id stringlengths 2 115 | lastModified stringlengths 24 24 | tags list | author stringlengths 2 42 ⌀ | description stringlengths 0 68.7k ⌀ | citation stringlengths 0 10.7k ⌀ | cardData null | likes int64 0 3.55k | downloads int64 0 10.1M | card stringlengths 0 1.01M |
|---|---|---|---|---|---|---|---|---|---|
anzorq/sixuxar_yijiri_mak7 | 2023-10-11T01:05:15.000Z | [
"task_categories:automatic-speech-recognition",
"task_categories:text-to-speech",
"language:kbd",
"license:mit",
"region:us"
] | anzorq | null | null | null | 0 | 15 | ---
language:
- kbd
task_categories:
- automatic-speech-recognition
- text-to-speech
dataset_info:
features:
- name: audio
dtype: audio
- name: text
dtype: string
splits:
- name: train
num_bytes: 337947909.07
num_examples: 6579
download_size: 727728499
dataset_size: 337947909.07
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: mit
---
# Dataset Info
audio-text pairs from the book:
```
Къэрмокъуэ М. Щихухэр иджыри мэкI. Япэ тхылъ. Нальчик: Эльбрус, 1999
```
Audio sample rate: `16,000 Hz`
Audio source: http://www.adigabook.ru/?p=1148
audio-text pairs for this dataset were aligned using META AI's [forced alignment algorithm](https://github.com/facebookresearch/fairseq/tree/main/examples/mms/data_prep). |
amttl | 2023-01-25T14:26:23.000Z | [
"task_categories:token-classification",
"task_ids:parsing",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:zh",
"license:mit",
"region:us"
] | null | Chinese word segmentation (CWS) trained from open source corpus faces dramatic performance drop
when dealing with domain text, especially for a domain with lots of special terms and diverse
writing styles, such as the biomedical domain. However, building domain-specific CWS requires
extremely high annotation cost. In this paper, we propose an approach by exploiting domain-invariant
knowledge from high resource to low resource domains. Extensive experiments show that our mode
achieves consistently higher accuracy than the single-task CWS and other transfer learning
baselines, especially when there is a large disparity between source and target domains.
This dataset is the accompanied medical Chinese word segmentation (CWS) dataset.
The tags are in BIES scheme.
For more details see https://www.aclweb.org/anthology/C18-1307/ | @inproceedings{xing2018adaptive,
title={Adaptive multi-task transfer learning for Chinese word segmentation in medical text},
author={Xing, Junjie and Zhu, Kenny and Zhang, Shaodian},
booktitle={Proceedings of the 27th International Conference on Computational Linguistics},
pages={3619--3630},
year={2018}
} | null | 1 | 14 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- zh
license:
- mit
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- parsing
pretty_name: AMTTL
dataset_info:
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: tags
sequence:
class_label:
names:
'0': B
'1': I
'2': E
'3': S
config_name: amttl
splits:
- name: train
num_bytes: 1132212
num_examples: 3063
- name: validation
num_bytes: 324374
num_examples: 822
- name: test
num_bytes: 328525
num_examples: 908
download_size: 685534
dataset_size: 1785111
---
# Dataset Card for AMTTL
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [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)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [Github](https://github.com/adapt-sjtu/AMTTL/tree/master/medical_data)
- **Repository:** [Github](https://github.com/adapt-sjtu/AMTTL/tree/master/medical_data)
- **Paper:** [Aclweb](http://aclweb.org/anthology/C18-1307)
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
[More Information Needed]
### 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
```bibtex
@inproceedings{xing2018adaptive,
title={Adaptive multi-task transfer learning for Chinese word segmentation in medical text},
author={Xing, Junjie and Zhu, Kenny and Zhang, Shaodian},
booktitle={Proceedings of the 27th International Conference on Computational Linguistics},
pages={3619--3630},
year={2018}
}
```
### Contributions
Thanks to [@JetRunner](https://github.com/JetRunner) for adding this dataset. |
bbc_hindi_nli | 2023-01-25T14:27:06.000Z | [
"task_categories:text-classification",
"task_ids:natural-language-inference",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|bbc__hindi_news_classification",
"language:hi",
"license:mit",
"... | null | This dataset is used to train models for Natural Language Inference Tasks in Low-Resource Languages like Hindi. | @inproceedings{uppal-etal-2020-two,
title = "Two-Step Classification using Recasted Data for Low Resource Settings",
author = "Uppal, Shagun and
Gupta, Vivek and
Swaminathan, Avinash and
Zhang, Haimin and
Mahata, Debanjan and
Gosangi, Rakesh and
Shah, Rajiv Ratn and
Stent, Amanda",
booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
month = dec,
year = "2020",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.aacl-main.71",
pages = "706--719",
abstract = "An NLP model{'}s ability to reason should be independent of language. Previous works utilize Natural Language Inference (NLI) to understand the reasoning ability of models, mostly focusing on high resource languages like English. To address scarcity of data in low-resource languages such as Hindi, we use data recasting to create NLI datasets for four existing text classification datasets. Through experiments, we show that our recasted dataset is devoid of statistical irregularities and spurious patterns. We further study the consistency in predictions of the textual entailment models and propose a consistency regulariser to remove pairwise-inconsistencies in predictions. We propose a novel two-step classification method which uses textual-entailment predictions for classification task. We further improve the performance by using a joint-objective for classification and textual entailment. We therefore highlight the benefits of data recasting and improvements on classification performance using our approach with supporting experimental results.",
} | null | 0 | 14 | ---
annotations_creators:
- machine-generated
language_creators:
- found
language:
- hi
license:
- mit
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|bbc__hindi_news_classification
task_categories:
- text-classification
task_ids:
- natural-language-inference
pretty_name: BBC Hindi NLI Dataset
dataset_info:
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype:
class_label:
names:
'0': not-entailment
'1': entailment
- name: topic
dtype:
class_label:
names:
'0': india
'1': news
'2': international
'3': entertainment
'4': sport
'5': science
config_name: bbc hindi nli
splits:
- name: train
num_bytes: 2990080
num_examples: 15552
- name: validation
num_bytes: 496808
num_examples: 2580
- name: test
num_bytes: 494432
num_examples: 2592
download_size: 3815652
dataset_size: 3981320
---
# Dataset Card for BBC Hindi NLI Dataset
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [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)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** [GitHub](https://github.com/midas-research/hindi-nli-data)
- **Paper:** [Aclweb](https://www.aclweb.org/anthology/2020.aacl-main.71)
- **Point of Contact:** [GitHub](https://github.com/midas-research/hindi-nli-data)
### Dataset Summary
- Dataset for Natural Language Inference in Hindi Language. BBC Hindi Dataset consists of textual-entailment pairs.
- Each row of the Datasets if made up of 4 columns - Premise, Hypothesis, Label and Topic.
- Context and Hypothesis is written in Hindi while Entailment_Label is in English.
- Entailment_label is of 2 types - entailed and not-entailed.
- Dataset can be used to train models for Natural Language Inference tasks in Hindi Language.
[More Information Needed]
### Supported Tasks and Leaderboards
- Natural Language Inference for Hindi
### Languages
Dataset is in Hindi
## Dataset Structure
- Data is structured in TSV format.
- Train and Test files are in seperate files
### Dataset Instances
An example of 'train' looks as follows.
```
{'hypothesis': 'यह खबर की सूचना है|', 'label': 'entailed', 'premise': 'गोपनीयता की नीति', 'topic': '1'}
```
### Data Fields
- Each row contatins 4 columns - Premise, Hypothesis, Label and Topic.
### Data Splits
- Train : 15553
- Valid : 2581
- Test : 2593
## Dataset Creation
- We employ a recasting technique from Poliak et al. (2018a,b) to convert publicly available BBC Hindi news text classification datasets in Hindi and pose them as TE problems
- In this recasting process, we build template hypotheses for each class in the label taxonomy
- Then, we pair the original annotated sentence with each of the template hypotheses to create TE samples.
- For more information on the recasting process, refer to paper "https://www.aclweb.org/anthology/2020.aacl-main.71"
### Source Data
Source Dataset for the recasting process is the BBC Hindi Headlines Dataset(https://github.com/NirantK/hindi2vec/releases/tag/bbc-hindi-v0.1)
#### Initial Data Collection and Normalization
- BBC Hindi News Classification Dataset contains 4, 335 Hindi news headlines tagged across 14 categories: India, Pakistan,news, International, entertainment, sport, science, China, learning english, social, southasia, business, institutional, multimedia
- We processed this dataset to combine two sets of relevant but low prevalence classes.
- Namely, we merged the samples from Pakistan, China, international, and southasia as one class called international.
- Likewise, we also merged samples from news, business, social, learning english, and institutional as news.
- Lastly, we also removed the class multimedia because there were very few samples.
#### Who are the source language producers?
Pls refer to this paper: "https://www.aclweb.org/anthology/2020.aacl-main.71"
### Annotations
#### Annotation process
Annotation process has been described in Dataset Creation Section.
#### Who are the annotators?
Annotation is done automatically.
### Personal and Sensitive Information
No Personal and Sensitive Information is mentioned in the Datasets.
## Considerations for Using the Data
Pls refer to this paper: https://www.aclweb.org/anthology/2020.aacl-main.71
### Discussion of Biases
Pls refer to this paper: https://www.aclweb.org/anthology/2020.aacl-main.71
### Other Known Limitations
No other known limitations
## Additional Information
Pls refer to this link: https://github.com/midas-research/hindi-nli-data
### Dataset Curators
It is written in the repo : https://github.com/avinsit123/hindi-nli-data that
- This corpus can be used freely for research purposes.
- The paper listed below provide details of the creation and use of the corpus. If you use the corpus, then please cite the paper.
- If interested in commercial use of the corpus, send email to midas@iiitd.ac.in.
- If you use the corpus in a product or application, then please credit the authors and Multimodal Digital Media Analysis Lab - Indraprastha Institute of Information Technology, New Delhi appropriately. Also, if you send us an email, we will be thrilled to know about how you have used the corpus.
- Multimodal Digital Media Analysis Lab - Indraprastha Institute of Information Technology, New Delhi, India disclaims any responsibility for the use of the corpus and does not provide technical support. However, the contact listed above will be happy to respond to queries and clarifications.
- Rather than redistributing the corpus, please direct interested parties to this page
- Please feel free to send us an email:
- with feedback regarding the corpus.
- with information on how you have used the corpus.
- if interested in having us analyze your data for natural language inference.
- if interested in a collaborative research project.
### Licensing Information
Copyright (C) 2019 Multimodal Digital Media Analysis Lab - Indraprastha Institute of Information Technology, New Delhi (MIDAS, IIIT-Delhi).
Pls contact authors for any information on the dataset.
### Citation Information
```
@inproceedings{uppal-etal-2020-two,
title = "Two-Step Classification using Recasted Data for Low Resource Settings",
author = "Uppal, Shagun and
Gupta, Vivek and
Swaminathan, Avinash and
Zhang, Haimin and
Mahata, Debanjan and
Gosangi, Rakesh and
Shah, Rajiv Ratn and
Stent, Amanda",
booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
month = dec,
year = "2020",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.aacl-main.71",
pages = "706--719",
abstract = "An NLP model{'}s ability to reason should be independent of language. Previous works utilize Natural Language Inference (NLI) to understand the reasoning ability of models, mostly focusing on high resource languages like English. To address scarcity of data in low-resource languages such as Hindi, we use data recasting to create NLI datasets for four existing text classification datasets. Through experiments, we show that our recasted dataset is devoid of statistical irregularities and spurious patterns. We further study the consistency in predictions of the textual entailment models and propose a consistency regulariser to remove pairwise-inconsistencies in predictions. We propose a novel two-step classification method which uses textual-entailment predictions for classification task. We further improve the performance by using a joint-objective for classification and textual entailment. We therefore highlight the benefits of data recasting and improvements on classification performance using our approach with supporting experimental results.",
}
```
### Contributions
Thanks to [@avinsit123](https://github.com/avinsit123) for adding this dataset. |
bn_hate_speech | 2023-01-25T14:27:23.000Z | [
"task_categories:text-classification",
"annotations_creators:crowdsourced",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:bn",
"license:mit",
"hate-speech-topic-classification",
... | null | The Bengali Hate Speech Dataset is a collection of Bengali articles collected from Bengali news articles,
news dump of Bengali TV channels, books, blogs, and social media. Emphasis was placed on Facebook pages and
newspaper sources because they attract close to 50 million followers and is a common source of opinions
and hate speech. The raw text corpus contains 250 million articles and the full dataset is being prepared
for release. This is a subset of the full dataset.
This dataset was prepared for hate-speech text classification benchmark on Bengali, an under-resourced language. | @misc{karim2020classification,
title={Classification Benchmarks for Under-resourced Bengali Language based on Multichannel Convolutional-LSTM Network},
author={Md. Rezaul Karim and Bharathi Raja Chakravarthi and John P. McCrae and Michael Cochez},
year={2020},
eprint={2004.07807},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | null | 1 | 14 | ---
annotations_creators:
- crowdsourced
- expert-generated
language_creators:
- found
language:
- bn
license:
- mit
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids: []
paperswithcode_id: bengali-hate-speech
pretty_name: Bengali Hate Speech Dataset
tags:
- hate-speech-topic-classification
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': Personal
'1': Political
'2': Religious
'3': Geopolitical
'4': Gender abusive
splits:
- name: train
num_bytes: 972635
num_examples: 3418
download_size: 974312
dataset_size: 972635
---
# Dataset Card for Bengali Hate Speech Dataset
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [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)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [Bengali Hate Speech Dataset](https://github.com/rezacsedu/Bengali-Hate-Speech-Dataset)
- **Repository:** [Bengali Hate Speech Dataset](https://github.com/rezacsedu/Bengali-Hate-Speech-Dataset)
- **Paper:** [Classification Benchmarks for Under-resourced Bengali Language based on Multichannel Convolutional-LSTM Network](https://arxiv.org/abs/2004.07807)
- **Point of Contact:** [Md. Rezaul Karim](rezaul.karim.fit@gmail.com)
### Dataset Summary
The Bengali Hate Speech Dataset is a Bengali-language dataset of news articles collected from various Bengali media sources and categorized based on the type of hate in the text. The dataset was created to provide greater support for under-resourced languages like Bengali on NLP tasks, and serves as a benchmark for multiple types of classification tasks.
### Supported Tasks and Leaderboards
* `topic classification`: The dataset can be used to train a Multichannel Convolutional-LSTM for classifying different types of hate speech. The model performance can be measured by its F1 score.
### Languages
The text in the dataset is in Bengali and the associated BCP-47 code is `bn`.
## Dataset Structure
### Data Instances
A data instance takes the form of a news article and its associated label.
🚨 Beware that the following example contains extremely offensive content!
An example looks like this:
```
{"text": "রেন্ডিয়াকে পৃথীবির মানচিএ থেকে মুচে ফেলতে হবে",
"label": "Geopolitical"}
```
### Data Fields
* `text`: the text of the Bengali news article
* `label`: one of `Geopolitical`, `Personal`, `Political`, `Religious`, or `Gender abusive` indicating the type of hate speech
### Data Splits
The dataset has 3418 examples.
## Dataset Creation
### Curation Rationale
Under-resourced languages like Bengali lack supporting resources that languages like English have. This dataset was collected from multiple Bengali news sources to provide several classification benchmarks for hate speech detection, document classification and sentiment analysis.
### Source Data
#### Initial Data Collection and Normalization
Bengali articles were collected from a Bengali Wikipedia dump, Bengali news articles, news dumps of TV channels, books, blogs, sports portal and social media. Emphasis was placed on Facebook pages and newspaper sources because they have about 50 million followers and is a common source of opinion and hate speech. The full dataset consists of 250 million articles and is currently being prepared. This is a subset of the full dataset.
#### Who are the source language producers?
The source language producers are Bengali authors and users who interact with these various forms of Bengali media.
### Annotations
#### Annotation process
The data was annotated by manually identifying freqently occurring terms in texts containing hate speech and references to specific entities. The authors also prepared normalized frequency vectors of 175 abusive terms that are commonly used to express hate in Bengali. A hate label is assigned if at least one of these terms exists in the text. Annotator's were provided with unbiased text only contents to make the decision. Non-hate statements were removed from the list and the category of hate was further divided into political, personal, gender abusive, geopolitical and religious. To reduce possible bias, each label was assigned based on a majority voting on the annotator's opinions and Cohen's Kappa was computed to measure inter-annotator agreement.
#### Who are the annotators?
Three native Bengali speakers and two linguists annotated the dataset which was then reviewed and validated by three experts (one South Asian linguist and two native speakers).
### Personal and Sensitive Information
The dataset contains very sensitive and highly offensive comments in a religious, political and gendered context. Some of the comments are directed towards contemporary public figures like politicians, religious leaders, celebrities and athletes.
## Considerations for Using the Data
### Social Impact of Dataset
The purpose of the dataset is to improve hate speech detection in Bengali. The growth of social media has enabled people to express hate freely online and there has been a lot of focus on detecting hate speech for highly resourced languages like English. The use of hate speech is pervasive, like any other major language, which can have serious and deadly consequences. Failure to react to hate speech renders targeted minorities more vulnerable to attack and it can also create indifference towards their treatment from majority populations.
### Discussion of Biases
The dataset was collected using a bootstrapping approach. An initial search was made for specific types of texts, articles and tweets containing common harassment directed at targeting characteristics. As a result, this dataset contains **extremely** offensive content that is disturbing. In addition, Facebook pages and newspaper sources were emphasized because they are well-known for having hate and harassment issues.
### Other Known Limitations
The dataset contains racist, sexist, homophobic and offensive comments. It is collected and annotated for research related purposes only.
## Additional Information
### Dataset Curators
The dataset was curated by Md. Rezaul Karim, Sumon Kanti Dey, Bharathi Raja Chakravarthi, John McCrae and Michael Cochez.
### Licensing Information
This dataset is licensed under the MIT License.
