datasetId stringlengths 2 117 | card stringlengths 19 1.01M |
|---|---|
ejbejaranos/Reglamento_Aeronautico_Colombiano_ChatML_QualityImproved25 | ---
license: apache-2.0
---
|
SEACrowd/minangnlp_mt | ---
license: mit
tags:
- machine-translation
language:
- min
- ind
---
# minangnlp_mt
In this work, we create Minangkabau–Indonesian (MIN-ID) parallel corpus by using Wikipedia. We obtain 224,180 Minangkabau and
510,258 Indonesian articles, and align documents through title matching, resulting in 111,430 MINID document pairs.
After that, we do sentence segmentation based on simple punctuation heuristics and obtain 4,323,315 Minangkabau sentences. We
then use the bilingual dictionary to translate Minangkabau article (MIN) into Indonesian language (ID'). Sentence alignment is conducted using
ROUGE-1 (F1) score (unigram overlap) (Lin, 2004) between ID’ and ID, and we pair each MIN sentencewith an ID sentence based on the highest ROUGE1.
We then discard sentence pairs with a score of less than 0.5 to result in 345,146 MIN-ID parallel sentences.
We observe that the sentence pattern in the collection is highly repetitive (e.g. 100k sentences are about biological term definition). Therefore,
we conduct final filtering based on top-1000 trigram by iteratively discarding sentences until the frequency of each trigram equals to 100. Finally, we
obtain 16,371 MIN-ID parallel sentences and conducted manual evaluation by asking two native Minangkabau speakers to assess the adequacy and
fluency (Koehn and Monz, 2006). The human judgement is based on scale 1–5 (1 means poor quality and 5 otherwise) and conducted against 100 random
samples. We average the weights of two annotators before computing the overall score, and we achieve 4.98 and 4.87 for adequacy and fluency respectively.
This indicates that the resulting corpus is high-quality for machine translation training.
## Dataset Usage
Run `pip install nusacrowd` before loading the dataset through HuggingFace's `load_dataset`.
## Citation
```
@inproceedings{koto-koto-2020-towards,
title = "Towards Computational Linguistics in {M}inangkabau Language: Studies on Sentiment Analysis and Machine Translation",
author = "Koto, Fajri and
Koto, Ikhwan",
booktitle = "Proceedings of the 34th Pacific Asia Conference on Language, Information and Computation",
month = oct,
year = "2020",
address = "Hanoi, Vietnam",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.paclic-1.17",
pages = "138--148",
}
```
## License
MIT
## Homepage
[https://github.com/fajri91/minangNLP](https://github.com/fajri91/minangNLP)
### NusaCatalogue
For easy indexing and metadata: [https://indonlp.github.io/nusa-catalogue](https://indonlp.github.io/nusa-catalogue) |
cnbeining/sentence-segmentation-dpo-raw | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: chosen
dtype: string
- name: rejected
dtype: string
splits:
- name: train
num_bytes: 9349781
num_examples: 8250
- name: test
num_bytes: 1063441
num_examples: 931
download_size: 3606658
dataset_size: 10413222
---
# Dataset Card for "sentence-segmentation-dpo-raw"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ZongqianLi/qa2qa_sc_simple_pro | ---
dataset_info:
features:
- name: answers
struct:
- name: answer_start
sequence: int64
- name: text
sequence: string
- name: title
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: id
dtype: string
splits:
- name: train
num_bytes: 13071626.0
num_examples: 38783
- name: validation
num_bytes: 3262468.0
num_examples: 9697
download_size: 6565635
dataset_size: 16334094.0
---
# Dataset Card for "qa2qa_sc_simple_pro"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_PulsarAI__MetaMath-OpenHermes-2.5-neural-chat-v3-3-Slerp | ---
pretty_name: Evaluation run of PulsarAI/MetaMath-OpenHermes-2.5-neural-chat-v3-3-Slerp
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [PulsarAI/MetaMath-OpenHermes-2.5-neural-chat-v3-3-Slerp](https://huggingface.co/PulsarAI/MetaMath-OpenHermes-2.5-neural-chat-v3-3-Slerp)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 63 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_PulsarAI__MetaMath-OpenHermes-2.5-neural-chat-v3-3-Slerp\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-12-10T02:45:05.724710](https://huggingface.co/datasets/open-llm-leaderboard/details_PulsarAI__MetaMath-OpenHermes-2.5-neural-chat-v3-3-Slerp/blob/main/results_2023-12-10T02-45-05.724710.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6464664842416276,\n\
\ \"acc_stderr\": 0.03217172590988582,\n \"acc_norm\": 0.646376680571289,\n\
\ \"acc_norm_stderr\": 0.032836550184029964,\n \"mc1\": 0.39167686658506734,\n\
\ \"mc1_stderr\": 0.01708779588176963,\n \"mc2\": 0.5514034273421413,\n\
\ \"mc2_stderr\": 0.015341235748555455\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6220136518771331,\n \"acc_stderr\": 0.014169664520303098,\n\
\ \"acc_norm\": 0.6459044368600683,\n \"acc_norm_stderr\": 0.013975454122756564\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6632144991037642,\n\
\ \"acc_stderr\": 0.004716449792353795,\n \"acc_norm\": 0.8539135630352519,\n\
\ \"acc_norm_stderr\": 0.003524710243768616\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.3,\n \"acc_stderr\": 0.04605661864718381,\n \
\ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.04605661864718381\n },\n\
\ \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6296296296296297,\n\
\ \"acc_stderr\": 0.041716541613545426,\n \"acc_norm\": 0.6296296296296297,\n\
\ \"acc_norm_stderr\": 0.041716541613545426\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.6907894736842105,\n \"acc_stderr\": 0.037610708698674805,\n\
\ \"acc_norm\": 0.6907894736842105,\n \"acc_norm_stderr\": 0.037610708698674805\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.56,\n\
\ \"acc_stderr\": 0.04988876515698589,\n \"acc_norm\": 0.56,\n \
\ \"acc_norm_stderr\": 0.04988876515698589\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.7169811320754716,\n \"acc_stderr\": 0.027724236492700918,\n\
\ \"acc_norm\": 0.7169811320754716,\n \"acc_norm_stderr\": 0.027724236492700918\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.75,\n\
\ \"acc_stderr\": 0.03621034121889507,\n \"acc_norm\": 0.75,\n \
\ \"acc_norm_stderr\": 0.03621034121889507\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.45,\n \"acc_stderr\": 0.05,\n \"acc_norm\"\
: 0.45,\n \"acc_norm_stderr\": 0.05\n },\n \"harness|hendrycksTest-college_computer_science|5\"\
: {\n \"acc\": 0.55,\n \"acc_stderr\": 0.05,\n \"acc_norm\"\
: 0.55,\n \"acc_norm_stderr\": 0.05\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.36,\n \"acc_stderr\": 0.048241815132442176,\n \
\ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.048241815132442176\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6416184971098265,\n\
\ \"acc_stderr\": 0.03656343653353159,\n \"acc_norm\": 0.6416184971098265,\n\
\ \"acc_norm_stderr\": 0.03656343653353159\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.4117647058823529,\n \"acc_stderr\": 0.048971049527263666,\n\
\ \"acc_norm\": 0.4117647058823529,\n \"acc_norm_stderr\": 0.048971049527263666\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.8,\n \"acc_stderr\": 0.04020151261036845,\n \"acc_norm\": 0.8,\n\
\ \"acc_norm_stderr\": 0.04020151261036845\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5914893617021276,\n \"acc_stderr\": 0.032134180267015755,\n\
\ \"acc_norm\": 0.5914893617021276,\n \"acc_norm_stderr\": 0.032134180267015755\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.49122807017543857,\n\
\ \"acc_stderr\": 0.04702880432049615,\n \"acc_norm\": 0.49122807017543857,\n\
\ \"acc_norm_stderr\": 0.04702880432049615\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5448275862068965,\n \"acc_stderr\": 0.04149886942192117,\n\
\ \"acc_norm\": 0.5448275862068965,\n \"acc_norm_stderr\": 0.04149886942192117\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.41005291005291006,\n \"acc_stderr\": 0.025331202438944447,\n \"\
acc_norm\": 0.41005291005291006,\n \"acc_norm_stderr\": 0.025331202438944447\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.42063492063492064,\n\
\ \"acc_stderr\": 0.04415438226743744,\n \"acc_norm\": 0.42063492063492064,\n\
\ \"acc_norm_stderr\": 0.04415438226743744\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \
\ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\
: 0.7806451612903226,\n \"acc_stderr\": 0.023540799358723292,\n \"\
acc_norm\": 0.7806451612903226,\n \"acc_norm_stderr\": 0.023540799358723292\n\
\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
: 0.5123152709359606,\n \"acc_stderr\": 0.035169204442208966,\n \"\
acc_norm\": 0.5123152709359606,\n \"acc_norm_stderr\": 0.035169204442208966\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.67,\n \"acc_stderr\": 0.04725815626252609,\n \"acc_norm\"\
: 0.67,\n \"acc_norm_stderr\": 0.04725815626252609\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7454545454545455,\n \"acc_stderr\": 0.03401506715249039,\n\
\ \"acc_norm\": 0.7454545454545455,\n \"acc_norm_stderr\": 0.03401506715249039\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7828282828282829,\n \"acc_stderr\": 0.02937661648494563,\n \"\
acc_norm\": 0.7828282828282829,\n \"acc_norm_stderr\": 0.02937661648494563\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8756476683937824,\n \"acc_stderr\": 0.02381447708659355,\n\
\ \"acc_norm\": 0.8756476683937824,\n \"acc_norm_stderr\": 0.02381447708659355\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6615384615384615,\n \"acc_stderr\": 0.023991500500313036,\n\
\ \"acc_norm\": 0.6615384615384615,\n \"acc_norm_stderr\": 0.023991500500313036\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.3592592592592593,\n \"acc_stderr\": 0.029252905927251972,\n \
\ \"acc_norm\": 0.3592592592592593,\n \"acc_norm_stderr\": 0.029252905927251972\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.680672268907563,\n \"acc_stderr\": 0.030283995525884396,\n \
\ \"acc_norm\": 0.680672268907563,\n \"acc_norm_stderr\": 0.030283995525884396\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.31788079470198677,\n \"acc_stderr\": 0.038020397601079024,\n \"\
acc_norm\": 0.31788079470198677,\n \"acc_norm_stderr\": 0.038020397601079024\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8495412844036697,\n \"acc_stderr\": 0.015328563932669237,\n \"\
acc_norm\": 0.8495412844036697,\n \"acc_norm_stderr\": 0.015328563932669237\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5231481481481481,\n \"acc_stderr\": 0.03406315360711507,\n \"\
acc_norm\": 0.5231481481481481,\n \"acc_norm_stderr\": 0.03406315360711507\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.7892156862745098,\n \"acc_stderr\": 0.028626547912437406,\n \"\
acc_norm\": 0.7892156862745098,\n \"acc_norm_stderr\": 0.028626547912437406\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.7974683544303798,\n \"acc_stderr\": 0.026160568246601443,\n \
\ \"acc_norm\": 0.7974683544303798,\n \"acc_norm_stderr\": 0.026160568246601443\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6860986547085202,\n\
\ \"acc_stderr\": 0.031146796482972465,\n \"acc_norm\": 0.6860986547085202,\n\
\ \"acc_norm_stderr\": 0.031146796482972465\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7709923664122137,\n \"acc_stderr\": 0.036853466317118506,\n\
\ \"acc_norm\": 0.7709923664122137,\n \"acc_norm_stderr\": 0.036853466317118506\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.8099173553719008,\n \"acc_stderr\": 0.03581796951709282,\n \"\
acc_norm\": 0.8099173553719008,\n \"acc_norm_stderr\": 0.03581796951709282\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8055555555555556,\n\
\ \"acc_stderr\": 0.038260763248848646,\n \"acc_norm\": 0.8055555555555556,\n\
\ \"acc_norm_stderr\": 0.038260763248848646\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7668711656441718,\n \"acc_stderr\": 0.0332201579577674,\n\
\ \"acc_norm\": 0.7668711656441718,\n \"acc_norm_stderr\": 0.0332201579577674\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5,\n\
\ \"acc_stderr\": 0.04745789978762494,\n \"acc_norm\": 0.5,\n \
\ \"acc_norm_stderr\": 0.04745789978762494\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7669902912621359,\n \"acc_stderr\": 0.04185832598928315,\n\
\ \"acc_norm\": 0.7669902912621359,\n \"acc_norm_stderr\": 0.04185832598928315\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8717948717948718,\n\
\ \"acc_stderr\": 0.02190190511507333,\n \"acc_norm\": 0.8717948717948718,\n\
\ \"acc_norm_stderr\": 0.02190190511507333\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \
\ \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.04351941398892446\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8237547892720306,\n\
\ \"acc_stderr\": 0.013625556907993452,\n \"acc_norm\": 0.8237547892720306,\n\
\ \"acc_norm_stderr\": 0.013625556907993452\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7225433526011561,\n \"acc_stderr\": 0.02410571260775431,\n\
\ \"acc_norm\": 0.7225433526011561,\n \"acc_norm_stderr\": 0.02410571260775431\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.3877094972067039,\n\
\ \"acc_stderr\": 0.01629533232815581,\n \"acc_norm\": 0.3877094972067039,\n\
\ \"acc_norm_stderr\": 0.01629533232815581\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7254901960784313,\n \"acc_stderr\": 0.025553169991826524,\n\
\ \"acc_norm\": 0.7254901960784313,\n \"acc_norm_stderr\": 0.025553169991826524\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6977491961414791,\n\
\ \"acc_stderr\": 0.026082700695399665,\n \"acc_norm\": 0.6977491961414791,\n\
\ \"acc_norm_stderr\": 0.026082700695399665\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7376543209876543,\n \"acc_stderr\": 0.024477222856135118,\n\
\ \"acc_norm\": 0.7376543209876543,\n \"acc_norm_stderr\": 0.024477222856135118\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.48936170212765956,\n \"acc_stderr\": 0.029820747191422466,\n \
\ \"acc_norm\": 0.48936170212765956,\n \"acc_norm_stderr\": 0.029820747191422466\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.45697522816166886,\n\
\ \"acc_stderr\": 0.012722869501611419,\n \"acc_norm\": 0.45697522816166886,\n\
\ \"acc_norm_stderr\": 0.012722869501611419\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6617647058823529,\n \"acc_stderr\": 0.028739328513983572,\n\
\ \"acc_norm\": 0.6617647058823529,\n \"acc_norm_stderr\": 0.028739328513983572\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6683006535947712,\n \"acc_stderr\": 0.019047485239360378,\n \
\ \"acc_norm\": 0.6683006535947712,\n \"acc_norm_stderr\": 0.019047485239360378\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6727272727272727,\n\
\ \"acc_stderr\": 0.0449429086625209,\n \"acc_norm\": 0.6727272727272727,\n\
\ \"acc_norm_stderr\": 0.0449429086625209\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7428571428571429,\n \"acc_stderr\": 0.02797982353874455,\n\
\ \"acc_norm\": 0.7428571428571429,\n \"acc_norm_stderr\": 0.02797982353874455\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8606965174129353,\n\
\ \"acc_stderr\": 0.024484487162913973,\n \"acc_norm\": 0.8606965174129353,\n\
\ \"acc_norm_stderr\": 0.024484487162913973\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.85,\n \"acc_stderr\": 0.0358870281282637,\n \
\ \"acc_norm\": 0.85,\n \"acc_norm_stderr\": 0.0358870281282637\n },\n\
\ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5421686746987951,\n\
\ \"acc_stderr\": 0.0387862677100236,\n \"acc_norm\": 0.5421686746987951,\n\
\ \"acc_norm_stderr\": 0.0387862677100236\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8538011695906432,\n \"acc_stderr\": 0.02709729011807082,\n\
\ \"acc_norm\": 0.8538011695906432,\n \"acc_norm_stderr\": 0.02709729011807082\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.39167686658506734,\n\
\ \"mc1_stderr\": 0.01708779588176963,\n \"mc2\": 0.5514034273421413,\n\
\ \"mc2_stderr\": 0.015341235748555455\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7963693764798737,\n \"acc_stderr\": 0.011317798781626915\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.7164518574677786,\n \
\ \"acc_stderr\": 0.012415070917508124\n }\n}\n```"
repo_url: https://huggingface.co/PulsarAI/MetaMath-OpenHermes-2.5-neural-chat-v3-3-Slerp
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_12_10T02_45_05.724710
path:
- '**/details_harness|arc:challenge|25_2023-12-10T02-45-05.724710.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-12-10T02-45-05.724710.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_10T02_45_05.724710
path:
- '**/details_harness|gsm8k|5_2023-12-10T02-45-05.724710.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-10T02-45-05.724710.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_12_10T02_45_05.724710
path:
- '**/details_harness|hellaswag|10_2023-12-10T02-45-05.724710.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-12-10T02-45-05.724710.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_12_10T02_45_05.724710
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-10T02-45-05.724710.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-10T02-45-05.724710.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-10T02-45-05.724710.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_12_10T02_45_05.724710
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-10T02-45-05.724710.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-10T02-45-05.724710.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_12_10T02_45_05.724710
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-10T02-45-05.724710.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-10T02-45-05.724710.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_12_10T02_45_05.724710
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-10T02-45-05.724710.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-10T02-45-05.724710.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_12_10T02_45_05.724710
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-10T02-45-05.724710.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-10T02-45-05.724710.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_12_10T02_45_05.724710
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-10T02-45-05.724710.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-10T02-45-05.724710.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_12_10T02_45_05.724710
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-10T02-45-05.724710.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-10T02-45-05.724710.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_12_10T02_45_05.724710
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-10T02-45-05.724710.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-10T02-45-05.724710.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_12_10T02_45_05.724710
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-10T02-45-05.724710.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-10T02-45-05.724710.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_12_10T02_45_05.724710
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-10T02-45-05.724710.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-10T02-45-05.724710.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_12_10T02_45_05.724710
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-10T02-45-05.724710.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-10T02-45-05.724710.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_12_10T02_45_05.724710
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-10T02-45-05.724710.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-10T02-45-05.724710.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_12_10T02_45_05.724710
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-10T02-45-05.724710.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-10T02-45-05.724710.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_12_10T02_45_05.724710
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-10T02-45-05.724710.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-10T02-45-05.724710.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_12_10T02_45_05.724710
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-10T02-45-05.724710.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-10T02-45-05.724710.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_12_10T02_45_05.724710
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-10T02-45-05.724710.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-10T02-45-05.724710.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_12_10T02_45_05.724710
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-10T02-45-05.724710.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-10T02-45-05.724710.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_12_10T02_45_05.724710
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-10T02-45-05.724710.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-10T02-45-05.724710.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_12_10T02_45_05.724710
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-10T02-45-05.724710.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-10T02-45-05.724710.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_12_10T02_45_05.724710
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-10T02-45-05.724710.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-10T02-45-05.724710.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_12_10T02_45_05.724710
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-10T02-45-05.724710.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-10T02-45-05.724710.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_12_10T02_45_05.724710
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-10T02-45-05.724710.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-10T02-45-05.724710.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_12_10T02_45_05.724710
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-10T02-45-05.724710.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-10T02-45-05.724710.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_12_10T02_45_05.724710
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-10T02-45-05.724710.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-10T02-45-05.724710.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_12_10T02_45_05.724710
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-10T02-45-05.724710.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-10T02-45-05.724710.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_12_10T02_45_05.724710
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-10T02-45-05.724710.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-10T02-45-05.724710.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_12_10T02_45_05.724710
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-10T02-45-05.724710.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-10T02-45-05.724710.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_12_10T02_45_05.724710
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-10T02-45-05.724710.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-10T02-45-05.724710.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_12_10T02_45_05.724710
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-10T02-45-05.724710.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-10T02-45-05.724710.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_12_10T02_45_05.724710
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-10T02-45-05.724710.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-10T02-45-05.724710.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_12_10T02_45_05.724710
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-10T02-45-05.724710.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-10T02-45-05.724710.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_12_10T02_45_05.724710
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-10T02-45-05.724710.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-10T02-45-05.724710.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_12_10T02_45_05.724710
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-10T02-45-05.724710.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-10T02-45-05.724710.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_12_10T02_45_05.724710
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-10T02-45-05.724710.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-10T02-45-05.724710.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_12_10T02_45_05.724710
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-10T02-45-05.724710.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-10T02-45-05.724710.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_12_10T02_45_05.724710
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-10T02-45-05.724710.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-10T02-45-05.724710.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_12_10T02_45_05.724710
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-10T02-45-05.724710.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-10T02-45-05.724710.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_12_10T02_45_05.724710
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-10T02-45-05.724710.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-10T02-45-05.724710.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_12_10T02_45_05.724710
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-10T02-45-05.724710.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-10T02-45-05.724710.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_12_10T02_45_05.724710
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-10T02-45-05.724710.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-10T02-45-05.724710.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_12_10T02_45_05.724710
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-10T02-45-05.724710.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-10T02-45-05.724710.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_12_10T02_45_05.724710
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-10T02-45-05.724710.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-10T02-45-05.724710.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_12_10T02_45_05.724710
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-10T02-45-05.724710.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-10T02-45-05.724710.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_12_10T02_45_05.724710
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-10T02-45-05.724710.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-10T02-45-05.724710.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_12_10T02_45_05.724710
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-10T02-45-05.724710.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-10T02-45-05.724710.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_12_10T02_45_05.724710
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-10T02-45-05.724710.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-10T02-45-05.724710.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_12_10T02_45_05.724710
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-10T02-45-05.724710.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-10T02-45-05.724710.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_12_10T02_45_05.724710
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-10T02-45-05.724710.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-10T02-45-05.724710.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_12_10T02_45_05.724710
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-10T02-45-05.724710.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-10T02-45-05.724710.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_12_10T02_45_05.724710
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-10T02-45-05.724710.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-10T02-45-05.724710.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_12_10T02_45_05.724710
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-10T02-45-05.724710.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-10T02-45-05.724710.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_12_10T02_45_05.724710
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-10T02-45-05.724710.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-10T02-45-05.724710.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_12_10T02_45_05.724710
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-10T02-45-05.724710.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-10T02-45-05.724710.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_12_10T02_45_05.724710
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-10T02-45-05.724710.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-10T02-45-05.724710.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_12_10T02_45_05.724710
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-10T02-45-05.724710.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-10T02-45-05.724710.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_12_10T02_45_05.724710
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-10T02-45-05.724710.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-10T02-45-05.724710.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_12_10T02_45_05.724710
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-10T02-45-05.724710.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-10T02-45-05.724710.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_12_10T02_45_05.724710
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-10T02-45-05.724710.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-10T02-45-05.724710.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_12_10T02_45_05.724710
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-10T02-45-05.724710.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-10T02-45-05.724710.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_12_10T02_45_05.724710
path:
- '**/details_harness|winogrande|5_2023-12-10T02-45-05.724710.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-12-10T02-45-05.724710.parquet'
- config_name: results
data_files:
- split: 2023_12_10T02_45_05.724710
path:
- results_2023-12-10T02-45-05.724710.parquet
- split: latest
path:
- results_2023-12-10T02-45-05.724710.parquet
---
# Dataset Card for Evaluation run of PulsarAI/MetaMath-OpenHermes-2.5-neural-chat-v3-3-Slerp
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/PulsarAI/MetaMath-OpenHermes-2.5-neural-chat-v3-3-Slerp
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [PulsarAI/MetaMath-OpenHermes-2.5-neural-chat-v3-3-Slerp](https://huggingface.co/PulsarAI/MetaMath-OpenHermes-2.5-neural-chat-v3-3-Slerp) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_PulsarAI__MetaMath-OpenHermes-2.5-neural-chat-v3-3-Slerp",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-10T02:45:05.724710](https://huggingface.co/datasets/open-llm-leaderboard/details_PulsarAI__MetaMath-OpenHermes-2.5-neural-chat-v3-3-Slerp/blob/main/results_2023-12-10T02-45-05.724710.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.6464664842416276,
"acc_stderr": 0.03217172590988582,
"acc_norm": 0.646376680571289,
"acc_norm_stderr": 0.032836550184029964,
"mc1": 0.39167686658506734,
"mc1_stderr": 0.01708779588176963,
"mc2": 0.5514034273421413,
"mc2_stderr": 0.015341235748555455
},
"harness|arc:challenge|25": {
"acc": 0.6220136518771331,
"acc_stderr": 0.014169664520303098,
"acc_norm": 0.6459044368600683,
"acc_norm_stderr": 0.013975454122756564
},
"harness|hellaswag|10": {
"acc": 0.6632144991037642,
"acc_stderr": 0.004716449792353795,
"acc_norm": 0.8539135630352519,
"acc_norm_stderr": 0.003524710243768616
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.3,
"acc_stderr": 0.04605661864718381,
"acc_norm": 0.3,
"acc_norm_stderr": 0.04605661864718381
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6296296296296297,
"acc_stderr": 0.041716541613545426,
"acc_norm": 0.6296296296296297,
"acc_norm_stderr": 0.041716541613545426
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6907894736842105,
"acc_stderr": 0.037610708698674805,
"acc_norm": 0.6907894736842105,
"acc_norm_stderr": 0.037610708698674805
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.56,
"acc_stderr": 0.04988876515698589,
"acc_norm": 0.56,
"acc_norm_stderr": 0.04988876515698589
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.7169811320754716,
"acc_stderr": 0.027724236492700918,
"acc_norm": 0.7169811320754716,
"acc_norm_stderr": 0.027724236492700918
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.75,
"acc_stderr": 0.03621034121889507,
"acc_norm": 0.75,
"acc_norm_stderr": 0.03621034121889507
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.45,
"acc_stderr": 0.05,
"acc_norm": 0.45,
"acc_norm_stderr": 0.05
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.55,
"acc_stderr": 0.05,
"acc_norm": 0.55,
"acc_norm_stderr": 0.05
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.36,
"acc_stderr": 0.048241815132442176,
"acc_norm": 0.36,
"acc_norm_stderr": 0.048241815132442176
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6416184971098265,
"acc_stderr": 0.03656343653353159,
"acc_norm": 0.6416184971098265,
"acc_norm_stderr": 0.03656343653353159
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.4117647058823529,
"acc_stderr": 0.048971049527263666,
"acc_norm": 0.4117647058823529,
"acc_norm_stderr": 0.048971049527263666
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.8,
"acc_stderr": 0.04020151261036845,
"acc_norm": 0.8,
"acc_norm_stderr": 0.04020151261036845
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5914893617021276,
"acc_stderr": 0.032134180267015755,
"acc_norm": 0.5914893617021276,
"acc_norm_stderr": 0.032134180267015755
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.49122807017543857,
"acc_stderr": 0.04702880432049615,
"acc_norm": 0.49122807017543857,
"acc_norm_stderr": 0.04702880432049615
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5448275862068965,
"acc_stderr": 0.04149886942192117,
"acc_norm": 0.5448275862068965,
"acc_norm_stderr": 0.04149886942192117
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.41005291005291006,
"acc_stderr": 0.025331202438944447,
"acc_norm": 0.41005291005291006,
"acc_norm_stderr": 0.025331202438944447
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.42063492063492064,
"acc_stderr": 0.04415438226743744,
"acc_norm": 0.42063492063492064,
"acc_norm_stderr": 0.04415438226743744
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.35,
"acc_stderr": 0.047937248544110196,
"acc_norm": 0.35,
"acc_norm_stderr": 0.047937248544110196
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7806451612903226,
"acc_stderr": 0.023540799358723292,
"acc_norm": 0.7806451612903226,
"acc_norm_stderr": 0.023540799358723292
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.5123152709359606,
"acc_stderr": 0.035169204442208966,
"acc_norm": 0.5123152709359606,
"acc_norm_stderr": 0.035169204442208966
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.67,
"acc_stderr": 0.04725815626252609,
"acc_norm": 0.67,
"acc_norm_stderr": 0.04725815626252609
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7454545454545455,
"acc_stderr": 0.03401506715249039,
"acc_norm": 0.7454545454545455,
"acc_norm_stderr": 0.03401506715249039
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.7828282828282829,
"acc_stderr": 0.02937661648494563,
"acc_norm": 0.7828282828282829,
"acc_norm_stderr": 0.02937661648494563
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.8756476683937824,
"acc_stderr": 0.02381447708659355,
"acc_norm": 0.8756476683937824,
"acc_norm_stderr": 0.02381447708659355
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.6615384615384615,
"acc_stderr": 0.023991500500313036,
"acc_norm": 0.6615384615384615,
"acc_norm_stderr": 0.023991500500313036
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.3592592592592593,
"acc_stderr": 0.029252905927251972,
"acc_norm": 0.3592592592592593,
"acc_norm_stderr": 0.029252905927251972
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.680672268907563,
"acc_stderr": 0.030283995525884396,
"acc_norm": 0.680672268907563,
"acc_norm_stderr": 0.030283995525884396
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.31788079470198677,
"acc_stderr": 0.038020397601079024,
"acc_norm": 0.31788079470198677,
"acc_norm_stderr": 0.038020397601079024
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8495412844036697,
"acc_stderr": 0.015328563932669237,
"acc_norm": 0.8495412844036697,
"acc_norm_stderr": 0.015328563932669237
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.5231481481481481,
"acc_stderr": 0.03406315360711507,
"acc_norm": 0.5231481481481481,
"acc_norm_stderr": 0.03406315360711507
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.7892156862745098,
"acc_stderr": 0.028626547912437406,
"acc_norm": 0.7892156862745098,
"acc_norm_stderr": 0.028626547912437406
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.7974683544303798,
"acc_stderr": 0.026160568246601443,
"acc_norm": 0.7974683544303798,
"acc_norm_stderr": 0.026160568246601443
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.6860986547085202,
"acc_stderr": 0.031146796482972465,
"acc_norm": 0.6860986547085202,
"acc_norm_stderr": 0.031146796482972465
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.7709923664122137,
"acc_stderr": 0.036853466317118506,
"acc_norm": 0.7709923664122137,
"acc_norm_stderr": 0.036853466317118506
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.8099173553719008,
"acc_stderr": 0.03581796951709282,
"acc_norm": 0.8099173553719008,
"acc_norm_stderr": 0.03581796951709282
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.8055555555555556,
"acc_stderr": 0.038260763248848646,
"acc_norm": 0.8055555555555556,
"acc_norm_stderr": 0.038260763248848646
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.7668711656441718,
"acc_stderr": 0.0332201579577674,
"acc_norm": 0.7668711656441718,
"acc_norm_stderr": 0.0332201579577674
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.5,
"acc_stderr": 0.04745789978762494,
"acc_norm": 0.5,
"acc_norm_stderr": 0.04745789978762494
},
"harness|hendrycksTest-management|5": {
"acc": 0.7669902912621359,
"acc_stderr": 0.04185832598928315,
"acc_norm": 0.7669902912621359,
"acc_norm_stderr": 0.04185832598928315
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8717948717948718,
"acc_stderr": 0.02190190511507333,
"acc_norm": 0.8717948717948718,
"acc_norm_stderr": 0.02190190511507333
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.75,
"acc_stderr": 0.04351941398892446,
"acc_norm": 0.75,
"acc_norm_stderr": 0.04351941398892446
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.8237547892720306,
"acc_stderr": 0.013625556907993452,
"acc_norm": 0.8237547892720306,
"acc_norm_stderr": 0.013625556907993452
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.7225433526011561,
"acc_stderr": 0.02410571260775431,
"acc_norm": 0.7225433526011561,
"acc_norm_stderr": 0.02410571260775431
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.3877094972067039,
"acc_stderr": 0.01629533232815581,
"acc_norm": 0.3877094972067039,
"acc_norm_stderr": 0.01629533232815581
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.7254901960784313,
"acc_stderr": 0.025553169991826524,
"acc_norm": 0.7254901960784313,
"acc_norm_stderr": 0.025553169991826524
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.6977491961414791,
"acc_stderr": 0.026082700695399665,
"acc_norm": 0.6977491961414791,
"acc_norm_stderr": 0.026082700695399665
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.7376543209876543,
"acc_stderr": 0.024477222856135118,
"acc_norm": 0.7376543209876543,
"acc_norm_stderr": 0.024477222856135118
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.48936170212765956,
"acc_stderr": 0.029820747191422466,
"acc_norm": 0.48936170212765956,
"acc_norm_stderr": 0.029820747191422466
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.45697522816166886,
"acc_stderr": 0.012722869501611419,
"acc_norm": 0.45697522816166886,
"acc_norm_stderr": 0.012722869501611419
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.6617647058823529,
"acc_stderr": 0.028739328513983572,
"acc_norm": 0.6617647058823529,
"acc_norm_stderr": 0.028739328513983572
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.6683006535947712,
"acc_stderr": 0.019047485239360378,
"acc_norm": 0.6683006535947712,
"acc_norm_stderr": 0.019047485239360378
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6727272727272727,
"acc_stderr": 0.0449429086625209,
"acc_norm": 0.6727272727272727,
"acc_norm_stderr": 0.0449429086625209
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.7428571428571429,
"acc_stderr": 0.02797982353874455,
"acc_norm": 0.7428571428571429,
"acc_norm_stderr": 0.02797982353874455
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.8606965174129353,
"acc_stderr": 0.024484487162913973,
"acc_norm": 0.8606965174129353,
"acc_norm_stderr": 0.024484487162913973
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.85,
"acc_stderr": 0.0358870281282637,
"acc_norm": 0.85,
"acc_norm_stderr": 0.0358870281282637
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5421686746987951,
"acc_stderr": 0.0387862677100236,
"acc_norm": 0.5421686746987951,
"acc_norm_stderr": 0.0387862677100236
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8538011695906432,
"acc_stderr": 0.02709729011807082,
"acc_norm": 0.8538011695906432,
"acc_norm_stderr": 0.02709729011807082
},
"harness|truthfulqa:mc|0": {
"mc1": 0.39167686658506734,
"mc1_stderr": 0.01708779588176963,
"mc2": 0.5514034273421413,
"mc2_stderr": 0.015341235748555455
},
"harness|winogrande|5": {
"acc": 0.7963693764798737,
"acc_stderr": 0.011317798781626915
},
"harness|gsm8k|5": {
"acc": 0.7164518574677786,
"acc_stderr": 0.012415070917508124
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] |
rkumari/corrosion_segment | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype: image
splits:
- name: train
num_bytes: 3474520.0
num_examples: 30
- name: validation
num_bytes: 1479472.0
num_examples: 14
download_size: 1484482
dataset_size: 4953992.0
---
# Dataset Card for "corrosion_segment"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
atom-team/shit-nlp-sentiment | ---
license: apache-2.0
---
|
distilled-one-sec-cv12-each-chunk-uniq/chunk_132 | ---
dataset_info:
features:
- name: logits
sequence: float32
- name: mfcc
sequence:
sequence: float64
splits:
- name: train
num_bytes: 1113346344.0
num_examples: 216942
download_size: 1141314887
dataset_size: 1113346344.0
---
# Dataset Card for "chunk_132"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
zolak/twitter_dataset_1713015235 | ---
dataset_info:
features:
- name: id
dtype: string
- name: tweet_content
dtype: string
- name: user_name
dtype: string
- name: user_id
dtype: string
- name: created_at
dtype: string
- name: url
dtype: string
- name: favourite_count
dtype: float64
- name: scraped_at
dtype: string
- name: image_urls
dtype: string
splits:
- name: train
num_bytes: 20751131
num_examples: 51891
download_size: 10368398
dataset_size: 20751131
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
kira/rayquaza-big | ---
dataset_info:
features:
- name: conversation
list:
- name: from
dtype: string
- name: value
dtype: string
- name: sys_message
dtype: string
- name: tkn_len
dtype: int64
splits:
- name: train
num_bytes: 3493078029.54713
num_examples: 993983
download_size: 1710059593
dataset_size: 3493078029.54713
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "rayquaza-big"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
irds/wikiclir_nn | ---
pretty_name: '`wikiclir/nn`'
viewer: false
source_datasets: []
task_categories:
- text-retrieval
---
# Dataset Card for `wikiclir/nn`
The `wikiclir/nn` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/wikiclir#wikiclir/nn).
# Data
This dataset provides:
- `docs` (documents, i.e., the corpus); count=133,290
- `queries` (i.e., topics); count=99,493
- `qrels`: (relevance assessments); count=250,141
## Usage
```python
from datasets import load_dataset
docs = load_dataset('irds/wikiclir_nn', 'docs')
for record in docs:
record # {'doc_id': ..., 'title': ..., 'text': ...}
queries = load_dataset('irds/wikiclir_nn', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/wikiclir_nn', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@inproceedings{sasaki-etal-2018-cross,
title = "Cross-Lingual Learning-to-Rank with Shared Representations",
author = "Sasaki, Shota and
Sun, Shuo and
Schamoni, Shigehiko and
Duh, Kevin and
Inui, Kentaro",
booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N18-2073",
doi = "10.18653/v1/N18-2073",
pages = "458--463"
}
```
|
juniuuul/leety | ---
license: openrail
---
|
joey234/mmlu-nutrition-neg-prepend-fix | ---
configs:
- config_name: default
data_files:
- split: dev
path: data/dev-*
- split: test
path: data/test-*
dataset_info:
features:
- name: question
dtype: string
- name: choices
sequence: string
- name: answer
dtype:
class_label:
names:
'0': A
'1': B
'2': C
'3': D
- name: negate_openai_prompt
struct:
- name: content
dtype: string
- name: role
dtype: string
- name: neg_question
dtype: string
- name: fewshot_context
dtype: string
- name: ori_prompt
dtype: string
splits:
- name: dev
num_bytes: 7518
num_examples: 5
- name: test
num_bytes: 1003777
num_examples: 306
download_size: 16915
dataset_size: 1011295
---
# Dataset Card for "mmlu-nutrition-neg-prepend-fix"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
gsstein/75-percent-human-dataset-opt-og | ---
dataset_info:
features:
- name: id
dtype: string
- name: url
dtype: string
- name: title
dtype: string
- name: summary
dtype: string
- name: text
dtype: string
- name: generated
dtype: bool
- name: prompt
dtype: string
splits:
- name: train
num_bytes: 86033257
num_examples: 15326
- name: test
num_bytes: 3055340
num_examples: 576
- name: validation
num_bytes: 3252533
num_examples: 576
download_size: 57122017
dataset_size: 92341130
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: validation
path: data/validation-*
---
|
lsr42/mscoco-blip-dense | ---
license: apache-2.0
---
|
autoevaluate/autoeval-staging-eval-project-glue-c88eb4d4-14065928 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- glue
eval_info:
task: binary_classification
model: mrm8488/deberta-v3-small-finetuned-cola
metrics: []
dataset_name: glue
dataset_config: cola
dataset_split: validation
col_mapping:
text: sentence
target: label
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Binary Text Classification
* Model: mrm8488/deberta-v3-small-finetuned-cola
* Dataset: glue
* Config: cola
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
nandovallec/df_ps_train_new | ---
license: apache-2.0
---
|
tyzhu/fwv2_squad_rare_train_10_eval_10 | ---
dataset_info:
features:
- name: inputs
dtype: string
- name: targets
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 4812
num_examples: 30
- name: train_doc2id
num_bytes: 3534
num_examples: 20
- name: train_id2doc
num_bytes: 3594
num_examples: 20
- name: train_find_word
num_bytes: 1218
num_examples: 10
- name: eval_find_word
num_bytes: 1174
num_examples: 10
- name: id_context_mapping
num_bytes: 2954
num_examples: 20
download_size: 25115
dataset_size: 17286
---
# Dataset Card for "fwv2_squad_rare_train_10_eval_10"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
brjezierski/benchmark-pl-szl | ---
dataset_info:
features:
- name: translation
struct:
- name: pl
dtype: string
- name: szl
dtype: string
splits:
- name: test
num_bytes: 19051
num_examples: 100
download_size: 15302
dataset_size: 19051
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
license: mit
task_categories:
- translation
language:
- pl
pretty_name: Silesian-Polish MT benchmark
size_categories:
- n<1K
---
Polish-Silesian benchmark for evaluating MT models. Data was taken from bilingual websites and aligned by hand. |
CyberHarem/brynhild_fgo | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of brynhild/ブリュンヒルデ/布伦希尔德 (Fate/Grand Order)
This is the dataset of brynhild/ブリュンヒルデ/布伦希尔德 (Fate/Grand Order), containing 319 images and their tags.
The core tags of this character are `long_hair, purple_eyes, very_long_hair, breasts, blue_hair, white_hair`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:--------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 319 | 434.92 MiB | [Download](https://huggingface.co/datasets/CyberHarem/brynhild_fgo/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 1200 | 319 | 389.52 MiB | [Download](https://huggingface.co/datasets/CyberHarem/brynhild_fgo/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 719 | 692.24 MiB | [Download](https://huggingface.co/datasets/CyberHarem/brynhild_fgo/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/brynhild_fgo',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 8 |  |  |  |  |  | 1girl, looking_at_viewer, solo, bare_shoulders, hair_flower, large_breasts, smile, gloves, black_dress, blue_rose, blush, cleavage, closed_mouth, braid |
| 1 | 14 |  |  |  |  |  | 1girl, looking_at_viewer, solo, white_bikini, bare_shoulders, cleavage, side_ponytail, bridal_garter, glasses, hair_ornament, large_breasts, multicolored_hair, navel, smile, thigh_strap, blush, collarbone, thighs, day, neck_garter, purple_scrunchie, sitting, sky, under-rim_eyewear |
| 2 | 5 |  |  |  |  |  | 1girl, armor, gauntlets, skirt, solo, spear, thighhighs, hair_over_one_eye |
| 3 | 11 |  |  |  |  |  | 1girl, armor, solo, spear, gauntlets, skirt, thighhighs, holding_weapon, looking_at_viewer |
| 4 | 8 |  |  |  |  |  | 1girl, armor, skirt, solo, gauntlets, looking_at_viewer, thighhighs, large_breasts, sitting, bare_shoulders, blush, smile, white_background |
| 5 | 21 |  |  |  |  |  | 1girl, solo, cheerleader, looking_at_viewer, pom_pom_(cheerleading), bare_shoulders, midriff, navel, miniskirt, blush, smile, crop_top, pleated_skirt, heart, underboob, thigh_strap, black_skirt, large_breasts |
| 6 | 9 |  |  |  |  |  | braided_ponytail, earrings, single_braid, 1girl, see-through, solo, blush, looking_at_viewer, black_gloves, corset, skirt, smile, alternate_costume, handbag, long_braid, bracelet |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | looking_at_viewer | solo | bare_shoulders | hair_flower | large_breasts | smile | gloves | black_dress | blue_rose | blush | cleavage | closed_mouth | braid | white_bikini | side_ponytail | bridal_garter | glasses | hair_ornament | multicolored_hair | navel | thigh_strap | collarbone | thighs | day | neck_garter | purple_scrunchie | sitting | sky | under-rim_eyewear | armor | gauntlets | skirt | spear | thighhighs | hair_over_one_eye | holding_weapon | white_background | cheerleader | pom_pom_(cheerleading) | midriff | miniskirt | crop_top | pleated_skirt | heart | underboob | black_skirt | braided_ponytail | earrings | single_braid | see-through | black_gloves | corset | alternate_costume | handbag | long_braid | bracelet |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------------|:-------|:-----------------|:--------------|:----------------|:--------|:---------|:--------------|:------------|:--------|:-----------|:---------------|:--------|:---------------|:----------------|:----------------|:----------|:----------------|:--------------------|:--------|:--------------|:-------------|:---------|:------|:--------------|:-------------------|:----------|:------|:--------------------|:--------|:------------|:--------|:--------|:-------------|:--------------------|:-----------------|:-------------------|:--------------|:-------------------------|:----------|:------------|:-----------|:----------------|:--------|:------------|:--------------|:-------------------|:-----------|:---------------|:--------------|:---------------|:---------|:--------------------|:----------|:-------------|:-----------|
| 0 | 8 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 14 |  |  |  |  |  | X | X | X | X | | X | X | | | | X | X | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 5 |  |  |  |  |  | X | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | |
| 3 | 11 |  |  |  |  |  | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | | X | | | | | | | | | | | | | | | | | | | | |
| 4 | 8 |  |  |  |  |  | X | X | X | X | | X | X | | | | X | | | | | | | | | | | | | | | | | X | | | X | X | X | | X | | | X | | | | | | | | | | | | | | | | | | | |
| 5 | 21 |  |  |  |  |  | X | X | X | X | | X | X | | | | X | | | | | | | | | | X | X | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | | | | | | | | | | |
| 6 | 9 |  |  |  |  |  | X | X | X | | | | X | | | | X | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X |
|
mihirch/SWE-bench__test-style-3__fs-test | ---
configs:
- config_name: default
data_files:
- split: dev
path: data/dev-*
- split: test
path: data/test-*
dataset_info:
features:
- name: instance_id
dtype: string
- name: text
dtype: string
- name: repo
dtype: string
- name: base_commit
dtype: string
- name: problem_statement
dtype: string
- name: hints_text
dtype: string
- name: created_at
dtype: string
- name: patch
dtype: string
- name: test_patch
dtype: string
- name: version
dtype: string
- name: FAIL_TO_PASS
dtype: string
- name: PASS_TO_PASS
dtype: string
- name: environment_setup_commit
dtype: string
splits:
- name: dev
num_bytes: 1001011
num_examples: 23
- name: test
num_bytes: 22730863
num_examples: 300
download_size: 9176758
dataset_size: 23731874
---
# Dataset Card for "SWE-bench__test-style-3__fs-test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
louisbrulenaudet/code-propriete-intellectuelle | ---
license: apache-2.0
language:
- fr
multilinguality:
- monolingual
tags:
- finetuning
- legal
- french law
- droit français
- Code de la propriété intellectuelle
source_datasets:
- original
pretty_name: Code de la propriété intellectuelle
task_categories:
- text-generation
- table-question-answering
- summarization
- text-retrieval
- question-answering
- text-classification
size_categories:
- 1K<n<10K
---
# Code de la propriété intellectuelle, non-instruct (2024-04-15)
This project focuses on fine-tuning pre-trained language models to create efficient and accurate models for legal practice.
Fine-tuning is the process of adapting a pre-trained model to perform specific tasks or cater to particular domains. It involves adjusting the model's parameters through a further round of training on task-specific or domain-specific data. While conventional fine-tuning strategies involve supervised learning with labeled data, instruction-based fine-tuning introduces a more structured and interpretable approach.
Instruction-based fine-tuning leverages the power of human-provided instructions to guide the model's behavior. These instructions can be in the form of text prompts, prompts with explicit task descriptions, or a combination of both. This approach allows for a more controlled and context-aware interaction with the LLM, making it adaptable to a multitude of specialized tasks.
Instruction-based fine-tuning significantly enhances the performance of LLMs in the following ways:
- Task-Specific Adaptation: LLMs, when fine-tuned with specific instructions, exhibit remarkable adaptability to diverse tasks. They can switch seamlessly between translation, summarization, and question-answering, guided by the provided instructions.
- Reduced Ambiguity: Traditional LLMs might generate ambiguous or contextually inappropriate responses. Instruction-based fine-tuning allows for a clearer and more context-aware generation, reducing the likelihood of nonsensical outputs.
- Efficient Knowledge Transfer: Instructions can encapsulate domain-specific knowledge, enabling LLMs to benefit from expert guidance. This knowledge transfer is particularly valuable in fields like tax practice, law, medicine, and more.
- Interpretability: Instruction-based fine-tuning also makes LLM behavior more interpretable. Since the instructions are human-readable, it becomes easier to understand and control model outputs.
- Adaptive Behavior: LLMs, post instruction-based fine-tuning, exhibit adaptive behavior that is responsive to both explicit task descriptions and implicit cues within the provided text.
## Concurrent reading of the LegalKit
To use all the legal data published on LegalKit, you can use this code snippet:
```python
# -*- coding: utf-8 -*-
import concurrent.futures
import os
import datasets
from tqdm.notebook import tqdm
def dataset_loader(
name:str,
streaming:bool=True
) -> datasets.Dataset:
"""
Helper function to load a single dataset in parallel.
Parameters
----------
name : str
Name of the dataset to be loaded.
streaming : bool, optional
Determines if datasets are streamed. Default is True.
Returns
-------
dataset : datasets.Dataset
Loaded dataset object.
Raises
------
Exception
If an error occurs during dataset loading.
"""
try:
return datasets.load_dataset(
name,
split="train",
streaming=streaming
)
except Exception as exc:
logging.error(f"Error loading dataset {name}: {exc}")
return None
def load_datasets(
req:list,
streaming:bool=True
) -> list:
"""
Downloads datasets specified in a list and creates a list of loaded datasets.
Parameters
----------
req : list
A list containing the names of datasets to be downloaded.
streaming : bool, optional
Determines if datasets are streamed. Default is True.
Returns
-------
datasets_list : list
A list containing loaded datasets as per the requested names provided in 'req'.
Raises
------
Exception
If an error occurs during dataset loading or processing.
Examples
--------
>>> datasets = load_datasets(["dataset1", "dataset2"], streaming=False)
"""
datasets_list = []
with concurrent.futures.ThreadPoolExecutor() as executor:
future_to_dataset = {executor.submit(dataset_loader, name): name for name in req}
for future in tqdm(concurrent.futures.as_completed(future_to_dataset), total=len(req)):
name = future_to_dataset[future]
try:
dataset = future.result()
if dataset:
datasets_list.append(dataset)
except Exception as exc:
logging.error(f"Error processing dataset {name}: {exc}")
return datasets_list
req = [
"louisbrulenaudet/code-artisanat",
"louisbrulenaudet/code-action-sociale-familles",
# ...
]
datasets_list = load_datasets(
req=req,
streaming=True
)
dataset = datasets.concatenate_datasets(
datasets_list
)
```
## Dataset generation
This JSON file is a list of dictionaries, each dictionary contains the following fields:
- `instruction`: `string`, presenting the instruction linked to the element.
- `input`: `string`, signifying the input details for the element.
- `output`: `string`, indicating the output information for the element.
- `start`: `string`, the date of entry into force of the article.
- `expiration`: `string`, the date of expiration of the article.
- `num`: `string`, the id of the article.
We used the following list of instructions for generating the dataset:
```python
instructions = [
"Compose l'intégralité de l'article sous forme écrite.",
"Écris la totalité du contenu de l'article.",
"Formule la totalité du texte présent dans l'article.",
"Produis l'intégralité de l'article en écriture.",
"Développe l'article dans son ensemble par écrit.",
"Génère l'ensemble du texte contenu dans l'article.",
"Formule le contenu intégral de l'article en entier.",
"Rédige la totalité du texte de l'article en entier.",
"Compose l'intégralité du contenu textuel de l'article.",
"Rédige l'ensemble du texte qui constitue l'article.",
"Formule l'article entier dans son contenu écrit.",
"Composez l'intégralité de l'article sous forme écrite.",
"Écrivez la totalité du contenu de l'article.",
"Formulez la totalité du texte présent dans l'article.",
"Développez l'article dans son ensemble par écrit.",
"Générez l'ensemble du texte contenu dans l'article.",
"Formulez le contenu intégral de l'article en entier.",
"Rédigez la totalité du texte de l'article en entier.",
"Composez l'intégralité du contenu textuel de l'article.",
"Écrivez l'article dans son intégralité en termes de texte.",
"Rédigez l'ensemble du texte qui constitue l'article.",
"Formulez l'article entier dans son contenu écrit.",
"Composer l'intégralité de l'article sous forme écrite.",
"Écrire la totalité du contenu de l'article.",
"Formuler la totalité du texte présent dans l'article.",
"Produire l'intégralité de l'article en écriture.",
"Développer l'article dans son ensemble par écrit.",
"Générer l'ensemble du texte contenu dans l'article.",
"Formuler le contenu intégral de l'article en entier.",
"Rédiger la totalité du texte de l'article en entier.",
"Composer l'intégralité du contenu textuel de l'article.",
"Rédiger l'ensemble du texte qui constitue l'article.",
"Formuler l'article entier dans son contenu écrit.",
"Quelles sont les dispositions de l'article ?",
"Quelles dispositions sont incluses dans l'article ?",
"Quelles sont les dispositions énoncées dans l'article ?",
"Quel est le texte intégral de l'article ?",
"Quelle est la lettre de l'article ?"
]
```
## Feedback
If you have any feedback, please reach out at [louisbrulenaudet@icloud.com](mailto:louisbrulenaudet@icloud.com). |
rjaiswal/watches-dataset | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 16329292.0
num_examples: 186
download_size: 0
dataset_size: 16329292.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "watches-dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
sudiptabasak/alpaca-vectors | ---
license: mit
---
|
cristiansales/urafeminina | ---
license: openrail
---
|
OEvortex/Bhagavad_Gita | ---
license: mit
dataset_info:
features:
- name: S.No.
dtype: int64
- name: Title
dtype: string
- name: Chapter
dtype: string
- name: Verse
dtype: string
- name: Sanskrit Anuvad
dtype: string
- name: Hindi Anuvad
dtype: string
- name: Enlgish Translation
dtype: string
splits:
- name: train
num_bytes: 697874
num_examples: 700
download_size: 287784
dataset_size: 697874
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Bhagavad Gita Dataset
## Description
This dataset contains the Bhagavad Gita, a 700-verse Hindu scripture. It is part of the Indian epic Mahabharata (chapters 23–40 of the Bhishma Parva) and is written in the form of a dialogue between Prince Arjuna and Krishna, who serves as his charioteer. In the dialogue, Krishna provides guidance on how to deal with moral dilemmas and the path to spiritual enlightenment.
## Contents
The dataset contains the following columns:
- Verse: The verse in the Bhagavad Gita.
- Chapter: The chapter in which the verse is found.
- Meaning: The general meaning or theme of the verse.
|
open-llm-leaderboard/details_DopeorNope__LaOT | ---
pretty_name: Evaluation run of DopeorNope/LaOT
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [DopeorNope/LaOT](https://huggingface.co/DopeorNope/LaOT) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_DopeorNope__LaOT\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-28T14:15:27.511951](https://huggingface.co/datasets/open-llm-leaderboard/details_DopeorNope__LaOT/blob/main/results_2023-10-28T14-15-27.511951.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.26321308724832215,\n\
\ \"em_stderr\": 0.004509873314169961,\n \"f1\": 0.3275041946308728,\n\
\ \"f1_stderr\": 0.004460519232677794,\n \"acc\": 0.3705603788476717,\n\
\ \"acc_stderr\": 0.006155257905496687\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.26321308724832215,\n \"em_stderr\": 0.004509873314169961,\n\
\ \"f1\": 0.3275041946308728,\n \"f1_stderr\": 0.004460519232677794\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\"\
: 0.0\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7411207576953434,\n\
\ \"acc_stderr\": 0.012310515810993374\n }\n}\n```"
repo_url: https://huggingface.co/DopeorNope/LaOT
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_10_04T06_28_47.978535
path:
- '**/details_harness|arc:challenge|25_2023-10-04T06-28-47.978535.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-10-04T06-28-47.978535.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_28T14_15_27.511951
path:
- '**/details_harness|drop|3_2023-10-28T14-15-27.511951.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-28T14-15-27.511951.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_28T14_15_27.511951
path:
- '**/details_harness|gsm8k|5_2023-10-28T14-15-27.511951.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-28T14-15-27.511951.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_10_04T06_28_47.978535
path:
- '**/details_harness|hellaswag|10_2023-10-04T06-28-47.978535.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-10-04T06-28-47.978535.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_10_04T06_28_47.978535
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-04T06-28-47.978535.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-10-04T06-28-47.978535.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-04T06-28-47.978535.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_10_04T06_28_47.978535
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-04T06-28-47.978535.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-04T06-28-47.978535.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_10_04T06_28_47.978535
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-04T06-28-47.978535.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-04T06-28-47.978535.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_10_04T06_28_47.978535
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-04T06-28-47.978535.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-04T06-28-47.978535.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_10_04T06_28_47.978535
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-04T06-28-47.978535.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-04T06-28-47.978535.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_10_04T06_28_47.978535
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-04T06-28-47.978535.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-04T06-28-47.978535.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_10_04T06_28_47.978535
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-04T06-28-47.978535.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-04T06-28-47.978535.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_10_04T06_28_47.978535
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-04T06-28-47.978535.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-04T06-28-47.978535.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_10_04T06_28_47.978535
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-04T06-28-47.978535.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-04T06-28-47.978535.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_10_04T06_28_47.978535
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-04T06-28-47.978535.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-04T06-28-47.978535.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_10_04T06_28_47.978535
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-04T06-28-47.978535.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-04T06-28-47.978535.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_10_04T06_28_47.978535
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-04T06-28-47.978535.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-04T06-28-47.978535.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_10_04T06_28_47.978535
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-04T06-28-47.978535.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-04T06-28-47.978535.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_10_04T06_28_47.978535
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-04T06-28-47.978535.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-04T06-28-47.978535.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_10_04T06_28_47.978535
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-04T06-28-47.978535.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-04T06-28-47.978535.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_10_04T06_28_47.978535
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-04T06-28-47.978535.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-04T06-28-47.978535.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_10_04T06_28_47.978535
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-04T06-28-47.978535.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-04T06-28-47.978535.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_10_04T06_28_47.978535
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-04T06-28-47.978535.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-04T06-28-47.978535.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_10_04T06_28_47.978535
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-04T06-28-47.978535.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-04T06-28-47.978535.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_10_04T06_28_47.978535
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-04T06-28-47.978535.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-04T06-28-47.978535.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_10_04T06_28_47.978535
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-04T06-28-47.978535.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-04T06-28-47.978535.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_10_04T06_28_47.978535
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-04T06-28-47.978535.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-04T06-28-47.978535.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_10_04T06_28_47.978535
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-04T06-28-47.978535.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-04T06-28-47.978535.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_10_04T06_28_47.978535
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-04T06-28-47.978535.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-04T06-28-47.978535.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_10_04T06_28_47.978535
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-04T06-28-47.978535.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-04T06-28-47.978535.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_10_04T06_28_47.978535
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-04T06-28-47.978535.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-04T06-28-47.978535.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_10_04T06_28_47.978535
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-04T06-28-47.978535.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-04T06-28-47.978535.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_10_04T06_28_47.978535
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-04T06-28-47.978535.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-04T06-28-47.978535.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_10_04T06_28_47.978535
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-04T06-28-47.978535.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-04T06-28-47.978535.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_10_04T06_28_47.978535
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-04T06-28-47.978535.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-04T06-28-47.978535.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_10_04T06_28_47.978535
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-04T06-28-47.978535.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-04T06-28-47.978535.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_10_04T06_28_47.978535
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-04T06-28-47.978535.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-04T06-28-47.978535.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_10_04T06_28_47.978535
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-04T06-28-47.978535.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-04T06-28-47.978535.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_10_04T06_28_47.978535
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-04T06-28-47.978535.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-04T06-28-47.978535.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_10_04T06_28_47.978535
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-04T06-28-47.978535.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-04T06-28-47.978535.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_10_04T06_28_47.978535
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-10-04T06-28-47.978535.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-10-04T06-28-47.978535.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_10_04T06_28_47.978535
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-04T06-28-47.978535.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-04T06-28-47.978535.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_10_04T06_28_47.978535
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-04T06-28-47.978535.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-04T06-28-47.978535.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_10_04T06_28_47.978535
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-04T06-28-47.978535.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-04T06-28-47.978535.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_10_04T06_28_47.978535
path:
- '**/details_harness|hendrycksTest-management|5_2023-10-04T06-28-47.978535.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-10-04T06-28-47.978535.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_10_04T06_28_47.978535
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-10-04T06-28-47.978535.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-10-04T06-28-47.978535.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_10_04T06_28_47.978535
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-04T06-28-47.978535.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-04T06-28-47.978535.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_10_04T06_28_47.978535
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-04T06-28-47.978535.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-04T06-28-47.978535.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_10_04T06_28_47.978535
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-04T06-28-47.978535.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-04T06-28-47.978535.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_10_04T06_28_47.978535
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-04T06-28-47.978535.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-04T06-28-47.978535.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_10_04T06_28_47.978535
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-04T06-28-47.978535.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-04T06-28-47.978535.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_10_04T06_28_47.978535
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-04T06-28-47.978535.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-04T06-28-47.978535.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_10_04T06_28_47.978535
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-04T06-28-47.978535.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-04T06-28-47.978535.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_10_04T06_28_47.978535
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-04T06-28-47.978535.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-04T06-28-47.978535.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_10_04T06_28_47.978535
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-04T06-28-47.978535.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-04T06-28-47.978535.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_10_04T06_28_47.978535
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-04T06-28-47.978535.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-04T06-28-47.978535.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_10_04T06_28_47.978535
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-04T06-28-47.978535.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-04T06-28-47.978535.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_10_04T06_28_47.978535
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-04T06-28-47.978535.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-04T06-28-47.978535.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_10_04T06_28_47.978535
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-04T06-28-47.978535.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-04T06-28-47.978535.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_10_04T06_28_47.978535
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-10-04T06-28-47.978535.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-10-04T06-28-47.978535.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_10_04T06_28_47.978535
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-04T06-28-47.978535.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-04T06-28-47.978535.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_10_04T06_28_47.978535
path:
- '**/details_harness|hendrycksTest-virology|5_2023-10-04T06-28-47.978535.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-10-04T06-28-47.978535.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_10_04T06_28_47.978535
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-04T06-28-47.978535.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-04T06-28-47.978535.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_10_04T06_28_47.978535
path:
- '**/details_harness|truthfulqa:mc|0_2023-10-04T06-28-47.978535.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-10-04T06-28-47.978535.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_28T14_15_27.511951
path:
- '**/details_harness|winogrande|5_2023-10-28T14-15-27.511951.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-28T14-15-27.511951.parquet'
- config_name: results
data_files:
- split: 2023_10_04T06_28_47.978535
path:
- results_2023-10-04T06-28-47.978535.parquet
- split: 2023_10_28T14_15_27.511951
path:
- results_2023-10-28T14-15-27.511951.parquet
- split: latest
path:
- results_2023-10-28T14-15-27.511951.parquet
---
# Dataset Card for Evaluation run of DopeorNope/LaOT
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/DopeorNope/LaOT
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [DopeorNope/LaOT](https://huggingface.co/DopeorNope/LaOT) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_DopeorNope__LaOT",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-28T14:15:27.511951](https://huggingface.co/datasets/open-llm-leaderboard/details_DopeorNope__LaOT/blob/main/results_2023-10-28T14-15-27.511951.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.26321308724832215,
"em_stderr": 0.004509873314169961,
"f1": 0.3275041946308728,
"f1_stderr": 0.004460519232677794,
"acc": 0.3705603788476717,
"acc_stderr": 0.006155257905496687
},
"harness|drop|3": {
"em": 0.26321308724832215,
"em_stderr": 0.004509873314169961,
"f1": 0.3275041946308728,
"f1_stderr": 0.004460519232677794
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
},
"harness|winogrande|5": {
"acc": 0.7411207576953434,
"acc_stderr": 0.012310515810993374
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] |
tyzhu/squad_qa_rare_v5_full_random_permute_8 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: id
dtype: string
- name: title
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: answers
sequence:
- name: text
dtype: string
- name: answer_start
dtype: int32
- name: answer
dtype: string
- name: context_id
dtype: string
- name: inputs
dtype: string
- name: targets
dtype: string
splits:
- name: train
num_bytes: 9763990.32814238
num_examples: 6305
- name: validation
num_bytes: 345326
num_examples: 300
download_size: 1469044
dataset_size: 10109316.32814238
---
# Dataset Card for "squad_qa_rare_v5_full_random_permute_8"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
liuyanchen1015/MULTI_VALUE_qqp_acomp_focusing_like | ---
dataset_info:
features:
- name: question1
dtype: string
- name: question2
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: value_score
dtype: int64
splits:
- name: dev
num_bytes: 1396999
num_examples: 8022
- name: test
num_bytes: 14576606
num_examples: 83799
- name: train
num_bytes: 12672982
num_examples: 72367
download_size: 17791138
dataset_size: 28646587
---
# Dataset Card for "MULTI_VALUE_qqp_acomp_focusing_like"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
autoevaluate/autoeval-eval-alex-apostolo__filtered-cuad-alex-apostolo__filtered-cu-fd7768-3096988007 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- alex-apostolo/filtered-cuad
eval_info:
task: extractive_question_answering
model: alex-apostolo/legal-bert-small-filtered-cuad
metrics: ['accuracy']
dataset_name: alex-apostolo/filtered-cuad
dataset_config: alex-apostolo--filtered-cuad
dataset_split: test
col_mapping:
context: context
question: question
answers-text: answers.text
answers-answer_start: answers.answer_start
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: alex-apostolo/legal-bert-small-filtered-cuad
* Dataset: alex-apostolo/filtered-cuad
* Config: alex-apostolo--filtered-cuad
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@pankajm](https://huggingface.co/pankajm) for evaluating this model. |
huggingartists/selena-gomez | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/selena-gomez"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [How to use](#how-to-use)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [About](#about)
## Dataset Description
- **Homepage:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Repository:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of the generated dataset:** 0.587236 MB
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/622e6858ce207990b4eb25cd9cdf8f8c.1000x1000x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/selena-gomez">
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
</a>
<div style="text-align: center; font-size: 16px; font-weight: 800">Selena Gomez</div>
<a href="https://genius.com/artists/selena-gomez">
<div style="text-align: center; font-size: 14px;">@selena-gomez</div>
</a>
</div>
### Dataset Summary
The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists.
Model is available [here](https://huggingface.co/huggingartists/selena-gomez).
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
en
## How to use
How to load this dataset directly with the datasets library:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/selena-gomez")
```
## Dataset Structure
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..."
}
```
### Data Fields
The data fields are the same among all splits.
- `text`: a `string` feature.
### Data Splits
| train |validation|test|
|------:|---------:|---:|
|397| -| -|
'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code:
```python
from datasets import load_dataset, Dataset, DatasetDict
import numpy as np
datasets = load_dataset("huggingartists/selena-gomez")
train_percentage = 0.9
validation_percentage = 0.07
test_percentage = 0.03
train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])*train_percentage), int(len(datasets['train']['text'])*(train_percentage + validation_percentage))])
datasets = DatasetDict(
{
'train': Dataset.from_dict({'text': list(train)}),
'validation': Dataset.from_dict({'text': list(validation)}),
'test': Dataset.from_dict({'text': list(test)})
}
)
```
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@InProceedings{huggingartists,
author={Aleksey Korshuk}
year=2021
}
```
## About
*Built by Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
|
joey234/mmlu-moral_scenarios-original-neg-prepend | ---
dataset_info:
features:
- name: question
dtype: string
- name: choices
sequence: string
- name: answer
dtype:
class_label:
names:
'0': A
'1': B
'2': C
'3': D
- name: neg_prompt
dtype: string
splits:
- name: test
num_bytes: 104255
num_examples: 124
download_size: 20178
dataset_size: 104255
---
# Dataset Card for "mmlu-moral_scenarios-original-neg-prepend"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
NaNames/ljs_mode | ---
license: apache-2.0
---
|
Gae8J/gaepago_s | ---
license: other
dataset_info:
features:
- name: file
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: label
dtype:
class_label:
names:
'0': bark
'1': bow-wow
'2': growling
'3': howl
'4': whimper
'5': yip
- name: is_unknown
dtype: bool
- name: youtube_id
dtype: string
- name: youtube_url
dtype: string
splits:
- name: train
num_bytes: 8774740.0
num_examples: 12
- name: validation
num_bytes: 8774740.0
num_examples: 12
- name: test
num_bytes: 8774740.0
num_examples: 12
download_size: 26037015
dataset_size: 26324220.0
task_categories:
- audio-classification
size_categories:
- 1K<n<10K
---
# Gaepago (Gae8J/gaepago_s)
## How to use
### 1. Install dependencies
```bash
pip install datasets==2.10.1
pip install soundfile==0.12.1
pip install librosa==0.10.0.post2
```
### 2. Load the dataset
```python
from datasets import load_dataset
dataset = load_dataset("Gae8J/gaepago_s")
```
Outputs
```
DatasetDict({
train: Dataset({
features: ['file', 'audio', 'label', 'is_unknown', 'youtube_id'],
num_rows: 12
})
validation: Dataset({
features: ['file', 'audio', 'label', 'is_unknown', 'youtube_id'],
num_rows: 12
})
test: Dataset({
features: ['file', 'audio', 'label', 'is_unknown', 'youtube_id'],
num_rows: 12
})
})
```
### 3. Check a sample
```python
dataset['train'][0]
```
Outputs
```
{'file': 'bark/1_Q80fDGLRM.wav', 'audio': {'path': 'bark/1_Q80fDGLRM.wav', 'array': array([-9.15838356e-08, 6.80501699e-08, 1.97052145e-07, ...,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00]), 'sampling_rate': 16000}, 'label': 0, 'is_unknown': False, 'youtube_id': '1_Q80fDGLRM'}
``` |
kawagoshi-llm-team/giji_hakusyo | ---
license: mit
---
|
maxolotl/must-c-en-es-wait4-01 | ---
dataset_info:
features:
- name: current_source
dtype: string
- name: current_target
dtype: string
- name: target_token
dtype: string
splits:
- name: train
num_bytes: 1018568678
num_examples: 5239386
- name: test
num_bytes: 10221278
num_examples: 57187
- name: validation
num_bytes: 5552288
num_examples: 27549
download_size: 183161579
dataset_size: 1034342244
---
# Dataset Card for "must-c-en-es-wait4-01"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
vwxyzjn/cai-conversation | ---
dataset_info:
features:
- name: init_prompt
struct:
- name: content
dtype: string
- name: role
dtype: string
- name: init_response
struct:
- name: content
dtype: string
- name: role
dtype: string
- name: critic_prompt
struct:
- name: content
dtype: string
- name: role
dtype: string
- name: critic_response
struct:
- name: content
dtype: string
- name: role
dtype: string
- name: revision_prompt
struct:
- name: content
dtype: string
- name: role
dtype: string
- name: revision_response
struct:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: train
num_bytes: 200025
num_examples: 100
download_size: 104751
dataset_size: 200025
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "cai-conversation"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
CyberHarem/liliya_olenyeva_honkai3 | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of liliya_olenyeva/リリア・アリーン/莉莉娅·阿琳 (Houkai 3rd)
This is the dataset of liliya_olenyeva/リリア・アリーン/莉莉娅·阿琳 (Houkai 3rd), containing 248 images and their tags.
The core tags of this character are `long_hair, bangs, horns, blue_eyes, blue_hair, tail, single_horn, hair_between_eyes, thick_eyebrows, breasts`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:-------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 248 | 310.29 MiB | [Download](https://huggingface.co/datasets/CyberHarem/liliya_olenyeva_honkai3/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 248 | 178.93 MiB | [Download](https://huggingface.co/datasets/CyberHarem/liliya_olenyeva_honkai3/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 552 | 365.70 MiB | [Download](https://huggingface.co/datasets/CyberHarem/liliya_olenyeva_honkai3/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 248 | 273.20 MiB | [Download](https://huggingface.co/datasets/CyberHarem/liliya_olenyeva_honkai3/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 552 | 513.17 MiB | [Download](https://huggingface.co/datasets/CyberHarem/liliya_olenyeva_honkai3/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/liliya_olenyeva_honkai3',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 23 |  |  |  |  |  | 1girl, bare_shoulders, solo, black_gloves, looking_at_viewer, thighhighs, white_background, open_mouth, purple_eyes, sword, navel, sleeveless_dress, holding_weapon, simple_background |
| 1 | 14 |  |  |  |  |  | 1girl, aqua_hair, black_gloves, mechanical_tail, thighhighs, asymmetrical_horns, solo, bare_shoulders, small_breasts, prehensile_tail, purple_eyes, very_long_hair, sword |
| 2 | 12 |  |  |  |  |  | 2girls, bare_shoulders, dress, looking_at_viewer, black_gloves, open_mouth, pink_hair, twins, sleeveless, thighhighs, mismatched_gloves, hair_ornament, mechanical_tail |
| 3 | 6 |  |  |  |  |  | 1girl, bare_shoulders, belt, halloween_costume, long_sleeves, looking_at_viewer, solo, black_gloves, jack-o'-lantern, open_mouth, pumpkin, black_shorts, night_sky, white_shirt, :o, food, halloween_bucket, moon, navel, sleeveless |
| 4 | 5 |  |  |  |  |  | 1girl, bikini, closed_mouth, hair_ornament, solo, twintails, bare_shoulders, looking_at_viewer, nail_polish, simple_background, white_background, full_body, purple_eyes, flip-flops, gradient_hair, purple_nails, toes |
| 5 | 5 |  |  |  |  |  | 1girl, blush, navel, nipples, nude, pussy, small_breasts, solo, looking_at_viewer, mechanical_horns, aqua_hair, bed_sheet, dakimakura_(medium), open_mouth, arm_up, armpits, asymmetrical_horns, black_thighhighs, full_body, mechanical_tail, on_back |
| 6 | 6 |  |  |  |  |  | white_shirt, black_skirt, long_sleeves, pleated_skirt, 1girl, black_thighhighs, closed_mouth, full_body, hair_ribbon, looking_at_viewer, serafuku, shoes, solo, brown_footwear, collared_shirt, neckerchief, open_clothes, sailor_collar, white_background |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | bare_shoulders | solo | black_gloves | looking_at_viewer | thighhighs | white_background | open_mouth | purple_eyes | sword | navel | sleeveless_dress | holding_weapon | simple_background | aqua_hair | mechanical_tail | asymmetrical_horns | small_breasts | prehensile_tail | very_long_hair | 2girls | dress | pink_hair | twins | sleeveless | mismatched_gloves | hair_ornament | belt | halloween_costume | long_sleeves | jack-o'-lantern | pumpkin | black_shorts | night_sky | white_shirt | :o | food | halloween_bucket | moon | bikini | closed_mouth | twintails | nail_polish | full_body | flip-flops | gradient_hair | purple_nails | toes | blush | nipples | nude | pussy | mechanical_horns | bed_sheet | dakimakura_(medium) | arm_up | armpits | black_thighhighs | on_back | black_skirt | pleated_skirt | hair_ribbon | serafuku | shoes | brown_footwear | collared_shirt | neckerchief | open_clothes | sailor_collar |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----------------|:-------|:---------------|:--------------------|:-------------|:-------------------|:-------------|:--------------|:--------|:--------|:-------------------|:-----------------|:--------------------|:------------|:------------------|:---------------------|:----------------|:------------------|:-----------------|:---------|:--------|:------------|:--------|:-------------|:--------------------|:----------------|:-------|:--------------------|:---------------|:------------------|:----------|:---------------|:------------|:--------------|:-----|:-------|:-------------------|:-------|:---------|:---------------|:------------|:--------------|:------------|:-------------|:----------------|:---------------|:-------|:--------|:----------|:-------|:--------|:-------------------|:------------|:----------------------|:---------|:----------|:-------------------|:----------|:--------------|:----------------|:--------------|:-----------|:--------|:-----------------|:-----------------|:--------------|:---------------|:----------------|
| 0 | 23 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 14 |  |  |  |  |  | X | X | X | X | | X | | | X | X | | | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 12 |  |  |  |  |  | | X | | X | X | X | | X | | | | | | | | X | | | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 6 |  |  |  |  |  | X | X | X | X | X | | | X | | | X | | | | | | | | | | | | | | X | | | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 5 |  |  |  |  |  | X | X | X | | X | | X | | X | | | | | X | | | | | | | | | | | | | X | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | |
| 5 | 5 |  |  |  |  |  | X | | X | | X | | | X | | | X | | | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | |
| 6 | 6 |  |  |  |  |  | X | | X | | X | | X | | | | | | | | | | | | | | | | | | | | | | | X | | | | | X | | | | | | X | | | X | | | | | | | | | | | | | | X | | X | X | X | X | X | X | X | X | X | X |
|
vinisebk/hannah_model | ---
license: openrail
---
|
rubio21/latas_roboflow | ---
dataset_info:
features:
- name: image
dtype: image
splits:
- name: train
num_bytes: 17092635.0
num_examples: 429
download_size: 17108209
dataset_size: 17092635.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
blinoff/medical_institutions_reviews | ---
language:
- ru
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
task_categories:
- text-classification
task_ids:
- sentiment-classification
---
### Dataset Summary
The dataset contains user reviews about medical institutions.
In total it contains 12,036 reviews. A review tagged with the <em>general</em> sentiment and sentiments on 5 aspects: <em>quality, service, equipment, food, location</em>.
### Data Fields
Each sample contains the following fields:
- **review_id**;
- **content**: review text;
- **general**;
- **quality**;
- **service**;
- **equipment**;
- **food**;
- **location**.
### Python
```python3
import pandas as pd
df = pd.read_json('medical_institutions_reviews.jsonl', lines=True)
df.sample(5)
```
|
BangumiBase/musaigennophantomworld | ---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Musaigen No Phantom World
This is the image base of bangumi Musaigen no Phantom World, we detected 27 characters, 2442 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 237 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 26 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 538 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 23 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 11 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 25 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 13 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 18 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 14 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 307 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 12 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 31 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 10 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 61 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 16 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 221 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 268 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 49 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 7 | [Download](18/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 19 | 12 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 190 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 10 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 8 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 13 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 5 | [Download](24/dataset.zip) |  |  |  |  |  | N/A | N/A | N/A |
| 25 | 11 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 306 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
Atipico1/mrqa-adv-test-adv-gpt-passage | ---
dataset_info:
features:
- name: subset
dtype: string
- name: qid
dtype: string
- name: question
dtype: string
- name: answers
sequence: string
- name: masked_query
dtype: string
- name: context
dtype: string
- name: answer_sent
dtype: string
- name: answer_in_context
sequence: string
- name: query_embedding
sequence: float32
- name: entity
dtype: string
- name: similar_entity
dtype: string
- name: similar_entity_score
dtype: float32
- name: random_entity
dtype: string
- name: random_entity_score
dtype: float64
- name: rewritten_context
dtype: string
- name: valid
dtype: bool
- name: clear_answer_sent
dtype: string
- name: vague_answer_sent
dtype: string
- name: adversary
dtype: string
- name: replace_count
dtype: int64
- name: adversarial_passage
dtype: string
- name: gpt_adv_passage
dtype: string
splits:
- name: train
num_bytes: 4308472
num_examples: 684
download_size: 4164438
dataset_size: 4308472
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
zhenza/guanaco-llama2-1k | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 1654448
num_examples: 1000
download_size: 966692
dataset_size: 1654448
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
davanstrien/quickstart-10 | Invalid username or password. |
open-llm-leaderboard/details_Stopwolf__Cerberus-7B-slerp | ---
pretty_name: Evaluation run of Stopwolf/Cerberus-7B-slerp
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Stopwolf/Cerberus-7B-slerp](https://huggingface.co/Stopwolf/Cerberus-7B-slerp)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 63 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Stopwolf__Cerberus-7B-slerp\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-02-05T09:08:12.484477](https://huggingface.co/datasets/open-llm-leaderboard/details_Stopwolf__Cerberus-7B-slerp/blob/main/results_2024-02-05T09-08-12.484477.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.629678937070562,\n\
\ \"acc_stderr\": 0.03233409511653798,\n \"acc_norm\": 0.6376621349483272,\n\
\ \"acc_norm_stderr\": 0.0330388306090536,\n \"mc1\": 0.44920440636474906,\n\
\ \"mc1_stderr\": 0.017412941986115302,\n \"mc2\": 0.6135075599156891,\n\
\ \"mc2_stderr\": 0.01558423055084616\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6723549488054608,\n \"acc_stderr\": 0.01371584794071934,\n\
\ \"acc_norm\": 0.6953924914675768,\n \"acc_norm_stderr\": 0.013449522109932487\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6920932085241984,\n\
\ \"acc_stderr\": 0.004606843344517468,\n \"acc_norm\": 0.8733320055765784,\n\
\ \"acc_norm_stderr\": 0.0033192094001351165\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.28,\n \"acc_stderr\": 0.04512608598542128,\n \
\ \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.04512608598542128\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6,\n \
\ \"acc_stderr\": 0.04232073695151589,\n \"acc_norm\": 0.6,\n \"\
acc_norm_stderr\": 0.04232073695151589\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.6776315789473685,\n \"acc_stderr\": 0.038035102483515854,\n\
\ \"acc_norm\": 0.6776315789473685,\n \"acc_norm_stderr\": 0.038035102483515854\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.58,\n\
\ \"acc_stderr\": 0.049604496374885836,\n \"acc_norm\": 0.58,\n \
\ \"acc_norm_stderr\": 0.049604496374885836\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.6981132075471698,\n \"acc_stderr\": 0.02825420034443866,\n\
\ \"acc_norm\": 0.6981132075471698,\n \"acc_norm_stderr\": 0.02825420034443866\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7291666666666666,\n\
\ \"acc_stderr\": 0.03716177437566017,\n \"acc_norm\": 0.7291666666666666,\n\
\ \"acc_norm_stderr\": 0.03716177437566017\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.44,\n \"acc_stderr\": 0.04988876515698589,\n \
\ \"acc_norm\": 0.44,\n \"acc_norm_stderr\": 0.04988876515698589\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.54,\n \"acc_stderr\": 0.05009082659620333,\n \"acc_norm\": 0.54,\n\
\ \"acc_norm_stderr\": 0.05009082659620333\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \
\ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6416184971098265,\n\
\ \"acc_stderr\": 0.036563436533531585,\n \"acc_norm\": 0.6416184971098265,\n\
\ \"acc_norm_stderr\": 0.036563436533531585\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.3627450980392157,\n \"acc_stderr\": 0.04784060704105653,\n\
\ \"acc_norm\": 0.3627450980392157,\n \"acc_norm_stderr\": 0.04784060704105653\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.76,\n \"acc_stderr\": 0.04292346959909283,\n \"acc_norm\": 0.76,\n\
\ \"acc_norm_stderr\": 0.04292346959909283\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5276595744680851,\n \"acc_stderr\": 0.03263597118409769,\n\
\ \"acc_norm\": 0.5276595744680851,\n \"acc_norm_stderr\": 0.03263597118409769\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.47368421052631576,\n\
\ \"acc_stderr\": 0.046970851366478626,\n \"acc_norm\": 0.47368421052631576,\n\
\ \"acc_norm_stderr\": 0.046970851366478626\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5379310344827586,\n \"acc_stderr\": 0.04154659671707548,\n\
\ \"acc_norm\": 0.5379310344827586,\n \"acc_norm_stderr\": 0.04154659671707548\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.42063492063492064,\n \"acc_stderr\": 0.025424835086924,\n \"acc_norm\"\
: 0.42063492063492064,\n \"acc_norm_stderr\": 0.025424835086924\n },\n\
\ \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.42063492063492064,\n\
\ \"acc_stderr\": 0.04415438226743744,\n \"acc_norm\": 0.42063492063492064,\n\
\ \"acc_norm_stderr\": 0.04415438226743744\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.36,\n \"acc_stderr\": 0.048241815132442176,\n \
\ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.048241815132442176\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\
: 0.7774193548387097,\n \"acc_stderr\": 0.02366421667164251,\n \"\
acc_norm\": 0.7774193548387097,\n \"acc_norm_stderr\": 0.02366421667164251\n\
\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
: 0.4975369458128079,\n \"acc_stderr\": 0.03517945038691063,\n \"\
acc_norm\": 0.4975369458128079,\n \"acc_norm_stderr\": 0.03517945038691063\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.68,\n \"acc_stderr\": 0.04688261722621505,\n \"acc_norm\"\
: 0.68,\n \"acc_norm_stderr\": 0.04688261722621505\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7818181818181819,\n \"acc_stderr\": 0.03225078108306289,\n\
\ \"acc_norm\": 0.7818181818181819,\n \"acc_norm_stderr\": 0.03225078108306289\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7828282828282829,\n \"acc_stderr\": 0.029376616484945627,\n \"\
acc_norm\": 0.7828282828282829,\n \"acc_norm_stderr\": 0.029376616484945627\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8860103626943006,\n \"acc_stderr\": 0.02293514405391943,\n\
\ \"acc_norm\": 0.8860103626943006,\n \"acc_norm_stderr\": 0.02293514405391943\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6230769230769231,\n \"acc_stderr\": 0.024570975364225995,\n\
\ \"acc_norm\": 0.6230769230769231,\n \"acc_norm_stderr\": 0.024570975364225995\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.32222222222222224,\n \"acc_stderr\": 0.028493465091028593,\n \
\ \"acc_norm\": 0.32222222222222224,\n \"acc_norm_stderr\": 0.028493465091028593\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6176470588235294,\n \"acc_stderr\": 0.031566630992154156,\n\
\ \"acc_norm\": 0.6176470588235294,\n \"acc_norm_stderr\": 0.031566630992154156\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.3443708609271523,\n \"acc_stderr\": 0.038796870240733264,\n \"\
acc_norm\": 0.3443708609271523,\n \"acc_norm_stderr\": 0.038796870240733264\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8201834862385321,\n \"acc_stderr\": 0.016465345467391534,\n \"\
acc_norm\": 0.8201834862385321,\n \"acc_norm_stderr\": 0.016465345467391534\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.49537037037037035,\n \"acc_stderr\": 0.03409825519163572,\n \"\
acc_norm\": 0.49537037037037035,\n \"acc_norm_stderr\": 0.03409825519163572\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.8235294117647058,\n \"acc_stderr\": 0.02675640153807897,\n \"\
acc_norm\": 0.8235294117647058,\n \"acc_norm_stderr\": 0.02675640153807897\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.7763713080168776,\n \"acc_stderr\": 0.027123298205229962,\n \
\ \"acc_norm\": 0.7763713080168776,\n \"acc_norm_stderr\": 0.027123298205229962\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6816143497757847,\n\
\ \"acc_stderr\": 0.03126580522513713,\n \"acc_norm\": 0.6816143497757847,\n\
\ \"acc_norm_stderr\": 0.03126580522513713\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7709923664122137,\n \"acc_stderr\": 0.036853466317118506,\n\
\ \"acc_norm\": 0.7709923664122137,\n \"acc_norm_stderr\": 0.036853466317118506\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.7768595041322314,\n \"acc_stderr\": 0.03800754475228732,\n \"\
acc_norm\": 0.7768595041322314,\n \"acc_norm_stderr\": 0.03800754475228732\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7870370370370371,\n\
\ \"acc_stderr\": 0.0395783547198098,\n \"acc_norm\": 0.7870370370370371,\n\
\ \"acc_norm_stderr\": 0.0395783547198098\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7607361963190185,\n \"acc_stderr\": 0.0335195387952127,\n\
\ \"acc_norm\": 0.7607361963190185,\n \"acc_norm_stderr\": 0.0335195387952127\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5178571428571429,\n\
\ \"acc_stderr\": 0.04742762361243011,\n \"acc_norm\": 0.5178571428571429,\n\
\ \"acc_norm_stderr\": 0.04742762361243011\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7669902912621359,\n \"acc_stderr\": 0.04185832598928315,\n\
\ \"acc_norm\": 0.7669902912621359,\n \"acc_norm_stderr\": 0.04185832598928315\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8760683760683761,\n\
\ \"acc_stderr\": 0.02158649400128136,\n \"acc_norm\": 0.8760683760683761,\n\
\ \"acc_norm_stderr\": 0.02158649400128136\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.73,\n \"acc_stderr\": 0.044619604333847394,\n \
\ \"acc_norm\": 0.73,\n \"acc_norm_stderr\": 0.044619604333847394\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8160919540229885,\n\
\ \"acc_stderr\": 0.013853724170922531,\n \"acc_norm\": 0.8160919540229885,\n\
\ \"acc_norm_stderr\": 0.013853724170922531\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.708092485549133,\n \"acc_stderr\": 0.024476994076247326,\n\
\ \"acc_norm\": 0.708092485549133,\n \"acc_norm_stderr\": 0.024476994076247326\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.4212290502793296,\n\
\ \"acc_stderr\": 0.016513676031179595,\n \"acc_norm\": 0.4212290502793296,\n\
\ \"acc_norm_stderr\": 0.016513676031179595\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7222222222222222,\n \"acc_stderr\": 0.025646863097137894,\n\
\ \"acc_norm\": 0.7222222222222222,\n \"acc_norm_stderr\": 0.025646863097137894\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7202572347266881,\n\
\ \"acc_stderr\": 0.025494259350694912,\n \"acc_norm\": 0.7202572347266881,\n\
\ \"acc_norm_stderr\": 0.025494259350694912\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7129629629629629,\n \"acc_stderr\": 0.025171041915309684,\n\
\ \"acc_norm\": 0.7129629629629629,\n \"acc_norm_stderr\": 0.025171041915309684\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.4645390070921986,\n \"acc_stderr\": 0.029752389657427047,\n \
\ \"acc_norm\": 0.4645390070921986,\n \"acc_norm_stderr\": 0.029752389657427047\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4589308996088657,\n\
\ \"acc_stderr\": 0.012727084826799804,\n \"acc_norm\": 0.4589308996088657,\n\
\ \"acc_norm_stderr\": 0.012727084826799804\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6727941176470589,\n \"acc_stderr\": 0.028501452860396556,\n\
\ \"acc_norm\": 0.6727941176470589,\n \"acc_norm_stderr\": 0.028501452860396556\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6405228758169934,\n \"acc_stderr\": 0.01941253924203216,\n \
\ \"acc_norm\": 0.6405228758169934,\n \"acc_norm_stderr\": 0.01941253924203216\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6636363636363637,\n\
\ \"acc_stderr\": 0.04525393596302506,\n \"acc_norm\": 0.6636363636363637,\n\
\ \"acc_norm_stderr\": 0.04525393596302506\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7224489795918367,\n \"acc_stderr\": 0.028666857790274648,\n\
\ \"acc_norm\": 0.7224489795918367,\n \"acc_norm_stderr\": 0.028666857790274648\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8407960199004975,\n\
\ \"acc_stderr\": 0.02587064676616913,\n \"acc_norm\": 0.8407960199004975,\n\
\ \"acc_norm_stderr\": 0.02587064676616913\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.85,\n \"acc_stderr\": 0.0358870281282637,\n \
\ \"acc_norm\": 0.85,\n \"acc_norm_stderr\": 0.0358870281282637\n },\n\
\ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5120481927710844,\n\
\ \"acc_stderr\": 0.03891364495835817,\n \"acc_norm\": 0.5120481927710844,\n\
\ \"acc_norm_stderr\": 0.03891364495835817\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8304093567251462,\n \"acc_stderr\": 0.02878210810540171,\n\
\ \"acc_norm\": 0.8304093567251462,\n \"acc_norm_stderr\": 0.02878210810540171\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.44920440636474906,\n\
\ \"mc1_stderr\": 0.017412941986115302,\n \"mc2\": 0.6135075599156891,\n\
\ \"mc2_stderr\": 0.01558423055084616\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8129439621152328,\n \"acc_stderr\": 0.010959716435242912\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.17968157695223655,\n \
\ \"acc_stderr\": 0.010575119964242236\n }\n}\n```"
repo_url: https://huggingface.co/Stopwolf/Cerberus-7B-slerp
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_02_05T09_08_12.484477
path:
- '**/details_harness|arc:challenge|25_2024-02-05T09-08-12.484477.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-02-05T09-08-12.484477.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_02_05T09_08_12.484477
path:
- '**/details_harness|gsm8k|5_2024-02-05T09-08-12.484477.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-02-05T09-08-12.484477.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_02_05T09_08_12.484477
path:
- '**/details_harness|hellaswag|10_2024-02-05T09-08-12.484477.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-02-05T09-08-12.484477.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_02_05T09_08_12.484477
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-05T09-08-12.484477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-05T09-08-12.484477.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-05T09-08-12.484477.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_02_05T09_08_12.484477
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-05T09-08-12.484477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-05T09-08-12.484477.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_02_05T09_08_12.484477
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-05T09-08-12.484477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-05T09-08-12.484477.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_02_05T09_08_12.484477
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-05T09-08-12.484477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-05T09-08-12.484477.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_02_05T09_08_12.484477
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-05T09-08-12.484477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-05T09-08-12.484477.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_02_05T09_08_12.484477
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-05T09-08-12.484477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-05T09-08-12.484477.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_02_05T09_08_12.484477
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-05T09-08-12.484477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-05T09-08-12.484477.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_02_05T09_08_12.484477
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-05T09-08-12.484477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-05T09-08-12.484477.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_02_05T09_08_12.484477
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-05T09-08-12.484477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-05T09-08-12.484477.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_02_05T09_08_12.484477
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-05T09-08-12.484477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-05T09-08-12.484477.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_02_05T09_08_12.484477
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-05T09-08-12.484477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-05T09-08-12.484477.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_02_05T09_08_12.484477
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-05T09-08-12.484477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-05T09-08-12.484477.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_02_05T09_08_12.484477
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-05T09-08-12.484477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-05T09-08-12.484477.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_02_05T09_08_12.484477
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-05T09-08-12.484477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-05T09-08-12.484477.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_02_05T09_08_12.484477
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-05T09-08-12.484477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-05T09-08-12.484477.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_02_05T09_08_12.484477
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-05T09-08-12.484477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-05T09-08-12.484477.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_02_05T09_08_12.484477
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-05T09-08-12.484477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-05T09-08-12.484477.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_02_05T09_08_12.484477
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-05T09-08-12.484477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-05T09-08-12.484477.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_02_05T09_08_12.484477
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-05T09-08-12.484477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-05T09-08-12.484477.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_02_05T09_08_12.484477
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-05T09-08-12.484477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-05T09-08-12.484477.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_02_05T09_08_12.484477
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-05T09-08-12.484477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-05T09-08-12.484477.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_02_05T09_08_12.484477
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-05T09-08-12.484477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-05T09-08-12.484477.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_02_05T09_08_12.484477
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-05T09-08-12.484477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-05T09-08-12.484477.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_02_05T09_08_12.484477
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-05T09-08-12.484477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-05T09-08-12.484477.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_02_05T09_08_12.484477
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-05T09-08-12.484477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-05T09-08-12.484477.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_02_05T09_08_12.484477
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-05T09-08-12.484477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-05T09-08-12.484477.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_02_05T09_08_12.484477
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-05T09-08-12.484477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-05T09-08-12.484477.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_02_05T09_08_12.484477
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-05T09-08-12.484477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-05T09-08-12.484477.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_02_05T09_08_12.484477
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-05T09-08-12.484477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-05T09-08-12.484477.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_02_05T09_08_12.484477
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-05T09-08-12.484477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-05T09-08-12.484477.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_02_05T09_08_12.484477
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-05T09-08-12.484477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-05T09-08-12.484477.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_02_05T09_08_12.484477
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-05T09-08-12.484477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-05T09-08-12.484477.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_02_05T09_08_12.484477
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-05T09-08-12.484477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-05T09-08-12.484477.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_02_05T09_08_12.484477
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-05T09-08-12.484477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-05T09-08-12.484477.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_02_05T09_08_12.484477
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-05T09-08-12.484477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-05T09-08-12.484477.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_02_05T09_08_12.484477
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-05T09-08-12.484477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-05T09-08-12.484477.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_02_05T09_08_12.484477
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-05T09-08-12.484477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-05T09-08-12.484477.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_02_05T09_08_12.484477
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-05T09-08-12.484477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-05T09-08-12.484477.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_02_05T09_08_12.484477
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-05T09-08-12.484477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-05T09-08-12.484477.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_02_05T09_08_12.484477
path:
- '**/details_harness|hendrycksTest-management|5_2024-02-05T09-08-12.484477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-02-05T09-08-12.484477.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_02_05T09_08_12.484477
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-05T09-08-12.484477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-05T09-08-12.484477.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_02_05T09_08_12.484477
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-05T09-08-12.484477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-05T09-08-12.484477.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_02_05T09_08_12.484477
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-05T09-08-12.484477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-05T09-08-12.484477.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_02_05T09_08_12.484477
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-05T09-08-12.484477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-05T09-08-12.484477.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_02_05T09_08_12.484477
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-05T09-08-12.484477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-05T09-08-12.484477.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_02_05T09_08_12.484477
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-05T09-08-12.484477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-05T09-08-12.484477.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_02_05T09_08_12.484477
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-05T09-08-12.484477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-05T09-08-12.484477.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_02_05T09_08_12.484477
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-05T09-08-12.484477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-05T09-08-12.484477.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_02_05T09_08_12.484477
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-05T09-08-12.484477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-05T09-08-12.484477.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_02_05T09_08_12.484477
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-05T09-08-12.484477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-05T09-08-12.484477.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_02_05T09_08_12.484477
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-05T09-08-12.484477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-05T09-08-12.484477.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_02_05T09_08_12.484477
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-05T09-08-12.484477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-05T09-08-12.484477.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_02_05T09_08_12.484477
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-05T09-08-12.484477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-05T09-08-12.484477.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_02_05T09_08_12.484477
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-05T09-08-12.484477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-05T09-08-12.484477.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_02_05T09_08_12.484477
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-05T09-08-12.484477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-05T09-08-12.484477.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_02_05T09_08_12.484477
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-05T09-08-12.484477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-05T09-08-12.484477.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_02_05T09_08_12.484477
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-05T09-08-12.484477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-05T09-08-12.484477.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_02_05T09_08_12.484477
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-05T09-08-12.484477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-05T09-08-12.484477.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_02_05T09_08_12.484477
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-05T09-08-12.484477.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-05T09-08-12.484477.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_02_05T09_08_12.484477
path:
- '**/details_harness|winogrande|5_2024-02-05T09-08-12.484477.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-02-05T09-08-12.484477.parquet'
- config_name: results
data_files:
- split: 2024_02_05T09_08_12.484477
path:
- results_2024-02-05T09-08-12.484477.parquet
- split: latest
path:
- results_2024-02-05T09-08-12.484477.parquet
---
# Dataset Card for Evaluation run of Stopwolf/Cerberus-7B-slerp
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [Stopwolf/Cerberus-7B-slerp](https://huggingface.co/Stopwolf/Cerberus-7B-slerp) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Stopwolf__Cerberus-7B-slerp",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-02-05T09:08:12.484477](https://huggingface.co/datasets/open-llm-leaderboard/details_Stopwolf__Cerberus-7B-slerp/blob/main/results_2024-02-05T09-08-12.484477.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.629678937070562,
"acc_stderr": 0.03233409511653798,
"acc_norm": 0.6376621349483272,
"acc_norm_stderr": 0.0330388306090536,
"mc1": 0.44920440636474906,
"mc1_stderr": 0.017412941986115302,
"mc2": 0.6135075599156891,
"mc2_stderr": 0.01558423055084616
},
"harness|arc:challenge|25": {
"acc": 0.6723549488054608,
"acc_stderr": 0.01371584794071934,
"acc_norm": 0.6953924914675768,
"acc_norm_stderr": 0.013449522109932487
},
"harness|hellaswag|10": {
"acc": 0.6920932085241984,
"acc_stderr": 0.004606843344517468,
"acc_norm": 0.8733320055765784,
"acc_norm_stderr": 0.0033192094001351165
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.28,
"acc_stderr": 0.04512608598542128,
"acc_norm": 0.28,
"acc_norm_stderr": 0.04512608598542128
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6,
"acc_stderr": 0.04232073695151589,
"acc_norm": 0.6,
"acc_norm_stderr": 0.04232073695151589
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6776315789473685,
"acc_stderr": 0.038035102483515854,
"acc_norm": 0.6776315789473685,
"acc_norm_stderr": 0.038035102483515854
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.58,
"acc_stderr": 0.049604496374885836,
"acc_norm": 0.58,
"acc_norm_stderr": 0.049604496374885836
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6981132075471698,
"acc_stderr": 0.02825420034443866,
"acc_norm": 0.6981132075471698,
"acc_norm_stderr": 0.02825420034443866
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7291666666666666,
"acc_stderr": 0.03716177437566017,
"acc_norm": 0.7291666666666666,
"acc_norm_stderr": 0.03716177437566017
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.44,
"acc_stderr": 0.04988876515698589,
"acc_norm": 0.44,
"acc_norm_stderr": 0.04988876515698589
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.54,
"acc_stderr": 0.05009082659620333,
"acc_norm": 0.54,
"acc_norm_stderr": 0.05009082659620333
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.35,
"acc_stderr": 0.047937248544110196,
"acc_norm": 0.35,
"acc_norm_stderr": 0.047937248544110196
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6416184971098265,
"acc_stderr": 0.036563436533531585,
"acc_norm": 0.6416184971098265,
"acc_norm_stderr": 0.036563436533531585
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.3627450980392157,
"acc_stderr": 0.04784060704105653,
"acc_norm": 0.3627450980392157,
"acc_norm_stderr": 0.04784060704105653
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.76,
"acc_stderr": 0.04292346959909283,
"acc_norm": 0.76,
"acc_norm_stderr": 0.04292346959909283
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5276595744680851,
"acc_stderr": 0.03263597118409769,
"acc_norm": 0.5276595744680851,
"acc_norm_stderr": 0.03263597118409769
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.47368421052631576,
"acc_stderr": 0.046970851366478626,
"acc_norm": 0.47368421052631576,
"acc_norm_stderr": 0.046970851366478626
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5379310344827586,
"acc_stderr": 0.04154659671707548,
"acc_norm": 0.5379310344827586,
"acc_norm_stderr": 0.04154659671707548
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.42063492063492064,
"acc_stderr": 0.025424835086924,
"acc_norm": 0.42063492063492064,
"acc_norm_stderr": 0.025424835086924
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.42063492063492064,
"acc_stderr": 0.04415438226743744,
"acc_norm": 0.42063492063492064,
"acc_norm_stderr": 0.04415438226743744
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.36,
"acc_stderr": 0.048241815132442176,
"acc_norm": 0.36,
"acc_norm_stderr": 0.048241815132442176
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7774193548387097,
"acc_stderr": 0.02366421667164251,
"acc_norm": 0.7774193548387097,
"acc_norm_stderr": 0.02366421667164251
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.4975369458128079,
"acc_stderr": 0.03517945038691063,
"acc_norm": 0.4975369458128079,
"acc_norm_stderr": 0.03517945038691063
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.68,
"acc_stderr": 0.04688261722621505,
"acc_norm": 0.68,
"acc_norm_stderr": 0.04688261722621505
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7818181818181819,
"acc_stderr": 0.03225078108306289,
"acc_norm": 0.7818181818181819,
"acc_norm_stderr": 0.03225078108306289
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.7828282828282829,
"acc_stderr": 0.029376616484945627,
"acc_norm": 0.7828282828282829,
"acc_norm_stderr": 0.029376616484945627
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.8860103626943006,
"acc_stderr": 0.02293514405391943,
"acc_norm": 0.8860103626943006,
"acc_norm_stderr": 0.02293514405391943
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.6230769230769231,
"acc_stderr": 0.024570975364225995,
"acc_norm": 0.6230769230769231,
"acc_norm_stderr": 0.024570975364225995
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.32222222222222224,
"acc_stderr": 0.028493465091028593,
"acc_norm": 0.32222222222222224,
"acc_norm_stderr": 0.028493465091028593
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.6176470588235294,
"acc_stderr": 0.031566630992154156,
"acc_norm": 0.6176470588235294,
"acc_norm_stderr": 0.031566630992154156
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.3443708609271523,
"acc_stderr": 0.038796870240733264,
"acc_norm": 0.3443708609271523,
"acc_norm_stderr": 0.038796870240733264
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8201834862385321,
"acc_stderr": 0.016465345467391534,
"acc_norm": 0.8201834862385321,
"acc_norm_stderr": 0.016465345467391534
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.49537037037037035,
"acc_stderr": 0.03409825519163572,
"acc_norm": 0.49537037037037035,
"acc_norm_stderr": 0.03409825519163572
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.8235294117647058,
"acc_stderr": 0.02675640153807897,
"acc_norm": 0.8235294117647058,
"acc_norm_stderr": 0.02675640153807897
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.7763713080168776,
"acc_stderr": 0.027123298205229962,
"acc_norm": 0.7763713080168776,
"acc_norm_stderr": 0.027123298205229962
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.6816143497757847,
"acc_stderr": 0.03126580522513713,
"acc_norm": 0.6816143497757847,
"acc_norm_stderr": 0.03126580522513713
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.7709923664122137,
"acc_stderr": 0.036853466317118506,
"acc_norm": 0.7709923664122137,
"acc_norm_stderr": 0.036853466317118506
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.7768595041322314,
"acc_stderr": 0.03800754475228732,
"acc_norm": 0.7768595041322314,
"acc_norm_stderr": 0.03800754475228732
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.7870370370370371,
"acc_stderr": 0.0395783547198098,
"acc_norm": 0.7870370370370371,
"acc_norm_stderr": 0.0395783547198098
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.7607361963190185,
"acc_stderr": 0.0335195387952127,
"acc_norm": 0.7607361963190185,
"acc_norm_stderr": 0.0335195387952127
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.5178571428571429,
"acc_stderr": 0.04742762361243011,
"acc_norm": 0.5178571428571429,
"acc_norm_stderr": 0.04742762361243011
},
"harness|hendrycksTest-management|5": {
"acc": 0.7669902912621359,
"acc_stderr": 0.04185832598928315,
"acc_norm": 0.7669902912621359,
"acc_norm_stderr": 0.04185832598928315
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8760683760683761,
"acc_stderr": 0.02158649400128136,
"acc_norm": 0.8760683760683761,
"acc_norm_stderr": 0.02158649400128136
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.73,
"acc_stderr": 0.044619604333847394,
"acc_norm": 0.73,
"acc_norm_stderr": 0.044619604333847394
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.8160919540229885,
"acc_stderr": 0.013853724170922531,
"acc_norm": 0.8160919540229885,
"acc_norm_stderr": 0.013853724170922531
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.708092485549133,
"acc_stderr": 0.024476994076247326,
"acc_norm": 0.708092485549133,
"acc_norm_stderr": 0.024476994076247326
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.4212290502793296,
"acc_stderr": 0.016513676031179595,
"acc_norm": 0.4212290502793296,
"acc_norm_stderr": 0.016513676031179595
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.7222222222222222,
"acc_stderr": 0.025646863097137894,
"acc_norm": 0.7222222222222222,
"acc_norm_stderr": 0.025646863097137894
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.7202572347266881,
"acc_stderr": 0.025494259350694912,
"acc_norm": 0.7202572347266881,
"acc_norm_stderr": 0.025494259350694912
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.7129629629629629,
"acc_stderr": 0.025171041915309684,
"acc_norm": 0.7129629629629629,
"acc_norm_stderr": 0.025171041915309684
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.4645390070921986,
"acc_stderr": 0.029752389657427047,
"acc_norm": 0.4645390070921986,
"acc_norm_stderr": 0.029752389657427047
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.4589308996088657,
"acc_stderr": 0.012727084826799804,
"acc_norm": 0.4589308996088657,
"acc_norm_stderr": 0.012727084826799804
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.6727941176470589,
"acc_stderr": 0.028501452860396556,
"acc_norm": 0.6727941176470589,
"acc_norm_stderr": 0.028501452860396556
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.6405228758169934,
"acc_stderr": 0.01941253924203216,
"acc_norm": 0.6405228758169934,
"acc_norm_stderr": 0.01941253924203216
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6636363636363637,
"acc_stderr": 0.04525393596302506,
"acc_norm": 0.6636363636363637,
"acc_norm_stderr": 0.04525393596302506
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.7224489795918367,
"acc_stderr": 0.028666857790274648,
"acc_norm": 0.7224489795918367,
"acc_norm_stderr": 0.028666857790274648
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.8407960199004975,
"acc_stderr": 0.02587064676616913,
"acc_norm": 0.8407960199004975,
"acc_norm_stderr": 0.02587064676616913
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.85,
"acc_stderr": 0.0358870281282637,
"acc_norm": 0.85,
"acc_norm_stderr": 0.0358870281282637
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5120481927710844,
"acc_stderr": 0.03891364495835817,
"acc_norm": 0.5120481927710844,
"acc_norm_stderr": 0.03891364495835817
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8304093567251462,
"acc_stderr": 0.02878210810540171,
"acc_norm": 0.8304093567251462,
"acc_norm_stderr": 0.02878210810540171
},
"harness|truthfulqa:mc|0": {
"mc1": 0.44920440636474906,
"mc1_stderr": 0.017412941986115302,
"mc2": 0.6135075599156891,
"mc2_stderr": 0.01558423055084616
},
"harness|winogrande|5": {
"acc": 0.8129439621152328,
"acc_stderr": 0.010959716435242912
},
"harness|gsm8k|5": {
"acc": 0.17968157695223655,
"acc_stderr": 0.010575119964242236
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] |
open-llm-leaderboard/details_starmpcc__Asclepius-Llama2-7B | ---
pretty_name: Evaluation run of starmpcc/Asclepius-Llama2-7B
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [starmpcc/Asclepius-Llama2-7B](https://huggingface.co/starmpcc/Asclepius-Llama2-7B)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_starmpcc__Asclepius-Llama2-7B_public\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-11-19T11:08:00.198126](https://huggingface.co/datasets/open-llm-leaderboard/details_starmpcc__Asclepius-Llama2-7B_public/blob/main/results_2023-11-19T11-08-00.198126.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.43616440221247377,\n\
\ \"acc_stderr\": 0.034450168116826926,\n \"acc_norm\": 0.4429356406607476,\n\
\ \"acc_norm_stderr\": 0.03535922633415619,\n \"mc1\": 0.2974296205630355,\n\
\ \"mc1_stderr\": 0.016002651487361,\n \"mc2\": 0.43308620079593113,\n\
\ \"mc2_stderr\": 0.015567429964446104,\n \"em\": 0.030411073825503357,\n\
\ \"em_stderr\": 0.0017585282619462322,\n \"f1\": 0.13804635067114085,\n\
\ \"f1_stderr\": 0.0023911010858403406\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.47525597269624575,\n \"acc_stderr\": 0.01459348769493774,\n\
\ \"acc_norm\": 0.5085324232081911,\n \"acc_norm_stderr\": 0.014609263165632182\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5856403106950807,\n\
\ \"acc_stderr\": 0.004916043838455664,\n \"acc_norm\": 0.7652857996415057,\n\
\ \"acc_norm_stderr\": 0.004229538929090431\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \
\ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.35555555555555557,\n\
\ \"acc_stderr\": 0.04135176749720386,\n \"acc_norm\": 0.35555555555555557,\n\
\ \"acc_norm_stderr\": 0.04135176749720386\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.32894736842105265,\n \"acc_stderr\": 0.038234289699266046,\n\
\ \"acc_norm\": 0.32894736842105265,\n \"acc_norm_stderr\": 0.038234289699266046\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.51,\n\
\ \"acc_stderr\": 0.05024183937956912,\n \"acc_norm\": 0.51,\n \
\ \"acc_norm_stderr\": 0.05024183937956912\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.4716981132075472,\n \"acc_stderr\": 0.030723535249006107,\n\
\ \"acc_norm\": 0.4716981132075472,\n \"acc_norm_stderr\": 0.030723535249006107\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.4305555555555556,\n\
\ \"acc_stderr\": 0.04140685639111502,\n \"acc_norm\": 0.4305555555555556,\n\
\ \"acc_norm_stderr\": 0.04140685639111502\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.35,\n \"acc_stderr\": 0.04793724854411018,\n \
\ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.04793724854411018\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.45,\n \"acc_stderr\": 0.05,\n \"acc_norm\": 0.45,\n \"\
acc_norm_stderr\": 0.05\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.38,\n \"acc_stderr\": 0.048783173121456316,\n \
\ \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.048783173121456316\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.2947976878612717,\n\
\ \"acc_stderr\": 0.03476599607516478,\n \"acc_norm\": 0.2947976878612717,\n\
\ \"acc_norm_stderr\": 0.03476599607516478\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.2549019607843137,\n \"acc_stderr\": 0.0433643270799318,\n\
\ \"acc_norm\": 0.2549019607843137,\n \"acc_norm_stderr\": 0.0433643270799318\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.52,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.52,\n\
\ \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.3702127659574468,\n \"acc_stderr\": 0.03156564682236786,\n\
\ \"acc_norm\": 0.3702127659574468,\n \"acc_norm_stderr\": 0.03156564682236786\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.2631578947368421,\n\
\ \"acc_stderr\": 0.04142439719489362,\n \"acc_norm\": 0.2631578947368421,\n\
\ \"acc_norm_stderr\": 0.04142439719489362\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.46206896551724136,\n \"acc_stderr\": 0.041546596717075474,\n\
\ \"acc_norm\": 0.46206896551724136,\n \"acc_norm_stderr\": 0.041546596717075474\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.30158730158730157,\n \"acc_stderr\": 0.0236369759961018,\n \"\
acc_norm\": 0.30158730158730157,\n \"acc_norm_stderr\": 0.0236369759961018\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.30952380952380953,\n\
\ \"acc_stderr\": 0.04134913018303317,\n \"acc_norm\": 0.30952380952380953,\n\
\ \"acc_norm_stderr\": 0.04134913018303317\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.28,\n \"acc_stderr\": 0.045126085985421276,\n \
\ \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.045126085985421276\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\
: 0.4645161290322581,\n \"acc_stderr\": 0.028372287797962956,\n \"\
acc_norm\": 0.4645161290322581,\n \"acc_norm_stderr\": 0.028372287797962956\n\
\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
: 0.3054187192118227,\n \"acc_stderr\": 0.03240661565868408,\n \"\
acc_norm\": 0.3054187192118227,\n \"acc_norm_stderr\": 0.03240661565868408\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.44,\n \"acc_stderr\": 0.04988876515698589,\n \"acc_norm\"\
: 0.44,\n \"acc_norm_stderr\": 0.04988876515698589\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.5878787878787879,\n \"acc_stderr\": 0.038435669935887165,\n\
\ \"acc_norm\": 0.5878787878787879,\n \"acc_norm_stderr\": 0.038435669935887165\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.45454545454545453,\n \"acc_stderr\": 0.03547601494006936,\n \"\
acc_norm\": 0.45454545454545453,\n \"acc_norm_stderr\": 0.03547601494006936\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.5751295336787565,\n \"acc_stderr\": 0.035674713352125395,\n\
\ \"acc_norm\": 0.5751295336787565,\n \"acc_norm_stderr\": 0.035674713352125395\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.4230769230769231,\n \"acc_stderr\": 0.025049197876042338,\n\
\ \"acc_norm\": 0.4230769230769231,\n \"acc_norm_stderr\": 0.025049197876042338\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.27037037037037037,\n \"acc_stderr\": 0.02708037281514567,\n \
\ \"acc_norm\": 0.27037037037037037,\n \"acc_norm_stderr\": 0.02708037281514567\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.3949579831932773,\n \"acc_stderr\": 0.031753678460966245,\n\
\ \"acc_norm\": 0.3949579831932773,\n \"acc_norm_stderr\": 0.031753678460966245\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.31125827814569534,\n \"acc_stderr\": 0.03780445850526732,\n \"\
acc_norm\": 0.31125827814569534,\n \"acc_norm_stderr\": 0.03780445850526732\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.563302752293578,\n \"acc_stderr\": 0.021264820158714205,\n \"\
acc_norm\": 0.563302752293578,\n \"acc_norm_stderr\": 0.021264820158714205\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.27314814814814814,\n \"acc_stderr\": 0.030388051301678116,\n \"\
acc_norm\": 0.27314814814814814,\n \"acc_norm_stderr\": 0.030388051301678116\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.5,\n \"acc_stderr\": 0.03509312031717982,\n \"acc_norm\": 0.5,\n\
\ \"acc_norm_stderr\": 0.03509312031717982\n },\n \"harness|hendrycksTest-high_school_world_history|5\"\
: {\n \"acc\": 0.5611814345991561,\n \"acc_stderr\": 0.032302649315470375,\n\
\ \"acc_norm\": 0.5611814345991561,\n \"acc_norm_stderr\": 0.032302649315470375\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.484304932735426,\n\
\ \"acc_stderr\": 0.0335412657542081,\n \"acc_norm\": 0.484304932735426,\n\
\ \"acc_norm_stderr\": 0.0335412657542081\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.4961832061068702,\n \"acc_stderr\": 0.043851623256015534,\n\
\ \"acc_norm\": 0.4961832061068702,\n \"acc_norm_stderr\": 0.043851623256015534\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.6363636363636364,\n \"acc_stderr\": 0.043913262867240704,\n \"\
acc_norm\": 0.6363636363636364,\n \"acc_norm_stderr\": 0.043913262867240704\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.4537037037037037,\n\
\ \"acc_stderr\": 0.04812917324536823,\n \"acc_norm\": 0.4537037037037037,\n\
\ \"acc_norm_stderr\": 0.04812917324536823\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.48466257668711654,\n \"acc_stderr\": 0.03926522378708843,\n\
\ \"acc_norm\": 0.48466257668711654,\n \"acc_norm_stderr\": 0.03926522378708843\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.38392857142857145,\n\
\ \"acc_stderr\": 0.04616143075028547,\n \"acc_norm\": 0.38392857142857145,\n\
\ \"acc_norm_stderr\": 0.04616143075028547\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.5048543689320388,\n \"acc_stderr\": 0.04950504382128921,\n\
\ \"acc_norm\": 0.5048543689320388,\n \"acc_norm_stderr\": 0.04950504382128921\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.6367521367521367,\n\
\ \"acc_stderr\": 0.03150712523091264,\n \"acc_norm\": 0.6367521367521367,\n\
\ \"acc_norm_stderr\": 0.03150712523091264\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.56,\n \"acc_stderr\": 0.04988876515698589,\n \
\ \"acc_norm\": 0.56,\n \"acc_norm_stderr\": 0.04988876515698589\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.5964240102171137,\n\
\ \"acc_stderr\": 0.017544332237926424,\n \"acc_norm\": 0.5964240102171137,\n\
\ \"acc_norm_stderr\": 0.017544332237926424\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.476878612716763,\n \"acc_stderr\": 0.026890297881303125,\n\
\ \"acc_norm\": 0.476878612716763,\n \"acc_norm_stderr\": 0.026890297881303125\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.28156424581005585,\n\
\ \"acc_stderr\": 0.015042290171866117,\n \"acc_norm\": 0.28156424581005585,\n\
\ \"acc_norm_stderr\": 0.015042290171866117\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.434640522875817,\n \"acc_stderr\": 0.028384256704883037,\n\
\ \"acc_norm\": 0.434640522875817,\n \"acc_norm_stderr\": 0.028384256704883037\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.5434083601286174,\n\
\ \"acc_stderr\": 0.028290869054197604,\n \"acc_norm\": 0.5434083601286174,\n\
\ \"acc_norm_stderr\": 0.028290869054197604\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.4228395061728395,\n \"acc_stderr\": 0.027487472980871598,\n\
\ \"acc_norm\": 0.4228395061728395,\n \"acc_norm_stderr\": 0.027487472980871598\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.3475177304964539,\n \"acc_stderr\": 0.028406627809590954,\n \
\ \"acc_norm\": 0.3475177304964539,\n \"acc_norm_stderr\": 0.028406627809590954\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.3428943937418514,\n\
\ \"acc_stderr\": 0.012123463271585892,\n \"acc_norm\": 0.3428943937418514,\n\
\ \"acc_norm_stderr\": 0.012123463271585892\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.4375,\n \"acc_stderr\": 0.030134614954403924,\n \
\ \"acc_norm\": 0.4375,\n \"acc_norm_stderr\": 0.030134614954403924\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.4166666666666667,\n \"acc_stderr\": 0.01994491413687358,\n \
\ \"acc_norm\": 0.4166666666666667,\n \"acc_norm_stderr\": 0.01994491413687358\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.4909090909090909,\n\
\ \"acc_stderr\": 0.04788339768702861,\n \"acc_norm\": 0.4909090909090909,\n\
\ \"acc_norm_stderr\": 0.04788339768702861\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.3795918367346939,\n \"acc_stderr\": 0.031067211262872478,\n\
\ \"acc_norm\": 0.3795918367346939,\n \"acc_norm_stderr\": 0.031067211262872478\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.6169154228855721,\n\
\ \"acc_stderr\": 0.034375193373382504,\n \"acc_norm\": 0.6169154228855721,\n\
\ \"acc_norm_stderr\": 0.034375193373382504\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.6,\n \"acc_stderr\": 0.049236596391733084,\n \
\ \"acc_norm\": 0.6,\n \"acc_norm_stderr\": 0.049236596391733084\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.41566265060240964,\n\
\ \"acc_stderr\": 0.038367221765980515,\n \"acc_norm\": 0.41566265060240964,\n\
\ \"acc_norm_stderr\": 0.038367221765980515\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.6374269005847953,\n \"acc_stderr\": 0.0368713061556206,\n\
\ \"acc_norm\": 0.6374269005847953,\n \"acc_norm_stderr\": 0.0368713061556206\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.2974296205630355,\n\
\ \"mc1_stderr\": 0.016002651487361,\n \"mc2\": 0.43308620079593113,\n\
\ \"mc2_stderr\": 0.015567429964446104\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.6827150749802684,\n \"acc_stderr\": 0.013080598411332118\n\
\ },\n \"harness|drop|3\": {\n \"em\": 0.030411073825503357,\n \
\ \"em_stderr\": 0.0017585282619462322,\n \"f1\": 0.13804635067114085,\n\
\ \"f1_stderr\": 0.0023911010858403406\n },\n \"harness|gsm8k|5\":\
\ {\n \"acc\": 0.003032600454890068,\n \"acc_stderr\": 0.0015145735612245386\n\
\ }\n}\n```"
repo_url: https://huggingface.co/starmpcc/Asclepius-Llama2-7B
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_11_19T11_08_00.198126
path:
- '**/details_harness|arc:challenge|25_2023-11-19T11-08-00.198126.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-11-19T11-08-00.198126.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_11_19T11_08_00.198126
path:
- '**/details_harness|drop|3_2023-11-19T11-08-00.198126.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-11-19T11-08-00.198126.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_11_19T11_08_00.198126
path:
- '**/details_harness|gsm8k|5_2023-11-19T11-08-00.198126.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-11-19T11-08-00.198126.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_11_19T11_08_00.198126
path:
- '**/details_harness|hellaswag|10_2023-11-19T11-08-00.198126.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-11-19T11-08-00.198126.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_11_19T11_08_00.198126
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-11-19T11-08-00.198126.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-11-19T11-08-00.198126.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-11-19T11-08-00.198126.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_11_19T11_08_00.198126
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-19T11-08-00.198126.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-19T11-08-00.198126.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_11_19T11_08_00.198126
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-11-19T11-08-00.198126.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-11-19T11-08-00.198126.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_11_19T11_08_00.198126
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-11-19T11-08-00.198126.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-11-19T11-08-00.198126.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_11_19T11_08_00.198126
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-11-19T11-08-00.198126.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-11-19T11-08-00.198126.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_11_19T11_08_00.198126
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-19T11-08-00.198126.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-19T11-08-00.198126.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_11_19T11_08_00.198126
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-11-19T11-08-00.198126.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-11-19T11-08-00.198126.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_11_19T11_08_00.198126
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-11-19T11-08-00.198126.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-11-19T11-08-00.198126.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_11_19T11_08_00.198126
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-11-19T11-08-00.198126.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-11-19T11-08-00.198126.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_11_19T11_08_00.198126
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-11-19T11-08-00.198126.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-11-19T11-08-00.198126.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_11_19T11_08_00.198126
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-11-19T11-08-00.198126.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-11-19T11-08-00.198126.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_11_19T11_08_00.198126
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-11-19T11-08-00.198126.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-11-19T11-08-00.198126.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_11_19T11_08_00.198126
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-11-19T11-08-00.198126.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-11-19T11-08-00.198126.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_11_19T11_08_00.198126
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-19T11-08-00.198126.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-19T11-08-00.198126.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_11_19T11_08_00.198126
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-11-19T11-08-00.198126.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-11-19T11-08-00.198126.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_11_19T11_08_00.198126
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-19T11-08-00.198126.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-19T11-08-00.198126.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_11_19T11_08_00.198126
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-19T11-08-00.198126.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-19T11-08-00.198126.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_11_19T11_08_00.198126
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-11-19T11-08-00.198126.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-11-19T11-08-00.198126.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_11_19T11_08_00.198126
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-11-19T11-08-00.198126.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-11-19T11-08-00.198126.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_11_19T11_08_00.198126
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-11-19T11-08-00.198126.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-11-19T11-08-00.198126.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_11_19T11_08_00.198126
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-11-19T11-08-00.198126.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-11-19T11-08-00.198126.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_11_19T11_08_00.198126
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-11-19T11-08-00.198126.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-11-19T11-08-00.198126.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_11_19T11_08_00.198126
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-11-19T11-08-00.198126.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-11-19T11-08-00.198126.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_11_19T11_08_00.198126
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-11-19T11-08-00.198126.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-11-19T11-08-00.198126.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_11_19T11_08_00.198126
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-11-19T11-08-00.198126.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-11-19T11-08-00.198126.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_11_19T11_08_00.198126
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-11-19T11-08-00.198126.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-11-19T11-08-00.198126.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_11_19T11_08_00.198126
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-11-19T11-08-00.198126.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-11-19T11-08-00.198126.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_11_19T11_08_00.198126
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-11-19T11-08-00.198126.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-11-19T11-08-00.198126.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_11_19T11_08_00.198126
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-11-19T11-08-00.198126.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-11-19T11-08-00.198126.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_11_19T11_08_00.198126
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-11-19T11-08-00.198126.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-11-19T11-08-00.198126.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_11_19T11_08_00.198126
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-11-19T11-08-00.198126.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-11-19T11-08-00.198126.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_11_19T11_08_00.198126
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-11-19T11-08-00.198126.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-11-19T11-08-00.198126.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_11_19T11_08_00.198126
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-11-19T11-08-00.198126.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-11-19T11-08-00.198126.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_11_19T11_08_00.198126
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-11-19T11-08-00.198126.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-11-19T11-08-00.198126.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_11_19T11_08_00.198126
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-11-19T11-08-00.198126.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-11-19T11-08-00.198126.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_11_19T11_08_00.198126
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-11-19T11-08-00.198126.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-11-19T11-08-00.198126.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_11_19T11_08_00.198126
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-11-19T11-08-00.198126.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-11-19T11-08-00.198126.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_11_19T11_08_00.198126
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-11-19T11-08-00.198126.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-11-19T11-08-00.198126.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_11_19T11_08_00.198126
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-11-19T11-08-00.198126.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-11-19T11-08-00.198126.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_11_19T11_08_00.198126
path:
- '**/details_harness|hendrycksTest-management|5_2023-11-19T11-08-00.198126.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-11-19T11-08-00.198126.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_11_19T11_08_00.198126
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-11-19T11-08-00.198126.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-11-19T11-08-00.198126.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_11_19T11_08_00.198126
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-11-19T11-08-00.198126.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-11-19T11-08-00.198126.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_11_19T11_08_00.198126
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-11-19T11-08-00.198126.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-11-19T11-08-00.198126.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_11_19T11_08_00.198126
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-11-19T11-08-00.198126.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-11-19T11-08-00.198126.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_11_19T11_08_00.198126
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-11-19T11-08-00.198126.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-11-19T11-08-00.198126.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_11_19T11_08_00.198126
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-11-19T11-08-00.198126.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-11-19T11-08-00.198126.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_11_19T11_08_00.198126
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-11-19T11-08-00.198126.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-11-19T11-08-00.198126.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_11_19T11_08_00.198126
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-11-19T11-08-00.198126.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-11-19T11-08-00.198126.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_11_19T11_08_00.198126
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-11-19T11-08-00.198126.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-11-19T11-08-00.198126.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_11_19T11_08_00.198126
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-11-19T11-08-00.198126.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-11-19T11-08-00.198126.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_11_19T11_08_00.198126
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-11-19T11-08-00.198126.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-11-19T11-08-00.198126.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_11_19T11_08_00.198126
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-11-19T11-08-00.198126.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-11-19T11-08-00.198126.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_11_19T11_08_00.198126
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-11-19T11-08-00.198126.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-11-19T11-08-00.198126.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_11_19T11_08_00.198126
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-11-19T11-08-00.198126.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-11-19T11-08-00.198126.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_11_19T11_08_00.198126
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-11-19T11-08-00.198126.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-11-19T11-08-00.198126.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_11_19T11_08_00.198126
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-19T11-08-00.198126.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-19T11-08-00.198126.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_11_19T11_08_00.198126
path:
- '**/details_harness|hendrycksTest-virology|5_2023-11-19T11-08-00.198126.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-11-19T11-08-00.198126.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_11_19T11_08_00.198126
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-11-19T11-08-00.198126.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-11-19T11-08-00.198126.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_11_19T11_08_00.198126
path:
- '**/details_harness|truthfulqa:mc|0_2023-11-19T11-08-00.198126.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-11-19T11-08-00.198126.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_11_19T11_08_00.198126
path:
- '**/details_harness|winogrande|5_2023-11-19T11-08-00.198126.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-11-19T11-08-00.198126.parquet'
- config_name: results
data_files:
- split: 2023_11_19T11_08_00.198126
path:
- results_2023-11-19T11-08-00.198126.parquet
- split: latest
path:
- results_2023-11-19T11-08-00.198126.parquet
---
# Dataset Card for Evaluation run of starmpcc/Asclepius-Llama2-7B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/starmpcc/Asclepius-Llama2-7B
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [starmpcc/Asclepius-Llama2-7B](https://huggingface.co/starmpcc/Asclepius-Llama2-7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_starmpcc__Asclepius-Llama2-7B_public",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-11-19T11:08:00.198126](https://huggingface.co/datasets/open-llm-leaderboard/details_starmpcc__Asclepius-Llama2-7B_public/blob/main/results_2023-11-19T11-08-00.198126.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.43616440221247377,
"acc_stderr": 0.034450168116826926,
"acc_norm": 0.4429356406607476,
"acc_norm_stderr": 0.03535922633415619,
"mc1": 0.2974296205630355,
"mc1_stderr": 0.016002651487361,
"mc2": 0.43308620079593113,
"mc2_stderr": 0.015567429964446104,
"em": 0.030411073825503357,
"em_stderr": 0.0017585282619462322,
"f1": 0.13804635067114085,
"f1_stderr": 0.0023911010858403406
},
"harness|arc:challenge|25": {
"acc": 0.47525597269624575,
"acc_stderr": 0.01459348769493774,
"acc_norm": 0.5085324232081911,
"acc_norm_stderr": 0.014609263165632182
},
"harness|hellaswag|10": {
"acc": 0.5856403106950807,
"acc_stderr": 0.004916043838455664,
"acc_norm": 0.7652857996415057,
"acc_norm_stderr": 0.004229538929090431
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.32,
"acc_stderr": 0.046882617226215034,
"acc_norm": 0.32,
"acc_norm_stderr": 0.046882617226215034
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.35555555555555557,
"acc_stderr": 0.04135176749720386,
"acc_norm": 0.35555555555555557,
"acc_norm_stderr": 0.04135176749720386
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.32894736842105265,
"acc_stderr": 0.038234289699266046,
"acc_norm": 0.32894736842105265,
"acc_norm_stderr": 0.038234289699266046
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.51,
"acc_stderr": 0.05024183937956912,
"acc_norm": 0.51,
"acc_norm_stderr": 0.05024183937956912
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.4716981132075472,
"acc_stderr": 0.030723535249006107,
"acc_norm": 0.4716981132075472,
"acc_norm_stderr": 0.030723535249006107
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.4305555555555556,
"acc_stderr": 0.04140685639111502,
"acc_norm": 0.4305555555555556,
"acc_norm_stderr": 0.04140685639111502
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.35,
"acc_stderr": 0.04793724854411018,
"acc_norm": 0.35,
"acc_norm_stderr": 0.04793724854411018
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.45,
"acc_stderr": 0.05,
"acc_norm": 0.45,
"acc_norm_stderr": 0.05
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.38,
"acc_stderr": 0.048783173121456316,
"acc_norm": 0.38,
"acc_norm_stderr": 0.048783173121456316
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.2947976878612717,
"acc_stderr": 0.03476599607516478,
"acc_norm": 0.2947976878612717,
"acc_norm_stderr": 0.03476599607516478
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.2549019607843137,
"acc_stderr": 0.0433643270799318,
"acc_norm": 0.2549019607843137,
"acc_norm_stderr": 0.0433643270799318
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.52,
"acc_stderr": 0.050211673156867795,
"acc_norm": 0.52,
"acc_norm_stderr": 0.050211673156867795
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.3702127659574468,
"acc_stderr": 0.03156564682236786,
"acc_norm": 0.3702127659574468,
"acc_norm_stderr": 0.03156564682236786
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.2631578947368421,
"acc_stderr": 0.04142439719489362,
"acc_norm": 0.2631578947368421,
"acc_norm_stderr": 0.04142439719489362
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.46206896551724136,
"acc_stderr": 0.041546596717075474,
"acc_norm": 0.46206896551724136,
"acc_norm_stderr": 0.041546596717075474
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.30158730158730157,
"acc_stderr": 0.0236369759961018,
"acc_norm": 0.30158730158730157,
"acc_norm_stderr": 0.0236369759961018
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.30952380952380953,
"acc_stderr": 0.04134913018303317,
"acc_norm": 0.30952380952380953,
"acc_norm_stderr": 0.04134913018303317
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.28,
"acc_stderr": 0.045126085985421276,
"acc_norm": 0.28,
"acc_norm_stderr": 0.045126085985421276
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.4645161290322581,
"acc_stderr": 0.028372287797962956,
"acc_norm": 0.4645161290322581,
"acc_norm_stderr": 0.028372287797962956
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.3054187192118227,
"acc_stderr": 0.03240661565868408,
"acc_norm": 0.3054187192118227,
"acc_norm_stderr": 0.03240661565868408
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.44,
"acc_stderr": 0.04988876515698589,
"acc_norm": 0.44,
"acc_norm_stderr": 0.04988876515698589
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.5878787878787879,
"acc_stderr": 0.038435669935887165,
"acc_norm": 0.5878787878787879,
"acc_norm_stderr": 0.038435669935887165
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.45454545454545453,
"acc_stderr": 0.03547601494006936,
"acc_norm": 0.45454545454545453,
"acc_norm_stderr": 0.03547601494006936
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.5751295336787565,
"acc_stderr": 0.035674713352125395,
"acc_norm": 0.5751295336787565,
"acc_norm_stderr": 0.035674713352125395
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.4230769230769231,
"acc_stderr": 0.025049197876042338,
"acc_norm": 0.4230769230769231,
"acc_norm_stderr": 0.025049197876042338
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.27037037037037037,
"acc_stderr": 0.02708037281514567,
"acc_norm": 0.27037037037037037,
"acc_norm_stderr": 0.02708037281514567
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.3949579831932773,
"acc_stderr": 0.031753678460966245,
"acc_norm": 0.3949579831932773,
"acc_norm_stderr": 0.031753678460966245
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.31125827814569534,
"acc_stderr": 0.03780445850526732,
"acc_norm": 0.31125827814569534,
"acc_norm_stderr": 0.03780445850526732
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.563302752293578,
"acc_stderr": 0.021264820158714205,
"acc_norm": 0.563302752293578,
"acc_norm_stderr": 0.021264820158714205
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.27314814814814814,
"acc_stderr": 0.030388051301678116,
"acc_norm": 0.27314814814814814,
"acc_norm_stderr": 0.030388051301678116
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.5,
"acc_stderr": 0.03509312031717982,
"acc_norm": 0.5,
"acc_norm_stderr": 0.03509312031717982
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.5611814345991561,
"acc_stderr": 0.032302649315470375,
"acc_norm": 0.5611814345991561,
"acc_norm_stderr": 0.032302649315470375
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.484304932735426,
"acc_stderr": 0.0335412657542081,
"acc_norm": 0.484304932735426,
"acc_norm_stderr": 0.0335412657542081
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.4961832061068702,
"acc_stderr": 0.043851623256015534,
"acc_norm": 0.4961832061068702,
"acc_norm_stderr": 0.043851623256015534
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.6363636363636364,
"acc_stderr": 0.043913262867240704,
"acc_norm": 0.6363636363636364,
"acc_norm_stderr": 0.043913262867240704
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.4537037037037037,
"acc_stderr": 0.04812917324536823,
"acc_norm": 0.4537037037037037,
"acc_norm_stderr": 0.04812917324536823
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.48466257668711654,
"acc_stderr": 0.03926522378708843,
"acc_norm": 0.48466257668711654,
"acc_norm_stderr": 0.03926522378708843
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.38392857142857145,
"acc_stderr": 0.04616143075028547,
"acc_norm": 0.38392857142857145,
"acc_norm_stderr": 0.04616143075028547
},
"harness|hendrycksTest-management|5": {
"acc": 0.5048543689320388,
"acc_stderr": 0.04950504382128921,
"acc_norm": 0.5048543689320388,
"acc_norm_stderr": 0.04950504382128921
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.6367521367521367,
"acc_stderr": 0.03150712523091264,
"acc_norm": 0.6367521367521367,
"acc_norm_stderr": 0.03150712523091264
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.56,
"acc_stderr": 0.04988876515698589,
"acc_norm": 0.56,
"acc_norm_stderr": 0.04988876515698589
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.5964240102171137,
"acc_stderr": 0.017544332237926424,
"acc_norm": 0.5964240102171137,
"acc_norm_stderr": 0.017544332237926424
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.476878612716763,
"acc_stderr": 0.026890297881303125,
"acc_norm": 0.476878612716763,
"acc_norm_stderr": 0.026890297881303125
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.28156424581005585,
"acc_stderr": 0.015042290171866117,
"acc_norm": 0.28156424581005585,
"acc_norm_stderr": 0.015042290171866117
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.434640522875817,
"acc_stderr": 0.028384256704883037,
"acc_norm": 0.434640522875817,
"acc_norm_stderr": 0.028384256704883037
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.5434083601286174,
"acc_stderr": 0.028290869054197604,
"acc_norm": 0.5434083601286174,
"acc_norm_stderr": 0.028290869054197604
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.4228395061728395,
"acc_stderr": 0.027487472980871598,
"acc_norm": 0.4228395061728395,
"acc_norm_stderr": 0.027487472980871598
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.3475177304964539,
"acc_stderr": 0.028406627809590954,
"acc_norm": 0.3475177304964539,
"acc_norm_stderr": 0.028406627809590954
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.3428943937418514,
"acc_stderr": 0.012123463271585892,
"acc_norm": 0.3428943937418514,
"acc_norm_stderr": 0.012123463271585892
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.4375,
"acc_stderr": 0.030134614954403924,
"acc_norm": 0.4375,
"acc_norm_stderr": 0.030134614954403924
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.4166666666666667,
"acc_stderr": 0.01994491413687358,
"acc_norm": 0.4166666666666667,
"acc_norm_stderr": 0.01994491413687358
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.4909090909090909,
"acc_stderr": 0.04788339768702861,
"acc_norm": 0.4909090909090909,
"acc_norm_stderr": 0.04788339768702861
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.3795918367346939,
"acc_stderr": 0.031067211262872478,
"acc_norm": 0.3795918367346939,
"acc_norm_stderr": 0.031067211262872478
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.6169154228855721,
"acc_stderr": 0.034375193373382504,
"acc_norm": 0.6169154228855721,
"acc_norm_stderr": 0.034375193373382504
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.6,
"acc_stderr": 0.049236596391733084,
"acc_norm": 0.6,
"acc_norm_stderr": 0.049236596391733084
},
"harness|hendrycksTest-virology|5": {
"acc": 0.41566265060240964,
"acc_stderr": 0.038367221765980515,
"acc_norm": 0.41566265060240964,
"acc_norm_stderr": 0.038367221765980515
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.6374269005847953,
"acc_stderr": 0.0368713061556206,
"acc_norm": 0.6374269005847953,
"acc_norm_stderr": 0.0368713061556206
},
"harness|truthfulqa:mc|0": {
"mc1": 0.2974296205630355,
"mc1_stderr": 0.016002651487361,
"mc2": 0.43308620079593113,
"mc2_stderr": 0.015567429964446104
},
"harness|winogrande|5": {
"acc": 0.6827150749802684,
"acc_stderr": 0.013080598411332118
},
"harness|drop|3": {
"em": 0.030411073825503357,
"em_stderr": 0.0017585282619462322,
"f1": 0.13804635067114085,
"f1_stderr": 0.0023911010858403406
},
"harness|gsm8k|5": {
"acc": 0.003032600454890068,
"acc_stderr": 0.0015145735612245386
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] |
jtatman/tinymistral-hypnosis-instruct-preprocessed | ---
dataset_info:
features:
- name: text
dtype: string
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
splits:
- name: train
num_bytes: 1031463407.6346276
num_examples: 2832454
- name: eval
num_bytes: 9103.973159269555
num_examples: 25
download_size: 307894933
dataset_size: 1031472511.6077869
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: eval
path: data/eval-*
license: apache-2.0
task_categories:
- question-answering
- text-generation
- conversational
language:
- en
tags:
- health
- medical
- therapy
- hypnosis
pretty_name: 'hypnosis instruct '
size_categories:
- 1M<n<10M
---
Dataset created for accelerated processing. Embeddings from this fine model:
- Locutusque/TinyMistral-248M-Instruct |
lurosenb/medqa | ---
license: mit
task_categories:
- question-answering
language:
- en
tags:
- medical
size_categories:
- 1K<n<10K
--- |
NoraAlt/Mawqif_Stance-Detection | ---
task_categories:
- text-classification
language:
- ar
pretty_name: 'Mawqif: Stance Detection'
size_categories:
- 1K<n<10K
tags:
- Stance Detection
- Sentiment Analysis
- Sarcasm Detection
---
# Mawqif: A Multi-label Arabic Dataset for Target-specific Stance Detection
- *Mawqif* is the first Arabic dataset that can be used for target-specific stance detection.
- This is a multi-label dataset where each data point is annotated for stance, sentiment, and sarcasm.
- We benchmark *Mawqif* dataset on the stance detection task and evaluate the performance of four BERT-based models. Our best model achieves a macro-F1 of 78.89\%.
# Mawqif Statistics
- This dataset consists of **4,121** tweets in multi-dialectal Arabic. Each tweet is annotated with a stance toward one of three targets: “COVID-19 vaccine,” “digital transformation,” and “women empowerment.” In addition, it is annotated with sentiment and sarcasm polarities.
- The following figure illustrates the labels’ distribution across all targets, and the distribution per target.
<img width="738" alt="dataStat-2" src="https://user-images.githubusercontent.com/31368075/188299057-54d04e87-802d-4b0e-b7c6-56bdc1078284.png">
# Interactive Visualization
To browse an interactive visualization of the *Mawqif* dataset, please click [here](https://public.tableau.com/views/MawqifDatasetDashboard/Dashboard1?:language=en-US&publish=yes&:display_count=n&:origin=viz_share_link)
- *You can click on visualization components to filter the data by target and by class. **For example,** you can click on “women empowerment" and "against" to get the information of tweets that express against women empowerment.*
# Citation
If you feel our paper and resources are useful, please consider citing our work!
```
@inproceedings{alturayeif-etal-2022-mawqif,
title = "Mawqif: A Multi-label {A}rabic Dataset for Target-specific Stance Detection",
author = "Alturayeif, Nora Saleh and
Luqman, Hamzah Abdullah and
Ahmed, Moataz Aly Kamaleldin",
booktitle = "Proceedings of the The Seventh Arabic Natural Language Processing Workshop (WANLP)",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.wanlp-1.16",
pages = "174--184"
}
``` |
DamarJati/indocorpus-sastra | ---
language:
- id
task_categories:
- text2text-generation
tags:
- corpus
- indonesia
- text
- parquet
pretty_name: Sastra Indonesia
size_categories:
- 10K<n<100K
---
# Indonesian Literature Corpus
## Description
This dataset contains a corpus in the Indonesian language taken from [Korpus Indonesia](https://korpusindonesia.kemdikbud.go.id/), provided by the Ministry of Education and Culture of the Republic of Indonesia. The corpus specifically focuses on literature texts, including various genres such as fiction, poetry, drama, and literary criticism.
## Contents
The dataset consists of texts in the Indonesian language that are categorized under the field of literature. These texts encompass a wide range of literary works, including novels, short stories, poems, plays, and critical essays.
## Usage
This dataset can be utilized for various research and development purposes related to Indonesian literature analysis, literary studies, authorship attribution, genre classification, sentiment analysis of literary texts, and other tasks within the domain of literature and humanities.
## Compatibility with Other Datasets
Please note that there might be some overlap between this dataset and the dataset available at [DamarJati/indocorpus-mix](https://github.com/DamarJati/indocorpus-mix). Some sentences or passages in this dataset may also appear in the aforementioned dataset.
## License
The dataset is retrieved from [Korpus Indonesia](https://korpusindonesia.kemdikbud.go.id/) provided by the Ministry of Education and Culture of the Republic of Indonesia. Please make sure to check and comply with the applicable licensing terms from the original source.
## References
For more information about Korpus Indonesia, please visit [https://korpusindonesia.kemdikbud.go.id/](https://korpusindonesia.kemdikbud.go.id/). |
Falah/invention_prompts | ---
dataset_info:
features:
- name: prompts
dtype: string
splits:
- name: train
num_bytes: 96461
num_examples: 1000
download_size: 2138
dataset_size: 96461
---
# Dataset Card for "invention_prompts"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
minh21/cpgQA-v1.0-unique-context-test-10-percent | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: title
dtype: string
- name: id
dtype: int64
- name: question
dtype: string
- name: answer_text
dtype: string
- name: answer_start
dtype: int64
- name: context
dtype: string
splits:
- name: train
num_bytes: 1292366
num_examples: 988
- name: test
num_bytes: 143063
num_examples: 109
download_size: 188281
dataset_size: 1435429
---
# Dataset Card for "cpgQA-v1.0-unique-context-test-10-percent"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
liuyanchen1015/MULTI_VALUE_qqp_your_yalls | ---
dataset_info:
features:
- name: question1
dtype: string
- name: question2
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: value_score
dtype: int64
splits:
- name: dev
num_bytes: 393794
num_examples: 2522
- name: test
num_bytes: 3429419
num_examples: 21436
- name: train
num_bytes: 3495533
num_examples: 22352
download_size: 4034122
dataset_size: 7318746
---
# Dataset Card for "MULTI_VALUE_qqp_your_yalls"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ibranze/araproje_arc_en_conf_mgpt_nearestscore_true_x | ---
dataset_info:
features:
- name: id
dtype: string
- name: question
dtype: string
- name: choices
sequence:
- name: text
dtype: string
- name: label
dtype: string
- name: answerKey
dtype: string
splits:
- name: validation
num_bytes: 80031.0
num_examples: 250
download_size: 46916
dataset_size: 80031.0
configs:
- config_name: default
data_files:
- split: validation
path: data/validation-*
---
# Dataset Card for "araproje_arc_en_conf_mgpt_nearestscore_true_x"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Tensoic/HINDI | ---
dataset_info:
features:
- name: input
dtype: string
- name: response
dtype: string
- name: instruction
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 2728502216
num_examples: 1034565
download_size: 821920341
dataset_size: 2728502216
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
adityarra07/swiss_data | ---
dataset_info:
features:
- name: id
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: transcription
dtype: string
splits:
- name: test
num_bytes: 70673964.0
num_examples: 556
download_size: 70468004
dataset_size: 70673964.0
---
# Dataset Card for "swiss_data"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
OrdalieTech/MIRACL-FR-Reranking-benchmark | ---
dataset_info:
features:
- name: query
dtype: string
- name: positive
sequence: string
- name: negative
sequence: string
splits:
- name: test
num_bytes: 1937467
num_examples: 343
download_size: 1177516
dataset_size: 1937467
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
---
|
CyberHarem/sucrose_genshin | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of sucrose/スクロース/砂糖 (Genshin Impact)
This is the dataset of sucrose/スクロース/砂糖 (Genshin Impact), containing 500 images and their tags.
The core tags of this character are `green_hair, glasses, animal_ears, semi-rimless_eyewear, multicolored_hair, hat, orange_eyes, breasts, long_hair, hair_between_eyes, cat_ears, ponytail, beret, streaked_hair, yellow_eyes`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:-----------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 500 | 969.13 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sucrose_genshin/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 1200 | 500 | 797.89 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sucrose_genshin/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1277 | 1.60 GiB | [Download](https://huggingface.co/datasets/CyberHarem/sucrose_genshin/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/sucrose_genshin',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 5 |  |  |  |  |  | 1girl, long_sleeves, looking_at_viewer, solo, vision_(genshin_impact), white_gloves, black_thighhighs, cape, garter_straps, smile, blue_dress, fur_collar, blush, gold_trim, holding, open_mouth, white_background |
| 1 | 5 |  |  |  |  |  | 1girl, blue_dress, garter_straps, long_sleeves, looking_at_viewer, simple_background, solo, vision_(genshin_impact), white_background, white_gloves, blush, black_thighhighs, fur_collar, gold_trim, adjusting_eyewear, open_mouth, white_headwear |
| 2 | 10 |  |  |  |  |  | 1girl, garter_straps, gold_trim, long_sleeves, looking_at_viewer, solo, vision_(genshin_impact), white_gloves, boots, white_footwear, black_thighhighs, blue_dress, simple_background, white_background, blush, cape, open_mouth, smile |
| 3 | 18 |  |  |  |  |  | 1girl, looking_at_viewer, solo, vision_(genshin_impact), long_sleeves, blush, cape, gold_trim, white_gloves, simple_background, white_background, blue_dress, open_mouth, smile, adjusting_eyewear |
| 4 | 6 |  |  |  |  |  | 1girl, blush, fur_collar, open_mouth, simple_background, solo, upper_body, vision_(genshin_impact), white_gloves, :o, holding_test_tube, long_sleeves, looking_at_viewer, gold_trim, white_headwear |
| 5 | 9 |  |  |  |  |  | 1boy, 1girl, blush, hetero, penis, sex, solo_focus, spread_legs, vaginal, nipples, open_mouth, medium_breasts, navel, thighhighs, cum_in_pussy, nude, uncensored, gloves, missionary, on_back, sweat |
| 6 | 7 |  |  |  |  |  | 1girl, blush, looking_at_viewer, solo, black_thighhighs, medium_breasts, navel, open_mouth, black_bra, black_panties, underwear_only, bare_shoulders, cleavage, short_hair, simple_background, sitting, stomach, sweat |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | long_sleeves | looking_at_viewer | solo | vision_(genshin_impact) | white_gloves | black_thighhighs | cape | garter_straps | smile | blue_dress | fur_collar | blush | gold_trim | holding | open_mouth | white_background | simple_background | adjusting_eyewear | white_headwear | boots | white_footwear | upper_body | :o | holding_test_tube | 1boy | hetero | penis | sex | solo_focus | spread_legs | vaginal | nipples | medium_breasts | navel | thighhighs | cum_in_pussy | nude | uncensored | gloves | missionary | on_back | sweat | black_bra | black_panties | underwear_only | bare_shoulders | cleavage | short_hair | sitting | stomach |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------------|:--------------------|:-------|:--------------------------|:---------------|:-------------------|:-------|:----------------|:--------|:-------------|:-------------|:--------|:------------|:----------|:-------------|:-------------------|:--------------------|:--------------------|:-----------------|:--------|:-----------------|:-------------|:-----|:--------------------|:-------|:---------|:--------|:------|:-------------|:--------------|:----------|:----------|:-----------------|:--------|:-------------|:---------------|:-------|:-------------|:---------|:-------------|:----------|:--------|:------------|:----------------|:-----------------|:-----------------|:-----------|:-------------|:----------|:----------|
| 0 | 5 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 5 |  |  |  |  |  | X | X | X | X | X | X | X | | X | | X | X | X | X | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 10 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | | X | X | | X | X | X | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 18 |  |  |  |  |  | X | X | X | X | X | X | | X | | X | X | | X | X | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 6 |  |  |  |  |  | X | X | X | X | X | X | | | | | | X | X | X | | X | | X | | X | | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 9 |  |  |  |  |  | X | | | | | | | | | | | | X | | | X | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | |
| 6 | 7 |  |  |  |  |  | X | | X | X | | | X | | | | | | X | | | X | | X | | | | | | | | | | | | | | | | X | X | | | | | | | | X | X | X | X | X | X | X | X | X |
|
Mike36Theone/GAIOFAFINAL | ---
license: openrail
---
|
lixugang/cm_fine | ---
license: apache-2.0
---
|
Rayyukii/crisvoz | ---
license: openrail
---
|
liuyanchen1015/MULTI_VALUE_wnli_comparative_as_to | ---
dataset_info:
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: value_score
dtype: int64
splits:
- name: test
num_bytes: 1568
num_examples: 6
- name: train
num_bytes: 586
num_examples: 4
download_size: 6636
dataset_size: 2154
---
# Dataset Card for "MULTI_VALUE_wnli_comparative_as_to"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
tsdocode/common_voice_13_0_vi_pseudo_labelled | ---
dataset_info:
config_name: vi
features:
- name: client_id
dtype: string
- name: path
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: sentence
dtype: string
- name: up_votes
dtype: int64
- name: down_votes
dtype: int64
- name: age
dtype: string
- name: gender
dtype: string
- name: accent
dtype: string
- name: locale
dtype: string
- name: segment
dtype: string
- name: variant
dtype: string
- name: whisper_transcript
sequence: int64
splits:
- name: train
num_bytes: 82967897.192
num_examples: 2462
- name: validation
num_bytes: 8286788.0
num_examples: 392
- name: test
num_bytes: 33940654.55
num_examples: 1225
download_size: 112186625
dataset_size: 125195339.742
configs:
- config_name: vi
data_files:
- split: train
path: vi/train-*
- split: validation
path: vi/validation-*
- split: test
path: vi/test-*
---
|
acul3/Oscar_Indo_May_2022 | ---
pretty_name: OSCAR_Indo_May_2022
annotations_creators:
- no-annotation
language_creators:
- found
languages:
- id
licenses:
- cc0-1.0
source_datasets:
- original
task_categories:
- sequence-modeling
task_ids:
- language-modeling
---
## Dataset Summary
This dataset obtained by language classification and filtering of the [Common Crawl](https://commoncrawl.org/) (May-2022) corpus using the [ungoliant](https://github.com/oscar-corpus/ungoliant) architecture. Data is distributed by taking Indonesian language only.
### Loader
TBD
### Licensing Information
These data are released under this licensing scheme
We do not own any of the text from which these data has been extracted.
We license the actual packaging of these data under the Creative Commons CC0 license ("no rights reserved") http://creativecommons.org/publicdomain/zero/1.0/
To the extent possible under law, Inria has waived all copyright and related or neighboring rights to OSCAR
This work is published from: France.
Should you consider that our data contains material that is owned by you and should therefore not be reproduced here, please:
* Clearly identify yourself, with detailed contact data such as an address, telephone number or email address at which you can be contacted.
* Clearly identify the copyrighted work claimed to be infringed.
* Clearly identify the material that is claimed to be infringing and information reasonably sufficient to allow us to locate the material.
We will comply to legitimate requests by removing the affected sources from the next release of the corpus.
