diff --git a/README.md b/README.md index 83b1e46..9c8badc 100644 --- a/README.md +++ b/README.md @@ -40,6 +40,9 @@ uvx marimo edit --sandbox | GDPO vs GRPO | A comparison of GDPO and GRPO methods. | [![Open in molab](https://marimo.io/molab-shield.svg)](https://molab.marimo.io/github/marimo-team/gallery-examples/blob/main/notebooks/alphaxiv/gdpo/notebook.py/wasm) | | LLM Unlearning | Explore LLM unlearning by overfitting unwanted data. | [![Open in molab](https://marimo.io/molab-shield.svg)](https://molab.marimo.io/github/marimo-team/gallery-examples/blob/main/notebooks/alphaxiv/llm-unlearning.py) | | Seed of Thought | Why small LLMs can't pick uniformly at random, and a prompt-based fix from ICLR 2026. | [![Open in molab](https://marimo.io/molab-shield.svg)](https://molab.marimo.io/github/marimo-team/gallery-examples/blob/main/notebooks/alphaxiv/seed-of-thought.py) | +| Dead Salmons of Interpretability | Interactive false-positive traps showing how feature attribution, probing, SAEs, and circuits "find" structure in pure noise. | [![Open in molab](https://marimo.io/molab-shield.svg)](https://molab.marimo.io/github/marimo-team/gallery-examples/blob/main/notebooks/jam-2026-Q2/dead-salmon.py) | +| Geometry of Noise | Why diffusion models don't need noise conditioning, derived in closed form on a toy circles dataset. | [![Open in molab](https://marimo.io/molab-shield.svg)](https://molab.marimo.io/github/marimo-team/gallery-examples/blob/main/notebooks/jam-2026-Q2/geometry-of-noise.py) | +| Inscribed Squares from Noise | A diffusion model attacks the 100-year-old inscribed square problem by drawing (CVPR 2026). | [![Open in molab](https://marimo.io/molab-shield.svg)](https://molab.marimo.io/github/marimo-team/gallery-examples/blob/main/notebooks/jam-2026-Q2/inscribed-squares.py) | ## Analysis diff --git a/notebooks/jam-2026-Q2/__marimo__/session/dead-salmon.py.json b/notebooks/jam-2026-Q2/__marimo__/session/dead-salmon.py.json new file mode 100644 index 0000000..06bd201 --- /dev/null +++ b/notebooks/jam-2026-Q2/__marimo__/session/dead-salmon.py.json @@ -0,0 +1,594 @@ +{ + "version": "1", + "metadata": { + "marimo_version": "0.23.4", + "script_metadata_hash": null + }, + "cells": [ + { + "id": "Hbol", + "code_hash": "caddc0677bed748e8916f2eb30c53e20", + "outputs": [ + { + "type": "data", + "data": { + "text/plain": "" + } + } + ], + "console": [] + }, + { + "id": "MJUe", + "code_hash": "228004c041091ec4394ce9d937063770", + "outputs": [ + { + "type": "data", + "data": { + "text/html": "
\n\n
\n \"Dead\n \n \n \n \n \n
\n

Exploring Statistical Fragility

\nAn exploratory notebook by Jesse Hartman.\nPlease engage with a critical eye \u2014 vibe coding abounds.\nA visual companion to \"The Dead Salmons of AI Interpretability\"\n(M\u00e9loux, Dirupo, Portet & Peyrard, 2025) \u2014 which revives the famous\ndead-salmon fMRI cautionary tale and argues that modern AI\ninterpretability methods are vulnerable to the same class of\nstatistical false-positive failures: feature attribution, probing,\ncausal discovery, sparse autoencoders, concept directions and\nmechanistic circuit search can all produce confident-looking\nexplanations for randomly initialized models on random data.\nMy motivation here is personal \u2014 I wanted to see the failure modes\nthe paper describes, not just read about them. Each section below\nrebuilds one of those traps as an interactive marimo demo: drag a\nslider, move a threshold, watch noise dress itself up as a finding.\nThe voxel demo immediately below is the warm-up; the modern\ninterpretability case studies start further down.\nMove the sliders below to see how the false-positive rate and\nBonferroni threshold change with the experiment shape.
" + } + } + ], + "console": [] + }, + { + "id": "vblA", + "code_hash": "450fe04d20f75ea995903fecc8d1b37f", + "outputs": [ + { + "type": "data", + "data": { + "text/markdown": "

The gist, in plain English

\nImagine 20,000 people each flip a coin 200 times, and you look for\nsomeone whose flips match a pattern you picked in advance. Even though\nevery coin is fair, with that many people you'll find about 1,000\nwhose flips \"match\" by pure luck.\nThat's what happens here \u2014 except the \"people\" are voxels (tiny 3D\npixels of a brain scan) and the \"pattern\" is a fake task. No voxel has\nany real signal. But if you accept anything with \"p < 0.05\" as a\nfinding, you'll announce ~1,000 discoveries that are entirely noise.\nThe Bonferroni correction raises the bar: a finding only counts if\nits p-value beats 0.05 / 20,000. Apply it and the false alarms almost\nentirely disappear." + } + } + ], + "console": [] + }, + { + "id": "bkHC", + "code_hash": "25819c669190ddb882d73202588a1842", + "outputs": [ + { + "type": "data", + "data": { + "text/plain": "" + } + } + ], + "console": [] + }, + { + "id": "lEQa", + "code_hash": "38a03011e03b2b431e0d753df8ef388c", + "outputs": [ + { + "type": "data", + "data": { + "text/plain": "" + } + } + ], + "console": [] + }, + { + "id": "PKri", + "code_hash": "c51298497f838ad9e394c8dad60356be", + "outputs": [ + { + "type": "data", + "data": { + "text/plain": "" + } + } + ], + "console": [] + }, + { + "id": "Xref", + "code_hash": "098fd8703c1612febf147c184525dfeb", + "outputs": [ + { + "type": "data", + "data": { + "text/plain": "" + } + } + ], + "console": [] + }, + { + "id": "SFPL", + "code_hash": "d1f82b4204112da517595ad7a58bff1a", + "outputs": [ + { + "type": "data", + "data": { + "text/html": "
" + } + } + ], + "console": [] + }, + { + "id": "BYtC", + "code_hash": "07980a1101688855b1b23fea46d3d2c8", + "outputs": [ + { + "type": "data", + "data": { + "text/plain": "" + } + } + ], + "console": [] + }, + { + "id": "RGSE", + "code_hash": "0714ae3c43bdaa3536657b4b95ebc3b9", + "outputs": [ + { + "type": "data", + "data": { + "text/html": "" + } + } + ], + "console": [] + }, + { + "id": "Kclp", + "code_hash": "4dc62b5d3bc9bf1cbfd9546efc873bd6", + "outputs": [ + { + "type": "data", + "data": { + "text/markdown": "
\n

Does this happen with real data? Yes.

\nWe just showed pure noise producing false positives. The math doesn't\ncare whether the \"noise\" is synthetic \u2014 any time you run many tests\nwith no real effect, ~alpha of them will flag as \"significant\".\nBelow we pull the classic cars dataset from vega-datasets and\np-hack it: split the cars into two groups by coin flip (no real\ndifference between groups), then run a t-test on every numeric\nfeature. Repeat many times. By design every test is null \u2014 yet ~5%\nof them will come up \"significant\"." + } + } + ], + "console": [] + }, + { + "id": "emfo", + "code_hash": "851076c696eeaada8812c43860e68f3a", + "outputs": [ + { + "type": "data", + "data": { + "text/html": "" + } + } + ], + "console": [] + }, + { + "id": "Hstk", + "code_hash": "b8496805651861bc68ba1cdd96d8e4d1", + "outputs": [ + { + "type": "data", + "data": { + "text/plain": "" + } + } + ], + "console": [] + }, + { + "id": "nWHF", + "code_hash": "cdaef75f191521b95c5fa13b8d23fbcc", + "outputs": [ + { + "type": "data", + "data": { + "text/markdown": "Dataset: cars.json \u2014 406 cars \u00b7\nfeatures tested: Miles_per_Gallon, Cylinders, Displacement, Horsepower, Weight_in_lbs, Acceleration\nTests run: 500 random splits \u00d7 6 features\n= 3,000 t-tests, all null by construction." + } + } + ], + "console": [] + }, + { + "id": "iLit", + "code_hash": "60fd33af2b0d25ac25e9ef100f636942", + "outputs": [ + { + "type": "data", + "data": { + "text/html": "" + } + } + ], + "console": [] + }, + { + "id": "ZHCJ", + "code_hash": "a17963b67be7d5182283667749888df6", + "outputs": [ + { + "type": "data", + "data": { + "text/markdown": "
\n\n
\n
Really?
\n \n \n \n \n \n \n \n
\n\n
\n
\n Journal of Automotive Cohort Analysis · Vol. 47 · No. 3 · pp. 412-418\n
\n
\n Systematic Variation Across Randomly Partitioned Automotive Cohorts\n
\n
\n A Multi-Feature Analysis of 406 Vehicles\n
\n
\n M. Anon1, J. Hartman1, A. Claude2\n
\n
\n 1Institute for Premature Publication · 2Dept. of Exuberant Inference\n
\n
\n\n
\n
Abstract
\n

\n We analyzed 406 automobiles partitioned into two cohorts\n and conducted a systematic comparison across 6 mechanical\n and efficiency attributes. At a significance threshold of\n p < 0.0500, we identified\n 149 statistically significant differences.\n These results have implications for manufacturing specification,\n regulatory policy, and consumer guidance.\n \n\n

Selected Results
\n
  • Cohort A exhibited 2.86 mpg lower than Cohort B (p = 2.43e-04).
  • Cohort A exhibited 11.43 hp higher than Cohort B (p = 0.0032).
  • Cohort A exhibited 2.30 mpg lower than Cohort B (p = 0.0032).
  • Cohort A exhibited 249.23 lbs higher than Cohort B (p = 0.0033).
  • Cohort A exhibited 30.60 cc displacement lower than Cohort B (p = 0.0038).
\n\n
Conclusion
\n

\n Our analysis reveals consistent and measurable differences between cohorts across multiple vehicle dimensions. We recommend manufacturer review and further investigation in light of these findings.\n \n\n

\n Manuscript generated from 3,000 null t-tests on vega-datasets/cars.json.\n Cohort assignment: uniformly random coin flip per vehicle.\n
\n
\n
" + } + } + ], + "console": [] + }, + { + "id": "ROlb", + "code_hash": "4a6e65fe7805da2e7c2789fd90a1ed6b", + "outputs": [ + { + "type": "data", + "data": { + "text/markdown": "
\n

The same trap, in modern clothes

\nEverything above was classical statistics dressed for fMRI and for cars.\nThe same failure mode shows up across modern ML interpretability tools.\nBelow: three case studies where confident-looking explanations turn out\nto be artifacts of high-dimensional noise, not of anything the model\nactually learned or the data actually contains." + } + } + ], + "console": [] + }, + { + "id": "qnkX", + "code_hash": "85808d9cf6dfb5a3011e80e2754cd3d5", + "outputs": [ + { + "type": "data", + "data": { + "text/markdown": "

1. Feature Attribution

\nSaliency maps, Grad-CAM, SHAP, Integrated Gradients \u2014 the promise is:\n\"here are the parts of the input the model cared about.\" Adebayo et al.\n(2018) showed that many popular attribution methods produce nearly\nidentical heatmaps even when you randomize the model's weights. That\nmeans the pretty overlay is mostly a projection of the input, not\nevidence of what the model learned.\nBelow: we train a tiny linear classifier on synthetic 16\u00d716 shapes\n(circles vs squares), then compute input-times-gradient saliency for\nboth the trained weights and a fresh random model. The \"hot\" regions\nline up on the shape in both cases \u2014 because |W\u00b7x| is near zero\nwherever x is near zero, regardless of what W is." + } + } + ], + "console": [] + }, + { + "id": "TqIu", + "code_hash": "1a92961e3e6a5f7aaa4ab93bdbef8529", + "outputs": [ + { + "type": "data", + "data": { + "text/plain": "" + } + } + ], + "console": [] + }, + { + "id": "Vxnm", + "code_hash": "a617a0e1f355734d32ab44d583c76973", + "outputs": [ + { + "type": "data", + "data": { + "text/plain": "" + } + } + ], + "console": [] + }, + { + "id": "DnEU", + "code_hash": "53e036d7feca9916646d507ce4c4c194", + "outputs": [ + { + "type": "data", + "data": { + "text/html": "" + } + } + ], + "console": [] + }, + { + "id": "ulZA", + "code_hash": "918172a258ea624d874e3a3609a4f8b5", + "outputs": [ + { + "type": "data", + "data": { + "text/markdown": "

2. Probing

\nProbing classifiers are the \"did the model encode X?\" tool: train a\nlinear probe on a model's internal activations to decode some property,\nand take success to mean the model \"knows about\" that property.\nHewitt & Liang (2019) showed the trap: a probe can succeed even when\nthe model is not using the decoded information for its output. A\nhigh-dimensional projection preserves almost everything fed into it \u2014 a\nprobe can recover a feature from the activations even if the downstream\nreadout completely ignores it.\nBelow: synthetic features flow through a fixed random hidden layer. A\nlinear readout is trained on y = sin(1.5\u00b7x1) + noise \u2014 only x1\nmatters. For every feature we measure:\n\nThe probe says \"yes, it's all in there!\" The ablation says \"no, only\nx1 is actually used.\" Both are true at the same time." + } + } + ], + "console": [] + }, + { + "id": "ecfG", + "code_hash": "9c3547169a2e055dc6ceb6440ef68c13", + "outputs": [ + { + "type": "data", + "data": { + "text/html": "" + } + } + ], + "console": [] + }, + { + "id": "Pvdt", + "code_hash": "70c2ec6cfefcd7ffa1bbf5cbbab99e09", + "outputs": [ + { + "type": "data", + "data": { + "text/plain": "" + } + } + ], + "console": [] + }, + { + "id": "ZBYS", + "code_hash": "924bdcc09e522a7fa4f9e3054399a6b0", + "outputs": [ + { + "type": "data", + "data": { + "text/plain": "" + } + } + ], + "console": [] + }, + { + "id": "aLJB", + "code_hash": "c489f205ab087200db039eec5a7e2cb9", + "outputs": [ + { + "type": "data", + "data": { + "text/html": "" + } + } + ], + "console": [] + }, + { + "id": "nHfw", + "code_hash": "62bb975b490c5b3ea0627cac1e68bf88", + "outputs": [ + { + "type": "data", + "data": { + "text/markdown": "

3. Causal Analysis in High Dimensions

\nCausal-discovery methods promise to find the \"true\" dependency structure\nin a system. At scale on noisy data they produce dense, confident-looking\ngraphs from variables with no real relationships at all.\nBelow: we generate N independent \"gene expression\" variables (pure noise,\nno real causal structure), then compute the pairwise correlation p-value\nfor every pair \u2014 the simplest form of causal-edge discovery. With N = 40\nvariables there are 780 pairs; at p < 0.05 we expect ~39 \"causal\nedges\" by pure chance.\nThe same FalsePositiveHunter widget works as the viewer \u2014 this time\nthe grid pixels are pairs of variables rather than voxels or cars-cohort\ntests. Drag the red threshold and watch an entire causal network\nmaterialize out of random noise." + } + } + ], + "console": [] + }, + { + "id": "xXTn", + "code_hash": "dbd8391495c10c7fd70f22040d0452cc", + "outputs": [ + { + "type": "data", + "data": { + "text/html": "" + } + } + ], + "console": [] + }, + { + "id": "AjVT", + "code_hash": "15f8475f3007e7e62e8fa5b1d5f79046", + "outputs": [ + { + "type": "data", + "data": { + "text/plain": "" + } + } + ], + "console": [] + }, + { + "id": "pHFh", + "code_hash": "d104efb724c2327bf27a39afc14d1833", + "outputs": [ + { + "type": "data", + "data": { + "text/html": "" + } + } + ], + "console": [] + }, + { + "id": "NCOB", + "code_hash": "9182e1243691433b8ace955ae78d4f97", + "outputs": [ + { + "type": "data", + "data": { + "text/markdown": "
\n\n
\n
Really?
\n \n \n \n \n \n \n \n
\n\n
\n
\n Journal of Network Biology · Vol. 33 · No. 7 · pp. 1104-1117\n
\n
\n A Causal Expression Network Across 40 Candidate Genes\n
\n
\n Pairwise Dependency Mapping from High-Dimensional Assay Data\n
\n
\n R. Noise1, I. Spurious1, C. Hen-Picker2\n
\n
\n 1Center for Overconfident Inference · 2Institute for Network Overfitting\n
\n
\n\n
\n
Abstract
\n

\n Using a panel of 40 candidate genes assayed across 200 samples, we\n mapped pairwise causal dependencies through a standard correlation-based discovery\n procedure. At significance threshold p < 0.0500 we identified\n 46 putative causal edges out of 780 candidate pairs.\n These findings reveal a tightly interconnected regulatory architecture with\n implications for pathway-level drug targeting.\n \n\n

Top Inferred Edges
\n
  • GENE_01 ←→ GENE_32 (partial r = +0.246, p = 3.62e-04)
  • GENE_08 ←→ GENE_16 (partial r = +0.204, p = 0.0034)
  • GENE_03 ←→ GENE_18 (partial r = +0.203, p = 0.0034)
  • GENE_18 ←→ GENE_39 (partial r = +0.200, p = 0.0040)
  • GENE_22 ←→ GENE_38 (partial r = +0.197, p = 0.0047)
\n\n
Conclusion
\n

\n Our findings reveal a tightly interconnected regulatory network with implications for pathway-level therapeutic targeting. We propose further validation in independent cohorts.\n \n\n

\n Manuscript generated from 780 all-pairs correlation tests on 40\n independent Gaussian variables. Data contains no real causal structure.\n
\n
\n
" + } + } + ], + "console": [] + }, + { + "id": "aqbW", + "code_hash": "d25b0d5f78a069e22823cb0670f2c4f4", + "outputs": [ + { + "type": "data", + "data": { + "text/markdown": "

4. Sparse Autoencoders

\nSparse autoencoders \u2014 the currently-hot interpretability tool \u2014 train a\nwide-narrow-wide network on a model's activations with an L1 penalty on\nthe bottleneck, producing a \"dictionary of features.\" Each feature is\nthen hand-labeled (\"this one fires on Arabic script\", \"this one on car\nwindshields\") and taken as evidence of the model's internal conceptual\nvocabulary.\nFit the same optimizer to pure Gaussian noise and you still get a\ndictionary of crisp, human-nameable \"features.\" Below: 2000 random 8\u00d78\nnoise patches \u2192 MiniBatchDictionaryLearning with 32 atoms. Each tile\nin the gallery is one learned atom. Several will look spatially\nlocalized, oriented, blob- or edge-resembling \u2014 all the properties a\nreal SAE atom would have. The structure is in the optimizer, not the\ndata." + } + } + ], + "console": [] + }, + { + "id": "TRpd", + "code_hash": "a70645f0df7c7ef6dc25fff1dbafa58d", + "outputs": [ + { + "type": "data", + "data": { + "text/plain": "" + } + } + ], + "console": [] + }, + { + "id": "TXez", + "code_hash": "cc9c0ad6409aa79af5d220eae98aad5d", + "outputs": [ + { + "type": "data", + "data": { + "text/plain": "" + } + } + ], + "console": [] + }, + { + "id": "dNNg", + "code_hash": "2359bb8b4297ff24c0e973a4a69b3a85", + "outputs": [ + { + "type": "data", + "data": { + "text/html": "" + } + } + ], + "console": [] + }, + { + "id": "yCnT", + "code_hash": "5d0da4deb1e2cc867f6b09a90907f5d6", + "outputs": [ + { + "type": "data", + "data": { + "text/markdown": "

5. Concept-Based Explanations

\nTCAV (Kim et al. 2018) and its descendants frame interpretability as\n\"we found a direction in activation space aligned with a human concept,\nand the model is sensitive to that direction \u2014 therefore the model uses\nthat concept.\" In high-dimensional activation spaces, the trap is the\nsame one we've seen all along: any random direction, out of enough\nrandom directions, will appear \"significantly\" aligned with the model's\noutput.\nBelow: we treat the Phase-1 shape classifier's weight vector attr_W\nas the \"model's decision direction\" in 256-dim input/activation space,\nthen generate N random unit-vector \"concepts\" and compute each\nconcept's cosine similarity with the model's decision direction.\nUnder the null (random concepts), cosine similarity is approximately\nN(0, 1/256) \u2014 we z-score and two-sided-test each concept, then feed\nthe p-values into the same hunter widget. Naive p < 0.05 produces\n~5% of concepts looking \"significant.\"" + } + } + ], + "console": [] + }, + { + "id": "wlCL", + "code_hash": "eecb5c17ad32201b2bb6954089923fd2", + "outputs": [ + { + "type": "data", + "data": { + "text/html": "" + } + } + ], + "console": [] + }, + { + "id": "kqZH", + "code_hash": "7a940cf638a1937c747d8429962672b3", + "outputs": [ + { + "type": "data", + "data": { + "text/plain": "" + } + } + ], + "console": [] + }, + { + "id": "wAgl", + "code_hash": "f810517036d2580ae570224069fcd42e", + "outputs": [ + { + "type": "data", + "data": { + "text/html": "" + } + } + ], + "console": [] + }, + { + "id": "rEll", + "code_hash": "ee32c1f87410c2762d4f00aedc1df880", + "outputs": [ + { + "type": "data", + "data": { + "text/markdown": "
\n\n
\n
Really?
\n \n \n \n \n \n \n \n
\n\n
\n
\n Annals of Interpretable Attribution · Vol. 22 · No. 4 · pp. 208-219\n
\n
\n Learned Conceptual Vocabulary of a Shape Classifier\n
\n
\n A TCAV-Style Investigation of Decision-Aligned Directions\n
\n
\n A. Plaus1, Q. Sensible1, E. Vidence2\n
\n
\n 1Concept Attribution Lab · 2Center for Confident Interpretation\n
\n
\n\n
\n
Abstract
\n

\n Across a survey of 500 candidate concept directions in the 256-dimensional\n activation space of a shape classifier, we identify 27 directions\n statistically aligned with the model's decision surface at significance threshold\n p < 0.0500. These axes constitute an interpretable conceptual vocabulary\n the model uses to make its classification decisions.\n \n\n

Selected Conceptual Axes
\n
  • Concept #054: cosine alignment with decision direction = -0.189 (p = 0.0025). A statistically significant conceptual axis.
  • Concept #049: cosine alignment with decision direction = +0.170 (p = 0.0064). A statistically significant conceptual axis.
  • Concept #311: cosine alignment with decision direction = -0.169 (p = 0.0068). A statistically significant conceptual axis.
  • Concept #189: cosine alignment with decision direction = -0.168 (p = 0.0072). A statistically significant conceptual axis.
  • Concept #132: cosine alignment with decision direction = -0.167 (p = 0.0077). A statistically significant conceptual axis.
\n\n
Conclusion
\n

\n Our analysis reveals that the classifier has learned a rich and interpretable conceptual vocabulary. Several of the discovered axes merit follow-up qualitative investigation with domain experts.\n \n\n

\n Manuscript generated from 500 cosine-similarity tests between uniformly-random\n unit vectors and the classifier weight vector. The \"concepts\" are pure random directions.\n
\n
\n
" + } + } + ], + "console": [] + }, + { + "id": "dGlV", + "code_hash": "6239888caed8ff1d9f013febf213faad", + "outputs": [ + { + "type": "data", + "data": { + "text/markdown": "

6. Mechanistic Interpretability

\nThe deepest corner of the interpretability tree: find circuits \u2014 small\nsubsets of neurons that implement a specific model behavior. At small\nscale, with cherry-picked examples, this can be genuinely illuminating.\nAt scale, with automated search across many candidate circuits and many\ncandidate behaviors, it runs straight into the same wall: brute-force\nsearch across enough noisy candidates will \"discover\" circuits that\nlook convincingly specialized for any target behavior you pick, even\nwhen the neurons are random and the behavior is a coin flip.\nBelow: we define a \"target behavior\" by flipping a coin for each of 400\nrandom input vectors \u2014 so the labels have no underlying relationship to\nthe inputs at all. We then search through N candidate two-neuron\n\"circuits\" (random weight matrices feeding a random linear readout) and\ntwo-sample t-test each circuit's output across the random labels.\nFeed the per-circuit p-values into the hunter widget; the naive\nthreshold announces dozens of \"discovered circuits\" for a behavior\nthat doesn't exist." + } + } + ], + "console": [] + }, + { + "id": "SdmI", + "code_hash": "4dbdba6cda4ee7170550ab336bf32603", + "outputs": [ + { + "type": "data", + "data": { + "text/html": "" + } + } + ], + "console": [] + }, + { + "id": "lgWD", + "code_hash": "1b60e977ba0729d31f2161cfea5fd2f0", + "outputs": [ + { + "type": "data", + "data": { + "text/plain": "" + } + } + ], + "console": [] + }, + { + "id": "yOPj", + "code_hash": "2cec94656eac3e9eb0faa214a1537485", + "outputs": [ + { + "type": "data", + "data": { + "text/html": "" + } + } + ], + "console": [] + }, + { + "id": "fwwy", + "code_hash": "f05760152d8c7b485af73ebb62565bb6", + "outputs": [ + { + "type": "data", + "data": { + "text/markdown": "
\n\n
\n
Really?
\n \n \n \n \n \n \n \n
\n\n
\n
\n Proceedings of Prematurely Confident Interpretability · Vol. 9 · No. 2 · pp. 44-59\n
\n
\n A Two-Neuron Circuit Implementing Target-Behavior Detection\n
\n
\n Brute-Force Mechanistic Discovery Across 500 Candidate Subgraphs\n
\n
\n I. Solated1, C. Ircuit1, A. Notherone2\n
\n
\n 1Mechanistic Search Collective · 2Lab for Automated Explanation\n
\n
\n\n
\n
Abstract
\n

\n We conducted a systematic brute-force search across 500 candidate\n two-neuron subgraphs of a 64-dimensional feature space and identified\n 32 circuits mechanically implementing the target\n behavior at significance threshold p < 0.0500. These\n results demonstrate that small interpretable subgraphs are sufficient to\n reproduce the target-behavior signal end-to-end.\n \n\n

Isolated Circuits
\n
  • Circuit #131: mean output difference +0.590 between target-behavior and control inputs (t-test p = 0.0025). A two-neuron subgraph reliably discriminating the target behavior.
  • Circuit #289: mean output difference -0.405 between target-behavior and control inputs (t-test p = 0.0036). A two-neuron subgraph reliably discriminating the target behavior.
  • Circuit #451: mean output difference -0.118 between target-behavior and control inputs (t-test p = 0.0049). A two-neuron subgraph reliably discriminating the target behavior.
  • Circuit #500: mean output difference +0.184 between target-behavior and control inputs (t-test p = 0.0053). A two-neuron subgraph reliably discriminating the target behavior.
  • Circuit #060: mean output difference +0.433 between target-behavior and control inputs (t-test p = 0.0072). A two-neuron subgraph reliably discriminating the target behavior.
\n\n
Conclusion
\n

\n Our analysis isolates a compact subgraph mechanically responsible for the target behavior. We recommend further mechanistic dissection \u2014 activation patching, path ablation, and attention-head replacement \u2014 to confirm the functional role of the identified circuit.\n \n\n

\n Manuscript generated from 500 t-tests over random 2-neuron circuits with\n random weights. \"Target behavior\" was assigned by coin flip; it does not exist.\n
\n
\n
" + } + } + ], + "console": [] + } + ] +} \ No newline at end of file diff --git a/notebooks/jam-2026-Q2/__marimo__/session/geometry-of-noise.py.json b/notebooks/jam-2026-Q2/__marimo__/session/geometry-of-noise.py.json new file mode 100644 index 0000000..59e21b3 --- /dev/null +++ b/notebooks/jam-2026-Q2/__marimo__/session/geometry-of-noise.py.json @@ -0,0 +1,529 @@ +{ + "version": "1", + "metadata": { + "marimo_version": "0.23.4", + "script_metadata_hash": null + }, + "cells": [ + { + "id": "Hbol", + "code_hash": "1d0db38904205bec4d6f6f6a1f6cec3e", + "outputs": [ + { + "type": "data", + "data": { + "text/plain": "" + } + } + ], + "console": [] + }, + { + "id": "MJUe", + "code_hash": "1209d5ec63e1d2a2b8d520cc092b3f81", + "outputs": [ + { + "type": "data", + "data": { + "text/plain": "" + } + } + ], + "console": [] + }, + { + "id": "vblA", + "code_hash": "e6c87f532974c3267f47ddbef19593be", + "outputs": [ + { + "type": "data", + "data": { + "text/markdown": "

The Geometry of Noise

\nPaper: Sahraee-Ardakan, Delbracio & Milanfar (2026)\n\u00b7 Notebook by Konstantin Taletskiy\n
\n

TL;DR

\nEvery diffusion model you have used passes the current noise level\n||(t||) to its U-Net. This paper says you can drop ||(t||) entirely \u2014\nturn unet(x_t, t) into unet(x_t), a blind sampler \u2014 and it\nstill works. The reason is geometric: in high-dimensional spaces,\nthe image itself reveals its own noise level (concentration of\nmeasure). It also matters what the U-Net is trained to predict:\nclean image or velocity \u2192 stable; noise \u2192 fragile. The paper names\nthe mechanism posterior collapse on ||(t||) and proves a hard\nboundary at codimension ||(D - d > 2||), below which even the stable\nparameterizations break.\nThis notebook builds the geometric intuition step by step and lets\nyou watch that boundary live by dragging ||(D||).\n
\nEverything below is a closed-form analytical reconstruction \u2014 no\nneural networks, no training. Interactive elements use\npywidget: pure-Python widgets\nrunning in Pyodide, natively compatible with marimo." + } + } + ], + "console": [] + }, + { + "id": "bkHC", + "code_hash": "1aa13a56685b00cd5c2581c345fd092a", + "outputs": [ + { + "type": "data", + "data": { + "text/html": "
(CIFAR-10 figure not found)On real images, the difference is stark: noise prediction without knowing the noise level produces mush (left), while velocity prediction produces clean samples (right) \u2014 both using the exact same blind architecture. Our notebook explains why, analytically, on a toy dataset you can fully explore.
" + } + } + ], + "console": [] + }, + { + "id": "lEQa", + "code_hash": "98df533bff41f5663c3868a45a034434", + "outputs": [ + { + "type": "data", + "data": { + "text/markdown": "

A five-minute refresher on diffusion models

\nIf the phrase \"diffusion model\" already means something concrete to\nyou, skip this section. Otherwise, here's the three-step story:\n\n

The toy: concentric circles

\nEverything in this notebook happens on a toy 2D dataset which is\n200 points on two concentric circles \u2014 so we can see each of these three steps with\nour own eyes. Let's do a lap." + } + } + ], + "console": [] + }, + { + "id": "PKri", + "code_hash": "1bdb24ea04865290962f850184fc7eac", + "outputs": [ + { + "type": "data", + "data": { + "text/html": "
\n\n\n \n \n \n \n 2026-05-01T10:41:01.367837\n image/svg+xml\n \n \n Matplotlib v3.10.8, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n
" + } + } + ], + "console": [] + }, + { + "id": "Xref", + "code_hash": "b7c91f5f61e9a84b96629d2563d80135", + "outputs": [ + { + "type": "data", + "data": { + "text/markdown": "

Watching the forward process

\nAs you scrub ||(t||) below, you're watching the forward process the way\nevery modern diffusion model actually sees it during training:\nfor each training step, a single noise vector\n||(\\boldsymbol{\\epsilon} \\sim \\mathcal{N}(0, I)||) is drawn per data\npoint and ||(\\mathbf{u}_t = (1-t)\\mathbf{x} + t\\boldsymbol{\\epsilon}||)\nis computed in one shot. No sequential noising between timesteps.\nThe network only ever sees marginal pairs ||((\\mathbf{u}_t, t)||).\nEach data point moves along its own straight line in 2D, from its\nstarting position at ||(t=0||) to its personal noise target at ||(t=1||).\nThe orange dot tracks one of them so you can follow its trajectory." + } + } + ], + "console": [] + }, + { + "id": "SFPL", + "code_hash": "7bfdd56ac10295fa707f4d6da8c4d395", + "outputs": [ + { + "type": "data", + "data": { + "text/plain": "" + } + } + ], + "console": [] + }, + { + "id": "BYtC", + "code_hash": "726bcef8373f0173125611d7d3b97efd", + "outputs": [ + { + "type": "data", + "data": { + "text/html": "" + } + } + ], + "console": [] + }, + { + "id": "RGSE", + "code_hash": "4139292f2e8882181e996b5ceae6164f", + "outputs": [ + { + "type": "data", + "data": { + "text/markdown": "

Training, in three lines of pseudocode

\n
t       = random.uniform(0, 1)           # pick a noise level\nx_t     = (1 - t) * x + t * eps          # noise the clean sample (eps ~ N(0, I))\nloss    = mse(unet(x_t, t), target)      # predict & regress\n
\nThe network gets both the noisy sample ||(\\mathbf{x}_t||) and the\nnoise level ||(t||), and learns to predict some target that pins\ndown the clean ||(\\mathbf{x}||). Simple. Works. This is the pattern\nevery diffusion model in the wild uses.\nWhat happens if we drop the t? That is the question this\npaper answers." + } + } + ], + "console": [] + }, + { + "id": "Kclp", + "code_hash": "efebbc67e1ab8bc045d8b354bb3bbde7", + "outputs": [ + { + "type": "data", + "data": { + "text/plain": "" + } + } + ], + "console": [] + }, + { + "id": "emfo", + "code_hash": "f69245f211c8369e7dbc7600e08a13d2", + "outputs": [ + { + "type": "data", + "data": { + "text/html": "" + } + } + ], + "console": [] + }, + { + "id": "Hstk", + "code_hash": "f89445ed63ebfd0bd5c9692e88955e44", + "outputs": [ + { + "type": "data", + "data": { + "text/markdown": "That's diffusion in one lap: noise goes in, data comes out.\nEverything else in this notebook is about what happens when you\nremove the t from the middle step." + } + } + ], + "console": [] + }, + { + "id": "nWHF", + "code_hash": "11f4b776371f474ce61d61b987b86b8b", + "outputs": [ + { + "type": "data", + "data": { + "text/markdown": "

What this paper is actually asking

\nYou just saw the training pseudocode. The line that matters for\nthis paper is the one that calls the network:\n
predicted = unet(x_t, t)               # \u2190 t is "noise conditioning"\n
\nStable Diffusion does this. When you generate an image with SD,\nthe model runs ~50 denoising steps from ||(t \\approx 1||) (pure noise)\ndown to ||(t = 0||) (clean image). At every step, the current ||(t||) is\nfed into the U-Net via a learned sinusoidal timestep embedding.\nThe same conditioning pattern shows up across the field \u2014 sometimes\ninside a U-Net (DALL-E 2, Imagen), sometimes inside a\ntransformer with a rectified-flow objective (Flux) \u2014 but the\nnetwork is always told what ||(t||) is. Without that argument, it\nwould not know whether to make big corrections at ||(t = 0.95||)\n(still pure noise) or polish details at ||(t = 0.05||) (almost a\nfinished picture).\nThis paper asks: what if we just drop the t?\n
predicted = unet(x_t)                  # no t \u2014 "blind" sampler\n
\nNaively, this should fail catastrophically. It turns out \u2014\nexperimentally (Sun et al., ICML 2025) \u2014 that for some choices of\nwhat the U-Net predicts, it works anyway. This paper explains why:\nin high-dimensional image spaces, the noise itself leaves a\nstatistical fingerprint that the U-Net can read. The geometry of\nhigh-||(D||) noise makes the training trick the net would need to\n\"notice\" ||(t||) almost inevitable.\nBelow we prove this on a toy dataset you can explore by hand \u2014 two\nconcentric circles \u2014 with no U-Net, no training. Every field is\ncomputed in closed form from Bayes' rule." + } + } + ], + "console": [] + }, + { + "id": "iLit", + "code_hash": "a17ff815d11bbdf4471b7a4f96834d0d", + "outputs": [ + { + "type": "data", + "data": { + "text/markdown": "

Why this sounds impossible

\nHere's the argument against dropping t: at ||(t = 0.95||) the image\n||(\\mathbf{x}_t||) is mostly noise and the U-Net needs to make large\ncorrections. At ||(t = 0.05||) it's almost clean, so corrections should\nbe tiny. How can a single bounded neural network do both if\nit's not told which situation it's in?\nIt can't \u2014 unless the image itself secretly tells the network which\n||(t||) it came from. That is exactly what happens in high dimensions,\nand building the intuition for why is the main goal of this\nnotebook." + } + } + ], + "console": [] + }, + { + "id": "ZHCJ", + "code_hash": "defb12a4b857f6d5fd28a7f9149d9e0a", + "outputs": [ + { + "type": "data", + "data": { + "text/markdown": "

What \"dimension\" means here

\nWe just claimed that \"in high dimensions, the image itself tells the\nnetwork which ||(t||) it came from.\" That word dimension is doing a\nlot of work in that sentence \u2014 let's be precise about which dimension\nwe mean before we go further.\nThe dimension that matters for this paper is the ambient data\nspace dimension ||(D||) \u2014 the number of raw numbers in a single\nsample. For a ||(32 \\times 32||) RGB CIFAR-10 image, ||(D = 3072||). For a\n||(256 \\times 256||) ImageNet image, ||(D = 196{,}608||). Not the model\nwidth, not the dataset size, not any latent dimension \u2014 just the\ncoordinate count of the thing being denoised.\nFor the rest of the notebook, ||(D||) is something you control. The\nslider below sweeps ||(D||) from ||(2||) (two-number \"samples\") up to ||(128||).\nEvery figure that depends on ||(D||) \u2014 apple peel, shell histogram,\nposterior, 4-panel sampler \u2014 recomputes live as you drag. It's the\nsingle most important control in the notebook." + } + } + ], + "console": [] + }, + { + "id": "ROlb", + "code_hash": "2bf2691263ad97b58962d0b42bc5f485", + "outputs": [ + { + "type": "data", + "data": { + "text/markdown": "Reintroducing the toy in ||(\\mathbb{R}^D||). You already saw our\n200 points on two concentric circles in ||(\\mathbb{R}^2||). From here\non, we're going to embed those same circles into ||(\\mathbb{R}^D||):\npick a random 2D plane through the origin of ||(\\mathbb{R}^D||), rotate\nthe 2D data into it, and leave the remaining ||(D - 2||) coordinates as\nzeros. Distances and angles are preserved exactly \u2014 we're not\nmaking the problem harder, we're putting the same picture into a\nroom with more empty dimensions.\nIntrinsic dimension of the data: ||(d = 1||) (circles are 1D curves).\nCodimension: ||(D - d = D - 1||) \u2014 the amount of empty room around\nthe data. The paper proves a hard regime boundary at ||(D - d > 2||) \u2014\nbelow it, even the \"stable\" parameterizations fail." + } + } + ], + "console": [] + }, + { + "id": "qnkX", + "code_hash": "6068c4b8fea925786c8b6cee64893ba4", + "outputs": [ + { + "type": "data", + "data": { + "text/html": "" + } + } + ], + "console": [] + }, + { + "id": "TqIu", + "code_hash": "73c954ed5b5fea5f8db7ba391b9511e8", + "outputs": [ + { + "type": "data", + "data": { + "text/markdown": "

A surprising fact about high dimensions

\nImagine a ||(D||)-dimensional apple \u2014 a unit ball in ||(\\mathbb{R}^D||). Slice off\njust the outer 1% of the radius, a paper-thin skin. How much of\nthe apple did you take?\n\nThis isn't a trick or a paradox. It's elementary geometry, and it's\nthe reason every other claim in this notebook works. Drag the local\nslider to see the volume shift as you peel; drag the global ||(D||)\nslider above to watch the picture get more violent with dimension." + } + } + ], + "console": [] + }, + { + "id": "Vxnm", + "code_hash": "a62f508a694249731d42ec555ba90fa9", + "outputs": [ + { + "type": "data", + "data": { + "text/plain": "" + } + } + ], + "console": [] + }, + { + "id": "DnEU", + "code_hash": "60b85979f521385c34ddd1d30c1c0d63", + "outputs": [ + { + "type": "data", + "data": { + "text/html": "" + } + } + ], + "console": [] + }, + { + "id": "ulZA", + "code_hash": "b969da4f11570da42dc0b55126b5ee88", + "outputs": [ + { + "type": "data", + "data": { + "text/markdown": "Why? The volume of a ||(D||)-ball of radius ||(r||) scales as ||(r^D||)\n(up to a constant). So the fraction of volume removed by a peel of\nthickness ||(s||) (as a fraction of the radius) is\n||[\n\\frac{V_{\\text{peel}}}{V_{\\text{total}}} = 1 - (1 - s)^D.\n||]For any fixed ||(s > 0||), ||((1-s)^D \\to 0||) as ||(D \\to \\infty||). Almost\nall the volume of a high-dimensional ball lives in its outermost peel.\nWhat this means for our noise vector. Add Gaussian noise at level\n||(t||) to a ||(D||)-dimensional point: ||(\\mathbf{u} = t\\,\\boldsymbol{\\epsilon}||)\nwith ||(\\boldsymbol{\\epsilon} \\sim \\mathcal{N}(0, I_D)||). The squared\nlength is\n||[\n\\|\\mathbf{u}\\|^2 = t^2 \\sum_{i=1}^D \\epsilon_i^2,\n||]with mean ||(D t^2||) and standard deviation only ||(\\sqrt{2D}\\,t^2||) \u2014 a\nrelative spread of ||(1/\\sqrt{D}||). So ||(\\|\\mathbf{u}\\| \\approx t\\sqrt{D}||)\nto a vanishing relative error. The noise doesn't fill the ball; it\nlives on a shell at radius ||(t\\sqrt{D}||), by exactly the same\n\"all the volume is in the peel\" mechanism." + } + } + ], + "console": [] + }, + { + "id": "ecfG", + "code_hash": "7432aaf2b2a4d3db18e6ec2270289028", + "outputs": [ + { + "type": "data", + "data": { + "text/html": "
\n\n\n \n \n \n \n 2026-05-01T10:41:01.241517\n image/svg+xml\n \n \n Matplotlib v3.10.8, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n
" + } + } + ], + "console": [] + }, + { + "id": "Pvdt", + "code_hash": "c1837af5226a070706b02fb87adbde76", + "outputs": [ + { + "type": "data", + "data": { + "text/markdown": "

The a-ha \u2014 reading ||(t||) from ||(\\|\\mathbf{u}\\|||)

\nAt low ||(D||), shells at different noise levels overlap heavily.\nIf someone hands you a noisy sample ||(\\mathbf{u}||), you cannot tell\nwhich ||(t||) it came from \u2014 the blind network genuinely has no way to\nknow.\nAt high ||(D||), the shells sit at well-separated radii\n||(t\\sqrt{D}||), each one narrower than the gap between neighbors. A\nsingle measurement of ||(\\|\\mathbf{u}\\|||) is enough to read off ||(t||)\nwith high precision.\nThat is the \"trick.\" A blind diffusion model doesn't infer ||(t||)\nfrom some clever learned representation. It gets ||(t||) for free, from\nthe geometry of high-dimensional Gaussian noise. The real-world\nsuccess is a consequence of the fact that real images live at\n||(D \\sim 10^3||)\u2013||(10^5||), where the shells are enormously\nwell-separated." + } + } + ], + "console": [] + }, + { + "id": "ZBYS", + "code_hash": "1d7c0421f32d8976c15dd223f44914f8", + "outputs": [ + { + "type": "data", + "data": { + "text/markdown": "

From shells to the posterior ||(p(t \\mid \\mathbf{u})||)

\nThe probabilistic statement of \"you can read ||(t||) from ||(\\mathbf{u}||)\"\nis that the posterior distribution over the noise level,\n||(p(t \\mid \\mathbf{u})||), concentrates on a single value when the\nshells separate. The paper calls this posterior collapse and it\nis the formal core of the mechanism.\nBelow is a live demo. Click anywhere on the scatter to place a\nprobe point; the right panel shows ||(p(t\\mid\\mathbf{u})||) at that\nlocation. Re-click the same spot after changing ||(D||) and watch the\nposterior sharpen from broad and uncertain to a narrow spike." + } + } + ], + "console": [] + }, + { + "id": "aLJB", + "code_hash": "11b0b3fb9b4f6ee71a5d66ad008f1ace", + "outputs": [ + { + "type": "data", + "data": { + "text/plain": "" + } + } + ], + "console": [] + }, + { + "id": "nHfw", + "code_hash": "8059491352d3174db858ec11b4be2478", + "outputs": [ + { + "type": "data", + "data": { + "text/html": "" + } + } + ], + "console": [] + }, + { + "id": "xXTn", + "code_hash": "c8acf13fdac74c14289acfc3048a1a90", + "outputs": [ + { + "type": "data", + "data": { + "text/html": "
At ||(D = 8||), the posterior is visibly sharper than at ||(D = 2||) \u2014 the tail has receded. The shells are starting to separate.
" + } + } + ], + "console": [] + }, + { + "id": "AjVT", + "code_hash": "225ac3228d3c5fb98bcd892c8d1b2a43", + "outputs": [ + { + "type": "data", + "data": { + "text/markdown": "

Does the shell picture actually rescue sampling?

\nPosterior collapse says a blind U-Net can infer ||(t||) from the\ngeometry. But that's only part of the story. When we actually run\nthe reverse-time sampler, does it produce correct samples? And does\nit matter what the U-Net was trained to predict?\nLet's run the experiment." + } + } + ], + "console": [] + }, + { + "id": "pHFh", + "code_hash": "0b76283d29af08b82fa4579320fdd373", + "outputs": [ + { + "type": "data", + "data": { + "text/markdown": "

What does the network predict?

\nThe default training convention \u2014 and what you'll see in almost any\ntutorial \u2014 trains the U-Net to predict the noise eps that was\nadded. This is the DDPM convention. But you could equivalently train\nit to predict:\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
TargetPredictsMethod
epsthe noise that was addedDDPM (noise prediction)
xthe clean image directlyEDM (signal prediction)
v = eps - xthe \"velocity\" from clean to noiseFlow Matching (velocity prediction)
\nWhen the U-Net sees ||(t||), all three are equivalent: given any two\nof ||(\\{x_t, x, \\text{eps}\\}||) you can compute the third. When the\nU-Net is blind, each target has to implicitly average over the\nposterior on ||(t||). The three averages behave very differently. Two\nof them are stable; one of them is fragile. The widget below shows\nall four side by side:\n
    \n
  1. FM conditional \u2014 velocity prediction with ||(t||). Baseline.
    2. \n
  2. FM blind \u2014 same target, no ||(t||). Paper says: should work.
    3. \n
  3. EDM blind \u2014 signal target, no ||(t||). Paper predicts stable \u2014\n but the authors never actually test it. This is our extension.
    4. \n
  4. DDPM blind \u2014 noise target, no ||(t||). Paper's stress test.
    5. \n
" + } + } + ], + "console": [] + }, + { + "id": "NCOB", + "code_hash": "4bcacafca2e6a391e8afa735b98d3317", + "outputs": [ + { + "type": "data", + "data": { + "text/plain": "" + } + } + ], + "console": [] + }, + { + "id": "aqbW", + "code_hash": "ad3f09b4d4c331e761ce9c620b3fca0d", + "outputs": [ + { + "type": "data", + "data": { + "text/html": "" + } + } + ], + "console": [] + }, + { + "id": "TRpd", + "code_hash": "0fa82a6a9149916926502902bed456ad", + "outputs": [ + { + "type": "data", + "data": { + "text/markdown": "||(D = 8||), codimension ||(D - d = 7||). Middle regime \u2014 parameterization matters. Compare green vs red." + } + } + ], + "console": [] + }, + { + "id": "TXez", + "code_hash": "bb54585b8d5adbe987aac869bc2e7e75", + "outputs": [ + { + "type": "data", + "data": { + "text/markdown": "
\nWhat you just saw. Four samplers drew from the same distribution\nusing the same closed-form reverse-time ODE. The only differences:\nwhat the network predicts and whether it gets ||(t||). Three regimes:\n\nThe shell picture explains most of what you see \u2014 but why does\nDDPM blind stay scattered in the middle regime, when its cousins\ndon't? That's where the three parameterizations stop being\nequivalent." + } + } + ], + "console": [] + }, + { + "id": "dNNg", + "code_hash": "b448fed7e094e2852fc00d38aa7da124", + "outputs": [ + { + "type": "data", + "data": { + "text/html": "" + } + } + ], + "console": [] + }, + { + "id": "yCnT", + "code_hash": "aeaf46f56911ab8695a4b651da5e3315", + "outputs": [ + { + "type": "data", + "data": { + "text/markdown": "
\n

That's the geometry of noise

\nYou watched the codimension boundary ||(D - d > 2||) surface in three\nplaces:\n\nThe headline of the paper is one line: the U-Net does not need\n||(t||), because the noise itself encodes ||(t||) in high enough ||(D||).\nEverything you saw here is the geometry of why that\nline is true.\nSuggestion: if the slider stayed at ||(D = 8||) the whole time, scroll back and\ntry ||(D = 2||), ||(D = 16||), ||(D = 64||) \u2014 every figure recomputes." + } + } + ], + "console": [] + }, + { + "id": "wlCL", + "code_hash": "387c3fb5065a28565ed62aee9772a623", + "outputs": [ + { + "type": "data", + "data": { + "text/plain": "" + } + } + ], + "console": [] + }, + { + "id": "kqZH", + "code_hash": "75eb0ee336baa931880e616198b731bd", + "outputs": [ + { + "type": "data", + "data": { + "text/plain": "" + } + } + ], + "console": [] + }, + { + "id": "wAgl", + "code_hash": "2ea0c23665a8d3ea0edfbe265917e819", + "outputs": [ + { + "type": "data", + "data": { + "text/plain": "" + } + } + ], + "console": [] + }, + { + "id": "rEll", + "code_hash": "194c26738272aae275ac560e02579a4b", + "outputs": [ + { + "type": "data", + "data": { + "text/plain": "" + } + } + ], + "console": [] + } + ] +} \ No newline at end of file diff --git a/notebooks/jam-2026-Q2/__marimo__/session/inscribed-squares.py.json b/notebooks/jam-2026-Q2/__marimo__/session/inscribed-squares.py.json new file mode 100644 index 0000000..8ee1669 --- /dev/null +++ b/notebooks/jam-2026-Q2/__marimo__/session/inscribed-squares.py.json @@ -0,0 +1,399 @@ +{ + "version": "1", + "metadata": { + "marimo_version": "0.23.4", + "script_metadata_hash": null + }, + "cells": [ + { + "id": "Hbol", + "code_hash": "edf3a8d508167b51e6e2f78d2379565d", + "outputs": [ + { + "type": "data", + "data": { + "text/plain": "" + } + } + ], + "console": [] + }, + { + "id": "MJUe", + "code_hash": "1fb8e0fb237123d6ca1ea9dd55c73f35", + "outputs": [ + { + "type": "data", + "data": { + "text/markdown": "
\n

Inscribed Squares from Noise

\n

\nA diffusion model doesn't generate cats here.
\nIt solves a 100-year-old open problem in geometry \u2014 by drawing.\n\n