### Citation Information
```
@inproceedings{karim2020BengaliNLP,
title={Classification Benchmarks for Under-resourced Bengali Language based on Multichannel Convolutional-LSTM Network},
author={Karim, Md. Rezaul and Chakravarti, Bharathi Raja and P. McCrae, John and Cochez, Michael},
booktitle={7th IEEE International Conference on Data Science and Advanced Analytics (IEEE DSAA,2020)},
publisher={IEEE},
year={2020}
}
```
### Contributions
Thanks to [@stevhliu](https://github.com/stevhliu) for adding this dataset. |
so_stacksample | 2022-11-03T16:30:57.000Z | [
"task_categories:text2text-generation",
"task_ids:abstractive-qa",
"task_ids:open-domain-abstractive-qa",
"annotations_creators:no-annotation",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"license:cc-by-sa-... | null | Dataset with the text of 10% of questions and answers from the Stack Overflow programming Q&A website.
This is organized as three tables:
Questions contains the title, body, creation date, closed date (if applicable), score, and owner ID for all non-deleted Stack Overflow questions whose Id is a multiple of 10.
Answers contains the body, creation date, score, and owner ID for each of the answers to these questions. The ParentId column links back to the Questions table.
Tags contains the tags on each of these questions. | null | null | 3 | 14 | ---
annotations_creators:
- no-annotation
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-sa-3.0
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- text2text-generation
task_ids:
- abstractive-qa
- open-domain-abstractive-qa
paperswithcode_id: null
pretty_name: SO StackSample
dataset_info:
- config_name: Answers
features:
- name: Id
dtype: int32
- name: OwnerUserId
dtype: int32
- name: CreationDate
dtype: string
- name: ParentId
dtype: int32
- name: Score
dtype: int32
- name: Body
dtype: string
splits:
- name: Answers
num_bytes: 1583232304
num_examples: 2014516
download_size: 0
dataset_size: 1583232304
- config_name: Questions
features:
- name: Id
dtype: int32
- name: OwnerUserId
dtype: int32
- name: CreationDate
dtype: string
- name: ClosedDate
dtype: string
- name: Score
dtype: int32
- name: Title
dtype: string
- name: Body
dtype: string
splits:
- name: Questions
num_bytes: 1913896893
num_examples: 1264216
download_size: 0
dataset_size: 1913896893
- config_name: Tags
features:
- name: Id
dtype: int32
- name: Tag
dtype: string
splits:
- name: Tags
num_bytes: 58816824
num_examples: 3750994
download_size: 0
dataset_size: 58816824
---
# Dataset Card for SO StackSample
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [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)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://www.kaggle.com/stackoverflow/stacksample
### Dataset Summary
Dataset with the text of 10% of questions and answers from the Stack Overflow programming Q&A website.
This is organized as three tables:
Questions table contains the title, body, creation date, closed date (if applicable), score, and owner ID for all non-deleted Stack Overflow questions whose Id is a multiple of 10.
Answers table contains the body, creation date, score, and owner ID for each of the answers to these questions. The ParentId column links back to the Questions table.
Tags table contains the tags on each of these questions.
### Supported Tasks and Leaderboards
Example projects include:
- Identifying tags from question text
- Predicting whether questions will be upvoted, downvoted, or closed based on their text
- Predicting how long questions will take to answer
- Open Domain Q/A
### Languages
English (en) and Programming Languages.
## Dataset Structure
### Data Instances
For Answers:
```
{
"Id": { # Unique ID given to the Answer post
"feature_type": "Value",
"dtype": "int32"
},
"OwnerUserId": { # The UserID of the person who generated the Answer on StackOverflow. -1 means NA
"feature_type": "Value",
"dtype": "int32"
},
"CreationDate": { # The date the Answer was generated. Follows standard datetime format.
"feature_type": "Value",
"dtype": "string"
},
"ParentId": { # Refers to the `Id` of the Question the Answer belong to.
"feature_type": "Value",
"dtype": "int32"
},
"Score": { # The sum of up and down votes given to the Answer. Can be negative.
"feature_type": "Value",
"dtype": "int32"
},
"Body": { # The body content of the Answer.
"feature_type": "Value",
"dtype": "string"
}
}
```
For Questions:
```
{
"Id": { # Unique ID given to the Question post
"feature_type": "Value",
"dtype": "int32"
},
"OwnerUserId": { # The UserID of the person who generated the Question on StackOverflow. -1 means NA.
"feature_type": "Value",
"dtype": "int32"
},
"CreationDate": { # The date the Question was generated. Follows standard datetime format.
"feature_type": "Value",
"dtype": "string"
},
"ClosedDate": { # The date the Question was generated. Follows standard datetime format. Can be NA.
"feature_type": "Value",
"dtype": "string"
},
"Score": { # The sum of up and down votes given to the Question. Can be negative.
"feature_type": "Value",
"dtype": "int32"
},
"Title": { # The title of the Question.
"feature_type": "Value",
"dtype": "string"
},
"Body": { # The body content of the Question.
"feature_type": "Value",
"dtype": "string"
}
}
```
For Tags:
```
{
"Id": { # ID of the Question the tag belongs to
"feature_type": "Value",
"dtype": "int32"
},
"Tag": { # The tag name
"feature_type": "Value",
"dtype": "string"
}
}
```
`
### Data Fields
For Answers:
-`Id`: Unique ID given to the Answer post
`OwnerUserId`: The UserID of the person who generated the Answer on StackOverflow. -1 means NA
"`CreationDate`": The date the Answer was generated. Follows standard datetime format.
"`ParentId`": Refers to the `Id` of the Question the Answer belong to.
"`Score`": The sum of up and down votes given to the Answer. Can be negative.
"`Body`": The body content of the Answer.
For Questions:
- `Id`: Unique ID given to the Question post.
- `OwnerUserId`: The UserID of the person who generated the Question on StackOverflow. -1 means NA.
- `CreationDate`: The date the Question was generated. Follows standard datetime format.
- `ClosedDate`: The date the Question was generated. Follows standard datetime format. Can be NA.
- `Score`: The sum of up and down votes given to the Question. Can be negative.
- `Title`: {The title of the Question.
- `Body`: The body content of the Question.
For Tags:
- `Id`: ID of the Question the tag belongs to.
- `Tag`: The tag name.
### Data Splits
The dataset has 3 splits:
- `Answers`
- `Questions`
- `Tags`
## Dataset Creation
### Curation Rationale
Datasets of all R questions and all Python questions are also available on Kaggle, but this dataset is especially useful for analyses that span many languages.
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
StackOverflow Users.
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
This data contains information that can identify individual users of StackOverflow. The information is self-reported.
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
StackOverflow answers are not guaranteed to be safe, secure, or correct. Some answers may purposefully be insecure as is done in this https://stackoverflow.com/a/35571883/5768407 answer from user [`zys`](https://stackoverflow.com/users/5259310/zys), where they show a solution to purposefully bypass Google Play store security checks. Such answers can lead to biased models that use this data and can further propogate unsafe and insecure programming practices.
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
All Stack Overflow user contributions are licensed under CC-BY-SA 3.0 with attribution required.
### Citation Information
The content is from Stack Overflow.
### Contributions
Thanks to [@ncoop57](https://github.com/ncoop57) for adding this dataset. |
tanzil | 2022-11-03T16:31:41.000Z | [
"task_categories:translation",
"annotations_creators:found",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:am",
"language:ar",
"language:az",
"language:bg",
"language:bn",
"language:bs",
"language:cs",
"languag... | null | This is a collection of Quran translations compiled by the Tanzil project
The translations provided at this page are for non-commercial purposes only. If used otherwise, you need to obtain necessary permission from the translator or the publisher.
If you are using more than three of the following translations in a website or application, we require you to put a link back to this page to make sure that subsequent users have access to the latest updates.
42 languages, 878 bitexts
total number of files: 105
total number of tokens: 22.33M
total number of sentence fragments: 1.01M | J. Tiedemann, 2012, Parallel Data, Tools and Interfaces in OPUS. In Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC 2012) | null | 4 | 14 | ---
annotations_creators:
- found
language_creators:
- found
language:
- am
- ar
- az
- bg
- bn
- bs
- cs
- de
- dv
- en
- es
- fa
- fr
- ha
- hi
- id
- it
- ja
- ko
- ku
- ml
- ms
- nl
- 'no'
- pl
- pt
- ro
- ru
- sd
- so
- sq
- sv
- sw
- ta
- tg
- th
- tr
- tt
- ug
- ur
- uz
- zh
license:
- unknown
multilinguality:
- multilingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: null
pretty_name: tanzil
dataset_info:
- config_name: bg-en
features:
- name: id
dtype: string
- name: translation
dtype:
translation:
languages:
- bg
- en
splits:
- name: train
num_bytes: 34473016
num_examples: 135477
download_size: 9305292
dataset_size: 34473016
- config_name: bn-hi
features:
- name: id
dtype: string
- name: translation
dtype:
translation:
languages:
- bn
- hi
splits:
- name: train
num_bytes: 18869103
num_examples: 24942
download_size: 3542740
dataset_size: 18869103
- config_name: fa-sv
features:
- name: id
dtype: string
- name: translation
dtype:
translation:
languages:
- fa
- sv
splits:
- name: train
num_bytes: 29281634
num_examples: 68601
download_size: 8550826
dataset_size: 29281634
- config_name: ru-zh
features:
- name: id
dtype: string
- name: translation
dtype:
translation:
languages:
- ru
- zh
splits:
- name: train
num_bytes: 59736143
num_examples: 99779
download_size: 16214659
dataset_size: 59736143
- config_name: en-tr
features:
- name: id
dtype: string
- name: translation
dtype:
translation:
languages:
- en
- tr
splits:
- name: train
num_bytes: 255891913
num_examples: 1189967
download_size: 82954694
dataset_size: 255891913
---
# Dataset Card for tanzil
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [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)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** http://opus.nlpl.eu/Tanzil.php
- **Repository:** None
- **Paper:** http://www.lrec-conf.org/proceedings/lrec2012/pdf/463_Paper.pdf
- **Leaderboard:** [More Information Needed]
- **Point of Contact:** [More Information Needed]
### Dataset Summary
To load a language pair which isn't part of the config, all you need to do is specify the language code as pairs.
You can find the valid pairs in Homepage section of Dataset Description: http://opus.nlpl.eu/Tanzil.php
E.g.
`dataset = load_dataset("tanzil", lang1="en", lang2="ru")`
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
Here are some examples of questions and facts:
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### 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
Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset. |
tunizi | 2023-01-25T14:54:36.000Z | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:aeb",
"license:unknown",
"arxiv:2004.14303",
"region:us... | null | On social media, Arabic speakers tend to express themselves in their own local dialect. To do so, Tunisians use "Tunisian Arabizi", which consists in supplementing numerals to the Latin script rather than the Arabic alphabet. TUNIZI is the first Tunisian Arabizi Dataset including 3K sentences, balanced, covering different topics, preprocessed and annotated as positive and negative. | @inproceedings{Chayma2020,
title={TUNIZI: a Tunisian Arabizi sentiment analysis Dataset},
author={Fourati, Chayma and Messaoudi, Abir and Haddad, Hatem},
booktitle={AfricaNLP Workshop, Putting Africa on the NLP Map. ICLR 2020, Virtual Event},
volume = {arXiv:3091079},
year = {2020},
url = {https://arxiv.org/submit/3091079},
} | null | 1 | 14 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- aeb
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
paperswithcode_id: tunizi
pretty_name: TUNIZI
dataset_info:
features:
- name: id
dtype: string
- name: sentence
dtype: string
- name: target
dtype:
class_label:
names:
'0': '1'
'1': '-1'
splits:
- name: train
num_bytes: 211166
num_examples: 3000
download_size: 162781
dataset_size: 211166
---
# Dataset Card for TUNIZI
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [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)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/chaymafourati/TUNIZI-Sentiment-Analysis-Tunisian-Arabizi-Dataset
- **Repository:** https://github.com/chaymafourati/TUNIZI-Sentiment-Analysis-Tunisian-Arabizi-Dataset
- **Paper:** https://arxiv.org/abs/2004.14303
- **Point of Contact:** Chayma Fourati (chayma@icompass.digital)
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
This dataset uses Tunisian Arabic written with latin script (BCP-47: aeb-Latn)
## 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
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### 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
Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset. |
ShreyaR/DepressionDetection | 2022-03-24T11:31:29.000Z | [
"region:us"
] | ShreyaR | null | null | null | 2 | 14 | Entry not found |
bazinga/bazinga | 2022-06-20T08:33:34.000Z | [
"language:en",
"speaker-diarization",
"speaker-identification",
"automatic-speech-recognition",
"named-entity-detection",
"addressee-detection",
"region:us"
] | bazinga | Bazinga! A Dataset for Multi-Party Dialogues Structuring | @InProceedings{bazinga,
title = {{Bazinga! A Dataset for Multi-Party Dialogues Structuring}},
booktitle = {Proceedings of the 13th International Conference on Language Resources and Evaluation (LREC'22)},
author={Lerner, Paul and Bergoend, 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},
year={2022}
} | null | 1 | 14 | ---
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", ... ]
```
|
metaeval/blimp_classification | 2023-01-09T10:50:25.000Z | [
"task_categories:text-classification",
"task_ids:acceptability-classification",
"size_categories:10K<n<100K",
"language:en",
"license:apache-2.0",
"cola",
"region:us"
] | metaeval | Acceptable/non acceptable sentences (recasted as a classification task) | null | null | 1 | 14 | ---
license: apache-2.0
size_categories:
- 10K<n<100K
task_categories:
- text-classification
task_ids:
- acceptability-classification
language:
- en
tags:
- cola
---
Blimp with the coarse categories and recasted as a classification task (Cola format). |
midas/ldkp3k | 2022-09-27T18:29:25.000Z | [
"region:us"
] | midas | This new dataset is designed to solve kp NLP task and is crafted with a lot of care. | TBA | null | 3 | 14 | A dataset for benchmarking keyphrase extraction and generation techniques from long document English scientific papers. For more details about the dataset please refer the original paper - []().
Data source - []()
## Dataset Summary
## Dataset Structure
### Data Fields
- **id**: unique identifier of the document.
- **sections**: list of all the sections present in the document.
- **sec_text**: list of white space separated list of words present in each section.
- **sec_bio_tags**: list of BIO tags of white space separated list of words present in each section.
- **extractive_keyphrases**: List of all the present keyphrases.
- **abstractive_keyphrase**: List of all the absent keyphrases.
### Data Splits
|Split| #datapoints |
|--|--|
| Train-Small | 20,000 |
| Train-Medium | 50,000 |
| Train-Large | 90,019 |
| Test | 3413 |
| Validation | 3339 |
## Usage
### Small Dataset
```python
from datasets import load_dataset
# get small dataset
dataset = load_dataset("midas/ldkp3k", "small")
def order_sections(sample):
"""
corrects the order in which different sections appear in the document.
resulting order is: title, abstract, other sections in the body
"""
sections = []
sec_text = []
sec_bio_tags = []
if "title" in sample["sections"]:
title_idx = sample["sections"].index("title")
sections.append(sample["sections"].pop(title_idx))
sec_text.append(sample["sec_text"].pop(title_idx))
sec_bio_tags.append(sample["sec_bio_tags"].pop(title_idx))
if "abstract" in sample["sections"]:
abstract_idx = sample["sections"].index("abstract")
sections.append(sample["sections"].pop(abstract_idx))
sec_text.append(sample["sec_text"].pop(abstract_idx))
sec_bio_tags.append(sample["sec_bio_tags"].pop(abstract_idx))
sections += sample["sections"]
sec_text += sample["sec_text"]
sec_bio_tags += sample["sec_bio_tags"]
return sections, sec_text, sec_bio_tags
# sample from the train split
print("Sample from train data split")
train_sample = dataset["train"][0]
sections, sec_text, sec_bio_tags = order_sections(train_sample)
print("Fields in the sample: ", [key for key in train_sample.keys()])
print("Section names: ", sections)
print("Tokenized Document: ", sec_text)
print("Document BIO Tags: ", sec_bio_tags)
print("Extractive/present Keyphrases: ", train_sample["extractive_keyphrases"])
print("Abstractive/absent Keyphrases: ", train_sample["abstractive_keyphrases"])
print("\n-----------\n")
# sample from the validation split
print("Sample from validation data split")
validation_sample = dataset["validation"][0]
sections, sec_text, sec_bio_tags = order_sections(validation_sample)
print("Fields in the sample: ", [key for key in validation_sample.keys()])
print("Section names: ", sections)
print("Tokenized Document: ", sec_text)
print("Document BIO Tags: ", sec_bio_tags)
print("Extractive/present Keyphrases: ", validation_sample["extractive_keyphrases"])
print("Abstractive/absent Keyphrases: ", validation_sample["abstractive_keyphrases"])
print("\n-----------\n")
# sample from the test split
print("Sample from test data split")
test_sample = dataset["test"][0]
sections, sec_text, sec_bio_tags = order_sections(test_sample)
print("Fields in the sample: ", [key for key in test_sample.keys()])
print("Section names: ", sections)
print("Tokenized Document: ", sec_text)
print("Document BIO Tags: ", sec_bio_tags)
print("Extractive/present Keyphrases: ", test_sample["extractive_keyphrases"])
print("Abstractive/absent Keyphrases: ", test_sample["abstractive_keyphrases"])
print("\n-----------\n")
```
**Output**
```bash
```
### Medium Dataset
```python
from datasets import load_dataset
# get medium dataset
dataset = load_dataset("midas/ldkp3k", "medium")
```
### Large Dataset
```python
from datasets import load_dataset
# get large dataset
dataset = load_dataset("midas/ldkp3k", "large")
```
## Citation Information
Please cite the works below if you use this dataset in your work.
