lbdxQYHPcN08IaxdayQSP3rmaR+9cfcPfY2K+TLFisbozjr6Qo2PZLlJKNFWhYTmUqxZ/8LVXGZzI0dMW5wsf2rvItvgC0okQjz2w4ZrbO3Quz3Ons6iK4KsvT7OlO0oi/NaHLf75C1NMOkEE8LXDWR7e2U5yOWvBtwiariMQ2Asuaw/fvpZssYrnSSypMJ2rIT1Jpngpo0VKmJvLMzdfAODeHQP8+sf2UahYPHl4islMhbZUiLXdKTpbohwbzhIwVGxX0hLQSV7xG6TjQUZmy6iKQipy65rdU2MFzk+XWNcZIxkxOD6cpTUZZF33rRHhm8HZ0QyDE1nW9DTR37k8qbUdl6dfH6FYabB/SxdNsRV98gp+evC23WUGBgZ47bXXlqTdP/7442zcuPGG1/ODH/yAH/zgB0v+LoS46nsrWMFbiQc3xHGl5HTGJu8p/O3xMu9ZF2bVNVrOcjXPD14rO8yXHOqWyy/sSXF3rz87mqk4NIU1Zko2rRGd1qhOU1jjsZ1xhnM2nXGd4MKMd+dlVZWYyZIKxwWULY9Xx2t4UrK7K0hsweFsVUIjPSco27A+pRM1/HyXmYZveSUE1OXVW16aggpNQYXZqseReYeIrtAVVak7kn8cqjNX9eiK+m1Oy2kYwK9mvDjjkW1IWoOC29LKomU3dEbY0LmYKSRMhWhQZz5no6gqWVvh9YykLeRXHMIRE0upMT1TAyERwsOR8OJohV++rYlNnSFmanBozqNe8zhXkNzfpWB74mJlZarmaxyaA1CwfQLT8PxZ9uUwVlOYsfzt52seiipoCvuWxhJIhA32b2lnqCLIAWeKks0JmGwoqIkmeuPQ1pXGXtNBPGySEIJ3DZgUG5LOqHrxmAgh2NgZIVf3eG7axvFc9rTqtIUvVXGqtsTyFjJxHEnDhYTuYnkCSwqadJdZW2XOUmnYLn/x5Ahzc0X2r0ny6G2dSwiMogju376YhJwdyzGTrdLUFGHQDVEfqjBbEKjBANVylbMTxbeNuBiawq+8cy3vy1RpipnEgjrS9luGJkoewWAEx/VIxMIM5wtsblMvHrufe3A9T7w2hutJtIDJkeE8W3rj5Kou8xWbRFAjX3OYLlqowQAlB1oDkqHxPNO5GvdsaiER1jEUMK7TCVprODz1xhh1y+GOrZ20XOYgdmwowx99+xiVms3eDa380rs2I4Tgnj39TM6VKFYbPHJwAyPTBU6PZEhEA5wdzXJ+IseOtW23dNw0RUERAtuRaKp4W9q4XE9SkCYSFynAE29tuOqVCARDROO+y2ClXKRSKrK2t5nf/ux9ABwbnONL3zuOogge2HupvW5NZ4yOpEmpoBAO6Hiuh6GrtCSC/PNHt3F+ssCq9hg96Shf+OAuTo1kKDc8SnWXgfYYva2L70mfvG8N/e1Rv6Vu862d9yOzZf7gH0+RKTZojpkEFMnkfJl42OBX3ruZDb1vne3w+GyJ85N5VrXH6WmNMT5T4L99+RDz+SqtqTD/28/dSWvT0hmap18f4UuPH8VyXE4MzvEbn9j7lu3TClbwo+JtIy5f+MIX+Bf/4l+gaf4mjh07xte+9jV+93d/l//23/7bVT93edtBT0/PWx4CtYIV3Cw64jof2dnEHx8uouDb0U6WnGWJy3zFoWpL9nYGGMxaSAmtEYWposN82bnYx98U1vj0vmZGcxY9SYOmBUHyuazNsyM1dKXOw2vDeAhGCg5dUZUNzfo1r4cnByu8PO7PNubrHo9uimG7ksNzNp6isq5Z4Z4eEyF84tIUUqm5vuZmffO1R2dSSnKFOuOTNRIhBaMnxVjJZbToEtYFQwWH2apOx1WqQLM1mK5JAipMViXZBqSv41glhGBNUwBd1/0qh/AduTwJdQ+smksm30BXoGJ5NHSBoUrSC7PLpqbg4tFwJUVL4niS56YkB9pUTIWL2TOhBf1Od0gyXPYtlFuXmYTPlm0OTUocCbGIiYdA9xxqlkJcU0hqDprw82O8BcOCsuN7uNqeb6SgKAJVVQg3JxDCr5CkQyrpZfTQDdvjubE6Y9UL393mvf2XZj57oiqDBY+i5bE6oRLRfYe2DvMS68pYKiAZHM9x+FyGhCl44tg8e1an6G1ZXoQ9krN48lyZkK6woSVGOhkiW3UIt5kkAhrVkEGxoBIPG2zrf3uzHQxNYdVlg0eBP4se1gXZukfQ0KhZDoYZpC50TFlDAD3pKB+9dy3/z6+d5Olz48SCOp97cIANXXE2tgU5MVWjJ2nSlAhytuSbPnzv8CxPvDRM3Xb5xiuT/OFntt+QuP7xQ0P8/bPncD3J0FSBX//IpXjx02M55vM1klGT48NZqg2HcEAnnQzza584cHG5uVyF5kSIyfkSrakw7csMJm8U+9YkyJQtpvIN7liXJBZ8ax7zqm6gKiq21UAIl809CWpDRRzX496Nybe12mIGgiiKgpSSQDBEpVQEuEjKtq5Os6GvCRAXw0nBv4c89tBm/ua7x5BI7tpxaSK1qyVC12Xi+0QkwL5Ni22Fn39zlGffGKanLcEH7t1IKmry7tuuboN9I8gUGxQrNs0xk2ypgXB8B7Nc2WJivvKWEZfZXJX/8pXXmMpWaEuG+Y2P7mEuVyFfqtOcCJIv1X0Cs8y5VijXsRyHoKmTK9Vw3FvTcq1gBW8H3rY7zec+9znm5+f5+Mc/TrVa5ZFHHiEYDPJbv/VbPPbYYze0juHh4bdr91awgptC2BD0xjXOZGwSAYXuZayPz8w1+PsTJRquZFdngM/sTvCl17Pkah69SYP0Fe1lbTGdaEAlU3Wp2i6vjlb5xukqqioIGoJD43Xyth/O5rqSR9aF2NFx9dF+qeEhFsIOSg3/QTNacjmVddAUwWDBY2tN0hb2B2qrogqKgLgh2J2+9q2g2PB4bbyGImC6YHNius6G9iBhXVC2JOGAylQNwobEcn13r+aAoGWhXSyykA6ftyBhLB9WCf5+jVX8FqeuEKxLCFxU5mseQkoMVSFh+iTD0QXJgEKm6pAOKdzZH6Y9prO94xLr6AwJkoYgW5ckTZ+sSAl704K5qiRmSGIL+pENCeiO+MYCAXWhKlV2URVJQFd54Vye4YKgrTVGrtSgNabjSkEEi1WGZCJTZd71aE9EKNoCD+gN++SkO+AxVoGGK7Bsj7aQx6V0l2V+y7rDf/v+CNOuTiwVIx03uDKKJmoI3tmn+1UXdXlNTtrwaHiCWEAhYgjKdYeWmLkot+dyWI7LV4/kmSo6SCBiRviNj+1lJlthzA0yVHTZ2RVm25Z+4gGV7lsU79ddyNgKmpC0GPKGdTh1qVLwTIQpido18hWJqhl0pCJYQqDgYki/lShftcmULBIhnULVZrbQYEuv4LHb0syUbJIhlZyjMtrw2wOHJ/KUGw5BTTCWbTAyW2ZtV+K6+1SoNHC9BTezYh0pJRJ4ddpmxAmgmAaVus3uda2ETP/EH58t8swbw0RDJu/Y209LMsyvfngv58azrOpI0vYjEBdDU3j/nlur1gAYgTBCUbGtGp7jt3Vrhkk4lkJRFexGg3J+nk/saaY7ZRIxVe5fF73l7d0ILKuBGQiCgEa9tuwyF9rwrsS+zV2s6U7heZKW5I2fr5lClb/7/jFKlQanRzL0tMW5Y/ti0jKTq/Lsm2METZ17d3YTNK/fkreuK86WviSD0yV2r2lG8RyODWfpSUfZ2HdzpKVhOZwdy5KIBuhKL271nclWmC/USSfCzBdqTGcrrO1tZlN/mqHJHJtXt9J/FUe8fVu6OD44S6HU4F23r8HUNeruih3yCn468LbmuPzmb/4m/+pf/SuOHz+O53ls2rSJUOj6VosrWMHbgULdZb7i0BG71IZ1o1CE4N1rwky0OUQNhabQ0ofkuaxFruYSNRVOzDR4YE2Ez+xrZq7s0BnXCVyxzULd5e+OlZivegRVyUy+QcWS2Ci0qxpRUzBVdZnM1inXXb5St2kOqXQvl3EAHOgJkq+5SOCO3hClhsdQ1qJueT4Z0vzKSMPxeGnKYbrqkQgo7E0bRI1rjxxDukIiqDKaswjoCsmQSmtY5cFVAc7kXWYbKmeLMFXzsF2oOpKILri7QyFuCBqOx3zFoeYJAkIg5fKDjOEynCz4FYtMA25rhtY2wdmc5IVJmwo2G+M6cUMDQ+eX9iY5NVunPaazqyu4ZPBuqIKDHepFe+SkKYibgkLN5ZkzRUqWxx29IXqTBk+P1pBC4faeAL1RlWeHKjw1WKXsCRJhDaoOwxMF5nNVmsIqHzjYDoogoMJzJ+f52+dGcFzJwzvbeeeeLjI1l3zVIyhUEgGFgy0uT4w7lKoegw3oDum0hpY/D89MVTg1WSEVNckJheZglLPnZvj/vFbggV3dbFnlC4ZVRRC8xqkcUiVrQw5r1oSJu90Mz1XY1psgHV/asz6ZrfHF753nTEUjGA6SCOtICR3NETqaI2yXkpojMdWlOR03AynhfEVhsuLheZKBiGR1/NrXo0TgCIO8a+BInwzGIya9cYuqEqLUcCnWXCZmCuzrNQgZKq3xALsGkrx2PsdAW4Qtvb6QWlMFnQvXkKFBNgR5G+5Yk+D8SIa649GXUGlL3tiz6uD2bgYnC9Qth/fePoAQgvGSy2szNoFkkg27N7KnWXDHuiaEEHie5E++8QZnRuZRVQVFEbzr9rV0pmN0ppfqzN4KHB3K8syxKdpTId61twfzKla+uhlCC8YoW37waKNQ5vTILIYR5N79cUwkmq6jKCo9KeP/z959x9d9lgf//3zH2ftob8mW5SHvPWLHdvYOIRAIAQIUaJ9QRsvzo7S0pe3T8bQ8hY6UQlsopQMKZSVhhWxnOI7jeC/ZkrX32fO7fn8cRV6SLcsaHvf79crrpVg6+t7n6Iz7uu/rvi4eCV+438tUyaSSGJqGJEnkctlLvn1R4OJ/y0ze4Im9g/TFNbY0Ban2y0hS4YyjLJ99hg8Ku9Df+tkB9p8cHEnNM7h/87yLXsfjVPnkvQuJpfIEvY5CgYahFCGvY7Qa2kSYpsU3ntjDG4e68brtfOwdq2ieUzr6/TmVQebXhmjtjjG/NsTcqiAel51PPbyB4XiGooB73PLVVSV+fvdDW9ANE6ddJZ/PT3hcgjDdpi1wMc3Ciq/dbmfFihXn/bssT29OrCCcaTCl8+9vRhhKG9QGbbx/Zei8QOJibIpEfdBGVjf58eEknXGN5eUONtYWJsxV/kLvlaxuMa/YjiIVOkIHXWNPEjrjOj1Jg5CzkEqW0y18qoQGbKlzsaXBjX4sxZFOk2K3St4w6U3o4wYu9SE7v742hEXhvMV/7I3TFtEwgPklDlImvN6jkTVMEvlC35WMZtGTMqkaI8XLMC0ODukk8xbzQioPrwxxsDdL0KWwZKSyWo1PIWVIDOQt7DKktMIhfK+t0AU8pYHfZvFcp8ZQ1kLGpN+SOTSoMZjIE3LJrK12jZ7tyBiFXRdVGtkdobAvcXBQJ6UVqokdGdJpDBbeuuaVOJg30mXdsiyOdieIpTSaa/x4nIWf8doktlepxPIWYYeEQ5HY15ulPabhVGVePpVmd2+OkzEDE4hqEu9d6GJPd5asYZE0QM0Z+N0O1s8LomKxem6IkxkFRYKwleXJnafoG04T9DnY1TLMlmWVPNWSZjhrUuJWeHC+G7siEc2ayEAsZ9GRMMcNXEr9DsIeG4PJPEGfn65ojkgU8t1JBqLH+OMPrj2vj4RlWXRHc7zVFuO141Eqgg4e3FSD12XHJRlsWVTMFsYvC/368SGO9yRwOO3ksFg6t5RNDadXqCVJwm27/NRdC+iKafQlNRRZprs7ydzVoQumQeZkN7rkwJIsTMtAxkLBIpnK8s8/f4t9rVEqq8uIahKmVsLNi8LIssSj2xq4a1UlfrftrF2mQugjocgWS95edC4tY0GxSmtfgg3zi/F7Cq+zt9P+xtNQEeAPHt2AaVoYlsVTr7fTlzbJe8OFQ9xhH80NztGyuoZpkszksdtV8ppBOnN2sZreoSTP7W7DaVe4Ze1cvJcwmR1LMqPxrV8do3s4TSJvcWDQ4MNb66gOnL87IMky0YxBfzxHTjP5l1+20n3yFJIk0RvL8eF715BIJnn5YDclAReNldMTaI0ln79wb57LtfNknGcPR7Es6I3l+d276njPrUvYsbeduvIAqxed350+msxhU2XymkEsNfHJvarIFPnf3hmWKA26ON4xTGiMnZPxJDN5jrQN4nHZGIqlOd4+fFbg4nHZ+NS7VtE3nKY05MY1sttnUxXKwhff0VMV+bL68gjCdJm2wEVV1Qt+EBmGMV2XFgTiGZ2MZqGqhe3t1sEcPXGNsEuhI5KnK5qhZowP7ok41J/nrZ4Mqgw7Thk0BCSK3QoLwhKuRW6yusWcsO2i1fPCdpOAg5G+LQoV5QptwxqLyhxsrrUhmTq3N9iJxB20DmuE3QpVXmlCq1/xnElfQsOlFg7th+wW8QzIWEQyJqZl4bApGBaEbQb5/PmNLA4PG7zcY6Cb0BbTuH+OyoaawiRKP+O+ldkhbJdI6zDXC/E8DOeh1AEBRSOftzAMAwUJA1Axee1UmsGUXmi6J5ksLSv83jIbnLJkojmI6Rp7LJP5IRW/zaTTLFSJ8tvMMR+DN1uj/NvzHaRyOsvqA/yvWxtGc+BtQLENMCGfB5diokqQ1QxK3Cpp3SwESRbkdJNkTqfKJ9MT15ApVOXy2GRuawpS4pbZG1eJpC0M3eBffrWPYycHiKZ1ZCnAtuYSeuM5hjMGNkWmI2FwbChHc1gmoJp0pcEmgUcxyOfPzx0fzMt0GA7u3DKXwcEYJ9IynR0DDMfzuCQZ0xy5/+fsWj13OMKP3hzkeE+SoFthQFOQD6e5Yb6LEptFSL7wSrXPIWNXJfKZHHODNh5c7EFVDPL50+/VmglJQ8YpW7gm2PNmLJGBOFnJiWEYdHUMk1nsQR2jp9DbdLsXUzJwKhKKZaAaGRzkeG5fJ7sPddITy9PWG6N+Xj35vH7W8yPkloHT98MCTJsPVCcYGnI+hjQSyjRW+ZlT5ccpWWRyeTpyNpKmTEgxqLTrXOzI5S/f6OR/Xj6FYVo0NOZYvbiKWr9M2Kaf9Tjec0MjT7x0jIDXyfrmClLZQk8euyLxraf2sK+lv7C7kNd4YOuCST/OfZE0w4kc+bxOOm+RyBq0RXL89EicR1ecn96l6zE6Mxo6Cq8e6eVUfxLVKiwYHDvZRXt7Kb/91R209yepKvbw6XcsZkFNcNLju5Jouo5pFgq9G0bhNbZyfhkr54/0wLHOfi0A3LW+nh+91ILLaWNTc/mkdiZM0+JrP36Lt4734XPb+bV7lrGg7uI7WXYF5lYF2XO0j5DPSV2577zry0BF2AmM/Z45Ubou0sSEK8e0BS7f+MY3zgpc8vk8b7zxBj/84Q/5gz/4g+m67FWtr69v3Iprjz32GI899tgMj+jqtOPYIP/5yilSyQy3Nxo0FqmE7QZBBwylNap8Cl5ZJ5udXPBs6QaSZZLRIOCQMLU82Wzhuf52L0hTy5E9J26JZExe7iisGjaFVY4N5glJsLLGTkNAxe+QoL4QTOVzp1cX759vpy+pEHLJeBWN7Lm/eAw2y6LBD0cjBmGnxFyfSUqX2BMz0DSLXF7H5lKpDiv4JXN0/GdKZkEzCk360nlIZQwcZ8yVTQsORwuBSp0XAh44nlBIahL1LpNGr4mRBwNYGYLCWoXEkqDJr6I6Ntkir1skM3my2cIE3jIgkVHpjBkMp3VaBqG3OM+6MgWfUth9afCZZLPnf5Ce7IkTSeYIuG2c6EkQSaTwOE6/xSXzFvuHCzseC8MSN9WrpDWLhcUqg1mLX7ZbWLJElVPnf17qQTctNlb5sLsUbKpMhQd8cp5sFjTdBZZEKp2jfyhFZdiBLMGmeQFuX1ZEzszjUaErY+FUZdozMjWZHKtCUOEopJdV2Cyy58QSpgUtaTdZ08JUbCyo8nHklTZajnWjazplXoUHts/DNDSyxtnPg5ePRchqBrphorr9lFQVkZZVDg5kWFVqx2VeOHBZUedB08qJZXRW1gfQtTz6GZcwLGjJu0mZKg7JZI49jUue3KHdhUGT/9x5inTO5JZFAZLpDPs709hVicVV7vNS0SwjBg4/MhaObATJyJEHZMlAlcHvkDFkhbX1blZU2cie+8CeSbEjOxxgmiDb0Q0JS8uQMFW6DRcmUCTnsWPQEodIPFMoqRzSKHKOvQKdyup87+V2Xj08QCJb6IOTHhxkS2kZqmKSO2ezYNncIpY0rEeSoC9t8e19SfImrC9XiMYLZ8kMwyQaT5PNZkcPR1/KCvjuliG+v6MNTbeYW+EjroPNbydc5EfX9XEfo9RwhJ/si5HTLW5v9vP6vigA6xcU88RLxzh8KgJYtHTpHO0cpr7ESTyVJ5bKUVHkmfQqfSRjktUtyrzyeWlZM2FppZOupmL64zk21jtQrIu/1y6pD7CwZgWyVKjedsHn3ThiyRxHTg3icqgMRNIcbh2gvmxiZ3Hee8sC1iwoI+B1UF3qm9T1J8rhcFw3mTKPP/44jz/++JjfE+02Zt+0BS6PPvroef/20Y9+lOXLl7Nz587puuxVraysjEOHDs32MK56v9jfRyyjo2GnUw/yjiWF8pgLF2bpjmaZW+Ih6J58X4NFpkWwfIjOSI4VNX6WVF38YKpmmPzTi6fY2TtAVjd5qT2H164gSxLz68vYtPr8NIQzZTWDb7/QytHuOFubS7lrZdXowkBeN+mMZAh77Gfdr0WLLbqjWXriOdK6yQ3lMu1vdOPyy5wcyuB02sgoDgacIW5fcH4KUU1GI7d/gEhGZ1N9gNV1Zzdb29ud4EjPAHnDJJWzs6U6RF8iimSHflnhlqZS/CPpWkuAO864bah0iOeODVEZcPKO1ZVohsme9hgOp4zXm4FEEkm2cDlVdJeHVctrWXWBxydvmOjucg737yWezrN9eTXrVi05a/Hk39/o5lAiDhIES4N84JazDzA/YFlkdZPHnzrMwT4TJCgqdvLFdyw/73qlyTz7+lKoZUFya+fyxqFuVpaV8ImHbqChspB7pJTG+dXxCH6nikOVmds0h9BFKjxlNIO24xGsnIEsSZSGw8xvyBPPQi6eoCzo4aG7No952009Dp58o5O5lXbqasMEg278bjuGJFNdGmZ+SfUFrw2w4vy7Omo4o9HaGqNElUnmDUqqyin3Ogq9UtRLm9AsAW7epJPXTAIeO3/75GF+uW8AVZHxhCt4x/rzKzfpZqGfkiKXjP7bgoWLCBWV0zUQZ/vqOSydd/EStZph0Zsw0A1QZCgp9+OySbzVk+TArg72nxikusTLB2+oJdbdi2q3E0/lSPo8bF3SiGlZDKdNcpqFxyERcMr89LUTHOhM4/F4SOZTVJcV8dF7lrNiee0Fx6IbJk88dYT2hEHY56Al7+KxR27hey8fx+W08/6bmsE0+ep/v0peN/i1+9awfvHEGj0++dYO8pYNp0vBUt1874/v5Udv9ZLVTO5cXMqcM6rK9QzE+elLB7HbFO7bvpQ7NxfSL2VZIjLSFyWW0fjs11/CdHiQ9Txel8Jtm5bjcap85UfPEYml2bS8nvtvbOaZXS0EfE7u3bJoQgfW93fG+Z/9p8jkDW5aGObdawvP1ZmMXwaTBo/WFwJEr12myHPhPllTxTRNNh6M8tq+U9RX+7ntxlUsaaqc8O1Xrxz732PJLEOxFLXlQVTl8u+LLMvYL1Yb/BpxoYXi6upqurq6ZnhEwpmm9XD+WG677TY+//nPz/RlhetIddjF8b4kqiJTU+zB6SzkEjc4nTSUTc017lw29iQwldX4950dRFIa2xaUsG5OmAOdMf7t5Xb2dUSJ5QwkIJ3R8TpsIIFmSaNjHM9rLb38Ym8viizx/dc6WdtUTm2Jl0Q2z29/Zx9twxnmlHj5/fsXUR06XXnMiOv8/FgvqZxOecDByrogx/tShDx2inwOQCKRZ8zrlzudfHSTh/3dcSwLZNWO/YwJqiGlMQG3XUW3QFZVFEXGtEBRFJxOJ07n2G8xty+t4rYllbzREeO7+wfYdXSQTFbH61S5aUkZepmXk4Mpwm47N8wrvuDjs687zosnh3GqCr/3gY3YsGiqDp238pvRCyvWJpDIw69OxolkdFZV+1k0UnLXBSiqiiQX+tvIsjLmtatsdiTFRtCpckvTdtp6I4T9bsL+04+9352n1O9AM0xCbjtlAfcFVywzmsH+7hQ2VcEDNIQ9dESzeIpDNC1UGOzo5x1r68d9LD5+RzNLG0qwqTKNVQF+eniQaEZjaaWPpdUXPkMyESU2O6W+HP3JPCG3HUWxcXgwiyRJzC1yU+q7tCZ1Z96Noz0JHHaVTM7g1GDmoq+H0d8BvP/uNZd2XUBRDdJ5A4cq43UqhRLhZpJX9vWQyetEYjm6F5bS39FP1oJULMXtTStxOp0kMjppLYeETDJP4VC1x4XdVjizsnpBBX/36TsmdCD8Jy8f4+V9HWguN+mszoraMGV1xayVPJiWxQAOdr28lxNdwyiyzPefPcjW1Rc/AA6wpLGCnYd60E2T5U0VVBf7+cTNY5+f+Pr//IIdb7YgyxKmJfHxd58OjitG/hb/+p1dDKcMAn4vDsXiTz64njXNdXz7p2/S2h0lFHCz82AnLR3DtHQOYlMUXE4n775l2UXHeri3l4GERlXIRWnIhy7ZsCkSDlWasZYIUiaLPLIJLysqTufMNV38vV+7jf0tPZSEPDRUXX7Bg/beKH/89V/SO5Rgw9I6fufR7SjivIpwjZjxwOWVV14RlcWEafW+DbXUF7tRZJnNTTNT9QYKh9n/9KmjvHxiGFmWODmYpqHYw1N7e+mMpFHkwoew16lSGXRS4rFT6nNw29KLly61qYX0ibxu4nWqox9C//NaB3tORQGLvRmNve2RswKXSFojnTfwOVWSOYMPravFocqcHErz80P9qIrMuvrguNd97tggT+zrxTAtbmwq5t2rTu8MLa30c2IgTXc8y8b6EKuq/WR1k/5knqUVvtHdlvFEMzrPHBskltZoH85Q4rGRzOq4VYXP3dxIJK1hWhZFnvFX+UzLYkdrhETWYMjQqAk6uW1ByZg/e+vCUpI5A0WWqAm7aBks/E1ebYsyJ+zCOXLY/X03NpLTDAzD5IM3NZ33ewzT4j93d3GwO0HAZePR9TU0Vp//PNNNk5BLRZYlvPYLn/kDiGU14jmNkNuGU5OpCTo4MZQm7HPidKjcv6KazfPGvm8ADpvC1iWndxzev6YKzTBxjVNB6kJ0w8S0OCtQtSkymxvCDKXz+BwqxwZS5DWD/kgKO9YlBy5nun1FNf/14gl8Thubm6euoeXxzgiRZJalc0oK6V4j3A4Ft+Psx2VukZsir52hmInboVLkd/GR25bywpsnqV9Wyw3LCrtAkiSNVgdTRwLcbSvrGYylOdUb4+Y1cyYUtAD0DCXRojFcEtgsjQeWldMWy2KYFjZVJpbVCQXcSEjopkVJcOLlfB+4cRGVxX403WB984V326KJNDZVQTdMoomxyw3bVQVVkaku9bNpcSUPbmsGoLgogN/rJBLPsHhuGYlkBpfdRiavkUxP7EB9XZEHv0ulKuRiTqkXSSrssMkGqAqXlDr2wp5WvvvCUTyqxKfetY7qsiBQKF7xy1eO8Oq+VpY1VXH/9qVnvSbDHhvmSMW6kGdmp0Yup421iy+8O3cpXt7bysGTfVQU+3j9QDtdA3Fqy4Oj3z/YFecXB/sIe+y8c1UlPufYu2J5zeC7L7bQMZDglpW1rGkqHfPnBGEmTdur8wMf+MBZ/29ZFl1dXbz00kt85jOfma7LCgJep8ptSybfx+BSZPIGB7vieJ0qYY+NU0NpZAl03SSZLUy8Cx+I4HOqzKvw4nPauWFemFsWlY5Ogi5m3bxi3rdlDsd64mxZVEZVuDAxssuFA/eGJKFIUBE4u8/LwnIfjSVu+pN5llf5KfM7kCWJsMfOonIfssTohH0srUNp8oaJXZFpGUie9T2vQ+X9a6sL5UJH7sO2xokHiqosocoykixREXZhaga1YRerG0KYloXTJtM+nOGpg/0EXTZuml+E55yGgBIQdKoMpvKosoTXMf59mV/m5XO3NgJwoDfJyeEsumFhc8pndRevLfHyxfeOn5gWSWsc6U3hUGV64zmO96eoDJy/Q1AfLjzummGysMx70b+z32HDa1dJ5nX8DhvFHjvzwi6ePjJALm+wsvTiv+NMhcf30oOWkwMp/vmFVjJ5g4fWVbN+7um/qV2VqRiphuRWZf7xZ29x+GQ/9eV+/urj2yifQLWisTy4qYG1TSU4bBMvQ3wxrx7q5kvfeZ1EJs/W5TV8/uH1F3z8SoNuPnvfEp7Z183cMh83LCzHpsqsWnB2GqfHIVPstZHTTDxOZTS4e/iWJZc8xq3L63jtYCeJVJ4HNzRSHnShqApd8Rx53WRxmZfqxpUEvS403eDOGyZ+UF+WJTYumVha2QfuXc8/fX8HToeNtUvq2XOkk+a55dhtp19vj9y0AEmWyGsG7916OqBfs6iGz3zwJoaGk2xcWsvxtj6+/dQbFAU83Lph/oSuv2VBMSGPDcOC8oADc6Q0Yt6yMCxwqmP3KTpXa9cQn/3nlzl5vBUzn2PnnuP84EuPUhzycqJjkK9+bwfxRIZdB9qprQizatHpx8dlV6gJX6Qr7hXCsqxxH4/OgTjff/EYndE8A8lhbl5ZS3Hw9GtKM0z+Y2cHXZFCgFrktXPPsrEXC57b18W//eooed3gwKkI//DYFoLemduJEoSxTFvg0tHRcdb/y7JMWVkZX//6188LagThamRZFt94qZWdJyO47QqPbKxlcZWfaFrDssMdS8op8zt497oainwODMPkVCTDyYE0O09GWF0fpsg7sZxhVZF56IaG8/799hXVHOmKc7w/wT2ralhZf3ZDsZDbxm/cUE8qb+B3nr3q7x6nCeGZVtQEONGfQjct1tSN3axssgdpfU6Vu5tL2dsd55b5xTQVewi4bCiKxI/299OTyNGfyCJZFhYSRR4bm87p1C5JEncuKmV/TwKHKrO86vxUmN54jrRmUBs6XXZ5YamHrGYQz+osKvNiv4Q0Cr9Tpdxvp2UghdOuIMvSmBOJIredW5pKME3rrJ2L8bjtCmtrQsRzGgGnDZdNobs/yev7ugEY7Eswv8pP2G2b1vSZZw/109KXwKbI/PCNrrMClzMpusapzkGCHhvtPVF2H+3lrg2NE75OKqvx1BsdZHI6t66spq50apsYHmwdpC+aojTgZs/x/tGu9RcSsEOd16IuqI5b5UySJOw2mbd64uimxcpqP0WTLFk8v7aIv//07WTyOkUjaYYlXjv3LipFN63R3bL33bFi3N/xwq7jPPv6UZrqSnno9lWo4zRivJANyxpYt6SOXQfa+at/fYZkOsdN65r47KM3jT7XykJufuuB88cR8tjYuLimUC7bLlNfHmD7mkZkeeJpXpIksbQ2iGVZ6CbohoVhWiiyhGUVilZcoOjcqKFYmkgyi5nPI9sc9AzG6OiNUBzyktcMdN3A4VDRdYO8dnVUyrIsiydeOsxPXz7KwoZSGqtCfP/pt1AUmXfetIyb1zed1XzzjSPdDMUyLKgvIZnJ89F3bsDtHPv5ebG6gDnNQDdNnHaFvG6gGZMrxiEIU2laAhfDMPja175GVVUVHs/kOisLwpUur5sc603ititE0xqdwxk+eXMjtzSXUey1U+JzcLQ3SUXQyQOrqmgdTPHsz47hsiucGEhxpCfBpnmFSWFON3hiby/d0Syb5xWxojY4oTGEfQ7++L3jT2qgEPQELtSl8ALW1IWoCbrQTYuq4MTOHVyKrG4SzegoskTQbcPjUDncl+TEcBqnKpPKm4USuPLZK666adEZyeB1qBR77Ww+J6B525G+JD8+0EdON1le5eee5sIul02RWTvBx/htfYkcxwfT+B0qj6yp5qeH+jk+mOaVtgh2VWLdGYFd3xnB0kSClre57cpZAaVFoWGjosrIHifffauXBaUetjaGp63yUsBlQ5ZkNMMifIHV1bKQh5oSP8c7hyjyuaguGT/wyGgGhmnhsSujf8fvv9zKt549jmGaHO2K8mcfWEvPYIJsXqO+4uJncizLYiitYZomu/ef4mTXMGuba1izqJAWtWROMeVhD4l0nm3La0e71gO0dEXoj6ZYPrcM90gw0zec5M/+9Tk6+mKE/S58nltYNs5h/9fbo+zqiAMWsazOgxNI9zxTNKNhmBZhtw2Py47HdfbE0qbITCTDr2cgxt//1/MMRlO8ureV6rIQN66Z2BmYc8myzP6WbnqH4pSEvOw62E46m8fjuvAKuyJL+M5JC53seYrCa7NQNCGnFZ7/snThPjpnWtJYwbqmMp4ZHMDS8qxtrqextpBeuaChlIduX8mLu0+wamENq5unLjWrP5rmf148BsADm+dRFhp73pPO67T2JakIuc57bWm6garI5z3vuwbi/OuTu0ml8xw9NYBs6kQTaTr7ouw6cIpfa1/Pbz584+jP15QGCPlchRTJxnIW1J6dXmpTZB5eVzOSKmZj6/zxezttXVrFwVPDnOyJc//GBkoCV8eOlHBtm7Ydl8WLF3Pw4EHmzZvcm6ggzATTtHi1ZYjhVJ61c8KUjZHyMx67KrOyLsRLxwcp9TtYVOnH41BZXR8ildP5o58c4VhfgoqAkz++fyHFXjvFPgftQ2nCHjsVZwQCO09G+PnB/kJKZSTD3BIPftfEKp+ZpkUyp+NzXvwcxWSUX8JjcilSeYPnW4ZI5Q16Ezlqgi7W1QXxOpSRoMWgMuAg7FQp9TlGd1Msy+J7u7t4vS2Kx6HwwfU1zCsdO0WpbThNLKPjcyocGyjsHNkusnSrGybxrF7Y/ZFPV277xdFB+hN57KrMLU1F6FZhEhDN6Pz4QD/RjM6a2gAHexM8e2yQbN5kbV2Qd4yThjERtyypoC+aoSOex+V3IklwpD/F8pGdl+lw1/JynDaZrGZy4zjnhQD8Hgd/+Ohmdh/toa48wLLGsStfDCRzHO1PYVoWdSEXdSNpjkOJHKZp4rDJ9MeyvLq/nS//1w5yeZ0Hty/m/XeOUy5pRMtQmn3dcY639vKzX76Jqem89FYbX/7M3VQU+1i3sJK//NhWhhNZljQUj7423jjaw1/8x6tEkzk2NFfyxUc3oygykXiGWDJLcdAzUpEpPe618yMrz5IkkTdMLMuitWsYh12lqjQw7u0ATg6l2dURxbRgcbmXJRWTb+KoGyaGYWJXFQzTQtMvrz/agoZySkNeEukcW9fMG3elfrrJkoTTVthpsUzrgqlRZ3LYVb79+bvYe3It6XSW5fPKRwMvWZZ5/91ref/da6d8vN/8+QGe2nkSkIgmc3z+4XXn/Uwmb/B/f3SQQ50xyoMuPv+OZipH0iK/9/RefvDcAWrKgvzvD9xISej0+5lt5GxRXjdQZBmX3capnmxhJ9emsvNAO5844/FZNb+C3//QFjr746xeUHleUAywuMrP4jF2p8/ld9v5/EMXqucoCDNvWgIXRVGor68nkUhMx68XhCnz8vFBvv58K9m8wesnh/mD+xdhu8Bq4cm+JO1DaRZV+yn1O3nfhho2zivC61DPCkQOdMfZfSqCYVlE0hrPHx3iXaur+MT2ORzpSVIVcjKn5PSqnDWytC5LEsZIasREpHI6X3+hldbBNMuq/XxgU90Fx/823bR47sgA3bEsq+uCNE+gA7ZpWbxycpiWwTQLyrysqwteVqCkyIWJv2boKHIhdeiHu7tw2RS2zQ0TzerUh1xUn/G4GqZFVtPZ1xVHVST6EzkO9ybHDVxqQi78zgRZ3WRBqXs0VWw8yZzOv+/qpCuWZV6Jh/esqsKuyOQNk4xm4rTJ5A2LtGYyt8hNRzRLLKvjsyvs7owzmMoRzehIkoSqSBzsSXDP4rJJ97bwu238r9vm0zqU5qdHBknlDUq8dlJ5nUxeJ+i24VSV8/qeTNb+lh7auiMsn19Jzcih5gupKfVTc5FO3/3JHFnNwMLi+GCKIo8dr0PlrjU1HOuKksrpvG9rIy/vPUHfcBKvy84vdrbwyB0rznp+nTt57Y5liec0ImmdVF6nwuckndVIZU432ptTGWTOOeM5cmqIvkiKspCHg22DRFM5ivwuGqvD3LSmkVf2nWJdczWrFxbOtpzsHOSNg+3UVxWxdnHhgP6q6gCxrE5et9hUH+I/f/4m//3LvdhtKp967w3csOLcq57WEc2Q1UwUWaJ1OH1ZgUt1WZAP3Leen+84yMI5Fdywcu6kfxfADSvmUBLyMBxLs2JB9YxV9BqLJEm8eaidr/73DlRF5lPvu5HmuRdfBJBlmRXjBNFvS6Sy9A0lqKsMn5VmNVmJdL5wXhGIpcdu9Ng5lOJIV5yA20bbQOHrypCboVia7z69l3Q2T89gnOd3n+RdNy8dvV1Z2Msn3rWRX+1qYV5NESuaKvjqf+/g9QOnCPtdbFk557y/08qmClY2TV2RC0G4kkzbjsvnPvc5vvCFL/Cd73wHv3/yb8yCMJ0GEnkyOQNZltjfGeelowNsW1g65gd2S2+Cv/jJIQYSORrLfHzxnYvxuWzMKzt/0uy1q9hUGT1voCqnUynK/E7K/OfvYKxtCNERydAZybB1fvGE+8wc7I6ztz2G16myszXClvklY47nXHvao/zwrW5ymsmRngS/c0cTgYvs8LQOpXn66BB5w+BUJENVwElNaPKpA05V4a5FJeztShB0Kbx0aIB97TEUWeK962u4d2UlumGSzes47Sq7TkX51dEBfE6VEm/hjInfqVIddBJJ5+mMZqkOOgmdcd6gudxHwGkjrRk0hF0XnYi1DKQ4PvJ7D/Um6YpmaShy47ErrKjysa8nQbnPRlNJ4d/CHjtPHOzDMC1kqZD65nMqJHIyWc1gTvH5zRQnoz7s4rb5xUTSGhYWPzsyyFBKw6FINJV62N5YNKEzSxdy6GQff/T1pxmIpJhXU8yXPnM3Qd/4f1/LsmgbTjOYzFPstVMfdo/5+PocKl1kSeZ0FEliX3ecNbVB5lcF+duPb0QzLDwOldhwFLfDRjavs3hOKQOpPL3xLE5F4mc7T7GrZZDNi8r5tVvmoygyqixxuDdFxu5i2drFpNo7uWlVA3Oqxj6L9bYlc0uoKPISS+bYtKSakLfwelRVhc+8dxMfvmcVPrcDRZEZjqX4o6/+jNauIYoCbv7osbtY2lRFscfOu8/YSfvD146h6QbRRIYdb7VeMHAp8drpjGUxLYty3+XtZkqSxP3bl3H/9ouXHJ6o+fVTVDP+EuU1nUQ6T9h/+nX6nz99g9auIUzT4nu/fIvm3zh/Mp7O5Pn2T16jtWuI7Rvmc8v6hRd8nfcMxvniV39GV1+MVYuq+b2P3npWEYLJeO/2BQwnsljA+24au4BCRchFdZGbYz0JygJO6ktGSrA7VHweB71DSdxOG37P+al5W1Y2sGXl6TOOj//uuzh+aoBMTqN57swUohGEK8W0BS7/+q//yt69e6mqqqKpqem8sy4vvvjidF1aECZszZwQLx0d4M2OKE5V4Tuvd1Lsc7K05vx0j/ahNAPxHKUBB92RNH2xLL5xJvsLK308tKaKV05EmF/mZUtTMVnNKFR6GmP13WVXeGT9xCoAnSnktqOqMt3xHBV+x0WDj7fldBPdsHDZFfKGSU6/+KFL0yrsuiiyhGmCYU1wW+gC6sNu6sNuTNPiR6934VBlsrrBcCpPa1+CL/1gL9GUxsNb57J3OEcso9OXzLNtXhFb5hXjdxbOuPzTy+10x7NU+J18bFMdoTMCv+pLOJsT9tjxOVQiaY1Sn4PgyOO5o2WIn+/vo8Tn4B2LS/GNnJdoLveiyhJvdcUJumxYmMSyOo3FHuYUuVlc7puSVWtJkmgsLqSV/OrYIImsTkbTyesSHZEM7ZEMC0YCVs0wyWomnpHCARPV0RdlMJqmLOylZyhB31DigoFLNKPRNlxIp0oO6wRdtrOCxrfVBF