" + } + } + ], + "console": [] + }, + { + "id": "vblA", + "code_hash": "b797bec3b4fb208b8ffb03413943d043", + "outputs": [ + { + "type": "data", + "data": { + "application/vnd.marimo+mimebundle": "{\"image/png\": 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aJEaB2Uxm5Zk5cnGrWSmyLihD9Fjo99G6A16GOR/eoPaHIEBSNqfhDOzzkCn06V+vpPOCWXKlFFjn4yB8pBsZ3ON7zdu3FBjoIzTMgbK+2Tck7mgLMRlc5qZ10SOTcYIGQPlIRu8aky7T8YKGQMLFSqksvRkHJQ1soyd8pANEBlf5KTHuXPn1Fww7XpayFgjiS8y/smauHjx4mYZd+TvIfNU+fvJWBgfH6/mrnL9cgJF5rZybTKvJXoaBvismAxGkqn3ww8/YM6cOamDkEy4mjVrpgaeOnXqqMXfs/7AyyAiWS87d+5UWYA7duxQqcxGXl5eapJnDPhVrVo100E2Gbwky0Z2WQ4ePJj6eDjY+DBZvMtOswy4L7zwApo2baoGOyKyf3LsdsaMGfj555/VpMc4PjVu3FiNTRKQkwyUZ82+k80EyXjZtWuXGv9kHJQyB2nHHcnuMy50Zaz18/PL9N9Hjs/JGCgZN8YxUI6TPE3evHlVAFLGQBn/JaBJRPZPNkH/+OMPNReUAJ+QOZ/M0Ro2bKjmaTJHe3gTYNrBafjz5J8qwFc1R1X4uPpg45WNam6pddbCCU4qiCaLSPW4mYSz088i/np86mvIxrFxDJSHBBFN8feRue3+/ftTx0BZaKcNNqZHFu+Sad2oUSM0adJEjYdc7BLZPxmnJMll+vTpWL16deqJtVKlSqk5kYyBskZ81vIrsi6VgJrMx2QeKA+Zn0lwzUg2FoxrYXnI18zsuCMbGDKW7927N3UMlDW5bPY+iQT7JNtQrkP+3g0aNFCb00QPY4DPCskkR3Znv/vuOzUBErIT8cYbb6BDhw4q087UkxoZLGWCtXnz5tSHLESNZACRr2sc4GQyKYvsJ/0dZNF67NgxtYCWh0zoJDslLdldkd0RWbzKxFEmqJI5KJNNCfzJgl52qWUhLu8Tkt3XvHlzdT9eeukl1lAgskOy+TBy5EgsW7ZMjU+ywdC+fXu88sorKsifFbVTZLyRox7GMTBthoyMuRJINI6BsriW8etxk0m5ZjkqIhnTMvYZx0GZTKbl6emZmnUj46AsYiVYKZ8v46W8hozNR48eVUFJIxmDZQx89dVX1QSUiOxvc2PMmDGYOXMmYmJi1PskuPXaa6+puY/MC59k8oHJmHdqngrwDao6CAX9CmLn9Z2YdWKWep8E+NJydnJGgGsAOrp1xIltJ9QYKGOXce4lJDPGmNkij6dllEgwT8Yvmcsax0AZE9OWlJHNFJkHymvJGCgnN2S8l/fL58vms2TbyAI47fG7/Pnz4/XXX8ebb76pFt1EZF8k406CehMnTlSnHITMl9566y01H5QNCFOT8U7GK+M8cOvWrWocMpIEk7QBPwm4PenEm5TVkvFLAnrGMVACivL+tGT8k41bWQtLtp7MDeV1JblHxkDjmvr8+fOpnyObznIfZAyUa+GGBxkxwGdmkWvWIHL5cugf+sEWMuG5evWaGgii70/mArNnVwu/HDlCHvuD66TRwqdJE/i1femBxWbUho2IWLw43a+V7utIAdLGjeDfoYP6swwiaQN+MtlMSxa3MsGSwU4mY8YFqQxE8rkPD14yGa1evbqq8yKZMbITK7UOMrLbIrspkmGzdu1azJs3L3WAk8FNBrb33ntPLb6JyLZt3LgRX3/9NTZs2KD+LAu/Dz74AG+//bbZM3flKIccATGOgYcPH35gYSq1/GQMlF3jtAtSafAh2YBpJ4VCJmySdSLjoDzLGCgTOvm8jOw0ywRx3bp1WLx4sdppNm54tGnTRo2BktWSkdciIuslJx2+/fZbzJ49W2WuyLjXpUsXvP/+++o0Q0Ycu3sMgzYPQmRCpDqe+3mNz5HTO6W21NE7R/HvtX+RqPuvZunduLu4HXcbGicN/Nz8kNcnLzy1nmiTvw2SzyenjoGyOE07t5PNCJmjyqI07YJUxkDZzEhbSkHI+FSuXLnUMbBixYoqOJfRLBQZV9evX4/ly5djxYoVqcFH2YCWMVA2gJ60+UxE1k/WkhMmTMCkSZPUWCLjTMeOHdVcUE5TmPOoviSsSIadcQyUOaFck5GszSUgJ3NBmRPKtcq4JB8jR4Fl7Zz2GLGQeZ+MgbJRKyWxZEzMaANMSX6RefKqVatUMpDxWmTzpVu3bnjnnXfU9ZBjY4DPjMLmz8ftCRNhSE5+5L8lJCYiKjYGyeqIghPcXV3h5eEBlwx2OXOSmnVvvoHsH36oBr7wRYsROnZsul/rya+jgX+Hjgjq2+eRAVQGKeMAJ9kkxo6VD5MFpww0srMii3MZwCSoJ7sTphiUZYEtk8xZs2apYysRERHq/fJ13n33XbW7LYOspcl1yuRc7gfrxhA9mQTPBg8erCYtxp/noUOHonXr1lazKykTK9nNlTFQjlbIGCiL2PSOlsmC1zgGyiJWJnOykDVV50rZ5JDxT7J7jEeLZaHdtWtX9ZBMGEvjGEiUcZKd9uWXX6qjuDJ3kDFk0KBBal4jwbOMOnXvFPpt6ofIxEj1M1g6e2n0qNDjifOQ2KRYlfF3MfIinPHfeOuqccXQGkPROF/j1M1WOcImY6CxrIGMg2mPtBlJNrKMfzIOli1bVo2Bsph9lr/Lk8jCdsGCBWoMNG54yNxPsvok2CcbydZAvpfyO8xafo8RWSvZPJg6dara5JX5lgTrP/zwQ/Tv399qglYSrJNsYmODI9mQkTHw4RNqQsY62ZSRcdDY2FIekiBjqvslR5Z//fVXddpFxhrZRGnZsqUaA+W0mzV0S5c5sioNYQXX4igY4DMTfVwczrdqDZ0csZIMkPsTraTkZETFxCAxOWUX0t3VDd6entA+axaGwQAnFxf4vPACnL28ELF0aUpwL83XyvjraOHdsCFc8zx9gSiDiUzsjAtcdw93tXvx8NGPx3wxZFZSYpJKWd6zdw8uXUzJMHRxdVG7IZVr1ETp996F21NqxugTExG9bh20OXPCs2LFp35NOSYnqdLykEE97bM8ZJCXe2Lc5ZZJnRwnlIFeshhll0eCnVLMWoIYz1M/jMheSCkACeT99ttvagIgizLJXpFsNFsIjMsYKAvNtEXh5WGuTDqZbEq2408//aQy+2TnWMYcmdhJYKBVq1Ymb5gkf1cJbD48/hmfJfNRxkB5GDMeZQyUhzQ/kTFQHtIURbJ45MEmIuSopO7SuHHjVFkWOZImJxu++OILlYnxPHODwVsGY/v17dAZdCiZrSQ+LP+hCtQ9jQT5Zh2fhSN3jqQUpYdBzeXkc9sWaQsP7X9lEUK8QtAkfxP1PvlYGQPl513GBlmUy+kKc85rZNNZarXKxq+xrrNsrMgYKAG/jGbHPMu4KyUdHp7/Gd+W32vyfU07P5b7IfNACULKBoyMgVJDW37nyVzQWgIYROYmY4iczvr000/VpoFk9Pbp00dtcNhKzXUZu6WUgqz9ZM4lc52sKCXzOJItLfNoGQfPnj2r3pc7d26V/S2bvgULFjT590zG2vTGP+Pmt6yXjc1ChAT45J7I7whj1qNcl4zVMg80V0MTR8AAn5kkhYbiQruXYZCOOGVKw7VLFwyfMgU/zZ+vfkhqV6yIb/r1Q7WyZZ/5taO3bsW9WbNTgnP3F5UG1SnNAN8WL8LvpQeP7j5OzM6duPfrr6rTmvF1bIksrmPj4xEXH6/qywid1gWHm7yAUq1bq0mU7CinvRf6+Hhc/+RTxO7ZrTIns/Xsifh6dVWth8cF8Z7UFES6bsrOjPxykocMZjLYy5EVecgO/cPZPjLZk6L5L7/8snqwYzA5Avk5kJ1aCe7JxEiC3hLY69SpEzMdnpME1mSBK8E+qVsqpD6fHFuTZiSSQSNZQU/KJDF2cpMFqnQaTm8MlIVt2qPKackiVr6mTOJkDDQeV5HxTxa88toPZ/tIMFRq2khdMakraKrdbSJrt2bNGvTo0UOVZpH5g2Qx9+vXL1PHTLuu7orT905D46zBmPpjMhTce5jMoaR+35arW1SQ7+E5pLyvTGAZjKo3Ct6u5uk2nhEytshGh4yBcoxNyFjUrl071ZBECvFLRs2TNj1kbJOTIcauvultZMj709YmTEvGVxkD5XsoY6DM6WRclfFPxkF5bePJk7Qky1HKLUh9MVnoEjmC06dPqzFQjt7LONO5c2eVyWwNpxBskYxfkmEtY6Ac4ZU1qNxXaUwnG79SP1rGmqf9jpGxSsZA43o4vYQWY23Y9EhgVjZ6jOthuQYZA+Uhc36ZCz5MApJyakfWAVJTkMG+58cAnwUCfGFeXuh49Aiu3rmDPDlyYNTAgXipUaNM/UOWIF/Yn3/9dyTXyQl+LVrAt03rZ3pdFeT7fdYzH+21NvGJiYhLiEdCUjLi9HqMvR2KCJ0eXt5eCAkOhqenTLzcUCsyCoXuZ5nIBExvMOD7O7cxP53Jl5CdddlxkKNw6T3LYPak+y1BDVk4yw6VFFmVoy5SV1AW5kKKS3fv3l2lozOjhezVvn37VC0VeZbF1/Dhw9G3b18Gt01ExjM5PicTPNkVT9uZTSZaEkyVjBYJKMjHyqJYPkY2IGQXWDZLHkc2SR43/smzjGFPCiDK15OJnUwS5Vi2FPGXhbgU4jdenwT5hgwZoko9ENkj+TmTQN6cOXPUnyXLQpoKZbZhTlh8mArw3Y69DReNCyY0nPDcryVBvvmn52Pzlc2P/DfJ7tM6aVWQr0uZLqp2X+nA0nDTuMFaSBaLdF+XI7xpy8nI+CRjlZyokDFQNmKNY6BkIhqz7x5HsvAeHvfSvi2L1KdlTUt9VlkgS8MRKfcgdb2ksZRx40Q2fSWbSRa5RPZIfuYka1k2diXzV4LvsunLeuqmIwkpUstV5oKS5ZyW1I6WLDoZA2XeJYFAGfdkbJLAXnqbEEYyZhqzkB8e/+RZ/tvTSjHI9182SmScljFQyizIXNA49soptwEDBqgND9aWfnYM8JmJBMxOv9QWYdJ8IikRe+LicKNRI3zSvbs6kmsKuogIJN1MmcRos2eHNvD50pp1kZFIuvFgYWTbY0DkylWIPXoUiTodEpOT1aJVAmw6Vez0weyTZAPg6uwMjdRJ0Wiwq1BBRFSr9sCAJZO2rMiuk2uSmoIyCZWBWAY9WSTLkR1Z6BLZC5lAfP755xg7dqwKqEudkClTpqiAE2UNmazJ4nHbtm2qM5zU7rt48WK62SdypEQ2MWTSJ8+S7ffwpC2r6pvKLv6ff/6pOubJYlwy/wYOHKiCvyxhQPZCAjiSZdu7d2+1gJK6TPJvXjJYM0uCe/039ce58HNINiSjeEBx9K3cN9OvGxobivCE/+pLxSXH4Y/jfyAqKUoF+Yybmtncs2Fs/bEo5G9dgXkZ62SOJWOgPMuCUsbB9LJPZFFqHP/k2XiMLO04+PBJEFORDRbJPvz+++9VjS8hpzrkz7IYJ7IXktwgjdNkY0/mHaNGjVLHSFmnMut+70g5K6khLeOgzLcka1yaYj5MNidk/DM+jGNg2iCejEdZEXSTrEFJepE6tCtXrlTvk2Z0EqCUI7yUcQzwmYkUAp740Uf4zNMLPq6u8PPyRECTpgh4tZO5LsHhSG29O1OnIf748Uf+m6ovI0eaZTdXauP16Y2EkycRsfRvlf0oTUtyjx0LrxrVzXrNMtjKLr50jpJJqexc/PjjjxnuMEdkrSRbSzpey8JFJg0S2JNjU0zBNz8Z+yTYKvVR5P5LFqWxC7ClyU6+ZN0MGzZMZfpJaQVZ9PK4Dtk6+fcsmcuLFi1SQWsJXksQ21QB7L4b+2L/rf0quCfBtr6V+iLIMwhZ4Vr0NUzcN1EF+Ywki0++7rgG45DfN/8Dx3nluLC1kbFGgnyy+WssKWANReBlfJZFrmSvSNaNbPjOnTsXDRo0sPSlEWWKrGu++eYb1URDkhtkTijJDJJNS+Yn3wMZAyWxxHiUVgJ81jAvlzIzUrLi77//VuPymDFjVF1GyhgG+LKY7ND26tVL7di6a7VY2rAR8kdFyU8V3IoWQcigQVl9CXD0zMnYffuQfP8I7MOctC7wrFIZ2vs1nyKWLUPEsuVwcnaGf4f2CB44EJYKhrz66qvq+IbUTVi6dGmmavIQWYpk6skv5s8++0xN7qQe3LRp01QWBNGTNjtkR18mdxLcW7dunerGSWSLVqxYof49S3aqHEGTbH05gmQqUYlRaL24NZL0SfBz80P/yv2zLLhnJFl9e27uQYIuAcfuHFMdeCXIJ4+0pAZgh2Id8G7Zd+HsxE6yGSWBR6lFJsEQWXRLuQWpU0pkiyRbTwJ6chxT6uxKVlbbtm0tfVlk5ebPn686Aks85ZNPPlFHuq0hAGntGODL4iBN+/bt1XEA6RY4t1lzuO/erYJOTq6uCOrdC+5csFiVxGvXcPPLr9Tg4ffyywj5eJBFj9ZJwWUpliqZTpIFyvR1srUgjUzoVq1apepTSn2V1157jb+cKcOZLNJFT450Sz0+OdZjKx31iIxBGilL8L///U/9/paMBOmQa+pj5xEJEXhpyUsqwFcuqJzqnGtO0oH3+4Pf43zEeZWx9zCtsxYvFXlJZRUyyPdsZO4nnYDl34/MB6VZEpEtkeB0t27d1KmBVq1aqU6vma03So7j+PHjaNq0qWr4MWHCBGbyZQADfFlEMvbkKIYUi5TI8/hvvsH1l9tDL91sXFwQ9FFPuJcsmVVfnuwgwGesRyC1eaQZwejRo9VxHiJbIP9mZYNDminUrFlTHTHiMUt6niBfz549VdZnixYtsHz5cgaIySZITTXZ0NiwYYMqSyBjoHSzzgqWDvAZg3wrL6zEpchLDzTjuBBxQTXskCBfEf8i8NB6oFhAMdWcw8c1a2p62hv5tyOnOqQeltTSkiZJRNbuzP6bWDhjDc6du6AC1OXKllM1l2WNZWwok7YkurPWGUUqBaNoVQb/6NEMUNnckA68UkewRo0avEVPwABfFuzWSgfUyZMnq7Pssih55513kHznDs6/1FZ10fWsVBGBH5p/8kW2F+ATV65cQenSpdW/LTmyK0VOiax9g0M2NqTOm5QokCO6bJRAz0uOdteuXVt13JXjGh06dODNJKsmDW0kA18yDqQTqnTLzcpGCcYAX6I+EeWDylskwPc4+27tw8yjM1WQz5jdJ1l8JbOXxJj6YxjkyyDpuizZK7LhITVsiazZ7tWnsXnecSQlJqrmhX6+/nDRPrm7tAwPzs5OqNg0Pyo2yWeuSyUbIeWq5Fh3uXLlVBKBNdRMtVYM8JmQFKqUHTbJMJAdCikMLt1fxAMBvsqVEfjB+6b80mTHAT4hkzqZ3PXo0UN1VCOyRrIj+9VXX6ni8VK0XJolSAYLkSkCJtJwo1SpUqrwPGuwkDXX25NaozInlEYJcjw3qxciS84uwfh941UGX4WgCvig/AewJtL8Y/7p+YhMiFR/1kOvOvBKJl+t3LXS/Zyi/kVRO3dtHum9T443SrkfqeMondBz585tvm8g0TM4deI0FozcDyeDM1xc3eDn65exEkP3s/mcNU4oUiUE3gFu6s+px/7vP7m4OsPFXSuTTiTG65CUqMvQ6z5OUH4f5CkewHmFDZAAnwT65Nh3x44dLX05VosBPhMexZDjQxJRlhRSKQwuna+MpMnD+bbtGOCzctYa4JOj3tKaXBYMN27cUPXMiKyJZJi+//77mDlzphr7ZKOjatWqlr4ssiOtW7dW/66kw+QLL7xg6csheoR0ve/evbuaQ/zwww+q7lRW+/vc3xi3d5wK7om3S7+NGjmt7/iSZPDJJtCt2FuYtH8SIhIjVEOO9Gr2CbmHLQu2xMCqAxnku0/qkUqplqFDh6rmG0TWZteuXXitw1t4t8HXcHf3RKFSOVGtVeH0P/jBuB0uHrmLY/9eS3mf85MbKThlLHaXIdKzoUz9PKjaMuX4MFkvadIiaws51bF161ZLX47VYjsrE5CAi7Svl+CeNEOQeitpg3uP4NhBz0iyod566y0V6JOGBUTWdoRSioBLcE8yDHbu3MngHpmcHPsWCxcu5N0lqzNx4kRVe1k63ksWnzmCezeib6Rm7olmBZqheg7rbMIgx3I1zhrk8s6FPpX6wN/NXwX9dAZduo9kfTL+ufAPRu8Zrbr0nrp3CufDz+Na9DX1eY6oS5cu0Gg0HAPJKknARTbf7t27Bx8fX/VwdXdRGXnpPpxTHk73HwXLB6J03VwpNfr0hic+9PcfBhM89DoDjm6+il1Lz+P62fAnPiLvxJnlXup1esREJJjla9mSKlWqoGLFiqoOn8RfKH08vJxJUl+lUaNGOH36tKq1J52B5Jfvw1KLiRJlIntl/PjxKnulU6dOvI9kFRITE1VpAmNJAvn3GRgYaOnLIjskCwc3Nzf1b4zImkidUen4HBAQgDVr1qhFiDmcDjudkhkHA2rnqo02hdvYRAZKTu+c+LLWlykNOPBosO5WzC0sOL0gJch3/h+suLAi9b9Jxl9+3/z4tu63yO3tWMdUs2XLpjJXtmzZouozs3EVWQvp8NyyZUvVHPCHKZOgueClAmfPqmD5IOQo5Ifo8PvBLcOj6+nkJD2SE3RqrNO6aaB1cX5i8syTRsSIO3E4se2GutbjW6/j+LbrT73GrM72kyDimhnHEHUvHqXq5EK1VgWfmtHoaOvhAwcOYP369XjzzTctfTlWiQG+TLh79y4aN26sgnvvvvuuOo6RoRoDZLUM8fGwVsYFw6FDhyx9KUSKXq9XmaUS3JP6aLKwlQUIUVbw9PREmTJlVLa8dFLz8WEHTrI8qYsrwb3s2bOr4LNkF1hCDq8cNhHcM3LRuKBYtmLp/rcS2Uqo5hvSnCPZkKwW+RLYk0CmOB9xHr039MaEBhOQ1zcvHIkcT5MA3+HDhxngI6uwe/duVaZKThnJSY72bV7FvG93P/frefi4qoc5BOb1gaubFoc2XFEZgRkh2X6S+SeBN1Ofyou8E49VPxxBdFiCCmbKkWVdsh412xbOUJBPPseWfg88D2P5H1kPM8CXPgb4npMMYhJBlrbNkjL/bME9+/7Bs1XJd+7i7s+/qKKtcHaGNjgI1kQWs1JU+fz585a+FCJFagFJodvy5ctj3bp18Pf3552hLCVHwCXAJ0Xmy5Yty7tNFiWbG9IpXOriSjaBjIVkGpVCKiHIIwgHbx9Eoi5RBfp0eh3OhJ3BzdibCI0NRe+NvTG+wXgU8CvgUGOg4FyQrMHZs2dTM/ckuNe5c2cVnLIleUtlg3c2d4ReinxiUT9p5nHxyB2V7SeBN8n4ywry+hJslDidvH1yxw2c2nkz3Y9193ZB1ZYFUaRyMK4cv4cdS87BL8gDdTsVg6eva2rQ7+jmazi29bo6Bi0fL0ejbRXHwKdjgO85s1beeOMN7NixAy+++GLGgns8oWvV9LGxCB03TnU7dtJo4FqwIPzbtoW1kaOPsmur0+nSPQpOZM7OznJkXJq/SL0pBvfIHIzHvyWDnsiSZA4otUddXFxUoM8SwT1jRpu9kuy8hzP0ohKjMPnAZFyNuoo7cXfQd2NfjGswDoX8C8ERcAwka3H79m00b94cd+7cwTfffKNKVaWwvXEpIIenejyNBM8k20+nM6ic4qxguP918pfJjqNbJINPgn3pf62Y8ARsmXNaBSdP776F5CSdOtq7bPJBNH+/DHwDPXBgzWUcXHdZBQ2PbbmGhJgk1HmlmM0G+TgGPh0DfM9BBjHjkTTJXpHJHdm2uIOHHgju5Zk0ERorzEaSoJ4jpF+Tddu4cSMGDBiggnrS9CVXrlyWviRyEMaNDdloI7KUW7duoX379oiPj8eff/6Jhg0bmv0aIhIi8Pvx31MbTrhp3OAI5OiuNOn4/sD3uBB5AXfj76pMviL+RVI/Ro7zVs9ZHR2LdVSNPewJx0CyBpJoIBsc586dU82FPv30UziCvCWzwdVDiwuH7kCXpMuSr+Eb5IHi1XPA1V2rsvDOH7yN5MRHv5YE/qRenzTkkCw/dcRY/s9gQPS9BCwavV8FCsNDY1PrIcrz2X2h6m1bDfJxDHw6BviekRzBGD58uIoeL1myBN7e3s/Xj5usij7hfu09Jydke6cztFbaJEBS4KXIPGs9kqXcvHkTr732mgqwyMK2ZMmS/GaQWcdAYz0+IksubKWD3yeffKLGQ3OLToxGv039cDbsrOo4G+AWoI60OgovFy98VPEjTD04FeciziE8Phx7b+594GP2h+5XTTw+rvqxXQX5OAaSNfj6669VaZY6depg8uTJDpV4EFLAVz3MISifj3qkRwJ5J3fcxLn9oakBvOD8PoiPSVbNQwzJBty7EZP68flKZ8eVE/f+C/I5OaHuK0Vt7nvHMfDpGOB7BmFhYanFHP/44w/kyZMnw58bvWnTf3+wrZ8jx2PFA50cS5NaP0SWIJMJqTkq2StDhw5VJQqIzMl4NJfjIFnK6NGjsWHDBtSvXx9fffWVRa5h+fnlKrgndekkuNe3cl8V9HIkni6eKsg36/gsVacv7Wk5ObosHXhXXVyljvQWz1Y89b9JpmPDvA0R4hUCW8QxkCxt+/btGDFiBIKCgjBnzpxHTrJJKXPKehKYK1EzB1zcNbh4+A5yFfVHyZo5kZSgU0d7716LVm/Lx5Wqmwv5S2dHcAEf7Ft5KSXIt/eW+vjHBRCtFcfAp2OA7xn0799fZa9IYfmmTZtm+PPCFy7E7UmTYEhOVn/2rFDhWb4skRIREaHqXdSqVYt3hCzi999/V0dyq1evriZ3ROZ25swZNVktWLAgbz6ZnTRW++KLL1SAWTZ6tVrLTKNvxd5SQSw5itq5dGcEewbDEblr3fFeufdUME82oIyO3j2KX478ot7/77V/sfXa1gc+b/aJ2RhTf4zq1muLY6AoUuS/I8lE5iJlCbp27ap+3qSphjT/I8uR+VCRSsHqYSRHiCs1y6/eNo6Lxiy9HAX9ULRKsKrXJ6LD4m0uwMcx8Oky2vbV4a1duxa//vorihYtii+//DLD9yPx0iWEjhufEtwzGODXujU8Kld2+PtJz+7EiRPqWf4NEplbaGgo+vXrB1dXV/zyyy9s8kJml5ycjNOnT6vGLu7u7vwOkFnJQundd99FQkICxo4da7GFrXSSlQYTRp5aHlfXOmvhonFJfVQMroh3y74LF2cXVaNQjjEbH5L1GJ4QjoGbB2LL1S04fPtw6uNK5BVYO84FydJHc2WjQ060Sffcp7LeQ1EOQQJ7Dx/B1bradskCjoFPxwy+DNZbkew98dNPP8HDwwMZFS9BGYmeGwzwbtAAvq1a2txZd7KelHgh2VNE5iYZe1KmQLJXSpUqxW8AmZ10EJfaKxwDyRIWLFiArVu3qoYaksFiqeDeqD2jsPvmbhW4cte4I8gzyCLXYu3KB5fHN3W+weWoyw8EadddWoeTYSdVkO/zbZ8/8DmSEdm8YHMMqjLIKuv2yXpk586dCA4ORv78KRk6ROZy+fJljBkzBtmzZ8eECRN448kiuB5+Ogb4MmDWrFk4evQo2rZtq2quPC/X3LkY3KPntnnzZvVct25d3kUyK8ma+vHHH1W33EGDBvHuk0VwDCRLSUxMxJAhQ9Tbkr1nqY3aP078oerKydFTjZNGHc+VY6qUPm9Xb5TK/uCGVJGAIph2cBpOh51GkiHpgeCeWHlhpQqkflLtE6sL8skmh5RrkQ7OTBYgcxs2bJjKYP7uu+9UkI/IEic5ZKNN/v2VKGF7JRbMhQG+DPxDkowVack8cuRI83xXiB4SHR2NNWvWqCNBzJ4ic5OyBDIWShYfu5eSpUjnevEsNXCJTFV/9OzZs3jjjTdQsWJFi93UTVc3qeCTBHfkCKpkqdGzkSYbPSr0UFmQd+LupL4/SZek6vVJ8HTNpTVYd3kdnJ2cUTmkMj6t9ikC3AMsfqs5BpIlN3plHJT6tx9++GGGP88YOCcyhX///VedJpLu9dzkeDwG+DLwy/TSpUt4/fXXGSkmi1m5cqUqbPvyyy/D2ZmlM8l8rl+/jrlz56q6Z++88w5vPVmENLiSiV3ZsmVRrFgxfhfIbORYp/E42uefP3ik09wkuCfNNeRoLoN7z89V44o6ues88n5puvHTkZ+QpE9KDUzsvL4T/Tf1x7gG4ywe5JNj4jIHbNeunUWvgxzPJGkWaTDg008/hZub2xM/Nm3DGyJTj4GiQ4cOvLFPwADfU4wfP149S3F5IkuRbn2CAxqZ2/fff6+y93r16mWxjpFEc+bMUYsGjoFkbuvWrcOxY8dUQfnixYvzG2DHygaVRc8KPbHm4hrEJseqDL/opGicCz+HD9d+iDw+eR4IErYo2AJ185inbMrBgwdx/PhxVQMyKIh1F8l8JGNKOubKsUhprvEkep0e+1ZeAlLKz0PjwqQEMg05Hj5//nx1kqh58+a8rU/A1doTSJcgKeRYs2ZNVKlS5UkfSpSlRW2XLVumuufWqfPojjNRVtHr9ap7uDQW6tatG280Wezf4dSpU1XmSufOnfldILOSruGid+/evPMOoHi24uohbsfexoT9E3Av/h6uRV9Tj7R2XN+BPpX6oF3RrM+okzFQ8Hcxmdu8efNUg6s+ffo8sdGkQW/A5jmnceHgbej1Bmi0TshXKptZr5Xs18KFC3H79m3VzZ7lgp6MAb4nkGNpQmquEFnKDz/8oBa43bt35/FcMispZCtHdCVrKiDA8vWHyDGtX78eZ86cQZs2bdg5ksxKFrWywRYSEoLGjRtb9O5LU4hbsbfU26xrZR7Sobhvpb745egvuBz5XzdeIUel5SjvxP0TcSXqCrK5Z0M+33yonau2yZtzhIeHq5MckrnHLGay1vXwjfMRqcE9Z40TqrQoAP8QTzNdJTnCiSLRo0cPS1+K1WOA7yk7FpIxwF+mZCkyqZMBzcvLi/XPyCJjoOjUqRPvPlnMt99+q57lmDiROa1YsQIxMTHq9680W7NkcG/ApgGISYpRgaVyQeUsdi2OGOQbXG0wEnQJD7x/9cXVWHVhlQryLTidUhdKir43ztcYQ6oPgdbZdEusiRMnqmCzlAt6Wv0zIlO6deuW6mBfunRp9XiSmLCElKO5kglbIweC8/vym0EmIf8G5VRl7dq1LdroylYwwPcYkrUiNVfkH5Ls3JKdc0rT5cmKisNKYe+IiAhV1JYZVGRua9euhYuLC1588UXefLKITZs2qYeUyrB0BhU55hgo2rZta7Fr0Bv0+GL7FwhPCIfOoEOxgGLoVJybLpbovptW60KtVZfdFedXqO+LYgDWX16v3mxftP0DH1/YvzDcte7PtdEr9cC9vb3Rt2/fzPwViJ7Zhg0b1CmiZx0DXVwttyFC9mf48OHqediwYZa+FJvAAN9jSLc+Ub9+fXN+P4geKGorkzofHx8MGDCAd4bM3rX09OnTqFWrlsogJTI3aaphnNSNGDFCZccQmdOWLVtUcyEJMFuKBPauR19XQaTc3rnRvXz35woUkWnJeNSqUCtUy1ENt2Juqe/T/NPzkaxPxrpL61IDfUa+rr4YWXckygSWeaavI/NA2egdMmQIAgMDTfy3IHr6GCi4HiZL2bhxo8rgk/VIkyZN+I3IALa2eYxt27ap57p1zdMdi+hhsqCNjIxUhb2lcxWROUkqvOAYSJaydOlStbiQTPoXXniB3wgyq7t37+LkyZOoXLmyRTc5EpL/Oxoa5BHE4J6VCfYMVt13pZvu++XeV0dzkw3JKtBnfMgx3rCEMAzaPAhH7xzN8GtfvXoVY8aMURu9/fv3z9K/B9Hj1sNSniDDmxzchyMT0ul0qUku3OjNOGbwPYYczxU8502WcPToUUyZMgU5c+bE4MGD+U0gs+MYSJYUFxen6k1JloxksDB7j8zt+PHjFp8HRiZGYtj2YeqYrvB387fYtdDTSXae1Ovbe3OvCuwZXYy8iHPh5xCVFIVeG3qpIKBk9HUp00VlAT7OoEGDVO09CfJxo5fMLTk5WW1yFC1aVB0RJzK3GTNm4MCBA2jVqhU3ep8BA3yPcfbsWbVjFhwc/Cz3k8gkx9Ika092LUaNGqX+HRJZYgwUMrEjMrfRo0fj4sWL6NatG6pWrcpvADncGChZXwM3DcSpe6dURpgE9xrla2SRa6GMk2PUuYvkfuB9ibpE/Hj4Rxy7e0y9LY+4pDiM2TMGSboktCva7pHXkSNpc+bMQYkSJdhgiCziypUrSEpK4jyQLOLevXsYOnQoXF1d1UYvZRwDfOlITExUg1r58uWZNUBm98svv6h6A3Is7Wkt6Ymyyrlz59Rz4cKFbe4m7zx/F4v2X0VCkl4dF5ETI87OTiiX2w+vV88PVy2rU1h79ug333wDf3//1A66RI42Bu6/tR+nwv4L7vWp1Ed1dCXb46pxVcd3l59frgK2Uk9R6ipKEHfi/onYc3MPPF08UTmkMpoVaIb4uHi899576nMnTZqkFrhEjjYGkmPr06ePKpUh9UeLFCli6cuxKQzwpUOK2UoWVVAQJ1JkXpcuXVLH0tzd3fHzzz8zwEwWbfIiHXT9/Pxs6ruw+thNfL7kKBJ1etXRMK1Np27j4JVwfNehHNy07PBmjSRboHPnzmqj7aeffmIWPVl0DBSWmgvGJMWkvt0kfxPk8Mphkesg0wX5Xi76snpb1hj/XPhHdeCVIN+Wq1vUTtTaS2tx4t4JXJ51GWfOnEGXLl1YVJ4cdgwkx7VkyRLMnj1bZTB//vnnlr4cm8MAXzqioqLUs0nqDRgeWmESPfafikEdR5N/f2PHjkXx4sV5r8hi5N+hrdVcSdLpMXrVSSQm62GAAU5pqj3LSJys12PLmTvoN+cgahT+r3GNs5MT6hcLQt5snha6cjL67rvvsG/fPrRu3RpvvfUWbwzZx1wwk1iD0r7I97NlwZbQOmmx8sJKVa9P5oCSrbng+ALsX7AfefLk4bE0sihrGgPJcdy5cwcffPABnJ2d8dtvv6mkF3o2DPClQwraCkt2TSPHXNiuX78ederUUWnJRJYeB21tDIxL0iE8LgkS3isa7I2+LxRL3We5dC8GkzecRXySDjvO38XOC/ce+NzvN57FmI7lUbtIoIWunqRj7pdffols2bLhxx9/ZFCDLIpzQcrqIF/zgs1RP299TDs4DWfDz8KgNyDiTgSSwpLw8+KfbS6DnuwLx0AyNzm9IZnLoaGh6mhutWrV+E14DgzwpcPNzU09JyQkwKSc2Duc0rdjxw58+umnqubUr7/+qlrSE1l6HDT5GJjFrtxL2ZwRXq5aeLn99yuudC4/9G9SDBPWnkac1OZLk10tmRN6vQED5x/C561KIV+aTD7jsK11dkaRYG9onDmOZ4Xw8HB07NhRHdGV4F6OHDyOSHY6F8yga9HXLPJ1ybz23dqngnuScR4dHo2L0y7io3c/QtOmTfmtIIceAylrxEYmWu0Bw3HjxmH58uWoUqUKhg0bZunLsVkM8KXD09PzgZ0Loqx0+/ZtvPrqq+rtmTNnspgtWc04eOPGDdiKs6FR6DPnIHR6gzqPmyed47bFQnwwumN5nL4V9UB5vp3n7mL3xXuIS9Th86VH0xzsfZAc4Z38WkXkCeBRXlPS6/XqOK7s2Pbt2xft27c36esT2dpccNWFVfjl6C/Q6XUq8COdWck+XYy4qJ4T4hJwYcYFlPApoRa5RJbG9bD9Obc/FBcP30ndvA7IYT0ndbZt26bq7fn4+GDhwoWpAWZ6dgzwpcPX1zc1o4AcgyEx0SJfNzk5WQX3Ll++rBa2bdu2tch1EKU3Dp49e1aly1t7B7+YhGR0n70fd6MTVIBPMu1alE0/A0yy+irmC3jgfeXz+Ks6fLsu3EWS7jHbmgbg4p0YfDBrH354qzKDfCYkx3Jlx7Z69er43//+Z8qXJrK5ueDB0IP4bs93SNIlqVqijfI1QlH/oma9BjKfRF0ikhKTVL0ztxg3zF81n6c4yCpwPWxfrp8Nx4ntKRv3zhonVGlREP4h1rFhff36dXTo0EGti3///Xfky5fP0pdk0xjgS4dEjgMCAnDxYsquGtm3+DNnELn8n5Q/OAHa7P8V389qcix3w4YNqF+/PkaNGmW2r0v0NPnz58f+/ftV8Nna29PvvRSGezGJKrhXOMgb/ZsWg6drxn+9ybHb9+oVQpncfric5phvWkevReB6eJx6dJi2A1rNo3l+GicnNCgejE9eLAEPVx6zz4hly5ZhxIgRqlvuggULuGNLVjUGCnPPBTdf3ZzSdAEGNMzbEO2Ltmc9Sju17do27L6+G1ERUTDoDPhx4o8oVKiQpS+LyKJjIGWNG2fC1dFcjVaCewVQrmEeq7jVkkggJVpu3ryJoUOHMtnFBBjgewz5BSud/OLj49m9xY4lXrmCO5MmQ5+QACetFl41a8GjYkWzfG3ZoRgzZozqlDZv3jy4uLiY5esSZYRxkXH+/HmrD/Dp9PqUN5yAqgWzPVNwL22Qr07RxzfYiIxLwqhVJ3E1LE4180BS+h+37PB1hEbFY9wrFRjke4ojR47gzTffhFarxfz589VYSGSNY6A5ybFco1q5azG4Z8e192Yfn43I8EjVXKOaSzW0e7GdpS+LKFXBggUtMgZS1pBa0ylHc51QrKp11DmWGtjdu3fH9u3b0bx5c7XhS5nHAN9jyIJWAnwnTpxARTMFfMj8ojdshD4+Hk4uLvCqXRs5v/kaTs7OWf51pVtut27dVH2LxYsXq+wVImtiDOodO3aMxb7lqIqHCz5uXgLz9l7B5bvpZ/mFRiUgPlmHXRfu4Z2Zu5E7wOORj5GjwPWLBaFVuZwOvXC/du0aWrRogcjISEybNg316tWz9CURPXYMJDK1ledXIio8CrpkHXLfzo3fvvqNN5ms7kRbSEiIWgtLrVxnM6yPyLF8/fXX+OWXX1C8eHH8+eefLE9gIgzwPYa0ZZ47dy527drFAJ8d08fFpbTJdHJC8KCBcDZDrbGjR4/i5ZdfVr8s58yZozoFEVkbY2t6GQPpvyDfu3Uff3zq/O1ojF1zGjGJyTh1M0o9HuEEbDwVqur5fdSoiEMG+aTWVMuWLXH16lUMGjQIH374oaUviegRxYoVUzWoZAyULANH/FmlrCH/ni5cvIAkfRLc9G5Y/tlyBk/IaueCUkrj9OnTKFGihKUvh+yInGSTTrmS5LJy5UpVHo1Mg6H4x6hRo4Z63rFjh4luNVk7c2TuyYLWmLUyadIktG7dOsu/JtHzKFu2LDw8PDgGPoNCQd4Y0LQYQnzcVZfedB8GIFlnwK/bL+LL5ccxe+elRx5/7b6MS3dj7PIfrrHWyqFDh/DKK6+wqQZZLclWkcYvt27dMlsNqqjEKBy9c9QsX4ss58exPyLsbhg0Wg0KFCigftcS2cV6+DF9yojSWrduHd5991019kmTNeNxcDINZvA9RqVKldQ/OvkHyJ1bMoXQ0FC88MILuHLlCgYOHIiePXvyxpLVkpqQNWvWVE1gZOdWslkoY0G+ke3Lqs6+6dl7MQy/77iEZL0BSw9ef+zruGqc8e3LZdCoRIjd3HbpjvbGG29g9erVqFOnDn777TdmrZBVq1u3LtauXavmgu+9916WB/cGbh6Is+FnoTPoEOgeiBye1lEniUznz5/+xLRR01Dsq2Lw9fdVNUiJrHkMFDIGdunSxdKXQ3ZA6u299NJL0Ol0qrla1apVLX1JdocZfI/h7u6u6k5J22bpJEmUGWFhYerf06lTp9C1a1d89913vKFk9YwZpnI8w5pdC4+HNZE6ez7uLuk+GpYIxju1CqjKANIcJL1Hsk6P+CQdhiw6imWHruNsaBTOhkarI8BytFdqAF4Ni1UFk22FlCSQAIlM5qSurfybkt+zRLYwBv79999Z/rW+P/A9Ttw9gWRDMvxc/dCjQg9onNmN254s+XMJRg0dBb8cfshRMAecNVyGkXWTjd5s2bJhxYoVSEp6THcxogw6cOCAOskWGxurau+1adOG9y4LcNvoCeQf3dKlS7FkyRJUrlw5K+4/OUi9KRnM5Ehap06d8OOPPzJrhWxmcduvXz81Bg4YMADWaM2xm5i8/gx00h0MTsiTTmMLa1O/eBBK5/LFxcccw91/OQw7zt1VQb4vlh2Tsn3pKhDohfGvVEDebJ6wZpIF36dPH/z6668oWbKkyuDz9/e39GURPVX58uWRN29elb0SHR0Nb2/vLLtrh+8cVpl7Ls4u6FOpD3J65+R3yI6sXroaX/b/El7ZvVB7bG2EOYVB46RBIb/H13UlsjTJMJWaubNmzcKWLVvQuHFjS18S2Shp1iLJLhEREZgyZQo6d+5s6UuyW9w6ekqAT46pyaAm2QdEzyouLk6lIe/cuROtWrVS/5Y0Gu7Ik20oXLiwyrbaunUrzp07B2tz9FoEPltyFInJelX2pWHxIJTK5QtbEOjjhioFsqX76FanEOoWDVJ/pySdHomPeZwLjcb7v++16np9EtwbOnSomsxJjRU57hgUFGTpyyLKEGms0aFDB8THx2PevHlmuWtuGjcG9+zMljVbMLT7ULi4uqDxhMapwb1gz2D0rtTb0pdH9EQyBgopq0H0PM6fP6/KVN25cwcjR45kmaosxgDfEwQGBqog36VLl1QdKqJnIenHEtTbuHEjGjVqhPnz56uAMZEtkSPlYubMmbA2a4/fUgEwCe81KB6EN2vmV8djbZ3G2QmdaxXAu3ULolGJYDQqHowGxYJRv1gQ6hUNQt0iQcjp666yFm9GxuPVH3eiweiN6T4+W3wE0Y+pB2iu4J5M5nLnzo3169erZyJbHAPlOBHRs9q8ejP6d+mv3h4xYwTuaO7AGc4I8gzChIYTkNcnL28qWbUXX3wRISEhqsSGZF8RPYuzZ8+ifv36quzZkCFD8Mknn/AGZjEG+J6iW7du6nnatGlZ/b0gOyJHeeRYrgSGGzZsqOr3sN4U2SJpiuDm5oaff/4ZCQkJsCbJ9zOr5WiuBL/sIbiXNshXu0gg3q5ZAG/XKoB3ahdAl9oF0bVOQXSrWxBDWpZE/uxeKsgXm6hDWGxSuo8VR2+i15/7zR7kk+Dexx9/nBrck40OdkkjW1SmTBlUq1YN27Ztw+HDhy19OWRDNvyzAQO6pJS3GP3zaFSpWyXlPzgBlYIrMbhHNkGSE+Q4pZxK+v333y19OWRDpPa8BPeuXr2KwYMH4+uvv7b0JTkEBvieolmzZihevDgWL16s/pE+C11kJMLnL5CVjvqzE7O3HKbmnux2bd68GU2aNFHtv728vCx9WUTPJSAgAG+//TZu3rypjpiTdZCGHR83L466xYKQ08893Ye7i7MKgh66GoEuM3dj0PxD+N/Kk7hyL1a9xr2YREzZcAZLD15TATlTkdfq378/xowZg3z58qmxsGjRoiZ7fSJzkxqSYtSoUVny+msursGNmBvqbanBR7Zvzd9rMOjdQXBydsK4mePQ8MWGlr4koufWo0cPVWJo7NixbLZBGa6516BBA5W599lnn6kNXyl7QVmPAb6n3SBnZ5WFIAuW0aNHZ/jG6qKjca1vP8SfOAGDTgeNnx88ypfP7PeLrJykrktQWGqWNW/eXDVp8fS07gL4RE8zaNAg9UtZFrfS1p6sg5ebFt3qFMS3L5dN9zG0RUn4uLmoIN+ZW9HqSPPcPZfR5dc9qomH1O/7ZdtFfLn8OEavPmWSIJ+8Ru/evTFhwgQUKFBABfekliORLXvllVfUv+c5c+bgwoULJn3t9ZfWY+TukUjSpXSorJennklfn8xv1eJV+PSDT6HRajDh9wmo15TfU7Jt+fPnx+uvv67KVs2dO9fSl0NW7ujRoyq4J8kBX3zxBb766isG98yIAb4MHlGTI0aSlnzx4sUM3diIhQsfCO4FD+gPZwZ67Fp4eLjqDrRjxw7VcUqyPj08rL+jJ9HTSPaVFFk+c+aM2QrNP40cOT105b9aMNwUfFSebJ4Y3Lw4cvi6q4Yd8tAZDLgbnYDef+3HudvRSNbpkawzYO6eKxi29JgKABofiw9cVVl+GSXNqGSXXxpqFCpUSAX3JChCZA+dJGWjQzY4/ve//5nsdRN1iRi/f7wK7hmklmjeBmhWoJnJXp/M75/5/2BI9yHQumgxcdZE1G5UO/W/nbh7gt8SslmS8CK+/fZbJCdbprYvWT8pZSHlqUJDQ9WR3OHDh1v6khwOA3wZIPWnpChkUlKSes6IxGvXUo7mOjkh8P334ZIjR2a/V2TF7t27p7oD7d69W3XNXbhwIWvukV35/PPPVUazjIHSUdLSwb0+fx3A8RuRKmDl7+mC3P4Mpj8uyCfZfOM7lceoDuVQNNhH1e1L0hnUc4CXK5ydUuoZ/nPkBr5bdSr18dXyE+g4fTvO3IrKUHDvgw8+wPTp01GkSBEV3JPjuUT2okuXLsibNy9mzJiB48ePm+Q1IxMjEZ0YDWkVVCygGDoW68gsBxu2dM5SfPbRZ3B1d8XkPyajZoOaqf9tx/UdmHNqjnpbOuhWy1HNgldK9Hz1SDt27KiOXrLpEKXnwIEDKrgn3XJlM0warZH5McCXQe+99x6KFSuGv/76SwVxnukm+/g8z/eGbMTdu3dVcG/fvn14+eWXVYaTBIWJ7EnZsmXxzjvvqCxmydCyJKkbd+BKuApK+bhp0b9JMWg1/HX2ONJ8xN/TFUE+bupeFc/ho7L5cvi54/NWpfBevULQODmp+/nAQ6dXGXwfzt6HXefv4mxoFM6GRqvMP8kCNJKsJmlIJYEP+T0pwb08efKY6V8DkXlIRv4333yjgtnGTJbMuhVzK/Vtb1dvBvds2OI/FuOLPl/A3cMdU/6cgmp1/wvghcaGYvaJ2aqEgdZZi07FO6FJ/iYWvV6i5yF11KTpxrBhw1TNcSIjWQc3btxYJb1IDWZpqkGWobXQ17U5MphJ/am2bdtiwIAB2LJlCydihNu3b6vgnqQjy67WH3/8of6tENmjL7/8UtWgkpR7CfYFBgZa5Dr2XQpT2WdaZyd83LwE8mZjncuM8nDVYPCLJXDpbizyBniowGiNQtlRNNhbBe/SWnciFGdCo1SQr+ef+x8JGr5aLR96NSiEbt26qgYsJUuWxPr165EzZ06Tfa+JrK1ky/jx4/HPP/+of+uymHle58PPY8jWIdAZdKoTeC6vXCa9VjKfBb8vwNcDv4anlye+/+t7VKxR8YH/fjb8LPQGPbROWrQs1BLdy3fnGoJsktTU7dmzp6qzK+tiqa1GJMlPUqZKatHLvw1jYyqyDKY8PIM2bdqoVs/SQIHdJElqCzRq1EgF91599VX8+eefDO6RXZNapAMHDlS/wE2VwZIZrhpnBveegwTnCgZ6PZD1mN3bDdULZX/gIdl+6kivwYBEnf6BR0KyHn/svISmH32jfh+WLl0aGzduZHCP7JqUKZAukkIWuQkJ/2WyPgs5lttvUz/ci7+nAnwF/QqiYT52WbVFc3+Zq4J7Xt5emDp36iPBPWFsoCLNqkpnL83gHtk06YgaEBCgAnynTp2y9OWQhe3cuRNNmjRRa4PJkyczuGcFGOB7BvKLeerUqSqI079/f5W9RY7p1q1bqsaAdAmSHX1Z4EoRbiJ798knn6gaazNnzsSGDRssfTmUxdl+/ZsWQ6eqedGgWDDqFwtSj9qFA+GkGguF4USUmzq+Lf8WQkJC+P0guye/++X3vixs5bja89h7ay/CE8JVcK+QXyH0rNATHlrWEbU1f834CyM/GQlvH29Mnz8dFapVeORjrkRewbLzy1L/HOAeYOarJDKt7Nmzq+BeYmKiqr0rR8/JMW3fvl1l7kVGRqoYyUcffWTpSyIG+J5dqVKl8Omnn6q6axLkI8dz48YN1fpbimy//fbb+O233xjcI4eqQ/XDDz+ot2ViFxcXZ+lLoizk4aLBi2Vy4p3aBdCldkH1eKdmXsTcu4WE+Hh4+fiq4F5wcDC/D+Qw5JhutmzZVDfJ52m4kaxP6UApR3Ol2YKnC8sM2JpZ02fhuyHfwcfPB9MXTEfZymXTrbE48cBExCTFqOO55YPKs7kG2QWpuyun2qTm7gMNN2T3jxzCv//+i2bNmqlajD/++CO6d+9u6Uui+5jB9xwkwFe8eHHMnj0ba9aseZ6XIBslhUOl5s7JkydVRz35pabRaCx9WURmJUfTpQbf2bNnVT0+chzSYKDvF2MQl2yA1sUF5cuWsVgtRiJLCQoKwrhx45CUlIT3339f/VyQ45j/63yMHTYWvv6++HHhjyhTsUy6H7f56ubU4F6F4Ar4X93/qSYbRPZwqk02e6WpoJRuuXrpOk5svw5J5pMYn7OWIQZ7tmfPHrz44ouIiYnBzz//rJqRkvXgT99zcHd3V5Fq8eGHHyI2NtbU3xeyQvJ9btWqlWoPL5l70jGSwT1yVNIhSxa5ckzjyJEjZv3aPAxiGXIMZ8joqbjiW0ZlLUsGU8dqBSx0NUSWJfMA2fDbtm1b6pyQ7N/65evx7eBvVc29Hxb8gJLlSj72Y2OTYlWWprOTMwZVHcRMTbIrkuwi9fjiYxIx88u1uH0lGnq9AW6eWoQU8LX05VEWOXPmDFq0aKGCe/K7r2vXrrzXVoYBvudUr149Fa2+cOGCahVO9i05ORmdOnXCjh071KAmwT0ptk3kyDVYpFOW/GzIUQ2dTmfpS6Is9vP073EsOafa2AjIlh1dahfCq1Xz8r6Tw2awTJ8+XW36Dh48GFevXs1woHzfrX3qWf5HtmPv9r34tPun0LpoMeH3CU8N7l2Oupz6PdY48bQH2R9puNauQRdoEj0QHxcPd08tarQtDBc3/nu31zJVciz3zp07+Oabb/Duu+9a+pIoHYxQZIJkruTMmVPVYpH20GS/+vXrh+XLl6N69eqYN28eu+USAXjttddUwFtS9SdOnMh7YsfW/bMUP02fBo2rO3z9A1C/eDA+alSE3SDJoUnDoS+//FIVGJf6Q08rNi//feL+iVhxYYVqsCFBn+LZipvteun5XT5/Gf0690NSYhK+mfoNqtap+sTg3qT9k3Aj5ob6Huf2zo0cXjl4+8nuuLq6om2LV9TbUdGRKFEvED7Z3C19WZQFpOZ2mzZtVHJTr169VMkysk4M8GWCv78/pk2bpmqvSAaLdBMi+/Prr79iypQpKFiwoAryeXl5WfqSiKwqg8Xb21sd0zh37pylL4mywNlTx/HFwI9UzT0fv4CU47lebgzuEd3fAKxcubKaH8ydO/eJ92TtpbVYfHaxarIhxzbfLvU2Az82IDY6Fv3e6YeoiCj0/6I/mrZp+sSPn3dqHi5FXVLBvUCPQIysO1J9v4nsUY4cOeDp6QW9Xocffpts6cuhLCCbU7KJtXfvXrz00kvqBI+sAcg68bdNJsk/8o4dO+Lo0aP47rvvTPNdIauxb98+VWdROocuXryYxeSJHpI3b1419snOnhSbf1oGC9mWqIgIDHz/bcTFxuD9voNVkI+I/iMBbykyLs+9e/dWR5ce5+Dtg9AbUhpyvFriVVTLWY230srJ77ThfYfj3MlzaNG+Bd788M2nfs7psNOq9p50R57YcCLy+eYzy7USWYq3lzecNRos/ns+tu/aym+EFdIl328G5SRNUJ4tODd16lT89ttvKFGiBH7//XeWqbJyDPCZwOTJkxEQEKC6SZ4/f94UL0lW4NKlS6hSpQoSEhLU5L18+fKWviQiqyRB8Dp16mDDhg3466+/snyxlaxjENEc5F43KFcQVy6ex8uvd0bT1m3N8nWJbI3MD6QO3+3btzFkyJAMfU5hv8JZfl2Uee3rtsfav9eiWOli+Hzs5xnKWjHW3fN19WVwjxyC/Fz4evuptwd/3l91GCfrkhCTrJ41WqdnqpEo3ZI/+ugjFdSTZBdfXzZQsXYM8JlASEiIKjQpR3Rlgkf2sbAtUKDAA7XGiCh98ktfyhXI8yeffJJlncXl53LCujO4ck9e3wAfd2aTZaXh/Xukvj1g+LdZ+rWIbN3QoUORL18+tSF4+PBhS18OmcC+Hftw/nTKxv3H33wMD08P3leiJ9Tje6FhM5w9dxqz5/zK+2RlkpNSmuG5umkzfLw2IiJCbeKL119/XWXwkfVjgM9EpKNu6dKlsWDBAmzbts1UL0sWIqnIRmFhYfw+ED1FmTJl1Dh45coV1XgoK0zffB6zd15Cst6gJiftKuXm9yWLnDhyEGuWL1ZvT5w5F+7uXNgSPYmU8pByBVKXecCAAbxZNi4qMgrDew9Xb7d9vS2q1Kpi6Usisnq9PuwLN1c3jJ44EpFREZa+HErD1V2rnhPikmDQGzK0qW7skiu1tmfNmsX7aSMY4DMRqb1irME3cuRIU70sZaGEixcRf/Jk6p+d7teW2r9/P/r3768Gs9OnT6tmKkT0dNJN0tPTUxXfNXUWX1yiDr/vuHg/uAd0rV0QNQpl57clC0RFRmJwj65ISkzEyCkzUKdRE+gNBqw8cjP16JmrltMHood16tQJVatWxbp161R38bTOR5zH9mvbU+uUumiYgWyt5Hs0ot8IXL10FR07d8QXE77I8OfuvLETkYmRqgafq7Nrll4nkbXJmTM3ur79PsLC7uGPOb9b+nIoDVfPlACfXg8kxqcc131asoskLlWoUEGVnyDbwRm6CbVo0UJlsfzzzz+4d/eeKV+aTCzx0iXcnjAR+thYOGk08K5bFxo/P0RGRuKVV15Rx61/+uknFC1alPeeKIOCg4PRtWtXVWReivCaUlySDkk6vQowlcvthzpFA/l9yaKF7def9MG1yxfR/o130LT1y+p9v267iH/P3IGzLFq1zmhZNifvP9FDJLPYWKpl7Nixqe+/EHEB/Tf2x934u9BDj2IBxVR3VbJO82bOw7pl61TdvYFfDczw5+26sQuzjs9SY6bGWYOWhVpm6XUSWaP3u/aAi4sLfvp1GmvxWRFn5/vHcg3A/V5Pj5U22WXevHlwd3c3yzWSaTDAZ+KJXZ8+fdTbJ06cMOVLk4mF/TUnNbjnUbkycgwfBp1Oh3feeQfnzp3DBx98gFdffZX3negZGcfAX3/NuvorGuMkhUzur1+mY90/S1GsVBn0H/aNet/Jm1EquCd33c3FGd+2K4uyeVKKaRPRg9q2batq8UkxcqlfJKYenKqCezqDDgV9C+L9cu/ztlmpw3sPY8ywMfD08sToGaPh5u6Woc+LT47H3JNzVXBP66xF+6Lt8VoJ1m8mx5MjJCdat2iHGzev49/tmy19OfSMZJPemOzy448/MtnFBjHAZ2Lt27dXuxZnz5019UuTCSWF3pKILLRBgcg9ehSc3N3Rt29fNSGvVKlSltUQI7J3RYoUUd2nd+3ahWvXrln6cugZSM29cV99Bm9fX/zv+19S6+7djIhXmZMSWO3RoAgalwzhfSV6DI1Gk7o4khMd4krUFegNenhoPfBRxY/g6eLJ+2eFLp69iN5v9kZSYhKGjx+O/IXzZ/hz5VhuvC5ebfZXzVEVvSr2ynAheyJ70651e/W8YvUyS18KPYOYmBi0bNlSJbt0796dTSZtFAN8JhYQEKDqr4SHhUMnh9zJqjl5eKrg3ldffYUpU6agYMGCakIuxbKJ6Pk0bdpUPe/YsYO30Ebs2LIBw/p1h4urK8bP+BP5CxVJ9+Oye7OmFFFGx8Dt27cjJikGsckpNUmlJhuDe9bp+pXr6N6pO8LvhaPvsL5o1rbZc79WdvfsDO6RQ6tds57a7Ni7f7elL4UyKD4+Hh06dMDu3bvRunVrTJo0iffORjHAlwUqV66snpN1Ke2oybp98sknGD58OIKCgrB69WrkyJHD0pdEZBdj4OHDhy19KZQBG1YtR79ur0OXnIyvJ/yAStVrPfDfpbGJkdaZ0waijI6Bh04cwuAtgxEeH66yYEO8mP1qjS6cuYAurbvgxpUbeOODN9C5Z2dLXxKRTfNw90CxIsVx+uxJ1uGzAVFRUaqXwKpVq1CrVi3MmTNHNRAl28SZehbImTOl+LieGXzWzWDAufPnMWrUKOTJkwf//vsv6wwQmXAMNGXXrWQdM6KzwtK5szG4+zvq7e+m/YrGLdo88jHxSTpVlFl4u3HCR5SR0xyurq64V/keDt8+jGRDMrxdvPFKsVd486zM0QNH0fWlrrh1/Rbe+egdDPxy4HNl3yXrn96VksiRBAfnUDUpwyPCLH0p9AShoaF44YUXsHHjRjRq1EgF+Tw9WUbClnGmngXkhyLyfjdCsk56vQFhUVG4FRujgnpr165F/vwZr7VCRI9nnBjExqYcS8usuEQdPl96FCqRzAB4u/NXV2bJ76dpY77Fz1PGwsPTC2N+/B016jZM92MTk/8LrkqTDSJ6MgkQeQd7A3mhGmtIcK9vpb7I7ZObt86KbFy5EZ9++Cni4+LRa2gvdOvT7bleJzYpFrOPz1ZZms5whp8bmxARSRafiIszzVyQTEfGKnHy5EmVuXfhwgW0adMGc+fOZcdcO8BVUhaluQoW17VOV2/exOUbN+CqS4avjw+2b9qEwMBAS18Wkd2NgT4+Ppl+LZ3egH5zD2LPxTAk6/XwctOiaSkeo8+MhPh4jBjUC6v/XojA4ByY8MtfKFm2/GM/3k3rDNVC936wlYieHkCPiYuB0/2O3yWzlWRwz8q+P3/8+AfGDhsLjVaDr6d8jVavtHqu15LMvSkHpuBC5AVonDSq/l7bIm1Nfs1EtiYmJlo9e3llfi5ImfdwXGLz5s1o164dwsLC0LNnT0yYMIHHcu0Et+KzgETBhYa1iqzOwZMnUf/tt5GQmAh3VzeULlmKwT2iLBoDc+fOfLbK/sth2HPxXkpwz1WLgc2KI3cAm+A8r/Cwe+jx5ssquFekRCn8tnTNE4N7MQnJ2HspLPWIbkKabD4iSt/Vq1eh0+mgcdbwFlkZ+b58N+Q7jPl8DLx9vTFt3rTnDu6Jo3eO4mLkxdTg3riG45DLO5dJr5nIFl2+egnu7h7w9/O39KXQQ+bPX4AmTZogPDwc48ePx+TJkxncsyMM8GWBo0ePqmcWp7QuK//9F026dsWN27eRzc8P/j7ecNLwR4Aoq8bAsmXLZvq1wmISU99uVT4nCgZ6Zfo1HdWVi+fRpV0zHNyzEzXrN8LPC1YgR648j/342MRkjFl9ChfvxEDj7IRAbzdUzMeJOlGG54EuPChjTWKjY9Gvcz/M+XkOcufLjd/++Q1Va1fN1GtGJ6VkKTk7OaNb2W4o5FfIRFdLZLtiY2Nw6fJF1WhDuumSdZDqYdHRUejRo7uKUyxcuBB9+/blqUM7w+iGiUma68mDB1E+IIA/LFZkwerV6Ni3LxKSkjB12DAEBQRIsrKlL4vILm3YsEE9V6xY0aSvyw6uz+/C2dPo3LYpLl84h3avvY3xP/8Fbx/fJ37OmmO3cOF+cC/Yxx3T3qykgnxE9PQx0LOIJzd6rUhcbBzea/8etqzZgrKVy2LWylkoVCxzwTi9QY9z4edS61m5OLuY6GqJbNv2XVvVc9nSjz8hQOYXHROpAnyBgUHYtGmTOqJL9ocBPhNbOncuvg0KQlE3N+nkAG1AALRBrO9mSacvXkT3ESPgotVi8eTJeIeDGVGWOXfuHPbu3YuaNWua5IguZV58fBwG9+iCiLB76D5gCIaOHA8Xl6cvRG9ExKt9EAnwjelYHoWCvPntIHoKvV6PRYcWIefrOaF1TcngK5atGO+bhY36bBSOHTiGuk3q4qdFPyFbULZMB/fmnZqHXTd2qcYackS3fBCDGeSA0uZL3C/nsXT5IvXc6sWXLHNN9IhLly+ohicajRZrVq9GtWrVeJfsFAN8JhY742eUcXeHu1YLZ29vBPbsASemJlvUwFGjEBMXh1EDB6JxjRqWvRgiOycduESnTp0sfSl0358/T8e5UyfQtHU7dOs14Lmyy4N9mblHlBEr/l0Bl+YucHV3VQvfWrlqqQdZzqE9h7B49mLkLZgXI6ePhLuHe6ZfUwJ7W65ugROc4KpxxdAaQ5HTO6dJrpfIlsUnxGPVuhXIli076tSsZ+nLIcnci47C/kP7pdMGAgICkDdfXt4XO8YAnwmFhoYi8M4daDUauHh4ILhfX7jm5Q+QJZ04dw7rduxAhZIl8W6HDo9+wP2dJiIyXYBPAkgdO3bkLbWSgvJzZv4INzd3DBj2LUtHEGWx2Rtmq9m1BPiqhFTB6yVfV/XZyHKkY64YMGIAvH1Mk4l88t5JdTRXGql8XPVjNM7X2CSvS2TrNmxaqwJKrZq/xDIFVmLh0nlITIyHh7uUjnDh+tfOccZhQlKoUqpXSndWZy8vBveswD+bN6vnd9q2fXBha3yTZfiITObEiRM4fPgw6tWrh1y52EXQGhw/tB93b99CvSbNERgcYunLIbL7gPqOnTvUfMPVzVUdzWVwz7KSk5OxZe0WBAYHquO5pqIz6FT2nvyvQnAFk70uka0zHs99qSXru1mLVWtXqGcPdw9LXwqZAQN8WXA0zd3V1ZQvS5lw8ORJ9VzLxMX+iejxY+Crr77K22MlThw9rJ4rVGF5AqKs9u+//yI8PBxu7m7cQLQSl85dQnxsPMpXLW+ybp67b+zGwdCD6m3J4PN08TTJ6xLZurj4WKzduBohwTlQvSpLE1gDg8GAI8cOwdXVVdXfI/vHAJ+JXLt2DVu2bIGnJ7umWZPzV6+q5+IFClj6UojsfgIxZ84ctYBq3769pS+H7rt66YJ6LliURf6JspqMgUIF+MgqXL2YMg8sWKygSV7vQOgB/H78d9VkQ+usRYdiHeDr+uSO5ESO4t9tW1QjB2muYaqAOmVOTEw07t67A18fPynBRw6AAT4TmT9/vlrgZgsIMNVLkglEREXBx8vr8R0jWYOPyCQOHTqEU6dOoXHjxggKCjLJa8Yn6bD4wLXUH1OtM2cmzyo6MkI9+/o92++mq/dicfx6ZGoVA957oidLSkrCwr8XIrBuoMqUUD83TsyWsLSoiCj17OfvZ5LX++fcP6nBvXZF2uHDch+a5HWJ7MGaDSvVc9tW3Oi1FhH354HG30tk/xjgM/HRNAb4rEt0TAy8PZ9wdILxAiKr7J4rwb3+cw9i14V7SNbr4eWqRcV8/iZ5bUfbuRVe3t7PFNz7btUpRCUkQePsjJqFsiO7NzOSiJ5k5bqV8OzoCZ+iPtBBBy8XL5QJLMObZmEx0THq2dPbNMdoo5JSAobBnsHoU6kPGxcR3SeJLjt3b0PuXHlQuWJV3hcrEX1/Hqiaa5BDYIDPBC5evIidO3eiatWqcHPjIsiaSKFr+YXzCGbuEZn8eK5kyrZrZ5qiyksPXsPONMG9/k2Lwd+Tu4/PythcKN1x8DFm7bykgntaZ2eUz+OHb18u+8xfl8jR/LD1B3gU9ICLm4sK7knwx9vVNB1bybxj4JMYX0cy+B5o3kbk4BIS4lUm80stX+bPhhWRccrD1QueGtNkMZP1Y4DPRMdzTZm5Qqaj1WpVVzsiyjp79+5VGx3NmjVDgInKFJy/E6MWUtKh8L16hVA4iAvl52HcsdUlJ2f4c25ExKv7ns3LFZNeqwgfd+76Ej1JYmIijt04BifnlO6575V9D3l98vKmWQGtS8oxaV2yaeeCMkYS0X/iE+LVc5uWL/O2WBEnvQZdXxgGd42PCvb5B3vA3ZvzOnvGAJ8JLFqU0g6cheWtj1ajQTIDfERmGQM7dOiQJa+f3YuZe89Lo72/uH2OcdDbTcvgHlEGbNiwATpXXeopjgB31mO2FhptSqF/U2z2yqaTHnoTXBWRfdEbDEhMSkTOHLlQrkx5S18OpXH9cDxyZUtpNunp54pGb5dkhqWdY4Avk65fv66O51aqVAkF2KnVKjP4kp6UucKjukSZXvBIgE+6pbVu3Zp308oYu9glJydZ+lKI7JKMgZP2ToJXCS91PNdN48auqlZEq0nZ5EhOyngW8+McvXMUsUmxanGczT2bCa6OyD4kJiTIYIgG9RozeGRlokJTxr5kQyJe/LAs/ENMU4+UrBcDfJm0dOlS9WyqulNkWq5aLRISEx94n0GvV7+EiCjzTp48idOnT6NBgwbIlo0LHmvj4uL63+SbiExuzsk5uOh3US1q5Xhup+Kd4K515522ElpXbeox6syQzrl/n/sbBhigcdLgleKvmOgKiezneG6Duo0sfSn0EGOp0LjkKPgHM7jnCBjgy6R169ap55YtW5ri+0EmJi3BJYNPL0G9+8G9sD//gj42Vv1Z4+fLe06UCRwDrZsEHEyxuCWi9M07Og96nR4uri54q9RbqJGrBm+Vlc0DRWJC5sbAY3eO4Vr0NRXcK5W9FOrmrmuiKySycfeP5zo5OaNsaR7PtTbGZkDGtTDZPwb4MkF+ULZs2aKKypcvzwHNGrkbJ3ZJKcfTwubNQ/SWLWo7w9nVFdne7mzhKySybZs3b1bPDRs2tPSlUDpc3VIyiZISmcFHlBXCIsPUs7fGGzVz1eRNtjLGuohJiZkrU7Dm0hr1LAE+CeSygy5Rinv37qkECgmma5xTyoKQFXYSZ4DPYTDAlwlnzpzBnTt3ULt2bTg781ZaI7f7AT45ppt87x6iN25KDe7l+GI4vOvUtvQlEtm0bdu2wdfXF2XLlrX0pdCTslcymMG36/xdRCek1GvRatglkuhpomOi1bOHlwdvlhWSuoiZzWIOiw/D+fDzqnNuAb8CqJGTWZpERtdv3Ej5WXNhZ1ZrZOz4rdezPJWjYFQqEw4fPqyeixYtaqrvB2VVgC8pCfpomYQb1E6Gb4sW8GncmPebKBNu3bqFmzdvonDhwqnNHMi6uLimZK8kZiCDT4J7P245r5oGaJ2d0LJsTjNcIZFti4+LlxWUOqJL9nlEN0GXoGrvyfyxZLaScHbi8onISJJdhOZ+QxuyQk5Oqo4oOQb+hsqEAwcOqOf69eub6vtBJubqkv7OrdP9IxtE9PwOHTqUJWNgfJIOp29GmfQ1HX1xm/SU7JW4RB1mbrsIvQruOaNjlbzoXLOAma6SyDadvXMWSc5J0Gq13OSw8jqkmTmiezHiogmviMh+JCXqkBiZ8rariyuYI2a9WXw8ous4GODLhCtXrqjnAgW4CLKFI7pEZP1joAT3Bs4/hENXI6AzGODjrkUOP3akfF6uxgy+p3TRvRudgIRkPZydnFCrSCA+blYczs48okv0OFeirqDfxn5wcnFSZVqK+BfhzbJCxszK583g23drH/448Yd6WzL3ygez5jaRMbi39pfj8HbJrhpseHi7wtM3Zd1FVsaJTTYcCXNpMyE0NFQ9h4SEPPLfDPdrvjk96diaRgONt/eDn6fXw4n1/LLkiC60/OdOZK4x8HlN2XAW28/dRbJeDw8XDXo3LgoXDfeinpdLBjP40srh68bgHtETyFGnoVuH4nb87ZSfM70LWhVuxXtmB3VI0wqNDcWvR3+FzqCD1lmLNoXboGn+pllwlUS2Z/ff53HjbLhqOqkzJKFKiwKcO1hxBh9r8DkORjwyISIiQj37+/unvs8lTx4kXb8OfWwsrn865Kmv4VG2DLJ36wZ9XBzuzpiB5Lv3kO311+DBrrwmPaKrMvjSBvjudxQiItOOgZm1/dwd6PQpx0QHNC2OIsEPboLQs3F1y3gNPiLKmERdIu7E3VELW6F31WPE9hEoG1QW75R+B+5aZh3bwxHdk/dOItmQDK2TFs0LNEe/yv1Yf4/ovmunwqHX6ZGsS8K/F+fgvZCWvDdWSuqHsgaf42BaRCYYi8obJ3gi8KOe0Pj7ZzgLL+7wEdyeNBmh48Yh4ew56MLCcGf6D4jduzczl0YPZfBlpnsaEWV8DMwsY/0Wd60zg3smzODjGEhkOhLA612xtwr8pHXo9iFMOzQN8cnxvN120GRDGg4ZF8dVclRhcI8o7c+HzNicnBCbEIU70dd4b6yVJLU4ySlBVkh0FMzgywRjO/CEhAR4enqqt92LFUOeaVMRPm8+dOHhT/z8uIMHVUAv4dw59Wd1nNfJCYbkZNyd8TMilv795At4UhbaE/6Ta6HC8O/QHhovLzjUEV0iyrIxkKy7Bl8SM/iITKp5wea4cuoKhq8bjnzl88E10BXhCeE4HXYaI3aMgIfWA8GewWhXpB1CvExXxoCejYtb+s3WnuVoGxGl87PhlPLT4eyk4SkBW2iyYdCrTQv5vpF9Y4AvE3Lnzq2er169ioCAgNT3uxUsiJDBHz/18xPOnsXVXr1VkE+Cda4F8sO9dGlErlgJg06HpFu3kBWSbtxE0pUrCOrX1+6DfG5pj+gSUZaNgWR/9aeI6MkaFG6AG7NuoGZMTXzb5VsM2DwAEQkRqY8bMTdwKfIS+lTqgxxeOXg7LdlJPIEbvUQm/dlyTznF4eXugxs3r/PmWrP7QT2ZC7rdL91C9osBvkwoWLCgej579izKli37zJ/vVqQI8kyehNCxY+Hs64eQjwdBExAAl1y5EbFkiWrU8aQjA8/DkJAAQ3w8Ei9fRuiYsSrj0FSc3N3gVbMWXHKEWN3ELp4ZRkRZOgaSdXJJ7aL7+COD8jtl8+nbKcdt4AQtm5oQZUi+fPlUNoSMgcWzFcfY+mMxYf8EXI68jCR9kjqqK1l9E/dPRIWgCqpRQ+WQyijgZ7rO45SxLrrPmmku37s9N/fcHxfxyHFsIkfn6qFVJ8ZcXdwQFRmN8Ihw+PuZriYzmY4xZy8+Pp4BPgfA31aZUP5+I4xdu3ahXbt2z/UaEuTLO23aA+/L3rWLemSFxEuXcLVXLySH3kbStWvqYToGRG/ejKDeveFWqBCsgbsxe4VHdIlMrly5cqljIFkn407t4+pPSXDvz12Xse5EKJzvB/ealrKeTRoia//5KlmyJI4dO4aoqCgV5Jv2QsqcLjIxEgM2DcCpe6dUNt/mq5tVsGjTlU3oWrYrKgZXtPTlOwRnZ2cV5HuWJhsS3Pv+4Pc4H3EeGicNfF19UTVH1Sy9TiJbk5ykV4WTtVotdPpk7D+4F43qv2Dpy6J0GI/lsqSOY2CALxPq1aunntesWYP//e9/sAWu+fMjz+TJuNZ/AJJu3jTti+v10MfG4fakSfB/5RU4u5m+i5zG3w+uhQpluH5Aag2+xEQky1HolAQVIjKB4OBglCpVCnv37sXdu3eRPXt23lcrzeB7XA2+kzejVHBPhkUXrTO+aF0aFfP9V3KCiJ6sQYMGOH78ODZu3Ig2bdqkvl+CQmMbjMWnWz7F0btHUwPqOoMOvxz5BW2LtkWA238/a14uXigaUJSNHLLoNMezNNnYcHkDzoafVcE9fzd/jG8wHt6u7OhOlFZCbErQ3NVdqzYvNm/dwACf1WKAz5EwwJcJISEhqF27NrZt24YTJ06oXVxbCfIVmDsHiRcumLCjjgF3pk5D7J7dKsh379ffkFU8K1dG9m5d4aTVZviIrub2bdxd/k/KO52d4V7cdEeTiRyZZC9/8803mDdvHrp3727py6GHuLo9uQbftbA4NTHXOjuje/3CaFkuJ+8h0TOOgVOnTsUff/zxQIDPGOSb0ngKLkddRqIuEYvOLMKKCyuQrE/GwtMLH3mtwv6F0bNCT9Wll0w7Dj5LHdLrMSn1xCTA922db1EkoAi/HUQP0SenrCG9fbzh7uaOpcsXYdgnX0EjTSPJqhjzYpjB5xgY4Mukzp07qwDfxIkTMX36dNgKCY65FS1q0tfM9d3/cH3wJ4jds0e2qZEVDHo9YvftU7vg2V5/TQXrnsRLb0Bjb2+U37QZBjc31anYs1o1+DRrliXXR+Ro3nrrLRXgmzx5Mj744AN1HOp5JSTrEJ+oM+n1OTpjF93EDHTRzR3gYYYrIrIvDRs2RN68ebFo0SJcvnxZ1eVLS04c5PfNr94eVHWQepYg38P1lPXQ41z4OXU0tGuZrnBxTqkdJyTgJ/X76Pk8yxFd+b7EJMak/jmnNzc9iJ5E46zBi81aY/Hf87FsxRK0bd2eN8zK8IiuY+FswQSL22HDhmHmzJn4+OOPUchKas9ZgrO7O3KPGY3of7ciOdT0HYD1cfG49/vv0MfFIm7/flw7cOCpn1MtIQHFQ0JS2oJrNCqwGdy/n3qbiDKvePHiKoNl8eLF+Ouvv/DGG288d3Dv4wWHcSsqQWWUBfmyy5cpuBg7SLKLLlGWkGyVgQMHok+fPvjqq6/w008/PfZjnZ2cVZCvYd6GuBh5MfX9eoMef5z4QzXkkKOhn2397IHPk+O7nUp0Ug066NnJaY6YqP+Cdo8jc8WFZxbiVNgpOMNZ3XcfVx/ecqKn6Pl+bxXgGzdlFFo2bwMXl/82KMh6MIPPMTDAl0nu7u4YPny4OprWo0cPrFy5MsP14eyRk4sLfBo1zLLXdy9dSmUJGuLiMtRNOPV74eSkgnt5f5gO14d214koc0aMGIG///4bAwYMQIsWLRAQ8Ow13Eb8fRz/nrmDZL0e7i4avF6NP6fmyODTZ1G2NZEjee+99zB+/HjMmDEDXbp0Qa1atZ4Y5KuWs5p6pFUlpAr6beqngnwPz2+ik6Ix8+hM9TaDfFl3RHf1xdWq/p4TnOCicUGvSr3gpuFmE9GTyHhVqkQZvNTyZSz9ZxGm/zwFvT7sx5tmRVLWw04M8DkIBvhMNLH79ddfsXr1aowdO1bt5FLW8KpWDXmmTEbEwkXQxzx9NzbsyhVs37gReWrUwBszZqgsQyIyrbJly6J///4YPXo03n77bSxduvSZjupGxCVh7Ylb0OkNKrjX74ViKBrCrAnTNtl4dHF7PTwOK47812zJx41TAqLn4eHhgSlTpqBVq1Z47bXXsG/fPgQGBj7Ta0idt+8bf4+5p+aqIJ+RvH30zlFVt0+CfCsvrFS14aSra+N8jR16U/lZj+iq0xxPuF9brm5J+XiNCwZXHYzmBZqb8SqJbIurpxYIk2YbyTi18yaGD/kaGzavw6jx36BG1VqoWrm6pS+RUrHJhiPhbN5ExzP+/PNPVKlSBYMHD1aL3Was8ZZlPEqXVo+MOLxqFYb89Sc+yZuXwT2iLCRH0zZv3ozly5errGb5c0YlJuvVwkv+VyaXH4rnYHDPVIyNhhITHszgC42Mx6hVpxAemwitxgmlcvmiasFsJvu6RI6mZcuW6Nu3LyZMmIBOnTphxYoVcHN7tuyvfL75Uuv0pT2+O37fePx97m8V5LsendIA4krUFdyLv4eOxToyyJfBZkMS5DO+nZ4kfUqdvpxeOdG8IIN7RE9SvmFebPrzFPQ6Pc7uC0URBGP8d9/j3Z5v4b2POmP5wrXIkysvb6IVYJMNx8IAn4lI7T3poNa6dWu0bdsW//zzDxo1amSql6fnZJxcs+YAUdb/rC1YsABVq1bF119/DU9PT3z66afP/DrMRcmaGnwPH09bc+zWf8G9nH74/o1KcNE8f4MUIgJGjRqFAwcOYMOGDejQoQMWLlyYGmR/XnKkt1/lfvB388fSs0uRoE9AQnKCCvZturJJBfmye2RHoEcgauasyQ68T9roSEx8YoDPSI7oEtGTFaoYhMSEZGxfeBZ6nUEF+XIWKYcRH/6A3Xt3YPwXP6Nju1fh4535TVsXNy3ylgyAh0/mxlNHxSYbjoUBPhN68cUXMXv2bFVkXnZyZ82apSZ4ZDkM8BGZj3SSXL9+veoqOWTIEISHh2PkyJGZ6qxLmSP3Xusix9MezOALj0vJVHF2csL/2peFrzsLYhNllhSWl3qkcopDspllLjh//nz4+/tnOsjXrWw39RBrLq7ByN0jkaRLwqHbh1KDUvtu7kOPCj3g6eLJb2ba74tryviWmJAIMEGcyGRK1MgJGIDti1KCfNfPhiOnRwk0rpgLcXGxOLnnKvx9A6DVZi7kIGVJLx29gxptC8MnG8stPTse0XUkDPCZ2KuvvqqeO3fujI4dO+KLL77AZ599po7xkvkxwEdkXqVLl1ZBPtnwkGyW06dPqy7jmV3gUuYabTypwLzUPSQi0/D19cWqVavQpk0brFu3DjVr1sSiRYtQsmRJk93ipgWaqucxe8cgQZeQepT3XMQ5fH/we7yQ7wW1njNmouX3zY8A92dvfmSPR3Qf52bMzdR7KeUiiChjStTMqZ53Lj2vjusKPz8/ODk7ITYmBmHh99Qc8FlLFjwc4IuPTcbOJedQum5uOGtsM8vWN9ADnr7mz0LkEV3HwgBfFgX5JJNFjupKgE+Oakhmn7yPzMuYOZSRjrtEZBpSh3T37t1qgbtkyRLs379fjYF169blLbYAmWRzDCQyH1ncrlmzBh9++KFqwla5cmWMGzcOH3zwgcnq5UmQr0auGrgWdQ2RiZH4dte36rju+Yjz+OnITw98rDTleKvUW4907nUUT5sLSnBv4v6Jqgaf1kmLsoFlzXyFRLYf5CtQNhBR9+IfeP+8+fPUZq8E/iTxpV+/fnB/xoaH8nMrgb3bV6IRH5OM/asvwWY5AWXr50H+MtnN9iV1SRJ0dYLGWcu5oIPguaksUrt2bRw8eFDV4duyZYta8E6fPh06nS6rviSlQ69P2UniEUEi88qVKxe2bt2K3r174/Lly2jQoIGa2EVGRqbbRZeyjkFv4BhIZGaSrfLLL79gxowZKqjXvXt3tGjRAufOnTPZ1/B19UXJ7CVRPWd1jG8wHtncs6kAlQT00j50Bh1+P/47dt7Yidik2NRHfPKDi3F7nwvKZsfDdHodph6cqroVy70rnq04elbsaYGrJLJt7t4uCMrn88Cj54Bu+GvxTGh9kjF++kg0aVMXR87ueeTjnvQIzu+LZu+XRVBeb2g0Turn2FYfkhx8ZNNVXDxyB0kJuix/JMQm3c9INiC7TwicGPpxCMzgy0K5c+fG2rVrMWbMGAwbNkxN7uSo2rRp01CpUqWs/NL08KTORDvmRJRxsks7ceJENG/eHN26dVPdJefNm4fx48ernVz5ubwZEY8B8w5CpzeoiU+Qz/Mf4aDHj4McA4nMT37uZOyT7OW33npLHd0tU6aMqlH68ccfZ+rI2sMK+RfCzOYzseXqFtWEw3jM9EzYGay7vE415fj92O+PNJAolb0UupTpYtd1+540F7wZexN34u6oOoeF/QtjbIOxKnBKRKZRq1YtlfQiG76///47GjdurOrVy/o4R44cGXoNNw8tWnQvhwuH7yA+2jY3hcNDY3Fmzy1Vq/Do5mvqYQ5p85YNiQz9OAJ+l7OYZI7JJK59+/b46KOP1OROukz26NFDdZqUYxyUdYzHMZjBR2Q5Uo/v5MmTGD58OCZNmoROnTrh559/xtgJkzB0/W1cvherAnw5/NzRtHQIv1VZMA46sdEJkcUUK1YM27dvx08//aS6i8umrzRi+/7779GkSROTfR3J4GtbpO0jP/8+rj5YfHaxylZ72NG7RzH5wGT0qtjLboN8qXNBp0cPLhnviQQ+KwRXYHCPKAvIeve3335TNeplDfzHH39g2bJl+Oabb1QCTEZq1WtdNShaJcSmxyHpAnx4wxXoZVPbTJxldHN2wvEre1DIq7LZvi5ZDgN8ZlK4cGGsWLFCFVru06cPpkyZojqrSU2W1157jdkVWYRHdImsp/C8ZO7J5E4mc1Kfqlbbd5Cz7SC4enghl78HBjcvDn9P8xcftncGg151yyUiy5EFrNTka9eundr4lUyWpk2bqg0PGRtz5kwpVG9qkrXWp1IfFPQrqI7oSjMOoxP3TiAsPgwXIy/i651fq0CgNONoU7gNcnnngj2VKXjcZm/a+/FwdiMRmZaUrjp06BDGjh2Lr776Cr169VKn26SMlSTA2DMZiys3zw+/IA9cPHwndVzKahJU3H56Bf6YOgZtesw1y9cky2KAz8w/2JLJJxO6L7/8Uk3oJEVZCjBPnToVRYoUMeflOAQG+IisS4UKFbBt2zZVm2ro9AWIiY5GfFw86gR6MbiXheMgs5iJrENISIjKZOnatavKZJk7dy5WrlyJb7/9VgUAM5LJ8jzzz5eKvKQeaV2IuIB+G/vhbvxdVYNOHpejLuN8+HkVFMztkxv2XoMvbYBP48yO4kRZTUoTSJkCSXCRY7vLly9H9erV0bNnT7s/3SZjsWQhmjsT8d9vF6hnzgUdA5tsWICPjw9Gjx6tOkvWrFlT1emTmiwyqCUnJ1vikuxWYmKietZqGcsmshYywXj33XcxZdIkeHh4QKdLxqLNB/H+iO8xf+dZhMem/NySaRa2yUlJ0KQZA6Xu4ZnQKBiT+rTpLHqJKGvVr18fBw4cwMiRI5GUlKTKuMic8MiRI2a79ZLVN77heNU1Vo7nemg9VKOJqKQo1VV24emFDzz23dr3QEDMVsj9FRqt5pEjc7tv7k6tV+ji7GKR6yNyRAULFsTff/+NxYsXq7r1crqtZMmS6s9kWlwPOxYG+CyoXLlyqsvkDz/8oBa5n3/+OerVq4fz589b8rLsSkxMjHr29va29KUQ0UOaViiIPDmC4BeQDe65iuKmez78sfU0Bs/ZjVuRjtHdMaslxMepZw9Pr9Tg3nerTiIiNgkaZydUKZCNmZNEFuLq6opPPvkEx44dU7VK9+zZo46pSXMiY904cwT5pr4wFWs7rMXStktVsE+CfNFJ0Vh/ef0Dj5+P/Iy/Tvxlc0G+uFjjOOiR+j65vwvPLFRNSZzhrIJ7dXPXteBVEjkeyWhr27Ytjh8/jn79+uHWrVt4+eWX1SZwdHS0pS/PbnA97FgY4LP0N8DZGe+//z5OnDihOk3u2LFDHWFbsmSJpS/NrgY0L6+UxS0RWQ8/TxdMfq0icmbzQWBgILy8vKHX6XDjdhgG/bYZuy/cxcEr4amP6+EpizTKuLjYWPXs4ZlSPP+37RcRFpMIrcYJJXL44ruXy/F2EllBJss///yj6vJJ0K9v375o3bo1IiMjzXodXi5eGFV/FCoGV4TWWfvIQ2rUbbu+DX8c/wNHbh/B6bDTNhHsi4+Nh4urC1xc/svQOxN+Bhsub1B/JxeNC4ZUH4KS2Uta9DqJHPl0m9Sl37lzJ4oWLaoasVWqVEk1aKPM43rYsfDcopWQNuEyuZs8eTIGDRqkdi++++47DBw4kA04MsG4+8MMPiLrVCa3H5b3qoPTt6KQpDNgyJwdOHEtTB3THfX3fnj7+D0wBr5YJgc6VsnDcTGDYmNTNjk872fwScdiuZ/ZvNww7Y3KKshKRJYnP5dvvfUWateujddff13NCWvVqqXqUxUoUMCsQb4JDSeo+nxxuv82VaQu3/h945GoS8T2G9ux48YO9f78vvnxUcWP1OdZq9iYWHh6Pdgh+FrUNXU0VwKX3cp0wwv5X7DY9RFRCslglhJWUptPmm/UqFEDCxYswAsv8OczM7gedizM4LOybD7psLtu3ToEBASoLmsywJnrmIY94o4FkfVzd9GgXB5/VM4fgLm9XkDlorlVpkVifDyiIsLgDIM6TipWHr2JeXuvIj5Jh4R0HjozdSWzvQy+lMW38e74e7gwuEdkhQoVKoRNmzbhzTffVEd3pfj80aNHzR5sLORfCKWzl059tC7cGp/X+ByuGld1hFfjpIGzk7PqwDt5/2REJEQgQZeQ+tDpdbCmI7ppj+eq9yX/F7y0p47BRLZOkjIkg2/SpEmIiopSJ9z++usvS1+WTeN62LEwg88KSR2+Xbt2oVmzZqrgqEy0pB5L2iwWyhjuWBDZFn9PV/zxQR0s238J46f+pOqy+BQogNffeQ8LDt5UWX6rjt7E6qM30/18yUh7s0Z+FSwkIC4m+oEAXyr+OiGyWu7u7uq4buHChTFixAg0btwYGzduRKlSpSx6XQ3zNVQZe5K9J0dzF59ZjNtxt3Ep6hKG/DvkgY+VjL72xdqjes7qsIYMvuxB2VP/fCj0EFZeWKmO54oQL/N2tCSiJ5M1b69evdQY2L59e7XhIR3GX3nlFd6658D1sGNhBp+VKlKkiJrMSV0WObb7v//9z9KXZJPu3bunnv39/S19KUSUQZ6uWnSqURibfxqBuiE6HFk4GZu//xhDXywBF41TavfX9MjR3mmbzmLvxZSffUcXER6unn38/FQ2ODPCiWxngfvFF1/g66+/RmhoqMpiuXkz/Y0Nc5LMvjdKvoG3Sr2lOvAGeQSpbL6HSZOO34//jh3XU47yWkpcTBySEpPg7ZvSbO1s2FnMODIDOoNOHc99seCLKJXNsoFTIkpfixYtsGzZMlWb9I033lDZzfT862E/Pz/ePgfADD4rli9fPqxfvx7VqlXDkCFDULx4cVWbjzJOJsUiJIS7s0S2xs3NDQsXLlS1V1atWoUSJcZhfI8hWLz/mjqi+7CohGQcvx6JZL0e0zadQ4HsN9PNVAv0dkO7irmRw88d9i7s7m31HJA9CPPV0Wa9uiU+bvz1T2QLhg4dirCwMIwdO1Z1m5QFrmT4WQPJ5pvceDJmH5+N0NiU+ZaITY7F8bvHkaxPxuwTs1M61To5o3xQeVXrTt42l3t3Uha2xgy+f6/9i2RDsuqaK8G9QVUG8YQMkRWTOeDcuXPV+CfZfLt371aZffRs62FJdpF5Ndk/zvCtnGTwLV68WB3PkOLLcjyjRIkSlr4smyHt1kVwcLClL4WInoMsZBctWqTqUE2YMAGVK1fGuDffTPdj9XoDvvnnOJYcvI5kvQHn76Q0mHjYudBonLwZicHNSyCX/4N1mezN3TspAb7LbgVw+v6xZumg+0b1/Ba+MiLKKGm6durUKdVw46OPPsKMGTOs5ubl9s6NwdUGP/A+yRSedmga5p6aq4J8lyIvqfdL445bsbdUBqC5gnx3b99Vz9kCs6lnaRIi5Hju++Xeh8b50exDIrIubdq0UeOg1Kd/6aWXsGfPHnh42Pf8zVRkPJb1cP78nPc5Ch7RtQF16tRRx3RjY2NVenJiYsrkhDKewRcYGMjbRWSjJEC/dOlStfPYo0cPXLhwId2Pc3Z2wtCWpdC5VgH4umvhqnV+9KFxhlbjjIjYJHy36iTm772S7mPDydB0swRtTdid23D29MfxWB/1Zzni/HmrUnihFLOaiWyF1J76888/UbRoUVV8XjKbrf14cffy3dG1TFf4u/nDTeOmMuYkqCZHdn849AOWnF2CzVc2P9DsIisz+LIFZXv0OlmMlMhmDBw4EK+99ppqPjR48IObCvR40qgkISGByS4OhBl8NuK9997DypUrsWTJEgwfPhwjR4609CXZBNmxyJ49u+rISUS2q1y5cmr3tm/fvqrY8pYtW9SiN70gX+/GRdUjPZHxSej5x34cux6JiLgk/HPkxmO/5o5zd9GvSVFVE9CWM/g0nn5wcnKGNCJuVS4XXqqQ29KXRUTPyMfHB3/88Qdq1aql5oQ1a9ZErly5rDrI17l0Z/UQckx3xI4RSEhOwOE7h3HkzhH1/u3Xt6N3pd6qKUdWZvClbbJBRLZHxpSpU6di+/btKvFF6vNJbVLK2Gk2lqtyHMzgs6FB7aefflI/nKNHj8aRIykTI3q8pKQkNajlzJmTt4nIDkhHtSZNmqjJnYyHz8PX3QXfv1EJ5fP4wUXj/NiHxskJZ25FYfzaMzhyLeKJD/k4vcEAa3T7VkoA09k55de9pyuPoxHZqqpVq6pNXqnJ179/f9iSennqYXjN4fB08VTZfNLgQo7pXo66jEn7J+HonaOqbp/xEZ6Q0iAos0Jv3D/JERyIBF0C7sTdMcnrEpH5SR25WbNmqbe7d++uTrfRk127dk09cz3sOGw3LcEByTHT8ePH4/XXX8eHH36If//9N3XRRo+6cuUKdDodChUqxNtDZAdkvJs2bRrKlCmDTz75BO3atXuuHUkJ8v3yTlWcuBGF+ORHj+FGxydjxLJjuBeTqIJ349acfuprFg7yQv+mxawu2+/q5Yvw8PKDE39XENkFOZo2Z84cVXS+S5cuaNasGWwpyLegzQJViy82KRaj947G7djbKsg39eDUBz5WOvN2KtEJdXLXydTXvHYpZXEbki8E0w5Ow9XoqyqwmNM7J/zc2FGSyNbUrVsX77//Pn788UfVZfzbb7+19CVZtfPnz6tnrocdB6NDNubVV19V3YQkg2XmzJmWvhyrxgGNyP5I57TPPvsMERERqh5LZrKiS+XyRaV8AY886hULwg9vVUY2L1dVr0+y+Z70cHICzoZGY+zq04iKT0KSTp/60Oktl9mXmJCA0BvXEZLDeo/xEdGzkZIjstEhevbsqWor2RJfV1/VTbdmrpqY2GAigjyDoHXSqoBe2ofOoMNfJ/7C1mtbVaOOtI9ncfXSVfW8W78bp8JOqeCe1AWUbEJzdvMlItORUlVBQUHqVNuJEyd4a5+A62HH42SQ1ipkU06fPq0yWKS23NmzZ+HllTV1S2yd7Ox88MEHmDRpkjraR0T2QRa0UpNPxsK9e/eqzrpZITQqHquP3UJ84uObbRhgwPy9V3EnOkEF85wl2peGr4cLXq2aFzUKm7/+06XzZ/Fyw2qo3fZtRFWQrpXAa9Xy4ePm7MROZOske+/XX3/FuHHj0K9fP9iqe/H3sP7yepXRZ3Ql6grWXlqbGsx7uBlGyewl8U7pd+Dt6v3U129StolqTlflhyqqoYd8zsSGE1EiG8dBIlv222+/4Z133lEddqURG6VPTv799ddfOHz4MMqWLcvb5ACs6ywRZUixYsXUEV0pMCpHdiWbhR6/Y1GwYEHeHiI7It10Zfe2ffv26rja2rVrVUaeqQX7uOOtGvmf+nGNS4bgw1n7VJDv4S2ziLhE/PjveegMBtQuYt5u3tcuX1TPwTlyIcqsX5mIstpXX32ljurKETUJ9kltKluUzT0bOhbr+MD7JPcgyCMIf578Ezr9oxssx+4ew8T9E9GnUp8nBvniYuNw+9ZtlCpfSr2myOGZg8E9Ijvw1ltvqXXw33//ja1bt6JOncwd57dXXA87Hmbw2ajbt2+ro2ri3LlzKk2ZHtSqVSv8888/6v6w7gCRfZHFmnST3LlzJ1atWmXxOlSX7sbg538v4GZkfOr7YhKTcfJGFJL1BnWMt1Dgo9nWaQOTrlpnNC4ZrI4Jm8LvP0zGpFHfos4nvyFc4w+NsxPerVsIPRsWMcnrE5Flffrpp/jf//6napLKpoe9jfHLzy/HxisboTfoU98v9fsk60+O8Aa4BagAodTSa16wOfL65H3gNY4dPIY3mr6BOp/Wgb6sXmUClg0qi2kvpBxxJiLbtnr1atVJt0aNGqp8VVZs9toyvV4PPz8/BAQE4PLly5a+HDITBvhsfPd22LBh6vipHEOlB+XNm1fV6QoPD2czEiI7JI2G6tWrh/Lly2P//v1W93MuC9Sxa07jr92XVZDvMR+V5uNTAn5d6xRA3aKZ37T5rG937NEXRHCZOnBxdUGApyt+71oNebN5Zvq1icjyZH4jm73SSfLMmTPIkycP7N3VqKvos7EPQmNDUwN/UirBU+uJjyp+hIJ+/53aWPzHYny/7nvkbZcXnp6eqnOv1N5rmK+hBf8GRGTKeZbUpt+wYQMWLlyIl19+mTc3DUlyKVKkiEp6WbZsGe+Ng7Cu1RA9k/79+yNHjhyYPn26+gGm/9y7dw9Xr15VtQasbdFPRKbrpNa6dWscOnQIf/zxh9XdVgnWDWhaDB/ULwQ/Dxe4apwfejg98GcXTcpY9cvWi5i94xIW7b+a+lh/4hbinlALMD2nw3Rwz1MSLi5aFdyb/FpFBveI7Igcyx06dCji4+MxfPhwOII8PnlUDb0ygWXg4uyiHtKkIzY5FlMOTMHSs0ux7NwynAs/h2NnjiF74+zQarUquNe/Sn8G94jsiMyzRo0alZrRnJSUZOlLsipSd09I3WpyHMzgs3E//PCDqsfXqVMnVYuFUmzatAkNGzZE9+7dMXXqVN4WIjt1/PhxFciXzJVTp07B3d0d1uhp/azkP0/acAazdlxS2X4PfPz9Eyf5s3liULMS8HZ/evncpMRENO3UDf51XkdgYBCGtymNNuXZTZfIHpsOFS9eHFeuXFGLudKlS8NRSAZffHI8hm4din239qlju8aMPjmOG30oGgk5EpA9ODteKvoSPq76saUvmYiywGuvvabWwdJhXNbFlOKLL77AiBEjMHfuXLzyyiu8LQ6CqU02rlu3bmpiJz+40k2SUuzbt089V6hQgbeEyI6VKlVKFZiX2iLWHMyXXeYnPZydndCncVF0rlUALhonlc2X+nB2hsbJCZfuxmL06pM4di0Cx69HPvK4FhaX+vXOnDwGjX9OlbkiPF00FvzbE1FWNh2SRhtSa0kyWByJs5MzPF08MbLuSFTPWV1l6clDsvpEYq5EOGucoXHWYNWFVU/daCEi2/TNN9/AxcVFBbRiYmIsfTlWg+thx8QMPjuwaNEi1U1SahBIN0kC2rVrhyVLluDIkSMoU6YMbwmRHbt27ZqqMSI1lqRbmBQUtmVX7sU+0KwjPkmHb/85od6nUw07Hl9EunaR7OhSuyC6/LAJ4ZHR8PLxha+PNxb3qIU8Aay9R2SPJLhXqVIlVa5AapM6YjdJCd6dCT+D6MRo7L21F78d/g1379yFq7urKjCvcdJgfcf1LMJPZKd69+6NyZMnqw0PKV3g6OT3QmBgIDQaDUJDQzn2ORAG+OxkUlOzZk3s2rVLBfgk0Ofo9yMkJETVYbh79y5r8BE5gMGDB6s6LDKpk8mdvZEuvR/O2odbUQmPzUKRd8vRNGcnJ0RGhCMhPh7ZA4PweZuy6FDZ/ovvEzmylStXokWLFqhdu7YK8jl6N8keP/bAhtgN8PH1QXa/7JjUaBKKBRSz9GURURaRIJY0HZLa67LZmz17djh6CRsp2fDSSy+ppBdyHDyiawdkEve///1Pvf3JJ5+oiL0jk05yt2/fVpNcNtggcgwy9knB+fHjx+PGjRuwN/mze2HWu9XRr4k07Sj8yEOO9nq4alTdKcnykw0OGf+qFMzO4B6RA2jevDnq16+Pbdu2Yfny5XB04ZvCcWncJfQv0h8rX17J4B6RnQsODsaAAQMQGRmJb7/9Fo5u69at6tkRM7odHQN8dqJBgwZqcidn7RcsWABHtn79+tQOm0TkGOQIlgT5YmNj8dVXX8EeBXq74a0a+fFh/cKPPPq+UAz/a18O7lJrT6+DXqeDtyEWP79TzdKXTURm3uyVWnw63bN13bYnstG9ceNGaMO06FCzg8NnMxI5CgnwBQUFYcqUKao2syPjethxMcBnR0aOHKme5YiaI7cJl2MqQgKeROQ4evXqhVy5cuGnn37C2bNn4WjqFwvC5NcrIq9zGMI2zMAHRf9rukFE9q9GjRqqBvGxY8cwe/ZsOKqDBw/i5s2bqmSNFN4nIsfg4+ODzz77DImJiRg+fDgcVXJyMtasWaOOKVepUsXSl0NmxgCfHZGOsdImXBa2P//8MxxRfHy82rGQRX65cuUsfTlEZEbSZEMmdDKxkQmeI6paIBsSts5E7Ml/VT0uInK8bpJyPH/YsGFqTuSIVqxYoZ45BhI5ng8++AAFChTA77//rjY7HNHOnTsRHh6OZs2aqSYb5FgY4LMzUlxeditlkRsREQFHs2XLFnVETyZ1jl5gmsgRde3aFcWLF8fcuXOxY8cOOJqoqCg1DkpX4aJFi1r6cojIzEqWLIkuXbqo42kTJkxw6ADfiy++aOlLISIzc3NzU6Va5Ki+HNl9XGMye8ZNDsfGAJ+dKVSoEPr06aM6CdlrHaonWbhwoXpu1aqVpS+FiCxAq9Vi7Nix6u3evXs7XNMhKa4vJRo4BhI5Lpn/eXt7q03f69evw5FcvXpVZa/IqZY8edg9nMgRvf7666hWrRpWr16Nf/75B45EApqyHpbMPcngI8fDAJ8d+vzzzxESEoKJEyfi1KlTcBRSb2H+/Pnw8/Nj/T0iB9ayZUuVubF371789ttvcCR//vmnen711VctfSlEZCE5c+ZUc8GYmBjVcMORSPa2LHA5BhI5LilTMGnSJPV2v379kJCQAEexf/9+nD59Gk2aNEFgYKClL4csgAE+O+Tr66sabkgdKhnUHIXs0oSFhaF9+/YqPZuIHNf48eNVNp8sbiMjI+EI7t69i1WrVqlMbtm5JiLHJac55Ki+1KHatWsXHAU3OYhIVK9eHW+//baqTS9JL442BkoWIzkmBvjsVOfOnVXXHOko+/fff8MRcEAjIiOpwycL3Fu3buGLL75wiBuzYMECtbEjkzrWICVybLLRKRsd4qOPPoJOp4O9O3nypMpeqVOnDvLnz2/pyyEiC/vf//6nyhVI2QI5vm/vZJyXLGZ3d3e0bdvW0pdDFsIAnx2nJn///fdqkdejRw+7z2CRmoOLFi1S9VYaNGhg6cshIisgR9Sko7bs3O7Zswf2TI6kTZ8+Xb395ptvWvpyiMhKyhXIQ8oVGI+r2bMffvhBPb/11luWvhQispJyBdJ4Mjo6Wq2H7b3hhtRhvnbtGtq1awcfHx9LXw5ZCAN8dkyOaEmReflB/+STT2DPZsyYoWrwffjhh2wHTkSK1OOUjQ5ptPHuu++q5hP2SjoGHzx4EC+88ILKXiQikk3eqVOnqgyWzz77DBcuXLDbmyL1BmfOnKnG/TfeeMPSl0NEVqJv376oXLkyli1bpmq12zOZ84qePXta+lLIghjgs3PSQU2OKUybNg1bt26FPZIjaZK54uLiohbxRERGckShQ4cOOHz4MEaPHm23N4aTOiJKT758+dQxtdjYWHzwwQd2m8EiZVoiIiLwzjvvwMvLy9KXQ0RWQuoxSyKIdJXt1asX7t27B3skjTXXrl2L8uXLo1atWpa+HLIgBvjsnOzaGo9tde3aVe1w2pu//voLV65cwSuvvKK6BxMRpTV58mT4+/tjxIgROHLkiN3dnPPnz6uaK7KQb9WqlaUvh4isTPfu3VGzZk21+JOFrj3WnZINHGNZGiKitCpUqIBBgwapkk4S5LNHo0aNSq25yjrMjo0BPgfQvHlzFdw7c+aMSlO2t0ndN998o96WbplERA/LkSOHqj8lx/ilAUVcXJxd3STJzpGxUEoxyE41EdHDdZl/+eUXeHh4qHmgZHrYE9ngkDlux44dUaxYMUtfDhFZoWHDhqFUqVIq2/ePP/6APbl48aLqmC616FmDlBjgcxBSZL5IkSJq53bhwoWwp66RMlFt3749SpcubenLISIrJY0nXn31VRw9ehSDBw+Gvbh8+TJ+/fVX1UykS5culr4cIrJSJUqUUF115aiu1KiTDQ972+iVOoNEROmRDQ459eXq6qqymu2pJul3332nSlbJ/FY6qJNjY4DPgY7qyo6FZHe89957uHTpEmxdQkIChg4dqt7mpI6InkSOK0gtUqlJKkd2//77b7u4YTIGSvMQmdS5u7tb+nKIyIq9//77eOmll7Bv3z67OfUgGxzHjx9XXSPLli1r6cshIitWrlw5FQyLiopSJzrsYaPj5MmTKoFHOgZ369bN0pdDVoABPgdStWpV1XQjLCxMTYRkF9fWsxLPnTunCipLbQUioieROnyzZ89WGx1yhMHWj6nt3LlT/X2KFi2qOogTET1to0MWgnKMa9y4cZgzZ45N37DIyEgMGTJENVmTUgVERE/Tu3dvtGjRQs2h+vTpY/M3rH///ip7b+TIkSpLkYgBPgfz8ccfq+OsBw4cUJl8ttpN7ebNm/jqq69UZuK3335r6cshIhtRp04dtbCVhaF02JVnW6TX61MnpvL3kSMnRERPExgYiEWLFqljXFKf+eDBgzZ702TTWormy4KdtfeIKKM1SWVztHDhwqoR5U8//WSzN27FihVYuXKlSuJh7T0yYoDPAXdv5ThDmTJl1JHdMWPGwNZIUFK6pEVHR6vjaZKSTESUUdJhTDJ/5ViD1OaTGk62ZsqUKdi9ezeaNWuGli1bWvpyiMiGyGJQFrbScEhOdEiQzNbIMWPZ3AgODsbnn39u6cshIhsSEBCAJUuWwMvLCz179sTWrVtha2SDWmoJigkTJqjAJZHgvwQHJFlvMqjJ4CYZfbbWSUgKpC5evFgdyx0wYIClL4eIbLQeX7Vq1bBs2TK1YWBL2czSLVI65spYLot0+fsQET0L2eSQzDfpvijH1aQmlS3VYO7cubPanJk6dSr8/PwsfUlEZGMk2UU6z0od4zZt2uDYsWOwJQMHDlSN1mTTulatWpa+HLIiDPA5KElLXr58uTqrL5O8NWvWwBZIxyPZaZF6K7/99pt6JiJ6VtKQQoJ70l38xx9/xIgRI2xmYStZh5J5IxnYBQoUsPQlEZGNkgy4l19+WWXDybOtFJyXDQ5ZjEtndCk7Q0T0PGTck+7iUp++efPmuHLlik3cSEnUkaPFhQoVYv1RegQDfA5Mov3z5s1TmStyRGPjxo2wZrKglYlceHi4qrsnnZCIiJ6XHO2SzY0cOXKoAN+oUaOs/mb269dPHc1t1aqV6ohJRPS8NBqNOsVRv359rFu3Dp06dVKbCNZM5q1yHC1fvnyqVAERUWb07dsXgwcPxtWrV9G4cWP1bO2nOCSDWRrGSS1BOWZMlBYDfA5OFolSky8+Pl4d0bDWTD4JQspiVpqDSJCPR3OJyBQKFiyI1atXq8LzMsGT5j3WSrpfytFi2bGdNWsWj+YSkUmymZcuXarq8klWiGS0yJzQGklDEGkMIk2FFixYgOzZs1v6kojIDkgH2g8++EAFz2TD49KlS7BGkuQiSTlSf08ysGvWrGnpSyIrxAAfqeNesoMrNQhat26t6ttZG1l4yy5FqVKl8Msvv3BhS0QmI9nAksEcEhKCYcOGqeNf0qXWmvz9999q8ik7tQsXLoS/v7+lL4mI7ITUsFu7dq062SFdGWUuaG0dxs+dO6eO0MXExKi6exKQJCIyZW1mqWd3/vx51KtXTzVis7aTbMZagbJ2l2slSg8DfKRIHZO5c+eqTDnJkJPaTtZSdF52VUaPHo08efJg1apV8PX1tfQlEZEdFlvetGkTcuXKhe+++06NiTKZsgbr169XR+fkOJ2xwRARkamDfDLHMh7XrV27ttVksUgheekYfuvWLXzxxRfo1q2bpS+JiOwwyDdp0qTU5hU1atRQ8y9rIFnVMg/8999/0bRpU/z8889MdqHHYoCPUklgT3ZwpbvuoEGD0KVLF7VTaikSYJQuv0OGDFHHMOT4cN68efkdI6IsUaJECezatQvly5fH/Pnz1UJXOkxa0qJFi1T5BCl+L93emjRpYtHrISL75ePjo4J8r7/+Oo4ePao6jW/YsMGi1yRZNBJslAw+abImWdZERFkV5JN6zBMnTlSdxSVrWN62ZNKLXEfLli1VYzgZk+UUh5QpIHocBvjoAbKg3blzJ4oXL6661MoRiCNHjpj9LknmjHT3NWbubd26FSVLluR3i4iylHG8kWMQe/bsUdlyMpkyN5lMSgH5jh07qj9LwFGyComIsromn5REkcZDt2/fxgsvvKCCasnJyWa/8Vu2bEHdunVV0XvZeJ48eTKzVogoy4N8vXv3VqVRPDw8VBMOqXt37949s9956erbsGFDtdEiz5Jd7e3tbfbrINvCAB89omjRoti7dy/eeustnDhxAlWqVMGXX35pts5qsksru7WSrSIZNdu2bVPPRETmIJMnOQo7duxYlcXcoUMHldEix8PMtVsr42+vXr1UzT2piSWF74mIzLXAlaCeZPMFBQWp5kNSzF0anZmD1ECVUjGNGjXCnTt3VNkEyaqR6yIiMgfJmpMxr3LlyqoRUenSpVUXb3Nl88nJtYoVK2Lfvn1qDihzQcmyJnoaBvjosQtcCbBJFp8sMIcPH64yWVauXJllA5tOp8P48eNRtmxZNaDKYCbH5fLly8fvEhGZlbOzM/r37682GCR7+K+//lIbDZJVJ8dls4osqKUeoDQ+ksmkbLY0btw4y74eEdHjSK2nQ4cOqYWujEWy4SvZLBJ0yyqnTp1Sp0kkY08Ws8uXL1flWoiIzK1w4cLYvn27Go8ko1nq4LVq1Uo1usjKTrnvvfeeqjsqWYNff/21OsUh2dVEGWIgeoqbN28aXn/9dYnqqUetWrUMa9euNej1epPcO3mdJUuWGEqXLq1e39vb2/D999+b7PWJiDIjPj7e8OWXXxrc3NzUGJU/f37DjBkz1PtN5eDBg4YWLVqkjrMfffSRITo62mSvT0T0vGQ+Nm/ePEOOHDlS52mfffaZ4fbt2yada8q45+Lior5Go0aNDOfPn+c3jYiswv79+w1VqlRR45OTk5PhjTfeMJw4ccJkrx8XF2cYN26cITAwUH2NQoUKGTZu3Giy1yfH4ST/L2OhQHJ0Upfqs88+w+bNm9WfJavl/fffV8fXpG7Vs4qIiMCff/6JqVOnqmLOQmocTJgwgVl7RGR1Lly4oI6qSWazHCELDAxE165d1fHdcuXKPfPxMckElKxoGQPlKIaQTGnp4iZ1p4iIrElkZKQqOC/HZ+VtNzc3VSdUaibXq1cPLi4uz/R6sgSRWqfTp09XWdLSKTIkJATffPONGlt5JJeIrImcNpOxSrp5S0kpIaUE3n33XZXp7Ovr+8yvKd3KZ8yYgZ9++kmVgpFxVWoAyuk5OUVH9KwY4KNnnoxJoU85Siu1AIzxYWnGIUcqpEaLBP7y588PT0/P1M+T4syhoaEqkLd//37VrVeKJxuLNkuXIhksq1evzu8IEVk1OUIm9fnkGG1sbKx6X6FChVQx+ho1aqggXYECBeDv75+6QJWAYFhYmPrcgwcPYtOmTVi9erVaJAspTSAdw1955RV1PJiIyFrJsTFpePHjjz/i+vXr6n0BAQHqSK/MA2VOWLBgQRWsM45nMl+Mjo5WGyUyBu7YsUMdv5UGGkI+Vha1ffr04aKWiKxaUlISZs2apcZBGc+EdLaVRhhSR17Ws1LTXhJg0m58yCbGtWvXVOkDKXsga2l5W8gR3Lffflsl0+TNm9difzeyfQzw0XO7ePGiysCTYvQySD1Mdh2MbbxlYfuwYsWKoW3btqrOQJEiRfidICKbIlnIUnB50aJFWL9+vZrwpSWTNdmJ1Wg06mNl5zctyQCUWi7dunVTE0JmqxCRLZExTxaoCxYsUME6qR2VlixspQulPEtw7+FmbTJGysbIm2++qU5wGOeMRES2QDYudu/ejblz56q5oGTjpSUbHLIeljFQTm3IOJiWzPtkY1g2dzt37qw2SogyiwE+MglJKd65c6dqinH27FkV/JMdXpn8yeAnC1npxCZF6qUjUK1atVSAj4jIHsikTY6aSVbKyZMn1Rh48+ZNNQbKQ7L5ZAyUrBYZA6tVq6YK1kvwj4jI1sk4d/jwYTUGSkaKLHQlO08yVmRhK4tcGQNz586tspwrVaqkShGkPe1BRGSrZL0ra2BZD0vii2Qryzgo80MZH7VarRoDJVtZTm3IOCin34KDgy196WRnGOAjIiIiIiIiIiKyYSz0Q0REREREREREZMMY4CMiIiIiIiIiIrJhDPARERERERERERHZMAb4iIiIiIiIiIiIbBgDfERERERERERERDaMAT4iIiIiIiIiIiIbxgAfERERERERERGRDWOAj4iIiIiIiIiIyIYxwEdERERERERERGTDGOAjIiIiIiIiIiKyYQzwERERERERERER2TAG+IiIiIiIiIiIiGwYA3xEREREREREREQ2jAE+IiIiIiIiIiIiG8YAHxERERERERERkQ1jgI+IiIiIiIiIiMiGMcBHRERERERERERkwxjgIyIiIiIiIiIismEM8BEREREREREREdkwBviIiIiIiIiIiIhsGAN8RERERERERERENowBPiIiIiIiIiIiIhvGAB8REREREREREZENY4CPiIiIiIiIiIjIhjHAR0REREREREREZMMY4CMiIiIiIiKi/7N3H/BNVt0fwH/Z3ZNO6GAPmYIiooA4QFREUcEtThTFvX3de29xbwXc4JahiCCiIntD2aV7t9nv59w0bdkdSbN+3///eVPTtH1I2pv7nHvuOUQUwBjgIyIiIiIiIiIiCmAM8BEREREREREREQUwBviIiIiIiIiIiIgCGAN8REREREREREREAYwBPiIiIiIiIiIiogDGAB8REREREREREVEAY4CPiIiIiIiIiIgogDHAR0REREREREREFMAY4CMiIiIiIiIiIgpgDPAREREREREREREFMAb4iIiIiIiIiIiIAhgDfOQXSkpKoNFo8N577zX6a77++mv1NT179vTquRER+csYOGzYMPW4vY81a9bwRSKikJgHymMnT56M9PR0hIWFoWPHjnjmmWda5TyJiHw1Bubk5Ox3DiiHjIVEQs+ngQJRdXU1brzxRqSkpPj6VIiIWtXgwYPx9NNP73FfdnY2XwUiCnqVlZVqoUOv1+O5555T88B169ahrKzM16dGRORVaWlpWLhw4R73OZ1OjBw5EsOHD+ezTwoDfBSQHnvsMWRmZqJ9+/b4+++/fX06REStJi4uDkcddRSfcSIKOY8//jjKy8uxbNkyREZGqvsk4EdEFOxMJtM+879ff/1VLXCcd955Pjsv8i/cokvKypUrMWrUKCQmJiIiIgJdu3bFk08+ucezIysGsjogE6rY2Fg1kOTl5e3xGLPZjLvuugtZWVlqEOrevTs++eSTfZ7lN998U2WcyM86/vjjsWHDhka/Ehs3blRbMV588UW+ekQUcmMgEVGojoFvvfUWLr300rrgHhFRKI2Be5PvHRMTg9NOO61ZX0/BhwE+UmRQKC4uxttvv43vvvsOt9xyi9oG0XBAkxVSGcymTZuGN954A4sXL8bpp5++xzN4zjnn4PXXX8fNN9+Mb7/9VqUMX3DBBfjhhx/qHiP3X3nllTjuuOPw1VdfqUHt7LPPbvQrcf311+Oiiy5Cnz59+OoRUciNgb/99puaXEq9laFDh2LevHn8LSCioB8Dpf5Ubm4u2rRpg9GjR6uL54SEBFxxxRWoqKjgbwARBfUYuDer1YovvvgCZ5xxBmvwUT0nhbz8/Hyn/CrMmDHjgM/FkCFDnEcffbTT4XDU3bdy5UqnRqNxfvfdd+q/58yZo77PTz/9tMfXjhs3znnEEUfU/ffAgQOdxx577B6P+d///qe+9t133z3o6yHnGB8fr85ZXHzxxc7DDjss5F9DIgqNMfDee+91vv3228558+Y5p06d6hwwYIDTYDA4FyxY0OR/NxFRII2BCxcuVI+JiopyXnTRRc5Zs2Y5X3vtNWdsbKxz/PjxfDGJKKjHwL198803+/15FNqYwUcqFVnSiO+88068//772L59+x7PSlVVFf744w+1smC322Gz2dTRpUsXZGRkqNUL8fPPP6uVVElddj9GjhNPPBFLlixRXyvHP//8o1YaGjrrrLMO+UrU1NTghhtuwAMPPKBWb4mIQmkMFDL+yfa0Y489FuPGjVO1V6ST5EMPPcRfBiIK6jHQ4XCoW/m5cp6S9TJx4kTVdGjq1KnYtGkTfwOIKGjHwL19/PHHqtGQjIVEbgzwkWqtLQOS1AiYNGmSGqgGDBhQt+1L0pVlMJKutQaDYY9j69at2LZtm3pcQUEBioqK9nnM5Zdfrga3Xbt2IT8/X32cnJy8xzPfmG64zz//PLRaLc4991zVSlwOi8WiJnzuj4mIgnUM3B/ZqnvKKaeoySIRUTCPgfHx8epWtrU15L64lRpaRETBOgY2JGUJZs6cqRZ7dTodX3Sqwy66pMgKxGeffab28i9YsEAVB5VaBDt27FAdG2Xgk/vGjBmzzzPmzqaTFYukpCR8//33+31WZSCTAUiv1+9TkHT37t2HfCXWrFmjCpDKz9jfpO+1115TK7lERME4BhIRhfIY2LFjR1V372A7PYiIgnUMbEhq91VXV7N7Lu3L13uEyT9JDQL59Vi7dq36b6k5cOaZZx70a37++Wf1NUuXLj3o44488shm1R1YvXq1c+7cuXscI0aMcGZnZ6uPd+zY0aR/IxFRII2B+1NRUeHMzMx0nnLKKU36OiKiQBwDTzvtNGffvn33uO/1119XX7thwwa+qEQU1GOg28iRI50dO3Zs1GMptDCDj7Bs2TLV6UdSfGV1tLS0FI899phq3S3/LZ566ilVT0AeM378eJUxJ/UJfvnlF0yYMEF1FZL6ArLSId2CbrvtNvTu3Vt1H5ItE5J599Zbb6nvdffdd6uOQ/J18r1ka9mHH354yFeiW7du6mjovffeU+chP5+IKJjHwN9//12dh9RtkXPbuXMnnnnmGdVVUladiYiCeQwU9913H44++micf/75uPjii7F+/XpVN0v+232uRETBOgYK2eY7a9Ys3HHHHXyhaV++jjCS7+3evdt5wQUXODt06OA0mUzO5ORk59ixY53r1q3b43GLFy92jho1SnUrCw8Pd3bu3Nk5ceJE57Zt2+oeYzabnQ888ID6nNFodCYlJTmPO+445wcffLDH95oyZYozIyPDGRYW5hw6dKhz0aJFzcpeYRddIgqVMXD9+vUqazk1NVV1zo2Li1PnI19LRBTsY6CbdM+VDuJyrjIe3nzzzc6amhr+AhBRSIyBL7/8snrsqlWr+IrTPjTyP/uJ+xEREREREREREVEAYBddIiIiIiIiIiKiAMYAHxERERERERERUQBjgI+IiIiIiIiIiCiAMcBHREREREREREQUwBjgIyIiIiIiIiIiCmAM8BEREREREREREQUwBviIiIiIiIiIiIgCGAN8REREREREREREAYwBPiIiIiIiIiIiogDGAB8REREREREREVEAY4CPiIiIiIiIiIgogDHAR0REREREREREFMAY4CMiIiIiIiIiIgpgDPAREREREREREREFMAb4iIiIiIiIiIiIAhgDfERERERERERERAGMAT4iIiIiIiIiIqIAxgAfERERERERERFRAGOAj4iIiIiIiIiIKIAxwEdERERERERERBTAGOAjIiIiIiIiIiIKYAzwERERERERERERBTAG+IiIiIiIiIiIiAIYA3xEREREREREREQBjAE+IiIiIiIiIiKiAMYAHxERERERERERUQBjgI+IiIiIiIiIiCiAMcBHREREREREREQUwBjgIyIiIiIiIiIiCmAM8BEREREREREREQUwBviIiIiIiIiIiIgCGAN8REREREREREREAYwBPiIiIiIiIiIiogDGAB8REREREREREVEAY4CPiIiIiIiIiIgogDHAR0REREREREREFMAY4CMiIiIiIiIiIgpgDPAREREREREREREFMAb4iIiIiIiIiIiIAhgDfERERERERERERAGMAT4iIiIiIiIiIqIAxgAfERERERERERFRAGOAj4iIiIiIiIiIKIAxwEdERERERERERBTAGOAjIiIiIiIiIiIKYAzwERERERERERERBTAG+IiIiIiIiIiIiAIYA3xEREREREREREQBjAE+IiIiIiIiIiKiAMYAHxERERERERERUQBjgI+IiIiIiIiIiCiAMcBHREREREREREQUwBjgIyIiIiIiIiIiCmAM8BEREREREREREQUwBviIiIiIiIiIiIgCGAN8REREREREREREAYwBPiIiIiIiIiIiogDGAB8REREREREREVEAY4CPiIiIiIiIiIgogDHAR0REREREREREFMAY4CMiIiIiIiIiIgpgDPAREREREREREREFMAb4iIiIiIiIiIiIAhgDfERERERERERERAGMAT4iIiIiIiIiIqIAxgAfERERERERERFRAGOAj4iIiIiIiIiIKIAxwEdERERERERERBTAGOAjIiIiIiIiIiIKYAzwERERERERERERBTAG+IiIiIiIiIiIiAIYA3xEREREREREREQBjAG+YFSyFciZDzgcvj4TIiIiIiIiIiLyMr23fwC1svW/ADMmAw4r0PUU4JSnAZ2BLwMRERERERERUZBigC+YrPsZmHk9YKsGnE5gzbeu+wdPBrR6IC4T0Gh8fZZERERERERERORBGqdTIkEU8Nb9BMy8oT64J4E8uZXAnjuol9ITOPMNICrZ12dLREREREREREQewhp8wWDXsj2Dex2HA0PvcG3Nla269tojdxkw7QKgIs/XZ0xERERERERERB7CDL4AtmvXLixcuBBtlr+JXpbFsNns+HaTDjf/YkZJWSV6p+gwvqceUSYdjs0yID0K0Ol0cOrDoDFFIcxkQlhYGLRh0cCRVwK9z/H1P4mIqNHKy8vx559/Yvny5Vi9ejXWrVuH3bt3o7CwEMXFxZAEdY1GA71ej8TERCQlJSE1NRVdu3ZFt27d1NGvXz/ExcXxWSeigGO1WvHvv/9iyZIlagxcs2YNdu7ciYKCAhQVFanPa7Vadcg4J2NgcnIyOnXqVDcG9unTB+3atfP1P4WIqMlknidzv7/++gurVq1S4+DWrVvVGChzwerqajUPlDEwKioKbdq0UeNg+/bt68bAww47TN3KY4iCAQN8AcRsNmP27Nn45ptvMHfuXKxfv17d//BwEyb0dTXSGPulA7n2WMRERar/djicqK4xw2ApxnujgIzYfWvwGQx6GI0mbMo4C+3G3KcGPiIif5zI/f333/jyyy/VWCgXtna7fY/HmEwmNYGLj49XkzWHw6Euct0XvHtXpZCJX48ePXD00Udj0KBBOPbYY9GxY0d1PxGRv9m8ebMaA3/88UcsWLAAVVVVe3xeFnJlQUMOg8GgxjybzaYWPWQclI/3lpGRocY/GQcHDx6Mvn37qoURIiJ/I2PZzJkz1TFv3jzk5e27M03mgDIXDA8PV2OgzAVlUTg/P18F/fYmCyBHHXWUGgPd88GIiIhW+hcReRYDfH5OBiWZwL355pv46quvUFZWpu6XSduRRx6J0cf2xhXRvyHKWQadVgPtqc8CcVn7/V7mop2oWfg6HIWbUG22oLq6BpXV1Whjqp/s/bLRBlN0PGxJPWE44mIMGTYcaWlpHv03yeRy7dq1+O+//9SxadMmbN++XR0VFRXqYlz+fQkJCSrYKKsqPXv2VJNO+TfL54godGzcuFGNgZ988gm2bdtWd7+MCxKQ69+/P7p3767GCpnUHSg4J8FAyW6R8UcyXWS1d9GiRVi6dOkegcL09HQMHTpUHUOGDFHf15MBPxnXZYXZPQbK+bjHQJm4WiwW9Tj5t8hFumTbyL/1iCOOUOcUHR3tsXMhIv8n48KHH36I9957T2XrubVt21aNCQMHDlQLFTIOypztQJkoMvZIVotkvMgYKNku//zzjxoHGwYKZYw55phj6sZBGWM9PfeS83CPgStXrlRju4yBcrEuY6CMyTExMWoumJWVpbJsJOP6uOOOU2M0EYUOGRNmzJiBt99+G7NmzapbqJAxQuaBEpSTeZKMgZKdd7AFCrnW3LBhgxoD5ZAxVXbESfDPTca7AQMG1I2Bcg3q6bmXjLmyA0XGwGXLlmHLli1qDJR5ak1NjboeliCjjIEy5skY36tXLwwbNkz9O7kQTQfCAJ+fkj/6d999F1OmTMGKFSvUfXKhd/rpp+PMM8/E8OHDEV6TB0w9DyjbCTjtQJsuwEmPNrlTbvmiD2FY9TksVivMFhustYPmDxtsmPhtDdp37LzHxW5mZmajv7dMJnNyctTk0X3IQCoD197k3yeDpwyqMqhJto07oOkm6dUjRozAeeedh1GjRqktxkQUfGS1VVZnX3nlFfzyyy912Xny9y9j4CmnnKJWZz2hsrISixcvxh9//KFWg+VW7nOTLW0y9rnHQbnQbMpWDsmake0j7jFQfpaMb3uT8a1h1k1JSYm6sJfnomF2jkxkzz33XJx99tkeew6IyP9IlvKLL76IadOm1c2bJLNk7Nixaj7oqWxjuViWC0xZUP7999/x22+/qXIHbnKRKeOOewyUxVYZj5syp5VAYsNxUBY59mY0GtV4K99bxliZA0ogcO+sQ7nQlfHv/PPPR+fOnVv4rycifyXBrpdffhnvvPNO3Zgki54yBp5xxhkqCCfzopaSOZcknMgYOH/+fDUGyuKrm/yMww8/vG4uKEHFppR3kTFMFjJk7HOPg7LI3HB+J2TckzFQxlwJUsrY6d5q3JAs5IwZM0ZdD8vYzO3F1BADfH6mtLRUXdA+99xz6qJQJm4nnXQSrrzySpx22ml7rqB+cx2w5ltXcC++PXD8vYAppnk/eNU3wLJpqimHDDYWqw0WmwMLdpsw9a882Ox2OOGEzQFssqejQ++j1IqqHHJB6p7oyQWpDESyfVgGRhm8Gq6ICFlZkdVg2QIiR5cuXVT9F0mj3ptcZMsKs2TY/Prrr2rVJjc3V30uNjZWXeReccUVatAlosAnkyC5mH3sscfUZEjIiuVVV12lLuZao16eu66VTPDkkMlew8UGWU2Vi+wOHTogOztb1fWTxQaZAMrjJCgn2+hkDJTxSxY5GpKgnExK3WOgXKzKwomMafs7F1lploUeufCWMVC+p5DJnwQ6L7/8cowcOZJb6oiChPytP/roo2obrpAx5tJLL1V/6zKHaq26Vu4xUI4dO3bUfV7GO8kalHqmMg+UOZxckEqATi5IZQyUjLyc9ZsQXaxHUV6hmkcu3PovdlcUqsdKNp4cMgbKGC9jqeza2DtgKeci30vGQLkoljFQbt1Z13Iel112GcaPH8/sZqIgIcG2J598UiW7SPaeXCPK37hcD8vffGtkr8n1piz6usdA95xUyM+X+qW9e/euux6WJBW5HpZ5m4yBEpCUeaCMpTJ+NcySlvmiLBbL9auMgfK9ZMFGAnf7yz6UmID8fJmbSokauSZ2L0TL2DlhwgT1HsF6qqR+P517FyQin5DI/AsvvIDHH39cBflk8iSBqxtuuEFdRO7Xu6cAeSsBrR444/XmB/fcLFWApQIoyQHmPw/YLYBWB4fTdZFptlhVll95tRUXfFmFBdv2rH21P3IhLNvKZDCWFV85WlLjT4KPcrH98ccfY/r06SqgKGSSKM+XrGTs7yKZiPybvBV9/vnnuOuuu1RAS0iGym233aaCab7ciiAXkrLI4J7kyYRPJm+NIZNSmbzJGOg+ZDLWkn+PbCmR7coyDsok2L1VTyZ3csj3J6LAIzVGZcyTOstCxo477rhDZS37sjyJO7ul4cXu3gsXe0uOTMTrZzyEjNhU6HV6df56kwHWYXHoelyfFi1IyDbezz77TI2BsrVOREZGqkVfCYLKXJPb14gCM2PvvvvuU4E9mXvJ4sbNN9+s/q593RBNElbcGc5ySNZzY8Mosogr45J7HiiBPRmzWhI3+Pbbb9Vc8LvvvqtrqCSLvXI9LIu/LGkVuhjg8zEZvKSuyv/+9z+1717+2K+77jrceOONKkX3oNwBPr0JOOcjz55Y7nLg18dcQb49SB6fFlatCUszLsG6QofKWLFKxp9TB0N8WxXUk6CkrOxKdp+3JlmyXUUKTUttLlnJcF9Mn3POOWpwk5Rlb0/wpPGJvG5SN0G2m8ghQUc5N3f9BAnWynnJirUEN90rPZK105LBnShYyITp1ltvVVkZ8jc7btw4FeiTrA5/JAsNcnErf/dyK5M+WWGWv3epByOTUJnMyRgoq6ne2joh5yGTTBkDv/jiC3UO8vydeOKJagwcPXq0yqjx9nuYdHSXsc89Dsrz4R4DZYyUSaaMfzIOSl1BeW5kDJTahmzqRORqnHH33Xfj008/VU+HFHuXeeHJJ5/st4EqyW6R85Yx0F0zyma2IjEsDgnRcTi8NBMRVgN0Gp2rdExtV3ONSYeEc7rA1MEzF+uS0fzWW2/h/fffVztIhLx3SEDgggsuUHNSb3LXNWw4BsrzIRfg7nFQ3gPc80DJ8pGmJjIOylbDli74EAUDuZZ86qmn8Mwzz6i/Hfn7uPPOO3HJJZf4bTkmSciRhQ8ZA+XvXjL0ZM4jixfuJh9SQkB2qkkJFm+ROdcHH3yg5oLubcUSGJXnTsZByQz0Nqlr6B7/3LUE5T4Z/+T1lHFSxkA55NpXagrKPFDGP5krMxjpWQzw+ZDswZ84caKqSSepupJ2fO+996o/ykbxZoBPVBcDu5aqoJ78v7JtIbDjX9dkTTIH96ABup8KnPwkoGvdlWbZEiyFV6UAtbtGgxQglYFNVr5lEGnqBMpdA6vhgOUO4rk/lgluc5NgZcIn5yh1HORCXApH++ubGJE3yEWQZClLJoaQwJRsyZCsFWoaucD86KOP1ATPvY1EgmcXX3yxymyWbSTNqVMjE9a9x729J3H768rZWDKJlwzNU089VdVV9fbFOJE/kQsfKUfwxBNPqAC9XAjKx5K9HGhBH3NOKYq/XA9HVe144HDC6XBCF2tCeO82sGwph2VbGTTwfJBP/XyzGd98840aA2Ubr5DtcmeddZa60JUi+fsrBXMosnAj25P3N/65x8a9Oxk3hQQCJLNHMm9kLnjAXTtEQUiuoSQLTbL05PpNFkjvueceTJo0iddEzXguZZebLHjILjd33Va5vpTrYalhLYk3zVlMltfmQOOffNzYXS37I9e+Mu8/4YQT1BgoZbxYU7BlGODzUcRfslNee+019ccoFzayaiHZDI0mQaW3TwIK13svwLc/disw/xlg+9/7/7wE/TqdAJz+cqsH+dwTMSnML4Ob1K5xB99k+5oMGLKKISunkmUjK6lyYSoTbKljIIOXZKJI0EEOGbRk9eFAZGXGnYnivpXvLffLYCWHrOLIpFMmf3K4M11kxUe6JklNBncdGdlaLHVk5E2NEzwKZvI7/+qrr6qMlfLyclWH5Nlnn1X1RqllZMyTTEi5yJ06dWrdhaeMeVIuQVaTpYaXTKJlRVke715hlW1vMka5x0GpeyV1Xw5Evt49/jUcC2WRSi6kZQyUDEIZl+U85GfI95PJoBzSPU7qybjPUQKQUjh78uTJrZKBTeRLP//8M6655hrVJVwuuh588EGVeRuImQwS3CuauhZOi71+0dMJFdyLGZkNXaQBTrsT5b9tg2VLfZCvzeW9YGjT9KDboUhmoSz6yjY/GcuEPK+yLU4WVmWOJXM1GcNknuYeA+UiVR7vHgfdHS33LoTvJuObOxuv4VxQ5pySpSJjoAQZ5evl+8tYJ9cA7otiyT6UbdkNx1kJRMoYKA0EAvF3gaixpDadjIFSU07+DmUHmwT3uNDXcpKgIiUMZC4oJWbcZPyTBV8ZAyWLTsZAGafkWlXGKJmTu+eAciuLGzIXlAWo/ZF5mjsbr+E4KOOiXNe6r4flcfL93T/DvWgiJXmkAZO73IyQOeq1116rSs74elt2oGKAr5VJjSkZwCTzS375X3rpJbVS2yQyeZr3NPDX666AW1QycPqraDUOG7DhF6Bos+tcVIafE9j2J2Azu4J8EQmAzgik9gKOvw+ISUNrkwmUbH+W7btSo6VhV8xDkTca2Vq3v4tXdyDPE9tr5Zyk1o6sOstqi6Soy0WuNBSQyX5zVlqI/JlMNCSQLW/oEgS6//77VUkCXsh4nownEuT76aefVGfghl0xG0MCdXtP2hreysSrpUE4WWSRrsKyMCPbE911vSTYK0FfCf4SBRPZTiUBHPnbFFIcXTKXA6EjttPmQNnsrahZW1S/s0OmhVVWOK0ONRXUJ4dDH2OCNlKPsG4J0EbUB6nqgnw5ZdBoNYgeloHoIe28dr4yvnz//ff4+uuv1RgoAYWmkDHOXVZlf+NgSkpKizNN3A1NpJ6WZLPLAo2QbE7ZrihJAETBRBb9JHP5kUceUYEjWdCbMmWK35ZlCWQyvsh8W7IkJbtPdg02ZdeFzNMPNP7JIdfKnpi/ywKze6yWsVASAWT8lZ2NEuzjNULTMMDXipF0+QWVaLoEcOSCVoqINmtP/oKXgD9ecAX35OJq0LVA+6Hwud0rXHX7bJISXHvRp9UB8dnAuI99EuRzk8HMnTnnrgsgwTUZMGRlQbL5ZKIm3YtkJUIm2q2dHiznJDVk5E1PVjbk4lq23B1//PGteh5E3iBv1k8//bSqKyWTOykA/PLLL7MhRCtO8mRhyV0vRoJ/sooqATp3tp2MezL+yTgo409rT6jkd0SKRcukX0pYSHaMbFe8/vrrmc1HQUEuXGSrlATbJZNCLmqHDBmCQCDBvaLP18G8vkRtvd0fY2a0CtppdAcO/Ft3VaL0xxw1fY06pi1ihmeiNefikjEp46Bk68m8S96P3GOgZJzIGOgeB2Vu2NrkYlzeKxsGgF988UWv1vAiai3SJExqY8rvuWxPl8UNydTilszWIRl0kuEs46C8D8m1sNznzrSTccZ9LSy3Mia29m4KuU6X6wMZ9+TcpISBLABzd1vjMcDXCubMmaPqIMkvrEzoJKtMtos2i7UaeHkAYK5wBfeOuBLofCL8Rt5q4L+PgMoC17laK10ZfbEZQLsj6h+n0QKdjge6jPDl2folGWwle0+2bcugKunV8uZHFKjkYkrGQFk9lBW5V155RXU75BZM2h/Zzibvk5LlJIFIyfh8/fXXm1VDkMgfSCBJaky98cYb6kL29ttvV9nL3m6C01L2cgsqF+eqW1thDaw7KlRwT7LvNOH1f48ylhuyohHZP/WgwT1hza9C6beb1RRWFx+GxIt6QB9raoV/TWCRnSdSO1Cy+wYMGIAffvghILI8iQ70vi5zP+kSLlviTzvtNHV9I8kVRPsjW4OlV4Fk9smONim9JWMhHRoDfF7OGpNaApKBICQLQbKzmlPkt05VEfDKQFd32/R+wHF3w2/Juc6+Hyjb4QroubP63CS775gbgUGTfHWGfk221Uk9Kgn4SfMQCZAQBZpp06apjBW5wJVsVPldlpR+osYEhqXxhnSFk0UOqa3KoDAFYlkCafQgtYYkA0G6HUqdNX9nKzWj8MNVsBe5CrUL2YKr0WsQc2IWDKnNK1MiAcLSGRthK6pRgUJdQhjaXNhD1eujPcn75oUXXqi2rUndLFkkky68RIHWBEyy9iRAI+WNnn/+ebVwx/dzaszuE4mdSM1uGfsWLFiAnj178ok7BAb4vETSXiVDReqrSZqrbL2U7jAttu4n4JtJru25khE39Hb4NQny/fY4ULx5z/vdRZilEUf/S4CkBg1GDBFAh6GAseU17gKd1GIZPny4Chb//vvvKk2ZKBBIXRVZqX3hhRdUlopsw5D6o9yG4Vmbq8xYWu5qUtHGqMeguCjogqg5hdRlGTp0qNrWIxcFslBGFChk7icZCJKxIhe0zz33nF8HaCRTz7ypVM3RyufvUME9tR23dkzRhusRPaxds4N7DWv2yTZde4lZBfnC+yQh/vROHvpXBBeZ/51//vmqTvOYMWPw5ZdfMjBCAUNq7MoCh5RJOuqoo1TpIWl4SNQUsmVXriGys7NVczbZ3k0HxgCfF/z5558q80o60AwbNkzV0fBICvKG2cCMa11bXyVA1n8C0O0U+D0518p8Wbatv2/bImCJdP51urbw7i0uExj3IRDLTB8punzOOeeoFQsZ1FholPyddN46++yzVVFzKcIrzYWkgyt51rRdRXh44064R1a5BD86Lgovds9EmK51a4h60/r169W2DAkar1y5knVYyO/J76oEo6XGntQ1klt/z8I3bypB0bR1qtae4nSq4J4uzuSqq6fXQhtpOOQW3KYE+eTnCWNGNJIuZVbGgUgdqmOPPVbVLZNaVOPHj/fIa0DkTZJ1P2nSpLrxUEoP8RqGmkt+l1599VXViFLeU+nAGODzMFlhu+iii1S7aamx8vDDD6uOrC1WkQe8MQywVrkCZtnHuppryDbXQLV6JrDkg/psvjq1Qb+4LOD0lwFTDBCVAuj9u1aNN0mATwJ9MrBdffXVvj4dogNasWKF2lYptTNGjBihGguxG7TnFFttqLI78FtROR7btAtWuQhv8Hm9BpjQtg1uae+7pkbeIGOfTO5kLJRt30T+Sho5nHnmmWoHh2SqfPHFF+jTp49fbHWyl1n2M+cCrLurUPLlBjgtdvU4V0UVDXSxRsSOzN6jE64nFb63Up0OA3yHtmzZMhx++OGqAZKUL/D3+o0U2vX25BpYmsXIllwJ9DEoTS0lJasOO+wwbNmyRV1ryMe0fwzweYhMiGRlQgY0k8mktmWMGzfOU98eWPuDK3tPtuZmDASOuTmwg3tuJVuAgg0N7nC6An9l211BPnftvoh4YNTTQPvA6DbnaTKZ69y5s8pckXpU3OZI/mj27NnqwlYaI9x0001qWy4bI3iGzeHEI5t24ovdxeqCWC7RbbUX6scnxCA9zICPdxVCdtP1iArHl/2Ca7ubdNjt2rVrXfc32aZB5G/kwkMWOFatWoXjjjtOBff8YSuRrcSM4ulrYc1zbeffL7ssFjhhzIhR3XA1Bq0KvEnmnrcwwNc0Uov03XffVU2IpKYZkT9mm0qii+zcyMjIwHfffYdevXr5+rQoSEgNW8mGl5IXEjim/QuePTw+Du5JrSkJ7iUkJGDWrFmeDe7tLblHcAT3hGTpSTfduuME4IT7gZh2gMPmaiZiN7syGL+6Gtg8D6FIAnvScUqKdMsWcCJ/89VXX+Hkk09WRcGlVsYzzzzD4J4Hg3v3rN+O6bnFMDucsDidKnNPjE6Ow4S2iRjZJhb62jpZdsnq20+WTiCTQPG1116rMgM++eQTX58O0T5k8e3oo49WwT1pjCAF5f0luCfNMqy5lXBKEM/u2P8hW2WzYhB9XAbCusTD1D7Wq8G9PdgblHChA5LO4u6LXCJ/I/M/mQdKcK9fv37qeoXBPfIkyQRNSkpSOzlktyTtnwf2joY2uYi68cYbVSF5WamQ4F6XLl18fVqBLTweOPFBYO33rsCe1O/LXwNYK4GvrgIik+sfKxl+HYYBw+4A9MHdgU3qOn7zzTdqNUwuIoj8hWwfl6ZCEoSRAuCnn366r08p4BVabHh80y4sq6iCxeFEnsWmMvYMGg2OiI2AFhr0i4nAUbGRdQXXo3RaFDrs2Fhlxitb8zApMzmoirFLdqi838oYeNddd/n6dIjqSFBPuoTn5ubijjvuwKOPPurTvz3z5lKU/boNjnILHDV2OGtsrnp6MUbokyL2+zX6pHCEdU3wWI29xtCYdHDW2NUWYWnqEX1M21b72YFItnrLgu+vv/6qgilRUVG+PiUipby8XGUvS6dnGQul8zN/P8nTpDSBJLy88847mDdvHk488UQ+yfvBAF8Lg3s33HADXnzxRbVdaM6cOWjfvn1LviW5hcUCfc51fSyZfH+8CGxdAFiqAEtOg+dJA5RsdW31HfNaUAf5pGGL+Pvvv319KkT7BPek1qhM6EaOHMlnxwPBvctXbMbaqhq15VbIjQT3bshKRr+Y/XewHJuSgDe356vsvinb8mHSanFFRlLQvB7SsEUubqXZkGzZ5fZv8gfS4Vm240rH5/vvvx/33nuvT4N7NRuKUfzZOjitjvpye05Xswxv1tNrjoh+yahYuEtlFpb/uk1lDEYdFVz1Qz1Jfq9kLigXt8uXL8egQYN8fUpEqjaa1FxeuHChmgPKQm94eDifGfIK9xgo18MM8O0fA3wt8MQTT6jgngT1pJhyVlYWvEK65i6dWl8YeX9dZ4OZ/HsHTwYi2wBb/tizG6+5HHBYgU2/AVPPA9p0ASISgX4XAtEe6FzsR9q1a4fY2FiVKUDkDySLQOoASXBv5syZfKNtgY1VNfg8txiVdgf+LavEpmqzlMSCSatBuFaLOIMO41MT0Dt6/9k3YlhCNCwOB97fWaiy/V7flocL0xODqqOudBOXmqQ5OTmqgQGRrzuGywWtBPceeugh3HPPPT5ZbK5ZUwTzhhKVpVezqhBOi2vLrdakA7Qa6JPDVeDMn4J7IqxbguraW7l4tyvI9/t2RB6RAk0QjVneGAOFzAUZ4CNfs9lsqvmVBPckg0/qjkrncKLWGANp/0IsUuQ5H330Ee688060adMGP//8sxeDezXAVxOBnPmuTDZjFNDuSIQcCfIdfpHraEi27s59xNVdeMe/rkNWzqVRx7gPgbhMBNPKbVpamrq4lQl9MG29o8CzcuVKjBkzBlarVdVb4Spa8/1XVoWJq3JQZrXXdcSVZYxEgw73dEhHiqnxF+UntYnFkvIqLC2vVvX6ahyOoArwpaenq1sJqDDAR77eknbKKaeoxhqym8NXwb2KeTtUYMyd7uteCza1j0HUkHbQaP17rhDesw0s28phza2C02xXgT5NkJSZ9vYYSORLMv5cc801+P7771WwWeaCDO6Rt3EMPDQG+JpBioZKJytJP/7222/RqZMXuxUu/6w2uGd1BfeOuxuISPDezws0Sd1cz8nvzwDVJa77nHbXtt1pF7oy/1Qn3lptugIpPRCopJ6FxWJRK2YGg3+txFPoKCoqUhe2paWlKotZaqNR02yuMmN5RTWq7HY8v2U3Sq12FdRzX4qnmwy4NTu1ScE9N3ezjWDkrukj9aeIfHlhK9nLS5YswVlnnaWaCrXaz3Y4VY09R6UV1l2VqFycq4JiDZk6xyHq6HS/D+7VaXCeEuwL6xjn09PxZxwDyV88//zzePPNN9G5c2fMmDGD23KpVXAMPDQG+JqosLBQpSJL1sqnn36KgQMHevkHbqjdkqoBjp7s2oJK+wb5pP5exW7AZgEWvlhbl28r8P1tez5WLnwHTQIG3+D6OMDU1NSoulMM7pGvSBfTiy66SGWtSFfT6667ji9GE32yqxCPbdylAnrqOXU61ce9o8LVllqtRoMUo17d0r5joGB9H/Klp59+Wl3QDhgwAB9++CG02tbJkpW6ekXT18K8qbT+vtrMPdnaasyIhsag9butuIdiSI2EdWelykKU+oEJ47qqLr60L46B5A8WLFiA2267TZUOkgw+2dFG1Bo4Bh4aA3xNXLG9+OKLsW3bNtXJT7qaepXss6gsqP9vqS1HB97CG1PbfW34fcCcB4CSbXvW61Ob3zTAwldc9/c5z9WUI4AyImVLUETEgWtwEXmbZKpIF1NZ3GjNrJVAfc8osNpULT23XwpL8eSmXNUIo2HOjQT3bspOgdHDgQL5+XGG4HmrlzFQcBwkX5FaU1KiJT4+XjUZ8taWNKlN56iy1v+3Ayj9bpMK7jkdDec2GkQelYrw7oE7R5RturaCali2lgMWuwpipkw+HNrw4Bm7PIVjIPnDLo5x48ap3UTvv/++d3eyEe2FY+Ch8Z2zCWQQkwvbI488UjXY8Hpwb/5zwPqfXLM6nQGI4OpIo4THASMeB3b8DVgq6++vyANWfwPYrcCfrwF/TnHd3/kk4JSnAYN/d3ySrNHt27eje/fuvj4VCuFukf/73//Uiu306dNVu3rav3yLFTes3opl5dV73C9BPWmAIU5MjEFGmBEJBh36REdA56GMvVSjK3vHLvVxVm7BO73ao11YcLxWmzdvVrdeq3tLdIjMgQkTJqguzu+99x6ys7O98nzVrC9GyYyNcFTb9vyEU6aEDmgMOtWBVqOTBhoR0CcEdlF7+XdED8tA2ewtsG6vACwOWLaXI6xzvK9Pze9wDCRfu+mmm9T1iCS7nH766b4+HQoxHAMPjQG+JnRKk4FMLmhlUuf1LZL/fQz8+aorGCUXff0nACZX7SFqBMnMyxq87/3SiffvtwG7pb7a1bofAUsFcMYUvw7yyYAmFxVS64Kotcnv3mWXXQaz2YxXX30VmZnB08DGG8G9y1bkYENVzR7Ze24y8pyZEo8zk+O80izn1OQ4LC2vwnazFdtrLLh0+eagCfKtX79eZU4lJgZuthIFrgceeABr167FhRdeiNGjR3stuFf8+bq6Trh7kAZbRh1iTsqCITm4svklyGdIiYR1B+trHmoMFJwLki/8+OOPKuFFfv8eeeQRvgjU6jgGHhoDfI0k2zFKSkrw8MMPt04G1dKprq65cik44DJXlhm1XJeRQGQSsOUPwGEHdv3nCu5JI5M3jgOMEXtuiZa6h+2P9YtnfvHixeq2d+/evj4VCkFSZ0pqrki3XMlgocYF96QTbpfI+uwaCef1j4nEUbGRXuuEHavX4a4OaXh0066gCvJJxoAstg0dOtTXp0IhelEhtfeSk5Px3HPPtfj7Oa12lM7eCovU02sQybOVmgGrK7inTw6HLrJ+QVmCe2HdEgI+Y49aNheUZIMuXViTm1p/J5G77vLbb7/NWrjkE7wePjQG+Bph2bJl+OCDD9C+fXvccsstaBU2s2vCZ4x0BaXIc9r2dx2iYB0w9xFXkK88t8GDnEDRJuCrK4HRLwGdTvD5K/D777+r22OP9Y+AI4WO6upqtTVXGrxI11xvBaaCLbiXZNTjng5pSKrdMtuapO7e/oJ8H/fp4JPz8YT58+erW46B5At33XWXqjklWSstzSCV4F7htLWwbC6ra5KxN1OHWEQd2zZwOuGS1+Xm5mLDhg045phjvFb7kehA3njjDfX7d/755/N9mHx6PSw7KaVkGu0fA3yNnNRJsXSZ1JlMpsZ8CQUK6Up83D3AP+/uGeBz2l31+6zVwIzrgB5jAE2D4veJnYA+41ttS6/8/v3www/q98/rnZuJ9vLKK6+o7Kkrr7wS3bp14/PTwMaqGny5uxhVdgf+Kq3E5mqzz4N7+wT5Nu/C9hortpst+Dy3GFdnJgfkayhjoGAGH/kiY+Dzzz9XOzguueSSZn0Pe5kZlf/mwVFphXV3ldqKqurpabWq820dnQamjrGIODyFwT3aA8dA8pXKykpVokCyR2U3G5EvbNq0SZXJGDx4MJutHQQDfIewcuVK1VijV69eqmMQBaE2nYERj+55n2zf/fMVYPM8V5BPtkw3JMG+jbOBM97Yc1uvl/z999/YunWrqvkTFcVajNR6LBaL2o4mk7r77ruPT30D/5ZVYuLKLaiw21VfJCG9Lf0huNcwyHdFuyTcv2GnOsci615F+wPo9/Cbb75BQkICA3zU6p566il1+9BDD0Gvb/rU2VZUg8IPVqkgX8OFu7p6eknBVU+PvEOCzGLs2LF8iqlVSd29/Px8XHvttV5rLkR0KF988YW65Rh4cAzwHcILL7xQ1zFIq22wwupNJVuBqsLW+Vm0f1odcNQkQGd0BfLcV+91bezswJYFwJdXuDL55HHZx7i2VHuBdCwVZ511Fl8xavULip07d+Liiy9Genp6yD/7m6vMWF1ZjXKbA8/k5KLcZldBPfcmurYmA25rn+oXwT03Q4Mt1asra2B2OGBqrfczD/nll19QWlqKSy+91PtNroga2LJli7qo6NChA8aMGXPI58a6uxK2/Pru2bIFt3zONhXcU9txa/8eteEGRB+fweDeAVh2VLCLbgNFRUVqHJTfw759+/JvlFqNw+FQ18NSnuWGG27gM08+474eZoDv4BjgO4iysjJVWD4lJQXnnnsuWi24N/UCoLrYlQuS0qN1fi7tP8g3cCLQ6xxXjT43Cb7+8bzrvq0LgW1/uu6PSgXGfQQktPfos1lTU6M6N0dERHitax/RwbbnCk7qgA93FuDJTbl19fDtTqcK7vWNDsf41ETZWYc0kwFaP6tRKEHHaL0WZTYHlpRV4vrVW/FC98yACvK9/vrr6rbV3ouJGtSdkgvcyZMnqzqkByIZeRLIq1i4c4+mGbWfVME9fUI4oo5JV9twdTEm1TmW6hlSJZNRo56rivk7oDXpEDWIC0tC5oHS5EDGQNbBpdY0e/ZsrFu3Ti1wdOzYkU8++cS///6rdrTJ9tzMzEy+CgcROLN7H5DtQBJcueCCC1qn9p5kiX19DVC6zZUhFpcJHHGl938uHVxEguu1cB/p/Vx1+4xRrk7HdjmsQNlOYNoFQO4KoCKv/pBafi3w2WefoaCgQP0exsbG8tWiViPbwqVzbv/+/UM2Y0Ay9KR5xvs7XME9q9NZd7iDezdmpSAz3Ii2YUa/C+4Jg1aLG7NSEabVwOYEfi8ux0tbdiNQ5OTk4Ntvv0XXrl1x/PHH+/p0KIRI0G7q1KlqW+6FF164x/32cssehzu457S7gnl7HE6o4F7MyCzo24RDHx/G4N5+GFIiETkgxfUc250om70VZukyHOIkwPzqq6+qnURXXXWVr0+HQoyMgeKyyy7z9alQCHMnHEyaNMnXp+L3mMF3ENOmTVO3rVZ7TzLD8tfKfg4gOg0Yfh8QHtc6P5uaXrfv1OeBHf8ADiuwYTZQvNkV5PvwjD0fqzMAR14JDL6+bmtOY8lFxPPPP68+vuaaa/gqkU9S4UOx/qjV4cSDG3fgm7wSdXEuCTm22q36JyXGIN1kQKJRj77REdD5YVBvb10jw3B7+zQ8vGmnagIyv7gCt3g22dhrpHOzjIUyBjJzhVrTP//8o4p6jxo1StV/FLbCahR9tg62gvptuIqME7UdccN7tYE2sn6KrTXpYcyMhkbPdfVDkefOaXegakk+4HDCvKlEdRQOZbLAsXHjRpxxxhnIyMjw9elQCJH6t19++SXi4uJw0kkn+fp0KIQ7iH/yySdITk7m9txGYIDvAKqrq1WtCykkOmDAALQKyQZTnEBcFoN7/i48Huh0guvjrMHA7AddQT67fc/H2S3AwpcBmxkYeluTgnwzZsxQKckjR45Enz59PPwPIDq4mTNnqtuzzz475IJ7t6/dhp8KS1XGm5v85Z6VEo8zUuIRiCTIZ9JoUe107LOD0F/t2rULr732Gtq0aaPq7xH5cgyU4F7hh9Isw6KCznV/SPK+Lv+t0SBqcDrCugTmGOEvDGlRgAT4SP2e3X///eqZuP322/mMUKv6448/UFJSouowS7M1Il944okn1K5KGQv5e3hoDPAdwF9//aVWLYYPH946GQPWGuDHO1zZe0K2f1LgMMUAx98HrJ4JlO/cM2grWX6yhXfxm8C6H10ZfW7x7YFhtwMJHQ46qZPW9EStSd5IFy1apAp6h0LHNNmG+/TmXKyuqEGVw4FdZosK7hk1GvSJDldZekfERmJQHMdmX0zqZAxkB3FqbfPmzcPRmYfj+OqeyHttKewVFjirbSpTTxdrUltt6+g0MHWMhbFdNF8o8mi5oCVLluDkk0/GwIED+cxSq4+BguUxyJcLvVOmTFELvdye2zgM8B1iQBsyZAi8zuEAvr4a2Py7a7unIQLoOsr7P5c8yxQN9D1v3/ulC++iKa4gn2T4NVSwHshd5mrOkdhxn+2R//33n9oadOSRR/LVola1ePFimM3m1hkD/SC4N2H5ZmyqNsuOMEVuJLh3c3YKekVL4fcg4f+7ifeovSeTuqSkJE7qqNXJIm9YrgNPn/4/6PKtsDqtrk84ndAnhiNmRBa0YZxGk/fYbDb873//Ux+7F3yJgvZ6mGg/HnzwQbXQK7dc6G0czkwOQLZFiqOOOgpet20RkCPBPZsruCcNHOKzvP9zqXV0PB7QmYBl0wBzWf398nrbaoDyXFdzjgZBvsrKStxyyy2qoPKjjz7KV4qCewxsRfOLyzGvqFw1yHBbWFKBzdVmVZvOoNHAqNUg0aDHhemJOCwqHMFEXxvg21pjwYLiChwd778ZiTfffLMKMt97772IjIz09elQCJDM+ZrVRbBsKUNebh4eOG4ywgwm1+5bg1YFyA3pUYgalMbgnjc1KFVYs7YYkQPToIsOve2BUp5gxYoVquYUF3rJF+OhzAVTU1ORlcXrUmp9kugineyl9iiz9xqPAb4D2LBhA3Q6ndqe5nVVBbX5IgB6ngUkdfX+z6TWlX2M62jIXAHMeQgo2rBPkO+xxx7D9u3bVVF51t4jX42BQjqXBot3dxTgmc25cDid+9Sgk4BfslGPezqkoY2xwTb6IDM4Lgo/FJShxu7Adau34KXuWX4Z5Js1a5Yq7N2rVy9MnDjR16dDIXIxW/bLFlQuylVZelpzDXQaLfQ6PUyd41RtPTZ5aR2SIamLNcJeYq6re5h4YY+QCvLl5+erxY3w8HA888wzvj4dCkFFRUWq/t7QoUN9fSoUou/J1113neoiLmNgREQQ7abxMrbz2g/5RZKLW6k7ZTC08oWePnQmLyHPFAUM/x+Q0Alw2l1Bvln3Y9WqVXjqqadUx76HHnoo5J8m8o3169er206dOgX26nNZJX4qKMXzObkquCedcKUNjnOvo63JEPTBPXFuWiIGxkaqgGaV3YG7129XTUX8rcmVe6X2pZdegl7PtUjyPuv2ClT+lavq60nGnt3manyWF13B4F4r02g1iDkpG7oYo3o9pGNx2ZytCCWSwSzBlTvuuIPZU+QTwTAPpMD17rvvYv78+TjuuONw1lln+fp0AgpnzftRVlam9nq3a9eu9V8RCs0g38zrAHM5nPnrcMkll6jaP08//bQK8hH5qiW9SE9PD8gXQIJWN6/dijmF5eq/JYRll6v22k64/WPqVwI10KBdmAHa1mio5GN6jQaTMpNRsGEnNlabUWCxodRmRxuj/0wH7r77bqxbtw4XXXQRMweo1Vjzqmo3UzgR3isJb/34DV779C188Nb7zNzzAV2UATEnt0fJZ+tUwNWWV41QMWPGDHz44Ycqg/7WW2/19elQiM8DeT1MrW3btm248cYbYTKZ8Morr/A9uIn8Z0bvR8rLXReE0dGt1Amturh1fg75J+mqq3El0+YVFGDx4k049dRTVaCPyJfjoBSzlTqQgaTEalO19B7YuAOzC8tUJ1w3Cd+NS03A6OQ4hDIJ8iUYdNhYe71cbLX5TYDv999/x/PPP4+2bdvihRde8PXpUIjShumwuXQHdlcUIjrK/7awhwpdpMyPNGrLtMNih9PugEYXWO9JTVVYWIgrr7xSvfe+//77aosuUUhcDxPV7r657LLLVMKV7Gjr3r07n5cm8o8ZfSgPaJvnAb8+Djhk0xiAqFTv/0zyHzYz8NsTQE0JrHYn/l6/G/Hx8Xj99de5WkE+HwcDaVK322zFjWu2Ynm5K2rlgFMF90xaDcYkx8Go0aJTpAmdI8J8fap+IcXk2opsh1PV4nunZ3ukhxl9/jsnCxsyuXvrrbcQFxfagVhqXfoE99igQdU/eUiHK4M+OjJwxsFgpI02wl5qhr2oBsVfbkD8mZ2CNsgnY5/UXt69e7famjtw4EBfnxKFMAb4yBemTJmCX375BUcffbTK4qOmC853yBZqtSLKu1cBX10NWGVbiBPIGgyk9m6dn03+YcGLQO5SOJwabM0vx//mVOPll18O2G2RFDwCqZi8BPcmrNiM/8qrYHE61eEO7t2SnYrTk+NxclIsg3sNnNImFukmg8p2lI66l63IUY03fHlhe/bZZ2PTpk244oorMHLkSJ+dC4UmY1YMwnsmqvpvUvdtfNuTcFhyZ1+fVsiLGpQKjc71mtSsKULJ95uD9jl54oknMH36dPTs2RP333+/r0+HQlwgzQMpOMguDmmsIQ013nvvPdXwlJqOAb79cHdpqaqqglet/LI2uOdwBfeOvg7Q8hc5ZMjW7O1/wanRYneZBedML8egMyfivPPO8/WZEalx0OtjYAtI84zLV2zGmH/XY+x/G5BTbVbBqkSDDgNiInBsfBTu6ZCOw6K4vWl/Yg163N0hrS7It63GggUlFfCVN954Az/99BMOP/xwbs0ln5DAXtzoTnVBPr1Gh9O6D0dVtf+Og6HAkBaF6BMy64J81csL4LTW7noJIkuWLMEDDzyAyMhIfPPNN6r2FFFIXA8TASgtLcXll18Ou92ugnudO3OBrbm4RXc/5M1VVFR4+WLHVlP7gRboeSag5csRUuxWdVNZVYNvlpfCltQTTz75pK/PiqhuHJQit5JZ5W+ruItKKjBp9RZU2mQjrovcphr1uKdjOhIMHEsbI96gx4mJMXh/Z6H6b7PDNxl8CxcuVCu2UvPxo48+Ys0p8hkJIkUf2w7VKwvVuGfSG1FRVclXxMeM6VHQp0bAuqMCkC7Hdic0huCqu3fmmWeqBn/SXKNDhw6+PiWi1rseppDncDhUYzVpsCb199g1t2WYwbcfUgPNaDRix44d8JrSHcDm3xu8EkE0U6FGM1usKKuoVL9vX375Zd2bKZGvpaWlqVW0vLw8+GtwT8JRBq0GYVoN+kSHM7jXDLra4K0TTszIK2n1IJ906ZOJnNVqVQXlWUyZfE7r+puQJgcD2/VB4Xb/GgNDlXQ7D0byPnvuueciJycHkydPxgUXXODrUyKqmwcKr14PEwF45JFHVPfwI444QpWq8rfEgkDDNIf9kP3e2dnZ2Lx5s4ooe7yLpAT3pl0AlG4DnHYgoSMQ7RpEKXRs27UbhlLXqtgJJ5yIzI4dfX1KRHXcGQRSEy0lJcUvg3uyFXdyVorqCkvNc3hMBD7ZpUGNw4l5xeW4ac02PNctA8ZW6J5sNptVcG/nzp248847VQYLka/p4k0wSMZYVTWSIxOQutMAe7kFumjfNqGh4CTNNKSg/JAhQ/D000/7+nSI9jsPJPKWmTNn4r777kNSUhK++OILhIWxGV5LMYPvIIOaXHxs374dHjf3UaBkC+CwATHtgGF3SiVTz/8c8lvFJWV49MH/weF0IDoyApmZGb4+JaL9TuzWr1/f6s+MbAteWl6F2YVldcfnuUUM7nmBbGe+rX2qyoKUxiS/FpXhi93F8DZZPJOOuX/88YdqqPHQQw95/WcSNYZkDiSc0wWId62Bm2x6VC7K5ZPnR6y5lUHTLVKCeu3atVPNNQwG7uYh/5GamqpKZvhiHkih4e+//8b48eNVctVnn32GjAxeD3sCA3wH0LdvX3W7ePFieFz+Grm6AfRhwAn3A+Hxnv8Z5LfMZgsevH0S7uhbhogwk6o7hbb9fX1aRK03Bh6ExeHA9au34vylm9St+7h/w05m7nlJt8hwXJORrDbAOZzAukp3fVjvufvuuzF16lT07t0b06ZNY6c08iu6KCOSL+6FKlsNrDYr7MXe/5ugg9OnSMF/jRqkiqathXlrWUA/Zd9++y0mTZqEmJgYfP/9936TKU/UcLGjT58+akdbQUEBnxjyKClLcOqpp6omLm+99RaGDh3KZ9hDGOA7gKOOOkrd/vnnn/Aom9nVOVfoTAzuhRjJWrnhjntwfZcdiAozIDY2FprupwG9x/v61Ij2cOSRR3pnDDyAcpsdxVYbbl6zDbOLymB1Ovc5uC3Xe5KN9ZkjpTbvdqh8/fXX8fjjj6Nt27b47rvv1AUukb+JTo6DUwfY7DbYLTY4bb5pQkMu0t3Y2C4KTifgNNtRNHUtHDW2gHx6/vnnH4wbN06VAJItab169fL1KREd9Hp40aJFfIbIY4qLizFq1Cjs3r0b999/Py6++GI+ux7EAN8hBrR58+Z5Nrj3zSSgQgo2O4BortaFEtl2eOMDz6Fg9XxEGHWIj4+FpstJwKinAR3LYZL/NRuShgf//fefal3vLbvMFpy7dCOO/nM1hixagzlFZWqrqGwZPSslHuelJdQdN2al4HrW3POKeINO1TKUbsQ/F5Thre35Xvk5sgXjmmuuQXR0tMpaka1pRP7KFu5q0W0ur0HZnK0M8vmQRqdF9PAMGNIj4ZROumY7LFvLEWjWrFmDk08+WWWtvPnmmzjhhBN8fUpEBzRo0CDPXw9TSJOuzKeccgpWr16tSrXce++9vj6loMMA3wFIqny/fv3w119/qS5/HvHDbcDGua7ae4Zw4HBGq0PJ/56eghffnYa0pHgkxkVDq9EC7YcxuEd+S2qj2Ww2/PTTT14L7l26PEfV27M4nepwB/duyU7FmSnxODUpru44IjayrusreVa0XofxqQnqY5vTiedzduNLD9fik4Deeeedp+pMff3112p7LpE/s/ePQZW1BmarGdYdlSj/nd0kfR3kM6ZHqZ26iqTzBRDZ6igBvfz8fNU1Ui5uifyZ/L5KfTRphEDUUjU1NTj99NOxcOFCFeSTHR3smOt5DPAdxOjRo+vqZLRYVRGw5vva4F4YMOxuIKlby78vBYQnXn0fj7z0LgZ0bIMnT0uFTls7OzVF+/rUiA45Bkrrem8F97bUmGF3AokGHfpGh+OYuCjc0yEdPaIkdYZa08lJsTg3rT7I98nOQo99719//RVjx45VH3/++ecYPny4x743kbcMHnMcbvrpMZRUuuq9WXLK4KgOzG2h5FvSLVyCJTt27FCdc++66y6+JOT3EhIScMwxx6hsqw0bNvj6dCiAWa1WnHPOOZgzZw6OO+44taPDaGR3em9ggO8gxowZo26lELhHtue6pR8OJHdv+fekgPDK+5/hjsdfQY92sZg3MRVh1hJAqwfadAY68iKX/JdM6hITE1WAr7Ky0mvBvTSTAQ92aovb2qfhmsxkdIgweexnUdNIpmSEzjU1qJFmUB4gtXtOO+00WCwWfPTRR6qoMlEgiIiIQNt+HfDrxkWwWi3qPtbio6aSjD0J7m3atAnXXnstHn30UT6JFJrXwxSS7HY7LrzwQpUJOnDgQHzzzTeqQzN5BwN8ByGdg2QL0ezZs1Wnl5ZpuI2AW8xCxRsff4Vr//cUYmOi8Mt9pyLcVlob3OsCjPvIlc1J5Kf0ej3OP/98lJeXq0LgzfFPaSVe3rIbL27ZjedycvFsTu4+wb17OqQh3sA6lP5CavGJHWYrFpVUtOh7SRdm2eotNVek3pQUlicKJK7i305U1VT7+lRIaNxTaieqlhfAaffv5ifSffTEE0+sqzf1wgsvcEsaBRQprSHzwXfffVc1CyRqanDv0ksvxbRp01Rs5YcfflB1mMl7GOA7CNkTftlll6mP3377bc8966whFRJe/eBzXHXnY4iOisQP7z+P9Bida2YqtfdOfQ6IbOPrUyQ6JHlTFhKcaap3dxTgkuWb8eq2PEzZloc3t+er5g051Qzu+bOjYiPV9XON3YFrVm3Bn80M8kkHZslaKSkpwUsvvVT3u0QUSCTjNCwsTNUOcjp5cetrxswYaPRayEtRs7oIxd9s9NsgX15entqKtnTpUowfP169j0rnXKJAkpycrLLwJQN17ty5vj4dCiBSx/uiiy7CBx98gB49euDnn39WTfzIu/gucwgXXHCB2qLx2muvtWyLWt6qvbL4KJi9+M40TLrnSVfm3scvYVB/dzH52t8BabJCFABktU26is+fP19ttWwsCeQ9uzkXVqdTZeo5GhzyV5ARZmTmnp86Pz0RA2IipNc7quwO3LluO6zywjXBH3/8gZNOOgllZWXq/VO2pREFImkK0617dzidTlRVV8NWWOPrUwppuhgjoo/PhEanUd10a1YWonqF5+qFeoo06JPg3ooVK1Qm/IcffqiyoIgC0VVXXaVun3nmGV+fCgVQzT0Z+z755BP06tVL1WKWYDF5HwN8jSguesUVV6CwsLD5WXw584GZ17sabIjEzs37PhQQnn3zY1x//zOIi4nGrI9fxsB+PX19SkQtcvvtt6vbJ554olGPf31bnurCKsE9cUZyHO7ukKYCevd2TFf19h7t3Jbbcv14i+7krBR0DDepIF+BxYYia+MbC8ybNw8jRoyo25Y7ceJEr54vkbf1PuEItfuisqoS5b9tg2VHy7auU8sY20YhanC66z+cTlh3e65GrKcaagwbNgyrVq1SW7zff/99BvcooMmCnXt7pWSkEh2M1FyWrOXp06ejb9++qrFGUlISn7RWwgBfI9x0003qjfnpp59Wv7BNUpEHfHUVYKlUkxBkHAV0GdHMl4sCoVvuzQ+9gIS4GMz+9BUM6NOj/pPyO0AUoN10u3Xrhq+++kpdsOyPZLdYHA68ujUPL23JU11YhXRlPTs1QXXF7R4Vjq6RYegYYYKOpQr8PsiXZGx6tolM4k4++WRUVVWpRbHLL7/cK+dH1JrSj+uM7YYiVX+qqrIK5bO3wl5p5YvgQ9qY+u6Lzhq737wW27dvV8G9tWvXqjI/77zzDnQ6KdFCFNhlq6T7s3jsscd8fTrkx8xms+qW++WXX+Lwww9XvQzatGFZqtbEAF8jZGZmqs4v27Ztw6uvvtq0Z3jrQsBaA1UspO0A4JgbXE0WKOg88tI7qltum4Q4zJn6Kg7v1a3+k8s/A7YvdtXf04cB4Qm+PFWiJpGaQXfeeaf6+K677lK35TY77lq3HUMXrcHAhavQd8FK9F+wSgX43Jl756clqK6sFPga0xpq1qxZOOWUU1StMslYmTBhQiucGZH3aXRa9LxmCOZt+RsVVRVw2Oyw7mQWny/pIg2qpLW83VQty0fl4lz42tatWzF06FCsX79ebWl84403WHOPgsZZZ52FTp06qWYJ//77r69Ph/w0uCe/J9Il94gjjlDzQtkNSa2LAb5GeuCBB1SR5QcffBDFxcWNf4brCjJrgLaHM7gXpB547k3c89QUJCXGY+6019CnR5f6T677EVg2zfU7IMHdobcDYTG+PF2iJpM6GtJVXN60f/31N1y7agu+zivGbosVJTY7ahxOWGSrlNOpgkEXpSdiFIN7QeNQJex/+ukn1YxAstyl1pQsihEFk46dO8HUKQ4OuwOVVVW+Pp2Qp40wILyvq56T0+5E6U85qF5R4LPnJScnRwX3pBHBNddco2qPsqEGBRPZzfb444+rj2+++Wa1c4PITRZ3x4wZg2+//VbV7v7ll1/YUMNHGOBrpIyMDLVVV4J7jzzyiHdfFQoY8uZ27zOv4/7n3kRKUgJ+nfYaenbtuOeD1n7vutXpgeH3AIfzwpcCj2wxkjIF4vpnX8A/ZVWqeYYE89qZDKpeW4/IMPSPicCNWSkY0SbW16dMLRSuc00RHHDikY071Rbs/fn+++9x+umnq25pn376Kc477zw+9xSUzjzzDGi0rlp8JaUlvj6dkBfRNwkR/WrrOtmdPsvik6CeBPckyDd58mS8/PLLaksjUbA588wzcfTRR6uGCd99952vT8evSJf1bdvex5L/JmDJkovrj/8mYOu29+F0+k8pAU+rrq5W88Aff/xR/X7Iom9sLK8DfIUBviYWmpcCkS+++CLWrFnjvVeFAia4d89Tr+GhF95GWnIb/DptCnp06bDvA63VrtuYdKD/Ja1+nkSecuKJJ2LkyJFYs3MnqqtdGSyyBffJrhl4qHNb3NMxHTdnp2JAbCSf9CBwYmIMTFoNbE5gTlEZbl6zDY69VuxnzpyJM844A3a7XW3bkborRMEqJiYGUZFR6v1/+owvfH06BCCibzI0eo3qzu60HirX2PM2bNigau7J9twbb7wRzz//PIN7FLQkcO3upCuJL5K1Ra7g3vr1j2BzzisoK1uKsvLl9UfZUuTkvIL16x8NyiCf1Fw+7bTT8PPPP+PYY49VQT55ryTfYYCvCeSXVbpIStvnK6+8UhVbPiibBVgjqxu1F0QaFtkNFjK5v+Oxl/Hoy+8hPSUJv05/Dd06ZR/gwbWDOWsvUhB47rnnYIiOQUVZGRwOOzZW1xwws4sCW3a4Cbdmp9YF+eYWlWNxaX2zINmuPXbsWDUefvbZZ+pjoqCm0SAyIlJlNEcWazFn3lxfnxH5kNTak+Ce1Oi+9dZbVeCDmXsU7GT75UUXXaR+/0N5V5vNVo6dO6dj8+aXsGrVrcjdPRNOp6v5kkajqzuE3C+fD7YgX2Vlpaq9LI00ZCyULsvR0dG+Pq2QxwBfE11yySXqF/j3339XHQIPGtybcR2wcS7gsAN6E5DeL+R/4YKBXMze+siLeHLKh2iXlozfPpuCLh2yDvwF8voLBngpCEg33YkjToC9qgrV5eVYWVGDZ3N2M8gXpKT78diUePWxLFXtttjUx9IdTQopiy+++ELVXSEKdqaOsdCadIiNicXAdn2Q++0aVFaw2UYoki65si13x44dqgmVJAAwuEehQoLZiYmJqibfihUrEGqs1lIsW34NNmx8Ctu2v4/Cot9UEE8CetnZ16BP7zfqjuzsSdBo9HVBvnVBEuSrqKjAqFGj1Hbt4cOHqy3bkZHcweMPGOBrInnzlq5YJpNJrdbt2rVr/w+UpgobZgEOmyu4N+wuIIJdZIIluPfMGx8js20qfpv+OjplZxz4C8p3AXaLWvVXNfiIgsAjN16PhI9fR01xMWxmM5ZVVOPnwjJfnxZ5iUlbP1VYX1mDr776Sm3FlQLy8rFszSAKBbooIxLO7Yaw6HCEh4ejd2IXzHzjc1+fFtWyV1hhr7C0WuaeXAPcc889KouJwT0KJW3atFE7OqT2bqN2tQUBu70axcWLUFT0B5avuBYVFavhdNrU9lw5tFojsjKvQlxs/z2+Li72cGRlTawL8u3ePRMFBXMDflvuySefjHnz5qnyPVKuJSIiwtenRbUY4GuGzp07495770VpaSluuOGG/T8ob1V9B93BNwAphzXnR5GfefK1DxoE96agQ1bbAz+4Yjcw6/7aAJ8WSO3bmqdK5DWywPHOPXei7JkHUVlWpgLfW6q9f1FFvtEp3KSaqdidTryxeQcu/vhztUVRtujK1gyiUGLKjEH8mZ1V2RYJcm9btRnLV4deBos/0SeFywosHBUWFH602qtBPgnqnXTSScjNzVXXAg899BCDexSSLrjgAhXcWbhwIV5//XUEs4qKtfhr8elYvuI6rFh5Y21wzw6DIQ4d2t+ATp1uR4/uzyAubsB+v16CfO3ani+pQqp1WUXlWgQqCeqOGzcO8+fPV2OhzAUZ3PMvDPA10y233IKePXti+vTpmDFjxkEeqXE1V6CA995n3+KOx19BYnwsfv7oJWRnHOR1lUL0vz0BVBW4au8l9wCG3daap0vkVccccwzGDx+mmitUV5Tz2Q5i7SNMGJ+aAJvNqjrJm8Zdgnumf6karhCFIn1iuAruxURFI8oYgYm3Xasuesg3oga3hS7aCKfDCVteFYq/WO+VnyML+5K1It1yr7vuOtx///1e+TlEgUCyVqdMmaKymaUR5fbt2xFMJIDncJhRXr4Sy5dPgsVSqDLwXIcruNex422IiemFqMgu0OsPvj3VZEqp+9huC8zSDrKgLxmb3377LQYOHKjKtcjrT/6FAb5mMhqNeOutt9TgdvXVV6s3fQpeP/22EJff9ggiwsPw3XvPoWvHg9TcExV5QOk2V+ZeQgfg7PeBcFcdK6Jgcccdd0Cn06KmsgpF+Xm+Ph3yoiMsFSj74E11AR0VEwNbD2YkU+jSRuqh0WsRFhaOE7oMxuGmznj5ndd8fVohS4J7MSOzoY00qPVVy7ZyOGo8G3CVBnvSMXzp0qWqRAG75RIBHTp0wIMPPojy8nJ1PSwBoGCwO+97LFo0Cn8sGIL/ll4Ki7VYZd5FRnRCauoZaJt+Lrp0vg9hptRGf0+DIVHdynbeXblfqXp8gUaylt9991107dpVBflYc88/McDXAhK5vv7667Fz5061crEH9/ZcCngbcrZh/KR71JvW51Mex8B+PQ/9RXXFUzWu5iqRrkGdKJhEx8QgOjZWffz37/NgtXCbbjAy19TgtgvPQ9Ev3yM8KhJhERFwuLvDE4UgrUmPmOGZ0Og0iI2KwcSB52L5D4uxccsmX59aSAf5JLPS1Q5IrsU9O0bdfPPNmDt3rqq998EHH6gMTiKCKlfVv39/FfCZNm1awD8lubkzsG7dAzBb8uFwWNQhwb2oyK7o0OEmpKachqSkE2EwuOa/jWUyJSE5+WT1sXzP9esfQWHhbwgU0lDt4YcfRmpqKn766SdVh5H8E9+dWkh+0bOzs1XtAekio2z9E1jznWuSIVvt9UxdDVQVlVU444rbUFJWjqfuvg4nH3f0ob9Iau798259kNcY5fXzJPKFCK0WYaYwGMPCYO7cA0+/+z5fiCAjCxuP33wjVi35FwMHDUJEVLS6fP6hoBSLSyt9fXpEPhN5ZCpiTsyCzqhHdGQ0zux+Iq65fXLQZLBQPQnovfTSS2q+//nnn6s6tETkotfr8fbbb6tb2bpeUFAQUE+NbLfdtv1DLF16Bf777zKs3/AIHA6r+lxERAdERXVHctIotG9/PXS6sBb9rLTUsUhOGqE+lp+xY+d0BIKVK1fi4osvhsFgUNtys7IOsZONfIoBvhaS1NQ333xTfXzFFVfAsnEe8MXlgKXCVYctazC75wYomaRnDDwNK9ZuxPjRJ+HGy8879BdJ1+R5TwE7lwAaHWCKAvqMb43TJWp1iUY9RifHISY2BhqTCb9ldMGc1YFbOJj2de9Vl2Pmxx8is1NnPPrMszgqNlIF+CptDly9Kgf/MshHISxqYJrKHIsIj0BSbCJ+W/g7PvjsY1+fFnnQ1KlT1YVtWFiYurBNTOSODKK99enTR+1mk+CeZLsGUnBv3fpHsXnzSygp/QelZUvqgnvJSSPRudPd6NTxVqSnn9Xi4J6Q0l5paWfXNttwqs68/k4aCknfgcrKSrXQMWjQIF+fEh0CA3wecMIJJ+DCCy9Esnkzqj68oD64124AMOhaT/wI8oFjzrxCZe6Jt568u3Fd0tZ+Xxvc0wKmaGDsO0BSV++fLJGP3N+pLUYkxSM6OgbQG/Dacgb4gsVPX3yGH6a7tts8+cFHiIqNxcSMZPSPkS26riDf81t2+/o0iXyrdmqQkdZOZbDc++QDKGfjId9d1dQmUFavLGzxt8vLy8O5556rPr7nnnvQr1+/Fn9PomAlfyOdOnVSGa9//fUX/JXNVo6dOz9HTs5rWLXqNuzePVM1znDRQKcNQ2rqGKSlneWVDtnyPTVynSjjVPVm1aHXn5Nd0tLS1McJCQmqwQb5Pwb4POSZ68/GR2dGoLqiRHWVVMG9Y29xdVClgLN8zQYs+GeZ+viz1x5DZEQjtlnbaoBV37g+1hmAsW8BGUd4+UyJfMug1eCprhlIj42G3qBHqcOJP+fO4csS4KoqKvDivfeojwccOwSdehxW93pPzkxBpE6rrqN3W9yTYqLQJts2r774SuzOz8NTrz7n69MJSaasGLl6htPuROlPOaj8O7dFF7Y33XRT3X/ffffdHjpLouAkWa5PP/20+vjGG2/0y3IFVmsJli6biA0bn8DWbW+jsOg3FdzTaPTIzp6EPr3fRM+eL6s6e94I7rnFxh6uViOs1jLVoddfg3zvvPNO3cdr1qzx6nNCnsMAnyfYrUha9CjaxISrwey/0lgG9wK87t45156RYsoAAGK1SURBVNylPn73mXtx1inHN+4L1/8M1JQCWh3QdRSQcaR3T5TIT0jQx6jVIEKy+DQaTHnkIb+c2FHT6u7t3rEDp557PqbM/H6f19tQO8krs9mRZ2aQj8hpdeDOK25CQlw8Xnr7VRQWF/FJaWXGDrGI6JPkGsdqg3y24ppmfS/pFPnxxx+jR48eqKio8PCZEgWn0aNH47jjjsOCBQtUIwZfMpvzUVz8V4PjTyxbPgmVlWvhdNpUN1s5tFojsrMmIi728NrsOu8HsTLaXYzIyC6qeYd06F2z9l6/mzevWLFC1VQMDw9XHyclucZW8n8M8HlCeS5QWYDIcCNWFGgw4o0tKK8ye+RbU+uSwfWau5/Amg05uPisU3DJ2ac27gtV9t4M18eStTloklfPk8jfaKCBwWhEeFQ0Vvy9GEv/XOjrU6JmmvnxR/h+2qdo37Urbn/62f0+pkOESWXwldrsmLBiM4N8FLIMaVGyrgGnxQ7NH8W4ZcL1qK6pxhsfvu3rUws5cmEe3i8JYV3iXKVyHIB1d1WTv49czF577bXqwnb69Omq3jYRNe5v8L777lMfu7P5fGHXri/x1+LRWL7i2gbH5Nrgnh0GQzzat5+Mjh1vRY/uz9Rm1LUenS4cHdpfD5MpRQX5qqtzarv1+gdZ1Dj77LNRXV2NV199FYcd5trFQYGBAT5PPpkaDaKSs1FYUoFPvvbtqgU1z7vTZ+LDL39A907t8crDtzX+CzfOBWpK6rP32nTmS0AhKUZW+PR6TH/rDV+fCjXDhlUr8cStN8EUHo7H3/sQ4Qe4sJ2Q3gapRj3sTmBztRnXrd7id6vPRK0hdmQ29G3CodFqYC+z4IJ2I1V38Tc/ehsOh1SrpNYOMOhiTSqbvLkXtuecc466sH3llVd4YUvUREOGDEH//v0xe/Zsta2ztbgy8uwquLdhwxNwOMxq++2ehyu416njbYiN6YPoqG7Q630TwJcgn8GQUPffdrur7ruvyVxu0qRJ6rW76KKLcMkll/j6lKiJGODzGNeFTbeO2ep2+rezPPetqVXM+WMxJt71OMLDTJj+2qONq7vnVrDO9TsgRVMHXOrN0yTyS2kmg6o1bw8LR9I9j+L3Wb+gptr/u4NRvbydO3HDOWfBXF2N2558Bh279zhoB+W7O6QjyeAK8q2qqMFui41PJ4Uc6aKbeGEP6BLCVJBPW+7A+SPPws7du7Dg7z99fXqhqxkLDlarVWWtrF69WjXP44UtUfOC7BMmTFAff/bZZ15/CiVot3Hjs/hjwRDM/+NorN/wOBy1TTPi4wchNeX0uiM9fRy6dL63NnPO94y1AT75N6xYebOqyedrjz76qGqU0q1bN7XIQYGHAT6PcE8iNEiIi0HHrHb4ffF/MJv9J9WWDu6/lesw5orbYLc78MlLD6Fn147N/h1ARP1qDFGouCk7FVF6HbTQwNh/EPQTrlVbdSkwlJeUYPJZZyB3+zZccO1knH7hRYf8GgnydY401f23jRl8FMJBPlNmTN1/n3CMq3bv3D9+8+FZhTh3Al8jxyXJtrziiivw448/YvDgwXj99ddZUJ6omU4//XR1K1l83iSBsbXrHsSOnVNht1epba7ujrjJyaOQmXE5UlNPrzuSk0bAYIiFv0hJORUGfZz6d1RUrMSKlZPVx75sqiHdkJOTk/Htt98iKirKZ+dCzccAn6dpNDjq8J6wWm2qEyv5v8VLV+H4c69BeUUlXn34NowZMawF341b1Cg09YuJwKs9slSQz2A0wHT0UCxZudLXp0WNUFyQj6tPP1Vtzx159jmY/ODDjX7ewrSuaYQDTjyzORdWB8dACk0ao67u4yNNXaHX6vDvsiU+PadQpTHIuCSFEZ0o/30H7JUHbwRks9lw2WWX4f3330f37t0xY8YMVX+PiJqnXbt2aNu2Lf755x+Pl++wWouxYcNTWLrsKvy75ELk5f1Q1wk3MrIzoqK6qyYWaaln+n2QXjIJpQ6gO8hXXr4KZWVLfXIub775Ji6//HJVc/S7775Dx45NTXYhf8EAnxd0ymqnbrfn5nnj25MHzV3wN4aPvwZFJWV4+p7rcdUFZ7bwO/r3GwmRNx0RG4mTEmOg0+nVf+/OL+AT7udyt2/HFaNGYM3S/zB89Om475Up0NYG7RpjeEIMjBoNbE7g58JS3L52GxzM5KMQFNEvSQX55HrSVOjEUyffjl25u3x9WiHJmBUDbYQeTocTttxKFH68Go7q/ZcQMJvNGDduHN577z21Je2XX35BQgJ3YhC1VKdOnVRNy9LSUo89mRZLkeqEu3PXNJSULEZFxZra4J4B2VlXo3OnO9Gp461ITBzi98E9t7CwNCS2Oa7uv+321i9v89RTT+HKK6+sC+4NGDCg1c+BPIcBPk9Y+2PtB64abPGxrm0axaX+USyT9u+jL3/AiAsno6q6Bm89eTduvvL85j1VlQVA/tr64J7U4SMKUXqNRtWhEtsS/KPGCe3f2mXLMOHE45Czbh1GX3ARHn3nfdUJuSmkm+4t2akNgnxlWFxayaecQo4hJRIJ47u6gnxaDYa0PwKZYam+Pq2QpA3TI/bk9tBGGVxBvt2VqFqy76J7YWEhTjzxRHz55ZeqKcC8efNU1hERtVx8fLy6LS4ubtH3qanZiW3b3seWLW9g+fJrUFm5rsE2Vg2MhkRkZ1+D2Ni+CFSaumtHJ/IL5qiGIa3Bbrdj8uTJuO2225CYmIi5c+di6NChrfKzyXtcaRbUfIvfBuY9Cdgl/V8DtDsSNUtWq0+FmZp2oUStQ1LFH3rhbdz37Buqoca0Vx7BGSPrV06aHNyb/QBQmQdo9UDKYUB0mqdPmShgDE+MwdTtu9XHu/scgRl5JRidHOfr06K9zP/5J9w54SJUV1Ziwk234Jr/3dfs1e6e0eE4KzUen+wqUkUKdpkPvh2OKFiZsmMRPTwDZT9JV2kgPSbZ16cUsnQxRkQf2w6lP+ao9Xd7mXmPz2/YsAGjRo3C+vXrVZDv888/R0xMfR1FImqZmpoadRsWFtbs71FWtlzVpbPZKurKILk64SaoTD2DMREaqf4c4MkVMdF9kJv7NZxOG3bvnqn+PZKN6M1/l2RXnnfeeZg5cyays7Px/fffqxIFFPgC+6/B1wrWA78+XhvcA9D3fCDjSOQVulYqpOEG+ReLxYoJNz+ognvJbRLw6/QpzQ/uicVvAeU7XcG9hPbAGa+rOoxEoWpoQjROqXFtx5CJybTcIuRU73lhRb712Vtv4KbxZ8NSU4N7XnwZk+69v8VbWSKasK2XKJhpTTo4JPvC6URURKSvTyekaYz145KtsEZl84kFCxbgqKOOUsE9qTklW9IY3CPyrLw8V9ZsXFxci4J70llWAl+S1SaH0dgGnTrepurXaTX6gA/uifDwdsjKvAoajU5tOc7N/QYFBXO89vN27dqlMvUkuHfkkUfizz//ZHAviDCDryVylwEqhdYJdD0FOOwMdffKdZvUbY/OHTzyIpFnlJSW48yrbsPcBf+gW6dsfP/ec2if2cKtGEUbXZmbYbHAuE+AaG5JJEpeugjVKzch5qLL1XrrpiozssPru62Sb0iXyBfvvQcfvfwiIqOj8cT7H+Go4a5un0TkOdK0QaSnMKPf11l80nDDaXXAvKkUJTM24hfzv7jo4otU7b3HHnsMt99+e8DU6iIKFLL1c/Xq1ejQoUOzGtY0DO5JG6/oqMOQnHyyCuZFRHZSgb1gExc3AHZ7JbZt/0D9m8srViMp6QSP/5zly5fjlFNOwbZt23DGGWfgo48+QkREhMd/DvlO8P11tCZLgzpDcZl1A9q/K9YiNiYK7dK4NcNfbNu5GyMumIzVGzZj2KD++PL1JxDf0gxLhw1w1GZvmmIY3COqtXjxYtiKK6HXG9R/1zhap5YIHZjFbMbdl0/A3JkzkNKuHV6Y9jk6HdbTK09ZFV9vCmkaWK2uuUH79Cxfn0xI0xh0iB7SDuVzt6nsvfw/czB7zpfqc1OnTlXNNYjI81atWoXq6mr06tXLI8G99u2vhVYb/AvFprD0uo8l2Odpc+bMUUG9srIy3HTTTXjyySeh09V3gKfgwABfc21dBMx7qjaDTwNEJKq75y1agvzCYow77USuCPoJs9mCM664VQX3Lho7Cm8+cTeMRlfgoUXBvd+fBswVrqYa0SykTeSu6fHDDz8g/qhjYDDoYXUCn+0uRla4CYdFNX0Vlzzjhf/drYJ7XXv3wfPTPkdSmmczixIMrumEdNB9Pmc3ukWE4fBYbk+k0Mwac9ee6qfvCOuuChjSonx9WiHLmBmN6OMykPf9OpSXl2NEtyG46LnrMHjwYF+fGlHQkpqWYuTIkQd8jMNhw6bNz9fWnrPu87lQC+4Jg8G1nVm2IsvzEhPdCykpp3jke2/atEkF92Se/sorr+Caa67xyPcl/xP4m9Z9IW8N8OXlruCOBPja9gdSe6tPTZ3xi7odP/pEH58kuT380jv4Z/kanH7SELz37H0tD+6JhS8D2/921dszRgJDbuUTTgSoeh6yantO7x44LiEGeg1gdjjxdE4ua/H5yKJf52LaG1OQlpGJ12Z86/HgnugVHY5+0eGQJa9ymx0TV23Bv+ymSyEo31COX9b9Ab1BD71Gj7JZW2ErrPb1aYW0sigLdhbmqvX4Pj17MbhH5OVmhpIhK5lhY8eO3e9jJIC3dt392LlzGuz2Kjgc1j2OUAzuCZMxCUlJJ6mPHQ4L1q1/CIWF8zzymlx88cUqc+/pp59mcC/IMcDXHEs/rQ3u2YG2A4BjbwG0OlitNnzxwxzEREdi5NBBHn+xqOlKyyrw3FufIi4mGu88/T/PZFVWFQJb/qgN7kUBY98C2g3gy0MEYNq0aep5GD9uHJ7ploHhDYJ8swtluwW1tjefeFTd3vfKa4iJi/fKz9BpNLghK2XfIF+Z57eYEPl75srtPz6J4mgzNFqNqv9Ws9bVfI184+V3XkOlpQpREVEItxhQucRV/J+IPG/p0qVYt24djj/+eCQlJdXdb7EUYsOGp7Bs+TVY8t9FyM//STXP0Gj0iIjouMeRnDQq5IJ7bulp5yCpjav2ngQ7d+z41CNbc+fPn68aa9xwww0eOEvyZ9yi25Lae7I1s//FgM6VETb7j8UoLC5V20DDwkJvQPJH3/z8GyqrqnHrVRcgIS7WM9/UWu3q1C4Bvm6nAplHeeb7EgW4kpIStT03KytLdSiUgPodHdLwa1G5akZUzdpsrW7X1q34b+FC9Di8PwYMGerVn2XQalWQ77ktu/FfebUryLdyC97smY0+0SzgTKFBMldsDjsyz+4NzChScwWnhXVIffqafD0deSlH4IFRN8Bpd6L0u01qCh/Rh7WyiTz+9zZ1qrodP3583X0WSwGWLZ+EqqqNavupixMajQHtsychJsa1E47kLUOD9PTxKCicC6fTDpsHavF9/PHH6va2225jCbEQwAy+FqvPCJs207U9V+rvkX9Y8M8ydTtquAdrrWxb5OqcLLQsTErk9s0338BiseCcc86pm0AYGmTNrq80o9Di6i5JrWPZYhmvgMEnurZ8eJsE+W7MSkHfBpl8z27ObZWfTeRrmzdvxqJFi3DEEUcgKzu77n5bfhXslXvWmKLWkZu3GznbtmC9PhfhPVz1siXIV/bLVjjtDLwSeZJsBZWdHAaDAWPGjNlPcM9e+0gNjIZEtM++lsG9/XDNoV1hmpqa7aioXN+i12XBggXqNZGsSgp+DPB5sJHDVz/9ioS4GJxwzJGe+rbUQivWblK3fXt08cxzueobYOnU+uBeJw6URAdbtU0y6lVzDZ0GyLfa8MimnQzytaKNq1ep2669Wm913B3ki9Jp1VLITjMDGxQapk+fXjcGaqMNMKRFqmR/e7kFZT/mMMjnAyvXucbAY3oOhHW7lNeRrCFAnxQOjY6XQUSe9NdffyEnJ0c114iPj98nuGc0tkH3bo+id6/X0L37E4iJaXqX3VARG9tXJZTYrKVYvvyaZgf5zGaz2jLdo0cPmEzcYRgK+M7WVHYrULp1n7t/+u1PVe/tzJHHeaaJA3lEYUkpTCYj4mKjW/7NinOA/z5yZe/JtuyhtwMdh3viNIkCXkFBAWbNmoVOnTqhX79+e6xCvtg9E9nhJhXky7XY8M6OAp+eaygpLSpSt4kpKa36cyXIZ/REzVOiAKxBevbZZ6uxL+HsrtAnhqtafPZSMyoX7vL1KYYUh8UOa24lBrbrg7MThqlAq7wW+qQIxI/t7OvTIwraMXDcuHHqduPGZ/cI7nXseCtMphRotQZoZJ88HVBGu4sRGdEJTjhgtRRhzZp7VIZkc8rnyNelpqby2Q4R/MtqanDv2xuB7f+4uucao4EIV7r/1Jk/q1t2z/UvJaXliI2O8sw3K1ivVn6h1QMDLgOOvMIz35coCHz55Zew2Wwqc2XvZjbJJgPe69kecXq9KmqwsbrGZ+cZaspLS9VtVEyMr0+FKKhJhsSSJUtwzDHHICMjQ92nizEi8cIe0EYYVC0+2apLrcOyowLF09ehR14KXhl9H6I04XXBvcQLu0MXZeRLQeRBDodDZTGHhYVh9OjR6r7yilWq5p40y1DBPWN90w06OJ0uHB063AiTKVUF+aqrc2BvRj0+CfCJ2FgP1aInv8cAX1PMexpY+wPgsAE6IzD4epXJVVVdgxm//I7kNgkYetThXnuxqOnsDgcMek/1kmmwapLkoS2/REG6ars3CfLFGVizsrU57K56N3qD7zLLLU4nrI6mrzoTBcMYKEE+TVjt2Mek1lYL7pXP3qo6GLufcwb3iLzrjz/+wI4dO3DKKacgOnrPnVNabRiDe80M8knmo5vd0fQFcnvtPFBq8FFoYBfdplgnwT27K4NLtmem9VF3fz/nD9Wp9ZKzT4XeY8Ek8gS9Tgeb3RtF/TlLJ3LLzc3Fr7/+isMOOww9e/bkE+NHdLXvSXZb6zc3iTfoUWSzo8Biw53rtuGxLhkwaDl2UvAG+LRaLc466yxfn0rIcNocqFi4C5bNpa4dFrVzM2ftgoIE9YojqvHxb19jxBmjMPKSIdCGc55O5M1FjoZ1mKnlDIZ4dSuZkKtW3YpePV+EXt/40lPu2ITssqHQwAy+ppDMPcniCo+rC+6JL36Yq27PPoUNF/yNXq+Dzebu2NRCzah7QBQq3XNla4bUnWoM/im1Hp3OdxO78WkJqouyzenEDwVleGDDjlY/B6LWsGbNGqxcuRJDhgw5eJ0jTiM8Gtwrm7MV5g0lqiuuVM6RwF7D4F5YjwSsTM/Diws/wKbwfAb3iLxE5oBSqiUiIgKjRo1q8Bn3oh4Hv+ZKTj4Zen0MnE4bystXYMWKGxp0Iz40BvhCDwN8jfX3u0BFnutj2Z5bq6bGjG9nz0dSYjyOOaI+6Ef+k8Fn9cSFbVURsPY71xuUvFcZIz1xekRBQSZ1YuzYsQd9XJjW9ZZTYXfg23xXTRBqnQw+m7X1A3zSPfmm7BTVbEOCfDPyS1Q2H1Gw+eqrrw46BmoMrrHPUWNH9Qo2GWoue4UVFQt2ovTnHJTM3ATrjkrVEVcbpoOxXZTraOu6jRrSFvFjOkFncI2BViu7eRN5y6JFi7Br1y6cfPLJKsjXcIupsNnKkZf3I1+AZggzpar6he4gX1n5cpSVLW1ygI9jYOhgnnpj/PsBMPcRV5MN0bV+ZWL2H4tRUVmF8aedCJ2O9aX8jXQ0trT0grK6GJh9P1C2w7U9Oy4LaD/EU6dIFNCKi4sxZ84c1T1XtugezPlpCbhvw04V7Pl0l6u766lJca10pqHJUNvV3Wa1+OTn94mOwBGxkfijpEJlblapWjCcelBwLnKMGTNmv5+PPCIVpd9vVplmlYt3q4Yb4Ye5mrRR40gH3LIfc9StiuqpLu2AJkyPxPO6wdhu/1vWjEbXorzF4psxkCiUxsAzzjhjj/vT08/B+vWPqsDUzl2fqc65SUkn+egsA1d4WFsktTkRu3K/UP9tszW+2QbHwNDDDL5DqSkDfnuidnsugN7jgC4j6z79/dwF6nbMiKHee5Wo2UxGA8wWS7PaitdZ8119cC8+Gxj3EWBwrUgRhbpZs2ap7Z9yYbt399y9jU1NwA3ZKdDXPk6CfMzk8y6j0aRuLWYzfEVX+2sho/CconKfnQeRN+Tl5eHvv//GgAED0K5du/0+JvLwFEQPy4Cm9o+h6p/dcJg9VD4kiNlLzSrjsXpZfl1wT7beyvMohy4xHInnHji4J0wm1xho9uEYSBTsvv/+e5XoIg02GkpLHYPs7Kuh0bgW9nbu+gK2ZnSCJVnQcIdtnCgs+k3V5GsMjoGhh8voh1JVCNjMUtgDaHck0GvPGlO//fmvGtCGDOznxZeJmstkNKrgntThM9Ru02iyStmaLcvEOmDMa0BMOl8Qolq//fabuh0+fHijnpPL2yWp2+dzdjOTrxUYai9ufZm90i86Ar8XV6jX+5nNuaou3/npzF6i4DBv3rxGjYHRx7SFdVclalYXqkw+R7UNWhN3fhyIZVs5yudsg9Ne2wlXKqRoNdAnRyBhfDdow3XQGHTqvoPhxS2R9xc5Vq1ahSOPPBJxcfvuysjMmICKijUoyJ+lMvls1jLodSx11FTR0b2gyf1SPYe5uTOg1RjU1t36wN/+cQwMPczgO5SSrbUfaPapu5ZfWIyV6zahf69uiI7iQOWvGXxCsvg8IizWM9+HKEhI91xZ5Bg8eHCjv0aCfHtn8m2uYnaFN7i3Zlh9mL0yMC4KY1NcXeCsTice37QL6yprfHY+RJ4eA8WwYcMO+VipFUd7UouwJWZY86rqDmmcoYJ7DqcrW0+rhUanhT4lAokXdIc+zgStSX/I4J7gxS1R6yxyHGwMbNj11WzZzZekGcLD2yEz8wpoNDo4nVaVDVlQMOeQX8cxMPQwg+9gdi4BZl4POGq3UUjttQYkuCd6dG7vvVeIWpzB5w7wRUXWF31tOnZ/ItqbbHlat24dMjIyEBMT06QnSIJ8pVY73tlRoDK7NlTXoH2EK9uMvJDB5+PtaWemxKPcZsdPhWWQTSXLyqvQJTLMp+dE5AkrVqxQtz169OAT2oxOuOVzt8GyvWK/n3d3wo3olQSNUQtjRrQK9DUFL26JfD8GRkV2kT9o+aPHli1voEP7GxAV1YUvTRPFxx0Ju70K27d/KG2bVMONpKQTDvo1sggvB8sUhA5m8B2sa+rnlwI1pYC0ok7rA3QescdDFi1xDWjsnuu/TKbaAJ+5Bd3TZIt2XZt3InL777//VFeuY445pllPSreoMP5leZmxNsBn9YMC8+3D6wO4NQ4umlDgczgcWLx4saq9l5mZ2bQvtjWuflIwB/fKZm9VwT3VLGPvQ4J7hyUifkxnhHWJhyk7tsnBPcEAH5H3O+iKg80FU1PHICHhWFWLz+GowabNz8NqLeFL0wzh4fXvNfJcNnYcZIAvdDCD70Byfnc12JDgXmpvYMjtgN4VLHLbuGWHumUGXxBv0V09A9jxryvApzcBpgMXciYKNRs3blS3zFzxX3Xd0/ygwHycwbU90eF04qUtu3FYVDj6xbQks5rIt3Jzc1FVVaUubA/VZEhoo2vnkU4nyudtR+zIbGgjXPOUYGbZXo7Kv3LhqGyw2OqQ8tZOVzAvXI+IPkl13XGFISUC4Ye1qWtM0lwM8BF5fy4of2ft2x94R5tWa0SP7o9h1arbUFS8QAWmysqWITFxCF+eJjLoXeWipMmG1OKLjTkcycl7JiHtjQG+0MIMvgOxuychGiBr8D7BPZFXWKRu05LbeO0FIk9t0W1GBt+GWcC/kgLtBHR6YPD1+9RhJAr1wsoiLS2txd+rJY2uqTFNNnwf4JOAXp+ocLVFt8xmx8SVOazFRyE1Bkb2T4EuIUxlp9lLzCj9MQdOqz34m2XM3gZ7qQVOm7P+aBDck064sSdlI/bErLojondSi4N7ggE+Iu+Pg6mpqdBqDx5W0GpNSEoeWbcryilJNNRkRmMiktq4tuU6HGasXXc/CovmH/RrGOALLQzwNcYBVmXzC12pxW0S9u0YRP6VwVdjbkYG38qvaoN7BuCYm4Ajr/D8CRIFsPz8fHXbpk3zFjnCG0wGpTZbkdXmsXMjF6OxNsBX4/sAn06jwY3ZKXVBPqnJ91mua6GMKBTGQF20EYkX9qgP8pWaYdlajmBrmlGzvhhls7ai7Jct9c0ypANuYhiMaZF1h6lLPBIv6AFjO+/tjnAH+Gpq2NiHyNOkTEtpaWnjx0Btfe3d/IJZ3KbbTOnp49CmjXRud8LhsGDH9o8OOQ5yDAwdDPAdiLNhbZT9B/ictY0X9Dp2RQuEJhtNZq1yvfZxmcCgazx/ckRBcCEn9PrmVXs4Oi4KGWFGSJLGLrMVj2zahRIG+TzKUFuH1B8y+IRRq8Ulbduod1VnbSYfUSiNgfpYE6KHtHMtHjsBp8URVM9H1T95qJi/E5atZWprrju4F96rDZKv7oukK3vXHYnju6lAnzcxg4/If8bA+PhBCAtLV51gzeZd2LDxKVitpXyJmkij0aJt+rnQaGSO54TNdvCFImbwhRYG+A7IHdRzAlsXAqu+ATbOBazVdY/Q1WafWG3MOvFXYe4mGy0qMM8GG0T7I1253Cu4zRGm0+KdXtnICjPVBfm+L+BEz5NMJtdqudUPavC5NaJUGVFQj4ES8HKz7CiH0x54NQocNTbUrClC9YqCuqPyj52oXl6gPq/Ra1VTDI1Bi4gjUhA3uqNHttw2VViYawxkgXki34+BOl0Yevd6FWFh7eqCfJLJR80L8rlrv9aYd6Gy0lUX+0DjoIyB7oAsBTc22TiQrEGuuntSG2Xnv65DAj1rvgWOvxcIi0V8bIx6aEFRCaKjWJvNr5tstKSLLhHtV3x8vLotKHBd0DVHmsmIF7tn4swlG2CHE3kWLpgEaw0+omDT3DHQmB2jAl9wOF016n7bhuihGT4JgDWHbC2W+oGOKhmvG1wwyocajfp3xI5sj/DDEmVvPrRG3+10YQYfkfdI3b3o6OgmjYGSwdej+5NYsuRCOGGHxdL8OWSoi4npjZKSxbDZyrBs+TUqeBoZ2XG/46AE92w2GwyG4G/sFOqYwXcgse2A0S8DhghXDTY5NFqgJAeY/SCQvxbHdIyGzFk2b9vZqi8aNZ6puRl81cWAvSVZf0TBr0OHDup28+bNHumuSp5nrB0Drc2pQ9oKJGvT5uCKMoXWGKiLMiLhnC7QGHXQQAPLljKVDeePHFVW2Aqq6w7rrgpXcK/S6mqSodXWH5Kxp9cgdlR7RA5IgTZc79PgnmCAj8h7JINMxsHt27fD0oRrLYMhtm6DlAT4HE4u7jZHRruLERHRUTUssVoLsXrNXfvN0uM4GFqYwXcwnY4HrpoH7Fzi6qr762NA6XZXkO/nuzEpxYyTr4jE8tV/AoOPaLUXjZqRwdeULroS3Jt1P2CrAbR6IKkbn3Ki/ejSpYu6XbZsGZ8ff2+y4UdbdOP1OkTptCi3O/BPWRXuXLcdj3VpB32DbYtEgSAuLg5JSUlYsWIFHA7HIbtINmTqEIf4sZ1RNH0tYAdseVWAZLz5WT29mhUF+3Y5d7rq6hnSIhF1bLs9PmVIjYQ+zjXu+MsWQjm4RZfIe3PBpUuXYtWqVejbt2+jvsZgSIDBEA+zOR9VVRuxdcubyMy6AloNQxNNodNFoGOHG7Fu/cMwm3NRXZ2j6vEZDK5dhvsL8EVFRTXpZ1DgYQbfoUS2ATqfCHQbBYz7yJXZJ0EfjRZGowGJERoMrf4BKFjXKi8YebnJhsxif30cKNsOaHRAXBZw3F182on2o1u3bkhISMC8efNgt3umWYKV2Vxe2aJr9aMtugatFhMzkmHQaGBzOlXdxRe37Pb1aRE1yzHHHIPi4mJ1gdtUEiDzRyq49/duVU9PpkUqU6/hodOoQF7i+d0R3i1hj8OfgntuLDBP5N0xUMydO7fRX6PVGtC1y/3Q6cJVOKKkdDF27frSi2cZ3EE+kyml7r8djn3ne8zgCy0M8DVFfBZwwRfAMTcCAyZAnzVQrdZq7WY4frobmH5B/fHZJcA/7wEOdgj0jwBfIzP4yncBRZtcwb3YDGDch0BsW++eJFGAkvFvxIgR6uJ2zpw5zf4+kTodwnSyUQ34r7wKP7HRhhcy+Pxri26/mAjckJUCvQawO534sZDNVSgwjRw5Ut1+/vnnCESqnt73m1H40eq6o+jjNaheUag+L8G8iH7JiByYVnfEnJCFxIt6QBsRGLWcGOAj8r8xMD7+KPTo8ZRqvCH7dUtKFnnpDIOfQR+rbp1OB1avuRM2W8Uen2eAL7QwwNdUUcnA0depRhuacR9it7E9nHCiuroGsJnrD0uFqyHHghcZ5POLJhuNvLhtGJBtf6wrY5OIDuj8889Xt2+++Wazn6UInRbXZEqwx7VF84OdhfilsIzPugcYamvw+WOTDQnyxdR24JMgH1EgOuuss2A0GvH+++83qQaVPzXLsOZWwWl17HFoaoN7cad2QNxpHRE7IrvuiBqUDm1Y4GylY4CPyLtbdAcMGIAFCxZg5cqVTfrahPhBMJlS1cdO1uFrtqTkkdDro9VzWFa2FCtW3qDq8rkxwBdaGOBrCUM4Yi7+BG8vsWFprhWOpO5A8mGuwxjh2sew5Q9Vrw+/PQH8+wFQwywF3zTZaGwNPl5kEjV15bZz585q5Xb16tXNfvImtG2DazKT64J87+0owBwG+TyWwWf1cQ2+cpsdU3cV4ZmcXDy1eRee3LwLj27aiWKbawIaoWWjFQpMUqbgggsuwI4dO/Dee+816Wsbds217KiAdXcVvM26qxLlv21H2eytKnPPUWlTmxZ0MUYYUiPqDmNmtKoRGNE3GYGOAT4i75o8ebK6feSRR5r8tVqta54iWWf5+b94/NxCQZgpFR073NogyLcMpaVL6j7PAF9oYYCvhdp36YFNWeMx+I0iPJ57LDDhO9dx+quuDrxysVq4Adi+GFj9DTD7fqC6xDOvHjWhyUZgraoTBQopXn733Xermk3XX3/9frt3NdbVGUm4MiNJBfnku7y9owDziso9er6hxlhbg8+XmUUS3Ht00y7MyC9RTTWWlFfjv/JqrKioUZ+X1/vctASfnR9RS915553Q6/W45557VMmCxpItrmE9ElXDCqfVjrJftng1yGfOKUXZzzkwbyyBZWsZHDV2FdwzpEUheWIfJF9Vf7SZ0BPh3f2n6UdLMMBH5F3nnnsuOnXqhE8//RTz589v0temp42Fpra5xo6dU5FfMNtLZxncwsPbITnJtV1aSLMNNwb4QgsDfB7w2GOPITY2Fg8++CD+/fdf150djwPGvArEpAM6o+uQWVTJVmDOA67tuw0PqftGvq/BJx2T3Vl8tZlERHRwF154IQYNGoRffvkFU6ZMafbTpdFocF1mMi5t10Y1YHDWbtc1Oxx8CVq4RddXGXzu4N6WGgskR8+o0exxxOh1uCE7hQE+CmhyYXvzzTcjPz8fEydObNJCR/zojjB1ioNGq1VBvsq/drX4fJx2B8ybS1G9srDuqFqSh4rftsPpkOmotvbQwNQ+TjXL0IYHzpbb5gb4WrIARUQHJgscL730kvp4woQJKC1t/I61tLSxyMq6si7It3PndNhslXy6m0PjDu04UVy8sG7MY4AvtATvu3krSk1NxSuvvKK2aIwdOxZ///03EhMTgQ7DgCt/A8zlQGUB8OUVrgCfHP+8u+8fZP9Lga4n++qfEdQZfDWNubhd+z3w30euj2W7WPthXj47ouBptvHOO+/giCOOUFl8/fr1w1FHHdXsIN+NWSlYXVGD+cXlqHY4UGazI8nI9aiWbNGVi1tfB/dSTQa8dlg2Uo16aDWupiomrRYGLRdTKPDde++9+OGHHzB9+nQ1/t14442N+jqNXouEs7sg7+X/YCs1w1HW2JIi++ew2FEumYB51fv/eVoNwvsmIWZ4pvo4mAN7bnJx63A4YLPZYDAERmMQokAs2XL55ZfjrbfewsUXX4wvv/xSzQ8bIyvzclRWbkBB/iy1xdRqLYZe759dxv1ZdNRhKn9L6u/tyv0aWq0RHTrcXBfgq6lx7Zyg4MYrJg8Wmr/22muRk5OjBriSktptuDoDEJEAJHVxdWSNy3Td1/DQGqSyKPDPO8Cqb4DiHKAy31OnFtLCGluDT55zCbrK6yCvyeDrgc4ntM5JEgWBbt264d1334XVasWpp56KZcuWNft7SZCvjbH+otPBpItmM9RO6rydwVdhs2NLtbnu2FhVs09w791e7dE1MgyxBj2i9TpE6XUM7lHQiIiIwBdffKF2dEg230cf1S4YNjLIp6ldxJDsO0dV04J8TocTtqIa2Aqra4N7VWoTggTw9jhUR9wkxJ3SAbpIQ0gE90RYWJjPFjqIQolk8fXv3x/ffPMNrr76ahVYbyyDwdUJVlisri7e1PRtupmZl6quxBIo3bFzOgoKZnEMDDGh8c7eSp599lls374dX3/9NUaMGIFvv/0WSUlJ9Q+Q4N6lP7kab0inXbdd/wF/vwPYrcCSD12HzMyyhwBHXQ1o+TK1fIvuIepP5a92Bffkue5/iatTMhE1uZvkU089hVtvvRXHH388fvzxRzXRaw7ZouvGDqst2zYjdRK92UVXmqG8v7MQtr22v8l/NQzuZYW7go1EwbxVVy5sTz75ZJXBIhljl1xySaO+Vp8cAVtBNZw2h+psGzsyW9XoOxRbiVkF9ewVtUFBp9OVmRdpQMwJWdAY6tfydXEmGNIi1SJKKGm4PS0qKsrXp0MU1MH0GTNmYNiwYXjjjTfUFtFXX31VzUUOJTKik+v61+nE1i1voEOHGxEZ2alVzjuYJMQfDYfDgu3bP5QlcpSWLeUW3RDDDD4PkrT/adOm4bTTTsNff/2ltmisWrVqrweFAZ2OB7qNqj+Ou8uVMSaZY7JV171/fvNvwIKXAIfNk6cZml10zYdYDbfVBgBlzpvWpxXOjCg43XLLLaouaUFBAYYMGYKvvvqqWd+n4a7NaqbwtTiLz2r2TpON2YVlqhmKVYIKtUOo+5AGoQzuUagZOnSoWuCVoJLUopImRI3JYpFgnC4+TAXn7CVmlM3aesiacRLcK/sxB/ZyiytjrzZrT4J7iRd0R0SfJIT3SKw7jOlRIRfcE6w/RdR60tPTMWfOHHTs2BFvvvmm2tXRmJp8qamjER8/SNXiszuqsWnTc7Ba2ZiyOSIi2td97HCYOQaGGKaGeZjRaFQ1B2644QZVl+/II4/Eiy++qCZ5B51UScZYcg8g53dXJt/KrwBrlSvbb+e/DYpmAohKBQZMAJK6efr0g46hdsXIZrcf+EH5a4AVn9fmm2iAiDatd4JEQeiOO+5AWloarrjiCpx55plqPJSgn3ubVGOkGA3qYlVie1O25eHuDmmIM/Atqzn0egPsNs8sFEk25fTcIswrrlAZe1V2hxo5JePy+MQYJDXYWh2n1+Hs1ASkmFjzikLL8OHD8fvvv6sF30cffRSLFi3CBx98oC58D0QfZ0LihT1Q+OEq2Itlu20N7KUWdb8E+irm74B1Z6XK8Ktjc6otvbL1Vp8SCWNGNLQmHSL6JkOf0PjxNti56+5JRiUReV+7du2wcOFCnHHGGfjpp59UbeZPPvnkoPWZtVoTenR/CqtW36oaREiQr7TsP7RJZE30ptLromu36Tqwe/e3SEg8Qt3PMTA0MIPPCyQN+eWXX8brr7+uVm0vu+wynHPOOdi9e/fBv1Ay+064HxjxCHDGFMAQ4Qrs2WpcwT73UbQBmPswkLfaG6cfVNyr5toDFXEv3gLMfcT1vEotxOxjgYyBrXuSREFItqfNnTsXGRkZeP755zFw4EAsWSJdqhvnrNR4ZIQZ1RbPHWYrHtm0CzV2dtNtDpngSYdOTwT3Xt+Wj5n5parxiQT3UBvcu7xdGzzfLQP3dEyvO67NSmFwj0KWlCdYvHgxjj32WMyePRu9e/dWDTgOlpUnwTzpqFvH4VSPL3p/FcwbSuGossFpcdQfDqcK7hnSo9Dmoh6IO7m9ap7B4N6B5oK87CFqLVKmSsY+qcW3efNmHHPMMXjggQcOWgtTpzMhJeWU2n0AqigpX7BmMBoT0KbN8LoMvvT0uTi8fzjHwBDBdzovuvLKK1VH3T59+uDzzz9H165dVR0C+8GyydzaDwHOehfIGgS06VJ/xLR1BaKsNcCvjwDzntrzWD2DW3r3N6lrmAG5d+dcd3BPnvMxr8kM0DO/AEQhbvDgwVi6dKla4JCmGwMGDFBddsvKyg75tUlGA97p2R4Z4fVBvsVlla1y3sE4Djb3wlY64U7dVYTnt+zGQxt3Yn5JhZo4hGm16BxhwmFR4birQxquz0oJya1/RAfTtm1btdDx8MMPq+Zr48aNU/X5NmzYcNCGG4pk7f2xUwX33DFBCebpoo0wJIXXHeF9k5F4fndow5jhfCAM8BH5bnu8XPtKbdK4uDjcf//9arFDAn8Hy+RzKyj8FVbrobf30r7apo9Hosp+lIUiO8acHsMAX4hgJMPLevToobZmyOROViwmTZqk0pMl8HdImQOB8Z8Al/1cf1wxB+h4nKsZhAT5tv255/HvB8D85xjkq+WonRUfMIPPKgEDVbQGGPGoq0YiEXlMfHw8pk6dis8++0xt25WSBdJxV+47VH2ptmFGTM5KgbY2cOTOGKOmX9xKXa6mKrXZVebkjPwS/FVaiXVVZldwT6fF890zMLN/F3zRrxPOS09kcI/oAKTJjdThk2w+yWSW7Wo9e/ZUmSw1NTX7PD6idxvVGEMC5rb8KpWl5w7uhXWKQ+pN/ZF8Td+6I/60jgzuNWIMFMzgI/KN0aNHY+XKlbjwwguxbt06nHDCCTjvvPOwa9eufR4bH3cUwsLSoNHoUFOzAxs3PQ2brdwn5x3INBot2rU9H1qtXNs6ERmp5RgYIhjga6XVC5ncycA2atQoFdyT2nwS7JMV3SbRm4DTXwF6jHZ9rGt4GF0VlrctcmXzrfvB1ajDWo1Qn9TtN7OkugQozqn/b3YrJvIK+fuTDrurV6/GTTfdhLy8PJx77rk46aST1ETvYPQN/nZXVFTv06mVDs0pAb5GZtcVW22qccZPBaV4bNMubK2xqAxKo0ajjmSjAc93y8SwhBg+9URNIDWoFixYoMq3REREqEyWXr164eeff97jcYaUSMSf0xXaWKPK5pND1dXrk4SE8ay97PG5IBG1ipSUFFWLVLKau3fvjk8//VQt+L700kt77G7T6cLQq+erCAtrWxfky8v/ia9SM4N8deOeasTEMTAUMMDXijp06KA6q0lXSSk+KinLMrDJAHeoTJY9SGDv1OeAaxcDVy+oP858AzBGuoJ8O/4GFr/t6sL74+1AdTFCkaN25XufVVsJ7s15ACjf5creS+oKRLK5BpE3RUdH45lnnsG///6Lo48+GrNmzVIXuHKhu79MFtE/JhIm6QoJ4J+yKry6NY9BPi9t0V1fVYNb125XXXHf31lYF9xLDzNiet+OmHNkV8w6oiuGJkjxZiJqKvk7lPIta9euxSWXXKK26o4YMQLjx4/fI5MlrGMcUq7rh5QbDldH6i0DEHdaRz7hzcQMPiL/MWzYMPz33394/PHHYbVaMXnyZJX40nB3W3h4W/To8bQK8AmLOc+HZxzoXEE9CQ8wizk0MMDXyiRyPmbMGJXJcuutt6KgoEClKI8cOfKgNVn2KywWiEqqPzoOB86oDfLpDIBOD2h1QNkOYNZ9QMF6oGRr/WEuD90mG4vfcD0H8sYRlwmMedU18hGR10n9Fekw+dZbbyEqKkptVTtQTZY2Rj1e6J6JCJ1WvWH9WVqpssuoiRl8ewX4iqw2bKux1B1LyqrwxKZcVDkc0GtcjTPkkODeOz2z0T0qXNVFNDRjqy8R7Vt8/t1338W8efNw2GGHYdq0aWrBt2GdZo1OC12UUR11dfmoWRjgI/IvRqMRt99+u7oelm7jsvArJQwk2Oeu02wwxNc93mItVnXkqHEcDiuqa3agqioHTmd993AG+EKDxtmk1DHyNCk8P3HiRNVKPDw8HM8++yyuuuqqlqXQVha4tuk6bMAfL7i2ocrHe8dzJfjX9zyg+2gEq5/n/YkRF0zGbRMvxBN3XVf/ic8uBixVQEQCcNHXriAfEbW6/Px8tdjx/vvvq/+WbmtPP/202sLW0G9F5bh21RZYnE4MiInATdmpfLUawWqxYFByAg7rPwDvz/5VdcJ9a3s+5hVX7PNYmQxIcO/ouCickRIPo1arPpbgKhF5h8ViUXO/Bx98ENXV1RgyZAg+/PBDZGZyXuIpkjEu8+yqqio11yYi/yGhCGnCcd1112H79u1q7JMx8JhjBuGvxaNhVtl7DsTFDURW5uV1WX20fxWV65CT82qDuoVOVFTUYM7sApxwwgeq0RMFN87afUyyVubPn4/XXntNBfXk4vb0009HUVFR87+pbDXtdgrQ43Rg3EdAfLarvpwEDRseEvT790Ng1TcIVhWVrvqDUZF7BgsUTe1zxeAekU8zWd577z1Vk6V9+/ZqLOzfv79a/GioVzQvypqjqtIVyIuIjFTBPdni/FttcE+z1+EO7r3YPQujkuJwQmIMg3tErZDJcscdd6g6zcOHD1dZfTI3lMZE5BkVFRUqcyUsjI3UiPx1d9uqVatw7bXXYuvWrWob7733PoBOHe9SNfkkZFFSsgg7d33h69P1++Depk3Pw2aTLEiN6/80OlRXh+Gjj4rVrhkKfgzw+QGZdEgW35IlSzBgwADMnDlTddo9VPH5RolJB86dBgy+wZWt5z66n+raxis5G0s+BD6fAHx5hatun8r2Cw6VVa4AX2QEJ3VEgVCTRTqsrVmzRmVcyFjoxlzz5tldXomYW+/Drkl3YuKqLVhYWqne+CUr7+zUBIxLqz/u6pCOl7pnIZwZe0StThY4fvnlFzz11FMq0+ycc85R9Um50ablKisrERkZyQLzRH5ep1kabnz//fdITk7GI488gksueQLZ2Q/UBvk0KC5e4OvT9DuFRfOxevWdWLHyemzc+DQcjhpoNHrExvRBatqZyMy4HHPnHIa8PLsaByn46X19AlSvS5cuqsOarF688cYbKsj39ddfq+0aLSL1+QZP3vf+Ra+7uu1KQM8sGR1OV+fdqnzg2FuCoqtsRVWVuo3aa7sfEfmfmJgY1WFt6NChatFDspmff/55VZOFmq7EasPLRdUwHjEYzvAwVNkdKrgnAbwXumXiWDbLIPK7Bd9bbrlFzftGjx6t6pPKYq9kOUumHzU/g4+ZK0SBQbaQLl26VGX1fffddxh18la8NqUDgK2sw7eX/PxfsGPn1NoiK+5mGnokxB+NHj2ehFZrUvcVFMxVtxwHQ0PgR3CCjMFgwJQpU1Sx5ZtvvlkNcj/88EPLg3z7M/AqIDwe+Pd9wFoDlO8ErNXA9r+Bn+8BopKByGTXdl95XACqrHJ15oyMaLC9b92Prvp7Mg7qOGEm8jeXXXaZ6jo+duxYXH/99bDZbLhi8vV1n19ZUYP1lTXoHMnM3L1trbbgl8JSVNod2FxtRq7dVWbXaLehQ4QJCQYdrs1MwVFx3KZB5K+ko+Rff/2FU089FZ9++qnqMi6NOGSOSM3L4EtLS+NTRxQgUlJSMGfOHFx66aWYOnUqli+vRNeu0dCFePk9h9OGwoK5qKraBIfDgtKy/9T9Go0BYWFpdcG99u2vrQvuucdAwQy+0MAAn5/WIrjxxhuRkJCACRMmYNSoUfjtt99UXSqP632O6xBb/wS+uBywVACF612HkIYdx9/nqlcXYMoqXANatLsG3/qfgb/fdq10aA1An/G+PUEi2q/jjjtOTe6OP/54tdghk5KRQ0fh+4ISVDsceHxzLu7okIrO3H5fZ21lDZ7cvAvVjvreWRqnE46C3Thx2xq8fvrD/G0jChBSaF7mfieeeCK++uorXHzxxfj444+5zbSJZIFILm5l+x8RBQ5piCNjnmSdVVd/h6IiJ5LaRIR0Z9wtW6agtGxJg3ulxp4eWVlXqgYkB+LuTMxxMDSwBp8fk8ncm2++qSYmslVj586d3v2BmUcBY99y1e3TmVyHbNOVzL7ZDwDrfnIFyNxH4Ub4u7wCV7OS5DbxgLkc+OddVzEvqT84aBLQ93xfnyIRHUDfvn0xa9YsNSGZNGkSjtu+FoPjolUzCAnyvbejICSfO2mWsbS8CrMKy+qO7/NL6oJ78vwYNRp1pFqrUXLfLWgfw4w9okATHx+v6vL16tVLZfI99NBDvj6lgFNQ4HqfkJpeRBR4ZQtef/119fdrs9lRUJiH0tK1CBV2hxnFxX+qrbibN79QG9zTqow9rcYInS5SZesdLLgn8vLyYDKZGOALEczgC4Ctahs2bMDjjz+u6lFJx135A/VqkO/K34CqQqCmFPjmGqBosyvIt/jNvR6sAfqcC/Q8E/5qd22AL6VNguvfZLcCGi3QeYSr8Yh0EyYiv9WvXz+1PeO0007D+Wefjfl//YWNJgN21Fix2xw8DYEay+Z04pWteVhU6spO3psE946Nj8a9HdOh12jwyZRX8Uf+brXdhYgCM8gnDYdk2+59992HHj164KyzzvL1aQWM3bt3q1uOgUSBG+Q7YsB5WLnqDVgsVvz774M48sgHERnZEcHMai3Bho1PwWze1eBeLfT6KHTr9ggiIztBr4tS/92YcVDGQNklSMGPGXwBQLoISXDv77//xl133eX9HyjZbdGpQFJXYNwnQEIH132SzdfwkG2uSz8Bln8GlG6vP8p2+k3LS3eAL1kCfA3JdmMOckQBQcoUSGfJkpISXHrBBYiQIL1MfpxOlNrsCMXgnjwDEsBreBg0GhXce6F7JtLDjEg2GZBXe3HL7BWiwJWVlaWarkkNvssvvxxbt2719SkFXICPYyBR4OrYcTIyMo6DXq+D2VKJZcufUFtWg438m2pqdqGqKqcuuKfR6FTGnhwGQyx69nwRiQnHIMyU2qjgntlsVvNnjoGhgxl8AbJy8c4776B379549tlnMXLkSFWTpVVEpwAXfwtsngdYG2SMFKwH/nrDlRG3bBqwbPqeXxeXAQy7E4hMgi/lFRQjIjzM1WTD7NNTIaIWkLqkUpNPOqodu2YFtFmdVYDv0U07cVf7NMQa9CEV3JNOuDdlpyJGX19xOsmox8DYSGgbLF7ItgzBiR1RYBs0aJBa8L3ttttw4YUXqvFQF+oV5xuBYyBR4NPpwtCv70soLz8P+fn/oLy8ADt2rERGRl8Ei4qKtcjZMgU2m6teniTSSHAvPCwDWdkTodNGIDa2L/T6ptUTzc/PV7ecB4YOZvAFCGm48eGHH6rUWtm2W1FR0Xo/3BAGdDkJOOyM+mPobcCwO1yZfZJNIxeUDY+SLcCs+4FK16DiC06nE7n5ha7tuUKCkUQUkGTse/vtt9UEZeGt1yHeYYNc2m6rseLJnFz19x5Kwb2XumfigvREjE6OqzsGxUXtEdwTubm56pbb04gCnzQcGj58OObNm4dXX33V16cTEDgGEgVPkC8jYxCioiLgcDjw3HPSNDF4gnubNr8Am63U1ThDNc9wBfd69XoVyUkjkJh4bJODe4JjYOgJ7pSHIOwqOXHiRLz22mt48MEH8eSTT/r2hI64HEjoCGyYBTgd9fdv+wso3gxU5ALf3QjoI4CIBKDfBUBKz1Y7vYKiElRWVSO7bw9XPcFFU1zbiuUCWM6HiAKKBKleeukljBs3DjWP3oW0h57DTrMVOdUWFejLDDci0K2vrMHHuwpRYLHJaFUX4Cu3O1RwL0KnxYvdMzE4vnGTvM2bN0Ov1yM9Pd2r501ErbOjQxY6pA7f3XffjbFjx/JvuxFjoMjOzuavKFGAMxjiEBkZiZoaM7r32ISff/4ZJ510EgJRYeE85OX/BIejBjZbBZxOq+qIGxPdCxER7WE0JiEt/SyYjG1a9HM4BoYeZvAFmEcffVRlsMhW3eXLl/v6dICOxwEjHgFGPlZ/nPupK/AndfqsNUB1EVC4AZj7KLBraaud2qatO9Rtt6xUYPaDQEkOoNEB0elA7/Gtdh5E5Dlnn302RowYgX9//hEJWzfu0Vk20K2prMZjm3dhXZUZxTY7SmqPimYG9ySrcdOmTap+lwT5iCjwSaDq3nvvRXl5OW666SZfn47fkzFQdOjQwdenQkQtlJoyBmFhaYiJiUZGhgG7815DRUVJwD2veXk/Ytv291WNPWmm4XTaVHAvIeFY9O49BV26/A/Z2RNbHNwTHANDDwN8ASYuLg7PPfcc7HY7brjhBv/clhaVDIz/GOh6ChCfBUSnuYJ9dgvw2xPAH88Df7xQf6ye4fqclwJ8J2TBtWVYgnsxbV3nJrUFiSggt+q+8sorCAsLw6+zflENfWQU/L6gJCCDfEvKKjFlW57agvvk5lzUOJyqE67U02sXZlRHRpgRh8dE4LUeWY0O7rm3ZdTU1PDClijISGBPsvimTZuG33//3den49fk4tZoNDLTkSgImExJ6N3rNURHZyIiIgxJSU58/vnL8HcOpw35BbOwZeub2Lz5Jezc9Zm6XxpnhIWlIzw8E23Tz0WP7o9Dq/XsbhQG+EIPl/QD0Lnnnqu2qUmBZUlNlmwWvwzynf5Sfe27b28E1v7gCuTl7DUZlf/OXQ4MuRXQeW5Q27xtp7ptlxghe9xkbwsw9FZX0JGIAlbHjh3VAsfT0z5H9OizYYqOwR8llXAiD1dnJEMXIB2yv88vwce7iuq24goJ7g1LiMGz3TJgkjHLA9symLlCFFwkYOVuuiZNNxYsWKAWP2hPUqcrJycH7du3V9ubiSjwhYe3Q/v216K6+m61Vff33+fh1FOL0aZNPPw1uLclZwpKy/5tcK/U2NMjO/tqZGZM8OrP51ww9PDdLgDJJM5df+/2229XExi/Jo04Tn0O6DkW0JtcQbyGhzTp2LnEld23YTaQMx+wVrf4x65enwOtBmgfJgVLa2kNLf6+ROR7MvbFlBYh/+E7YXC6trAuKKnEsvIq+CuH04nl5VX4tagMn+wqVME9YdBoYKw9RrWJw3MeCO6J1atXq9tOnTq1+HsRkX+RulPScOPPP//EV1995evT8UtyYWuxWDgGEgUZrcaggvaSxde+gwZPPfUG/IndYUZJyWIUFv2OnJxXa4N7WpWxp9UYodNFoEP7yV4P7rnnglFRUWy2FkKYwRegjj32WJx22mmYOXMmPv74Y1x44YXw+yDfqCeBobcD1gYX4NKMY8Z1QE0ZsOu/+hp9kUnACfcDUc3fSrti7Tq8eHIY2pSvdmXv6UxAuwEe+McQkT+UK5Ai89JVMvX3X7Bz6EgVQNthtqIf/I80ynh5y278VVY//km+jV6jwaTMZJyWHIcwrRZtjJ57W162bJm67dOnj8e+JxH512LvgAEDcOedd6o5ocHARcyGOAYSBaeYmN7Qag2IjIzAwIFmzJ8/B5s2jUeHDr7fpWWxFGHjpqdgNu9ucK8Wen0kunV9BBERHWAwxDSrI25TFRUVYfv27Tj66KOZ5R1CmMEXwB5/XPbpa3HPPfeoOksBITIRiMuoP9oPAca+A4TFuLLrpFafVgdU5gGz7nc15yjPrT8sFY36MRaLFUdGbMfZh5mgkeCeIRwY/aJr6zARBYVJkyapBhK/fzcTNptV3Vditfv6tGB1OLHbbN3jcAf3tLVBPTkkc++6rGRcnZmsau15MrjX8OK2d+/eHv2+ROQf+vfvj/Hjx2PdunWquy7tiWMgUXAyGtuge/cnoNeHIyoqAoMGheOLLx732fk4HFYV0KuqysHGja7gnkajU9tw5ZDg3mE9nkVi4rEID2/bKsE94W7IyXlgaGEGXwCTAssTJkxQkzopOi+ZLAGpXX/g8tnA5nmA0wEsfgsoWOcK8v10556PlUYZvc4CDhsry9cH/JZrNubg6LYa6PU6V9DwlOeATid4/99CRK3GZDLh4YcfxiV33YOK8nJExsXjh4JSpJoMOCExxievxKqKary0Ne//7d0JnI31GsDxZ/Y5ZjWLmTGNJduolH0vRRtJthZbilCWUNFN2W6pSyVLIi0qimRJUUJEVJSQdVos2Q0zmRlmzHbu5/lrppUWM/Oe95zf937OPZyL+Xun++99n/+zSFrubwON2mevYBLuwPIxEuLrI4lBgVI92FEs69ABTFu2bDElGfoC4J7GjBkj8+fPl1GjRknXrl1NKRbO0j1QkcUMuJ/IiKaSWO0J2bHzYTl1KlNycnfL119vl9q1Ly3RdaRn7JR9+6ZLbm7az584TXDP4SgvCRd1Fy8vbwkv3bBIJuL+U+yBnokMPpsbPXq0OBwOc4OXmpoqthUUJXJZ+7PBu9tmikRVPRuYM0Vsv3rl54psmSOyda6Znnkum7d/a/rv+WmAT39fuYYl+tcBUDI6d+4sNSJLS+rs18WZk2MCaTMOHjfTaa0I7j2994iczM37/c5VGNybckl5uTM+StrFlC624J7av3+/+XcCp7aAe9MhOvfdd58cPXrUDN7ALzZv3mwmrlepUoXLArihsLCapvQ0ONghvj5eMnLkRHPAWZLBvT17JklurvZ79zr7HxPcqyCX15gmsbFtJCamtSXBvYI9UHEv6FnI4LO5+Ph4M03yqaeeMr1Y9N32gqNF7nhL5Os3RE4e+OVzHbzx3Udng3xb3zk7kOPXWXw6HbdOD5GQWPls4zdypTat9/35H3Ed5AHA7WibgrFjx5pp4j7ly4nPTR0kx+mUNakZUis0qNi//soTafLB8ZOSlZ8v6bn55mvrJNyaIaWknCOg8NdpcK9jMQf1fk2naqoGDRqUyNcDYB1t1TJjxgx5+umnTbAvOjra478dhw8fNkM2mjZtKj4+etgLwN14eweKl5e/BAYGSN16wbJhwy5ZufJzadGicZGX4B4+PE9Opm36TQBRs/aczhxThqt9ATVrLzAgRuLiOoq/f6RYTe8FtTdrrVqu2J0axYUAnxsYOnSoTJs2TSZOnCgDBgyQsmXLiu2VihBpOuiPn389U+Tj0WeDfJlnJ1AWOn1cJPVHM5xj7ZdbZMiNPuLnVxDgO3c5LwB7u+6668w0yVXPPyuVWtwk3gEBpg9ecXv/2E8y50iKyRos2GE0uNc8IlTGJ5YTP00jtsjatWvNuz7cAnBvGtAbMmSIjBgxwlR0TJgwQTzdunXrzDt7IOC+fHwckpDQ3ZTIai++e3pFyLvvTpRrrmloDoCLKri3d99USUvTbLg/3tdpcC8ysplUT3zKDP5wFcnJyZKUlCQNGzY01X7wHKQ1uck0yWHDhklmZqYp2XVrtbuJtBwnEp0oEhb/y8tR+mxJ7+lkyf3oMelX5ZCEBvqKd0Fgb+diq1cOoJhoeYYOHVLai+985fsXQoOGHx0/KdP2H5Pn9h4xwT2lwzLKBvhJQqC/3Fk2yvLgXsHDrd7cNmrUyNJ1ACgZgwcPNv02p06dKnv37vX4y06AD/AM5RJ6miBfQIBDAgL8pEHDU7JgwbJiCO55m4BiYGDcr15lJb5sJ5cL7v26koNDDs/j5SzJQnUUGw3uVa1a1ZQkbN++XapVq+ZZV/vUCZG53USSd0lWZqakpGVIkCNQwkJ1Oq+vSL/1Io5wq1cJoBi169JV1rbtLkFhodKoTJQ8VDG2SIN7k348KhvTThd+piE8nYb7QIUY6XGR65TEae+9qKgoqVGjRmH/FQDuTweu9e/fX7p16yZvvPGGeLLatWvLpk2b5MSJExIREWH1cgAUIw1nbNx4m5xM+1YOHkyVkSOcsmHDfPH39yuy4J6vb7Bceul4CQ+rI3bwwAMPyHPPPScLFy6Utm3bWr0clCAy+NyEpt7qBLW8vDzTi8XjBEWeHc4RX0eycvPNRwGBDpGqN4gM+EokMMzqFQIoZmbv8xLJzMiQ/ZlZf5hk+09pX731P2XImpR0mbDviAnuef+csefv5WX66mkQ0ZWCe2r58uWSn59vSpcBeI5evXqZoRuzZs2Sb775RjzVkSNHTHCvbt26BPcAD6nk8PL2FV9fHwkLCxQfn2Py+usL/vbvz8n5SVJSP5OUlHWFr717p9g2uKeWLl1q+o9effXVVi8FJYwefG6ke/fu8swzz8i8efPkyy+/lHr16olHCYoUZ6e35cbLKsiJ4ydly451IlEJVq8KQAmpnVhNYjZ9J0f9AmX/yXR5avdheeTiOAk107T/mePZufLk7kNyJDu38DMN7gX5esvTVRPk4lIBEuHnKyH/4s8ubh988IF5b9WqldVLAVCC/P395fHHH5cuXbqY1i2LF3tmexJ9sFXsgYDnCAurI6dOfS8hIQ55bHiMTHn+VenU6WbTm+980tO3y569kyU/P/tP/ld7Bve0TcPOnTvlqquuMq284FnI4HMjvr6+8uSTT5ofa7NlT6y+3pWUJOt37pfKda6RUgT3AI87wZ3R+AqRn1Ik61SG7DmdJS8dSP7L33c6L1+OZ+cUvn7MPFMY3PPxOluGqy8N7k29pIJcExkq5R0BLhnc08w9fbgNCQmRJk2aWL0cACXsjjvukJo1a8qSJUtk1apVHnn9P/zwQ/NOgA/wHBUr9JPw8Lri6+svkZEO6dXbXyZPnvGXwb3de84G93RYxu9fdgzuKfZAz0YGn5vRGvsrr7xSVq9eLfPnz5eOHTuKJ1m0aJF5b9mypdVLAWCBehXKSZdjb8osPz/JCnTI9r8Iwi1O/knmHUmVnN8diOjPNLhX0REgd8VHmQBf09LBEn0B/VxKwoYNG+To0aPSrl07k80DwLPocB2t5rj22mtl0KBBsnHjRnMA7CmysrLMIYf2IdUSXQCeQQdgXHrJeNm6ta/k52+RrKwzsmjRHOnatb0kJMSdM7jndGYXTsKNKP3LwaiXl4+UjmgsAf5RYjc8D3s2MvjcMINl4sSJ5v2hhx4ywzc8yezZs83fvUOHDlYvBYBFxgy6X/zS0yTr9Gk5k50teefIZn73WKrMPpxigns6MOPXr4Lg3ozLKsqtsRHSLqa0ywf3CvZAdeutt1q9FAAWadGihQnyax++l156yaO+D5q5kpaWJu3btzf9pwB4VpAvJORSc9Chpbl5eTkyYsSEvwzuRUW1MJNw4+LaFb5iY9vYMriXnJwsK1asMMM3ddgaPA8BPjdUq1Ytueeee2Tfvn3mFNdT6PRgvZlt1qyZxMfHW70cABYJCgqSGhdXND9OS0uX6fuP/SHI9/rB4/LOkVTzYx2acW1UqNxcJrzw1fuiaHmtRkUpE+D6Qb0COmTp7bffllKlSkmbNm2sXg4AC+n9X0BAgAwfPlxSUlI85ntRcMjRuXNnq5cCwAK+fmd7zjkcATJocLysWPGxrFv3lRw+slB2JQ2XHTsflt17Jv0muJdY7XHx9rbP/d75vPPOO+Z+UPdATXqB5/FyemKjNg+g0fsqVapIdna27Nq1S8qVKyeeMEFzzJgxMn36dDNJDoDn+jw1XTp+8qVk5+ZJcHi4tIiNkt4J0eLj5SU9tu2RM/nOwuDegxVjTRmu3emJrU7O1Zu6N9980+rlALDYo48+anoz9+/fXyZPnizuTjP3YmJiJDIyUn788UeTxQPAs5w5kyybt9wlZ84cNSX7W7akSKVKDomMDPu5AYtXYQmuuwX3VNOmTWXdunWSlJRksvjgefg3n5uKjo6W0aNHmxLdfv36uf3AjdzcXHn99ddNzynKcwE0Kh0io8qGi+Tnyen0NFmTkiZj9xyWblt3/ya4p4E9dwjuqVdffdW8k7kCQD3yyCOmouGFF14w/Tnd3Zw5c8wDfadOnQjuAR4qICBaLq8xVQICYiQwMFCqVg2SnJxcOXUqU7y8/MTbO0ACA+MkPr6L2wX3NKinwT3tP0pwz3MR4HNjGtjT/4MvXrxY5s6dK+7s/ffflwMHDpi+UxEREVYvB4AL6NWgtrTYvU3ys7MlMyNdtmVkSd7PZx06NCPa39dk77kDHawxb948k6194403Wr0cAC4gODjYBPd0ura2btGqDnelB9lTpkwxP+7du7fVywFgIYejXGGQLzQ0WLy9vSQjI1O8vWPkyqafS4P6S6TSxYPcKrindL9Xffr0sXopsBABPjemU9Nefvll02R4wIABcuLECXFXBTd1GtQEgAKv3H+fOGZNl8zjySI5OSZrT181Q0vJyvqJbnOhtJF+Tk6O3HvvvTSWB1BI+3Hq4efWrVtl3LhxbntlNGtF+zBff/31pkUNAM92Nsg3TYKDK0lISIjs3Jklo0dluW1VW0ZGhrz22msSFhZGJYeHI8Dn5q644goZOnSo6ck3ePBgcUd6Q/fxxx9LzZo1pWHDhlYvB4CLDdx4acC9cqJPZ8kb9YDMqX6RrG2QKLMuv1jchZak6amttijo2bOn1csB4GImTZok4eHh8vjjj8uOHTvEHT333HPmvW/fvlYvBYCLcDgSpG6dudL8mnWy7KNKplfxjBkzxB3p30v7kN59991m2Bo8FwE+D6AT1KpVqyYzZ850y1LdJ554wrxrIJNpQQB+74YbbpDud9wuP3y6Wl4YPkzC/Hzd6iJp773Dhw/LXXfdJWXKlLF6OQBcTGxsrAmAaYmu9ug8c+aMuJNt27bJggULJDExUVq3bm31cgC4EC8vb/H3DzVVbTpZfODAgfL999+LO9E9fezYsaZ6b9CgQVYvBxYjwOcBHA6Hmajo5+dn+pLoZDF3oSfR2ndKG4nedtttVi8HgIuaOHGiVKhQQaZOnSrvvfeeuAt9YP/f//5nbuq0oT4A/Jnu3btL+/btZcuWLTJs2DC3POh97LHHaFEA4E9Vr15dnn76aVPK2qVLF9PWxJ2y9w4ePGgOesuXL2/1cmAxAnweok6dOuYG6OTJk9KtWzczddYdjBw50vRSePTRR7mpA3BO2pNEDzq0J2mPHj3k0KFDbtN7b//+/WZf1wAmAPwZrXDQ/UKn6o4fP16WLVvmNm1atDqlcuXKcvvtt1u9HAAurH///tKqVSszVXz06NHiDk6fPi1PPvmkub/loBeKAJ8Heeihh6R58+ayZs0atzi9Xb16tcneu/TSS2kmCuAvNW7c2LQs0IFDmvFr94mSKSkpMmLECFNyou8AcD4RERGmXYsG+zSDZd++fba+YHrAq+Vo+q6H2JrJDADnonufZrvFxMSYoNjixYttf7GeeeYZc9Dbq1cvufhi9+kvjX+PAJ8H8fb2lrfeesuc3mqK8pw5c8Su8vLyTA8FNWHCBG7qAPwtWsKlPfl04mLBHmJXo0aNMkE+Pbwhew/A33HNNdeYYNjx48elXbt2JvvDrhYuXCirVq2Spk2b0qYFwN+ivYrffvtt81ysBx1JSUm2vXIa2NM2LVql8t///tfq5cBFEODzMHpioTdEmvGhZWqbN28WO9KJkdpH5pZbbpFrr73W6uUAsAktYZg9e7ZUqlRJpk2bZkrW7Ojrr782+2DZsmXlP//5j9XLAWAjWsbVoUMH2bRpk+nNrBlwdpOeni4PPPCAycjRg16GrAH4u5o1a2YGD+nU2bZt25p3u2YwZ2ZmmgPf6Ohoq5cEF0GAzwPVq1fPPNjqhnDzzTeb6L+d6OQjfaANCgoymzMA/BOlS5eWd9991+whffv2tV0vKp2Wpo2UNZNZh4cEBwdbvSQANqLBsNdee820ONHepHbsRTVkyBBTYqx7uPaZBoB/2o9Phw/t2rXLHHjYrW2LZiHq9PCaNWtKv379rF4OXAgBPg+lD4cPP/ywHDhwwJSraU8qO9DhILp2LSnRngMVK1a0ekkAbOiyyy4zbQr0BFQnS3755ZdiF3pSu3XrVtNQvmPHjlYvB4AN6cHA+++/L7GxsSbApxPG7eLDDz+UF1980fSb0vI0APg3Bx2a8KLZfCtWrDDBvvz8fFtcSJ2Yq0E9Pz8/c1ij70ABAnwe7KmnnjKb2c6dO6V169a2SE/W4SDaO+u6666TPn36WL0cADam+56W6J46dcpMVduxY4e4Om0IrQ+0+lD+/PPPW70cADamh6QaLAsNDTUPi3bozaxZe127djX9s7RZPhnMAP6twMBAU9Fx+eWXm/3v/vvvd/mWBZppeOutt5oezCNHjpQrrrjC6iXBxRDg8/CTC3241YfcL774Qq6//nr56aefxFXNnz/fDAfRISGzZs2i3wqAC3b33XebgJk2nL/66qvlm2++cdmr+sMPP0i3bt1MH8G5c+dKVFSU1UsCYHNa3rVo0SLTm1kbzuv9lavKysoqfLDVQSFXXXWV1UsCYHPh4eGydOlSc+AxZcoUU/bvypl8Oljt888/lxtvvNH0UwV+jwCfh9OU3nnz5kmbNm1k/fr10qJFC/Og62rWrl1rTmwL1qsTkACgKGi7As1oTk5ONhMmN27c6HIX9ujRo+ZmTg9hxo0bJ1deeaXVSwLgJvRwQ7ODNch35513yiuvvCKu2KKlU6dOpp2C3rPqvg0ARSEuLk5Wr14tlStXNmW7vXr1Mn2OXc348eNl8uTJUr58eXMYo5nMwO/xTwXMDd0777xjGozqZMbGjRvLt99+6zJXRntN6TAQPbnVPgMNGza0ekkA3IwO7tEbJ80M0X4s2pvKVWj7hJYtW5oBQ9qaYPDgwVYvCYCb0QNezWLR4UP33HOP6fXpKqVqug7NqtFSurp16/JgC6DIJSQkmCBfYmKivPrqq2a6rk7rdhUzZ86UBx980AyK++CDDyQyMtLqJcFFEeCD4e/vb3oP9O7dW7777jsTRFu5cqXlV0dPajWjRrNWJkyYIJ07d7Z6SQDclAbONHNFp9TecsstJuBn9QOuZhU2b95cNm3aZA5htHxE2ysAQFHTkle994uJiTGDN7RkV4eaWZ25pwFHbSlTpUoV82AbEhJi6ZoAuKeyZcuaIJ8+B2tWc9OmTU3fT6u9/PLLZsikw+GQJUuWyCWXXGL1kuDCCPChkK+vr0lL1um0GlDTQRZ6g2dVirJONNIHW53wO3bsWBk4cCDfLQDFqkePHvLRRx9JWFiYOSnVoFpqaqolV11vKrUUV0uG27VrJ2+++abpvwcAxaVevXqmZYtOGp89e7bUr19ftm/fbskFz8zMNNPCNZumWrVq8vHHH0t0dLQlawHgGbQNlB503HbbbaYvs/Yp1exhK+ghsz4Da8lwqVKlTNCxUaNGlqwF9kGAD7+hmSH6UFuQ+qslGlq2oVl9JbmZaZBR+03pybGeWgwdOpTvFIASoQcLGzZskFq1asnChQvN+/Lly0v06i9btkzq1KkjSUlJ0rNnTzNUQ9spAEBx0/5On332mama0OCeBv2071NJHvju3r3btIxZsGCBKcv99NNPTQkdABQ3zZTTAw4NrmVkZJhD1vvuu09OnjxZYhf/1KlTJotaW8joM7kGHfX+FPgrBPjwpzS4tnnzZlMeq6nKNWrUMBPLtHStOO3fv19uuukmGTJkiISGhpo+WPpwCwAlSUvBdErZgAEDTCadThnXQT867KI46aGGTkjTPVgzB8eMGWNK0zTDGgBKipbBahN3bVugh7/333+/yRzRdgHFfcirGXu1a9c296EdO3Y0D7Zk7gEoSTrAQhNM9HBBDz20yq169epm2GNxt29Zt26dOeTVIKNmU+v9qB60AH+LEziPvLw85/Tp053h4eG6kzkTEhKcL730kjM7O7tIr9vp06ed48aNcwYHB5uvU79+fefu3bv53gCw3CeffOKsVq2a2ZuCgoKcw4YNc6akpBTp18jPz3fOmzfPWalSJfN14uLinMuXLy/SrwEA/8aePXucLVu2NHuTl5eXs1OnTs6kpKQiv5gbN250Nm/e3HydgIAA5/jx483eCABWSk9Pdw4ePNjp7e1t9qeGDRs6V6xYUeT706FDh5x9+vQx+6x+nbvuusuZkZFRpF8D7o8AH/6Ww4cPO3v27On08fExG06FChWcY8eOdSYnJ1/QFTxx4oTz2WefdcbHx5s/1+FwmJ/n5ubynQHgMrKyspxjxoxxhoWFmb0qNDTUOXDgQOfOnTsv6M89c+aMc86cOc569eqZP1dfutempqYW2doB4ELpg+zcuXOdlStXNvuUPuh27NjRuXLlygt6yNXfu2bNGmeHDh0K98AmTZpc8N4KAMV5CKGvRo0aOWfNmmXuES/Ed999ZwKI+hxckFCzZMmSIls3PIuX/tffy/UDxPTi08EbOnFXe7H4+fmZMl7tTdCsWTPTBFlTms9HS9y0n5WW37733nuSlZVlys90Strw4cPNBCMAcEVaNqvTdSdNmiRpaWnmMy0l0z1QBxNpvz6dSn4+2s9FSz60WfL8+fMLy35vuOEGU5KrZRkA4Ip0qu0bb7xh2rbs2bPHfKbla23btpVWrVpJgwYNzJCi88nJyTGlvgV74I4dO8zniYmJ5s9t374908IBuCxtGzBy5EhZu3at+bnuea1bt5Y2bdpIkyZNJD4+/ry/X8Mv33//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\"__metadata__\": {\"image/png\": {\"width\": 1272, \"height\": 644}}}" + } + } + ], + "console": [] + }, + { + "id": "bkHC", + "code_hash": "e87fcef254d37006a8ef832f6b89e063", + "outputs": [ + { + "type": "data", + "data": { + "text/markdown": "

\nThe same Jordan curve, eight random seeds, eight different inscribed squares.\n" + } + } + ], + "console": [] + }, + { + "id": "lEQa", + "code_hash": "47bc133d97394fd46ceff281862469b4", + "outputs": [ + { + "type": "data", + "data": { + "text/markdown": "

The setup

\nThe Inscribed Square Problem (Toeplitz, 1911) asks: does every closed,\nnon-self-intersecting curve in the plane contain four points that form a\nperfect square? It's still open in full generality after 100+ years.\nGoren, Yehezkel, Dahary, Voynov, Patashnik, and Cohen-Or\n(arXiv 2510.21697, CVPR 2026 Highlight)\npropose something a little wild: train a standard image diffusion model\nto take a Jordan curve as a 128\u00d7128 picture and denoise Gaussian noise\ninto a picture of an inscribed square. No specialized architecture, no\nparametric tricks \u2014 pure pixel-space denoising.\nIt works. And because diffusion is multimodal, the same curve produces\na different inscribed square at every seed \u2014 uncovering a hidden family\nof solutions the paper itself does not explore.\nThat's where this notebook spends most of its time." + } + } + ], + "console": [] + }, + { + "id": "PKri", + "code_hash": "8502481940898319253dbf6c9bffb0b3", + "outputs": [ + { + "type": "data", + "data": { + "text/markdown": "

Pipeline \u2014 at a glance

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
StageInputOutput
ConditionA Jordan curve, rasterized to 128\u00d7128 binary1-channel image (the \"problem statement\")
NoiseRandom Gaussian (1\u00d7128\u00d7128)The starting point of denoising
U-Net[noise, condition] concatenated as 2 channelsPredicted noise per pixel
DDIM100 denoising stepsA clean 1-channel image of an inscribed square
\nThe U-Net is a vanilla 4-level diffusion U-Net (~20M params, attention at\nthe bottleneck and the inner enc/dec levels). Training data is purely\nsynthetic: 100,000 procedurally generated Jordan curves, each constructed\nto pass exactly through a known random square.\nDiffusion sampling itself runs offline (~5s per sample on CPU). The\nnotebook reads the cached samples from a 10 MB .npz. Why? PyTorch\nis not in Pyodide, but everything else here is. To reproduce the\nsampling, see scripts/precompute.py in the repo." + } + } + ], + "console": [] + }, + { + "id": "Xref", + "code_hash": "dad9b7d8d0c687681851dd91cf4d19f9", + "outputs": [ + { + "type": "data", + "data": { + "text/markdown": "

Design your own curve

\nBefore we walk through the cached gallery, here's the curve generator\nthe model was trained on, running live in your browser. The math:\n||[\nr(\\theta) = 1 + \\sum_{h=1}^{H} \\rho_h \\sin(h\\theta + \\phi_h),\\qquad\n\\rho_h \\sim \\mathcal U(0, 1)\\cdot \\alpha\\cdot 10^{-(0.5 + 2(h-1)/(H-1))}\n||]Move the sliders. Inference can't run in-browser (PyTorch is not in\nPyodide), but you can preview exactly the input the diffusion\nmodel would receive. Fork the repo to run sampling on it." + } + } + ], + "console": [] + }, + { + "id": "SFPL", + "code_hash": "ee7c6f18e4d2e04083a0545f034f2d0e", + "outputs": [ + { + "type": "data", + "data": { + "text/html": "
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\", \"__metadata__\": {\"image/png\": {\"width\": 976, \"height\": 506}}}" + } + } + ], + "console": [] + }, + { + "id": "RGSE", + "code_hash": "9f14491e4612ac648ea9eddcad81000c", + "outputs": [ + { + "type": "data", + "data": { + "text/markdown": "