```
@article{dl4srmahata2022ldkp,
title={LDKP - A Dataset for Identifying Keyphrases from Long Scientific Documents},
author={Mahata, Debanjan and Agarwal, Naveen and Gautam, Dibya and Kumar, Amardeep and Parekh, Swapnil and Singla, Yaman Kumar and Acharya, Anish and Shah, Rajiv Ratn},
journal={DL4SR-22: Workshop on Deep Learning for Search and Recommendation, co-located with the 31st ACM International Conference on Information and Knowledge Management (CIKM)},
address={Atlanta, USA},
month={October},
year={2022}
}
```
```
@article{mahata2022ldkp,
title={LDKP: A Dataset for Identifying Keyphrases from Long Scientific Documents},
author={Mahata, Debanjan and Agarwal, Naveen and Gautam, Dibya and Kumar, Amardeep and Parekh, Swapnil and Singla, Yaman Kumar and Acharya, Anish and Shah, Rajiv Ratn},
journal={arXiv preprint arXiv:2203.15349},
year={2022}
}
```
```
@article{lo2019s2orc,
title={S2ORC: The semantic scholar open research corpus},
author={Lo, Kyle and Wang, Lucy Lu and Neumann, Mark and Kinney, Rodney and Weld, Dan S},
journal={arXiv preprint arXiv:1911.02782},
year={2019}
}
```
```
@inproceedings{ccano2019keyphrase,
title={Keyphrase generation: A multi-aspect survey},
author={{\c{C}}ano, Erion and Bojar, Ond{\v{r}}ej},
booktitle={2019 25th Conference of Open Innovations Association (FRUCT)},
pages={85--94},
year={2019},
organization={IEEE}
}
```
```
@article{meng2017deep,
title={Deep keyphrase generation},
author={Meng, Rui and Zhao, Sanqiang and Han, Shuguang and He, Daqing and Brusilovsky, Peter and Chi, Yu},
journal={arXiv preprint arXiv:1704.06879},
year={2017}
}
```
## Contributions
Thanks to [@debanjanbhucs](https://github.com/debanjanbhucs), [@dibyaaaaax](https://github.com/dibyaaaaax), [@UmaGunturi](https://github.com/UmaGunturi) and [@ad6398](https://github.com/ad6398) for adding this dataset
|
solomonk/reddit_mental_health_posts | 2022-01-11T15:40:01.000Z | [
"region:us"
] | solomonk | null | null | null | 8 | 14 | # Reddit posts about mental health
## files
- adhd.csv from r/adhd
- aspergers.csv from r/aspergers
- depression.csv from r/depression
- ocd.csv from r/ocd
- ptsd.csv from r/ptsd
## fields
- author
- body
- created_utc
- id
- num_comments
- score
- subreddit
- title
- upvote_ratio
- url
for more details about theses fields [Praw Submission](https://praw.readthedocs.io/en/latest/code_overview/models/submission.html).
|
FanFan/sentiment-amazon-test | 2022-03-08T05:56:20.000Z | [
"region:us"
] | FanFan | null | null | null | 0 | 14 | Entry not found |
Khedesh/ParsTwiNER | 2022-03-11T16:25:50.000Z | [
"region:us"
] | Khedesh | null | null | null | 0 | 14 | Entry not found |
tomekkorbak/pile-curse-chunk-0 | 2022-03-18T21:40:36.000Z | [
"region:us"
] | tomekkorbak | null | null | null | 0 | 14 | Entry not found |
pere/italian_tweets_500k | 2022-05-11T14:32:46.000Z | [
"region:us"
] | pere | \\nItalian tweets. | null | null | 0 | 14 | # Italian Tweets Test Dataset
This is a test dataset that is available for debugging reasons only. It contains errors. Please do not use.
## How to Use
```python
from datasets import load_dataset
data = load_dataset("pere/italian_tweets_1M")
``` |
JeremyAlain/123_test | 2022-10-25T10:29:11.000Z | [
"task_categories:multiple-choice",
"task_categories:question-answering",
"task_categories:zero-shot-classification",
"task_categories:text2text-generation",
"task_categories:table-question-answering",
"task_categories:text-generation",
"task_categories:text-classification",
"task_categories:tabular-cl... | JeremyAlain | The Fewshot Table dataset consists of tables that naturally occur on the web, that are formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance. The dataset consists of approximately 413K tables that are extracted from the WDC Web Table Corpora 2015, which is released under the Apache-2.0 license. The WDC Web Table Corpora "contains vast amounts of HTML tables. [...] The Web Data Commons project extracts relational Web tables from the Common Crawl, the largest and most up-to-date Web corpus that is currently available to the public." | @InProceedings{huggingface:dataset,
title = {A great new dataset},
author={huggingface, Inc.
},
year={2020}
} | null | 2 | 14 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: Fewshot Table Dataset
size_categories:
- 100K<n<1M
source_datasets: []
task_categories:
- multiple-choice
- question-answering
- zero-shot-classification
- text2text-generation
- table-question-answering
- text-generation
- text-classification
- tabular-classification
task_ids:
- multiple-choice-qa
- extractive-qa
- open-domain-qa
- closed-domain-qa
- closed-book-qa
- open-book-qa
- language-modeling
- multi-class-classification
- natural-language-inference
- topic-classification
- multi-label-classification
- tabular-multi-class-classification
- tabular-multi-label-classification
---
# Dataset Card for Fewshot Table Dataset
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [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)
## Dataset Description
- **Homepage:** [Needs More Information]
- **Repository:** https://github.com/JunShern/few-shot-pretraining
- **Paper:** Paper-Title
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** junshern@nyu.edu, perez@nyu.edu
### Dataset Summary
The Fewshot Table dataset consists of tables that naturally occur on the web, that are formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance. The dataset consists of approximately 413K tables that are extracted from the [WDC Web Table Corpora](http://webdatacommons.org/webtables/) 2015, which is released under the Apache-2.0 license. The WDC Web Table Corpora "contains vast amounts of HTML tables. [...] The Web Data Commons project extracts relational Web tables from the [Common Crawl](https://commoncrawl.org/), the largest and most up-to-date Web corpus that is currently available to the public."
### Supported Tasks and Leaderboards
Since the tables come from the web, the distribution of tasks and topics is very broad. The shape of our dataset is very wide i.e. we have 1000's tasks, while each task has only a few examples, compared to most current NLP datasets which are very deep, i.e. 10s of tasks with many examples. This implies that our dataset covers a broad range of potential tasks, e.g. multiple-choice, question-answering, table-question-answering, text-classification, etc.
The intended use of this dataset is to improve few-shot performance by finetuning/pretraining onour dataset.
### Languages
English
## Dataset Structure
### Data Instances
Each table, i.e. task is represented as a json-lines file and consists of several few-shot examples. Each example is a dictionary containing a field 'task', which identifies the task, followed by an 'input', 'options', and 'output' field. The 'input' field contains several column elements of the same row in the table, while the 'output' field is a target which represents an individual column of the same row. Each task contains several such examples which can be concatenated as a few-shot task. In the case of multiple choice classification, the 'options' field contains the possible classes that a model needs to choose from.
There are also additional meta-data fields such as 'pageTitle', 'title', 'outputColName', 'url', 'wdcFile'.
### Data Fields
'task': task identifier
'input': column elements of a specific row in table.
'options': for multiple choice classification, it provides the options to choose from.
'output': target column element of same row as input.
'pageTitle': the title of the page containing the table.
'outputColName': ?? (potentially remove this from data)
'url': url to the website containing the table
'wdcFile': ? (potentially remove this from data)
### Data Splits
[Needs More Information]
## Dataset Creation
### Curation Rationale
How do we convert tables to few-shot tasks?
Unlike unstructured text, structured data in the form of tables lends itself easily to the few-shot task format. Given a table where each row is an instance of a similar class and the columns describe the attributes of each instance, we can turn each row into a task example to predict one attribute given the others. When the table has more than one row, we instantly have multiple examples of this task by using each row as a single example, and thus each table becomes a few-shot dataset for a particular task.
The few-shot setting in this setting is significant: Tables often do not come with clear instructions for each field, so tasks may be underspecified if prompted in a zero-shot manner, but the intended task becomes clearer when examples are provided. This makes a good two-way match: The few-shot format is a perfect setup for table learning, and tables provide a natural dataset for few-shot training.
### Source Data
#### Initial Data Collection and Normalization
We downloaded the [WDC Web Table Corpora](http://webdatacommons.org/webtables/) 2015 dataset and focus on relational tables. In the following, we describe the steps we executed to filter the WDC Web Table Corpora and create our task dataset. Given a set of relation tables, we apply defined preprocessing steps to ensure all the tables can be handled consistently. Each table can then spawn one or more tasks using a simple predict-one-column approach. Finally, all tasks produced in this manner undergo simple rule-based checks, i.e. any candidates that do not meet some defined minimum requirements for a well-formed task are rejected. Following this approach, we start with 50 million tables in the initial corpus and produce a longlist of 400K tasks.
1. We select only relational tables.
2. We make sure all tables are vertical (horizontal tables are simply transposed) and remove duplicate rows.
3. To create task we use what in the literature is referred to as verbalizers. For example, a table with 3 columns may be cast as three different tasks: predict column A given B and C, predict column B given A and C, and predict column C given A and B.
4. Rule-based-checks to reject tables:
a) We reject 25M tables that have fewer than 6 rows (so we can do at least k=5-shot learning)
b) We reject tables with > 20% non-English text as measured by [SpaCy](https://spacy.io/)
c) Given 2 Million passing tables we consider each table column as a potential output column, and concatenate all other columns to form the input (which produces 5.6 M candidate tasks)
5. Rule-based-checks to reject tasks
a) We reject a task if it has less than 6 rows. Note that tasks may have fewer rows than their origin tables since we remove rows where the output column is empty.
b) We reject tasks if any input maps to multiple outputs.
c) We reject tasks if it has fewer than 2 output classes.
d) We reject a task if the output column alone has >20% non-English text.
e) We reject a task if the classes are heavily imbalanced.
6. Lastly we apply domain-level filtering. Initial iterations of our dataset found a significant imbalance in terms of the website of origin for our generated tasks. In particular, we found that the mos-frequent domain in the WDC corpus, Cappex.com, was emphasized by our export criteria such that this website alone represented 41% of our total tasks. Since we want our dataset to represent the diversity of all the tables available on the web, we apply a hard fix for this imbalance by limiting the number of tasks per domain. Starting from the initial corpus of 50M tables from 323160 web domains, our resulting longlist of tasks comprises more than X for a total of 413350 tasks.
#### Who are the source language producers?
The dataset is extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/).
### Annotations
#### Annotation process
No annotation Process
#### Who are the annotators?
-
### Personal and Sensitive Information
The data was extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/), which in turn extracted tables from the [Common Crawl](https://commoncrawl.org/). We did not filter the data in any way. Thus any user identities or otherwise sensitive information (e.g. data that reveals racial or ethnic origins, sexual orientations, religious beliefs, political opinions or union memberships, or locations; financial or health data; biometric or genetic data; forms of government identification, such as social security numbers; criminal history, etc.) might be contained in our dataset.
## Considerations for Using the Data
### Social Impact of Dataset
The purpose of this dataset is to help develop models that are better at few-shot learning and have higher few-shot performance by fine-tuning few-shot tasks extracted from tables.
While tables have a similar structure to few-shot tasks and we do see an improved performance on few-shot tasks in our paper, we want to make clear that finetuning on tables also has its risks. First of all, since the tables are extracted from the web, they may contain user identities or otherwise sensitive information which a model might reveal at inference, or which could influence the learning process of a model in a negative way. Second, since tables are very diverse in nature, the model also trains on low-quality data or data with an unusual structure. While it is interesting that training on such data improves few-shot performance on downstream tasks, this could also imply that the model learns concepts that are very dissimilar to human concepts that would be useful for a certain downstream task. In other words, it is possible that the model learns weird things that are helpful on the evaluated downstream tasks, but might lead to bad out-of-distribution behavior.
### Discussion of Biases
Since our dataset contains tables that are scraped from the web, it will also contain many toxic, racist, sexist, and otherwise harmful biases and texts. We have not run any analysis on the biases prevalent in our datasets. Neither have we explicitly filtered the content for toxic content.
This implies that a model trained on our dataset will reinforce harmful biases and toxic text that exist in our dataset.
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
Mention all authors
### Licensing Information
Apache 2.0
### Citation Information
[Needs More Information] |
sude123/twitter_dataset | 2022-06-09T20:40:16.000Z | [
"region:us"
] | sude123 | null | null | null | 0 | 14 | Entry not found |
MicPie/unpredictable_baseball-fantasysports-yahoo-com | 2022-08-04T19:37:41.000Z | [
"task_categories:multiple-choice",
"task_categories:question-answering",
"task_categories:zero-shot-classification",
"task_categories:text2text-generation",
"task_categories:table-question-answering",
"task_categories:text-generation",
"task_categories:text-classification",
"task_categories:tabular-cl... | MicPie | The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance. For more details please see the accompanying dataset card. | @misc{chan2022few,
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
title = {Few-shot Adaptation Works with UnpredicTable Data},
publisher={arXiv},
year = {2022},
url = {https://arxiv.org/abs/2208.01009}
} | null | 0 | 14 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: UnpredicTable-baseball-fantasysports-yahoo-com
size_categories:
- 100K<n<1M
source_datasets: []
task_categories:
- multiple-choice
- question-answering
- zero-shot-classification
- text2text-generation
- table-question-answering
- text-generation
- text-classification
- tabular-classification
task_ids:
- multiple-choice-qa
- extractive-qa
- open-domain-qa
- closed-domain-qa
- closed-book-qa
- open-book-qa
- language-modeling
- multi-class-classification
- natural-language-inference
- topic-classification
- multi-label-classification
- tabular-multi-class-classification
- tabular-multi-label-classification
---
# Dataset Card for "UnpredicTable-baseball-fantasysports-yahoo-com" - Dataset of Few-shot Tasks from Tables
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [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)
## Dataset Description
- **Homepage:** https://ethanperez.net/unpredictable
- **Repository:** https://github.com/JunShern/few-shot-adaptation
- **Paper:** Few-shot Adaptation Works with UnpredicTable Data
- **Point of Contact:** junshern@nyu.edu, perez@nyu.edu
### Dataset Summary
The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance.
There are several dataset versions available:
* [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full): Starting from the initial WTC corpus of 50M tables, we apply our tables-to-tasks procedure to produce our resulting dataset, [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full), which comprises 413,299 tasks from 23,744 unique websites.
* [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique): This is the same as [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full) but filtered to have a maximum of one task per website. [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique) contains exactly 23,744 tasks from 23,744 websites.
* [UnpredicTable-5k](https://huggingface.co/datasets/MicPie/unpredictable_5k): This dataset contains 5k random tables from the full dataset.
* UnpredicTable data subsets based on a manual human quality rating (please see our publication for details of the ratings):
* [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low)
* [UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium)
* [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high)
* UnpredicTable data subsets based on the website of origin:
* [UnpredicTable-baseball-fantasysports-yahoo-com](https://huggingface.co/datasets/MicPie/unpredictable_baseball-fantasysports-yahoo-com)
* [UnpredicTable-bulbapedia-bulbagarden-net](https://huggingface.co/datasets/MicPie/unpredictable_bulbapedia-bulbagarden-net)
* [UnpredicTable-cappex-com](https://huggingface.co/datasets/MicPie/unpredictable_cappex-com)
* [UnpredicTable-cram-com](https://huggingface.co/datasets/MicPie/unpredictable_cram-com)
* [UnpredicTable-dividend-com](https://huggingface.co/datasets/MicPie/unpredictable_dividend-com)
* [UnpredicTable-dummies-com](https://huggingface.co/datasets/MicPie/unpredictable_dummies-com)
* [UnpredicTable-en-wikipedia-org](https://huggingface.co/datasets/MicPie/unpredictable_en-wikipedia-org)
* [UnpredicTable-ensembl-org](https://huggingface.co/datasets/MicPie/unpredictable_ensembl-org)
* [UnpredicTable-gamefaqs-com](https://huggingface.co/datasets/MicPie/unpredictable_gamefaqs-com)
* [UnpredicTable-mgoblog-com](https://huggingface.co/datasets/MicPie/unpredictable_mgoblog-com)
* [UnpredicTable-mmo-champion-com](https://huggingface.co/datasets/MicPie/unpredictable_mmo-champion-com)
* [UnpredicTable-msdn-microsoft-com](https://huggingface.co/datasets/MicPie/unpredictable_msdn-microsoft-com)
* [UnpredicTable-phonearena-com](https://huggingface.co/datasets/MicPie/unpredictable_phonearena-com)
* [UnpredicTable-sittercity-com](https://huggingface.co/datasets/MicPie/unpredictable_sittercity-com)
* [UnpredicTable-sporcle-com](https://huggingface.co/datasets/MicPie/unpredictable_sporcle-com)
* [UnpredicTable-studystack-com](https://huggingface.co/datasets/MicPie/unpredictable_studystack-com)
* [UnpredicTable-support-google-com](https://huggingface.co/datasets/MicPie/unpredictable_support-google-com)
* [UnpredicTable-w3-org](https://huggingface.co/datasets/MicPie/unpredictable_w3-org)
* [UnpredicTable-wiki-openmoko-org](https://huggingface.co/datasets/MicPie/unpredictable_wiki-openmoko-org)
* [UnpredicTable-wkdu-org](https://huggingface.co/datasets/MicPie/unpredictable_wkdu-org)
* UnpredicTable data subsets based on clustering (for the clustering details please see our publication):
* [UnpredicTable-cluster00](https://huggingface.co/datasets/MicPie/unpredictable_cluster00)
* [UnpredicTable-cluster01](https://huggingface.co/datasets/MicPie/unpredictable_cluster01)
* [UnpredicTable-cluster02](https://huggingface.co/datasets/MicPie/unpredictable_cluster02)
* [UnpredicTable-cluster03](https://huggingface.co/datasets/MicPie/unpredictable_cluster03)
* [UnpredicTable-cluster04](https://huggingface.co/datasets/MicPie/unpredictable_cluster04)
* [UnpredicTable-cluster05](https://huggingface.co/datasets/MicPie/unpredictable_cluster05)
* [UnpredicTable-cluster06](https://huggingface.co/datasets/MicPie/unpredictable_cluster06)
* [UnpredicTable-cluster07](https://huggingface.co/datasets/MicPie/unpredictable_cluster07)
* [UnpredicTable-cluster08](https://huggingface.co/datasets/MicPie/unpredictable_cluster08)
* [UnpredicTable-cluster09](https://huggingface.co/datasets/MicPie/unpredictable_cluster09)
* [UnpredicTable-cluster10](https://huggingface.co/datasets/MicPie/unpredictable_cluster10)
* [UnpredicTable-cluster11](https://huggingface.co/datasets/MicPie/unpredictable_cluster11)
* [UnpredicTable-cluster12](https://huggingface.co/datasets/MicPie/unpredictable_cluster12)
* [UnpredicTable-cluster13](https://huggingface.co/datasets/MicPie/unpredictable_cluster13)
* [UnpredicTable-cluster14](https://huggingface.co/datasets/MicPie/unpredictable_cluster14)
* [UnpredicTable-cluster15](https://huggingface.co/datasets/MicPie/unpredictable_cluster15)
* [UnpredicTable-cluster16](https://huggingface.co/datasets/MicPie/unpredictable_cluster16)
* [UnpredicTable-cluster17](https://huggingface.co/datasets/MicPie/unpredictable_cluster17)
* [UnpredicTable-cluster18](https://huggingface.co/datasets/MicPie/unpredictable_cluster18)
* [UnpredicTable-cluster19](https://huggingface.co/datasets/MicPie/unpredictable_cluster19)
* [UnpredicTable-cluster20](https://huggingface.co/datasets/MicPie/unpredictable_cluster20)
* [UnpredicTable-cluster21](https://huggingface.co/datasets/MicPie/unpredictable_cluster21)
* [UnpredicTable-cluster22](https://huggingface.co/datasets/MicPie/unpredictable_cluster22)
* [UnpredicTable-cluster23](https://huggingface.co/datasets/MicPie/unpredictable_cluster23)
* [UnpredicTable-cluster24](https://huggingface.co/datasets/MicPie/unpredictable_cluster24)
* [UnpredicTable-cluster25](https://huggingface.co/datasets/MicPie/unpredictable_cluster25)
* [UnpredicTable-cluster26](https://huggingface.co/datasets/MicPie/unpredictable_cluster26)
* [UnpredicTable-cluster27](https://huggingface.co/datasets/MicPie/unpredictable_cluster27)
* [UnpredicTable-cluster28](https://huggingface.co/datasets/MicPie/unpredictable_cluster28)
* [UnpredicTable-cluster29](https://huggingface.co/datasets/MicPie/unpredictable_cluster29)
* [UnpredicTable-cluster-noise](https://huggingface.co/datasets/MicPie/unpredictable_cluster-noise)
### Supported Tasks and Leaderboards
Since the tables come from the web, the distribution of tasks and topics is very broad. The shape of our dataset is very wide, i.e., we have 1000's of tasks, while each task has only a few examples, compared to most current NLP datasets which are very deep, i.e., 10s of tasks with many examples. This implies that our dataset covers a broad range of potential tasks, e.g., multiple-choice, question-answering, table-question-answering, text-classification, etc.