0kcgZtQylcNoWsbpDTTfriOf59ZwepnMG9y8q5d/MCSkMeMjmNlQur2deXIKuZHOuI8KOd7ficKj/ZeYoNC8pYWh/GJsvYZJmioB2CLj747pWUTyAHf9ncMv7qN7YzFMuwqL74rMdIkqSz7vNQNMVQNElJ0MNQPE3PQJylTec3i106r5KndhzG7bQzv+7CJWMXlnqRTcjkTUIuO+mccVZp5uOn+jnS2svixkoaqsc/f3At6RtO8H/++Rm6B+LctLaR33hwA5IkEfa7MQwTSZII+ceuNver1w7zz0/s5kTE5GvPnOChG47wt5+9F5uqoBkWpgU2RRpdQPjFjkM89/ox/F4nr+0zONUdYV7d+CmRE9FcX8zjn7wJGL/6mddp43ffsZhDnTFqiz3Ujey4u512PvfBbTy/+wTlRT5uWjOxAheXO2ZBuFpNW+DS2NhIY+PEK8wIwkzIagY/2t1FbyzH9kWlLK0J8Imb5/KnTx3Frsik8jqxjDbmbRdV+ZlT6qU3lmFxdZCaovHLtsqSxIc21fPwulrsisSrJ4d5Ym8vbrvCBzfUUnuB214Kt0PF6VKxGSZOl4oykdI7FKqFHekN0TaU5sZ5RZRMoLrZnCI3WxrDHO9PsajcS13IhWVZPN8yxN6uBHOL3dy+sGRCqWrnkmWJ+1dW8r1dnVQ5XGyeX8wv3+xg/6kIbofCfz1/gpUra+nXTVRFIuSysbouCMChngQ98RzFHju98Rw9sexZgculqA25eGRNNZ3RDHOLPYTcNlI5nR++2U08o9MZyTC/3MsdZ5Tbnl/qYX5pYRKS002iGY2AU71gmenLURlwcmwwTTynF5r02U4f6E/nDfZ0RUlrBsUeO8sqAhMOXpbPr2RhQymdfVHWLKqmoWrsggdvkyQJWZLQTbNQ1nqcCZskScwpcpPM6aTzBmU+Bx67wpP7ejnen8KmSDy5v5fVdUE2Lq0buR86pgmKVLh/DptMPJOnyOfEMxI0VgedlPnsxHOF3bSiSyjTWlcWoK7s/MUJw7QYSOXxORRe2d/FD146huUKkMsmWNZUxarmsRcXfuPBjayYX4XDrrJ+yYUPfbf0xNl5dJCw30XKo2NXpdHA5VT3EH/wd0/QPRCjtiLEX332nZQXn52xkMnrfPfVdjqH0tyytIJ1l1CC/Er1yt5T7G/pIeB18svXjnHvjc1Ulwb4+Ls2UVkaRJEl7t++dMzbGoZJTwpypoQiKzz9VjcHWnpYPK+KdN4klc0jS1AeLOzkPL/rKIlkhuFYGo/TRlnR1FSzm8gCRZHPweaFpwPbVCZHMp1nfl0JC+pFjxRBmIgpD1za29upqanhm9/85lT/akG4JB1DaX66twe7KnP/qipCHjsvHh3kx292Y5gWbQMp/vRdi2ko8XLTwlJ2nhxmXlmQ5bVjH64tD7r44oNL6I9nqSly45jAxNShypiWxVP7+4ikNXrjOV45OXzBwCWSyvOdXZ0MpzTuXFJ2wQpjA8kcINFU4iWR1xlK5S/YsHE4lWfniWF8TpUNDWEMwySVM8gbJo4L5Hpn8gavt0VQgfeuqhxdWe+MZnmhZZisZnCgJ8GxviTvXlk5qRSyGxeUsGZOCFWWsasyu70O7KpMJmdQUu3kPasq2d0Rx+dQWX9G2efqoJOqoJOeWJaqoPOSdlhg5OxOW4RTwxmaSr2srvbTeMb5I0WWcNoUBhJ5bCNfj8ehypRdRiPGiVhY6iHoVOlL5tENk7DbTs3IfR5O54lndRyqzGAqX+gJc04JYMsqVMKyKTKeM9LLysI+vvTpuzneFeFE0uTHhwbZWB+kapziDAGnytxiz+hzLnCBhqMum0KlSyZmaswvKaSU+ZwqsgSaYRE4Z4wum8KcIjddsSwbm0qo8dk51BFl5dxi5o6cCakMOHnfqioiGY3qgHNSAfOZTMviZ0cGaBlMI5smzz63n1Qmj2YpfOCu9bzvpmYc9vPvYzqr8YMXDhNNZrlrwzxk+cLjePVIP+2DaYr8LmLpPIZx+rXS0RulbzhBRYmfvqEER9v6+NaTb9DRF+XBm5aydXUjLxwe4Ce7u7Asi47hNAur/BMu5AHQP5zg9f2nKC/2s2pRzbgTbsuyyOmFFEibMr0d7EtCHrwuB5FElqaaYoIj6XulYR8fecf6C9725g0LWfjkAV4/MYwsSfhdNkI+F6YFv3j5IH/+jz/FsEx+76O38Z47VuOwKdSW+UnndG5a04jfOz3FRy6mpX2AP/unXxBNZLjnxsV86B0bzvp+Lq9jtynnPe6WBYZZqLimyBOvuCYI14opD1waGhro6emhtFSsHgiz61s7TrHnVAQASYJHNzeMdgRXFQnNMLGswmr/w+treMeqKpyqfMEV6oDbRmACq/nd0QypnMGcEg+yBEUeOz3RLDZFGk0/Gs9LLUO83hpBkSW+t7uL5kr/mCV1W/qT/OjNrpHDoBaLK/3UXaCBmmVZfPmXx9nXHUeRJHx2GUVVUaRCCdxtF6gg9cO3uvnFoX4Gk3nKAk5+fUs9a+tCoykYQymNZE6ndSjNT/b38diW+os+RmNxnzExvHdtLYoskUhr3Ly8ksqAi8oxUoH8Lhsf31RHdyxLRcCJ/wIT6LG0DWfYcTJKTjN4+Ugv3xwc5KEt87lxReE+OG0KH9pUx47jQxT77KyfG+LEQAq7KlMddF5wQpfOG+zqiJLKGywu91E7EtAZpkV/Mo/bJhNw2bAsi5/v7+WFo4M0lnp534ZaXOOcWZEkicqAc8ymlx6HgkOVyekm/nF2fXZ3xdnXncCmSGxrLKL6jN/jcdkZslTaYgkk4KWTEdZXeXl2Xw9Br527VtWMBuySJFEddFEdvHiQ+vrBDr707RfI5DTu2byIjz2wju3zS1BkiXTOYFNj+KzHUZIk6sLu0QpkiysD3LHq/J2MIo/9goH6pYhndVqHM9hkicGURs6w0HQTRZYpDfnGDFoAfrzjKP/y5B50w+DIqUH+9tN3XPA9pLrYwxOvnyKeylFX6uPmJac/KxfPq2BpUyUt7QMsX1BNR1+Mn798BEWW+Fr0VVYtrMGyLCwKj5FpWlxKxmYur/MnX/sZB4734Pc6+Z2P3MqGZef3hwKIpnXSeRNZgoBbvewzVOeOwzCt0VLUm5bV878/cCPdA3HWL6nD65548O/zOPne/3k3X/rv1+lNadywvAZ3wIdNkfjH77xAZ98wIPGVbz/He+5YzUfftZlv/M/L+DwOPnj/xim7T5dqx5snON4+QMDr4mc7DvHgrSvweZxYlsV//OxNnnzpEA2VYf73B7YRDpxe6DItMEb+5pYJNrnw+SYI14spD1ysKch7F4SpkNMMZEnCtCxyWuEMx+amYtoG03RFMty5tBzvyCS30Dxt7A/mTN4gldMp8tontOq4pz3Kv+44RVYz2LqghPeuq+H962t4rTWC26Zww7wLp3bYRmr5G6aFTZHHXVH76f4+Tg1lUIBqv5MHl5dzsj9FbZF7NJ3mTFnN5HB/Ch2JnGFh5AzK7DYG4jm++WIr+ztifHBT3ZiBWVc0SzJnkNdNYhmN544NsbYuRJnPwd3Npfxwby/ECyWizSl6D3DaVd65cexJ1bl8TpX5zsl1bbeswvtWJJFhIJom3hXhaz/aBT4vnfE8tUEXSl7DpmvUB308e3SIZ44MoMoSD6yoZH1DYfdHN0y6YlkCTttocYWWwRStwxlkSWJ3Z4zKgBNFgmeOD3KoL4XbrnDnghJky+Inb/WS1wy6o1kWVvrYNO/SzzeEXHZWVAVI5g2K3LbzGmualsXhviS6ZZHK6LQOp88KXIDRymuWVVjN/fJPDnKgPYpdlZElifvX1V3yuF4/2E7PYJxQyMezb57kw/euxmVXuL156g+C9ydztAymcKgyi8p8o00cL8ZjVwi7bXRGs4R9Tj5+73LeOtJNbYmPm1eOn/6VTOcwDBOnXSWWymFaFjLjv09sW1KB06YwlMiyYUEZDpvC4ZO9fOOHr+CwqfzGQzdiU2UqS4P87OUjSBLoZiGA6hxOc7gzRthto8jv4L5V1RNaSHlbJJ6mszdKyOdmKJaivScyZuBijbxnShSC7LxmTlngcrC1n//3X6+Szet85O4V3LS6UBXrxlWTr4gW9Dp49+1LOTGURpbgcF+CG+cWje7cALhHygKvbq5jdfOlP4enWkVJAK/bQTKdZV5dyWjp6Z7BBD94Zj85Tee1/e28tOck921dPHo7McMSrnfTdsZFEGbbezbU8J3XOgrVq5YXqtH4XDb+100T/4DsGErz+DMniKbzbF1QwkPrCqkV/fEsu9uiFHntrGkIja56yrLEwa44g8k8PqfKrtYI71pdRYnPwT0TbFK3ZV4RiaxGLKOzbX7xuKV03XZl9EPMZZP58tMn6ItnmVvi4VM3N57VcA9AUSRK/A66ollURWJ1bYB83qR7KI2mSLxyfJC5pR7uGqPvyI1NxRzqKRyWDrhslPpOr3KvqA5QF3Lx88MDpDWDm5uurgPFDWEXmxpCPJNKc3xgiFwqja2olL09KSRJ4lBnL3v3d5LOalSG3VTXFmNZkMwZHOyOs74hhGlZfHdPNwd6EvgcKjc1FdNY4il0D+d0UQMJ6Evkeeb4EDndxGNXaRtOs6DEg02RiKdN7Db5kppWnivkthMaJxNRolDV6uRQBrsqExpj929VtR/dtMgbFovLPDz5wnHcDoVM3iCeHvv818U0VhdTXFOBGg4RDrhpj+WYc066pKYbpDJ5At4L72JdiGlZHOpLkMjqWBSCkQWlFz/DkMrpHO+Js6HaT7rCR8Bloybo5IZFFTx7ZICfHuzn5gUlY6Zk3b5+HofaBhiOZVg+p4hv/vAVli+oYc1I6eT+ZI4DvQnsisSyygA+h8rGhWcHbP/yPy/zxsF2TMvC63bwO792GwC3bZzPwHCCrsE492xp5j9faefN1mEkCTbNL2bT/Es7oF0a9rJpxRxe3H2CuTXFrF08dkAmSRJOW6EBrCJLOGyXl4Z3pp++epyWrmEcNoX/+tUBblo9fgW2S1Fo3sjIYfzC4s+ff+Y+/vDxn6IbBr878pheKW7ZMB+HXWUolmLzyrmjTTs9Ljtet4OBriQet53AOalsigTWyMtDEbstwnVoWgKXV199lVDowiUpt2zZMh2XFoRRzVUB/uSdF24GdzFvtEU4OZDC51R58eggdy4rrJb+/a9OcLwviduu0DZYxt6OGLIEj2yoo6HEg9+lktVMVtYFL7mHh8eh8u7V1Rf9uQdWVo6WWQ67bexui1Dqd3ByIE1nJENT+dkTNrsi86ENtTx5sI+Q28Yja6rx2lW+8D8H6BzOoCoSjnEmzGvrQ8wpcrOnM44yEvScKeyx8/Dq86stTZdUXh85G3H5TTdlWWLL3DCrKr38Bzk6B/xsXTuPY0nI6CbZrEYyo1EacBJPa1R4bZyI5XHalNH+MbGMztG+FA5FJqUZvHIqyqloli1zQiwu95HWDOaXeFBkicO9cZJZA0mWSOZ0PHaFYp+DD26s5Ws/28+ht9r498QAcz50I0WBqSni8DZJktg6J0xtMINDlWkYI7XQ61C56YxdwYe3zOE7L52kyOdk+9LJ9Ya4bUMTb8QsYhmNeFrnWy+c5GM3zqFqJHjpGYzzZ//yK3oGE9y+cT4fuX/dmH/XdE7jv547Tn8sw93r6llSf/bupUShMIZ1xv9fTE4z+L8/OsC+UxFK/E5+74Elo2eGvvdGF6+3RZAoFPZ4eO35h/NrSv389W/exr5j3fzh408wFE3x9KtH+H//+53UlIfY3xNneKSMtduusLIqeN7vUFVldKfyzL4ibqedjz14Op3pO7v7UeTCbmw2f/FKgOeSZZnPvH87D96ygnDAc8HzHQG3istuIUlcViB9rpKgp9BQUTOoKJrcLulYGosK59I0w2ROuPD1gvoyvvtXH5qya0wlWZbZuub8Bt0Br5PPPbqNl/acpKo0wJaVZwd2kgTTVPdDEK4K0xK4PPDAAxdMGZMkCcO4vA6/gjATSnwOPA6FZE6nJuzHZVdIZnUGkzl8TpV4Vudn+3rpjedQ5MJZkd++vYmwx046p0+oO/FkhT12HlpTCHC6oxnK/U76Ezkait1UjHPuYG19iFW1QWTpdBWcj2xp4PkjA5T6HWy+wG5Jsc/BLQtnvwRn61CanxzoI29Y3DAnxIb6Cy+SnMuyLI70p+hP5phb5Bk9zO9x2fnYfatHf6asL8XJ4Qxrq7y4NY3jPXE2LyrjY9saOTmYxqZKzBs5xO9zKFT4HZwYSuNz2XDIFk88u5fnnzX54G1L2HxGNSqvw4ZThqxhUOR1sGCkIpnXpfDm3uMkEll+MRRn8ZxSPnrv1HetdtoUFk2g18/bbl9RzU1LKlFk6ZLKK59JkiQWVgV59mAfbx7uxSFJDEdS/MUjq/A4VF5+q5X9x3vweRz8dMdh7tnSPGa1p5/uauc/nj+Gblgc64rxj79541lFMiRJwpbPk4pnqCv1UR/2nPc7ztUTzXC0K07I46BrOM2ek0P82xM7OdgVw1NWhuwsVM9L5fQL3r9EKkMilaMo6CGZzhFLZKgpLxSbePvTcLwGqB9/1w143Q7sNoWH71oz7nU+sGUO/76jDbdd4d4JLG5AYSKv6RYOm4wiSyiKTH1VEcOJDLuP9TK3MnhWStWZ98lhm/rl/Hdvb8bndpDNa9y69vTudzyVpWcwQV15EKfj0qsC2kdSA68FzXPLRX8WQRjHtAQur7zyCsXFV1e6iCCMZWNjEaoicbIvhVOVOTWYYm6plxvmFfPSsUHmFLs52pdgKJXDNOGZQ304ZIkPbK4n4J65D9HKoIvfum0eJwdS7D8V4W9+dpTtzWVsHCMQObch4qIqP4umMcCaagd6kwykNJyqzOunYqyvC17SrkvrcIYnDvaTyuvs607w6Jrq0Z2rt0mSRHO5l+bywgR/Q0OYWDpP0FM459RcefbfVlVkHllbzdG+JG2RDC+90cK+fW34nSp/F0ux+HfK8Izk2K+sCaAZJkNpjeVVflwjh75zuoWsKFhWoW+FdJlVsqaSbQpW3O9YUEJnT4wDikxlyEV/NEskmcPjUCkOenC77MSSWZrqSvCN0YQPIK8ZGKaFXZXJaQaGaQKnA5c3j/XyV9/ewWAsjWRoNNcVcd+Ni7htw/xxx1UWcDGnzMuBjhjlQRd9gxGeOZVCcniRB5Msm+um2O/h5oUXLjizcmEtN66Zx4Hj3dy4uokFDYV0sOWVfo4PJlEVmabisQPGhupivvDxOy7yCEJzdYBGR5odb57gx0aUjz246YKd33OaSVc0i24UdnsqQw5kSWIwluF3//lF2npjNFYF+dOPbCF0mc0wJ8rttPHOrQvP+re+4SR/8LWnOdE5hAXcsKye99+xgsaaq7/UsyAIU2taApeGhgZRVUy4JsiyxPxyH995+RRtAylK9zn5/L0Lee/6Gm5ZXIZDkfndHxwgq5n0xbJkcjrPHOyjMuTinWOklUyn8oCTg50xnt7fh2VZtA8VSqWGpqjq0pWi2GPDrkjkDZNy38QKJpwpmdPJagZeu0Iqp3OkP0WZr1BSeLzfJcsSoYv0CfE5VFbXBlle5SfW2ctbLrXQrNOyRnegLcvCNE02zjndI8U0LX722nFae6LctH4+R08NUF/mZ/GiWvZ1J5hf6hk3he9q4rYrPLS2ltauGL3RDBsWlFA5chhny8o56IZJ72CCTcvrRw8qn+vWVTUc74rSM5zmoRsbcY+szGuGyQ9f7+Cnb7TTEc/jNHWOt/Vh6hr9kSTL51dRFh47aHDZFT7/jiUc6oxSGXaz40AHkt2Bmc+jupzcu7ScrQvLL/o8c7vs/P7H7yCT1XA5baM/H3DZWF1zabuC4zl4oocfPbsXGYmfPLePuqpili+spXqcgCijGeR1C7sikdGMkZ0XiaMdw7R0Rags8nK8M0JLd4Q18yeXBjgV9rf0crRtgEQ6R0d/jHgqRyqr8aVPXjyYmw5HWnv5+vd2IMsyH3/XDcy7SFNRQRBmzpQHLtNZ610QZkN/LEtfPEdZwEl/PEdXJENDqZeSkX4dD66u5oe7u9B0EzsgMbGeB0d7E7zVHqMq5DqvHOykjeSkSNLpalkTcbw/yf6uOBUBJ+sbQpc1FsO0zkpFA0hkdTTDJOS2Xfb9XFsbxGNXyOkmi8ovPUd+brGHucVu+hN5XHaJZ1uGkCWJbY0hVtcEL2tsUNh9edfWhUSjKTr6Yzy4rRmv20FeM/jbH7zBG0d7WLugkt98YBU2VWHHvlP8/fd3kszkqS8P8tVP3cGRwQy7u5MARDIaN869cDPIq0V5yMWfvW8l0VSe8qBrNPVMlmVuXtd00dsX+1384SNrgULA1x3PIgEnehJ8b2cHqZyB4fCRT+TwexyoSqES2sWecn63jeYqH6+8dZKwQ2FBZYDeZJ6GEh/L6oou+py1LAvdsFAVabR61XRwOWw4bCqReJqcbvGVH+0j+HwbH79rMbeMUfnMaZOxKRKaaeEa+RqgutiL32OnrS/GgtoiGsqD0zbmsRimRSyjoxsWPqdCfUWIsiIvXQNx7KqK3a5imNNfP0vTDYbjGUqC7rP673zrx6/x5qGOQrEEl50vPnb3tI9FEISJEeWQBeEi6ko8LK72c6grTlOFj4WVZ6dVbV1Qwvq5YY50x/n53l6KfXZuWTx+mdfO4Qxff/4kr54YxutUKQs48TpVltdcXiEBgI1NRbQOJHmrLcqm+cUT2m2JpjX+7bUOeuNZ3HYVj0NhadWlj8WyLF46McyOk8OU+x28a3mheMDRviTffbOLnG5yy4IStk6izO+ZFFliaeXkU9s8doX3rqgkq5v8155u0nkTzTBpG86yqFQnmdEoCUy+shWA3+Pkt993A5Zl0Tac4UhfkuGhOE+/0YrLYeOXu1q5cXktC+qKOdwVI5XVCHgcJLN5dE0nljNGz0VEs5Or5HWlcjvU8yreTcaRgSQnBlMAxOM5TKtQ2GJRfTEf37qKw8c6ONk1zO0b51MauniA+5VvP8dzrx/FYVf50Ds3s2RJMxUB50X7LpmWRedwlkRWx2VXqA07J1yQI5nR+JdnW2jtT3LXyipuWVZ5wZ9vrC3hsfdu4Y2D7exsiZAwbUSSWZ7Z0zFO4KLgtknsa4vQWOFDll3ohsk3f7aPeDKL16nya3cupXiM/kjTKZ03SOUMJCCasZhTHeZPfv0WXj/YyZtHe1BVmUfvWjmtY4gmMvyfbzzLiY5BVi+s5n9/4EbstsLz0mm3jRZLcDlt5PVCIYTpbsR5puPtAzy14zBhv5sHb1561i6kaZpYMBKUi8Vi4fpyWZ8ee/bsYfny5We9cEzz0iudCMKVzGlT+OxdC+iKZCgLOMfskeK0KSyvC7G87uIpIT/b18u+9hixtIZumAQ8dhJTNDl12VUCLhvDqRw/faubIq+dW8+oBNUdzfLLQ33YFJk7FpcR9tjJaAbpvIHXUaiElspNrnBGLKvz7PEhcprBUCpPU2mcG+aEebMzRl8ij1OVeenEMDc2XnwFe7rJcqFvT1OJh9fSMRyqhFu2+M2vvkQ0meO+DQ184Kbxz0VM1L6eBM8cG0IzLMpcMm6nncFompKgG1VV+ObOTlpSEo6gD4dkct8N86ks9pFX1NGAZUn57B44HkjmOdyXxO9UWVLhO++M1HTKa0Zh12SMaw6n86PBXVWJhztXVHC8J8G25jJuaC5n8+KJV7kzTZPDJ3twOW0kUll6eod49y3LJnTbrGYSz+hIUmFnMZE1CHkmFri8dKSfp/d2oygy//bCSVY0hCn2nz5rYlkWe9pjdEQyLKr0Ma/Uy83rF3Dz+gV87an9fP+lFmRJYsE4qWjJrMaff+8tDnVEKA+6+OLDq3CqEnuO91Hkd9IfSdM5EAcuHDBNF4tC5TcJaKotoam2hEfuWDEj195ztJvdhzsJ+928vLeN+zsW0zynsOD0kXduwuOyI8syD9+zntRIBTenKo3bGHYq6brB//v2Cxw82YeqyDjsKg/durzwPcMko1kjzZMLO2nnviYty+Jg2yCSBIvqimf9/VYQptJlBS6rVq0iGAyyefNmtm7dytatW88LZAThWuCwKcwpnZrSnc6RPh0+h4LNprCiNsCK2uCU/G6A108MY1qQyxnsbo2cFbh8/80u3myPktEM+uM5Pn3zXMr9DrbMK2Jna4SmMi9LJnlQ367IOFWZaFrDocq4Rno/lPns2FUJzTSpDrqm9P3BsizebI9yYiBFU5mX5eOkemmGiWlx3lmRTQ0h6kIuFFniFztbOdYVJeC28+Trp3hg4xy8F1ltv5jeeI6MZuKyyaQtmU+/cxUH2wZZOrcUl9dFZ3SQ8pAb29pm3ruygpW1hQloQ9hNxcgE1jmL51s0w+SJg/30xHPYlcIEaUnFzARSP3z+EP/61B6KAm7+z6/fREWRD82wRkvzVgdcJHI6EtBQ5GZr4+R38mRZ5o7NzfzP03uoKAmydtnEe4uosoSqSGR1E5ssYVcn/vy2jaSyabqBTbGft1NztC/Jt15tJ57VeO2kg8/e2kSRt7Dy/uiti2iqDiFLEhsXjV2Bqns4TVt/grKgi+5IhpaeONsWlzO3Ksie432UBN00Vc98GqLHrmCYFoYJHocyK3OGsrCXkM/FQDRFTWmA4uDpCnTVZUE++6FbAEhkDfSRtDVjhtZlTcsimcnjsCnkNYN0Nj/6Pd2weDuLzjBBN63zApfvPX+Ef/vlASQkHr19Me+8ccHMDFwQZsBlBS733HMPO3bs4IknnuCJJ55AkiQCgYAIZAThAu5dUVjdzOkmNy0qpb7YPeHXyGAiRzStUV/sHjcdZWVDiI7hNE6bzJIaP6+fGKZjOM3y2iA5zWA4lSeVM3ildZiNbWHWNYS5b1kFdzSXXVYqhNuu8NDKSt7qilPssbN8JN3sxsZi/E4bOd1kefXUVi9rHUzzHzs7iWY0drZGKPY6qA6dnfZyajjNTw70o5sWt84vpvmMibcsSdSO/Hxl2IPXaSOZ1ZhXFZySldXqoJNXWiOk8wZb5ni5cWEJNy4vNCaMZ3VKfQ56YjlKvA7qis4u3TubAcvb8oZFKm/gVGVyxuR34y5VPJXjb//7NY51DKIbJg67wsJVi+gYzrKpMcx9yyuoC7kochcm8edWhZuMR+5ey+IFtXQlNQyvh954lnL/xStt2VWZ2iIXyayO06aMuSM7ns0LS+mPZWkfTHHTknKC56R2xjIa6bxOwGkjkTVIZLXRwMVhU9i27MIlkWuLvSyqCbKvLUJDmZfFtSEcdpU/+OANvHW8j4oiL/NrZ75ylyxLBN1nLwr0DcU50THIvLoSSkLTHxwvmlPG7354O8fbB1kxv3Lc4g12VcLUTn89E+w2lY/ct5ZvP/UGxSEPt204HXi8/fZsATKF97BzvXygC003MC149VC3CFyEa8plvdv/+Mc/xrIs9u7dy/PPP89zzz03biCzbds2Pv3pT0/RsAXh0mQ1g1hao9TvmPVAOuC28f5NdZd8u6M9Cf7u6eMkMjqbmor56NaGMe/Lu9fXsqg6gCpL6IbJl39+nFhG48UjAzy8qZbXWqP43YUdkfbhDOsaCrebiiZzDUVuGs7piK7IEqvH2FFK5wud53e3DoMFNy4oOev8kGlZHO8vnGFoHGneeK5UXieVN5BVmeGswcnB1HmBy672GH2JHIos8XJr5KzA5Uy3rqzBbpMZTuTYvLhitJP122O1KRK2SyxRfLQ7QfdgCgvo9NuB031w/E6VD62roSOaoSrgpOgKrP7msSusrvHzZmecMp+dBWUX74syFRw2hVQ2Ry6voyoyBzpj5MviuGwKzx0dZP2cMOUB55QELG+TJAnF7cZp5sjpJp0jvZEmwm1XcE8i0HXYFB7e3DDu95sr/Syu8tMxnGF1fZDqMRqGjufVfW384pXD1BcHeOd7l1NfHqBk5P4EvU62rrj096Dp0jcU53e//GNauwaprQjzV599gJLw9AcvqxdWs3rhhYM/hyqjyoUtjplMk9y2ppGtq+eOvsfn8oU+QnabgiRZGBaoEoz1tr12YQXHO4dRJFgzX/SDEa4tl/2uL0kSy5cvZ/ny5Xz6058+K5B5/vnn+eUvf8mTTz7JU089JQKXi+jr62PRokVjfu+xxx7jsccem+ERXRt6oxm+9ORh+mNZNswr5uM3z5t0I73ZtK8jSnckS9hjZ3drhPeur8E3RiqTIkssGwkUnjvUTzKrE3LbiaY1ynwO3rOmih0nhnCqCotmKO3nXKZl8Z03u3n2YD8ne+OEnDaO9Sb50wcXj+50PHd8iGeODgKwramIW+YXJv2pnM4P9vXSE8uyoT5EWdBJRyyHyylzYijDlnOaUZumRTyro8gSzeVjv+XtPtZLa0+UVU3lbD9nFfu1tggvt0Zw2xXuX1JOhf/CZZHPNJDMYZoWqizTn8yd9/2Q20bIfXnpaFMpkdN5vmWYRN5gdbWfphIPG+pDrBwJhGdq4uawq3zwjuV87Ye7UGSZG1bUEVNlEjmdyoBz2s4ZuB0KUkrCgkvaOZkuXofKb26fSzpv4LFPPKUqEk/zt//5Ir2DcRRFpq48xJqmsQuGmKaFaVkTLigwHU50DHK8vZ/haIqjbX18+d+e4U8/dd+sLzK9bSYDljO9ff/fONTO3//XS5iWxSfes5m1iy8cdL53+yIW1hUhIbFsrijlfKkef/xxHn/88TG/19fXN8OjEc415e/MPT09HDx4kAMHDrB//36y2SwAijL9B9qudmVlZRw6dGi2h3HN2X1ymGPdcQJuO68cG+S+NTVUhma2is5UqCvyEHLbSGQ1lteFJjSxWlEXZGltkNaBJJIi8fUdbdy8sJTP3NSI265Q6pv4JHwqRdIanZHMyK6QhQUjDQVPVyVsGUiNVPaRaBlIc8vIWfk9nXF2t0dRZYlfHR1kabUfS04gAQ7b2ZOvdN6gK154D5IliTnn7AYB7DvRz5/+28sMxTM0VAb58mM3jzbj0wyTV9qiZDSTSFrjQE/ikgKXLfOKaR/OoBvWRRsYXgkO9aU4OVz4u7x2KsqcsKtwOHgW0tZ+453rWLWwinzeYE1zNbvb4/TEMiyvCRI4I2C3LIvjAymGUhrzSz2EL2PnqiHswaUqWEDZLL02ziVLEt5LDKIM00I3DGyqjG5YaMbYKX5HOqP8zU8OkNMMPnb7AtbPHzu40Q2LrGZgV+Up2Zk917y6EtxOO0eG+wgEXLTEDb77RgcbG0tGUzmvZ99/ei+nuodBgu/98q2LBi6yLLFynthpmawLLRRXV1fT1dU1wyMSznTZgUtfX99omthzzz1HS0sLlmWhKAorVqzgwQcfZNu2bWzevHkqxisIl6w04MTrshFN52ks952XW30l0w2TXxzqpyuSYW1DiN+6s4loSmNpbWBCu0ZBj53fvXcBT+3r5cd7e4ikNH66v48v3BWalcfBsiyePjrAyyeGSeQMgj47NcUeStw23rW2Bu8ZqT+LK3x0RbMjX5/OP7cpEookkTcsgqrEDXPCuO0qummxof7s6koZzSCnW5T5HWS10yWGz9QzlGQ4kaUk6GYwmmYwlh4NXBRZIuRSiaQ1bKpM4BJTkxZW+PjCnfMxLWtKV/CPDaR46WQEr13h1gXFhC6ziMDb7IqELBUO/NoVeVZXvGVZZv3i0yV+b5g39lmMY/0p/nN3N6m8Tm3Ixcc21uK0TW6hTJElqoJX/0S5OOjh196xgR89t5+51UVsXzNvzJ/78c5THO6MosgS//5cy5iBi2FadEeyZDQTmyJRFXKetUBwqnuYTE6jqa7krF4ol6Ik5OM337eNP/unn+EvLyVUV01rPE/6+BDvW1l5TTRgvRxlRT7e7jRRXjS7VQYFYbZd1idpc3MzR44cAU6njH3mM59h27ZtbNmyBZ9PvMCE2bd6TpjP3LmA7kiG1XPCU9JDYqbsPhXlx2/1oBsmLf0pfvfOJpovsceKqsgU+RzYFJmsZhL2yjN2yPRckbTGf7zWwVAih8ep8uDqKlZtbeCFlmEODCSpCLuYX1YIUjY2hEZXW6uDp88arKgJEMto9CfzrKkNUu53cueisc8ihN021tQG2NMZpzrgZMEYleFWzS9n6dwS2vvibGiuor789OMrSxL3LynnQG8Ct01h2SQqrl1KWlMqr/NGRwzLglU1AXxjPFdN0+K5liGGUhrdpkWJ1872cSb1l6q5zEt+5BB+c7l31lJkLsVAMk8yp+N3qgylNJI546KBSzSjcWwgiSxJzC/1jvk4X+l0w+T7r3eyrz3KusYw966sOivQvG3jAm7beOFD2UGPHWnkd4XH2WHSDJPcSMW0vG6S1YzRwOXF3Sf42/96iWxe5503LeVD962d9P25c0szDrvCvt4UKbcfh6qgjaSxXe8+fP86ykeClzs3j51OLgjXi8t6tz58+DCSJLF48WJ+//d/n7vvvhunc2KHGQVhpkiSxJq5M185ZzLeaIuw8+QwdUVubl9cRt4wMUdWv3XDRDcm9yG+riFEMqvTn8ixfmSHYiq1DqT4712dSJLEQ2uqqSs+PyUL4NRQmr5olqxuFlZwJYkDPUn2dceRJXjyYB9NpR6kkcZqNWOkidgVmVsnmHYlSRI3NRVzw5wwNkUaswJPccDNX/76dobiGcpDnrMO5UPhHMrmOTNTMvb5lmHe7IwDhXM5947VyFQqPAa6ZSFJjHZDh8IE9Nu/2M8bx3q5cVkt79q64JJ2TeyqzLoxCilYlsW+7gQd0SxzitwsKp+a0uBTYX6Zh+qgk0haY3Glj7Dn4rtPxweSDKQKJWZVWRqtgDcROd0gni0ESg519lKg97XH+NEbnViWRcdQmkVVAeZdYs+f926Zi9uhktcN7lp9fgNLGClzbpPJ5E0cqnxWULjzQDsDkSRet4Nndx3n0XvXTHqXTpIkbt6wkI15g2dbhhhOa6yqDuC6hN0zy7L41WtHaWkfYM3iOlY3j32frjYBr4v33rFqtochCFeEy5q9vP/97+fFF19k//79PPTQQ9hsNtatW8e2bdvYunUrGzZswOG4MvKEBWG2DSVzHOiMUxl0jjnBGEzk+PYr7Qyn8uxui1Lqc7C6LkRLf4pTQ2lubCoeLYV6qWyKzO1jTYKnyI/f6mZ/Z4ycbhJJ5/m9uxeMmZfvtitUBpxE0nk8DpXFlX6OD6YBMC0uuWrXRFiWddFUE6ddpap49neIU3kdsECSSOZPn0uIpApNIEu9DhpLPdyxoITdnTHcNoXVNacn3buP9fLd5w5jWdDeF2fp3FIWTEG5245oll8eHSStGRwbSFHksU3qDEgkozGQzBN22yieoipqJV4HH99URzKnU+Sx09YT45s/34csSXz4jqXUlZ8flJwZwF5KdlNWM3itPUIsqxFw2lhfG5p0WtrlUuRCIK4ZhQB2rKD8YvxuO+/fNnYa2dtkWaIy5CSrmdgVCdsZr6UFDaU8/0YLec1g5YLqKUktdNsV7l40ufNgbx7u4G/+43liiQzP7zrOlz/3TipLLm2H+mpmjexOXSlFDQRhOlxW4PKtb30LgNbW1tEzLi+++CJ//Md/zJ/8yZ9gt9tZv379aCCzZcuWKRm0IFwtjvcm2dU6TMhj56UjA7T0pwh5bHzmtiYWVJ49UdZNC900sasyhmGiGRYuu8KHJlE6eabZVZm0ZjKczpPpivONHW18Yvvc8yZTTWVe3re+hsPdCZqr/Mwt8VAecJLXTVJ5g81zw1P2oRtJa/x4fy+RjMa2xuIp7yEzHdbXhYhlDSzLYkNdEChMlr++o42Tg2kCThu/dkMdTWVe7hpjcqfIhZ0q3TCQJXVSk9mx5HQTzbRw2RTyhkVOv/ROfMmczgstw0SzGl6Hwk3zpu5szpnliL/xs728uK8TKKRJ/v4HNp338/NLvagjO3BziyZe4jma1YhmNNw2hWhGI5bVZi1wWVIT4N3razjQEWPt3CLmlE5fqWpFlvA4zr+fd29eRGVJgEw2z5pzdjdM06KjP07A6yDonZlMjHgySyaTJ+Rzk8rmSaSycJ0ELjndJJ03kaVCU0/1KkjzFITJmJJ8kYaGBhoaGvjwhz8MwIkTJ3j++ed59tln+eEPfzgazOi6PhWXE4SrQjKr89VnT3CiP0UklUM3LBpLvQwn83QMp88LXMr8Du5fUckLRweZW+ph5cjE9WrwzlVV7OmMM5TJY8kSR/qSZDXjvJQ0SZLYtqCEpGby3PEhuuJZ3rWykvuWTn0FnDc7YxzuS2KTZZ4+OkBD2MV/7+zg5ECKm5vLuKn5yqvyVRty8ZG1hXLMbxdfiKY1+uJ5gi4bkbRGVzRLU9nYqVor55Xz6O1LeKulj42Lq5lXHRrz5y5VQ9jF8iofJwczLCjzUB249IloMm/QH0+j5zV03UE8q09Z4HKm0cB3JJVuLF6HyrLKS5/Q+h02fA51NFXM75zeAhfprMaPdxwlns5zx7q51Jadcf5Klrh/dTX3r75wH5LpJEkSq8bog2JZFn//wzf51ZunCPudfOGRDTRWjf9czGiFYP1yU1jXLqljy+pGDp3sZevqecyrLbn4ja4BlmWRyZuYVqGYQk4zUUcCzY6BJMc6I8yvCVFdfOWkeArCZE35icQjR47w3HPPjfZxebscsiBcb9J5g1ROJ5PXiWd0bIpM21CatXNCNI9xyFuSJG5pLuOW5smndFmWxVAqj2+G8+9LfA6KAg68sSyabhJw2cbNTe+IZHjx+BBgsbM1QnOFnxU1AUzT4sk93expi7KyIcjdKyova/fFocookoRmmjhtCrtaIzxzqB9Flvjv1ztYUuOndIINBmfSudXiin0Omit87O+OUx1ysfAC50tkWeKh7Yt4aPvUHuBVFZk7F5ZiWdak/yZ9vcMcPHQKxeXEKYN3mlIXP3LnUlRFRpYkPnDbksIZkGiWjG5Q5XdecmnhM7ntCuvrQkQzGsELPMenyg9ePMK/PPkWumFy5NQgf/2JWyb1+JtmIYibqRSi4USWF/Z2IEtwsifKzsM94wYuXbEM+3sSWFgsKvNRFxr7fNxEuJ12fuN923EqEn7P9ZWmLklgjfyd334P6RlO8YVvvUpbX4L6Mh//9yObKL+Mx1cQrgSXHbgcO3bsrEClv78fKEygXC4X27dvZ+vWrWzbtu2yBysIV5MSn53bl5TzLy+24nGqlPrszCvz8YX7FuKfhpVm07L4j1c72NUWocRn59e3zqFkhnpRSMC8Ui+pXGHl9Jbm0nEnSS6bgkOVGU7n8dhVnCMVio50x/nezk7yhklLX5K5ZT4WTaKK19vW1AbJ6ybxrM7q2iCtfcmRMwEmiiyhTLJ060xTZYkPbqihO5Yl5LZPabf4S3U5E9/27iH2vn6Q6ooiEokU8RvnUeSb+sCxvjzIH37whtH/bxtOs6szim5YdHgybGssvqxqaR67imeKi1uMJ57KoZsmTrvKcDyDVTj+dEmeeu0E33nuCJXFXn77XWsoDU7/xNXvtlNT5uOtln6CHge1peOfH+uIZshoOrIk0TacnnTgYpoW33r5FK+1DFHqd/CJWxopuwIXJqaDJEl4HQpZzUSWJZwjVSPb+hJ0DSapLvbQNZjiVF9cBC7CVe+y3n2rqqro7e0FTgcqb59n2bp1K+vWrcNmu3p6ZgjCVJIkiXesrmL1nBA/3t1F3rB4x6oq/C4bvbEspwbTzC31UDxFwUV/PMfrbcMAnOhPsa8zxk3T2PTQMC1ebBmiK5phRU2QB5dVMLfYg0uVWVkzfhpOqc/Be1ZXcagnTm3YzYKRtCdr5L+pWhN2qDLbm4pH/7/MZ2cgXnjcb5g/+UIHs0FVZGrDV/eEY/mCauZWhekZjLFmUS3VZcEZuW4qb6AZFg5FJpk30EwTRb46GiLfub6Rw6cGiSazPHrHsov2bsrpJql8IY1NlWUS6Tz/9suDRFM52vvjPPPmKd67feG0j9umKnzhkY28dqib0pCbNfPHTwX1O230JXJYQPAyFnQGEjlebRkC4Fhfkj2noty+5PppwjjWOaQFNSEW1IRp6Y6ysDbE/DFSRzXdYP+JPgIeB3Orr47qm8L17bICl0gkIgIVQbiImrCbT9xyunJPXyzLl352jO5ohroiN5+7az5B9+VPov0ulZDbzqmhNF6HSrF3endb9nXF+OFb3eQ0kyO9SX775ka2Nk7sg29plZ+lI7sp0bSGqkgsrPTz4NqqkVSxEAsrp7bKl02ReWDN7J0HuN7VlAX5f799P31DCRqqwthmKJWxKuAsrOrrJnOK3DimoXLddKmvCPK3n7oNw7RQFZmW7hj/8OQBdNPkY7c3s7j+dJnudN7g9fYIybxBkdvGmtogNlXG67LTM5zCpsp4LnAmJ5nT6YrkAKgOOcc8jH8pigMu7t4w96I/N7+k0EfHtCwqL2OHxOdSKfLaOTmQwudQKZ2h3eYrWcjr4M8+tIH2/gR1pT5853zOWJbF3/33a/xy53E8Ljufee8mblh25ReDEa5vlxW4xGKxKQ9ULNFsSrjGdQyn6YlmKfM76Ypm6RzOTEng4rar/PrWBvZ3xin1O1g6zVW0slqhr4zLrpDTTfK6AVza+8HzRwf48Z4e7KrMBzfWzvphY2F6BX0ugr6Z7UwfdNm4aV4xecPEbVOuulKxkiShjvTq+a8XjvNGSz+yJPGtZ47wVx/ZOPpzw+k80Wyh4tlQWiOe1Qm77fzvh9bw9O5TlAZd3La6ftzr9MZypHI6FtAbzzG3ZGZ2+BRZoiZ4+c8Jt13lN29pZM+pKGV+JyuuouIm08nvtrO4fuwFpbxmsPNgB067Sv9wkv0nekXgIlzxLitwmY7dFdO89DKbgnA1mVPipa7YTVckw9wSz7jNGiejMuiicgomAROxrDrAkb4kbUNpNs0JT2qF8+lD/SRzOvmUyasnh1lUOXsli03LYn93gmROZ3GFj8A0nEO6knXHsxzrTxFw2Vha4buscyBXGpsiY1NkDNMirxs4VfmqC2AAXHYFkDAtcJ1zzsbvVHHbFNKaQdBpGz2Hs6C2aEK9fBRZ4u1lw6u1lO5Mvv9dC+w2hVULq/jV6y0UBz00N1x5lRYF4Vyzd8pTEK5TYa+dz901n65IhtqwG+8sHra+HG67woc21I5ZaSqrGUQzGsUeO+o4qTl53UQzTHrjWQIuG+V+B73xLG2DaeqK3FRMouTu5XizI8YTB/vI6xaH+5J8eH3NhPugnBpO0zqUoSLgYH7p2FW/8oaJLElX5KQwp5v88ugQg8k8NkXCrsg0X6B62dUoldN5vSNKOm9QHXSxtMJ31QUvH7hpAQ6bimaYvHvz2WlYfqeNdXUh4lmdIrftok1Xz1UZdKLKEpKESLO6TkiSxKfevYGtKxvwux0sqL8+ykcLV7erc8YkCFe5gMt2zazonzv5i2U0/uXVDnrjWZpKPHxgXQ32MSZRr7dHyUkSfq+DEp+DpdUBvvpCK92xLGU+B5/cPndGJ1DDaY2sZuK2KwyltMKBbvXiE9vhVJ7v7+1lOKXhd6k8vFKhJnT2qu++7ji/OjaEU5W5d3EZ1cHZr3akGyYHepMYpkVdyElON3GoMpppTqrB5JWuO5FjKJ3Hrsh0RDM0FrtnrDrYVCkNuvjNe5eM+/2A00bgIr1legbjxBIZGmuKUc84Z+RQZWrCk9ut0HWDl3a3kM3rbF7ViNctAp+rhcOusnaRSM8Vrh5X17u2IAhXvOMDKU4Opgi5bRzuS9IVy9JQdH46XE4zUSSJ6rAbn1NlMJlnIJGj1GtnMJmnN5ad0cClucLHod4kac1gbV1gwivWiZxBJm/gd6mk8wbx3NmNdi3L4oUTw8SzGkOGxZudsSsicNnRGuGFlmEsC1ZU+1ld4+etrgRht5OmGTrfMJM8NgWbLJPXTUJuG/ZJHNLPawYH24cJex3Ulc1eWuNk7TnSyf/95jMk0jlu3TCfT753y5TsOv3gV2/xL//zMpphsPdoJ7/za7dNwWiFmWRZFsmcQV4vLNh4HFffeTDh+iACF0G4Trxd+GK6P4xKvQ6CLhvDaY3KgJOwe+wV4FW1AdqG0wyl8mxrKqapzMucEg/tQxnmFLupn8KzPxPhd6o8sroSmyLjv4T0vaqgk+ZyH8cHUzSVeph7TpAmSRJBp0pvPIcsSbPah+VMA4k8hmWhyhI98Rz3LSljeaUfeQYbFc6kCr+D1TUBkjmDCr8T2yUGLpZl8dc/3MPze7vwuex87qFVrJ53ZZ8JONQ2QEvnMEvnllFfEWT3oQ66+mMUBdy8vKeVX3vHBjyuyy8M0to5SDav43baOHKydwpGfnksy2L/8R4Go0lWLaoh4BXnXi4mb1ikcyYWoBsWdlXGPoEdZ0GYaVfGJ6ggCNPqaE+C/9zZjoTEIxtqaSybvvMLtWEXH95QQ2c0S1OpZ9yUuKDLxkfOOSPz2I1z6I0XKq657TPXa2Nfd5wf7+vFAu5ZXMaK6vH70Jzr1HCGIq+N5opyGkvcY56LuWdxGdWdcRyqzKoL9LiZScuq/HTGsuimxZrawpiupQP555IkiarA5CewyYzGG8cGcDts9EbS7Ds5eEUHLi2dw3zxGy/QN5ykvjzIlz5xC/NqSwj73STSOTYtr8R9kbSyidq6tok9RzqJJLJkJTt//m8v8cE7l1NZPLUlzceS00wGEnkkqdD0167KvLqvjb/85jPEklnWLq7lz37zbpSrqAz2rJOmrp+WIEw1EbhchmQyyVNPPcXTTz/Nrl27OHnyJLlcjnA4zPLly3nPe97DI488gqqKh1mYXT95q5uWvhRI8OTeHj5967yL3+gyzCn2MKfYM6GfPXN132VXaJjg7abSa60RohkNCYnX2iITDly6oll+sK+3cCDaY+OD3mpCY+wwBV02NtQHkWVpUilK06Gp1ENloAbDtK6Z81bTyeuysaS+iB0HuykJuFhQc34zvytJR3+MgUiK8rCXvkiK3qEUW1bNJeBzEYmnWdNcO2U7a+uWNvD3v/cQf/iNF9h/sp/u11pQFJn/732bpuT3X0hfPEds5LVrWVAddnKiY5ChWIqSoJcTHYPEklnCgWsv/XEq2RUJr7NQ2t5pk0dLcAvClUbMqC/DihUraGlpGf1/u92Oy+Wir6+PX/ziF/ziF7/gH/7hH3jyyScpLb1yV+aEa5/HoWJhgVX4ujeeJZbWaCj2jHlw/npT4XdwrD+JhUXZJZyriWU1krlCp/JkrnC+ZazAZV93nGeODeFQZe5uLqU2NPmV/4FkntbhDMUeGw1h12VNPr0O8REwUZIk8f+9ayW3rqol7HOM2YV8Omm6gQRnHai/kKVzy1gyt4zWngir5lcwryaMJEksn181LeMrDnnxuOxIgCWBac5MTzbLsgpBCxTe44CVC6upLQ8RTWTYtmYeIb9IFbsYSSqca7ncxqOCMN3Ep9Zl0DSNxYsX85GPfIQ77riD+fPnA9Db28tXvvIVvvSlL7Fr1y7e+c538uKLL16TeePC1eHda6sJumxIkkRjuZcv/+oEyZzOypogH9pUO+Gyv9eqWxeWUuJzYFnWJaWJ1YfdNBZ76I7nWFDqodI/dtDzcmuEZE4nmrHY0xWfdOCSzhv85GA//ck8HrvCfYtLqTvnd2U0A82w8F3G4dq0ZrC3O45hwpIK70UrVV0vXA6VDQvLZ/y6L+4+wT/98DVcDhu/9ciNLGgou+htigJu/uLXb6JvOElViR+7bfonpL9290pURcauKjx86/jVz6ZSqd+BRQ4opIoBNM+t4K8/+w4i8TSNNcXisxc42hlh17F+akt8bF5cIR4T4aolApfL8K1vfYstW86vylJeXs5f/MVfEAwG+fznP8+OHTt4+eWXueGGG2ZppML1rtjr4OENtQA8ua+XwUSOkMfO4d7EyI7B9T0xdagy6+svfQXdbVd4eFUlyZyOz6mOGwCG3bbRw/nByzicn8wbJHI6XodCKm8Qy+hwxrDbIxmeONhPzjDZWB9kfd3kdgV2d8bY253AsiCe1bhzodgxnk3//tPd9AzEyesGT7x4aEKBC4DbaaOhcuZ2hppqi/izj980Y9eDQnrpWIU8yop8lBVN/xmbq0EkmePPvrOb1r4EQY8dt1O9os9nCcKFiByRy3DjjTdecNXiIx/5yOjXu3btmokhCcJFNRS7CXnsJHM6c0s8Il3oMimyRMBlu+Cu1Z2LSrl1fjF3LCxhQ8PkJ5JFbhtNJZ5CLn/ASUPR2bsth/qSDKTy5HWTN9rjmNbk0nXe7uMiy5C9Bnu6XG1KQl7ymoEiSxSJsxoXZJomJzuHGIqlZ3soV4xoMkckmSPsc5DIaAzEMrM9JEGYNDFjmUZO5+leDYZhzOJIBOG05ko/n9w+h0haY36Z97pPE5sJPofKpjnhy/49iixx+4Ji4lkdj105r6RveKQ/Sd6wKPHaJ10ZaFmFn2ha4+XdJzicSGFPxLlrwzyRXjJLfvO9m2msKcbpsHHvjc1j/kxGM0hrBkGn7ZquDnchlmXxD997lV++doyA18nnP7SdRXMmtjt1Lasr9XHT8mpeOtDNysYS1jaJx0S4eonAZRq98MILo18vWTIz+b6CMBG1YTe1lz+PnlY53aQ3nqXYY8dzDe8KmZbFns44/ckcC0q9YzbrPJMsSQTHqQK2qjqAy6aQ1U0WlXkmHWiUeO3U2AyOHDyFpht09kaYX1PEvJqiSf0+4fKUF/n40H1rx/1+LKvx6qkIGc2g3OtgXV3oulyQSKRzPP/mSSQJ2noi7DrUIQIXQJYlPnnfUj5w8wJ8LhvqFVLZUBAm49qdDcwyXdf5whe+AMCcOXPYvn37RW9jWRbxeHzS13Q4HDgcM9dpXLi2aIbJj9/q5uRAmg1zw2yeVzxrY8npJt94pZ2WgRSlfgcf3VhL2HP5jfKuREf7Uzx5sI+sbnKoN8nHNtROukmlIkssqZiavP6sZhLLaoCE07AmnXYmTL+BZJ54VsdtU+hP5a/bc2sep505lWF2Heog6HVRV35ll6yeSZIkEfKK+UEulyOXy0369pZ4H5x1InCZJp/85CfZu3cvkiTx+OOPY7Nd/EOku7ubQGDyzen+8A//kC9+8YuTvr1wfXvzVJSf7e/DsqArkqGpzEuZ33nxG06D7liW4wMpPHaFU0NpTgymr9nAJZ030EwL98hOSVY38F0Bb81RyU5lfSU9vcOsXl5P0yzttvQOJfjer/YhSRLvunkpZeHpa566Y38nh04NsWROCRsWVU7bdaZa0GXDZVPI6AbFHjvuGaggdiVSFJnPf2g7Ow+0Ew64WbOoeraHNGOe3n2Kn+1qo6kqyKO3NeO0z/57yJXoz//8z/mjP/qj2R6GcBnEM3sa/OVf/iVf/epXAfj93/99br/99gndrrKyksOHD0/6umK3Rbhs1umOybO5sFTssVPitdMRzVDksVMxTpnha8HCMi9H+r10x3KsqPJTfIUEaNGszuqVczHMuSy8jLSzy/UvP3qdX7x2DJBIpnP8zqPbpuU6R9qH+Ovv7WIgmqEs7OavPr6VhorgtFxrqhV77GyqD5HMG5R47Nd1KlDI7+L2jfNnexgzqnc4xdee2kc0mWPfyQHmVAS4dXX9bA/rivT5z3+e3/qt35r07RcuXEh3d/cUjki4VCJwGccDDzzAK6+8ct6/19TUXLBC2N/+7d/yuc99DoDf/u3fvqTIXpIk/H7/pQ9WEKbAyrogty8p4+RAmo2NYcoDl7bboukmP93fS8dwhk3zilhRG5z0WHxOlY9uqqNlIEVV0En1ZTRsvNK57QoPr6xEN63zDtvPplU1AZ5vGUaWYVnl7L0vJTN5JElCAlKZ/LRdJ5rMkcxoFPmdJDMa0eTk00lmQ9htJywKjl2XCq8PCcsCWUIU0biAy02pF4/t7BOByziGh4fp6+s779/PrBR2rr//+7/nU5/6FACf/vSn+dKXvjRt4xOEqWZTZN61evKpFbvaIvzkrR50w6J1MEVDsYfgGF3kJ6rYa6fYe2XsPkw3SZKwKVfWB+LiCh8NRW5kCVzTlHp0snOQzv4Yy5oqCXjHDk4fuXMliVQOSYaHb18xLeMAWN5YyrYVtew53scNS6pZ3DB7Z7wE4VKUhdz8+j1L+fmuNuZVBdmytGq2hyQI00ayxEmjKfF3f/d3fPKTnwTgU5/6FF/5ylcmfNvq6mq6urqoqqqis7NzmkYoCNPrhaMDfPPlUyiShNeh8gf3LqBIHAYVxvHSwS7+bccJsppOkZXjLz52M07H7B4otyyLbF7HaVcveWXVsixe3d/Oic5hVi6opFlUs5oSrV1DJNI5FjWUoarX59kd4coh5muzT+y4TIEzg5ZPfvKTlxS0CMK1Ym1DmBMDKdqH0mxbUDLtQUv7UJq9HVEqgi5W1QXFFv5VZuepCIZqw+10MBwz6RtKUFc5uzW6JUnCNcng6cCJPv7y2y8yFEtTUxbgrz99F+Wic/tl2fFWK1/5jxdIZTXu3LSA33zP5tkekiAIs0wELpfpcnZaBOFa4rIrfPiG+hm5Vjyj8Q/PnqB9OIPfqWLfPpelNZOvyCfMvNoSPy2DGXTDoNjnpKLk6j7fNxhNEU/lKC/2Eywv4o3OGOudjium2MLV6K2jXfQNJwn73by8t43feHCj2HURhOucCFwuw+OPPz4atHz605/my1/+8iyPSBCuD4msTjStEXCpJLM6Q1fZQWoB7l5WRcBtIxrPsr25HLvt6v44WrWwinXN1cQtmfkLaslYEm91xdg+r/i6bAY5FRY1lBH2u0lnNTYua5jVoCWR1emL57DJEhVBJ3b1yimkIQjXE3HG5TLIsjzajKis7ML5zA899BB/8zd/M+b3RM6kIFwa07T491fbeb11mPKAk/+1fe412+flatc1lCKvm9SXeq/5dD7DMHmzI0JbLIcsgc9h46Z5xSjytX2/p4tlWRw62Uc8lWPlgiocs9SbxLIsjvamyGoGlgXlAQcVwdnpcSXMLjFfm31X9xLXLDsz5hurAtmZYrHYdA9HEK4bsizx/o213LO8Aq9TvaLKCAun7Tjcx+M/PYymm7xrUz0P3TBntoc0rRRFZnFVEEuOk9EM5pd4RdByGSRJonlu+WwPAwBJAgtON7oSBGFWiMDlMojNKkGYPZIkERK7LFe0Vw73MxjP4nIo/Gpv9zUfuEChdPTa2tBsD0OYQpIkURVyMhDPoyoSxT7xviMIs0UsUwqCIAjToqnKj8uuoOkWi8VkXriKeR0qDSVuasKuKdvhTaZz7HjzBEfb+qfk9wnC9UDsuAiCIAjT4t41tZQHXeQ0g3VNpbM9HOEK1TMQ4yfP7cNmU3jg5uUEfe7ZHtK0MwyTP/vnp9m5v42gz8XvfOQW1jTXzvawBOGKJwIXQZhGpmnRF8sS9Nhx2UUZT+H6IssS6+eLgEW4sH/87ks8t+sYEpDO5PnEw1tne0jTLpLIcKStj6DfzcBwgmNt/SJwEYQJEKligjBNDNPiH351nM/911v84ff30xfLzvaQBEEQrjiRRBplJP0qlszM8mhmRtjvYtXCGlKZPJWlQZY2Vc72kAThqiB2XARhmnRHMrx2fAhVljjaHWdPW4Tbl1XM9rAEQRCuKO+/Zx1f+++XsNtVHrxl5WwPZ0bIssxnP7idu29spijgobosONtDEoSrgghcBGGahDw2SnwOTvYnCbhtlIu6/4IgCOdZs7iOVYtqkCTpmu/1cyaHXWVZU9VsD0MQrioicBGEaeJ12vjs3QvY0xahKuxiWW1wtockCIJwRZJlkbkuCMLFicBFEKZRVdhNVfjar5AjCIIgCIIw3cQShyAIgiAIgiAIVzyx4yIIgiAIwlWnL5GjK5rBaVNoLPZgV8VarCBc68SrXBAEQRCEq4pmmJwaTpPWDAaTOfqTudkekiAIM0AELoIgCIIgXFVkSUKWJAzTQhr5WhCEa59IFRMEQRAE4aqiyBLzSjz0JXI4VJkyn2O2hyQIwgwQgYsgCIIgCFedgMtGwGWb7WEIgjCDRKqYIAiCIAiCIAhXPLHjcgXp6+tj0aJFY37vscce47HHHpvhEQmCIEyOaVmk8wZuuyLOHwiCcNV4/PHHefzxx8f8Xl9f3wyPRjiXZFmWNduDuN5VV1fT1dVFVVUVnZ2dsz0cQZhWmm4Sy2qE3XZkWUxor0WaYfLkoQFah9JUBZzcv6QMhyhVKwjCVU7M12af2HERBGHGRFJ5/uHZE3RFsqyoC/KhG+pQFTGhvdZ0xXIc6UvitMkcH0zRNpxhfqlntoclCIIgXOXEjEEQhBmzvzPGoe4EErCrdZiuSGa2hyRMA59DwW1XiGV03DYFv1OZ7SEJgiAI1wCx4yIIwowp9jnwu2xE0nmqw26CHvtsD0mYBkUeO/cvKaM9kqHS76TC75ztIQnCJbMsi2ffPMXu470snVPKbWsakMR5LUGYVSJwEQRhxiys8PHxrQ10RTIsrvKLUqbXsLqQi7qQa/T/c3mdlo5BSsNeSkLeWRyZIExMS1eEv//hboYTWXbs66S6xMfihpLZHpYgXNdE4CIIwoyRJInltUGW1wZneyjCDNJ1g7/45rO8duAUJUEvf/ixW5hbUzzbwxKEC0rndLKagc9tJ6cZpHP6bA9JEK574oyLIAiCMK16h5O8eaQTr8vOqZ5h9hztnu0hCcJFLa4v5h03NBH0OrlnYyMrGktne0iCcN0TOy6CIAjCtCoJephTVcSeo12UhDw01hRN6/USGQ2nTcEmSjALl0FRZD52z3I+evcycbZFEK4QInARBEEQppXDrvIHH7uFPUe6qCwNsKB++lauv/vSCX74ShtFfie/8+Ayakpm7jyNZVm8uKeV7sEEG5fWUVcenLFrC9NHBC2CcOUQy1GCIAjCtAv53WxfO29ag5ZERuMHr7SRzGkcbB/m+QM903atsex4q42//PaL/OP/7ORPv/Es6Wx+Rq8vCIJwrROBiyAIgnBNcNoUiv1OkhkNp02l2DezZZj7IimS6TwBn