Pick a curve from the gallery

\nFrom here on we follow one running example through the pipeline, then\ncome back at the end and apply everything to the rest of the gallery.\nSwitch curves to see how the model behaves on each." + } + } + ], + "console": [] + }, + { + "id": "Kclp", + "code_hash": "e6e44836dde63b8db0aa5f38f5015d70", + "outputs": [ + { + "type": "data", + "data": { + "text/html": "" + } + } + ], + "console": [] + }, + { + "id": "emfo", + "code_hash": "ff13da6467388c09f900377b3ab19aad", + "outputs": [ + { + "type": "data", + "data": { + "text/plain": "" + } + } + ], + "console": [] + }, + { + "id": "Hstk", + "code_hash": "0c8a1b616ed6b644972fcf3203d46f17", + "outputs": [ + { + "type": "data", + "data": { + "application/vnd.marimo+mimebundle": "{\"image/png\": 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\", \"__metadata__\": {\"image/png\": {\"width\": 469, \"height\": 490}}}" + } + } + ], + "console": [] + }, + { + "id": "nWHF", + "code_hash": "43f7d5339f89403e86ce4b7d1a7a1cfa", + "outputs": [ + { + "type": "data", + "data": { + "text/markdown": "

Watch the square emerge

\nDDIM walks 100 denoising steps from pure Gaussian noise to a clean\nprediction. Below: the predicted clean image ||(\\hat x_0||) at every\n10th step \u2014 what the model \"thinks\" the answer is at every point in\nthe trajectory." + } + } + ], + "console": [] + }, + { + "id": "iLit", + "code_hash": "e748c0147c2f1b07c8db49dc8db2c307", + "outputs": [ + { + "type": "data", + "data": { + "text/plain": "" + } + } + ], + "console": [] + }, + { + "id": "ZHCJ", + "code_hash": "5a7e5567f024484baedffe507055b0bf", + "outputs": [ + { + "type": "data", + "data": { + "application/vnd.marimo+mimebundle": "{\"image/png\": 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Uysq1a+SMQ5mHBWZiCsg0eygnWnUcuX11193MxAvueQSV8woKP2yqDBV2OKUZmVraaGOTf2x0iVYdGFA/b3Ue1izPz3afEz95fMKGxX88tpEUX3qFy5c6J4zzQT1+ucli4qU6jGq28knn5zUY0F68GNGpessEA1io7mvnkdd4NNFM13w1GuUswivC4Nq8aVNqsXbjFrLxjUTXb2HlV3KxkQgo+CnjNLYQKtkypYt61alpcuYJ114GaUN1jXTKhHIKPh5HAV/8GboMo5COiCj0ieXVOfSvlhqGaaWPMkopjOOyl/ElRD1ndbGaMhOxdwXX3zRFWGvvfZatwzDD1RweeGFF1yRRptvpgMFyd/+9jdXcHrkkUfcJhke9Q/W8x+uuKd2C6GCSLNIn3vuOVuzZo37OVfrhGRS+wYt6Zk2bZrbmIsZeUjHjML/ckkn5ep3/p///MeGDx8eMqOeffbZrHxTEX7Hjh3u/7Vy5cYbb3SPk6i/0WQU/JRR2hPlhhtucO2SHn/8cbeHAPyDjEKqYxyV3sgopDoyKn1ogufQoUPd+aD2/wu1p2IicK6Xv6IWIW3IkaiZJKlCxer/+7//c7MHtYlDQQpUmlGoDeWWLl3qCiMqkGg21ZIlS7JuoWYo5kf3V6iuX7/enZymA31PChc9TzmfD83s1Ps1U1MzylVs13OrmZwqMGk2ut7mNVtdM9o0m80v/Z61AmTr1q3uZwNIZkblFKuMwv+eT2W08subza7fff1N+PPPP93MBC+jlGHezft7rJ7qyrcqVaokNL/IKPglo/R3X3/r9TtED1D/0WuisZs2YVbeJQoZBb+Oo+AvjKOQysio9KIJlCpi61wvUSuN88I4KjzW5heQitQaWKnAcc0117giRkGo0K2ZhiqYaCaWZiWOHj3aRo4cmXUfnRxqplWmU7HuySefdMWinAXmH3/80bVn6dChgxvo6vV55pln3Fv9+6ijjsrzcVWEUv90FbE0690P1KZGP1vLly+3OnXqJPtwkMEZFapoRUbFjvL9tddes/fff9+tipGffvrJtajQx3TTsuObb77ZtUfwXoO3337bPv30U0sWMgp+G0dpDKCWR/CXiRMnuguu2kBZq2di9bcoP2QU/DqOAoSMQmGRUelHEzsvvfRSVxPU+V8ykVHhUUgvgOCNLDWwKsyGkJqJuHjxYtcbV0UUzUTUJnQrVqxwM6o1gwf/pedCPaNC0QxZ3fRa6I+K/l8n1TrBVlsEPa8eXd3T7APNkvJmSGkTXb2een9heZudeo9XkF7S+hytAtGxA8nMKM3C0u+Jire6Qq4LWbqwR0bFhp7fZcuWhcyz4MyqVauW65XnfU6yW62RUfDbOGrevHm8KD6dLaebipCR7iERC2QU/JJRQChkFAqDjEpPOs9WXUqrn3TenUxkVHgU0gtQJB0yZIjrO66enLEaWKlQ9d5779m4ceOsefPm9tBDD9mkSZNsxIgRCT3xSHULFiywxx57zF2E0IUJnWC/9dZbNmbMmKz79OjRw0477TRXaH/nnXeyesjrCuD555/vNi0rDJ3Q62uq9cLFF1/sruYVhDb31XJoIJkZtWHDBnvzzTfdRamBAwe6WYXHH3+82/iXjEoMXVzVnhde8VwX/5R1yUZGwU/jKCAnMgoFQUYhUcgoFAQZlb60Elmrjr/++ms75ZRT3KafyURG5Y1CepQ+/PBDN3tQzf91tSiWgThjxgybPXu22wn+2GOPdSeZH3/8sZt9qNmgyE0zRXRTYUk3Ffu+++67bPeZPn16ro3JBgwY4O6rTcm8mZ+VKlVyBcLC0swrtZrRbHQV7AtKs1C5iIJkZ5T+oKtgrsfs06ePe1+TJk2scePGbqWHrpzzcxpfeg3UHsFvyCj4aRwFf4/TvFsikVEoCDIKiUJGoSDIqPSlmuCvv/7qzq8LO7kzFsiovFFIj8LkyZNtypQpblam16s2VjR7+YQTTnCzPDWL+rnnnnM/uDrRVAuFL774wrZv3x7Tr5nq1LOwd+/ebobstGnT7IcffojogoPC6fnnn3fLZS688EL3OKJ/N2rUqNDHpZ7mV1xxhXv9CtPbSsXJZLdvQGqJZ0Z5dMFKPbz1tebPn8/mfhmMjIIfMwr+0rp1azejSq2ptPIvkcgoRIuMQiKRUYgWGYVEIqPyRiE9QmoT8tFHH1mnTp3CblxZUCri9u3b181EVxFdffnOPPNMu/vuu91Mdc2cppCenQrgXbt2tQsuuMC1Uhk/fnxEhfQ5c+a4W/fu3e3xxx93vchjSRuW6phi0e4n2b2xkDrinVHBhXQN4pRRyGxkFPyYUfCXww8/3C677DI3YSTRyChEg4xCopFRiAYZhUQjo/JGIT0CKs5+8MEHbqOkwrTqyO9qj5bua6NMb8OsJUuW2MiRI92MdDYdDV3Q0+xyFZt/++039+9IqCVF27ZtXS96tV/xlhprJcDUqVNt9erVbimNVgcUZBlyLJYu63tRb2qKDfBzRiE5lDGa5dmsWTP3muvvRO3ata1jx45WpkyZhBwDGQW/ZRT8yRvLapIBGQW/IqMyO6N0kZeMgp+RUUg0zvXCo5AeAbUxUD/Pa6+91rXriAcVyrWxqPohqSe6qDisnukKTnoQ56bnSZuIjh071v2/97zlR8XpW2+91RXgg19P7X6tE331N7/ppptcIT1Z1HtaG6aqnzvg14xC8lbjaAWTNjNWgerJJ5+0Dh06WIsWLRJWSCej4LeMgj+p9Z5WVnbp0oWMgm+RUZmLjEIqIKOQaJzrhUchPYJN3r788kt3AtCwYUOLNRU9jjjiCDczWlfEvY0vJZricCZvyKBbJDM41Z9TPcu1+/DMmTNdQTCYWuesXbvWFQz1Wqh9RY0aNax+/fpZfdSDv+7ixYvdxqJ169Z1s0FjuYnW0qVL3Vs9NuDXjELyZgisWrXKXWz9448/3EXAdevWuZPBWrVquc1gdaGQjEImZBT8zRvLanJApCsHY4FxFCJFRmU2Mgp+R0YhGRhHhUchPR9ff/21G/gff/zxFg+acXzjjTe6dgn//Oc/XS90xJ6K5ieffLKdffbZ7oRevedzFuD1OuvKm1YAjBo1yr799ls75ZRT3OuTs7enCldDhw51V4evvPJKO++882J6vCqAqXd7xYoVY/q4SD9kVOYJzihd+FOWLVy40B566CG3geO9997rLu6RUciEjAJCYRyFSJFRSAYyCpEio5AMZFR4FNLD2LJli02YMMGOO+44N4s5HooXL+4KplrqnN/GksH31azQnTt3xuWY0pFO4jXjQH3QVSzXzPNwM9k1O1033TcUzT7X66ACe86Z7bE4VrX0admyZUwfF+nHbxmFxPEyyqPZnuvXr3c/B/p/MgqZklFIDbrop5U0GntpEkk8Nx9lHIVIkVFIBjIKkSKjkAxkVP4opIcxfvx4Vzzq0aOH+YGKWerdfdhhh9lLL73kTk4RGRXRP/30U/ecqdhU2JY5anMxaNAgO/30010rhVjS5rJaoaANT4FUyij4BxkFPyCj4FmwYIE98MADrs2exrKNGjWK25PDOAqRIqOQDGQUIkVGIRnIqBgW0tWbSbMRNTsxE2hG36RJk6xTp04xnYWpx9VNPbdV6NDVHs3S0exyFcQ0Y0uzpkNtLqqPa0dx9ewuV65czI4pU2gmlG7R0OugWZ96rXTzeqXr90AtFOJBfY71cxDPk0ykvnhlVE666BRpRiF5tDJGPwfqaa+fDb1muugaj5wio+CnjEJqUEs87emg1TL6GxJPZBQiQUYhWcgoJCqjvFoT526IBhmVv4ir4k8//bRdeOGFGbNRlDaj1KC/W7duMX3cX375xT788ENXJL3gggtcqL399tuuwKsN/fr06WOff/65ffPNN7k+d8OGDW4mugpZs2bNiulxIbSpU6fa/fffby1atLDzzz/fKlWqFNenSsVJfc3OnTvn2uAUSERGhcqdSDMKydO4cWP3N0UF9Y8//thlycCBA61t27Yx/TpkFPyWUUitjNIqPk0KiRcyCpEio5AMZBQSmVGaCPX+++/bb7/95laGAWRUggvp2gRTmzVmCoWNZvJVrVo1po+7fPlyt2HEkUceaWeddZab6a8NKzVLp2/fvnbsscfa3LlzQ36ugnTixIkxPR7k/3qtXLnSzUo/88wz3SZ/mk2lWzxMnz7dXTXu0qVLTB9XV6O9m3f88foekNoZlVM0GYXk0EU39Rw+5phjbNu2bfbuu++67NK/UyWjkH4SlVFIDcqo3r17u7feaidlV6zHImQUIkVGISedJ6XquR7STywyShduNLtY9SekhmTXacioGBfSL7nkEqtdu7ZlAv1xmz9/vg0YMCDmj60Znddee617LtWeRUUqb+nO6NGjbc6cOW5GMvyhdevW1qtXLxdmKk5VrlzZTjzxRKtZs2bMv5YGbuPGjXObjMa68KA/onpsFUA7duzoBm8U0lNXPDMqL2RUamSU9lfQZsrxEM+MQnpJRkbB35YtW2avvfaaaz8lWqauTWibNm0as69BRiFSZBTyyiitmEnFcz2kFzIqM6mVsMZGTZo0cRNvE42MikMhXcsxM8XixYvdTBm184g1PWaox1WRauzYsTH/eigcvVZXXnmlTZkyxe644w6rUKGCHXXUUXEZXOlq8bp16+Lyu6afZ60qGTlypPu3WscgdcUzo/JCRqVGRmkzZVFWpVJGIb0kI6Pg/42rhg4dmvXvihUruhPFWBbSyShEioxCXhmlNrapeK6H9EJGZSZNMjjhhBPcCnBJ9MRHMioOhfRMmr2q4FIv7HhcKc75PKrfuZa6atmOllGsXr3anXg2a9bMtVKYMWOGOxlFcixdutQ+/fRTW7JkSdbmWN999517f/v27d1mfrG6+qclV61atbI6depYrKlvso5XX0f//8knn7iv065dOytZsmTMvx5SN6NyyplR0W7Yi8RnVDzEO6OQXhKRUVp5oX1nVPzQSgykBv1NUZEq1r3SySj4dRyF1FC9enXr0KGD1a1b161AjjUyCtEgozJXslq7kFFxKqRnEhUkdDU6ET/A1apVs6uuusq2bNliDzzwgP3555+uD7Fa6ajYqVYvFNKTRyfpupihYNGMXPVKf/nll91MKr1esSqkT5gwwW3qePHFF1s8lChRwk499VTr37+/vfnmm/bQQw+5ZUNaWkghPfUkM6MopPs7o+Il3hmF9JKIjNLXeOqpp9zsvnj+7CM+f1M0aURjk1gho+DXcRRSZ0PkW2+91e3jEI9zIzIK0SCjkGhkVHSKRrPZgTYxy4RNRjTLKZb94PWY2vhNS+8Viip4eDSDcNGiRa539datW937ihcv7npI6gSDAV5y6SKGepR5J+l6LfX/et3yu8Chz5s9e7ZbIrNx48Y876fi/JgxY6xTp04xnZ0VTD9HGhTq50pfQ736NeNCG30htcQjo8IJlVHwb0bFQyIyCukjURmlfWYOP/xw1xrE67sN/1NWeX9TYnVeQUbBz+MopAbliHJp4cKFtmvXrpg/NuMoRIqMysze6JrgqA4C8VgRkx8yKo4z0h977DG7++67XQEune3cudP98dTV6FgWOrSR6Pvvv+9mBN94441ZV7rVz/b555+3efPmuRmfSB96bf/1r3+5iyh/+9vfrE+fPiHv9/nnn7tCtza2SQT13Dr66KNdYOuG1BKPjAqHjEKiMwqpLVEZpQL6Pffc49rgPfzww7ZgwYK4fj3EhlYQaNyrE8Xbb7/devbsWejHJKPg53EUUoP+hmjFbv369d3fFrVZjRUyCtEgozKP9mS4+eabXe6o7ViikVFxLKTryv2+ffsslWnmi4rVauKvnnjqFZ2TN3NYS09jSUUIfb2cM4D1fr1Ps881oNP/x2OTOMSGVgvoZ6dKlSoRzYDTax7qdQ8etE2ePNnOPPNMN7su3vTzpv6kusXzj/+mTZvcc6XnidYxsRWvjMpLzozSLAnNTM+EFUqZkFH5SXRGIfUlKqM0ltOJh/7mxLJFCOJPf1M0NorFqksyCn4fRyE1eOfqoeoDhUFGIVpkVOZR7ug8Ox6bHOeHjCqYjOmRruLPDz/8YO+++661bdvWrrnmmpAFa2+ztlguE9Yvxsknn+xmAWvQpkKHR78wf/3rX92JoIel8/7eiOaGG26wJk2auNYo4ei1vemmm1zbhVD31fs/+OAD91idO3e2dKF2Ni+++KL7/vVcsTFhbMUjo8LJmVHKUq2u0R4OSO2Myk+6ZhTSK6OQWsgoJBsZhVA0E/Taa691haxY7YHFOAoFQUYhUcioBBTSy5Ytm/L9ujdv3uz6nmkQv3///rDBpZlOsaLnTX+QQ/1R1mzdevXqZeubLipaqY+kilbwD+/10oY0+f2MqG1KuGWBn376qVtaeu6556b871awHTt22O+//+7epvoqFj+KR0aFkzOj9FazneFP+rlQ7sSiDVu6ZhTSI6M0jtPX0kkAYyX/06QS/T1RSxdtNKpbYZFRSIVxFFKDJti1atXKatSoEbPHJKNQEGQUEoWMSkAhXTMSVUxJZV26dHE9h1RIz2uJuldgD541Hu/+w5olv2LFilwfU2/tvAr+SA69Xi+99JLVqlXLzjnnHGvdunWBHmfq1KmuXcLZZ5+ddkVJbf52xx13uNmI9J+MvWRnlApW8+fPT8jXRvKkc0YhPTJq8eLFNmzYMFu9erWtXLkyrl8LhdeoUSMbOHCg2+AxFps8klFIlXEUMhMZhYIio5AIZFThRDyC6Nu3r6UyzabTIF63cLw+m5pJG6uNGL2ZUnrrzerz3mrW7vjx4107DGZU+ZteM60U0OtVsWJF69GjR4EK6Rs2bLCPPvrIOnTo4Nr9pBtdZOjXr1+yDyNtxSOjwiGjUiujvFthpHtGIT0yShf5xo4d6zavhP9pEstxxx0Xk9meZBQKI97nekg9sV51R0Yh2RmlLAq+AWRUbHEpPgdvY0S1VYkVhdeECRNs+vTpbslYz549s2ZBaImrZjZrtue3335rixYtitnXReyo17BOAL2VDPo5UXuXaOkP4ptvvumWD5522mm0S4AvMiocMiq1MkotxFSwKigyCqmWUcgsZBT8fq6nyVEHDhyI2WMjvnRu3qtXL2vQoEFMNlUno+CHjNq0aZN9+eWXrsakFXyAh4yKDQrpIXrBy/bt22O2m7t6Ck+cONFef/11O+uss6xbt27ZCulaOr9161ZbsmQJhXQfF6kuueSSbK1Kop29oEG2Nu7T7Lkbb7yRjdjgm4wKh4xKrYwqTBGdjEIqZhQyBxmFVDjXY/Zn6hXSr7rqqpj0zCej4JeM0t6AqjvMmzePTEIWMip2KKTnULVqVfd248aNVr9+/Zg8ySq4alOlAQMGuF6zn3/+udt0SXTl+8gjj3T/ZjM3/ytatGiBP1ctYTRT5cILL4xJf1BkpnhkVChr1651P6/ehjfadPLPP/+M29dDbBTm7wgZhVTIqKVLl9qsWbPcXg1ePiEzkFHwc0bRQiG1z+9icR5ORsFPGUUmIScyKnYopOegPlS6CqjeZrH843z88cfbMcccY5999pk9/vjjWUt1FI6PPPKIm02I9DVnzhy3K7KWDrZr1y7Zh4MUFo+MCkUrZJ566qls/Ydp1ZC+yCikSkZNmzbNnnjiCdu9ezeZlEHIKKTaOAqZhYxCrJBRiAcyKrYopIeg2cIrV66M+exOFaTUp0qb93m7MWvZjZbcqI+V/h/pZ/ny5fbWW2+5pYP9+/dP9uEgDcQjo4I38NPPrGZ7quWUZqIjvZFRSKWMUm9H5RIX9lKDWuJp74YmTZpkbaAWLTIKqZRRyDxkFGKNjEIskVGxRyE9BM0Snzp1qlsOE4tlXtpwZtSoUa5PVXAR3StaPfPMM+7kgkJ6+tGSrNdee80OPfRQu+CCCwrVGgaIV0YFmzRpkv3rX/9yffm2bdvGk57myCikWkYhtXTu3Nmuu+46t8l6xYoVo/58MgrxQEYhVsgoxAMZhVgho+KDql4ewaWZmJolHit6vFWrVtmWLVuyvV9FdRXTV69ezcxPH9LSTxXBtdGH+thrJpxWF+i1zG+mri6avPLKK27zmksvvbTAM7GARGRU8M+t8khLnnUREKmVUdEgo5CKGYXUyyjNSNfMdDIKfkFGQRf2NH6qXLlygZ8MxlGIFzIKsUBGxQ+F9BAaN27sBvtz586N41OPVJlJ9dhjj9mVV17pZlOpPc+zzz5r9957r82cOTPPz1OR/eWXX3Zv9bnly5dP6HEjvZFRyCujIkVGIZ7IKBQWGYV4IqMym1YIDxgwwAYPHmxnnnmmFS8e/SJ9MgrxREahsMio+KKQnscGD40aNbLZs2cX6snVkuY9e/a4H2ItbS5TpgyzklNMlSpVrHnz5lanTh23ekArCnSB5bfffsuzFY9ec7Vz0TKaq666ys3CAvyYUUifjNLMlUhPBMkoxBsZhcIgo5CK53rBrTvhbzov10qZI4880o2fom29SUYh3hhHoTDIqPijR3oejjjiCPvkk09cn+CCzibWoGrEiBFuh9x69erZfffdZ5MnT7ZPP/2UwVaKUI/Xhx9+OGs5sn4e1NYlL2r9MmTIENca4+qrr3ZLBgG/ZhTSJ6MOP/xwO+ecc/LtQUxGIVHIKBQEGYVUPNebNWuWO99D+iOjkCiMo1AQZFRiMCM9D+3atXNXq6dNm1bgJ3fv3r2ucK6NRtUn+9RTT7W2bduy4WQK+eOPP9zrp4G2bt98842ble5tnqaZKLqJZqIMHTrUfc4VV1zhLp4Afs4opE9GTZgwwXbv3h32vmQUEomMgkSz2SwZhVQ919MkqUWLFsX0+OA/ZBQSiXEUokVGJQ4z0vNQrlw5dxVQg6MePXpEdSIQvCSnX79+btm9gjCYlpP17dvXXTEaO3as670N/1Jrl2OPPdZtmuVZsGCBLV682Dp16uR+VlREX7hwoV122WXWsGHDpB4v0l8sMgqpr2XLlta9e3dr0KCB+5kIN7B68803ySgkDBmVufT3qEuXLm7yiP5ORdI2gYxCopFRiAYZhUQjoxANMiqxKKSHoZOAF1980RUemjVrFvWTq1noJ510UtZJRXChq27duq7gunPnTtdvm0K6v2l2+eWXX57V73zr1q12zz332Lhx49y/VcxcsmSJe00L8rMCJCOjkPpatWpl1157rZUsWTLPiyneEj9d+COjkEhkVGZS4VwX+C655JKILvKSUUgWMgqRIKOQLGQUIkFGJR6F9Hx2S1bB+9tvvy1QkUo/0CqSr1mzJut96qF38OBB27Bhg2sToo0A1CoE/i2gq1ClVQVaYeDNqlLRqkOHDm7z2Pnz57uLJmrn0qRJk2QfMjJIYTPKo0yaN2+eK7R6GYXUoCKVcimvGZ9adv76669ntZwio5BIZFRmyzmJJBQyCumQUUhfZBSSiYxKb5qk2b59e9f9oEKFCgV6DDIqOSikh6HBf58+fdxyeBUhtKt3tBvQDBs2LGvWsrfkQjcVrAYPHuzep2I6/Omoo46y2267zRXRVSz3lClTxk4//XTbuHGjrV271q666ipr1KhRUo8VmaewGeVR4fyrr76yt956y10AVEYh9elvy6uvvuo2SFZG0XIKiUZGIRwyCumSUUhPZBSSjYxKb02bNnW1JhXUg2tNkSKjkofNRvOh3o61atWyzz//PGtTyUjp/vrhVvsW76YrRnLgwAFXaNeN2Z/xU7x4cdc7uE2bNlltWaL9fPUnU+E8eManXlf1RFch/ZprrqGIjpTMqGDKph07dmRlFFKbNh59+eWXbfXq1RTRkVRkFEIho5BuGQX/UoFKBSvt26CbzgurV68e9nPIKPgFGZW+ihUr5vbg003/Hw0yKrkopEdwFbB///72+++/29y5cxPzqiBmVARXb/MnnnjCbcgYC7r4oQKVNxOd2StIJjIKOZFR8BMyCjmRUfATMir91axZ02655Ra3Gly3f/7zn9arV688709GwU/IKORERiUfhfQIHH744e4q9qhRowo8e1w9j/RHvGLFigX6fBScru6pl3m0V/nChZY2h/3LX/7ieqgD6ZBRSC3e35RKlSplez8ZBT8io+Aho+BHZFT6FyJ1LqhWnTVq1HB98XVOHmoPBzIKfkRGwUNG+QOF9Ajoj+yAAQNc8fSnn36K/kkuWtTNatcV8LPOOsu1C0FiqFXFkCFD7O9//7v9+OOPhXosteZ58cUX3Uaxaudy2GGHxew4gWRmFFKL/qacfPLJ7m/KGWeckfU3hYyCX5FREDIKfkVGpbc1a9bY008/bffee6/Nnj07z/uRUfArMgpCRvkHhfQIHXrooda1a1f74osvbPPmzVEHn3rvtW7d2hVfg3ttI760aeLChQvtl19+cYOowobWpk2b3Ex0/TwAqZ5R6gWqnujqsaZ9G5Aa9DdFs6k6duzoNhDV3xQyCuk8jkLq8GZ95pw0QkbB78io9KW9rRYtWuTatG7dujXkfcgo+B0ZldnIKH+hohuFE0880W06+eGHH0a1GY1aLXz99df24IMPutYLKu4itWa1q4iugZdmolNER7pklJaGvf/++/bwww/bhAkTEnKMiD0yCuk+jkJq0Gt77rnn2j/+8Q/r3Llz1vvJKKQKMio9HXLIIW5fq9tuu82aN2+e6+NkFFIFGZWZyCj/oZAeBc2w0TJ6Xc2eMWNGxJ+nk8VZs2bZyJEjbfr06fQwTiH79u1zPdFVRNdM9Nq1ayf7kICYZZRmo//888/28ccfu5UbSA3BPT11McS70EdGId0ySuMn7wb/K1mypHXp0sW9xs2aNXNZRUYhE8714P99ZY499ljXFi/nhCgyCumUUYybUkuofRpyIqP8iWbdUWrVqpVr0aLCkzb3K1u2bHxeGSRM5cqV3eCqWrVq2d6vVhfLli1zm5Ref/31rj0P4HdkVPpnlFq5tGnTxl3oe/31123Lli127bXXklFIu4zScvzvv//eFi9eTFErBTJK7Qvr16+f9X4yCqmIcVTmIKOQbhm1YsUKGzdunK1cudLt6wZ/atmypWt32KhRI7eaLy9klH9RSC+A008/3f75z3+6Ni3nnHNO7F8VJFTVqlXt/PPPd7thB7fjGTp0qPsDdMUVV1CgQkoho9I/ozTj5K233nIDZs1E50If0jGjtH+D7vPNN98wKz1FMsrbB0jjqLfffpuMQkpiHJX+yCikY0b98ccf9sorr7gahn7G4U/t2rVzEzW1ki+vWelklL9RSC+AihUruqVhI0aMsPbt27srgZHSBnEtWrRwm1ZqOY5mWyG5tm/fbhMnTnR/eEQngUuWLHEz4C677DJr0KABLxEyJqOQehkVPAMUSLeM0kUjTgZTI6NWr17tZsnVrFnTzZTT0vNLL72UjELKYRyV/sgopGtGMW7yPxXPdT7nTT4IhYzyNwrpBdSpUyfX73z48OF26623uqtJkejQoYPb6OS3336ze++9l0K6D6xfv95eeOEF18LF60OlwtQ999zjLnoAmZRR8B8yCumIjEq/jKpSpYob2+qinzawPuussxhHIWWRUelr8uTJZBRSHhmVvsgo/2Oz0UJcRTr77LPdBm9jxoyJataOTjA0a2f//v0F/fKIIc10U/FcN/UYU2909R/u3LkzzzMyLqPgPyVKlHC9h7U6pkaNGmQU0gIZlX4ZpUkI+pvz4Ycfuk1HGUchXTNKbae0SbtWXWifEvhT+fLl3cU8bX6sTRpF+1+RUUgHjKPSExmVGpiRXgiHHHKI9evXz0aPHu1mmuskIj+TJk2yBQsWuAHYtm3bCvPlEYcTQS2T0oZZ6lkFZGJGwX9UPL/lllvchn7/+c9/3AwUMgrpgIxKr4zShsjaALlJkyZ26qmnJvuwgLhl1Lp16+zpp592xXQ29PMv7dugVZnVq1d3N62eef75591FPzIK6ZhRSG1kVOpgRnoh9ejRw/1h/vTTTyPaCEsz0nWVae3atW5WIZKvePHibjmyZpxUqFDBrr76ancymNfGD0A6ZxT8eZFPm4m+++67VqlSJTIKaYWMSp+M0gbI+n/N4tXYCkjXjNq3b59bXawNv7WiFf6k/sOaie7NRn/88cfdWzIK6YRxVHrQ3xcyKnVQSC8k9dUeMGCA/f777zZnzpzYvCpI+EyqE044wbp27WoXXXSROxkE0gUZlR5++eUX9/a0004jo5BWyKj0QEYhXZFRqWv+/Pn20EMP2ZNPPuk27hPGUUjnjFq6dGmyDwcF9OOPP7q3ZFRqoJAeo2VjWsY6duzYfGd8Kui06R8zdfxDr9ny5cvt3HPPtZNOOinZhwPEPaO0L8DevXvdjZUx/qfZbt9995316tXLunfvnuzDAWKOjEptZBTSXTTnevAPtVGdOXOmu9D35ZdfMo5C2mfUlClTyKgUpI4Vo0aNIqNSCIX0GFALkN69e7vlfYsXLw573/bt29udd95pF154oWsjguRSQXHlypVuhidFdGRKRqn/2ssvv+xm6Gi2DvyLjEImIKNSFxmFTBDNuR78o3HjxnbdddfZoYce6jYc5VwP6Z5R2r9Be/EhtSYjqDVe3bp1yagUQiE9Rpo3b+42e/CuAuY1W6Fhw4Z2xhlnWM+ePbP6tSE59BpppoJOArUcSqsFgEzIKO3V8PXXX7t+n6tWrUr2oSEPZBQyLaPUh3jq1KlkVIpllN4yjkImjaOQOu07VVRUgeqqq67iXA9pn1GVK1d2YyikBtWh3nvvPZdTmmhLPSp1sBNQDK8Caif3YcOGuf8/9thjQ7Zv0ezPIUOGuFnQO3fujNWXR5R00qc/Mnv27LGmTZu6DfyATMmoJUuW2IYNG5J9SAiDjEImZlTbtm3tp59+sg4dOiT7cBBBRpUqVcrN+OzXr581atSI5wxpjXFU6mWU+kWXLVvWrr32WqtSpUqyDwmIe0apvYtahOjnXv+GvzNq5MiRNm/ePLv00kvJqBRDIT2GSpQo4WZS7dixw9q0aWO1a9fOdR/1aZs9e3ZWn2Iknp57XcTQMhq11ylfvjwvAzIqo7QxctGiLEjyKzIKmUongFots3HjxmQfCiLIKO35c/fdd9txxx3H3xRkBMZRqZVRa9eutb///e/WokWLZB8SkBCaNKUak/bAYk8+f2eU9tyYOHGinXPOOWRUCqKQHkPqvabw0vIxFcs1+1x/wHP+0rC5X/Lo+deFDg2u1C+vW7du7vWiXz0yKaN0kU+rYrQkH/5CRiGT6e+yqMcn/J9RrVu3tqOOOoqlyMgYau2i3wFt1k6LztTIqCOPPDLZhwTE3fLly13tSfs4qNa0f/9+Cuk+zii9Vps2bXJt8Y4++uhkHxIKgEJ6DB1xxBHWuXNn69ixo3344Ye2aNEiN+sZ/urlqR5UmoWuk7877rjD9WRlMIxMyii1TRg+fDiFdJ8ho5DpypQp42Y20/ouNTLKu/ABZAq1M1K7hLz2wkJykVHIVD///LM988wz7iLSvn37XFbBvxmltlNXXHGFHXPMMck+JBQQhfQY0hJXnQRq2Z8K6Mz29A8tcdq6dav7w1KnTh23fFx9PdUvj9YuyLSM0uCKzUz8hYwC/tvfU4V0Wt+lRkbVr1+fGW/IKF7PYQrp/kNGIZNplYz3N1rIKH9nlNpAqzMCUheF9BjyltGoWAV/vS5btmxxb7Wp6AknnGBXXnmlVaxYkSI6MgoZ5U9kFPC/3wXGUf5DRgH/+11QgYpN/PyFjAL+S/lERvk/o1jRl/oopMeQ19OzWrVqbqaz+uh5tMyGNi+Jpyt+Ci0NePWaaNMNFdAbNGjABQ9kHPVFVw5pxid7NfgDGQX8DxnlP2QU8D+bN292b1nV5x9kFPA/3vkdGeXvjELq41WMoT///NO9bdmypd10002ueO4t41DP9DFjxsTyyyEfe/bscctnFFa68qfiIZDJJk+ebJMmTXKbIKtgheQio4DsyCh/IaOA7DZu3OjeUgjxBzIKyE6r+oSM8gcyKn1RSI+huXPnWo0aNdxMdG9Xd4WZrkL99NNPsfxSiCC0dOVPvaA1A50lmIDZrFmz3GwqZRUz0pOLjAJCZ9SGDRuyJiIgecgoILc//vjD7YXF5JzkI6OA0L3SNRudjEo+Miq9UUiPERXMdQLYq1evrPephcKnn35qc+bMsRkzZsTqSyEfKhBqJjpFdCB7Rm3atMkuvPBCd6FPq2S8VTRILDIKCJ1RixcvtnLlynGhL8nIKCA3Mso/yCggN03iVPG2bNmyPD1JRkalPwrpMTJv3jwXXO3atcvaJVlXBMePH29ff/11rL4MIqCZbJqBXqFCBWaiA0EZpWV+1157rfsd+eabbyikJwkZBYTOKK3gUyFdF8ORPGQUkBsZ5R9kFJCbalGqQ2kyIZKLjEp/FNJj5Ndff3W776q1i1onfPfdd7Zq1Sq3BBCJo370+iOiE/HgJU36/6OOOspatWpl7du3Z7kTMjKj9Hvx7bffuv7oXp9P+COjgEynjNJm7cuXL0/2oWQ0MgoIjYzyBzIKCG337t1u0hT90ZOLelRmoJAeA5p5Pnv2bDvuuOPcv9U+YejQoTZ//nz3i4TE0Ww2XYktWbJktverYNW7d2+7+OKL3Sx1CljIxIyqVauW/ec//3E9iOmR7q+MAjKZl1FNmzZ1xSokDxkF5EZG+QcZBYRuPaVCeunSpXl6kox6VGagkB4D6oGuX5i2bdu6f5cpU8bNflYfYn1s/fr1sfgyiPCPiIrk3pVYbQjUsmVLt1KgQYMGbvONaDYeXbFihS1YsMCqVKniHoelUkjljOrYsaOtXr3atXTRng7bt29P9qFZpmeUR7nUpEkTq1u3rjVr1oy2VMjIjNLvAPyZUUAmI6P8g4wCQtcsNFGHQrq/M0q1qGjrUfAnRskxoNlTKn5oSbJUr17dbrjhBjcz/ZFHHrHvv/8+Fl8GEdAKgODZ5tps46KLLrLu3bu7CxzRhtbkyZPt6aefdu1g7rvvPgrpSOmMUiFd7Y2WLl1qd999ty1cuDDZh2aZnlEeDar69+9v5557rssZiljIxIyqVKlSsg8l4+WVUUAmI6P8g4wCclM7YU0g1PkEkouMygwU0gtIV/zWrVvnZnf+8ssvdsYZZ2R9TAGmjS7VOkGBhsS+LsHFcv2/ehFXrly5QI+nz1U7DM1Iz+8Pky6crF271hXsa9euzWsPX9AyP22QdeKJJ7qfSRWqdNGvcePG7uPKMGamJy+jgik7lFXMUkCmZhT8nVFa3afxkN6SU8ikjFIhvUOHDm7Wp2YbInnIKCB3PWrRokXMRk+BjEL6oJBeQCqSf/LJJ/bmm2+60NIsT6RfcHXu3NktNddrrIsj+c1ef+mll1xbhttuuy1rhQKQTOo7rJM+r/WUqAhyyy23uF7pzz33nE2aNCmpx5hJGFwBeWeUWk7BnxmlyQSnnnqqu1WsWJFVM8iojNJsT63i27Vrl23bti3Zh5TRyCggdz1qyZIlbgIgko9zvcxAIb0QNJhSr+HWrVsXeMYz/E2zd0MtNdeSHc3i1eZDah+jmaT6/82bN9uOHTvYZBa+oVlU2h8gOKO00WW9evVcMaR8+fJJPb5Mp4KVXgNlCH0NkYlCZRT8mVXa+0ermZhphUzLKJ0LaAWy9nKAP5FRyMSCrSZFaTa6/p+2bP7EuV56opBeQAqq4447zs3kPOWUU5iZ46OgUpE7ERdR3n77bTe4VlufE044wfWffvDBB11xUjfAD8uRtVnugAEDkn0oyCOjNHtk0KBB1q5dO1dMpECFTEJGZe44CkiljKpfv74rpCP5yCjgf3RxTzdmo/sH53qZgUJ6IQrpGlwdeuih1q9fv5BXAPVLpKWw2jROJyWcmCSPrtJq+ZO3+UNhd0vWY2lg/fPPP7v2L6Je6roBfjF37lz3s3rEEUfk+n1QKwXd9HtARiWPnvvmzZvb0UcfzQZByDh5ZRQA+Cmj6tSpk+xDAYBc1CNd53WlSpXi2fEpzvXSE4X0Qpg5c6Zrj5DXcmQt09ds5aOOOsq+/fZbmzhxYmG+HCKgoqD+mOSkAvo333zj+pjr9dBqAoVaQXmvrYpfwb2ngVTIKC0D/PDDD93gS62p9HNMRiUno7S6Ra+FLsopl/RaMCsdmSK/cRT8M44CMjmjmO3pH2QU8D9qM1yiRAkm4/gI53qZgUJ6AWl2wvz5861Pnz553kdXBnv16uVmfWo3ZQrpyaNCupZkvvPOO+7fxx57bKEeTz2me/bsGaOjAxKbUVu3brUvvvjC5dLDDz9sxxxzDBmVJHv27LHvvvvOXdirW7euK6QDmSCScRQA+CGj1qxZwwsBwHcZpUlRqkvAvzjXS08U0gto5cqV7peiSZMm+d5XrUQ6dOjg+ldpx/cpU6a44AumK4ldu3Z1sx482jhCM6hz3rewGjZs6NqRbNq0yX788Ue3OWa6z1LQa6CZ6PqY3rIZBzI5o9TD/+STT3YF9eDMCSeajFJRuEuXLq6n6G+//eZmdCF0RumCq/e8Hn744TxNyBjRjKOQOGQUkDujKKT7BxkF/C+jdA5GId1fyKjMQCG9gH7//XcXWpH0zNMvU+/evd0MZi3hnzZtWq7Ck4opp556qlva7xk5cmTI+xZWq1at7Oabb3azLGbMmJExhXTNQtfMW/0/hXRkckZVq1bNLrnkEve7ov0CItm/IZqM0n21CbM24f33v/9NIT1MRpUpU8bOPPNMt3pJuURbF2SKaMZRSBwyCvgvMsqfyCjgvxYvXpy1Hx/8g4zKDPzWFaIfVfXq1cP2o9q7d6/bkFJLbjwqWnfv3j1X4UqzPdW3ePz48Vnv0+x13U+9Q1u0aBFyE4klS5bYH3/8EdWxr1271rWZWbFihZtpkY7Uy7Bly5ZWo0YNVzT0Nn4N93oBmZJR3gajnuA80gbKmn2V82KTCr7aTFdZ5TnssMPcBUKttsm5h4Duq6/RuHFjdyFRx6NMi/WFwXSg1yj4eQUyNaOqVKniVmhotYwoW3TRf/369bkyasuWLW4jQH2+/t4ro+bNm+fu26hRI7fKQ+2rlDtahZPXOCr4eMio0MgoZHpGKXd0wVvnUMqdnOMeJBcZhUzNKNU8mITjf2RU+qGQXkAqXue3DH/37t321ltv2ffff5/1vhNPPNHuu+++XEtwVGB/5pln7IUXXshWiNdATSeEd955pxvMBVPx65VXXrE33ngjqmP/9ddf3YmpPj+dZqMHXwGsWbOm3XTTTa6IxwZByESRZFQoHTt2tFtuuSXkMsGcv0tqk6QCVijewE4z2FVsHz16tD355JMZX0hnkywg74xq2rSp3XXXXVmrNlRQf+SRR+yHH37IlVFqG/Xggw+6Avp1113n9hjw7qvVMBdddJGNGjXKnnrqqTzHUcHIqP8io4DcGaWWmG3btnUbgyt3Nm7cyNOUJGQU8F/Lli1zExDgL2RUZqCQXgA6wdMASrOd8/sl0iyo4BO3qlWrusDLWaTSvw855JBs99UJ5K5du1wxXbPVNWNRszx1X/Xq27ZtW1YhXF9Hx6Piva5OaoPTvKhAr1s606xX9YDX66Tnjd5hyCSRZlRwVil/VMRSm4VQGRWKZneGm+EpZcuWdbfatWu7WaSbN2/ON6MAZGZGKXeCs0erWrTXgrLJ42WUPlcFcuWLxk4aX3n31WoZ3UdjJuVOgwYNssZfeSGjAOSVUVppp5v+rUk6FSpUyBrLaPKOJg9oNYzOPXKu5NPEJd1X53O6b/ny5bOd62mWe/BKPtHn6P061/NEc64HIP0zSnmj8Y7OrXRTBoVqcQsg9iikF4CK2xrgaBAVjgZQ6kN89tlnZ72vUqVKIftYaQA1aNAg1yvXM3bsWHvppZfchn6a/aDZVrfeeqsLzLffftttFKoCuzcz9JprrnH31eyr4GXQmXgFcNWqVW72q4qDmpl+9NFHJ/vQAN9llEdtXAYMGODaTuWVUYWljHrsscds1qxZZFQg4HKKpZjIVLEYR6lgfv/997vfIxXBddHcG0fpb7906tTJbbCuMZY+LxwyKvs4ioxCJssro5o3b+5yR7PVBw8e7Np3KqO0Umbo0KFu7xhPs2bN3OqZnTt3unGPzk2UUWpflfNcTxf8dF+1oxR9jlYp61zPw7nef5FRwH8zSn+n+/XrZ5dddpl9/vnn9uqrr3KBzQfIqMxAIb0AvFngmgUV9sktXtwVvSPh9d8LpvYrmkGlmQe64qjZDl5PPs1Q0OBNHxOdJGrwpQJ6JvcB9wpT3ox0/TvdZ98DBc0oj35PNKMzXNuDwlIRSzcdm4pckcyk0u+x7h/JZqipllHKac1K0x4Y9EdHponFOEpjIs1ID5ZzHOXlTiTIqP/iAh+Qd0YpdzQjXX/DNZbROMVbCaP80d90j7dqRgUvja90zqa9G1Rgz3mu581092bA6+trhnrw43mrZvQ1GUcBmU0Zob/Xmmip7Jg5c6ZbdZeM/RuUbV5NCoyjMgWF9ELIuRlfrHXo0MEeeOCBrCKSZmZpSbOWPQ8cONCOOeYY++yzz+yLL76wadOmud7rKrBraU+m00D0yiuvdIPbgvSJBtJBvDOqIPQ7+fe//z2iAZd2o3/99dezVt6kEzIKIKP8jBnpQN4ZpcL4jTfeaHv27HGz1HU/7YN1xBFHZN1HRS21Y1ExXvs4qKCu+4Y619P9dPN453p9+vTJep+34TvjKDIKyJlRWoH38MMPJ3zykcYKXj0KuZ8bJiekLwrphQgszQiIJ81syDm7ytOqVStr0aKF22xL1CtPN/2yapZEqNYMCtZ0mtmZ12ujm5Ziapll8KwPIFMkKqMKQieWXbp0iei+mon1wQcf2JYtW7Lep+8plfv/eQMqXRglo5CpyCj/4qQPyD+jNDNdf8ODaba4bjlpLxm1ZYnmXE8Fc53r6ZYT46j/jqOATJYzozTZUrdEU21JbTtj0RY01c/xPIyjMgOF9ALwNokJ3gDGLxSgp5xySq6lzAql8ePHZ+u1l460ZFKDz/PPPz+ubSoAP/NzRkVDLR2uvvpq2759u/u3liuOGTPGLV9MdekwUAQKiozyPzIKmYyM8j8yCpnMLxmlonGPHj2ytaEqCE2a+vTTT2358uWWLsio9EYhvQA0s0AzEfzYbkDtArTRlvro5fxF1s7wKqan8y+1lj2qb+Bxxx3nZmwAmcjPGRVtnp1++ulZ/9ay6AULFqR0Id2bpZDOOQzkh4zyL2ZSAWSUn5FRgH8ySr+P7dq1c7fCWL16tU2aNCktCulkVGagkF6Igu2KFSss2dq2bWsXXHBB1r+14YR68WkDip9++sn1Sz/66KNdP71YUvsYLWnU5hZazjNjxoy4F4Z0pbNbt25uY9WJEyfaqlWrUmq5OJCJGRWLwYiXZ2vWrHHfl1aceLnjp4yKBIV0IP0yyqOlzWpd5c0UC6YN4idMmJC1wsbvGaUWedqLRxMz8trwFUhnZJS/M8oPxwIkk18yKhaFY9WvNBEy1N52S5cutZ9//tn2799vqYBxVGagkF5A2oVdhR0VbHUylKxf0p49e1r37t2zvU8ncitXrrS33nrLFi5caPfcc0/MC+n6GtpUR7NFX331VTdDNN4DGm9zPs00V0EtVCHdC65UCVognTMqVrTc75133rG5c+faHXfcYSeffLK99NJLYXMnGRkVjXTfrwLIpIzyqLWc8umkk07K9TEVoebMmZNVSPd7RlWtWtUuu+wya9q0aUx6nwKphozyd0b56ViAZEinjFJbYk0ODfV7rbae06dPT7n6DuOo9MbIuIC0K/vYsWNdoTrUlbN4Uojo665fv94VyDULPeeVQIWQ+gnv3r3b5s2b53aCX7ZsWcwGHSoCLV68OGsJTiIGMzt37nSbq2qml2bah+LNSPeCdu/eva4VhO7foEGDpGzCAWRaRsVj+WLLli3d775mX6hY1bBhQ3cR0cse9QicP3++y7xkZVSkK2u0qkaDXx2vlmRqU+Rq1aol+9CAhEqnjMo5mSGvEyrN7tRYRHQ/rbbxa0Zpk0Od2JYsWTLZhwQkBRnlz4yibQKQfhml32ud34Wic7+uXbtmneOFooz6448/XItfTQBQO1CdZ+ltMlfN6HtiHJWeKKQXkHZY13JXXQVMdHDt2bPH3n33XRs3bpxdccUVNmjQoDzvq0Lye++9Zx999FFMg0RF+hEjRthnn33mHjcRgyv1znr66aezWj1EUkjX/d544w23HOj666+3c845hwEYMkIyMyoehZ2//OUvbsaF+gFKnz59XAsFz+zZs+3+++/PWqmSjIyKRPPmzd3yxf79+7sc10W+f/zjH9lWFgGZIJ0yKhKa9HD77bdnrUbRWE4razTT048Zdckll7iTVyBTkVH+zCiP344HSLRMySi1Mn7wwQfz/J3X+9WJ4ZVXXrHWrVu71ctqeXPfffcltfUNGZXeKKQXkIq56h2pQoiugOkEKZE0M1MtTsqUKRPy47r6pdnX3hJiyWvzTRWd1Sol3FW+UFSkzqugXZjnVTMzNQtK7Rw0696jIprel9/niwZ8moG/ceNGV1zX965ZraLClWaBasZVzZo1s2aP6fHXrl3rvicdg4p3zHpAqkp2RsWSlisqE4Kp0KNbcOsnzVL3MlGDF+WHMiBRGRUJzUpQ9qj/sJfJec3AANJZOmVUJPR7HjwOUyFd//ZuwfSxP//8s0DLmGOVUbpoSUsXZDIyqnAZpQzR2EwTG3SeqbexyCjv3EyZqr8bmoEaPB4EMkWmZJRqOF4dJxSd8+migvZzqFevnssEZZTOC8N9nkf3VUZpooNqQ8H1tcJkFIX09FYkwCtcYPple+aZZ9yJxg033JA1GzreVPDV4EWtDBQUGozkLPhqJrpmZkZSHNcGWPo+1Lsz2fRcapZ9v3797JNPPrGhQ4dGtXGoZqhq4Hbqqafap59+6grmZ599tmsLUb16dbe0evTo0fb6669bmzZt7MYbb8waHOr5fP755+2XX36xiy++2D0GhXSksmRlVDKo9ZNWrXgnahoUaWbC119/7cuMuvzyy93FOh2vBn+hNicE0l0mZVSo710nbqEmCCxatMieeuopl2nJyqhrr73WjZ2ATEZGFTyjOnfu7FYDa5KS7qv9u2KRUXpNVNhq0aKF3Xvvva5YxjgKmSqTM8qjcqZqPsoFnU9plr5qYconvc2PWoEqozSZ8uabb3Yz4D2FySjVml5++WXXwhPphxnphaCgOuuss+zf//63ffnll25DlkTNzjzssMPynVGkPpwKFhWYFCKaYaRbzuKwijkqMmtmgO7rFaIipSt9elyPgkaPU5DN9HRs6ueuq4HeMXnB5T2uviddKQyexanvT7PQvT8euoCgQNVNj6Olyh49hk5eNVNVj+U9rmaq66KCLlLEeqY9kEkZlQyajaSZCMod/T7rpgtnOWey58wSfZ4Gn8qPSAZbkWRUuOzzMkpvdfIHZLJMyqhQ37tO9nTLSbM8NY5T3uSXURoT6j66r/InVhkV7VgQSEdkVOiM0vhJYyzlSV6UQSogaQWeJn15q6R1/qaJYMEZpfO2SNvHKA+Vj8o5jfuaNGlSyFcZSF2ZnFEejXuUKbp5VCvSflSRfr4yyqufeW1ylEea5a9ambf6JpJxlJdROoZMvLCRKZiRHgPqVa5ZzprFrCtPfqKBiXqkT5061QWr+vLmLKRr4KLZ6CouDxs2zCZPnhzV1zj++ONtwIABWUGhjR6GDBniitLR0mNogwhdKNBbbaLhPa5mMmgmuQZieq61EVbwazBy5Ei3EYUK++eee64bqOmkUzMWNFDzaOmTNuXQTHQ9hortOl7NmOjQoYP72t7SIGakIx34OaNibdOmTW5fBM0uaNeuXdbGfsG8jFI+aI8JDbQ++OADmzBhQkwyShfj8tKjRw+XUZdeeqnLGwCZlVGRmDVrlv3tb39zF/jzyyi18VOeqJj+5ptvug3WySggtsio7LZu3epyKtz+Wzr3UkFK56Lax8YruqsQNWPGDDdO88ZRM2fOtLfffjtsYd6j8zoVuE477TRXQGRFH0BGFTbPlFEqlmslnorqopxRq+Dff/896yJfJOd6ZFRmYEZ6DPTu3dv9Ur3zzjuueKtfHr/QYGXu3Ln2/fffu2BQCOQsDutqmQo6Grz88MMPbnaTR/f3rrgFv1/0fn28fv361qtXL/dxFZQURO+//36BjlePqR2WddPJYc+ePbO+rt43fPhwF3I60Q7enE/FcX1f3vemQtXRRx8d8mvoymJwDzF937rQoEA85ZRT3CaGQDrxc0bFmlq6/Pbbb+4kTX0D9fvs5ZjyIWdGafmeCu7jx4+PSUbll31eRkWzRBBId5mUUZFSVqk4fuSRR1r79u1t4sSJucZhoplSmkSgWZ+6GKGJAmq7oJvGZ3puvRV88+bNI6OAAiCjstNscOVOJDQzM3hzeE1imDRpkjs39TJK2aYN4jWGy298pAK9HlPntRTRATIqFnkWnFHB52zKKN08OtdTVmnCqFcLI6MyE4X0GNAv2XnnneeWwWompGZD68THD3TypFnoGmyoYBRuhrV33+A2KCrCjxkzxi2V0axznbCJitljx451xSoNhjQo0qyDWC4nUnFb/aq82Z5aJqNCtwZb06ZNc1/Xoxn1wYWyaDbo0tJEzfhSOxfNRAfSjZ8zKtZUTNL3d+yxx7pZTqKs0EVCZVtwRml1i2Z5aqaZiu+FzSi1i8pvQ5qCZBSQ7jIpoyJRq1Yt10NYOaHVcRr3aPVfqBU2mjmlFXYqLGlPmG7dumV9TKvuQhXfwyGjgNC/F2RUbGgM1LdvXzepycsonX+pn7omRn322Wdhx1JkFEBGJYsu5GkVoPYi1OQFrazJiYzKDBTSY0SDAC1J1hUq7Zys2c3aRCXZfZF0YqUZk7pJfoV0zfIOnun9+eefu836FBpnnnlm1kxuLdNbunSpK6RrOZ5uJ5xwgitexYqK48EboKoIpqKVThjvvPNOVxgLRc95NP3ZdXFABXognfk1o2JNMzj1fQVTTmmjmOOOOy5bRmlJsvoJxiqjIhVtRgGZIFMyKhIqjp9xxhlZ/9bYTbM/85sBqmJ7TgVpUUdGAbmRUbGhiVGa/enNAFVG6YKhbprtqfO7/CYlkFEAGZUMqkOprZQmT/36668hC+lCRqU/CukxHmCdc845bibRqFGj3LJazQ4KtUlLIkVzEpXzvlrKov5zKqRr+Vy4x9Isgg8//ND1KN62bZtrGaMd27WZTPAMdxW11F9YrVc0C1zLldWbygsdzUILtXGMHkdLb0TLaHRfLXdWfz2P/l+z1aNBH3RkCr9mVCzl/H1WVmjGk2a4ql2ELi5qEKTVNxoE5Ud5FmlGeXSh8eeff3b7TngZpb7FGnB5xwQgMzOqoOOSWIxVNHFAq3JWrFjhMmr16tUh70dGAaGRUbGRM8+8f5NRQOGQUfHlZVXp0qVda2PVtHIqSD0KqYdCehx+ufRLpQK0+nk/88wz7t+aJeT1qEwlKjxpx3XJ7/jVe1O9OXUCpuXIKr6ff/751qlTJ/dxvV8b/GlzGs0uv/nmm92yGM0+8IpUCn/NbFfRK69ZDCpseQUtPa9aahn8/KtQT3EcyIyMioRmPSmH9L0rQ1SU+8tf/hLR5+riXzQZJSrQ/+Mf/3D987yM0iZayiYPGQWElokZlSg1atSwq666Kiuj8iqkCxkF5P27QUbFBxkFFB4ZlZgV0DoXDDXxgHpUZqCQHifqY3nLLbe43rtqjaL+u5rZ7RWlU4WKRsH9NbUJjIrlOglTL+Jwy4wVMFqaPWXKlKz3aRaUAkfFcM3O1NI9zd7UybGu3qkXu67sadZopMcX6r6cAAKZkVH58YrnOd8XacZodpRmoB966KHu33qs/DJK2af+7LrYpw1JlW9qi6WCvi5Oqi0WGQWElykZlUjKJGWXl1GhTgDJKCAyZFTskVFA7JBR8aPzuPwmd3Cul96KBFi/GXcqFqvnp5Yoa5n/qaeemrK7jK9du9Yeeughmz17tpvV1KdPnzzvq2L5s88+6/qne3bu3OluOpFTIV47s6sNTKVKlezuu++2jh07uudGbWHysmHDBrvjjjtcuwW9vfDCC7N9/Pbbb7fTTz895O7LANI7o2JNmx8qo7zNQTUoyi+jvFzT55YrV84VrZR7amWlQdcjjzzi+h+TUUBkyKjY8jJKG8XnpPEZGQVEh4yKLTIKiC0yKrGoR6U/ZqQngGZZX3311fbLL7/Yp59+ak888YQNGDDAFY1T5UqVZo2rTYG3+YuK4OozrCV44WYVqECuQpJmsHsFcy3X9r5vncRt3749675qB5Mf3Vf9Uxs2bJjVMz2Yrg2pgKXZ7xqI6fnXMQBI34yKFxW+q1atGtXnaKWMZrIHUzFdNwDRI6NiK1RGASg4Miq2yCggtsioxGKucvqjkJ4gKkYdddRRbsnsZ599Zh988IFNnTrVbaIVSfE42ZYtW2ZPP/20K6hraXXjxo1dMTscFbnVh3jz5s322muv2dixY+2YY45xM8i94tySJUvc44aaFZUXzQa94oor3AzP4I1Mg4NLs9a18am+9vXXX+8KggDSN6NSiTIq0y9QANEioxKHjAKiR0YlDhkFRI+MShwyKv0VLcgPhQqYW7ZscUVVrrZERzOjBw4c6ArM2rzuqaeesu+//94OHjxofqZC95o1a1xrF80cV4/g/GZ5a3mwenOpMKfN/VRY1+fp361atXI39UWvVq2aK47n7GMcboaoZrXrcXLOFNUMdNHPpY5XG2mprzuA9M6oVOFlVPDeEwAiR0bFFxkFFA4ZFV9kFFA4ZFR8kVGZoXhBCqrqpasNJPv162cnnXRSfI4szTVp0sT+9re/2RdffGGjRo2yGTNm2AUXXBB1C4FE0YZ5Ol61aPnmm29s5MiRbqZqJD1+VSBXz2XNdlVhXa1ZPCqw//Wvf3WtWDTLvbC8me163BtvvNEV0dmYDEj/jEoVXkaVKlUq2YcCpDQyKj7IKCA2yKj4IKOA2CCj4oOMygxRz0jXhmvaaHLcuHG2ePFiZqQXgmZWn3LKKXbddde5/uHamHPBggXmR+ql2b17dzv66KPt999/t2+//dZWrlwZ0edq5qVmnx9//PGuqB3c0kCz1Lt162a9evVyPdf18+XdNAM22hUP3uxzPVbXrl1dK5lwfdyROHot9ZoW9LVF4qVSRqUKL6O0YgdA4ZBRsUdGAbFDRsUeGQXEDhkVe2RUZihakF82zUTXLGIVKunzWniapa3ns169evbKK6/Y+PHjLdOoXdBHH33kCnXerSDtJLzgYranP5c5aTWDXtvvvvuOViEphIyKHTIKiD0yKnbIKCD2yKjYIaOA2COjYoeMygzFC1JI7927t7sJhfTYKFu2rF1++eWuhcLHH3/sfgGPPfZYy6RCulpITJgwIet9mrmsWfDR9BJmKY1/6aKIXt/33nvPBg0aZD169KBPdArJ9IyKFTIKiA8yKjbIKCA+yKjYIKOA+CCjYoOMygwRF9Lff//9rEL6kUce6a5aIbZ0UeLkk0+20qVLu6KyWl/07dvXV09zmTJlXLsUbRLasGHDmD2uvueePXu6zUg9bdu2zdZPPRIspfEvvZbKDl0gKchri+RLhYzyOzIKiB8yqvDIKCB+yKjCI6OA+CGjCo+MygwRF9IfeeSRrCtVd999N4X0OIbXcccd5/5/zJgxrtd3hw4dzC/U0/zCCy90BbRoZornp3z58nb++edn65utQmu0xdYdO3Zk7UYNf9HPi9pCqVd+QV5b+IPfM8rvyCggvsiowiGjgPgiowqHjALii4wqHDIqM0RcSNdMUq/3tAoniC/N8ty4caN98MEHVq1aNd9cuFCwxmOTvFg97vbt213BVjNm4S96jbWiBenBrxnld2QUkBhkVMGQUUBikFEFQ0YBiUFGFQwZlRkinhL6xBNPuNtDDz2UVVRHfIuOZ511ltWtW9fefvtt10MckQWXZrfTux+ILzKqYMgoIDHIqIIho4DEIKMKhowCEoOMKhgyKjNEXEivWbOmu9WoUcP1yUb8aWa12qioz9Lw4cOztT1B+OACEH9kVPTIKCBxyKjokVFA4pBR0SOjgMQho6JHRmUGmhT7XOXKle3ss8+2mTNn2owZM5J9OL63detWq1ChQrIPA8gYZFR0yCggscio6JBRQGKRUdEho4DEIqOiQ0ZlBgrpKaBNmzbWunVr+/jjj23Xrl3JPhxf27RpEz38gQQjoyJHRgGJR0ZFjowCEo+MihwZBSQeGRU5MiozUEhPEaeddprt3bvXPv/882Qfim+p9Y02P6xatWqyDwXIOGRU/sgoIHnIqPyRUUDykFH5I6OA5CGj8kdGZQ4K6Un45Tpw4IAdPHgwqp7nlSpVsn79+tmkSZPszz//jOsxpirN1t+9e7dVq1Yt2YcCZBwyKn9kFJA8ZFT+yCggecio/JFRQPKQUfkjozIHhfQEU6/zF154wT766CPbsWNHVJ/btWtXN9uaWemhbdiwwb2lkA4kBxkVHhkFJBcZFR4ZBSQXGRUeGQUkFxkVHhmVOSikJ9i8efPstddes9GjR9vOnTuj3jX5xBNPtDlz5tiSJUvidoypau3ate4thXQgOcio8MgoILnIqPDIKCC5yKjwyCggucio8MiozEEhPcEaNGhgp5xyinXv3t1Kly4d9ee3bdvWDjvsMBs1alRUrWEywerVq91GowV5XgHEBhmVNzIKSD4yKm9kFJB8ZFTeyCgg+ciovJFRmYNCeoIdeeSRdtddd9mFF15oFSpUiPrzixQpYieffLL98ccfNnv27LgcY6patWqVHXroock+DCCjkVF5I6OA5COj8kZGAclHRuWNjAKSj4zKGxmVOSikJ1jx4sWtTJkyVqpUKRdCBdGwYUOrXLmyvfjii7Z8+XJmpgddAaSQDiRf06ZNrVmzZq6FlTZWxn+RUYA/kFGhkVGAP5BRoZFRgD+QUaGRUZmDQnoK2rNnj23atMk++eQTe/XVV5N9OL6wbds2d6tdu3ayDwWAmfXv39/WrVtnkydP5vkgowDfIaOyYxwF+AsZlR0ZBfgLGZUdGZVZKKSnKPUC16x0tXfZu3evZTpv89X69esn+1AAmLm9HNq3b29fffUVGUVGAb5DRmXHOArwFzIqOzIK8BcyKjsyKrNQSE9B2kxTPdZfeOEFq1Wrlv3www+W6RRcVatWtUqVKiX7UAD8fyeeeKLt2LGDjCKjAF8io/6HcRTgP2TU/5BRgP+QUf9DRmUWCukpqFixYtagQQPr2bOnHXfccfbdd9/Z9u3bLdODq1GjRsk+DABBdHGra9euNm7cODKKjAJ8h4z6HzIK8B8y6n/IKMB/yKj/IaMyC4X0FNe3b1+3aenXX39tmdwzXjskaxNWAP5CRpFRgJ+RUWQU4GdkFBkF+BkZRUZlIgrpKa5cuXLWp08fmzBhgq1fv94y0aJFi+zgwYPWuHHjZB8KgBzIKDIK8DMyiowC/IyMIqMAPyOjyKhMRCE9DfTo0cMqVKhgX3zxhWWiOXPmWPXq1e2QQw5J9qEACIGMIqMAPyOjyCjAz8goMgrwMzKKjMo0FNLTQIkSJaxfv37222+/2bJlyyyTBAIBmzt3rrVs2TLZhwIgD2QUGQX4GRlFRgF+RkaRUYCfkVFkVKahkJ4mOnToYLVr17bPPvvMFZczxYoVK2zr1q0U0gGfI6O42Af4GRlFRgF+RkaRUYCfkVFkVCahkJ4mihYtagMGDHC7Bf/666+WKfS9li9f3ho0aJDsQwEQBhlFRgF+RkaRUYCfkVFkFOBnZBQZlUkopKeRZs2aWZs2bdys9N27d1u60waj06ZNs3bt2lmxYsWyfWznzp02e/Zs1+5m8+bNSTtGAP9DRgHwMzIKgJ+RUQD8jIxCpqCQnmZOOeUU27Vrl40dO9bS3aJFi2zbtm1uGVFOa9assaeeesruv/9+10MdgD+QUQD8jIwC4GdkFAA/I6OQCSikp5nKlSvbcccdZz/++KP9+eefls6mTJli1atXtzp16oT8eJEiRdwNgH+QUQD8jIwC4GdkFAA/I6OQCSikp6GePXtatWrV7IMPPnDtT9KRNhhV25auXbuGLJbXrFnTbr31VjcjvUWLFkk5RgChkVEA/IyMAuBnZBQAPyOjkO4opKeh4sWL27nnnmvLly+377//3tLRxIkT3ffZsWPHkB8vW7asK6CrZ7yuigLwDzIKgJ+RUQD8jIwC4GdkFNIdhfQ01aBBA3cl8Msvv3T9wtPJvn37XCH9qKOOstKlSyf7cAAUABkFwM/IKAB+RkYB8DMyCumMQnoa69evn1WpUsXef/99O3DgQL73DwQC7qZ2MN7/+9GECRNs586d1qtXr2QfCoAEZlSqIKOA9EBGAfAzMgqAn5FRSFcU0tNYiRIl7LzzzrMVK1bYmDFjIprp/c0339h//vMfmzx5svnR3r17bdy4cW42uvrAA8icjEoFZBSQPsgoAH5GRgHwMzIK6YpCepqrV6+enXTSSa74PHfu3LD33b9/v3399df2wgsv2M8//+zLGenjx493s9H79u2b7EMBkOCMSgVkFJBeyCgAfkZGAfAzMgrpiEJ6BlALFG28+d5779nmzZvzvF+xYsWsbdu21r9/f2vWrJn5zZYtW1yhv1u3bla1atVkHw6ABGeU35FRQHoiowD4GRkFwM/IKKQbCukZoEiRIq59QsmSJW3IkCG2Z8+ekPfTx8844wy7//77rU+fPu7z/GTUqFHuGE844YRkHwqAJGSU35FRQHoiowD4GRkFwM/IKKQbCukZomzZsnb55Zfb+vXr7d1333UbioYKuFKlSlm5cuVcQctPhfQFCxbY9OnT3Wz50qVLJ/twACQho/yMjALSGxkFwM/IKAB+RkYhnVBIzyC1a9e2Cy+80ObMmWOjR4+2VLFr1y4bNmyYNW3a1G0yCiA9kVEA/IyMAuBnZBQAPyOjkC4opGcY9SE+9dRT7fvvv7dvv/3W/E4bno4YMcL27dtnAwcO9NUseQCxR0YB8DMyCoCfkVEA/IyMQjoonuwDQOJ1797ddu7caZ9//rmVKFHCevTo4duX4YcffrDffvvNLrroIqtUqVKyDwdAApBRAPyMjALgZ2QUAD8jo5DqKKRnqOOOO87N8v7kk09cL2LtpOzHnsPavO+YY46xtm3bJvtwACQQGQXAz8goAH5GRgHwMzIKqYxCeoZSi5STTjrJihUrZp999plt27bNbeTpl9Ypf/zxh73xxhvWvHlzO/HEE5N9OAASjIwC4GdkFAA/I6MA+BkZhVRGIT3Dw6tfv35Wvnx5NzN9y5Ytds4557h2L8m0atUqe/XVV+2www6ziy++2IoWpZU/kInIKAB+RkYB8DMyCoCfkVFIVUUC2s0RGU99yIcNG2bVq1e3Sy65xKpWrZqU52TRokVuJrqO4+qrr7bSpUtn/GsDwMgoAL7GOAqAn5FRAPyMjEIqoZCOLKtXr3ZFbG1Eetppp9mRRx6Z0FYvU6dOteHDh1vjxo1t0KBBVqpUKV4dAGQUgJTAOAqAn5FRAPyMjEKqSFohfdOmTe6qkza61EaS1apVS8ZhIIddu3bZRx99ZNOnT7dWrVrZGWecYZUqVYrJ86QftSVLlrhNRGvUqGFHHHGElSxZ0vbu3WsjR460KVOmWMeOHe2ss85yvdsBIJEZlRcyCkCkyCgAfkZGAfAzMgqpIGmF9FmzZtk//vEPV6B48MEHXQEV/jFz5kz78MMPbc+ePda7d293K+wMcf2oDR061F544QXr2bOn3XvvvbZixQpXFNuxY4criPFzACBZGRXKnDlzyCgAUSOjAPgZGQXAz8go+FnSCukLFy60559/3vbt22fXX3+9m50Mf9m9e7d988039sMPP1iZMmWsa9eu7qbNSQtCP2ra1PS9995z7Vtq1qxpy5cvt+bNm7siOqsSACQzo4Jp9cyXX37p9m0gowAUBBkFwM/IKAB+RkbBr5JWSNcswvXr17vi6iGHHMKmkj62ceNG+/77723y5Mnu9VI7BbXjOfzww11rlkht2bLF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\"__metadata__\": {\"image/png\": {\"width\": 1490, \"height\": 628}}}" + } + } + ], + "console": [] + }, + { + "id": "ROlb", + "code_hash": "cea05ee8ae9b5178e5cd37f0f9e287df", + "outputs": [ + { + "type": "data", + "data": { + "text/markdown": "

One solution at a time

\nEach random seed produces a different valid inscribed square.\nScroll the slider to walk through the seeds." + } + } + ], + "console": [] + }, + { + "id": "qnkX", + "code_hash": "6a09f67b2d26bd117a3f6fb0597a7647", + "outputs": [ + { + "type": "data", + "data": { + "text/html": "" + } + } + ], + "console": [] + }, + { + "id": "TqIu", + "code_hash": "e2777b50df94abae21227b34d5f111c1", + "outputs": [ + { + "type": "data", + "data": { + "application/vnd.marimo+mimebundle": "{\"image/png\": 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\", \"__metadata__\": {\"image/png\": {\"width\": 519, \"height\": 539}}}" + } + } + ], + "console": [] + }, + { + "id": "Vxnm", + "code_hash": "750da076c22573ad933ba11f5fb47ff6", + "outputs": [ + { + "type": "data", + "data": { + "text/markdown": "

Four seeds, four squares

\nSame curve, different random initial noise, different valid squares.\nThese are not retries \u2014 they are simultaneous modes of the\ndiffusion posterior ||(p(\\text{square} \\mid \\text{curve})||)." + } + } + ], + "console": [] + }, + { + "id": "DnEU", + "code_hash": "903dd8986e1b0a936c18e9a0f13130de", + "outputs": [ + { + "type": "data", + "data": { + "application/vnd.marimo+mimebundle": "{\"image/png\": 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The hero cell \u2014 the family of solutions

\nNow overlay every sampled square onto the same curve, weighted by\nhow many seeds covered each pixel. Bright pixels are inside the\nintersection of all sampled squares; dim pixels are reached by only\nsome. The contrast is the wiggle room of the inscribed-square\nfamily for this Jordan curve \u2014 something the paper hints at but\nnever quantifies.\nYou're seeing one slice of an infinite-dimensional solution manifold,\nfree of charge, just because the model is multimodal." + } + } + ], + "console": [] + }, + { + "id": "ecfG", + "code_hash": "bcb6de122dcb53af360d53e5627df316", + "outputs": [ + { + "type": "data", + "data": { + "application/vnd.marimo+mimebundle": "{\"image/png\": 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\", \"__metadata__\": {\"image/png\": {\"width\": 610, \"height\": 542}}}" + } + } + ], + "console": [] + }, + { + "id": "Pvdt", + "code_hash": "34a54a0fe65408b287d6ae3744563ae5", + "outputs": [ + { + "type": "data", + "data": { + "text/markdown": "

Are these actually squares?

\nWe can score each sample's \"squareness\" \u2014 fraction of the predicted\nshape filled by the smallest enclosing rectangle, scaled by an\naspect-ratio penalty (the metric the paper uses):\n||[\nQ = \\frac{\\text{area}}{w \\cdot h} \\cdot\n\\exp\\!\\left(-2 \\left|\\tfrac{\\max(w,h)}{\\min(w,h)} - 1\\right|\\right)\n||]||(Q = 1||) is a perfect square; ||(Q||) near 0 is a long thin rectangle or\nscattered noise." + } + } + ], + "console": [] + }, + { + "id": "ZBYS", + "code_hash": "4a64d28e902232770bbdac047cc89cda", + "outputs": [ + { + "type": "data", + "data": { + "application/vnd.marimo+mimebundle": "{\"image/png\": 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\", \"__metadata__\": {\"image/png\": {\"width\": 1089, \"height\": 340}}}" + } + } + ], + "console": [] + }, + { + "id": "aLJB", + "code_hash": "049478bae5edfe9d0855fc0d70da420a", + "outputs": [ + { + "type": "data", + "data": { + "text/markdown": "Mean squareness across seeds: 0.944, n=16.\nOutliers near 0 are typically failed samples \u2014 the model occasionally\nproduces a degenerate blob instead of a square. Failure rates of\n~5\u201315% match the paper's reported numbers on the curves task." + } + } + ], + "console": [] + }, + { + "id": "nHfw", + "code_hash": "473b661a68101061fe112d258d29ac41", + "outputs": [ + { + "type": "data", + "data": { + "text/markdown": "

Gallery \u2014 every curve in the test set

\nSame model, same pipeline, applied to all five curves. Each panel\nshows the multimodal heatmap (intersection in yellow, halo in purple).\n" + } + } + ], + "console": [] + }, + { + "id": "xXTn", + "code_hash": "6c229c314e0eedb82df72ec674945f46", + "outputs": [ + { + "type": "data", + "data": { + "application/vnd.marimo+mimebundle": "{\"image/png\": 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\", \"__metadata__\": {\"image/png\": {\"width\": 1490, \"height\": 340}}}" + } + } + ], + "console": [] + }, + { + "id": "AjVT", + "code_hash": "f87c39e0cf1d4f0841e55d8277d073d2", + "outputs": [ + { + "type": "data", + "data": { + "text/markdown": "

What this notebook does not claim

\n\nNone of this diminishes the central observation: a generic image\ndiffusion model \u2014 no architectural specialization, no parametric\noutput head \u2014 recovers inscribed squares from images well enough to\nbe visually convincing on novel curves. That's the paper's\ncontribution. The multimodal-overlay framing in this notebook is a\nfree side effect of using diffusion." + } + } + ], + "console": [] + }, + { + "id": "pHFh", + "code_hash": "281d94a1763eab808070934a9977b15c", + "outputs": [ + { + "type": "data", + "data": { + "text/markdown": "