The intended use of this dataset is to improve few-shot performance by fine-tuning/pre-training on our dataset.
### Languages
English
## Dataset Structure
### Data Instances
Each task is represented as a jsonline file and consists of several few-shot examples. Each example is a dictionary containing a field 'task', which identifies the task, followed by an 'input', 'options', and 'output' field. The 'input' field contains several column elements of the same row in the table, while the 'output' field is a target which represents an individual column of the same row. Each task contains several such examples which can be concatenated as a few-shot task. In the case of multiple choice classification, the 'options' field contains the possible classes that a model needs to choose from.
There are also additional meta-data fields such as 'pageTitle', 'title', 'outputColName', 'url', 'wdcFile'.
### Data Fields
'task': task identifier
'input': column elements of a specific row in the table.
'options': for multiple choice classification, it provides the options to choose from.
'output': target column element of the same row as input.
'pageTitle': the title of the page containing the table.
'outputColName': output column name
'url': url to the website containing the table
'wdcFile': WDC Web Table Corpus file
### Data Splits
The UnpredicTable datasets do not come with additional data splits.
## Dataset Creation
### Curation Rationale
Few-shot training on multi-task datasets has been demonstrated to improve language models' few-shot learning (FSL) performance on new tasks, but it is unclear which training tasks lead to effective downstream task adaptation. Few-shot learning datasets are typically produced with expensive human curation, limiting the scale and diversity of the training tasks available to study. As an alternative source of few-shot data, we automatically extract 413,299 tasks from diverse internet tables. We provide this as a research resource to investigate the relationship between training data and few-shot learning.
### Source Data
#### Initial Data Collection and Normalization
We use internet tables from the English-language Relational Subset of the WDC Web Table Corpus 2015 (WTC). The WTC dataset tables were extracted from the July 2015 Common Crawl web corpus (http://webdatacommons.org/webtables/2015/EnglishStatistics.html). The dataset contains 50,820,165 tables from 323,160 web domains. We then convert the tables into few-shot learning tasks. Please see our publication for more details on the data collection and conversion pipeline.
#### Who are the source language producers?
The dataset is extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/).
### Annotations
#### Annotation process
Manual annotation was only carried out for the [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low),
[UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium), and [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high) data subsets to rate task quality. Detailed instructions of the annotation instructions can be found in our publication.
#### Who are the annotators?
Annotations were carried out by a lab assistant.
### Personal and Sensitive Information
The data was extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/), which in turn extracted tables from the [Common Crawl](https://commoncrawl.org/). We did not filter the data in any way. Thus any user identities or otherwise sensitive information (e.g., data that reveals racial or ethnic origins, sexual orientations, religious beliefs, political opinions or union memberships, or locations; financial or health data; biometric or genetic data; forms of government identification, such as social security numbers; criminal history, etc.) might be contained in our dataset.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset is intended for use as a research resource to investigate the relationship between training data and few-shot learning. As such, it contains high- and low-quality data, as well as diverse content that may be untruthful or inappropriate. Without careful investigation, it should not be used for training models that will be deployed for use in decision-critical or user-facing situations.
### Discussion of Biases
Since our dataset contains tables that are scraped from the web, it will also contain many toxic, racist, sexist, and otherwise harmful biases and texts. We have not run any analysis on the biases prevalent in our datasets. Neither have we explicitly filtered the content. This implies that a model trained on our dataset may potentially reflect harmful biases and toxic text that exist in our dataset.
### Other Known Limitations
No additional known limitations.
## Additional Information
### Dataset Curators
Jun Shern Chan, Michael Pieler, Jonathan Jao, Jérémy Scheurer, Ethan Perez
### Licensing Information
Apache 2.0
### Citation Information
```
@misc{chan2022few,
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
title = {Few-shot Adaptation Works with UnpredicTable Data},
publisher={arXiv},
year = {2022},
url = {https://arxiv.org/abs/2208.01009}
}
```
|
MicPie/unpredictable_mgoblog-com | 2022-08-04T20:09:03.000Z | [
"task_categories:multiple-choice",
"task_categories:question-answering",
"task_categories:zero-shot-classification",
"task_categories:text2text-generation",
"task_categories:table-question-answering",
"task_categories:text-generation",
"task_categories:text-classification",
"task_categories:tabular-cl... | MicPie | The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance. For more details please see the accompanying dataset card. | @misc{chan2022few,
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
title = {Few-shot Adaptation Works with UnpredicTable Data},
publisher={arXiv},
year = {2022},
url = {https://arxiv.org/abs/2208.01009}
} | null | 0 | 14 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: AdapTable-mgoblog-com
size_categories:
- 100K<n<1M
source_datasets: []
task_categories:
- multiple-choice
- question-answering
- zero-shot-classification
- text2text-generation
- table-question-answering
- text-generation
- text-classification
- tabular-classification
task_ids:
- multiple-choice-qa
- extractive-qa
- open-domain-qa
- closed-domain-qa
- closed-book-qa
- open-book-qa
- language-modeling
- multi-class-classification
- natural-language-inference
- topic-classification
- multi-label-classification
- tabular-multi-class-classification
- tabular-multi-label-classification
---
# Dataset Card for "AdapTable-mgoblog-com" - Dataset of Few-shot Tasks from Tables
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [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)
## Dataset Description
- **Homepage:** https://ethanperez.net/unpredictable
- **Repository:** https://github.com/JunShern/few-shot-adaptation
- **Paper:** Few-shot Adaptation Works with UnpredicTable Data
- **Point of Contact:** junshern@nyu.edu, perez@nyu.edu
### Dataset Summary
The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance.
There are several dataset versions available:
* [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full): Starting from the initial WTC corpus of 50M tables, we apply our tables-to-tasks procedure to produce our resulting dataset, [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full), which comprises 413,299 tasks from 23,744 unique websites.
* [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique): This is the same as [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full) but filtered to have a maximum of one task per website. [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique) contains exactly 23,744 tasks from 23,744 websites.
* [UnpredicTable-5k](https://huggingface.co/datasets/MicPie/unpredictable_5k): This dataset contains 5k random tables from the full dataset.
* UnpredicTable data subsets based on a manual human quality rating (please see our publication for details of the ratings):
* [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low)
* [UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium)
* [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high)
* UnpredicTable data subsets based on the website of origin:
* [UnpredicTable-baseball-fantasysports-yahoo-com](https://huggingface.co/datasets/MicPie/unpredictable_baseball-fantasysports-yahoo-com)
* [UnpredicTable-bulbapedia-bulbagarden-net](https://huggingface.co/datasets/MicPie/unpredictable_bulbapedia-bulbagarden-net)
* [UnpredicTable-cappex-com](https://huggingface.co/datasets/MicPie/unpredictable_cappex-com)
* [UnpredicTable-cram-com](https://huggingface.co/datasets/MicPie/unpredictable_cram-com)
* [UnpredicTable-dividend-com](https://huggingface.co/datasets/MicPie/unpredictable_dividend-com)
* [UnpredicTable-dummies-com](https://huggingface.co/datasets/MicPie/unpredictable_dummies-com)
* [UnpredicTable-en-wikipedia-org](https://huggingface.co/datasets/MicPie/unpredictable_en-wikipedia-org)
* [UnpredicTable-ensembl-org](https://huggingface.co/datasets/MicPie/unpredictable_ensembl-org)
* [UnpredicTable-gamefaqs-com](https://huggingface.co/datasets/MicPie/unpredictable_gamefaqs-com)
* [UnpredicTable-mgoblog-com](https://huggingface.co/datasets/MicPie/unpredictable_mgoblog-com)
* [UnpredicTable-mmo-champion-com](https://huggingface.co/datasets/MicPie/unpredictable_mmo-champion-com)
* [UnpredicTable-msdn-microsoft-com](https://huggingface.co/datasets/MicPie/unpredictable_msdn-microsoft-com)
* [UnpredicTable-phonearena-com](https://huggingface.co/datasets/MicPie/unpredictable_phonearena-com)
* [UnpredicTable-sittercity-com](https://huggingface.co/datasets/MicPie/unpredictable_sittercity-com)
* [UnpredicTable-sporcle-com](https://huggingface.co/datasets/MicPie/unpredictable_sporcle-com)
* [UnpredicTable-studystack-com](https://huggingface.co/datasets/MicPie/unpredictable_studystack-com)
* [UnpredicTable-support-google-com](https://huggingface.co/datasets/MicPie/unpredictable_support-google-com)
* [UnpredicTable-w3-org](https://huggingface.co/datasets/MicPie/unpredictable_w3-org)
* [UnpredicTable-wiki-openmoko-org](https://huggingface.co/datasets/MicPie/unpredictable_wiki-openmoko-org)
* [UnpredicTable-wkdu-org](https://huggingface.co/datasets/MicPie/unpredictable_wkdu-org)
* UnpredicTable data subsets based on clustering (for the clustering details please see our publication):
* [UnpredicTable-cluster00](https://huggingface.co/datasets/MicPie/unpredictable_cluster00)
* [UnpredicTable-cluster01](https://huggingface.co/datasets/MicPie/unpredictable_cluster01)
* [UnpredicTable-cluster02](https://huggingface.co/datasets/MicPie/unpredictable_cluster02)
* [UnpredicTable-cluster03](https://huggingface.co/datasets/MicPie/unpredictable_cluster03)
* [UnpredicTable-cluster04](https://huggingface.co/datasets/MicPie/unpredictable_cluster04)
* [UnpredicTable-cluster05](https://huggingface.co/datasets/MicPie/unpredictable_cluster05)
* [UnpredicTable-cluster06](https://huggingface.co/datasets/MicPie/unpredictable_cluster06)
* [UnpredicTable-cluster07](https://huggingface.co/datasets/MicPie/unpredictable_cluster07)
* [UnpredicTable-cluster08](https://huggingface.co/datasets/MicPie/unpredictable_cluster08)
* [UnpredicTable-cluster09](https://huggingface.co/datasets/MicPie/unpredictable_cluster09)
* [UnpredicTable-cluster10](https://huggingface.co/datasets/MicPie/unpredictable_cluster10)
* [UnpredicTable-cluster11](https://huggingface.co/datasets/MicPie/unpredictable_cluster11)
* [UnpredicTable-cluster12](https://huggingface.co/datasets/MicPie/unpredictable_cluster12)
* [UnpredicTable-cluster13](https://huggingface.co/datasets/MicPie/unpredictable_cluster13)
* [UnpredicTable-cluster14](https://huggingface.co/datasets/MicPie/unpredictable_cluster14)
* [UnpredicTable-cluster15](https://huggingface.co/datasets/MicPie/unpredictable_cluster15)
* [UnpredicTable-cluster16](https://huggingface.co/datasets/MicPie/unpredictable_cluster16)
* [UnpredicTable-cluster17](https://huggingface.co/datasets/MicPie/unpredictable_cluster17)
* [UnpredicTable-cluster18](https://huggingface.co/datasets/MicPie/unpredictable_cluster18)
* [UnpredicTable-cluster19](https://huggingface.co/datasets/MicPie/unpredictable_cluster19)
* [UnpredicTable-cluster20](https://huggingface.co/datasets/MicPie/unpredictable_cluster20)
* [UnpredicTable-cluster21](https://huggingface.co/datasets/MicPie/unpredictable_cluster21)
* [UnpredicTable-cluster22](https://huggingface.co/datasets/MicPie/unpredictable_cluster22)
* [UnpredicTable-cluster23](https://huggingface.co/datasets/MicPie/unpredictable_cluster23)
* [UnpredicTable-cluster24](https://huggingface.co/datasets/MicPie/unpredictable_cluster24)
* [UnpredicTable-cluster25](https://huggingface.co/datasets/MicPie/unpredictable_cluster25)
* [UnpredicTable-cluster26](https://huggingface.co/datasets/MicPie/unpredictable_cluster26)
* [UnpredicTable-cluster27](https://huggingface.co/datasets/MicPie/unpredictable_cluster27)
* [UnpredicTable-cluster28](https://huggingface.co/datasets/MicPie/unpredictable_cluster28)
* [UnpredicTable-cluster29](https://huggingface.co/datasets/MicPie/unpredictable_cluster29)
* [UnpredicTable-cluster-noise](https://huggingface.co/datasets/MicPie/unpredictable_cluster-noise)
### Supported Tasks and Leaderboards
Since the tables come from the web, the distribution of tasks and topics is very broad. The shape of our dataset is very wide, i.e., we have 1000's of tasks, while each task has only a few examples, compared to most current NLP datasets which are very deep, i.e., 10s of tasks with many examples. This implies that our dataset covers a broad range of potential tasks, e.g., multiple-choice, question-answering, table-question-answering, text-classification, etc.
The intended use of this dataset is to improve few-shot performance by fine-tuning/pre-training on our dataset.
### Languages
English
## Dataset Structure
### Data Instances
Each task is represented as a jsonline file and consists of several few-shot examples. Each example is a dictionary containing a field 'task', which identifies the task, followed by an 'input', 'options', and 'output' field. The 'input' field contains several column elements of the same row in the table, while the 'output' field is a target which represents an individual column of the same row. Each task contains several such examples which can be concatenated as a few-shot task. In the case of multiple choice classification, the 'options' field contains the possible classes that a model needs to choose from.
There are also additional meta-data fields such as 'pageTitle', 'title', 'outputColName', 'url', 'wdcFile'.
### Data Fields
'task': task identifier
'input': column elements of a specific row in the table.
'options': for multiple choice classification, it provides the options to choose from.
'output': target column element of the same row as input.
'pageTitle': the title of the page containing the table.
'outputColName': output column name
'url': url to the website containing the table
'wdcFile': WDC Web Table Corpus file
### Data Splits
The UnpredicTable datasets do not come with additional data splits.
## Dataset Creation
### Curation Rationale
Few-shot training on multi-task datasets has been demonstrated to improve language models' few-shot learning (FSL) performance on new tasks, but it is unclear which training tasks lead to effective downstream task adaptation. Few-shot learning datasets are typically produced with expensive human curation, limiting the scale and diversity of the training tasks available to study. As an alternative source of few-shot data, we automatically extract 413,299 tasks from diverse internet tables. We provide this as a research resource to investigate the relationship between training data and few-shot learning.
### Source Data
#### Initial Data Collection and Normalization
We use internet tables from the English-language Relational Subset of the WDC Web Table Corpus 2015 (WTC). The WTC dataset tables were extracted from the July 2015 Common Crawl web corpus (http://webdatacommons.org/webtables/2015/EnglishStatistics.html). The dataset contains 50,820,165 tables from 323,160 web domains. We then convert the tables into few-shot learning tasks. Please see our publication for more details on the data collection and conversion pipeline.
#### Who are the source language producers?
The dataset is extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/).
### Annotations
#### Annotation process
Manual annotation was only carried out for the [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low),
[UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium), and [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high) data subsets to rate task quality. Detailed instructions of the annotation instructions can be found in our publication.
#### Who are the annotators?
Annotations were carried out by a lab assistant.
### Personal and Sensitive Information
The data was extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/), which in turn extracted tables from the [Common Crawl](https://commoncrawl.org/). We did not filter the data in any way. Thus any user identities or otherwise sensitive information (e.g., data that reveals racial or ethnic origins, sexual orientations, religious beliefs, political opinions or union memberships, or locations; financial or health data; biometric or genetic data; forms of government identification, such as social security numbers; criminal history, etc.) might be contained in our dataset.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset is intended for use as a research resource to investigate the relationship between training data and few-shot learning. As such, it contains high- and low-quality data, as well as diverse content that may be untruthful or inappropriate. Without careful investigation, it should not be used for training models that will be deployed for use in decision-critical or user-facing situations.
### Discussion of Biases
Since our dataset contains tables that are scraped from the web, it will also contain many toxic, racist, sexist, and otherwise harmful biases and texts. We have not run any analysis on the biases prevalent in our datasets. Neither have we explicitly filtered the content. This implies that a model trained on our dataset may potentially reflect harmful biases and toxic text that exist in our dataset.