### Citation Information
```
@ARTICLE{2022arXiv220106642A,
author = {{Abadji}, Julien and {Ortiz Suarez}, Pedro and {Romary}, Laurent and {Sagot}, Beno{\^\i}t},
title = "{Towards a Cleaner Document-Oriented Multilingual Crawled Corpus}",
journal = {arXiv e-prints},
keywords = {Computer Science - Computation and Language},
year = 2022,
month = jan,
eid = {arXiv:2201.06642},
pages = {arXiv:2201.06642},
archivePrefix = {arXiv},
eprint = {2201.06642},
primaryClass = {cs.CL},
adsurl = {https://ui.adsabs.harvard.edu/abs/2022arXiv220106642A},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
@inproceedings{AbadjiOrtizSuarezRomaryetal.2021,
author = {Julien Abadji and Pedro Javier Ortiz Su{\'a}rez and Laurent Romary and Beno{\^i}t Sagot},
title = {Ungoliant: An optimized pipeline for the generation of a very large-scale multilingual web corpus},
series = {Proceedings of the Workshop on Challenges in the Management of Large Corpora (CMLC-9) 2021. Limerick, 12 July 2021 (Online-Event)},
editor = {Harald L{\"u}ngen and Marc Kupietz and Piotr Bański and Adrien Barbaresi and Simon Clematide and Ines Pisetta},
publisher = {Leibniz-Institut f{\"u}r Deutsche Sprache},
address = {Mannheim},
doi = {10.14618/ids-pub-10468},
url = {https://nbn-resolving.org/urn:nbn:de:bsz:mh39-104688},
pages = {1 -- 9},
year = {2021},
abstract = {Since the introduction of large language models in Natural Language Processing, large raw corpora have played a crucial role in Computational Linguistics. However, most of these large raw corpora are either available only for English or not available to the general public due to copyright issues. Nevertheless, there are some examples of freely available multilingual corpora for training Deep Learning NLP models, such as the OSCAR and Paracrawl corpora. However, they have quality issues, especially for low-resource languages. Moreover, recreating or updating these corpora is very complex. In this work, we try to reproduce and improve the goclassy pipeline used to create the OSCAR corpus. We propose a new pipeline that is faster, modular, parameterizable, and well documented. We use it to create a corpus similar to OSCAR but larger and based on recent data. Also, unlike OSCAR, the metadata information is at the document level. We release our pipeline under an open source license and publish the corpus under a research-only license.},
language = {en}
}
@ARTICLE{caswell-etal-2021-quality,
author = {{Caswell}, Isaac and {Kreutzer}, Julia and {Wang}, Lisa and {Wahab}, Ahsan and {van Esch}, Daan and {Ulzii-Orshikh}, Nasanbayar and {Tapo}, Allahsera and {Subramani}, Nishant and {Sokolov}, Artem and {Sikasote}, Claytone and {Setyawan}, Monang and {Sarin}, Supheakmungkol and {Samb}, Sokhar and {Sagot}, Beno{\^\i}t and {Rivera}, Clara and {Rios}, Annette and {Papadimitriou}, Isabel and {Osei}, Salomey and {Ortiz Su{\'a}rez}, Pedro Javier and {Orife}, Iroro and {Ogueji}, Kelechi and {Niyongabo}, Rubungo Andre and {Nguyen}, Toan Q. and {M{\"u}ller}, Mathias and {M{\"u}ller}, Andr{\'e} and {Hassan Muhammad}, Shamsuddeen and {Muhammad}, Nanda and {Mnyakeni}, Ayanda and {Mirzakhalov}, Jamshidbek and {Matangira}, Tapiwanashe and {Leong}, Colin and {Lawson}, Nze and {Kudugunta}, Sneha and {Jernite}, Yacine and {Jenny}, Mathias and {Firat}, Orhan and {Dossou}, Bonaventure F.~P. and {Dlamini}, Sakhile and {de Silva}, Nisansa and {{\c{C}}abuk Ball{\i}}, Sakine and {Biderman}, Stella and {Battisti}, Alessia and {Baruwa}, Ahmed and {Bapna}, Ankur and {Baljekar}, Pallavi and {Abebe Azime}, Israel and {Awokoya}, Ayodele and {Ataman}, Duygu and {Ahia}, Orevaoghene and {Ahia}, Oghenefego and {Agrawal}, Sweta and {Adeyemi}, Mofetoluwa},
title = "{Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets}",
journal = {arXiv e-prints},
keywords = {Computer Science - Computation and Language, Computer Science - Artificial Intelligence},
year = 2021,
month = mar,
eid = {arXiv:2103.12028},
pages = {arXiv:2103.12028},
archivePrefix = {arXiv},
eprint = {2103.12028},
primaryClass = {cs.CL},
adsurl = {https://ui.adsabs.harvard.edu/abs/2021arXiv210312028C},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
@inproceedings{ortiz-suarez-etal-2020-monolingual,
title = "A Monolingual Approach to Contextualized Word Embeddings for Mid-Resource Languages",
author = "Ortiz Su{'a}rez, Pedro Javier and
Romary, Laurent and
Sagot, Benoit",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.acl-main.156",
pages = "1703--1714",
abstract = "We use the multilingual OSCAR corpus, extracted from Common Crawl via language classification, filtering and cleaning, to train monolingual contextualized word embeddings (ELMo) for five mid-resource languages. We then compare the performance of OSCAR-based and Wikipedia-based ELMo embeddings for these languages on the part-of-speech tagging and parsing tasks. We show that, despite the noise in the Common-Crawl-based OSCAR data, embeddings trained on OSCAR perform much better than monolingual embeddings trained on Wikipedia. They actually equal or improve the current state of the art in tagging and parsing for all five languages. In particular, they also improve over multilingual Wikipedia-based contextual embeddings (multilingual BERT), which almost always constitutes the previous state of the art, thereby showing that the benefit of a larger, more diverse corpus surpasses the cross-lingual benefit of multilingual embedding architectures.",
}
@inproceedings{OrtizSuarezSagotRomary2019,
author = {Pedro Javier {Ortiz Su{'a}rez} and Benoit Sagot and Laurent Romary},
title = {Asynchronous pipelines for processing huge corpora on medium to low resource infrastructures},
series = {Proceedings of the Workshop on Challenges in the Management of Large Corpora (CMLC-7) 2019. Cardiff, 22nd July 2019},
editor = {Piotr Bański and Adrien Barbaresi and Hanno Biber and Evelyn Breiteneder and Simon Clematide and Marc Kupietz and Harald L{"u}ngen and Caroline Iliadi},
publisher = {Leibniz-Institut f{"u}r Deutsche Sprache},
address = {Mannheim},
doi = {10.14618/ids-pub-9021},
url = {http://nbn-resolving.de/urn:nbn:de:bsz:mh39-90215},
pages = {9 -- 16},
year = {2019},
abstract = {Common Crawl is a considerably large, heterogeneous multilingual corpus comprised of crawled documents from the internet, surpassing 20TB of data and distributed as a set of more than 50 thousand plain text files where each contains many documents written in a wide variety of languages. Even though each document has a metadata block associated to it, this data lacks any information about the language in which each document is written, making it extremely difficult to use Common Crawl for monolingual applications. We propose a general, highly parallel, multithreaded pipeline to clean and classify Common Crawl by language; we specifically design it so that it runs efficiently on medium to low resource infrastructures where I/O speeds are the main constraint. We develop the pipeline so that it can be easily reapplied to any kind of heterogeneous corpus and so that it can be parameterised to a wide range of infrastructures. We also distribute a 6.3TB version of Common Crawl, filtered, classified by language, shuffled at line level in order to avoid copyright issues, and ready to be used for NLP applications.},
language = {en}
}
```
|
TokenBender/hindi_convo_high_quality | ---
license: apache-2.0
---
|
hackathon-somos-nlp-2023/ask2democracy-cfqa-salud-pension | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
- name: topics
sequence: string
splits:
- name: train
num_bytes: 7711587
num_examples: 3805
download_size: 880079
dataset_size: 7711587
---
## About Ask2Democracy-cfqa-salud-pension
Ask2Democracy-cfqa-salud-pension is an instructional, context-based generative dataset created using the text reforms of Colombian health and pension systems in Spanish(March 23).
The text was pre-processed and augmented using the chat-gpt-turbo API.
<div align="right">
Creado por Jorge Henao 🇨🇴 <a href="https://twitter.com/jhenaotw" target='_blank'>Twitter</a> <a href="https://www.linkedin.com/in/henaojorge" target='_blank'>LinkedIn</a> <a href="https://linktr.ee/jorgehenao" target='_blank'>Linktree</a>
<br>
Con el apoyo de David Torres 🇨🇴 <a href="https://twitter.com/davinci137" target='_blank'>Twitter</a> <a href="https://github.com/datorresb" target='_blank'>LinkedIn</a>
</div>
Different prompt engineering experiments were conducted to obtain high-quality results in Spanish language,
while balancing simplicity of language and source-based generation.
Two styles of questions were created:
* Conceptual questions about the text's definitions and concepts related to health and pension reforms.
* First-person questions that address concerns of regular people from diverse population groups.
Conceptual questions and answers are useful for understanding complex concepts commonly found in public documents. These types of questions generally ask for easy-to-understand explanations of a particular concept or subject, such as "What is the definition of concept X or Y?"
On the other hand, first-person questions are designed to address specific concerns of various population groups, including young adults, middle-aged adults, and older adults, as well as those from different gender and labor backgrounds. These questions are tailored to address the specific needs and interests of these groups with regards to the text's topics.
These kind of questions looks like this "How x concept can affect me as an y person?, like older adult, labour woman, young adult, etc
## Bias
The bias of the text generated by the Chat-GPT-Turbo model has not been evaluated, nor has the accuracy of OpenAI's knowledge about Colombia been verified.
Nevertheless, to reduce the risk of model hallucinations and biases, the text was pre-processed, and specific source based contexts were provided on every API call.
In addition, source based text was explicity insctucted during the prompt engineering exprimentation for each topic: Health and Pension reforms.
## Dataset Structure
### Data Instances
#### plain_text
An example of conceptual questions looks like this.
```
{
"instruction": "Given the context please answer the question. Context:'ARTÍCULO 73. SISTEMA NACIONAL DE PROTECCIÓN SOCIAL...",
"input": "¿Qué es el Sistema Nacional de Protección Social Integral para la Vejez?",
"output": "El Sistema Nacional de Protección Social Integral para la Vejez es una iniciativa del gobierno colombiano para garantizar la protección social de las personas mayores. Incluye el Consejo Nacional de Protección a la Vejez y el Comité Técnico, que trabajan para desarrollar políticas y programas que mejoren la calidad de vida de las personas mayores.",
"topics": ["protección social", "vejez", "políticas públicas"]
},
```
An example of first person questions looks like this.
```
{
"instruction": "Given the context please answer the question. Context:'Artículo 1 Reforma Pensional...'
"input": "¿Cómo me beneficia el Sistema Público Único Integrado de Información en Salud (SPUIIS) como adulto de mediana edad?",
"output": "El SPUIIS permite el procesamiento digital de imágenes y otras tecnologías de última generación, lo que facilita el diagnóstico y tratamiento de enfermedades, mejorando la calidad de vida de las personas.",
"topics": ["Diagnóstico y tratamiento", "Tecnología de última generación", "Calidad de vida"]
}
```
### Data Fields
The data fields are the same among all splits.
#### plain_text
- `instruction`: a `string` feature.
- `input`: a `string` feature.
- `output`: a `string` feature.
|
open-llm-leaderboard/details_abhinand__telugu-llama-7b-instruct-v0.1 | ---
pretty_name: Evaluation run of abhinand/telugu-llama-7b-instruct-v0.1
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [abhinand/telugu-llama-7b-instruct-v0.1](https://huggingface.co/abhinand/telugu-llama-7b-instruct-v0.1)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 63 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_abhinand__telugu-llama-7b-instruct-v0.1\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-01-23T18:07:17.451860](https://huggingface.co/datasets/open-llm-leaderboard/details_abhinand__telugu-llama-7b-instruct-v0.1/blob/main/results_2024-01-23T18-07-17.451860.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.24045369051307708,\n\
\ \"acc_stderr\": 0.029966214967594704,\n \"acc_norm\": 0.24113587081419893,\n\
\ \"acc_norm_stderr\": 0.030748707255235257,\n \"mc1\": 0.29253365973072215,\n\
\ \"mc1_stderr\": 0.015925597445286165,\n \"mc2\": 0.49051835602774946,\n\
\ \"mc2_stderr\": 0.015311522184683459\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.3438566552901024,\n \"acc_stderr\": 0.013880644570156211,\n\
\ \"acc_norm\": 0.371160409556314,\n \"acc_norm_stderr\": 0.014117971901142813\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5331607249551882,\n\
\ \"acc_stderr\": 0.004978795454216732,\n \"acc_norm\": 0.6792471619199363,\n\
\ \"acc_norm_stderr\": 0.00465812015223082\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.22,\n \"acc_stderr\": 0.04163331998932268,\n \
\ \"acc_norm\": 0.22,\n \"acc_norm_stderr\": 0.04163331998932268\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.18518518518518517,\n\
\ \"acc_stderr\": 0.03355677216313142,\n \"acc_norm\": 0.18518518518518517,\n\
\ \"acc_norm_stderr\": 0.03355677216313142\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.17763157894736842,\n \"acc_stderr\": 0.031103182383123398,\n\
\ \"acc_norm\": 0.17763157894736842,\n \"acc_norm_stderr\": 0.031103182383123398\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.3,\n\
\ \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.3,\n \
\ \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.21509433962264152,\n \"acc_stderr\": 0.02528839450289137,\n\
\ \"acc_norm\": 0.21509433962264152,\n \"acc_norm_stderr\": 0.02528839450289137\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.2569444444444444,\n\
\ \"acc_stderr\": 0.03653946969442099,\n \"acc_norm\": 0.2569444444444444,\n\
\ \"acc_norm_stderr\": 0.03653946969442099\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.2,\n \"acc_stderr\": 0.04020151261036845,\n \
\ \"acc_norm\": 0.2,\n \"acc_norm_stderr\": 0.04020151261036845\n },\n\
\ \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.26,\n\
\ \"acc_stderr\": 0.0440844002276808,\n \"acc_norm\": 0.26,\n \
\ \"acc_norm_stderr\": 0.0440844002276808\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.21,\n \"acc_stderr\": 0.040936018074033256,\n \
\ \"acc_norm\": 0.21,\n \"acc_norm_stderr\": 0.040936018074033256\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.20809248554913296,\n\
\ \"acc_stderr\": 0.030952890217749874,\n \"acc_norm\": 0.20809248554913296,\n\
\ \"acc_norm_stderr\": 0.030952890217749874\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.21568627450980393,\n \"acc_stderr\": 0.04092563958237654,\n\
\ \"acc_norm\": 0.21568627450980393,\n \"acc_norm_stderr\": 0.04092563958237654\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.28,\n \"acc_stderr\": 0.045126085985421276,\n \"acc_norm\": 0.28,\n\
\ \"acc_norm_stderr\": 0.045126085985421276\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.26382978723404255,\n \"acc_stderr\": 0.028809989854102973,\n\
\ \"acc_norm\": 0.26382978723404255,\n \"acc_norm_stderr\": 0.028809989854102973\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.23684210526315788,\n\
\ \"acc_stderr\": 0.039994238792813365,\n \"acc_norm\": 0.23684210526315788,\n\
\ \"acc_norm_stderr\": 0.039994238792813365\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.2413793103448276,\n \"acc_stderr\": 0.03565998174135302,\n\
\ \"acc_norm\": 0.2413793103448276,\n \"acc_norm_stderr\": 0.03565998174135302\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.20899470899470898,\n \"acc_stderr\": 0.02094048156533486,\n \"\
acc_norm\": 0.20899470899470898,\n \"acc_norm_stderr\": 0.02094048156533486\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.2857142857142857,\n\
\ \"acc_stderr\": 0.04040610178208841,\n \"acc_norm\": 0.2857142857142857,\n\
\ \"acc_norm_stderr\": 0.04040610178208841\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.18,\n \"acc_stderr\": 0.038612291966536934,\n \
\ \"acc_norm\": 0.18,\n \"acc_norm_stderr\": 0.038612291966536934\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\
: 0.1774193548387097,\n \"acc_stderr\": 0.02173254068932927,\n \"\
acc_norm\": 0.1774193548387097,\n \"acc_norm_stderr\": 0.02173254068932927\n\
\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
: 0.15270935960591134,\n \"acc_stderr\": 0.02530890453938063,\n \"\
acc_norm\": 0.15270935960591134,\n \"acc_norm_stderr\": 0.02530890453938063\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.25,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\"\
: 0.25,\n \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.21818181818181817,\n \"acc_stderr\": 0.03225078108306289,\n\
\ \"acc_norm\": 0.21818181818181817,\n \"acc_norm_stderr\": 0.03225078108306289\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.17676767676767677,\n \"acc_stderr\": 0.027178752639044915,\n \"\
acc_norm\": 0.17676767676767677,\n \"acc_norm_stderr\": 0.027178752639044915\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.19689119170984457,\n \"acc_stderr\": 0.028697873971860664,\n\
\ \"acc_norm\": 0.19689119170984457,\n \"acc_norm_stderr\": 0.028697873971860664\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.20256410256410257,\n \"acc_stderr\": 0.020377660970371372,\n\
\ \"acc_norm\": 0.20256410256410257,\n \"acc_norm_stderr\": 0.020377660970371372\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.2111111111111111,\n \"acc_stderr\": 0.024882116857655075,\n \
\ \"acc_norm\": 0.2111111111111111,\n \"acc_norm_stderr\": 0.024882116857655075\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.21008403361344538,\n \"acc_stderr\": 0.026461398717471874,\n\
\ \"acc_norm\": 0.21008403361344538,\n \"acc_norm_stderr\": 0.026461398717471874\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.1986754966887417,\n \"acc_stderr\": 0.03257847384436776,\n \"\
acc_norm\": 0.1986754966887417,\n \"acc_norm_stderr\": 0.03257847384436776\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.1926605504587156,\n \"acc_stderr\": 0.016909276884936094,\n \"\
acc_norm\": 0.1926605504587156,\n \"acc_norm_stderr\": 0.016909276884936094\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.1527777777777778,\n \"acc_stderr\": 0.024536326026134224,\n \"\
acc_norm\": 0.1527777777777778,\n \"acc_norm_stderr\": 0.024536326026134224\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.25,\n \"acc_stderr\": 0.03039153369274154,\n \"acc_norm\": 0.25,\n\
\ \"acc_norm_stderr\": 0.03039153369274154\n },\n \"harness|hendrycksTest-high_school_world_history|5\"\
: {\n \"acc\": 0.270042194092827,\n \"acc_stderr\": 0.028900721906293426,\n\
\ \"acc_norm\": 0.270042194092827,\n \"acc_norm_stderr\": 0.028900721906293426\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.31390134529147984,\n\
\ \"acc_stderr\": 0.031146796482972465,\n \"acc_norm\": 0.31390134529147984,\n\
\ \"acc_norm_stderr\": 0.031146796482972465\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.2595419847328244,\n \"acc_stderr\": 0.03844876139785271,\n\
\ \"acc_norm\": 0.2595419847328244,\n \"acc_norm_stderr\": 0.03844876139785271\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.2396694214876033,\n \"acc_stderr\": 0.03896878985070417,\n \"\
acc_norm\": 0.2396694214876033,\n \"acc_norm_stderr\": 0.03896878985070417\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.25925925925925924,\n\
\ \"acc_stderr\": 0.042365112580946336,\n \"acc_norm\": 0.25925925925925924,\n\
\ \"acc_norm_stderr\": 0.042365112580946336\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.22085889570552147,\n \"acc_stderr\": 0.032591773927421776,\n\
\ \"acc_norm\": 0.22085889570552147,\n \"acc_norm_stderr\": 0.032591773927421776\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.3125,\n\
\ \"acc_stderr\": 0.043994650575715215,\n \"acc_norm\": 0.3125,\n\
\ \"acc_norm_stderr\": 0.043994650575715215\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.17475728155339806,\n \"acc_stderr\": 0.037601780060266224,\n\
\ \"acc_norm\": 0.17475728155339806,\n \"acc_norm_stderr\": 0.037601780060266224\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.2905982905982906,\n\
\ \"acc_stderr\": 0.02974504857267404,\n \"acc_norm\": 0.2905982905982906,\n\
\ \"acc_norm_stderr\": 0.02974504857267404\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \
\ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.23754789272030652,\n\
\ \"acc_stderr\": 0.015218733046150193,\n \"acc_norm\": 0.23754789272030652,\n\
\ \"acc_norm_stderr\": 0.015218733046150193\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.24855491329479767,\n \"acc_stderr\": 0.023267528432100174,\n\
\ \"acc_norm\": 0.24855491329479767,\n \"acc_norm_stderr\": 0.023267528432100174\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.23798882681564246,\n\
\ \"acc_stderr\": 0.014242630070574915,\n \"acc_norm\": 0.23798882681564246,\n\
\ \"acc_norm_stderr\": 0.014242630070574915\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.22549019607843138,\n \"acc_stderr\": 0.023929155517351284,\n\
\ \"acc_norm\": 0.22549019607843138,\n \"acc_norm_stderr\": 0.023929155517351284\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.1864951768488746,\n\
\ \"acc_stderr\": 0.02212243977248077,\n \"acc_norm\": 0.1864951768488746,\n\
\ \"acc_norm_stderr\": 0.02212243977248077\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.21604938271604937,\n \"acc_stderr\": 0.022899162918445806,\n\
\ \"acc_norm\": 0.21604938271604937,\n \"acc_norm_stderr\": 0.022899162918445806\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.23404255319148937,\n \"acc_stderr\": 0.025257861359432417,\n \
\ \"acc_norm\": 0.23404255319148937,\n \"acc_norm_stderr\": 0.025257861359432417\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.2457627118644068,\n\
\ \"acc_stderr\": 0.010996156635142692,\n \"acc_norm\": 0.2457627118644068,\n\
\ \"acc_norm_stderr\": 0.010996156635142692\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.18382352941176472,\n \"acc_stderr\": 0.023529242185193106,\n\
\ \"acc_norm\": 0.18382352941176472,\n \"acc_norm_stderr\": 0.023529242185193106\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.25,\n \"acc_stderr\": 0.01751781884501444,\n \"acc_norm\"\
: 0.25,\n \"acc_norm_stderr\": 0.01751781884501444\n },\n \"harness|hendrycksTest-public_relations|5\"\
: {\n \"acc\": 0.21818181818181817,\n \"acc_stderr\": 0.03955932861795833,\n\
\ \"acc_norm\": 0.21818181818181817,\n \"acc_norm_stderr\": 0.03955932861795833\n\
\ },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.18775510204081633,\n\
\ \"acc_stderr\": 0.02500025603954621,\n \"acc_norm\": 0.18775510204081633,\n\
\ \"acc_norm_stderr\": 0.02500025603954621\n },\n \"harness|hendrycksTest-sociology|5\"\
: {\n \"acc\": 0.24378109452736318,\n \"acc_stderr\": 0.03036049015401465,\n\
\ \"acc_norm\": 0.24378109452736318,\n \"acc_norm_stderr\": 0.03036049015401465\n\
\ },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\":\
\ 0.28,\n \"acc_stderr\": 0.04512608598542128,\n \"acc_norm\": 0.28,\n\
\ \"acc_norm_stderr\": 0.04512608598542128\n },\n \"harness|hendrycksTest-virology|5\"\
: {\n \"acc\": 0.28313253012048195,\n \"acc_stderr\": 0.03507295431370518,\n\
\ \"acc_norm\": 0.28313253012048195,\n \"acc_norm_stderr\": 0.03507295431370518\n\
\ },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.3216374269005848,\n\
\ \"acc_stderr\": 0.03582529442573122,\n \"acc_norm\": 0.3216374269005848,\n\
\ \"acc_norm_stderr\": 0.03582529442573122\n },\n \"harness|truthfulqa:mc|0\"\
: {\n \"mc1\": 0.29253365973072215,\n \"mc1_stderr\": 0.015925597445286165,\n\
\ \"mc2\": 0.49051835602774946,\n \"mc2_stderr\": 0.015311522184683459\n\
\ },\n \"harness|winogrande|5\": {\n \"acc\": 0.6140489344909235,\n\
\ \"acc_stderr\": 0.013682036993397402\n },\n \"harness|gsm8k|5\":\
\ {\n \"acc\": 0.0,\n \"acc_stderr\": 0.0\n }\n}\n```"
repo_url: https://huggingface.co/abhinand/telugu-llama-7b-instruct-v0.1
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_01_23T14_56_34.848721
path:
- '**/details_harness|arc:challenge|25_2024-01-23T14-56-34.848721.parquet'
- split: 2024_01_23T18_07_17.451860
path:
- '**/details_harness|arc:challenge|25_2024-01-23T18-07-17.451860.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-01-23T18-07-17.451860.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_01_23T14_56_34.848721
path:
- '**/details_harness|gsm8k|5_2024-01-23T14-56-34.848721.parquet'
- split: 2024_01_23T18_07_17.451860
path:
- '**/details_harness|gsm8k|5_2024-01-23T18-07-17.451860.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-01-23T18-07-17.451860.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_01_23T14_56_34.848721
path:
- '**/details_harness|hellaswag|10_2024-01-23T14-56-34.848721.parquet'
- split: 2024_01_23T18_07_17.451860
path:
- '**/details_harness|hellaswag|10_2024-01-23T18-07-17.451860.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-01-23T18-07-17.451860.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_01_23T14_56_34.848721
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-23T14-56-34.848721.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-23T14-56-34.848721.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-23T14-56-34.848721.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-23T14-56-34.848721.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-23T14-56-34.848721.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-23T14-56-34.848721.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-23T14-56-34.848721.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-23T14-56-34.848721.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-23T14-56-34.848721.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-23T14-56-34.848721.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-23T14-56-34.848721.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-23T14-56-34.848721.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-23T14-56-34.848721.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-23T14-56-34.848721.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-23T14-56-34.848721.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-23T14-56-34.848721.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-23T14-56-34.848721.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-23T14-56-34.848721.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-23T14-56-34.848721.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-23T14-56-34.848721.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-23T14-56-34.848721.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-23T14-56-34.848721.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-23T14-56-34.848721.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-23T14-56-34.848721.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-23T14-56-34.848721.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-23T14-56-34.848721.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-23T14-56-34.848721.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-23T14-56-34.848721.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-23T14-56-34.848721.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-23T14-56-34.848721.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-23T14-56-34.848721.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-23T14-56-34.848721.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-23T14-56-34.848721.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-23T14-56-34.848721.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-01-23T14-56-34.848721.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-23T14-56-34.848721.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-23T14-56-34.848721.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-23T14-56-34.848721.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-01-23T14-56-34.848721.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-01-23T14-56-34.848721.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-23T14-56-34.848721.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-23T14-56-34.848721.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-23T14-56-34.848721.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-23T14-56-34.848721.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-23T14-56-34.848721.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-23T14-56-34.848721.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-23T14-56-34.848721.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-23T14-56-34.848721.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-23T14-56-34.848721.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-23T14-56-34.848721.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-23T14-56-34.848721.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-23T14-56-34.848721.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-23T14-56-34.848721.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-01-23T14-56-34.848721.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-23T14-56-34.848721.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-01-23T14-56-34.848721.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-23T14-56-34.848721.parquet'
- split: 2024_01_23T18_07_17.451860
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-23T18-07-17.451860.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-01-23T18-07-17.451860.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-23T18-07-17.451860.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_01_23T14_56_34.848721
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-23T14-56-34.848721.parquet'
- split: 2024_01_23T18_07_17.451860
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-23T18-07-17.451860.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-23T18-07-17.451860.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_01_23T14_56_34.848721
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-23T14-56-34.848721.parquet'
- split: 2024_01_23T18_07_17.451860
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-23T18-07-17.451860.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-23T18-07-17.451860.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_01_23T14_56_34.848721
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-23T14-56-34.848721.parquet'
- split: 2024_01_23T18_07_17.451860
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-23T18-07-17.451860.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-23T18-07-17.451860.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_01_23T14_56_34.848721
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-23T14-56-34.848721.parquet'
- split: 2024_01_23T18_07_17.451860
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-23T18-07-17.451860.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-23T18-07-17.451860.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_01_23T14_56_34.848721
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-23T14-56-34.848721.parquet'
- split: 2024_01_23T18_07_17.451860
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-23T18-07-17.451860.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-23T18-07-17.451860.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_01_23T14_56_34.848721
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-23T14-56-34.848721.parquet'
- split: 2024_01_23T18_07_17.451860
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-23T18-07-17.451860.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-23T18-07-17.451860.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_01_23T14_56_34.848721
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-23T14-56-34.848721.parquet'
- split: 2024_01_23T18_07_17.451860
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-23T18-07-17.451860.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-23T18-07-17.451860.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_01_23T14_56_34.848721
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-23T14-56-34.848721.parquet'
- split: 2024_01_23T18_07_17.451860
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-23T18-07-17.451860.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-23T18-07-17.451860.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_01_23T14_56_34.848721
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-23T14-56-34.848721.parquet'
- split: 2024_01_23T18_07_17.451860
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-23T18-07-17.451860.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-23T18-07-17.451860.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_01_23T14_56_34.848721
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-23T14-56-34.848721.parquet'
- split: 2024_01_23T18_07_17.451860
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-23T18-07-17.451860.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-23T18-07-17.451860.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_01_23T14_56_34.848721
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-23T14-56-34.848721.parquet'
- split: 2024_01_23T18_07_17.451860
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-23T18-07-17.451860.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-23T18-07-17.451860.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_01_23T14_56_34.848721
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-23T14-56-34.848721.parquet'
- split: 2024_01_23T18_07_17.451860
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-23T18-07-17.451860.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-23T18-07-17.451860.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_01_23T14_56_34.848721
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-23T14-56-34.848721.parquet'
- split: 2024_01_23T18_07_17.451860
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-23T18-07-17.451860.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-23T18-07-17.451860.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_01_23T14_56_34.848721
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-23T14-56-34.848721.parquet'
- split: 2024_01_23T18_07_17.451860
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-23T18-07-17.451860.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-23T18-07-17.451860.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_01_23T14_56_34.848721
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-23T14-56-34.848721.parquet'
- split: 2024_01_23T18_07_17.451860
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-23T18-07-17.451860.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-23T18-07-17.451860.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_01_23T14_56_34.848721
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-23T14-56-34.848721.parquet'
- split: 2024_01_23T18_07_17.451860
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-23T18-07-17.451860.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-23T18-07-17.451860.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_01_23T14_56_34.848721
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-23T14-56-34.848721.parquet'
- split: 2024_01_23T18_07_17.451860
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-23T18-07-17.451860.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-23T18-07-17.451860.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_01_23T14_56_34.848721
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-23T14-56-34.848721.parquet'
- split: 2024_01_23T18_07_17.451860
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-23T18-07-17.451860.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-23T18-07-17.451860.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_01_23T14_56_34.848721
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-23T14-56-34.848721.parquet'
- split: 2024_01_23T18_07_17.451860
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-23T18-07-17.451860.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-23T18-07-17.451860.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_01_23T14_56_34.848721
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-23T14-56-34.848721.parquet'
- split: 2024_01_23T18_07_17.451860
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-23T18-07-17.451860.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-23T18-07-17.451860.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_01_23T14_56_34.848721
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-23T14-56-34.848721.parquet'
- split: 2024_01_23T18_07_17.451860
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-23T18-07-17.451860.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-23T18-07-17.451860.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_01_23T14_56_34.848721
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-23T14-56-34.848721.parquet'
- split: 2024_01_23T18_07_17.451860
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-23T18-07-17.451860.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-23T18-07-17.451860.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_01_23T14_56_34.848721
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-23T14-56-34.848721.parquet'
- split: 2024_01_23T18_07_17.451860
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-23T18-07-17.451860.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-23T18-07-17.451860.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_01_23T14_56_34.848721
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-23T14-56-34.848721.parquet'
- split: 2024_01_23T18_07_17.451860
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-23T18-07-17.451860.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-23T18-07-17.451860.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_01_23T14_56_34.848721
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-23T14-56-34.848721.parquet'
- split: 2024_01_23T18_07_17.451860
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-23T18-07-17.451860.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-23T18-07-17.451860.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_01_23T14_56_34.848721
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-23T14-56-34.848721.parquet'
- split: 2024_01_23T18_07_17.451860
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-23T18-07-17.451860.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-23T18-07-17.451860.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_01_23T14_56_34.848721
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-23T14-56-34.848721.parquet'
- split: 2024_01_23T18_07_17.451860
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-23T18-07-17.451860.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-23T18-07-17.451860.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_01_23T14_56_34.848721
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-23T14-56-34.848721.parquet'
- split: 2024_01_23T18_07_17.451860
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-23T18-07-17.451860.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-23T18-07-17.451860.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_01_23T14_56_34.848721
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-23T14-56-34.848721.parquet'
- split: 2024_01_23T18_07_17.451860
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-23T18-07-17.451860.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-23T18-07-17.451860.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_01_23T14_56_34.848721
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-23T14-56-34.848721.parquet'
- split: 2024_01_23T18_07_17.451860
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-23T18-07-17.451860.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-23T18-07-17.451860.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_01_23T14_56_34.848721
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-23T14-56-34.848721.parquet'
- split: 2024_01_23T18_07_17.451860
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-23T18-07-17.451860.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-23T18-07-17.451860.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_01_23T14_56_34.848721
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-23T14-56-34.848721.parquet'
- split: 2024_01_23T18_07_17.451860
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-23T18-07-17.451860.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-23T18-07-17.451860.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_01_23T14_56_34.848721
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-23T14-56-34.848721.parquet'
- split: 2024_01_23T18_07_17.451860
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-23T18-07-17.451860.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-23T18-07-17.451860.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_01_23T14_56_34.848721
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-23T14-56-34.848721.parquet'
- split: 2024_01_23T18_07_17.451860
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-23T18-07-17.451860.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-23T18-07-17.451860.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_01_23T14_56_34.848721
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-01-23T14-56-34.848721.parquet'
- split: 2024_01_23T18_07_17.451860
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-01-23T18-07-17.451860.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-01-23T18-07-17.451860.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_01_23T14_56_34.848721
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-23T14-56-34.848721.parquet'
- split: 2024_01_23T18_07_17.451860
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-23T18-07-17.451860.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-23T18-07-17.451860.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_01_23T14_56_34.848721
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-23T14-56-34.848721.parquet'
- split: 2024_01_23T18_07_17.451860
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-23T18-07-17.451860.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-23T18-07-17.451860.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_01_23T14_56_34.848721
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-23T14-56-34.848721.parquet'
- split: 2024_01_23T18_07_17.451860
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-23T18-07-17.451860.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-23T18-07-17.451860.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_01_23T14_56_34.848721
path:
- '**/details_harness|hendrycksTest-management|5_2024-01-23T14-56-34.848721.parquet'
- split: 2024_01_23T18_07_17.451860
path:
- '**/details_harness|hendrycksTest-management|5_2024-01-23T18-07-17.451860.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-01-23T18-07-17.451860.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_01_23T14_56_34.848721
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-01-23T14-56-34.848721.parquet'
- split: 2024_01_23T18_07_17.451860
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-01-23T18-07-17.451860.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-01-23T18-07-17.451860.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_01_23T14_56_34.848721
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-23T14-56-34.848721.parquet'
- split: 2024_01_23T18_07_17.451860
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-23T18-07-17.451860.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-23T18-07-17.451860.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_01_23T14_56_34.848721
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-23T14-56-34.848721.parquet'
- split: 2024_01_23T18_07_17.451860
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-23T18-07-17.451860.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-23T18-07-17.451860.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_01_23T14_56_34.848721
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-23T14-56-34.848721.parquet'
- split: 2024_01_23T18_07_17.451860
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-23T18-07-17.451860.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-23T18-07-17.451860.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_01_23T14_56_34.848721
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-23T14-56-34.848721.parquet'
- split: 2024_01_23T18_07_17.451860
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-23T18-07-17.451860.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-23T18-07-17.451860.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_01_23T14_56_34.848721
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-23T14-56-34.848721.parquet'
- split: 2024_01_23T18_07_17.451860
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-23T18-07-17.451860.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-23T18-07-17.451860.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_01_23T14_56_34.848721
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-23T14-56-34.848721.parquet'
- split: 2024_01_23T18_07_17.451860
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-23T18-07-17.451860.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-23T18-07-17.451860.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_01_23T14_56_34.848721
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-23T14-56-34.848721.parquet'
- split: 2024_01_23T18_07_17.451860
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-23T18-07-17.451860.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-23T18-07-17.451860.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_01_23T14_56_34.848721
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-23T14-56-34.848721.parquet'
- split: 2024_01_23T18_07_17.451860
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-23T18-07-17.451860.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-23T18-07-17.451860.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_01_23T14_56_34.848721
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-23T14-56-34.848721.parquet'
- split: 2024_01_23T18_07_17.451860
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-23T18-07-17.451860.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-23T18-07-17.451860.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_01_23T14_56_34.848721
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-23T14-56-34.848721.parquet'
- split: 2024_01_23T18_07_17.451860
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-23T18-07-17.451860.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-23T18-07-17.451860.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_01_23T14_56_34.848721
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-23T14-56-34.848721.parquet'
- split: 2024_01_23T18_07_17.451860
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-23T18-07-17.451860.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-23T18-07-17.451860.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_01_23T14_56_34.848721
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-23T14-56-34.848721.parquet'
- split: 2024_01_23T18_07_17.451860
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-23T18-07-17.451860.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-23T18-07-17.451860.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_01_23T14_56_34.848721
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-23T14-56-34.848721.parquet'
- split: 2024_01_23T18_07_17.451860
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-23T18-07-17.451860.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-23T18-07-17.451860.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_01_23T14_56_34.848721
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-23T14-56-34.848721.parquet'
- split: 2024_01_23T18_07_17.451860
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-23T18-07-17.451860.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-23T18-07-17.451860.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_01_23T14_56_34.848721
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-23T14-56-34.848721.parquet'
- split: 2024_01_23T18_07_17.451860
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-23T18-07-17.451860.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-23T18-07-17.451860.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_01_23T14_56_34.848721
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-23T14-56-34.848721.parquet'
- split: 2024_01_23T18_07_17.451860
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-23T18-07-17.451860.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-23T18-07-17.451860.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_01_23T14_56_34.848721
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-23T14-56-34.848721.parquet'
- split: 2024_01_23T18_07_17.451860
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-23T18-07-17.451860.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-23T18-07-17.451860.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_01_23T14_56_34.848721
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-23T14-56-34.848721.parquet'
- split: 2024_01_23T18_07_17.451860
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-23T18-07-17.451860.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-23T18-07-17.451860.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_01_23T14_56_34.848721
path:
- '**/details_harness|winogrande|5_2024-01-23T14-56-34.848721.parquet'
- split: 2024_01_23T18_07_17.451860
path:
- '**/details_harness|winogrande|5_2024-01-23T18-07-17.451860.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-01-23T18-07-17.451860.parquet'
- config_name: results
data_files:
- split: 2024_01_23T14_56_34.848721
path:
- results_2024-01-23T14-56-34.848721.parquet
- split: 2024_01_23T18_07_17.451860
path:
- results_2024-01-23T18-07-17.451860.parquet
- split: latest
path:
- results_2024-01-23T18-07-17.451860.parquet
---
# Dataset Card for Evaluation run of abhinand/telugu-llama-7b-instruct-v0.1
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [abhinand/telugu-llama-7b-instruct-v0.1](https://huggingface.co/abhinand/telugu-llama-7b-instruct-v0.1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_abhinand__telugu-llama-7b-instruct-v0.1",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-23T18:07:17.451860](https://huggingface.co/datasets/open-llm-leaderboard/details_abhinand__telugu-llama-7b-instruct-v0.1/blob/main/results_2024-01-23T18-07-17.451860.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.24045369051307708,
"acc_stderr": 0.029966214967594704,
"acc_norm": 0.24113587081419893,
"acc_norm_stderr": 0.030748707255235257,
"mc1": 0.29253365973072215,
"mc1_stderr": 0.015925597445286165,
"mc2": 0.49051835602774946,
"mc2_stderr": 0.015311522184683459
},
"harness|arc:challenge|25": {
"acc": 0.3438566552901024,
"acc_stderr": 0.013880644570156211,
"acc_norm": 0.371160409556314,
"acc_norm_stderr": 0.014117971901142813
},
"harness|hellaswag|10": {
"acc": 0.5331607249551882,
"acc_stderr": 0.004978795454216732,
"acc_norm": 0.6792471619199363,
"acc_norm_stderr": 0.00465812015223082
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.22,
"acc_stderr": 0.04163331998932268,
"acc_norm": 0.22,
"acc_norm_stderr": 0.04163331998932268
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.18518518518518517,
"acc_stderr": 0.03355677216313142,
"acc_norm": 0.18518518518518517,
"acc_norm_stderr": 0.03355677216313142
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.17763157894736842,
"acc_stderr": 0.031103182383123398,
"acc_norm": 0.17763157894736842,
"acc_norm_stderr": 0.031103182383123398
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.3,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.3,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.21509433962264152,
"acc_stderr": 0.02528839450289137,
"acc_norm": 0.21509433962264152,
"acc_norm_stderr": 0.02528839450289137
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.2569444444444444,
"acc_stderr": 0.03653946969442099,
"acc_norm": 0.2569444444444444,
"acc_norm_stderr": 0.03653946969442099
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.2,
"acc_stderr": 0.04020151261036845,
"acc_norm": 0.2,
"acc_norm_stderr": 0.04020151261036845
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.26,
"acc_stderr": 0.0440844002276808,
"acc_norm": 0.26,
"acc_norm_stderr": 0.0440844002276808
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.21,
"acc_stderr": 0.040936018074033256,
"acc_norm": 0.21,
"acc_norm_stderr": 0.040936018074033256
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.20809248554913296,
"acc_stderr": 0.030952890217749874,
"acc_norm": 0.20809248554913296,
"acc_norm_stderr": 0.030952890217749874
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.21568627450980393,
"acc_stderr": 0.04092563958237654,
"acc_norm": 0.21568627450980393,
"acc_norm_stderr": 0.04092563958237654
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.28,
"acc_stderr": 0.045126085985421276,
"acc_norm": 0.28,
"acc_norm_stderr": 0.045126085985421276
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.26382978723404255,
"acc_stderr": 0.028809989854102973,
"acc_norm": 0.26382978723404255,
"acc_norm_stderr": 0.028809989854102973
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.23684210526315788,
"acc_stderr": 0.039994238792813365,
"acc_norm": 0.23684210526315788,
"acc_norm_stderr": 0.039994238792813365
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.2413793103448276,
"acc_stderr": 0.03565998174135302,
"acc_norm": 0.2413793103448276,
"acc_norm_stderr": 0.03565998174135302
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.20899470899470898,
"acc_stderr": 0.02094048156533486,
"acc_norm": 0.20899470899470898,
"acc_norm_stderr": 0.02094048156533486
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.2857142857142857,
"acc_stderr": 0.04040610178208841,
"acc_norm": 0.2857142857142857,
"acc_norm_stderr": 0.04040610178208841
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.18,
"acc_stderr": 0.038612291966536934,
"acc_norm": 0.18,
"acc_norm_stderr": 0.038612291966536934
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.1774193548387097,
"acc_stderr": 0.02173254068932927,
"acc_norm": 0.1774193548387097,
"acc_norm_stderr": 0.02173254068932927
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.15270935960591134,
"acc_stderr": 0.02530890453938063,
"acc_norm": 0.15270935960591134,
"acc_norm_stderr": 0.02530890453938063
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.25,
"acc_stderr": 0.04351941398892446,
"acc_norm": 0.25,
"acc_norm_stderr": 0.04351941398892446
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.21818181818181817,
"acc_stderr": 0.03225078108306289,
"acc_norm": 0.21818181818181817,
"acc_norm_stderr": 0.03225078108306289
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.17676767676767677,
"acc_stderr": 0.027178752639044915,
"acc_norm": 0.17676767676767677,
"acc_norm_stderr": 0.027178752639044915
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.19689119170984457,
"acc_stderr": 0.028697873971860664,
"acc_norm": 0.19689119170984457,
"acc_norm_stderr": 0.028697873971860664
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.20256410256410257,
"acc_stderr": 0.020377660970371372,
"acc_norm": 0.20256410256410257,
"acc_norm_stderr": 0.020377660970371372
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.2111111111111111,
"acc_stderr": 0.024882116857655075,
"acc_norm": 0.2111111111111111,
"acc_norm_stderr": 0.024882116857655075
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.21008403361344538,
"acc_stderr": 0.026461398717471874,
"acc_norm": 0.21008403361344538,
"acc_norm_stderr": 0.026461398717471874
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.1986754966887417,
"acc_stderr": 0.03257847384436776,
"acc_norm": 0.1986754966887417,
"acc_norm_stderr": 0.03257847384436776
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.1926605504587156,
"acc_stderr": 0.016909276884936094,
"acc_norm": 0.1926605504587156,
"acc_norm_stderr": 0.016909276884936094
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.1527777777777778,
"acc_stderr": 0.024536326026134224,
"acc_norm": 0.1527777777777778,
"acc_norm_stderr": 0.024536326026134224
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.25,
"acc_stderr": 0.03039153369274154,
"acc_norm": 0.25,
"acc_norm_stderr": 0.03039153369274154
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.270042194092827,
"acc_stderr": 0.028900721906293426,
"acc_norm": 0.270042194092827,
"acc_norm_stderr": 0.028900721906293426
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.31390134529147984,
"acc_stderr": 0.031146796482972465,
"acc_norm": 0.31390134529147984,
"acc_norm_stderr": 0.031146796482972465
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.2595419847328244,
"acc_stderr": 0.03844876139785271,
"acc_norm": 0.2595419847328244,
"acc_norm_stderr": 0.03844876139785271
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.2396694214876033,
"acc_stderr": 0.03896878985070417,
"acc_norm": 0.2396694214876033,
"acc_norm_stderr": 0.03896878985070417
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.25925925925925924,
"acc_stderr": 0.042365112580946336,
"acc_norm": 0.25925925925925924,
"acc_norm_stderr": 0.042365112580946336
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.22085889570552147,
"acc_stderr": 0.032591773927421776,
"acc_norm": 0.22085889570552147,
"acc_norm_stderr": 0.032591773927421776
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.3125,
"acc_stderr": 0.043994650575715215,
"acc_norm": 0.3125,
"acc_norm_stderr": 0.043994650575715215
},
"harness|hendrycksTest-management|5": {
"acc": 0.17475728155339806,
"acc_stderr": 0.037601780060266224,
"acc_norm": 0.17475728155339806,
"acc_norm_stderr": 0.037601780060266224
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.2905982905982906,
"acc_stderr": 0.02974504857267404,
"acc_norm": 0.2905982905982906,
"acc_norm_stderr": 0.02974504857267404
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.3,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.3,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.23754789272030652,
"acc_stderr": 0.015218733046150193,
"acc_norm": 0.23754789272030652,
"acc_norm_stderr": 0.015218733046150193
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.24855491329479767,
"acc_stderr": 0.023267528432100174,
"acc_norm": 0.24855491329479767,
"acc_norm_stderr": 0.023267528432100174
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.23798882681564246,
"acc_stderr": 0.014242630070574915,
"acc_norm": 0.23798882681564246,
"acc_norm_stderr": 0.014242630070574915
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.22549019607843138,
"acc_stderr": 0.023929155517351284,
"acc_norm": 0.22549019607843138,
"acc_norm_stderr": 0.023929155517351284
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.1864951768488746,
"acc_stderr": 0.02212243977248077,
"acc_norm": 0.1864951768488746,
"acc_norm_stderr": 0.02212243977248077
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.21604938271604937,
"acc_stderr": 0.022899162918445806,
"acc_norm": 0.21604938271604937,
"acc_norm_stderr": 0.022899162918445806
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.23404255319148937,
"acc_stderr": 0.025257861359432417,
"acc_norm": 0.23404255319148937,
"acc_norm_stderr": 0.025257861359432417
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.2457627118644068,
"acc_stderr": 0.010996156635142692,
"acc_norm": 0.2457627118644068,
"acc_norm_stderr": 0.010996156635142692
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.18382352941176472,
"acc_stderr": 0.023529242185193106,
"acc_norm": 0.18382352941176472,
"acc_norm_stderr": 0.023529242185193106
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.25,
"acc_stderr": 0.01751781884501444,
"acc_norm": 0.25,
"acc_norm_stderr": 0.01751781884501444
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.21818181818181817,
"acc_stderr": 0.03955932861795833,
"acc_norm": 0.21818181818181817,
"acc_norm_stderr": 0.03955932861795833
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.18775510204081633,
"acc_stderr": 0.02500025603954621,
"acc_norm": 0.18775510204081633,
"acc_norm_stderr": 0.02500025603954621
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.24378109452736318,
"acc_stderr": 0.03036049015401465,
"acc_norm": 0.24378109452736318,
"acc_norm_stderr": 0.03036049015401465
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.28,
"acc_stderr": 0.04512608598542128,
"acc_norm": 0.28,
"acc_norm_stderr": 0.04512608598542128
},
"harness|hendrycksTest-virology|5": {
"acc": 0.28313253012048195,
"acc_stderr": 0.03507295431370518,
"acc_norm": 0.28313253012048195,
"acc_norm_stderr": 0.03507295431370518
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.3216374269005848,
"acc_stderr": 0.03582529442573122,
"acc_norm": 0.3216374269005848,
"acc_norm_stderr": 0.03582529442573122
},
"harness|truthfulqa:mc|0": {
"mc1": 0.29253365973072215,
"mc1_stderr": 0.015925597445286165,
"mc2": 0.49051835602774946,
"mc2_stderr": 0.015311522184683459
},
"harness|winogrande|5": {
"acc": 0.6140489344909235,
"acc_stderr": 0.013682036993397402
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] |
aravind-selvam/rotated_x | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 112837730.0
num_examples: 4000
- name: validation
num_bytes: 28685240.0
num_examples: 1000
download_size: 140758572
dataset_size: 141522970.0
---
# Dataset Card for "rotated_x"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
GBaker/MedQA-USMLE-4-options-hf-DBPedia-context | ---
dataset_info:
features:
- name: id
dtype: string
- name: sent1
dtype: string
- name: sent2
dtype: string
- name: ending0
dtype: string
- name: ending1
dtype: string
- name: ending2
dtype: string
- name: ending3
dtype: string
- name: label
dtype: int64
splits:
- name: test
num_bytes: 3472206
num_examples: 1273
download_size: 1928988
dataset_size: 3472206
---
# Dataset Card for "MedQA-USMLE-4-options-hf-DBPedia-context"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Pixelatory/AllMolGen | ---
tags:
- chemistry
size_categories:
- 1M<n<10M
configs:
- config_name: default
data_files: allmolgen.tar.xz
---
Downloaded using PyTDC (https://tdcommons.ai/).
Contains the unique canonicalized SMILES molecules from MOSES, ZINC-250K, and ChEMBL-29, done with RDKit.
Distribution of tokenized SMILES sequence lengths below. The following regex string was used
to split the SMILES molecule into tokens: (\[[^\]]+]|Br?|Cl?|N|O|S|P|F|I|b|c|n|o|s|p|\(|\)|\.|=|#|-|\+|\\|\/|:|~|@|\?|>>?|\*|\$|\%[0-9]{2}|[0-9])
<img src="violin_allmolgen_cano.png" width=50% height=50%>
Included in the .csv (after extracting the .tar.xz file) is a column "smi_len".
If using the same SMILES tokenization regex string as above, you can simply filter using the values in this column ("smi_len").
I'd recommend post-processing since clearly a majority of the sequences are of a much shorter length than the highest, which is above 1400 (using my regex string). |
FreedomIntelligence/ALLaVA-4V-Arabic | ---
license: apache-2.0
task_categories:
- question-answering
- text-generation
language:
- ar
tags:
- GPT-4V
- LVLM
- Vision
- Language
size_categories:
- 1M<n<10M
configs:
- config_name: allava_laion
data_files:
- split: caption
path: "allava_laion/ALLaVA-Caption-LAION-4V_Arabic.json"
# - split: instruct
# path: "allava_laion/ALLaVA-Instruct-LAION-4V_Chinese.json"
- config_name: allava_vflan
data_files:
- split: caption
path: "allava_vflan/ALLaVA-Caption-VFLAN-4V_Arabic.json"
# - split: instruct
# path: "allava_vflan/ALLaVA-Instruct-VFLAN-4V_Chinese.json"
# - config_name: allava_laion_instruction
# data_files: "allava_laion/ALLaVA-Instruct-LAION-4V.json"
# configs:
# - config_name: default
# data_files:
# - split: allava_laion_caption
# path: "allava_laion/ALLaVA-Caption-LAION-4V.json"
# - split: allava_laion_instruction
# path: "allava_laion/ALLaVA-Instruction-LAION-4V.json"
# configs:
# - config_name: default
# - data_files:
# - split: allava_laion_caption
# - path:
# - "allava_laion/ALLaVA-Caption-LAION-4V.json"
# - split: allava_laion_instruction
# - path:
# - "allava_laion/ALLaVA-Instruction-LAION-4V.json"
---
## ALLaVA-4V for Arabic
This is the Arabic version of the ALLaVA-4V data. We have translated the ALLaVA-4V data into Arabic through ChatGPT and instructed ChatGPT not to translate content related to OCR.
The original dataset can be found [here](https://huggingface.co/datasets/FreedomIntelligence/ALLaVA-4V), and the image data can be downloaded from [ALLaVA-4V](https://huggingface.co/datasets/FreedomIntelligence/ALLaVA-4V).
#### Citation
If you find our data useful, please consider citing our work! We are FreedomIntelligence from Shenzhen Research Institute of Big Data and The Chinese University of Hong Kong, Shenzhen.
```
@misc{chen2024allava,
title={ALLaVA: Harnessing GPT4V-synthesized Data for A Lite Vision-Language Model},
author={Guiming Hardy Chen and Shunian Chen and Ruifei Zhang and Junying Chen and Xiangbo Wu and Zhiyi Zhang and Zhihong Chen and Jianquan Li and Xiang Wan and Benyou Wang},
year={2024},
eprint={2402.11684},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
mkay8/nlplab2 | ---
dataset_info:
features:
- name: Conversation
dtype: string
splits:
- name: train
num_bytes: 123649011
num_examples: 106556
download_size: 72564908
dataset_size: 123649011
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Gaeros/e6db | ---
license: cc-by-sa-4.0
task_categories:
- token-classification
language:
- en
size_categories:
- 10K<n<100K
tags:
- not-for-all-audiences
--- |
Pablao0948/Nascimento | ---
license: openrail
---
|
lukekim420/sshsbamboobot_final | ---
license: apache-2.0
---
|
susnato/go_PRs | ---
dataset_info:
features:
- name: repo_name
dtype: string
- name: pr_number
dtype: int64
- name: pr_title
dtype: string
- name: pr_description
dtype: string
- name: author
dtype: string
- name: date_created
dtype: timestamp[ns, tz=UTC]
- name: date_merged
dtype: timestamp[ns, tz=UTC]
- name: previous_commit
dtype: string
- name: pr_commit
dtype: string
- name: query
dtype: string
- name: filepath
dtype: string
- name: before_content
dtype: string
- name: after_content
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 23175811193
num_examples: 434774
download_size: 12420800789
dataset_size: 23175811193
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
nateraw/gradio-guides-files | ---
license: mit
---
|
distilabel-internal-testing/testing-distilabel-cli | ---
dataset_info:
- config_name: push_to_hub
features:
- name: instruction
dtype: string
- name: completion
dtype: string
- name: meta
struct:
- name: id
dtype: int64
- name: source
dtype: string
- name: category
dtype: string
- name: subcategory
dtype: string
- name: prompt
dtype: string
- name: completion
dtype: string
- name: motivation_app
dtype: string
- name: input
dtype: string
- name: model
dtype: string
- name: generation
dtype: string
splits:
- name: train
num_bytes: 1643736
num_examples: 981
download_size: 464347
dataset_size: 1643736
- config_name: push_to_hub_2
features:
- name: instruction
dtype: string
- name: completion
dtype: string
- name: meta
struct:
- name: id
dtype: int64
- name: source
dtype: string
- name: category
dtype: string
- name: subcategory
dtype: string
- name: prompt
dtype: string
- name: completion
dtype: string
- name: motivation_app
dtype: string
- name: input
dtype: string
- name: model
dtype: string
- name: generation
sequence: string
splits:
- name: train
num_bytes: 773596
num_examples: 327
download_size: 460946
dataset_size: 773596
configs:
- config_name: push_to_hub
data_files:
- split: train
path: push_to_hub/train-*
- config_name: push_to_hub_2
data_files:
- split: train
path: push_to_hub_2/train-*
---
|
Tippawan/SNOMED-CT-NER-V.2 | ---
dataset_info:
features:
- name: text
sequence: string
- name: tag
sequence: int64
splits:
- name: train
num_bytes: 226675
num_examples: 1228
- name: validation
num_bytes: 15534
num_examples: 95
- name: test
num_bytes: 18077
num_examples: 95
download_size: 75589
dataset_size: 260286
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
|
odatems/TSP_Tours | ---
license: mit
task_categories:
- question-answering
language:
- en
pretty_name: Tsp_Tours
--- |
Nexusflow/OTXAPIBenchmark | ---
dataset_info:
features:
- name: Input
dtype: string
- name: Output
dtype: string
splits:
- name: train
num_bytes: 19094
num_examples: 92
download_size: 9314
dataset_size: 19094
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/c7e359b5 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 180
num_examples: 10
download_size: 1331
dataset_size: 180
---
# Dataset Card for "c7e359b5"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE3_3.3w-r8-q_k_v_o_gate_up_down | ---
pretty_name: Evaluation run of CHIH-HUNG/llama-2-13b-FINETUNE3_3.3w-r8-q_k_v_o_gate_up_down
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [CHIH-HUNG/llama-2-13b-FINETUNE3_3.3w-r8-q_k_v_o_gate_up_down](https://huggingface.co/CHIH-HUNG/llama-2-13b-FINETUNE3_3.3w-r8-q_k_v_o_gate_up_down)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE3_3.3w-r8-q_k_v_o_gate_up_down\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-28T08:25:23.619577](https://huggingface.co/datasets/open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE3_3.3w-r8-q_k_v_o_gate_up_down/blob/main/results_2023-10-28T08-25-23.619577.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.3410234899328859,\n\
\ \"em_stderr\": 0.0048547488279167,\n \"f1\": 0.3798521392617453,\n\
\ \"f1_stderr\": 0.004781623142650588,\n \"acc\": 0.43779100069232807,\n\
\ \"acc_stderr\": 0.010221026790242026\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.3410234899328859,\n \"em_stderr\": 0.0048547488279167,\n\
\ \"f1\": 0.3798521392617453,\n \"f1_stderr\": 0.004781623142650588\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.10841546626231995,\n \
\ \"acc_stderr\": 0.008563852506627504\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7671665351223362,\n \"acc_stderr\": 0.011878201073856546\n\
\ }\n}\n```"
repo_url: https://huggingface.co/CHIH-HUNG/llama-2-13b-FINETUNE3_3.3w-r8-q_k_v_o_gate_up_down
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_10_04T05_37_11.185661
path:
- '**/details_harness|arc:challenge|25_2023-10-04T05-37-11.185661.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-10-04T05-37-11.185661.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_28T08_25_23.619577
path:
- '**/details_harness|drop|3_2023-10-28T08-25-23.619577.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-28T08-25-23.619577.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_28T08_25_23.619577
path:
- '**/details_harness|gsm8k|5_2023-10-28T08-25-23.619577.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-28T08-25-23.619577.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_10_04T05_37_11.185661
path:
- '**/details_harness|hellaswag|10_2023-10-04T05-37-11.185661.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-10-04T05-37-11.185661.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_10_04T05_37_11.185661
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-04T05-37-11.185661.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-10-04T05-37-11.185661.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-04T05-37-11.185661.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_10_04T05_37_11.185661
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-04T05-37-11.185661.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-04T05-37-11.185661.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_10_04T05_37_11.185661
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-04T05-37-11.185661.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-04T05-37-11.185661.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_10_04T05_37_11.185661
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-04T05-37-11.185661.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-04T05-37-11.185661.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_10_04T05_37_11.185661
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-04T05-37-11.185661.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-04T05-37-11.185661.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_10_04T05_37_11.185661
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-04T05-37-11.185661.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-04T05-37-11.185661.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_10_04T05_37_11.185661
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-04T05-37-11.185661.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-04T05-37-11.185661.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_10_04T05_37_11.185661
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-04T05-37-11.185661.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-04T05-37-11.185661.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_10_04T05_37_11.185661
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-04T05-37-11.185661.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-04T05-37-11.185661.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_10_04T05_37_11.185661
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-04T05-37-11.185661.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-04T05-37-11.185661.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_10_04T05_37_11.185661
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-04T05-37-11.185661.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-04T05-37-11.185661.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_10_04T05_37_11.185661
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-04T05-37-11.185661.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-04T05-37-11.185661.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_10_04T05_37_11.185661
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-04T05-37-11.185661.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-04T05-37-11.185661.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_10_04T05_37_11.185661
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-04T05-37-11.185661.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-04T05-37-11.185661.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_10_04T05_37_11.185661
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-04T05-37-11.185661.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-04T05-37-11.185661.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_10_04T05_37_11.185661
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-04T05-37-11.185661.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-04T05-37-11.185661.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_10_04T05_37_11.185661
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-04T05-37-11.185661.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-04T05-37-11.185661.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_10_04T05_37_11.185661
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-04T05-37-11.185661.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-04T05-37-11.185661.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_10_04T05_37_11.185661
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-04T05-37-11.185661.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-04T05-37-11.185661.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_10_04T05_37_11.185661
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-04T05-37-11.185661.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-04T05-37-11.185661.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_10_04T05_37_11.185661
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-04T05-37-11.185661.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-04T05-37-11.185661.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_10_04T05_37_11.185661
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-04T05-37-11.185661.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-04T05-37-11.185661.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_10_04T05_37_11.185661
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-04T05-37-11.185661.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-04T05-37-11.185661.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_10_04T05_37_11.185661
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-04T05-37-11.185661.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-04T05-37-11.185661.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_10_04T05_37_11.185661
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-04T05-37-11.185661.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-04T05-37-11.185661.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_10_04T05_37_11.185661
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-04T05-37-11.185661.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-04T05-37-11.185661.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_10_04T05_37_11.185661
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-04T05-37-11.185661.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-04T05-37-11.185661.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_10_04T05_37_11.185661