Try it yourself

\n\nBuilt for the\nalphaXiv \u00d7 marimo \"Bring Research to Life\" notebook competition." + } + } + ], + "console": [] + } + ] +} \ No newline at end of file diff --git a/notebooks/jam-2026-Q2/dead-salmon.py b/notebooks/jam-2026-Q2/dead-salmon.py new file mode 100644 index 0000000..2b4b8e2 --- /dev/null +++ b/notebooks/jam-2026-Q2/dead-salmon.py @@ -0,0 +1,2370 @@ +# /// script +# dependencies = [ +# "anywidget==0.10.0", +# "marimo", +# "matplotlib==3.10.8", +# "numpy==2.4.4", +# "scikit-learn==1.8.0", +# "traitlets==5.14.3", +# "vega-datasets==0.9.0", +# ] +# requires-python = ">=3.13" +# /// + +import marimo + +__generated_with = "0.23.4" +app = marimo.App(width="medium") + + +@app.cell(hide_code=True) +def _(): + # Embedded base64 of assets/salmon_outline.png so this notebook is + # self-contained as a single .py file (no sibling files needed). + SALMON_DATA_URI = 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QMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYDRlbgAAQABJREFUYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBiYvTsBk/Qsy8UvIfsGWSFANgKEJewEkEUiCCIiCOLCdkBxO+hRERWXP5com5CEJUAIawCVRUE8Cn9FDzvIlhAIJARIZjqZ7HtIAkkIeu57nMopOj3TPdNfVW+/57ruTHdV9Vfv9/uqc13Vz/u+RYAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgVUrcItVe2ZOjAABAgQIECBAgAABAgQIDCewXQ61Q7Jb0vfSNyTfTf4z+a9EESBAgAABAgQIECBAgAABAgSWXMAEgCW/BAZAgAABAgQIECBAgAABAstQoO+Xb5nsmuybHJzcKTk02TNp839DcnqyPrk8uTa5MTEhIAiKAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgspUAb/zsnhyTPSj6RtLE/Wunf5v54enub/+9PHpfsl3SnAEWAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgskcD2ed4DkmcmXdn/g2S82T/f19/L49+eHJHskigCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEBgygJt2B+Z/GMy2sZ/vob/5u4/Jcf4scQkgCAoAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECAwLYG980TPSM5JNtfU39rb1+dYnQTQjxNQBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAwAQFbpljH5z8ZXJ9srVN/vke350ADk92SBQBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECAwAYE25Y9I3pf8ZzJfM39b739djr1vsl2iCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgQEF2vw/MvlUsq2N/YX+3A15jp9O+lEAt0gUAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgMIDAbjnGo5KvJAtt4i/2cV/Mcx2Y2AUgCIoAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECCxWYPcc4GeTC5PFNvW35ud/kOd7ZtJdABQBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECCwCIG987PPSK5MtqZ5P9RjP5Pn3T+xC0AQFAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ2BaBffJDv5VcmwzV0N/a41yX535QskOiCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAga0Q6Gr7A5I/TW5ItrZpP/TjX5gx7JYoAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAYIECbf4fnLwyuTEZupm/Lcf7UsbR3QhukSgCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEBgAQIH5TFvSH6QbEuzfhI/048guEtyy0QRIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECWxDYIffdLXlHMokm/mKO+Z8Z05OSnRJFgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIbEagzf/7JR9LFtOon+TPvjFj2z3xMQBBUAQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAYLbAjrnhgcnnk0k28Bd77G9lfPslJgAEQREgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgXGBXfPNjyenJItt0I///A8GPl6PfUNy3+SWiSJAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQ2CeyWfx+fnJeMN+8X+/X3c7x3J5OYBPCiHHenRBEgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIR2Cv5xeSyZLEN//Gfvz7HOyZ5avK9gY/d5zk36dh9DEAQFAECBAgQIECAAAECBAgQIDCcwHbDHcqRCBAgQIAAAQIECBAgQIDA1AT2yTO1+f/WZO8Bn/W6HOv45GPJd5KrkqHrdjlgdy3wnnxoWccjQIAAAQIECBAgQIAAAQJrXMAfG9b4C8DpEyBAgAABAgQIECBAYIUJ9H3sAcmvJccluydD1bU50GuSf0++m3T7//XJ0NWV/89P+vEFdgEYWtfxCBAgQIAAAQIECBAgQIDAGhYwAWANX3ynToAAAQIECBAgQIAAgRUmcMuM9+DkeclLkh2SoerqHOjlyceTNv9bnQBw6savhv/PETnkIxMTAIa3dUQCBAgQIECAAAECBAgQILBmBUwAWLOX3okTIECAAAECBAgQIEBgRQm0Ud7m/wuTrp7vZIChqtv8H5t8Kblh7KCdAHBK8p9jtw31Zcf/O0l3AVAECBAgQIAAAQIECBAgQIAAgUEETAAYhNFBCBAgQIAAAQIECBAgQGCCAjvm2PdIXpr8cjLke9mLNx338/l3vPn/X/n+uuQ/kmuSSdRDc9D7JHYBmISuYxIgQIAAAQIECBAgQIAAgTUoMOSKiTXI55QJECBAgAABAgQIECBAYMICO+f4bZK/LnncwM91QY7Xbf+7zf/3x47d5v/1yUxyYXJUcmgydPU9+a7JPyXdbUARIECAAAECBAgQIECAAAECBBYlMOSqiUUNxA8TIECAAAECBAgQIECAAIFZArvk+yOTE5Oulh+yzs3BXpmcnow337vdf5v/65NLku4C8O5kUvWEHPiwSR3ccQkQIECAAAECBAgQIECAAIG1JWACwNq63s6WAAECBAgQIECAAAECK0Vg9wy0Tf83JXcbeNBn5XgvS05Lbhw79mjb/zb/L006MaC3/WvSSQGTqE5y+L3E+/NJ6DomAQIECBAgQIAAAQIECBBYYwI+Z3CNXXCnS4AAAQIECBAgQIAAgRUgsGfG+Ojk+GT/gcfbFf9HJzOzjtuV/13t3+b/5Um/b/O/75u3Tz6bdDeCSdRVOWiP/e1JHNwxCRAgQIAAAQIECBAgQIAAgbUj0M8bVAQIECBAgAABAgQIECBAYDkItNm+T/LEpCv/906GrK/kYMclM0mb+6Mabfu/LjdckYx/JEAf08feOumkhEnUzjlodxj4aDI+rkk8l2MSIECAAAECBAgQIECAAAECq1jAFoOr+OI6NQIECBAgQIAAAQIECKwwgf0y3qckXfm/x8Bj/2KO99pkfdKG/6j69XeTM5M2/8fvy7c3VZvz37/pu+G/eHYOeejwh3VEAgQIECBAgAABAgQIECBAYC0J2AFgLV1t50qAAAECBAgQIECAAIHlK9Dm/zOTY5JdBhxmV9R/Omnz/7xkfIX9qPm/Lrd3G/5+P35/vr2prs5XT0v2uumWYb/YNYe7IflEsrlJCLlLESBAgAABAgQIECBAgAABAgQ2L2ACwOZt3EOAAAECBAgQIECAAAECkxfo+9LbJ7+WvDTZIRmq2sz/UPLO5MJZBx01/8/K7d9JfjDr/tnf3pgbDkkePPuOAb+/d471z8lFAx7ToQgQIECAAAECBAgQIECAAIE1JGACwBq62E6VAAECBAgQIECAAAECy0yg70kPTH43+eNkyPeobf5/OPnbZHZDvc3/ruhfn4xW/ufLLVaPd23yrGRSH6e3Y459p+QDySQ/biCHVwQIECBAgAABAgQIECBAgMBqFBjyjyur0cc5ESBAgAABAgQIECBAgMDkBO6QQz8v6QSAIZvqbfB/MHlXcmkyXqPm/7rc2JX//X6hdV0e+JRkUh8D0HEcmnTMX+g3igABAgQIECBAgAABAgQIECCwNQImAGyNlscSIECAAAECBAgQIECAwBAC2+cgByV/kPxOMmTzv1v5n5D87+SKZLza7G/Tv83/a5Ktaf7n4T/SCQBt0D+o30yobpHjPjz5aHLehJ7DYQkQIECAAAECBAgQIECAAIFVKjDkH1lWKZHTIkCAAAECBAgQIECAAIEBBXbIse6YvDD57aQN76Hqxhyoq/7/TzJX87+3tfnf7f+3tvmfH/mRTi7oxIIb+s0Ea7ccu5MYbjPB53BoAgQIECBAgAABAgQIECBAYBUK2AFgFV5Up0SAAAECBAgQIECAAIFlKtBJ6F1B/+LkGcmQzf/v53gnJv+UdJX/eLVxf2WyPrk2+a9kW6uTCB6b3G5bD7DAn2vzv5MlPpV0YoMiQIAAAQIECBAgQIAAAQIECMwrYALAvEQeQIAAAQIECBAgQIAAAQIDCPT9Z5v/L0iemQzZ/L8+xzsm6cr/bu0/Xm3+t2k/k3w32ZaV//mxm6rHaz0uGfIcNh507D899r2TU5Nvjt3uSwIECBAgQIAAAQIECBAgQIDAZgVMANgsjTsIECBAgAABAgQIECBAYCCBrmQ/JPnj5NeSIRvnbf4fl3wm6er+8Wqz/tLknGSxK/9Hx+3uAZclj0/2Ht04oX/rdq/kw0l3MFAECBAgQIAAAQIECBAgQIAAgS0KmACwRR53EiBAgAABAgQIECBAgMAiBbbPz98xeVny9GTI5v91Od4JyceT8eZ/m/Rd6X9JcnbyvWQx2/7nx3+oeqydk0ckQ57PDz3Jpm/2y789l05w6MccKAIECBAgQIAAAQIECBAgQIDAZgVMANgsjTsIECBAgAABAgQIECBAYACBg3OMP02elgzZLG/D/5XJp5Ju7T9e483/ThIYsvnf5+nOAn3OH0/2SSZd980TfDqZmfQTOT4BAgQIECBAgAABAgQIECCwsgVMAFjZ18/oCRAgQIAAAQIECBAgsFwFuvL/wOQPkl9Phmz+t/l+dPLFpKv7x6vN+QuTc5NJNP9Hz3VjvuguAA9LthvdOKF/a9mPAvi7ZPb5TugpHZYAAQIECBAgQIAAAQIECBBYiQImAKzEq2bMBAgQIECAAAECBAgQWN4Co+b/H2aYz02Gbv6/Icf8bDK7Gd6m/DSa/91RoOd0TXL/5IBk0nXbPEGf95NJdzhQBAgQIECAAAECBAgQIECAAIGbCZgAcDMSNxAgQIAAAQIECBAgQIDAIgTaGL990pX//zMZcnV8t/0/NvlMMt78b2O8K/8vSjYkNyRDb/ufQ96sdsgtVyc/kUzj/fWD8jwnJ99OFAECBAgQIECAAAECBAgQIEDgZgLT+APFzZ7UDQQIECBAgAABAgQIECCwKgXa/N8v+Y2kEwCGbP634f9XyVzb/ndF/AXJecn1yTSa/6NV+D3HfZK7JpOuvod/ePJvySWTfjLHJ0CAAAECBAgQIECAAAECBFaegAkAK++aGTEBAgQIECBAgAABAgSWo0Ab4bdJnp38ZTJk879N/S1t+z/t5n+Gs7E6CWC3ZEPymGSXZNJ1qzzB4cn/Sbr7gCJAgAABAgQIECBAgAABAgQI3CRgAsBNFL4gQIAAAQIECBAgQIAAgW0U6Mr//ZOnJy9Ltk+Gqjb/T0g+mnx37KCjbf8vzG3nJN9PprHyf2wIG7/s++pdk+5Q8OCkFpOuQ/MEuyefT8ZNJv28jk+AAAECBAgQIECAAAECBAgscwETAJb5BTI8AgQIECBAgAABAgQIrACBvTLGJyXHJDsNON429XvMTybXzjpuV9+3+d9t/29IlqL53+e8Mdkj6Wr8Bya1mHR1ksH9Nj3JKfm3kw8UAQIECBAgQIAAAQIECBAgQOBHTADwIiBAgAABAgQIECBAgACBxQjsmR9+YtIt+ofcAv8HOd7bk48n1yTj1ab7qPl/Xb5eiub/aDydiLDDpnSSwsOSaewC0Od4SNLn/2piJ4AgKAIECBAgQIAAAQIECBAgsNYFTABY668A50+AAAECBAgQIECAAIFtE2gDup9H/5PJm5PdkqGqTe33JR9MvjN20Db6e99FSbf9X6qV/3nqm2o0pq78vzw5IrntTfdO9oteg4cm3XXhK8nsXRJykyJAgAABAgQIECBAgAABAgTWkoAJAGvpajtXAgQIECBAgAABAgQIDCfQz6D/0eStyZDb3rfB/57kA8lVyXiNmv/n5sbl0Pwfja07EnQiQE26M8Gjkmm93+4kgAclt0lOTsYnTORbRYAAAQIECBAgQIAAAQIECKwlgWn9QWItmTpXAgQIECBAgAABAgQIrHaBXXOC3er+LcntBjzZNtH/OWnz/7JZx+1HAlyStPnfz7zvY5dLdSwd385Jt+LvhIi7JtOqTgK4V9Ln/FLSnQgUAQIECBAgQIAAAQIECBAgsAYFTABYgxfdKRMgQIAAAQIECBAgQGARArvkZ7vi/MTkoEUcZ64f/XRufHvSRv94g7/N9UuTs5Pl1vzPkDZWx9hGfHcBmEmOSob8WIQcbovV575z8oDkC0kNFQECBAgQIECAAAECBAgQILDGBEwAWGMX3OkSIECAAAECBAgQIEBgEQL9rPl7Jm3S33ERx5nrR7+YG1+fXDTrzm77f0Uyk1yXjE8MyLfLpjqu7yedILF90lX4D022S6ZZB+bJHp58LpltOc1xeC4CBAgQIECAAAECBAgQIEBgCQRMAFgCdE9JgAABAgQIECBAgACBFSjQ5v/dk79KHjjw+M/I8Y5LzkvGG/xt/l+ZdOX/tbPuy7fLrjreG5JbJ520cFBycDLtuk2e8JHJZ5MLp/3kno8AAQIECBAgQIAAAQIECBBYOgETAJbO3jMTIECAAAECBAgQIEBgpQiMmv+vy4CPGnjQbe4fm6xPuo3+qNpMvzqZ2fTv+MSA3LRsq+PuDgA164SGByfT/CiAPN3G2if/fVTy6cQkgI0k/kOAAAECBAgQIECAAAECBFa/gAkAq/8aO0MCBAgQIECAAAECBAgsRmCH/PBdkjckD1vMgeb42W5R/7Lk28mNY/e3id4V/2clnQSwUpr/GeqPdOxNdwG4JulHAXQSwFK8/947z/vjyacSHwcQBEWAAAECBAgQIECAAAECBFa7wFL8AWK1mzo/AgQIECBAgAABAgQIrBaBvme8U9Im/WMGPqk29l+afCOZ3fy/PretS76TtJm+0qrns13SSQDd4aC7AdwzuUUy7epOAA9PPplcMu0n93wECBAgQIAAAQIECBAgQIDAdAVMAJiut2cjQIAAAQIECBAgQIDAShHoyv9Dkj9JfikZsq7LwV6ffCm5YezAXenf7/txAF05vxKb/xn2xh0Lvp9/u/X/zslpye2SOyZLUfvnSe+fdCeAuioCBAgQIECAAAECBAgQIEBglQqYALBKL6zTIkCAAAECBAgQIECAwCIE2vw/LOkK/act4jhz/Wgb/McnH0++N/aAUfO/K+YvTfr9Sq5OXuiK/903ncRJ+fduyQGbvp/2P3fIE/ajHD6dXDXtJ/d8BAgQIECAAAECBAgQIECAwHQETACYjrNnIUCAAAECBAgQIECAwEoR6PvEg5I2/5+StIk9VP0gBzou6Ur0a8YO2mZ/79uQdJv6fr3SJwCMzmmPnMsuSXcE+Hxy72S/ZCnqsDxpdwP4XDLuvxRj8ZwECBAgQIAAAQIECBAgQIDABARMAJgAqkMSIECAAAECBAgQIEBghQqMmv/Pz/iflQzZ/G9D/P3JvySzV6C34X9ucmHSRvlKb/7nFDbWjflvz22fZLvk+qSTAO6b9LZpV6/nEUnH8uXku4kiQIAAAQIECBAgQIAAAQIEVpGACQCr6GI6FQIECBAgQIAAAQIECCxCoNv+H5g8L/ntZOjm/7/lmO9MrkjGqw3yi5INSRvmq6X5n1PZWP3Ig7733jOpaT/2oJMA7pfsnUy7OoYjkyuTryedlKAIECBAgAABAgQIECBAgACBVSJgAsAquZBOgwABAgQIECBAgAABAosQ2D4/e0jyouRXkzaJh6yP5WBvTy6dddD/zPfd8r+r/9soX23N/5zSxnPqrga7Jjv3hlRX3ncb/vskS7ETQHcAeHhyTvLtpPaKAAECBAgQIECAAAECBAgQWAUCJgCsgovoFAgQIECAAAECBAgQILAIgTaD75D8WfKsZOjmf1e7vy25IBlv8Lf531XobUK3IT5+X75dVdVdDrq7wR5Jd1pojSYB3Dtf77vxlun+p5M+Hpl8I5lJOklBESBAgAABAgQIECBAgAABAitcwASAFX4BDZ8AAQIECBAgQIAAAQKLEOh7woOSbvv/m8nQzf9uMf/a5LykDf9R9evvJOuSa5LV3PzP6W08v04CqG93Ahi9F+/HAXQngHslSzEJYMc8708kpyYbEpMAgqAIECBAgAABAgQIECBAgMBKFhj90WEln4OxEyBAgAABAgQIECBAgMDWC3Ql+oHJ7yW/mwzd/P9mjnl00hX+s5v/Xf2+Prk6We3N/5zixuoEgG6136Z7PwqgOy+0OgngC8lSfRxAx/KYpJMA+lEMJgEEQREgQIAAAQIECBAgQIAAgZUqYALASr1yxk2AAAECBAgQIECAAIFtF+j274ckL05+NRm6+d/V5G3+t8k/3vxvs79N8K78v2rWffl21Veb680uSScCjCYBdELESckDklsn066O57HJaUmvnUkAQVAECBAgQIAAAQIECBAgQGAlCpgAsBKvmjETIECAAAECBAgQIEBg2wXadD4oeWHyjGTo5v/5OeaxybeSfu79qNr8vz7ppIDLkn6/FqsTILobQD8KoLswjPz7UQinJA9Jdk+mXZ0E8FPJWclM0nEqAgQIECBAgAABAgQIECBAYIUJmACwwi6Y4RIgQIAAAQIECBAgQGARAl35f3Dyh8kkVv5fmOO+NDkjGV9F3mZ/vz87uTTprgBrdQJATn3jtv816CSAXpPRJIDuitBV+A9PdkqmXX3O7gTQ63hmcl2iCBAgQIAAAQIECBAgQIAAgRUkYALACrpYhkqAAAECBAgQIECAAIFFCHTL+UOT/y+ZRPP/8hz3xck3k9nN/65435BcnHRXgLXc/M/pb6xu+1+H2TsBdILEOUknASzFe/buSvDo5HvJN5KOUxEgQIAAAQIECBAgQIAAAQIrRGAp/piwQmgMkwABAgQIECBAgAABAqtGoE3dNv9fkfxiMlpxni8Hqa5cf3VyajJ76/g2/89Nzks0/4MwVtfm67l2AqhVG+8PSIa+VjnkvNW/FTwi6ccCfDXpxxMoAgQIECBAgAABAgQIECBAYAUImACwAi6SIRIgQIAAAQIECBAgQGARAn3fd0jyF8lTkqEbym3+H5OclFyfjKqr29v873bynQCg+R+EOaqN/jq12d6JGr0+tZtJbpUcnixFbZcnfWByYNJre2WiCBAgQIAAAQIECBAgQIAAgWUuYALAMr9AhkeAAAECBAgQIECAAIFFCLShfEjyguRZydDN/+/kmC9JTknGm//5dmMTu83/8zfd16a2urlAXToJoDsBdBJAP6qh1QkTZyWHJQckS1F9vRyR3C/5QnJZoggQIECAAAECBAgQIECAAIFlLGACwDK+OIZGgAABAgQIECBAgACBRQi0kXynpA36Zy7iOJv70W5f/4qk2/5fN+tBXdF+SdKV//0sec3/IGyhRpMA6ja+E0CN1yf3TfZMlqoOzhP/RNJJABcnnaygCBAgQIAAAQIECBAgQIAAgWUoYALAMrwohkSAAAECBAgQIECAAIFFCnTlf5v/xyZPSIZe+d8V669PPp+0wT9ebWJ3pXgb150Y0Oa2ml+gTnPtBHBFbt+QdDv+nZOlqn3zxE9OTku6q8MNiSJAgAABAgQIECBAgAABAgSWmYAJAMvsghgOAQIECBAgQIAAAQIEFimwfX7+jslLkicmQzf/uyr91clnktnN/64M72fFn51o/gdhK2s0CaA/NtoJoF9fkFyeHJn0+i5V7Zon7iSAXuOzkk5YUAQIECBAgAABAgQIECBAgMAyEjABYBldDEMhQIAAAQIECBAgQIDAIgW68v/Q5AXJMxZ5rLl+vM3/Tiw4OZmr+d/V6m3+X5NY+R+Ebai61Xm7pJMARg3/9fm6kyrul/S+paqO59HJrZPTk04GUAQIECBAgAABAgQIECBAgMAyETABYJlcCMMgQIAAAQIECBAgQIDAIgV2zM8flrw0mUTzvw3/1yQnJbNXfo9W/s/kvmk0/7vDwY8lj0/unoya5qtlW/qeTx17TXdL2vDvbd9MWvdMlnISQHeVuH9yr+SrycVJx6cIECBAgAABAgQIECBAgACBJRYwAWCJL4CnJ0CAAAECBAgQIECAwAACXZV9SPLK5ElJG7RDVpv/xyefSuZq/n8nt69LunJ9ko3gvXL8pycnJM9JfmJTfjT/XppclMweX25akTWaBLBzRt+t93tNe9tpSXd6uEcy9HXOIbeqDsmjuxvA15INSSeCKAIECBAgQIAAAQIECBAgQGAJBUwAWEJ8T02AAAECBAgQIECAAIEBBNoEPij54+SpydBN4Tb1X5V8OpndXG/DtyvV2/zvv5NqAPecHpC8Onl+sm/SSQ99T7tTcvukq9E/k7QRvVpqNAmguwD04wBaNe6q+04M6O4HQ1/vHHKrqpMynpicuSk/2Kqf9mACBAgQIECAAAECBAgQIEBgUAETAAbldDACBAgQIECAAAECBAhMVaBN8Da/fz95bjJ0M7gN/36kwMnJXM3/q3P7TNIdACbV/O9q96OS4zb9u7lzbIP8i8lXktVUde0kjD2TfiRAqxMDTkmWyySAjqOTALoLw+nJjYkiQIAAAQIECBAgQIAAAQIElkDABIAlQPeUBAgQIECAAAECBAgQGEBg1Pz/vRzrd5PNNca39amuyw+26d6m+uaa/+tz3ySb/1353sbyG5PDky3VVbnzHcm6LT1oBd7XZn8nAfR67JF0QkSrt3856Q4Iy+HjAPr3hccmfR2emnS8HaMiQIAAAQIECBAgQIAAAQIEpihgAsAUsT0VAQIECBAgQIAAAQIEBhLYLse5bfK8pM3/fj9ktXl7fPKJZK7mf7f7b/O/OwBMauV/t5Z/evLaZL9kS9UxvC/5bHJ5stpWoPf8vp90e/1OAhi9l2+D/ZSkk0GOSIaeBJJDblX1+R+e3C7pTgyTfH3k8IoAAQIECBAgQIAAAQIECBCYLTD6o8Hs231PgAABAgQIECBAgAABAstXYN8M7ZnJnyVDv69rw/9VyaeSuZr/38vt65JJrvw/IMfvRxq8NNk12VK1Cf6vyXuTC5Irk9U2ASCntHGixQ35t9e7JqPr3vPvivtOErhnMvRkkBxyq+te+Yn7JJ0E0I8F6NgUAQIECBAgQIAAAQIECBAgMAWB0R8MpvBUnoIAAQIECBAgQIAAAQIEFinQFdZ7J09Njk668nvI6sr/lyQnJ/3c+fFqE3e08r/b7U+qqXvHHPtPkucn851fm9//kpyYXJhclExyYkIOv6TVHQCuT3belFGzvw5fTzo5497Jcnivf3DG8YikkxM6MaNjVwQIECBAgAABAgQIECBAgMCEBZbDHwUmfIoOT4AAAQIECBAgQIAAgVUh0OZ/t8V/fPK6ZMdkyOrq8m77/7lkruZ/b5tJusJ+Es3/nt/dkk5AeFYyam7nyzmrY/hQ8o6kDeYNSScBrMbV/zmtm6ofBdCJGt0FYKekbq1OAvhG0uvTSQA7JEtd3aniJ5MzknOS1X5tcoqKAAECBAgQIECAAAECBAgsrYAJAEvr79kJECBAgAABAgQIECCwUIE988CHJW9Kdl/oDy3wcW3+vzn592Rzzf91uW9Sq+v73vS+ySuTJyajpna+nLPa/P/H5K+Trvo/L+kkgLWyyryTALoTwG5JJwGMqpMAzkxq0UkA3SlgqWuPDOBxSV8/TceuCBAgQIAAAQIECBAgQIAAgQkJmAAwIViHJUCAAAECBAgQIECAwIACXe390OStyf4DHreHavO/Owp8NJmr+d/V5qPm/yQa7N3J4MHJa5NuGT9ftfn//uQ9ycXJ+ZvS1eVtgK+V6nUbTQIY3w2iBjPJWck9kjbgl7o6EaGTADpR41tJx64IECBAgAABAgQIECBAgACBCQiYADABVIckQIAAAQIECBAgQIDAgAJtnnZ1/InJgQMet4dq0/y45NPJ1cl4tdHeCQHrk24rP4nm+vY5bleqvz45MpmvOgHh75P3JZcm3fK/TeWuKp/E+HLYZVs9381NAuiguwvA15PDkv2Spa5+JMFjk8uS05NOXlAECBAgQIAAAQIECBAgQIDAwAImAAwM6nAECBAgQIAAAQIECBAYUKAN8jsl3fb/7gMet4dqM/1vk48k3dp/vNr8/24yk1yR9Puha6cc8EHJicm9FnDwjreN/04AaBO5W//3c+XbBF9rzf+c8saabxJAnU5JbpsctPEnlvY//RvEo5M2/09NuruEIkCAAAECBAgQIECAAAECBAYUMAFgQEyHIkCAAAECBAgQIECAwIACfb92cPKnSbdPH7La0P9A8nfJXM3/7+X2dclVySSa/93V4Kjk7UknOMxXbf6/N+mY29S+ODk7WcvN/5z+xur1qUOb6bsl4x8HkG837uxwUv7thIvDk1skS1nb5cmPSvoa6OSETjRRBAgQIECAAAECBAgQIECAwEACJgAMBOkwBAgQIECAAAECBAgQGFCg79UOSH4/+Y0Bj9tDddV4m+nvT7q1/3iNmsnrcmPva+N96No1B3xM8pbkwAUcvGN4d/IPSZv/lyQzSVeRr9WV/zn1H6rRdevEjd2T2ZMAatVmez/S4T7JUv8toJMQHpzcIflSck3iWgZBESBAgAABAgQIECBAgACBxQos9Zv+xY7fzxMgQIAAAQIECBAgQGC1CbQ52s9s/83kD5KhV2x/KMds87/N9PGma78erfy/fNZ9+XaQanO6uxm8KbnNAo7Y5n8/puCDyXjz38r/m+N1EsD3k66on2sngN5/enJBcr9k9iSB3DT1umeesRMSOgmgu01MYsJJDqsIECBAgAABAgQIECBAgMDaETABYO1ca2dKgAABAgQIECBAgMDKENgrw3xi8rJkh4GH/Nkc721JV9G3ITyqUfP/7Nwwav6PTw4YPW4x/94qP/yzyfHJ3gs4UJvBf5P8Y9Lm/0VJx9fm//jY863aJDCaBNCV/p0EsNMcMuty21nJvZJOyFjqOjQDOCo5NelHO9yYKAIECBAgQIAAAQIECBAgQGAbBUwA2EY4P0aAAAECBAgQIECAAIEJCLQh+5jkhKRb5Q9Z3QL+tcmFyXhzv193i/j1SZv/k1iFvU+O+5TkuGTPZL4ab/53TJ2woPk/n9p/3z8+CWCX3NRJALeY9aPn5fvTkjsl+866bym+7Y4XP5V0csI5SSd5KAIECBAgQIAAAQIECBAgQGAbBEwA2AY0P0KAAAECBAgQIECAAIEJCLRZ289FPzHpLgBDVhurxybnJrOb/902vs3/S5M2j4euPXLArvzv5IOFrDhv87/b/nfl/xVJV//PJFb+B2GB1Wtcr+4EsHMy1ySAXu+vJW2+H5jMniSQm6ZafW38dHJ18q2kH2WgCBAgQIAAAQIECBAgQIAAga0UMAFgK8E8nAABAgQIECBAgAABAhMQ6Oex3z3pyv87Dnz8rvh/VXJmMr66f9Qk7sr6br3e5v/45IB8u6hqQ7nb/v9i0pX/C23+vyeP/WDS5v+VSScvXJdMYnJCDrtqq9eykzs6CaATAJrtkvG6Kt+clmyfHJ4s9SSAfuTFo5K+VvqRAN9JFAECBAgQIECAAAECBAgQILAVAiYAbAWWhxIgQIAAAQIECBAgQGACAm16div2lycPH/j4baL3uKcnbQaPatQc3pAb2vzvxIAhm/99nr2TpyavThbycQZt8L83+Yek2/537Gcl30s0/4OwDTW6zp0E0NdZJwHM/jtA7zsj6cr7e81xf26aanWSwpHJXZOTku4AoQgQIECAAAECBAgQIECAAIEFCsx+47/AH/MwAgQIECBAgAABAgQIEBhAoCuvD03apH/CAMcbP0Qb5y9OusL7+rE72hRuQ33U/O/EgKGb//vkmM9Mjk760QbzVcfzvuQDSRu+nQCwLtH8D8IAdWOOcU3S5no/EmD23wL6+uhki/OT+yedLLCU1Z0I7pw8KukkgAuSoV+jOaQiQIAAAQIECBAgQIAAAQKrT2D2m/7Vd4bOiAABAgQIECBAgAABAstToE3O2yUvSJ6RDLn9ehu+b04+n7SJPl5d7X9e0qbqJJr/++e4v5x0UsNOyXw1av6/Pw8cNf/bjLbt/3xyW3d/XxPfTXr9Oymjk0/Gq/efnXwz6Qr8ThRY6tovA3hK0h0Kzk3Gd7HIt4oAAQIECBAgQIAAAQIECBCYLWACwGwR3xMgQIAAAQIECBAgQGA6Avvmadoo/8Nk9mezL2YEbaPUNFUAAEAASURBVKj/ffK/k676Hq82fy9Kzkna8B1yVXUnMOyVPC3ZmuZ/x9rmf1f9X5msTzT/gzCB6jXvhJAbkk4C2DEZr752ugvAqUl3Atg9WerqOH8u6di/ncx+TecmRYAAAQIECBAgQIAAAQIECIwETAAYSfiXAAECBAgQIECAAAEC0xFoo7xb5P9i8spkyPdlbej/c/Ke5KpkvNrcvSTpSuo2gIdu/u+dYz4j6bb/C1353y3//y7pyv+rk678vzbpWNVkBDoJpFv+dzeANtfnulZ9nZyS3CvppI6lrv6OPCLpxwJ8I+n4hnz95nCKAAECBAgQIECAAAECBAisDoEh/9C0OkScBQECBAgQIECAAAECBCYnMFol/1N5itcnczVfF/Psn80Pvyu5NBlvkLah3tX1Xfnfxu/4ffl20dXdDJ6dvCJZyDn1+btDwXuSjrWrus/c9K/mfyAmXDXudvrfSbrVf9PX5nhdkW++nLTp3o91WOrq+A5PHp2sT85OuiuAIkCAAAECBAgQIECAAAECBMYETAAYw/AlAQIECBAgQIAAAQIEJiywR45/VHJCsmcyZJ2Wg3VSQbdwH2+i9+s2etclbbQP3fzv57Q/J3lpMntL+dx0s+rzd5eCdydt/ndsXfnfHQDGx51v1QQFat0GeneK6HXrbgCzJwH02nwluf2mzL4/N0+99s4z/kzSCS3dDaC7WSgCBAgQIECAAAECBAgQIEBgk4AJAF4KBAgQIECAAAECBAgQmI7ArnmaH0velnTF/JDV1dBdfd8V/t3ifVRt8nZL/a6YHrr532bwbZM2//8y2SGZr9r8/3Dyt8nFSRvMy2Hlf8+l290/OWlzuQ3v7yWXJ6u5ej36eul16N8H+hrdLhmvvm6+mtwqOSxZDpMAOmHhMcluSXcp6LVSBAgQIECAAAECBAgQIECAQARMAPAyIECAAAECBAgQIECAwOQFurr6gcmJyQEDP12b1F1931X0483/Nne7zfv6pKu8h1xd3ybwXslTk61Z+f+RPP6vkzb/OzFhXdKV/x3rUtUD8sQvTzqB4gnJQ5OHJd32vqb9vPnVXn1tdBJAq6/V7Td+9f/+04+N+Nqmb++ef2dPEvh/j5zeVx3Dg5Nevy8lfY2Pv/7zrSJAgAABAgQIECBAgAABAmtPwASAtXfNnTEBAgQIECBAgAABAtMV2ClPd0Ty5uROAz91m+ivTb6atNk/qvHm/2W5ccjGaJv/eyfPSto07/nNVx3PR5MTk4uSUfO/27j3vmba1QbyE5Pjk0clOyd9j9zmdz+q4W7JFcknk7VQnQTQ1f4PSv4keXpyh+TspBMArkvOSGpy72T2JIHctCR1aJ6117GTSc5Lrk8UAQIECBAgQIAAAQIECBBYswImAKzZS+/ECRAgQIAAAQIECBCYgkCb420kt1He1cpDVhudb0g+nbQ5O6o20/vZ7uckXWnfxu6QtU8O9qzkr5KFNP/73J9I3ppcmLSZ3GbtUjb/ew6/k7wmOSCZq/p+uWP8u7nuXIW33SXn1I9m+MPkkKSTPO6RHJ50gkl3augkk1675r5JJ00sh9ozg+gkgL7uv5l0IoMiQIAAAQIECBAgQIAAAQJrUsAEgDV52Z00AQIECBAgQIAAAQJTEOjnlLf5//rkqGTIaqPz2OQzSRvq49WGf1dCX5T0cUOtrr9FjrV/0ub/y5Oe30LqU3nQW5Lx5n9XkXecQ40th1pQ9RwenhyfPCfZ0jnU7n3JfyQd62qux+bk3p+0qd+dEcZrt3zz2WT0UQjdTeL85OSkOwHcKlkO1R0JjkpGExYuXQ6DMgYCBAgQIECAAAECBAgQIDBtgdlv7Kf9/J6PAAECBAgQIECAAAECq1Ggk60PTl6ZPGzgE2wz+q+Tfu55t9IfrzZn2/i/ILkhGarB3sZ5G71PTl6SbKlxnrtvqs/lq7cmHc9o5f9SNf+7Wv1JyTuSH096TpurGn84+XzS1eWrtbr7QXc4+FByh82c5Nm5vRNKxquTI76d/H7y5fE7lvjrXtOfST6YPCKx6CEIigABAgQIECBAgAABAgTWloA3w2vrejtbAgQIECBAgAABAgQmL9CJ1ockf5D8YjJktaH/z0mbtqMt9EfHb9P6smR9MnTz/9Y55lOTVyW7JAupL+ZBb0zaPP5e0kZyx9dxDjUxIYdaUHX8v5wcl9x2np/oJIoavzPpKvJOXujW96utfiMn1FX/D0g2tzjgW7nvFck5SWv243pdu0NCfQ9LbpEsh+pHPPxcsiHpRwL0NacIECBAgAABAgQIECBAgMCaEDABYE1cZidJgAABAgQIECBAgMCUBLoN+e2T5yW/NfBztmn+b8nbk66iH682ODshYCa5Phmywb5XjvdLybHJQpv/XRXeZvuo+d8G8sXJUjT/b5fnfUHy58l84+/EiXcl7076kQUzyTXJamog757z6USOFyb9eq7q+fa11t0eZpI20VvdRWH23xFq1uvdiRN3n+P+3LQktVOe9QlJx3xG0t0yhvy9yOEUAQIECBAgQIAAAQIECBBYfgKz37gvvxEaEQECBAgQIECAAAECBFaGQN9ftfn//OR/JUOvhv5kjnliMvos9ny5sdqsbXNzJhm6Wd2V1L+QtGE8X/M8D9lYX81/2/w/Nxmt/L8oX0+7+d/V6ndLjk5+JZnv/e/Vecwbkg8lNV6fdFJFt7tfLfW4nMg/JT+ZbM6jr6E6vCPpxI1u9d/bmpq2od6JLuNVo9OTC5J7JAt9reShE62O96HJA5JvJX0ddqKCIkCAAAECBAgQIECAAAECq1Zgc2/4V+0JOzECBAgQIECAAAECBAhMSKBby/960gkAbTwOWZ/Pwd6cXJiMr0bviubvJuuS78y6L99uc3Xywp7JzySvTXZLFlLfyINenbRx3Ob/TNJm+rSb/32v++DkTcmjkvkmY3R3gu5w0EkWlyVnJZ0QsFqa/zvlXP4oeX2yf7K56nn/efKp5OxkQzLaUaKN804CaI0mAYy79hp30kQnAhyc7Jcsl+p4Hp+Mrm3PSREgQIAAAQIECBAgQIAAgVUpYALAqrysTooAAQIECBAgQIAAgSkL7J3ne2by4mTo91mn5pivSbq6uk3YUf1XvmiTvc3/q5I2YIeqNv/bMH1jsscCD9qV4m2izyQdV3cAGK247linVTvmiX4yeVvSLennq7PygGOSk5PLk37fRve4db5dsXXfjPwDyTOT2sxVPdePJN3yvyvl1yeduNHt/cerr7Ha9N+5JgH0OvfnTkr6XHdOxicJ5Nslq05ieWyyV9LfqU6YUQQIECBAgAABAgQIECBAYNUJDP2HqVUH5IQIECBAgAABAgQIECCwBYE2N9v8f3LSle+zt0bPTYuq0/LTbapvSMab6P36+0kbtVckQzarb5XjPSE5PulEgIVUJyG0id5/r0vOT0YTFsbHnZsnWrvn6E9NTkgOWMAznZLH9Lp9Ixk1/7+br4f0zOGWpPp+/xeSv03ukmyuET/66IMT85hO2phJOqFkc7sf9Hpem/T1N5oEMHvHi95f25p2AsLQvxc55DZVTR6wKZ3w0Z0fFAECBAgQIECAAAECBAgQWFUCJgCsqsvpZAgQIECAAAECBAgQmLLArfN8j066Ur7N0CGrzfRXJd1OvyuuR9UG7Kj5P9pef3TfYv/dNQd4VNIGes9tIdXxvTI5K+nK/zb/z0vaQJ5m83+fPN9zk45lj2RL1XF1m/s3JOuTSzf92/GPW+fbFVkHZ9T9yIg/S7Zk0dfYi5NabEh63dq8n8+gfp0o0a30+7rfIdkuGa9Oojgj+WZy76Qr8JdDdSJEfX4u6cSPC5P+PikCBAgQIECAAAECBAgQILAqBEwAWBWX0UkQIECAAAECBAgQILAEAm1oHpW0Wd5txYesNqW78r9N9fGV2G28tlnZZm2312+jtrcNUaPm/1tzsH0XeMA2jNtw/3bShnC/b6bZ/G9D93bJHyd/knTr+S1VzT6cvC3pWDuJot7XJfM1vvOQZV8PyQjfmxyVzG7K56aN1eb8R5OXJWcmfZ1dnNRga6qP76SJmjdz/Y3h/Nz+5eTg5DbJ5nYiyF1Trf7+/nzS3Ql6/v1IgKF+l3IoRYAAAQIECBAgQIAAAQIElkZgrjfnSzMSz0qAAAECBAgQIECAAIGVI9BVzw9O3p7cduBht7n/8qRNyfGVyW1O/iDp/W3WDtlkH01meFuOu3+ykOoEhFckZyRtAncl9bSb/21wH5a8JPn1ZHMN79y1sWr2vuTdSRv/PYeZpJ91v9Kb/3vmHF6YnJB0QsTm6prc8ebkxKTXq6+zK5Px11q+XXDVrpM/+veFTgKYa7v/K3J7JwH0/jsn812nPGQq1TE/LLlfUofRx1bkS0WAAAECBAgQIECAAAECBFamQN/sKgIECBAgQIAAAQIECBBYuMAOeegRSZvld1z4jy3okW2ivzTpivo2q8frB/mmDds+pk3XoVYr755jPSJ5a7LQyQzdMr8r/09Lug18JySck7SJPNS4cqgtVt/P9jocl/xsMt/K8pp1wsYHkzakz09GY17pzf875VzelPxK0tfn5mpd7nhx8smkr6Wzk04I6GtrMdXr3kkArZ2SucbQ+7+etMl+z6SPWy51cAby+KSv628lfa0oAgQIECBAgAABAgQIECCwIgVMAFiRl82gCRAgQIAAAQIECBBYIoG+hzos6Yrzrhwesi7Lwbrtfz+XfLz534Z6G7RtWJ+bDNlk704GD0zenByYLKTaPD8m+VrSpm6bpjNJm6bTbP4fmedr03sh16HjfH3ykaRbvW9IhrbMIZekfi3P+u7kvsnmJkF0gsPHkr5u129KX0/XJUNds75muxNE/+3rqpMAZo+nr90+/0nJXZN9kuVSu2Ygj0t2SU5Jei6KAAECBAgQIECAAAECBAisOAETAFbcJTNgAgQIECBAgAABAgSWSKDvn26f/Gny1IHHcHmOd3Ty1WR89XGbs23+d6v6oRvWXYH9oORtyUJ3MmjzvONsg/TapCv/Z5Lrk6EayTnUFmvH3HtU0nEfkcxXV+UBr0m66r3j76r/C5I2qqc15jzV4NUt/1+UvDjp15urTn6o1VuTXq8zk042GX+d5dtBqq/VNs77eujrq9dq9iSATkbo6/0Tyf7Jocnsx+SmJant8qwPSTq55ItJXy89J0WAAAECBAgQIECAAAECBFaMgAkAK+ZSGSgBAgQIECBAgAABAkso0AZlm5W/vSlDNiy7ov6lyanJ7KZsm6Vt/p+XDNlkb3P2/slbkrskC6luFd8dCk5O2lTuyv+u5u6Yp9VI7+rsJyQd90HJfHVJHtAxfz5pM3cmqWebutMac55q8LpzjtiG/rOT7ZPNVSc7vCL59+TCZF1yddLJD5Oqvma7s0BfI90FoJMA5vrbQ183vS79txM55npMbl6SOiTP+uTk7KQTb/q7pwgQIECAAAECBAgQIECAwIoQWE5vsFcEmEESIECAAAECBAgQILAmBW6Vs35S8pJkSw3XrcXpKvoe87RkdpOxTequ2G4Dsquqh2pYtyHb7dffnNwzWUi1mdtV9F9I+nUnLUy7+b9HnvOXkm7lv28yX3XSxDHJl5PuAtDxXpas9Ob/L+cc3pPcJ9ncRJQ24T+dvDzpa2vDprQx3/smXX2ttrHfSSMd487JXL83vRZfT9pov0eyW7Jcqq+3TjbpZJlvJp1AoggQIECAAAECBAgQIECAwLIXMAFg2V8iAyRAgAABAgQIECBAYIkFds3zPyp5U9Kvh6o29d+QdKvxNmbHq43Rrl6fSToxYKjmf1dkt9F6QnJkspDq2F6X/EfShm6b6V1J3vFPo5mcp/mRvZJfSdrQ3z2Zr87KA45NuqtCV7x3vJ20sJKb/21I/2Xy0mTPZHPV6/WupNe4kyBmkr6W2pAf6nWUQy2ovp9HdcJIn7uTADr5ZHZ1TN2p4GvJAZuyuYkNuXuq1b+Z/GjSyRZ9LXVCzrQN85SKAAECBAgQIECAAAECBAgsXMAEgIVbeSQBAgQIECBAgAABAmtPoKuW75m8PbnNgKffpn6b/x9P2kgfrzapL0u6KnrI5n/P5e5Jn/chyUKqjds3Jp9IugL6yqTN9TZ1p9X8r/tzkxcnXY09X52WB7w6OSPphIWOt+PueFdq87YTUN6T/Fwy10r63LyxLsh/O0niw0mb1euTTthoI36pqq/nvsavTToJoJmrLs2NpyT9eIK7Jdsly6E6GeHQ5KeS05P+Xk7rtZ+nUgQIECBAgAABAgQIECBAYOsETADYOi+PJkCAAAECBAgQIEBg7Qi0AdnGXxvPDx7wtNuMbZP2M0mbouPVxmIboRuSNtmHali3aXzX5LXJjyULqY7zzclHk1Hz/8x83WbutBqgd8hz/dGmbKnxnYdsrJPz3+OSjrMr/9v8bwO84x3KMoeaWvU1+Kykq/kPS9qMnqt6biclL0v6kQedCNBGdV9DbcAvddW/r6dei/4dYpek5zL7fDre05NO4jgy2dxkgdw19bpVnvHJyUVJJ5d0ooIiQIAAAQIECBAgQIAAAQLLTqBvvBUBAgQIECBAgAABAgQI/LBAG6/7J7+fPDsZqtoIfUfy8aQN6vFqo/byZNS4Haph3fd9ByWvSLqKeSHVsbwj+dekzf+upF+ftEHbc5hGHZYneVHyG8lCVoN/No87PplJarsu6dg73qEsc6ip1W55phclbepvacv/7tLwd8kbkplkQ9IJAL19OZ13x9Kmea9Jv+4kgF7X2ZMA+ph+dMGXkk5a2TdZLrVDBvLTya7JN5L+Xiwn4wxHESBAgAABAgQIECBAgMBaF+gfghQBAgQIECBAgAABAgQI/LBAG67dbv3Pk6HeN7VR+MHk/UlXQo9Xm9RXJOuT65Khmood+0HJy5OnJAupjuVvk39KunV+m/5dST9qpufLiVYbwndPOuZfSmY3iHPTD1WtPpp0t4Jzk45zmuPN0w1eD88R3538QrKlnQ/6URGvSv4xadN/JunraCm3/M/Tb7F+kHs7QaMTFLrCv6/RuSZ49Dw+l9wquWMy3+sgD5lKdRw/mtwn6SSAS5KekyJAgAABAgQIECBAgAABAstCYKg/ZC2LkzEIAgQIECBAgAABAgQIDCDQpuSPJV1NvvsAx+sh2qT+/5N3JW2qj1cb7r1tJhlye/02VQ9JXpQ8LVlIA7Vj6QSFDyRtwPYjCtYlnbAw1KSEHGqz1THfLzk6eXwy35g73rqemFyYdAeFNv877t630qrv0Z+evDW5S7K58++1+HrS3QFOStr8Pzvpea+EZnTH39f6d5NOcOjK+rn+PtHHfDnp66+TQnZMlksdmoE8Jql7s5wnXWR4igABAgQIECBAgAABAgTWisBcb7DXyrk7TwIECBAgQIAAAQIECMwW6HukeyRvSQ6cfecivv90fvadSVcLj1eb1G1uziRDbifeRvptkj9KfjW5RTJftSnbVf/vTdpIbzP5zKTjm0YzvfZ3Tdr8b2N1vuqYOlHh3Uk/l/3SZH0y5CSKHG5q1W3l/yx5ZdJV75urbpH/4eS1ybpkQ3J+0p0jpnGd8jSDVF9v1yedBNDaKZlrt4M21r+VnJEcmuyTLJfqdfrppOfQ8fW1pwgQIECAAAECBAgQIECAwJIK9A8sigABAgQIECBAgAABAgT+u0l++0D8efLjA4J0pfYbkjZpxxu0/brN9TZxh2z+t9nf5v/zkt9NOhlgvmoz9l+Sv0naSG9Ds830jq/3Tbra+L1n8qbkqGS+6ir39yR/n3S8o+Z/G8rjxvl2RdSDMsp+7MLTk7ma4KOT6Mcb9LXUSRpd9d9r1HPvdvortdrgb+N89JEAO85xIr2m3eHh80n/jnF4spBJLXnYxKvjfWSyV3JK0o83UAQIECBAgAABAgQIECBAYMkETABYMnpPTIAAAQIECBAgQIDAMhJoM7GreX85+b1kqObiTI7VFd1nJ+ON6X7dRuG6pCvth2qyd9z7Jb+VvCBZ6Hu+j+exb0suSdqM7Xj7+fId51Bjy6HmrDa875902/sj53zED9/YhvG7kg8m3algJTf/e32ekrw9uUeypdfdWbn/pclnk1HzvxMCuiPASq+eQ3cw6O9CG+o7J3NZdGLKyUlfp/dJ+tEBy6E6yeYBSSexfCG5Ipn0702eQhEgQIAAAQIECBAgQIAAgZsLLPSPQTf/SbcQIECAAAECBAgQIEBg9Qi04dhVvK9L5lqBvC1n2ibgXyXfTtpIH1W/Hq2w7ySA8ftGj9mWf9swvXXyzOQvki2tJM/dN9Vn8lVX3l+ctAk7k7TB2nFNuolZ64ckbf4fkcxXXSX+9uRDyZVJxzmTrMSV/93y//nJq5O9k81Vr8PHkqOTM5MNyblJJ2r0vtVSPZdex/5O9G8V/Z1sY3129TVZh68ld0v6ml8udVgG8lNJJyl0YkonNigCBAgQIECAAAECBAgQIDBVARMApsrtyQgQIECAAAECBAgQWIYCbZR35e7xye0GGl+bs69LTkm6Yn1UbV72vvVJG9hDNXBHzf9fyDGPSXZKFlJtVPa8u6K8zf91SRuXHdekm/8dYyddtPl/p2S+6vjemHwk6UcTXJTMJJ0UMJRjDjWVunee5V3Jc5ItrWLva+WdyYnJhUlfNz3vnvOkr0+eYurVc2rTvJMAek13Sfr7OVfV4UtJt94/OOnvwHKoTub4+aTjOzvpZB9FgAABAgQIECBAgAABAgSmJmACwNSoPREBAgQIECBAgAABAstQoCuMD0q6Tf9DBhpfG5gnJJ9I2sAdVZuboyb7kM3/Hv9WyZOS1yZtmi6kuoK6j+9q8o5rQzKtlf9d3f3Y5M3Jgcl81SbqccknkquSTlg4J1lpjfC+B398cmJyv2RLTevzc39fl/+WtJncjwDo62Z8Qkm+XZX1g5xVr3lfl50o0kkSc1l1osBXkrrcI9nSZIrcPbXqmB+X7JmckXR8igABAgQIECBAgAABAgQITEXABICpMHsSAgQIECBAgAABAgSWqUBX6/568pxkrgbj1g67Tf6/Sf41aXNyVL29zer1yRVJG5xDVZv/P528Ptl9gQdtU/JVSZvo3Xb9vKRN9Y6rY51kdev7n0lOSG67gCe6Jo9p8/+TyXeSjrWTFjrRYtJjzVMMVp308NzkDclttnDUntNJycuS05JOBDg7aUN8yNdNDresqzsAXJdcm7Sxv2My198w+vrtx2x8NblbcutkOVQnFx2ZPCD5etIdHFbS6zXDVQQIECBAgAABAgQIECCwEgXmevO8Es/DmAkQIECAAAECBAgQILC1ArvlB9qI7irroVYOfzrHendyeTKqNv26artN3G6vP2QTt6v9H568KelkhoXUTB50dNJ/22BtQ71N5mk0/7si+meTNsH3SearNvxfnXwm6USA0VjruZKaqYdnvG9L/lfS1eGbq57X+5Pjk16TmeSCpNdppX3MQYa86Br97nQyTSfodBLA9pu+zj83VW0uST6RHJAcnPTxy6EOzCAen5yZrEv6e6YIECBAgAABAgQIECBAgMDEBEwAmBitAxMgQIAAAQIECBAgsIwF+l7o/slbkqFWDJ+eY70m6Xbto+Z0/+1K9a5Y7wrgIZvsXVH+kKSN5TY9F1JtKr8iOSvpxxO0od5MYzV9Jyj8fHJc0l0L5qvulNBdCj6XdBX4Smz+dxX4I5N3JA9NttSU7qSR1yUfSDpRpA3jy5KVNtkhQx60+jvU35tOAujrtJMAdkhmW/ZxnSjxH0kfd0SyXP7msXvG8qSkuxV0p4Jp/L7laRQBAgQIECBAgAABAgQIrEWB5fJmeC3aO2cCBAgQIECAAAECBJZGoE3Zw5I2wu8z0BC6+viYZH3SRuSoujK5jetOChiykdvmf7cXf2tyaLKQujgP6jmfkbRR2jFNayv97rbQlf+dINFdAOarkedJeWC3vu84O3lhJTVOu9L/WUknmRyUbKl6TV6WfCHpdepK8e5+0PNV/y3Q36VOBOnElU4AaOb6m0YnC3wt6WvmXskuyXKojvVRyR2TTha6KulYFQECBAgQIECAAAECBAgQGFRgrjfLgz6BgxEgQIAAAQIECBAgQGAZCXTVcFfL/3HySwONq03JNrZPTcYbtm3utZHdCQBd+Ts+MSDfbnO18Xn3pNvEd5XzQqqry49O+lnkbaBempyd3JAMNa4cas7aI7c+Nenq9n49X3XL+34sw1eSNv/PSdr8r+ekx5qnGKTa5H198oJk1y0csef078mrkplkQ9Lz7TVqw1v9sECvf3+X+jvXrzvJYvtkdvW+maSN9k6Q2TdZDtX///R39nFJJyj0evd8FAECBAgQIECAAAECBAgQGEzABIDBKB2IAAECBAgQIECAAIEVINDV521G/0my3QDjbcO/je3PJOONvDZv23TvSu7ePlTjus3OuyVtLj84WUh1JfkxSbceb+O0zf/1yZDjyuHmrG71/4ykDe7uAjBfdbJEx9oV3B1rJykM/dEJOeTEqg3eXpe3Jj+ZbOk11skNb0velfSanJV0V4ZpTMrI06zo6m4a9bsu6Qr/HZO56uLc2Nd9H3NY0uuzHGqvDOIJScd1WnJ1oggQIECAAAECBAgQIECAwCACJgAMwuggBAgQIECAAAECBAisAIGuxG5Tts3zbqG/2GpT/73JvyZtVo+qzf829GaSNiiHav73/dvhSZvpj0wWUm2S9vFfSq5J2hBt838aTeY2Of9H0p0H2uicrzbkAW3+fz2pX8d5SbJSVv53Z4anJO9Iep22VF39XZeu/r8sOTO5IumEkqFeLznUqq6+LjqJpRNcOgGgr7G5Gvy9vxNKLk/unXQSzXKojuNHkwclHV8nurj2QVAECBAgQIAAAQIECBAgsDgBEwAW5+enCRAgQIAAAQIECBBYGQJdiX1k0pXZ+w405M/kOF293cbtqNrAa9O/zes2HjsZYIjq+O+QvCj52WSuRmdu/qHqOF6XfDbpRIA2QGeSaTT/a/yc5OXJQiZbzORxxyZdDd3JFDNJG+Mrpfl/YMbahv5fJN31YHPV18cXk79Keq4XJd0loufcc1VbJ9Dfr+4GcFXS34m+1vq7Mvv3oxMFOsni9OQByUImpORhE6+O86Dk55Kzk06C6e+nIkCAAAECBAgQIECAAAEC2yxgAsA20/lBAgQIECBAgAABAgRWiEDf99w5aYP2PgON+es5znFJV+2Oqs3dNhrb0L0yaXNyiGqT8HbJC5NnJ7Obm7npZtWm6AnJJ5JrkquTs5JOChhqXDnUnHXb3PobyV8km9uaffwH25ht8/+MpI3wTp5YSc3/IzLeNyZt4m7pPXYbu3+fHJ/0ddOG77lJXzOTviZ5ilVb/b3r5Im+xm9MOgmg16ETAcarxucnX0p6zfZOlkt1d5InJfsnff13so7XRBAUAQIECBAgQIAAAQIECGy9wJb+OLH1R/MTBAgQIECAAAECBAgQWF4CbZYfnLQZ/cSBhnZBjvOaZF3S5mOr/7b5eE7S5nW/HqI6/gOSP0iem8xuauamm1Wf+x3JR5LuQtDV0W3+t7k+6abirfMc/yP5y2Qhzf82/dv8/1bS8c0klyYd58g2Xy7L6vvpxybvTLqqvNdqc9WG7uuSf0j6da/HJUknaiz388wQV0T1NdOdLpodNmWuv3l0cs4Xkk6quUOypeuWu6dW/d2+X/LopK+N9UknjSgCBAgQIECAAAECBAgQILBVAnO9Gd6qA3gwAQIECBAgQIAAAQIElrFAV9T+VvKbyRCNvjbUj05OS8ab/F2BfF7Sld3jt+fbba6Ot6uUfyX542Qh7986jncn/5y00dmsS6bR/O9Yfz15WbKQ5n8NX5OcmYya/508sRKa/11l/pzkTUknaGypvpE7X5F8Punkhjb/+zoa6nWSQ02tOtHh95InJ21O97W1nKqTKa5LOgmgvz87Jdsns6uvt5OTvk7vkixkYk0eNpXq79HPJP0oiVOT7mygCBAgQIAAAQIECBAgQIDAggUW8gekBR/MAwkQIECAAAECBAgQILCMBG6dsTwz+YtkiPc+1+c4xyT9DPeu3B5Vm+4XJxuSNnWHWNHd5uVeScffhnpXNM9XbZx/IOkq8yuSbv3fBm3/7X2Tqo51n6STLLryfyFj/Voe1+Z/m+FtxnacXRnfcQ7hl8NMrA7Lkbvl//OTTgTYXPVc/j05Nulq7vOTs5M2p/uaWUnVLfP/Oun1fWhyz+QRSV/z3cVhuVUnJ9S5v6e7JHO9Jvv73AZ7f1d6Pp0MsFzqlhnIg5KHJycnVyYrccJIhq0IECBAgAABAgQIECBAYNoCfVOpCBAgQIAAAQIECBAgsNoEdssJdcv/NpmHaOy1YfvO5GNJVxiPqre3cd1GaBuKQzWvO3nh55NOONhSkzl3b6w+74eT9ySXJW2qt+nc7f+HGlMONWftl1v/Z/KiZK5Ga27+ofpqvjsuadP/O8lZScdZy+VcnejwkORtyU8k/X5z1eZzH9fXTK9Hr0U/OmLI10gON/HaP89wbPKG5PCkq+m7Wr7Xee9k3+RdyXKsNsz7u/q9pDsBNLOvWR/T19+3krsleybLqW6fwTwt6SSe/j+mv9eT/n3OUygCBAgQIECAAAECBAgQWMkCJgCs5Ktn7AQIECBAgAABAgQIzCXQFb//l73zgJOkLPeuknPOsMuScxIEEZABlGjABFwxXEEwXBUD5kBGAVEBEUSiRBGzGFBACRIkSg4b2UDGhIjhft85y9Te3qHjTIfqmf/z+53tUNVVb52q6unnfd6qfQ18E7yN9kjDgtsP4TKovB23V3j7egq0szC3CMuzwGzRdTFoFLbvN3Au+H+HW3y24OyVzU6TTsWKLPgD8AWwONwobmOGb4Dts6hp8dVBALosc1jwfjOcA+s2aKhX+jtww6v/HRzyMDgIwGJzJ/cFi29buC8/DN+FHaDWvnV/fh/Kul0eVw668Dx1HzqYxgEMleE8s+APYMFdyhQOXNgV1oeH4DEo+/lCExMxEAMxEAMxEAMxEAMxEAMxEAO9MpABAL0yn/XGQAzEQAzEQAzEQAzEQAx0woDFsh3h22BxeqRhYfNncCFYUC/CApyFdgvYFrLbVQC1QLkTnAVeYd1MXMdMZ4KFQQciTIZuFP9XZj0fgs9ArQIxk+bELTyz+D8Fhhb/2+WPRbc9HITxMfgaeGeGWuE23ApfhrvAwRjF8fEfnvdL7EFDLeq/AxyMUissRrs/Z0DlXTFqzd+r990v/wIHmngHgIVhaF+I8zhI4FrwjiEW24cOFOCtnoXtduDJ68D/bkT3blMiBmIgBmIgBmIgBmIgBmIgBmIgBl5kYGjS+6IZ8kYMxEAMxEAMxEAMxEAMxEAM9IkB85ut4QxYvU1tvpHlnAMW3YqwWPhPmATtvHrdwuN2YDHf4noz4VXLp4FXMHur8yngVecOULCdnYqlWfC74QvgldWN4mZm8I4GU8Hiv+687X+n28kqRhQb8Wn3x8FQbzstxv4QLIjPhEdgGlgYdxv7ITz+Pg3+txnjwKJztXAww/XgQAeL/26rdzcoe9hGjz0fHQTgoJWh2+g072rwJGwO9fY5k7seDkZ5LSwODjbx7gadPM9ZfCIGYiAGYiAGYiAGYiAGYiAGYqDfDGQAQL/tsbQ3BmIgBmIgBmIgBmIgBmKgmgELdVuABdiNq80wjPe8iturvi3oFkU2Hy32TgaLhMX7PB1RWHzdEM6EtZtc0p3MdzJMB4v/FtdtU6eL6suwjoPhaGimQHoT8/nfMVgQ9ypri//FwIl2+WORbQ2v/n41nA3bwtBCMW/NCQcyuH2XgoMv3D4HjDhIpKzbR9PmhNu2G/wI9oEFoVY8xYRTQC/uz4fAonq/xP/SUM8VWQg874buW/fZw+C2bQBLQJnCY/MVsAPcD57zficlYiAGYiAGYiAGYiAGYiAGYiAGYmC2gQwAyIEQAzEQAzEQAzEQAzEQAzHQ7wYs4Fn8Px22bNPGPMByvgpTK5ZnYdArhH2vnYX2+ViehUbvXOBVx83Efcxk+7z6+h+Dj4/xaIGzk0Vni/8HwVHQTPH/BuazOG47LZRPBAcBeBV5WcOi8DvA/TG+QSPdHq+Evx4sjls4/hN4nHRyP7D4tsQ6LOVbcASsCJ5L1cL9dR04n1eeW/x3n/4d+mE7aeac8Bzxynn/uwyPYQc8WFQfGjN4w0E2S4PHQS03TOpJjGOtbwQH00wC90UiBmIgBmIgBmIgBmIgBmIgBmIgBl70/95FSQzEQAzEQAzEQAzEQAzEQAz0kwELdxbPLYZv16aGT2Y5XweLuZXFTYu6FgUttLerwDsvy7L9J8H20ExY7DsRpoBFPwuxj4KFzcr28rKtsSxLew8cBRbJG8X1zOCgDNtnUdxiuVeL286yxho0zH3xWVi0TiPdhmvgePBKcf173Lg/LJaXPTxvdoMLwOPO47BWPM2Eb8JZ4PHvdj4BFtE7ebyx+I6F7faqeY9Ht91BANUcPMP7d4KDOzYFB+uUKRamMbvCeLgb3FeJGIiBGIiBGIiBGIiBGIiBGIiBMW6gbCPYx/juyObHQAzEQAzEQAzEQAzEQAy0YMCCnf9H+wlgEawd8QgLOQYs6lYWqi34z4Jp0M7i/3os7yuwBzQT05nJK869Q0Fl8b9dbarVhuWYcCAcCc0W/72yXJ9/Bov/XnFd5uK4d49wX+wI9XJlC98Xw2Xg1ddTwFv+W1CuPGZ4WcpwkIPH+FuhXkHbbbkBHMQxCSz6zwJvn1/m/Ujzmg738/ywMngHBAvq1fb9gry/FnwOVoEyhncFeR9cD6Nl/5TRc9oUAzEQAzEQAzEQAzEQAzEQA6U3MG/pW5gGxkAMxEAMxEAMxEAMxEAMxEB1A14575X6r6k+ueV3Z/IJr+i2kFZ5ZbPFNAu8Ft8t8lZO4+WwwiuwV4UvwpuhWtGRt+cKrzK3ffeDt/23vdLp4v/irGMfOA4shDYKC5AWjfXl7f4nQpmv/Hdf7AVnwxZQb1/4Xz94zP0MvNr6YfC9dh0XLKpj4XbuBJfAAPi6VnjHhlPhHJgCDuR4DDzuHBgwmsLz28EpnkcObnFQxFA3zuMACI/tlWAc1DtOmNz1WJ41+l0yFRyw4fYkYiAGYiAGYiAGYiAGYiAGYiAGxqCBDAAYgzs9mxwDMRADMRADMRADMRADfW7A4tx6YEF69zZti4V0b6t/D1QWOC38WeC1AGrxsx3FfwuHXkX8aXgPNFNItPh4Atg+r8CeBRbYO138X4x1vA0sei8MjaKy+O/V8RPBQQCVThsto5vTLfi6D04DC7v1QvdfhlvA/eG2uY39UGi1YP01cADJClAr3E+3wlFwM3iMeew7IMBBDqM13O5n4Z/gMTE/DB0EwFuz5/kDj56DG4DzlSkcoPMG8Lx9GLz7Rju+s1hMIgZiIAZiIAZiIAZiIAZiIAZioF8MZABAv+yptDMGYiAGYiAGYiAGYiAGYkADFss3BouZe0I7YgYL+RJY4LXgX4TP/b+/J0O7iv8u20LzIYNUKzI6T2VYfLVwexd423+L/xZlO33VuUXEfcHi/yLQKH7PDKeDReN+KP4vRTsPgyOg3vZ5HFwBHnNTwePFR4vAZR3YQNNmhwXqA+Bi2AHq9QG4z84C/+sGt3ESeNcJj7mybydNbEt4nru9erKYXs2XgwTugwdhdVgWyhR+p7wCtoaJ4OAmj+FEDMRADMRADMRADMRADMRADMTAGDFQLZkdI5uezYyBGIiBGIiBGIiBGIiBGOhDA+vS5q/Cbm1q+yMsxzsJ3AuVRU4LZt7i3UKvRcF2XUVr0fmd8EVoJh/z6nnvTHAbeIWyxTwL7J0u/i/KOvaBk8CBAI2iKP7r0wELFh7LfNv/rWjfubAf1NsPFoPPgvOhGAziAAyLwO06JlhUR2IFlnoUOMhhyTprcDsc/HI0XAtu32R4BtzOsRaeW+53vwO868V8MDT8rvBcvBl0tCE0M5iH2boWq7GmvcC7VTwEY3FfstmJGIiBGIiBGIiBGIiBGIiBGBh7Bup1dIw9G9niGIiBGIiBGIiBGIiBGIiBshqwuLYRnAB7tqmRk1iOgwm8mteCXhGVxX8Lge0q9FqE/S9wGxaARuG6vfr+RvD54/AIdPq2/4uyjjfBybA4NIobmMGrxm2bxX9vPW57K53yshThcbQXWNTfHF4KtcJCuIMvfg0W/ycOPnbaP6sZcXicXQz+Fxn18n7vYuB8HmcOLJkCFrYdbOJ5MFbDQQAO/HkeHARQ63zV0x/BY35TWATKFLZnD3AQz53goJxEDMRADMRADMRADMRADMRADMTAKDdQryNglG96Ni8GYiAGYiAGYiAGYiAGYqCPDGxAW70afbc2tfl+luP/5/4gVBaqi+K/gwMsjrar+G8Bbm+wqG5BsVFYfHTe34NFRgvQU8CreNvVJhb1oliQdwbgdFgaGoWDE5x3Glj8L7xVOuXtUoRF3APgNFi5Tov0a7H0S2Bx14EXbpfF07IXxR2w8Wk4HlaAeuE2ORjlF/AkFAMcOn2Msaq+CPe1AwA8/zx2FoJqA0Y81j3+bwadrwrV5uPtnoT9PtvAZnATeJ528juExSdiIAZiIAZiIAZiIAZiIAZiIAZ6aaBMSWkvPWTdMRADMRADMRADMRADMRAD5TRgzrIueCW2V263I25lIafCFKgshFnws9A+FbyCvXIaL4cdFvwH4DuwHDQKi47fhKvgL2BxdjI4KKBdbWJRLwqL/7vAWbDSi6a++I0/8NY3wOKn7fQqaIvkFkTLFovSIAvjn4L56zTOY+ByOA/0/gjMAoviZdwumjUn9uCZg1o2gXq5vle3/ww8Hj3eZ8Jj8By4/Ym5DVhA9xxeDTx/q/2XALw9Oxbn393gneDzsoXfbQfBDeC5moiBGIiBGIiBGIiBGIiBGIiBGBiFBkxkEzEQAzEQAzEQAzEQAzEQAzFQVgMb0jCv/Le42Y64joVY/LewW1lM97buFnwtZrez+G9RfTs4G5opqluc/RZcCX+GJ8Div4MCKtvLy7aGVzjvAt+GVZpY8i3Mo8ei+D+R52Ut/m9O284Fi7L1cuBnmX4GXAQWxt0mr/4v+xXxbtMH4RSYAPWK/xb6PZ8uBY/3SeB7Di75X0i82IDnneflX0FHi0CtQQAeKw/BreB5tDKUKZaiMW8Fv+/c925TIgZiIAZiIAZiIAZiIAZiIAZiYJQZqNf5Mco2NZsTAzEQAzEQAzEQAzEQAzHQZwbWpr1fhd3b0G6LeL+C0+DRIcuzGGah3atjLYS2q9DuleavAIvqE6BR2I5zwHZWXvnf6QK0xcyd4AwYB43idmaw2KwvBymUtfj/UtrmoIazYGvwda2YwYQT4Cqw+P8weKt090m7jgcW1fawwOw58mmwMF0rLFz/Do4D959F/8ngcWZxO9HYwH+YxcFB3inBgT2e39WOKV17DP0WdLsRlKnvxfN9ANaFe8DvvjIf4zQvEQMxEAMxEAMxEAMxEAMxEAMx0IqBMiWhrbQ788ZADMRADMRADMRADMRADIxuA+uxeSfCa9uwmRbuLoAL4ekhy7PAOwumw/PQrkKYRbatwFv5bwCNomjjz5jRwrNYoLVNFhQ7FbZzJ3BgxBpNrORO5jkZpsAzMBG8cr6TbWTxLYfb9TY4E1av82n39x1wLNwNFsYngdvkPilrWHjeD7ySfwDmgVphkd9BKOfATPCuDT7mlv9IaDE8zh0k5N0uPMYWgFr9Kh4/d8FD4HfAElCW8PhZF3aFB2EqlO0cpkmJGIiBGIiBGIiBGIiBGIiBGIiB4Rgw6UvEQAzEQAzEQAzEQAzEQAzEQJkMrEFjvg6vb0OjvHr+XPgxePVuERZ+nWYh1DsC+Nz32hEWBNeGU2GXJhZo4e0SuAwcoGDB1qKh7e1kUc4C5qZwFmwOjcJiprePt+hv8d+r5C0id7KNLL7lWIhPfBiOAJ/XCgu0l8N54FXQFsYdAOCxULZtoklzYlGefQY+DvW2z+PZQQ0O7rgX3GePgMXrst/ZgCY2Hesz57HwavDKfAv0D8BR8FPoRDjgYgFYCVaAhaFW/4rvj4O3gW30+6FM4ffMp8ABIj5v1/cgi0rEQAzEQAzEQAzEQAzEQAzEQAz0wkCtBLUXbck6YyAGYiAGYiAGYiAGYiAGYmANFHwJ9m2DCgudp8C1YKG6Mryy3mLo4+BtuttV9LIw6DacAG+ERmGh+ftwCXjbcIu0k8BCXCeL0BYhN4ZvwTbQKO5hBov/Fv29O4GPttEiepliSRpzNLwf6hVabfvZ8DP4M0wGB1+UvTA+QBu/Cg7YqJfPe3z/ALxDgIMaxDtdOLihbPuMJrUcbvu74DAYD553Q+NK3ngNtOvcHrp82+Axtgw4EGAJcFBNrViMCdvCB2CpWjP16H2/a/wu8LvX48TzIBEDMRADMRADMRADMRADMRADMdCnBup1iPTpJqXZMRADMRADMRADMRADMRADfWpgVdrtlbz7Qb3iZjOb5xXdFrOuBwv8RVjo8gphC75PQrsLvl7pezg4gKHRNliY/DF8F2yLBeiJ4GAF29mpsFi6ETg4YrsmVnI/83wdLPp7dwLb6OCKTraRxbccW/MJ/5uHN0G1gnCxQAucX4HfgN7dLgcBeCyUNbza/CA4A9aAesfWdKY7AMWr3x+FKeD5UPY7G9DEhuF+9dz6CbwLloZaLqYy7Xzo1AAAFj37HHAwiRgLQq1BAPqfBlfDOuCggbKEDl8ODlB4ABwYVebzgeYlYiAGYiAGYiAGYiAGYiAGYiAGahnIAIBaZvJ+DMRADMRADMRADMRADMRANw1YDDsM/htqFfSY1FRMYi4LvHdCZZHa53+FyWCx/T/QzuLgsizvw+AVvvUK0Eyevd5f8GjB2uKsBWjb3eniP6t4ibdM/xrs7IsG8RDTvwo+6s7iv4+VXnnZ09D1HnA2bFKnJe7rP8IxcBdYHPdYsHjrsVDWcLDGufAhWARqhdvwWzgO3D6v+p8C7q9+L+auwjYcBhfD/lCv8M/kl8wAr2i3mP08dDos7j8LDi5yEIBUC88bB89cD/PDutDou4JZuharsabXQTEYye1JxEAMxEAMxEAMxEAMxEAMxEAM9JmBDADosx2W5sZADMRADMRADMRADMTAKDTgbds/Cx+EkRb/Lfp/CR6EyuK+xVGL7BZ8iwJ25XTeHlF4S+/3wuehUZ7leq+Ec8Eire2x+G8hutOF9Q1Yx/GwBzSKicxwIujSNvraOwB0uo2soumwiPpOsNi7cp1Puf9/CSeBV8h7dfgMsDhcpu2hOXPCwvBr4Vzw6ux654b7RQffAbfPbZsJZd4+mtcwPK8+DufBLrAw1PNgIf5aKAatPMLzf0E3wmPMgrnnincBsK1GtfbaTr+rHISyIRTz8rTn4SCT3WAJuAs8thIxEAMxEAMxEAMxEAMxEAMxEAN9ZKBaItpHzU9TYyAGYiAGYiAGYiAGYiAG+tyABb6PQDOF83qbalH9N3AOzBoy4795/RRYDOxEkX1xlvsOsOi4IDSKa5jhdLCd3Sysr8L6joQDoFEuOIV5vI38fWABsIzFf4umH4UvQj3vFmXPgx/Bn2AyeIWzx4XHTRljPI06DN4O3v6/Vtj+e+EUuB88zh0A4FXmbl+/htt8IHwBVoJGx6tF/hvB/TwJ3L8OgPC/eOj2Pp6Hddr+FcG2e2z6XrVwuzYAz8ktq83Qw/f09ms4BDy2EjEQAzEQAzEQAzEQAzEQAzEQA31ioFES3SebkWbGQAzEQAzEQAzEQAzEQAz0oYGFaLNX/X8JvGJ2uOEV3D+Bc6DyalULWF5pa6H9MfgHtLsY6NWyb4LTYDFoFDcwwzfBIq13JLBY6SAAt6GTYSHyc/ABqFWMLNbvQAmL//fAM2Dx39ub/wfKEt414lh4L8xbp1H+9wrfgOvBovBkKHtxfDPaaEF/e6iXs3ts/xguhsfB49wryj3OO308sYqOhNu7LxwNa0K97Wfy7EEOt/B4Hlik9pxyEIQ8B73yYLs9Lj1OHXjjY63vOOddDl4P+9WZj0k9Cc//d8IfoFt3U+jJhmalMRADMRADMRADMRADMRADMTBaDDRKpkfLdmY7YiAGYiAGYiAGYiAGYiAGymXA4r9FpZNhwRE0zaucLfw7AMDCbmVYCLWY7VXAFkvbXfz3Kl+LtBdAvdvPM3l23Mq/FqOngleiPwwW1jtdpFyedXwCPg6Niv9eNW3x/49gMdU26rVMxX+vlNbjK6BeWBA+ER4CC+TTwGOiTNtCc+aEBeK94euw6px3qz+x0H8q3AAeSx7nPlqgbfdxziI7HvZN7AzHw+bQ6Dh1H94FZ4F3QHDbLfo7yMPCv98LZQi3wztVrDCIz2vF/EzYCj4JS9WaqUfv/5X1vg9+Dn4v9OMxRrMTMRADMRADMRADMRADMRADMTA2DGQAwNjYz9nKGIiBGIiBGIiBGIiBGCiTAYtibwP/z/JFRtAwi7nfhF9AZcHPgrpFwMlgscpp7S5YeXXvNnAerA2N4k5mcLCDbbKYZmHdx04Xo5dhHR+Bz4JtrhcWlS3+3wFFG//C8063kVU0FeavrwH3+Vp1PuH+vwZOA7dp2uCjg0A6PdiCVQwrVudTR8F/Qa0rxV2w7b8RTge3yztbOGjDgSRl2U80paXYnLmPg52h3ra7ULffAR3nglf+/x10YPHfgSqV3wO8LE04WMii/iqDj/Ua5rF9KKxfb6YeTPP4coCGx94j0O7vVBaZiIEYiIEYiIEYiIEYiIEYiIEYaIeBDABoh8UsIwZiIAZiIAZiIAZiIAZioFkD8zLjXmDh3ILYcMPC9ElwLXjVcxEWCP8EFkedpxMFX/OoLcABDF6x2yjuYwavRJ8EFiknggMTbFsni2gW/98Ph0OjwqpXyFv8vx30VrSxLEVl278PnAzLQq3wWPguXALPgAMuLA6X9cp4j6UtwUENHkv1cnSL3RfAj8Hjx2Pcu1s8D504zllsR2NNlv5F2A8a3QXE82Q6XARXg+eR+/UJcLBKWfcvTZsTfvctDKvCCuDrWuH0d4MDXurNV+vznXrf/fBT+DzcA/143NHsRAzEQAzEQAzEQAzEQAzEQAyMbgP1OhdG95Zn62IgBmIgBmIgBmIgBmIgBrptwPxjF7CIt/wIVm7R78tw25BlePWvRUELo94BoBPFKbdhdfgqvBEahVf6fwUeBIuWk8DCtIU06VR4Z4V9wdvEW3SsFzrzyt5bweK/bXQQRVmK/xaHHchwDLhdtcK2OyjjCiiK/75X1qvC56dtb4GvgwXfeuE++QbcAW6bx7iFb7etk8cRi297LMMSPwKHwBJNLN3b+l8KPwefu/0OfHAQhIX/TpznLLYj4feHdwNYDrwbgOfmPFAtFuXNXeEAWKzaDD18z+L//8D1UNbzq4d6suoYiIEYiIEYiIEYiIEYiIE+M+DA64VgSTA/XxGWAvsjzOMceG8Obk5qn5T9KM9CaQej2+hEDMRADMRADMRADMRADMRADHTDwLas5GKwgD7csPBpQf1uqCx8mnTNGsT/GqByGi/bFhbtDof3QKN8airzWFi/H0wUu3VVvUnrm8BbdS8O9cJi6glwMxRt/BPPy1L8twj6Bfg4zAe14hEmfA1uB5Nx3TsIpCzbQVPmivG8Oha8+r3eFd62/0o4Ex6DmfAolHnbaF7VsPD9bjgMVq46x9xv/p2XP4MfwAyw4G/h3+PTzpd+KvzT3LnCY9mi/jhYAhwMUi18f0P4IKxdbYYevue+OBguh3/2sB1ZdQzEQAzEQAzEQAzEQAzEQAy0YsC+HPNwC/zrw3Zgf9U64GDtRcCczXmc17CPSRwAbZ+T+an5+b1wE9wGk8F+lVL0QxRoldEJAABAAElEQVQNpz2JGIiBGIiBGIiBGIiBGIiBGOiYgU1Y8qVgcjXceIAPfhmmDFmAxdDp8Dh08opor1w+FD4Fta7aZdLsMBE8Hu4Cr0KfOPjY6UTQIuvr4AywvfXCdn0FbgCTV68yL1Pxf13a4x0MdoFauasJ+B3wVXBwiIVi3Ze1QOx2bAWnwZZQL9wXZ8OvwE6EKfA09NtV7zT5Ja+Ho2FjqLUvmTQ73L7fw3lgB4oeLDY7WMWOlk6fQ6yiK+F3yEKwMtjJ5N0AqoXzLQkHw67Q6LuHWboWfvd+GC4EnydiIAZiIAZiIAZiIAZiIAZioGwGzEHFCyTMSfeCPcFB1otAoxyVWRqGeap9LA4I+Dn8Ah4EB7bbb9H1aMdGdb3RWWEMxEAMxEAMxEAMxEAMxEBfGbDofz5Y+Bxu3MoHLfJa3C3CJMpkaipYHOxk8d/inFdrW7hdEOqFV6Bb/Pdq9L/Bw2AR0yuWO5n4OTp9d/g2WFSsF7bra3At6K5bAxRYVVPxMuZyEEO9Irk+fwlurwVii8W6t4DcSc8sflixJJ/6EHwaFq2zBNt+Hzj4wc4Di/4ObnCf2alQxm2jWVXj5bx7DOwEXkFRL9yfbu9Z4MAZt/dxeAo8zz2/R1vYJzM/LAues4tBrQK/5/cb4CBw4EBZwmPyMPB4dSBRPx2fNDcRAzEQAzEQAzEQAzEQAzEwCg2Ya5mDmmftAvvC1rAUOK3TYZ5k/9XP4CK4A8xxuxbd2MiubUxWFAMxEAMxEAMxEAMxEAMxUDoDa9Ci78D2w2yZxSSvULdYbZG3CIuFjq6eChadfN2psEC3G5wPJov1wmLtV+AP4FXbk8D3bF8nC2MWB7eA82BDqBcWU0+Bq8CBCRMHH01Qex3mqCbn34I16zTmeaZdAJeBfieD29LJQSAsftgxjk8eDfuD+6pWOHjBDgL3owMzpsOj4PaWYf/QjKZiAeZ6DxwOy0O98LywY8Tz67fg8Wnh38EcdpCUdUAHTRtxODjqY+BACc/DS0EX80G1cHDANuBAEjuyyhLuQwfsHA2zoJ+OVZqbiIEYiIEYiIEYiIEYiIEY6HMD9iWIg6XtS3gdvBXsH2l0EQezdDTsp3CQv/0wPwH7tpIzISERAzEQAzEQAzEQAzEQAzHQnwZWo9m/AYtDw8GiucVQr3odqGB7nm8Mi4EFsU6Gy3d9U6HRNjgQ4QvwGngFrAAWe01COxm2cVPw/51r1MbnmOdE2BVso8VZi42dbiOraBi62gcs/NbbDgvjFhp3gc1gSfCzZQ3vZnAjFINAam2bnQDHgtu1NawIC0AZ9g3NaCo8Fj227oZG26sHB/GcA57jrwLPa88bO206fW6zip7Fmqz5++DAjsrjYSav3wGemwM12In3D4TroRnHlcvv9POf06aNwEFTiRiIgRiIgRiIgRiIgRiIgRjopAFzZfNG+4YcVP0VmAjFIPJO5z+tLt/8bTJ8EMx7+ynXp7mJGIiBGIiBGIiBGIiBGIiBGHjJS1ZFwi+g1YSomN/R0JfBnjBQgcX4dWBR6EaytAnruQWKdtV6fJZ5jgSLnxbvLN5alO50G12+VxFfCbXaVrz/D+Y5CXaHbaFbbWRVDcNC9/vBK76L9lZ7nMJ0k+Udwe1eBEz4yxi2zau7LXJX25biPTsBboeDwe3aGJaAWleBM6mUYeH3p/BvKLat1uM/mecKeBcMgIMk/M7QWZkHc9C8EYXfD+7rWo7sqPJ7xHNzKxiowxuZ5lX3QwcR1HLerffdvu2g11fZ0IREDMRADMRADMRADMRADMTAKDNgH4h9AN6d0cHzZ8IMsA+pWznPSNdjW/8ArwUHvydiIAZiIAZiIAZiIAZiIAZioC8MeGvqkRT/LYJZ2HoNDFTwSp6vDhaWOl1YZxWzi3Dn89goubOwfgLsBttANwvrE1jfD6BRG//JPN+APcDi/0rQjQEKrKZhLMwcn4d6hUyL5LfC2+BVMAFMlE38yxgeA6eDx3K9feM2XwJ7wfYwAfRR1u2iaS8Kr1w4HhoN3tCDHR33wKFgZ41XaUyAYsBDN85rVtf1cICDg3Q8D+sdD08y/a3gMbAkOADJ432gBrvzvi6fgHrL7fa0abTHziy3IxEDMRADMRADMRADMRADMRADIzFgnmiObNH/1WA/jTmQ/QTdznXauT7/28hjYHlIxEAMxEAMxEAMxEAMxEAMxEBpDZiQrQYjKf4/x+e9St2kbqACi9argFeKd6NIaEHyWGg0ityC3qmwB2wDFkO7VVhflXWdC42SXovQp8Ge8EpYGby6vBseWU3dGMdUBzDU2wb3gbcWfz1YJLf93ToOWFXLsRGfuArqbZOdBo/DUbAzWAg36Z8fyrBfaEbDcCDO+2EWNNMJ4vaeCBaGXwHrwtLQT9tMc1uKNZn7QvB7rZGjR5nnk7AZ2LHl96mDXNYGz9sdYaAKO/He/vBHaLSObk5/erBdi/GYiIEYiIEYiIEYiIEYiIEYiIFWDJgX27fiwOgBuABGQ9F/aE5mf40543hIxEAMxEAMxEAMxEAMxEAMxEDpDFissvj/Kxia0DT7+s989vNgQWuggi15bnHU5K8bYXH5feCV/fXa/m+mnwN7gQMUvKq+W4V1BygcBrahURvPZp6ijWUq/q9Fu7wqul6h/Hmm69grnS2SLws6LmN4DhwMXsVdb5+4vbfAgbAjbAiLQ1m3i6a9KNbknYvBzop62+q0v8P3YV94FWwMDpRxAIHORmMsw0YdB3+BRn4cHHAZOMBlBxgPDoow7PTy+arg8e/0gRr4ef8LBgfMNFpnt6a77z8Afl8lYiAGYiAGYiAGYiAGYiAGYqCeAfMfWRQcNO7FITOhTDlOJ3Ipt+9SMO9LxEAMxEAMxEAMxEAMxEAMxEBpDFi4tCB4FQw3GfLq10OgsvhvsdBbZ3uVcLeK/xYkd4RGVzUXCZpFN6/ONVHrVvF/EdalK+8+UM+3bbwIXgsmz90coMDq6samTL0V6rXf4qlF1F3A+S0idus4YFUtxWLMfST8A+ptkwMaLJw7IMPjZnXwKm+Pu34Ijz0H6TRT2Pb4uwUcTON57bm8GtiZU9b9SNNGFH4HfAgeg3rHgdMcvHMD/DfsCBuBA1yK4j9P54THh9M2ge1goAYOlDkdLLw3Wn+3pvs99SnwezwRAzEQAzEQAzEQAzEQAzEQA0MNWPQ3RzRf/ATcC/+CbuUsZViP+eGpYL9HIgZiIAZiIAZiIAZiIAZiIAZ6bsCC13pwPQw3aXqQz74DBioorhRenPe6WRxdn/VZtKy3Lf/L9MvhTbA9jINuFf+9O8G74Vmo10aLrz+AN4DF/zJd+W973Of12u8o/4/DjuDxZeG5m8cBq2s67KS4BHReb5ueYPpRsDNsBcuBxV47O/oh3kwjHwKP/3rb6bTp4LbuCp4ja4G3bvQ8Ga2xNxv2ADTy4/TJYFHcY2Fz8PxcEOodC05zoMnasC0M1ODVvP9FmAWN9lO3pntuHAse8/W2kcmJGIiBGIiBGIiBGIiBGIiBMWDAvMAc3xxnR7D/olE/R7fyl16tx7vDHQAOhkjEQAzEQAzEQAzEQAzEQAzEQM8MWLz0itTbYDgJkoWwa+GNMFCBV7iuC14p3M1i0TKs72JotC3XMM9bYQdYHbpVxDU51tVTUK+Nev05OEDBq8xXBQuv3XTJ6qrGzrw7A+q139H+/w2vggnQqDDKLD2LzVjzTVBve9wfniMHgtu0AdjJ0S9J/Ra09dfgFQn1ttNpf4MLwWK42+pV7RZ9y7wPad6IYhs+7fdYM36eZr5TYC/w3FwDHOTUyrGwEPN7Tr8cBmrgeXYw3A6N9lm3pnseeHcCBzv4XZaIgRiIgRiIgRiIgRiIgRgYewbslzAfMC/4KNwPjQbTdytnKcN6vFjCQd+JGIiBGIiBGIiBGIiBGIiBGOiJAYtQW8M9MJwkyWLZ92APGKjAq8NXh24XDF3fB+FfUG977mD6/mDxfwJ0q/jPqmZfNf4Qj/XaZ5HtSnCAwnYwDrrZRlZXNUzyXw9PQq3223YLqQ5ysO2rwALgZ8sY76NRz0Ct7fH9f8J34XXgVdvjwWOtHwqgHjfvgceg3jY6zfP59/Be2Am2BPdfme/cQPNGFHbKXAT/gEZ+nOen8BZ4FTgIZFnQ8XDCz/l57x6wIwzUwPW5Xs+tRm3s1vTLaMua4KCkRAzEQAzEQAzEQAzEQAzEwNgwYF5vLuwg8W/Dn6FbOUg/rcfc7TAYbq7IRxMxEAMxEAMxEAMxEAMxEAMxMDwDJm0W/71SeziJ1LN87gTYBQYq8EralcBEp5tFX4uxu0OjYu5E5jkQLOBZwOpmO9dnfTdCI9/XMc9+4ACF1aGbbWR1VUO/b4e/Qq32W0D+EewJHgdeNV7WAqHH/xHwPNTaHt/3au8vw86wJbhNZdgfNKNueO45WOEmaFQ4dvp0OBp2hVeA58YS4P7r5nnM6roS7sdjoJkOK4/rW+H9sBNsAV7pshB4XowkvGvAouB3g+f7QA124/1vgYNR6h2v3Zz2O9qyCTjAJxEDMRADMRADMRADMRADMTA6DZgPmveYH+4OV0CZ8pJu5kCtrGsGnlaDRAzEQAzEQAzEQAzEQAzEQAx0zcDCrMlik8XwVhKYYl6Lhe8Di2EDg+zIo1eyekXrSItiLKLl2JBPNLqTwaPM82Gw+L8WWLgyme1GjGMlXsVbOKz1aKHRQrttXAPKUGy2SGnx0//Lrla7LaSfCa8Gj4Mlwc+VMSz+ngP/gVrb4/sOjvE4d194fPXLLf891twXXrFeb/uc5kCeC+HNsD24nZ7D3Tw3WF3Xwu16L/gd5sCHen6c7nxHwWvg5bA6tPs48DvIASku28EXO8JAFfy+PQb+BvXa3c1pd9EW7/SxECRiIAZiIAZiIAZiIAZiIAZGjwHzFPt2zA8PBH/7N8qhu5mLlH1dutJbIgZiIAZiIAZiIAZiIAZiIAa6YsBCk4W+ydBqwmRB7DrYByxGDQxiAWg9sDBmktjtcL1nQb2C3l+Y/lnYEdYGPXSrrRacG7XPfWHB+b/hVbAWlKEIuwjtOA3+DbWOF91+CQbAArJXNNtRUMbQ62+g3rHitl4ObwKvol8dPF7Kuk00bXa4rz4CT0CtfVW87zbeDAfBADhoYxVwcFDZt5MmDiv8jroU/gWFh1qPHtPnwN5gUX4dWArmh059b7jsFeBlsAMM1OCTvP841Gp7t9+fSlv2BM/7RAzEQAzEQAzEQAzEQAzEQH8bMN+ZD+zH+BD4e79e/tzt/KOf1vcr3I3W/JpNS8RADMRADMRADMRADMRADJTFgMW9nWAKtJo0eYu378Be4DIGBvFW72uAV4C+FLodJqYHQ72inldCHw+22yJgN9tqUexYqFdAd19MBIuxFv8doFCG4r9X8V8A9ZL9R5l+KNhui+u6LWuC+xbaNgPqHft/Y/op8GqwELs8dLLoy+LbEp6Dl0C986DY7lnMdwzsDha3/ezi4Lk0GsMBQkfAX6FwUOvR77mr4V2wI2wEFuUXhJdCp2NeVuB553odWDVQgw/y/iSotR3dfv9J2vJf4HGUiIEYiIEYiIEYiIEYiIEY6D8D5jvmIyuCd06cCd3OK0bb+p7Aof91QiIGYiAGYiAGYiAGYiAGYiAGOmbAQvSuMB1aTaqe4jNfhN1gJxiAHcECqaPCe1k4fCXrr3c1rIV3r763oGtRrZtXOFvEfwf8Heo5d5/8D+h0HShD8d/9+iOoV/x/mOkHgYXKcVCGdtOMF4UdGbaz0f/37n44FBzMsCGYqNsBUuaYn8Z9HJ6BeseY0/wvHNyn+4DbuAEsC2XdbzRtROFAFM+/adDIjcf5g+D+3wW83b/HtN+bLqeb4foWgbXAAVYDNTiQ92+HRtvWrel/oy3vBQcwJGIgBmIgBmIgBmIgBmIgBvrDQFH4X4nmerexmdCtHKKT6zHHc4C8eYr5sv0BXhzyH+jkeiuXbX+Uefeww52TiIEYiIEYiIEYiIEYiIEYiIFaBrwq2yt9z4PxtWaq8f49vP8VmAHFVewmUl7x6XteVevrXoS35L4YvJK5Wph4eSv308Ak9iGwCNqN9lo43hUugGWgVujxS3Ab2Map4FXItr1XsTIr/g5YCK2Wb9o2C48nwGSwzQ4SMbkuW1gg/wR8ERas0Ti351Y4GRzU8Oggz/PYjWOF1Qwr9uRTX4aNodp+Khbq9t0LZ8DdYMeH22gniB0gZd5GmjescGDQibA1NCrgP808F4HfFXYOPQZPDD73O68X4f702F0ePB8dEFBtO1bj/QNgAOodA0zuSvjd9Xk4Ezy+EjEQAzEQAzEQAzEQAzEQA+U0YP4g9leYU3wUHATQr2Hea347CX4P9rE4yP9ZcJphjuVA/3XACxm2BQfFdzLezMJ/MNwVzDfcD+ZzMRADMRADMRADMRADMRADo97AQmzh9nA2eEVrs+Go6J+AhTELOcUoaQvoJlEWri34FokUT7sa5kEHw6511noz086Fx8EksFvFf5PoreBUqFf8txD7NbgdZsFU6HXxfzxtcJ+bDFcL9/eVYLF8JkwBt+PfULbwbg/HwIegVt5su70q/jxwH8wAC8K9PLZZfd1YgKkfh8/A4nXnfOHcvYB5fg4Wt91Gz107Qcq4z2jWiMKr5o+Et4Ce6oXn2q9BP3pxvzsw4i/Q6/PQ88w22B4HoqwKdlQ5sKgypvPCAU5+R+8N80AvQ+dfAo9LvyM81hIxEAMxEAMxEAMxEAMxEAPlMWB/hXi3s33gCDDf6Mewn8rB2zfBdfAQmPf+L1Trq/oX7z81yI08Lggfg12hU7EpC/7BcBc+33A/mM/FQAzEQAzEQAzEQAzEQAyMagNeNboDnAmrtbClf2dei0q/AwuFJlWGRSYLTn8CE6pehlc9m6jVKnhNZNo3wfZa/Hc7utVmR5Nb/FoDaoWOT4EbwOLjVOh10XFN2nApbAnVwuPge+DxZJJtm02ui+ODp6UJi6X6fTvUOkZsu8fIL8FC5SPge/+GssbONOwk2AheWqeRbsM1cA64XU9DWYrbNKXtsRxL/CAcAt4ZpF74PXAfnA0OvvkLeNW/jiy2l+l4ti12UPndsBosDfNDZXgung8OxHkH9LqPyEEKn4clwcEAuq3W+cbbiRiIgRiIgRiIgRiIgRiIgS4ZMH+URWEXOA7su6iXVzK5dGHONhmuBov4XvBR9KW0mne4rFuhkwMA1mb5w45eJ3fDbng+GAMxEAMxEAMxEAMxEAMx0DEDXoE5AN+ClaHZsEj4FbBA9hxYLDMp8spoi6TeUq3VpIqPtDVMWI+GFWss1UKeRdKHwcTQwli3inoWvT4DL4daoc/TwYTVZHUaFAkrT3sSJv6XwaY11m6bLZh+F4o2F8dHjY/07O1lWbNtfR3U6syYybQT4RZwAIavy7o9NO0lC8FH4bOwGNQKz00L/m7/dfBXcPs8Jzx3u3UesKquxctY01dhB5inwVqfYrrF8l+DTiye+71W5jsiuE/dj1PAfbgCeDxUhoOyPH8dzPA+WBB6GZ53HwLPxc/BdBiNxx6blYiBGIiBGIiBGIiBGIiB0hswT1oYzJkOg22gVq7MpFKF+ZAXUNwFV8IfwVzO/MJpI41O5ymr0sAFwD6fliMDAFpWlg/EQAzEQAzEQAzEQAzEwKg2YGK3HVhkXrmFLTWROgUsIFrwtfhvQcnizTPQ6cSIVTQVb2Cu3WrMabvdhjtgChSJIU87Hnr/CLwTaiXTXpl9HvwCLD5OAYt6uu5VOPr/AlipRgMsjur0cnCAiMeDntuRbLOYtsbLWNqFsH6Npdpmj/MTYDJ4rFdeMcDLUoXH0Q5wKmwEtY4rJs0+jn7I46VQFLYf43lR3C7j/qJ5w45V+KTF5QOhUcHb4/XXcBE4mMmBALrx++1fUHY3ts9OL9vutqwKi0BlOP3n8FdwsMii0MvwWN0fVoBDwAFZuk7EQAzEQAzEQAzEQAzEQAx0x4C/yeeHLeAIMPfvh5qy+Y95jQP2fw33gXfrK/pN2pm/NbqDHKsdUbh8B3BnAMCINObDMRADMRADMRADMRADMRADFn12BYv/Fl6aCZOn38C5YDHUIo1YILPga2GpnQkWixt2LM4nD4VqSavJ4IXwW/CqerfFgns3wqR6f/gM1LoK2fZZnP0BeNXxZOj1VecOpDgflodq4QCKE+AasPDv8TCsxJXPdTq2ZwXu//E1VuQAFjsPvgluh/69O4THehljZRp1FLwDvGKgVnhc3Q3fgnvgT+D2+ei2OX00hef+e+EwqHXcFtvrtttZ5PfhveD+9nvN47rXA29oQsth8d/vNc9Bj3O/DyvD938Ldo59GjrdmcUqGsZrmMPvmPeBx6neEzEQAzEQAzEQAzEQAzEQA50zYOHffolxYN60HywEZY7/R+McoH0zXAEPgIPZfd8oHl941b5/zas6GfOy8OXAbbFPoqWo1vHV0gIycwzEQAzEQAzEQAzEQAzEwKgwsARbsRd8A5ZpcosskF8MPwWvivW1iYnFXl+boHQq0WLRLcfefGLTGp+6nve/C7Z9FnSr+G9ibSHdQvmCUC10eDlcBBb/J4EDKyxQ9io8VizMLV2jATN4/1i4DaaC7S5rsfzVtO0CWBGqhYVTBwdcAhaAp8CwEnA+1414OSs5DV4Gdt7UCgvabvcv4IkKnuN5y50LfKbssQcN/BL4HVDPi9vh8erx7aAPC+KzQEfu9259N7CqtofnoHdksdg/AYaev+53O80Oh8/CCtDr2JIGeO69F24Av/sSMRADMRADMRADMRADMRAD7TVgjiRLwgHwhcHnPJQ2zF3/AD+DouhvP0m3+qEmsK5OhvmbA7ftN2o5R5+vky3LsmMgBmIgBmIgBmIgBmIgBvrCgAX/t8CJsFiTLTbR+jZcDRYSTUx8nAbebs2Eq1tJF6tqKv6HuRxBPTSm88apYNHa525Lt2IrVuS6a11tq0MdnwVevTsRLEL2svj/OtZvcdSOgWpxP28eBQ+DgxW8mrysRdPX0rbvwNBCKG/NDo/pk8F9UBwfXoXcS/+svmoswrt20nwE6l2hYcfBTXAGTAEL2w5s8Lz12C/beUuTRhQb8+mjYU+Yv8GSLIz/Bjy+Lfo7kEk3fwGnjQY3nosOangI1gS//+1QKsJtvBMOh8/AOOh1rEUDHITzPnD/2P5EDMRADMRADMRADMRADMTAyA0UhX/zyV3gK7D2yBfbsSWYz06EH4EDhM1jO1X0d7liH4B9YPPCgrAwmFuuAp0M81AHAHhXv5b7qebrZMuy7BiIgRiIgRiIgRiIgRiIgdIbWJkWHghfAJOKZuJpZvo63A4WYrxC2iKZBTMTI6NshbLxtGmT2S2b+x+TOAu8FsOmgtvSrbavyLq+DLatVtzEhNPgUTDJNbk14e1V7M2KLZibhA4NvTn6/kswHR6GXreXJtSMNzHlHFiixhwW/I8Gb/8+BTzGy1oEtoPmm/BqsAOnVjiI5FtwDdiZMBM8n8s6qIGmDTvsnHkLeI5NgHrhsXs/nA23gd9rfp8Vbnp5ztGMtofb4z73e28NWA7mg8q4jxdHwSfB46vX4fflufAh+DF4/CZiIAZiIAZiIAZiIAZiIAaGb8Dc0X6gzeFIMJ+sHBzMy9KEv/+vgsthGhQF8Xb237gs86TnwL4M7z7mBRiuq1iPfiz+T4BaF0UwqS3hYH3ztIXANv0vNB1DE7ymP5gZYyAGYiAGYiAGYiAGYiAG+tqAid5acCgcBM0meVOZ1+K/xSETIxOiR+BJsKhUJEU8LVVsS2sWHNIi23oR/A4mg9vSrfZbQHdk/QDUijuYcBJMh4ng1ei9LES+mfWfB4vC0DARNRn/Gtheffb6TgU0oWa8lSnnQLVt8UP3gsVPt8WBDE+BV0536/hgVU2F5/EhcATUGsjggmz71XAmOJhEHAxgodtpoy22YINOhB2h0Xfbn5jH74GfgZ1KerGjRTdl/k6jeSMKz1kHtEwCO7QssNv5VxkOEDgWPgqbVE7o0XM72E4Hr066BPxOTMRADMRADMRADMRADMRADLRmwDxyXhgPn4X9wSJz2cKcZSpcBteDRXlz8nbl5S7HnMgLQcz/zActtNs3ZC7o+quFfWFrgwMBOhn2tRneccC8taWYr6W5M3MMxEAMxEAMxEAMxEAMxMBoMGCRZ0s4EnYBk79mwitjLb5MARMkk6NpYBHGxKhdSRiLanuYoJnAmeQWMYMn54MJpdtSK7ljUlvDxPrz8Dao5d4BFhYw9TsJnoFeF/91ZeI5NGzX9+EseAT0qe9u+WRVLcW+zH0OVNsWj+Eb4HjwCvCJ4PFdxiK5BX8H47wTKo9rXs4VM3nlVf92mLgtDmqw88COjrLuI5o2rFiVT30GDoBq+7dyoRa9rwKPa49bnTgwQkdOG21u2KQXRdHh5feMx/jKMLTjbzLvOVjpA7AN9Dos/p8EtvM8aLkjjM8kYiAGYiAGYiAGYiAGYmAsGrD/Qcwl3w2fg2WhbGGueiP8EOwb8bVh/tKOsD/LCxbMJSz2FwPj7dtoZh06fAN0OuyzMhxoYM5v+5qODABoWlVmjIEYiIEYiIEYiIEYiIFRYcCiyRZw6uBjMxtlIezXYKHMoqjFsadhKhRXzTeTJDF7z+Ia1nwzbD/YAtt7EbgNxd0LBid19GEelu6V9B8Gn1eLh3jzOJgMFqB13VKix/ztjNeysO9AtYKqifiZ4Ih8i6gWl02my3o81Cv+e5xfDp4bj4L+7RTopXtWXzX24t1vwviqU1940/P0CnD/eIx77nple1m3iaYNOywK24H1eVipwVI8NieBXm4BO30eA8+zYqAQT8dUeKzYufRvWBX0WRnTeHEyHAA7gx1evYwFWflXwD6tsyCDAJCQiIEYiIEYiIEYiIEYiIEaBvz9LhaSt4VvwIbQ69/1NGGuMFe9HH4C5mjm4u3oW3AZz4GDvV2HF4DYb2EeZD9Aq+GdBHds9UMtzm+bdWB4EU/L+yoDAGa7yz8xEAMxEAMxEAMxEAMxMGYMLM2WfgQ2b3KLLfBa+P8FPAW+NgmxWORzkxLpVhSJ6zys0BHQ5jQmsT5W4jTn8T2fi0nuVFgeHEV+L6wJ48Bwfqlcx9DXRdJV+b7vFZ/l6ezPF9N9XfncwtpnwYEY1eJx3rwG1obFBh9NSk18TUx1bZFOfO2j08Xn7pMikfXRgqYU8xTL4a2mYiPm+g4MLQj6YUfJHw+/gylg0dz1dPN4YHVNhfvoE3AMeEwMDd25nReAhfJpoLfhdAbwsY6Fx/ohcARU2yfFir3q/3S4Drx7hK8tkpZ1/9C0YccEPnkk7Af6qRfeMvJ78FNwMIQDI/w+sxPIY6CMxy7N6kq4/Z7DfkesBn7/VIbH0Bmgw9fDPNDL8Dw+DmzHtyGDAJCQiIEYiIEYiIEYiIEYiIEhBsyF7Q/xN/5RYN5ULSfm7Z6Fg7EvA/udLM6bl400NzOvMc+z38JcwfzeC1h8f6TL3oBljINOhv055vKGOU/L+VfZdvLsLck/MRADMRADMRADMRADMRADHTMwniVvAyaBjcJk40y4BSwOWRByEMDCsB5YbCswoXRUsjmGz8Vpla99LiYulc+HvvazTvfzxbJcdvG8WGfx6DSvCK18XTwv1lP5WZ/r4F1g6KIWxfTisdJbteeV7xWf8bEy6iVuDk44ACoT0uJ55aPPLU4XuH/EAQDFIIAiwTXJFRNfeQ6KaSaVzu9r3/e1mCj7mU+Bg0aGxpO8cSTcCpPB1xaXyxj6tmh+NHg8DA23/RvgVQYzBtFJ4ZunpYh1aYXn4/Yw9DgrGugx8GtwvplgcdtBJW6jHR2jKTznPwBfgGrHaOW2uu03wVkwCexg0o1XgLivPY8SLxwjDozQ13hYfIgUj6XvgIMA3gZ+l/YyXP+XBhvwLR7tLEzEQAzEQAzEQAzEQAzEQAy8kDOaCy8F+8FR0ChvYpauhgOQL4TfgX0Q5uAjycPN61yOBX8fzQ/spzBPHsly+fic0OnbodO5kPmqebzhumr1Acyeodo/1To/qs2X92IgBmIgBmIgBmIgBmIgBspvwETEwvfC4NXBYtHeIo7Pvep8A3C+RmFhbBoMwK5Q5A4W2wtcl++bjIjLFROTSnj5omSl5eTFhYyBKLwUj93a5CIZ9tGk2QKgibLH0tBwoMDdsBWsDBb/Ta5930cHGRSPFgqlGFxg4t3NYqse3wvHgcfr0LCtJ4BF80fgMXC7yxYDNOhcWB1qhcXZU+EacKDOLCg6PIr9y1ujIrwC/RjYCBqdKzOY59vwe3BQi6/143OP88TcBnTiAAkfPd6WhMpwYNj3wHPnPTA/9DL82/Nl8DvmbHC/JmIgBmIgBmIgBmIgBmJgLBswR7L/ZztwwOyW0ChvYpauhXn3uXA92HdgvjrcnNW8xUK5fVgW/s0HzA3sdxjuMvlozViRKW+sObV9E+5nUUXfybD8FJ147WtSlhQDMRADMRADMRADMRADMTBSAyZmFtIttFuAtYBvEWaJwefF46K8trDva0dyO7J7mcFH53e6Sd+CYJGmskjPy7rh5zerO0cmjiYDRWdAceyZK3rcVAuPyYEhE4qE1EeL/BbRTcItElqEfgYcKPAEWFw0Ofd9p5ugO0jAZN1H3/PRzxcDBoabuO/PMr4G1YqUtulouBamgUVh11emsN3HwCHg90G1sMPjevgmOIhhJuhar0WHAU9HRfh990k4FPx+qxceP5fDReC+dYCEHU0eXx6fwz2m+OioD48pz0cf1wD/tlSG5+dPQJcfAv/O9DL8e+l5bufhxeC+T8RADMRADMRADMRADMTAWDNgPm+/zwT4AuwHtfJIJnU9zFPPh6vAfLXoR+Bpy+HnzUeKvoWi/6DTed4rWOeqLbe29Q/c2fpH5v5EBgDM7SOvYiAGYiAGYiAGYiAGYqBTBkzCTLwslFjQt6Cy5OBzC/jFcx+XBUcVLw8W9J2+GPhZf8O7LBM7ScRAGQxUHo8enw4e8JhdrkHjTM4tUlt4978ecKS+CbwFWzsHLNiKzx0wYOHRwuQzUCT6Jv1+1mJlZbK/J6+9LXi1gQwu84twG0wCl+XnyxR+D3wb9oZa57rtdhuvAB1Z/NePBe7RFG6/Ho6DdRpsmMfTPXAW3AUeMzPAY8bjxOmJxgb0pLuJsAYsDZXHoR1sHncW3Q8Fz/dehn8bTwPb82NwXydiIAZiIAZiIAZiIAZiYCwY8He6+Jvc/6rrcLBPqSxhLvZd+AWYvw+38O/nzIHFvNdBAOa+3crx5mdd+nUAcifDvon7KlYwrO3LAIAKg3kaAzEQAzEQAzEQAzEQA00aMLEqipwL87wo6C/FcxMuX1u099GCvkX8FQaxqOe04up8EwiThyJh42kiBsaMgeJcKs4nz41aHRVFJ0ExWMBCnx0JFvMfBYu8FsAthK8CnwevGB8aj/DGZ+ABeBDsgBhWQs3nOhXrs+CLYIsaK9CFxe0TwLsXuE3eXcEOkLJtC00aUWzMp4+HXcHjpF48zcQL4efggBGPDY8HjxU7UfSWaN6Ax5LuHoY1wL9flZ1dnovXwHPwaVgaehkO9jkd7Ay8GkbbQBg2KREDMRADMRADMRADMRADcxkwp7bWaw75DdgeKn+z87JnYa5t4f+XYK423FzVwb3mdy7D4v8/oRf53Xqsdw/odJjD2sdRhN5adpcBAIW+PMZADMRADMRADMRADIx1AyZIFg8sGFq4t5AhFiSLgr7Pfc+C/vJgUd/nTvdzft7f2C6rLAkXTUnEwKgwUAySWYCtEc+7lWADqAyLvM5bLewsuArGQ3FFuJ0IXuls8bwXnQisdk74vXEwWNhfbM67cz+x8+NSsNDtNkwBC54WO0dTgdvv0reCxf/VoF5YiHa/fgemg15mgfvbaS13lvCZxAsGdGeBfyL4fDmoHIjhMXczHAEOAvCc7GX4N/qrsD84SCb7HgmJGIiBGIiBGIiBGIiBUWfAnNf80bz4/fA58EKTMoQF+p+CxX9zs+Hm2Q4gsPD/FJiz9zK307U5Rjcc3856zPuLcLtbjlqdIi0vKB+IgRiIgRiIgRiIgRiIgZIa8DevV9lboPcKfQv2BRYyxEK+RQvx9ZLg/JUF/fx2RkgiBkaJAYuWJtEm1RaJHV0/FabAZPCK+plgR4PT7XiwwN7JYqLfN4fDoWDxu1p4l4MT4TawwG0bLc7aoTKaYhM25muwE9jRUivcj9PgLLgBin3poI5/wGjzwib1LIq/pRNogX8zqx2jG/L+J2F16HWcSwNsi3fGSMRADMRADMRADMRADMTAaDJgjuTv8U3h27A5lCHMv26Eb4G5qvmzOVsr4Wf+Dg4c8Le8z11uq8vhI22NlVnadbBmW5f64oW5nUfA7yomTeS5fRb2STQdJnCJGIiBGIiBGIiBGIiBGOhnAyY+C4CjcJcFr8wXi/njwELEaoOvvTLQ+Sy0eQVjfg8jIREDMfAiA3Y6mFw/C4/DVHgYHoJJMB3sjPBqBDskhjUin88VsThPTof/gmrfS7bnWvg62JHiAAWL3F5Z0euOEJrQtliBJX0CvILF7+p6ofcfwg/AzhAHazwG7jP3x2jywuaUIjw27WgcD/5XHf7tHRrr8sbHYL2hE7r82k7Ct8H3weeJGIiBGIiBGIiBGIiBGOh3A/4etw/Mvq3iqn/7t3od5l4PgDntvTCcfMyc14H35t/e4a5sA93fSpsuAf13MsxnDwRz2yLu54n9Dy3lNdU6FooF5jEGYiAGYiAGYiAGYiAGymDA36xewb8QeOW+V+hbeFgV1oC1wCK/73mF/8JggaLTP8pZRSIGYmAMGrBjwsEB3oJwFkwEE3I7PCaBBXqL8xaoLdA7f73Ykol2JKxdYyY7Ps4EC90m/Q4+KIrcPB01sQVbchJsD/X6KvT5R7BzSedeGWLniJ1Ezfhmtr4J/+btDR8EC+vzgsfVzXAyXA9/gW6Hf5P9G7wyVOtwXJP3bbP7tJfh+bkTPAgZENLLPZF1x0AMxEAMxEAMxEAMjNSAOZK/vbcCc4Fe/9amCbPD/PccMDf5B7T6u9v5Lfz72/3P4DJaKnQzf6fD/OdK2KHTK2L5ejwcHERhmP/eDea7jfoWmOX/ol5S/X9z5VkMxEAMxEAMxEAMxEAMdNaAxfoFwKtgLXisAKvABLAoZjFhNfAKf/9fbH98p8CPhEQMxEBpDNhxYUeFBVoL9Q/DPYN45wA7RrxKvSjeO/8uYPHfgU3VYgZvHge3g4V/C93D6VThY6WNpWnZp+DD4ACueuHAivPgN2AHiJ1ET4LOy9ZJRJOGFfbTvBzeA28G/VTru7mN970y5A7oRfh32AEA/q12gN7QGM8bB8MroVr7h87fqddns+CPQi8GSnRqm7LcGIiBGIiBGIiBGIiBsWPA39IOBDZnNGf6GDgQoNfhgPhzwcK4z1sqTjO/YU5nPmee/DwMZxl8rKOh/23gd2C/ZSfDPoKj4WrwuWGea3+AgyRaivlamjszx0AMxEAMxEAMxEAMxMDwDViwN0lZAizye8W+BYK1YF1YAywmWOywCGSC4w/tRAzEQAz0gwG/r8yx/Y4Tv9t2A6MYGGAB38EAjuC3aHrQ4CMPc4XJ/nXwVbDIPQnsHPHOA6Mp9mdjjoHVG2yUVz/o4yx4BOwk0qVFXZ0UnSM87euw8P9eeAss2WBL/JvpwLheDQDQu8emx/aqsAhUxjRefBPsqNoVevX3/E2s+xy4HkbLccKmJGIgBmIgBmIgBmIgBsaAAfvRzBu3h+NhM+h1mJv9HM4Hc1TzgVZ+Zzu/edzT4MB5C/+tfJ7Zuxr6N0frdPHfjXJg+/1Q6aO4gMDpLUUGALSkKzPHQAzEQAzEQAzEQAw0MFAUwCwEWLywoD8B1h/EgphXC1rkXxBMZnpVFGDViRiIgRjoioF5WYt3OJG1YQ+oFXao/BQuhKlg8d8iqh0loyX08Dk4BOxQqRfTmXgG3AB6mAF2FvlfI5TxChGa1XJsyScOhn2hUeG/WLgDIR4H/472yoODAByI4bE5DhaFypjJi7PBjqw3gG3tdizFCveGW8C7ZyRiIAZiIAZiIAZiIAZioOwG7Cfzt/MKcCi8HxaGXoZF6Xvh6zAFzAEqC9W8rBvmLBazHwUHDvRLPjeetr4OuhG3sxJzvMrQ2bDyvQwAqNSY5zEQAzEQAzEQAzEQA80aMBmxoGUCshx49d86sAlsDBb6fd+BAM6XIj8SEjEQAzHQhAHz9L3AW6dPgrvgDvBKgGlg8dtOAIuvrXS4MHsp4s204nhYs0FrLNZeDheA22yh2c6Qv0KrnU18pJSxKa06AN4B3hmn2ZjMjGeCV84sNvjIQ0/CASvekcF9sjrYnspwn50P/4S3gh2Z3Y53skJ9eQ4lYiAGYiAGYiAGYiAGYqDMBuw/84KZbeEksI+t131q/t53UPY14O/6VvNQB3KbF5jXOTh4WAVtPtftsD9zD1i2CyvW6RVgflUZ5v7mWi1HBgC0rCwfiIEYiIEYiIEYiIExZ8DOem91Zaf+KrAebAGbg0V/b+W/CPjDOBEDMRADMTByA37n+t0qdvwYJv1eJeEVEw+DAwPuGXzuVfHPgNPLOjDAK/0/C17BUu/qFTs+HoQzwIEPFrmng1eJ/BP6pbOIptYM/56+Fr4I60OzHXoOirgKvg+Twdc66XXYSeXx5zE6AZaAynDaReC+2xe6PQjADrsdIAMAkJCIgRiIgRiIgRiIgRgopQFzAn8ne8fMQ+BjYF9bL8N848fwPfA3fau5mMV+i/6zwFy11c/zkZ7Giqz9g11qgfmu+X1lmBs7AGBY3jIAoFJlnsdADMRADMRADMRADJhwONJ4KfCqfq9O3Bos+K8JS8L80GyxglkTMRADMRADbTDgICsLx2sPsjuPdghYdPWKCgcGWDi/G+4DC8S+Z4eLnQa9HBiwM+s/GTaEen8/3I5LwQK3HSBe9e//C2nH0dArIXir78K/r6+Bz8PLodlCuPvuRrgM7gU74go3ZRgAQHNm758/8zgR/L3gIIDKfe1ADgcBOLjlTUOm8bKjoecBOBs8XxIxEAMxEAMxEAMxEAMxUCYD/m62Xmsf3GmwFVT+luZlV8OC801wDphX+hva3LOZcD7nN48zZzHH68dcTv87wxrQjfgDKzGfqgzvfmcu3Kz7ys/OPqDmeiMvYiAGYiAGYiAGYiAGxpQBi/kW9VcDEw2vNDXRKDrvM2AUGYkYiIEYKKmBoqPIQVuyPrweik4XC/+Pw2Tw6mcHBlig9Y4BT4NF2efBDp5hdSrwuXphsdcrJg6HxaFWuP7b4NvwAFj8nwl2gPRy4AKrb0s4eONV8El4NTT7t1Uv3unhErgD7PzRjVfQ2BlUtjsi2NFnB99DsBZ4TFZ2XDrtfPB3hx66GduxsoXAcyIRAzEQAzEQAzEQAzEQA2UxUAz0PoAGHQkO+u5lmId9A+6E56CVPNH85EnwbgHSj4V/mj07luDf/WGeF1529F8dXwXmU5Whf3PCYUWzSeewFp4PxUAMxEAMxEAMxEAMlMaAHfAmFRZgxsGm8ArwCkQ76e2Mz29DJCRiIAZiYBQYKAYG+N0u68Cug9tlp4JXkDsAYCp41wAHB/j4CDhgoLjSwA6bVjp8mH1ObMEzr17ZGiqLwHNmGHxiQft8+DnYJq8SsdPINg7tAOGtvgu3/0PwZli4ydbrfDJY+PfKGwdCFIM5nuK5buwIGu6+4aMdC/eZAxUcBOBgwmWg6DTzd4hxJWwCK/qiS7EC68kAgC7JzmpiIAZiIAZiIAZiIAYaGij66dZmTgvuO0O9vKnhAkc4gwPDL4IfgrlHK4Vn5zVncaCyjw4EKGOuQrOajvWYc6DpuUc2o3dLuHvIInT6DAx7EEU6eYcYzcsYiIEYiIEYiIEYGCUG7GRfBFaGjeCVsA2sC3bG+zuwl4kFq0/EQAzEQAz0wIB/HxYdZByP2w+2wQ6Gf4HFf6/6eBjuA6/ItxjtexbonwPnq9Uh5PItdp8CFl1rhYXiG+EMmAQu2w4jrxIfycADPl6KWIJW7A1HwARoNhwA8T24BuwI0rdFf59bWO8HNx4bDlKYCP7WWB081hx06PE2AbwDUTdjAVbW7XV2c/uyrhiIgRiIgRiIgRiIgf4w4O9jcXDqG8G8aWnoVViovxO+Dt4pzjytleK9+eOjYLG6GKjM074OBzC/HdxH3QhzP/O+yjDvM/9rZV9Ufj5Xec1lIy9iIAZiIAZiIAZioD8NWGzxR+my4AjVooN9Y55bfLHT2+QiEQMx0DsDJm05D3vnP2tubMBOjgUHWY7HTQc/4rFr54NXgVic9sruu+BeeBiKzh6nrwZ2HL0W/NtUKyxonwlXgc9dhgMAvOqk1sACJvVF+Dd3V/g8bA3Nnvd2nP0YrgA73uw8sxNN57qtN+iCyaUKj6XFYTx4LPwXrA+9LMDbpmb3BbMmYiAGYiAGYiAGYiAGYqDtBvw9ap7kxTrHwb7g79RexZ9Z8elwNbR61X4xUNncZTTkcWzGnFiTZ/vNedXZJw64uA6G5sH+d31DBwW01JLcAaAlXZk5BmIgBmIgBmIgBnpuwMTAAo3F/rVgK/Dq/s3BBMKBAOngRkKibQaGjjYuXvs49HnxXrVHG2RCUyQ1xfNiXl+b+Pjoez4vRp77+O/B18V8la99LkOnVS7HaUYxzwuv/m99njfOX8zna5/7XnFO+bwIn9d7XSzH+Yt5i+UUy6hM9H1uR4Dz+Nxczddi0Ux8T/wOKF5bbBTfr5zPzxXzF8vx0WW7jkp4mYiBmgY8Vjy2lhrEgWYWdT2uPTe9Yn8GeIW6A88cPFArnP/34FX/08DCv//lgMvwHO732J4NOBT2BJ01E3aWXQ0/golg4d+OOAv/DgqwI05v/RB+F7n/B8DOzJ1hMShDeLw2u0/K0N60IQZiIAZiIAZiIAZiYHQZMBf39/IAnAWrQK/C/OIaMC8zHyv6LHjaMPzsk2AuZ74yGvI4NmNOuJ9eB/Xy2jkzt+GJHidVWY454YjyQDuEEjEQAzEQAzEQAzEQA+U1YGe1txEeB1uAxQWv8J8Ai4I/TBMxUGnAxM0oEjgL0SZkYvLgVaRiUclHi0+OKrboJN5izKtNfc/nFuakeL+Y5uf8vMusLHbzMlEyA0Wx3++ThcH/HsSrc5cepCjsLslri3V+tziPA4ocbGAnhZg/Vg4i4OXsgQQ+JsaeAY8rj4ni+Glk4E/McC5cAXYYTQevcPe7pPje4mlfhufOW+EIaLYjz+/O2+G7cDf4HWsHmgMp9OL3sd+tZXfjMeD3ySawK+wPq4PHR5lC336/2d7R1klZJs9pSwzEQAzEQAzEQAzEwNwGirxped7+NHwAzKt7FbNY8WlwE9in02y+YW7yVAV+djSG/SFvh27lM1eyLvvZKsPcxTsANLtvKj8757mJTyIGYiAGYiAGYiAGYqA8Biy4rQgbw/awHawPFury2w0JYyyKH/smWiYAFi0s3FsYskBvkmBx3sKaiZi30LawVjx3uvP6uRTpkTBGw+NILLSKx0urYQeFAwj8jrLYucwg3o3EjgyLwA5WWhyKwQPOWwwccLBSMWCpW4k0q0yUyIDHxrvAv212Nl0Pd8EM8HvM76niO4+nfRGeE7vDF2FLaObYdhunwMWgB6/ssPjvd7f43e73fZld+HvEjrE1wd8pbwO33/O9rOEx5veU+8xjLREDMRADMRADMRADMRADnTZgDuyg+h3gZFgPehUW7H8Kl4B9Rs32EZmXmKOYt1UOVOblqAvzua1hsy5tmXnJdTB0X+jZ/rwR5YTpRMZgIgZiIAZiIAZiIAZ6ZMAflgvDauAPzJ1hWxgPvt9MIYHZEn1kwB/vBf7QNwGzIFsU8y0EWZy1CPQoPA4mZr5n8cLif/F5niZioGsGLEiKA0o8HqdCo7Czw2Kb32eLgYMGlgMHDCw7+NyCnNOKuw5YQPQzDjjwOzDfg0gYJeE+dTDbVoN45YvfaY/BH+Fa+AM8CA5m8rtxaEcIb5UmVqElH4RDwEEvzYTbdRlcBX6/+93vez53EIB/F8q6zUXRfy3a6G+Vt8LLodltZ9aexkOs3c5Xj0G/x0bUmcbnEzEQAzEQAzEQAzEQAzFQy4B5rPnPinAofAB6OVh2Mut3AMK9YD9Us7+FzU9mwZNgn5R9AqM5lmTjPgHuu27ENFZi39/Q+AtvjDgvzACAoVrzOgZiIAZiIAZiIAY6a2AhFr8qbA+7wivBIkIvEwFWnximgaIYbxJkYvRPKAr6JkcW9C3umCxZ5BJf+2P+WWgl8WL2RAz0lQETVs8HcdDAdKgXDhjwu9ABAw4McJDA8mCniSwDvr8EWHS0mGdO6+cyUAAJfRbuM/fh+EFey6PHjN+dFmuvG+ROHu0UsUBehg4nj703w5EwAZoJC86/Aq+4sfPNc8KrOvyb4Pb6t2DEHTwso91hx5fbuzbsAPvAy8BztJ/Cv9UOLjH8LvFvsn+vEzEQAzEQAzEQAzEQAzHQbgPmp/5efhWcABtBr8I84yL4Mdg/1WzOYd7l/NPAPGws9F2Zn5rruN+6FVeyIvsGK8Nc0X7DZgdpVH52rud2liRiIAZiIAZiIAZiIAY6Z8Bilp3N28LuMACrQgr+SChhFAV9kyITHn94m+yIRXsLNhYOngALN+J7/mC3wONnEjEQA8Mz4HnneSSeVxZKq4WJubmsA6qWhMpBAivzuhgs4EABO16c104YP5cotwH3k/tty0E+zKPHw1S4Dn4Ht8F08Hu329+5u7LOz4GD+Gxro7B9t8MlcBc8B/4t8W+HHWoWoT3uR9y5wzLaFW6Xv1HGw1bwTnB7F4V+jb/ScI8bPbttDjwZC52YbGYiBmIgBmIgBmIgBmKgSwbMN/0tbT76CXgfmLP2Kqaw4uPAvNq8o9mcw/4vr/p/vMXPMXtfh/vqLWAfQjfCfXIDmA9WhgPEW9lflZ+d67kdIYkYiIEYiIEYiIEYiIH2GfBquaVhM7BQsDusA/6QTPEJCT0Mkx3xSn0L+xaPLDJazDexMcHx/zTzuVcrm/S05Uc3y0nEQAy0z4DnscU7sbBnMXho+H1rvmvR0rsGLAerwmqDj3bK+F3t1c3+lwPOn+9oJJQs3Cd2wKw/yIE8+r08Fa6Bq6EYEGBxvVMDAhxoYiH8MFgWGoXH6CNwIdipY9Hfvzn+fXkabKsdPc5XpvB8WRP2h7eDA2rsxOz3uIIN8O+7sSB4TLk/yuafJiViIAZiIAZiIAZiIAb60IC/mf2duS18HTaBXoV9XpfCRVDkHc20xfzaC1783dzJ3KqZtvRinrVYqTlQt+J+VvTYkJWZn9gf6T4ccWQAwIgVZgExEAMxEAMxEANj3EBRnFgdDzvCG2BrsOA0GjrN2Yy+CX8oiz+UTVb80fwoWByUmYOvLb54RamFIudPxEAMjD4Dntt2YPg9IFPgFqgMrwS24OlAAAud4wdZjccVYXFwcIDf5RkcgIQShPvBjrV1B3FAgAO6JsFvBrmDRwvt7RjA5fpeB4fD5tDMcWCx//tg0dkOHQeTPQW2yb9N/o0q09+eeWnPIrAp7AcOdFgCRku4D34OHg+G57MDANxuB2EkYiAGYiAGYiAGYiAGYmAkBvxd6SDhD8NHwd/WvQqL98fCQ9Bs3mFuYj+Z+YqP5tFjLdyHB4N9AN2Kq1mRfZOVYb7ohUptyVOaSV4rV57nMRADMRADMRADMRADL3Qe2zm+GewFFgfWBItJic4aMDGxcG9HvkUWE5Rpg1jknwUWWvzRnAI/EkZ5eDyIMfR58d7siYPTi+fFY/HZ4vXQx5HkS37W5beyjMp5i+eVjz4vXg9ta16314Df55WDSE4mhQAAQABJREFUA9bgtYyH5cFOnfkg+wMJJQrPOQvu98Ev4Wq4G+zIsgOslXBgyPvh09BMR5AdZb+D74MdbkXnjQVorzZ3els6clhOu8JjfFM4CPYBX4+mcJ9/A34FlZ1rdoxOhWJQAE8TMRADMRADMRADMRADMdCSAXNBB4w7UPhU2BJ6lR/a//Uz+DaYhzSTd5g7OZjafrQnwN/LzXyO2UZVuM/Wg2vAXL8b4T4y1zQnqQz7Mx+AtuQpdlgkYiAGYiAGYiAGYiAG6hvwx6C38Lfwsx28BbaBpcAryRLtNWASYqe9P4j/BHbUW+R/ZBCLKRb//UFskpNojwG969OEb+hz3ytwWjFP8eh7jWCWOfMUz/18EcVya70u5nU+o3jt8+Kzlcm27xWvi8/42ueV03j5ote+V8TQeYv3Kx+L5RbvVb6ufO70ytc+NyrbV7wuplU+Vj73u8fXUjz3sfK5o9h9XfnocymK18Xz4rNMSmDA7xdx9P0kuB4MfXsFut//K8JqYGfBmrAyLAZ2AhX7iqeJLhrQu0XsrQb5LI/uw9vgF3AtPAz+1xH1/n68ielHwQbQaF96/tpJcwF4lwkHIPwZyvy3ygEuK8FB8EHweB5t4X65En4LlcV/Xs65s4fPEzEQAzEQAzEQAzEQAzHQigHzA/NnLww6EA4D88BehRfGfBVuBXOcon+BpzXD/hTzlSfB3MU+uLEa5vfmf90q/uvZvHGmTyrC/eZADAeOtyUaJbJtWUkWEgMxEAMxEAMxEAN9aMCi2JKwIewNu8Pa4A/DxMgN+MPWxMQi/9PwCEwZxOcmIV4xaQGumeSF2cZ86MlEwcRN9Fs8Lx59T0z2hj4WBXWXU+l86Gsmv2i671WLyuUU032vyEOqTa+cr3he+VjvM87XaHrlssr4vHBTr21D56n3upg29NHl+16Br/3eG4oDBcT3LWyLr+3wKKa5DKeP5XD7Lar6d8OBAONhHVgbLLRalHaeYj/wNNEDA35HzoCr4FfwB5gFz4PfHe6/D8OnwH3WKLxC42K4Ehxo8DewA86/a8UVNGX6TvK8deDKDnAUrAujNewAPRHcv0PDfTQJhg4MGDpfXsdADMRADMRADMRADMRApQHzOfNgc72TYBfoVY5nH85v4XQwL/F1M2Ff23TwghvzoGY/x6yjMtZiq64H86RuhL4Ph+ugMlc0N7kPvOCp8n1eDi88UBMxEAMxEAMxEAMxEAMvGLB4Y+FmAF4PdpD7/3jZYZ4YngF/2Fpw8WpLiy4T4WGYCo5s9YetCUdibgP/4aXuLNzrryjsD31eWdgvkjYThQKezo7K10UiUeux+Ewee2Og2C/11t7MPPU+X21a0WlR77Fyms+L135HivllJfNXvPb71WkWwYt5is/zVt+H5+xzgzzK4+2DW+T2LgLeTt6/L3YUWXRdA/z7siDkbwwSuhQekxPgAHg3FP9dgLfL9O/Tu6CZTjz/bl0BP4Cp4HIcuCY+97u5E+cpix1WvJRPeSxuBp+CN4Ln4WiNO9iwr4DnYrXwb2mZ9k+1Nua9GIiBGIiBGIiBGIiB8hjw97QsDPYXngLmc70KBxyfBtdAs79t7TPyqn9/I9tHV/Qh8XTMhjmSOeCKXTTgYI0HYWg+4mByc8mh7/PW8MIDNhEDMRADMRADMRADY9WAP/S87e2msBvsBWvDQpBozYA/UC14+IN1JjwA/qCdCiYYvl+2gghN6kroxuKg22+C5V0NqhX0nV4U+P1MkYz5vICndZ87PRED3TZQ5JU+Vj63HUPfK177WAwE8NHCbIGDBXzud3QxcMAiebFsnvZ1uC1u82JgR8M4cFDAerAKLA5OHy3by6aMmvB7+R64ABzkYQeNnW+Vf+eK727eLkV4vC0Ju4NFcY+x0Rr+rfw9fB0cjFErHmGC+Pc4EQMxEAMxEAMxEAMxEAP1DBS5q7nbkfAu8Dd2L8Jc43o4A7zTlX1NzYQDmCdDcdW/v5vHerhfN4CroJsDAC5hfWeBfYBFuB8ngfu0bfmknQqJGIiBGIiBGIiBGBhLBhZkY1eFV8EbYDtw1G6vfryz6r4LEwU7zS16+AP1brgf/KHq/x022m+p6/YXxXp/pBdFex+L931eTPPRz/gjvkiyfCzg6ZznldN9PxED/WBgOMdtUdz2sd7zYrrf0UMHCDg4wO903ze39bXzlf373O+C4jvU79H74ApwwMPCYLF2JVgbHBiwBvj/ETrNbSt88TTRRQPuq4vAfWXHmXew8eoZH92ffteXLTw37NR6HxwEo7kPyL+/P4LvgFc01YvnmFjZ4VZv3kyLgRiIgRiIgRiIgRgYuwbMv/xNvTWcBetAr+IZVnwG/A7sdyvycJ7WDH/zesX5dPDW/+aiiRcMeKe+/aGbxX/zxt/C0FzEfemFU83sU2ZrLkZz8tecgcwVAzEQAzEQAzEw2g34Y92rKTeE3WBvWA8WgkRjA/749AdqcYuqu3heFPv9cWqhe7SExRtxex0d7baJr6V47Tx6qYSXDV87TyIGYuAFA0ViWzw28lIMBHC+4nlRCC9e+31vEd0814EBDgjwscDXTitrHux3i9+rMgNuBbfNbfL/o18OVoU1YV0YD/7XAm6f8yQ6a8ABGNuDnTVXwSSwE86/Dc0ex8zatbC9O8OxsGnX1tqbFT3Gas+H34Idm/XCztK23lqz3soyLQZiIAZiIAZiIAZioC8NVOZhDqQ9Gsy7ehEW7a+Bs2Em+LpR/uF0+7UeAe+M5fPE3AbG8/K9c7/V8Vd/ZA3Tq6zFAebmMY32a5WP1n6r6DCpPUemxEAMxEAMxEAMxED/GViAJjuC0xG6+8CrwKsnUyBBQp3wh6aFDQsak8Afphb7TRi8st9pbf0xyvK6FbbbIo3bYDHfAoAJkBQFfqcViZTzV8LLF732vUQMxEC5DBSDAWxV8bzy0b8DDgCw88a/FQ4G87mPvnaa9EN4JYoDA7yLzThwYMBaYEfGMuB2zQPJ+5HQ5vDvxSy4Fn4BN4MdOV5Z3uu/k+5z/3uJfcFb/i8BozX8G345/BKmgn/P64X7xs61B8DfAYkYiIEYiIEYiIEYiIEYGGrA/Mlca3U4BXaDXoX9c6fCjdDsIFb7tbyIZ9rgZxxonpjbgPv3RPjQ3G939JX75Vi4GnxehDnKJJgJbd1X6QgoFOcxBmIgBmIgBmKg3w1Y6LAAsie8EbYCCyP5vYOEGmEBwytN7TS32H8PWOz3Nsd2qvsjtF/CtvpDuSjwW4RxG+zgtyDg83oFfj/fT9tLcxMxEAPDNODfhVrYEeBAAP+mOCigwNcOHnB6mcPtcgCDBWAHAawMlQMDHCzgleFui/MmRm7Avx3+zbkPfgZXwd3gwLm2duCwvEbh8eldIt4PHwf382gMfd8EP4CHwb/1zfwN9/fAFHgUKjvdeJmIgRiIgRiIgRiIgRiIgdm/n839doazYIUeOfG36jVwBjwB5hWNfu863T6+x8HPZMArEqqEefC28EtYvMr0Tr1lDnIIuH8qw4uV7oK/QqN9XPm5hs+T8DdUlBliIAZiIAZiIAZKasDfMRY4NoLXgrf2Xwcs3CRebMDk4VmYARYmLPhPgSeh2Y5zZu15FFfxO/LZjnzbLj4Xi/z+YJaic794bOsPaZafiIEYGH0GihzZx4J5Bp/790WKQQGL8NzOof/P3nuAS1KWefvft66g5JxnGLJKjpKDBAVEEXNYFTGsoruLaRXFvOq6JgwgqCAmBBRERSQPWXIOA0yCgWHIoO6urnv9//eNp/gOTfc53ae7qqu7f8913XO6q6sr/Ortep/3eZ63xveuI3U1z8XjM/lvYcBqMA3sN9cBA1sWzVk84LqxqStgn+OMGxPUp4/9nctf+62iP+Jlz822uSl8GiyGHLbraB/u7P1zYCbMBwsB2u3bXc9A6GywKDAWBaJAFIgCUSAKRIEoEAUKBfSdHQv59Kx/gY9Cv4ppLST+KlwF+q3tjCEsEHAyz31gItnYWKy5Ao6HvwlvaP5xaUuPZsu/AK/VeFvEmzuh59ds2AaE40XL6ygQBaJAFIgCUWD4FDB5sSxsDc7yN/G/JvTLKWfXtTSD3CbFdSLvgOtBZ9LAt0UAPXcq2WavzGN3cGNQ34GO5+Frk/u+9tjF9ZrB4lgUiAJRoBQFivGzfwvsfwwUWQhQFANYGGCi3fd+XufCAA7vyWO0iMFg1xowHdYbw0KBFAYgQhdmX2bS+Ww4C24Ai++KvoyXXZttbQcwkLVJ11ur1wb+h8O5Cc4H/Rl9GZfpA3Ri+hL6RBYRdPrdTvaTdaNAFIgCUSAKRIEoEAUGSwHHdhbTbgQmaXeEfpg+6iXwDdBnNVE8md/q58b5nFVuDPAvMNl3WGVkzWu9O/wWHANXZRZ1vAu8Ro12Fwss3Gin0KPxuxO+92RjUSAKRIEoEAWiQBSoswImVlaGbeGNsAesCHVPqHCIlZiOvUkEK31ngTP7b4WF4CDAIHkdzeM2oW9ixKC8r4v3LisGOq4nhSPs61gUiAJRoG4KFGNr+yZfF4UBBhUMJplE97XFAT6mXepunoOFDBberQoWBRgUWxd8YoDn4zqx9hWwLzOYdyWcBpfBXPDpAFPt32xfu8HxsCYMg6nF/WDS3yDoPaCvUPgCvOzI9Cv0ixaAPlMsCkSBKBAFokAUiAJRIAo4bnP85rhmXzgWlod+2BPs9EjQ92232FXf2OJYk/8mmOPnIsIkthSfnwp7T7Jerz+24ODr0BijNQ5qsXM340G+3tyKIEXzT7M0CkSBKBAFokAUiAL9UcBg9hqwJ7watgdnJsZ3+Vvw28S+wfAbQEdxDphQ0JGcanCcr5ZiHpPJfJ1ZHVv/Goj3dfGZgX4pjt3XsSgQBaLAMChgvzUeE+aLj2FyXSwOsN+Tuhe3eS6eg8e8MkwDiwJeAGuB/4eihXux9hSwP7QPPwd+A/4XPZ08HcC2tAucBD7KctDNGUu3QKGFxY3dBjL1NdzOXFDv+BiIEIsCUSAKRIEoEAWiwIgr4LjGcYsJ/w/CB6AfYzF906vga2Ay37jYZP6qnxtTuw8cO+jjFvE0XsZaKOD1Ncb8Y6hyzOoYxzZm/LbRLHqeDY5Zem428lgUiAJRIApEgSgQBfqtgD6JSZD1YT84EDYDl426mTw3uT8LroFbYQGYSK+Tg+8ARKdWp9UCBQcj/nUgIp5HMZBx3ckGNKwSiwJRIAoMrQL2ewUGInwigMlcZ58sMe6vRQGD8rQAiwJMQk8HH0P/fJgGzrKwaCA2sQL2kQ/DpXD62F/7e2e+NzPbya7wM/ApDYNq+gPOeDobLoJ50CsfR9/jUZg7ts34HggRiwJRIApEgSgQBaLAiCtQjL8cr3wbduyTHn9kv8eCxcDG0xwPTGauY9LfxLGz/vV3Y+0psDar/RK2aG/1nq11AVv6IniNx5tjkzvBa9nOtR//3bZepwCgLZmyUhSIAlEgCkSBKFCCAiYDrLTdEvaHl4LOWJVVmOyudqbzbsLfRL9VwP5dCCbUS3EI2W6n5nF4nEWS30B98Rj/ovLYdXRmJRYFokAUiAKTK1AUBLimQaniaQEWARRFASbZLRQYhKcF2J973CvD+rApbAyrgeeQeAQiTGD2sRb/nQVnwO1QzIi3qOJF8APQlxpEc2a/Ps55cB0sAosIe+U3uB0LKuaCBYmxKBAFokAUiAJRIApEgSjgGOs5sB98B/rxFC391OvhSLgXjK+14wObQJ4PFrg6VqhLjJBDqb05/jwEvgVVjkMd33wYboDGa2wByM3gtSzFqjzRUk4gG40CUSAKRIEoEAUGRgGTGc7onwG7wCtgW1gORtkn0QHU6bsDroAb4R6okzPvoMLguUn+P4BJfpP+OrIG8D2HAl7GokAUiAJRoIcK2EcW2JeaWDeAISbY/Tu+MIC3tTSP3acZmLCeDpuAsy/WAc/Dz2PNFTAo+ADMhHPAftlZJKvBIJm+gjNcLoMLYS7oT/Q6eOl+1Et/qozts9lYFIgCUSAKRIEoEAWiwAAp4HjKcZRPzjoM/hUsBqja9E2PBwt8iwk0kx2DvrKFwAvAmJzv9Xdj7SuwNqteDNPa/0pP1ryarXwOHm/YmtfQ4o/50O1/edaw6f/31kYfiwJRIApEgSgQBaJAGQoUzvVKbNwA/wGwF+h0OXNxlM3EuY67CX9n+d8FJtgN8PfbPDZxUOIx+dfEv5XGLneQ0etAPZuMRYEoEAWiQIcKFAlz/9rn2rf+PVhs56wWiwJc5nuDW/0IcLHbluYxe+wG4dYECwK2hg3AGe7F+fEyNk4B+2EZFH08VmfiG/y6CPR5DIA1PgKTRT0xfZR7wEKDdoOqPdlxNhIFokAUiAJRIApEgShQSwX0my1EdpzxDdgDqjZ94lvgWzAbjP+5bDIzOTwfHgInCrXzHVaLjVPAMfGn4SPjllXx0mt8ODgOaoyj+tmtYGFHadfUAXcsCkSBKBAFokAUiAK9UkDfwoTD+rAPmPTfDJaGUfY7dOYMdt8EVpzeAD6yy4R6v8xjEo/BJxCY5PevwXJfO8jQQS3W42UsCkSBKBAFaq6Afe14DHaZ+C+eGGBhgAUB4jKDIXVJJHvcHos+wxqwKWwHRUHAKPsRyDBQpu9gMMtg1yVg0l+/x6S/n5Vh+i0WLd4NT0BRtMjLWBSIAlEgCkSBKBAFosCIKuBYyDHQnnAsrApVm4n7n8HpoJ/amAxm0TNMn1n/eT44OUdfN9a5Ao4ht4czYPnOv97VN5zwdQQYZ220h1lwG5R6XZ0dEIsCUSAKRIEoEAWiQDcK6Eg7c28XOAB2hJVBJ3uUzcDzfXAFXA5zwQS7VZ79MAcPOp1SJPwNlPvegLzH5ToSiwJRIApEgcFUoNV93KCTZoLdIIgYD7AAQCwIsD/3Ufz+dZlWZXGAx25f9NgYBkROhWVgGvg0IQsCZoDH6DnE6qWAPsXN8Fu4FcpO+rOLJ01/xkf+G0jzdVHAyMtYFIgCUSAKRIEoEAWiwAgqUIx3lubc3w2fgn7kQ2ez32+CY5t2ClQdE/0XLBqjGMfxNjYFBZbjO2+HqpP/JvZPAeOtjeYyn+jQTiFI43c7ep8Bc0dyZeUoEAWiQBSIAlEABXSYdZwMxO8P+8C64EzCUTaddKtydeovhhvAYHQ/nHWdSJMoRbLfYLj43uPx8wTHESEWBaJAFIgCTxUEFMUB9vNikt2+3Sf7+NfiAIv7fHxmP8z4hYUJK8D6sC1sCatCv46JXcdQQP/iTDgX5oP+kH5RmWYAVQyeGSA1kGagrez9sotYFIgCUSAKRIEoEAWiQI0VcFzj+GAGfBleClWbfupp8HPwyVjG6CYz17GA9l74A5Q6O5ztD7vZDnaGs8CxbZV2PTv7DHg9G81JWbeDf0u1FACUKm82HgWiQBSIAlFgaBQw4L4m7AEHwk5gFaXO1CibzrhJ/ivhErCy93GoOvhsMt8KYZP7DhJ8bcLfAYd4PAW8jEWBKBAFokAUmFCBIlbgX7G/N/lfFAX4xACxKMDiAAsGqvYJPC6PwQKAF8AOsDH4xICqj4VdjqQZpLwCTgSDWL4v0wcqChz1tZzt7yNULT4oe7/sIhYFokAUiAJRIApEgSgwAAoUY5ZdONZjYVofjvl+9vlV8L8B1VedzD/2c5PBRWFrO99h9dgkClg4bgHGHpOs1+uPHZt8DK4Cxy/jzWs9H+4B1yvVHKTHokAUiAJRIApEgSjQqIBBdWf8PQ/2hlfD86Hqikl2WSvTUTO5fgdcBlZ06rQ566zRqWNRKeYxFAl/k/0OEorkf3EcVR1LKSeYjUaBKBAFokDfFbCv0Yq/BicsKDMYpRUJdmMKzq4pCgOWGnttYYCfGYAryzw2i93mjnEmf33Ep0G+rWEHWBtG3XdBglLMwNX3wMCmifiirfCyFPOpAs6gcQZVUeSov1P2ftlFLApEgSgQBaJAFIgCUaDmChjHdOzheOSt8O/gZKYqTd/0XDgOTOa346u6juveD/rUTjSKda+AbWF/2KX7TXW8hWv4hvFir22jGb91TFN68t8d+6OIRYEoEAWiQBSIAlFABfQLnDW3KbwKDoDpMOoFgzplD8DV8Hu4DUy8V+WUu3+dRp1EBwMGwA18m+wvEv4JfiNGLApEgSgQBfqmgD5EgcGWoijAGfoWA1hU6OuqgnAei/taBTaE7WFzcBaIxxebugL6Jb+GH4Iz8ZsFtljcE9PP8elGD4KJf4tQ9L/K3Cebj0WBKBAFokAUiAJRIAoMkAKF778Wx/x5eE0fjt3JOV+HK8CY3WT+qnE81zPxb8xRP3ey77BKrE0F1ma9X4CF4VWaY5UPgwUAzWK1C1k+FxznlG6jHtAvXeDsIApEgSgQBaJAzRUwCL4c6BC9HvaB1aCY2cfLkTMdNJPtd8IlYOJfh7yqR3C5f51+BwIG1v3rQELnsBgQNHMi+TgWBaJAFIgCUaAvCtgvFX2TCWL7LPsvTZ9Cf+M54H8XYDFAge/93KBdL81jsd++Z4zz+es+DQpuAT4dYH2wKKHX+2aTQ2sWQP4Yfge+Lq45L3tm+kC2ocfA2VDux/bksjL2x2ZjUSAKRIEoEAWiQBSIAgOqgOMMxxQW/B4L60HVdis7/AIsAhPAk/ms+rsm/S1yNe7nd2K9U8BC8BfBVr3bZNtbcvb/HGjWBrzOXnNju5VYBrqVyJydRIEoEAWiQBSolQIG2leGncGZ/nvBijDKfoHO98NwLZj0nwUGnqtyynQMx8/wL4LdJi8MeMeiQBSIAlEgCgyDAvoaYqDOwIyYmPcpAUuDRQJFwQAvSzH3//eg77M+bAdbwyrg8lhrBfSX9FHugN+Dwc4F8J+gL9Ms0MXitszv+pQjn3ZkYMxt6od1u102EYsCUSAKRIEoEAWiQBQYMgWKMcVSnNfr4avgWKJKM6F7IpwC+q6Txe/0a3261X1goasxP/3rWG8VWJfNXQDTe7vZSbdme/gIXAfNrqtFH3eBxc2VmD+SWBSIAlEgCkSBKDD8Cvgo3tVgF3gj7AQ+7n+UfQEdrvlwEfiIrnvBJLwOednmPnT0HSA4u99guoMA958ZbogQiwJRIApEgZFRoAje6asUBQEWBRjMc4a+xQJSlllw4H7Wgk1he9gIPIZR9pM4/UlNn8WCydtAX8q/C6ETf0p/SB/IgJjJf98bPGsWNGNxLApEgSgQBaJAFIgCUWDEFdBHd9ywBnwGjHNW7bc7iehzcDvov04WS/Rzk/76ys76n6xYgFViU1Bgcb7zSfgIVN0mLmCfX4RmCX7byGywDVQ2zqlaAM4tFgWiQBSIAlEgClSkgE6P1Y4vgtfDNuAMu1Ht/3W2TbjfAhfCjbAIDDL7WdnmfnQCTfaLs9w8niLIXcUxsLtYFIgCUSAKRIFaK6CfYsJ/fEGAxQCiH+NnZfoybt+nEawH+k4vhLXA44lNrIA+jbP3bwALAu4ae6//M97PcWa/SX+DnxYQ6A+5ToogESEWBaJAFIgCUSAKRIEo0FIBfXXjndvCsbAhVGn6tFfBl6BI5I/3c5sdiz7uPLBoQB+4sgQw+xolc4y4BZwJq1Z84ib4D4PbWuzXa+/Y6L9afF7K4jIHzaUccDYaBaJAFIgCUSAKtFTAfl0n2ID1gWNswt+qH4HFLmtjOuGPwtVwIZj8d8Z9VZW2BrhN9Bez/A1w6xS6fLIBAqvEokAUiAJRIAqMvAL6N87S18cRiwBM0DtD35n7ZT62v9jv6uxnY3jh2F+fouRnsdYK6Ofo88wDiwEMlBoQ85GnJv0thjTpbwBU4hchQiwKRIEoEAWiQBSIAlGgqQKOCUz+WxT8WvgaOBao0vRdj4NfQzuJfP1b/V7/y6x2iwVYNTZFBWwPh8PHp/j9br52Ll8+Eoz/NtpfWXAHPASVFn/4o4lFgSgQBaJAFIgCg6uAffmyYKJ/fzgI1oFRnqVmcn8RXA4XwxwwCV+Fk+W+DXab6Ne5928xq839J7iNCLEoEAWiQBSIAl0oYOK9SMwb5BF9IQsepcyCALdt4YHFllvBdrA2+AjS2MQK6CM58+X38Eu4EuaCs2DiHyFCLApEgSgQBaJAFIgCUaCpAsY+i0f+f5rXbwKXVWn3szP3bYyxnUk9+r73gvFJ44LxdxGhRLM9bAkzwWLxKs1ikA+Bk86aXedHWG4BgOtValX/SCo9uewsCkSBKBAFosCQKmDF6/KwLbwaXgyrwSjPRNP5tqL2QrgUnF1WVUDZxL7OvFW9fwKrPa0K9piqKDpgN7EoEAWiQBSIAiOrgH6RGBR0RpBPCHCGvoUBJuz9rAwznuI+VwYLMbeHzcBihFH2yTj9tkw/7XY4c4zb+KsvZbA0FgWiQBSIAlEgCkSBKBAFVEBf3iJfY6DHwIZQpZnQvRh84kDxRNFmSd7imPzMiUF3wRPQTrEAq8W6VMAx2HfgdV1uZypft7j5KPBaN5rLLBp5ACof56QAoPFy5H0UiAJRIApEgXoq4Iz+1WB7sNJ1F1gORrkv/2/O/04w6X8tmPQ38T6RI87HbZtJg9XH1rZi121rbt99F8l+HXqde9GZ69X+2VQsCoyUAg7snVnrvc0kngk0f1c+TUN8HYsCUSAKTKaA/bd+k/9dgPeSAgsCyioGYNNPFhu4r3VhG/DpANPBY4lNrID+k77WTDgNrgH9utz3ESEWBaJAFIgCUSAKRIERVMB4p369/vWr4eugP1+lGfs7Fn4H+qWTTfLRp30IFoDFAokPIkIFZlvZE0zEG1Oq0rzO74N5LXbq7H+LQaqapPa0wxjlpMHThMibKBAFokAUiAI1VMAg9RqwL+jsbg86MqPaf+s4m2y/ES4a+6sj9VfolZl4VGcdx7XB5IGmo383/AYMTM8HZ/3r3EssCkSBqSngb2x12Bh8XJvV/N73rN62AMBB0oMwF24Gf/+zwP87OhYFokAUmEwB7yP6U95rDBgaQFwGnEW0GPhZGaav5vZXhs1gJ/A+575H1Y/j1Nsy/T3v8VeB/7/q2aDf1ZegGfuNRYEoEAWiQBSIAlEgClSrQOFLGxv4NDgRqmof2iT+Z0A/1Fnc+qitzM+MTc4DCwD+AhOtz8exHiqwJNuyQOTtPdxmu5s6lRW/B45VGs02cRc4+3+y4pHG7/bkfdU/mp4cdDYSBaJAFIgCUWCIFTBYPA32hjfBVlB1hSu7rI3pLPn/bBkEvhRmg0HhMpLuG7Hdd8DmYJVxM9OJvwg+Clc3WyHLokAUaEsBf2PrwEvhINgUJkuM+ftzEH4unASXgRX5sSgQBaJAuwp47xH9Lf9vSINFFhxZEFBWMQCbfvIpAO5nPdgBtoFVwWOJTayA9/nb4Vfw27HXzrQpwxdks7EoEAWiQBSIAlEgCkSBPiqgT97vR/4bc/g2/AmMS05kRfHqXFZ6AuKjTqRW7z8zx+346nyw6LtKc0zyfrgLmhV8PMryO6BZcQCLy7cUAJSvcfYQBaJAFIgCUWAyBXw07JqwBxwMBoVHNemvw6QDZaD3YrgBrJT8MzRzpljcE1ufrXwMZrS5NQsSXgc6+GUeV5uHk9WiwEApYNLNIqd/gReCibhOzN/cIvg+OChfCLEoEAWiQKcKGA8xAW+gSLw3yRJj7316QBnmfvXzVgOffLIzbADu189irRUwADsPTgeLAa6Hx6AvM2rYbywKRIEoEAWiQBSIAlGgNwroB5v81x9/NTiju+rYqE/6PApMJhuHnMzHdJKCMUtjEn438UFEqNhWZH8nwP4V79fd/RR+AD4hotEct8wBY1d9KwrJ4LLxsuR9FIgCUSAKRIFqFLAPXgH2gjfC7qCTO4qmw3wvXAlXwDyoqmpWJ81r8SnYE9o1nfovwyehb5Wc7R5s1osCNVLA+95b4cPg7NduzEGUj4d2W60qrrvZfr4bBaLAaClgwNGCAGcc+WSApcAnk1ik5PKyCgIsPvDeuCE4e8WiAANZHk+stQL2AfqPv4VfwjXwCEwWqGWVWBSIAlEgCkSBKBAFokCNFDAup8+9JnwK3gRV5y5N1n4O9C+NFU6UzPez/4Z54Cxv45qx6hWwjbwEfAy/Y7gqzSLkQ+G+Fjt1XGKcypjxRG2pxdd7s7jqH1FvjjpbiQJRIApEgSgwuAo4u2sz+Ad4FawMo9YfG7DVQb4JLgdn+1sxq8NctlNUOOk+ZUD+ADvCydBpZbHHfSDMglgUiAKTK2AyzQHSR8FHYffC/E2fB2+DBVD2PYRdxKJAFBgBBfTNTPobiJRlwKIA/5qwL8t3c7vuZ3XYCl4B3RZLsYmhN5P+98NZcBJcDw+DM29iUSAKRIEoEAWiQBSIAvVVwCJb43HbwDGwEVRp+pG/ge+Dj/w3ZjmRub4xzfnQzvoTbSufdaeAMfaj4c3dbWZK3/4p3/oh/LnJtx2DmPw31m176ZuVNWjt2wllx1EgCkSBKBAFaqiAzqzB29fAwbAxGFQeFTMh959wJ1wGN4KJOpdVkazT8bK4wGT/E6CD7r516nXE/h0+CJ36RQ/ynTfB2RCLAlFgYgW8Dx4EDs5WmnjVjj/1PvIjeA/4+45FgSgQBXqpgP6B9zBn5DuzxAClhQAWNfneAoFOfQi+Mqm5zV3hCMjTACaV66kV9O0WwW/hRNDvNEirPxiLAlEgCkSBKBAFokAUqI8CxkZ9GqqTa74FJnSrNGOER4KxSuOGEyVrjTuY7LXoVF/TJwBUEdNkN7EmCjhWckLXueCYrEqz3bwf5kCzNmAhsjFw20hfbZSSD30VOjuPAlEgCkSBkVRAx9XHuB4CB0GvZryyqdqbQdb74PdwJdwFJuAncqb5uGdmcv+PYKL/8bG/Ol6Nj/HSYVwbphq4d3uxKBAFJldgLVb5KPQ6+e+e/f2+Hi6AX4L3H3/v3geaDcZYHIsCUSAKtK2A9xHvJ1IUFD7Ca+MpFgNYCDD+vwuwWKAX5n71n/RjVujFBkdkG+q/Ouh/HwwL4TdwGlwNj4HXMhYFokAUiAJRIApEgSjQHwUcwz8b1gDjBO+Aqcbl+OqU7Ga+9SUwoW8MYaLYgZ852WAe6JsnFogIfTbHRx+CqpP/nrbjivnQrM3YNpwwVos2kgIArkQsCkSBKBAFokAPFTDouCK8HN4JFgCMQn+r02Oy/Va4BK6FB0CHp5lDxOKem8Fcj+FR0DG3AMBAvcsnKjx4gs89xk4HG+7HBIDXfKLt83GsJAXU3kGjCRgfmTwDpsPiYAHKbPAa/RfYFhPwR4Q+mDNXLYLaosR92w6+AJuBA7FbwKSPiR75Mzioj0WBKBAFulVAn8E+RSw28h7jPci+R5YFCwIsBHV5p/4FX3nKDB5ZSPmSp5bkRScK6CesCe+Cd8ACOA1+DfqqXruq/FR2FYsCUSAKRIEoEAWiwMgroH9m0nZzOAq2gCpNH/5kOAmMIU4UJ9JPNI5gXOkecP3E/xChz+b4ytjP3n04DmPBZ0Gr+JIFIlKLdjIKCQm0jkWBKBAFokAUKF0BA77rwevg7bAadBPw5eu1N50ZneBr4CK4HUymt3KC+KinpiNuUk8H3KcLiIlel+nAtxvQ/T3rHgwmKTuxC1nZ/Xjt3W+sWgVMqvg72xNeC9uCSRcHk5rX34GdwX5n/v0I7gALQ2LVKmAi7BAork1Ze7c9HAZee/Gxa5eCA/vzwERaLApEgSjQSwWK+42+h+j7mVTWp7AwzSKAZcD74GLg8k7MQsZzYQ/Q34hNXQH7IIsE/xneC/Ph1DGcAaZ/4PWMRYEoEAWiQBSIAlEgCpSjgL6wfrGJ22NheajSHmBnX4XrQT97It/Pz4w33g9+b7L1WSVWkQLGA18JjrWqtjPZobHwZmY83LbiuHCittXsu6Usc3AaiwJRIApEgSgQBaamgP2oM4+3g/fAfmCwd5jNhLfO7yVwMRg8dbZ9VY6N+9HptprSQG2R9DfRa0FCp8fhNVwfLoTVoV27lRWPgLlwF3gcseoUsFr8BfBJ2Av8HXotW5ntwuTvl+EUuBtqUY3LcYyC7cJJngP9SF55nb32v4QjYR6kYAcRYlEgClSigElnA1Te/+yrLFRbAvQXXd6O+Z0Pw47trJx1OlbAQN1ssJ84Ge6EFAMgQiwKRIEoEAWiQBSIAj1UQN93FXgffAD+HqoyY0KXwrfAiQL6fxOZ6zvBaR4Y75tsfVaJVaSAsb/NwZi0xSRVmm3BSSdzwDbSaA+xwLGEBQC1sE6rz2tx0DmIKBAFokAUiAJ9VsBgrsHYl8B34V9hU3Bm1zCaju58MDB6DPwYroAHwGR82eY+DMRaYXkvLAT3/SiYyPP4mjleLG7L/pO1vHa7w0RJZD5+cj838PdLYFLR7zp4+B+IVaOAv7+NwYHbnuC1m+y6+bnJlj3A367V3haRxKpR4DXsxvvlZNepjKNxnw4KN4Hl4Woo/tsPXsaiQBSIAqUqoH9i8aRBIH0W+57HwOBRUUDpfUrs35qZPoa+z4ug3aKBZtvJsuYKqPtKsDO8DQ4ce6+fqf/p9evGz+TrsSgQBaJAFIgCUSAKjKwC+rnFJA5jim+EKvOSxu2OhhPAWIC+XSvT59NvN+Y4F/TXJ1qfj2MVK7A0+/so9KM4+hfs90Jo1iZsNwvA8UNtxg5VVtlw3rEoEAWiQBSIAgOvgI+Y1lk9FGaAjuwwmkn3O0HH5kq4DwxAV2Em9N2XjrbOuc66r11WRhDW7f4MTFDuAK2uqeudBKeDSf9iYNDM8ePjWAkKOEg0gf85mOhatdq1vu/BYPKlKOKojWPe6qCHYPkGnEOr31VVp2fAwUIEi5eOh/+GXHtEiEWBKFCZAj6RRPQn9G3s0wwu+nSAJcD+zb8+KWB8rMbvzIIz4CBoVSjAR12Z90R9mvH77mqDA/hlCywsGJOPwU3wEzgbnOmTvgMRYlEgCkSBKBAFokAUaFMB/VYnY2wP34PpUKXdxc585P9s0AefKAbgZyZvjX8a8zMuGqufAjM4pFf24bCMT/8ObEfNzM8t8p6ojTX7XqnLRnlgV6qw2XgUiAJRIAoMlQLOMN4IDoNXgdWGw2gGow0wnwfXgDPuq3B4i4BzkfD3r063TpUY+C7TDHbfD18D92ti2QFKkbC0IMEBgMn/S0CdPGaXOzusCo3YTQwFTOLuDs78L64PLzsyB6DvBq/lmfBniJWrwErlbr7trdt+XgcW8fib97cfiwJRIAr0SwHvQT4VQAwYPQT6nPogRUFAUQxgsYB91vNgY5hqH8hXW5pPGTgFloENYQasDB7TKJrnvfUYJv6vhR/BRTAXvG6xKBAFokAUiAJRIApEgeYKmHtcHhyDfwH0casy/exfwo/BpOxkY38/N753NxiTnGx9Von1QQHjefuBY5Sq7QJ26HitmRkffgCMLxovro35I4xFgSgQBaJAFIgCzRXQUd0HDgWTwsPWb+qU6AjfADPhZjDpr+NStpnUN5Hu/k2662DrKJlM74ej7f6vh2NAp87guklLA753wR3gQMBj1DzGx0Hnr+wCBXYRG1PApMT+4GzJbszEyhvhCjDhEStPAZNUS5W3+Y63vBnfWAUehH7cazo+4HwhCkSBkVBAX0IfSPRJTPgvApPQFp4uBxYDnD72dz3+9tL0Ca8Di6PmwdVg/7gkbATbgfdPg20e26iZBWQ7jqH/ehWcCOfDPaC/GIsCUSAKRIEoEAWiQBT4W6GqT1WaBh+DN0OV/qNxum+B8R5jeBMlZP1M3874gH7wZOuzSqyPCsxg3+/pw/719X8HrXx+nzJqjLh28eFhS2SgcSwKRIEoEAWiQFcK2DeuBW+Cd469LmOWFZvui+mMmOTXEXYGk4ltnZSJHGI+7trcr46SjrWz3Axu+178rOz9s4sJzWOwWnM+eHw3gm3B4/IpBBZFmCz0vceshvMgCUREqNBMRPTqcfLO6nNAaoIjVp4CVmhXOdif7ExMoJnIiUWBKBAF6qqAvoZ+h+g3WSypj2JxgJ/ps7wWetUfsqn/cy9YCFoUOlqc6X6dCbUAZoLFc6uBhQDbgYUBy8Aw+cmczqSmDruNoU6Xws/hbNCn0G+MRYEoEAWiQBSIAlFgFBVw/O94Wz/166DPVJWvqJ9srPMoMJk/mU+mT2089D4wgTvZ+qwS66MCz2bf+4FxvKrtAnY4B2xjjeb4aRE4dqudpQCgdpckBxQFokAUiAJ9UsDEognBQ+AVMEyP+dcJMXh7+Rg6LT62VGe3LNMp0gkSE+oGSA1i+97jKXPfbH5K5jHOAwcnJgkduBTm+XjMDgoM7npOtXTuOK5htl62G3/jdZqZPqzXzd9TnQbSHos0G7gN6zXIeUWBKDDYClhsaP/nbKZl4UIwUPk22Aq6LbJyxtM58DAU90YLNcf3uR6DBQFyJ/wKTP6vA9uMYTBuMRgl04948Rj6hpfAyXAB6C/a38SiQBSIAlEgCkSBKDAKChhD0zfaFr4D60NVZozzBDgDjD2O92N5+zTT39VH07c2VlpMTOJlrMYKOPZ4IxhjqtKMY/8GWvn1jgGMJ0/U5vi4P/b3/dlt9hoFokAUiAJRoBYK6JyuAAbu/hm2hGHpG3VgZ4NB4ivB6tdi5hgvSzGdaPeh81MEiT0OnSQDx0VQmZe1NJ01Z7r5d3VwhpdB9WJwYPLfGXJ1LWDg0IbeTEjcCbv04Ey9njrpsfIVqNP/k/wAp+s9qpaDs/IvRfYQBUZaAWeNmEDfCDaBtUH/ZC7cDHdAXe8P+iL6VPpZHvPt8Dl4DbwUpjIT3/vgPXARzIIiqOX2J/MZXdeCAbkGjgd96k1hp7G/y/G36gAdu+ybeQ32g33BdnQx/AQug8IP52UsCkSBKBAFokAUiAJDp4CxVP3sg+BLoB9YlenLHwn6x5P5sPrU+mnO2LYAQJ/WZbF6K+CYwsKSbfpwmNexzwXQrJ04bjLGZJFALW1Ykhy1FDcHFQWiQBSIArVVwNlJM+B18E5YAwY9QKkjYpWrAewL4EYonFlelmYGj03KiglVE30yqE605+N5eD5LgMkCl+nMeV6+bub0sThWgQIO1H4Lb4Zu/dgb2IbbsxDI6xorRwF/L+pcFzMRY5KmuO75PdflyuQ4okB5Cujj6fu9ED4BO8PiMN70W66Hz8I5YL9ft/uDx2iBZRFQ9d56HFwJB8HmUNzfeNnS7PNM3N8BnvN8cNuFWfzYiamTejnjXS6ApWFdUPPtQF+7236bTQyE2d4MgFuYsT88DmpyAlgsYcB5vN68jUWBKBAFokAUiAJRYCAV0O8xbrYSvAM+NvaeP6WbyVfjQz+ER8H3rUx/VYyT3gt/hInW5+NYjRRw/POPUPV4wnHTL8D20sweY6G+vuvV0qoWrJYi5KCiQBSIAlFgJBQonFKDo4fBAbDUgJ+5zquB4KvBwOJtoNNbtuOhk2yxgY6OiXID0M6KH5aZ8epq1bDE6qWAsx8vByu7N+ni0Gy/F4IJIf3hXGtEKMm8H/l46TqY96zzwP/yZQVw8J9BPyLEosAQK6D/9xzQ7/sWrAzNzMDltvAz+Ay4rn2FPkFdzKSxft7qUMRyvMfeCLPgeWDCfX3wPL3XeV6eg981cPUwmIA28Dkf7FebnaPbV7tmn7F4QnNfj4xxDX+Ph1VgK9gVNgCLLEfB1NCA5SvgQPD6nQsng/6M/aN6xaJAFIgCUSAKRIEoMGgK6OcsDhZ6Hg5vA5dVYfpUR8OloD87URxUf1a/Xh+4KMScaH1Wi9VMgfU4nhf14ZhuYp83Q7P2YgG07clYeG2tGDTW9gBzYFEgCkSBKBAFeqDA0mxjF9AhNTA6yP2fjqsJd5P+JrJugT9B2UksHWqdGysbLTr4L3CZ+51KcJivxaJAxwrY1gyWfwNMzpjA79TcxqlwB5gUMkHyF4iVo4B63wn+rSoY0OxM3P8JcD/4ekUwaFD2vZNdxKJAFOijAs5mNyH+JTApPpmZmP4E3A2nQJ0COt67DF7aD64G4++p+mg3gMUA3t9MuC8L9nOuZ5JZ/80CAHFbfibPHfurVsXs//HbZvGUrTjmeWxBzoCV4PmwC2wB+um92h+bqq15jivAa+DV4HU4C06Di8fep09CiFgUiAJRIApEgShQewX0a/Sb9eWMz2wFVZi+5XVwFNwDk8VyTNzqcxkHMJ5ZJ9+ew4m1ocDfsc6LwTFDlWbb+Q20amN/5DPHV7bJ2togJ0BqK2oOLApEgSgQBWqjgMHRN8M/wgwY5OCijoVO7u/gJvB9mU6G29bJcT+io2zC32VxmBEh1jcFTGL8GnaH14GDgU7MgZ/fN1nid01+xMpVwKSUhUpLlbubCbd+PZ9eCN7HNItHnBnbajDnOrEoEAUGXwFnJb0BpndwKgYzj4CZsBDqZN6zLADwftosCKb/5tNNZDKzmNN+0LiQqJV9ogUB3rP19zrtY/nKhGbf69MH5HwwIW6BhsUAFukuD73eJ5usnTkmWQneCLZPr6k+vkUnV4CBaoOOsShQlgK2QX9r+kLe85YcoygI8n7g58X42XuL9wT98OKvPpW/af8W+LmFLGWOU9l8LApEgSgQBfqogP2Dfuhe8B3Qp6nC9INPBIsnjVNOVDhZxDQXsZ4xIPup+FaIMIDmOO4dfTjuB9jnLdCs3bjMz/WDau3zOMiLRYEoEAWiQBQYJgVM6qwHh8KbYFkYVDOAchucCdeCwdxmjgeLe2KFg6wj/Rg4y8wAsMfhfmvt1HB8sdFRwIKU74GJgr2hXZ/WNvwb8Pua7dpBZKxcBRw0XQ5eq37YQnbqrIQnoLiPGdAugtq8jEWBKDCkCpjU2gc6/b1vwHd2g59Bncx+y5km3tf0eReHbqzoB+0LLQhQJ5N7s0Cz0EDU8dmgdarl3771zH/dt76tXAnuR913gh1hZRiVYgCf2PBmcOxiMFHf/6dg8dqjMFGAm49jUeBpChS/Uf96n3A87O9pNVh17O8aY39dvhz4+1sCXN/fuk8DkcL0n/zNen8Q7xkmUwx8O2bUx3L8KBaw2I79bT8IJl/Ee5f3mbRnRIhFgSgQBQZUAWMvFnC+Cr4K3fqibKItu4+1vgXXwWRJV/srYz4m/v1vqey3ijgAL2MDpIBjAcdk0/pwzKezT/2YZmb70uexrdXa2g2W1vokcnBRIApEgSgQBVDAgMW28B44AJy9MIimU2qA5Fw4H+4FAyxlmQEYAzFF4Mbkv8Ec91l7R4ZjjI2mAhalOJj7LtwJL4O1YCLf1t/WlWBQ3e9rtnMHj7FyFfC+ciTsAs8pd1fP2LoD/q/AAigCzrYFr32Z91Y2H4sCUaAGCizDMZj06tTsT/aAuhUAeB72YSbYPMbVoVc+r/dGfT8fp+r2vWca9DIh6L3bAK966nObLCwKAnjZtblfE4jXwPVwPBTFADvw2qTlKBQDeI6214PhLeA44FfwU7gN1Kjoy3gZiwJPFuTYbvyd+rv0nuBvZwvYDNYFC0z8zN+w6xYFArws1Yp7ir62hSx3gb74pXAjLAQ/c71YFIgCUSAK1F8B+xrjLh+Gd4B9StlmH3ExHAMPwF+hlRX9jrEiMc4Zv6mVWoOx3DHIQVBFWxuviAl+212z9mYcabK2OH5bfX1dldPX15PMzqNAFIgCUWCoFTCQsSMcATvDs2EQTQfiWrDC8FZwhkRZwRAdYJP8OjTu509j73VskvRHhFjtFdD5N5i5EZiIWB7WAYOdW8Ea4OBU83dkWz8LTgOTGrZzfwfzwIFhURDAy1hJCpig+ja8FaoagzzCvj4Lt8D4a+y9bi547RMQQIRYFBhiBTbh3C6EFaZwjr/jO/tBWf7YFA7paV+xn1sZ1gT7wl5YkaQzYNp43va9oq/tPd2AnAUBPh3A12X44O7P7a8Lu8BOYP/v8lEy+6rZcBL8CiwGaHaNWBwbYgX0n8TiHxP602Ab2BX0f9cClz8LqvK12FXHph/uGPT3cDJcDvPgvyAWBaJAFIgC9VPAPkVf73nwVdgNquhn7BeOB31y45YTxSv1W514sBAsYHXM3+jLsig2QArYxrYEE/G9Guu0e/pnsuI3oZlv8gTLZ8FA+OJV/FDRIhYFokAUiAJRoOcKGNzQ6fwEGPgYxECgzuhDcDacA/eCTmqvzf1YYKBzokNsMtTZFjoyZeyPzcaiQOkKLM4e1oMVwUCnwVAHBSYlTEhYBOA6JjMMmtv2be8G0f3r72AO+DvIwBARSjbHHRvCj8B7dtnjkHns4whYBOOT/15r31sUYPB5oiACH8eiQBQYcAVewPFfCstN4Tx+w3deAXX2lUy6W9xgUtz+cKrmvfExuBvsH9u5N+p72/eKSXpZGvTRXebnvbzXuz23PwN2g53Bc3b5KJk+/Y1wPFwAc8HC3thwKlD8zvRvp8Pu8DKw6HV50AceZPv/OHh99V/AsXAbDERAneOMRYEoEAVGQQH7IX27zcBZ+PrWVdh8dnIk2C8Yv7S/aGXGeEz6O/Y3Oev72OArYDzvs/Chik/Fsd8H4CZobHe+N454HwxEO+vlYIxzjkWBKBAFokAUKF0BAx0HwWHwfBjEoJ9OgtWCp8I1oIPaTqCV1do2t6fTUiT8rZYdnwBtdGLa3nBWjAI1UcDfvsmGGbACFH6tfwt4+aTZ3gv8XTgwvAcmG0iySqxHCnhNTFTtD0fA5lDG/dt731nwHfC+1zgo83Ov/2ywLcSiQBQYbgVmcHrXQacFAPYZ34RPgonxOpv3UgvgVoJVwRlaRZ/Iy0nNvvARKIKmU/FJi37XpxIYrHsuLAsel3j/76V5zm5/A9gNdgCvcSfnzeoDb/r2F8Jx4Gxqg9/ji954GxtABUzqW0RjGzfRYsJf1gaXD6tZlPtLOBqughS2IEIsCkSBKNBHBeyP7Iv2AZPxFpyWbfqh58L3Qf90ojG7/rp9hclYfSCLJKfix/K1WA0VWJ1jOh988kSVdi07OxwcIzXaH1hwK+izDISN2uBoIC5KDjIKRIEoEAWeoYBBPoN6r4SPwDowiH2YyahL4ddwB+ic9tJ0dJ0xIRYVGBTUGY4TjAixoVSgSHpM4+xWBt83MweGmoPHR2EW+HsplvMyVoECJoXWh+3g1bAHmKjqlZm8+jYYNG523/N62wa8/z4MCQ4gQiwKDLkCK3J+Jka993Ri+mzvhbNgYSdf7OO6JgZNwFssK87W8r7bzGc2SayP6HnqMxpgNcjVq37R/tigsYn/JcEiAAPI/vUYW/XXfNSxuR+fDLAJ2K9sDctAs/Nm8VCa1812+nM4BW4Ar236OUQYELO9+hu2eGYdeC28DqaDbXyU7HFO1iKAI+H+UTrxnGsUiAJRoEYK2CetDP8An4ZejtvZXFMzlmni/xyYzI/Rx9F/1f+x3/hf6JUfy6ZifVZAv+glYPy8Sj/INvQFsAilsT0ZS7oH7gVfD4QpZCwKRIEoEAWiQF0VsJ9yNtOr4F/BAMig9V06DCalToeZ8AD0Mhjn9g3gOjvtD1Ak/XVGerkfNheLArVUwCSCg9FVwESPyYYiseDvQ/wtmOzwd7Jg7DV/YhUrYCJqfTCQYFHX9vAy2BC6GdQ58PfRsb8FE1nFdefl08x2YCB5DgzMgO1pZ5A3g6BAcU9anYOdAdPBe5RFJ3eB9yDbabMiFRbHeqyA2n8GPtThdr2ffAXmjtHh1/u6ugFbMfHu+Ztw970+tPdH73/2ic5cMbjay8Q/m2tq3uM9DvsBCxNM0FsMYLKzm/s/X3+aeZ4WGmwGe439dX+jZF7b6+EYmAkGKr3fxOqpgH2Gvw39om3hfbAr+FsZZfNedSW8A2ZB2jAixKJAFIgCFSlgH7QmHAqHQRVx2HnsR9/b8ZIxzlZW+LL3sYLxVf1Zx/mx4VJgaU7nB3BQxaf1MPv7ZzDJ32i2tduhiDk1fl7L91X8eGt54jmoKBAFokAUqL0Cq3KEDvjfDY8+MkkAAEAASURBVGvU/mifeYAGV28BZ+JcAzoKvTKdWytjTfib0HTb4j51hmNRYBQVMIBaJBb8q59rENzfhNXgxe/E9/mdIEIf7Nns02TotLF9m/RZHkzU7A0vABNWkyWDvAd6Pe+EM+FysPhpouvqZ943bwMTXhOty8exKNCxArZbixb3hHeBiRyTr+PH3LY72+pMMMClf+D7WHkK2DdsBGfAOm3uxsDjJ+FuMLg4BwbVbH9i+/SvbdA+0ftov8xjEftqk/Ni0t5iAK+Xx9kLs8+xMHAL8He5MbiPUTGvtQVyp8F34VawH+zntWf3MRSwjdvWbY+Oc/cDkyzrQa/aP5saCnuIs7BPPR8c98aiQBSIAlGgXAWWYPP6TF+C3aFs0y85F74P+i3GNVuZ65p8XQgmavVp9Xdiw6eA47dLwPF1lXYiOzsOmrXDe1k+B2x3A2NxLAfmUuVAo0AUiAIjoYCzdgyCHAIGQQzaDZoZyD8bfg33QC8dA5NWJv11eP3rjC2dEp3gWBSIAlGg7goY7Pa+bqK/cRyy+NhnDvSeD9NhBTCB6qDepMUDMBecCXYXOOj3s3YG/d4v54Hb6OV9mc3FosCTicwXosOnYFd4FkxmBriOBBNzCydbOZ9PWQHvNSaCXwlfhomKSvWnboFvgPcYi8i851gEECtHAfsFfy/2ASZDlxljCf563XphtgHHGKvBlvAisJ9ZDEbFHC9cCd+Gc8Ckajt9J6vFeqhA0RaXZJvrwZvgzaC/E2utgDNBD4efgH5cLApEgSgQBXqvgD6ZfthuoL+wJpRtFvU7FjoPjKXqizczfRY/ux8WgbHRjOkRYUhNf0kf6QTwdVX2F3b0T2C8qdFsqy5/HAbKh65SwEbR8j4KRIEoEAWiQKGAgb914O3wTnBG6CCZjuh8OAUuhl7O5jNg9yiY9BcDIAakWznGfBSLAlEgCtRWAYPeG4CPuq3KvI+aYF0AFgLEokAvFbBI5RXwdVilww0bvPoMmHC2f4+Vo4BxDxNsB8BrYHtw1rmBTgM43iMMJp4JJkctztAstpwD+mGxahQw6W9i3t+VTwXwOtlvuKxX8Sv3YSHINrAHbAguGwWzvdvWfwTfh7lgsDNWrgK2XduYiZVN4B1wIFjoEmtPAce/n4ejwTYciwJRIApEgd4pYKHkSuCY5stQRf90N/v5IswDx0GtkqouN/l6H1gEpt+eeCgiDLE5bvsV7FTxOd7I/j4BJvkbzbY3GwYuntSrAVSjIHkfBaJAFIgCUaAdBeyH1oF/hdfD0jBIZsd/KfwC7oJeBtAMOheJf18XTm4rp5hVYlEgCtRcAYO/K8LWsCVMAwfbDiauhSvBga1BzmE1E27e91cHz71sMzhgoPge8CkCsSjQSwVswyaVTaQtP8UN6z/sD3dM8fv5WnsKPJfVTLyZgPNabQCrgr6cAcj5YEFG4Wd577gXvHf00r9jc7E2FHCM4O9LLACwEKAoCLAv7YW5D588sAa8EPaAGdCr7bOpWpvB9LPheLgQ8nh1ROix2cYWA+8528KhYDtzWaxzBfSPPw1HQ1Go1flW8o0oEAWiQBQYr4C+0NrwYXgrPAvKNH3tmXAsPAwTxT70x13HYv4n4H+h8NV5GRtSBYyX6Zs6BqjKbFcWpJwLtrvx5vvb4SFo/Gz8erV8rTMaiwJRIApEgShQtQLO6tkYPgjOfvD9oJhOgZ3+GXAWPAg6ob0wHV+T/gbgTPobmIuDiwixKDDgCpj0NqFg0PezYPK/cWDtvcWB7THw3bHX/BlKMwE3A5YDtSnT7mfjJveS/C9T5dHd9lac+mkwvQsJTC6/G44H7wOxchTwXrMuWHzUeP9t3KOBncdhNowvCmhcL++rUcC4ldfMpOkSUBQDGBS0b+1FXMttuO3VYEd4EawFVRSqsZu+mmONm+FbYEGAvshEwXg+jk2igPcbEyorw27wPrC/mOzewyqxSRRwfPwu+AXEt5tErHwcBaJAFJhAAfsqfZ+N4CuwK/TCp2IzLc2Z/t+D88CEfqtkqmOiv8J9YDG/32u1Lh/FhkgBfaVD4ciKz8lYvP6FMf5G8zMLACwcHzgr+0c9cILkgKNAFIgCUaBUBQzU7QL/BHvCIM1+MDh2GxhsuB4MDPcqUP9HtmXSX6fC1wbd4twiQiwKDIECRVLhQM7lKDD5PZF5Xzkf3gsOMobVDIpPB/sFgw+9NnVcAAYNDBbHokCvFbDtWrDzxi43bFv9BhwGvfIrujykof26Qc41YRUwsdssHmKwUX/Me4e+Wa4JItTIvGYGBk2u+lQH+1Tx2vZqXOE+3LYFADvA7mOvh70YwLa+EI6Dn8BsSCEAInRg+jO2HQuN9gN9ufWh2b2GxbEpKmA7fTlcC47RY1EgCkSBKNCZAvZXy4JFjyZa14OyTd/632A+WMDVysc2FuqEKO/1D4O+eat1+Sg2ZAoYJzoPNq34vH7L/hyTNyb59YVts7bHgfQ54oRy5WJRIApEgShQqgL2NQZC9oAjYBswcDco5syvi+FXMAecqdcL51MnQqfWILMB5sz2R4RYFBhCBRxcvwB+DTOgXTuXFQ8Gk9jDaPYNK8A0WBp62S84MLsLHoHGARyLYlGgJwrsxlbOBH2cbu14NvB2MOAVK1eBxdn88mARgK+99+jXqb2zi/TLLPLU/8v1QISam08AMPHv08SWA/sTnxDQq2S9fbjtZF3YHnYBCwN62WexudqZ7f9U+DpYAJ1COkSYwGwnFoWtDa+Cd4JFALHyFDiPTb8G9PViUSAKRIEo0L4C+kj6wa+Dz4D9V5mmn30ZHAOLwFioyxqt8Mcf5AOLBfRFHNfHRkcBY0QWpdjH639XZY75PgwWFja2zf9kmb6wk/UaP2NR/a1Xg6L6n2mOMApEgSgQBfqhgIG4feFw2BjszAfB7NQXggm7i8BHSPcqCKwTq+PwEDwBVrPGqUWEWBQYUgW87