5PBaJp4Kjej1xcEQbjWiVQxQRAE4ZpgU2V+58FlPH+gh2Kfk1tWVM3o9TcureXZXSfoG05w89pGysKi7LMgCMJUEoGLIAiCcM2oKfHy/m3zZuXalcV+vvyZu0ikcxQF3OJshCAIwhQTgYsgCIIgTBGHXcVhFx+tgiAI00GccREEQRAEQRAE4YonAhdBEARBEARBEK54InARBEEQBEEQBOGKJwIXQRAEQRAEQRCueCJwEQRBEARBEAThiicCF0EQBEEQBEEQrngicBEEQRAEQRAE4YonAhdBEARBEARBEK54InARBEEQBEEQBOGKJwIXQRAEQRAEQRCueCJwEQRBEARBEAThiqfO9gCE0/r6+li0aNGY33vsscd47LHHZnhEgiAIgiAI14/HH3+cxx9/fMzv9fX1zfBohHNJlmVZsz2I6111dTVdXV1UVVXR2dk528MRBEEQBEEQziHma7NPpIoJgiAIgiAIgnDFE4GLIAiCIAiCIAhXPHHGRRAEQZhRhmHyq9ePMxBJsXlFA3UVodkekiAIgnAVEIGLIAiCMKOe2dXCV/7zJdJZjZf3tvKV374Ph118HAmCIAgXJlLFBEEQriEDyRzPHRtkb1eMK7X2ylA0RTqr4XPbGYimSWe12R6SIAiCcBUQS1yCIAjXiLxu8q+vddA2lMZtV3hkbTXLqgKzPazz3LCigZfeamUgkuKeLQsJ+V2zPSRBEAThKiACF0EQhGtEMqcznNLwOlQSOZ3BZH62hzSmmrIgf/PZ+0hl8gR9ImgRhKnSH0kRT+VoqAiiKCKpRrj2iMBFEAThGhFy21hdG+CN9ih1IReLK/yzPaRx2VRFBC2CMIX2n+znL/7jFeKpHLeumcMnHliNJEmzPSxBmFIicBEEQbhGSJLEA8sr2NpUjMeu4LQpsz0kQRBmyBtHemjvixP2O3lxbzuP3rEUn9sx28MShCkl9hGFa1Iul+OLX/wiuVxutociXCWuleeMJEkUeewiaJkB18pzRpg50/mcaagIEvI5SaTzNFaH8TjtU34NQZhtknWllp25jlRXV9PV1UVVVRWdnZ2zPZxrQjweJxAIEIvF8Puv3HQZ4cohnjPCpRLPGeFSTedzxrIs3jzWy3A8w9pFVQQ8Yrdlqon52uwTqWKCIAiCIAhXOUmSWDW/YraHIQjTSqSKCYIgCIIgCIJwxROBiyAIgiAIgiAIVzwRuAiCIAiCIAiCcMUTgYsgCIIgCIIgCFc8EbgIgiAIgiAIgnDFE4GLIAiCIAiCIAhXPBG4XKcef/zx6+KaM2mm79+1fr3ZuuZMutb/htf69WbDtf6YXuvXmw3X+mN6PfwNhdNE4HKdEpPQqXetv1mL58zUu9b/htf69WbDtf6YXuvXmw3X+mN6PfwNhdNE4CIIgiAIgiAIwhVPBC6CIAiCIAiCIFzxROAiCIIgCIIgCMIVTwQugiAIgiAIgiBc8dTZHoBwWl9fH4sWLRrze4899hiPPfbYDI9IEARBEATh+vH444+Pe+C/r69vhkcjnEuyLMua7UFc7+x2O5qmIcsyFRUVM3LNvr4+ysrKZuRas3FNy7Lo7u6msrISSZJm5Joz/Zhe69eb6WuK54y43qUSzxlxvUslnjNX9/V6enowTRObzUY+n5+RawpnE4HLFUBRFEzTnO1hCIIgCIIgCBchyzKGYcz2MK5LIlXsCuB0OslmsyiKQmlp6WwPRxAEQRAEQThHf38/hmHgdDpneyjXLbHjIgiCIAiCIAjCFU9UFRMEQRAEQRAE4YonAhdBEARBEARBEK54InARBEEQBEEQBOGKJwIXQRAEQRAEQRD+//buP6aq+o/j+OuC8mv8UBQFnDC0lorAzHBKKSAl2HQu/COaTs1cW2Voq2ZrNqn+aHP+xh/9sNQmGdpKV20hJBgCIyn81dSpgFMTh/gLREDgfP9w3O9FLhpw4V4uz8d21rnn8/PsfPzEe+d8znF4BC4AAAAAHB6BCwAAAACHR+ACAAAAwOERuMDh1dbWKjMzU0uWLFFUVJR8fHzk5uamwMBAJSUlaefOnWpqaup2OzU1NUpLS1NERIS8vb3l5+en6OhorV27Vo2NjTY4E/SWpqYmHTp0SGvXrtX8+fMVHh6uAQMGyGQyKS4urtv179y5UyaT6bFbTk5O908GPa6nx0sr5hjn09jYqHXr1ik6Olp+fn7y9vZWRESEVq1apZqami7XyxwDWDfA3h0AHmfChAk6f/68+bebm5s8PT117do1ZWVlKSsrS1u3btUvv/yiYcOGdamNixcvKi4uThUVFZIkLy8vNTQ0qKSkRCUlJcrIyNDvv/+uwYMH2+KU0MMuX76shISEHm/HxcVFAQEBHaa7u7v3eB/Qfb0xXphjnM/NmzeVkJCg0tJSSQ/+vbu6uurUqVM6deqUdu3apcOHDys0NLTLbTDHAG1xxwUO7/79+xo/frzWr1+vM2fOqKGhQbdv39bVq1e1YsUKubq66ujRo5o7d64Mw+h0/c3NzZo9e7YqKioUFBSk7Oxs3b17V3V1dfr+++/l4+Oj0tJSzZs3rwfODj3F29tbMTExeuutt7R9+3YlJibavI2RI0eqsrKyw23q1Kk2bxM9oyfHC3OMc5o3b55KS0vl6+urzMxM1dXV6e7duzp48KCCgoJ08eJFzZ49W83NzV1ugzkGeIgBOLi8vDyjpaWlw/TPPvvMkGRIMvLz8ztd/1dffWUuX1hY2C79u+++M6fn5OR0un70vubm5nZjZuHChYYkIzY2ttv179ixw5BkhIaGdrsu2F9PjxfmGOeTnZ1tvmZ79uxpl15YWGhO3759e6frZ44BrOOOCxxebGysTCZTh+mvvfaaef/o0aOdrn/Xrl2SpPj4eE2ZMqVdekpKisLCwtrkhWNzcXF55JgBLPX0eGGOcT6t12nUqFF6+eWX26VPmTLFvD6KawrYDoEL+jwPDw/zfmdvydfV1amwsFCSNHPmTKt5TCaTkpKSJEnZ2dld7CWA/og5xjm1LopPSkrqMOhtvd4FBQW6d+9er/UNcGYELujzDh8+bN6PiIjoVNnTp0+rpaVFkjR+/PgO87WmVVZWqrq6ugu9hDOqqqrSxIkT5e3tLU9PT40aNUrz589XXl6evbsGB8Ec43yqq6tVWVkp6b9d05aWFp0+fbpLbTHHAG0RuKBPa2pq0sqVKyU9uGU/ffr0TpX/999/zfsjRozoMJ9l2tWrVzvZSziruro6/f3333Jzc1NLS4vKy8uVkZGh+Ph4LV682Cav6UbfxhzjfLpyTS3LdAZzDNAWgQv6tNTUVB0/flwmk0lbtmzRwIEDO1Xe8j37Xl5eHeazTOvOu/nhHIKDg7Vq1SodP35c9fX1unHjhurq6lRQUKDnn39ekrRjxw698847du4p7I05xvn0xjVljgGsI3BBn7V69Wpt27ZNkvTRRx+ZnxEHetqMGTOUlpamyMhI83cUXF1dFRMTo6ysLM2ZM0eStHXrVp07d86eXQXQBzHHANYRuMCukpOTFRgY2G6Ljo5+ZLlNmzZpxYoVkqR3331XH3/8cZfa9/HxMe/X1dV1mM8yzbIMel9Xx0xvcXFx0Zo1ayQ9eLb9559/tnOP+jd7jxfmmL7ncWPG3teUOQb92QB7dwD9240bN3Tt2rV2xy3fFPawzZs3a9myZZKk5cuXmyfwrggODjbvX7lyRZGRkVbzXblyxbwfFBTU5fbQfV0ZM73tiSee0NChQ3X9+nWVlZXZuzv9mr3HC3NM3/O4MfPwNe2IZZplGVtgjkF/xR0X2FVeXp4Mw2i3VVRUWM2fnp6ut99+W5K0bNkyrV+/vlvtjx07Vi4uD/4ZnDp1qsN8rWmBgYEaMmRIt9pE93R2zKB/s/d4YY7pex43ZoYMGaLAwEBJ/+2auri4aOzYsT3eb6A/IHBBn5Genq7U1FRJDxblb9iwodt1enl5mT8I99tvv1nNYxiGsrKyJMm8KBJ4lAsXLuj69euSZP6wIPon5hjnlJCQIEnKysqSYRhW87Re75iYGHl6etq0feYY9FcELugTLIOWZcuWaePGjTare+HChZKk3NxcFRcXt0vft2+f+Vb8ggULbNYu+qaO/kixTG9df+Xi4qJZs2b1RrfgwJhjnE/rNb1w4YL27dvXLr24uFi5ubmSOn9NmWOARzAAB7d582ZDkiHJWL58eafL79ixw1w+Nze3Xfr9+/eN8PBwQ5IxYsQIIycnxzAMw2hubjb27t1r+Pr6GpKMxMTE7p4KetGtW7eMqqoq85aSkmJIMmJiYtocv3XrVruyjxoz5eXlRnR0tPH5558bFy5cMFpaWgzDeDBeiouLjZkzZ5rLvvHGG71xqrCBnhovhsEc46wSExMNSYafn5+xd+9eo7m52TAMw8jJyTGCg4MNSUZ4eLhx//79dmWZY4CuIXCBwzOZTOZJevjw4Y/cUlNT25V/3B8VhmEYZWVlRkhIiDmfl5eX4eHhYf4dFRVlVFdX9/CZwpZiY2PN1+9RW2xsbLuyj/ujwrK8u7u7MXToUMPd3b3N8VdffdXqHyxwTD01Xloxxzif6upqIyoqynwNPTw8DC8vL/PvkJAQo6yszGpZ5higa3irGByeYXHb3NqbXizdvn27S22EhYXp5MmTWrNmjX788UeVl5dr4MCBGjdunFJSUpSammp+lz76t+HDhys9PV1FRUU6duyYqqqqdPPmTXl4eCgsLEwxMTFavHixnn32WXt3FQ6EOcb5+Pv7688//9SmTZu0Z88enTt3Ts3NzQoPD1dycrLee+89+fr6drpe5higYybDeMzDlAAAAABgZyzOBwAAAODwCFwAAAAAODwCFwAAAAAOj8AFAAAAgMMjcAEAAADg8AhcAAAAADg8AhcAAAAADo/ABQAAAIDDI3ABAAAA4PAIXAAAAAA4PAIXAAAAAA6PwAUAAACAwyNwAQAAAODwCFwAAAAAODwCFwBwMkuWLJHJZJLJZFJlZaXVPOnp6eY87u7uqqurs5rv008/NecrLi7uyW4DAPBIBC4A4GTi4+PN+7m5uVbz5OXlmfcbGxtVUFBgNV9reR8fH02cONF2nQQAoJMIXADAyTwucDEMQ4cPH25zzFq+hoYGFRUVSZKmTp2qAQMG2LinAAD8dwQuAOBkgoOD9eSTT0qyHpCcOHFC1dXVkqQpU6Z0mK+oqEj19fWS2gZDAADYA4ELADih1kDj/Pnzunz5cpu01iBl0KBBWr58uSSppKRENTU1VvNZ1gcAgL0QuACAE3rU42Ktv2NjY5WQkCCTyaSmpiYdOXLEar5BgwZpwoQJPdxjAAAejcAFAJxQXFyced8ycGlpaVF+fr4kafr06RoyZIgiIiLa5bt37575LWLTpk2Ti8uD/120vmGsMxsAALbASksAcEKBgYEaM2aMzpw5o0OHDpmPHzt2TDdv3pT0/7sy8fHxOnHiRJt8R44cUWNjY5t8kjRv3rx2bRUVFamsrEwRERGKjIzskfMBAIDABQCcVHx8vM6cOaOLFy+qvLxcYWFh5rsqAQEBGj9+vDnfxo0bVVpaqlu3bmnQoEEdrm/ZvXt3u3YWLVqksrIyJScnKy0trWdPCgDQb/GoGAA4KWvrXFr/GxcXZ36Mq/VRsJaWFv3xxx9t8vn7+3MXBQDgEAhcAMBJPbzOpbm52by+xTKoGTx4sKKiosz5amtrVVJSYq7DFutUKioqZDKZFBcXp9raWr3//vsaPXq03NzctGjRIkkPPoppMpnMvx+WlpYmk8mknTt3tku7dOmSli5dqtGjR8vDw0P+/v6aNWuWCgsLu913AIBjIHABACcVEBCg8PBwSQ8Ckr/++kt37tyR9GBhvqXWQCY3N1f5+flqampqc9xW6uvrFRcXpy+++EJjxozRiy++KH9//27VWVRUpKioKG3ZskUuLi5KTEzUmDFjlJWVpWnTpikzM9NGvQcA2BOBCwA4sdbA48qVK/ryyy8lPfhA5VNPPWU134kTJ/TDDz+0O24rxcXFMplMKisr06+//qr9+/dr3bp1Xa7vzp07mjt3rm7fvq2vv/5a586d04EDB1RYWKj8/Hz5+PhoyZIlun79ug3PAgBgDwQuAODELB8X27VrlyTrwci0adPk6uoqwzD07bffSpKGDRumcePG2bxP6enpGjp0qE3q+uabb3T16lUtXbpUixcvbpM2efJkrVy5UrW1tcrIyLBJewAA+yFwAQAnZrlG5VGPf/n6+urpp59uk89W61ssBQYGavLkyTar7+DBg5Kk5ORkq+mxsbGSZP4mDQCg7yJwAQAnZvmByVYPr29p9XBAY+vHxCQpJCTEpvVVVFRI+n+Q9fAWHR0tSaqqqrJpuwCA3sd3XADAybV+YFKSQkNDFRYW1mG+1atXm39bPmZmK56enl0u29LS0uGxOXPmyNvbu8OyY8eO7XK7AADHQOACAE5uw4YN2rBhw2PzJSUlyTCMnu9QB9zc3CRJNTU1VtMvXbrU7tjIkSN19uxZffjhh5o0aVKP9g8AYF88KgYAcAhBQUGSpLNnz7ZLa2hoUF5eXrvjM2bMkCT99NNPPdo3AID9EbgAABxCWFiYQkND9c8//7T59kpDQ4NSU1PN61ksvf766woMDNSaNWu0bds2NTc3t0mvr69XZmam+VE5AEDfReACAHAYn3zyiSTplVde0XPPPafZs2crLCxM+/fv16JFi9rl9/Pz04EDBzR48GC9+eabCgkJ0YwZM/TSSy/pmWeeUUBAgFJSUlRWVtbLZwIAsDUCFwCAw1iwYIEyMjIUGRmpkpISFRUVKTY2VkePHlVoaKjVMpMmTdLJkyf1wQcfyN/fXwUFBcrOzlZNTY0SExO1e/duvfDCC718JgAAWzMZ9lyJCQAAAAD/AXdcAAAAADg8AhcAAAAADo/ABQAAAIDDI3ABAAAA4PAIXAAAAAA4PAIXAAAAAA6PwAUAAACAwyNwAQAAAODwCFwAAAAAODwCFwAAAAAOj8AFAAAAgMP7H5hyCPJj9wIRAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# The residual version of the parity plot\n", + "\n", + "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(8, 10), sharex=True)\n", + "\n", + "# Axes 1 with scatter plot and color bar\n", + "\n", + "sc = ax1.scatter(true_test_theta[:2000], posteriors_all[:,0], c=posteriors_all[:,1],cmap='Blues_r', alpha=0.6, zorder=3, s=3)\n", + "# Create a separate axis for the color bar\n", + "cbar_ax = fig.add_axes([0.9, 0.5, 0.02, 0.38]) # Adjust position and size of the color bar axis\n", + "\n", + "# Create the color bar using the pre-defined axis\n", + "cb = fig.colorbar(sc, cax=cbar_ax, ax=ax1, label=r'$ Error\\ \\mathrm{w_{Pred}}$')\n", + "cb.ax.yaxis.label.set_fontsize('x-small')\n", + "cb.ax.yaxis.set_tick_params(labelsize='xx-small')\n", + "\n", + "# Get the original colormap\n", + "# original_cmap = plt.cm.Purples\n", + "original_cmap = plt.cm.Reds\n", + "kde = sns.kdeplot(x=true_test_theta[:2000], y=posteriors_all[:,0], cmap='Reds', thresh=0.2, levels=[0.2, 0.5, 0.8, 1.0], \n", + " ax=ax1, zorder=0,fill=True, alpha=0.8)\n", + "\n", + "ax1.plot([-4.0, -0.1], [-4.0, -0.1], 'k--', zorder=3, linewidth=2.0, alpha=0.8)\n", + "ax1.set_ylabel(r'$ \\mathrm{w_{Pred}}$', fontsize='small')\n", + "ax1.set_xlim(-2.3, -0.1)\n", + "ax1.set_ylim(-2.3, -0.1)\n", + "ax1.tick_params(axis='both', which='both', labelsize='x-small')\n", + "\n", + "# Axes 2 with residual plot\n", + "\n", + "ax2.plot([-4.0, -0.1], [0, 0], 'k--', zorder=3, linewidth=2.0)\n", + "\n", + "ax2.fill_between([np.min(true_test_theta[0:2000]), np.max(true_test_theta[0:2000])], [-1, -1], [1, 1], color='gray', alpha=0.3)\n", + "ax2.scatter(true_test_theta[:2000], (posteriors_all[:,0] - true_test_theta[0:2000]) / posteriors_all[:,1], c=posteriors_all[:,1], \n", + " cmap='Blues_r', alpha=0.6, zorder=3, s=3)\n", + "\n", + "ax2.set_ylabel(r'$ \\frac{\\mathrm{w_{True}} - Mean\\ \\mathrm{w_{Pred}}}{Error\\ \\mathrm{w_{Pred}}}$', fontsize='small')\n", + "ax2.set_xlabel(r'$ \\mathrm{w_{True}}$', fontsize='small')\n", + "ax2.tick_params(axis='both', which='both', labelsize='x-small')\n", + "\n", + "fig.subplots_adjust(hspace=0)\n", + "# plt.savefig('residual_plot_xtest_2000.pdf', bbox_inches='tight')\n", + "# plt.tight_layout()\n", + "\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:.conda-astro]", + "language": "python", + "name": "conda-env-.conda-astro-py" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/train_model.ipynb b/notebooks/train_model.ipynb new file mode 100644 index 0000000..5067f25 --- /dev/null +++ b/notebooks/train_model.ipynb @@ -0,0 +1,827 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "6655e2be", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "!export LD_LIBRARY_PATH=/opt/conda/lib\n", + "!export XLA_FLAGS=--xla_gpu_cuda_data_dir=/home/jarugula/cuda_data_dir\n", + "!export PATH=/usr/local/cuda-11.7/bin:${PATH}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c2386057", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# Import necessary packages\n", + "\n", + "import logging\n", + "import warnings\n", + "import json\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from sklearn import metrics\n", + "from sklearn.metrics import confusion_matrix\n", + "from sklearn.metrics import auc, roc_curve\n", + "from scipy.stats import uniform, norm\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib\n", + "import random\n", + "from tqdm import tqdm\n", + "import pandas as pd\n", + "\n", + "import h5py\n", + "import os\n", + "from tqdm import tqdm\n", + "import tensorflow as tf\n", + "from tensorflow import keras\n", + "from tensorflow.keras import layers \n", + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import Reshape,Conv1D,Flatten,Dense, Lambda\n", + "from tensorflow.keras.models import Model\n", + "from sklearn.model_selection import train_test_split\n", + "from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau, TerminateOnNaN\n", + "physical_devices = tf.config.list_physical_devices('GPU')\n", + "try:\n", + " tf.config.experimental.set_memory_growth(physical_devices[0], True)\n", + "except:\n", + " pass\n", + "from sklearn.utils import shuffle\n", + "AUTOTUNE = tf.data.AUTOTUNE\n", + "from sklearn.preprocessing import StandardScaler\n", + "import tqdm\n", + "import gc\n", + "import wandb\n", + "cols = [\"#DB4437\", \"#4285F4\", \"#F4B400\", \"#0F9D58\", \"purple\", \"goldenrod\", \"peru\",\n", + " \"coral\", \"turquoise\", 'gray', 'navy', 'm', 'darkgreen', 'fuchsia', 'steelblue']\n", + "\n", + "os.environ['PATH']=os.environ['PATH']+':/usr/local/cuda/bin'\n", + "os.environ['XLA_FLAGS']='--xla_gpu_cuda_data_dir=/home/jarugula/cuda_data_dir'\n", + "\n", + "import sys\n", + "sys.path.insert(1, '/home/jarugula/deeplenstronomy_cosmology/deeplenstronomy')" + ] + }, + { + "cell_type": "markdown", + "id": "d74d570a", + "metadata": {}, + "source": [ + "### Data import and pre-processing" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "f585fd63-bc24-4dca-8935-597ae91af163", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# The training data of 1M strong lens images\n", + "data_path = '/deepskieslab/stronglensing/hsbi/datasets/'\n", + "image_dir = 'w0_8param_fixzv_train_1M'\n", + "column_name = \"w0-g\" # Dark energy equation-of-state parameter \n", + "fig_title = 'w0'" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "d2e53fb0-4669-4f00-90a9-3664361c0ec9", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Read the images and metadata\n", + "if os.path.isdir(data_path+image_dir):\n", + " images = np.load(data_path+image_dir+'/CONFIGURATION_1_images.npy', allow_pickle=True)\n", + " metadata = pd.read_csv(data_path+image_dir+'/CONFIGURATION_1_metadata.csv')\n", + "else:\n", + " print(\"Data not found\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "c5235414-b563-4cd2-ad25-115c75eaa14e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Hence we use mean=0 and std=1.0\n", + "\n", + "images = images.reshape(images.shape[0], -1)\n", + "# means_image = np.mean(images, axis=0)\n", + "# std_image = np.std(images, axis=0)\n", + "means_image = 0.0\n", + "std_image = 1.0\n", + "images = (images - means_image) / std_image\n", + "images = images.reshape(images.shape[0], 32, 32, 1)\n", + "\n", + "\n", + "#manually standardize the theta (w)\n", + "mean_theta = 0.0 \n", + "std_theta = 1.0 \n", + "theta = (theta - mean_theta)/std_theta\n", + "\n", + "# save the mean and std for later use \n", + "# np.savez('mean_std_normall.npz', mean_theta=mean_theta, std_theta=std_theta, means_image=means_image, std_image=std_image)" + ] + }, + { + "cell_type": "markdown", + "id": "2f107ac1", + "metadata": {}, + "source": [ + "### Split the data into train and test" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "45fec1c4-5e9f-4438-bbcb-8d164817cacf", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Split into train and test\n", + "x_train , x_test, theta_train, theta_test, y_train, y_test = train_test_split(images, theta, np.ones_like(theta), test_size=0.2, random_state=0)\n", + "true_test_theta = np.copy(theta_test)\n", + "true_test_theta = true_test_theta*std_theta + mean_theta # unstanderdized parameter\n", + "\n", + "del images, theta\n", + "gc.collect()" + ] + }, + { + "cell_type": "markdown", + "id": "bc4b228c", + "metadata": {}, + "source": [ + "### Train the Neural network model " + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "50cf0320-99c3-420b-aee3-97ded0d4fbe2", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-05-19 06:40:04.905478: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "2024-05-19 06:40:04.905907: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "2024-05-19 06:40:04.906183: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "2024-05-19 06:40:05.312098: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "2024-05-19 06:40:05.312444: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "2024-05-19 06:40:05.312690: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "2024-05-19 06:40:05.312911: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1639] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 28808 MB memory: -> device: 0, name: NVIDIA A100 80GB PCIe MIG 4g.40gb, pci bus id: 0000:05:00.0, compute capability: 8.0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"model\"\n", + "__________________________________________________________________________________________________\n", + " Layer (type) Output Shape Param # Connected to \n", + "==================================================================================================\n", + " input_1 (InputLayer) [(None, 32, 32, 1)] 0 [] \n", + " \n", + " batch_normalization (Batch (None, 32, 32, 1) 4 ['input_1[0][0]'] \n", + " Normalization) \n", + " \n", + " conv2d (Conv2D) (None, 16, 16, 8) 80 ['batch_normalization[0][0]'] \n", + " \n", + " batch_normalization_1 (Bat (None, 16, 16, 8) 32 ['conv2d[0][0]'] \n", + " chNormalization) \n", + " \n", + " activation (Activation) (None, 16, 16, 8) 0 ['batch_normalization_1[0][0]'\n", + " ] \n", + " \n", + " activation_1 (Activation) (None, 16, 16, 8) 0 ['activation[0][0]'] \n", + " \n", + " conv2d_1 (Conv2D) (None, 16, 16, 16) 1168 ['activation_1[0][0]'] \n", + " \n", + " batch_normalization_2 (Bat (None, 16, 16, 16) 64 ['conv2d_1[0][0]'] \n", + " chNormalization) \n", + " \n", + " activation_2 (Activation) (None, 16, 16, 16) 0 ['batch_normalization_2[0][0]'\n", + " ] \n", + " \n", + " conv2d_2 (Conv2D) (None, 16, 16, 16) 2320 ['activation_2[0][0]'] \n", + " \n", + " batch_normalization_3 (Bat (None, 16, 16, 16) 64 ['conv2d_2[0][0]'] \n", + " chNormalization) \n", + " \n", + " max_pooling2d (MaxPooling2 (None, 8, 8, 16) 0 ['batch_normalization_3[0][0]'\n", + " D) ] \n", + " \n", + " conv2d_3 (Conv2D) (None, 8, 8, 16) 144 ['activation[0][0]'] \n", + " \n", + " add (Add) (None, 8, 8, 16) 0 ['max_pooling2d[0][0]', \n", + " 'conv2d_3[0][0]'] \n", + " \n", + " activation_3 (Activation) (None, 8, 8, 16) 0 ['add[0][0]'] \n", + " \n", + " conv2d_4 (Conv2D) (None, 8, 8, 32) 4640 ['activation_3[0][0]'] \n", + " \n", + " batch_normalization_4 (Bat (None, 8, 8, 32) 128 ['conv2d_4[0][0]'] \n", + " chNormalization) \n", + " \n", + " activation_4 (Activation) (None, 8, 8, 32) 0 ['batch_normalization_4[0][0]'\n", + " ] \n", + " \n", + " conv2d_5 (Conv2D) (None, 8, 8, 32) 9248 ['activation_4[0][0]'] \n", + " \n", + " batch_normalization_5 (Bat (None, 8, 8, 32) 128 ['conv2d_5[0][0]'] \n", + " chNormalization) \n", + " \n", + " max_pooling2d_1 (MaxPoolin (None, 4, 4, 32) 0 ['batch_normalization_5[0][0]'\n", + " g2D) ] \n", + " \n", + " conv2d_6 (Conv2D) (None, 4, 4, 32) 544 ['add[0][0]'] \n", + " \n", + " add_1 (Add) (None, 4, 4, 32) 0 ['max_pooling2d_1[0][0]', \n", + " 'conv2d_6[0][0]'] \n", + " \n", + " activation_5 (Activation) (None, 4, 4, 32) 0 ['add_1[0][0]'] \n", + " \n", + " conv2d_7 (Conv2D) (None, 4, 4, 45) 13005 ['activation_5[0][0]'] \n", + " \n", + " batch_normalization_6 (Bat (None, 