### Other Known Limitations
No additional known limitations.
## Additional Information
### Dataset Curators
Jun Shern Chan, Michael Pieler, Jonathan Jao, Jérémy Scheurer, Ethan Perez
### Licensing Information
Apache 2.0
### Citation Information
```
@misc{chan2022few,
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
title = {Few-shot Adaptation Works with UnpredicTable Data},
publisher={arXiv},
year = {2022},
url = {https://arxiv.org/abs/2208.01009}
}
```
|
MicPie/unpredictable_msdn-microsoft-com | 2022-08-04T20:10:19.000Z | [
"task_categories:multiple-choice",
"task_categories:question-answering",
"task_categories:zero-shot-classification",
"task_categories:text2text-generation",
"task_categories:table-question-answering",
"task_categories:text-generation",
"task_categories:text-classification",
"task_categories:tabular-cl... | MicPie | The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance. For more details please see the accompanying dataset card. | @misc{chan2022few,
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
title = {Few-shot Adaptation Works with UnpredicTable Data},
publisher={arXiv},
year = {2022},
url = {https://arxiv.org/abs/2208.01009}
} | null | 1 | 14 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: UnpredicTable-msdn-microsoft-com
size_categories:
- 100K<n<1M
source_datasets: []
task_categories:
- multiple-choice
- question-answering
- zero-shot-classification
- text2text-generation
- table-question-answering
- text-generation
- text-classification
- tabular-classification
task_ids:
- multiple-choice-qa
- extractive-qa
- open-domain-qa
- closed-domain-qa
- closed-book-qa
- open-book-qa
- language-modeling
- multi-class-classification
- natural-language-inference
- topic-classification
- multi-label-classification
- tabular-multi-class-classification
- tabular-multi-label-classification
---
# Dataset Card for "UnpredicTable-msdn-microsoft-com" - Dataset of Few-shot Tasks from Tables
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [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)
## Dataset Description
- **Homepage:** https://ethanperez.net/unpredictable
- **Repository:** https://github.com/JunShern/few-shot-adaptation
- **Paper:** Few-shot Adaptation Works with UnpredicTable Data
- **Point of Contact:** junshern@nyu.edu, perez@nyu.edu
### Dataset Summary
The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance.
There are several dataset versions available:
* [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full): Starting from the initial WTC corpus of 50M tables, we apply our tables-to-tasks procedure to produce our resulting dataset, [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full), which comprises 413,299 tasks from 23,744 unique websites.
* [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique): This is the same as [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full) but filtered to have a maximum of one task per website. [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique) contains exactly 23,744 tasks from 23,744 websites.
* [UnpredicTable-5k](https://huggingface.co/datasets/MicPie/unpredictable_5k): This dataset contains 5k random tables from the full dataset.
* UnpredicTable data subsets based on a manual human quality rating (please see our publication for details of the ratings):
* [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low)
* [UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium)
* [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high)
* UnpredicTable data subsets based on the website of origin:
* [UnpredicTable-baseball-fantasysports-yahoo-com](https://huggingface.co/datasets/MicPie/unpredictable_baseball-fantasysports-yahoo-com)
* [UnpredicTable-bulbapedia-bulbagarden-net](https://huggingface.co/datasets/MicPie/unpredictable_bulbapedia-bulbagarden-net)
* [UnpredicTable-cappex-com](https://huggingface.co/datasets/MicPie/unpredictable_cappex-com)
* [UnpredicTable-cram-com](https://huggingface.co/datasets/MicPie/unpredictable_cram-com)
* [UnpredicTable-dividend-com](https://huggingface.co/datasets/MicPie/unpredictable_dividend-com)
* [UnpredicTable-dummies-com](https://huggingface.co/datasets/MicPie/unpredictable_dummies-com)
* [UnpredicTable-en-wikipedia-org](https://huggingface.co/datasets/MicPie/unpredictable_en-wikipedia-org)
* [UnpredicTable-ensembl-org](https://huggingface.co/datasets/MicPie/unpredictable_ensembl-org)
* [UnpredicTable-gamefaqs-com](https://huggingface.co/datasets/MicPie/unpredictable_gamefaqs-com)
* [UnpredicTable-mgoblog-com](https://huggingface.co/datasets/MicPie/unpredictable_mgoblog-com)
* [UnpredicTable-mmo-champion-com](https://huggingface.co/datasets/MicPie/unpredictable_mmo-champion-com)
* [UnpredicTable-msdn-microsoft-com](https://huggingface.co/datasets/MicPie/unpredictable_msdn-microsoft-com)
* [UnpredicTable-phonearena-com](https://huggingface.co/datasets/MicPie/unpredictable_phonearena-com)
* [UnpredicTable-sittercity-com](https://huggingface.co/datasets/MicPie/unpredictable_sittercity-com)
* [UnpredicTable-sporcle-com](https://huggingface.co/datasets/MicPie/unpredictable_sporcle-com)
* [UnpredicTable-studystack-com](https://huggingface.co/datasets/MicPie/unpredictable_studystack-com)
* [UnpredicTable-support-google-com](https://huggingface.co/datasets/MicPie/unpredictable_support-google-com)
* [UnpredicTable-w3-org](https://huggingface.co/datasets/MicPie/unpredictable_w3-org)
* [UnpredicTable-wiki-openmoko-org](https://huggingface.co/datasets/MicPie/unpredictable_wiki-openmoko-org)
* [UnpredicTable-wkdu-org](https://huggingface.co/datasets/MicPie/unpredictable_wkdu-org)
* UnpredicTable data subsets based on clustering (for the clustering details please see our publication):
* [UnpredicTable-cluster00](https://huggingface.co/datasets/MicPie/unpredictable_cluster00)
* [UnpredicTable-cluster01](https://huggingface.co/datasets/MicPie/unpredictable_cluster01)
* [UnpredicTable-cluster02](https://huggingface.co/datasets/MicPie/unpredictable_cluster02)
* [UnpredicTable-cluster03](https://huggingface.co/datasets/MicPie/unpredictable_cluster03)
* [UnpredicTable-cluster04](https://huggingface.co/datasets/MicPie/unpredictable_cluster04)
* [UnpredicTable-cluster05](https://huggingface.co/datasets/MicPie/unpredictable_cluster05)
* [UnpredicTable-cluster06](https://huggingface.co/datasets/MicPie/unpredictable_cluster06)
* [UnpredicTable-cluster07](https://huggingface.co/datasets/MicPie/unpredictable_cluster07)
* [UnpredicTable-cluster08](https://huggingface.co/datasets/MicPie/unpredictable_cluster08)
* [UnpredicTable-cluster09](https://huggingface.co/datasets/MicPie/unpredictable_cluster09)
* [UnpredicTable-cluster10](https://huggingface.co/datasets/MicPie/unpredictable_cluster10)
* [UnpredicTable-cluster11](https://huggingface.co/datasets/MicPie/unpredictable_cluster11)
* [UnpredicTable-cluster12](https://huggingface.co/datasets/MicPie/unpredictable_cluster12)
* [UnpredicTable-cluster13](https://huggingface.co/datasets/MicPie/unpredictable_cluster13)
* [UnpredicTable-cluster14](https://huggingface.co/datasets/MicPie/unpredictable_cluster14)
* [UnpredicTable-cluster15](https://huggingface.co/datasets/MicPie/unpredictable_cluster15)
* [UnpredicTable-cluster16](https://huggingface.co/datasets/MicPie/unpredictable_cluster16)
* [UnpredicTable-cluster17](https://huggingface.co/datasets/MicPie/unpredictable_cluster17)
* [UnpredicTable-cluster18](https://huggingface.co/datasets/MicPie/unpredictable_cluster18)
* [UnpredicTable-cluster19](https://huggingface.co/datasets/MicPie/unpredictable_cluster19)
* [UnpredicTable-cluster20](https://huggingface.co/datasets/MicPie/unpredictable_cluster20)
* [UnpredicTable-cluster21](https://huggingface.co/datasets/MicPie/unpredictable_cluster21)
* [UnpredicTable-cluster22](https://huggingface.co/datasets/MicPie/unpredictable_cluster22)
* [UnpredicTable-cluster23](https://huggingface.co/datasets/MicPie/unpredictable_cluster23)
* [UnpredicTable-cluster24](https://huggingface.co/datasets/MicPie/unpredictable_cluster24)
* [UnpredicTable-cluster25](https://huggingface.co/datasets/MicPie/unpredictable_cluster25)
* [UnpredicTable-cluster26](https://huggingface.co/datasets/MicPie/unpredictable_cluster26)
* [UnpredicTable-cluster27](https://huggingface.co/datasets/MicPie/unpredictable_cluster27)
* [UnpredicTable-cluster28](https://huggingface.co/datasets/MicPie/unpredictable_cluster28)
* [UnpredicTable-cluster29](https://huggingface.co/datasets/MicPie/unpredictable_cluster29)
* [UnpredicTable-cluster-noise](https://huggingface.co/datasets/MicPie/unpredictable_cluster-noise)
### Supported Tasks and Leaderboards
Since the tables come from the web, the distribution of tasks and topics is very broad. The shape of our dataset is very wide, i.e., we have 1000's of tasks, while each task has only a few examples, compared to most current NLP datasets which are very deep, i.e., 10s of tasks with many examples. This implies that our dataset covers a broad range of potential tasks, e.g., multiple-choice, question-answering, table-question-answering, text-classification, etc.
The intended use of this dataset is to improve few-shot performance by fine-tuning/pre-training on our dataset.
### Languages
English
## Dataset Structure
### Data Instances
Each task is represented as a jsonline file and consists of several few-shot examples. Each example is a dictionary containing a field 'task', which identifies the task, followed by an 'input', 'options', and 'output' field. The 'input' field contains several column elements of the same row in the table, while the 'output' field is a target which represents an individual column of the same row. Each task contains several such examples which can be concatenated as a few-shot task. In the case of multiple choice classification, the 'options' field contains the possible classes that a model needs to choose from.
There are also additional meta-data fields such as 'pageTitle', 'title', 'outputColName', 'url', 'wdcFile'.
### Data Fields
'task': task identifier
'input': column elements of a specific row in the table.
'options': for multiple choice classification, it provides the options to choose from.
'output': target column element of the same row as input.
'pageTitle': the title of the page containing the table.
'outputColName': output column name
'url': url to the website containing the table
'wdcFile': WDC Web Table Corpus file
### Data Splits
The UnpredicTable datasets do not come with additional data splits.
## Dataset Creation
### Curation Rationale
Few-shot training on multi-task datasets has been demonstrated to improve language models' few-shot learning (FSL) performance on new tasks, but it is unclear which training tasks lead to effective downstream task adaptation. Few-shot learning datasets are typically produced with expensive human curation, limiting the scale and diversity of the training tasks available to study. As an alternative source of few-shot data, we automatically extract 413,299 tasks from diverse internet tables. We provide this as a research resource to investigate the relationship between training data and few-shot learning.
### Source Data
#### Initial Data Collection and Normalization
We use internet tables from the English-language Relational Subset of the WDC Web Table Corpus 2015 (WTC). The WTC dataset tables were extracted from the July 2015 Common Crawl web corpus (http://webdatacommons.org/webtables/2015/EnglishStatistics.html). The dataset contains 50,820,165 tables from 323,160 web domains. We then convert the tables into few-shot learning tasks. Please see our publication for more details on the data collection and conversion pipeline.
#### Who are the source language producers?
The dataset is extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/).
### Annotations
#### Annotation process
Manual annotation was only carried out for the [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low),
[UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium), and [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high) data subsets to rate task quality. Detailed instructions of the annotation instructions can be found in our publication.
#### Who are the annotators?
Annotations were carried out by a lab assistant.
### Personal and Sensitive Information
The data was extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/), which in turn extracted tables from the [Common Crawl](https://commoncrawl.org/). We did not filter the data in any way. Thus any user identities or otherwise sensitive information (e.g., data that reveals racial or ethnic origins, sexual orientations, religious beliefs, political opinions or union memberships, or locations; financial or health data; biometric or genetic data; forms of government identification, such as social security numbers; criminal history, etc.) might be contained in our dataset.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset is intended for use as a research resource to investigate the relationship between training data and few-shot learning. As such, it contains high- and low-quality data, as well as diverse content that may be untruthful or inappropriate. Without careful investigation, it should not be used for training models that will be deployed for use in decision-critical or user-facing situations.
### Discussion of Biases
Since our dataset contains tables that are scraped from the web, it will also contain many toxic, racist, sexist, and otherwise harmful biases and texts. We have not run any analysis on the biases prevalent in our datasets. Neither have we explicitly filtered the content. This implies that a model trained on our dataset may potentially reflect harmful biases and toxic text that exist in our dataset.
### Other Known Limitations
No additional known limitations.
## Additional Information
### Dataset Curators
Jun Shern Chan, Michael Pieler, Jonathan Jao, Jérémy Scheurer, Ethan Perez
### Licensing Information
Apache 2.0
### Citation Information
```
@misc{chan2022few,
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
title = {Few-shot Adaptation Works with UnpredicTable Data},
publisher={arXiv},
year = {2022},
url = {https://arxiv.org/abs/2208.01009}
}
```
|
MicPie/unpredictable_cappex-com | 2022-08-04T19:41:09.000Z | [
"task_categories:multiple-choice",
"task_categories:question-answering",
"task_categories:zero-shot-classification",
"task_categories:text2text-generation",
"task_categories:table-question-answering",
"task_categories:text-generation",
"task_categories:text-classification",
"task_categories:tabular-cl... | MicPie | The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance. For more details please see the accompanying dataset card. | @misc{chan2022few,
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
title = {Few-shot Adaptation Works with UnpredicTable Data},
publisher={arXiv},
year = {2022},
url = {https://arxiv.org/abs/2208.01009}
} | null | 0 | 14 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: UnpredicTable-cappex.com
size_categories:
- 100K<n<1M
source_datasets: []
task_categories:
- multiple-choice
- question-answering
- zero-shot-classification
- text2text-generation
- table-question-answering
- text-generation
- text-classification
- tabular-classification
task_ids:
- multiple-choice-qa
- extractive-qa
- open-domain-qa
- closed-domain-qa
- closed-book-qa
- open-book-qa
- language-modeling
- multi-class-classification
- natural-language-inference
- topic-classification
- multi-label-classification
- tabular-multi-class-classification
- tabular-multi-label-classification
---
# Dataset Card for "UnpredicTable-cappex.com" - Dataset of Few-shot Tasks from Tables
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [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)
## Dataset Description
- **Homepage:** https://ethanperez.net/unpredictable
- **Repository:** https://github.com/JunShern/few-shot-adaptation
- **Paper:** Few-shot Adaptation Works with UnpredicTable Data
- **Point of Contact:** junshern@nyu.edu, perez@nyu.edu
### Dataset Summary
The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance.
There are several dataset versions available:
* [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full): Starting from the initial WTC corpus of 50M tables, we apply our tables-to-tasks procedure to produce our resulting dataset, [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full), which comprises 413,299 tasks from 23,744 unique websites.
* [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique): This is the same as [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full) but filtered to have a maximum of one task per website. [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique) contains exactly 23,744 tasks from 23,744 websites.
* [UnpredicTable-5k](https://huggingface.co/datasets/MicPie/unpredictable_5k): This dataset contains 5k random tables from the full dataset.