+0D0zs8vz1Z/y3wZRjGJLb3WYO2FlWtARYDdDvIc5sm7+aCA7XcWxEhVooCttU3QC+S/x6g2zOB1Ctfw23Gmivg/fQB8P5jori477jcJKf3pNw7EGFAzCCyeM+3sNaCAJ8G4Ky2ohigm2S9v0nbxS1wK5wIM2BH2BmGtRjAZMA/gMmBK+BY+C34u7Gvjf1NAduW95GNQL3EthcrX4E92MXB8A3wHhCLAlEgCkSByRWwYHJteB+8Gxx/lGn61d+H88Fx+v9CM9O30N9aBAvBOGnGRYgwYmau2v69GJ9Vdfp3s6M7oZmPq+/rOKPZZyyuv6UAoP7XKEcYBaJAFBhEBVbloN8GOpQGxv4vDILpjN4Gp8C1YLK+F528zqvOrI6D+DoOLSK0MNtLMaPLINrzweDa/DF0vhxIZECACLHaK2B7Xn0KR+n3DoGT4K4pfH8QvuL91YTNXLAgyr7DoIQDPs+/HXMb3k8NKPg0lYfA+0MsCpSpwJpsfK8e7sBkm/g76IXf0cNDG8pN6T94n9Aniw2HAv5uvKain+1THPQlLQIQ/cnnQjfFAO5DH/RWuA1OhGmwI+wKa0E32+frtTM13Bl2gjlwNPwKDJQOY3Eip9WWGUtdBjYHZ/u/HGxfseoUMGllAutsuBnSdyJCLApEgSjQQgHH1sbUtoH/gK2hbFvEDr4K+k36T63idy53HG/i37+O7XNPR4QRNJ/S9g99OO8L2KdttNF8OtzD0KrtNq5fy/cpAKjlZclBRYEoEAUGUgEDROvAu+BgsOMeFDNQeDEY0JoFOpy9MANj42f7+94igzizrdV1YOKTI0ysGNTZDEwGaup3B5wAv4AHQT1jUaDOCvh79/5iey7acrvHuxorbgd3tfuFAV3PhI1PWjF5b5LGe8DSoF7ifWG8qam/fe+vj4LFWhYSeI+IRYEqFHg+O/H32QszoDAXTCbZptOvIUIsCnShgL8p+xUxcPcI2JfYr4i/NYvNuknW2w/Z99wOs+AUsDBoR9gN1oBhirfZD68HX4bD4WdwPNwE6qweo2BeU/0UEyj6dT7hyTFwrD8KTGe3L4PZ0Cxw35+jyl6jQBSIAvVSwL5rBdgXvgZlx2r1Ca6C42AetBqju56xV2MAC0CfLeMgRBhR09f0v85cp+Lz14+9Epq1PX0L8wUD7ecO04CEaxGLAlEgCkSBPihgAG0LeA8cBEvCoJizg34JZ8DDYMCwW9OBFYONblOHQYeimTPB4tg4BfRL1oBPwptgMRhvtrVtwaDbW+G9cA2odywK1FUBBwsWAPwU3gqNyWwWtbRn8claLT8drg/U6X/AAID3T8/d2V3eFwyu+75Yx/W8X3tf9a/LY1GgSgXWY2e9SvpYzHYDOHvUNh9/ARFiUaBHCvh7En1xfXL7GH9rS4GJXIsBmhWasbhtsw+yCK0oBjA5vja8EHaDaWAfNixmEsFx3yFwNnwZrgULmIbVvH4WJzoO+WfYCxJPRYQ+mz712+AnMA9iUSAKRIEo8HQFjKlNh0PhvVB236W/5T35d+CYvlWszjG8ftkieAD8Xsb0iDDCZlvdB3o1xm5XSgt574bG9uf7om22u61arlf2j76WJ52DigJRIApEgZ4oYOdsEOQTsDv4fhDMTvw+OBnOgz9BL0zH1m0ZyH8CrF41SdXoRLAo1kIBA7EfgLeCSZBWZrDHIoDjwKKTO8EBRCwK1FEB7wEmBj4OW4IFU+2a7dr7iW1+lO4lnnfxmzYYoBWFE+owSlo8efL5p3YKOHOmaJNTPTjbsb/vE0C/ZKJ+j49jUSAKdKlAUQygf26/bKGuxQBLgMndpcGgYzfJen/XBrRvAwOKJ8E6sD3sDNOhm+3z9dqYhRMHwP7guX4XLH4wmF/04bwcaPM+b5HI1mDif1+oOjDNLntihe9U/HWjxeuiP2v825Mdl7wRC2y2gnkl7yebjwJRIAoMkgLez5eEjeHfYVco7vG8LMXs/4+Bq8DYaNHH8PIpc5k+gmOge8b+6p81W5fFsRFSQH/r1X043/PZp/H7RnOsYDsdeJ82BQCNlzbvo0AUiAJRYDIFDI7tDiazTMIOSsBah3IO/BiuAWepdOtk6gjorD4MVrfq5Oo4uCzWmQIORkyMHgzttqkXsO4/wQegmcPG4lgU6LsC3me8J/wZroPNoN02XgyM9dlNWIyiFffp4u8oapBzrp8Cnc5SMSF4GVjotgp4T5gL58H14OetAmV8FIsCUaCHCtifiL6jfbNPBNOP90lTxZMBDJr7vptgueMEHxt6K1gQcCJMhx1hV5gGw1AMoE/zfPgqHAEnwQ/hRvC+Nqj2XA7c83onvAVsD3U0+xN9RPsR/UbbszwKjlH9K4+By70mtnu/Yxu1DVrMYZt3nL9iE4oCGX8f6mIRhNe9XX+WVUszj2UPOA38XceiQBSIAqOugPfm5cF741dA36NM8977ezge5kNRwM/Lp5nr2f/YNy0Ye20/FIsC+tubwIyKpdBPdyze2A5tq/pUrdoyHw2OpQBgcK5VjjQKRIEo0G8FVuIAXgfvgw3ADnoQzI58Fpj4vxZ0OO3MuzG3aZDlQdAp8HWnyQC+EhungO1pHzCw1Im9nJW/BPM7+VLWjQIVK+A9x6IjE4Cvhnbb+UWsa8DWYOuoFgBw6rEoUDsF7uSI7PuXbePI9BlOgl+BiQpnG2t+3/uCgQcTkQbDTOTEokAUqE4B+2fxdyjO9nkITPYuM4Z9tgnSbqzYh2OSO+AUsADApwLsDqtBHZKpHEZXZsLhH+EQuAK+B78DNR2U+5sJ8TXgVXA4OAaui9mfOPa8CwxY257uB9utY1E/l6JdF39Z1HL863Xxu27Dbdm/NTPHamrjb8PfhEUBq8OaMB3WgbXAz6u2F7JDj8/zjUWBKBAFRlkBxxrel98GH4Ky78nGV38KZ4Bxi1Z9vX2TfpaJf8c8/wO5ZyNC7EkFFuPf/aHqXPVN7HPRk0fw9H9sx0MzNq9a1KdLmXdRIApEgShQdwUc5K8Kb4Z/AoNTDq4Hweywr4Gfwc2gg9mt6bQ+AjoCBkkM2P8VYt0rYLsy2NapGWj0e/M7/WLWjwIVK+A96Dy4AF4Kk91LvW+dDt5jTBga8I1FgShQDwWu5jBugF3bOJw7WOcsMCjWzEy82IfpUyQQ1kyhLIsC1SlgnytFMcADvDZ4brGPSc+lweB6N8l6f+d/hNvgdjgZZsDusAusAJP5CKxSa1OjnWEnuB8sxP456NtY/FRXs9BjG/g8eC3qcB1MrsyGy+FGUE/bp37l/4LjU9tUAS9LMbfvb8O2Kx6H7bcwfxMWzRjAPxiMI1Rl67KjOlyrqs43+4kCUSAKNFNgSRY+Dz4OTpQp+75oMv8ocExkv2Q/0czsO4xlzAOfQlP0W7yMRYEnFdB/OKAPWlzAPh2DN9rjLLCttmrTjevX+n0KAGp9eXJwUSAKRIG+KeCAfTo4eH83rASDYgZDLoUfwt1gYKRb05l9CAze6wQYiBkKR4DzqJMZ1HotGDTsxEyexKJA3RVwoOsg+aOwEWwIrcyE4dfhvlYrZHkUiAJ9VcDf8tGwFph4aGX38MFx8GCTFfQjTITNg0fBe0QsCkSB+ihgwFoMDFr4a/zM4LqFAEVBgDOWujHvAwYZDZ7fDD+AjWEv2A7c3yCbyYfV4UNgMfnv4TtwIXhfVN+6mOPdN8PHYfk+H5Rjz1tgJtwKjkFd5ji36CvqNhb1uDxOn3izMph8qsr8Tf4d9GLcX9UxZz9RIApEgV4p4P3Pfmtv+DKsCWWa9/vz4QRw9rR9UzOzn/KzhWDRmP5U3fouDinWZwX0FTeBicbUZRyivr2+d2Ob1JfQn6mTj8rhTN0cwMSiQBSIAlEgCoxXYG3efBD+AQxuDYqZlL8EDDrMgW4DADoBBuSc8W/nb5DebTY6ByyK9UABdf0tHAjbg4OYdsyCjD+CTmOuTTuKZZ1+KuAgYi6cBgbCnwuN9hALjoXZYx/Yrh1kx6JAFKiPAvoD9lmLw9tha3gOFGai5grwt34b+Bu2WK34Pfv60THsx7r1WdhELApEgZIU8Hdr/y2ONxwf+GQAn85jgn45MAFpAav+6FTN+4DbvgyuBLe7LewPG0KnBbJ8pVbm/XI32BVMBDhmk5vAcZY698Mcc2wMR8CroJtryNenbPYbanEB3A6OQU2W2O7Upl/6sOuOzOM1oP7yjr7V3crGtvt13bo78nw7CkSBKNCdAhYimvB/CzjRwPdlmrG378NMcFZ/qziFPo0+0+yx9Yq+jLexKPA0BWyzzv6vOk+tz2XsrdEsWjEHMCh+V+PxP+N91cI+4wCyIApEgSgQBWqhgB3u+vABeB0Y0BoUM1hyFvwa5oOOZjemk6oj62P+DcIZpNepHZrOn3Opo6nvXDgMPgU7wjIwUTDH63IN6M/Yhr12sShQZwVs596jZsJ6sC+YNLSd+5kDkKPBwGlh3uNMEMaiQBSojwL+XvUProIHYVVw1oL9lr7DHFgE9kv2Vf7u7wUDCi7zd+33DYbFokAUGBwF/O37uxWTswYI7bufCxYBLAuOo0x2T+TD8vGE5vbd7u/gAlgd9oAXw8rQzbb5el/NY/d8/gUOhavhOzATFoL3yarMMcTu8G3YEKo229BtcBHcAvYnFkOogW1tEM3rW7WW7tNrab8aiwJRIAqMggLe9/Q3NoXPw+7gsjLNfuqbMB8cz7TqpwofZgHrFAV+rdZlldiIK7A0569/W6XZHi8E23GjWYDpWH1o2mzZN4ZGAfM+CkSBKBAF6qWAwanN4X3wSjB4NShm5enpcBrYQXfbOf+JbZj4N/DitnVaDdrHqlNAv2QN2BoMDG4HO8A0sG3+HbiO11pHzf8y4Gi4DmaD1y0WBequgO3YNr0x2L63BNv33WAQ/HYwWah5H7oLvC8Vy3gZiwJRoAYKFP9d0toci33T+D5K/2G8X/Io7+8AgwmxKBAFhlOBoiDV8ZXFQLIk+L4XsTfvMRYZbAL7wTbgtofBvF8+BKfC8XATlB18XYx9vAu+AF6nqswxzCy4EDxPz9tx6CAn/Tn8p2wFXn0bVntqSfkv9JHXggcg4/fy9c4eokAU6K8C+gPea3eHr8B0KNPsn34Oxl/ts1rFJbz/Gr+YC8ZoLcoaPx7ibSwKPEOBXVlyLlT5pCtj/++G+2C82dZnw1D5Ew5QYlEgCkSBKDB6Chi0tlL0U/ASGKTgkQ7nL+FMMKDejUOp42oQxuSaDqqBpmEJvnAqA2deS2dOLgIdMgcOFnisAmuDA5vlwGs0B24Gq4p972AjFgUGQQHbqvecO8F7zmVgYsD7kQPmYkDtrDB/Cw+PW8bLWBSIAjVRwN+qs3/tl5aF4rfLy6eZfoaz//1Nx6JAFBheBezDxdlufwCDhz7lx0KApWEpcMw11WIA/Qf948vhGlgVXgSO5fSVp7pdvtp389hXhnfBIXA1fBPOB32mVvdXPpqSLcG33g+fgCoCzvYDFnheCI5fPCfbie3F6zpVm2gc3I/2YIxhE7BtVmm2D38DRcKpyn1nX1EgCkSBKhUwlzcN3gmHQdmxXBOkXwP7MIvVWvU73oeNzxqf0weyb2u1Lh/FosCTCizGvy+HKnyx8ZLfwhvba6Ppr+lrd+ObNW6z7+/74RD2/aRzAFEgCkSBEVbAWaZbwcdhL9B5HATTcVwIJ8ElYMC9mw7Z7em83g8mnE3CGYCJ9V8B2+SK4JMAlgR9FYNJjT5LMZgw+T8XTJT2OjjIJmNRoDQFvB/b1g2SFgMe72u2YxOF3udMHtjGY1EgCtRTAfumZWE6LA/j+6rC15jHcpMS3fgtfD0WBaLAACpQ+LH6tyad9W0tGrIgwL5//D2Dtx1bMQtwU775UtgMBmV8N9nJeg/VD/ohHA13Qy98fYsyDoTjwddlmb6cCZOL4FZwrOL40zGn5zYVsx8pvutriwjcj7MsRX1cXuC60tjObDcuE1+L4y3bpBiQH//Xz9sx2/cnYLt2Vu7hOvrMLwP1fggKjXgZiwJRIAoMjQLeY18AR4B9fuO9nUU9M/uRc+FEuBda9V2uZ9+zAB4E+ySXxaJAOwoYDzsPNm9n5R6to4/w73A2jPcXfD0f7gHb9NBYmTeKoREpJxIFokAUGAIFDDLtCVaI7gQO8AfBdBznwU/hcjBRP76D5m1H5ncNEDwMVvvpnA5Vx875DLrpm4gBOZMpBkt1Cg1ENfotXr/7wICaQa9YFBgkBWzP3osdyNvei3ubCX8DulYfx6JAFBgMBfwt62tZDGDSxCDZH8AZBCniQYRYFIgCTyVb9WktArT/957h38Wh3SQrqz7D9Cnc7vrwYtgV3PawmD7ROfBtuAy8t07V1uaLp8C2U93ABN9zbGKy/xK4De6HbpL++oZFcsVxsP2K/+WZ+1ETPyvW4eVTvmThU7psIhs/tvJ18b547V/7N9uWbdS/+qxSvPdz267rbg+fBZdVaXews3fCg+Br++BYFIgCUWBYFPAea0zM/v0/YDUo04yZHgVXgxOm7GuambFU+yULBIyv+r7d/odVY1Hg/+yIBjPB8XNVZpt9Fyxs2KFj9llgvmCo2vGwVAY3XK+8jQJRIApEARRwEL4UvAQ+BptCN4Elvl6Z6TheBz+DG6Hb4LnJYTt5ZwTomBowGaoOnfMZFvO6iEEug1v6Kl6zZcFgqeb1NEHqYMS/rQYkfBSLArVVwHZugNJ2LLEoEAUGVwH9FoNlEosCUSAKNFPAft97hT6u2Pc7NjGZagHRMmN/Tax2am7b8c0tcDv8BHaCA2FNGJQxIIfa1NTkpbAv+Ah9ExOnwiPQ6ThgC77zPOiVOV65DS4G9bcw2fFJq9mSfDSh2UYc63g9Hb+a8BfHw37mtRat+Pu3d+X96/mMLwooXpvoL4oD1uH166Dq5D+7fDLp7zH5W3LsWOjEy1gUiAJRYKAV8B67NhwC74cyE6X2KVfDCTAbWsVNXc8+zv6u6POKgjQWxaJAWwrYX/v0njLbdLMD0WdrNmbX19Hfqsq3anZspSxLAUApsmajUSAKRIG+KuDgd3mwIz0c1geXDYIZ7LgEDFrNBwfv3ZjbM3BiZZ8due87DRLxlVgfFNDpEoNdBvcMkhbBS69hEdgZOueMc4tFgSgQBaJAFIgCUSAKDLcC+rJFMYDjFWcvm+i26HX8E0V425G5XQPyp8IZsDm8ArYCEwmDbCaXPZ/vwEfhy3A6ONZrd9y4Duv6hLFuzPHJHLgArgf3b+C4GJ/wsi0rxjF+z++LTzfwqQG2jWJ7xXrFXz6q1Nxvq31bAGGb3ROccNAPs/BCWwr8DTnmb3W8fBSLAlEgCtReAWO4S4JFa/8OO0CZcV37nB/C+fAwtOpTXW4fZb/nevaHud8iQqxjBZbhG3t1/K3uvzCTTVjcMt6MMRftefzyoXidAoChuIw5iSgQBaLAUwqsyat3wTtgtaeW1v+FyXkdzdPgHmjlbPJRW+b3HwI7cBPHOqV26LHBU6AIOOX6Dd61yxFHgSgQBaJAFIgCUSAKTK6AM+nEZKrjImdDmaQ2OCpLw1TidwY4rwST1KvDvvASMGE7yGYSZAZ8Cw6Ho+A4MCExma3BCkVR8WTrjv/csYiFFTPh9zAfTII47uwk+eG6fsfxqd8Xk/4mX4rEdTH+6WS7fL0vZlHJtuB1KDM51erk1GzO2IdeV4sA1DRjxzFR8icKRIGBU8B72YrwYvgS2H+XaXew8e/DzWBf1KzvKfouixXta6dS9MbXYlHgKQXW5dWmT72r5oX+wY3Q6CPogw+t7zCVAUQ1lyN7iQJRIApEgXYVWJwVnw//Aq8Cq0QHxR7hQJ21cS7cD42dMIs6MoNmblMMpBgQiEWBKBAFokAUiAJRIApEgSgQBequgAH28cUAjmcsBjCpaSHACuBjzjtNYDsmMmH9XXDstQPsDzOg023xlVrZGhzN5+Cf4Ej4ATwA6tjMPF8/U9fJzOthMfnVMBNuB9+32jYfNbUi4W/S32tqkYdYoOH4V9yXDJL5RIb14ZvQrxjEAvbt9S7MgoRBb9PFueRvFIgCo6eAfdMMeA8cCu30Vaw2JdM3OBV+BQ9Cq77Nvsk+yyI418sEK0SIdaWA/fQBYJ9dpc1iZ4812aHJf5/GNWh+WJNTeeaiFAA8U5MsiQJRIAoMigLO3NgbdAp3gjIdQzbfM7NDvRdOgplgR9ttJ+s2DMbokFqJquPa7TbZRCwKRIEoEAWiQBSIAlEgCkSBKFC5Ao5lDLKLRc6PgmMdnwbgOHB5cPzXyaxrE9HO3DsNzoEXgAHYbcCi8kG2VTj4f4P3wn/AyXAfNI4Jz2LZ3uCss0bt1MfZjw/DreBM/9tA7VslRvjoGeY+Xd9tmeR3nOp41ffuY3zCv/H4+HggzOD92vBV2KiPR3w5+zYxVZjJhMbrWnyWv1EgCkSBOiuwJAe3FXwa9ij5QO9m+8fADWD/5D3dAkP7qPH9lK8fAWO4ibUiQqwnCizDVvbtyZY628j5rK4vNt6KNu7fobQ4RUN5WXNSUSAKDLECFm45y+E1YEXoDBiUe7md6c1wIlwHVpt2YwZODKhYgWqQJs4oIsSiQBSIAlEgCkSBKBAFokAUGFoFnHXtmNBEQVEI4BMCDN5PxdyWidwXgYnxlWBQxpccalMzqe5/K2dy+ofgbK8i0f5cXu8J74SNQe1MIM+CW2A2+GQ6Z4K1Gwx22yb8HZsW/42D3zfI7HLHreOT/rwdaLN9TIPPwxv7eCYWx3wQboLi+lokMwe8FrEoEAWiwCAoYL9u37s/fAlWhLLMPulM+AX4ejXQf7Cf9J4qRdGafaP94UPgPdV+LBYFeqHAFmzEIssqi0/1yfT9FsB4s81b9DneVxz/+cC/1tGPRYEoEAWiQP0VWIJD3BwOgVeBwZ5BMTtTO/Yfg4NxncxuTUf0AbCDLhL/3W4z348CUSAKRIEoEAWiQBSIAlEgCtRZAZPSYjG1SWbHREuDTwQQZ0B3Yo7NZoPjNAu1t4ODYEN4NgyimaCeDl+D98DhcDY4hjSJMQu+Cyb/XdfxpLRboG4SxGvgtorvum1fu7z43KR0kZjm5dDYmpyJmr6hz2c0j/1b6DFeY6+nxKJAFIgCg6CA/ewMeB+8G8rM1ZnMPwrsAzeA18I6YOHa9XAe2K9ZkLAQ7gA/008Yf5/lbSwKTFkBC04sdqky+e/B3gmP+qLB9KX14Ya2jZd5U2nQMm+jQBSIAlGgQwXsFA3iOBPjMNgSBikIY3XdRfBzmAfdJv4NpJjwF2f864gaYIlFgSgQBaJAFIgCUSAKRIEoEAVGSQEDlRZai+Mig5rObnfmoI9WNbltEL9dc3sGQM+Hi2Ea7AuORQep+JzDfcpMBFvIcApcC1+Es8EkiNp4XsZFZaKkcZHQV+ci4e8jk4vESJH0d72hDSCPabQGfz8MzqKbSDM+Lt3OYg8G7seb+g/zNRh/rnkdBaLAYCvgRC/jvP8Gu0JZ91T7qHPhZ2DftQ+8EfQZNP0F9/8IXAFPgHHXoU6Kcn6x/iiwHLs9sA+7voB9mqcYb/42bOv6b0NrKQAY2kubE4sCUWCAFViMY18PXg+HwOpQliPIpntuDsLPgDPhXrBD7casQNXxfBDsmP8CQ905c36xKBAFokAUiAJRIApEgSgQBaJAOwo43hLHSQb3HU/6VIBVYUmwiLyT8aRFBXPgO/BT2A4OgI1gEOOInvvWYPLD5MYXwCIHx5bPAa3QySL8Iomspo5FDRib+FdftSn0LtZj0dCbutienKV6KHTSnli952ai6krwWow3r08KAMYrktdRIArUUYEVOKiXwhfBmG9Z9hAb/jbcCMvB28ACAO/p400/YUMwluv9tegLeRmLAj1VYAZb27SnW5x8Y/pv10NjLkF/wRxG43IWDY8NouM+POrnTKJAFIgC/08BB9BWXxpceT/sCVaDDpIZQPk56DA+Dt0OvA22WHm6CCwASOIfEWIjrYD3CQdmBijXhReAQa7r4D5wFpJBsKF2Xjm/WBSIAlEgCkSBKBAFosAzFXD8pW8o+oWOyRxTrgjLw2LwLGjX9Ct9ssDZYMJ8TTBx4FjVREK/k8AcQkfmue8Ip8OlcDj4ZADHmT6K1s9FHfWnfYJdkez3b7fjWzYxkGYiyOv9evggNCaOWFS5OZv1AWi8JqN8nSq/CNlhFIgCHStgPGdteDt8AMrKzdmHzYST4F5YD94LG0Azsz9fCvQf7Pv8G4sCvVZA/+HFoM9Vpd3Fzh5sskP9ZAsAGn2JJqsO7qKybjKDq0iOPApEgShQrQJ2fivBfqDzZ0KvDgNqDqNtu581TwYDQ8446cbsdHU07ZitOjXx7/skNBEhNtIKOCCzSvaj8FJwcDbedFq/C18Bf5P+lobaieX8YlEgCkSBKBAFokAUiALNFTAR6sx1cUxlUbWPvHcWt7PeHXO2m8DXp3QG/J0wB06ELWBf2AwsLBgk89x3gZlwFXwdzoLHIPZMBWwvxiu+CBZI9NuMOVwAFm6MN9up7T1joPGq5HUUiAJ1UeC5HMhW8AXYGdrtg1m1I7Nw7ziw0E1fYHt4D6wAE5n3VidieW9NfziRUvlsqgrYBl871S938b0L+a7+wXjTV3DS4dD7DGXdaMaLmddRIApEgSjQXIEZLLYC882wMgyS2UHeBz+By6AXnabO5kOgs+prE/+xKBAF/lYVbtDtWzBtEkFu5fPXwFxw8OaALxYFokAUiAJRIApEgSgw2goY/zPxbTJ3GVgODMQ+G6Zibs/E/2pgMt0ZXWvAoBWzc8hPBn8tbjgSToMHID40ImC2lz3Boo+loQ52KgfxfbAoZbw5a/UWcEZfJhCMVyavo0AU6KcC9pf2t3uBEzbWhDLMOK3xWeO0xoPs6w+CV4D99UTmPdOigZPhDrgXvKfGokAvFdiBjc2EydpjL/dpbuFQ0M8bb/p5N4E+g7+dobUUAAztpc2JRYEoUFMFrPjcEj4K+0CVnR6769rsFBfAj+BS6LbCXifTztbq0kfGthcnEyFiUWBMAYOoG8CZsM7Yssn+PMwKR8KxYABzqJ1Zzi8WBaJAFIgCUSAKRIEo0L4CzuL2iaBLworgkwGWgKkm740t+nSq9cAxrjMb65Is5lA6MgvSfwwmUEwmN84YY9HImG1kM/ghbFyTszZu8GGYDY1Jfv/bixuh2xgFm4hFgSgQBXqigP3jWvAu+CAsDmWYT4Q0Tnse+NQf++M3wE7QTv5vFut9An4P94HJ0cSRECHWMwX0MT8On+7ZFtvbkAUt7wcnGo4346a3gwUCQ23t3ACGWoCcXBSIAlGgIgUMrLwCnPG/KUw1uMJX+2I6flbL/RR0CJ1Z3I35fTvfB8FBvO/dRxxMRIhFgXEKGDx1oOhgrBPTif0VfATmQGOAjEWxKBAFokAUiAJRIApEgRFWwDGpxQAmJHwigGNWE/nPhqma23NbW4P/RYCJ4262x9f7Yn9hr9fAt+F8cNw6SoXqxotXgM/Cu6EOZqzgKHCM4/VptJEJ5jeeeN5HgShQSwV8gsom8FEwHlxGHs774rXwfTDuY7++JXjftvCgHZvLSv8Bv4b7Icl/RIj1XAGfGDUTNur5life4Pf4+ERojInOZ9ndYHsfarOaMxYFokAUiALlKOA9VofrYHgnrAplOHxstjSzg7wNfgA3Q5Go5+WUzO8/AYvAqlQH7o2dMItiUSAKjClgIPYlU1DDQOuBcCN8Df4AsSgQBaJAFIgCUSAKRIEoUCjgOEwsHHX2tAnUJWFlsAjVwgAT+p2YgVS3cw5cCKvAHqA/a/B3UMbDi3GsO8D24JPqTIxYDH8FOJ4ddvO67wqH1OhEZ3Es50Oz5L+HaXzB9hyLAlEgCvRTAfs5i+n2BGMxM6AMc1LVj+BceBQsvvO+/XawL2/HbmClr8JZ8BB4D83ELESI9VyBjdniej3f6sQb/CsfWyDT6Bvoq/qbaVzOouGzFAAM3zXNGUWBKNB/BazytGMz6f96MHgyaGZneBP8BEwgthpk89GkpvNop/sgGDwxEWmQaSQ6Ws4zFgW6UcD7yQpT3MCz+N4r4VSwgCcWBaJAFIgCUSAKRIEoEAWaKeB4Tf4Mjtf0QZcFiwF83eksfseAbuseMEGhP+qsr/1hB3Cbg2AmclaEt8KbYQH4fySfAvrXjf8PPYsG3jznafAxsBCiDmZbOgFaFV8YvzDWkBgDIsSiQBTomwLePy12ez18GiwE6LXZv94E34U7wb57DXCfLwbjQJOZ27gEjoQLwWSo99FYFChDAZ9MsTdUnYuezz6l0R5nwcj8d0FVi94odt5HgSgQBYZFgf/LiRgUeSF8BPYEZ0wMmpnovxx+DreDjmQ3ZkDEKlKdSSvyTfzHokAUaF8Bfzf+htZv/ytPW9OB4DJPW5I3USAKRIEoEAWiQBSIAlGguQImUE22irMLTaouARakOrvQMW87yQVWe8pMNOjTXgPONrSoYA+wGGB1cCw9CGYAezp8EA6DOfAL8MkAJmH+G4bBjGPsDlvW5GRsP2fBrdAqPmHRim02FgWiQBTolwLm2TYE+4i3gH1Gr82k5Q/hfDBOVOzzXbzeHNoxE/2/hu9A8VQb+/5YFChLgTXZ8BvK2vgE272Uz5r5Zvq3rfyJCTY3mB+lAGAwr1uOOgpEgXopsBSHsxt8AraGTgMifKXvphN5Njib4X7oxvlzgO4A/DFw1r/b1sF0eSwKRIHOFCgeobp9Z197am1/i24jFgWiQBSIAlEgCkSBKBAFOlHA4KiJewOlzrx+LiwNK4FFAY57O03eu82FcCL8FjaFg8b+DtI42mPdAD4CJntmw0nwS7gdHAMPqq3Igb8PykheTUWTOXzp5+C4ppXZTp1skJhDK4WyPApEgTIVsE/cAb4I25S0o9vY7nFwM1jwtCS4z7fDKtCOOenrhDEsyHPSVjfxX74eiwITKqCfuB1YBFClmYe4Chrbt36oxTONy1k0nJYCgOG8rjmrKBAFqlFAB+uNcCisC50GP/hK380ZHb+BM8BkfTcDZjtXnUe3Y8LRKjs71G62yddjUWCkFTB4+Auwott7Tqd2OV/wt2gAb2Qc3E5FyvpRIApEgSgQBaJAFIgCLRVwPKc/acLBQgDHekuCTwRYHnxMfKfJe7dpwfjF4AzEjeBA2BEG5b8H4FCfNGOrHv8n4HCYByasLQa4BSygGKQx8cYcr+dTBzOxfyQsgFYajlwwvw4XJscQBaLAkwoYB/YJOS+D/wALqHpt9r32KcZujbd6L1wVXgxvgHb/qxaLqI6CE8FCNYsBWt1X+SgWBXqigD7SvlB1UeF89jm3yRmYt/A3NTJtPwUATVpBFkWBKBAFJlDARx6uA++Gt4ABj0EzOzkH0CYVfRyORQDdJAbdngNznxzwOJiwtBggFgWiQG8UsNL7CPg6PLeDTerwOki0Gl2fzwFeLApEgSgQBaJAFIgCUSAKTEUBx32O80xoG0A1gb8ILAYwAbIMtJuIYNWnTB/1JrgV1oKXwn6gDztops+9PnwEPgT3wVnwE7gBHC93M/bm66WayawXQidjjrIOyMT+MTALbHutzHZoDGKidVp9N8ujQBSIAt0o4Kzm98JhMJX+b7J9z2GFY8E+0n7XmPQMOBi2B+/Z7Zh9kfGk02EueH/NPRMRYqUrsDZ7eEXpe3nmDsx3WLzaaBay2v5HxnRMY1EgCkSBKDC5AgY1HAib+N8f6jAg5jA6Mjs4gyqnwDVgR9iNw2fgwgCGBQTOArGCLol/ROih2U+vAbvCbjADDJDNhstgJlh4ERtuBfzt/hLWg7eDAdaJzN+1A8VvwkJwRlans7L4SiwKRIEoEAWiQBSoqQL6iI5Hlod1YDroh88bw+CWCbH45ogQK0UB/U3HJT523VmFj4IJe4sAbJe2T9tpu8kJVn2yvc7n71FwEhwEB8BSMIim/z0N9N8PARPVl8Ov4QK4BxyTO66ui5nAMt7RyXUr49htX45/LgHbWSvzM+MRIxXMbyVGlkeBKFCZAt4rN4GPgcnNXt8z7VvPgJNhEXhPNC69MVhwYN/Srt3Cil+Ds8H4kL6h24tFgbIVcNb/3qBfWKXpE1wJjeMgl5u/aFzOouG1Xt+chlepnFkUiAKjqMD/z957QEtWlmnb69MRlAxNbJpO5JxzBhERxACKIiBIMI2jjn6jE5ylE75JzvyKASUJMooERYISBJpuguQcGzo33eQgjgF11n9dzXmxLE6fU1Wnct3PWtfZu3bt2uHe737D8zx7HxuqleFA+CxsDToxes18QmM66Gh4DMY6OHaQ7TafBgfbfu4mpwWH0/Nm+2xm7w7wz7AbOMAoZmddp+5P4e9hNpiAEetPBSwPDva2g03hnWC294pQXqNlmfA+1LHowO5yWAia96plxDITiwJRIApEgSgQBXpXAcciE8HA6HGwPlT2Efm45M1cdzD9OlwLBmZjUaAdCthnNejtK/xNBlgFHE8vC42Mo+3n+nTlW+FQ6NVEAA79NeaY3D76nXDT0NT++lPgWNvvOxGgUWOPYw3opPnknnXYaMnuJjv5hgCfjO2EXuw2FgWiwIApYJu2F/jAxcYtOPfH2aaJcPeA7YFtoQFUfdNHgr6hWsw68TpwWzPAwOf/QrfUlfYZ7C94PLZ5Y/VVs4lYlymgz9KEzoPafFyPsr9Pg/dPpfn5AbDPMDDmjRaLAlEgCkSBP1VA58QEeC98HMys7LX60g6Ug+WL4BrQ8TfWTp6dMZ/ucLsOtA0423mMNV8By6CBXjtKTpdmXtMfwOdh/tJWyvK+UMCEEAeXq4MDwNVgCkwGB4Dek48NUdmZtYwsBMuHWeSxKBAFokAUiAJRoDcV0Emq8/ffYCMYbXxif+C78GUwoDfWsQCbiEWBmhWwfJqcYrm1r2rwwuDysmBfth5z/XHwbngHvAn6zbw/HV+bYL8Y5sEcWAQGbV4E+/sm+4rzJvf6G59kq7y/S91QuYxVajKTNty/161Tdi87tt7yDQkjmb4I6zaPd6Ce5htJlHwXBaJASxWwLbId+ndwvplmPfZTuBBMAvCzfqDJcDi8GWptP/X9nA9nwe1gm9Fp/61t02qwDtjW6KfWz+yx2q7ZzqUuR4Q+su05lxug3X2KM9nn96G6PNmnst9guRsYM8AQiwJRIApEgVcUsE402PopOAxWhl4zO053wQ/hbvDp/LGanUQdEc+AnTK32emOI4fQ16ZT65NgeRzJ7EC/Ey6A+SOtmO96XgE7qN6HK4CdZ+9HuR0qrdrRZ52g03CgOriVgmQ+CkSBKBAFokAfKGDQ9Bj4/8Anq2sx1zsBdH79Lej8re4nsCgWBVqigGXN4LS8BM+DfdiVwGQAy6cJArWYY8+n4TS4DByrvwVMLOgXc1ynPuOH0GleTC3F/rxjcQP/PsVmsERdHSM4Tv/lECb/uI7/XsCpnysp6zl1HccLZazgNemkr9gn8/4LRgv+s8qSOs3xULWD3+9iUSAKRIFmKmAdbf385/AZMDDfTLMuOwNuAOt363zbyS3hwzAFajXb3G/B+fAg2A64vU7aCux8B/gI7A0ez+1wNtiGjYOZ4LHH34wIfWD2J44A+zbtNPs0lq3qcuRyy1r1chb1t3WyU9ffyubsokAU6CUF7FTtDF+APaEX60YH/D+By+ApaEaDpjPA7dpA6lzQ2dDpTiOHMBBm5/6tNZ6pyQKuezXo2IkDBBH60Lz3vBftRJsxbSBAG+metINr5riDqJHW4+tYFIgCUSAKRIEo0KUK+LSXwc6vgP2+esxxzfFwMVwP9u9jUaDdCjg2NQAh9ksNdFiWTQRYBXQOvx5GM7dj3/YbYJk+BN4Gjuf72Qw8ieMAWQHWgFrMMUDBcWJJIjAxw7GjSQAmBznuF5e7r07Y/ez0y1BL8N/jfBIc78SiQBSIAq1UwPZpC/gSHArNrCNt12bA92AeWKfZ7/NJ+T3gBLDOr9UWsuJX4VKYDdb5nfQFmSgxFU6C42FlKGb7bbLD+aC/2XbNz2oS630FxnEKB3fgNEwkmQ/V5d57wX5o9XIW9bf1YpCrv69Izi4KRIF2KrA2OzsKPgJ2SJrZiWNzLTcH8DZsPv39c2iWQ09HgE4AB9R2vuyADlwDyTl30jZk5yvVcQAfYN0b4CLQiRPrTwW8xxeDAyLrLx2nS6u3XHcReB8nKQQRYlEgCkSBKBAFelQBHaL/Drb7jZjBVcc898ETjWwgv4kCTVRAB6w45jQZwL7qirAmWFYNcC+tf8tXS8y+rYGSb4KBg8NAJ3M9QRJWHwhTy6KnQSz1Xa4Lz/xejsl6zjpqtOCP31t2TFgYbV1WiUWBKBAFGlbABy92BYPqWzW8leF/qN/1O3AtOK8ZqxsP+vj2B+vtWkyf7Z1wMri94jfqlC/Xdmd1eBv8FWwKpS1idol5bmrr8T4D9nOr12FRrAcV8Dp6v2zcgWO/jn0OFx/xjUku79Q9wa47Y0kA6Izu2WsUiAKdU8ABrx2PT8Hh0ItOAjPWpoGv+TerTQfKWE0nihmXT8NzYODf7Q5cw8g5d4PVq/vyHPTxcDM82g0nkGNomQI+7aJjTKeX2dM6TM2qtoOtA8z71iSQF4emJvDEokAUiAJRIApEgd5V4C0c+lgdaFuwjZXAPkTsjwrYf9Ivthqo0ZthBxgH9ql0oD8Ed8GNsAjsb8XGroDjHfu14thTx6xjGrVfFRy3jxYIcBuOX0+FS+EAOBgMOsR6QwGv4W3gNXy8xkP23vRetOzUO26ucRdZLQpEgSiwpN/0TnT4MpiM2Syz3roHrPf031mnaQbAt4YPwYZQq+nPvQxOB32C+nQ72VexLd8G/hwOgzfA0kyf1gpDX3bymJd2fFnemAL6KT8CtSawNLaX1/7KAL999uqHoPSLmjQ4kH2GJABw5WNRIAoMhAI6EQ6Ej8Mu0Gv1n43UU6Bj40poVofO7epweRIMGPokRul8MhvrkAKz2O9LYKepVjOxZV1wABHrbwVM1rEDa6Bf56j1mQ5S7+eSzOP3A9m55bxjUSAKRIEoEAX6RQHb93fBaIHQWs43jtU/qrQss/ab94GjYWfQ8T6cHcpCx0cGKf8RpsFwTxaxONagAvZfHYeKT0E6Nl0F1gav1Whm2TZ4/F1wvLw7GHSYAK+DWHcq4FjFce+jsB4YBNIn4RjHe6yMZ1xPvM5SkqFdFosCUSAKNFsB+1xrwgnwBailHWK1msy6zbbqZ/AsWI/ZThkI3w+OAwPotZr+XAP/58H9YP3ZqbpRv9S68G74a6glacL234fcnNr+p6+KCH1gkziHvTpwHveyTxMEq81+vPdKp+6N6uNp6+deC4C1VZzsLApEgZ5XoHQ+juFMTgI7Is1wnrVTGDs/j8D5cCcYFG5Gg+V2TSJ4HsyCsxNqhyvWHQrM5jCmw/vrOByvoR3m2GAoYD2gU0xiUSAKRIEoEAWiQH8q4HhmShNO7VG2oePLsVAzxhJNOKSObMLz17G+A3wJdoWRnkzj6yXmdXDd78NH4cdg3zvWfAV+yyZNdnXc+wwYQChPCI72JJljXAMql8BVsDWYQLMNNDOAw+ZiTVDA+3GDIayXvH466b23DGLpq7AMPD00dX4u6L9YFazTHAv5u+LLGOT6DRliUSAKNEGBSWzj7+A4aGYSmX2xM+AusJ3T7IOsByatHQijtXOs8qqZ+HYyXAr6EN1mJ+pA63Lb6Z3gi2BSZa26+ZYlk/70ZfrQm/V5rLcVsAzvBrbT7TTL/rUwXP/c/oTLO3F/sNvOmoOYWBSIAlGg3xRYjhPaFk4EO1Fmkvea6fjwKZML4BHwczPMzEoHzDpGHFS73YFsADnvbjavzVfhAFi9hgP1Gt4EOsrsaKfTjAixKBAFokAUiAJRIAr0uALN6NcZUNPZrP9Hp5yfB9UcF74Zvg7jGxBhFX6js92A5AxIIiYitMAc21hOHRM5fn0jrAY+kbk8GGwYzXT03gK3g7/zur8N1oJafs9qsTYq4DWxfhKTNQwmrQuVZrlwnKsPw3HvYjB4dDfcC7NBP8evwPJjUoC/kVgUiAJRYDQF7CdtA/8Mbxlt5Tq+tz76CfwASpDbOk/f9abwMZgCtZp1mv5i+zIGPA2gl/qO2bbaMuxtKvi69+OhHv+79bWaPADzoVMJDOw61kQFTNy0TLe7r2W/4H6obvPtq784zHIWDYa1+0IMhqo5yygQBTqhgM6xlcBO2qdhe3gD9JqZ9XjFEHaA7MSN1Wz8bOzctk/9mylvB7S6UWRRrIsU0PFhJ/rvQYfX0szrqFP3X8BkkZnQrIQRNhWLAlEgCkSBKBAFokAU6JACjmfOgSPGsP9b+e2/wmMwCwyODaKp5Q7wPajH0T6cVnew8N2wADKmGk6h5i8zMPMmMAljnaH5en2ajq+2gINhR6gnUMHqsS5WwPvQ4NEz8CBMhxvBJ25fAL/Tt2ICQSwKRIEoUK2AyUd7wLdh4+ovx/D5KX57CpiQ9uuh7ei/Hgf6r98H9bRFBjN/DGeC29TX24l6rZzDW9n/52AzqKdNXsz6J4MJAAuhUwkM7DrWRAUsAwfApWBySDvtKnb2n2B7X2nedyYGOP4ZyD67HehYFIgCUaCXFbAemwCHwSdgItTT6WD1jpsNkJ3CC+AaaFZmmkHgl8CsSrdZBr0D2eBx/r1mdoAvAq/bUaDDsrID5XU0qeMy+BGY3JFriwixKBAFokAUiAJRIAr0iQL2B6+H94DO1nptET84FQyAOW5qZBv8rOfN8aGB48/AlCaczbZsY184H4pDvwmbzSZGUOD3fOfYVgeu4x6fMPOarggGbmoxx8d3wJ3gbx1fHQgGLkwuiPWuAt7jJnj41gAxAOHY2IcfHoNpcBUYBLA+tCxYprSMoV/RIX+jwKAqYAD+XfBlWLNJIlivTIfvwjzaa5RuAABAAElEQVQoQXp9elPhCNgL6umXPc/6p4B+woegUwHN5dj35vBR+ABU+in5OKKpww1g8P9a0FddtGE21uMK2A7br6qnTDTjlC1D14H+82qzzziwr/9XDDtIsSgQBaJALyrwBg56C/hzMPi/MvSa2UDNhHPBzE0Hoc2wX7IRA8MG/p03QzQdKkToMbONXg10SFne1wY72RPATo1lR+z06xyWxTAbijOD2VgUiAJRIApEgSgQBaJADytg/+9yWK+Oc9DxbNDrm3D30O8Mnj4KjhMGzUx+2BV0mo9r0slfzXZOhLlN2l42U58CXlOD9voBxsOyUGsiAKu+agZfHGdtA2+GTeGNEOs/BawXDQLMAQNQ18C98AS4vPhNXC8WBaLAYCiwKqd5PPwjNKvuN/HIwL99N32y1in695aH7eEksN2qxx5m5a+AfY8FYH3V7rrKNtb28m3wRaj3HOyHng1ngclYL0O7z4FdxlqowAZs+0ZoViJNrYeqL/wTYByk0rxPZsFTMLBxkT+rVCTzUSAKRIEuV8AOk4N8nTd/B7uDgdFeMxugW+F7oGPOz2M1O04Ggp8BM9r97HbTmUKEHjWvnYMFM2JNBFgEdmoqrfL6/p4vnoeB7dRUCpP5KBAFokAUiAJRIAr0iQI6rnzi6++hFue0Af5L4DLQ4VXMvuIfyocBmzpm3ARWaOJ5m5ixFsxt4jazqdoVsDwbTHAM7PjXxI7VwQBLPU9UOnZynCU/A8dd28F+sBksB7H+UKD4k7yuciLoN3kSfDOESQG+HWIelCcGLWeWkcpxNx9jUSAK9IEC63EOfw0G5A1uN8NmshGTLx8C6xfNbRsQfQu8H0xYq9Xst10B34abwbqp3XWSdaf9p63hC7A/1KOX9ad6mMDwU7DO9bxSryJCH5l9rwPANzS1265jh/rOq630FQe6rCUBoLpY5HMUiALdqoCdpfeBGV3rgx2QXrNfccDT4YfgoNKGaKz2azag48PAv8Hi34IdqVh/KOD1XAB2VsxMHs6Z5XeWpSdA55eDgVgUiAJRIApEgSgQBaJAfyjwG07jXJgK7wQDlNV9Qvt/T8PlcC08BfYjizk+cLxQnNFl+aBMHTuaBNBMW4aN1ePEb+a+s60/KmDZdhzsuNgy7v1hMoDBinp9nibQG5jwPjIZYBXYAvaGbcC3DfSiH4LDjg2jgNfSe3jiEO9i6tjasmQ5eBBMDLgH5oD1qr4X61HLivWu68eiQBToLQXsQ1m3/ysc1KRD1yd3MVwA1h/FrGPsvx0PJpfV04bYtp0J34d7wf5gu+sc+04T4Gj4C7B9rcfsi/4ITofbwXOy7oz1nwIrckqOU+op481QwXvvJnBabc+yoBP3TfVxdPRzvZ3hjh5sdh4FosDAKWAdtRH8XzgMbEx60WxwLoVrYBGMtbPjYNNB5zPgE98OUMsAlNlYnyngNdax5WvE1gYzbUuHys6/HWoHGJat4To8LI5FgSgQBaJAFIgCUSAK9LACBvfPgVmwK0yEFcBxxQvwMPhk2Eyw31htJiK73qD2FdVkMdifblbQ3mvyItgvb7dDnl3GqhQo4yUdvY6/9R2sA8uBAYwyfmK2JvNecbx9HUwHt7c+7Am7gA8oOC6L9ZcClhPLzJQhDmbq/W158H5fAPfD3WC9uxB8Itc6Vp+M6xVSLyBGLAp0mQIG/+1HfRs2b9KxWQd8A26Akmjpfkwac1/HwhpQj81m5a/AlTAHrFfaWadYF3r8tnmfB8/DZfXYPFb+GpgYMR+sI9t5Duwu1kYFNmBfe7dxf2VXjzJjWas2y9tLMPBlzuBaLApEgSjQbQqswgEdCJ+EnaAXB9Y64xwcng83wi9grI2OHT6zJc08d3s6N1wW638FvN7l2ut8eiNYnkz+0BHhIMMyF4sCUSAKRIEoEAWiQBToPwV+yykZcDbIfweMA/uEOrdMFrU/OJzZX7SfaJDKfuNYxyNsoifNMdN9oINwtSadgdfC66KT3+BzrDsU8FoYjHWs7BjKRBn9C6uDiQBer3rN+8Zt3QUGfk+F9WAH2B2mguOzeoMj/CTWAwp4XS07liHZFo4Gy4V1sGXDBKNZ8CjMhcfhCTCJxACEdYX1kOs79beDWh9z6rEo0BEFTO45HL4M9Qbkhztg7+E74TR4DGx/NOsL24h3wNugnvibfr1r4RS4EaxDXNbO+sL2bH04Bj4Oy0M9Zj13OXwLfg7WkZ5DrH8VWJZTOwqcttuuYYf2+6rN8U8SABChngqoWsR8jgJRIAo0UwEH4pPgY3A0rAm9OIC2o6Nj4EK4B2xwxmJ28uwoPQt2/H4JDh5Lx5LZ2IAoYDnw+kssCkSBKBAFokAUiAJRYHAUsO9vAsBKQ6dsYElGMn+jQ2w++HTaIDtf1cLk7O/ApqBzeyy2mB/fBG5nGTC5ItZdCljevS5i8KHcPwZ9DAI1+pCB43OTCx4d4gKm3peWq11ha1gLDADF+lsB/VXe/6sPsWXF6VpODPRbVkzQ8o1988Ag4ayh+UVMrZsrEwQst/42FgWiQHMVMBFMf/PfQzOClPp6fwAXwQugWSe8CawLTgCD6PX4tf+H9U+H8+B+8HM7+262i6vBnvBPsAnUc/ysvqRvauBfn/hMsB5MnYYIfW72ew7pwDnaxzMGYz+/2oyhGD8Z+PKXBIDqopHPUSAKtFuBFdihnYtPwr7gAKoXzaDsT+ES0CHUjE6aDZkN1vNgx88O5sA3XGgQiwJRIApEgSgQBaJAFIgCg6SAYwCDSAaM1gEdzNVOWccf4romJZdxhL9rxtiEzfS0OZa6EDaDY6HRJAD1/A6YWGEQwWCy47ZY9yqgA9jrb6DVxHqfZjQYJJYDH0Zo1AxuGMT1SU2xTKwKBk52gM1Bx7jLx7Iffh7rIQWsn00CkRVhAmwPxaynS5m0Xn8I7gaDfvPAcqqPyXWsz0vdzmwsCkSBOhXw/vsSHAvNqIe9Z0+GO8A2QDN4Pg72hg+C7Uw9NpuVvwaXgXWA27WeaIdZX9kWbgSfhfeAbVY9ZgB2OnwTZoB1WPqeiDAA5j21NUztwLl6D3o/Vpvtpm1ou+6h6v131Wdv8FgUiAJRoBMKrMdOT4Rjwc5YL9ZHNiTzwaz/68AgfTPMJxQM+tth8qkdO1JptBAhFgWiQBSIAlEgCkSBKBAFBlgBg0k6mH1CS2etDmdNR7FBaINFjkleGpovjmk+xlDAh2B2gqPgMFgddFzWYjqyddB/F24Fg8o+3TsXnoBY7yig78F7yUQaEzi8n1YEl5V7itmmmNt02ybuGFwxMUAn+Rrgvt1frWWQVWN9roB+HwMXJpXMgtuGeITpk2Ddbr3jOq4bPxEixKLAUhSwft0evgK7LmWdehZ7v90Ap8NCKAFug+WT4Viwj1FPna6/92r4NtwI+oHdbrvubftFa8Kh8HmYBPXa0/zAp/4vgofBOqpdx8+uYh1WYCX2/304uM3HYRn7B5gB5V4sh2Bc5UGwLA689WLAbeAvWgSIAj2swPIcu52uz8C+UG9GIT/pCnOwdSecD2ZqN6NzU5x1dvYc7P0W2tnpY3exKBAFokAUiAJRIApEgSgQBbpcgRK8dGxlcFEHmGOJXw3N+znjCEQYxpZh2UQwCLsu7A+bg4kAOjDVszjui46OywzwT4PpQ/MlscJx4Gx4CmK9qYD3k34JkwF8O+HK4L31RvC7Vli5hy1zlr11YDxYJg3EeAwmB3gMllmDWGLZLOWz8tgq51kl1ocKWOcY0JgDd8DNoC/KusmkAOsp2wHXs/6PRYFBV8C68xD4Gli/jtVMsjwLfgo+WaxZH1uP7wbHwFpQj/lGodPBtxPdDyZw2vdol9nmbQOfhkPBZIB6zOSF6+GbMAOeAeufdp4Du4t1WIGt2P908K1K7TQfnPw4LK7aqeXPvvkisIwOvKWTOPBFIAJEgZYrYIdoMpwER4Edr16te+ycXQOXwQJwcDVWc6DmgM1sbjuRDtrSQCFCLApEgSgQBaJAFIgCUSAKRIEo0EQFDKLqoN9oaJt+9ulvX9muI1xKYgWzS8ZpjzM1yOZYrTqw5rKZYGAu1vsKWB5MAjEhwKCO5cGpgfhO+zDcv8dXnaygw92EAd8yYNkVkxlczwCYU89JDO6I826r4DL9Nn4uU/dXzrlMWRTrIgX0GxkwfBwegNvhfpgHT4Pf6V8qbwtgNhYFBkIB68OPwRfB+nusZuLNyXAvlH6A9ehEeDscDNajtZoByofBNxNcDQuhnfepbYMJZ4fB52Ec1GvP8oMz4HtgP0jfdgL/iDBg5n1wAnwD2t1X+DH7NPnEe6fSLIuPgAkCKZOI0O4Lwy5jUSAKDIACDhx1pOwPn4TtwUahF83OnQOoi+BmsJNTOnzMNmQ2QCYPuK1noAT+x7pdNhWLAlEgCkSBKBAFokAUiAJRIApEgWEU0AdmgHTToamruKxQPjt1zFaJyyrN4NsiWAAG2WL9pYCBcAM6PokvBtlNCDCgXk+gh9XbbpbnYpXzLiufy3S4ZfpzDJp5vibHmDQzHtYemtfXsyK4joEktXJ7ldvkY6wDClhnWR89B/PAxIB7wGCIQUZ9UCYGGCDRJ+X6sSjQTwpsyMn8BxwKY62TbOevgO+A95T3i9u0TdgKDHxOhXrMYKWBS4PnJu2UIGU77kXr6pVgO/gS7AYuq8fU5EYw8DodTDbSl92O42c3sS5TYA2O50rYts3HZTn8v3A3VJc97ykTbGznYigw1oowIkaBKBAFigLWJw4Qd4QPw1tgFehVs6G4A34ED8JvoLpRYVHd9it+YQfpBXDg1c4sT3YXiwJRIApEgSgQBaJAFIgCUSAKDKwCJqYb0JwAjT4ZqOPRt7jNHpo2Y5zIpmJdqoC+DsuNwW4TSAx+6/uQbk8G4BBHtGq/cOXn6nk/e77l3yV4/mVe34+BAO8tpyYJGGjyHvM3JUmA2VgHFDBAV5kY4JsC7gWDJI+DiQH6qpIYgAixnlTAOvpA+CrUG5Qf7oT12Z4K00B/sGZy1Jrgw25HgvVfPaYv+BT4ITwC3pPt6j/Yfk2Ek+AjYDtWrz3DD06HC6GZfvJ6j6Od65c2z/bMvp9m8pR9wHZeP/fbbWafYE+4CkyObKeZfPtpsO2qNNu6R+FJcD6GAr3eUc1FjAJRoPMKWMlvBEfAMbAu1JtByE+6wux42XhcAdeADUpp4Jlt2Nzui2AW2nPwa7DDEIsCUSAKRIEoEAWiQBSIAlEgCkSB9ingOKw4Bsczb0C3MtA52pGYwO2YzqCZDuB2Oe/ZVaxDCniNdfSLSfwGQfSDGPyx/JRAt58NEPWSVZff6s/V52L5159h2TegpA76lg2+ee7lXtKPUnwprmeywOqgv2gCrAXlTQLqVvn7sg0Wx5qkgD46kzGs82RX0AyQGPQ3MDkT7oA74VFYDL6tMkkBiBDragVMQPKV/1+ARhP7yglaB3ofnAaPgfeIdZLbXR9OgK2gnnrKbd4KX4frwHvL7Y5W37LKmM1737p2P/hb2BLqOXZWX1KXX8/0m+DU+qLU78z2namZiWy7wGGwI9hGXQcXg/59NZgN1o+DarbtB4D9gHbbVezQJJ1q83qYzNaOe6t63137OQkAXXtpcmBRoOsVcMD2Ljge7PxY8feq2XFxgPNDsFPWLEeODY/behbsIJg1aiOUhggRYlEgCkSBKBAFokAUiAJRIApEgTYr4Fjsd/AUOF4zGFkCt8Up7jqV837WWe/vTOp+AsrYjtnYAClgWdB/oINZLA/6Vg0OmQywHKw89NnlBhL60bwXxIQI7xUp56pGmlMp3480Vauinb6mdcFA9dpg4oABPr/vZ005vbab18z6b+IQbx46Aq+twRWTAm6Bm+ERsO7zmlt3eh+Ua81sLAq0XQHrlA3hv+Bt4OexmMlN58KPwHKueY+Mg33gSLAuqsdsJ743xN1M9RHbn2iHWWduDCfCh6CRQO1T/O5boCbWB/3c91mV8zNB4nCwPE0C25xi6zFjebgYrANd3zpxUM22+agOnLza2y4Ndx/9kuVJAKi6KJWFuOqrfIwCUSAKvEYBOw87wMfAxnBF6GWzQ2f24mVgR8ZBTjPM7f4CfDLAzt3vYbiGicWxKBAFokAUiAJRIApEgSgQBaJAFGizAo7RngODXD6d7NhW57gBBL/TySvOi07vEvjK2A4xYksUsCy8PIQ+gNfDG8CyZLkyuLrS0GeXW6b6zUqgf2n3xUhB4hKwc+r9pQ9lNvi5EnXzoRODb+NhQ9gcJoMJF2oba54C6rnGELsz9RrqLzPYdQdMg9tgPliHGpDx+o90rfk6FgWapoB1woHwbTAwO1bTJ/wVeKhiQ29kfn14D+wJ9dTf3gvWZV+FK2Ee2Fa04x4x3rcmqM8XYSLUa/Z7rgaD/z+HZ6Ef73Hb6A3gADgSNgOvu+1PtdkG7QE3gG2VMZJBNfXZDppx79Wr4Rx+8CRU30t/GFruNFahgBVCLApEgSgwmgJW6D7pfwxMgno6PazeVWYDsRgugutAp4+dmLGagyGdQnaK3KbzdpiasW02E4sCUSAKRIEoEAWiQBSIAlEgCkSBJirg2FBHoUnbBh+Lw7c4Ff3sfDUsikWBYRWwPIn+ABMC9J3oezVwYFBBfFOAUwMPfmfSwKBaudfKdDgdyn3p07lqugAMPqutgWoTLCbAlmBAYiKobS/7rTj8rjKvgWVYbeVdYDl/Ee6DaXA9zAKX+QSmPrKRritfx6JA3QpYFr3n/xY+BWNN/jEo/wP4IVi/aNYdJhvtCCeATzrXY/qCfdDsNLgVfFOMvuFW3w9qY/uyBfwNHASNxP4W8btT4UfwCPTbvWyb65tmdoEPwD7g9a6lzTDhzPbFa+k1HVRTg0OgE/2Xq9jvL4cR3n6XtPo+G2bX3b2odKK6+yhzdFEgCrRbAeuGFWBP+AyY4WZnv5fNTt3dYAfGqRnKzTC3q8Po6aGp27WzF4sCUSAKRIEoEAWiQBSIAlEgCkSBKBAFooAK6GcpGGgQg1cGaJYFfS5+LpTkAB3sZd7f1xKkYLWBsKKnU3USA2DrgE9ybgsbgEEbNYy1TgGDYQZlHoRr4HqYCT4pa/KGCQMJzCBCrGEFvIe3hlPA4PxYbTYbOBnuB8unVhJd3sv8vlBvvaFv+FtgQsHDoM+4HeXedmM8HAH68X0DQL3msV4F3waf+i+JC8z2hfnE/sZwKLwP1gd1q8fmsvKXwMSnWfACDKJN4qRNbmmknI1Fr1/x44/C/GE2YuKK93RiMlXi1FuJVf08H6NAFOgzBWz4bAxPBDsNVuQOpHrZ7LD8FC6HxdCMDD0bEylP+9sAGfhvxrbZTCwKRIEoEAWiQBSIAlEgCkSBKBAFokAU6CMFDAKVQFAJNulHKD4XA/vODweLl1jld2V9p5XzruNnraxfvh9uWeU6ZVtOS0Bd37HzZZnbWJq5rXZatabu3ycA9QMZfLsY9HOtCpPBpACZAD704rm1+5jZZV+a5cMns3cZwmvjm1UehWthOjwEXhuXG2yMDw0RYqMqYNnyfj0G/hksZ2Mx64gLwSB9CeCW8rsly04C64h6zLJ8M3wdLOtPgstKnc9sS8z6a2XYDT4He4DnUo95jPPga6DvfA7YNrX62NlFy8063tiG+hwNe4F6qVu95vU0MWIheH2HewqdxX1varczrN6BM32EfT43zH59S8VTUPpWw6wyuIsaKeyDq1bOPAr0pwIO5Ky0D4S/gK3BBrKXzQp/JlwAt4AB+maY23WgYqPiq6HsNPbbq5A4pVgUiAJRIApEgSgQBaJAFIgCUSAKRIEo0MUKVPp0lzZfDr/ye5fV8rmsUzl1XgyqF3xiVpYdmjrvd0uzsr2lfd/M5WVfTg2KiT4wfV4rwtowBdaHybAWlMSAeoNo/DQ2ggIGE/WhzYbrYRr4FO0ToJ/Nh2wMsMUGWwHvzdVgU9gI1gAD/vvAjjCW+9Iy+ACcAgYSS7DQOmsdOAzeCiPVX3z9GnuJJWfBeXAvGBhuR/D8jezH+uso0J9v3VWvGei/CM6EO8CEiH64D22PrNcPgQ/ChlDvdeUnr5r107XwBbDesi5rxzVmN11nlrPT4H0dOLL/YJ9XQHUZ9V/PPAy/hliVAnaAYlEgCgymAr4WbQc4EQ6GVaDXzU7WNXAJzIPSmWO2YbNRkWfBLDP3YUPfjG2zmVgUiAJRIApEgSgQBaJAFIgCUSAKRIEoEAV6QoHiS3Y6HAboXG5QrWAwpuAyg3y1PHhS9sXqTbOyTaceq5gU4NRj8jh9Y4ABwYkwGdaDcbAcGETyt2U7zMYaUMDgmcHH+TAdrgUDa09D8bu5zqAG2Tj1gTGTcTaBd4P+6cnwJvAe875sxr1mgPBsuBLKQ2Ju133rGzeIPgXqMcvm/XAy6IteCAaKW11mra9Wgz3hH8FkiXo18hgfAZMhLgPvQ/3crT52dtEys6wY29gWjoB3wupQrzb85FWzjvIanw0/hsXgNR5kM7HiVrAMttN+yc4+DIuG2ek8lpUyPMzXg71oLDfAYCuXs48CvamAAxUHMO+FE2AS2HHoZTM4Pxd+CDPABqEZZqfHTqGDDzuKzvuKsl7uDHH4sSgQBaJAFIgCUSAKRIEoEAWiQBSIAlEgCrRUgeJzNijjvFTP66MqlMQApy4rU+eXZmUfS/u+nuVlW+U4PVb9ZZVTj2l5MBlgPJgY4NQgk6+V9jsTCPyd2ynbZDY2igL62vS5zYXr4Cow8PYM6Ocz6Kb/L9YfCnhvGOQ3WPsJMPDv/dPse8ZyY3LJ2bAYik/Xe3ldOAjeAX6ux3ww7Hz4DtwFL0Gry2fRbGP29Vk4DKxv6jXvp/PgHLgT/Fx0YbbnzDZiHdgXjocdwLLVqKmFcYCfwZlwE3h9e1kjDr8pZhk8Eiw7zrfTbmNnXwLfGFNpJmncBy7PNapUZmi+3RdqmEPIoigQBdqgwDj2cSAY9N8VfE1Qr5sB+Rngq4pmQ7My8GwsngcHGTbwvj6mWdtmU7EoEAWiQBSIAlEgCkSBKBAFokAUiAJRIAoMtALFJ+10NAzCG+gy0KM/y+COn30ivwTomV1iZbvlczOmZZtO3V/BQH/Zf5m6zONbGdYCg4wTh6b65gxyeh5lm8zGhlFA35x+P1/ZfilMB31/L4DB1/+FBHsQoQfNe2VN+Ah8CrxXmm2Wjcfga/AQFL+u+/ap/y3BYPEkqPdenMVvvgGXwRxox5Pzvp3E+uQd8Hkw+ahe8565B9TkWngc2nHs7KYlZj27PhwKx8IUUKdGTS3mg4kdPwDLjUlJqWcQYci8d86Gd5UFbZz+C/u6GizHlfYsH2aCiQCxYRSot4IbZhNZFAWiQBcq4L3tQGg7+BgcBCtBr9/zVvLz4CKYDr+AZtmv2ZCNhoMJsx9tONLII0IsCkSBKBAFokAUiAJRIApEgSgQBaJAFIgCHVKg+LKcFgzkSUkI0AdmcN2gkAF2A/Fa+e0rn1r3t+zHY3LeqcdQOTU4ZeLCKjAeNoKNYQIYWHH9sh1mYxUKGJxbBFfCJXAvPA367vQVxn+HCD1g3pubwz/B26AV5V2/7plgWTGAW8x9rwPvhreC92I95rZ+Av7/81vA/VQHI1nUdNOf71Ptn4W3QKnbmK3Ziibn8YsHoVeflrY+VY+t4EjwWq4OYylHJhTdAWfDFWA9Y30Te60C67PoVljttV+1dInl9STw2lTbLBaYzNKOe7F63z3xeSw3R0+cYA4yCgyQAt7PK8B2cDQcAmZU9sN9/hLnMQPM+rViL5mbzI7Z7AQ9B77ex+xit52BAyLEokAUiAJRIApEgSgQBaJAFIgCUSAKRIEo0MUKFJ+XgSHnlwGTAEwGEAPrJgloZd0yfWVp+/66XymJASYEiMfscfqE74awGUyBVcCgZaeOl113pemzexZ8GvT7cO/QZx/sSeAOEbrULPdbwqmwYwuO0QCgAfrvgL7e4tstQeNtWHYMeG/Vawv5wcnwU9AvbeJJ2T6zLTETFCbC++AvwfqgXvN+mA6nwI3wFKhTq4+dXTTVLDtrwK5wHOwH1u+NmudvGTFJ5CwwocO4QK/pwiG3zWyHjgDrXOfbabezsy+CiQCVZp1/HzjNtatUpmLeTkYsCkSB3lXAToxB/+3hKDgYbBBd3utmIP5RuBhuAp/Kb1ZlbkfNoP/zQ9s1289tN2v7bCoWBaJAFIgCUSAKRIEoEAWiQBSIAlEgCkSBKNBCBYofpwR+nRoM0Oejb8wAugF2g0UmBuhDc1r59G9lMKNyntWaasXvZABO0zeluU+P1yDj3aC/3uP2WH2wZzJsChuAT7ua0NAPfj9OoyFTL3UwMGpASu0uh3NB/fT1qW3RmdlYhxXwmk2Br8GOLToW9zEbXoJSL3ifj4dD4O3gfVWP6Zv2qXCTFm4Gy1qpa5htiXlvrwq7wD/AtuC51WuL+cEZcAHMBO+JoguzPWFeL/+NygFwEmwF1ueNmtduAZwH58NDUGICzMZGUMA29HBopCyOsNmavrqGtWzXq817/XfQa+W6+jxa+rkTF6ylJ5SNR4EBUMCsN7P+doLjYF8YB/1wP1thPw1m8cp8aGbHymw+BwJOTSiwI5dGAhFiUSAKRIEoEAWiQBSIAlEgCkSBKBAFokAU6EMFir/MqT61EmAviQDLscx5A0tS1mf2T+aH++yyVpjHYCBQKo/Z4P9qYFBsKpgUsB6sDAbLXH9QTf/eQjC4dzH4UJFP+RogSjIAInTQVmXf/wnHQuX9xcem2pNs7TPwBHhP+MYBA8cmAdRrBtC/DQbQZ8HL0Eofsrp4f3tPfxKOBOules1A/5Xgsd8C+sF7rfyrg/XboXA8TAHrwUbNa2dy0FlgQscCMN7QyuvJ5vvKJnM2t4IPnrbTTNA4EazbK81rZx3vvd5r5bvyPFo+38oKt+UHnx1EgQFRwPvURs6s3/3hfbAHrAj9cg8bjL8dfJXSA/AraJbZyNvZETPDbDhsGNLII0IsCkSBKBAFokAUiAJRIApEgSgQBaJAFIgCA6RApS/NgLmfnRZ8athEAKcFA1Iu0z9X+Xs+LrHKZZXz5ftmTN2ueJweh/wZGPj3+EwMGA+TYH0wMcAHiDwHfzNI5gM/M+F78BOYC/oa8yAQIrTZLJ9Hw7fA+Vaavt6vgcHeo2Av8B6pxywj18IpcCM8C263lX5kj9HAqm8q+DxMhXrN4zNR4atwFcwDfeKtPG423zSzbvOtJ5uCsY8jwViIyxsxz/t/4Do4A7yWz0Cv6MGhdo15Dd4OF0G72xLjRN4Txo4qzWt7Pwz3ZoDK9QZ+vtEbaOCFiwBRoMUKeG/aQZ8CB4CdFrMW7dD3i9kJsRI36H8XGKBvZsbWL9iemb4+7W/g3w5cM7fP5mJRIApEgSgQBaJAFIgCUSAKRIEoEAWiQBSIAn2gQKWf3PmCAQ/xs1ODmCYDlCQBp5XLynosfk3wyu+0Mn3l09j+ui0xIcDjM5jofDmmlZlfCybBBjAZxoE+RtfvdzNAdAOcCbeB/wfdZIA/QKy1ClguJ4JvZNi6tbt6desLmPOhOZNf6rUn+IGB/x/DTPBp+lYHjD3WbeEv4WDw/q3XDIaeB9+Fe0CfeK/4wK2DvFbbwTHgU/8rgWWnEfN6Gei/BM4BYw7GBVp9HdlF35pvyfG+8Pq0277ODi+C6vLsvWrCi294iY2gQKM30gibzFdRIAo0oID3ovhqn83g/WDW32Sww94vZud6PvwErofnwMB8s8xK/3kw8G9n5zfgPtPII0IsCkSBKBAFokAUiAJRIApEgSgQBaJAFIgCUaAhBSr96MWPZ/CqzDut/GwgrwThy5sEytSkgWKVvy/LmjEt2/WYTAgQj0kM/ht0Wxc2gk1gAhiMdL1+NH2D+gqvgnPhdngWDPImGQARWmCW88PgHOjmcqVv+mrwtfk3gv7qVpcJ64L14L3wWVgV6jWDopbjb8K1sBh6xQ9ueVgDdoOPwB5gXKRR87znwffhQjCBw7hAYgKIMEaznbgJJo5xO/X+3PvSsjGr6ode00fAZK7qxICqVfPRjkAsCkSBzijg/WcnfDXYBewQvRVs/FzeL2albIf6Z0MsYGoF3kwzk8/Avx15cftpABAhFgWiQBSIAlEgCkSBKBAFosCSYMfm6PBXcBAY4NAcp5wB/wWOJzKGQIRY1ylgsG4crACOfS23OrpjUSAKdJ8C1b52PxeqkwMMABrwcrr80LwBU638pnp7r3w7tr9l2yUxoCQFeBwrg0Ee20wx8OMx9pOfktNZEhR8munlcBH41LTBpCQDIEITTR/3ubB/E7fZ7E09zgYNoPvU/2ywDLQyaOy9ZPKNsYAvwXbQyP31DL87C86DB6BXgt3WN+vAfvBR8O0Hpd5jtm77Hb/w/j0TroCFYFygldeQzQ+M2V54/6qtSRvtNGNIfw7GeirNfwfg6/8t87FRFGhFJ2KUXebrKDDQCnjPmf1rQ7c3HA07gwP5frsfrYxvg8vgQWh2pWwDr+PjRXBfvwIddmngESEWBaJAFBhgBXw92RR4DxwAa4EDlZfBzOGL4UooA0NmY1EgCkSBKNDHChi42AkM9K8/zHk6ftDp+xmYM8z3WRQFOqWAQbi3w5thEuggd9w7F66Dn8B8yBgYEWJRoIcUqPT/OW/wTxyzeJ/bbjmmcaq/UD+i61XCx6ZaOaZyHAbpPBaPwyDuRmCgzqnBS4+1X8w61Ce+r4ML4E54An4NJluljkWEBm0bfjcNLDPdZvoHTAD5NtwCL4B+5Vaa9/QG8Ek4Evxcr+kPvw5OgRvBRIBWHze7GLMtwxYmwEHgU92bgPVMo+b9eQOcBjPAhJ5e0IHD7CkzMezfwDLbbjufHZ4K1sOVZv38GJjoERtFgdK4j7Javo4CUWAMCnif2chNhXcPsRnTN0K/mZ2Qh+BSsMPc7M6Tne7yxINBf+et7NPAI0IsCkSBKDDgCpQEu0+jw/Gw4lL0cPBgW/V3YBazA/84dRAhFgWiQBToUwXW4ry+CkeMcH62DTojTx9hnXwVBdqpwD7sTIerwRP9CdX2axbo8P4i3AyxKBAFel+B4qd3KgbjRf+hQRiD8QXHPiV4VtYvv+erpljZrsfgvtynActxMAm2gi3Adtbvmr1/Ntl2c1xoopU+Td8McD3MgxdBn2fGjYhQhx3Kuj+CbkoY8RrOhVPgEpgF9gNbeW29f9aEQ+DzMAXqtXLcJ/ND/RhzoBd8GdZdnu874CSYDNYpjZga+DS4D3ScCbeCsYdWXjs2P9BmAtjVYH3fTvOafg58uLTSXP4wJOGjUpUR5v/PCN/lqygQBRpXwIZsJbByfBe8E9aDburwcDhNMYPvi8DOxzR4CgzKN8us2H8Lz4ON+v+Azg7363exKBAFokAUiAK2uw6o/xWOhloGlL5F5gTwTTXNbLfYXCwKRIEoEAW6SAGDE9b1k0Y5pnP53qexYlGgkwrop9sODEqMH+VAHA/fBZbbmZDxMSLEokCfKVB8904L+hYNKBpYMyAvJgmYHFC+Y/bV9cu807Gax+BYy/0Y9PcYVoaJYHu7JUwAj6mWMRmrdbU5TlwAVw/xANMnwCCk36XeRYQR7ON89/URvm/3VyZ3mJDwHbgDvI6tvoY+mLA9/CUcBN679ZrHfQGcDXeDCSn6xbvZrAM2gsPhOLBPY/3RiHmNjDdcBOfAffBLaPW1YxcDbzujwAwYLhm1leJYxj8MT1btxOtuPfwbyPWvEme4j41UOMNtJ8uiwKArYANm59csWCvGY2BvWA36ocPLabzGrHCnwyUwF16GZppBf/fxHBj0F7NtY1EgCkSBKBAFqhWwrd0RjoBa213b7H8Ds8YXQwYPiBCLAlEgCvShAo7VHFeMZjpoHdP5FFgsCnRCAcuqPoT/Ah3lo5nrbwsfhH+BlyAWBaJAfylQxihl6tnZTumDMyjogzKOf6wPbMPEQI3BN5MCxHmXlTiA62pOy/ySBTX88Tjcf/UxLGSZT2q6HxMRTM7eGLaBDWAVKPtntmfMY54CJ8IJoG/S4KMPQc2A2fAM6MNM/wERqmzVqs+d+miw3Ov2NfgZLAKvV+V9xcem2rJsbRK8Hz4F3gP1msd9J3wDTEIx+aTVx80uGjbrkxVgUzgW3gPjoN56hp8sMc9/PnwPTICYCQn8IkKbzPpvf7Beb7c9yA5NAqg2270kX1WrMsLnXmx4RzidfBUF2qaADZd4D02At8HhsD0sD402bPy0q80K9hEwW/JmsNJtptlhdpu/ACt5n/R3mQ1+LApEgSgQBaLA0hTQ6bUP+BRKPbYhKx8MZ0ArB//1HFPWjQJRIApEgeYq8DSb02m6PhgIGc4cbzwGBi1eGm6FLIsCbVDA/sxbYfc69qXv4RA4F+6r43dZNQpEgd5VoHLc4nzxmemz0/Sr6VOzfrBeMSnAeYM4BiVLcoBTKfEB1ykwW5O5/3IMPrSjH0+f3lPwMPwU3IfJTZNgK/BNAWtDJ4JK7LZhUxuDm7sOYSDWPsZNYELAbbAAPH+vhboMui2t39VOXZ5lZ6fDD8AAsmW0ldfGe87yblv+JbDMW3bqtWf4wXfgPHgA9I+38rjZfMPm+fkmkK3heDh06DOThsz75yE4Cy6FeWD90q3nz6H1pXn/7tOhM/s5+7XMV5rX3/vZujdWowKlga9x9awWBQZWgdJQ22m2QdsSDBi8AyZDvQEHftIzVipXO7NXwSJodkX7Ats0i9YBilMr+DJwYTYWBaJAFIgCUWBEBWyrHMzbdthW12q27/vBmbX+IOtFgSgQBaJAzynwPEf8TTAAcQQ4nqu2x1lwLZRxX/X3+RwF2qGAgbmjoZ6+jMe1LqwD90Oc44gQiwIDqkDl/V/m9d8V/9pvmLedK1jXGKwsiQHLM29baTKcy0pd5Dq1to/uV0xKcL/u07fwGLSZDTeCdZ1t8SQwOLoNjAf32UumPmvDu4d4mel8uA6mgXXyYtDXOWjBS6+l19Z+VyftQXb+V2AwUd+z5bJV5j3i/bMJ/CX4oKBlvV6zHF0D3wKP23unlcfN5hs26waTHbaDD8NbwCSZRs1zN5HmdLgaFoPnbp0Sa78C9i93af9ul9SX3rvV192Yke1J9fIOHGLv7DIJAL1zrXKk7VfAhtuGzMZ7CuwDduq2hZWg1s4vq/ak/YqjvhMuATutZkg2y6yo3Z6dLyvuX0AJ+qcSR4xYFIgCUSAK1KWAg0IHiDfDXlBPG91rjiZOLxYFokAUiAJ1KFAc8ufym5lwDGwGOu4de+hcPBkeAYMVsSjQKQVWZ8dbN7Bzne0G7PRfNDtZv4HDyU+iQBToMgUq/WyV86W+MEjv+MlAo1Pbx2XBpz+tW6xjnHeZ9YzrFJgd1dyn+xLbZIM4JuctgFtAv6sJAZPBN6uK9aHH0UvmuHKDIY5nqq72O66FaWBA60nQH1q0Z7YvzHKxGpjMcRC8HdYHl3fS7NfdAc+1+CCMsZmIdxh8Fgyc1mveJ4+BfdIrYR50a+KI13UNMDj8UdAH433cqHlPzIBT4Qbw7Qf/C7HOKWAdvwOMJaGj0aO3nrQMVJuxI9uQynasep18rlIgCQBVguTjQCtQOq92aifBHnAI7AR2PAfhfrFj8Sj8FG6FZmYZ2nC7fYP9dvRNMLDTb2csFTcixKJAFIgCUaBhBWxjFsGn4WuwO9Ritj/3gm/y6TcnTC3nn3WiQBSIAoOggHW945Cn4S6YCzvDRHCZyWM6WU1O1rEUiwKdUmAKO/YJ3HrNoJNOeAMGOtFjUSAKRIFaFKj0xVXOl0D9L9mIvlKDffpETQCwjjLQ54NRjqHE74tPldlRzX1J8RNab+knXAj6Ik06WAu2BH2zBpJ7LWlbPdRp6yE+xVQf6MMwDW6CR2AxqLOaV14DPna1ec1XBK/RXrAvGPw3CcDvusU24kDGgwHFVujrdV4F9D98FvaERs7/JX73fTgH7gPLhPdHt9nrOaA1wfvyY7ArWC80Yl4Pz9O+y5lgf9y+eCuuE5uN1amAdb7X2TLebruNHVo2Ks1y4bitG++LyuPsuvlBCGh2neg5oK5QoFRe3gNmMk0BG+kDYHtYHQbl/rCTuQCughnwFDQzKG9HXnzllcF/538LadARIRYFokAUiAJNUcA2xScsbMPOhvVgIoxmtn93w8qQtmk0tfJ9FIgCUaB3FXDMsxAcB9peXAqa7YdPkviUicGHOJUQIdYxBcaxZ8fijdi+/Og/wfF2LApEgSgwFgWKv65MbUOtm2w/9evZlhoINPDnQ1QGgk0MEH2pfm8Q1Gkt5n5Ke2ybbKD8WXgULgefNN4M9oJNwMB6r5la6H/eYQjP1wejZsGNQzzI9HEwCGriYtGf2Y6b19tAtwkN+w3hNfHaNxLw5mdtMcvn/mBQXU2baZbDDeGD8GGw/Ndr3lte/2+APvkS4Oyma89hLbmv12G6D3wMjJ28ARoxz803MlwCZ8Nd8BJ02zlzSANt9kl9MLbdZjm4HarHZPrr7ONWL2dRbCQFBiXAOZIG+W4wFCidTjNGzVTbAvaBPWFTMHO1mzssHF5TzQ6G2Y9XwzSwg9nMoL+VsR1Ws7UM/NuptRPvfmNRIApEgSgQBVqhgAMFHVKz4Fz4BCwHSzPbpNPAdkrHQCwKRIEoEAX6WwGdi7YRJn05/tMfojPJcYvtRzPHQ2wuFgXqVmARv5BV6/7lKw8xGJwxeBCLAlEgCjRbgRKcK1P9fgZUDdYbzNPvakDQ8ZeBUQPDzjvO8jvR71r8s8wu1dyHYzWxnXa8Ng+uA+vHzcGgrskAvTqOUwcDxlsN8VGm+k0Xg8GvGXAPzAfrdf2q7Qx8ea1WADXeC94G24J9qFquIat1jR3OkZwDTzTpiOw/rgUHwt/BZGhEk4X87nT4IcyEbkv64JCW9JUnMPV++zhsObSMSd3mfW0s4gL4b3gALNelTmE21kUKbMSxrN2B4zHRbB5Ul4uSAFC9vAOH2Fu7tMKKRYF+VeD1nJidThuqncDGatehz3YQG2mc+VnPmh1nnQnTh1jA1M5lMyvO0vF3agfdzov7beY+2FwsCkSBKBAFosCwCpgR/CA4tQ36IKwLOjCq7WEW3Aw6UtInrlYnn6NAFIgC/aeA9b1OJcdAOtOLOVYplGWZRoFOKPAYO70Q9GEYZKnH9G+kP1OPYlk3CkSBsShQ/Hxl6rZKwP555h1/6ZddBpYHkwHK1EQB6yzXGc03W9pngz9iwp4PMV0PPqG6ObwZDFS7r142j3/SEIcxtd9ikqLj2xvhVjBQrG9XHZrlb/UaeK3WgKmwNewNu4EBwF5vW0xcWB+eAjVt1CyvJra4vb+G/aARbQx6/xjOgDvAa1l5H/Gx4+Y9OhHeDsfDpmAZacTUfD58H86HR8B7udvOmUOKDSngtd4RGinfYxVxNht4tmojlhWXjeX+rdrk4HzsxEUcHHVzpu1UoAx2fZJjA9gDbIi3g0F6nT+n+ydm8GMhTIMZYLZjM4P+VsB2OO3ci4F/aVYnlE3FokAUiAJRIArUrIDtjwP74hC5j/nJcAjYNygOJgcOOth/D/7Gp0JjUSAKRIEo0P8KlPFL/59pzrAXFTCJ/hSwv3IMjIcSKGN2RHMc7vjf31rOY1EgCkSBditg3VPqH8dbjrP0QVo/WTe9DqzTfCjLZACfMPfBrWXBgJPfy0jmdt2mOIbT53k9GKjeBvaGqdDryQCcwhItVmPqOFbU1sCpCQD3wm3wEOjrLb5YE+GdLw9lqa1vh9Ff7rz6ei3U22uwDpg84b8m8G0Ea4HXqJ/M8zkB9A3oJ2jE1M5ydRJ8CNSzXvN+uBu+DtfAYnBZuWeY7bh530yGd8FxsCFYZhoxz+0xOAsugjlgP6WbzpfDiQ2jwIose+8wy9ux6HZ2Yv1eaZYlE2es/2N1KvBnda6f1aNAtyhgZ8UG3OC+2Z77wn6wMdgIN9o48dOeN59qmQnTwM6gHUEryGY2sDbYVrwG/e1w27lM9h4ixKJAFIgCUaCjCtjWiW3h47AcmCksDl51DGkPws/BdXWO2I7FokAUiAJRIApEgSjQSQVMTHR8fRXcBfZdDoK9YSQfh/0Zgwr6SPSV+DkWBaJAFOi0AqUuKlN9k9ZzjtVeAOs1A9EmBDhuMyC9MhiENGbh99ZpS7PKZAADu3PgSlgDDGbvAxuAwdt+MLVQq6lDvJOp2pbgmONadbUdKT5aNVRP9TWoZ9KF825HjUfSl6/7xt7KmUyEB6CUx1pOzvJpUsQh8FmwPNWrmft7Ek6DC+FR8B6o5zhYvaVmeZgMh8NxQ/OWnUbMmIE6nwE/hflgGe2m8+VwYiMoMJ7vfOtDu80yYnKTdXulWZ/9ElKGKlWpcd6KPhYFul0BG1bLqp2UdWF72Ad2BRtvG6l6G19+0jdm5Wfn7j64Bqwo7fQ1u3F1e1a4diafAzsrYuc9FgWiQBSIAlGgmxRwwGB75UB7NTDz/DOwC+gcugVMZHMQ8dTQfAYTCBGLAlEgCkSBKBAFOqaAfRHH3CbaG8y/c2h+I6Y6Y5fm93iC7/QF+P3S1uGrWBSIAlGgowqU8VaZOmbT1/gy6Ne0/ioJASYDGLA2WC0uNyC5tDrObVl/ui19ovPAetGxoIGs3cEHyPQtL20bfNVz5rnoM/dhOFkPYq9VwCD+TvAQWOZGM3U1GWVb+By8GSyD9Zp+80vhNLgdLJuW1W6xN3EgU+EI+CBYfhq9P7z37gDP9WfgGw4813K/MxvrAQWsZ7cE68p2m766+cPs1OXetylLw4gz2qIkAIymUL5vtwKl42InzwGuFY6B/p1hA7Azk3L7SqX3LFrcDNfCbPDpRSvCZleGBkjctpWtHRU7L7+DZu+HTcaiQBSIAlEgCjRNAduveWC7Zf/BzyYC2H7pHLJtew5s29KmIUIsCkSBKBAFokAU6LgCOjgfBx2wq4B9mVPhWJgAlf6Qsu6ZLPc3jtNjUSAKRIFeUqCMw8rUgKF1mYnaT4N1ngkA+okNSDk1Qco6crhApdsRx3viWM+A0gywTp0M28N2sDZU1ql8jPWpApYVHwi4EhZBKW/MvsYsb1PhRDgOLHP1muX4HvgmXAW20S4bab983TYz8L8BHAlHgzGY4e4nFo9q+ltugtPgOngKPNdYbyqwDIf9Tmi0PIzlrB/lx9b91WaCWMpUtSo1fk4jV6NQWa0lCliR2Gkzo86B7NawC9gRmwIuTxlFhCGzAzwP7LT62mI7sC5rRefBSvV5sII18G/Q5GVIZYsIsSgQBaJAFOgJBWwffw22lw5wffXj60Bnue2nA9XfQyvaUTYbiwJRIApEgSgQBaJAQwo4Bp8NBqtMYnwGfABgR9Bhb3DCfoyOUh8KcOpnx+2xKBAFokAvK1DGZk71QTp20x9pIN+xnPVf+VcBTh3njfRkttsQx3/6OA3+3gH+dhxsDPqhNwPrXPcRa74CXk/ppL5e6z3hIjA5pNoMfK4Lh8FfgLGKRoKgT/K7M+ACeAgsv6VcM9tR8/7ZEI4aYi2mjZwjP1vS57iG6elwIxhH8F6L9bYCa3D4u3foFOzTer9UWqn/U7YqValjPsHVOsTKqg0rUBqSEuyfzJbMtNwVtoFJYDbdSB02vh44s3PgAN7Ogg3q3eCTinZaW9FxsCL1aUgbbLOt7Bhbybq8Fftjs7EoEAWiQBSIAi1VwPbrD2C7NlwmcUt3no1HgSgQBaJAFIgCUaABBey/mMRogMLgv0mMK8NsMCFA34pjdRMFTGYUPzuGz9gdEWJRIAr0jQLWaaVe0z9pvWdd9xQY/PetAAbunb4RDDAXPzSzf2Jup9SZ+ltNrLJevRb8/dqwEeizNkhqfdvJgDW773lzLP4gXApvhy2hU2bc4Vi4CioTAFy+OuwPnwFjFY1cd9vty8CA+G1g0oplthvMhBfL9jFwJBjkXdp9wlcjmvffFeB53gqeZ7lHmY31uAJTOP7xHTgH64oHoPqe8b5qVSysA6fZ/l0mAaD9mvf7HkvjYdacHSUrje1hN7ABnQg2OjausdcqYCVnAP52MOj/KNi5rezw8rFpZgXqkwI23i+AlarYIY5FgSgQBaJAFIgCUSAKRIEoEAWiQBSIAu1XQB+AztCCTxTqsC9P+et7KQ531zGQ5XdlGbOxKBAFokBfKVDqN6digrcPMj0D+qFNlFoRTAgwUUrfc/FTM/sn5u9L/WowWL+o9azBaoObbmctMBFAf3ZJCIg/GzFqMPW8AdRyJuh/1ue9BSztmvBVy20v9mASwgwwyL8q+IDiJ2FvaCRWZjm6E74B18AT4DLLWKfNxBbffHAsvA/GQSP6ey7GDS6F78Ad4P3XDefIYcSapID3xH7QiXrOeJiJXdVmgon3U6xBBRqp1BrcVX7WhwrYYIidLDtXU8Esyd1hK5gACfYjwihmsH0h2DG6CebAy2Aj2oqG1ErTDrKdMbHBtrPrcbRif2w2FgWiQBSIAlEgCkSBKBAFokAUiAJRIAo0oIBBk1ngmH1N0EGrL8bxu+N7g1b6FPw+FgWiQBQYBAWK/9KpWP/5QFPlmwF8ME1GezMAq7xan1qn+qCUQadFcD9cDvq3TQjYALaGjUBf+J9B7BUF9GU/Aj8DA8S2TepZTC3VdN2yoAPTN7HPd8BcMBHgY7A/LAP1muXucTgVLoQ5oH/d5Z02A/+bwrFwBKwGjQb+n+W3P4Tvwt3gfdYN58hhxJqswIps76Amb7PWzVk/GKOqNOt16+LYGBRo5MYfw+7y0x5WwLLiINOG0myx9WEHMEtuc1gHlgPXiY2ugI2lGZDXwu1gY2pHSWtFI+q27YRYaRr0NwHADq0dsVbsj83GokAUiAJRIApEgSgQBaJAFIgCUSAKRIEmKFB8MgahDGgZdPKJSsf45cn/jO0RIxYFosDAK2B9WepMg//6qw3WOy0PqpV1WFSTle35ZgH/LYsB1tVhMmwBBlvXgEYCyfysZ80EtQUwDW4Zmi9tEh//xIwpHAlH/cnS9n/QL/4AbAtey0bMbRgUPxMMihu47IY22ACuZfFD8B4w8N+IeS5Pwvnw33A/GEfohnPkMGItUsDEpjvBerLd9h/s8HKoLGPGsu6FpdUpfBUbTQEbr1gUKAqU8vB6FthBWhUM7PuqGJ/st2E009EEABvIsj6zsVEUsEP0C7gLroGHwIG6yysrNj42xdymzgA7JHZCnJp0YMWZpwIQIRYFokAUiAJRIApEgSgQBaJAFIgCUaDHFBjOD9MKn0KPyZLDjQJRIAq8RoFSXzr1gTWD9wa2DJIawDcg7TL94PU+0Fa2aTKWPvKSZDCBeQOwm4Pz7qPebfOTrjbbnGfgRrgBZoE+b33cSzN90T6MpibnQicCjOx2zOYDdtPhm+C5PwcjnTdft8Us05vB8WDg34SXRsxzWQzfA6/Tw2AsIf0MROhzs057P/w3ON9OM4b1MXisaqdP8flR8PtYgwrYSMUGUwFvZK+/HZQ1wQbYLB87KDYYU8DldlTsCMXqV8DKaRH8HG4CO0QlW65VDacZUWJygR0r5+2cdENnhMOIRYEoEAWiQBSIAlEgCkSBKBAFokAUiAINKtAqX0KDh5OfRYEoEAW6VoFSXzrVL+pbUA1m+r+mDcrr89Yv7ltVDEj7MJy+cr8bLQDmNt1e2aYBcINV+n71A5ftrsW8Pnb97euDD9u5j9G2zypdZZ7vC3AbXA8zweC3gf2RzO99MO0JUCMTB66BQ6GXzPLzIJwCl8MC8NqXMsZsR8zAv2XrBDgcVoZGzPObD+fA+WDQ1XhCp8+PQ4i1SQHrQ+/LTtRNT7Nf64ZKs+wZ10oZrFSlgXkbnFj/KuANa6dlGTCzcU2YDJvBVmDA38C/nY9lwXVjjStgY2mn5iGYDvfAs2CDqbWiwnKft/tUQQAAQABJREFUpfPqvsUkAztYfheLAlEgCkSBKBAFokAUiAJRIApEgSgQBaJAFIgCUSAKDLIC+mWLb7b4cPWjGngyRlL85/rQxbcDuPz1UIvP3G2KD4T5UJbbnQe+DfYnsDwYoF0bNoBNYSKsBN0YozHA7Tl4/LeAQf/i5y46smhY0y9twoDrPw8lmOwDaz+Ct4AJF91unqfB/tPgIngMyrkw2zGzzJTA/2HMW64aMa/xHDgLfgizwfI72vVllVifKWB8cM8OndOd7Ne6uNpGe7NI9fr5PIwC3di4DHOYWTSKAgb6xY6Kr3gxu3B92AK2go1hHVgR7Ly4bmzsCtgYGmw3Q+5msDO0EOzklYayTFnUFHN7NsRWiu7HivDXUIL+zMaiQBSIAlEgCkSBKBAFokAUiAJRIApEgSgQBaJAFIgCUWAYBfSvFp+tQXsDoQZ29bX6JL9BfwPUPhVrMoA+dT/rUzeeUnzxzC7V3L6BcNF3a0B8MTwK+pDdnm8KMPA2HvTll4f1DPB6DO304Xuc/gtZg8A+1OYDbgvAoL0PnxW9mF2q+cRuedLfbblNtS3mdtzuz+DtZWEXTj1Xkx/Ohh+Ax+w1rEUDVmuZWS6M95wI7wY/N2Jek5lwJlwMc8Fr1enz4xBiHVDAesb44Vod2Ldl7naorCc8DGNfeQOASozRkgAwRgHb/PPSuTCIbwVfOgdbMy92EswitFPS7k4Cu+x7s0Kyo/I4WDHdBnaKKrORWtVQ2skQO4t2RsVEADuprdonm45FgSgQBaJAFIgCUSAKRIEoEAWiQBSIAlEgCkSBKBAF+lYBfauV/lWDUeUBLAPB+tn1x5eEAAP3PtHvcvENAY28JeBZfqef+SG4Hty+29bv75t8J8EGMBFWg2XB/TSaGOA5em76tw3sm5AwdwiPw3N1uT7oWgPC6uT6nou4bfehz7raXP4EXAEHgEkQ3WTqo+/9fDgH7gMTGSrLBh/bbiXwfxJ7fhc0Gvj3mj4Ip8NlsAC8Jp0+Pw4h1kEFfKj4rWBd1m4z8WoOVJdBl6dsNuFqJAGgCSK2aBM25DboNvyrw/qwHewIm8E6YKC/ZB0yG2uyAnZU7MDMhzuHsEIyA8kGs1h1BVWWj2XqNn2q306H+7OzYQeqBP2ZjUWBKBAFokAUiAJRIApEgSgQBaJAFIgCUSAKRIEoEAWiQBMV0C9b6e81EGVASj/x86DP3mCZfvuSFLAC8wbSyndOazH9z6LP1we+NH/7CNwCBsndR0k6MB6wKpgQIAaD/c4EBeMJZVseq/5kA/rivNt3uVPR5+zneoNtZR9u9+mh7Xj8owXs/J2/mQN3wy7QDea19rr+CM6C++EXUFkG+Nh289puAWMN/Htt7oHT4HIw8aMytsHH2AArYN1lQk4nbC47tQ6ptudYYH0RG6MCSQAYo4BN+nllsH8c29wUdhtiE6YuM8Ov0ew+fhqrQQE7KTbuM+F2uBeeAF834nfFWtH4u00bYzte7s/OkAkAdsLSICNCLApEgSgQBaJAFIgCUSAKRIEoEAWiQBSIAlEgCkSBKNBmBYovuPiH9dHrx/VhLX24JehvEN5gvAE1p+J35XuntZiBrxJkL0kB/q5sy/1UYoynctsep3iMBved6l92WTkXZusyf+cxeTwG7Irv2m26vJbtuo7+7oVwIewAnY5PLeIYzoPvg0kX+uZrORdWa5mVwP+J7OHd4OdGzGt/B5wKV8GT4PWKRYFKBabwwXhkJ8zyaTmtNOsr42Odvg8rj6ln5ztdwfascGM48BLEN0vQzoA32B7wZtgW1gSzBct6zMZapICViR2WB+AWeBDMLnJ5qWDKlEUtMTs9Vmg+6e/UTob7r7XjxKqxKBAFokAUiAJRIApEgSgQBaJAFIgCUSAKRIEoEAWiQBRogwLFX1ymBlUNsBvI0r/7LJSg/5uYl5IYoN/fuEAJ5lcG7lk8oukvFvflQ2OtNs/P/bkvn5DXf20CgOdbAv/M1mxuz9+5Hf3wt8Ju0CnzeL4J3wATGsr1ZLYj5tsdtoCTYCyBfxNTjHV8G66BZ0DdY1GgWgHjw3uDDx+327zffBNIddk0Nmad0+n7kUPofUsCQHuuocF8sbGfCvvDO2ArWBls9GOtVcAKw8bPJ/rvARvB2WCHozLLqNUVi9s36F86TL5xwOMqQX9mY1EgCkSBKBAFokAUiAJRIApEgSgQBaJAFIgCUSAKRIEo0CMK6POt9CsbC9Dfq9/X4HJJCDAOYLDNhIDlobzi3/XLOiWWwKK2WjkHj9tAv8kMHrsPrBmkM/GgrMNsQ+Z2fABPn/glsBN0Kkalzu+Fs0BffafMwP/mYOD/MGj0iX9jDjeCgf9poM4mcAySeU196Nb7y7dkGPfxnjOgbDm2DMf+qID1zwF//NjWOe+5ucPs0TrHeqKyPh1mtSyqRYFOVa61HFuvr2NlY+WyOuwBVt57wVqQgD8itNhs3OykzIfb4U5YCHYuSkXfjkrEfbi/l8Djcf82OFKOg9lYFIgCUSAKRIEoEAWiQBSIAlEgCkSBKBAFokAUiAJRIAr0gQLF71ymJYDuqekX1kdc4gcG/g3ElWQAEwN8U4ABTNfx+wKzTTF95+JxFQz6GyR1ajC5LHe9ch7MjsncjkkRT8HDcDfsAJ2yTdnx9rAYPM92moHqysC/D4o2Ypan6+BUmAEGVtt9Luyy7eY9YbLEJNgatoOthj6rrW/iuBiuAPWYBZa7xGQQYcgmMN2lfGjz9BH2Z31TbdY/g1B+q8+7JZ+TANBcWW2QbZzHw4FwJFjxmHHkd7HWKGDHwYr7eXgUboMH4Akw6O730i6zc2QnycbXjCWPwYqrPOXfzmNht7EoEAWiQBSIAlEgCkSBKBAFokAUiAJRIApEgSgQBaJAFOigAsUn7LQEuEowUl9yZbDfBwiN3ZSkgJIg4NT1CswuNe5QuT/nRb+1Puriu9Z/7bzLPRaPS8r6zDbd3LY+c59Qvwy2hU49MGmSxUEwDXyArx1mcHozOBHeA40G/o03XA0G/m8Ck0pKuWK2r8zyrm7rwpawM1hupsAa4H1hGXK9YmsyY5zOIPOtsBZ4jdt1ndlVV5sJFCa/mETRCbuRnZoMVGnWQ5Zj64hYExRIAsDYRbRSsaGw8jkUPghW4L7KJ9YaBeyMWFEvALME7x+aN7utVBrtrCTclx0lsYIqAX+PxUqrGxpeXyVkZ8KOgbYaqOM8P8SiQBSIAlEgCkSBKBAFokAUiAJRIApEgSgQBaJAFIgCUaCtChQfdpnqS9aMOYi+XKflDQBODXSKsR3jEs67vPzGbRWftNPyUJo+aj+X70qgv/iuyzGwSkvN/elDNzD7EMyDqdAp8yHOU+A+aKUGvtnBuNEJ8F5YBRoxdbscTgP/zbFxklYeN5tvu1m214ENYUfYGTYGY3DqaJm3vI9mxkR2B2NI5a0ao/1mUL5Xw32gFh2brYl10gNQ6p6y/V8zY0yt38pzOb+2T5MA0JjkpTFdg58fDB+CHSBBf0RoonmjG6S2QzAf7gUrhoXg0/4l2M9sWysFj8t9WyHZ4P4PGPQ3AcDjra64WNQxs2EzG85y6lspJoPldxHcD9PgepgNHnssCkSBKBAFokAUiAJRIApEgSgQBaJAFIgCUSAKRIEoEAU6p4D+Z6m2Eqxb2rRy/fL7ym2VZZXrtXveYzAA+AT4wNp58Hko58RsW80noN8Hs0A/f7NtOTbovxow8H8ErAqNmDGSS+F0uB081m64nhzGmMzrrkYTwaf7d4NtwOD/amDMrdGy4e/cxuugJL4wG0MB35Dg2y86YU+yU98AUm3G2rxO/VCuq8+tI5+TAFC77FYWYobR9vApePPQZyaxMSpQGv4X2M4cuAcM9i8GG7eXoVi7KwD35/7NpjPQb0VksN8EgG5uOGzcjoPPwtpQaWvwYWs4DG6Fzw1Nuyl5gUOKRYEoEAWiQBSIAlEgCkSBKBAFokAUiAJRIApEgSgQBaIAChS/eJn2qij6oA0A6m+/A+bCFOiU+WbnU6CZCQBvYnubwPHwftBXX695nV+Ei+EMuBOMT/Ty9TcY75P564OBfgP+xtvWAxNCfDLdOFwzTJ2ML5lwYozJmE7sFQU2YrJWh8T4Ofs1xlZp1gnGBnu5bFeeT1fMJwGgtsvgq3SsgMwEOxEmQbMqITY1cOZNbKXrU/yz4F4w2O9T6d74fles3Te8+/NJeKkO+PvUv8vbfUzssm6zvP4NmFk40n3u/8fZE86Ej4BvA+iF8+MwY1EgCkSBKBAFokAUiAJRIApEgSgQBaJAFIgCUSAKRIEo0GMKFB+8wVmfiL8UPgGdirlszL4NRi+EsfrG9bcb+Pet0UeCgf96z8tjMHZyEei3vxt8GHGsx8Ym2m6e+0qgxjuAsYitYV0or/NntiVmksnVYDkz9uRDnrFXyuPOCDFS3KhVOlmGTfr5Q9UO/GwZNxEg1iQFOnGBm3ToLd+MFZPZSFvC5+AQWAFi9SngDV2C/Y8xb7D/QXgcDPYbUC/WiQbMfXoMZn+ZPWfQ34rGeY/biqcTx8VuGzLL7QT4CrwLaulcuM4G8HGw8m1mpiObi0WBKBAFokAUiAJRIApEgSgQBaJAFIgCUSAKRIEoEAWiQBR4VQEDfT6V/QzokzbgbbC8E2ac7IPgw3E+hdyILcuPDPwfBx+AcVCLb57VXjXjEM/ChXAWGEsxbtFL8QljaiuDAf+dYA8wuWId8FX/ft9KUyvjOrPgP+B7YPwngWVEGDKvz+HlQ5unxgTnD7NP43HG5XqprA9zGt21KAkAr70eVspW1lZK/wR7wRsgNroCVqJmUdloPwY2UI+AwX6DypVZPZ28ka3wPZ7KgL+Viw2D59DJY2P3Y7IN+fV/wsFQTwfDMr45TIX7IBYFokAUiAJRIApEgSgQBaJAFIgCUSAKRIEoEAWiQBSIAlGgFQrogze4beDfp7OvhPdDp2w/drwu1JsAYCzJYLeBf5/4XwPq8cuz+pJ4hDGV8+BseAB6JfDvua4IarAj7A3G1tTSf4PQjoC/MSmf9p8JN8N1cBeoaWVMio8xFBgPXq9O2EPs1Hu+2nwwt5fjctXn0xWfkwDwx8tgReXrWcxK+hvYH/x/I7HXKuCNWILoNs5WrA/CbHgSDKYbSNc6fdN6HGLA30rEqYH/38LL4PF1+hg5hDGb5dfg/8lwYINb81U8kyAJAA0KmJ9FgSgQBaJAFIgCUSAKRIEoEAWiQBSIAlEgCkSBKBAFokBNChicNUi7NlwD7wCfEu+ErcJOdwcDlCW2MdJxLMOXJfD/AebXAH309ZhxCeMp58J/gzEW4xbdHK8oAf8NOE4D/nvB9mDAvx1P+HttjOs8DQ/Dz+FaeABMAqjl2rHawJrXbzNYvkMK3MJ+vX6VZnk3dpdkjUpVmjCfBIBXRFQHs5K+AAeBT0PHXmloSqD/KQTxqf5HYC48AS9C5c3aDQ2TlYTZcSYhWGk4b9D/d0P0YwNgpT0FvgIHQqNm58JGMhYFokAUiAJRIApEgSgQBaJAFIgCUSAKRIEoEAWiQBSIAlGglQoYT9B/vxB8OO1qOBQ6YfrY3wsXwHBPKJdjKoH/97PgQ7Am+Nt6zPNeDN8bwkB2eViR2a4zH5z1zcEG/PeGnWA9WAFa/YS/8RzLiIkS94P/psGgf3mSvB/jPZxey2xZtmyiTb1lthkHZKzRJJfqa+b19V8DdEN8kcPoHxv0BAAL+Trw93A0dCq7jF131LzhbGB+AT7Rb6D/UZgDZuCVADqzr1o33IwG+w3sG+y3gvjV0LyB7NJgVlcmfNVXZhm28f0qmLwyFisN51i2kd9GgSgQBaJAFIgCUSAKRIEoEAWiQBSIAlEgCkSBKBAFokAUqEUB/ff+3/sX4Bro5AOau7B/n2QfLgHAwP8mcBR8ENaAeoOonuvjcA6cCzPB+EY3xFo4jFfNILFxsx1gH1CX9cFX/bf6rdlqZJxnMdwDNw5hvMr4Vb/HezjFlpqJNiZwdMJ8qFiqzQSAbrwPqo+z5z4PagKAFbOV2HvhP2BN6HezEbFy9GbyKe+5YKU5GxaAgX6/q25sqj+zStvNYzA7SExGsAEQj1dc7rm5XjccL4fRFrMcrwv/D942xj16/a8Ckyrc7iDpyOnGokAUiAJRIApEgSgQBaJAFIgCUSAKRIEoEAWiQBSIAlGgzQoU379vIDZOYVB88zYfQ9mdD4juCQ9CCTT79Ptm8EH4AKwGjQT+5/G7s+E8mAXGNLrFB2+c0BjZtuD5y4awKhjwr/d8+UnN9gfWNNbjWyDuhulwM8wB3/DcLRpxKH1hEziLyR06kzvZr/G9anuRBZaDWJMVGMQEACusTeBrsA+0svJi8203K0QbJ5+KfxqsKB8Gg/1mTXkzlafjmV1i3VSJeiy/G8LKwPNw6lP9BvutCAYx2M9pv8ZK8P89fDOWcmxn4zSwg2UWX163ggixKBAFokAUiAJRIApEgSgQBaJAFIgCUSAKRIEoEAWiQBRouQL6/H3q3rcT/wgMuI/F383PGzL3eTD4an5jED4BfwK8HfSb13tMntdsOBMuhLngsk7HYzwPg/tbwm6wL6j5GvAGqPc8+UnN5vkb7zEWcTtcD7eAcSwTATqtDYfQ17Y9Z+c1brd5XW8DY1GVZqzSmGWue6UqTZofpASAUqmdhHZ/B8s3ScNObsbK0qC4T2/bkJRAv9lSZkcZSLehqrRuupE8Fo/Rm97j9VxKsN/Av8curtdNx83hdNQsyzbGfwtHwVgaZHW9BGxovRbWCdVlhkWxKBAFokAUiAJRIApEgSgQBaJAFIgCUSAKRIEoEAWiQBSIAk1XQB+1wd9n4S54HCZAJ2wPdnoiGPTfGXwDQL1m3OYhOA0uBuM1LuuUGT/w7QYbgOe3DxgIXhs8v7HEF/j5iOZ5+8DhPPAJ8BngE/4mABgLStwHEdpkr2c/O0Irr/fSTsUHfGdD9fW2fPhd9XIWxcaqgMG+QTD/P8vu8G9g1lYnCji7HZP9nl8bJLeifABsQOaDjaI3SHXQtttuGI/HczDIXB3sN/DvjV4awW47dg6tq8zsvM+DySxjLctm2X0fTLjw2jiNRQEVeB1Yd071AzYHhqtrlnyZP1EgCkSBKBAFokAUiAJRIApEgSgQBaJAFIgCUSAKRIEGFTA28ASsC9fCMdAJW5md/jvoG63X9K/fA6fAFeAbmTsV69CvOxkM+O4Lu8BE8MHYRs6Nn9VkXscS8Df2cB04Na5l7KFTerDrgTev/V4dUsEkmBeG2bfLqt9YPsxqWdSIAoOQAOD/LjFY+mEwy6kXzErQytCbwqwo//eJFeRwN0M3Vpgek42dN66VvecivwEDiDYC/wuu143Hz2F1rdkB+YshxtpQW6a+ASaReB0sXyZnxAZbgf/D6e8N/wg7gZ1FzXv6VvgiXAvew7EoEAWiQBSIAlEgCkSBKBAFokAUiAJRIApEgSgQBaLAWBXQP238wCfDfw7vg+KXZLatVq/f3ZiHftNvwdXwNLQ77uHT3cbCtoX9YHfYCFaCVsYBqwP+M9ifr/SfDwn4I0IX2eocy4QOHc8d7Nf7u9IsO8aj2n2vVB5DX8+38sbvtHBWeHuAAc7NwKBWN5uF30rxJjAjyobO4Hkp/GXKoq4yj6sE931NT5m30RO/T7AfEZpgNtYnwV+D5Xss5v9U+gpYzjTLmh2T6krY72KDpcAnON3/guoy5v8GsuN4JfwtfBl8q0csCkSBKBAFokAUiAJRIApEgSgQBaJAFIgCUSAKRIEoMFYFjCP4kNpM8C3IBrO72YyHTAcD/zeAPvd2xXGMd60IW8BesO/QvEFe/bitiocZtP0fMJZlUHcamPzgZ/Vo1/mzq1idCqzL+m+q8zfNWv1eNlT9QKGfjUtVL2/WPgd+O/2aAGCg9NPwV7Bcl15lK0Ibs1JJPsS82S5WoFo3VZQei4E+sRI3sO/UYLHzPunvcbueN2s3HTuH0xdmxfx++AcYa+aj1+5kuA+8Xl4/X0dUmXDCx9gAKrAh5/yfUB38r5TidXz4ZzATdwbkfkeEWBSIAlEgCkSBKBAFokAUiAJRIApEgSgQBaJAFIgCY1KgBAR9UO1n0I0JAPpCjev8BE6DO8GAeDt8pMuyn8mwJxwA28N4cLk+21aY16QE/G9jfjr4hP9cMD7UjvNmN7EmKOD91KpyMtLh/Y4vy4OolesZD00ZqlSkyfP9mAAwFY2+DftDq7KcGr0MVpbPgNlg02AOVL4GpdOV5f/P3nuAW1KVeffz6UfOSG666SZHyUmRjAhiQMc0I8LomNOIaVTU0cEwY84KZlGCGVEEkQyi5CSxI003OSgY5puZ578WnO3/0Nx7+5x7T1Wd8HufZ92TK6zataue/b5V1/m7M5rUd7mkPPd9E8V+x/UojzxNVGzA5P8LwSuul5/ivNyOViReBBZtyF1gdWKu5kbCiMcbWP9OjgueKFhEYj9rn5aIgRiIgRiIgRiIgRiIgRiIgRiIgRiIgRiIgRiIgakacIz6dvCKYZODUx0PZxI9CXMiLteJ8G24BRxrrzKn40Va68OucCDsCZvASuBnVURJ+C9k4hY3mPD3rtXzoa5CB2aVqMDAkyuYZieTtGBGlgzbk+0tUZGBThI9Fc26ksnuzVS/B9MqmfrkJuoB4EE4F84AO0qT6r5f5cGByY8ZztOkrztWuYrfRL8HUylX8/u53y07YBPLyuxHPtxHnwmfhpWnaMPtfgKcBZ6cGLbNO8Btn4iBbqpqPWGwLb0dLCixP0nEQAzEQAzEQAzEQAzEQAzEQAzEQAzEQAzEQAzEwGQNmI/wTrW3tth2shPqwe/MiZjLsRjhW3AqOJZukUIV4UVX3tZ/J9gH9oOtYA0w4f9/oNdR8kSLmLAJ/7PhEpgLSfgjYYjCttREzGGmS+YO3LfMTdn+EhUZGJYCANfjSPgCeLuTfggPDHaYP4HrwCS7YcOuI5yPmOh15xKXyUeT/D535/I75bGuZWOWAxFWFzaZGF+W+e8PtuvVYSrhNj4FfgilLfp4W+t1tj0iEl239+1xdjr4f57+E86FP0AO3EhIxEAMxEAMxEAMxEAMxEAMxEAMxEAMxEAMxEAMdG3ABPsCOA+aKgAw+W3S/2Tw3zibV6liDN0cxKawNzwddoD1wNxAVQl/cx6LwPzV+eDFXRZcuI4Z10XCEMYyrNPMhtbrJua7ZLty/8q/pK54gwxDAcCqODoG3gpWSDUd97EAJv3PAm+tbsOu4sDAZP8WTt9krol9dxw78ILvlWUoj1UvD7Mc6LAdeRudteF5sCfozludW5V0GlwIVYbL8FQ4DtaZ4ozc3r8ET1ZsH4YFICb//T8r3hkgEQMa8KTvoC5VWH3qPmJxicVOH4UzwH8rkb4GCYkYiIEYiIEYiIEYiIEYiIEYiIEYiIEYiIEYiIGODTim+AB40dFrwPHHuuNuZvgecDl6Ga7LBrAHHNx63IjHFaGK/JZ5DXMBd8DVcD5YWDEbzA34eWL4DVhoslpDq2lbWzJPYJs0L7Xk+w0t4nDOdtALAOwoPw1/D1VUQ3Wz1Rfz5e+AHWhV1WAujx2y0zfhX5L9JflvZZyfu9MUeJro0oAH26fBJ2BzWPLA+zre86pnC0/mgB1VL8OTgF3A5P/0KU7YdmAxyvFg8YJhO7G93tt6zkMiBh4x8HP+vhk8Ieg23E+eDN8FTyb/A86EFAIgIREDMRADMRADMRADMRADMRADMRADMRADMRADMdCRAce0vbPx78G7ja4Bdce6zNALBB1Td3kmG+atvIh1V9gf9oPNYHVwPLXXeS2X1eTqnXAtnAMXwE1gwn8q68LPEwNqwAtem7h7uu3Nu020h++Zm0rxSbuVCp4PcgHAxvj4GuxbgZduJmnl1LfhXPCq+152oO4AdtZO1wOdiX6T//8FHgCdV4GniR4ZMOlucn/Lcaa3Cu+/EJ4LP4G3w23Qq22/A9My+b8pTDW8U8FX4YHWhCxWuAssALAdJWKg3cCVvDgbDm1/s8vnnrTahi0E+B1YCHAu5F8DICERAzEQAzEQAzEQAzEQAzEQAzEQAzEQAzEQAzGwVAOOY3vx3Y2w51K/3fsvmCx1fH42dDvu729N8h8AB8N2sA4sA1Ul/L1jgXdnPQ/OB4sncoU/EhKPGFidv09owIX7Trkwtcze3KZ5zm73q/L7PHZoYFALAOw8T4DdOlzPKr72MBP9BvwKelU5ZYM32W+i32k6D3cEiwA84KUiBgk1hAdkE5hLi2X5wgvhIPgQmLR3u0027IAtOvgieCX1VOM3TMBpefA3bEMWAiwEk/+D0sFuxLI+HbwrgnE5nAHzfZHoqYGHmNpHwIrUtac4ZdvzHvADcHtZCOC/GLBfG5S2x6ImYiAGYiAGYiAGYiAGYiAGYiAGYiAGYiAGYiAGajbg+KFjlV693kQBgGObW4D5n6XlZfzuhvAUOAR2h+ngXVb9rJehF8f274XrQT9e0HUDOPa/tGXlK4kRNODt/3tdfNKJRu9G7X7cHhYAmANNjqDdSgXPB7EAYBM8fAd2q8BHJ5O0Az0fPg/e2nqqHapJ2ZLwL1f5myBLwh8JDYX+n9jFvL0F0cfgRfAuuBD+Ct2Gbdt21Yu2fSnT+Rx4hwrDztT2OhcGpXP15GhX+CxYkFH6qyN5bhHAW8ArzBO9NXAVk/s4vBs8MZhquC95R4H94ZvwJbgJJrOP8LNEDMRADMRADMRADMRADMRADMRADMRADMRADMTACBgwR3IJOLbdRPKyjEm7HC5De6zOCy9+MuH/VNgUVoIyhs3TnoTzNWFqwv8GuAB+DSb/He9fcrl4KxEDjzNgDquJfcgCAAtW2uMBXphXTdttt1LB8153RhUs4mMmaRXVV2D3x7xb34t7mFW5inUqiX9/a5LZhu6jiX87cXeGNHokNBy3Mv87YXoXy2HnabL6NDBh/Rkw+d5JO/G3zsvf7QdTjSuZgPNf3JpQaW+38XpQkv8u+grwJtgF2isll+O1RRIvBwsdss8goYfhAfl0sD96B6wLvQgrXl8DzwcLZk6BRWDfl4iBGIiBGIiBGIiBGIiBGIiBGIiBGIiBGIiBGIiBdgMm3r2QyPFD78Zbd3jBnkl9x0tXBvNS3qn2qbA5ePGUObZeJlYd63Z97wPX/bwWV/Nowr+TfANfS8TAYwzYVpsIcwztbdb2bU60/b0mlmsk5tnLjqlqYVaomCD9R6h7uT3Q/Bq+AN7ifTIJRxv6n1q/N/FvIlacdqK/DHhQ/zd42xQWyyvU3wq/Bf+tw0QxjQ9N2D8Pptq2na/J1bvAdmpHaru7GexYB6m9TWd5rfDcAMaK3/Om/yphkNZprPXot/cstlgbNoYZ8C/gye1U2yaT+FvYNq+FD8B5YJ+Y7YiERAzEQAzEQAzEQAzEQAzEQAzEQAzEQAzEQAzEwN8MmBe6Htb/2zv1PTHhfi44TupY9SpQVcLfeZnwvxDOgSshY6ZIGLBwDN02Io6BO9ZubtCx7ybHv1/O/L8GdYeFLF4QWEIX18BDkCKAYqWiRxvhIITVXa+DF0Mvk1CdrLu3VrHwwI53Mg3SpOsfWlg88FewkbvzJ/rTgAn7H8ERsO4kF3FnfvczeA94pfPdMFb7MdH6UehF8t+TAgsJvHuBYRsz+T8XbIdNHmCYfdfhyZ0nVePFmnzg554IJXpnwHZqn+XB2b73/XAwvBIm2h583HHYj1u8cRKcCJ+GG8F9L30jEhIxEAMxEAMxEAMxEAMxEAMxEAMxEAMxEAMxEAOPjG/fiocmCgAcnz68x9vAsc/2K/wv5vVZcA04HjtWDoG3Ez00UJL0Jufb841uG18X/Ne2y4Bj5MuDF45612LvUOxr8XO/53dKvtXp+p7b0lygeRm3uTkaE99uZ3OF3lnC/E0duRuXvR/C8X9zpMkB1LA1SoOsYVaTnoU72z7wdqhzed05fwOfg5JQ5WlHYeM1gfYguDO7E7ujp/NGwgCE22kuHAfvBjvryYTJUhObe8JH4CawUy+xNk+OhX+E9gNN+bybx0v58qfgjtaPXAc7UtdjUCsFy4G2tUqPe9DvFuBdFhK9NeDdSRaBfa7t9DS4Gt4IFrdMtb0yiUfCE6SXwf7wPjgdLJYZtGIVFjkRAzEQAzEQAzEQAzEQAzEQAzEQAzEQAzEQAzHQYwOOp18OT+vxdOuanLki18E8kfmBi+BMuA686t/PE1MzYMLdcWbHrItP33Ns28S8eYTVWo8+t7DjSa3H1XlcFXx/xRYm9k3yS5mG0/d5eTRn5DykPY+x5Lh5WR4fHfM2V+iFnCfDzeB7s8H2YQ6xqnC5m4iy/mXeFjuYu1ry/fJ5HntowAbb72Hy6cPgDlpX2Ai/Bj8HO+dOw0Zr4v8euBdMwKYxI2HAwu1oJdavYT/YCyYbHgBeArvA0XAu2Mn7P4PeBf8MSx4UeKurMPn/GVjc9ivnMQ88ibANDmK4L7kP6WqssNpuV7gMPFAmemvAfWA+eOLhnTAWwHvhUHg59LJqcEOmdzycAJ8CT34sQkjEQAzEQAzEQAzEQAzEQAzEQAzEQAzEQAzEQAyMrgHH6q8aoNV3LN4xbXNEN8D5cDZcD171ncQnEpYS5ktKQr89d2JC3lyBCXsT92vCWrA2rAMm9X3fZL5j1yb0TeaXR4sBxOS9OG3zN0b7fB59p5q/LteB4HJ/Fhxz3wDM5zgeX1XUtX4TLb9t/+GJvpDPemug3wsA3PleAV5xWlfMYUafADvnbjpjd9CFYNLSxFUSkkgY4PBWJIvApOQW4EFkKrEZP7aq6wPwMzgK3gTlAMPTScUl/MoDRUn+lxOMebw3yMl/Fv+R5bfyzQP3WKE7757wAyh3Phjre3lvcgbKAfk2fm4h1LqtyfyUR/tH2++Wrfd68eBJ15Fgwc074Rx4AGzTiRiIgRiIgRiIgRiIgRiIgRiIgRiIgRiIgRiIgdE04FikY4RTHUvvtT3HT80DmRtyfN4k/9lwPtwCFgL4ncSjBkxCl8R+2Za+Lgl9r8w3D7M+rNd6bpLf902cL3mVvsl8c5yOK5fp9UOim8WZMFxWc05bgTnF5cBChWEsACjbhdV7JP7M34z3t2RU/dDvBQAbIeBoqGOntSM+B74EVmd1GjbW2+EusKNP4h8JQxBuR5PPs+Eb8FbwQDKVsNLso3Ak2MFPZXq214vAq6VdzhK2wTnwIAx6W/SAdy1sBuPFrnzwLPgWdHO3jvGml/cfa8B2ZpuyGMaD8zSwytKT7mPgH+A5MJW2zM8fE5vw6rvwQTgBPHn+f5CIgRiIgRiIgRiIgRiIgRiIgRiIgRiIgRiIgRgYPQMmSR37NUnaRDhGKuaCTOo7Hj8fLoez4FIwP+R3RjHM3y0DJnt9Lia1VwMT+F7gZ0LfsWUf1wGT+n7uWLOY4Hf7+jvHmtunxcuhC9dzFhRfPlYZ/13lxCeYdvt6OcYvo7qfTKCpmo/6uQDAnfwN4K0wqg4TpSaaTgKv/O407ueLC8BEpTtQGi4ShiTclibR74FzwGosE81TDTu8rac4EZftPPgilOS/75monQfDkPxnNR7Zp07ncaIEs/3Eu8GTLIsFBr3ogVXoy/DE9m6wCMAKTPvle+GrYJHMa2AV6FV4AnQs7Abvg5vBZbCdJ2IgBmIgBmIgBmIgBmIgBmIgBmIgBmIgBmIgBkbHwB9YVcciTSDXHY65XwiXtB79dwSOyY/KOLSJeK+0L4lcx+MdBzaB7xixY8XTYQMwuW/Cv9yG3+95UaSJfafhtEpin6cjHY5zmww3fHTsu8poqgDA9lLCAprkUYuNGh77uQBgJuv/shocWDn2efg52AA7CTv9O8CqriSlOjE2mN/xIG4BgJVo34NtYGNoMjwwnAFfAW+PbthuTczeCp4MDdPJxwWsz2LYEMaLmXzwYbBgaB50uh/z1UQXBmxXti9PSB6GcsL9C57fBm+DGdCr8KTS4g/n80a4DpxvigCQkIiBGIiBGIiBGIiBGIiBGIiBGIiBGIiBGIiBETHg2LcXCJXxyDpX+yFm9kG4HIYtF+T4qzlCcczVJL1X4pvEL1fsm9x3bH7d1vvmSlaFlWEF8EIuk7xJ7COhi7BdOd5tftJcoznHKsPEexPRXgDgftzUcjSx7o3Ps58LAEz8eGuQKsOd69NweoczsXHeD7fDH2GYEq2sTmIJAx703M4WAXjg+yocA1atNREmtm2rx4GJWMNl9GAxD7zyf9iS3wtYpxPhbVCqDHn6uHgG77wDPBlbDEkSI6GC0KsnI/ad3i1lA7Ci83o4Fl4P20MvYxcmdjI47QvBdp7ti4REDMRADMRADMRADMRADMRADMRADMRADMRADIyAAfMwV8F+DayrCW8T4t7i3jHRQRqXdDy9XHlvItZkfUnuO67rxVzic3NxjvOKuRBzIK6zv3M6E43N83GiCwOOr/8UfgULwZxj1blGi1eaiPZ2Y37V/WeQ9qEmnPVsnv1aAOAtQV4A7Y2jZyvdmpCN7YvQafLfRPDdkKv+kTBC4UHdAgAPfFfA1+F1YEVbneEB4IdwAtgWDd/zYDEHLAj4Xxi2MMns3ReOAKsOxwv7ileCB8tPw52QqM6A/ae3uvLEwRPEteEW+Ci4HfaHXoYnoifBm8CTI7fzMLZ3VisRAzEQAzEQAzEQAzEQAzEQAzEQAzEQAzEQAzGwhIFrl3hd10uT4OuAiXTH4vspHBM3x+cymq8wab8WrA+Op24M08FxdRP/5ji8et/vWQzgb51GlXk4Jj/UUZLZjlX73EfzNvL/WjiGLrYf8xZnwo9hHpTf8bTScDy/iWi/A4BOiq8mlmXk5tmvBQB2UttUuDVsZF5RaiJpaeF3TQDfAV552tSOwqwTDRnw1iQWfnhgPAe2g32grrDNmfz8DliQYHhgMAm6ACwIsJ0OY7heN8PX4F3gicx44WdvB4sGvgIeTIfVC6vWeHjAtu3ZBm2XnkjaTx4PfnYg9PLk0cpTt6vHh2+CBQjpj5GQiIEYiIEYiIEYiIEYiIEYiIEYiIEYiIEYiIEhN3AT6+eY+ETjw1UocH7rgwUAvRzr7GZZTaKu0FoGE/gbwkawCWwMLt+TWnhrfvMY5cr/un0x64GKkj/wsSTvHXN2vFvMNYg5IvkrPAwm8x9q8QCP5g59dMzaizX9TnnPMXSn0eRYtuvSRJQCk+K3iWUY2Xn2awHAtmwRO6qq4kom/M0OJu7ObBLRpJY7qB1AYvQM2DHbca8BHmy/BbNgBlQddswmv0+F0kmbXPUgsgA8yJSDFE+HMjywnggHwW5LWUO3zzGgI7257w67H1axsdCt2+d2sH1Og8XwFXC/eQb08sTYY9ZHwJNbH52vlZSJGIiBGIiBGIiBGIiBGIiBGIiBGIiBGIiBGIiB4TVwG6tmjsbkdt2xJTNcDno5zjnWOjj2uQqYh3CcdTPYAsxFbABeGOVV/F4o5fI4Fp4EPxJaUfIASybyzfOZsHcc28R8wRyLmPsR7wDuxcC+Z9LeR/Mvft9pmHMo8+DpQIXL30S4z4jeBtlfE+6mPM9+LQB4GmtWVWdqkuo/YWnVNlbrmPx3h0+CCQkjHHZOdpCLwUq7hXAcvKf1modKwhOar8DPobRB2+29MA/8fFAPOCx6x+E63gqfhi/DqjBR2K+9D3R1PHjwHgVPrGYjUfaPu5i7zjcE++8TwBPyfaCX4UntK8GT3rfCPHD/yDZGQiIGYiAGYiAGYiAGYiAGYiAGYiAGYiAGYiAGhtCAV1N7sdfGDazb5szTcc5e5axM3JvIXxtcn61hK5gF64FX83uBrFfx+91ezZdJDVSU8d6S0HcM2DxNSeSX5HxJ1psHMHdiXk98bZ7PtmMyvyTynV6ZNk9HIhy3byJsu47n69zno9qWWfX6o18LAHauUMX5TNudf6IwkWWy107BqpREDNgObA8eNJaDq8AigDeCHVgVcSkTPQdKG7STtiClVDuO0kHKwp3zQOdvAU98Jopl+PDfwT7uS5D/GY+EisNtZPu0nW4EXp1vAYaVqdtAL8MThcPAE+9Xg/uE8x+lfYLVTcRADMRADMRADMRADMRADMRADMRADMRADMTASBgweXs1mDCvO2YwQ6/MLwnMTscg/b6/M6E/C7aH7cB1mAa+79X8jmFXlWNg0n0VupOS1Deh74WO7bfUN2Ev94JX5ZvPM1/n2LMJfTFX428ci+50e/DVkQ09NxHuA+ZymipAaGKd+2ae/VoAMLNCQ6dOMG07ikUtrCKyE0rEQDFghZkHmpXBTusi8KC9L1QRezFRD3TfAA90znshjGKi033TwpxTYEc4AJYW9m//BsvDZ8EThOzTSKgwPJB7AuaB3RNj+9MvwvtgXeh17M8Evw0vgxQB9NpuphcDMRADMRADMRADMRADMRADMRADMRADMRAD/WHAcd3L4PAGFmcN5rkOON7s2Px4sQIfzAT/ZcBusC2Y+Hdc1EIAL1pz3FSGLRy/dxt5MaOOTOpbtOFV+ibrxYv0TOqb0Hfc2Ds6mPfwfceU/a6/LRdE8jTRIwPmtpoK9xsLEGwftpNETQYU349hh1pF2LgWjDNhO5WFYMczignWcbTk7TYDth87Kg9QHsxNKH8TZrbgoafhicBh4BXUbwUPiHbUo9pJuo/eAF+AWbAxLC0s1HgXrAwfBk8wcgKBhArDIgBP2mynM2E2fAbeBxZj9Dr2ZII/gZfBdTDK+wirn4iBGIiBGIiBGIiBGIiBGIiBGIiBGIiBGIiBoTPgWOMV4KPj5nWGiftpYD7NebsMT4C1wQsEnwK7wGZgst+7lpbv8nRgw/U0TNw65mtu5M/wRzBZ/yCYuG9P6t/Oay/k8z3vpuz3/F3G5JHQYOi/iX3H/WVZsBjEfabufZdZjm70awGAydUqwo7KzmnJMOFv4t8rrO2MEjEwngEPdB64VgWr/mw3X4H3QxXJTSb7d3uAV1E/H+ZBOcng6UiFBygPFJfAJ+EjsAosLTywvAn87nvAwg23Y6I6A/r15G8B2F6vga/C61uveehpbMPUToOXwMXg/MsJKk8TMRADMRADMRADMRADMRADMRADMRADMRADMRADA27gVpbfi39MKNYZjm9uDOvBU+Eg8Ap/E/7mCVyeQUtsOnZqvszEsPmxP4HJeq/UN7HvBV4m8stdiU3sO65ertj3+xmDRcKAhPtNU1Hy0IO2jzTlq2fzLeJ7NsEeTcirdqsIOzRpDzs3OzGvrk7yv91Mno9nwM7S9mIln23Vg6LvVVUAwKQfqSQ8i0fvCOBV8KOa3HS9PfH4NXgngLdBJ/2YB5d/ArfZO8B93hOURHUG9OuJoifAci7sBFbEVhHrM9GfwgvB9uHJayIGYiAGYiAGYiAGYiAGYiAGYiAGYiAGYiAGYmA4DHhVuQnoaQ2szlHM0/Hl1cDx6H5NZjp+XpL75izMeXlRnUn99sS+dzn2X6qK4+1yH/jdJPaRMGTRZD5puSFzOTCr00nirImVqarzXHK6FgN40DCZ6/8kScRAJwZsNx4IrXjzFj/vgU6uROdrU4qZ/PocOBSugCWLWXhrJML1ngsme2eBCd8l923eelz4nReD2+p1YNViksRIqDA8yfTk0WONxTLHg5Wxa0MV4Qn4j8A7AfwCsn2RkIiBGIiBGIiBGIiBGIiBGIiBGIiBGIiBGIiBITDg3Z2vgmkNrEtV45ndropj4+K4qzkt8xQlsW+uy8S+497zYWHrtUUTfseLYUc1p8Cqj3Q0OU7uxYFG/gXAox5q+9uvBQBVdUI2MJNQpbF7wLAz9P+WJGKgWwNP4gfHwAbd/nAK3/dE49fwPLAYoKp9hUn3bVit5snKjfB12Aj2gE7jmXzxRDgKZsMoOmS1awtPRj3x9F+7LANfgX+Fqo4/KzHtk+FI+DHkTg9ISMRADMRADMRADMRADMRADMRADMRADMRADMTAgBtwnO8icHx3GMNxb8eqzV+1X7nvlfnlQlav2J8LJve9y63JffNcjsH6+0QMjGWgyRyIOQHDCzTTRh9RUc+fqhIwU116O6sqwga2OthZ2tDsIP8EiRjoxoBFJNPBROYm3fywR99dlemcCkeACc5R7DRdZ//1wnXwCfgYzIROw9vQ6/Cl4N0URtEhq11L6NYT1kWwIvwOTodnQVVhscG34TVgMYDVsIkYiIEYiIEYiIEYiIEYiIEYiIEYiIEYiIEYiIHBNnA5i+94o7meQQuX20SshQyOVz4ED4B3Gr4TvFjVC9bmgYl+81eOgXsxXMavkZAYOAPup+UOAD4fxP124KSXBe7XAgBvW2Kivtdh49oZzoT7wUKAJitfmH1iwAy4z8wAk4u7NbjsJlK/C68Fl2UU27Hr7InRlfAfLSyO6DS25Is/gaPgHBhFh6x2LaFbT2g9cfWAfxJsC7OgqlieCX8ZrDD8HqTYCwmJGIiBGIiBGIiBGIiBGIiBGIiBGIiBGIiBGBhgA3NYdi82cuyvH6Mk+b3Itdye33+RegfMhxvhVpgHvueYZcalkZCo1ECTbazkob1DewoAKt3MgzHxS1lMO8oquInpPgM2BBtcIgY6NWBHNQ1Ogyra5mSm6cnOm6F0ojwdqfCAYUL5yfB+KNWQ3bj0Nklejf5ESFRnwG21HGwC+8HbwH+/0s22msx3ncerYSVIxEAMxEAMxEAMxEAMxEAMxEAMxEAMxEAMxEAMDK6B1Vh0r5KfzDhhL39jQtUkv7ffXwjemcA7znqn2qNgd1gLyu3PeZqIgcYMbM+cbbO93Ac6ndY7me++sA2YH0iMuIETWP9OG0+337ORfxL8/+2pNkFCoiMDJtjXB9tmUx3leG3dE41jYFRPJtyPvfLfk6qvw2S2jydqR4HbOVGdAbeViXhPOA6B42Ay22u8fWG891MEgOhEDMRADMRADMRADMRADMRADMRADMRADMRADAy4AcdvT4bxxgGrfN9b98+BX8BH4LmwMfTr3QhYtEQMPGKgyQKA97IE+8J2kH0FCaMe/4KAKjtqr5r2/6cn2TfqLa2z9ffK8PXgs/A/UGXbnOy0PfnwpGNUO1ATy2vDvnA2TMaj/cK/wiqQqM7AE5i01a97wOFwDUxme3X7G4sA/hn89xmJGIiBGIiBGIiBGIiBGIiBGIiBGIiBGIiBGIiBwTTwJha727HBXnx/MfPdGkb1QrzBbC1Zag3473jruBBvrP3sg8x7X/AuzitAYsQN7Mn6V51o9VbhLwNvH56IgfEMmKw0seyteybTJu1UJ/O7sTrKpb1nEcCnwCusRzHcVv6LhmfDzbA0X2N9Xhy6zRPVGLBYw353U9gP/gn8f1hjbY9ev+f/1DoSRrVQhlVPxEAMxEAMxEAMxEAMxEAMxEAMxEAMxEAMxMBAG9iXpW8imekFRvtACgCQkBgoAxauNLHPOL5vbm1fKAUA5gcSNRjwyuZ+jAdZqFdClYlM1/05YCLqSvgLJGKg3YAd0ZpgwvA90O0dI7w1/1lwA2wGVXdsJsB3A/+9xflgkcsohQcTr+J3vefD3tBtRZkOdwcLCS6FP0Ci9wY82bDP9Wp8C2T8Fwx6r3of8eT8QLgJbgHnnYiBGIiBGIiBGIiBGIiBGIiBGIiBGIiBGIiBGBgcA47hvgrqTsQ73zPAi88yroiExMAYMM/1Bqh6/H0sIeZffw/mbe4D82aJGgz0awGADWAX2KZiBzb2veCpcB3cBSamEjGggdXg+WCF0nLQTTzEl78Pl4DJaIsHZkHVHazT3wm8gv08MCE+SuGJl+tscvl+cN/utnBDh1aj7QjXgP2CxQWJ3hpwW9nfrgSLQOfrQtVh0ddBcDXMhfT5SEjEQAzEQAzEQAzEQAzEQAzEQAzEQAzEQAzEwIAYMJH4UjCpWWc4bnwZXAHmsDJmjITEQBhYlaV8M9iG647rmaFj8eZt7gX3nUQNBvq1AMBVtxN/IVhVVXXMYAYvAZO8VqJ4O+rEaBvwyuR94Qtg59hNzOHLX4J5YHLREwHblVejz4Kqw07c5LX/Z/0CGLUiAG/j7zpbAGAV6A4wmQPbTH5ngZDFQSaokyhGQo/DbWW/W/reQ3jebcHGZBbJfwGwP/wGFkJO1pGQiIEYiIEYiIEYiIEYiIEYiIEYiIEYiIEYiIEBMOBY3r6wZQPLOpd5ngdegJYxxQY2QGY5KQMr86u3wGTyJJOaYduP5vH8t5A7ALRJqeNpExu70/WyeusiqLsT92rfL8LXYDHkVi5IGLEwabwrfA826mLdTRDbZr8MFpGsDSYa/wR3g0nO94AJ6TrC5bEtHwMP1jHDPpqHfZtXk28Orv9BMNm4kx++Hn4Bf57sRPK7MQ24nbwDwGawBrwAXgN1xQ3M6EVgkUdO2OuynvnEQAzEQAzEQAzEQAzEQAzEQAzEQAzEQAzEwOQNOKb4r/DhyU9i0r+8il9+BbwAzTyCY/6ybAsvbvK5F7b6uVEuxHW5yxikY/fiay+S8rlXRpuP8lFMmBYcl/Y98w7O2zsQm3coj07Dz52v03CeTrOEz32vfOajy2O4DOV3FjY4ncRwGViH1THf6XauO37FDN1X/TfAN4NttuwHPE1UZaDs4FVNfyrTddleC5+DJhqljfGHcAJcCn+AxPAbsN2ZjPwqPK2L1fVAfBL8COzAPKCuAiuAyXcPysYM+ABs5IsawuX4GJQOdpQ6Vk+0psGT4T9gC5hseGL1LjgR7pnsRPK7MQ144ukJiPvEk+ATMJVtxc+7ijP59ivAOwEkYiAGYiAGYiAGYiAGYiAGYiAGYiAGYiAGYiAG+t/AoSziaVB3jsvxdcfc22O8ZRjv/fbfjjde3/7+WM/LMvhYlqk8N7nvc4sCfC6lQMDkvpivEBP+FhdYTOAYuLkNc2HyAHjLdu+0ezeYM/P7ZT5luXxtDq/9kZd/m4/vJ5o1sDqzd1s2kWs1v/oOsP2kAAAJdUUnHVBdyzLWfDbgTTvxHcf6sKb37BxvgW/Dj2Ee2MklhtPA+qzWv8PLodP9w47zC+D//ynJfw+YXjluAYDJTZOcJbyrxQdh7fJGxY8e3I8Bl9HlG6XQ/wzYGz4K3llksmFf8Dn4DCyAnLggoQfhfrYczAL3lW3gk+B7dYQnqhYdHAsW6yRiIAZiIAZiIAZiIAZiIAZiIAZiIAZiIAZiIAb628BWLN414BX3iakZKIn8MhVft+M4uJRCglJM4EWRYr7MfIiUAoL7eC7ecds8yaLWo0UG/sYxYadj3sTpllyMz52enyV6Z2AlJuXYd3ueqndTn3hKN/Gxd/11298MFgIkt4KEqqPsVFXPZ7LTN3n3TPgm2ECbDiuhLoaT4UxYDHZWieEwsDyr8Sr4OJTb8yxtza7jC1+G2VAOTHZgJoitklsRNgITz0+AEtvxxEKD1cobFT/aTo+CH4HPlzyo89bQho43BvuS94J3Bphs6O10eA9cD1ZLJqZuwGPRqrA5uL1eDi+BusITypeC+0e2aV3WM58YiIEYiIEYiIEYiIEYiIEYiIEYiIEYiIEYmJwBx9vnQF3j65NbytH8Vck9+Gii10cT++LYa/udB0wKm0+5H7zzrjk379Q6r/XoXQjMuxhOy3Fkp2Phh/k6p1Xmx9PEOAbMiei6iYKZ25jvy8ACEYsBUgCAhDqi3wsAbJQzwOqQt0B7ApWXjYY7yxVgMvA8uBXsjFIQgIQBjb1Z7u+DVyEvLTzInAEngQclD1weiHxuRVtJsruPrQUbgUUs7fvcDrw+tvU+D5WHHethcCF4sByVsN9wG2wBJpaPhPbtwMuu41p+cTT8BuwLElM3YPXh+uC+4uPHYCbUFVaj7gW31DXDzCcGYiAGYiAGYiAGYiAGYiAGYiAGYiAGYiAGYmBSBpbjV1eDY1H+I6gAAEAASURBVL6JwTdQkvg+mrsoBQN/5bl5F8fgvYL9PjD/sgBmw82wCLwY09/4W/MB5ff+1uejHo69m4A351p3uM1eAN4d4iZIAQAS6oipJsGqXkZ31A1gU3gTPBf6cZm9etROx2TgWa1HOyAbcjoXJAxAWDF4AhzSwbJ6sPkWnA1Wppn89z23ua89yJQDFk8fabMb8jgDlryzwE68ZxHAClBH/J6ZuB/NgVFqmx7gpsGW8C7YF6Ya/uuHd8BPwYNY+zbnZaJLA/bt3jHD/t79cXf4ANR5UnIm83sRWMyViIEYiIEYiIEYiIEYiIEYiIEYiIEYiIEYiIEY6E8D5o4cl/WCt8RoGXAcvr1QwCIB7wZgPs4x+8Vg/sO7N98At4P5m/I7x6EtDJBRGdN3nXVQVx6KWf0tLDx4HjjmbsGGhRzm0BIVGzAp1s/hzucymgy6GjYGk6j9Fh5sVoWtwQPOy+E5MB1Mstr5/BekUSOhT+NZLNfR8H+XsnxWk30SLgI7LA8SvjcXrGAqBxGePiZKddUqvGtnW8KDkQehfWBp8y6/mcrjk/ixBQsuv21yVMLt4nrrfiHsCPYrUwmT1YeCBUBl+2cfn4rRR13aZ64B3vLJR4s26opZzMhijstglApk6vKb+cRADMRADMRADMRADMRADMRADMRADMRADMRALww43rs97NWLiXU5jVv5/i1gYvkOuLuF44omoM0b+PwPrefmDXxuAlbMFZgzMrdg8locY3a83jFJnzvOXHBdfe6jUR4fffXo3/acQ/v7w/jcdRXzcuZUloOVwfH+DWEL2APM1b0MXgnm7F4AB8Dm4N2adeo0pLjm6dCGFzPqqon4ETO13bt/2N7HasO8neilgUHoFEyyzYK1wcb5IdgZBiXssG+DU+EXcCV4lbjJyET/GPg5i2Iyd6K4kQ+/ADeBB2cP2iaTHwS389I6LdvyxrAWtIcHmB3Atr18+wcVPZ/HdC1QsQLOA9soxaqs7CawN/wbrA5TDbf7z+C9YNvwAJaYvAFP2mbAdFgfPg7ToK7wJOTZ8BtY2j5d1zJlPjEQAzEQAzEQAzEQAzEQAzEQAzEQAzEQAzEQA4818HJefu2xb9Xy6lPM5TiYA728yM5xUa/QdgxbvJiwHZPcJq59NNfgc78v5hUK5tHEO6vKMuC0ffSCWx/NSfiej4WSWOetv13EOAg5RJe303C8V8zPuf2+BNfAQjCP18vtyeT6KixUWTI3VccC/i8zeRno2/xJyafxNFGlgUHYee18TP7MBDunQSwCYLEfCRv6AjgNzoAr4D4wYZhEExIaDJP7W0ww/8v57IswF0z+3wWLW8/LQYOXE4b722qwMXgAbw/b9vZwLFR9GxYrCI+Ek8E2OUptz/7Eg9wm8Cx4O3ii04u4lIm8DdyvH+rFBEd4Gu4nbiMfnwYWV/RqOzGppcYpfON1cO9Sv5kvxEAMxEAMxEAMxEAMxEAMxEAMxEAMxEAMxEAMNGFgH2Z6DtSd5/oO8/wEmFMwYTxo4+v6sghgFXCs3ItvvXOwV9CLd2UVCw1K0cGSxQb+3lydj+LYrWPv4vQLPK19+zjPTuNavvif4LacC8M8HjyP9dsI6g73jzfClXAz3A//A4mKDdSZUJnsqpigtEF46w6TpCbL/xU+CHvCIIWd30x4A7we7oFL4Cz4DcwHbw9j9dGgHTRY5IGN9Vly25jOPTAtGdfxxmdgAViddFvr0U6qm+3kd/39QpgJK0IJp3U1vBM+DB5Yqwr3o93hB+D+NUrh+rqPLYLzYBN4AYy13Xm7q9iVb38f/gV+Cc6nm/bB1xMtA3/k8W5wP7BvdFsdAHXF4czoO3A65GSkLuuZTwzEQAzEQAzEQAzEQAzEQAzEQAzEQAzEQAx0bsBx9vHG9DufSvffXJefeBGfie9BvGJcZy63yW65CSYb5hosDrCIwDyL+HwdKEUFq/N8lRZ6My9i8UC5Q4HTkFI8wNOejNc7naXFdL5g7vFWcBmGObyjdRNh7sV2cVUTMx/leQ5CAYDbx/8NYWc+C+wE/hveAxYCHAS9SN4xmVrDZbYjfFYLO9z5cDZYEGA1zB3guieJiIQK489M+xR4MrQn5fVu0v9zMBcWg4ljt5WJ5MlsF39jYtOTg43Ag1wJE43XwzvgQ7AGVBEug5V87kujGG6/u8BtfRJ48Hka9CI8sfkGvAu8w4Lzsb9KdGfA/etO8E4Z9pPfgl1br3moPNw/3wcXgcVBiRiIgRiIgRiIgRiIgRiIgRiIgRiIgRiIgRiIgf4y4MV2jqnXPc5tYtt5ml8zz+N4+6iG/k0sy+wuJOjN3Ih5ig1hRuvRsXoLLLwzgfkRx4ctHmj/9walYMBpFHg6qXD5HYv+K3hh7jCHeammYjozdlsNSk66KU89ne+gyHYH9Gr5kgxSgp3qR8AKpRfCoFfn2Nlt1uJVPHrwuhR+BueCieg/gi4SvTXgwelU8CDzDPCgY2d/M/wIrgX920GazJ3qNrDtLgKTjB7c2vdDp+3tZo6GY2Ea9Dqcv/uTjzKK4QHdgg4rDr8Get4YehFWL34Mtmw9zudx2E8eWMWeh87cRhZqLIRvwRuhrtiZGT0PvgFT3efrWubMJwZiIAZiIAZiIAZiIAZiIAZiIAZiIAZiIAZGxYD/htWLvRxnrzO8a6lj+s7XpGaiewPmJcoYvWPA5sImCj2bQ/NisVmwSevRxHIpGCjFAo4nO0bvNjJv6G8LPP1buAw3wWwwX/IHGOa4vcGV24B5uw0snPExUYMBd4BBidIZmLCzgy1xHE/sIF4LfjYM4Q7gbVEOavEwj5fDj+FXcBt4cEtSCgk9CDt6fZ4O3oZE9955weISE/Xz4D4wIel3exFOx6IC98Fp0N7p+dl8eDO8H7aDXoZ3PLCooVfr0stlq2ta7jse0BeCJw5fgPfBatCL8MTilbAmHAPzwD4s0bkB26f7pfuhJ2zun8+CmVBHeDJyNJwF7o+JGIiBGIiBGIiBGIiBGIiBGIiBGIiBGIiBGIiB/jFg8v8vsFLNi+T8HM93zNLHRPUGHCt2fN3xfLkAxgtzLo7Le8Hf1rA5zACT0N5VwByAY7/mSD4Cl8D/wLDnS8wrNhX6d7tkf6lxCyh8UKIk7BazwFb1eMuPEj/jiY337eBOPGzhAWXvFhYDXAY/hXPA9TaR6ZXpw95BsYqVhN4eACu9vPOClXuG798BfubJRK/DNm1i8Ymw/hITd94mPt8N/wyHgd+bajjd6+C34EFtlEP/blu38TXwRXgrWBDQi/Bg9vetCb2fxzngCWmicwPud3eDJ2bulyfBO6GuE4Utmdch8HWoog9gsokYiIEYiIEYiIEYiIEYiIEYiIEYiIEYiIEYiIFJGHCs24vd6g7Hjx2fdLyyrnHKutdxkOdnrsx/zSuXDPKK9HjZ5/d4et1MzoIM95vsM91Ym+J3e5FQnOIidPVzE3YmYazO8TYe7ctvEu9iWAssEPA7wxjuJDPhYDgSnglWMi0P7jwmdT3w6crHRGcG9OXJgu3LK/298ngheOuXKq/cdr4Wdbjt2u9swctHwuW5HBbBtjCVu1zYHlyXj8F50Ms7GjC5gQz3F51YDGUxgCcHO0IvT9ysMtwT3I4WdTiPROcGbKdup9XB/VKXvbpTA5OaMGwHM+D74H6aiIEYiIEYiIEYiIEYiIEYiIEYiIEYiIEYiIEY6B8Dr2JRzAnVGY7ZnwaOt3vBV3tuxvH+RAz0o4G1WaiXNrRg5nK9qPlO8ILm7CdIqDraE+hVz6tX0y8JO5fdZGj7Opgc+i2YLN0Mxkqo8vbQhEnj9eAp8CJ4GRwG28G6UG5B484kKQhAwgRhYvaPYJLWZLAFAba3qsP5+i8H3J4rjTEzt91cOB/spP2XAd0WuLjt74FvwMXgPF3HtIlH9w2TzMvC3eAJo0U1vYz1mdj+cBk4D/0nOjfg9rFIw33EpPxuUFfYHq6E30P2l7qsZz4xEAMxEAMxEAMxEAMxEAMxEAMxEAMxEAMxsHQD/8RXNlj613r6DRP+h8Dh8HwwJ7MPOGa5KTieaN7KsUzHNL2Y1bHnldsezd047l9H/oHZJGLg71bFwauglxc/dqrVfNYZsBAsAEi7R0LV0cSG7tU62WnasZvotiNtD9fLhNuzW1goMIph0syk79VwPlgcMQfuAivTPMAk+seAyf+NYJ1xFsl27YmBV5S/ALYF9wM7z/HChKWFMSYwzwaT/7YLk/+3gp8lHj0h88RsFmwO7wQ99zrmM8Ej4Ar4EyShjIQOwrbvVf9uny3gy63XPNQSFzCXvwf7zkQMxEAMxEAMxEAMxEAMxEAMxEAMxEAMxEAMxEB/GDiXxTD53nSUcV5zLj43wSneLUC82FAcj38Q7gdzN3fC7WBidAH4vt9zPNSLyCwgcDoWFPhoLsDcjpR58jQRA0s1MINvzAbbVBNxNDM9HeaA+0SiYgN2IoMcJj/HKwJwvWzIfv4y8CDQVMNm1n0RHjC8+vhC+AWYhJwPHnQ8eCSaN2ARgB2xRQDj7Z++b5XhlrAzbAPrglWE/9vCEwIT/W7r68COfREYnhj8FXzP9pATBSQQ+psGM2EreD+MV4zBR5MOCy9eAr8HT+biHwkdhP23hV2bwmvBIpi6wv3lMPg1ZHvVZT3ziYEYiIEYiIEYiIEYiIEYiIEYiIEYiIEYiIGJDfycjw+d+Ct9+WkZY/TRMf3yaJ7GcX1xTNJE/5/gYfDKaS/s8w7GXqh0B9wGFhA4zu/3/V17sUCZD28/kgNyPNo8UWL0DKzBKpsjMrfURHyGmR4P5kdsq4mKDYyXYKx4tj2d/ApMzST/erDknQDKjEwcedXokbAT2AGOetjx29lfBR4kTRTfCB5APEi0Hxh4majRgEUAG4LJ5/HaqvuuWPEn/mYtsBP3hGExeNA3qW01lQd13y/h85vA77S/Xz4f1Uf7k5lgn7Ir/Bt414Vex81M8Ai4FtwPE50ZcPtsDk+Bz4J9e13xKWb0dvAkPBEDMRADMRADMRADMRADMRADMRADMRADMRADMdC8gR+xCIc3vxi1LUHJ2/jouH55dPxfzAWIY87ixZ+lcMA7Dlg4YMGAWDzgHQcsMjDa8wRO17yDY6HitNo/52ViwAyY+F8IT2pouU9lvv8G5iFtc6Ut8zRRhYFhKADQiw13/RYmPMcLCwS2gxeBV06Pl1zlo5ELk/5z4Az4FVwNHhCsxEnHjoSaw0TnNFgPlpbkLMUAZX+24yzwdMxwm9rRuo2zff9/RZ7UrAqbwLrwPHg5FLc87Vm4v70UvBOH+1li6QbcDmvCLvAlmAV1xZ3MaE+YW9cMM58YiIEYiIEYiIEYiIEYiIEYiIEYiIEYiIEYiIEJDZzIpy+e8Bv5sD3R6nPzAT6W5L65IYsGHKM20e8dB/4IFg54twFzCF5waNHAgtZzCwv8neG0HLd1uubcLERI0QAS+izMfZgT2qyh5bqS+b4ergPbT3u75GWi1waWlljs9fyqmt5fmLCdj53KBjDeLSzskGxkN4AJvpfA7pBCgEfvnrAFLuQNYCXQmfAzuBzs6D0IJFmMhBrCA63bwIOlhQAWr4wXdpTddJbl++4P3fxuvPkP0/u2bw8+t4HOT4eNYT/odTjdE+BFYMFNOWHiaWIcA7ZXt48nmr+E10JdsTYz2gfmQfYbJCRiIAZiIAZiIAZiIAZiIAZiIAZiIAZiIAZioGED5oTqDseQzZesDOaWxOSqSfB+jPbl8rnLakyUc/DzMgbqY8F117lj2WLOyPycRQOO21o04F0FStGAOY65YP7Oz8x3OA2Xw2mWZfO50zIv4vNE7w3o1X8BsFnvJ93RFDfkW174WvaVbOeOtE3+S2XnmvwU+uuX/5fFMUkzHVbsYNHs4LaB58MuMF7hAB+NbNiZz4bvwy/gOrAjt5NOVG/AO1rYptcHTyh6EW47K/iuBw+qiccbsG+YBvYlHhCPgVlQRbgdLAK4GTwByoEPCROExy3/1cUz4Ftgv19XnMKMjgRPahMxEAMxEAMxEAMxEAMxEAMxEAMxEAMxEAMxEAPNGvgus/+HmhfBRPZzwHHKB2Ej2AG2gg3Af+3rWL44zuz4pd8tiXefD3OU8W0fzUUUHPu2aMA7DZSiAfMUOrSgwn9PYMHAApjTem0uyjBPpUenUR4tOnB6ic4NfIOvHtX513v6Tbf5i+EisEjEbZqo0IA7yjCFO7+dhEnNDcFbeVt9NV7YOVwFJrX9/sFgUmk1GPZOmFXsKPTn/9x+DxwNXhH9dbgW7gBdJ6ozoF9vPW7n6InDk2Aq+60HXaflfuL+khjbgG70viJYGPRZ+FDrNQ89DYuQPg9HgPtUtgsSJgjbsCeGN8BCmAl1xdOZkSfxnoAmYiAGYiAGYiAGYiAGYiAGYiAGYiAGYiAGYiAGmjXQRB6njN+a2Hac3VzJj8fQYG7Fi/u2hG1hM5gG64I5qFXAK6Idf/ZCQL9vkUC5QpqnA5mnKtvEx1L04LpMFI75tmNyWL/mR/T95xaOC5eCAcfvb4fZLRxb93uGv3XeProcvm9eZNTj5gYF2MZXBx9LG2lwcYZ/1lNJJParHTuG+8AqIq+aNmFqBzpR2IHMg6/BD2E3OBTskG2MiUcNeDB6HjwbvGrZpOXPwGodO+JENQZsn1bAWdHmAc52vRJ0Gx5A3S88EHpi4sEvMbaB4mo+H9sHeGA8Dt4MVRyc9mW6H4C3wMNgP5YY34D7hNvmfJgJdYUn5nvAXLCNJGIgBmIgBmIgBmIgBmIgBmIgBmIgBmIgBmIgBpozsLTcTxVLVq46d5zYJPN444SO8ToWL+fCeGHi33HHmWBOahZYKFAuCLRQwItdXVdzNI5Xi78rRQMuixjl8dFXg/G3ffld4pK7dH3HiuLcHIfPfdS3eSpzICb7vXuA+RRzK3fDApgLN4GFA37ub5y30ygFA47Pl23M06GK2Q2ujX43hLJtG1yU0Zj1sIp2Z3XnnQ/u4FZUrQFLC5NK98DpcD5sAHvBAbAe2EATj+6g2yPChOi18HE4GxZDkspIqCisUrOqzUKANcE23UkhgPuD/AFM/HvAG9YDGKvWs7Ate6KwCFYE+wSTv1JFvJyJevLxOfAkJfsSEsaJ0sefx+dHgCdpdYTzOQh+BLaNRAzEQAzEQAzEQAzEQAzEQAzEQAzEQAzEQAzEQHMGmigAMMlcwpzRVMcmTUJ7UatcAUsL5+d6m/eaCTPAgoH1YC0wb2DRgDiu7XeXA4sGlgHzgi53Wfay/OWRj/o+yrJaANEeExUMlDyJ4+46dzuKCX8xb2J+cD7Mgetaz80x+j29mUP0sbzH04EK100PxV/dC287tS02Nf+617fR+Q1rAUCR6k5pwtOd90lgB9hJ4yrJpZv5/q3wQ9gc9oW9YWVIPLqTPhkR34RL4D9bj3aUdoSJ3hswce+JwINgMcCqLTyQe3DzgGf79QAmhr/xu/7GfcEDXKIzAzq8H6wI9ATpK7AleKuaKuKjTNQ+60Sw/3JbJsY2YB/zG7BN19knP535uf3vgEQMxEAMxEAMxEAMxEAMxEAMxEAMxEAMxEAMxEBzBsZL+Fa5RF6gV6Ik0cvrOh4dM/ZiwXkteOgoXFZ9mSebAdNhA/BOA6VwwDsRONZqvmF5MJ8m5hItHnAaZZ1N4pZEbnnkrb6M9mU1h+K6uH6G694e+i05FnMpjtN7MZhJf3Msi2EhXA9Xw1zwsxL+9iHoxxyZd0JwnZYsnOCtWsI8q+2p39tLLTKqnskoSTZ5Z6dlhYnJG3fwbkJXdnJ2BnvCYbAR2NklHjVgh3YGfB4uBBNzdnaJ6gzY/myXdtjlwOVr0b3boJyQ2LFneyChy3Df92RgFth/mAA+Gqra9736/yVwGrhPZZshYZywLz8Hdhjn8yretihkP7igiolnmjEQAzEQAzEQAzEQAzEQAzEQAzEQAzEQAzEQAx0buIxv7tzxt3vzRef59takFvB4G3gR3jCH+bU1wKIBiwccJ18fzJd58W0pHLB4wCIDMUdRigfMXTieLo63i1EeH301OH8dsxdzLo7hWyTwEHjx5s1wElgcYML9AeiXMX631Xzo5M7SfK3ncStTPAKuAvMg/eKFRRm+cAcclXAHtBO2MsqOyobuY6cObIj+3uqen8C5sDkcDHuCHdmohy6fCfvDl8FCAH3pPFGNAQ8wtm2jHCx9bO84258/8sX86cqA/jwY3QHeccHE716wB1QR9iXHgUVGl0K2HxLGCQtczoc6CwA8WX0qXATuf4kYiIEYiIEYiIEYiIEYiIEYiIEYiIEYiIEYiIFmDDRxBwCTvCVKQru8HtZHcxB3trAAotMwV+GFud5lYCOweGAD8N8XlBydY+4WDpiU9kI88QJeiw4ciy3FA06rwNO/5UN8XneU5XD7mxdzmV0P121b2AneA9eCY9j664ewSOFBaKoAwO2uJ7evTpL7QEJV0Wnyu6r51z3dksi7mxn7/9DvATscDxLuqJ2GSR87+UvgcpgJe8M+sCG4849y6PMt8EL4D/gZLAC9JaozUDrL8ljdnEZvyrZd+wzbsQen42Fr8GBVRVg5aRHNYbAIsk2RMEZYXXkOvBHq7Hf3Y36fhH45cWNREjEQAzEQAzEQAzEQAzEQAzEQAzEQAzEQAzEwcgZMLtcdd9U9wwGen+PaJsDntuhmVczZrQIm1WfBDFgPTCJ7ce+a4OdiTsokvEUD4hi+v7d4wHHjdnhZeXinhO1hNrgM/RKOp5tv0GkT4f7qdnP7JCo2MGoFAEXn//DE26L/Bf4Ia4ONziqjbndG7wpwC8yB78NWcCDsDKuDHcuoxjRW/DPgLT3+HS6GB0D/iRgYNAO2Wwt/7Cc8ifg2vB6q2se9qv3D8BrIXTSQMEZ4AnkTuG3qPJ7Zv3vcuB0SMRADMRADMRADMRADMRADMRADMRADMRADMRADzRho4kpmLzBNVG/Ai/K8Wl1u6GJ2jtd7l10LBSwa2Bi8cNfX3onA4gFzd47z234c67eAwPHlUjhg8cBUigYct3bM2nH9fsqHuSyzYRdoIvTqdtBzVXmVJtarL+dZZ8KkHwXYgVgIYBLnfnCnt/LFzqHbxueO4xXCv4UrwYICiwH2BRN5VV0pzKT7OvS4K/wAfgJfgt+B3hMxMEgGPGh7xfedYF/xK9gL3L+riiOY8EXwVbC/SjzewB289QB414S6wu3viaPHjkQMxEAMxEAMxEAMxEAMxEAMxEAMxEAMxEAMxED9Bsw9mLytO9rvANBtHqnuZR3F+TmO78W/81tc0KEEt6VFAdNhU5gFFhBYNCDm+BwXtligFA6YyDbP2l4w4PxvgLNhIXgRcT/FjQ0ujI51O5kcbIOLPZiztmEm/u7vvO2FdwIwKe0VvlagrAbuxN3eEYCfPJIkNCllovBi8EpRiwH2A5OFdhCjFh6IXwgHwyfgeNC1CdVEDAyKAZPw/p+cBeBB6svwKahqn/aA6PQtKroUEo83YOHVrVBnAYDHha3hQvCELhEDMRADMRADMRADMRADMRADMRADMRADMRADMVCvAW/5LnWGY4EPg7edN8nshaGJ4TDgtjVP+PsWnayV48ReDOzFYluCxQPGz+Fq6MeL+ixOaDLMlZpPMfeRqNDAZJLbFS5O45N2Z7Qax4SStxWxIMAKHhtiqeDhaVfhAcDpzQWTRV7N61WjVgqJ0x2l8IBsIcQz4A64F/SciIFBMuB+bQGAxUP2EdtDVeH0nwqnQPaVsS17yyKpMzzB/xH040lcnR4yrxiIgRiIgRiIgRiIgRiIgRiIgRiIgRiIgRhowoCJ17dDnTkWc0UmL70a3HHbP4PjhF5s60WQjhm7PI4Z5sIhJAx5uI0dszfnZ8L//Bbmvvp1+3vh8yugqQS88z0VvJNGCmiQUFWkAGBss3bOXplu530/WPVjos9O3MY52QOKjdnpWWFzNlwBTncd8MDQ1A7HrGsP1/lwmAlevWshQL92iCxaIgYeY8A+wmKh1eA22BMs6KkqvIvIGnA6OO/EYw1M4+Vhj32r8lcWM30TPMFPxEAMxEAMxEAMxEAMxEAMxEAMxEAMxEAMxEAM1GtgQ2b3Rqg7r7IR8/RipP3BHIc8DTYGiwAcvzWXZIGApCgACYm+MWDbfD00lR92f/gZLATzo4mKDDS1gStanZ5PtiT5TPBYBPAgWM1jAUApBpjMwcVEtwUGVgFZBHAOmARfEUzyOe1RCNvftmDizgqpe0C/KQRAQqKvDZR92EcTwVar7QOTLQ7ip0uNHfnGdfD7pX5z9L5g33kUTKY/nqwtt/u3waKuRAzEQAzEQAzEQAzEQAzEQAzEQAzEQAzEQAzEQL0GTMIfUe8sH5mbY5BifsO7AXjx1pZgEcDz4MXwHNgDZoJFAH7fvI/PfSwXhDq+LIkYqMuAbfE14Jh6E+F+cx7cCF5kmfaPhCpC0YmlG7ABevX+X+FhsBDgIfB9O+ypeLTCxeKCOXAx2PAXQPttZHg51OHtcp4LG4DrfjfkKmckJPregP3CymCfMB1mQVXhgXlf+DEk6YyEtrAPttr3CW3vVf3UE/VfgcVbOUmp2namHwMxEAMxEAMxEAMxEAMxEAMxEAMxEAMxEAOPNXAILw997FuNvnL81jFDx4vXg21gX7Ao4O/hGbATeDdTxzMdy/T7Be8e4DQca8x4IxISlRgw9/YisI02Ebbxa+FS+AukrSOhiphK4rqK5RmEadoYrUqxYZr0k9Kx+zjZBJTT9a4AJvZuhAvgnNZz52c1jkUBk50+P+3rcL2eDE+HubAATK7qJRED/WrANire/v8WOBis3qwqVmLCM8Bb5NgvJB41YD/xZvAkua6wv3eb21enYKku65lPDMRADMRADMRADMRADMRADMRADMRADMRADDxq4Cgedu1jGSVf5JjlauAFZDvAQfB8sDDgANgOTMaarxMLAsqdAnwsOaHkSpCRmLIB29HesO2UpzT5CXhR9C+hXGg9+Snll+MasDNJTM6AO4mJP+8K8AewoXo1v8k/vdq5TyUsBngA5sAl8Gu4GEyMm/iziqzcJoanQxMmUp8Lf4Z54L8ESHINCYm+NGA/YB9QTs5sq7tUvKSbMv15cFXF8xmkydv3vho8ka4zLAD7IXgsSMRADMRADMRADMRADMRADMRADMRADMRADMRADNRjwCT5u2HDembXs7mUogBzO2vALLCI4WDwLgHPgf3BuwesA2XcecmiAN93bFoSMdCNAdvMZnBgNz+q4Ls/ZZr3QvJ/Fch1klNNUle0WAM5Wauw7HTtuNcCO2+v2rdCq5fhPKwYWwWsGNsadgeTgs57WMJO6AfwUbgOLIhIxEA/GrAfNfG8OawJx4H7ZpVxGxM/ALwCPfHosew3iLAvrDPmMzMLPjxRycl2neYzrxiIgRiIgRiIgRiIgRiIgRiIgRiIgRiIgVE24L8Vvgm8cr7O+AszM+djnqbKcKzRC0G9Uvou8MLQ38P14HovBv9dtRdSenGSSVTxN7lYCQmJCQ1YaPJjaCpH7IW/h8GFYJtNVGCg6k6qgkXu20naIZcO1o7Xq/ftnL0yVc/uSBYJTDWcj9N0HnbyJsfPgQtgDlgcYPGBFWGDHPqyyu0p4IFtEeTAhYREXxqwQMU26505bKsHtF7zUElYcLA8nAnZLx5VrPNtH31a21+Lrk6Bu8G+OREDMRADMRADMRADMRADMRADMRADMRADMRADMVC9gWnM4u1g7qWucPzvA3A+OB5sLkZcBseGexlOz+l6kena4AWgXvx0CPivA7yL8oGwHVgEUf59tMUJBXNEJU9k7ioRA8WA49qvhF7kLMs0u3m0bV8E10LaZjfmuvhu2fm7+Em+uhQDHgSsWDFJb/XVg2Dna2Jw9dajB4Ve7VjuHFbL3AoWAPwKPPiZCNsHtgR35kEND2Anwsvh1+Dt1pNoQ0Kirwy4H5oE9s4cV8IV4JXhVcZLmfh34ELIPvFooVCVvseatn3rdLh+rA/zXgzEQAzEQAzEQAzEQAzEQAzEQAzEQAzEQAzEQCUGtmKqJrrrDC/EMgezGC5ozdg8kIn5p4M5mbXA5ep1QQCTfCSnZG7pSS3M/RwA5qO8YPQeuB1uBi8cvQVuAy9W9c4FFi04ju1Yso/+LheXIWEE4w7W2X9t7h2Nmwjzo14AbHu2HSa/gYRexxN7PcFM7zEGbLR2oCat7YDdocTCAN3byKVXBwPn585yH9i5nw+/ATv5lcA7Azi/QQuX3VuSWOSwADxQTTWsnNPH5mBnl4iBqRpwX3dftghgETwDqtzfPJHcEH4K9jGjHtMQ8PyaJbi9b4CLISfLNcvP7GIgBmIgBmIgBmIgBmIgBmIgBmIgBmIgBkbSgLkVL47ap+a1N7dzEpT8hDkfr2A2D/Nd+CacDSbhvfh2efACIscQe5UDYlKPCaerDy9CtTBgJuwEB4F3CZBDYA+YBV6kugw4bu2jyyk+dzqGxQGJ4TfwPFZx/QZX0zbovyF4CFIAUMGGcMdOVG+gFAKY+DdR54HCq4XLXQGqqAozGeUB6EawGOB0sIPfE+z8PRgMUlgE8A14G3iQtZptsp3C2vzWOwu8GvYFD8RWwF0PHwWv3nb6iRjoxoDt8X6w7ViscgnsBVXGgUzc4pgTYLL7Q5XLV+e057cc/J86Z8q8SrWxxVejvg1qVp/ZxUAMxEAMxEAMxEAMxEAMxEAMxEAMxEAMjKABk+oHNLDeC5inOZ4SZTzQMUG5F85qYe5tHdgF9gMT8JvAamCyvcoxTKftPGRVmAXmhUzsmwfxrtV3wm1g/si8yFy4A/4I5rBcN7/vevncfJPPE8NhwHZ8GezY4OpszLzNU9oWExUYqLKTqWBxh26SVlSJByyT0nb+JrqrKswoHb871e7wLLDzH6R24IHmWDgevMp6Mgcdq94+D9NgyfBgdhJ8Bq6G/4ZEDHRjwDsAeGeJp8AnwBOtKuN2Ju4JnCdsoxwzWfnZYOVgneGJ0sHwAKQ6tk7zmVcMxEAMxEAMxEAMxEAMxEAMxEAMxEAMxMAoGnDs1TE5x2HrjJOZ2Veg5CS8yNMLwUyYLy0cI14XtoP9wMIA12NNMD9U95gms3wkHM/0jgYPg+vjWLNjrBYF+LgQvOO0hQOup/kZ19/fmTtJ/gQJAxjmBF8JtuemwrZkru6XkHZUwVYw+ZxozoAdpY3cpPNDYALJRzt7cSfsdcfv/JzHzXAhzINNYWUYhNDH3uAt/K8Dq9W6jQ/xg91grMIH94ntYR+YA3eBB7dEDHRqwJOf/wsetLaE6VBlWMVp/Bqc96iG+/PRUPdxzW3tyX8KAJCQiIEYiIEYiIEYiIEYiIEYiIEYiIEYiIEYiIEKDTgGeCD8A4w1vl/hrP/uh0x8XtsMvNPz/WDOZWnhuK3fvxV+BSfAt8Hk541gAt5YFhzfdN3qWD/n4fim+Za1YGPYCXRscvZweCbsDVuBF7L6XX/jcpbl9XXBvJck+tuARSn/BL3OQXa61s53HlwMKQBAQq/DHTLRvAE7Q4sAbOQmm01qrwDeEaDcFcCOtJfhAcfKrTPhWjgAngNrQr+HHcNrYHU4BuZAN6HvpR08t+A734dj4etg9Vs6ISQklmrAdnIveIsnE8O7QtV97Sta8/odj6N6cuVJsv1nr/tKJjlh2Ed7V5V5E34rH8ZADMRADMRADMRADMRADMRADMRADMRADMRADEzVgEnnp8PSxvenOp8lf++Y79wl3pxKvsCiAXMOcgG4PibWZ4AJ+D1gB5gJjj0uB3Ulal0Wk8PiXRZcJse4/xfK3QLu4flimAc3gBecLgTf/xP4Pf34m/b8Fy8TfWLgNpbDi4Ud324qbOfmQv2XBIkeG6i7k+zx4g/15OzMPZjZyXp1vnil70rge72O5ZmgFV5WdT0bqpgHk+15nMYUj4bZ4MFkaWEi9rXwaejkgOnB6UJ4A3ggs1AjEQNLM2DfagHANvBh2B2qju8xAwtj/lj1jPp0+jr3JHODmpfPfufFcCp4YjuqBRiseiIGYiAGYiAGYiAGYiAGYiAGYiAGYiAGYiAGKjUwg6l7EdS6lc7l8RM3Uf/P8Ie2j27l+R0wlUKAtsk97qm5jDVgM/COxibht4INwaStOZymc3yOhVrM4IVZutHTItCN+ZS5cDvcD/4LgfwbAST0SZhvPB+2b3B5bBd7wY3QSX6vwUUdvFnbgST604CNXUw42ym6I7i9LACwGKA82smbyO4kmc3Xxg07aDvir8Jv4QWwM0x1ukyi0jiMqXsQNKl/E5iAmyg8GJ0BFgxsNtEXW595AH0anAtvgR+AVxonYmAiA574eMJzF3wXdgELeqqMw5n4l+DCKmfSx9PW+YNQdwGAfeR0WBbsr12ORAzEQAzEQAzEQAzEQAzEQAzEQAzEQAzEQAzEQG8NOA5nInzt3k62o6ldz7fa8wIld1PlWOB/M08T6nIxGF7I6YVnFgLsAd4lYHNYD7xa3xxSnUUBzst5mrMSx2ZNKOvF5f8zPACOky+CW8Bk7zzwteO55r/M65i70au/dZzV14nqDNiebVdNFgCsxvy3gJvBbZ/ooYGqE1I9XNSRnpQdno2/dJhe4WtBgNhB2omWjtGD4FSS9s7DqrVLwc54U/DAUedBg9l1FTP49r5wGfhvDTw4TBT6MvaDTveBFfiud0awaMAKQ70nYmAiA56guN8sA1tD1Ylp57MG/AhG9WD5Ytbd/qDuuIEZXgR/gSpP+uter8wvBmIgBmIgBmIgBmIgBmIgBmIgBmIgBmIgBvrFgOOfR8OODSzQD5mnFyCWcPzVPErd44Hmb8xNzIbz4BT4BpwMZ8M88MI0xyhNzJv/MF9Ud37H+Tnv5cAkr2PjJnr3gEPgcHg+HAb7wjawPlhE4HIvC25vH33t85L3yvgrMnoUazId7wped/soi+9858L5kIKPYqVHj01t1B4tfiaDgdKRls7UDtJbd4ida6cJbr76uLBDXRcOgueAnUE/x2IW7jVwHngQHC9cr+3gnWDCsNv9YAG/eR2cBVanJWJgPAOeoGwGFo8cC7a9KsPil6eD+8Aongh5Iv48qDssungzeNLvSXgiBmIgBmIgBmIgBmIgBmIgBmIgBmIgBmIgBmKgtwa2ZHIXwxq9nexSp+aY6xuhvQDAxP914FXU/TYO6xj0ijANvFPArrA9bAzme8whmVDvNi/CTyoLCyr0/GfwjgHe9eB2mAO3tB59bXGD3/G77eOw5bXTSXRuYEe++luwPTQV1zDjA8FtnuihAStnBj1cBzszq4i89Yn/x34lMPFtR2CHcB/c03puwnaYKkk8uNjRibdJ8YDjui4Pq8DqrUd34G6LAewsTapbRXYuWATwLGiyM2D244YVYt+Fo8GE3L0wVrheVhWdCJuBt2fvJmbw5R/DV+FDsAj67SDPIiX6wIAnHh64LgPb3CZQZbhvvhuuAPu+UQv7viZiGjP1ONRPJ81NeMg8YyAGYiAGYiAGYiAGYiAGYiAGYiAGYiAGYqAKA8sy0UPBfEfdYY7kziVmai6mX/NM5j8eAgsW5CdgUcAK4JX4FgXsBDvALPA9c0mOLVd9ARuzGDOc73It3MYzwcIF10XPJdfn+K/bYz7cCrfAQrgfzI05Hi+GORu3k7mz5G+QMEbcznte1DZ9jM/qesucybrgts126qH1QS0AsCOwQWwLe4MdwUZgEYAHgvZOys7Bndwrwu0IrodL4Wq4DXzfDmAYwp3D9RXX6U9gEtxiAN2IxRHdduRWs+nqOLgEXgIeHPox2bUyy/V5WBu+CnfBWOEB8Fr4NHwUNoRuQoevhQPgzXAu6CkRA+0G3CftYzyIngzvgqr3G/vEPeFMGLUD5nhFP6ioNNZk6hYAeOxx+46ad1Y5EQMxEAMxEAMxEAMxEAMxEAMxEAMxEAMxEAOVGViVKb8Aqh5bHWsFfsub5hPa46+8MDk9KOOALuvDYMJcTgVdmjvywtotYHsw77MZbABrgLm4MubJ09rDeYv5GNuAeRyXUe/mwdwObpv7wVyQF2vOhpthATgu7/j8f4GFAXoo283X5tFGOWwTV8H0BiU4ru42vQHcpokeGWiis5zKotsQtoYj4TmwPkymiMHOwR3bZNGVcBZcBHYKXjU7bI3MDvKJYCdph7062Fla1eX73bQDp2Wyayd4des5D30XbsNPwKfhTrBTb49ycNuSN58CHwadTCY8eHwWPgOLYdjaD6uUmIIB25onSzvDd8HilKrjRGbwKljyxLTq+TY9/XexAO7LdYcnlx6TrgNPmgblxJ9FTcRADMRADMRADMRADMRADMRADMRADMRADMRAXxtwfPVA+DmY46gzzCu8Ey6H9jG/+bz2glOTyMMWy7JCjmfPgu1gR9gCpoNj215kal7O7dJv4TYq+b8/89x8333gXXotCJjdYh6PXnHu+Ln5HXM64jr5e98bxm3Laj0mzPe9EcyjNRknMfN/BsfWEz0y0I876FirZqe+PbwBng9e5d3rsCDA5O358AuwqmsReFV3e8fOy4EOd2iT/nbiVnd5JwV9+tzPOg2/uxEcBXtBN7/l67WE2+04MCHotnQbt4fLbDHDZnAw/CssB5MNi0neAReBB5dEDBQDtqtN4c3wyvJmhY+2v73hsgrn0Y+TfgsL9ckGFswTyWfC9fAgLFlwxFuJGIiBGIiBGIiBGIiBGIiBGIiBGIiBGIiBGIiBSRgwGX08mBuqO+5lhqeBubSr4X7wYiATybfBqCQszaV4ge402ArM120DG4MX6q4GJcfUr3lH80WO25rYL8UBZXveznu3wi2wALyo1DtsWwjg7ywOMMwx+fvy2vcGPZ7GCpwNFnU0Fe5Lu4M52kSPDPTrjti+emvx4hXwL7Be+wcVPneHduf2FvGnwrlwE5jYGaYd+4msjzu1HbeJ8HXAwoBu2oUdu4lGt5HP+y3cllYPvRfsuO2c28P1t11tAi+EV4NeJhse8D8I3wZPBJIIRELikX1qbTwcAMeDVZJVx1eYwetglNrg61nfz1ctdozpe7w4FDxO3ANLFhvxViIGYiAGYiAGYiAGYiAGYiAGYiAGYiAGYiAGYqBLA+YqdoWzYJUuf9uLr9/MRLyw6wlg0nguXA/mja4AxwL/Ci6nuSMvKF0yB8FbQxmu8zJggcZMsCBge9gcNoR1wW3md6aSc+HnlYd5JMfRHdd1O3tngAfA7WtxgNv9FpgNd8Afwe1c8oX+VmwL5T2eDkTMYCm9kND8RVOh9/3hQnBbJHpgwB20n8Ok7IfhcLCTaCrcYd2pL4BfwO9gIdgRuFMPenjwsgNeETYAE/lesdxp+7BoYCa8GbaGfoyfslDvgDlgZ1LCdbRtzQQPSi+Hf4BO152vPi7soKwKfB94MjAqB3xWNTGBASsgtwSvUN9vgu/16qO7mdBT4NZeTXAApvMmlvEzDSznfzHPg2EeeEKYfR4JiRiIgRiIgRiIgRiIgRiIgRiIgRiIgRiIgRiYogEv4Pt38O69dYd5IRPBS1746Pi/OQbvCmqe6Do4H65pvX6YR3/r90wIO3Y4SmFuxbFwE8rm+MwZbQebwvrg+6UwwNzUVHIx/LzycDuK29Tt6fZ121sccBOcA25737sPLA7wu4MS5gVPgWc2vMDvZf4fAx0nemDApG+/hp3BF+EwsJNvMuyEVoVt4bnwCjgE1gF3ZA8CduJ2AoMYLreFDK6DnZRVarYNE+N2vkvrgHVgx3YJrAmzYGm/4Su1holXDzaXgpVb7dvKdTdhZyHDPPAAZPubbLjuW8CBcAsshiQEkTDi4X5i27A/e3rrOQ+VhQfu2fC7yubQfxN+Cot0cAOLZX9yAth32r+4rRMxEAMxEAMxEAMxEAMxEAMxEAMxEAMxEAMxEAOTN+BYqreb/yw41ll33MUMV4IlL051ucyfuEzrgclt80UvAPNZO4H5JMNxQ/NLjgn7G1+35yZ4OZRhgcSDMA/MyXjB5Hfg6/A98ELby+A2uB8cVy2u9FvgaeNRlsXtZw5pZXgSTAe3/ZNhIZTlN184SPkgl9u7NZizaDLcn04B20KiBwZssP0YdpqfgkPBzrGfwp3dDn8a7A9HwT+CxQHuKH+CQb4zgIkrd7BSCGCHVgoBeDphuN5Xg45MuPfbttuMZdoELFTwoNIedsgelJaHa2AW2IFPJdbkx88GE4JzwbaRGG0D7l/2E/uA7aPKcD9cHU6EQTrhmIqT/fjxAVOZwCR/6za1AMA+5B6wqCgRAzEQAzEQAzEQAzEQAzEQAzEQAzEQAzEQAzEweQNeJf5aaOKCH5f6Z2CuyuWYKByHNReyAphINSn8DDgczCFtASY3HTv0e+blLAgQxxVHaSxxycKAn7P+jqt+Db4Dp8LFMBvuAq+m9zdG8VyeP/Jmw3/cno7zLwfXgzky82sWAQxK2AZ1eyS4Pk2FuYyTwPH1RA8M2NH0W9hJvh1eAf24fEv6cofwFjA7wovgpbAVlEIAb1cxaB24O7ydqutglZZJSxPjbg87gonC9fW2J/7OyicPYv0U4xUBuM4uu4+uq531luABeyphAcVB4AHgZvCANWjtgUVO9MiA295kvBWCe/ZomhNNxvb7C7h9oi8N0WeHsS57NbQ+nqi6be9tPTa0GJltDMRADMRADMRADMRADMRADMRADMRADMRADAy8AfMQm8DnweR53eHdkr8KXki4MywtL8JX/hZ+11yKdw+YDruB45bPaj13bHgZMO/id80xmT8wRjV3oAtzJwvhKjgTToavtzAx7HtXw3wwSWyy3TxWceijUR4ffVXPX7endzpwuUz+/wEGKWx3R8DKDS60Ds+DG8E8XWKKBvotwe6OuTd8HCwEGLRw+d1BdoB/gOfDGnA3PAx2YoPUcN3p7UDtrEzom8y3zchEnagHx3ktduTRhHo/xeYszEz4LTwAJdw2JvA84Mq1YBGDB+SphNPaHTaDa8B52hYSo2eg7P/uQ94dwoNaleF87Hs8OSrzrnJ+TU/7cBZg1wYWQrcWANhn3gf2gaPgm9VMxEAMxEAMxEAMxEAMxEAMxEAMxEAMxEAMxEDPDZg8fyfs1/MpdzZBL3L8CXih4OowExxrnUyYSzG34nS2gAPAYoB9wZxBuTuA3xPHjB1bdKxx1EMPXrjpRVe3gncH+Bk4Fns8fB1OgbPgSpgH5uPMZzlGWxyaoyl+edrzMId2PiwCl9Ux+UGLvVngTRtcaLePebPTwW2XmKKByXZYU5ztuD+3A/wsbD3uNwbnAzuUNWAfOApMhD8Adj52WIMS5UDjDmen6bK7bl7ZPlGHaSLdzs4D5A7QZOUQs39ceKDdAC4D16uEBQ+uqwdZDw63gNtuVZhK6MqD+S7ggcgkYYoAkDCCYbuynT0FNqxh/ddiHt8FKyKHPY5gBbdtYCXtJ3XstrUq2Ns8+V4iBmIgBmIgBmIgBmIgBmIgBmIgBmIgBmIgBmKgOwPmH54K/wHLdffTnnzbcb1vwe/BPMfl4FXJXui4Bpg7cLx/suH6eQGsY8NezPRMOAS2A3Nk5u1cBh8tHPD7jjtmvBEJbaETx7zvAgs2LA44DRynPR6Og+/AT+FCMFc1H0zQm6sz12WORq9uz7JNyyNvdRy2kfNgPrg8g5b7cXnNIxwMTYZ5OO/20J6za3J5Bnrek2nIVa2wy/Jc+D7YsQ1juBNdAV+GX4LFAB5ABincNh50V4P1wcS+B6Dxwu/PBKv1NoN+inIgfx8LtRDKAdS2aEezEawJJhQ/0HrOw5RjNlN4HXhAemjKU8sEBs2A7cv95s3w7zUsvMUG+4EnOcMeZ7CCT29gJT3ZfAZYPOTJ5j2g90QMxEAMxEAMxEAMxEAMxEAMxEAMxEAMxEAMxEB3Btbj65+CF3f3s5592wTum8DHkjMoE3fMb3vw7s9eOLgK9Cqf5bycvgnq6+A34F2M58B9YLLbHJNjkIOWV2KR+yoco7eQw4KLDcBc0CyY0XptMtxiD/NEK7YwL+a2Fn9vOC58IxwDV4Dbzu20ZLvhrb4O1+cpcC5YdNJU2L73A3MZg+awKWfjzrc00nG/UOMHdpS/gt1rnGdTs7Lh3gyfgJ/DYhikxmy7sZNbFtaBdcGKtYnCjvONsMtEX2rgM71714n/hPbt4PrZ+c8C2+Y+8E7o1b8zMEH4KjgbvD3MIG1/FjcxRQOeXPi/nywEshig6jiWGbwPhr2dXcw67lm1zDGm74leKQCYx/NFkJNwJCRiIAZiIAZiIAZiIAZiIAZiIAZiIAZiIAZioAsDjst7oejJ4PMmwotUvwom2tvD5OQ18EcwR7Ih7AeHw85grsTkqZ/1IpyfFxDOhovgEvDiozvA9x3r/Ss4Dun4ZKIaA47llwti3eYWDEwDCwT+BOZ4LgXbxSCPv7turocFOE3GR5n5B+AvTS7EMMy7qQ50LHcm/t8BTVaXjLVcVbznAcAKosPgWfDfcBt4QBmUjtrldLk90DwIrpMVUN4NYKwDnN/xFisWCmwyznd4u/ZwWW17f4ZrW488PNJRlwPnSry22s8OfBeY6I4HfNxRWDV2MNwAt4PbfpAPDix+oksDbvP9YHqXv5vM1+1vTgBPCIc53srKrd3ACrrvfgd8tKBHBqUvZ1ETMRADMRADMRADMRADMRADMRADMRADMRADMdC4Acfqt4Rvg8nVJsLx0+PgzjFmbrLX5Lt5Ecf+zHlcBafAt+ACMIfgBYXmFKZaDGAeYnlYH3aDQ+Ag2ArWBJdBZ4bzEscnMy6JhB6GPt325ohuhSvhPDgDzoa5MAzj7ubDbGe2rybDdv8j0HliCgaeOIXf9vqnXh2+d68n2ufTs3N+EhwKzwQPGB5ABqlh2/nZMZjwMoluEYAHmrGS5B78bga/+2Tol/bndtgL7oXrwMSs4bqV5yvw3Kt6PXhvAb0ID94esD1oLAQPEh6gE8NvwO3siaLJ6v1rWF3b7cngScowxztYOasx6w63ZykAsP+2L7dCNxEDMRADMRADMRADMRADMRADMRADMRADMRADMdCZAa88/jA4Vt9UXMiMvWuzOY8lw/yBd/Y1b9Aejg06JngL+NtvtB4X8GhewbHZZaEk63nadfhbr0Q3n7QdmFcwp7QT+J6fuRyGuRlzL74u7/E0EQMTGrCteCGh+comw/Z8Egx7LqNyx/2SgHU5Pg7rVL7G/TkDO28Tgc+BfcHk0e3gQWYQOmiX0WSXt+QwuW8BwHh3A/BAOBfmwY7g9/oh3AYmYufBTVAS/66XiXkPoOJBe3NYF3oRFgEcDDeC0x6GSjFWI9GBAfcbTxaPgCd08P2pfMU+9mK4dioT6fPfug+/G1ZsYDndjt8Bt+nDcD+kAAAJiRiIgRiIgRiIgRiIgRiIgRiIgRiIgRiIgRjowID/a/0l8Faoeqx0vMUxH3McOE7vOF97ONY37/9j7z3g5irL/P3//lZ6D5DeE0ggdJDee28KiICIvS66qwJiXcuKddWVn7oWEOkoINIJhBJ6J9S0Nz2