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-04T05-37-11.185661.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-04T05-37-11.185661.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_10_04T05_37_11.185661
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-04T05-37-11.185661.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-04T05-37-11.185661.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_10_04T05_37_11.185661
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-04T05-37-11.185661.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-04T05-37-11.185661.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_10_04T05_37_11.185661
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-04T05-37-11.185661.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-04T05-37-11.185661.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_10_04T05_37_11.185661
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-04T05-37-11.185661.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-04T05-37-11.185661.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_10_04T05_37_11.185661
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-04T05-37-11.185661.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-04T05-37-11.185661.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_10_04T05_37_11.185661
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-04T05-37-11.185661.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-04T05-37-11.185661.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_10_04T05_37_11.185661
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-04T05-37-11.185661.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-04T05-37-11.185661.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_10_04T05_37_11.185661
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-10-04T05-37-11.185661.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-10-04T05-37-11.185661.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_10_04T05_37_11.185661
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-04T05-37-11.185661.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-04T05-37-11.185661.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_10_04T05_37_11.185661
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-04T05-37-11.185661.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-04T05-37-11.185661.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_10_04T05_37_11.185661
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-04T05-37-11.185661.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-04T05-37-11.185661.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_10_04T05_37_11.185661
path:
- '**/details_harness|hendrycksTest-management|5_2023-10-04T05-37-11.185661.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-10-04T05-37-11.185661.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_10_04T05_37_11.185661
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-10-04T05-37-11.185661.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-10-04T05-37-11.185661.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_10_04T05_37_11.185661
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-04T05-37-11.185661.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-04T05-37-11.185661.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_10_04T05_37_11.185661
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-04T05-37-11.185661.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-04T05-37-11.185661.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_10_04T05_37_11.185661
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-04T05-37-11.185661.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-04T05-37-11.185661.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_10_04T05_37_11.185661
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-04T05-37-11.185661.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-04T05-37-11.185661.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_10_04T05_37_11.185661
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-04T05-37-11.185661.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-04T05-37-11.185661.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_10_04T05_37_11.185661
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-04T05-37-11.185661.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-04T05-37-11.185661.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_10_04T05_37_11.185661
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-04T05-37-11.185661.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-04T05-37-11.185661.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_10_04T05_37_11.185661
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-04T05-37-11.185661.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-04T05-37-11.185661.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_10_04T05_37_11.185661
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-04T05-37-11.185661.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-04T05-37-11.185661.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_10_04T05_37_11.185661
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-04T05-37-11.185661.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-04T05-37-11.185661.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_10_04T05_37_11.185661
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-04T05-37-11.185661.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-04T05-37-11.185661.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_10_04T05_37_11.185661
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-04T05-37-11.185661.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-04T05-37-11.185661.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_10_04T05_37_11.185661
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-04T05-37-11.185661.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-04T05-37-11.185661.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_10_04T05_37_11.185661
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-10-04T05-37-11.185661.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-10-04T05-37-11.185661.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_10_04T05_37_11.185661
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-04T05-37-11.185661.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-04T05-37-11.185661.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_10_04T05_37_11.185661
path:
- '**/details_harness|hendrycksTest-virology|5_2023-10-04T05-37-11.185661.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-10-04T05-37-11.185661.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_10_04T05_37_11.185661
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-04T05-37-11.185661.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-04T05-37-11.185661.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_10_04T05_37_11.185661
path:
- '**/details_harness|truthfulqa:mc|0_2023-10-04T05-37-11.185661.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-10-04T05-37-11.185661.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_28T08_25_23.619577
path:
- '**/details_harness|winogrande|5_2023-10-28T08-25-23.619577.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-28T08-25-23.619577.parquet'
- config_name: results
data_files:
- split: 2023_10_04T05_37_11.185661
path:
- results_2023-10-04T05-37-11.185661.parquet
- split: 2023_10_28T08_25_23.619577
path:
- results_2023-10-28T08-25-23.619577.parquet
- split: latest
path:
- results_2023-10-28T08-25-23.619577.parquet
---
# Dataset Card for Evaluation run of CHIH-HUNG/llama-2-13b-FINETUNE3_3.3w-r8-q_k_v_o_gate_up_down
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/CHIH-HUNG/llama-2-13b-FINETUNE3_3.3w-r8-q_k_v_o_gate_up_down
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [CHIH-HUNG/llama-2-13b-FINETUNE3_3.3w-r8-q_k_v_o_gate_up_down](https://huggingface.co/CHIH-HUNG/llama-2-13b-FINETUNE3_3.3w-r8-q_k_v_o_gate_up_down) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE3_3.3w-r8-q_k_v_o_gate_up_down",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-28T08:25:23.619577](https://huggingface.co/datasets/open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE3_3.3w-r8-q_k_v_o_gate_up_down/blob/main/results_2023-10-28T08-25-23.619577.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.3410234899328859,
"em_stderr": 0.0048547488279167,
"f1": 0.3798521392617453,
"f1_stderr": 0.004781623142650588,
"acc": 0.43779100069232807,
"acc_stderr": 0.010221026790242026
},
"harness|drop|3": {
"em": 0.3410234899328859,
"em_stderr": 0.0048547488279167,
"f1": 0.3798521392617453,
"f1_stderr": 0.004781623142650588
},
"harness|gsm8k|5": {
"acc": 0.10841546626231995,
"acc_stderr": 0.008563852506627504
},
"harness|winogrande|5": {
"acc": 0.7671665351223362,
"acc_stderr": 0.011878201073856546
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] |
huggingface-projects/DELETE-bot-fight-data | ---
license: mit
---
|
infCapital/viet-llama2-ft | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 2088825146
num_examples: 1932833
download_size: 874832201
dataset_size: 2088825146
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset mix from:
+ databricks/databricks-dolly-15k
+ ewof/alpaca-instruct-unfiltered
+ garage/bAInd_Open-Platypus
+ gbharti/finance-alpaca
+ Honkware/oasst1-alpaca
+ medical/chat
+ pankajmathur/WizardLM_Orca
+ teknium/GPTeacher-General-Instruct
+ LIMA
+ Chain-of-Thought
+ Dynosaur/dynosaur-full
+ nam194_vietnews
+ quora_chat
+ stackoverflow_chat
# Dataset Creation:
+ The source language dataset was translated into Vietnamese using the OpenAI GPT-3.5 API.
+ 2% of the translations got translation errors. These translations were skipped.
+ The remaining translations were merged into 1 main dataset for Fine-Tuning
# Important Notes:
+ This dataset was translated by a machine learning model, and may contain errors or inaccuracies.
+ 2% of the original dataset could not be processed automatically and were skipped. |
Gan1108/agriparts | ---
license: apache-2.0
task_categories:
- text-generation
language:
- uk
size_categories:
- 10K<n<100K
--- |
kaleemWaheed/twitter_dataset_1713131586 | ---
dataset_info:
features:
- name: id
dtype: string
- name: tweet_content
dtype: string
- name: user_name
dtype: string
- name: user_id
dtype: string
- name: created_at
dtype: string
- name: url
dtype: string
- name: favourite_count
dtype: int64
- name: scraped_at
dtype: string
- name: image_urls
dtype: string
splits:
- name: train
num_bytes: 20293
num_examples: 47
download_size: 12417
dataset_size: 20293
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
ise-uiuc/Magicoder-OSS-Instruct-75K | ---
license: mit
task_categories:
- text-generation
- conversational
size_categories:
- 10K<n<100K
---
This is the **OSS-Instruct** dataset generated by `gpt-3.5-turbo-1106` developed by OpenAI. Please pay attention to OpenAI's usage policy when adopting this dataset: https://openai.com/policies/usage-policies.
|
Maani/MoeinPersi-Fa | ---
license: cc0-1.0
language:
- fa
pretty_name: Moein Dictionary
size_categories:
- 10K<n<100K
--- |
astronomou/me | ---
license: other
---
|
datasets-maintainers/dataset-with-standalone-yaml | ---
license: apache-2.0
tags:
- should-be-overwritten-by-standalone-yaml
---
This is a test dataset used in the `datasets` library CI |
JeanOre/Breakman | ---
language:
- es
pretty_name: 'Breakman '
size_categories:
- n<1K
--- |
bazinga/bazinga | ---
language:
- en
tags:
- speaker-diarization
- speaker-identification
- automatic-speech-recognition
- named-entity-detection
- addressee-detection
---
# Bazinga! A Dataset for Multi-Party Dialogues Structuring
[Read paper](http://www.lrec-conf.org/proceedings/lrec2022/pdf/2022.lrec-1.367.pdf) | [Watch video description](https://www.youtube.com/watch?v=R5m2OF7ksO4)
This dataset provides audio soundtracks and time-coded manual transcripts of episodes of the following TV and movies series:
[24](https://www.imdb.com/title/tt0285331/), [Battlestar Galactica](https://www.imdb.com/title/tt0407362/), [Breaking Bad](https://www.imdb.com/title/tt0903747/), [Buffy The Vampire Slayer](https://www.imdb.com/title/tt0118276/), [ER](https://www.imdb.com/title/tt0108757/), [Friends](https://www.imdb.com/title/tt0108778/), [Game Of Thrones](https://www.imdb.com/title/tt0944947/), [Homeland](https://www.imdb.com/title/tt1796960/), [Lost](https://www.imdb.com/title/tt0411008/), [Six Feet Under](https://www.imdb.com/title/tt0248654/), [The Big Bang Theory](https://www.imdb.com/title/tt0898266/), [The Office](https://www.imdb.com/title/tt0386676/), [The Walking Dead](https://www.imdb.com/title/tt1520211/), [Harry Potter](https://www.imdb.com/list/ls000630791/), Star Wars, and The Lord of the Rings.
## Citation
```bibtex
@InProceedings{bazinga,
author = {Lerner, Paul and Bergoënd, Juliette and Guinaudeau, Camille and Bredin, Hervé and Maurice, Benjamin and Lefevre, Sharleyne and Bouteiller, Martin and Berhe, Aman and Galmant, Léo and Yin, Ruiqing and Barras, Claude},
title = {Bazinga! A Dataset for Multi-Party Dialogues Structuring},
booktitle = {Proceedings of the Language Resources and Evaluation Conference},
month = {June},
year = {2022},
address = {Marseille, France},
publisher = {European Language Resources Association},
pages = {3434--3441},
url = {https://aclanthology.org/2022.lrec-1.367}
}
```
<p align="center">
<img width="75%" src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAA4EAAAYpCAYAAADo3RnoAAABOGlDQ1BrQ0dDb2xvclNwYWNlQWRvYmVSR0IxOTk4AAAokWNgYFJILCjIYRJgYMjNKykKcndSiIiMUmB/xsDGwMsgwKDFIJqYXFzgGBDgwwAEMBoVfLvGwAiiL+uCzJpivO1cjMWGdoHpqRe3pXw2xVSPArhSUouTgfQfIM5OLigqYWBgzACylctLCkDsHiBbJCkbzF4AYhcBHQhkbwGx0yHsE2A1EPYdsJqQIGcg+wOQzZcEZjOB7OJLh7AFQGyovSAg6JiSn5SqAPK9hqGlpYUmATeTDEpSK0pAtHN+QWVRZnpGiYIjMKRSFTzzkvV0FIwMjIwYGEDhDlH9ORAcnoxiZxBiCIAQmyPBwOC/lIGB5Q9CzKSXgWGBDgMD/1SEmJohA4OAPgPDvjnJpUVlUGMYmYwZGAjxAa2wUpFhHmN3AAAAOGVYSWZNTQAqAAAACAABh2kABAAAAAEAAAAaAAAAAAACoAIABAAAAAEAAAOBoAMABAAAAAEAAAYpAAAAAK0ILpYAAEAASURBVHgB7H0HYBzF+f13Ot3pTqfebLnKvXeDKzYYMJgaSkKAAAkJJYUUSOEHgSSEwD8kkF6A0EMJoZpuSnABY+OKbdx7ly2rt7vT3f+92dvTSVa1JWNL39in29udnd19Mzszb77mCCNJM+mtt96Sn/3sZybXv/71Lzn55JMbPCMUCsn3vvc9WbBggTnes2dPmT17tjidTrnqqqtk5cqVZvu///2v9O/fv8EyuHPDhg1y6aWXmuMPPPCAnHHGGQ3mXbdunfzpT38y5a5atUrKyspMvi5dusjpp59u7mXSpEmHnXv55ZfLmjVrJCMjQz788MPDjnNHeXm5TJ06VWpqaszxBx98UBoqiwdvvPFG+fjjj4XXfffdd03+Cy64QLZt2ybjxo2Txx57zOxr7s/nn38uX/3qV5vLVuf4KaecIn/729+i+6699lpZsmRJ9Le94XA4xOPxSEpKigwaNEiuv/56GTlypH24ye8dO3bIk08+Kc8//7xs2rRJKisrTf7u3bubMk466ST5yU9+IklJSYeV0xbPdFihkR2x7bJ+nri4OHM/o0aNkhkzZsgll1xSP0uDv/Pz82XmzJnCtky85s6dK16vt8G8sTv5Gv3zn/+Ut99+27TH7du3CzHv27ev8B5OPfVU0x65Lza1Jz6x19FtRUARUAQUAUVAEVAEFAFFIIoASWBz6ZlnniFRNJ8PPvigwewgS+Grr746mq9Hjx7h9evXR/OCQEWPgVyFmb+xtGLFimjeF1544bBsmKCHQf7CmJxH89n3V//7iiuuCFdUVNQp4/vf/370vK1bt9Y5Zv947bXXonlY5q233mofqvPN50hNTTV5zz333OixgQMHmn3Tpk2L7mtu49NPP61zzfrP0tBvkMA6xU6fPr1FZYCMhC+88MLw5s2b65xf/8df//rXsNvtbrbMfv36hRctWlT/9HBbPNNhhUZ2xLbLhrCJ3ce2WV1d3VhR0f2///3v6zzro48+Gj3W2Ma+ffvCII51zou9tr19/vnnh4uLi+sU05741LmQ/lAEFAFFQBFQBBQBRUARUAQiCMRjgnrUiVKTr3/96/LUU0+ZsvLy8gRkUfr06dNg2ZQU/vnPf5Yf/vCHDR5vbielZZRKMVHK+I1vfEOmTJkigwcPlkOHDplrU2KJCbeAKBjJ4vz5841kh+dQssjrM/FeeL/1EyU6sYlSp3vvvTd2l9mmdJPXYaIEqa3SWWedJaNHj262uMYwplTuu9/9bvT8YDBoJKUgvfLRRx8ZSeerr74qlPKBvInL5YrmtTduueUWoSTWTpRuUvI4YsQIk5/P/vrrrxu8QSZNHSxcuFDGjx9vn1Ln+2ifqU5h9X5Q0mdLl9G2jbQS5Ezee+89KSwsNJJMnvLEE0/UO7PuT5C+Ojv+8Y9/mPZVZ2e9HzfccIPMmTPH7B07dqx861vfirZ9Sk4pRV69erVgYcFIJRuS1PLk9sSn3i3rT0VAEVAEFAFFQBFQBBSBzoxAS+hwrMSlviSQkjCoekalIAMGDAiDWBxWbKwkEHgbKR7UPg/Lxx1NSQJBXKLXGjZsWBgT6gbL4D3k5uZG8/7oRz+K5qM0Jj4+3hyDKmd0f+wGJVu8T6jyRcvYs2dPbBaz/Yc//CF6fO3atdHjRysJBHGIltWaDVsSyHpoLO3evTsMwhG979tuu+2wrCBPYUoLiUFOTk6YuDeWiAHIuMkLNd462WIlXUf6THUKjPnRVLu0s0EtMzx06FBzbz6fL1xVVWUfOuwbBDaKyU9/+tPodmNtjAVAbTgqkQYRDVNKXT/xHaEUkFjyA0IYzdKe+EQvohuKgCKgCCgCioAioAgoAopADAJxmJQecaovAcRk29hQ0RawuUS7MkrwWEZLEybwUelhQkKCUJJFm7uGkm2PmJiYaA7TdhDqqWabdnG2XaNtvxhbBqU3lGwx3X333ca2i9v1pYPcN2/ePH5Jr169jCTS/DjO/3Tr1k1eeuklY6vGW/3tb38rS5curXPXtO1EOzHPThtOSgEbS5ToXnbZZeYwyyV+x0tivVx33XXmdmjn2VB92/dqSwFBeuWOO+4QkEZziNLAxtL//ve/qI0kr1Pf5o/n0T7Rljzz9yuvvMIvTYqAIqAIKAKKgCKgCCgCisAXgsARk8D6BJDOL+hkBdK3Jh+Eqpg2oaBa4h//+Mcm88cefPrpp4XqjEw//vGPBdK62MOHbVMt0Z58836fffbZaB7b2QwdxBQVFUX3c4Oqn0zp6ekya9YsoYofU30SSJJENVOmtlQFNQW28x+S41/+8pfmKpBUyTvvvBO9Igkhne4wwZZOYNcYPdbYBiRn0rt3b6MWDNu7xrJ9Ifu5YGCnrl272pt1vmE3Kv/5z3/MvrPPPts4lYH0zvxmu7FVfuuchB9sI3ay2439O/abKscklr/61a+i7Sn2uG4rAoqAIqAIKAKKgCKgCCgCxwqBIyKBJFTXXHNN1AaQ3iEpEcnOzm7RfdOLIj1zMv385z83NnstOZGEzU60w2pJop2VnWIlMDYJJJGjHVtssske89Dm0C6Dnj9JmOxEz44HDx40P080EsibhiObKInhs9gJznjsTSEhakniIgC9oT7yyCMCNd2WnHJM8rB+Hn/8cXMtSgUbuzc+c0lJicl35ZVXmm96tGUiQaR31IbShAkTJC0tzRxiu6aX1MYkoXfddZfceeedZmGhobJ0nyKgCCgCioAioAgoAoqAInAsEGi1YxiSIBLAf//739H7owpgrEQkeqCRDUoLKaH72te+ZlTp6FSGanpUm2sqMXQEEyU78D7aVNboMeYjOT1w4IBs2bIlun/ixIlG3c9WEaTEj4lSLDtshE2ASALvuece42CETlQmT55s8jJ8ABPvmyEp2jKRcDbkrCX2GrBrFHg/NUQ1dn9Lt1k+pal0VBJLAjdu3BgtYsyYMdHto91oz2eilHjZsmXmFkns/X6/0DHMc889Z+o+OTnZOGhp7BlIXpkY9sJeIGC9s63u3bvXhH+46aabDjudiwTwKCpclGDb4TY/DA3BcmCjaZzBNCaBjC2wPfGJvY5uKwKKgCKgCCgCioAioAh0cgQwYW42xTrgwOQ26uAC0JltxN1rtgzbMQwmxtG8DFFgl/G73/0uur8xxzDw/mjy0+lKa1Ks+/7S0tLoqeecc44pj85U7AQvj9F72rVrl9kNQhEGiTD7odJnZw3DDs7sg31hdJ+9cbSOYWxcmvumk5fY1BLHMLH5GdaC12C4DUh4zSEQZLMPKqMNhvKgoxM6UeEH8RHNB6q9YfsDVdLoJWIdnzT3LPbx+s8ULazeRmy7tM9t7BtSunpn1/4E6TXPy3P/7//+r/YAtiDZix7D4kCdY7E/3njjjTAWQqJ5Y++DDnYgMQyDkMaeYrbbE5/DLqY7FAFFQBFQBBQBRUARUAQUASDQtOgNM9n6yZamnXnmmVGX/LSZsp1q1M/f1O9YtVDaS9l2aI2dY9vuNRSUvLFzuN9W1+N2IBDgl0m29G7x4sXR/bYqKNUGKRViosTstNNOM9sMfWEn2ylMe6iC8poMUt7cpyFHJPb9teTbDvxOCS/agznF3of4hw1KZ3kcpN58KBXlhyE67A/rsqHUns9E9WJK7eyP7dTFvo/vfOc7Ag+xRrXT3md/P/bYY/amkXJHf2CDzovsxPbaWMKCguzcudOELuE5vA87EVdKkBnaBAsfRkppH4v9bk98Yq+j24qAIqAIKAKKgCKgCCgCnRuBVpNAwsWJLOPDQRIjVElkoqocQiSY7Zb+oYrcX/7yF5Odnj85eY61uatfTlZWltkFCV39Q03+tj19klDFqq3aan8kNbYqoU0CbTtAu2D7N52mkEhSZZJqgkz2MTtvW3wjSLshLLRHa+oTSzaO5Lo2NnRcYqvj2uSXKrQ2MYwt+0iJZ3s+E236EMIj+ikrKzMxI+m4h3ENacdKJ0Q333xz7KOY9mbHDuTz0xMqPcLanxdffNE4ieFJVHvOz8+vc37sDxLPL3/5y2ZBhPdCFVs+86mnnhrNNnv2bKFtYEOpPfFp6Hq6TxFQBBQBRUARUAQUAUWgcyLQahLIoNycKLvdbqFDmF/84hcGORIVhgkgmWtNok3bl770JXPKJ598Ivfff3+jp9P7JBMn4rZUsNHMMQdsRx317QhJDhgOgImeShk43baNs+0B7WJsosfnY5B0WwrIcBO0LzwRE+3mKL1iig06TwcqTAwwv3z5crMd+4eeRYkVHcHEflpi9xZbTntvk/BPnTpVKOm1nRZRYs3nthO9okL91PwkUaQUs/6HhJKJ59m2g2ZH5A9UjMXOE7t/yJAh8t3vftc4TaLnUUpWmV5++eXYbLqtCCgCioAioAgoAoqAIqAIHFMEWk0COaml2pqdYENl1AD5e9WqVdE4fvbxlnxTzS4zM9NkpffExiSK9GZpJ0ohW5IYS9B27287f7HPo0QrViUUAdLNIUoMTznlFDub+aYDFTskBckqSSMT1URtaajZcQL94fOS+DANGjQoeuexOMWG1YhmwAbjMJKUx36OVxw8Hk/U2QuluAggH30UW42Z904Jd2MflsH00EMPRTEjdiR2XAigumlT6Stf+YoJn8E8XGggidakCCgCioAioAgoAoqAIqAIfBEItJoE1r9Jekd86qmnhN4XmR588EGJDTFQP39Dv7t06RJVC6WHxa/DW2hDiZJGm2hQWlNQUNBQtug+Sm4YT9BO9EZaP9kqoXDQYQLd8zg9OtqT/tj8tjSQISVsEtge9oCx12yvbZKhW265JVr8N7/5zeg2SaCteksVxdWrV0ePnagbtuSYKp9sb0xUd6V6JhMlvwwh0tiHEnAmSj5tlWGWZUsYudjQXHxEkkU7UcqqSRFQBBQBRUARUAQUAUVAEfgiEDhqEsibpiqhbdvH39/61rfMZJnbLU3wMCoXXXSRyd7YZJqqm5Q8Mh06dEg4MaftVUOJ0j/GebNVQSnhofpq/WRLAunwxo4jaJO9+nltwkd7SDtchb2vft7j+TdVF7///e9HHfHQqcnIkSOjt0xJL0NiMFH9lUHT6diksUSsSbZtG8nG8n1R+6m6azv0GT58uJHc8V4Y5sR2FGTHBGzsHmNJ8j/+8Y9otgsuuMBsM8Yg27AdazCaIbJByZ8dVoUE21Ztrp9PfysCioAioAgoAoqAIqAIKALtjUCr4wQ2dkOMHUhyRCkgSQE9IdIpR6zqaGPn2vs5ueaEvSkJHyWAlMTYkjuSF9ol0lvl4MGDTSxAxr379a9/HSWidJ7yr3/9y75MnW9OxgcMGGAcvdgT+Pr2gPYJM2bMMM9j52MsOIStsA83+E37xeZUVxlw3FY1tQuhHVtLvaBSTda2N7PPp51a7HVJrGn/R1XIt956K0rYSEgQnsM+Lfp93XXXCdVFn3/+eYMjbeuo+spvfuiNE6E8hI5yGIuPQdntNHToUHuzzvfRPlOdwur94L3GklA6GCIGlNjSBs9eWGA8PzvZqqCU0Nlkzj5W/5vOXVjfXCx48803DY5sO5Sm0t6PixG8DlWir732WmF8RbYN4k2M7r333qgd609/+tMGYzu2Jz71n0d/KwKKgCKgCCgCioAioAh0YgTg/bHZBDLB2AHmA4lKo/lB3sLdunWL5sVkN5q3oTiB0YMxG7BBi57Pa4JUxhy1NiHJCkNqWCeffX/1vxnDDxPxw8qI3fHtb387WhacosQeOmx72rRp0bw33njjYcftHXacwPr309Dvhx9+2Jx2JDHjWN6aNWvsy4btOIENXaf+PqhFhj/77LPoufU3ICUL33777WGoPUafuX4Zsb9Hjx4dBomvU0xbPFOdAmN+xLbL2PtobPvqq6+Ong3JZvSZQNqi+5vawMJC9JzbbrstmpVxDSFljh5r7PrEEYHko+dxoz3xqXMh/aEIKAKKgCKgCCgCioAioAhEEGiROijt/uzUlGSP0qHHH39c7BAClDDZtnNNnWeXzW9KEG37K/5u6Dy64qfrfrrsp4SmoTRu3DihwxlKI21vlw3l4z5bJZTbjamC8hhT7PGmVEFjMbPOPDZ/G8LLvjK9ZVIdkpJDerlkeAh6SG0s0f6SoRIodQXhrRNv0T6HKrqMGUmHKZR41XeoY+drj++mMGYbpO0fJXK0JUUwd9M27fuIdXjTnCqofQ6l3bQDZLLDSnAbCx9Ggs3wEyDCh7VZ4k7bU0rKY+0wea4mRUARUAQUAUVAEVAEFAFF4Fgj4CAZPNYXbevrUe1v69atRiWPjjqopmc7Nmnra3Xm8qhiyXAKjNPI2IrDhg2T4y0sxPFQP1Q9pTOd/fv3G6+r9VV9j4d71HtQBBQBRUARUAQUAUVAEei8CHQIEth5q0+fXBFQBBQBRUARUAQUAUVAEVAEFIHWIdAiddDWFam5FQFFQBFQBBQBRUARUAQUAUVAEVAEjlcElAQerzWj96UIKAKKgCKgCCgCioAioAgoAopAOyDQbIgIuv+nQwxNioAioAgoAoqAIqAIKAKKgCKgCCgCJwYCDIN24MCBBm+2WRJIpxaxgbIbLEV3KgKKgCKgCCgCioAioAgoAoqAIqAIHDcIeL3eRu9FHcM0Co0eUAQUAUVAEVAEFAFFQBFQBBQBRaDjIaA2gR2vTvWJFAFFQBFQBBQBRUARUAQUAUVAEWgUASWBjUKjBxQBRUARUAQUAUVAEVAEFAFFQBHoeAgoCex4dapPpAgoAoqAIqAIKAKKgCKgCCgCikCjCCgJbBQaPaAIKAKKgCKgCCgCioAioAgoAopAx0NASWDHq1N9IkVAEVAEFAFFQBFQBBQBRUARUAQaRUBJYKPQ6AFFQBFQBBQBRUARUAQUAUVAEVAEOh4CSgI7Xp3qEykCioAioAgoAoqAIqAIKAKKgCLQKAJKAhuFRg8oAoqAIqAIKAKKgCKgCCgCioAi0PEQUBLY8epUn0gRUAQUAUVAEVAEFAFFQBFQBBSBRhFQEtgoNHpAEVAEFAFFQBFQBBQBRUARUAQUgY6HgJLAjlen+kSKgCKgCCgCioAioAgoAoqAIqAINIqAksBGodEDioAioAgoAoqAIqAIKAKKgCKgCHQ8BJQEdrw61SdSBBQBRUARUAQUAUVAEVAEFAFFoFEElAQ2Co0eUAQUAUVAEVAEFAFFQBFQBBQBRaDjIaAksOPVqT6RIqAIKAKKgCKgCCgCioAioAgoAo0ioCSwUWj0gCKgCCgCioAioAgoAoqAIqAIKAIdDwElgR2vTvWJFAFFQBFQBBQBRUARUAQUAUVAEWgUASWBjUKjBxQBRUARUAQUAUVAEVAEFAFFQBHoeAgoCex4dapPpAgoAoqAIqAIKAKKgCKgCCgCikCjCCgJbBQaPaAIKAKKgCKgCCgCioAioAgoAopAx0NASWDHq1N9IkVAEVAEFAFFQBFQBBQBRUARUAQaRUBJYKPQ6AFFQBFQBBQBRUARUAQUAUVAEVAEOh4CSgI7Xp3qEykCioAioAgoAoqAIqAIKAKKgCLQKAJKAhuFRg8oAoqAIqAIKAKKgCKgCCgCioAi0PEQUBLY8epUn0gRUAQUAUVAEVAEFAFFQBFQBBSBRhFQEtgoNHpAEVAEFAFFQBFQBBQBRUARUAQUgY6HgJLAjlen+kSKgCKgCCgCioAioAgoAoqAIqAINIqAksBGodEDioAioAgoAoqAIqAIKAKKgCKgCHQ8BJQEdrw61SdSBBQBRUARUAQUgU6OQBjPz49J0Q17h34rAopAZ0dASWBnbwH6/HUQuPPOO+W+++6rs09/dA4EVq9eLV/5yldky5YtneOB9SnrIPCLX/xCfvvb39bZpz/aF4H33gvJgAEBee65UIsuVFHRPkymo777jnBY+BF+HC2CuFNm0nG/U1a7PjQQUBKozUARiEHggw8+kAULFsTs0c22RCCMyciBm26SnSefLLumTJGKOXNM8QEQr9J//1sq589vy8u1qqz8/Hz573//K4cOHWrVeZq5YyCg7/6xr8cXXwzLpk0izz7bPAlcuTIsWVlBufLKYJvf6P79+827X1hY2OZlf6EFOsD87I+5kfYh0V/oM7bBxfXdbwMQtYgTEoH4E/Ku9aYVAUXghEQgsHmzFP/1r9F7L3nkEQkHArL3kktEqqvN/pTrr5ecBx+M5tENRUAR6JgIVFVZpKSqqvnn+/jjkFRWirz/vhKZ5tDiYltFeakUHjooYUdYUpJTJTklWeLiEpo7VY8rAopAJ0JASWAnqmx9VEXgi0bAmZoavYX4Pn0k7Wc/k/1XXhklgDxY8tBDkvKNb4hn4sRoXt1QBBSBhhBonBAFgzUgTRXidrvF5XJDIATFHwqGYCUWDoXFj8UXl8uF/Q4jLKqpqZEqsKwQjiWnpFgahMhPQsFjzBfndH5hWoW4BZPs74bQ6Hz7WP+oz4hSV6zGZ2lpqbzz9uvyzusvScmhfMnO7ip9Bw2WEaNPktFjJ0jvvH7iclukkCWgFUTg4y9KZvnbUhazj0Qy6JcioAh0EASUBHaQitTHUAROBAQq3nsveptZDzwg8Tk5Eli3zuxL/vrXpfTxx8126XPPKQmMIqUbikBDCMQSQAfIW8gQNubk9maoWM9+9WXJ691bRgwfId179BBvYiKkQU7ZsnmrrF69EvuHYH+eVFVXyqYN62Xe/z4QtydRrr/xO4YkVpSXS2FREYT0fumW21UyM7PE4Wx7K5I5c0Bj4gINPWR0H83aNDWMQH2SRsLeNbe7fPP678u5531JnvjX3+W1F56QZYs/lBeffkRS07Nk+OjxMnPWBXLKjJkgiN3A95wgfxb1A91v+EK6VxFQBDoUAkoCO1R16sN0DgRiZ0MY/u2R+4gevl5ZR1RGy08K7toVzZx4+ulSOW9e9HfazTdLFewxAzASotqopuYRoISmGmq0JSUl5lNcXCw+n0/69esnCQnWKn8gIvFpvjTNcSIhQFIUBtkrLSs1ErxDBQch+YO+JFIcSEAAksB3YXP74dx5MnRgHzn7rJlywUWXSHJyuvz89jvkk/nvyPhxY+WSy6+WbXjnZr/yvOzPL5VrrrtO1q5dI6/PflUWfrJQ3JAizjx7lsw65xxIjihRdECCGI/25RGnIYT1KciRoagkrzW42f02sccCQE1QiosL5MC+vbL/wH4pOHhAysvKTfuIj4+XvD550m/QEFm7CpLeYLVUlRbIJx++LgvnzpHe/QbJRZddLRdcfLlkQVqIJgVC3jZ12ponam3eEJwExSUe//fZ2ufS/IrAsURASeCxRFuvpQgcJQKcKMUOeyHs2Ltnt7zy32clWFUu/QYOlP4Y7N0gABWlZVId8JuVf58vUZzxLqmuqpSiwkPirw5K38FDpFevvMPuiNMLmJHUvdBhuY5sRzjW+CcuTvxr11oFQc3MPWiQOCkZxIQ0HJnMHtlVOvZZVM8j8du6dascOHDAeDNdv369rFq1yuzrDcnP9bCrzMvLMxKhYDAow4cPNyp9PJeJaoCcHPJb04mJgL+6StasXiUvv/CsLF+0UPbu2yN+f3Wkf3BIalqGBMNOcaLDWLNuk+zetkXenP2SkQLRG2ZKokvWrFwiq5cvA5sMil9c4knOlPKSIvnm1VfK2o1b5OQJk+Rr138NpDIsjz70D6morELf4pH+/Qfi2ETp268PyKAXZNB5VCCi25Jf/rJlZUCY2akT32FKeisrSmXLpnWy+JP5snDe+7Jr+1Y4tSpAG6iUGrzz4TAlwyG0B8j14uPE5QSBh4CPJN4RDyLvTMB2WHZu/Vz+eM+t8r85r8l3b/65jJ8wzRBBllFZWSZOEn63F2rFnnbFfe/FuGcIg7v8G202NXaUO/yyey4IwqlYWHp86ITGSGeTWlp9OIw9UZm22i7xwn5ziPuJGfWn6UTJjw93ECcuDOLbnIvNaOKJdlmdDc8oCJ1yQ0lgp6x2fegTFQGrq7c6ebNdEwCpy5et6z6T91+fjYE6XjyJPgzw8ZAEVEsNpEDQ3zIE0IHV3XAoKFVVQcnIzpErvnmDjJ98CiRHSUZNLDk1TTwerzV+sHAzqJiNyKBy9KjFg6DYqXr5cqlescL8dEFyRYJY/dln5nd89+52Nv2uhwAngAsXLjShTD7//HOhV9OqGHK9Duq1S5Yska5du5rJeQ+oAX75y1+WiooKQwRJ/igtzMrKMnkyMjKgDpYtXq+33pX05/GIABd+gljc+WDOW3LHrT+WXTu2CtZ3JB7vOVMYE7xwOE4OHYSny7gayUpyS3l1SGrCNVJSUCilBfskK9mNt5v5QAicNfh2CwSHEqoqlTdefkYOlVShjaRLz9ws+fv998rWLVuloMwP8uCSbl1zZcDgQbISBPS0GafKtKmnSEZGlimP14/0GNxsccrLc8jll+vk83DAaJ/HmmJy4P2F5LekUFYs/UTeePUFWfzxB1JwYI8h6U6oc8ZhYc0FGF1unhGPM2vrA5ad1uIe9hmuwG9sxJPAQ6K7fPF8+dP/u0N+esdvZRc0NhbOf182YFyhevCZsy6Wcy78suTkdDUkEqe2aQpsCUv5y9ZdFf89JO7BTbciEkCBH7HK+WGQwDa9leO7MAMRsAF5DxsCGGe1DSziEJCwo0zCNQckWL1fgv5CLAaUg9BTO4DnuMWb0k/i3Zk43Yd9ydgLUugguWdbse19rXow5+CIpo6NgJLAjl2/+nQdDQH25Xim8rJimf/BHHnzlRdk1dLFUg1PcCk+l5nU1VRXGOlfRjJWbzEBLCmvkApIDWqoxoXBPs3rlOrCvfLg7+6Sh6Hq5cIKL8lfZg4md5AYTTxlmowdP0EyMrMx4GOCgPPaKiWMGhUt6iDUP/2QXjHRCcxeEJVwWZn1e+pU861/6iJQBPusDRs2mHh277zzTt2DkV8kiZQQ8sO0Zs0aeffdd4USQR6jJIDOQlLg/INSw1Gok3Og6jd58mRM8nLMOcyj6ThFANKdtbDn+/Wdt8n+3VvF5wGhC2EC54iH6mYcJHMOiceCjxNzOT9IQ3FlQALxbhCFAPIEIRVKEDcl75DwVEEzwE8iiPpOcoUkiOOYRUqKN15qQhUyF9KhOIhnuHCQnuxB2QmS1zNThkO9dPz4MTK4f38QULfAlwzKIF6cQLa87djNzP4+ThH/gm8rDv06HPnAbnPl8oXy/NOPyoJ570l5cSHqBdN4LPzFYm5P4e2bjv0du13/uCfBLRvWrpQffvtrUlx0SALV5ZLoS5EJ086UpJRUEArL5tSqq5bXsX2dpr7R1KLp0G2USLUsGe7TsqwdIxdVdPg/KskrwXYRnm2P1PgPir9iB+oN/X6wFIJArOrEkSxGzoEE0JFchfe0C4pg/ZH4JWPlB6TQkYEmlIZ9IITQHjCpbavYKlP/HncIKAk87qpEb0gRIALWcM2/mKIZSKjaU1x4QBZ9tED+88SjsmbpIokL+SUtCVKdjCTLlB9ZKfHj+qAHfXmiJ0HSQQRLMREsrqgyebLTU0EIApJfDHJYVW0cQFSXlUhVeZF4Epzi9XhgYxIn0884E+SQzgJiPceZWzniPySB3jPPlEqQkupPPzXlOKDflQwPoXvOOitabgie7TTVIkCbv+3bt8uHH34oTz/9tCxatKj2YDNbtAmMTZxQUnLID6WIS5cuFcbJuuaaayCNuVzy8vJUTTQWsONom3W3c8d2ufdXd8iOresNAQDdgxTXIz4vCAHIXTzUuuIgJQhDDzQOKn9Z6QmwG/TLnsKglOO9ToKkqAazZ08iSJ0rSQqKSgx59OC9L6mAzRje9wQX3nszd2SHAg+i2E504w+kChtXr5C1K5bLS1BBnzp9hnz/llvhbfIkSBRtSULLARs2jH1bWKzvlp/XaXIaqW4INn775bWXn5P//vtB2bNjE8gf1CBB4tk3c4w4+mSpAlI9tBSaJU6Qh3hoiHzn5tvl0iuuhx2p5dW5ba519Hdrl9DZFg+Iv1HnDZdiQW8ffuEThnmHfz8+JRjfKyXOVWMWZSwPr5H5A18z1K0D0mKqfcY5/Gg3nP5X4DsfHw8WktIwa+iOY12Rl5JCTZ0BASWBnaGW9RlPKARMR487tr7h4AGqX6tAmN567QVZuugj2bdzm0B5S7qm+bDi7xUvVoJTYfMXZ3T67UeNTA5AHF0YANJ9CZKamCABSIJcGAy8HhcmeslSWFYlReXVUu3H1bCc7wZpHD9xiow5eZKRDrI0jh9tmRgDcO+FFxopYDzUQDPvuUcSZ86UjF/9SvyQWsVDfTH5iiva8pInbFl+v1+2bdtmyN+zzz5r1EBpD9iWidLBLVu2yAPw1rpx40a56aabjHSQ0kKVCLYl0kdf1kE4/fjdPXfJ/A/niMcNogYjL072/IFyqYLpD0miWeXHS0tpYDxmySlevPs+jyQlumXngSopxGKQQBuATmTSEDsuCceTQSKdIBblUBl2O13ihcSvCtoDLCsR7YCqhgH0D+Voj+Ewpg0ou6LSL/97/3+4do3cc98D0qffANwALtyKDuNHP4qTqVMdMnZsK046ehhPmBJC6L93bNsoTz36N2h9PCOV0Pjge8nBgeMDP22RiL4ZMaBGDI1SUy6J/6plSyS3Zz+ZMGEKwoakQepLm7K2T04IouzU9WWnJIyy2gOE0rL/sqBUw2yVKeuPceK7oFZt2Jlr7e/4f633GgrBIGv7UT/b8JoV4F3jWODHogBXdvCOBpGvBm8tFw9wxELKailhLOZAn9s6FlEdJ24k/lQldYBQhqUQ04B8bPfG/hz8Jmk0PQq+a3HHD00dBAElgR2kIvUxOgoCVodLc/4w1Pe2bdkoj//9zzL37TfFX1VibP26pLqlS2omJm1Y6cckgV04HTuY7wZgsI5j9Q9lJtBLBM5hv+/GRC4nNREE0QOboYCUgVxsWLZA7lryiWR1z5NLrrpWpp95rnTJ7damZMDVp4/0gu1fzcGD4oRdmp0y7rzT3qzzHcLENK6TeYPgZJ6qn5+C/D/88MPyxhtvRD0/1gGnDX9Q2vjiiy8a6eCtt94qkyZNMup/SgTbEOQjKIptgXVwAATw7394QGb/999GAsjYcIz3R21Adxzkf1AJDSBvNYz7KqprMF0MSSJWgPw1FXi3/ZKdmiR9crxStRsOP/w1koB+IIRZdte0JJSPOSSEQckgfylYMKJtcUUFSCQkgokgm4GQQ/ILy2FjjH4GE8i4uJBkpGdIVtfe8tnaDfLEE0/KbT+/w5zXmskivVBOmGBN+I8Amg59CjU/tm3dKI/98wF5+7X/QN2vyqjz0ya0rVNtiSQb1hgUDgXk3bdflA/efV2GjhgtF3/1Gjnj7IskJZWMjWSx7eotvpsDi4Boz9BG9s2C7gtUmu3U9ZV42TEI7Q6mbdUrwpL2g9pjdp6O+82asWrHEa7GO74Dv0EA46ACalS3oSYc9GNuUI4xHUSO73QAizzwFWCqJwYqox9Ez0B1Zgp2Bl6DauGVeL93o184BJ8z/VDHfZAbHQxVRO2syKmp4yCgJLDj1KU+SYdAwOqMSQA//2y5/P5Xt8vnKz/FZC4BE3IPjP4xCGKS5qLBDztzGuPEJnbUZhf+2Ic4wzMdOAZ35OduQwnBBDlokBgmoMx0n9c4jwjC9qOoaLe89swTGPDT5fRzLjTqgW1FBvZefDG8wAXgBe7fsXfe4PaeCy6AF7g58AL3YaeJG0jJXEFBgSFk999/v2yCt9RjkUg2KB2aO3eusR+84447jJ2gHVD8WNyDXuNwBKgJQNf/f/vLA/LMow8aVT26eWR9xcPG1wt7Xtr4OaACytec2gF+vOd7CyrlYHkAGgBU7UQZJaWSm54s2Sle2ZJfJAGSO0jx/JDyVcA2kP1KRrJPKvBuVpdWSypsAMuxMFQBYUOlPwCVctoVc8rAvgVuRkBKeuR2kSlwDLNs+UrZiHY6bOjQwx+gBXuCkGB88EEYqslUVRYZN84hp54KKWaK6bhaUELHy7J390557ol/ypzXX4STj2rUO3Q9uHpn9+vt8shW4fYl3PQejDHCeJH9bIW8986bcsP3fgpSOBaE34M2x5xtU0fJ14CgYCEilgDyEV09Ycs+GPKu5TB1294uD30cF4rxmtJ1ELywrAM420Du6ACGuEMdGIs4TjiBS0LYl0AVpPchqITSNwA0A7ADeNJDqJXCeM/pOIgrwKYZ1a82B/NSpZtkEA5mZD1Or4FKeW+cQ5e8JJCtS/tkrSyWxyVPJspIuajZk5fI01BoLZZhco5k4CxN7Y+AksD2x1ivoAi0GAFrDdYhJcVF8tLz/5aNaz8zqlksgJM8jydekn3JMmDUYAlXBOAgAt40QfKiYzHGBgcGhURIztxQ8Qpi8lYOZyshrOA77aVBfFPNyHTpUP8xXsYw+FKqGI+CqFqSA1XTooqD8tpzjxuHANPOmCn0HmquZWYh9UeQlj1iYMsWeIF72WQu/vvf4QUOo3sTiQQQ8RDgBW5+pyGBlMg9/vjj8pvf/Aaxv4qbQKd9DlHd9KOPPpJ7771X7r77bqjqjTWTh7ZaBGifuz4xSyXhp8Oehoh2AKqXbAtLF38sjz30d5k/9x0s/tDOj28u5YCQ7YOMlfH95uPzlcT778bhFHh/HdgN6uL5VbKnqEziklzCWOzVkPgnwpGMAySyBpNLqneWQD2Ua0kedwLIHtTDS8vFBS+grgSXFJVUSmJSMgggVMYx6UR2CYZ4ZTinqiqTJVBP92HxaPKkcbIBYUqOhARu3RqWK66okU8+wU3EJDoSfuEFJxzQmJ4q5kjH3ywrLZY3Zv9H3nn9eTiEKUWdWzaaX8iTo125oSpMyeSC92fL5vWfy8233SXTTz/f2KIa52FHeWOhyrDsGAxpH3hIrzVwcDSgdnypKQ5LIBJJKL7nUV7ohDmd7wK99+IdByELhUEAw7ux9kMCaL8PtO2DeifIXU0QYUEAnsebiFBRJehToDZKxzCRsZpoOqEtQE/hlqS3tgyr42BW7DMeR6kphDMcIJFxn0P6WI3rDMPv1qsCr5bZslU+htuaVc2SQCj9ykJ5GH0ZiCf+TZEbcVOa2hsBJYHtjbCWrwi0AgFr6IPNXqJXJk6ZJh/BDby/ogg2O244AnBKJlS6Jp0+Q7xJibJ4/gIZNG6cdEG4BwYD2797jxTkH5RpZ52BGICDkccnVWUVsCdcKnPfeVsCIIRDR4+SwSNHyNZNG2XlJ5/Ck1iV8fyWnJYOpzOFwskH7Q6oOhoPdaD1yxfBTgwxxnZtk0uu/LpkZsFOgKSzFc8Um5WqnXY6dNtt9maz35SMdoZEQjAHxJcE7IsggDbGJIIff/yxsRO86667ZMCAAfYh/W5jBD5dvFh69uwhaXgH6dSJxJAEcOWKZVgIelrmvPUagnsXWyqgtOXB6n4C7MKSYL+L1x7qnyEQsipM/DC9g4YAzXsPgrz5IUHsmZkqhbD5LasMihceQiuxGJQMCU48VvyZvwJSBcoPOek6WFIu1dAJrcE91ECdrBAiwCqU7UC/UYHXDxqluK5TskAOOUUtrYJzqfxiqAu+hz4kWaZOOdWEIGlNzEA/bnbWrKCAPx6W4AdJpk+vkXXrHMDnSHucw4o97neEUL8L5r4LG8Bn0ScfBCGHqi/IfrTTJRSsgGOWeEFKh7BQAHXhA3u3ym9uv8UIhk47/cKo7fjR3E5gMx4JnINp/1fhwTY9Ut/gMVVL0K4jxxJOiuy3snbIvxZJw7IOVDDpzTcc3gr0twNvikn5/HblgyQa0sa6gWVwdZn4yw+g/4AXYNj1xiWkicuTYvJXleyHhgDLw6IvUUN+vvWmErEn2rxw0AgeDcz4w/JDu9BHeEAE++EgiSAymftgQU0nv0AiiVQtpfJXmdF0ZhwlAWSqjpxnfuifdkVASWC7wquFKwItR4DqXTVYvSsvK0XMpgXy1IN/krLiAumSlizd0uH4BX32kLGjZOwpk2T5JyvlGzd9R7r3yBMn7HewSItAwflSUVYuPXv3NVogtBZMg/AuA8TNh43tmzfLGeefK9179pLREyZLL+RbOHeeDBszRmace7bs2bVTFuH36sWfSjzchWf3SJVQ3DbJLzgkrz/7qPhAKi/66tWQDHBgObapM0ihWP979+6Vv/zlL1IIQv5Fp/LyckNIu3XrJrfffjtiwcV4b/iib66DXJ/qWYWw/fzZzd+XcWNGSwKIXQU8dG7euEE2r1stlZACUTLvhtdeoyWAFx1TP0z0aqSM3n7RJ7iwOESPvpTWMQ4o52/kimWU+iXAFhDqn1tB1spgB+iFjWCSF86hEEeuwo94bAnxmFuC4UGKUA5SGAya2R8kPw6cD/sgtEkHVEYpkUhPgoQGKmMMNUHJUBbKraj2yN7CUpn77hwZNXwMFq4m4+q4eAvTn/4UihLAb37TIY88Yk1wTz/dYdRDuWb04x/XyH/+0/GnKpz88//6tavkrdnPyzYEgndC6ksJnEkWNC1Eti2z0fVQ7cUZLqSsJF8euOcXiDPaU0aOnmDu0xAKZrOa0BHfgOUEpvZ6dkGeaQ5JvYHEpWMnvt+U85vv8C5sb8F7HkA7ILDEpRYDLscGq4ukonC71IAExsFsxJvSU+ITMxAKhjF/4dmXi7oI41KJ/oHn1wSg6gk7Qm47EC4m3oV8cBrDWm6o8oz0EXaI4TDL6xPJ0vqKrpG6XqpxMU3HAQIdv2c9DkDWW1AEWoKAUcP78F157rF/yAbEz2NcKC/cgPswAYyHdCAB5GvKGWdJ19weMuui7rDJgA0fQj2E/JaULAXOYtIyso0EwBqHOXTjXEgNJp86HfGeTkFnD6+AWOF3QSVs4ozTpP+IkZj4+aE66pM+A4dKbo8+0hte/tYgiPvUM0+XZR8tlEXzPpbdBcXy6fy5MmzUeBk17mRILGoHopY8m53HGUMkukIt1I4bSNWV/ZddBi9wlhu4rD/+EV7gLrBPE2dubnS7o27QE+irr75qPIAei2e0phT2xOLwK5IAHDp0SF5GPQ1H/Mgrr7wSbQ6eCTW1KQInnXQSPHv65anHH4OqJqZ+wJ0Svni8+24QPMy8rLkfr4ptkkE/JMb8NlMxuAWlkw4G/Q7hvURIcCwmYRoJHU9+16DvwBdIIqWBYfFBgucF2SxEWJiAH16CSTTwrzoYBxVwTBhRVnqSU5KwEER7wxqQR7BOCcIzRxnmcUaCgD7HVxOEHXEiAs97pWB/vrz51qty6pmzpHefPi3G5913+QQio0eL/PWvTpBAqy+76aY4tLmwkCTOnk3Si+fBfXXMZGHACX1pWZHMff8NWYbA7ZHaNXV8PD0321081IX37tgsTzz0Z7nznj8jpmwX6xYhYYqwhCO+5YTxaGEx603ObIQnmYnQJl8CQljE6PiJz4jadxRgUWcLxlp66eV4yw/xrW0vQX+xlBdsMzaA8d40SczsDUKeZOWlszhIEkMhxAJ29WTrkrKineKvxAJjECqe7GQQLN7hToIaaRrmAAgeH5eA0mMxxjakgVgSwr1sw3Yqtlk51j1go8Wpr0w1eXfIp/BtTkJqJZ9kQu53CD9qy4S7OvuwfrczAkoC2xlgLV4RaBYB9n0YPP2BSky6C6UcUqAg1LsYByrJGwePfQjcjIlaanamdO+FGG4YgGnTRwJYJ2HyGIItQLQLt20HUD4OGQLIDaPJgUU/qobkdLEGb54Hl2JwPuOWsZMmSVa3HBzLldNmnW3sE8uXrJH+gwdJDjyFUl2NEgxz23VuoPkf8ZAqMRQE1Tt9s2bBzKDWzqDrK6/AC9wgeIGrhBe4FfAC94PmC+wgOTjx37Fjhzz00ENGGlznsVihZhWYkwBTcfgdkQ6Y2mZN2IlTSdQNjfztc4zKEOrdVJjT2JX5Erm44IbHx2p4fsTEG8VZK8GxZVll7tq1S5588kljG8jA8praDoGiokJI69Pkuhu+J7f95GZMcmsgbcOkC5UVnYyZKuEvS0LAv5itocKwD/Z5aanJWBjqgu8UYyNUDccuB9CPHIItYBWaQQkmfZQoVUGid6CMpaBvYSxQbJUgQ5InDpN6OInBglFaRqqkQ+W8J8obNbyf9M3rKTu274BE+H05cJBkE4qjkBqGcP0KEMF42Ch5YUtYWFppVMwXL1rYKhK4bp3V3qZMYduum2bOdIAEQh0QqoBQYoBKct3jHe0XVT5XLF0s8957C5N1eE5GP2/q+nh7UA4guFeP2yULPnxHVixZJKecdrZZbGTrQuNs9R27oGno8KJYtNcuzzrF3b/1ZbT6osf1CXjHQjvxPoMc0W4fjp34zoN+G+y5HUKM4HI4cJNAqTg9IIBZ/fF+It4fniscxJyh2oePG/4AaM/JxaRUaPgkiiejFGYgiC1YXW76gTCInz9QgYWlAH5beWoJJ0FCXaCfEQfCRwjuSeBNGEGqcBXrGLO0IJ0v95pcL8vNQiLINFoulenyA9DT/fKG/Bx/15n9WHIy3/qn/RFQEtj+GOsVFIFmEaCqR1JiCoImj5CnoLaR4IQL9uREqHLBwQtWP0dOmCTd+/fHZA3DLAZg0/82WmpkAGUfHUlmk+fFJFNMlExYB0jw6KSiX/9BIJro7pNSZfzkSQhVsUPWLP5Eho85Wbp0taRyvApLbO1wnYyg5BjB6hBAXt3Vsye8wA2GF7jl8AK3nbs6TWJA99mzZ8vq1asPf2Ya7ANpR5yllheHiiPuVm2S9DGB+kFSkuiF+g92VFbGSQCSGqoCGTUiTBpoW5SS7JZuWT4ZmpcpqWleeX0+7D330Q60nsTJlGn9oYryZwjp8TSC1Pfr1w9tgivNmtoCgRVLl8HWrkrOOe8c+c/zz8vihfNBzlFX8AIchzpzwg4oDttWQsUiVEMoom7JZYBePbvJpRddKNNPnSgZ1P3GOQEssOzas182bd4la9ZvlI2bt0sxPIMWw+aP7uTDUPuEnxjpg3ebbSM1yYNzU817PXRwPxk+bJAM6NNbuuRkmKDxxSWQAsS55ZHHnsZvSCipa4p2xYWDKtgLxsW5jBSytKRIVq5cLhd/+TKQTuZpPkHj2KRevQ7PaxarIrvRJXXgxDcYEdoK9svCeXNk0/rVxhGMeefNkePs0TFmmBaJCqqCvu77c16TcRNOkRRqCbCvsjqkVt10nNchvdZjOoo25ep9BAW06mrHe2Y8f/gAbnIv+nKEesE7y3HZCZVOAxD+stevhv2fVBdKGJK8xMw+eA+tBdVgpRu8kATQh4W/yHvIFcAwogsHOT74xIuFHifOrSzZBwlgoiSldTd9C22BDRM3K4YxCzPoh9jzhMMMTt8VN8DF4+brKU16IJ9ImvQ03/yzT9aYbY45J8nVZjtZusAf6N3yGEghEx3JaDo2CCgJPDY461UUgcYRiPSl+/fvlgf//Dsp2rtdeuWkmuDucez8ofY5etIEGT5uPOIBMVizPSlsvMijOWKKxwDPSRhX/vsNHiKjxoyUt15/R155+lFJS8+U0Qgmb+hH7EytBRcNQcq3A0SP6p+9EBjeHbO8XwNPmIG1lhu4eBDCzpQYEuJ5kICG6xb1bQZxp6QkeuCcAySvOgibMEhmODBjMPUhEHi3bK/065WDCWRYNm4pkE07D0EY6BAPnASkJMdLD7SpoX3TpWf3VDgawgQei61pSV7ZDZfcpJX2kF6/dXECQhvFd955R2bOnCmnn3462oaduzPVUts/ayrUo3/0vRvkvFnnyJRJJ8uiRR9LMbz+MvRDQnwITlwgBQAJZCgXtg0I8+AGHhQMldS7e0+56aYb5cJzZ6DuEB8MO1l3rJqe3brI5JNHIgRECB5Gy6UUdoYFBYXw9lmKgOPwBop8HPzdaE9ZuIeM9DR8J8Pbo7XCH6KDGEiI/ZD2JXpccu6smfL8q3PkYH6+iRvoguTQGRc0qqbVUGV1YNGKCxn5UAulWrPXiwbWgtQDc0RoHMP5VP1WJ/Lee9a+dGiG5eV15PaGekPdrli6SBZ//CE8uFYaUk07TFOZJNymZlsA6DHNYsWoXLnsYykqzEc4Ict79JHeAkNBMDHYedUiqCd/GpYEhArxTMRIg8XPTpPCsP+DI5g4R7kEIaELgph5PInYh/bAwRkeQUOhKgmUHTSQeNN6YFeyaSIkf9X4SA3t95AMmYsgR42QGrf4y7CIhz4lAac405zwO7AL2qHF4kvvARX0pMgYVE/LCJK5MKSBcXGlWBzegxEHKqFhrMyYi5heJ3KRul8nyVWSLQNB8QaZA3T44hfLOVym5Ems2mcKcrnFh+PlUD4lCdZ0LBBQEngsUNZrKAJNIMAuNITV+08//lCWwg38gNw0Y4/DQND8l4qA6rk9ehr1zzabfLPztuZYDd+ZYYKYeELykIJZWJ9BA6T78hWyafUyOIF4U3rBbpCeQlubAtDrClO/C2n/V78KL3AR3X+QwqolS6LHEmAn1VkSPYIuwbOvXLmy3iNblcS/VL3KSffJsAFZ0i3HB4JXKp+s3G4mi2mI5zawd5aMHpwt2WlQ/wFhoLSvFF4hQQ2kd266DOmTJb1yvXAUgmqHUxEICWF/FJTS0ioTHDwRk/8E2J8FMPkvxr7ySk4CahsIJ6lUCyURnD59upEW17tZ/XkECIwcNVJGjxkrv/vNXdINUr1MkHV/NV2/w6UDHMLEGfKPgtEI+EoG8CmB3V5WZqZ877vXyoUXnIEJHCZVCBRfOyHDXA/vk5XCWADwSDqcuPTulg1BIVf3Iy9/pHrZXtjXMHA8vZLWTcwUJ4MG9JKzzzhVHnnqBamBCmkKVEidmJjTm18VvBGam4NU0XJeUbeEpn7R6+dnn4UlsvYTzfrggyG0NesGTzuN99uRkwNenfeCAH4g27dsAK5Qy0Xfyol/UUG+VFWW4eHbFgPTvbPUoyyW7SkfMSx379oOJ2V9QSKOTmRb9kpI8r8JVUiaiEVSXKpI5m9hDnFDy6TL9nkn6jcssdHzguCF4K0XQeC9iSBmIUzVaRNrKgyLQeUHQRARyiUlG86k0pAfizDlyeIvgc0eJH44GXm51BMjzYu0IUeNy0gK47Bw40qMw/jugqOfnVJycIv4UrvBaVQ63mNcL5ZA4t0OwFzFBWmkw5GP6xXhm7aBTdeJS7wyQE5FPiu5Qfvi4Wk0CJX0QyC6VVAE9QjYKFIx5H+2N9Fkaf3cwrqC/m0tAkoCW4uY5lcE2hgB9rVBzMpXLl4kLnTcCRxIMUpz4h2Hyf+w0WNgeJ+NPPbELuYGcC40wJDgAALu3NFXo9uHK2js40AfO8izPH7oWRDZsXrP1fzYQSKmXDNgcFKHbh75smEf2AV2Qtu27sJ9fiRjJ06VU04/G4XwnCObSdhOYGKvym3PtGkY8G+ov7vD/qbkhFJAOgaykpGxQqIXJ90yk0D6kqVnTpL06ZkiacmoW2TKSXVLho8SQJDDrCQTQiQO6qIBqA7R3r9HlksuOLUPbDwZVsRj7ABrUO8QJGNsR70iTznIxPABmZKV7pWstAQQTRAQiJrWbi2RuUu2IewAPdKhAUQSvYWuhy///fv3Sw+KcDQdNQJOuG3/xrduQJiFt0AEdoH4wdqGczi8uLZXSEp6EcHTmuih9hm/78pLz5XLLpoFAogKpb0OJ30mxbyL9suPXUGQPDJ/1jtT7PzOZIt0AzxcW+Mmp/lN++SLzjtDPvpkqWzestmaj/KOcLID7YrFu2HDltUl2xBQNhv78iylsTR4sEPeeCMsa9awb6rN9dZb1g+YLsp99zU90aw968TdWvXZUvkU9pTxrkQ59dQZktd3IFSD58n+XTva9KFsjGkPTrXuKizI0THYkSYuSlZXVcje3fvMGHY0JLByQUj2Xw71dWuNMHpLoWKRA9+GOiJURlOubmy8imY/8TbY1PFC8p+R6IcP4t2pgKptsbXQglc7Vrs6DDJWXXLAeAJU4s/qAABAAElEQVT1+LrjXMwXgnDw4of6dgJeGL7E0N5gSCi+l7UJBzC/oHfXuLhk2N5jRdALUwAUnpyeh3BSB6S8tEA8iXAUB9tAhwNl2Qkq4LQr9leVSgKk/CHco5POWyIB5u1szX1zZMuSvlAJRfxBUMFl8pxMlutMCIm35a7o6dmgjpqODQJKAo8NznoVRaAJBLD2dzAfYSEWSzr09NDXoq9Gz4+Vv9zeeTLxtFPRYeJ3vRkaJ+gcxBkfcM+enVJaXEKTIcnO6Sp9+/UHcczENS01P9BDTPrLZAskcVTZCgfhgAKzzYysTOnWPRf2YZlmcGjsJhMQt9AH24FkqI8dwMRkzYqlMhYqoUkpWKY9wpQwfjy8wNW6gXNmZ8ML3Ex4gfsSxrWjW1E+wls65qexDqkK+umnn0avjek/6hoEMDtJTp/YS7qkJwB72F3BLoOqmazRRMRrG9AnA4NyjZSWVEtBYSUGbZBCjNtpKfDqCCLRKzcROTFRx+IBvYMXlQakkhMsSH64QMwBeXj/LKiYwuEAJyH4+Lzxktc9WVZu8CK2XN3JIcnqZrQf2i0qCYxW19FtoKpHjhotl19zrfztd/8PRAhSNUysLfLNGmJbQH2hwqqh2llWHpKxo4fJjdddhvYQxASex5AM40LLiMyR6RU0BDUyE/QZ+xgoui4ps/oFLitRC4GSQNqUxoGUWsHo0UyiE0jmdcjQgX3lS+edJb//0z+x2ID+gyp6yMMyAjUgFb4M6QnjvlWwC5w4eQr24sIsg/1WI+mccxxy//2QAmCiX1paN9PQoQJvoU7YoTZRQN1TTshfxUWHZDlUQeOx4HfL7XfLgMHD5bmnHpTVK5fgeVi/R//8pi5ZXahnX1KynDbjNJl2yiny3LPPyWerVkCtlw7Fjuw6NWgLxUWMT9fAImUramTfVyIEENwk+8/wTnuRQyrnhiX/O1hkgHZg/rUIcYIwEa68I7vPVtzKF5SVz1WFWihGGAc4hwsUY/EuB0QQnTokc+wLwpDWV5TtxKJOJcI+5ZlFg5pgkmxdVy1rVy6THXtRDyiF8YRPHj9CumZjfI6+xwKbYJFlq9fL5+t3oD/xS2JyjQweCk2RobmSlZUnHh+cxqCfD0LlIN6N9z7aaWApCmYFwTgQ0Kr9kD4iXnC4L45HpI6m7bSsXsbK5fKm3GEw/lSehIXg6+hDAoYIcielh2PkK+a4/ml/BJQEtj/GegVFoEkEKOFbDinggd3bZUBXePhDrw2FMPEmp8gZF14oWTk56JQjk71ISXSZzmDilMpwUp/Xt58kJkLVAqt6HM65akfCwImdGQMgAkiAF7/+sMEbMgg2eTinClKE0rIyxCkrwXYApDEHtjzUF4wZNSLXo71PAIOGB/HFkBkxBzfIvj27pX8rSaALjkUcWEmkTWCXZ5+FF7j+TWLT0Q+yjrZt22Y8g8Y+qxP1NrhvlgzqlQr7Lzh4obQFc6wQ6xMTgaoqBPqGcVh1JYZPquOhzhkcIAxVnxq0DZrxR5eBMZBT5S8OHyfsBblSwPqn2pkfq8XlkAZ7YHvmZD6cxm9KBZPAKENoN4xbx0RiUoSYdmuhu3f22Webffrn6BEgrmfPOh8BwufJ8kXz8J7SA6epQby7mJqDnHPF3YV3OiMlQb7xtUth85eDugez57yLn0gKIBi8H0y/vBy2f4gZilZjAnynZmQatbJoVvAzEsiy0iIEJIctLqRBdPuf5IPTCMQDTYAXwVjnLrzHpKQEOX36SfL2O+/CE+gGXDEO0h8EqvejVASi756HSSna5zsIbj9m7Hi4nYddYPSC9h3W/Z4xI07++EeovKfC8TzWg773vTjh2tWoUQ4591xKRZspoG5xJ9wv9rQb169DTDe3fO+WO+GUZ7Q89ejf5MN338C7bHljPbw3bvljsis35+MPJbUjxwyXSy65VCZMmID68RgV9PUb1uMdh4eeI4Sa0isjTeSF+DmCcvzwEluz13quzLug+nm9tZqRdCnGsmyR3aeh88P/yg/C4rr2CC7QcsiOfU6+3wQONqAY1QHfQakszTdjtychBXjSKyhDPcCaruQQCGKV+DL6ituXK/v3VsiyRbtk765qSYe5wKgRA0DQ0G8jBERpSaHkZCajHGsxhoQuEKiWzPRkOWPGBCPEKyoqlb1Q5527exPe3wRDBlNBIK0lolooaBbiwAJRgjtZKkoPSiBuF2KM9seY0NXqn1pR6VQPHSbngvy9YS5QYcJDWNeCn2I5XX4CBVHLa3ntHehWeyGgJLC9kNVyFYEWIkCVzL4D+6PDTgOJw2of+R5W2YcgiPswuOQPYqXVJOpymfEPAwa+U+A5NC0NHkMxYcOU3gwWZiChO2mq+MSszDogTaA+vwvW4GGog3DccSWHJDUDBAMkowbSAMqY0KsfdtckKsVwOV9wqAhu5rk6iODVIJSUGvCUVvT/EoeJYS+oFGJEgxe43oddq7PtILaUrlXCYU5s4sQ/O82HaTYmWBGHH8TZzBewEQepoBcrtakpPklOTQV5h+MAtJOKihKQA8Z0whSdbQUfLhjQw2y3Hl2xUOAD0UO4AJCEitJieKYM4hrMaFUjq5MaRUP7ZkASmSYbdhbg3hCnKtIueJ+0DbQWGKyJmjlZ/xwZAqZ+QrJg3v9k5tlnybp16yHRL4B3YDiDxztrqtC8Y1iZR52PGjFYzjtrGlbrIwTQviry0BtsIbys5O/bJ+VlpebVjEff4oInoTDqPAE2ZlH1b+Snw4niIgSRh4YApcU1cDV/qLAAbSRJMrNzEHIiHeqpVEXlBBA9DMrol5crF55zmjz84GYsEFCBzQmpRA00GNKkf98+8smCD0FAy1BuARYZuhjtglppgn2zdb9/8ANL3TOAvuWSS7hAITJlSscngETBDxVwd0KCnH3uRfDIPEDeeOW/8s7rL8B5T0mEhAMMNgJ8tTTFduFcMHK5PNIfi23nn3e+TIdWSRK0OsrLK6B+7pfevXqLD8TfkMCWXqBePhIGepS2Gmu9gy38WTmv9gFTvlW3X/FORxsegsWuz0UqPghJyrV1j7fwEsdvNlaY6dg58GNBphrSPH8lQj70owgf+/ieIYQUFuxcIHi+1EGAOhEOYBIkVJooE0b1kfRTs2UHNIKWrVyLui2X7l27yJDRQ2FagpiiOI/vIC+TAaduRSVVsnLFaqlAX96/Tx+ZMXUKPIRC/liyB4MF+w3WBecctTgHQCqlpgL9APoRLDZXVuTj/d6LNkqJIG7fGkJwTsvSGXIrogP2lfXyruTLRjiESZSuMhSqodfDGnBgywrRXG2CgJLANoFRC1EEjhwBB2br6enZmMzDu5ofLp8xIHCyPnbiyeho4dYZEjiTTEfLDpqqW/jrwCAAG5I4byYGhySocXEgxrosOuxAeZEEKwogQaL+HwYAxA9y+tLRgWfCBgBuorG3BhKkEGIF+StgiB4uFycmDKZH59FIp86vELwEFhYckF1798v2/EJJ79oT9zYFQeUhxWtt74/yGAqCqfKjj7Cy+4H4IVly9YH9GlanfeefbwYsk6ET/CGZ2l4vHEZ0FRajKwdY1ndtQt3jpwcS2x65edJ74GjYawI7b5KxxSop3Cc7Nq3GZ5mZSLD+EhJTpc+QMdKjz0iogmFZHbaDtP3I370ZakTLpKRwvyGClAJS9TAZTj9GD8yQfYcCsn0PAhbbhmS4D0qEKYGm4xGzwlx7Y7p1BAiwZuOdiOkVrJL33npTevfuKYsXw74KJNANlV/QN0y0kAkZScLOOuM02ICmQK0X6r9c+Im0Dcp+/dVVpl8wWgG+ZAjnYCeEviSMNmb1IbHtCOWDHNDBUzz7DXOI9evHBK/cqJn7sR2PPA42QrQBIw0EYTh12gT58P0PZO26jYYkpvvixRWukE/nvwe7xn3Sd9BQWfXZChk0ZIT06NmrWVRY7g9+EJInnqAnUys7o5BcfLEDcTMhfUiIdEbNlnTiZaB91tDhI827tGrlUnn7tedl984tqBM8M+uuhY9k9RPMbNUTHQt5MYYMHDRIpk6eJDNmnCZdunQF2avA+1tkFo18vkSoIg+XbBD+AwfpjbGlV6t7U1QfTka4EbMoaMpofX2FSmqv7UisWz5/YfgyifaBHS4ZAogB3QGNjhAWWsugbpmYjJAQPlMlVNMNQ9XfgzGexCwED5/VZZgXlCZJNqR6O/bky31/vl/yD1VIj159sdiTIO8teF9+/9cn5Ff/920ZMbQ3iCWWcvCe//aPj8j8RZ/JmJMmQuqfKEvXLJDHn5sts06fLGefPkXC/iQJINSLKwnzBqibsz2xSt0g+SGEoKjivKKmTNzosyrL4UgmZQDy4D6PIFHlkx86l6IfA01fDAJKAr8Y3PWqikAUAUpq8vftkp3ozNNdfumaDhf+mVkIDN8LHT474pjEsdL0y7Dd8aQj3k8uJnscNe0VQ0wWMfnjABJwJUh10S4OIRKf0k08SVmYFNZ2tk6nB5M8xAvypMCr2H4QxwKUguuZMZx/rIGZkj+XG6EJ6KUBhJWe67Jze0RWqrl6WbtiiB/NpjBsDg7efLMU/+1vh+VNPO886fLkk+K0vYYelqNj7eAEOB9u9xtKtOsyqbYq8JMTQ6f0GTxOxk65ULr3HS5JkMIwVhuPVYMI5A3YKEs+SpE1n75rJvojJ50jYyafA+lOb9Q31H2Rj6SDdjzZ3frLZ4vfkgM7NqLaKRPkPzonwqpxcpz0z0uXguJK4y2UVyZpJRG0JYPm/lrwh0QksB+kIQfqwJhAaLIQ4LvJF+688y+Wp/71TxOCISMJTmD4WiFREmzUQZGNEt8JE8daUli8k6xH1hYJGl/BZKiPp6RloQNAf4D31YHYfpQG0+Yv3gVpn78c4sKIzSEXb0DwnPQTT5sjZoR6aLy7SpIhCk6BlJDeQvEH18ExpBAWCChJ7NkjVyYiZM06xCBkW0EoajioKYFtcpEhtPl798iLzz8nV12bChvB3ubcpv7ccktI/vKXyANHMkJLXZ58kurHNfLii05IFK17aKqcE/GYpXLrhJ0XvP1i4r5m5WJIgFGrkcU1ot9owsHa42wndPaSLHl5eTJ27FgZP36cDAIJdMPrbwU8BVdCQ4BSfR8kve4ELDxAlTcbnqe7Iu7rpo0b4BQK0h62KENK6uJNkhm5pTq3w/1utLUsEMxY9eE6mVrwwwPJr50q5kD1+ILa38Hd6NfWWEe902v32/lP/G++y3yTA3AGsxf1Anu/xN7Yx7HacvaGlxN9boLUIAagvwwxACuhto2FwA8WLJX/vDxHLrnsKpl51iy8J7Xj+/z58+UfD/9V7vzp9VgwzJInnn1NqlDGf196BVpEUDNFYj++dcsWeeqJR2XJiofkh9/+uviCGRJEO3ElV6BrIBnkIjRoWnwiSJ8L9+iUymLEMHRuRSQKOK/BHKK1KQgLQDqF2S2fwc/oToSfz4EkcIiMla+CUtKXgaZjhYCOxscKab2OItAIAhxcPVj5c3m8cAcOtUD8ppc9D5bDjUZndNyLDPmwHXCAuHngztkJAmimghyNuWXnJXHzppnJPg32E3xpOMYBwi7DXMaM7DQ8T0jtYgaEIALQWpTOyse/9GBIBzLdcU8H9+yTBKiGMcyAVYJ9QfxsYSq8774GCSBPr3j9dcn/xjck95VXWljaiZ2Ng3AZbLeYbJWdSM1gQs3aQg1EqoyEoQYT/q55Q+WUWVdLj76jMXe3Bn0rC+w+E7wghiMwYMfJoQOoK69PJky/VNKze/AK+DAnZEjxXtRpLxkz6TxIFX2y4O1npOTANhxF7ZNg4L58kPAMg1ro9j0lsmlHjM92+4ZQUktT6bxHpeR/D4lv/MWSfv6tdU6rKcmXojfvl4Q+4yRpQudzCMAa6T9wiFx57XcQKuLXeN9A2syryjebG7D1RH30hAS9Z4/uUOOiDSidBNVYquIogDyQar5UDY/zpUCq7wWZcBo1vQQfpQfpiCtWhIWefHEEAyB/PolP7opJHtTDUTZVMf2wOQqGElC7CBVSBQ+F/jLTJjkBJMGgdIpckbbFw4YOMQHmKyrZdnGfuAGEL0NZDqizFsmeXbsheWrezfvs2SH5wx8sAjhzpkP+8Q96IRT59a9r4BQmLLNnh+VXvwrht9XOcbEOmTZtXAPbrvlSApvbeJA21kmjydQ3SYNF/Kg1MmjgYBk3biw+Y+AUrA8kc5zk01bPajuM+VheVg3poBdtAliyweC4B+2le7fu2OfGugGcf0HV78CBfLMAwBxMWL8xxDGAdhOkWmC0H0ExOJgEM4auXe1FQXNKq/94xuN5YEIaxvB38PtwOjQEkuoBUGVci0UyhIwAZzDJO8ManVp9geP6BAvpEENCVOxG/bvwDvjMu2Sg5r3jvQyUeaECmgK1cDcWhLzy/rxl8vQLc+TOX98r/fr2PewJT4Hjn0yQ/A/mviETxw2VkDtN7r7n51Y+07ys9tO3Xz/5v5//Qp547Em5676/yG/uvFk8qAx/MRYK/DAlgBdqRzwW/rg4AE+knkSEmoEJSXkx7BP9+bjf7iiTBdot5rBbqbOjRPbKq/JTWAJui+7nvj2yUtbJHDlPfiO5Mjx6TDfaFwElge2Lr5auCDSLACdYGeis01JTMMkvhZ1elkw+dQZUreDkw7YHtEtBPxuGWkaCD0HB3ZYahul6Dfur1wlD4hLvy8IEkOqjlBQxRfLUyYpJntMr7qRMXK8cKiGY/EWoIM/gFI1SyYFDB8m+nTulCs4k9uzcbtTPXJAa1CmKJzSRgrBXKrznHpMjDs/MoPD+5cshjYBKGQaywIYNUv7qq1Lx3nuSeMYZTZTUcQ5RusaA3Cm+JKhgIv5SGJN3SD6SfLbnNT4rnAZAMuNNSpdJM+AYBASQs246eGH7sQZh6ysOv7NzB8jYqeeg3hGON7u7Rds4hpt2YmYALBSTwhQZNGIqnIMclIXvPgX1YMy2WKEokmqnCS44KPJwmDAnGylUNVQR6SmU3mVbmgL7N5mslAbWT5Xr50vl2v9J1balnZIE2tLAq6+9XuYvWCgLFsyD5LXCvIFexvIizKg32vR9snQVvPNB3RsT8hpOwCGdp42XA8QsRMmfpxRe/3aCrIVRbwnihfpmJqTq9AiZhAk+F4aCUAF3ejIQQD4sJft3wytnqRRBxbcK1/TDTowSHdoQOyoLcQ9wNAPpcgmcAwUgKXTBxsgFT6M7ducbhzOUXrjQzyC7CWBfQQkC7mXAkKHwOsyFh6aTHQswL0/klVeckG5Yvcm//hUvBw8G5dVXwwgh0bFJIFV116xaDmkcHMRA0tpowivIiTjtsliXvXvnyRjYjY8ff5IMHzEcnn2pMwmpLCS/ftqM8kVmfmy54Xm0Ek6kKClyQH2TJJM0kotF3bojNhwaGcucNHWyvImFuHKSe5wYh7qugTZKBsYkLEHJXjgDo/oez2WiV9Heef0lM6cL8pKo1/YtJkML/zjQrjJ+EScFt4K4bhfZOSoozq6Qf22tLSDpa3gHxtT+7jhbxAy9AGzuwoz9B9X+MOqYRAtvId4tOPBCAPhACSVuLlmzcYssXrZR5i9cKXffe6/07dPHoG7VSC0qHC+GDhkiIQSc37Vzq1x2+TWR6sH1zDhQ29bYl1/zjavl7xWl8oOf3COXXnSmjBoyCA6bsmArXIHA8qVYYPKb9kf7YldijriDUCwI5gt6Hly0tqzaO2h46wO5vw4BjM1FJzFvyS/kankGLmKotaKpvRFQEtjeCGv5ikAzCHAifwgqgeWFh6RbSrIMO3mMDB45DL5dqLpVv3MFPXMnQWrYvB4+pQjxLiyvxluDTNO3gXKhKuhKSIHTiYo6Wbnam5KWJv0woGxcvUY+/3yTrPxkvowYN0EGDx9VJ29zP6qXLsVqr+UEJfsPf5DKhQsNCYwHyez27ruyPeIspuLttzsNCXSBAKci3t/Avjmyv6AQdR6WzBQPPEHCyxtW8JlIFOips9egsSBtU7DDcu5jDeY8HpkCREgeHTUMGDIZByhVwFHuN1kgW+QE0OTDDjQNL1z7Dxg2STZ/vlB2b1plVP4oKWCrqagOwmEAVIJwcgJsSnyIPVFSDKcysC1inLEWp4hkI3hotxS+/ts6p1VvXWJ+h0FOOl9ipQBp9AEMAH/Pb++T+/7fvfLic0/jXcRkCyScPgMpuFmyap18dPMvIQmEox5MvocM7itXf/ViuHbPktQcSHNg7wsNPyNZLoPUbx8WXAoKDhnHECnw4puVmQ7HEPAgjLLKq3fCDqwAxw9ikk/PnyCLuH46XHSyXj0gkFKTK1VF+6DmuV2WLV8tz730phSXVRjPsV7EDXQioFt8uFoSsFjFpwiijivhkCo9M1fGT5gCQkebJjybaWsN1+z8+ZxAilxxBRYbIgTQzvntb8eBBNbAg6XIoUNhEBFepeOlAoQH2rRuNZxvHcC7Z713dZ6SzQP/SLJSoPI7ZPBgY+M3DiqfdPRBu0LmCCCup5mLA3PjDZhjRwR/2n16PJEFI0IeyRPG2JCdlQ5Cj5UGEI+BKHvB/AUgjCAk+EdV0X179xlzgO49u0F1fR8cUFl1ZjQXEKNy2PAxKJvjEXuMI6+j9J/BydAeuEb5MzRNMETEEkAfHAZ1eZyLE0dePm7uuEzEmf13TbAQzwdHXW5LHZLvvCOMwO5llALS5APvWTxsyHfly/MvvyM//PEtIOB9GsXENv0YNmIMvIIPNhofFgANY0gieO2118o3F8yXV96cLwP695Z0xAqsqYITGnqUToJ6qI9zA7YjhHxPTEc7qcTd+3H3EaPNZhDeLSsQIn6RycWg8QwNwcUFpkSogVZIAcLH5yPXCzJerjT79U/7IqAksH3x1dIVgaYRwLjJoK7zPnwXLpsroZKTLulQvaQ7ZksGV/d0Goi7oMJDe5+WJEMO7Al/EydwWGDMHyfKDpRBash4ZTGJksE0rAanpafBxoTOZ+BAIkLmYrI1u+lfty6axzNliiGB9g4XiKBr4EAjDfSvWWPv7tDfnNR4YaBPyUuv7ph8r0DAdszkeyBUiA/x+8ANzLSKCwV0Iz8IZM0Nom4SKs2aPtDUq9rYdgThPpwDPqUxyVjF5RwwBAdAe3btlEKEAugPL7Rmcm6VivNZ76jb9K6QLo6S3VtWc4/51ODkkvKAFMImkCrBqSClmbBXLQMBrESA6CNJofJDUv7pi0dyagc+B3hHJrec4F/ypfPlvdeeA7mqgVQOsbnQPzBsw8mIvVcBac6GzVtk67adcs2Vl8qNV1+K9gKJbUYv6G03TMoPFRbK53if3sbCyksvvWSkQjmQ3Fx11VVyzjnnmZiPHtr71k9oc8HKfKkZ0gNqhsPl841bZflnn8vIYQMRizJJdmzdLHu3bzV2y7QfrGF7dCbAIcxImTxlqlUam1IT6cAB6yDMHQ9L9hoD1qBAVmGjWhtS9LC8J/KOPbt2yLatm+C8p9pIVflOWimyBXKWDEc/Q4cNl5kzz5SJJ0+AI7E0eIOFSjDU88JQ7WbzIdTs72kXvHrNakMQu0N9mAtCLjecbwBISnmpQs7QQrv37pKB/VCXsBV2e1O4XiS5PfJA4rEYtR9kD3U6fNgwLALBGwvGg4H9+smyJYsgaYx4m4S9KKXTY8ZNtuzFDeHknTdT6dbDNfg364+QcoLwlb8FarEaqojD4QcTasKUCpZDKuw7H6Vj/OlQCeYdhKwmgEYOlet49PNmBzRCAvAASimgCQaP2frBgyWy6vPN0rf/ADnz9JmRBYC6aFASXA2NDoaM4vjC/p0q/xwt6H+A4zbf98NtOBlDMkm+df2N8off3ytbd+yRLHiodqMPCvu9uBfcJNRD4r2UEkNpHNLjeD8WGRHKxAH/Ai1J9ARqpwnydSiBrpYtsgDL1S6jBvq83GgOc7+mY4NAfTHDsbmqXkURUAQsBDie4ZPojkegbxf6WEy8KAGkq8bIxLAWKg7x+EdHDjyJg26TCSuqnJyZOHLW6m3j2Slt4IABj4KYKNRNuA6WJUksHFgt7NK9p5x21nkIMjscd9HcPdQrieqGkRTfrZu9Gf0OYwBjYizBzpIYm83vD0laElRCk2DThV65eza8O9L2EnViEz2X2ys53fIisFi42+gz4PTDD/1N/vzA7+FkaKcZ/KuM2maFlIO0PffsU/L7390nmzdtMpMC035QEpsYr+CGLWFmTjfUPWYadrvChIGSwCrEqHRBbSwTE4JknwsOJKCKGLBWb1tdR5hQONNhy4pPHLzVxqb49O6xPzvldgXcu89+5QUQK6heQcrSs2cPo3bbo0uu/PqOm+Wff7hD/nDvzxGOobfk9cxFXwE7PiwI0ftvQ8k48cHkn3HhbrjhejP527p1m0yfPh3StytAJtJNWzn8XLQsNg6ongcgKYxH/ef17iGXXTRLHv7rb+TuO34gY0aOwIKUR7rmdpUgGIQfdkvd84bBScUV0gsLOi1JDAXBNG+e3ZJrz5o719qXmysyYICVr/boib1lnsy8Z2HZs2dH9J217K54lFI8xmXzyVD0szdcf4Pcftv/yayzzsZiTDIWDhFUHOOAD+Q/KRsxYrP6Rj+u1N7y+dZiOVjhEndqLxNqIDErTxIze0hieh/xIm8wIUv2FkJ6mJAtabn9YPcFpx+pmZLVfYhk5aLuONmHhLFPn77iRR1TI4Gea2lTaBLunWrs3eGNctDQUUad1NJaObp6Moti0+Ik616ndHst3ny7Bzlk73k1su+SGil78fB2cmK3BKv/BctDnwr1a4yvDhB7vNDw7o2+lhLAGmBuYMU+zA18kNJfdeXlxkN0Q8/+/vvvy5e//OWowzHUlFkIZCEbN26UC7/0JVmxYkW9U4krPRE7ZTK8kp+CT+F+2A+bXPyLhd9qjFNwShPyU00T+yANdLpocxpx6WvyNv2HTmDsVN/uj45hbBXQQtlhZ9PvdkZAJYHtDLAWrwg0hwA73l55eZAIeUHY0Blj0nV4iuzDChw7a5OsHvrwrPYeZAtCtZMTdicdBfC06Pn1139sqhEp28pslUS9FPymLQo7f0oEu0Ntk/HELNVCU7CVt5m/8T16RHP4Yf8XmyglDG7ZYnYlwNalMyRLEuiTSohzqBaUleGVwvIyxH/EpB6TrEilWbUBtTs3QoJYyap81gjPw9oBbLcCWAGmV0fUO8j0vj0bzKp/WmZPtIMgXP9XglxggSFaXZG6Rptg3cbDy5+TJBA2QFRBJB0PUb8QEz4G7c7APVVW+hEOAPmxunwkyZ07WHKue8ScGqoqk/3/vEpqCneb32nn/uRIiuxQ56xbt0bmfQCvrtDZZO0MHz4MdRuGtK4n8Mf7hro5ecxgOWncKCwQYHKHd9MRj8UZinHqJb6be/bsMdI/TgrpQGQoHLrQocxgSBwPIabgM888A9XCGTJixIi6Z5s2QhVxSJlgT8y7ycpIl1HD+kuPnAzZuh1u4rFwNQQq4j179ZDtr76JUCW5ctZZM0Ewp6Esu5E13UlNm+aA90/a/YXhDTQkV19ttav33qMdINu/yGmnNV2GyXQi/sFjUWpHz9AlhbAFJunm5BqPzbALXSGtPW3GaTLr3FmSBy+r1AaIhgtCPr6jJj/eRau1WOcnQWX0hhstiYqlSgDyAOldoAq2oFjECcFD5ABoC/DDtHfPduPsh2OQD56G0zNy0EYgfYS0N7dbLuwG0Seg6OxshDGCV8lDUDEmsaDTmfEIFZQGT9bWvZvijvpPYDukVVgUqJwbkqq5IKqbaosMbOIzd7wE/6fAvASLcbCxx7tcE4RGSDklgCCACOnDhPVcyUX8v+/feKW40nqZNtIQEqkQq+ehLvl+8j0MwRM07Xqp9p8IEt+3D+rZFrNHC2DbYW6ohvsS5Mc/+hYiVpSbeKQGcfQ1bAOhag+0AxDbEuSPIS2c8RglwpAUS+24bgpqwR/4IK6TC5asbFVmHyxU6xzTH+2HgJLA9sNWS1YEWoAAbD0w4J809QzZ8aXLZO7Lz2JiACkA/tVN7KS5+oq/RlpGj35YPUS8tgZVuZCbq7c1iAFYQ3fvCCbOeIRUPWVvfrhTD04pOOmqwSSBcXusQQE7TOdPjRUzocCRFKgLJcMWhVmsXDF5zQmN/6EjGDv9f/beA0qu47rzvp27JycMZjDIkSBAMIBZzDmJSjQl2Vp5RctJa8lhvbaP195v7bPWsWwfe+Ww6yRT1tqWFSw5UBIpkWIQCBIkSDAg5xkMJufQ07m/379e9wRgkDGUCHQBPe/1e1X1XtetunXzVX7AYhHzd/Suu4pfLQZhejEUEU/yx0HZRoCOtNVWR83fFiBQjIK+ML5uGqARZDAUoCGJk/7xRYx5nf3Mz32KoCEJm99MsJ3kqO1+/Tkky2V2x/0r7dGPfszuvLvXVqzwCL/pfchn0EWahFBQShLBPocWUhEfYwSUUIh5JQ2vLAtbb/8oxGkEhvH0zJGnP+fYcz95r+o//PvW8zeP8ePSNvStP7Smz3z92GoX1fcM/lYtC5fYgT0ywTPMLivthuuvYzET5AkYpNzazzj/vrxblPLPOZ4B1KCJCRzEFHTrq6/CnN2LGXAUInKeXb7+EebaMEmlR+21rVttHSZ/xzGBaq9O6Fum6WI5qirLXfAqjxHxWcO8eluGdqi/rw9hAxEdCSXf29lqPZgSimFQUR8nww4f+5ifiKA5Q/5jP/mTigiaQ4ORd9/VHjdF+83fnP336f67tRTHZJzUEL093Zj5KhKr+D8/jFiVbbz2Bnv/Qw/alZddgotA2KX5KO4JHlxg+2DmvSjN7srsQ+GENeD0xCjE+6BlMOELkVpAUYC9d1C+SKJN1jTaVdfchPCpzqUigfOwxnlNVi8bXJhPmZBXMhfLsBhQO+0Qyk16/Y23sP+cu9VGtjdvff8165g/BYa5qAqAzykojCWI9g3jB8zScWCO5s3ByK1zRoQvWWCRxRTfh0WNBHOzleuvv970UdHMyMZH0CqSf5gI5IvI2/nXf/037t7xfyQI9uZg2qWTSfBNV+gFoYCO2hMyCVJQRZMEimGvYJ/I5Qa4hZ8y/oOeMIKqrp2OMwuZDScvTNcK6qISxyvokEqjrXLH0p+5HwFBtlRKI1AagR/aCCDJ59khNtdrbrufKFwEZnH5mmZ/ISVuzqTHSOYct29/50n7vd/7vVl880Da+HBkUoSEVyJ4GAIRAPID+Zu//Vt7/PHHHfM4/QmOjBDTKOQPcXFc0XORJvvRFDUtWWH182XK6VodV/VkF8Jr1kzeTm2fafefPepphCo+/GGL4S94sZSoM7fKW2f3iM1vYKNurLRygoKIOeM/RUek+cC8ow1qeZai/H/rCNJz5dXXOjOuzradtnvbc3Zw+4vW133IViD1v+aGm8jzJOcrOtUUgZAToynqM4mWoKfzEKD3hAR6MLIJmJAIZqAx5xMownA0nrWaSqJNFs3CZnmXWS+Jwp2lhJvXWMW1j7g7mf42y/QfmaXWxXPp2uuut//+//0vCG6i+qIBHEMrfPvtN+MPtpJBELBEqPmsubHBxonqKcGOTyoCwfSYokiTYu7+4v/8H7Q+q4gAOmIN1eV253uusSG0gAsWLLQ//fM/s1tuueWYlnz1pA+cSDIPoYewScRCPUFEFJVUaQYuxTfw2ms22uAQRCbM6VB/Dwmkx2EeGtUBn2nduG/H/1Gwl6eeCvIu3j2ZhYohVJGf4He/S5qSdcf/Nq/Gu//v6OiIDZKoXWa9YrQlwPnxT3zKfv23P2c33noH5nZE50TYJ4a+AH6OYuDEOJAiJE1gjtnw9eTQoFsBb2QSw6T84Kj9AM0QranhjasEiVcQYfTKq2+wMnwPyzDFl4Z53fpLnYAqheBwPhooaZEksHSFNi2Llthq/D/lc3iuJdONEOz/sce0zuyp7F4sEP4XwZEKluP+mpn3L5RvLigMvo7yAc5l5AcMs+2Yv5lzX/u/4JbNwDTCEOrbyUo+myTtBExaatgy5AHWWj5RG+0HKjnoC+UWLc4Pd9G14l0wUbYMDGpKewfnXPL5xrg75FU7Ye/ebeUDLJZ+O1w8hfVL2zP2h5PfF9nGyfPSydyOQGFFz+1DSr2XRqA0AiceAYfWIbpq5y2whqaFNkiaABFVsxXQP4waTB2S3fj4CAET+p1Zjld3SlacSY7hUN7nNCwy78vE+y0JIziEZmBoaBhUPXMrEFGQk/kHdXzOh3Da09l4tBXFx+NWBmW2ZPWlmJNUH9PDtPonOZUfYPjyy12NbGfnzJoQrdWf/rQ1fuELM69fwN+cOShaWhH8hzuGMLUrs5uvXoh0HYiwyWuQBSmnpwUuO157AWK73Y2IrjuawIHSg6gIwq4je2zLc9+04f4OG4AB3PLsN8kZeBgY6h9tmGsu1LwjBNn0yf3V3bHfDu9/k/vyQ3Td88dntSQuX7einvQVEUxWCdtOgvGrryWf33HmRMU2sx+VJF4lNN87Tq8VqGiY/Jo7JjLt5I2L5iRvt999r92B75dy93Xjl7Nu/Rq76aar0eKnGAWAw1xZvWqJ7d1z0EbGJmASSOtCOPnZikzNa4jsq5QeO7a/acuXNNrihTW4fKWsra0VzZ4CPc1uECSpfh7/T5mStXcPuLnT1EAiaUyEy8sidtXl66wCIUEPeeXEM9Y1NNntd96HWeh8XsVNSve6s73X9GuLF/uIABq0v//7AH6KPvd5/PGA7doVJOn55GSc3uQCOfe5JPFjBF7JQfAvWbnWfuYXft0+/snPWFPLMtzCsQYoCk90LHy8ZO4MAcx/fmLYsgj5PAytMRdz542+LDsU3j893oFWadyt7SzMYHKMPICsec/yg6BCRPa8/8Efs5aWpe4R8iMvhxG88cYbLYFVSpIUFuvWwRASTET4JhSJkE8y43wBq2vJGee0je6xZ/1HbnBugh3Tg3veShjB3yVgzEdIm/OBC49kFdSymQHnVynT6xwRlvIEXHFQxDdzRtGAMA+k2csh5NUaPVHRXpAYQrAKvaCpkxzp9oQBJ2zADYTHyUQ/QeqOwSdahoWl6OIMZAjfCuOmiz6fIoQqtRFf9T4nfiWrtcWq5Eq/HSqeFo5ewwW2AT3g7cfcK32dqxGYHfvP1dNK/ZZGoDQCx42AJH4ivGMkeV56yWV24M0X8d8aQ/JKImcIviLydQ2p5ycCJMYz9ugHHrCPfuSjblPwMC91YRRSE0Pg/U42EiS+DnHD4KHhq4fZ+PVf+RQJoqUN0mbqIV1JFMVYJtgk8skJHqcNaHpRJzI/TVvz0tW2eNU6nlk0J5pe7/TO53/xizaKL1LVT/80yYB3Ee2N6HUriVqJRiKyYcPpdXIB1VJ4/hybe1cfMCDM+8olFRAFSP8d7PRDvRMfpjcdh7bb5me/Ytfd/AiBXBY5OBSHIhEftrbDO+3Nzf9uh3ZuQWov4iKFRvBZtIN+u/yGBwnqsxJio+hXCEzZzI8e3GHbNv+bDXQedj6Ek1og6MlImBDwK+qcyVhr9zimYjV24w1EKD1Dc9DKWx+zyCraNU1pgovvPfbKV91pcN4yk2bw4i4QVQDu3gfea9/453+wttajSPInCNVf54IHSWiAcZ9tWLvKtmx9i4TRr8AorbeKdBn4osoRktLMeOseoT0MWxxt4pHWw9a2f7c9dPfVLvfjZWuW2KZnn4aZK0PrWG0RCHvn1wVKUO43rXXlg5wAJ3QfabWXX33D1q5ZTuCicvpM0QdmhaSb6GzvIIVAtwsatQZG4ZY77wJ8kxP3tEHZ0ODDH1CfC4/IP/EgEHgJk9xxtKfLV6+3T/z8r9jd9z3sIm6K25LAJUu01TxRo2crYgCU1zU52m1RLDQCSgfk8LqER7LqGIPh64VJHHHmuupDWuOUNELg/BB5YYPggirMPOVHJjGSnjUxOmA33HCNXU7gn5df3kKU0VrbeNWV9IFGjol1zcarbcurW5x1QQhz8fNRFPxlyeGgjf59zkb4ZA54vU58F9/A72at/nP4SH75QiVXxXARGVaB1+QPKEFYHn85grR5m4C3T7sRYfzdbswaTAy2W6yWaK8Rwa6wR7hKsPfg9YnhbsuOY6rNXa3JPIz/BCl6YrUE/YmiUnVIorhW6SGXIAdwFxrDIQUpPaZ4fcAd0m4MSyJUtz6pZwUTuS5IsCgBhJjCQt1jetDX+baGVBC1pIIYxPh1hJYz3QousXvsFvvMLC1Ll+ZqBC7UVTVX41XqtzQC53kEQJgOaZITjKhfq6/aaG+/uolAAcOY7lXByAmhFguY2dUFYSP9H8d8LohfhjZyF0USMy2ZcmQSg1ByGHcUKUFtEJIgQhRMDB4g1QBtIpWEo2b5c13JZJVAWmZCs5JgNJcpaAwGYEXLOiJUwnycQ5EmsJ6PiM4wZmoVDz98Dr29u5tqDKqAs6TpY4mMbXmj0xYQ7Q+lKLCZ+m06lT+GYPXmC9+EcXvLlq652qrrFgFntLSjQ9betsO6WndbfKjXEXt+p0nAxAoi8I0Xn7CDe1635mXrCQO/nBxPlS5YTOeRvXZk7zZylCExljZpcs7wlXM/kujqCkUGDdvmbW22aOV8m0fAijMtYmwiC9fP2iy88DK0DQmrvqe0+YsQF5m3CJPrMPhAmsA3ScvQ1HwboPESgGselJdF7ScefZjk8pttyyuvW6TskNVhSVDXMJ8IjuWYlXkTaDyeIEBQGwx+q9183TrMjTHnJKDHFeuXw1TutOe//6QtXLIKM895zvQQlbTTOMpnsK+700YHepgnY0QjXWQ3XH0FxB/pAQRFiFVFKuzg/Xp7+jBjbLEHHvqgC3A1K5BPcPHtt/P22c9m7TOf8cN4zMQ+KXDfP/xD3mkDr7jCPfUEvbx7LyvQUhM+oA898uNTDGCBaPcH8QkLISBibUhDz6Af90N9MHO51Ah4PefhdIJHyWQwi0tBNolfKcyC35kLqzntmTsB1nl6HBPUJHnfopU8A+aD6wr1n0GrWIXv7y0f+YgLAqNck7ffdoctXbqY3KBJayZU62133Gq79+4DDzR5gaSOe6uzuxBCI1zxiN+yA+QK/N/6vdPKNFw47eoFcSqxTjgWBI9H+T2svxyaWmAUgGECgHymrwtvDmi/yKcmbHyglciuDfgSItgTbQAcMwhyU1j0kOvJ7Q3FeaM0RPnMmMUHDluwrBp6o8q5d2gQZXKcSgxZLg4DqPlC/zOL5g7XwEG5XCePUkRQWXAIMEQ197GH8e9UYJLJ8/32O7bfnrdL7QHYwC6rhDWstgXWbOusiU+pvLMjUGIC39nxLj2tNALHjICQuifdCyFdv+zqW9j0s465E9923L7vsCxtQPj+7LhlSd6cLWgSVVdomG2Uk9mXtg9mLp8ZBuGPuj7cy0BIqN3xGsDCq7oXCUAsXmKRmkXOb6Fw54wPSjzfhp9SbnjYluzcaX4YoJOVPA7wGcxGQ0uXnqzau/ae/LbmNc5Di1OGRmDMDhwdtsNdo7Z6YZnT4giiiGjd5u7OReCh3etu3Qth306AFkmPpfHBBxSiLosGx5ln0a9jKAQ7EQz4dg13t0E0dNvh7ZtdKggRlvIFzGLu5W3mdDS9OMmu2QRWP/uPjNgwBOtaNEHVaI7OZ6l/9LMoJpiDjvM9nz2/e/sqI21IEHwQxyJg80uv2D333gZcWaUFKksmWY1o4h553wM2Tt6vfqJLDo0gWx9ot97uIhzRqMAMNFTH7KpbrnLpPTQ/NGFCMInvue4K6+oftiMdzAm0Bi4yLPNNQoUoWuQFdWGbt3KNVVdWYgKIb5pM1Au8iOacAlgd2I8fJ21uvPU2u+v+hzgrPvv0xv7v/i5n//zPCmCTsz/6o5ltlUj+U5/KGfIiQtqfH43T6b3VO1ELQALMxuYF9shPPGar11zqaQBZqa6A333kiwsgrMtj2i8BnSPyjx1fAOFofwQ9aaw5XGv2A4fPZUoIU+AR7/Ra6JqO2CPADhnajMlMVPdk/QFDCfNx7z13udQhCgDUiGmvTEEjpKcZwPfzOgKOXHnFRlu8ZDk45PzBJNONJcQjmC9umnxJvZX5kFVWPMp8Xe6zoT/PWtUn0EaWz5wnruK7+I8ig8LRsSyLvx3Nbw5Nm38hA4B2t3j52N8o2OMTmhzqoO40pg1NvqMBVH86MweM3bzATzA7inBnDNcPD/pMRT1EYYlmFzZozngvwhzL8zzVg26QMMjbZ9DoCXE4pvVYSyIuF0rSxuw7MIEqG+wDthK9nz7F8qL9FWzhDqcNnMedUpn7EZidUpz755aeUBqB0gjMGAEP05eVV9sNdz6MWUaHpUDuDq9O1lMdEK1wrdsZhNRVhMqFvFW4kie5q6s2c/eQZseX15KHwJBZh6S/usbHEQqF5h6l6fWsS6rvJyFxpKbJwqSF8JD/tE3Hq3Raf9PkKUrD/KkkXnvNym6//aTtjt55pyVefNEWfO97VjYteuhJG72LbmrsF7YstPkQWwcPjtk4TO9rb3fakvlrLBggSiwg9OUEJ/0owc395RyJMVJ7j5kv3AP2fiaMNmVvpqix6nOkAx9EYQZiMo1mYbJwXxpj1Z9evPbMKp7d1TNhuw/1OeZfIcrr688ua7f8khJ7N/MuWYusuI5w9YVoDzy4xAB6o6+VrCKfy3KYr4mxYXv1tW2keui1pvl1DF1xnTMDBCS0N9FwwBY119viBRD/mA+i0tOAMndgrDEPz2cTMHDyD5PZmQdr9xT8uprrqlzKh7w/6MFAE0z+QGonLRMBQORblJVFghrx0UFv0dM7YG+9tcvWb9hoH/vEJ4kIOr8w77h5mmVoyJt5Tz2VJ0CM3u/40tZ2/LV3/xUNpM9Wrr6E8SzgYPejNLpeUVJ3WWxkowTqGE+Cu72xcgAoVipeKhD7rnWRmfCQhgewYv3Jo54KzIvfCwKfAMLDelKBKAeg5tett9xMZGDM/ZgDlRWVdv0N11l9y3K7/0MfcybBzgyQPib7KfZ3hsdsL/vBMQyguqj9NaKlPui3jgcxb+0EV1ViOfGT5/q0M3y5Oa9O0B4/jB8/S6JYb2H3sI7bOZc28OTFMXysZQcEaeucEFh7BvRBcc6oCzcvWLnAVWIAnyw/CpjfG1H99c6OW8hq40dgmIc5xZQTDEUV97a0QIiIiainGdTzhR1mpw96iAEaNwWoMXvZvoAucYU71x9FBt1q/8hZ3kUKLTGBk0MzpyclJnBOh7fUeWkETncECshX2JcNPVxWS2RPArjIqbuImHVUNap4J4W+HXIvttctDzkX7k4eXI0igSAkXehu2olXV4RgoWiT90EY+GM1aJ3Kuap27gWKVc7oqEATk2X6+eTFmScJQtirJMUwXqBMoAJ3bMD/5uDBA+z/eTvQPmR7Do/Y5avQBkK8O87PA3rhb2GMgNMUqDyYTYfM9HNNGm/aTG/j9TOznnfN9QZDOTqStdf39Fv/0AQmg/VoBdYTQRDzYzcvpuZJ4Y1OeMgSpKj3iz9vmb5WV8eHGVrDR//QIkuvstzEiCX2bXbngaqp6HEn7OwiuFFbV2+rV6+z7qNHCODSbk9//zn7+I//GIQdJl2OsCuMfQF40OjMExg2fLp8RqAGCDwn3HEIQ2tWzJ8Is5m4QcS9cki6yTEdnJMTiwdATIqfmFz2nMfj4/bq1jdsFO3wz3z6l+0qIkvqVTxhlJ5z/or7ueevux+pnuRnd7LiJwdkMFaFySeWG5jlOo2PI/RP1urc7okBVJEA0k9akqJbenkE4UK43nzhGnvv+z9qY5gMh1wuunN7nlrLElJavzw/c3oZ+O2c6VMsmfbZsFXx7rvpWGSUMoyv9ngibuZJ4wSL5IQ7pFvJ4hjp9zdyrYb1J6ZNv73YrvBbJ9epvmuR6ljQxB03VGrLxck2quwaqNExpTDmLgIoe4erJi1gK02EL+jHaQJFnUhQBM7hsQ5HTPZ/TJd8LeYB1J199qz7HF+Lqc44lMo7MwInx0DvzDuUnlIagdIITI6AiDXkdMEyi1QtwNwezdsPoXjaI20YmN/E8B8oq+edwo7Qm0lGnv3LZRXZFFPPk33ko6Qic8ELtSjIyp133un9PDbXeDJrW7YfMVLyoX2T4dYPobCRJ9D+7Gwdsn2tPS4tgLSVSicgE1aPwTj99xr+/l9NMoBqlcccuf8rv+6iFg499Xkb+Jf/YYPkCSwVbwQ0Jx58/4fQBkdcbs+vf/3fbN+BVsYeSssRYJoVWp8cih9OHK2mSwhuPGKwsMVDxXmYhcvTi2vLH1H8It6Kn8k6qsDl4iTka4a12NrWZXsOttsHP/Kf7O57H/TquL9nRlLU1Hj930sqgLffDk5+/uZv0GJ4t0g+Xzhx/V9cf2TaHQxX4buH9l1aXkeNF2D/TmAG5ppj7AFB1he2YHk9ruRlBIaKWl39fOYnPojnASThlT5bPhi0hVuD1vAnaP8+yB44lVJu6gnFeTh15V165sEwD7uTJweoL98CbLW2EZKiifdFELThMZfN72R8yQvozCzZA/OYXc5ajoeC1vvMojpan8fXnVlP31SPjxg+WDefjzQw+UOcowXUwsRSQDllJ4vTJB/7vMm7pZMf4REoaQJ/hIFTerWLcQQ8FktS+zDMl/K2JTENDRDZix2gMCAnQ+Kqc6L7p2pfbFt4B0kfwxXwoXI8L+fpHoHnSfvOHTbdP/7j597JBdCDojnec8899jABcp555hkXLbC9a9iee/Ww3f+eFUR81GYLbBwBKNgW4XiiH1+EI3UFLH2lbb6QAP5ErXTdBYOhQQYz0INHR20rpqnjE1miB1Y5BvDKK688WfMT3kvse9HdCy1Ya+GF62z8la8ThICE5a9+g3f05lXy0Fb4G+Zekfo/YW8Xx43b77jbrrz2Otv22ku2d/c++9L/+2f7tV/7BXJIRtAWSChyonU+2/icas7M1ubYa8ILPpjSnI1hAXb7vQ/bHXffjzYI4dBZwu2xx/wEv8nbL/6i39avn/o9Ot+/P2+f+1zOvv1tSGUEEidKjn3sW15o3/1+IrEihMth+psl55vHlGldT43XiX9zEe6qq3OtteI1HU+nD7EBMKPsA2GYQOdCMNnLlOCBS+dUfISkjG7EH3UjaU1+yesqtZvooD/IW/9/IyYucW4CszGG5/TUH2ZjRK1odQN+Am3l+XFOk8/CgsHywwQqjYdlj8B8lxPcZx0vWmTK3ql3lsuIWATFIz7CTGnlFdD68R6KMpvHBNVjNCUyVp7LY+fX8e8ZwYy0WG6xT9siu9p9zWC98C37bTwGe933ummpJIr1S8e5GQFv952bvku9lkagNAJnPALepuz+YoYVKq+zSHmj5fDZycv5e9qenYdQP66Ah5X/SYEjilu9ruhcZiZSIoC9j2vmLkw14DmEiYEBDFc24ZdSzVcQP5W895pecfauSldPfwTE9Cxfvtz+4A/+wP7iL/7CPvCBD9jS5SutbyxoOw6SBwzhsJ9or270HQBP1rfCuHtoXcSi/AkFOV3y5U6O7mVu5EfLk0n57EDbqD2/td36hsYJWBG26wkI8YlPfMJFMj3Z02e7J5PDnHJWUqLLr7XqO36Ol/HeJdW9z0INS9y9PD6D2cGj7rz0x6wZX9Ff+bXfhhF8j1VV19uTTz1t//7v3yV6IMNXGL9Tj9Nsa1XM3GzXT9Sb6uqjVn6Lksj+upvutfsfer9LLaFWZ8u4X3aZz/7pn4J23XXHz80HH/Twm6zGcSW+eAvDEEDzpr3Ar0AxjIQw+ql5wAKeB794I8lf/p+6XbGO155kFRaI1VoUJtBHQr9pvRX6nTvQhC/xWfVP+60RzXD1p8kV+MHj58ncPX2OewYQrCbGEHNfH2YfvjTRdxH4wVwFFBkWbb5jtvL7URLu5xx4nA7wND/c8vagfuyvOOUW4hqoLZpJaSp9UgEzhAAAQABJREFU++hvD+9D0DFnigwegJiYoQkkPJREBV5xDy+czzw0EvF0vT3sPmuJE9pgy91HUUHfa78/WfkQHoOl8s6MQEkT+M6Mc+kppRE4gxGYQt7IXy1SOc/h/hSR/wLSksAM5iU1BEdPmmk50sB7xPDohPX2DVkDDv4RovrJx2NkNG5x8n4tam4g0tuxJiUe0s4rmhzFxYwJRC1cvcBFpzs+aujU+3lPPLu/VT//8xa7+ebTahy97rrTqvduraSk3mvWrCFhc4vd+J4bbQBT2VEivyoh8MCRV+zA25vZiFNsy2y+IgRmAYGgl0xmgHOeSJARpokkufhvJdHsQVzUVRAw6AQD5Lpz9FXIusay9vL2ARsYztj8pgV211132E998pN2GVFdz0bjo/QQPvya8vj+ZccHMC+usjAawdTRHTB9HRZcf/fkW2XH+i1Yt3Dy+8V8IlPgm267w5oXLLRv/fvXbdvWV+0HW960a2+40ZYvbkZAX7QOEPQE2WMnhQdtRR101tQS7PARA0G8F6ERCLsiwTh9pIt9eUfvLxVp4Mc0MYRgSOHlvet0VPRBmt7FaZ4/+2zOPv3prP2P/xGwRx+dSeDv2TPVCcFzL97CQMsnzKX1qcwSSxJim0jAUOEeyI8Fe2GkhCcUwVVRX5lKwBrIs2kkUikX7CUcmjne0wfY7SsIjfKYe/rx341VNTsXBW+eqeYJHjq9k7M4T27LW2JLzsIb0AreALupCUqp+DEYwB87iw5/ZJto8y68XJ4UIHkStGudou2ViCZIzseUAjwR3dd8KfjC3Y4G8NtSQKDFwDrGBLPYxeTPdIAjTQQBpAaJ5lpdVUEaCDFzoAsEicNE5a6vxby4cG2ynTsRkwnMMQ13zKfSQdgh5tlulrhwjZ7JnAnyVPF7jptE4Kxr05hTz15gZs/Fb6JnrrfH0C1mYH9xAp1WGm01ySZipKCfwBC2Y9qd0ulcjkCJCZzL0S31XRqBcxyBHEjdlw1acjhp+/7jaWu5fJVVLl1CrxBkQsKTu0DhhPqS0h092mXbt+8lSTj6IOrJd2PRokboOExPVGg6oy3fJeXPwHgMHzhsyWzIVt6nyGTeBjKzvjo491Lx/vdbGWaQpQIoisQOUSFXrVxl+RUrHPOeTiWtfV+j7XluE8GCYPxjMFTAV+A7tmgGyGxrbCxpg8MKOe7VUALiajGAovlma6hqVM5nmCe5MMngb7fb37+WxPUpIj7WwZyutoULF51xgnjv6d5faftSR9625MFXCF6C1FgcCMXnclUWJci64F13N0t/GCafrbrkEvvFNf/dBgYH7EDrEcsQrXHCH7MwwV/84uYE1CKwp4+ZNyGIOJux3v4BlwBauUdTpJQYY51HolFrINJrDGZg5rSg4aS6QCSd5hV/QzWYhjciGFJal6I+qIAfpj/3DM6/9rW87dhh9pu/mbVnn516i5GRvD3xhPed1HS2bJne4iIt/HT369EMKcl3fCxnibFO4EduQBHkHkXOUeeFMWQdpUk1tOXV7eTgTEH411I3aMMjw3xG7KorL7MFTZj5T7Yojr3XRx7BTXqCPphn9Y0wgOQrnCrFulNXzsfZ0J9kre+/ihHxeqv4qM/mPw6+ixDqaBPRSklNV/7AhYcf8qZoOPL7y4CDYbZY8woI5EfrKnwuEx6fjzy+ub2WhgEkqRCaQjSIRcsONzkKEFBdmqQxF3j5lTfdMq5UHlrGdHRslD7zdudt7yFFjIZZAz2tMVYjeUxynTUI5id5XxvvshscDWNKPABMRFz9AHMil+M9FZF0sv3pzYlxIoN+wT7Ek7P2Uftbk2awWDrsLccA6vt0s9Hi/dJxbkagxATOzbiWei2NwHkZAVg9FyX08L9/w7r+7u9tdPUyW/C++2z+FZdboBJfAUf8gYAdDvaOdbWVIPpr2QjkSyNmwG+RGDmnwPdZ8gQ6qV0R97sjyJ/w8fHuXut+/iXrfvpZdtsqq19+mTUQDXIyjcR5+UWlTk5rBNisAzBKOYixt5/8gQ28ftiq5kUtuqTGfNVsyC5KpJgndnyBHTgCRYuGfNY0j2Ai8AbyGxMTEWKOBCT1F/PIQUwkk4K2fNx3TuM5S3WSoqJ/0Boi7Xb3+z9ulfWNTmMQIAKgSybtteDvmZcIZqBiArPkoxt75WvuqF7yEBbj256Y7DBYA8VfKoUREIyAlTLxwQAEotXWsKTaEqzr/uwYCZZzFiVgQ5BgEoK9x65NGzwnJApYX9+Avfb6DgjHoEUjUUuQhiSZRPvAHNh4xTpbuWwhBKaYyWLRMwsF3JGFVZgIVFnaN89CaeYgV8rC+CxLvXSOZWLCe9aBA2YHDkwTBhQfzxD82Z+dG6N5jq/4o9HcwRJ/TGjvtzYP2YFX++ySK2K26nJgSmRND/ZFuDFo1I+QOuS66y639qM9NjAwaBmEOkr/cNn6NVZTJWaiKEzSPKMULAy0X4wMBOyVZyZgGMfsoU8utDqWpZKNO5wxSfi7VuflT/pQ3vp+Y4oBVKdjX8YP9LKcVf64347ewvzk5y16w2eRywvve16e/KPQiUxBlbZH8PC0ewGC8PhCBALK8hGTj020z19LnJaIxWHi0eeSsqkGLZ/886b9BmCnIGrl7PcPP3in9Q8MIPBR/xidVq2yulpyvLLWvfU+c13JsiiXQdM8OgYzlrBoOYxmgDyFQVL6YA6cHEdbyX4UCOGvyB6inJLefJj2/FOc9tkBfqOHa/7FflF2Tq6FfveEDU22brbLJs9LJ3M7AiUmcG7Ht9R7aQTOaQTySOR6d7xpPd/4hs2DoM/ua7WOv3jc+sigvODOW61q1RILVZVj8qMw7jKfYSvB7COLhoDqEH0sce0S9JN1mzwXtYdCJOS5nk8iVe7rs77Xt1vPC5gc7jtoFfILGx23zueetgr8kmIQDuerhNBw+aBa8omEhdfJ2b1UjhsBR/CxIbPR7n/9Vdv6lS9bhFD8qU6Su4+mLdpUYZH5wLxcMGf7xIyXmC8UpLhs5BE+kBBokLkvJk8ghyF0tALSXoGfW2iS+UCEpwcSluwct8wgYb4TWWt/ZYvteOopu+0Tj9EfJkl6n3Ms6W6o/EIZfvJPiqeWat8+eR5eLMFGw+T30klxBDxt/ghzYAztjA9mLu8vs6Fsg8WAZ4VvSAHmHVHvEZLFdhCEwL25sd4eeuB25280AFFYFiuziqpqF4BICcG9IDOCsWaGVzRL1GMaIi3up26+0tIZuA1pi2FKyVNvDVURzzy92OgsjvwUV+rrjfQjU8+XtdqyZWYKHPOe95w7s3kWr/aj00S4GmhoGR7dP2g7nu+zQ1tSdvBNGMGb8Ke8rYWk88wLJ9DxxAGaB/ofC4fsklWLwQFLHUSh/l10V1mHOITgzPg4Z7PIc55Nh+3ggTF7+hu7bWBHA1GB/da8+Ijd8sgqi1QALKp6zOD5hUn8aTqWoolS91m/Df85WeOwCBz6A5jAj/AsbqvEn8nBBDI53vVFP0jzHdgqvYKHwBGKidmTkA94wgiGYL6yBf9+X2Y5ZtjzLA2TtvvJ523hutVWu3YFpqMaH8aE7lyvOha0dPNg+hrratxoubyfmQLjpoAvRe5RcwEGPwej2b/nsPUdOmqr77rOQpF5Tmicwxw0GKkhJ20SDSV9837BMH7e0gbqiXqopImnVVxlVxPxHyAfP65VmdXbtfbx466XLszNCBRQ8Nx0Xuq1NAKlETi3EUgODVvrN75u0T6StEYhuuiuDMIt8RJ+Ym9tt+jq5VZ37RVWs/YSi5DHLRTDhwMTu6J54RSSLm44MBcg+wwmYfGObut/e6cNvfKG5VrbLMz1IHmhfKLA2ESGNz1vvesus0W33eEkgOf2S7zW/ljMFu/aBfOZtCD+b6Vy/AiIsdOmPjE2ZJu+9LglejrQyGHOCXPvH0lhpjVk6d5xfOfKCKoSs2AljBp0gyT7WW3oMITyL3FSAHXvgspoJqBXpqo/zVwYw09wKGHxrlHLDrGZkytOvqASHCT6++ytb/+HLb/2Glu24Sq3x4uRPJcyNR9n7yWABrD2vb8x+82L/iqDz//KaBAtYNZSrE0FicoFK2DQOKZDVpaPoxVUjkAiCzIBZALsJgLQC4W0zTMfZBLe3MQZBB/1aipj1CMUvTTDtHQwZgJIKZhhoiSJShjPV+ODVmEZX4T7YjCYTgiJUuSvdGlbnDbw7CfH3Xf77Utfytpv/ZbffumX9M6lMnMENOJaz36bGE3aHjSAXbuSFgFuyf68bfrXDju0Y8LuemitLVoTtIpqTISDrGfwhxMKwvRlsfLw8sxpHjnkwH3BDMLdj9kfDEE6FbKBbr/tfHXcnnximwVHG6w6gLYw67NXv3XEVl/daIvXonkSFzAHJXPE+53quvZXmMHEkRr6Y+YpyiEpnHwElcyP4QqJz+CFUfQ7BAP9uCRnnpAuJ+EtuF73fKzBAB8/ay+P3j87iitHlmiiYdZi16DFK9qsbAn5hIkuHSJwkDT98t8XMy/Yaj27/UB7gisyKy2egSO4nGOx5/A1T6fwP4dBHNl7yPz94+wTJIOnH39gAVNliGMU31B8CZEc+jAH1atn2MMlngAZ0akC3HjTysMShQd5j5v8Oz2+QK0twQPQY1BVIUQfDbbSrrIPz7g+2bh0MicjMDcrek5etdRpaQQurhHIwuz17njDxjb/wKpcGHbv98tEFF7PoskJy77xtvW8tdO6kPaFli6yKvwFK5YutGh9LUgbZC3VEJuACLbsxDgMxYCNknx6DL+/TNsR8w+NItnHrAzmD06DujyDjwJIJA8fsIHNm6xu7TqraF5w3gY/tHTpeevrguyIzVfmOge2brF9zz3rOfGzp2pb1SefgkofgBBHK5jqHjN/edj81SHLVsUs0DIPJp568t/A906RQnNRGEgEA9mJjGVbuyw8jBAAv8F8nH7oy0cQAVdE5PNsH8RA3/69tu3b37GFay5FIozmtvBsr+JZ/C0EIlAy+JpjmL1AWa2Fmlah4YKTLZVjRsCDusis6jLgyFp+i9yN0PVWXoFQCIJxwt8Aw5a2Mv+ohaGaIwSMkeYX/pD70iBBEArEaBsERxVFjdUXMQpSCSsyrBi7eBKLgQCRCcvm2bivRpiBj9oRykFaBOpXRn2Ym0nY5PXhJqXX7Rn/VTCYD3wAk2XMmEvlBCMAta512b5/2A68PoQ1AL6AwDYSqIDBm2dtO/rtXzrbbNnqSluxodIWrSq3uvmYUqK4FfzJL0THEhEBR2mYdJ5RwDBD0BS1nqOk43h7xPa9OWZHDh3B9LDWqspb3FwLMGdGSNC+9anD1rjoMiurnhuScXrqh0wPGsy7fTCB3nhkWhFkrAJVbQOldZ1gjN7Nl8nFp2WIlA8IZdmPARxwcsJchCxBf70lRrHGIZ+gkwKrKsIaf6TcyiprWJdYiSTgkIGnn4kRDIBHJQgGzj4hARXXP+1EC+g57C9Z2inCp+ZWOIIwUfkoSQWVQZjERYcX8ln2FBgzH/kBw1GZndM/5qAZgo/l0Ax6HDp0hgtWw8YziWHcU4/7Mx8fQDF+WYRQ77XPWm0pFcRxY/ROX5ibFf1O/4rS80ojcMGNAJqa+LgdffoZC46RQ4jcYCrS1Dg8y1HS4SBIPihibnDQsj39NvTqNutDMujqy1lE4j6K/MB8ktyBuAMQc0LXFZIcKnAM246ro6r06x0gzHBAGXnzNevb+baVz29iQ9HDVYpH79vZ/NVmNP71r9vEiy9ajncPNDZa9KabrOJ97zub7i6oNtK4JInk+uo3v2nZsRELh8QceQQ8+7ybAw5GaIXyEO42Ti5JtAJtwQmrXXOzzd+wxvr2HUZzQCROmIS6xYstSoqBjudftOxbb9pC+lM7Ef6S2rrobg7qfHegRZ+EH8mBlzZZ+/0P2TJMj2U+ho74rEEfXXG9Tex81sqv+ZDFVt90QcHrnfoxgo0Y/Be27rX+YT/+PVErgzGsIhJsc32FzauvtNpgJRrBURseQsgTx7+zuswxjwGlmNFcKa5dN4H4DicQByf0j6bsKLkpOzANXnLJRptPAJiJZNbGSNsRj8uHMGtxOM8IyOb6SxqtmgBFYis8puLcRkB06ksvQf4ik7juuhJDOGM0HZwIjDKO+ecb/dazP46AjpXotC95hIFVYs9tfCRuB1+P2ZG3RlnrQ1Y1P2D1LWGrXV1hVSvrSC/BIANudZfsIvjX/hEbaE/bwNG04abLXBEaIR0NduL15U0uOIgTEADlsD9i25/vt8tvH7blGwhKEqAXD1HMeNVz+aJUEMUS/w5M37LiNx4lxZhenOIU3N7pBfPX53Lx8XPEeCG88Tl8LwaONcv45/ADzGecqcfUuLOvI67jH1G8ERCHwpUusmgmlXBxAGRiqkErau9ZqdRlDrhx5Fx0A77eYfyM/X7oBFEECtjlWqku5zmlA2HSZKtgNNMIAwV33gj/ZCdcLoAsn4c28Sl1iRhOPWAKlupveokQEfSn7F/cbhYq+ANOv186f+dHoMQEvvNjXnpiaQROPgLg0SybQV/vkO1+Y58tQDKPzA4TII+IQ3Dn8XZsxM70Qt9hCC2C9FCEPdq/fFklGybR5DrR9oHwnTme6lM3j5+I0LQ+KjoKdcsqrHgtTT9JTEA68RVL72m3hmsmiBWDxI+ajgA5ByIgC9OnRPHxJ5+kv2nlj/7Iyh54wOZ/8YskBb6gsgJP+5GnPpXWtuvAPtu36UWnBRRsJL9X8WClKx6wRIhDicuiyPwEfug+0mextVdZcMF6C4wMcheJbVUTvmQQ+od7bH5Cnl7SCLhmak3RvFLf3nxy8wtJ8UjbYduzebMt3bCeNhIbnH0pv+phK9twX0nbd/ZDCI2Yt0M9CYK0LLAgTnmDWAocbR+1VLybNd5uSxZU2lXrFtsSoj4ehQF86lvfsXk1MbvtimW2tLnGYvIlBK84JKBZQDj67rGEbdvTYVt3HbbxeN6uueFOK69fYocxNzvYNmBHOgZtGAbERzTCcFmN1ROUaNG8hDVXy0RNk0jz0JubZ/PTxsfz9r73Ze2ZZ7yZuHKl2be+FbTVqyFD8T98+eW8XXklmsfys3/G2bzXj0objbFwfM+RYTR+aAGJDBqGCNe6Fg4QLi4L4rOZHrV0gPCZ+XLMuYFrz4TtegsN09Yyq7lhuVWubSGqKya+3aM2sKXbUnv6zA9jL4I+SuRPaQknMqNWFYFhxJRUUam9AuZB4zvem7e3Nx2xllXMowp5HJ/fEr2R3ymFErzL4B9kXW7A4hMSW9BS7/S+BZec7ycXn/JOH4u/Awyel1k2UAbnai0FAwWBL8c8jFhmgoHJwAk7Hs5rJ39uzY1iWid982NJEUYLqJXptL78lZCnCEnXUppg0QoOgsLpXl3MP9wAuLQTeg7voeijeInyfgSHisfwRwQ4jgbRk6kkKyP2BRISMY+mksB7fbvujvuTJB38l+xj7vqH7PNWh0no9PKi/ZV12Q67xT5DDFSQQanM+QiUmMA5H+LSA0ojcCYjICINJpAIgDsOD9m3U6tsUVWtLU0csYU4StQTSpqI2Q6xOwZwRtege+75WhZb/QcfsYnOo9b/9a8iLUbjxwYTRAPoJZz1SAvVnV70VYRmAsTe6auyXZHFdii82JZ3VdjC7hFbQ0Q5tTy23fQ+Tue871d/dZIB9JESQeah6f37XbCY+Le/bd0f/7gt+M53TqerC7JOFji9+fR3LdHbaVGktdqKNe4K4BFgA9cG782A4jxgP2ZPrsOsc+8b2y1z6z02f+Uya9+BeB9nweqm+bb/mWct/fY2q8U0VBKEImFQHMDiVNB13RVRkh4dsb2bvm/XPPwQWoVFxaqndZzY8wPL9LVaxXWPTjJ+JzL3zKcTNv7GtyzUvMYiC9efVv8XR6UilDzopFCV7TyKqSepGsolyIHgr66tZ14stPFhIka2H7X2jjftzptX2bolq61p8Tp7+ntP2NHeQbv7xsvtquUtVk0wIdGA4wQAOtSbsGe27rbX3z7ABIravfc+bJddfbNt29tjb+8+CvPnw9ys1irnVVu0TPnGZDietb1dKVvTkrEqzSU3k4qz58yh8od/mJtkANUaNGDvfW+GtBFB/ARz9rnP5exnf9Zvf/mX5yaEOPM3+xFpwVoVb9C+v89a97fbRJpxCGKiB7wkmNFaDaAqqwrX2nCqG/zdx1VMdUkxIOuPiY4OG39xyJpDRHVtqLXul3dZYkebRdIwkhDxSRi/icyAwy2V4XorCxQ0OmICkQZl8RfLELcxkZmwbS8O2vXvXUbSeKJUnusmcMzwpg/xOCmvKBmmY78ihRZK/69PnVc8evZzrdjfj8ax+DtYj+y1IEkHS4CL6653HgRmuWQEzZ4sQTT/mQhqBmjQF/IPPC0UAa52RcPEqdujqe+ustYLd706ajxZ6I/i7tM2JxNTpZ2giuw+ZCauvJIquQxCI/xG/RGkja4Fb+CYQOYZRp0+jDy9MvNphYuThx7bAwXT776/TLKIBlsxeU+4Zav9I9/z1PpeiQmcHJm5PSkxgXM7vqXeSyNwhiPgsXYpAnVshxjriSyyofJltid9qbXED9itQ5ttRYBoYqDd6ejWQ+2YkIK0sw34XV220eouv9pCdfMx7RsmAEiPJQ4fNH/rPouQNqKwe9CPV7y+0P6B/Pf4m21z1UbrLl/iwkwEkSwf7Rqy1SsxCXUbTrHVGf40qqf27LHRxx93DSPXXGML0AYG6uos09lpXR/+sCV+8APHII5hKlrxyCNn/oALoEUKU9BDmzYjXS0yfbjpMOR47x1nQOORAvxoABjjUzU2aKPtbVa7oMXio6O4hgQtPjhkI3v2WiPmvUHCjju/PyA/HYo6VxABkQVYCDuf0Bzmf8OHD9vBbdsKTKBqeTOFkxMW5QEc+Ppv47sYt2DjcoutuvGEdXVjYtdzNvTE5yxQs8Caf/lfT1r34rrp4QKZ7SpYUO9Yxo70kPwbk08p9CS1B5gQ+/jp1TSgqa+2no5We+b5/Vb9YIXdettdtmvvLttJxN/ewTFrvXqd3XXVpRaLRey1fW32vZe228FD3eSbC9s999xv6zfebK8f6LcDR7IWq11pNS2VmP5BFEJyCvLSVGR4cO8weUj7E1bdgo/iqafDSUH29a+LcjW77TafXX+9z37/9xFk7DX78peV3sZr+tRTquMRo96Vi+ev1ncc/92eDkzzRzIEBhqziewQPtxlFgvUYvyBYA7gxNAGZoigIgFRmfI5ElBEAV9ysYSNDfXb6Mv7bJTAQtGutNWGluID5uIHs94zNpruc35lZQECv+QFU80pv8WzgzaRG7YUeEMBSoZ6o9bVOmaNC6n3Q/DhrPlVtJ53aj5eWMVHAKZ8rhxGfAABDwJbzG9dQQOXSeGPjSmoC/SlVUiQr/RE3Jnr5lJp9vY48NKYcE9rUQt1ssz4wv1jFqsmjitc1y2EBhm5GCBMVL7gAKbkXnAhukUTmUmiaYxoUdLOCSSdCIKmyht6eoWeJyvus2dNn9lKClaxVN6ZESgxge/MOJeeUhqBE46AULEj95wdnoePx+IJaz/SZS3z/ARswNSDfD3DvQ02PoD03TGBNPIauoPQMSyDZRYvsQUfeMT8+AkMowkMwhCO9HTDEG6wyO13WO9T30Yy/CyeBMj6REBObgTaI3gLCMohAg70li2wrDYkuIL+gRS5pmAkMTcsh4AsPPaEv+dkN5IwFI5qoVL1pz7lGEDVD5IRuumrX7XWlSstPz5uEy+8cHExgQyqI6jZXAc72q1j707Hp8sXQ4BOA9s0ktoIMJq513vQkH5QwSLmQbC1Pv20BathrDH3UgqAA8+9YKmtL1kj5mBOcuyI+uN7QU+MVoAcU1SSObICR4z3ktnptVfsqnvvR0Itzc+pSz6FkAIGUCU30nvKBhk5JVGyI97xlA0uqgreys5BlB/uTWL2RxAgdm3NFb8j2tAMEvXTl4NJwmGqaeFy6++J2ZZtbfbwXZfaNddea4NDA9ZPovnvPPeyHQSn1NbW2Rtv7bbh4WGrINfYNdfd4jSAO9tS1jUUtSqEMj4FkWECKFphFrNRMQHuwYz9eNpvrX0ZW73AE1KcCzhaW73W993ns898xm+f/zxmiWiEXnklb+sLSmHkEOS5y1tdnebsxVdGWMejaOjKmldYeTRhifEB0nv0uiitIfC8gj/5CBpSFcaEXugA5iEvB0sI7hz30rm4Jdv7QLt+qylbipBHEWG5T11FnqwJLsD9C1gSeTIrwQI3sgQa6o+34zOGT2ldiwWrCBJUG7JuIgtnmBeyTzifBUXYZKn5DQKf3TQN1tyLrMdMsmXatcna7/6TPJpc51fHT/G7yJsFpi5P4pekrHi44f0B5hnrful1C48R3ZtInoe60ap5SH1qIDRMmgfHFMfQFa8ddx/6AXySHR7DtYD9ftMrtuy+m1nyBcBkESiAe7yos2IEETa7gGK6L5/CUnm3jsC0pfdu/Qml9y6NwLt9BAoYubDHCbkOQqBtINrbz/3EZdbVNWIh/POe++dvW30bSJp6wvtFPO5wPl+SlZVWcd/D1tPZY53bd5gfQjxC1K/AYL8dfvIJK9t4jVWS8sFXN8/yfT02/tZWK8OMzCWcL2wcAbi+CvxLQml8AXkm3iEuuXQHPkJDw3HHBLo9p/CuZzryMvssFvn/TS/BpiYLQ/klt2zBB6TgBDK9wgV87vh/YKiNuL2v33rnNVtw0WL2fsxwiOaWgBmr7O8U1TZjFLw54P3VvKjgT2XbAWt7/mlr2HitJQeHbeS5Z20lAT7CmJaKpfBK8eh9c71yabwSjcKSVRasIeAENqYTRJpsQ3g7Go9bTXX1jGeXvszxCAAPD0o+lxpi94EOi4/j5+sQAEwfGtdxtL11TYuRonhEvTw+6wmyNNiTtYPt/bZ61aX24pZXXOqY0Z4he2P7/oJJMfL7+iZbtGSpXbnxRsOVlHknYVM5P0oMoPfbpBUa6+tEAOAnMAQaSOZiCoK0lYiSg8uiNq+ShIEzp+QZDYryA44R2JBUpWgofXYTxP/3vpe3Q4fy9qEPTTEaGArABJ5R1xdI5byNIphLLq232B2szc4xi7ZXWbo1YMFhTPMYewXkyEtaV4AZE6Pw22GcmEAuDQB4JYB5nxhESfYEMid0oq4sAzTRPAZQTTXuGJvGola5Yo2FFs6zyKIq8zWErR9wp5l/k2S/Jug5wF9PU1H0zyCWgXnymtfCBAaqz0OnXtfvgr/y0Uf7hxY2SEhX+dtpWJUaI4vZrqfBQ2AL7s+ioUu0dZmPdE7ZQaJ1Et0bABbq6KdKGIygkGuOlTxmGKWHU0qYgEw/HeC0Iwj+HiB1XUbG6dZ2IoCmLFyB8Me9jHyIoQYwR/aHlWoIxhXhk7SHXNCDT6tESDdTLLfYp22RXe2+yuT4W/bbeAx6QsO6UtTQ4jDN+bHEBM75EJceUBqBk4+AtIAOZSNxz2SS4FUIrIF+u2x1jS1p8PGpsATh/Q9XjlmEBK3ETivsCyBg6sqJX+1DK1ZZbNkq6/3aP1msmiiB9Q02+NYbtupjH0fjVk8qgWpr2rDBwteVWds3vmYJIXJeTbSDGDvtF6ITykngGk4TbbSynmiAATSRWbQG4zY0NGYtTbWOeDhmbzn5D5x21181ZToiRi8IwVos8kfMitqjBEQdUnQtDwPiLxdxegEXgRJI5CCwBmobbN5/+S+GxRZwxucC/5627zxlmW982duQHRc+cywER5UQ7Vsg1Md3brdhxix5tMOa+tqtGh8yr7GrdtwfB0+Z/i1ZYg0/9ZiVM1/SabR5jH+0rNqG0ChMZXQ6rvnMC/iOFUtmuKt4etrHxIFXLN2NsAAmtPL6j5x2uwuuInAWXKSdb+8bt7feOmCDoxDwjrpjboALpJNpWLjUrWH3++He1Kq8Zp4dPDpot1xRbzWsud4BIsfiGzo2PEjk2bjVsu7C4TJbcykBhGJNdrgtjiIRQhQGz9GD7skePkhODNvQgQMWqWygb/LKkTsu3R+zfUvCpI9Z6JjKsx37tWt91tqat+9/X1gowDt5PcnHtZBVxF0Q33sxFoxAbBj/zThzIDi/zMJNhPBfSn7IEOpSUkP48NU68boWUvEjuKsjL+sAGkH5c81WitjDu6ehDuNWkKussuj1C8hF1wCHhuYHK4F+cPEYmsmKcMHnbLbuzuKags+0PA/7kQDuFxUDqH1XFAAaP/bwAIJX7e+CaY51lkfD6019/cWHE02dVOUJNLkDWAtVlUUxCw5zx5sFCZiyeDLu4gBUktpH1gLF4oNWSIIfOsaHrK68yspDivDrlQyC5xFSRpAD3mrJQ5ibIMoobiThakxVaeOTbyCa4hwWB2IelTcwTDoJzYlQZFL6UHzUCY+NpIhYbw+7+2vtfoQJlZN132u/b18mdqjKIXvZrrAfm7xXOpm7EZjarefuGaWeSyNQGoGTjIAYOG0DyQTR34a6LM+GmxkZshqlBgI5ZyHq0/gI+uNo7WorncbPh59XJKtEv3TMJ8smGlsMMYg/Waqny2oWtljNho2WYYOItSy1q266xfr37bWu1151KSIGnvimVWJqGFB+QNoXuoFmyFsZtODK+Rm79M5GW7ao1uIThBEnQqChFSrIDU/ya05+K3zppZMVxr72NSu77bbJ78Of/zy5C9vc98jVnoSw8+GHLf6971nLs89a7IYbJuteUCca/AIEZII32L3Dcu1bkMSOmi/KJtywghDw3dboz6AFikp27+p7LAKEuvteIBU4yGB3EXmjjvzgOWtAW9QMs6+77jF6lPvmXZm8LmabT46UFOOdu8xPVhLr74Dhx0R0wRqLL1+Iw6GCRkjOXCQ81fr44g8TSW7+Khi5fRZ/+ymruPqDFqg6/WivEzuetvHX8A2EmbyomUBGWvy+AiYcHUlbrPky1rXGW4xa2CbAB2OYeTozTUeHedCUZidANM8JcneNTOStobHeugky5EebV1FLoJc8wWTQBpWjxVu+7BI72JXAzMwLGDM5lYqTBfjHqurRHCAEWLAEk0FMDAF/EJvUznFyUzJnpDnw5tTxc+FUV97/fp89+WTeXn9dUUFzdqSQNJx4UfaVrxRfwowsJxdlSUNkjycwCc9hio/JptbjeFgaQHS+zAGNvgvjP+voaD5krXf0CMIkiHUId8+ne9bKkxeF4yWM8mP2m0IgWIkpaoR9gcehmcI0NZ6xRjREyiV7vkqOvHNta8Bs8LZL22F4m2f2PfyXMMLfZS5/nrA3i2beO1/v8MPrR/Mcv0vcL4TNTSkXgJv8Ad3Sci+mOozBwBAawoxVsr7zZQHrgynvVToIMWqCBwijEiYuSqRQMYBTK4jWwDCCyXhDrIKowMPWiw1uhL0hK1/SNNHHwbcV5YVn4m+YIoq3NSGkLfQrhjSX4h3JEyrT83C4RjMFXJRwVdxrnuIPiSnsentMv24GA6hmjbYa9jeGmGnCRqzjFD2Vbp+vESgxgedrJEv9lEbgrEZAaJqPiPDUOJY6SOTxu9iwuhrCi+TebP7aGGSaf8k9N1rFQ7fCCOAf9DePW/bwUeg/nMdB0knywFUQwbGieb41YRJat3K5Na5ZC7LNWtcrL1tqoNf6yfd3+D/+zZbdfIdFkfIG0Qq4XaKwp8oU0WqrbO0Dd9pNt77HykgJEZHPCYEo0vgE+KKwF0pCjsbgbPf/6DRGbuT//l8k1EmLXnWV8wEc+8pXJkfQD7E69i//4hhAkpRZYtOmC5cJdHs3mynkXG/nATvy/NesYqDdmXIlEQIk9m2zpWya2eZaS5AcPkNIcXZhKw9AvGsbRtqfzCH1hbCPBeRDCBhhBDJs2DECOIRkKkY9+QSNZxVGHIIQnx/1keVaIhtF0J8wfyxP6P+k+Xc/7UzIYmonE6S+Q9ZWEbPVjf/JAswHpirw17wtTBzOji3RNTc5JjDL7+j8k4dhCldSXczj8SU7VNrwjx+VqSsi7hrLcnb14iTml9qys9ZHqoCDbSMw/J5JlqT8juQTWCD20mjv+nparatplbU0ttgONMMqqoO+3a3fRkyOU0Qm7BuGgJRpF4SkV6bgqllZSS5CMIytmR+3xkqZixE0BGaAYMHnXDZv1gt75aGHNE+9osAwHnIye+ABn1VUTL1Tsc7FcMxg9lcBUby+Pm4VMdY5y/YQzH8uMEDWAISH+P5JLHPC0WHuLKxaBbzE6JPqwwlwTj1yMiesi+asJttjy0kJ0VItn2PwEyk9fCnYfvANXOCpOzrNGukDQHvcq5zazrOOYQIH/ieiEFyGY7fzTp+W3uvCKfl8GhimQbXFtSBGDMYsq7XNemeYczmYsviEhesICHTJIkvtaCUcSwif3mp881jRCIplLhpEfQ56pwF9qbvJPkELXJcwoIZ9vArf/pSCwLhKuH1UwFyDnhVRVqswsrjRfBUIkpSnNopLCQIESX/yCAbyaAR95IpUnALhhzyAO116YJzIoF+wD9Eqax+1v4XxWzMJyA57yzGAujDdbHSyQulkTkZAO0qplEagNAI/tBHQBi6zD8wvMONwZDJEXEhmV25PEGZG8o453+prr4S4S1jHppewERrFWg6EDPbNiCm7+npbcMPNVoYJ6ETbEevZsZ0UEe125B/+0UKDPTa85lKrQBsYw2R0YowoZITeY89wpp3FrUfPCcyrt/nXX0GyYZJFE4AglWJDhsD0sVH5iBCWzVRbCPPEsy3pQ4emmvIDR7/wBfeZuuid9f70T8+85AIdzLx0wXwDAJLoZzMJe33zE5boOGghduQ85pk5qD5oMAuXk7pjUbl1dudsKLcGgixNSoj9MIIp8sXV2mBwsYUIANForUjtU9afqbeh8BKIxj6kvV30nrH+FGHkQwstkhmxpuARPdF60k02QUCJcLrHGipH0fYAW8zGFIxIPkJRvcdovx3a8QPr2HCtLV52hZuPEkycrFTd+klLd+6Bgd1Mf2iyOT9l8Sa8SygfalotiuWUTS7sChBYGgLGrzk6ZnWLJ4j8yGSBmB+MpWxZNANRHrZtgyMWz1d7w0V9F+AJLX9yHHUui9yLHjh9pES4Ca8QeTKFmAjiUMFmCg+jf87d2CNAIEXAysYxGAEY0ZpeCEjhohiCA7QC4Ckf5usWOHtuUGafJyurmQafR/tzcRUPI4tAD5EAdHk1UX4riLRLLrcUgZ6WBFPWR5Tf3RwPbmOtpoSPT7RWpHupRHNUMKcvVPOeMPuouujB4RG76ZaYLd5o1tDQBsMfg8EIWaKGiYLGCkNt3k7CQPV0CiDO/piZV6e90NCf5mzsm9MuUFMMoEqOVIgXRhGrJWBo7MREKX2TNLtafBUY3SQtO6HFjKaXMU4TbEtrtG7xMisjXUvr0QGEc9AAVFeg1hACNjdiLGZ3LMCZzqYK19w9rE10OyoCoDhvEADBRzoG0MrC1njzRqtavNy5BKShSwLlCB4xSc4mSVtB8DE/TKDhIODkRj6ZjYAHSEfkIY8Tr1fCjPEO+u1GyvhfZG56tIR+94QNuev602yXTZ6XTuZ2BEpM4NyOb6n30gic1gj4oIbCsRqxfkjbQPjCyNOKiMEM0ty2FzZbx+P/aGUkefahBdQWolQC+X07bfuf/aE13vGAtX3r38wXh2h8+H3C5xbDByi5f4elmltsxcOP2sDm5y0XR+xawNXaCrxCiGj8/kb3k0dqXiOEInJjaW+8nYMdQv5A0iDpwlSrQuM5PchU8YItbPISBORgtLuPyhcOCa12fDZdH4xgFp9Mpe7oHCFBOBvvAzfCHGaC9vRrTTaKf06ossLed53fhofKbPO2RRZk/kQaquwjVxBgo7vG3touz7G8xeqr7NErfNbWW2avvbkA4iJnLYtr7Ko1ftt+sM4ODeDnMUbQhypJodMWjKiVjM3YoEcHbQCTQscEuk1cM+/Ec8AXDFn9R/7AJva+aKnW1wlo4OWGOikMIXhUwuQL1KdUNMIQXgRpCmeGYcgxx2ZOZDLjVg0DMH9BjY2lcnZobNTGElWeeZ6EQhMkeO87as3LVqPF89shArvMhBVwgwkcGRnAvDOFkCBsqQJhqDmRLxJzaILDMCGXzk/Z2nlllkhAmCKoUJTY8gBEK0xgNkXQqsjZM4HF4IO4KttnPzuTeGxqwiKC66EfQjqCH+7ck1aXIiuQ1BB+WhDZwEcBogzY12GK2XhFLabb49Z9MEkCeKCr5XiC4hiLEy/V41opcEhlXcauuH2ezVsYsUQcrSPm5b5oJfMB/A8+yibRQ2lzOQkOOK7j07wQf0K//gLG98VxcIIYrbdh9lpZWOBz6x9iRCucJUZedtfgWrmL+GHAc0QGzcCAJQkGo1yyRZCf7Ugd207fWdIIhXJMuzEYwAT7AGbHCt0qZl//+eTQBAYIYpPjus+/CJw0ytyEMRRD54RHVDrhvNA9r8jUWJ9jSxnZkK+1jx97ufR9jkagxATO0cCWui2NwOmOgEOLvrBFq5sgvPH5G+3Btw9t3SQjKE0cDt/9g9b37WesfHTcEWLaPkQkhiSZ27ubJLv7kKUFbNFt+P/tP2ThhmYrX73Wxjo7cTNAukiqCBvvt6qFTTaOiYcl8SUo7iS8rB+mL0sUys5vfc9CTQSUWLkcJO8FjxHR6IMxUcJo76mn++tm1vPHeG6hNOETqFyBp1P8hLK/cIsoNEaVUOHK9ybNjBfH04ffBea+bPjjo1W2a0+L3b9hwq5a1IFk2GddycX2zOvl9hNXDNmGui6L1wQIFtBim3aH7D9fMWaX1JNipDJm3fHltutQwB7bOGSr6vox+YzY0dHFtqc9Yj+xodsWlw1a1eoK63qLfJQHxuyyDf2YfcI4umTAHrSl/akgcbhXPNNDzb2TFSWHL7v0dvc5Wb3SvRONgDcvsqzvvPxxHSHGgoXih04j0Te+uhNBS0KUeRo1z7s4jZlXanzEFjZdgsZ/0A61kh9UQX9mlKwNDcDYd+21RfMvt8PdMHj4IsnY04FV1B4f+YYNTxAlNkXEDkzSnJaRfvQqORhIvzSBog5PMRdmPHral7vv9tuXvpS1T37Sbw8+eOw7Tqt4kZ0K8jmALGGMmDg3E5y2J4g5OMK68bQlRjAFhSfzWMbiWnS7yYzR0gqeWbw63vqdXr9Qj0Mak8+xwaxVNyVcEnHtA57UkHeCGRHsPbAf2/fMJ53Nt0Azv6mwTWSO0IPkjhQyW5j/gtkGinN9HAZqDEZrPut7jCWXZS1Xs14HOWqsWY98gljfpPHbixPtuevFNy03inaO/drDCd74TP97MqhMh/j0NhI0C6Y+gtD1bHmb4GxVFpvXgJAH5s6p/MSM8n4u8BdOu1xTnFifn2Bx7FjHTbPpnRfOHX4pnNfaEjwApwAaoq8GW2lX2YdnXJ+lm9Kl8zgCJSbwPA5mqavSCJzNCEwhbJB9tAqzj4Dh0mNZzDFhy0CuROMjNHTnc1vMd+QICb9hEWikTVxbicy9AjALYhySW1+2Mfy3Ft/zXitfvNTsjvvM19hs0XnzbGTnDjv8V38G04j0P4mUz5mDTHtjvgflE9DRbYOvbLfqZctdHUdkyASojHcLTTFx01qe9mloxQore+ghzHpGLHb//ZZ6801LvvqqRTZuJBLd9Wg3L0aUpG2ZbRRp75U33GcHdm1D2tvBd4BMVLZstsY691dYZaLbujvR+C3BVwM/kM6OIYJ8DBuZA+ySpqgNjfswF+2zcoiGw50BW14XtfaxoI30DTi/ov1HY7awOma9I0EbIiZ/JX2094QwMYoZXREIgGeSYHqgt9IamwhBDpOh+ReIVNnyNVdby5JLnCTY59QOcCGl8o6MgF9hMiWNR1OsBR/QOZrWUfxzXj0atYEUhBRr14cz0OhAN75Do7Zq/Vpb2hSwt7a+YSNoCgOOoS+8riP2lPoljWDhdbuhYZ61IDBqx9Q4h6DHh/mp+pPJaBJ/0e3Mh4ayjC0sR/sAYy8GUPPVJ82t+tX3KSRWeMjpHR591G+33EL8iaYiUXx67S78WgwqeD/vtOMaY/mMAXusPxQkrH33uO1+NWWjw8ADGDgczaA40LjBmQLIzLMisIo1vZYyJhSzqL8SKIxiFfDaU0NWVhsjbyQCIQWXEiOgd9LzJu14i/2dP4jM/2LAyu7x5sPQn+P/+mkP10Su9JFb9kKYJ4UxQ9OeUVCVfCXLugmD/UOcK1p2jH2W/J/47uXigi3MFjg3QkCXiT6igzL20Zb5MMcIAbAEyBOsSQHF4CZdXSe8EajcY/jjuDsPPo5p5FRzQvu/1rCOwjGCqYLSBdh3fISmRulvkSi4RXgHwUNefoBRPtLM+xbhj4oG0DdIO/KKyjeZueN9ZsIojv1KzDBX51fOxwdQjB8OAvZe+6zVHpMKQgFjUiSKKJV3bgQuRorrnRvd0pNKI3AWI+AP4vxds8BSHLOpUQLGEAQCZmnfvz1h9SB+OeQHJan3UL5D6PojpjAMck0+/V1r27vXGt/3iIUw68yRc3Dw5RcsCwNZBmEvglAhud3Wqj2CnnSuDUKS/wkYzr5NW6zyPTdZ87o14H82BjSUMleVWcq5lgX/8R829q//aq2E/MsNEN2wUPzkoav/3Oes+md/tnjpIjm6LdltyMtWXm2P/OdftZe+/6+OoPcFK+z1LeOkethtl8QI1d9dbl99cRHAzhMFts1WRiasu7XWvpZqxgxwwnKDR21xKGV799Va32gLUUWJIjvWaS34Du7dN9+6h5stGe+3svEOQ3For73dZLs66pAw91l1ssdiSPxHOlfYojUbrbp6HEY0aCsuvdY23vSwxUgV4SaLg4reuVTmfgSQnYfKLBgjR5wkQ6xvRD6kFKsk2EetjfgJoYC/TgZCMEVI+cz4GCZ7o3bZJcuIMrvfdhIQxi/uQYvcwxTegXUuwq63p9O2v7nZrrzmDssi9e8gCbxM0PwwgOLv5IucDtVbP6ZpCyL4pWH25Xx6ZAoWrkBoRYh39XWW5fHHc/bYY1n7vd/L22/+Jg+cVkZG8vY7v5OzW2/12cMPzyQsp1W7IE8ddochDwL7LJEgPU0MnsME6IhjBdC6K264DiOoERMII+Ckgg7Ibh8QwMU4uOuTIwScFOyJor3CbSHuvMgA0g/fXS+Y+x14O2mrri+3mqYQDKh2CD7AWvuTn/fy4H72sKfDU5aaXwhY4gfkqvwqPtEvcvxnTFU/OrfPPOVLnXOF4vuLaWNsAy0MOoFdWGM+fz1w0VwPWqhc55hdwo3JLSOZRCtvCau45XaEtZhjS1iDb785s00YQoR6PjGEaYKIoUGWaScPKADUeybW5O67XzRAULBkHbOn+6KY9sIISgiUJaI4oj8bI9ZAVY5Io6SayCEAUGCgIDLgvB/T4DyMH2rZfE6CahhZxwSq85lr+E37hj1nf0I+wI32QfvfGIxW2iftm26OIfKm/lTJwQD+A2agQ3aE5BH/k1ihd07dLJ3N2QjMhMKcPabUcWkESiNwuiOgrcFHsIVoNekAFM4dM5BM9R7bBuNWQwhnArVZFUxAlPDhYaR/bBMQZwgI+Shyn18n7a125PG/toykhvhzhagXBuHn0egRjRsJJFs6ODsDYk+zwaTYd0Y54hJGeGZyBZKn77qWSy1Wu5yNRAQAmwb3vUJjt7sUvp7hYWLTJuv+6EfJCYWocVoRs9r78z+PKRDS549/fNqdC//UwRw4BkJRW3HJe6x58RVI/EfYZCOWDX3VvrlnB0eiwQGLngTwxJS3PoLElMAdlUiBh9vxA4TAqwp6yYOriC43eIQw3P4JIoaOOdOhilyXDbUnCRU/QT1yzAHrbG7IOgYk3U1ZrpxIoZgdrVy7wB795C9bwzzliMLgJ4b/TyTmEYduLoiE1IZfKu/ECPihvMIV88EJYXCBJyXv6x2zXZh5VwbnWXW5BDoQb5hzTmBSHgvV2mjfQXvxxR/gMwrspU2CeBepr+JBTn/lZ2Z2+PBBi2IevuGq22hbRVAg/JMJQBMgWIWf8PM5fEx7+gYJOeSzFU0VCJGAPoxJMFYLkzKV/Nl1foZ/XnxRuMVs0ybv3aY3V+qIP/7jnCEzusiYQGEDljaMdhBNjHzB0gQJ8UHYa7SGiOY6SsCuigXAHcYgk1TgEMaPfUKm5E7zI82NgoQUUbYGdpJR9GaCND/uOewXfuaH0Lu0PKEI1yNZi1SUGYYFtixNUJgKCQdgMLDTDFfNd3D3cJbg5s0oPeKsy0m6mPfnMCT/xvOxPB4njUjlR6f/qLN+4o9AQ4SrPgXYwbxbQVGI1KtsrIRLA++Cc6Pz0fyWwwRikYEw7uCeTvviN7eTqaeCIN7QAeTwq60qtyry/8aQE0TY28Mwc9r/A9AHEv5Iq+eyzvNrFUWay8wL7Ds4Zln8Gfw70wiZ48yf0TFMxEcSNkBO4L7BCYtjQfDYJ+6z1c3NtIFJpS9/kPzBOUxPxESi3fMjhBLTeiI50FF7041zu70xOd6yWZpeZO68z54lM+DfOQZQ99SuxAROH6W5O58Jjbl7Tqnn0giURuA0R8BtzIW6PmRnysMzTqjwHkyxRsq4gn9PDP8x+QyGkAYG2OzFDIY5D4Ldw3xkGuojsEAaAiFDuyhBPmRPJHfyFJtEGgIg7dJL8B3knkEKKNeLBG2VlqAOhjGlPmQiUkDy2u7ZQs55y+969FGPAcTXYN6f/qlVfOADNvH889bzqU9ZrrfXeh57zGLYiIWWLnVPvBj+QII5WkqwF7FfUYlkFoIgpXxNpH3oJzdTjnxtGUyCIsAzTaqOvmwdMWRw3JffENJfAEXOKIlq3Q6PRpcgPzCISeXoA/Y5qLw8cE4Q2jsexgcFwiKL5DcHrOXZNTpG1EeIgkYkCiGkv1VoowXvKWKPrw76estSmfsRmFpx0rj5YbykFRDRNNK6yzY9QSqRapI+1zZiOVCLuVi1i9wZRxuwc9db1tXdDREIwex8i5lhogBV3MGDof5mIDL37t6JsCBoy5atJR/pOIzFCPNn0OLDAzbOJzk+arV33WVr11xCKHlaMW/8CAxOSP25B536TzHo7759ebSBnpaq2OqFF7z3xXL5IiuCiodn5VcbLm/A3L/M5YdLTCCs8R21uiWEiFoYJSpkDP/PIDkjwelJkrn3IAxCWyif4RySvhwD7EcIkErhNy4tnnz7mAcBtEDCCcGI7o2TMzIK00dKmbK8lVfLDBh4NyJ0IChNOgDTV8n8c2CH+Y8gHGIfKUyk8wKb0ApeS6gLuWB4vX7/VIFPMj/BTQlabLjPXUBFvxNmyqlk8a3DUNKLsMl1n5jBMk/YIk0wZSyRtS2vdxEhVu1SMH7lFiJlTzSsuUIKEQL1VFSQ+gkGLRxGk4jQMBpByJuGqUQ44IehTDMn0viYp9kvUhmfpRDExpMpjsBcfDbzZIII5NLsRaOkcYB+CEj9N8kpgEcEJL2zXtNXwcET5LiXPO6Pt4aLEUGn39bOcsBegPn7AokjDk2/xR2v3YyLpS9zMgKToJ2T3kudlkagNAJnNQJC86BtNl7PH6C8ohKEjumPpMLg3AyEXZ57QpXyIRRG9vnDhBF3Z27DVw9piABFewuHvToy61A+QP7zKaBaxzR4JmDIel2eoSibSxhTMBXvXYrH4jd364z/pHbvtiwaDJX63/1dq/6Zn3HnFY88QnqKeXb09tsRUWZt4vvftxDM4MVVjh1bEe5ofwmKE1NAHcxzxmH0J9DMuAIdFsA3zOeLOAItAJPnxf8Q4S+mjhxP3M+VlRVgLaKQ7bgIfwWe0CMhCjQvZGjoRyBQFivHF0RMqArvUDi6Q+nPOzgCnvZOfwUH+ePpo4AhFazPgf1vWz8BY4Jo8crKIdJZr36YhRQq3tGkknzDpIlyF/MPbNWLiME0TGJIvj/S/tBL4fUAAEAASURBVDAXXO/gk0Nbumx8z1Z8gUYtNQEjyCdBlOEkRKH8kCvvuwetcDX1p4sF1Prcy/79Zr/1WycjJs/9Ge+uHrTqvJH2A0c/giHNgUx+xPZsfdFe+NpXLMaeUN+ywBrnN1nDwqUWXoCJXuWINS9cAk5grvDJMRei5ITrRnMchikoiym9A8wfG0WWyRBA4NN+pMsqK3gGfuLp8SHrwyf8yKHDlurtQSiYseVr/9TCZctgIMX4qQj+bjbxV72de/Ej5Fq8B3IU68YgGs7pZYyckVgdulL5E8V3mF7jXX6uJSphr9ao9nRMQH0wgcTF5cOakDoXxj0ILo+yD8j3V9FEgwr2Bj0wFs/YRMJnPf0Z1ik5JJGlOFNgepXgJ4VLiZjNSFjcNCwb+EP9aiTzWIzoayhMrkDmWYDUI2EJffkXjTA/2A+8eah3VJH5cT1HMaZFzOTNhWIN1Zqt7LIn8Qi8m+cGDDsFe4k8gX3Gwp+lFHedWW6VLp3nESgxged5QEvdlUbgfI5ABqJ9FL+uIwd2uTDdJAwgTDQ2/yLcZe6jDYSPyIUAUl4/G4UINkkBA0j209RRElkfGp5MIuUiCmbRKmrzcLtF8WWdlBj0THv5CcRHeq2ro83qGxfQH9cgJM9HmXjhhcluqj75yclzncRuvdVCa9daeudOi8MEVj322Iz7F9MXpcQQA6gNu729HViiwUtM8IFQww8kA6Os+4K8NvwQDICYdvlvRDHdjED4TWAu7I+GLM4ciuMrNoHPYFJSXwIDyWfE29WBudoD47CkytGojaMJ6u8fsMZGgg+Uyg91BGYSQ1MMl9IFBNHo5xQcAnjFSQOj4A4iFNPMmUpgmeEoAZFINfkAiXTPMCfSaP5izAuRei4CJYxCHoZAOKTr6EEXkChFf1MlB9OIIEFzplD0Jh5R6P0tXj/bo2QcWJ25EufRXV3euab40qXe+cX3dzr0C6PNwAcV1Kuny/oO7rPBN1+3HvB9LhKyCUzJs5iQVtdWIy9CSwgOiIALomXl1s/6D7LOK7G+CAJr4Y+UctGBSwYHyQcLXgnTb4h8dNIWDXIvjESpEp9yCeVmliLMp7/fzBpn8y206P9n7zzgpCi2t32WJEGiiIgoKFElSRBBMCsqiFlRBHNAzHIVvSqi15zQD714UVHMXMxeMAIK/A2IYAZFSYKIIIKA5P7OW2v19sz2zE7e6em3+A3dXbme0zPbp0/VKVtvZOkqe6ruosuRqx1cJDX6+ueJLBG0K4xJWZqh4WUNPG4iBo/n+v1Wea1ft17WrF6hy/9W6/69Kg/MDlq1XkUDjU9vir+/kJV0u5cKupSksq4ZrqQWxIqq3G1XdXujwG/V2SPYb9DRbUa2bFqv98B6Pdf5Plr/3w3q34JK+gwAN6x6VIVv9R+rZa1uRVKjuip9+hsCk2AFmGaNCmnlj/iywztym3wiY7SGqmr5+ymiQD1pKvvJOaoaPqrrHpeom5y9ItJ5kT0CVAKzx5Y1k0BaBPCAtuznxfLJB+/KrI+mmT/ceDG4DfsHqdUPVh88uiEUv49VKw+mfql+hwe+Krrgu7JaCWAJxDSPTfrwt03f7GqSmRbkaB3FAbmL/45s1vUC+mwpq1etlenvvq1tFUmrtu3NNBNT8O8SqR7gFdSGIrVQRYcKqoQgYH1gmANkP3v2bHnttdfMZ/HixeaBHdtFmD0TVWDu3359esA/POjjBQCshrAe4p5Yq1NC1yjztX/qvk8q/60qfxh+sdbPTA/UI+6gYoORrtZQpeFXnUaIt/4nnHCC9OjRQ2rqVDCGfCCAb6l69VTL3Hp9IdCkRWuVp+7ppd4iV636Q5brw3wFTPfSqQKYpLURD/JQ/vCgpy79q1VRy6BKGxagDboFwCZ9mVRBFYd1+nKomq7/wgsCjdD8ur6oWj1p3KiRbkytTml02jimlW7W34K/VFkwMxKKn1gzBqVnzyJ5++3ix5H1OrVx3323yDffqP1Cny/HjuVjigtav/T4xlZS/jVVnlX0e1pRv9CbVOYV1PqrrwVkw59/mDXgW/WHXCUq61Wj2Kx/F1Ss+qOhzj+0ji36N0HfB6jSqFMC9QcAat42Y/XRNeQq6x30oX+jvjgs9gCL+678QtX9Ksjuv+vvFX6kQhBgwcX3vDgUyarVf8iXs7+QyZP/T9as/Fw9wuq6PDiDUYcsxZLDLzgC+Oh3RdfpVa5SQ2cH1JHadZpIjVoNNV6d6qxfKn+uWmb2fdy0EdZBLALAix2UR1n96L0Cq2RFrPXdUE/ef7eW7gm5v+zTsaP+Tan1d9bUv4+rZam2UxJqSyNV/s5VC+Fh2q7KWbrrxFbdskjVQIbcEEhdmrnpH1shgVATqKxvePdsv4+06djZeGo0v9WWiP2baP8GmHi81dcIE2d/3JGgDw94cDNl/i4ILcKGv09tCbwdNBaD6jW1SPFUUps1nWPV/fd3i69/5x3Zvm9f93rLkiWyEU9+GmAVDHOADKHIHXPMMeoUo69RypLhYS2J5l5QCZoHKNwXCQSUwQfKZLG1MYFCzJITAnjzD0vuDg12kWNOP1sf+HQtL5RAfWO/cPHPMn/+j/Lzop9lnVp+4NF3rSpt29R50Ead2rm9ThWtrw9y+G7//scaneq3SWptX1cfBXXLGH1gdKps0rWoNaVZixZm7d9uu+6mSiCsEnjbpBvH6xqkzbpGtYq+aDBrDbM04uq67nn8+ErSpYtOddM1YJdeulUfgMP9qIKXQniZs2ThAvlp2XJZuEFlvnaDrvndapQ17B2ItWX4vmIKX3W15NTWFzq1KleUBrrur66u38T033X6Eug3vV9WqAV5tR7/0vKb9MUBtiTX4ubPA5T+anpf1amsyqCuQZwzd65OM60tOzVsqJZFvEjIXsDvzrrxjvylnkC3rdLZLQ10WmIP3T79WL0HQxPwO21/q4vk95UrzYu5Jo13kksGnWoo4KUf/uFNoM2JhCLzUq/497s4o84M0j1eoWBt3tRcq1Wvon+/SMAUUXgEx2OB9SJrXglqhXiZgOmpWFO6XD0Ir9I9RWvr+uMina5afJf8/QxhGon9H6x+NmBfQLiX8gZYG3X+iukf4vWVhl7rtACGnBEI9y9rzjCzIRJIngCsMTs13MV8ki+dnyWqdu5svH86OjVxxWWXSRWd/llFHzo3ffedLD/3XH05ibeTqgQeckh+DiAHvcKDEKZ/7r777tK0aVPzYJeDZn2bKFkH5JvMyBwTwMwtOINo2KixNN61iT6PlTyMbdywUZYsXSqLFi+S1WpJr6T3EKx7yLFJp4BDeatTq5ZagFQBUKURj3PV1K2gPgbqrAKd5qWWlmrVq8me+p2sr96BcQ+WBFUU1OqIKeg4wgNhJl4Q2O7bo22vdesiueqqCnLLLdvkgw8c+V092NarVzJWmy8sR1h1v9dtfybpNPlvdc3eXy1bq0Mw3S5BfytqqPzAz9DBfypYXKvhVtZp7CrdQ7aCTguEi//VOv0Tyh+mCsOZmFkXqkVUFzDKhKMFK2idKA8F4U9dGvDKO+/Kz/rS4Oijj5addtop6RdSicpoqyp9v56+VdarV9iSoOf3ilQ/eptg/8CKO2KAYQhW6S2SFs1bmE95jxp/l/6+yxLuyp5ypK77+1GaywHSRQbIXHnfTAnFNhAIv8sCeVP+KTtKS+mmFkFMB8VU0aNkuMaE9xkgYcAZyEglMAMQWQUJZIsA3gDj7b99GLcPZvhBRhqOlfQP9Sb94451XX4PZsiDaYSoAx+/PNnqf3S9RdrHesOGycqhQ2XLQt3Gon17qahvmLfMn+9m3f6MM2S7ffZxr8N2YuVjj2EbP8cbmwDW/W2HJ3Rv+PvZbDvdXLpFi+bmU/zABmUAMwOKlQIUsfeUTbdHW51J1wJb9bclMsDCpA/h+rF1RKandtW2LcbiSJsoj5CorWXL4nGi/3+oB/169VJroxBKgXuTJk3kNN1ap4L+Pprf8TIGhvXixZodMsJiVMzT3D56CsUvXrDJZv253g81aqi6GX3vxasgybQVQ0oUQHU6KZWb6mTFedpznfW4foIqiAO3SqOJYXhkLZZToviwTnzdunVm5oh9Pki0bDL5UpH9rtJRTlfvnza0VscwUO7myrtGGbTTQ3+T7+V1udZm01WBs6kEujSyexKGb1R2CbJ2EsgSATyg4YcXSp4N3oc2qxgiDgoggjfdlsExVh3ePH7nqfzw+9Xjjat77bWyZelSWa3bQ8Ai6FUAa5x4or7xfTKrDxvevuTzuZWlVwY2zvYbaYjz5rFp3rzR6bHKoKy3HK6jyyKOoXwI2Bc5ZbXulZnfc7tNt8eI+vCboy+LchEuv7yCHHBABdF3QaXCiBHFimjXrkWyxx7JPRiXqizAEfg+4ve9QQN10qLBV2Y+44v+HkdnQT02j1+dNi2ZNqPbSPR601y1Oo4pVju361Ikjd7SFw5q+d3yizorOnWr2TAeFsK143W960m5uTcT7Xt55oOMMEUXTr288kykT34yT6Rcunkw5RMWQqwD/E7eVmXwSd0Z8W9PUH9XHjnJNd0WWT4egZKny3i5mEYCJJBzAuX1I52LgdYfMUK2V4Vv3cSJsunrr3VvqDZS/YgjpFrPnuq7wDsNLRe9yc82/OSfaBxG5JfXjjTVNFueRxLIBAFMQe3Uyb8mKIa6TFjuuCPcD/3xvqv+5IpjEykXL0+8tHjtppK2cZYqgH+bHmtfrGrC31N/K+1cJA3HVZSFzdWh1TqRvz50VAlMpYXCLGNlZI9BGiU2jd9bektr6SXfygSZIU+pMrjcDMGsdwzSYALcVyqBARYeu04CQSWAP1rYEB4fBhIgARKIJvDYY7r1ga5bg6LIUNgENs+zk0+x/i9S3pUaqqdrnS688RP1dvptSb7CJhKe0alPa2krfY118Ek5RdexrtQ1gs3DA6CcR0olsJwFwOZJIGwElg0YoJ4qNkmDxx6TClHbD6x7+23ZMH261LnqKqmo3jEZSIAECp/AmjWOvPmmI9/qQ77OcJMOHYrkqKPUK+T2kQpB4ZMI5wgr6O4DNkDRq6ReQW3AlMetOi0UoSL2KdeAOEf3k6xQoyRfcQr/DyqBSuop9Ax5WtaqNbC+NAvqMALXbyqBgRMZO0wCwSWwWR3ArH3mGTOAqt26mc3hvaNZfs45slXXC1babTepHbWZvDcfz0mABAqDwP/93zbp33+rLFgQOZ499hB56qmKuldluKeDRlIpzKsqe5Uoc2v/60j1g0rGufpB3dx+UfH1dp2L8/3SV53IvOvILpMrSrVuvD9KaAX7rKrU1A0juC9tLqVIJTCXtNkWCYScwDb1YmbDiiuvtKeljlt1w3IGEiCBwiawcqWj+2BuFd0KzYRWrXSPavUGqY6D5aefRHr12ipfflkkzZqVKAmFTSSco6varUS+a/6tXq83OlK1Y5FZA7j2xZIpoBXqFsnal7YZBRB7nW+YpvuZdgsnM46aBDJBgK9QMkGRdZAACZAACZAACSRF4LbbtrkK4KuvVpQ5cyqrRbCyjFNnINiXfL1O+Rs0aGtSdTJz8Ahsnu/ps+p8fz7uyG+Dt4lXAUSO387fKstO0vuheDtZ0V3uGUiABNIgQCUwDXgsSgIkkBwB7zq/nZ57Tnb96qviz6xZxkOora3agQfaUx5JgAQKlMDHHxdbedQ5sBx7bMnjyMknV5Drriu+njJFHYJsKs5XoBg4rBQJYP0oAwmQQOoEOB00dXYsSQIkkCSBSo0bS51rrhHdvV5qHH+8VKha1a1h5//9TxbpfDBH54OtfeEFqdajh5vGExIggcIjMFf3h0Po06dEAbSj7NWrSG6+WTcM3yzy/feiG8rbFB4LjUCFaiUjavhftQLrXoGJhAr0HZYIJuYhgZgEqATGRMMEEiCBbBCof9ddvtVWVmcwVfbaSzZ+/rlsmjvXNw8jSYAECofA9tuL/P471v+VNun89lvJOOvWLTnnWeERqKxrPqv3KZJta3SLCD1WqJqYElh4JDgiEsgtgdKv33LbPlsjARIIEQFn2zZZtM8+smD33fUPvv7F9wRn40bZhFf+Girg6ZCBBEigoAnsvXfxw/6ECY46hClRBDdvdgTrBRF23llkl12K1BroSKNGm+Wqq7gQrBBvikZvVJLGH1SiAliIwuWY8pYALYF5Kxp2jAQKj8DmefNk0+zZZmBLjz5a930q3vgJ+z5tnDFDnLVrTVrV/fcvvMFzRCRAAhEEDj20SCZOdARf+8MP3ypXXFFB/vpLZMSIbTJzZrFSePjhRfLxx9vktdcc+eUXkRdf3Cb3318xoh5ekAAJkAAJJE+ASmDyzFiCBEggRQKObhJvAzaF9wuVdUpo7cGD/ZIYRwIkUEAEvv66xPo3Td39T5tW2so3dqwjY8eWxHt+QgqIBIdCAiRAArknQCUw98zZIgmElkBRpZKfnCrt20uF2rVdFhVq1ZKqnTtLHd0/sEI1j6cANwdPSIAESIAESIAESIAEMkGg5IksE7WxDhIgARKIQ6ByixZSqVkzsx5wl8mTpSI9PsShxSQSKGwC1aphTaCjm8IXyaOPJjbFswI9GRT2TcHRkQAJ5IwAlcCcoWZDJEACRRUrShM4f1EHMV6rIMmQAAmEj8AppxTJ1KkiF1xQQZo0oUfI8N0BHDEJkEB5EqASWJ702TYJhJBAEV7l83V+CCXPIZNAJIGDDqogX31F014kFV6RAAmQQG4I8Nc3N5zZCgmQAAmQAAmQAAmQAAmQAAnkBQEqgXkhBnaCBEiABEiABEiABEiABEiABHJDgEpgbjizFRIgARIgARIgARIgARIgARLICwJUAvNCDOwECZAACZAACZAACZAACZAACeSGAJXA3HBmKyRAAiRAAiRAAiRAAiRAAiSQFwSoBOaFGNgJEiABEiABEiABEiABEiABEsgNASqBueHMVkiABEiABEiABEiABEiABEggLwhQCcwLMbATJEACJEACJEACJEACJEACJJAbAlQCc8OZrZAACZAACZAACZAACZAACZBAXhCgEpgXYmAnSIAESIAESIAESIAESIAESCA3BKgE5oYzWyEBEiABEiABEiABEiABEiCBvCBAJTAvxMBOkAAJkAAJkAAJkAAJkAAJkEBuCFAJzA1ntkICJEACJEACJEACJEACJEACeUGgUl70gp0gARLIewLz58+XOXPmyNKlS6Vu3brSt29fqVSJPyF5L7g0OkiZpwEvoEUp84AKLo1uU+ZpwAtoUco8oILLcLf5BJdhoKyOBAqJwObNm+XOO++U0aNHy+LFiyOGdtttt8n1118fEceL4BOgzIMvw2RHQJknSyz4+Snz4Msw2RFQ5skSK/z8VAILX8YcIQmkRMBxHDnjjDNk3LhxvuW/+OIL33hGBpcAZR5c2aXac8o8VXLBLUeZB1d2qfacMk+VXGGXC5USuGTJEnnllVcKW6IcXVoEfvnlF1m9erWMHDkyrXoKofDLL78skydPjjmUihUrFhSnuXPnmrFC6f34449jjruQE8Imc68sMc151apVBXVPe8cX6zzMMrdM7Hf/xRdflI8++shGF+yRMo8UbRj+7lPmkTL3XvXr10/q16/vjQrNeZG+HXDCMtopU6bIwQcfHJbhcpwkQAIkQAIkQAIkQAIkQAIxCMyaNUs6dOgQI7Wwo0OlBG7btk02bNhQ2BLl6NIicOihhxqnJ+PHj0+rniAXxlvRzp07yx9//OE7jFatWsmHH34o22+/vW96UCPxkqh3794ydepU6dixY1CHkVK/wypzL6zDDjtM6tSpI2H57lPmJdLHjIc+ffrItGnTZJ999ilJKLAzytxfoIX8d58y95e5N7ZatWpSVFTkjQrNeaimg1aoUEGqV68eGuFyoMkTwBRHeLwM830ydOjQmApgzZo15dVXX5UGDRokDzfPS1StWtX0EMewyT+sMvfekvju4xMW2VPmJdIPy3efMi+RufeskP/uU+ZeSfM8mgD3CYwmwmsSCDGBCRMmxLWEjBkzRlq3bh1iQoU3dMq88GRa1ogo87IIFV46ZV54Mi1rRJR5WYSYTiWQ9wAJkIAhgKnSgwcPjkljyJAhcuKJJ8ZMZ0LwCFDmwZNZuj2mzNMlGLzylHnwZJZujynzdAmGo3yopoOWp0hXrFgh8E6KaQdt2rQpz674to0fDOshDWu+7PQY38xZivzkk08E+9j06NEjSy2w2ngEHn74YVmwYIFvlj333FNuv/123zRGBpcAZR5c2aXac8o8VXLBLUeZB1d2qfacMk+VXLjK0RKYI3mPGjXKeB9q27ZtjlpMrpmZM2ea/sFD0qeffppc4QzkhlOObt26Sa9evTJQG6tIlgC2xbjjjjtiFsMflMqVK8dMZ0LwCFDmwZNZuj2mzNMlGLzylHnwZJZujynzdAmGpzyVwPDIOm9H+s0338gpp5wiIdqtJO9kcc8998jKlSt9+4U9dLi1ii+aQEdS5oEWX0qdp8xTwhboQpR5oMWXUucp85SwhbIQp4PmSOxwOz1w4ECBh1KGEgIvvPCCXHLJJTEVkJKcPMsWgWXLlsmIESN8q8c2EPfdd59vGiODS4AyD67sUu05ZZ4queCWo8yDK7tUe06Zp0ounOWoBOZI7th/DB+GYgJLly6VQYMGyeuvv04k5Uzg1ltvlXXr1vn2YtiwYdKoUSPfNEYGlwBlHlzZpdpzyjxVcsEtR5kHV3ap9pwyT5VcOMtRCVS5YzPNzz77TL788ktjqcO6uC5dukj9+vVL3RWLFy+WefPmmU2FYd3D+fTp041TlcaNG8shhxzi60L/559/lh9++ME4hjnggAMi6t20aZO8//77MmfOHPntt99kjz32kL322stsWotNLGOF+fPnm/V7KAdHLu3atTObfO+4446xiph4bIaNdX/YDBzj7NmzZ9z8NjEZTrZMrCPW/y1atMgkN2vWTJo2bWoYxMrP+OwQ+PHHH2X06NG+leMevOKKK3zTGBlcApR5cGWXas8p81TJBbccZR5c2aXac8o8VXIhLqfrsEIbtm7d6qgzDEcdXjh6C0R8dBqc8/jjj5dic/PNN5t8nTt3doYPHx5RBnUUFRU56ma/VDl9O+Pm9SbOmDHDadiwoZvm7cfuu+/uTJs2zZvdnK9atcq0oZ5GS5XTjY6dkSNHOtu2bStVbu3atY563ixVpnbt2o4dF9r/4IMPIsqmwimiAp+LHXbYwalSpYqj1kBnzZo1jm5oavqF/pdn2H///Z1jjjmmPLuQ07ZPO+20UveDvQdfeumlnPalvBvTFzGGBb6ThRwoc3/p4rexT58+/okBj6XM4wvwvffeM999fRkcP2OAUinzxIVVKH/3KfPEZc6cxQRCbQk8/fTT5cUXX9RnXhFYzw488ECzRQEsZb///ruce+65xlqlCpLJ4/0PlkN84DGxb9++stNOO8nEiRMF1jl4UmzZsqVcdtll3iKlzuGIQx865NdffxVViIxnTFWOjGURVkPUhT7BYman5KlyJ8cff7zAmyYC2u3evbv89ddfogqjqKJn1tjB2cojjzxi8tj/zj77bJMH1+hfx44dBfm++uor8RujLZcOJ1tH9PGqq66SAQMGyK677hqdxOscEcD9NW7cON/W4MUW9xlDYRGgzAtLnomMhjJPhFJh5aHMC0ueiYyGMk+EEvOUIhBWbfjdd991LSC6Vs+Bdc2G5cuXOzpF0qRvt912jprYbVKExaxmzZrOpEmT3DRYtA4//HBTrlKlSs5HH33kpvlZAtUpituH1157zc2Lk0cffdRNg7XSBt2rzY3XjbuNFc2m6Y+Ao1NUTToskt7277//frecKqfOli1bbDHnrrvuctP0BomwBKbKya08wRNaAhMElcFsOtUzQu6Qvf2ocpjBloJRVRgsgZR57HuxUC2BlHlsmduUQrMEUuZWsokdC8ESSJknJmvmiiQQWleVN954o1GIYUl75plnzBo/E6H/wSoIr5VYZ7dx40aBu12/cPXVV0e4zlelUFR5E1UARZUsGT9+vF8xN867MTcsc95w3nnnyQUXXCBDhgwR796CTz75pMmmU0hl7NixgjZtwLq65557zqw7VDELFgjbgPEg6BRT4+0Rm9bbcM0110SMw8bjmAlO3vp4njsCsCbDGoz7O3r7B31hITrd2bczWAuoLxh80xiZ3wQo8/yWTzZ6R5lng2p+10mZ57d8stE7yjwbVFlnKKeDQkGCExgEtdwJ3OBDafOGBg0aGKcpmBr69ddfe5Pcc7/pnlCy4BzmnXfekVmzZrl5/U68Dlm6du0qmK551FFHmSmgUEChUHoDpo1+//33Jur8888XXT/nTTbnrVu3NnW8+eabZponIjGF1I7hzDPPNEpqdEFdxyiTJ0+OiM4Up4hKeZETArpeVf71r3+59zWmGY8ZM0Z0vaNpHwrgn3/+6duXG264gVuZ+JLJ70jKPL/lk43eUebZoJrfdVLm+S2fbPSOMs8GVdZpCEQaBsNxpZ463WlvCqHMc7UWumB07ZzJr9ZCNy765MILLzR56tWr5yb5TQfFlMyTTjqpVPtwjqLrDJ3HHnvM0Qd1t4633nrLzfv000+78dEnaqE0+TAlVF3/O+o91C2nlsTo7OZ69uzZbh7rGCYdTr6NxInkdNA4cJJMuvvuu11Zeu9v9TTrzJw500wFVquxb55WrVo5cAQUxhDk6aCUefp3bNCmg1Lm6cvc1hCU6aCUuZVYZo/5PB2UMs+srFlbJIFQTgddsmRJUq8AYIFTbBFl6tSpE3HtvbBTNLEFA7Z/iBUwJROOOXRNnrRo0cLNtn79erN/HqaEqhdSs3UEElWpc/PYNtwIz4ntG/qMPmB7Chtq1KhhTyOOsHxGh0xwiq6T19kncO+99/o2AudBxx13nPznP/8R71Rkb+Zrr72WVkAvkICcU+YBEVQGu0mZZxBmQKqizAMiqAx2kzLPIExWVYpAKJVAr0dKtUCZvfmwP1+8j1cBA0V4D40VsBE6AqZmwutnvIB6sSYP0zzxGTFihPESqg5pTLG5c+fKwIEDzbn1EIoL9DVW8Cpv2Otw7733drOuWLHCPfee+MVngpO3DZ5nnwBeOsS7N7DP5cUXX+zbEd0qRE499VTfNEbmLwHKPH9lk62eUebZIpu/9VLm+SubbPWMMs8WWdZrCYRSCdx5553NOkBAgJIFRcnvgzVyOgXTXYdnoeEIRxuwEPoFnVppojt16uSX7MYhHxy9PP/88yYO1sDLL7/ctAnrXfPmzU28TuEz7WFTdauMfv7552490SfIjwAHN1BC4URml112MXG2b+bC89+3337ruSo+zQSnUpUyIqsEIO8OHTqk1Aa2AvFbZ5pSZSyUMwKUec5Q501DlHneiCJnHaHMc4Y6bxqizPNGFAXbkVAqgZDmfvvtZ4SqWzOY/f6iJaxbI8gpp5xi9rLDFDm/4Gemf/3118UqVGUpgfAuCmcwZ511lixcuDCiCSilBx10kInDtE5dpyW6pst9wIeTD+80T1v47bffFt3s2lweffTRNtpMK8XFU089JdZS6SbqyX333ee9dM8zwcmtjCc5IXDbbbel1A6mHzMEkwBlHky5pdNryjwdesEsS5kHU27p9JoyT4cey5ZFILRKoC62NWuf4Dnz2GOPNdY3KFswv0OR8j4QwzrnFzB1Ex8oaAgTJkwwCh3O1cGGO40T137BuuFHmxdddJHZJB51wX3/K6+8Is8++6wpBkXMrtmzytqGDRuMF1JsWI+Acbz66qty2mmnmWtM7cOG7DZgyimmmKLcEUccId99951JgodIeAy1iqPNb4+Z4GTr4jE3BOBhVp0TJdVYx44dBR+GYBKgzIMpt3R6TZmnQy+YZSnzYMotnV5T5unQY9kyCajiE9oAT586vdL1klirVi1Ht4twrxWeM2jQoAg+1jso0mxZdbbi1K1b1y2n7videfPmRZTz8w6qCpnTrVs3txzqVOXNwUbzOMcHdet2DxF1XXrppW468qA9dRTjxuHcu1G8Laz7xbl5UE6nibptwXOkbdN6B7XlUuFkyyZ6pHfQREkllk+dCzlt2rRxZWplG+uo+wkmVnEB5wqyd1CIhTJP7+YMmndQyjw9eXtLB8U7KGXulVrmzvPZOyhlnjk5s6bSBEJrCdSHYRk2bJjoj79gc2ystYMFbu3atUiSxo0by8MPPywjR44019H/VahQQV566SXBZvO6DYOsWrXKZOnSpYuxCGL9njfYzdlRzgZY5jB9FJvCV65c2USvXr3a7O2GtO7du8vHH38svXv3tkXM8aGHHjKWQuxJiID1ibDoYQoprIvYo9BO4zQZ/v6vf//+MmrUKLNGEFHLli0zbbVv316mTJnydy4xm827F3qSDidvPfHOLR97jJeXaWUTwNThF154wVh/y8qNvFgPyBBsApR5sOWXSu8p81SoBbsMZR5s+aXSe8o8FWoskxCB0nphOGOwH58qXM6kSZOcBQsWmL3U/EhYS6Aqc26yOpdx8Cbxp59+cuOSPYFV8IsvvnB0Sqmjm8w7OkU0oSrUS6nz4YcfmrI6JTShMhs3bjT5dd2js3z58oTK2EyJcrL5g3bM9zeCyfK86aabyrQG9uvXL9lqCzJ/0C2BViiUuSWR3DGIlkA7QsrckkjtGCRLoB0hZW5JpH8Myt99yjx9WbOGSAKVEtIUQ5BJp4FK165dUxppy5YtBZ90Aix/7dq1M59k6tFpqNKzZ89kihiPoWgrlZAOp1TaY5n0CFx//fXG++wPP/wQs6IBAwbETGNC8AhQ5sGTWbo9pszTJRi88pR58GSWbo8p83QJsnw0gZK5idEpvCYBEgg8AbxceOCBB2KOo0mTJmZfypgZmBA4ApR54ESWdocp87QRBq4CyjxwIku7w5R52ghZQRQBKoFRQHhJAoVGAGtK/db8YU/AsWPHlloDGmv88+fPl4kTJ8rjjz8uL7/8sllPGisv48uXAGVevvzLo3XKvDyol2+bmZJ5+Y6CrSdDIFMy59/zZKgXbl5OB01StngTU7VqVdeRS5LFmZ0EyoWAeoaVwYMHG2dAmzdvFkwj3nfffaVOnTpx+4O8d955p4wePVoWL14ckRf7F2F6CkN+EqDM81Mu2ewVZZ5NuvlZd6oyz8/RsFeJEEhV5vx7ngjdcOWhEpikvHUrA8GHgQSCRADeb+FtNpmgy4fljDPOkHHjxvkWU0dGvvGMzA8ClHl+yCGXvaDMc0k7P9pKReb50XP2IlUCqcicf89TpV3Y5UKlBGIrB5jAGUggFgHcI7pPo3z99dexsoQm/u67746pAALC3nvvXVCc7G+D7vFprP2hEbRnoGGTuWfoZqsfbOETtu9+mGVu5e/97mO2D0O4CITh7z6/57Hv6ebNm4f2b34RnIXGRlNYKdgL7+CDDy6sQXE0JEACJEACJEACJEACJEACSRPQbdmkQ4cOSZcrhAKhUgJ1Tz2z+XohCI5jyA6BIUOGSM2aNWXYsGHZaSAAta5cuVIuvvhiWbt2rW9vd911V+NxFBvYFlLA9FascRwxYoS0aNGikIZW5ljCKnMvmH/84x9So0YN0b1gvdEFe06Zl4h29uzZ8s9//jOU3/0SCuE9K+S/+/yel31fY5s1PPeFMYRqOmi9evXk6KOPDqOcOeYECdx+++0S9vvk5JNPjqkA4ofynXfekdatWydINDjZ4PAJQTcOls6dOwen4xnoaVhl7kV3xx13GEdJYfkbQZmXSN9OAe3Ro4d06tSpJIFnoSBQyH/3+T0PxS2c8iC5RUTK6FiQBAqPwIQJE2T8+PExBzZmzJiCVABjDjgECZR5CIQcNUTKPAoIL0mgAAnwe16AQs3wkKgEZhgoqyOBoBLYsGGD2UYiVv8xZebEE0+Mlcz4ABKgzAMotDS7TJmnCZDFSSAABPg9D4CQ8qCLoZoOWp68V6xYIUuWLDEbc7dp06Y8u+LbNn4w5s6da9JatWqVM09JW7dulQULFsjSpUtlt912Mx+4P2bIPYGHH37YyMKv5T333FMwZYahsAhQ5oUlz0RGQ5knQol5SCDYBPg9D7b8ctV7WgJzRHrUqFHG+1Dbtm1z1GJyzcycOdP0Dx6SPv300+QKp5AbSt/ZZ58tcC4C97wHHHCANG3a1DhmuPLKK2XNmjUp1MoiqRJYvXq1YE1UrIA/KJUrV46VzPgAEqDMAyi0NLtMmacJkMVJIAAE+D0PgJDypItUAvNEEGHqxuLFiwXK8JNPPimbN28W7M1Vv359g+Cvv/4yHtr22WcfwQ8ZQ24I3HPPPQIvYn6hX79+3FrFD0zA4yjzgAswhe5T5ilAYxESCBgBfs8DJrBy7C6VwBzBh1IzcOBAOeuss3LUYv42079/f8F2HZj2edNNNxmr32+//SbLly83jNDzn376SS6//PL8HUQB9WzZsmVG8fYb0vbbby/33XefXxLjAkyAMg+w8FLsOmWeIjgWI4EAEeD3PEDCyoOuUgnMkRB69+4tTz31lMC7YpjDwoULZerUqQbB+eefL8OHDzdTQBGx4447Gusg9mxB+O9//ytYM8iQXQK33nqrrFu3zrcR7JfYqFEj3zRGBpcAZR5c2aXac8o8VXIsRwLBIcDveXBklQ89pWMYlcIvv/win332mXz55ZdmaiLWxXXp0sWdougVFKYyzps3z+wnBesezqdPn26cqjRu3FgOOeQQXxf6P//8s/zwww/GMQzWv3nDpk2b5P3335c5c+YILGJ77LGH7LXXXoL6423IPX/+fLN+D+Wwx1m7du3M/mZQpuIFKGFY9/fHH3+YcVqlK14ZpCXDKVZdH3zwgZt05plnuuf2BNbB448/3iiK69evN8wKcU86O97yPv74448yevRo327gHrziiit80xgZXAKUeXBll2rPKfNUybEcCQSHAL/nwZFVvvQ01Ergtm3b5O677zZTErE2zRswDe7BBx+Uc845xxstTzzxhNx8881G2TrmmGMElhJvgBJz8cUXy8iRI73RxsJ14403mjjHcdw0KJ+oByb86LD77rvL008/bTav9qZBebvhhhsEzmaiLWXVq1c3Y0Ifor1swtpz5JFHyrRp07zVSe3atQXOWGKFVDjFqgtK8muvvWa8gUJp9QurVq1yo9E3huwRwD0Zfe/b1vBGsVKlUP9EWBQFdaTMC0qcCQ2GMk8IEzORQKAJ8HseaPGVS+dD/YR3+umny4svvmjAw3p24IEHmgdiWMqwZu3cc8+VRYsWGaUvWjpQ3vCBx8S+ffvKTjvtJBMnThRY5+BJsWXLlnLZZZdFF4u4hiOOPn36yK+//ipVqlSRXr16yQ477GAsi7Aaoi70CX2wU/KgkMFSNmXKFFMX2u3evbvAoQqUu7Vr18oll1wi33zzjTzyyCMR7cEbp1UA0b+OHTuafF999ZXvGG3hdDjZOuwR1lJ8YgVsVYFpswi77LKL7LzzzrGyMj5NAri/xo0b51sLHPfgPmMoLAKUeWHJM5HRUOaJUGIeEgg2AX7Pgy2/cuu9WqVCGd59912Y48xH1+s5an1yOaiDEkenSJq07bbbzlETu5umVkC3XM2aNZ1Jkya5abqtgXP44YebdLWgOB999JGbplYVt5yNfOGFF9w4tY7ZaHN89NFH3TR13e+m6V5tbrxu3O2gTRv0R8DRKaQmXa2AEe3ff//9bjlVTp0tW7bYYs5dd93lpoGJTtl001Ll5FaQ5Ik6g3H7ok5jkiydfvb999/fUcts+hUFoAad6umytt8Fe1TlMAAjyGwXdUq24TFjxozMVpxHtVHmsYXRo0cPR1/Kxc4Q0BTKvGzBvffee+a7ry92y87MHAVHoBD+7vN7XnC3ZU4GFFrHMDCbI8CS9swzz5g1fiZC/4NVUBU0s85u48aNAne7fuHqq6+OcJ2vSqGo8mam0KmSJePHj/cr5sZhk3QbYJnzhvPOO08uuOACGTJkiNlOwaZhWwWEhg0bytixYwVt2oB99p577jmz7lDvHsF0PhswHgRMMYW3x4oVK9okueaaayLG4SboSSY4eeuLdw7OmIKLgKmimPLKkDoBWJNhDcb9Hb39A/ZhfPzxx30rx1pAfcHgm8bI/CZAmee3fLLRO8o8G1RZJwnkFwF+z/NLHoXSm1AqgVCQ4AQGQS13gvV/UNq8nwYNGhinKcjz9ddf41Aq+E33hJKFdW8Is2bNKlXGG+F1yNK1a1fjhOPtt98WTInE3nlQKKEYwbMoAqaNfv/99+YcnjWx/i86wInKUUcdZaIxzRMBU0jtGOCMxW+d1+DBg01e73+Z4uStM9b5LbfcYpRRpGNKLJRZbk4ei1bZ8fC6CmUOch0wYIC0atVK3njjDbcgFMA///zTvfaeQPnG/ccQLAKUebDklYneUuaZoMg6SCC/CfB7nt/yCXTvcmJvzLNG1FNnzGlwKsxSaWotdEdgp4OqtdCNiz658MILTR316tVzk/ymg2JK5kknnVSqPVXuHF1n6Dz22GOOPqi7dbz11ltuXnUY48ZHn6iF0uTDlFB1BuOo91C3nFoSo7Ob69mzZ7t57HTQdDj5NuITqU5JHHW+47YNruhLeYVCmBaizo5cnt77WT3NOjNnzjRTgdVq7JtHlUVHnQ2VF/5ybTfI00Ep8/RvnaBNB6XM05e5rYHTQS2JcB7z+e8+v+fhvCdzNepQvu5fsmRJUoo7LHAqkIgyderUibj2XtgpmvDiie0fYgVMyYRjDl2TJy1atHCzYWuE119/XTAltHPnzmbrCCR6vX3aNtxCnhPbN/QZfcD2FDbUqFHDnkYcYfmMDpngFF2n9xqWKFg54XEVoXnz5qLrKKV9+/bebDxPksC9997rWwLOg4477jj5z3/+I96pyN7M1157La2AXiABOafMAyKoDHaTMs8gTFZFAnlKgN/zPBVMgXQrlErgrrvu6opv6NChZm8+7M8X7+NVwFAY3kNjhaVLl5okTM2E1894AfViTR6meeIzYsQI4yVUHdKYYnPnzpWBAweac+shFBfoa6zgVd7q168ve++9t5t1xYoV7rn3xC8+E5y8bXjP1fmOYDrsO++8Y6K7detmFMBmzZp5s/E8SQJ46RDv3sA+l9g+xC9gO45TTz3VL4lxeUyAMs9j4WSpa5R5lsCyWhLIIwL8nueRMAq0K6FUArHtANYBIkDJgqLk95k8ebLoFEx3HZ73HoCjDVgI/YJOZzTRnTp18kt245APjl6ef/55EwdroHrHNG3CegfLGIJO4TOOPaAgWWX0888/N2l+/yE/AhzcQAmFExlst4Bg+2YuPP99++23nqvi00xwKlWpRsA6ibWYX3zxhUk+5ZRTRL2sGhn45Wdc4gQg7w4dOiRewJMTW4H4rTP1ZOFpHhKgzPNQKFnuEmWeZcCsngTygAC/53kghALvQiiVQMh0v/32M6LFxuXY7y866NYIAuUETjUwRc4v+JnpMY3TKlRlKYHwLoq9+8466yxZuHBhRBNQSg866CATh2md2BRe13S5D/hjxoyJmOZpC8OxjLq4N5dHH320jTbTSnGBPfispdJN1BN4DPULmeAUXe8//vEP1zEPPKBaT6zR+XidGoHbbrstpYKYfswQTAKUeTDllk6vKfN06LEsCQSDAL/nwZBTUHsZWiVQF9uatU/wnHnsscca6xuULZjfoUh5H4hhnfMLmLqJDxQ0hAkTJhiFDufwxminceLaL1g3/GjzoosuMpvEoy6473/llVfk2WefNcWgiNk1e1ZZgwdReCG1CizG8eqrr8ppp51mymBq31VXXeU2iymnmGKKckcccYR89913Jg3r8uAx1CqOboG/TzLByVsn1vzZrQnggRLr/0aPHm3WqWGtWvQn3tRGb708LyEA77DqnKgkIoGzjh07Cj4MwSRAmQdTbun0mjJPhx7LkkAwCPB7Hgw5BbaXqviENsDTp06vdL0k1qpVy9Fpou61CtUZNGhQBB/rHRRptqw6W3Hq1q3rltMtDpx58+ZFlPPzDqoKmaNr4dxyqFOVNwcbzeMcH9T95ptvRtR16aWXuunIg/bUUYwbh3PvRvW2sO4X5+ZBOZ0m6rYFz5G2Tesd1JZLhZMtG3084YQT3HZse/GO06ZNi64iq9f57CUsmYGrcyGnTZs2CbPW/QSTqb4g8wbZOygEQpmnd1sGzTsoZZ6evL2l6R3USyN85/n+d5+/7eG7J3M14tBaAlXxkGHDhon++Jv91LDWDha4tWvXIkkaN24sDz/8sIwcOdJcR/8HK9ZLL71kNpvXbRhk1apVJkuXLl2MRTDawYndnN27/xosc5g+iimRdk+81atXm/0Kkda9e3f5+OOP3X0CbR8eeughYynEnoQIWJ8Iix6mkMK6CGcrdhqnLYNj//79ZdSoUWaNIK6XLVtm2oI1bsqUKYgywfbVXqfDydZhj3afQ3td1jG6L2XlZ3oxAUwdxjRb62AoHhfkxXpAhmAToMyDLb9Uek+Zp0KNZUggWAT4PQ+WvILU20pB6mw2+oopld98841R/nDE9gx77LGHUQLLUkCOP/54wQeKDbwuopxVzKL7et111wk+0QGKGzaFh2IHJzXw7AmHLPDoaRXD6DK4hqt/fKB8YiN4TP9s27at6zjGrwziME0Q6xB170CBh04ogHAgg6BvHszR7790OHnrsxvYe+N4nh0CuIewnvWWW26J2wCmQ+P+YQg+Aco8+DJMdgSUebLEmJ8EgkeA3/PgySwIPQ69EmiFBG+hXbt2tZdJHVu2bCn4pBNgsWnXrp35JFOPTkM1Wy0kUwYep9BWKiEdTqm0xzLpEbj++uuN99kffvghZkVwfsRQOAQo88KRZaIjocwTJcV8JBBcAvyeB1d2+drzUE8HzVehsF8kkCkCeLnwwAMPxKyuSZMmZl/KmBmYEDgClHngRJZ2hynztBGyAhLIewL8nue9iALXQSqBgRMZO0wCyRHo3bu375o/7Ak4duxYKWvas21t/vz5MnHiROPd9eWXXzbrSW0aj/lFgDLPL3nkojeUeS4osw0SKF8Cmfqel+8o2Hq+EOB00CQlgTcxVatWjbteL8kqmZ0Esk5APcPK4MGDjTOgzZs3C6YR77vvvlKnTp24bSPvnXfeabbxwLpXb8D+RZiewpCfBCjz/JRLNntFmWeTLusmgfwgkOr3PD96z17kEwEqgUlKY+jQoYIPAwkEiQC838LbbDIBjoLOOOMMGTdunG+xL774wjeekflBgDLPDznksheUeS5psy0SKB8CqXzPy6enbDXfCXA6aL5LiP0jgXIicPXVV8dUANGlI444opx6xmazRYAyzxbZ/K2XMs9f2bBnJEACJJBNAkXYkDCbDeRT3dOnTxe4w2cggVgEsE8j3rLVqlUrVpZQxG/cuNHdM9NvwPAwW7NmTb+kwMZh6iv2CsV2GZUqhW+SRBhl7r1Zw/jdD7vMrfzD/t23HMJ6DON3P6yy9hs39slu06aNX1LBx4XqSQf7751zzjkFL1QOMHUCzz33nFnzecIJJ6ReScBLrl27Vp544omYo6hXr55gWwkogoUUFi5caCyfffv2lYYNGxbS0MocS1hl7gWD7z7WfJ944one6II9p8xLRBvm734JhfCe8e9+eGWPke+www6hBRAqS2BopcyBJ0ygR48eAiXn9ddfT7hMoWU8+eSTZfz48b7DgvXv008/ldatW/umBzly0qRJcuihh8qMGTOkc+fOQR5K0n0Pq8y9oHr27GkcJb3xxhve6II9p8xLRPv+++/LYYcdJp999pl06tSpJIFnoSDAv/uhEDMH6UOAawJ9oDCKBMJKYMKECTEVQDAZM2ZMQSqAYZU3xk2Zh0/6lHn4ZM4RkwAJkEA0ASqB0UR4TQIhJbBhwwazjUSs4Q8ZMiQ0U+ViMSi0eMq80CRa9ngo87IZMQcJkAAJhIEAlcAwSJljJIEECDz88MOyYMEC35x77rmn3H777b5pjAwuAco8uLJLteeUearkWI4ESIAECosAlcDCkidHQwIpEYB3tDvuuCNmWTw4Vq5cOWY6E4JHgDIPnszS7TFlni5BlicBEiCBwiFAJbBwZMmRkEDKBO655x5ZuXKlb/l+/frJwQcf7JvGyOASoMyDK7tUe06Zp0qO5UiABEig8AhQCSw8mXJEJJAUgWXLlsmIESN8y2y//fZy3333+aYxMrgEKPPgyi7VnlPmqZJjORIgARIoTAKh2iewPEW4YsUKWbJkiVSsWDEvN6WEs4C5c+caRK1atTJ75eWCF9r9/vvvBZv1tm/fPpSbdOeCc7w2br31Vlm3bp1vlmHDhkmjRo180xgZXAKUeXBll2rPKfNUybEcCZAACRQmAVoCcyTXUaNGSYcOHaRt27Y5ajG5ZmbOnGn6hz5iH7hsh59//tlsOF6jRg2j/GFfttq1a8shhxwin3zySbabZ/1/E/jxxx9l9OjRvjz22msvueKKK3zTGBlcApR5cGWXas8p81TJsRwJkAAJFC4BKoGFK9u8Hdmvv/4q3bp1k2eeeUa2bdsm2223nbE8rl+/XiZPnmw27H7rrbfytv+F1LEbb7zRWGH9xgTLQaVKnCzgxybIcZR5kKWXWt8p89S4sRQJkAAJFDIBKoE5ku4+++wjAwcOlLPOOitHLeZvM+edd57AEohw2223yZ9//im//fabPPHEE7LrrruaqYknnHCCYKooQ/YIzJ8/X8aNG+fbACzWxx9/vG8aI4NLgDIPruxS7Tllnio5liMBEiCBwibA1/w5km/v3r0Fn7AHWAEnTJhgMJxxxhly/fXXm3NsP3D22WdLgwYNpE+fPvLXX3/JlClT5Mgjjww7sqyN/6GHHpKtW7f61g/LQVFRkW8aI4NLgDIPruxS7Tllnio5liMBEiCBwiZAJVDl+8svv8hnn30mX375pVSoUMGsjevSpYvUr1+/lPQXL14s8+bNkzp16gisezifPn26carSuHFjs6atdevWpcrB8vXDDz8YxzAHHHBARPqmTZvk/ffflzlz5hiL2B577CFYj4X6q1WrFpHXe4E3vFi/h3JVq1aVdu3aCdbW7bjjjt5spc6nTp1qyv3xxx+Ccfbs2bNUHr+IZDj5lUccnMBgbHBCA8todOjUqZMbhbWBVAJdHEmf4H579913pVatWnLUUUfJDjvs4NaxZs0aefzxx91r7wnkc+KJJ3qjeB4QApR5QASVwW5S5hmEyapIgARIIEwEnBAHtYI4ukG2o1YoR2Ue8VHX+I4+JJeic/PNN5t8qmw5w4cPjyiDOtR64gwePLhUOV1f5eb1Js6YMcNp2LChm+btx+677+5MmzbNm92cr1q1yrShnkZLlatevbozcuRIR9falSq3du1ap0ePHqXKqEMWx44L7X/wwQcRZVPhFFGBz4Uqvr59fPPNN93+4TzXYf/993eOOeaYXDeb8fYgT13P57JUBdB5/fXX3Xbuv/9+N817z+H8ueeec/OF6URfxBgm+E4GMVDm6UkNv406CyG9SnJcmjLPDPD33nvPfPf1ZXBmKmQtgSJQKH/3AwWdnc0LApIXvSinTpx66qnug7Baz5yTTjrJOfbYY5169eq58eoiP6J3XmUJD8xQINVq4lx88cUOlDb7QP3ggw9GlPNTAnXbCGennXYyZapUqWKUD10z6LRo0cKtB4qebi3h1gWF7KCDDnLTUV7XbjlqMXOguNr2Bw0a5JaxJyeffLKb3rJlS0c3AXd07ZcbZ8tGK4GpcLJtJnpUpzBGSbE8MBbE5ToUwh+Du+++u5RMIVu1KjvqBdbZsmWL07RpU988uj2Ig3ssjCHISiBlnv4dGzQlkDJPX+a2BiqBlkQ4j4Xwdz+ckuOo0yUQWiVQp8m5D8G6Vs+Bdc2G5cuXOzpF0qSr50r1og5kAABAAElEQVRH3WvbpAiLWc2aNZ1Jkya5aTrFzjn88MNNOVhhPvroIzfNTwl84YUX3D689tprbl6cPProo24arJU23H777W48lE+0aYNOD3V0CqlJh0XS277X8nPZZZcZRcCWu+uuu9w6oSx4lcBUOdm6yzrq/nSObgvh6HRWtw/qHMYpL2tMIfwx0HWVLkur2Nsj2D7yyCMx09U5T1kiK9j0ICuBlHn6t2XQlEDKPH2Z2xqoBFoS4TwWwt/9cEqOo06XQGi9g8LxBYJansxWBVjjZwPW1KmCZtbZbdy4Ue655x6bFHG8+uqr5eCDD3bjVCkUVd6MW321tsj48ePdNL+TBQsWuNFqmXPPcQIPmhdccIEMGTIkYm/BJ5980uTTKaQyduxYQZs2qHVHdCqfWXeoN4bAxb8NGA+CWivlvvvuM3ls2jXXXBMxDhuPYyY4eeuLPse6RlWkIzyBYm2lWmOjs/I6AQJYXwpPq7EC1rSq1do3Gfs0qtXXN42R+UuAMs9f2WSrZ5R5tsiyXhIgARIID4FQKoFQkOAEBkEtd6JTDwVKm/cDL5VwmoLw9ddfm2P0f2pRi44yShY2PEeYNWtWqXRvhNchS9euXc3G3G+//bZRiOCgBgolFFDrVRSeNeFYBeH8888XXf/nrc6cwykNnIAgfPXVV+aIvfjsGM4880zfvd90HaPJ6/0vU5y8dUafw/mOWimN0g1PoVBq1YIpe++9d0zHJdF18LqEgE4rNo6NSmISPzv99NN976nEa2DO8iBAmZcH9fJtkzIvX/5snQRIgAQKgUAolcClS5cKNiZHwIbl2J7A7wMvmgjwvhYdYC2sW7dudLS5hrUNYfbs2eYY6z8ofroO0STDW6OuIzTeMOHFUdcmGiVInbm4xb31RVsO3Ux6ouu6zCU8kmKc6L8dL6yFfqF58+alojPBqVSlURGwxF555ZXGAoU9AzFGWGWxRyDif//996gSvCyLADimEmB9ZggmAco8mHJLp9eUeTr0WJYESIAESCCUSqA6WklK8rDAwSrmDd7po954nNspmtiCAdN2YgV1+mI269Y1eaLOYNxsUNjUk6OZEootH7AFBIJ33zbbhlvIc2L7hj6jD3ZjdmSpUaOGJ2fJKSyf0SETnKLrLOsa22Nce+21Jhs2kcc2EQzJEYAl+MILL0yqUMeOHQUfhmASoMyDKbd0ek2Zp0OPZUmABEiABEKpBKpzDFfyQ4cONWuosI4q3sergKFwPAsVLGgImJqJaTvxAurFmjxM88RnxIgR0qtXL1GHNKaYdz+9Ro0auVXFW/flVd4w3RJTK21Qj6T2NOLoF58JThGN6AUsfN99953ZFzE6zV63b9/engrWsDEkT+CBBx6QNm3aJFyQVsCEUeVtRso8b0WTtY5R5llDy4pJgARIoOAJhFIJ3Hnnnc06QEgXShYUJb/P5MmT5a233nLX4XnvhpUrVwoshH7BTtv0bnweKx8cvTz//PMmGdbAyy+/3LQJ652doqlu/QXtNWvWzLUGfv75535VmjjkR8CUVSihcCKzyy67mDjbN3Ph+e/bb7/1XBWfZoJTdKV33nmn2Sz+sMMO8+WK/F7LZZMmTaKr4HUCBHQ7CLPO0r5MiFcEebEekCHYBCjzYMsvld5T5qlQYxkSIAESIAEQCKUSiIHvt99+OIhuzSC6Qaw59/6nWyPIKaecIgMGDHCnJ3rTcX7vvfdGR5lpnFahKksJhHfRs88+W3RvQFm4cGFEXVBKdT9AE4dpnbp3m+APfocOHUzcmDFjIpQlWxiOZXR7BXN59NFH22jBtFKEp556Sqyl0k3UE3gM9QuZ4OSt94ADDnAvdasC99yeYPrsQw89ZC7hHMe2b9N5TJwALMB2am28Ulh/Cs+gDMEnQJkHX4bJjoAyT5YY85MACZAACYBAaJVA3WhXoGTAcyYegmHxg7IFJQSKlHd6HKxzfgFTN/GBgoYwYcIEo9DhHM5ZBg4ciNOYQff5M2lo86KLLpLp06ebuuAk5pVXXpFnn33WpEMRsmv2rLKGaZXwQmoVWIzj1VdfldNOO82UwUP9VVdd5baNKaewCqHcEUccYaZkIhHr7uAx1CqOboG/TzLByVun7sVlpskiDsoePug7AiyAffv2dT2ZXnrppVRODJnU/4PHVe96U7+a8KKDoXAIUOaFI8tER0KZJ0qK+UiABEiABFwCqviENtx8880ONlVXGOZTq1YtR7eLcK8RP2jQoAg+KGPz27LqbMVRT6FuvHr3dObNmxdRzm+zeFXInG7durnlUK8qbw42mrdtoO4333wzoi5Vjtx05EN76ijGjcO5d6N4W1g9obp5UE6nibptqZXRTfNuFo+yqXCybfoddesKB+OyYwT33XbbLUIW3bt3d8An16EQN43F/WNZRx91uq2jW6PkGnNethfkzeKjgVLm0UTKvg7aZvHRI6LMo4kkfs3N4hNnVYg5C/HvfiHKiWPKPIHQWgL1YViGDRsm+uNv1qjBQQsscHZLBmxY/vDDD8vIkSORtVSAFfGll14ym82vW7dOVq1aZfJgb0FYBLF+zxvgCRQB5WyAZQ5eQLEpPLaoQFi9erXZrxBpqgjJxx9/7O4TaMvBegZLod2KAusFYdHDFFJYF9955x3faZT9+/eXUaNGmTWCqGvZsmWmLThimTJliq0+YiN5RKbDya3UcwKHJV988YU7LnBftGiRscTCgglr54cffug6x/EU5WkKBLDPpN+aP+wzOXbs2FLyTqEJFskzApR5ngkkB92hzHMAmU2QAAmQQAERKIJeWUDjSXkoUP6++eYbs58etimAEmgVN2+lw4cPF7WMGWXOTgOFV094sUQ5q5h5yyRyvnHjRuOkBp494ZAF6zysYhivPJRPbAQP5alt27au45h4ZTD9FNtOLF++XKAAwoFMoiFRTonWB6UX3kKhSENxhiMYKOTlFTBdtV69ekY5L68+ZKNdfM3VOmwU/82bN5s9Lvfdd1+zJ2Oi7c2fP9/cN1hTij0yMXVXrdaJFs/7fJMmTZJDDz3UTI22a2jzvtNxOpgJmcepvuCSevbsab4Pb7zxRmDHRpmnJjqdBSBwVoblFWWt5U+thfwuVei/7WXRL9S/+/HGHXaZx2MTprTCeYJLU2o6DVSweXsqARu3x9u8PZE6Yflr166d+SSS3+bBwzgeXpIJ8BiKtlIJ6XDyaw/KK52/+JHJbBwUa1iWkw1QGOHRdfTo0aW268Bm1ViLxJCfBFKVeX6Ohr1KhABlnggl5gEB/raH7z6gzMMn87JGTCWwLEJMJ4GQEoBV4YwzzpBx48b5EsCUXgYSIAESIIFgEeBve7DklYneUuaZoFh4dYRqOiime2J9WzoB2z9g+iKC9e6ZTn0sm18EsDYSltJUrGb5NZL0ewMlTx0cxayoY8eOKU9/jllpOSZgevTUqVON111Y2BnCRQDffUzBVycR4Ro4R2uWRoTpux+23/aybvEw/N2nzGPfBdjurWnTprEzFHBKqCyBWHcHByTpBDhgwZQbfNKtK51+sGx2COAewVrPsMv2999/lx9//DEmZChJWDNbSJzgoAgBDpPwPWcIFwF899VTbkHd0+GSYOqjDdN3P4y/7WXdGYX+d58yj38HYJpsWEOoLIFhFTLHnTiBMC4Qj6YDxy/w4Go93kan77nnnvLpp58K1ocWUig0xzCFJJtcjKUQHMPkglMhthEWxzBh/W0v654t5L/7lHlZ0g93esl+BeHmwNGTAAn8TeDyyy+PqQDqHpTy8ssvF5wCSOGTAAmQQKET4G97oUu49Pgo89JMGFNCgEpgCQuekUDoCWCPy/Hjx8fkMGbMGGndunXMdCaQAAmQAAnkHwH+tuefTLLdI8o824SDXz+VwODLkCMggYwQ2LBhgwwePDhmXUOGDKEzpJh0mEACJEAC+UmAv+35KZds9ooyzybdwqk7VI5hylNsK1asEGwED2caWG+VbwE/GHPnzjXdatWqlVStWjXnXVy/fr388MMPxjsn1p0x5JbAww8/LAsWLPBtFPK4/fbbfdMYSQIkQAIkkL8E+Nuev7LJVs8o82yRLax6aQnMkTxHjRolHTp0kLZt2+aoxeSamTlzpukf+ginH+URzj//fNMHOGhgyC2B1atXyx133BGzUfxBgft8BhIgARIggeAQ4G97cGSVqZ5S5pkiWfj1UAksfBkHYoTPP/+8PPfcc4HoayF28p577pGVK1f6Dq1fv35y8MEH+6YxkgRIgARIIH8J8Lc9f2WTrZ5R5tkiW3j1cjpojmS6zz77yMCBA6VCBerd0cgXL14sF198cXQ0r3NEAPvijRgxwrc1bANx3333+aYxkgRIgARIIH8J8Lc9f2WTrZ5R5tkiW5j1UgnMkVx79+4t+DBEEti2bZtRjv/444/IBF7ljMCtt94q69at821v2LBh0qhRI980RpIACZAACeQvAf62569sstUzyjxbZAuzXiqBKtdffvlFPvvsM/nyyy+NpQ7r4rp06SL169cvJXVYrebNmyd16tQRWPdwPn36dONUpXHjxnLIIYf4utD/+eefjdMTOIY54IADIurdtGmTYLPaOXPmyG+//SZ77LGH7LXXXqb+atWqReT1XsyfP9+s30M5OHJp166ddO7cWXbccUdvtlLnU6dONeWgeGGcia7BS4ZTqUZjRNx7770yZcoUwTjRj3feeSdGTkZng8CPP/4oo0eP9q0a9+AVV1zhm8ZIEiABEiCB/CXA3/b8lU22ekaZZ4ts4dYbaiUQVqi7775bbrrpJtm8eXOElDEN7sEHH5RzzjknIv6JJ56Qm2++2ShbxxxzjMBS4g1FRUVmauPIkSO90fLkk0/KjTfeaOIcx3HToHyiHpjwo8Puu+8uTz/9tOy///4RSVDebrjhBoGzma1bt0akVa9e3YwJ0yvRF2+AtefII4+UadOmeaOldu3acuWVV0bEeS9S4eQtH+t89uzZLpO77rpL8ANGJTAWrezE456MvvdtS3ijWKlSqH8iLAoeSYAESCBQBPjbHihxZaSzlHlGMIaqklA/4Z1++uny4osvGoHDenbggQeaB2JYyn7//Xc599xzZdGiRUbpi74roLzhA4+Jffv2lZ122kkmTpwosM7Bk2LLli3lsssuiy4WcQ1HHH369JFff/3VbIvQq1cv2WGHHYxlEVsloC70CX2wU/KgkB1//PHGeobK0G737t3lr7/+Msrd2rVr5ZJLLpFvvvlGHnnkkYj2zj77bFcBRP86duxo8n311Ve+Y7SF0+Fk64g+or/9+/cXWEEPP/xw0+d4imh0eV6nTwD317hx43wrghdb3GcMJEACJEACwSLA3/ZgySsTvaXMM0ExhHWoVSqU4d1334U5znx0rZ6zatUql8Py5csdnZpo0rbbbjtHLVRumloB3XI1a9Z0Jk2a5KatWbPGUYXGpKsFxfnoo4/cNLWquOVs5AsvvODGvfbaazbaHB999FE3TV33u2m6V5sbf+KJJzpo0wb9EXB0iqpJVytgRPv333+/W06VU2fLli22mKNWODcNTD744AM3LVVObgUxTi699FLTZt26dR2dKmtyXX755SZOFeEYpbIfrVZXRy2z2W8oD1rQqZ4RcrffBxxVOcyDHua2Czol2/CYMWNGbhtma3lBoEePHo6+lMuLvrATuSXw3nvvme++vtjNbcNZao2/7cmBLYS/+5R5cjJn7mICoXVVaadmwpL2zDPPmDV++vBrAqyCqqCZdXYbN24UuNv1C1dffXWE63xVCkWVNzOFTpUsGT9+vF8xN867MTcsc95w3nnnyQUXXCBDhgyJ2FsQ00oRGjZsKGPHjhW0aUPTpk3NNgtYd6jiFUznswHjQcAUU3h7RB4brrnmmohx2HgcM8HJWx/O33rrLbHTZWGt3GWXXaKz8DoDBGBNBl/c39HbP+jLA3n88cd9W8FaQH3B4JvGSBIgARIggfIlwN/28uVfHq1T5uVBvfDbDKUSCAUJTmAQMBUR6/+gtHk/DRo0ME5TkOfrr7/GoVTwm+4JJQvOYRBmzZpVqow3wuuQpWvXrsYJx9tvvy0bNmwwDmqgUEIBtV5FMW30+++/N1VgY3Ws/4sOrVu3lqOOOspEY5onAqaQ2jGceeaZvuu8Bg8ebPJ6/8sUJ2+dK1asEExLRd2nnXaaYA86hswTGD58uHEuBLkOGDBAWrVqJW+88YbbEBTAP//80732nmC9Kbcy8RLhOQmQAAnkBwH+tueHHHLZC8o8l7TD1VYolcClS5fK+vXrjaRhJcG6Pr8P1gYi4A1MdIC1UKcyRkebayiCCHB8Ei9A8TvppJNMFlhm4IgGjluwLvDYY481lhqs8bPBW1+05dDmwREP/AjwSIpxov92vLAW+oXmzZuXis4Ep+hKobzCCQ6sf1g7yZB5AnhxoNOWzUsNWzssgaeeeqp8/vnnxpnQQw89ZJMijrh3kI+BBEiABEggvwjwtz2/5JGL3lDmuaAc3jZC6RhmyZIlSUkcFjhYrrzeNrFFRKxgp2jCiyccn1SpUsU3K6ZkwjEHvuSPPfaYq2xCYXv99dfNB2mvvvqq2XbC275tw69i2zf0GX2AMmhDjRo17GnEEZbP6JAJTt46dd2FGQvi0Md//vOf3mT58MMPzTUUYrt5PKbEYssOhsQJYNsNvwBnPMcdd5xcd9114p2K7M177bXX0groBcJzEiABEsgTAvxtzxNB5LAblHkOYYewqVAqgbvuuqsr6qFDhwrW9pUVvAoY8sJ7aKwACxoCpmbGUgBtWdSLNXn4wGI3YcIE42UUe+dhPeLcuXPNZuqffvqp6yEUZbGfYKzgVd6w1+Hee+/tZsV0TL/gF58JTt62Vq9e7V7Ceyk+fgFbFvz73/82SfCOSiXQj5J/HF46xLs3sM+lVbCja8BWIbQCRlPhNQmQAAmUPwH+tpe/DHLdA8o818TD114olcCdd97ZrAPEVEsoWX6bwuNW+O9//2sUMWzeHp0H0+tgIYRjmehgp2126tQpOiniGvnwUQ+kZn1cixYtRD1kmg+Usm7dupnN6GfOnGkcezRr1sxYI2Hhw7S+WAH5ETBlFUoonMhg+iWUQ9u36LLffvttdJRkgpO3UmxzgemusQKUQigpWI92xBFHmGyNGzeOlZ3xPgQgbyjNZa1H9Skq2ArEb52pX17GkQAJkAAJ5I4Af9tzxzpfWqLM80USBdwPVShCGQ477DDjEloVDsfPJbxuWu66z4frcBu8W0So504b7R6x1YPeLuYzYsQIN95viwh1IGPy6Rfd0el5bl57oh5CTbpaCx1VOE203QKiatWqjipMNqt7VM+bbvvqBMaN1zWGJh7lVBl04+1Jly5d3HLeLSJS5WTrTebILSKSoRU7r1qTXVnaezGRo748iF1pCFK4RUQIhBxniNwiIg6cAk8KyhYR/G3Pzo2Yz1tEUObZkTlrLSYQSscw0OnvvvtuY3GC50w4YcG2BYrErOGDh05s0WADrHN+QZU8wWfr1q0mGVM5zzrrLHMOBxsDBw70K+bGWTf8MPlfdNFFZpN41IU1ca+88oo8++yzJu9+++0nds0etndAgAdReCHFhvUIGAfWDsLjJgKm9l111VXmHP9huiksjigHK9t3331n0uAhEh5DVRF283pPMsHJWx/Ps08A3mEvvPDCpBrq2LGj4MNAAiRAAiSQnwT4256fcslmryjzbNJl3VB8Qhtg1YOVTW8D86lVq5aj20W414gfNGhQBB+vJdCWVWcrDjY9t/Vgs/N58+ZFlPOzBKpC5uiUT7ccyqvy5mCjeVsX6n7zzTcj6rIbrds8aE8dxbhlcO7dqN4WVk+obh6U1WmiblvVqlVz07yWQJRNhZNtM5kjLYHJ0IqfV50LOW3atHFlau+VWEfdTzB+hSFIpSUwBEKOM0RaAuPAKfCkoFgCIQb+tmf+ZsxnSyBlnnl5s8YSAqG1BOrDsAwbNkzgsRKbY8NBCyxwdksGrEXDFgZ2U3Pk9wasW3vppZfMmsB169bJqlWrTLJOqzTOXbB+zxvs5uze/ddgmYMXUHjAxBYVCHCegv0Kkda9e3f5+OOP3X0CbX1w7w9Lod2KAusTYdHDukVYF3Uqq8B6GB369+8vo0aNMmsEkYatGtBW+/btBY5obLB9tdfpcLJ1JHK07dpjImWYx5+AKvXywgsvmPvIP0dJLPJiPSADCZAACZBAfhPgb3t+yycbvaPMs0GVdYJAEfRBohCj/MExCbZngCMYKIF+ygg27VTLmJlKaqeBYgN3ODRBOauYJcvUegKF8xY4ZIFHT6sYxqsLyic2gsf0z7Zt20ZsYxGrHKafzpkzR5YvX24UQDiQSTRASU6EU6L15Vs+tQZIvXr1jHKeb31LpT9Q4G+55Za4Rfv16yfPP/983DxhSJw0aZIceuihZmp0586dwzBkjtFDoGfPnmbrmjfeeMMTy9MwENBZAKLr383yirIcuuULD/62Z04SQfm7T5lnTuasqZhAKL2D+glfp4EKNm9PJWDj9nibtydSJyx/7dq1M59E8ts82LAeDy/JBHicQluphHQ4pdIey6RH4PrrrzcKHrYfiRUGDBgQK4nxJEACJEACeUiAv+15KJQsd4kyzzLgEFYf6umgIZQ3hxwyAni58MADD8QcdZMmTaRXr14x05lAAiRAAiSQfwT4255/Msl2jyjzbBMOX/1UAsMnc444ZAR69+7tu+YPewKOHTvWd9pzyBBxuCRAAiQQOAL8bQ+cyNLuMGWeNkJW4CHA6aAeGImc4k2M7rWX0Hq9ROpjHhLIBQH1DCuDBw82zoA2b94smEa87777mjVQuWifbZAACZAACWSeAH/bM88032ukzPNdQsHpH5XAJGU1dOhQwYeBBIJEAN5v4W2WgQRIgARIoHAI8Le9cGSZ6Ego80RJMV9ZBDgdtCxCTCcBEiABEiABEiABEiABEiCBAiJAJbCAhMmhkAAJkAAJkAAJkAAJkAAJkEBZBDgdtCxCTA8VASy6rlGjRqjGzMEWE8D+nGeffbbUr1+fSEJIAN99bMrMED4CjRo14nc/fGJ3R8y/+y4KnoSMADeLD5nAOVwSIAESIAESIAESIAESIIFwE+B00HDLn6MnARIgARIgARIgARIgARIIGQEqgSETOIdLAiRAAiRAAiRAAiRAAiQQbgJUAsMtf46eBEiABEiABEiABEiABEggZASoBIZM4BwuCZAACZAACZAACZAACZBAuAlQCQy3/Dl6EiABEiABEiABEiABEiCBkBGgEhgygXO4JEACJEACJEACJEACJEAC4SZAJTDc8ufoSYAESIAESIAESIAESIAEQkaASmDIBM7hkgAJkAAJkAAJkAAJkAAJhJsAlcBwy5+jJwESIAESIAESIAESIAESCBkBKoEhEziHSwIkQAIkQAIkQAIkQAIkEG4CVALDLX+OngRIgARIgARIgARIgARIIGQEqASGTOAcLgmQAAmQAAmQAAmQAAmQQLgJUAkMt/w5ehIgARIgARIgARIgARIggZARoBIYMoFzuCRAAiRAAiRAAiRAAiRAAuEmQCUw3PLn6EmABEiABEiABEiABEiABEJGgEpgyATO4ZIACZAACZAACZAACZAACYSbAJXAcMufoycBEiABEiABEiABEiABEggZASqBIRM4h0sCJEACJEACJEACJEACJBBuAlQCwy1/jp4ESIAESIAESIAESIAESCBkBKgEhkzgHC4JkAAJkAAJkAAJkAAJkEC4CVAJDLf8OXoSIAESIAESIAESIAESIIGQEaASGDKBc7gkQAIkQAIkQAIkQAIkQALhJkAlMNzy5+hJgARIgARIgARIgARIgARCRoBKYMgEzuGSAAmQAAmQAAmQAAmQAAmEmwCVwHDLn6MnARIgARIgARIgARIgARIIGQEqgSETOIdLAiRAAiRAAiRAAiRAAiQQbgJUAsMtf46eBEiABEiABEiABEiABEggZASoBIZM4BwuCZAACZAACZAACZAACZBAuAlQCQy3/Dl6EiABEiABEiABEiABEiCBkBGgEhgygXO4JEACJEACJEACJEACJEAC4SZAJTDc8ufoSYAESIAESIAESIAESIAEQkaASmDIBM7hkgAJkAAJkAAJkAAJkAAJhJsAlcBwy5+jJwESIAESIAESIAESIAESCBkBKoEhEziHSwIkQAIkQAIkQAIkQAIkEG4CVALDLX+OngRIgARIgARIgARIgARIIGQEqASGTOAcLgmQAAmQAAmQAAmQAAmQQLgJUAkMt/w5ehIgARIgARIgARIgARIggZARoBIYMoFzuCRAAiRAAiRAAiRAAiRAAuEmQCUw3PLn6EmABEiABEiABEiABEiABEJGgEpglMAdx5ElS5bIX3/9FZXCSxIoXwLbtm2TZcuWCe7RdMKmTZtk6dKlsmXLlnSqYVkSIAESIAESIAESIIGAEqiUar9vueUWGT9+vCl+4YUXyuDBg+NWNWDAAPniiy+kYsWK8t///leaN28eM//3338vJ510kkm///775bDDDjPn//d//ycXXXSROe/Vq5fcc889MetAwpVXXinvv/++dOjQQcaOHRsz75w5c+TBBx80/fvqq69k7dq1Ju9OO+0khx56qFxyySXSrVu3UuXvuusuefbZZ0vFlxXRv39/ufbaayOylcVzzZo10qNHj4gyiVxUqlTJMKhbt64Zx4cffphIMTfPc889J23atDHXM2bMkHPPPddN855UqVJFatWqJWgHzM455xypWrWqN0vc83RZQm7gGisUFRVJjRo1TP923313Oe2002T//fePld2NX758uRxxxBECBQzj+eCDD6RatWpuevRJpu/R+fPnC9jMnj1bMMb169dLzZo1pW3bttK+fXu59NJLZc8994zuRqnrTz75RB577DGZMGGC/PLLL0aRxL3RpEkT6dOnj7kfd95551LlMsX1f//7n1x33XWm/ieeeEI6d+7stoV75bPPPnOvvSeVK1eW7bff3sgN/TzllFPMfYY8mfhOeNviOQmQAAmQAAmQAAmEhoBaFZIOGzZscHbYYQeYI8ynadOmztatW+PWo0qUm1+Vmbj59YHXzauKplvv22+/7cajbVzHC8cdd5zJr0qMbzZ9sHdU+XP0oT6iXjsu7/H000939AE8op7LLruszHLeOuy5PvRG1JMIz99//z2lttDmokWLTHu9e/dOug5Vot2+Tp48OeHyqlA4I0eOdMuWdZIuy48//jjhvoGJvoxwHnnkkbK65dx7770R9aoCE7dMJu/Rd955x6lXr15E+/YeskdVCJ3XXnstZp/Uou1ccMEFcetAXargOi+++GKpejLF9ZlnnnH7MGXKlIh2DjzwQDfNjivWsXr16g64IGTiOxHREV6QAAmQAAmQAAmQQEgIpGQJ1IdOWblypT6nFYcFCxbIW2+9JUcffbSNinucNm2aPPTQQ3LFFVfEzVdW4nnnnSdff/21axkoK390er9+/WTcuHEmGhbKs88+21iHWrduLfqAKZMmTTLWk9WrVwssYrBQTp061bVwwULpZxWC1RHWlgYNGpg6o9v1WkGQlghPWKGirYco++OPP7oW2eOPP15atmyJ6IgAC503wCIG62YiAZYiv3DkkUcaS5RNg4Vq1apVAmvh3LlzzfjRBqyp1qpr8/odM8USdcPqHG01tVMpYXGCVUpfWsjFF19srGpnnHGGX5dMHKxW3vDvf//bV6bePN7zVO9RWM2POuoo009VfIwFtmfPnlK/fn2BdVIVKRkzZoz8+eefoi875NVXX5W+fft6mzZlYTG31l9YaTFmWLUhl59//lmef/55c//oiwgBB1gZ0a5fyCRXv/ph8YueUQA5YYzvvfeeuddxn8Ea+Omnn0rjxo0z9p3w6w/jSIAESIAESIAESKBgCaSi7OqDpXlzr9OzHJ2K5p7Hq8trCVSYxvqmSpVvkUQtgahHpyf61oHIeJZAWE9QHp+9997bUeXAtx5Y0WDVsnl1iqlvPm9kly5dTH6drueNjnmeCk9bmSo0bt9eeeUVG+17tJZAjCeV4LUE6jTYmFXA6mOtq7AY6/qzmHnLSkiUpddipdMe41b7+uuvOzo91HBTxTlm3o8++shle80117jnse4VVBRtCUz1Hh02bJhpb7vttnNU4fHt4/Tp0x2dLmnyqaJdKs/w4cPdPnfv3t1RJb1UHkToCw6Xhyr9jq4VdPNlimsilsAWLVq47UafrFu3zlEl1B3P0KFDo7O418l8J9xCPCEBEiABEiABEiCBEBFI2jHM4sWL5d1339VnWxFYg0499VRzjrVGqjCZ80T+g+MVWN5goUknPP7446IP3klVAauHtULqQ7axxHXq1Mm3jl133VVUaRBYYxCwdhCWrkyFTPHMVH8yUQ/W5mH9GQIsxoMGDcpEtRmr45hjjpGDDjrI1Ic1o7EcpFgrICy6N954o1lTiEKwBiYTUrlHYa1E2GeffUQVYd/mVLETnaZs0mCJ37hxo5sPVtnbbrvNXDdq1Mh8R+rUqeOme0+wPvL88883UQsXLjRrdr3piZ4nyjXR+rz58P3DGmCs7URQBd2bzHMSIAESIAESIAESIIEkCCStBD711FOu4obpn5hSiQBl7j//+U+ZTWPan522ppYMGTFiRJll/DLAYQymcCJgyh2mbCYa4MwFDjcQhgwZIs2aNYtbFNM3MX0VAePEFLpMhXR5Zqofma4HygkclyBASU9X2c90/zDNEAFTDb3Kk20H0w51jZy5xMsOTFWEkoMA+Sdyv6Vzj2LqJsKsWbPk119/Ned+/+HeV6uYqOVQ1FrmZnnppZcEXkARMI0Y/Y8Xrr76atG1ecZ5S6tWreJljZtWFte4hctIxBRW61Bq3rx5ZeRmMgmQAAmQAAmQAAmQQCwCSSmBaiE165BQGdYVwcsiHhitpQIWj82bN8dqy40fNWqUqMMLc33DDTeYtXZuYoIn++67r+gUPZMba5uuuuqqBEuKfPPNN25eeDZNJGBtlQ1Yf5WJkCmemehLNurAGjIEWF5/+umnbDSRUp2QP7x4IqhTI9fC560Mnm/hfRLBeh2144GCGM/brK0nnXsUiicCFFSs0cM957dtCdY+3nHHHeZlhv1OodzEiRNxMOHggw+2pzGPWEuKdYa33367sT7GzBgnIRGucYqXmYTxY/sWBKsMllmIGUiABEiABEiABEiABEoRSEoJxEOifZg/66yz3MrsOfYw03VpbnysE7iit5Y1PNihfCqWIlg/dD2faQZT97wPvrHaRjwcvCBgKiicSyQSkG/HHXc0WS2DRMrFy5MpnvHa8EtbsWKFUebhWCTeB9sSpBPgYMeGb7/91p5m/QjrHsbo/UBmcC6i3j6la9eu7hTQWA5y8EIDYZdddnG3KMGLANy7CHiRkUhI9R4dOHCgu40CrIFw+gMlD9tVQOmDEmutbn79wIsRBFjL/ZwF+ZUpKy4TXMtqI1Y6lGHICgo4gn3xFCs/40mABEiABEiABEiABGITSMo7qF0jBU+Vdi0gqsaaIlji8KCG9VLw3ldWgHUF+wXCMybW92DqHKZmJhOgxD355JOy3377mQdirGuCt9BYa59s3XZNHzxf2jVGNi3eEeuz1D29mUKIvQTLmmIXry6kZZJnWW1502Gtxd5sZQXwTGSKb6x6MH3PBiiBdhqwjcvWEftD4hMvQDnCVMrLL7+8VDZMNbQeNaGMVahQ/K4EZeBBE2vTMB7sGYgplPFCqvcovIDiJQHaf/nll00TsKhiPa5dkwulEN+1m266yVVObV+wGTzCHnvsYV522Ph0julyLattTHu16xhtXkxxhUKLtZt2TOpsyNcrqC3DIwmQAAmQAAmQAAmQQHwCCVsCsQYK64wQ4JK+du3abs1Yv3Tssceaazy4YvP1RIJ3WigcbyRazls31uvZrRMwVaysh3+U/eOPP0wVySpxXuUykWmv3n5Gn2eDZ3Qb8a6xtUVZH2wAn07wTl+MZ7VKp41UyuL+xRYMuP/sulJvPbCO2nDmmWfaU3OEMyMbErUGpnKPog1s5YHvHDZsh3KEqZ/Y4N0GbGOCPmBKNl5OeIN9uZHufeqts6zzsriWVR7TbzE93PuB1fPpp592FUBMQcdaTSjJDCRAAiRAAiRAAiRAAqkRKHmiLKM8nGHYh3o8fP7rX/+KKAErhQ14ME3E4UvDhg3l//2//2fWXKE8HrCxh2CyAVPu4METVkBYBrEvnW6HELMaPEBiqqCdMhczY1QC9uRDgPJkHXdEZUn4Mhs8E20cUxqtVSXRMqnks7xQFhapXIV//vOfrtURFlvd5sBMA4XHTOyZiKmVdhpxdJ+grMJZDwIsgLBWRwe8PEC9sNBhzz54Dy0rJHuPeutr06aN4HP99dcbKzS+I/AGCgdH8L6KaZoYE2RqX85AxvA8C2+fsKZBoUw3pMM1kbah4MIbrzfgRcRuu+0msNrD0RCs07CuMpAACZAACZAACZAACaROIGEl0E5dRFOwOkRbHrxdwEM03uBDWSorwIskHrTh+AIP6/fdd594nbCUVR7peFC000Lh7v+CCy6IcP4SXQceKGF1xAM8rIJeC190Xu+19UiY6DpCb9no82zxjG6nPK+9SiAsOLkKaAtOWWw45JBDBJ5s4ZkWShNeNuBFAOKiAzyZWucjWKcKC3WsAO+bWDt43XXXxcrixid7j+I+xsuWaAXTbuYOZzFQynCvY0o11srhO3nyySebNqE4YUN1OB/67rvv3PWFbod8TvAiBUoYlC07BdabLR2u3npinaN+u143Vh7GkwAJkAAJkAAJkAAJpE8goemgmI42Y8YM0xosEpj66ffp0KGDyQPFKpltFGA5xDofBKxvwkNrsgH7/NlpobCI+K31snV6rYS6UbaNjnvEg7bdFgAP4OmEbPNMp2+ZKgvlw74ogEKhG4FnquqU6sG9iWmFCFDusI7V7z6zyjmsUn73uI3DulgErJlM1KlRovco1hxCacSaSq8ibRr0/AcFEfsx2imtXsdI3hcpmPpaVsB6vBNOOEE6duwoiXgTtfUlytXm55EESIAESIAESIAESKD8CSRkCfSukYLFLdbG6j/88IPriRCKXSLOR4AAD7uYFgqrIJzLnOXxPJoMIiiQsGZAyYILf+/6KW89cGoDRzawtsDSg2urhHrz2XNYfLxOa+AcJJ2QbZ7p9C1TZTFVEZYoBKwVi8c3U22WVQ+U94suusiso8P6M9xveLlh75PffvvN3D+oB1s0xNsKBPcAxrhgwQIzNdPPqujXn0TuUUzlhBKNACs59gGMFbCJeuXKlY1jJO+m93369DHKIaa3wiMq1jbacfrVBWXWrtuEMphMKItrMnUxLwmQAAmQAAmQAAmQQPYJlGkJhAJkLSh77rlnTAUQXYW154ADDjC9xsP1zJkzEx4BLDNY14QARTCVAOsJFCz7sOt9KPbWBwuKncKHKXcnnnhizDVysP5hfzg7FRSWoHTc0+eKp3e8uT7HmrV//OMfbrPxlBg3U45OoBDZ9YnYAgPTj2145pln3H0u7Z6ANi36eO6557pR8IibaEjkHj388MPFWhrvuusueeONN2JWD6+6dj0uHNDYgPW2dg9MTH3GeazvAzxv3nLLLaYopnBHO8OxdcY7xuMarxzTSIAESIAESIAESIAEck+gTEsgHkDhRAUhEQsYrH/WvT4ejjFdLdGA/CiLdVupBlgpoXREO66Jrg8WQDjXgLIKV//t2rUTOO/o1q2bYH877Cv32Wefya233mqsPSgPC00y44luE9e55OnXPuLg4CfRabBYWwavlNHBbraOeFitcI8sWrTIrMXEujobLr744rSUZltPpo5wkAIrNfbbQxg+fLh5CYDNx+1UUDiPKWs7i4MOOsgok7hPJkyYYBywYK1pIqGsexSWcUxtRt8wtRovHqCYYTsKTL/E9FpszA4Zvvnmm6ZJ3Jtez6WIxL0L5zXYvxNjg4UcFm3Ug7WFsNxjTSO+d1ZBxHmia2S9Y43H1ZuP5yRAAiRAAiRAAiRAAnlAQB/g4wad5oZ5aY66nHfU02DcvEhUT4SOPmCaMjpVzdGHWFNGlSsTp8454tahawlNPrSJz/jx4938qly4aWptcuOjT9SS6KhS5+bVdYzRWcy1enh01Pro5rNt+h3V0UhC40fFaik0daqDjVLtZoqnrfh///uf2/9XXnnFRvsedS2km9dvjH5x6mjErWvy5MlJlR88eLCj6+Xc8qmcxGPprU+dCrl9U0Xdm+R7rpY+N7+ugXM++eQT91pfZPiWiY5UJcsto547TXIm79FHHnnEUau224affBCniqGjCnh098y1KnqOWj7LrAPfb31xUqqOdLnaCtXK6vZhypQpNtocVSk1aTqTICI+1YtkvhOptsFyJEACJEACJEACJBBkAnGng2KqpLXqYJonrEJlBaxR6tevn8kGj4XWxT7WLSUSUBbTM21ItJzNj2P0lDtvmvfc7sMGawksO34BVhtYjqZOnZrQ+FFHrD5nkqfta6y2bHqujugH7o/u3bvLpZdeKrNmzZKRI0eK3a8u1X4kOr5E89l+YBql3WtOlVtjdbNpZU0FtflgnbNeNO22EjatrGMi9+igQYOMcx2suYN10BvQ7l577SWqaJttVaK3VrB5YeGEtRuWcT+vtqgHVlFYveFtNDqky3XcuHHRVZb6fiTbRqkKoyIyXV9U9bwkARIgARIgARIggcATKIIGG/hRZGgA2G9t/vz5Zn3gjjvuaPYms4pChppgNSSQMgF4vcU0UKzbw7RQ7FeYTMBXHV5AsT8mplxDcWzWrBn33UsGIvOSAAmQAAmQAAmQQAEQoBJYAELkEEiABEiABEiABEiABEiABEggUQJxp4MmWgnzkQAJkAAJkAAJkAAJkAAJkAAJBIMAlcBgyIm9JAESIAESIAESIAESIAESIIGMEEhoiwjvRukZaZWVkAAJkAAJkAAJkAAJkAAJkAAJZI0A/EfE2re9TCUQ+4nBeQQDCZAACZAACZAACZAACZAACZBAMAhg14ZYgY5hYpFhPAmQAAmQAAmQAAmQAAmQAAkUIAGuCSxAoXJIJEACJEACJEACJEACJEACJBCLAJXAWGQYTwIkQAIkQAIkQAIkQAIkQAIFSIBKYAEKlUMiARIgARIgARIgARIgARIggVgEqATGIsN4EiABEiABEiABEiCB/8/eewDIcV1Xoqdz9+Q8CINARAIEiUSCAAkmMIiimMUgUV5/rWR7LUv8a+pT6y8HyVTwrr8tryRnSZYsS1aivFamJGZSBEEQgQABEkTOaTCYHDr/c6unZ3p6ume6e6qnu6vvIxtT9eqle96rV3XqvnevIqAIKAIWREBJoAU7VUVSBBQBRUARUAQUAUVAEVAEFAFFIB0CSgLTIaPxioAioAgoAoqAIqAIKAKKgCKgCFgQASWBFuxUFUkRUAQUAUVAEVAEFAFFQBFQBBSBdAgoCUyHjMYrAoqAIqAIKAKKgCKgCCgCioAiYEEElARasFNVJEVAEVAEFAFFQBFQBBQBRUARUATSIaAkMB0yGq8IKAKKgCKgCCgCioAioAgoAoqABRFQEmjBTlWRFAFFQBFQBBQBRUARUAQUAUVAEUiHgJLAdMhovCKgCCgCioAioAgoAoqAIqAIKAIWREBJoAU7VUVSBBQBRUARUAQUAUVAEVAEFAFFIB0CSgLTIaPxioAioAgoAoqAIqAIKAKKgCKgCFgQASWBFuxUFUkRUAQUAUVAEVAEFAFFQBFQBBSBdAgoCUyHjMYrAoqAIqAIKAKKgCKgCCgCioAiYEEElARasFNVJEVAEVAEFAFFQBFQBBQBRUARUATSIaAkMB0yGq8IKAKKgCKgCCgCioAioAgoAoqABRFQEmjBTlWRFAFFQBFQBBQBRUARUAQUAUVAEUiHgJLAdMhovCKgCCgCioAioAgoAoqAIqAIKAIWREBJoAU7VUVSBBQBRUARUAQUAUVAEVAEFAFFIB0CSgLTIaPxioAioAgoAoqAIqAIKAKKgCKgCFgQASWBFuxUFUkRUAQUAUVAEVAEFAFFQBFQBBSBdAgoCUyHjMYrAoqAIqAIKAKKgCKgCCgCioAiYEEElARasFNVJEVAEVAEFAFFQBFQBBQBRUARUATSIaAkMB0yGq8IKAKKgCKgCCgCioAioAgoAoqABRFQEmjBTlWRFAFFQBFQBBQBRUARUAQUAUVAEUiHgJLAdMhovCKgCCgCioAioAgoAoqAIqAIKAIWREBJoAU7VUVSBBQBRUARUAQUAUVAEVAEFAFFIB0CSgLTIaPxioAioAgoAoqAIqAIKAKKgCKgCFgQASWBFuxUFUkRUAQUAUVAEVAEFAFFQBFQBBSBdAgoCUyHjMYrAoqAIqAIKAKKgCKgCCgCioAiYEEElARaolOjlCLdb6yAUSaTlPF/41ejcmH4SjxO/hrRiRESl3Sup4qAIqAIKAKKgCKgCCgCioAiUDoIOEunqdrS1AgkUjLbmCShUAjv7N+PVze/gmWXLsWKyy9HbW29kSYSieL8+fM4c/oEFi5chJraOgSDARw9fBCvvPwSKquqcf8DD8PhdCIcDqOzsxM93T2orqlGU2MTYB9b15iK9UQRUAQUAUVAEVAEFAFFQBFQBIoWASWBRds1mTVMNHU2W4yQCVnr6e7C4OCgkVlio5EInnzy+9i6dRs2XLkSd919F+648z70Dwzhzz/9Kex+7RXccMsNePedD+KtPTvxk//zA3T3hfCh3/k9HDt2BD/76Y/x8ssvwwYH3nMX877nTtjGEUAhokoKM+sxTaUIKAKKgCKgCCgCioAioAgUFgEblwEmqpIK2xqtPWsEIiR+R44ewg+/8y28+psXcfrkCfgDfhJDUdbZUdfQhMFgFNvf2ININILGahfmzJoFl68Kb+99C9WVDnAQkCwCERLGkM2F1tnzceNNN+LlF1/AoSMncf1NN+BjH/0Dpgkyz170kUBWVVbj8itWYc2Va9AomsEyIIGPPx7Gjh2pb5dHH7Xjvvustbq63OTN5OZ79tkIPv953iwpwqJFwFe+Yq3vas88E8Ff/EVqeRcvtuGf/9mRAonSjSq3/n3uuQg+97nU/WvF8TyRvAsXAl/9qrXu3+efj+Czn03dvwsWAF/7WvnIq/1buvNyvOUvvBDBZz6TejxfcgnwL/9irfH84osRPPFEfuW1FmLxkVIGfyMkbuFgkETtWfzRY4/i+OFDcHkAp90eI3RRG0mdjcs9z1FzF0VLjRv9/giCgQjOnzlDkhgkIXRyf5+hL4TNEWV6FyJhoKv9FH78/X/DhV4/WmbOQUtdDT7/J4/jwMHD6OwPwun2YM6cOZi34BJcfvkVePe734V1V61DdXXNcHnWpIRvvBHF88+nJoH33586vpSH4q5d6eW97z7ryZtJX507h7RjgCumLRcmkrerS8aAtUjgRPKWW/9evGi54cwtEOnv33KT98IF6/XvRPdvR4f15J1oPFuxfyeSt729vPrXLHmVBJbquKHq7uA7b+FT/+//gzPHD6GyggQuxBeyiBMelw0etwMupw127v0bCkXQPRCA3e6CxxGBwxHkzw23wwknVYYDgSHItwY7l3lW2iOI2EOIUsNY53PA330OT//8P/iqF2a5DjTUeFFfX4crLluI9ddchw0br8PCRUtIQD0xIzLGqlB5OTQOShVdbbcioAgoAoqAIqAIKAKKgCJgWQSUBJZk10Zxjtq8zz/xJzhy4G14PS4u/XShqsrH5Z02kjsSQNI6u43UzmGnls+B2REf2jsHcborhL6gDbWis7OFaQCmEi6XHd29A6ANGLg9XgwMkRRyZaOH57KslCUZJkHl2EeSONhzAa8892u89Oyv0TJjJj7wwd/Ff/mvv4cZM2exVKV/JTmktNGKgCKgCCgCioAioAgoAmWDgJLAEuzqrq5O/NX//Aye+/Uv4PM4ECU7C/O/wUAPBvzi1iFGxUSz5yQJdPB6rdeDGXUVqK7w4tj5fvQGAiSINvhJ+CorfAh5Q8Y1MfoyRMMysqzUXeFBgOmi1CZWeL0sCwhH7VxWKnsOSQzDUZw8dQ7/+Hd/Sy2iA7/7kY/Ryiitj3IpqioCS3BgaZMVAUVAEVAEFAFFQBFQBMoCASWBJdLNQuzECmhvbw/+6Yv/G09+++vUAMqePtkDGIWL+/6cdif39TkQZOxAMERSGOayzwiqPHb4Q/2MC6KprgqXtFbiwBmm8YfgobawmpxtVkMViZzdiKshYaytqjCWk/YP2rm01AUvtYVBqgcvdPUhxOWloiJ0Oe2YOXcB+gZD+P6T/4mFS5fjnnvvM9qpLLBEBpY2UxFQBBQBRUARUAQUAUWg7BBQElgiXS4EsKuzA1/6wv/C1//xy9TKUdtHbZyQQ7fLiUpq6jxcz2kjqWNS7vVzop9GYI639+JcTwQNNAJjG6KasDtKgzBVqK9y4WxXgITRiVA4gkCIRDEwiGqfG66aKu4jDCE8GDW0g4PDhLKfGsJuUTWKdpH1hqO0IhMJ4M477sDJsxfwm99sxtXrr8XMmTNKBFVtpiKgCCgCioAioAgoAoqAIlB+CCgJLII+F9cMooVLF8SJ+/bXXsXff+mv8NzTP4dbDL6QiEVpHMbGpZchavi6ZHmnsD/+LxvzPExTV1mFy+fWYt/pPrR3D8JR64SD7iKCTF/pdTEh85LM9ZAcdvYPcg8hDcq4XOjjEtGu/n5UuLyIcsloT98gvF4fl5CGECDxHBiwUStIosn8vYePwed9AeuvuxFNM1roW/CoksB0HanxioAioAgoAoqAIqAIKAKKQBEgoCSwCDpBnLzv3LEDl112Gbw+30iLhBzufXMX/uWr/4Cf/+hJGmTpji0BtYlvv4hB2Gp8XjHbQl+ANPhCTR2zkLgB9AaB890DqCOBnNdShd7BIB3ER+Dlxr4Al3M6nS5j2WaIpHAg6jesgzpZ0rmeQUMLyOwYoF/AyAA1hGxflOX3+8M0GgNUUVtYwaWogWCYZNGPnTvflu2BuP/B96GutoZtmJjUjgioB4qAIqAIKAKKgCKgCCgCioAiMO0IKAmcdshTV7h79y782Sf/B26++UZ4uLSzq6sHu7dv5+9V9PZ3w+3m3jwvjcCIqk80gIbGL2o4bheDLS63G7XU/HUPDFK7FzYUgvQPgV5q+Zx0INhS68PJi32oJAH0h2gVlD3voDZxiOzNxbLtJG7iTH4w6KeGkVpGliCO6Aeig2xwLJ3o/prr3PDYw9yDGEJzlRuzmypw9mI/9u/bh9e3bMaNN94yoVYztfQaqwgoAoqAIqAIKAKKgCKgCCgC04WAksDpQnqCepzcy7fu6vX0+fc4Xn/5eRpcIc+jZs0gdzzx0gKoweoYZwQSQFI2DAlhEz1gkJdJ9gyLnZJANHfCEmmx08b/XCzMSTcRAe796xkIgdsBuVyURI5+Avv7aR2UJFDopZTlDzvQPRhmehv3B5IIyjJTOp33Gr4HnQiGekkcWSeXoToHA9xr6EEzfQdGwyHs3LoFL77wDJYuv4yuJ6pjbdV/FQFFQBFQBBQBRUARUAQUAUWgqBBQElgE3SFGXxYuXIiHf+tD+MqX/wbVdR7YIiG2TIjZcBg+sA2TNUMjyHw0CkoNHi13kqy5SNSqafTFRkrnl32C9P3XQ+IXoT/ACz1DBrHs45LOcz3iOzCASp8H7T0D6PPbUeWlgZhIGBf7A7QqyuWi1BLW1jZi3bplmD9nFg7ufwd79r5l1EUvEmSdYhgG6KIT+voKJ11IuFlHL7a9vhU37d+PVWvWxlte9H//+q/D2LdvBOkx7X3kETs2bUq/X3NM4uGTL3whjLffTl3e+99vp7Y3/cE7xAAAQABJREFUu/JS1TGVuInalyivfIjIJPzN34Tx1lupExeDvJnI8NprEXz1q7IIenyYO9eGT32KH2KyCFu2RPC1r5lXXhZVZ5R0InnnzLHh058uH3lz6V+z8cuo07JIZHb7tm6N4CtfST2ecxkvWYiSUVKz2zdReW1tNvz5n2d3f2QkRBaJXn89gn/+59T9kUv7Slne2bNteOKJ7Pqj2OU1u30TjZdc8MtiqGaU1Oz2bdsWwT/9U+r7oxjkNbt927dH8Y//SEONKcKsWTZ85jPp7w8lgSlAm86oEK1wvvqbl3HJokX4+Ccex49+8hO0nzqCOhIrhzA8wwIol3eSdAkpFJWgGIORpZlRIWL8uagtvP++u3HvPe9GS0sz0zowSOMu7xw8ih27uExzx24M7D9Coy70Izg4hE7uD+z1B1FNtw9ym1zsC3CfoJP7+aqx8oq5uHLN5diwbg1WX34p5rS1wksXEQcOHsFn/+eX8dwLL6GSLicMozQkl2FaiBkK0CkFNyKKsZqjh/bj4MH9WEkSKK0shfDUU1E891xqErNqVZQkMDsppLxnn01d3sqVUZLA7MozO/Vk7YvLayw5zqDyX/4yiqefTi3vFVcUXt4MRMChQ8C//EtqGVavjmZNAs0uLxMZskkzUftkzGdLAicqLxf8spElk7QHD5rbv4cPpy8vF/wykSGbNBP1Ry7tm6g8mdOyHS/ZyJJJWrPbN1H/ypxWaBJodvvMLi+TPssmzZEj6e+3yy+PZk0Ci11es9tnNn7Z9F0maSeS1+z+XbEi+/GSiQzZpJlI3lzad+RINO37y/Ll0dImgWLJsr29HefPn+c+uS76yetFT08PhkhyxJG5/MQQiQTRqHk8Hlqy9MJHAyuJf2tra9HY2Gj8KioqsumvvKcVgy7/5eEH8Icf/zh+//d+F5/80z9Ffyc1e/TD56Gfv1ovl2tSy2fjXj95TaVyj9Y5o1zeCVT4KvGH//2j+J0PPkDSFeY+voiRRoyNLl+6APfftQl++gO8cLEX7Re7cObMOVzo6ERfb7+s8uTePju1h9WYOWsGZre2YEZrA6qrPOSbEVodDVE7GERgKISZrU1417tvw69e3ELNYRg+d2ypqZ0uKWSPoWgHnSStXd1dRn+JxVIXLY1qUAQUAUVAEVAEFAFFQBFQBBSB4kKgKDSBfr8fu3fvxt69e7ksbx/2cznhsWPHcPz4cVy4cMF0xIQcxglhc3Mz2traxv3mzJljpDG98qQCZT/gxo0b8ZW6Ovy3DzyCOYvmY0adj5o8unQgs/JwY6CDBl6MtZwkuULyBoM27tuL0pKoFx/9yO/gwx96EKHAAPflUeU7rDHktkAGWVIqIYrm+grMaKrCykvnkbBJOtHTyXJT0koWHyHBjHB9ZzgawtDgAK+N6vFk/6H4Ilx/5RW4+abr8H9+8ms6oHdyCWmUJJWENBygwpIHDKIZDFIzGCfmRqT+owgoAoqAIqAIKAKKgCKgCCgCRYNAQUhgZ2cnl8s9ixdffJEOxn+DPXv2QJZFTlcQLeKpU6eM30R1ioZsEZdpLl68eNzf1tbWibJmdU00k7/30Ufx2qsv4sKZo3AL+fOShJHQybJLIWRitCXKvX529piNewAdrijuu+NmfOR3H0E4wP1+BumTtIlB8sXInlyJ0DKoaPiiNlqSYTD4onE0/I9sVTOWmiZGyrGUA8xorscjD9yFF19+DZHgALWIQiOH28f9hGGW7aEGtrq2ji4o3MmF6LkioAgoAoqAIqAIKAKKgCKgCBQBAtNGAs+ePYvvf//7+I//+A9s3ryZBiwN1lIEEKRvgiw93blzp/FLTiWaRPHrF/+tWLECK1euRB01etkGB5ndDTduwh33PICf/uDfUV0Zs9UZNQiZ2Pck0eLeQKFbAwP0zTcYwtJFl+B//OGHjeWiQfr6M0Kc1RlaPomh4RjhkgaBFELJdMZ5LHn8XxstgcpSWoPpMVJcRSQHyevmnr81K5dh7ZqVeIF7Az3UKLrdQjJZLNtHX/JonTWPS3JdXL57mk7jZ8fKjCVJLlLPFQFFQBFQBBQBRUARUAQUAUWgAAjklQQKcXj66afxt3/7t3jqqadKgvhl2gcdHR146aWXjF9invnz52P16tVYu3YtrrzySlx11VVoaGhITJLy2Ov14LHHP4nXXtmMzvZj8MieO+51jFkDFZIVIqGzc5+gAzWVbvz2I/djyaI5GBLv7UKy4sSPx5JO8g1xSWkfiWyEWjo3/QhWV9fSOSC7XMigkYV6PB4P9g2gl/ssRRsreymra2qoifQwX8wAjJFYdH5sT11NBW64di2d2O9GaLDXuEQbMyAv5T5FB+ZcsgDt589iK30G3nn3ffRFmNchFmua/qsIKAKKgCKgCCgCioAioAgoAhkjkLc39B/96Ee02PQE3njjjYwbk5hQlkjKvjz5ydJL2bvX1NRkGDGpIUmRfX0O+r6TPXXyV352GjmR/YWy3HOQxlbif+VYlqAKcUv1MzRkiZVP4fjo0aOQ33/+53+OlHLttdcay15HIpIPSNwGabXz1c0v4dGPP44/odN48b/gplN2Y8OepBclHjV09OSAyxZfgofufRf33vljBHCkPO7p457B8+3ncfTwQVzsaOc5fQQ66efPV4H5Cxahbe487jHkOk7WKQSwv68Xx9je3u5u+gAMIsyfncStsbkFs+fMRU1NnaFNNPwRcm+iiwTzxmtWY8fWrYZGF1H6DmQdfWSBa+nrcP78Odi+9VX4B/txy623o4IO7A0t40gb9UARUAQUAUVAEVAEFAFFQBFQBAqJgOkkUIy7fOxjH+NywRcykkuImyypFI3ZqlWrIMsqlyxZgtmzuZRwGoJYH5X9gSdPnhzzE6M0Bw4coOn4QwahnEpThMBOFEQvJ8Ts4P69OH7kKK5ct4F7Jp9DBTfdiUbQQ6ubDi7ZFObmpsXN2265EbNmNGDIP8QYYyOfUXyUFkP7eknmwkNYcukyavTWwUUNoJCwKLWBIVrxHNEYGitDbXTqXoMVXMbqEMMujBNCHAgG0NfXRxJNn4HOfqYhkRPtIZenykLRpYsX4MaNV2H/22+hs7vHcBnhcXsx2HkWP/3+t9Fx4TxmzpiB06dOYMasNhL3GqN9+o8ioAgoAoqAIqAIKAKKgCKgCBQeAdNIoCwV/MIXvoA/pXsDcdswURBrnHfddRduv/12XH/99Tnto5uo/GyuiRuD+fPnG79U+YQUCUk8SEdT8hNi+PbbbxuWTI/Q+UomWkSx/jlRkKWbbpcbd919Px655100/uJEa63LcAUh+YR6SRpyORKqelxzzVWGNU/ZLWgwN/nLdsr12toG1Nc1ImIn+XPRFQYNtEh8lBo+m43LN2nJk5kNMhjlhahYCnVWIuz2GRq/SDBMQtiPOhc1qyR9xs5N2SPILBLEmqiHjuFXrrocC5cs4rLP15k+5h6i8/wxox12hxvbtm5BfUMz7nnvw1i2/LJY5mn+95vfjODw4eGGJ9X9nvfYsG6dEOjChYnad8cdNlx9dXbtm6i8XOQV3m9mmKh9ucibSdt2745yH/L4Pa6Sd+ZM4Pd/P2bVNpOy8pHG7Pa9+WYUP/yhyit9xe9Q+MhHCtu/E/VHLu0zuzyzx/SePVE8+WTq8WdFec3uD7PxK/b+nUhesb33B38Qu3/NfhZlikum7cu0vL17o/jBD1LfHy0twEc/Wtj5aqL2JfZHpvKajV+m9WaabiJ5c+mPYpc3FS6mkEDRGn3gAx/AT+joPF2orKzE+9//fnzwgx8kibmGhITMpASCtDPuQuLGG28c0+KBgQGDEIp10127dhlLX8WQjPgzTAyTkUBJa+Ny1rVXbsCdD/4WnvzWv5LsuQ1zLnItTEfsZHGSCm1z2rB4wXxyPhuXeYqvPplQSBa4TNPghNToOTz1cFZzBrXR399IoM8/fy/8PVwiOthNgkcH9CSJnpoZcPpquZRWJp/4cKA/wgA1in1n4Qj0MV4oKI3VyEzMfHZSw/nz5uCyZZfird176SeQ+xL5EcAowTBfasMhuvq4YtWVlKNqpAXTffBv/xZJ6wS+udlOEjjdLRpb37e/HcEzz6RmWk1NdpLAseknOzNbXrNv0W99K0INd2p5pT+ylXcyPOS6vKR95jOpH7rcultwEjhR+9asyb59E5XHhRYFl1cekmb2x0TlibyFJoETtY8LMLJu32T9W8zyXnFF9vJOhJ8V+3cieXPBL5M5Mps0E7Uvl/EsL+Hp5oPLLx8lgWY/izKVeaL2SX/ESaoZ5XERXFGQwHT9Ie0zU97E/s0UP7PTvfVW+vHHBYpZ98dE5RWDvKnwi7/1p7qWUZzstbv11luxffv2lOnFiuZjjz1mLBEVh+1WCrJvUQzAyC8xHD58GNu2bcNW7pvbsWMH1sjb3CRBaJaH+xz/++N/jKeffQnHjx5GVQXdLHAZpywHFSucoiGcP38+6LsdnSfbuYcvjCG6h+jt7OHevxCCJGlBEkZnRSNpmgc+GndpaqxHMz9ptHBJqpPk0F7vwxCO08WDH95aEsCKZsPgS3d3L87QgmvHhXb00EhMhNZboyR3Nn8X9yHS0AxJqrfCh6qaani5TFV8DdbTFURTQy0CfpexH9NG7/NhksHuvkG6iajF4qXLjLonEV0vKwKKgCKgCCgCioAioAgoAorANCIwJRIoGsBbbrnFIDrJbRZDLY8++ig+/elPF3S5Z3K7puN8wYIFkN9DDz2UYXWiFRWPflEsZL6vfv0b+JM//iS2vbYFPnpjd7tJukgExejLD378K3znJy/QgAv98kVCJGLVePjeO3H1VatR3zITtdVN3BMIaiO7cfLEUbz22qs4f+48iV6U+y0vp/ZrNRbOn0XloQ8nz/Xw+jPYTUufvb39NAJTjVmzZqGFen9xdeHz1pMs1qKv4zQOvr0fP/3VC3h9x5s0HCMO7B00EhOimwi/sXfR7XTJilT4w0H0cTnwsksvweJLL+OeRm+GGGgyRUARUAQUAUVAEVAEFAFFQBGYDgRyJoGyNFCWgIqmKzmItuo73/kONmzYkHxJz9MiQCLINQ/kV9hIa6J//md/jA9/4L1wOUj2SP5k1aeTmr3mpjZ0dA9wn+IZ+KiZe/wPfxcf/dD76aOP2sL6NlqYqR5Xg+zXbL9wAc8+8ww++9m/MPY1VjCv1+vDPffcw378L7jkkksM9xDjMwcR6j+Hm69fjeuvuxqP/fFfYvPW7WhpakQtjcr4+7vQ199HwzF+kkO6lKAYUYcLK1atxWWXc72E8FsNioAioAgoAoqAIqAIKAKKgCJQNAjkTAK//OUvp9wDKPv9fvzjHxvuHIpGyhJrSJDWOX/5s59yuacfTu6xa25uRGdHFxbNm49/+Kcvo8LnxTPPb8H/+uI/Yin3BwZoJTTkqILL7k3JucQCayuXhMqeTCHoH/jAI2hvb8fHP/5xY6luenio2uNew4jdZVgWraB/wkUL52PTDevwwd96L/a/cwhf+uI/4a09b9IKqYNuIvzo6g8Z1kZve/edmCWWN6jd1KAIKAKKgCKgCCgCioAioAgoAsWDAK2JZB+OHTuGT37yk+Myij+8X/3qV0oAxyGTeYRo7Xbv2oFf/PSHcNMthBjmXL1qNZpIBOe2zUVLYxVqfA7ctmk9Nq6/kpq3CI19cs8el13abOM5vZR35swZg5h30xeg7NFctmyZ4cB+JXdynzt3zugzsYA6Lgh/Iwm0i5VRWhsV4y/NjXW4mRrB2S11cNrC9D9IS6GrV2EB3XqEIi5cvuoq/N9/+BhuuunG4eJUFTgOV41QBBQBRUARUAQUAUVAEVAECohATiTwE5/4xDjfefPmzYM4iK+iTzkNuSMgpO30ydMYGqQrB1lbSVucM2c2kwiuihl3oXEYw8MDrYWK0RfDibvsJ7S5UlYaCoUgFkuFtB87dpTLTR1YunQx3nvf3ejnMs43uR/wLz7/OcOyaaoCZKcircKwGTQ4Qyfy9XU13CvoRogbD4M0HrNgwSXYQDLaXFuDKp8NFQ76XTx2AGfPxEil6gFToapxioAioAgoAoqAIqAIKAKKQOEQGK86mqQt4g7hhz/84ZhU4kbhm9/8pmoAx6CS24mTJO89d9+DTjpc//QffZwULEoFXBibbroBnV09sm3QCA5a7Gyg8ZYoiRidSDAdVYYpFoO66Sz+tttuw4svvmgYezlw8ADaWhtx+WXLcLajB3feeRdW/edq1KRy6G4zVIFGG2RZpxBUB/f9VVZWGG3ycVnqhg1XUUMYxWtbtmGQpPIQHd73dt9I1xAxS7DDRcQarf8qAoqAIqAIKAKKgCKgCCgCikDBEchaE/ilL30p5i8uoeliIOaGG25IiNHDqSAgDuwforGW9dfdgCF/xPA7eO3Gq7jEcj01cNQQGvo/O+a0teDA/oPcj+dFJDTAfjFcu4+rWohlM11EDA0N4dSJY2ibUYv5bfXo7+vCIOOaGpvorD719wAhl9Gwn78QBuibIsD0YkU0ROfzc2a30v3FMtAwKLp7ug3n9pcsuBTr1l+HBi47NYKuBh3XHxqhCCgCioAioAgoAoqAIqAIFBKB1G/+aVokJOLJJ58cc1WWFz7xxBNj4vRkqgjY4Pb4cPf978UrLzxDVw9nuBTTgbZZdP8QpL6P6jXRyK2+fBm+/Mp2PPPia9i48WojPl3NYs31xInj2PPGdtx165V0JeHlEk4vfv2Ln+OBh99n7P0bn5ffCIRYRoLMewbPvLCF+xJnorrSRxcVAWoiuRyVRmc6L15ET3cXXCSSa+jxe+Ua8ZuYP/b3sY+F6YMxttBU5Aob2tBY6x99NIj77ouR4WBQ3FNwKWuKMDAwSHJNh4sMxZ7u8cfdtMIb+14jWne55+LhE5+w48EHJ/+Wk4u8gcD04hcIuOkOxU0HrWG8/nrqhcSPP26n6xU7nnoqQvczov0eHy69FPi3f8tqahtfSJHF/PKXEXzqU6nlXboU+Na3JpdX3MR0dXUZkvX3yxhK7b4lRPczXV39054uEfJf/SqCP/uz1PJy+zG+/e3J5U0sL0C3NenmgkLJK/NWHOfnnnPgc59LvaR/4cIovvpVf1b9EQj4mH7ieaG3t5dbAqTY1OMgsX0TjZfM08U+aEqNzz7rwOc/n1reRYui+MpXspM3k/bFtqqkxyQXOTKpV+Q1o3/l/s0kiDXxTO5z89ONjufnn7fTEjntCKQIS5bY8L3veVJcGR/l9/uNrUd9fennK/PlyB6/VPKKQT75LVoEWsuffL4qBjkmHs+juHz5y0785V+m7t+Ghii3GQ0anZlpeWamm+w+Hz/K0sfIc2NgYAAvvGDHZz6TWt4FC6L42tcmn6/M7t/0rc7uyuQjM6E8sfopxkUSw7333mv4xEuM0+OpIRClps/ByWPBoqXwVHhx/PhJWuI8iLVXriDRG9X2tTbX43c++D787Oe/xN63D6ChdS4uWbgEs2bPobauFk4uGY3SskwPnbcf2LcXh/ftxvXrl2P2jCY6g4/g6rVL8cLLO/CvX/8qVl95NWbOmGkQDFl+KgO/vf0cjh05hNNHj8A/cBELFs7DLddvYN4Q2xEjeUMkU6dPn0MHl5YuXLwcm265HbPa6Koij+Gdd6JJJGGUFB050o0LF3qN2oPBFv6Vl6HxoZ9vP6WSbu/eFu7rTJRj9GXg3LnR4/FSjsbkIm8oNL34yXu6kMDx/TsqR1zejg4kjYHRNJFI/j5AjNYyvUcTycttvxkF+WByga5iJPT20tjTBC//o+kqTU6Xvl5pVzxMJK98CMs2xEhg4j00WoK8/I/Km759ZqcT8hmv9/jxCt7jzaONSjga5P7weLpM+y0YbGUJqcldvOienh6OA7lXUqdLbN9E9WaeLjQix0TyDg1lL28m7fN6Rc7UL3KCSS5yZFKvlD2RvJn2r2zHyCSIHYBMxov56UbH87Fj6cez3z/6DjOZPEICRZa+vvT3pflyZI9f+v6NcgXWZFLGrheDHBOP51Fcjh6t5ztiaiIforuywoy/WPt8vsk/gGXWI4A8N0SW9P0LfkjLbL4yu38zlWGydFmRQPH9lxw+/OEPJ0fpuRkIkGRVVFbB4XKjm47cX3t9B9Zdvcp4UMUMxrASErzF85rw2Ef/K7qY5tTZdpy/eBpvtp/BYCD2wBASKFq7GY3VuP/OG1BT6UI4pk5EQ20l7n73DTh04jz27XmdxmFshsZRyid/RIXHiZZqB1a/6yo019cZmr4QyyJHNYJ85eru7sXJk2eMj+w33347rr7mOuoArfcSbkaXahmKgCJQGATiH60KU3tx1irz98hkXpxNNLVVOgZMhXNaCtM+mxaYtZI0CMTmyDQXLRKdMQmUr1AvvfTSGLHraJjk1ltvHROnJ1NHwCBR5FFeftHwUDPSPyQkcDt+u/cR+NzU7o1UEQU/JjP4aZnThcsWzcYKPthtDn4JcfKrJ907yD5BWyTAFZ0D3E8YJAGUZVHDZbBPnfYoll0yA5ctnkMix6+kzG8nAY2G+bmdfgpFKyiavwiXhIYCshZ1VOtms9twnMtEDx46gfUbN+Ge9z7MvYBNRvuUBo50kh4oAopAgRHQl8nxHRDDZPRpMj6FxigChUVA79vC4l/utZfD+MuYBIpV0Pg68/jAEKuTYnREg/kIyOBrbmk1lna+1dmOffsOYNu2nbj5xmsgSyRiYZhqGa4kSAiphifjQzTYS11cb4wADi/bNPaHCCk0tHSRYV0dyR6ziKZQfoj2jd3KZxQ//LVY8rIsw9onjcXIF5Kenl68/JtX4aMl0A/9tz/AiivWsPxYDWMLMh8fLVERUAQUgUwRKIeHeaZYxNPFvnJntsQwnkf/Fg4Bq4zhbOQoB01M4UaUNWvOZnxNhoCZZU1WV6Guyxt+RuHll18el27Dhg3j4jTCPASq6bbhjrsfRJj8rK+vj8YQvo+ungFR1pGwkb3FP+IKWROyZxA+0SPGyKHN0NqJ5k5+jCNZFJcTseuxNLwwmlcKJtkb+Y0YFpC0zBWvTzSJTPvq1p04caYDD//Wh7Dx+ptiRUk9xs841X8UAUVAESg4AuXwMM8WZMUkW8Q0/XQjoGN0uhHX+hIRKIfxlzEJ3Lp1ayI2xrGSwHGQmBohriLuvOdetM6ejUBwCNu37cAPnvxPON2yIZeMbJSVpag3FRkbYXEp0mceZecy0FMnz6KzN4CH/6/f5TLQB+kmwkVeak75mbdEUyoCikCmCJTDAy0dFuUmeybylpuWJRNM0o0fjS8MAtpnhcFda40hUA7jL2MSePDgwTHjQgjK6tWrx8TpibkIyACcv2AhPvEnT3DJZQONsPThX//129i8eQfcXrGAJERvugNdMtD6YmvbIjz82/8Nt9x2h2HRUVpRDjfMdKNdTvXp+Cmn3p5eWW2ywkHDGATKjQSOEV5PSgIBHaMl0U1F1Ugz3yPMLKuoQEpoTMZPxsOHDydkA+bOnTvy8j/mgp6YioDsuXzokd/GN7/zY9z9wMMIcSnmX/713+LQkZN055DJfswU2jlRIhprSVNcS9P6WMrYvw5vDSob2uCrrGUpEqf7StLAptGKgCJQBAgYK+WLoB3F1IRyeMEpJry1LdkjoGM0e8w0h3kIlMP4y4RFGD7jzp49OwbZ+fPnjznXk/whIE7YN2zcSPcL1+DcObHGeRCDTh8C9grYI920BSMaQZKxcW86EsdL4mSWDsYD9B8krh88Pi/9nwQR8Afgcnvpk5CUUJyNjVEsMs+wwRmRzCCNLN/mqoandjYNiYr/sNjuwqSMklyDIqAIKAJFg4BqAsd3hWpZxmOiMcWFQDm8hBcX4qXfGjPHjJllFSuyGZHAY8eOjWu/ksBxkOQpYpjgga4e7A7UNMzEHF8rhvwRtIe70EBroC5bX/rtgQaXs+HggWN4+vnN8HorUFtTg86uLvT09qChvgY3Xb8e8+fOotvBRCeu8XqFSJICRh3wO6oRdrUgFPHCF47C5YiTwDyJrsUqAoqAImACArKP2fhQZkJZpVGEyDtxKDcSWA4vdBP3eOld1T4rvT6zUovLYfxlRAKTXUNIJ8+msRIN04mA7MWL4Hz3EHqDtPZJzV7YXoML5G1V1ARW2nsgnUnKNj7QYMucOTPwoQ8+hMHBAXRc7EBdbR2q6Nqhu6uHmsJgCqMu8ZIcCNncGLBXoy9aSw0i9yL6g6hwRdFU5UK116l7AccjrjGKQNEhUA4PtKIDvYgbpOOhiDtHm2YgUG4fKrTbiwuBcpgjMyKBvb2943qmurp6XJxG5BMBOoGnRs7nsaM/QD9/sg2Pxg4C9lr08Ct3KGxDtd0Pt018CMpOPdHgyZZPuoQgCfS5pauZv6YSTTXV3MVHf4IsxNcc68coCWaMQvILsvzP7FEp3+ZDr60eA6glGaQDemMJqA2DgRB/EVR6oqBC0IiXfzUoArkiUA4Tbq7YaL6pIRB7mUxc6TC18qyQW1+wrdCL1pZBnwnW7t98SGfmmDGzrHzIakaZOZPAykrZE6ZhehAQlmUzyFZTtYdHfpzuDtBXnwMeNzV1rkb0hmrhD/XCF+2E19YFN5d22iPUGNpCiDpJCXlsE6JH4hfmfwbh42k06jTInrA+uxA/8sYI9wL6oy4MORvQH21karqkYBPsduYjERVDMB4W7fE4GcdzURpKtAZFQBFQBBSBgiNgTNOTtKIcXnASISg3eRNlL+Vj7bdS7r3Sb7vVx1/OJLCqqqr0e7cEJXCQpDkdEfz82W1o7wZmzajkHr9KNDfWYFZzLWoqZlNDVw93+CICQ2dQwR6uodN46gqFxVFi+cWCuI2XYIuSKJLY9VGzd+JcFw6f6kHLvMswg/sEQ3ybELsyQ1wCOjTkR+/QEOor7FjWVo8aMkExGCMaQ+WAMUz1X0VAEVAESgEB1QSWQi9pG63+Eq49XNwIWH38ZUQCQ2I5Mil4vbI0UMN0IyD7Ak9cDKDPMRu2Oh+Od/rRd6wTQ/3HUeWN4orLZmPdykWo9rThrf1nsfmln+GyRTNx7Yq5aKEWUbR2orgzgpC3SADdwQjeOHgOr715COcuDODKq2/G0tlLcKHfjwOHz2D/kbPo6BygltAHT0UtViysw+z6KtT7qEUU+idO4jP59ByvV/8qAoqAIqAIFBQBJYEFhV8rzxABHacZAqXJDATMJm1ml1ds3ZQRCRRfdckhHNb9FcmY5Oc8TtliurZAKIJ9Z7nU09OAGrcbthqgacYMhMMkczT48sr2o9h/8Czue89qLL50Fbbu2I7v/mIz9h8/jwduuRqLZtTBaQsYSz5l2efJjiB+vfVtvLJjP8IhB951xz1Yv/FmbHv7BLbuPIquXhqdqWtCfet8+Kj9tTlcuBjw4xiJ6Ix6H9wOad/06gEfe8yOhx4aRXtgYAB9fX1GxJo1sifSWuFDH+rFnXcOGEK52ed1dXUjAl5zTf6wF25fiPDxj9vx8MOpa47Lu369DV/5CtckpwhNTSkiSzzq6qunLm8pfaeZSN7GxhLvzBTNv/zyAD73uY4UV8D7PT/P2kK+3FxxxfTLmxLcaYosRP9Ok2gpq5mof+fOlVVk/CCdYZBxWqhnUYZNRKr+raEVdlGWNDRkWkrppNu0aQAXL46uKktseUNDfuarxDqm81jG34oV6eer2lqxp1G6YTy7SyGLy+UaFxsIBMbFaUQ+EDA89BnGXSLct9cxEMKxM4Ow011DVPb0iYUYzpB2LvlsaJ6Jmrp6nD52AD/62U68/971eNftd+Pw0SPYsvMQOnqHcMfGK7Hx8kUkc1Fs3XcEP//NLuzbdxJenxu33vYerL7qBry65ywOHI2gqnkFmi+ppSFSIRo0JMN/xY3EQCiKk+1BLGklEax1sw35kDt9mXfcMXby6e4Oo709RgLT5yrdKzfdNDjS+IqKCsyaNVb+kYsmH0w3aYi/lCb3byqxFi2yQX7lEsySN/ZCVSB2n0VnLVxog/zKJcybF8K8eebNYfF7aSL8CqlhMVveieSMX8sEk3has/8WQl6zZcimvLlzQ5g7N/V4bmmpyKYowyDedD+LsmogE6eSt7W1EtXV0/Oszra9U02/fr0f8ivGELvPzXvGSXlz5wbTjudixCCbNmVEAlNpAoPBYDb1aNopISC2PiOGBdBj7QH0+l2wk5cL+bJzw16UbDBKiy5CCu1ON9oWLEP7mePYvP0QbrluKa5atw4vv/wiDhw8hn+jtnD3oWP8QuWjlnAvOtovoJqfqtatvwErr74Bb50M4lxvBTV/zTQuynJJ+iKyKdCggDKhOXgUZZog3VUE0Vqb+Re9KUEwQeZCvsxM0Cy9pAgUHQLyQIsW+2f1okPNmg0qJCmyJqIqVT4Q0HGaD1S1zEwRsPr4y4gEptIEKgnMdAhNMR35V+ybhg1BauCOnu9BhH777BE/v5A5EAkF0dfdicqaRthcZIWSmPHNM2fhzIUzOHW+G0svvQI733wTbq8LXec68NxL2+GgkZhIOIK61lmYf8kiXLFyPZeG2nGinUtNHbJcQwhgrO3hMI3CdHXAV10Fh7uCWkcakRmI4FxPEIu5n9DnFCukU5RzCtnLiQRafUKawjDQrBkgoOMnA5DKKInNcCNURgKrqCWHQDk930uuc8qgwVZ/ZmZEAlP5BOzpoZNxDflHgEtAhV9RIYeT5zqx+blXaBWURM3w58BrkRBcvhrUrLmRtG04GOzNCW9tMw4d78aaJc2oqalFVzSMxtle9HReQDjoR9Ps2aisqsWiJcvhrJ6JI+9w2aFd3EGIZlHKkpr5ozGa7vNHcHT3cdZVS55JQ0GMc3XPxIL6VVgwq4mpCscCrX6TDveq/lEEpoyA3itThtBSBRgufiwlkQpT7AhkOwdlm77Y5df25ReB2HiJqU7MqMnq4y8jEtiYYif+hQsXzMBXy5gUAdIrjucQCdyRLjp9n7seslWGjhtIxhwIDvUYmkCuAyUxk8KEjBnqQDicFeijFm+QGsTm5npc7GyHw+VAfUsry6Qm0BZGXX015sxbwn2GfoTFl6Cs+IxlH1ZBcr8hy65qnImKmnpUNs5iPUEalnEgXGnHuX4H5pExOg0OWBgiqF8Kpd81KAKTI2D1B9rkCJRLikzn4kzTlT5uOvZLsw/NfqmfLhR0vE0X0vmtx+r9mDMJ7OhIbcksv91RvqWLps3ntqGpOoLaSnYbNwR2D7lwwR+Fu4JWO+U/kjH5LxaoJSTJC0TsuNAbRWvzLLzzzn7jkqQQl/FuhwONTTPoTL6ORmPChuXPkTWgBpkcLon7iFyeSvhpgbS22kWrpMxPzaTsUxS3g/EaY6mn/18lgdOPudZYmghY/YFWmr1SuFarJrBw2GvNmSFQqiQwM+k0VbEjYPVnppLAYh+BQuv4sVacxC+s82PG5YNoriD94jLQU1wWutfWgXODVegK+ZmO7EwomWTgks7ujtM4f+gtzL/jOlRWVhuELUbZhLaRJNrdcDp96KLF0RDJYmx7yPCXYWM9aMwyqdMWQl1lDy2BduDSOVFc0uQh/XOia8iBymof7EZaUSEWJlj9Jk1E1cqyWlm2xD4s5LFiXEj0i69uHQ/F1yfaorEIxD7yjmx2GXtRzxSBPCNg9TkyIxJYRf9wHo8Hfv+oSVhdDprnkZdQvEHLIoPggk40+AYRpY/GYGgQbW4/lqytxZkBB3701kV0BFu5dFMIIAkdSWBFRQ3mLFlK9w8OnD/dG+N/CRo+sfo55Kf/Oc6vNllzyjyyGVConyw1jYUINX+DuGluFMua6xDw9wOBfjhpIKaiwkH3EXQYH6okSxVjMoUJqgksDO5aa+khYPUHWun1SGFbrOOhsPhr7ZMjoGN0cow0RQyBfIyVfJRZTP2VsfpmBh2SJ4YTJ04knupx3hAgqSMxCw71I0zyFeHewIiswZTln3buy+Pevgp7GFWuoKEAjDUjjP4LR3Dkzc1omjkTVd4gDh59h5rAxO620eVEGF0XzsEd6QYVeiSDoiEUyilEUkggCSGNzLjsAVS56ReS7YiIRVJJKmloXTTk76OF0iEjLf8pSLD6TVoQULVSSyKg94olu3WcUJn6VSun8VBOso4bEEUUkW0/ZJu+iETVplgAAauPv4w0gdKP8+bNw7Fjx0a69NSpUwiFQlxOmHERI3n1IAcEuPwzFmT3n/A5O4LBMPoG/XizvQJn+um+QXgbfftx9Sbcla2Yu9iF+U12tJ88iFNnT3NJ6WhfieYvyjIvdHbg+JGdWHrFjXj7+BACoQpERZtIsikVRbm0tDfgw55zHlTau+G1+bk/sMJog0ESSRSFHMqf4chYM6fx33SawC9+8QICAUFrfKipKfzykv/9vy9Qu25e+773PQfLGy9rd3cP79XUjnvHpx6NkW41M4i8E/dHrZnVGWXdd58NJ06MjvvECtyyerrAwez23Xtvenld9C0aDqceb9MFw623DuKll06OVDeTH6m83piv0Vz64557JpZ3pKICHSTLm9gMlyv7G8zs8sx+wbnllrH9O1V5zS4vsT1mHJvdvonKmz27mU0urF/eidqXy3i+++7J7195vpv9LMq072++OffxnOreuuuu1PJ2dnaiv78r02blLd2mTbnLm6pRU8EvVXmTxaXCfKI8d96Zuj8kjzwvJWRTptn4xVqQ339Tvx2lqFNIYGIIU4t08uRJzJ8/PzFaj/OBAD/ryvLLgMNLbsa3fBI4h8MNj8+JkwMVeOOcF35bPRxMZ+P1c0feQSjYi023rkdj1QCe+8VrzEMtYOI7B98FbUIkA2Ec2L8HDQ2NWDp3DfYdCyEYdnHgh5icrJLaxsFwBQ50N6GtzoZlTayfxHDE4bTTA5uTdwvrLmSQG3WkTcMNaWwsPNGbCJOGhuzbN9GE1Nycug+quR20vT2x8ydq1eg1s7u0EP1RUWHjsuVRmYrtyOz2TVbemTOpx8h04eLz0cKxjx+YhkNbG0gCc2/TZPLG6ynUX683ipkzR+WdajuS8ZtqeRPNJ7mUXezy5iLTRHnM7o+J8Gtpmagl03Ntovbl0gKfzwaZAyYKMkbNfhZNVF/itemSt7LSho6O7N8HEttqxrHZ8ppdnhkyJpaR6fhLzDPRcbHLm6rtiesDU10fiUsmgXIhUTM4klAP8oAASaCnGu6qFkSFCHJGlGWgg5EqDDiaMHt2C+a1hNBQMQCvM4rG1hm47LK5mN0YwEsvPoNzF88YBNGYSYUMGhZgpOtJDEj2+gZ6sX3bKxjs3Icl8+xwOQbpED4Mr30Itd4AZjZEMaO5EkPuRvREqo1lp6xe1I1Gm5xusowCh3TawAI3qyiqN/tFL19ClUo78yX/dJSrGE8HyqVTRzmNh3KStXRG4OQtLYd+cxjLuCbHQlNMPwJWH385awKlK44cOYIbbrhh+nulrGqMaXCiNnH+3gqHpwoRfy/9Bkaw/63D2LJjM5rb5mNmy0x46qgN9HgRttUh6h/Ei0//Crv37qK/QK5743oKMfkyvJtvGMHRbwBdPT149ZUXcfOmKlyzfAG1gUFjL6DsQxzqvYiejnbsOdCBvrYm3LB2GZzUlTtdtAxKIhgjlYXtFCGBop3WMB4Bq09i4yXWmHQI6FhIh4y14jPt50zTWQsdlaaUECiHMaofsc0ZkfkYK/ko0xxpzSklYxK4aNGicTXu27dvXJxGmI1AzFan/EsTnHB5a7h+qgaOYBAVOIw3f/Y9auaCcFbVobK+AR7+9VTVotcfwtmuTvoClI1ANOpiGJORlZxcWsGiZOGBlGmPcUzjrOv8Kbz0yx9g4Zx5GOxqRw+dy/eR/PV1XYS/v5uO5etxxe98lPXMYU6hlLG2MXPBg9Vv1KkArNhMBT1r5dWxYK3+nKo0Oh6miqDmzzcC5UCQykHGfI+TfJVv9TkyYxK4fPnycRi/9dZb4+I0wnwEhGyNhhhrkzXyLu7Fq/CS2AW4e2+oB47z/QieO4ohY8mnAy1OkjTu2RO3gbGloA6hfTTOQb+A1PT56PZD/A9ybSmJIculMZjQ2TD2vfkSyWGIlJOpWVElPcO7aUSmhml9rlHtobRkbNtGWzndR+UyieYyIeWSZ7r7T+ubHgR0LEwPzqVSi83YGlAqrS3vdlrl3s1WjmzTF8soyabduhy0WHptfDuy6cfxuYs/JmMS2MJdyQ0NDbh48eKIVG+//fbIsR5MFwLC6MRZvPj4G+Qxl0FSreenpdYQNX5Otwtet5P+/Pyo8FUgONgDN8kiU5HQxQicOxKhN4gIPLT0GSEB5P9cXmoUa+j3QizPYXehq6+PBmjoC5DWQm02F0IkhH39A4YBFiGhxRTKhQTmgrlikwtq1sxTbA+0YmuPNXs9vVTFNo+nb6leKVcEymGO0Ge0OaM7H2MlH2WaI605pWRMAqW6ZcuW4ZVXXhmp+fDhwxgaGqJ1N6qjNEwbAhFq5rq7u+mzz4ZN972PPNCO/j4/jh4/ib3vvI2zfT303ecCDfHRqbwTDqYPDg0gTMfw9VUVNH1L/4IkkaGQH4PBEFy+KvSRNNbW16G3fxAeL11A0PLL4stXYNXKlVwGWksiSAJJbaDTV4/+gQFU0uRiMb1AWP1GncrgKhVsSqWdU+mLQudVjAvdA9NVf2Zf6cpFE6jjfrrGnfn1lEPfKQk0f9yYVaLVx19WJFCWhCaSwAg1SrIvcNWqVWbhreVkgEA4FIaHJGz1Vddi/bU3jhhmERcJFzs68fq21/Ha1q04ffo0CZ0HvfQTJ68E/dTiVVdW0GR5KwYHh3Dm7Bm4Se5qa+ifjVpCj9sLF7WIM1pbcPsdd2Dp4sVj/UBSexigb8JwKGgYYREtYbHcIDqJph84xdJH6VuoV6YLAR0L04V0adRTTB/ySgMxbeV0I1AOz/ZykHE6xk0+nm9W75usSOCKFSvG9eOOHTuUBI5DJc8RJGwuEjAhfUN+MfrCQK2fjcs2q6orcdtttxo/uS4/GcT8w7+xr8PxG0VIvBzLX7kuhci5EUdLmyFZYspfcrAXoTnjuEzJbc3k/NOfvoi+vtG9jol52trGy594PdXxpz5lbnmJdeQiZ3KeTNsXGxOJtac+/rM/u4je3tT4zZ6dPX6pa8lv7LveZcOWLfSLmSKIL7psw0TliU+oQoX4WLjuuiE8+eSZlM0Q32fZhuuvH0xbXkVF9uVlW/9k6W+/Pda/J0+eMubExPS5tG/jRnPxS2yPGce33hrNaDxnqgksdnnNbt+11xZ3/95yy/j5SuZr8d2cy/1bzPLKnHXNNeb2R7HJKzLKe5q8i0kwu31m42fGHJVYhtntS3V/xOsT34DZhPgzM5s8k6WdSF7xNZht2LRp/HwQL2MyebMigWvWrImXO/JXSOCHPvShkXM9yD8CLrpnkN9UQ/wLR6pNyXJN3ECUSojLkkt7Fy0yl6iYXV4uMiXmSZ7EMm0fn0sZhYULzcUvo0pNTtTYaIP8zApNTTbIr9hCfCzU10dQXz/8AcmERppdnglNGlNEvH9b6E/VDFcyxS6vjL3Zs1N/mEkEJv5hMDEu1XGxyzt5+7K7FycvLxVK+Y2L37tSS3w8J9YoH3ybmnK7p6dT3kQ5Etuf7ljS19VFqGzITbZU5U6HvNnKmUgCzW6f2filwnQqcWbL29Bgw9VXZ3fPT6X92eY1uz+mIu/kT4kE6VavXm18rUiIwvbt2xNP9biIEZCHxEQh/hUqXZrJ8qfLNx3xUyGB09G+QtaR7cOokG3VuvOLQLmPhXKXf/zoKt4XpfFtzT2mHPpdZLSinOXybC8XOXO/iyfPmY/xb/V+yYoEVlZWYunSpWN6YteuXaZ8WR1TqJ6MQ0AIWPwnF+PHmf6NE7xM0yemy6S+eBr5W4iQj5u/EHJMVmcuclp9EpsMM70+ikAu42c0t/lH090evRfG9mGmmsCxufSsWBGY7vtpOnCwokypcNO5KRUqhY+z+vjLigRKdyQvCR0cHIT6C8z/QJWBGP9JbfHjTP/KBDNZ2rihl+R08fomKsOMJVZTQVEn0PToSX+WQiiVdpYClunaWO4Yl7v8yeNC8UhGpLTPrdifVpQp1ShLtS0nVTqNm14ErD7+siaBV1555bge2Lx587g4jSgvBORGKeTNoiQw/XgrZL+kb5VeKQQC5T4WymWeyLSfM01XiLGqdWaPgBX704oyperZcpmbUsluVlw+xko+yjRLXjPKyZoEXnPNNePqTXQbkXxR/Nl94xvfGONkPjmNnpc+AoWewKx+o051hCg+U0XQGvnLfRwUep6yxigqPSnKZdxbUc5SlSnbduvcVJzzSrb9WJxSpG9V1iRQjMP4fL4xJSaTQFki+uSTT+K+++5Da2srPvaxj9G6U92YPHqiCJiJQLlMoLlOSLnmM7OPtKzCI1Du46Dc5S/8CNQW5IpAJmO3FJ6DmciRjFEpyJXc5mzPdTlotoiNT5/L2BpfytiYfJQ5tobCnmXlIkKaKq4J1q1bhxdffHGk5YcPH8aJEyfw5ptv4rvf/S5+9KMf0e9a38j1DRs2jLMqOnJRDxQBExCw+o06VYgUn6kiaI385T4OyuFl0hojVaXIBQGr3t9WlSuxj3VuSkSjeI6tPvayJoHSNddee+0YEihxy5cvH0P8JC4eRHuoQRHIJwI6gU6MbilMZKXQxolRLv6r5Y5xucwT5d7PyXdiueBhVTmtKlfiOC2XuSlR5lI4tvrYy3o5qHRaS0vLuL6La/48Hg/uv/9+fPaznx1JoyRwBAo9yBMCOoFODKzVJ7KJpdercQSKbRxMd3umu7447vpXEZgOBKw6vsvh+V4OMub7HsjH+M9HmfnGIZvyM9YE7tu3z1jqKcs9Dxw4MKYOWct888034/3vf79BAGtqavCtb31rJI2SwBEo9CBPCEzHjfqRj/TgwQdHlzknirJiRSDxNG/HucqZywPmIx/pxgMPFFbevAGZY8HXXmvDd77jSJm7vj5ldFFFZjt+1q4dwt/8TXtKGWprIynjizlysvtg7Vq/peSdrC+uucaGv//7Hvj9/nFJS7F/xwmRFLFhQ+r7t7OzEx7PYFLq0jtNvr9Xr04/nmtqSuf+TZYrXc+sWVM88mba5rgsuewJtEr/xjGY7O+qVZP1r/nusLLtx8lkyOb6ypWp5RW7LHPn1mZTVNq0E5LA48eP43vf+55B/t54440xhQgwYilUiN+DDz44Tju4c+dOI73T6cSKFSvG5NUTRcBsBCZ7uTOjvg0bhswopiBl5DKRrV8//sWwII0vokrnzbNBfqUash0HbW1htLUNlKq449o9mfyzZ4cxe3bpyzuZnHFgZCzfd18A/f2lL3Ncpon+prt/z54NcjtLcc/vmfRpcppZs8KYNau4+ja5jRP1V/xapnmKUd64DJP9zeUdppTlnQyPVNcnl3es0cpUZWQbl+nYy7bcTNKnk7e62kGjmzkt5BxX7TgS2N7eblj2FI2fWP2MRqNjMq1cuRKPPPII3ve+95GJzh1zLfEkTgJlr6AsEdWgCOQTgULeqPmUy6yyFR+zkCztcsp9HOTyolXaPT5568sBk8nG/WTXJ0exOFJYRY5kNK0qV6Kc5XAfJsqbj+N8jRMpN5kL5aP9hShzHAl8/fXX8Ud/9EdjjLwsWrTI0PiJ1m/ZsmUZtTOuOdSloBnBpYmmiIDcpFa+UacIj4HNVMvId/58TeD5bncplV/uGOuL1vjRqpigJObH8T03Psaq93c5jNFcloOOHwEakw8ErPxuOU6feMcdd+Dll1/mkpjZI1guXLgQn/jEJzImgEeOHEFXV5eRX0ngCIx6kGcEyuFBketDPtd8ee4yLX6aESj3cVDu8qcabooJLOPCyqrPwHIYo+UgY6r5x8y4fGGYr3LNlD3XssaRQClo1apV2LJlC2Tpp4Rf/epX2Lhxo+EL0IiY5J+4FlCSKQmcBCy9bBoCVr5RpwqSYjNVBK2Rv9jGwXS3x6ovycmjMxtcywWTZIwSz7PBKzFfsR1bRY5kXEtRrmzbLOn1Xkzu+eI4z7Yvi6PVmbUiJQmUrG1tbYZG8N3vfrdR0u7du7F+/Xrs2LFj0pLj+wEFuDiRnDSTJlAEpoiATqDpAbTyJJZear2SjEC5jwOdI5JHhHW0YOMlG42ZbNxPdn20pMIdZdLGTNIUToJYzbm0MZc8hZYzl/p1fsoFtfznKbbxZ2Z70pJAgbW6uho//elP8ZGPfMRA+fTp07j++uvxs5/9bELU4yRwwYIFqK01x4zphBXqRUWACJTDBJrrzV8O2OhNkBkCuY6hzEov7lTlLHu6nlFMdE9gurFRLPHl8vzSfYFTG3H5msvyVe7UpDUn94QkUKqQQfkP//AP+Ku/+ivjJbu/vx/33nsv/u7v/i5tC+IkUJeCpoVIL+QBASvfqFOFS7GZKoLWyV/OY6FcXiazGa2KiZLAbMZLIdKWy5yl92IhRtfkdVp5/E1KAuPwPP7444brCHFSGA6H8eijj+Kxxx5DJDLW4ai4mDh16pSRTUlgHD39Ox0I6ASaHuVSmMRKoY3pES6dK+WMc7nIno2c2aQtnVE+tqWTyTjZ9bGlFe+ZVeRIRrgU5cqlzfoOk9zzxXGeS18WR8snb0XGJFCKuv/++/HCCy+MOIb/4he/aMQNDIw6I41rASW9kkBBQcN0IWDlG3WqGCo2U0XQOvnLeSzoS9b4cayYWGcrgVX70qpyJd+N5SJnstxmnefr2Zavcs2SeyrlZEUCpaJ169bhtddeG3EX8eMf/xg33HADzp49a7RDSeBUukPzTgWBcphAc52Mcs03lf7QvMWJQDGNheluy3TXV5wjYGyrymHeHCvx+DOrjAuryJHcQ1aVK1lO3ROYjEhxnFt5/GVNAqVL5s+fj82bN2PTpk1GD23btg1XX3019uzZgzgJrKurw4wZM4qjB7UVZYGAvsyk72YrT2LppdYrqRAo97Gg88TYUVHu40HQKAUMMmljJmnG9v70n+XSxlzyTL9kU69R56apY5iPEopt/JnZnpxIoIAsJO+Xv/wlPvjBDxqYHz9+HNdeey2effZZ41zcSWhQBKYTATNvjOls93TUpQ+X6UC5NOoo9/ukHO6FbPpY8SgNEpjJ7JJNv2dSXrGksapcyfiWw72YLLOZ5/kaJ/kq10zZcy0rZxIoFbpcLnzjG9/Apz71KaP+np4eXLhwwTjW/YAGDPrPNCKgE2h6sEthEiuFNqZHuHSulDvO5S5/8kjVeVNJYPKYKLbzUhyjucwzuhy02EZerD259GVxSjK+VVMigfHinnjiCfz7v/87PB5PPEqNwowgoQfThUApPiiyxSbXySjXfNm2T9MXPwLlPhbKYZ7IZhSW+3gQrKyCgVXkSB6/VpUrWU6dm5IRye48X+MkX+VmJ11+UptCAqVpjzzyCJ5++mk0NDQYLVVNYH46TEtNj4CVb9T0Umd2RbHJDKdySFXuY6EcXrSy6eNs0lr1/rAKBlaRI3mcWVWuZDnLYW5KlrkUzq08/kwjgdKR1113HbZs2YI1a9Zg4cKFpdC32kYLIaATaPrOtPIkll5qvZIKgWIaC4VoSyHqTNUPxRIneFgdk8nkK4Vnx2QyyHiyihzJ90YmsifnKcXzUui/UsR1qm228vgzlQQK0IsXLzYsh1oZtKkOKM2fHwR0zKXHVbFJj025XSn3saAvWuNHfLljYpV7wipyJI/QchmfuicwueezO8/X+M9XudlJN5razPaYTgKlmYl7A0ebrUeKQH4RKIcHRa43f6758ttjY0svhTaObbGelSICOs7G91q5Y2IV+a0iR/IILUW5cmlzObzDJPdtKZzn0pelIJe0MS8ksFSE13ZaCwGdQNP3p2KTHptyu2LlB1omfan3wniUyh0Tq9wTVpEjeYRaVa5kOcv9PkzGo1jOraqIZmkAAEAASURBVDz+lAQWyyjTdkwZASvfqFMFR7GZKoLWyV/uY8HqL1q59G+5Y5ILZsU4I1hFjmRsrT4+E+UtJ1kT5TbjOF/jP1/lmiHzVMtQEjhVBDV/0SBQDpNnrpNRrvmKpnO1IaYhUO5jodzlTzWQyh2TUpA/kzZmkiZV/09nXC5tzCXPdMpkZl26L9BMNM0py8rjT0mgOWNESykCBMqBBOYKs5UnsVwxKdd85X6flLv8qca9YqK+AlONi2KJK6fnl96LuY+6fI0TK/eJM3e4M8kZTUhkw9CgH2/t2QX/YD/q6uvQ2NxMIzI+BAJ+hIIhmYXhcrngcDoRCYfhHxrCwEA/6hub0djUNKashBM9VAQMBPI1AVgBXsXGCr1ojgzFNBYK0RYrP9BzHSFWxySTcSZpotHEd5Zc0SxsPqvIkYxiqcmVyZhLllHOrX4vppK52ONy7ct8yWVme/JGAmUutSUiwJOenot46ZlfYte2zaitqcOM2bNQUVmNwOAAhvyDTG2Hx+uFy+lCOBJCf38/urt6semOu7Buw7VwuT1wkiAaaVxuyHRtG/0nsTY9LkME5MaQnxUe5GZ3n5mThtlt0/KmF4FyHwtWlz8X+XLJM72jNv+1yct3JBLJf0V5rsEqciTDJGO0HJ7tuhw0uef1PJ8I5I0ECgGMkgZGomGcOnEUB/buwdtv7cYbW17AfmoDEbHB6XHCbnPA47TDZbdh0E+NIBzMKS/zzE2C56eWsK+zHW9u2wJfRSXcbjdqGxrRPGMWZs+di7nzLkFlVSVzxPKNZZ75hE7LLkYEyuVBkS32xf6SV+ztyxbvYk5f7ljrl/bxo1MxkYVIYz5bjwepRGKsIkcy3FYlt6nkTI7T88wQyNfYt/L8mDcSGAwN4dyZM9i1/XVs+80LeHv3Tlw8fxp2avjqKz1GjxpKPB5VeRyoqnDD77ejdyiMgUAI0UgYDbVViIScOLx3B/Nvh93hpqrcATe1hQuWLMUVV12NK67cgDVr18Hrq0hSPWY2aDSVtRCw+oNiKpOc5LXil9RwezvOPvQQQmfPWmsw50kaWWoP+RVBOM7l/7ININfgWrgQM777XdirqzMuYir3UMaVlFhCxURJYLEP2XIZo1YmHMU+xtK1z8pjb0okMEbiorFlmcMquAvnzuLg/rdx5NA+HNyzB7vf2IrO06dhiwRQ6XWjrrYipuUbfu4LuKLDcztt8Dm9qPBGSALDCAQDqCNZtMMNLzWG3f1D6PcHuXcwwD2Ffei8UAcHl284HE7YSAyn8iKRruM1vvQQ0Ak0fZ8JNuEieflP38rsrggBPLVpEwKcazSUHgLBKTY5uG8fTr/73Zj11FMZE0GdI8aDbnVMMnmJyyTNeOSmLybT9mWabvpaPramXNuXa76xtRf/mdXvxeLvgfEttPLYmwIJlMWesSWf8tc/NIid27bi1eeexp43tuHMiePwD1xEjceFGXVeEjb52VDBPX2y1HNsiBFJKc3jsMHtcyPsdbH8CHiKWp57nA6SwBAG+BsKBtHffQEH975JzQYNzvT3YcXqtWigARkrd9ZYzPQsFQLa/6lQicVZDRslgOn7upyuDL3ySlZE0OovWbnc51bHJJP7IRfcMil3utNYRY5k3EptjObaD7onMLnnMz/PFfPJashXuZPVOx3Xp0ACR5vX19uD55/6GX7y5Lep/duLgH+AyzaB1jofmqo8tPbpgJ28L0b1IobmcDQ344ctcgnQQg+jw+QvdmSjNjACn0v2DjpQTXIYCHPpaCCA/dtexIFdr2Fzy2zc88jv4O6HHqFmUPSKGsoVgVJ7UGTbT1OZjKaSN9t2Tkf63u98RzWA0wF0CdQhRLD/5z9H9fveN2lrrXYfTCpwBgkUE10OmsEwKWiSchmjVn+HKeggyrHyYht7ZrYnZxIYI3R27t2LYMvLL+Db//J3OHHwEFxU3TlI2FxkfZXUAjbNaDGsfbZzSWh42PIWzb7AxnRy7uRSTheNvQgPlCWg4rjQJgySy9YijIzIfhE5l3hqEEVT6HG6uY/QZZDHYDiC9pMH8ezPfoiWmbOwZt16w4CMkMkYpRQ9pYZyQcDMm8NqmBUzNsXcNquNg3KXR1+yxo8AxcQ6pvmt2pfl8oywav+Nn3XMj8nXGMlXueYjkH2JOZNAIXKiqZPQ39+LrosXQUUdnC5a/ZRln/x7ycJLsGj5Upw4dBhVDQ2ooBVPh82JQe7p6+7spnXPNrTNn48Kbuq3kfCdOX4K7+zZjcCQn34E6zGX+Xt6enDy6DFEQyHYWYHXV0lNo5++BQe5vJTaQRLOltpKnDqwB1/74v+H2+97CLffcx9q6xrYulgrjUbqP2WBgE6g6bvZyhNZotT2xkbYaDxKQ2oE5MNdsZjCF5+w2YbowAAinZ3ZZhtJb/U5Ipf7XDFRTeDIDVKkB7mM6yIVZcJm6Wq2CeEpyEUrj73sn8DsAlm+KaDI346O87jYfpZWPMNwu+xorvHBRY1dfXMDrr31FpK4XsxevAhXzZxN1w51hjP49nPncJaawctXrUZr2xxa+6S1UPLJ7o4O+GqqsHfHTly16SasXX81erp78PpvfoM3X3sd9S3NuIbxXR0XcfCdfTh19Ig0BjNnzUDvoRM4um8HvvOVM1QiBnD/+38bHhJGDeWFgNVfZqbSm1aeyBJxaf3mN1H5nvckRulxAgIDJFGnOf8WQ7hk0aKsm9Hz9a/j/Ic/nHW+eIZyuQ/i8mbyVzEpfhKYaR9lmi6TcZGPNLm2r1ye7eUiZz7GVr7KzHXM5qs9ZpabEwmUBpw6eRx7dr6OvTu34ZXnfo2Bvk4SwCo0+jwI879112zA8tWrcOLISbTNnU3NHq15iiVPksd5iwYwODCEWsbZaNjF2BPI+Io5lbjlrrswZ8FCGnpZjfqmZrRGI2jg3/rGJrhIFq+95RZqEoew9NhK7OQ+kF07tmP9phtRO+sgdm3ZivNd5/Cj7/4rFi5ZjquuuS62tNRMxLSsokbAyjerAD8V+aaSt6g7XRuXFQI6DmJL/4pFG5pV5+Upsb54Tm1uzVO35FSsVe/vUpMr1/bqvZjTsDcy5Yr5ZDXmq9zJ6p2O69mRQGOjHffp0QH8oQP78ePvfgtv0RJokA7dZVlmrZfrQbnUyEUieMVV61FdXYPlK5dzmajLWH4U3xPocnngrvdyP6HsLIxw9jUKpgbPhuaWVjS2tFDDxz2DARoQ5zU5v/H2OzBIC6RkdcaevyXLV6C5tQV21jVnyWIsXnEFbOEgtr26DX39/ejr6zHM4csNJaVL0N2BMRys/K9OoOl7V7FJj005XbHyAy3TflQMxiKlePD9QOwPWCBYRY7krrCqXMly6nM6GZHCnxfb2DOzPdmRQPaFuGSQ/+q572aArhkiJIBiAKahiv78uBy0YWYr6ltb0cSlm3Zq/mS5ZqovrkIARwLLjAeJlvKNq0Y0HUUw0lvhI/nzDZdFgkjyWE+XEDfddhuPuAy1dQY20V/UqWMncexcD4a47zA+p8dLlzLjx/H69K+1ENAJNH1/mjlxpK/F6lcS5i3OJmEug7/I/dCBwX64PG5Uyr5nGq6KhkP8CCUfuGjdmB+i5CcrHkLc2xwKBeHxxOaz0T6ZvplptE6r91V6+aQ/rOYzMy5tLv1r9XkzE0wySRPHuJj/WkWOZIytKleynHIviqzGCrnki3peEASsPPayI4HD7ylC4M4cP4aO82dR7SMBrPbSdYObBC2AFWvXYsmaVTTg4qNW0GB0k3Ta+JefxNeseGa5IWLxo+nlId7Q3GJUI8eXLLkU665bj1Pf/Ql+8f1/x9z5i3D56qsMIjrCCOMF6l9LImDlm3WqHVbM2BRz2+K4cwoa+xGJU1EfjWI9+9RP8eYbr6OWBq4WLF6CSq6ACPqHMDQ0xLkpAq+3gqTPTfIX5oezXlzs6MJy7odeueYqY54U2V0uF4miw5jjhhdGxKs1/W8pYG260EkFWp30JIk76aniId9rRt8tJgWsiBNYRY5kiMtpjIqsVv1IldyvZp7nc+xL2VYk5lmRQCFhMk2eOXUM3/36PyE60ImWukpUuqSYMGxc5rngsmVYculyw1DM2DemHLtaKkzFCoeL4zsWq5EEUX6Bd2LV+vV4/aVXse/tHfjeN76CuZcspKXQ+hwr12ylhoDVHxRTmeSmkrfUxkE+2hubimS2icbIXHs7jh05gH1vbsOWZ59CHy0Zi5ErJzWBDlnmHgkjRG1gxEY/qXyoM8LQDvr9AVx3y+38kHaUc1MjiaAH1bX1qKMFZflbW1dL4ijGsoZ9npr8blos46CQ7Shk3fkYm1MtU/EofhcRmfZRsT8DM5UjeUznmi+5nFI4VxJYfL0k46/sSWD8C/XuHVtx5O03MK+5ElUkXlFbhC87YbTRoEvbnHl89wknfTKPdaiNriNGgDQYJd9uZLkof8nvOUY6IXf8X47j+SbqBHk5a53RhgWXLsKpU6ew/eXnsOWl5/Cuux8wyonvPSy+4aUtMgsBGScaUiOg2KTGJdNY2Qs9ONCPc2dOY+fWzdj84rN458030N/dSdIXRg1XRZDycW+yH1U+N88ruI/Zj76hEIZIBrmwnXEeRMjvdv7mGVo9fpZubzz0o+rkUvdqzKWlzJX0c7puw3W4dPll8FCDKHugzQ7F/pJotrypylMMxqKS+Iwde6V8zqwyP1pFjuSRZ1W5kuWUc52fUqEyeVw+x0g+y55csvylyEoTKExNfPRt37IFHjqD9xrWPvnaQxLn8HixbuNGVNbUCGuLka54u3ke4UvQYG8vXT50YYAvUrJdxu320BBMCyor6coh/u4uafkF/SLdQHR3dvGY/gHpW7CisgI1tTWoqKjgexG/kMvarJEQyyxRTrcLM+bOhY9/WRGe+sn/wcZNt3GvTs1IFSPZ9MByCOjkmb5LrTqJpZc41yuxuUX+lf3JQ4OD6OrsQPvZU9j/9h689PSv8RYtI/v7uoz90M1VFXA7ZB+HkcHI4+SHMZ/HyeXyTtQGw+gdDMHPJaL1dKHjdlSgs9+Pnj4uGQ36McT4/t4uEkEXLp6dh2P0izpv4RK6uIlpHaUNZgYdB9Z+ycq1fyXfRB9ZzRyD011WJphkkma6251LfVaRI1n2Unq2T7UP1Fdgcu8X/nyqfVp4CVK3IDsSyDJOHDtMtxDbUVvhpVN46t74phTmPyvXXkmLoFfyRYhfrg2CFn9xsRn7Yzq7etDT1cUXKjr6pRVPeWMSgzEDfMESghd/0RFto6yFFrIp/v4iIWoZo0HupwnQQfwQqrjfpqamFi63O4kIDgvIcl20RupkM+oqPdi/aweOHNhHlxPrUiOgsZZCoJQeFLkAP5WJaCp5c2lrKeaJ0b9Yy0O0Trxv7y68+tzz2L3jVZw4eggDXRdR4Y6iucINd1U9P1CB+/2ccBgau8TcLEPmQf7vpeVksZ4cERUgJzgHfy01oil0op9awgF/GP6gnRrFDpw7dRRVtXXYSWK5YuVaWkZu5Uev+FxqDqI6DuTxYy6m5vRMYUuRuTOVEbfCtmr6arfKmLCKHMk9b1W5kuWUc6u/x6SSudjjimn8mdmWrElglMTNTgMwPr7A2GnVM8glUjPnL8R7HnyALy+1wy8+fMAOP2Nliab8V1lJ4zE1bVz+5ILDTgInFpCYNxoJMqm8PEkGOYqQwNm5rHMWZsyeZ3xdF9cSkSAJoZDCMNMKUzSI5vhhE6blvXOnzyLAdE6+ePk7++iY/gxJINPGqxmfTWMsgoCZN4dFIBkRo5gfLMXRb7JkU+Ygav+4WmHLS8/jZ//xPezZ/jr6ezqJI93V0Apyc7WPhlyE+MVmLUN7IpuT4yFxbhKywXMpVz6aybFYWJYg5NDtdFBbGEEg5IaflkOP7tqKo2/twtYXnsZt978f7/3AB7k/kEa2NJiKQDHfC6YKmkVh5Y5JccxBWXRYmqRWkSNZPKvKlSynnJf7vZgKk0zi8jlG8ll2JrLlK03WJLBlxgw0Nbdi8Ew32yT0Dliz/mo0NLYaLzgSN7r3ji8/XBbldNFQgqcKnop6ONxVsHEPjOx1ka+O4cFuhAYuMgu1g8wrr2F2bw2XRjUyndcggZFoCFFqAkODXQj76fpBXrKMly6q+2LvUzF8+MLl58vbsSNHcLa7F8GwA/OWXYGVV9JCqAT9+hvDwcL/6uSZvnOtOomllzjbK8OTCeelN9/Yjie//Q3s3fGa8QHKxg9T5GyopBXkxuYGVND6cWf7BS7x9MemPGYVjZ2xNN5OgiiJOU0FAyFOOzInsi38MCV7BoUE2mV/NKPEiyk968BNbWKVh9pCbgMUYzIXTh/B87/4D7TObMP6jTegSpbZmxR0HMijYLivTcLUCsWUOyZWkd8qciTfU1aVK1lOOdfloKlQKWycVcdf1iSwt7cHF7lXL9A7AFcVfV3RLPri5cuF08lHbobhh6sckwBGo/SR5fLCXT0TTl8103kMLibfxh1M4yIpDLor4O86zpekAOwVLfDVzjTy2GhVT96kjHKpNXR6qhHou4BgfweJYGi4KqnPqNjgnlKu/ELchzNA5/NLLltFn4UzmEa+1EtaffgTBMsGJYHpu9aqk1h6ibO7MjyLyIxD65/93JPcwW9NYbhJ6JwkbV63nYav2rBkxRJ0dXTCT4JXRx+p4t7BPziEnp5uw1/pTO5JrqmTvdF2tHNVwoG9ezDI8sR1RNu8edwHOITTx04gOBTgw94OH5fDB/1BY7m79JGHrLC5zof2E4fwzb//ApfgH8FdDzyMxqaW4e9YU5/DpJ5C7/8q5Hi08jyRK67ljkmuuGU3y+SeOtP2ZZou95ZMLWeu7bPy+ExGtJxkTZa9WM9zHbfFKk+8XVmTQMkYjdjRRzPn9bR0V1tP0+b8Mh6N8MVEvngnBln2RG2eu3o2XJV1vO4a1uKJhpAJ5R/u33NVNiIcHECQWkFfbYuhLYwTO5oejdE2O5eRurm3ppbWSMkKQ/3t/IouIbHOKPfneNE8s5m+CysR7gvQkIxoEyWlpJNKNVgZAaveqPE+m4p8U8kbr9/Kf2OzQ5TGqzrReeEsQtyD7OKaz4YKDwkg9xjTMNXV123gx6gKeGnNc/mqVVwB0cQ5x4Ourk6cPXUai5ZdilltbXD7Kg1tX8/FTtTS9cOu7du5b3ot1l67gdrDAHa9thW7X3uNH9EqGXcd90AP4ejB/Thx6Aj3QgfR0tyMvmOncerg/8/edwDGcVxnv+uHDhBg750URUlUl6hebFmWVWwndop7SWKnOcWO/1Sn2qlOYjt25BY7LnGTLMmymtW7KFESKVIsYi8A2NAOOFz9v28OAy6WC+AOdyCAuzfkYdvM7Mw3u2/nm/fmzWvyw68fxqBWXN79vo/A5L6pJFKMz8JEk8CJfJa0k3Uq+pWOSbnUv1zq4X5Cp9L3q9iylmsbutu01MfF4j5SecYz75HuO97XCiaBDU3NUjetUWLtmBOIEfDz1l8K06g68CvHnBiWGscZOE4Pw6wzVIOOCzx8mpBjf7l9+xcaPxLBLLzmBcPwFGrCAGEbwtswjzBQJZG6mYY0ZpMwDR2ggkxCmhfAGl3rLjhXtm14GfyxWw7s2WVG2MPhqNJAg2t5/1HhOXz7lqsQG77G+V8hITp6tF22vPySvPYK1v177CFpPbBf6iN+aaoJSxADXGeec7asOf88aAnj0tjUKM3NzZBXEUP24vE+WYnF4euh7aMGMGvM1X0yE4Tw8hveJLOhAVy8fLnQnJ7tMA0kr2nadCwskZHLrrsaBQ3I4QP7QAyfl5cxB/HCK66QWfsOyUvPPSutbSfkru9/W5auWG3WFwxhXnWxodKfhUqvv9fzU+mYlEv9y6Ue7me0XOvlrieP1RzUC5WJPVeuz1/BJDCBda+Svd1SDQK4+IwVcv76y4yHvFOaB4zMmIEaDeDIt6GuLwCT0EgA3vPQGRo5gPaF4ZihulGSnb2I6tLw4XDlmrWyeOUyiW/aJLtfe1U2PPOUXHrltSNnq1fLAoFyfVFL0TiTGZuJLhvnJ+/bu1seuPvHsuHJx7BkQxe0gFmpjUKjB4IYiIRl5dqzZcbMWRhoipiPNGYAwgIC85ox/hUKYb0/mL1nQP58hgDmzM9T8G48DWacF6yfbgwRGJcDZNNapsul110jffCOHIYm0Q9nWUtA8lpaZkqoKioLV6yQtRddBGmYlWefeEra4VW5J9YDjWAS6woqCSz2fSjnwaKxvkuVjslYcSv2WSx1+nKphxuXcq2Xu548Lud30au+U+HcZHr+SlmWnEVlPi0AcsXR8pdffFZ6ThyXSNgncxbOl4ZGeAQ1BkokYyeDmfMXrYM30Kq8zJdAA9Gxgs3VKLGpGGTcUBVH3L06Q1mpwpqAi9CJ8mMeYSbRLbtgZqWhchBQAerd1qUUHN53mIJnjdjCHwiWxvpGM5c4DdPMKMatmuEFtAYm6M3TW2TBiuUwM58NogetIN2CguhBHJ4MPCABtGdogg6NH/9lM8yfaXgbSEZsuW5qJBqVRpiKUp5x7mEGS+M0NDfJJVdfLQ0tLXAKM1+uvvEGmbdonkThmCYe6x404UQWzG7ModKfhUqvv9eDU+mYlEv9y6Ue7me0kr7rlVRXdzsXczyez/545l1MnYtNmz8JHOjdxOD8IBKEoSdSwjgTPRF2dryL4YfpJuw3EWe07gqdv3BEHc5ejAMX7/xyZ3MeSf1+kMthRsRBJ9GJakKePqy1da6cf9FlJik7YBrKH4FyfVnZcsXUTT8s3s++WbIBoqG97bC0Hz6IxdwzMr2+WlpqsRYqZNui1SvlyhtukJaZdMzCuXTe+Qw5yzjOeB6JOKhGTeLJkJV0Ki1NTdNkGkxFU+mULFiyVC6+/DKpCfnkgR9/Vza//IIhi4ZMum5xMp/R94p5jkbPffLH0Hfh1DaqdEzK5Z0ol3q4n9ByrZe7njyu9HfRC5OJPleuz1/+JBAtQBCmz5gOZysw20QHJ5uyPR2yQBcTxOHgIseuS6c2Jgyr4Bk02deDS8yYMfjH2UHiOQbqGBE4sm7mA5rIPJMLprMFk1GM2HNuzsozz5KlK1ea0fgcfbQRdVuuCKgA9W7ZchVi3rXN8yzFCH5H2g/KPT/8jhw/uFOaMQewsTZiCGAwHJSFS5fK8tWr4d2zNs9Mi4tGiWbEG+UfNIBnYwmeJYvnSeue1+X7X/0yynoYotEl9wq85WR4FiayDCojTn1gKh2TiXweT22NsZ8pl3q4EZhK9Sq2rDon0N36+R0Xi/tIdxnPvEe673hfK4AEkkJl4BHvfDgyeCs6KUG4SO/HOa/OCEe4Qddg4pQjc4a2DVsX5pDpx7p+Pe1mlDvnZdSSPHcyrqzFAMczMPc0PThzPPAHrtzZqeO6zOIPyJzFy40JFztVA35GnbF1vwwRqPTOzHBNWq5CbLj65nOeYoKSZtvmTbJ900ap9qelDoNcQZylJq555myZPX+BBGF1kJMfjH0yENMABpvMun/Y9+NncDZCKiepbGyeN6kpi/Djc+rZJkyGwSyajaZhIto8fRbmXy/HQvXV8vKzj8sTjzyAslH2jT143