* UnpredicTable data subsets based on a manual human quality rating (please see our publication for details of the ratings):
* [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low)
* [UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium)
* [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high)
* UnpredicTable data subsets based on the website of origin:
* [UnpredicTable-baseball-fantasysports-yahoo-com](https://huggingface.co/datasets/MicPie/unpredictable_baseball-fantasysports-yahoo-com)
* [UnpredicTable-bulbapedia-bulbagarden-net](https://huggingface.co/datasets/MicPie/unpredictable_bulbapedia-bulbagarden-net)
* [UnpredicTable-cappex-com](https://huggingface.co/datasets/MicPie/unpredictable_cappex-com)
* [UnpredicTable-cram-com](https://huggingface.co/datasets/MicPie/unpredictable_cram-com)
* [UnpredicTable-dividend-com](https://huggingface.co/datasets/MicPie/unpredictable_dividend-com)
* [UnpredicTable-dummies-com](https://huggingface.co/datasets/MicPie/unpredictable_dummies-com)
* [UnpredicTable-en-wikipedia-org](https://huggingface.co/datasets/MicPie/unpredictable_en-wikipedia-org)
* [UnpredicTable-ensembl-org](https://huggingface.co/datasets/MicPie/unpredictable_ensembl-org)
* [UnpredicTable-gamefaqs-com](https://huggingface.co/datasets/MicPie/unpredictable_gamefaqs-com)
* [UnpredicTable-mgoblog-com](https://huggingface.co/datasets/MicPie/unpredictable_mgoblog-com)
* [UnpredicTable-mmo-champion-com](https://huggingface.co/datasets/MicPie/unpredictable_mmo-champion-com)
* [UnpredicTable-msdn-microsoft-com](https://huggingface.co/datasets/MicPie/unpredictable_msdn-microsoft-com)
* [UnpredicTable-phonearena-com](https://huggingface.co/datasets/MicPie/unpredictable_phonearena-com)
* [UnpredicTable-sittercity-com](https://huggingface.co/datasets/MicPie/unpredictable_sittercity-com)
* [UnpredicTable-sporcle-com](https://huggingface.co/datasets/MicPie/unpredictable_sporcle-com)
* [UnpredicTable-studystack-com](https://huggingface.co/datasets/MicPie/unpredictable_studystack-com)
* [UnpredicTable-support-google-com](https://huggingface.co/datasets/MicPie/unpredictable_support-google-com)
* [UnpredicTable-w3-org](https://huggingface.co/datasets/MicPie/unpredictable_w3-org)
* [UnpredicTable-wiki-openmoko-org](https://huggingface.co/datasets/MicPie/unpredictable_wiki-openmoko-org)
* [UnpredicTable-wkdu-org](https://huggingface.co/datasets/MicPie/unpredictable_wkdu-org)
* UnpredicTable data subsets based on clustering (for the clustering details please see our publication):
* [UnpredicTable-cluster00](https://huggingface.co/datasets/MicPie/unpredictable_cluster00)
* [UnpredicTable-cluster01](https://huggingface.co/datasets/MicPie/unpredictable_cluster01)
* [UnpredicTable-cluster02](https://huggingface.co/datasets/MicPie/unpredictable_cluster02)
* [UnpredicTable-cluster03](https://huggingface.co/datasets/MicPie/unpredictable_cluster03)
* [UnpredicTable-cluster04](https://huggingface.co/datasets/MicPie/unpredictable_cluster04)
* [UnpredicTable-cluster05](https://huggingface.co/datasets/MicPie/unpredictable_cluster05)
* [UnpredicTable-cluster06](https://huggingface.co/datasets/MicPie/unpredictable_cluster06)
* [UnpredicTable-cluster07](https://huggingface.co/datasets/MicPie/unpredictable_cluster07)
* [UnpredicTable-cluster08](https://huggingface.co/datasets/MicPie/unpredictable_cluster08)
* [UnpredicTable-cluster09](https://huggingface.co/datasets/MicPie/unpredictable_cluster09)
* [UnpredicTable-cluster10](https://huggingface.co/datasets/MicPie/unpredictable_cluster10)
* [UnpredicTable-cluster11](https://huggingface.co/datasets/MicPie/unpredictable_cluster11)
* [UnpredicTable-cluster12](https://huggingface.co/datasets/MicPie/unpredictable_cluster12)
* [UnpredicTable-cluster13](https://huggingface.co/datasets/MicPie/unpredictable_cluster13)
* [UnpredicTable-cluster14](https://huggingface.co/datasets/MicPie/unpredictable_cluster14)
* [UnpredicTable-cluster15](https://huggingface.co/datasets/MicPie/unpredictable_cluster15)
* [UnpredicTable-cluster16](https://huggingface.co/datasets/MicPie/unpredictable_cluster16)
* [UnpredicTable-cluster17](https://huggingface.co/datasets/MicPie/unpredictable_cluster17)
* [UnpredicTable-cluster18](https://huggingface.co/datasets/MicPie/unpredictable_cluster18)
* [UnpredicTable-cluster19](https://huggingface.co/datasets/MicPie/unpredictable_cluster19)
* [UnpredicTable-cluster20](https://huggingface.co/datasets/MicPie/unpredictable_cluster20)
* [UnpredicTable-cluster21](https://huggingface.co/datasets/MicPie/unpredictable_cluster21)
* [UnpredicTable-cluster22](https://huggingface.co/datasets/MicPie/unpredictable_cluster22)
* [UnpredicTable-cluster23](https://huggingface.co/datasets/MicPie/unpredictable_cluster23)
* [UnpredicTable-cluster24](https://huggingface.co/datasets/MicPie/unpredictable_cluster24)
* [UnpredicTable-cluster25](https://huggingface.co/datasets/MicPie/unpredictable_cluster25)
* [UnpredicTable-cluster26](https://huggingface.co/datasets/MicPie/unpredictable_cluster26)
* [UnpredicTable-cluster27](https://huggingface.co/datasets/MicPie/unpredictable_cluster27)
* [UnpredicTable-cluster28](https://huggingface.co/datasets/MicPie/unpredictable_cluster28)
* [UnpredicTable-cluster29](https://huggingface.co/datasets/MicPie/unpredictable_cluster29)
* [UnpredicTable-cluster-noise](https://huggingface.co/datasets/MicPie/unpredictable_cluster-noise)
### Supported Tasks and Leaderboards
Since the tables come from the web, the distribution of tasks and topics is very broad. The shape of our dataset is very wide, i.e., we have 1000's of tasks, while each task has only a few examples, compared to most current NLP datasets which are very deep, i.e., 10s of tasks with many examples. This implies that our dataset covers a broad range of potential tasks, e.g., multiple-choice, question-answering, table-question-answering, text-classification, etc.
The intended use of this dataset is to improve few-shot performance by fine-tuning/pre-training on our dataset.
### Languages
English
## Dataset Structure
### Data Instances
Each task is represented as a jsonline file and consists of several few-shot examples. Each example is a dictionary containing a field 'task', which identifies the task, followed by an 'input', 'options', and 'output' field. The 'input' field contains several column elements of the same row in the table, while the 'output' field is a target which represents an individual column of the same row. Each task contains several such examples which can be concatenated as a few-shot task. In the case of multiple choice classification, the 'options' field contains the possible classes that a model needs to choose from.
There are also additional meta-data fields such as 'pageTitle', 'title', 'outputColName', 'url', 'wdcFile'.
### Data Fields
'task': task identifier
'input': column elements of a specific row in the table.
'options': for multiple choice classification, it provides the options to choose from.
'output': target column element of the same row as input.
'pageTitle': the title of the page containing the table.
'outputColName': output column name
'url': url to the website containing the table
'wdcFile': WDC Web Table Corpus file
### Data Splits
The UnpredicTable datasets do not come with additional data splits.
## Dataset Creation
### Curation Rationale
Few-shot training on multi-task datasets has been demonstrated to improve language models' few-shot learning (FSL) performance on new tasks, but it is unclear which training tasks lead to effective downstream task adaptation. Few-shot learning datasets are typically produced with expensive human curation, limiting the scale and diversity of the training tasks available to study. As an alternative source of few-shot data, we automatically extract 413,299 tasks from diverse internet tables. We provide this as a research resource to investigate the relationship between training data and few-shot learning.
### Source Data
#### Initial Data Collection and Normalization
We use internet tables from the English-language Relational Subset of the WDC Web Table Corpus 2015 (WTC). The WTC dataset tables were extracted from the July 2015 Common Crawl web corpus (http://webdatacommons.org/webtables/2015/EnglishStatistics.html). The dataset contains 50,820,165 tables from 323,160 web domains. We then convert the tables into few-shot learning tasks. Please see our publication for more details on the data collection and conversion pipeline.
#### Who are the source language producers?
The dataset is extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/).
### Annotations
#### Annotation process
Manual annotation was only carried out for the [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low),
[UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium), and [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high) data subsets to rate task quality. Detailed instructions of the annotation instructions can be found in our publication.
#### Who are the annotators?
Annotations were carried out by a lab assistant.
### Personal and Sensitive Information
The data was extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/), which in turn extracted tables from the [Common Crawl](https://commoncrawl.org/). We did not filter the data in any way. Thus any user identities or otherwise sensitive information (e.g., data that reveals racial or ethnic origins, sexual orientations, religious beliefs, political opinions or union memberships, or locations; financial or health data; biometric or genetic data; forms of government identification, such as social security numbers; criminal history, etc.) might be contained in our dataset.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset is intended for use as a research resource to investigate the relationship between training data and few-shot learning. As such, it contains high- and low-quality data, as well as diverse content that may be untruthful or inappropriate. Without careful investigation, it should not be used for training models that will be deployed for use in decision-critical or user-facing situations.
### Discussion of Biases
Since our dataset contains tables that are scraped from the web, it will also contain many toxic, racist, sexist, and otherwise harmful biases and texts. We have not run any analysis on the biases prevalent in our datasets. Neither have we explicitly filtered the content. This implies that a model trained on our dataset may potentially reflect harmful biases and toxic text that exist in our dataset.
### Other Known Limitations
No additional known limitations.
## Additional Information
### Dataset Curators
Jun Shern Chan, Michael Pieler, Jonathan Jao, Jérémy Scheurer, Ethan Perez
### Licensing Information
Apache 2.0
### Citation Information
```
@misc{chan2022few,
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
title = {Few-shot Adaptation Works with UnpredicTable Data},
publisher={arXiv},
year = {2022},
url = {https://arxiv.org/abs/2208.01009}
}
```
|
MicPie/unpredictable_en-wikipedia-org | 2022-08-04T20:05:44.000Z | [
"task_categories:multiple-choice",
"task_categories:question-answering",
"task_categories:zero-shot-classification",
"task_categories:text2text-generation",
"task_categories:table-question-answering",
"task_categories:text-generation",
"task_categories:text-classification",
"task_categories:tabular-cl... | MicPie | The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance. For more details please see the accompanying dataset card. | @misc{chan2022few,
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
title = {Few-shot Adaptation Works with UnpredicTable Data},
publisher={arXiv},
year = {2022},
url = {https://arxiv.org/abs/2208.01009}
} | null | 1 | 14 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: UnpredicTable-en-wikipedia-org
size_categories:
- 100K<n<1M
source_datasets: []
task_categories:
- multiple-choice
- question-answering
- zero-shot-classification
- text2text-generation
- table-question-answering
- text-generation
- text-classification
- tabular-classification
task_ids:
- multiple-choice-qa
- extractive-qa
- open-domain-qa
- closed-domain-qa
- closed-book-qa
- open-book-qa
- language-modeling
- multi-class-classification
- natural-language-inference
- topic-classification
- multi-label-classification
- tabular-multi-class-classification
- tabular-multi-label-classification
---
# Dataset Card for "UnpredicTable-en-wikipedia-org" - Dataset of Few-shot Tasks from Tables
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [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)
## Dataset Description
- **Homepage:** https://ethanperez.net/unpredictable
- **Repository:** https://github.com/JunShern/few-shot-adaptation
- **Paper:** Few-shot Adaptation Works with UnpredicTable Data
- **Point of Contact:** junshern@nyu.edu, perez@nyu.edu
### Dataset Summary
The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance.
There are several dataset versions available:
* [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full): Starting from the initial WTC corpus of 50M tables, we apply our tables-to-tasks procedure to produce our resulting dataset, [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full), which comprises 413,299 tasks from 23,744 unique websites.
* [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique): This is the same as [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full) but filtered to have a maximum of one task per website. [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique) contains exactly 23,744 tasks from 23,744 websites.
* [UnpredicTable-5k](https://huggingface.co/datasets/MicPie/unpredictable_5k): This dataset contains 5k random tables from the full dataset.
* UnpredicTable data subsets based on a manual human quality rating (please see our publication for details of the ratings):
* [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low)
* [UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium)
* [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high)
* UnpredicTable data subsets based on the website of origin:
* [UnpredicTable-baseball-fantasysports-yahoo-com](https://huggingface.co/datasets/MicPie/unpredictable_baseball-fantasysports-yahoo-com)
* [UnpredicTable-bulbapedia-bulbagarden-net](https://huggingface.co/datasets/MicPie/unpredictable_bulbapedia-bulbagarden-net)
* [UnpredicTable-cappex-com](https://huggingface.co/datasets/MicPie/unpredictable_cappex-com)
* [UnpredicTable-cram-com](https://huggingface.co/datasets/MicPie/unpredictable_cram-com)
* [UnpredicTable-dividend-com](https://huggingface.co/datasets/MicPie/unpredictable_dividend-com)
* [UnpredicTable-dummies-com](https://huggingface.co/datasets/MicPie/unpredictable_dummies-com)
* [UnpredicTable-en-wikipedia-org](https://huggingface.co/datasets/MicPie/unpredictable_en-wikipedia-org)
* [UnpredicTable-ensembl-org](https://huggingface.co/datasets/MicPie/unpredictable_ensembl-org)
* [UnpredicTable-gamefaqs-com](https://huggingface.co/datasets/MicPie/unpredictable_gamefaqs-com)
* [UnpredicTable-mgoblog-com](https://huggingface.co/datasets/MicPie/unpredictable_mgoblog-com)
* [UnpredicTable-mmo-champion-com](https://huggingface.co/datasets/MicPie/unpredictable_mmo-champion-com)
* [UnpredicTable-msdn-microsoft-com](https://huggingface.co/datasets/MicPie/unpredictable_msdn-microsoft-com)
* [UnpredicTable-phonearena-com](https://huggingface.co/datasets/MicPie/unpredictable_phonearena-com)
* [UnpredicTable-sittercity-com](https://huggingface.co/datasets/MicPie/unpredictable_sittercity-com)
* [UnpredicTable-sporcle-com](https://huggingface.co/datasets/MicPie/unpredictable_sporcle-com)
* [UnpredicTable-studystack-com](https://huggingface.co/datasets/MicPie/unpredictable_studystack-com)
* [UnpredicTable-support-google-com](https://huggingface.co/datasets/MicPie/unpredictable_support-google-com)
* [UnpredicTable-w3-org](https://huggingface.co/datasets/MicPie/unpredictable_w3-org)
* [UnpredicTable-wiki-openmoko-org](https://huggingface.co/datasets/MicPie/unpredictable_wiki-openmoko-org)
* [UnpredicTable-wkdu-org](https://huggingface.co/datasets/MicPie/unpredictable_wkdu-org)
* UnpredicTable data subsets based on clustering (for the clustering details please see our publication):
* [UnpredicTable-cluster00](https://huggingface.co/datasets/MicPie/unpredictable_cluster00)
* [UnpredicTable-cluster01](https://huggingface.co/datasets/MicPie/unpredictable_cluster01)
* [UnpredicTable-cluster02](https://huggingface.co/datasets/MicPie/unpredictable_cluster02)
* [UnpredicTable-cluster03](https://huggingface.co/datasets/MicPie/unpredictable_cluster03)
* [UnpredicTable-cluster04](https://huggingface.co/datasets/MicPie/unpredictable_cluster04)
* [UnpredicTable-cluster05](https://huggingface.co/datasets/MicPie/unpredictable_cluster05)
* [UnpredicTable-cluster06](https://huggingface.co/datasets/MicPie/unpredictable_cluster06)
* [UnpredicTable-cluster07](https://huggingface.co/datasets/MicPie/unpredictable_cluster07)
* [UnpredicTable-cluster08](https://huggingface.co/datasets/MicPie/unpredictable_cluster08)
* [UnpredicTable-cluster09](https://huggingface.co/datasets/MicPie/unpredictable_cluster09)
* [UnpredicTable-cluster10](https://huggingface.co/datasets/MicPie/unpredictable_cluster10)
* [UnpredicTable-cluster11](https://huggingface.co/datasets/MicPie/unpredictable_cluster11)
* [UnpredicTable-cluster12](https://huggingface.co/datasets/MicPie/unpredictable_cluster12)
* [UnpredicTable-cluster13](https://huggingface.co/datasets/MicPie/unpredictable_cluster13)
* [UnpredicTable-cluster14](https://huggingface.co/datasets/MicPie/unpredictable_cluster14)
* [UnpredicTable-cluster15](https://huggingface.co/datasets/MicPie/unpredictable_cluster15)
* [UnpredicTable-cluster16](https://huggingface.co/datasets/MicPie/unpredictable_cluster16)
* [UnpredicTable-cluster17](https://huggingface.co/datasets/MicPie/unpredictable_cluster17)
* [UnpredicTable-cluster18](https://huggingface.co/datasets/MicPie/unpredictable_cluster18)
* [UnpredicTable-cluster19](https://huggingface.co/datasets/MicPie/unpredictable_cluster19)
* [UnpredicTable-cluster20](https://huggingface.co/datasets/MicPie/unpredictable_cluster20)
* [UnpredicTable-cluster21](https://huggingface.co/datasets/MicPie/unpredictable_cluster21)
* [UnpredicTable-cluster22](https://huggingface.co/datasets/MicPie/unpredictable_cluster22)
* [UnpredicTable-cluster23](https://huggingface.co/datasets/MicPie/unpredictable_cluster23)
* [UnpredicTable-cluster24](https://huggingface.co/datasets/MicPie/unpredictable_cluster24)
* [UnpredicTable-cluster25](https://huggingface.co/datasets/MicPie/unpredictable_cluster25)
* [UnpredicTable-cluster26](https://huggingface.co/datasets/MicPie/unpredictable_cluster26)
* [UnpredicTable-cluster27](https://huggingface.co/datasets/MicPie/unpredictable_cluster27)
* [UnpredicTable-cluster28](https://huggingface.co/datasets/MicPie/unpredictable_cluster28)
* [UnpredicTable-cluster29](https://huggingface.co/datasets/MicPie/unpredictable_cluster29)
* [UnpredicTable-cluster-noise](https://huggingface.co/datasets/MicPie/unpredictable_cluster-noise)
### Supported Tasks and Leaderboards
Since the tables come from the web, the distribution of tasks and topics is very broad. The shape of our dataset is very wide, i.e., we have 1000's of tasks, while each task has only a few examples, compared to most current NLP datasets which are very deep, i.e., 10s of tasks with many examples. This implies that our dataset covers a broad range of potential tasks, e.g., multiple-choice, question-answering, table-question-answering, text-classification, etc.
The intended use of this dataset is to improve few-shot performance by fine-tuning/pre-training on our dataset.
### Languages
English
## Dataset Structure
### Data Instances
Each task is represented as a jsonline file and consists of several few-shot examples. Each example is a dictionary containing a field 'task', which identifies the task, followed by an 'input', 'options', and 'output' field. The 'input' field contains several column elements of the same row in the table, while the 'output' field is a target which represents an individual column of the same row. Each task contains several such examples which can be concatenated as a few-shot task. In the case of multiple choice classification, the 'options' field contains the possible classes that a model needs to choose from.
There are also additional meta-data fields such as 'pageTitle', 'title', 'outputColName', 'url', 'wdcFile'.
### Data Fields
'task': task identifier
'input': column elements of a specific row in the table.
'options': for multiple choice classification, it provides the options to choose from.
'output': target column element of the same row as input.
'pageTitle': the title of the page containing the table.
'outputColName': output column name
'url': url to the website containing the table
'wdcFile': WDC Web Table Corpus file
### Data Splits
The UnpredicTable datasets do not come with additional data splits.
## Dataset Creation
### Curation Rationale
Few-shot training on multi-task datasets has been demonstrated to improve language models' few-shot learning (FSL) performance on new tasks, but it is unclear which training tasks lead to effective downstream task adaptation. Few-shot learning datasets are typically produced with expensive human curation, limiting the scale and diversity of the training tasks available to study. As an alternative source of few-shot data, we automatically extract 413,299 tasks from diverse internet tables. We provide this as a research resource to investigate the relationship between training data and few-shot learning.
### Source Data
#### Initial Data Collection and Normalization
We use internet tables from the English-language Relational Subset of the WDC Web Table Corpus 2015 (WTC). The WTC dataset tables were extracted from the July 2015 Common Crawl web corpus (http://webdatacommons.org/webtables/2015/EnglishStatistics.html). The dataset contains 50,820,165 tables from 323,160 web domains. We then convert the tables into few-shot learning tasks. Please see our publication for more details on the data collection and conversion pipeline.