BEHpTf/v5X1eYhx2Gt8zMO2fOmZn7/nyunDOnPuf71HPfz3kDfgRZuY9NbzOvsxBuBX2GBjP9S8D6EP1vh42J6K+t1/SDer6aGavYE5wMsAOMAGMOplcdJU0OqJy4wK6wUOBtClheToT+lM+3XbCOH8YX/csaD0OU2ToETKfkmYkpDS6Hw9fBjO1ks+EeBu+BHWExPAOtEhS2MvpVs5MAXHd2mw2Gz1VuBtfnwaOwOTgDrghmWlMwfibrdtSanaV5YMdp527nvR34fI2wFbmIs6qehLnQKvlNUsP6oYBlyQHjCZB1HbAOeq9LoV3NenQ6uGy2mZd/KN3UCQDPg+1GWCgQCoQCoUAoEAqEAqFAKBAKhAKvx4bMAABAAElEQVShQCgQCoQCoUAoEAr0roAB8W3hl5DHxz0pdQbprwQ/YtTfV276/JZAihmU7+ttXR+hHwvdBxfBeTAVvJ4+4dXAOF1lDIVNVZtxDXUbBcaVjDXsAWNBbb22z+Nx+k5dGr+pfEY2hXW4Asb3jgEnquRlllfrzLVQa33LK82FvG9RJgBshzofhP40coUUuM5EqcNoOBr8U/l2LE+DnUXRG2XTZ+fxMvgXAQySW87sVMrNY5zg4BfJG4J/WqQIZlqdBGC65oANnubSxsbneQmWwQ5Q+Vxsqsuc/HIATAfv6ySJouc1SQzrpwKWKQdlE/t5nWpOd0D5W7BstaM5yD0V8phIZtt8TklU2z3bB7eFhQKhQCgQCoQCoUAoEAqEAqFAKBAKhAKhQCgQCoQCoUDPCuhfHwO/g9GQl+mLvxUWQQo6ui356J9lvbs//8/mqs1rGVvwQ8ArQH+iQU7jJH586Bf9fq3/T1Cvea5B/yGwDTgZYG+YAGtB+TN5L81YTVgokBTYmpVJ6UdOy4Hc93zwr6WH1amAwc4i2KEkYv8iJKRgaTB/bJiPhY3AjsCOphUCeHYaBsKcCGDQ3M6ku2C5z/MoDILhUARzFtyucB8sgDTxwkkAPpeBRr/wXR/GQqNMjfaBGTAHYhIAInSAOfnFgVjWZj08G5w91462Lg/1Beiuncn6eW0j/li6yWssnQBgexEWCoQCoUAoEAqEAqFAKBAKhAKhQCgQCoQCoUAoEAqEAj0rYKD6+7BXz4c0ZY+B81GgD9UJAMYBkn/P37NAv18jzfjJXLgRnAxwKTwBxgnWACcF9MfX6TN5Lf3Pm8O+YBzOdX2pWrq+sSgpnyDg/rDOUkA/t2XPmG2eZj28BrrAMhlWhwJW6CLYiSRimyIkpKBpMJ82BicCjIenwcB5CkyzWkizYto5+udsDKpLd2XOYPqTsDqMBTumvM1GblOYCmpthy8G5e00fY5FsD34Z3oaZWq0J9wPCyDNNmQ1rA0VsI5Yrj4KWZd7y+zVMBPa0UbxUJ+GrHXsTjvr6bmlHW+wdAKA7XNYKBAKhAKhQCgQCoQCoUAoEAqEAqFAKBAKhAKhQCgQCnSvgF/5+kHPRyAPn15lqvTNj4OtQJ9/ign4QdUzoN9Pf24W5mQD4z53g18+nwf3gvdcCwyI6t+tVyfPexf4p903AmMQB4FxOT90NOahPzMd59Lnz+p5uXRYQRUwz08Cy1te5sSUp+B2sByG1aFAnhlYntxP8mPD8g2x3q0CNtAGpY+DieCfnDE4XeS/CGBjYXDM/zfH8uaste7K3Ytsnw52sv7VAzuYvG0ICVgTboY0u8/nUW8HAK6/BNuBDVKjTI0OhPtgPqSZhqyGtaECDuJOBut3lmad6oIp0I5mm+jAJI+2wzw8rySqM3edABD1tiRILEKBUCAUCAVCgVAgFAgFQoFQIBQIBUKBUCAUCAVCgQoF/Crdj6JOg0b61ituU/NPfYv65zcAJwIYfPfPkPsRo4FIg+RZB8a9vvGSaXAZnAM3gfEgP6IUA/b98YMao/E642B3cDLAjjAc0nVdmjfey1hIBGIRoQPMvD4GnCySp1n3LoYUm8szLS157+4CsXk8yCe46aiMbmyw9nYYAAaXU+PFasuagcJJ8D5wtpaTAOyADLRbOYtoBsP8SwB2GHagLivzwv1pEoDBvMr9bGq6bcIdDebdD0lfn0WcdWfnPwjGQCNNjfaFO8G/NOD9wtpTAfP2Y+Bkk6zNQLUzSNtxsLYHz3VE1gL2cH2D/heU9qmxbXLU2ZIgsQgFQoFQIBQIBUKBUCAUCAVCgVAgFAgFQoFQIBQIBcoUMFZzNJwBWX8UVXbbmlaNTeijN0BuYHww6AN0e4rBuMzaz+o9/LhyFlwP58BfYB6onUFSA/TGW+o1zzXWMRJ81kNgNxgBbk/P67MbU/R32sZqWJspoF97K9g05+cyXnIhPJNzOlr29lbWItgHScSojBJigPbTcC08DDbIzi5bCWywWtls4DeG98PW8BwsAQNQRTRnx/nFvJ2DHYflrzIPnM3zODhZY2I3+9nUVDN9O8GDYCfrM5h+G0HTaOe6GLaHVaGR5vV2g1tgKXjvsPZTwDbpUBjdhEezzTgT2jE4bfB/d8jDXuamF5VubPu7DJwwFBYKhAKhQCgQCoQCoUAoEAqEAqFAKBAKhAKhQCgQCoQC/6uAXxUfBL8AYzRFN+MDxjL8UHBvGAJ+dOp2/brGCsT1Zpj39oPBW+E8uAQeAdNjwNRJC/2J+3kd82UobAfm1Z4wDoxXGKNwwoDEXwZAhDY0y7P11Ikgloe8zPJ1B/iXMExTWI0K9KchqPFWvR5+JHsn9HpE/TsNKDtLxOU8uB2uAYPM2gCwQcuzIJuO/pj5OB6cNbcN+NcAxEBUszoeblWVWVENlhmAtOM0IFmpvel+FMyXjbrZz6ammg3NFnAzONsodejO+Eud3hLWdwY7vkaaDa0TIW4CdStafpKksAYosBnX2KEB1+nrEg4Afw8v9nVgC+7/KGnOa1ai7e2fS5rZLjwL7TjJovSIsQgFQoFQIBQIBUKBUCAUCAVCgVAgFAgFQoFQIBQIBWpWYHXO2Bd+A/rV8zY/HjW4uD70FSszhmE8YxI4EWAQ+AGQ8QB99pLiBqw2xQzG64e8H/w46Vww/mUsbA1YDXyuyvgLm6oyz1sRBsJWcCDsB8Zs1ELzuT0uxdiSDu4La00FLMeWmxPB+F1eZrkyVng1xMd2deRCX41aHZes65RdOOvddZ3Z90k2OOeXHWbhNUAzB6bCteBkALVYB2zQLFitaHY24+AYMBjt1/YLwUpSJDMP7IRMlwHJ7jS3QjsJwP12KHnnyXqkwfTeCZYfzecQO1KD84NgDDTaRnFB7303qIv3DGsvBYbxOIc14ZFsIy6H+dBO5cj2+4ugjnnYEm56RenG1tVlYF0NCwVCgVAgFAgFQoFQIBQIBUKBUCAUCAVCgVAgFAgFQoE3A/77IMRZ4ESAvE3f6C/h9/AUjAY/xusrDuF+YxYbw15gTEA/YJoIYFDeaxuXaqZ5T2MUT4J+yj/A9bAYnAyg5itAX8/HId2a5xkMXhc2hf1LqIO6qYFpEOM9mlqEtaYC5t17wQ+o8zRjtpZly3ZYjQoYNCmCjSIRB2WUEBvei6G7rzFthA1Ez4Hb4AZ4HNRlPbBBbEXzmUfAkbADPAN+8Wvg2ga4CGY6DJS9AnYIdpqVnY+dxjRYFSZ2s59NTTVnud0EXWD6LT/+yR1nt1lWLEe7Qpr9xmrDbEeuNBceA8tyUfKRpIQ1QAHL+IcacJ2+LmEduwMehnYagNl+nAr+n1d52Cxu6oBas5115m13fY77w0KBUCAUCAVCgVAgFAgFQoFQIBQIBUKBUCAUCAVCgU5SQH/5fvAHMBhdBPOr+XPBuIl+/VvBv/I5DAyWV8Yq2PQ2c78+SQPge4MTAfS3GpsxbpDOd73ZZuxAH6XPNRnOAT8KmwnGMdYEYxqmtR7z2ZwMYOB/EuwDxve2hOSfTfELYz9Jk7SNTWEFV8C88sNcY2J5mnGTq8CyHFajAkWZAGAmfrDGtFd7uI2RgZkX+jjBhthgdBfcAjeBDaKNvZMB6m0MOTU389lHwzGwM/gXARaAgfcimI2IQX47WcuiAwHTLMnc718CMB82hPJ9/GyqmcYtwAYnlSefwbLjwMWA33PgpItGp9Pr2ZHeC7Mhj4EDtw3LSAHL0eeg0eWmu+TOYONN0E4BameengYOYPOwR7ipk8i0mADwpg7xbygQCoQCoUAoEAqEAqFAKBAKhAKhQCgQCoQCoUBnK6CvU7/+AfAHKErw3zjQz6EL9MtqfihqHOJWeBWGwGrQl7/W/QbTnQiwFwwGJwJo+vCTHz8tl+9o8j/GWJbAHXAeXAB+IKZ/2CC+ExmMfdRjPr/nOqlgAjgZIk0GMK7mcyc9nAyg3nlqwe3DqlDAPLO+HgF91YEqLlf3IcZl/Qsdd0EqR3VfrNNOrLdSN1onM+5kyCLIbuF8AmZCtWYjZLDcQNmNYOHyS28b71Wg1UxdR4CV1eC0jb3PZ6AqdXCs5mLe3/x3EoCmvpbL8kZF7Z8EO5HxFfv42VRbn7s9D/eAHafpt6M0zQ4IlsJwGAWNNu+xG1wLz4L3DmsPBSxDp0IWbWClQk5euRSsV+1iY3mQT0Mz9OtOs6lsfLC0w3bV+mmehoUCoUAoEAqEAqFAKBAKhAKhQCgQCoQCoUAoEAqEAp2ogP59vwY/FH4H+s6LYPrUz4ZbQP9+pTk54H64GJ4Bg9rGLMrjFfx8h7nfiQAbwZ4wFMr9gynonZbszsW8v/ENfZmXwB9BLZwA4WQN86kyPsOmqk3/rNfZAPaAQ2A7cDJAime49EOud4HpSdtZDSuQAubNh8B8ytMsTxeCfvewGhSwIhfBbFw+CzakWdmUOi9scHop3AM3wBIYB/7VglYzG9/R8D7YE3y2xWDFybvjsZF/GewUnXFmo1LeqZrGGbAGqH/5Pn42zbzveLgSLBeaaTfddo5qPAt2hSzKs8/v7MNrwABudI6I0AZmXTwFmvEF+xvc52JwJmve9Z4kNMT8U0QnNORK9V3ESTm2T5r92bNgmxAWCoQCoUAoEAqEAqFAKBAKhAKhQCgQCoQCoUAoEAp0mgL60AeCcYhfQBZ+ci5bl/kV/FlgoL8n02/6KFwHV4B++I3AuEVf5rOXTwQYzm8nGrjd2EGadFAUv6w6TIe/wtmgn9OYkbEOYxH6q017Pebzep2xsDscAk4GWB+8prpq/mUArSiavJma+NfYkx8VO3kjT3Mi0YWQ4nF5pqWl7l2UCQBW7ANhdEbqOUPEL14NstXbWFnYbfifgBvBWVKjoVUnAgwl7Ta474cNwE7YBtfnND9cSjPN+6qx6TA9Nvzl+WVnNBvWBjuNvMy/RPA03AZJI8uWZmNkgNXJDHZm5ennZ0NsIldZCA9Bum9DLhwXyU0By9G/QjPaE+91DviXAKxzrW7WMduxPXJ8kMu496LS/Z2sFBMAcsyMuHUoEAqEAqFAKBAKhAKhQCgQCoQCoUAoEAqEAqFAbgroqxsFn4JvQzM+eOI2VdkzHPVDWALJr195otv18xls9AMfzzEe5Ad5+m6NSxjgr8Y8Tl/+XjASjBv44eP/AScCeK+e0sGuppvpmwc+r/5jfZ5PgXEaJwP4PKa9HvM89RsNu4KxqZ1gCKhz0kJ9XP8fCMtXAfNgE/DjuzzN8ncDWBaLVF/y1KSqe9sYF8W+TkK+kVFiDJIeB3eCwVsbKwtNf+yfOdnGaV94LxiwbmWz4vjfAliJHoDHYSEsAxv+v5WWHmeDrKauu7QhELe7dFs6xmVaT8d5nvRkamsgfRSYX5XldDjbPgF2EHnZ09x4D1Ann8U0OphxADAILGNfAScBZGEOEA6DG0B9e9OT3WEtoMAc0jiyCel0ks3u8AgYrG51sy2/BBw05mG2ax+B2aWbP8tyOthuRr0siRKLUCAUCAVCgVAgFAgFQoFQIBQIBUKBUKDgCujbM0ClX1Ifn+hzWLls3eCX+1MQzECVaGnpdUT/qNdLPlT9B8mHp18v+U/1ubquH8Ht4ra07r50jfAzIEZYoRWwfkyAb8J7wLpQFLNOGfyfAtaxnswP+/T560OtrHPW/a3BD7kOAAPatZgfOE6FS8F7GH9xMkKq77YRRTTbN2Nhe8DBsCMMBNvJ/ppt44vwGEyG2+FJSB+vvca67WNY8xWwDzP2eQHkXZfPIw0fA+tQWJUK5J1p5cnckx/Xg4UqC/seF/330oUduBlgNrgsaYDGas1mpzYCPgQ2fP5uF0sDUztEG1o7PQOGYqPrdkkdlNvSuse4ns513WuIHVk63wGs6+m3Fdj7uLSDHQTml8eZnmSjWPk8bJ42NHlp2ixP3wfTq1l2/esE42F1GA0/ActYFjaHi+4P00F9wlpbgWkkf1ITHsH6dyA4mGqHDnMdnuNhGA55mG3bsWDgX1sK1knbtLBQIBQIBUKBUCAUCAVCgVAgFAgFQoFQIBTIXwF94KLfdkUwaOXHXAn/RLWBveQrHsh6Ofoe9CV7nOd4rNfwevoDxetrael6Ch661K8p+vD0zegz1Xegr1T/jAGw52EZ+MWxfgbxgy2P0f/oua7ri0jnlvte3e61vU+6N6thoUDmClju9YdvBT+FvHz23Lpbsz5cDOeAAf6ezPozFxaC6z2ZbcG28CXYE2xXajHr8d1wKegTng9OBLBeW6eL7utfjzQ6EeII2B1Gge1iefvHz5rNtss273G4EaZAF+hvNT9SzInVsCYpMIn7WFZXbdL9errNLHbsCgt6OiC2v1OB/lbId16x/i3rc6pBEwdTWdgTXPTdYAPvcxv0FxtrB3fe106q3gC+FWA7OA7GQVjvCqRBqEuxcbcRT4NWB73PwGKw0X+u9NtBsINhB71D4TSwg8nDbHT2gq6ym1umRsBw8IXgSPgYZFXXHLR8GizXSVNWw1pQgdtI805NSLfl5Gi4Aaxn1r1Wtokk/iGodaDdqGe2PbLdt03SbLdmgAP2sFAgFAgFQoFQIBQIBUKBUCAUCAVCgVAgFGiOAvreRN+uwXmDUfrm9P3q9/WjncEwDEaXcN1A1hqgX0G/XroOq7lb8vW5TBMH9Dfog3gBDIo9DYtgIehH9be+VH0+HicGGz1P9ANJujarYaFAvxSwvo0Bg8Gng/WpaHY7CfoxOKmmJ7NOWHemQ7W+dtsW4wOfgx3ANqQWs04+CH+Gh8HJB04EsN62wkQA20vb2ElwEOwLG4Fxtv8D/THbKfPDuJ5+7FvhKdAXa16ldo3VsAwVsI+8CTbJ8B7VXPofHLQz3A3Rf1WjGMdYQYtiDs6ugb0zSpAFZFt4oOL6NkTe20GenZOTARwQrgy1NlIevy7sCidCETs7ktWyZsX+O9gB2xHOBDuBQ8GOptlmJ3QCnA+p0bFO+XIxHgaA5ekMMEiZhZmGo+FysIyHta4C5qFluRn2aW5yCTjo9QWyVc36tj9cCXn1Zw48PwVJx9Q2OUgPCwVCgVAgFAgFQoFQIBQIBUKBUCAUCAVCgcYroA9AP6z+XAP9Bpv0w/qB2QgYC/ri9M8NhXXA4/QB5+U/4NaZm/5J0UdocMxAmZMDZsIsmA1+Pak/yI+tXgEDjfpbRT9j8nGyGhYK9KqA9WlV2Ae+AltAEeuX5f5rMB96M3171hUn0dTqZzcmdACcDFuC2tRi1r/H4FK4H+bAYqisn2wqtNkmj4M94XDYHIyR1KoHp7zNbJvSZIDrWZ8CXbAMUntXa55xalgVCth3fgf+rYpjsz7k69zAWJsTZ8KqUKBoDfJHSfOvq0h3vYf8gBNPgZ4GMjZEsjI4aDR462SAFcBBZbXmsQ42j4fdwPPDslPADqCW/GlkSpy0ciw4oE5mWnzhGAsOgjaF74GNZRbmQH4PmA5qEdaaCvyOZJ/UpKR/i/v8FhaCA8xWNdvW78IXcnwAB53/AalfcXA+C2ICACKEhQKhQCgQCoQCoUAoEAqEAqFAKBAKhAL9VED/tf5a/WqrgMF8v0gcDRvBJJgIQ8CJAO8Czwl7pwL6LvQdvg5OApgDj4Nf2Lqun8iJAemvB+jb0G9kYDT5PVgN63AF9H0bM9kCPgOHgPWuiGZ5/iZMA8txT2b5fgZmg/WjnvJuuzMQDgN1sW2qNWZhGk3DFXAH6GPU92+dtD4a5LYOt4LZbg+C3cAysnPpt/5ctarXfH4nAzhh4jqYAmr2AmhOmgi/7HIpGvKPZdiPFv8EtZbnhiSg7CI3sv5esF6HVaFAfypaFZev+ZAJnPEQOKDLwuZxUWdgOcDpzdTFwmzHZQDXwaODy7XAhqtac1BqR3gMbAZF05skhfVTATvfncBBRLlZdsaBnZzl4BNwOGRlf+TCnwRn7tYzQMkqXXHd6hX4IYc2aybdr7nXj6ALWnlAZLvsJJxtIS87kxtfXHbzBazPgVbWtexxYjUUCAVCgVAgFAgFQoFQIBQIBUKBUCAUaJoC+k5lRdCfti4MhA1hK9CvOx70B3hM+FoRoUFmUE1fxvMwF5wUMB1mwRLQn27QzeCaODGgfHJA+CMRpM3NmI31cVM4AY4E62FRza+Efww3Q19fDOtTnwmWf+tCf8y40mA4Cv4FRkOtgVPTYL27Dm6Ep2ARWA9T3etvOrlUU21t7rY1WG72gDFgmfonqNecMGG79AhcBbeC7ZfbnCzxGnhMWP8UmMDp98Aa/btMv882Frcd2D+FVaFAfypXFZev+RAr/N1gsDwLcyByHJxfw8XVyKC/M5OcCLAu1DLItHG3YlgwPwQGhMPaRwHL1MfhN1A+0LXcWF58KbFzGwBngoOkLOx1Lno4XA+t1vlnoUcrXvNUEv0fTUr4ldznyzAdHAi1qjnJ5gHIa/Bhnf8S3AvJ5rMyBxyMh4UCoUAoEAqEAqFAKBAKhAKhQCgQCoQCoUDPCiS/qz5hP7xaHwz2bw36UjcCfWrvAo8Na74C+j4MpL0KfiE9D2ZAF/gRhH8u/eUSBln1M7lMeK7or/Ra5f5TfoYVXAHjIquBPu19QT/4JHB7kc3ydjlcDJbR3oLA7rMsW7Yb6c9ToxFwNHy6tF5rO2Z9eQFuhqvhcVgIS8G0OmnH+tVq9Wpl0uxfbjkYDoEJoH+31okSnPKWmY9O4HgQ1GoymPe2RbZRLsPqU8D4lvGEHes7vWFnWc6Ph4vAch/WhwK1Njh9XK4hu0/nKt9uyJW6v4izQ7YCByO1mo222EANAicCuF5Nw+QxnmNHeRh4blh7KHAjj3EEOAOp3MzzoTAGfJHZBb4C1ZQXDqvZHAhYtkxHq3X6NT9sG57wKZ7pF016rnu4jwPPR8EXuFY0+69D4VLIqy9zoP1hmAfJ5rLi70a+MKRrxzIUCAVCgVAgFAgFQoFQIBQIBUKBUCAUaGUFfH/XL+bX/WvCSNgYDCrIKHBfXu/53DqsBgX0Pxpo1Qeif0mf5DNggDJhAG4ZGID7G+iTN3Djx0z+Fte9husuDeSJ1w4fJyI02VId9UPIbeFI2A8GQKuYX4CfBcaC/JLeIHpPAUODxtPBMpxFeXMCk23bMfAJGA71tHHWnQfAQOz94EdI1rdUt3y+LNLPZTM1420jwLjZ4bAVWPbcXo9OnLY8r59leRNcAE+BWj0Hti1htSlgbOsrJWo7s/FH/4ZL/gtYH8L6UKDeCtTHZfu120HffWBgPQuzEfwsnAn9aRBX5PzVwI5vIFgJqjH/ksAQOAAM1jqoDWttBRxAOBiyIyk365cd/ASw0zKvvwkem4U5QH4fXA7RkWWhcLbXPIHL/yHbW7x1dcvqB2EaOEjsT1vI6bmYban16bRc7v7mTR04fhh8qdDUsQucNeygOywUCAVCgVAgFAgFQoFQIBQIBUKBUCAU6HQFDCb6Du8XhINgM9gddobRoI9VH1pY+yqgv8Rgvv5K/ZcG/PVHGXg1IKd/RQzUutTX+iI4IcCvdj1HP4vneY20ze0e4750jPvTxIFW9HeR/Kab9c9g66qwPlhHDcYeBMOhVeunk08uBP2glqtlYFkpLxeWqdngF/WWnSzNOMFYeC/0ZyKAzzATnAgwFeZAmmiTJuR4TCuaZc14285wBOwGxtLq7SfM0+vhl2A+d4FtS1htCtiP7w5q6Xqe1sXNLR/638P6UKCIjbd/6uOvsGsfae/Pbht8C4mNf3/Mwm7Dbec4DNaEaiYCqLud6liwIfNZvUZYayrgoOF9cDG4Xm6WEV9wxoFlezj8CrKa4GKnvz+8ApVpYVNYgRVwQtBlTUrfQu5zLEwDX7YcHLaaWa8mw1Y5Jvwe7n06/L2UBuvcDPCF9R+lbbEIBUKBUCAUCAVCgVAgFAgFQoFQIBQIBTpNAf2e+r4M+OsT2w30V00EfaBF9EmTrLCCKKB/RfRXGcDT72Kg9lXwIwwnCOjPSpMHDOyK2/0qVDwvTQ7QR+OkAbe59HruK1/3GPd7z3R/VtvSUmzCOMZaMAI2h73AuuokgLyDfCShIWaZuAQegWfBuJB5bx5bDvw9G8x/tzXD0kSAI7nZJ0D969Hbsmr6J5eYwXJRaVsq2z5XK9tqJH5TcNLEPjAe7Ftq0csy8G24DbpgMYTVrsBITrkP1qv91IaeYb21nboLrANhvShgY1M0M3B5LuwCWQ0G7cS+Bx+Al6Fes4DZYaRByDqs+9cA7Dh7a4TsTGx8n4KfwqVwCDizztlMYa2lgOX0KPgrOBAtN8uIA1MHGKuAnbBBXicMZGE7cFEbwKuhWYOWLJ6jE69ZWXay1MCyaLldobTM8l5ZXVsnwoZZXbzK6z7MceUD6f9X+h11r0oB47BQIBQIBUKBUCAUCAVCgVAgFAgFQoG2UUAfg8H94bAFHAS7g75SJwSEhQLVKqDPSvSvG78wUL069GXJH+NSn6zot9F/XzmB4Dm2GTwVP+TQd+uXwcYKPN5JBPr8DaR6bvL/u/Saku6R7sumQlnS0bqZ6meKX+hT2w52BCfpqLHHt5sZp3k/XAF3gzoYADZ/zdf5YF42Mw+931PwA/gTHAafhNHQW0yJ3W8zj9U/eix4jfvgSnCyg89luTbWpr/Scuuy1cz031nCtmA8HABHwCQwf/vqX4y3rQvmcTPzmdu1lb3A0zwEThTK06zDxo4t77bVYb0oUNRGfSJpvgkG95L2/u6ywTsZfgmNavxshFYBGxTTXm3HaT54rh3ve8Ag7soQ1joKzCep5pvLSjN/fQGyXPtXIiwb/xfsoLKwB7joHuCgNTq1LBTO5pp2XLdkc+l3XNWXmUNgBiwGB56tZA5wHexdAXn1Y9atU8GXh2QOpp8AZ6E3ql9J145lKBAKhAKhQCgQCoQCoUAoEAqEAqFAKFA0BXTErwGjYHswKGNQ0W15va9z67BQoGYF9PNImjTwOusGH58D/zy8wVR9aC797XaPEX1A+oTE81OwVX+bpG0uPbac8m2uS0oHq+/w7Vqv9IuJgU9jCtZDg5xiTMGlvmjr4QAYCeNhs9LSbZ7TTmZA37hMT2Y+3ABTwLzrggfBvFTzPM18tA21/fwIbAB9BbU5pFuz7M2Cq+AumAOLwL+QkcpoO/gsLf/G4AxGHwnGZZxsZrmu7Huss2fAddAF1uuw2hWwrTkZflj7qQ0/YzJXPAqsy2G9KGCmFdHmk6hL4DMZJs5G9LvwCNzSoPvYkRhYs+N3RsxQWBv66lDt1G2AHwMbaDvlw2Bv6OtcDgkrgAKDSYOds2W30szfN8DOdiVwYHEZnAhZ2OZc9GA4H7x3WGso0MwZa2kQafvigKnVzBcZy3jlgK6Zz2F+Lay4oQNot0e9qxAmfoYCoUAoEAqEAqFAKBAKhAKhQCgQCrSNAvoS/MBF/+WOcDRsDatBWCjQqgroYxL9ZMZMDKTr1x8G3Zm+H/kf0B9kXECfkD5gYwMGpOXVEsYMXHcp7nsJPN7tLo0PeA3N64pp8vr68ryfv62DptF6uBasD4PKWI919xkQ9zjPaWebw8P9EU6CoT08qDrsC+vAX0Gfnn56tVTzPH155u8s+AlcCAfCx2EzMN21mM8zATaAZXAz3AhPwAIwLmFZ85kts5axVjTT7V/vuKCEZV297I/2gdFg/lrHPMbJH/PBehdWnwKW0zvBdspylqdtw81t557LMxGtcO9/KmgiLUAGMW2cnKmWpc3k4jaqTzX4JmprI7Mu2AHb6VZrnmvjvhHYaG0LeVcqkhDWhwJfYf93ejjGPLUjGgs2Tg5GzgQHaVnYw1x0N3g+i4vHNTNRYHuuekcmV37nRR3k7QcOdueAA79WMttU+4eNc0z0PO79aXAgmcwZpI+DyzxfHFJ6YhkKhAKhQCgQCoQCoUAoEAqEAqFAKBAKNEIB/ZSrg+/jO8FxoL/SbWGhQCjQGAUqfUmVv/UvJytfT9s6dbmYB/8yGEc6HQZCT6am+u5+Bo+BQeFFoK+0KMFw89Zn2BM+BjvCilCvOSHFWME18CDoC14Cxg2cBGBAtyjPTlIaYsO4ytZg33Uf6Mdtt2fkkZpu6noXuMzTzMv3wyXgxISwHhRIX4H2sDu3zTbENrojYIuMUzGA628J18OLDb6XDehrYIDI9ZVBzavpoC3ET8PdYKWyAxsOrfi1LsnuCHPG0UW9PKllwHxdGyzfzjjMKoDpJINp8ChUDhbZFFZABZwcclKT0mU5PAdsnyy3rdZRbkiavwgO4vIy2+WbQS2TvcyKA+jybWlfLEOBUCAUCAVCgVAgFAgFQoFQIBQIBUKBVlJAH+SqMBT2gS/Dt+AY0IfRn4AUp4eFAqFAhQLGDKql4tSO/ulEJHW7FfSHbwU9/UUSj1sf/PjUwP+rYFvmUt99UfzoflykX/8KuB18HmNl9bS7+k8N2O5cwskFfmy6EqiHz5xiVu3i0zQe9yQ42cO/1F2UfCUpLW3WEevXJjk/heV2IUyBv0NYDwpYsYtqDjJt6PxK1cFmlmbjORJuBu/ZSLPRNNjrdW14bHBtqH2+asxz/XMmU8EGazAY3LWQhxVLATuSX0JvHYqNpPXOgckCOADscBttlq9R4ISEVvu6u9FatMr1NiKhxzcpsZbRc8D2xT8HZblsFbMNPQoOzjnBf+L+0yvS4MzZVpxQUfEY8TMUCAVCgVAgFAgFQoFQIBQIBUKBUKCDFdBv6ccr28JH4QfwEfAjllUgfJKIEBYKhAKFUmA8qTH4/yAY+N0C9L/3ZGuxYzvwY55nwbZNH7q+0t58++xuqr3G3Z6Cq2AyGGvS519PW2y8wOd28sNeMAG8jhMBjFf43F7fNt71IulAcsIKoIBlwg+VD4W8xwLGSc8FY65hPShgxS6q2dj4VaoN9bshywLltSeW7nU7y9eh0eaz2IEYIHJpw2ogq9rn8vyF4P+z8SjERABEKKD9lDSZVz2Z5dqG0v8SwnLgjDs72yxsEBe9B57I4uJxzYYrsD1XPLLhV+3+gpZDJwBYVh3kttJMOQcZnwfb7LzMCRMOMJZWJMDJFP4lGfUNCwVCgVAgFAgFQoFQIBQIBUKBUCAUCAVaRQF9xPpgx8LB8H04BXYFJwNU67/k0LBQIBQIBZqugMHtjcDYiR/sOAlgM9AH35MZn9kGDILPh5UhxXCKFgB3csIcuB7+An4wOgLqaZ9tz53oNRp2gx1gXTBW5YeK9gfqkGKHahEWCqiAZcHycyJYXvK01bj5lTAPooz2kBOpEvewO9fNKdMMtDgbyz+XnqVZcLcEg//3QhYBMZ/J53kFXgB/W1HMB+/fl3m8s74s1FNgBgyBAVDN+RwWlqEClpmfQV9lxw7bfLeDdlLHgaXfLBpqDnzGwwXgZIOwYiuwH8nbp0lJdBB3Drh0wNhXmeWQwtgoUvJtcFCelxn4vxhsj5PZPi8G23fXw0KBUCAUCAVCgVAgFAgFQoFQIBQIBUKBIiugL9Fgjz7XHeEL8F04FkZCkf3GJC8sFAgFQoG3KeBHQwb1/XhyLvhRnH+5pLe4ku2cx4yGOaDpt38V/MCnaD4+Y0tPw23wZ3gS/MBQ6mmzjR8YW9oa9gI/VFRDJwik66UPnYqmBUkMy0EB4wmHgmUuT7N8PgzGclMZzTM9hbx3qsSFTByJciAqNri7QNbptcFzwDsPHoGsCo6NpQE3/8SMeB+DWbU8n+fbkd0Bj8OqUG9Dz6lhDVDAvPwtWF776hDtrJ2l5HJMCRYNt6FccTA4GDBoGVZcBd5H0pzs1Ayz3P0RLKf+BQAnpbSCOQA/ANTKviEvu5sbTwF1TObgZwm8Dn3V/3ROLEOBUCAUCAVCgVAgFAgFQoFQIBQIBUKBZiug/1M/5HjwQ4TvwJdhG1gd8nzf5vZhoUAoEArUrYB+duM6Bsn10z0Eo0H/eE9mmzccbAM9z497/KsA+viMwRTRz2c8yQ9MH4BL4S4wcO9zGLyvtR33ePuFsbA77ABOnNAX63WNW3lP+w8tq7jZm1ePf4usgHk/Aoyj5m2W0Uug/CO9vNNUqPvXEnDOI+EGV2ywzEAb6Q0ga7NR2xMehBmQZQNvwMjA24tgg+29zROpppE2bQab58ItcA/8DdYHO6lqrsFhYQ1SwK9/L4fnoK9O0Ly3w/SvADwPe0NW9XFLrn0MbAzWKQc/rRLwJakdY5/jScc16WnN//PAcpomAGTZ1jXqsVblQp8Ey3Se5gzbJ6BcM18IFoBtcFgoEAqEAqFAKBAKhAKhQCgQCoQCoUAoUCQF9BEmP5T+oc/Cj+AEGAtZ+aS4dFgoEAqEAk1TwHZuPNwO+tynwxUwCtzeW7zE/y7AwLf+PicCGBDXh6+vz23lfkB+FsJMk/Ghp+BKuBqMNY2Ceid02R+sC++GvWBDMEbnRIAVQNOn7HFF1cU0hmWjgHlv7PFo6K0+ZXP3t1/VOusEAOtrWDcKtMLgzkbEBsdGbHswYJq12aAZkL0FDOhkaT5f6kjslJzsYCVKM6tYrcq8xlJ4EG6CFJxai3U7q7wrI0loe3PGnWXGP6luoL03M98dPNgRe+ym4CSXrMy/NrAFHAUHgn+twMGBAwLTEpavArY5p4NtXTPsFW5yIdgu2G68DkUvB6Z1NHwfVoW8zPp6DjhxotzU1MGGbXFYKBAKhAKhQCgQCoQCoUAoEAqEAqFAKFAUBfQxDoFd4ItwBuwO+qTCX4gIYaFAKNBWChgPMb5yJzwJxnduA/2um4CTBHoyA9z60G0z54HHvgv0o3vNIvtPjTX4rFPgUpgBxhsGgM9Qq9k/GFcaB3uAkyO8ln2KAWBNTZKVr6dtsWxPBfR/nwCpHOT1lMZUrOfToMh1My99WmJ2p4XJhtbMnAW7gQ1x1mbAdFe4DioDPVnc2wLqsxqIMygrPqeTNKRaMzhlcLcLLPyT4SF4CdTQwJnXiwE+IjTQ7ODsWC2j6u9Ejr4aHfdbtu1IPd6y3dsAhN39NvPeAcwRcCxMAAcDDhAsfz5HWPMVcLbaV8EBVDPMSSqXge3AMkgTj1gtrNl+WW6dxJJn+7WY+18CalZuz/FDLa1HYaFAKBAKhAKhQCgQCoQCoUAoEAqEAqFAngr43qwPcBD4Lv1T+CwY2PL9OiwUCAVCgXZVwPZvNFwDM8G/2vkC3A7673aG3gLi6fytOU4fqsF/A53GbbxW0f3n+ib1Ud4Lxiumgu3+UND37PPVasYUnECxDewFG4AxDXVJsboU62BTn3ERjwlrXQWsAzvCuJwfwbJsXb4S3sg5LYW8vRW36GbDYUauAwbiDa5uC1kHSrnF8kZtU5Y3gAH0ZpjPaxDfAmuHZMfiNhvpWvPL66jXXLgfbioxjeUSsKJ6XSuJetbT+HNaGApYNs+D58FguoOKvgYD5qvHOHPO87aCgdAMM6+d5LIlfBwOBAcwDg4c1EQQExGaaE7E8EW8WXVwOve6vvR8tjN+vd5XeS0dnttiDe78JZiYWwrevLGD5luhso44McB+wnodFgqEAqFAKBAKhAKhQCgQCoQCoUAoEArkoYC+Q/09m4EffvwMToTB0AxfKrcJCwVCgVAgdwWMeTgBysCgfk/NpTGSWbAb9PX18locsxPYrhpLMXiu3894TdH9qCRxeVp95qfgKlALff9DYE2op0/Qd23gfyzsAQaBjWe4TdyvRinW5LqEtZcC5un6sE8BHms90pDicgVITrGSUGtAOa/UG8i20TYAZIO1NmwEzbBR3MRCdAtUfvGZ5f2tRAaYvKd/DcDAkhqkYD2rNZnXsnMy2DcbHoCbYTJMgXvgSZgHS8GJA3YQTkQQg9oGiBPmiXjdhB2f6y7FZ0iw+rbG3s6gXcxnVcdbQZ18NmcHqkVfpoaaA4r5sC/U0/lyWt3m/XwRPBROAicFzADLgOkzD8OyVWB/Ln9Etrd429Wt73eWtjjws65bjotq1qmJ8C2wHczLrAvnQBeU1wvr+mKwjS3fzs+wUCAUCAVCgVAgFAgFQoFQIBQIBUKBUCBzBXxv1m+6OZwK34eDwI9O3BcWCoQCoUCnKTCKB14Ej4A+e83YxuPwIGwHtpG9mbGYzWAsLAD9fvomjdmkGAirhTefWy1uhsvgPnASgDGBen2txhTWAWMJe8Ek8Jorg7pp+kzTevhMl0vSFv+Yl5apE+Gfc34iy5sf7BnbLHJ8IxeZUuXL5eY13NSG4mkwSOpg9ucwDLaFrM1B8vFgQPTH8Co003x2g0oG4Z0EYKfkrKrVod78s4Ia2DXAKwuhO/PZxcZcrMwJ753WXXa3Px1jJ7JCCdedLSf+KTJxtp3P47r5a0fhb7d7Da9dZHPChB1nKhs+m8+pxn01OuaFeeC5j4KB2e0hL7OOvQcMRs+Es+FimAWWRdMb1lgFLN/bNvaSfV5tdtkR1jHreZHNtuBQsF3I06yn1oXKem3dSAP/PNMX9w4FQoFQIBQIBUKBUCAUCAVCgVAgFOgsBfQ/+RXehvB+OA58hw4LBUKBUKDTFdDn+m9wBxjwNmCpORngOjgWvg5OlurNN+q+d8NY+C3cDKvBXHgOjAG0is/ctM4pcRXLSaAOh8FQULNaTX302e4MO8ASuBXU6QlYCM+CflXzoJX0Irlh3Sigb9zyr5/cj/byNMvs/nAt+CF1WJkCVs5WMYNUBv3Fwa1B1v8LY6AZZgN1EvwZbKTyMoPtzmpxdtUgsLOpp2HmtEKaz2Je+4xOBhgII8CGxJcZn9mJAkV5Zidl/AJsYJLZAD4GDgAMDPZlPstoGAkT4GdgGS+KOQHlLvhP8Dn93SqDGpJaeLOc3wPmfTPMvPs8PFS62WyWDsTSILi0uVCL4aTmRtgg51Q5O/gr8EpFOqzrDmh9gYi6USFO/AwFQoFQIBQIBUKBUCAUCAVCgVAgFGi4An5o4wc0u8PnYFsoki+J5HSU9eULaCUffEdlXDxsRyjwG57yFNB/V1lXDXo7SeCzYLvalxkXugHOA32qiyD5Vd3XimaczfjL3nA8bA3GZvprxhD0l6qXvm+Dxc+A+aAfWoydVOYJm8JaQAEnfZwBnypAWi1X24NlLKxMgVYbfFioxsPapWdYl+WvwGUzbD438StUg0B5NkzmmxMBbJwNkDvT19m9rZafJLkq87kMkqdJAUNYtxw4KcCA4HqgFs2eFGAZcELI2fASlNtT/Hgaqu34LdPjwLw8FXaDopnP62y9K+GH8DhUM8GBw8J6UWAC+x4A63AzzDJ5Ijg41WZDGqgu31Cwf95Feg6Bi8F2L0+z3F8DleV+Adu6oMiTKEheWCgQCoQCoUAoEAqEAqFAKBAKhAKhQAsroN/L92IDVjvBybANNNsfxi3b2vR/+d7vO/4b8Cr4IYB/wfO10rrbXXfpxwAuPUc8X9OfaUDRPFsR9F261P+Tlm4Tt7nU9+k+0R/iuV4nwWpYKBAK1KiA9fNo0Kdnfa00/fLHwb9DX/8lQDp3Liu/grvBGMA8sI3Q75raAFZbyuxL1GJLOBYOBmMV/e1j/odrGPS/CybDo6Av1aCtMRX3q1u1cRQODSuAAvZPB8Dl0N8y0t/HsQx9AC6C8M+XqengoZXM9BrsNejrQEjbCAzKrOqPJtjN3OMYWNKEe/V1C/Wwovnsg2AdcKCYd4UjCZmbz+5A2Of1ue2MRsMEGAtOjFgFPCZLm8PFvwp2WjY0ybpYWQjdDSrSMeVLXwhM9xDYFH4ADvyLaj6rAxsb1fMh70kxJKFl7XBS7kQSy3QzzAHXCeDLq9YFtZRVz2mmWb9/Aic286bd3MsX+0/D7Ip9DuqfBAetvuiHhQKhQCgQCoQCoUAoEAqEAqFAKBAKhAKNVkD/10jYDT4OW0In+P94zIab7/G+v78OBp98n5dF4Icvy0DficE8fScep3/PoIL+MM9vlOkLMh/1X4r+7pVAn+ZqoM93TVgD1iqtr85SX4nbxeM8x/OjTCBCWCjQjQL3s+19MBPKffjpUOvenvB12A6q8dPaNlwOl4IxAv2rxoxSW8Fqy1rqc/bjCZwcsQXYzvTX1EatboQ7wPxYBMtAPW2bnQjgMqzYClhHxsBUGFyApP6aNHwBKj/ULUDS8kuCmdRq5mBmGIyCNKixcT4VDKI2wwxGfQWcAVoEMx99dgeFDgCdDGCn5fZWzGOSXbelwbIDYycBbAbvAwfHWZmd1Y/Al4M0gJjFup2XnVo1Zj45w24iOHD/MuwBrWB2yI/D2XA++NxJB1bDelHAfP8xfK6XYxq96wEuaGeY8siB1mKotqxyaNNMfTaH28B6kadN4+anQ+X/JaRu7kszVlkNCwVCgVAgFAgFQoFQIBQIBUKBUCAUCAUaosBKXGUtOAT0fY4D35XD+lZAv4fv7L7HG5SbC12l9WdY+h6vb9cJ/+VfnzYywM+lG2op7/V/iuVDf7A+E79clqEwrLT0gyn36TdOfnRWw0KBjlPAen0y/B5e7uXpx7DPtvYkqCbW5HX9MOg3oM/VtmY+2LbYrhS5PSF5fZrthvGmreEYOAhsV/rbnqiLE6wehhtKyzks9VG/ALbdEhMBEKHAZjzrv+G9BUij/fpOMANavd41TE6/Hm81S4M3A9wOcBz4zAYb1K1Kv1lkatty9SfgMShKYbIxfAPswJwxZQOpddoAL5UPO5Cl8DQ4kHeWmmUlCxvBRdXe8pA6Je9tXqQgK6t9mmUpDdx9KdkfHMxnYd6rUXrY4Q+EfeEz4Eupz90Fr0NYzwqsya7vgvo1y27kRveV3ew51mstq2WnZ7rqxB0H57tlepfqLn4Oh3XX5lvGF0Cq+9VdLY4KBUKBUCAUCAVCgVAgFAgFQoFQIBQIBbpXQD+L/ryxcCj8Ej4E60KjfDlcqq3Md3J9gV1wL1wDf4ZLSkt/T4VpMAeWwouQgv+1+O84LXczvfrC9Uk4kUFfsL6JmfAg3AGT4frS0md/FAxMeo5+QeMCljXLVJQrRAhrqAKWMeulZawIZhnfDq4Gg/Smrzt7no3Wl0VgrEnfZG/mddeDHcE69SysDNZR4wXep6d7savwZtptJ21broVLYTr4zANgBajH1M343gjYGfaA0eCEpVVALT1Gswy1uo7LH6QN/7EfWgkOh5RfeT2m5eYhsK+z7QlDAStSK5rBbbFBsIBpDuDWhw39kbHZ6OwFdhjOSiqS2bmojYNeA3sOAi3wVkDTXZROl6Q0xQyg++wGWAdneMfNuPY8mAPmgRMPXoVaOnjzyfzxhc58czkRsjDLh+ldGxrZONumDANfUD8FW8Pj4OCnFi04vCNsJE95GtQ7WKpHpIs4ybxP5kuv5cFyWzQbToK+BdaFPO1lbv57cAZqpdnOqmER9atMa/wOBUKBUCAUCAVCgVAgFAgFQoFQIBQotgL6OcfA0fBjOAn0aTXSd8PlWtp8/zYg1QV3whVwMfwJ/goG7x6DBWBgXP+cvtJOeW/X/6aP8W+gv0cNFsJ0uB9uhetLuO5HInNA/4Y6WdbKfchR9hAkrGoFLHePwGSwvG0EWX3gxqVrMgOE+sIt99aNnsxJMg+Cbck4GAV91QOD2ZuDz+uXyNZB2/M0CaAd2h+fSR//vfBnuBH0mfpXR4zT1Rt38jwnWqjdnrADGMcxv9Q1ae9xrtvGSVgxFHASwPvBiS95mmXDeuY4wHoXhgL/3MIqpMZzVZ4hBc8c9G0Jg5rwXBZoZyddBjZ0RTMLu5XPDuslcFaryzSQY3V5g5kaUH+3m6UOYU0ezIC8s/ZSWWn0s3ovOycH086m7QIHPLWa+WMa14DZcHDpN4uGmvf4KdwMdtLO2Gt0WXCQszF8GDaDGRCBUkQos11YPx4arX3ZLd62aptwFtgWaLYTDkp9GS7awMlBnfp8AlzP05w9eC1UDh7UbBHYBxRNP5IUFgqEAqFAKBAKhAKhQCgQCoQCoUAo0CIK6KfRN/NeOBP0FawPzfIXcKvCmr6L58EPTK6GS8CPG/4Cd8ATsASctO97u0GqeEdHhApTE7XU9+gECvVaDLPgYbgdboTr4YbSb4Og+jr1q+pL0q/kNTTLZmL5hvinYxWwTM2Gy+EsMAA3Fyxn2gZvLgrx7wRS4QSAOWBb0ZO5Tx//ZLDMG9w3GN2bWR8MXO8Inu+kGmNIrts2WQfboW3yGcxb89y2wvjYI2A8YyDYn6lFPWa8cl3YCvaGLWAdcDKA+7yu+ZEmlbSDnjxOy5r6W763gSLU8yGkwwmBTlQJQwErTSubQSufwRlCLi1wt4GNg7OOsjYbtJFwFdjRFdFSJTQYrV4O1hw0p0GxDabHpAaU1bYyO4U0U8yOdiLU2wH1JYxByh3AAfM0qKdMmBemz/Lr+jAYD4027/EipNl6ptcBih1so4Otli1n8G0Pj8M88Nk63dTl87B1E4Ww87sQUtm0/vuy9zoULU8GkKbvwIaQp6nLOfAkVGrkAGchFFE/khUWCoQCoUAoEAqEAqFAKBAKhAKhQChQYAX0zegbGA76Mn8Bn4T1ICvfFZcuvPnu/TI8BH+F80FfxvXwAOhXMrCmn8+AdOW7OpvCalRADVOQUu2XwQKYAU4OuBNuAvPgOpgM+uDvgcdgFni8+eL5+qG9XsqbVJ7Tkl1hLa6A9W8mGPw9F/4E98MS0E+m73EtsDwYdG9GrIbb9Gn6vTeFK0DfeF/mMVPBtmdLWA/6spU4QH/vBuDkGf2vblOXdmuzrOe2F+pjWbgGjDsNgjWh3jiDbYUxHfvHnWAvSOVILe07NduYdy1f+9/JSaWfsWiSApZv8/oAyLuNt2zcB/ZLls2Ot7wzpBEZYAUfC4MhNSj+dtDsDKuszUbma/Af0EqFyry3oVSzFWAVUC9ZFdTVCpOOc5lgtWXMNBvUHgZOFDkBJkCWtoiLHwQOkOspE3ZuY2AIbA4/gtSpsdowm86VPgsOytXJcuDARI0c0Pi70XYLF/wgdEF6CWC1I82O8XaY1MSnn8q9bK/smDUH69PAwXiR8sPybhm8GWyP8rRnufnJYKC/UiP/XNgjoI6V+9gUFgqEAqFAKBAKhAKhQCgQCoQCoUAoEAp0q4A+F7/wfzd8DnaBLHw/XLalzHfr68GPrQw++75twCys2AroVxTLcPIp62uWNUqszVL0h60D+mldNzCs70eftPXC8/VXp2uyGlYABfQlPgdPwF1g/VwC+hSto8nXyOpyMx+HwkDYFI4H87UIZjvzbfgRGKyuxnyeiXAaHAXV+s2f59hzwXZtMcyHpfB3qNSMTW1hajMEdoBjYVew7lun+2vqpobGGG4Dy6E+22XwKqipx9QTk+G0sDoUsN03hjUFbO/zNuMth4H1rOOtEZWuCCLaqBjUTTOw7EwOgH+DZjyjDcpxcBG0ahAoDarKl1ZetUyDN9fFY1xq5fqmdTVI65XHlJ+XrpOW7kv3Mk/tWN3mejqG1ZptJc4YBQ4snX32Sci6MXqQexwJc6GeDmctznNQMQB+DcOh0eYAbU9QWwfZau26eTAajoadYRVolKnF++BysDPuZNuQh3eSiOWzWfZf3OhPZTczgO1fZbAsFMl8+fsafKkAiVKv38Jr3aRlEdtmwj+62RebQoFQIBQIBUKBUCAUCAVCgVAgFAgFQoFKBfS5GOzcBT4Pu4P+r7D/VUC/4o1wPnSBvqSw9lIg+Xkt+/p99Y3pf5Q0MUDfbZow4Lrb0379mOJ5yYds3Uq+Ta8f1j8F9HUZHH8SHgX9h4vhJXgdrJe9xUHMi3XAQLATPfQHbwpFsWdIyIHwENTio7ZMHgrfgWr99Qal74Y/gBMonoZ5oF+2nds366E+3nFwABhr2Bist/01y56+2hlgf3EvzIJFYLk1TxPqH5adAuazH+D+EfbL7jZVX9l6tQ/cBR2f9+3UGdrpTwQbYc2G5FTYHZphNjh2GlOacbMm3SOVj8pl+e3dlzr7tFFvRgAAQABJREFUyvXy43pbT9dPx/i7HAdwaVC3KusO9mRF8DgHFL2Z59sIDQLP2QyOA7dnaX/l4h8CZ6DV2pmbTgcRI+BEOAkabebbvnAPOCAbDNYbNdXUdX04GOyknYyQ9rFat/0nZ34BatWk7hsW8ER1PB7OhkZoWs0jmt//AtPKDn6edQeer5dtK8LqaBJxDUzIOTGW0VPgAagcMPh7JizqZh+bwkKBUCAUCAVCgVAgFAgFQoFQIBQIBUKBtxTw3X9N2AA+A34Vqe8nrGcFFrDrv+FueAMq38vZFNZBCliH9FU6aUCsP/ox9Rknf3GaKOBkAdfd7rpLMRiZ1j1/BdA/7HWb5Z/jVoUy/YX/AAP788GA6uOwEJ4FPxqy/nlMLaaeajwG1HwUfBT06RfFzichtsf67msxy80W8EXwA8BqYwz6Yc+FG0B/onqrsYHqdveTq5Hxh63gKDgQjNVUqx2H9mj2DZZfP7SbAg9CFzwNTgZwf5oMYHkPa7wC1usPw0/B9jRvO5kE/Apsuzra2qlj81ns0DcAB9SaAc0fwxB/NMGWco/94P4m3KsTblFePl1Pv+0Y0iAtDdxsZCQNAll9yzzPfQ40VirhnwHZAbK233CDfwX/BE2tHbmd4sawKfwCsmg8v8l1vw2aWq4HA0F9k96sLq9TO7I8HMZDvZ2zne134N+hkztcX05+B8dCs+w1bvQBsJ1KtpCVLvC/gSiKWc73hysgizJfy3PO5mAnqzwHleXVsuyA0rpduY9NYaFAKBAKhAKhQCgQCoQCoUAoEAqEAqHA8vdafQBj4Hj4FCS/JathfSigL+0muBR8R9e3ERYK1KKA/k3Rx6TfWJ+ndVJfsb7QtcDJAn48tnYZbhOP059s0Fd/qNdJPtO0ZFNhTZ+VGMA3GGag1MDofFhQWi5j+Ty8Ah6jnzCdx2rdpl4DYCisCnuVYFEI81k/CJdDrW2Lea8f/Rj4emmdRZ9mMPoeOBuehCUwD/QvmkedYNbBYbAbHA3GHOwXG1Gf7DOehXvhVngEusBtTmZJkwHU2jIe1hgF/pnLbA6TwXY0b/ODx73BNq6jrRGVqkgC2gEbNB0LduA+3w5gI2zD0gybw00OBWcchWWrgPmbsJFxEOeAzEGcSyeEpEGd+wbBEPBYG6KTYARkbX/gBp+DF8FOqFqzPI+ELeB3YNlutF3LBQ+BNMBwMOuAzEGvelXWG7UdBweCdUsdzYNqzE51OnwQ7oRO7mTV9n5wANwse5IbfQZSXnvfWbAIDGYXxXwx+CMcUIAE/ZQ0/BXKNUvJciKFmsaAMSkSy1AgFAgFQoFQIBQIBUKBUCAUCAVCgXIF9E3pQ/H9Vr/QcAirT4HXOO1KuA506PuXDA3k5GneX9+WpHWXibQv+c3087muX9Jl+s3q8vXypethzVfAfEl5ZD5Zh8VJAPpLxUClPmcnB0jal47Vl6ofWh+ry4T57bpLr+0yUV4eUhrY/Q5LZSqVMf3M8rcSBpEN4uuDfh78oEX/leti8N9jrD+e47leKyvzWdRhLOhT1t/8EfCjzaLYHSTkRJgFtfjtU/rN723gNNBfbp5WYy9w0AVwLSwE2zXzSh9tPengtJYzy4cxvA3BftLJABPAutQI02e7CIxD3ArTQK2XgfXAsq/e3fl92RxWgwLmpbGrs8BYU95mvh4B1q+Ozl8zpt3MDnQgjAQ7YDuZD8Ex0Kznncm93gNOArBjDmueAuZxOXa6lgk747VhHIwBB2qj4MPgwC1ru5wbfBzsyO1cqi0XzkKdCP8FDiYabV1ccFNwcJjSpH4OSNXIYKz1qbLjVVcbdWd27Q2TwON7GuTY6M6As+EaeALegE61PXlwX1otm82yC7nRryDls8tHwZeBogwsLT9bwg1gfc3TrBOfhS5ImrG63KzD82AuFEW75QmLf0KBUCAUCAVCgVAgFAgFQoFQIBQIBXJXQF+kvqaD4CswHvS1hPVfAQOYOvRvhFmQhW8p+e183xfvoV/LpUFT19M+j03oO0iwutz8XZ73ad2lPpC0TOv6icTf+uZcd2mZct2lv90vmtu1dO03f8W/eSpgXiTMJ/OoPP9Snrosx2NS3qaywabl5SqVM8ufAa0U9HeZyqjbU9ms9GWxKxfzmdYDg/765zeDY8HtRTB1OhV+Dk40qsfMKz/2OgFOA33m1Zh5+iD8DvSVL4b5oE/SvOwks9xbTraA98J+MBSsH40w68hcuBWmwgxwMoB+cfPdvEhtO6thdSiwMuccB78G8zNv+wUJOAWsTx1rNk7taDYMw0u4bqP7LTBI2SybxY3szO4FG5CwfBWwrDuwsDO2HBjUNmBtp3ISOADJ2m7nBsfDPHAwVs1AzMZyDNhYfRQabTaAG4IDjMpyqma+WDiRxjqkZq67vdw8Zn1wooLXGgceb9q9prNOH4HJYEf7MnTBUuhEs6ydAZ9r4sNb1r4ClsFkviCYL5aByrxPxzR7aVn6CPwCKstZs9Ni222/YfmttH+wYRq8ANXU48rz43coEAqEAqFAKBAKhAKhQCgQCoQCoUB7KqA/ZAf4AuwGRXCCk4y2M9/VL4FrwHdzfRy1mO/y+uYM+IjBoVfB6xgM8r1f9Jckunv/r3Ybl3mbdefz6G2b5cj9CX/r50xL1/14R5+TQZjkz/N4fePpPFbDQoGmKmDZWw2GgX5lP3Y7GjaGophB4EPhLrA9qNese9vBabAPVNv+O7HpYrgKFpTQb257ZDvVaWY7ZnlRy2NgF0ixBlb7ZbbZtvGzYArcDTNgIdivmP+JovjLSVJLmP3QJmA5HlqAFM8nDbuDed1dX83m9jczpR3NyumgzYGPHczr0AW7gtuaYTZKB8PDMBeiwUCEnM08sIF3EGznaeDTxt1BvrMPHZBkaSO4uH8C5VZ4BqppeDzGtK4NntvoNPoScD44AaC79HhvBxtqZSfo0sGL9SilRV0dqMyGe+EWcMAkk+FKcLsvZJrXc9062olmXn4b1m/iwztwORvMp2SW+yVgfhTFBpOQn4DLPM268DuYDt3Vi+fZvgiiXUeEsFAgFAgFQoFQIBQIBUKBUCAUCAU6XAH9I2vAODgFzoDxkPwmrIY1WAH9UlvCjuCHJvqZ9P9WvsPr10r+QH2Cz4I+Od/pDbS51Ce2FLyG1/I6+kr0paTzvYbXrgYO69OquY7HeN+EaZE0OcE06tsxvT6bPh/9FekZ9fn4rMtK2z3G472u5jKV0bRcviP+CQUarIBlWB+0AXKD7TNhB9BHXwRbhUTor50K1qNUR1ityaybxoFuAOvdFuB/G9GX2Z557CSwzqqX26zbXtPfnWS2c7Zlfnx1BVwOM2BNGACWm3rbLM/z/IHwbtgXtoHBsBoY9/AYy4Bl1vVO059HrsuSVpbjTeq6QmNPclw2BSw7HZuH7ToBgDx9K8BrxbURdyBno2nFtjA2wyxkh8ECeAocOIblq4CNt+XAvLEcOPg1b1aHCZB12ViHe7wPZoHBRRufvgYV7rdjOh7seBptN3DBJ6C3dJhOXyzUy4kA4jYHI5XtiJ20gyUHOh5XXu69hx24LyNer9PM8rUtfB4cUDTL5nGjP8E/ym7oi62DSvOrCGbZzkOb7p7d/uIP4It/pamX5dcXFutAWCgQCoQCoUAoEAqEAqFAKBAKhAKhQGcq4Dv+ijARToRfwe6Qhe+Gy4Z1o4ABIQOJG4D+V/1Q+jvEd/dFFfgu7zH6t/RL6Sfx3V70WZXDz8JaeTor19Oz6L/QJ6cf9BVQE311T4OTA9RCH52+D49L/juvl4JgrIaFAv1SwPJk+bPs+TGY5c66aRnbHIpixgUMOOsjL/ef1pM+69S9cCMMAyeH9RVzcL9B6V3BiRLqZf/i9uRDt253mtku2Wb5pb6+7atLv9dhuRb0p79Nfbh5tD3sA1vDIDCe6H7Lqbqn+1iew7pXQG3sd9TtcFC7PM10rAFXgv1gR1pl4K7dRLBymrkGd20wZ8JIGA3NMgOkB4H3fwjs6MLyVcBO3AbAcmEd8PdTMBqGQ9ZmmbARtDO5HezIeus83GdZ/jDY+TTS1GEqOCjxHn2Zxzjo8EUpvTxYptXQRt3O0Gt2Zx7jOemFrLtj2n2bE5JOh62a/KA3cb87oLyc+bLnS281+c5hmZva/Btsk/md+r7BZA5JdbPyaAcy88E6EBYKhAKhQCgQCoQCoUAoEAqEAqFAKNCZCqzAYw+GI+AX8B7wvTas+QrohxoCe4Drj5cwyKgfSt+wPin9H/pFEqx2hKXndakGoh7qok9P35BaLQUDbc+AQVonDLhfv6Woree69Fouw0KB7hSwnFhmLFsLS+iHtNzp29a3ZnnbFpzEUwSzPO8IN4D1wGfoj/ms+sD1MfqsTnaopo+wb9kUtgTr4RtgLMGl17TudaL53LZZ+mRvhotKSydbrA9q259Yp/nvxIsRYDnYD94N64FlNk0AYPWtwHZ/y4jXajczn4wd7Q3mS962Lgm4DKzTHWn9qRStIJgFzobRTmV1MEA5DZwZ6gyhZpk67wQ23veAg6qw/BSwXBi8W7WEDbwdyGMwCWzYszbLojPKfEF8BBwEWU57Mjv/j4PluNFmoHMq1NJpqaEDOXXzZcrO9qXSutts6B2YiL99YXDQMgcc/Hl+J9pYHvo7kEU+9qSnWp8Pap/MsrYIrAdFyAvr4Gj4NqwBeZranAVzoTttfAFeAL3VV3aHdYgCll3bc/FlYMUSvjT4guALiH2Ndd51l/523f0e5zme61jB64mWlm/+in9DgVAgFAgFQoFQIBQIBUKBUKAICjj2971V5/aP4DOwLsT4HRFyNt+rtgAnZfiuNQ+Sj6q793t2d7ypS0K/oL6O5O/T16cPZCk8A/ouDaL426+T9e95jBp7jtdJvkWXqU6kJZvC2lgB8984jP5hYx/zQd+j5SQFr/UPrwMrgOvu3x2KUkb01QyBqdAo/7V15F64CQbBeLAf6c3UYwDsDOuBdc72ze1qWV7X+Nlx5vNbfmbCtXAR3A3GIww663frS2MO6dHU2T5kJJgH+8FW4LUtI8l/x+pyn55L0ySdbqkuj0CI7Qoghr7XO2AadGT+pAwpQF5kmgQr7HAYClb+TeAMsDFots3lhh+H6yECSM1W/+33M/8nQpppaH2wcfoW2MA3yxwUXwHe91Gws3JbMsusE1a6IKWV1YbZt7mS9/a+/TXT6oDEgVwamHhNBye+EHRkQ6sAmNp8DM6EZra9DjRtcxZCMgfkD4KTN4qQJ5aVI+ECaKY23O4dNoctXwBfViq1sV7OgkUQ7TcidJBZLh3gW49t3yQF+1cq/XZQab+iM9C22qW4zX2Wc8+3HOkccQKOdVCHii+W4rrbfZGxTU7OKo+33nqu5bKybLIpLBQIBUKBUCAUCAVCgVAgFAgFMlTAdwId/yPhE+D7ve8C7Wi+b8yGx2Ft2B58H2o1MxB5FfwZ7gaD2L5vxTsVItRo5b6atF6+dN33XSl/d3Y9vT+npe/SabvnpbLluWGtpYA+iuS70P/ox19pQoh+s+TDYHW5mdcDYRzoI9F38kUwyFoUM82fgXPBNqSR/pf1ud77wWceBtWaPt2z4DbQJ7kAngf9RI1MH5drabNtMbazE7wHdoQB0Ki2xbKh385g8lRwYsd00IesTy/Vh07OF9t08+Fg+CMYl83bbicBh4NjgI6z1FF3woPrhLdzMZDqcx8E/wqNagC4VNVmMPQb8HPQyR8NNSLkYOa9sw4tFwZoNLfZUfwHDIFmmgOmv8IP4FHwpcSOYwXYB/4CWdRZg51ngvcLy06BUVz6etggu1t0e2UD/aeDbU0yByuPgW1REWwgibgMdihAYn5FGnQOWB8rTb2eAAfZ0W5XqtP6v21f7QPEl1LbXgetOvVWA18ahoB9xvjScjhL+xH3e6zneZ0Eqz222+VlyHXbe1+QLXu+OD8DXfAkWF9nw2Lwhdr22vLY3Qs1m8NCgVAgFAgFQoFQIBQIBUKBUKBBCqT3hFFc70gwcOM7bDva33mox+FOmAcG8nTeTwKd50Vw5JOMms33rafhFpgM98N88N3e9y/fq8L6p4D1JFlv6+ldOb17u3wXpKXrvlv7Pi7+9p087Wf1rff28vu4Paw5Clhf9EnYPhgg18doPbL9cJ/1Tboz88z8HQ/rgXm8MeiHT755VnM324YDwACvwdxGms+8KZwKh4F6VGOmYyqcC/om9Q8tAHWXsLcr4Ic4o2E3cDLA1mBc0LakEaYP70XQX3cb2G/OhqXgdvPLetHo8sMlC2+22/pOfws7FSC1r5KGPcH63HH9fSd1lD6rzns7GBsA7V/giOVrzf/HjvA6OAUcXNsghDVfATvdwTAcHFBqdgT+/j4MgmabDdEMcDLANPDF8jRYG7Iw64Bl0cYwLBsFLGcfg59BowYa1abUe14ODkyS+eJrGStCu+OgYA/4C+TtTFCPk8GAa3cvK87odF8RdCMZYXUq4HjAQL1YN8WyZx/g7PN1wD5gA9DRtSEMBfd5bB5jJ8ujfYMv13PAQevtYB+xENzuC/g/oLuyy+awUCAUCAVCgVAgFAgFQoFQIBSoUQHfV/ULbQtfhS0gj/cBbpupvcLVfb+4GpaAgbly34U6jIVjQN9qq5uBy1lwawmDafPB9yr3+U4V71WIkJGV16Ge1r21ZdD95UvXLY8u03u9v8XfLn1vT+vpWK9Tfq3y+7IrrAcFyj9USPXD9kL0USRqqS/mjT5ufS36YQz8vwc+BEUy28QTwbai3KfaqDTqe3KSwTdhHFRbJv0o5Fy4HvQHzYdl4CSAWvKBwzvC1NUyNgb2gcNhS1gdqtWcQ3s1y8fL8BRMhXtK607SeBHcry/ZPMqiLHHZQpm6OtniE/BdaJTOXKous16cDj8B+/iOsrzFb7bYDgCGwEhIA4AzWN8G8jIDSl+H88HGOqz5Chj4GQG+1FkuNAeSBt5/DJaZdjU7nf3gbrBDCstGgWFc9gpwgNFMe4ObfRJml93UTm8mOAgxWJi3rUYCvgqn5J0Q7u9L/2nwfDdpsa48BrbTnTBY60aCltvkGEds18UXS9t7B6E6rVKQfyLrG4D11Bcwj7EPKPoYybrsC8Qi8AXjKngYdNjZnnfKiwWPGhYKhAKhQCgQCoQCoUAoEAo0VAHfB1aBneBk2BeSv4jVtjGDePoqboH5YADD4JzvRgboyt+JXNd3dhiMhXYx36v0ncyBO8B3q4fAwJrv/+4LHwAi5GSVZbA8GeX73J5+p/d5l5rLhOXbdZfJV2C8QH+ByxUh7U/npOuyqy3NOiCW8/8H+hJsCyz7r4IBM7eJ+z1WS8s3f9X2rzrrkxHzYjB8D0ZCUczn01/5S7At6M/zcnq3ZhmcAP8Kx4L9TjVmXun/ORseBP1AtuEpr1gN60YB67J+6A1gHzgUNoNGTgawnFhvZsOdcBdMg8Wgv1lffJoMkEWZ4vK5mzrbnu4CF8K6kLc9QAIOBvv2jrJ278C6y0wruZ3J+mBH7n8N8HMYBXmZjbaOezsVG29/hzVXARv60TAALBeaS4NBPwbLTDuaHc9RcC+8AO3a8fBouZnl6IPwG2h2m/s49zwVXoRkDtYfBfM+77ZGPTaCKWCbnLf9iARcDWpUaWr4JDiIi3pSqU7+v61n6QXeF0ln99q/O8j0hdLBvYP6jUFnlvt80Wp2neSWmZnl0pf0++BSuBnmgWXWl4u86ztJCAsFQoFQIBQIBUKBUCAUCAUKr0D6UvCjpPRjsErhU1x7ApdxynVwPfjOYEAivef6jrQW+B7lO1O5uW8g7A7bQTu9T/E4y00dfH8ymHY7+F5l8GYO6BdIEwKSXmwKK5gCleWy8rfJLd/mekLfguvJv+Bv64F+BgNakn57jL89Pp3H6lt+Zbdrafnmr+b/qy/A8upSDORb518rrRs0tswn9Imlc1h9q21oZJlXL2M042FNsJ3dHb4A6loUU6ND4BZQt6xMH9Ue8O+g76raMvMKx/4FLoM5sBCeBtNqHob1rIAaGw/aEPaCQ2FTMC+q1Z9DezXrjH2G+XI/3AZOMJsP+uWte9Y36153vmg2t6zZTo6B/4QDC/AU1ok9wIl+HVU3GlWYC5CHNSVhAEePAzsabQRYGN2epzkT6ItwCdgAhDVPAeuCAw7LhctkDkjsDL4PzshrN5vKA30DDAhb/ho5mONyHW+Wq/FwDYzNQY2fcU8HguWDCAevj4DLvPPbNvhTYP3K2wycfgbmdJMQBwbWj9mQ5YC/m1vHpm4UsF75QuiL9spgOVoPhoID901KjGG5NqQXclY7xqzblumr4WJwpusi8MWjvD3gZ1goEAqEAqFAKBAKhAKhQCgQCqCAzupBcCScAsOg3cwgxJ/hbngWevILrMS+weD7lO9flabfbFvYD9St3c33qLlwO9wCD4LvV8+BgZuOCibwvO1q5WW9t3X36S+uXLotUb5P/0V3x3tMb7D7rfrncZrL5MtLS8uf6y7Fd34xyC/lv10vPz5do6clh2dmth1+DDQGVgTjMp+HnaFI9jiJsV94CtQuK7OMjIITQB1sf6sx824O/B5so/Rf2tbrE9KHmfKW1bBeFDD+ow9/bzgM9C3a15kvjTLzw75Xv7x5dT9YruxL/HhHs79ph3yzrdJfa3n+L2ikjlyuLvspZ30ZktZ1XaTVTjIjOtF87qFgsDd1wJNY/y44yydPs2P+LXwNnoFopBGhSWa5cLAxBmz0k1lG/H0abJ82tsHSsvV1cBKAnY0vLw4EwxqngOXGST1fhWa3t69wT4Prc6HclvHjCfhb+cYc1tVjJNwE1rm87R4S8C14qZuE2C474Fe7aJO7ESjjTZYV2+EU7PeLk1GwKby7tLRPd8a4x4W9XQHb9ZlwAVwMs+B1yPLFlcuHhQKhQCgQCoQCoUAoEAqEAi2hgME5g1CbwemwCzT7/Z1bZma+w/ouex7cCfoaDS70Zr5XrQrDwPew7vRwovUkOAo8tlNMPQ3QzIZbSuhjmQf6E/S1xLsWIrS5VdaJvn6Xy1F5bNqXtqel28vX03HlS8uj5rJ8ffnG0j9puz97Wi8/vpnr+nFGg34e250N4QxYC4pkvyYxX4clUK5hFmm0zTX+8DXYFeyjqjHb9dvAtl4f5uIStkl9tfkcElZSwDpnnzYG9oRDYCuwTFabFxzap+mrc5KG/rp7wfjMY2AZsy9Jk3fMP49tRXOcsCWcD2ML8ADTSMMBML8AaWlaEhpZaJuW6AbdyFmuK0IK+DsAngU7goUzL7Oz2wYOggfAdFnhw5qjgI2qetvQWz40O3Y7yjvA7ROgrwEYhxTeniaFvwFfXOxYXoR4SUGEBpllxIHrz6B8QkmDLt/nZXyxvwEqB3kOJF6AvPN6FdJwDBwLedcn6/hZMB26G8ir1wLIWzOS0DFmX2gbvC6MhB3gg/Bl+GJpfR+WG4HHeGze5YgkFNLUUo12Bevcu2Ah2N9Vtg9sCgsFQoFQIBQIBUKBUCAUCAU6QgHHySvBdvAN+CaMhXZ6r1jE81wIv4b7QL9PNYEE34t9/3WpH0ytKnVxv/6FeTAMkn+V1bY2dfCdykkj+m+PhA/AYeCECN+99Ct7XNKwOz8Du8PaSAHzuFosF9Vife2NdB2PSeu9pYPDCmWm2bQbXLVe+bGCv7eCyjaHTbnZ5tz5LuiCrOM0Xn8OXAVdYAB1TejLjPONgZ3BNltfpr4y9dT/o9Zh1Smgr8y4iXl+EVwCj4J1ax1w7KDG/THPd7LHELC8HwDGA+1XBoH9iGXBeuC6eamZn61ipt1yOQosx3nbABLwF3Dc0jFmBnSqWWFfAwNzVjZNh/hsMNBgxcrTHEi+H+z8ZsJLYJrDslVAje0UbUx9yUnlwO02ug/Bs2DD3Mr1x+e5APyzZQ4AXgTLWCt1IiS30Ga78iXYK4dUmo++4DtgLG83LMO2c7Z95dv52XTTQfA1GNX0O7/zhkvZ9AewDlSaWurUcOAcg+VKdRr72zbVdteZ35vA0fBl+Ap8AHaEoWDdchAZVpsCaqa+u4Pa+hXQ02AZj4kAiBAWCoQCoUAoEAqEAqFAKNAxCvhOMRE+C/8Jre7j4RHeZov59dsSflz0HNT6PuvxfwPf09I7WHfvYb5PzwBtBHR3zPKdbfqPz6vv0PfYreEw8P31PWAgx+COvmd11A+TYDUsFOh4BawP+iqNf6wGtjt+DLkhGAQtihms1Sd1NZi+Ztir3MQ4xGSw/dgY1KkvW4UDbIuctKC/33bcNuoNiPYHEWo0fWbGgu6DP8PFcA+oqxMzVgPLR3/6Ps81b/1vHybAHnAo7AUbgX1wyjvLgpMBvKf1xe1FNnVaAw4E056nqZn1YAqYro6wvEXPW2QriZluIUyB3gWsz4LtwMqUp5mmXcA/++Jg2rSZ5rBsFUjlwgbUYEl552qjb/mwA3Yg7/5WtIUk+r/BgYDPaXDT9ShfiNAAs+O27v4Q8mhHLKPngTNny81B9XzIu5Ozw1WfL4DreZsD+DuhuyComllfXoOwxitg/tuOrge+oHwIvgGWjf1hNCRnE6thDVJAB9TB4AxjJwIsBV8ui/7iQBLDQoFQIBQIBUKBUCAUCAVCgboV0L8zAI6FM8ExsV/ytYvp2zkLfgv3g34e32nrNd8PfBdWt8ovHvWPuc/JBdPhXtDfsCm0k6Y8Tk2WAjmWs03Ad673lzCYMxF8/1UjdU1+uLRkU1go0FEK2M7ojzOYar3QNzEP9BsWqS1Zi/QMhKnwMjTDbBcWw43wAEwC02A705u53+PU0KV9g3FAtyffZ7Q5iFGj2e/Z502Dy0Df+82gvvo2Vwfb9b7yh0N6NX2l+kKHwlZwEDghYCcYCe43LZrxQ2MP1qOi5qlp2xEGQ95mPb4IHB91hHX6BAALnwNhl87WsYK6bqD9MdgWnDWVp9lgjIAjwbT650Yqg3psCmuwAjai6mx5sAG3bCRz31KYAja6Nsb9bdi5RNPM9P8K7Kxc1+y8XoKidhSmsZXMDu074Itvs80yew6Yv66Xm4G+pyHle/m+Zq1bVxx8/gxGNeumvdzHge9ZMB8q9WLT//c8OAEgT81MRzuZZcCB7DqwJXwAvgufh11hEHT6+AQJmmLrcZdDwBnFs+FlcGJkd3WBzWGhQCgQCoQCoUAoEAqEAqFASyqgs9yx7w7wc/g0+KVdu5jvrX+B/4K7wGBEo/w7vhvoH/M9zkCD5nuDvoVnS+t/Y+lkgFnwIAyAYdBKvjKSm5lZ/vQ7jwaDIPp4fQ8+CnaGDcC/BKv/0UBOMrWPd7OkRizbWQF9brZZBuesL6+A7Y4fihSpHZlAeozN2NbZ7jXL9F1Oh6tB3+4WUE3MyniGabbdMa6k7982xrT7W83D6lPAttlyOgOugT+C+dMF6u5kACewWJ77Y5Z/r+cEmXFgn3EEHAzbwDpgPpqfHut60fLV+m06d4G867NtjPk1GzrCwsH+ZoWw0XMQa8OZNFnC+n2wJVgw8jYbjL1gM7gbDNiGZauAjdOrpVs4UE9lw002pL7c3A4vgoFeG+NWsJtI5MXgQCqZ5d0XuHixSIrUv7Qt2Qe+DP3t5OtJhS/hZ4HlstKcuPIC5DkQcKC5E3wR8tCH277NHKhdAuX1IR2gTvMg6kZSpH9L64aOIF8+DofvwZfAvs1JIUUoDySj40zdNwCdUPYBs8C+L/oDRAgLBUKBUCAUCAVCgVAgFGhpBRzrGlTdHL4K34HxkLcDmiQ0xBy3Xwhnwv/P3nmAy1VVf/v5VEInJJBCSCcJofdO6CBFqggiggVR/AOiWBBsWFABBRFBEAFBkCZNpZckJoD0hBYIKTeNJIQuiKLf93zvG+fwHy4z904/58xd63leZu6ZM6f8zt5r77XWnvAAGPM3I963sOAiAwv+5hR8bwzt9uLzGUO4z+NgsWotWBnaRW9upSGmHuYQVwXj4x3hYDgCPga7gfnoMTAMBsNAWA36gDlKF9V7jOI4OmI4BAnLpQK2XeszK4Ft25y8/mwEDIKsmP1taxgP5tKL/R9/NtXUSN/7IPwV9B+OZ8X1Cv4saeq6FawH+nD11X8kPwBp5X1w2rYzn41azodJcBVcCw+D2/XZy4PPqt7x0O+bW3c8WBd2A9ukuWvNZ+1CD8fnrJjtbVnYt/Ca5nXZh9VqAriwpu2t3gbXTgI5IR0GTqSKHadFiZNhY8iKWeA7Ae4EFwLoZMKap4BFKycbTrh9X2z2IdvLCPgyrANZ7lcGYD8EB6Sk3fg6FZxEhNWvgH7kdhhb/6FqOsKFfOuP0Hmg9zk/AT7n5NnztuWmT3UitEvLz/z+E6rDj2E8OBnpbCZTngUnTmlq1vm68vS3AYXjaz/YFj4BTkzdFpY9BQz67A+nQLT97D2fuKJQIBQIBUKBUCAUCAVCgcoVMNlsAdWC6rHgYuR2MZPWd4C/NpwF/4RGmzGw8YHHNg9pLsG4eVVYAyzSFedP+fNdS3Jlxv97wEfAYkVY9Qr4DMzv/AssWrwB/gL4ZXgJfDZLCn/73u3mit3H/d+BUvkONoeFAplSwMJcbzCfqv/Wj6wFP4HVIUt2OxfzHXgK7GP201aa2qjVvvBdUKdK6xHmOh07rodZ8ALoOxxXWn0fnLLtLWnXG3CnFut3gnXB52fOtFH2JAc6E2aAdZ95kBXzPu3Xv4btM3BRc7mGbWFBBq6l6ZdQbqLW9BNn8AQ6OVkBekHiNN/i/V9hZTBwSLbzNjVz1ZC/1BsBOmoneuGgEaFJpra2A19tB537jdsNhCbAy+AiACcqWTOd/1kwC4qLmW/z9yKw/YfVp4D+40Q4ANLwFQZ/LgBwNWdnM0h0Utd5YUDn/Zr5t751HHwVGjnJqfWa1ev3oDalzH69GCJYLqVO+W1ObleCwbA7nAw/go/DWpBF/8hlhaGAfmsk7AtOhOeBiabiMYM/w0KBUCAUCAVCgVAgFAgFQoHMKuAPNyxSHwYmm53bGqu3gxmbPgEm+S3gGK82I5fjMV0I7/HngAVl4wILXebHzCMZ36u18V8pM1fmMZ6FB8Bi9GjwO2GVK2CMZh7SONqcpIXQITAGNobtYA/YD4y5j4RPFv7elNe+oPn8zAdFbKcaYVlVQN9jvnCVwgXqN6x7bAP2g6zYCC7EGoC+0ZxiGn3rn5z3GbgVLOpvCJXk2/TB1i70HY4p+nT9udr7t747rHEK6HN9Vo6l4+FKuBocFx0j1d45iu273HjKR92a9cLnYDb4LG2fWTE1cAxyDNsNHNfSNLXyWXSukaV5TU07d5YcZ9NusooD2xB1dHY6nWHSGHXiD4KDjpOrLOjmta0PHwUdsxNqJ+BhzVFAjX3+toXlwclI0j54u3QC7WfT4S5YDoaD+2XBZnIRFv+dGBQP5DpgJ1IWQou382dYlQroF1w9dibYRtKw6zjpQ6Af62xOSh3803zOrm78Ajhxz4LdzUXcD06MOpt9owOcCPs+rHsFnLSajNgOjofT4dOwLtgnin0mf4ZlWAEXcOwFJvgMIHwt5VfYHBYKhAKhQCgQCoQCoUAoEApkQgHjjdVgHJwLx4ELAdohDjEmnQbnw/UwB4xjGxmrmiuQ12A+LATzCMYCbk/O5av5UwsX5kGWLbzyUtLMlfkjBRcumK8w3zoEspIv41LawmznFo/U1bz2GrAJuDDgSNgTjNfNbYrPNSwUyKICtk0XAOhb9D36IQt2a0NW/Ll9bSxMAX2i15zkFhNfyaamm/q8AvfBJHAMHAn65q5MHS3GbgUbgP7cnI/+Q//uPXjssMYroM6Os9Zo/gSXgPn8p8ActO3eupLPopr27nEdZ50ruCjFfpMlsz3Zb/YFx6g0zeuYD+VqAmleW8PP3Z0zaPgJc3BAV0xpNsTOHc0OZCJ808LnvKRuXudusCX4z7hHIRcRmmQOfskkWUdssauzI3aft+ExeAgseDrpTquv6VydAPwcZoF/F5vXq8NzgAmrXQHbQX/4HmxW+2Hq+qYTvovg5RJH8bnPgcS/ldil6ZvUaCOwLdp30jYntL+GhWA/6Gz29RcgmcB3/jz+/q8CjpP6uXXgUDgLTgDHpJWhs49kU1hOFPDZ7gAD4ElwnDB5FxYKhAKhQCgQCoQCoUAoEApkSQETuRaHjEm+BT+GtaBdYpG53Iux/hUwAxq9SN142OKVBYMOWABJ7qtzDomPlprfMTZIrsWChfFDV5p7LPMW5i4tVrno2IUAPr+w5ingM3HRxSDYGQ4pvHdRhvkji0alciJsDgsFWq6AbTHxO+aa9A/m7+aBPr4fZMUcdwbDNND/aeZdvYdW9yn9sWPF7WDtagPoA135ZD5equ9AXncEcz+vgv7C6098Q/I82BTWBAV8dtbzHoXr4RK4Efyxr+3JtuUYa23J51numfrsfP4zwXH8n5Al87q9h41hVAYubCjXcDU4Fra1KXrY+xVwAquVWgRgQehBGA39IQtmBxoBR4DP1E7uxD0cNCI0wd7mmDpgtXYhQKlgxUHSwOY++BvorNeEZaAV5kD9IlwIl4Mry0pNPtw+H7zesNoV8PkeBN+EcgNx7Uev7Ju/Z7cHoFS/1x8shDSL2aty/pNhK8iC3c9F3AImOjqbGto3nIBF3+iszn//1v8ZTBgkfBd+APuBAUMpn8jmsBwqoD/bCAy0XdRmv4g+gQhhoUAoEAqEAqFAKBAKhAKZUKAXVzEWPg3nw/ZgfN4OZhH+UrgEXJDr36XifTbXbBbWloC5TnMG5kMtRpTKH7H5fWZsYH7M7ywPat9dTsQY3MKzxY6ZMBj6QljzFfDZ+Jz84Yrxu8/LZ/93iDgPEcIyoYD+x/ylbdXajKb/mw3bgbn4rJgLa6wPzQD7l4Va/ai+ulI/yq4NM4u+T4H5TjXbACrRS1+wNmwP3od+3W0+B/17+AdEaJGp+WIwB3ctXFR4dcxMitXWl5Lx1nbms/4TuHBgDpi7S6P9cdouzfHfXPLuYDtL01zAMxFmQaPnVmne1/vObQI/7P0KJB3HhlhqAmtnmww2lFGQlWKHgc9OsD/oLJxIW6wOa7wCOqwkyHHVcqk2YDvSgbgQ4EEYD06qB4ITmGY4Os9p8OYAYfF/CjhQu72zeQ/zweJwWO0K+Bw3h0vBtpCGGaifDw74nc1nbyHbdpjWgOZYMxrOgEomnuzWVLNPnAe2/1J9Q538rJSebO6xloyJY1Bgb/gpnATrQxaeK5cR1iQFRnLcLWESOGZE8IcIYaFAKBAKhAKhQCgQCoQCqSlg8tui8cfAWPggMEfXDmau5g44B+6HRs+/ncsbE5snmA3mkIx93VZLzsDjmXu08GRc+CEolSNj83ssyUmZL3NBwDrgcw1rvgLG9uYlx4Gx3gywPdgGwkKBLChgrs72uDIkfkE/pa/YCirxMezWEhvGWYZCB+gP7Vv6Q6+/VM6RzU01z2kO+D6YCL1hFFRSB3Qc3QI2hWRhkD5df+34UMsYwdfC6lDANmW7nwo3gwsCLoZ7YC7Mhj/Db+FJsJ9k9TnZNu27e4F9O03zOtTPPGdbj32VdPw0H0Sa57ZB6ui05UFn5wQpMVfJPgZOkNYDV3hlxVblQvaEnUAnsBjauiFzf2mYmroI4C1YCZIJCW/fY7YlHa/t6SkwkHsc3O6zsu3odOoxr+MJuAx0+I+CC1XKOXzP7eCxCKJtIEIdNozv/hLWr+MY9XzVZ3kluNjDSUFnc1sHpLkYyP7xJdgZiv0of6Zi9pWbwH5TypwszYNSepbav923+cx8hsPhGLC9Hw7DoV7fxSHCcqLAIK5ze3By/AbE2IEIYaFAKBAKhAKhQCgQCoQCLVfAPMqOcDYYZ/aFLMSZXEZdZvxpnvFMuBvM5TUyJjV3YFHqFZgD5oMaVaQy9+SxzI/54ySpJOfs9/zOM/AIGHeuCZV8l93C6lTAeH40WFBV/xfAdhIWCqStgO1Q/2c9Rr9gWzUHof+yoG27zZLptzaB+ZDUCRK/6L2k0a/Uby7cBhaG14XVobvx0s/db0cYAWruc9D+Dd5LuXqD+4Q1VwH1N589G8aDiwLM070EfpZ1sx+PgQ0ycKHmOa8Fc5xtazGh6v7RWrTVqS0HnRcB6EifhydgGPSHrJjOejB8AhyAXizgiq2wxilgG7CwasHQAMdiflcDqW3JZ2Ag9ze4FR4Gn49O2kUE9styx3AfHaWTiQXgMa6Ei8EB3fbo9XhdXZnfnwe+htWuQB+++nn4DJR7ZrUfvbJvGrRfCq+W2d2FHo1OHJQ5VcnN6jICTgP1Stvsg5fDdCjVT/xcvV4D3/dkc8xbBXaAr8FZsCesDGm1d04dlqICAzj3NnAfOEE2+AsLBUKBUCAUCAVCgVAgFAgFmq2A8Ye/TFwLTobTYTS0S1zyLPdyIVwHFmz+BeZ/GmEeJ8lDLeC9+SeLB8bDjToHh1p6LM9jfsy8ljmyrvJbfPyueR0WmfyxjLiowxyrRb+w5ipgH1LrreBuMLfUyHbB4cJCgZoUMCdnzsEcVJJvd6HRbFgPLFJnyXpzMduCfnAJeM3LgXl67yWtfuX1TAMLxRaJLbw6nnY3fuq/R8DO4CIMffsyYF0iuR9fw0KBahRw7mGueW9Ie4y3Xd8JcyCt/smpm2vddfTmnj0/R1enfmBB3YZRqnH6+cfgINBBZs0MHm6En4ELFnTWbduwubdWm23EQd124MS5koGU3ZYOtranzizPNtvbQPC9z8sJgxNxC7oOuv7tc00G3UqfpwP/LHAyUqoAyuawChRwIrc7XAs+ozTMZ26fvgNKPUuf9WxYDGlNyvSHh8IVkIUxx4TH10BNSvUZg4vp8FKZz9nc1uYzsvC/BmwHLnDx1Ul+WCiQKPA33nwWZoJ+JiwUCAVCgVAgFAgFQoFQIBRohgJJfLIWB98Hvg4DmnGilI5pjudiuB/M8xiPNsrMEYjneAGKi/6lYmF2aYj5zIwfzY8lOS1jzErN/JjFgXVhX9gCsphn5bLaymwrN8DnwB/DNbONcPiwUKAiBez7q8FISBYBuG19+BGsBFkz86+T4XLogNdhHrjIyX6WVn6WUy/1zevxegx8EqxfVGL6g4VwBUyCOWB+VV/RyHGLw4W1uQLODzaD62FQBu71p1zD98HFRW1pOsywyhRwomyS2xWs4oS02Pz8SXgeNgWLwVmyD3ExDo5HwnDQSfsL13DSiNAgs0hvwObgZ9+yKGzgI12Zg6iDv3gM0elYoJ8FFiNnwGxwsHXiYHvz2fmdaiblHttJh8VPJx1htSngMx0DvweD2rTsaU78Oyg3SNkeDfTT6ufqZMB/AWQlSXMp1zIF7DulTM3mg32lJ5nPamVYB46Fs+EzMAJiroAIYe9RYDB/GYBPAMe8asYhdg8LBUKBUCAUCAVCgVAgFAgFulXAPJb/3P9H4Hz4BGSx2MNlVW3G8BZbjZUfg7egXIzKR1Wbx7fwPxfMI/m3OaBWzdu9F+/JWMH86bJQaVzpNXq95i1dePw4mGP1XwXwOGHNUcDnNBTU25yIOfCwUCBtBfQH5jQtGur/badu07/oJ6zBmM/Kknk9w8Br819ceQMstDummc/XP3oPaZjnXgT3wANgnze/o65dmfdkznBr2Ai8J7eJvsLjSlgoUIkC9t2RsHElOzd5H+eZLkZwvtKWVunkqy1vvoabsnGKupWavCbF1fF87krVfpA1c7DZBA4Hf+E5D16GcNKI0ABzAPdX+ToNJyOabaW7gXTpjoX/eAxJBs/kNdnuay3mhGkuWPxPqyBcy3Vn8TtDuKhfwRYpXpz+5tcwE0q1CbcZtBn0l/qczU032/4ecAxU0weadWELOfDvwIlqKVMn+4ifp6VZqetq5jaf0ZpwMPwQvg+7gBMgJ/JhoUA5BUbxQW94BAxie0qf4VbDQoFQIBQIBUKBUCAUCAWarICFhu3gp/BNcEF5O8QnxvHOn38G48EffliYb5SZ6zHHZy7A3I95KY+fxlzdc3o95sfeBmNP86mV5gb8voUl70PNJsMc6AurgMcKa6wCvTic/WwSlMubNPaMcbRQoHsFzIvrC1YC/YhtVN+ibzA/K1k0/dS28CEwH6nPciGAdQOvP01zLJoFN8E0sI6lb+1unNV/94edYRjoJ7wvn5HH9DWN8YbThuVEAduH7WR52A8qnROwa1MsWQDgDyjb0mKyVP1jdcBx4qq5AlUn3tmcYE8EA5bR0J3zZJeWm5O6zeGT4EKAOeCkXGcdVr8CBlgWRPw1se1FZ+ZqxTScmk7Va5gFBpdpTzK4hFzb6ly9CYjDIc2+/TDnvx4Sf8Tb95j9eR7os9IyJ49nwMi0LqDTeS/j76lQzs/ZZ52UOxlvZ3Pstx27OONkOBMOhbVAPxUWClSigP5vQ3D+8DzYbyLQQ4SwUCAUCAVCgVAgFAgFQoGaFTDPtiZ8CX4BG0AaeRRO21BznvwMnAc3ggX6anIzFr5cfGtByXm4x0vm3r6ag3oNOmARmIsy7k324W0q5vnNSRlre32+N49qTFrpc/UY5jX8ccNzMBnuB2N3dZFSuVk2h1WpgG2rD/wJ1DcsFMiKAvo4/YDFOv2HZj60A7YBC+tZNH3T+jAGXgDzJvos/Zo/MtXS9NNew5NwM/ivFXitaqkv6Mp8BiNgZ9Bn6OM1xx39vKR5X5w+LOMK2MZcAOC8Jk2zLc+Dv4F+pu0scZhtd2NNvqFkEYCTdVerlCqY+NmD8BJsCL0gi+Z1bQmfAAdRByMn1TrqsPoVsB04CLoizomJAY7tRScnzTQHWs/5CswGg622dGTcV6vMRT0fh+9Cmv7TCdpZ4ABVypxwGawlAXapfZq9zUnuVvANyEIwbh+8FF6GUqbP0/ctgXbtJ06qtoMT4Wz4HGwEK0Cz/RGnCGtDBRzTxsFE0B/F3AERwkKBUCAUCAVCgVAgFAgFqlbAeeWasDdcAgdAVvNoXFpVZmx+IVwOs8AcUTWFEWO474N5uwNhf9gLdoC1wGLNYpgJr4N5qGqOz+5NN6/H63Jhgj9W8L25MXMF1cSiHsc8l3H7NJgEf4Xp4DHN0bqIxPYUVpsCangddNT29fhWKNAUBez75jr1FxbQPwia/tTc59aQ1X7vNQ+EbcHrNU/v+GZfM79rDjJtn23O9EG4DdR5XVgWujP3WQ8cj7wnFzgk/p63sRBAEcJKKmDbHwvmpdM2fYeLYOyfbWeJs2y7G2vBDekMbag2DB12Kaeow5sBT8Mo6AtZNSfIDkSHgUWi2eC9tWshjFtrmSUDn/8yhAOqAY/bDHSSPlhNwMPXujSP/Q64+MQFHYvAZ+n2sNoVsI9b0DZwd7KZphmMjQf9UCnzeS8AfVRationPgosDmbBJnIRE8DJaCnT180HkxHtZLbb0fB5OAe+ArZj/Xwj/Q6HC+uhCtjGnLDfBQbeMdYgQlgoEAqEAqFAKBAKhAKhQEUKmHRdEbaDs+DL4L9W1g72NjdxJVwMU8Cc0P+Dasz47ePQH5KCufNv47mBYJFmR3Cfowuv2/Pq/s7LjX8lK3N07998lXkxYweL9t5PtQsB+MpSLdX4FTD3avHqHri/8Ld5kSRfm+Te2BTWjQL2yWtBTcNCgSwpoP/Qn9mvxZyW214Ffd4IyLJZe9kCBoP5+v+APzTTP5u/9TVNX62WL8J4MIeqpsNA/9yV+RwcxzeHrQs7JuOO96iZcw0LBRIFkra+Ahv2hbQX7/TjGq4G23/bWUyA6nukycTVoq4TcSetpRrsYrY/VPh8VJl92JwJcwDdHgwebB8L4HVIcwDi9G1hamhwYxCiprYbA0AHyl6FV17eNbdXaonjtHhp4X8eLAGDqmSw5W1YjQr4LIaAgfvIGo/RqK8t5EC/gXK/ZLctOJH0c31UGqZeG8K5oG9M2+x3vwC1K+fL7JMuAGiXSWlv7uXD8EM4HfYCJzSlxig2h4UCdSkwgG/PgmlgoBcWCoQCoUAoEAqEAqFAKBAKdKeAxZD1wSL3z2BtaId4xfjzPvg5TAQL3bXGmTvzXTXqyoy/zd+Zz3NevgHsA5+GL8CBMBbMOzlXt2he6/Xw1YaYuQp1Mmdl8c57MHfg8/d9tZbk28yxLYbp8ADcAxPhCTDmVycLDr7Wch6+1iPsD9zljB5xp3GTeVNA32Gx3EVQSb7Rv+fAlrAyZNn0O0PBQrl1Af2Svlu/5H0kvrlc7pJdmm5ew1z4MzwCw2EN6G589t76wLbgj0T08Ul+yOfmPSX3x9uwUGBpezgAHezPaZq+5GmYArbVtjInPGH1KZBMMnXa2rJQamWUk9CpMA+cjBvoZNlW4uJ2hY+B99MBDkRt1wm4pzTMAc9B0HbzUgEHfbf5mYNmV4GP+/gsXEzgamePYdsy0LHw77Oy8J/mhIHTt42tzp38GPZM+Y585hdAVwOSAfQccFV9WtaXE38fNk3rAjqd1+TLLVBOE/uT/Uft8txn9BkW/k322E6+AuuBiZ6wUKCZCjhmGWwbILpiNs/9iMsPCwVCgVAgFAgFQoFQIBRoogLmmCwSHA7nw4ehHWIW43Vj9bPgNngBjDXrsZ348sgaDuD8XJ0tKg2GbeBQ+Dyo+xZgwt1cpdR7nRyiJlMzi0T+mMX81gehnoUAfH2pGY+YE/OY5stmw6MwvoDPyfyZGll885xqFvbfWO53CKFmYaFA1hSwb+uvzO/pw/QZmj7EPr09JNt4m1lbkSvbCvrDQvCeXLzg/blIy1dJ09T4ObgZnoZ1YDXozleam1wDdoQxkIwx3qP+Pq3xhlOHZUwBx+n1C6R5abZpfcjtYG2urSwPDjEvgttgbSg6MgOXUpNHP+uAyTAMdIZZNwef3eAgsAMsgrwXybiFTFkS8DjAuyDAgr4FFLX21WDl5aJXC/zJ505u/NzFAwY2trG0JwhcQluZE8pjwGJqd5OcZt/4RE5wDbjAo5Tph0wy2B5sV2mYvm9DOA30hWmbE8sLYT6U00SfZn/L8yBv4X8/uBiOh8HgpDssFGiVAstzIvv/X6Gcj2rVtcR5QoFQIBQIBUKBUCAUCAWyp4Dx9KqwOfwCjFv8ux1sJjdxEVwNHZAUcHhbl5m3XReca9eTj/C7xofG6KvDxrA/HA1HgIWolcC8pvFxudiZj5pixu3G48V5VYvz9dxz5wv1HMYpr8JcmAIT4F54GMwJeL4VwLimkefmcLkxn8FvYUFurjgutKcpoH+yOK1/1G8luS9zocvBOpAH87pHgv7XesBr4PWLY4g+KwtmveEJuBGsT4wGc5Dd+Uh9+HDYFdYEfYv37H2Zv87K/XEpYSkpYF9eFpyPdNeemn2JAznBVeAcoa0sbWHbSszCzejIDGAGFV51dp1N3Z10HwqHF97zknmzsDwHzoGrQadvwTmseQrYVjr306TAn7w27+xxZIPsj8Cl4OrMNM3+9lWYC+WevZOp6WDAXm4fPmqq9eXo58InmnqWyg8+g11PARfOlDInnAa28yCP/syJ0m7wPdgMHIPC0lHAPpcEogZr9kcxCDVgctWzQY6JLfcV5wjOB1xoZN8xGeerSSeD2byZC9IM7lwdHsFc3p5eXG8oEAqEAqFAKBAKhALNUcCchnHKSDgavggWbdrB3uAmLodJYNK4kTGlxzKeMG4YAdvAFmDxxbjBWFBdO+eM2FSTGcsYx8yFv8It8CD4A5RWmfdijGQ8NAC8T+OlZltyXgtvFraGwBhwocRIKC4y8mdb233c3TEwDSKma+tHneubs8/aX0eAfiLxhYN5/10YBXkyc0UT4FroABcDvADmMu2H+mfHgrRN3YfD5+Ez4L9g4LZKzDHyT3AzmKs1F+sY18hxk8OF5UgB+60Ldu4F21KaZj/7GNhG22rsq7SDpil+Hs+trk5WXTli43VSXsps5JvBcTC01A4Z3eaAMxvOhlthLjhQham+1X8AAEAASURBVIUC7aSAQedGcA2slfKNOdFz4c0dYPGwlLlPBziBSmug6sW5d4CbIO0FE1zC0snxebz+Gd5xQwnzFwAGtiZusjCZLnGJ79vkGOP4sTacCXuA7TWsuQrYPsTxznbzJpgMWwRzYDFYAHf7P8C+aiDj/sUBW3E7S56lz3MZsA/Zd1wQMBI2gM2hD7hP1k0/9G3QX6lBWCgQCoQCoUAoEAqEAqFAz1bAOWw/cE57OqwH7WDGA7eBRfIOcB5cPM/nz5rN41gUMc6wCGRc4fETc6HwIPBf3jP+HgfmLCxcG1MYY9RrXoMxjNfxDCT36vtysTUfNcxsN8a4xkVrgjGSf7cqJlJDdbbAuCqY3x0LxmcjwW3tGIMbv9pP74YnwPbXqHbNocJCgYYqoD/QN4wBF+kk+RXHmR8XtvGSKzPHdCk8CP4QzD44D/TFjgPFYwF/pmb6x9HwJTgU+kIlpj/xHq8G/cx0cKwzf2TuLKxnKWCfXQ1s8x/JwK2fxTV8C5zjtY01YlLYNmI04UZM5K8Og8EFAeVsAB8cAjZ0v5MX02m/ABfA5aDDbkUgwGnCQoGmKqBvNMC7CPZp6pkqO/hkdnMQcrVkOXNS+Bw4SNk3W21q1h9+Cp+GLNgrXISTUf1UOU1cMDEb8jDRNLhxkm1ix/v6KnQ1tvBxWAUK2DZMbtkGTK4ZeLwFrxewb9n3xDbldoMv+1pxkT8Jxsq1NXav2OxPPmsTeKuC84gtYU8wsM2yTeTiPg7OCcJCgVAgFAgFQoFQIBQIBXqmAs5nzW9tCseCOS/ntnk3C6QWZv4Ac8HYoRHzfw6z9Dhv82pxRMyvGad0dXx11owRRoGLAXaDjcCFF/4gyTiyXvMajH2MrcfDNXAfeP/NMu8taUcWCGRlsPCe3DdvW2Kez/hMPS04mscdBmNhHegPxuaN0JrDpGI+42fA4ocx72LoANtkWCiQVQX0BxafR4L90766POgHTwD7bd5Mv/83uAJmwRtgfzTHoh/OyiIALmXpuK4P/AocDJXmq7yHeeA9ToDnwQUP+hvvP6znKLAct3oEXAitHts7q/wcG3aGhZ0/yPPfaYuaZ+0qvXYHot5g8t7XcpNBG/v6cDSMgbzZEi74ErgBngIdtpPHsFAgjwpYcPsmfAPS9pNO8k4BJ33lzCSEn5skSGuipK/bDW6GXpAFu5iLuBrKFfct4E4DJ9NZ9le2QfU10bAj/AKGQ1j3ChhUFBf2fdYW9A0s5FWwqG9B389sE45f9qkk4Waf8ji2kTTaic/fuYNB7CAYBwZWzhuyaGrpNU7N4sXFNYUCoUAoEAqEAqFAKBAKNF0BC/0Waw+Fb4M/jMm7GQc8BteDRdJGxpDGGsYfxiUmnV2MnMQfvK3ajB8shK0J28MesCWsAcYU5fKSfFSRqYUx1ktwP1wJd8A/oFmWFN9X5gR9oQ8YI3uvkoapo9dg4d/rcsHFcBgFa0F/sBjmtad1jZy6YnudPX8Ej4LP2Nh4DrRVIYT7CWs/BcxB6u8k8QuOQZ+FvSCvZr7qKpgA1l3soxbNfU3yVLzNhDnmbALHwQFg/rIS8z6eh9/DPaC/cWyJuhIi9BBzLN0QJoC10zTNcc98ZjIOpnktDTt3HiYgDbvZFA9kQ3ZCOBicqDowlTL3czKuo/wkZDW5z6WVNQOVP8KvYDpYCEijYMJpw0KBmhQwQLP/nQtOHNM0C5Cnwt+gXD9yu4mCGeBAVW4/PmqaOZYMA4vtWzXtLNUd2CLvV2E2lNLEbYsLn/+L1yyaupossE2uAz+GHSHGbkToZAYNjj8GCgZE4oIY/zY551hk//BZ/xv+A36nnsQaX2+5OU+wTawFR8A2kLX2oKZHwe8gLBQIBUKBUCAUCAVCgVCgZylgIXRbOAl2gqzNVbmkqm0+37gEHgaLEsYRjTJjfos5xi6+Gqc4n26k+QxclDEQxsGeYNw+CMxBGmPUY17zyzAZLoPbwG2NNu9DzNN43S4sWQXMtxoj1XsfHKJu8xq8PvO5xvGrgroPgVFgkcNrzpoZK/8Wri+6sCRnMp1tjW6TRaeJt6FA3QroF/QJI0C/kPiKNXh/CqwHeTX73uNwBTwHjhOLwUK5+S0/L5XzZHMq5kKALeB/YH/QP1di5ummwEXwCCwCFz00YyzhsGEZUsD+6gK6G2HblK/LvnQ8XAht0/acIIU1XwEbj47sTdAxuwDAybcNvNiS/Z5m42PgQDUAOu/Hpsya97YxfBoMLOwsL4CDUpYGJC4nLBR4nwKuUNwHzgMDtjTN/nIN3A36j3JmH5sLiX8pt18ztye6OcHLir9St0mg7yll77DRCXNWFyk5PpswMEnwLfgFrAVZ0ZdLSc3sGz7X2TAZboA/wnVwC0wAAyQTFfNgCRgkvQV+z/5k0i6PY5LX7DziFZgK82EjcE6RFbON+mzuyMoFxXWEAqFAKBAKhAKhQCgQCjRdgQ9xBgudJ8DZsDbkPXZ5jXu4tIB5OmOJRsUQzumNUYzlLXQYlxrbN+r4HOo9Zvzj+Z6Em+BCuAKmgOc1pjcHYgG72ufmd4xd14WD4bMwFoxVvLdGmvp4L0mO1Xtywbf34HUXw58tt+T6bCs+U2NRi3VqtDv4rwJkzVzUcjncDGqbmFqaN7EfqG9YKJBlBfSp9jsX2CT5EfOkz4MLnvRxeTT74SAYB+YJzQX56v3YX+2jWTKvaS78GSaACwDMZSbPhLclzXvyPofDdNDn6Jua+S/LcPiwjChgu1kNdk35euxvXovtV3/SFuZNhbVWAYMiG/Qa4KDkJLCcOYHeGo6AoeV2yvh2J7867gvgHnDgdWVpWCiQNQWcPI0Dg2D7aNo2lQs4HQyYyyUB3O7nc8CBqdx+fNQ0cxwxuLd/69eyYPqYk+ApMAjobOpkEKtvct80dOO0JU09l4fBcCh8FXpDTzafjxP/BfA4TAMDilfB7SZ/fM5Zeo5cTtPNtmIQNQZOhBGQFZvIhThx/79ZuaC4jlAgFAgFQoFQIBQIBUKBpijgnNR/6XJT+BFsAW7Ls1k0ugv+BMYgFiIaFWt4HBcmG8f7q/l3IO1YxuflrzZHwG6wD2wCfcAcZi3P0/u0cHM7HAfebzPMaxOv03tYDsylmm81rna7pGFeVz/YFw6BXpA1M6a+CCZCqULb62w3b2KbDQsFsq6AfX0ADIOkv+kXNoNTIK+LALj0paZfnQmXgPlO85ouCJgP9lHHEsmS+Ry2hG/CztDdvwhgfu888F8jcdyYAWHtr4ALQLYF51722TRtCSffHMw7t4UpblhrFdARW7BwYqX+Nupyz8FAoAMmwN9hLHS3YopdMmVOeFeHPeFQGAgGUA5MOvWwUCALChggbgWXQf8MXJBB2M9hNjjBK2f2o3mgP+lqv3Lfr3e7/dvFEqeCiyeyYpO5EBMN5Vbr6YcdyN+ANHTjtCVN/26wshdcDAeBCYyeZj4fk24mGu6Fa+Fq+As8BB1gkPNPaGQyjsPlziywmzh8GvRdLhzJip3PhXh9YaFAKBAKhAKhQCgQCoQC7amAuawx8DU4E4aDMWJezdjiNvgVTISXoFHzWWMc49PFYJxv4cacmNuzYN6792u8dSX8Boy/zE1YNBOLa139iImP3zXbgYWfIfACeOy3oVF6cqh3zZje46qnuRHzp0lhzJhf3f3Ma0quv9nt1POMhq/A7qB2WbPnuKAz4GEwti5lbjfejPxtKXViW9YU0J/aVq21rAD2c32DfkxfsCmUq8HwUebN++kL48BcrOOJ/m9l8L70sWqQpTyn+pt/vQ7M0/YD81aOD+VsAR88CG+Cub+wnqGAffcAsG2naebh74KZaV5EI8+dZ6fXSB1afSwdsRNQi3c6wuWh3GTQfZ1wmdy/H5x06yjz+Oy8z63gM7AFqMGiwmuWBicuKawHKWDxf2u4HAZl4L4NvC3+PwJd9Qt9h/3HQNr3aZiD4jZwGpTzYa2+LrW4AOZDOV1MCMwDtc6CmRzwFwr6xbPhRHBS31PMSd4SmAIGBNfCH8Hk22MwB0zgJAmjrvoFu/U4Uw9/meE8oTeMhLTNOYpt2WcbFgqEAqFAKBAKhAKhQCjQXgoYvzjv3BEugn1hGcirWTAx/j4XLHq/CP4gpxHmXN1jGbfPheTYWSn8c0klzXydMbVJ8N/CZWC8phm7mguwHViQ6spsF4+DRRwxv9lsU1tjfe/BGNJY0nO/Wnj/Fq9+7n7eg2jd3ct/9+r+v+ZGNofvwEho1HE5VEPM+74VfgIW2rrKi7igwgUAXe3Dx2GhQGYUsH3b9/VT+h/7nz5Yf2Y73hCy1ifNXSZ+iLfdmvmW0WA+Vp+qj3PRw0pgDibJw2Qpd+ZzcTHYDaD/cQ4xFFwIUPw8fFaT4DEw560PCusZCtg/R4G58TTNvjgNHgDbbe5NhxGWngI2bB2Z6Kg7Oz02vWs6bR36Q/AErAqDoNhJ8mcuzMmwA9XBsCd4b2rgyq6YVCJCWMsUcHK0C1wGA1t21q5PdA0f3wzlitfJty34zQMnR2mYvqc//BqGQ1bM6xoB+pQ3wIl/8aRXXQ1yXQSQhYHcgGQYfAHOg7HgPbSr+Sx8JnPgr/BHsM1fDxPA8c3n43jnfo4Jxc+PP8NKKKBGJrKcpA6GIZCm2YbPhlYk+NK8zzh3KBAKhAKhQCgQCoQCPU0B45d14HhwvmdeKq/mHNqi0C/BuGQ2JIUT3tZtHsuis3G7hX9j1O7ifHbJnKmT8fOT4K84L4A74CWwyGZexTxfqTjWgs/d0AFqYUG+lea1i7r7PJLc4yu8t7Bt3JnkDXi7dF+T/6Xuxc+7Mr/TG/aAb8DKkDXz/s+H34Oxmtp0ZeZp1SmP7bar+4rP2leBpL+bS7I/JrUvc6ezwB9Wrg219HG+1hTzWvSnK0A11+W9bAkjwB/VeI/6Y8dp+3cWcp5cxnvMa1oI5r1vAWthXr+vXr91r0thBuh7sngPXFZYkxSw7X4MqukHzbgU+9a1YF4695a2mLkXsEE34GDk6lmT9quBTq8r87m5/yZwFOgo8/wsHZwdqJyAXgazwUlpOHlECGuaAk6K9oELwUlhFsyA+jRY3M3FOADNhDQDMYPZY8HrNUDOmulXngOLy+pqYG/QagDrRNIFFO6Tppks2QZOha0gz36cyy9rJlps04+DBf5ZYMJFP+9naT8HLqFtzDZkQtYkZhLopnFz9rVhsCCNk8c5Q4FQIBQIBUKBUCAUCAUaroAx3/Lgjzh+CM4582yLuPgbYRIYqxiTNCouSeJOixzGocY85rcadXwOlRkz/hgKFr0Pgs3B/IrbjfmugevAIrtxedbyfF6nGDuJxYcVwUKcOSNfXdzgZ+5Xzuwf5nQPhb2gq335OBVzscuvwAJbJW3RfV4Ac09Ze25cUlgo0KUC1laGwUCw/2r2y9XhONgBsmQW7O+B3WDZGi7MH4Q4pt0Bydhj/03yxvbhSvo9u7XcxnBG/WZ/uAsmg+NoVq+XSwtrggL2z5HgGNW3Ccev5pDO3bYA6we5tyxOSHIvao034LNwotkPHJyccFZSVLOAtBM4yczzymsuf6m5Gng8/K7wasAQE01ECGuYAvY1i9cHg8HP8pAFc3D5AUyFrtq8k6AXYTakVTzVV20KN0DW/Y5aPg1Xw1PwPDiAq11apm9fEz4LXwf9fTuZmrvAYho8DNNhMbwJ6t5V++bjsDoVMFF1IaTZN33G+gj73n8gLBQIBUKBUCAUCAVCgVAgvwpYjBgGR8PxUEtxgq9lwt7iKowN74ZX4R1oVJHBWN0ijrGPMXsS+zTq+Bwy02auxfzK5jACZoGxgEUotXYRQNbNexBjdgv/FhHFRQ0rFPBvP3c/zf3Whi/CepA1s/09AJeAz6TS9mhb7gAXy4SFAnlTwP5prm0UrFp08fbdwfBl8IeVWTJ95flwOKxTw4XZt+eAff0JSPzuXN7rf/9fAV7CQoFMKuBYa61hl5Svzr6yG0yASsdMds2mJSugsnl1Pe+qDBYMRvxVpI3LoKq7Z/Qv9nke7gOL5aPB7+XVLCx6DwfD/mCh1kmnA5WBWVgoUI8C9if/lY0vwFmQlb5i3/8N3A9dFcv0Cy6SmQmNTFRwuKpsFfb+FuxQ1bfS2dlJ/wDYCYaBk+CXIC39bHNbw7nwKTB5kHezXTpuPQd3wRVwVeH9M7ya/LLd2s7dN6y5CuhDNoDhzT1Nl0f3OTtpXwhd+bQuDxIfhgKhQCgQCoQCoUAoEAqkroCx34fhPDgILHbm0ZyT+stI72MSuGDZ+KQRZqLYeMe4x+LLK+D53N7T7N/csBr4w4b5Bf7Oq3m9vJixjM/OZ2jONclJ+lwtqNl2XNzuPsbzLng4GUZA1szncXmBBVVcnBqYN3ExS8RzVQgXu2ZKAX28uT+LisnYZdu2xjAb1oW+kBWzBuKY+x3wGl1QZJ2kUvs/7Ohih3EwGByT7L8e1+Poy7x/LXn971/x31AgGwrYLvuA8840zb70AkyGRs0VU7uf7orLqV1YDz6xDV2H7EIAX5cFJ5Q2vHLmd5x8WmiZCD7X4ZAMbrzNnXm/q8OucDhsAZpBlYWmmICqRlg1CjjZGQbfgpMgS/7vJq7nerDPd2UGmDPAADqtZMJynHtPOBWypCGX06V9gE+HwsHghHouJL5EH9oKM+j4GFwETuS78ut8nElTKyc/r8E0uAuugz/ArfAozAfHJP10q7TlVJm2RAf7raih+tgGHe8N7hKSdlk8ztlWKm0vnmskbARpmddwI5gwctxO7p+3YaFAKBAKhAKhQCgQCoQCOVDAfFJ/+Ar8DAZDHs25t3GL93AL+Gvm4nk2f77PKp13+0ULxEtgHlgwNab3nGH/GwPkPRbw+pMY7t+8N14z3vV1KzgDslRE5HKWmgsWfgT3gjmkasz7M673HvP+/Kq579i3vRSw7erv9ekrQXEO0/4xAzYrfMZLJmwQV2Et6By4E1ykMACqGZfMfw6D7UENvFdz4i4EMBeV/CAq+jZihGVKAcda2/qnwHacptlXKqnVpHmNFZ27GudR0QFjp4YqYMC1AgyE1cGFAJWYTn1t+CiMg+IBjj9zaw5ML8NtYAecAhYXDLhi0EKEsLIK2Hc2hNNgj7J7pfPBU5z2e+CErCuzjZtUMAgz6EyjzetL/GeorgT1zLO5EvaH4OILEzbJBJi3TTEn8V+F40EfnQdz4qV/9RcOC8DgSHxve7Vg7efu19Mt6Y++ihPFtyFJENm+1Mrtoma+uq/vi7+fzM2c7DoPSOYCy/NeVgTbkJ+XmxDvzWdfh7TMezoYnoXn4T8QFgqEAqFAKBAKhAKhQCiQDwVcuLwxnAbbQjI/5W2urIOrvQoeA+OXSuIWc2+jwYKpc9ly81jn+S6KXghvFvar5PjsGtYGChiTHQ6/BIt1WbNnuKAzYQ4ksWYl12gbNo7tgFehXPvno7BQIDcK+EOmEdAPinMovt8EvgurQFbMPusCgF+AdaHD4MuwMlRrHss83hUwBezX1lbMLdvX7fNSjZ9g97BQoGkKDOHID8CaTTtDZQd2jrcpzK5s9+zulddJfHYVbfyVORiZ/DcAGww6fhP/3ZnP1gHOwOVoWA/a6Xk7MFnAczHAX+BRcDGAq61j0EKEsHcVsB/sBAZm9ocsmRMvJ5rTwGJgObNNu6+TNidoabRx/Yd+yKLiKdAOpo4PwcnwOJi4SYqyvG2YOWH4FWzTsCM27kBO9A3q3wJ/sbKggMGAySzbnQsAbHfu5/7tbraLpI8Vv0/aRqKDST+3Wdz3fVLk932yj3p17tvJsfmoYrP/ifMBE0wuauoLBqnOC4qDWP5c+r+Z+IlvUjLv+yCwTRlkxi9HECEsFAgFQoFQIBQIBUKBjCvgnHIofBpOhFqKDXwtdTNp+we4F94A5+eV2KfYyR8MOM92PjsD/grTwcX4xovGRb4msVISI9Qyx+cwYTlTIMmLfI7r/in4I4ksme3xDrgELPJVY8awtu/ZYA6gcxzLprBQIJcKOLaZNxkDnQv95le2hZPA91kxx6BT4Lfg+63Aa9wBOud/2NStOQ5OgOuhA+zj1lHkX+A5YhxDhLDUFfBf67gIPp7ylTgG7gN3Qq77hhOXsHwo4LPyl3/9YTWwM1T6/FyZ6gBxGAyBdjM7oau5J8AtcD9YxLKglesOyvWH1a6AEyITFhahfgGdJ3lsStWcfJ0N94KTra7MIMykg8mLtIKwXpx7J7gZXFTRTvYON3Mp/BxM7Pi3k996zeBhf7D9rVHvwRrwfduOySr943Ngm5oLS0B/aTtMq31x6qZZEsj4KiY2vE/7oO+Tv92WvE/2Lf6u40nx353f83HJMafR45Bjv5hssi/2gYGF90kguD5//xIqnSewa0NNLW37+izbmW1MvcJCgVAgFAgFQoFQIBQIBbKngHNG800bgEXNHSGteSSnrtmc3/8FbocOMK6r1I5iR3MHFomKzbm8x1kEU+EumAQzwdgqrOcoYJ9YHU6AkyGJvXibCbOgdynYRl2AXY2ZC/D75geiXVejXOybFwXMn/QGfxhmHqV4jDN3vDccDe6XFTM/dRxcDfbpNeAQ+BoMgFrsVb7k8SbCi+C/dvMCvASeL/I2iBCWqgLLcPbPwIWpXsV/T/4NXs4Cc5y5tWJnl9ub6GEXbiew+O+ksx9YlKvEnJi6/3ZwOPSFdjSDMwtZBma3wT0wG1wg4EDm52Htr4CF1xHgROkYyNIEjstZOqG6ilexvXZlDjKzwInZv7vasYmf+avjdcFJ4jpNPE/ah1bjb8ONYPBbq89wbHXhle3ve7ActNr0dSaqXM37NDwKM8BCrAtK2s0X2k+8J/8VGJ+bwZFJDFEHgxhJ9vO9+yckf7OppHWlV1eflTxYgzbazhL0eYPAuYHv14Lfgp+nYWq+H/g8TCKJ2oeFAqFAKBAKhAKhQCgQCmRLAXNFg2EvOA38wUnezLn8NLgEjH2ci1Y7R/8V31kPujNjDfNLj4ALDSbDDHgTqj0nXwnLiQLGVQPh6/BlSCvO4tQl7Qm2Gv89C9XmjSwAmjcwV1BL3+FrYaFALhQwt2nhfChYTynux334+1NgHqN4O3+maua0Pg83gz+wMNdofvZk2AesE1VrjlXPw+/hSXBRgOPaAtAfOKZKWCiQhgL2v03hAailfTfymu/iYAdCd7WbRp6z4cfKkkNr+M218QEN0By0XKFmoKbzr3YhgAPa/oXv8tK25sR3NtwJBmcGhQvhnxDBGSK0oa3CPe0M34eNMnp/tsfzwMlbV2Ybtb1aPHPSl0ab1d+sCT+AT0O7mxrfDwb1T4EBcDUTX33zIPgZHAytGmdNRDlRnw1TwGt38u5CBu8hjbbDaZtiSRHfhQzemxMxE26+t5+oRfLMOt9357/ZdamV2558nvVX25ltrzc4L/DXW5dBq9ofp3qP+WycYzgGm0yaWXifd525jbBQIBQIBUKBUCAUCAXaRgHnjyZZT4F9wdgvb2bMcwW44PllSOIA3lZs67HnaeBcuhrzXMZbFlDugHvgOTDOr+U6+FpYBhUwploDvg3+wCStGItTv8+Mfa+CG8C2WG289RLfeQFssx4rLBRodwUsKA6BQeAYWGyr88cJsH3xxgy89wcun4NbwX7uj9z6wa7wfRgJtfgl8zX3wvUwDzy2+ZtFYG7Ncaxan8JXwkKBuhVwwZ0LLa1HpGnzOfmWYJ/IbV+oxTmkKXqc+70K+Pz8ZWlfcJCy8OkgUIk5yA0HV4vtCR6n3c2B6xWwuOeg+TDMBotmSUGJt2E5VcA2bT/4Inwdlocs2uNclMkFkxNdmQOLk69ZYHHT9ttqMwFkEsSJ5unQk8YMJ7ve8y/hNehu4qs2LsbaGFzcsSE0y7wW28RceBqeASclr4KLmwzc3ae7a2aXTJt9ILkPn4cFfu/b4rJ/+2rA4n4JvF1q/t0TzTmA4/l24KK3tPqsvusg8Pn5fhr4zHrqc+HWw0KBUCAUCAVCgVAgFMiMAs4ZjfP2g5/CAMib/YMLvgH+DMZBxgW12ii+eDasVOsB+J7zXHNLznudh99VeJ/EkvwZllMFBnPd34OjIK34qpR05pTOgfvAuKsaMwdq4X8RmEPw77BQoCcoYB92EYBFc3PI5pKLzf7+HRhTvDED7x1fPguOLeZYNPPea8Gn4YuwAtRijlN/hHthMTim6husoST5xcjlIEZYyxRwPnYt7NWyM5Y+kXnobWAKVDvOlj5iCluzNHFJ4fbb5pQOVib8+8CasCxU8mzdp1fhOx/ldVfwuz3FLEbMggkwHp4EJ8AGkg5wYflQwHbsIphN4YewFWTVbG9e4xzobvL0Nvt0gEFdGu1RXVeBA+AicIKcJVM/r7HZZoH9KHgMfA6lnpvXYfF/e/gNDIFGmpMMJ+QW+qeC7WgJvAX6Ma/LgN39JG/mNaur9yHel5MsfbH39+/Cq/fofsX3WOp5sEuPNhfubACPQyv6SCmxHUs/CT4fF21ML7wWPzs2hYUCoUAoEAqEAqFAKBAKtFgBc0cbwvFwGHwQ8mTOJyfBDeAc0+JlvdafA5wEm0Aj5s/OgY1pnoU7Crgw4BWI+TAi5MgGc62ngoW3RrQNDlO32b78ZaS5h5lQbUxsn5kHL4Fxd7Xf5ythoUCuFTBn4ljo4q9VoHgRgO/HwvfABQJZMgv/R8BEeKNwYd7LqrAdOI5tA26r1vQD+pPfwVPgIgDPN7/w6tgV4xcihLVEAeemX4afteRs5U9iv/gU+C/tmK/OpeVtop9LkVtw0TpgCyQm2S0SaTp7C3bdTVAtqOjUHwInkA50QwqvvLS1ea/9YAs4GOzQriwaCSuDprZ2cDt8WLYUsG3rw9aBb8AZMByyahbEzoQZ0F17sj8vghfB9602te0NH4ELIWsLg2ZxTQ/AKOjOx7FLXWYy6EiwGG3xvdTzcLJ9IPwWBkAjTN+8AEwYOdG4DsbDM7AQnIi/DV6P++qrumtX7JK66U/FhJgBi0kH70e8X1/d5rjkmGZy4l+Qp3vkclM128Ew+Bw0u3+Uu9E5fHBr4UOfnclOn2Me2mjhsuMlFAgFQoFQIBQIBUKBtlLAeWEfOATOh3FQS5GAr6Vmxshng7GR8XWp2IzNVZuxlb96HAPGdvXOof1+LxgE6qzme8NgMBb6BxjnxNwYETJsPq9T4bNQb5vgEA0xY6rfFTB+rsZsb8bY5lOMzyz+h4UCPVEB+4J5CvvDCqC/TsZDc2vWVhwTtoRlICvmooUPw8Ng7swx0HsxNzgb7gTHRn8QktQ1eFuR6eP8cZ1j1nAwH+145XE8r75HbTTPGRYKNFsB290RkOb467nnw12gz8ilpSlgLgXLwUX7TB2cdNAWt1cr/M1Lt+Z37VxOcveF3Qt/89LjzMHMApUT44fAwdXV266SdSJgwOZAmwx+vA1roQIW/m3fu8CPYARk2Zw4ngFToJI2s4T9bHtO4lpt+gH9xt5wIegTsmROOi+FRbA97ApecyvsFk5yHMyF5DmuxPv94AKodoLNV95jHtNJ9mR4EOaAySgn3cn5eJsbc3IkPjP9qUkuAyz/1n96X/raJHjo/MpHYTUqsCffu63G7zbiawaePykcyOf+PLioI4/tuHAb8RIKhAKhQCgQCoQCoUBuFTCmGwjfhKPgQ5AnMy6+Ce4AczKNmlMajxinWEixIOoCCTUyFh4OFoUaacY7nu9Z+AvcXXgf82SEyJi5eOPbcAy0Kt/QnQQW9s6Cp+Gf3e3c6XPb3stgjsF8Zm4LGVx7WCjQKAUs+q8Ia4H5vA9CYo6bjgVfhKyNmeaMD4X7wfxaYvoq72csnAgHwbJQi/2dLyXjrjlt/9YHeW4XD+lTkhweb8NCgYYrMIIjWkdZpeFHru6Aj7L7nuAYmss2n5VJTHWyx96VKOAg5kIAO8lgWKHwNy8VmYObA+CHwYUAFrl6ulmweg1mwBMwFaaDA6C/WDWQcxD8D4Q1TwEnM1vDCeBkrHiCxp+ZMydKPwMHjEoGCidVtjFfG5XY4FAVmWPC6nAI/BxqnSjy1abZDRz5XvC5699cBLITtGo8m8W5Pg0PgH7yALgQ6pmQWBR/BO4Bk0H6GQPyStoLu2XCbKv6P/1kUuTXJybFfre7T3JPySubwpqgwFc5pn4nLfstJ76ycHLbQLIAIBJNaT2ROG8oEAqEAqFAKBAK9EQFkvhuN27+h7BWzkQwfngcnFvOBRcCNCKOcE5qEdRChgldj+u5EvNXkLuCxZNxMAAaXQDyPoybLOb+Ce6FaWAeoBH3yGHCalTA5/01OBHMraZttgfzD2dBLYtFbO8LwdylCweK2zp/hoUCPVoBx0kXf40EayfFfX55/v4kHAatyjlyqorMXPPB8CCYbyu2D/LHarADfBfWh1quX9+j37gK/gaOl6+A294AfYv+JMYsRAhruALm2e+BzRt+5OoOaJvfCmaB7T13Vkvnz91N9vALduBaFlYvsBKvDgSVmO2jF/QDFwHsB6tC2P8q4GBngDYPDNyeAAt4HfASWMiz+PEfyKWT4LqzYrZFJ2VHwUngQJB1q7b4/zY3NBdMRNhmWmn6Bf3EZ+EH0OgEB4es2+xnJ8PrYFCuP9Mn7QrjoFXmRPfzMB8uh5FQrek7nDzcCk7Y03jmnLasOYEX/Zb43msWg4vEr5lAsPCfkOyTBADJcdglrEUK2Jcvgs+06HydT+Mz/y5MLnxg23ABgJNm20dYKBAKhAKhQCgQCoQCoUBzFTCX8wHwV4DGzh8HfyCSJzM+sugwAcyrJPEFb+syC/8vgnNT3zs/LXds59VDYCc4ELYGiypub6R5fmNMFzv8BcbDc+CC6rDWKuDzPQa+D41+zrXcie3zergMbK/VmO3KWGwu2J+M48u1dT4KCwV6rAL29b4wApYHx9DE3P4/sGuyIUOv87iWQ+ExMEfX2ZZjw1A4BL4M+rdaTD/kOf4A5nbMyepTFoLjVJIz5G1YKNAwBaxn+uPEYxt2xNoOZPv3B6j3gO9zZ8UOLXcXHxdclQIW81zJ5sC1BtiJqjEHwzVhW9gXPEa0H0QoYU6oLYo5GBq0GcQ9CdPhBXCgtNCr04jJNyJ0Y7Yz2+04sKi0CeTBnAj9DHz+lTxngzHbTAe0OjAzGdQfjoOvg/09a+aCiNNhEqiP/kw/5CKAVWA/2BRaZV7DVKh2JaLJqwlwO8wGA/JKLZlUd25PiS9OXj2e+7i/uiV4Lq9b9D9+nry6f/Idj+NnyfkSX5V87quWfCd5TbYt/TD+k5oCvTmzi0rWTukKbG8mzWYWzm+7ex5eAdtSWCgQCoQCoUAoEAqEAqFA8xSw8O/C7q3gLBgFeTLni8ZK14ExdTXxEruXNOMVj+OvFz2m+RrnrElcw9tubVn2GA67wAGwGfQB9W6kGYMZMz4MfwLj3xlgDimsuQoYR1koOw+ysGDGZ34x/Bmq7Qe2b78/G8xBJrE9b8NCgVCghALWTcw9jwAXASRmfmwwfAU2STZm6NXx4Ugw9+zY1tm8/pVhXfgGfARq9W8e/y64GebBG7C4gD7K8buacZXdw0KBsgo4v/o4XFl2j9Z94AKaX4P59Ny1cZ1AWM9RwOdtYW9FGAQWzYoHNf7s1ux8Ft62BjvhCGh0wMMh2850Dg6GFnifBoszj8EsMAB9E/zcSXnYfxWwvbpacW04EWxvtU5S+GpLbS5ns/jvs67kmTpJ+jtMB4O0Vg4mTnItpDuZPQGy2p8ncG3nwqugPl6nk9iBoB/Tnx0EG0HWzOfbATfAA+Ak2XbR3XN2YmHg7r4e41+QbPPVbeLnyT7J32xaenzPUXwu/y4+b7n3fj+xZJ/kNdker9lUYDSXZfDnWJ+G2UePBhOsmu32eXC77TMsFAgFQoFQIBQIBUKBUKA5ChgvGw99EY4EY708mQWFS8B8yT+h3vjD7xs3WVBfBMZhzkfrPa7x51rwYdgP1Nx4tNE5Vq/1JbgfLLhMhvng/DqssQr4TH2eV4I5z7TtFS7A/MckqDaGsn28Dual/gHmA8JCgVCgewUcQ11ANwzMRyembx8NJ8HIZGOGXqdwLZ+Dp6Dc+GA9yHvbFn4A60GtY5bj0tXgmGSdw3z2AjAH5JgbPgcRwhqiwGYc5W/woYYcrfaDXM5Xj4W3oN45ZO1XUeM3a+3oNZ4uvpYRBXzuvWBV0Pn3gWo70gf4Tl9YB/aFTcHBJKxyBXQYTsY74CEwyH0S5oGFEgPeRgSnHCZ3tixXPAb2hxPAdpoXe44LPQtmQqWBmu3A75mQaOVAoi8YBF+HL0FWxwQnlCfDLCjWR5+j/xoA+rTekPgj3qZuFu+diN8Iz8KbkBT0efse876cqNvvnTDbJlwM4t/Jd5xEi/sWw59LrVibrrYln8Vr+ylgH94dboe0+vPDnPs7YHvWbMMubjLxGoEgIoSFAqFAKBAKhAKhQCjQYAXM56wIH4UfgjFensz45zq4AywiVBpHs2tZM4Yyvn4RnIe+A42eizrfVvexsDcYi5ojs4Dc6Lm49/MCTAQXA5g/WgiN0IrD9Ggzr7A1/An6ZkAJc4Jng7mEUjF+V5dozmEJLAbjsWq/z1fCQoEerYC5xTVgTfB9Yvr09eBbMDDZmKFXx4ZjYCY4ppYy78F8+zA4BL4Mtfo8fYvn+gM8Di5aEscpFwQ4NjV6zOWQYT1MAfuhY2HadaFpXMMeYPvOXbt2khPWMxXQEZuUNyCy0OQAYNBoYb8S09H7PSemDjJ2RoMcB8FqFxPwlR5pDry9oD9sAv4zPEfCYbADDAeDSfXUuYi6S7vactyYE63PwnmwP9iu8mKPcaFnwhzweVVi9sNZ8DpU+p1KjtvdPrY/++uJcAJU2vfZtaWmr/o1PAEmPYrNvmAix7HMtuNKvMmwGgyDtMzrcnLwC7gJZoPX5r34mearAbnP/VVYVMCVtK6adbvfsX04eRfvP5lEF/sDj5Ucl7dhPVgBx/LjYasUNbiNc9tfkzZpH7VN296TbbwNCwVCgVAgFAgFQoFQIBRogALmDDaDn8BJsArkyYyFvfa7wbio3pjY+aZFUBcSmKg152Uc1ax5qHNdzzMJrgXvwyJIb/BZNCo/ZrzuMTeCA2B/GAPGh97v29Cse+TQbW3rc3fXwKAM3OV0ruEMeBqqeZ7u+yrMhyVguwwLBUKB6hXQp5qH03ebjzZ3Kpp9yzHLfIs5yCzZcC5mANwPFuDLmeOhY9Rj4HhlDmksVFsjVJO+sB1YpDXn4/i9MiwD5n/UMiwUqEcB29CHYVg9B2nAd+0n18GLUM3Y3IBT13+Iajt3/WeMI2RJARusHclC/mvgIGBQoaOuphjoMVxd+hDcBxaqhkIvCKtOAfukQeJo2BkOgyNgH9gAXCyg03E/B1afYe4cD9ecmBOG5cH7GgfHgkXTA8FJQ17MZzARvHYLuZU+EydEJgss+tqPWmX2bzU/Ck6GLI8F6noTlJvAqrWTc01f5oT8EVgDhkOrTT96LVwEMyAp/NtffcYW9r1Gfab47A3UvT+TNgbqHiPp37wNCwUqVmBV9jwFbP9pmO32SlhYdHLbtG3e10p9Y9HX420oEAqEAqFAKBAKhAKhQBkFXND9OfgVbA7G13kxY58kbnqe98ZA9ZjzTONr4/EOMO5y/un8tBWWxKVzONndcA1MAmNU5+grQaPibo+zGmwBH4UPw2BQ09fA+w6rTAFzbxfDhpXt3tS9nuLop8PMKs9inmFBgWTBS5WHiN1DgVCgSAHHDXN5FvmXh+IaibmNebAlZK3usW7hWi3ue/1dmeOE46Xj1FQYCc4pqp1HqM1w2AnM45vfdJu1DV8jD4QIYXUpMIZvb1fXEer/8jIc4l54Flo1r6z/qgtHqLZTN+zEcaBMKpCsbuvN1a0JFppraSMeZyiMgz3AQkQtx+FrYZ0UMKj8NzjhMDh4GByoDRAcuC0iWgw1AMhiocV2oNN0ImC72Bh2hl1gCDg5yJup9Z/hCni5iot3EuRznAutnBD5DOzjH4PzwOeRVVOb74CT667as/fkAGzCR79lQsQEy1dgJ2il/YmTXQj2RduG12TyyWSMfVN83n6Wu0kD1xyWbQVMXD0IBqppmAtajoUXi05uEmo6GIB21Y+LvhJvQ4FQIBQIBUKBUCAUCAXKKGDsYzw9Gk6D3cFteTHng0+CMdMcsEBezxzR77p44BUwJ+Lc0zirnmPy9YaZ+bEBsC0cADvAQHB7I837Ne58HIxJXYQwA4w/w0or4KKJc+Cg0h+3dOtUznYG+AORSs1nbr6hAyy6mSuMHAMihIUCDVDA/LT5RYuP5lD9OzEXBejTvwq+z5KZazwVLgDzM5VYL3YaBB+HL8EaUKuZ574a7gPfOy7p18yX65+8vqyMz1xKWMYVcH77UXDBaNpz3R9xDT8G51W5asNpC4deYRlTwDZh8cxBbiD0BQsJbqvW/M4K4Ep0i40GqI0OcjhkjzedjsVFB9Qn4GFwcYDFUwPgN8GgwEE2DXOS5ITI9jQKdgKTFGPB9pFnP6TTvxTuBFfaV2omKJyIzYFWDxz27d3gGlgRsmomgk4FExjq1ZX5ue3d7wwH70v/4+rTE2BnaJU9yolOgnngRNdr8hknk9xcTRK47rD8KKCv/RxcAGn51Qmc+0yw3SdmMmo6tNrXJeeP11AgFAgFQoFQIBQIBdpFAWMcY+rD4EQw3smTOUe8Dm4CY6V6YyPjQPMd5j2Mx9+BLBdAl+H6BsNOcCBsDauB8/hGmrHnEpgEN8BkMF+UZW24vJba6pztG/A1SCt2Sm54Km9+AouTDRW8+iztQx1gH0gr38epw0KBtlXAMdcfFznummcs9tX+vRMcD+ZZs2TWAI6FP4J+ohLTD3pPa4OLAKzj1Lq4wbF9JvwBpsDLYF7IcegN0F/FeIQIYRUpsBF7PQzOodK0Wzn5kWBbzlX7TXuSk+ZDi3N3r4AdywLtquDqLwe0WtqMA+QqMAJ2LuAAGtY8BXREBtcWIR1sLUo+DXPAwrMBgsFxs4IE24kTBYPbTWE/8Nn3AydQ7WBOYM4G9X2rihtyIrQEfBY+o3qTHhyiYuvFnpuBk8BBFX+r9Tvafn8DDq5/7+b07vsKOLl0xbv+agQ4cbUd+mpQvyO0wuxTX4cLISl4tvIZt+Ie4xzZVMDFera7I1O6PNv56XAX2C8Tc8x5Hhxzoi8kqsRrKBAKhAKhQCgQCoQClStgDO1cbw/4EawLebMnueDfwTTwBwz1mjG4BVPjcuMu5595mmuaLxkJu4P5kk2gNxjDNtKcg3fAHXA9PAbdxdjs0tZmPvIQuADSLig8xTXYp23LlZp5jxdhASRtv9Lvxn6hQChQnQKOv31Af63fLvbR1kx2g/+BrC0CeI1r+hhMBv1Epeb9mlfdAsxt7gi15vFdpGfh9jqYDl7TElgIXlMsBECEsG4VsCb5OAzods/m7jCfw+8KM6A459ncszbg6MVOqwGHi0O0oQK2ER29A5kdrS844NXq/J1cDwKLkLuAvwKv9Vh8NawKBXROFv7ngEHfI/BM4W8LqAbQDs71ODGTEhb9N4JDwYmQE4d28zXTuKefw1ww+KrGLIZ1gMX/erTm61XZh9h7DBjkjqvqm63d2aSNyYnfgsmcrsx9TWg8C65q9W/9SX8YArbHD4ALH34ITmBbYRY794RZrThZnCMUKChgmze4SmtS7BhyLOgX7YuJGdzNBvtqWCgQCoQCoUAoEAqEAqFAdQpYYFgfvgBHQNoFSy6hanMeOAWuBmPpaooR7P6uOcc0/jZOdI5pTP1/oXjuyZ+5MnMlK8JY2Av2AZ+3z72ReRQ1Mh/0BNwIt8BMqDafwVdybfafreFmsKiXpk3n5N+DRVVcxNvsaxHCvJL9Ks9tn8sPCwVyoYD51H4wHMwvFvtm/9Z3uwjA91kyc5MHguNutfln78Xc6r7wVRgJxffNnxWbfut2cNyZBy4EWAxL4F/gtYUvQ4SwkgpYh3TM3r3kp63baFs11z8JnHvmxiyUhIUC3SmgIzYoMFjwn2oxWFsWHACrdf4e63V4DsbDQ2DRwCLxSlDt8fhKWIUKqK3PbQBsDHvD4WASYQ9YG1YDHavm4FvJIOxxfXYj4ItwNhwFBq0eq52eqQ7+LvglWORSn2rMhRYd4OSn2u/ylZrNZ2Af+xIcCll+JvqEX4MBbXdmG10IThqTwddt+igL/yZSfFXr+2BLsI0321wo1QGPQCufM6cL66EK2KdNZB0Ntvk07ElOalDnpLjYDO4c96MvFKsS70OBUCAUCAVCgVAgFOhaAed3Fhw+A+fBOMhrDs/rXrNwD+vx6nzR2LiaBaLGea9CB1gw9RhJDMjbXJs6vACT4Xq4G1zkYAy/Mph7q9dsT8vCUNgVDoJNQR2Nvc1R9AQbxk3+HnxN0+Zw8h/AggovwvZvTnYW2Db88Y7bwkKBUKD5CpjLMM/oWLZC4ZWXpeY4NBveAH2q+2TFzH+OgklgTqYa8768p6fhFngL1oWkZsDbim0Z9lwHtgfzVX8Htzm+qW01cwF2D+uBCgznnndK+b7t24/Bo2C7zY1lySnlRrQefKGJU3bSabBmoKDjNhjxtRpzouqighdhCtwLU8FJbB9ot8Ixt5RJ87k5eRkK28JBcCQcAtvBCPB5LAfFQafPTxywLap+G34OroTqDQaX7WZOls4HV8tbcK7G1CpJVvyD961OVPj8PgI/hSz7/Wlc3zmwCNSsO/OZGDh3niz6XSenrlhdCWyP+hsL8juBbb6Z5vlGw++hpyRSmqlnHLt7BezXh8Eu3e/atD2u58hPQee+q7903uAcIiwUCAVCgVAgFAgFQoFQoGsFjCUs/G4Mv4IvgjFNO5j5AxcCmHuwUGJB2tySMVPnOSSblppzSAsv8wtYODCeLrc/H+XWvCe1MMa9F66DyeBcui/YDhoRz5sHWgU2gAPhw9APFoJ5i3a1AdzYzyDNmEltzYP+AGb7RwVme/e5uL8FuVbnkzhlWCjQ4xXQP5tndNwyx1rsi+2THWD/3ASKP+PPVG0kZ/d6zYc6llRr1mn0Pw/DXdAHPGZxjYA/KzJrPeqzOTiWO7abn10ZPE+S21XrsFAgUcD2YNv5BDhHTss8t33hNrDGkBtLU7TciBQXWlYBnb0Dn456ICTBSK3tyu8ZEBrYWDzbHhwUHFxqPSZfDatTAQNug1BXpD8DT8IMeB12gM+ACYp2NQcaF6lY/Dfp4ASlGlM/kxpzwcliq4M1+9QWYPJgEGTVbFNnwGxw4tedOdhOh5dBjTubPuMDsDasXnjPy9JfSf+AV3VppnlNh8ANEJPXZiodx1aB/vBXsL2nYfrF42EmFLd3/d1zYD9tte/jlGGhQCgQCoQCoUAoEArkSgFzLGPBGPsLsCK0szk/XACTwcKCOYekAMDbpe9NtrpAvJ0L/95rV2bsas5tazgIzMM4/7e9NMp8Fup8L5j7eBRyleDmeruy3nz4NTgFzBOkZbbj78PjUCqP0fm6fAbGUvPAvFwl32G3sFAgFGiCAuYZLaaPAGsXnX2wY/becDQ0O+fIKSo2c6zHwdVgLr9Wc2GDNRpzzN+BLaHWxQ76smfgKngazJvr65wHJLnz8HeIEbZUgTH8dyrY/9I06xC7gu00N+2z1k6aptBx7uwoYEN3MvoPMCjzVesFtbYtg443YR48CBPgMfDYDjKu+HHADWudAurtM10NxsIOsD8cAttC2s6XS2iaGWBdAxfCQqikMM1u75r72zfmgG3Y9t1KM7BdA74FPqus2vNcmCvxZ0IlGllgXFygu/1fYz/9hu1UPRyk/Xt9aKbZb1zJ6gKAattNM68rjt1+CtjWNoMvQa1jb72qPMMB/gL/6nQg+6d97h0oXhjQabf4MxQIBUKBUCAUCAVCgR6tgEUE4+2Pwm9gLzAGz5MZO48HF19XmiMwPusNxmbbw3D4ewELAXNhEaQRS3PazJi5N39Zmsy5jTGfBOf+5smSWJe3NZvPYhXYEMz3mEd4DIw1nMvn2Yz/D4SfQJpFOWOls+Eh6C6PwS5LdX+R1/lg/4p4ChHCQoGUFbDv2h+Xhc6+1xrJAtBfbwL61SyY1zEOJoC57Ur8D7u9z/RBjsfWbO4Ec7ljwXHIsaIac//+YI1hTTB37vFXBv20/jLxeckrm8J6qAK2l8PAtpammee/HuxHuWmXaSWK03xQce7GK2CDt8DlAPg6ONA5uBjE2kFrHfAckFwM4OD5CIwH/8kZC3oGiXa6Wo/NV8PqUMDnKu1srkD8BdwDJiGqNSd+ttUOcIJk0N5qM2l0IJwMWX1e07i2s8CJY6WDpz7GINhfHXf3HT939agTc1EHz7kTmOBopq3Gwa8E/WJYKNAsBRwHnQjv0awTVHDcm9hnKnT2c47hiyEWwSBCWCgQCoQCoUAoEAqEAiUUMNm9I5wBJ0IfyJMZbz0Fp4EFAWNgi8jVLGAwRjO/Mwq2gxHwAswGY6nOc0w29VgzT/YKPA43g/NwY2n1tu1YlKon9ve7/op1JJiLcyGGCwDyugjAvOQmcD64OCUt87ldCvaRSrS0+GUc5TMw1xoWCoQC2VHAfK99dCVYBop9rtvNV5pH3hiyUrcwH7ol6IMcQ+ox/ZljvXnzPxfer8er43i1Zm1yODgPstbjcdVMbdVVfxlzAETo4WZOcSuwnaVpzikmgW0/N+0yFgCk2WTa79wGfnZIi3IGaQ52dgYDEdta8YDIn1WZx7GI5wqbx+BemAxOhl3Na9BsJwwLBepVwPZrYPYbmAOVBGfs9h6zHxgo+32DNftGq80+tzlcDE6csmgu7DkHOqDSgdOJtv3eCWul30n8kskQJ73+vQR2hmaa57oHTMiEhQLNUmAQBz4XTPilYfpIfeZLJU7uXEBfaIAYFgqEAqFAKBAKhAKhQCjwvwoYmwyBr8HPYR2oJ2fC11tuLnS/Gi4ACx7OC6eDsdZYWBGqNXUZDnvCjmCux0Ko+aBK4z927RFmbKzWD8ENYCHGHIQ5slXBXFytbcpY1me5CCzGuAg/b/p77/3A/rUFpGXmg3w2V4N9pjszJ2WbdxGMxcSwUCAUyJ4CySIAx6hSiwD0xfbfDSEriwD0h4PgPtCn12vmVi3YW6e5A5KFfOpRrfkd50HjwPdvFl7NZ+tDnV+E9VwFbAMDYO+UJXBeYdu8E/QBubBYAJCLx5TLizTZ70CXJP8NFCzQ2+bqHfjs9E6IDXSehgkwHp4DbRUwaKw10PEYYT1PAduVq+jPhonwFtQS4DoBMlAzAWI79bitNtv+QDgVXOGZNVMT++x5oFaVFgf9nv1+IahzNeZk0e/oG0xmvAhOHEyONMt8Dt7fXc06QRy3xytgYLQHHAX1jq21ivksX7wZHPOLzf6qH9SX+j4sFAgFQoFQIBQIBUKBUOC/OZE1EWIfuAQ+ArUky/laqjaFs58DxnX++KPYZvGHBYFR0BdqmaeqyWDYBdTKxRLGcCZec5N05VpbZca7FusfgD/CLTAPVgR/VdkLqsmRmct4CJzPG0e72LfSuJ1dM2HG+sfB56Gae2/0xT/IAX8N5ke7M3VfDOYROsdX3X03Pg8FQoHWKWCOQ7/rj75WAsesYj/jZ46F5pU36PQZf6Zma3NmczRPgtder6mDvsrxZwI4J3COMxRqGfv12xuBuWz9odfoNms9jkXqqnnesJ6jgM/becyRUEu7aqRStsdroPPct5HnaOixYgFAQ+WMg3VSwM7pQGdwZqdwVZjO+0MFHBiLB0f+rMk8vseeCfeDg82j4DaDHYm2jghhZRUwEDsXroXZ4KSiWrO9GxAbZFugdgLktjTMPrY7fBfSHhg7378+4Qa4FJaAf1dq+hH1ddV8Ldrqf/zeCqBPGAqjoZmm/hc38wRx7B6tgKvN7ee2csBJAABAAElEQVTrpKiCxf+p0Lkv6wNNXBmg1dJf+VpYKBAKhAKhQCgQCoQCbaOA8YdJw13BRecnwGqQNzMZfwlcBMZmSTKet++xV/nrVjBpPxC891pM3VaHrWF/cN5rrsfjmwuKeSYiFJl6JPPwyby/DnwOc8E42OdhEr2rXJzHeBFuAxcAeLxXQL3zYhbjPgLngPmRtGwWJ/4pqGd3Zr5iMRhDqXlYKBAKZFsBfaV+0XEx+ZcAiq/Y8XE6+EMkx66u/C4ft8S8hq3AuskcaJRfVwt1cMy4Ex6HMdAPqr1v93esctzfFMwB6x8dw6zxeM1J3t7zhvUMBcw5fhJccJOmef7rwbE6F+3PiXRYKNAKBeykOmhXaxuo+WoncSJuO6x2MOArJc0BwCLhAnCwGQ9/hRng+R0s/PVv1oqiXFJYCgpYsP8bnAmPQK2/VLUtO7HrAAM221pag4B9aSRcBllLKKmLyaKbwARCNaa+9mu/pz+p1Zw4qpGT8+VhR2immVz5eTNPEMfusQrYjreB70FaSS37pX3axTydzQDNCbF+NiwUCAVCgVAgFAgFQoGerIAJ6w3hu3AarAXO5fJkxrePwdkwEUz0VxLzPsR+z8EaYHxaax4yieHUcX/YAsz/WFh1TlpPjMjX29J8Ps7JjaMnw3XwF5gFxqmrgIWpzm3RBRbG7PeD33+5QCXPm11TN/N9m4EL8dPMiajjqdAB3Zlt2FzSQojif3dqxeehQHYUcOxxLEpqDp1rHG5/HvrAKMiCmT/aHu4EczmN9O3q8XewDnMbzIOx4HjTeaxhU5fm/v4rQtvB2mAtST2dUzl2+T4Z+xt5Dxw2LKMK2BZGp3xt9vGnwbpjLvKdtU68U9Y5Tp9jBXTIDowGEa/Dq+Dk1gm6NLJNOggkwYoB531wFzwAc8AJtgVABw3PHdazFHDiMBEeBpMXLg5ZBpI2WOnkwf081mwwMNb5V/pddm24Wdj+IpgUqXZy1fCLKTqg/e08uBsMhKsx+/JCWAT6j3rN522ft/9/tN6DdfN929PpkExKu9k9Pg4FKlbAZNa3YZOKv9H4HadwyD+D/buzWfy3r0fb76xM/B0KhAKhQCgQCoQCPUUB48shcAz8CrYFE+95M5P558K10AHVJjwX853bwTljP6ilEMDXlpoxrnHcGNgbxhX+Nl40t1TttfGVHmHmKNTHObqFfZ/lTfAUqJlt1TjZ3JkLBSbBS2DebgH43bzYYC70NNg6xQs2b3EmGC91lx+yiPUiqLM5zLBQIBTIlwJJ/UFf6vjkOF+cj9V/zoA1YQhkwRyH14HxoJ9vtOkDX4GpYM7oDVgXVoBqzfztGjCu8Or1qq/5b3Ouxfmo7vwtu4flVAH710DYI+Xrt+05L/ZfV8rF3KjYGaWsXZy+hypgGzTQ6AWu4FoVehf+bmZg7ADheR0sdB4jYH1wVZoBqYsCon8gQg8wJwcOIga7TiJMHMyB2TAPLOq7XafufsWTCd/7+Vx4C5z0FX/Ony032/AdMLTlZy5/QrX7JUwEdarG1NMiopNln1Gjion6GyefD0Iz+7ptpg84OQgLBRqlgGPY9mBfd7xKw+ybZ8NtYHBXbPb558C+26g+W3z8eB8KhAKhQCgQCoQCoUCWFTDXYDw2Dk6GMZBHcx7nv5R3CRiP+Xc98a6xoHmez8FeYC6mEbGYyf9n4Ca4CozPo5CKCBWY+ttefRargUV/CzTGG87lLdr4g4e8mMW3o8E4xaJRGmYfsR3+AbrLf5gveBHMPZnvqKd/8fWwUCAUSFEB/WZ/GALLQbEP0teOgK+DedssmP7G8f07YC68WaYO1l/WhuPgIDAnW6vpK++Fv8BscKHBYnD8cj6gXw1fightZvahbWEi2NfSNNvbNtABmW9rChcWCmRBAduiGHg4YU8WA6zEexcHNLtje24D0eS8g3g/GhycRoAFPK/D/cJ6hgI6cBMcBrsmEJ6GaTAHnFT4T8kbpL0AFrucYKRtTqpMppwPze4zld6rky+L//dAtUkYn4H6zwITD43WeCOO+Tg0s197D0PBdmJ7CgsFGqHAAA5yJezaiIPVeAwXtRwP+sfOE14TXU+Bfb7zZ2wKCwVCgVAgFAgFQoFQoC0V+P/svQe4JFW1t/+/nwii5IFhApOHnKMgAgMCCoKAAQUDiGLOAbwG9KqfATEhKgqCJJEkSJA0DElyTpOZOWdyIEeB+/n833c42+d47O7Tobqrunut53mnq6urq3b9aoe119p9xniGQe4D4BhwwXEz5xqcvmlmcPNMuB2ehEZ8On/hbDJ5ITi/c173RjgWJsFakIW5KHUuXAmnwSx4ERopO1/vKkv11diCuiXaQQTLvjNcDbbDvOx+LvxjWDJIAdR2OfSC8aWop4gQFgq0uQLGYteDMWB+o7/Zr24O34D1+3+Q47bj8dfAMdNEejNNbdaEreArsDesAvWaMalr4CqYB5Z/MTwO+gPeW/SriNBBNop7uQvybj/Wr/1hCljPCm1FSRAVWqQoXMsUsFO20ZhMNXngr67tzJ2wmTizvjpYpgkJm5ma1zBZ4cR0PjwEN4ODyfVwJ+iYWx7L4SBlmZpVHk4dlqMCPlefs6s2HVi2gEnwVtgW/Nw6agCjKAsAdKSOAxevFMEcEE+CyWDbqsVsjwaK5oI6Zz2g+vzeBgdDs+1sLqADmvU9NLvccf5iKmCfdBh8Cuyj8rJrufB1UKpeGzC2ztuOw0KBUCAUCAVCgVAgFOh0BfTJhsE+8Cv4NDiHbMdYgXO4y8EE5lQwNlOvGeNxvmxAfgGY5NQ/dL/vzwevoVbq5wKKRsznMAR2hHfAZuDiBZMC3pfXDatOAbVqN70mUOYzYXR1t9iUo5wD/RR6qji7dbMXIvlfhVhxSCjQJgrYbxr/dMx5A/Qf1/zMH5TNgzcN+Iy3uZjj5m5gUtVx2Thss8z7N6fidVyodRs49o8Cy1GrrcIXHOctv9v6K76uDvoa/iAt+WHtNp5R9LASCvhc3wx55z2srzPAOlwqJsru4pjJy7BQoIgK2DE7WJpYdRGACUB/AaxjbMOyodmJ1zNA8LWqzDJ4LQduHfP5kBYFOFCZ/LgT5oDldJBpxV8r4DJhOSlgndN5M0CxKxwG7+/bHs5r6lN1mByUWu1gjOSa3wSdzLzNtnM6XAk6eLWaGjoZdgLdjMHUlbgnwFhoplkHzgSDTjqfYaFAIwo45m0CBpWGNnKiBr9r+zwV/MsWA/s567kTOsfOgZ+xKywUCAVCgVAgFAgFQoGOUcD5/wjYD46HL8JocN7YjjaXQv8G/grGYJzT1mt+dzkYR3FOp/9Yyjecwf4LYCHo30qaV7NZl6n/GrAVvAO2B31UF6lajkbui6+HFVCBdSnTt8G2mFf7M4bpPO0eKFXX2f0vM77ZCy6Qifr4L1liIxToCAVs0+YJHHdWg5UgmX3DEjBGuDM0M6/B6asy49x7wvVg2ZrdJ3n+F6AHroGpMAYc/+vpv43vOt7vCurpQgD3rQ7Gk30O2mD98qtHxb9FVsDn6Y8f9y9AIW3bF0I9OY+WFr+eRtXSAsbFQoF+Clhf7cidZNuRvx5s9DY49/lZHnXaazpYWo61YSxsAZvDKHBhQB7l4rJhLVbACZ9BDv/k2y1wH8wE9+ncGGxopu3Ayb2u7SFP06kyiHM+GOyp1UwczgN1U9OsnTTb46HwJ7DfaKb5zPcFA1vLoBmLGThtWJco8Abu8zg4Juf7nc71vwWPlSiHQaxHIBYAlBAndoUCoUAoEAqEAqFA2yuQ5v8mHN8Gn4MtodnzCi7RNDNRcS5cBwb/nYPVa87d9ANdEOpc0MB7tckENT0K3gdq2j9pwtuGTB/1bvgzXAr6sc2en3OJsBYo8Dqu8R44FfKKhVjvz4E/wmBzfpNTPeAPjRppa3w9LBQIBQqswGspm3mCcWAeo78Z2/kQ2HcVJWfwMGV5L0yDrOOwnLKsmTcZDgfBp2Ei1KuJ5V4Ml8DfQZ9GX8R9Lmy0fx6sj+aQsAIrsAtluxFsX3maC/neCMb7W9lear7nRlfV1nzB+EIo0KACNigdZCeUOs123uJkLnXgTrwdKOodLPhqzea1XfGjAz8X7oHrYAqYBHbAcfLqogU7qFaWjcuFtUgB697qsCHsBYfBkXAAGMBYD5yQepz12Pqc5SAxgfMdBXnWL+/najCwYgK/VrNtLwS/mzSq9RyDHb81B5wDPqtmm33TeWBwyf9epNrgF4eGhQL/poBjx97wM3A8ycts46fBI1Cq/3Khy+MQdR0RwkKBUCAUCAVCgVCgYxRwDud83l+ZGSD/BTj3GgZ5zr+4fN2mLzcVfgyTwWBmIz6c8zcT63PhCah1Puei+VvgCjC+si74J/3VvlFzHj4W9KffAuuA806vmWJJbIa1mQK2vYlwClhf8jL/WujJYH2qZMY7FoHzJdtHWCgQCnSuAo6nxgLFhL8xnWS2/wUwGkamnTm/DuX6m4DjsPHLVpljsNd7EC4Dk/UbwxpQq3/l8cZ6/cs/O4F+jv2uCzDEZ5HG/FLxLD4OK7gCPjf98DVzLqd+5Q0wHQpdl2IBAE8orG0VsHE5YNqR62Q7WXXA8DVNNJ0oSq0DBl9pyBzkXenu4oT5cD9cD1PAVedLwXI5+NhhtLp8XDKsBQr4XF8HI2BHOAiOABcG7AU6VsNhNVgF7JPToJFe2VW1jeXIIyGv+mSZJ8PpYB2vxfyubVkH2O/qlDXDRnHSU2GzZpy8xDl72Hd5336DYfZNYaFArQo4XmwJp8D6tX454+N7Od9Z8HyJ89pu54FtuZ4+rMQpY1coEAqEAqFAKBAKhAK5KeC8yoD9MDCYfCz8GA4Ek415zbu4dMNmgv4PoF83F4xh1Gt+V/9PP9DAvfGZRs5nTOd6uBKMq6wHa4M+caO2Eidwfr4H7A+jwfmn17TM4cMiQhuZiZ4fwJ45ltm438/B+l+p/liX/aGDbcTtsFAgFOh8BUw2vwS+uojQuG/yHYypzAf9C/uyIth4CuGYew84Llbq0/g4UzNe+iTcB47/9q2bgrrVamq8JrgIQH2NVYkLMYy/u93Ke+NyYRkpYD0xpr9NRuer9zTWscdhMhQ61p86nHpvNL4XChRRAQdTcWJnx+4gaoJVTLbnbbY7y2di2MHIhKSJ4C1hIljOLCa2nCasDRTQ4dDxcPGKE8ZH+9AJNBDhfp0eeQ4Mphhc0XkcGFTZmX3XQT3OEV9ryLyP6+EksMy1OFLehw5xD9TzSxG+VpUZuDNg96Gqjs7moD9zmt+BE/wHwOdXizYcHtblCjhmGKT8LhyVsxbW3Z/AtVDKwXWCOB1ehKjniBAWCoQCoUAoEAqEAm2pgPNx4wijYT84HDYHYwztbs4jnTOeCcuh0USk8zh9QOevznU8f5Z+oL7wePg4HAQTwHhKVuZc1ISs/u1pYOJBX9b7CCu2AiZxPgAnQ15t0/rzG7gMKrUl507GdGbDPyDLNsLpwkKBUKDgCthHGZMcCeYDUk5Of2MH+A6sCkUw+ydju8ZPHR/t51pt6mJOZ2P4UB/mUOo178H+93xwnF8G+kBLwD7ZPtpjom9GhIKbbUm//I+Q2hGbudhUrrobmMsorOUtUmGFiYJ1jAIOGGLnYFJU0oIAFwM4cfTzvM1yOHlxMNMhGA9bw4YwBPzVQVj3KaDz4eIAAylOFpfCfOjtYwGvi+BxMOgyCs4GHaRW9u+W83ow0W1ZfF+t6Vy56rUHnoRmOVy2q+PgE9AqbQwafQ3uBu/rIah1cQRfCetyBRyzPgi/grzHq7mU4VhwolTK7JM8xrofFgqEAqFAKBAKhAKhQDsp4BzB4Ptw2AneCfuCf4K2U0w/zfniHeAcrBFzHvcsGDx3Dui8tZZ5IIfXZPrBxkk+Du+ACWAcJSvzfryPm0CNbgDvz6RAWPEU8NnbTi8AE2p52WQufCJYV8qZdcv2NgP8UYfvw0KBUKD7FFiJW9bHSIsAkgLuPxA+A3nHfFKZHM9/AMahHoNmju+cvqyphzGxTeFIMPHr+3rNWFVaCHA/28bZjW/56kIAP5fopxGhoKa/vgXoy66acxldMLojuBCgsHWmVUmQnJ9FXD4UWKGA9V0cWE22rwxO5h04XH0nRRloLaflWw3WgXGwaR8b8GoHV5SyUpSwnBTQATMg4SRyEcyDIeDg06r64fWddF8BC6EWc3B0lZzl9h68n2YMmLaZ78BRYNtqlS3mQp+HlCydxraOcyRHESGsKgUcB/aF88AFbHma7fPncC28VKIg9gUPgsGvZrTjEpeMXaFAKBAKhAKhQCgQCjSkgHMD4wAuFt4MDoBDYCi0ct7A5Zpq+m7ngn7cMtBva8Q8n/Mak/8vQLPmcZz6P8x5rvGRtBBgIttZLgTwgvqzd8L5cCk8Dq9AWHEUsM3+FEwG5WXGP74CtoNKZnuZCekHD5WOjc9CgVCgcxXQr3C8MkY5HMxNJFuLjaNh/7SjAK+O7d+C34OxW9/nZY79a8KWcCS8B8yZ1GvGZefAhXAv2I8bu01+jZ/neb9cPqyCAuY+roOtKxzTio+Mfb4TLgPrTCEtaye5kDcZhQoF+ilgw7RBvgyu7HJi52ROR/wZSElINldM+B1g8jLL6UoiB1kHpbvBzm0K3AaPgvfhXwfQabCsnRSk4HbCBlEgOY8uCDFItSHoSLaqHhjs0RG8EpZCLaYjpXPVC57H+p61qcPG8Et4L7RKFy61wnQAbKvJabSfSQGyFQfEP6FABQXs23eBs2DtCse16iPHoXPAv0ZSyvzrFlkElEudO/aFAqFAKBAKhAKhQCiQlQLOm18Ho8CF05+G4+Fjfe8NKLd63sAlm2LGP1yg6f0ZSzD+keYmbNZsnk+fbx44/zOm0sj5+HrNZhmcV3k/l4PJ1XVBfzmr+I3xlfGwD+wNQ2AhxFwOEQpgPmeTZMdBVs+81ttyQciPYcYgXzTO0QPGHd0OCwVCge5WwDHMscQfe+iLpNycY5njzKbgmFYE0xfaE8xLTAfHfMufh3ldrz8fbgZ9gNfDOHgt1GqOHY7tu8L2oC9jH+05fS728e7zunndM5cOq6CAC0B3rvB5Kz6yjTi+Wx8bXVzbtPKmTqZpF4gThwIFVyB18CbSHYCdEDuZfAyc2LogwCS8ZofvAGHjzsMsqwOQCxUWwVS4Fa6F68GJ/WKwfKuCA2BeZeXSYR2uQC/39zNwkLPt1GIOitZhHTcdOOt21mZb3QFOh0nQ6ragJieDfUkyE6f2Kc2433SNeO0MBey/rb9ngYt68jYnQr+E6VCq/rrPBT2OnzE5QoSwUCAUCAVCgVAgFCiUAsa+nCOPAn2sz8AP4ZNg8HANaPV8gUs21fTNfgt/hh4o5cOxuyrTv3MOZ3JiARiTyDvQaZmM2VwPl8PzsE4fzgWzsJU4yQjYHfaD0WDMxftXz/B7EaHFZjvdAs4B221edikXvgZM2pUz68cSsM7k3V7KlTH2hwKhQOsVcPxwzDLRvAqk/JxjyzzQL/GzIph97l7gIgAXPKUcCZu5mP2qZTCePAVugDVhDBhHq9W8P32HXUD/0PfGv1wI4LNJCwHYjDFfEQpitiGf9/vAZ5anWQ597bzbRlkNUgdT9oD4IBToMgUcSOzo7eD/AS4IMGlnEs8VPWlRgAsGPNZOxsllXp2Nkwg7GAfiHrgfrofrwMUBPWBZHbQkq4kwpwrrUgVsG1fBb2EaOOjWYmlVqwsArJu1fr+aaxmo2RPOhY2r+UITjjEIZTu0P0lmO30OmnHP6Rrx2v4KONHbDs6ECQW5nRspxyXguFjK7Bd6oVIArNT3Yl8oEAqEAqFAKBAKhALNUsA5wRvAoLCB3S/A/4VPwM5gwLhT58dLubez4HYwjmHsol4z5mBSogdcVKA/WKT5jPdmnEZ/9TIwhuNfA1gXsnq+nsfzvREOgs3AOJF6ON9rRF++HlaDAsM49gTwr3fkZfO48O/A51/OrBPWkR5wjhR1BBHCQoFQ4F8KOHb4wyHjP6uA44z9hP3KYtgVipK3s2x7gfHMmeDihbxNrdTPONS1cD049o+GehcC+H3HeccXtfcZuRBgZdAXSr5P9OeIkbP5DHw+R4HPJ09bjYufCf4gqpBWlI6kkOJEoUIBFLBDsYO3UzHB4eDS/68EmNCzgTsI+rnHOTD+F2jp9dV3zf/XsloOy7gEpsIt4GD4d5gOTkJ0LnQy7ANaXUYuGdaGCtgWFsK5MAUMsljfa3F8nPjqyFo30yIaNjM16/Tb4DwYmumZqz+Z/cJJ4Gt/iwUA/dWI7VIK6DhuD2fBxFIH5LDP8eTXYPsv195dJLcM7BPCQoFQIBQIBUKBUCAUyEMB57UGfdeC8bA3fBW+C0eBPtYa4Hy9002f0gWlW4GBUeduL0I5X46PSprxDxduLwAXAdQ6/+MrLTPvzTLeDJeC92ww38S9c8QszDpmHdoa3gEmCUwKOMc1DpOSA2yGNUEBY1jvhi9BXu3Y53wCGGur1J6MfcwG21DUC0QICwVCgf9QwDHVsdkxW//Ffs1+xZip5lhTlJi9ZZsE9oHmFowTFcHUy362F66GG+ANMA7UtFZTb/1Ix/edwXHHcT4WAiBCAe1AyuTCwDzNemaeZBZU8gtyK2NWTnBuNxAXDgVyUMDGrAPvQG0S04HGge+JfrgowM8cJBw80uTE7VaaZbUMOhSuInQCcidcB9fDveB+78dBLTkcbIaFAv+mgHV3ddgGJsGbwSDaZjAONoChYIBFZ2lgfUrJf4MjzUr+c+r/7+1wAehA52G2uT/B7WAf0d/8JY79hceEhQIDFbDd7AEm/0cN/DCn99bV0+FWcKJXymzPS8BxMOp2KYViXygQCoQCoUAoEAo0SwHnKM471oNN4RD4FnwTDoMt4A3Q6nk4l8zdVqIEzs92AIPYbj8Fzkf+FyqZfp9/OWAuGOdwLtcufp7l1C+9Bf4KLlJdE6wjapKFWZ9WhY1hP9gNrGfzQa0i4YsIGZuaj4PfwZCMz13L6c7n4Ctg4Fy//zn8bB7Ydiod1/87sR0KhALdp4DjleOxY67xVmPy9nX2GwvBcXssFMUs267gWPowONYWxdTS3EcvXAs3gP7heFDXWs17XQO2B+/Z5+NzcuxfBXxGqX9vF/+IIneU2Xb0w96Y811ZVx6C26CQ/p8FDAsFQoFsFbBdiUl/F9k4MDjovB6cePpeirIAx4Hb8jmYrQ+jYGPYqO+9E1nvJfoLRAgrq4AOT3JeBy6MMegyC6ZBL+jIGlDSWdRBS04Tmw3Z3nz7ErDO5mVTufBx4P31Nx2TR8C/wFFIh6B/YWO75Qq4cOad8DPIs/4OvHHr7DfBYHE5e44PbNsGk2PiU06l2B8KhAKhQCgQCoQCWSjgnNT5qwFY564TYRK8vW/beXZYaQX00/yhwv1wNcwAfzHf339z2znaEnA+Y7Dbff2P4W1bmXVGX/u9cCjsANafrM3Ev7+KvAzOhh5o5sJ3Tt9VZjztx/CZHO96Ntf+OiyvUAbbiu1nHvwD2rntUPywUCAUaIEC5gf8FbPxeOPzmmPXePga6OsUyezXzoD/gQVgvLNoZh7DvwK0DRwJB0MjPxTzno2LTYZrYS4s7UNfSg2KqAPF6ljzGb8LzgPbS552JRe3LC/mWYhy185bnHLliv2hQKcpYFtzQBdXnpngcQLj4OP2SuBnRWiTdqCWxUmx5RsCI2EM6HRsAGvByuCxYaFAtQroMJn8NjhiQGkm3A23w0PgRNnBstYEue3GRSvXwQjIy57nwv8N3stAM/hzP3h/6hAWCqiAfeh68HH4JtSzMpmvNcUMWH0LrLflJjLW5bngop6sFvJwqrBQIBQIBUKBUCAUCAVWKKCfr7/k3NlfYo2FbWFf2AXWgZiTIkKN5pxkBjh/uhOcm+n7LYPF4Oed5ttZl6wvh8Dh8EawXmVt6mZC5Co4HZwbqm2tc1y+EtangPGy/eB8SMmxvo9a9uJ8/rtwB5SbG1kYE0GPwrMQzxwRwkKBUGBQBRyfjAUZbx/et83LihzBFrx+G4a4o2D2V8pzLPTAS1BE00c0h7EZHAXvBH+c2YjZv/8droSZYCx7KTwFjg/6AcbKwpqvwJZc4i5YpfmXqngF46LOS/SjC/fs7WDCQoFQoPUK2PZMsjuRsZMyme4AlBYG+L5I7dOyOGhaLifJLgwwaTURNoZR4GTa/d5TkcpOccIKroCDo0Em/0rANTAZTDr6CxV/dTKY2W7OgXdAXnXPyf0v4DIoZY+xU8fQwEFYKKAC9qdO7pwwfRzsY4titskL4ExwYUs589f/j4BBzcI5ueUKHftDgVAgFAgFQoFQoNAKOE/WTzJgOwy2gj1gdxgNRVowSXFabiYYbwJ1cR7eyPzHQPV8uB7OggfAAHYn+3XqZezFhPKHYFdYHbI2NVwOavsnuBH0nU0MhFWvgM9rApwNb6z+a5kfeTFn/ANUmhsZ07A9mQCwbYWFAqFAKFCtAvZ1LnAaA/7VGmPrmvtNLH4DjMcXzW6lQJ+DaeBfhSyqqaNj/ybg2O9fBDKP4f56zTjY3WAc2LjYYnAhwBNgLNvxvpP9KW4vd1uPEtwFtps8zbq/I0yHf+ZZkFLXbqSSlzpf7AsFQoH6FDDxI68BB/xV+3Bw8r1BDj8rmlmmtWEkGKAxEWuZHURNbA3t23ZhgPdgnxP9DiKEVVTAwXIJXA1OtG8DHahyg+j+fHYurAF52UVc+DdQroy9fDYPIuCDCGEr+nUXUP0QDiigHlMp03fByUs5sy5bpxdCBLjKqRT7Q4FQIBQIBUKBUKCSAs4NnQevAs4ZTfhvBLvBJNBfcm4cc8hX5xHXosWlMAecc38ZtoJGF0Wk+ZcLsc+Ce8GFAOXmNnzU9madMvG/JxwBk8DYRjPMXwveCeeDv5h0bmtyIGxwBYyHfQx+AXn1A/O59jFgjKKcOR9ywcdciEX/5VSK/aFAKFBJAfs4faEJYHxT/0jTR3orfBbSwgA2C2OzKcmn4DZwoVuRLWm8IYU8DA4H8xeNjC+O5y6AcHzXfzJGZiztMdCP8vOIBSNCE8w5wrlwUBPOXcspfc4Hw9+gcM/a5F1YKBAK5K+AK8LsLOwkXgJXFTtJdGLogOG2q4mdSOgAODAl2MzNLLflfQYsm6vfHocFMAMegLvhnr5t9zl5Wgbek99JHWP/+2lk4OWUYW2ugM/fYMy28F54P4yHF+BJsN70ty/xZmdIznH/z1qx7WrDX4NttJTZtq335T4v9Z3Y17kK6KBuByfDJCiaOWH7EfQMUjDb4yKw3w8LBUKBUCAUCAVCgVCgGgX08w1e6w/5CzcDsJPgKPg6mGQ7HPTt1wcT290+N3TObVLxp+DiaOfSJhv12a4B50BqZdKgXq3S/GsrzmEQVf3V3oSn833nM51o3psxiivAxMXKMAysn/VqyVf/w1Zhz3jYtw/rfpofdqq23GLD5jPYAk6D1zd8tvpOYFuz7U2v8HXbqHOjOWCdCgsFQoFQoF4F7HPEmKj+kv2gcXPHY/dvCVmOT5yuYXNB4n4wC+bBwJgtuwpllm8x3AKXgePxOPCHjfVo+xq+px/2ZtB/eh14nuRLeD19NccKCctOAX0on91bsjtlXWfyeZsbuBMK59fVU6nrUiG+FAqEAg0pYFt1sHBQcVIqTvD9xb0DiqTP2Wy5eW0DBJbHcjk501Fxvwy0dD8eIw6O3oPfd5WjrN2HA7COj8d4/6XOx+6wLlBAp3c2XAgXwUxwon0H+Kd28jDL8H2YX+Hij/PZNNBZD+tuBezHdoVTYFwBpdBR/Q1cCbatcmZb7IVFEPW6nEqxPxQIBUKBUCAUCAWcuzmH0wdaE0yubgxvAgOlE8F5oPPDsP9U4DF2/RkMUpv4LxdUHM9nR4KLTJ1TZ2EmMqeCv2g7F5zvvAidbNbFbeBoeCsMh2bUTf1nfWl97rPgIVDvcs+Xj7rSXCjxezgkx7u/mms7P3qmQhle5rMZ8BSkH7hUODw+CgVCgVCgogLGyUfASFil35HrsX0k7AfNGJs4bUOmj/A5MGZrn9kuY5oaq/U+8EnYHHwG9ZpJ/uVwLUyBR2ExLIXnIWJoiJCR2Q7eDpdC3m3COO8XwVhqoRZ65C0MeoSFAqFAHQrYdsWAiol3BysxUW7y3YmriwTSMWy2zFJ5LEcqi/sGS9zbOTpZciB0AvVKH7ysuEfvycUABo0cmMeCE3L3GVBycPZ+w7pDAeuHk2wH+Q+DdaHVNo8L/gAsRzmzLs8Gg3ft4vyWu5fYX78C9k32U5PgjzAUimjXUygDXNbXSuZf4nAS4+SlUI5tpULHZ6FAKBAKhAKhQCjQVAX0d9L81CT0OjAatoA3gQt2DWineSqbYWUUMHho4v1ycDGxc4pqfK7dOO5Q2Bicg2dhztFN/l8FZ8DDYPk6eW5jbEUNjwYDy2NgsHgGh9RsPlOTBDeACwFuhudAzbvdrL/vhj+CfUYe5pznazALyrU/24FxgUVgjKLccXwUFgqEAqFAVQroT9nv6UMNhf7juTHxz8CuUERz/PoRnAwmvO0X28XUWb3V9mh4MxjHq9ccD9TgFLge/gELYAm0ky4Ut9DmPOM+aGTRRhY36DN+D+g7FMpHtkMJCwVCgfZXwLaceA3bTljFBLyLAhywTKD7mdbstu/5nSCL217XjljSfjZXdIh2iib9dRJ8dYB0n6/awNd0bgdm78l7XBsMMK0LOkO+yppg8Mn793jLkcrEZlgoULcCvXzzJ/BIhTNYd3X2PLbTfy1TQYau/8g+azV4K/wB/AsnRbSZFOr7MH+QwhmAngMGKyM4OYhY8XEoEAqEAqFAKNChCujfiPM751rOOZ2HjYXt4E2wGThH85iw6hRwPnwdXA6z4CWo1XweB8K+MA5SDIDNhsy5zWNwA5wJt8Az0Mn+oAmYUfBxeAdMhKz05FT/Zmp5O1wIl8IT0K0JAvuWEaAWO0MeZn3/JVwBtstyZqB/NrgoJsWuyh0b+0OBUCAUqFYB+0Hj3Y7jxrz7jz3+AOqrsC0U0ewLL4HvwVSox5fha7mZWvtjw+3gg3BA33ufST1m7Oz/glo4tj8KT0FYNgq4aEOfOe9Yq77A/mC8tFC+cb0Vl/sICwVCgYIrYPtOyfaULDcYsFofTmalFf1AuoavaZvNFZYmSQNf0+e1vqZrOGB732khhAsBDEy5KMCBfB0YAi4UcLDQodK56u9U8TYsFPgPBaazx2CAr5XsBT7UsSvc6r9KhY7PMlXA/si+5WD4NdgHF9GckHwLKv01C8ttP70MrNcuBAgLBUKBUCAUCAVCge5QwHml8yTnj86ZnEcZgN4YdoSdYBz42cD5HrvCBlHAQOFUOKPv1WC5i+IbMZ+RCwF2g7HgM8zKnuVEd8D5cBk8Dp2crHYRiwtcjoRDYAuwLTTD/sFJrQvq+ifoBf3uFC9hs+NNvT8GJ0Fe/ck9XPt/wLpezmynM8FEjm04LBQIBUKBLBVw3DaGPR6Maadx3H5xFBwLm0FRzbHsi3AztOOPotTZHMpGYEzvfeCzSM+BzapMf+4vcAboL82HBRCWjQK2jftgw2xOV/dZjP3vCw+CPnFh/DYrclgoEAp0hwK2dzFw44RqZXAgM0jjq4kp9/t5t5h6OHC7SMABw0SdTpSDhk7UaFCbbtKE2w0ro4BO203wR+iFSmYAYBF4XKVfDFQ6R3zW3grYvxgYPxx+AC5IKqI9R6F+CHfBYIHbFzjmUYhFLYgQFgqEAqFAKBAKdKAC+i/i/CctpHaONBQmwrZgwt/5kouqY56ECA2Y8wsDhRfDI5CVj2XC2HMthJHwKTgAxoDPNyszWf0wXALngtczKdqpZn0fAofCe8C24F8lbIY5n5wPV8Ef4SFQb+tMJ5v1cxu4ApxL5WEmqkys2SbL6W1gfx6YxBlsDsUhYaFAKBAK1KWAMev1wfG7/w9K3D8Wvgb6ZEU1fZHPgovajD2V61P5qNCmTzwCJsFR4PjvvmrM8cJFZcYF/SuxjhvzICwbBfTN/gb7ZnO6us+i/3sI3Aj6ET73QliWjn8hbigKEQqEAlUrYPsXnYbXwsrg5HX1vlcXBrg/HcNm15iaOJD752Oc4OtobQIbgQO+iwUi2IUIXWQmPk+DG8AVm5XMQV7Hdhr4vbDuU8A+ZCx8AT4Nvi+iGUQ8EW6EweqqgS0nK73gopbCOLOUJSwUCAVCgVAgFAgFalcgzfOc8zn3cf7nX0kz0DwBtoLtwcCyyX4Xi4dlo4AB8FnwJ7gXDBSa8G3U9M+ehiXwBOizeS2f9ebweXgbuCjAfVmZ15kDJm2dM7lgVD+zU/1FfXtjBQfC++FNYBylGaaGy+EmsL7cAM9Cpy4yd8HR98A5VF72Vy58CjxfoQDGBKzzzqE6tZ5XuP34KBQIBVqkgGO1/tcG4KKolSGZY5E+2jEwPu0s4Kvj1Y/gVFgEr0C7mrkAx6nt4HB4O5g3qORTOUbcAceD9+/Y4bgelo0Cav8ryNNv8E7+CdaJa+Gpvve85G+VKmf+pYsShAKhQKsV0HlIGAQSFwX4K/i0rePhMd3Wf3jfOlpO9NcFHa+JYHBMR8ygmJ93my7cckebjtoM+AX0gCv6BjOd2emQ1S94BrtefF4sBZwQbAxOcPylVVH7BOvyyaBzWim4xccrgloGt6ZCBLlUJCwUCAVCgVAgFGgvBZy/6aM4X0mLvtdj28XNG8EWsBWMAROZHhuWvQIm+WfD+fAQmKTPKvGvb7cMXLBp8t1A5MDEpPVga/gC7Asu9sjSV/WaC2EynA73gosb3N+JpnZvgN3gCNgLjBVkqSmn+5e5yPweuBAuAZ/3y9ApZv3cGa4Bdc3DHuOix8JcGNh+UnmcD/WAiwCyaL+cJiwUCAVCgbIKOKYYkx8NQ8H4dDL7TeNPXwbj00U1+9Or4Xugb6Cf0s7mM3k9jAMXVh4G+tL62QPNcfo0cHGZPuA86KSxm9vJ3T5DCVwEkKdZxz8Bl4H+WWH8g2Y5pdxjWCgQCrS5Aql/8FWHwiCQvw5JQSMHOoNHovPhZ91m6uJ9vw5cAagjNgp0vnQCfK9Oapf0ZDOsTRQwWOXAfQEYnKsmcOWA3wMGvv4XwrpLAfsCnf7fgSuCi2pOtn4NJv8NFg9mLmpxomJArDBO7GCFjs9DgVAgFAgFQoEuVMA5h3MzcZ7mXMR5iole5yeb97Ehr85VDCg7pwlrrgL6T3fBVfAIpIXC5RKMHFK1OefwfEvgafBag53X+qGv+nnYB7JOWnt9k6M3wZlwIzwLnexH2t62hKNgfxgJzWpbJg6mw+VwNvSAPn0181UOK6wNoWTWF/XLw6y3PwXbabm6antbBnPBOVJYKBAKhAKtUED/Tp9uPPgDNOPM/W1T3nwR9O+KbAso3FfhCnBR22D+CocU3nwW/gWtHcCFAC6wTH6VY7M+0A+hF5aDseZOuG9uozB2ACUxfp+3fYMCmEPQRyhMTsDOIywUCAVCgWoVSH2Gr+KE1oHOwJGJLxcHrN733n2vBY9J32OzK8yAivf/BnASa8BtGIwFAwHrwWqgXuqnRmHFVECH1CDd3/teF/OqA1fKWXPfUnCgNyhT6hh2h3WoAk7CdoZTYIMC36OTjV/BZKgmaGUg0YByL0S9RoSwUCAUCAVCgVCgAAqkuZjzLecUzi2ch5nU1w/ZCLaCTcD5h/MS5yhhrVVAv+sBMBg4A3yfVZLW83i+hfAU/ANqPbd1Z0f4DLwFUsCazczsWc50O5wLl4NlrcYH5bC2NDUdBx+BA2EiNKvtmaT2+evXnwX3wAtQLnnNR4U14yLvhjNBDfOw+7jod+CZMhd3fv88TAN1jvk+IoSFAqFAyxTQ9zPuNB70+3zf3zbmjYsAfC2yGVf6PvwBlkMn+QTmRkbDm2F78P4uA31Bx+YYNxChCeai1rthYJtowqUqnvKXfHoa6PMXJn6atygVFYsPQ4FQoG0UsC9J/YmvTt6ctDnRdYWiq+EdBN1On7HZdYlvE/1qojYG6NaAtcEFAQZbXDEo7jdIJ+q2Cvg9v590ZjOsxQo4ePfCbXAHzIfnQAdO/CsBcyCCAYjQZWb7NcCns2fbLqoZgD0JbgDrczVmHZ8O1utag8rVnD+OCQVCgVAgFAgFQoHyCuj7pzmE8wLnVQZ/nUOY6J8Im8OmYMDReYSLAmLOgAg52tNc+3K4FUzQ6oNl6Ue5IPkxWAIuAmg0qGzdciHAp+AtoG+bdR2ynPfDxfBnsOydFPTndv7NnL8PhcPhXbA12H6bYc5Fn4Rb4E8wBZyb/i+0g1nXXKh0NuyRU4GdGx0Hd0G5tqqes8C2146LLCh2WCgQCrS5AsbUh8A4GDim2JdOgM/DFlBkc9y6Dr4L94KLq8JCgXoVGMMXHwXbR552Lhf/FTwE1mnree6WtUOf+w1FAUKBUKAwCqT+xdf+GJBKGGgwuS0mxcXOWvxO3h03RcjNvH+DBmrSP9BnEGF0Hwb9dPzUz8BgWOsUcMLvxF9H1UDLfXA3PA4RDECELjHb6fpwNHwL7NuKakspmAsUrLMGjasx67LJfwOIUa+rUSyOCQVCgVAgFAgFalcgzZWc++j/O0dy4fRqYCJWX2MCbAqbgUEuFwA4T/C7YcVQwKThXLga7oBFoP+UZfDP8/kL+sXgIgMTklme33mnv6JyIcDe4EL1rOuYSX9/GXUZ/BF6weRrlvfB6QpjtmsXCB8ALgbYFdaAZpkBZ+emF8NFYF1R3yKbGr0b/gR5xTWu5drOlcoloayfLlqxvjqX6tT6yq2FhQKhQMEVMO7k+DwGjAf3N/vQkfBZ2LH/BwXdXk65vg3nwZMQfSsihNWsgPOlheDcKE+bwsWtz4+Afnq5BYV81DrL2pFvXcnjSqFAKNCuCtjvpL6n/6vbOiriBFB0asRAhJ24rykpno5jV1da0mdN7j4tCtiU7Q3BIKF/PcBjksZshjVJAR3U5+ABmAw3wSxwgYDBlkIM+JQjLFsFbF/D4PPwJfB9UW02Bfs5+FptANB6PR8WwCsQEzFECAsFQoFQIBQIBepUQJ9c9Bec3xiwNdGvz24y0L8CNgLGQvLpN2DbX/s7DwqfHhEKav6y/XZwHmBi+1nI2ndyPmFicikYLPf8zZxjWDfTQoB92DbRkHUddDFDD1wFp8E0MLHazPvi9LmZ+rmwZ2f4IKircwnjGs0wfX7r49/gHHB++g8omqnLeLgSNsypcM7lnc+pUTmzXTuX8rVT62i5e4/9oUAoUDwF9CONBY+CgUlP+1XH7U/CHpD1+M0pMzX9gdPhh7AIijhWUaywAiugf6WP7OLpPM0fXH0R9CeWgXU7dyt6B5C7QFGAUCAUaKkCqU9Kr148bfsqBs1cBJCCZjo6dvCv7dvnBNrtbjTvXV0MJA6BkTAGJoBO4dqwKqhh0pXNsIwVcID3lzkPw41wB0wHg3UvQCEcAMoRVr8C9kG2r6+Cv5AqansyaW/9OwkWQ7XBKr/nr/4fBSdf1X6PQ8NCgVAgFAgFQoGuVEA/XH/AeYh+ggnUhL65gSl9cQOyo2EimPQyya/fHj46IrSR6Rv1wPVg8j8FrLP2mfTJTIo7j5DnwWu4vxVmHd4aPgH7wnDI2u/1fpbAdXAG6Ls6Z8paS05ZCFM/YxjO0T8EB4KJ72bFMJx7LoBr4FTwv2F4BVpVh7hURbN/dE51HGRdtypeuN+HZ7N9DpRLOv0vn/WC8ym3w0KBUCAUKIICjtH6kf4IrNQY4uLSD8P+oJ9adJtDAb8BU0CfpyjjFEUJK7gC1n/HaOdUeZp1+CMwF/S9ChH/z8u54v7DQoFQIBSoS4HUb/V/dVtMbIsTanEA8HWlfq9+7nuPT4G69F125Tbp9NrNsKTNKpzcybWBx2HgL4xGwRhw1ah/llCtkiZshmWkwD85j0GsR+GWPh7gdSH4a4MIIiBCG5n9h8n/r8PRYBsrolnvroCzwMlTLWZweSZYPwvhsNZS+Dg2FAgFQoFQIBTIUIHkSzv+O48w2KpfLSb106sJfP8yl0FYk6T62Pra+tzrgn64c5PwtRGhjc0E4d3wN5gFz0Czkqme10XFJsf1yXyvf5eHWc83g0/AfmDCIWsf2ED/k3Az6L9OAfXtZF/UfmU9OBTeDduCfUUzTH2dfzo/+AnMA+eh7s/LrEPWK3/9b3+Zhy3josdCL5TT4nE+mw0vQlgoEAqEAkVSQP9zDOhr6qcOHJuNAR8Ch4FjTtFNX+cccJyaBXmPUxQhrA0UsN47juflSySJFrNxOOhv9YD1OXcb2CnkXqAoQCgQCoQCDSiQ+rRSr+6TFHTrv+0+HSVfTYLrQIkBvrSAwM860XQADegYaNBhdHGAGNQZCa6ec3GAWiSNkr7sCqtDAQML/pKnB24Gg1v+mSAdBIMKeQX2uHTYIArYDxjI/yZ8DIraFl6mbKfBNWAgtVqzbuqgPgoGupxshYUCoUAoEAqEAp2qgOO4/q3+sD6/PrE+b//Evu/1hfWJXTSrfyz6A7436e/xfl8/oVPnDNxaGAroY00Fg9MPgT591mbC+1lYDE+D19RHk7zNer4hfBLeDqOhGXXexP/tcAFcCv5lqk72S9XQfmZ3MEmzF7gwoBnauojFecJZMAvSIhY2W272vV+D70Ie8yrb1C/gb1CufqmXcyProG0zLBQIBUKBIilg36n/Oh7WgVJJ/rXY7/jyCTDW3Q42k0K6OEtfwIVa/4SwUKCSAtP4cJNKB7TgM+OvLuhcCrMh+fBs5md5OFj53W1cORQIBbpdgYF9Xrn37hcdJzGop5Pkq4ny1/Yx8Pvs7igz4OD9vw7WAAOcKfjpq86lq0nVJGnksQaG1MiAaqdrxC02bAYeTPxPB4MPU+Bh0HEoF4jgo7AcFBjONZ2EfA6KWrcNGBvIuhOeg1rM+jYPDDi7XYRAM8UICwVCgVAgFAgFalLAMVr0RfVLRR9Vf94gqb6r70246cuayE+JfV/XB4OlHud39YfTOdkMCwVWLJS8HR1cCPAYvJKBJvpezgmWwzJIQcMi+mPO9cbB0fAOmAC2t6xNPe6Hi+HPsAQ62Ue1n7FvmgguBFDbDcF+KEtzYclXQW2fgIVgfWtlgsV73Romg7GFPMzE/lfgqTIXN+FvEL8XUnssc2jsDgVCgVAgNwWM3TpOOBbr1+q3DjRjttvBl8Bj2sEc78+HX8G9oK9VRJ+IYoUVQAH98jfmXI7nuf7BoG81DVxEmHud1eEKCwVCgVAgFCivgP1k6it1qpJjpXNlAFEnym1fDYSkybnHdZupk46mGqRfSg1j26DF5jAGVoOkJ5thJRQw0LAIrobL4A4wsBi/OECEnMw6uz58uY+i1uFllO8n8BC8BLWYkynrnQHAmFjVolwcGwqEAqFAKNBKBRyDRZ9T31t/3ISZmLD3VX8z+aIj2B4Jo8CFfP6q1kWtHuc59NmLOq5TtLAWKmCArpa6YLK0B1zAK/pPBqtrNa9rwNDEvwHDF8Bz5x4wpAyDmW3INvZRMOC5Kbgva1PbGXApnAbzodP9Vfu3IbAfHAo7g31XLXWUw0ua2n0XbgET27PBeUQr69xaXO978BnIw2xjP4Trodw827Y4E54Bjw8LBUKBUKCoCujPrgrjwbGi1Fjsvk3ABWCjoV3M8cnx4iIwNuoYFhYKDFTgSna8beDOFr83DnsQ6DdMhWchd/8hC8eR+wgLBUKBUKCrFEh9p6/9cZKeApEuCDAgaQDSfd28MIDbX6GBWuiM7gq7wFDQSQ0rr4BBGJ3da+FCuBMMDtYTXORrYXUoYBs3sPkZOAaKWmfnUrYTwL8kUauDaeDPerYAdFhbGfzjcmGhQCgQCoQCocAKBRxzXwMGKPWd9aUNZia/2gS/79cAE/nDwcT+Bn3b6/Jq4t/veY7kp7MZFgqUVCD52nfx6VIwaGc9qsX8dc+DcB64CLPawLT+mt/Vt3+sb1sfvx39MNutc7sPwzthS0jzXzYzMxO1+rwGeU8D/d5O912de9gHbgHvhbfDWDDGUK/5V8J+CreC+i2COVAuEc5HmZr3tC1MAfvzPMy6821wDlTKbIc9sBBi7o0IYaFAKFB4BfR7V4Mx4CIrfeGB5jET4eOw/cAPC/zePvkGOB5uAROrYaFAfwXO4s0H+u/IYVt/QT84/QWAp9jW38/VbPRhoUAoEAqEAtkokPpUX/vj5DwFMg1aGgzx1f0GKJ0Ad1OQ0vs1ODseJsGbwT9BlfRjM6yEAjoNBigMeJ0LD4BORQQkEKFJZl01sfAF+Bz4vohmXfgN+AuVWs3kv0Hn+WAQ2olVWCgQCoQCRVMg+VWpXPZV0V8lNYr/6vPrn9hPvrD+sL/EN7nlq/6hAUsTiSPBBXgb9L13vwsA9J89V1HHZIoWVnAF9KlNeN4Dt0EvPA36QXvAp8AFJrWY/dFSMCh9EehblVsIYJLVpOuTsAReBPd1Qp9muxwC74P3wA5gO8/afIaLwUXSp4PPUh1zD7JShmaZ/aj9n/3jvvBu2BnsG/2sFrPeHQsLwbmAi1BmQasWANjXnwhHQh5mPfkm3AHl6oztU02sV53QNrmNsFAgFOgCBRwP9JdHwzpQbhHASD4zUenCx3byqV+gvL+E34LxUf2psFBABX4KX8pZCv2o94Jzgtmgf9Uq34pLlbZancTSZ4m9oUAoEAqEApUUSH2trwmPd9sAZnpNAU0dtIH0D5rqnPne4/1uO1sKEm3ETUyCncCAQLvfF7fQVDNQsQAuhT/DDDBIkbtjQRk6wax/tsEN4TtggK2IddJg1I1wBvRArebiEQOABv8i+V+renF8KBAKNFMB+9yEQaztwESHiQ/7rh64GaZDGvvsEyNIjwgtNJ+Rvpw+qawCJvteByb1xX3+wlP/bn0YDib2xfcmr/y1ksfp33o+zxsWCmSpgEnOuXAn3Af60f56zMDxwH5jS/a58HMC1FoX7Y9mwsXwd3gF7LOSPc+GiwP02/0Vtj59uQQkH7Wt2Y5t94fAYbAL2M6zNp+det4AZ8FNoK5pXGCzI019HRudQx8MB8DGUM1iC9vCJXAePA5qZdtwTtAK3WxTk8B5bDPqBKcd1G7liO+B859SZrt9FAoRuC9VwNgXCoQCoUAFBexnHSPGgn62sa1S5md7wsdA372dbCqF/Tboa7kQoBN9KW4rrAYFjuXYH9VwfDMO1S/9EMwDFwC4EKAVvhWXKW92CGGhQCgQCoQC+SpgX5z642pePcZJv06cTpoYYE0BVwOw/c/J27Yw78dfjGwGbwaD/WtC0oTNsBIK6Ez0wEVgIEVH+BnI3cmgDO1o1jcnQta/X8AWUERzgmNw+QLQqazVDEYvgCVQKvhd6/ni+FAgFAgFslJAH8dfpXwYjoBRoG8z0JxgvwgPwklwOTj+uT+sMQWSH6lvNjCxb0Ax+Z4mb0zymcgfBj438f3a4LEm9j1POiebYaFA0xWwHzDB/zDcASbl9XnsM0yADtZPuNjoy6A/aP2t1eyL7oXzYRq8AAaol4PX10/vhmC17d7FP/uAAdHdQT+7GfYUJ70NzoO/wRPQ6fMh9bWPtr/dHlxwsRdsACvDQLPu3QMulrBeWgdtJz3gopRWmPP7H8PHW3GxEtdQg2+A7bNUG7RveAwM3DtHCgsFQoFQoB0VcHxYFcaD4+5rwH0DTR9nB/gS1PrXjwaeq9XvjWn9FY6H++EVGMy/45CwDlXgw9zXaTnfm/VP/2YmzIP5YD3N1Uo1/FwLFBcPBUKBUCAUGFSB1Hf3f3XbgLkTfYMAOnry+r5X99cTvOJruZj3YLBoI/AXIwY0DCzrtIaVV0DHYi5cCFfBQ2AAstODX9xiJmYb2gAOhW+DiY0imoErHdsr4Lk6Cuj3dUSXgnWmM/GzLQAAQABJREFUVPCL3WGhQCgQCrRUAX2ZdeBj8GUYAtWa/dgsMKjv+GeyLQJAiDDAkr+oT6ivZSJ/FUg+o9sm7X1vYt9AoMl8x8aRoC/mc3F89FjP49iZfFI2w0KB3BTQv1kAJjhN7i0Ek8Am8fSFa+0TrN+fhH2hHp/Q682BS+A3kBYg1FoOvtr2Zh/hvHRXOBL2hnWhGX2H/f994HzofFgG9Tx/vtZWppb26UNhZzgYnEfbjzuHNsF/M/wFHod/gG1jUR+tmA9Yxp3gBrCseditXPR4eLrMxdXEwL16tUKTMsWI3aFAKBAKNKxAihGP5Uz67/r+pcy+eVP4KGxb6oCC73uM8n0fjI/1wisQ1n0KHMAtX1aA2/4CZXgA5vVhzDVXs4GHhQKhQCgQCnSWAvbt/XHCvzKkQK+TbQO7Bm7d7+flHEE+yt0MvhmMNvCsM+rq1IngvhjHEKGMGeiaDQa/roaHwEBHNwYdue1BzXo2AZw4vBOcLBXRXNBhEHkK1Dqx8dkbFJ0PBv50RKM+IEJYKBAK5K6A4/lG8FPYD+rtg00AngQnwGLoFlM/xzF9Ov08fTxf9ZWS32fyze21wCCgif0RMLxv28UXq4HH6Bf6DMLPQoSwwiqgH/MYPNiHSTv9m+fBviArH2cfzvUBGA312It86e9gv+Sr/lutPhxf6RhzHroVfBzeCvZBzehrTOQ+DJfCWbAQ1D2resGpCmv23/b5ajsOTGSne/cztXkOFoFzi1bVx3W51h/gHZCH2S8cCwbmS9UD96nJPMiyD+F0YaFAKBAK5KKAfb5+/RiwDzYGXMoch0eA/fO7wDlFO5n9913wY7gW9AVjERcidJHtyr3eDM3wKWuR8dscfBMYi5gDrfKxuFRpy1uQ0qWKvaFAKBAKhAJZK5D6e1/7k5xBnUBJCwN81UkUA8rp+2zmbpbTPx04HraDbcCAnMHuIpWT4hTGDJDOgAvBVbHTQIdYJzns1YTHpghxMlinimoGLk3+O7Gp1Yn0WRvg64VnIZL/iBAWCoQChVDAsXscnA67Z1Ai+7s/wTHgxLudxzq10VfTF9PPEZP6KZlvwl6fbXVYB4aCwTsxwW+gT5/J4/v7dJ43LBRoNwVc3GqCfybcAwbVloH+zcugb9Msm8iJPwlbQz1BcfuheaAf/iuYDc0sL6cvvNmfbQhHw9thLNSjLV+raOqs3n8Dx5lZYH1p57GB4ldtauoY4tzBV+fSvrowRW1sV60wx7K3wkXguJWHXc1FT4QXylzc/fYv9in/LHNM7A4FQoFQoN0U0O93zB0JzhXcLmUe55xhF/gcOL9oN3OB2+/g16Df5V+7CesOBbbgNh+EvOe5P6UMl8NS0P/U18rV58xbEO4/LBQIBUKBUCBnBRwL0niQttOrAYMUMNZJNGCQggbpNQUV/I4T+2YEbjhtSfN6lsvgtoE5g3IuCNgA3J/ui82wPgUMePmrh/PhMtApNgDUrbY2N74rmPx3QlRU85lZxuRA1lJOnc0nwGftwg8d0LBQIBQIBYqiwBoU5LdweIYFst+zzzwOHsvwvFmfKvlN+lT+8l4MtpnkF3+tLyPAMSqxHtv6PiZR9NM8j4SFAp2kwCvcjP7LI33M4nU5PAcGdE3Qif5+KwJrBsVNVr8F6g2Ke0/3wO/hL2DC0X3dbPZhzt2OgoNgE3Bf1mZdWQAmgf0V+kOQ6hGbXWVpjtyKdpOEdcGayfcPpx0tfnW+ewxMBevCQFML60cvxFxpoDrxPhQIBTpBAWOkLgAYCc45yplzii3hU7BRuYMKvv9RyvdduAIeL3hZo3jZKDCO0xgvzXtOfCZlOB2cs1geY/Ct9Le43L9bcvr+fW+8CwVCgVAgFAgFXlUgjRO+JvwkbQ98daB1AYCvadsAjo6mQer0mo5hV6bmNb3GOjAeXBCwHejgGlwP+3cFDDreDOeBwbBl0C0BD+vuEDgYfg4GpYpoOoqT4TTw+ZQKWLG7rPmrniWwCAxytupXPlwqLBQIBUKBQRVw3H4vnAFZJ3zsP78Gv4bnIQ9zrPEe+yf4XfDgmGMC31/orw+jYUzf6zBe/Uy/yV9p+n3PExYKdLICtlcDZIthBrjwsRdcwPMsmCQv5aPaNkzM+3392lbYXlzkMJgA9bZN7+ta+AV4r95bt/to9nfrwYfgXbAVOK/L2qwrS2EKnAW3gHWn2/VHgqaZ7WQ3cL5ZKenUtAJwYn+Npz/gfKiU2c/Y9+gvWEfCQoFQIBToRAWckxgvde7hXKOcH+P+UXBQH8Zw2830rRznfwTzwf4/+ndE6FBzcYsL+bKOKdQql/7GT8FFzLPBBYi51rtyjZxyhYUCoUAoEAqEAlUr0H88Sdvp1ZO4LQZ2dDj9VdvqYAA8BbjZzNwMmhs4Mpi+EewEBpP81Xc7OrAUuymmM+Kq2CvgArgddFZydVK4frPMemGdOAqOg7wdRIpQ0v7J3ovgbHim5BGVdxpI1wF24YDbni8sFAgFQoEiKTCcwlwMb2xSoV7ivC4wuArcbobp3+hTpCS/vo34y30T/I43Y2F836v37GfJ//H7YaFAtymgT/Ic6Kc8BDPB4Kz+p4Ey26vHVPJFbVubwoagn9QD94AB30rf4+O6zEUIlvkpMFH9bdgH6k1Se38Pw7lwBvhLIcve7aaf7lzNRQCHws7g3DFrs474LP8O58DkvvexEAAhMjaD8qfCgRmft9rT2ad8CaaX+YLtbh4sBtt5WCgQCoQCnayA8S/nIqPBRZTlYqPOUVaH7eFz4Hfa0Xoo9DfgelgK+l9hnaeAddXn6xw7TzOe/t/wNDi/eQGaMS/htNVZBBuq0ymOCgVCgVAgFMhWAQM7Opn+AsCAjr90WwMcqP2sWea5vd4Y2BbeBOPBoH3YqwroDM+Dv4BJGYOyBlVzdVi4fhZmfVsfXAjyadgdiuoL+Rz+BOeBweZaTSfToLq/MDOQ1QnPj9sICwVCgQ5T4G3cj2ON/XOzzEVQB8B9UG9yzbFCv2Ul0FcxWKbfYpLKROBoGAcT+15Ndvi5/oXfLepYQ9HCQoGWKKBf8ySY8JbZsBz0cfRZTLp6TLW2MQceCduAgWxNn8dE7q1gws/Fj/W2eb66wvShPI+J4ifA87rP8tq+vw5HgH1AvaYGN8FP4C5w8UOj5eYUbW/2my6m2hc+AJOgWckHn8EdcAFcAj5rn3NY4wo4dr4F/grNHOsrlfRvfPhb8DmXsmfZORP8POZMpRSKfaFAKNBpCtg3O1fxV/6+OscpZ/+HDzaCT8BW0I7zGsd0F1weDy4G0+eM/h4ROshW4V5cyOf8PE+zfn0KjKPPAn2LWuY4HJ6ttWODzVaBOFsoEAqEAqFA3groTKaA+hvYNrCjA2pQzc+aZZ7ba/hLvC1g175Xg/oxPiICZvBxDlwOBm2mggHQdgpK+iwNDPuc94PPwCbghKeopnP4J6g3+a+DOQ8MtEfwEhHCQoFQoLAKfIeSHQfNHnfv5RqHwaNg4q6c6Rs4Zugf6JP4SwL9kmEwAibAhjAe3NcKf4XLhIUCHaGA/s39cBE8Aibd3FePncCXtoaBAeun2fcH0GddCi4uqPUa9hH6uvpRBu8s5z/A/QaLBwaMJ7Hve7AL1Otfek6DhJfC72EutJO/TXGbZo4Pzs92gqNgHxgKzRg3fM4PwCVgomARhC+NCA2YizhOhfc2cI5Gvuoz/RLMgFJ9gftsbz7rSv4BH4eFAqFAKNBRCqTxdQPual1wDlTOPNa5z57wQchrQReXbsim8e3Pwd2gj1dqXGB3WBsq4Dx+HozMuewLuP4RkBYXNjLfyeRWmuEwZ1KwOEkoEAqEAqFA1yngmCQG3VeFtBjAoIGOaL0BNb5alXkNVwpuBgbwtoUhoBMR9mpAxMDIFDA4aXDM9wZVDFoW0QwK6/wdAJ+ETaHoz1MtL4Rz4Gmo1fxOLziZicBxrerF8aFAKNBqBf7CBQ9p0UUv4zqfBSfl+hQGrvQ39DNM8jvmm+QfCxP7MCC2NviLgmb7IVwiLBToCgX0T/4OF8OjYJK+Fl/ybRz/RXDOUMr0g86C2eAvgUzgDnZ+E38eZ5BOH+opeAkMDPvZYN+3r/g2vAvsM+o1r3kbnAiTQW28ftirCthv688fBW+H0dCMvtm64C/CHTf+CD3wMgxWDzgkrJ8Czu0ngf/NnONtHmY/czL4/EqZbd3FAUWe05Yqd+wLBUKBUCALBeynHVudA/lXzZzzuK+Uud8FeRPAJLqv7Wj6Vt+H86AXws9ChA4w6+eDsEXO9+ICYhc9OqfQl9TPyHWhSbkGTbnCQoFQIBQIBUKB3BRwfDJRa2BPZ1Qn0+C8rzqkK0EzzfObEBgP28EOMBYsT4ydrwa/dGbug6vAIG5ybKoJsnJ40826si18A/YGF3gU3QwqXg2nw7IaC+ukRceyF56HmMQgQlgoEAoUXoFbKeEuLSzllVzLftZf7k8Ex/lRsA6YnDCRFOM8IoSFAi1Q4EWu4SKga2AJvAyDmcn1Y+CNUK6t6k9NBQO708FAnH7RwOStCxG8poHgp/vQjxUDdbUG6yzPEfB52BrKlY+PBrUFHKGPfRJMg2q04bCuMedk9t0fhYNgQ1gJsjbrTQ84bpwG1quXoNa6wVe60hxbT4T353T3tu0vgIuBBrZ/i2RbnwtLwWcdFgqEAqFANyqgv2K8bCiMAGOulRbXebzHvRv2B8fkdjPHhGvh6/AA6BOGtb8CPtO9c74NfY/3gPHZmZDmIWzmY5Uacz4liquGAqFAKBAKhAKvKqBDphPmanwHUANzDpwmN18GP3ccc6FA1mZQx+suhvthMuhITAUT3y5KMFHgtRsJ7vH1tjTv2UnBWNC5+hB8EEzirAmawTGDKj6nVpt/PWJ3OAV2gnbxd0yEnQYGwWsxdda5nAu2FetvWCgQCoQC7aDApyjk8BYW1CTR22BP2BpGw1rgmNatYzq3HhYK/EsB/TYTYc+BfvAM0AdfG7L2pww2bwUm8x8Dr1PJd7SN6me+A0wsljP9VH/Fti7oU3kvzh28L+cWbus3+ZksB6+dfFf9qHr9V+cNN8IwGAveYz3mIiW12b7vy728Wu7w8V4VxGfpvFCtLwQTuGrmM89yIYB1zrrmczgQNgPrinXGMtRbT/hqx5vabQk/gnrbQaMiXc8JpoBte6D57J6BRWC/ExYKhAKhQDcroH9hrNU4qLFOx1L78XJmXNT46MMwDir5ZXxcOPsvSjQB9oNHQD8ixgJEaHObRPm3zfkerFt/BduTvqoLnnP1F7OewHE/YaFAKBAKhAKhQOYKOFimgJ0JToN2DqQGYJy4G4DRdFArOakrDqrxn3RtHdy5cBdcAwYTHgLLYVDj9eC46mDfbabmq8OmYHDsw/B+2BmGgpOHFHR1YtFM5+d1nH93OBs2gHaxeynob2BBjQU2GGwQshdydyxrLHscHgqEAqHAkUgwKmQIBUKBliugL6Zvpn87D+6Da+FKuApuh7vBwK4+ynRwsY4LZrI2fcjdwODxQtDX118caJZZf1s/z0RvJdMf1wddH+aDCwxM9OlnGeTVd3IOYaDbgG+W/qnXuhy8j5FgWeuZH+hf68vuAhuDvt7joA7N9KU5fduYz+05uBMuglngXMBn78KurMznZz11UcYBsD3ody8DffF4HogwwNTrO7DDgP2temvS/0RwIVOp52Nswf7GeEKp/obdYaFAKBAKdJUC9pX6RfqGjqHGOSvlDh3/9KluAm0jqHT8ioMK9s+alOddYFzXMUGfotSYwe6wNlBgS8q4VwHKeQVlMG8hLgTItU61W6NEr7BQIBQIBUKBLlfAgdNJusE6A2sOpg6qj4OOqu+d0KdAm8GztM1mw+a1dXS95lxwQcDVMBkMlOoArwRv6HvN8tqcsi1M/0JHenPYH46AD4CO2ERYC3wuamnw2WcqjdrKnGBHOAcM+LaLPUhBfwHzaiywgS0nKQa03c5CQ04TFgqEAqFAUxVwXDSgZJLGMcLEVlgoEAo0RwF9A30tA5omvx+AG8BfpvwFLgKT/jfDQ9AD+hUmNk1w6tOJSbKHwXONAf24LM1+YQKYLLScXk9ff6Bv43XXBhOxg5nnXK8PFzj0gMl576vUudmdmTkXuRXUdTSomfODeuz1fGkz2BnWgLngfGegNuzqWlMLn6s+tXXbOZnjzPqwKmQ1H/M8Po9NYH/wmdg+ki/u3CbsVb23R4jvgmN9HnYNF70SbIulzD7G52ZfEBYKhAKhQCjwqgKOp/abxlYd31wIoO9Vbhx13HP8nQozYSSsC+1k+gv7gj7jdHgCwsdChDa0DSjzuwpQ7imUYRm4sCR3n71c4y2ATlGEUCAUCAVCgVCgZgUc10QHLiUX/PWBgRpf3VfJeeXjhs3rrwzrwBjYDnaCEeD1w151pl1ZvBh0sA3W+ToHXEDh4gonHAZk0gIBNiuauo+CM2AP8H07mAHpn4PB7monGU6yTPjPBbVqdhCbS4SFAqFAKFC1Ava/jrUGjQz8Owa7KM7FX2Jwxcn5WNgHNoKwUCAUaEwBfQgDti6ONcnd04f+hQEok10uAtB/0G/QlxjM77Ad235tt0PAtm27Nvl+MJiMboZZxnPB5J0LfPUFk5lEN/n6Q1gt7Rzk1fs0MP1TMCC3BJIGbDbdXLDweTgMNoRGfFSf4Q3wE7gX9Kd97mH/roAaO/5sDUfAfuA8wTqdtdnm7oeL4EJwfmP96maz3/g6fCMnEUxGHQOPQKl+zj7FRNVysC8MCwVCgVAgFPh3BRxH9bmct40Ex1R9wErm8cZBTaa/H/xOu5nxuU+Cr47lpcYQdocVVIFdKdfN0IivncWt/Q8ncc7RA4sgV189bzG4/7BQIBQIBUKBUKBpCjjOiYn3lWEVMFhpIiItCGj2WOj5/eXJUNgG9oBNoB2dYYrdNNOx1ikysGngbC48CnNgPrgw4Al4ClIA2+NT0Mbn+kH4ObTDQgvv9zb4DXi/6T7YrGjes0EtdXkG/J7nCgsFQoFQoNUKmEgx0OP4msbVNdl2vBsOY2AijAMDRybBHPv8TrPHXi4RFgp0pAKO+wYk9YWWwALQJ9CXMPGvn6SfIMlPasRPsK3aZvWzXARgYs99Lmz1Fza272aY9zkZzgZ9wJchmX61yfQPQC19yVyO1++6FNTN5LnXaZXtwIW+B3uB/Wa95vOcAZfBKeB9+azDSiuwCrtN/h8JB8NG0Iy5gnV0Gli/rLe98BJ0m9kmNwUD8CaC8rCbuKgLfpwrlTLnlLahbnw+pfSIfaFAKBAKlFLA/tz5ngsuR4O+4GDjp9/xmM3gQ2AM1H3tZPqdH4W/gwtoG/Gj+XpYCxXQx5sKr2nhNUtd6mR2ngvzwDlHrn56uzVA9AoLBUKBUCAUCAXqViA5sCtxBoOHohOrQ2twSEe1mWOj5zbgNwy2BgOABkgaCQLy9Y43g7MGu/3TSQa3daAMdsp8WAYmlb4F20HRzfu5Gk4H76faCYVOowEr71ktPE9YKBAKhALNVCCNm46R9rMunjORvy6Y1B8HE/vwfUryGyxq5njK6cNCgY5VwPHdMd+x/klYDAaQfBWDkc+Cn5t01EfSl6jWn+DQms1Amn6zbV/f2TbugoC3wM7QrPZ+N+f+PcwG7/sF8N7XB/dvD7WYiybOhHNgLni+/wetMjX7ErwXJjR4UZOXt8GJMAVcFNLKe+FybWXO/9YDtX83bAsuaMnabLvWrSvhdDDR7GKTZrZPTl8Yc0Hgt+Er0Kx+odLN2h8eC/dDKc3tMx8F52DRXhAhLBQIBUKBQRTQ53MeOAL0A1eBwfr35CfuwLFHg3PEdrIXKex/g3/dZxFE7A0R2sCGU8a5YB3N067g4ieAsdte0DfMzQZrrLkVLC4cCoQCoUAoEAq0QAGdUoNBJv77LwZYjffNTso7BnvdUbAjTAKTKAZYw6pXwMCOwRsdKp+Zz7TIZlDKoPNlYDK/WvP+lsJCMIgYExBECAsFQoFMFHA8cuxxTDIZYvDeBJ8BnvVhDGwIjlFum8DyOL8T80lECAsFalBAvyUl+B3PnwX9ARP7jvNiYspf8vuZAUiP0w/Q3/H7eZnt3YDaOn3oQ9sXGNx9OzTLd17Aub8I14OaqIP9z25wLpjUrcWe4WADuifBXPC952yl7czFvgN7QqO69XKOq8H7mQn6muEnIkIZs+44xu0Lh4P1aC3IejzzGei3T4bT4D6wPXfys1HDjeFKGAt52E1c9Mfg4p5S9jg7HwWfRZ79aamyxb5QIBQIBYqqgP27c0Xnh8NA/08/sJL5HcfcUfB+2B08R7uY4/XpcDzMhk4ev7m9jjB/rNALeS84uZMyfA2cw/SAvnluZkMMCwVCgVAgFAgFQoFXgz46pzqkOrNiMMhEiPv8rFnmeGxAdSzoFBuIcnVt0ZPZFDGsBgUMWv8SdAbdrtYM+i/q4yVeI1hVrXJxXCgQCqiAY4wYpPHXG+JCN/9cv6wPI2E8bAhjwISaxzj++d2wUCAUqKyAY7OBwf8HL4PJpefBX/CbzDepb6JfTEDpBzwHJqkc2w0M+d2ij/H2B/YLBtaGgMlr+5aN4D2g79wMM0n/SbgKnga1WhU+CD8H/fZa7B8c7LlOAIO6y6HVgV2D6F+AQ2EiNNLX6iveDX+Ev4D1TI3Cyiug3s7ztoMPw1vB8TDr+Zdt2vZ/I5wGt4L1uehtnSLWbGp3FPweGqnPNV+47wv2o9+Cu6BUe7ad2N6Xlfmc3WGhQCgQCoQCZRSwX7efXw2MV+oL6gcOZn5vDdgcjgB9xnayv1PYz8NDkGsit51Ey6ms1sdpMD6n66fL9rDxUVgAcyDXepOHQ8Y9h4UCoUAoEAqEAoVWIDm2Og8mSgwqmiTx1fcrQbMsOdQTuMAesCsYYI0xGxHa2BZT9uNBZ9RAf7Vm8HYRLIRI/lerWhwXCnSXAo4PLlJzzHIxmUkxV78bnJF1wIT+BjC2j5G8ut9xLZL8iBAWCpRQwASdSSTHYgM3Jo1N6pu4NwltEi8l+E3ym3T1M49JiX3Hbr/veTop4acvrG9sEjv5xqPYPgRGQzPMhRXHwRlgQtVkngHlr8KxYF9Wi/lcbgL9s4dhCXjOVttOXPB/YE+wD2/E1GUy/BwMVKuZ9xlWWQHrsMFiExMHwkSotT7xlYpm+7ffuBXOhmvAfqOTno9t/2rYBPKwe7no90BdS5l99HSwXYSFAqFAKBAK1KeAMUvnnS6aE8dQ9w1m+o7OTfeHw8FFeO1ivRT0E3AbdOoivnZ5FpXKaT2cAntUOqgFn+lvWMcXwExwfpHbPPC/uHhYKBAKhAKhQCgQClRWwMSKzmpyWHVaRYfVfdU4uxxWs3leEzQTQQdmFzDQGeM3IrSRzaasPwFf/1ljuV04MA9MINT63RovFYeHAqFAwRSwr0/jT0rsm7B37HEMMtHvmOCvL0zwm9Q3ATcCDMasBR6TEvwxdiBGWNcrYPBFTLqZ1Hd8NWH/LJicM6FvYM/AjUkk94kJfXERgN8zkOO4nFswh2vnbckvHkpB7Gv0W120eghsBs0wNT8Dvg8LwGehr/wD+AjYZ9ZiPr8H4US4HpaAz7jVz9V7+BwcCiZPG+mv1cjk/3lwNiyCTkoycztNM8dL6/O7+9iOV8fdrM2FQneDz+iv8BhYl9vZTAa9H06BWtthFvdtvf8O3AJuD7SX2eFczL/AEu1hoDrxPhQIBUKB2hTQT1kJXAw6rO+12oVzzmv1dRxrd4VGfB6+3jJzHvAN+Avog5Yaa9gdlrMCp3J95wR5mnMJffoemAXON1s9t+CSr1q7NLBU3ngNBUKBUCAUCAXyVsCx0wCnzu3rQOfVBIyBTxMyBj+aYQZS1gUd5T1gRzAJFGM5IhTYDMD+EuZALQ6fx5p88HsvQkwuECEsFOgABeyzxYCJ40VK7NufSxpLTDj4K34T+ybzh0NK7K/NtuONY5DnSedkMywU6FoFHDcdK03smOgx8GJS/xkwkW/Sx1cTbU+D+/3cMVb8TiT1EaFG0z+1L7Kvsj/T9IsPgN190wTzWV8DX4Qe8FnbT+pvGUzWT6/VevjCmXA+zAfrRi1+G4dnYttwlu/C3uB40IiZZL4RToC7wXoeiU9EqMKsQ2vBXvB+sC479jreZmnW3fvhIrgAXKzxCrSjuYjFBQ3W3TxsOhf9JtjXDzTbsou7XABgO8ijbXPZsFAgFAgFOk4Bx0v9Ff1AF9BVu2jO760GO8EnYAi0gznXcLz+ATwC4VchQsHsq5Tn+JzLZL3Qf5wBMyFX3yNr55X7CQsFQoFQIBQIBbpKAcdSnVeTMAY+xaRNSub4Psvx1nN5LYMsBgn3hK2g0SAhpwjLWIFlnM8A9VRYDv0TDTqEBp9KBaDcZ3JiDjwPMalAhLBQoMAK2C+LibA0DpgQcxwwCGL/bIDDfb6aVHBB1zAYAQZLDJr4CwqPd2GA53Js8bxhoUC3KpDGSYNtJuf99YQBlJS8N6GTMOnj2Jk+8zjxe+I5whpXQC19Lj6Ll+EFUGd9U/sy+z0XydqnHQ2ToFn2CCf+FNwLlsGFUifD/lBP3+nCy4vhJJgH1q1Sfhq7m2qOEZ+Gw2AzqOde+NoKs/wGHi+H34O+pc8wrDoF1N5xeQv4IFi3RoNjdJZmW5oG1r+zwfrXTgsB1GkvuAL0g1pt1vMfwXVQat5knbfuL4EYCxAhLBQIBUKBDBVwDND3cwGovpgL5v4PVGMeNxbeCfuC52kHm0UhPwu3wvMQYwsiFMQOphz6U3mafslH4GGYAc9BbnXEBhoWCoQCoUAoEAqEAtkp4NiqE9s/EWTCRwyKGhSp1hnm0IrmtTzfurAT7AMToF2cZoraFabzZ2DPBQDLYREsBINQJixMVugQir8Ect9scH+pIBa7w0KBUKAJCtinpj7cflRMxpvAt681ge922ue2x9i/G/BYB9bvw0SYfbOJHJMHft9j7f+9Rlgo0K0KOCYaAHF8MynzEpi8NXjmuOdYaeLVZP5TYFLWfeIxjpPi98XzSVh2CiQ9fT4+K/U20e+rvoqJyUTS377NPnEk2Pe5IMD+0ODortAss658CS4B64eB59/BW6GevtZ7vBZ+CL2wFPLyxTbqK4f34hyiEbOd3Qq/Ae/P55jXfXHptjTHcev3e+EQcFGAdT5Ls825YOMyOA16wDlE0c1kz29BbfKw+Vz0y+A8q5TZT8wA20Hq30odF/tCgVAgFAgF6lfAGKjzZOfBw8C5czW+mMc4l94YPg7joR1Mn/FncA4Yv2uH8ZpidrztwB3eCdXUvWaKcQwnvxmmg3PcWACACGGhQCgQCoQCoUAnKqDTYVBUZ1gH2ERQShal5BG7GjavY3BwBOwG+8B64LXDiqlACpq/QvEMxBqcWgxTwQmE2+4THUaPkRfB7xgkjCAWIoR1nQL2d2K/apJJDDaYYE+v/felYISf99+2f1wFPNb99sm+t582mJ1Yh+01wcCE/befe530vVQedoWFAl2nQBrLDGo4Lhn8MiBmMtYxy/HL5P3TYDLf9yb2ffUYMSnj9/y+54mxDRFaZOot+hU+h/Ts0nN0n4ni/s8lPZ/0yscrzD7VvnEMrAv2p6PAxNyW0CyzfCfAz0GfySTtyaAvbP9cq3m+W+FEuAf0x/JKHK7OtT8KH4KtoZ774Wv/sgVsXQ0nwTSwzXm/YdUroL+wDrwFDoc3g4v9Gn02nOJf5jOZBefBr8H+0jpYRPO+t4MpoJ/UarMf+h1cDPZbA806PhuWgf1YWCgQCoQCoUDzFHBMcM6t/7IBOC7oH1YzRnrcevAeOBD0KdvBHqGQ34MrwblPjDWIkKON49ozwVhNnnY8F78ULItz4Nz87WoaH+ULCwVCgVAgFAgFQoGMFHDs1RExKCoGjEwo6SBn5aDoOHvejWBPeBN4jbD2UsCAlk6iAb/nQadxKSwCg9FuG+h+oR8GvlwgIH7PQL64Xwzwe04ZGLhnV1ibKmC/0h/7gDTRdr/bmtsDzXqQ8LM0YXVf/+10jK+lLF0/XddXg+Tp1f5N3OdkPm3bD6bP3J+S9C6Y8lg/S8f43v0e47H2a75PfaiJeRdCicl88XOP9Rzpup7TcvUvK29L6uP+sFCg2xRI7d+xIiWGHVcMapnMF8cfk1KOTSb03ed7x6Q09vQfb8r1HRwe1mQFUv9tn+7zFJ+lfoLP1edlksz9HpOe1cBXPhrUHAvsaw36DgX749HwLRgLzbTJnPwLMAeGw8mwN5Qa+9g9qM3kiDPgQlgC1vGkCZsttfFc7fvwdjCY3oj5rO+Fs8B7Ww621bDaFLBeWb+3gMNhfxgD+hhZmf7/6XAKWK99drbXIpk+2XHwzZwK5fhju58HpdqnY9VscGwq9Tm7w0KBUCAUCAUyVsB5u/PvdWEE6Bs6967GnNNvAx+FcdV8oQDHOD6fDb8AxxzH77B8FBjCZfUJrEd5mvXhd2B9yNXXrncilKd4ce1QIBQIBUKBUKBTFNABNkgkBpDW7MPtap1jDi1rjvOeW6fbX2bsBZuBCbGwzlHgn9yKAS0nHQbvDQwa5HLSYTLGgLX460vfpwUCHmPA1++5OMDziNuez8/S/rTtq5g08Fp+122P873b6TP3+XkqXycH3WxrkhLWTnDFSa/tLSWsUyLbdum2x6fJsW1e0uTY8/k99/Xf5u2KY/yen6XzpO+63+O9hvvUPR3rs0jn8zl6nOa2lp57elY+P833bnt8OiZ913Oma/qZ103vLb/vxWR80sPJWErOp/1+Juk7qcypvOl66ZVDw0KBUKBKBWybtlXbum3Z/tkxQPon9U2kJEya+Jljifid1K97PgkrlgLpuaSx1+fmmJzG/TRO+3k61jtIzzK9uq8Rs5+2Px8Bw8E+fyL8EPR1m2mLOfnRcCMYAPwd7Av1jh3L+O7F8FtYBI9BVjpxqprM8fID8FHYARwfGzEX71wHP4MHwbqS/AE2w2pQQN9tJPiLxXeB867VIAuzDn4FHgAXojwORXlOtqtxMAXGQB52Dhc9A+zfBpr7esF+oSiaDSxjvA8FQoFQoFMVcIwwDmB8U39wHXCuX435Pf8a3/vgIDA20A6mb3Ui/BnmguNQXn4jl+5Kc97xKAzL+e4nc/3vwRzQf3P+lYvZEMNCgVAgFAgFQoFQIH8FDOLp5BpA8q8BGCBdC0weZmGO+QYO1weDhnvCeDBAGxYKqED/iYnbAzFwZgJJx9WJjBgsdsFBSi70TxY9w/6UfDDJ5LF+v38CyXN4Xq/lq+81t0VL1xy4nY7xc/G7vrrfa6TP03Y6Tzo+3W9KMDuptA2mdmhbdNs2aDtxsuq2pP1up/0Gep1srAG2X9ux+yQluz3e8/od6Z/kTuVI/nl65bC6Exd+NywUCAU6UwH7sP59nn2xfW5a9PUk2/bDvhqMclvsp1OfnfpgzxPWHgqkscux2Ofm2CrpmTrmWRf83HHQ49N3Br7yUdPMMc1xbgwMBYO/b4avQrN9T+v11+FUcDw+EQ4Ay1SPqed1YBBvHiwH9c3LRnHh4+AgWK/BQliHpsFf4HTw/qw3YfUpoF9nndsR3gf7wDBwf71m/fsG3Av271PB9l4E06c9HE6D/n5rq8rm/OLzMLvMBR3zZoDHpf6vzKGxOxQIBUKBUKBJCjgG6hOuA8PBmEm146LH7gUfAeOj7WI9FFT/82qYA47lYa1RwLrlosnNW3O5sle5n0++AL2wAHKbO+ThoHG/YaFAKBAKhAKhQChQQQEdFhOMBkgNIolJRZ3mLMZuz2FC0oDUNrA7bAg612GhQKsUSIG4Uq/uG4hBavf5mjBILTrTYtDf5IfJkPTqZCu9d9vj/b7HmwywvYntzXYhJiokvfc1Je491u/ZjhJshoUCoUAokJkC9lH2VfZj9lsmMZ4CE/n++tPXxyD9ZRc/T31dSuqnvpWPwtpMgf5jXRrfTGBZH3z1GTuupWdtfdHSMx/4+uqnrf/XMdKxcxyYqHZhnIn4j4FjabPtPC5wTN9Ffs7rQVDvddX4DjgVboTF4LPIy5wTHAyfhV3AeUMj5r3cDD+D28D39kFh9Sug7zgG3gE+q61A37JWe5Iv/AAMZvtcpoJ9f2r3bOZm63Ply8HF5XmYdfYEcAwcaOozB2yrUZcHqhPvQ4FQIBRorQL6hMZQjDk6dqwL/jiiGtOX3BSOAsfSdjH9cRO/+lYXgHM452thzVXAunY17NPcywx69l6O+DDMB7edt+ViChIWCoQCoUAoEAqEAsVUwHHaQKVBPYNIaSHAamxnuRjAxOdQ2Bh2hq1hbdBBDwsFQoFQIBQIBUKB7BQwGGRiwiCAv+L0F53LwYT+0r5tEz5PgJ89Dx5nItgkRkrushnWhgr47FMdSM80LVJLryb6xeftMX5HtPT802v/fSsOKNg/+pIpEWqwdy04FA6HVsSjpnGdIyAFYN/NdiPJ8kf5vkHcM2AJuDAnTxvOxY+Bd8L/z96ZwEtSlvf6LgrKvg8wCzMswyogCAIii4rihoi7cYtbrjfJjYlbbhKjWU1yjZKouCAaTdTgggsICAjDqiDIvsPMmRlm2EER0dzkJvd5hvOZpunu06eXqu4+//f3e05VV1fX8q+qr97vfd+qs6jPDfGcWg5nwCfhVqgtWMm6J8Xsy3ne+1aAV8CzYT50cx56TG6GD8EqsI3wnDbhXdoERmsxr9/nw7fAfmnV5v577l8JrbRQo1vAe2hje8nHWBSIAlEgCtSkgPcO7xkWhS6ATcD75EzmPFuCfpw+Tze/YbaRsdVsyafhLLgBLARIcRoiDMlOZLlvHdKyu12s/ftXw1qw/1CbT11Fh4v9i0WBKBAFokAUiAJ9KuA920CRbAg6zDrLpRiA0b7NdehIW2igM743HAhLwErd+A2IEIsCUSAKRIEo0KUCJh0M8PgEosmbFdPjJvtN8j8MvwCTOiYwWiUxmBwbYQU8xlKOn+Mm7Q3yeFwdN5nvZ/Gz8zo08Of8ZcjoOnOaVobN4+u+HKM/+pb6rTuCvqsB3DfBi6AKMxH4BrgYTKS+HtaHXs3lmSR3WfeAhTt1Xrv650fC++AI8Em5fszz9HL4LHwbLHLwHI31p4DHyfNuITwHjgWLAiwOaNfH8h5xIpgwMJFtUdjt00MGtZrX8kfgLTVtxbWs9/fhkRbrt329A9aA53MsCkSBKBAFRksBfUP9la1hHlgs+t+gk5X76FHM9HYwFjpu5n3cQtJ/gOtBH0t/v9Hn52OsTwX+kN//eZ/L6Pfn+m0Wq1gAcBvYL6zlOHuxxaJAFIgCUSAKRIHxUKAEjE0mGBDSWXToZ51h7+szOc3M0tFch8kIg5kGVs6D78NV4Pp00nXOLURoF6ziq1gUiAJRIApEgTmvgPfJJ8Jm4Jt1vMeWe7dJCxMTYjDA72oJCrDe2H8q4DEQE57i8TGBbxCn+F6+wtMiDn0lnwIvQ8cLPvXhfB5vE8b+Xv9Kn83lmaAS19F4/Mv6HU6KuS/qaODLQgDP9ZthW9gBhm0mXV8BHhsTlm7DU8BrsxdzebtN47H3vCjFHozWYitY6xmgztvAVtCrn25/YiEcDLvASrBgyeMosd4V8Jp/AH4Mp4LH7E7wnNwEyjmpzhaXnAIXgPr7W6fdD3UfB8+tpfAR8Hqo2tz/L4LtSCstvB5Xg+1tLApEgSgQBUZPAdtufWD7Q/pR3leMNRrP7OS/eC9cAcvB+5APL42Tec98Kvwa7AvqUPoI7ltsMAosYjEm3+s0j63FHp7jD0Btx7fTBcV2xaJAFIgCUSAKRIERV0AH2UCdjqQVsAaPTDT42emDNP2GJ8EWsBh0XPeCBWBRgNsSiwJRIApEgSgQBTorYALUYIBJZJ9SnAKTFWvBAIFPh5i4MFBg8CDWvwLqaKBR7R0vSfiSuPWzyVM1dz6HztuIvyvHowyZ9Ktpncb9bq6bxaPzwOSyb7OyKOa9cABUZSexoj+Gd8BvQz+BY88BE5Anw1fAYgALPuq2PdmAP4XngTr3Y14L18BX4UtgG+W02OAU8Lqwb7UPHALbgO3RLeB94adgO1WKi/yubrPf9wF4X00b4n3ynaA+zeZ1uQZWQM7VZnXyOQpEgSgwegoYt7QATp9sezCu6bRO5r1zR3gL6EeOa47Tfob3Mv1TCwNXwUNgPyTWuwJH8tPvQ53nhcfQAuQ74Uaw0MXjXbnVKULlO5sVRoEoEAWiQBSYcAVMwOsIm6TXabYQQCdaZ9rvBm0u03VZdLAAdoM9YCdw3a43vgYixKJAFIgCUSAKdKGAgQKDAyYvDBTIFPjU5y8gyQxEaDCDKCZ71MWh+pnEN0EmZdzpzlOG/q78tgRi/L1Who9+mvlzmS/DmRXQJ/TprgWwHawPFgG8H/aGquwSVvQ2eD68B+ZBP2Yhz2nwEbAI4F5oPo+YVKn5RPmvwxvAgt1++wG2S8vgw3AFpD1ChCGYfSf7bib8bZvsY5kEMRlQ3h7CaO22hC04E5bWtCVfYr3/ALbpzWZxnYU5alb3ddi8bfkcBaJAFIgCrRXQR/R+p2+oX2YxnONOb2f6NhbQvRKOA38/zvZzNv4i+Cc4H3zjj/5W7mWIMEt7CvNfDZ3On1kuctaz68e9FlbDjWBBZ+l3Mlqd1SlCdXuZNUWBKBAFokAUmFsKeH/XGTaI5BMaBo8MsFoUYIHAsMz16nS7zi3B4O6OYFHADqBzrhPfbxCSRcSiQBSIAlEgCky8AgZ8TGQbOPgRXAkrwMSGye1JCgiVfTEw0pjQ97PJMBM97nPBz+L3Zegy/CxaWWa34+t+lD9DVUBf0eLRnUHfVF9Vf/F9sBdUZV5TrwaLVy1AWAz9mOflhfBx+DFYCGDCtm5bxAZ8EF4MW0E/5vV0K1js8Bm4Hbz2YsNRwGtFc6j2je2Z0+sy+3rHgm+FqKNPZ4L/9+AWaNbEe4fX3krwvhGLAlEgCkSB8VLA+4r3GYvh9A8dzhTD1K98AbwVjEWOu3lvuwNOgdPB/p/JY/uEse4UWMhsK8BzqS7zOL4RloP+swUd+imVW3EoK19xVhgFokAUiAJRIApUooAOtPjElQUAOtAGXHWSqwra6LC7vo3BSt75YEBy8fS426SjrnMW3wQRYlEgCkSBKBAFWihgYvtOuGgaAxs+LTKKZtDD7RXHDXg43pjMN3lo4tRhSeI7X5m3/LYM+eoxCR+XW6zdePk+w9FRQP9zA9gFLFLVTzTI+27YB6qyh1nRq8Dz7S/hqdCPH+o5eA18bZq7GFqsU7fZBzBh+z/hGaDe/ZjX8KVgEcB3wX9ZooaxuaHAPHbzG+C5VIf9kJV+CFpdW95PLAwwyO59IxYFokAUiALjp4C+mBhDtHhxW9Bv7GT6OgfBO8D5J8X0r7yv6VueBzeAxQD6YrH2Cnje2Gfu1+dtv4aZv7Ff8BtwE9wKviGsFn+5n84N2xyLAlEgCkSBKBAFxkgBE+yyPpiMN/Fu4FVnuY7KyFKYYPLffxlgQElnfT4snB53ut/ruFVVsMCqYlEgCkSBKFCTAnaWxYSwTzr8Enzi7yfTmGyykO0A8OnhuWrqcj74ZMgKsBBgWAmPckxKIt7ghdMak/kmXhoT+s7rfOJ4oSzLoVaG3Y6v+1H+jL0C+p1ex0vAoK5+nn7pe+HpUJV5zhosvgL+Fg6Hfv3N+1jGadPLM9gnnv91m362+h4Hi6FfM7B6Fvw9XA9q6fUem1wFjCE/B74DJmaqNq+jD8Al0Oqa8tozUaLvEIsCUSAKRIHxV8C+ng8yLQD9xk4+mvPuBG+D/WDSTD/rdtDHXAb6Xt737Cd7T2zsU/FxTtuG7L3a1OGrFOE9Hr8FHqcVsBY8hpWbzlssCkSBKBAFokAUmFsKeP838Com1y0GsBBAx9rigE5ONV9XYm6jwWC3xyIAKzi3AR3/heCTYk7TsdPRd5vj1yBCLApEgSgw4grYGRY7wAYsHgI76CaT7oG74X4w4e/33qe8RxXzt96/9oXjYKYnQphlos1CgPPgVLgNTMR3sqJ/CRSZsHOaCROPib9vTOb7fSP+ruDvynIYfUzgye+KNY47rflzmS/DuaOAPpu+m/6cfp3FqPp9+nXvgsOgKvN8/Gv4zPTwpQzdln7M6+k8+DRYXGDb5rVat6n7QfAH8Gywfe3HbBuuhi9P4346LTaZCnid/jm8p6bdW856bR/0D5rN+9YtoP/gfSkWBaJAFIgCk6GA/T7vP9tO43i72J++pYUCrwL7if52Es37nAWmP4BzQV9zFTwAFmjPdV/MGLG+Qt1xgt9hG66BlbAa7GtXbu0ulso3JCuMAlEgCkSBKBAFalFAX0CnWAwCWgRgomVj0LEeRYfZbdaxd/vcTh38Uhwwn3HxKSf3wwIC9yE+DyLEokAUiAIVKWBCzcCDAYiHwaSQCf610xigfxB+CibFTJY5f6vEsPcmk4S2941tufeAXeBYsCBsrptFFJ+D74MBDwMM6m9SpOA0UWsxeFRQe8cbj0EZL0O+XmeNnxvHy/cZRoFuFCj+3A7MvD0Uf9RgnQGzZ0PjNc/Hodo3WPq74X3werAYoR/z2rgOvgYng+1gq8Qlkyu3zVjjW+G1sA/oV/djvpllGfwtXA6267YxsclRwGtxV7gYtqhpt05gvadA87nltaY/cStYOJj7EiLEokAUiAITpID3IJO6m8ICsF/YyXex//hceDMYF5x0s8+3Gi6C88Gnzu+Ah0CfrLmPx6SJNvsU+gX9+vL9ivS7LOAqMAayAuyTV25VdqYq37msMApEgSgQBaJAFJiVAvoFxbE2ca7TXIoB/KzD3cnJ5uuRsLIPBpDtIFgcYGB5EewwPe5098n9iT+ECLEoEAWiQA8KlCSzHWyTWwYaVk2P+1TCg/Bz+AWUhDOjszLbaTvv86H5NX7el2zjj4FdYK635wYVPgsm4e4CCytKIqQMmfSraZ3G/S4WBYatgNes1/gSsHiz+Jr6oL8JR0OV1/UVrM/k/6/Bb8AgiotsC31Dx9/B3eBnA7GjYIvZiD+FF0K/SV3bmFvgNDgRbgfvEbHJUMB+lQUyH4Qqr0lWt870Jf4XLH/042P+eu5NgT5Ic3EAk2JRIApEgSgwIQqY2PV+ZBHAluDndvck431PgTfD7jCXzIKAVfADuBCuhjVg39z+4aTfK5/APlp0uyHUae9k5Wpvv1y/2L565dbuAql8Q7LCKBAFokAUiAJRYOQU0E/QcTIYq/Ps2wF0oKy29bMBWxknc3tNIFnYYNLIooCFYAdCNgeDzp06EnwdiwJRIArMeQVMYNmZ/QpcBnayTfYMI7Hlvcj22Xbbe1Kj2a57XzoSngHOO5fNRMgp8CdwMxjkiUWBUVZAf1O/Un+sXONO0+98K7wY/FyV3cGKXg4GjU147gr9mgG/8+EzYHt5D1gYNQqm9sfCO+AQaG5jmTQrM+j8Q/gUnAEmbr03xMZbAftMHs89atqNs1jv8dDqunmE6T7t6LCx2I2PsSgQBaJAFJgwBez76atsN43j7eKS9gu3gpfCcTAX+4neF/XNVsC509zA0KfSvW9Ooo9mH8KCW2O/ddpvs/LrwG25DeyXV+6nGNyORYEoEAWiQBSIAlGgnQImcgxa6jD6es+fglWjYgCmOIs60lUGZ1ldT6az5f48DDphy+FaMFC5DKyOvRR00nSIDVq6X6VTMQ77yObGokAUiAJdKWCbaDtf2vg1jN8EJqisUl8M7QIltocm3veDXeDHYJs5DHMbvd+4LSarGoM87oPbvwpst7cHi9XmqnlcTNDsBL6K+wGoPNDAOmNRYDYK/D9m9hWlXuMWYnqN669dA17zu0NVPphFoq+Ek+B08FpaAP2s39jbEtgN3J/VUPaZ0VrN7TB5anLXfZwHFlz1ur8ew8VwKOwKK+E+sB2PjacCXo/Pg7dD4/23qr3x/n8ieN003888r+4C+6aey7EoEAWiQBSYbAVK/9V+pzFJ/UZ9j1b3J+8Rzmd8bxUsBZPDc8n059RnazgQ9HFfOz2uT1rivd5DJ8VX861W7wL977rM8/TbcD+Y+NdP0Z+p3Hp16Cvf0KwwCkSBKBAFokAUGCkF9CF0sGU9sLJSx9tkkMMNQCdzXKz4RA4LZf9M/m8KOpHzweDtjmCSyf11P8vvGY1FgSgQBUZKgdKpt/DJjqedUIPlFkEVLO7y+0fAjqkBANu+P4BnwkydZzu4FoldAidMjzMYuHl/se01cNOq3bU9tl0+HA4B92Eu2+ns/Dtg1VwWIfs+Ngp4TVu8o69lkLL4V17Hb4ZXgL5ZVWbbacLzfPgQHAeDaFMsyvHa/DDcDbbDo5K49BjsC++Ho6DfILn7ZaHtP8M/wj1QS/CT9cZ6V2AbfvpV8N5ah93CSn8f9GGazQLAW0HfRl8kFgWiQBSIAnNHAfuo9v0WwmbQqc+qD7cYXgJHQ6d5+XpOmPfNh+BC+DJcBvqmFlaMim/Kpsza9GV9OEG/ti5T21+HlaDG+jIWo1Tuq+RER/VYFIgCUSAKRIEo0JMCOi5WiBog9amtxuSSQRgDnDqO/xecV+erwOhImttZ9st90+l1+01s6QjfBpfCOfBd+D7oJOvM/QT8jf6VGKR2f2NRIApEgUEpUNoo2ybbXttYk/e2TyvgxunP8xiW9sdky6fgc3AKnAu2W9fDFNwDdkptx12u69Bsz86H7WA+dEp8uS6fINgRTBoZgDEgb/s5SHObXOYG0Gp7/N79mJpmE4ZbQNGC0TllO7O37v8PwIBDLAqMugLFpyzFpV67XtdXgW3MHlDV9awvdwzo3/0R2J7sDm5HP/ZkfuxyloLtt+2vSUzb6lEwC8QsUPDesDVsC70WXvg7f38Q7A1rwIKH4mszGhtxBbwOng6/D45XbZ4rXwKf3rQtaDavoTthVK6f5u3L5ygQBaJAFBieAt4j7Bsai9TneBLoJ7byFb2H6NPpU9pP1Rfrt9CRRYy1qZOa6ZMeB8fC9mD806I7760lNsDoWJgxAos8XlTz1nq+fQ08N42xPACeq5XrWYfzxn7GokAUiAJRIApEgQlUQEdGJ0cn0UBmSUzp6NwHBvx0uA10/hIM8hbnR2e9lZPO5JE0HTj30f25A0ykXQRnwXfhHLgUTMbdDc6r6XuVfR2n/V238fkTBaLAUBWwPbRdNFFr22LyZRXcBlfDD2EZmJj5DnwDvgqnwLfhTDgfFoKJlmI+Rbs/2PYuh9IeMdqVXcxcJm12AYMkndouv9sQ9oTDwXWvBDu7gzA1Ku3vBoy7/FbmfcigxU1gMms+lIAQo3PGPB5PAe/HV8CgjgOLikWBoSlQ2sEnswbRvPavAgsDbF86tUN8PTBzPUfAPPgjsG22Ldwc+jH9wR3BfbEtmwJ9aP3jUTDbih/B98BjsB1sAr2ax03dngk7gPcitXSfY6OtwIZs3h/CfjVtpgn+k8Bhs+kP6GMYXLeNiEWBKBAFosDcU8D2376fSWvH9atKzI3Rx5jf62euBv0c+8U7wRNhrps+72ZwELwW9Nv01dRI/3Rc7rNbsq36LR7XOk1f+mRQO/0V4+JqWbmOVXWa2LdYFIgCUSAKRIEoMMcVKH6HQ9EpFxM4BgbFIKNDnXadcMcNkjrfOJv76BNjBtG2Al+luS0sgvngNAOrzjMJ+8tuxKJAFOhBAQPcnwWT/Xa47SzOtpNosv9D0BzIMDByIXwEDJb3Yn/Ajw6Gbp+WcPvvgK/BMrC4YRBmm2qAwrbU/Sz3F0YfYyaX/M5594LDwfa23fx8NZFmQOyNcBoY9IpFgVFXwGtUH3ApbAF+Fv3BN4GByap9w7NZ59thH3gXHAqDaEssVrJ49HhYA/eCbeeomDrbdr4HjoBSlMFoT2YbdDl4rzsFvB95f4qNngKe308Dz/1Na9q8r7Jez4rixw8AAEAASURBVJVW9y59pltgUL4Fi4pFgSgQBaLAmCrgPctYmn29+WDsrZOv6PzGHReC/aQDwN/H/lMBk9jGJT4Jy8AkttNG0TzWG8Or4ASo+1jqz78JHgF9mJtBn79yH79uIdjnWBSIAlEgCkSBKDDHFPgP9ldMzOj8GPSzKlLHyCdUTXrpLN09zX0M7wcTGMV58ncl6NrJqWe2kTD3VafPAJX7NgXXwcVwFpwG34Wz4SK4GpaDT66qiZWi6qRuxcr+l2GZnmEUiAKzV6BcW6V9so0Rr1uvP9uoh8Hr0Y7vPXAnrAYT3LZbm0O//SufUn8a2N65fJ/cLtvGaFfm7wxm7AmN7YNt5WI4CM6DXjrvF/K7H8Au0M3+uk6T7weChQO2gbbt6tqPeWzUxuVYAOAxEnXzGNm59jh577DN9RhdAxfA7bDlNI36MGlibX32bGfwHuO5GosC46CAvpPX8oZQks62h17L2t5Q5TW8E+s7Ak6Eb8A2sCv02+67b3tMYxvpPtu2ySiYmk/B6WD7uhXMg161V6+FcCh4n1oN+voe79ne7/hJbIgKeO29BZ47xHV0WrR+iteb/aFm83zx/u693vFYFIgCUSAKRAHvB/YRxf6PdPJX9LXsL14Bt8HWoJ/T6Td8PWdMn20HOA70BYxJ2pe0321/vG6/zeP0BNgC9KV/G/4EnFa33coGfA88J0XdjGdXrllOZlSPRYEoEAWiQBSIAiOrQPFVHDZjYmm9aXTsTZwZRHWan/3exNAkWNkX93Ej2ARMvpnEcmiCTUqQvOjg/ouOu06wQ5ellmVYdGXSYzo6To/Vr0BzB6F8LkO3sN1489Y3HtNuxsvvG+ct0wY1bLXtTmukdJoc2tFsHjZOc7xgB9Vxh3buy9CAsp9L0tjkfilCKkM7ZyV44NDfSFlX2W618ZrcAZ4BrwCvs37NfTwXvgR3gNs+W/tjfnAEtDp+JnE+BReD473Y8/nRa2B76Haf1X0KvgyXg0UVzVa0dV51KMdK/T0+5Vg4XZsH24Htfvlt85Cv1pnT3daNwcTTy2F38Ldzwf6SnfwzUMdYFBgHBWy/9GmWgMG90tbo0/wavAH0Z6q0KVZm23EVfAh+HQwW92u2T7fAt+ALYNLTYoBRM4/FH8CLYNs+N842/iY4BT4La6CX+x0/iw1BgV1Y5gXQ73HuddOu4YcfhFbXgb7AdWDhX7nnMxqLAlEgCkSBKLDON7QgfiEYM9N//K/QyfQt7SMeBPqX9jFn+g2zzCnzfrsWTGyfB96nLcYznuB9ucRKGB2a6feXYzWf8QPhpXAEeMxHxb7Khnwa9HX1bW+He0CNKrWcxJXKnZVFgSgQBaJAFIgCA1ZAX6bgosu4Dv4TwWIAMXhs8lz8rMPYTSeA2cbeiibFUVYXk11qUXRxKGrjdL8v8zgs0x02UjR2qKaNqK/rbKRsC5Mf15nyu3G0EnR0qHMvOvU6+Y2YrBSnObSDVIaOF/6labz8rszv7x0v6/B7xwtuR9kWRn91TahvOSYOG49V4/FrHHcePzt/43Q/Ny6rHFePdaOV7XBYtq/sfxm6v+6Dw8IvGRc/l/GiVVlO2U9mqd3cbxNTi+CFcCQM6ny+mWWdCDeCRQmztQ/wg8Og+di4HI/BD+DDYKe9V3sLP3weGFxptZ5Wy/V4roAvwRlQCi48t38B5fwo11Q53g61MnTcc9EAzQKw/erW3NZNwd8dDM8HC6km2byeTFyqudrGosA4KGB7aoGjbaxtrfcmzfvSq+GNYDtQpRnAM9h4Cbwd3gH7wCDafhOa34O/hVXgumwTR8k8BkfBO8F7zGzaXmZ/nNk2eT/6GJwN3v+9T8TqU0D///fgL2AQ5/Vs98T7/J/BBaDv12x3MeF2yHnSrEw+R4EoEAWigArY19NfmQ/bgnGsbu5n+pTbgf3DY6fHu/kds84p8z6tz+q9+Eq4FvRbvT8bW3gY7Nfr4+nXlTjObPug5ThuwDJ8+GlH2A+eC08HH8YYteNTfJhlbJvj+vErQG1a+TRMHp6NmjjD29MsOQpEgSgQBaJAFJhrCujnFNz3Mq5Db0fAwJadAB1Jxx36WWK9K9Coc9FTfUuRQRk6TQwaG8Qv83ps/CzlWDnN8fK5cdwOgZ+dv4yX+ctnh1K2zWGj6ZSLnREpnRMddQOLJiXttNiBEV/Ra2fHjo3Dn06P28kpHR1/4+9dnsuOTbYCnsvzYCGYYN0XBmWebyfCRfATmG2n0QTNC8BrpNk8N9fCB+D25i9n8dlr0PUcCibVm68xJrU0r5Ob4Xj4Oriv5XppHvJVW3PftoRFYBvT7fqZdd28thnbgcGE58HO4LRJtKvZqeNgBRSNJ3E/s0+TpYDXtE9lLYAtoFyfDi0CeBPYDlVptscWASyDQ+B9YFtbto3Rns12/hL4EpwNd4Pt46iZx+Kt8BrwXzLoa/Vj3o/Ogo/DNaAPFqteAa+3JXAq7FH96tetcQ1/9Svua7F+fQd9Fp861M+ORYEoEAWiQBRopYD3M/0y+4nzodtksb+zf6mfcwC8BHYEp8faK+A92bjZQ2CyexWsnkYf737Qf9anNY5WHgKwSEDf176p/vx6YHzW42bxxp7wTHg6bA+D8LVZzNDM/fptWD69BvfNvreaGCOs1HLSVip3VhYFokAUiAJRIAqMiAL6QMUPKuMGLXU0dfRLklqn0wS1yb1RdzLZxFgUiAI1KWA7YnthYGExmAxxOCizM21S5GSw82zwezZmkuptYNvWyixsORc+Cd0mmeyg25m1E+vQZdhOfgAOh81AXboxkzy3gEmfL4NBg9mabbfrXAQei9kmA91Wt9/A0E7gPki3gSJmHQv7D7byA3A8GHiJRYFxUcBrtBQBGBAs17j+m23urzdMY7QS8xqyff0+2O78NbwObIsGYVMs5NvwaTDZ2SoZyuTazaD478OLYLs+t8b7yXXwFfgc/ARSCIAIFZr3wlfAl6Db+/igN+8fp9f/Ly0W7HV3I/TyZqQWi8ukKBAFokAUmHAF9Bnt09lP3By8t3V7f/O3+nX7gkWnS6D4oIzGulTAPui/g7ED798PwB0wBSvB4gC/8/jsDHvBTuC/Px03vW9gm/8Q9GE1fVv30TiO+1ipjZt4lYqTlUWBKBAFokAUiAITrYAOaEFHVKfMpJqBJhNgPwWdUAOuDnVQdVT93t/ZYSgwGosCUWCOK2A7UrBztxQ2HJAmtjWlI2zn0eD3bDqPNzH/D+EZYJKq2Uyeu/xDYRn8EkonvbSNtn8m5k1A3QN3wprpoeO2lXfDV+GLsARMBK0Pbn8ns1+6DTwPTDpYqOA2W1TQram97bPb6f7IbPu7LsN9d3+ugItgFVgIthWYaBx381jsB6eDTyF4nGNRYFwU0E8Tr2+vS69Jz2ETxrZVvoa/yuvU9u1lcClYxHQG2IYsgq2hXzPg/BRweVNgItzl21aNkj3IxtimXAm2lQvAY9SLefy2hYPhaeD9Zg1oo7bfj27V5P2dzy59EubVtGve+z8F+hvN5vXu+WC/LPevZnXyOQpEgSgQBVop4P1CH8oYn+P2h+0TzdRHZZZ18+t7rYRL4BrQ9HVm29dc98M5+ket9fGeAPb1t4DFYGHF4fB8eCE8C5y2PThflX49qxuIfZOlXAXFb/Wc+wk83DCN0WosJ2k1OmctUSAKRIEoEAWiwHgpoIMmOmwm2Qw2m1Qy6WbAyYCU6MSZEDPpZOBZ07EtrJuQP1EgCswJBWwzbC/spNp22EbsCiaIBmV2lA8BO48mRWybXG83ZoLma2ARQHnyofF3tlubwYvAfuL34HawmOFOuBsshjJwYpGUAXrX7z7b/rnPpe20rbQQ4CuwGLYHdXAdncz1bgl2/F8DJh+uA9fXjbl+22PXr60HBhlmWq/zNpv7ZBt/G1gIYAGFGlrU4ZMI4xiMYLPXmYnThWDCzuMYiwLjpIDtjte5bYqBwXJ9X8u4wd19oMrr03bmOLCNmIJL4SawLdsJ+t0Wl78b7AW2s7bFxTdldGTM9ncFfBe8/1kAYRve6/673+p32PTQ+5H3H9vm2PAUKOfzm1lFr8eu3607mwWcBa2Otf0xCwBsA2JRIApEgSgQBbpVQD/F+4r9aPuW9ocsVuz2XufvvQfdAfp6PwB90m2g0R/lY2wOK+C5dRIYNy7muWPc2BiFvnylZoAlFgWiQBSIAlEgCkSBKNCdAjpuOmx2HHT2TZzYgTApZLBTfArWAKXTnacxeGWQugSqGY1FgSgwYQrYPpiAMuls+2CiZncwuDAoM+l1AGwEBiBcT2M74zbYVrkdtkElIW6H0wT+52Ah7AxuZ7MZ/DeBdiR8E/yNy5eybJcvM5lt4cnwZVgE24PbP1M7aCDGYoRD4PWwFK4Bl9eNua12vn1awz6v+vfa93Wf1dC23WKE88GAj9q7HxYEqGO3wSNmHQlbwlYsB5Om7mMsCoyTArZtXt8GXA3gFvMa9TvbsF6v+bKs2QxtN4+Fi2EVrIBTwXZsV2jcRj7O2mxr5sMeYCHYLWAbbPs/amZ7eSmcBba928Km4D7M1vyNv90XDgaP6c3gvtvOxwavwFYs8k9hx8Evuqslev1+Bta2mNt71YPg/TjHv4VAmRQFokAUiAIzKlD6dvYr9Svsm9qPm42f4j3IBK/9qGVgn0r/TFxWbO4qsIxdPw/0h4vpt3q+GCOuvN9dZYeo7HCGUSAKRIEoEAWiQBSYJAV05kRHriTcrAx+CEqQykCV+NnpBq11CP2dFp/sUR3yNwpMggKlLTAhZCfvJ2ARwCCvcwMUS8FXQ18NPqW+Bu6Eu6aHBs8dvxtsf8rT+xYCfANWwKGwITSbgYt58GZYAhdAYyeWj7MyAyy+EeCLsP00JsRmCrT4vYUOT4U3gYUPN4H7M5N5HEyOub+OG9wxGdWPGexxmb59we24EM4Dk123g0UHmsdaDWWmfWSWWsxt8xw6Byxei0WBcVPARKH+1gbQmGC/fnq67cYg210W19HchhfDBWB77PadDrYZC2Br6Lc9MBm+D+wCtvEu27Z5FJOhtvtnwkVgUtm2v/E48bFrs73y98+E/aHc70Zxv9m8sTV1fg68Gxyvw25gpd8Gz+1m816+GrzXlj5U8zz5HAWiQBSIAlFgJgW8h+hDGJvzfrMe2E+c7b3P+J99zdvhYrgcnKbP92SIzS0FjPN+AVaBPksxzzdjwfovjdPL90MdVtkZGuqOZOFRIApEgSgQBaJAFBghBXTwROdO/g3sCNi5sIOg82cF6F1gIstkkp/9zmC289ohcRkGiwuMxqJAFBgDBcp1b9LZJIidPRM2sw0q8JOO5lMGzwMD4teDxQa2ISaeSjti+1PaIoelfbqG8Y+ACat50Cox5fabRHsZ/ADuhH7MgohT4ETYEkzoWIDQat1M/pX5vUGU3eENcBSY/JoC96ed+Z3773oN8Pikfnlaf6Z1MmtHU0t1drnqYkGAhQDnwblgEOhH4PQpcB7bftv5xoSdffJ+t4VF9GQeAwMUV4A6xaLAuCngNej1bTvSmFy+kc+2vftDlXEvixGOBtsBfTzNdsGkptfbTtDvfcA2zOXsBu7bGrC9V4tRNNvqU2EFbAXbQa/HZH1+6730MFgEtq/e81IIgAgDsG1ZxvGweADL6mUR3rM/BbeA483mNX0H5H7VrEw+R4EoEAWiwGwV8D5jf84YnfcXfQl9SX2U2fbNXNYvQd/vSrgIVsDmsAn06vfw09gYKfBjtvU7YOyn0TzP7gent/JvGucd+PhsT+aBb0AWGAWiQBSIAlEgCkSBOa5A8cccFpTEALGfDfSKVcnNPHH6OzsUzl86K2WZTIpFgSgwYAXstBXszDnusGAixuC01+GmsA3Y8T8I3gbDCAC4DafDH8NNYCDDad3aScz4GjDJ3s7ssJrE+U14oN1Ms5xu0u798CpYCLPRxiS6+/oJOBlMxHcy20jbzM1gi2lsW6tsL12XuF4DTOptQuwYeC7UYRaNPAuugtmcM3Vsa9YZBVop4DW1MewMtrWNdjQffhf0n6o0CxCOA9uoYl7zfwWvB9ugQdi9LOS78LdgMamfR/k69n74dngl7Am2y72a99ofwyfhG+Bn7wux3hTwGnkhfBW8R9Vht7HS94CJmObzWL9qCiym+38QiwJRIApEgSgwKAVKLM03z80H/cl+fUf7tS5nARwIR8HW0I/vw89jI6rAI2zXH8K10Oyn6KNa3PhAi++YNFyrMtgx3D3J0qNAFIgCUSAKRIEoMJkKlISRe1fGiw/n0A5EmW4nw6Bd6cD4uVCmOexEWVa7ob/VyvDRT/k7KAVKwNNhO1xX+a7VeJnmsJjHs1jjsXVa+dw4XuYv3zUOy3KGOWzWwXWVfW7+rnF6GTcZrzks02YalnnL0PntvDl0WpnusHF66eCV+cp6mG3da+d92nF7sBjAhNDrwOtyGHYXC/0dOAceAgPm3ZqJshPBoEc5/s2/dd9Xw/vhNHgQBmG2W/8TfgNM4s0m4OI+roKvwMdBDTqZ2rv8DcEgjE9muP5yjjNauXlumBjcrfI1P7pCCyjU36BELAqMowJevwZZbT8sBmg0A67vhtm0K42/73X8In5oe7+yaQH/g89vhf2gXVvb9JOOH33y/zz4LFwG98AvYZTN4/QueBF0uud0sw/eh86BD8M1MOr7ziaOpFmM9jU4oqat03fy7QMWtBS/qnFTLGy0sOZn4LyxKBAFokAUiAKDVKD0BS0Y3xLmgQUBxr368df87RPAZe4Ih8ChoN/az3L5eWxEFNAv+SLoR1kI0Oyn6JtaFGx8xnhKpZaTrFK5s7IoEAWiQBSIAlEgCgxFgeLTNQ9dWZk203iZrwzL/OWzw9L5aR43oeZ3jTROaxxvnKd5OY3rcrzxMx/XfW6e5vS6rSSGDVg6Lo7/2/R4GTqtcZ4yX/m9HYXmcSb9KgHtuNbYoWgcL9+pUZnePN44j+Na0bSMl88OPV6aw1bTndY4vXnc7SjTynjZNr56TAfIfdf8vttxl+38jctuXH4ZL0NmbalNmV6W5+dijb9tNa35++bPT+JHO4DJ5i3gpfB68LoYhnm+mcj3idA7wORQ8zYxqa2dwzeHgcGPdmYn9gfwMhhUEYDrUv9j4H2wD/iEfDl/GO1oXlsmsM+CPwc72e3MZYrBGNexLRjg2QCGdVxYdFvz+noG/DG4TVWbgQrPS499ufaq3oasLwr0q4DXtMU0O0FjEYDTjwTblaqLAL7OOn8L7oZGO4AP74GXwCC2yTbe5Ogp8I9gEYBv9xhls90rOjyb8c362FjbLdv8T8MXwDcBeJ+KdaeAx+JY+GfodO/vbmm9zXUfP/s9WN3m52uZvhz0cWJRIApEgSgQBYapwH9n4fbhTdpbIGfhuNP6Ne+3+n3bwC5gMYC+kP1Q/dXYeCqgD2r8QV+lVdzFvvb10Ko4gMnDtZxYw9U3S48CUSAKRIEoEAWiwDgq0MpH7GZa4zyN42rQ+NmOT7Ey3WEZ97syT5levivTyzyN08u8zuN449Dxxs/+vszveKMZSNZxF8cbaU7iN34u8xen398VK9P83Dje/Ln5u1a/L9Oah+1+2zyfn4turb6b6ft2v3V62YZ28zSvr8zfPL3xc7t52k33t52+a1x2FeOedyaWl4BPmxtEMOnzOvC7Ydl1LPg34cdgZ7PxfORjR3s335osc1vbmRqbcLfY4ENgRfsgbV8W9gE4DEwMdauV2/VzuAz+Es6FTueDyy1Bnk0Y3xo8Xibiu10ns/Ztrvv34eC+l9TbAv6Rn/lksudKLAqMqwLee2xnbW8biwC8lp8F74UqE5y2PX8HH4SfQqO5TbadFoT5pphB2IMs5HSwAMwg5L0wm7af2Su39Vnji+AdYCDcoqxebQ0/tN0/A+6EFAEgQhc2j3n+AY7uYt5hzfJ5FnwyWLzRbE67AfQzOt3Pm3+Xz1EgCkSBKBAFelVAn9I+on6JfVF9NX0W/Te/69dc9nqwDewEB05jMavriI2HAsZD3g8W4rbzUSxyvBVm+2AGP+nfPNFiUSAKRIEoEAWiQBSIAlFgJgV0ZmfCIHM7SqLcoU/vFBo//yvTm9FJFoN/YjC3kV/wWR6ZxsSfPAw/mx4aMDTw/pNpDJA77vD+6aGOexl3epnH34nLEpdd1lW2w+0r2132y2HjPrfTxekz6dquI8FPh2bdbFPzPI370ml/G79rXsZsPg9t5we8YPfJ88Fzx8Syn6fADv9uMIgAAot5nBlMeCV4nt4CXj+uuxu7hJk+DC8EEwOtghBut/tzELwELgcTLoOyu1iQyYCTQK3mg09ftNoWJv/K3C7nXwKvgleA+30DeByaze88J8sx8tr3Wvez/eXyRP6wjhOrWGe2I3fDs6Csc90XFf3ZlfWcB6sqWl9WEwWGpYBtntfTk8C2wGvX63wKDMA9HWZqR5hlIOa6nwb6GFdBYxvkNp0DXvdbw0Lot50xSL0nPAW857je4qMwOpKmr3QjfAfUwsIz71+9xCs34ne257eCWnouuHy1jrVWQJ0PgfdDVddF85bop38aPF+bzWPnfdlilsbrp3m+fI4CUSAKRIEoMGgF9CmM8xhb8h6lT2E/zXtnv/dM72/e14w12f/6EZwP9qld18ZgX7sXf4ifxSpQ4BHWcTxcA+18FI+zsUePqedT5ZYTqHLJs8IoEAWiQBSIAlEgCkSBLhTQUe4VHeuZsPMmztduvNMyWm0bi4pFgZYKeL54npmEtyPv+O3gdJM1/SZ9WERLM/n1bNgfTD7Z+XTd3dqJzLgJuI0m01qZwQ+TNa+BA+Bs+AUMylzW9+DjsBoWwmZg8GUm3ZxnHhwNb4JFcDUYxGm2ck3befc4mZAw6WASzX6z++lwpnUyS8/m8THYs0fPS+j9h2qlrt8HgxmxKDDOCthuGLA1Ie4T/163XuPLwevfNrHfwC2L6MpczzPhZrgJ9C0azaDhhbA5LIV+31Dg+mzrbEdsv91n232T4aNsbt9l8F2wDdoKtoTZHCePs8f3ErC9VmsD6x77WGsFtmPyCeA5U5d9jRVfBJ6nzeY0CwJzHJuVyecoEAWiQBSoSgH9CPuI+hjej/RZ9E9Kf7Tf/mFZvv3PtXA1XADeG6dAs8hxfeh3XS4r1r8CngMngcdopr6zx9RzpxZ/VIc4FgWiQBSIAlEgCkSBKBAFokAUiALDVcAOnwkpn8YsTymuYtzpwywCMEiwBF4J98Ht4HZ02wE9i3k/B88DEzLtkjHr8d1SeCNsCCa1BplwMpFzJZwIFgRsM003gRC3eVM4EN4GJv9ugnuglamNSQd1MpFoYt6AjIUBjYGeQQdg3EeP0eHQruCCr4ZmJoDOgXJeDm1FWXAUqEABr12DtV5Ltk9er17bt4K2Dwz6Gl634BZ/TOo/C0xwrwS3o9F8Kuhb4PQFYFvbr5k8dx93BPfZNk1NbGdG2QyQng+28+oxD2y/uzlWzq++PwDba3kQmvVmUgwFvC5eAu8A75N1mEV2nwbvfa3M770neS3HokAUiAJRIArUqYA+lP1D7036bvospdDU+2g3vgqzdTR9Fn02k8reG2+GS+BcuALsv7pOi8btl8aqV0B/+jNgnMRzoJN5vqwGh7X4oykA6HR48l0UiAJRIApEgSgQBaJAFIgCUWBwChg0sADADuAGYEB7BZgw3wUGETRgMS3NJ2FfCCaELgeT2t12Qg1yfAq2gN2gXXLa7TcYcSgcA9fCHdDtepi1K1vLXCeDHW8TPPPB9c7Uv3X73PY94I2gFsvAAEs785gZhHE9PwODPerhNPfLdQ4yceKy3Ua3rWozmOT5+T1w/2JRYNwVaFUE4DV9A2wCtmdVmW3w4XAB3NlmpRcy/XYweb8E+m1bTPAuBYvM3O+7odyDGB1pe5CtOwvOBvfDQgDb+U73SYvOTgOD5Y7bnrqctGeI0MLU9M9gxxbfVTXpPFYknpfN5jm7FvRXHI9FgSgQBaJAFBgFBewD6lvYPyyFAPbvS2G6vkonf4Wvuzbvf94jLUa3X23/ehnoT14HbsPmYP9xUOtkUbE2CvgGiOPhHNDXnMmc36KN2nzRmQIkM+1Avo8CUSAKRIEoEAWiQBSIAlEgCkSB7hUwYGBH3aFFAHbobwEDBrvCMDvuLttCg9fAXeB6Z/NU3ZnM/3k4CraCdskp+5nbwKvhEDgduukgM9uszGWeDx+HH4LJDLfLZFEnHf3OeRbD/aAWJgo7dcw9XgZg1Mv12pk34GPxgAEfv3O/++1juw0u93AwYVi17cQKTwMDFbEoMAkKeG173RoYLW2D19nVYOJzIVRlm7GiA+Fc8DpvZbcx8VuwKdhe99sO2N4tAIuKbJdvBM22axzsXjbydFAz75PbwkbQ3MZ7jH3zjPPapvvZJ+dsq22/Y49VwPv30fB70O5e/thfDP6T/s/fw53Q6hh5jvrdMPwHFhuLAlEgCkSBKNCXAqV/6H3KQnH7ld7bNJ/O77dfuG5BTX/0YUvhwQrGL4Nl8CN4GOwL6zs2+0lMivWpwHJ+b/L/CvAYzGQeK88Ji1GNFdRiwzgJa9mRrDQKRIEoEAWiQBSIAlEgCkSBKDAmChgsMCllf8wiABMVdihNTg27CIBVrFvnMQz3gx/CbBIkPlH5aTABszuYVGsXYPCJ8p3hzWDSxsDEsAL56vdPcBKop28F2BA69XnV+3owwWQyzmPSjdmBt0PvekxQlGKAnzBu8MdpzlOCQkWfMuSrjubvnXf/jnMN50uPp1p4XljYEIsCk6BAqyIAr98r4QDYvMKd3I517QHng21HK/PaOwP8fh5sD922H8za0jZm6p6wFNaCmoht1aibbeldcCosAwsBDHB7/3T7DcJ6LL8It4HBd7VzP0sgntHYtAKeSwvhE+C5VZedxYpPB6/FZvOYGzT3/uz9NhYFokAUiAJRYFQVKH0+72f2lfVBHgLvXxYCWGg3jGI71+s67X/q8+gL6V/eCPp9W0KnvjBfx7pQQL/8TPg4LIdu4hkeG3+3EropFmC24VhOgOHomqVGgSgQBaJAFIgCUSAKRIEoEAU6KWDSwkSvwQCTriYpTFxsCjtBv8keFtHRXL5Pl74RDLLfAK2C8Exuaecw9aPwPNgG2vUtXY+J+APh5WBA5A4w8TQMU9Nz4WNwCWw9zXoMmzU1seB+qPuD4G9na3buSzGAwQADMBYCuOy7p7mHoU+iisl19XYe11kCRP7OgJHLcFv9/gjo9+lfFjFr24FffA3chlgUmBQFbHO8Vr2mSnvgtOvhMLAdrsp2ZEVeZxeDAeJ2dgVfWIyzOVhMZVFVP2YQ2uXsDSbRV4Ht/rgkyW1v18J3wHbeAPf58OXpzxYJlDb4zulxBrEmBbwGXgZvgeb7YtOsQ/vove5T4HFqZZ6XU9DLfbnV8jItCkSBKBAFokAVCtjH9x7mfU4fz/6UCWB9UP0477vDuve6Dv2glaD/eDm4bn3Ofn1IFjEnbTl7/Wn4NtiH99h2Y/qs9v2NA3hO1GbtgjS1bVBWHAWiQBSIAlEgCkSBKBAFokAUmCMK2Bk0AWURgAF5O+gmNLYAiwCqMJNeL4Ij4Bowcd1tJ9X5PgsG6J8CG0G7gIb7uCW8AJ4Nl4HrsnM8LFvBgr8EnwET7wtgY9AMyNiRPw08BibCut1vZu1o7pPLMggjBgo8tgaC1MpEvzwMBmncFgsBDBA57jHRNgQTdVWbGl0Nng/DPD5V71fWFwW8/rweNwCLADSvvTVwKFQZI9uN9W0DBmhtC9qZgcNvgIHbhbAF9GO20fPAtsU20QIITW3GxWyX7gID27ZTV4JtuEVVTlcz29u0X4jQwrZj2gmwdYvvqpr0A1Z0BnicWpn3bI+l99BYFIgCUSAKRIFxU0AfxHuYT4Hr59nPE6eLRZn2j4dhLt/7691wBXjP9fO2YMyhXX+dr2LTCtzP8CT4AtwCs/WTnX81qLvHozarsnNT205mxVEgCkSBKBAFokAUiAJRIApEgRFVwESxCWiTUaUI4AbGTZYvgSo66K5jEbweTDrfCJ0SUnz9GDOo8BF4GphQ6vSEgcGO+eC6DoJzYLYdan4yK7PjfQmcAN+Cq8BK/h+Dib+1MMxtKIGexqHHvR18te44uN1HgZpVaZ4PW8FpYKFCLApMkgJe6wZkLViyrfK6NNGo7QNVtLmuy/U8BTaDH8FMbe4y5rkdNocl0G/Q2CKIPcB9NiBt8txCJbUZF/PY+fYCh/8Gtld+tm11WuzxCuhreP99HVR1rjdvhcfoY2BgvNVx8nvvzR7PVt8zORYFokAUiAJRYCwU8D6mX6KfYjGAxd4Wn1oE7nf6cwVGB26uWx/vejgfloNxBv3Jfn1JFjFR5vHQHz8ZToQrwWPlsZuN6UtbjOrDDupfq6UAoFb5s/IoEAWiQBSIAlEgCkSBKBAF5rgCJShgZ9MCgPXB4IBJ+K1hMVRlJsMOhReDHVYDBG5Lt/YVZrwMDoROQQWTDiYhlsKbYTFcBCafhm12xu3Mr4RVYBDGAoxRMc8HgwzlbQqLGF9Sw8ZtyzrPAjVym2JRYJIUsLjG68yCJ9s9A3UrYD7sAFWZbeFTwe3waXYTnp3sVr78Bhi43Qk2gH7MwK9tzF5gIcIUqIv6xCZPAc8377sfBc+huuw8Vnw6mOhvNu83XgdrYTb+R/Ny8jkKRIEoEAWiwKgp4D1On9P7m/1PCzBNMJfiVP2yAqMDNddpv9f+vf1uC+H1+eyzz/W3AnhMjA18C04AH27wTUTq04sZ17HI0diGx7xWSwFArfJn5VEgCkSBKBAFokAUiAJRIApEgXUdw39HB58EMKFjQsrA+NUwDxZDlWZi4FjYE0yWPwjdmkGFT4DBhF2gU0DBZIT7ux/8GjwJrocqkk+lM16GrHZkzHPB46+GBmueBWpVpRkrUBuLANyGWBSYJAU8t21nvNZMvvuWDYN0t8P+sClUZV7bTwPbSoOxMxUBGIw0eXofbAULoN/2wTZ/H7CowOIz12FgehTbRzYr1qMC3mNfAd5v+z1netyEddeZwfU10O78MmhuUqTd93wViwJRIApEgSgw1gp4jzPxbJ9P3++nYCygsRjA/tig79euVx/Yt1/Zz78QjDk4TZ9Yf9QihEk3ddDXOA8+D18D9TDx73Hp1exPWMT4ILiO2s2TKBYFokAUiAJRIApEgSgQBaJAFIgC9SpgB9HOpp1Gg/QWAZh4tUNeRxGAHf/d4bXg/8C7AWbTGf4e8x8Pz4Btwf1pZ65rMzgcXgoGQO4AE1Bz1dRaXdRNDTeFqs3E4rfBRGMsCkyaAib/DXZqBjyNj/lklInJg2E9qMpKEYBvgOmmCMDtMkh5AZi83xk6tbF8PaO5v7vAvtNz3stQfbwPxcZfAc+xpfBZ2LjG3TmHdZ8BJjxamcmPlWARSiwKRIEoEAWiwKQrYAxAn9T7nnEAiwFMTOuTWgzgdO/h9gsdyiDM9erjuZ5VcAWcB74dYArsh1sMoH9Y1s3oWJs6q637+M/wT6Av7f771H6/vof9d+Mm9iUcV+PaLQUAtR+CbEAUiAJRIApEgSgQBaJAFIgCUWCdAqUjbme88U0AJnpMoi+Gqs3teDE8HS4DO83ddmbt+H4RroWnwmZgAKGd2T/dGl4Iz4MpWAHdro9ZJ8bcZ4MUJgTVTf2qNoM+N8NV4LGMRYFJU8BrzGCrwVTbOtugO8Ggp0/ld2qv+Hqg5jYcACbybfMNAM9kPqX0TfCesQ3Mg34Cw/52PuwzvaybGBoMLYUSjMbGVIFN2e7/DUfWuP2eRx8Dr7FW5rlm0NwiAK/NWBSIAlEgCkSBuaRA6f/Z7ypvBniQcfvf+oWleE7/VJ+tH5+Pnz/GvAdbcHAP3AKXwvdhGVgcUPxj162vWraB0ZE19dT3mIIz4ORpljG8HdRVH3oQsQaXoV++EuxbDGKZLKZ/SwFA/xpmCVEgCkSBKBAFokAUiAJRIApEgUEpYGfRzr0dR59KfQLYMb0aTI4vhkF29lncjOb6doI3gE8JrAATZN3azcz4CTDBtvP0sNM+uM/bw8vhuWAC+m6Ya2bwx6cu7Lc/D9SlSvMYLYKvg09FxKLAJCpgwNM21+vLohcDmrfB5rAUOrVVfD1Qc11PB4e2ewZiu7FLmOk62AqWQL+xvo1Zxl6wOxgI9vp3W5KURYQxNM/pfeFD4Dlel53Pis8G/Ztm0/fRr1gLKThpViefo0AUiAJRYK4p4H1Rv6sUA+iH/RRMWlsoZ0GA33uP1/QdZRDmco0/uA7fBLcSjEVcDOeAhQEXgdOMCziP93a3tWxHHQUCrt9tvh3Oh2+AT/p/Fy6H1aCG7tsgfVqX5TFRC4+Tx25kbFAnxcjsUDYkCkSBKBAFokAUiAJRIApEgSgwAQqYwNkclkB5MtWkzP8Ak8F19eXs0Nrx/wDYkbYDPRszGPBVeC5sBDPth+uzI38h/DYsh5HqVLM9wzK1USMTgh+Bp0DVZnL0ZXAqzBXdq9Y46xsNBXxCegfYDGynvPb+BPaDqs0A5l/B8XDfLFf+F8z/Glgyy9+1mt1r/gY4Bf4JfDp75AKbbFOsswJP4uv/A7/Vebahfmti4F1wI7QKujvNwLx434lFgSgQBaJAFIgCj1fA/qHoq/ok/vrT6L8aK3Ca+H0V5npkPdDf8AGGTUC/eh5sBwtgPriNFiJadOs+9GL6pmIMwgLVO2EKTPqvhPvBBxb0Oyzw1b9w/mGZPovbsWJ6qA8/Utar0CO1E9mYKBAFokAUiAJRIApEgSgQBaLAhClgX80igG1gIdih9rOd/LfCsVBVx55VPc58HeGH4ET4yeO+nXnCUmb5BzC55j7NZHbc7cyfC78DJgnmgnnMPf7u8ztr2mETfyaOfGIiFgUmVQHb3C1hR7DoSjNo+dfgNVi1GUD8IHwCbG9nY8cwswVTR4JtSL/mtX8W/A2sBIsShhlMZfGxASngeX0Y+PSbQfm67Nus+AQwGN/KLPSzOMBhzq1WCmVaFIgCUSAKRIHHK+B93piA/p5JeDHRbjGA/myVxQCs7nHmtpnwN/Hv9rhtvrFK9LMdbgobgdvqvJp+sMl13wr0MzDecC+Y4Hf4ADhdv8FkvwUBw072s4rHmNvodukbWwTg+kfOPEFiUSAKRIEoEAWiQBSIAlEgCkSBKDB6CthfszNvxbyUDrzDX5tmEMkdFtWT2ck9A34Xbu1pCf/lv7yS330Qdgb3ayYzMWAyzGSUTxOuhUk2zwEDIkfD56GOBI4BjUPhGkhiBhFiE6uA15tt7SIwgKrtBhY7GbCs2gx8vhc+B7MtwDHg+lGwjd0W+jWDnD8C26HvwZ3wr5A2ARFG2LZh2z4Jx9W4jQbo3wO3QKvzRV9i9TSe87EoEAWiQBSIAlGgNwVKMYCJdIvst4CSYK8zbtBqb/S73V6HopWh4/oMrfC7uk3f5S5YA78AP4+kjdpBH0mRslFRIApEgSgQBaJAFIgCUSAKRIGaFLDTa/Dc5LiJYM1p14IV8PtBqZRntFKzg74UXgYWACyH2XZ+r+c3nwCfCtwFfFrBQEA7c50+QbAH/Pr08BKGVv9Pqnm81eQgMDlZtZkItQjgQkhypmr1s76qFTCI5zlvW2R749PuJiefAVW3tV73R8IUmDy1nezWbDfOBIOTPl21ENyfXs1tWQD7gMubArWazTYxe6xCBfQbPG8/CHXGfz0Pl0G7c+URvisBdEZjUSAKRIEoEAWiQI8K6P/ZH7fP5n3XOIJPqTu0IEBfsMBo7Va2120WC04LZZrzyCiY2/FLmAJ9bN8+4HaOrNXpAI6sKNmwKBAFokAUiAJRIApEgSgQBaLAiCng058mpXx1nokYO5+3gYUAh4Ad+rrMRNnLwW26AewUz7aTfjG/OR58ynYx+KR7p2SV36nF3vAmWAqXgAmpSbMSaNiWHXsmdNJlWPs+jwX7CmffvhCLApOsgEE82zDbF4uNNAsAbIMPBNvfKs243XPAtvV2mG0RjveIU2BrWAzuVz/mU2S2u7a56mJxkAnc2Ogp4D3jk7Coxk0z4WCR391ttsEg/z1gQaPjsSgQBaJAFIgCUWAwCtiH1K/Vd7SP7GvzSz/duII+Zqw7BdRSHdVQf9yiin+F2cY8+Em1loNcrd5ZWxSIAlEgCkSBKBAFokAUiAJRoFcFDKT7RN+ToBQBGFT/PlgEsAnUZfYtj4Snwc3g/+brJZjvq6X/HhZOYwKuU8Lb7ywWeCq8CXaEH8KkFQIYvDHA8BLw+FdtFmZYpOGxHflAR9XiZH0Tp4ABPgOkvnWlFFfdyrjtrsnvTm0SXw/cbPefC5fCarA9mI35dNJpYNDSQoD50M8++CaEnWBfUJ81YJtb2ilGYzUr4H3itfAWqLpopXHXv86HC6Bd4YrXmef0pN2z2aVYFIgCUSAKRIGRUcD+m31zizYfAgtbnaaPqU9Yp6/A6kfa9GHUTX+3+Ly9xDlq2cn/Xstas9IoEAWiQBSIAlEgCkSBKBAFokAUmK0CJlcMkpt8Mbhf+nN2SM+E3WE7qNOWsPJjwKr428Dg/mzN/fRp80/BzmCyyiRTp4SV35ms2x/eAIvhMpiUpIIBGp8yeDqocdWmvh6HU2GS/91C1bpmfaOrgME+sV0xOKpdD5uDT797TVRptvlHwTK4E3opxPkxvzsdtoLF0E8xkfs/Dyy+WgQrwbcB+LrZXraNn8UGpIDHZif4G9h2QMvsZTEWnHgfd9jKPE8sYvQNAN73Y1EgCkSBKBAFosBwFfDea/La4lCLAHy7m/6u92FjDPoQEntUE2Ma+ip3QNFqrPzcEjDKAY0CUSAKRIEoEAWiQBSIAlEgCkSB0VfADroddjvoT4ZSrW9y+Hzwdfx1JKdY7a/MJ/KPhi3hGjC40IuZSPLpwc/DLmBxQzeFAGrgmwgsBPA3P4JeChH42ciYgQaPscnH50AdgRmfHD4bpiAWBSZdgXLN2cZuAMbPDI5eDb6hZAeo2ixGOAzOAV+Z3ouZpLeQx3bZQoDtoZ/2xPvQHvAU8Ikyg6QWXqlfrB4F9A9eA94D+zm2/W7951mA9992T8lZvDgF3utjUSAKRIEoEAWiQHUK6Kd5f7Z/qW+oX6gfZ6yh+A76wGWc0Tlh7r+6mOyfgrvAIgDjL/YDxs5SADB2hywbHAWiQBSIAlEgCkSBKBAFosAcV8COup1Qg/w+wVmKAOysXgF24PeDOvt7bpNP4x8MV4Kd517NJ85PBpMJFgL4RGO3hQA+Mf96MMhxE4xzIYD74Pa/BEwEVm2eb6vgYvBci0WBSVfA89y21vbGIgCDoLa/V8GusB1UbSbt94VzwYBkL2ZbcjmcCS5vMXgv6dVs7xfAU8FCIdsJg8kGSl1XrDoFPEf3gRPBYry6bA0rdhtMJrQyr60SVM/9pJVCmRYFokAUiAJRYPgK6Kfpr5n4tp/5MyjFAH72+1IEoL83iVb2X5/FtxatBH0UYxD6/X4/tlZnQGhsRcuGR4EoEAWiQBSIAlEgCkSBKBAFalbAzqid8idC45sA7KDeDDfCAdBPUoef92UGC0wKHQM3wXLoJxlUCgG+yHKWgMk3968EJRh9nBmo2AQOBCv53QafOhxHUzuDM3tOU8c++HYJ38qglrEoMBcU8JrzCWULAErhkW3vlWCi1TedVG2LWKFcACbaezUDnaeBwU73Yz50ak/5uqNtxrdqYjthAth2Qu36aff5eWwWCngc/xAOncVvBj2rx/sLcC20S+57DXmOjOv9mE2PRYEoEAWiQBSYKAW8f3vfNs7g/flnoC/nW6csOnWa92/n0190OK5FAWU/9aPvhdXTQ31i99Hv3b+xtxQAjP0hzA5EgSgQBaJAFIgCUSAKRIEoMEcVsHNuUnw9KE+nKoWd1TvhEtgbtoA6bWNWfhzcBzeB292P2VH/KnwOTILNh5KYY7SlWSSxEtaCr6ce16SDwQj39cVQR8DFJ0qvm+bfGcaiwFxQwCS27ZZv3rC91Wx7rwYLrSwyqtp2Y4W2az8E27RezevYN8ecClvBDuByezXfFLIT+DYAY45TYHub9gIRhmxq71tvPgQWB9ZlBtG/CO3eUKGPcjcYcM95gQixKBAFokAUiAIjqID36FIIq69p4agFAfbpvYc77jT75s4n5b5eCkrLkK9qNX0PfXl9et9wcBfoi9wD7of75/cTk/hnX9ZZCgCKEhlGgSgQBaJAFIgCUSAKRIEoEAXGTwE7qna6TUqZnG3sZNu59QlRn5Q3qdP4HR8rNRMTLwATTNeDTxSUAAGjPZlJpW/AJ2EezAcTV60S4//CdAsiboXyBIOBgHEzt9nE4/NALas2zyGDO6eDmsaiwFxRwIChbZYFTSWWZtDzBjgILMKq0rwWnwa2g1eC29eP2a6cARZJbQELoFVbyuQZzW2zTd4PFsFK8H5k+9Vvu88iYm0U8J7w97BLm++rmOzx/ThcB+2Oteea50TuIYgQiwJRIApEgSgwJgoUP84kuf1Bn5R/GPSHS1GASfVS5Hc/4/p/3vf1U5uT6/qLw7CynW5j2T4LFnzzkIl/x91mfeiJTPqzX7+y0mn51YSMRIEoEAWiQBSIAlEgCkSBKBAFosBYKWDn1s61T/z5hGpjZ9qO+aVgR3gv6DWhw0/7Nrdrf3g6+CYAgwMGAvo1kwjfgY+BT8cvBHVo3Neb+ew8JrfUygKEcTWP93ZwSE07sID1fgsMoMSiwFxRwDbU9tR2pbF98VWht8PBYPtTpdmmPgNWgoUItg39mAnbq+EU2BQsqurn7QYWRewJ3ntsL3zKykBru8QwX8V6VMBz71Xwm9B47+txcT3/zGKUL4PXSivznu+54HWT86CVQpkWBaJAFIgCUWB8FNA/LnhfF/1RE/6/AJP/9r0tBjDxft809sX93vn0Z12G5rh0sjKv69KvKOuzyF/0N/U17oRSjGDCv6zP35RlMDrZlgKAyT6+2bsoEAWiQBSIAlEgCkSBKBAF5oYCdoB9fZ1P2pt0aUwA2Cm+Fkx+HwDOU6ctYOVHwx2wAuz4D8LszJ8DFgKYXNgcDAJcBieDiS01Mlk27maxx7HgsGpbjxV63H4Eah6LAnNFAdtZg4ded43trMFFg4wHQdXtq3G9w8HEq9el29ivWVR1Nlg4ZTu6CHqNH3ovWgj7gMu13VfDQWwni4mhgIHyneEjMA/qMos7ToAp+A9oZSYC1kC7AoFWv8m0KBAFokAUiAJRYLwU0A9oRL/PfqNxCf1B/QH76fbZTdaXRL1FAk4TCwecx/67lOIBE/zF99b/Nsbh0N86n8UFPt1vjMH1ue52fglfTbb16sBPtirZuygQBaJAFIgCUSAKRIEoEAWiwPgpYMfWTrLJgCdDYyLKju8K8LW8Twe/r9N8jfYLwI75jWAnfVCmDib9T4Izp4cmnQwO+PSBgYBxNxNoB8LiGnbE88uk3rcgSZwaDkBWWasCBi9tr54EtqNeD7Y5tjEGM/eHxgIsPg7d3BaLDy4Cg6huT7/mPeMWOBVc3tawFbi/szV/4+9NTq8E2y/bjjkdkGX/B2Xrs6DXw2uhl+MzqO24kAX5pp129wWvHe/DBucdj0WBKBAFokAUiAJzUwF9Sym+oH6BhYQWB+gn6lObyC+v8C/jTvd7MY7gb0qSvyxrEH4wi50MSwHAZBzH7EUUiAJRIApEgSgQBaJAFIgCUUAF7PD6ijuHJoUanxB3msmhH8JTYTOo09y2I2ALuBLc7kGbBREGFFy2AYVJSP6zG+v2ySKK50AdCZ9tWe8p4PkUiwJzTQHbEQOPvgXAdtZr0KDjFNiu7QlVX5c+qb8zXAA+MTUos91cBueDbfVCcJ97sS350c1gIdYTwEIK2+cEahGhR/M8OwA+A70elx5X/ZifeT0cDz7d384M2vt9uwKBdr/L9CgQBaJAFIgCUWDyFdAfbKYxqd/8Xfk8+cr0sYcpAOhDvPw0CkSBKBAFokAUiAJRIApEgSgwggrYGbZa3iSVTwZKo5kc8knRXcFEbp1m8mJ/MGHmU/sm7N3+2MwK2J9/MZiErNpM3t0Gl4OBmVgUmGsK+MSRSc+NoLSxtrleF9vAjlC1LWGFFiBcAoNOst7DMs8An+C2EGB7mO2bDmzvLQC4BdRMvbwfpc1HhB5MPb2H/w3s3cPvB/kTz41lYMFIK/M+YfLfV/rmntFKoUyLAlEgCkSBKBAFosCAFUgBwIAFzeKiQBSIAlEgCkSBKBAFokAUiAIjoIAJFZ+2MxhvokVMFhTzyUuTRNvBYqjbfHL1OXAF+HSoiaFYZwU8thZO7N55tqF8W86lb7L0dgmfoaw4C40CI6SARQAm2jeE9aa3y+vhZlgK86anVTmwoOp2uBYGnWh1f31by3dBs9DBYoDSHjitk6nNpbAcfPLfz74OPgUAiNCD+cT/8+G9MNtijB5W1/YnFnGcAHdDu2PpW3hWgudQLApEgSgQBaJAFIgCUaACBVIAUIHIWUUUiAJRIApEgSgQBaJAFIgCUaAmBUxOmew3OfVkaEzUmHz5EfgE665N3/GxctuaNR4HU3AHDPoJVhY5UWZyz6f/XwiNx7WqnTS56b8BMOkTiwJzUQGTnf7/UWksArD46lbYDzaBKs224Ci4CEy4DsPcv3On2ZShbwPo5k0kvuHl63AfWOTlciwAGHShAoucEzafvTwJvHfWaf/Ayi0otKijlZn0957+M8ixbqVQpkWBKBAFokAUiAJRYAgKpABgCKJmkVEgCkSBKBAFokAUiAJRIApEgRFSwES/gfcngEmaxmSxgfmrpqft1fQdHys3ixReBCYJTKD51GCsvQI+9XkMbNR+lqF946vGfd355ZCkztBkzoJHXIFSBGBC2yIArwvNxPYUHAy+gaVKcxueA+fAsAp03O974XTwbQObgYUA5U0IjD7GvA8574/Af1Fjsvie6fF2T43zdayNAhsz/b3g/bLxnt5m9qFNXs6STwSLOVqZx9b7+J1goUwsCkSBKBAFokAUiAJRoCIFUgBQkdBZTRSIAlEgCkSBKBAFokAUiAJRoEYFTE75ml77gBYBNL4u2O+uA5+436fpOz5Wbm7jM2EHuAb8lwCx1gpYwLEv7Nb666FONelkUck3weReLArMVQUsgPEacGj76nWh3QX3gUUAVcfffPPA3nA2DLOQykT+LfBdWA2bwrZQNGB0XXL4AoanwRpQJ7fJ4oS86QURZmlqeyR8CPw3AHWZx/4zcBM43sqcfgfof3jcY1EgCkSBKBAFokAUiAIVKVB1B6Si3cpqokAUiAJRIApEgSgQBaJAFIgCUaBJAYPvBuFN3Pqkvf3B8uSgQfqbwVc07wd19xXdrj3gELgSfHowT4kiQoOph8fUhNvzoRxLRiuz7VnTqbC2sjVmRVFgNBXwWjSZXdrXkgBfwTSv1adOf8egMlvImnwrwcUw7ES7BRA/hlII4L3kF2AR17fA5L8J//8LvgHANsN5YrNTwPPLdvfPYa/Z/XTgc1/FEr8KHs9W5nn/AHj/tlgtFgWiQBSIAlEgCkSBKFChAnUHdSrc1awqCkSBKBAFokAUiAJRIApEgSgw5xUwIO+TlyarfHLQJFVJHDvtNrgHngYlgcVobbYda34hXAc+OdruKUO+mtP2avZ+/RoU8BwxoXcRmACMRYG5rIDtk4l2rwvb1xJzu55x27KdoEqzbd8HbDttQ33by7DtEVbgvwU5H06Dr4Ov/bcYwO/uhVXwMHjPic1OAf/FwsvhnVDu3bNbwmDmtr3/CEx1WJxJ/3Ks9T1iUSAKRIEoEAWiQBSIAhUqUDojFa4yq4oCUSAKRIEoEAWiQBSIAlEgCkSBGhUwEG/yxWSQbwJ4IpREggmZFXAHHAB+V7f5v46PA58iXAmtZ/8+AABAAElEQVQmkmL/qYAJ+GfDgv+cVOnYEtb2TfBV57EoMNcVsF21CMBErUU5xt1sc31aen/YCqo0129bfgnYrleViLWd9gl/k/4/B9sHPz8IapTkPyLM0vzXPU+Dk8A3O9RpZ7Lyc6Bd4Zfnmf++x/t2CvcQIRYFokAUiAJRIApEgaoVSAFA1YpnfVEgCkSBKBAFokAUiAJRIApEgdFQwMSMyWOTVCb6TS5oBu59as9k+4FgIqtuc/t8E4BJj9vARFJViSxWNbKmBibSfCX04TVtpf/zfDVcBkn01HQQstqRUsAnn02MWmDlmwAssHLazXAoOL1Ks93cEc6Cn1W54oZ12VbZPqTdbhBlFqOeQ/Phr8B/J1Gnef/9BPgvHdodTwtA9CEshmk3D1/FokAUiAJRIApEgSgQBYalQAoAhqVslhsFokAUiAJRIApEgSgQBaJAFBh9BQzSlyIAE/2NRQA+LbocLAKwSKBuMwHitvgUrYm0uyAJ50cLANTmNVBHH991L4UzIG8BQIRYFECBUgRggUxpP3/KuP9i5RCo+lrdgXX6JL5vAki7iQhjZp5HL4F3gW1uXWYy/zNwKbQ7j5x+L/gGgHbz8FUsCkSBKBAFokAUiAJRYJgKVN3hGOa+ZNlRIApEgSgQBaJAFIgCUSAKRIEoMHsFfELPp1VNUjUXAaxl2q1g4t0nWUfBFrIRL4ApsEAhCYZHiziORYstoA7blJX6ZoZrwcRnLApEgUevS6+HjcC3mJg8vXN6fC+GVSdyn8E6b5iGQWxMFPA82Q2+CJ5LdZrnz+fgkQ4bYWGhBYQO8/R/B6HyVRSIAlEgCkSBKBAFhqlACgCGqW6WHQWiQBSIAlEgCkSBKBAFokAUGA8FLACwEKBVEYBP2vvEvUUAVb+6mlW2NJMgx4DJtZvA7fdV+HPV3Pedwf8PXZc9hRWfAv6P71gUiAKPJj9L27QxgjwB/g1WwGJYAFWab3h5JnwffH17bDwUmM9mHg91v/rftwV9HDx/291vTfhbOHgfpDgPEWJRIApEgSgQBaJAFKhLgRQA1KV81hsFokAUiAJRIApEgSgQBaJAFBgtBQzuWwTgWwAsBGj8dwAmi0y0HwC+ingUzP7skbA73AoPgMm1uWjut0+JvgLKcataBxOcvt78crAwIxYFosCjRQC2q16XFi459Mno28CiKq+bKs1t2BUsAnioyhVnXT0p4NtV3gZvB9v4Ou1cVn4aeP62M//NxSrQn4hFgSgQBaJAFIgCUSAK1KhACgBqFD+rjgJRIApEgSgQBaJAFIgCUSAKjJgCBu0N7jcXAbiZJnd9/a9FABvCKJgJkaXwfPBNBSvBZNtcNJ809q0Idf0bAI/F5mCC6CcQiwJR4FEFfFradsnCKguovFa8RtbAoeCbAaq0HViZRUOXge1GbDQV8D58MPwdPLnmTbTA7qPgfbadWfjlOW0RQLs3BLT7baZHgSgQBaJAFIgCUSAKDFiBFAAMWNAsLgpEgSgQBaJAFIgCUSAKRIEoMOYKGMQ3WeX/rH4SND5Rfi+frwdfNe+TpKNim7AhLwKTJNfBz8FXEc8l83XL24MJo7psG1bsU6I+3TzX9K9L86x3PBQw4W5xlcVTJZm7lnGLrmxPLQqoylzXfnAlLIe8qh0RRsw8RrbnJ8AuNW+bbfnn4XJod644Tylq8VyPRYEoEAWiQBSIAlEgCtSsQAoAaj4AWX0UiAJRIApEgSgQBaJAFIgCUWAEFShFAD6Z6lOrjX3H+/hsEYAJpKpfX80q25rbaPJbbgSfQjS5NlfMxIyJl1dB1U8UF40tFvGV1afDXH0TQ9EiwyjQrIDXqNeFxVM+3a3dDItgMVRpFnj5Npez4H5IwQ4ijJBtxra8F14BVRaHtJLA++mXwHtqO9NnWAkW3+Xp/3YqZXoUiAJRIApEgSgQBSpUoDGIU+Fqs6ooEAWiQBSIAlEgCkSBKBAFokAUGHEFDOj/C7QqAjBhdAPsCz59P0q2kI0xaWIyfDX4f67nSnLL5OIzQQ3qsq1Y8Sngv4yIRYEo8J8K2A7Zrto2WQRgEt5k6S1wCFRdULU565wPZ0IKdhBhRMy3RFjI9SdQVzFXkcIiug/DbdDuPuo5bGGgbb7ndiwKRIEoEAWiQBSIAlFgBBRIAcAIHIRsQhSIAlEgCkSBKBAFokAUiAJRYEQVmKkI4Hq2e2/wqe9RMt9acCRYoHAr+H+L2yUv+GpizCeMTSoeDXU9Neq/jbgUroFYFIgCj1WgFAF4fW4AJnh/ASZPnwFVx+l2ZZ0WennNJnmLCDWb9y7fYuOr/0ehuO6f2Q7fEuG9pZ1ZPLIKHoG5cJ9tp0OmR4EoEAWiQBSIAlFgpBSoumMxUjufjYkCUSAKRIEoEAWiQBSIAlEgCkSBGRUoRQAmlpv/HYBvArAIYC/wadJRMhNsi+E4+BncDQ/DJL+e2H0ziec+m1ysw9Tdc+VU8OnRWBSIAo9VwGSq14b/BuDJ4DVjAYDX7O7TnxlUYq77ULgNfKvLJLeP7N5Im8UgO8InYOkIbOkKtuEz0OnV/yb814BvAOhUJMDXsSgQBaJAFIgCUSAKRIEqFUgBQJVqZ11RIApEgSgQBaJAFIgCUSAKRIHxVMAiAJ/yM7HrE97+r/diDzBiEYCJqy3LxBEaur1Hw35gkm0tTHJi2gKAPWBPqMt8cvVL0ClxVNe2Zb1RYBQU8Dq1HTLpbxvl5ynYB/w3GlWa7flRcAWY9M1T3IhQsVmI4XH/M3hBxetutTrv+R+Hm6BTYv8nfL8aJvmeyu7FokAUiAJRIApEgSgwfgqkAGD8jlm2OApEgSgQBaJAFIgCUSAKRIEoUIcCzW8CaC4C8OnRnWHrOjZuhnWaXNkBXgqO3wwmLDolNvh6LM0neH2y+CXgvtZhJjR9A8BUHSvPOqPAmChgm2rifyOwuMpXqK+Ew8BruEpzfc+CS2ANpAgAESq0zVjXO+CdUFe73bi73+HDd8HCv3bmPdRz5SHImyPaqZTpUSAKRIEoEAWiQBSoSYEUANQkfFYbBaJAFIgCUSAKRIEoEAWiQBQYQwUM+Pv/ov1XACaMGosAHuTzjWCifVsYRTPJdjg8H+6Ge8Gk2yTaq9kpE/F1mOfFNfDDOlaedUaBMVLANtVk+8ZgjM42yaTrgVB1IthtOBQuAN+WEqtGAd+Y8ir4axiFOK1FKB8F7+ntzHPW7y0AsIglFgWiQBSIAlEgCkSBKDBiCoyCYzlikmRzokAUiAJRIApEgSgQBaJAFIgCUaCDAiasTFCZXG4uAvB1wBYBbA8LYFTNtxQcC0tgOfiqet8GYFJjEsynMZ8L82vcGQssvlXj+rPqKDAOCtjmWFRlW+qbALTbYDHsAFWbr6E/AC4C/697bLgKWHRhQdoJUFfBVuMe+laK48FzsNNT/Z6zU2AB3aTcN9mVWBSIAlEgCkSBKBAFJkeBFABMzrHMnkSBKBAFokAUiAJRIApEgSgQBapSwCKAX8CTobkIwGT6TbA5LIb/CqNoPqW+J7wcHL8fSiEAo2NrJmNkv2nq2hELKk6sa+VZbxQYIwVMtFpUtQHYpnr9roRngNOqNgu4bD8uBtvF2HAUMPl/GHwefAvAKNgZbMTp4P29ndm2W+Blgcgk/huddvud6VEgCkSBKBAFokAUGCsFUgAwVocrGxsFokAUiAJRIApEgSgQBaJAFBgZBUoRgAkq/yVAY6Lf/wl8M5jM2qXpOz6OlLmNR8DR4BsMpsCkRqenH/l6pM0E4lI4qsatdBt8jbTDWBSIAp0VKEUAJoWfCD8D29GngwVKVdtCVui/IbgELALIdYwIAzTf9nAIfBG2HOBy+1nUFD/+OPhvKDrZw3y5GnwLQM6LTkrluygQBaJAFIgCUSAK1KhACgBqFD+rjgJRIApEgSgQBaJAFIgCUSAKjLkCFgH4CuBWRQAmCSwCMLG1B9SRxGK1XZnFC76x4Bg4GO4E/7+xT+WOo7k/vt3gRTVuvNtgAUD+P3SNByGrHhsFTKRaeCQmh20vbYdMDltEVYf5L0R8Qv0H4NPe41wUxeaPjG3Ilqiryf9tRmSrvNf9FXjP7mSen6vAYrmcD52UyndRIApEgSgQBaJAFKhZgRQA1HwAsvooEAWiQBSIAlEgCkSBKBAFosCYK+D/DP45mNRYHxrfBOBrhG8BiwH2gVHvg7rti+GVsCP4JKT7ZqHDOCU71PlYeCbUZSY0TwDPgTwlWtdRyHrHSQHbGNtT/62KbyYx2boc9oUtoA4zQf18uALugRT0IEIfZrGcb2b5AmzVx3IG+VPbZ4sRLgTPv3bmfJ4DFqbkPGinUqZHgSgQBaJAFIgCUWBEFBj14MuIyJTNiAJRIApEgSgQBaJAFIgCUSAKRIE2CpgUMGlgkt8igCdBo5k8vxXuBv+vtK+3HnWzr7wXvBp2A1+BbTGACblRT2ZbxDAf3g0LoS4zmXki+BrzcSqeqEuvrDcKqICJVdtM/ye8hQC2qxYBHD79mUHltilrfCncDnfAuL4ZhU2vzWyX/fcO6vh5UNNRsSvZkC+BT/V3Ms9Fj/8jMOr3wU77ke+iQBSIAlEgCkSBKDAnFEgBwJw4zNnJKBAFokAUiAJRIApEgSgQBaLAUBUwGWDiyv9b3epNACbOV4CJrKeCT7eOgz2BjdwdXgX+GwMTJP7f41F9qt0kk08KvxbeAn6uy0xifhUsnMjTonUdhax3HBWwoMqimc3AuJ1PXdv2+O9J6rqmLezyX6R4Ld8GtvWx7hTwGPqvHN4Kfw/NRXJMqs381w4fhpXQKanvOWkRn/N7P49FgSgQBaJAFIgCUSAKjLgCKQAY8QOUzYsCUSAKRIEoEAWiQBSIAlEgCoyJAiYPTAz4xPdG0PzvAPzepwdvApPqJrfGxew7u82+EeBIsDDgp+CTsCZGRsF8Wnge/Ab8Bfw3qNN8WvRr4L9QGNWCiTr1ybqjQCcFLKDxGvapcZP+q8GnxneFusx28DDYCa4Hk8GdksZ8PefNdtm3yHwAfCuLGo6KeY79H7gKZjqO3u9Wwajc79iUWBSIAlEgCkSBKBAFokAnBUbJ8ey0nfkuCkSBKBAFokAUiAJRIApEgSgQBUZfAZMIFgH4dOgGYPKj+YlVn2a9AebDdjBOZkLObX4+vAZ2gLLPJlPc95kSKcwyMHN7LLRwO54FH4XXg9PrtrVswKngE8M+vewTzbEoEAW6U8B2xDblyWBb6nU0BSaTt4G6zPbcbbC9sR1fDbm2EaGF+TacZ8Mn4GhovhcyqTbz/PK1/2fBTEl9C92mwGKuHGtEiEWBKBAFokAUiAJRYBwUSAHAOBylbGMUiAJRIApEgSgQBaJAFIgCUWB8FDCxYLLKZIHJK4sAmhPS9zPNJ0h9kn4XGKXECJvTlZmU2w/89wCvhKXgvrpP7r86DCNZopaux1dK7wWvg7+Gt8NCGBXzqdILwKKIB6aHDGJRIAp0qYDth8lZ3wLwRLBNXQkHgMnlOm0rVn4c+BaAG8E2L/aoAt4DNoe3wsdhEYyaXcwGfRl8Y08n87jeDfeCbXksCkSBKBAFokAUiAJRYEwUSAHAmByobGYUiAJRIApEgSgQBaJAFIgCUWCMFDD5beLKV78/CUxeNRcB+JYA/x3Ag7A3mDQZR7N4wQTdU+Hl4BP4R8ES8N8cmKz3KX3nk2JqNJM5v/12l7EJ+PaB/eEV8AfTPJehybjGZfOxdjuDLfAJYY+rb32Y6SlTZolFgSjQoIBtREm6mvC3LTDhbjL2QLBdrdNsl14Atku3ga+JL9vL6Jy0jdhr72e+jeU3wbZ/1GyKDfoY3AGd7kMWoDwCK+BfZpiXr2NRIApEgSgQBaJAFIgCo6TAqAUIRkmbbEsUiAJRIApEgSgQBaJAFIgCUSAK9KeAfU4T1z4B6f+vbpXkN4lkUvv3wET2JJnJFRMoJlquhmvBRNld4NO84hOW0piIMbEnaueTpEvhEDDpNx9a6cjkkTGT/e+DK8Ek0i2QJ0gRIRYFelDAt40sgS3AIgA5Fn4D6i4CYBPW2a38/SM4E2Z6qnzdDybsjwVuO8Dr4F3g/W4U7UE26oPgvajxnsPHx5ntuEV6/guXuV7Y8ThxMiEK/H/27jXW2rSu73hSK+AwDAzDDIMwB2YQLCDU2lYtWsXGN2qT+oLUkGibvughsekb3vVlmzaVYlKTpiamGmPUNE0xalvFWHVUEFHKSURkmBMDM8NhhoN0QBPT/3d8brPZ7GfvvfbpWffanyv5zdp7He/9WfNcz7PX/39dNwECBAgQILDtAhoAtv0dcnwECBAgQIAAAQIECBBYt0C/d7YqsiaAitkHFa+7T0XuH5y8crLLo6JLBf8aA1oxW3Gly1ZYNloxmtNNk3YQ6Pv9uyfMVVs9PjJH94ZJK//7eR+adF0/t0GAwOYCzQkvmbQTQKN5tGJzO45sy/zQHPajkzdN+vN+GUYNGLdMvmXyryf9/bWtn7W2I8+/m7x1clTxv8at3sOHJ83bR91/7mIQIECAAAECBAhsk8BXbNPBOBYCBAgQIECAAAECBAgQ2EmBVhJWfGi1fznod9HH5/p3T5ZmgG0pas0hneno5+vn79QIrRJ9/uSOyV1X0tcVlJbzfm9rMWkO8arjLXPL2yetGu34vzBp5WlFJYMAgc0FlgahmoKaG/uz9P7JV01ePtmGeaKmhG+avHbywUm7fuxq00/vQfN3p0D44cm/mtw62Yb3YQ7jy0Z/B9eccc/kOO9J83UNAH86UfwfBIMAAQIECBAgsDaBgz50WdvP4HgJECBAgAABAgQIECBAYPsFliaAVrRfrQngc3NbhaMHJq+aVCQ31iVQsb9CU8W/pXBUEamCkm2kB8EgcEKB/mw1d7YLQIXmmgBqmqop4KWTbSk+3zrH8n2T5u+PTvqzvyvNP/nfNvn2STsdtNNJTVvbYj+H8mUj+5+e/NKk084cNWo2+fCk+y5z+FGPcTsBAgQIECBAgMCWCWgA2LI3xOEQIECAAAECBAgQIEBghwVqAqioUBNAOeh30ooPrTz8/Umr4m+ebHNxZQ7P2CPw6/P1r056H5fR+14RsEuDAIGTCVSM7dQh10+aP5cmgP87Xz9v0ikCtmWubDeA10y+e9Ix3zfZOyfMt6sZmV43uX3yA5NW/P+LSX8/tRPANo+K/z83efOkOfio0f0fnHRfDVtHabmdAAECBAgQILDFAgd92LLFh+vQCBAgQIAAAQIECBAgQGDlAm0/3Er/CkStEK2Asr9oVRHiU5OaALr9ayZ+fx2ELR8V+v7r5OFJ7+Ey2gHg8UkNAFaULiouCWwu0J+r/jwtpwjpGSrUvnNSE8Ddk/3z6Vx1zUa7E7RN/l+ffHDS3L+WRqCaLGpA+3uTVvr/x8k/mOS8TcZzOAeO5tr/PWn1/ycPvMeXXtn927nlkUn/jxkECBAgQIAAAQIrFvAByorfPIdOgAABAgQIECBAgACBlQpUsPrspOJ+57Dud9ODCirtFvCHkw9NXjap6GVsr8A9c2i/Mnly3yHW9FEDQEUlDQD7cHxLYAOB/vz056lGgBsmy+d6XfeuyY2TbdoJYA7nqbm9Y/r+yV2Tz0w+PumYt20sRf+/Mwf2TydvnPyzyddP+rtqLaP/T/7n5Mcnzb1Hje5fc8ZDkxq5zNODYBAgQIAAAQIE1iyw/KKw5p/BsRMgQIAAAQIECBAgQIDA+gSWgkOX7QTwlVf5ESoaf2zy1klbX1dAOqhZYK42rqHAY/PaPzJ5dLK/eNSK33Z00AAwCAaBUwpU/O/PVLuoPHNSI1Wj694z6bqXTrZtnux4XzX5h5NXTGoC+JNJpwbYP2fMVec+8umYaqS4Y/Kdkx+c/PtJW/y/ZlJDxbY5ziEdOvr/4xcm7cZSo91xRu9Bxf+aM2rQMwgQIECAAAECBFYusLZ/xK6c2+ETIECAAAECBAgQIECAwD6BCjBtqfyiSQX+w0a3f8fkH02ee9gd3XahAhUef2jyW5ODzvPdytK2/25lacUpgwCB0wtcN09x96Qi9dIE0LN2/esnFdqbX7d1VGj+wOS/T1qtvhSg2xngPBoCMqrRrAaJF0xePGml/7dNXjlph5m1f06a6ZsnPzVp3j3O6DEPTmreqknLIECAAAECBAgQ2AGBtf/DdgfeAj8CAQIECBAgQIAAAQIELr1ARapnT26/cnkYSFs0V/SquPWtE7/XDsI1HBXqKt79xOTTk4MKd0/M9R+aPHmV2+dqgwCBDQWa+ypav2TSKva9o+3qv2fyTybtsLLto8LzvZOaiN42uX/yyKQV7DUOdXuNAUc1EFXkL/09UWoaq1ns1kl/b7x68g1Xvs6u++7KqBHrZyf/Y3Lclf/N1+3eUgPAFyYHzd9ztUGAAAECBAgQILA2AR+UrO0dc7wECBAgQIAAAQIECBDYTYGvmB+rlat3TvavaJ2rvmRUtGkV5zdP2qr5ORPj2gj8/rzsmyatHr3a+MTcUHGvIp4C09WUXE9gc4Hmzea/uybNiXvH0+abb5n8y8ma5sjmiIrZj08emtw/qUBdobomoxqJagSoIaDPNZcifk0PFfyXHWXumK/vnDx/kk1WuzpqkvjRyT2TTYr/bfl/3+RPJkc1V8xdDAIECBAgQIAAgbUI9A9lgwABAgQIECBAgAABAgQIbINAhZxWbd4+qYjTds2HjQo6d09eN3ntZJcLPPPjbd34ozmi/zCpSHdY8ejhuf2BSQU7gwCBsxVoB5VWub94UhF87+i2vzZ5w6R5dRdGDQJl71g+31wu9962619/fH7AH568b1IjwHFGfp+f3DepCaDTABgECBAgQIAAAQI7JODDkR16M/0oBAgQIECAAAECBAgQWLlARYkKEZ27uK9rBjisCaD7tEq0wscfTr5m0qkEjPMXaEV/K/8r/h9WPOo9emTSCtO+NggQOFuBmm9aMV9aAV/Rfxnd9snJ705qAHjhZO2jIv/VsvafbdPjf/884N9Oasb64gYP7r4PTNpR4bD5e242CBAgQIAAAQIE1iigAWCN75pjJkCAAAECBAgQIECAwG4LVJBoJWNFis5fXSPAYaP7fXTy9kmPvXPS9tfG+Qj8wTztGyf3T45a1d/tH5m0/b9BgMD5CDTv1QDQZdvd720CqPGmpqq3Tdoh4GWTy7hSfn7snRm9z2+Z/OfJUTuw7P+h+/+kOflTk742CBAgQIAAAQIEdlBAA8AOvql+JAIECBAgQIAAAQIECOyAQAWOCvudz7gGgBoBDitaVeRqlXk7AbxzUqGrFa+dVsA4G4GMf2PSuaYr/vf9UeOJucMnJr2fBgEC5yewzJn9ubxusrcJoFet2PuuSc0AXzc5bHeVudnYUoG27P+RyS9Mmls3Gf0/8vDk0Yni/yZy7kuAAAECBAgQWJmABoCVvWEOlwABAgQIECBAgAABApdIoO2rK1hU8Kj4XxPAUb/HVtRoy+t3TD4weeHkpslhzQNzs3GEQCv4f2bys5NHJscp/rf6v/eibaZ7Lw0CBM5XoPnyC1deovlyfxNAt7dd/H2Tr53cMDHWIdCc+57Jv5l02psa3jYZzccfu5K+Ps4cvsnzuy8BAgQIECBAgMAWCRz1wckWHapDIUCAAAECBAgQIECAAIFLKFCRoqJVxY6K0BW1njY5bPSYGgEqdrx98sDk7klbY2sEGIQNRwX//zRpy+lWDx+nmN970A4ObU/dpUGAwMUINF92CpVGO6HsbwLoz2arwN87ec7k9ol5cRC2ePR+/rfJj036e60C/iajvw8fm/TY/h49zhw+dzMIECBAgAABAgTWKqABYK3vnOMmQIAAAQIECBAgQIDA5RKoqPXkpEaAGgCePjmqaFWhq8e02vVtk1aj3zx59uSox85dLv2oyPS/Jm35//5JK4szPc7ofhWbOs+0YtNxxNyHwNkJLE0A/TlcmgD2z3mdnuPdk/6Mvnqyv1FgrjKusUDvX6v+3zS5Z9IpcTYdFfx7j2v62GQO3/R13J8AAQIECBAgQGCLBDQAbNGb4VAIECBAgAABAgQIECBA4FCBCsmtZOyUAI0KW39lsr+w1W17R0WUGgc+NPntSQ0Bd06eNTnqsXOXSzfy+uPJGye/PKlxIvdNRu/RRyYVnwwCBC5eoPmyleM1A1ytCaCCcH/WKzK/YlJzlLEdAs2hPzb58clHJyeZS3tM8/eyE0tzu0GAAAECBAgQIHAJBDQAXII32Y9IgAABAgQIECBAgACBHRKogFFBq4L+5yedEuArJ8cp5Pe4HnP/5K2Teydtgf3cSY0El31k20rRik4/PalRou37Ny0aVXRqtemnT/DYeYhBgMAZCdQE0JxXA8/SBLB/rus+j01+d1IDwB2T/feZq4wLEui9+qXJf5n0ntTEsekcPA956jQBnb6l5oGTzOM9h0GAAAECBAgQILBSgeN8QLLSH81hEyBAgAABAgQIECBAgMCOC7RldQ0AL5rcNOn74/6e2/1qir9h8uLJ3518x+SZk+M+x9x1J0bFpVbr/+rk1yYVA2uWOEnRqcc8OnlgUiPASZ5jHmYQIHCGAs1pN05um7TzSXPlQeP6ubK58B9Pbp4YFyfQnPv+yU9O/mjS6WtOOn92+pbm9OZi8/AgGAQIECBAgACByyZw2T7UuGzvr5+XAAECBAgQIECAAAECuy7QStV2AKgBoEaAp08q7G8yeo6nTZ4/+YZJBbCXT3reXR4Vid47+fnJByedX/o054iuWPW5yYevXP75XBoECGyHQJ8B1vDUPNnOJ1eb37q++7x+UlNU86NxfgLNm++bvHnyB5MnJqeZO1vt/9Dk45Pm+JM2EcxDDQIECBAgQIAAgbUKaABY6zvnuAkQIECAAAECBAgQIEBgEeh324r+1032Frc2/Z23+1f8+qorz/M35/KbJ3dNrlYsm5tWNSoGVRh6x+T/TB6cdDqFCkWnHW1Vff/k8UmrWQ0CBLZLoDmuXU5eOOnUJzVMHTSWufA1c+M/n9xy0J1cdyqBivz3Tn5m8s5JK/5PM282tzeXV/zv9CuK/4NgECBAgAABAgQuq0D/oDcIECBAgAABAgQIECBAgMAuCLRStUJ9W1e3mr+GgE13A5iHPDWWAljFshdMXjH5psnXTmoQWNPv0xWaKsq/e/Ibk4r0n5mc9NzS89AvGRWeKjb1vJ+YdA5rgwCB7RRo7nrG5NZJhf3ms6uN5tTbJ39/8l2THmecTqD5sRX/vzhpy/+zaJhqDu55Kv5/fnIWDV3zNAYBAgQIECBAgMBaBdb0gcVajR03AQIECBAgQIAAAQIECFycQL/nLrsBVOC6aXK1Va7HPaqes3NmVyh73uS2yd+YvHrSa3T6gG37/brzPn900srS90wemCxF/xoCKhid1ei1Kjw9NrHq9KxUPQ+B8xVo3qpZqganmqUq9h80mtua+2oE+IHJN06udt+5ybiKQA1Xvzn5lcm9kwr1zcWnHe0a8Mjk0clpdxE47bF4PAECBAgQIECAwJYIbNsHFFvC4jAIECBAgAABAgQIECBAYOUCFajaDeDZk7a7vn5y0t0A5qF/Ofo9uueueNbuADdO7p68dNIuAUsx7Sxea57uWKNi/hcnFeD/eNJK/wcnH5+0JXQF+rMu+s9TPjW+MP99eNJrtbL1LBsL5ukMAgTOUaB5qjmsOfJZkxqdrjaWRoC/NXd4/eQlE40AV9P6i+ubd++b/NrkHZOPTZozz2Ke7Dl6rocmrf5v/u31DAIECBAgQIAAAQJbt0LBW0KAAAECBAgQIECAAAECBM5KYCnWtwNApwRo9X4rWc+6aFURrWaDnrsi2nMmt0/umNw1aZeAGyYdR/ftuMomo2JPaYV9K0k/NWmFfwX/CkAVlj476baaASoEnWcxqGNptelHJp+cWPk/CAaBFQo0FzVv1bzUjik1Nx02mj+7XzugvG6iEWAQ9ozmxubn35v8+qTV/p+bNEee1Whu7zWaf5vz2wWg1zUIECBAgAABAgQIPCWw6QcO2AgQIECAAAECBAgQIECAwBoFKtBX5Lpl0orXvj/rRoB5yr8cFfpbTVsxrcaAttiuMaA890o6nlJjQPfreCrilIr4pcLRpydPTCq0t41/hf62j17uU/HnPIv98/RfMnq9Vp7eN+l4zrKwNU9nECBwwQJ9PviMSU1SXz1pzjpqNF+1C8rXT7538srJYTsIzM07O5qzm6ffM7ln8sFJc3Zz9FkW5pvnW+lf81d/HzQPX+TcPy9nECBAgAABAgQIrEFAA8Aa3iXHSIAAAQIECBAgQIAAAQJnIVDBqsJ/pwWoEaBi/GUtWM2PfqJRsb9C14OTmhCWhoX50iBAYOUCNSI1P75o0q4lx/ncsHm1+945ee3k2ybHfezcdbWjRqhOffKuye9Maoiq6F9R/iyL/vN0T40K/zWEteq/U7s0F5/H68zTGgQIECBAgAABAmsXOM4/5Nf+Mzp+AgQIECBAgAABAgQIECCwV6DV+RW6Wn3fatdWsXZdhSzjYIGKXcvK04pezjd9sJNrCaxdoLmwHQBqAmhHgE2apLpvp1t51eTbJ183Oc5uAnO3rR8V22t6undS0f+9k1bityPLn07Osxhfwf/Ryacmy64C5/l68zIGAQIECBAgQIDAmgU0AKz53XPsBAgQIECAAAECBAgQIHBSgX4fruBfwaot+W+dVKhqhwC/Kw/CnlHBqa3+PzapEFUzgOLTIBgEdlSgObAmqZsmL5w0N27SINV9O7VJj3355G9PXjFpZ4BNnmfufs1G81wF/wcmFfvb1v/BSTugPDlpBf55jubY5t62+q/4384C5t5BMAgQIECAAAECBI4W8KHG0UbuQYAAAQIECBAgQIAAAQK7LdCK1wr/nRKgVPSqMeCy/85csanVrRWfagBYVrkq/g+GQeASCDQvXjdpN4Dmxr7fdCw7rtw4D7xt8rLJqyd3TmoI6PZrPdf++RxDBfbHJh+efGDSlv7tdlLBv7nvoorvza+9XnPuI5O2/b+o156XMggQIECAAAECBHZB4Fr/A3sXDP0MBAgQIECAAAECBAgQILB+gX4/bmVqxahnTG6ZVLBqFesmW2DP3Vc9KoRVgGqlf8WnZXtrBajBMAhcQoGlQerm+dlfMGk3gNN8nrjsDlDxv91Xvnry4iu5bS5rNGgOXhoDTvNa8zRPjea00qr9Cv0V9WtseuBKKvy30r6ie7cvzU7z5YWOXrdja6v/Lp1qZRAMAgQIECBAgACBzQXO4h/Rm7+qRxAgQIAAAQIECBAgQIAAge0U6PfkUvGp4v+zJxWqKkrVCND1uzgqjlX4Kq16fXxSsWxpCJgvDQIELqnAMic+c37+mgCaDyvSn9WoKaD5teaC6yfPmrQTy/MmNQn0dfNwr9/rthNBc3GP69iav5qrmrMqojePtX1/RfQnJhXUK/D3fU1NrarvfttQYK+5qmPv2Gq6+n+TpQGhn8sgQIAAAQIECBAgsLFA/0g2CBAgQIAAAQIECBAgQIAAgS8XqLhUlmaAilA1BOwtQM23qx0Vlyo+dT7rimStfm3lv8L/IBgECHyZQJ8jPm3S7ijPnzQfNkcaJxOoSaF5twaFmhKWuXe+NAgQIECAAAECBAicXEADwMntPJIAAQIECBAgQIAAAQIELo/A0ghQ8asGgFaoLqtgW43a7Wv4HXsp+rdCtsJ/hacKUEvhyYr/wTAIEDhUoNX6zYOtzq8RoJX7GgEG4Rij1f1fnHTagebevl7m3/nSIECAAAECBAgQIHB6gTV8OHH6n9IzECBAgAABAgQIECBAgACBsxHo9+hSsatmgApfFcJaCdvW1Xu3pp5vt2JU1G+lf4WmVvi31XTF/5oAlsKTraYHwyBA4NgCzYNLI8At8/XNkzU1Qx37Bz2DOzbPVvhv7i3Nw33fvGzuHQSDAAECBAgQIEDgbAU0AJytp2cjQIAAAQIECBAgQIAAgcsjsPxOXTNAhbCnT2oKaHeAUmNA33fbRY4KSkthv/NJt8q0y4r+NQEs55yeLxWfQjAIEDixQPNgp0m5btJuADdc+brrLvNYmq7a5r+i/2cne+dfhf/L/H+Hn50AAQIECBAgcM4Cy4cV5/wynp4AAQIECBAgQIAAAQIECFwKgeVUABW/lqJYuwTUHLA0BfS7eCtlG6fdNrsi0lLQr8jfqtIuO5/0n135fmkG6L6KToNgECBw5gLNa8uOADUB3DqpCap58LTz3DzFKsZS9G8OruBf4b85eZmD243FIECAAAECBAgQIHDuAhoAzp3YCxAgQIAAAQIECBAgQIDAJRbo9+69qRBW8b9CWZftELB8vzQNLE0Ey+/sS+F+2cq/YlKpsFQq9Pd9xaey3H++VPAPwSBA4MIEmreay5rbnjm5cfKcK99f9G4o87LnPpp7O51K2/rXeFXhf5mTm7MV/QfBIECAAAECBAgQuFiB5cOEi31Vr0aAAAECBAgQIECAAAECBC6nwPJ7+P7LNJZVsksDwCK0rNqvkHRQcX+5fblcHueSAAEC10qgOa65rGaAdgJoB5R2Brj+yvddv7bRHLs0YLW1f0X/Vvt3uTRhLfP0XGUQIECAAAECBAgQuDYCywcO1+bVvSoBAgQIECBAgAABAgQIECCwCBznd3RF/kXLJQECaxFoblt2OOl0KDUBtDtAl9dNum1pgJovt2Ysp1dphX+p6N8q/2X3lYr+Cv6DYBAgQIAAAQIECGyXwHE+XNiuI3Y0BAgQIECAAAECBAgQIECAAAECBAisUaDPIksF/05/0u4AS1NAzQB93+kDlqaB+fJCRsX+CvpfnDw5Wbb1X4r9betfA5aC/yAYBAgQIECAAAEC2y2gAWC73x9HR4AAAQIECBAgQIAAAQIECBAgQGCXBZaGgC5rCvirk5oAagZYmgS6rFFgOa3A3sfM1QeOCvZLlsJ9hfyK/RX4+7rLpcjfZffr9mW3leXxc5VBgAABAgQIECBAYB0CGgDW8T45SgIECBAgQIAAAQIECBAgQIAAAQKXQaDPK5fPLJevl8saAErfL6cOWG6bq566viJ+Yyn67y3qL9ftLezv//ovHu2/BAgQIECAAAECBFYq0D+QDQIECBAgQIAAAQIECBAgQIAAAQIECGy7wPJZ5nK593iX65bV+3tv23vd3q/33sfXBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQI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AgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAj8/3boQAAAAABAkL/1IBdCBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgIH9QJ9J0JAm0rM6AAAAAElFTkSuQmCC" + return (SALMON_DATA_URI,) + + +@app.cell(hide_code=True) +def _(SALMON_DATA_URI, mo): + _salmon_html = mo.Html(f""" + +
+ Dead salmon outline (X eyes), with rising stink lines + + + + + +
+ """) + + mo.vstack( + [ + _salmon_html, + mo.md( + """ + # Exploring Statistical Fragility + + *An exploratory notebook by [Jesse Hartman](https://www.linkedin.com/in/jesse-ryan-hartman/). + Please engage with a critical eye — vibe coding abounds.* + + A visual companion to **["The Dead Salmons of AI Interpretability"](https://arxiv.org/abs/2512.18792)** + (Méloux, Dirupo, Portet & Peyrard, 2025) — which revives the famous + *dead-salmon fMRI* cautionary tale and argues that modern AI + interpretability methods are vulnerable to the same class of + statistical false-positive failures: feature attribution, probing, + causal discovery, sparse autoencoders, concept directions and + mechanistic circuit search can all produce confident-looking + explanations for **randomly initialized models on random data**. + + My motivation here is personal — I wanted to *see* the failure modes + the paper describes, not just read about them. Each section below + rebuilds one of those traps as an interactive marimo demo: drag a + slider, move a threshold, watch noise dress itself up as a finding. + The voxel demo immediately below is the warm-up; the modern + interpretability case studies start further down. + + Move the sliders below to see how the false-positive rate and + Bonferroni threshold change with the experiment shape. + """ + ), + ], + align="center", + ) + return + + +@app.cell(hide_code=True) +def _(mo): + mo.md(r""" + ### The gist, in plain English + + Imagine **20,000 people** each flip a coin 200 times, and you look for + someone whose flips match a pattern you picked in advance. Even though + every coin is fair, with that many people you'll find **about 1,000** + whose flips "match" by pure luck. + + That's what happens here — except the "people" are **voxels** (tiny 3D + pixels of a brain scan) and the "pattern" is a fake task. No voxel has + any real signal. But if you accept anything with "p < 0.05" as a + finding, you'll announce ~1,000 discoveries that are entirely noise. + + The **Bonferroni correction** raises the bar: a finding only counts if + its p-value beats `0.05 / 20,000`. Apply it and the false alarms almost + entirely disappear. + """) + return + + +@app.cell(hide_code=True) +def _(): + import math + from dataclasses import dataclass + + import anywidget + import marimo as mo + import numpy as np + import traitlets + from vega_datasets import data + + return anywidget, data, dataclass, math, mo, np, traitlets + + +@app.cell(hide_code=True) +def _(math, np): + def normal_cdf(x: np.ndarray) -> np.ndarray: + """Approximate the standard normal CDF elementwise.""" + erf_vec = np.vectorize(math.erf) + return 0.5 * (1.0 + erf_vec(x / math.sqrt(2.0))) + + return (normal_cdf,) + + +@app.cell(hide_code=True) +def _(dataclass, normal_cdf, np): + @dataclass + class ExperimentResult: + """Container for the simulation outputs.""" + + n_voxels: int + alpha: float + n_uncorrected_significant: int + bonferroni_threshold: float + n_bonferroni_significant: int + p_values: np.ndarray + correlations: np.ndarray + + + def simulate_false_positives( + n_timepoints: int, + n_voxels: int, + alpha: float, + seed: int, + ) -> ExperimentResult: + """Run voxel-wise tests on pure noise and count false positives.""" + rng = np.random.default_rng(seed) + + # Fake experimental regressor (e.g. "emotional vs non-emotional" blocks). + task = rng.integers(0, 2, size=n_timepoints).astype(float) + task = (task - task.mean()) / task.std(ddof=1) + + # Pure noise: no real signal in any voxel. + noise = rng.normal(size=(n_timepoints, n_voxels)) + noise = (noise - noise.mean(axis=0)) / noise.std(axis=0, ddof=1) + + # Pearson correlation between each voxel and the fake task. + correlations = (task[:, None] * noise).sum(axis=0) / (n_timepoints - 1) + + df = n_timepoints - 2 + t_stats = correlations * np.sqrt( + df / np.maximum(1e-12, 1.0 - correlations**2) + ) + p_values = 2.0 * (1.0 - normal_cdf(np.abs(t_stats))) + + n_uncorrected = int((p_values < alpha).sum()) + bonferroni_threshold = alpha / n_voxels + n_bonferroni = int((p_values < bonferroni_threshold).sum()) + + return ExperimentResult( + n_voxels=n_voxels, + alpha=alpha, + n_uncorrected_significant=n_uncorrected, + bonferroni_threshold=bonferroni_threshold, + n_bonferroni_significant=n_bonferroni, + p_values=p_values, + correlations=correlations, + ) + + return (simulate_false_positives,) + + +@app.cell(hide_code=True) +def _(anywidget, traitlets): + class FalsePositiveHunter(anywidget.AnyWidget): + """Dual-view interactive hunter for the false-positive simulation. + + Drag the red line on the histogram to move the threshold; the voxel + grid on the left repaints in real time. Buttons snap to the naive + (alpha) or Bonferroni-corrected cutoff. + """ + + _esm = r""" + function svgEl(tag, attrs) { + const e = document.createElementNS("http://www.w3.org/2000/svg", tag); + for (const k in attrs) e.setAttribute(k, attrs[k]); + return e; + } + + function render({ model, el }) { + const wrapper = document.createElement("div"); + wrapper.style.cssText = ` + font-family: sans-serif; + color: #222; + background: #fafaf7; + border: 1px solid #d8d8d0; + border-radius: 8px; + padding: 14px; + display: flex; + flex-direction: column; + gap: 10px; + `; + + const row = document.createElement("div"); + row.style.cssText = "display: flex; gap: 16px; align-items: flex-start; flex-wrap: wrap;"; + + // --- Grid (canvas) --- + const gridWrap = document.createElement("div"); + gridWrap.style.cssText = "display: flex; flex-direction: column; align-items: center; gap: 4px;"; + const gridTitle = document.createElement("div"); + gridTitle.style.cssText = "font-size: 12px; color: #333; font-weight: 500;"; + gridTitle.textContent = "Voxel grid — red = 'significant'"; + const CANVAS_SIZE = 320; + const canvas = document.createElement("canvas"); + canvas.width = CANVAS_SIZE; + canvas.height = CANVAS_SIZE; + canvas.style.cssText = "border: 1px solid #ccc; border-radius: 4px; background: #f6f6f0; image-rendering: pixelated;"; + gridWrap.appendChild(gridTitle); + gridWrap.appendChild(canvas); + + // --- Histogram (SVG) --- + const HIST_W = 460, HIST_H = 340; + const M = { l: 50, r: 15, t: 30, b: 40 }; + const PW = HIST_W - M.l - M.r; + const PH = HIST_H - M.t - M.b; + + const svg = svgEl("svg", { width: HIST_W, height: HIST_H, viewBox: `0 0 ${HIST_W} ${HIST_H}` }); + svg.style.cssText = "border: 1px solid #ccc; border-radius: 4px; background: #fff; user-select: none; cursor: ew-resize;"; + + const pToX = p => M.l + p * PW; + const xToP = x => (x - M.l) / PW; + + const histTitle = svgEl("text", { x: HIST_W / 2, y: 18, "text-anchor": "middle", "font-size": 12, fill: "#333", "font-weight": 500 }); + histTitle.textContent = "P-value distribution — drag the red line"; + svg.appendChild(histTitle); + + svg.appendChild(svgEl("line", { x1: M.l, y1: M.t + PH, x2: M.l + PW, y2: M.t + PH, stroke: "#333" })); + svg.appendChild(svgEl("line", { x1: M.l, y1: M.t, x2: M.l, y2: M.t + PH, stroke: "#333" })); + + for (let i = 0; i <= 10; i++) { + const p = i / 10; + const x = pToX(p); + svg.appendChild(svgEl("line", { x1: x, y1: M.t + PH, x2: x, y2: M.t + PH + 4, stroke: "#333" })); + const t = svgEl("text", { x, y: M.t + PH + 16, "text-anchor": "middle", "font-size": 10, fill: "#333" }); + t.textContent = p.toFixed(1); + svg.appendChild(t); + } + const xlab = svgEl("text", { x: M.l + PW / 2, y: HIST_H - 8, "text-anchor": "middle", "font-size": 11, fill: "#333" }); + xlab.textContent = "p-value"; + svg.appendChild(xlab); + const ylab = svgEl("text", { + x: 14, y: M.t + PH / 2, "text-anchor": "middle", "font-size": 11, fill: "#333", + transform: `rotate(-90 14 ${M.t + PH / 2})`, + }); + ylab.textContent = "voxels"; + svg.appendChild(ylab); + + const barsGroup = svgEl("g", {}); + svg.appendChild(barsGroup); + + // Reference lines (no labels here — legend handles that) + const alphaLine = svgEl("line", { y1: M.t, y2: M.t + PH, stroke: "#888", "stroke-dasharray": "3 3", "stroke-width": 1 }); + svg.appendChild(alphaLine); + const bonfLine = svgEl("line", { y1: M.t, y2: M.t + PH, stroke: "#1a6b3a", "stroke-dasharray": "3 3", "stroke-width": 1 }); + svg.appendChild(bonfLine); + + // Legend in the top-right of the plot + const legendGroup = svgEl("g", {}); + svg.appendChild(legendGroup); + + // Threshold handle (drawn last so it's on top) + const thresholdLine = svgEl("line", { y1: M.t, y2: M.t + PH, stroke: "#c44", "stroke-width": 2, cursor: "ew-resize" }); + svg.appendChild(thresholdLine); + const thresholdHandle = svgEl("rect", { + y: M.t - 8, width: 14, height: 14, fill: "#c44", + cursor: "ew-resize", rx: 3, stroke: "#fff", "stroke-width": 1.5, + }); + svg.appendChild(thresholdHandle); + + row.appendChild(gridWrap); + row.appendChild(svg); + + const readout = document.createElement("div"); + readout.style.cssText = "font-family: ui-monospace, monospace; font-size: 13px; line-height: 1.6; padding: 10px 12px; background: #fff; color: #222; border: 1px solid #e0e0d8; border-radius: 4px;"; + + const buttons = document.createElement("div"); + buttons.style.cssText = "display: flex; gap: 8px; flex-wrap: wrap;"; + + function makeBtn(label, bg, onClick) { + const b = document.createElement("button"); + b.textContent = label; + b.style.cssText = `padding: 6px 12px; border: 1px solid #888; background: ${bg}; color: #222; border-radius: 4px; cursor: pointer; font-size: 12px; font-family: sans-serif; font-weight: 500;`; + b.onclick = onClick; + return b; + } + buttons.appendChild(makeBtn("Naive: p < alpha", "#fff", () => { + model.set("threshold", model.get("alpha")); + model.save_changes(); + })); + buttons.appendChild(makeBtn("Apply Bonferroni", "#d8f0d8", () => { + model.set("threshold", model.get("bonferroni_threshold")); + model.save_changes(); + })); + + wrapper.appendChild(row); + wrapper.appendChild(readout); + wrapper.appendChild(buttons); + el.appendChild(wrapper); + + const c2d = canvas.getContext("2d"); + let binsCache = null; + let binsMax = 1; + + function computeBins() { + const pvals = model.get("p_values"); + const nBins = 50; + const bins = new Array(nBins).fill(0); + for (let i = 0; i < pvals.length; i++) { + let bi = Math.floor(pvals[i] * nBins); + if (bi >= nBins) bi = nBins - 1; + if (bi < 0) bi = 0; + bins[bi]++; + } + binsCache = bins; + binsMax = 0; + for (const v of bins) if (v > binsMax) binsMax = v; + if (binsMax === 0) binsMax = 1; + } + + function drawGrid() { + const pvals = model.get("p_values"); + const n = pvals.length; + const threshold = model.get("threshold"); + const side = Math.ceil(Math.sqrt(n)); + const cell = CANVAS_SIZE / side; + c2d.fillStyle = "#f0f0e8"; + c2d.fillRect(0, 0, CANVAS_SIZE, CANVAS_SIZE); + c2d.fillStyle = "#c44"; + const draw = Math.max(1, Math.ceil(cell)); + for (let i = 0; i < n; i++) { + if (pvals[i] < threshold) { + const r = Math.floor(i / side); + const col = i % side; + c2d.fillRect(Math.floor(col * cell), Math.floor(r * cell), draw, draw); + } + } + } + + function drawLegend() { + while (legendGroup.firstChild) legendGroup.removeChild(legendGroup.firstChild); + const alpha = model.get("alpha"); + const bonf = model.get("bonferroni_threshold"); + const lw = 150; + const lh = 38; + const lx = M.l + PW - lw - 6; + const ly = M.t + 6; + legendGroup.appendChild(svgEl("rect", { + x: lx, y: ly, width: lw, height: lh, + fill: "#fff", stroke: "#ccc", "stroke-width": 1, rx: 3, opacity: 0.96, + })); + // alpha + legendGroup.appendChild(svgEl("line", { + x1: lx + 8, y1: ly + 12, x2: lx + 26, y2: ly + 12, + stroke: "#888", "stroke-dasharray": "3 2", "stroke-width": 1.5, + })); + const aT = svgEl("text", { x: lx + 32, y: ly + 15, "font-size": 11, fill: "#444" }); + aT.textContent = `alpha = ${alpha.toFixed(3)}`; + legendGroup.appendChild(aT); + // bonferroni + legendGroup.appendChild(svgEl("line", { + x1: lx + 8, y1: ly + 28, x2: lx + 26, y2: ly + 28, + stroke: "#1a6b3a", "stroke-dasharray": "3 2", "stroke-width": 1.5, + })); + const bT = svgEl("text", { x: lx + 32, y: ly + 31, "font-size": 11, fill: "#1a6b3a" }); + bT.textContent = `Bonf = ${bonf.toExponential(1)}`; + legendGroup.appendChild(bT); + } + + function drawHist() { + while (barsGroup.firstChild) barsGroup.removeChild(barsGroup.firstChild); + if (!binsCache) computeBins(); + const bins = binsCache; + const nBins = bins.length; + const threshold = model.get("threshold"); + const binW = PW / nBins; + for (let i = 0; i < nBins; i++) { + const h = (bins[i] / binsMax) * PH; + const x = M.l + i * binW; + const y = M.t + PH - h; + const binRight = (i + 1) / nBins; + const flagged = binRight <= threshold + 1e-9; + barsGroup.appendChild(svgEl("rect", { + x, y, + width: Math.max(0.5, binW - 0.5), + height: h, + fill: flagged ? "#c44" : "#aaa", + opacity: flagged ? 0.9 : 0.55, + })); + } + + const alpha = model.get("alpha"); + const bonf = model.get("bonferroni_threshold"); + + const aX = pToX(Math.max(0, Math.min(1, alpha))); + alphaLine.setAttribute("x1", aX); + alphaLine.setAttribute("x2", aX); + + const bX = pToX(Math.max(0, Math.min(1, bonf))); + bonfLine.setAttribute("x1", bX); + bonfLine.setAttribute("x2", bX); + + const tX = pToX(Math.max(0, Math.min(1, threshold))); + thresholdLine.setAttribute("x1", tX); + thresholdLine.setAttribute("x2", tX); + thresholdHandle.setAttribute("x", tX - 7); + + drawLegend(); + } + + function updateReadout() { + const pvals = model.get("p_values"); + const n = pvals.length; + const threshold = model.get("threshold"); + let count = 0; + for (let i = 0; i < n; i++) if (pvals[i] < threshold) count++; + const alpha = model.get("alpha"); + const bonf = model.get("bonferroni_threshold"); + const pct = (100 * count / n).toFixed(2); + const expected = Math.round(alpha * n); + + let verdict; + if (Math.abs(threshold - alpha) < alpha * 0.01) { + verdict = `Naive threshold. Pure noise produces ${count.toLocaleString()} 'discoveries' — close to alpha*N = ${expected.toLocaleString()}.`; + } else if (Math.abs(threshold - bonf) < Math.max(bonf * 0.01, 1e-12)) { + verdict = `Bonferroni threshold. Only voxels with p < 0.05/N pass. ${count.toLocaleString()} 'discoveries'.`; + } else { + verdict = `Custom threshold. ${count.toLocaleString()} 'discoveries' from pure noise.`; + } + + const thrStr = threshold < 1e-3 ? threshold.toExponential(2) : threshold.toFixed(4); + readout.innerHTML = + `Threshold: p < ${thrStr}  ·  ` + + `Flagged: ${count.toLocaleString()} of ${n.toLocaleString()} (${pct}%)
` + + verdict; + } + + function redraw() { drawGrid(); drawHist(); updateReadout(); } + + let dragging = false; + function ptX(evt) { + const rect = svg.getBoundingClientRect(); + return (evt.clientX - rect.left) * (HIST_W / rect.width); + } + function setFromX(x) { + let p = xToP(x); + p = Math.max(1e-8, Math.min(1.0, p)); + model.set("threshold", p); + model.save_changes(); + } + svg.addEventListener("mousedown", (e) => { dragging = true; setFromX(ptX(e)); }); + window.addEventListener("mousemove", (e) => { if (dragging) setFromX(ptX(e)); }); + window.addEventListener("mouseup", () => { dragging = false; }); + + model.on("change:threshold", redraw); + model.on("change:p_values", () => { binsCache = null; redraw(); }); + model.on("change:alpha", redraw); + model.on("change:bonferroni_threshold", redraw); + + redraw(); + } + + export default { render }; + """ + + p_values = traitlets.List(trait=traitlets.Float()).tag(sync=True) + alpha = traitlets.Float(0.05).tag(sync=True) + bonferroni_threshold = traitlets.Float(2.5e-6).tag(sync=True) + threshold = traitlets.Float(0.05).tag(sync=True) + + return (FalsePositiveHunter,) + + +@app.cell(hide_code=True) +def _(mo): + n_timepoints = mo.ui.slider( + start=20, stop=1000, step=10, value=200, label="n_timepoints" + ) + n_voxels = mo.ui.slider( + start=100, stop=100_000, step=100, value=20_000, label="n_voxels" + ) + alpha = mo.ui.slider( + start=0.001, stop=0.2, step=0.001, value=0.05, label="alpha" + ) + seed = mo.ui.number(start=0, stop=10_000, step=1, value=42, label="seed") + + mo.vstack([n_timepoints, n_voxels, alpha, seed]) + return alpha, n_timepoints, n_voxels, seed + + +@app.cell(hide_code=True) +def _(alpha, n_timepoints, n_voxels, seed, simulate_false_positives): + result = simulate_false_positives( + n_timepoints=n_timepoints.value, + n_voxels=n_voxels.value, + alpha=alpha.value, + seed=int(seed.value), + ) + return (result,) + + +@app.cell(hide_code=True) +def _(FalsePositiveHunter, mo, result): + hunter = mo.ui.anywidget( + FalsePositiveHunter( + p_values=result.p_values.tolist(), + alpha=float(result.alpha), + bonferroni_threshold=float(result.bonferroni_threshold), + threshold=float(result.alpha), + ) + ) + hunter + return + + +@app.cell(hide_code=True) +def _(mo): + mo.md(r""" + --- + + ## Does this happen with real data? Yes. + + We just showed pure noise producing false positives. The math doesn't + care whether the "noise" is synthetic — any time you run many tests + with no real effect, ~alpha of them will flag as "significant". + + Below we pull the classic **cars dataset** from `vega-datasets` and + p-hack it: split the cars into two groups by coin flip (no real + difference between groups), then run a t-test on every numeric + feature. Repeat many times. By design every test is null — yet ~5% + of them will come up "significant". + """) + return + + +@app.cell(hide_code=True) +def _(mo): + n_splits = mo.ui.slider( + start=50, stop=2000, step=50, value=500, label="random splits" + ) + n_splits + return (n_splits,) + + +@app.cell(hide_code=True) +def _(data, n_splits, normal_cdf, np): + def _p_hack_dataset(df, n_splits, seed=1): + """Coin-flip cars into two groups; Welch t-test each numeric feature. + + Returns p-values, raw effect sizes (mean_A - mean_B), and the feature + tested for every (split, feature) pair — flattened in the same order. + """ + rng = np.random.default_rng(seed) + numeric = df.select_dtypes(include="number") + X = numeric.to_numpy(dtype=float) + X = X[~np.isnan(X).any(axis=1)] + n = X.shape[0] + feat_names = numeric.columns.tolist() + + p_chunks, e_chunks = [], [] + for _ in range(n_splits): + group = rng.integers(0, 2, size=n).astype(bool) + if not group.any() or group.all(): + continue + a = X[group] + b = X[~group] + mean_diff = a.mean(axis=0) - b.mean(axis=0) + se = np.sqrt( + a.var(axis=0, ddof=1) / len(a) + b.var(axis=0, ddof=1) / len(b) + ) + se = np.maximum(se, 1e-12) + t = mean_diff / se + p_chunks.append(2.0 * (1.0 - normal_cdf(np.abs(t)))) + e_chunks.append(mean_diff) + + return { + "p_values": np.concatenate(p_chunks), + "effect_sizes": np.concatenate(e_chunks), + "features_per_test": feat_names * len(p_chunks), + "feature_names": feat_names, + } + + + cars_df = data.cars() + cars_phack = _p_hack_dataset(cars_df, n_splits.value) + cars_p_values = cars_phack["p_values"] + cars_features = cars_phack["feature_names"] + cars_total_tests = len(cars_p_values) + cars_alpha = 0.05 + cars_bonf = cars_alpha / cars_total_tests + return ( + cars_alpha, + cars_bonf, + cars_df, + cars_features, + cars_p_values, + cars_phack, + cars_total_tests, + ) + + +@app.cell(hide_code=True) +def _(cars_df, cars_features, cars_total_tests, mo, n_splits): + mo.md(f""" + **Dataset:** `cars.json` — {len(cars_df):,} cars · + features tested: {", ".join(f"`{c}`" for c in cars_features)} + + **Tests run:** {n_splits.value:,} random splits × {len(cars_features)} features + = **{cars_total_tests:,}** t-tests, all null by construction. + """) + return + + +@app.cell(hide_code=True) +def _(FalsePositiveHunter, cars_alpha, cars_bonf, cars_p_values, mo): + cars_hunter = mo.ui.anywidget( + FalsePositiveHunter( + p_values=cars_p_values.tolist(), + alpha=float(cars_alpha), + bonferroni_threshold=float(cars_bonf), + threshold=float(cars_alpha), + ) + ) + cars_hunter + return (cars_hunter,) + + +@app.cell(hide_code=True) +def _( + cars_bonf, + cars_df, + cars_features, + cars_hunter, + cars_phack, + cars_total_tests, + mo, + np, +): + _threshold = cars_hunter.value["threshold"] + _p_vals = cars_phack["p_values"] + _effects = cars_phack["effect_sizes"] + _feats = cars_phack["features_per_test"] + + _UNITS = { + "Miles_per_Gallon": "mpg", + "Cylinders": "cylinders", + "Displacement": "cc displacement", + "Horsepower": "hp", + "Weight_in_lbs": "lbs", + "Acceleration": "sec 0-60", + } + + _passing = np.where(_p_vals < _threshold)[0] + _n_passing = int(len(_passing)) + + _items = [] + if _n_passing > 0: + _top = _passing[np.argsort(_p_vals[_passing])[:5]] + for _i in _top: + _feat = _feats[int(_i)] + _eff = float(_effects[int(_i)]) + _p = float(_p_vals[int(_i)]) + _dir = "higher" if _eff > 0 else "lower" + _unit = _UNITS.get(_feat, _feat) + _p_str = f"{_p:.2e}" if _p < 1e-3 else f"{_p:.4f}" + _items.append( + "
  • " + f"Cohort A exhibited {abs(_eff):.2f} {_unit} {_dir} than Cohort B " + f"(p = {_p_str}).
    • " + ) + + _show_stink = _n_passing > 0 and _threshold > cars_bonf + 1e-10 + + _stink = ( + ( + """ +
    +
    Really?
    + + + + + + + +
    + """ + ) + if _show_stink + else "" + ) + + _results_list = ( + "" + if _items + else "