4, 4, 45) 180 ['conv2d_7[0][0]'] \n", + " chNormalization) \n", + " \n", + " activation_6 (Activation) (None, 4, 4, 45) 0 ['batch_normalization_6[0][0]'\n", + " ] \n", + " \n", + " conv2d_8 (Conv2D) (None, 4, 4, 45) 18270 ['activation_6[0][0]'] \n", + " \n", + " batch_normalization_7 (Bat (None, 4, 4, 45) 180 ['conv2d_8[0][0]'] \n", + " chNormalization) \n", + " \n", + " max_pooling2d_2 (MaxPoolin (None, 2, 2, 45) 0 ['batch_normalization_7[0][0]'\n", + " g2D) ] \n", + " \n", + " conv2d_9 (Conv2D) (None, 2, 2, 45) 1485 ['add_1[0][0]'] \n", + " \n", + " add_2 (Add) (None, 2, 2, 45) 0 ['max_pooling2d_2[0][0]', \n", + " 'conv2d_9[0][0]'] \n", + " \n", + " separable_conv2d (Separabl (None, 2, 2, 64) 3349 ['add_2[0][0]'] \n", + " eConv2D) \n", + " \n", + " activation_7 (Activation) (None, 2, 2, 64) 0 ['separable_conv2d[0][0]'] \n", + " \n", + " input_2 (InputLayer) [(None, 1)] 0 [] \n", + " \n", + " global_average_pooling2d ( (None, 64) 0 ['activation_7[0][0]'] \n", + " GlobalAveragePooling2D) \n", + " \n", + " dense (Dense) (None, 64) 128 ['input_2[0][0]'] \n", + " \n", + " tf.concat (TFOpLambda) (None, 128) 0 ['global_average_pooling2d[0][\n", + " 0]', \n", + " 'dense[0][0]'] \n", + " \n", + " dense_1 (Dense) (None, 128) 16512 ['tf.concat[0][0]'] \n", + " \n", + " batch_normalization_8 (Bat (None, 128) 512 ['dense_1[0][0]'] \n", + " chNormalization) \n", + " \n", + " activation_8 (Activation) (None, 128) 0 ['batch_normalization_8[0][0]'\n", + " ] \n", + " \n", + " dropout (Dropout) (None, 128) 0 ['activation_8[0][0]'] \n", + " \n", + " dense_2 (Dense) (None, 64) 8256 ['dropout[0][0]'] \n", + " \n", + " batch_normalization_9 (Bat (None, 64) 256 ['dense_2[0][0]'] \n", + " chNormalization) \n", + " \n", + " activation_9 (Activation) (None, 64) 0 ['batch_normalization_9[0][0]'\n", + " ] \n", + " \n", + " dropout_1 (Dropout) (None, 64) 0 ['activation_9[0][0]'] \n", + " \n", + " dense_3 (Dense) (None, 1) 65 ['dropout_1[0][0]'] \n", + " \n", + "==================================================================================================\n", + "Total params: 80762 (315.48 KB)\n", + "Trainable params: 79988 (312.45 KB)\n", + "Non-trainable params: 774 (3.02 KB)\n", + "__________________________________________________________________________________________________\n" + ] + } + ], + "source": [ + "seed = 128 # random weight initialization\n", + "inputs_img = keras.Input(shape=x_train[0].shape)\n", + "inputs_theta = keras.Input(shape=(1,))\n", + "custom_initializer = tf.keras.initializers.he_uniform(seed=seed)\n", + "\n", + "# Entry block for ResNet\n", + "x = layers.BatchNormalization()(inputs_img)\n", + "x = layers.Conv2D(128/16, 3, strides=2, padding=\"same\",kernel_initializer=custom_initializer)(x)\n", + "x = layers.BatchNormalization()(x)\n", + "x = layers.Activation(\"relu\")(x)\n", + "\n", + "previous_block_activation = x # Set aside residual\n", + "\n", + "for size in [256/16, 512/16, 728/16]:\n", + " x = layers.Activation(\"relu\")(x)\n", + " x = layers.Conv2D(size, 3, padding=\"same\",kernel_initializer=custom_initializer)(x)\n", + " x = layers.BatchNormalization()(x)\n", + "\n", + " x = layers.Activation(\"relu\")(x)\n", + " x = layers.Conv2D(size, 3, padding=\"same\",kernel_initializer=custom_initializer)(x)\n", + " x = layers.BatchNormalization()(x)\n", + "\n", + " x = layers.MaxPooling2D(3, strides=2, padding=\"same\")(x)\n", + "\n", + " # Project residual\n", + " residual = layers.Conv2D(size, 1, strides=2, padding=\"same\",kernel_initializer=custom_initializer)(\n", + " previous_block_activation\n", + " )\n", + " x = layers.add([x, residual]) # Add back residual\n", + " previous_block_activation = x # Set aside next residual\n", + "\n", + "x = layers.SeparableConv2D(1024/16, 3, padding=\"same\",kernel_initializer=custom_initializer)(x)\n", + "x = layers.Activation(\"relu\")(x)\n", + "x = layers.GlobalAveragePooling2D()(x)\n", + "\n", + "# embed the theta value to the image\n", + "y = layers.Dense(64,kernel_initializer=custom_initializer)(inputs_theta)\n", + "\n", + "#concatenate the theta value to the image\n", + "x = tf.concat([x, y], axis=-1)\n", + "\n", + "x = layers.Dense(128, kernel_regularizer=tf.keras.regularizers.l2(0.01),kernel_initializer=custom_initializer)(x)\n", + "x = layers.BatchNormalization()(x)\n", + "x = layers.Activation(\"relu\")(x)\n", + "x = layers.Dropout(0.1)(x)\n", + "\n", + "x = layers.Dense(64, kernel_regularizer=tf.keras.regularizers.l2(0.01),kernel_initializer=custom_initializer)(x)\n", + "x = layers.BatchNormalization()(x)\n", + "x = layers.Activation(\"relu\")(x)\n", + "x = layers.Dropout(0.1)(x)\n", + "\n", + "outputs = layers.Dense(1)(x) # Outputs the log likelihood-to-evidence ratio : log p(x|theta)/p(x)\n", + "\n", + "model = keras.Model(inputs=[inputs_img, inputs_theta], outputs=outputs)\n", + "model.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "a188f530", + "metadata": {}, + "outputs": [], + "source": [ + "# Custom model for training and testing\n", + "class SNREmodel(keras.Model):\n", + " def __init__(self, model, **kwargs):\n", + " super(SNREmodel, self).__init__(**kwargs)\n", + " self.model = model\n", + "\n", + " def train_step(self, data):\n", + " image, theta = data\n", + " labels = tf.ones_like(theta) # generate class label=1 for p(x,theta)\n", + "\n", + " # shuffle the theta to generate p(x)p(theta) which is assigned a class label=0\n", + " images_shuffle = tf.identity(image)\n", + " theta_shuffle = tf.identity(theta)\n", + " theta_shuffle = tf.random.shuffle(theta_shuffle)\n", + " labels_shuffle = tf.zeros_like(theta_shuffle)\n", + "\n", + " image = tf.concat([image, images_shuffle], axis=0)\n", + " theta = tf.concat([theta, theta_shuffle], axis=0)\n", + " labels = tf.concat([labels, labels_shuffle], axis=0)\n", + "\n", + " index = tf.range(tf.shape(image)[0])\n", + " index = tf.random.shuffle(index)\n", + " image = tf.gather(image, index)\n", + " theta = tf.gather(theta, index)\n", + " labels = tf.gather(labels, index)\n", + "\n", + " with tf.GradientTape() as tape:\n", + " predictions = self.model([image, theta], training=True)\n", + " #use a binary crossentropy loss\n", + " loss = self.compiled_loss(labels, predictions)\n", + " \n", + " trainable_vars = self.model.trainable_variables\n", + " gradients = tape.gradient(loss, trainable_vars)\n", + " self.optimizer.apply_gradients(zip(gradients, trainable_vars))\n", + " self.compiled_metrics.update_state(labels, predictions)\n", + " return {m.name: m.result() for m in self.metrics}\n", + " \n", + " def test_step(self, data):\n", + " image, theta = data\n", + " labels = tf.ones_like(theta)\n", + "\n", + " images_shuffle = tf.identity(image)\n", + " theta_shuffle = tf.identity(theta)\n", + " theta_shuffle = tf.random.shuffle(theta_shuffle)\n", + " labels_shuffle = tf.zeros_like(theta_shuffle)\n", + "\n", + " image = tf.concat([image, images_shuffle], axis=0)\n", + " theta = tf.concat([theta, theta_shuffle], axis=0)\n", + " labels = tf.concat([labels, labels_shuffle], axis=0)\n", + "\n", + " index = tf.range(tf.shape(image)[0])\n", + " index = tf.random.shuffle(index)\n", + " image = tf.gather(image, index)\n", + " theta = tf.gather(theta, index)\n", + " labels = tf.gather(labels, index)\n", + "\n", + " predictions = self.model([image, theta], training=False)\n", + " loss = self.compiled_loss(labels, predictions)\n", + " for metric in self.metrics:\n", + " if metric.name == 'loss':\n", + " metric.update_state(loss)\n", + " else:\n", + " metric.update_state(labels, predictions)\n", + " return {m.name: m.result() for m in self.metrics}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "83ca40a6", + "metadata": {}, + "outputs": [], + "source": [ + "# Run the training\n", + "snre_model = SNREmodel(model)\n", + "snre_model.compile(loss=tf.keras.losses.BinaryCrossentropy(from_logits=True), \n", + " optimizer=tf.keras.optimizers.Adam(learning_rate=1e-2),\n", + " metrics=[keras.metrics.BinaryAccuracy(name=\"acc\"), keras.metrics.AUC(name=\"auc\")])\n", + "\n", + "callbacks_ = [tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=20, restore_best_weights=True), \n", + " tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.1,patience=5, min_lr=1e-6)]\n", + "\n", + "history = snre_model.fit(x_train, theta_train, validation_split=0.2, epochs=100, batch_size=1024, callbacks=callbacks_)\n", + "trained_model = snre_model.model\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "0608764d", + "metadata": {}, + "outputs": [], + "source": [ + "model = trained_model" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "42a972ac-724a-4533-ba85-47945dd6f879", + "metadata": {}, + "outputs": [], + "source": [ + "# save the model\n", + "model.save('working_model_1M-2-034_seed128_v2.keras')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:.conda-bison]", + "language": "python", + "name": "conda-env-.conda-bison-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.4" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}