#### Who are the source language producers?
The dataset is extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/).
### Annotations
#### Annotation process
Manual annotation was only carried out for the [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low),
[UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium), and [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high) data subsets to rate task quality. Detailed instructions of the annotation instructions can be found in our publication.
#### Who are the annotators?
Annotations were carried out by a lab assistant.
### Personal and Sensitive Information
The data was extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/), which in turn extracted tables from the [Common Crawl](https://commoncrawl.org/). We did not filter the data in any way. Thus any user identities or otherwise sensitive information (e.g., data that reveals racial or ethnic origins, sexual orientations, religious beliefs, political opinions or union memberships, or locations; financial or health data; biometric or genetic data; forms of government identification, such as social security numbers; criminal history, etc.) might be contained in our dataset.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset is intended for use as a research resource to investigate the relationship between training data and few-shot learning. As such, it contains high- and low-quality data, as well as diverse content that may be untruthful or inappropriate. Without careful investigation, it should not be used for training models that will be deployed for use in decision-critical or user-facing situations.
### Discussion of Biases
Since our dataset contains tables that are scraped from the web, it will also contain many toxic, racist, sexist, and otherwise harmful biases and texts. We have not run any analysis on the biases prevalent in our datasets. Neither have we explicitly filtered the content. This implies that a model trained on our dataset may potentially reflect harmful biases and toxic text that exist in our dataset.
### Other Known Limitations
No additional known limitations.
## Additional Information
### Dataset Curators
Jun Shern Chan, Michael Pieler, Jonathan Jao, Jérémy Scheurer, Ethan Perez
### Licensing Information
Apache 2.0
### Citation Information
```
@misc{chan2022few,
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
title = {Few-shot Adaptation Works with UnpredicTable Data},
publisher={arXiv},
year = {2022},
url = {https://arxiv.org/abs/2208.01009}
}
```
|
MicPie/unpredictable_cluster28 | 2022-08-04T20:01:54.000Z | [
"task_categories:multiple-choice",
"task_categories:question-answering",
"task_categories:zero-shot-classification",
"task_categories:text2text-generation",
"task_categories:table-question-answering",
"task_categories:text-generation",
"task_categories:text-classification",
"task_categories:tabular-cl... | MicPie | The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance. For more details please see the accompanying dataset card. | @misc{chan2022few,
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
title = {Few-shot Adaptation Works with UnpredicTable Data},
publisher={arXiv},
year = {2022},
url = {https://arxiv.org/abs/2208.01009}
} | null | 0 | 14 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: UnpredicTable-cluster28
size_categories:
- 100K<n<1M
source_datasets: []
task_categories:
- multiple-choice
- question-answering
- zero-shot-classification
- text2text-generation
- table-question-answering
- text-generation
- text-classification
- tabular-classification
task_ids:
- multiple-choice-qa
- extractive-qa
- open-domain-qa
- closed-domain-qa
- closed-book-qa
- open-book-qa
- language-modeling
- multi-class-classification
- natural-language-inference
- topic-classification
- multi-label-classification
- tabular-multi-class-classification
- tabular-multi-label-classification
---
# Dataset Card for "UnpredicTable-cluster28" - Dataset of Few-shot Tasks from Tables
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [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)
## Dataset Description
- **Homepage:** https://ethanperez.net/unpredictable
- **Repository:** https://github.com/JunShern/few-shot-adaptation
- **Paper:** Few-shot Adaptation Works with UnpredicTable Data
- **Point of Contact:** junshern@nyu.edu, perez@nyu.edu
### Dataset Summary
The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance.
There are several dataset versions available:
* [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full): Starting from the initial WTC corpus of 50M tables, we apply our tables-to-tasks procedure to produce our resulting dataset, [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full), which comprises 413,299 tasks from 23,744 unique websites.
* [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique): This is the same as [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full) but filtered to have a maximum of one task per website. [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique) contains exactly 23,744 tasks from 23,744 websites.
* [UnpredicTable-5k](https://huggingface.co/datasets/MicPie/unpredictable_5k): This dataset contains 5k random tables from the full dataset.
* UnpredicTable data subsets based on a manual human quality rating (please see our publication for details of the ratings):
* [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low)
* [UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium)
* [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high)
* UnpredicTable data subsets based on the website of origin:
* [UnpredicTable-baseball-fantasysports-yahoo-com](https://huggingface.co/datasets/MicPie/unpredictable_baseball-fantasysports-yahoo-com)
* [UnpredicTable-bulbapedia-bulbagarden-net](https://huggingface.co/datasets/MicPie/unpredictable_bulbapedia-bulbagarden-net)
* [UnpredicTable-cappex-com](https://huggingface.co/datasets/MicPie/unpredictable_cappex-com)
* [UnpredicTable-cram-com](https://huggingface.co/datasets/MicPie/unpredictable_cram-com)
* [UnpredicTable-dividend-com](https://huggingface.co/datasets/MicPie/unpredictable_dividend-com)
* [UnpredicTable-dummies-com](https://huggingface.co/datasets/MicPie/unpredictable_dummies-com)
* [UnpredicTable-en-wikipedia-org](https://huggingface.co/datasets/MicPie/unpredictable_en-wikipedia-org)
* [UnpredicTable-ensembl-org](https://huggingface.co/datasets/MicPie/unpredictable_ensembl-org)
* [UnpredicTable-gamefaqs-com](https://huggingface.co/datasets/MicPie/unpredictable_gamefaqs-com)
* [UnpredicTable-mgoblog-com](https://huggingface.co/datasets/MicPie/unpredictable_mgoblog-com)
* [UnpredicTable-mmo-champion-com](https://huggingface.co/datasets/MicPie/unpredictable_mmo-champion-com)
* [UnpredicTable-msdn-microsoft-com](https://huggingface.co/datasets/MicPie/unpredictable_msdn-microsoft-com)
* [UnpredicTable-phonearena-com](https://huggingface.co/datasets/MicPie/unpredictable_phonearena-com)
* [UnpredicTable-sittercity-com](https://huggingface.co/datasets/MicPie/unpredictable_sittercity-com)
* [UnpredicTable-sporcle-com](https://huggingface.co/datasets/MicPie/unpredictable_sporcle-com)
* [UnpredicTable-studystack-com](https://huggingface.co/datasets/MicPie/unpredictable_studystack-com)
* [UnpredicTable-support-google-com](https://huggingface.co/datasets/MicPie/unpredictable_support-google-com)
* [UnpredicTable-w3-org](https://huggingface.co/datasets/MicPie/unpredictable_w3-org)
* [UnpredicTable-wiki-openmoko-org](https://huggingface.co/datasets/MicPie/unpredictable_wiki-openmoko-org)
* [UnpredicTable-wkdu-org](https://huggingface.co/datasets/MicPie/unpredictable_wkdu-org)
* UnpredicTable data subsets based on clustering (for the clustering details please see our publication):
* [UnpredicTable-cluster00](https://huggingface.co/datasets/MicPie/unpredictable_cluster00)
* [UnpredicTable-cluster01](https://huggingface.co/datasets/MicPie/unpredictable_cluster01)
* [UnpredicTable-cluster02](https://huggingface.co/datasets/MicPie/unpredictable_cluster02)
* [UnpredicTable-cluster03](https://huggingface.co/datasets/MicPie/unpredictable_cluster03)
* [UnpredicTable-cluster04](https://huggingface.co/datasets/MicPie/unpredictable_cluster04)
* [UnpredicTable-cluster05](https://huggingface.co/datasets/MicPie/unpredictable_cluster05)
* [UnpredicTable-cluster06](https://huggingface.co/datasets/MicPie/unpredictable_cluster06)
* [UnpredicTable-cluster07](https://huggingface.co/datasets/MicPie/unpredictable_cluster07)
* [UnpredicTable-cluster08](https://huggingface.co/datasets/MicPie/unpredictable_cluster08)
* [UnpredicTable-cluster09](https://huggingface.co/datasets/MicPie/unpredictable_cluster09)
* [UnpredicTable-cluster10](https://huggingface.co/datasets/MicPie/unpredictable_cluster10)
* [UnpredicTable-cluster11](https://huggingface.co/datasets/MicPie/unpredictable_cluster11)
* [UnpredicTable-cluster12](https://huggingface.co/datasets/MicPie/unpredictable_cluster12)
* [UnpredicTable-cluster13](https://huggingface.co/datasets/MicPie/unpredictable_cluster13)
* [UnpredicTable-cluster14](https://huggingface.co/datasets/MicPie/unpredictable_cluster14)
* [UnpredicTable-cluster15](https://huggingface.co/datasets/MicPie/unpredictable_cluster15)
* [UnpredicTable-cluster16](https://huggingface.co/datasets/MicPie/unpredictable_cluster16)
* [UnpredicTable-cluster17](https://huggingface.co/datasets/MicPie/unpredictable_cluster17)
* [UnpredicTable-cluster18](https://huggingface.co/datasets/MicPie/unpredictable_cluster18)
* [UnpredicTable-cluster19](https://huggingface.co/datasets/MicPie/unpredictable_cluster19)
* [UnpredicTable-cluster20](https://huggingface.co/datasets/MicPie/unpredictable_cluster20)
* [UnpredicTable-cluster21](https://huggingface.co/datasets/MicPie/unpredictable_cluster21)
* [UnpredicTable-cluster22](https://huggingface.co/datasets/MicPie/unpredictable_cluster22)
* [UnpredicTable-cluster23](https://huggingface.co/datasets/MicPie/unpredictable_cluster23)
* [UnpredicTable-cluster24](https://huggingface.co/datasets/MicPie/unpredictable_cluster24)
* [UnpredicTable-cluster25](https://huggingface.co/datasets/MicPie/unpredictable_cluster25)
* [UnpredicTable-cluster26](https://huggingface.co/datasets/MicPie/unpredictable_cluster26)
* [UnpredicTable-cluster27](https://huggingface.co/datasets/MicPie/unpredictable_cluster27)
* [UnpredicTable-cluster28](https://huggingface.co/datasets/MicPie/unpredictable_cluster28)
* [UnpredicTable-cluster29](https://huggingface.co/datasets/MicPie/unpredictable_cluster29)
* [UnpredicTable-cluster-noise](https://huggingface.co/datasets/MicPie/unpredictable_cluster-noise)
### Supported Tasks and Leaderboards
Since the tables come from the web, the distribution of tasks and topics is very broad. The shape of our dataset is very wide, i.e., we have 1000's of tasks, while each task has only a few examples, compared to most current NLP datasets which are very deep, i.e., 10s of tasks with many examples. This implies that our dataset covers a broad range of potential tasks, e.g., multiple-choice, question-answering, table-question-answering, text-classification, etc.
The intended use of this dataset is to improve few-shot performance by fine-tuning/pre-training on our dataset.
### Languages
English
## Dataset Structure
### Data Instances
Each task is represented as a jsonline file and consists of several few-shot examples. Each example is a dictionary containing a field 'task', which identifies the task, followed by an 'input', 'options', and 'output' field. The 'input' field contains several column elements of the same row in the table, while the 'output' field is a target which represents an individual column of the same row. Each task contains several such examples which can be concatenated as a few-shot task. In the case of multiple choice classification, the 'options' field contains the possible classes that a model needs to choose from.
There are also additional meta-data fields such as 'pageTitle', 'title', 'outputColName', 'url', 'wdcFile'.
### Data Fields
'task': task identifier
'input': column elements of a specific row in the table.
'options': for multiple choice classification, it provides the options to choose from.
'output': target column element of the same row as input.
'pageTitle': the title of the page containing the table.
'outputColName': output column name
'url': url to the website containing the table
'wdcFile': WDC Web Table Corpus file
### Data Splits
The UnpredicTable datasets do not come with additional data splits.
## Dataset Creation
### Curation Rationale
Few-shot training on multi-task datasets has been demonstrated to improve language models' few-shot learning (FSL) performance on new tasks, but it is unclear which training tasks lead to effective downstream task adaptation. Few-shot learning datasets are typically produced with expensive human curation, limiting the scale and diversity of the training tasks available to study. As an alternative source of few-shot data, we automatically extract 413,299 tasks from diverse internet tables. We provide this as a research resource to investigate the relationship between training data and few-shot learning.
### Source Data
#### Initial Data Collection and Normalization
We use internet tables from the English-language Relational Subset of the WDC Web Table Corpus 2015 (WTC). The WTC dataset tables were extracted from the July 2015 Common Crawl web corpus (http://webdatacommons.org/webtables/2015/EnglishStatistics.html). The dataset contains 50,820,165 tables from 323,160 web domains. We then convert the tables into few-shot learning tasks. Please see our publication for more details on the data collection and conversion pipeline.
#### Who are the source language producers?
The dataset is extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/).
### Annotations
#### Annotation process
Manual annotation was only carried out for the [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low),
[UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium), and [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high) data subsets to rate task quality. Detailed instructions of the annotation instructions can be found in our publication.
#### Who are the annotators?
Annotations were carried out by a lab assistant.
### Personal and Sensitive Information
The data was extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/), which in turn extracted tables from the [Common Crawl](https://commoncrawl.org/). We did not filter the data in any way. Thus any user identities or otherwise sensitive information (e.g., data that reveals racial or ethnic origins, sexual orientations, religious beliefs, political opinions or union memberships, or locations; financial or health data; biometric or genetic data; forms of government identification, such as social security numbers; criminal history, etc.) might be contained in our dataset.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset is intended for use as a research resource to investigate the relationship between training data and few-shot learning. As such, it contains high- and low-quality data, as well as diverse content that may be untruthful or inappropriate. Without careful investigation, it should not be used for training models that will be deployed for use in decision-critical or user-facing situations.
### Discussion of Biases
Since our dataset contains tables that are scraped from the web, it will also contain many toxic, racist, sexist, and otherwise harmful biases and texts. We have not run any analysis on the biases prevalent in our datasets. Neither have we explicitly filtered the content. This implies that a model trained on our dataset may potentially reflect harmful biases and toxic text that exist in our dataset.
### Other Known Limitations
No additional known limitations.
## Additional Information
### Dataset Curators
Jun Shern Chan, Michael Pieler, Jonathan Jao, Jérémy Scheurer, Ethan Perez
### Licensing Information
Apache 2.0
### Citation Information
```
@misc{chan2022few,
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
title = {Few-shot Adaptation Works with UnpredicTable Data},
publisher={arXiv},
year = {2022},
url = {https://arxiv.org/abs/2208.01009}
}
```
|
MicPie/unpredictable_cluster09 | 2022-08-04T19:48:52.000Z | [
"task_categories:multiple-choice",
"task_categories:question-answering",
"task_categories:zero-shot-classification",
"task_categories:text2text-generation",
"task_categories:table-question-answering",
"task_categories:text-generation",
"task_categories:text-classification",
"task_categories:tabular-cl... | MicPie | The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance. For more details please see the accompanying dataset card. | @misc{chan2022few,
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
title = {Few-shot Adaptation Works with UnpredicTable Data},
publisher={arXiv},
year = {2022},
url = {https://arxiv.org/abs/2208.01009}
} | null | 0 | 14 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: UnpredicTable-cluster09
size_categories:
- 100K<n<1M
source_datasets: []
task_categories:
- multiple-choice
- question-answering
- zero-shot-classification
- text2text-generation
- table-question-answering
- text-generation
- text-classification
- tabular-classification
task_ids:
- multiple-choice-qa
- extractive-qa
- open-domain-qa
- closed-domain-qa
- closed-book-qa
- open-book-qa
- language-modeling
- multi-class-classification
- natural-language-inference
- topic-classification
- multi-label-classification
- tabular-multi-class-classification
- tabular-multi-label-classification
---
# Dataset Card for "UnpredicTable-cluster09" - Dataset of Few-shot Tasks from Tables
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [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)
## Dataset Description
- **Homepage:** https://ethanperez.net/unpredictable
- **Repository:** https://github.com/JunShern/few-shot-adaptation
- **Paper:** Few-shot Adaptation Works with UnpredicTable Data
- **Point of Contact:** junshern@nyu.edu, perez@nyu.edu
### Dataset Summary
The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance.
There are several dataset versions available:
* [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full): Starting from the initial WTC corpus of 50M tables, we apply our tables-to-tasks procedure to produce our resulting dataset, [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full), which comprises 413,299 tasks from 23,744 unique websites.
* [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique): This is the same as [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full) but filtered to have a maximum of one task per website. [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique) contains exactly 23,744 tasks from 23,744 websites.
* [UnpredicTable-5k](https://huggingface.co/datasets/MicPie/unpredictable_5k): This dataset contains 5k random tables from the full dataset.