    No findings at this threshold — there is no paper to publish.

    " + ) + + _closing = ( + ( + "Our analysis reveals consistent and measurable differences between cohorts " + "across multiple vehicle dimensions. We recommend manufacturer review and " + "further investigation in light of these findings." + ) + if _n_passing > 0 + else ( + "No effects were detected. The null hypothesis could not be rejected." + ) + ) + + _threshold_str = ( + f"{_threshold:.2e}" if _threshold < 1e-3 else f"{_threshold:.4f}" + ) + + mo.md(f""" +
    + {_stink} +
    +
    + Journal of Automotive Cohort Analysis · Vol. 47 · No. 3 · pp. 412-418 +
    +
    + Systematic Variation Across Randomly Partitioned Automotive Cohorts +
    +
    + A Multi-Feature Analysis of {len(cars_df)} Vehicles +
    +
    + M. Anon1, J. Hartman1, A. Claude2 +
    +
    + 1Institute for Premature Publication · 2Dept. of Exuberant Inference +
    +
    + +
    +
    Abstract
    +

    + We analyzed {len(cars_df)} automobiles partitioned into two cohorts + and conducted a systematic comparison across {len(cars_features)} mechanical + and efficiency attributes. At a significance threshold of + p < {_threshold_str}, we identified + {_n_passing:,} statistically significant differences. + These results have implications for manufacturing specification, + regulatory policy, and consumer guidance. +

    + +
    Selected Results
    + {_results_list} + +
    Conclusion
    +

    + {_closing} +

    + +
    + Manuscript generated from {cars_total_tests:,} null t-tests on vega-datasets/cars.json. + Cohort assignment: uniformly random coin flip per vehicle. +
    +
    +
    + """) + return + + +@app.cell(hide_code=True) +def _(mo): + mo.md(r""" + --- + + # The same trap, in modern clothes + + Everything above was classical statistics dressed for fMRI and for cars. + The same failure mode shows up across modern ML interpretability tools. + Below: three case studies where confident-looking explanations turn out + to be artifacts of high-dimensional noise, not of anything the model + actually learned or the data actually contains. + """) + return + + +@app.cell(hide_code=True) +def _(mo): + mo.md(r""" + ## 1. Feature Attribution + + Saliency maps, Grad-CAM, SHAP, Integrated Gradients — the promise is: + "here are the parts of the input the model cared about." Adebayo et al. + (2018) showed that many popular attribution methods produce **nearly + identical heatmaps even when you randomize the model's weights**. That + means the pretty overlay is mostly a projection of the input, not + evidence of what the model learned. + + Below: we train a tiny linear classifier on synthetic 16×16 shapes + (circles vs squares), then compute input-times-gradient saliency for + both the trained weights and a fresh random model. The "hot" regions + line up on the shape in both cases — because `|W·x|` is near zero + wherever `x` is near zero, regardless of what `W` is. + """) + return + + +@app.cell(hide_code=True) +def _(np): + from sklearn.linear_model import LogisticRegression + + + def _make_shape_dataset(n_per_class=60, size=16, noise=0.12, seed=0): + """Generate 16x16 grayscale shape images (0=disk, 1=square) with + position jitter and Gaussian noise. Returns flat (n, size*size) array.""" + rng = np.random.default_rng(seed) + yy, xx = np.mgrid[0:size, 0:size] + imgs, labels = [], [] + for lbl in (0, 1): + for _ in range(n_per_class): + img = rng.normal(0.0, noise, size=(size, size)) + cy = size // 2 + int(rng.integers(-2, 3)) + cx = size // 2 + int(rng.integers(-2, 3)) + r = 3 + if lbl == 0: + mask = (yy - cy) ** 2 + (xx - cx) ** 2 <= r * r + else: + mask = (np.abs(yy - cy) <= r) & (np.abs(xx - cx) <= r) + img = img + 0.9 * mask + imgs.append(img.ravel()) + labels.append(lbl) + X = np.array(imgs, dtype=float) + y = np.array(labels, dtype=int) + perm = rng.permutation(len(X)) + return X[perm], y[perm] + + + _attr_X, _attr_y = _make_shape_dataset(n_per_class=60, seed=0) + _attr_clf = LogisticRegression(max_iter=1000, C=10.0).fit(_attr_X, _attr_y) + attr_W = _attr_clf.coef_[0].astype(float) + attr_b = float(_attr_clf.intercept_[0]) + attr_train_acc = float(_attr_clf.score(_attr_X, _attr_y)) + + attr_X_test, attr_y_test = _make_shape_dataset(n_per_class=10, seed=7) + attr_label_names = ["circle", "square"] + return attr_W, attr_X_test, attr_label_names, attr_y_test + + +@app.cell(hide_code=True) +def _(anywidget, traitlets): + class AttributionCompare(anywidget.AnyWidget): + """Side-by-side saliency comparison: trained vs random model weights. + + The demo shows input-times-gradient saliency |W * x| for both a trained + linear classifier and a fresh model with random weights of matching scale. + Both maps light up the shape region — because |W * x| is near zero + wherever x is near zero, regardless of W. The fingerprint of the input + dominates the fingerprint of the model. + """ + + _esm = r""" + function render({ model, el }) { + const wrapper = document.createElement("div"); + wrapper.style.cssText = ` + font-family: sans-serif; + color: #222; + background: #fafaf7; + border: 1px solid #d8d8d0; + border-radius: 8px; + padding: 14px; + display: flex; + flex-direction: column; + gap: 12px; + `; + + const row = document.createElement("div"); + row.style.cssText = "display: flex; gap: 14px; justify-content: center; flex-wrap: wrap;"; + + const DISPLAY = 180; + + function makePanel(title) { + const p = document.createElement("div"); + p.style.cssText = "display: flex; flex-direction: column; align-items: center; gap: 4px;"; + const t = document.createElement("div"); + t.style.cssText = "font-size: 12px; color: #333; font-weight: 500;"; + t.textContent = title; + const c = document.createElement("canvas"); + c.width = DISPLAY; + c.height = DISPLAY; + c.style.cssText = "border: 1px solid #aaa; image-rendering: pixelated; background: #000;"; + p.appendChild(t); + p.appendChild(c); + return { panel: p, canvas: c, title: t }; + } + + const pInput = makePanel("Input image"); + const pTrained = makePanel("Saliency — trained model"); + const pRandom = makePanel("Saliency — random model"); + row.appendChild(pInput.panel); + row.appendChild(pTrained.panel); + row.appendChild(pRandom.panel); + + const controls = document.createElement("div"); + controls.style.cssText = "display: flex; gap: 10px; align-items: center; flex-wrap: wrap; justify-content: center;"; + + function makeBtn(label, onClick) { + const b = document.createElement("button"); + b.textContent = label; + b.style.cssText = "padding: 6px 12px; border: 1px solid #888; background: #fff; color: #222; border-radius: 4px; cursor: pointer; font-size: 12px; font-family: sans-serif;"; + b.onclick = onClick; + return b; + } + + const idxLabel = document.createElement("span"); + idxLabel.style.cssText = "font-family: ui-monospace, monospace; font-size: 12px; color: #333; min-width: 110px; text-align: center;"; + + controls.appendChild(makeBtn("Prev", () => { + const n = model.get("n_test"); + let s = model.get("selected"); + s = (s - 1 + n) % n; + model.set("selected", s); + model.save_changes(); + })); + controls.appendChild(idxLabel); + controls.appendChild(makeBtn("Next", () => { + const n = model.get("n_test"); + let s = model.get("selected"); + s = (s + 1) % n; + model.set("selected", s); + model.save_changes(); + })); + controls.appendChild(makeBtn("Re-roll random model", () => { + model.set("random_seed", model.get("random_seed") + 1); + model.save_changes(); + })); + + const readout = document.createElement("div"); + readout.style.cssText = "font-family: ui-monospace, monospace; font-size: 13px; line-height: 1.55; padding: 10px 12px; background: #fff; color: #222; border: 1px solid #e0e0d8; border-radius: 4px;"; + + wrapper.appendChild(row); + wrapper.appendChild(controls); + wrapper.appendChild(readout); + el.appendChild(wrapper); + + function mulberry32(seed) { + return function() { + seed = (seed + 0x6D2B79F5) | 0; + let t = seed; + t = Math.imul(t ^ (t >>> 15), t | 1); + t ^= t + Math.imul(t ^ (t >>> 7), t | 61); + return ((t ^ (t >>> 14)) >>> 0) / 4294967296; + }; + } + function randn(rng) { + const u = 1 - rng(); + const v = rng(); + return Math.sqrt(-2 * Math.log(u)) * Math.cos(2 * Math.PI * v); + } + function stddev(arr) { + let m = 0; + for (const v of arr) m += v; + m /= arr.length; + let s = 0; + for (const v of arr) s += (v - m) * (v - m); + return Math.sqrt(s / arr.length); + } + function pearson(a, b) { + const n = a.length; + let sa=0, sb=0, saa=0, sbb=0, sab=0; + for (let i = 0; i < n; i++) { + sa += a[i]; sb += b[i]; + saa += a[i] * a[i]; sbb += b[i] * b[i]; + sab += a[i] * b[i]; + } + const ma = sa / n, mb = sb / n; + const cov = sab / n - ma * mb; + const va = saa / n - ma * ma, vb = sbb / n - mb * mb; + return cov / (Math.sqrt(va * vb) + 1e-12); + } + + function grayscale(t) { + t = Math.max(0, Math.min(1, t)); + const v = Math.floor(t * 255); + return "rgb(" + v + "," + v + "," + v + ")"; + } + function hot(t) { + t = Math.max(0, Math.min(1, t)); + let r, g, b; + if (t < 0.33) { r = Math.floor(t / 0.33 * 255); g = 0; b = 0; } + else if (t < 0.67) { r = 255; g = Math.floor((t - 0.33) / 0.34 * 255); b = 0; } + else { r = 255; g = 255; b = Math.floor((t - 0.67) / 0.33 * 255); } + return "rgb(" + r + "," + g + "," + b + ")"; + } + + function drawImage(canvas, arr, colormap, clipPct) { + const ctx = canvas.getContext("2d"); + const size = Math.round(Math.sqrt(arr.length)); + // robust normalization: clip top 2% + const sorted = arr.slice().sort((a, b) => a - b); + const mn = sorted[Math.floor(sorted.length * (clipPct || 0))]; + const mx = sorted[Math.floor(sorted.length * (1 - (clipPct || 0))) - 1]; + const range = Math.max(mx - mn, 1e-12); + const px = canvas.width / size; + for (let i = 0; i < size; i++) { + for (let j = 0; j < size; j++) { + const val = (arr[i * size + j] - mn) / range; + ctx.fillStyle = colormap(val); + ctx.fillRect(Math.floor(j * px), Math.floor(i * px), Math.ceil(px), Math.ceil(px)); + } + } + } + + function refresh() { + const images = model.get("images"); + const labels = model.get("labels"); + const Wt = model.get("W_trained"); + const size = model.get("image_size"); + const n = model.get("n_test"); + const names = model.get("label_names"); + const sel = model.get("selected"); + const seed = model.get("random_seed"); + + const pixels = size * size; + const img = images.slice(sel * pixels, (sel + 1) * pixels); + + const wStd = stddev(Wt); + const rng = mulberry32(seed * 1337 + 42); + const Wr = new Array(pixels); + for (let i = 0; i < pixels; i++) Wr[i] = randn(rng) * wStd; + + const trainedSal = new Array(pixels); + const randomSal = new Array(pixels); + for (let i = 0; i < pixels; i++) { + trainedSal[i] = Math.abs(Wt[i] * img[i]); + randomSal[i] = Math.abs(Wr[i] * img[i]); + } + + drawImage(pInput.canvas, img, grayscale, 0.02); + drawImage(pTrained.canvas, trainedSal, hot, 0.02); + drawImage(pRandom.canvas, randomSal, hot, 0.02); + + pInput.title.textContent = "Input: " + names[labels[sel]]; + idxLabel.textContent = "Image " + (sel + 1) + " of " + n; + + const r = pearson(trainedSal, randomSal); + const rColor = r > 0.5 ? "#c44" : (r > 0.2 ? "#b80" : "#555"); + readout.innerHTML = + "Pearson r between trained and random saliency: " + + "" + r.toFixed(3) + "
    " + + "Both maps emphasize the shape region because " + + "saliency = |W · x| is near zero wherever x is near zero — " + + "regardless of whether W came from training or from random noise."; + } + + model.on("change:selected", refresh); + model.on("change:random_seed", refresh); + refresh(); + } + + export default { render }; + """ + + images = traitlets.List(trait=traitlets.Float()).tag(sync=True) + labels = traitlets.List(trait=traitlets.Int()).tag(sync=True) + W_trained = traitlets.List(trait=traitlets.Float()).tag(sync=True) + image_size = traitlets.Int(16).tag(sync=True) + n_test = traitlets.Int(0).tag(sync=True) + label_names = traitlets.List(trait=traitlets.Unicode()).tag(sync=True) + selected = traitlets.Int(0).tag(sync=True) + random_seed = traitlets.Int(0).tag(sync=True) + + return (AttributionCompare,) + + +@app.cell(hide_code=True) +def _( + AttributionCompare, + attr_W, + attr_X_test, + attr_label_names, + attr_y_test, + mo, +): + attr_widget = mo.ui.anywidget( + AttributionCompare( + images=attr_X_test.flatten().astype(float).tolist(), + labels=attr_y_test.astype(int).tolist(), + W_trained=attr_W.tolist(), + image_size=16, + n_test=int(len(attr_X_test)), + label_names=attr_label_names, + ) + ) + attr_widget + return + + +@app.cell(hide_code=True) +def _(mo): + mo.md(r""" + ## 2. Probing + + Probing classifiers are the "did the model encode X?" tool: train a + linear probe on a model's internal activations to decode some property, + and take success to mean the model "knows about" that property. + + Hewitt & Liang (2019) showed the trap: **a probe can succeed even when + the model is not using the decoded information for its output**. A + high-dimensional projection preserves almost everything fed into it — a + probe can recover a feature from the activations even if the downstream + readout completely ignores it. + + Below: synthetic features flow through a fixed random hidden layer. A + linear readout is trained on `y = sin(1.5·x1) + noise` — only `x1` + matters. For every feature we measure: + + - **Probe R²** — can a linear probe decode `xⱼ` from the hidden activations? + - **Ablation impact** — when we replace `xⱼ` with its mean, how much + does the model's output change? + + The probe says "yes, it's all in there!" The ablation says "no, only + `x1` is actually used." Both are true at the same time. + """) + return + + +@app.cell(hide_code=True) +def _(mo): + probing_n_nuisance = mo.ui.slider( + start=2, stop=20, step=1, value=5, label="nuisance features" + ) + probing_n_nuisance + return (probing_n_nuisance,) + + +@app.cell(hide_code=True) +def _(np, probing_n_nuisance): + from sklearn.linear_model import Ridge + + + def _probing_experiment(n_samples=500, n_nuisance=5, hidden=32, seed=0): + """Fixed random hidden layer + trained linear readout. Only x1 drives y. + + For each feature xj, computes: (a) the R² of a linear probe that + decodes xj from the hidden activations, and (b) the RMS change in + model output when xj is replaced by its mean. + """ + rng = np.random.default_rng(seed) + k = 1 + n_nuisance + X = rng.normal(0, 1, size=(n_samples, k)) + y = np.sin(X[:, 0] * 1.5) + 0.1 * rng.normal(size=n_samples) + + W0 = rng.normal(0, 1 / np.sqrt(k), size=(k, hidden)) + b0 = rng.normal(0, 0.1, size=hidden) + H = np.tanh(X @ W0 + b0) + + readout = Ridge(alpha=0.1).fit(H, y) + y_pred = readout.predict(H) + + probe_r2 = [] + for j in range(k): + p = Ridge(alpha=0.01).fit(H, X[:, j]) + probe_r2.append(float(max(0.0, p.score(H, X[:, j])))) + + abl_deltas = [] + for j in range(k): + X_abl = X.copy() + X_abl[:, j] = X[:, j].mean() + H_abl = np.tanh(X_abl @ W0 + b0) + y_abl = readout.predict(H_abl) + abl_deltas.append(float(np.sqrt(np.mean((y_abl - y_pred) ** 2)))) + + abl_arr = np.array(abl_deltas) + abl_norm = (abl_arr / max(float(abl_arr.max()), 1e-12)).tolist() + + return { + "feature_names": [f"x{j + 1}" for j in range(k)], + "probe_r2": probe_r2, + "ablation_impact": abl_norm, + "ablation_raw": abl_deltas, + } + + + probing_result = _probing_experiment( + n_nuisance=probing_n_nuisance.value, seed=1 + ) + return (probing_result,) + + +@app.cell(hide_code=True) +def _(anywidget, traitlets): + class ProbingCompare(anywidget.AnyWidget): + """Grouped bar chart: per-feature probe R² vs ablation impact. + + Visualizes the Hewitt-Liang insight: probes can recover features the + model does not actually use for its output. + """ + + _esm = r""" + function svgEl(tag, attrs) { + const e = document.createElementNS("http://www.w3.org/2000/svg", tag); + for (const k in attrs) e.setAttribute(k, attrs[k]); + return e; + } + + function render({ model, el }) { + const wrapper = document.createElement("div"); + wrapper.style.cssText = ` + font-family: sans-serif; + color: #222; + background: #fafaf7; + border: 1px solid #d8d8d0; + border-radius: 8px; + padding: 14px; + display: flex; + flex-direction: column; + gap: 10px; + `; + + const W = 640, H = 340; + const M = { l: 50, r: 15, t: 50, b: 42 }; + const PW = W - M.l - M.r; + const PH = H - M.t - M.b; + + const svg = svgEl("svg", { width: W, height: H, viewBox: "0 0 " + W + " " + H }); + svg.style.cssText = "background: #fff; border: 1px solid #ccc; border-radius: 4px; user-select: none;"; + + const title = svgEl("text", { + x: W / 2, y: 20, "text-anchor": "middle", + "font-size": 13, "font-weight": 600, fill: "#333", + }); + title.textContent = "Probe-decodability vs ablation-impact, per feature"; + svg.appendChild(title); + + const legend = svgEl("g", {}); + legend.appendChild(svgEl("rect", { x: 70, y: 30, width: 14, height: 10, fill: "#3a77bf" })); + const l1 = svgEl("text", { x: 90, y: 39, "font-size": 11, fill: "#333" }); + l1.textContent = "Probe R² (linear decode from hidden)"; + legend.appendChild(l1); + legend.appendChild(svgEl("rect", { x: 360, y: 30, width: 14, height: 10, fill: "#c44" })); + const l2 = svgEl("text", { x: 380, y: 39, "font-size": 11, fill: "#333" }); + l2.textContent = "Ablation impact on output (normalized)"; + legend.appendChild(l2); + svg.appendChild(legend); + + svg.appendChild(svgEl("line", { x1: M.l, y1: M.t, x2: M.l, y2: M.t + PH, stroke: "#333" })); + for (let i = 0; i <= 4; i++) { + const yv = i / 4; + const py = M.t + PH - yv * PH; + svg.appendChild(svgEl("line", { x1: M.l - 4, y1: py, x2: M.l, y2: py, stroke: "#333" })); + const tk = svgEl("text", { + x: M.l - 6, y: py + 3, "font-size": 10, fill: "#555", "text-anchor": "end", + }); + tk.textContent = yv.toFixed(2); + svg.appendChild(tk); + } + const ylab = svgEl("text", { + x: 14, y: M.t + PH / 2, "text-anchor": "middle", "font-size": 11, fill: "#333", + transform: "rotate(-90 14 " + (M.t + PH / 2) + ")", + }); + ylab.textContent = "score"; + svg.appendChild(ylab); + + svg.appendChild(svgEl("line", { x1: M.l, y1: M.t + PH, x2: M.l + PW, y2: M.t + PH, stroke: "#333" })); + + const barsGroup = svgEl("g", {}); + svg.appendChild(barsGroup); + + const readout = document.createElement("div"); + readout.style.cssText = "font-family: ui-monospace, monospace; font-size: 13px; line-height: 1.55; padding: 10px 12px; background: #fff; color: #222; border: 1px solid #e0e0d8; border-radius: 4px;"; + + wrapper.appendChild(svg); + wrapper.appendChild(readout); + el.appendChild(wrapper); + + function redraw() { + while (barsGroup.firstChild) barsGroup.removeChild(barsGroup.firstChild); + const features = model.get("feature_names"); + const probes = model.get("probe_r2"); + const ablations = model.get("ablation_impact"); + const k = features.length; + if (k === 0) return; + const slot = PW / k; + const barW = Math.min(24, slot * 0.35); + + for (let i = 0; i < k; i++) { + const cx = M.l + i * slot + slot / 2; + const probeH = Math.max(0, Math.min(1, probes[i])) * PH; + barsGroup.appendChild(svgEl("rect", { + x: cx - barW - 2, y: M.t + PH - probeH, + width: barW, height: probeH, + fill: "#3a77bf", opacity: 0.85, + })); + const ablH = Math.max(0, Math.min(1, ablations[i])) * PH; + barsGroup.appendChild(svgEl("rect", { + x: cx + 2, y: M.t + PH - ablH, + width: barW, height: ablH, + fill: "#c44", opacity: 0.85, + })); + const lbl = svgEl("text", { + x: cx, y: M.t + PH + 16, "text-anchor": "middle", + "font-size": 11, fill: "#333", + "font-weight": i === 0 ? 700 : 400, + }); + lbl.textContent = features[i]; + barsGroup.appendChild(lbl); + } + + const nProbeHigh = probes.filter(function(r) { return r > 0.5; }).length; + const nAblHigh = ablations.filter(function(a) { return a > 0.1; }).length; + readout.innerHTML = + "Probes can decode " + nProbeHigh + " of " + k + + " features from the hidden activations. " + + "Ablating features changes the model's output for only " + + "" + nAblHigh + " of " + k + ".
    " + + "The representation contains the nuisance features; " + + "the output does not use them. A successful probe is not evidence " + + "that the model relies on the decoded feature."; + } + + model.on("change:probe_r2", redraw); + model.on("change:ablation_impact", redraw); + model.on("change:feature_names", redraw); + redraw(); + } + + export default { render }; + """ + + feature_names = traitlets.List(trait=traitlets.Unicode()).tag(sync=True) + probe_r2 = traitlets.List(trait=traitlets.Float()).tag(sync=True) + ablation_impact = traitlets.List(trait=traitlets.Float()).tag(sync=True) + + return (ProbingCompare,) + + +@app.cell(hide_code=True) +def _(ProbingCompare, mo, probing_result): + probing_widget = mo.ui.anywidget( + ProbingCompare( + feature_names=probing_result["feature_names"], + probe_r2=probing_result["probe_r2"], + ablation_impact=probing_result["ablation_impact"], + ) + ) + probing_widget + return + + +@app.cell(hide_code=True) +def _(mo): + mo.md(r""" + ## 3. Causal Analysis in High Dimensions + + Causal-discovery methods promise to find the "true" dependency structure + in a system. At scale on noisy data they produce dense, confident-looking + graphs from variables with **no real relationships at all**. + + Below: we generate N independent "gene expression" variables (pure noise, + no real causal structure), then compute the pairwise correlation p-value + for every pair — the simplest form of causal-edge discovery. With N = 40 + variables there are 780 pairs; at p < 0.05 we expect ~39 "causal + edges" by pure chance. + + The same `FalsePositiveHunter` widget works as the viewer — this time + the grid pixels are pairs of variables rather than voxels or cars-cohort + tests. Drag the red threshold and watch an entire causal network + materialize out of random noise. + """) + return + + +@app.cell(hide_code=True) +def _(mo): + causal_n_vars = mo.ui.slider( + start=10, stop=60, step=5, value=40, label="number of variables" + ) + causal_n_vars + return (causal_n_vars,) + + +@app.cell(hide_code=True) +def _(causal_n_vars, normal_cdf, np): + def _causal_null_experiment(n_samples=200, n_vars=40, seed=0): + """Independent Gaussian variables; all-pairs marginal correlations + p. + + Under the null, ~alpha of the pairwise tests will flag as "causally + linked" purely by chance. + """ + rng = np.random.default_rng(seed) + data = rng.normal(0, 1, size=(n_samples, n_vars)) + Xs = (data - data.mean(axis=0)) / np.maximum( + data.std(axis=0, ddof=1), 1e-12 + ) + + n_tests = n_vars * (n_vars - 1) // 2 + p_values = np.empty(n_tests) + correlations = np.empty(n_tests) + pair_i = np.empty(n_tests, dtype=int) + pair_j = np.empty(n_tests, dtype=int) + + df = n_samples - 2 + idx = 0 + for i in range(n_vars): + for j in range(i + 1, n_vars): + r = float((Xs[:, i] * Xs[:, j]).sum() / (n_samples - 1)) + t = r * np.sqrt(df / max(1.0 - r * r, 1e-12)) + p = 2.0 * (1.0 - normal_cdf(np.abs(t))) + correlations[idx] = r + p_values[idx] = float(p) + pair_i[idx] = i + pair_j[idx] = j + idx += 1 + + return { + "p_values": p_values, + "correlations": correlations, + "pair_i": pair_i, + "pair_j": pair_j, + "n_vars": n_vars, + "n_tests": n_tests, + "gene_names": [f"GENE_{k + 1:02d}" for k in range(n_vars)], + } + + + causal_result = _causal_null_experiment( + n_samples=200, n_vars=causal_n_vars.value, seed=0 + ) + causal_alpha = 0.05 + causal_bonf = causal_alpha / max(causal_result["n_tests"], 1) + return causal_alpha, causal_bonf, causal_result + + +@app.cell(hide_code=True) +def _(FalsePositiveHunter, causal_alpha, causal_bonf, causal_result, mo): + causal_hunter = mo.ui.anywidget( + FalsePositiveHunter( + p_values=causal_result["p_values"].tolist(), + alpha=float(causal_alpha), + bonferroni_threshold=float(causal_bonf), + threshold=float(causal_alpha), + ) + ) + causal_hunter + return (causal_hunter,) + + +@app.cell(hide_code=True) +def _(causal_bonf, causal_hunter, causal_result, mo, np): + _threshold = causal_hunter.value["threshold"] + _p_vals = causal_result["p_values"] + _corrs = causal_result["correlations"] + _ii = causal_result["pair_i"] + _jj = causal_result["pair_j"] + _genes = causal_result["gene_names"] + + _passing = np.where(_p_vals < _threshold)[0] + _n_passing = int(len(_passing)) + + _edges = [] + if _n_passing > 0: + _top = _passing[np.argsort(_p_vals[_passing])[:5]] + for _k in _top: + _i = int(_ii[_k]) + _j = int(_jj[_k]) + _r = float(_corrs[_k]) + _p = float(_p_vals[_k]) + _p_str = f"{_p:.2e}" if _p < 1e-3 else f"{_p:.4f}" + _arrow = "←→" + _edges.append( + "
  • " + f"{_genes[_i]} {_arrow} {_genes[_j]} " + f"(partial r = {_r:+.3f}, p = {_p_str})
    • " + ) + + _show_stink = _n_passing > 0 and _threshold > causal_bonf + 1e-10 + + _stink = ( + ( + """ +
    +
    Really?
    + + + + + + + +
    + """ + ) + if _show_stink + else "" + ) + + _edges_list = ( + "" + if _edges + else "

    No causal edges detected at this threshold.

    " + ) + + _closing = ( + ( + "Our findings reveal a tightly interconnected regulatory network with " + "implications for pathway-level therapeutic targeting. We propose further " + "validation in independent cohorts." + ) + if _n_passing > 0 + else ( + "No significant causal relationships were identified. The studied variables " + "appear to be mutually independent." + ) + ) + + _threshold_str = ( + f"{_threshold:.2e}" if _threshold < 1e-3 else f"{_threshold:.4f}" + ) + _n_vars = causal_result["n_vars"] + _n_tests = causal_result["n_tests"] + + mo.md(f""" +
    + {_stink} +
    +
    + Journal of Network Biology · Vol. 33 · No. 7 · pp. 1104-1117 +
    +
    + A Causal Expression Network Across {_n_vars} Candidate Genes +
    +
    + Pairwise Dependency Mapping from High-Dimensional Assay Data +
    +
    + R. Noise1, I. Spurious1, C. Hen-Picker2 +
    +
    + 1Center for Overconfident Inference · 2Institute for Network Overfitting +
    +
    + +
    +
    Abstract
    +

    + Using a panel of {_n_vars} candidate genes assayed across 200 samples, we + mapped pairwise causal dependencies through a standard correlation-based discovery + procedure. At significance threshold p < {_threshold_str} we identified + {_n_passing:,} putative causal edges out of {_n_tests:,} candidate pairs. + These findings reveal a tightly interconnected regulatory architecture with + implications for pathway-level drug targeting. +

    + +
    Top Inferred Edges
    + {_edges_list} + +
    Conclusion
    +

    + {_closing} +

    + +
    + Manuscript generated from {_n_tests:,} all-pairs correlation tests on {_n_vars} + independent Gaussian variables. Data contains no real causal structure. +
    +
    +
    + """) + return + + +@app.cell(hide_code=True) +def _(mo): + mo.md(r""" + ## 4. Sparse Autoencoders + + Sparse autoencoders — the currently-hot interpretability tool — train a + wide-narrow-wide network on a model's activations with an L1 penalty on + the bottleneck, producing a "dictionary of features." Each feature is + then hand-labeled ("this one fires on Arabic script", "this one on car + windshields") and taken as evidence of the model's internal conceptual + vocabulary. + + Fit the same optimizer to **pure Gaussian noise** and you still get a + dictionary of crisp, human-nameable "features." Below: 2000 random 8×8 + noise patches → `MiniBatchDictionaryLearning` with 32 atoms. Each tile + in the gallery is one learned atom. Several will look spatially + localized, oriented, blob- or edge-resembling — all the properties a + real SAE atom would have. The structure is in the optimizer, not the + data. + """) + return + + +@app.cell(hide_code=True) +def _(np): + from sklearn.decomposition import MiniBatchDictionaryLearning + + + def _fit_sae_to_noise(n_samples=2000, patch_size=8, n_atoms=32, seed=0): + rng = np.random.default_rng(seed) + X = rng.normal(0, 1, size=(n_samples, patch_size * patch_size)) + X = X - X.mean(axis=1, keepdims=True) + dl = MiniBatchDictionaryLearning( + n_components=n_atoms, + alpha=1.0, + batch_size=64, + max_iter=200, + transform_algorithm="lasso_lars", + random_state=seed, + ) + dl.fit(X) + return { + "atoms": dl.components_.astype(float), + "patch_size": patch_size, + "n_atoms": n_atoms, + } + + + sae_result = _fit_sae_to_noise(seed=0) + return (sae_result,) + + +@app.cell(hide_code=True) +def _(anywidget, traitlets): + class SparseFeatureGallery(anywidget.AnyWidget): + """Render a grid of dictionary atoms as tiny diverging-colormap tiles. + + Pedagogical demo: an SAE fit to pure Gaussian noise still produces + spatially-localized, feature-looking atoms. The structure is in the + optimizer, not the data. + """ + + _esm = r""" + function render({ model, el }) { + const wrapper = document.createElement("div"); + wrapper.style.cssText = ` + font-family: sans-serif; + color: #222; + background: #fafaf7; + border: 1px solid #d8d8d0; + border-radius: 8px; + padding: 14px; + display: flex; + flex-direction: column; + gap: 12px; + `; + + const title = document.createElement("div"); + title.style.cssText = "font-size: 12px; color: #333; font-weight: 600; letter-spacing: 0.5px;"; + title.textContent = "Learned dictionary — 32 atoms, fit to 2000 patches of pure Gaussian noise"; + wrapper.appendChild(title); + + const grid = document.createElement("div"); + const cols = 8; + const tileSize = 72; + grid.style.cssText = + "display: grid; " + + "grid-template-columns: repeat(" + cols + ", " + tileSize + "px); " + + "gap: 8px; " + + "justify-content: center;"; + wrapper.appendChild(grid); + + const footer = document.createElement("div"); + footer.style.cssText = "font-family: ui-monospace, monospace; font-size: 13px; line-height: 1.55; padding: 10px 12px; background: #fff; color: #222; border: 1px solid #e0e0d8; border-radius: 4px;"; + wrapper.appendChild(footer); + + el.appendChild(wrapper); + + function diverging(val, maxabs) { + const t = val / (maxabs + 1e-12); + if (t >= 0) { + const k = Math.min(1, t); + const c = Math.floor((1 - k) * 255); + return "rgb(255," + c + "," + c + ")"; + } else { + const k = Math.min(1, -t); + const c = Math.floor((1 - k) * 255); + return "rgb(" + c + "," + c + ",255)"; + } + } + + function redraw() { + while (grid.firstChild) grid.removeChild(grid.firstChild); + const atoms = model.get("atoms"); + const patchSize = model.get("patch_size"); + const nAtoms = model.get("n_atoms"); + const pxs = patchSize * patchSize; + + for (let i = 0; i < nAtoms; i++) { + const atom = atoms.slice(i * pxs, (i + 1) * pxs); + let mx = 0; + for (const v of atom) { const a = Math.abs(v); if (a > mx) mx = a; } + + const cell = document.createElement("div"); + cell.style.cssText = "display: flex; flex-direction: column; align-items: center; gap: 2px;"; + + const canvas = document.createElement("canvas"); + canvas.width = tileSize; + canvas.height = tileSize; + canvas.style.cssText = "border: 1px solid #bbb; image-rendering: pixelated; background: #fff;"; + const ctx = canvas.getContext("2d"); + const px = tileSize / patchSize; + for (let r = 0; r < patchSize; r++) { + for (let c = 0; c < patchSize; c++) { + ctx.fillStyle = diverging(atom[r * patchSize + c], mx); + ctx.fillRect(Math.floor(c * px), Math.floor(r * px), Math.ceil(px), Math.ceil(px)); + } + } + + const label = document.createElement("div"); + label.style.cssText = "font-size: 10px; color: #555; font-family: ui-monospace, monospace;"; + label.textContent = "#" + (i + 1); + + cell.appendChild(canvas); + cell.appendChild(label); + grid.appendChild(cell); + } + + footer.innerHTML = + "All 32 atoms above were fit to 2000 patches of pure Gaussian noise. " + + "No underlying structure. If several of them look like \"features\" — a blob here, " + + "an oriented edge there, a localized cluster of positive or negative weight — " + + "that's the pedagogical point. Sparse dictionary learning produces feature-looking atoms " + + "by construction; the L1 objective makes them localized regardless of what's in the data. " + + "A real SAE fit to a model's activations would look indistinguishable from this."; + } + + model.on("change:atoms", redraw); + model.on("change:n_atoms", redraw); + redraw(); + } + + export default { render }; + """ + + atoms = traitlets.List(trait=traitlets.Float()).tag(sync=True) + patch_size = traitlets.Int(8).tag(sync=True) + n_atoms = traitlets.Int(32).tag(sync=True) + + return (SparseFeatureGallery,) + + +@app.cell(hide_code=True) +def _(SparseFeatureGallery, mo, sae_result): + sae_widget = mo.ui.anywidget( + SparseFeatureGallery( + atoms=sae_result["atoms"].flatten().tolist(), + patch_size=sae_result["patch_size"], + n_atoms=sae_result["n_atoms"], + ) + ) + sae_widget + return + + +@app.cell(hide_code=True) +def _(mo): + mo.md(r""" + ## 5. Concept-Based Explanations + + TCAV (Kim et al. 2018) and its descendants frame interpretability as + *"we found a direction in activation space aligned with a human concept, + and the model is sensitive to that direction — therefore the model uses + that concept."* In high-dimensional activation spaces, the trap is the + same one we've seen all along: **any random direction, out of enough + random directions, will appear "significantly" aligned with the model's + output**. + + Below: we treat the Phase-1 shape classifier's weight vector `attr_W` + as the "model's decision direction" in 256-dim input/activation space, + then generate `N` random unit-vector "concepts" and compute each + concept's cosine similarity with the model's decision direction. + Under the null (random concepts), cosine similarity is approximately + `N(0, 1/256)` — we z-score and two-sided-test each concept, then feed + the p-values into the same hunter widget. Naive `p < 0.05` produces + ~5% of concepts looking "significant." + """) + return + + +@app.cell(hide_code=True) +def _(mo): + n_concepts_slider = mo.ui.slider( + start=50, stop=1000, step=50, value=500, label="random concept directions" + ) + n_concepts_slider + return (n_concepts_slider,) + + +@app.cell(hide_code=True) +def _(attr_W, n_concepts_slider, normal_cdf, np): + def _concept_experiment(n_concepts=500, W=None, seed=0): + """Random unit-vector concepts; cosine similarity with W as TCAV proxy. + + Under the null, a uniformly random unit vector c in d-dim space has + c . W_unit ~ approximately N(0, 1/d). We use this to get analytic + p-values for each concept. + """ + rng = np.random.default_rng(seed) + W_unit = W / np.maximum(np.linalg.norm(W), 1e-12) + dim = W.shape[0] + C = rng.normal(0, 1, size=(n_concepts, dim)) + C = C / np.maximum(np.linalg.norm(C, axis=1, keepdims=True), 1e-12) + cos = C @ W_unit + z = cos * np.sqrt(dim) + p_values = 2.0 * (1.0 - normal_cdf(np.abs(z))) + return { + "p_values": p_values, + "cosines": cos, + "n_concepts": n_concepts, + } + + + concept_result = _concept_experiment( + n_concepts=n_concepts_slider.value, W=attr_W, seed=1 + ) + concept_alpha = 0.05 + concept_bonf = concept_alpha / max(concept_result["n_concepts"], 1) + return concept_alpha, concept_bonf, concept_result + + +@app.cell(hide_code=True) +def _(FalsePositiveHunter, concept_alpha, concept_bonf, concept_result, mo): + concept_hunter = mo.ui.anywidget( + FalsePositiveHunter( + p_values=concept_result["p_values"].tolist(), + alpha=float(concept_alpha), + bonferroni_threshold=float(concept_bonf), + threshold=float(concept_alpha), + ) + ) + concept_hunter + return (concept_hunter,) + + +@app.cell(hide_code=True) +def _(concept_bonf, concept_hunter, concept_result, mo, np): + _threshold = concept_hunter.value["threshold"] + _p_vals = concept_result["p_values"] + _cos = concept_result["cosines"] + _n_total = concept_result["n_concepts"] + + _passing = np.where(_p_vals < _threshold)[0] + _n_passing = int(len(_passing)) + + _items = [] + if _n_passing > 0: + _top = _passing[np.argsort(_p_vals[_passing])[:5]] + for _k in _top: + _idx = int(_k) + _c = float(_cos[_idx]) + _p = float(_p_vals[_idx]) + _p_str = f"{_p:.2e}" if _p < 1e-3 else f"{_p:.4f}" + _sign = "+" if _c >= 0 else "-" + _items.append( + "
  • " + f"Concept #{_idx + 1:03d}: cosine alignment with decision direction " + f"= {_sign}{abs(_c):.3f} (p = {_p_str}). A " + f"statistically significant conceptual axis.
    • " + ) + + _show_stink = _n_passing > 0 and _threshold > concept_bonf + 1e-10 + + _stink = ( + ( + """ +
    +
    Really?
    + + + + + + + +
    + """ + ) + if _show_stink + else "" + ) + + _items_html = ( + "" + if _items + else "

    No conceptual axes survive this threshold.

    " + ) + + _closing = ( + ( + "Our analysis reveals that the classifier has learned a rich and " + "interpretable conceptual vocabulary. Several of the discovered axes " + "merit follow-up qualitative investigation with domain experts." + ) + if _n_passing > 0 + else ( + "No concept directions were found to be statistically aligned with the " + "classifier's decision surface." + ) + ) + + _threshold_str = ( + f"{_threshold:.2e}" if _threshold < 1e-3 else f"{_threshold:.4f}" + ) + + mo.md(f""" +
    + {_stink} +
    +
    + Annals of Interpretable Attribution · Vol. 22 · No. 4 · pp. 208-219 +
    +
    + Learned Conceptual Vocabulary of a Shape Classifier +
    +
    + A TCAV-Style Investigation of Decision-Aligned Directions +
    +
    + A. Plaus1, Q. Sensible1, E. Vidence2 +
    +
    + 1Concept Attribution Lab · 2Center for Confident Interpretation +
    +
    + +
    +
    Abstract
    +

    + Across a survey of {_n_total:,} candidate concept directions in the 256-dimensional + activation space of a shape classifier, we identify {_n_passing:,} directions + statistically aligned with the model's decision surface at significance threshold + p < {_threshold_str}. These axes constitute an interpretable conceptual vocabulary + the model uses to make its classification decisions. +

    + +
    Selected Conceptual Axes
    + {_items_html} + +
    Conclusion
    +

    + {_closing} +

    + +
    + Manuscript generated from {_n_total:,} cosine-similarity tests between uniformly-random + unit vectors and the classifier weight vector. The "concepts" are pure random directions. +
    +
    +
    + """) + return + + +@app.cell(hide_code=True) +def _(mo): + mo.md(r""" + ## 6. Mechanistic Interpretability + + The deepest corner of the interpretability tree: find *circuits* — small + subsets of neurons that implement a specific model behavior. At small + scale, with cherry-picked examples, this can be genuinely illuminating. + At scale, with automated search across many candidate circuits and many + candidate behaviors, it runs straight into the same wall: **brute-force + search across enough noisy candidates will "discover" circuits that + look convincingly specialized for any target behavior you pick, even + when the neurons are random and the behavior is a coin flip**. + + Below: we define a "target behavior" by flipping a coin for each of 400 + random input vectors — so the labels have no underlying relationship to + the inputs at all. We then search through `N` candidate two-neuron + "circuits" (random weight matrices feeding a random linear readout) and + two-sample t-test each circuit's output across the random labels. + Feed the per-circuit p-values into the hunter widget; the naive + threshold announces dozens of "discovered circuits" for a behavior + that doesn't exist. + """) + return + + +@app.cell(hide_code=True) +def _(mo): + n_circuits_slider = mo.ui.slider( + start=100, + stop=2000, + step=100, + value=500, + label="candidate circuits searched", + ) + n_circuits_slider + return (n_circuits_slider,) + + +@app.cell(hide_code=True) +def _(n_circuits_slider, normal_cdf, np): + def _circuit_experiment(n_circuits=500, n_samples=400, input_dim=64, seed=0): + """Brute-force search over random 2-neuron circuits for a target behavior + defined by random coin-flip labels. + + Under the null (random labels), t-tests yield uniform p-values and + ~alpha of the circuits will appear "selective" by chance. + """ + rng = np.random.default_rng(seed) + X = rng.normal(0, 1, size=(n_samples, input_dim)) + labels = rng.integers(0, 2, size=n_samples).astype(bool) + n_a = int(labels.sum()) + n_b = n_samples - n_a + + p_values = np.empty(n_circuits) + effect_sizes = np.empty(n_circuits) + for c in range(n_circuits): + W = rng.normal(0, 1 / np.sqrt(input_dim), size=(2, input_dim)) + v = rng.normal(0, 1, size=2) + out = np.tanh(X @ W.T) @ v + a = out[labels] + b = out[~labels] + mean_diff = float(a.mean() - b.mean()) + se = float(np.sqrt(a.var(ddof=1) / n_a + b.var(ddof=1) / n_b)) + t = mean_diff / max(se, 1e-12) + p_values[c] = float(2.0 * (1.0 - normal_cdf(np.abs(t)))) + effect_sizes[c] = mean_diff + return { + "p_values": p_values, + "effect_sizes": effect_sizes, + "n_circuits": n_circuits, + "n_samples": n_samples, + } + + + circuit_result = _circuit_experiment( + n_circuits=n_circuits_slider.value, seed=2 + ) + circuit_alpha = 0.05 + circuit_bonf = circuit_alpha / max(circuit_result["n_circuits"], 1) + return circuit_alpha, circuit_bonf, circuit_result + + +@app.cell(hide_code=True) +def _(FalsePositiveHunter, circuit_alpha, circuit_bonf, circuit_result, mo): + circuit_hunter = mo.ui.anywidget( + FalsePositiveHunter( + p_values=circuit_result["p_values"].tolist(), + alpha=float(circuit_alpha), + bonferroni_threshold=float(circuit_bonf), + threshold=float(circuit_alpha), + ) + ) + circuit_hunter + return (circuit_hunter,) + + +@app.cell(hide_code=True) +def _(circuit_bonf, circuit_hunter, circuit_result, mo, np): + _threshold = circuit_hunter.value["threshold"] + _p_vals = circuit_result["p_values"] + _effects = circuit_result["effect_sizes"] + _n_total = circuit_result["n_circuits"] + + _passing = np.where(_p_vals < _threshold)[0] + _n_passing = int(len(_passing)) + + _items = [] + if _n_passing > 0: + _top = _passing[np.argsort(_p_vals[_passing])[:5]] + for _k in _top: + _idx = int(_k) + _eff = float(_effects[_idx]) + _p = float(_p_vals[_idx]) + _p_str = f"{_p:.2e}" if _p < 1e-3 else f"{_p:.4f}" + _sign = "+" if _eff >= 0 else "-" + _items.append( + "
  • " + f"Circuit #{_idx + 1:03d}: mean output difference " + f"{_sign}{abs(_eff):.3f} between target-behavior and control inputs " + f"(t-test p = {_p_str}). A two-neuron subgraph reliably " + f"discriminating the target behavior.
    • " + ) + + _show_stink = _n_passing > 0 and _threshold > circuit_bonf + 1e-10 + + _stink = ( + ( + """ +
    +
    Really?
    + + + + + + + +
    + """ + ) + if _show_stink + else "" + ) + + _items_html = ( + "" + if _items + else "

    No candidate circuits survive this threshold.

    " + ) + + _closing = ( + ( + "Our analysis isolates a compact subgraph mechanically responsible for " + "the target behavior. We recommend further mechanistic dissection — " + "activation patching, path ablation, and attention-head replacement — " + "to confirm the functional role of the identified circuit." + ) + if _n_passing > 0 + else ( + "No mechanistic structure implementing the target behavior was identified " + "at this threshold." + ) + ) + + _threshold_str = ( + f"{_threshold:.2e}" if _threshold < 1e-3 else f"{_threshold:.4f}" + ) + + mo.md(f""" +
    + {_stink} +
    +
    + Proceedings of Prematurely Confident Interpretability · Vol. 9 · No. 2 · pp. 44-59 +
    +
    + A Two-Neuron Circuit Implementing Target-Behavior Detection +
    +
    + Brute-Force Mechanistic Discovery Across {_n_total:,} Candidate Subgraphs +
    +
    + I. Solated1, C. Ircuit1, A. Notherone2 +
    +
    + 1Mechanistic Search Collective · 2Lab for Automated Explanation +
    +
    + +
    +
    Abstract
    +

    + We conducted a systematic brute-force search across {_n_total:,} candidate + two-neuron subgraphs of a 64-dimensional feature space and identified + {_n_passing:,} circuits mechanically implementing the target + behavior at significance threshold p < {_threshold_str}. These + results demonstrate that small interpretable subgraphs are sufficient to + reproduce the target-behavior signal end-to-end. +

    + +
    Isolated Circuits
    + {_items_html} + +
    Conclusion
    +

    + {_closing} +

    + +
    + Manuscript generated from {_n_total:,} t-tests over random 2-neuron circuits with + random weights. "Target behavior" was assigned by coin flip; it does not exist. +
    +
    +
    + """) + return + + +if __name__ == "__main__": + app.run() diff --git a/notebooks/jam-2026-Q2/geometry-of-noise.py b/notebooks/jam-2026-Q2/geometry-of-noise.py new file mode 100644 index 0000000..e6b229a --- /dev/null +++ b/notebooks/jam-2026-Q2/geometry-of-noise.py @@ -0,0 +1,1932 @@ +# /// script +# requires-python = ">=3.12" +# dependencies = [ +# "marimo", +# "numpy==2.4.4", +# "matplotlib==3.10.8", +# "plotly==6.7.0", +# "pywidget==0.1.0", +# ] +# /// +""" +The Geometry of Noise — why diffusion models don't need noise conditioning. + +Interactive companion notebook for Sahraee-Ardakan, Delbracio & Milanfar +(2026). All analytics are closed-form over a discrete 2D-circles dataset +lifted to R^D via a random orthogonal projection. No neural networks, +no training. Pure NumPy + pywidget, runs entirely in Pyodide. +""" + +import marimo + +__generated_with = "0.23.4" +app = marimo.App(width="medium", auto_download=["html"]) + + +@app.cell +def _(): + import marimo as mo + + return (mo,) + + +@app.cell +def _(): + import io + import json + from functools import lru_cache + + import numpy as np + import plotly.graph_objects as go + import traitlets + from pywidget import PyWidget + + return PyWidget, io, json, lru_cache, np, traitlets + + +@app.cell(hide_code=True) +def _(mo): + mo.md(r""" + # The Geometry of Noise + Paper: [Sahraee-Ardakan, Delbracio & Milanfar (2026)](https://www.alphaxiv.org/abs/2602.18428v1) + · Notebook by Konstantin Taletskiy + + --- + + ## TL;DR + + Every diffusion model you have used passes the current noise level + $t$ to its U-Net. This paper says you can **drop $t$ entirely** — + turn `unet(x_t, t)` into `unet(x_t)`, a *blind* sampler — and it + still works. The reason is geometric: in high-dimensional spaces, + the image itself reveals its own noise level (concentration of + measure). It also matters *what* the U-Net is trained to predict: + clean image or velocity → stable; noise → fragile. The paper names + the mechanism **posterior collapse on $t$** and proves a hard + boundary at codimension $D - d > 2$, below which even the stable + parameterizations break. + + This notebook builds the geometric intuition step by step and lets + you watch that boundary live by dragging $D$. + + --- + + *Everything below is a closed-form analytical reconstruction — no + neural networks, no training. Interactive elements use + [pywidget](https://github.com/ktaletsk/pywidget): pure-Python widgets + running in Pyodide, natively compatible with marimo.* + """) + return + + +@app.cell(hide_code=True) +def _(mo): + from pathlib import Path as _Path + + # Prefer the PNG on disk; fall back to the base64-encoded text file if + # the image asset isn't shipped alongside the notebook (e.g., in molab). + _src = None + try: + _dir = _Path(__file__).parent / "figures" + _png = _dir / "cifar10_comparison.png" + _txt = _dir / "cifar10_comparison_b64.txt" + if _png.exists(): + _src = _png + elif _txt.exists(): + _src = f"data:image/png;base64,{_txt.read_text().strip()}" + except (NameError, OSError): + _src = None + + if _src is not None: + _figure = mo.image(src=_src, width="100%", rounded=True) + else: + _figure = mo.md("*(CIFAR-10 figure not found)*") + + mo.vstack([ + _figure, + mo.md( + "*On real images, the difference is stark: noise prediction without " + "knowing the noise level produces mush (left), while velocity " + "prediction produces clean samples (right) — both using the exact " + "same blind architecture. Our notebook explains why, analytically, " + "on a toy dataset you can fully explore.*" + ), + ]) + return + + +@app.cell(hide_code=True) +def _(mo): + mo.md(r""" + ## A five-minute refresher on diffusion models + + If the phrase "diffusion model" already means something concrete to + you, skip this section. Otherwise, here's the three-step story: + + - **Forward process.** Take a clean sample. Add a tiny dab of + Gaussian noise. Add another. And another. After enough steps the + sample is indistinguishable from pure Gaussian noise. The + **noise level** $t$ labels how many steps in we are, from $t=0$ + (clean) to $t=1$ (pure noise). + - **Training.** Ask a neural network to undo one step of noising. + Given the noisy sample $\mathbf{x}_t$ and the noise level $t$, + predict something that reveals the clean sample — either the + clean data $\mathbf{x}$, or the noise $\boldsymbol{\epsilon}$ that + was added, or the velocity $\boldsymbol{\epsilon} - \mathbf{x}$. + - **Sampling.** Start from pure noise, run the trained "undo" step + many times over, arrive at a fresh sample. + + ### The toy: concentric circles + + Everything in this notebook happens on a toy 2D dataset which is + **200 points on two concentric circles** — so we can *see* each of these three steps with + our own eyes. Let's do a lap. + """) + return + + +@app.cell(hide_code=True) +def _(X2_data, io): + # Static view of the toy dataset before the reader starts moving D. + # 200 points on two concentric circles in R^2 — the entire substrate + # of every figure in the notebook, lifted into D-dim by a random + # orthogonal embedding when D > 2. + import re as _re + import matplotlib as _mpl + + _mpl.use("agg") + import matplotlib.pyplot as _plt + + _fig, _ax = _plt.subplots(figsize=(3.8, 3.8)) + _ax.scatter( + X2_data[:100, 0], + X2_data[:100, 1], + s=18, + c="#1f77b4", + alpha=0.85, + linewidths=0, + label="inner ($r = 0.6$)", + ) + _ax.scatter( + X2_data[100:, 0], + X2_data[100:, 1], + s=18, + c="#e67e22", + alpha=0.85, + linewidths=0, + label="outer ($r = 1.2$)", + ) + _ax.set_aspect("equal") + _ax.set_xlim(-1.6, 1.6) + _ax.set_ylim(-1.6, 1.6) + _ax.grid(alpha=0.2) + _ax.legend(loc="upper right", fontsize=8, frameon=False) + _ax.set_title( + r"Toy dataset: 200 points on two concentric circles in $\mathbb{R}^2$", + fontsize=10, + pad=8, + ) + for _s in _ax.spines.values(): + _s.set_alpha(0.3) + + _fig.tight_layout() + _buf = io.StringIO() + _fig.savefig(_buf, format="svg", bbox_inches="tight") + _plt.close(_fig) + _svg = _buf.getvalue() + _svg = _re.sub(r'(]*?)\s+width="[^"]*"', r"\1", _svg, count=1) + _svg = _re.sub(r'(]*?)\s+height="[^"]*"', r"\1", _svg, count=1) + _svg = _re.sub( + r"{_svg}' + ) + return + + +@app.cell(hide_code=True) +def _(mo): + mo.md(r""" + ### Watching the forward process + + As you scrub $t$ below, you're watching the forward process the way + **every modern diffusion model actually sees it during training**: + for each training step, a single noise vector + $\boldsymbol{\epsilon} \sim \mathcal{N}(0, I)$ is drawn per data + point and $\mathbf{u}_t = (1-t)\mathbf{x} + t\boldsymbol{\epsilon}$ + is computed in one shot. No sequential noising between timesteps. + The network only ever sees marginal pairs $(\mathbf{u}_t, t)$. + + Each data point moves along its own straight line in 2D, from its + starting position at $t=0$ to its personal noise target at $t=1$. + The orange dot tracks one of them so you can follow its trajectory. + """) + return + + +@app.cell(hide_code=True) +def _(PyWidget, traitlets): + class ForwardDiffusionWidget(PyWidget): + config_json = traitlets.Unicode("{}").tag(sync=True) + _py_packages = ["numpy"] + + def render(self, el, model): + from pyodide.ffi import create_proxy + from js import window + import json + import numpy as np + + config = json.loads(model.get("config_json")) + if not config: + el.innerHTML = ( + '
    Loading…
    ' + ) + return + + X2 = np.array(config["X2"]) + eps = np.array(config["eps"]) + k = int(config.get("highlight_index", 75)) + N = X2.shape[0] + vb = 2.6 + W = 520 + + hx, hy = float(X2[k, 0]), float(X2[k, 1]) + ex, ey = float(eps[k, 0]), float(eps[k, 1]) + + # Straight-line trajectory for the highlighted point + traj_line = ( + f'' + ) + traj_endpoint = ( + f'' + ) + + regular_dots = "".join( + f'' + for i in range(N) + if i != k + ) + highlight_dot = ( + f'' + ) + + el.innerHTML = f""" +
    + + {traj_line} + {traj_endpoint} + {regular_dots} + {highlight_dot} + +
    + + + t = 0.00 +
    +
    + ut = (1−t) · x + t · ε. ε is drawn once per + point with independent components per axis; the orange + dot tracks one point along its straight-line path. +
    +
    + """ + + svg_el = el.querySelector("#fd-scatter") + slider = el.querySelector("#fd-slider") + button = el.querySelector("#fd-play") + readout = el.querySelector("#fd-readout") + dot_nodes = svg_el.querySelectorAll(".fd-dot") + + dot_by_index = {} + for j in range(dot_nodes.length): + node = dot_nodes.item(j) + idx = int(node.getAttribute("data-i")) + dot_by_index[idx] = node + + state = {"playing": False, "interval_id": None, "direction": 1} + + def update_positions(t): + a_t = 1.0 - t + b_t = t + for i in range(N): + ux = a_t * float(X2[i, 0]) + b_t * float(eps[i, 0]) + uy = a_t * float(X2[i, 1]) + b_t * float(eps[i, 1]) + d = dot_by_index[i] + d.setAttribute("cx", f"{ux:.4f}") + d.setAttribute("cy", f"{-uy:.4f}") + readout.textContent = f"t = {t:.2f}" + + def on_slider_input(event): + update_positions(int(slider.value) / 1000.0) + + def tick(): + v = int(slider.value) + state["direction"] * 8 + if v >= 1000: + v = 1000 + state["direction"] = -1 + elif v <= 0: + v = 0 + state["direction"] = 1 + slider.value = str(v) + update_positions(v / 1000.0) + + def on_play_click(event): + if state["playing"]: + window.clearInterval(state["interval_id"]) + state["playing"] = False + button.textContent = "▶ play" + else: + p = create_proxy(tick) + state["interval_id"] = window.setInterval(p, 40) + state["playing"] = True + button.textContent = "⏸ pause" + el._py_fd_tick_proxy = p + + _slider_proxy = create_proxy(on_slider_input) + slider.addEventListener("input", _slider_proxy) + el._py_fd_slider_proxy = _slider_proxy + + _button_proxy = create_proxy(on_play_click) + button.addEventListener("click", _button_proxy) + el._py_fd_button_proxy = _button_proxy + + def update(self, el, model): + self.render(el, model) + + return (ForwardDiffusionWidget,) + + +@app.cell(hide_code=True) +def _(ForwardDiffusionWidget, X2_data, json, mo, np): + # -------- Act 0.1: forward diffusion (interactive) ------------ + # Draw one fixed eps per data point. Scrubbing t = 0 → 1 then gives + # each point a deterministic straight-line trajectory in 2D from x to + # eps. This matches what the network actually sees during training: a + # single (u_t, t) pair per example, sampled from the marginal. + _rng_fd = np.random.default_rng(0) + _eps_fixed = _rng_fd.standard_normal(X2_data.shape) + + _fd_config = { + "X2": X2_data.tolist(), + "eps": _eps_fixed.tolist(), + "highlight_index": 75, + } + _fd_w = ForwardDiffusionWidget(config_json=json.dumps(_fd_config)) + forward_widget = mo.ui.anywidget(_fd_w) + forward_widget + return + + +@app.cell(hide_code=True) +def _(mo): + mo.md(r""" + ### Training, in three lines of pseudocode + + ```python + t = random.uniform(0, 1) # pick a noise level + x_t = (1 - t) * x + t * eps # noise the clean sample (eps ~ N(0, I)) + loss = mse(unet(x_t, t), target) # predict & regress + ``` + + The network gets both the noisy sample $\mathbf{x}_t$ *and* the + noise level $t$, and learns to predict some `target` that pins + down the clean $\mathbf{x}$. Simple. Works. This is the pattern + every diffusion model in the wild uses. + + **What happens if we drop the `t`?** That is the question this + paper answers. + """) + return + + +@app.cell(hide_code=True) +def _(PyWidget, traitlets): + class ReverseDiffusionWidget(PyWidget): + config_json = traitlets.Unicode("{}").tag(sync=True) + _py_packages = ["numpy"] + + def render(self, el, model): + from pyodide.ffi import create_proxy + from js import window + import json + import numpy as np + + config = json.loads(model.get("config_json")) + if not config: + el.innerHTML = ( + '
    Loading…
    ' + ) + return + + traj = np.array(config["trajectories"]) # (n_frames, N, 2) + ts = config["ts"] # length n_frames + X2 = np.array(config["X2"]) + n_frames = traj.shape[0] + N = traj.shape[1] + + vb = 2.6 + W = 520 + + target_svg = "".join( + f'' + for i in range(X2.shape[0]) + ) + + dots_svg = "".join( + f'' + for i in range(N) + ) + + el.innerHTML = f""" +
    + + {target_svg} + {dots_svg} + +
    + + + t = {ts[0]:.2f} +
    +
    + FM-conditional sampler in D = 16, projected to 2D. + Faint black dots are the target dataset. +
    +
    + """ + + svg_el = el.querySelector("#rd-scatter") + slider = el.querySelector("#rd-slider") + button = el.querySelector("#rd-play") + readout = el.querySelector("#rd-readout") + dots = svg_el.querySelectorAll(".rd-dot") + + state = {"playing": False, "interval_id": None} + + def update_frame(step): + for i in range(N): + d = dots.item(i) + d.setAttribute("cx", f"{float(traj[step, i, 0]):.4f}") + d.setAttribute("cy", f"{-float(traj[step, i, 1]):.4f}") + readout.textContent = f"t = {ts[step]:.2f}" + + def on_slider_input(event): + update_frame(int(slider.value)) + + def tick(): + step = int(slider.value) + 1 + if step >= n_frames: + step = 0 + slider.value = str(step) + update_frame(step) + + def on_play_click(event): + if state["playing"]: + window.clearInterval(state["interval_id"]) + state["playing"] = False + button.textContent = "▶ play" + else: + p = create_proxy(tick) + state["interval_id"] = window.setInterval(p, 55) + state["playing"] = True + button.textContent = "⏸ pause" + el._py_rd_tick_proxy = p + + _slider_proxy = create_proxy(on_slider_input) + slider.addEventListener("input", _slider_proxy) + el._py_rd_slider_proxy = _slider_proxy + + _button_proxy = create_proxy(on_play_click) + button.addEventListener("click", _button_proxy) + el._py_rd_button_proxy = _button_proxy + + def update(self, el, model): + self.render(el, model) + + return (ReverseDiffusionWidget,) + + +@app.cell(hide_code=True) +def _( + PARAMS, + ReverseDiffusionWidget, + X2_data, + conditional_field, + json, + lift, + mo, + np, + random_lift, +): + # -------- Act 0.2: reverse diffusion (interactive) ------------ + # Precompute a full reverse-sampling trajectory on the circles at + # D = 16, capturing one snapshot per Euler step. Then hand the frames + # to a pywidget; the reader scrubs or auto-plays — no kernel round-trip + # per frame, rendering is entirely in-browser via Pyodide. + _D_demo = 16 + _P_rd = random_lift(_D_demo, seed=42) + _X_rd = lift(X2_data, _P_rd) + _cd_rd = PARAMS["FM"] + + _n_samples = 200 + _n_steps = 80 + _t_start, _t_end = 0.99, 0.005 + _ts_full = np.exp(np.linspace(np.log(_t_start), np.log(_t_end), _n_steps + 1)) + + _rng_rd = np.random.default_rng(1) + _u = _rng_rd.standard_normal((_n_samples, _D_demo)) * _t_start + + # Capture every 2nd step so the timeline stays smooth without bloating + # the payload. For FM-linear (mu=0, nu=1) the Euler step is u += f*dt. + _frames_2d = [(_u @ _P_rd).tolist()] + _ts_captured = [float(_ts_full[0])] + for _i in range(_n_steps): + _t_i = _ts_full[_i] + _dt = _ts_full[_i + 1] - _ts_full[_i] + _f = conditional_field(_u, _X_rd, _t_i, _cd_rd) + _u = _u + _f * _dt + if (_i + 1) % 2 == 0 or _i == _n_steps - 1: + _frames_2d.append((_u @ _P_rd).tolist()) + _ts_captured.append(float(_ts_full[_i + 1])) + + _rd_config = { + "trajectories": _frames_2d, + "ts": _ts_captured, + "X2": X2_data.tolist(), + } + _rd_w = ReverseDiffusionWidget(config_json=json.dumps(_rd_config)) + reverse_widget = mo.ui.anywidget(_rd_w) + reverse_widget + return + + +@app.cell(hide_code=True) +def _(mo): + mo.md(r""" + That's diffusion in one lap: noise goes in, data comes out. + Everything else in this notebook is about what happens when you + remove the `t` from the middle step. + """) + return + + +@app.cell(hide_code=True) +def _(mo): + mo.md(r""" + ### What this paper is actually asking + + You just saw the training pseudocode. The line that matters for + this paper is the one that calls the network: + + ```python + predicted = unet(x_t, t) # ← t is "noise conditioning" + ``` + + **Stable Diffusion does this.** When you generate an image with SD, + the model runs ~50 denoising steps from $t \approx 1$ (pure noise) + down to $t = 0$ (clean image). At every step, the current $t$ is + fed into the U-Net via a learned sinusoidal timestep embedding. + The same conditioning pattern shows up across the field — sometimes + inside a U-Net (**DALL-E 2**, **Imagen**), sometimes inside a + transformer with a rectified-flow objective (**Flux**) — but the + network is *always* told what $t$ is. Without that argument, it + would not know whether to make **big corrections** at $t = 0.95$ + (still pure noise) or **polish details** at $t = 0.05$ (almost a + finished picture). + + **This paper asks: what if we just drop the `t`?** + + ```python + predicted = unet(x_t) # no t — "blind" sampler + ``` + + Naively, this should fail catastrophically. It turns out — + experimentally (Sun et al., ICML 2025) — that for some choices of + what the U-Net predicts, it works *anyway*. This paper explains why: + **in high-dimensional image spaces, the noise itself leaves a + statistical fingerprint** that the U-Net can read. The geometry of + high-$D$ noise makes the training trick the net would need to + "notice" $t$ almost inevitable. + + Below we prove this on a toy dataset you can explore by hand — two + concentric circles — with no U-Net, no training. Every field is + computed in closed form from Bayes' rule. + """) + return + + +@app.cell(hide_code=True) +def _(mo): + mo.md(r""" + ### Why this sounds impossible + + Here's the argument *against* dropping `t`: at $t = 0.95$ the image + $\mathbf{x}_t$ is mostly noise and the U-Net needs to make **large** + corrections. At $t = 0.05$ it's almost clean, so corrections should + be **tiny**. How can a single bounded neural network do both if + it's not told which situation it's in? + + It can't — unless the image itself secretly tells the network which + $t$ it came from. That is exactly what happens in high dimensions, + and building the intuition for *why* is the main goal of this + notebook. + """) + return + + +@app.cell(hide_code=True) +def _(mo): + mo.md(r""" + ### What "dimension" means here + + We just claimed that "in high dimensions, the image itself tells the + network which $t$ it came from." That word **dimension** is doing a + lot of work in that sentence — let's be precise about which dimension + we mean before we go further. + + The dimension that matters for this paper is the **ambient data + space dimension** $D$ — the number of raw numbers in a single + sample. For a $32 \times 32$ RGB CIFAR-10 image, $D = 3072$. For a + $256 \times 256$ ImageNet image, $D = 196{,}608$. Not the model + width, not the dataset size, not any latent dimension — just the + coordinate count of the thing being denoised. + + For the rest of the notebook, **$D$ is something you control.** The + slider below sweeps $D$ from $2$ (two-number "samples") up to $128$. + Every figure that depends on $D$ — apple peel, shell histogram, + posterior, 4-panel sampler — recomputes live as you drag. It's the + single most important control in the notebook. + """) + return + + +@app.cell(hide_code=True) +def _(mo): + mo.md(r""" + **Reintroducing the toy in $\mathbb{R}^D$.** You already saw our + 200 points on two concentric circles in $\mathbb{R}^2$. From here + on, we're going to **embed those same circles into $\mathbb{R}^D$**: + pick a random 2D plane through the origin of $\mathbb{R}^D$, rotate + the 2D data into it, and leave the remaining $D - 2$ coordinates as + zeros. Distances and angles are preserved exactly — we're not + making the problem harder, we're putting the same picture into a + room with more empty dimensions. + + Intrinsic dimension of the data: $d = 1$ (circles are 1D curves). + Codimension: $D - d = D - 1$ — the amount of empty room around + the data. The paper proves a hard regime boundary at $D - d > 2$ — + below it, even the "stable" parameterizations fail. + """) + return + + +@app.cell(hide_code=True) +def _(mo): + D_slider = mo.ui.slider( + steps=[2, 4, 8, 16, 32, 64, 128], + value=8, + label="ambient dimension $D$", + show_value=True, + full_width=True, + ) + mo.callout( + mo.vstack( + [ + mo.md( + "🎚️ **The notebook's main control — drag $D$ here.** " + "Everything below recomputes live: the apple peel, the " + "shell histogram, the posterior $p(t \\mid \\mathbf{u})$, " + "and the 4-panel sampler experiment. " + "**Come back to this slider any time** as you read — " + "the notebook is built to be played with at multiple " + "values of $D$, not consumed once at $D = 8$." + ), + D_slider, + ] + ), + kind="info", + ) + return (D_slider,) + + +@app.cell(hide_code=True) +def _(mo): + mo.md(r""" + ### A surprising fact about high dimensions + + Imagine a $D$-dimensional apple — a unit ball in $\mathbb{R}^D$. Slice off + just the outer **1% of the radius**, a paper-thin skin. How much of + the apple did you take? + + - In ordinary 3D: about **3%**. Most of the apple is still on the table. + - In $D = 100$: **63%**. You took most of it. + - In $D = 1000$: **99.996%**. You took *almost everything* — the + "core" effectively doesn't exist. + + This isn't a trick or a paradox. It's elementary geometry, and it's + the reason every other claim in this notebook works. Drag the local + slider to see the volume shift as you peel; drag the global $D$ + slider above to watch the picture get more violent with dimension. + """) + return + + +@app.cell(hide_code=True) +def _(PyWidget, traitlets): + class ApplePeelWidget(PyWidget): + config_json = traitlets.Unicode("{}").tag(sync=True) + _py_packages = ["numpy"] + + def render(self, el, model): + from pyodide.ffi import create_proxy + from js import DOMPoint + import json + import numpy as np + + config = json.loads(model.get("config_json")) + if not config: + el.innerHTML = ( + '
    Loading…
    ' + ) + return + + D = int(config.get("D", 8)) + + AP_W, AP_H = 280, 360 + CX = AP_W / 2 + L = (CX**2 + AP_H**2) ** 0.5 # corner-to-apex distance + R_OUT_FRAC = 0.85 # outer arc radius as fraction of L + R_OUT = R_OUT_FRAC * L # apple's outer radius + + # Curve panel + CV_W, CV_H = 380, 360 + PAD_L, PAD_B, PAD_T, PAD_R = 38, 30, 12, 8 + plot_w = CV_W - PAD_L - PAD_R + plot_h = CV_H - PAD_B - PAD_T + + s_arr = np.linspace(0.0, 1.0, 200) + f_arr = 1.0 - (1.0 - s_arr) ** D + pts = [] + for s_v, f_v in zip(s_arr, f_arr): + x = PAD_L + s_v * plot_w + y = PAD_T + plot_h - f_v * plot_h + pts.append(f"{x:.2f},{y:.2f}") + curve_pts = " ".join(pts) + fill_pts = ( + f"{PAD_L:.0f},{PAD_T + plot_h:.0f} " + + curve_pts + + f" {PAD_L + plot_w:.0f},{PAD_T + plot_h:.0f}" + ) + + x_ticks = "" + for sv in [0.0, 0.25, 0.5, 0.75, 1.0]: + tx = PAD_L + sv * plot_w + ty = PAD_T + plot_h + x_ticks += ( + f'' + ) + x_ticks += ( + f'{int(sv * 100)}%' + ) + y_ticks = "" + for fv in [0.0, 0.25, 0.5, 0.75, 1.0]: + ty = PAD_T + plot_h - fv * plot_h + y_ticks += ( + f'' + ) + y_ticks += ( + f'{int(fv * 100)}%' + ) + + s0 = 0.10 + + # Cone wedge (cream): full V from corners to V apex + + # Outer arc wall intersections (constant — arc radius is fixed) + t_out = R_OUT / L # parameter from V apex along wall + x_out_l = CX * (1.0 - t_out) + y_out_w = AP_H * (1.0 - t_out) + x_out_r = AP_W - x_out_l + + cone_d = ( + f"M {x_out_l:.2f},{y_out_w:.2f} " + f"A {R_OUT:.2f} {R_OUT:.2f} 0 0 1 {x_out_r:.2f},{y_out_w:.2f} " + f"L {CX:.2f},{AP_H:.2f} Z" + ) + + def peel_path(s): + R_in = R_OUT * (1.0 - s) + t_in = R_in / L + x_in_l = CX * (1.0 - t_in) + y_in_w = AP_H * (1.0 - t_in) + x_in_r = AP_W - x_in_l + d = ( + f"M {x_out_l:.2f},{y_out_w:.2f} " + f"A {R_OUT:.2f} {R_OUT:.2f} 0 0 1 {x_out_r:.2f},{y_out_w:.2f} " + f"L {x_in_r:.2f},{y_in_w:.2f} " + f"A {R_in:.2f} {R_in:.2f} 0 0 0 {x_in_l:.2f},{y_in_w:.2f} " + f"L {x_out_l:.2f},{y_out_w:.2f} Z" + ) + handle_y = AP_H - R_in + return d, handle_y + + peel_d0, handle_y0 = peel_path(s0) + f0 = 1.0 - (1.0 - s0) ** D + + el.innerHTML = f""" +
    +
    + +
    +
    peel the apple!
    + + + + + + + + + + + + +
    + +
    +
    + volume removed vs skin thickness, at D = {D} +
    + + + + {x_ticks} + {y_ticks} + + + + + skin thickness s (% of radius) + + + volume removed + + +
    +
    + +
    + s = {s0 * 100:.1f}% → removes {f0 * 100:.1f}% +
    + +
    + Drag the inner arc up/down to set the peel thickness. + Try 1% peel and watch the curve as you raise D with the + global slider above. +
    +
    + """ + + readout = el.querySelector("#ap-readout") + peel_path_el = el.querySelector("#ap-peel") + handle = el.querySelector("#ap-handle") + dot = el.querySelector("#ap-dot") + apple_svg = el.querySelector("#ap-apple-svg") + + S_MAX = 0.95 + # handle_y = AP_H - R_OUT*(1-s) → s = (handle_y - (AP_H-R_OUT)) / R_OUT + Y_OFFSET = AP_H - R_OUT + + def update(s): + s = max(0.0, min(S_MAX, s)) + f = 1.0 - (1.0 - s) ** D + d, handle_y = peel_path(s) + peel_path_el.setAttribute("d", d) + handle.setAttribute("cy", f"{handle_y:.2f}") + dot.setAttribute("cx", f"{PAD_L + s * plot_w:.2f}") + dot.setAttribute("cy", f"{PAD_T + plot_h - f * plot_h:.2f}") + if f >= 0.9999: + f_str = f"{f * 100:.4f}%" + elif f >= 0.999: + f_str = f"{f * 100:.3f}%" + elif f >= 0.99: + f_str = f"{f * 100:.2f}%" + else: + f_str = f"{f * 100:.1f}%" + readout.textContent = f"s = {s * 100:.1f}% → removes {f_str}" + + state = {"dragging": False} + + def svg_y_from_event(event): + ctm = apple_svg.getScreenCTM() + if ctm is None: + return None + pt = DOMPoint.new(float(event.clientX), float(event.clientY)) + return float(pt.matrixTransform(ctm.inverse()).y) + + def y_to_s(y): + return (y - Y_OFFSET) / R_OUT + + def on_pointer_down(event): + event.preventDefault() + state["dragging"] = True + try: + apple_svg.setPointerCapture(event.pointerId) + except Exception: + pass + y = svg_y_from_event(event) + if y is not None: + update(y_to_s(y)) + + def on_pointer_move(event): + if not state["dragging"]: + return + y = svg_y_from_event(event) + if y is not None: + update(y_to_s(y)) + + def on_pointer_up(event): + state["dragging"] = False + try: + apple_svg.releasePointerCapture(event.pointerId) + except Exception: + pass + + _down = create_proxy(on_pointer_down) + apple_svg.addEventListener("pointerdown", _down) + _move = create_proxy(on_pointer_move) + apple_svg.addEventListener("pointermove", _move) + _up = create_proxy(on_pointer_up) + apple_svg.addEventListener("pointerup", _up) + apple_svg.addEventListener("pointercancel", _up) + el._py_ap_down = _down + el._py_ap_move = _move + el._py_ap_up = _up + + def update(self, el, model): + self.render(el, model) + + return (ApplePeelWidget,) + + +@app.cell(hide_code=True) +def _(ApplePeelWidget, D_slider, json, mo): + _apple_config = {"D": int(D_slider.value)} + _apple_w = ApplePeelWidget(config_json=json.dumps(_apple_config)) + apple_widget = mo.ui.anywidget(_apple_w) + apple_widget + return + + +@app.cell(hide_code=True) +def _(mo): + mo.md(r""" + **Why?** The volume of a $D$-ball of radius $r$ scales as $r^D$ + (up to a constant). So the fraction of volume removed by a peel of + thickness $s$ (as a fraction of the radius) is + $$ + \frac{V_{\text{peel}}}{V_{\text{total}}} = 1 - (1 - s)^D. + $$ + For any fixed $s > 0$, $(1-s)^D \to 0$ as $D \to \infty$. Almost + all the volume of a high-dimensional ball lives in its outermost peel. + + **What this means for our noise vector.** Add Gaussian noise at level + $t$ to a $D$-dimensional point: $\mathbf{u} = t\,\boldsymbol{\epsilon}$ + with $\boldsymbol{\epsilon} \sim \mathcal{N}(0, I_D)$. The squared + length is + $$ + \|\mathbf{u}\|^2 = t^2 \sum_{i=1}^D \epsilon_i^2, + $$ + with mean $D t^2$ and standard deviation only $\sqrt{2D}\,t^2$ — a + relative spread of $1/\sqrt{D}$. So $\|\mathbf{u}\| \approx t\sqrt{D}$ + to a vanishing relative error. The noise doesn't fill the ball; it + lives on a **shell** at radius $t\sqrt{D}$, by exactly the same + "all the volume is in the peel" mechanism. + """) + return + + +@app.cell(hide_code=True) +def _(D_slider, io, np): + import re as _re + + import matplotlib as _mpl + _mpl.use("agg") + import matplotlib.pyplot as _plt + + _D = int(D_slider.value) + _rng = np.random.default_rng(0) + _n = 5000 + _eps = _rng.standard_normal((_n, _D)) + _eps_norms = np.linalg.norm(_eps, axis=1) + + _ts = [0.1, 0.3, 0.5, 0.7, 0.9] + _colors = _plt.cm.viridis(np.linspace(0.12, 0.85, len(_ts))) + + _fig, _ax = _plt.subplots(figsize=(9.0, 3.9)) + + _all_r = np.concatenate([t * _eps_norms for t in _ts]) + _max_r = max(_all_r.max() * 1.05, 0.1) + _bins = np.linspace(0.0, _max_r, 70) + + for _t, _color in zip(_ts, _colors): + _r = _t * _eps_norms + _ax.hist( + _r, bins=_bins, color=_color, alpha=0.55, + edgecolor="white", linewidth=0.3, label=f"$t = {_t}$", + ) + _mean_r = _t * np.sqrt(_D) + _ax.axvline( + _mean_r, color=_color, linestyle="--", lw=1.2, alpha=0.9, + ) + + _ax.set_xlabel( + r"$\|\mathbf{u}\|$ where $\mathbf{u} = t \cdot \boldsymbol{\epsilon}$, " + r"$\boldsymbol{\epsilon} \sim \mathcal{N}(0, I_D)$", + fontsize=10, + ) + _ax.set_ylabel("count") + _ax.set_title( + f"Where does a noisy sample land, at $D = {_D}$? " + r"(dashed lines = theoretical radius $t\sqrt{D}$)", + fontsize=11, pad=6, + ) + _ax.legend(loc="upper right", fontsize=9, ncol=1) + _ax.grid(alpha=0.2) + _ax.set_yticks([]) + + _fig.tight_layout() + _buf = io.StringIO() + _fig.savefig(_buf, format="svg", bbox_inches="tight") + _plt.close(_fig) + _svg = _buf.getvalue() + _svg = _re.sub(r'(]*?)\s+width="[^"]*"', r"\1", _svg, count=1) + _svg = _re.sub(r'(]*?)\s+height="[^"]*"', r"\1", _svg, count=1) + _svg = _re.sub( + r"{_svg}') + return + + +@app.cell(hide_code=True) +def _(mo): + mo.md(r""" + ### The a-ha — reading $t$ from $\|\mathbf{u}\|$ + + At **low $D$**, shells at different noise levels overlap heavily. + If someone hands you a noisy sample $\mathbf{u}$, you cannot tell + which $t$ it came from — the blind network genuinely has no way to + know. + + At **high $D$**, the shells sit at well-separated radii + $t\sqrt{D}$, each one narrower than the gap between neighbors. A + single measurement of $\|\mathbf{u}\|$ is enough to read off $t$ + with high precision. + + **That is the "trick."** A blind diffusion model doesn't infer $t$ + from some clever learned representation. It gets $t$ for free, from + the *geometry of high-dimensional Gaussian noise*. The real-world + success is a consequence of the fact that real images live at + $D \sim 10^3$–$10^5$, where the shells are enormously + well-separated. + """) + return + + +@app.cell(hide_code=True) +def _(mo): + mo.md(r""" + ### From shells to the posterior $p(t \mid \mathbf{u})$ + + The probabilistic statement of "you can read $t$ from $\mathbf{u}$" + is that the **posterior distribution over the noise level**, + $p(t \mid \mathbf{u})$, concentrates on a single value when the + shells separate. The paper calls this **posterior collapse** and it + is the formal core of the mechanism. + + Below is a live demo. **Click anywhere** on the scatter to place a + probe point; the right panel shows $p(t\mid\mathbf{u})$ at that + location. Re-click the same spot after changing $D$ and watch the + posterior sharpen from broad and uncertain to a narrow spike. + """) + return + + +@app.cell(hide_code=True) +def _(PyWidget, traitlets): + class PosteriorCollapseWidget(PyWidget): + config_json = traitlets.Unicode("{}").tag(sync=True) + _py_packages = ["numpy"] + + def render(self, el, model): + from pyodide.ffi import create_proxy + import json + import numpy as np + + config = json.loads(model.get("config_json")) + if not config: + el.innerHTML = '
    Loading…
    ' + return + + X_D = np.array(config["X_D"]) + P = np.array(config["P"]) + X2 = np.array(config["X2"]) + T_grid = np.array(config["T_grid"]) + D = config["D"] + N = X_D.shape[0] + + def a(t): + return 1.0 - t + + def b(t): + return t + + def compute_posterior(px, py): + u2 = np.array([px, py]) + u_D = P @ u2 + log_p = np.zeros(len(T_grid)) + for i, t_val in enumerate(T_grid): + at, bt = a(t_val), b(t_val) + diff = u_D[None, :] - at * X_D + sq = (diff * diff).sum(axis=1) + log_g = ( + -sq / (2.0 * bt * bt + 1e-30) + - 0.5 * D * np.log(2.0 * np.pi * bt * bt + 1e-30) + ) + m = log_g.max() + log_p[i] = m + np.log(np.exp(log_g - m).sum()) - np.log(N) + log_p -= log_p.max() + p = np.exp(log_p) + return p / (p.sum() + 1e-30) + + def posterior_to_svg_path(posterior, w, h, pad_l, pad_b, pad_t): + t_min, t_max = float(T_grid[0]), float(T_grid[-1]) + p_max = float(posterior.max()) * 1.25 if posterior.max() > 0 else 1.0 + plot_w = w - pad_l - 10 + plot_h = h - pad_b - pad_t + pts = [] + for t_val, p_val in zip(T_grid, posterior): + x = pad_l + (float(t_val) - t_min) / (t_max - t_min + 1e-30) * plot_w + y = pad_t + plot_h - (float(p_val) / p_max) * plot_h + pts.append(f"{x:.1f},{y:.1f}") + line_pts = " ".join(pts) + x0, x1, base_y = pad_l, pad_l + plot_w, pad_t + plot_h + fill_pts = f"{x0:.0f},{base_y:.0f} " + line_pts + f" {x1:.0f},{base_y:.0f}" + return line_pts, fill_pts + + vb = 2.5 + LW, LH = 280, 280 + RW, RH = 340, 280 + PAD_L, PAD_B, PAD_T = 44, 32, 22 + + data_dots = "".join( + f'' + for x, y in X2 + ) + probe_x0, probe_y0 = 0.9, 0.0 + left_svg = f""" + + + + {data_dots} + + + """ + + post0 = compute_posterior(probe_x0, probe_y0) + line_pts0, fill_pts0 = posterior_to_svg_path(post0, RW, RH, PAD_L, PAD_B, PAD_T) + plot_w = RW - PAD_L - 10 + plot_h = RH - PAD_B - PAD_T + x_ticks_svg = "" + for tv in [0.0, 0.25, 0.5, 0.75, 1.0]: + tx = PAD_L + (tv - float(T_grid[0])) / (float(T_grid[-1]) - float(T_grid[0]) + 1e-30) * plot_w + ty = PAD_T + plot_h + x_ticks_svg += f'' + x_ticks_svg += f'{tv:.2g}' + + right_svg = f""" + + + + {x_ticks_svg} + noise level t + p(t | u) + + + + """ + + el.innerHTML = f""" +
    +
    +
    + click to place probe +
    + {left_svg} +
    + probe: ({probe_x0:.2f}, {probe_y0:.2f}) +
    +
    +
    +
    + posterior p(t | u) at probe +
    + {right_svg} +
    +
    + """ + + svg_el = el.querySelector("#scatter-svg") + + def on_click(event): + pt = svg_el.createSVGPoint() + pt.x = float(event.clientX) + pt.y = float(event.clientY) + ctm = svg_el.getScreenCTM() + if ctm is None: + return + svg_pt = pt.matrixTransform(ctm.inverse()) + px, py = float(svg_pt.x), -float(svg_pt.y) + px = max(-vb, min(vb, px)) + py = max(-vb, min(vb, py)) + + marker = svg_el.querySelector("#probe-marker") + marker.setAttribute("cx", str(round(px, 4))) + marker.setAttribute("cy", str(round(-py, 4))) + + readout = el.querySelector("#probe-readout") + readout.textContent = f"probe: ({px:.2f}, {py:.2f})" + + post = compute_posterior(px, py) + new_line, new_fill = posterior_to_svg_path( + post, RW, RH, PAD_L, PAD_B, PAD_T + ) + el.querySelector("#post-fill").setAttribute("points", new_fill) + el.querySelector("#post-line").setAttribute("points", new_line) + + # Store the proxy on the DOM element so Pyodide's GC doesn't + # collect the Python closure while the DOM listener is still + # attached (`self` isn't bound in the extracted render body). + _click_proxy = create_proxy(on_click) + svg_el.addEventListener("click", _click_proxy) + svg_el._py_click_proxy = _click_proxy + + def update(self, el, model): + self.render(el, model) + + return (PosteriorCollapseWidget,) + + +@app.cell(hide_code=True) +def _( + D_slider, + PosteriorCollapseWidget, + X2_data, + json, + lift, + mo, + np, + random_lift, +): + _D = int(D_slider.value) + _P = random_lift(_D, seed=42) + _X_D = lift(X2_data, _P) + _config = { + "X_D": _X_D.tolist(), + "P": _P.tolist(), + "X2": X2_data.tolist(), + "T_grid": np.linspace(0.005, 0.99, 80).tolist(), + "D": _D, + } + _w = PosteriorCollapseWidget(config_json=json.dumps(_config)) + posterior_widget = mo.ui.anywidget(_w) + posterior_widget + return + + +@app.cell(hide_code=True) +def _(D_slider, mo): + _D = int(D_slider.value) + + if _D <= 2: + _explanation = ( + f"At $D = {_D}$, the posterior still has a mode near $t \\approx 0$ " + "when the probe is close to the data — the geometry provides " + "*some* signal. But notice the **long heavy tail** stretching " + "toward $t = 1$. At this dimension the shells overlap too much " + "for the posterior to commit." + ) + elif _D <= 16: + _explanation = ( + f"At $D = {_D}$, the posterior is visibly **sharper** than at " + "$D = 2$ — the tail has receded. The shells are starting to " + "separate." + ) + elif _D <= 48: + _explanation = ( + f"At $D = {_D}$, the posterior is **concentrated into a narrow " + "spike**. Shells are well-separated. The sampler effectively " + "'knows' $t$ from the geometry alone." + ) + else: + _explanation = ( + f"At $D = {_D}$, the posterior is effectively a **Dirac delta**. " + "The high-$D$ regime has fully kicked in." + ) + + mo.vstack([ + mo.md(_explanation), + mo.callout( + mo.md( + "This posterior is recomputed **in-browser** on every click " + "using [pywidget](https://github.com/ktaletsk/pywidget) — " + "pure Python running in Pyodide, zero kernel round-trip." + ), + kind="neutral", + ), + ]) + return + + +@app.cell(hide_code=True) +def _(mo): + mo.md(r""" + ### Does the shell picture actually rescue sampling? + + Posterior collapse says a blind U-Net *can* infer $t$ from the + geometry. But that's only part of the story. When we actually run + the reverse-time sampler, does it produce correct samples? And does + it matter what the U-Net was trained to predict? + + Let's run the experiment. + """) + return + + +@app.cell(hide_code=True) +def _(mo): + mo.md(r""" + ### What does the network predict? + + The default training convention — and what you'll see in almost any + tutorial — trains the U-Net to predict the **noise** `eps` that was + added. This is the DDPM convention. But you could equivalently train + it to predict: + + | Target | Predicts | Method | + |:---|:---|:---| + | `eps` | the noise that was added | **DDPM** (noise prediction) | + | `x` | the clean image directly | **EDM** (signal prediction) | + | `v = eps - x` | the "velocity" from clean to noise | **Flow Matching** (velocity prediction) | + + When the U-Net *sees* $t$, all three are equivalent: given any two + of $\{x_t, x, \text{eps}\}$ you can compute the third. When the + U-Net is **blind**, each target has to implicitly *average over the + posterior on $t$*. The three averages behave very differently. Two + of them are stable; one of them is fragile. The widget below shows + all four side by side: + + 1. **FM conditional** — velocity prediction *with* $t$. Baseline. + 2. **FM blind** — same target, no $t$. Paper says: should work. + 3. **EDM blind** — signal target, no $t$. Paper predicts stable — + but the authors never actually test it. This is our extension. + 4. **DDPM blind** — noise target, no $t$. Paper's stress test. + """) + return + + +@app.cell(hide_code=True) +def _(PyWidget, traitlets): + class GeometryOfNoiseWidget(PyWidget): + payload_json = traitlets.Unicode("{}").tag(sync=True) + _py_packages = ["numpy", "matplotlib"] + + def render(self, el, model): + import io + import json + import re + + import matplotlib + matplotlib.use("agg") + import matplotlib.pyplot as plt + + payload = json.loads(model.get("payload_json")) + D = payload.get("D", 0) + circles = payload.get("circles", []) + panels = payload.get("panels", []) + + if not panels: + el.innerHTML = ( + '
    Computing…
    ' + ) + return + + n_panels = len(panels) + fig, axes = plt.subplots( + 1, n_panels, figsize=(3.2 * n_panels, 3.8), + ) + if n_panels == 1: + axes = [axes] + + cx = [p[0] for p in circles] + cy = [p[1] for p in circles] + + ROLE_COLORS = { + "baseline": "#555555", + "stable": "#2e7d32", + "unstable": "#c62828", + } + + for ax, panel in zip(axes, panels): + sx = [p[0] for p in panel["samples"]] + sy = [p[1] for p in panel["samples"]] + role = panel.get("role", "baseline") + sub = panel.get("sub", "") + color = ROLE_COLORS.get(role, "#555555") + ax.scatter( + sx, sy, s=14, c="#1f77b4", alpha=0.55, + linewidths=0, zorder=2, + ) + ax.scatter( + cx, cy, s=5, c="black", alpha=0.85, + linewidths=0, zorder=3, + ) + ax.set_title( + panel["title"], fontsize=11, + color=color, fontweight="bold", pad=8, + ) + ax.set_xlim(-2.2, 2.2) + ax.set_ylim(-2.2, 2.2) + ax.set_aspect("equal") + ax.set_xticks([]) + ax.set_yticks([]) + for s in ax.spines.values(): + s.set_edgecolor(color) + s.set_linewidth(1.2) + s.set_alpha(0.55) + + fig.suptitle( + f"$D = {D}$ · closed-form analytics, no NN training", + y=1.02, fontsize=12, + ) + fig.tight_layout() + + buf = io.StringIO() + fig.savefig(buf, format="svg", bbox_inches="tight") + plt.close(fig) + svg = buf.getvalue() + + svg = re.sub(r'(]*?)\s+width="[^"]*"', r"\1", svg, count=1) + svg = re.sub(r'(]*?)\s+height="[^"]*"', r"\1", svg, count=1) + svg = re.sub( + r"{sub}' + ) + legend_html = ( + '
    ' + + "".join(role_labels) + + '
    ' + ) + + el.innerHTML = ( + '
    ' + + legend_html + + svg + + '
    ' + ) + + def update(self, el, model): + self.render(el, model) + + return (GeometryOfNoiseWidget,) + + +@app.cell(hide_code=True) +def _( + D_slider, + GeometryOfNoiseWidget, + X2_data, + compute_panels_for_D, + json, + mo, +): + _D = int(D_slider.value) + _panels = compute_panels_for_D(_D) + _payload = { + "D": _D, + "circles": X2_data.tolist(), + "panels": _panels, + } + _w = GeometryOfNoiseWidget(payload_json=json.dumps(_payload)) + widget = mo.ui.anywidget(_w) + widget + return + + +@app.cell(hide_code=True) +def _(D_slider, mo): + _D = int(D_slider.value) + _codim = _D - 1 + _regime = ( + "**Regime failure** — codimension $D-d = " + + str(_codim) + + " < 2$. Theory predicts its own boundary." + if _codim < 2 + else ( + "**Regime I** — high-$D$ concentration dominates. Even DDPM blind converges." + if _D >= 64 + else "**Middle regime** — parameterization matters. Compare green vs red." + ) + ) + mo.md( + f"$D = {_D}$, codimension $D - d = {_codim}$. {_regime}" + ) + return + + +@app.cell(hide_code=True) +def _(mo): + mo.md(r""" + --- + + **What you just saw.** Four samplers drew from the same distribution + using the same closed-form reverse-time ODE. The only differences: + what the network predicts and whether it gets $t$. Three regimes: + + - **$D = 2$** — shells overlap completely. Even the green panels + fail; **EDM blind** collapses all samples to the centroid. + - **$D = 8$–$32$** — shells start separating. Green panels match + the conditional baseline. **DDPM blind** (red) is visibly noisier. + - **$D \geq 64$** — shells are disjoint. Even DDPM blind cleans up. + + The shell picture explains most of what you see — but **why does + DDPM blind stay scattered in the middle regime**, when its cousins + don't? That's where the three parameterizations stop being + equivalent. + """) + return + + +@app.cell(hide_code=True) +def _(mo): + mo.callout( + mo.md( + r""" + **Physicist sidebar — the Brownian walker's rule of thumb.** + The codimension threshold $D - d > 2$ that the paper needs for + near-manifold concentration is *the same threshold* that separates + recurrent from transient Brownian motion: a Brownian walker in + $\mathbb{R}^k$ almost surely returns to the origin for $k \leq 2$ + and escapes to infinity for $k > 2$. What the paper calls + "Regime II" is rediscovering this classical stochastic-analysis + fact in a modern diffusion-model setting. The posterior at + $(u_1, u_2) = (1.2, 0)$ is Inverse-Gamma with shape + $(D - d)/2 - 1$; it concentrates iff the shape is positive, i.e. + iff $D - d > 2$. + """ + ), + kind="info", + ) + return + + +@app.cell(hide_code=True) +def _(mo): + mo.md(r""" + --- + + ## That's the geometry of noise + + You watched the codimension boundary $D - d > 2$ surface in three + places: + + - **Apple peel** — almost all the volume of a $D$-ball lives in its + outermost shell. + - **Shell histogram** — Gaussian noise concentrates at radius + $t\sqrt{D}$, with shells separating cleanly as $D$ grows. + - **4-panel sampler** — even DDPM-blind, the most fragile + parameterization, recovers once $D - d > 2$. + + The headline of the paper is one line: *the U-Net does not need + $t$, because the noise itself encodes $t$ in high enough $D$.* + Everything you saw here is the geometry of why that + line is true. + + Suggestion: if the slider stayed at $D = 8$ the whole time, scroll back and + try $D = 2$, $D = 16$, $D = 64$ — every figure recomputes. + """) + return + + +@app.cell(hide_code=True) +def _(np): + # FM-linear schedule: a(t)=1-t, b(t)=t. t=0 data, t=1 noise. + def a(t): + return 1.0 - t + + def b(t): + return t + + def adot(t): + return -1.0 + + def bdot(t): + return 1.0 + + # Parameterizations: network target = c * x + d * eps + PARAMS = { + "FM": (-1.0, 1.0), # velocity + "EDM": ( 1.0, 0.0), # signal + "DDPM": ( 0.0, 1.0), # noise + } + + def make_circles(n=200, r1=0.6, r2=1.2): + n_per = n // 2 + th = np.linspace(0.0, 2.0 * np.pi, n_per, endpoint=False) + inner = np.stack([r1 * np.cos(th), r1 * np.sin(th)], axis=1) + outer = np.stack( + [r2 * np.cos(th + np.pi / n_per), r2 * np.sin(th + np.pi / n_per)], + axis=1, + ) + return np.concatenate([inner, outer], axis=0) + + def random_lift(D, seed=42): + rng = np.random.default_rng(seed) + M = rng.standard_normal((D, 2)) + Q, _ = np.linalg.qr(M) + return Q + + def lift(X2, P): + return X2 @ P.T + + X2_data = make_circles(n=200) + return PARAMS, X2_data, a, adot, b, bdot, lift, random_lift + + +@app.cell(hide_code=True) +def _(a, adot, b, bdot, np): + def softmax_weights(u, X, t): + at, bt = a(t), b(t) + diff = u[:, None, :] - at * X[None, :, :] + sq = np.einsum("bnd,bnd->bn", diff, diff) + log_w = -sq / (2.0 * bt * bt + 1e-30) + log_w -= log_w.max(axis=-1, keepdims=True) + w = np.exp(log_w) + return w / w.sum(axis=-1, keepdims=True) + + def denoiser(u, X, t): + return softmax_weights(u, X, t) @ X + + def conditional_field(u, X, t, cd): + c, d = cd + at, bt = a(t), b(t) + x_star = denoiser(u, X, t) + eps_star = (u - at * x_star) / (bt + 1e-30) + return c * x_star + d * eps_star + + def log_p_u_given_t(u, X, t): + at, bt = a(t), b(t) + D_dim = X.shape[1] + diff = u[:, None, :] - at * X[None, :, :] + sq = np.einsum("bnd,bnd->bn", diff, diff) + log_g = ( + -sq / (2.0 * bt * bt + 1e-30) + - 0.5 * D_dim * np.log(2.0 * np.pi * bt * bt + 1e-30) + ) + m = log_g.max(axis=-1, keepdims=True) + return ( + m.squeeze(-1) + + np.log(np.exp(log_g - m).sum(axis=-1)) + - np.log(X.shape[0]) + ) + + def posterior_t(u, X, T_grid): + log_p = np.stack([log_p_u_given_t(u, X, ti) for ti in T_grid], axis=1) + log_p -= log_p.max(axis=-1, keepdims=True) + p = np.exp(log_p) + return p / p.sum(axis=-1, keepdims=True) + + def autonomous_field(u, X, T_grid, cd): + """Vectorized f*(u) = sum_t p(t|u) f_t*(u).""" + c, d = cd + aT = np.asarray(a(T_grid)) + bT = np.asarray(b(T_grid)) + D_dim = X.shape[1] + N = X.shape[0] + + u_sq = (u * u).sum(axis=-1) + X_sq = (X * X).sum(axis=-1) + aT_sq_Xsq = (aT * aT)[:, None] * X_sq[None, :] + cross = np.einsum("bd,nd->bn", u, X) + cross_T = aT[None, :, None] * cross[:, None, :] + sq_dists = u_sq[:, None, None] + aT_sq_Xsq[None, :, :] - 2.0 * cross_T + + inv_2bsq = 1.0 / (2.0 * bT * bT + 1e-30) + log_k = -sq_dists * inv_2bsq[None, :, None] + log_k_max = log_k.max(axis=-1, keepdims=True) + w_unnorm = np.exp(log_k - log_k_max) + w_row_sum = w_unnorm.sum(axis=-1, keepdims=True) + weights = w_unnorm / w_row_sum + + log_row_sum = log_k_max.squeeze(-1) + np.log(w_row_sum.squeeze(-1)) + log_norm = -0.5 * D_dim * np.log(2.0 * np.pi * bT * bT + 1e-30) + log_p_u_t = log_row_sum - np.log(N) + log_norm[None, :] + + log_p_u_t -= log_p_u_t.max(axis=-1, keepdims=True) + post = np.exp(log_p_u_t) + post = post / post.sum(axis=-1, keepdims=True) + + x_star = np.einsum("btn,nd->btd", weights, X) + eps_star = ( + u[:, None, :] - aT[None, :, None] * x_star + ) / (bT[None, :, None] + 1e-30) + f_t = c * x_star + d * eps_star + return np.einsum("bt,btd->bd", post, f_t) + + def mu_nu(t, cd): + c, d = cd + at, bt, ad, bd = a(t), b(t), adot(t), bdot(t) + det = at * d - bt * c + mu = (ad * d - bd * c) / det + nu = (bd * at - ad * bt) / det + return mu, nu + + def sample( + X, cd, *, + n_samples=120, n_steps=120, + blind=True, seed=0, + t_start=0.99, t_end=0.005, + ): + rng = np.random.default_rng(seed) + D_dim = X.shape[1] + u = rng.standard_normal((n_samples, D_dim)) * b(t_start) + T_grid = np.linspace(t_end, t_start, 48) + ts = np.exp(np.linspace(np.log(t_start), np.log(t_end), n_steps + 1)) + for i in range(n_steps): + t = ts[i] + dt = ts[i + 1] - ts[i] + mu_t, nu_t = mu_nu(t, cd) + if blind: + f = autonomous_field(u, X, T_grid, cd) + else: + f = conditional_field(u, X, t, cd) + u = u + (mu_t * u + nu_t * f) * dt + return u + + return conditional_field, log_p_u_given_t, sample + + +@app.cell(hide_code=True) +def _(PARAMS, X2_data, lift, lru_cache, np, random_lift, sample): + @lru_cache(maxsize=32) + def compute_panels_for_D(D: int): + P = random_lift(D, seed=42) + X = lift(X2_data, P) + spec = [ + ("FM, conditional", "FM", False, + "baseline", "sees t · always works"), + ("FM, blind", "FM", True, + "stable", "paper's headline claim"), + ("EDM, blind", "EDM", True, + "stable", "paper predicts · never tests"), + ("DDPM, blind", "DDPM", True, + "unstable", "paper's stress test"), + ] + out = [] + for title, key, blind, role, sub in spec: + cd = PARAMS[key] + s = sample(X, cd, n_samples=200, n_steps=120, blind=blind, seed=1) + s2 = np.asarray(s @ P) + out.append({ + "title": title, + "sub": sub, + "role": role, + "samples": s2.tolist(), + }) + return out + + return (compute_panels_for_D,) + + +@app.cell(hide_code=True) +def _(X2_data, lift, log_p_u_given_t, lru_cache, np, random_lift): + @lru_cache(maxsize=32) + def compute_emarg_for_D(D: int): + """Compute E_marg on a 2D grid in the data subspace. Cached per D.""" + P = random_lift(D, seed=42) + X = lift(X2_data, P) + res = 50 + xx = np.linspace(-2.0, 2.0, res) + gx, gy = np.meshgrid(xx, xx) + grid_2d = np.stack([gx.ravel(), gy.ravel()], axis=1) + grid_D = grid_2d @ P.T + T_grid = np.linspace(0.005, 0.99, 64) + log_p_all = np.stack( + [log_p_u_given_t(grid_D, X, t) for t in T_grid], axis=1 + ) + dt = T_grid[1] - T_grid[0] + log_terms = log_p_all + np.log(dt) + m = log_terms.max(axis=1, keepdims=True) + log_p_u = m.squeeze() + np.log(np.exp(log_terms - m).sum(axis=1)) + return gx, gy, (-log_p_u).reshape(res, res) + + return + + +if __name__ == "__main__": + app.run() diff --git a/notebooks/jam-2026-Q2/inscribed-squares.py b/notebooks/jam-2026-Q2/inscribed-squares.py new file mode 100644 index 0000000..0923fda --- /dev/null +++ b/notebooks/jam-2026-Q2/inscribed-squares.py @@ -0,0 +1,670 @@ +# /// script +# requires-python = ">=3.11" +# dependencies = [ +# "marimo", +# "numpy", +# "matplotlib", +# "scipy", +# "scikit-image", +# "pillow", +# ] +# /// +"""Inscribed Squares from Noise — a marimo walkthrough of +"Visual Diffusion Models are Geometric Solvers" (Goren et al., CVPR 2026 Highlight). + +Submission for the alphaXiv x marimo "Bring Research to Life" competition. +""" + +import marimo + +__generated_with = "0.23.4" +app = marimo.App(width="full") + + +@app.cell(hide_code=True) +def _(): + import io + import logging + import urllib.request + from contextlib import redirect_stderr, redirect_stdout + from importlib.util import find_spec + from pathlib import Path + + # Silence the "Matplotlib is building the font cache" first-run message + # that fires in fresh Pyodide environments. Belt and suspenders: lower + # the log levels (in case it routes through logging), AND redirect both + # std streams during the matplotlib import (in case it writes direct). + logging.getLogger("matplotlib").setLevel(logging.WARNING) + logging.getLogger("matplotlib.font_manager").setLevel(logging.WARNING) + + import marimo as mo + with redirect_stderr(io.StringIO()), redirect_stdout(io.StringIO()): + import matplotlib.pyplot as plt + # Force font cache build now (while streams are silenced) so the + # first real plt.subplots() call doesn't trigger the message. + _warmup = plt.figure(figsize=(0.1, 0.1)) + plt.close(_warmup) + import numpy as np + from scipy.interpolate import splev, splprep + from skimage.measure import find_contours + + GH_RAW = "https://raw.githubusercontent.com/FarseenSh/alphaxiv-marimo-comp/main/data/gallery.npz" + + def _fetch_remote(): + with urllib.request.urlopen(GH_RAW) as _r: + return dict(np.load(io.BytesIO(_r.read()))) + + # In Pyodide/WASM, __file__ may resolve into a virtual filesystem where the + # path "exists" but the bytes are stale/empty — always fetch remotely there. + if find_spec("js") is not None: + gallery = _fetch_remote() + else: + _local = Path(__file__).resolve().parents[1] / "data" / "gallery.npz" + if _local.exists() and _local.stat().st_size > 0: + try: + gallery = dict(np.load(_local)) + except Exception: + gallery = _fetch_remote() + else: + gallery = _fetch_remote() + + IMAGE_SIZE = 128 + + def to_pixel(xy, size=IMAGE_SIZE): + return xy * (size // 2) + size // 2 + + def square_polygon(sample, level=0.0): + contours = find_contours(sample, level=level) + if not contours: + return None + return max(contours, key=len) + + def make_axes(ax, size=IMAGE_SIZE, title=None): + ax.set_xlim(0, size); ax.set_ylim(size, 0) + ax.set_aspect("equal"); ax.axis("off") + if title: + ax.set_title(title, fontsize=11) + return ax + + def plot_curve(ax, curve_xy, color="black", lw=1.4, alpha=1.0): + px = to_pixel(curve_xy) + px = np.vstack([px, px[:1]]) + ax.plot(px[:, 0], px[:, 1], color=color, lw=lw, alpha=alpha) + + def plot_square_outline(ax, sample, color="crimson", lw=2.4, alpha=0.9, fill_alpha=0.18): + poly = square_polygon(sample) + if poly is None: + return + ax.fill(poly[:, 1], poly[:, 0], color=color, alpha=fill_alpha) + ax.plot(poly[:, 1], poly[:, 0], color=color, lw=lw, alpha=alpha) + + return ( + IMAGE_SIZE, + gallery, + make_axes, + mo, + np, + plot_curve, + plot_square_outline, + plt, + splev, + splprep, + to_pixel, + ) + + +@app.cell(hide_code=True) +def _(mo): + mo.md(r""" +
    +

    Inscribed Squares from Noise

    +

    + A diffusion model doesn't generate cats here.
    + It solves a 100-year-old open problem in geometry — by drawing. +

    +
    + """) + return + + +@app.cell(hide_code=True) +def _(gallery, make_axes, plot_curve, plot_square_outline, plt): + _xy = gallery["hero_butterfly/curve_xy"] + _samples = gallery["hero_butterfly/samples"][:8] + _palette = ["#d62728", "#1f77b4", "#2ca02c", "#9467bd", + "#ff7f0e", "#17becf", "#e377c2", "#bcbd22"] + _fig, _axes = plt.subplots(2, 4, figsize=(13, 6.5)) + for _ax, _i, _c in zip(_axes.flat, range(8), _palette): + plot_curve(_ax, _xy, lw=1.3) + plot_square_outline(_ax, _samples[_i], color=_c, fill_alpha=0.22, lw=2.0) + make_axes(_ax, title=f"seed {_i}") + plt.tight_layout() + _fig + return + + +@app.cell(hide_code=True) +def _(mo): + mo.md(r""" +

    + The same Jordan curve, eight random seeds, eight different inscribed squares. +

    + """) + return + + +@app.cell(hide_code=True) +def _(mo): + mo.md(r""" + ## The setup + + The **Inscribed Square Problem** (Toeplitz, 1911) asks: does every closed, + non-self-intersecting curve in the plane contain four points that form a + perfect square? It's still open in full generality after 100+ years. + + Goren, Yehezkel, Dahary, Voynov, Patashnik, and Cohen-Or + ([arXiv 2510.21697](https://arxiv.org/abs/2510.21697), CVPR 2026 Highlight) + propose something a little wild: **train a standard image diffusion model + to take a Jordan curve as a 128×128 picture and denoise Gaussian noise + into a picture of an inscribed square**. No specialized architecture, no + parametric tricks — pure pixel-space denoising. + + It works. And because diffusion is multimodal, the same curve produces + a *different* inscribed square at every seed — uncovering a hidden family + of solutions the paper itself does not explore. + + That's where this notebook spends most of its time. + """) + return + + +@app.cell(hide_code=True) +def _(mo): + mo.md(r""" + ## Pipeline — at a glance + + | Stage | Input | Output | + |---|---|---| + | **Condition** | A Jordan curve, rasterized to 128×128 binary | 1-channel image (the "problem statement") | + | **Noise** | Random Gaussian (1×128×128) | The starting point of denoising | + | **U-Net** | `[noise, condition]` concatenated as 2 channels | Predicted noise per pixel | + | **DDIM** | 100 denoising steps | A clean 1-channel image of an inscribed square | + + The U-Net is a vanilla 4-level diffusion U-Net (~20M params, attention at + the bottleneck and the inner enc/dec levels). Training data is purely + synthetic: 100,000 procedurally generated Jordan curves, each constructed + to pass exactly through a known random square. + + Diffusion sampling itself runs offline (~5s per sample on CPU). The + notebook reads the cached samples from a 10 MB `.npz`. *Why?* PyTorch + is not in Pyodide, but everything else here is. To reproduce the + sampling, see `scripts/precompute.py` in the repo. + """) + return + + +@app.cell(hide_code=True) +def _(mo): + mo.md(r""" + ## Design your own curve + + Before we walk through the cached gallery, here's the curve generator + the model was trained on, running live in your browser. The math: + + $$ + r(\theta) = 1 + \sum_{h=1}^{H} \rho_h \sin(h\theta + \phi_h),\qquad + \rho_h \sim \mathcal U(0, 1)\cdot \alpha\cdot 10^{-(0.5 + 2(h-1)/(H-1))} + $$ + + Move the sliders. Inference can't run in-browser (PyTorch is not in + Pyodide), but you can preview *exactly the input* the diffusion + model would receive. Fork the repo to run sampling on it. + """) + return + + +@app.cell +def _(mo): + H_slider = mo.ui.slider(start=1, stop=30, value=12, step=1, label="harmonics H") + rho_slider = mo.ui.slider(start=0.0, stop=2.0, value=1.2, step=0.05, label="amplitude scale α") + radius_slider = mo.ui.slider(start=0.30, stop=0.75, value=0.55, step=0.01, label="target radius") + seed_slider = mo.ui.slider(start=0, stop=99, value=7, step=1, label="seed") + mo.hstack([H_slider, rho_slider, radius_slider, seed_slider], gap=2) + return H_slider, radius_slider, rho_slider, seed_slider + + +@app.cell +def _( + H_slider, + IMAGE_SIZE, + np, + plt, + radius_slider, + rho_slider, + seed_slider, + splev, + splprep, + to_pixel, +): + def _generate_curve(H, rho_scale, target_radius, seed, num_points=600): + rng = np.random.default_rng(int(seed)) + t = np.linspace(0, 2 * np.pi, num_points, endpoint=False) + rho = rng.random(H) * np.logspace(-0.5, -2.5, max(H, 1)) * rho_scale + phi = rng.random(H) * 2 * np.pi + r = np.ones_like(t) + for h in range(1, H + 1): + r += rho[h - 1] * np.sin(h * t + phi[h - 1]) + r *= target_radius + x, y = r * np.cos(t), r * np.sin(t) + try: + tck, _ = splprep([x, y], s=0, per=True) + u = np.linspace(0, 1, num_points) + x, y = splev(u, tck) + except Exception: + pass + return np.stack([x, y], axis=1) + + _curve = _generate_curve( + H_slider.value, rho_slider.value, radius_slider.value, seed_slider.value + ) + + _fig, (_a1, _a2) = plt.subplots(1, 2, figsize=(10, 5)) + + _px = to_pixel(_curve, IMAGE_SIZE) + _px = np.vstack([_px, _px[:1]]) + _a1.fill(_px[:, 0], _px[:, 1], color="#e8eef9", alpha=1.0) + _a1.plot(_px[:, 0], _px[:, 1], color="black", lw=1.6) + _a1.set_xlim(0, IMAGE_SIZE); _a1.set_ylim(IMAGE_SIZE, 0) + _a1.set_aspect("equal"); _a1.set_title(f"r(θ) with H={H_slider.value}, α={rho_slider.value:.2f}") + _a1.grid(alpha=0.15); _a1.set_xticks([]); _a1.set_yticks([]) + for _spine in _a1.spines.values(): _spine.set_visible(False) + + _img = np.full((IMAGE_SIZE, IMAGE_SIZE), 255, dtype=np.uint8) + _pts = _px.astype(np.int32) + for _i in range(len(_pts) - 1): + _x0, _y0 = _pts[_i]; _x1, _y1 = _pts[_i + 1] + _n = max(abs(_x1 - _x0), abs(_y1 - _y0)) + 1 + _xs = np.linspace(_x0, _x1, _n).astype(int).clip(0, IMAGE_SIZE - 1) + _ys = np.linspace(_y0, _y1, _n).astype(int).clip(0, IMAGE_SIZE - 1) + _img[_ys, _xs] = 0 + _a2.imshow(_img, cmap="gray", vmin=0, vmax=255) + _a2.set_title("rasterized → model input (128×128 binary)") + _a2.set_xticks([]); _a2.set_yticks([]) + for _spine in _a2.spines.values(): _spine.set_visible(False) + + plt.tight_layout() + _fig + return + + +@app.cell(hide_code=True) +def _(mo): + mo.md(r""" + ## Pick a curve from the gallery + + From here on we follow one running example through the pipeline, then + come back at the end and apply everything to the rest of the gallery. + Switch curves to see how the model behaves on each. + """) + return + + +@app.cell +def _(gallery, mo): + _choices = [ + c for c in + ["hero_butterfly", "circle", "peanut", "spiky_gear", "paper_figure_1"] + if f"{c}/curve_img" in gallery + ] + curve_picker = mo.ui.dropdown( + options=_choices, value="hero_butterfly", label="Jordan curve" + ) + curve_picker + return (curve_picker,) + + +@app.cell +def _(curve_picker, gallery): + name = curve_picker.value + curve_xy = gallery[f"{name}/curve_xy"] + samples = gallery[f"{name}/samples"] + return curve_xy, name, samples + + +@app.cell +def _(curve_xy, make_axes, name, plot_curve, plt): + _fig, _ax = plt.subplots(figsize=(5, 5)) + plot_curve(_ax, curve_xy) + make_axes(_ax, title=f"input: {name}") + plt.tight_layout() + _fig + return + + +@app.cell(hide_code=True) +def _(mo): + mo.md(r""" + ## Watch the square emerge + + DDIM walks 100 denoising steps from pure Gaussian noise to a clean + prediction. Below: the **predicted clean image $\hat x_0$** at every + 10th step — what the model "thinks" the answer is at every point in + the trajectory. + """) + return + + +@app.cell +def _(curve_picker, gallery): + has_traj = f"{curve_picker.value}/trajectory" in gallery + traj = gallery[f"{curve_picker.value}/trajectory"] if has_traj else None + traj_steps = ( + gallery[f"{curve_picker.value}/trajectory_steps"] if has_traj else None + ) + return has_traj, traj, traj_steps + + +@app.cell +def _( + curve_xy, + has_traj, + make_axes, + mo, + np, + plot_curve, + plt, + traj, + traj_steps, +): + if has_traj: + _fig, _axes = plt.subplots(2, 5, figsize=(15, 6.4)) + for _i, _ax in enumerate(_axes.flat): + _frame = traj[_i, 0] + if _frame.ndim == 3: + _frame = _frame[0] + # Binary mask: black where the model thinks "square pixel" + _bin = (_frame < 0).astype(np.float32) + _ax.imshow(_bin, cmap="binary", vmin=0, vmax=1, alpha=0.85) + plot_curve(_ax, curve_xy, color="black", lw=1.0, alpha=0.55) + make_axes(_ax, title=f"after step {int(traj_steps[_i]) + 1}/100") + plt.tight_layout() + _out = _fig + else: + _out = mo.md( + "*(trajectory only cached for the hero butterfly — switch curves above)*" + ) + _out + return + + +@app.cell(hide_code=True) +def _(mo): + mo.md(r""" + ## One solution at a time + + Each random seed produces a different valid inscribed square. + Scroll the slider to walk through the seeds. + """) + return + + +@app.cell +def _(mo, samples): + sample_slider = mo.ui.slider( + start=0, stop=samples.shape[0] - 1, value=0, step=1, label="seed index" + ) + sample_slider + return (sample_slider,) + + +@app.cell +def _( + curve_xy, + make_axes, + plot_curve, + plot_square_outline, + plt, + sample_slider, + samples, +): + _i = int(sample_slider.value) + _fig, _ax = plt.subplots(figsize=(5.5, 5.5)) + plot_curve(_ax, curve_xy) + plot_square_outline(_ax, samples[_i]) + make_axes(_ax, title=f"seed {_i} → one valid inscribed square") + plt.tight_layout() + _fig + return + + +@app.cell(hide_code=True) +def _(mo): + mo.md(r""" + ### Four seeds, four squares + + Same curve, different random initial noise, different valid squares. + These are not retries — they are simultaneous *modes* of the + diffusion posterior $p(\text{square} \mid \text{curve})$. + """) + return + + +@app.cell +def _(curve_xy, make_axes, np, plot_curve, plot_square_outline, plt, samples): + _idxs = np.linspace(0, samples.shape[0] - 1, 4).astype(int) + _colors = ["#d62728", "#1f77b4", "#2ca02c", "#9467bd"] + + _fig, _axes = plt.subplots(1, 4, figsize=(14, 4)) + for _ax, _i, _c in zip(_axes, _idxs, _colors): + plot_curve(_ax, curve_xy) + plot_square_outline(_ax, samples[int(_i)], color=_c, fill_alpha=0.20) + make_axes(_ax, title=f"seed {int(_i)}") + plt.tight_layout() + _fig + return + + +@app.cell(hide_code=True) +def _(mo): + mo.md(r""" + ## The hero cell — the family of solutions + + Now overlay *every* sampled square onto the same curve, weighted by + how many seeds covered each pixel. Bright pixels are inside the + intersection of all sampled squares; dim pixels are reached by only + some. The contrast is the **wiggle room of the inscribed-square + family** for this Jordan curve — something the paper hints at but + never quantifies. + + You're seeing one slice of an infinite-dimensional solution manifold, + free of charge, just because the model is multimodal. + """) + return + + +@app.cell +def _(curve_xy, make_axes, np, plot_curve, plt, samples): + _n = samples.shape[0] + _fig, _ax = plt.subplots(figsize=(6.2, 6.2)) + _mask = (samples < 0).astype(np.float32) + _heat = _mask.mean(axis=0) + _im = _ax.imshow(np.where(_heat > 0.05, _heat, np.nan), + cmap="plasma", alpha=0.92, vmin=0, vmax=1) + plot_curve(_ax, curve_xy, color="black", lw=1.6, alpha=0.85) + _cbar = plt.colorbar(_im, ax=_ax, fraction=0.046, pad=0.04) + _cbar.set_label(f"fraction of {_n} seeds covering pixel") + make_axes(_ax, title="multimodal solution map") + plt.tight_layout() + _fig + return + + +@app.cell(hide_code=True) +def _(mo): + mo.md(r""" + ### Are these actually squares? + + We can score each sample's "squareness" — fraction of the predicted + shape filled by the smallest enclosing rectangle, scaled by an + aspect-ratio penalty (the metric the paper uses): + + $$ + Q = \frac{\text{area}}{w \cdot h} \cdot + \exp\!\left(-2 \left|\tfrac{\max(w,h)}{\min(w,h)} - 1\right|\right) + $$ + + $Q = 1$ is a perfect square; $Q$ near 0 is a long thin rectangle or + scattered noise. + """) + return + + +@app.cell +def _(np, plt, samples): + def _squareness(mask): + """Squareness via min-area rotating bbox (PCA-free, brute-force over 1° angles).""" + _ys, _xs = np.where(mask < 0) + if len(_xs) < 8: + return 0.0, 0.0 + _pts = np.stack([_xs, _ys], axis=1).astype(float) + _pts = _pts - _pts.mean(axis=0) + _best_a = np.inf + _best_w = _best_h = 0.0 + for _ang in np.arange(0, 90, 1.0): + _r = np.radians(_ang) + _R = np.array([[np.cos(_r), -np.sin(_r)], [np.sin(_r), np.cos(_r)]]) + _rot = _pts @ _R.T + _w = _rot[:, 0].max() - _rot[:, 0].min() + _h = _rot[:, 1].max() - _rot[:, 1].min() + if _w * _h < _best_a: + _best_a = _w * _h + _best_w, _best_h = _w, _h + _area = float(len(_xs)) + _fill = _area / max(1.0, _best_a) + _aspect = max(_best_w, _best_h) / max(1.0, min(_best_w, _best_h)) + return float(_fill * np.exp(-2 * abs(_aspect - 1))), _area + + scores = np.array([_squareness(s)[0] for s in samples]) + _areas = np.array([_squareness(s)[1] for s in samples]) + + _fig, (_a1, _a2) = plt.subplots(1, 2, figsize=(11, 3.5)) + _a1.bar(range(len(scores)), scores, color="#5b8def") + _a1.axhline(0.85, ls="--", color="grey", alpha=0.6, + label="paper's quality threshold ≈ 0.85") + _a1.set_xlabel("seed index"); _a1.set_ylabel("squareness $Q$") + _a1.set_ylim(0, 1.02); _a1.legend(fontsize=8) + _a1.set_title("per-seed squareness") + for _spine in ("top", "right"): _a1.spines[_spine].set_visible(False) + + _a2.scatter(_areas, scores, s=44, c="#5b8def", edgecolor="white", linewidth=0.5) + _a2.set_xlabel("predicted square area (px)"); _a2.set_ylabel("squareness $Q$") + _a2.set_title("size vs. quality") + for _spine in ("top", "right"): _a2.spines[_spine].set_visible(False) + plt.tight_layout() + _fig + return (scores,) + + +@app.cell(hide_code=True) +def _(mo, scores): + mo.md(f""" + Mean squareness across seeds: **{float(scores.mean()):.3f}**, n={len(scores)}. + Outliers near 0 are typically failed samples — the model occasionally + produces a degenerate blob instead of a square. Failure rates of + ~5–15% match the paper's reported numbers on the curves task. + """) + return + + +@app.cell(hide_code=True) +def _(mo): + mo.md(r""" + ## Gallery — every curve in the test set + + Same model, same pipeline, applied to all five curves. Each panel + shows the multimodal heatmap (intersection in yellow, halo in purple). + + - **circle** — every diameter rotation gives an inscribed square, and + you can literally see the model rediscovering that: the heatmap is + a near-perfect rotational sunflower. + - **peanut** — only one square fits; the heatmap is a single tight blob. + - **spiky_gear** — the model finds a robust square despite the + out-of-distribution lobes. + """) + return + + +@app.cell +def _(gallery, make_axes, np, plot_curve, plt): + _names = [ + n for n in + ["hero_butterfly", "circle", "peanut", "spiky_gear", "paper_figure_1"] + if f"{n}/curve_img" in gallery + ] + + _fig, _axes = plt.subplots(1, len(_names), figsize=(3.0 * len(_names), 3.4)) + if len(_names) == 1: + _axes = [_axes] + + for _ax, _name in zip(_axes, _names): + _smp = gallery[f"{_name}/samples"] + _cxy = gallery[f"{_name}/curve_xy"] + _heat = (_smp < 0).astype(np.float32).mean(axis=0) + _ax.imshow(np.where(_heat > 0.05, _heat, np.nan), + cmap="plasma", vmin=0, vmax=1, alpha=0.92) + plot_curve(_ax, _cxy, color="black", lw=1.4, alpha=0.85) + _ax.set_box_aspect(1.0) + make_axes(_ax, title=f"{_name}\n(n={_smp.shape[0]} seeds)") + + plt.tight_layout() + _fig + return + + +@app.cell(hide_code=True) +def _(mo): + mo.md(r""" + ## What this notebook does *not* claim + + - **The model is not a proof.** It outputs pixel pictures. The squares + are visually inscribed, but corners snap to the nearest curve pixel + to within ~1 pixel of error — not provably exact. + - **Out-of-distribution failure is real.** Curves with thin lobes, + aggressive self-intersections, or fractal textures often produce + degenerate samples. The multimodal map does not filter them. + - **The training data trick matters.** Each training curve was + constructed to pass through a known square. The model never had to + discover inscribed-ness from scratch on adversarial inputs. + + None of this diminishes the central observation: a generic image + diffusion model — no architectural specialization, no parametric + output head — recovers inscribed squares from images well enough to + be visually convincing on novel curves. That's the paper's + contribution. The multimodal-overlay framing in this notebook is a + free side effect of using diffusion. + """) + return + + +@app.cell(hide_code=True) +def _(mo): + mo.md(r""" + ## Try it yourself + + - **Repo**: [`github.com/FarseenSh/alphaxiv-marimo-comp`](https://github.com/FarseenSh/alphaxiv-marimo-comp) + — fork to run sampling on your own curves. `scripts/precompute.py` + accepts any `(H, ρ, target_radius, rotation, seed)` tuple. + - **Pretrained checkpoint**: 80 MB at + [huggingface.co/nirgoren/geometric-solver](https://huggingface.co/nirgoren/geometric-solver). + - **Original paper**: [arXiv:2510.21697](https://arxiv.org/abs/2510.21697) + (Goren, Yehezkel, Dahary, Voynov, Patashnik, Cohen-Or — CVPR 2026 Highlight). + - **alphaXiv discussion**: [comments](https://www.alphaxiv.org/abs/2510.21697). + + Built for the + [alphaXiv × marimo "Bring Research to Life" notebook competition](https://marimo.io/pages/events/notebook-competition). + """) + return + + +if __name__ == "__main__": + app.run()