nfs2U25lOVc/7HWbazppkLj51O3fOJMVF0LKVshcU93fYopWzFpT3c9i72f9mGKRbD06cv1+SuABOY6MPUNzbL+hlslCi946VRyGNOoXNwUln0gG3z55Vfki1/8ItbR6nK1DHs7IHPpPrP+nySwcHzfcRPnvvvvk9tvv910gpyJmIKmUOkU8jbmo86r2EeErC8N9+5JiWKOz5LVa3MRzPIVQztwrpR6WCYIlOvLWmzzKC6nIpgjWimYWb4Eh1edWBMQXo+NmWYKS0FEZc1562TG7DmGsJ2ypAK0cbGeHjl4+IC8sX2bbHt9q+za9YZ0dnYZBzE5epn7S8czx7Hu4K7db8jO17fJzu075OC+fdLd3TFo3jm0dJRV+EGeBQNBmT1vgdRgTcII/Ik+cOdPsIbhUXNt4M/QpHkcVfqzoJ2sUx+SSsekXOpfLvVwP6GUWZUityqpru52nqzH5frsFUACbXcjA4cwS2X63IXS3dUjyRTn8Z0afOhdpfp7sJRDTA4eOiBPPfUUPOHFByJa/SHMQEHkEj1H4MCl13jVS0AbmE7FZOuWLfL0088OdKZO5m9oXBYLK/dgwfh0/8kLdg+dpgw6cd0dnbJw1RqZvWCJ0RySPGqoDATK9SPI1itGEBWTdryfnAkrGwTKiWPHQAI3ih+avygWhif3oiH6EpiAnnX+BXA0VZ1jcjk9noEikUzJMawN2N7aJsePHpUeDHD19/ZKH37x3hjk2kmJk0XnhRIvDs/KKaRLYR3A/v5+Myh25MhROQZy2I9Bq1NI5gDoZrlVkNMgJmI31VXLji2vyPbXt+AeaUeJCmuhCcO7sGKOW+xylhFjBa3SMSmXd6Jc6uH1HJdz3dz1rfT30Y1HPsfj+XyMZ9751M0Zp5RlGXntBuddse+DExh2SMJwm75k9Vny2nOPSH9fLGduyT6PYWgDidDxCaTiEu84JBecc4YsXPBpODtocuQIRzBYjDkRa5V07ASSwryJ6bEocvz4Prn1rdfJDXDGwFFwG3iLLOYO9mOh+DRG7QeMq+zlgS1ioQPWgzW7LrzqzRKK2Hk8BfFdV556OJUQUOHp3VqlFBzed5iaZ5PJhKTiMakK+eHwCoQNWrsaeO284LLLZebceTgH751pyBUj33JCLktzTAw21dTXSSPWC6SnTz/jAYIsBrZoFpobNsMW8Rha4Oxl+gysE4h9mrL3xxNYJL7PXKfZZyjs5e2YlzMgqsclAacxEXgrTR3rlrZDB80AGQxRTd6F/qn0Z6HS6+/1vCgmeMU5YDPwvnphNFXOlUs93HhX0jPKeYH8LmiYHAiU67N3kmGNijM7QezMcB6LX1ZdcKE88+jPpQOj6HWNIHfuZ9X0lTD+DW1gTTgrS+chTqobHRl2WmACmuyDySZMoeK9ZimHXA/LdI9MmhkNWDsrVCXJGMyeQAR5JYsR9BSWfEjHu7HUltVAmhudLD2EeCrVJ9PmLIRDhatOnte9ikGgXF/WYhtQybE3gvUNDTILnj8PHN0DEQdTc4i5ZatWy2IsCxHG+oCc3wyhN5iYc6ODIGNNVVwsvhZyCppCM2cQWkTIpSxlWz9M3wfmRNOVVRBrmwbq6iDL4NXYiMCMVGUSsICIQabBQiKbyolX0kjHvXjXJOZeH9izT452Q9OYzMo0rFW4eOlyQzoN6zxZtMEyjrZT6e9IOb8LY21bxYSvnpLA0WTHRF4f67N9ustcinKW8/t4utujFPcrRZuWohylzqMAEsiehtW9ZbFw8kVy7a3vNh0gmjvR/HNIoMrQhDRIXZeZw5eJBc2KEiSSYHToJPWBALJHZOMyAfbREcv2x2BK2i/pvi6zygTPc4QdGZmOGiK50jEtgulDheXsi66Rhmkzcuf0b0UhUM7CsxhBVEzacn6AOPLfDy1bfyIlYax/GoJWb9nqVVJL0mbkmFO25fa5YLy/qhFxm8QfxoLyGLU1fospuyC3fBjAomk7zB1gOlEjwdrpiNsAWUYHMxB9iEfCyEEt6e3AwFbnAGkk0owxcE/IVq4f2BvrlW6Y03fE0rL+wivhrXQNSKBTbjJd/mEyPAsTXQbevxy0Pvm3+sgxJ7o9Ri7d6bk6WTEotFyFxj896EKy4Z0rJpTzt92NSyXV1V33sR4X+3yNdN/xzHuk+473tQJIoC1KrnNS19AEEvgedHjgyKW/w14cunW+7xwh55C6Masa6OBk0XFCHGc0ZmDO4RrNOsXXZ/iiIZrsGA0kNaPldt95V9wjUN0ks5uWkDY6r+h+hSBQri9rsc2nuHgjmEomJdYbl84YFon3R6QJJp7NM2fCAAHmmSRrNnCXg13wjByAFjAEYhcIV+NUToKZ/g13I7BcQNpMCnMA410Srm2RSM10pOOAFwKyMPwNccLBMAhkVDi7OdPXAZlFtaPznlkJIU4jFpFvqK6WWF+ntEzHfXEuF28gTxwVEvRZyHlnVXOrk0+NdjrxihZJUk6iObF75VIPN4rlWi93PXms76MXKhN3rlyfvTH0INjLyf0ampqlvmmOCMyhzIgq+y72xx12jgY6SCaN6UDZRsS13H97YnDL3AdukTs3GG9gxwpqEzHXYTJ/ectAWKI1M2CdFUUeY6jeYCl0Z6oioMJz+JabrIJsIssViVZJHTwJ+2l2jmUbFi5bJk2Y50ez95x0sXhCywd5Fog0SKimBSaeNRBTkDGUR0YWMR53YBkBORSsbRY/yF+oCqbwPgxqDVzLxbcJAsivFkRxZk6OMgvHXXl/vz8oq9aeITMxp7qlvkqOH2mXJLSNuOKIyXT5h4nEO/9Sjm9MlRND8VU88IbavsVQaKbcUbnUww18udbLXU8e61qBXqiMfG48n49ylY9j0ATaRsjp2Tgfhh2Y/k6Yaabp/ZPdllxHyMbMHZ88OvXYec29bztL9vzQY5pgmbthxD4DAW46Z9V1A50ja75q0+q2EhAo15e1FG1HIakmcEORJCY+mKdXY55fy6xZcu4ll0hDUyM6hCR9jrgUNH6Qu2oQRmgAh8qxoXKJkieA+YJhHxzGwAR05ODHchQ1IIv18LZMRzHUBp4MaTiqWXP22bJhCTwsw4HNvm1bsK7hq7LuoktBWkkuCw/j+bEsvDQTk8KJQVtbG+aS5+aZ19fXSx1NgadocNarkCrkky4Wi0lHR87yh/HnzMEg8BQI+dSN1Rgt3vHjx+HlHO8owmR+Tmw9+Ezz2WaYSu1lCuzxZ7Rv++msb2srnBoOOG5xPwsWf48q5H3Kq67d3d2DS63x+uzZs/POb6pG7IXH7RMnTgwWf+7cuYP7p3OnFG1aqvKWsixFkEAzAwZkyy9hdIoyWK6Bc2ACnANjaFmpqjtyPrmuF101YEweI+7hOswD9EVMIjNNcWjfbOTM9GpZIGBfkH1Yh+21116Tw4cPS2Njo8ybN0/OPfdcLPwdFHZmampqplx9bd3GWvBi04/1vpMyHQgeaJ4caWuVzqNtEvRnZN7i+TJ34QKsFxg13jdz5R6QMtAC0llVEATQn4eVgR/aPz+IJTV2IwWTO+IGo/WS6oV5PcxInYHeShumtcjSM1bK3h2vS1/7EXll4wY567yLUBYlgU6sCtlnJ4qE5uabbzYywqb91V/9VfnMZz5jDytm69XpdFb+3/7t3+S///u/B8kyr7344ouGDDnjTeX90TD4wz/8Q3nyySdNFT/0oQ/Jn/zJn+RVXa4TumHDBjnjjDOkttZ6LM8r6Zgi2Xps2rRJfvmXf3kwj1deeUWqYVY+VcNo369i6st+Akn+mjVrRoTnGJwh3nrrrUISaMP73vc++bM/+zN7WJKtbUOb2V/91V/J9773PSwNlBskJBbbt2+3l8ty+4UvfMGsMW4H6FjJZ599VpphqaOhNAgUQQLZdckRQfSM4PtgOgawsUh7dytGnDjqxMs5rWA2g3Fxv3NI3SRF3ygL9+t+SXPun72MdHS1TmcIJr3JyFlZRDDZ2gR4IXCfQNV0idTPwlwZmIHaoCzQIlFRWwrzj3zkI4Mfa2flZ2KuF0evN27caOJ88pOfdF4u+/3RPqJlD4CzgkZGiXRgnb5UvAck0C8ReAOlk5fhgj9I756Yj2dk0HCxeD6nRSTJ9I8qhxgLBJN5w4w0m+JAmpVvuXv4MEd6OkZ9s5CVCxfOl/kLl5g5Izl7DFaksKDPQU4zsmvXriEEsDAUyyv2aM/ECy+8MIQAllftc7UZDYOx1pkE+stf/rKsXbtWfvKTnxScTaHlKjR+wQUaY4Jiy1Vs+uGKzUHh6667ThKJhHz1q1+VK6+8criosnPnziEEcNiIRV5wm4M+99xzgwSwyKynTHIOnDgJ4GgFH6/ng/cdz7xHq9d4Xh95iHrUO5/sfPjRMYpACxdqwDpY8JxnRr8H1hXMBoZ2aEy2SJqER749B9rhlQ/XOWcGv0QC5/YfljjconsrFDPwMAqPo3SgQIII0kcCGG2cjXmAHGE7Waah+6NWRiOUAQIcnXvb297mSQBZPZrGkAAy/PznPzfbSvpTroJsrG1IPKqqohidhxwBWcukIFeGrAnolCeQKCCKuVGu0e4Iepbhkja94HMc0GJ8/vGQhTjLu3AOIp3OnBoHafCfXks5YLby7HPlgosvNWTVOzdkMUrQ54CDjX5jGfCJT3xCLrroolEQmzqXi2lbt/bBWetPf/rTctttt0lTE+a4TrGQLyajxVu4cKGsWrXK/AoxxbPfnM2bN2M+b3Lc0bP1oLWLLe/q1aun/DwzW6/hABxrfWkxRALI8NJLLw2XvTl/4YUXyu/8zu8It+MZ3O8irRNuueWWKWnBNFac/viP/1je/va3Y43xaWPNomTp3O1RsownOKMiNIFDS55BB8rvR0cq3Ch7n3hCWua2SHQWPOKB6Pmp6XPRzVynJyB79x6SzZt3SDSamzeTgJv2aHVEZsAbHuMYj6JO0yvTH4KZVTAg/UeOy4m247Lw8huhjIRpHzSOJgxshpZQj8odgSNHjsi1115rRupYV5rdcETvnHPOMXb0Tz/9tDFfsjjQ1rzSwmQVZKN93MennaiBE5k+a46ccc4F8vKxdjPqmHbNyTt5b8bPWR7kaNvJK+495su1UJOxTgk2zDDyKpeGV04NubPI39zbSD5HJBzjPxegZ7x5S5ZJbT2Wm+DBqFpGRzaO3YnB21EAFv0UK4+h18f7yL4LH/vYx+Tyyy83nY3xvudkz5+YWHMzd1mpxfrHf/xHef/73y9PPfWU+3JZHI/2TNIkbyzBajM4H3s4fMeS73BpbD1WrFghd99993DRptx5+84OV/Cx1te2D/O18/yGuwex/d3f/V1DFt/1rncNF63o8+66knTyRyK4ZcuWovOfChnQNPdzn/ucsdp69NFHJ7TI9p2a0EKMw81LRgL9YHnkaq3Qsuz96rfk2IIZMufGa6UZiy774DIdQ9hkdANVwBb/A4h/9pnLpbcvgdExLJyMc+FwBDbrEWyhGWR8Z0cB+z548PNjpP74a9vk0J33yYn9ByRSO0/mX7IejkhhVOWMPw6AaZaTFwGOGlnhyJGj//mf/zGjoLbEFNzf//735c///M/tKc8tJ1//7Gc/M/MJSSxbWlqEo6g33XSTNGBhcRv4MefILs1DGBjv6NGjQmHFieK//uu/LtPhzv+73/2usd3niOxb3vIWWbJkic1iyHbHjh3yzDPPmLhdXV3CDxrnkFxwwQWDjiqKfb690udbX1vYQ4cOmQ+gHTm95ppr5J577pGtW7dKZ2enLF26VC699NIpoF2h+5Ys2rRJLnvzzbJvy0ZJJbmUDeSOR6Bn0AzkFIna4UOHZceO7ea5YBufxDWXllrAZN8JrP/XISlo8MIw89yydZu0tbfJRRdeNGReDk1BWY405lVnuWi8oXpDC5CF6TzL5o9UYRF7rhEI+WgG1syfIZHZiXn++ecH54uwbOzAr1u3bjDe3r175Re/+MWgqQ2f68suuwzmsLn51DZiIc9Gf3+/mfdkHVEwj7e+9a2DefL55oi77QRHgQvfyYkKJ9vMuwScH0QTSJqK2bBgwQI5//zz7aGU6l0oBGeau1NbQbwZqP244ooroNGuGixXPB6XJzAYy/IxsENJOUIZNFIYDRN32oMHD8p3vvMdeeONN0znmfmzo+p81txpeFzq+hILtlV7O9bmRGB9+cxTFhUSvOrP94lzzWg67AzE/c1vfrPz1Cn7e/bsEc7D43fEhjvvvFNCoZPOonjP5cuXy5lnnmmjFL3lvLVHHnlk0ImNzZDPLp/h0QLf0YceesjUe//+/eadnTFjhlx88cXmOaPjpLPOOmu0bEa9Tq0ov3m7d+82cYkFB23deRN7zgXjO8nBW7e8Yn0p86zTHnvj0erL+DQ5tPM8mY59CLfJLt8tDhQNN5+Tz8gDDzwgNNnk+0n/AxyA5txBZ5/BliufrZsEeqUheb3//vvNM3bgwAEz1YXv4FVXXWX6I+40/GZTdli5wOuLFy827yvbgHlRRrMvQw0c3yF3YB78drz++uvmneD8PPZTGJf9pOEC07366qumrLwX2zIcDsv8+fPNt6cQawx334v3ZF6cz83ntNjA8vG5tFr7lStXDsq0np4eI2uczmr4vC6DV3E+T5RD7Acy8Hm+8cYb5cEHHzR1ZxvNgvM520YjlXWkd5BTmmiJQJlebCgZCURdpedYm+z93/+VyIF9kmk7JAf2H5Lj550nM6+4WOrmz5FAFHNpIFxyo90ACJ2throaqautyXUMBka7OeKdQUfLdKe4xAT6OVxkOZtKStfufXL4qeek59kXxYeXrSreLzu/+VWQzTOkGh79NFQmAuxU/PCHPxys/De/+U3PDs+73/1uM7magt7rY0jh9vu///vCTpQ7/P3f/70ZlSIZZCC5G8mBxB133GEEE4UGw3333Sef//znhWZVH/zgB805/iHh+9d//dchk7553pqr0vzKmmLxfDHB3ckppL68L4Ub505Y4chzdDTg1qp+6Utfkl/6pV+Sv/mbv5nUJkiUMZRdC5edITPmLZZ4F5df8CaCnNuXTvVKOtkre/ftkrvuusuQc34EAxjRypE5ULgsCCAWf0/3dWHACgNcsWOwkKhBZ+N5/DaaD6bTOQNujwEvzKfGGoDZVBwk0CtkpaejU2bOXyTzUVafuV/uju7Y1Nb8v//3/4acZruThLFTzA8cO+lusvsbv/Eb8kd/9EeD6Qp9Nqgl+dGPfjSYnjuXwNMqP1bUwtN5gjOQcE4kCRypk8VOHR3GOAmtLfu3vvUtU69SvQuF4MzOrp27ZMvDLd81yicbKF/oRMIZ2JElaRvJnHMkTJx52X1i5AwcDGKguRx/bnnDa+NRXzposffmPRj4fFFjWUgn3Ku8/JZ89rOfzWXq+MvO3EgkkJ1pkgB3GM6ByL333mvIoDt+oceUzVdfffVgR9SZns+I13fPGYfl5oAq5YU7cGDVBpJMkp1iAgdkqelxBs6FI+mz5STZIMlwy6uPfvSjppysLweb+G64w2j15bf+4YcfHpKMgyf8uQMHcP/jP/7Dfdoc/y/6vfw5wze+8Q0heeB3ggO6hQb3nEB3euJBWcB32h04CP0Xf/EXRmvovMbvMQfCnYH1IgZf+9rXhnzXWZ+//Mu/NIPZNv7jjz9uvukc3PAK119/vRlkd5tKs4wklSR+XoHzZa/BYPIXv/hF47jPK47zHOvAsrnDj3/8Y8+2c8cb6ZiD+mw3O1jJuJSn//Vf/2WSffjDHx5iUcaTfF9IAinzHnvsMRPP/vmXf/kXz+8IBxTYr6PsdsudfN9BfssXLVpkbzWm7anDyGPKJpdo7/33SerlDVhwGUs14CPqP3hYeh58SHZ/4Wuy4xvfk7bnXpT48Q4QPHRzgtAchuAAhl0edK7MwDZLg31zDoKACy5TXZiI9cmRV1+T7d/+kez6wu0S+9mDEgCj5j2ioaCkX3xe9vycHx/kpaEiEeBLY4kIBe5II1LsrL7zne+UT33qU0Ow4kf4t3/7tz0JICNyJOsP/uAPBju67hd3SGYD8S0BdF77p3/6p8F7cOToPe95jxlNdwodZ3yOONGBDUfciw3OMhdaX96bZXSX0+LuLhtJOYnyZA4QN4a8RaprZe6SFbBK6Je+3h7IKNJDd4DGDiQtBVI3Y1qdXHnF5Rh1nGm0DrmYnFMIL8m9J+AgC27ZB7x8Zvt7JNnTJiuWzpdrrr5Kaqphuj4QeJdsJiWJ/i5oDjtAGjF/0K0JRFk4YNbR1SlnXXyF1E+bAUmX+2fzcW6dpk32PDsM9jzbz92hYjznwMdYng2bv72nc1souXCmHa/90cpErbZXsNqGUrwLheLM99er7SibnMFLY8H2GamNmH40TJz3GGn/P//zP401hTvOeNXXOahi70kib+vrlHv2utfWK57XOa+07nOFpmN5SxH4fHg9I/nkzWebTtW8CKA7PbWjxQbnYKIzL+c3hhYDTs2pjWef+WLqW0gbOeWjLcNo223btpnBpNHieV3P5130IoDMiwPLHND7+te/PiRrr/pysJkkzKstOJhrA82K6RF3OALIeNR4URvn7hOwnMMRQJs/iShJYD6B9fMKXnXwijfSOWLk9f5Y7EZqFxvHmb/XQCKvs2/4p3/6p0IPqM5QyDtILXaxoWSawM5D+6X9np9KhA5dQALZuWHm/lhcMrv3SN/hQ9L30svSNnumRBYtkNoF86R6ziypam7AYskwY4H5BpA39cnAO15/R4/0tbZLD8w9+97YK6kDByVz/IQEoPkLQktonCggNtpLohh5b7/nJzL38qukAaplDZWHAE1WbOBo1EiBWhC3uRJHjOkgwn58aJpAgceRTprOkcxwdJDCgaM3HGnjfACOVjq1erw3R4p+8zd/c3BtGzoT4EgyR4Sstyu6dqYJAUfmrAkry/xrv/ZrxpSS6n6aEdF06OWXXzbVIXn8wAc+MDhCOlIdh7tmhdRY6vumN73JmFrQVIYj7zT/tIGjidSy8mPNj4XVQvz0pz81JNfGm1xbyBszbgTPnBiFWrhqjWx8+mHpxGhlZlFKgrQ+cBYYIsqXAZmCieccWB3Mu/ZSCWDJiEwyhniQeZl+Sfd3SxIk0MfF3MkwzQ2yRht49sq5su6sNfBgjM5pf8zILpp/phOYO9iHEdL+4eao+oypaLi6Xs658nqMlMKiwgTvQS+2DbUgdpCDI8o0peLoJgO1gRytpMmQ/dhx8IMj6wxjfTY48kwz4Ntvv/2UTiTNyNgh4btEgjBah8AUZJz/2HfBfRt2YLgUgLPTR3M9vpuco3I21mxkoClPMe/CWHGmZoHzGDkKzEBtCp1h0VyNuFLTR5nBwTCOUDNQTvFdpOnySGE4TEZKQ3OnX/mVXzHPEgd+7Bw0PmPWaoLpx6u+XP6HcnLx4sWDWiVqItixLdSVvFcHj2b9XJvMmnRS024HAkbChdoralNIqOhx0nbYqaV3khq+n3ymRjLVLaRdaBZHrSjNE/k9o5mktSoZqby8RnM9W0960iYhpJkfTV/5PeI7TNNfBp4rNLjr8Xd/93fmmeQ3leE8WI5RI8XpGHwHGagpZF0oz2huyUBZRa0LA+tLzRDN9wqt7z/8wz8YzTSnddhBVlov8FvnDHxvKNtGCnwO+b5RRnBQmnKOjupIBPmu8vksJHg9i17pmS81mpRRNPP8yle+YkwSGZd9DsoFXmPgYDKXyaK2i/HcgXKEWlX2dSjbrCab5bdtxDTUenHpEebFQWq2C7XEJGE0eaRlk1PjTbNc9oloKsu0fDdYbg58UF5YDSs1jb/3e7/nLtaQY5ru8n2ygc8U+2A0pyYBLTbw+8h7sFxOCwD77P77v/+7MbXm8jhus2E+qySzJM/OPinrym8s24GabcpiykMGkkA+W3z2GQp5B70G+0wmBfwpAQnMdZMO4aHJ7N5uuFwGBSA5I6fzBzhiDYUebPYzbUckexSLrW7bKX3RasnWVkmoHoIkWiVZdLjsCLgPcdMw70t3o3OFkSmm9ePhCrBiPmgQ8TOZ8hghCCGaemOH7PvFvbL2fR/J3dxc8e4kmUv6p6wQcHfYCq0c17+yBNBtWsUXlw5n+MGgiQcD4/NDsX79+sFbsaNFAcEPPOf9UUgwMA3t3kn67MgNy8uf7TAxHkkoOxw2MD/el8SPQptzk/75n/95UGDaeIVsrSAba315Lwpw1seSQLcZIed08CPO0bqRRg0LKff4xKV8IH1jyMqilWfK6vMvhcPhOuGY/CnCkaKO8odL4cRB9FIxSfkjxjQTE5KNN9BMsgfqUkQ0mZ6UPySPaWj60okepKmBlQOvwYQUWsAsNIbZdBJJeANzE2wdAVGTyYysOPtiLA2xMueh1HHZvcvnj50Rdmr4QeGHls+icy4bvRVaAmjNWGynY6zPBucJ0Xsk5xJ5aRLYGeGP5ZoMJNDW140fCYUz8D1nx9+r01vMuzBWnNleNPezJJC4s33tumUceWYHY/HixYPVYB34Xo4WhsNkuHTUNvD9t4EdYBIOauBIPpxhvOrLeYCsv9M0kfNb2TaFBisfnelIMpxmn+y05UMCmQcHE/kjKbMkkB1V99xb5/1Ksc9vFud1MbBN8yWBznJxDU0+Y9SysmN8ww03mHluJBw0hXUPpI6l3NTy/dZv/ZbRiPD7S7lAQvz+979/cM4rO/zUOjrn5tJ6hmlt4LNun/dC6ssBE1oFcdDVkkB+p53fYXuPkbb83rOc/C4yEBuSI36vGTggYstnTuTxh88if1ZOeyXhwB5N8C0WbHfODybhIHGmtpT9D2sqTtJAYkf57CSBlCEcZGZ/g4Fbmlvy/gx8fu0cZA5+0TTSeuxkvUgGuaVmi4Em82xXOwjDNiGpZ5+AhJH9GRJNytTcdIqA+U6RJI4WnFMWWB9OsbFLethnfrQ8RrtOUkZMvEggBwRIgHndTQKJCZ8nTgWyJJBtxEFxxmeglRrJNgcx+K3kc8/rlgTm8w6SzFO5MNrAxGj15PVT+jn5JBqMg/4KnbHEe/tl1xNPSzXn0uCZgaWn6Vpxn32a3MMMYWSO05KJpyXd2yfp41mJBTCq3dAo4a7jA/EQCU5kfND2BQc0gwG+DHiIbBfJkMtc1sZsi1eyqYxsf/g5WXTbu6WuoR4mpRksQ4i8SBg1lD0Czs5Lvh9oCwpfQkvOKFSo0XAHPsMUPiQ37HRwVJRC0Tmiyw+KPbbCk/kMN/mXHRcneeVaUvyNFEZzXz1SWl5juYqpr1NA2XtRQ+IMxIDCmAI/X/MMJ17OvMZ/f0BI4UZNzTPl6re9S7JJePQMUJZhOItCxxkYnWIFRBDqO1zG4Bav8xyOTG78Y/a4zQVm46M8Quc4K124Bz2NDpwzBvAmEc/kEjj/4vn0wfvxUnRqgyGrBXRGOHWfeHK+KkkZw9/+7d+ajhXbj88tSY0NnJdn359SPxv2HpNxm88zxw47zcfzics65vsuFIszna9YUy86InCTampTSBZtIPnOJ+RbT5sXNYDOQAsJdupIRJ2ybbzrSw0kO0U2jNVhQqH1t/ebbNux1oPEmUSCJIFywhIjWz9iTIsPal6szLDXxrrlnE0SPw5MkXSScFjrF+ZJLR3NiKldYyDZJwmdTIHvlyWALBfxt5YXPC60T8I0DNQSW5Pm3Jmhf6lVtgTQXuG9KbNIAhmclkY2jnO7ePFio311lp/Xnc+Q03EO+z+jWVuRuFLjZQfJORBJksyBoOFMOZ1lymeffTWSMJbfBmeZ7blSbceaNwc0LAG0ZQkGg8YKjPOjiZXzec/nHaSGnprZUryDRTAkdH3QSYIhlew/dFwebK2Vp8NrZLuvRToyQUmhYuzOsIOUG+G21cdZPKQBmowGw9J0021Sd9310tOFeTgw9Uz3J8QPBzCYEZiLx5F3xGfgX/szoyO4RwyLKO+URnmi6gx54MRM2bL9iIkF2mjSM52G8kfAOQrMF8o+M/nUnHN/7PyCRYsWnSJUbR4cEaZ2zobh5gzZ66NtnSObo8W112lGWkyg0Jio+hZT7vFNm5MqtDCYBecwM+evkmBVA4QzzkPGGLZGxsZgjilbaI3A7eAmdx0njOwZOLIbI8IQ16bheR6aPyYv7ufy42kGc0ve3h+Uqvo5Eq3B+mwmo9z10f5yxNeOLtJEx4788kNsvcOxo8IRfhsq6dnI5wNK5xfW/M1iVIptsTg7tbrU8jz66KNDisUOoLMDny8JzAeTITfK82C868tOKs1kbXDiw3P5fg/yjWfvM1m3Y60Hv3HsrNPE1+tZoDk3Td5IBL0csYwVD6vJYXpavrhqyxtGAABAAElEQVQDO9JWI+ZuW3fccjr2agNn/YbTdlNbRfNUhtHkFy0E3ATQeQ/uj6WvYi0VmJ4m69SKlooAMk/OqePgj30ueG48w1jfKVpHeAUqDUgGGZzvUj7vIE1tKdOtN2Sv/PM9lytBvrGHxMtRO3Zktmw7LNsys2VPywzZCgcHsxL75ILuV2SBv8+kYGfGdHgG0nM/jdHtGDo/c9dfLnWz50i6Jw436hnpbj0qsf27JXj4gETpAQ+RnWltX4xZHclWy3NVq2R/9WLpDE+TODpLL23eJxddsAxpyG+dsZlCQ7kiQGHIF4qjZjSx5Ly8RYsWDVtdmslRUHLuCIUlXzwSQQpMjqpbUwdnBvRAas3cqOli+mIEEEdcnYHzpkbzCEazlWICBVkx9S3m3pM/bU6m+YMRCdZMh5OXJLwBxVBsGodaKWS3rI1z3+uY54YLw6e18pLOYLj0aaimWYKYD2gNV90ph7sDz1MbaOdl0ZyZmipubaAW0Plxq6RnY7gOFrUM7GDwR/Mkzv2l+RO1b6UKxeJM7QnnkHG0neV0B468W+0gB8gWL17sjuJ5PBwmnpELODne9aWmkUSTge03Wqd2uKI734Xh4kyF82OtBz3ecgCBc05pCsfni5okfhc5AElLFFp3UGvHeXgf//jHSwIHSSDN+hise31nxnaeIs+NVcvrzG+q7I/2PpKU891yB8oE9lcYSqE1ZV+FcxsZqO33WjrCXHT8sX0VWiqQrNlACwWarFKecv41rQZoYp0v0WR6mpMycP45ZTQHJtiHG88w1neKc0Pp8MadnmW3llLOZcPyfQfpcZsDus65l2Opf0GawBylwt8BbsXOSAofm5de2W3mxcSg2TtS3yxb6lbIwQyWfcB1RuXgOYPZ5w7mx/TCGcyc975fms9Yi49Yt7Rcf4N0+auk6UpMYv3zv5XIW26RHmgFU+iYs6M9mJbpceMANu0gga80nCMH65ZIDC7YE/5a2fRqq3T25BwsDNyWKTSUOQIkZHbhVgoW2qN7eS+jictf//Vfyzve8Q7jkpgaEb6cnAzOQLMNfticIzM8zw4GO8y2o8GPlvulZrxCAkmq00yAx9/85jc9fxQkNP1wju4Xci8bl2WeqPraMri31NyyvTi6zJ81zXXHG/9j0izKGr+EInUSqm2RTJjzTqyYpMQb/5C7C70k487RBgnXzcQOnGchGF8zZi+/P5zkzzlJDJwzxn0774IDJ26znlI8G873gp0UG/ju0XmAnRtlz0/U1llOZxmuueYa+b//+7/BzhNHr6mdoElaqUIpcPbqDHt1CPLVArJuw2FSbL1PZ32L0RSNV/2d+Nm5m85zJFUcaBhOa1NouQqNb8tCbQ2dtdDcjN9Azm3jwBGnSNDJjdN7Y77zDG3e3A5XLj6j7muc92rN2Z15eD33zuvF7lv56MyH8yN/8IMfmB8HWE5XGG1QmI7lLNmzZSKxoObNlpPz9YoNbtJHOePVV6GJ+l/BFJWkjHKU4VGHlQIdyPG5oSkj5x1yrjLnnI5k8uouO+cbOrXFNB+meTJxcD9D7rSFHDvzsqTTnqM/BPdSG8PlbT2m0lqB/U32dzinj057bGC/x4ZC3kEnubbp3VtqC72eaRuvQE3gAK0yvZRcV+XoiU6ZXi/y6Y+fD9Vkp9TUNcjjP39Wph/sRdeJa/3l5vLxhiYFsojhJaq58W0Snz5HtuGBSLTvl7oZcySC2Pv+8/Ny7IJLpObMsyRwDUwSkK5r00tS03kccwrZgcUJFgO9oep0TIJw2c7ekg/HAX9CWtsTcuDwcWlYXp3rMOWKiQQayh0BznviWnz8ePGl5bo0dBDAUSzaj9M8gS8gTeMYKHjYweOIFSdSc8Iy57GQhLBzzDWw6BWOLy7nAtrFQTniRBLJib/0gmUDR035saQAdgZ6u7ITpu15jrLS2xmFgY1PoUKByQ8vhS6FOIUN50Ts27fPJGW5rK2/zauQrRViY6kv70MSzA+A9dLGcyTNFMLUOHEEkh8A29EnISd5Zprh5kYSW+sgh5pZ63yHeZ/ekBMuRmTAqiBY1QSHLWlJplshYxInZQ8EUDYDM3Us4D4k8BACCwYMnNack1OMgAzpfTTDzsOA7BqQhryKgAiD53mMA8yJDkSnSaR+NjyQVvNkLhgWaEpoz4y65aCHdWzh7GS6tYA2o7E+Gza9c1STmkh++DmSz/kPzhF9kkKuHUjNpB01tnmcji1H2SkD6BDBvS4YPWvyA8v3nB9RWgnwPeZcO3pNpNMEzsUp5l0oFmeOpH/7298ehIrar/e+971GVpFU2FAICRxJ80BCT7nlHKShxQU9NXK9MsoqdmCsS3RqIlkeOimiPBuv+tIMlJYdNniRQCv3bJzhtu76s84cgHMuFG81rHwu+I1hYDrOhaQsd05NsPfhOc4BZ+Dccr6PHNzmt4VOQ6wTCX6/6DmwmECixkEMEhcG5ztHL4zWczPnbvPdpKmlDfyGMbDTyA46TcXpFIbEgnPyOBfeBg68lioQO5rN8VtnA8vFe9DJhg08Xrx4sT00W5JT4sZvDUMh9TUJ8IffeRv4faUjFZ5jWxMTygertWFZ2b9wthO/X9TgUJbwWSARoryzge+FfX/suXy2zueRTkTYdpaQMD3bg+8eHY1QhrIMXPDd9hfYxnwHGSjDWD72gZzEkeW0zzHjcT1Rfs+tkxieI0mhh3TWgX2mW265xTheYl+FOPF7z0F3ao1tO7CsfBcpL23gO8BvAs/xW0Ri5NT8cp/3YnnofIVerUk2qU20gZhwQJxOZezyCuyP8FlmX49OfZzOqmy6Qrf8jhE/tjvLSTlHmU/ZymNn4OA8NbIss1egzHQSV2cc4uwc7CjkHRxNy8t+J/tjlDXsp1kvrM77w+LI6umcp4fbz5E6LmzMZRwCcFKwcdNO6WzdJ+etbkYnJynhSFTu+Nq9UvWjb8ucIDpLmPvHNbdyc/yQbyotvfPmysyPf1r2P/YL8fd0yHQQvje++205/zN/J7vu+KFkMOq94OabZBpe9gMP3S+t99wptfv2otMFEogs+IPTUWlLiHx72pukvX51zlGDjyM0GfnDj10pb33TOeyqIS5ja6gUBCis+UK5NXle9WcHxrrR53UKR77olux5pSGZpOtxalToxMW5jo6Nz44iR3WdnSVqutjptc4cGJeenagZodcs53wWm4/XliZg1iun1/XRzrFuFptC68u8WTenxyx7PwojYs9ROXo5dQeO/PHD4hXY0bDkhLjRlXQ+oQOeWI/CU507zEYZaqABKzZkYbGQycIaofcofseNF0+yuyzIn+F7/OMKSRC9I0c7zAehKhIyI5P8iLTDK3JjY41UQz6agSyXXLJOaHw+ssgwlpGokXDDLMhYeBTLJXDdqbBDEgHnh4ujw+woDhfG8mzYvNgZ4IfczrO154fb8iPFwZB8QxdGm9s/9KFTos+kBzbHiOopEVwn2C4ceLGdJHuZzmDYUWFgJ8fL3JLEhqP0xb4LxeDM95gm5DaQ1NPjnPsdZHvQfXo+gR284eaZsJ04X8wd2Flix5PvPe/tDhy4snJyPOpLDY1zkI2DSu7FuSmfKLtHC2xrp6aODpRotpVvYEeL3jPdgbJxOPnnjEvtF0283IGdUafjG/d1e8wOOM31rBbInh9uyykUbBPr8IteN9nxHi2wM8y2zqdMzrxGqgexs88J24rtQEJC0mdJDec5k8jYQMsdzmmjOXA+wV1fZxp24J2DJ85rdp+DCSSdbgdQ9jo72fQbYJdWsOftlqSN3kfzDSS01vqIZMHikG96DlJxoI2Byy98yENueuVFKxEnyWUcyhHKSi956JUHsbADgCSNTnLuFd95jtpBYkXHU85+FOPw+SQRZ1/KObXBpic5LJVWkJ4+uQB9PoHPKsvMZ4TPh/WOzGfZCzO+C6wf+6BOU9Z830GLkdMBmLuclAWW8PMdp8mtW7uMHkf+gVo9Uqok5snEju2VROc+6WhrlZpITt8XAEmDS07xHW+FFi4p3SCJCXSUqMGz/aVUOiG1i1agoxOQnk0vS6a7SxpWrZXo0pUSqJsmF//l38s5H/tNrCt4WLoP7JOD3/2W1BzYz+hDuk3on8G1aVJqU+1y1gqf/Oa7lsvvvG+l3HbtPOnuwFIUKKfSv/zbtlxisrNLYcWRIC9beb6oHJ1i548aEn7M+DJydIkfE3aMaerp7jBQsDBvjm5Z87prrrnGjFRa7JgHO40U8s4RPHaSrrrqqiEvH7Vidq4WzQIoMJ0jZjZPu+UcRX6k3O7r7fV8t8Rk0aJFZrSa5IujnhxJdJqlMi+v+vI8iat79ImChhoTBmLD/J2BzkeG00ZwJNwSQAozEvPJEjLQuvl9YfGHG6T99T2QR4eNN2TON/ZzvMkj+HwBjIq2yeNPviCPPP7swO952cG1TpN0pIVgxt2sROQxT0G2wpw+gfVR27fuhPVnC+4LAmic03jcqMBTzjWemJRac3auqKFge3KOK+eZsd35sbAkMd93wVkcan1I5vmRcgfm6zzPuNQ4TETge8/OibtTxvJx0j7x4Dvn/miSYNDCoBTvQqEyx4kTO0LU0thgR5PZAbXvM+XGcI4JbDrnlun4XLAjyxF+auYpq3gvdojc2h/iZDu8bEe33OSzZbXQvM941JdylM8tA9vMPdfaXMjzD/Ox8pF196rzcFlRfvF98QqUfzSz9Jprzk4jO93UbljNhlce+Zzjs8tpEc7vz3DpGJcaF0sAGY9ynGXkc+V+7m0+/O7xO1koAbTph9s6seMyTWwL1sNJXPgdtc8nNV9sK9ZhrPV1loUaX+vN0nme+3wvKCuolaQGnvvOe7INOdDC7z8HammyynM2UNaQxDk9htprI20pl1lfYs1BBCdZYDri4RWsJQO/p2xHloVly+fdYPtbWeLMm7Kapo2s+3CBmPAdp0bcynXKDBI2t5xlHiwXB7KIjw2UIXZqD+/FPpszWFz5Tnn1RVhnG8eZbiz7JPycUuEOxIh42sAyc+DT675cN5DvPgkfvxusEwf+OXBGnNxtms87SHLNZYBGIoAsm9PpG59tr3e6AE0gR7+pWcOaxt3tWMz9oARgstTd75NwMCWhAK6hQX3+oGx69Bmp7otJ/cxZsv97P5TA9l3GGyj1iL3wdFBz6zuk5c1vlYNQQ9fjY7Pyppvl8GuvSOfugzL/ogsl1npAnv7jT8g5v/NHcugb/yW1J47gaaGXUNNfMn2oBEbcIxevk6q33izRmc0yo6la/MEqKBr90pcKyexFS3EMk9CT76FtL91WCALURNCEhaYKNPOkkKZwdApvNxQcQeWoIrUEHCGlmRGFOj/SfL553cax+wUp0903dBwzH5aVpkc0qeIHgELOOq9huUkoxyOwzhzFYn2JEwU2y8M60mTO/pzH3C+27uxMcLSSgQSbgjTfMO6aQJAzyo/WV1+UbV/8PAa2+mXWtZfL9HVrMV8QWgXIP85vHgzAi5yttzcuMSybk0wk4VImKyGQu+rqiNRURSTkJ6+zQglb7PtgKppNZ6Vj125p/dlDcuyNXbLyt/9EFl5xFUbQKPds/ME7FbzDj5DFmR9dLqw8WuAzwXanBpsj5Bxc4UeH7wPbnddGCrxO8y6aL3GAg2n5LNvAzudYHHiUShNoy1Ho1r4XxMfKgNH2eX20d4VxqOWnAwbKKa4n5fXRHq28JEJusjZammKvW0xGwsOJkZUjpahvPmWnHHUT1HzSDRfHPv9ecpHnbP3cbU5NHc29OCWB7zXJFmU6ByNHCuwkl5p08X4j1YOaG77z1MixPhwwYhmGM+sfqfz22njVw+bv3Npn0raHbROvrbOdaPLI59JOFWEbOeWW8x7D7bM9vcyCh4tfzHmaEtMMk30H3pPymXLV/d1gHfkOWllk90faOtM441G7xOk17KvwPsSHP8p5J6Fz1ot5sZxMxzbhc8RvQjHPkzN/+z45z5Vqn+8ALWlYbr6vJIBe9WQd+XxxoITaf4Y9e/YYose09lnMt1w06SZmfBeJv30HuaSKHfgaKS+WhR6u2Z/lYL9XmQsggblbZTE3JnZsHzxoHB04gY3ppLDHxFMw/4TJpx+Lv59AY+/5/JclgBF0agnjKawpeP7Fsvbjvy+NGN04uvlV2Q0b4QXnrZPXYQLh37tTQrPmytz3fVBe+a8vyaJrr5aeO38itd0wxSIJRO4cgPeh49UbDsjyv/h9mbZ2naT6e1DJPqnGHB6OtWOFQZhSwaV6/SwWSIMiMK4I2BefLylfOv7y2S+0UONJAgsti43PujqFm923ws5ubXz3lvOEOCJGbQU7HO4Plzu+83i8SSDv1Ye5yK9hnnLP/fdIINGPRR+bJYIR0RmXXSQNyxaBDELbBdEH0c8Vc8w+eBu4IddjpEykNhFcDvKLZvEDkXkCzrRCMDFNSuxQu7Q+u0G68JP9ByTT2yeZcy+S8/76c1I/GzIMaYsJ7MwsWrRo0MyZ2l+3Q5ix5M/n3tne/NDw2G65zzjDhalKAoerz2jnrUyw23zkBOMWGiaCBBZaRhs/HwzccUZ6pmy+7m2pSaA7/+GOWXb+nO+J1/Fw6e15agucyxPZ86drWy71GA4vvmdsI2c9bTu5zw2Xh/P86SSBzvuern2+g/wRt5F++cYpRbnHkwQWWr6rrrrKzPVlOppgOq1fiImVB85vpfMc44wUSLjzIYEj5WGvBe1OvlsCHYJnz/4EVLTpOPoy/EiRnvGHLg46PcGqsBzd9obs/NI3JHiwNdcJYowgtIaxTtn+7a/Jgre9U3b97C7pePoxWbAGc/o6j0k9Olz9HUel/a475Ozf/iPp2PG6ZDo7jOdRkznz4A9/Msm09LWfwIOYe3FhoIWOGMoC7OivIZ3g8hQsW3EdKGSgQREYEQG+E3a9lxEjui5SePIDY7ej7buST4pDair4c5oUuQtGgWY/qFbQ8ZgC0GqkaApVCAF032O8jg8/87T0vvCMhON9Zh5yqu2IxDuekv2vb5f2ZUukYd0aaVyOkfzpLZi/R7MbSJ3cSBUGwk6WynTlA9TqQU6h3ZOxPunej+VIXn5Nel/ZLOmDhzE/ugfWFRBewYDEXn5J9txzp5z5gQ+LH2b1hQQ6M6BW1a4pSc2SnefKkUBq9WiaNJJGPJ/7mW8B8vMaXbTpbTvbdnd+9Mai5bL5TsUt8S4Uc9uJGk028LqNU+g9JhJLKz9Geobc5bPyJF+5SVwmSrbY+rlNvpx1svXhO8Ky2nfFeTzRbVou9XDi7twnviO1kY1r28q+b842cp4rd9nG94m/UjyXxJQ/LzI53HlnXBvHttFEbmn1wjnKdjkNloWmnxwU4PQBmjk7v5tuU1dbdrcc4HPm/HaWUp4VRAINN/WF4LJ8Bjo8NRLvbpNsHCSNzMsEPBj4l4HzhAN33S+BXVCD4kGxNDHIDs6ObZLZulkOJFKy8NqrpQtEzxeuloY1a6Vv6ybJgmB2w1xiTiAls847W058DyPtMC01TBL3IAmkUWoUPa39379TovNnS+3CuRJEeTIkfPSeBzMtvoR2DmOubPpXEZhcCFCAlkKITq5anVoaCiySZDdRplCz8wnpjGOyhZ4jbXLk4QfFf6TdDCXRyoC8LhDvl/S+g5Joa5f2TZvl6KwZEl4wT2oXzMdvnkSb6yUYhdxC+0LiQx7hAwdHWomuXukDieyBKVj8jX2SwDYDhzGBvrgEQQwZ1Ug4bCOYU33kZ3fIiSuukWbMqRy4mBdEdOBBhxZegZhzIjrNuuy8C694pTpnO4+lyq/S8uG7oxgObXUrT4aenbpHtj5u+TjValQu9RgJ90qo40j1H49rxJS/cugL0QkP1+9zBjoSZPh3OLKjz4R8NPpW5o80uO68RzH7BZFA00fh3UAEg1UNUo1R4N4OmDnFjqEROT8kaD5YbS9ukuSrr0mYlI0NzCT40UFMBFQtgxHxxHNPSBsWh1/zod8AiVsss6+5QQ6gM9R01tnSic7R7q9/WcIYSo9gBB4+E4YEZCNBdLAyh49I28PPSOOHfs1YTJFj+nxJyYbRCatuGJJGDxQBRWByIcDRf3o74wdgcoXcoNaRlzdK/+ubJZDsN+boRojxEkw5KTizyYQhcVksk5PYsVuOYQL70doaCdWDAEarJAtzT5MGcX2Yn5qGpi/d1SNZeLQLYJ6dH4TMaP4oI4mBA4YgBrGS+/bIvgfvkabFi8SP+XO54Ig0cMa9Gc35ADubp3vOmLuMeqwIKAKKgCKgCJQTApyP6SaBtn708eDlGMpen6htQSRwaCHB3OHGvLpxtvQHI7AM7YC5VFD2bt4qO/7n+1J3vNN0lvyYF5NBB4d9J9N9wT7JYDiTlOTTT8mB7TtF3vtBqcOi8ZF5C+XEC09L/LUtEurukCBMp8LQ7FHzZxR8gwXAGcy5CeB664OPS2b+Ajnz1hvNml4SaJRozXSYrI7uDnowO91RBBSBCUFg0hFACCqatCfiSdn/4suSAMGrxuiS3z9gWg4hxsEmDm7l5vphH0vmZBMw4wKxS584Lr2c61fXIKHeLqH849I2sHXBnEHMVmZa2ijgFOcJWicxZvogs8WPcwdp3E4T9+2PPicL3/lrMg2OLTIwvffT8wzSjRToRe6KK67wdMlNvDn/0nqPHCkfvaYIKAKKgCKgCCgC+SHAJXS4VI9dK9GmokaPzvYmo7a/CBLIrgz6IyCC0XqQwNppcP6CuSt1x+QVTISswbzAengCbYDmL4zOTxW839FAE05EzY+dqCCcIgQO75fWb9wu++FqOnxovwR7kRadIC7+nkXnKoE82DOCVb9Q15hE76kXGsJu7Hf7AtIF+6wb56yU6mkr0c9KoX+ErhPW2UIXCz9TQmw1KAKKgCIwGgIgekbc+KXtSJc8fjgEmbNc5gUOy+xMh9T7+iUEuZULhs0N7OdkFOVbErKn/uo3S6axTjp+/EMJJzBvGlo90ELMbUaMgfxJAhm4YU5GWkHucb83G5A2X43srZ4tOzvnyYxtx+SKGTORDxMPJES8kQK9rvGnQRFQBBQBRUARUATGHwEOstJz51QKRZHAXAcGWjqQMr+v1vRPYsmU7MdxoL5KojD7rIXpqB/z98L4hUAEQ5gvaPYx9B2CA4UQ3azD+2e684RxRV7NBZbRQ0qD4PVj9J3bBBzKJJFnbgtTUuTbh3ziyCOGEfueIBwuoPPlx/IUbAR0t0wbOPSPU6lNtKyKgCIwIQiclBi79h6Rbb0N0tF4gWxLxWRm/0E5O7ZZlmROSMRYJ+QInLOYnKDeC5Y34/wLpGklnF3BaNTX1yO9R49J7MBeCR46AHN4CD0jo4amt5TyWLZKXoouk13Vy6Qj0ixdWPLmxc37Zf2lq2BqT3FtYzrvrPuKgCKgCCgCioAioAgUhkBRJJC3ytEtaN+Ml1CQvnpo9DA3JtmH9aVA4JLo8JgRcBA3o6BDJ4mj4EYriNQ0e2JIgtQlk1GpipLI5c6m0KliwMoSMIXCDrbmDPJM4YfZhcgzLA1Nufl/ubLYMjHlyGZTjKFBEVAEKhcByhND/XIqOiPP6Jnrjd1tEutPSxyeOfsjYekKhqQ5tl/mS4fR6tEDMZMMBgxI9WM9wJYbb5ZpZ55lBrTqL7xEjry0QWrnL5SaG98mJ554TPoevV9CWLMyCE+iQ8w6kRfz7JCobK1ZKUdrFkDmwZQ+HcCaVe3S0d2L1Snqjfxz3nbw/rqjCCgCioAioAgoAopAAQgUTQLtvTiDhZ2TY237wOj6xJ9OYI5Mzt1xxvhMz3WaaOZJBzIhOCcIwDFEAFvuY0qNZNAx8mM/DScKSWgU+Uulk7gAQ1AmGwg+EkrMu2HaAMjngb275OwLr0SnLFcGG0+3ioAioAiMjMCAYDHMKkevurB8QxCmn+++abH0x1OQMxF5/eUd0tyB/Th9hA7MU0bGJgWyiMOiIXjuBZJZukoOYsmH+JEDUl3fiLUF4eXz3p9Kz4ozJIw5z74L1wvW15HeXTsl0nl0wDQe+SAPjGtJKBnHPoe3OLAG2Qf7+db2uBw4fEJamurMAFrupiPXSq8qAoqAIqAIKAKKgCIwEgIlI4Enjh2Tjc88IY/97P+k9eA+yWAJiAxG1HPKPIxoD7A46AVNeejbgPP3giCCUWgOA6Eozvukp6Nf4lgSIoXOE+cE0lrUmncyITtgzMEQPmgaAzATfeind2CNlzpZt369tLRwgfjcPRhfgyKgCCgCwyFgZAkvQtZwsCoAM4Wjx07I/JlRWTG/SiKQkEFoA2s6shJ/PmbM1+nKOs35ewPaQwqp7LRmqb3gUjm6ezfUecekEctFHLznLln2zl+WxBEsJ9HbL83z58v89ZdhbcAN0tqFNU5hBs/ldMy4GIsAuVUj/RJJ8vx8LCTPZSOy0g0vpLv3HJGzVs3DHGwd6BquLfW8IqAIKAKKgCKgCOSPQMlIYBoau8aWZnnLu98nb3nne8HDBkbYBzan8jJ0vwxDBC00nM2abqJDxTQmPeNgn3ZSzjBwyE6Tj5FhWRWJ1ko6yQsmM2ds3VcEFAFFwBMBI0NwJQUNXKLnGLR+Ip3trVIdTkkVVniIRmF1gHORvhOwSuiTPrP+KIghxQzEDSVOFgu5RmbPkyA0f7EH75MwLBrqrrhajm55XSLTZ8vaj38CeXfL8T27JYklb44++rCE9+/D2oAY5HLIK1iUYlkd3Dd1TFYsyMjZZyyQqiqfHG7tlljnMeM1tGQC2xMNPakIKAKKgCKgCCgClYJAyfoUzc3TpaGxKdelybG6QQy9aBk7T4OBBzaSY9+eGow3sDMkLc9xVB6bnOOEgUi6UQQUAUVgRASsJOFi7nHJxDuh1OuXOZhi7GvCen8hXAdRA8eTGQtbJPX2t2Bx+0Y58exzknltG9YPhLkmQhym6VHIv2BdtTSsXivVLdNkOjyEBepqpOv4camdO1eSKcwz/P53ZOGbbpRMJ2b+9cdNWopKyjkSwCS8KNecuUzeddV6qVoyT2bPrMfgVlT64tOlPwkNYDYOUZdzwGUS6x9FQBFQBBQBRUARUATGiEDJSCDdoIfxcwd6zHOvBeZ1Lp907jh6rAgoAorA2BHIGadnYc6Z7u8BEwPJAumrqwIxo706ghlewrnFa1eJnLVa+o4elc4NG2DejgD2RuOD1NKV0nz19TJj7TmGtB3evkM6D+6XQw/9Qvq3bZKuB2ZIyw03wsEVlrc51i4BmMkbc3ikNQSQWUFOpqFBnH0dTNovXYe50VloJ/skGopAK+mXFDxjZeLd4gvX5G7M+2tQBBQBRUARUAQUAUVgjAiUjARmMIrNnw12UUQSQHrbI/HjuSRGz+01G9e55XWmGSmOM77uKwKKgCJQDAIkYn549kwFoiCBcGoFZ1PW+QtpGqf+hbHkTfehdtl/9/2S2LZTgpBTfswfZNoA5hMeff5p8VfVSNuG5+XoM0/KtJYmSezfJdF9uyW9Z6ccDwdl8S3vlEQXtI09PYb05VLniKDxNIoldNJxzEvEWFqa8wxBEjkvmnMOA1kQwUSfRHjsK5nYLgY2TasIKAKKgCKgCCgCUxgBOxGv6CrQWQKJm/05MwygV2NJXQiOYNyaQWdcXrdxned1XxFQBBSBUiMAnmWcTIWqp0m0ca4Eq5twHDTm5dQD8keilwVBO/LUc9K/YaP4u3oMMeSQFxezCcERVv/D90nrT38kDfPniK+6GqQyIrVLlsHMFA5nQBg7t2yVaHVEWlavkkxNtSF4SD4YSAgDIHttDzwix7dtN4NmoQhUklxMB4NidK3FpXXohVmDIqAIKAKKgCKgCCgCxSKgQ8rFIqjpFQFFYMoiQIJHXZwvEBE/1ij1YzHTfngcTsWOQVuXwLWAGbTq2r1Pel7aJIHuHuNBlMTMaAGx9cN7ZwBqu9TG56W7JiqL3nqT1C1bKaFwVA51dko95gT2dnVL20P3SgiDZX6cQ+bmvry7CcgsAnVgfO9hOfbUi9KwbJn4oT3kXEGsiirZYDUIaj0OciW2yXSrCCgCioAioAgoAorAWBBQEjgW1DSNIqAIlB8CXHIGc+4itTMwZy8gmf4uoww8tu+A7PrpfZJ5Y7dEodmjJQOpmKVxPA7wHJy9JJ9+Qjr27sfSElhWYsVKqVq+Uvr375bE61vF13bIkLpohg5lkAMyYB65kMstCG3gkSefE9/SpbL0zdeYfMWPMlW3SDBaj1RKAi1iulUEFAFFQBFQBBSBsSOgJHDs2GlKRUARKDMEaPzpD1dLBBrBdFUj5v1hTUAs1r7tWKf0g6A1hENSizpHsR9OwRSUBprgbzTSJD0LYKH5zI4tcuQHfdI2c7b4Dh8Q//FjEo7HEQ8GpNQYGh4HRy9IAcejkoKdZz88xfTgfAw5dUKzeFn1dKluWIIMQftASLmOqs/MBcyRRSTToAgoAoqAIqAIKAKKwJgRUBI4Zug0oSKgCJQbAuRnnHfn5+KA/nrMw/NJAscHQdSOwdSzBqSwBqQsDCdYQThpCWOuYAg2myF4EA3BZ0uI52nD2X4I5LEV6wJi8Xcs8B6A58+MLwRTUzghBflLgNylMFea2wQIYAILD/YiPReO6OlPyLlVmFcYqUU5QAJRBpYpR/+UBJbbM6f1UQQUAUVAEVAEJgIBJYETgbreUxFQBCYtAkZRRyIIL6HU74VByAKRaizhEJIYCFmc8/owVxBLxuNqFvMBGQsejUHveAauXIydZzKdkVR1WCJY4oFL6JC+JaEJ5DaFeFj1wezzOAMimML9MvQQGo5ITT30jbieM//M/UU0BHUMk8NB/yoCioAioAgoAopAMQgoCSwGPU2rCCgCZYsAtW8khLGu4/Dw2Se+dAJLN2QlgdXjzbI3uAY+aDx90mw04IdjGeMhGeab2KZBGFM4H43AaDSVxPI4aaRLmYXjJZsc4iGU6UkU/ciDZPII5g9moVHMBnJlKFuQtWKKgCKgCCgCioAiMCEIKAmcENj1poqAIjDZEYh1d8uOLZvkqft/Kvve2CaxjhPQ1A2sh0qNnvHwyVrkdIc+QwRBBuHVMxLBHD44h+Gi8/FYt8TjvZLqT4JEwtMntH1GBTgIAGYigjByBXkfndOA+D354P1SVTNNzr7oYmlumWnyGYyuO4qAIqAIKAKKgCKgCBSJgJLAIgHU5IqAIlCeCCThoCUIQnf+VdfJeeuvBkmj4SbCwGaA++XOmb8gciCHxojT8EL+4Q+EkeftshCMQm8yzjBwSMc0PsaFtWkoVCUJEMdcHs7Iuq8IKAKKgCKgCCgCikBxCCgJLA4/Ta0IKAJlikBDQ6PUnnlOjthRU+cIQ49yF4bQOh7YSI59e8qRldkdkpZnqGnEJgAHNUZLaGLpH0VAEVAEFAFFQBFQBEqDgJLA0uCouSgCikCZIWDm6GGenjtQq+cmZl7n8knnjqPHioAioAgoAoqAIqAInA4ElASeDpT1HoqAInDaEOj62tek75FHir4fnb+k4ATGEr4gnL3QgQtDKon5fVgOIgSPockEzEaxDXgQRsbtxxqB1OpFIpHBvHhew9gQSGzePLaEmkoRUAQUAUVAEVAEBhFQEjgIhe4oAopAOSAQu+OO01aNvgLuxDUANSgCioAioAgoAoqAIjAZENBFpyZDK2gZFAFFQBFQBBQBRUARUAQUAUVAEThNCCgJPE1A620UAUWgtAhEzjuPNpalzVRzm5II+OrrJbx27ZQsuxZaEVAEFAFFQBGYCASUBE4E6npPRUARKBqBqssuk9l33qlEsGgkp3YGJIBz779fImvWTO2KaOkVAUVAEVAEFIHTiICSwNMItt5KEVAESotAzQ03KBEsLaRTKjdLAKMXXzylyq2FVQQUAUVAEVAEJhoBrEvMlYk1KAKKgCIwdRFI7t0rmY6OqVsBLfmYEAhMny7BOXPGlFYTKQKKgCKgCCgClYyAksBKbn2tuyKgCCgCioAioAgoAoqAIqAIVBwCag5acU2uFVYEFAFFQBFQBBQBRUARUAQUgUpGQElgJbe+1l0RUAQUAUVAEVAEFAFFQBFQBCoOASWBFdfkWmFFQBFQBBQBRUARUAQUAUVAEahkBJQEVnLra90VAUVAEVAEFAFFQBFQBBQBRaDiEFASWHFNrhVWBBQBRUARUAQUAUVAEVAEFIFKRkBJYCW3vtZdEVAEFAFFQBFQBBQBRUARUAQqDgElgRXX5FphRUARUAQUAUVAEVAEFAFFQBGoZASUBFZy62vdFQFFQBFQBBQBRUARUAQUAUWg4hBQElhxTa4VVgQUAUVAEVAEFAFFQBFQBBSBSkZASWAlt77WXRFQBBQBRUARUAQUAUVAEVAEKg4BJYEV1+RaYUVAEVAEFAFFQBFQBBQBRUARqGQElARWcutr3RUBRUARUAQUAUVAEVAEFAFFoOIQUBJYcU2uFVYEFAFFQBFQBBQBRUARUAQUgUpGQElgJbe+1l0RUAQUAUVAEVAEFAFFQBFQBCoOASWBFdfkWmFFQBFQBBQBRUARUAQUAUVAEahkBJQEVnLra90VAUVAEVAEFAFFQBFQBBQBRaDiEFASWHFNrhVWBBQBRUARUAQUAUVAEVAEFIFKRkBJYCW3vtZdEVAEFAFFQBFQBBQBRUARUAQqDgElgRXX5FphRUARUAQUAUVAEVAEFAFFQBGoZASUBFZy62vdFQFFQBFQBBQBRUARUAQUAUWg4hBQElhxTa4VVgQUAUVAEVAEFAFFQBFQBBSBSkZASWAlt77WXRFQBBQBRUARUAQUAUVAEVAEKg4BJYEV1+RaYUVAEVAEFAFFQBFQBBQBRUARqGQElARWcutr3RUBRUARUAQUAUVAEVAEFAFFoOIQUBJYcU2uFVYEFAFFQBFQBBQBRUARUAQUgUpGQElgJbe+1l0RUAQUAUVAEVAEFAFFQBFQBCoOASWBFdfkWmFFQBFQBBQBRUARUAQUAUVAEahkBJQEVnLra90VAUVAEVAEFAFFQBFQBBQBRaDiEFASWHFNrhVWBBQBRUARUAQUAUVAEVAEFIFKRkBJYCW3vtZdEVAEFAFFQBFQBBQBRUARUAQqDgElgRXX5FphRUARUAQUAUVAEVAEFAFFQBGoZASUBFZy62vdFQFFQBFQBBQBRUARUAQUAUWg4hBQElhxTa4VVgQUAUVAEVAEFAFFQBFQBBSBSkZASWAlt77WXRFQBBQBRUARUAQUAUVAEVAEKg4BJYEV1+RaYUVAEVAEFAFFQBFQBBQBRUARqGQElARWcutr3RUBRUARUAQUAUVAEVAEFAFFoOIQUBJYcU2uFVYEFAFFQBFQBBQBRUARUAQUgUpGQElgJbe+1l0RUAQUAUVAEVAEFAFFQBFQBCoOASWBFdfkWmFFQBFQBBQBRUARUAQUAUVAEahkBIKVXHmte/kikGptlWws5llBf3OzBBobPa9N1ZOVVt982imD9k/jOfAM4bCE5s/3vDRVT2p9HS2n7esAY2ruZnp7JX34sHfhQyEJLVjgfW2KntX6OhpO29cBxtTcrbTneWq2koiSwKnacoPlzg7uifhEeIjN2IIrr7FlMilStX/kI9J7zz2eZWn+3Oek6ZOf9Lw2VU+2f/Sj0nv33Z7Fb/7sZ6XpU5/yvFbOJ3sfeEBa3/52zyqGVq2ShVu3el6bqid7779fWt/xDs/il2V9R2rflStl4euve2IxVU/2PvigtN52m2fxQytWyMJt2zyvTdWTpr633upZ/NDy5bJw+3bPa1P1ZO9DD0nrLbd4Fj+0bJks3LHD89pUPante7LlyrF9+37xCzl8880nK+nYCy5dKot27nSc0d2JQkBJ4EQhX4L7ZsHZnHwvgxOHDx2UO3/4PUnFY7IUHYOly1dKtKZGeru6JZ7oF78/IDU11RIIhqQ/3icdJ45Loj8lS1atlgULFp1SKsMpzZ9TLukJRUARUAQUAUVAEVAEFAFFQBGYgggoCZyCjWaLTAKYJQ3M/ZdsOglS1y67X39VfnHPXRIOByVaVSOhUFBSyYT5iS9gCKDP75NsJiXxeEqmTZ8hv/qh35DzL70cBLFWqqqrpa6hUaLRqhzJNEwzg5sNTCE1x7YUulUEFAFFQBFQBBQBRUARUAQUgamEgJLAqdRa7rIOkL9YT6c88fADcu+dP5JNLz4v/bFuqa8JSTbrk0yyVxIJn0yrr5ZIbY10xXqltz8uaZ9PQgG/NFYFpP/EYfnKP/213B7CPKlw1JC/5hmzZfmZZ8rFl18h555/kTRNa4EWEQXwqS8hdzPosSKgCCgCioAioAgoAoqAIjCVEFASOCVai/aYdrpfTg2XzWak88QRee6pJ+X//ufr8tqLz4k/k5BGEL2WabViqBpJIjR+/Bf1Z6WmKipNtWHp7ktKZ2/cxJne1CCpVFLaO0EO4/2SSCalv6dL4rEOiUYCUhWNSibtl/VXXS21tbXQPFL3qKrAKfHYaCEVAUVAEVAEFAFFQBFQBBQBDwSUBHqAMplO2el4ua1PkjDr3PTCC/Lzu38kLz73lLTu3yNhScmsxhoJ+qukCiagDZjz5xeYbw6GARIJ4hgCf2uqiUhDdUSSGR5nQfRCEgnVyYmeuHTE+qU/gfiZrISjETn/4vWy7sJLQADrTG5K/wZB1R1FQBFQBBQBRUARUAQUAUVgSiKgJHBSN5uZ8Qc6B90bCNyh/fvk9i/8q/zirjsk0dctoWBQZtSHZFYTljwI+MSPOIbuZVO5rUfdctczIIk+iSANJgYib5EwNIYzGqpBEKPSE09IT3+/bH3hSfnLF56RWfOXyjU3vk3ectu7ZN6CRTALVZNQD2j1lCKgCCgCioAioAgoAoqAIjAlEFASOKmbiZQNRC2dlm2vb5Z//LM/kU0vPSvV4YhEYKYZhIlnI0ibP5uEUxgQM8zz8wzwGpqlK1EE3yCBA8HkeZzLUUIeZwwxjNRFZVpttaSzaUmmMtJxfJ/c/d1viD8Qkg9+/BMmH/2jCCgCioAioAgoAoqAIqAIKAJTEwElgZO43XJ6QJ9ZxuG73/hv2br5JZhuRgxxy4K6VYWx3EO0Wlaes1p8yYxsfmkjroHW4X8gCEIIT6BpEEgSxmhVlaQS9Abaa+YCcqmIdCotaZiEZqEFDAUDQMIPspgWKBKNpjAIXhnEn0hjrcRSWXni/jslAscxv/TeD8CDaI0pB3yMIt0w5HMSY6tFUwQUAUVAEVAEFAFFQBFQBCoVASWBk7jlc9QqK7X19XL19W+R5x55SOLdJ6S2OiRVIXj2rKuVS669SuqmNcrjDzwkS85eK7NmzYJC0C+tBw/Isbajcsl1V8kZZ58tNQ31Eu+JycZnXpDH7rsXzl4ysuq8c2X1OWtl17Yd8srzGwQTDiWCPGtq6qSzs0PivT0ggQHxQ8MYSKekbfd2+cq//b2ksLTEO37lPdIAM1QSVaWAk/gh0qIpAoqAIqAIKAKKgCKgCCgCLgSUBLoAmSyHNN/0gXylQb42Pv+kfPNL/yLdHUdlBrRyc5tqcC0rK85aK+ddeZm8+NRL8tE/+D2Zv2CJBOAYBladcuxYm8S6e2Th4mUCZR9+yK9JZFrLLKmDR9A3tu+QG9/xdszxW4D1AbtkwZJl8vQjj8hZ558v17/tJjmwb688/fDD8uoLG6S6ukrmL50hyS07xN/RKXd86ytSBU+jt737PRLBOoRTOVgzWa86EP+JDqUuX6nzmyh8Sl2PUudXalxKXb5S56f1LQ6BUrdHqfMrrnanpi51+Uqd36klLu5MqctX6vyKq92pqUtdvlLnd2qJiztT6vKVOr/iandq6lKXr9T5nVpiPTMcAkoCh0Nmgs/TjPOZJx+V797+n7Jl40uS6O+TKBZ9rwHJC4LlheGt8/I3v0XmzF0gs39pvkQiVTDvTEoGJp8MDY0t0tQ8A0s+JAY0dTD7TAckFAnL5dddJ5decw2IZED6evslVBWRy66/XlaffY4kkEcYpp5LV58pc0EqFy9fKZtfeVmuv+lGWbFlqzx4x92yt71TnsK6hCvOOEvWXXCxY57hBIM2htsfvuUW6b37bs+UzZ/9rDR96lOe107XyRHL97nPSdMnP1lQUUbMbxLUN5/K9Nxxh7S+/e2eUUOrVsnCrVs9rw13sufHP5bWd77T83Jo9WpZuGWL57XTdbLnzjul9bbbPG8XWrlSFr7+uue14U6WGr/h7jPW8z0/+Ym0vuMdnsnH1L4lxs+zYEWcHLE9xtK+P/2ptN56q2eJQsuXy8Lt2z2vna6TPSUuX6nzKzUOPXfdJa34zniFsbTHiPJgxQpZuG2b161O27kYvqeHb77Z836hZctk4Y4dnteGO1lq/Ia7z1jPl7p8pcZvrPUaLt2I9R1D+8buuUcOv+1tnrcLLlkii954w/OaniwNAkoCS4Nj6XIxU+yysMyMS1tbm3QfPyapeFzCMMusiQakLhI0Szs0NzXJ/EWLMZcvhNUcMoYADikENIkZzPkb1GXZRd45ZRA/EkDuGGUXNIX0+Dlj9myTBdPBGw3mEmKJiMsuk5a5syRSUytXXvdm6enqls57fyGr154FkrgAGka4lUFaFpth8H65Q/2rCCgCioAioAgoAoqAIqAIKAKTDAH19T/JGoTFyWZ9cABTI+vXXy6xeFJC/rS01EVk/rQGCcMZzDmXXiLnXn4ZloWgIxeyupEqQVqGH/K08Ux0pnMEHpLQ8WcD98PhsKxYuVpmzp4HIlgjl1xxpUyrr5PNzz0tO7duNmVg/IG72FvYLHSrCCgCioAioAgoAoqAIqAIKAKTDAElgZOsQSyb6urqkH/7h89I297tMnd6I9YCxGLw8AgaDIdkHUjg9TfdJMFQOEcCx7EOOY6JNQihMqTWsHnWTDnn3LOkffdW+fG3bseSFXAoQ+rnIpXjWCTNWhFQBBQBRUARUAQUAUVAEVAEikBgypiDdnR0CH/d3d3S1dWFpQ7ikkgkzM9qr+jIIxKJSBRLIlRhSQTntqGhQfibDM4+Rmov6ucycAbz4rOPyRP33Skr5jRJPZeFAMmijq6uaZrMmb/AmH+WrC5U4w1VDA4toiF4vLtPwsB3yaoVMveVjbLz1Q3y2AM/g1OZpdIIT6EaFAFFQBFQBBQBRUARUAQUAUVg8iMwaUjgkSNH5LXXXpPX4eRgOyau7927V/bt2yeHDh0SXkti+YJiQyAQkGnTpklzc/Pgb/r06TJv3rxTfo2NjcXebkzp4fRTUqmUbHj6aQlJSqIwx6SWLUuXn1jbb/U566S5ZYak4DjGKwRhIopJevAQCkcwyItEkWnT6YH5fwOJqNWDfg/EkvMG8Q95cx15xjP3GpJ5ztiT+fmxpuB0zB2cPXuO7N1zSDY++4Ssu2i9XHbNm3JEkhVAfhoUAUVAEVAEFAFFQBFQBBQBRWByIjAhJJCau5deekkee+wxefLJJ2XDhg1y4MCBcUeIHjdJKPkbLdTW1srixYtlGbwdLYdHNed27ty546dRBH/qOnFMnnvycWmqqxYu2O7Dmn7gazJj/nxZf+01IG44Js8i3xoIPIz3J2QvcNy/fze0pp247pNGEN6VmNM3feYMxAARxF/QQ6NRfWPnTmk93IrF4dNm7t8MxJm7YJ40NU7DOvN4NIwGcOAGjk20uhprCVZLfXVU2g/slddeeVHWXXix1NTWK/1z4KS7ioAioAgoAoqAIqAIKAKKwGRE4LSRQJpu/vznP5cfwx07t0ePHp2MeAyWqaenRzZt2mR+gycHdqpBglbDdfyaNWsGf2eeeaYsXLjQHbXgY2oBn33qSTmwe6esntsI7peVNLRvUXjnfNOtt8l0zMlLJWmaeTJwDcDOzhNy8FCb+LIpkNdlUl1bJUEQuQxpGTyDknhTi2d4I8hhOByRFctXyBmrV+FaVvpBIGlme+TIcYnF+qRl+kyzFqAXEWQZ09DMRuGkxtfXJ7u3bZXDB/bLslVrThZK9xQBRUARUAQUAUVAEVAEFAFFYFIiMO4kcBvWrPnCF74g3/nOd+TEiROTEoRCC9Xb2ysvvvii+TnT0oT0nHPOkXXr1sl5550n52Ph9RVYt6eQuXsB2GQuW7FcZrU0gcQhdyr9YOK5Avmedd65MBUdMAOlt0+q9UjrsK2rq5U1ZzTAWUxUMjDtNKrDLL2Hwow23Y98TqoNfb6MROBURqK1kvUHUb6sRKD1a5zWgqzSIJlpKBER30MTyLmJncdPyLHjHdIP01GaqPppgporjCmO3WXpNCgCioAioAgoAoqAIqAIKAKKwORCYNxI4MaNG+Uzn/mM3IWFUs0yBmOoNx27cL7ezJkzhXP3WlpaQHbqpL6+3jh94Ry/YDCIZQoC5sf16vr7+43TmD5oqOg8hlv+SECPHTs25Me4pQx0XPPoo4+an82XxPB6LMT+gx/8wJ4acevjnDto4Zqap4svfhQUDwStqlouuORiCQTDIGiJXHpLAHHd7+d8v7BkglXir2mRULQOxCwEXgbNXzopydgJ/I6IL8P64pw/IoHqRiz5ANIXCBvOls4kJNMfk0TsOAhjjwRILg0JtGQzR/MyOHesvV32HTose1pPSOPMOXLB+iuxqPyKgXKZgo1Yx6l4se3975feBx7wLHrTn/6pNH78457XhjvZ9r73Se+DD3penvZnfyYNH/uY57XTdXIiyhe7915p//CHPavIRZXnwXx8IkOpyxe77z5p/+AHPavERZXnPf6457XTdTIGi432D33I83ZjKd+I9V26VOY98YTnvU7XyVKXr9T4lRqH2P33S/sHPuCZbWjJEpmHqRqFhFLjV8i984lb6vKVGr986lBInFKXj98rfhe8QmjxYpn31FNel07bOX6f+Z32Cv+fvTeBj6s674b/sy/aJVuSJVuSd2yDjc1isA2YJWyBBMjCm7QJbRPeps3adEnb7/t+7ft+Tbc3bdKGhoYmJaUhJIGQHQIJm7EBg9mNV7xbXmRrl2Y0+/t/zmikkTQaz0h3Zu7MnGOP5s69557lf869z3nOs82kfRn3N8Vmeao2GH0ubfs6OjCffiWySWnxm8H7IJu6M8nr+81vcPrjH0+Z1TGD/qYsqAxPGs4ESoDzL33pS3jggQcyZv7Ea6dIzkSKJmqVIj0TO7zGRrFjy10aHh7GqVOnaEN3TNkkil1i4iNOad6lzZx4I51NEsZQVEszTaKaeaLzGG37OlHnCGFebTWq5zSgtb1DeQ2dUA75NOp/Emc7rO4aeGpaGULCy5MimRP2kZfJ8LlrK2Dj+ZGeA2T4LLBXtcBdRSZTAsaPJpvNTSliFWzuagQHTpNp7GYplDpaJIP8kdJ4RMbSQQ+hLpdb1VA3txmNLQsUE57IM3qTyl8qfyI9PYicPJmyO7EsxjdRQLryojMoL1GuUd9p28fnJhcpxk2b6TC28h1R6GR0+9KWx42uQiej25e2PG7uFToZ3b605Zl9PtMmPtuUtr/lNr4zwC9bvM+VP+14zKB9actjDOFCp2g6+jGD9qXrr4UmQWNJvOwVIGXcvgzblha/GcyXDKvNOFva/jIagE4zQ8BQJvDhhx/Gpz71KfRwwZwuiYTv6quvxo033ojNmzcrxk+kePlOFXwxLOYOtHymS8LU7t+/XzGEwhTK8e7duyFqrmLnmEnatGlTJtlUHnmfuL0VDMVQAZ//rLLlq5tTDw/VPaOiCTr2vokzZYjRLo+SP3fNfMXoiesXi9qZmugN1OGpQbiySTGSrsq6UQYwUcZosazcRmmiq7pZMfBhSg/joxLPJ3+tLL+uoR7z5s5B14lTcNl4VjyXjjGKYw1U/dF/NAIaAY2ARkAjoBHQCGgENAIaAXMhYAgTKF43v/CFLyjbv+m6JxKka6+9Fr9Dcf373vc+pdY5XV4znRdVVPlMZuTEOYowhRLWYufOnXjjJcrOhAAAQABJREFUjTfw5ptv4tChQ1OaP/neKRmSTljoxGVOI9VB62oRjPZTYleNTVdfB4fdwdiAk8JCkN+KWZ1wVTbC6ozvxCkWTO1MTWTGYizXTvXPKD2NWq1UFVVpNM+ErGTzyAg6K+fQ+cswYvxIKIlEEkZwPh3gLF25HKc7jyMw2IfTJ47RVjFE1VxHXPqYyKy/NQIaAY2ARkAjoBHQCGgENAIaAdMhMGsmUOzq7rjjDjxGe55Uyck4dx+nHu+f/dmfKRXPVHmK8ZzYIp533nnq84EPfGCsC2J7KPaQEvbi5ZdfVqEwLrnkkrHr5zqIO17pxmDPGTRWeHDB+nVYuXY1wkE6eCEjNzGRPXNW0gbw3Ko7wsjZHRSZ24WNm8D1TSxS/WJuh4vlVjM+o3/CdWlfTV0dmcAVOLBrF3btPoBXtz2HlRdegvNWrZ6QV//QCGgENAIaAY2ARkAjoBHQCGgEzIfArJhACeB+22234Vd0cJAqybWvfOUradUtU91XzOfqyCBdc8016pN1P8ifhahi+vSTj/PAT5XQSjQ00XbPKrZ7onI5McXovsVBtVELpYGZJLEHFGcx52ICVQ46mrGx7NAQpwjDTiQnYSjr5sylN9F6OG3vIuAbwtAsbSeTy9fHGgGNgEZAI6AR0AhoBDQCGgGNQO4QmBUT+JnPfCYlAygeMb/5zW/iwx/+cO5aXoolj/JnXpcdlR4HrLS1CwcoAYwmmLfkTpOlI0Nnsbt5ktfFDlAxeMl5ko/FgUyEkSKisDEsRNx5TPL15GPmlbItrnjQeKq+jifWw/AUDqebUkgHmhe04/pbbsfKC9aoLPFIhNIRnTQCGgGNgEZAI6AR0AhoBDQCGgEzIjBjJvChhx7CfffdN6VP4tnzF7/4RUmpfk7pZA5PSLiLto4OBnv30ilMkCEeRH1zcho9J7H8Rr12nlPDkznDQR/t/CKwVdLzoNw6dv9kNVPlXma87AmWfsLg8bpy5GNRsQVbaSPo9TLmoDiIEeaROXTSCGgENAIaAY2ARkAjoBHQCGgEzInA5NV/Rq08e/YsPp0iLprYyD3HWF4S3kGnmSDAmH8MvH7Jputw84c/hjAZwMBIgCzXZEYwzmZFyXPFlJQuRtu9sIqLOF2twqBFfAxzwNiD0UiYJUaVd9NgMFnKl7ibQeZVnSI5nOCSVGVgDHpWLOqpVlQ3NKKqrkEJI4X90wxgAkP9rRHQCGgENAIaAY2ARkAjoBEwJwIzkgT+vwxmLQ5QklNDQ4NyDtPc3Jx8Wh9nhYAwUTHa+Xlw2XW34tmfPUyvm9MHtJdQEOHQEGIjlXjiyaex45WX8Zd/+ZfwTIiZIgwkGcBgP0JkAoVJC/t70TMUw48efRR2mx2fZIBukUAmkmI5o2Hew3hwKi5F4srot9RLb6BW3tvYvph2iy28wLsUB1jebGDo8GFEBwYmARb/aZ83D7a5c1NeK9aTmfQ30t+P8JEjKbtoYcxJ5/LlKa8V68ly66/Md5kHqVIpjq/u7/hI6/Edx6JYj/R8Hh85Cx0ZOinMyCqJKY6JU9rxnUl/TdxXaVq59Xe2w5E1E3iYxP7b3/72lHrFBnDhwoVTzusT2SEgrxML1TTr5s5DU2s7urv7yHClktYJzxUhozbIUIGMKTg8wLzdzJsIIyGyvLhkLhLyITDQBeqCSukI+7oR8FvQR0bebncy38SXmEWYRoaGCJFZtETFK+k4g6hUQVmKb9gHLwMety9biYrKmrESypsFBM589rPwUR06VWr4x39E3Z/+aapLRXsubX//4R9QR6/A/qeewqkkD7rJnXWsWIF2epktpZS2v1xgtDPOaCkl39NP49Ttt6fskoPmAe2MqVpKSfd3fDQd3MBp37Nn/EQJHPmeeQan6NQuVSrJ+Zyuv9Tqat+3LxUURXvOT221kwxTlirZGTO6g6G/skppfTFkVVJOMvu3bMHJW29NWbZ90SJ0HDiQ8lqxnkw3vo4lS9DOWN86jSOQNRP4r//6r1OYkptvvhnJYRLGi9dH2SJgoa4l/a7A6a7AkgsuwptbH4Of3je93ipqYCakbaOlMp81NEIG7Cw+fPvN+Mj/+IhwkLwoTB0/ZOBCgUEygCcQC/jil8jgRSnha/B48aUv/iHs7hrmVfqdqlClNkrGcmTgNO/xk2VMZgAlS7x88Qw7r2MZ2pau4ikJIa+TRkAjoBHQCGgENAIaAY2ARkAjUAwIZMUEikTqwQcfnNKvv/u7v5tyTp+YCQLC5AkDZ4WNcQhXXHopXnz2MQz09qOiuhqxoFxLJLJdo45dLJTaDXcfhd1TyViAXuXZM0aDwShVRcMBqu2GaWs4tlvF+0R9ITgEf+8BWO28x1UFVqjORyk1DAcYJD48oljDRG3J36IK6qmpxeLWVWhsWZB8KWfHwnRKTMpMU2Qa6ancL+UMDQ2posyaT9rooqqkTtkjEIlE4Pf7McLPdCnK5yMxB4zOl26e5rJeo/tRzOVNN+4zPa/HLTVyGheNiyBg9DxIjao+mw0Cw8Ncx3Gtl+49LuvEXNFBI+rNtH3Z4KLzTkQgKybwiSeeQFcX1QqT0vXXX4/Vq3WQ8CRIZnEo8rS4Cqd431x54Qbc+ak/UcydUjufLG5TPGGcGbRGhhEZovfPUUmiiObEvtBKlVHGkUjZJguZuVi4H8GRwVGGktno8EXumyoBTBQhldowv/08uGoX0JbQkbiQ029Z1E+ee2krTMMwSkzDoVOn4rebNJ+NdlZzS8x+MO14GXhRNgxOyfj29U1bqjD/Ko/kMDifk8R3upTLeo3uRzGXNx3+Mz2vxy01chqXMsJlkh+I5J5PmAeZ5kvz3k0uWx/PDIHTp08r5pwOPKYtQDZMc0UHM6430/alyTdtB/WFcyKQmjuY5rbvfve7U67cfffdU87pE7NFIC7xc7q82Pie26iaeRzBvhOU5iWXK3kUp8fvOGMWvyxMZCKwPM/EHEq9NB6/b/x+FQcwJsNPhs9CRpE6qPHYgHLPqNNYKTAF92llbEJXbTOcDAuhvIRSHTTXyapCUuS6Fl1+KSAg8TMLmcal7oVsha5bI6AR0AhoBMoVgULTwXLFvdj6ndXq/Wk6AEhOFRUVuOWWW5JP6WNDEJBF7PhC1umtg8VZEY/DJ0yZ+oxeF/5PjAgTSVRE1SJ49Bx/T2YAJau6qvLKL04D3hO/g3/lIP5DnZcckpQDGTKIVk8t7GxP4j51Mcd/NBOYY4B18cYhUGAm1LiO6JI0AhoBjYBGoBgR0GumYhy1/Lc5YyZwLz28TVbHu/baa+F2u/Pf6rKpMa6YabV54aqeh5jdU5Cex72HkpkU/tPhgsNbT1tC8SoqjGGCW8xt08ptV6vc+mvk7Ck0dloSaORo6rI0AhoBjYBGIFsECk0Hs22vzl8YBDJmAp9//vkpLdywYcOUc/qEkQjEpXgxqls6KX1zVjYhYhNnIflhvBI9iddGhpR12yvnwuasJPMXnzrCGOYj6V2tfKBcGnUUmviJPa9OGgGNgEZAI6ARKBQCes1UKOSLq96MVysvvfTSlJ5dfvnlU87pE0YikGC/WCZj9Tkq58BZMQdRqpvFlCpnXBYnNcaiKRhDqo3GaB8o2mnJvJo6ljISqqVTmpx8Q7wOi9UBR1UzXBVzWV48JISqMU9cYKEX9lMg0idMi0Ch50qh6zftwOiGaQQ0AhoBjUBeENB0KC8wF30lGTuG2T8pwKLNZsPFF19c9ACYvwPjzJ0VdqqFNjIsnxXBgVMM5B53ABOzRimli6trTu6Pzx/CiVNn0dw4h6q7TuUtanBwGP0MkTC/uQEup3PSLcL0sTwrv2n/ZxGJH9VQHZWN/DTw9+QpM96+SQUZ+lPvahkKZ0kXVmjip9VBS3p66c5pBDQCGgHTI6DXTKYfIlM0cPKKftpGHTx4cMK1+fPnM4C5d8I5/SO3CEQpdbNGHQj5otj32DNYsGYpKha0QqJAkA9UflriLVCyvrjWKA+PHTuJ117fCYfdhiilfzbGBGxva0ZbS+NodmYac2YhUkCRNFoRDQTRf/gQgmEHOq5ewjycLnG+M152brs7ofRye6EVmpGZAH6R/Sg0dlodtMgmjG6uRkAjoBEoMQQKTQdLDM6S7U5GTKAEP+7s7JwAQkdHx4Tf+kfuEaBfToRHhnDoZz/GiXvvw8CKxVjwwVvRyDiNVq87LhkUFc+EzSCPKytduP7ayxEKRVWQdFHldHtcELOlSCgY1xMdYwCFFxQ1UQsCXd04+exWnPzVUyy7mmqgjWjbdIVyRFqIl0sh6sz9iOoacoFAoedKfmTjuUBOl6kR0AhoBDQCpYBAuW2cl8KYFaIPGTGBR44ciduPJbVQM4FJYOTpMBYJ4+yet3Hy4R9iLpm46J6DOPpP38Tpi9dh/g3XoGZJB+xel2IBlS0g2xWLxBCOBJWgz+2yKf4wFgmARfGYhciKlcxizMYQEREL/D09OPvaTpx6agvL34+qaARh5yCO/uj7qF+2HJVNo9LDPPU5UY0s7OWj7BgTJ/W3RsCMCHCe6qQR0AhoBDQCGoFCIVDozdBC9VvXmx0CGTGBvb29U0oVdVCd8otAcHAAhx9+BJ6uk7C6Kc1j9RWhAEa2bMP+V9+Ae+UyNFx+EepXrYC7oQ5Wp4MSPy5IlXAwocfJm4T5k7tlrUq7wvBIAMMnu9D95k70vLgD0YOH4AqFYaP6KP/ATo4xvPMNHH3ycaz4rY/z9sIscjUTyPHS6ZwIaOJ3Toh0Bo2ARkAjoBEoYQS0JLCEB9fArmXEBA4ODk6psrq6eso5fSJ3CETDIZzZ9RYGtj6NWjpziQpjxySOW9wOfgJ+hHe8hlOvvYkTDfVwLuqgZHARKtta4G1qgNVF+00rmTqR+sWiiPiH4Tt9FoOHjmPw3QMIHzoKC5l9B8t0C/PnsEtWiH8YGyUb4YE+9Dz9BPqoElq3cLGqO99/5KUWHXWGk++6812fZmRmh7jaMJhdEfpujYBGQCOgEdAIFCUCeg1RlMOW90bPmAmsrKzMe2PLt0KqdPp8OPmb38Ax2I+Yyx0X7lEgpyJFiLCP0j0HmSSHMHlU6YycPoPel15Gl53SQEoN5R4Ic8c7lVdRSv/g98MapsSPrGQFr1n4iUcmjDN/SlKoQKdPUJEYHjmIzufIhHYsVKqZ8fHIn1RQ72yV7xOQbc8LyQRq4pvtaOn8GgGNgEZAI2AkAnq9ZCSapVvWjJnAioqK0kXFTD2jJC7Kf91nerDv1d2YZ3XxVwx2xXtZGDOQjRWpIKV1tJhT33Jsc9GNDBlCYRKjFicsToZ5OHmc6qEM+8Drkkdc2cdGQ0QkWDn5FsVRkQImio5Qcshog+gfjqLrtX1Y9MEReCrJVArLyDASUlY+UlEsrv/93+PgpQJEvPGUWJr305+es78Vt9+OxdxsSJlyNHcKOVfc73sfvMeOwTc8PLXLOerv1IryeOa664A9e1JXqPubGpdiOltu43vtteU1n8usv95bbjGWHsliyYRpjAZec01ZzWfDx9eEY2tkkzJiAoNBepGclHR4iEmA5OQn2THyV+FgDLuODODngSVoq6pCR+A42qJnUWcJKmZQXkFWxQnGG5Fg3hQXVzsHDbTjCw0Poeu+e+Gx28UaUEn2pPAIX2B2yTjKxyW/ziScRIC5z8aqcMjehP3uDliGFmL16QEsq/QICzh2X066P6nQotjZKkFGb9IwTPiZSTgERYwYV7RckvTXLpsrIyPl0WVh9MpofNWml+5v6c5tPZ9Ld2zZM8PpkUk3uiasl8rofWX4+Jb000BeIJP+2ck4TE6RCIPT6ZRjBJRsD0E6adm5rwsnXe3oqViC3SEGeh/ejyv7X8Iim18xY8K8jfJxo20Sr6DU+PSQabxgLbw1dBRDu8DI8CAGj51A6OBeWI8fgZfePyfdqO6XsgJUFN1nbcbWqnU4U9GGIKWQ1RTonDjVh6WLm0dvS2YbR6vO0deEl1qO6jBLsepFZpbGFGE7Co1fOc3VIpweuskaAY2ARqCkESg0DSxpcEuocxnpp6ViAkOhUAnBYJ6uxFkq/h3lrYQZ8/kDOHiwEw1VMbhcQVirnTjjbcJANM6cq6yj+eVL1EVFfTPUOh/tv/cJeOc2oafzGJxNLfCPBNG4aQM6PvtFODduxiBVPUOU+IkKaMJ/qGImqUcapapnj62KDOAChKhOaqGXGL/fjuOdfaocQW20WjnMedIvtZxDXDIVFHqu2Mpo57VkJo3uiEZAI6ARKBEE9EZkiQxkjrsxVcSXokKHQ3xGTkyaCZyIh3G/RtmqOCdG27wYevr6sHpZFT71W6tx+lQ/ozZ4sOUHj2PuMV/cHI+3yF1yi3x4CwLeSlS+9/3oHg7g9CMPwzLcSzu+WsROncSBn/8IFZdsgPe8lXBTQojebgy9/Tq89AAqdoJSiIUB4+2WMCrDg3CEhhGyeagYSq+iMT86KQns6x+G101bw0TFxgEwbUn6pTYtNPrCJAQKzQTquTppQPRPjYBGQCOgEcgbAoWmgXnrqK5oVghkxARqSeCsMM7qZlEAjYnVXiyMSGiE0jcH+np7sGZFHRY3W7CoqQoBfxjHa4bhivJ6jN4/ybSJ/Z4wbsKVyf32JUtRsXglzvz4hwz5ANQtWY4zL72IJR+7C9aaajiq6jHvkkvg9lTgyM8eQWAfPYiypSINFMaOJcHGb0/MB0ewlx5G62CTZlFy2N09xDYNo6Wpjl5J43mz6uQMM5fTS62c+jrD6ZD2tkLjpyWBaYdHX9QIaAQ0AhqBHCKgNyJzCG4JFZ0RE5gqHESq2IElhEvBuiIMnDCCQX8//D2dcHicGGbIh2YK7CLhGCVxUdoIhhDrP0MOjdfoKMZOL4SMHKjEgSIFjNAHR0XbQoaVGMLIgb3wrlyJmvMvxPCwD5UdS3Hp5mvR++5+nH7jNTqv8OHsIz9ANQPCS3B4uV+SfEXJDXpsIbTV+dB6eRXWrmgl0xfG6bN+Mqhii5g/BlDapF9qgoJOmSBQaCZQz9VMRknn0QhoBDQCGoFcIFBoGpiLPukyjUcgIyawoaFhSs3d3d1TzukTs0VA2Kr4JxYcgpWSvsigDxedJ1K4ECL8LX5A7TYLVrxnAypvvRJOuwuHv/0dRA4eg5VMXJiSuUBFLdzNzahpbsS8934ANYsXonnNalHmZDD5HQgN9qLr9Vdx6CePYtHm6+Curoatvy8uAeT90oIIo9HHaitx/o3X4orNV8JT7aH6Jz2LOqoRIpdpdTNWYYReY61UCeU9+Uh6YZ0PlEujjkISQKlbz9XSmEe6FxoBjYBGoBgR0DSoGEct/23WTGD+MU9To8T6oxwwMoLACJlA0BWnBIBHkBI64bQo4qN0zu504LyNFyPM2GunXtgOGuipRWeM18I2N6wbrsLC99wMT109YkMD6HxzJyzhEbz7jXvh6GMQ+eXno+qKK+BuXwj/QA+s9D5KvlKpdgoDKEkWso7muWi68lJUtzYh4KNk0j8El4SWl9iA4SFKA2vgoF1gvlIhF/b56qOuxxgECj1XNAE2Zhx1KRoBjYBGQCOQPQKFpoHZt1jfUQgEZswEnj17thDtLYs6Jfaa01ODoIRvCEukPgnHIeyZMIJkEvkV5vljz23HsW89gApKCy2UAoqrV6uV8r53d+Otr/495l5zM47+4icIHjmIls99HpW2KDwjwwi8+zaC81qw9IO/je6tT5NRHGKwECk7XkOcEaTX0L5hDB3uhHtOIyKRKEOBiYMgJSekqJAqqfyMt0vuzm0q6MJ61SogRbxM1eO2tuw7LuVN52F3wYJ4LKPsSzXsDte6ddO2z7FwoWH1JBdka2qC94Ybkk+NHdtngvHY3QYdzJkDbNqUurCWlgnnM7IJFA2L6cqbN29CeQX5ka6/M2mfyfs7ef6JUy6/zxeHfib9NRo/oydB0ngIzfF4GPs10WdqkmSdzN5fo9uXhN8UrGaC35RCZnmC7bNedRWcjFk64vdPLGwm87m+fvr3Fd/dhU72xsZp6Ydt0vs5k7baMi0vYUOTSaEG5jlX+7JeL6UbXzPM56T2Jd5XCThtM5nPiZvL/DsjJrCa6oLiITTZI6hWB83NzFEMmMVJr50tlLLVYGSwC1F/N6VvwghKoqyQLx3f2R50/ewJVA4MwcaxkbAQIkV0BMk07t5JiaANfZEYFt/0HhzZ+hLsNXNQsWIVhrpOI+asQOT0STQMd6OmrRUnSfzpbYYc5DgjaCWnGek6gxM/fgz2+mrULFlIaaFD1aLaYKVqqM0+Wmu8Zbn+m/VLzcgGfe5zRpYGfP7zxpZncGkN/+t/GVziuYvzbNwIz69+de6MGeTIyS7oRRcB99+fQe2yGSNbMudIwmhnWN45SsrNZaPbZ3R5Bvfac/nlU+bfgQMHFGM0o6pM3l+sXTs2/5wuF1q4+STp4MGDiEYTAYOy6HlSeVnclb+sRrfP6PKMRoLtcz34IBrJzBw5cmT2pZu8v+7LLkOLQfRDwHKvX59Zefmyh5k0ghm3b9J90/40+fjiwgvH3lcumiO1zJ8/bVf0hcwRyIgJlOLkRdLZ2TlW8rFjx8aO9YFxCMTZMCnPCpurCl67HT6qeUaGe8jikRHkCyc05MPJZ7fDwjGwO8iI8SZhAGXZGbPwPiftBsmeBXa8hDOUDC66+Q5UzF+AhmtuhKVxHtxz52Jg1zs4/M2vM/xDEK7ACGKjDOBYT/jbTrvAyInT6H15J6raOmDzWEG/NLCwTqu3mvaBZB7zmHKysM9j+3VV+UPADHNFpIGRSGLzJn991zUZh4Aw8+UwhsnPi3gDD06n9WActLqkPCGQPLZ5qlJXYxIE5P01ow0dk7RfNyP3CGTMBLa3t09gAo8fP64mV0Y73rnvR8nWYGF8Pk9tC4J2LyLBQcSCURx8803s/+kv0BDiApPGfHaljmAR/6BkBeN/hCl0IoTgtq04smcvmj78Ubha5iPa34/el7YgQgbSGw7R4Qxt/FiG2velGFIkkXIs9oVRMpx+Mpxnt5LhXLocS6++krviVAt11VJdtZaSwKnxI6X6XCU913KFbOmVa4aFT7kwEKU3e8Z7ZIZ5NN6a/BxlpMqcn6boWmaJgMzfcpzDs4StZG7XY18yQ5mzjmTMBHZ0dOCFF14Ya4iohp44cQLztUh2DJNcHAhTZrF5qR7qZniIBkRDUYRr9uJ1Mm61FW7UMEM1GTZ3OAonJXeiGGrnOSs/YiVo5Q9H71mceOA/EeIOr5ueQR3M5+RxjBK9EeZj5AkyfPym9C9ESWKQIsVBfg/x3ADrr6Btwcr28+GtX8J4hBESFTKOvB5PvDnOeo7+zt1XOb3QyqmvuZgxZsBPFtPJKvS56KcuM7cIlOPGk2YCczun8l26Gd6F+e6zri+OgN6I1DPhXAhkzASKJHByEj1zzQRORsXY3yLZEzZLkoW+OWOxEQyTAe+yOzHg5RlGcPfQVk9sBh10JGOjAxdhBp08ttMm0MmPstxj8PlQIIQw73O7OOxk+MJkEoPkFkNUGQjZ6YtUGEAyl2JPKC5fRnhvgOXVk2EcIaPJyPWK3ROiEm+T2CHmL5Xjgix/6JZWTYVc+CTq1vO1+OdUYiyLvyeZ90AzgZljZfacMn/LcQ6bfVzy1b5SHftS7Ve+5kVyPbNiAg8fPoyNdOagU24RiDNalOpZokoCV1FZRUaO8fmiZOyouxkmAxjjNWHMJFagsIsWxu8Th5+iFmolIZAUon1STBhEZzxPlKqdUf7mf34YF1AyiWop81kZBiLMM3QoCje9i7k9Lrk6xvTFS4z/VRfy8EcvqvMAsq7CMAT0YtowKAtWULm8c5IXVXreFmy65aTi5LHNSQW6UNMiUC7vL9MOQBE0LGMmcNGiRVO6s2fPninn9IncIRCmZG5woA/HDuxGWMURjCDMGH8RxguMUmKnHMSQLxN2zmZ1kCF00HOoDQ5K/xxU/QxSZdRCCaAlzPtGggjQJjBCqWKEzCQLGG84mUZ5edh4v5VSQN/AGRw9uA9ti1bAQYbQRuljIZImZoVAvTjrNMNc0QS4OOdOcqvLcQzFMYxOpYFA4j0o81g7CCmNMc2mF4nxz+Yenbe8EMj4bb9y5copyOzatWvKOX0iNwjIC/zU8WPY/tyv8fqLWxEWr4OU0kWjZMgo9RO+LC4L5E8lr6NnTwkrQb1OKnzSQNDOkBOMA0WxX4BhJIJkAKMRMn9kGq1kGGMsI57iKp4iFQxFyAzyoL93CNufeYZFeLBm/eWor5/Lq/mVAkrbymlBpl/eMuIzT2bAT0tUZj5+ZrnTDPMo31joeZtvxHNfXznO49yjav4aymnNZP7RMGcLM2YCW1tbIfECBwbEVUg87d69O3Gov/OAgMPlwIo1a3H+uothJ1M3gQ9L8GRKpzPRGBELUi6ozsmf8UyKKKifo+fE5i+RRg8Td4hH0DADw9tsVEGlheF4OYkb8vOdE0ImQUaXLEndgbq61OfzeNbO2F2OFBsw0gSbBCvOMjkYdH3a8iSYchEka1XVtH1wjGosZDVXKiunnwOzCFJvGAGuqJi+fTNxzGV0eUbPmXTjMRrLLpsq086XhQvTFmXYGKarJV1/jR7fDIJm55wJTDf/uM7IOqXDL4P+Zl1ftjek6+9M2peuv5PwS7wHE9/ZNn1G+dO1bwZBvdM+vyl8VcyozbO4yej2WdLRtyzpUU7GPd18nsH4Il150wSpz0m/ZjEHivnWjJlA6eR5552Hl19+eay/7777rvJ+J4HkdcotArIYaWpuVZ/c1mTu0gUHQ9Va/vqvTd3hxn/7N0PbN/eeewwtrxCFea+7Du3vvJO26qyIxKZNwOOPpy1vJhcNW0xv2GBs+8SOOwf9nQlGKe8xuH3ea68953xJ2Q6ezAsTaPT4Muh9tuOb/LwYNm+nA3UG7ZuuKHWeQcKz7W/a8oy+aHT7ZoBf8vga3b0p5THIupHj4b366hk/v1PaloMTRrfPu3mzYf3NyfvL6Pls8HzJwRCXdJFZGXdNVgkV9+f79+8vaYB058yFQE5eaubqompNXom2Cfs/2yaZAb9ymauzHSsz32+GeZRvfHLOBOa7Q2VcX2L+Jr7LGIqy7Loe97Ic9qw6nRUTuGrVqimFv/7661PO6RMagVwhoF9quUK2tMo1wzzRi+nin1PlyMhLn83w/BT/7Cl8DxLjmPgufIt0C/KJQDm+v/KJbynUlRUTuG7duil9fvXVV6ec0yc0ArlCQL/UcoVsaZVbyEVPom49V4t/TiXGsvh7kr4Hk/upPYSmx6vYrup3UbGNmDHtnfxcG1Nq4Usp1X4VAlnNBBYCdV3njBHQxGzG0Okb84yAlgTmGfAcVFeu7xs9d3MwmQpQZGKxnPguQBN0lQVEoFzfXwWEvOiqzooJrK2txeR4gaIOGou7nyy6zusGFx8CmpgV35gVosVmmCeaABdi5I2ts1zHUDOBxs6jQpdmhvdhoTEox/r1uJfjqGfX56yYQCn6oosumlDD4OAg9u3bN+Gc/qERyBUC5bIo0y/v2c0gM+AnbTBDO2aHZHnfXa7jp5nA0pj3ifmb+C6NXuleZIpAuayXMsVD55uKwKyZQCnyxRdfnFqyPqMRyAECmpjlANQSLNIs80Qvpot7cpXLImry86LnbXHP28mtnzy+k6/r36WJQKmOe6n2qxCzMGsmcIPENJqUtm3bNunM+E+J6fb000+jv79//KQ+0gjMEIFyWZTNEB592ygCZiESejFd3FOyXN832jFMcc/bROsT78HEd+K8/i4PBMr1/VUeo2tML7MKFi9VXnLJJZDg8BIjMJFSMYGvvPIKvve97+EHP/gBuru7MTQ0lMiuvzUCM0agXF5qmmjPeIqoG82CX7nM19mNlnnvNss8yjdCevMi34jntr5ynce5RdX8petxN/8YFbqFWTOBbrdb2QW+9NJLY23fs2cPenp6cOrUKcX4ff/738eBAwfGrktoCWEcddIIzBaBjF9qLhfAuZoy2bOe9imLMdNJK/tq8XpTN6lcnz2bbfo5MN3cSI1gVmeT52heF9Myr6fr13Tns+qZyTKPjq/Cm/aXyWnaZyE5UwbHpmLiGb9v2vH1eDLoTeZZ8jpvp2tWgZ7f6ZqT8/M56G/iXWSqeZwAcnJ/k2yojXp+E1WZ4dvC/k7Xr+nOz7bdBR33yeOb3JlSpEfJ/SuiYws9e8aybe+f/Mmf4J/+6Z8m3NbR0YHDhw9POJf48YlPfALf+ta3Ej/1t0Zgxgj09fXh7NmzM76/WG5saWmBdzqmrlg6UcB2joyM4Pjx4wVpQVtbG5xOp6q7q6sLAwMDBWlHuVQ6f/588kbTbPjMEoRIJIJDhw7NshTz315VVYWmpqaxhgaDQRw9enTstz4oTgQaGhpQV1enNullo97Mqb6+HvLRyTgESvU5rqysRHNzs3FAlXFJWdsEClYrV66cAlkyAyiSv8985jNjedauXTt2rA80ArNBoKA7W7NpuL43rwgkdsDzWmmKyvR8TQFKEZ0q1/EzhSSwiOaJWZuaeA8mvs3aTt2u3CBQqu8vPZ+Nmy8ZM4Hi2OX+++/H9ddfj7vvvntKC5YvX46/+qu/wt69e/Hqq6/iiiuuGMujmcAxKPTBLBEol4e/XPo5y+kw7e1mwU8vpqcdIsMu5HKsc1m2YQDkoCA9b3MAagGLLNd5XEDITVG1HndTDIOpG5HWOMrv9+PnP/85HnroITz++OMIBAITOrNgwQLceeed+MhHPgKR/iUnCSIvSXYi1qxZk3xJH2sEZoxAqe5szRgQfWNKBMxC/PRiOuXwFNVJeeeIl+tyS+IhNBwOl1u3S6q/ifdg4tvMnSuGNpoZv1Rt0+ulVKjoc8kITGECxevnr3/9a8X4/eQnP5ni1XPOnDn40Ic+pBi/TZs2TRsMOcEELl26FBUVFcl16mONwIwR0IRixtCV1Y1mmSeaCBf/tCsHJjDV8yIbGJoJLP75Kz1INb6l0TPdi3QI6HFPh46+JghMYQIffvhh3HXXXRNe/mI0fttttynG7z3veQ8yiSGUYAK1KqieaEYioBfVRqJZumWZhfjp+Zr7OZbrsc51+blHaGY1aCn2zHAz012JuZv4NlPbdFvyg0ApbmLp+Wzc3JnCBH70ox+FeJQSad/g4KCq6fbbb8e3v/3tjJg/ueHEiRMQr3iSNBOoYNB/DEKgXBbV+iU3uwljFvz0Qnp242iGu8vlnTMZaz13JyNSvL/LdQ4X74gZ13Kz0ELjeqRLMhKBlI5hbrjhBmzduhXielvSAw88gJtvvjljV+cJKaDcq5lAQUEnoxDQLzSjkNTl5AqB5DmqF9K5Qnm83GS8x88ad1SuC+hMNH6MQ1mXlAsEEs9G4jsXdegyzY1Aub6/zD0q5mldSiZQmrd69Wps3759jIkTO8GNGzdmFDtIM4HmGeBSa4l+oZXaiOamP2ZZ9Oj5mpvxzWepZplLuexzqj7qDYxcIp7fslONb35bcO7aiqGN5+6F+XJoXM03JmZq0bRMoDRSAlY///zzuOWWW1Sbd+7cifXr12PHjh1p+5BgAkWSKI5kdNIIGIVAuSyq9Yt7djPGLPiVy3yd3WjN7u5cj3W5jqFmAmc3L81wd+LZSHyboU26DflFoFzfX/lFuXhrS8sESrfEs6d4CU0Efz916hSuuuoq/PSnP5221wkmUKuCTguRvjBDBDQxmyFwZXibWeaKXkwX9+QzyzzKN4p63uYb8dzVV65zOHeIFk/JpTj2pdinQs2oczKB0jAhBl//+tfx1a9+VcX98/l8uOOOO/Av//IvU9rd19eHQ4cOqfOaCZwCjz5hAAJ6Z8sAEMugCLMQCj1fi3uylev4aSawuOettD7xDkx8F3+PdA+yRaBc31/Z4lSu+TNiAhPgfOELX8Cjjz4Kr9ergufK789+9rOIRCKJLHjjjTfGjjUTOAaFPjAQAU3QDARTF5VzBPRiOrcQ5/p9UA6LqFQYascwuZ23+Sw91fjms35dV+EQ0GNfOOyLoeasmEDp0Pvf/34899xzaG5uVv275557VAzB4eFh9TuhCio/NBOoINF/DEagXBdlBsNY8sWZhfiVw3wt5clklnmUb4z1vM034sbXl5i7eiyNx7ZYStRjXywjVZh2Zs0ESjMvvvhi5Tl01apVqtW/+MUvcMUVV6j4gAkmUALMt7e3F6ZXutaSRiBB2Eq6k7pzs0agUPNkcr1aEjjroUxbwGS802aewcVyXUQJrnruzmDCmPCWXD8jRnS5GNpoRD/zXUYp4lqKfcr3vEjUNyMmUG5ua2vDtm3bcN1116myhPm77LLL8Oyzz6rfwijqpBHIBQLluijLBZalXKZZCIWer8U9y8ph/KZ7VjQTWNxzNzGuie/i7o1u/UwQKIf310xw0ffEEZgxEyi319TU4PHHH8fHPvYxVdqxY8cgH0nr1q1T3/qPRsBoBPRLzWhES7M8syx89EK6uOeXWeZRIVDUc7cQqBtXZ2LuJr6NK1mXVCwI6LEvlpEqTDtnxQRKk8V4/IEHHsDf/u3fjnmikvPaHlBQ0CkXCJTDS60c+piLuZFcplkw1JsWyaNi/HGux7mcx08zgcbP10KVmOvnpFD90vWmR6Cc31/pkdFXBYFZM4EJGP/iL/4CDz30EFwulzqlmcAEMvrbaAT0S81oREuzPLMsevRCurjnl1nmUSFQ1B5CC4G6cXUmz93kY+Nq0CWZHYFSHPdS7FOh5pFhTKB04M4778TTTz+t7AWXL19eqD7pekscAc0ElvgAl1j39HzN7YDmekFQDuM3HYZ6AyO3czefpU83xvlsQ7q6zN6+dG0387VyeH+ZGX+zt81udAM3bNiA1157TXsVMxpYXd4YAppYjEGhD9IgYJZ5ohfSaQapCC6V8yJKz90imKBpmpj8Dkw+TnNLUV/avTuGH/wgWtR9MLrxwaAdQ0M1Rhdb0PIuu8yOj360oE0omcoNZwIFmYaGhpIBSHfEfAiUw6IsG4Id6etDlB+dJiIQO30a8PsnnszVr7o6oKIiZenlMF9TdrxETmbzLJZIl8e6oZnAMSiK/qDU30O7dsVwzTVhyGtfp2QEZJlfm3yi6I/vuSeGaDSK3/5tQ5UZix6XmXQgJ0zgTBqi79EIZIpAOS/KJmMU3LMHnVdfjcipU5Mv6d/5RGDhQuC73wUaGyc4yJIm6IV07gYiH++CUl88pxsdPXfToWP+a8nPR/Kx+VueXQs1A5gdXsWeOxaz4K67IqobmhGc3WhqNnp2+Om7C4BAOS/KkuHWDGAyGgU+PnQI3JYEurqmNETP1ymQFNWJchi/6RgE7RimqKZq2sZON8ZpbyqSix/8oJYAFslQGdZMCgIVI3jkSMywMsuxIM0EluOoF3mfy3lRljx0J268UUsAkwEp9LEwgp///JRWyOKrHObslI6XyIlSXjyfa4i0JPBcCJn7evLcTT42Y6tn077BQTP2SLcp1wgII+jz5bqW0i5fM4GlPb4l2bvZEItSAiRy9mwpdac0+tLbm7IfmglMCcusT+brXVCu4yf4lmvfZz05TVZAvp4Vk3VbN0cjoBFIg4C2CUwDjr5kTgT0omT6cbGKUyar3tuZjFCMW4axmMFqI+EwMDAwuaqUv0WiEpb8OhUlAuW8gJa5K04YdCo+BJLnbfJx8fUk+xZffrkF3/ueLfsbS+yOSCSCY8eOFX2vvve9KvzHf5SWl1MzDIpmAs0wCroNWSGgmcDp4Wp75x3Ym5qmz1CmV3p6eiAfQ9NbbwEf+EBGReo5mxFMWWfK18JWxk8WU6Wa0uEoTGAoFCrVrpdNv9KNcSmC4PEAHR0WA7o2/eahzz+C3p5uOoau4KcSdrsDYJUWxBAJRzBMXUUvG2Kz2+kwDAiMMD+1RWRTZV5LK9tmUedlg1A+MkZOl0uKMCzFYjZugBb/u6u6Wm9EGTYpkgrSTGASGPqwOBAoB2JWDn0sjtlmTCu1bZUxOBaqlHJm4rVzmELNutnXm0xHynkOzxzJZAbQohi16OhmkDByx44exUMPPYjamlqsvmAVFi9egrmNTWT6nHj99dfw1muv4oILz8fSpSsxPDyId956A9u2bcPcOY34n5/+LPrIEHZ3n0Vffz8cvKejo40OppthsRmnzZM8B2aOg76zVBHQTGCpjmwJ90sTsxIe3BLoWiqiq+dscQ9sqjEt7h5l3nq9gZE5VmbOWc5zeKbjIhYEoWCQsQdPoq9HGLYz8I/EY8/GpXVWbN2yBc8+u4UMXCs2X7EBN733VtTVzcXf/t3fY+cr23D+6vNww3s/gONHDuOp3zyO7r4RfPx3P8H7nsWvfvU4du/ehca5Tdh01WZ4K1wIBAIUEFrgdrtRW1sHl8vJ5s9ONij0R6t0z3QWlPZ9mgks7fEtyd5pYlbIYZ24M0qtl1nQp0llFbJbOa5bL6RzA3C+3gWlzsSnw1HP3dzM3XyUmjyuycf5qDvbOszYvoH+Prz0wvN47Gc/xq63X8cZhgAKhYKKJYvBiqrqGlCJE26nDUeOdOLRUw/jheeeQk1dA/bu3Y9Krx379+zCvl27gFgUwZgdrspadJ08hj/5wmdx8GgnNmzYiA2XX4qgrw/fuvcb8JEJdNpd6Fi0CBs2bcSy5ctRR2bQ4RBmcGbJjNjOrCf6LqMR0Eyg0Yjq8nKOQKkvyHIO4AwrkF3RCfuR/CGqLL/86Y8QCfjR1rEQbYsWcgezAv7hIQSCAVisNnjdtIlwOhDm7346UvENDmMB1Wba2xdOaYniKWfFWE4p0hQn9Jw1xTDMuBHlPH6aCZzxtDHVjZoRyHw4oiR2I34ffvmTR/CPf/v/4/TJ47T3A+wWOxinnHSQVn88GO4f4D5oFHVeB4aCDvJ5YZ4bgm+gBw2VZNqYNyrmePYY8zloJ8jjoA/P/vrn6B0MwOOtQQ3v/c6/f53OW46jzxdClDSzpakZKy44H30D/VQjHcYll1yC+voGlsECmeJ/1WFGf+T9Vco2zRmBoDOlRCBHTGDyDv94veKdTwxp+/t6lSGt11sxY0NaMVSXSS2T2+F0Zv1QjLdKHxUbAuVAzMzYRyE8igjxQB3T2Lyv9yx2vfYytj39JA3g3fByZ9RF4/hgOIBwIMiMVjKATtj4LcbpI37uojrsuPNjn8BlV13Neyrg5H1V1dVkHr3x51hROBqBx0btItTvYpulE9urF9IT8TDqV76ek3zVYxQuRpaj566RaOa3rOR5m3yc31YUX21Rcms7XtyKr37l79DTdZwbmXT4ErXDZqMkz26l8xYL+AVr1IIQJXxDgbBiCsNc+sYsIWUT6ODa1E1VTn9gBAE6fbGSulU5YwhSdohYGNUeGyJRP7ZvexpW/rbbbaipdLEOKxrrPWis9mB+Uz1qqkgXrRZEWTa1RJmy3yXVY198czBfLc4BE5jMAE40pBXvRwcPHsSjjz6C+ro6XHD++VhEkbcY0lptjowNabvOdGGQ0UG9XDS20ZC2oX6OoYa0+QJf1zMzBOSFJh/DXf7PrDnlcxcJkNCgQMBHArkNT/3q53h7x8s4e/oEEPZx93MEvqE+EjQriZkbbocVA0M++IXm8RzpJ5wsIBiJ4sFvfhU/+t5/kpi64XJ4UDtnDpatOh8XXnIpzl+zFjU1dfFIF2QeSyGVsyRJj19xI6CZwOIev0TrNSOQQCL9t9jO7d75Fr7yD3+DU8cPwclNSwts3KR0kqkDHJTU2Sn9s1m51nXE4CZhq6nixudIGCf7YhgIReElzQvbo8xLb5+VFejpG6SwwgoPPX/2D/t4N1VIHQy9wiJEomihhFHGx0UaGeVm6YkjB3D0wLt4+qlf4+L1G/AHn/0j2gxeM2OBh6Y/6ce8nK8azgSmM6S1cpL7/QE889RTeOGFl8gAzsfVV2zC9Te/FzXV9Rkb0u7dswet8xfgChrSujwO+Cgut3DXxUNXvNUiiTDAkLacJ0Ux9F2rN+RylOIbOfJX1F4SaWiwB2+/9hp++vD38cZLWzEy0EtbCCvqSBytrso4h8jssuPpsVso5XOhlqoug/4QBvjcy+5qY0M9Yvw+M+BD74lB7poyjpMlRqN5JyIhH9VpovTAZsGlGzYobQFRokluQ6ItxfatF9K5GbF8LWzzVU9uUDp3qen6p72Dnhs/M+aYPKaaEchslA4ffBf/9I9/i9de3sYNSqp/kqLBQns+0qdAkBI5/ouITigJpE2YPH4qSANrvPTu2eRCV68fXYN0HsPQKuJEpr6mBlVeF6pID8Xr57AvxksOCjFcSkoohLOS2jKy6xmKxDDEMBJRSgWpPkP7wwh27HgVX//aPythyQXcIB3VR82sM6O5Js+FrG7WmUsaAcOZwLSGtFzwVVVVUxJA7WguEg8ePIqznQ9hC1XJqmn4mqkh7fXXX4+L1q5G96lj+Pd7tvDBDCo7pCVLl+KiSy42xJC2pEe9BDqnX2q5GcQ446fom2K+ZFd039tv4ZnfPIY3Xn0Jx7k7OTJE5k9UVyrs8DjtqKsQNc7xGD6JsZFYSaLaUkcVl2oSwCDVZtykbVYSVqe9kvYPVA0fClJ1lFYVYmxPYnoepYGrVl9ABtLLDpYC+xcfJ70Ay818zVep5Tx+egMjX7Mst/Uk3su5raU4SxetIsoocJxB1f/tn7+CZx7/KSWAjK8nDCD/OsiQuWxOeti0kAGkNgzpoj8Q4dozQlpmQSDiVw5dGqorMH+Oh+qhjBHIdSm1RhGmUeCcGtIziv1E6ldNG3mvx8n7rKjgvcIYitZMmGWf6RvmJmiUfJ6VNUfpYKYaLW1LcOJ0N7774PfwV0uXwetlAETVrsyxLuf3V+YolWdOw5jAjA1p+/r5SMWUIe1wkEJ1PiBDfQM0pj2bsSEtgsP41j1fReeJk+inrpmV8VXmt7bg+IlOnKaqqBGGtOU5HYqn16X+UisMwY6bnYuqipC+Hj5LD33nP/Cbn/8YfWe7EA2HSMCA9jlVJIpU7+RzLMlKSZ4wbBNT/JoQUJEMUoOGKjRSqpQOJSl0VHpRQ+n9EL2h+Wg/ePCtV/ENqou3LFyCjddcj8033MKAuvOV3e/Esovvl15IF9+YJbe41N83yX2dfCx9l/eRVr+fjIy5f0+mIZN/m631hWyf3zeMo4cP4b57/wWP/uAB0ivSL5nzZNxsnP/irVNs9iykdULuaoQhpPLLmYEgpX5BhNxC28I4OzCstGPqq1zoOz2CMDVlgqEwhXp0IkPbwDk0k6iucGNwJMjA8TGqkZL+UfInGnS+kRDXs0FEKSyJCadJQhmkJLGmqoLCjfU4dOgw9u7bj7UXXpj10BUS26wbq2/IKwLGMYEZGNJys4NrQNoJUiIwSP1p2RVROtEWPiRk5DI1pN2xfQvtjqhvzQJrbS646Hmwsc6DplqvYYa0eR0FXVnWCJTzoixrsDK+gYSHlMdCinT86GH869/9NbZveYa0LURWjsSQ1K+WRuouqzBytJIQIsl/cblhUiW8P7FgFDVtuS656BpGZYoqZlCIK+Cm9M9Nm8AoCWNIVEFDI+ja9zp+0nlMEcaP/O7vJxVcHIepCK6er7kZu1RY56KmfNWTi7YbUaZsYohNv07Fi0C5zmHRZhHBQEVFxZQNRb/Ph1OnTuIVmjf84MHvYPsLzyktNbXxITSO0jqSKIyQgRseoYROSBh/O/ippLZKW0PckcvRs0NwVMU3S/xk5CoY40/MnyR8hEgEB8Ucgvd6HZQg8ncf63Va7bC7HOgfHIG3slJthgZj8pyJ0JAMJ9fHPnoSff3Vl+FheWvWrFHB6WfCBGr6U7zPba5bbggTmJUhLb0jWTj5ayodGCEjeKqfnpVClBZkZUgrDyafQn7c7EEkGsDhd/fgwJ7deOLxXxpiSJtr4HX5s0OgXAna7FBLf3dcDmjBCHdFf/jg/Xhp6zN0YkY1TT5qQvzE/s/jcGHJ+csp1bNg95tvKfZOdkYtVGuhEQMl+2E6e/HARRuJCHczAyR2csVKUWCEDmFE8i9OP+3iJUZdIWEldeQTTVVR5uRi0+OyYSg8hC2P/4R2vpW44db3o5K2vnH6G/+bvifmu6qJsPnGJJsWlfr4net9qpnAbGaLOfJOHtPJv83Ryvy04rUdO8gEetHU3KxMFMJhqmsODTL235t47Jc/xZZnnsAInQ26aN6g7NRJp5wULnjpX0IoVYg0SuL3haiqGaVDGC5Z0Ts8wt9BNNPmr2c4iIHhANzVdvhpx1dDyaCNEr0wTR1G6C1UjCWExp0d8ivHaLKNGhDv2r4ARujl3kJpoS9EVVOGk3BS4lhFb6RCDoeDFJj0+bD1+edRXVOFjRs3K6/42WqWlPPY52eGFW8ts2YCZcc/O0NaK3WroQxpxU6oo9GJ0zSkPTs0krEhrZX60l4HXfZSyhDgQznMhzPGB0e2bIwypC3eIS2Plpf6oqwQoyjMmkjtnDRYX3vRpfjVI9/nDqif9gsOeCi2q6QdwyWbNqBpQQu2PfUMWpctQ6N49uVz13X6FPrP9mLtxvVYunIVKqqrEAmGsOetnXj+CRJYvx/tSxbjgovX4eSxTrz5yiuIckfU6XWjsqoWQwODdBo1SAItKjdUv4mF0Ll/J+772t/jIDd4fucPPkvD+HlsXfHaCUrfdKwmY2d2vhY3+arHWHSMK02cwwRIZ3UqXgTKdQ7LWmGQksD/50t/TF8SF3KD0gWfz48D+/fh4N6dGCbdEa0yJxemaiOUWmai4yIblj5K8ESZxUH7QBc9ewYpcJBYgEIrxXG1LxhGJTVl5la5sZ9r2CEygN5gDNWUPjooLvSRiXOxXCvvoY4nPWXHyBjGKS35TTJ5ogrKzVLeJ5ui1XSk5hDNONI/O7nA1no3fBUOnOwd5Kbs87j0kg3YeMUVzMv1bhZJr5eyAKvMss6YCYyre8VwkI4ivpGhIa2PsVSCNKR1iSFtWAxpgxBD2tYGj3pYMjWktZLhi5fBh9A/rCQMIkuw0YNTpUGGtGU2D4quu6VM0ArRN3mepV5hUva+8wZ+/ND9GOrvRT2NAFtrKknwImhfuhiXX38tXt/+Oj5w129jQdsiFeNPCOaZrtOKkVuy7Dza6HKDRsgkRYTz5negoqYab7z8Ct77wTuwiNd9g0NYsGgxnn38V1h03jLcfMcH0dV1Ai+SsdxDJzQebg7Nq52DwN5D6B7sxjM//yF3R+345Of/mGozNUU3VxMNFkKsmcAEGsX1Xe6LqGwlD8U1uqXZ2sl0ZPLv0ux16l5JsPURbjr+93fup1dOUizSO5G02emkzOUkQyV8mSiZSOKxMINB6mXKt9I9oQ2fbHbauZEXlfc4z4umi5USQvkOi5YLbx30heMbprTv85DZ7B0aQIhB5F3cRBWjiEDYSumfxLe20GGaDZWUNHroKCbCdTG5TppK+VgvmxBlfkoGgzwnjtfm0Haw+3QXHqPDms3vuQntCxeqpmb6p5zHPlOMyjXfjJlAEaUf5E7Kfd/8V/yCEoNMDGkjFWJIG8CZ4TBCLnkkwlzkDaOlrhLZGNKK5C8SsWCIrucHAxS1iyGtKFzz4TXKkLZcJ0Sx9LvcF2VGj5MwJ6/R++fPHvwv7HzzVQbIPUVDeLquZuAi8ejJAEXYdP3NaF3QhvqGufTyW0OmUUgkHzrF7LWr50+paZPAxikqPaqRobt881VYw/h/FVVVPG+Dl0zlZVdvRtuiJSSK3Aia14jauY1obm5TAXr3796FK2+4Dgvf2UuV0Cdxmh7Ttm99GqvWXYKr2YZiXZBKu0OhkNFDp8vLAwKl/r451yKxWJ+5PEyNoqnC7HP4XHNwpkD39fWitrYWd//+Z/CXf/pFWBxU9aTAQOiXUDCVhGSpX3IuzrApUZ9sjpIhq6UqZvO8Jn5XU+GMUhejk9IAAEAASURBVHG+x8/09DL+3xDVOUGHLn1KzTRANdOzQ6SKlhF6rHeTMWTcQGaopHqoOJZhGHnU1tegjjRwActbc/5iLOpYgKNHjuLJJ5/CmTNkNskwWrnpGqWoUSSNdmuAXrjJUDLsxKF39+Pl7S9mzQSafezjg6D/FgKBlExgOkPa4aEhnDx5Atu3PYcffv8BvLZ9m1osyiRTziLSGNJWeb1Y0ECDWWsQnb3DcHAnxMZdDz8ZuQrummRqSDswFKBI360MbgM0oKVPGsMNaQsxGLrOzBHQL7XMsUqbU4gfiWEoPILOo50MULsXZ44fV/GPKsj91XCnMkS1mJraeiw97zzumoqXNMco6YzvkkrcIm5sxpNiAKXMxG/untKzWk2tK25Uzz1UcQ7lIFPZvniROhelioxkr51Tj/VkGJvmt/B4Lq6+oQP+4UE89+QWzG1uQtvChUqSJgtSRbOTqhmtzdRfeiFt/PDkauE4uaX5qmdyvWb5reeuWUYi83ZMnrOTf2deUnHnfOPV1xjKYQQ333IzfvDDH+LlF59HhctKR4SkO2S07HR0ZpXA7yqREpE+MRS8+iXSvbYFLfjg7e/HVZsvQz2ZSWEOQ5QSHj9xGu8eOI539u7Huwx31k+Thr6BIW700cMnaRrdXmAhNVfEAWINvYLW19bQJnEeVp63GOevWo6lC9vR1FjPWIQ23jtEW3kn/uP+B+FhE+xSv0VoJZ3S0KzCanUoKeTgQB/efPN13PGhO7PaDC3XsY+Pqf6bDoGUTKDcMNGQVmztaKDKCfgOJ+Bjj/0U27Y8zUgNg4ylkq0hbRhN3AXp89GQ1heiIa0NI9SHruZDmakhbZBSiwh3XPwUl3N9Su+Cdu600FUFn2OftNMAQ9p0oOlrhUdAv9SMGwMhUm5XBdauXYv/vjeg7BHq6Ultbm0FCU8My+mVrKm9g7YNTsW0ZWSVl6Cpo81M8Ibj3CEpnfyXhzaRmKmSXtLOu+ACRYQddCZz+ZWbcXDfQXpFO4ydr72CJctWqNy8VSW5O3E8esq0X3rjwrRDc86GlfvYaSbwnFPE9BnKlWbW1Nfjjz7z+7jlppux8fJLsX37C+jn2tNOiZvLRhMirj0twhDSFlAInJjn0RGoigfY3roAn/3sp/D+915DOkOVT6p+KppDorOgpQkbLl2t1sYDDA0x5B9Bd3cvGcFB+OgAhkUopzJi9z6Hbaivq+V3Fc0dGBiepUSpQhqhrWCQ0j4vHcG896br8egvn8KJzpNkUm1wkhG00WNihCqhEgvbwraKJkkX1UKD/O1heKVMU7m/vzLFqRzzpWQCZcIkG9I66elveNCHA+/uxaF9uym5G+TuPndSqEut9vAzNKSV1ZqfuxpeVxj13Bk51BWgNI+GtDSgrcrCkFZ2amJ8UqWd1V4+LNwxsdKQVrwq1VU5DTGkLcfJUEx9LuWXWl6J9SgHNTQwgO/e/02cPX4QzXNq1PMpln3iyXPN+stx4WXr+fhSTWacm5tmumTLko3nj7ODojYjRJKJNorNC+bj0o2XofPBR/CLh76DuU3zcMW1N7JhJLFimW/CNN34lfKcLdQwTIe10e0p97ETxzA6FRcCk5+Nyb+Lqzczb+3qNatx4dp1+D9f/t9ooVSvocqOYIDSP9IXWcdK6KM4Zxf/Es+fA7Tbm9PQgM98+vfw/vddh3DAR6mcSOekHXFKNW7fTUcwdAxTW+1B27w5SrootEvli2elplrcC3aUzmFCZOAmJslkxfKlbfjQbe/FV77+nxjwj3oY5dqYrCKZUpoRCO3lhq1s2mabynXss8WpHPNP+2ZPZUjL54VxU+yU/o0+DKMTXB4MYQYzMaSViRyhqFx2N7ipwvgpNKS1x1CRjSEtXdCHxXo25mcZfnpkkmeTjeO5CuqGGmFIW46ToZj6XO6LMqPGSh7hKCXrb7+2DU//9IeYX1+BOq9H0TAJ91BRXY8OevUUd9kReuI1JI3Sx2nLGmU0JZubu51LVq1CS9tW7D1wGD/74YM47/zVZAZbpr3drBe0NMWsI3PudpX7IkrP3XPPEbPlmDxnJ/82W3tz1R4bNUp+95O/j6d//Ti6u46rOIBO2WckoxaTzUQmuonhxy6yPtrt0VyB8ft+64PvxZ2330QGkEwb7QKVbqbKncSEKWaPJ3kqopzE0JnM6OVkJReVbXTPUi4nls6qON4sv0Wj7UO33YBfP70NO3fvViYWciXKmy1UeRO3F06aYsxpmqvMIoRMJqqPlzP9X71emh6bcr8yOi0nwpBsSGthIGcrwzG46CDCLl6UlPEPp/HoTI9P3/jfxM583JC2FsvoQn7durW49OJLsW7NhWhr74C3bi78Fjd3OmgiS3H8CNU3zwxFcWpghJI92fWIG9L66fglTANcCu1pSFuP9vYFWHX+Wrzv/e/D3XffhWvfcyUdTVQqd7sWPuRWGvqKPqgY0kpgTg8XrWLbmDCkndhD/avYEShXgmb0uAmhitC+4ZWXXkSIrrIrPRLklrWQODKiH5avXsPQDM1q4yZV3fIMi4c1+RZCI4tF+R7l4ybcIucV0WKdcizSBfmeLkkZVjp9qmuYQ3tAqqPy7XBg11vY+tST8Vt4fTI5jV8w5990fTVni3WrEgiU+tid632qmcDETCje73ONcfH27BwtJ51YzfXnR+76PVKQuEmDCC2iJDCKhMjf0fVjkCEdhobCuIBeqz919520FxRhw+jm5yjHJQoo8pH7w3RsFmGMvzBpqNAiySLkUz7qN8/JmjbEMBJBehgNU51T1qUqXzyTyhnPC7S3zsPHPnIHz1kRFL1UycjmicAkGGEYi4p6euVuw9s0yxpjSuOdGC0n9VfZjn1qOPTZJARSSgInG9K+QkNaL70bObhilBh9thka0h49fhL7aUi7i4a0Bw4dU4a0/dSlDvJBimVpSHus8xTd59rwyI9+RsNa6l5zschHRT0XRhjSJmGkD02IQKkvyvIGOWnMQF8PXn5+C2or3LSTIN1Ru44x1DAG4BXXXUvxv1A8EsLEFicbJ3aBQRK2k8dOo7PziHqWhaGsqqvDYnr9rKeTl0QSOuYfGcGRI0dw+sRJ7piGaJTvRB03d1rmN9NpTINiHsdVTZOoIw8d3LatbaiDlxs7Pl8/4yU9i2tuvlXdl5QzUZ1pv/VC2vihyefiRuoan6PG98XMJer3rZlHJ3XbJj8bk3+nvqtwZ3PZPnlub7zpVmx9bgtDHG2hgzNuWpK5E/ohDGHcNj1CKaGVYZFc+N3f/iBt/hoRoEBBZUoiNCEyfUHa/w3TJ4Z/iCHKhLUkbaqrnwNPRZXKrlAk2QxTe2ZosA/9vf0qsLw4Vaukvb2nsoKCFa+iewnEpY2UZ+C6zZfiojUrVbgk8aYt4SeGg2wAzSRaOzrImFrwxOM/x9p1FytNmfEKEyVN/dbP71RM9Jk4AimZwJSGtMNcuHEx6LRHUcUHKDZqSCsTN2ND2tYmbFx/IY1bIxikjeEgA3YmDGn9wyN8lDIzpJWHcA7d7F53zWY88cyLONvVxQWijQ+wcYa0eoKYG4FcEoxC9zyffRMp4Ks7XsaR/fuweC6Jktq5pHI1vXded+utmN/RrgzY1XbkKLXhI4+BwQGcOnWKatlBzJ3bhPlt88kr0msoJXsSx1N4xoSQL35sRXNTI+ZRlUX2agJkIP0MIH+WhvTDvhE0zGmkobub1Uzd1pR3TJjvDDFL8nCz6PjBdxnkdw/Wrt9Y6KHKqn5NiLOCy3SZZfzG7YBM17ycNkjeSbKJUa79zym4eSo8n3QlT13KrBryTyJ927rlGVx/4w3Ys2cvBvu76RSGc5oSQPJqyqmg+hML42I6QrvlhitpuzfKACZqIWkK0dyot6cHXaR9EiZNqJWd7wU7mUAJLTHPQxoqnJwkXgxHQujv68eQb0iZXURCg+jp7YbXW4kGhkWqralT61ZhRy0iXOF389xafPSDN+MfD+walVaSEeTGbF1VLZYsWqg2QYeHh1huN82zmpRGzbnG9lzX4w3Wf8sRgZRM4BRD2koa0lJtUyao7JRYZctfZj8fLlmzZWdIG79RDGnraEjb3jJXLRzj2y2j5bLodIa0IsR3cEV44epluPG6zfj2fz+CCGOxVFNaaWNsMyMMactxMhRTn/WC2pjREqatZV4zn0UvwzaIxI//SRgXr1qJSzZczueQD7k87PIth/FfjIHkwMKFC7hzSeMKG9U6LRTHq/cB1WJI+LhNxJzxG2SX1cE89uoGUkx68eXpCjFwiIitBb2fcbfUJnbG0zCAgREfzp4+S8mjqNZIJeIlTdRvmMarif828V8tCTR+cPK5uCllJjATHDUTaPz8zWWJqcZU5rAwROWUhETYSX8iDIP0m8cfU6ZFL7/MDUwygeLfQjTclAYM80lohxuuuxqNc6qpveIntRGGTkqQv1E6lBGzJaBj0WJ4KfWzM2SSijdITEXVUxHQMaIk5bvRtngpyyd9VMVQaMIQEn7fMEaoHRPksZ15LEL7SGMli40VXLP5cjz28xV4482dikmsq7DDwUDyrzz/G9o1nsKi5Svx9ltvYPmKCzCfsXvPlfR66VwIle/1lExgSkNacRWo1GHiLxAxpI3SXk8ejOwMaWXfhVOda7mwWgjO1JA2hgYG7rz9luuw7aVXceDgAS5EpVi2iO2crSFt+U6J4ui5fqkZM07iIa2xuQX1VGWxhnr59HA/k2on6zdsgNtbTcJGRk2SbPzIQyvXuWNpdzDun80NV0UdbO4aMoMkllQVj5IQRkb6EPJ18xkUoshNGZ63e2sYF7SB+UjwWFaUO6qRsB/h4T5YggPx4hUTqB5i3jdaG88N9vfj6NGjONbTD5urEpvWrMOqC9epPIqjjB+Z/q+es6YforQNTLWoTntDiV0UG15xTa9T8SJQjnM4zlpZcMutd+C/v/XvSnOsnoKNhKkfqZaiSSRt8FZ6sP6ydYpRFtMnoXdcTSoGjTubqKqqRnXtHHJqXnppIS2jWYOsO6PUqLE7hxENDFMqwmdE3cI/ZPBsripyodRykYzc8LQ7R1DlcqOam6XiLZR/WA+vSW3KNtGCuXPqcPU1V+HNt3byikQtpDZMYACnO/sUQ9vFWN0/+uH38bHfq6GNYLu6N90fTXvSoVPe12SWT02ck2JI+6X/739z7tYo1a6YYgBlskoSA9gIP9wZ4c6JzxfG9Zs34Yufu4uTlQs/ebpkTvMeSRZOfnH6IqokI/5hBoAepq71CPNNMqTl4lAWiBI7RcTdQxSjS94wF4zy/CS/wERFTHSjL1i5BJ/4+J3UyXZT51oMaeNOKcR5qC9sg6e2iTZKi/Ds00+yH7wuKdGN+C/9twgRSJ4LRdh80zRZjNvPnjmNk2fOoJf2uRITorK2Dh3LlvIxHpW2xR/m0eeGZu4xOn8hM+atb4erpoV0roaSvAoyj17YPTVw17Ty/HwSNJIuvgvsFc1w13fA4amHzVlJVdNKMo61ZCCbeL4NzspG5h19FalXRvy9EQdJ3h2sj7YUMRJLm9OLRYwVWMF4guO7rqaBM21DtCQwLTymv1juCyk9f00/RSc0MBWNTHVuwk0l+kOWfBJj9rd+7w/pYEXMFchYMe6ezcp1LOmLMHpie7dgwQIsmN8KHx0XhsiwBRjSbIhmS0M+MV8aYXzBAIaCdvis1QjEaui8sBIxRx3pHWlc7QJF62LC8FGqZ3PS7k9oYW0rHHToAnstgtZK+FEDHz99XAIPDnKdy7KHaBoxPBLgJktEmVjIGnXN6gtUgHkxc5KPm9JML2P1OrjhOtjfhxPHO9FEE4tMUrmOeybYlHuelJJAAUUeknnzWnHDLbfhx488CDdd5NIiQql5yd6IWhdygeekSL21sR53feQ21DMIfJAPTXKSh08Yuf7eHvRzRz9IEbgkp8dFWyLqRNc3KgZPnWSxEjizu7sL/T19NKplWVwberhrUkmpXzXd1bt4PMpbcn8kBq/HwYCdF2DDxRfgle3becbKh5kP10gMTm8FVl5wPgYGerH7nbe4s/IeLh65K5O8xlQV6z/FhkApL8jy+cKWR8FO+z8rdzR93MUUyX7t3HpU1dZQUseLk58VCRzo9MBdTYLnqVLPW0KNUzZw1A1Wqq6Q6EVDPqq+jMBVJRJAF6/JdX74XxVLJlGYR0t1E4ugmszQWXncR/PFc8uzLgxfc3MjGg54EWHcJAnyO96wyQ1UBZjyTynP2UIBntdnJUF4CtXZAtermcACD4AB1efzecm2ublsW0Ia+PHf+594fuuL2Lp1C4b9PkVv3GKLJ+SJyVtZjZdefRsBMmTi0VNCmVW6uf6UsElU/YyK5M89iEDoGNetMXjcLjqDqSCzVqvWllU8dnhrER4Z4kZnPRlHers/3UlmbxB9XP+OsM4gQ07IsyRCFIu/V1asSs10gMxmkItXJ02uxHyil0IQt4vqphSsyG8REoqXUB8FKA62ZemKlWhpnR9v+Dn+5hLbc1StL5scgdRMoKyr+Nmx4wU0NjWgsXURjh0+yDgmXIBR+iY3CT1UqmN8EDZeugZXXL5WPTQT+ssJGwj4McDJP8zdDgt3+z0VlASIQS4XilKILP5UYXKjWhyKfrZdMX0xrkKjnPURisxH/CJiH0QN73HxwRBGVB4iEdnPp13hrTddjQN7dvIBoR43zzm4yyMPro3Sv2ef/CUfGg+6KfEQtTW3W+KgFc/icQKm+odCQC+ojZkIYhPYMHcumb5aRHoHSMCobnnVNZSsu5St3oRa+LxFafsnEjybp5qXlKLM6PM76Xkig2evpBMYqo/ZZGdUpdE8E7KyDKrWiDQwHBiil2Cxw0jOYEFldQ1Wr1uNQ7v2YJAOYjqPHGKxAdVGvjIm5B6tyJRf8s6ReVtuNjmmHIwZNKqU3zmZ0EPNBM5g0hTwllRjmupcAZuYp6qFnpBScK0pAeDv+ca9+M9v/Qe+9n++TM/ytNvjelRyOBgKbf/hTnzuz/4GfWTARMhw03WbcPfHP4x5zc2omkvnZ+46vr9lXRugY7PhOHPX06ukcn4yZz093crMwc2yJPxZBZnKKoYyq+C618v1aC3prFe8gzL+rYOhlWSnNTBwCv1nOtH51h7827e+h/2HjtAbvwc1VQzPFvYhFiTTx41PGbsI+xAgozhv3kJcd+MtLKdC9YsXz4mlpj3nhKgsM6RkAmVhJXaB8+e14Bs//Ce4uFMiesmhMJk27lIoHWUxAeJDIi5vr7v2SlRRl3qEDN+YIa165ngfmUYPJXI1dQ1wcYFpoy2ReiQ5+SNhel+iNyYRnaskiyTaI82Zt4A7JczHDf8YmcBwiLsy3FkJUYdavcTIeFpEj1oMaXnspVTx0otXY+XKFdi+fQclG1w40kmMJTyIt3ZsxdDAABYsXILntzyDiy7diOXnrSjLwS6lTpcnMTN+BGUTRp6PwEAfGujZbAWZrdXrL6ZvF1GtnkRY+LxZqeLipK3guVgv5uSzTiKmJIBx+d70rWdu2k6IZDE86J+QTdonmz5LuOvZvqQDu3fuxt43duDNHdvpuObKCXmL4YcmxMaOUj7fA6XMBGYyKmITqFNxI5DP58VcSJGWjZKz1tZWLKCneg99mrno7d7rrYKPoR7cXi9+566PkNGy4u2de/DrZ7fhfTddi+uuXI8gTSDcDR3UTPOk7JYIOU50duLll1/Gl7/8N9ykDOKiiy7Gpz/9aRUvu57hkJwqQv2k26lNF/aT7jVV0LZvHp5/+XX4KIW87ZZrMbehlmZMz2LXm28pZzEimQxQZdVGk4uLL9uIiy++JF7YJDI9qYaxn+U79mMQ6IMUCKR8qwtzJ0/MNe+5CQ/85304zMCU1Z6E9z4yYLwswdllR6ShoR7r1q7mJovoV8c9KckCUD1xXETaHVWU6okonWqYNn5TAihryyjtjaxB2vuNDCBGNTSZoGIXJDaINlc1LFRRE4meGM3aotz1D1NVLUCXvFFKBIUBFJUw4QOpoirSjObmubjsskvw+utv8KQotTHUNcsf5MfGoPNnTp3EE7/8JZatOF/VlQILfaqIEJD5ouYMx7rUUt5e1oROPJW98OwziPI5cdZ40dRK2wa3VwW2VQ9YgnISZFG1dripvknnLpkkeQ/IszlGfae5SdEwPqN22guGLb2sKGGLGL9Byqige+x5bQuw9+2dSmJ4+vTJaUoz92mRpsQDC5u7nbp1UxHI23M5tWpTnNGSQFMMQ8aNSDVfy30jQ8A703UaP/vxw1zHiiDCSiZtKfbu2cfQDE34OM2a3Az3cKTzDE53nWXoIqp20hN1hDQxZuGaNAX6PjKARxkDt729HbcyrNK3v/1tvPHGG9i0aRPWr1+v4uOK5G8qE0gCLOsYuwdh2WClwKRpTgMZz834s89/gjVFcPTgYRw/egStLS3YuXsv7FwbX7T+Ctz50Y9hzhw6qMkiydjrEC9ZAFYmWVMygdJ3WVo30ybwE3/4R/jjz3yaRqsD3DURRpDMnjBnfBxEgNfW3qaCPvf1+5RDFwmOGaDdnzhxiPJ6hJPc7qJRrIsLSEdEicS93HER8biFziWstAkK4bRahDo8dVQho6oZVTdD9DI4OOSn0xkfXen6OXn9iPkGqV7Wq3ZFhEG0cWfSSUNZaVY0YsHChQvRQHG7zz+k7AxjbIC0NERuNcAyAtydaeEukE6lgYAQOaVOXBrdyX8vhKLxQY/y2apw2WFTx9zZkYc/FbXjOVHdVBeF+Sb+0ycqi1ONW0K9KNVv9SROl1sUy0kMaW9hoSo4uaQJGWXTyU71Gg9tA92eSqyhBPDiSzepPIkNqwk3mPiHXoSZeHDO0bRyHzvNBJ5jghTB5VSMYRE029AmvvvuPuykYENsy0PBKJYtXawYvdqqevqd8CoP14s7mrHqvGVxUsfaxWZeCSUmtURU+w8fPoyvfe1r+OIXv6gYwSVLFilHLk1NTThDh2v3fuMb+OCHPoSNGzdOvFvRWdI9hlmySIxdXq2q9DIWYBs931dSJbWP9M5FIcuFZEbr8c6ufRS61GHl8qVoY1zecUKdjg6PV6nHfhwLfTSOwLRMYHxxBdx40y3Y/vFX8KNHHkFP10klgKtUIcFk4sXQ1T2Ae7/9fSX+FkcuFupXL1vYgdZ5TVT/pNRAJIDOQdoE9ij1TwlOzWUmKqh6NrexCS10+FAnnv4oCQhYvDh88DhOUmrX308VNTJt8qCKjaCLxrkOlh0L0K1uoJ8BN3tx8GgnzjDYtHhLknzdZ3vo+YnOa7g4dVHf2kImMMyHdIR1OtyVWHHBGhXYerz7+qiYEZBFmbavmt0IysKulR7RXG5xmU0riDH+azJhEaYvie+bfDlFMyKixk3bCU9VzSi9EqonH9maSU6KBeSJRKGKOo5nUFWLVJHefitIJFesQhN3RmWjCdbEvePZzXykF9LGjk4+FzalzARmgqOeu8bO3VyXlmpMU53LdTsyLT9fbZs7txlXbX4Pnnzsx4rm2ShFuOqKjbTnG6LPCxu9fkrQsyglbXW8Lq0XAjSZZsV7JW12c20qUkAvpX3iyb6WTgwv+8Ct9IXRS+mfg3H85lPoQdu9KSm++amkgSxfNjudNH2Q8BDhcFCtk1tbmrF61QrsY4D7GEMuhUf6cfLYIfrIGDeb4G1jlHNKFUknSvn9ldRNfZglAtMwgbIY49Ti/6qqKuo1f4ZOYVy4956vwM1g7Fw3qkln4cHJM734r4d+PMGQ9vrNG1Ma0vp8Q8qQdoj618P0GLp3zx689OILZA79LH+qIW1DfR13ZqqnGNL6+zpRdfo4jnWexhO/3kLR/UkylR5Ue2lHyIcwRuZRvIzKAzpmSNvShMs2XqmcSYi6aHopRpYo6uwFQUC/1GYLu8QEtOKyK6/H8d1vYsuPH6LBO6X48uBPSdyA4aaKxPeLM3IJhm1KRnVC1MMjfnr4pQp3lJtBVlHfjr81UtwgoeWlTqqY8z6Swwl5aI6hbhUi6aAkcE7rAvVsZ9KOCQWZ4IeesyYYhBk2IV+L1Bk2L+e3aSYw5xDnvIJyn8MC8JKlS/H5P/lz5TH+3b17KTw4i9vfdwtOnDyttNnidMVKQcYcqo6eUbQrSsYwVRI8Fy5ciM9//vOU2nlw6MBBSvFcuOyiVXj86VdQRadmn/qDP4ArlT3gKJmLKb8YEfq8oN8N+tVopgQxQl8YNm5wrlmzkmvgKrz4wnbS3oiizzU1NTR/msfmxAsQujiJZKZq6ijNTHlJnyxjBKZhAgURTrDRSdpKN7TzW+fCSwmg005HLFTn9FFVs5qT8aO/nb0hrajwiSHt8WPHsG3bNnzlK99Q6p+ZGtKG5njQPq8atXV05/v622r35Kbrr6BHJRde3PaCYYa0ZTwviqLrpUrQ8tcvqqKQ+XKLR9Cb7sCLT/6chCak9khSTQALn9twKB6389lntuD557fgS1/6knofjOePM3OR0BCCvjPK+1louAdhWxV+9Oij6KUnNTGWT3YyoWiYMI0MKcGou+NFJY6YIcpd0CiJZE3TPLQtopqOJEX9Uu/QxjOY76+RC2mRgp86dWpMJbqBnu9kV7qcUv6eFVEJK565lou5IVgLBmbQvujp6aF3xrg0RDaKZbM6F0k2q0UtT5L0v4UaCMWSUj0bqc5l059czKts6jcmbwyrGAf7xlvfj6/v+UccO3ac0roWtM5vJA0S+sOFL9+tF6xYhp/88tc4dOwUWtsYp5bmDRZqrE1Ogmkltdl8nCsvvLgV5y/rQPOcCtRSZe7N117FZRuoBiqChykpTn8tlPrJvW/tOqCELS3NDaw+QrMrG1U/l+D0mS5+6Nme9TQ2teCa629Wa++xTdDRdfqU4iedKKb316Sm6585RGDqjE5R2akTnXjsFz/hnKN0LebA4sWLcfDAISxobsVdH72NqpeZG9IqBpATvpOelESELi/w//qv/8LOnTszNqSln15xB0MVUBvaKC5fe/5y/MEn/wdf1r043XnCMEPaFFDoUyZCYPJLTdw2D9DTpbyU5SMeuSYTvbPc9ZM5mMgj809cQ5drUgwYiciclvlonNdGlZg+7kKmYMQIkIRkiYwMKpWU7u4z2Mtd1LAE5VRp1K6Px5HwCL2NdtEYN6iYzJCvGyHaAx+jgXtXV8+o1G/0Nn4JDYtF/QgxZpKFzB51yscvMreFxsdiIxykWvfiFRegbi5VQUdzZEj/xsqTeE0jo7FKk+dG4ljmVF1d3Vh+ow8mz9mZlv/666/jk5/8pJrviTK+/OUv48Mf/nDipym+j3Gj7/777x9ry91330335rKLnT7JYnPHjh30+LxSLbDS587P1cQcSVVbd3e3Yo4SeRLfwpjnO+VybsjmjXg+LHT64z/+Y8Z626qa8YlPfAJ//ud/nlGTsplXX/3qV3HfffdNcOT06quvqjVLRpWZMFNiXs6kabmcVzNpz8zvia8PNl6xGfd9/Z/p1OUEzpw+g6VL2xCkV+w4axbD4o75WLqMXuVf2IEldBJT3TRI/xeN3HCoVptt4shFkgSU7+k+S1Om/YiN9GHdZWvhpIf6decvwdvvvEaHZw60UntF1hrq/U/iJZ4+fb7R+NndJ3D2JB3AnDiJDZdfhAq3k2qlIRXOzFvhQP++PpyilNLNcBDrLl6P9Rs3sdZsKZ/Q7+zvkf7pVNoIZMQE7t+/F3t2xt3UihnOMj4YorA8j7vyVVXZGdKKd6J9+/aphcHnPvc55V10KQ1zJehmVoa03JGRjdmG+lrqTC+lXWGFCkhvpCFtaQ998fcueUF9zz334N57752wQEleFP9f9t4DQLKjOhc+ncPksDObdzZHSatVzkIIISQEElE22AhM8E942P6NI2BMNGCDH7xnYxNNEAYkJIQQEmAkhKRVXGl3tUGb4+QcOof3fdVzZ7tbPTPdPT2p99Ruz01161Z9t+qeOnXSwYMH5R3veIcx1E5vOVfw/vu//1vWr1+ffvqs2bfBHTYdPHkR92/VlvNk37OPwN4A7rJhQ2ts7tLpBoiIIx6SyGCbvP6m6+W1r70ly9YBKi1B2PIOQToFR1IpmkOKBw+9YLw//P53Y/2mBkbzZ5huso4MKh8a6sQ9iCUq6QwgXwMrAB/AYACdkFiec8k1MM+wPluFSWbe9a53QXr5+0nf7cUXXyw/+MEPJs1XTIZSSQJ3796dwQAWU5eZuIcM9/e+972xR3EM5pM4Af/a174m55xzjvwUEuS5kNK/N1Z96PjhzXD6wEXNXIltv/TSS3NdmrZzxfSNfCeIpeq/09b4SQoupF8988wzGQzgJEXPucu53mmuc/lWvJh+lW/ZM5nPWq5sXrRU3D6vCcr+1DM7ZN36VaA2o5JAEEW3xyE3Xnep7N93CGqaUWk/sV964JXaW1FrQp2RCaR3+lAQoRtGIC2ODslVl2ySGjh3SWCivLKlyUj0Ok8fkv7ebtxXZRacjdNE0LNINCghhGaSeADqok65cOtmWQunMKR1KdqJ2oDBPH26U3qgQbN81Wq55bY3SyPi+haTcn2/iilH7ykvBKzZ1IStWgiJ3+VXvkIefuh+M2H0wCPnVVdeDnWMKAxp7TCk5VQtf0NaqpMuX74cjJ8XKx5xaVrQgIDz5+dtSGs+ZMaQljEIfVJXW41yGEzeBle6pTOknRAUvTjrCKQTtEcffTSDAWTlPv/5z8t1111nXCnTZTMnbNlpeHhYuLp7djKBHLj4wQqPk7uNcGe9/eEHpb+rVxatqoITJl5LS4YywWYPxCsyeFoc/hrET0KAd0juklDjTFBVNIwQD1hNHV0kxc0pLtIORi+ZPAUCN4CVUziKMTHHQI6jIRMDNIl7aRmYK9FOELoxsmztFlmzeWuuLJOeoyRx+/btk+ZjBkqgKDGcDhWzUhHiW2+9VU6dOiVPPvmk7Nu3L692zUamZXA6lJ7ykQIyP6UOTNQQ4bsbT1qf/g0wN0zjn1zvjtiPxwCyKnfeeeeMM4HT2TfmChNILSJqdTDl26eYN99+xbx/+7d/axYwHnnkEemDI7pySLn6cL7tms5+lW8dSpmPTsbcbg9Mm4bl949tl7e+9VYjLRvT3ATdqYKfiYsvPE/CcHI2TG/1IWikgFGMwT4+pY4JO3V3WJohDGmGlNALZo62e5yLOjFHXb92mSwcCklPfwD3BbHECaczII42V0Jq3XaEZaqBR9Bm8cMhjMOVqWrNbxsD0O8/cFjcCM104y2vk4svuwoUNUVTC8ViJr+VhdZN888eAnkxgRuhkvP+D///cmDvHjl5/CiYtQF5/Wtvkq7uHhjSUh0sNZHMx5CWRGTdunXy/ve/H4PHLQehUtZU5y/QkBbPhH421cMwq8Qkv8EY0nrh5r6UhrSz91r0yfkgkE7Q/uqv/sqoFVPSQzsOJqqGfvKTn5SvfOUr8prXvEb27t1rJs60Q+XEkqpNlA4xts9cSjP3sSYxSZEUOnnacv7V8qb3fAihW3xg6sylTFgMj4Z7oBZqj49IYiggYYFXUVMMyoG6OCIR4XquzwrUOqECI2ACIwjhQq+efHZKxRzXcGfuhHx4QHVNs2xZDDUbD0NUFJ7IRPzjP/6j/M///A++FfxmibFHZsw+fpMY04mJC1TXX3/9tDCALL9Uk2iqFv3d3/2d/PCHP5SPf/zjLHpOJtooLsDKNRdgmpqacsTKyl1tK5YiVbfngg0aa5lrXF5++eVCdcTDhw+bNu7ZsyejQb/+9a+FqqIzqRY6nX2jVP03A6QiDj7xiU8UcRejz1DSg09KHv2KUugvfOELcscdd5hvRVEPnMWbcvXXXOfyreJ09ivWYSp1y7cN5jmjTFRNda3UIc71YF+PvLBzt+x/6ahs2ZQKF2GVR4aQC5wueA2tr62UBpI/u4cGwiYLvk6ge/CqzUVQ2KybbxWYt1HqZmwLayu8Ug8mMUkNFtxm1l2ZA/SS/dB45UYZCTiDwUnr0dgm5MSJ01gIOyDXXH+jvOmtbze2gJYkMy1jXrvp86W8btBMZwUCuWZrL2s4Pfudu3WbvPI1N8k3/+9Xpa2tXZYtX4zfwqIMadkZ6TK3H6trzz33tGxet6IgQ1qJh3HvgLx06KTxCtrcUGuYUT8mHKU0pH0ZEHpiTiGQTjQuvPBC4Y+qWZT6WemXv/yl/Pa3vzUSwX/4h38wp6+55hppbW01ffAjH/mIlTVjyzJ+85vfGKaRH+qlS5eaMi644IKMfOVxkCI8PqirXHPz7RIdbpXoIA3ReZ4cnpVwbKSBPKYPTzJxtNkjx8jEYxjQk1BmEDOcxn22JD83yG8DM4iVVHPO3DfKAPJRpLosJz1hRdVThVXWmoW4TiYzW2U0PfP4+7SXS7eZY9ymzs5OM0H/xje+Mf6NJbxSLCHesWOH8LcfHpXZH1etWiWvfOUr86oZpZq/+MUvhAwKmTEGGd64cSPUeV8r9DSXnTg2+CzL9uu6666T+++/30gcB7AASJtwMj8MhJxPojSQz83HqQZjbu3cudPkt8q+9957MySBHPdr4eFvy5YtMzZxZF1yvTsuJFn2aFRHz2YCyXT85Cc/kT/90z+1mjO2LRXOU+kbVmXSv6XWuVzbtrY28z2dat+gej4l8zQN4WIdF4Zp/3nRRReNuwDDxRuqJB45ciSjapxLvPrVr844l31QaL/Kvt86ptSXquJk+lmfDRs2CNXHzz//fCtLzm0x7eWz+G65YGmlm266yYwF0jWqq9JpDZ/9B3/wB+KBNGmylO97ZjlT6VeFfnMmq/d0XKdjw4suuVqOoi/2INTYPffcB8ndh40WizGFIB0iKRolRyRNKaYtbE5RFXR0CTVVPTJ/ZkWUi5pnEhlFIyyBnV/GhbED0ryUWQbt363EEGmPPfmc1DQ0yTvf/T5ZuXqdoapnclg589uSQaUk/OjRo+YG9gWOu82bN48VcOLECTN/shZLSB+4QJrdtwp5v/TVQO2ajo6OsefcfPPNY2VybPC7aS32ccF2ri3Mj1W8DHfyYgLZc50uN7wcXSnf/c9/g4OH09IDlbHVq5cWZUgbCkfglrdLjh0+IPbYMGz6zivIkLav84R0nj4mvXAEc8G2c8TrcRnXvuw8dojUS2VIW4bvu6yalGtSlquBZP5IqGn/N1mixzl6r8xlO0YnASyHtkrToSo4Wd2m77pFVpKQjrrEAYYrBgcwycjwGJkyzwbROMPbjd6TzSji2Cotvb7mnMnLs1wOtejh2E4qO58xmsyKJ4iizV0hbn8dJvxkFkkw528qVJJCputTn/qU/OxnP3tZoxmgmM6PJkqUfP7Zn/3ZmDOc9Lyf/exnjco0mUErUcWOktD0iafxBg1vzunp3xAAmQsurNtkbaLqPyeUXEiZKHEiQJWz7PTRj340+5Q5fuCBB/JmRHMWUODJib43nMD86Ec/GiuRmPP9MPH8e9/73gwmshQ4T7VvjFU2zx1O4rjQNpW+QYbvS1/6kpFgW5M+Pp5MDROdMlEN87bbbjPH6X++853vyD/90z+lnzL7CxcunJAJLKZfcZEhV3rd616XcZqLI0wf+tCHzC+bySq2vVyc4ji0JuPWQznH+frXv56hAs5xQNtTLpak06XsurCMXOessq3tVPtVod8c67kzveUCzq1vfKs8cM9d+D4OyQO//LUxc3rFNZfCp1nYIlCo1hmaxH3r6GX266RV49KnLDqX0dhUiWPkEdcgF0Qc7HYJx53y9nf9qWzddqG5YzRnxt35Hnz1q1813+vs/Fzspno1nXixz5HRTU/ve9/75C//8i/HThX6fim1vwtxxtPTZZddZtS4n3jiCeOrIf0aGU5lAtMRmd79vJhATsZIAOme1gkHLt1YNXnu+Z2It9KCTk/1CnRNDIB8DGkTWMkPByMSCvSLEypll1+4UaorfFgFKMyQtqrSJytbFksLXPtSBzs1d7SX1JB2eqHX0qeKwESTMpbNyQFd6PP3L//yL2JJAsd7Lle36L2QNoLjpaefflr+6I/+yDg2mk4vkuM9f3rPp0iYDd53vdWLJdh3EnE3Q3hkaqVyep99pvSUugu+KnhsEotP7soF4nBDncZkOUOEz9wxf/Ym67PpLeEK6u233y6HDh1KP52xT1f54yVODulFMXsiaeWnNOcv/uIvDIP4pje9yZzmxDx9cs6TgSwG0LqfEi5KFDkeJkqWXeCSJUsmypbXBDW9AEpi8pnUpt8zlf2JnkUVc8s2kJJSLiSRQeC7o+0mPVleffXVY4+fKs5T7RtjFSlgZ6p15gIb+wrV8sdLtL2jaj/73Nve9raMbBPhn5Ex66DQ+yx18axiJjzkBJvS+fQFlam0NxfWrADHa65ECQ6l/fxeTJQmw2Kq/aqYb85E9Z3uaxdcfIm8/s1vlbvv+r50tHXKN7/5X7J6zQpZhrjSqe9miu3Krx6ZzFN+92TnYhmUKDrgSKZBbn7D7bLlnK3iAMNK5myy95ddWvrxeP3aOs8+l80A8n7Lmzb3i3m/49EfllcIPWR+TaVHIC8m0HqsD2oXbtjxjQwPQpXjaazW3Ww65djCAdTC8jGkdXsjsqimQhbULYQhLYxhoUtNPjJfQ9rqqoUwpHVDKwwi9DFVNBQBTrCUhrRWu3U7NxGY7IP46U9/2rjRZ+2pwsNV3InUdrjiZTGAlH7QbpVqb/w48jxtC2lvyJVl2olw5XWyOhSD3HSUmV89UlI8LPmIy1+LkH1heC9rF0diZl3Cp8guCKHDKc6KBeKqqAcDmFIBNaulhdDl/Bo+Y7lI9Ph+cxHb7EpQYmIxgFy1Zv+8DqqZvPfhhx827uvTpTLp95Mp+fM///Mxho6qm7RdozTu+PHjxmEJpd0si5IX2vvccMMNxm6P3jip4pjucIY2tZxgUhLx85//3Ehy+DxKKCdjAqlCTWnhRGOPZVEd8Pvf/75QPYjquRZTRdtHPtdKLOu8884zqnjWuZnYTjRhoW2mlaiax/SHf/iHxiaZ+7yezgTSPnIqOE+lb7A+xSQ6YGG/euc731lU36DUOJ0BJJPH7yvVhKkCzO+ppcr/xS9+0fT1dKcvb3/72+Fqf8mYqjDt9axYgRO1p9T9iiqZfMccO1wI4Xhg+t3vfpfBBE6lvVzA/PGPf2wcI9F7NdXA0xMx43imdss3v/lNOC0ZfpkDolx0JNe59HKn0q+K/eakP3+m9xnu4b0f+HPxVVTKE4/8Vl7cd0C++7275AP/3ztTwom0+eX4dUsxbmPXzaFFS/MlVhYDCdoAjReXvx5O0DYghm81ik1dm+zdjT1/nB0uCJJecGwx8XtGCR0XL5goDeQ36Y1vfOMYffrgBz9otBh4vdj3S5t1jnNKsPltT0/0nPytb33L0CQupEy0qJl+n+6XDoG8mECz9o6+XFNTiwDtdTI82AdD2hfl4KHjsnH9SgSQpjQwlcgQTmhIi0FFT6JJE/yZRv+4AWWzm5vhggn3ZIa0nJSjAPznXfyRAYSPGDipOXGidIa0pmD9M2cRmGhSxkpTl53qZZxckGD//d//fU61OuYlk0cbCyYygPwYckXfSpx0cgL+1re+1XyoOJnhJDp9Ymflnb9bjkBLndMhLqiFMkh7FCEhbOC+jLSdAxxMYhI2fTbj3CWttbwGjU0nFmdiNHJPDU0zsDlxp+vr1EA3Iz3tRhzzXnOaN+EbYXeLsxKORCqa8Ny0z9R85wKJHoivtfqaBkLGLlfkGT/VSlRFvuqqq6xDwwSRuaJjo1yJ+c13EheptknVTytR1Y32hJ/73OfGYvgxP5lAJk6aKb2zmMBsdaCtW7caqQNV3Y7Bhm+yRNvLXbt25eUUhswqf5SiWUwgmalsm5TJnjkd18ebhFF1j3bHTHSEY6ky8ttDRoUr6bxOjQRO7q1ULM5T7RvW89O347UtPQ/3t23bVlTfIAYWs8RyuEBBps5KVBlmnySDyYUPLrZx0pju9IgL0Om2fwwLlA8TyGeUql9RLY7jwUq0p6IqK6Ud1BKxUinaS5rD31NPPZXBBL7qVa8yatyW6ucb3vAG67GTbid6z1PtV1P55rDiE9Vt0oYVnSEpa9dvkA/+2V+BUbkKixS7ZBAaQR39Mamo9sCZWTBFm0icctKeFJ2jx88YhBmkk1yw4vc9FoOXbCxkmlASo4KOM9XkfSR4pHdg/Mxf/AGddHrrxAWPoU5PRfZVZCg+cfxQ0+m+++4zMX5JH+h9mQ6QrESbW86VmNasWWNUnK15VrHvl/2U30SqnWYzgXwO6Rp/XKhXJpCIzGyikU1eiQO0tq4ewSovRwdPSkdnFwj1L7GywI6PIthxUn0nNZnDOZ4ikxeDRCEeCUJVcxhu5INwJhOB9A/34EZKHTjtTFfyShnSRk3eBO6Lm3tCmETGU1JD00lxH5/LwTn66x8YLKkhLUvXNHcRsD5OE9WQUgTLboofIH7IcqV0Ak4nDukMoJW/paVFuDJmpfR7rHPzf2sGlWmGjSopNYvEXbtU7G4vRhmYMTh34bhLOqzBntniQCgih462IQwERjW9ocGJy0ggLAePnJQQ7CzMtyLjFpYTh+c0fhBwD4imzVMNBnQJnME0g4C6M3KnxnrWqXl2OJkNHZvDlX+LUSRzls4AWs3lOYtxs85xS+JOQ3wmSgrSJ9LmJP7we84JLSVSTJTEcBKYK2Wr5XGSQ+cyTONJIrPL4QRkOtJMThz5rFzPozTIelec/FNKRMcHlJzymInvhJKdiVK+OE+lb0z0/HyuZffdfOvMRTYLCz6H8fq4iJH+Y3+2JN/MQ42LuZYsKa9VL0rQLc+v6e2brvZSmkN7XIsBtOqRa5urr+Y6Z907lX5V6m+OVaeZ2SZh6tSEEAy3yrs/8Bfyhj96v9jqV8ug1Eic9McQLdKpM7RxrF44xcu9A8PyzPMvyrMv7JP9B0/KC7sPyhNP75Knca63bwi0MPteHo/SUC6w4ihmc0vEWS8J3wKYQdD8wZoV5z1NH6tWrh3Ol/j+P/zhD49dZl+yvvvc0rmVlRjH25pjze/3a7VIt7kQKKh3+Xw+ee3r3wCvihgccPf+wC9/I089vcM4kzAd2qyUZE8OrY7MIUSVLj5ydEBgNkk2MHt4pCqKs2bw8ar1S12x7jeP41VcDkMaefh4a0kNaa2n6XZuIjARQbNqTLs9SgCtxI8evfJlJ67mW4ke38ZLXB2zEj2zTUfKp13T8dzsMhMcYEkSQb8c/NVjEuzpEruXjCACOhh31tYdHPOpcc/FnYOHTsg99/1KfvqzB+Xu+x6SBx76PTDvwDhNqXSa1SHrVo5tBqwXlyRQ7sCx09Kx/7g4fHB4QgJ8puixO+b7jkVYJ2qH5cGNeSgxGi/lcmBBxw6W98aWlhYjncp1PxkzSmCsxPs0TYxA9tjMxdzR46X1Sy8tnVlMP1/o/lT6RqHPys6fT9/NvofHVtieXNfGO5ePlHm8e2f7/HS1N93mcLI2ZvdV5p/o/U2lX83fbw7nlkxchBTEvXWLtx6LntVLZEjqwAjWSyjpMQufKXmdyXbmDwQS9HJNSbAReqCYjs5uGR4JiB0q/EH4vxgchIM149TszG0WveSZBObFEbtfhh310icN0h3ySM9QVIYRoD5FVdPvK37f6g+UJFseQamdQHVjJjqw4jET4ybfeOONZp9/5u/7HWuC7oyDAGZ0+SfapVx4yWVy8623yc/u/bGcPnkaKkt3yoqWpbNvSAspRV3jMhjSbi6ZIW3+yGjO2UBgIoKWXh/aAlIllOqb1uQ4/Tr3m5ubx05RLfQVr3jF2HH6Dr0cWmmiybmVZz5v7UbtMyrHfvWgHP/Kv0vfxlWy/K2vl+ZzzxWb141A71TvJJmyCGlSKhBc91XXXYLVRTiAooc1lOGFMyk6jUqOEsrU4s4oMiCgJJbxkZCc+u0j0vbzX0N8VSMerIYuuehiEFgsEnGVp4xStjQlV9PSPWnShm+8lGtBo7a21qhesq/TlT1VbCxpeHo5dIRkqedQssd4fvMtzXTf4DeHjJ+V+E2x3gHrkm67aOWhtJQqVpxgPfLII3mH9rDuz95OpW9kl1XMcb7f3fSysxcraAs02TiglG2+prna3onGy1T61fz/5pDFSyL2NOgWHYNCIyVu88qQrR6uDxEMItEvXlsMfitSGitnmDMINbAgWocYghdt22Lo4eDQIDzWe0HDfJB+h6G9hhiCDG1kWLoztIzvIo55a1gqZNhWI+FkJYLQg66GKS5JwkNpTPwNDnHgm1OKlD5uKQ2kx2ImejvnHIlbK1EKmN5X5v/7tVqm22wECmICeXND4wJ5z/v/XCoqq2X7o4/Izhf3FmhImz5pHK2OOZWSCI4nF8yu+JmVFAxe2Az5ahtkw8LNGHiVyJoaoumd+OX365n5jkAh75dG+nRuMZ4NCQ2X6UGUiU4pKA3MdgfOwM+0Q7ES4w1OlOjOnHG1mOjQgF7v5lMiWew9flhO/Ne3pRExjpJ7DsjxL35NOi66QJbd/EqpWblCHF447ID9QwIiQJI3G5lC/DxueAt2eQ1/SBtgiaUxcxye+PJQ1TMWGJGuvS/K6YcekehzL4gXFDju7pETP/2R1K1eI/76uvkEWV51TSfG493A/sf+TeaBzlfYF62A9tY9ZEByhY7gfXTBTUcV7O/0VMl+a6mt8X6u7DKMgSX9Y18uZDxZdZiJLZknOi1IT4x39eSTTxqVS8aWy5XIrL300ksmFtZkDEeu+3Ody3536Q5h6H04Wz2SZdD5Ap3vMN15551TZgKn0jdMJXL8me5339LSYlQYufDAxGN+k3Ml5iFjbTmsyJWnFOcm6ldUs85lEpDvc+dCe3O901znrDZNpV+x3Pn9zUkxWtV+l0TAtA1HYKIA6Z3d6ZWQE7bxUT8YtBHxQD7okhCYNNI7EDEwhUlHQpwwnTDTTtjJN9SlYq8mJCoVWCy12b2woefCEdXJ8Rc281w/jdk8EgTzFyTzJz6onsIUAmqjdmjEJHAmwcymUBJM7k8tpX+7uMhNe1OaATBcDb1DM5YrExe3KS1MT6V4v+l9jwubltMnqqFSGmnZgKc/dzr2p4MuTEc9Z6pMzNk4ayskYb0Et7QjYPyOZ56U/ft2yzA+2re9/iZZtawRsZyDKfvZ8Yo0Kma4aJwjpFZU3TSkxSSSjiNsdq58YB3EGA2OV0halR0eccKToauiWRzQo06l0gya8Z6u5+cGApR00DU2E/fJdNGhi6WKQ5UH2jxRHZSTyG9/+9sZDjIYCNWynWIZtPd76KGHuGsSYwLSoQA/XrTxoHcsK9EhDL2yjZeoKsr7reFF20Q6PcgnUe16Mnf6LOcw7L2ScKCQnlowYXamSTXTrxW6H8fHeceXvyAj//0deOP1kJUzDF4IO3Gs0ns2r5MFV1wkDZs3iru+Fiv7DCCPLEZVlLnTxilVwUHgiGUCquSh7n7p3rNPuh97WqJ7XxIn2uGi3QSv0464foE0v+f9sv5Nt/OpU06UhjFuG9+vtRBAo3i+H9aJgceZqO3AWEnWKumED4azE7hSy8wCZ0Jr4NhiokSim48qMb3bpjuHYb0sI346WmG8pvTEPsPrn/jEJ0zMJ3rttOyUaENERpJ5OMGmO3m642eiWuh3vvMdE6ibTCHj8jGQt8Ugskw6oKEqGr0Qfv7zn0dg5XvGbEm4uMJ7LPvC9DoVu097RYvBpSMaOgThu2LdydwyphXTuZBKcyKTnXidk1JOLFivRx991Kg4Zecr9JjlctJC/LlyzndgSQZXrlxpMPzkJz9ppFw8/8///M9GA8Hy7si+xu8C48rRE2qxOE+lb6QvBljtb2lpMX3fOs61ZX/huKCDB6v/FtI3ONH72Mc+NlY0neTQYyzLoE0lHRFxTFrfdDoUYr9k4qSR3gPTA8Vb45eTW0u9jftsH5+TLtkyheDwewnkAABAAElEQVRPvv2KE2F6gyZN4WKLZTfF+hIDjkXWl++XfctK7HMf+chHTJum0l7Smu9+97tmYk41TY47JpojkD4wcWGDdO0zn/lMTsdJlOyTxqUntsMaO+nnrf2p9CuWW8w3x3p2Pn3Qypu9XbYsilAsmWevu86G8QlGrYDEb8wAVDj/57njMhKiHwyPker5fR6pwZpmlTMizuiAeOLwb5EIitcB6b+bYXUco35jztC8lGNRMpegMdiw7JFQVNp7h2Du0CCVjSslZAcNh7ppDEwlfV6EYxEzD15Y75dGv1OqvCw3RRcLaEbOrFRZTVexZr/O5ViM3zU6acpO9Nw7lfdLz7OWzSH7MekGGU9+Qy0G1HomnWpxrKZraH3ta9VYqH/5ovDevU6EKuLMY/I0XXRh8ifP3RxFMIFsDDs6f3bpgzOWEx29mEjh41uZlMp4n9gTcADB6+a9pL+c1ADBWMB9w3Lg8DF4EnQa9+QjkAiMDI2IG6pja1e1SH0tPCMx41jCfvohzsewwpLw1ImnshGSB9/YJHTsFt0pawTSP2rjfdAIAIk2CTMnZVzxogcspmwmkOVxMnv33Xeb6+P94ceLxH8iZxeMp2MZYHPFi1LEfD0c0jspXYBPlqaTCeTKZdf+3bLjg++TusF+SYyqpJjRbIYiGDWMz5jLJUkY1XvXrpLq1S1SvWKpeBfUicODBRks5mAQmx+dOwU6e2X4+EkZPHhUQoeOiK29SxxgCMn8payD+UVhSkoI3xPX1otk88c+JTVLl00GxaTXGeiamFoT9olusJj+SZ0vFMkEkiHNxwsaJ2xvectbMtzqT1Rv6xr78Dve8Q7jbp9932L2rOvpWzqOIdHnYgcTFzbGC8jNMUavnfTsmJ0o6co1ocjOl+/xROM5vQwyhw8++GD6KbPPCQedaFiJUifiMtVEppKLCFSXsgKcZ5dJaR+lk5RW8v3lSpS6WIxh+nUyRvngPNW+kf5M7uczAaemBCdm2SnfOvM+vhN6J8wnURJoLcrRyyq9heabKP2mpDs75duvKCmhJJBaIdnJqhfHgRUsPj0PpSi0PWcqtr30Qp1uepBefvY+n5UtuWEeLn5kq9SmL5xml8PjqfYrMgqFfnOseuTTB6282dtSMYGkPW0DQfnavS9K/4gb5g1QzbQlxA1pXwO8ha5YXifLG6qkwhbGgtR+6Th5UJY31cjy5hqEO6PwwqqZmQFDqpeAWqlNujDfPdbeJ8dOdUvCWStbL75WqusR7qQ/IN19g9I/GIQqKi0TIQQB2XzltoWyrrkiVRiYQNKkqSbSvvRFFJZH51Vc4LYSF0O5wDdemsr75byLz2MfzCdxzsa+ZKVSMIHTRResOs7HbWrOVVTN6cMTk0CItH21i8WB4NJDMKIdhHg7AqF5aizk6ric7tGrHDSt4WY+Ate5nVgZDwwHTVywQDAkA3A9TolAZrKOISmAg4mwrRL62nXSn6yTnpBTekZiEgjTODeLU8wsRI/KCIH0DyOlBdl2GGwqCfZ1111nWs1VYrrKt1TDrK0FCaVAnACTcKc7gLGuc1LAFXxKlCZiAJk/fXWYzGC+DKD1rNndYmyDOTv94EPi7u2E3xY6bkEaHYLkB+n22g3Jnw8r+P7W05J8+DHp/tb35cBnvix7/+6zsuvvPy07P/YZ2fnx1G/v335OjuAa80R/95j4TreJLxETt2EuU98EM+T5DOy4qBlw5IC0Pfow6LIlVSx+bNNWi4bu7AMTJa6yU2I2KQM4USGTXMvud+NlZ5+hMxFOZnP1N9aRzER64jElKEwcEzT2p6onmb30VIGYr/TGSHVGiwHkdapFp4cx4DlOJBk7iol5W1pazL71h04Ecnkvta4Xs2V5lHDksmXkuKekhtJ1StNyJarzWe+a+UsVysUqk94/s/sI3yuZP0sqxTpaEub0OnJRiH1sKjhPtW+k1yfffUq/sqVrhfYNMpKUsKWv8Gc/n++cYU0sqQGv89m5JJjZ9/KY3+7xVPXz7VdUn2YfsrzgWs+hozFKKZh4PXtc8Z2mh7Eotr233HLLuA6drLqwL1Klj2E78k3pNDPXPVPtV8V8c3LVY+bOkaak05WEHOqMwEtns/jqlsBhywIZjNbJ6T6nvHB4UH77xFF5fPdp6U14Je5dKr/ffUq+98AT8ttn90s7vIAmMZ9NxMNwFBOCDXwEzl1CsuvwSbnnkR1y5y+2y4snhmTBinOgBlotT754VH775AH5/Y5Wef7goLzUGpPjPS5p7ffI4daQRE0ItNIhkevd0xN6esq1cJJ+fSrvlxJ/jmkucmcn9uH08+vWrXuZ+UP2PcUcTxddKKYuc+WeKUgC0dnh1a+tPyS9Aczc8EHi/M2RDENU3ieViV7x2mNY2U+bwDEDEuP7xY2EAJNLTPAGh4fE5/GJB04lQhDFUxW0Ch2Fhrom8T6TH4a0YABD9moZkWpICypx7MJzaUibgLg+Kc01HqiT8obRh6VK0L9liAAZfjq+mK5ECQpXybiCRlfm+U5CWB/ab1H6xAkJVe+syWM+dZ1VSSCGXBIrn/0dPfIrrKYvPPy8eFx2rIaSEUytbnIokqCkjlIklCOVapxRXIw5PWIHg+Lu7UI+uqXGRd5j+K80hm8UDLNgZMocLQvfBL7bQXuFRK+4Xl7xDx+Fr5gqjPCEsZcYLSgfKMfy5Ct9G7shn50iJYG0eWLfKCRxhZ5qYezvlFjTqycZPks1bLKy2IepvmbZyG3cuHFsMWSye2fzeiAQMBKzU9D1Yp/jQgwXdhiPj4nMRDYzZtWXqqMPP/ywGYvZNoVWnkK3tCOzVPMKvXe68k+1b7BeK1euzLs/UE04W32r0LZxfFOqSskExwKZLTJQZJDphGK602T9qtTPn4325hobVLvldySfNNV+Veg3p6VlcpXk8eo9FUkgaZexZQedCgOf7z/aLUe6QcOwqGNoHAkYvp9UrR8CTYsEOmXrhga54oJ18tyTj8ld99wpNRUeuWzberninLWydnGtofed8Aq648BpMHkvyeGjp2VBMxwXvvY2qWleJbte6pZTHcNQ//QYPxb+Cr+4sOiXom0JafIH5E2X18siSB85D4al4HhNL+h8eggW3kgpMsO1MJHB46JjvqnQ92uVS/pFVW7WhX2UcyTLPtDKk2tbCkkgy50OupCrvvPlXJFMIJtnho4MhWLGne1IFAMFzJcZOAnoTceGxJsIiB/yQYctaMTimEriLkzw6PEP/1iEmUzimBNLfig5aUxNNvHXHCMnGEyuisTgrSmE2C0BMIBR7Cfpcp5jA+JyhF+WKjijWFTnwlNQtpl5zpfXoPUsFgESKn6MSNz4m2y/2OcUch/dw1NSwDSeqs5E5c0eE5iS3wNOefa5Q/KVj/2nLB9uk1WRk7I00SdVWNRxYlxhKGJMZ7YgtdQDJrC6Qarf+GaJYGGn61tfl0qPC2PUAd0AJBBVmNHDnB5jevR+fkX44x8SO5oTYvlIjjsa5YCnRZJrzpE7PnSLXLh1Ja4iJinHtvnxpvwTCTgn7/TUaP34vZlSKpIJZB+1PEZynz/WxdrPPp7o2pTqXwY355roTmezOIHhL9/vzZT72HQ2Jq3slSvzZwLZP63+a/VNbqeyb/X5+YJXGnRzdjfX2CC+VMez+rHVl9OP+S5mI7W0zBYTSOLDuSdsT/vC8t1H+iScqMAxqU0Ki6RRyYSoIYHQDQM9MtLbKq+8ZLmsXl4D282vI6bli+KB+eHmDcvl2gvOkZrqSnn6xQPy1Av7pbt/BJ6Xm+U1N79RmpdulhePDUhHD9zKuD2wJ0T8XbNQSqEKUDckKQETibBcv9kll62jDRxnxKVhAq13z3fMRW5K5yzbb6qxc/Ha+rbNtbFYKiZwNvr2XH5mYVazGS1JzeIqYLgax8r9c88eg+gbhrQ1iLPi9UuFzyu1cJMbdVXCkBYucxMwpI3BkNaFiGB4ajI+KlUgU2jNxVAkisIcDyHk0ec55RuGIW1Hz5DY/Q1S1bjIGNLGk4gpBmlhDF6cImBCUZg013mlBp6d7GQoMWBStcuosB6UIQKFqlny42d95NKZxvTz2fs8LiQ99thjJjtVdXLZakxWFonP7KTUmIxDpWXn/lY56VohXY1rZF9sWBYFDstVA0/KSkcIVUuRxkyyBAYulkCsI8Se23aJ+LGa7/BWwnka4iSdOAU7wP3iOnVc/E7claN5PBXB8s1BxwJ5svI86fCvkKDdJ1VRh7x0sN0wgSlCaH0sCkOIkiNLemTdyYmPxRBmbwt951aZ+WwpFS60345XrjXxZn35K+R4vLzjPUvPi3GeQrXxfBMxLvR7w/xzOZWy/+Zqp9UvuU3ft/p3+rlC99PL4L3lnHLREZ7jt2ei7w9xsZhC9kVrn9v047LAL6ML2OVIRwDSQAgSnLwAJhCb4Mgw1PF9kgTtIsNWXd+EdU2ohx7slRXL6mTrtgvkZNtpCQ0PyI6dB6UVtn+1tTWQcp+EuVNcauDk7NLLr5Gm5bADPhGV/qBf/FXwGgqNG2KYTEYlDJ8YbmjD2aFFw5lvAnPco11xuXA17BGxiJqLZhbad6kNQ/V/LlIzUSvEYgBpLkGnKXQcZmktcaxY3670fesct9nnC62T5p99BPKnZuPUlZO3ICxadx3pHzWkDUBK0JtlSLtAKhzVcrptr3S3HpFlMKJdvgDeBQsypF0P1q5aunoD0tnTIQNDIYlgbk6vTBUVDmmqbobUYXR2yY/7rE2kxwFKT88JBPiB4y9XPK/xKsgPdfYHL/3jZ+1bWzIbVB2ly/i5nlKkDn+NB7IUrQlHorJn70nxuSmZi8oI3FyftC2R3j4vmEDY7o4SRxIm3s9lF66URpqapeVP3iNVS5dL74ljUrFpi3Q8+ZQ0XnWFVLz5dmn/6U9k6Fc/Q7wluzhBdDhGOWLNqAW1jSfs0uWA/UXlSolg8QjGFRKAve/hw3AoEwhKpR+E2MpfAmA5mecvl0ol33c2Y2gd89pcSZzU0RYtXzvDfOrNfsw+z631Sz9O358sr3U9n+cWkyfXRLeYcqbrHtbP6meFPKOQ742Vl1gXm+YSjlafLrYthdxn9U+rT6dvuZ9+nCtv+vXx9qfyXgppS6nyEn/Sx8loJNs1HoOYzjDmW69Z6YNGaIAagrAEIHDYAcldVzvoE9YqkzB3spNBg6+Jhas3IVNqfNGcqRJeV4f7YnLoeK+sXLkWKulwhob5rH1gBExgr7S2dYoLwpBaqDmvXA0V0U0XyNH2BLTmUCYYSJpBcZpqpaHuVomHoTXnhTdXCDWoLRfrTUj72g2yDI7WUpTSyl3cll6d6WApVyJt++u//mvjbZZOiZiKmSvxW2R9j6z99C37TPZxrvrouZlDoAgm0Oq5KYaLL/WMIa1fIlS3grpBeHBATnYNyuFT/bK2pUEu3LJUwq6F8rudj0A6MCyXnbNSzl+/TBorOLFjME1qdtplGKtNB093G6PZfYfbpbpuoVzziquMIe1eGNIePzUAZhOTEyiVOTwVWKHxSlXYIcc7w7K03ovBm6rXzEGoTyp3BEicCln9//jHP25clHNSkP1BTP8IWvvW1so7s3iOjucUJ2YmPV29/bJ6iVvecdvlsNvpE19FlTxy9/9I48lhSNpT6qDpXwESszBWMf03vV76sSjTc+89khjskYq6BnEMDciRz39O/BdfZphC5w23ig1B5Id2Py8Vg/AkjPLIBdINtgMxlyqgRu6IhhBLieo4IBhww93eNYDfoKxZ4TMrsymucXpRshirbOkhn8r3RaIZgE1Tz/RWY1ZKt1aCS81YEjdrosz97OOJruXKOyvgzNBDrf432UQ8vTrFfG/4zTmbk9XXpxsDvhvrZ/Vlq7+nb6eyb5VrtWW6mSpiR4dVuZxWWXVge9jH0plCi3G0zvOYdZ+dBP0SEjPQoBM9QTnRCelWGHPYFFmCx86gxCDksNuhfWYqiAtc9IRTQ19lvZyCdG3V0ho4sKqTkdYhqaithm2fC3PgMKR9VeL1VSB8wbkSiFVKRy+j0GPxk+pupHsWETUEDY4NQQ/90KRJQuWUi6r9SaccbAvK4sZamGHw4eaPqUUxf+jAa6LEOQ7DikwlWd+tfMuw+gffP/tDrp91zQFHdJpKj0ARTGBKZcwypA1ABezAqZDY3dXGHtDtcaOvchW/dsyQ9pndrZg0heXSratk7YYL5Of33wUVzxFpH4zIFVvWyKqFNdCBtkvKkLY9w5D2ymtfI5VNK+XZfZYhrV+8mJTScYwTHyAG9MQnRo50RmXz8pgsqOS50VFdery0REUgLwRIgPkrdJLBjyJ/M5U4mqk+zdXHRAxECsSuu7tLtm1ukE0rHMbInWqYrY8E4Ak0ACbMbewB6dgpAUJlaBnbicDulRvOk5MP3Ccee1yql6+U1vvulS1/8RdQ2w6L3VslC8/bKtU3vEZOPPhzGTn6kiQH+uDpF58LNJfkjRbDftgRu6L9MuyqwXmsiIJg9sKNdmtrP5jAZktgOVPw5HwO3ynVqZI5vJwVMmnPWXiZniRmhY6FiaCwxsl0T3QnqsNcu0YsClmssurPSVYp341Vrm7PIGDRA54p5eLKmSec2ZtLY8Pqk5P1y5mkeWeQSt9LwsFhTPz1S6XRy2kxFvqgFjoMxmwEAg36pcAa5Rm+DRTL4YSX+qRP+iCUWIQwSSdOHwMhA2MMuuAB84dYaTCPqpaFS1bB82cUJcKJ4Th8nLeqAcxfUhqXrhSXLWLoYRySyH7EKiStpR39VNO73vUu4yU5l1Mnvid6NR7PydZUnz3e/Vb/GO96+vm6Oi5YzdZiQXpNymu/CCaQAHDiiJkb/rcPROHSFtZ7GCSpxPO8asMqSKW4F3lgSOuTXQdaZWFDn2y94CLZg/hj+1/aJ7969AVp7eqVq7edI1WV/pcZ0l736lukunm97IYhbedwhfjr6qUmy5CWE8g4JqPgKSF5DEkjmEBNisB8RSB9sjATbUjZz8JZQHBAgv1tWMV0Sx/i9zVVg8mLpcZ4jMxYdxvGNDz5YuHFBXVRD6kZLvMHLU6paFkl8WBAhvfuFPe69VKz4RzpO3VafM3L5bJPfE6G207J6eeelWhnu7T94DtSDUctDniXsfhdFsUVUncyIE3efjnv4s2ybeNis8DT1R2Q4HCvedzUSSEfNI1pPCo/jY88G4ue6XFSzhgrA1heb3c+jg3WeXYS5qp4NBc663xxOX8ZBQmU1MWlZzguh+HPYhDXjPomF0sNFcJlTDwjgWEZ6DouPYvWQBLYlOJPUoQMuTAnBhNXU9sgIQSDp/aajbZ95vpYMdghHY3DlAmB6CvDsqZ+SBbWYDEHeAQgjqzyGzeHqXz4O9VET5z8aVIELAQKZwKtTowSyHwd6wzmbUi7+0gfXMEul3O3ni+nO9okDEPaZ184KCdbexG4uzpvQ1oThyUSwoQVhrQUn2PyGI455WRvXLYsL50hrQWSbhWB8kSAgzn1S0aHxRYLSDwyDDXtGtj0wglAIuUExgYub/1V50nVNedJRX2DHPvWf0ns8HEjdUBkTgl5EA6ioVF8TfWy8IZbpH79Oll8ycXiqKmQzr17xQZdlv79e2Xfv39VNt7+x1gcghQf8ZOMBJA0kLWgZLGqQjZB9fuSa6/Fc6qkusoLCYUHtr+QANr9xrGU3QGV0NmaL5RnJ9BWKQKKgCJw1iJAe7tkMibNriGpWR6QCjgvBCGUPl9YVnoRxxpB459HDMBAEtoppD0kWFzARJzb8Mgg7oZNNoUg5BENbUIG5uEhbgiGQNswV+ZlcopkIGlvaBL2ndBkW9MUkFWVkARWd0mtT8zcNhrHFUgTYSyI5708tl6qAP2rCEwNgcKZQHZgPhN/BkfC8sJOGNJ25mdIGxxAMMzTg9KyYrVUVddBndMhgYEAHMZ0y+nWjrwNaekZdLDzpMRhO0S7QEoQBPZDrhEnVnJWl8yQdmrQ6t2KwFxHgOQPxCweknAQHnglCkKGAPBQR6FaiyRTXsnsjqRsvuYys5LZuXs3Bj7yIh9XUKPIY7/qFbL6dW8Sbx08gsIWom3vfnFs/70c+ta3xNl+SnpaVsuC198KrYBlEujrFHssblRKef8orTRAOZc0ydIbrpTKZUslGhqUaBhe2eB92GUDq4nnxUIj4qkEhdSkCCgCioAioAhMGQHDtUksGkR86wHxOsOYTtpgwoBAZI6wNC2pkWXgwY4gtutIpNrQSxOybKRPBjAHXbRyjVRX2GT33lOoSaosa0u7voHBXmiFhuAV342wE2AAWV/OV8H6GdEhFj/dtpBsbArLpuYKCYcQaD4ygrmxEyZS0MAB7U2GB0XgndQqFzuaFIGSITC6HFFIeVjR4OwN3ZmGtKe6YNSLWG1RdN54OCihwJBEOaGEbVEqpQYGB5bbXydtfXHoS1dJHVzIUxWtEoa01Y0N4q+plRpuqyrTDGnJ3FGEzm3qmVZNE4hFGBrqM4wgB00E6mXdUE2lIa0JRJ8xvbTu0q0ioAhkI0Ci5kY4hwRWG2EdlEZrUixakmMPw7D9+Rfk4P/+T7F39xn1b5zC8AQzePKIvPgfX5ETT2yXl777fTiGuQvOXSLiARNXGxgQx4FdMvDE47L53R+Am20fnMYMjI7ntEdhvMbh8TfQ3gOJXwSOAhi3iU/A2Mf4t8dhU0GbRR5rUgQUAUVAEVAESoRAAgIF0h2qcRqqRAeDIH+RaBwhHbClzQOTUV+BgyuEJwsHh2Xp4npc7JUjxw9DKy1zOs2y+vr6pa/zoCxZAGkhgiCxTBPfmjssC3noFHEAtn8hOKSJQ7oI1Rk+CDlwgXQP/jS4q0kRmA4ECpcEjtaCKpj9oYRUNCyHIW1qspaPIW0w4ZMhDKrmpkY51XYCk0s4WfBXGPvBfA1pbXa4dYchrcMFV/wLl0B/OppaYYH0opSGtNMBuJapCMwlBAxtsSG2Z+0ycfnqJDgA279QP8bTKDFEZbn8EsMiz8kf3ye+dkjsEeQWZBDn4UocRMq263mJ79kl3bAVXHvra+Wle+8TR2WtVG7cIsOID5iEkXzs0CFZDAlj87bz5NB9kN4HYcQ76smX5fMpibYOOXX3z8XZUCWVSzGu7VD1NkwpagmCaYdNRcqG0dygfxQBRUARUAQUgSkjQNpCgUNKzy0JTRWEcXC6If2zybOtXukL1yKGH7KADo30dMMecEDWbV4vK5tssvMZLHIODmIqy3nwaAJRo/PEcCgi+/c+L5ddsUCWIM71qU48AdozpK8pvg7MX9Ive7pqZIG/V5ZVwAENpICsB+liagGWC7MpKmkVr1tFoFQIFMEEonuyg2Ny1uCnIW0UzljgtScPQ9oQ9Kf7+lqlr2m51NXBFS3nmWYksMvnb0hrR95Kb1I8zpCsahyGE4uUdDIQs0l1iQ1pSwW0lqMIzEUEzpAWO1Srq7Go45KRPpCokR7QHaxKYv2S4RvaH39a5ODhlDtwM/7JAuIqiBO99CZI8B57WDrgb/ucP3kfVDpXmG9EOyX86zdJ/8GX5MC/fl7cCETvhQOZZFYoF9pOuHAtfqpd+p/dJ7XLV5KKgmDSoB4P8lSK0189FyHUOikCioAioAjMYwScMCtyeGslgQVQGMeDdoHuIVbtsK0WjmE80FDzQWUUfughtqOmm9M+IudsWCM97Qfg6HAXaCWJ1BlqmoKCk1vYFHa1ye5dT8i2i6+TOLTdWnsg3UM5tCMkY+nCn6izXrqhLbfYExRnErb4oHvIIXbSPdBlTYrAdCFQBBPIrk5D2ogsdg9Jw4qA+J3o7PkY0kJqEIa66FQNaX3OiKxZFJIVVTFprOqSGh8msJBO0JA2roa009VXtNyzAAEbHK9U1C2VkNMv8RDUPrFqeeDxJ+XIt34gjYj/mWBgcjB8ZNpSK5lcrQQwOHYjvlH4N7+Sg7t2ydI73i0V6zaCgPmk694fSfTAS1IBtRYXVlttiMTLe6kNM1YG9riwFBsclhMPPSzx5kWy5vprYOoLVVB3lXgqGuGS23sWvAFtoiKgCCgCisDMIDBKgaB14q1plggcDSYQq5az3M7uIdnf2i5V7iaphYQuAROnWMIji6oXid9VJ8Pdh2T79kdlBIua9lH10bE6j9JEEjgukB6FuqgHtPDcbdfCPrAGc9WEeBwMzQKGMwE7xBjMmXr65CDs5dcvqxU3FlNtcIrm8EPjDYygJkVguhAogglMrXbEowHxJAbFC+PZfA1ph3vaZVHLSqnwJRHXrxVtslZOUtt8DWn9rpBsWhiT1Q0+iNtTutxul90Y0mKGqoa009VbtNyyR8CQRNgG+mq9sK1dAAIF9ZSmU/IMFjrr/F6pBcdXg9VNH4lYnJ7NkkJnagy4S8YQvp7E3dUup//vVyTi84q/pwvSP4SUgGE83Ili8QiB5XEfGcc4xj9c0UgYntZGoEqDNVgZxNbX2Cjr1myTysaNIKDIhVXWlH0g4WcNre8GjzUpAoqAIqAIKALFIJCyvSNFcbgrxVMDU4dYLY4SMnx8nzx2/0/gxLBa/A3N4qsGcwZtFAeYuWEENt+3Z6e0dcDRGeae1JYhbRozoiCZwrGhVNhPROJy6MAeqQANbFm5UUIjwwgoPyThoX6YYPQgFmEvzg2J5/IrZevmLbSSAhPogqQQsSOMlJHlaVIESo9AEUwgK4HOjlV9GtLa4ZXTeDrKMKSFCWwOQ9poJCCLF1Lk3iHHTh6dxJD2XDneAckDglMnqWvN2aMRHSSwGoPJIiTm4Qjd2HOSSF3u1CDMMKQ1I7D0oGmJikC5IjA6ikzzbFCDScJ99UAwJG1QWemtdYsPAXJ9CHrrAHPmwthzghl0Y0B6QBRdYO7ckNyRKYS3KIkPhiUCxtBXWWVsG+IgZmGsfsYwXsMueGBDmWGEj4iC4kVxLYBywiivDoRyBOWT+LE+6VLHlPxRB3a59j9tlyKgCCgCM4lAyrAh9US7A4wXflTX9MPMoe/QbukBLSPj56+shDaKByqaPsxvbTIYDkgVQ5SFQY9A58w8GHNUkEV4F41hzRPqnDhP20AQUpHhhBx5qkOGX3rGeLqOwrEMf6FAAE5mAibkUuXVl8NxYg2y8warZtzXpAhMDwJFMoH0JF+4Ie36jWtkaX1CXnjmRXiZHyrakHYoViH7uqul3oe4gz6ql6UMejkM1ZB2ejqKlnr2IJBisWCvgAWeBIhYfV0jHDchJidiKcWwuDNMAoXzSaiG2sGwIXK88fFiBwEk22bGIbLEwBDGod7i8TgNI0cEY6MEEbyeIXNmcYfF4QcekpYY4vf6pLqmygCeqgvJoZVoe6FJEVAEFAFFQBEoNQKGeqUKBTfnMIQJXF1gREJg1GygeS6XS6KgbdUet9k6MRemwxhwj7gP9vSgeaFYTPweMJSgkwlqvvAf7iH562g7AmeGEGIwVi6fhJNkFF0uzGNj1I1Jp3cvPzIZ9I8iUCIEimQC4QzCGNJCMSxEd+/5GdJuXLtKuk7vk/0Qi0/NkNYNm6V66cOEtMkY0tKFLgYYPDrZIdJXQ9oS9Q4t5qxGgCxdAnZ+bScPgnsLglDFJArj+CgIVQKSP2rAgMYZwubA6il/Tjd+Lqc4HQh0C06PDmAYdoLeRaPRqEQovYf9MCjlGLZkG2kn6IQ3NgfuCwd7pfXkcVm1/lw8IyUNHMusO4qAIqAIKAKKwDQhkARDFgoGZRhM2vJ1GxAOImHCN/QNDEhXL+zkISWMhePCZcpwOCIuaLIkYS+fBF3zgjGkUhwlieEoNF4Q9sHu9koAXkK9FRUSRBxduw2SRtzrcSMG4dKl4vP7jNTQQY0YBJEPBILigykFaasmRWC6ESiCCcRSBpPDK95qy5B2GCcmN6Tt7zggTz/9OAZEcGqGtHEwfbERaYchrRdi+VWLwPhh5NngOMLhrVNDWvOC9I8iUDwCJITtbW3y+K9/IY/+4sfScfokiBxUXbCaaQSBKJr+y5iorpk6CX9mdByDlVKP1w+GMOXIpQ92u6HQMDS1EfQdizW8jQymlVIqniyL2gV2eBAekgd+fCeYwaicf8WVUl8PT8KpNVPrFt0qAoqAIqAIKAIlR4ASuwhoVWPTInn9H77TmCPRM2hf34AcP3lCjhw5IidPnZKR4WHQuCoZxgJn0hmTIIK6VyGoe2NdHdY4Y9I7OCRBCCqqPbUSRHgkT1Wd2MAsVlRXyZo162TD+g2yfPkyqQATaCPniIVVOx0cYrHUxYVUaNmkaGvJm6gFKgJjCBTBBHLKlrLAo0t5r7sCQjhO7pISPLlTHvyv/4MVDqe4ahuksroBhrY1iD9WJZ0YRIePH5FhrHLYob5pA/PGRHUzs8elkdFysYFJUVIO7N0pYRjMrl+zCfZFfdI11CuB/l4Z7u2QII69Xq/U3fpGqdyy1QwW4zwCbnc1KQKKwNQRoAxvzaZNsuHcT0KyB8cuqSFrCja71rFh6ngaJ/AdIJ9n9DvN8eg+xjnpXCoPNvQkM5rM3ughvy0wBIYUMWK8gdLpVMaDrZt0qwgoAoqAIqAIlBgBevN0Q6Nl4ZLlsnR5C0onXUttQmD42tpa5cSJk9Lf32+0XsLhMHLYJIItF0Eb6utAv+IyMNAPE4ek+GDewDKo5UJpnw+mFRs2rJcFCxZAGJI5X6WGTRxMqFlsxV2aFIHpRqAIJpDd2czmTN2M5z4ax2Li5oUEIDHQKcPRkLh6WiUK0bhhzND543AG4YKxbB1UvpLwBogLuB+Px+CKQcIQgZMZD3SoYWWE+SNjBnI+iYHUe0xe3PekJFFmEnmS8DQYxWDjYPQtaILxLsJDQAJoJo8ZNZtu6LR8RaB8EeAK5MLFS82vfFupLVMEFAFFQBEoRwQwTZTWVnJvhSUGc09CLTNXstkQQqlijWyEfwsmCj/yldYhK+8Yy9/ZaSQfPJmWUlNylmmmyIb7TLt8Fu8ODp7FjZ/GphfFBGbWh8xcigWLRcGkcRUDqxkhiMrD0BK1w8OSz+OVEK75ILmLQS3MySCZHAxYNeG4oCEt7Ye8iPPHRENarqCkBo3IEKSIFI8PD6EX8AYOEEgpuNoSgaoZ0xm21BzqH0VAEZgmBHIRvlznsh+fT57se/RYEVAEFAFFQBEoFIHHH0/KkiVnbM8LvV/zKwJnAwIlYAK5GpKQoaFhGYGt33lXXI0TNgmGotLa3ilHTxyT6AAdQYj48YvBMQQ9I8WwTJOIBKUKsccoIidDFwjCNXwwLG5fpQyMBKWmoU4GhwNgAD0SgX3QgoZlsmnTRqmFiimDczrhTSlu98oQGM4Kvz9lZ3g2vDVtoyIwDgKxo0clAc+7pUiMwRmEgTy9oZGBq6xKeexk2cN4Bs9V4Vxvb6/UNzTkfCQXhag2QzWZaozbckqxkyfLqTnaFkVAEVAEFAFFQBE4ixAoCROYgBSPDh1Wr98sLSvXGFe5cXpUgspme0eH7N69W3a/+KJ0d3VBfRMSPTBtDj+8JyE4Zhj60YsXNhmPg+2d3RK2x8RX1wDj2Zh4EV/MUy+YPFbLFVdeKZs3boKTiHpxQypoYnNCBdUGqWLKiy/jBVKErjLBs6j/alOzEDh12WVZZ0p32JujqL7Rc3QNNVliMHhNioAioAgoAopAKRGA401NZyECNKkEC6FpCgiUhAkEK2YYM48HEjqLCTNqmyLNixbL2nXr5PpXvcq4vnXAKUwcnpOowJlAHBYaxvrxFhOQJgbhNIa3UWrAZEcwab5kGtQ2LWiEx0G6zc1k8kzsFeSlVEKTIqAIKAKKgCKgCCgCisDZg8BPfuKU666LSXf32dPms72l5A2+8Q2HtLRk8gRnOy6Ftr8kTCCZtmwvR1ZFqErmhwvcxsZGc6oQu6AUX3fGkHYij0kqBbQQ1+3ZgoBn2zYJ/f73Z0tz50U7+U40KQKKgCKgCMwcAuecY5Pf/lYZwZlDfHafZDGA73xnbgc+s1u7+fV0G5gyFaHNr3emtVUEDAIJuMtqvfFGCW3frojMAQQqXvc6WXjXXWLDwpcmRUARUAQUgZlFYPfupPzwhzBPKiLR/n1gYNAILRirj9pqFGJE4dTQ4/EYQUeczggjvIbQD9Bgo/CDeRkmohLmSwwtEQgETH6eo+CC1xnOjIKKYDCEa26JwRkiNeFYnh/+LKKIS8i8vJ/5mTcGjTkPnCp6vR5TjyKaVLa3XHSRTW67TRnAUrzgsmYCC5E6lgJMLUMRmGkEyAi23XabRPbtm+lHl+Z5XIPKUvEuTcEzW4oPNsvNP/iBMoAzC7s+TRFQBBSBkiDA+SIZL2q2kQkjU8Z9nmPg9nR5iTW3ZD7uW3lZEeuald+6lp6P56xERpLX+OM+GUPruVZdrLy6VQRKjUBZMoFH4SFx0aJFZjWGg1STIqAIKAKKgCKgCCgCioAioAgoAopACoGylKe2t7ebFRVlALWbKwKKgCKgCCgCioAioAgoAoqAIpCJQFkygU1NTapDnfme9UgRUAQUAUVAEVAEFAFFQBFQBBQBg0BZqoPSwJdGu5oUAUVAEVAEFAFFQBFQBBQBRUARUAQyEShLJjCziXqkCCgCioAioAgoAoqAIqAIKAKKgCJgIVCW6qBW43SrCCgCioAioAgoAoqAIqAIKAKKgCKQiYAygZl46JEioAgoAoqAIqAIKAKKgCKgCCgCZY2AMoFl/Xq1cYqAIqAIKAKKgCKgCCgCioAioAhkIqBMYCYeeqQIKAKKgCKgCCgCioAioAgoAopAWSOgTGBZv15tnCKgCCgCioAioAgoAoqAIqAIKAKZCCgTmImHHikCioAioAgoAoqAIqAIKAKKgCJQ1ggoE1jWr1cbpwgoAoqAIqAIKAKKgCKgCCgCikAmAsoEZuKhR4qAIqAIKAKKgCKgCCgCioAioAiUNQLKBJb169XGKQKKgCKgCCgCioAioAgoAoqAIpCJgDKBmXjokSKgCCgCioAioAgoAoqAIqAIKAJljYAygWX9erVxioAioAgoAoqAIqAIKAKKgCKgCGQioExgJh56pAgoAoqAIqAIKAKKgCKgCCgCikBZI6BMYFm/Xm2cIqAIKAKKgCKgCCgCioAioAgoApkIKBOYiYceKQKKgCKgCCgCioAioAgoAoqAIlDWCCgTWNavVxunCCgCioAioAgoAoqAIqAIKAKKQCYCygRm4qFHioAioAgoAoqAIqAIKAKKgCKgCJQ1AsoElvXr1cYpAoqAIqAIKAKKgCKgCCgCioAikImAMoGZeOiRIqAIKAKKgCKgCCgCioAioAgoAmWNgDKBZf16tXGKgCKgCCgCioAioAgoAoqAIqAIZCKgTGAmHnqkCCgCioAioAgoAoqAIqAIKAKKQFkjoExgWb9ebZwioAgoAoqAIqAIKAKKgCKgCCgCmQgoE5iJhx4pAoqAIqAIKAKKgCKgCCgCioAiUNYIKBNY1q9XG6cIKAKKgCKgCCgCioAioAgoAopAJgLKBGbioUeKgCKgCCgCioAioAgoAoqAIqAIlDUCygSW9evVxikCioAioAgoAoqAIqAIKAKKgCKQiYAygZl46JEioAgoAoqAIqAIKAKKgCKgCCgCZY2AMoFl/Xq1cYqAIqAIKAKKgCKgCCgCioAioAhkIqBMYCYeeqQIKAKKgCKgCCgCioAioAgoAopAWSOgTGBZv15tnCKgCCgCioAioAgoAoqAIqAIKAKZCCgTmImHHikCioAioAgoAoqAIqAIKAKKgCJQ1ggoE1jWr1cbpwgoAoqAIqAIKAKKgCKgCCgCikAmAsoEZuKhR4qAIqAIKAKKgCKgCCgCioAioAiUNQLKBJb169XGKQKKgCKgCCgCioAioAgoAoqAIpCJgDKBmXjokSKgCCgCioAioAgoAoqAIqAIKAJljYAygWX9erVxioAioAgoAoqAIqAIKAKKgCKgCGQioExgJh56pAgoAoqAIqAIKAKKgCKgCCgCikBZI6BMYFm/Xm2cIqAIKAKKgCKgCCgCioAioAgoApkIKBOYiYceKQKKgCKgCCgCioAioAgoAoqAIlDWCCgTWNavVxunCCgCioAioAgoAoqAIqAIKAKKQCYCygRm4qFHioAioAgoAoqAIqAIKAKKgCKgCJQ1AsoElvXr1cYpAoqAIqAIKAKKgCKgCCgCioAikImAMoGZeOiRIqAIKAKKgCKgCCgCioAioAgoAmWNgDKBZf16tXGKgCKgCCgCioAioAgoAoqAIqAIZCKgTGAmHnqkCCgCioAioAgoAoqAIqAIKAKKQFkjoExgWb9ebZwioAgoAoqAIqAIKAKKgCKgCCgCmQgoE5iJhx4pAoqAIqAIKAKKgCKgCCgCioAiUNYIKBNY1q9XG6cIKAKKgCKgCCgCioAioAgoAopAJgLKBGbioUeKgCKgCCgCioAioAgoAoqAIqAIlDUCygSW9evVxikCioAioAgoAoqAIqAIKAKKgCKQiYAygZl46JEioAgoAoqAIqAIKAKKgCKgCCgCZY2AMoFl/Xq1cYqAIqAIKAKKgCKgCCgCioAioAhkIqBMYCYeeqQIKAKKgCKgCCgCioAioAgoAopAWSPgLOvWaeMUgTmKwEc/Gpd///dEztr98R/b5ctfduS8Vs4n9+1LypVXxnI20QE4OjtdOa/N15N79yblqqtyt9eJL3NHR3m1d6L3W47t3b8/KVdckfv92rH82tVVXu/3bGvvgQNJueyy3O/XZhPp7i6v9/vSS0m5/PLc7S3H/ny2vd/5Ske13lNDQJnAqeE3R+5OjluPaDQmoVBQ3G63uFxusdkw+wCBsklSEvGExOJxcWIGZgPVIuGKRaMSCodx3SYVlZWSRNE8n0gksJ96jh0zcpzSNAUEAgGR3t7cBQwPj/8+c99RHmfRFcfFhExguaWJ2kumqNxSDPPH8fr82fZ++U0ttzRRfz7b2ltu75btOdve70Tfq3J8v9qmsxOBMpxqnG0vMp1hsGUwazF8xV46cEB+9dAvZfmyZXL+1vNl4aJF4vP78UFPyMEDh+T48cOyedNGaWpeLCOBYdm/50V5cvsT0rxwsbzl9j+UCJjCkeFh6R8YkFgsLgubm6SxcYGIvQxnMWdb19H2KgKKgCKgCCgCioAioAiclQgoEzjPXzuFcwks0Q2PDElgJCj9fT0SDAZNq+xYfh0aGpb7779fHn/8Cdl23ka5+TWvkVffdAukg1H59Cc/Jft2PSWXX3mFXP/qW8AA7pYHH7hPIjGnvPO975UdO56VB+7/uTz/wg6pqaqVG266Sa699lpxDbrFDv0Pt9uFnwf7ZAiVKZznXUmrrwgoAoqAIqAIKAKKgCJwliCgTOA8ftFUz+zr7ZYnHntUHrz/Z7L/xZ2wQ+iUKKR3ZMmSNodUVVVLJJ4Ut8MmL+zcI8cO7Je7fvg9cXl8cvzYcfH77PLUYw/Lk48+bNQ9o0mH1DUtkT27dsj3v/1N6egbkGuvuUauueZK6e9qk3/+p89KOBKV2tp62bh5s5y/7XxpWblSqvEch0O70zzuTlp1RUARUAQUAUVAEVAEFIGzBAGdtc/TF50AAzg00C/3/uS/5X//y+els70Vtn0iTpsTzB/lcrD6w85I/wCs/5JS53fJSMQJNdC4DPT0QnoXk7oKF66AWUzCQYkdNoJgALkb6O+Sxx9+SPpHwOw1NItERuQb/+fLcrq1TQaCMfH4KmTVqhYct0pXTzeYxKtl8+ZzpJI2hIb9VLngPO1WWm1FQBFQBBQBRUARUAQUgbMAAWUC5+lLjoZDsv2xR+TfvvIv0tfVKn6vG3qhDuPkxe2AqqbHJi74gJGETWLg7IZCMTB7ZPjwAwNod0CVE94Y3C6nBFEWpYVOqI+6XUmJ4h+MC6UaUsL4SK88+9Sj4BETcCxjl1qHB9JFjyyo8cmixipZ3Fgrfr/XcH0sO8UDju3MU3S12oqAIqAIKAKKgCKgCCgCikD5IqBM4Dx8t7FYVHY+/5x89UtfkI7W42DcXPDTYhcPGEGfB9JAO5hBSYgD0j1xQzZnd0pNJSSBgZh0DCYkGHNQ8CcJe0LcToe4HH7pHx4Rjxv7KGswAJtCSBO9LkgGkS+ZtKN8ihdt4kWPiQYGZc8Lz8ou1OGBn98nN772Vnnne94nm7acJw56Gp2HmGqVFQFFQBFQBBQBRUARUAQUgbMFAWUC59mbpjrngf175StgAHfueEo8kORRBTOBf5FoAPZ6qf04dULBwDngwMUD9/oVYBBr/B7xwplLxwAcyAQQBiJuFy+kgFWVFbjmlUpwkDFwfcFgUlxON5hClwQjYXGAwfSDOYQOqYRjCRkOhSSJawKX/kMjAbn/vp9B8uiTD3z4z2T5ipV4LhnGeQasVlcRUAQUAUVAEVAEFAFFQBE4SxBQJnCevGg6gUkkyADuk3//1y/J73/zACSAkNQJ4/7R8QtUQKHimaD6J+z6IlDfDIRjEonExeO0gXkLSggOYxqqKmRRnU9CEUgE4eDFh2tVwKC+ykeRnzhwf43PD4cxLkgUbVIJFVA7pIWpMmAvGBwRRJcwPB5jey1ctFi8FXWy/elnZdPDD8vtf7BIPB6II5ULnCc9S6upCCgCioAioAgoAoqAInC2IaBM4Dx442QAhwYH5MC+PfL1//iqPHTfT2HTBzEf1DOTCdjyQf2TkjsnmDUbGEIfLtkdbklUiHQNhqVrJCZRD/NHxT4ckMZqv1RXuGWoJwyG0SFhBJSnymcE2zqcr/C5ZSQchUopJIiQDgajETiUsclwMCpDOA9FUzCfqeDxIcQW3HbhJWAyRV566aC0trXLypaWeYCqVlERUAQUAUVAEVAEFAFFQBE4OxFQJnAOvPcEnLCMjIxIRUUFNC7pzeVMYqD2UydPIAzEw3L3j74vO597CsyezeSjB1Anvb+ASQxBrXMkRMYM90IV000pns8nyxq8yBuR030j4q6k/R8cvyCIvM/tNHZ+EeQfDoclHoAUEEylG2EeApAQ0i7Q53ZLEhLGkZEw7A298C4akTA8iMZi1PWkvWBC2to75entj8kll10h9fXV0tPdfVYzgV/6Ulx+8AOISnOk226zy0c/CvFpAenLX47L97+fu7xbb7XLxz5WWHkFPLpkWQ8dSspb34pVghzJgdAlTz9d2Gfo4MGk3H577vI4Np56qrDyclRrSqcOH07KW96Su36MqfnMM4XVr9T4TalxOW4udf1KjV+OKk/pVKnrV+ryptS4HDcfOZKUN785d3+2gWY8+2xh/fno0aS86U25y+Pjn3sOpgazmErd3lKXV2poSv0+JiqvmP5S6vaW+n1M1F7Wfbb7c6nx0/LKC4HCvt7l1fY505o4mLIdzz1nbPMWNDUh3p4Nsf7g0XOwX/bsfF7uu+9ueerx30kEUjd682T8PxsYMDqE8XsgAURLopAIjsBWLwYvnwl4fSFz1zcSMgxfc00lbAAjkOSR+aPkL+Xp04ZyYjEwkPAGSjYD7mKkGwHno9D3TIC4h+IxScB2MAY7xHgsDokg7A1hB1jlc4oPqqjgXcFAxuTEsVO4eztUQxeJz+vB+cTLmNk5A/Y0V+QUoNixI/dDtm0jh15Ymqi8888vvLzCnl6a3EH4GRoPEwcl2gWmicpzOgsvr8DHT5p9ovrZ6ZGpwDRRecXgV+DjJ81e6vpNVF4x+E3agAIzlLp++GyPOz6o2THbaaL2FlO/icqb7bby+RO9D2NoX2AlJ2pvMfgV+PhJs0/c3klvf1mGidpbDH4ve8AUT0zc3sLH28TlTbGyersiMM0IKBM4zQDnU7wN0r8uSNA+/rcfkfPPPUdccOIyMhSQw4dekqMH9iGEwxCkf3ZxgYEzcfjAAIoNUj8wW4FgGAyXwKsn7PY8Xonii8TzlNXxD+3+/J6Y1Fd65Xj3IOwE47DzoxdQKHWC2QyBIXS7YfcH7o6SvVAUkkKofkJggdvhJCYaxnfbLslo3DB21X6ElbDFwTAm4GzGJQ3VPukdckj76RPy3JPb5eprrpMNGzfl02zNowgoAoqAIqAIKAKKgCKgCCgCs4CAMoGzAHr2Ix3wsLLt/PPhaXNEvvudbyPmHwO909YP0jmEXKADGMPVWYtUYNDIDIYRKoJbnraFImDSoB6KshLgCuM4H4dUkPqhcUj74mAMY5AWDkIa6HchhqDTZbx/Dg1EJAbVUDfuSSBvMGZHnjjKsUldldN4FfW6vRKLQH0nGZQ4fsOQCPLfMM7V+OPG62g87pG9L+6S3z/6sGw57wKhRFOTIqAIKAKKgCKgCCgCioAioAjMPQSUCZzld0JmbxBOX8g0veOO98gn/+Hvxe4CEwZJH1VFjOSPdTQMYIrlo0dQXkEAQMPk2WGnV1tXLYsWNkl1dRXYMydUPuEQpndA+gaGwdiR+es3krwgJHqdQ3GJCUI/2F1g6nAtBOmgF9JAOJiJ41x9o1/qa6qlZdlCOXfzGlmycIEcOHBQfve7x6W3rw/3kQs1rmFkCM5iHLjPA9XUocFh2fXCDjl08IAygbPcr/TxioAioAgoAoqAIqAIKAKKwHgIKBM4HjIzdJ5x/55/9jnY3UXlxptfI3ffc488/8x2SAOh/gkmkEHgHQjqfsYWBgwYwjjQQydTEhK81S3L5A233iJXXnWJ1IF5o9fQMKR0p1rb5eDhU7IXXjsPHz0pff0DMgjvoFGEioDYT/x4+57aOjCNdqlBiIj6ulpZsnixbNywRrZsWmfKXdBYh4Dydjl5ul0i8CR61933IYi8DU5kKJxMQNoYQ7gJGvk7IT20y4njx+TYsSNyyeVXpALMm1rqH0VAEVAEFAFFQBFQBBQBRUARmCsIKBM4y2+C3rLc8OL5mb/7mNzwyhvk0osvgDepp2VgJApPnoj950xIFWz2knAoQWcwlBwi9J+EYRZIieCalavkgx96j9x8w9U4xnWogFJoSGHdCkjyrrhkK5i+uAzBxnBgaER6IMkboHQQtoRM9Ajqq/BJQ309frVSX1spXtj6gUs0aqSJZBT3izTW18j1110rDz28Xfq6u8AIgkkFH+qwxyUCxzZ2MKw21JHeTHt7+ySGcBNuBpTXpAgoAoqAIqAIKAKKgCKgCCgCcwoBZQJn+XXQHvDc886Vdes2yJf+6VPStHihNFQ6EbMP0j/I2lyQwpHBMpxdSgsTEjmBGmYCUrtm+cD775CbbrxGohG4mEtAPRR5mBlZBGI6s8+T1VVeqUN8wJXLmsGsIZ/JOFoujhJJOIZBdvgBRVkRnDmTyFa6YJu49dx1cuP118o3v/cT4wG0EjHh6VwmloAkkF4P+R8/uC89c7PuKQKKgCKgCCgCioAioAgoAorAnEKA3ICmWUbA7/XJ2+54lzQuWSL93e3iSobgwTMhfncCtoExsGtk5uC4xfjkRFgHHFVU+uVtb7pZbrv5ejhtoXonG8E/5MLSEu0GyZPhF4NzmCgkdGHG+4uEJRKOSAT7ZPqolgrZn8madvfoLp3PJKUBqqa3vfZ6WbKoCeqpKWkjzxu+D95F45BUVlRVS2V1JZhE1lmTIqAIKAKKgCKgCCgCioAioAjMNQTmrCRwaGhITpw4Ia2trdLV1SWdnZ3S398PtcYhOFIZRCyfkGFgyMQwLh0TVSs9Hg/UGb3ig4pl+rampkYaGhpe9qutrTX3zeaLoUOWbdsulDf+wdvkm1/5V0jTwNShLVT9JPdm/H8iLAMZwRB0QUNhu1x+ySZ55x/fBg+iMcT6Y/vB5VFiyD3D2sMjKG5JwNbQRskczjntjDFocqbyjTJwkAFKAiqjSdgJMli9Az8bVFFNaakiTV1o87dp3Sq5+ILz5Re/fAjlp8JMsJoMShFCEPmFS1pM3uPHjsr6DRtSUkE8c6bTt7+dkJ/9LNUvsp99ww02ef/7Cwuy/q1vJRCvsXTlZddpqsezUb/jA6kgywAAQABJREFUx5Py4Q/nZvYhOJa77prdz8tE9YMAXu6+u7D6lbq8qb7z7PtPnEjK//pfud9HMe2dqDwuAv30p4Xhl13fqR6Xun4TlVcMflNtX/b9J08m5UMfyv1+QS7knnsKex8Ttbcc32+p8ct+P1M9LnX9Tp1Kygc/WLr+MtX2Zd9f6vqdPp2UD3wgd3v57HvvLWx8ZNd3qscTtXcu1G+q7dP75ycCszsqgFkfbNR2IJI0f3v27JH9+/fDE+UBc34mIHXBq+ViOENZunRpxm/ZsmWycuVKWbNmDTxuwtnKNCeqhb76xlvkyceekN07njAhG+yQrJF/ok9QE1QWjCCdtDRX1sgf3f5647UzEoJtHzONMVqI8wdGMRwKwj5vSEIjAcj3EuIFc1zbsEC8Pv9YVoT/M7Z7QwP9hrGOwbaPAegrKitgJ1gJmz4v1D3PMEtktiuhA3rtlRfI7p0vSF9Xu0EljNiCAfCtXtRr+aqVcurEUdmBoParVq9GPEL3NCOXu/jdu5NgAkc52KwsCxZkncjjcKLy5kI0jNmo38CAjIsxmcDZTlgrGrd+ad0672qWury8H5xnxlLXb6LyyCTMdsJ64Ljvt5j6TdTeYvpLqfGZqH5kAgtNpcav0OdPln+i9hbzfidqbzH4TVb/Qq9PVL9Cy2L+ifArprxS31Pq9k5WHhfVKSiYrQR3CeN+r2arTvpcRWDGp2qBQEB+85vfyK9+9SuEHPidYfxSEq/ZeRn0lHn8+HHzG68GTZjlkxlcu3bt2Hbz5s2w41sHRqkETA6+S9FYRJ59artc96rr5cDBQzI81Csexv1jqAhUzAj58MfjtMkF52+UG667HAzcKANoVRw8TwSxA/t6eiE5bZcAvjpkg5ygmC6PG6En3OJGQHlK+kzCRzESjcBRzACCzgckASZwKBaH85geMHvVCBXRBK+h1YhVmApSb4OXUgfEjJdeuEWeu+gc+fWvO43UjzWMQSq4YkUL6ivyzPbfo75xtGFQqqprEeh+xruZhYhuFQFFQBFQBBQBRUARUAQUAUUgC4EZmZ2Hw2Go0t0nd955pzz44INGlTOrHnP6kKqo/D3xxBMZ9SRzQ8aQDOGWLVtk69at5rdixYqMfJMdkFFzQFUzHB6SR3/9a1m6bIk8/1yHxOB0xe12iBusnFmJRkaqu17/iqvhrbMSaqFhsF9k6FhCyjovEg6BabNDCrdWfP5KcSK/DcxeElLFGBi+VF5sTLJDbbZCVq7dgPLRFVAM80WRLxgYSanbgqn0wRMoy6AuKRn2FcuXyhWXXwhp4C5pbesw1ylBHOltlUcfOiWDYP6WQLK6b8+Lsm7jZmlsLEL0ZlVRt4qAIqAIKAKKgCKgCCgCioAiUFIEppUJbGtrk69+9avy9a9/Xbq7u0ta8blQGNUn9+3bZ3533XXXWJXqEW5h27ZtcuGFF8rFF18sl156qSxatGjsevYObf6olnrDDTfLD779DemFDWQdPISCHzOJLmGMOihYvlqEarjwwvNgB8kYgpQRGmVRbMAMQlJXXV0jNfVN2PeJQJ3TboekEtmSqKvDOSTJyAh0QOFaBvcyxqDN6RO7uxLiQrj6hOTRBkmgyx0UF5zV2OKj9pZkAFkIUgKSSQ9UPbds3ijr1q+VtvZ2XKHLGkj++jtYHTCULtn57DOycPEyaWxaqEygQU7/KAKKgCKgCCgCioAioAgoAnMDgWlhAnt6euRTn/qU/Md//EfBUj9K11paWoyEbfny5ULbvObmZlkAQ67GxkapqqoyNnp0+kJ7Nebnlj86NaHUkU5jgsHg2Jb7tD1kvbJ/7WBiTp48aRzQmCDqJXgvvb29RuWVaq9Mq2Ebd+jQoQlLZtiG9ZvOlbe87U/kK1/6ItQuYwi+jlvAgCE0O3boudMGPJbDhnGRBEMpqR69cMbAuJH5Qgx58GrAwlshdn+NOJJecUHCSIw8FXaJR2skOoIYfiPd4BfjYvdUiauyWRyeCsNURhGWIpqIQrXTjZAR8EIa7BZbdHhUjx6hKuDABr5h4GwmLk14H6tXrYLEcofxNMoK0AENGdMYGNT21jYJA/ea2poJ260XFQFFQBFQBBQBRUARUAQUAUVgZhEoORNIqd/f/M3fIGB476QtIeN27rnnypVXXikXXXSRUaXcAI+SlIoVm+gVtJhENceOjg45deqU+dEz6cGDBw3zxi3tBin5KyaxfZMlSgNpX/j2O94tTzz1jDwJ1dMR2OlR2dPnSEA6B0EdGEWH0y3bn90FBjcM9c4oZHBw1gKpXYXPC8kfA/fh5x6EbeAJw7B5YQvor6xEEPha2OfVSAUYRIcP90Hl0+Gulf6hqAy2njReVwdgSR7CMxnonc+yCTyLhgYRAjAK1dOQDAWCYBBhl4jg9aiJ9CHoPMt3wxDQeBSF5DIK28ChQEjqwLCvWb9R6urqJ2u6XlcEFAFFQBFQBBQBRUARUAQUgRlEoGRMINU977jjDvnFL34xYfUpyXvd615nfq961avAJNRNmH+mLtJr1MKFC82PapzZiQzgsWPHDGNIFVB6MuVv7969hoHKzp9+PDkTSBEeJX5JWYJYgZ/69Gfli1/8gtx/94/h0AVOXWCTx4ANELDJE8/tkt8+uQeSwJDJf86WtYgX+HpphGpmdeNi+X/svQeApFd1JvpVDp1zT+yenLNmNKORQDlLgGQJ0AMh48Bi/BZsnNfPuzxsrw1+a8yymMXmmSjAZCwQIIQklMPMaHLS5J7p7uncXTnud251dazurq6uqq6/6t6Z6vrD/W/47v1PnXPPuefY3LUU1ACv10PvYH0Qk9ze3j7u7wujuroGDfU1qK6kmShrHAqcZfiNbobe6KMmENSylittaxUFxnIKjg4KeAgH4O1vxyBDPjz/4ht44ufPYMjrp/Bng5MKSkuMpqM0Q7VTSyhGo2FqGH3Eat3yVVizbiO9jbIMnTQCGgGNgEZAI6AR0AhoBDQCGoGCQSArQqAIQ/fddx/OnTs3Zcf27t2LD3/4w3jwwQdV/L4pMxboDTGpFA+h8rnrrrtGWikaRNEavvnmmzh48OBIuAsxMU2mmYVAyUlBkIIot+Vxv90GvOPeO/H8z75HLVtUOY0Rk8+Kygps33kdNW1hnHrrDLq6e/HY//Ub+E2GiwjFzHDULKFUVpasduRb2tjHGIv73nhDCelP0fmM3W5jCIzleO9734u3ve3tas+iOJ2ZlGgeGvYtwrYNrYz7txwXr3TgrbMXsG71MrY4hvOnT2Ggpwth7hVUTmVYgL2sCjt27mb+dcmthJOK1Rc0AhoBjYBGQCOgEdAIaAQ0AhqB+UFgzkLggQMHcOutt05p/nnLLbfgE5/4BEQILMYkGkTxBiqfd7zjHSNdFNPS119/XQmFYuI6mzRAge3JJ35E7VyIzmFsWLR0ETopfC1f0oK//q8fh5MC3Kv7juFv/8fnsXRhM715hhG1VdDRCz2BpqhI7sep6rvxxhupDaxiaIen6KinB7/7ux9SbfZ6vcP7/iY+TN0eQ0LE6VxG9vk5WG9ry2Lcfdvb8NCDd+Lo4RP47P/8Is7Rg6iFmwU7e/u5L9GJHbtvwu133oO6ujoWKPpBnTQCGgGNgEZAI6AR0AhoBDQCGoFCQUCsAjNOslduKgFQArD/8Ic/VA5SilUAnA44cWZz77334q/+6q+myzbpXpROV44fP4KXn3+G8f3EQBQMQbEeCxcvoqnoYtRUueFyWLBr+zps37KRURu4K5AOWUz07mmiU5iJScqT/Yxf/epXlVOcqqoqiFC6YsVyagKXKac43/nOd1JrcaVylmm2Olm2lZ84Q1PU4BrGKayvLKfAGaWTHmond2zH0tZWnlNbumoN7r3nbqxflxR8U4mlE1s5f+d/9EdRakXDKT+/93u0qy2y9Md/PHV/P/zhRH8l8PxUmJSVhYsMEeDIkan76+L+2WJLR4+WVn+PHZu6vw5HaY2v01l8/Z1ufMf2lwYxRZGOH596PgvdLrZ04kRp9ffkyen7K5ZdOmkEsoVAxppADwORiwloKgcwcv0rX/lKwez3yxZY+SpH9udJeIXzpwZVmIi6mmrsouMctzh1oTMdCcxuovBXy3ARcQkNQbPMVAKgtFcIhuzXfINaSRkX0VyuWtmCPbu2w+MZpNfUHrxJba7EOUyVlBwowiX3/Jn5XVlRBjfNRqNRcZJjwtp1q1HN/YMvv/wKTVcpkIYG0XH5AgPQ9ytHNOr5VAUXyDXKyNSkpm5MJFJ8xHa6/kbp9EeS/MZMhUkx/v5M1195F4st6f6OjijJYdGl6caX/r6KLk3X37HjO/bYyCBM118j92uqtuv+ToWMvq4RmDsCGf8kfOxjH8PJkycnteCjH/2o0gAWisOXSQ0s8Asi5F13/Q344z/7f1BeUadCNwRDQZpy7lV78hLmlYwRyF/zJppb+jxDtNikJjAuWpzJHI3sZRTPq//r85/H4sVLlMOYxU11uHbregwNDGLTpi3473//dyrPJGio+UvUJ2XHEKXDF5kwZRQERcNYUenGtRQmFy9ZDA+dxQQDfsY47KQG04yqympVnCpCHek/GgGNgEZAI6AR0AhoBDQCGgGNQCEgkJEQ+MILL+BLX/rSpPZ/5CMfwWc+8xkloEy6qS+kjYCVAQJvuu0OvO2WW6mRiStt68ZNa3HttdtUWAixEbVwWXNZ60Kcfus8hjx+xgD0UYOTOoSFCILV9PjpocB47uxpLF/agEULKtUzHR3tFNiqYFNBCSc30cRluFgkBL/Ph/aufiVmVlHzF6EmcGFzIzasW0HxkGEmBgcoL5qweu1GXHPtXlRUVSYKmyyXTq5EX9EIaAQ0AhoBjYBGQCOgEdAIaATyhkBGQuAf/MEfTGrgjTfeiH/6p3+adF1fyAQBE0P+OXHzbXcyLiBw6eIVRBgcvqGhkoHaRTfHcBJ0xLJhzQpEKHg998IbuHy5jXEO25UZpp9B2sUZTIQOW+Qj4SR6e3tw+uQx9HVexub1K1FV7sSSpiq8+sKvIYKglzEAE89EVJzAYDDI0BdedHddRXvbJRw/cQZHT5zGujXL4XLaaaYaRyWFQYnL2NvFMBN9A9QQVmDXdXuxbtMmdlpLf5mMvH5GI6AR0AhoBDQCGgGNgEZAI5BrBGa9J/CnP/0p3mCogbHJ7Xbjy1/+stqvNva6Ps4MARHyxNxz8dJlsDsdjPXXiSPHTuCmpr3c0ycb2MT8E3QSU4GH3nknXmZw+dde3QdH+VnU1i9CTV0D3GXlLIOOZeIxJeB1trdhqPsy9uxYw8Dxlcpb6Ob1rThw+DReePYpeiBdgWoGdrfYOCW4ESpIwXFosB/dV9vh6etCPBrAegqA2zat5+2o2lvIgBY0L/XiMj2XDg55sGnbNXj7TbehvqEhs47rpzQCGgGNgEZAI6AR0AhoBDQCGoGcIzBrIfALX/jCpEb9yZ/8iQqRMOmGvjAHBEwU5MpgZbD1QZ8Xr76+HzfdJEKgCHbJYmNYSO3gb9x/JzzU5PVQG9c/5MNgTxu6OhOaOBpz0pwziLpKF67Zu5nOZWw8pyDJcirLXNh77RZl5nml8wq1ee2MSSgeRk00NwWcnB0tjW7Ur92gtH4WBq2PiWeR4fplL2JfTz8uXmpHBQXI2+6+l4LgDhFRkw3U3xoBjYBGQCOgEdAIaAQ0AhoBjUCBITArIbCrqwtPPvnkuC5UVlZCnMTolD0ElBBFOaq8vEyZWPZ2DmHf/jfR3tGLpsZqauVGXRbSKhPc3MewEVbGDKxDCzWIJsYLhMWuLDLFO6iJ9+OxAB27iECY8Oqp5Djes1KiW9JUi9aFDYwHaKOZKSuWm2J3quIUxhFVxxHEwrQeHpbv5CvK50+dPkdz0l7ccsc9uOPu+xgygh5LeW84G4900ghoBDQCGgGNgEZAI6AR0AhoBAoJgVntCXz88ce5x2y885EPfOADkNhzOmUXAdH41dY3YPXqDQhHoozjdxHPPf+CCtMg8frGJTnlJ0YX/yLohUN+RPz9CHt7EPX3IRLy8joziBaRYR7i/E5q69SjdPISCVFI9A8h4h1ExMdPwKv2BooXUKV6lOeo+UuId3yawmZHRwdee+NNLF62Au99/2NYtmI1m0GhU4mB41qoTzQCGgGNgEZAI6AR0AhoBDQCGoECQWBWQuB3v/vdSc1+7LHHJl3TF7KDQBn39d3zzgdhtTjg8/rwgx88gTPnLnGvH002uddvxC50RDZL6N+SfyXAO1WC/AwPM53IiICWuD+mjep5/hEhjwLiyGcki2TgZZEYJfE0EArj4JFTMNnL8dAjH8DmLduHb0neRH51Qf/RCGgENAIaAY2ARkAjoBHQCGgECgqBtM1BA3QU8tprr41rfGtrK7ZvTzD/427ok6wgIKEdbrr5NmzevgOHDr6OY0eO41vf/h7+83/+XZQ5uLcvKZSlXdusH0hRMp3W8N+gJwBnRQMeeM9ebN66g15MaVgqpqciROYgyfwbGhpKu2Sfz8m88pmc/P4Aurr86kah5vN4xBy4fHLjS/SKzC0xR5fU2yuLGsMhSNSV0T+FkG9gwMYGlY02asxRIbSv0PHLRvvGQD7tYeGPx7TNH7lZ+P1I7/2VDolnaoeDWxpKIKU7bgJFOvRvvvIZox/dan17OvqSC/ykTJ00AoWKQNpCoAiAoVBoXD9uu+22cef6JLsIiEC1YNFi/N8f/3N87p8+jXMnTuDJnz2F1atW4f7772BQdlEGziTYyf3JgploBBNPTr6XuhfJekyIUcNYv2AZbl15LRyuUUElVwKgtEfCVwwMMBZhmikUEkEhtRAo8zhZVqHm8/ujWgicMNbJMfN4RMhKLQTKI/OdLxisYCtSC4GF0L5Cxy9b7ROs00nzPV+m72+S7s7ck8Lux+h7OTQ0/fsrW05KRQiUUU2O20y45C6fsIEz09Psty/derOXT9ao892Pmd9cnUMjMH8IpC0ESoD4iWn37t0TL+nzLCNgYTxAiRe4ePFSPPkfP8DBN/fhxdcPYfuuXWhd3ETvLAHWKIJcKmFvlIGQfYSM/KA0dWZSwhjzi78XFi8x3hOPj2t7srzEd+IvM3IvoMVVC3tFI8NJuJQomdgDKEJX7lIuBczctVqXXIoISHgXnTQCGgGNwMwIyI+vThoBjYBGYH4QSFsIPHDgwKQW7tmzZ9I1fSH7CFitFqzftBlrN2xED4O+n7t0GRGbDX56AbXHIzDTsYsSBFP9nsg12o16gxF09fRyf6FZhZ4I+Hzw+4NwMsZjfXUV7Az/MD7xwREtY2IfYczEPLZq2MsbYba6VJ2JKlNVPL60uZ5pxnquCOrn84WAXrDIF9K6Ho2AsRHI0e4JY4OiW68R0AjkDYG0hcAzZ86Ma1QZY9itXbt23DV9kgsERMASPVyEWjwrrO4a1LXUIBiKoTfmQRV1eg7TIKwUBhNuXya0gY/K093dvQwzcRQ2Co82mwOBYIBlBBhawoFtW9Zj6aJmynxUDY4keWo4UfiLmETorOR3HZ+1wsW8LpuFJqm5FwClFaUmBGpBIjn5kt/5mWfJ2ubyrcduLujpZzUCpYWA/LbFxExHJ42ARkAjkGcE0hYCz549O65pra2tOXMCMq4ifTKMgIlx+WIMHB+CN2CBidrBuNmN/mg9XDETykwDsFMg5M+Jyp8Q4ZICJLCgsQ733n0T93UGMTg4iDJ3GVz0Pur1euloNCrGoXxOnkow24knxdDTgpDJCa+pCr54OUOEcJ/dEAVPSxQNFXFUuqx0Kpp4ZrihOfnSjHVOYNWF5gCBUluwyAGEusgSRqDUaL04gJvob6GEh193XSOgEcgjAmkJgb29vSObl5NtEyFQp3wiQD0fha0KpwUBiQVImS1O086opRw+WUkM26id88Np8lOMC6mgEGNlM5tNhjpORy5OVLpdFPlkV2AEtVVuagApxIlnTxEAafEpRzEKlhGLDUEKfj5TJYIoR9RkV20QUTESizB+IZ3LiMmokgHVn5wBkivG+jd/cxD33ONN2e66uoRAnfKmQS9+7GNmvPvdqceqsTFxfeVK4JVXJLTI5DR2Tk2+a8wrLS0RfOc77Skbn0l/TWI2XcBp6dLs9reAu6qatnz5+PkcYnibzs5OdS+T8S30/pb6+I4dn7HjO7K7YWyGAji2WFLT2qmatmTJ1O/vVM8Y+frixdEp6bOR+zVV21tbx9OriflKbZFkYv/1eXYRSEsIbGtrm1RrS0vLpGv6Qq4QEObcBAu/qsuo74sF8OzBC/AHTaiudsLhdKDMWYMqfsqtQdjC1ApGPSqfkw/Z7OIURol4LEe8glJwE9ktblYCoAo+T741yr2DHoZ+6BoMwFbWCFfNEgTNZdxSaEOUQmOEHjqDwRAcNgayr3SiwsVgEWIOyrISgmCu+p87c9CFC6OQT6mkpUtNkM90ye024dprp88z3fNGu+d0xrF163jPx3Ppg3on5lJAjp/Ndn9z3Nw5F+9yjZ/PoZAJFy9mb7zn3MAsF1Dq4zsVnGMFwqnyzMf12QqBpTa+pdff8fRqPuakrrN0EEhLCBTzwYmpubl54iV9ngcERIDzhqM4dKYfg3473E6v8vBpF/NMCoQti2uxuK4JLksFOjpO4eqVt7C0uQZL68pht9M1d8JFqGqpaADFfDTMJdLOfg/OXelDW0c/bOVN2Lh9A6+Xo7fbi6v9gzQhDSJEWSlG89ClTS4srHTAwRgVKtQENR+5FhlKbfWr1Pqbh1cnb1XkSmudtw4UeUX63SryATZY92YrBBqse7q5GgGNQAEjkJYQmCpItw5kPT+jGonGcfZqGCF7s9IAhiiZhcXJS2AQbV2DONPWj7XL67F57UIErE349cFfAfuOY++W5dixdilq3HZE6U3UJCagNCcd8odw/OJVvHL0HL2O9qBxQSv20OHPIM1LD7x5Fm3tHu5DjDA2oA1WhxtWuwtRav+WLwijvsJBIVK0iqJWzK0YqBm3+ZlvhVJrjqdXVrup52pW4cx6YXp8sg5pVgsstfHRQmBWp48uTCOgEZgFAhkLgeIdVKd8ICC2lpJEyKK5JveznG4PwWIt54eaPZp6oryCJps1CPn9GOjtwqsHr9DzZxC7NrVi1bpr8B9PfA9dvR70+WLYvWEZljRUsbgoLvd4sO9EG1568zQutXejpXU1dl53O8zlC/Ha8R50dAUQiZfBQQcybu4jFK+i8gM9GAziYncILfURtUdRNS/Hf6Re+ag9iDmuSxdfmAgYx4tebhdECnN0jNOqUhMyjDMypdlScQyjk0ZAI6ARmA8E0qI+qTSBWgjM13AlQrGb6BmUSkB0DERpuhmF2eKkSCgCojgvoVkn9/c53ZWwO50Y6nPh8KmrqK/pw9btO3H0+CGcOHkCT/56P6509WHPlnWQ2IOvHj6FfUffwgBNPRcvbcH1N94OR3ULjl4cRH+gHOW19dT8JQS/xMY/UfrFaRZqRntfFN2DIQqB7oR8mgc4RAiIRktn/14eIDVUFZp5N9RwFWxj9Twq2KEpyYZpTWBJDrvutEagIBBISwhM1VK73Z7qsr6WAwQSrlzi4FZAauBo+hmzUQsoV6kd4x6/kN8Hi92pHL1ILMGq+mZ4uLp45EwvFja1YPPWHbjc0Qmvpw8vHziOc5evcn+gExcuXlEiZEPzQuzccwPKG1bg5KUofKFyuMo5NUwUMin0RaNhxBhT0Cp1WDjuNCPtpVaxyxPD0noGkchTrMBSYt5Kqa/pvjJGwcRIpqvpYl9M+Ywyj4oJc92XqRHQQuDU2Og7GgGNQG4RSEsIlADjE1OYniJ1ygMClMMShqBmDAz5cPDQSfR0U/On3NDzJvf3RSgcNrWsRdwkcSP4n55Ay6qq4R+M4UL7IFqXrkAFzy1WE695caGN7tGZz+Fy07toHVau2YhFSzfgfGeczmboJpSCZKKgRP8kjuDA1UusKkhBsIyPRtHH8BKnbdVYUdeKukqaBufBAk473MjDfCvgKjTzXsCDY6Cm6XlU2INVauOjhcDCno+6dRqBYkYgLSEwlc26FgLzNC1EG8cUYWzA8z1+tHczPl8gpvywKM+cEe7boxBooqdO8c+SSKIjNMPurkF7zwAW1ZejpqaKISU8KOO31WlDjGaVrooy1FbXYvnKtfBG3OgZpGDPWICiAUyUJZJdIrRELBZGYLAXzirWw2MJOXGRewbb+/yoqXAxfEXuY6NpITA5vqX5XWrMYWmOcn56rfeX5gdnXcvMCGghcGaMdA6NgEYgNwhoITA3uGaxVAphot1jiUPBOMoalqDBJj45GdohYsPQUD+CXg9lNQacVUJgQm+owj9YrAjAiYFAHA0Njbh8uQ1mPusqL6eQRzGRcltNXS2qahfjdEeEjw9PByknUQwPeMKyXRV1sNAxTF3zIthMFAK5B9FMpzRDYYsyKbWoynOrDpyNECCxhSorU+8fdLsVULMao2yXN1Pls+mrlOXm1syamtSllpXlZlzEn8FUdebC18FsMZH5PdUcyKR92S4v9WhlftVsnnrOzzIetWpEtsvLvGepn0y2z8QFqImxGWWspkup5lKyvFTPZYJfqnLmck3aIO+bLNDFaJ0xNs3U37F5k8fT9TeT8pLlZus72d9U5WUyHtKnqejVfPZXFiRkPlosU7+/mZiYZ7u8VOMwl2syhlPR50zKnam8TDDMpB1TPTPd/JvqGX1dI5BrBNISAlPt/wuFijfYbq5Bz6R82XbXWBbDtsUh1HO/XjweRddQGOeijOFn5jAy0LuEfVDOYpQQF0PQP4T+3ivct9eChpp6RlxP1JzYY8iIf/QuWl5eyz2GDngDvKcESXE0IykhOEhRQrzKKFTVWv1Y3uBBcwWjC7KuQIRE3M5wEyp/7v/MRhP4h3/YD/lkK3384/2QT6Gmv/kbC+STz7R+vQm9vZNNxXPVhlSM+3R1rVoVxr59bdNlmdW9bJc3q8rTyLxyZSSr/c12eWl0YVZZVqxI9LeGnH1dXd2snk01lwq9v2vXjr5vZ85c5G/A7BezxoKUxG/stUI6XrdutL/ZaNdY/LJRXjbLEGur5cuzS6+WL88uPchmf6WsZcuy277W1uyWl+3+rl6d3fmc7fbp8koTgbSEwOrq6kno9PX1TbqmL+QCAYpslLLM3IO30OVBwxI/XDZeiwbR5w5juSuCDq8Jh/uH4ItXKjNRkcqEyYmHQwj6Bvkwj8eZaw4zDyyYVp3wBWNKqEtsM+QFeiJNJDkGXOYgVi7wo6UiivrKLsYatNA7qQO+qJX7Cuk9lI5jYHbkovPjypyNEDjuQX1SFAgYZfxTCRhFMQBF1Amjj5G8C8XqKdnoY5PJayImoXqLTSbI6Wc0AhqBuSCQlhCYapW1p6dnLvXqZ2eBAEUxeuf0whEdoAAYpowW5z5AL6qtETQvqsICfxwXaRLqCVRwb57kBj2G9sHTdxULWlfA7QTOdbfzauJe8jvKmBPeoT5Y42HYrHZE6GlUpD4TzcricZkasvcwCrctgA0LIlhe40IwGKIAGoKVHkIrrWFqH62IRVgvTUVznUqROcg1pkYqX4+/kUarsNtq9Llk9PYX9uzIf+v0vsD8Y65r1AhoBETBlEZKJQR2d3en8aTOMncERBtHf5x0ACPClzIBEtUg1XYiswXopXUwYEKQsfvEbFOEOPmKheg1NODBwsZKxAO9OHfhLO+PDreIg+LspZfj6O07j4W1svdQTHylPuaTDKIGZN0R1jfop/kn6xKnMMKAKF0ihdEYPYbKNZVXHslhGtv+HFZTEEVrJm/yMGhMJmOir5BUZbDZJ5NnCgnrUqKFhYR7rtqihcBcIavL1QhoBKZDYFQqmCZXKiFQawKnASwHt8x08jLi/IWjZrFIrEArBkMW7LviRF+oinKYCIcxBovvhN/nwer1q+kZFDh14hAGPUNklsY0TDyA8nSI18+eOQinqQvNNeJcJpYQI7nnMK6EPQs8IReOdZej02NhGSJs2igwJsqScxFIE1LhmPJzcGh0xi0HkJRUkXr8S2q4c9pZowtR+l3I6fTIe+FaCMw75LpCjYBGgAikJQQ6HA46ECkfB9jVq1fHneuTHCJAYcxiK2Nohyru4RNvnDTTpCfPuK0SXnMDPOY6lJU54RInLRTiIhQAXWYv1q2qQE/HaZw8eZyCm2j42EYRFOXDEoSRiNLL3JXLF/DWiddR4xpEU40IdKLZi8ASj8BujcLmdCBgrUN/rA4xRw1lUfoCpbAZo0Mas6uSbXOxuLESZm6wMDrjlhtUSqdUzfiWzljPpqeZzItMnplNm3Kdt5hpodHHJpOxTxWGK5Ny9DMaAY2ARmA2CKS1J1AKXLRoEYWJkyNlX7x4ceRYH+QaARECXbCVNzEeoB2xsFcUdrjaPYRTHR2otDWgmjJ6DDaabtoQrFnAvYMB9HW+hVdefQmeoJd7BUVwE0FNYghKkr8JBzChSARn3zoFp9OFVev2oMxRzv2Bce4TjMHKPYGI+gDW2d7dB0fMjJXN3APIPYFmCqY2Vy3MVm46zEMqZsYnD/AZvgqjMIdGaafhJ8QcOmD0MTJ6++cwdEX5qNYEFuWw6k5pBAoegbSFwJaWlnFCoGgC/X56qnRRC6RTDhFI2F3KTj+baN0ocMViQQaPj6DvTDde+sn3UF5VCXdNI9zVNbBTW2ixO2kmGsTxk4dx9WqXEti4oVB0f5T9hu041Ze6ohSE/oAPJ48eBn19UuBfTq+iHgalH4TP2wdvfy8/PTCzjIobbsD6tWspHDK2EUNMmGgamg8toABcSoxPKfU13ZdHY5IuUjrfTAgYfS7pBbGZRthY97UQaKzx0q3VCBQLArMSAid2+sKFC1hLgUCnXCKQiP2XcMXCAO/0wmmmqAZLGG6aZfacOohu8e5JLZ7LXc4A7nZ+6MWTSj5P0I8Ki12FiJB9eyJIqkDD4mgmSi0fA8dbaBpq4rn8Mw9dxZnBy+grr0A0MMQ4gz6alnoR8Ht47EdNfT0qbno7HBQ0Jb+IkEkRNZcIJMvWjE8SidL81uNfmuM+U68zEegyeWamduTzvtHbn0+sjFCXFgKNMEq6jRqB4kNAC4EGGNOEvi7Z0GFNHk9j1MxZGc5BPIFGqbnzUWhTZprU0EXpOrSCAqGEfTBb6dBFhDyz7Cc0IRKOUpsYhcPBPX08F4FQpDnxPBrvj6MzzP2AVBqGKUQmk4l1WVhWTGICDidpyahpafJq7r6NIAT88R/3TRmk3mIZHbt0Ufq7vzPjr/869dZd+gUyRNq4kbEofXNvbJLxXbMmjMOHC9ccff36RH/Pnz+feLfmOEqrVxd2f7PdvlWrct/f5FzKZGiy3b6VK6fub2trS8om5pMWTte+lI2b4eJM5ZlMS2coIbe3163LDr1KtjJJD5Lnqb7nUwhcsSKSVXo60/im6n8+r2W7fanwq62tRU3N5Pja+eynrksjkA4CaXNmra2tk8o7e/bspGv6Qq4RoDjHDYEBauaCDAbfsnqdYjSDQYZ76O1H90AfzBGGdYjE4KaWLxwOcqcgNYAivDGUg8vGPYWU+KSMWMCMMGMFcsMhy6ITGJcTIQqUlBqVxrCsohELmprgdrvBLYBwlrkRZnT5EOu1iSloQgLMdYdHyp8L4zZSSI4PbLSOtdlmL+xN1SybzcTyprprjOsybtmwGk+Ov8w7pzN7GGcbxUT7THTWZOH7xD21c0zG6G/2xmO2/U3Oi9nAnMkzyfJn277kc1N9T1eey5WayM6l/VO1Y6rr07Vvqmemu57t8qarK5N7gm026FWy7nTKm08hMNvjke3ykjhm6zvb7UtVnsyfqd7dbPVDl6MRyAYCaQuBq1evnlTfsWPHJl3TF3KPQDQaQTgSRdOiFtz3yAeVuWcoGEVvXx8uXGrDuXNncPnSZWpf6BDGWcEg8n4KfmGEgj5UOspQU1nOuIIR9AwMKU1hmbsKQQu1imUV5K4jqKqpwcpVq7B29RosXrgITkabT7AicdgdFAQpKFppimqis5l8pnyufuezX6nqyieTl6r+QrxmNExKab4W4nyZrk1GHxujt3+6sSnVe+IhVPb662R8BIz2W2V8xHUPMkUgbSEw1d6/48ePZ1qvfm4OCMi+PofTiYWLW2BpWcaSKKLxmuzTCwSCuHLlMi5RGBwYGOC+PyuCwaC6H6YGz2F3oIpCYIiav8GhAT7LHYYMASJlSOxBq9WEiopKrF69CmLSMJHZiEVpSipaRHoPNdHJTD6JXT7rmsPw6EdzhIBRxj/Zzvlc3c/REBRksUm8Z9O4TJ6ZTfm5zmv09ucaHyOWL/RCC4FGHDndZo2AcRFIWwisqKjA4sWL0dbWNtJbrQkcgSKvBxYGjldB2sfWSjlQ9g663S4KcKvVR27LPr8kw5D8Tj6WvCffibzqr8ov12LcAzgxmflDZTFJ/axN7CDymCYKpHmsWldVAAjke77Ntct6vs4Vwdw9b7S5NBEJPbcmImL8c71oZPwxTPbA6PQl2Q/9XfwIpC0EChTrucN5rBDY3t6utE1VVVXFj1QB9VAYgHSZgOmIUfLe6Ld0MiHYybV068gXNIXWnlz2OzkmuazDaGUbDRPN1BXuDDPKXJqqnVNdL1zE029ZMfdtOhQ0vZgOHX1PI6ARyAUCqd0OTlHTOnGbNSEdPnx4whV9misExFQkSnPMTFIgEKCTipDSDEo5ouUba3oi9/r7+1XRogWcWI/kl7iQkm+qlNQoTnV/rtdLlTmYK27F8rzRFgGM1l6jzpNM6EImzxQSPnpuFdJoZKctWgjMDo6FUIrR6UshYKjbkB8EZiUEbt68eVKr9u/fP+mavpB9BETAkh8J+fGX49l+ZN+fbdjNpJQjREq+k+XIPdHoyrmkifVIfif3IUq+5DPyLcJhT0/POIEy+71PlCht0MQ1V+gWfrlGG3vN1BXunDLaXJqIpNHbP7E/+hzcjz8rwywNmUZAI6ARmDMCs6I6O3bsmFThvn37Jl3TF7KPQKH+6Is2saOjA5WVlXkR0ASHpKCafZR1iYWMQKG+AxMxS7ZTa2smIpOb8yTesyk9k2dmU36u8+q5lWuE81++XjTKP+a5qtHo9CVXuOhyCw+BWWkCN2zYoLRBY7uhhcCxaJTesQhkoiHM1ypmqTA/+kdk8rtkNEw0Uzd5DAvlitHpiNHehdmMezH3bTocNL2YDh19TyOgEcgFArMSAoXR37Rp07h2nDhxgvHofOOu6ZPSQUB+uJqbm/OiBRRUjc68lc7MyH5PjcYc6rma/TmQrRKNMpemaqeeW9maCYVTjhYCC2cs5tqSqd7buZarn9cIZBuBWZmDSuViEvr666+PtEMciBw4cAB79+4duaYPSgcBYUbcbnfeOlwsxPV3fmcADz3kSYlbZaWE5mhIea+YL65aBRw5kpok0QqYCw3GCqQ8E1PX2hrGT396pZiHdFzfctXfTGhCJs+M60waJ0uX5m5889H+NLo4Lksu+zuuogI5Wb58anqVSRPzZU2TSdvkmVIb31Lrb6bzQj9nbARSc1zT9Gnnzp34whe+MC7Hiy++qIXAcYiUzkm+mZFiWQFvaIhBPjqNIuBwmECL8ylTNDorw4Upy8nXjZnmKn01YdWqcL6aM+/1FFJ/80G3ctnfmebWfAx2Lvs7H/2Zqc6Z6NVMz0+8X4hjOraNpTe+mdPnfNCXsWOjjzUCmSIwa64qlcZPhMBUSZyGPPnkk3j00UfR3d2dKou+phGYFQKF/kM5q85Mk1n/iEwGxyiYJNtZKnN18kgV/pXkGBV+S1O3sJjnltHHJvWIzXxV+j2T9cDMpegcGgGNgEYgfQRmLQSuWbMG9fX142p46aWXRs7FUcjzzz+Pj3zkI1iwYAHuvvtuJQhOfGbkAX2gEZgFAqXKIMwCoqLNarSxl/YWM7NeKBMtk3mRyTOF0l9ph9HbX0hYFlJbtBBYSKOReVv0+5k5dvrJ/CIwa3NQad51112HH//4xyMtFS3fD37wA7z88sv41re+hUuXLo3ck4Nt27aNO9cnGoFMEdBMdabIGf85I/6wClMnsTR1KiwEjDiXJiIofdDhciaiYuxzLQQae/x06zUCRkMgIyFQTELHCoHS6QceeGBc38f+QG3fvn3cPX2iEcgUgWJg3jLtu34uoQExEuOrFy1yP2szoQlGGZfp+iZ9EMdsOhUPAloILI6xnO69LY4e6l4UCwKzNgdta2vDxYsXp+z/7t278dnPfhbf//73R/JoTeAIFPpgjggYhXmbYze1udcUABrtx1UzdVMM5DxfNto8SgVXMfShlPqVqq8TrxW6h9CJ7dXnGgGNgLERSEsTKOae3/3ud/HNb35T7febuBIvsQPf+973qk9ra6tC5J//+Z9HkNm6devIsT7QCMwFgVIRAueCUTE/azTGV8/X3M/GTOZEJs/kviezq0HPrdnhZYTcetHICKM0cxuLgb7M3EudoxgQmFIIHBoawg9/+EMl+D311FMQT59j03IGyUkKfhtS+HWX2IGSysrK6AadAcB00ghkAQFNXLMAooGLMML4j22jZuoKc7KNHaPCbOHMrSqGPszcy9LKoelFaY237q1GYL4RmCQEPvvss/j85z+PJ554An6/f1z7xNvnww8/jEceeQS7du0ad2/iSVII3LJli/aQNxEcfZ4xAnr1O2PoiuJBo42/0dpbFJMkzU6IEDXRqiXNRwsim55bBTEMWW2EFgKzCue8FaYXaOYNel3xLBGYJAQuW7YMx44dGycA3n///fjoRz+KG2+8MS2BTrSGR44cUU3R+wFnOSI6+7QIlArjo39EUk8Do+GimbrU45jNq5nOCXlOC4HZHAld1lwR0PRirgjq5zUCGoHZIDDJMUxLSwsk+Putt946Uo5o9RoaGtISAOWh48ePIxAIqOe1EDgCoz7IAgKZMnxZqFoXUQAIGG38S2XRogCmxqybYLS5NLGDRm//xP4kz4u1X8n+TfetHcNMh45x7pXyHDbOKOmWCgKThEC5WFVVhZ/+9Kf44Ac/KKcq7t/111+Pn//85+p8pj9JU1DJp4XAmdDS92eDgGaqZ4NW8eU12o+rXtnP/RzMdE5k+lzue5SoYab2zXQ/X+3U9WQPAU0vsoelLkkjoBGYGYGUQqA8ZrPZ8KUvfQl/8zd/o9zVDw4O4t5778UXv/jFGUtNCoFSxsaNG2fMrzNoBNJFoFQYn1LpZ7rjnsxnBFzGtlEvWiRHrvC+jT42Rm9/4c2I+W+R0A49rvM/DnNtwdjfgLmWpZ/XCOQSgSmFwGSlf/EXf4HHH38cDodDeQj90Ic+hD/90z+ddi9FUghcv3497HZ7sij9rRGYMwL6B3LOEBq6AKP9uOqV/cKdbkabSxOR1LRwIiLFca5phvHH0ei0xfgjoHuQLgIzCoFS0Hve8x48/fTTqKurU+V+6lOfUl5Ck/v+JlZ28OBBdUmbgk5ERp/PFQHN+MwVQWM/b7QfVz1fcz/fMp0TmT6X+x6lV4PR25+ql8XYp1T9nO6aFgKnQ0ff0whoBLKJQFpCoFS4d+9evPLKKyMx/yR4/E033YSurq5x7Tl37hz6+/vVNS0EjoNGn2QBAc0kZAFEAxdhNKHKaO018NSYddONTkv03Jr1kBviAe0cxhDDNG0jjU5bpu2cvllUCKQtBEqvV65cqQTBG264QYEgQuHu3btx4sSJEVCSpqByQQuBI7DogywhUCqMj/4RST1hjIaLzFejtTk18oV7NVN8M30uX0jM1L6Z7uernbqe7CKgNYHZxVOXphHQCEyNwKyEQCmmtrYWTz31lAoYL+dnz57FddddBwkyL2msECiB4nXSCGQTAc34ZBNN45VlhPGf2MZSWbgw2myaOE5Ga7+eV0YbsfTaq4XA9HAq5FxGpy2FjK1uW3YRmLUQKNWLk5hvfOMb+Mu//EvVmr6+Ptxxxx342te+NiIELl68GJWVldltrS5NI0AENPNTutPAiD+umqkrzPlqxLk0Fkmjt39sX/TxKAKaXoxioY80AhqB3CJgnUvxn/zkJ7FixQr89m//NkKhEB599NERb6B79uyZS9H6WY3AlAiIEBiLxaa8r28ULwJGZHz1okVu52Omc8Lo42L09qeaFZmOZaqyjHpNC4FGHbnRdut5PIqFPipsBDLSBI7t0mOPPaaCyEuAeUkiDErS+wEVDPpPDhAoBQJbCn3MZGoYERfN1GUy0rl/xohzaSwqRm//2L7o41EENL0YxUIfaQQ0ArlFYM5CoDTvlltuwcsvv4zW1taR1mohcAQKfZBlBIpxBTzLEBVtcUYceyO22UgTKFNhKNPn8oXNTO3T8ypfI5HferR30PzinYvaZnp3c1GnLlMjkAkCWRECpeJ169bh1Vdfxa5du1Q7tm7dmkl79DMagRkR0MzPjBAVbQYj/rjqlf3CnI5GnEtjkTR6+8f2RR+PIqDpxSgW+kgjoBHILQJZEwKlmY2NjcpL6Ec+8hE0NzfntuW69JJFQDM/JTv0hgi3MHF+6kWL3M7XiXinW1umz6Vbfq7zFeO8MvqYZGPMZVw1DtlAcn7K0GM3P7jrWjNDIKtCoDTB5XLhc5/7XGat0U9pBNJAoBiZn4nd1j8kExFJnBsRF72yn3os5/uqEefSWMyM3v6xfdHH4xHQNGM8HvpMI6ARyA0CWRcCc9NMXapGYBQBzfyMYlFqR0Yce83QFeYsNfpiktHbX5izojBapWlGYYxDJq0w4m9UJv3UzxQHAnMKEVEcEOheGA0BzfwYbcSy114j/sDq+Zq98U9VUqZzItPnUrVhPq4Zvf3zgVkcMRzFTxBBYD6qT7vOgcrBEU/raT+kMxYEAvJeDqJOtcUKB9bjHphhKYi26UZoBCYioIXAiYjo84JHoBSYas3gpZ6GRsSlFOZr6tEq7KuFPpfSaZ/MrWKKmZpOnzOdVSIA/gJ/ixP4eaZF5O+5RMSt/NWna8oZAm04gNvxl1oQzBnCuuC5IKCFwLmgp5+dFwRyySjMS4eyUOkzz8TwyCNRrh5nobACLiIet5DpXVzALQSWL4/gySetaGoyqXZq067CHK5ioCPF0Id8zA5DCYD5AETXkTcETuKXqi4tCOYNcl3RLBDQQuAswNJZCwMBrVkZPw4iAN57bxQ+3/jrxXkmglVhm9YcOADcdFMEzzyTEAT1fM3dTJyLEDSXZ3PXo9mVLHMrGo3O7qESzP00Pm0MDWAJjk0pdFkEQTOs1Aj+l1Loru6jgRDQjmEMNFi6qQkENFM9OhMOHYqXkAA42u9CPzp+PCEIRiJx6PlamKNVDEJgMfQhH7PjIl7PRzW6Do3AlAjoOTglNPrGPCKghcB5BF9XnRkCpcD4pNvHU6fiJaIBzGyuzOdTIggGg1Axv7QgmJuRSPc9SVX7XJ5NVd58XCu2eVUMYzIf80DXqRHQCGgEMkFAm4Nmgpp+Zl4RKDbGZ17B1JXnBQHZF1hMDjzyAlqOKyl0OpKOQJROnhzDaOjibXBBPjoB8Th3TmrT4jlPhag5hIi5sL3PzrmTuoCiQUALgUUzlKXTEc34TD/WO3ea8NWvFva+uel7kPnd3t5eeL3evAtcwaAJ73jHwikbXugCx5QNL+IbxUBH9Lya2wS9Fo9hBx6ZWyFF8nQoHMLFixeLpDfz142Ldb/E4ZpvzF8DdM0agVkgoIXAWYClsxYGAprxmX4cXFzYXrs24Zly+pwz3Y1Pk2F8+VxEpt2j/B8+GH5SVpcZNilxc/iafEn+xPXRi+OfHL0+m6OurhiGhiJ5FwIDgfF4TGyz9hA6EZHsnM9FkJvLs9lp/dxLKYY+zB0FXYJGQCOgEdAIZIKAFgIzQU0/M68IlIIQOP/MnYhkyTRZwOno6MBLL7+Ecrcb69atw4KFC2G12viACYFAEG+dPomamho0NTer6wMDfXjjlZdx7vw5PPjQe1FTW6sK93g86O7ugd1mRUNDI2x2KaP4UinMWaON2vy/Y3NHrNjmVTGMycyjOj1tnfl5naOwEZj8e1nY7dWtK2UEciwEjid2AX8Qx44cRNDvRXVNNeoaGuBwuBjbLIhIOKJUAzabDRarVdmmBwMBOr3woqauAXX19WPGSb9kY8AoucPSYBTmd1gTmrrEeybavGAwAO+QB+FIeLhhJrz26iv4wQ9+iJqqMtx4w/V4x7sexIZNW/G1rz+Or3/pf6Ouxo13v++3YHc48NSTP8BLL76Ozdt24rbb78aLLzyLp37xC7S1XcbOa6/DAw8+oATG8b3Ohm5wfInzdaY1gblBfi60YC7P5qY3sy+1GPow+14X2hPDfE6c9NIUY+OEbsqHx3E5l/sxWj/QSxQiPEsGczXTc7Cb17jwxfinMImfvuSziWcS59p/H4HRSSOgEcgBAjkTAhUTObbBpG2Dg7349S9/hoNvvISqymo0L1oId1kFQn4fAkE/c5vhcDpho0YhGouovT0D/UO4+e77sGvPXmoJHNQqWBN5bPaE4ZniE9WfsbXp4yJGoNhWvwtxqIS59Ho9eHPf6/jVU0/i5LEj6O3pUe+lvNvlFZXw+kPo7b6Kc2c8OH/mNJ59+udobV2BA4ePYqjnKjrswKf+3z9Xtp9DPj883DfnKnPhL/7kY3jhpTdQUV2FD3/4P2HliuV48flfUVB8ApFojAs+Tbhm5zVYuWol33cWUgRJz9nCG8RCF6DSaZ+eV/M8r4T1EMHNFEd8WAA0KeFPBL4gYvEBxCJXEQ13IBLq57eHwiCdhqjnyMtULeciWTNLEGGwjN/lLMvJYyezmHkuQqHKzG851qnQEZi4zaHQ26vbV9oI5EwITJAuksN4FJcvncfpo0dw/NghvPnKszhFbSBiJlgdVphNFjisZtjMJvjpTz2iAkGT9JGo8j+C1BJ6+rpw+I1X4HKXwW63o6q2Dg3NC7Fo6VIsbVmGsnIhnuIIQ4hxaQ9oKfReMz65G2XR+slmve6rHfjW176Cb37933C14zIXtMP80HscF7ajfHeFQZWFazvPK1w2auyDaDt/EV1XLjFPFHYrX0QW5fX0q/cyTAWi3WLDq7/+Bbr7PRQI49h93bX49c//A185fxZXOvvgC8dRWVGGFStW4Olnn8HuPbtxx223YunSFtaVIFVGfb21JjB3czbTktMRsjItO1/PFUMf8oVVTuoRJkX+8wNTlH9EyOvlpxOx8FWEfZdo6XQVCHnJ85CGkmYKb6OeIc9i4oMmszwj2j4bLzuYr455aC4vHwqGiPOa5m2IgU4aAY1AthHImRAYjgTQ2d6Og9QkvPHCszh+6AB6r16BmRq+mjIhaooOKpmt3GHh3iI7Tc7MGApE4QvRZIKMZG1VOVfRrDh7dD+f3wezxU7zCQvs1BYuX70Gm3dei83X7MH2HbvgdHElzagcYrZHtcjLKwXGZ776KPX6aYL9kx99H49/9V/RfukCXysu5sQssFjMcNr5/lHAkyWXaIROWOIRhCgNOnjNbI4gSpMoh5V5KPCFIlzUiZLh4UKP0xbluk8UQd8gWZ0YKvjO73/xV4hHg4hGI4hELLAya11lDZa3LMS1u7bzswuVVVVkjKQFkhSnpY6M9kcvXORmxObynszl2dz0ZvalFtu8MtqYKIpEDaAp7iGNbOcAUuCL95Du9SIU5AKYycftLWYuhlsRJ70ccZxFgiYaI7NJWDCahbKMhJloiN9D/FwiD1TJ/E28t4SZK5kvZ+za7CeefmIaBDQjOg04+laBITAnqpJgyWgGwU4l2bTuzg68deo4TcRO4K0jR3DozdfQd+UKTeVDKHPaUV3lVithQgAlCdEXhlIYS5fVCbczRiEwCnFXXE1h0Qw7nNQYDni5JykY5t7BEPcUetDXXQ2L2UzG1MqVNLGn1y+eArRE/gjzo+OuZX+wo+dqEoQAAEAASURBVJEInnvmKfz7419BR9t59Y5ZrQ646LDF5TDDxVdN3lclsVG4a+Shxx9GR28Q3giNmvgexqnPl4Ual8ONQa+fDFGU77UTwUgUYa6G20l17CQakTBNwLlnRuiHy0oGiWM6wIWiZ3/2H3jpuaexbuNG/OGf/BV27t6rFn+M/IZrTaBMmsJLRqcjRhOaCm8GZNqiJN9DoS12lTTsInmgLvIhYgYapeAW57YWK0IBWk9Q+EuoCoVPkiQUT8xHKRjyY+LCWVzxL3KXS14qkyx99fGM5qQUKk1YwusLeS5mo3JPkmgPddIIaAQ0ApkjMAchMEGI5K8QpGDAjwNvvIaXf/UUjrz5BjUIF7nq34tKhw3N1U4yk/Ihk0jCqMwhxrU5QVCFuDksJthddkSdNpYbA09RxXPRLniDEfj4CdC2zDvQjbeOHiZtpTdC7l3auG0HaulARv8ojgO2aE/0OOdiaON09vISvvwv/4wTRw9SKOMbSY4kSsHNFwzRXHt4uYfvpJVaQfnnpkRX4XKgfIEN7X0B9HoCiPOanU6dairLqTm0wWqyocztRO8gV8tpMeVyOelgRrR/0cR9rpSLWbifzqGC1ApG+X57Av14/rkXuQ/xj/DZL3wR6zZsIf2QxZ5c9Dv3ZRabxib3iKVXw1zpwFyfT6+Vucul51XusE1dckKAU4JYfIgLkW3MdkmZdMJE53Zc1JKFLqFhce5vVtwRt8SQiCaOyeOMJtI9LnyNT0kCl6S1XCQzdVMQ7OdCeg/zr2QJFayH1lTJrOML0GfzjIDRaco8w6erzzMCcxACR1vqGRrEM3Tq8OPvfJ3av6M0g/CRWAFN1S7UlztoDmGBmTQtIeolVv5Hn+Z1ZVBPmqa0CJIvIfwljmhmxnOXTfYOWlBB4TAUpeloKIRTbzyH0wdfxUuNi/COR34b9z/8CDWDSk8xtnh9XIQIyI9nlBqmYk35/CFJxPIzYf/rr+N/f/Z/4NUXn+PqtDAfZFLIwFgpfFHnjkjMjACFNG+YuPO6aAa93LM7xJAQtZVlWFrvJlME9AVC8FLaKycjVFfhgJMLP2Im6qLn30oKgJXU8A95grDYuSjE99rM/X59Q9z76/Px3ec+GTOFQzqEWbZ6Mw4dOYa//9Sn8ZnPfJYOY+o43BOZJmPMAE2XCnOc8vme5QIBo7c/F5jktkxyMcKvUACM4yyZlktcwOLClxLILLxHSkk+xko+JcKtLGHuo7a66O2cTrHi3CJDaY7Nkw+T0FXZ60ytodomOFGok4ukdyJImkwUKilsxvibZ7as4DPiLT0r7Ju0RKdsIjBxHLNZti5LI5BlBDLmqESgEy1gnFzfK88/S5fwn8PR/ftIpMjckbGz28wooxawvrkRjYwVFidTKUy7fGJcFZOnI6IWYLLR2YuV3j6jJK5JhlS4SckXlW+ph/lEgyiawgqHncylGwuqy1DnNKG/7S08/cR3VTtkP5OkBJkdJrbqiv5TTAho5iebo0kN4Msv4DOf/iSef/bnSsATpoaKe5piU8tXVkaHLVzQqbZjaWMFVjVV8x2kZs9Dj79hGzV4UTp7GUIgEkJNuZ0MjRiEcocL319hVAZ8Ib73cs9Nx042BnMPolIWh6j9C3PPoJehY/qGvIjwXFgeYaJAOiJmo3/6Z3+KA/vexP4DBxEW4dOgafKKv0E7UmTNLmQ6kk7b9LzK14Qc5ihIGE0mOnLBadLJyzwOqsWyxOIU/XnKwhm3qAi/EyfNs3D1Ox4Nk50hRZQi6NcAFgePSetkP6Ba+JYbwoqJ9DCGZ1GSpdDQKGmq1Csm820s6xSzicMtuaeTRkAjoBHIHIGMl5JkdSpJsLzeIfT39tJEjCyfjatgYvbJ72UrlmHl+jW4dOYsyhkc2k0vnhYSPj/39A30DdC752Isbm2Fu6JCMY7tFy/j5JFDtKMPMo5gDZby+cHBQbSdv8BVtAjMrMDpKqOmMUiPW35lHuagwNnIOGWXTx/Bv37mU7jzXQ/jzne8C1XVtSSniVZmDo9+slAR0MxPeiMj+yanw6q76yp+9sQP8PWv/CvDQNBrLzV8Zi60iCfQOL2ASuD3ADV7ikchf0LLTZRzf9+yJjfk3Wvr8ymP5rJQ4/eFGfLFBRu18bLME+SCT9egl1r7MBor3WxHBL0U9qIU/OxcKR8Y9CmHTn6amgap2ZVqAiFWIqvpljjePPA6mpub8MAD9+HYsaPYvftaSBxRIyatCczNqKUjKE1X81yfn67sfNwzevsnYlSI/ZEFa7UMTdJkEq1fnOafuEyaSJfHSluX7AVziYMXCoAxav0igUFE/CKsUTi0OGF204+BrZxlmOgstIuhAbnkxYUvyU8GR9E9XpD/vDbMuwg5lHNht1irqo9moTEKoWbTGt4QD6KqYZJBpwJAQHOdBTAIuglpI5CREJjU1sl3D+OB9XZ1kOjRLTy1fw2VZAJJGGsaarGX7t0HB4ewiPG+di5YxNAO1YqJ6+rsRAedxWzaug1Ni5eQIaR9O+nYAOOQubiP6Oj+A9h5803YQaZvcGAQr7/wAg6/+jpqGhtwHa/39/TirZMncPn8OT4Xx4KFzRg6cwnnT+zH419sV1qHB977KBwUGHUqTgSmE2yKs8eZ9cozNITLfNdWrWTMvTECVG9PNx3A/BLf+dbXsP+1lxD0ernKLJwGvYByNdvNfbhOquIoQ3IvblAJcnxFwSgO6Gc4iDD379XTe+8Q9+h6/AHYRYijAwQVrIXlRMMmOniiVEdaYKJQ109tnyjywlIgLQD6qB30sYyYlft8eWPIy9VumkbVUUNopRDqDUTQz/2FP/nRT3H3O4H1GzZnBkCBPCWMn3yUKVmBtEk3g9NTOG4DJ00Hcz94MkOE9omPgni8ncc0ATVTAygXJyYKdJFAH4JD5IlC3BbjYFgrVy3NQ+npk47vxA2elGeis60Q/ShEI9w/GB0kYaSDGaGV1CJaKSiaRVsoe6DV6tv4SkwUPuNgO+IulsX9gaJRHG7h+Jz6TCOgEdAITI9ARkKgFHm57SKOcKX+6IE38OKvfgGfp48CYDnq6CSCRp/Ydd0erN+2FZfOtWHx0kXU7HEVTDx58ke3ZaWPWoMAqnhNecYSasrr7iVluPW++7Bk+Qo6etmGmvoGNJGo1vK7pq4eNgqLe2+9lZrEANZc2IIDL76IgzRB3X3zjaha+BYOvvIarvZ34off/DJWrF6PndfdQMYyY4vX6ZHTd+cVAaMzb/kCL0AB7qmf/Qz/cvYUVq1eSYaD7y6dNh3a/7ry4CtaeTErSgiAwujIqjd5Ei7qiKm3uDd30/xaHLf4yKjwIjPRhJPHDpZd46ZGjwJdIBKnwyaaevMZGz39DoVoEcBnuBuQpcWVQCjBj0UIEnNPX5Ar5dQ6hlheiHmraSrqZIwIC8t2UAvYVONmyBg3zl0dwC9/8SS2bNqq2pUv3HJRj2gDI7Ro0Cl7CMyVDhhdiJpr/7M3EsVeEmmXaZDkjyagZq8SuUYFtIQ0qOhmcAjBwSuIhgKwuaoYDH6hEuogISJoARHjglg8SlPQ+AJaTPgQ8jOWYJjawgj3Q1MbaDY7ELR5uVjuhs1RTs0hA8fTsdZoYi0iLEK8krbxuJ7Hi0Zv6yONgEZAIzALBGYnBKrlMCFkUZw5fQo/+ubXcIyeQMN0DiGmYVVOrlwJE0hBcPPO3aioqMT6Leu5ss/IYLwu+/sk2WwO2GucJGJiaJFgKknNyKCa0NDYhLpGOp6n/UOUZmRC8OT8xjvvhp8rZ6TAKmj86vUb0dDUCDPrWrJ6FVZt3MxYrWG88fIb8FCr4fEMsjyatlEITC7YCZHWqTgQMDrzNt0oZJOxq6ys5CLMYvzD3/43FaZFXJfLe6f2l/CFMIvHJqbkOyLfEb5HITqAkXfSRLlPLaPwvYvzfVL5ZN8L78mjdgo20l7RCDqpwQsEwmRurIiQJkSoEbTRLFyEzChXq4d8cWoC6e23jCZQ1BLG2I4YYwk6GDomAj89gvJ95V6ZQT7npBlqc10lFtW60dk7iG8//nXcce876SV0E9ssK+TGS8U8Z403GokWZ/Ndmw8M9JzKF+rUvkmoBlOPsmSIUWCThTMudZF4khCSJkYjfgQ8neRb/EqAs1cupBBXQTpH83junY4GyPOE6PsgSlpKOgdq8Sx2N9xuehllOIhw0MPFNtJAKx1sseSQCJIUGq10wDUcmGe4s6SpLBMmL/NdIC2uYW6xehLqrLmcYZDm7Ys/hzppBAyDwOyEQHYrYatuomauDj6GZoiR2RMHMLV0COGkOWjtgibUNDWhnqabskFaCGSqeG7CiI6khMG7OpXLQlrVXfUySaDqOJxuF4U/13BZFBBJ/moYEuKm22/nEc1Qm5px81134fKFNlzoHESAGo7ky5h8JzWJHEHc8Aea+UlvCJ3cv7dlyzZqxjfi2KFXaeZJAY77ViQl3w91rK4IGyH7WijiDb+HEZp4Rvn+ubiHz05tnfhyGvL7KNDR0QHL8QX9CNKsM8IFnl6fhVrAAKq5x9fM/S5DjPdpppAomsFB5h/w00aAVZu5yr1p7Xps27QOfTRL3b9vv4qnJfE+TSwnxvr9lEF7uGewyl1Gs1QHLl46h2eeehqty1eirKx8uLXG+tL7AgtvvApZCEynbcVGB9Pp87zMIgaDV4HgESQNYyxAxUwwjJUQygTVVGag8eAgzHZuiaEAaLWXceGMHkL9FPx8ZfR1RXNQCnVKUJPnqB2M8j7ggIN7pi22fgqRHfQDw6017kpaTjnJ75B7UXUJzVbLcfyWQxETeSvO2ITxTha5jGdJTkcy6DR/COhxmD/sdc2zRWB2QuDw3BYBrv3iBfRc7WCMMAqAFU6GbrCTYIWwcccOrN6+lQ4fXFytIvWa8X2YnEFo3sQkZmSJ66P5RdNX29CoqpHjZavXYtcNu3H5mz/GT7/9DSxtXYlN23YKpWQ7Rp+bWLY+Nx4CBcssFBCUAwP9OH3iJBYuWYzb77sfJ986SVPqQbXXj4vYfJ8o8ql3IyEUivZd/sl/WZcR773LlrXivnvuws6dW1FRWUH+JI62yx04cvwsDhw6jiPHTiFo6sWgx6u8hF7sjaLfG1au0f1U+0mgeAkLUc/39Lo1q7Br+2bs2bkNq1cuRQ33FEYpFf7DZ76Ab3zr+9RQBvmayp4Z7g8kk+TzxxiHkPtelOYxzriBT+Ph97/fsEJgsTHshTDV50oH5vr8fGNg9PbPN35p1S9OW9DNTxf3Oov2jdZO9GaueAphSmjlEKU5Z8TXp2ino7yJDkArKQDaEB5y0wkMA7xHHUJW1X2hvCqJIEiNXsTP+5TvHNze56rkAhgFQf9QJxzuWmoUq5hVzEEnmpHT42iE1hhWP/mfy9xp2MxiWY6qRDUqUYf+qxHQCGgEpkFgVkJgkrS0X76Ab/7/X0CcRK+RYRrKbFIMGTeaeS7fsA6rucovjmISBGma2tO5JURtmGamyq62KKkM3E/EeGRbd+/G679+GSeO78e3/u2LWLpsBT2F1qR6VF8zMAKaoZ558ES+O0Kvmv/rf/4jbrrlNmzdvhNP/fIpWKiRs1NTJx58nYzVZ6EZp7xCkp9b+5TJZogC3E4u6Hz0I7+J6/dsU85axJpbXscNa1fgtlv20EtvBN19HvTReVPn1V50dXejv2+IDmC4t4Xl2LmXsL6hDgtotr2oqYFx/iqpiSRDw8Uh5UKdFbpoRfDQb7wTP/jpc7jSfhmVdgqBbI+ZgZdlDUmcy4inUCsZnnOXLsBDR1MN3CNsROZXawJnnrP5zmHEeTQWI6O3f2xfCvNYlspkD2APF6kGEaLXTxudutCjFS00Kbkp/iRKQY7mnGEfbCK4Oat4mcJdkJZLAWr0RIgjQUwsZJOojTBG8rDconm8v4zm86SZlPnKKmwIeDv46SIfFaFjGfpOMIvQqbIP/6HgGfMrp6JmC9vG9plMzKNjB44FaV6Otb5hXmDXlWaIwKyEQGX5QEJ0aP9rOHf8TbQ0lKGcgpeYhcmK/2I6dFm8pIUmY6kFQGVDzzdEechTtJCFkRGM8TOOvrEz8uMm5Ff+y7E6Zz717BSdlfxNzYuxfO1KXL58Gfue/xVe+fWvcMf9v6HKUTZuk2qaojB9uaARKGbmJ1t9q6qqwpatW/D4v/0L/r9P/hWiNM2sq+D+O76fNqoCRRA00axI1H58C5XnzqEgA8DTM+c1O7bg9/7TY7h+73YyKTQJpRBGW015HWW9RyUbncYsaKjAosZKbFrVIi9q4kYiF/+KXlHeb16W1RomtTikjuQP9wjSwcwaPvvgu+7EZz7/VXR6QqhyMLC8CKfcH+MPB/m80k8iyH2CRnasohcuRgY+awdzfVfm+nzWOjKHgmRepdpyMYci9aOkTUK2xCohDjqEYVy+SJimnkL8aK0gdxIprrSDIV8vF6Gd9IdQzz3LLgR8Nnh6LPAPUlPH/c5Cb520lrKRXxohkyxAahHnXX7S3CitKOyMoeouj3OfYCPLc9ErM7e1BPu5L7CSpvW0rhpJEkbHRjP6AXpXpxBp7qSwuYDNEuJszD3TI13TBxoBjUDeEJiVECh0T2L07XvlFTi4suXknj9xMCFCnIXBo3ddfz3K6IhCUTnFLQ73g1RPAr/76a5+kCZqPgZ0l71Bdu71aaDTlzIGox6hqZKXZmi9DAMx0NfPY2oVaG7hLnOjsoqaBLebjCuJHOscTQmCLJesDEbdvHQpmUiuvvl8ePLH38f1N9+OsnK6aB59QB8ZHAHNUM88gMLgtrYuw33vehCf/uR/oTYvijLR+snrw68EgyOOk0QYTDh7kb22LXQk89h73oUbrttBAZBsipLihLkYfYOSyzZSR1LIkzIlj1osUs1LCH5jt7Koy2P/0NRTHM48+u534RdPv4RjJ07RsQx3+bKQOMuOyiZEJmlXRVXNsCnoaDvGFlXox1oTWHgjVAxCYDH0ITkzCq8vYVK0IS6C9XJP3xCtneixUwK+U+NGysfrYQR91NhR+HK4m0j+3OjtNeHKeR8un+vEVTq1ipIwVrrtDNPTiqUL61UIHkUnWUqIjND5S104f/kqvSGzLkuQC9k2xk+uRFNTOZ3rcV8haWCMTvNM5LlG8aHnZvJfFpqlBv293H7TSXoZ4H1xECN0V2ikMekkG27wpHE3+ACWVPPH7DROr9+XLpxlWIh9dNjgZOBoMmpkEqP8bNxxDT2CXkMixCJFGhtJJnq9CqCbpmLt7R2MK9gLr8dDxy1eehL0c98PTRr4L5mEgZT9fSJsRqMhrrJFeOxXwqMEtu5lUPqwij82xYtGxtHGFTIJal1d5sCpg/tx7vSJZPH6u0gQ0EJgegNZTcHpljvuQMuqtdS60ZmAEq7GPssXju9KmHag/R5646Vp0t2334A7b93DkA3yKsu7yXdNXkz1kVMyJLxkoeAoTIkcqyzqS/IPf7j6baK20Sx7+lRelY33x6coTZ6WLm7AB9/3EN2p22kCOqxxZDZVNBd9vH4Ttm27BhcunOGKPF2WSpJqDJT0nM3+YI0yxZmVXchjkm7fCrkPmY1KgTxF+hKP0yM5vYJGggzQHgszFrIsQicWv6PRIEM8SDB4wFm+iPv3muHpt+HssUFcvsgYgbS8WLywEa1LF6C5kcIfl7IS5FSoWmKRS56tKHdi1YrF2ECLiFUtq+A0NaPjXARn3uoHLe1JE8lrccF87HyQtbEoF8ht1BbSeJ48koSuoCCYWIljqVKHThoBjYBGYHoEZqcJZFl+r49CnRcVDiu1BCbuIaKXToZ1eBu9dJbT/GyYyo3UKgKeeA4UszOJC9jQ3AyHxB0jIVX7gtRKv3BzCU5SzNKEuRRHEo3NixSDGeH+wrB4IaTLZFFhxMS0bJygOVKdMjfro6AYoXrCxrAVgd4hBqZvZ9xB5klWM5pdHxkUgbE/iAbtQl6aLTi1tq7A7Xfej69cbqMHuwG1lUXenwSbwJeCwp0S1NiizfTYefdtb0NTQw2FLQnRwlzq1aTAx38WCnYhCmE+D/f+8b4woOKt00pLAJER5RUbLpjvYhCD3MMXDARUvgp6DRUPv2ZzImRMAgD1hFrYufeut+Hxf/8hjh45pG7JK+5j0HmJQRjm6vs11+7ED7/3PSxfsRLNzWL6lOhBopzC/6s1gYU3RsVAR4qhD4U1M4QmDVsx0JQzGu5m2IchFc7BLA5hRICTHMxicbjgsNbyxImwjzH9AgxZtaAR9dUxdHT14NwFOnnhYrfTbsfSRU2orQujXPYSKmpKr8lDPqUJ7Ozp46J4EPXkkVqXLsQCbmtxOmgRwX2IMe6xNllJi8ckCVERZ0gKcd5lIZ8T5J5Es+Ui92EvJWmXsD1jMuvDvCKg38e8wq0rmyMCsxYCly5rxaKFi+FvP8WqRWSL47qbb0bL8lU8FdKY/AgVEvOFGIU+xgV0LaK5RA0JaTkJGquldkDFDvQP0KtWL7MKkZPyuK7lrES5u475yFiymATBC3HzdT9NMmgjL/WoPUYkpmOJHTMHfIM4ffwkrvSLgwoLWtdtxpZr6CFUkqaMCRyK4G8xr35n80dE2JXevh4soofQa/a8Hc8+/Qs4aLpkVfv75F1l4oKJmIY21pbh9ht3YsvGNcP7ekdfLnnl/AEfLpw9g86OTqXFF1bGSuamkY5fWhi6oVzMuuWF5H9Zpe643E6HMZ3ct+KnplEcvcRQXlHOuIXLUVdXT/PUpCkqW8l7DTXleN+778GnL5xSlgDidc8fjGKA+2Uefez96O3uxIvPP433P/ooGrnwJFYH2cRKoMhlKuY5m0vcclm2kebPVDjoeTUVMnO5LnSM3E10kAvQ9AzKFS6buO9kiAdZDIvLOTV08RjD5kQcCHsZzoGOYEhGce58G/YfOgVuB0RdwwI4yqpwpbMTL+17FjfsWo+br99G/saJrp4hfO+JZ9De7WHom1V0YNeA9v5+HDvzOhY1VGLXtrVYsqSeHka9sDp9MDkkNqEsgMdIO8UXg5MLbENc2BuiLjBIRzKnSY+3snXVc+m4flYjoBEoIQRmLQQODQ2SqexHiCtYtnLG7uPq/qr160WmS8iAwgFKEv5SiCiJppnE0l6xAFZXBfOJWYPcplaBeWwUCsMMmBrsv0gCGoKZG6JdVQvUM+IuXnImZEsyriTCIU83CS49YXElLFGV1JdgZhOaCHFFQQ1lOAof7ehXb9jKmIV0n6xW9iTvcPt4pJNxEdCMT5pjx5dNzKff3PcK968EUVPfjEsXznNl2swVbDNsfGnEZFNWm1csa1UhHKoYs0r28I5NEvzd6x3kSnYDlra0kCFyck+KbC7knmCGjaB6bzQ7T02MgdW8eBEWcX+uej15TQRDP7WCQx6fMgmXQPYqzpZ6fc3ULEZxzx034udP/AQHDhwWfkoFlne5Hbj81gkc+PXP4PH50X6lTYWGWbBALAWM8z5rTeDoFMnW0VzHf67PZ6sfcymnmGhhYYzHMJ8QD1IAvEr62UvnK2XUuMkiFxeuFKPBZbMwnWwx/EMsTJPMmAs93QP4xTOvIcp9g9fecBfWki9S/g6GB7etrQ3PPfs0Dh4/j7Url+DFfUexaft1+O0910OceCUIJbWDg4M4dPBN/OzXz2NBfRnuvmU3XKFyxO1+ah6pkXRy7yCFQTOtI1xlNYiSfwoMXeEi+TlEXFfonEZ7Q5/L+6Sf1QiUEgKzFgIFnHjMDE8whBqXgyaeNahuqOU1Ek4hjmOT2KdTm2evoL18GVenTAyuqrR4ZDwVneUf7t+zldWRoNLMlFpBVxW9YlEwTBBE+ZJ1NyaakFnoPt5RJWakNDGl++QE2zm2zji1jk40LGhg7EIGZ6WnQTdX3NQ+RcWJqpKkNJ0MjkBhMAuFD6IsidTX1ytzpP0vP6vi/FW5raL8S7yL1Aqq94hmnitWLMOy5Uv5vsl7Iu/VsGAnK882G+prG7kQzv0wsg/FTiGQgp94Ao7RLCke5rI3j+VRcegiL7jFyveYAZMtVnqvo6Bo4h6aMpuHe2DcLF3KlszD7y9pR5T2VXV1Vbj19ptxmNr8qD+gTFcdljDeYsgXyWvhgtLjX/8y7uwfxG889B5lZlr4o5BoYTEx60bBfKZ2FgMdKYY+zDRO83I/Lj4JuOdP6B9pXlIANMmWlDBjAHoY1iHgxkC/Hz20tnjh1UMMFF+F22+/Gy0trYlFsjENX8xFsTvvupfe1V/ChSv9jGu8Gdt27FaWUorcSl6SRFkcu/6Gt/G7Bt/99td56yVs27gKjQyx46JW0UZaa3HTxN4m1lP0EmpzwVnRxEU0moeG22l1tX64Vhamk0ZAI6ARmAaBYS5vmhwTblXV1KGithp2mnSayRju2HsdY3/RTIJav3GJ5zFq8mw067RxtcokAqBkUAyiVDvMBMpVyUdB0FpObSGZxkSS3HJv+KMu0hSDbpIdJHgm5hPj0bFJ2EmL1Y5tO7fTJr8a9RUutJ0/i5DsJWRZw+zm2Ef0sUERKGaGOttMXXlFFd5+y51Ysmw1QyyEKBDGUMb9Jtyay2DDYpYdp5lmGZYvb2EMvjruDzSR6bHwQydL/LbR5NPudCizpvLGFShvWoOymuVwVrXAXdsKd/1y2Kjpj1sZrFi9ZHyO77OzfhnK6xbDVb1U5StrWIOKBetgq2ykZ2ArP2IJwEUg0hEbtZE2IQlkuPbsuRYL6DVY6lYG4hQuxUupg/nM3APz3FO/pLt1WhRQcDVS0prAwhutbL9r89HDYujDfOA2dZ1CxEh5YgHuu/NRmCPfIVtTeE2csMTCVlokcZFZgrwz2HtbRxe+86On0dblwdtuulUJgOIUZmKSxeu6ujps3HINF6qXY+Pm7aTBjDkoC2GK3RlPzzZv2YS77nsnnnzqFTzxixfR1y/tcSLkK6M1FPmfIJ9lktaaqX10lTeSvnNrjQosP74syadTfhDQ72N+cNa1ZAeBWWsCQ9y8HPYNMdC0DcvWr8Y1e68n05iiMaRMygxUaQCnr0Y5nKBJg8MiRG0y8RxfOsmw3UXhshrhAR9vCQkc0wCertmwCcvWrETg8GGcO3oIb7z8Iq57+y3ji9FnhkZAE9r0h0+Yl71vuwn7DxzCibcuQEy67RTwLGQ+JJKKvD3V1TWora3HgCeI7h4GRaZ5qM/jhZ/ee6M0DQ0zb5zmUHHrRSUUVlVUUsNYq/YDupxuOCupHeRiTmioiyvR5Qx6TFNNC/es0Mtvd08nuru6GEi+lzGxQpTz6N2TJt30LaXMyG1khMoZ/sVBTb+TQqmTzzdyz6B3sI8MGF9omnWL45oAPQX309FM/aIlWLtuPbWAM9GK9DHKR049Z7OP8lwxnevz2e/RaInptq2YF8RG0cjnUYKfiDMYeww+LoZxAZsWS2LhEAtZKYDRBNTP/YB0wBKic6xzF6/g7MVOPPK+R2lJsWySBjDZ8oRFEuj0rhk1XEwXT8iJNIZ/SWYe/r521y68dM0u7oV+Frt3bUZ9XRlscQctoUj7olw4I300W4OKDbK5KpWX5xjNWM1cdNdJI6AR0AjMhMD00tnYp8mLiZMJ2VvkITPnZjDnhS1LuJm5KmEKOkEYkzUzq7OCBGpsgNOxBY4/Jks6TDynJojyROIutRNC8Aa72Khhd/EjxcW5IlaJ1tWrcfLQmyTaQzj71iktBI7gUxwHmvFJdxzljYmjmntO3veBD9BZUhTf+NpXcLX9MrWBVhVKRbTtl65cxX/7+8/B9A//wmDyCU90m7jIc8eNN2Dl6pWoqKH2nXtyPR4/uujs5fjRw7h6tZuhOP2Ma9WMLZs3Y/NGOodxVtOfnh37X9uH/fv3o63tilLmi0lqAz3+1tTVoKqyCmZ6FA0MXkVP52U88/xr+OVzLyEQCisaYGV7on4KquagMmO1KidS3ONLcydPKIpbdu1hDK0FY5d+0gVjXvMJUy/zVgf2ntdhGFd5uoLWuIcK7KSYaGHhjAc1gVFxQsfYe1bxYyAm7VY6sXMhyk88QiGLGkKv188FrgFs2rwRm0kDXS5qByek8+fP44033sDb3/52ZZpPMkCLJWG96LSrtw9PP/00tm/fjtbW1gkCpOz7s+BdD7wLxw+/icuXrmDt8oW0mGDdMbbFz0U57g10VEW5mCbxlBkay8bCo15uw5EtNTrNCwLyk6uTRsAgCKQvBMrEpiDoHRzgCj7DONAvi/jJEm9Zk/YCDnfeTNNNMfVMmDtM92YkvAOK90CLWt0Xu7CpkoiiwkxR88AVOu7anpSR4iSq6mtYrYmmF9voWv56lSdhEDpdOyYVpS8UKAKFwywUKEDjmsU5z/+tdOjygUffh/7Oi/jR975JpoF7+GTPLbmS8kou2Ngr0NnVpxw/7d2zAx/7yGPYu3MrLK4qWMopBJrpAp1JhBj5kBxQqziEUydP4sknn8Tf//1/l2qUtm/btm24k/EJ77//ndQw1irmRswhpa7k2EV8dL3uvYI7bt2LP/qvJnzz33/E/S1WmoLSlNRRCV8fnUDRM57KzyD3MT4r+1/ueccDcFOIVJVJgwyUBAMtBGZvwJJzKdMSi0GAmisGmWJX3M8xpp8IU2aGYLCI2aeVfutECBSPoOQ7xPyJ1hEO7o3esWUdKpuWcZGrPiUk7e3t+MlPfoKNGzcoc9B4PEonXYzxx2eFfv6UtLOOi2RLliwZIwQKdRVqCrTQs/MjD78DFa6oioFMgsirpN0USsUkNWIP0/zeS3pME36b8EfcxwgGrtdJI6AR0AjMgED6QiALkh+bhsYGOluhOcLAEFfDhFCNEqtxdZFOjezZSdCycbfHn5DBo2dQCSpvKascLjJZ9kSBUERAinOKEKpNRCxqTAXKvp5CophaUIOwZuNmrFizhoSb5TG4vTyrk/ERkLkon0Qwc+P3Z2wPcsXUSbniGfTsmdN0DBNGJTXm4sspSqct737gfrz3vQ+hu28Q//Jv3+YiC7CQAY7NXLE20QzUSuFLln0kjd3b5uDGvtrdu9Uq+O/93ofxox/9GHv37sXv/M7vYN067v+j2fjkNEwzGGMr7LWirNxMAXURtmxaiz/7+IewbesGfPtbP8QXPv9Fmoa6GCYiAD+1hL1DYTz0yLtx3Z7rEs4UFO2ZXHohXykGoaOQ8Z1t23L1rs22HXPJr+fUXNBL/Wyc9DEKLzVxorWzkz+xKC1gPMotK4r3IA3jf3E8t2XDKtiqFtJ7eSpaByylh+SHH36YYW0a1aOREEPm0NLBam2iWWgNHn7oQaxcsYLnY9kx4VNkoY08CwXO3dduginkJa3m6rtKvC/e10U7yfAUFgc9q1v5sYhH9uS+wLHlDT+mv3KOgOYxcw6xriCLCMyCSgg5imP91mtw/a334Lnvf4PB24Pq2jghTDWOOemzJT4SCH6iIDe+B8ISxoKM6+fpU66YzTSzSJSZSmCT2GKSqI3giprSRqrz4T+yQsf/lPf4x4KFy0igRSAU4VALgGORMvyxMD9R8Ug5z8nr9aKf8Z0kCVO5cOHCeW5R6uq7u67i+ed+idMnD9PBCtDc1EQmpgweattalixCc2MVFi+oxZ237MXpcxfYGb4z4tCJTFBSABxbssfjwWuvvUZHCC2K0RGh7+mnf4WtW7eqFe9XX32Vew2rsXLlSjpySWgR1fPDr6LZ5qahgJ2vJk2u+D5LeIgbdm1BJR06ORgAuaq6kgLlHvzi57+Eh9747rzvZvz5n/8FNYtJF+ip6MPYFhbe8VgBerrWyeJGR0fHiNZQcBzrbn66Z+f7XidjokWo6ZAkng4rGEYoV2muQtxcn89Vv2ZT7lz70Nvbq/b+Sp25HC+j0EnBIRaTfXZ+0j6ySCYKgXQGEw1TAKTlRCIlvoUnitJ83kTBzuoSwjY5LVrEUDn8SBJrpyhjGUf9vYg63MoD6F133TP5IXUlIU7EyEdFw0F6V04KgKxHLLBIM038jrFdsRC9p1t5nzQ7Hu/jfS7Sm+iQj3l4cfijCtV/NAIaAY3ACAKzEAKFIMVRWVWHvXe+E68+9R80aQgr2UotjI0UKQeJvBGGfXCQ6L158DBefPEFvP/971c/MqNZhWiK7X1Qxf/jH9q5MyYPTc9+9vOf4dLFS/jgBz84TvOQ4B8ZIoJeu+ibfrSo5JGij1FEqDlwVlZj+bpNiTvC0A5rMpJZ9bexEZjI/PT09CimWa7Lp7y8fFhjlOinMNaSJ5kkjzA9qbVVyVzTf//jP/4jvvjFL44wvZJ73759E+b59GXk667En+q62kuNe+K9dVHLtmrlCrXPr5qOWSTkg7yPEsJBgror5oHa9ES8zsmtFDOnT3/603iAe1Yefvjd7HMFHnzgnWiiBtHn8+GfP/85rFm7Hh/60IfGC4GqFimey+xqf0sQFQw0X1aW8C4a4ziFaeZ9w/XXY8OGFXia3kAbapwwk56cP3uaXkxl7wxDxSRaOLlh01wR86sAYxVKGjt/kseysCCr87lK6WhtLly4gHe/+93j5upHP/pR/P7v/36umpWVcmUh5P7774fMi2R65JFH8IlPfCJ5WnDfyXEvuIaxQem2LZ05NV3/Pv7xj+OFF15QWX7rt34Lf/ZnfzZd9pF7YtYse93WMx6e0NrpkpHopPQjHiN/YQoqLSC9YSlTUDG/VPLUmI5KyCuxL4qKlo70SYK4T8VnyO9PxD/AsBL9MDGsTsTHeMd0hmehU7xJBUsblKk+nXIFB2h1RUFzTL1CpxOJNJseSqPcK211UeAjTTWZfLzbS3mQdEz4HhFcxz88rqSJJ/I7IQ69JCXnoHzLApYsRuk0PQKESieNgGEQmIUQmCA7srK0sGXF/2HvOwDkuspz/ym7s72pd8my3GQj446NewEbjA2Y0MFASEIIJCGUQAp5j0ASXsgjkIS80ExvptvGBmNkGxe527Isq5eVVn17m93Zmfd95+4Z3R3NVs2uRqvvSLNzyznn3vvde//5+2+zFiyxjvZOZKNKIcMgLQVDG4tAp5KdIIxdthvFnR966CG75ZY3DDLHAeGkqJhO90P2OwBNVjfoVRrL+1EMtcrWv/CCPffcOrv1XbcyZUx2cvd+wVWjrxMEFMIjqFR2n1sAzSMT2dHaZktOW2nzFp+UJZc5PYeO09pxh4Bnfhh4TwUDLSfhRuvTV77yFbvwwgudYPanf/qnCMSnq8zhRivFj370I9TIW3544ziWHn/88SEC4DiGTnnXpctOslvf815rP7TffnvPXRC0+m3R4gVWiWyc9Sj74t+PqspyJI1JOEt+oEnOf6rLli2zL3/5y7DcQZBG/xje+esuv8h2Nh10gvVn/8/nLIFSDvV1OUKVU8jwaGSfyKRQmz5gs2fNgOtTUHy+pr7GbrngfFu39gXsS1sPaMS6Zx+DsPhRCKsB2cpOk//0jthKhdKDDz54xPbcDRcgI993vvOd3M0FWR+LJXD9+vVDBMCCHHgKJtm6desQAXAKDnnUh/BM7lFPdAwnOFbXQMHuv//7v+2ss86yn/zkJyMiMFY6eayuZejJ05rWA5d45ChAUhjWOE0z9IW1kB2VxLJvTlBD/1QvsqYfxBiUznLlqwJq6mlqBgrr/mQXvJ32IY8BBUx8wSWUdVdLEb8XQ8bzYG4/MdegzO6Fh1QP5oXXkzu03+2IH1ZAO2GGxG9QsyVcRlAmsQENpSur43yoEPWDRv6mUE/lExN6Ddco7J90En5Hbr3VbrzxxuG6absQEALHCQJU/Y+50fWARZ5Ly1BT7PSX2IF9ByzZw4BktBBddBNScwTC2NvaZOeffYZ94hMfD7lxsQfcIiD4Jdt22QDSypPgOSLXjzHNO+3mV11jH/3oXw1q1tyM7hCMHexB/4HuNnTPR91wIiBmndD2X3DFK8CcVrleiBIMJtHfaYOAFwKfeeaZIwRAXiQtPtRUs7FPrgDI7bQMPf3001ycUPv4xz9ur33tawtqPZosRoh4rUIWulciWcu8RUvhwtqJ8662K6+62E5attgJW4ydrYMbZhpMSwsUKRH4VdNSf+QLjgR0cJWim1MNrIg7d+6w0tiAnXEKstehuHs7EkgxBmbmYFKYXHD55mbwLkP7Y31QYLccarZ5c2cj3wLSrqOu5/nnnIVECsvtQDPedQiIfWDCLrzocjv1jDMRnziou8r3+uceaHCdrmiPPPLIMHuHbuYzw+diMpp/Zkea+8orr3TWU7rVHk+NGQ7/8i//0ildpuq8j/ZdOdrxU3WdIx1nLM/USOPpzn3aaae5z7x580bqOmSfp5vPP/+8UygN2ZmzMhl0MucQBVyFmyUSwpDOROGpMMBSEWm4WBo9CMjohJidQRoUAd0c6G51Suz+3jYIjT0QDJP47oUyvAu1/Q6hfE6TpXvbs3NEQGNTXRjTsdftp8KctJafAWRC7utpsST2WS8S1ISPibVgHQeHMEj3z8wAMqVn+SHEcaOMhFOwZfu6hRH/7N+/f0QBkIMZAvDcc8/Zhz70IXvf+96HY4ewGHH2E2nnOH6YTiRYdK1FicA4LIF42Z32ie5hUTvt/AvskdW/sla411VT058bmuXeAxAoWAMrSzO2fCH6pDrAzFEYg6YKRC7V1wqi2B1ouQZFNe7jmNm1CXiKMXHEQUZmu70Z+N6nUPJhANoxEtCg5bxwED5TIMAN85fYqouuGOyjr+mIgGfgrr76anv1q19td9xxh7tMZqNkum7GolGzyfa6173OaDGktZBMy8GDeK7QrrvuOnsFslhOtFEL/tnPftZpRmntLvYWQ70rZsxdedZKe+yh+60dguAMWAFZrD0D5QmSjSMucBase9X2+NMvWsPM2bbiDLoA8cc+513jFrxvFLYfWH2fLZ7bYDPrq+z0kxfb4w8/gJpWM23+YCzMUFwChUw0AyYIbts/u+PXVonjLZqH7Ho8BA61bMlilxChqXEXmCJ4G8DSePMb3wyBszbveQyd/8g1xtPRLZHp2H0cKe8XY9donXs5XE/ZKioq7Jprrpm0OLaxWAITuNYPf/jD7lz/5E/+5MiLKeIttLZfeuml7n0r4tPMntrRClDZiY7hgqeDEz2Ff/iHf5jQUB/3SUFgtIy3xxedRK4BCIFUHBPbTKYb3kWMW67EOmKbs3GBHjYQLdItxO6luhHrB8EviuLy6Ow6pOHaTqHQINzRQyroHIyJQAmWgqVvoL8DbA7mBy1iI54UBGEGBNtF3iuYy+10f7COOoUWQQH59C6QzM5BEs35ed5wzwWfRn5qOPdUN03oz1yU+vm7v/s7u/fee7MKM7p/rlq1yvWiuzcFQC/4sd/XvvY1owux2mEEjrhVh3dpSQgUHQLjEAJJhLztLWOnnnWhXX3zmyCoVYAm0uWAxCfUsoQSqZaTCIRGDF8amQBdPDOtcnRvgIYt6jaECRyJG4VH+thDIwaXCYYO8dh0qcBEAVEchinl5ggCuVddeJXVNswOnZAWpxsCnoGjSyfjpbwQSAGQbor8sfrBD37gmH7+mH/yk5901qsPfvCDrqQB8aDlohQZLikQMKGFb6961auy8YSbNm2ydevWZRkdxhCO5Aqze/du5064ZcsWd2xq2eliyLIJx7rhzUI68tk2Z94C64HwtmXLdjt08JDNmz8HryQYBrw/JfGYXXrxBfbYE8/aPffcZw8/ttbmLDzJFi5eZnPnzgcuSBYDPJMQ4HbtarTnnnrcZlXHbdXpy1B3MGpno15gD2IC7/rF7bb8tLNs+cmnuHg/koh+xLY0txxCzatG2719qzXvb0QG0BL7g5uvtwpWj2cD8xLD/M2t7bAw7sZ5puyaV96AgveX49hgribYmKGPH9+YxZTa7xkzZji3Yb99Mr/9MzuRY1CJcffdd+OebXGWFz5XZNBe9rKX5Z2OzyFdu+j269sNN9zgXHVZ0oMuemTs+Fy++c1vzj7vvi+/aRFlens+/wcOHHB1zpgAiEqXWieQh3sPv0wrPI9Hi6xvzJp43nnn+dUh33znaLnduHEjrMrtdgrqvjL27Pzzzx8ioB+tAMTYp4m++3R/JVPsBaBTkYWaygbeI+7j3NxGeuSVDEMucoIru3YF4RU+tpVKg5UrV6JGHV0Kg8ZjE28+32x87iiIebd3KkLWrl3rzjMYEfzl+Y+mFONz+Oyzz7rnwY/92c9+NiS2mvdlxYoVsOaf6bsc8Z2PTl5xxRVG2ntsG+LvIFxBaw1ySN6EvAri8mwfPBVm4ndlGLaJXSEIpqGkTkcglLlGfgaWRSq3gQn5mGxzvA/2QeCD2c9S8ILKclgU/NAOW/OyowYXME+EinAo0iN73fyOeGMLbJLgkehZFcwRbA8dN3eq0Po73vEOI43wNIW0gTHvvvE95m8rwyzYGEqRKwT29fU5BdaLL77oni/SV767fP5IO3Ib+5NONTU1uV3Lli1zvy9U2G3bts3FIfI9uvnmm4e8++x8NGOpCOY70tMDYRuN7w9rOVIRyMZ3iF4hni/gO8R3me6wakJguiAwDDUb6fICwlJdWw8h8O2W6UeMVTLIjHjEqDDdgQCXATE0+NdniRM0WZQVw904h9tGLRdcLCwCtwrQMydokkMNDo/tGOWXwwfGMWIV9Tav/qQRCGh4gJaPVwTCDGA4I6f/MaG7EjWbvrEOE60Ufj+3cxw14bfffrvv5r75I0jXqIcfftjeiSLr4UamayQhkMkxws0Lpx/4wAeMn/B5h/tN1TLPvww/dGRNNm/bbjsbm2zBwnngX6g1xmsFBqSmqsSuufx8633ZS+0QhLH9B9ttz86NtmXzRvda8uVLQaCrSETtgrNPtoXILFri0pNz1oi97AL8WB7qtC079tpDDzZCrqPWOgr1T8bKIGSWxzN29sr5NvvSlWCckfSFDEtweHcOtJg17d7nLLe1sFS++33vh+tqg9t3PP8ZiyUw3/V94xvfsC9+8YtZgYN9KJyxXXXVVfaP//iPqFM2y63zD5l/WjS9tcbvoAKDTBzjDn2766677Fvf+paRkQ9n8iQT9hd/8RfZRDq+P78/85nPoC7kvzhhMLw93zIFDr4TnpkK9/nmN7+ZZTi5nQLfv/3bv9n3vve9IddKoZWNSXu8a6HbcJR/qDz6+te/PmSWsbz7FP5e//rXDxnHeCkmRPJCIXcSWzYKgZ/+9KfHlTk4H52gIE7hyguAbnL8ecMb3uDuiV9nchdPd/w2vvcUeCm833bbbfbP//zPflf2m9agkYRAKgPIjOe2v/3bv83d5NZ5/TzffC0fnfzXf/1XR4///u//forp5GFmImNMrELBAElWHPPBs4fFDUJgJrMEyzXgR7jPj+H3YS7GCW7cx02+S64AiF0s8eA6DPYJZLaACIZnY1ccePAQtO7RBRR2ymgP2KmdOAxc19NQxjuKTrsfhddOd+hIwExhzOEZOd1EG71sSBMYA0qBkMoOPos++/MDDzxgn/rUp2w7FAX52rXXXut+k8Nux6Ql4Rhs0qiw4srPQ2H085//vJ177rl+k3vmJzKWgh+fc9KbcLvpppuMzyDb3/zN36Dk0c/Du52izL9DQ3ZoRQgcpwhMQAg8TExq62fArbPMug71WQaZPb0Wy2FBikYC5BqpHJazmilu5Lr773qE/7hR7s/g1mw/LgzOxV2uT7Du/uJPBinnyypnw5+fFgNuVZuuCIStKtTi8QeKP0xeyPMMmL9+MpJhIZDMELV+uYyy78/v8DHC2yeyTCaeWkRaUUZq+Zi/kfqPZx/f0Xi8BD/aCXzHbFfjLtuweZudc84qWPFCemf36qRRDDlmC2bXQciDSygzeaJmIHyd8GpBs42kTshcAF6G2UadpiZQ4OCEyDzNbai0uTNOQXK9Ush/cOlm+RYodiIDdGFiMWaMoeTHsVliwJcakcc4t8cefwYCaI99AK6RLz3vIrzTRyagQufjqk30efIlSPJd7H333efcL/m8eyGOgkhYGPHjGMuTr+3cudMJlW9605vcbs7FrJHDvRvUwHMuMoC33HJLvimHbGtraxuy7le8Fp7rXGaCpxeQFGy41tLSgljxjzph661vfetw3ca83bsG5xsw0r3K944yXmq4xuybFBopmNEyMtHG4+a7r7wf4eatGeFtvFZ/P/Odf7jvcMvjHTcSvsMdg0o5Wn78szhcv8JvJ9EDXcoAywxoTaQalAjxdlRekyxFEYoC18uYLceq90jgDn4Gm1v065jPCX/BPr/Vd81+D9kxZCXbJbsweC4ugVYG8dKZJiTKolBIegovqTjH8zrCz8Moc2YnH9sCLWT+OaLig540bL/85S8dTRhplt/85jdOAUUFVr5nlGPzCYDczlAOeu7cc889Q6ze3OfbeMb6a/Bj+c1rG6mF36Hh+xUW7+GPoz1C4OgRmIAQ6A8aMIx0B2VJh2QbCBCYu4AA8SUIvwjhZY7PXee24Vpu36HrdNfgFmrl0iC4JZUzLV5B4s02RCx1W/Rn+iCQy6TRqkchkMwYmU5vPfBX/Otf/9qosaY2nc1bD6l1vvjii52FhG5o4XYRCqEz7oFp+ynEcf6xNLrU0MWOVjW6zPAHku3+++8fVQgcy/xH06cCLl9z4NZZXVNrhw7stWefed4uv/QixOEtxI87BTLf8P64F4nvOoU+vONMVw5GJMTbOCt9xkhKAuepgAZwFYMR9xdJwm0JEwXvJDb5V9h9Y258M+lUYArETGBkdjftt5//7A57DQSMt936XneuQaZS9jt+20QtgbxiMuC33nqrXX/99Y7xomDxH//xH04QI4PEmEevxaZF54c//KGLf/3+979vdM0KNz77dOMiE/fVr37VvTN81tmo6Saz5YUNZtdl34ULF7r34Lvf/a7Lsspnm1Y5lllhbG2+RssYhcmw5YqWIQpwdGH08UYcSwtCWABkH76XPFe6H9JSyQRPbCxNchUsoFT8HE37whe+4FwWaR0dz7tP1zZaT0lT+B1u73rXu4xxyqRPdGmlpZE0ie5nH/nIRxw9Cfcfz/LMmTMdBkxGtWHDBjeUWTrp2kwFF+kT47iI1bJly5y1lp1oeWEmTy+Avu1tb3NJnTwtZFxzWCAf7pzo1vftb3/bYUWXQLp0sn3iE58Y4g7K55z3li7LI7Xh6CRp95QKgSROjh6RZlHAQ6ZiCIMZ0C8mr3KKL0cDd6Af9mcWow+VYaBHQ5Tb4at1E4Y35FkeSx8OYz+cm+uOPygIn7at8KBAPUDuwXky0zr8LQYbhUBaFbmFPY6+8VmhpY+WN29Bo1WOzzndNkkLfGM8Pl3vmSyKihvWjKU3A4U0ujPT2u+tx1Tq0EsnbJkmTeF2uhPzOedvL8ex/AxdOC+77DJ3qImOpdKYVj7GXNO9no1WQIaM+MZlvhN8x9mWLl3qLJH+HfL9cr/HqyjJHa91ITCVCByFEBgwfC5baEUdXMlY6w/lHQZIfBylmpLrCI4UOEJEy+uttBpxgJGEO3bYaDglJ6ODTCkCucSWmSqZ9IWNQpePh/EnRcEwnMrcu6TQekKmioHuuYwgx5K54oc/fmMRApnUg7XxfCOzS6aGmkcWVz/WjVk9V571Ujv5lJW2Hz+qTz75tD308BM2H4IDdtHIh4Y3iy9X9lVGsoHBE2diqMOMBbZCigvZEA8Pyg7gQL/C5aEt4KHIqAQCYQapzr/5nR+hFM0K++CH/trFMAajj28BkFedq7gYisTIaxR8yKj4xueKQhKZLT5bZGr47FEAZCMTzg8ZsLAQSJcsunJ6qyGTJoUb3a68AJjrZkgBjgLOP/3TP2XdKNl/OCGQwki4sR8FDsaehRuFRK8o4XYKoRRUfGP8II9LAWvz5s0utpCCG93DjqbRG4AWuom8+xSaSYPCQiBxCVtGGcNI91KeN60MLFFChRIzck60+ZhMLwRS6GNtUl8ih3SP6xTafaOwTc0TAABAAElEQVQ7KgU432i9Cbt9UpkwFiGQ46kU4IdWTS8Esh4k3U3H00aik1SWHZOGeL9oBPyMwbshgk+KSV368N4idCVWBs+nFljf1rtyOIEgOA6ahGfFKcbCF5ZvW3h/dpkUEDTSacz24RzWgZjsc/Kn48QYiwj3fNSbcP0ysAQepskBbc1ONY4FKoSYrZgWsHD9T05BF1Bfu5TPgreiMSv0l770payCZtmyZU4Y5DddLNnoBs7sohSoaBEMx8xS6UPFiY+9I52jIOmFRLqaeiHwaMZSqCOd8ddA4TL8DHOZcbO+0Q2W56ImBKYTAuOgYLmXTaIUsHYRuImVVs6ykgq4hw4ycvhCI/HBX1dfxy0e/uPoUgaZ+dAxmCrYh+UotIiu0ZJwREOHrCmBO8GxgmuNlTegOPw8ZNgqc9O5KYfV0B0xqTYchwjkMtTessdL8YHrXPZMMZfJOPoW7u+3FeKbFsBwo1bTaw/DFpFwn/ByrnAb3leo5VNPR5KNC15mVTi3vXv3wMXmXntuHZgbZOJ1zb07+d4/fwZ8w9xbhu+R+vn+Y/tmltJnnlsP74IG++Q/f86WngTXKzJK06RN1BJIjXtYAPRw0CJFQc23sCXNbwt/0yr3X//1X1kBMLyPyxT+fFkVWglpJc9tvB9k4MnssdFK5xnA3L7hdQoKFDZyBUD2odASfjdo3eI1hz9UxFAA9I3xaYV4NgoxB8+JSqhcgZrbKQiGBUPidbTNM8Gchxbh3Jhmeh+EFU48h2Jr+egkLZ1sYxVIC3ZNjsaAHaKCC+Qmgjp7hqygdFXnOxGJljreAtwJdnZaKsPncBdWETtIfgQWwbxUkKQLH4Zb9yQhTIK3iSHmjZ8o3OR7epEd1MVi5xuNbfzvzg0TgCZnIhAA0xQAD2JirkPUgyXO7XP8EhPQeFSCOb1/ht86nm+6GXsLXHgcf8/+8z//M2vJ5zPoG5UQVDSF310uewGQ/ehFEFZM+bH8pmLLC4B+e/g3fDiXz4mMpVLKJ6thGAm9J3zjshd8qfyi1VpNCEw3BEA9jqZlqQ00ZaWWgBWupHYu/NKpEcTUJIzokonlIXDY3g/Xs+279iPLIPZHIPjh09eHbY17rLcPvtmHpw+dJIgc0jfT9gcOAB4ZZRAAZ1lZ3TzEDFWhX3hQeDk0hRanBQK5QiCZMN+8hprrtBZ4Rs/HC3L7ZAmBnLvYW21tnd342lvsmutvtAhKsTzx+NP205/caY1IxhIHgxK0qX9/+gaidsqZ59uf/9XfgRFY7ixn/t4VO6ZjOT9ey0SuJ2zFyT0OGRTfvGuTX8/9Hi0eldZyH1+2dOnSbMKH3HloSaJ1zrfhYv78fn7/7ne/y7pehbdzOZw1NHffcOvDJZ8Yrv9w2ydyP/LNRWYylyb5fl644fohlFU62kaB2J83BcDVq1cPmZJM+S9+8YvstuGysGY7aCGEAHgPJFtB/mPol2ENZLZNKMeijImmkEjWA1k5B9IbIcDtxQoEQZaUIJuTq7hGX2y0vv6UPf7EWrv3/sft0cfXIvPyOvvdA4/BA+NJ6+hE8rs8LBLjpzOIo3YWPQiiAzjWQAaWqRieH7JXwdTw3ijDMjw1EBN4mP/JNyEHjK/xeWYyJn74vjN7Ji1njO0LKyIm8v7ShfRYN75Df/7nf549DVowqdDih+7TvjGjuH/f/DZ9C4HpgMCg2v/oLyUNChiNQiArrbMdcHmZuWCmlc2dhcQRqLkDYkaiFW5kLyMQ+nbsaIIL3yYwGwHj2deXQubChM2eUe/EuSAoOzQYU8F+6BJHJA80W8u+Zlty6Q3IWQH3Im9xnHreNXxpWp4iBHKJclgI9KdA5plaSDJBdPcItxNZCCQOpwKbv/67T0Gbe5798qc/sN+veQKlI+ba2996i9VVV4IHcRxMGLI8y2Q2Bl+40CIZF65m97nlI/9QSx24LSGRCYhEadUsxP/BnRFKJZeaPRTlcuTo43MLrYH5khKMdDV0IRyueW0194c15sP1H2k7XQsp4FEQpEBJ9+d8cXcsHeFdpynghDOThufn+TAejh+eJ+t2ktFiyZRwCwuy3E5Xy9GsprSwF6INJ7iNd27GSzFm8eyzzx4ylEJf2MKQa+UY0jm0kkvfQrscU8509bQq5hPAab3y23kPGHOlNlYEKATCEkh3UEcD4WkEAZCCIDRkEAzJ0yBeMAqFdH8aSYraEQ9ZZSVllc4yx8Lx2QbSyCSgiVK44J++wlpa26D0hnCJDjW1M5CFuRJxucyOjLkck3SYecnAS4qu92n07+3ptFiiDxZEJNWCwFmC0JdUF5ItcX7kZYj0D0BAhDtogRuFvnCJiOGm5/vr3ZP5XtJDYbRWLL+/dDPn+dL1k1ZMhn2QJvhsxoxrDbtOj3ZdI723o43VfiEw1QgUTAhk8ncqyfYiLf+Or3zTDi2ebfNvuNpmnHY6QvRwGLo8ZNVdIIH4D88vW3XmCuvu6UPAMHzvsa20NAEf8QS+YRlkf1r7fKMWnRo5lJloXrfBmn52t7Ugu2GiaqEtetklIMBgKcP9/Th9T0sEcpm3fD8qTKLBRleOXCHQxwR6cMLPDpluv59aQSbXCFsX/ZjJ+GZpC7rb+cblydDkE7+Fixbbu/74T+21KMS+Y8d2MDSd1huttRQ00FHE+QbvLDkNvIte2OOJ8bUEowkfKRcvwux0CQTbs39vNzTjMbiIg2FiXU9ntQ+P5VSODaIAyEQ0EBhjCbzHcyxRMwdzBwohTMKO067lPrdjuUAKGIy7ybXksVQB42t8YyKGo2l8BxjDxpgsuuS9//3vdy6c3p2Zc1O4YHyMFzLIKIbfnfDxr7rqKpcE5j3veY+LWWNCCcbHMb4x7F61dOlS56JK4ZKN60wUk6+xDy36FKaGO26+ccNt4xzheSb67lNwZjkZxjqRsaQwzfpnTILh4/UYpxeOfxrunMaynZaYXNdSJttguZBwmwzaEZ6fy7y+3DhH0rFHH33UxYv6+oS544prPaBxmUwSvEgyEMwg8DFplRMAIQjGSqtBruAiCmV1NL0QuqqZSJCcsd1PrrfKmfXWgORasTIosAZ9Mp0qDAJeDOsz6qusrhbZqCGwkXxG41GU1QFvhBUna3Kja1ggLwO1WAee80Nbd9ucM5eifuscbO6EsLcZAmedpXgOrBUYRabnUgisaQqvaI6+ZicLtk3yXwpRpE++8TlkcpjcRuUEMxEzMcucOaD1RdJIz0ij2OjmGqYHtBSG1yfrlCmAMvbS8x2TdRzNKwTCCIACFabhd9Q6D+2zHcgclti109L7mpB+vsmaYYWZc9lFVr1ofkAcQQQC7RrpXMZqYXGohjaMxIHEMIqJ+KEVIuAVuRG7wFRmEPjcvm2n7XlojXU++qRFoFku703a5tu+AmHzDKuoryvMxWiW4wKBXMKczxLoNXivfOUrHVPpnrPBq8sltmENPTMtUoBkRjTWS/NZ9DiUQiGz/FE4YxwTA9Z9HBX3/+Ef/qH90R/9kavTxnpszNjotYq0rLAgL8cPpy2lGxeL3LPxHHPrmLkdBflD5jcDLXbM6hpgtYd2ubUnbX1IhNCRPmSVkVbkv+sFUxEwR0MOiW1RMDAHmlvsgQefQGH3TmcxooWL18ii8OedcyYKx59q5bDyuymGTIAVCpb4n4qWWzI+y/piDWCQokYeajLEPwpLTC/u4438PeW3j+Vi0hzW1+P9m6w2moWLVjM+M0wiEm5MYvDjH//YWbZ5nny2WJjcP9N81v07wIQOvF5eW9jt6r3vfW82vTrPg4w769eFEyLQ3YvJZPhs87lmfA9runFuCl9M785EDWwUdHy9PN57Cne5581C74xRYz8yOhSWyFgxKykZQWbh4zcz/fm6nlS6rF692hWy53tC5ojXy8RPZCLZmBEzzHi6jRP4QzoykXc/HxPLbKif+9zn3Cf3VJhIg7RitPufO264dQrfzJroG619pC2kH1QO+JYrBFLI5bitW7f6LtmEV7w//l2gsoLCP+9JOMmMH8RtjOVkIy3k88dYLz4jVCI0Nja6fXxGmYV1LHTSC8t8bvkeckzu+btJC/2HBMrJTeBDXFIVuoGicTsX4LUUicRhCASvgo7RCOry9S7E7iq4Y/ZZbyNqCB5qgRILyqw6bku4uD/0xPgYnJQCn4dYNA4+aJDtwtzZ+D0o3YIVlnZJ2gAKx6eQjbll/Wbr3r7HYqtOx3MDS6Mtxnm0QoCsgnK9F7Mj+zIS1sTKMtYPpY1TuDFjKTLE8HI4f+Bp4S4OW0ZudIFkkiTfGFfqnwcKbizhwt/S3MZMrswazGeLih7GL9MLh8XmSTeoQKWljTHLfEfYWAuU9/a2227L1jzldv7esvwMkyyRLtH1NBzLz3H0WCA9OZqxPJZvVKjwXKm48JlPuY+JYPgcjquNDeohUzJeksm6+P5Q8cb3RU0ITAUCkMNI5QrT1n33G7b/P/6vlacCjRR1/KlyuDrMmWflp51s9WAKa5cvs5K6WhgQaAUAkWInOtkHJJcraGABKfjBtJiBcNjX2WXt2xut+cnnrGftOks37UM8dneWUexMR20+sgie8Za3B8P194RAgMx8rnWOrlg+PoHMpy+oTUD4A0bNNBsZMf4gha0y/JHij5mPiXIdR/hDQY7McDgJje9OppICBxn3fIwqGWsm6MjXKHz6BBh0nWMa6+Ha7benkRjEvUTZLpddFgETNlb9DsciwqU7ZfvbByyJ7HgUzuJgRMoGmq0KqcjLIkhogPeTbMzQlrEkYl36+sl6pJEC/wCYo6g1zJhlSSQ86MU7OntWvZWXQggMD3QrGAOmqi9aieibWuuJVmO9xOJQ/lSXmc2uKYOWnMzM+BqZR1qKvGDkR/OZoJUsd7vfH/6mUEDm1mfPDO8bbrm3NwKhfvERuzs740iEMhQ3ukX6Z/SIAdjgs3zm2zfcNsbrMPMts12y0e2SVqixND6HfB7DjW6NFIS9sBfe55eZOIZMIzNFsrEcAoWQcGMyGCpU2MhkUcDNbXyPvNDNxDXhWLbcvuF1vmO8Rn/N4X3jWSYNIbM73nef50xh2WcxJU2hsJqv0SpCy+Z4BBo+f/kETT8/n3WfnIfbGLf0gQ98wLmehpNwkAaSFvrG7KxhptpvH+6bTCktJbmNwv673/3u3M1HrDO7JC2BE6GTzNoczuh8xORj3PA1uwV0Zt8RvV9u77Nz7S1gP0CU3GtKOrcDn/Ugg3Dd7NpvqWSHJSphhYMlMJXuggdTjfV31lmmqx5CFgqbd8N1+sf3WAIeTHOuONviVQkIcf2YLuJ+ZyLwcmB2UYa/uFqpPAsei3SQym4cOw1GKANvqBQ8J9J0N4WSp6S0zHb99knr3LbbTnnba62kiso0DIrttcpZvaC9LaC3CI6J1yIzaKv1tB2wmjkrMGkC/VbimKfiELBiugONTkupFBjNYrt06VInlOEgRzT+fvL9z/eOH9EZGyjoUHFAYTHfe0PlFDMfM3lLWJHFuZgVlALbRMfmWtA558MPP+ws+Vz2ja6wfH7H05qXrrFH4ofjCf3YSpQd+UP7mV8d8k0XWu8FQcGXvw+FUhYNOZBWhEAOAqNThpwBw622NTXa/jt+bgkmdHFkB0Wp8V3aBW3Vtu3W87sHrOkr37aNX/gf2/zNH9jue39vzS9stl4wjv0d3dbflQRh7XWfZGuLtW9ttL0PPW5bfvhz2/jFr9quL6HW0t3QUG3ZbvFuvCCOigZuPGXwhd9/x0+sbVDzONw5avv0QiAswPkrC7uEeiug3xd2PyNzlTueFgcKXfmK2FKACG8nU0W3Lv5AhZM+8FgMor/55pvdYbmfzHK4MUYn99z8fmrRvQBIxpFWxclt5EZQXxMCVyljUKB9RqgLBLKE9cAy144frq50JVyQEBPjemInLXj84H8CzEpVeYnVlidsOSwRyxYusJrKEps1o9IWL0TCJicAoiMbx/ALEw0g5q8bqdibbbZ1RRrgFEpX0rj1ZxLWgXCc/mEz5rkpxv2HGSmpwc6957kTUdtNq9d4BMDcOUZbH+3Hne6Yua51fBbyxeZxLj4jLK8QFoZuvPHGYZO6+PMjFiwhkc+FlMoUWqNpbcp9foklE5NQI+8FQM6Zry5c2FpPhj732v175M+JVjQyhyMJP8SBGVH5rhai8Rwn8u7nHpturhRg6Q5KRc4VV1zhXF///d//3SmjxiMA5s6db51xmD6zIfcTXza6DPsMrHwmcuMtadkIu/e6QcP84XPHZyBf4zNARj3fc0lMyaCzfiAzwk6ETtKy6a3M+Y4/WdsceQOZcwQOMSpURlMAdPEu5GrS5UjCApoOoc71ISHkILyLJTHUTUZ8YGl5LayGZS4Kpq8PLvaI3+vpOmQ9nc3WjU9XxyF8t+DD7a2W7OmAgImYv9I4xtYghrAW7p+VgfCIowQ1UvlNGj0LQh9iEEtrLJ6oxnESODUm4yN9pVBJRV4V1nh+bKTcozdadmkRC7+z4VH8XRvpfvAdotUuXxZjPw9pDmkLrct8JrhOQS+XLvDZ8RltaYmkx4FvpEdUch3NWD9X+JtZSXkNvjHmdrwCIMfyHo23ha2r9HDIxWO886m/EBgrAgWwBJLwmK3/wfes6fOfsUpoQukuQUbPEVN8ux5O24VlEMoMGENkf7FMVbmV1CChS1k5tmNb0BPFpeEOAW3+ALjBDKw9MaxHnWYNXUiQecDB94xf1I71giDPeN8H7Kx3vhf7Bnf6TuyvNu0QoMXOu4YV8uLo1ka3MwpjZEbJCOW6jhbyeOG5yHj7IrpkskcrmHz0lsDg6HxHu1DU/WBHyjqRXyAGF1H+yDJON9bXY6WpFiuHG1J5BgoYuDfx5c5E6TIFpojGD0qOg+8vXkgsMjMw+7ErGSnME0f2X3TrNVr/ZlgyXY1uZLBcN/BUcG5CNr7KEsQq1pVaiSMi/l0OznO0v8NZAkcbd7T7x2MJZNHw1tbWcR+SzztdIulmR4smtfbLli0bopwY96RjGMBj8bhM/EChjYLHZDMppOm00NE6QVdqKlqoPOF7SFcw3ygIhYVfv30832HL7Hjf/bAlkML4xz72sfEcesS+o1kCw4OpPPJuduHtU7HM4zLdP0sJUIDgc0krLd1fx9t4b3OVauOdI1//0S2BGEWGBTQsk2nC5wWQroPW27kXxroBK6ueiz2IYEaiu0zfDEu1wRLohC0orJDTYNvt91gJLLfzr7zAymohIOL5df8wluEv/DgBDQSVR2FzlG2QxsFmCOyYgRR2O+7A9ijo587f/N46tu60FW99NeIAEY8Irwxkp7FYdSeOA5fsKNxBMTbV12ZdrU1WM+sk0OxZmOci9KNXAM96bDS0kL+ntAbSesf3l88E31t++HtaUkJer/gaLXEU+nys89e+9jWn7BrvmbYsfcwejh+poBrJEkhLKLMns/wFlTTFitF4sVD/4kdgrD5j+a+EvB4IZ2930rY++LBVwDWMBAzxzo70OGKGPiQCpHV0e3dB1b3IZNXdYwPNGeuKQcODdPWl7c2D/dAJVoAIiGaczCRajOPBLHLNffCHJkwup9HHuaql0rbxvjW29LVvsuraGueeFnUO8eypNh0RoJAyGY3xVtRW8jPVzcdT8djUdk5V46tZkYjZ7o2N9ps1O/CCwZo3Cz9bdXBJm1Vns+pmoXxyHeIhD9pA3z6riKVhAUTNKwgIaQhvwRs5eLaYLAMXbWqvY0iKwP0d3f2ICdlm/XBdWnLaCiQyKLcoYgVToBk9yX7rRvxZa0ennbGkweYjrqYkSmZpUOEzVSBM0XEmKkBRG+4LwE/RqbrD8D1jbMxUFkrmbwYtE/li0cLXzn5H28JzHMt3P/c6wueVuy93fTx9c8ce7To9JGhNzmdRHu/cx+w63GPEPxSZ4JUQQdkFx3+Q9wCbhHcgSssaMqAn+yDcUhFG4QrPXwZ8BhXf5EiCX6RAACQzxLHcRl4l+BssudUhf9wJuC1uidq3Qb04T8NZ9UATKfBx9nRv3NKVmLv0cFmICFxOSYcjxszqEBgPzza4PHVftNbRqha2rE3d0Sd2JMZQewGQruu0dE+kTYQk8Tdh3LGHEzk5jRECOQgchRAIzRZpIIhOY1Oz/WZvlc0qXWkLU/tsXroNSSVSFsfbQP0Xydbh5ga5zKD9YDTrX/VaSyXidui2r1plAgIhiC0jiJh0YgBd6fbpXypHHAcnCgg0rBcgxvvg+rClfKHtbpljCzcesAvPr+UoR6APH1dL0w2ByRICjxVOfKYZl8DGoPipZYgy1g6N9t6uUuuKL7BkKmp7t/dYd/suaFo22KJ51XbuqqV2xmK8Zwd67aEH7rSGCmSSPGuRLYLrJ9kPsihsgfAGt1IIfzsPdtrjGxrtyWe3WGXtfLv+NX9gmUSFbWk6ZOs37rJde1tQExTvfEWte+crE+W2qC6oewXualq+w9PtuXU3/Tj+M5H7wXeVsXZMeuIbk/Ew7o/xPbTgj+TS6scU6nsi11CoY0+7eSAAwk0JdA/iGzTXUfApjOdz3g0DoE0pxtxhnbUfyNpAYR3E3TG2b3ATaeGg9HeY+wlzMBjnVkPbXEf+4VhOhG8KklwnI4WDpcEM0WCZGUD8aRIF50sZfuM2kHXCEjmmemzjwdVGQoBWfCpdfaKwn/70p9nuDAvIV+4l20ELQmCaIHAUQmBA9kjCXtiwxzak59n2mbNtfbLd5vbttPM7nrXF0R4H0yBZy0LGMQOwIHTBD2zBJZda9bz5NoB4wFJYFzr2HrSuxm0W37PLysBEkg6yv2+cy7cDmQpbU36aNVYss7bSBqS2j9tTz++EEHgyxgxSYN9Z39MOAQpJZH7GkuzjeLh4uqIxloexi+EYgck7d/82BW/YvrYB29/FUg1lcP2MW6YeOud5EAh7umwvsv3+7O511n9tys5YusyqZyy1X6++29ZuabQbLjvHLjhlkVWUpC2VofIHsX3phD1Kq+Kja+359Ttt2fLT7fqb3ghX8Br7wS8ftq2NrHtVa7X1C60WlsYSCH8gCbZud5eduTRts6rIzORSjslDYipnprWJzy0FCa/MmsrjT6djFUJRMhHLLAU+Jn0KN7rM8sPGLJdU5ExV4zUQCz1PhUCcbFECchcwZUIXCIGBUIXfmj7sg4IscB2FkhteSy2btlmmpd1S7V2274EnkLVz0IQ3yqmM7KgJ2oD72b1tj8FFwvY+tdYWXHQ2aOagK+VACZLPwGMig4yggT++c1ul2txsaAz6KKdxQu72pWrofpmvURnLd5jJ3cbfwhzr+EdrhBCYSgTGJQQGLBn+DkpmfNRT8GV+6tltLp6H6Vp6EjOsOVlm89s220IIgezjFfqe5aS6rBtM3/y3vNNmnHGWNTdut5nXvtJ2rf6dNVx+qdUte481/fKn1vnLH1sZNHHOPxoEMduwGMNk+yEEPlt7tvWUzQBN7odgWWJrn9trbZ3dVluFmEMMCI3KDtfC9ECATA9jT8j4UBCkX73/HstysTFMfM5ZDy43Ecfk3a1BMQua7CTwa2rutUMdVDlD0z34vtEJqryy1hYur7FWlIC589frrOS6uF18ydW2o3GnPf3Uk9a0f7VtuOgldtPFL7VFc2baLpSNuOORtfbAI+ttH35ITz/zJfYHb3yHtafL7LHHEWeTXmSLT29AsD+TGSBmhv9wfLqL72+N2rZ9XdaA5AhxJKoZb6MGl40/7hSq+Sk2JQEFfT63bP7Z9d88V37C6+Hl3H351t3E+jNmBJgkhclNSDP8MzPcMvFmY9ISCvPsn6+N5saab8zRbGMcHT88P557+JNvm9/vr+dojj39xlKQQrIV+CRFIqXwUBhMSgKGYoBCIPkfchf4SiOjZzcUYWA6kDwrbT0Q2MJMh6NgJGx8bKj0cRsOIxbwKLT1cSlo2SV2RkxhHC7zXes3WWrVqbD8ofSD60CBtAS7EbsdhyUSNVlBQdEfY+CyOpHG31O6nPOZIM3xdGcicxX7GNJgWuoZxzpcCyeZG66PtguB4x2BcQmBjvDxih0hC6jZwZY2m1Vj9vH3n4caUHADra61B371qM3a3Q2SBGKCv56ouRFY6cKPVOUNN1rvrPm24Ve/sr79jVY9ez5ZT9v5xc/bofNfZpVgHGNX3UCyZu1rn7LKtmYwiSCWnIQTghJWDHRZPNXrCCzrncWicGfb32e79jRb7YoKr6xDZ7XpjAB/vKgJH69Gnz9yYWZorMuTiSWvZWob31Gzlq4B29OGpC3QcrtELc7nKNiHoD4nFNbNnAulj9nja/fY7CtPs7PPfakdaDlgu7ZtsTvvfdB2Nu2xU09ebi9s3GEbXtwIq2DaTjrlVHv1Ta9DIpg6W7+rB8kT8J4zORQUQWlkw+N7i9uARsYLDCyYrk1NfbZyIWIO4RI+3kYBOleI5n3Ox9xzG++538d+U938s1vI43rmzTNz/Pafse7LHVvI8yvkXIV6XzgPhTp+Rmr+WWLts40bN7rkUf458vSDCWzCpRtGmq/Q+2hh5mesiSX88+DPnd+89+H13GWOmd6NNBgJr/ChABhBGQhaAjMQsDID8JBwDBD7RCyFxHXpzg5XVqg92WOVqA9YDoGMoTBEqY8K7/4kYqMNCa8gUGKbbxT9ekAjm3u7rBox0pVx7sfs6MtcB52otUqsa5E0L93WgQzqnVaK8lqgmI63yQygdwrnFu/Hbx8KxidQwxD3D/m3JtT4zLDUTG7jOfjnpJDLnsaE58499mStk1e466673DvM88htdOkerVxG7hi/PuU/4f7A+hYCE0Bg5F+8nAlJ1lzoM2rapFFQOgaitmfPATvr1Do7+2S4Tyyrt9JEmTU/1WvlaQiB0ICxbikJGoOnHe0EkbJFcAE7/zJrvP+3Fu1stVkQ+LZ886t23v/6tCWb98EVv8waVpxiJ1/3Ctt17z3WsWOTZVqbnZKLvCnJL4lyNfIMlvYdsoGy2e6Hi2kKW5EgZvu2g7ZyBQq5Dh4y5zK0KgQcAmNl/HLhymWS/Lr/zmWkuJ2fYmt4LfE+832KWEtnn7WgViBjcWM0s+PF7UV9zjQEpZLKavRAIgIE6c6aO9sO7e23rU2tyPa21GbMmofi8klr23fInnpmk617YTvqAyYR/1djM+tnoozGlcj+O9s27h6wnv5q0ATQAocFGBkwqx0H91tFZYWVwPJH7oeJFPa0pay9F0JgaWFeYN5nMjijMca8R14g9Ix9vvViu4+558PrnYhSJHee8Lpn1IhReHm09ZH6huc/XpbDz9KyZcuMn3yNNGCkZ8jvyzfWb+OxJrtN5FnhPc0VDPkc5G7z6/4ZGcu1TMU1j+U8qJBiTb8YStnQKmjwMkpT6KKlbdCcx9IRvS0o7YASVyVYLgff0pzsRdKW3kFrHfgfMCwlmKcOfBH5IUdwB0+AOQ9Aba0U7qYHkGH1EDynSsBTkT6mmWQP9LEBNeNIn62n13qam61y/lxHJwMpMAqXUKSrKSNnFkVhdcQCgt/KZJJOkBzbdY7ei/eEHyoXJrvx2fI0I/xdqGX/LHI+eo0w0ZaaEDiRERiXEBgkfIB2q68LxK8JlR0S1rpvr1UlAnufK4QKJi/SvBc0qt86SlAzB9qsUhAQMpwgZbAk9FnV0lOg+I9Z59pnrGpmg9WedpaVLT8VaY8b7KJPfsa6Dx2w3Y8/jvHIVvjdb1pNexu7uzn8zWIANiKPrCq13+aecoZdsmoFGMkMzPsd1tF6YJC59b31LQQKh8B4te08Mn90yBSFmSW/7L/9/tGElUJdCa1wfCu7e1kSY5M9tnqddSFzL3nPCNyQ6J85a/EZNqO6xp0/j8vNVXWzrXFvsy2a1WD1dQ3wANhrdXPnWUl7mbU3H7SZ82e7dOZLly23RctOt60HYyg/gRcY85IOBA3MC5ifZHez7XgWyqA4BM0YGB9q22EhPLXqfLv2ZWcgY+mgK5YfNonfvK90hwrXpMo9nL+PvFdhAbGzE8BM4zYRYWE0ODxjx+efn/Gs+75TwZiOdh359o9FCPfPkn+Ows8Ul0ezSuY77lRs47MwFqtp+FzCNM7TOX7nfsbrzRE+RuGW6QGBWDEolSnoGWqkQpMF4kfaFLBMLBvRl0QJqxLcp7m11tfeiUg8CIKI2WO+A14X6WccZrk4hMY46R70gBQKfWNVHVoIZ5RXWA3KafRjjNuL/lGEy5SCZHJsGtbC6Axk+8Rp9EBpnkD/WAxu7/TQ6KNXBZVnOEZJNQ4BGprpLKgQ6M93Kr6nWuAM0x2+j56uHM2y+6GbCrB0DCFQAATGIQSSPAUELNPXjaCbLuvv67DTl1RaKVwgAtcuECxosJadc7JVnP4eq5kz1xq/9yNLb9wKho/EDOUk4uUWQ22n0poKm3nxpVYD94M5q15i6fe82/YjqD5RU21de/fY81/4nJ39gQ9DS0YiSgYLbi5kInkW+JMEway7+Fz74KtebWVzZtjsemjaMHfq7JnWAxeJdD8skXG4hGKMmhA41gh4xulYn8fQ44N5wMt0sKvfWqNwYTulAe8L3ZHI7MBy137A4q42FTUwfJH4IwnGJF5mvQOo9QfBrq4esXslEetFmYdKuCpV1dU7poWxfitPO91aekoM+RKwjdpkDA6mcafB2llllfU2d9kZcAdfBMGT2e8gLEPzvr07bd0oKlgOBRNGDj3tY7jm7yOZ4AS09L5VVJAy5Y8P8330PRQBYjkWYWnoqOmzFn6Wps9V5b+SiSjO8s80SVtJ2Jjtk6Y0CICgaPjGNlcKAnWMB8BboI6qc22CYJhKgQdCv9r5C6zs8krb1/WgJbfvsxLQsFIwKhl4NJAiBDIf6GY+EoZt2ONoMFVdpU7TzUGDZJICIPanUTd19mXnWf3yFZhnAHxXj0XLyxxdTCXL4Q3aC6GRWUJRuxAeWJFoM5YDnin4Hgebh5EnSvP0p9DXe9AeKfSUmk8ITBoC46AOZMUCLX1/bycyGLNQNKwCpF4hSYvM4xkQzqLwY2/Ztg2qK2QIJQHEpw80NHXehbb4Nbcg+csyWAlTtu3JJ23fumdt4//8j0V3bLbWOxbYgne+2+Kz51nnwSaLQthzwt8gYSSZJuM6gBpjC15zjTWcdSqIcycSQXRZAq4UMQRJl+Cq+nvarQyCppoQEAIjIUCXJ4OwBY10XcQqSqNQsESttT1jPVDuxEuhSMFwMiu+RcCIZBArcxDWr+pqKHQgFPbABQoyHVkWfKetYUaDVdXPtT0dMSSdYYIFzuJbsEwNd2l5NSz3zcgOmrCa8ggyAkP4TFIQpUAYHuPH6lsICAEhUGAEHKnBHwhemQhcOikIpuGdEEliHdkK0hVQLIPGOWUWj41+cOWMoChyHLRrAF5LDH0hDfRtQtRrcBCpLWkuHKuCUlkg0FHwNwNwvXc6cexz/0GrmbE0Vgp+DGUsItFT4a1xCCZG8GgsFk9LJjs6Ku7PTN9CQAgIgQCBcQiBwQAyc0znnuyDO8IANFBOe0bKFVAvujvEy8Egbthim//r6xbfvdcJcSRFkTiyT3W12cZvfdUW33iLbb3zF9b68P22eOXpFmk7ZDVI/pJsPWj7f/FTW/VnH7bWTS8iILrVZR4Njh4chbxhuh/M4v4WEOUguQOzGJIBJb1zhBPaMud/4TR7frS+hYAQOIxAoKGuLU3bRUuSFl/YbTWwvHVCCNu4q80270G2UJR66KVrE8o+8H3iu5fEO7xt7eNWdcGZtrChCoIikig4EkBmA659+FdRXol5oAOCV5VTsjv6QMaFfdjwvmK5Mt5lc2c12+nz0nbGgnK4f8aRnAYxMl0QSOEvVUxWwOC89VcICIHpiQCJGK197VA0YzkKrwZrB6WqhoCFmnyI2aNlDn+gbIbgB4+o3vZu5EB4wfqaDiJJHSVAzlG4xtlKkijd88hzFqupQshMlZWWMVkNj0XlGuL/qMUzJIWBYj5mDbCuM3kMWmFPhTOqCQEhMM0QGJcQGBCWEiutno2kMJXW27HPMr0Q0gKSA2gCayFr5+z6xT0W27rdxQN60hgnA7hpg6XXP2+7YBZccvWV1g5BLwJrQ+3Ks6xn/VoUki63jhdftPmID5p77ipr+R6seahT5iRJdwTKebAYwOLY+P2fWdmieVa1ZAFc0kAEKfDR/wLqOLoZ+RjGaXbPdDlCoHAI4F1JZDpsXrwV706fZVBaoXqg2y5YELfzllTYE7uTtqapC1nsasBwkDFCAoKySltw8ilWNwOxMH2N0E7DMpjVkCO7KN7DJBikATBFFPSwwQl/zoJIdTk3cBu02ucu7LRLFs9E+E3SUr374BJVDWtg3GprkWEv1YJxMFE6bXbhLlkzCQEhIATyIUAr4EAaCbJspqNZTmkVrQPZg5tlCV0uWX4hSJJShsQiHXv2W9v23Sjc3ucS16FOBPaS4yEfEpA5ks2sMsvRQ2zAvqAFijhucMnz0NPvYpkeV68e2/qaDlj77oM2b9VcnAvHcxz4HFgAY4hLtMgyeE21YtsB0OgVGAEXfjcTZyPNVRMCQkAIHInAuIRA8m2uQSsWL69FcegS60Zdr3TXITCIjIdhZsGY7XtyrfU/t86grwJxCsgfx7LEA0uwpqG56lvzoO1DcfiV7/ljCHHLbN5VqBOIDFj1L1llbajdsu1r/+3iARO9KIaaQ8NIVONgRtPITLrvvkes7j1vdXxiQF/hFoGC1PEKuEKoCQEhMAICYDKQ5Zfu3WlY9fmmDtAdCt8ZxOIytqUGTEYZisB390GlAmGsH4qfHeuetPmnnGmzGyrthWe2IrEMGKQscaCcGLVm1Aoc6DlkteX1lkzF3bx044baGrwJmCR8ICoiKQKYGDAzGbiPD+CbW6N44TMDqPPXA6amCgIiykaoCQEhIAQmDwESMNr5QIEiEPrIqWTgVhmpxLYq0KAeFGpPWH9pJTJywlUUtCwaKbOyhvm27JXXWBrZPVkuor+7F+kSoADrQ91iKscQK51GcjzmSnACJQ9DK+Ngc4uOLILuIYlMDLEsdPssqUhYrByWPoa4VJRbCUpIlDSgHjKU3Sh4A/KZAilFSZNK0MY44hMzc8AD1UOA3Q/xj/WZkUjGNQqDakJACAiB/AiMSwgcOgW0YfFKq6ibZ0m6RsAiGIPL2I7n19umb3zfqptRdAxMZNQlewi0W470gVukMFia7rf+hx+yXRs3m73j3VaNovGJhUus5fGHrXfdC1bS0YpaO8jWB8semVJn4MueALaAesawf+9vHrD0osV25s03gHGERixWh2QTs+CyWpXtrQUhIASGQcAJZEwiwLcT7ybeWTph9vUlbV9H1DY1VyABDFyhWDYCe+JlDTZ76UpbsRjFtbuarHHnDsTjst7fYcaGWfX6YQVcv+4Je/kVqC0Ioe5QO5I1QXkU4fHYFd9pCHfP7S23mYkWq0YMSwmSKQTzODHU9cHLryYEhIAQmGQESGgYVALPo0gF5DXGAoIeZlB2AdY/xv9FS2Zaef08J9CRTu3e126rf/80SNmAzaqvsLqaGVa3IGbVFXErRzKXOJTdUcQMRhnEh49Tr4H2BdwQqCkO6cgbtdxQjtGyR/I4APMfc9B0JdPW1tFvLR2gxU2dduCprfaK6y6w5UsXOdoYhcI9lmiFgm0Acd2sa1iPb5SYgIAaJseTDJymFwJC4DhG4CiEwICARSAIltVACKyCLzpr6lQfsme7uqwScYE1SF1cC8axFIkiyuEmQQdN8pKOnwSVikNDFtvTaHu//mVrrEeNwaZGKLUw1hExukcgDTPmINNIEkpbYz+Y1G5YKTqw3AHC2Q5F1w3zT7WKBgREUzsGKwQLvJKgD54hvtWEgBDIh0AUhY2ZVTdtnXjf8P5Q8ZKoQIrzqG0+kLAtLXXWF0EsCt7X7pZ9tnfbelt1/hl22rIae3D1/dbaBmXPEY1ZH0ts1+5d9sJzD9tLLrgW72aJNXeiIwREegw4agCl0Za2WTa3OW0XL0QdP8QM01hITojvewlqa8kV9AhwtUEICIGCI0DNFJtniZCQDkIVAk9AqxDY7NxAUcy9FLQQ2ZApZbXDg+K3DzXazl0t8EwCn4NYvSpk7awohx0RHhSV5XGU0Km1UnhMJVB6JoHyEWVIvJVOw7uJEmA0gWR5GSjcUpaEIq2/HzlJkWCrqytpnciO3NOTRsmeXuvogps+BMNUqtfOuehcO6Ws1inAyRcFNQzbHc3kOUUiyPAciJbcqSYEhIAQGBEBT/FG7DTcTtIgZ6WDUBYFowgaZF1we2jEeqymHLE9UatiQDU0ZaX4lEAQLEG8oFsGEaQrewlVXx3NNtDWYslkEokhYC2AoDcAAS8JwsrvPjCH/Zgz+IYrKebtwTy9mKOrt986oW2LQPCLDloSeE5sATEMlt0G/RECQmAIAhHU6iutbHAuSwO9TLTErL9w80ZygfL6BluMd6oDTEk3irdnyhK2bMViW3VKvW144VHbtGW9pWgFxPvqlC5Orc3pae1LIXmC2XPPP2uV1RW2YuWlNrAnba0diGOJoHYoal5VIMMv42qiNcgEHG8D04V6oMzGh6VYWZ0lquZj7qMiUUOuVStCQAgIgbEgEAHfkkF9wAxpG5RjtA46gcsNDhTMEfAmaVjxkvB6SMIzKY1i8XR9P9QOoa4XdAzlGkrgqonqsC5ZHV0/6WnR39eGffCGKmVt1DiEwoBwMo6P9HcAfJLLyhxDRlBYJnvA47jyFRAYmf7O/afx0J0L+kRnYSmIVXT8GI7oOg32cN30RwgIASGQB4Gj5rACQgTrG1wZyAhW1cCiB8auvweEEESyn9opJmmB4OYMdKCEpKvOKggi5YgaRvZDqOvvL7PyMrqEBVtTziyA/BGgezQIDtI/UD9YBfFhBFEMGrba+iD+LzgXTxx5tSTWakJACORHIGA+qN0ubyhFYhbWmUrafrhyr3l6HUpApGz2/MU2f/YcV8ohE2WR4npY9x6zRx79vXV2IY4QypmgQUgcfAEZW8g3nF8RVEles+YJKwfDs2rlxdhcBsYITBWOk+xuQUxxs+16Yaehnrxde/FZVl9d5jLvlZTVoO/hOnz5z19bhYAQEAKTgQAUy4P0jEoxi1SDm2DReDS3HZ4KKAtRDl6nHDkImPSKcdX9iAF0SjAIdm48PJNKQPPi0Uqskw8CV1LKmEN6LJF2YjLQwtQAEstAoZYeYLF3CIKMIcR/unmWsR4pBkYTiNNGbCD5o8MKbirgZ2AesnIB5XW0F2tqQkAICIHREDhqIdAfgDoq0sZD+3aySB8yVfUh+d8AXBhS0HRRMwU6hg6uYDQIVgmKLcfgJhHDN5f70CUNohrF8kAfXSNS7pNCggiYKQLFlpsFc1CghLsZxzKpxK4dW23VBZdj/uAcBrvpSwgIgRERICsR2M0jELhKKwOhqxIxgG3bfmkP/+qXUH6jKHpNg1XAKliB7xQYmr1I+pKEizeTQPmsTXz3nH7cyZWDtngso3IVjpC2Nb+/1zoO7Ia+OmWtB/Za+4F9cADYjwzDbdbd2Wqvf/OtVtfwSiurYF3CgMVxzNSI56+dQkAICIHJRSACt3UErwwehO6bfdaI5HX33fugPbXm57Zly07r6213lr9Mpg/9qBAfbKgxGIGFMIFEelXwuKhtWGpVtfOc8qy7Y6+1HNpjXe174fqJ7MywHGYyLFLPFijoWJonAitkCZRoldV1dtedSBpj19ppp58GARTWP3bDMdSEgBAQAhNBwFO2iYwdMqbl0CF7+pEH7f47f2B7d+9EAVMIf0jUEhjzyAYGRM3rqsAzghAieBqCIF3CYiWBO0Nna9J6URIihexajAmkt+ggS+mOR/aQwqYT+KBZi8ESce/Pf+pcK156ySU2c+Zc7GUPNSEgBEZDwL+PQT/HUeCdghMS/DUrSrE33WelnQcs2nnQuvAypqGZro3hLYwjAQGtgIOuoIzxG6CLNhikEmS5K2WWOwiKTOjEiN4M9u3Y8TS02ylkHUW8DQhAFbaXwJWqDOvV8ABwU6F3cBYcJ0s+UVATAkLgWCPgqVIEMYA77aEHH7I9u7fZ5RevsEsuOMmprZzlzyWyO3yuTGBHjsUlawFdjcYqUWIHmTtBR/t64E6aqse+U9ADim7QO9ZZpqI8w2QxaBw9+B/bS6y3s8VW33+fU7mfueosKOLI6/iPG6I/QkAICIExI1AwIXAAFru6mTPs+je9066/5R2gS477Czg6ns4Rchk2OAkRjKbb5xk+iIuO3g4SXX6x+nu4Da6SuLq08+BFE2VgKfsHx4T7alkICIFxIZBCwqZkH1ybkAqdqc47IMBFYHWvgJUuDSGurBTp0/uQMh0vLhMiRCkM8nVGvwQEP0TnWgz+2wPw40YydbedbqMppEwvwdjmNmS064fpH2NcPU+88Ewwk6bQOK4zVWchIASEwFQgQMoUUKcli5bYnNfNcUqvwwzOeM4BHktuKgp7HJfD34wwVZD5MwJaXAm663mmEQZolxAQAkJgBAQKJgTOmDHLauuYTQstoHDZwwakM7vqFoaQvbDsFlrON46Dh4x1G+jURoayYJfDWdWEwAmFABmMZLLXWlrbbe7S5XbNG96GUg8ZO3io1dZv2GTbd++Ei3cGbkh43waogEEcDEpJJDs7rAY1rcqR1IlJDhgD3IF6WYmKamvr6rHqhnoklumF1b/Metp7bPbsRXbxhRfZvAXzEe9SCsEyiUQIlXaopQ2ZScstDoFTTQgIASFQHAgM5URK4L3Ej5oQEAJC4HhHoGDcFoOcS12g81BIvOYqvDXftvB+Lo+lT+4YrQsBITBxBJi2nMqU+QsX2dKTlkMYg4VvUPvdl+yzLVu32Z133WkvvrgeWyPWheLIGVgGu7vxDVfRGQsXILFTwnbvabJIZ5eV19ZaJWJ3E7D+lSKBUxwuolddfbVdc9WVVlUVruOZcYWVe3sRR4z5Yoj5ZfIDNSEgBITAZCKw1n5u221N4Q4BAsq6qaSJTBpDujdIQkc+BgnvUFlz5P7aW7QIdNi+oj03nZgQyEWgYEIgXcdY4sG7KNB1jI1FVXvALNLVq7Ky0trb2626Gj7xw7SOjg43ZiiTOExnbRYCQqBwCOBdZYwts+5SGOPHSYWDzMmKFcvtQ3/5F05Bc/igQYF5rvuC8c6tE3Px2ze6PdFBgFP1Y17Ww8ptMZR6CZq4oVxstC4EhEDhEWizJhSmaSrcxCRdLFOMD2sZj7mJ5I0ZKnUUAkKgcAgUTAgcyUWCGjHfamqQ+n2ENpKAOMIw7RICQuAoEaACx2muCzAPp3DZQ/PMlcjjMZCnmzYJASEgBISAEBACQkAITBICBRMCJ+n8NK0QEALjROCBB5CrM8Z6VWpThUCQ4GGqjqbjCAEhMB4EGmwpLHNy0xsPZupbWAQabFlhJ9RsQqAACEgILACImkIIFBsCIU/MYjs1nY8QEAJCYEoReJV9yr5ir7U+65rS4+pgQoAIIG2avdo+LTCEQNEh4INwiu7EdEJCQAiMjsBclMVkrJ1a8SEwcyZ+/JVEsPhujM7ohEOATPhMO+mEu25dcHEg0GBLECYa5MkojjPSWQiBAAEJgXoShMBxjMDLXx61r3wlJkGwyO4hBcD77osjxlISepHdGp3OCYoA0l6doFeuyz7WCCB92rE+BR1fCORFQE9mXli0UQgcPwi8+90SBIvpbnkB8KyzJAAW033RuQgBISAEhIAQEAKHEVBM4GEstCQEjlsEKAjedFMEZR3Gfwks5dDd3W0DAwOwXCVcCQhm9mStTrYS1PfjMgvBsy/3uXIP+NPbm3SFk9mH432ZCPZnGQifGZhlYsrKytwcLD3B/eXlZaiplXJzlpaWuP6JRMKVlCkpKXXHHS7D6PivcupGoBIO6iBKAJw6xHUkISAEhIAQEAJCYLwISAgcL2LqLwSKFIEZMyYqeNBNavjanSNfbpiE5DoWhAPiwvOzkJZvYRctX0A+PM7307cQEAJCQAgIASEgBIRAoRDI5doKNa/mEQJCQAgIASEgBISAEBACQkAICIEiREBCYBHeFJ2SEBACQkAICAEhIASEgBAQAkJgshCQEDhZyGpeISAEhIAQEAJCQAgIASEgBIRAESIgIbAIb4pOSQgIASEgBISAEBACQkAICAEhMFkISAicLGQ1rxAQAkJACAgBISAEhIAQEAJCoAgRkBBYhDdFpyQEhIAQEAJCQAgIASEgBISAEJgsBML53SfrGJpXCAgBISAEhIAQEAJCYJogUG51Vm+LrcTKrcsOWrvtsT7rHvfVVdlsm2HLrNfa7IBttrRNoNjtuI+qAUJACBABCYF6DoSAEBACQkAICAEhUAAEKm2mXWJ/bHNtpcUGWayMZYbMnIGo02H7rMV22m571jbab7P7Ixh1mX3AFtk5ELDKstu5kDvPkJ2D+39j/2RNmHOxXYAZ/sAJar5f7vh+J3rttlacxSa7D2e03ncd5jtiZ9qNdpG92yohuoUbhbct9nuL4h8xeNS+ZjtsTbhLdrnMau1S+1OIfpdAhKzNbk9BjDwIQfBZ+4m9aPdkt3PhNHuFO3Y1hEY2XkvaBuyX9tcOR7cRfy6y92Dei4Fc9RC8eK3P2y8w9499V30LgRMeAQmBJ/wjIACEgBAQAkJACAiBQiBwlt1kp9srR52qzhZC0DvXXmKvtbPtFrvX/sWabbsttQux/vpRxw/XIW4Jt+vl9j6bZScP1y27faad5JbPsTfaBpzF7+zfIIp1Zvf7hZiV2uvs8zbfzvKbhnxHIfCusCuy2863d+QVAk+xq9HrLyD81WX7+oU4jjHXznCfJcDhd/avzroYgWB5lX34CKGY4662j9rt9mduipm43gvtVrec78/LIXiuszshrvbm261tQuCEQ0AxgSfcLdcFCwEhIASEgBAQApOBQA/sUuNt82Djusn+D0SgKkvCRliINt55IhaBte1au84+nvfwV9tHhhUA8w1osCVHbF5qL4N4/Mm8AmBuZ57LG+1/IFomnM2P7qL52gJbBfRe43Yl4ZRK6+BwbQDo0n6oJgSEQIBA7B/QBIYQEAJCQAgIASEgBKYrAi/YryBe7Z30yztk2yBmpKzfeuCKuSh7vFbbBefPx437k4iiq4KdjoKXbwlsoXXsObhCUuDphTWOsXK+D11HG+0JN55z+E83hM5aiGe+vWi/xujdzrWTY2nBq7AGvxsumw84l8tDsDpybCmOGnY7pfBGiyQ/vlXgSq6zv82eC7cfgAPpOrsDLpZ3wJ20EQ6gJ+NYJX4IEEjak/bd7DpdRF9rn0OP8uw2umiutZ/jmn/mXFjjcOKssTnZ/cSD50/c9toL2B6BSFjlXD2znbBAQfAFu9vFJvJceGxiGMaF8Yb32Kdc7GJ47FQs817TjVZNCBQbAnIHLbY7ovMRAkJACAgBISAEjksE6Gq4xr4O8WWRi03zF7HGbhsS58a4uJPtcrvS/hLOjgErRldQNsbE8XMyogO9e+c2e9getP/002W/58OhlG6luY1CHF07z7e3Q/xant19t/1vJyT5DRSyLrb3In7wTX4ThKqzIeL9LrvO+b0wyo0U7n5vX8ru5wLj7d5o/29YK9919jdD9lGovdP+DsLs1uw8T9kPEcn4DhdzSBdQtpfaGxAzeS/SzjzvPtfCUhkW7tiHgiFxvBOCKuMr+cnF5WFYFZsgcqoJASFwGAG5gx7GQktCQAgIASEgBISAEJh0BGipouDEGDXfaDHKFyvn9/ObtrJrkAzlHRDEVtqrnDUvvL8bNrzxtAFE3VG4pPXMN1ogwy0asvBxO61qua0NItYLdld2czOslb5R4A0LqjzWT+2vhgiAQd+MPWbfsKchDPoWhS3wPHubXx32mwL1SUg3oyYEhMDYEZAQOHas1FMICAEhIASEp9MBUAAAQABJREFUgBAQAgVDwFv6/ISjxaydChGQwh9dTS+0dzlXTG/h2g8bGLNrjrdxvvB50KUy3HbbM+FVZ3U73a4f4trJDrTkbbWH4HL6IIS5b2XH0CoXtiRS0BvJNZeZRTshavoWtmT6bfm+r7QPOefXfPu0TQgIgSMRkDvokZhoixAQAkJACAgBISAEJhUBCl8sfeBbM/Jp9iJqLV9jFtEZsHWFXSGrET9Hd84fIeslSzZ0wbY2WmNdPiZIqYHDJC1+tKCFrXQcvxlxg+HGGoA8N5/sJYHyC9fZJyCOftTFJjJerwmRfY0QA1myIbctgBAYbutzyj+E93GZ8ZQUJFchHylbHRxUeZ20WoZbEqIi8fKYVMHx9RJkRf0dYg/VhIAQGB0BCYGjY6QeQkAICAEhIASEgBCYMAIsn8CaeIz/o9sn4+5ySzjsROKX4RotdUsQMTdcG4sAyLHvDCVryTdXC0S5RkT95bbf2GdcBlO6o/rGa5mFwhD8sDQGa/ftgjPnvfbPQxKw1IcyhbKgfKft91MM+033Ut8YH0hBL5yshvsYf/lbZFV9nf1f39WdxwYkx1ETAkJgdATkDjo6RuohBISAEBACQkAICIEJI3CSvdwVgWf9PtYFzBUAmW3zYSRWOZbtIOxvP0UNP2Y3zW209v3I3j+kMHtuH7p8ssj9m+zLTsj1+8MWvMwIJRx8f377ZDl+23ClH5gxlZlffeM5sHYgLYdqQkAIjIyAhMCR8dFeISAEhIAQEAJCQAhMGgJ0o/w56vDRDXK41gU72D7kFw3Hyg3ty3IQh0s0DN03tjVmE+0YwUpHS9w37a0QBv8MMX/fdFa/fOdMi+f19g/Zg1K49I2upCw5MVrL7dM9gqvrA/bFIQlyGmwpsqKOnkxmtHPQfiEw3RGQO+h0v8O6PiEgBISAEBACQqBoEGDylw6IdHS9ZK29cDmG4U6SLo7M4kkr4uX2564bXStpZWNs32vgFlmB6Dn2eQ72vOHaA/Yfzo2yzha6ucIWtxvhxvlj+wCSyxwW2jgPBbIVsK/1IA0NyzU0oYAFP2x01QxiC69AIYk3ZxPMMEZxtp0KkXLDEdlEmcWTmVGHa8wIugyF5X1rR5VAXutwLQk077cvQPD8ZLZLbpxjdocWhIAQyCIgITALhRaEgBAQAkJACAgBIVB4BFbbv0McCmLVaD0bgN1vIm2TrbbFsHPV2DxUvQsEqeVwNPVF1lmUfCQhcC0Ks/tyEJshOt1kn0W0YbU7lTJ832j/gqjBdyF1TEf29K5DPb8lOCYbBbQXQ4ldKNBSaOSHtfxutn/NjluC/KWBELgpu40Ll9gfI/nM/Yjoaxuy3a/QobQeV+lbuJag35b7TeH0NKSrCQuPuX20LgSEwFAE5A46FA+tCQEhIASEgBAQAkKgoAhQqGImS34mKgDyhLogbv3CPmbfRqXAZ+12d47M+Okbk86MtVFo+wmKrIddOilMstRCuIWze16A1DLDHWMmco2GWylsiGwsB8Fj+cbkMq9GopkKWAtzGwU5lr4It2fggDqW9jsIoCNZDMcyh/oIgRMJAVkCT6S7rWsVAkJACAgBISAEJg0BRubRFfEUu2rIMZgd1Fu3mHiFmUD3wJaXr821M+CseU7WtZJ9WGvvZfbefN1h/br4iO0UtJZiBIWqcKOA1QdRcqPdBzvcbmepu9s+BaHs09lafqxF2ANnVdYfpKtqONaQ9QnfDhGUFkUKdnTVrEDxCJa6yL1mWgF9u9v+t73Fvg6rY6XbRMHyLagH+CTsjuzHOMLlcHQ9za71Q9z3ervbYcWVhfZSiI0nZfeX4MjEJImzeNF+A2FzP5LrfBlIB+6y2Y5aEAJCIC8CkQxa3j3aKASEgBAQAkJACAiBaYDA7Yh1yy16PhmXdSai865GkpfR2gAEwa/Z6xHp1jykKwujv9VuG7JtPCuM2/sfOHUyvu8kOF4O11ogWjHJi28Xw0UzN5kKSz58194NYe2rLvbP9x3LN0tWfAMxgmErIwXFV9jfjmW468NkON+GyEnrKQXr19nnhx273R51yXUYpfhG+5LNtZVD+v4cGUO32yNDtk3VCoX6Nx7jzK9Tda06zvGFQPT4Ol2drRAQAkJACAgBISAEihOB6pBr5khnGIOdj0XQcxsTthSijTZPrSvAfjib6KP2Fdj01g85NMstzIZ9j9a+8TS6vt4JYS8sAHI8Ywl/DwEtBVvkaG03nF2/DysfBUC2aripjtSYETRoGUQHfhYut0eWuRhpvPYJgRMRAQmBJ+Jd1zULASEgBISAEBACBUeA7osspUAr2nCNQhLdF5tCcXK+7zZYq3YjtQsTrkykdcONk+0J+05WgMqdh26Tj8IOGY5NZB2+e+AW6pPG8HuHrUG6lwdRwOL7LjMoLXMbUZ49nDQmPDcFL8bv3YbELuEYwHAfun9+x2515SXyYcTzfwSWxx/bB4cUld9sD+B8HsueX3hOZlldA1dT35hI5kkcJdxyLa7hfVoWAicqAnIHPVHvvK5bCAgBISAEhMAJgsBUuYOeCHCyEPtMOJtWw+mS5SPogkr30laIdl6IHAsOJYgEnIXiEywxQYsfC9KzdMZ0a3IHnW53dPpcjxLDTJ97qSsRAkJACAgBISAEhMCkIsDahCxcz8/RNLqLNsHqyY+aEBACU4+A3EGnHnMdUQgIASEgBISAEBACQkAICAEhcMwQkBB4zKDXgYWAEBACQkAICAEhIASEgBAQAlOPgITAqcdcRxQCQkAICAEhIASEgBAQAkJACBwzBCQEHjPodWAhIASEgBAQAkJACAgBISAEhMDUIyAhcOox1xGFgBAQAkJACAgBISAEhIAQEALHDAEJgccMeh1YCAgBISAEhIAQEAJCQAgIASEw9QhICJx6zHVEISAEhIAQEAJCQAgIASEgBITAMUNAQuAxg14HFgJCQAgIASEgBISAEBACQkAITD0CEgKnHnMdUQgIASEgBISAEBACQkAICAEhcMwQkBB4zKDXgYWAEBACQkAICAEhIASEgBAQAlOPgITAqcdcRxQCQkAICAEhIASEgBAQAkJACBwzBCQEHjPodWAhIASEgBAQAkJACAgBISAEhMDUIyAhcOox1xGFgBAQAkJACAgBISAEhIAQEALHDAEJgccMeh1YCAgBISAEhIAQmAoEzrE3WdTiU3EoHUMIZBGIWMzOtbdk17UgBIoJAQmBxXQ3dC5CQAgIASEgBIRAwRE4yS6xV9k/ShAsOLKacDgEKADeYP/LTrbLh+ui7ULgmCIQyaAd0zPQwYWAEBACQkAICAEhMAUI7LQnbLc9MwVH0iFOdATm21m2xC480WHQ9RcxAhICi/jm6NSEgBAQAkJACAgBISAEhIAQEAKFRkDuoIVGVPMJASEgBISAEBACQkAICAEhIASKGAEJgUV8c3RqQkAICAEhIASEgBAQAkJACAiBQiMgIbDQiGo+ISAEhIAQEAJCQAgIASEgBIRAESMgIbCIb45OTQgIASEgBISAEBACQkAICAEhUGgEJAQWGlHNJwSEgBAQAkJACAgBISAEhIAQKGIEJAQW8c3RqQkBISAEhIAQEAJCQAgIASEgBAqNgITAQiOq+YSAEBACQkAICAEhIASEgBAQAkWMgITAIr45OjUhIASEgBAQAkJACAgBISAEhEChEZAQWGhENZ8QEAJCQAgIASEgBISAEBACQqCIEZAQWMQ3R6cmBISAEBACQkAICAEhIASEgBAoNAISAguNqOYTAkJACAgBISAEhIAQEAJCQAgUMQISAov45ujUhIAQEAJCQAgIASEgBISAEBAChUZAQmChEdV8QkAICAEhIASEgBAQAkJACAiBIkZAQmAR3xydmhAQAkJACAgBISAEhIAQEAJCoNAISAgsNKKaTwgIASEgBISAEBACQkAICAEhUMQISAgs4pujUxMCQkAICAEhIASEgBAQAkJACBQaAQmBhUZU8wkBISAEhIAQEAJCQAgIASEgBIoYAQmBRXxzdGpCQAgIASEgBISAEBACQkAICIFCIyAhsNCIaj4hIASEgBAQAkJACAgBISAEhEARIyAhsIhvjk5NCAgBISAEhIAQEAJCQAgIASFQaAQkBBYaUc0nBISAEBACQkAICAEhIASEgBAoYgQkBBbxzdGpCQEhIASEgBAQAkJACAgBISAECo2AhMBCI6r5hIAQEAJCQAgIASEgBISAEBACRYyAhMAivjk6NSEgBISAEBACQkAICAEhIASEQKERkBBYaEQ1nxAQAkJACAgBISAEhIAQEAJCoIgRkBBYxDdHpyYEhIAQEAJCQAgIASEgBISAECg0AhICC42o5hMCQkAICAEhIASEgBAQAkJACBQxAhICi/jm6NSEgBAQAkJACAgBISAEhIAQEAKFRkBCYKER1XxCQAgIASEgBISAEBACQkAICIEiRkBCYBHfHJ2aEBACQkAICAEhIASEgBAQAkKg0AhICCw0oppPCAgBISAEhIAQEAJCQAgIASFQxAhICCzim6NTEwJCQAgIASEgBISAEBACQkAIFBoBCYGFRlTzCQEhIASEgBAQAkJACAgBISAEihgBCYFFfHN0akJACAgBISAEhIAQEAJCQAgIgUIjICGw0IhqPiEgBISAEBACQkAICAEhIASEQBEjICGwiG+OTk0ICAEhIASEgBAQAkJACAgBIVBoBCQEFhpRzScEhIAQEAJCQAgIASEgBISAEChiBCQEFvHN0akJASEgBISAEBACQkAICAEhIAQKjYCEwEIjqvmEgBAQAkJACAgBISAEhIAQEAJFjICEwCK+OTo1ISAEhIAQEAJCQAgIASEgBIRAoRGQEFhoRDWfEBACQkAICAEhIASEgBAQAkKgiBGIF/G56dTGhEAm1CtixlV8TazlzDWxSYpi1MEPf9h6Hnww77nU/tmfWc3b35533/G68US73rHcp+7Vq+3Qxz6Wt2vJ0qU29wc/yLvveN2o6z1856bj/e25/347+NGPHr7I0FLJkiU294c/DG05/hd7HnjADn7kI3kvJL54sc370Y/y7jteN/L3inQ8X4svWmTzbr89367jdtuJdn+P2xulE5/WCEgIPI5vbwYyW1jey0ACPHjwoD20+l5L9SVt/sKF+Cy2RFmZJbu7LdnfZ9FozMqwHo1Frb+vz7o6Oq0v1W/zFy21OXPmHoGGkymPSrA8Ysop2dC3YYMlH3ss77EG9uzJu/143ti3cePw19vUdDxf2oTPPd3SMiwm6fb2Cc9brAPTzc0n1vWOdH/b2or1Nk34vAZGut7W1gnPW6wDR7xeYDHd2ojXi3d7urURr3ca3t/pdv90PdMDAQmBx/F9pACYoRgY/Lf0QMqaD+6zJx56wJ565H6rqqq22oYZVlpSAoEvaX3JpEWicYuXllosFrGBVMq6u3utsqbWbnz9m+wl551v5YkKKylLWGVVlZWWJgIh00maaRxs0HvYrR/HwOnUhYAQEAJCQAgIASEgBITACYyAhMDj+eYPCn+9PZ327FNP2JoHV9vzTz9hjds2Wfuhg9Z+cL/tadxusUjUqspLLR7NWHdv0pKpqPMajUMQjFgagmHMvrV/j9XOmGkliTIrL6+w+hmzbcnJy+30M19iK047w6qra2FFBFiYS00ICAEhIASEgBAQAkJACAiB4xcBCYHHxb0LYvUCr8zADJeBL2hXR4u98Pxa++1dd9gzax6ylv1NFkn3WVk8ZrNrKyCwUUqErTATsYrSqFWWlVqyotQ6k/3W1d0PeS5jDTXV1t+fspZ9jba3cYfBTxTCXtTKqyttf9OZ1tnRbsm+ATvnfFgJIRzS5ZSio5oQEAJCQAgIASEgBISAEBACxycCEgKL/L75cDwvANKFc8uL623NI6vtheeesR2bNljzvl0WQVxfaTRtlbD41VaWOwufv7QohUE0GP5g6YtboiRmteiXgiBZjmVLIE4QQmIbXEM7egacUNjXDXfRgX5buGiJLUHSgdLSMswg8c8BqT9CQAgIASEgBISAEBACQuA4RkBCYFHfPBfxR4dNWPOQ9GX/XrvjJz+wB+75le3bvcN6ujutPDZgs6vKEfeXgAUvYxDpDDlfMCLXbZM2PG7PWEk0gk9scB2xfmhVFA5jVVZTnrLO3hRcRvutcdM6+/Ft/88e+/39dv7Fl9mFl11ps2bPdZZCN0h/hIAQEAJCQAgIASEgBISAEDjuEJAQWNS3bNAOmE5b4/at9o3//qI9dN+vrae91dKZtJXEMlZfW2mViagL1YMjJwQ7jgn+Zi8NAiTdR11zgX2HBcLBjU44LI1DOIyXwHU0bgOZBLKGwip4sNFePLjLdm5+0VKwQt70xrdlp9WCEBACQkAICAEhIASEgBAQAscfAhICi/ieBXbAiHV0tNkvf/J9W/3rO6y3qxOWPlgG4eJZBlfOSiRyWXLqUov2Z2zrpk1O/KPzZ4S+nxAK0+kBl+WzNJGAEAehLtkLgQ97YAmEv6el0hAI4UYap/nQ7Uk7a2Icx0jAOghZ0PoGEH/YusdW/+qnVoGMo1ded4MrO+Eti8E4DFcTAkJACAgBISAEhIAQEAJCoOgRkBBYxLeIwhqtegmUdJi/cBFzvCCuL2MViOErhZBXX11uq84/z2bOn2nPPvaUzVu+zOrrG5yA2Ix6gR2tbXbGOWfb0lNWuJIPvT09tvG5F+y5x9egPETaFmP74uUnIYPobtv64gaUmBiwRHkZBLxy6+7qQkmJHpSSiOJYURcfuH3d0/aVpn0QJvvtimuvt6rqGpyd4gSL+BHSqQkBISAEhIAQEAJCQAgIgSMQkBB4BCTFsYHum7T2UTDbjNi8R1EAPtnbbbXI7jkHLqAxxP8tPeVkO++KS23Lhi12zU2vsQUQFMuQFAa7bP/+fdaOgsmnnn6mlVdWujIQGbiQLlqy3Cprq233zka76obrbclJ/5+99wCQq7jShb/OaXJQDqMsIRFEFpgMDmAwwTbGeI1tWNY4G6/DLm/922vv+r3F6/Cedx3WARubYKIJNtGAQYBAgARCOSNpNDn1TOfu/zvV06MJPaPpnu6ZnulTUk/3vbeqbtVXdW/VV+fUOQvR3NSM9c8/j02vv44lxxyDU896F+oPHsSbr63H3m3b4XY5UTOzAoGd+2iEZi/u++3PuQfRjnPffYlxKVEYiGkpFAFFQBFQBBQBRUARUAQUAUVgNAgoCRwNShMQJ859gG+/9QaeevgBvPXaK9i/ZztlbjGUUErnk717JHZrzrsACxYvRnXtDFRXV8LhcBriKCLDkspq7gOMUxXUSWEiWaHoblIFtGbmDJz3vvdR0teDqmm1vO7BbK8XpWUXYxH9AbpoZGbBihWYvWAx6hYvxWsvrsW+PXuw5px3YfbmbXj5mb+hYf8uPPfkXzCXcZYdcywJ5mAjNBMAWJa3bPv+9xF67bW0qUs/+lH4Lr007bXxOpnr8o2UX8k116DkssvGq2pZ3yf46qto/8EP0qa3z5qFmv/8z7TXhjsZfOUVtP/wh2kv22fPRg37yESG4Pr1aB+mTlnVN8f45RqbEds3i/bINX7jWt9s+jPfZ+3D9FnbzJmoHebZyXW9hstvpPbIpnzBQq/vSOWbMQO1w7x7xgu/4e6T7fkgF5Pbb701bXJbNvXNMX5pC6YnFYEiRUBJYKE1vJA1krhINIR9u/dgw6trsXvzFpK7BErddpQZoy2gQZhyLD1mJbxeH9z032elE3eRHhoDMMzDbrMxDS2AkkyaILqlPC+O40srKs1HjhPxKHcOWlFONdIVx5ebSLwVVUKpgjpvHjxeD2bs2IoZ/D2HUsQwpZF/+9urqGAeImFMGZxhEhOSKqy9B5PgK/C3v6Hn4YfTltR1wgkTTgJHLN/q1RmXb8T8WF9MAhIYPXAA/rvuSttmjuXLMyaB0XfeGT4/LohMNAkcsb7LlmVe3xzjl7YhxnByxPaQ9h2G4Ax3y1zjN9x9sj2f6/KNmN+SJcAEk8AotUyGfX6zKN+kr2+GJHDE+i5dOuHtGxupfblojUzrO1J+0l8yzC/b51TTKQJTEQElgQXYqgk6d3faXVjCCV5Pd5DqnVFUUkJXVeYhObNRDXQZZi9cRIKWdAgvpC1FxvpXRwSAfaH3t3yZ+H0XxGmEmHahdJEEU+yGmnT8I+4FK6qrcazvRLJHh1EBXXP2uXhr41Y0vrMHLY31mDd/ockpRf6SefVlrj8UAUVAEVAEFAFFQBFQBBQBRaDAEFASWGANQi5mQiDQgyce/RNa6vdjWoWPBNANB6V4Drsdq884A8dQCmR3OEBfEUepQYqeHYmWLoU5Z/4ciS9k0EpVTzeljSbw0lzuIVyxfAnWvboRT9BnYUVlDRYtXZ5UN+11Sn/kTvpLEVAEFAFFQBFQBBQBRUARUAQKDYHJu5mr0JDMUXmEhyXo1mHLW+vxl3t+jxqfHbVlPjhJxmgmBt6yCtRRBaK8otwQNMPbxnrvI7xvSE4pqaCIB0WC6PF5sWTlCpSXOPD6i8/Sb+GT8Hd1UpQ4QiZDctUTioAioAgoAoqAIqAIKAKKgCIwUQgoCZwo5Ie5rxAtccq+/qW18Lc0oIJqoHaa+zTqnnTVIEZbaqfNoNXQ3r1+g/IRyZ3NTrfxQhopORR/gPJtyFy/uIay8bxIHuWeYtzFJr4D5dxwgfGsNjtmzpmDmbNmINzZitfWPoMdWzYnU/B6UiQ4XAZ6XhFQBBQBRUARUAQUAUVAEVAEJhoBVQed6BYYfH+SMn9nO9a98AJKvS446A/QQuMu4o+vonYaTj/rbG7Ps9MJ/FASGI5E0d7chMaGw/DTqbxwstKSMrqFqDOSw75b8R7dPT04dOggWptbkaAbCqfTYYzD1E6rhs9XRrJHQtjHHA1lNPkJRfTSxUR5WTm8TLN/1zZs2vgaVhx7HA3U0BVF3030hyKgCCgCioAioAgoAoqAIqAIFCICSgILrFViJGRvvfEG9mzbjLnltPpJ6hUntXJ5PDjzwgsxb/FCoy4KGo9JhQT3Bfrp3L2psQmhUBAexi0tK6EU0E6poIPpmQc5I4V9hqTFGV+siVbQwmhZic9IFaN0Hh+NhOkzsBU9gRAqK6rgpH/AI0QwdTd+k12K+wkvjdT4u7qwe8smNB6mkRi6jNCgCCgCioAioAgoAoqAIqAIKAKFjUBBksDOzk7s27cP+/fvp7TqEJqaKN1qbER7ezu6SDrkejAYRDgcNp+UVEycq7tcLrjdbkOE+n+Xk/BU09Ll4E9tbS1m0neSTSRfBRBka50QOK/TRmucVOUkeUtQDXQeLYWeTIMwVv5OxBlJxHy9PFD4oMSdPrOaRM8JK/0F2kkARbVTXEBYQDcQovPJX+Yv47soxXNUTYeFqqNC9ATDWCREqWDUxLFSApmOAIpaqr9T2sCPmGTG2MFAAOFQWA4GlCt5Qv8qAoqAIqAIKAKKgCKgCCgCikAhITChJFCIx1tvvYX1dIb8Oh2Mvv3229i6dSsaGhrGFSMhgDPoxHQO97qlPnPnzsWCBQuwmH5t5OMVdwzjEGT/3szZc0hWa2CLdfCOCThIbFefeirKyqu4X1DIFtmXMcRiWBiJX5xx3PQNWE5LnlWwOr0kd2xassNYPIJ4oBPRQBv3/kWZluZluPfP7q6kdLECFps4mBcSGEU8EmC8Dn73mH2CSRJIMsj/qSAuJFqbGnGw/jAOtXbBXVqF5cevNvsETZwpaiCm89e/Rnhz797HFBi9395LLoH3vPMGnR35cMT83v9+eM89d+QM8nx1IsoX4rug67e/TVsz27RpqPza19JeG6+TI5Zv+nRUfvWrGRUltGkTum67LW2agqhvjss3Yn25GFf59a+nxWK8Tua6fCPmVwj9meNt129+kxZeWxbtMWJ9s8gvbcHGcDLX5QvlGL8xVC1t0lyXT8Y/GRfSBVtNDSq/8Y10l/ScIqAIFDAC404Cd+/ejUceeQRPPPEEXuC+t44OIToTG0QF8yAdkspn3bp1QwojEkaRFi6hVU4hhPK9cuVK86mrqyOJ6seShqTO8AQlbR0d7Wih1LPMEkQ11TVLKyuxkE5gRQVzECPjIZU9E1T7dHnhKJ0Ou6uUAkCH4Yiyj9AmPNFZirDDTUMuBw1/dHinwVlSCyt9EQrplJPG8IwrCpuzBBF/M6LBDhJB3s9UTf5IbvzL/OSX2KUJhqKorZuG+QuXwOsrMXH4hyGHeCQznPC//gcfHNapvEyYMiWB/gceQA+fg3RBCMBEk8ARy0fCk4/yRXbsQPt//mc6SCBO4CeaBB61fBmSwMjOncPXl5L/gq5vFuUbsb58v000Ccx1+Y7aXyZ4UWPE+nKMy7Q9RsxvKrbvSM9vFvilffGN4eSI7ZFF+SK7dg3/vuK8SEngGBpLkyoCE4TAuJDAw4cP4/bbb8cdd9yBDRs2TFBVs7+tECRRS5XPc889NyAjn8+HFbTYuWrVKpxwwgl9H1E/HUuIxazwU+pXVeI1DtvLqiq4dy8p+RuQb4JWQCn5c5TOhsNTSv5lJ3kTpkaqJlxM/pDsOXzVVPfsMWqfzpJqGpcRyWZvfpQYyj8ySeZBFVRKCoVwxoKd3JMo4ch9hfCWlpehtqYShykN9LioVkp/hSkyORUJoIFA/ygCioAioAgoAoqAIqAIKAJTBIG8ksA3aODk1ltvxb333otIJJIzyISIOJ1Os4/PTrVHUeeUj7hFCIVCZr+gfI9H6KZBFlFnlU8qSPkWLVqEk08+GadSjXPNmjU4/fTTU5dH/LZYLaisrkUJ/QDG27vgdHuw+rTTWF/3ILcQJGbkbXFK/VzeGpK3MhKx3n2NQvwGSeNE7dPhq4VIPW0OT28ZJB5D71fygHhSGihx4zHuEYwGeTkZQaig/J5FtdmFNFDT8M5BxAJ+WhhtZL6UItJ9RDJOMif9qwgoAoqAIqAIKAKKgCKgCCgChYdAXkjg9u3b8TWquvzpT3/KqMZCnmQv3lKqjojK5bx588zxdKqfiQGXGuqdl5aWoqRELF8mZVTD3UCkd2I8JkCjJfJpa2tDS0vLkI9IKd955x0cOHDAqINKmrEGufdOqorI56677jJqo5u4v2Y0QYR4IZZXyFUZCeCiY1fguFNOpsEWUQUdFCjBE4mew1tGdjayYRsLZXp2N3Ez+YyMnUgTbW4v7KFS7kEchAcLWMW2WLLiGOzdth173jmMt19/FceddDrVQhcPKqAeKgKKgCKgCCgCioAioAgoAopAoSGQUxIo1jq//e1v4/vf/76x2nm0yoqlzrPPPhvvete7cMopp+D4449HWRkJTQ6CEEpxlSAfCbNnzx5Vrs3NzYYQimXSHdyjJEQu9S1kMWWJdFSZ9UaS+o0qkABGKTFd//ILNNDSDU+1C7Pq5qKExDfC/XdJ/c4jOSW4n0/2AFrt7iMnR/hFymgkpoNEf0NSiNzPZnVRO9SHaE8btUFjfXFE0me3O1A7axaqaqqxd99+tDbS32BLs5LAPpT0hyKgCCgCioAioAgoAoqAIlC4COSMBApR+vCHP3zUPX8i4ZN4l112mVGXPJpEb7yhE2mjfGR/3+AgKqZi2GbLli3GkqlYM5XPtm3bRlR3HTUJJPsSKWJ3Zxs8TsruSGS5448n+RFmNijIPjyLTQggJXsiQjRqoIMi9R2K8ZcY6CKQBE8kgSNJAxmXeVutbubPLhI9QgIlOwvL46G1VKfHjZra6Vh98hrMmVcnl1hMoYlpCmuu6h9FQBFQBBQBRUARUAQUAUVAEZhoBHJCAp955hlcddVVRuUyXYVk397VV1+Nm266CWeeeWa6KJPinPggFCMw8rnyyiv7yiz7HTfTfPLGjRuNqwtxdyEf2S8oYdQkkHFlb+PsObPp69DBDX+0/BlJkao0xEq4IfcQmpDmcvLCkb/RcIj7/Lh3z0tLniZb+SOfwYRQ7H9K5pKpfFJl4E/zWxzPs+vQgEzNjJk45vgTSAanGWMykmYURZGMNCgCioAioAgoAoqAIqAIKAKKwAQgMGYS+Pjjj+Pyyy83++8Gl1+kfJ/85CfxL//yL5g/f/7gy1PmWKxjiiqrfD7+8Y+beonaqBBDIYN1dXWjrGuCqp1WnHLm+dj7/ivx0qP3IUjiJr75BobkcYJiveRewf4kbWDM1FHS2mcbDbhE6FPQY5zOJwleOspGJ/PmnnFKDuNJaWQqI0MOeWC4IS2FVtagjIZsLCJdFGmkBkVAEVAEFAFFQBFQBBQBRUARKGgExkQCxafecARQCNFv6Ih29erVBQ1AvgonBFjcRshn9EGkaAmUlFXhrEuuwvq/Poa4cQ4/OAfDwAzpikb8tCRTgr89/yI2vfWmkba63f33CAoxo7uHSDf397UY9xFRVwk6Agk8/tgTxsCOSGlFApkKhsrRsby4lGABUqf7voUgxlguC9NMn7MA1dNm8BpTGT6ZjlT2JdUfioAioAgoAoqAIqAIKAKKgCIwwQhkTQKbmpqGJYCf/exn8YMf/MC4cZjg+k262wsBE//t02fVoWbmXKrYdhj3C+kqQpftiIX9dBPRiYbDh8z+xEgkSlVSiS1UTUglY9HCZ7irkaqldJtBjhbtaUW3P4Fdu3aS/DlNzP75CxGNkwBGAu30RS8GaQariwKBngDcvjLMXbyCfgOrmCIZip0CNrLvBwf5kkxhW/GVr6CMkvGpFJo+9zkEnn02bZUqbr4ZZZ/6FHqeegrNX/pS2jj2hQsx66GH0l6brCdHrO+CBZj18MOTtWppy93z17+i+QtfSHvNXleHWY88kvbaZD1ZdPV9+mk0f/GLaZtL2zctLJPqZA+38zR//vNpy2ynBtesRx9Ne01PKgKKwORHIGsSeMMNN0DcKwwOQv6+/OUvDz6tx6NEwELn72IHxuMrxcJVx2PH6y8gSJcRbrpsEPVPIXF9gb+t4QDjt+CySy7Ce9/7PrrP8PVdBklihK4mwv56xINd3K4nbiYsiIc6UeXx4rOf/iQc7nLYxfhLbzBSvpAfIX8TEqFuxu5/Q4kk1DJBf4xBTJu/APOXrCJHPJI+lU+xfkdpVTZMY0HpQoyWZ6daiOzbd9T6xjs7h42ToNr0VAsj1pd+OqdaGLG+OfQPWyi4FV19u7qGf36jQzVFCqWdsi1HfKT6Flt/psV3DYqAIjB1Echq9i7+/x5Ks3r/ve99TwngmPqKkDyRqdEyJ90wrDj5FLzy3BPopI9Dz1wfEuGUvK33JoafxWEhWROznz4PXUn0kMZRcpeIx+jo3Y9osMNIAI2VUUPomIfs86N6qMcegSUaQaiLEkKqdlq4py8WCSIW6jKSQCvjDWSdcl9KFrmv0EWSumjlIsyYWycnNSgCioAioAgoAoqAIqAIKAKKwCRBIGMSKAZPxBH84PDBD34Q3/jGNwaf1uOMEBBWl5S9yR69lSeehUv/7kYSNHdS3XKAUI4HKUMslhgskS46dvdT9mcjCeQ1+XBfn0U+PHck9GZCgpeIUIrIvX2xQEfSEKjwQ+ZgIaEU0zBDCWAylwSllbPmLYOrYg6crv77D4/cRX8pAoqAIqAIKAKKgCKgCCgCikBhIpAxCfzjH/+I7du3D6iNOH3/2c9+NuCcHowFgaTETwzEnPf+axALNiDmb6bfwDR59jtnfpLcWUSCZw74JyGkcCidE6Io/v5EKkgFUUoSqeRJKaRJZm7TmyhZlH43ZhybCyXlM+H0VTI9Iwjh1KAIKAKKgCKgCCgCioAioAgoApMCgaEWP45S7F/84hdDYnzzm9+EEEENuUJASFWSWLm9PnhK6ILB6SHfIuESUmY+8odxDJGT+/YSMVEnNT9Tx31XJFJf6IvSG03S9O3/M5LE3qhGPTX5W+4o97O5y2B3lZH7UcKoBLAXKP1SBBQBRUARUAQUAUVAEVAEJgcCGUkC99PoxbODLAFOmzYNN95IlUUNeUBATLBwh6DDB2fJdIQ6DsISk43a5my/+6WYXOrU4OPU+XTfg+MOPZa7SUmEDybsLjh81bA5xKqohD7qaI70z+RFoL29HV18xiVEaIxouNDOPaqZxIvTkvBwIUJDC/JekTCe8fpUqYcrWIbnJ6oeo71vNMcGPMSgz0S022jr2z+eL8Q9zxomBQL922083wejvW+hG7IabT0mS7zS0lI4JkXP1UIqApMTgYxI4O9///ukNKpfXa+//npartR9Yf0gyeHPpHpmghI3p7fS7N8L+Rtgo9GX8QxJKkoCaHPA7quBzVVKApjcZ2gEhYN543gWTu+VMwRiJAqxlDW4Eax2xmjhMqN4IxEQSrfDqXuOZ7ycodab0UTVY5T3NVoEOa7zhLTbKOsrJD9VPu8IfTnHkGh2Y0WgX7txk/vwuU1UvOFLVBhX8onLSJZR+993tPFG0b4y1igJLIyupaWYmghkpA56//33D0HhuuuuG3JOT+QKgSS7kr8WqxOOkmnch1clO/iMFqgRzfXeKsE9fUMCX8yi5mm380+/yxZuLrQa5/Ai2huSiicYWdRMTZAIJKMkog4f7y+qqRa7yc7E6Kcu2ptAvxQBRUARUAQUAUVAEVAEFAFFoIARGDUJ9Pv92LBhw4CqrFixAsuWLRtwTg9yjUCKjFEtlETQWTaDn+lUEXWRiFEaR7JmtgXa0rI5BIIh7NrXgBC1SMWfn9j9bO/owb4D9QhH5OTg8ko+tBxqFWkjyZ+VriMo+XOU0hBM6TTYrIOlvkMyGJyhHisCisBUQ0D3Ak+1FtX6KAKKgCKgCBQZAqNWB33ppZfoH26gGuIFF1xQZHBNbHXjlLpZEx5E4y7s/dsrmHHMAnhraujbLwKrOJJPQ+mlyXbt2o/1r22Cy2mjO0FRMbVg5qxpmFFTxQoxnfC+/pM6YZWU/MVpOdT/zmGEY3bMOWWxsQpqxJACg3I/QUGDIqAIKAKKgCKgCOQBAUv/eUke8tcsFYFiR2DUJPD5558fgtXpp58+5JyeyB8CdCFPwhfAgaeewN6f/ALtxy7B7CsuRi2lsRYXpXxRMj5RATWsLlkOr8eJs848EaFgBEHuvbLSkbyLezhdTitsQuSMyugR9iiSP4r/EOnw4/DLr6D+z08DvnK4vNMwc/VJlDqKaqgywPy1suasCCgCioAioAgoAoqAIqAI5BeBUZPAt956a0hJ1qxZM+ScnsgjAokY2nZvx4E770KFvwtYvxH79hxA82mnYNaFZ6F0ziwabeE2ahqOidMYghh0sVKqZyPbc/ic8HmdRoJnNVeELEpIEkDZJwiqi0a6utCyaSsanlmL8KbNcHbRAT0dwu+//x5ULFgET0V5Mpn+VQQUgUmDQK6XbXKdXz6B1EWrfKKreSsCioAioAhMVgRGTQJ37do1oI7l5eVYuHDhgHN6kF8EIt3d2PfQn2Dfuwt2qnaKxM/W3Izux5/C9tfegPeYZag+7QRULFkCZ0UZ9w0m3TckYqICGhugwWn2E1pJBzmbi4Uj6D7cgta3t6LtldcR2boTNpJMlxBJEkhrJIjga6/gwDNPYckVH8xvJTV3RUARUAQUAUVAEVAEFAFFQBHIKwKjJoG7d+8eUJC6uroBx3qQXwTisShat29B69OPo0wse8q+PQb56+aewEQ99+41NuHAK6/h4PRpcC+uQxnbyDd3BtzVlZQQeijpk3QJ/o8jFgwg0NSK7v0H0bV7L4L8WBqaYA0F4TT7BpO5i/FPkRVaWhvRzHtPP3UNymbPlosaFAFFQBFQBBQBRUARUAQUAUVgEiIwKhLY0NCAbkqh+gclgf3RyPfvBGKBHtQ/8wzszQ2Aw2Hss4gUz0qSlqAqp5BBB9VF7bTiysZCdM8+NNrXIs79f3afGwm3h37+ktJD6orCEggh3uM33xbuJXQyreQlQeSGJvR+CdW0xyKI7tiC+hefQ9kHr0myz4GRkmn0ryKgCEx9BPjyETXLfPgfnPrgaQ0nLQIy6GoYNwRUlXvcoNYbFSkCoyKBhw4dGgLP/Pnzh5zTE3lAgMQszn8dLR3Yve511JCQyTBkp4hOdv2Z/0LeZFImt+cfyvroQpDqn6EoIsEgwt0uWEuicLa3mImbmbzRmqiVeZikyeTmmvggNPnIBQbzRcmhnO3pCGDXq29i/iVX0FCMy1y1JCgn1IFRoDKh4uabUXr11anDAd+uE08ccDwlDq6/Hrj44vRVWbkyef7YY4Fbb00fp7Q0/fnJfPa444qivn0kcNWqoqhvqku6TjoJ02+/PXU44NtSVjbgeCocFFt9Ie+tYd5XFQsWTIUmHViHEeqLkpKBcfVIEVAEphQCoyKBXTQWMjhMmzZt8Ck9zjkCJF9kZBTCYduBdvy5azbmeRKoix7CzFgnSq3RPgJmTdK13hIYGidGPoGSSlRfdgVCAT/abr8NHqeT561wShS6gUjK/XoJJI8kiVA+E6gWyu2EaIUb79iqsdM5D4nWmVjR5MeC+ZQuSuGSt0qlKPpv73nnFRcGp5129PrOmgVcfvnR441njHwuXMycWXj1zQO2fav0RVLfFISOuXPh+NjHUodT/rvY6osR+rN33ryp194j1HfqVVZrpAgoAv0RyJoE+ny+/vno77wgIPY9QafuEby1oxm7bXU4PG0R3g61Y05gN87oegNzbEHGSMZL8TH5FhlfLJpAj9ONeaesoVVP7gt0UCU0HIR//wF07d0JR+NhuMVPRCqhScc/vSFMZ/S7rTV4tWQV6j3z0GP3wRd24tDhdpLA2t5kvSLDVCL9VgQUgaJAoI8EFnpt80n4C73uWj5FQBFQBBQBRWAYBETwc9SQThKoJPCosGUVIUmp+LeXWwk/6+H+va3bDsLptiDMFuv0lWKfdw5a4nT5wGAkd73x5ct8+CdcU4u5134cpfPr4Ke1T/fSYxAiEZz23ndj/ue+AjuNvPTQd2AsEjV7e1JpJU+ZN8Wp6tlgr8CeksXo8lQjanOgO2jDgYPt9DlI8SSDpNGgCCgCxYfApCGBxdc0WmNFYEogoO+YKdGMWokCRmBUksDBRmGkPiWqK56nZu2lVUY6lzS80NHZicVz3Pjge05GQ2MXJXpurH/or6g61JOU4jGJpJIk8olTjTNIa6De91yCLqcHXY89hkhrA7ylFYgdPIB9T/0ZvpNOhWPFsbC5SxAjQQzu3AK3v5MO5JmDZMI8rJYYPNEe2KJhROxyzN2J8RAOUhLY0dkDd0059x723pRfGhQBRaB4ENAJWvG0tdY0iYD2ee0JioAiMJUQGBUJTPfis9tHlXQqYTUudRHFTtItkjA6fCf5gs2O1rZ2nLiiHKuXeRBeYEc0HEfn2gDcsSAterIdSNrESl9C3EYYYy9WWOnDseSY1Tjw9GNw0Edg+ey5aF23FvMu/QDqKTu0lVRg1mmnw3nOOTj4xF8QOrgPCZLAGGspxE4cyttYEl+8G45oB/yxMmNsRszR1Dd0oq2tG9NJAs0txwUZvYkioAgUEgJW7i3WoAgoAoqAIqAIKAKTE4FRMbl0hC/CfWoaco+AEEAhguFgJ4Idh+kU3omu5ibUlNJ4C4mejXv4ookILG2NJGAx+O1ekrQIHCLBE4mgfOjrwTd/EeJU9fS//SYqFy9G2bKV8Ld1oHLF8Zh79nnoeOcdtO3bg57uLrT++SGU0K2ETbinSPZ6gxBLZyKAGmcbVp20DMcumYm4JYrOrggNzXTL7YzQMBVfvxUBRaB4EEi3OFg8tdeaKgKKgCKgCCgCkxuBrElgmARDQ64REFqV/CTC9PfHD7UvsbKuBC5nDNEYD0gSrfQLuPis41D2rlVwe704cOc9SOyvp98Iq9kfGHT54KiuhaumArVnn4/q5csx85RT6CvQgeZdu3iHOJrf2oC9Dz+EuWecRUuhlPrRDYSxESqqoAwxupCIl3ix/Kw1OOHsc+CrrUBlqRdWuxsBbkx0l9E6qJgttTrUQ0QSMv2rCBQVApOFBPa+0oqqbbSyisBUQGCyvGOmAtZah+JEYFQk0EHn5IODSgIHI5KLY6FhlANSzTMc7CLdS7qAKHGJmifz79W9dDjtWHnOGiO2a3rzbSAQoNsHpmOcsM0Fy5qz6cvvcnirq2Cnn8D6nTvhfOMV7Pn1r2Fta0b7McfCvYq+zLivM9jeDCudxzN5KntDJOV+jlk0LPPus1BSNx8REtJELAC7ww6fk0QUAaqrBmF3De0buUBC81AEFIHCRkAnaIXdPlo6RUARUAQUAUVgJARGtanDSZXEwSEUEqmUhnwgYKGDP4fLi7jNTUJGNtZnfYW/KcdLkLFZSN8b33obe391O6x0JC8+AcXrn4WqoGg4iO1/+DXqX38DO+67F60PP2CIor2jBb6mQ4i+/jKi+/Zh0ZUfpREZOxJUBZUJneQuQTqFyCNjPSGSxE4k4rQeSslg8u5SIu4cjIaSexZNTEmlQRFQBIoJASWBxdTaWldBQPu89gNFQBGYSgiMShJYVVU1pM6tra1DzumJsSMg5AtWJ9wVdEjsLkegswGJYDuJlziCEHqWJGvhTj8OPvAoHAfqIZLaOMmYkDRHOITImxsQ3fImGgJBLLjwPOx4mBY+veXw1i1AYO8exKw9JIG7UXvBBbCuXI6Dz7gASgxFHJgkevzJgkQON+LQA3+GvaoUvlkzaTnUwbskKaJsQbRYxXTMEfLI20/ZMOOOO+hjMb0KtIUquZmGGXfemdP8Mr3/UeP/8IfsAMPs+/V4jpo8qwjnnw+8+mr6pDZZ4pjY4Lv0UixoaRlQCNGIOMD9tdysO+D8qA7OPbew6/v+9w+pb1+9WN9meWdkEgq9vpdcMmJ9M6mqxPUdBb9M88t1fN/FF+e2vjnGL9f1RY77X67xy3l9afQtp+/Ts8/ObX45r7BmqAgoApkiMCoSWF1dPSTflkGToSER9ERWCAgJSwYb3TdUwmd3oruN+/N6SAQtMilnk0WjaHzlDWD7HrhopTWpJWpJUkSKBGlQlK4caFzmhb+ihWzumOs+hbIFSxB9z6Von1MHz5y56Ny5HTt/8gM4Q0G4gmEjXUzdWb5l36EjGkds7wG0vbYFZbPnGWljgnsHeRFWTxlsdERfLMGaY5couc4vJ+0gzD4VfL7Ur/H7FrXziorxu1+Gd7KwfLbBC2IxSsW7ujLMqTf6ZKxvv5paMtUGmeT17Vf1Uf1M219GlXJ8IuW6fLnOL+co5Lj/FVt9udo87u9nlbzm/CnQDBWBAQgoCRwAR+EdWOw+eCvmIEQroLFwB6VHwM51L2PfHfeikivxCZr0tJDwiXUWI0WUKoiUjsdOSgXDTz2BPZu3YM7HroNzxhwjt2t78lHEaCDGQwJoJ9kTwidpRes0lYc4qrCQXUY7/DjwxHNIzJiFheeewXuRhrqr4PRVwsr9hxqmDgLSZ1LtP3Vqld+aFPMkZbK4iCjmNspv79fcFQFFQBFQBCYzAqMigenUQVUSOD7NbsiZoxSeCg9iiemIR2gltHwX1lMaWOV1oZxErYzSOTeNuzhjCfoEpBsJJhJ1ThHsOOhv0H5gH+p//l+IcG+nq6WJEr4QXBaqr9HdBJMhSBIp94mRIoqsMUxi6ScxFPlGlxVwl5dj8fxjUVK9gnsIGctqZ968IGzTpJRvDZMdASWBmbdgMROMYq575j1FUygCioAioAgoAoWFwKhIoOw5KysrQ2dnZ1/pGxoa+n7rj/whkKJZootpAd0yIIj2UAQHqQba6uXeQRI2D/fq2YXs0YCLI0rffjEhhDE4hBiK5E4YXqALkc4EXT/E4HZzTxfzizLzELlglL8jzCdktyFip/SPBDFEjhdgXgHmU2V3wHgFFPKXYDmMxChZMqV/+Wv78c5ZJ/WZI17MmBVz3TPvKZpCEVAEFAFFQBEoLARGRQKlyHPnzsXbb9MdQW/YR+uSGsYHgSTR4j486mKKClZ5BdUxSeQSiShC3AlozHeQyCVs3LNEEiciQJHT8Qw/8psskP/DJIUJIYYOm5HkkR9SskcpIK9xZ5NE4Z+kVFCkfFGxTUpC6KYhkNKyErlqZH9HvpUCGlCmyB+d1GfXkIJbQh6iIgvaX4qswbW6ZgFUYRg/BPQdM35Y652KE4FRk8D58+cPIIFiHdTv99PVXJIcFCd841troXQxqoE2Hd4DSyTAm0dpwDGOSIyGXSj9MwZihP0xWEQ6SGmhnVI8O/0K2ijFi8hElSTSwk8sFEaIlg2j/MQoQaS/h14WKESP5I5SQbuQSloBDAfa0HDoABYtP573EFqpYSoioANudq2qJDA73DSVIqAIKAKKgCKgCEwcAhmRwMHFFGngypUrB5/W4zwgEKcEr/7gAbz818fxwmP3ofHwQcSF+PG88R7Be4qbCAnJyTwpIwmhhSTORiLo9nr4TXVS7v+LRoIIBrrN/kIqiPKc8L9e9ijpeSSfBKWAVpLHjo5uPHbfH+kwPobjTl2DyqoaE0vupUERKHYEipU8F2u9i72/a/0VAUVAEVAEpgYCYyKBe/fuVRI4Xv2AUjwK4VC3bBnqln6DlE0Y3pGbixEYE/pppQmVM7qefdEGReJhn5c/45D+SB6pbOTbQpYYp6UZl8tnDMkMuHFf3vpjsiOgk/rsWnBS4Nb3gsiujulSTRbroFwVS1d8PacIKAKKgCKgCBQ1AqMmgXV1dUOA2kU3AxrGBwErJXozZ83F9Bmzk9wv03mNYXO9Ze3/e7TFZxpJNmkmfqOsV+3//b+If+c7aWPbZ81Ke348T+a6fCPl10YDUPRAUvhhzRrgoYfSl9OVudsSz4UXYu6GDWnzs7iP7gsz78/E6afntr7nnz+m+qaAyhv5PeOMnNbXfe65Oalvqt65/vZecEFOy+c977zh88vi+ch5fdOUT4zOdbS3A7RgnXE47bTh+0s2+WVcgJETeEfqf1m0h3fQ8ytbOurr65OFyCK/kUufxdVTT81Ze8g7Jtf4ZVEjTaIITFkERk0Cl1ECNThs3rx58Ck9ziMC8kKUPXqDgxilGDwhS3duNOkGx5nqx440ixuFVOdcl2+k/OzNzYBMxAo9lJYCK1bkrJQ2ukCxHX981vkNfvayzmi4hAVW31Qx81bvHNfXSsvWrtraVLEL7tuUbwz9b3CFcp3f4PzHepyufA5578j7J5uQ4/4yUhGy6fPp6jvSPY52bXB+dlrwRkXF0ZKN3/Uct8fg+o5fRfROisDURyAjEigr3rI3LRW2bNmS+qnfeUZASJ18BP/UQNSfEMZkIGCQNpLfYhQmXZA85Lrk0T99urh6rrgQyLtEa4rCmXoep2j1hq1WsdZ7WED0QtYIaF/KGrq++UD2OWhKRUARKFYEuLFsdMFDNwF1g6QmKgkcHXa5iCWDpEzShdwJeetP4ORa6pz8Ho4ASjlS1/unz0X5NI/Jj4D0DQ2ZI1CsuBVrvTPvIZriaAjoAtTREBr+uj6Hw2OjVxQBRWBkBEZNAiWbFYNUsJqpviEfDYqAIjD5EdCJWHZtWKyTsMlS78lSzux639RIpW2UfTsKdlMVv6lar+xbW1MqArlFYEwkUIry5ptv5rZEmpsioAhMCAJKArODvVgnKsVa7+x6iaYaCQHtSyOhc/Rrit/RMdIYioAiMBSBjEjgCSecMCSH1157bcg5PaEIKAKTDwGdSGTXZsWKmy4aZNdfNNVQBCbLM1So5dRncWif0jOKgCJwdAQyIoEnnnjikBxff/31Ief0hCKgCEw+BHQikV2bFStuhTohzq4VNdVEIqB9aWzoK35jw09TKwLFikBGJFDcRPh8vgFYqSRwABx6oAhMWgR0IpFd0xUrbsVa7+x6iaYaCQHtSyOhc/RrxboQdXRkNIYioAiMhEBGJFBeNKtXrx6Q386dOyGOXjUoAorA5EZAJxLZtV+xTmAnS70nSzmz631TI5W20djacaq+u7VfjK1faGpF4GgIZLaxa/MAAEAASURBVEQCJbPBKqHid279+vVHu49eVwQUgQJHYKpOJPIN+2SYqOTD+cdkqHe+217zzw0C+u4ZG476LI4NP02tCBQrAuk9io+Axqmnnjrk6tq1a3H++ecPOa8nFIGJQqDqf/0vlN1wQ9rbO1euTHt+Mp+suuUWlF1/fdoqjLa+U24iIYasfvrTIZiIH83ahQuHnM/2RMHgJloaaeor9XLV1GRbvWHTTXi9h2lfU+BB2xaGrcQkuuA+/XTMePDBtCW2lpSkPT9ZTqbtS8cfP2x/htc7Wao26nK6ObfKtn0nJYkusvYddUfQiIrAOCJgoSQvkcn99u7diwULFgxI8p73vAePPfbYgHNyID4E77nnHtx55524//77UZOHiciQm+oJRUARyAqBeDyO3bt3Z5V2MiVyuVyYO3duzorc1taGlpaWnOWXj4xKS0sxffr0nGe9a9cuZDiE5LwMR8tw/vz5cDgcR4um1ycQgVgshj179kxgCUZ368WLF48u4jjHamhoQFdX1zjfNf+3mzlz5hA7FPm/q95BESgeBDKWBNbV1WHWrFk4dOhQH0ovvfQSZAIpq1HyInqQq5VC/J588klEo1ETXwlgH1z6QxEoSATSrsYXZEnHVqhc13NSrsKPDcK+1IJloZPAvsLqj4JFINfPZMFWNE8Fm6r4TdV65akbaLaKQMYIZEwC5Q5nnnmmkfCl7iaGYX784x9DyOAjjzyCQCCQumS+BxuTGXBRDxQBRaAgEJABVz46qc+sOYp5oiIEWBYANSgCY0GgmJ+hseCWSlvMC1EpDPRbEVAEMkcgY8MwcgshgYPDzTffbIhhigD2dyWhJHAwWnqsCBQmAjqZyLxdJsMENl9lzFe+mbfC8CkmQxmHL31xXNE2Gls763t7bPhpakWgWBHISBIoEgKR9r344otp8RLid9lll+Gaa66Bx+PBRRddZOIpCUwLl55UBAoOAZ2MZd4kxYxZMdc9856iKUZCQPqSaiGMhNDw1/Q5HB4bvaIIKALDIzAqEvjmm2/ijjvuwF133YV9+/YNyM3pdEIMwwjxEwKYkgCKemgqKAlMIaHfikBhI1AMK8q5njDlOr/C7iEDS1fMdR+IhB6NFQHpS0oCs0Nxqr639f2SXX/QVIrAaBEYlgSK1Tcx7iKfzZs3D8hPXjjnnnuuIX5XXXUVKisrB1yXgzfeeMOcq6ioGGJNdEhkPaEIKAIFgcBUnUzkE9xinqgUc93z2aeKMW959xTy/tJC7uuFXLZi7MtaZ0VgsiAwhAQ+9NBD+O53v4tXX311SB3ER6BI/K6++mqI6d6RQooEniC+nDQoAorApEBAJxOZN1MxY1bMdc+8p2iKkRDQvjQSOiNf08W7kfHRq4qAIpAegSEk8Oyzz0bJIMezH/vYx/Ctb30LixYtSp/LoLOhUAhbtmwxZ1UVdBA4eqgIFDACxTCZyPVksxgwG67LToa657q9h8NCz48NAW2n7PFT7LLHTlMqAsWMwBDroKK++fjjj+O6667rw+Xhhx/OyJHrpk2bEIlETHolgX0w6g9FoOAR0MlE5k00GTDLVxnzlW/mraApJjsC2peyb8HJsBiTfe00pSKgCOQLgSEkUG7kcDhw22234dvf/ra5b0dHB973vvfhV7/61ajKkVIFlchKAkcFmUZSBAoCAZ1MZN4MxTx5Lea6Z95TNMVICGhfGgmdka9NVeymar1Gbk29qgiMHwJpSWDq9t/85jfx+9//HmIBNBqN4oYbbsAtt9xyVAteKRLodruxfPnyVHb6rQgoAgWOgJLAzBuomCcqxVz3zHuKphgJAe1LI6Ez8jV9b4+Mj15VBBSB9AiMSAIlybXXXounnnoKVVVVJod///d/N8ZhZN/fcGHDhg3m0qpVq2C3D9l2OFwyPa8IKAITjEAxTMRyXcdc5zfBXSCj2xdz3TMCSiMfFYFCJzKF3NcLuWxHbXiNoAgoAhOGwFFJoJTsrLPOMk7iU4Zh7r77blxwwQVobm4eUnAx8bxx40ZzXlVBh8CjJxSBgkag0CdihQheMU/AJkPdJ0MZC7Ffj3eZtJ2yR1zf29ljpykVgWJGYFQkUABaunQpXn75ZZxxxhkGr7Vr12LNmjXYvn37APx27NiB7u5uc05J4ABo9EARKHgEdDKReRNNhslrvsqYr3wzbwVNMdkR0L6UfQtOVeymar2yb2lNqQjkFoFRk0C5bU1NDZ5++mnjJ1COd+7caYjg888/L4cmpPYDyoGSwCQm+lcRmCwI6KCbeUsVM2bFXPfMe4qmGAkB7UsjoTPyNcVuZHz0qiKgCKRHICMSKFmIsZc777wT//RP/2RybG1txUUXXYQ77rjDHKdIoEgUjjvuOHNO/ygCisDkQKAYJIH5mDAVA27penCx1jsdFnpubAjk47kcW4kmT2rBTvGbPO2lJVUECgWBrKy2yMtGDMTIHsFPf/rTECMxYkBm165dSJHA+fPnw+v1Fko9tRyKgCIwCgR0IjEKkNJEKVbcirXeabqAnhojAoXelyZD+RKJxBhbQZMrAopAMSGQFQlMAXT99ddDyN4VV1wBv98PcSmRelHKfkENioAiMLkQUMlOdu2Veu9ll3rypirWek/eFivckmtfGlvbyLtbDPNNpaB9Yiq1ptalEBHIWB10cCUuvPBCYzBm3rx55lJqJUr3Aw5GSo8VgcJHQElgdm1U6JOVfJUvX/lm1wrpU02GMqYveXGd1XfP2Npb8RsbfppaEShGBMZMAgW0lStXYt26dTj55JP7MFQS2AeF/lAEJg0CxTBhzkcd85HnZOg0xVrvydA2k62M2pfG1mKK39jw09SKQDEikBMSKMDNmDEDzz33HD7wgQ8YHJUEFmN30jpPdgR0NTm7FizWCVix1ju7XqKpRkJA+9JI6Bz9mr67j46RxlAEFIGBCIxpT+DArGAMwdx///34/ve/j6qqqsGX9VgRUAQKHAGdSGTXQMU6gS3Weg/uJTvwDLbhycGn9TgDBCLeKEIzQhmkGN+oVhrEexvZG7uzw42z8XnmUJmXgk/FZ3Eq1ikvja+ZKgJZIpBTEihlkEnk1772tSyLo8kUgbEjEA4ncNNNMVqrHXtexZaD7OkNBqdP6WrbbDZ85StxXHVVzhQhzHtvSoM2TOV00QDYjqfxGP4VCUwtoxzDNHn+TjuYtXymcGjGTlyJH+eFCOqzOIU7jlZNEcgTAjkngXkqp2arCIwKASGAV14Zw6OPqqnsUQGWNpI77dmpdPIjH4nhrruQMyJYrCvWk6He+SyjEsCp9FbIf11asAf344t5IYL57Of5R0bvoAgoAhOBQO6Wwiei9HpPRaAfAkoA+4GhP0dEIBoFhAjed19upDeFPgHLV/nyle+IjVcgF7vRohLAAmmLyVQMIYLP4oc5L7JKAnMOqWaoCEx5BJQETvkmLp4K/vKXcZUAFk9zj7mmKSIYj49dalysZKhY6y2dL4awqoCO+SkszgwiCOS84lPxWZyKdcp5w2uGisAYEFASOAbwNGlhIRCJFFZ5tDSFj4AQwVyEYp2sFGu9c9FnNA9FIJcIqCQwl2hqXopAcSCQpz2Bw6+sy6p7NBqhIQUbxEADYOn9L2ksiHFWZjXnecRLtFOBKGf3cf5wuVzmWM5LiMfjjCPpLZKLBkVAEVAEJgSBYiVDxVrvkTpZKabhfHx1pCh6LQ0CkXAETc3Naa4Uxik75yXTpk/LqDDN2IW1+FlGabKNrM9itshpOkWgeBHIAwnsTwAtJG1HjqMkeFu3bcPTTz2JpUsW49hjj+VLdQYcDgfVaiw4dKgeWza/iZXHHMPzMxGOhLFvz078lfEtFjv+/tOfMWQwFAyirb0DgUAPqumKorq6BhabCjWLtxuPXPObb7bixhu1f4yM0pGrDQ0NtBAaPHJiivy65poZaG2Vhafch2KdgKn0YWhfctD2Yx1OH3pBz4yIQJjqtZae/SPGmciLMk+Zz3+ZBBucmUQfU1x9FscEnyZWBIoSgZyTwBTnC4fDiPDT1tqCHpI1CTJRCgW68acHH8TatWuxasVivP+Si3HFBz+CsvJK/OM/fg1rn34Up59+Mq6+7kbsJmG8/57f41B9Oz7x9/+AvXt346EHH8DTzzwNj9ODSz5wOd773vcYsmiJWoyZdpEuWq0iF1TZoAFd/6C2Fli2LB/94cgCx1CYB97PPBc8xWWRAX1TFkmSku2h8ZPnj+Q8MOWR87n+VVoaR09PjvQkc124MeRnt4/UXmPImEkLfQJWrCR1bK2qqccTAe2jY0N7KuI3Fes0tlbW1IpAbhHIOQkUdc5du7bh3jv/gFdefB4HDx1AOBQylMxisaK8ohrBGNU4ORneuGkb9u/ZgQfvuQuVNdOxYcMGlPsc2PjaOmx49VVKEaOI0HGQp6yGefhx7dVX4a23d+Ci97wbf3fdxxGNhXHbL39OkhmgNNGNRUuWYs2a0zFvfh3sdochnbmFS3NTBFII9CcUAwmcxNi9Zw/+8udHsXjRApxwwmoj2U4qLVvQ3d2DN15fj2VLl6Bm2gzGtqC56TAe//MjOHSwHl/+6jdg56qzhM7OTtTXH4bP68GsWbPHReJd6ITGAFNgf4p1siL1lk9/jY9CaprJ3S6975iErB6l3jfyruFvcyjnBW2xcCufGD9yQj69Wy1MWh72BUkon1S8vgsT/mNyt9WEw1fwC1ETj5CWQBFQBAYjkDMSKHv24rEYnn/2KXz9y1/Avr07ScwAO4mfhHhcJgpWNDU0cdk8jtpSJ7pDCbMHsKOlFZ0t9agpTaqFMhUnuzHGd4JZIh7oxIN3/w4tHQH4yqpIFJ249du3cKK9F23dESRsdsyaOR1LlizH2pdexAUXXoDzzjmHaqK1HO7MKNn7d3D19VgRyA4BkeylJi0yAe7saIff7zeZSY/zejx45pm/4ktfehiL62bh/Re/D9de9yksX7EKX/3qV/HH3/4SK1cuxue/cguaGxtxx+0/w4Y3duDKj3yU+XThgfvuwUMPPUgC2IhLP3AFPvHJT3Cv7GCV1tRkLrs6DJdKSeBwyAx/PtUXho8xda9I3QuVBE5a1OXRllGL5C/RSwDNIlJCyF6U58IcGLsRj3bx08PxNcg2CCWry60TDtd0vi98POYgnHAxJ37zPAffPgrYyyR5LjlGJhNP3F9974wN+2J+B40NOU2tCBQvAjkjgSLZ27V9C/7l6/+I+nd2osTtICmU1UgHnHYr3G4L7FTTtHLCHI4m0B4I0zCMgxIPK6wWGopxuOCycvBy2MxevygHJnmp+exxxKxUTUvEUO61U/rXjeeeeJj5xOB02lBlczNvF5Zwon3y6mNw2pp34djVJ9CIjJv7Bzm8mfEtP5Pl4u02WnPpm01NDfgTpdjPPP049u7aSQlfd3LSxu5WWcV9qnYnHOzzW3fuR/3//ByP/ul+zK1biJfXrUepx4o9O7fiy5++jpM5kXiz73u9mDG9BpdfchFeeOl1LF2xHP/xf76HivJS3HPX7Sb/SCSB2XPm4tzzzsMSShLzMYHTyUTm/buYMSvmumfeU0aZQoif/DeSPCF+XOxMiFuBDg6FHYhFGmkwrRHxcKshgYlEmLJAmkdm/ATfJdbqJXz/TIMlLuTPy3NeXitjehJDi4ffHPq5KGtCYXDAvkW1ZKEK72+h93Ml0YXXZ7REikChI5AjEphAw+HD+M43/4mT4c1wOSnJI/kr8blR4rHAZbdxyImT7MUptSPpI9mbATfaO0I42B5GV8yCMo5HsXiYUr4yuO0laO7ogsNugcftQVcgyNQJkkkLnDIwUrookhjZ+md3cICMBrD1zfXY9PqruPsPv8U5F7wbN3/tf+GYVccS/6QSXqE3hJZvciAgEm8rCeDmTRvx9S99Dq+/uo5djErL1uQKe5yTsDj7c+PhJk5q4qj2udDttCIWjqKrvRNbNq5DdQknZuyXRuJtVLgo8Sa589gSuOf3v0Bjaw/sXNg476wz8f/+z3ewefMmPg9hBNnVayrLsXT5Cjz+1FMkgufiyssvo5roXD4dyZlcLuZzOpnIvC8W+gQx8xqNPkUx1330KGUWk8ObIUUWSvcSiS4etXDga6VmTAP32pP4hZqpRdOFRKQbCS4iyeN/5NnnYiu4CCUqoVZq1IgUUH7HS5hnCfOr5HEVr5MUWsbPcAlvOmLQfjQiPEe9qPgdFSKNoAgoAoMQyAkJ7Oxsx3/827fxzJOPwk3pnCFpnNwGw10IUENFVIVkkiovKQdJoI3f5R4XKktdJIou7G/qoWQwSOmIHYGeHpSXl6HEFUKp102SZ0c3z9npUsLDfVGBUJBSQJJD7gG0UsLI7YXoMQ7ikqzQ3x3AY3/+C8KcdP/v7/8Is+fOF9bYf4QcBIEeKgKjR0AIYGtzI751y1fx1uvruMDB7hV3UepMSR77ttttNYsXdqo/h9k523tERcvB83SJYg2zzzrhovqyi7rS3T1+xJif/PNyMSPOCZtIBY3Em6keuud31JymjNBuRwWfFT5AWLRwFk47aRXOPOtcnLrmDJSWledc4q2TidH3h1TMQscsn+XLZ94pfIvnu3es5IiZSHSy2o38Psxv+e2n2mcnF5n4QRBmpwXHxQS1Yvoriid4TlQ/jTCRC1EWqo9CHNtbA8y1je+YJn5XMM4svrxIFoUYMpaE/n/NiXH+I31JVYuzA30qLt7puyW7vqCpFIHRIpA1CZQXtTygYrjiv3/4Q9z7h18ZCSDlJJwUc+2RUg2n1UGSZkOEccORGLojVPK0ROGjZCREtc4uGoypparbwuk+bD1Ai4Qkbk7ueSjjRHhmVQknzTYzkS6lf8DyEg9VPK3cL0XJImfebqqRhilxae7oRpT+hRIWsQpKyUttDaqmzcbL6zfiN7+5Df90yy1UO5Uhsv8wOVp4NJ4iMBABMXL0H//+/+G1l9eyv4lUkITO5UWpjwSQqszOPom3WKt1YRY86PSHsK85gI6IBZUUX8diIbh9Hlq4LUcTpYOUHaKkxAd/T4BLJ3xuHOzjXNNI8PmKkzDyFBc9xCemDQd2b8XurVtw5+9+jVXHr8Z//OhnOOHEkyVK7wRuYHmzOZqKk4lscMgkTTFPVgq57oVctoH9S55g+fA5TtAvbryev/aR6LXwIyupVPaM0uJ2RFy38F3A4xitb1McmBzZ+sSAQuc41lHbJvlGSI171t73A1dNLVQrtVCCiGYSrjKqjC7ku2oWj0keE0IgmXSCgrSXksDswJ88fT27+mkqRUARyD0CYyCBcbS1tOD//ej7+M1Pf0zCxgHMqGlyRwLdNPgoxROJh6jEyZhi5+9AOI53WrrR6I+hysdbU/2zsaMTs6pKUVPmwp6mICKUJIYjcZJIGzooAazwulBdXoIgCWS4K0Iy6CaZDKOHRmX8wRA6KWmJc3IswSISRw6SC+fNw9nnXoAXX3wZO3buwvJly8x1/aMIjAWBcDiEH976b7jv7j9wXw4nZlykEDXlMNWymts5f+on8Zb9rzaqiJa4nKgqcWLlvErsb+xBM/cN+qjq6e/qQk11FRdEHDx2JqXc7O8ygSvxefmssF9H4/Bxb6vNxieIfbybk74QFatdzDvEfbWvvbYR1//dR3D3AzQ+s3QFCyDxxlLDZFolgZljWMyYFXPdM+8pw6UQyR8fXhJA7q7nc7yPC0wkalQ1l4darln5DnC5S7noSS2DKKV/1DpIcGGJJ5hOLIMmQ4IE0CZiQhl7OSxLtgMCz0ueIiG0WFp4P75raIHNaqNk0EKNgwlcMFUiM6ClMjrQ5zAjuDSyIqAIEIG0JDAe59SWE1rxuTc4yLUAXTJs4F6on/7XD/DsU4+SsHGAorQtwdVJ7vjjomWMEkLuV+CqnpmUciAit0OFrxTHzC7FjsM9aOjsQW0ZSSLHuFAwAp/HyQGJ6p0csXpI8mQfYIwSRbfTSbXSIFo7u7knkIY2KAXs6AxSvc7DSXGMSi6UKsqgyDGQiqboCnZi3ct/Q0mpF2efcwY2v71JSeDgRtTjUSOQkniHKAH8/a9/id/98r8RDQWMVC4l8XZQGhjnoxRlfw1xcubngoWs3Pu4gTUY7aYV3BBqKkqwaIYP4Xdi6OJ1O1U/o+zn0ynxdpIscksg4zupAu3iAoqTfZxKpOzrJZSaxy0OSgy7EeGih0zwLBYaRSKRXH3aqXj5ldfwrW/9K2773e+oNirPa2rlf9RVHBJRJ2JDIDnqiWLGrJjrftSOcdQIfPANyaOkjhK6eGIP3x37uZ7a2Uve5HkWjQM+29wTn5D9DxznnG4vJYGUBjK5jLv8Y/IRvmeTuGaPcq9UkOdEepgcjPllosqfJEmEhXsOrVsofeRoalnEaBO3T7CQ+1Ihl42Nyf4zmO3LWQ2KgCKgCAyPQFoSKNFffeUVulxYTDJFdRG+W2QyLA7gN7+5EXfe8Ts8+tA9CHZ2JFVAZdAh8RPCVsLJKccqTn7j8JMsxjhKiYZJmGNOE4lfedSOubUlJHkRqr9F4S61kfTFUMrJro2DIbVbOGkWQzAiE7GiuTPAvKK0ECoGNyJopeRPJtoxTqS7QzHuEaTqHBlmDY1tyDDX2R2mWf02PPGXx3Dlh0ox7V3nsAyxtIR2eFj0iiKQREAmWAH69bvtVz/Hf//oP9Dd1cYFD9mHIxJvKw0XuWjIiAsYVEWW6ZpIvCPs8wdaetDYFUYFpYCgcYfGtk64asuo/kyDSA3tiNI6aJD93k1JYFNXAFVeB4liqZF4t7UHKP32opPksYdS8XC4h4sg3A9Ea7oy2ZPBXiTeIgH43ve+h3/7t+/hjQ1v4pSTT8pJs+mKcuYwFvMErJjrnnlPOZKCIyNHOSFwHPm4sJNIHODxbhIyahkMmM/zeadIT0ZErjNx7CNZDIpVUNlvzKh2H+xOWv9kCPe0mrFOVMeF5okoUNIlF4dE2ihZmwHd5GkWak28Ho7h+1kaJ4ngXJZJiKBEHlAQiZnXoH0pe3inInZTsU7Zt7CmVARyj0BaEiiTwH379uEWuns4/7xzqILiph+0Lmza+AY2b1xP8tZBVw5WOF0y0MiAklT5lBGmh5ZgRCBhdzhRRpP3YhgjTqfvZijhRFlUOB10B1FT5sbepi6UcGUzJCSQdRP3ED08djBfKyWOkq8/ItbROIjx0MJ9hj0kiDKQyRIonU6gptxB9TiZgMe4N8uGGZWl6Ohy40BbB5796xN0Hn8WF0VlENSgCAxE4GgS7wgNDm3Z9CZ+/Yuf4M8P32skgDYaIzoi8Wb/9HNvq4UTKOng7Jd8LNjvfVg+mxK+wwHsb/XDSom39NZAT5hSPi8nWcnJXIgrHofbugzx85AMyl7Bxg4/F0Os8HIPbFubH2XlFVww6UGUBDDIlZRQlBID3sfGZ+GVdWsxfcY03HjjJ/HyS2uVBA5s3nE9KubJSjHXfSydjNSO//g8y3eigX/3cKzq4W8Z4CQcIWBC3GJhP0Ld9Yh2U02AKp0OTwVsnjJu/+M7xepmDK6gckEqwvFUXkZxWs1OyKoqf1v4/rDZ6S+QWgu8MiBvHjAwjpXaOyShiYSHeYlqaPJ8/3KYqHn8o2N19uDKcyifI/0n+7w0pSKgCBQHAmlJoFT9jDPPxD9/9Uv44Ssv0pIhhwiOS4bc0VKhSN7MANFvrJLhLCiETVYdZRsDyaC80GUcsYh6KX+J1VCR9tm5nCn7pWLMtL0nAg/3E4ZCYSMVaW/rplTQTmmi5MhhLW5nHO5X4MutgsY3QKmgTdTvONA5KT2MRP0I0kCMhUTRErRQEhlGJY1sTI950HDwIB68726ced75qK2dLtXSoAgMQGDb1q2oqqpEeUUliZUQPK430DDRzm3bcN+9d+HBe+9Ay+F6WvykXNrG1XFKvJ18Bko9tFzLPhzhooU/GODEi32cj4WodbZQsidGHObUVhh/mH5Kp5207ulnn53GPizWcZkNuoNc0JCBm/9a/UEEeJ1dmX0+ihY5Zt52Gkvq5nk/eaaN953JRQ95djr53LR1B/HAPQ/gyqstuOzKa3Mm8dZJ/YAuMqqDQscsn+XLZ96jAn+ESIVctuQgKuMiVT/j+5IqoBz1RN3TrHr21kveDzG6gujpOIB4oIPamvSNWz6TfI9aOjQ+ZUikTP7j3IdPq9sWmx8BfyPC3BqBKK1p832RoIqn1cmFKWcpF1lJ8mi0TcbXI4G/KY20iB9ClgW0GGpcSJjyHImV71+F3V75rv3Y85e2Fs0nDYqAIqAIjAaBtCQwSqI1Z84cfOKGz+DW734bpZx4Wow0T4hZb+j9YQYgEj8zoMiElueTkrs43Jwol5WWksDR2AvzbGnv4j4+rkzSqEtThzi+Fa4YpxFsSvLsIXg93OfQ2oXOYAJldBcR52pnR4AGYeI0uy+WF0sqcdJxS7F4YR12bduONzZuoPooyR/vKz4IRR4o+wRtdERf4nGgg5Pkrds245WXXsIll13eW3D9UgSSCMiAebihAf/8ta/g2FXHkAhWoEf6DP3ybXpjHdram0gM6ZvSKb2ck6ReibdMnQLBMMmYSLwdJIS0AMrdqWGqK0uwUkrXRYm3xxXEtHIPdtTTxx8l1yH2ffEz6GJf7qS6tIOGZazMU1bh/TQEI8+NCMDtvKc/2GMWSUJki5GIleqhnPjZuBjCXu6wxTGNksZg0Mu82/HIQw/gpNWnmcmeKcAY/wguGjJDoJgnr8Vc98x6SbrYMWMJ1GppNO8Xcfcgg6jx8SfvBtEaoJuYnq56EkC6d6AWjbtiLhxuunggWUzE7JQQyqIoP1ESO75DEolyqoi2cN+8g/uOqXrObRwOWtbmMMl3SReiMS5KkRDaSCZTaqLJkjGC6JtamvmWOcR3jUgExcegDPby1st/0L40NowVv7Hhp6kVgWJDIC0JfOG5ZzFj9ix87gufxx/vvRf7d242pEwsgIrxlgRVL63C9lKDgzE/llQNTZCMuWkR8bL3X4LLLn0vpk+v4eTUhiAlfdt37sPGTTtI3t6GddduuGnspYvqdDEOdoc6Yyh3+mkl0U33EZQqxmyoKC9H3eKZWH3cSpx28vE4ftUyzJ0zA163HTt3vYPvff8neOyJv8LromRFVEa5kimilCAn4FbGEcM2nR2teG39Orzv0g8YaWKxNbDWd2QEVq1ahaamRvz8J09S+iyEjFMrIXeUujlFt1PmPtLVJfC3LIOEKOUzEm85J32NpEmipSTecT4jVvZHGydU4t9SkndQFdTN8wEaPPLQGmhTYxdiJJBWGoiRtJQvorU7auJWlfCxjEb4rHHxhHtnvTSaFIn70Ul1UAvFjSI9FJI5u6YCc2tKUN/chl/9z3/hAx/+CGbPmcfcxhZ0IpE5fsVMnLW/ZN5fkinkhSL+/w7zWQ9RgkPLoFz0EV+iKUmgSAEjwTYSQDqLt9PfaMUcEsByXrchFqIxqm4xEMP9gCSD5uVhMnZTK4GSv5JSeMoqTPpwsB12kkKxLmrUDeSlY0imJJCD3mBeebLlgi4qErW8VM0L/a6n4uXpW/vS2ICdau8h7Q9j6w+aWhE4GgJpSaDN6aLp+WvxhS9+EZ+67uP4+i3/jJ52riZSJc5lj6Ocq4oJ+jKzcNVSJriifUBtOBp/SVD64cPnP/9ZXH/dVZxM03oZJR4SR1Yhly6ah0vfdzZVP2OUCnainfsM6w83o7W1jfv4qO/G9DIB93Lf1IyZ0zGTPv9mTK9GKY1r0C6M2esQ4x6HcDCGGdOq8e53X4jHnlmHTu6Z8nE/oKiaxkkEoxzcwtzPJfumxK/boUP1tEBKyQwlkxoUgRQCojZTU1ODqz96HW79t39l7+mmqqb43cpM4u3koodIvO3cdxNhnm004uLvCaKZVmvFj6Xs0QhRT7S5m+v79iAdv5fxgeikxDvGxRUSQUr4OrojRq1ZLHwmqLJ18vFLufixCgf378ff/vY81cH4vJFsyl7ZOL+DFKg3Mu8q3reV+28PHNyPvzzyCG749GdS1cv6e6pNJLIGIoOExTxZ0f6SQUfpH5XaNQkc5NjWxnFSjKhF4KBxNcO5zKBJFzS02Bn2N5MT0oduxTwSQJKyBMlfj5vnvTQQ5eG13u0ZfXkzcdRLgsgRkQuhTi8HVS5GhWhMJkGNHHdpNcdjGQuF3MmeQVE/TQUhouJzt50Lqw28QsLJ+5moyZE8FTEv34X8HBVy2VKNMRnKmCqrfisCisDEI5CWBJ5x5hlYsHABPn/9JzF/yULMoEpbgGbxbWRiLg4q4iQ7uYpINTnuWwrGLWjvpoN4mrT/x5tuwA3Xf4hGNMSJuwweMprJh8MNB6BkSNCgi5fm8X1YsWiOkXhYTLzeqySUcRI5Mdwh+7NC3DvVOwqZCLKx3UmVulNOWoULz38X7r7vz2aSXeax0FIj03I1NUAjGzL5FuFORNTwZBOWBkWgHwJvbdgAK6V9H/3YtXj40UfpAP55eGjd02Fnf5VVC/Zbm5EOSv/lca/EW7Jgl+dzAFx00UW48opLMH8+V+hJxsK0Wrt3/0G8+fYuvL5hEzZv3UGJdxddPtC6J/vzYb8YQuqEm64ghBgGqM5c7i3DstnTcOyxKyjxPgGnrF6FuvmzUEJXEVEuonzzOz/Ar397J92opCZ7VHzm8xIIcLGDiy5WltlGC6RPP/kXfOofbhqzxFsn9dLCmYVinnwVc90z6yUDYydk/x03Q4BS/hDHVycXX7m6yleNiOOS42aUxC0a6oLVW8F3Bgkgr0UDLkS6ykjoSOQ4viXjyvuqXxC10IgL0U7qLFDbwOXmAhQ1C4I9TejmVgyPj3sKHT6Okal3Siot8+c+QrudGgoWMVYzk99ctBpAFFNxc/+tfWlsmOq7e2z4aWpFoNgQSEsCbVw1vOnzX8bzzz6FpkN7jR9AJ1UuZbAxPok4IRZ1OE6ROTaIihqzsUZw5cUX4TM3fpSDFgmYWLigkhsT9cO0d28VU8rYFafDa45UnFsn91L144HJNBwLkxPvflmYn5IPMKO6Eh+58v14cd0baG9pNpYZ5UqcE3gLSZ/sv3JQra+8ptpM0HmYnNsPzk6PixIBK9Uxb/337+Dss8/GZRe/F2++tRGNjU2cAFHizQWPUjpjttD4kLXXEbOsI4irEzH+YuE+1Ztu/AQ+9+lrUU6flKLGZbbTsLsvXjAH7z7/dLp2iKOV+2A7urpxmBLvptYWdLT7jf9LbvujNVunkXjPqKnFrJnVKC/zmD2IUS5aRCnxDvRwYsj++7Frr8bt9/wZbf5OlFOlSyZ1Vkq85X49fNZE4m0nK92zd59J46Oz+bEEnYhljl6hY5bP8uUz78xbooBT8Hk1YyjHKDNOJVo4HnVykZIkjxoIR6RzUgfGoAuIcBfjUD3G7ZvFbxfHVUoKQ7QKSsMtNu5V5soSSRvHUMm7f+BgKtshaCqN7iQ4Prs6qQ5qh4eaBqHuNvSQDLppRM3u9PG+on6aCnzf8WckTL+8rg6+Y1o4yovmgozl/SWGqfi5/da+NDY8Fb+x4aepFYFiQyAtCRSGdcaZ78KVH7kWd/zqFzSyQi4m/4wkRHYpkMKRaAkR7KFqZ6s/hIV1c/D1m/+exitEFdMsT5oBzyxVysomg6Q3Ej+TT++oxXGM/wcEYzFRGKGJIt9DI4mUz8HB8YRjl+FK7j38yc9v48TdyoGuNzemiXI11Mv9EwvqFmDnji04ZuVxyftIvoNvOqAEelAMCKxctRLLVyzHd2/5Bqqm1cDLhYxEGUkWKy99S6zhWnpXDhKcbIXZp9oo8RZ148/9/cfxmZs+ZqRz4RAXMUx/Mh2WkzKZMMnvBCppFbSakvQl82ew7yeNMyQjy/PEGMw3xo9IqsN0f5IMRzpnmA7lly+Zh49feyVu/dEvKT0Eyinx9skEkBOzHvoLNE8bSaBIvEVdOhdBVpRFcqlh9AjIBKwYzbMX8sSzMMsmz3eQrwzu9YuSBIY6SejE0qcYYbHzfHKxNdTdwC0Q9C9aOpOErJTPtg8N78Sxb9te1DfRoihjl/k8fD8sQGWFL/nKMd2V7wJq6OzYewD7DzQyjzBcFRFqK3gxv66Cqus+qp92m+c7xneVzUFSyr6bDCSPDhqboWQyHG6hdLKFfXour6f2KUq8VNzeJDn8OlKOHGZaRFmpJLCIGlurqgjkAIEkOxsmo3+46QtYuuoEs/8oQVUSmRAba4YcfWi0mgMD1Uy42lhGs9T/8KmPYtniuRw4klK95DjBwUIGF4osYhyI/DTS0nDgHRw6sBctDfWctCad3fbdvlfS2OPvwKF39mP/nl2oP3gA3Z3tHACT6p19cflDJqkiPbnkPWfz3vN4RoZFkj/OwcX4aDfVUWtnz0VJiZeuIu49MsnO3xjWv3j6u8AREIn3ZVd+CLVzZqGt6SAsnHCVOeMocVHd2CZ7A4XMSa8iG2Rc+UQpFbzo7DPx+U//Hd2ROGSrDoOQpYGEKSkpF1ue0h/FBUqEKtV0/s69qWIcJkAXKmLAKMTnJdorRUxOrgZ1Tj53UT4nN33yo1i6eAGllKKSLYZoOFHksxVjWrEoKMS0avp0ShdpFEIegzEGnYxlDmCxYlas9c64hxhVFz6cXExKJLr4DLciFKA6KLVhnA66ZCAJlDE1Hue7gfsAw7Tk6S6ZwT18fD+1xrH+xYNYt3YvGpo7uPjkMYbTHCRsbe0kk0YNobdEfC+Ij1NZcJ09ezrmzZ2DUts0HNwTw/qXDmHzlib6HHVy+4aXhE98Bx555yREz50umOwODw3O0D9p5BDLRf+BshBrQuq79zDHX0pixgZo/7YcW04Tn3oq1WXi0dQSKALpEUhPAvmeF3W0Z55+En/3iU/B4iqnxIHWyLhKKUbqZYILscZJEmbnoHXisQvwoQ+8m6SOkoxBY4T4T2tqbsZbG9+klc71eHvLFuzcvgM7d+7CofqDRgrSVzSmDVOycbi+nqb7D6OxoRH79u7h/qqN2LplG1rbWo0qncQ3CjXcm2in2t6yhbNx5SXn02gNi8RyxbnPIcT7llZW4aSTTsIbr76INze8hk5DJmXSnINZcl+h9cekRYDdYNWqY3HZBz/MlfgSQ55IrWTNwvQvQ+xk4sZ+KY7am9tDqK6sxM2f/wT3s5aY/m/qbiZRyXgSV/qmTMBEZt4X+FOO+p0xJM6IHZnUhGH6ZUxctlBd9Is3fYpEMmqk77xBbxrej5M2uibEWe86G2+8vq7vEguSddDJWObQFeukpVjrnXEPMc+3PJSyYNRBgtWEWKALdlcl9yYnXTHIRglR76S+J7xls0gA59DVXymCHaWYXj6Pe4ZPxBLu049w8aejs4Oq4VZa4J5uXChxHcgEeWWVlpTQSrcXzS2tVEWvh4fuII5fdhxWLjkR5Y5ZlARy/yGle4PbLkZ3FJEQ9y/TwJVIBMPBBi7ANjIeDbyN4X2SLNnR/w4uz9FTjF+MQi5bCgV9b6eQ0G9FQBEYDQJp1UHlXe+gOerWlsN4/dV1WL78GLz44osIyV4pWuF0cnYp/tNkSivGMd594QWYOb2CvtMCPEOJSWr2yVEpFKQpfA4sCxcvgY+WDB3c/C7zV9lDFaUTbNkj0Re42uh0erFo6XJjot+c52VRZ+nuprPcQABBsQRKyZ5R05MVVf4rKyvBheedieee/iv27HuHUhwbrSbSHDZCePmZx9BQvw9LVhyHXTu2Y/7CBAfNGX231B+KwMev+3uusL+It15/iWDQ6Ar7bbJnsXtykYMiQPYp+qq0RnHNBy/DifRVKTMi+Wc6s0Aoqp78JwsjoVAP/F2dnMxF2Y9pX6+cjui5sm5UoU1c/mH6SDiAdlrGDVFC6KRRmdLSEhps4B4doxaWei6S30E+W1dedgF+/qvfGQm57M+RSZk/ZKF/QvoOLCnHqaedgv/52U+x+sST+ZxSh7uXJ8otMw06mcgUMekKYwA889sVTArtK6NsCjPWccDk/j/E+dx3N3LxyUlVT7HAKW8PvnX4HrHTRYTDzv1/CQ8iAfr66y5BhduLpp4uPP7U8zjQ0E6n8GVUTLDilTd24uHH/obrP3Y55s+rpcYN30pWO+558Ele24yZc+uMy6a3ttVzf3EMa05aidUnLKXWAw23dZOAeqiNQyve5mXB94mNA3pMVMtp2C0eD/DNx/dLcA/3JC5gnPxb1y7WZ2iUPeio0RS/o0KkERQBRaAfAmlJoEyAZVC44soP4/f/81ND7mpIqjjXNCGp5MY4nPP4fGU488xTOWDIxJkDHNOaybDJgsY1Sis4Ca6hWXuSPxI8K/0XSRzRo7PaaD4/QtcQkjEnUKJxYvwb0WpZnKuYJjfma7X1oJST5IryMkppOMglRTWmlDIRl8Fw/vzZOP2M07Frz34ei4SS5JG+kQ7uayFRtaPh0EHccftt+NA112HGjJnJiujf4kaAfUTcRKxd+xyuoo+93Xv2kZQ1cG+p9OWk/z4zbxNSSJXLZQtm4aNXvpcLFULAks9ICkBRx2pvb8U7e/egpbGZkvSI6ZdOrvDPmD0bdQuXkOjJaj8DO3aMq/0H3zmANq7URyIhRHgsfbu0rAxz5i1AVVW1mZCZp4nxReVT9hbedP1H8K/f+i7jsvCUzIfCMXRxL+AXb/w0pe3r8dLzzzDPZkyfOcfcKttJQbbpzE2L9E8hk6F8tmc+855aXUlGNBkdafE61Eh1yy54yqfR4AvHRL5OzCMtCuQWF+IhupsJiJsHD8mbBxu27sJfnnoRC5cdi8vPuoTviXIjBezp6aGGzav4w71/wWdu+CCqK8rxxDMvY+c7zbj2E/+A6RzrbBwfw3QYf/DAfqx7+QVs3r6PmjsXwmoczdMSqIeWv51hvpfEOA3dw8s4TTIYCdJtjb+N5dtDotpJ/4VcWMpz0L40NoAL+R00tpppakVAEcgHAmlJoNxIprjHcD/gh6+7ET/78Q/o4JqSEFohlCDqluJfiHNjzOF+g0UL5nESK0ROJqs0dME9UCIZkXmyvNSt9HtmL5nOSbHbHIvVMrFoGKPqp/hAivhJ1IQUOtywc/+Dw8tN8kwXi3FVkpN06qbwdl0I+RvI7DrlNiyfWD4T2smbUHXVR9+Cq084Ho8+8piRxIiLCfFkYaN1RRFXdrS2Yue2bail83oNioAgwO5B4y92NNUfwPpX1mHRkkV44fkG9FCCl5J4yx48K/tiNBbG+ee8CwvqZAFBeuCRIEfBgJ97/Howb34dVh53HAkfJX9C3riIYVyj9E/CR8VqcWABpeNLlvU+giyMqGD39HSjs8uPbr+fiyciIeAFKSj7u+wrvOLSi3Df3Xdj69adRl2rqtSG0oQbm9a/hIe3b6JlXCv27NrJfYayH2jukUJm+EsnExkCxujFOoEt5HoXVtmSL4G4uITwvwML3Rw5ODaarRXGFQ07UZzkj8Qv7C+lRqiTi6xerF23CXfc/yRuuOlzWEYtGR+1BfqHBQsW4I3Fi/Hci6/Sv+hyHG6P4Oav/TPfHxVmjGSm7JxWLGS8lauOw71334Mf/vR3+OoXr4cr7DN7D20keQ4vx1Q7x3WufFntbrqkmG4WXINdTdyXzMUx+3TeVl5G/V9m/Usy9t+F1V5jr8945zCV8JtKdRnvfqD3UwRGi8CwJDAlDfzMF76CJ596Fm/TfD4XFI1ExE0O6KSzeFHlrKmdhn2HmmmaXjaRcz8BCVucxi4cVIOLiyEN2WTuqaTK2i64uMdAVDfLua+qpqqKJqo9dH5bCwcJW5STaLtvGtP40NFBdTqqfzY3NfF3O3q6e8wqqZVqpYlAG5zihJ6TY/Ew4XBS7ZNqqrLaGaAH7XLmH+ymehzvL5vZRWropxGOBCf7i5avwJw580eLjcab4gik+vh7L74Mf7z916g/dAjlXJSPsu9IYA/mdMcwMO65cWDNGaeQHHIfDa8ZVVAR6fGy7OPxektogKicJIzXnVwxpzqXUe+iBM/CPTZc1k8uZsjiCDOQfXwWrqzHXCWGZBrJItVDvZRal5ZQEi59V1ZZkrc3z5oIzCsrS3DpZZdg0+Yf0juL+Avks2gLY/vb6/mbzwHv/dOf/AhXfPhjmHPVPFOPbP4oCcwctWKdtBRrvTPvIfIw87mOBThGitVPkjk+76J1IG8acTsjDuCjfu5PpiuInfsPYdOOA3jiyRfw2S/+I44lgbOntAn63dzptHPv+8l8X0Ww52A93v+BD1FrppIxeD95WRmdmuSCaG1tLT58zdX4+U//G9/53z/FpRefgyV1NJ5WWmUshjpLeug2Isz3E99T4hfYV8tFLL6+oiSBLGUqL8k1H0H70thQ1ff22PDT1IpAsSEwDAlMTnNF1Dd92jT84Ec/xne/+x08++RjcFMVzkkH2+IwXtTXnn3xNTzxwo3GymEiEcWs6f8/e98BH1d1pX9mRlPUuyxZbnI3tjHGNgZMMaZjWoAAIYSQBLK0zZIASZaUX9qm7CYhm+S/ySaQ7CYQWCAkIVTbEEwHY2MwLrg32VavI02f//fd0ZNH0kijalTOtUfvzXv33ffeN/fec849rUg+efVlsmjBfMnOLxF3RgG0EjYEZWmS+roqBITZLlVVMJdDCM+S8aUyC9qXiRMKTC42LyI0bt+xVnbt2g0fQJiAwkeKTu/5BQUIa50FRhvMdahQmqsPysHdO+W5F1+VF199G6YuIZjM4LmgtXFFfBAAw0gaj0hrCBITgobRC1OY7MICOXHxKVhFRUCPmIpyrP3W+r4JECBbNvu4+XLZNTfIf//8PnxDIuU2jXcYGm/0IDBHESlBBNGZCMhgZ5RcLH6AY4v1IzJYjBxKjTd8e1yZWC23w2eV36GBZkCFSNgnPuT7CjVC4x1F8CQ7/IBQz5WJfo9rI5GYeakRBCNeCTQeguBYT3YxJiwaTo73C+NZQggAc5rcn/8/RmsYppCJhQ4X2qEfbwDn/7FmtdzxpXsSvG3vDykz1nusrJpjFbOx+t7W797bLReOuDAUDmNsIzeuw5WDecPYsoBWpUiwxQXzT+QchQDowCLrvoNH5I8P/12u+eR1Mg/WBVyISlRsmJOc0CouWHgS5oQmyc6BtYsR/syfLpfkYQH2hk/fKF+44zb5yzMvyx03fVyybR4sxGaIP+yUaCYWZGEiymKDG4cHiepp5QAvZjSLgDJDWIazEDMS+vlIeMYh7D7atCKgCPQRgW6EQLYCAgJGlmXx4sVyy003yqY3XjACFs9FoZZwpXpkxuy50tgSkD3wxatraJRbb/6k/PNN14kfjLM7D5oIB8xdOhUyu9U1NbLx3Xflib/8RV566SUj4KWmpsnVV18tl1xyqUydOlVSEQa7S6HW0HtITlkyW048cZ7UNnjlrfXvyvjx4yUvN1PqqyqkDuknQtCqkORS6xJ2OGVy2Uw5fflZseZir9WlaT0w9hCgoMX+/Jmbb5N/rH1N3njtFZhhxXwAXVjo8CAfH+LioX+Virc1KDt2H0Lf9pugRq0w3cRKiITRn0IQGB3QeAejO8UN7TQ10rn5+TIOK+/UeLszSzmiYM1cg4h/EP7SxyEwDPL8QQNeUVmJEPBMJI+IgdCkMzBNxItkztS2Y6xEoeVOxeKFm+khsOofxuIGo5QyMq8bzB/kQAxVaMKhgfc2NMnU2QtkFsblQMpwZsYG8l5Dee1YZcDG6nv3uS8xojaGdCRQR3deCHoUqDjGocPzuiTYBA1gABYEGOc1tY2yaetuycrOk4suvBhzSPKgLC6kh7ECr/X0bCTr9BW89rrr5XcP/Ba5BCskLzsNga8wl/ghDPJiPGqKG/76WPBKQbspQQirSNPEhPVDWbQvDQxdnbcHhp9erQiMNQR6EAKPQkHn82efehIMqh8maE6ZMH4c0jhUSimibP7Xfd8zefjeeucDufsbP5TpU+EfiGiHoZR0aCfgA3i0mfY9BpHJgF/D2WefLTNnzpS1a1+SDz/cLnfeeafcfvvtWHWENi/hqic1MGDKYXLHBNupqS7jjzhlcon86123mHxq9/3st/L8M8+CAc+Wqpo6Ad8uRUVTYB53nUyDYKlFETiKAHsn+hSEriIIa//xk5/K9//te/Ls03+PadeMxptdzibr3v1ALv/kbQjiAm0ctMvM03XFpefLGScvkaLxE8STPQ79MqVN412NlCZbpAJpTpgPcMLEiTDlmiezZ04SJ4I+1MNnZ9Orz8mmTZukBn00FYsp1HgX4ZML4S4D4d1t+XnSWn/Y+Cs+t+ZVefL5F2CSBYYMAqETPrUuaBRpBpoGBo36SkbgZeL4Fiy+nLb8HBORdCAab2XGjvaS3u6NVczG6nv3tl9Y9WILToiKHao2wWDoF0wBMMQgMM3pCJIGQc8svNrF1+KDz3xIrrnmSswJ0BgmKGvXrpXf/e538uMf/1ho5kmzdJqQ2zAP7dq1S77xzW/KPXffLQsXLoy7moteUdDXFFl+5pnIG7hWDiNA1YI5UyD4QfLDubAP2ki4XNgRKMbuRMAYSKx25E+NRrHoJVn4DF3RvjQwbEcTfqPpXQb2q+rVisDQIdArIXDzpo3y4upnsFIIYxbQkAULFuCJ3pdJEyZJQT7MKxG85cxlC2XZ0hOheYMWA+o3m0lC27V5CoAHDhww2r8LL7zQMLzz588zCbRPOOEEqaurk6eeekpOPvlkIyB2eHXSLxBJO4Ju4AagV1EphLB3/LxZUjahENpIpIdw2WX6zOlSNrVMnnp6lRQVT5Crr7lGLr/sMlxsGmAjHZrVL2MZAfQFw3iJEdTu/MLtsv3dN2C6fMj4k1LTZ4fP6ZQpZdC0iew/UI4gQy1yy+euk3u/eBPCr3vElVsK51T64HTtVw0NDfI+hL0/P/643HXXi0bj7fcH5OKLL4bG+xKkX5kNP9nEjFW4BXnEvOVy2cVnS8ttIYR9f0qKYW49CQmg/d5GqUFAmyZG12VByhbGM/Wk58hV114XO9b1cWLHe/FXV5R7AVKnKsOZaRnKZxvOfWUo37vTz9+rrzSpjIQbjBUNhbVwKAU+gB5oAEHTKIOBRtHKprioUD7/maslvXCqSTOTqHHiTquaWLomiIChgIk07EnNNj7yTMPU1YcwNinQNLUgP1Pu+pebkfKpFQtLMbPUmJk74n9TI4gJj2klmNIihdtoAx5jaCNrD7ffKxHuw/nYcB6Lwxk3fTZFYKwiYMhOspdvgD+fO5W+dFjLhByVX5Ari5GEvXhcCYhDCvlkMMxBKSzIw/nYaqKNQlqCwmifu3fvlj/+8Q9yCIE4yH+XlBTLp6+/Fn6ATVJTXSUPPvhHaAY/THA1SSQKfCDoh8Uoo9nZmZID38EgIieSsOXk5MqKFWfI+KI8JJIPS7o7LNVH9kPw3Nvenmmj/ZvuKAIxBLhA8cKq56QRYdHD0LqNG1do+ncO0pz8+Htfl+f/8oA8/sf/wqLDbJkN/0BGrQV7hGBGNNXqKnGRQUtD1NrTli2Tf//3f4ewlynr1r1jVubvvfdes6XWr2tp66EILuMPRBDhVuA3WyJLFy2Q5/AMa576X7nisoslgMTx+QjMxAqtIZtUgke7/rO3IEruCV2b7OMRZSb6CBiqj1XMlHHvZV8BsYuE6Vvnw5BFUBiYcFMLGPZzDsD8wWHPD6cSLLimISevEwtQieYWHJSTTjpJfvrTn4Lmge7iorCvESallaDBIZk4cZL84he/lNmzZrFqp2IcJdCsUzLSkAMVJuWxhTDcHMFpzENgG/J5kMaCgWvo3wwjURvMWKMIsmZasx62U9MD/Kp9aWAAKn4Dw0+vVgTGGgKc8ZOWc845T+79+rdNOGvwvSYS6DlnI6rYrLK2a0FUQLTGQQgM+PwgFjGfKux0aZtmnmeccYY8+uhj0LzMl+bmFinITpfzlp8itdU10OBNk4cfeUTOO++8LtcaNSSOUud2c1HtAABAAElEQVRBQhVBcBn6QuUV5EAQBbOM1czZx80A032y1NU3Y1U0KNWHy6W64hA0hmCW256H2kwtikBnBLZ/uBWmxH8FU9YCZi0s8+fOk+KSEkSUnYgASTniQf7JZUvnyYrTTsaiiJN54WMabwR6SVSo1X700UelvLzcmDdTg85gR0uXLjUr+E888QRSPWwzOTY7XN/WP+1OBJhh0mjeCI5/H7/sApk3czKeA1/B6DHdycpLViLEO84jqu7Vn/iEMf/i2IiVruOvw316+KLMRA/gdHOqJ8y4wMBFL/YFfmjynqgwnYhVh/UHWqiJZnv79++Xw4cPD7S5hNf39N4JLxjDB8MhBIVJgXUB6GAkhETwfswdEVrMcKxaH+xhCHMRKQJNP7fWiI6Hzu12SwGCphmNIBZhAy31yFVTBw1enaGFDKiWyK0Cdzf3ioS8MDnF4ilXdjsUPAfMVCXowjOm4DyFRPy3NeA5Gttqdr6mQwP9/jKQvnTkyJH2sdPU1NTvZxhpF1ZXV8u+ffvMh1ZWPc0vQ/1udB3i79Dd/DbU99f2FQFFoG8I9EoITIH/wMWXXymnLl9uGM5KBLI4cdHxctZZJyNABoQ+UIgocgNOnzZJPty2HfnOsOIJAsME14kKtYf5CJrBBNnbtm2WSeNzZdpkRBEFwWPb+UiUzeAaiQr9KiK4LhTwS10jk80HYTqDayEQZqR7ZMmieSaMfkVVpeGFs3OLZPmKC5CwezyaayNcpLVaFIFOCNRgESLMKC/QeDMdQ3ZOpiw68QSZWDoRqSEgjKGf0+QqvzAXjBeYIx7pRuPNpkmMv//978tW+AdSCKAm8NZ/+hz2EbEWKVB+dt9PkKj+tVguzE7PYnqq0XjT7BmeOAg0U1iEMQNTUkblZTqWlSsvkhlTJ0oU46Eo3yOtdRXywQfvtbc0EDaNjCW19lVI00ImoxZ5NvkOnUsNAjzxPLdjnfB3x8C+iwBYS5YskTPhg7Uccyg/Tz75ZGcozXf6iVp1WJ9MVX/LHXfcYYJ6sb25c+cazVF/2+rpuu7eu6drBuMchdrNmzcPRlPHpA2Ox0i41ghmdjuEQMw1UQhaIJRYxOk0WrGoScEr2FIHQbCBu90W+gH6G45I1MeooxFEF66EcNnU/TXmVhAaITDaMMbNjazWeSNzM1r9wLUDfsYIe4UPIx1Ti4l7mOvxp9MjW00MZNufvsS55/TTTzcfa+z853/+50AeI+G1/Xm2hA0N0kEK7ytWrDD+oFOmTBF+GGOBGPztb38bpLskb6a+vl5+8IMfGBceLnTyt5g/f74cj4i2H//4x+Xpp59O2MhIG78JX0IPKgIjHAF7754fqRfg+3ThykvB/EaxqlxuQtWXlhQYszm2QYJx4vFzpK65VVa99IY0NtQa804yhtTIkYG0PlztZl7B8oMH5L0Nb8nJi+chCqNd5k4thSbmSWnGKl4ATvFWfW7JkDIVBK9rbqyTnbv2yZvvvI8oopMlF8w1NYEeFwJsFCCFRHOTVBypMM7vZTNnyIrzL8ATtlO33r2y1hpzCCw7/Qz52je/i6BCJWB0bMhV2SynnrJU5sydBV9TrtaD60E3KszNNn6wDChjMwsd7FtdCxlvRr49a/kKRAH1IdqnXT524dmIBFqLXIAZ8uRTT8v111/fdbW+XVXdZrYFYs8xVFKCPJpQxXM8FI7Ll09edxWEtHrDi9VXHZFN772DoE3wT4xxcW3MWtfn6s2Rv//970ZwOfXUU+WUU04x2ksKJdYKO7WcDOxE312e55YBKHjdWC3dMYkU7BobLQ3KsUPnjTfeOCY36+69h/LmXEQ555xz5PLLL0dgsbVDeatBbBuJ2KOtyMOXCoHKAS0cFlAphMFM1ISxjr8TjvEfpDkIeIcRUTSm2aLcFS97RRCxs7X+EALLVKB27Ewk0CI+HAv5E/c5m7DNSkQkrcWt41vjA7R9R+RhsXlNEBv6BMaO0weRbheMl4xn63Itrx9Y6Y9J9c6dO432aWB3HnlXt7a2yrEa492hw0Wq6667zgQoojAeX/h8GzdulB/96Efxh81+b8bvRzGvdHlQPaAIjHIEyNkmLdR4cHKePHW6uNLccrgcGodNW2X5WaeAiFgriTbJykiTm2+4Vp5ftQYrUdWSnvuelE6cKgVFJSb4Bc01KSw2eX1yYM8O2b97myyHed0EMLchCH1LF8+Wl157Vx5/9EGZPfcEKS4eD8d2aFyglvFD81dfX20imVUc2gP/B6/MgObx1CULIYgGSJIMMaWG5MiRKkRmrJS8whK5aOVlMm3GzKTvqBUUATI6F1x8qaxZ9aw89tCDiIBbIXMRdGghBED65nDBnjkDp5ZNkHXr35PQaUsgkPmMP6wN/qmdC8cMo/axbN6wHsJjqhw3s0TWvASNNzRsExE11GLcOl/L/kwGL4qIvE2+kNRX10nZxFJoKKkJ98kZuPe06aVy/+8OmL4PRTgWaa6Uqaavk5Ejk9a51d5/J3NhCXzWVTTzIcNFYW/Lli3GxNA6xy0Xd6jZZMCbsVi6Y1ooqBw8eFDefPNNaIW39ghNOqImM1gQC9uj33N/y3/8x38Yofy5554zv01/20l2XXfvney6gZynBjCA/K8sGzZsMFrWgbR3LK6lG4MTttwO40MclmCkCouXiMApU3F7DtauA5byYQRpZFqiB8SZVoC0EvA/ZjVaKwR8RpsXhbAHvT2Ox8Y9tYE81lobRL6/LHyyQb/ZjxD5E/NXEBrAMDSMjjD9/HC4Q0EbJpUFBFY8n0ThByhY9MK1ghAydlsmLulu1urQUL++9Kcv0Tfyn//5n+Wtt96St99+u1/3HYkX0d/8EbjO/PWvf8XCd7OZmyl0HctCl4YdO3a037KsrAyLlSXGoouaPgp71FZ2LiNx/HZ+B/2uCIwGBHolBJoXBTVKS8uAUObCwG6R1998W85asQxEBFSE9AGFZm6lRRnyuRuukSasEFVUwVQMGoPyvQ2yK9hWCUJgKBwAQ5wml5x3KoK6IEEuiTnaYYSy885aKvuP1Mue/bvlwP6dCMwBwoMbkPCkwoG9AD70C85cKDnZWSZBfIiEjFZqeAwSkAACxOzdc1B8EAZPXX6yrLz8KnOteUD9owj0iAB6GZil8y+8SJ5/6i8w5zyCXH4BmTxxHBbs2cFghglB58QFc+TFV96W1WvXyZlnLJH8NARIQt8ko9WZiaEGm5rp1c89LStXnIA+a5OlJ86RJ594VG787OeR/y/daNfjH4vCJsdSJOAVL0yeH/nzMzJ12mQZPw4+t1gMIa83dfIUPFtQ9u/dyxgS4oE2/JOf+SyawZdBKGSqKPSRweA7s5DZYgRfFpo38rvFdNH36Nprr5VPf/rT5vxY/NP5t7cwYPRXBgJ6+OGH5ZsI299ToTnXYGlTyXzxQ+GTJrtDWfjuXX3Lhu6OVp/kHTjGuivd/Sbd1R/K41Fo4Gwm4TrvgnGKlAsMqGazl4DCITooBLujxdrHFkKd+Jox9mGaaRYFYuei4aDYMU8YkYx02Br7aMccC1KT14q8ujQT5Xks52IRif7E9nZXjVhbHe/L6+EvGD2ILWgzaDaDsRkqHIUQygnH+OX3f4Hi6P067vXn9+I1X/jCF8xiwDWIBD6WymWIes4Py29/+1v5/Oc/f0xfn6buVmHws4997GPWV7OlJViiHJe9Hb8dGtMvioAiMOgI9EoIJEng/7S0VOQ0SxMfoni+tW69VFY3mCSz9FGyCn2pEF9aUl0OmTapSKZPhi+BA4SDAS4QxIXmcyReEm6FOQyiK4KRZeNsgaQlCqFuyrhsKSstYDZdw5TTZIXhrqNBrvzCrBRMKZnkEANi8CJ+UNhGbV2DbHxvq5RNP05uvPkWBPYobW/bVNI/ikA3CFDjzb4+Yco0yYRQdQT5szZ9sFUmTyrFYkRME8iu7oZ2+p9u/IQ89exqefyJZyQrd72UTp4uJaWT4I+aB/PO2Kp7iy8g+/fskffXvyHLFs2Q2dBcB5BD89ST5ou3+XX54x8ekPkLF8ukiVPE7XGja4NNpK9rXY0c3L9PDu3bI411hyUvL0euuOQ84wdruroZR1Gpqq6VvXsPShAC6rUfu1ZOXHJSN2/W98OTJ082fh7UVt53332mAQaxoY9gUVGRMOBIfARfMmG33HJLwhtRo0i/EK7+8noGs5gzZ45Jk5GdTS3D0eIHPu+88w40+RXtB1euXGl8MnmAq85shyaxLBQ+h4vm0TJlo2aKH+JFwWjq1KnGdNY8cDd/aEpFgZomVPFl8WL0j0mT4g8l3Ofv8Y9//MNoGhkcYvz48XLcccfBb3RlwvoDOUgNHDW+DDbDMgsRKBnEZvXq1UjTswfRKnPMMWpAMzMze7wVNRdr1qwxmlJiNWHCBCO4Llq0KOF1xIf949VXX20/T600NRLxJRV5POmbRF/e4VOaQc/w+5KekWiZPlyNgJyIkh2Z0cNjctTD9BwCI1aG2gglZ6oY/YvR1bjL2+ghTRdsFODC9OuL0WieMvS87bvZWPXZBAVIPGM4Cp96Wy0OpLZXoRmpwC8w1pYdW45BbgevWEIgxzf7BU2p2Z/5nfMOzc7527Jf0eesu8KFgVWrVhntIDVS1ng499xze+yT7NsvvPCCGbu7d+82sQs4jujjxrE42kpP78s5OlHhOOUcvH37dpOP0qrDuT1+HHIe4BiML30Zv2eddVb8pbqvCCgCQ4BAr4RA3peTc0HhOJlcNl2qKw8jzcM+efmV1+Sqyy9qM8uxxDhUjtEbmJ7wQghvoRaxIeIiVyBjkzypDogH2oxFKjMkEcdixazs8mL4T5BWthezmslvuAFXJnGOWhEWnvL7fbJ+4/tSDnPQz9/+L3Lq6SvaCNjgE6vYXfXvaEQgLS1dUjwe8YORePvtDXLeucuNBry9X+N4QY5HPnv9leLFSueRymqpqG6Sfbs3yzYf/WVYomZc5GW55LKLTpPCnHQseoAZa/MFOu/sZVJeUS9bd+2VA3t3m1yECMSO/oy0Em6npCM585mnzpZx+aeKJ9VtBEDyc9Z4YHClPbsOGKFqYtlEue3Ou9uYO3PzAf+xBJp/+qd/MkzR+++/b/zaqNG6//775Rvf+IYRBHkjMmM333xzwnuSobrzzjsTBo1h0Bz6izBnolW+9a1vyePIqRhf6HNIE6PXX3+9i6aRTP5wEQIZIOFuJOdOFJThZz/7GYT5vPjXat+nvyeFtc4+NaxAjJIJgS+++KJ8/etfN32hvdG2HZqEDrYW8Itf/KL813/9V/utXC5XGw1oP2R2fvOb3wjfO5FAR2bw9ttvl1deeaXjRfjG66hl/vWvf92FYWdf4vvGF7aRqB3mof3v//7v+Kofwb5Fe7BwiTx7NqaUgTbNhjQRGOoYzxC4QnuQzqgYXzLaFkp5wpw8+ryGDsZmFnPQfDd/UJXbuHNmH/c19LLzOV4d33bbdcYEFMIproE3NJ5tH66H0BmlhpKmpi4cx9wWAR3HGpehu+302DzRoPwhf0AB45577ulgZmg1/r//+7/Wrln04KJBovLggw8i1dSDXU6xT7FPlJWVdTn38ssvy3e/+10srO3tco4H2J/YN7u7Z8KLhvHBZO9LgZnzPOfe+ELrkC9/+cvxh8w+55rOhYuDtCqxSl/G70UXXdRtUBmrPd0qAorAwBDotRDI2zCn2cpLr5INb75uzOQeevAxOWXpEkTnzAUho8SHQprTgTjE1h3NKSTH7VAss5UOB/GFbfCP2XY+2XaONeLoHkRNJPKulLfXfyCXXHG1XHZlzCwk1sTgrlYmeiI9NvIRsHoqzVdS8UmB6eY7GzbK4YpqKZtc2qE70jcvDH9AF5K0l00olKkwGbVD2y0paeiY1Bpy5T6ABRCfEeCMAIgWLPYrBNOukvwMmThuPiLEu43G245xQ403EnRhi2BIGFNRaLyDPqy+o82j4wqtwN/wH/94HXH7nPLVb35PyqbNbG97MH4JSwikTxqJ+6WXXopFFr8JwnHTTTe1B+OgAEAzoES+a88884zcdddd7eaknZ+Lq9Bf+tKXjIB41VVXmdPxZkKd61vP1Pn4cPhObC644AKjQejueRhhNVHhyjo//SlkyMgwd1cGWwBMdB/+jokKzYkpMD7//PNGe2PVoWaYiwbr16+3DnXZUiv6qU99Sn7/+99Du57bfj62iNj+tced4RGtlr8rNX8IqBLBMo9tHPYP4RhMQbGYZHe5odyrl1B0B0byXBx3xd4pClpp/O1jX/GlbceaQfg1diw2q1j1uI2vw+/WtdzvprT59tttCORGAVBq8axoB9rHCBZk4amBgu80Te3SPs8NTuHiAPsGtUrJCrWEfRXIqKnmwha1hPGFJtici3oqzz77rElvxfvSd3ckl968LzX79GGmFQf9D63S0xxt1bG2nBfjy8gbv/FPr/uKwOhDoJNU1vMLpsD06vwLV8rDf7xfdu/YbIJD/PHBR7DSf4s4yPj2mTiQWA20QIsI5tnf6pdDNQ1y2tkXySWXfxzRHF2GserLpDPQJ9HrRwcCuUhRMnFSmez8kMFPDsprr74lkyZc3h4cpv0tzQo8rK2MxhsCG4Iu2IQab+avtBgvbM3Y4HdqvK0+j3V1MIEBXuzrrPHmHVif1+IKXM9opTE/HOgSUhyyfccu+euTT8kX7rpbrrzmU20jz9I68PqBlaPPL8ackcIctVIs8dEYyThNmzaty81oLkgBwDLbZG7Ez33uc4ZpY06rP/3pT0Z7Q+HnX//1X03gKOYGpc8cI5LSvyU+4ABvQFOw3/3udyYf1i9+8QuTtqLLjT+iA1/72tfaBUBqacloroA/Ht+PZprUIFDjl6hwrmKABwbjIV4UgMhwJis0c/vOd77TXo1ma1x1pykvMSZGNJUc7EKhnyau1HpahWa9FEbnzZtnzIR5bwbD4TOuW7fOMM9WXWJjCYBkLm+77Tbzm/PdefznP/+5CShBjdCNN95o/FKt/shQ9NQuf/DBB/LQQw+ZJqkp7pxXlgGZ2I+GR6GQjyA/1PZFEbHTdgACIJhjLBLZnDC3pJlnaKdEbFgMitIEj8JWd36OnAeSld7Usdpom1codOK+ESnHrLMbi0wQ/mCZYIf1DheiYvMWKTz9GjmHUSq05jKrrYFv2VcsAXDcuHFGIKQpJoWu9957z4z/Xbt2mRslE8Rojsg+ySjNvJZ9kosiFARpYkpTdxZ+5xxklenTp8vVV18tJ554Ikzz64xJKTWQHL+sy7FODfdILX15X47hn/70p8bSwHpf+v1xYYaLPP/3f//XPu9x7om3duA8RI1+fOnL+GU+aS2KgCIwtAj0SQgkIZ4wabJ89evflX/79r1yaO9+JMP+K3xP5shFF5wNwpaYyen4CiQciYiURVASnevYQuzb0fo0KfVkFMgZZx8nGdmxaIysYzEOia7WY4pAdwjQxPC8Cy+XN15ZizDr9fLIY08gDcJJMn0qTI8sjQ2FvA5dFYKaaRD6RJh8xkpbBTCA8cJf+33NafxpFxjbz8TtUADkV/7BQguqB6FR+O/fPiinrzhf7rz7XlM3diezXG++D/RPZ60bmXH66FiBYNg+maTPfOYzCW9FoccSAJkryhIgWXnGjBnGR44MATU9LKxPRp6+PmQyeK/OQiDrWfnAKAB0p1ljvWNZqHGKz0vGd4n3hWHuLJpEfvazn+32scrKyoQfFmLfGyGQgWasCK5ktsioUgBlIcbLly835rPxv5k5OcA/ZL7jhS4ye//zP//TrpUh003m+Yc//KG5E83rLIaOQh4ZfRYKgPQhil9EIFYrVqwQBvjg70shlqae1vVkPqk1po+XJQTSHJmpVhKVYUEDMGhBofB4LgnbDmO4BxHMBrQSwpUjJQuWAnCXgOtDRLZDow5BMFSG0Y4RHW/qkujl2o7xHTtrkhMdS9wEZw7OV1iMsu2DqepmdEAGn0KbuD8DyVATeLRwn4tNvIZzEq8fvBIfRITm1RRY2E/YR6hpZ5oamhQWFxebKMXd3ZmLFDRbtwQ99kn6sD7wwAPmEo4J69xTTz1lrBx4gn6Hv/rVr9qFGY5JznPcUvhjoTDJfSvyszk4gv709X3/8Ic/yK233mr8I/manGOYnoWFONL3meWKK65onwPMgQR/+jJ+6e+tRRFQBIYWgT4JgXwUaiHOu+gSmTBxijz60O/l7bdekwcf+avMhiA4FQE0Ioj8eZQ4dCYS/G4Vro5iHwSsfU2RHK6hKZ2v4zXWsdjW+sbrbTDBc2WPF1dqTlstrqJajDiv1aII9A2BFeeeJw//YZ68v/F12bVjJ/xLHpG777lDMmAmGmPO2ANZEjFBnY9ZdWNXDOQvI+i+sX6zTJwxV27/l6/AXxCaBAykoWB24xlJ7pOpJyNG8z8SaGqEOguLfDcKfwzewUIT8kQRMdkeNUk0NaqsrDQr9TQdGl6BPMwrJP1Df0nLRIrCUbwAaF3MYzzX2QzNOt+fLTVlViHGlgBoHeN3Ho/3ubTODeaW+SKpPYk3vySTbpV4DWi8QMpAQvECoFV/ypQpwkT3lpaT11hCoFVn5GxBh6zpIArNX7QGYxW+dRACKegx5YMdAdCi8BGkf2AotA1WNYg0bENAswgFR1A+aAW5jNShmK+IVhzGlQiulpOV0T4HRLBIVI9AQWkeJ7SPpK6drqUQBx/AKKJ8mjyndjhTRA7CvH0zXJa9pnYE1gfiwBOSSJvpC8/MY6TRbYXPf/SbdXRgW6afYYRcBh3hfGAJ+larXHBgFGIuqCSae6x6HG+WkGcdo2Bolfi+Gh9kiHMRfeF6KpzfqFm0BKGe6g7Hc319X9IXCnrLli07pq8zFDTtmL6A3kwRGAEIkEL0udhBPOZhxfbbP7pP/vjnp+XL3/mJ+D2l4renw6QFZIGEIibhdW2bVANEJYgVxjqEv29A+PwA9puxml5VW4fvPkQmA+Hswje3tcnjZHrxiULQizgzxZldKk4IgCR2bF4FQAOC/hkAAoVF4+Suf/2mTJ05H8mdobH4y9/lmafXmH57lDjFetsAbtPHSxFsJuKUhUtXyJfv/U67X8rR5+ljc0mqd2ayyFRZ5j5Me0CGLFFhpErLT4wMffzqfnx9mkHGBz3hdSOxxEdJpUlmd4XaucEs1JKw0B+TjHOi0t3xRHUHcqy3fZBMtlWsfIjW9/gthUqrUCM0GgqoHF4DWj+kV+BiKckk/YjtNmo88AXCm81Wj0WUDxDx9wjOt0Iog4m5oaedEIAgQmrn94fluedfkcf+vEpWvfCWrFrzljz+l+dk9ZpXcI51EpB4IwPyDGgoTNLDob3QAL6HjuRlZzKkl207HKkQF/lcXFTF1pQuhLnt+OBsOCfQOoBBQTrPP7wDzZwZTIqCYKJASv15Cuay62uhkDpSS3/e15prRuo763MrAopAYgT6rAmMEQMSAvo9OWCGmS/jZ+VJawAh68NNkodVTqetCYuIJBysZxGPtgcwh6LIAXhEXn39HfhZeSQDq5jeZq80tzTju0OWnbJYZk6bCNrDNqwSR3wgaIajTgk4MiVoL4BfVap4AhHxIIx/islhZF2jW0WgvwhE5fTlK+RX9/9J/u/B38lLa1bJ/zz0GFKPTJdFC44D40aTqGQl1v+t9RBj3cXhwANmWHQaGwmai622814wywKz6M4qFncmg0uwiaHVeJMJ6ykHW4LHNYfoi0NmjoIg/Xdo1mcJj/HX0JTRMvlk2oiRal5VVlbW/lpkUrsrTKMwmIVBMejbxN+ovLw8oSkWjx+L0lshkH5eVqFZaHdh4Jliwyo9CdZWnZGxbcKghcYPolU0yvyA9BNE1M0UBIOBZp3Lmnb4FUft0O4HHdLcUC+paVniQLRiSGkxrZz1oqCBDB6VnuqRS1eeI9W11SZhOIiyzDluiuTn5iDvLs1EYXZqrGKOzjVRWBPYoNVjwKnWlgZxpeE5XDiGQFhOT54EvTV4TmgjnWkQSFtwa1r3HJvC1DDUltNPlJYH1EDRJJjzCAUv9gtqlpmfjn60jDA70MLFGWshh4tb9EFMVLjYYlkqxC9eJao7nI/19n3j34Hm11oUAUVg9CHQDyHQAgFMKZjZ2ma/NPqx2uhIkTA0gTXhIknFimamrQHRzizBzdqSEMXMQEvHF8o1V19izKgqKiskKzNLsnNypbGhCZFHY9ERuwqRIGpoIWhLlRZ7lnijWRIMIWpVUxSCZ0DyQMzyM7myepTgWU+rW0Wg7whEZQZyoH39uz+S27/0ZQRj2UkeS/xYfHBFm8VhVsjRI01/Y8+M63eU+MC4RZFAvoWBX9Bz09PTTMTPZm8LeD63eNypCLwAJq1zf7WkRlxjwrLzaqzKu2Hy7M4oNPeJ3WloTZ57y9jjgToUXsdgHQwgY6UC+OUvf9nuU8LK1PrRt8fS/tHXJ/5+8fsUrKww5TQRI/N3rISbDi/WzRf6sVFgppkY00Mwkuppp53WoTb92hKljuhQqY9fKBwxmAwLQ7nTlyle60rsefxYlERam0T3ZbCWn/zkJ+YUfbaoDSRe8YVRCdlfrMK+0VNh8JnOhdpDChM0W2bwokTRa3kNA4dQkGahZptapsEt1ryAuSDSivkg5l/HIFJGAwhhy2Z3w5IFEbYxnzA0iz08VRyeIgk1N8vmZ16WCXNnSh5yjNqwQMoAMyymVS5EQUBjhOLxRQVwaCtom4EoTrZVwmIppEd+Q6Gmkc8RkJpdh6Rmz0EpO32BON35RjMZiew0zxH0B2CiimuQFiLFiby8RgjEfU0zcXNcrNFB/csALdQEcrwzeBTNQ/mxCoMsWQnR6Tc7GEIghT76yVmF6VbiNdHWcfrCNuM34XYkC0W9fV/OZ/SjZF7G+MUbC4/B2nY3fpkKJj8/X2688cZuxy8tC7gIZtGHwXombUcRGCsIDEAIhOAHepDuBpMLLVwETu7ghCTkgFaPrGsoRTJh+uKyMZoYNRYgWG30gzTJ07by6YE/RNbkiSBuCImPMNoFuWkgVDArha9D+wW4FcNrR0EwKQA22aB5ROshRFMjA81mQ2C4W2EaE04HEcVKZ/u12NOiCPQdAfYq9sGYts2TnivjZy+CQIfg6eFmyY1UitvWCI03mbq4vmrdCIKcI8UpBw9XyqrVr6J/Is8mtF1ebzP8deoN4Vp++lKZN2ca+i+vjy+8N8cLV/JtEsDiit9ZJAHJwQJJRFKdDvgNxdcfmn2LsWd0uLfeesuswFvmfNTuMRAAmWsKI9/61rfwuEcfij5dvIa+N/QPpJ8Nmf3S0lKTWJy+gAwewkKt4ZVXXtnhJeL9d7797W+bHF2MGsjIkFb0QF5AoZCMPP0Lh5JR6fBwnb4wmA0j4zE4DBknBsuhv5ClUaDPIJ87vjDPHgVD4kZm9rnnnpOWFqQCQYl/P0bKZAAYFgo0zFVGpoiFKRQYnY+MKf18iDHN6IgDI/cxRQc1K1Yhs0VfKfpTdRfQx6rb05bvSUHcKk8++aT5LZljjZoSCnGM7moVPj+1w/yNGcTl/PPPN2kj6GvEqLN8B0aPZf9h4BhGlrUKfQHZZzqX+GMU9hh4iMfYL6k9IraWLyKT2XcWNNke67Jv8zdjue+++8x2cP9YcwPmCZp3YqzzCOmlHQunpFM2agNBO6GHAw3NknDTeGRigD+fCxrB/RXS7LSLuyhVUtwuceJYihP14TIR5aeN/lkziLWlsMe2zX/mAMR8Egq3YP5okTA09PUfbJNwrRfaPmgZoSm0OSYC/3poJlPF5snAEVyPhSc8FoJjIUIo5yMTQZQ5DrFrmm67B74PVuFYYWFfpQ8tfZDpN8rfkj6w9MWzSrzlAHP/xS+yMAUCI4EyvQ3nMQahYr+0CgPEUDPPuYOmpRQ4udjU2Ngol112mZnTKHyyT3HBiWkhqJHkGGU/ZY69zgs9VtvHass5hDlVOf5ZOOatwkUUjisWzhscc1Zgqr6+L9vgGF68eDF3Tb5WBqHiPBVvJsrUHhQYWUgXuKjyb//2b+3aU3Oi7U9fxi9/50Tjl8F5uGjDeYSLAZwrtSgCikDfEIBrnVE79O2quNphXF7X5Jfd1X7DLqe5QdgcJFIwzwx7JVUaEBetXlxwhCeZszH0dQpMXiIgPBDcoAo52hofxRA2UB6scsb8/kCEcIkfZCnsKjQCYJgO87iO0cuobaHbgyslKrmeFMnPSMEpUiiSKi1jCYH//M8wtEsxhi7+vX/wA7t89av90Zqxb3J13iHlta1S74O2x47+BYYoBQxVWqhKMsA4OdlXDWsX3+d4rUiAuf6QH5OLHGQm0qANLCgolqbGZmnxNkgpAmjEGDZTPfanbUiGsRLvs2dKk2Rji0TSuLMD7WSDzpbkgEEb4j5OJonmV71Jxv7nP//ZMPhxbyEbN240q/aWsBd/ztpn4BgmcKYQEF/IdJFZsXwL488l2iczZ2kIEp3nsWXLShGEhox3xxIO4zcdoBk5hV1quWim1pfyla98xTCqliCS7FoGeyGullkamV0KwL29nu0znUN//ex4H97bCoQT/7xk+Bhan8x7PHPIOozwaDHwvJbaFvaZngoF3h//+MdmkSBRPd4rGd5k2N98880uoerZ3qOPPmqikHKf5n00N7Rw5bFkpVEOy+/l6i7V8mSKfEr+2Hac8xGFMKRciG7AWN8PGheUpuodCOSTJqnZEyTgRxJ50DJHSr74anMl2poFoQzJGGBls/n+R6VwzlQZf/YiYB6QoJ/mpCChzhRcj0iiWESldx+FSFM4BXHqwRzCVDUUNkNBP65FIBosrLqY/9ThkX2r3hL/kWqZ+ekrxe4GPQ7jwpQqSS/0wrqmHs8D14qUdAiAteJrbpDMwiloFHNQdB7GylTcgouvvFHbfbE3GIVBhqgBSlao8ab2joIGNd5cXEhUKBjwt7388ssTnRaOH2qjOd/ccMMN7QJVwspxB9l3GPU4WTkgG+QJ+Zcu1abIyXKZ/EeX4709wHfm4lNvzfUplHH8Wf27r+9LHDkeWRi5N95cu6dnpqDaXaCd3oxfCvC0duicaoL3pOmuFR2Z70Vfx+40/j09o55TBMYyAgOewck7ud02WfXyu/Lwkxvl6bVbZO26bbJpxyEphwtEja1I6iKTIQYWSnlzq1Q0toCxw6oocyNRADSCX2wb26XvQxBEyy/1mOi27auUZ97YKh8ctkmTfbwEoi5phdlMLQLIHKpqll2Ha2VvebVkwqchL50MOoVDUkItisBgIBAbImkuh6SQUQNTxbWHELRzXkeRNERzxRdJQ6+jcEGmCEwfK7APokO7wbRDWS6pMOWaWTZFSrGqmQLT5fwcD3IPUgC0hFZeE2uB1waxIt9ky5c6KZJWGxhDhJdHhkCIpG5p9iIqoNFe8H5DV8hA0yyKIdK7KxRKmL+P2pbO5YQTTjCr0TTno7AXX2hSRa0UV5g7C4CsRy0azRspPHQufJ744wx+8lGvypMppQaLJq7UbHYuZNg6B0Lh9+XLl8vKlSuNtqLzNZ2/852pfbUYOZ6ngM40C90xwUxNEc8YUSPHFfv+FjJlN910U4dIpOwnzA+4ZMkS0yyfMR4D/vZkHK3CPkN/L2oBE5ndUfNDzSpzscW3Y11vbRmqv7uIhcSbGh1qQxMxkGyD2lerUNscj6t1fDC31N4ZFZohdLBYgaUAhS3635mFopAb8iGsW+ycQ9rujLmEApfdhgjYrixJyywUT3o22rGLr8UrLQ114m2sFm9DFXwIq6SpvhJb+Ac21OB4nbR6QYQxJ3nSMyU9uwimn3noaxAe24Q3zDQ4zeVZ3CVcIOFgOrSNmQiGlW6iliKRIZ6F8wyFSjybLbYYFXs66yHbnnUQNtRy03+YfSC+38Y3vWLFCjOvWIGpqH3ib21ZLrCu1SepzeM44ziIL+yDNFkvKyszhznfUFPIdrorbJ/XUCBiH/8oC9/5uuuu6xaj+GfjvEEtfHz/7u37cg6nSTkXdqzCOSfe7Nw6Hr8lVjST74l2JBu/xJh+w92NX2qJrcJ5oLv+YtXRrSKgCHRFYMCawAg0E1sONctDL1aAGU7FymEAxKcBxKcagVpCMntmkZx0/HQpgvpi2+Z18vI//i4zJxbKsvkTpQSmnw4QQcPKGqaZXHBAmmDWuXF3hby5abccrmiUhYuXy1nnXoIIokHZubdKtu06Ig1NPonQTyo1S4ryPHLx0nFywpTM2BtCE0gioGVsITD4msCj+NH/tcEbkIP1QSxE2MQNoZAabzjyiDMEjXekTlJtteKCv43D+OFg5T0FKmyYdcU03ti3CsYMTato3kwGixpvdldG1g1ifcTnzEUcwUJEAoVpNGVQoy2nkMg+HZIsaNtLc5xDrvGmKaG10mo9en+31CBt3brVBGCg0DZnzpxeEW1qjZgYfOfOncbMkUJDf/0/hlITSFz4jrt37zYmqtSEMZgFn5+aCDKilqlUfzFMdh1/L96TGmeaUJHBtRhlml8N5f1petdf7SI1xTQDJX5k1ukH1JdCMz72LZry0VeLAoTVR/jO8aZn8e0ysA6xojkzNSPxQkR8ve72e6cJJHXjuGVE0K0Yzx9C69YizbW7xJNZjLRGCMQShK8gBLFwa7GEG7BYAgYaUp8EmlpkKzSB+XNmSum5p2D4W21BZOOcQcEM8wH/Ajz+NcVQPkwcRsCEFQMmqlhdXm+mkKjsfX6t+CpqZNYNl7YJhYhWCsHQntEiqbm1qGfigiJ4TIyWZ+aVQZNZiroUpmhCyvubO5l7DuYfmjXSxJH9iSaYNDtk3+Dvxf7MXH5DWXhvjl+OZfIR7Ev8MKBKd32pu+cZKk1gd/fjcY6nvkRO7e59aVZOU9KhLt2NXwqtndN8xD8LtaD0EaWpMM3vj8Wzxt9f9xWB0YBAV9uopG/VgdQYjcTmcuQ/cuVCG0czUJG8wiJM2lOwKlkn727ZB8bkdbnkwvkydfp8eefd9+TPa16WXQcr5cIzF8nxE4vE7SQhA3sLv6lDtUFZ884OeXX9VkQuC8u5510qZ5x7kby/87C8/e5eqWmMSCZWNLOKpkgqtAk0h6E34YdHfDJjfLqkkzk3rQ0NgUoKj1YYlQhQ/spMd8orz2+QDw+0SmF+KszqYNqZlwXtXg7ydBWLL5gj7midBFoOQvsXlRwP831R481hRmkuVshs8bvJ0QUmLggGsKqhxYyVzHFTZdqcE3AUN0S0vlZ/UHwQPr3wfWtobJKlc0pkfDYEQPRx5u0ayl4+mAspZLCZsJmfvhSu2HM1mp/hXiy8yLxQ6OPnWBYybR+VX6T17v15XyaQHkjONQoGlrDb2/tzYYECIAvNWPsqAPb2PjGpi7UhiNmwSEmtH+kTtg6YdHI/BUGiIgGPBHycJ/jBghEnHCw2xQzNKXBZBde2LRrxCK0DzDmSvQ6FFJXFUFazZ+ph/SmCsUiXCdJqhFTDVISnMP5+OIcgbxFEJrW7aKHAK/AEZv6i2WkePhQAWY4+Uez74P21+hK11jSxPtaFmmtqyviJLyNFyOhrX+7ufePffSj3uxu/Vj/o7t7U/A1k3uiuXT2uCIwlBPohBMY8AYy/HqhITUtI9h5uhelIFlYzSbf4h0TLLtl5RRDYcuTQgd3y5NPvy/VXnoJBe6Hs3LNL1n2wTw7XNMmKUxfKuYuOk1TYzG3Ys1+eeuU92bLtABzUBdq/C2XZWRfIuq0VsmVXQDy5s2TahGyYIpGJjq18RmEaGsSNK2qjxm9rVjHMVYaOPo2lvqHvGocAez39AusihWLPdUlFa1h2VzZISyN8exD8iBrv05bMluLsSVjgaJTVzz4iU8bnyZknTJEJ0FTbmJzZag8MoC0CH1q7Q7YcqJXX3t8lm7bslRkIPHP50oXSDO3irr0Vsnl7uRypasDiiNOkYnFAICrOa5XJuR60hU6OcWaYRavdQd72lZkY5NuPuOaSMS0j7oX68MAj7d2tICE0Re7OX6wPr9+rqjaaUkK7H8FCESN90izTFJiJhgMwBQ0hKIuhXRjXJKPQfnHeMRo/ErXYydh6UvtkwhY6fDlaL9Z6bJ4w+2iDVgU0UIBPvhHkwjA9wLximmczYSeseVwwPcVxtgva6gCdp7m7TbJMK0P9Z6T1paHGo6/tK359RUzrKwJjF4F+CIEEi0SBoaPtsrfSD/NNF6KM4ShpDAK10BmdwTQY/IVakAlTZkrFoVR5A5q8806bLovhO7J2bbOUH66Sx597UbbvK4dGJV/e3LBJquGsnpaTKQsXnSrLzjxPNu8PyIFat+QW5WNFkgFloPeDGYDRooCJ5gorS1PAIfurgjK9mHoWQ0nNcf2jCPQPAYuxsvpSVLYeRmAGe7ZkZlHjjXVxmNxFomXG/Hnzjt0wWXxZPnYpfFBmzZf1GzfIX198VrbtOSSXnbNUFk0tQSh3BocAUwchsAmr/i+u3yar3tiEnJlVctoZ58hVn/i07DlcJ/947W05VBmE/08B7jFL0rOyETTChXFlkw1762ThtGzJQIRQw6QNYV+n3wf9STje+KFJFj9aukeAq+o0T6IZKDEbLmWoGUNqP2l6ab33cOkn3b03n5da0/hUFEP9W0UR6Cka9Zjb2B30deUYxvwC8/FwAEIWhxYDp+FYBMLZoTffEQ/cHrzb9squWgaP4VxkzUvY7aZQbLRmrc5VSLlpSho4Ui925Anc/+rbMmU5IrNCq2IKhMAIfBMlijRNtGIANQ1jUQoxJvGJRX409YbwD+cdawyxPw0wdt0QPunwbJp+tNa8zXFozd3D82n1qRQBReCjRKDvQiAXB80T2xC1MyI7D9ZKEIQjhaHywdxGoJlrQujtrPxx8CmHxg6V4QIv4+CrUVlVLgcrG2XmzHmy4b33Yc7plrojNfL6W5uw8piC8NUhySkqluLxE2XpyWfKwVq77DkSAgNMXz8KgDGoIvC7aqmDzyEYVIbR5gkfnuVAFYTB1nSY4dGB/aOEVe898hE4qvGm3VSdLyS7D7WiFyJsO4QxE5kWfZ4mVZnZeZIxP1sOH9gjf/nbRvnEFR654MLLZPe+PdDmbZfD1avljFMWyCVLFyAgTLpsPnhI/v7KRnn3/T0I7tAspy4/Ty7/2LXwg62Rd7c2S0rmbJk1PhcL9GTMGJ2UXTymFahucMh2RFxaNCXHLLoMJc4UaPiJL2TILMbCYi64TbY/Vhi5YkR7tQrfmbhYghG31sc6zu/DRWCynrs/Wwb64ccqfKdE7xr/3jz/URUmI7/11lvbBKtj9RQM7gQ/Yphe0gQ05vALoQy0K+wHKTZCHs0vIQS2+sS386DYgkjUXlcvkdp6zAMxysunNeSNvoNYdKVFABdg4wtJJRdDY7NY7Ix1NRewnLiHDbTav2OfhE8+UVIQVC3WPO4Nc1CmeLI7w/gg2y8XW3kfS3MZf6Mh2GfUR36sEt+X2Gc69yHru1V/rG8pRHfOY2jNRcSKn/g53Dpmba1zY2XOHuv9Rd9/bCPQdyEQXC/pDRnh8uomeeu19fBnArGgmQn+08zNhoAtucXjwTC3FQhpvMqTXSB7DiDQy+wCyUZy+JqGgBSML5WG+mrxgxkeh3yBTuQtmn3cXEnNmSjvbfHGCA+WSEHnUGKUjoE2mqoPSHnlAXGm5YA+gZlAhWb4ac0rPlEWzZlIDwzr7rpVBPqJANkuCl92OVCN3FqtGC4Mh4v/RzXeMPMkg4SDpZOnyxGXR15Zt0uuumghopothR9fvdRWHpG/r35Z9pUfkbJJE6Hx3gKBsRwJoV1y/IlL5PyLL5Mdh6Oy6whMqAtKDW9IM2cS4yijkTI6DNJMcCEkjNyY2w76EQTpo9F4UxtBX4y+RmLju1jMRWcmpKfj/fzhhsVlxIo+jfz0VMhsWQJTT0zuSGLKaEpMjURP0T2JCfuC9e7x+/E48PhQlO60hENxL7YZE8koCCLVSwq1auwXmGHCnEPoc8dCVweH+BqaoYxrkVZoBOuaWiUXAnYGAlFx+iEtbCUNBM10Y6Eoy410MWYOMg2Y+/iQHuJwU50UZsBXH1YDXKyijBlGmog6pJqAAark4BmiyHvnb22QlCxEOAadptm6hCEE4kPrHjtoudMJawAkt3e6DRGO3eQY/u1NX4oXcqz+FN+HrGOca8Zi6e1cFI9N/Lzcec7md+sTXy/++oHuH+vxOdDn1esVgZGIQM/cScI3AikDLQA7KnsQxMUzYbFMm8iKnFydJudRY001qAeaNvMtSRuYaaw+OpAXqRlMbUswKgVFeVJVV4FqNsnOLxAb/AcjEPYyMtwya+Zc2XXIh0iJcGCnMoS0J9YMdrCLttPzimEuB7M4Ms1hRDYzDJdTDjQhkAQmeocmjDdY6Z9+IoA+F2N5sJaOnT2HEG49YAPhC6L/wdQZDFNTXS1MNfMgzIFxQv8mg1WMKHKV5SHZCRPPmTPmyRtIlJ6CADF1h2pk/bsfyqbNuxEAwg9NeYHk5BfJmcvPlepmt+w4xAAyCP2OQYOmTbEhI3xD5WGY2SG3F6LgUivIh9qLIEi1yCFWmA5GkuNiBBQycvwkE4jiX4WMHRmMeCajN/sjSVji+5LZYdCJZIEnLKbLYmgTMbmsM5KKtaBA88zuCn/P7t41Hovh/7tzRgkiIAxNKzFwke6IAzocpHbw6NtTWGuprJGI1ydZyOkXSbXLEUQKDUeb2oQ5WiIgQJrLLdnIFWgHPvGawDAItAeLRrlI+n6woRbjDj7FiCjMvhEKhiUV81deBiIP46YRL4TAyjrJQMRNY93ABSe4cVAbKJzX8CxuD1PUkIb70Fc5Rw2/Ei/k9KYvJetPifrSWBNKrDk72bxk9QZi1t38bM1d3HauY12vW0VAETj2CPRDCLQeEnHCQLvGZYUlFwnauYLZ2ApiVRlCTiJE7eQ/Eif8ixXsITegD5NAbRPMQwtKTFhvnmMNTB8gMBAOC2FG6sqXKoTit4PAtXPEpEZWAZV0u+E3EPVLXibc1d2MlhhBYu6wOLEyat3Rqq5bRaDPCKAvmh4H7qqywSfr3t4ku8ubjVUU+6kt7Ec/c8rUhWe0Nc1eR4EFSWzzi2UHUpmcs3SC5GbnyiFfs+TDHLqpsUaaEb67CBpvG/yDZiM0fX7xNHlzayvEO+TuQrsYMm0FiybQFARb66V88/vizigGk4Z7IpJoFObQGyYukXNOmjWqNd5kuiwhobeMCMEjM5KI2SBTM5KLhUVP2jXr3TszuX3V3A4nnHorJJO5tIRCI/DAdHA4vXcUFiugUtDmcZWHfZH+gVj0CXPBFEIXJhzSyBaMeQciALumFksQ6ZByMFdk5bnRp/F+0PDRXNRJbTyboQaQcwbmDquYNIO4Rz4Wj3LTPOIPwKTcnIdm2pECU1DMX7gujHHiLMmTaKoNAa7qkW6JKWlivvzRIOcZ0HDcy05BlM8ZbcEcZd1lZG7705essdSXOWhkojOwp46fr3vbkjVfdZ6v+X04jd3evo/WUwRGGgL9EALBqIIQ0N9gUqZfLp3rk8I0UCMQpyONYdlir5GKllSpCiH3kY1EjkQKxAQh85trKqVizxaZWrwMjsvwH6GWzxRSM4qLDpgPpUldaxB+hiQ+PNlGdQx3HNtPgcN6QVazFOfXycwJyB+U70I00RRphnM9aBdMY9Ce5ehu2tc/ikBfEeAiBq4BM7W3xif2wnkyOZuCIfoWEiiHAl5pqD4CK02u5rOQqYsJcU6nR7yhDGnwRRB8okDKK+B3g3MZWTDNys43DBUj7i04/gTZjWAzvgBGk7kcN2QXb+PnqG30ZOZL4eQ5kjthhthCfnM+iKTNO+tCciYJpfEbbBsjfAwtYFRjZpidobDbGehidBfr3fuicR0tiFDI740J6rF9X2pmOT45wFvRN5FOyUQKJnFLR0AYCIatHPcgxZgTgoEWaO5sUjB1JuaKXNn74DMSqW7ENATBD804MVGY6QECodkmGvo4Zs6BDvJ0KiwKYs+ADeg0/tOmQCTNJSXnniqZU6ZCQ+iFhYIXC7ig2bhX2J8KbaAfboAcM9AE0s3DXof9SfjwXdhCP9gHXDUSyvDsSyMBub4941ier/qGlNZWBIYGgX7N4mCP4TPglexoreSktoCogHiFW2Vcik/KFuRIrd8mj22qlapgiSFoXD7kio8L/lKTZs4SD81b4BtIfjomCLYRKRCfVl8TEgbC38kcQgXSR0RMi0ZIJFEfhDLL6ZMzJ4dldkEWzE9bJOJrRNLdLGgHKWyibhBtO/uWcHho4NVWRz4CNkmFnFeQERYPfGscYDSbIbRVIHqnG4GJ7FypwCIHlzBiBdsoFjGglT5SF5CigvEw13rX9FsunlDjzei5xfCZdacVSsVh9HEHNd4WUtxhR+ciP4TA1HT4B0HbnWbDWHOKG6v4XgSpcaWACRzpy/LmLfWPIjDKEaCfnRn7jaCDSMcQhemlvR6HMhCABfTLSGQU2GjmiQWhtnmC6548ZTQsnFb6CVPn6/idLvxYZ2XjfDQ0zpualShzI2oBaRLqQL5ARjW12SfhOesxVzVDBIRJaGzVig3wai2KgCKgCCgCIxCBfgiBnPQh9EH4CkMbQtMW5hAihWIAC8hv4oLQlsYE8G3B30gvfLWH5ODOLbLozDMl3RmUnXs/BEEh0TlaSIdqKqvEFqiSvKwiqWkiYUQdnqCPIZhrUFDkZYMvYgpsy7HPyKP0gTAkFPx0JOLDKiYCyqTloWElUEfR1b2+IRDTeJNFKk4NyAWz/PCxCaPvhaXaG5ZtKTVyuNEpFdFWCdgYFZH9FD0OMmErTKt2v/eGTCs+y0RMpG8fldNGi4hK7LepnnSpbQ3BhBn+Qaab8g8awP/YH6z4Y8k+z9MsMyY2y6yS/TK9CCZi8CdqxTXVLTCVhCZQNd7ES4siMFwRsGgcLGOwaGqzQasG+hUFLbPDB9jm8FIOw5DnQifGNyxhfFgIbUE00MMvvo2ooA3ipgn4IJMykExxNPnlEO4hK2FJg/QeHpiPxlJUMLE9zE6NNQ1SRoCQw4sQU00+pie8zyA/y3D95fS5FAFFQBEY7Qj0Qwhsg4Q2JYYaxFYS7SAYwWBEgr6AfFCZJoe8mTH+FA4KERy3u7KgBZwrk/OhBdyPsPmImJhCwc4qICw2EJsmr1e2bH5TTlq2Er4MIWluRT4lEiNKlIZYijQGPbL5iBvR0hokDX5STicjrrHQXI+MdBszrcTKQle3/UCAPTsS9kkGkjtneJrQrRigwid58I0557gsaUbgoie31Ms+mD8brRxWO1jHge2kWXMkI9stjQcajKY8prRjh2Sr8MFphbaa4wLcGBcx2G1tZA7BCFLbjSrQiPvlpCleOXligfj9fvgH1iHXYC4+doRQR/AGfw1WXEr68WZ6iSKgCAw9AhjHHMhYKGVQF+YIdMCcMiz7MN4LcS4d5twws3RhnmlldFAsZWKiSM/IlOqd+6Vh7yFo/hEplGad8OEztK0tCijnE04Rhvqa2+BPvKSIOYSHSQt5JWua72atCuae2NKlI1hRK82H62X8gomYe0DT8YmCZjvcEPyY4sk2SexhzGE25F+ywySdQqBpia1xX4sioAgoAorASEUgTgrryyvA5wYrlgGGmIbvH4mcA35SbphkVvjTZWMVnNjtuWaF0waTtsr9OyTQWi3nXAD/A2g2Vq3H6iPVg4Yqtd3X0Cn8gUnK7l3bkTy+QGbNPUO27oGjPDQfdq6U8hp8fNE02estlEmtNpiEwscCDZH5ZhMRBJNxImS2uXlfXknrKgIdEGBvgpwWQNAWaJa5QGF8YNhn0QcRMd1ovFNdOI4hwMIgCoHGCtm9aZ3MP/Usyc+wy3r4wFLIi09ZwgWT6qpqCbUckfysSVLVSG06xwPu2a7xpo47iKTwyIsFxi8Kn6EwxpnReKNeFFFKE1seFwAAQABJREFUQwjtLpnMSxd7VvMQ+kcRUASGCQLWuATtwmh2OCZjjDPwClMqFYJmIUE8pLmUjCwsLjE3H2kZBSuPZE+cLvNvKpEIfAQDXiwANTdLsKUVFjgBRBOFsBYI4BzmgCA0imZOwmVxQiCsOfEd66fwGba5cG8slNrxcWcity4ihdqRS86JQDApMDd3pGXiUiSytyPKNi60pyCKL/wFxeFDGwjUZs/BHFSDOQzzoDBnLxunT6MWRUARUAQUgZGMQD+FQJAAV7q4wIAGmitBOOjXAO0GIpw1hLKlpDhdskGgWvxBaPPgA1FcJNlIOl2U6ZMX16w2qSFSjKkJiYlFKAkjtYsgbGB639nwpqRnpsucqYtl226/IFsanOLhCwXalAqiloaQ4l57ltSH4Jvo8oMkgSu3g7ClFYgTIa21KAKDgoDReLMlCmlg5dBvaZXsBxO2vTZV9jcwmTr6ME5HmNjZkS6lM46XWRMQHKl8hxwsPxgTAOO6uQ292Q+76Xc3vCpnnXuVBIIhqfciT5cRAKnxRmUsnkSQE3D9oXQp8NRLut0LP0BEEDUvpRpvA4P+UQRGBALIFWnLgUxGKkWT0HRMFzStbAbFy4Q/+3ikTsmCcIfIoRDatu+qkr88uU4K87OlKD9D8nOLJK+kVLLTnZIH2segP9TSGc2hoYyxKYPfORdRWUi50AicJggNFplwMBiCzz2sDxphzl6HiMeVFS1yqAJ5TGv3ylVXnSnTJhdBEIWAirRNdmc1BL8KE3ANSZywZR7B2H1GBOT6kIqAIqAIKAJJEeiHEGjIiyFo7swCCINIJOuHqQu4031bdskLr62VwpJJkltUIsU5edAYpkpoohvhrB3y0ppnZcu2zVidhCRHigKCRW8+lhhzy1XQWPGDMX7t1VfkXHeGLJoxW/xY8XTCXC6MKGYtDVXSdKhaPkAOJP+UUlm2aC5MQpGHDJpJaiixYzWjW0VgQAikOFMliGTJUfRxmmliTR2+MxlIuJwq71e7pTWKBQdoBh0Q3KoO7UEKiENy5rmnIHKtyJ9feB0LGtToxXp3jDPj4zB6pVP27d8nmzeslXknnSfb9gWlKQCzZqPxxio72rMhYMx+b5HsaIzKSeNjAmBM482k8fAfwnMYgXFAb6gXKwKKwNAiwPEPrZ+heRzHpfjOvIjNOJaFaSEDAYfTQMNAu1DqvEfkqdW7TJ5c5ubzwLIlNdUt6Ujl4IR2Lgu5dPNy84x/sNvtwnkXfIxhnYAFUQp7Yk8FvUQyCixU+bEY60OgtlZE/vQ2B5BgPiytrWGYowekBflK7Vho8sDX+fyLzoBgmYEPn4C0mYteiEbMRTDORcgPGBMyeV6LIqAIKAKKwGhAoB/SkjG+BEGIEYoUCGn8MEdTprtcPlzzN9kO4mGDiUlGVrY40zNgdpKFYBYRqW5sRMAYUBkTloyhrtsc3kEcIyBeDI1tTFvakA0j6udrTz0kUyZNkWATfBca6sSHoBveegh/rc2SDUK4+OY7JDO3FG1ROxJ7Kuhk0EIb4z0afiV9h48MAQeEQGdGEfTTYIFCWOxAkIZWfOoCWVJQgL7tj0qr3wemC6v8hfkycYJbppU45JWXn0dqiANkpdoENfTH9i4Z03g7EPP9nY3vIOF8hsyZc5ps3esXP8ZGCjXeGCapWNhwulLEl5Ij1cEmaAF88KNFwCXkJ0zx5Ik7fdxHhoveWBFQBPqIgJkCsKBk6B60czQHRXAYGJbjA5ploohCXwg/QA/MNW0w+4xGMyU1PQ8aw4g0eEPi9TKoDPMfVpmciGYZlXIfJhq/HykcYG7q8eTgACwWcJxGBXZGq8IMRlNPtwe++khh40wN4651ZkpywfyTKWtwMf5Z05QHbTLCNlwrUCt2PFbHqoETWhQBRUARUARGMAL9EAJJAtq5Wbw6CUOMLDDYpwfMa8QP7QeENPoz+KtBnEChItCW5MHUJYytDUSHK430h4iC8IUQ5TAQCkoqkr47SCBhCkdHeP6jcLlnz/s4jjuF4TMBYuWkjBcOSRokxhjxwneUowTMsN6xg/pXEegXArF+TYbNnZ4L7XUqouE2gbeKyp6d5bLm5eckFTn/8opKpQhbpydNQqUucdnz5LW1z8u7723AoDC9Fh3TsGrmKWIj52j/ZP7MV199FebNGbJg2kKMA/Rvux+mpS3ia641Cx7bNx+Q+oJcueTsk43G2+Zw4X7wzbGbZft+vZ1epAgoAscaAY5+atV4Xy6CpkIIhLBnhEBq3HjcBs1eiqSmQTvoI6VFuhlfTUzgA+1zGSMBaASh/XM4spH8PRNtQOCDv3AoXArBDRQUNJZCJXP7hYI+XOvDfAIxkqmWQGfpUpiCuSMNGsYohMa09Ii4PDFhzzIppebSjkimMd8/i+JzPtOiCCgCioAiMFoQ6JcQ2PHlSbnobxCCmQlMVXJyQXQykUw7jHxmrdCQ0I/PgUS0AZizOGGi0gKTFqyARuDQjuvouJ6CxLcU+lKQCzAEnwT6RvE6J7SGQeQgjIKJZrjqQDgFpjAZyAcIExbQubScHJi6wIcQ905BO1oUgcFDgIJbm+8demqKG0EU8GHJSm+UvW/8Q+qQ9sThyZBU9HlPZrakZeQKrK3kcF0t+jgENJOkmb2ccmCMkYqgP5NHM2KgkTPBdmGh4/U1TyKS6E4Ei6mXxtpq8dZVw7S0RvzeRmnB59O33inZeRPanim2Mq8ab/Nz6B9FYEQiQFPM2Bjm49Nk0yfbP/xQ/vyXZ2X9649JXU0V6CaiCEsLPkzabhXSXMwv8NPzpGVLJtIp5RXMlKzcCWgmivljv1RX7JfmpiOwmKF2ECmTzPWxha1YKyD9tjSYlOdKft44eeyRkFx+yUUye/YsaAvb8pbC5FyLIqAIKAKKwOhFYBCEQJAxRDvzepFEFiuL5338eqx0OmAiF5QjRypl244diIRYAT++JvFC2BMbIpHBVyHsR3CMFq/kZKQjl5oH7u1wmW8OirfVj9D6adLYEpHcghxpgg9gWloWQuJnyvEzZsjc446TrOws44NFjWFGdi7u3SIZaMdhgs2M3h9L3+zYImCtf8fuajFQMNeEZjsNfJIPjJsd/dNZixX3mkNSD+mOEWzzEVghAm0dltLR39GK0QhS4x3BuPBD4+3CCj7EQAh/XPwgI0hfv+373xc7Esk74IdDLXkq+rcD1zixyk/TUKvwCqM0iImS1mHdKgKKwIhCgCP56Lyy7cOtsmbVGmj+muSmGy8wYxwzjJkLKNxZNfmKNmNdELOUMd/tjIoN7SHq+33IWxqdhukHC074UKsXbdun5Y2pz9Yw55CGMmCNz9sgq1c/j/p2mX/8PGwtl4rYTGMu0j+KgCKgCCgCowqBwRECyai63DJz9lyZN38BKNRRhtXbjLx/W7fKpg8+kIqKI9AKuqDZwMomaJAPmkImvy4pHgdtHoTGigoTHzQ7KwsRzmDyggAyTqcDeZPS5QwkmS+bPNlERrN+ARKwMHwNQzANDcOk1A7mnMyzFkVg8BFgv4r5rvr8rdBMw3QZGukQbKsafEGYWkFo87iw8u6X9DSkT0EgBjc13vDfsRtBEMIjmC47+qqHK+zYD8NHJwAh0JhFg30LQAvudnmktqkR48Rtgsc43A5oy5FvEOOIGkP6zWpRBBSB0YKARSttsnDBQvP5qN+MC1LWMtNH/Sx6f0VAEVAEFIGhQ2BQhED6JDCQBQu1HcbEpY2O0LRkyZLF5hMjLiAvYIpJZyyG1hLcyORyn9v4Ys7jAvoO0vSzQwFPzJDZVhsdzukXRWAQEeCiQzOEMaY+WXb+xaavB2D2fBga783btklVdSUSOyP4QsCBfopInz6keYBZcysCIuVmpUJ76EHS6KjUtwalAe1kIrBRdV0joumOl/rGBgSAQKTAYFSmzFksJy1eLHn5eVgIQX5A+PRwlb+mtg6mWznG72cQX0ubUgQUgY8EgeG5oKO09CPpDHpTRUARUASOOQKDIgTSDHMwTDGpyWPpri2Xmnse8w6iNzyKQJg5tNAHZ8w6TubOO94EZLDOtrb4ZON7G2XdO+/I3r17oBVMlcYmBJJBhSYkenZioWTyJPrsCHIHlksqQrfnQqArhXmWB8Khyf2FgEkXXnQRtAELjD+s1TY1kLy3D361QeNjqxrvo9joniIwvBBolmpZLT8Y3IfCommz1yv1dXVSVFQkLpiU96qoUq9XMA1VJa/UDFXT2q4ioAgoAgNGYFCEwAE/hTagCIwEBGDmTM0cSxBCGULUmn2aNjsgwJ188lLzocabHy5qYJNU4806FPTMIgi+MMgSc3x1LkwXoUURUASGNwIB5P/bIs8M7kNSaYi0oPwwVEyvy/BUNvb68bWiIqAIKAKKwNAh0G+ukkyumo0M3Q+jLQ8eAi+8AMGKcdEHXMhR9TRkjDTXdhfWtb5b2/gH6Hwsvn5P92AbHc2l41vV/e4RgGJWiyKgCCgCioAioAgoAooAEEjGbXYLkgqB3UKjJ4YZAmvWRIUfLYqAIqAIDBYCHsmWLCmBZu7wYDWp7YwRBIpk9hh5U31NRUARGM4IWKHJhvMz6rMpAoqAIqAIKALDCgGXpMmV8gsjCA6rB9OHGdYILJCr5BT53LB+Rn04RUARGBsIqBA4Nn7nMfGW06czJ9+YeFV9yUFCYNo0RisepMa0mTGHQJaMU0FwzP3q/X9hCoDL5V/634BeqQgoAorAICJgg1mn2skNIqDa1EeLwG9+E5FbbgmbgCwf7ZPo3Yc7AhQAX3opRSZMUClwuP9Ww/35mqRSDsn7w/0x9fk+QgSc4pGpctpH+AR6a0VAEVAEOiKgQmBHPPTbKEBABcFR8CMO8SuoADjEAGvzioAioAgoAoqAIjCsEVAhcFj/PPpw/UXg2WcjsmfP4Ci5w+EIcvQhYbvTaSLiRiIRs880Dm63W/idxdoy1QMj51LJHgwGxeVC4nh853nrOOvz+tRUJJBH+9z3eDxIMh8y1/E872d9Zx5B1mFbfr/fnON97HbVYhGrvpZLL7WrBrCvoGl9RUARUAQUAUVAERg1CKgQOGp+Sn0RRUARUAQUAUVAEVAEFAFFQBFQBJIjoIFhkmOkNRQBRUARUAQUAUVAEVAEFAFFQBEYNQioEDhqfkp9EUVAEVAEFAFFQBFQBBQBRUARUASSI6BCYHKMtIYioAgoAoqAIqAIKAKKgCKgCCgCowYBFQJHzU+pL6IIKAKKgCKgCCgCioAioAgoAopAcgRUCEyOkdZQBBQBRUARUAQUAUVAEVAEFAFFYNQgoELgqPkp9UUUAUVAEVAEFAFFQBFQBBQBRUARSI6ACoHJMdIaioAioAgoAoqAIqAIKAKKgCKgCIwaBFQIHDU/pb6IIqAIKAKKgCKgCCgCioAioAgoAskRUCEwOUZaQxFQBBQBRUARUAQUAUVAEVAEFIFRg4AKgaPmp9QXUQQUAUVAEVAEFAFFQBFQBBQBRSA5AioEJsdIaygCioAioAgoAoqAIqAIKAKKgCIwahBQIXDU/JT6IoqAIqAIKAKKgCKgCCgCioAioAgkR0CFwOQYaQ1FQBFQBBQBRUARUAQUAUVAEVAERg0CKgSOmp9SX0QRUAQUAUVAEVAEFAFFQBFQBBSB5AioEJgcI62hCCgCioAioAgoAoqAIqAIKAKKwKhBQIXAUfNT6osoAoqAIqAIKAKKgCKgCCgCioAikBwBFQKTY6Q1FAFFQBFQBBQBRUARUAQUAUVAERg1CKgQOGp+Sn0RRUARUAQUAUVAEVAEFAFFQBFQBJIjoEJgcoy0hiKgCCgCioAioAgoAoqAIqAIKAKjBgEVAkfNT6kvoggoAoqAIqAIKAKKgCKgCCgCikByBFQITI6R1lAEFAFFQBFQBBQBRUARUAQUAUVg1CCgQuCo+Sn1RRQBRUARUAQUAUVAEVAEFAFFQBFIjoAKgckx0hqKgCKgCCgCioAioAgoAoqAIqAIjBoEVAgcNT+lvogioAgoAoqAIqAIKAKKgCKgCCgCyRFQITA5RlpDEVAEFAFFQBFQBBQBRUARUAQUgVGDgAqBo+an1BdRBBQBRUARUAQUAUVAEVAEFAFFIDkCKgQmx0hrKAKKgCKgCCgCioAioAgoAoqAIjBqEFAhcNT8lPoiioAioAgoAoqAIqAIKAKKgCKgCCRHQIXA5BhpDUVAEVAEFAFFQBFQBBQBRUARUARGDQIqBI6an1JfRBFQBBQBRUARUAQUAUVAEVAEFIHkCKgQmBwjraEIKAKKgCKgCCgCioAioAgoAorAqEFAhcBR81PqiygCioAioAgoAoqAIqAIKAKKgCKQHAEVApNjpDUUAUVAEVAEFAFFQBFQBBQBRUARGDUIqBA4an5KfRFFQBFQBBQBRUARUAQUAUVAEVAEkiOgQmByjLSGIqAIKAKKgCKgCCgCioAioAgoAqMGARUCR81PqS+iCCgCioAioAgoAoqAIqAIKAKKQHIEVAhMjpHWUAQUAUVAEVAEFAFFQBFQBBQBRWDUIKBC4Kj5KfVFFAFFQBFQBBQBRUARUAQUAUVAEUiOgAqByTHSGoqAIqAIKAKKgCKgCCgCioAioAiMGgRUCBw1P6W+iCKgCCgCioAioAgoAoqAIqAIKALJEVAhMDlGWkMRUAQUAUVAEVAEFAFFQBFQBBSBUYOACoGj5qfUF1EEFAFFQBFQBBQBRUARUAQUAUUgOQIqBCbHSGsoAoqAIqAIKAKKgCKgCCgCioAiMGoQUCFw1PyU+iKKgCKgCCgCioAioAgoAoqAIqAIJEdAhcDkGGkNRUARUAQUAUVAEVAEFAFFQBFQBEYNAioEjpqfUl9EEVAEFAFFQBFQBBQBRUARUAQUgeQIqBCYHCOtoQgoAoqAIqAIKAKKgCKgCCgCisCoQUCFwFHzU+qLKAKKgCKgCCgCioAioAgoAoqAIpAcARUCk2OkNRQBRUARUAQUAUVAEVAEFAFFQBEYNQiMaCGwtrZW6urqRs2PoS+iCCgCioAioAgoAoqAIqAIKAKKwFAjkNKbGzz77LPyla98xVS9//775aSTTurNZXLHHXfIyy+/3Ku6VqU//elPMm/ePOtrh+3+/fvlD3/4gzz66KOyc+dOaW1tNedLS0vl+OOPlyVLlsg999wjGRkZHa7jly1btsi1117b5XhPB04//XT5f//v/7VX+dSnPiXvvfeeOBwOeeyxx2T69Ont5zrvbN++Xa666ipz+Kc//amcc845Zn/dunXyuc99rnN1893lcklWVpbk5ubK2WefLZ/97GfF4/EkrKsHFQFFQBFQBBQBRUARUAQUAUVAEegPAr0SAuvr62XTpk2mfa/X2+v77N27t/263l5UWVmZsCqFsS996UsSCAS6nC8vLxd+KKw+9NBDQkGys6Da0tLS52fJycnpcK9du3a1t/GZz3xG1q5dK3Z7YmUqBVQLs4aGhvZ2iJ91vP1ggp0nnnhCvve978nXvvY1uf322xPU0EOKgCKgCCgCioAioAgoAoqAIqAI9B2BXgmBfW+24xXp6elGK9jxaOJvkydP7nLirrvuEmrTrHLppZcKtXTz588Xp9NptHNPPfWUvPjii0JBbdmyZfLGG2/I4sWLrUs6bM8//3w54YQTOhxL9KWsrCzRYXPs1VdflZ///Ody5513dlsn2YkLLrhAFixY0F6NgirNW6kt/PDDD+Xw4cMGt3HjxrVrFdsr644ioAgoAoqAIqAIKAKKgCKgCCgC/UDgmAiBNHH84Q9/2I/HE3nhhRfkvvvuM9cWFRXJb3/7W6EQGF9WrFghX/ziF+VnP/uZ3H333RIKheRHP/qRMdmMr2ftX3HFFfL5z3/e+trv7b333isrV66UGTNm9KsNmpded911Ca+lRvPmm282Jq+33HKLEWxLSkoS1tWDioAioAgoAoqAIqAIKAKKgCKgCPQWgcS2jL29+hjUo19hNBoVm81mhLrOAmD8I1Ard80115hDNKek3+BQFpp80iw0EokM+m0++clPCv0vWWpqauTWW28d9Htog4qAIqAIKAKKgCKgCCgCioAiMPYQGNZC4Pr162Xbtm3mV7nhhhvkjDPOSPoLffnLXxaalN54443i9/uT1u9PBQZ5sYTR1157zWgg+9NOsmuoJbTMRZ9//vkhETaTPYOeVwQUAUVAEVAEFAFFQBFQBBSB0YXAsBYCH3/88Xa06T/Xm0KhiQFpHnjgAZk7d25vLulXnV//+teSl5dnrv36178ujAY6FIUmoyw+n0927949FLfQNhUBRUARUAQUAUVAEVAEFAFFYAwhcEx8Aqurq+X3v/99UlgXLlzYIWDLjh072q/hucEqq1evNgFlemovJSXF+OsxHUSiQv88Boa5/vrrjd8eNY8MFtNdtNBEbfTm2OzZs9urMc1FT2kp2ivqjiKgCCgCioAioAgoAoqAIqAIKALdIHBMhMBgMGhy3nXzDO2HGQjlN7/5Tft3pn1gSUtLSxh8hb54b7/9tqlDv0EWa8v9/Px8mTVrFnc7FGoY47WMHU7GfWGuvvHjx8cd6bhLvz3mC/zb3/5mopEygikD0wxmYWRQq1AItMxQrWO6VQQUAUVAEVAEFAFFQBFQBBQBRaAvCBwTIZAPlJqamvS5mCw9vljJ4LOzsxNq2Hj+lFNOib+kw/7HP/5xk1i+w0F8YVoJavqSFQajSVZoFvrKK69IbW2tfOMb35CLL75Y4rV3ya5Pdt7CgPXC4XCy6npeEVAEFAFFQBFQBBQBRUARUAQUgR4ROCY+gTSdZA68ZJ9f/vKXHR62tLTUfK+qquqg4bMq9UZIs+rGb3mfZM/C871JyVBcXCy/+MUvTPP022O00MEU1pj30CpTp061dnWrCCgCioAioAgoAoqAIqAIKAKKQL8QOCZCYL+eDBdNmjTJXMq8f++++26XZmgmun//fhMIhsFgrA8Fs2NZGMXz8ssvN7d888035Sc/+cmg3T5eCOwpef2g3VAbUgQUAUVAEVAEFAFFQBFQBBSBUY3AsBYCL7zwwnbwH3744fb9+J2JEyealBBMC2F9emPqGd/GYOzTLJQ+iCzf/OY3ZevWrQNulv6Nq1atMu0w4Ex/k9IP+EG0AUVAEVAEFAFFQBFQBBQBRUARGDUIDHshsKCgwIBNE84PPvhg2ALPAC6WWSjzEzJa6EDLQw891B74hppGS8gcaLt6vSKgCCgCioAioAgoAoqAIqAIjF0EhrUQyAAu3//+982vQ3+7Sy65RN56661uf62GhgYTnfPw4cPd1hnKE5/4xCfkYx/7mLnFQBPVM93EPffc0/64X/3qV9v3dUcRUAQUAUVAEVAEFAFFQBFQBBSB/iKQPERmp5bXrFkjPQlZ9NOz/OOsSxnh8k9/+pP1tcct/QBPO+209jpMG8F7Pvroo8bnj+fOOussU4f7TNi+ceNGWb9+vTzyyCPCnIRWOe6446zdDlumlcjIyOhwrLsvK1euFEYn7W351a9+JS+//LLU1NQkveT1119vr0PTTz47fRw3b94szz//fPu52267TZYsWdL+XXcUAUVAEVAEFAFFQBFQBBQBRUAR6C8CfRYCLc1cdzdkRM/OQmB9fb0wp15vCtM6xAuBvIZmkfSH+8EPfiAMEsNk7/x0V0444QSTyP30009PWOWBBx6Q/8/edwDGUV1rn5W0u+pdstwl9w4u9GYwNi1AwCG0hFBCICG9vTyS/wEJ5IWQvCRA8gIkgQcECDUJxKEZgzFgY8AUG1xw71ax+kqr3dX/fXd15dF61S1rVzrHXu3OzJ07d76ZO3O/exo/XRESsu6QQJqF0nSVWsHO5Pe//73w05HccMMNrWamHZXTbYqAIqAIKAKKgCKgCCgCioAioAh0BYEumYMmJiZ2pa4+K8NAL7feequsXLlSrr/+esnOzj7oWIWFhTJ//nyTbJ5awfYI4EE7dmMFzVO7IpdccoksXLiwtWhX92M5akKPP/54+cY3vmEiopJQ9jQVRmsD9IcioAgoAoqAIqAIKAKKgCKgCCgCLQi4YIbYHG9oMA/fzp07ZceOHUJT06lTp8rhTgsRb5hpexUBRUARUAQUAUVAEVAEFAFFQBEgAnFJAvXSKQKKgCKgCCgCioAioAgoAoqAIqAI9AyBLpmD9qxq3UsRUAQUAUVAEVAEFAFFQBFQBBQBRSDWEOg0MMwrr7wit99+e6y1W9ujCCgCioAioAgoAoqAIqAIKAKKgCLQDgJpaWny9NNPR93aKQmsr6+X7du3R91ZVyoCioAioAgoAoqAIqAIKAKKgCKgCMQeAh2lxFOfwNi7XtoiRUARUAQUAUVAEVAEFAFFQBFQBPoMAfUJ7DNotWJFQBFQBBQBRUARUAQUAUVAEVAEYg8BJYGxd020RYqAIqAIKAKKgCKgCCgCioAioAj0GQJKAvsMWq1YEVAEFAFFQBFQBBQBRUARUAQUgdhDQElg7F0TbZEioAgoAoqAIqAIKAKKgCKgCCgCfYaAksA+g1YrVgQUAUVAEVAEFAFFQBFQBBQBRSD2EFASGHvXRFukCCgCioAioAgoAoqAIqAIKAKKQJ8hoCSwz6DVihUBRUARUAQUAUVAEVAEFAFFQBGIPQSUBMbeNdEWKQKKgCKgCCgCioAioAgoAoqAItBnCCgJ7DNotWJFQBFQBBQBRUARUAQUAUVAEVAEYg8BJYGxd020RYqAIqAIKAKKgCKgCCgCioAioAj0GQJKAvsMWq1YEVAEFAFFQBFQBBQBRUARUAQUgdhDQElg7F0TbZEioAgoAoqAIqAIKAKKgCKgCCgCfYaAksA+g1YrVgQUAUVAEVAEFAFFQBFQBBQBRSD2EFASGHvXRFukCCgCioAioAgoAoqAIqAIKAKKQJ8hoCSwz6DVihUBRUARUAQUAUVAEVAEFAFFQBGIPQSUBMbeNdEWKQKKgCKgCCgCioAioAgoAoqAItBnCCgJ7DNotWJFQBFQBBQBRUARUAQUAUVAEVAEYg8BJYGxd020RYqAIqAIKAKKgCKgCCgCioAioAj0GQJKAvsMWq1YEVAEFAFFQBFQBBQBRUARUAQUgdhDQElg7F0TbZEioAgoAoqAIqAIKAKKgCKgCCgCfYaAksA+g1YrVgQUAUVAEVAEFAFFQBFQBBQBRSD2EFASGHvXRFukCCgCioAioAgoAoqAIqAIKAKKQJ8hoCSwz6DVihUBRUARUAQUAUVAEVAEFAFFQBGIPQSUBMbeNdEWKQKKwABFYPXq1fL5z39eNm3aNEDPUE9LEVAEYhWBm266SW6//fZYbZ62SxFQBA4zAkoCDzPgejhFQBEYvAjs27dPnnjiCamoqBi8IOiZKwKKQL8gsGTJEnn99df75dh6UEVAEYg9BJJir0nx1aI77rijwwZfe+21kp2d3WGZWN747W9/W+688852m3jDDTfIXXfd1e523aAIKAKKgCKgCCgCioAioAgoArGFgJLAXl6PH/7whx3WcMEFF8Q1CeTJNTc3t3uOHW1rdyfdoAgoAoqAIqAIKAKKgCKgCCgC/YaAmoP2G/R6YEVAEVAEFAFFQBFQBBQBRUARUAQOPwKqCTz8mOsRFQFFQBFQBBQBRUARGNQIrFixQkpLSwc1BvFw8lOmTJExY8bEQ1O1jd1EQElgNwHT4oqAIqAIKAKKgCKgCCgCPUfggQcekGuuuUZCoVDPK9E9DwsC+fn5snjxYpkxY8ZhOZ4e5PAhoOaghw9rPZIioAgoAoqAIqAIKAKDGgElgPF1+cvKymTevHny4YcfxlfDtbWdIqAksFOItIAioAgoAoqAIqAIKAKKwKFA4Prrr1cN4KEA8jDWQSJ44403HsYj6qEOBwJKAg8HynoMRUARUAQUAUXAgUAwGJTGxgbhd6Q4TeQYgdmPcj6fL7KYDqQPQkRXxAMCzvs7HtqrbQwjoNdt4N0J6hM48K6pnpEioAgoAopAzCDAFDvhNDvNzS6TcofErryiXNasXi0ej0dGDB8m+fmF4vF6Zc/u3bJ33x4ZPmy4pKalyZ6dO2XLls2SmZUtM2fPkcr9FVJXVyduj1dyc3MlDWVcLlfMnK02RBHoLgL0Obvuuuu6u5uW70MEamtr5Xe/+10fHkGrjgUElATGwlXQNigCioAioAgMSAQCgaBUlJfK/ooKqa+tkUa/X1z4t3vfXnnwoYfkk48/kROPO1oWLJgvRcNHyTNP/V1WvvGqzD31ZCkeO0FeWfyirF/3qZx93melqrJC3nprhdT76mXylKkyZ84cQwQTE5MkIzNTUlNTJCEhcUDiqCc1cBEoKCiQW2+9deCeYBye2a5du5QExuF1626TlQR2FzEtrwgoAoqAIqAIdAGBhgafrP7gfXn+uWdk1TsrZc++XRLwN0mCK0Hc3lTx1TfK5k83ytbNm+R1kL3CoiGyZfMWlKmT7Vs+kaSEJKn1NUpG3hBZ89EH8sTjf5MgCOQ5Z50p+8t2y4MP/FkCgYAUFBTJzDmz5YgjjsDvIUa72IXmaRFFYAAjAO07NO9GC6+a8gF8nfXUeoOAksDeoBen+z7zzDPix2x0ezJ//nwzu9ze9vbWV1ZWygsvvNDeZklKSpKFCxe2u103KAKKgCIwUBBobGyU9995W+74+c2y4o2lOC2XuBPgho9xqQskkMPTEP7mZ3jF19Qs+yv3i6+2XNzQ6qWkJBtyFwo1SbInSRpr98uKZYulvjFBRo4aLu8uWyJPbd4oFXVN4k1Nl+nTp0oFzESbAk1y0okn4fmdbwxQ1Uh0oNxNeh7dRQAW1+hn3Et7QXex0/KDBwElgRHX+rnnnpNbbrklYu2BRc60/ulPfzqwohu/brrpJlm0aFG7ezBnDqNm9bVcffXVQsLWnrz99ts9IoFbtmyRSy65pL1qje+KksB24dENioAiMEAQCEI79+mGtfLgn/8oK99cKl5PoiS6vOLFRFhKcoJgUcADJRhwSV1TQGrqESQm2Ix12IYyqR63+Fwh8QdD4k1MlEAoKAH8zvA2S8Xe7bJfApKA9fmZXnG7RfzVZVK2c4uU7dkhdfDlyQEJtH6IOggeIDeVnkY7CNDfFr625m+4SAgMcPvWjfLq4hdk7+4dUlBYKMVjxknJmEkyfMQo4097cGWsgaKkMYyD/h0MCCgJjLjKDIP7zjvvRKw9sOiF435PZfPmzR3WfdZZZ/W0at1PEVAEFAFFIAYQYAS97du3yJOPPigvLvqHJLrpAZgISwg0LjEAn8BmaQg1S2KiSzzuJMlJTZLsFLfsrXZJTYMf2kJGC4WPX3KK+JugCUz2SHVtvSS5QSTh7xdANNGkBA9+J4AcInJosFF2bN8kfL+89957sndvqdzwze9IVlZODKChTVAEDg8CTuqWABXgyFElcsbZ58kLMMV+8pH7pWzvNknLzJWx4yfLMSfMlRPnng5SONEQQieBPDyt1aMoArGBgJLA2LgO2gpFQBFQBBSBOEeAGsDdu3bKP554XB57+AEJNDVCY5cEH8CQNIcC0hQCcQPHawBRDOKT5PJLGolgukeGZqeIq9IllfV+SU5sQjTQNMkAOaQEk6HxS0rEJ8GYfCa7PVjrkv11DdKIYybAdzA5zWP8DBcvfkUmT54h51+4EOudQ2NTlf5RBAYYAi6pr6uVfXt3yU5MvuzZs1PKy8qlAcGTmmFwTf/bouFDpXzfdikvhRYdZPDdtxbLow/+QY4/aYFcdNlVMhH9JcntbTEfHWDw6OkoAh0goCSwA3B0kyKgCCgCioAi0BUEmMdv66ZP5bl/PCkP/eVeqa/ZL4kgbrD6NIFa3FAFkpORloUQsKKqPiD7QfiaoNlrFp/kprtABt2G2DF+aCPYYqKr2fgRpoMENsJstBkmo8mwRgngd1NTSKrrGwyxZJ2J4IsFhXkyZsIkeXP5WzJvwRmSmZnRlaZrGUUgDhBoa65Jkrdm9Xvy6kv/kg9WLZdd27dKTU2lBBr9yJ9JbXpz2EQUnSPBxaEutO/QnrMWbinbu1P++fj9suKtJXLDt2+U0xacb4jjxk/XSkNDvdESFg0bCXNrTrioKAIDEwElgQPzuupZKQKKgCKgCBxiBJjYvaamxhAxb3Kyyfnnq6+XsrJ9svrDVfLs04/LksXPS0N9LQaPIIAw30yCaRo1hA3+RpA/GnoKgr24pRABYZIwKN1VVQ+iJ5IC08+M5CRo+5JQFgTR5YP2UCTN68YgNgDC55MU5Ab0NCUa4uiH35MvmIgBL4gljtFQ65cNa9dKdna2jBlTgjaVKgk8xNdfq+svBFoIHSZHGnx18ubrL8s/nnwEgZfekJqqcvRDhFgKR4FBA2F+zVkRfrc2l6QQ5M9h98kJmWb0v73bNskf/udWeeFf/0ROzi2yDb6E2XmFcuZ5l8jCz38JZqWj0Y85laOiCAw8BJQEDrxrqmekCCgCioAi0AcI+EHkVrz1ptSD+I0ePUIaGxoRgGIrBqVLZNnSVxCEYpskIWBLIj4JGHAmJ3kQDAZEDRq9hsYm+ANCg4cNTUgTwWAv6fD7S2tAGghsy0iBhpAmohid+gIgi0Fo/lBH0BcymkMOg0Mgig1Yj9ErNIDwNISpWyZ8CpPBIgMY8tbUNcrK5ctlGMzfmo02pA9A0CoVgcOOQJjQrV+7Wh76811Ip/JPqaquRB8LszoGVDpA+LreOBfMtF3oq/t2bZM96LshTNYcffypcsP3b4KJ6JHiTUnFBExPau56G7SkItCfCCgJ7E/09diKgCKgCCgCcYMANXuNMDf78Q+/I7mZacY/r6qiXOrqqkH8mk2gF54M4nySskm93ye+RleLb14i1uFfgKo7RPREZFBPYhAkkSagjcgH2ISooIlYlyhVyA2Y4gHJwyDXD2JY35QAs1KXpCUHMSh1mwAxCdB+JHtAEEONMF9zIVG8W4blpkpppU+WL10mp857X8aMHefQkMQNzNpQReAgBF59+V9y712/kPUfv4/ZEuTaRH84oP07qHiXVpDeNWOChqF6XehP7N979uySJS89D1Nut0yYNF283hT0WtNlu1SnFopdBGjJ8dprr8lpp53W2kiumzlzplm+8cYbD4pwv2HDBmPOP3r06NZ9BtIPJYED6WrquSgCioAioAj0CQLNIGTU1B2BAcOESdNk2ZIXQMrgb5SItA7M+WCGigeGi8YrCdo6/ktK9EpuTg6IGv2LEqQOJK+21ofgMDDpbKoz6R/K4SOYiIAwCQlu+P81m5yAyYgs6kdk0VCiRzKzM2T0sDwpGVUkIWgSV6/5RErLS8ODYRyjCYnnM6FNzErxSFnpbnnlpUVy2vyzJD09vU/w0EoVgcOFwPPPPSl//N1tsm3TOtzpJG1h7Rx7W2/kwP40J0W1iNi7c9tGefDeO+TZJ/9Pjj1lgVz8hWtlytQjNXBMb4COgX0Z9f8rX/mKsdJYuXJlmxZ99NFHZpnZAaw0weqC6eJ+9atfyVNPPQXLj9F204D6VhI4oC6nnowioAgoAopAXyAQhBnmVqRhKC3fJxdderksX/E2fPdqxANyyJx9jACaQK0CBV+hZmj+MLDMzcmWU046Xk479UQpLMg3Pnyl5ZWyfsNmWbtxq2zeulMqq2ugzWuECSe0e3gre7NzjVlpHiKE5uTlSklJscyYOlGmThono0cOlSrkef3TA4/JQ488CX9CHB/HpfmnD1pKJpcPIGn8+rWfyPZtW2XylKlskYoiEF8I0NQTzGzZay/KfXffbgggGCCEbA3bWrpaX5wUNYxVFWXy76cflvdWLJUvXP1N+cyFl8HflmlXTCP64rBaZx8icNddd8mqVatkzpw5bY7Ca52Xl4dnZqDNhNmOHTvktttua1N2IC4MChLIC79///52r9+kSZNk2LBh7W6P9Q1Ub3//+99vt5klJSXy+OOPt7tdNygCioAioAh0jAA1er6GBrnn7jtBxiZLydgx8sH7q2C+6QJxC0mKl8EjEOkT1p7014N1p+Tk5Mq5554r111zqQzJzzaaPw5gWdcZp58o9dAI7q+qkcrKaimvqIJZqU9CME9iDsHU1FQpyC/AJwtEMkPcIJrURNKXMMWbL/PnnybPv7pCtmzegoAyCSCNLpM3sDHQSOdCqQax3Ldvn5LAji+rbo1VBDA4/3T9GvnLH38tWzeuBR8k6yMB7NsG47DmOCQHSfC93Qt/wbt/dbNUVlXIpV+8TvLyh/RtA7T2w4oAg/44NYCH9eAxcLBBQQK/973vyZIlS9qF+5577jFq4nYLxPiGSswKd5TgnkEMVBQBRUARUAR6jkAionaOHTtWRsEs6O7f/krSYXuZmwY6x8EiA1Ng8NiMQWMzNIK+ADRzSaky99ST5NqrLwOJSzWavsgBrBcBXYYWZMlwpHZIAIlrHeG2aDlCzUEJBun3h6AyDAjTIsz/N2l8sXzmrHnyu//9P5iOBiUz2SWhhBB8CFmOfoeMTjooXvEWFv2OewRaiB7UfHW11fLw/X+QdR+9Y+5/9jOj/mvpG317qsZ712jyOWHT6KuRh/98p+TmFci5n71UMjKzcHjDFvu2GYOodk5wbdy4UXbv3o08p5OloKCgy2dPJQ9NOkeNGiXFxcVd3k8L0jlBRRFQBBQBRUAROEwI+P1+2bt3r3zyySfy5ptvyiuvvCIvvvii/Pvf/5Znn31W/v73v8tzzz0nixcvljfeeEPee+89+fjjj2X79u0mKudhambUw6TBv27hxZdL8bhx4qutggYQwWDwYQoHJILA0BW+ecEERANNkBlTJsi1X1wohXmZEkROPw5gDc3Dn/CAlqQRg00MagMgeH5ECCU2fph0MgqpH4nm6ZfSDCLogl2pGQO3tKoZvoSZGWlyzvxT5Ihpk40ZKSvn+DiI/ILNSFCfCZNSD9JL+P0NUc9FVyoCsYcA72B+XPLCc0/LW68y3Up9a3/pj/ayryaiPzUi+NMTD98D7eTHRiMfbifbqtIbBJhy58tf/jLS2WTKhAkT5JRTTpHCwkJD6KJZsP34xz+WJEzIXX755fLWW2/JtGnTjDkn9yspKZH8/Hz5v//7v9Ym0f+P5R966CGzjgoTLvN4FAaG4TI/f/jDH8w6Wm+MwzPeynnnnWe2/+hHP2ot+5//+Z92c5tvPrOHDBnSWr7Nxhhc0GnCGLwo2iRFQBFQBOIZAVonvP3224a8bdq0yczwcpaXfhZ1dXW9OrVk5OejD0dubq554Q8fPlxGjBghI0eONN/8zdlgbj/UwtQPY2EGevlVX4FZ6G9kf+lOY4YJ/mXEhJzHIHZk0TD58pWfk+lTx4HI+VuUBtDUwVa0Hvn+aquq8F0X9gFEGPqcvHxJSUFUFwrM3vyNMBPF7HZ9Xa0hjsxJmJ6RKSlp6cZXkENPKg7HjxkhV192gfxs22YJNdZgX5fUgvPlYBA1fuIUeev1V4FVDgY0E03V+kcRiG0E2JNcsm3zennu749KeeleqCrC5tP9R7fCWkEXIodu3rBO3lr2ihSPGS85uV3XVMU25v3Xui1btphInZvha01JS0szBJDrOel38cUXm4nA3/3ud62N5MQZiRsnCjlhSEs3EjiSdRKw8vJyufLKK02Zq6++2qzn9kY8UyksZ0mfrZT1UaiNpGRkZLT+5rJJ+QNrD5K7WbNmCYnlX//6V/n5z39u6mMZK5zMpBk+5fzzz7erY/ZbSWDMXhptmCKgCCgC8YEAX3qLFi2SpUuXynLkqVuLpOV8WfeFNMAvb+fOnebTUf05iMY5fvx4M6PLb36mTJki9AFvJVwdVRB1G3L8NQWltqYKJGuqLN29V5IwcGCyd5po0iEw1euVo2dOlpOPnYXF8OCCQ1sSQJ/PJ9UgyD5fvRmk0BQnhDJ2EGIOicIhag1RniHriSM1hLUghFghaalp+IK2EeuTU9xy9OypctIxR8qrry5FpHukksAnBW2o2r9PlmIwPfvoo0GQRyOCqfegAUvUU9SVikA/IkCf2H/982+yETkBA4EGpGrgfQsz6+YD5tD90TxwB/TTJnln+VI585wLlQQegotAVy0SQJKyX/7yl/Ktb33LPL9KS0vliiuukOeff17uvPNO+dznPicnnXRSmyNWV1cb0njvvffKl770JUPaHnvsMbnmmmvM79/85jdCEshAMHxnsMyDDz4os2fPNiSuTWURC4888ogJCjNmzBiz5ZlnnpFzzjnH/OYkJEkgSSrjccydO7fN3jwGhVrN4447rs22WFxQEhiLV0XbpAgoAopAjCNQW1srjz76qDz88MPy+uuv9xnp6ykM1KRRG8mPU0iS+HKfOnWqHHnkkSZHFPNE0Z+kc3GJ241ZZV+t7Nm5FYMXJGkHKaTfkJsBX8B7MzLS5ZijZiKgS46J0sk6DR/GIJJ+fxlZOZJXOFzcyWkgbQh4D3+/EIK5kOyxHiaC93rTZcjIXHEhVxlGNNAMNuADrSCIoaHWPBAmrRNQtqgoT+bB93DlyndgOteABPRIK1FbJu+9tRRh7T2y4o3XUWa4jJug2kDn9b377ruFA0hqkjl7rxILCLhkLfIAvvrCc1JVtV/GTZlpTDF3bN6AgTwiLbF/9FICMJdmmomEpMRwf+tGfUlIB7Nz2xZom0qhDUTOTlgGqPQMAboCPP3002ZnmlZ+5zvfaa2I/oB0DeAzev369fIf//EfxnWgtUDLj//6r/+Sa6+9tnU1NYCciLz//vtl9erVhvyRtB1KufTSS+W73/2uqZvvPicJ5DuHGkoKSWc8iJLAeLhK2kZFQBFQBGIEgT179sgdd9whf/rTnxCBsrrbrWLeOmrp7IfLNLfhbDC/+aFZD2dvqTmz3zwWTX2qYErZGy0jTX4+/fRT8/nHP/7R2n6amB511FFyzDHHyP/7f//PtKN1Y+uPZjP7fPLc+fLM40+IK+hHUnfq8zA8BUGjqVF+fp4cceQ0ccMfz7BC+O8FENwFm+Gjly6pIIGJaVkw6/RS3wcy1yRBfx3y/CGCNZLLC4ibJy1XEjwZIIl4RYMcJoEkehorJViPc0dyeESdQfuSMAhtlvSkNJl+5FQZP7ZE1qz5GGaiyGeI+nwNzUaLsvyNZXLagrMOwfC5FYQB8YN+qQwmsWvXrgFxPgPhJNg3Fz37uDSg/9/wvZsxGeKVJx97QBob0C96eQcb8of+OWvmbPTDZJiqfwjTdEysQMvYZUHZmpr9iOZbYVIKePCsUukZAvT3tnLDDTfYn63ffB984QtfEBK9d99912j3OIHnlC9+8YvORfOb1h5WaCp6qElgdna2fPaznxVqHZ988knhZJI9xt/+9jdjdsp2UpMZD6IkMB6ukrZREVAEFIF+RoBk7PbbbzcfkrOOxO12y4wZM+RomCIy0ltJSUnrh6kPeiM0nayoqDCEkGaoNMuhr6H9cJn+h/RL7I6QYNL8iAFrbr755nZ3dYOkHTFrjnzu8ivl93f9Ruob6iQJXM6LP0wc7wI527J9n5TvXy5NCPBCk84sEF13ihcELw1MsAGpHEox+ERQGeCUlZUNP5hcSclIkuaGKmxPl4ZgipTuLDd+gcanEHQRCSIQQQaDVn+t8RmsxgC2KRCCmWeSVFXXSkpquiR7k8SL9lFR2ATiiaChWJcsw4aPaPd8dIMiECsIrF2z2qRX+fFPfwONu1t+/fMfQ/P2Kegf7v0eitX8jRkzTs4771xZMH+ebEVuzjuQBLyurnuR041JKHJwNjXWwxw7bOrdw2YN+t02bNjQisGJJ57Y+tv5wz7DaQ6/bds2KS4ubt3MCYKioqLWZfvDuc76+Nlth+r7qquuMiSQE5LU/NFclWJNQefNm2f80w/V8fqyHiWBfYmu1q0I9AMCtJ3nw4m5I+1slJo+9cOFGECHZIRORmOjr180oQbs2GOPNX4Tp59+ujGz5Eu6L4SaQpoL8UP/vvaEuZ+sxo8DDrZ9zZo1xryImsb2pL0BSbg8zdEQmRMh4i+5/ArZsWu3/P2pRyXYgAiGLcG2t+8ulbvueRgELYByaXLGaSfK7COnS2pKriSm5iBXIPL5gUT7fA1S4auAb+MuEM+wRhAuftKA7aEQTNWAKc81OdkL378UaBZTJSElTUINyVJXv102fLpdFr3yhtQgHyA8pqSuqhw+S6gbaSJIAv0gy+7kTJkx5xiYpha2d7q6XhGICQSYCgU6bPnS1V+FNjtBbv/pf8gGmIY2U8OOf+amxn3dFQmhriAi7nJCZuzYcXLGgjNl7mlzJSc7E/W5ZEhRIXJDD5WdO7ajP3bDzBTHZ790JbrDbepKY7RMVAS2bt3aup7P6c6Ek3vFxcWtxbKyssy1aF3R8oPXp6+F7zgGIOPEI01CSQJ5DoxWSrkSZqnxIkoC4+VKaTsVgS4iwHD61JTYiFvcTU2fugieFjsIAUZBo7O9ja7mLMCInF/5yleM/wN/x5IwVDg/JKdOIQEkKfzggw9k1apV8v7775tvmzC4YxLImkD3YJU0cuQIOe2UE2Xxv56QAAYeSTDdpJnZrJlHShrSM6xesx6mrI1y8vFzZOKkiZKcPRLawGzTFGoziWdNTS00mmVGc/ni4iWGpDKgwOc+dxEGqcNAdPNhfppuzOJoYsTxTZMPkUUrckEO3fLSa2/B1DQos46cJOXQir63vBQaigDMafFBnsDJE8dh8DsfZqF81XME3bsBEs1wOfDhs4VmURMnTjRtMyfVyR9GibUDudHItdiZcBafGt3u5g0jtmwfB5kMAETzMLb1UAsn2uh3xFDz1HbTfK0rQu01saDpMUm+ShgBk/tyynSkW0mQ+/5wu7y7/DXcxw1moA/e1qHgtsTESQDpUULGD7agcAiuyRQE5jhGpk2dZvoq+xvJIU33MocOw707GebAH0p1jR/HJLnjISIP1LbP8P73YjImPT2jpU912Czd2AECjLRJoRk+UwR1JnwuxorwWUyfv9tuu834E1Njyfckhc+DCy64IFaa2mk7uvbU6rSaw1uALwea7nQkzkSTvfEf6egYfbmNmhvaQbcnF110kZx99tntbdb1ikAbBBjymDNn/KgoAl1FgHmTvv71rx/kg8dgGj/96U+FPhk024onYXtJDPihk7+VLVu2yIoVK+SEE06wqzr4dpkUD++seEsa62skMz0V4cOLoH1olss+fy5yV02RpW+8I2+8vUpGDh8CLV6yJHpaUkCgVg7+SVCsWS3NZhkAgVHoTjzxJDniiCOEEfJoKurxeBztgFmoOxmpIjKg3cuWGdMnyajhRbJg3vHy8ktLZdO6dfC1zJL1n26VoaPHyHkLPy/TYZZ7KOS+++4z15wk0ArPg2295557TBQ+u565vGg6zMHQ5z//efnmN78p9CW1wgh9vLd43pHCvGEMEkGfG2c6EU4y/AomfKwvmrz88svC45LU03zMCjUDnLn/3//9X2iFxtrV7X6TLNDn56WXXjJl6B960003tZbngJXnsw5YW6HWm7nFeAxOPDjlBz/4gTBSIfsKn7933XWX8W/i4Jd96Gtf+5qz+KD9bTXfa1evQmCYZzFBUmnIGRM0mPmLCGRI/Ej4GV03JTlVRowuRr+bbkL4T5o0QQqHFIrXk4w+VotUK9DUg+mlggAymAustmXsmBJJhQm139+ESR0EU0JeTubftJKQ4MZzDzk36cxryCFbEkJU0Dz0sTzjk2vLxts3+wdz6VmTTPaL66677rCehiV1HMuzbzvH7LYhfCbSgoOTRn1lWWKPFfndGW+4Eto+kkBiycjY1r+cz6eeR5+ObEXfL8clCWR48M4iudF/xd40h0M9fKgvFRMoc0DQntAMSklge+jo+kgEfvjDHwo/KopAVxGgr0MkAeSzlOv++7//2wRI6Wpd8VCuuLhY+Omq+BB0YMvmTYgO2gRTzzRD/CoramTUSGjwcjJk+rRxsmXHbnbsckEAAEAASURBVJibcfAI8zEGeXEIB7D0c2GAhAsvvAARS0vkeGgti0ePMsFKlr2+TIYOHdqWBGLk64KGrxkD1AR4CRYV5MmsIyZLblY6ZqDTQchmSBoI6YZN25CkvkDGlhRjQGKj40VqORyN6eQnZ7mp8aXwvVpSUmI0gowQS1NhhkJ/4YUXTM4vluEAiufHwAmMAMiJW+5DLQwHdZzgJGnivpxQsEIiftppp7VaMXQlbxj3/eMf/yhf/epXbTVmEEZNKjWPHKSR0FEjTO1gRz6pLHvhhRca31BW9u1vf7sNAbzllluEH54f+wLHIdQCUCvIc122bJksXrzYTDDYxvDcicUDDzxgV5lvDn4jCWObAoNyoVmWvvoCTKS3hgkYMI6UEPpTMOA3EysTx02SOZhQmHPU0SB1Y8wESBJInsnsx34HrTh9eH3Qnqd6kVoF0Xwp7JO8P3gvpOCeHA6f2dWrP4BWED65NK7GNSuEuXkDAjUxHQyJJokgNY0jisdIRmZOi+aQtcWf0PfZ9me2fu7cuYedBDJ1g5Vbb71VnLkAuZ7PljPPPNM8C0iqOO5nMLGeCrV3FDvx1lk9Ti19tH2YTJ5WI+zzTGNBqxIKyWE8SRiVeGqxtlURiHEEGCaYudKWLFligkxwENBXwkHTq6++agY3vT0GB0A0b2KIZWsa19s6df/4RIDmdPQndc6GcsD01FNPmRceB+eDXTKQvH3uvAWSXzAMOCXI6FEj5Ljjj4KpWCoGmUHxJCVKBn7TNzCBTnoRwkEJSUA4mh0igEIbcebpJyLHn1syoTGaMmVy60Rm666OMTE1G/QXzMxIk2b4PxUOKZC5p54gydAchqDV8FWXgaAe0Ii11tGDH9TAURiynYMxBs8hiWGyZp5HAOdILVg0oeb1oYceMiaQNFWnxo6DOZpFknDxuWPFmTfsf/7nf0z0WZpO0pydA0IKB1xMSWKFBIuTEhTmgmToeZIy+uhwP1rNUPhMY/S+9oRmwvTt4aw+hWHpqcGzQg0jNXfsE9Rgkszy+cvnPbWWjHJLbec3vvENu8tB39TqUrPIyLrsX+edd95BZQbzigqYRn/47grx1dLP1XGzAxSSvyZo7VJTU+SUU+bJLTfdIrf/4ja55uqr5ag5syQvJxt7cPIBvoDmHyYjoJmneKE9T4J/IOugsBw15pk0tQYJnHHEdMnKzIa5aLPpk6lYTxPiEcNHIngTNfFoCwgptYNTph4JEnjozYtNww7TH1o7MO9df05CsD/PBfmk0PKNfcv6avP9Q5LKbwr90XtDAFmHfWfxeUJtPp9FHYktzzJ8znFsFGmBeNVVV5kqaEFCITHsmiWJKR4Tf5QExsRl0EbEOwIcdDB3DO3cc3Nzzcw4Z7Q5wOODlg85Jxnkw4Q+JPy0p/GleQS3M4lqpJBk0qSqpKRETj31VCkuLjYmE5zhiyYMs8y6bPJTZxnOll9yySVmVnT69Ol4wZ5iTDNoomGjXTnL6++BjwBfbhzcWqEGh7nU4snXwba9r77TMOg/8+xz5biT5sK/rwkaOzf64glI5p5iiEIKTECz09Nky7adaAJyk7naRhMkeWIfmzv3VGOS5qspkyOnjkGUURTHYPVUPD8O1lrxlQ3NEnylSiuqzEA5F4NZ1jW2ZLhMmTRO9sGMFCFlpGDIUJk2/QiUbzuYxopuCUkWB0CU888/3/jw8DcJDZdJeqgJbG+QRjLFUO9W+Fy0pI05HDmTTiF5i8wbxvOi8FnIvGHWhIx1WiGppEaVwucs22JNlDmQZw4yK/QxjCYcfNKMi8eg0PzzF7/4RZuitKTgM5xmnM62UCN48cUXG/NW7kArnvZ8nPgc5mCXPrY0x7Oh5dscaDAugFhTNm34RHbs3Awi12g0rdTKga+BHMDkMzVZ5i84Q26FCd5NN/0E/eZkyR8yStIKisWbM1o8ucWSnDtGJHW4PPPiCvl4a6W4MkZISt5YySgaK8k54e3vrNklyz/aJu6sEZKeV4RgvFkyZvJMycwbYjSIND0sLCiEf68H71WaIYb7czP8CbNz8mXqEXNAArPi+irxHqalBydh+lN+//vfmzET+xX7HM2lOabhOIU5aCkckzAlUW+F/ssUavXOOOMMM6EVSeqcxyBGliTTEoLtuPnmm51FzDPDSRbjJTeg8yTa2qc4t+hvRUAR6BICfKgw6TRnha1wcEeTZPr1cDDNgRLL0T/ECgdXFKffi93Gbz4YWYZmEU4haeMMMuum8EFFPxYOhD7zmc+YfZzl+duaZ0XmdaNfC8mkbQPNLvjwo98PP3yo0czLOj1H1qvLAw+Bf/3rX/Laa6+1OTGa6px88slt1ukCghoUFsoxx58gy155EVqgvVI0JA+DV5qhIX9fWgqI2ShZt3GreFM/lLyhPsnKKYSpYhpISvjV6wf5KCvdK1s+XS95Wckyeni+0Xas+egdcLdm9EUMRkEuORKmtq2uvlaqkKi6fOd6E1V0/NiR8HPyYntQckF49u0rk127dxu/pbmnL5BR8JPqrVi/P5pwcjDGZw21awxsQpL229/+tt1DkCg6k0Dbgpxk4LOQ/n80oyIx7GneMA7o+Iyk1iCsVbVHCT9D+Ry1/pdOraMtRVzpG8rZfsrPfvYz+clPfmI3t36TpFJ47oW47pHCiTQmriY+K1eulAULFkQWadVKHrRBVxgE1q9fIzX7K0z/ScDcBcm5F5OXRx1/vCwEYTly5hG4lkgxAz+9ZrwbE2Hq7EnLh5mnNXkGbcTmuQsulIyMdEnDh/2oub4S6TdhvZDklZnHnQbNvMukZ0mDRi8ZQV6GjUbwIPQ1+guOGD6i9R06csRwo3msqoIWMhCUydOPlJGjxxkTU+cloykwtUG819hfbP9gGT4LSPiZk5LCCQr6h1KrTuE7nhMHtByiFQ4nPNgfeP9Y4mIK4g9JC+uiNp7C/sc++c9//tO8ozk2WLhwodEyO33sGCiOEyTsw3yvk2RRE2g1b6ayfvjD/sqIzd/61reMlQnHSHYcxQkS9idOnEQL7NTWV/pA4+0E0IE14V/XX3+9wdeap3Mtxz80E+cEOZ8Dzjo5uUMNP/u1tYxy+gFzf2r/OTHKCKEsb6Oxc1u8iJLAeLlS2s6YRYAEyT646C/y5S9/2fgbsMH0D+HDlgMDPoSdJLAnJ0RiyYdOOGhElnn4kPjx5XPvvfeagAWWXHZWP8uR5JEA8uH361//Wvig5AORvjM0weDA7JFHHjGkk7PdKgMfAafmhGfL2WKn/8jAR6DrZ0j/uCGINAjFG4IsbES/rIAfX77xHfKA6I0bMxIDS5fUIo2D379BymAC6UnNQB9zo8/SvK1BGmr3S3Jig4yZMAqJ55OkZNQQCW1D/sNNn4Ag7jERR6koCWJA3NRQIw118FsK1puAMGNKRkDPB3NzDEDor7RvT5lUlFfK9JmzZMFZ5yJSIglk74WDNPq6cNDI5wQ/nCw666yzzMQTn0HOGXF7RPrMtTeAK4b1AgfGNLOk2CAV/N1ehNb28obx2PSTpykn/QwZUIIDNn5INK1wQB4ptOKgmbOVaINIJpS3E2X333+/MWm15Z3fdlDtPBe7neSAWg6VKAjg/qXf3Y4tm5AYvh7ESaQR9/OEKUfIRRecLyeecKxkZ2ViRxA6vOt4FTnobkbOPhJCrg8LPGWhPGT6B26nBJhXs7ZMXKkBSQJhzGrR4vF4adACjp84CX14OO7fTPQ1BJgZMdJMpDC/Z25evtHW0tw7Aeaks489EesKwodq+UtSZU2O7YZX4aJBLTflnXfeEWs2aLfTFJHEjZMSnICwGnC7nRMSvF8eeOCBNlp0mhH/6Ec/ssXMN82SqdWzwjQFDD7EPsD3OgkmNfGMsmuFfYTENRaEzxGSKFod0YSbk9n012RfiRZg5ec//7nw055Qox8teBT79RNPPGEip9MUnVZb1nrB9tvIOpnvjyblNCHl/k7/ZVuWBJvCsp3FKrH7xNK3ksBYuhralrhE4C9/+YtpN82QOGvlFD4YOFPNBz4fbpxtIsnqqXCm0EaNpZkoB18UzgiSwHEmsKMHpPO4jz/+uJm95DqSVwb8sEKTGJo0MWoY/VyYc1BJoEVn4H7TNM/OWPMsORCxpnsD96x7fmYcKKamIuAEBojbtu+RT9ZtwEBhKPRyfjMsTUXiv2mTxkgDzEXrfPXiQ/b2oAuD3CCDVyCcfWITNPleyUGgCY+bASngu5Lskcljh0t5tQ+J6GslgQPdBCSWR/6/DGg5krOzJD0FkQ+NBpBj4BCOnyA+TBCt37hZMhG58KJLL5PiMeN6fmIRe1JzwQETJ7E46KRQK8HBGz8MYMNB5fHQ2DjFmlM519nfHPxRbL4w+811HAx2JnzWFYNIUmiqzOiGXBcp9NPpSn0kqySZNEtj0BqnVtHZNk7odVZftHZw0BmNYEa2d7AuUwtUum8PSGAD0i945bOf+6J84crrEfwoQ6S+DPd5oBUaw++QSiIIn9cAEre7k1JaSB8JIVOphAlgc9AvTXVlYJRV4kekT0bWFS/qQ5kA+tWRs4+TI2bNRlClbBwTgZaQfHwkfHtXIOpvIX4zzyd9b0kY8wphXj0DpqDwBXYKiQGDEjE4EScZeL/R8od+tNQgUaNHUsL3LYX3AUkhxwKMQst7l8JJDFpbsF/RxJmTE5ykJVG8Gn6PFFoAcRtJJCcmKJYAsl5a9dAlhCSHePL49HO1pv0ci5BcUWtpA5mYSmLgD8cwNPe2Jt991SS66/DTVWG7eE2jCSf/aSlF4eR/PErPR6PxeLYDrM184PDTmeaHWiLOhrIsf/PTkVDLxAeRfUl3VFa3idGWWfOMSDyIIwdIVvhA7w0JZG4zCnPRXImZ+Uih6RV9WTq7xtzvww8/NLvTpIEhzyOF/kj0c6QvDGc7OZPoPJfI8roc/whE+oBedtllff5SjmfUSJIzkcahAOkhtmG2ePnydxHh8yhjasbQ8njkQpoRrCUJUQg5eHRhgAq1IQawRpsBusjcZXiSo89itQkg4xI3tIdD89KxjHIY0LYMacNaDxQM8tlPDSAqQREEognBRGy3rF23Vc678GKZO/8sEHhsOITCCSd+qOWiyTAHrzQbJinis+E0mLDx3cF0NFacWji7zn7blBGWbHEQS+F7pz2fOrsvv+1gkZpEmsBx0MvrcdJJJ5ngDExdQZNVzs5TU0grCr4DI4WEgb5JHECTxPJ8+GylRoX1UWzb+PvGG280x+Pv9sSJgS1j67LL+t0WAV99jewv3yfelCy58ivfkPMvukLyQbya6vaJvwH3srl04Z5g90xAond/XSnMQd2w9ET/aiF/3B4K+qSxZp8E6sphEYr+0lgjvqrd4s1CrF4P8vwh4ftpZ5xnooMGm3wglI1yAq5/Xm6e6U+TJ08yUX+98O1NwURPCQhKYdEwkMW22nXeP0x3wglamjby/qOJob0/aUZM4kY/Ut6jfNfSmoflLQHkOmrX7diA5ILR3xmAiYGELAlkPkpGuqX1kdPP9oYbbjD70zKBfq+81+hfx0BLJIBcpnbRThqzH9DkuasTxhZv/RZjisuYDHyeMEUEx99MHN/f/pU9vTZKAnuKXBf3Y6enGQk7XXsqZ1sV/Rr4YmRZ7teRkFxYJ3fO+nQknHGiSttKtBeh3cZv3tydHd9ZfrD/5kuADwFeD84GcpaOphj88GHuxNv5uye4WRJYjBlwp/26rYsz7xywOE0/7LbIb75gKJzlOjgARbj0DEeOMZpWKQkM4zIQ//L5ZGer7flFmjHZ9fodRoB9f0jRUJlz9PGybvUaeXvFu7LyvQ/kJEQJDQbCNI8l8UgPW62ZkSxN2LDSEL4DRA1VtQo30+cJ9K51nflhCrXUy69wRYiiWSOr122WMROnQgt4BQaAPQ+l3vaA4UAKnDBi/+fglRE4mTqBH5I8mgrTd4bkiaZnDBZjhc8/rrfpmux6vuusdo3ki2IHzXy30afa6dNk9+MzlZpIZ94wmqvb9xUtLiJTJ/G5zHcaJdqEKbUCNr0EzV4ZlZQ+fbS0sKbRPB6ft5zEo4Zm1qxZtkmt33y2M1cYNaY0Z1PpHgLVVZXIpZksX/3ujXL2eRcDR2hrcL8nYvIkob4CkyTQBFJV3jIlYmpHn2n2V4lvPyxsUnMkiX5/2M7ASYHGSmmGJt0l2A/avDARrJSGCmxLhuYPpDEZEyVB3Bv7S7dKycgiTAIcjZQQtZg4GA0//yNgOrhfZsK0OhV+iKOKx8E0FKr4doRaPJJAkjtqFK0wkq3zHrW+1ZYAsj/xnnNOEhTj/c57jz5m1PixXutDaOu13+x7TisdWu9YIVmhXIlJDUsAucznFn1faQbNft0fwn5EwkyhzzkD3FHoNkPXFmpYLUZmQw/+dLeurpTns5DaVSvUFHISIF61/AfeQPaMBuE3X0hUm3MWkzdmR8KXCR3QaSNsSVhH5UnQOCAnubMvqvbK8wXDtpA0RntZOffrLZlw1hXtd2fHj7bPYF3He8JqTDiYoD0+TQR4n9C52flC6A5G0a6xvec6SvreVaJmzUk6Mo1wBkBwTiR05zy0bHwgQDNj5/OP9xEjxap0jAD9huadfqaMHjtONm7eIs88/SwiHO6Bb1JYi2T2dhA8M4g1ZC7a69dRkGUiPwdVBlM1kMFAsxvJ4SfKwku/KCOg+TqUQr9gajZomhYZBIYaL+dEQWTgKb7L6McUKdS82fehJYGRecMi9+G7kWHlSTJpes9livUppP/Q/PnzI3cz6SnsSk6IdiQcGNsIyjfffLMZfLM8B+gzZ840u9JPK5q5J1NkkCTPnTv3IJw6OqZuCyPgRR6/r3zjh3L2+ZcgsFE+7n1qy2nqnIIAMNlYdvQnB2jUlrsC9RKo3iUN5RvFV7ZRmqq2Yza7GsSPkyjoZ2bChBp1/IPWL1ALs9OKTaasf/9mSQrWybnnnCFjRiOCKMLznnfuZ2QYnn/JySnw9TpVjjnueEnPYBsc/dPRBv60z0q+o0nqnEJXDQonQ6idptgUBQz+4iSAZiP+0N/WijXBtsv2m/s5CaBdb785BqEwsmWkkLzQBLW/hH2Rrgf8OC2pOM7gOk7E9Fa6W1dXypeUlLRqbDn5z2cZzcfjVaK9heL1XHrc7nok/bUJX+2MYXuVcWBOgkRzu2iD9Pb2i7f1nb0s4+18+rK9NNVgOGPeE3wo0H+GM0qcEbSRQe3xo90z0Qg370MbBMHuy2/OGlKsM7JZiPjjHMhHbGqzaAleR3VZky3u2BFZbFOxLsQlAs7cazwBalQ4UFDpGAFO9Mw++ji59oZvyYxZR8u69Vvk2X+9LNV1PjPj3vHeGFeyQAvZC3+1kL/OduTIljsjMmLuMCSaP3m+jBs/ASvMiLfTvbtagINbG0CBg1tq2/jOpFBDYX1GOSCN1MKxDM0nqSGzQlM2+iBTGDSLkZUpPc0bZjUfJJV87tp3FwkotXn2WDxGJEnlOqfQIoJaCAonZa+EBsXWZ1NGsF6225rTU9NJvyyazVOIg9UsmhX6p0sIDB0+AkT7WNxrYV9R7oTMfKZruFNyJcH48vHejri/zSLpHcpDUwjdHn63lGklbWYrq0QhZiDEPxBEF5LJQ+UuXmh5M+HrRxPuEvjSjoM2jf6+o0YPR8CmqXLGZ78I38FjQRCTbc3huhx/rYaPq3hPUKw22y4zv6TVitvyfO5asmZ2avljUyRwkZMePREbXZRjkUhhH7YTKJHb+nOZE0uc5O5ooruv2teVY5M4cwKKlgycDLIEv6/a1Nf16hu+rxGO0/pJVrriVxanp3fIms1BhTWh42CJphX0q6KNPk0d+MC3Zpc8qCV8TlNOOyPubBRnE6MRRjtbTjMrOxBz7se6os1SO8vY35ZQ8ljR2sBynJGz0lUNoy2v3/GFgM3XZltN7Y9K1xDIzc+TSy+/Un57z/1y22//KBPnnCoV/jQJJKSEK4gYt5qVDF2Pjw9BY0rLK6Te1yh1DX7ZizQP1TUgWdE0H6zH2JaGK3R5YNKWPUoycoZDU8HcZY7BbvjIvf5LMyeaa1E4MWVzBdLCYdq0afIqIiFSGAwjmh85n5EMgMHyHBTTl4nPG/rs0ZTNOdHQk7xh1yDnnjXFog8zJ7dorslAL8wnSGJnA9SQtHYmDOZlfbAY2fH22283u1DDx3Ok8LnI9tNslXVTE0BySLnnnnuMuapZ0D9dRoD3AVM0RJMEBIphZE9XohebTSeIVqyX68J9KokTXyCK7GdeTwpSSBTIsFHj5eTTzpQcaP3Zw6IJ34/2ncrtvN8ZNMkp1vyR6ziRQOGkLU0hGbmSlmN8H//4xz82/vjcTg20NS+m9Q5NOOmvSuEYgX3IfphmwjluYCAYCqOPMjgN92X9dFuhD681yeZxGVDGTniYnfrpDydT+JxhOw+3dPXYHNfFYyTQaHgqCYyGiq4zCFjConC0jwCTKFucTj/99IN862jmQD8ZK/YhyxknawJiH+i2DL+dIcud6y0J5ICDduiRwoc7Z7C7IrYu+vXQhDVSOFCz5l/UcLbnkxC5ny7HJwKRA+Sezj7H59n3ttXIO4bk0iNACqZAmzF25lxpzhknVZKLOKEeCRn/P+cxms1gMtgUkl17y+TDNRtl+TsfyZvLP5AP8HvVB0gPAX8kE0SmdbfwIJVEL5iQJPUJmVLpypfKQIrUN2HSzm5uLX/oftC6gQPakpISUyktFax5OM0nGSH5N7/5zUEHJCEjKSIhY3m6XdAfiRpDas8YlMopNm8Y09NQw8pnECe8KFwmCePz1Jl2gs8xRia1baP1BSMf8jgkZ7yvqY2k0OTZTpJZ4mg2RPxhkA5rxs/k7lZTc+edd8ozzzzTajLKgFnWLJX+09R4kpRGk46OF628rmuLgCc5S5Iy4GuZQCIIcdE/sDs3fXv0zdQW/mPmUELoYQighzVJaYXwNcwDOfXgfZ0EbhidpNoarEkolznZQfNkZ2RJJwmkRonpRmxKJpI0+vOzPAO2cKzAvkVySCG5o9ac/micLKFwop4pJuyHxM4ZVInRym3gJdbDfVk/NdWMEGqFpqk0P7WRzu36WP7muXP8xfOINiHel23v7rE5XqPWlROt9nnRl+3rTt1KAruD1iArawnLIDvtbp2uNUXiTkziav3suMwHK4mhc53THIkzyZSHHnrI7MuBFQdJHEzRHyWa0J/Avmj4gKfDuRUSx8gUFXZbtG++OKwpFsOi0wzKan/ZZg7UbIAZ5ibioEplYCLAiQymMLHCCQprSmTX6XdnCECzhzGpP0iDNJiNwZepVpB2wFUgDaHkqCQtkVFAkWB+9hGTZMaUsTJj2jiZPWsKJlzGIQ0ETc/o0+QUJI13eVBvDgjmUKkKZUpZXYLsqw5Kgx8BZ/pQSMzoC08SxfynjAxKwsUZe6dfYGQTGDiGwV5oPkmtIZ8tjC7KiaVoQm0iCScHTrSsYDREEjmarZOEWa2ec1+GzmfUUqZuYCRGHov703S1uLjYJKznIJofau8ofFZymc/cSCHJ5LOP22nKZwkmy1GryXPmuXNQxw/PiUG72I5IIaFkPf2h2YhsS1wvQzPuSc2VxHT6Cyahb+B9hPx97QlfV0kI/MJvkjoWT0IaCC63Sx7JKUMwJkW9bpA/bwYTyHtaD2F2bV06+Ic18eSWK2FKzHemTR3AZ2pkChWW4cQB38NOjTgnialxZ2RRO8ZgXZyY6+g9zLL2/mYb6CdLc1O2IXK/yABGDMTi1GRy//4Q9ksSY+ubyzZwYpvrOJFC4kcSS/cU+jrSYoXR0kli7cRUZ+1mn2Z51smPNRk/1Mfme5URWjkZRo0uJwFo5spxIZ8b9vh2oquzdvfFdo0O2heoDpA6rYZrgJxOn5wGZ+44KGD4ZQ5AOLChiRSDDFl/OkaWs2YXHMyUlJSYtjASHYMtcIDAlwEHSyTeJGJ8qPEBEemvx4cgyR4ffDweB2aM0scXCB+AzhdJZyfMstQc0heH5hfMs8V8gDTZ4sw320Whzw/NW1UGLgIkgM7oxTQ/4v2n0h0EYM6GMWmqJyBpiCJfz3Gn2yNNoQKpgVloIFQpyc3VkuhqkCQMMtm9THnkBWRuQCaKoC+TC/+oi4B7khEziIUGIojBqc+VKvUggI2udAkgxH24rEhTkJpADojZZzsbqobr7elfPuPaI3Dt1clnTbTgFO2V53ruw4ihNmpoR2W5jYNsDoLtoLmz8r3dTqJ4wgkn9LYa3b+rCODWdoGQedOHwMwa2vD6cgkhrUPLa6pNLSQ8NfXIO1i2X7LS02Chkwxz61qpqKyWIYV5yLfZYqbdZi8soOsk4h3rSs4FASzC8Viu632KGnN+nEKTZH7aE0628kOzUEbz5CQHSaG1FHLu5/Stda7v6DfJ0n333WdSSDDADK2T7DglWjL2juo6HNs47uDY0zlhzuPa8ShNXO3EOk29qQXkNpqzkiQzgnBH7y4SSqZzYHkKx0+cBKcc6mMzwqsNEsTrSaJtJ9EYcMuekzl4P/1pfxqlnxqkh40dBFQT2LVrQZMOzoRz0MKHCP1FSAA5kKZ2jWTNBlagKZEVhn/mjLd1HufDifvTrIgz5lbjZ8vbb86UcyZ97ty5ZhUJJwkgH/bUKp544om2aOt3e6ZIxxxzjJll5ww2y9A0lLP9/M1ZK5pyUQuoMrARsCZy9iyLi4vtT/3uBgKkX6nICZiU0CTvfrRBXl+5QVZv2i0bywNSGsyVqqSRUktS6Edy96YAzMu4B2MgcqDJX2ECyEVjeQYz0qq6Bvl48175dL9Lat0jpMEDE1OYxNU1NEkFwtlzcJubmSDIS99Si6lK/ygCAwsB01Wa0S+8smeXR95d2iiV5UnI2xfW9h04WWh8seDGJFYtfGvf+2i9vP7mKphYrxVfPSZg8J4+SFC325MotZVeee+NJrwHGQkUieVNTTxw3ws1RCQ4jJYbjQD2tgWcWGZgGkawpUVRLBLArpwjCSDNbEn2qO2nht1OxnDyuiOizIlOmsQuWrTIHIrkPJoZe3vt6M6x6ad5xx13mKpIOmlxQGUALQyoCewsCGV7bTjU63Wq91AjOoDqi4VZiniAkzPCtKXnjA8JH22+aYdvo2/yHCI1eva8qMnjhw8vmgRwBtASRjpztyecHacTOAfvPCb9V+hjwBlQpquIFJo58BNNSFb54OQDkuZXNHOin028viSinaOu6xiByFlX573b8Z66NRKBIFR4FbUB2bAvEUFfEmT9rjpoLPZJRkpIJo7Olylj85H8ulnef3eJ5GDd7AkjJDcd5qIhmn5iwGn4IMwUQf7eXbtdNmwrl4JhE2QOcphtKWuUT7dtl33lddAsYgCcjPxlhakypjAdA0fqEVUUgYGKQHiqxO9rkuWLdsnHL1RL2tAKOeHcXJlyZI4kp4agWUEPQDdilE8vTD+nT50g02BZw+UE9I+woAwdaOmniw+Tvzc1gfy9vUeW/wvaw/UeKd2xXU69PFky80AEUYYad5XYQIDWV/QBJqml0GyUbjG0uKIwmNPChQvNb+cfjm/od/nss8+a1dT+3dyO241zP+fv7hybkYkpNLFl8EBL7FkH28D19Cnub4lLEkhVr73g7QHotH+mdiXehDbczuSeke0/HP46qgmMRL3jZZJBG2yl45IHby0pKWk1Ez14a/traP/v9AFov2TnW6j9oymryuBDgBpgp0QG7HBu09/REOA7JjxQrG+Ef2VFUJoSc8UNu9BmkLtgwC+V9TXy1kelsru8WuZMHSpBz1B5eski+XRHucw/eoqMG8oAFEHxI8jLJzvK5OXlq2Xd5n0yftIMGTXxKFm1vgKEsEoamjwIVpEtXhDAhGaP7K1tlq2lPslLY3RQ2pD2/4CVE1l8pmlE4Wj3iq7rPgLoX82gYpjk/OTtvbJ5ea2E6pKkbH26PPq7LTJlVoPMOqFISqZ6JCMLGvZEEEJ2hWAT9uG8Cv/AyBpfSD8vCR7U53JLfV2ibF8dkreXIiH72zvE4xsqHleKfPjSPhk9PVumnTBU3F6o2FViBgH6SloCaBvFSJ1paWlGM0jtYKRwLMsYCHTboTAf6E9+8pPIYp0ud/XYtOqygdZobmoJoD0Ag1x985vfbE0rY9f3x3dckkC+WLrjSOkkhP0Bck+OyRDX/S2qCezvK6DHVwQODwKRJJAvVJXuIGC1cEGp8gWhsQtCw+Axg1bmOuMEi9ubIo2+VNkG8+3k9eUyDvnHPvrkI3n9nfWyu6xG5h03TSaMGCarNm6WV99aIzv3Vcq4SVNl5nELZEdtqmytQOCXtAxJQ7h8mrrBuRApzpBiIhCUTfv8Mnl4ACahUUzdunMah6jsD37wA+FHRRE4ZAiAwdVW++SDV/ZIXRl850HnkpPgG9uULx8uK5ftHyEv3/B0TJh4ZfRkr+QPd0sqYsgkGcJH0geNYDBR6itF9u8Nyva1AdnycZ3s3lotZaV7Jc1TJJ7EdNJF8VWIvP/CLhkxPkcKRnKYzLUqsYBAewogWkPRNNQGt3O2lf6WzojrfB73RLp6bAavIRGk0EIrmsRKtPW4JIHRANV1hx4B1QQeeky1RkUgFhGwLyzbNjUFtkh08dsoKhC5M+iSstqg7PclggQiEiF2D/obMTCB2ZnbC5O1TDMrvLu6SobAL3DGkbNkx64dsn7rTtmLIBZFBfmycw/yBCKoxYiS8TL72HlSFciVXZXwhQKJTEa0Q2o0gk1+SWxOlCTUGYKGpLTGJeUwQR2RQz8mFUVggCGAe5ydafOHFbJ3XYM0Ixd7Syo/BGHKRWCkRqmBG0bzxhTZu6lGlr+0V9zpLsk5arhkzxkuCWnoi9hn/3vbpXrVPmncDw2hPwV9yCNl9RWS7M6SFARvgle/0Rq6EXRp6wd1su2TSskqSBVPcsepIQYY2jF9OtZdJrKRXVH20FqLQfBoCsr0MTZ9RmRd7S139diMq2CFwWuiCWM4xIIoCYyFqxCjbVBNYIxeGG2WInCIEYiMpqZ9v5sAG78hwUDUJx99/KlsXVsO004MPI1NmkjmkNGSijyCdENyw4yzuckle8pqZWjhcMktGAI/3CTZX10DX7+N4k1JlbyhI2TKtJnIUzZSNu+iCxMiFjIvGv43g1D6Kkulat9WcadkYFtAarwJ8knGUBk6Z6wkmmAzqrno5hXU4rGMAG7ngD8oq95cL/v2ViC4S7p4YbYZoqsP/memFEqFb4fs862XBKSPSASJC9aEZMtz2yS7coYMmTNNyj5YJzVvrxN3LU80iImZgAnElubJkvTEbPQvDocx2QJ22YQIvtXlFQg+0yyjpmXCJ5em1irxigAJInMrMugO03QwRcSViMjOHM2RppqH4hydLjoM3BdNbPqtaNsO57rYsB05nGesx+oyAhwIxqM/ZZdPUAsqAoqAQSDSPMaZLkIh6goCMM3EgLQSAV92VsFcE/nMvEhu7YbmjwOQcORgErPwx5WYjCihGRiKehHVN0cSYN6Zju+s/AJJy8lGUKkCGT5ysuzeD+1iiK9pjHTp0GRqQH1u+DUhebU3PROkEfV4s2QLiGRtY8CU0T+KwEBDoHxvrZRuCpiUABU126TUtxnpV3zmNBNCiZLtLQIZLIJv7GgpSC6RovSxMso7XtwfVMjuJ9+QhHfKpCgwSoamjpGhaeMkHxMs6ck5ku7OR69EInj884caoRncIqXVW+DH2yg719ZhsiWsyR9oeA6m86HWjbkFGR2VqbkoK1euFBu85VBj4cy3yONEk7fffjva6sO+TkngYYc8vg6oJDC+rpe2VhHoCQKRmkAlgT1BkWkdXJKd5ZYjxmXL1LFZUpCfIUnJMDtzw0zTBCgzISrwMySNDQGpakiQnGwOQuHiB/PRJAQM8HiTJScXudCSsmR/Lc3QuBUksEWaQQaTUF9yerqMKMqU6ePzZNKoTEmGyZqf0RFNeVtavxWBgYHAvqYECU4aKxlTp0lW0XiB5bU0BBEgphmT1TjFJEmBRi9P3M3sa6B0zUnS6K+Vqv07pA7mecn+RHG7POh7mLABafRKlmS5h4kb+3GChf2qvqlSgonNkjG0WLLmzBCZPVkqU7wgmy3970A3HBigDsKzYFAYm4ie0UFtAJdDCQVJp03xddddd5n0EM76mdKLmslYEDUHjYWrEMNtiOZkG8PN1aYpAopADxCI9FtgAl6V7iAQHh3mev1y6phGSUkMCH2q91Y1yrbMgGxtrIfmj/56GLnifyjol7K9WyUXkStSYB5qxFRBczSQPE8Kkl2zbLg8tRSGQ2IxETahuek+pIVolJGFeyQ/HYnmYcrWhLxmKS4Ej8HwVkURGEgI0FOvlH6AU4okY+wQCVU0im9Zs7g3+iQBptUUZtt0zJWABAaRszMbWvMEmIruMmbU6DwtwhrR4fifNtoQBBSVRA8mV2aMksyjxokHEzhBmGDv8wekMdCMlBMtu+pXXCPAdx3zNzNXH33hrVlo5ERob0/ytttuMzmb6YNIDeSvfvUrYTCYDz/8UL7//e+bnMy9Pcah2F81gYcCxQFch2oCB/DF1VNTBFoQiHRSLy8vV2y6hQAGovAxcjftl2HJtZLnqZM8d5VMymuUE8YlyZjs+taodc0IW99YUyEZOQhGkZ4k1XUIRchxbMtglEFk6n01kuQKYjVGpmZkyxyCCFuBbe7EJpmY3ygnTXBLSWajZCVUwfytQYZ66yUxUI3i3EdFERg4CDQEQgh+1ISASLjHEzDRkQvK58VkB/wCm12tzK7NCXN9LTR7pXVbEbDJj74UJottCjkWQtjsavZKwIMulIHJmIR6+PTWS1m135BAR1H9GecIzJs3T66++mpzFswrePvttx/yM2IC+1//+tfG55B5oJm7kJFCmatw27ZtUlBQ0HrMQ01AWyvuwg+d2+gCSIO5iGoCB/PV13MfLAjk5eW1OdWysrI2y7rQOQLNIIGhpgZEHAybpxnyht/BkAdmZhhgtpC8ZiSvDjY1SnZeurhDtbJj+zYMOA8MZAMgieWle2R08R7JyRwu+6sQ2j6B87Ugdxio0pQUlqTQJgag2PBjG/yZoCaERxOiZ/iwHb87Hu92fjJaQhGICQSopXMhDYRfxmdUy7SsRklKSpBNO+tkddpuqUtIlmAz0tlEueGb0fcyvXnw+ctC/6sDeaSGPKz1i3Zq7DepCUHJSdgr0zI8MmEU8nAiyFItzEiTmkE26Tdo+rB2rmj4dXcdiQ/9pSMVDZH+6V2tN9p+0dbZ+kjQFi1aJHv27JGf/vSnctlll5lUPnZ7d77bO853v/tdmT59utx3330miT2PxfRvF110kWRlZclVV11lDhOZ97A7x+5tWSWBvUVwgO8f2UEH+Onq6SkCgxKBSBKomsDu3wYuRBVMRLCWJmjsEjBYTEKewDrwsnUVyPFXmW00FsFAvVTv2SlDhqRK8bBUWbvmXalGVFAXUz9AOMikz1INTIi2bFgpk2dlgTBmSSXiXzB8Pf83QFWxqSpbhmU0yrQCDkgTzUCKPk0mIqmJDmqq0z+KwABAICTuQJVMzK6AZi6A/lArRSNCcuy1Q+X1J6uQNxDmmr4woTjoZOEXyGihmTCvDhmt+kElWlZA5x7yy7STRE5aiOBMuRXoVfthlo1IvqkeSQzCbzCE3HIJPcsv195RB/N6kqBoSob/+q//En6c4vHQl7N9As+yH3/8sXMX8ztaXbYQ00VERuiMVr6nx96wYYPR9s2fP1/4iZRf/OIXZhWjk2ZkZERuPmzLg4IEdnbzHDa0++hAJSUl8rWvfa3d2iMHeO0WjLIhWieNUkxXKQKKQBwjkJ+PrMoOoeO6SvcQSEBY+qS0PEQVbJJmkD3m76uVZGlOK5Rhw9KksakZKSQSJWVIupSM8CJB9TrZsAEDF6PlI5njhwOdEAK8+KEh3CSp6W/JmEmnYG2y+JtC4kUqiBSPW9KSs8XnDiGH4H7J8PhRBQgo/J/cKbyO6uUBEFQGCAJM5RDyVyKtSiO03okI0oI7HKae3lSXZGa7MfESnhwx3eegcw4TB+jSD9py8IpmSctCLs9MTqa44dPLHol/oSYcvxqJ5nOQmxC2oiqKQBcQOO+882Tt2rVyySWXyKOPPtpmDyavv/vuu806aga7kuOwTQWHcGFQkECqfJ15r3bu3CkNDQ2tMHppWx7HMnbsWPnOd77TJ2cw0Al0n4CmlSoCcYYAk8OTCFoz0Lq6OqE2sDcTSHEGQS+bCw0eNIFukLMEDBSDTT6pRvLqTz74SFZvfEMykQ8wNbsA5p3Q7GHSt2znWlkNLWA9EsknYKBpJrmhBTRUEPVw2YegBZ9uWC/J3lSZMOlo8Tf6pamxGjkCkUx+6z75CANTz7RxMmfGRBwTudEQMTTBDY2FiiIwYBBgj4C43EaT7gIhTEryQoPkk43vV8tHywLSUIck72YihUSPdK9ln/CO5u+BCRZLBlkmRIqH8pw0wV7otx8tq5Mho5tkzJEgmWmpMLPGdhSllp39OzxJ46y/pXr9UgQiEJgxY4YhgX/7299kzpw5Jjn9iBEjZNmyZXLnnXcKeQjliiuuiNjz8C4OChLIAY5TqIbuiwSRzmMMlN+qCRwoV1LPQxHoGIHRo0e3kkCW3Lp1q5LAjiFzbOXAEANMmoR6M/BJlyQ/NIG7tsonzz+GADBpkpyaJq6kZAkhAmE91AwIOCjpGLxSu8GBKKPQh5BcnmPNJKx3Md1Dbal8unSblL37sggijPoRMCboqxNfXa1kZGfJnPFfRaqIIjNQNYEvsIuKIjAwEGD8TubYhIbdmy3BxjpoA+uhpYOm3J8l7y+tlD1bMeHiZxCXsL9smAyGqR07kqVrrCccZClM48L4sN81w2cXxBG7c0vVniT5YJlLhozPlpQM+vbig6i77pRcHINaQFtjuAb9qwi0h8Ctt94qL774ojA6KKOB8hPpA/mjH/1Ivv71r7dXxWFZPyhIYCSS7TlxRpbTZTwWO7HDVowUAUVgYCBAEvjuu++2ngxJIE1VVLqGgCFhjqL0EXQhxDwclhBdsFH81VXiccN8zZ0kHmgWvPiwjFFikAxiMNoUCKJMYnjIClbI5y8jgtaQIeJ3IwhgkOvgGyWY3GS6CFRyYGiqY1THFdCf8Y4A9XQkcJ60bHA6lzQ1wCwT5pn1VUEZfpRLsoqhMd/vkZpyrmuQukr0swYEXoIvbgh2oyZtJmowsySml4RMLs8EBJdxgTi6k5ElEClWUnOSJD3XI+mFyNtZhF7lhQ8aTK/h4ovcnVn45MIGlcGbOMuinSze76vD0X4mjH/rrbeEqSKeeeYZoXUNn+dDhgyR4447Ts4880y57rrrDkdTOjyGksAO4dGNqgnUe0ARGBwIkAQ6ZePGjc5F/d0NBAIgc/U+n6TnFcgxZ5wrDf6g7Ny9T7Zs2yqN1eEInl6QPX9DjSSA0CUgImg6EsUHoZKg+VkD/AcT4KbQCM2gF8nmm7DsAekrHjdBxo4bAxNRr7iRXD6Y6JW6ep+kpba1dulGU7WoIhCjCJBskQKSeCHRO7Rx7pQc/HbJilcek9cff0zcMNksGlUihWNHSv6IYimvbJDMnCL0DWjjkRie+wcQJbSupk7S09KNBVhikgsTMYlSXrYDRBARdkPV0lRbAR/dXbL9k02y5rXdcvqXrpZh406H9r4lh6dpQ5iQKgUErCpdQoApIR566CFTlq4WdEOjSWgsiZLAWLoa2hZFQBFQBPoJgYkTJ7Y58ieffNJmWRe6igBymEGrl5aeJSeefo6cPP8zRoHQCJ++Hbt2yqpV7yEgzEZpbGwwQWSampD/zN8kuTDvTEtPRcjyvRKA5i8dEeNc8PWjiduQoiFIOHyUTJgwAcnlSfgwFEUewRDTB4I4BpkugmoLFUVgQCEQSblalmvrpG79OqnYu0vKEcQlxe2SRkTjrYG2Lg1kLwmTJAnJXjNxwkQqdfC9zcK6ZGgTmW4ihP5Whzp89bXixbZkmGfXI6BTLc2x3R7xX3CRSbtyAMrwcSNbc2C7/lIEOkYgMvhax6UP39ZB+dZQc9DDd4PpkRQBRSA+EJg8eXKbhkYLud2mgC60gwA0DQj7nZySDKIGU02YAFGoVcgC0ZswYSJ8mhrNOmtuT5PPBKSJYF4yBjHj+rA5KU1Gw6kfvAiTTkJo9zEVtP7R4WkrFPpjwCPggTmnF2bVyfCvrYPqvNIfQNCkBlpMS0NNvaTBcpP+tonoU8nwscV0itEo+mE9XU0tPcr5QPj86HfsX270Ozf6LMeGbpDAFBBKmp+qKAIDHYFBSQI1KMxAv631/BQBRaC7CEyZMqXNLqoJbANHtxb4jmnvPYMxpqSq+Wa38NTCigARYMTixYtfkWf//qy8v7dC9tf6pQHMj75/nGzhJAotNxPxlQqtIDJIyOi0ZMlFp6uEBnCbr0HK4Cvog6ae2naWZUwYM9HiChg/3UxPUPbAhO8cmFkvOOMMKSwsNNv1CigCAxGBQUkCzcwPZnxohqOiCCgCioAiICZFhDNNBHMZMYz18OHDFR5FQBFQBPoMAZpP19TUdFo/farGjx8n377xJybAUoIzKFLL3uB1RpjWgSwPLoE0ng7/wQ/+5hjQCFWHZk14r/A+Aq1iWFMfgJko29VaPrzXoPhbixQ3KgMfgUFJAnlZqfZXEjjwb3A9Q0VAEeg6AtOnT5clS5a07vDee+8pCWxFI35+hM1JWwa68dNsbekgRWDdunWSmZk5SM9eT1sR6D8EBjUJjAb7mDFj5LLLLou2yawbNWpUu9s623D88cfDaTmt3WJMLnk45PXXX+/wMJF5FTss7NjI9ndlNs+xi/5UBBSBGEJg9uzZB5HAc889N4ZaqE3pCgKDUXPRFVy0jCKgCCgCisABBJQEHsDC/Jo5c6bw0xdy4YUXCj/9LR0R0d60jUEL0tPTe1OF7qsIKAL9iABJoFOceQOd6/W3IqAIKAI9ReCoo46SN998s6e76379hACvm8rAQgBxkwanaITQwXnd9awVAUWgfQQik8MrCWwfK92iCCgCPUPg3//+txx77LE921n36hcEvvWtb8ktt9zSL8fWg/YdAqoJ7DtstWZFQBFQBOIKgfHjxxvfnOrqatPuXbt2ybZt26Q3ZvBxBYA2VhFQBPocAfr/vfDCC3LNNdcgL+aePjue+sYeGmhPPvlkue222w5NZVpLTCEwaElgUtKgPfWYugG1MYqAIhA7CNCX7LjjjjMDNNuqN9544yASWFFRIU8++aQUFxfLggULbFH9VgQUAUWgSwiQCD7xxBNdKquFFAFFoG8QGLTmoPRfay+PU99ArbUqAoqAIhD7CJxwwgltGkkSSKmrq5NHHnlEGCimqKhIrrvuOtHJtDZQ6YIioAgoAoqAIhA3CAxqdRj9AoNBJJFRUQQUAUVAETAIMIqxUxYtWiSXXHKJPPvss1JfX+/c1GdBtNocRBcUAUVAEVAEFAFF4JAjMOhJIJOPqigCioAioAiEEZgzZ47QUoIJnCmbN282H/6mb+DEiRPlpZdeMqagOTk5XK2iCCgCioAioAgoAnGGwKA1B+V10gihcXa3xklz9+3bZ0zn4qS52kxFwCCwfPlyYQQ4kjxLALmhoKBAvva1rwnzi27ZskVOPfVUU76vUumYyvWPIqAIKAKKgCKgCPQpAoNaE6j+LH16bw2qygOBgHz9618Xhr5mNEUKA2YwAtpAlpNOOkmqqqrk+9//vlxxxRUD+VQH5Ll99NFH8uijj5oPCZ4VBm244IIL5NJLL5V58+a18f1btWqVKaYk0KKl34qAIqAIKAKKQPwhMKhJoGoC4++GjdUW//CHP5R77rmnTfNGjx7dZnkgLnz88cfCSJE0GVSJHwT++Mc/yt133y1r1qxpbXRycrJ85jOfMcTv7LPPFi5HEyWB0VDRdYqAIqAIKAKKQHwhoCQwvq7XYW/t2LFjhTliqDX1er0HHX/q1KkHrRuMK5555hlz2iNGjJDbb79dJk2aJLm5uYMRCj3nOECA2r1f//rXrS2lFpekMCMjo3VdtB81NTWyceNGs0k1gdEQ0nWKgCKgCCgCikB8IDCoSSBTRDAvFhOK9lQefvjhDncdMmRIh9tjfSPNwfhJT083YeFjvb390T7ePzt27DCH/uxnPyuXXXZZfzRDj6kIdBkBJoWnD+D5558vTAHx17/+VY499lj56le/2mEdH3zwgXle0k9w+PDhHZbVjYqAIqAIKAKKgCIQuwgMahJIAkiTUL/f3+MrdMwxx/R433jakT5vKtERYBANi8/kyZOjF2pZu3fvXlm3bp3k5eWZABxd8Utl/e+++65Qyzh06NCo9W/dutUE7SgpKTHlGN2xI6Ef3+rVq4W+X2xzV9pBnzF+eIzBYOraEX4DYRvvwcWLF8uVV14pjz32mAn+Qi3fL3/5SxMdNNo5WlPQI488MtpmXacIKAKKgCKgCCgCcYJAxyPFODmJ3jSzK4Pf3tQ/UPbVfIrRryRJmdNMlsFheE9NmTKldQdqCh988EEZOXKk0aaecsopMm3aNKNdZfCN3bt3t5blD5rcsQ5+nnjiCUO4jj76aKN5Oe6446SystKULysrky9/+ctCk93i4mKZO3euKTtu3DiT1LtNpS0LL774ojFVzc7OlhNPPFFmzJhh2nHRRRcJ64sm1BjNnj3bkD9GhuSxSAKff/75aMV1XRwhwHuXCeBvvPFG02qaiPJe8Pl8Uc/CkkA1BY0Kj65UBBQBRUARUATiBoFBTwI1OEzX7lWr6epa6cFTypI1e8Y0MeY6p3b56quvli996UutJqM0o0tNTZXGxkb5+9//bgjhihUrbBXmm6Sbn8svv7x1P5JJavBI4Ji0+5xzzpE///nPsmnTJlNfYWGh2ZdBWrjffffd16bOW265Rc4880yjiaQWnEQuKyvLtOPJJ5+U6dOnCwO9OGX79u1y3nnnyXvvvWdW5+fnG78xRkBlEBEGhVGJbwR4L9x2223mXuLz8OmnnzYTCtRaR4qSwEhEdFkRUAQUAUVAEYhPBPrUHJTmZhzIDhs2zOSaikWIlAR27aqQgNAssTMzw67VNnBKkSTxHifxo9x5551t/KoeeugheeCBB8w2+lzR92rMmDFmH5K0b3/724ZIXXvttYZo2XrMDvjT1NQkxx9/vHD7hg0bjBaP14LamrffftuYMzMQzTe/+U1zbZYtWyaXXHKJ7Nq1y+R8428G+3j//fflpz/9qfHnolaR7ZowYYJZfvzxx41Gcc+ePfKNb3zDmAjy+A0NDSZNQGlpqSGL9H8l8eN9cO+995pj8txVBgYCnKzgxMDChQvNvcX7ddGiRcZcmGfIiQ07SaCawIFxzfUsFIH+QoDvFZ1E7C/0u3/coqIiMw7o/p66RywjcEhI4Pr16+UPf/iD/Pa3v21zrvQb4SDxpptukptvvrnNtlhZUBLY9SvBa6kksOt4sSQJGoUaNJpi2uiL1Bhef/31RqP3ve99T5ivjWSR5p1OYWChZ599tk2kUWr+ODinkPx95zvfad2Feft+9rOfyTXXXGNM+v7xj3/IF77wBWEKC5I3+oGxPqs1pBbo4osvNgN8Roh85ZVXTDuZ43Dp0qXGF5GV00+MBJDCe4BtJwH++c9/btbpn4GBAKOGMlAMtcz0/6T5MTWDp512mkknQSKYlpYmDCyjoggoAopATxD48MMPzTOlvLy8J7vrPv2AAJ/5S5Ys0YBg/YB9Xx6y1+agf/nLX4wZmQ2R35eN7Yu6lQR2HVU1Ce06VizJWU6rOSFZswTQWcsNN9wgOTk5ZtXKlSudm8xvDr4jU01Qq2eFBDJSGOiD/ZEvWmoMKW+++ab55rIlgGZFyx9qDK1vo20HI0FSGDyGdUYKyadOCkSiEv/LTPtC8+SjjjrtOQNlAABAAElEQVTKmB+fccYZZoLC3nf0OdXrHv/XWc9AEegPBJQA9gfqvT8mLZEYE2Dnzp29r0xriBkEeq0JpEma0//JeWYcONKcbdasWc7VMfU70vwuphoXY41R07/uXZB33nmnNf0IA7BEExKviRMnmnD9jBoaKdE0LtYvi+QsWrRQDtCZqsIKTUPr6urM4v333y8vv/yy3dTmm32Vwoc9xZJABoLxeDxmnfMPtZvUVEYGtnGW0d/xiQCv66uvvmp8S+m3etVVVwnvAwoDCqkoAoqAItATBOhjrhrAniDX//twbMBx/UsvvdT/jdEWHBIEek0CO2rFn/70p442x8Q2DphpmqcEp/PLoZrAzjFylnBOjkRq85zlrGYuWiAOpoWIFJpfU+x+kdsjl5k+wgqD0Xz66ad2Meo3zTwpNik4g8e0JyShSgLbQye+1zN40VNPPWVMjn//+98b81CekaaHiO/rqq1XBPoTgerq6v48vB67lwjo9eslgDG2e49JIKMT0mTIDhi5THth+hjRL4nE6vXXXzf50xjCftSoUebUWZ6DUPomUTvCGaHXXntNqN1g2Py5CHPvTLDOAS/rZTJuakXOPvts418VDUeSFNZDDQYjJNJsiQMWftiu9oQmoUoC20PnwHrF6AAWXflFR+r/3955ANhRVf//bN9NsptegEASJIQQkA4RkA4i9QdIE/iBoCLIX5Ci0lH4Kf4EQfhJUUGKUqUK0gRCkyJFEJLQQUhCQnpCytb/+dzNebk7O6/sZpPN7p6TvJ15M/femfm+92bu91STXAHwJGRB0ogiv6Ok2O+DwPpCxNrTllIAJP7IJea2yu+N8hC5zp1spS7dFwGUZP/3f/8XksOcfPLJ4T7pSWG67+ftV+YIdAYC+eZonXFOfszm5HCTJk1yKLoxAu0mgcQ6Ea9kQo0xe8+klokn/sMQhzgxDDGEJImBKB5++OFCXTUSVsRy7bXXCpnqSFRx6623xrsCAaR2GmQxlsmTJwfXJUtlH+/bbbfd5MYbbwxZSuPttg4JJBOiS24E3BKYG5/k3tiVk8Qv+++/f7JJ+N6Z+2Waa2erDroB5QYCAZsxY0aqRZCEHtR64zfE5B13TiyT9ElzzybjKElkiE8kmy+y5ZZbhiyiJAhByYNlKBbGNyVQvN3Xux8CxK7iDnrUUUcFZV33u0K/IkfAEegsBFA2Wkx6Z52DH7c1AmT4p3SUS/dFoN2JYahVBskzKwNEive8qqqq8iKGlfDEE08MBJBU9RTSNiGJBuNAALGOQC6prYZANkmXb/FLbMP6x8TWCCAp9Zn8MolFiIHaZJNNJJvlxJPDBJjy/nFLYF6IWjTAjdII25VXXikLFy5ssZ8311xzjcyZMydsJwFHIRKTOMZNCgk8eKhiEed3ijXRrDc3aAbSNOJ29dVXh3IQKFcsy6/9fognJPtvUjj32OU1ud/fdy8EyBjKvdYna93rc/WrcQQcAUfAEeiZCLSbBDK5JZ28JaDAisF7XiSsKESwTuAKSkIM3Dd//etfh27ELVHvDNdP0uE//vjjYeJqx8KdNDZRQxqxSjA5If09Kc6x/JHlkCXHgTyeffbZqaflJDAVllYb3RLYCpK8GyitgGCx+/rXv54hYFjesIrjnolgNbRMnmFDjj8k5jDCeMkll4RxGI/P58EHH5RvfvOboTdunQThIxdffHFYQuiYzJOhDeG39sADD4QSEryHMJ5wwgmshuyQO+64Y1g/77zz5JZbbgnr/CFWjG0uPQsB6gi6OAKOgCPgCDgCjkDXR6DdJLAjLp0aYzvssEMYisnnMZp1yFKPQ+gopm1JKYjpwyXJBHKIPPLII4Ewso7bqdUy4z2CRZCaZsh1110XimiHN9GfOEMoZJR2vLBWuixHAEsgGS8vvfTS8MLa5JIbgQMOOCC4PdMKxQaTaIrFY6GzWn7EzBL3yrZCBWsdiWFwY2YcLObEIPL9NwUJVj8yeCJY+CgEj+CaimUc6zv7991330z2UFyxbaKPcgSyh8IHJcsRRxwRjslxvvGNbwQX0TCg/3EEHAFHwBFwBBwBR8AR6FIIdCoJZIIcC/FIG2ywQdhE2nyLTbI2scuopby3NPa0wZKCNST5YpKLEHtoE+SwYdmf2BJIbCFWE15YVVyWIwAJxNJ7+umnhxfFz11aIhB/l2wPFjRKM/B9xmKHooEMW3yfUVKAqbk7Wx9bpo3HPn4nkLkDDzxQaDN37txM2u2xY8cG5Qj7YrniiitC/UBIKEKyJXNRJUkTMYEQylhI4MTvABKJ4FJNFlNI58033+zlAgIq/scRcAQcAUfAEXAEHIGuhUC7E8N0xGUmSR5jWvKJtNT4ti8+tiXVYFuciCNuE6/Tftddd403CZZALI1M0F1yI5BM4pO7dc/YixU733cHKzcv3EJJqgQBxAKYJrhx5huPflgCsdQRH4tyg0y7xNdmI5T0waWaF6TxrbfeYlOwTOZKSsOYWCotsy+WQEgovxlzPQ0D+R9HwBFwBBwBR8ARcAQcgS6BQKeRQCaQlZWVWUGKXTSzNtIdU6ZMybW71b5sNc2wpniSi1ZwtdpQCDlp1ck3ZBCAuBVa3y/TKc8K391sxeizdcX1dLvttsu2O3U7xDW2xqc28o2OgCPgCDgCjoAj4Ag4Aqs9Ap1KAjsCHZuUQhpxb8Mqk0vSrIm0dxKYC7Xl+9wSuBwLX3MEHAFHwBFwBBwBR8ARcAS6IgKdGhPYEYDhqoYQB0isEokuki/iB//yl7+EtPkLFixIPWyhlsfUzj1oo5PAjvmwqdeH9Y4XrpYmFOZmG5lEXdqPgMWvtn8E7+kIOAKOgCPgCDgCjkD3RWCFLYFGnixRy6qGavz48ZlDnnPOOXL//fdn3rMCaaHAsWX6pGag1TaMG2IJdMmPgJPA/BgV0gKlBYldkNilGUUG26dOnVrIMN4mBQEy2H73u98NXgGUiXFxBBwBR8ARcAQcAUfAEWiJwApbAs29kqQUN910UyghsCpj64hrsiyj1Ag89thjM8W3P/roIznttNMyBHCjjTZqlRTG4HASaEjkXjoJzI3Piu4lKQxlUaw0yoqO1xP7X3nllaGoeU+8dr9mR8ARcAQcAUfAEXAECkFghUnguHHjMsc5+uijQ4Hpf/3rX5ltq2Llsssuk1GjRoVDkYqftPZkO2Qb9dQQtt1+++1hPe2Pk8A0VFpva29iGEoLuORH4Ec/+lHI3Pn+++/nb+wtHIFOQ4BMys0vrNq1S5e24UwsC7Mt29DVmzoCjoAj4Ag4Ao5AhyCwwu6guFpOmDAh1AwjDgd5++23Zeuttw7uWGyzAvDss8QttmRbLLbdlvG+eJx4neLWb7zxhjCBpnYZtc8+++yz0JUspBS2vvDCC4Xag9nE3Fqz7fftzQjYZ9wWPCCAZMQEf9z0KCtAmYHuKnPmzAm/AQqsc51cd/x9XdHr/vjjjwUrN4oOi4ktdEzODXfTddZZR0aOHFlot1Dagt/1ZpttJn369GnRj+8Eih8UBJSOSO5v0VjfEA/55ptvSk1NjVDTsC2/vfaef/Ic/H37EaCSThHd9d6KUKLk5j9cI1M+fl/W0e/UmsPXlqX63V+8ZIl+70ukV+9eod1CrY05b8EXctChR8igocNCiZGwI/kHbtg8dHKPv3cEHAFHwBFwBByBDkJghUkgk1usb1jciGMiKcvgwYPD6S1N0Q4Tt8crm7zwwgvZdoX6Z9ksUUw8r7rqKvntb38bJsjvvfdeOA8KYzPZzCdcR1smo/nG6677s+Gf63r5PhxyyCFyxx13yE9+8pNQaH6XXXaRb3/727L//vtLVVVVru5dYh/E5qc//an8+c9/DoQpPun+/fvLz372MznxxBPzksHzzjtPfv7znweS9sEHH8TDhMRGJ510krzyyiuZ7Vi4f/GLXwSXZ4rSU3z++uuvD/vPPvts+eUvfymHHnqo0O873/lOqFFonyF9L730UsGCH4v1o57g8ccfL6ecckroRxt+I9tss01ItMTn+oMf/CBcM9ePYFH/8Y9/HK4XBUwsjz76aGgPmTSpqKiQfffdV66++upw77DtLO08Cj1/4v++8pWvhDhg+hMbyPnisj5fCYhLByEQPtbmz/b9dyfJcxMel6cfvVc+mjxZmhqVv5UUS5XiLk31sqShXu2FJVKsrA5X8uLSEvn47Tdl2NojpbpPjQxGkbHBWBmx3npaMkjJYjAOrrCDSgddqA/jCDgCjoAj4Ah0XwRWmAQaNKtLHBMTz1HqBsqrreIkMD9i7Y0JRElw3333iSkGyIj51FNPBdJw2GGHhULqX/3qV7NbB/KfWqe1wOK36aabBuWDnQSlS5aoJQQrKNar//f//p/Q7owzzrAmqUsIGpa1JGnB+gdZmjlzZugHgcMNj1hcrKvl5eWhX/z52FgPPPCA3HvvvbJo0aJAiviNYL2hLwXsOR6xtCbWj8+HOFtifFGSMDbHfO6554J1HevuPffcY93CknEvuugiWU8n9TG5hCDzYmyOjyWSgvWQRzL3Pvvss/L444/LhhtumBnPzqPQ82dcfsP2HbP3/rvOQNqOlcDKtN9yQq90Tt6ZPEnuueVG+ccTj8i8WTOkorhJaqqayVuRtu1VXiz9etfIotqlMndhrS7rZdiA3lLf0CjP//1BqW0skmL9vIpKSmXtL31JNtt6e9n34MNl/TFjpVhJpIsj4Ag4Ao6AI+AIrFwE/Gkb4etxgREYWVaZmLdHcF084ogjAumjvxEKiNGf/vQn2XHHHWX48OFy/vnnC1bcriRY/z5S90wEokO2z//85z/BIkg2WqxdCOUf2iNgRPIjCCBF3iHTrEMucX/G0pUrGROEEkL0u9/9Tsjiy3us9+aiSkxtmnAMCOJ1110XCNvrr78uu+++e2gKEYQAYi3EA4B2f/vb3zLX+vvf/z4zJK6iWEL57uAmPlktRuDF+d92223BfRT3bYhymhR6/ltuuWUg3lhDkS222CK8nz17dtqwvi0PAslf+tzZM+Vv99whZ5/yPTnj24fLY3+5SermzpDBvcpkxOC+ss6gfjJiUF991cjQvpVSoSrG/r0qdXuN7q+Wfr0qZM1+VTJiiK5XlUqpfh8alSQOUEv5NtvtJGutPUIJYIfpJfNcne92BBwBR8ARcAQ6HwG8uCgNhifTqhYngRHiTgIjMFbCKhYrLEVJsW2QiYsvvlhGjx4tTOivvfbaQD6S7Ve39+Z+yQ8Yd84111wzc4q77rqrfO1rXwvvIYZY0toqjz32WCbb5W9+8xvZb7/9whAQuyOPPDK4fOYbk/PCHRSLYWVlZbAAmqWO+DyslmkCecNKSNZSblKnnnpqphnWTm5eEHwIJbUNcd1E4sQ2xOpC+rFeYlm0OEbOn/a4cSNPPPGE4DKaJu09/7SxfFshCKjFVn0z+YsV8Km/PyxnfO9bcsk5p8lTD9wrpYvnyKjBSu6G9JUhfaukvERdhYsapaSoSS18MX1UZ1B9yvSpKNM2jNYo1boOMVxnaLUM7t9L3nvjRR33ZDnvh8fL048/LLV1tYWcoLdxBBwBR8ARcAS6PAJ4ZZGrIVc43Mq6SCeBEbJOAiMwVsLqVlttlTf+zyxaxL1hGSLuDCsY5KE9BGolXEarIbGKffLJJyHmMbmTHzckycSuz94Xsnz11VdDM5LMQPqSArkm7jCXkMApKbHrJa6iaULcZiyUZIG8IXvuuWerz5MkTQhWPpN//OMfYfXggw8OCYJsuy1xBzZraba6fu09fzuGL9uHQEN9nfzlzzcoSTtDJr/6ktTX1klFWYlU99JYy6pyKStp/i6Yh0Bw4W3UbKFK5Or01ajunwT6Qf6aGhukoam5fam6j/atLJPh/XrLqIH9ZFhlvXz88tNy89WXy3S1pNt47Ttr7+UIOAKOgCPgCDgC+RAo2PeGSSIT2u4sWEPiySvXPG3atO58yW2+tjhWjXguyE9bZOONN5aXXnqpoC5mIYQA3n///dK7d2/51re+FWLNNt9884LGWBWNIEW4smL9uuaaa0ISlXfeeUd44fYYT2jj9ULPzUggFlJz4Yz7Yt0jBi8bgYJgpWVjjbfFsYQ2Ngl7iPuLBYsgMXZ8Nkb44v3JJExYd3FBRXBBxT02Teyzfvfdd1vtbu/5txrIN7QZgUWLFsu7k/6tGZfnafyeqJVPLXnlZSF2b9jwofLGS6qgKKnX7cUai7lE25TI8LVHysChQ5X0NcoH+nl+obGf2lP6Dugvi5YskqV6Xy3Rf3WiSWMamqS0uEhjCktlWP8+Mv3jyXLuycfLCaefLVtuu71mk0ZP2Uwc23zy3sERcAQcAUfAEXAEsiJQMAm86667QubBrCN1wx0PPfSQ8HJJR+DXv/618FrZQrwZsmDBgkCyrrjiikB6cG/EMha7X67sc0kbH+UBLpPUoUySKYgUSZOsZEla/3zbLEsosX/ZZMCAAdl2heOb9S5ulLYt3p+v1APkM5+Q0MaEhC354j3TlArgl3auadvsWL5sPwIoKpZjWyeLv1igxryG4PbZu6xY1hm1tmyx4w7y4Tvvyp6HHqwKkHWkTK2CE199TQYO1lIwG39ZKlVZUKLk7cN33pbbr/+jDB2+piZ+OVDmz1kg9996i3z26VT5+v77SV3tEnnlhRdl9oxpmsW5r1RrApn/THpFfnrGiXLer66QrbfdSeMElX26OAKOgCPgCHRLBDC+tKV0FXPCDz/8UJhfMMfCq4l8CYXIjBkzQgmvjih3xXwP5T/GIspdWWWEXOdBCTsMBMyHMGaYF1SuPitzX8EkcGWehI/tCBSKgLlTQiYoRUCJg+nTpxfafaW0gwDeeuutYWwsgjvvvHMoo0AMHYlQOEfKHSDtsQQSe/fWW2+FTKNhkJQ/K8NivZwIpBywwE1D1SJkctZZZ8lBBx1kb1OXWBpdOh+BmZrV9omH7pUn/nafTHrjNaks1pjOqkrRCg+y8557yYgNxsgmW2wpNdU1ook+le4VydC11gmWuyJcPkNYYJOsq9k+j/3hyfqgq1RlxAAZsmaRHK1lRV5/+TXZZoftpJeWidh6x53knpv/LH379ZWB6jr90F13y8effCa/u/xXMmLd0TJszbUjUtr52PgZOAKOgCPgCLQPgbaWfoqPgicR/Uk2Z3NB9jNX2W233UKpqS9ptmkTO1ZHl7vCIPHDH/4wJLYzTyeOyVztkksuCSXR7BxsCWEkOzzeYhZ+AwE88MADQ74Fa7eql04CVzXifrwVQoC4TWIDcVM87rjjgnvoCg24gp1xj6X+IUKGUzJkJi12kyZNyhzFrJqZDQWs4Ab68MMPB+0RLrhYxmJBi5bPwha3X5XruIxiMeSGzbmnufFCjMl4SlxjZ1t1VyU2q+WxIG/K4f7z8Yfy5EMPyFuvvqKung3Stw+1POulT/+BMnL9MTJ02BrSqGRPI/0C4SOFTKkWhm8mf9pUSSMDNakFcejQNZs3kw1UYwT7DxoiO+2xh+7WXvVNMnjYmnLoccfK25MnyhbbfEWKNKbwT9ffLGsMG6pWwLLMkHpaLo6AI+AIOAJdGAGe98yDCi39ZJcKeTrhhBPsbbAAMl/Ae4j5BQn0xo8fH6yDNgezY3VkuStCfHbROtdYIhHClJiPsp1zIdkdeSJI4hcLhNQ85wipIVfEp59+GgwIZkSI26+q9YJJIEkguLDuLkxG//d//zdc5k477dTiS9fdr72Q6yM2j5IICCQsri9XSH+Ki8fFzgvpg5aHWLgSdQuzmoJ8Nh1hqSrk+LnakFnTiB2aKLv5WB/KRZD10qQ9yW3QFF155ZUal7UwaJkuvPBCGy4syahqmqUWO1aDN3xmuF28+OKLcsMNNwTrLdqyWCgU//3vfz9swrrL9ayIWNwkpTVc2o4AVr31Rq+vHYulQjN6Du3bR/pUFsu6um3T7beTPv1qAtkje2hWaVqecwyCGCSwOKyE+t7WNcZQ88VITb/+suV40mOXyo6aZXbi6/+Wf77ykrw/+d8ySF1McQllFCeCzVD6X0fAEXAEujICKNAhUJSuIlM5ljJKRjGvZJ3SVTa/ZI71i1/8IlwuSvEbb7wxZJDHKEC9YZLj3XnnnaF0FmE55I6IhXJXzEUod0WCOgjc6aefHoij8RqshWQqx3vpkUceCQkJcdmk3JVlUmfM0047LfSHyMEVTj755DA/pSY05alQ2BOy9I1vfEOofY1wbjavYT5HRnmU+ZBGzof5UWdJwSQQX9dC/F0760I66rix1YaU9mmWi446VlccJyZwFPzedttt23QZaYk/sg2ABYmEIVjYyFJJltAkycrWd1Vtj10PuDFx0zJr1sSJE4P7I8lRTLjx8b1qi0B4IZi4QlCInTG42aDl4mYCiVqdhZsfLrK4Tey9996hLiSustxg0d5RQgLhJh1r+tp7TTxYEGIpKTmBi26cCbW94/aIfsqyGhrq5A9XXiIfTnxZhg+s1pIOJUHTOmKD9WU71YAWaYaYpqZmS19HYcJ3WQfW4eqloqq37LTnHuqGOlEu+9mZctkf75C11ll32f6OOqKP4wg4Ao6AI9CZCFjpJzuHY445Rp5++umQRM5KV1HSirkPJbYQ6i3H9fSIBTzzzDMD0WJ/XJ6K9yZW7or3Vu6K+Qdi5a6IL0Ss3NVNN93UYjwynd99992hDcfEJdQEfkQSw3HjxgWvLRTalhndajEzZ4boWiUCjov32MiRI0POCxtrVS6Xq2tX5VFX42PB7l1WDgIE/kJgcoklG0HbA+HBXP7kk0+GQvOrGwHkOtAaoUFCcMmEcHCDwdTPzYDC6HEWTWL72iNosDbZZJPQFS0TdRQpuQEBjAOiV8fvLyTWCsHzHeA6uPkNGjRI9t1330z2UOpCxli1Byf6jBkzJnTFEkiNRj6H7p7ZuL1YtezXbLGbPu1TeXHCozKkT5nW9ytRt88mTfxSKettsKE2x8WzpU0Oi3yJ3jdLVSvLyyyxmbG1OdlDbT/f0SJ930oYV1/1dfXypbHjZOR6I2TOpx/KHX+6XhPIeO3AVnj5BkfAEXAEujAChZZ+4jmOJxTzBxTisWA15GUELo4VjNt1RLkrsxoyrnkvxcfg2WZlvDCYcF48LzlvBCW3EUDrR1I/yG9niTOeBPKtJjCJ/f62/Qj84Q9/yMSHxaNgAeKHQnkBLFz8IHAh7CpC6QPi2bAE8qO3HzwWwQsuuCC4JuAzTuzePffcI/vss08os8DkuXlCvfxKkzcI24MGiRsQrqDPPvtsKB6P5gmNGGSZGyM+6ckSDfQ3Ym1j2TLbsWx/tn62P61/2jbaQ1zxo8eVAgsd5N4E0sx17bfffrapxTLbeWQ71ve+972gTURjx+eBvP322222Wrc4iR705l+vvCxfzJkpfXoXa9hek9TW18qmW2+vpGy9ZmtcM1cM7ti4ji5cuEBmTJ8q8+ZqGQm1Eg5bay0ZpNbuTFZP5Xuz58yWKZ9+IkuXLJZelX00DnCwDBg4QAljafQbwBKov4nGJqnqo3EWw4bIYL0nPPngfXLIkce6NbAHfQf9Uh0BR6B7I9DW0k94+GywwQbBckbZLDJs8lznRaIWk+Sciu0QxBUtd8U4sSfb9ttvz6ZWgnsqAhnFekkIEAQWMQV1eBP92WijjaJ3q3bVSWAC79XRkpI4xS75dvbs2cEfO9bS2CQeUoQfONqerog/ljjcMgn6xRrIDx73w/imw/XHgj+4EZR4O+4RvGLBeoq/OS4D5lce78dfnrTHSGwV/PnPfy68sskhhxySmsUqX7/4M0yOjX88rzTBYsqLm6RZRNddd91gNU1rn+88sp0/3yt88MEcXLDWQtJd8iFQpA+upTLh0Ye1bl+jlKtWs14TuwzUxC677bePVPbu1WIAHm7Tpn0mszTeolTb91HiVqKJXCgo36Dku3hZjT88RzUVgLpBDwi1A5csWaqkcYbM1URBa6iipErdP1vGF2IRxC21SfqpNXLap9O0jMQ/lFyuo/eHshbn4G8cAUfAEXAEuh4CbS39RLm2448/PrU2NXWScyXH64hyVyAcl7zKdTz7NIj5i/M1JJP6WTs8xzpLnAQmkMcq5dLxCBCES0wYE3Ti/HBnpM4fE/mYuHT8kVfdiFwH19XRws2PhDhYxCgIj+Uslssvvzxzo1kZx4+P1RHr4LTddtt1xFA5x8DNIlf9xJyde+hOlDBrrTFMpleqW6dioMk7Zdvdd9MyDesoUWu20oXYvWC0a5JhQwfK2mqlLi6r1ELx+jjRjKENdYukqW5JBkFyiA4YMFSKSrWNFoZvIhNMQ63eBzRxD/Ul1NqoQ2ekSJNAfbFgntbWnC6L6hukpCy2Fmaa+Yoj4Ag4Ao5AD0CAkhCUlyLEgzk6CVeYQxBaQlgMnlJYCqnZnGYJ7KgkglbyirwO5BvIJ+uvv35GQU/bpDHA+pM5vbPESWAC+Y76siSG7dFvsWLhMrmWuonhl40feJxQpUeDU8DFG+nDAodP+TnnnBOS5ZB5lIDpc889N4zCjZAkOi6OQHsRKNYyDwMHDtYgvhKpb6iX/kMGyzh90BaXFgeuFgggg6t5r0TJWlNFfynrM0RKK/sqCWwu59BYu1iWzv9MGpfMCu2KdV95zRpSWtEnnBYksH7JfGlYMF2KahfpNkyFqnzTkhFIsRLFBWotnqJWxnenzpZ1Ro+THXffc7WyAt5yyy3BKs/E4/nnnw/nXcgfJi888FGK4fru4gg4Ao6AI5AbAe63lu2bDPV77bVXiw4kg4EAIpatvUWDDnoDqUPIMUBeg7RkmbipWi4IXF7xdkIZDQEkWWCa0KezxElgZyHfg47LD4U4sFGjRvWgq+64Sx07dqyceOKJIX0x2aa4ASbjCSHYJNCxzJgdd3Qfqach8JHGMXw2a56sWV0ug9RNpVrLN1iVh4CFcjXqwReVVUuvAVogvrxaN2PKa9S/Ws6lolqKB5TL4pm1ShwbpbL/SLXmLXMlVasfZLG8d6WUVFTJopmfiDJGtRA2E0DGDzGBSq769+0n5cXTZOiaa0lVLwgkbSKTIY07SZgEWOxvW06BSQCTAasx1Za+3tYRcAQcgZ6IAJZAhNi+3XffvRUEN998c2Zbe8pwZTrnWYk9rcjFkKwFSCgQ5fS4v3OuKOoJRdliiy1CJvTf/va3QQEYJzmE3FKGorMkJUVbZ52KH7c7I+AEcMU+XW42FEslMNoIIK573JROOukkmTBhQtA4rdhRvLcjIDJqvdHqtl0eyNr64zaU8oryBCxKxoorpKLv8EAAldYtI4nLCVpxSbmUVg+T0r5raebQyqh/c1voXElptVT1X0uaSiv0nVoDl0mjJoap6T9AxmhJinUG9ZMv5s3U4Hqygy4f39r60hFwBBwBR6B7I2CeYxAmSkQY0SPEiFp9P/3pTzMA5MtAn2nYjhUIHtnOEc6DshOENyEQP+oVmoLviCOOyOQioJQEbqzkKCABHnULEZIFUk/ws88+C+8744+TwM5A3Y/pCLQRAQgfQdHUscTtAcsqNztiBCkkT2C0iyPQEQh8MX+e9CopkjGbjJNtvrqDlBLrF5kCKf5e2qvvMvdObH8qmT9G1Iq0rIS6gaorqJr5lp8Wu/XFAiJYqlZDxmrCtGii1sISTQCz/e67yuCB/WTmR+/JM088knnwWzNfOgKOgCPgCHR/BEgcaIkETz311JB0jxre5BegHh+WNUpOIZZ0bmWhgjWP2EAS+51//vmh6PuoUaOCEv7WW28Nh914443lV7/6VeYUqDtI4kDk8ccfl2HDhoU5G8kDqRPYmRI9nTvzNPzYjoAjUCgCJIjhpmN1cQrt5+0cgXwIzJ07Ryb/6xWpLC+WNddZWyr14RoYW9xRy0CUVmgMoMYN5pJibUd9wGzSTPuKpayyX4hBzLTTHY2aeXSt4SOk3+CBUqWH+VTLn6Rl0830KXCFeBGyuvEgxrXa0nnn6o6LDynJif1bunRprqYt9lGyBQt9nFGuRYMsb7hOlDvTpk1LbcF+UpVTVJl463xCogSy1NH+jTfeKOgauObXX39dnnrqqaznkTxuW8/L+hMjSfkb3GtNw2/7fOkIOALdE4G2lH7C4+kvf/lLJqQIC9prr70WvKKoNQzxO+usswJQ1OfjfhdLtmNZGyOY9p5l2ja2k/md42Hpo5A91knu9QjvqYn8xBNPtEp4+IMf/EB++ctfBusgzyHiGLln4t5KNvPOkuxP6M46Iz+uI+AIOAKOQKcgUFTUKBUVasVTS2CjumBqhYhlElnqlPwVlVTo9mibNcssm/QBV6svXGWw+aVJ8/YidR3V1KHaIGqnq8Wq7ChVV9TRYzeUfQ48dNlDOWqTNmSWbSRQ2mabbYLGePTo0aGuJtnlCNjfY489wgM52ZUHNHUt0foS07HtttsGre83v/nNkOk42d7ev/DCC6E9ipqdd945lHYZMWKEPPzww9akxZIaV1j6eTEZoO3WW28dEmlRB9SIKu1IrEUtUBIUkAQKTTIJoe64444WY9obYk3Yb0mjyKZH3DDX8/LLL1uzzBLiuf/++4cJzKabbhpcn6h3SlzLT37yk1D7KtN42Up7zouuZNfDvR1tPjW3SIBFKveDDz444y6VPJa/dwQcga6NAKWfTDGVdiVkjGc/L7Pu0Q43SpRfKPG4n6PQwh2URDGUz8Ll0vqRtAXJdyyS7dHnlFNOCe3jP5S6Yp8lnIn3kR30T3/6Uzg+dQofe+yxQAyJ96Ymcnzecb8f/ehHIU4QpSIKQsgs90FcQu3c4/arYt0Tw6wKlP0YjoAj4Ais9gg06SS8r2yyxVYy4f23pLGuUWlZGukiAQzbIXpNms0zTZdYJHWLKZDbJBW9tD4ga/pATW/bPFYSniK1Ijbo+ENHjJKBg4eG3bTMRT2TY/CeWFqy6ppgQYfYoC1mEsADfPz48cFiFwfsn3322RkXHggatZw+/fRTweXH3H5sTFsyJpMVs9AxGcB6SNFg6qFmy1xn29EuW4wJeGElgyShad5ll10y8SYQOQgg2znmoYceGqxpcaKCP//5zyFGhXMjSx2klPM3yyYE85FHHgnj0oZ6VrgtYQFEuF6wwvUcIooWGwslVlST9pwXfYnh4cU1EuMMSeUYXC8a/2effTYcB627iyPgCDgCIEBcHfGBFiPY2ajwPEMhZ1lDCzkf7qmbbbZZIU1XSRsngasEZj+II+AIOAKrPwIlGv+3z+Hfkfdfe0Hq6+qlUbN7JqVJi7g3NixVklcqb02cpA/A0VJdXRM1Q4tbLw2LPtelFp3XYvAzPp8b6v6NG7dRsHg1N26mc41aU5B2y6ILw64m5ZVN9Vp0Xg2JIzfYWCtWqPUxNG8bBYRc/eIXvwhjYgG88cYbQzIlXH0gHQTyY30jUP/222+Xb33rW6Et2y6++OKwfuCBB8r1118frIAQLixVL774YtgX/0FjfMABBwQCSFFgNMUQPyyKv/vd7wR3ICN7cb94HQKIxZEaqmi9sZQhWCRJOAAZJRECWmomIJBNSk1gZUQDjUaZMhTIJZdcEpbjxo0Lbp1oryG91B3lmnC9vPrqqzMkEDJsBBDiTAwyAoElO/GDDz4Y3JxwhWJMpD3nRaY/EipAALF4ktmPSRTvsWhi7SRRAm5VMeEMB/Q/3RIBrCkoKlxWLwQsycnqdVZtPxvupbiE4mVhQmIX7stkVueeGAv3aVzhUbx1d3ES2N0/4ZV4fUxuXBwBR6C7IADBapA+Nf1k7fU3lDlT3w+F3XVu3kKKdEPD0nny+fy5gWCdc/a5svGXNwpa2tBQfUhrF8yQhiVqCdT1Rm377HNPy/33/U0Iqsflb7nUKZmcLUW4jVr8oB6PkoG1i5dISWW5bLjlVzRRjLmLto0E4jYEiUF46GP9MsHCduaZZ2biMYjRMLnsssvCKhaq2267LRMfgpsRgfy4H+EGGQsxd8SjIBA1CCACWfve974XLHa4J+USXE//+te/BjdVa0fs4t133x3ecr64PZlQfof2kDJqTZEkgfZMYt58883QDPdOCCDCRIj3EKyXXnopk72OfZaGHaIZ1zAEAyyMxDYyicJllOO157w4Di5RPDs4J84diyaCRRCLJkSV4xNXg6sU7rou3RsB3JRdHIGORgClGh4HKMTuuuuuFiRw+vTpIQ556tSpLQ6LmzzKQayOeD50d0nz4+nu15zz+nCZOfbYY+Wwww4LGuOcjXvgzjFjxgRtMxpnv3H3wC+AX3L3RUCJlzpshusbvdHm6po3XxZ9sdBquGeum5p+tQtny8DqEvnD765aRgDp12whXLLgM6mdP00tew3Berd49qey23abyxWX/Up6k2im2TlUCWatLJ43TRoXzdO2kSgDxBV04YL5MmLDTWWtEWPCe6VTUaPCVr/2ta8F90eSjuy2224tOkFEeFmCJcgHgkXKagDiRppMEEAc4THHHBPaxn/MikbMXtp+yFu6O+zyUdA8M34sJE0x+f73v2+rmSWk7cgjjwzvIaFcExMYuz+TpY7i9Fgv2YdcfvnlgcShCTchThDBQog1EkumubXifgUmDzzwQAbH9pwX40MeESyqRgDDhmV/ePaaVagnTMLia/d1R2B1RyCfN8PqdP64v//P//xPajKs6urq4N2B10YsZFsn6UxPEbcEJj5pshBdd911wT3IAvITTXr02x122EF4IZjRXRwBR6CbIAD5WnYpX9poU3nyb32ViH0h/Qfr9riEg1psipVM1H8xUyqqaqVu4VKpJ1NoY73ULpknTXVf6H4dSNsFUatUSdNsKSlaonXhl6jBr0yZVr3U1S6UxiXalhqBZgUMHdQxVM+lXuMBt939v6S0fMXcxIifw60SCx4B+VjMcD/jFVvzIH8IFkHi5hCUXmmy0UYbtdpsJBArYVo2OuIDsfRly/rJgLisJgVXJhMSqKSJPasgslg+OQdcRiGjaMMvvfTS8ML6RtwfVkpeYGMCASUZDBZBXkcddZR+DkWy1VZbhbb/9V//JaQ+N2nPeYELyRyQP/7xjyHBg40XLy0uMj5GvN/Xuy4CWK9JiOHSNRHAcoYyKZ9Ca3W/OjwSePV0cRKY5RuQ1P5madajN3cljVCP/qD84h2BghAwCtgkQ9YcKUecdKZUyPzWPeFK2hTy1rBotlrO5gbC16TErViJFMQhwybpzbDK83APbVLSZ1KkcYChhATtl41p+xo0HnDIiLFSPXj0Ck82iPcgvo14vqRQX5Nsc7HEbqFJLbG1I2lKUqxftj60p18uEjh8+PDksC1KTCTPtVVj3cB1jhw5MpA4snqeccYZMnny5NB01qxZwcKHlY9zIQkLVj+EpDjPPPOMnHTSSSE2j9TnEGPcRnmdd955IVYRt1rIXFz6otDzwmppQsKcfP3SPjPr78uuiQBJmVDEuHRNBLh/oaQxa/2KXgXJoHBdx4Ni7NixUcx47pEhohMnTgxKJZRTcVKv3D19b4zA8jtyvNXXW7kAOSStEfCaTq0x8S2OQHdBYMR6G8nSLz6X2tkfB8tdIHNYBJXsZVie1gKEwyEhsUtYb3YNbd667G9oVKxtmq1tob/2zciyMZpH1sQyReXSu98amhBGrYaZPpnWBa9g0TrooINCLSdcJEmaQmkIXCWxcBHvhjWMpC5mCYytcaT8ThMmLkmhH+UhsvWhfVq/eBzOMSlYDxGseMTI5ZM4U51Z/LCokdgFQkzCAwgYkzmsf7h84hqFEK95ww03hIQxTz75ZOhD0hkyhCKUnMCSg4tVe85rxowZYRz+UNeLzyaX2HnlauP7uhYCad/xrnUFfrYdgQD3MpJl4ZFhArGk7h8Jq+IyC3g4QPJQMOKqSWw5GZrtfsp3inp73LvsvsQ4cSF2sjbTnyzHJLRCqUWMNs8A7m+4nhMzbi7zxAaitOK49CU2nPObMmVKqxqAnD8u9rjd04f7HKS2K4iTwCyfklsCswATbXZLYASGrzoC3QaBZkYGISuv6iv1VQOkcfEs5X4NuiUigKnXS18jenED27aM7cW7lq2HkdXyhFNqac0grRFYs+xo2fukDNNi0y233BIIIBupJ7XXXnu12I/1zupA2f1s3XXXDXF5kDk0zWmSZskglIBMlx999FEot5DUTGNZa49ly0gdVjwS00DCksL5YO0jcQsTFY5FHS0mWGQshaBSC4sXLrAkPmBSAxkkAQvJYjhvMn9y3tQ3BCvDi5INxFTSnokVJLA958X5YUVkUscEbvPNN09eSiDj9913X0hag9XIxRFwBLoXAu0pEWP3Z+K8LZkL9ypK27APZRUkjnshRAwFkhE60IMo4sJqbqwo/eg3f36ztwsEkX7c4xB7zzZqw1Jyh2PhPUEG46TcdNNNYbxdd921yxBArqHtkfbJK++m7/ngXXIj4JbA3Pj4XkegKyMQrHbF5VLZVy1ylf3UOsfjwkgelM2IXfIqs21Ptmv5nuM16YO4uPcAqaxeQ9fVytiySZvfWcZLkr+gKU4KpM0kvp9ZkhQ0zjz4Y4FgYRFLCiQQIebtqquuSu4O9Qot+UyrnTk22Lg0ueiii1q1JH5xzz33DESOSRDvSdpC7cOjjz46aKjjTkyOrBQG220SRHIZLIdY52yb9SMW0Qib7WvPeTERsxpZaO3TSDFWAIjrTjvt1Orc7Xx86Qg4Al0TAe7JcYkYlFcooIgTRTGFN4KViEm7Qggg93LIHvdaFHl4dyCUtECBhKAANA8G3t9zzz1B4RdnV2a7CfczFIKWGZlnAO9RBkI8LQQAV/qk4M5qyWSOOeaY5O7V+r2TwCwfj2kBsuz2zYqAaWYcDEfAEeieCEDCSsqqpKxmmBRX9JEm1aQ2+38qZUtyvfBeM4RC5rDoBauevtdlc71BGsSddD1+y7GqBirpHC5FJRUrTAD5RKyoMMSNWDYjekweKOOARtrEyA3vmShAWHDrwRWIOoIIExUyIzNJSQrupTvuuGPYjKsRkxAT0pOzrT0CwYMQIVwDEyhLnMKkB6ue1fOi2DxxgJwHS+TXv/51sIIamcXaZ7UTuUaz9pH4BeEacdOyOEIsh2i5n3/++bAfNyukPedFP6u/yGew9957B4sl29HAk33UkjVwbmRndXEEHIHug0CyRIx5FDDnpkSMKdCsREzyynH3xBpnbvt4bsT3Wtw4O1q4Fxk5pBSQlR2y43B/RIgHt/uo7Vvdl04Cc3xC7hKaAxzdZROq3K18ryPgCHRNBJbZ4ZSolZX3kTIlZ/VLNRlMHTRP9xUv2x9fnG567/2p8t6HU2XGrDkybfpceXPSezL98zlaEkKzw7QQJZJaboJ/9UoAmkr6SmW/tbQmYFWLVivy5rjjjsvEd5966qmhJAEWLWoEUlMPdyKLPYEcmZBBE/KEULB82LBhQhIZShrEcSbWniXPC8ge7SCdEDLa0xfiaCQs7lPoOhZJJj+4N51//vlhsjFq1ChhAkRsDEJyBMpBIJwLtf0QModarUDOhcymEyZMCPuoF2g1BKllaGUlbrzxxpCkgQzQlKzAoojgnmlF5Hnf1vOiD4SW4yKUneCYuLnyOUAwIYfItdde26KuV9jofxwBR6BLI7CiJWK4lyXj7Sy2G2Ds/tHRIB2zzMKHcjMmndyTcRVFDjnkkEzJoY4+/soaz0lgDmSdBOYAR3e5JTA3Pr7XEegOCJD1E9JXO3+RTPzjbTJ1wtNSR6H0BmWHCR6INrd/v5pQY/Cddz6WD97/SGMJS6R3nyo1IEaPm9BPSWBDsSyZPlPevvFOmfLcK+G9HqzDBBcftMYQJgQrF247nCeEA+JHghKEGnuxeyLWMJIIYFHjXofbEQ98XJHuvPPO0Cf5B0JFAhaz3JF0haLEEClcT7OVeLBxsj1zNtxww3CuEMvKyspAMnGhQngPqUJzDrk1ocQDrkt27bg2cS4I5PH666+Xyy67zJoHNywILm6haL4RXK9Q9pE8B7wosUEGP5P2nBd9r7jiiuCexXkg1POyshxf/vKXg0sXBN7FEXAEug8C3E+MpFEiBmte8sU9xTwd0krEEFecJii4kDgOMK1de7dRZgh3eyR2Cf373/+eiVE0otjeY3RGPw98y4F6tgdyji49ahcTI7QiTKhcHAFHoHsiQNbPpoZ6+fi+u6T2MX3g/b1R5r8xWYbtsYvUrDtCSqvKl5X5o53IsMHVssawTZT8qcWQe4OSOko+GLcLJQcbGrW+4EKZ+dok+ezBh6Rx4jsy76U3ZJAWhx8wdsPm0hEdBCfunLgdQpp4YZ3jgW73d1w/s8WJ4LoEwcI1EndR4tlMC829L01wbyKzJoSSEghMTjge90kSDCSFGL1sY8VtIZhMPnA9YlxckrDMQfKs4H3cnnVIIy9IliVMgGTFZDHuw3iQVYrIT5o0KWQOZdLFNVlChbg96+05L/rhNsULS6VZYSGFFntDGxdHwBHoPgjEZWVw/25PiRhzc0+isirmocRS4xbP/YrYRuqqmisoZNbK7STPbXV+7yQwx6fjyWFygLNsF0TQccqPk7dwBLoyArPffVtmPnCfVCrLK1Jr2JIJz8r7r78lfTbdWAaN31z6fGldKe/XR0s66CMF4teo7p36r1k9pOSQi9fi7421dbJ07jyZo6Rv1tPPS+2bk6WsrlYqKjUGcLqSpj/fKFucdZ4U92kuWdBRmGHZIj7QYgTbMi4Ey5KZtKUfLo68OlogY5Ayi6UpZHzqD6bVIMzWl2u2RDDZ2iS3t+e8GANCaokdkmP6e0fAEeg+CFj5Bq6oK5aIIWbx5JNPDp4YuIRyDybhDGIu8+FNF/rjJDDHh2Wa4hxNevwuJ4HLvwK4LpD+PJu7wvKWPXeNxBZYGYg1omZZR4jj3hEoZh+jSRU9/3ngHimZPkWte0rt1K2zolxr/s1fIEuffEr+88I/pWj4GlK9/mjpNXK49FpriJT31hpJgRCqIVD71y1cIIunTJf5734gi95+V5o+nSYlal2sLCuVIs3EDEmsLKuQBU8+IjP23k/WHL+tJodpdknMfma+xxFwBBwBR6CrILCqS8QU4mHRFuzwAiF7Mh4Z9957b3DvJ9YbBZgljmnLeKtDWyeBOT4FJ4E5wFm2i3gR6lL1ZMF/nSyDl1xySUgM4SQw+7eBmCSSQVidn+wt8+9x3PNj1BEt5kz5VOaqC0xlvSZ20fhA3EODha+kSMpLykUDODQbzEeyUAnePH0YNlVVSpEmXAk+otqwSEmgLFoimm9brYhK/CgSX6rxgcVlzRZC2uiJNummssUL5cP775Uhm20hpb00jjBcQPPfjrgWH8MRcAS6JgLEpeIKaBP7eIlLMcmYkoKLIO7FTNKtvbVJe88zxTIBW19zM7T2LImvJb64EMGqTrmBMWPGhNjgadOmyeuvvx6eg1xPrnGIJcYLgaRPxMxS0JxyBCjfk0JmSsorcH68eNbG8XGcB67btp+5G9mPV6XgkcH1vPjii0KJGJJzJb0lKBHz/e9/P5wW+y2bcHvO02Kb6Zv2/Ugbk+9KrvbE/UECUT5TbB7ZZZddWl1H2NEF/jgJzPEh8QXiCxH/kHI075G70m5GPQ0I4m0onuySHwHin3hY8VpRcdxXFMFc/dU2h3lOJ1Fvvv6BvDdbY/2KqqSf1Ep5kcb6Bf/OZQlCyRJaXCIVmgCmXl0+62sGSMkwrfP31htSqtY+aGOxZgEtUtLYVKrxg8sOG4ZYdhimNLXaf2bpQHn/3zNlnelzZPjISj0+vd0imOuT8n2OQHdHgMn26aefnonjTV4vxIYY2ccee0zOPvvsQG6I13r00Udl8ODByeZZ35P4CMLGi+Ljljk4rYPFslJuhWRQaYLViCzDZK9MCkmjODdicJNWJLIWUwrmlFNOySRpsv7z5s2Tk046qUVykjvuuCNkIDbCSlsIFGVwbB1FdVJhT4wepW04l1UlkLqdd945JIghVhtCRZwyMYJ8fh1ZIgbFgQmWO2KzUQpAhrOJ9aHGIN8f3OhJVmMC4UPRD3aQWQRi2FWlmfJ21bNfBeft1sDcIDsJzI2P722JADd4Hp5kWnRZzRFQtkZ9v+ffWygP1uwi9/TZWZ4pHy2zmsqV2Ck50/3LuGDzhShhq9XJWM2ee8vQI78lDZtsLrXrbSB1Sgjr1NWTAhHLogMzFx4IoR5jWlO1PF61idw96OvyWNFm8tok1WIHZbc/ojJg+Yoj0EMRIDNtrrkY5IeJ+be//e2QAAkCSCbethDAGFr65iKAtLVYVmpbXnnllXH3sE78GJmJ0wggDezcKB8TC4mrsBSedtpprQgg7VCgQhwp42L5GCibEBNA2l1wwQWZGGhqgSYJIG3AbFUnM9lpp1VXIgayZ58jZRywqIJLLsFii2A5pEj8uHHjZNasWZku4BzH/6HYPuCAAzL7u9qKP2HzfGL2I8vTrMfu9lqBbfvocb944YUXUt05GAnXEPaTXZDsfIVaoWmHmwiaqULqkeFawoPmqaeeEtxTChW0X/RB61rIueW73mzH5Rqee+65kM7fUkpna+vbVxYCRTJr5jyZOXuh9B7aX75Yd2N5beh4mSq9pUEfhFgDMyRQ39Rh8VPi12fsJlIxYJAMO/gIqdp5d+l90OFStsd+srTfAGkor5IGUe8KPeXQFxaoK/8uHykvDtxBPu8zQhrKKuXl1z6RunpNQrOyLs3HdQQcgS6DAPUocdNMPnN4NtTW1ra4DkgStTOx5JHFEQtTLPSJX7h2JuW+++4LfZNj0y/t+YpljizEJswbcWuMBfdMygngwRILbppG4FiSbZJaoyY8qylQTgmbWOmO9RCiiFDqBeVqLIxLrU2EOHyOn7xW5hm8VrWsjBIxaUoC8LztttsyRJDrfPvtt8PlprVnB/VSIebmFso268M6Elv+qA2I5baripPAPJ9cti9Knm49Znd8U+oxFx1dKBrK+IbNg4AHwKWXXhpanXHGGeE9qYVx7cAVgVozZMm66qqrQhvcOyhkzTZiANiPywEuCGixSKYSP/x4MHEMvpsQvxNOOCH0Q8s1fvz4kMKeYtfc9JMC4UNriBYTbSlaOdLCE5vwk5/8pNUDlf48fNGwkllx5MiRoQ8aRK47LppK23zXe95554Vzt/pg9LHr4ZpwvwBTzg9tLBkK0YwyboxBPtwZ16U9CEDN9AXD0+Wn02bK+I37ybcOUC37QSNlqy+VS9+iWo3twxZIm9BarXaa+XPtkVK1w64y6z9aiuHxR2XuxLfki7delwWqmCjT70vVHvtIw5c3l6WaPAn3rXAY7a/ROlJZt1DHVBrZVCclalF8/6NZMnf+wjB+8xGWrfrCEXAEehwC11xzTbCc8bwz4flADByv3XbbLRA/24fbH5a2jTbaKEOU2AfBsj623HPPPa1bZkkpGfry3DGhrh19cBfk2XfiiSeGsjG2n3InJltvvXV4ptr773znO+HZT41Rnn1JgmjtqE2K9ckE0kj7HXfcUah5itUuLrNw/vnnh+cyz26e4UnCu+uuuwbCghWMUjXxtaIEZp5B6ZvOEPDCIwjF97PPPhte5ArgvGJCzbmRcM9iGZnvpMnEiRNDG+ZLsYABcyFi+PhcqYWKMBdhTIsBtT7Mq6gDi4ssivjZs2e3spbGlkHmdl1ZSrvyya+Kc+/JJJAbCj+Ul19+uUVQLQG9lp68p1sCcQWIyYnFkZoWiX0QZTCLhZsIBA+XA8gYNycTAqXR2HET4gZJnTLaxQ8kI988MCzJCtootJTsQwsKmbTaYIzNPsghN1kEQoq2EN93tIgUxibw/PHHHw/7+UMf/PZfeumlsI1j8CDEwvfhhx+GGmRoR3nIIfmuFxLK+SUfPHY9xFCg+aQA9iabbBJu3JBkku7gznLRRReF4+TDPTTyP21EwGid1vBbslBKNYZv4bxZssW4/jKkvyod521jEwAAGBVJREFUKsql/s3F0lSiiWA0BlDUjXOZj6fUa+xf9ZZfkQULFsnCN/4pQzfdXOa+/Y6M2md/mT3pHSnX79kau+8pH999mzSUat8laklUM5/yPbX2FUlN01ypKVsofQYO08++KExm5s5ZIEMH9VONbBsvw5s7Ao5Aj0GAZC48syASkCxz/+P5QU23NDn44INDvByk8swzz8w0SRKCzI7ECuQFIgdRhAwiY8eOzbTi+RULpCIovnQj5wtpO/LII4XnGM9f28c2E+rQQdrs2ch2nsPE02Hh5NnNi5IFkMdsgkIa4rOqk8BkO5/kdnOrTW7vyPfMx2JlfSFjo5DnlSYoJRA+865e3sYfr2mfcLStJ5PAf/zjH3L33XcH1z8Iib2Y9JvENyjb1pOWWMK4iZtQMwYCl1Z8Gm3WueeeK3/4wx9CIDjaLjR0RgAJ3J4yZUrAmxs2WkDz409qt+x4EEAefJA9PhceTnZTgqTh1mJC0LURQG5i9KU9mkWIHvLEE0+EBwzrPJh4WPLg4XdAgDvk7bPPPgvuKVgQER5CCxYsCOvxn7TrjfenrUMAv/rVrwaXGQgppPGwww4LTUkOYDV52oJ72nF8WxoCOF8WSWPdUlkyb4osnv2RVJcslYqyJi32Xi+1dQ3Sq6JJ+mptwKKxG0hDKAGBRU9jASt7aXmIUVJUv0TKq2tkgFr8+uokrKz/EBmjxcqXzJguM157RRZNeFwqtFxEsSaT4Wh8x+pKimXt9YfK7uP7yvGHrC2nHrOeHLXvKE0Hs4gW4Zz0j4sj4Ag4AlkRgBDF7ptpzySUmDy3eD7j1bLPPvuE8XieQAhxOy1UsGTF1j+yXpsQ0hFb5V577bVA1Eg4w7OU5+gRel9kLvDd7343dEO5ikLYhAyZafMrnuuW8IW2eADlEojM5ZdfnquJ7ysAAYg08Z8o46+77rrQAwV9Vxe3BOb5BHsyCdxmm22CxQ+CkU16uiUwGy5p27GqkrkMOe6448Ly+uuvD0usduyPBTcGLH33339/IIZgjctkLLiQEnxO/RoE1xEeaLhrIlhxsa4haBYRxoizkRG4/pvf/CaQQfpBvAiG5mFjrhNoGmNiC1G78MILw3VgpYRsxlpMjpN2vWzPJcRzcL1oBxEe7LjwoJAgDpHYh64chJ3r2jt/H5k4i6ShbrEUNyyVxvqlst46VYHkcW5NSg432HlrKd51vMx+6VWZ+cFHoTxELTxtvTFStc4IWWvkl2TukKFS0rtSmtSC+8H118rQnXaW2ZMnSvWw4VJaVq7unugetZOywKZGJYE1fWSLYw/SgvPriCiJLKsslbWHaPygNsM9VKRCXy6OgCPgCCxHACUjSlKeVXi4fPOb3wwEz1o888wztppZEuuVTMTCswZClk947kDscKuEeBEeEQsKcxM8aMhGSXIYZNSoUeEZyzrPSxSrEAosguZaSJZOe75DYHnmZZNHHnlEcAVFspFAQiv22GOP0Obwww8PWThjBX7Y4X8KRgAlvXlE0Qllu83jCh5kNWzYcka5Gp5gZ5+S/Sg7+zw64/hozUh9jHYNf3RuXklBk48LoLk/Jvf7++UIYFVLCslPsP6lCQ8HXDZNLHbO3rMkvs8IoG2H1BG3wA0/vumjhUQgk8QWEFQOySR2ggdarMmknZFG1i0AnXUTgqPRMtLX3INtH8u06433p60ff/zxGQJo+1HEEAeA2w6WTL5zFkhvbXzZEQhgm1MpUiqopRyaSM+pdf2atylh0+01gwbIjH+9ITMfeUKK9H5QFPw5S6WkV29Z+MH7UtJ/kEz7+yNSVLtU5j3zpJS8/7ZMm/qxDNz7ICWXdVK3VJMwLDtM88D6t1gLxnNM/VenpLC4XhPCUIZC2zUoKSwtcRKYwcpXHAFHICCA10o2ueuuu4LSMNv+9myHLJI0JE0oEQUxi4UMkjy7DjzwwHhzUGwS48cLax8eQTx7sQSaoIjNJXG8P3MvXnFYCn1R2pJQxsoh4L5qrqu5xvZ96QgwxzGvKD47lO0oIrq6uDtonk+QycmqIIJojghcxY2ATERprgx5TjV1N+PgVvfee++luhYkO9Ee14U33nhDiMVC+KLHRTeTfdJcFpJtevp78EMbmBS+X9ShQcOIiyYWN+IAsOhBziy7F/0gP0kxi19yO9pKJH4wEARu7iYQvKOOOioko8Hii1UvSQL5HiCQzJiMho36hwcP7jBoIs1t1fZlu17bn21JvaA0MZKJG03sfpvW1re1HwG+YWWVNVLeb20ldoO0gPsyPaHuKFbCVzt/nky95yHRzC1SrMofpWxSpoGBRa+9KFN//3/yxeR/S2W1JmtQ5UXNhhtJuS4btVB88eIvpO8gLVSs33fcRxG4YFAezZwtUx54WBbPmSslmhm0KFgKdb+6idp66OB/HAFHwBHIgwBkjGdbNmGOEz8XrV24F9mbNizJyH3OOee0ej7zTEcRShIzauHFCV1s+JEjR8qECRMCWYyfa8wJcik611prLRsizB3Srofwndh7B+Ww1zPOwNbmFcpyEEKDcp7QGivx0eaBVrMObgks4ANBm4MV5oILLgjBx0bQbAKPi1o8GefHzqQaH3MjSNxgMPeTGje+2WDyp94LGpvYtZKxsdbg4mAm/eSpkq0KV8BkwC9fUIgkfuOQORMSjlCnLc4QxT4ClfFxRrOR1EBBRtBoxf7tNp4tOe+e7DZrOORa4raZhhHxg8cee6zcfvvtrR5MuELiHkkMXjYhI1iapD1AsOziIkM6a4rLYtmFWKLd4oX7JgleiD+E+JsbMNk52yrZrjffOBbUn2xn2ky28/tKuuIk2/v79iEAMVMzoCaBqdHcL+VSq2rChkVaI0ktc3W1DfLpM/+Uhvc+lFJtWKz3KOgcfSo1xq9MJ1cLn3xUBmiG0AotB9F3q6/IvN69pFhjCee98pwsVDJYSjp3iKAKfbH2VSjpW/ji6zJ7i81kze2/ottKRH1GpbTXACkuU3dUF0fAEXAEsiDAM4yyCzyvKIXAsy2bEFrAfIZ4e8ItEJKiYa1jHsYkn3laMnEZ7QitIMELcz08U+wZixKVQvYkLzNhrkXWSJ5VjIvrJ8JzkQzeWBRtHoY3DZlOmQMyHyCpDM9q5n1J66KNH8cixhlTbb8tIS6EadgckmQ5Lu1HIE0Z3v7RVo+eTgIL+ByYvHODofZMUsjeiOsBKf5NcKGkllpS+EFjgSHtPcQLn25uKmnCjQ1XQV7crH71q1+FTFJxWxJ1cNNICn7KMfmz/Z988knINElyDbJaIVwXiT2wFKYJ2inzPU/bzzYjutn2+3ad06olME0ggPa9QvtH5i8eKljESDNNZi+LI0yzBKaNmWsbLic3aKZSXEOoD8R3lUyipoX8/e9/HzRcaAx5YCFoFNsq2a433zimYEm2i4kwpTNcVj4CJaWVUl4zTOpLlYg11srcmZ/Iv/7+lFSo10K1KrXKsQ7qfUr5ny71VVYiRdOmyty7NAPorM+lfNT60qjva995W0r0fQnZRDUzKDGEWAQbtW+dLpfqAAsWLZY5T78sw766h1RU99bBKqSi9wCdaPkjauV/0n4ER6DrIcCzEiU684+2zkFQ6DMXQxFKGQAseTwb8TiBkFksfIwKnjKWkAUSaeUmaENmbRSpEDmE+DErHUBsvW3HjZP4eV4c30jZTjvtFJ7HHAMSiqCMZR6QDMNhW5wNlHFyCYQTogixdHEEkgi4O2gSkZT3kEBuDruoO11sxaMp24mrQvuEJY0JPVoac8eLh+PHTRAzkkYAMdejJUKLFAvpj/nRJ2903GTSfJKTBJBx4/PmpoSg7eIGERNAYslwGeRGk0xzHJ9TvB5bMOPtPWW9veQM/E1riY851lseLMQJkHgFF0seUCbJz9+2F7okCymkD/KHlXGvvfYK2dDIEIqF0Fw67QFoKZVxC05am+2YJLTh+4JCoiMkWZTVxiQlNkJmN/tttRd3G9OXuRFQrqbWwEolZYOlou9w9QseIu+Ulsk/+1TKqzW95d/VVfJ270r5oLJCPq4ol0814dDnupyvT5VPnn5SJt38e5nywjOyQMtMzCvXfZXNbT7W5ftVZTJZ+/67pkpe1nFerK6UGQOHStXAUXqsEVKp5FNCLCBn4eIIOAI9HQHCcmLlInMfnlk8y8wql4ZRPEdiLkdZBmLLIWqQOshR7FHDfpM4To9xmB8hKNJ5ftrch/kVz24rKRCXi8DLyqyO9EW5St1dmwuyzTytUPab8PylxIWFQrCd+MEJ6j5qIUpYMa0msfWzJedOO+Z7yaRz1saXjoCTwAK+A9w4+JETn0XiChN+xEyqmcxjrYHY8WNDM8OkmgKmJiTwgCRy0+KHHVsAuWHwHrKH+Z6AZzI7mZaIMaiBg8UxFqyPTI7RQKUJFiU0ToyL5gpXP5JrmBvCz372M8E6aIJVCs0a58KxOOYhhxxiu7MuV5ScZB24i+yIH0xJrV2uS4jdhfmuJDV1JIyJg9/tgZNrzFz7cAshJTbZQpPuLlaYnf62D4u1yZVXXmmrmSXfc2JYeaBaNs/MznauYKG0B6INQXIbtiOW3Ib19uJOX5f8COC0yav5r67p/zolcwuU9H1aUSbvKuH7d68KeVUJ3D9rKuWF/r3kxQHV8uKgvvLa4GolilXyz4H95IUh/eX5AX3l+X595KW+vZREVsgrEMDeSgaVEH6mr/l6X1yqRr8idQMNyWYyxw4n4H8cAUegByNw1llnhZq5MXHCs4pnFd4jZI5Ohi3gMYKCO1ZQkiWTPsmXuUsaxMS5oxyNY+jIIEoJI+ZFkE7meGT3NMFNlHgx5nCxQOaYf1F0HM8rQm7wuDEiR1v2I8y9CPMxYQ44efLkcC54wzCfiwkr4T1s57nIXM4UufTHuweSyHyPEhGvvPKKDetLRyCDgJPADBTZV+Ifq9Vgo/XEiRND8hRqvcVCEDDEiAmyCRYTEzRGJrgA8t4Sdtj2UaNGyW233RYscrYteRzbHk+G2YbGipsTNyNznbN4QIgepBZCATk0IZAaghgTEeLRuAlaLR1rm1yuKDlJjtfV3pt2kPMmLTTkztI+57qWOK7N4hGsPd8tiCExCiZGzux9W5cWR4ALM5ZlHi4ID1G+g1ZYl0B2BGKIlRsh3oFyFljf+LxRfhCviqA5RUPZEQLxRfFgSYlwRWVsivjyvaZGj0l7cbf+viwMAf3IZc6smfLuW/+SBTM/ly/mzZF5M2fIbH3NmjldPtfX9Fkz5DOdAE2bN1umLpgn02uXyCwt7zBHk8l8tnSRTFs4R6bM1X2zP9d2n+vnqX0/1zG0fuD82bNk6RfzZcqH78kHk9+SRV9QH7CZfi5fFnau3soRcAS6HwI8a2KrXPIKmUfhUhkL1rf4GRvvy7dOX/IhpAlzQOZnCFY4q11rbTnXtGc15G306NGtrJYkhIsV+Tyb7VnMmBBOzsXCM9hGIhjq5lJnEIHEgkFSeEby3Gc+ivWxp8/Vkvj4e43QcBDyIwBpMtlss82CmySxeEyIqWlmWhxrQzZOLGlojUxI8mLCBN+EH2Y2Kwrk85RTTgmJQ2iPFokfc5L02Vi2ZEIfW0xse7zE9zw+v1xFL0kkYoHN8Ri23tMtgSQtIaEJRAWizgvM0qxnhhlLburcoCGOaCy5iROrCfGxGDi0i5ZVDKuvPXzicQpdx/UXBQWWO0gnL9wrcfW0hwPvY2s3GkRcVWlDrCmlIvj+xST3Bo0xzJbQpdBzi9vxmyLzFm7MH6kLq32/eODhJmvSXtytf2cs+XxR7rR3ctIZ59ygE45Z+vkvULfgDbbYWuq1zAPxfPonJHbhnJopG1Y8fen3o0zvXcXqOtqkmV8atN4gxeYbQ8kJ7aeN9W+G5mkn/V8sFeqWNW3Kf2TAkGFS1at3GKszrteP6Qh0VwQu0Fi4eD7TVa4Tl0pIT1qICoSIpC24ScaCohJvqZEjR8abC1pnvsPcK074R0fmfQ899FBwsbSBCMuhhBaKduZUzL/I5cC8jmPzDCO+H8HLxax1uGmieCcpXCyMgQIWMsjnhTI+FpS3hIzEXkI8Myn/kCSCzCvM6wzPHUjrj3/84zBcXGYiHt/XexYCTgIL+LyZ9OIOys0GawSxfbgCILhUpokl82Afk9k4q1Cc/CLOepg2TmztYKJOQpl8JDAZU5g2LiUpTLixxi4Gtt2WcTpi2xYvbZIeb+tJ60x8mdgfdthhgQhy7cnYtmwPXlw/wB5CxvfLyjRAxngA8IDBzQXrHRrHfFZZwz3teGhSiffjIUDsKp+bWRr5nhHziptKrHHcYIMNwjmRDe2vf/1rcC+xY+DGDElMutKwP+34ubbbmLhT89vigY47DsL5gEUy7XchuNu4q8uSz9IKCK8u55TvPIq1Qb9BA2WLbXeWrbbfdRl7U+KW0hFu2Cy2Yq0oRK+iv5VYmlvpXyWLKNUaNXlMVa8+TgBjkHzdEeggBLjHd0UhAzuvtgjukKNGjWpLlxZtIXCFCF4rzPGSQviPCftR/GEhJG4QhT4urNni2pkL8GwlmzzWQ9xTIYcYGGIFrI2PgriQeR8eYrELq/X3Zc9FoEi/hPa07rkoFHDl+FtbrBJaqYsvvrhVLwgipSSSwqTvoosuymwmHbFN9nfYYYcw8Y4Tt2Qa6gq+8KbJ4eZBTZqkcGOJLX9ooZL+8ck+3EhIQmNCqYk4BtG2s2RCTjyhCclMsIiaQEpX5GZr43T1JTduHjwQIIhz7Eac79rw3Udrx40eF958n1++8fLtt6xoWB3RdhJ8nu07aGOhgCBRDd8d2udTDli/fEt+M6Ydveqqq0L6bUgvmkssjGPGjElNgGTjrgjuNoYvHQFHwBFwBFY+AoQ5xKEoK/+IfoSORgCLqD2zO3psH2/VIuCWwALxZmJvJDB27Yy74xaH5cba2b5ke6w5RgJJHUyNGSyHsVUQa+FvfvObDAFkrEKtQHbcXEuOhXYKbRRCUVGSbzDhjoWC4fk0R1iU0CVgmenJAomyjJptxQHXEVxKVpWQUS1O/FLIcfkNkDV2VQjWUVKAFyIrgnsh43sbR8ARcAQcAUfAEXAEuhsCTgIL/ERjqw4ucljlyPZkwgQeN1GygFraf9tHls5YcBvEuoflB8HNDv9u/MZramoEV0183LGGmGB1+e53v2tvBXJGsg78w7GExMLkGaukCRobsmJRDzAWXOyI84LAYemEZHKu+JXjesq2V199Ne4S1jl/zvXcc8/NpC+GCMYYterkGxwBR8ARcAQcAUfAEXAEHAFHYLVAgHAPlwIQiGOcsHjF2T7pbpkSjz766BajYVmLLXzsJAMnmRbj5BCkwad+GymAH3vssRYEkGMRDxbHahGDRkYpXEFxIYwF9zq22Qv3vbTU+yTZiIvcMwbupiS14TzSCCBtIJ1kPo0Dmnt6XCC4uDgCjoAj4Ag4Ao6AI+AIOAJdAQEngQV+SjEJpEvs4olV0FLpEysVx9olyaIdjkQxZHQi2yJJQNKEeCiyTFrykLgNtd4KjRvj/P77v/871YebjJFkp7TsVfExWMfVLumWBwkmbm3vvffONLfskpkNvuIItAEBYkp5YQl3cQQcAUfAEXAEHAFHwBFYuQh4YpgC8SUphqXqL7BLm5pRNoL4PFxMiYfC/ZN08vmSdbTpIHkac2zSGfPCRZRzIMatkPT/EFKfwOcB2Hc7Ao6AI+AIOAI9GAFPDNP1P3xPDNP1P0O7Ao8JNCTyLFd2vBuWNV4rKpacxZaMl7aetg3rJC+sl7bflvE4advyla1Y0evy/o6AI+AIOAKOgCPgCDgCjoAj0DEIOAksEEeIDzF5xMMZCbIlQ6Std/S2+Dg2ti0LvAxv5gg4Ao6AI+AIOAKOgCPgCDgCPRwBJ4Ft+AJUV1e3obU3dQQcAUfAEXAEHAFHwBFwBBwBR2D1Q8ATw6x+n4mfkSPgCDgCjoAj4Ag4Ao6AI+AIOAIrDQEngSsNWh/YEXAEHAFHwBFwBBwBR8ARcAQcgdUPASeBq99n4mfkCDgCjoAj4Ag4Ao5At0OAMlouXReBddddV1Z2osSui07XO3MngV3vM/MzdgQcAUfAEXAEHAFHoMshcMUVV8g3vvGNLnfefsIiEMAJEyaIZ4PvPt8GrxPYfT5LvxJHwBFwBBwBR8ARcARWawTq6+vlzjvvFJYuXQeBXXbZJdSP7jpn7GeaDwEngfkQ8v2OgCPgCDgCjoAj4Ag4Ao6AI+AIdCME/j9nrtpPaqbmogAAAABJRU5ErkJggg==" />
</p>
## Usage
```python
import datasets
dataset = datasets.load_dataset(
"bazinga/bazinga",
series="TheBigBangTheory",
audio=True,
use_auth_token=True)
# iterate over test episodes ("validation" and "train" are also available)
for episode in dataset["test"]:
identifier = episode["identifier"] # TheBigBangTheory.Season01.Episode01
# knowledge base
kb = episode["knowledge_base"]
kb["title"] # Pilot
kb["imdb"] # https://www.imdb.com/title/tt0775431/
kb["characters"] # ['leonard_hofstadter', 'sheldon_cooper', ..., 'kurt']
# annotated transcript
for word in episode["transcript"]:
word["token"] # your
word["speaker"] # leonard_hofstadter
word["forced_alignment"]["start_time"] # 14.240
word["forced_alignment"]["end_time"] # 14.350
word["forced_alignment"]["confidence"] # 0.990
word["entity_linking"] # sheldon_cooper
word["named_entity"] # None
word["addressee"] # sheldon_cooper
# audio (when dataset is initialized with audio=True)
audio = episode["audio"] # path to wav file on disk
# annotation status (GoldStandard, SilverStandard, or NotAvailable)
status = episode["status"]
status["speaker"] # GoldStandard
status["token"] # GoldStandard
status["forced_alignment"] # SilverStandard
status["entity_linking"] # ...
status["named_entity"] # ...
status["addressee"] # ...
# get list of available series
datasets.get_dataset_config_names("bazinga/bazinga")
# [ "24", "BattlestarGalactica", ..., "TheBigBangTheory", ... ]
```
|
eDsny/LUCASSS | ---
license: openrail
---
|
bigbio/swedish_medical_ner |
---
language:
- sv
bigbio_language:
- Swedish
license: cc-by-sa-4.0
multilinguality: monolingual
bigbio_license_shortname: CC_BY_SA_4p0
pretty_name: Swedish Medical NER
homepage: https://github.com/olofmogren/biomedical-ner-data-swedish/
bigbio_pubmed: False
bigbio_public: True
bigbio_tasks:
- NAMED_ENTITY_RECOGNITION
---
# Dataset Card for Swedish Medical NER
## Dataset Description
- **Homepage:** https://github.com/olofmogren/biomedical-ner-data-swedish/
- **Pubmed:** False
- **Public:** True
- **Tasks:** NER
swedish_medical_ner is Named Entity Recognition dataset on medical text in Swedish.
It consists three subsets which are in turn derived from three different sources
respectively: the Swedish Wikipedia (a.k.a. wiki), Läkartidningen (a.k.a. lt),
and 1177 Vårdguiden (a.k.a. 1177). While the Swedish Wikipedia and Läkartidningen
subsets in total contains over 790000 sequences with 60 characters each,
the 1177 Vårdguiden subset is manually annotated and contains 927 sentences,
2740 annotations, out of which 1574 are disorder and findings, 546 are
pharmaceutical drug, and 620 are body structure.
Texts from both Swedish Wikipedia and Läkartidningen were automatically annotated
using a list of medical seed terms. Sentences from 1177 Vårdguiden were manuually
annotated.
## Citation Information
```
@inproceedings{almgren-etal-2016-named,
author = {
Almgren, Simon and
Pavlov, Sean and
Mogren, Olof
},
title = {Named Entity Recognition in Swedish Medical Journals with Deep Bidirectional Character-Based LSTMs},
booktitle = {Proceedings of the Fifth Workshop on Building and Evaluating Resources for Biomedical Text Mining (BioTxtM 2016)},
publisher = {The COLING 2016 Organizing Committee},
pages = {30-39},
year = {2016},
month = {12},
url = {https://aclanthology.org/W16-5104},
eprint = {https://aclanthology.org/W16-5104.pdf}
}
```
|
INo0121/low_quality_call_voice | ---
dataset_info:
features:
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: transcripts
dtype: string
splits:
- name: train
num_bytes: 9302913443.561954
num_examples: 111200
- name: test
num_bytes: 1119354595.6598015
num_examples: 13901
- name: valid
num_bytes: 1125525152.5452442
num_examples: 13900
download_size: 9232284149
dataset_size: 11547793191.767
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: valid
path: data/valid-*
---
# Dataset Card for "low_quality_call_voice"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
TinyLlama/English_plus_Chinese | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 36224369.0
num_examples: 18818
download_size: 22780087
dataset_size: 36224369.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
autumnjohnson/ceti_audio_whale_recognition | ---
dataset_info:
features:
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: label
dtype: string
- name: path
dtype: string
- name: sampling_rate
dtype: int64
splits:
- name: train
num_bytes: 229769546.19874716
num_examples: 2458
- name: test
num_bytes: 98526078.80125284
num_examples: 1054
download_size: 162178422
dataset_size: 328295625.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
Minecraft3193092-organization/translates | ---
license: mit
---
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.