* UnpredicTable data subsets based on a manual human quality rating (please see our publication for details of the ratings):
* [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low)
* [UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium)
* [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high)
* UnpredicTable data subsets based on the website of origin:
* [UnpredicTable-baseball-fantasysports-yahoo-com](https://huggingface.co/datasets/MicPie/unpredictable_baseball-fantasysports-yahoo-com)
* [UnpredicTable-bulbapedia-bulbagarden-net](https://huggingface.co/datasets/MicPie/unpredictable_bulbapedia-bulbagarden-net)
* [UnpredicTable-cappex-com](https://huggingface.co/datasets/MicPie/unpredictable_cappex-com)
* [UnpredicTable-cram-com](https://huggingface.co/datasets/MicPie/unpredictable_cram-com)
* [UnpredicTable-dividend-com](https://huggingface.co/datasets/MicPie/unpredictable_dividend-com)
* [UnpredicTable-dummies-com](https://huggingface.co/datasets/MicPie/unpredictable_dummies-com)
* [UnpredicTable-en-wikipedia-org](https://huggingface.co/datasets/MicPie/unpredictable_en-wikipedia-org)
* [UnpredicTable-ensembl-org](https://huggingface.co/datasets/MicPie/unpredictable_ensembl-org)
* [UnpredicTable-gamefaqs-com](https://huggingface.co/datasets/MicPie/unpredictable_gamefaqs-com)
* [UnpredicTable-mgoblog-com](https://huggingface.co/datasets/MicPie/unpredictable_mgoblog-com)
* [UnpredicTable-mmo-champion-com](https://huggingface.co/datasets/MicPie/unpredictable_mmo-champion-com)
* [UnpredicTable-msdn-microsoft-com](https://huggingface.co/datasets/MicPie/unpredictable_msdn-microsoft-com)
* [UnpredicTable-phonearena-com](https://huggingface.co/datasets/MicPie/unpredictable_phonearena-com)
* [UnpredicTable-sittercity-com](https://huggingface.co/datasets/MicPie/unpredictable_sittercity-com)
* [UnpredicTable-sporcle-com](https://huggingface.co/datasets/MicPie/unpredictable_sporcle-com)
* [UnpredicTable-studystack-com](https://huggingface.co/datasets/MicPie/unpredictable_studystack-com)
* [UnpredicTable-support-google-com](https://huggingface.co/datasets/MicPie/unpredictable_support-google-com)
* [UnpredicTable-w3-org](https://huggingface.co/datasets/MicPie/unpredictable_w3-org)
* [UnpredicTable-wiki-openmoko-org](https://huggingface.co/datasets/MicPie/unpredictable_wiki-openmoko-org)
* [UnpredicTable-wkdu-org](https://huggingface.co/datasets/MicPie/unpredictable_wkdu-org)
* UnpredicTable data subsets based on clustering (for the clustering details please see our publication):
* [UnpredicTable-cluster00](https://huggingface.co/datasets/MicPie/unpredictable_cluster00)
* [UnpredicTable-cluster01](https://huggingface.co/datasets/MicPie/unpredictable_cluster01)
* [UnpredicTable-cluster02](https://huggingface.co/datasets/MicPie/unpredictable_cluster02)
* [UnpredicTable-cluster03](https://huggingface.co/datasets/MicPie/unpredictable_cluster03)
* [UnpredicTable-cluster04](https://huggingface.co/datasets/MicPie/unpredictable_cluster04)
* [UnpredicTable-cluster05](https://huggingface.co/datasets/MicPie/unpredictable_cluster05)
* [UnpredicTable-cluster06](https://huggingface.co/datasets/MicPie/unpredictable_cluster06)
* [UnpredicTable-cluster07](https://huggingface.co/datasets/MicPie/unpredictable_cluster07)
* [UnpredicTable-cluster08](https://huggingface.co/datasets/MicPie/unpredictable_cluster08)
* [UnpredicTable-cluster09](https://huggingface.co/datasets/MicPie/unpredictable_cluster09)
* [UnpredicTable-cluster10](https://huggingface.co/datasets/MicPie/unpredictable_cluster10)
* [UnpredicTable-cluster11](https://huggingface.co/datasets/MicPie/unpredictable_cluster11)
* [UnpredicTable-cluster12](https://huggingface.co/datasets/MicPie/unpredictable_cluster12)
* [UnpredicTable-cluster13](https://huggingface.co/datasets/MicPie/unpredictable_cluster13)
* [UnpredicTable-cluster14](https://huggingface.co/datasets/MicPie/unpredictable_cluster14)
* [UnpredicTable-cluster15](https://huggingface.co/datasets/MicPie/unpredictable_cluster15)
* [UnpredicTable-cluster16](https://huggingface.co/datasets/MicPie/unpredictable_cluster16)
* [UnpredicTable-cluster17](https://huggingface.co/datasets/MicPie/unpredictable_cluster17)
* [UnpredicTable-cluster18](https://huggingface.co/datasets/MicPie/unpredictable_cluster18)
* [UnpredicTable-cluster19](https://huggingface.co/datasets/MicPie/unpredictable_cluster19)
* [UnpredicTable-cluster20](https://huggingface.co/datasets/MicPie/unpredictable_cluster20)
* [UnpredicTable-cluster21](https://huggingface.co/datasets/MicPie/unpredictable_cluster21)
* [UnpredicTable-cluster22](https://huggingface.co/datasets/MicPie/unpredictable_cluster22)
* [UnpredicTable-cluster23](https://huggingface.co/datasets/MicPie/unpredictable_cluster23)
* [UnpredicTable-cluster24](https://huggingface.co/datasets/MicPie/unpredictable_cluster24)
* [UnpredicTable-cluster25](https://huggingface.co/datasets/MicPie/unpredictable_cluster25)
* [UnpredicTable-cluster26](https://huggingface.co/datasets/MicPie/unpredictable_cluster26)
* [UnpredicTable-cluster27](https://huggingface.co/datasets/MicPie/unpredictable_cluster27)
* [UnpredicTable-cluster28](https://huggingface.co/datasets/MicPie/unpredictable_cluster28)
* [UnpredicTable-cluster29](https://huggingface.co/datasets/MicPie/unpredictable_cluster29)
* [UnpredicTable-cluster-noise](https://huggingface.co/datasets/MicPie/unpredictable_cluster-noise)
### Supported Tasks and Leaderboards
Since the tables come from the web, the distribution of tasks and topics is very broad. The shape of our dataset is very wide, i.e., we have 1000's of tasks, while each task has only a few examples, compared to most current NLP datasets which are very deep, i.e., 10s of tasks with many examples. This implies that our dataset covers a broad range of potential tasks, e.g., multiple-choice, question-answering, table-question-answering, text-classification, etc.
The intended use of this dataset is to improve few-shot performance by fine-tuning/pre-training on our dataset.
### Languages
English
## Dataset Structure
### Data Instances
Each task is represented as a jsonline file and consists of several few-shot examples. Each example is a dictionary containing a field 'task', which identifies the task, followed by an 'input', 'options', and 'output' field. The 'input' field contains several column elements of the same row in the table, while the 'output' field is a target which represents an individual column of the same row. Each task contains several such examples which can be concatenated as a few-shot task. In the case of multiple choice classification, the 'options' field contains the possible classes that a model needs to choose from.
There are also additional meta-data fields such as 'pageTitle', 'title', 'outputColName', 'url', 'wdcFile'.
### Data Fields
'task': task identifier
'input': column elements of a specific row in the table.
'options': for multiple choice classification, it provides the options to choose from.
'output': target column element of the same row as input.
'pageTitle': the title of the page containing the table.
'outputColName': output column name
'url': url to the website containing the table
'wdcFile': WDC Web Table Corpus file
### Data Splits
The UnpredicTable datasets do not come with additional data splits.
## Dataset Creation
### Curation Rationale
Few-shot training on multi-task datasets has been demonstrated to improve language models' few-shot learning (FSL) performance on new tasks, but it is unclear which training tasks lead to effective downstream task adaptation. Few-shot learning datasets are typically produced with expensive human curation, limiting the scale and diversity of the training tasks available to study. As an alternative source of few-shot data, we automatically extract 413,299 tasks from diverse internet tables. We provide this as a research resource to investigate the relationship between training data and few-shot learning.
### Source Data
#### Initial Data Collection and Normalization
We use internet tables from the English-language Relational Subset of the WDC Web Table Corpus 2015 (WTC). The WTC dataset tables were extracted from the July 2015 Common Crawl web corpus (http://webdatacommons.org/webtables/2015/EnglishStatistics.html). The dataset contains 50,820,165 tables from 323,160 web domains. We then convert the tables into few-shot learning tasks. Please see our publication for more details on the data collection and conversion pipeline.
#### Who are the source language producers?
The dataset is extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/).
### Annotations
#### Annotation process
Manual annotation was only carried out for the [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low),
[UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium), and [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high) data subsets to rate task quality. Detailed instructions of the annotation instructions can be found in our publication.
#### Who are the annotators?
Annotations were carried out by a lab assistant.
### Personal and Sensitive Information
The data was extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/), which in turn extracted tables from the [Common Crawl](https://commoncrawl.org/). We did not filter the data in any way. Thus any user identities or otherwise sensitive information (e.g., data that reveals racial or ethnic origins, sexual orientations, religious beliefs, political opinions or union memberships, or locations; financial or health data; biometric or genetic data; forms of government identification, such as social security numbers; criminal history, etc.) might be contained in our dataset.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset is intended for use as a research resource to investigate the relationship between training data and few-shot learning. As such, it contains high- and low-quality data, as well as diverse content that may be untruthful or inappropriate. Without careful investigation, it should not be used for training models that will be deployed for use in decision-critical or user-facing situations.
### Discussion of Biases
Since our dataset contains tables that are scraped from the web, it will also contain many toxic, racist, sexist, and otherwise harmful biases and texts. We have not run any analysis on the biases prevalent in our datasets. Neither have we explicitly filtered the content. This implies that a model trained on our dataset may potentially reflect harmful biases and toxic text that exist in our dataset.
### Other Known Limitations
No additional known limitations.
## Additional Information
### Dataset Curators
Jun Shern Chan, Michael Pieler, Jonathan Jao, Jérémy Scheurer, Ethan Perez
### Licensing Information
Apache 2.0
### Citation Information
```
@misc{chan2022few,
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
title = {Few-shot Adaptation Works with UnpredicTable Data},
publisher={arXiv},
year = {2022},
url = {https://arxiv.org/abs/2208.01009}
}
```
|
gorkaartola/SC-ZS-test_AURORA-Gold-SDG_True-Positives-and-False-Positives | 2023-03-22T21:22:16.000Z | [
"region:us"
] | gorkaartola | null | null | null | 0 | 14 | Entry not found |
gorkaartola/SDG_queries | 2023-04-13T12:49:05.000Z | [
"region:us"
] | gorkaartola | null | null | null | 0 | 14 | Entry not found |
jakartaresearch/cerpen-corpus | 2022-11-28T04:15:40.000Z | [
"task_categories:text-generation",
"task_ids:language-modeling",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:n<1K",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:id",
"license:cc-by-4.0",
"cerpen",
"shor... | jakartaresearch | This dataset is built as a playground for beginner to make a use case for creating sentiment analysis model. | null | null | 1 | 14 | ---
annotations_creators:
- no-annotation
language:
- id
language_creators:
- found
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: Small Indonesian Short Story Corpus
size_categories:
- n<1K
- 10K<n<100K
source_datasets:
- original
tags:
- cerpen
- short-story
task_categories:
- text-generation
task_ids:
- language-modeling
---
# Dataset Card for Cerpen Corpus
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [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)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This is a small size for Indonesian short story gathered from the internet.
We keep the large size for internal research. if you are interested, please join to [our discord server](https://discord.gg/6v28dq8dRE)
### 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
Thanks to [@andreaschandra](https://github.com/andreaschandra) for adding this dataset. |
roskoN/stereoset_german | 2022-08-30T14:53:55.000Z | [
"license:cc-by-sa-4.0",
"region:us"
] | roskoN | \ | \ | null | 0 | 14 | ---
license: cc-by-sa-4.0
---
|
Osaleh/ArSAS | 2022-09-05T07:09:56.000Z | [
"region:us"
] | Osaleh | null | null | null | 0 | 14 | Entry not found |
gfhayworth/wiki_mini | 2023-01-28T23:28:54.000Z | [
"region:us"
] | gfhayworth | null | null | null | 2 | 14 | Simple English Wikipedia it has only about 170k articles. We split these articles into paragraphs.
wikipedia_filepath = 'simplewiki-2020-11-01.jsonl.gz'
if not os.path.exists(wikipedia_filepath):
util.http_get('http://sbert.net/datasets/simplewiki-2020-11-01.jsonl.gz', wikipedia_filepath) |
matchbench/Abt-Buy | 2022-11-16T09:03:32.000Z | [
"region:us"
] | matchbench | null | null | null | 0 | 14 | Entry not found |
antoniomenezes/go_emotions_ptbr | 2022-11-21T14:27:31.000Z | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"task_ids:multi-label-classification",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:2 languages",
"size_categories:100K<n<1M",
"size_categories:10K<n<100K",
"source_datasets:modified",
... | antoniomenezes | null | null | null | 4 | 14 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
- pt
license:
- apache-2.0
multilinguality:
- 2 languages
size_categories:
- 100K<n<1M
- 10K<n<100K
source_datasets:
- modified
task_categories:
- text-classification
task_ids:
- multi-class-classification
- multi-label-classification
paperswithcode_id: goemotions
pretty_name: GoEmotions
configs:
- raw
- simplified
tags:
- emotion
dataset_info:
- config_name: raw
features:
- name: text
dtype: string
- name: id
dtype: string
- name: author
dtype: string
- name: subreddit
dtype: string
- name: link_id
dtype: string
- name: parent_id
dtype: string
- name: created_utc
dtype: float32
- name: rater_id
dtype: int32
- name: example_very_unclear
dtype: bool
- name: admiration
dtype: int32
- name: amusement
dtype: int32
- name: anger
dtype: int32
- name: annoyance
dtype: int32
- name: approval
dtype: int32
- name: caring
dtype: int32
- name: confusion
dtype: int32
- name: curiosity
dtype: int32
- name: desire
dtype: int32
- name: disappointment
dtype: int32
- name: disapproval
dtype: int32
- name: disgust
dtype: int32
- name: embarrassment
dtype: int32
- name: excitement
dtype: int32
- name: fear
dtype: int32
- name: gratitude
dtype: int32
- name: grief
dtype: int32
- name: joy
dtype: int32
- name: love
dtype: int32
- name: nervousness
dtype: int32
- name: optimism
dtype: int32
- name: pride
dtype: int32
- name: realization
dtype: int32
- name: relief
dtype: int32
- name: remorse
dtype: int32
- name: sadness
dtype: int32
- name: surprise
dtype: int32
- name: neutral
dtype: int32
- name: texto
dtype: string
splits:
- name: train
num_bytes: 55343630
num_examples: 211225
download_size: 42742918
dataset_size: 55343630
- config_name: simplified
features:
- name: text
dtype: string
- name: labels
sequence:
class_label:
names:
0: admiration
1: amusement
2: anger
3: annoyance
4: approval
5: caring
6: confusion
7: curiosity
8: desire
9: disappointment
10: disapproval
11: disgust
12: embarrassment
13: excitement
14: fear
15: gratitude
16: grief
17: joy
18: love
19: nervousness
20: optimism
21: pride
22: realization
23: relief
24: remorse
25: sadness
26: surprise
27: neutral
- name: id
dtype: string
splits:
- name: train
num_bytes: 4224198
num_examples: 43410
- name: validation
num_bytes: 527131
num_examples: 5426
- name: test
num_bytes: 524455
num_examples: 5427
download_size: 4394818
dataset_size: 5275784
---
# Dataset Card for GoEmotions
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [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)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/google-research/google-research/tree/master/goemotions
- **Repository:** https://github.com/google-research/google-research/tree/master/goemotions
- **Paper:** https://arxiv.org/abs/2005.00547
- **Leaderboard:**
- **Point of Contact:** [Dora Demszky](https://nlp.stanford.edu/~ddemszky/index.html)
### Dataset Summary
The GoEmotions dataset contains 58k carefully curated Reddit comments labeled for 27 emotion categories or Neutral.
The raw data is included as well as the smaller, simplified version of the dataset with predefined train/val/test
splits.
### Supported Tasks and Leaderboards
This dataset is intended for multi-class, multi-label emotion classification.
### Languages
The data is in English and Brazilian Portuguese (translated by Google Translator).
## Dataset Structure
### Data Instances
Each instance is a reddit comment with a corresponding ID and one or more emotion annotations (or neutral).
### Data Fields
The simplified configuration includes:
- `text`: the reddit comment
- `texto`: the reddit comment in portuguese
- `labels`: the emotion annotations
- `comment_id`: unique identifier of the comment (can be used to look up the entry in the raw dataset)
In addition to the above, the raw data includes:
* `author`: The Reddit username of the comment's author.
* `subreddit`: The subreddit that the comment belongs to.
* `link_id`: The link id of the comment.
* `parent_id`: The parent id of the comment.
* `created_utc`: The timestamp of the comment.
* `rater_id`: The unique id of the annotator.
* `example_very_unclear`: Whether the annotator marked the example as being very unclear or difficult to label (in this
case they did not choose any emotion labels).
In the raw data, labels are listed as their own columns with binary 0/1 entries rather than a list of ids as in the
simplified data.
### Data Splits
The simplified data includes a set of train/val/test splits with 43,410, 5426, and 5427 examples respectively.
## Dataset Creation
### Curation Rationale
From the paper abstract:
> Understanding emotion expressed in language has a wide range of applications, from building empathetic chatbots to
detecting harmful online behavior. Advancement in this area can be improved using large-scale datasets with a
fine-grained typology, adaptable to multiple downstream tasks.
### Source Data
#### Initial Data Collection and Normalization
Data was collected from Reddit comments via a variety of automated methods discussed in 3.1 of the paper.
#### Who are the source language producers?
English-speaking Reddit users.
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
Annotations were produced by 3 English-speaking crowdworkers in India.
### Personal and Sensitive Information
This dataset includes the original usernames of the Reddit users who posted each comment. Although Reddit usernames
are typically disasociated from personal real-world identities, this is not always the case. It may therefore be
possible to discover the identities of the individuals who created this content in some cases.
## Considerations for Using the Data
### Social Impact of Dataset
Emotion detection is a worthwhile problem which can potentially lead to improvements such as better human/computer
interaction. However, emotion detection algorithms (particularly in computer vision) have been abused in some cases
to make erroneous inferences in human monitoring and assessment applications such as hiring decisions, insurance
pricing, and student attentiveness (see
[this article](https://www.unite.ai/ai-now-institute-warns-about-misuse-of-emotion-detection-software-and-other-ethical-issues/)).
### Discussion of Biases
From the authors' github page:
> Potential biases in the data include: Inherent biases in Reddit and user base biases, the offensive/vulgar word lists used for data filtering, inherent or unconscious bias in assessment of offensive identity labels, annotators were all native English speakers from India. All these likely affect labelling, precision, and recall for a trained model. Anyone using this dataset should be aware of these limitations of the dataset.
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
Researchers at Amazon Alexa, Google Research, and Stanford. See the [author list](https://arxiv.org/abs/2005.00547).
### Licensing Information
The GitHub repository which houses this dataset has an
[Apache License 2.0](https://github.com/google-research/google-research/blob/master/LICENSE).
### Citation Information
@inproceedings{demszky2020goemotions,
author = {Demszky, Dorottya and Movshovitz-Attias, Dana and Ko, Jeongwoo and Cowen, Alan and Nemade, Gaurav and Ravi, Sujith},
booktitle = {58th Annual Meeting of the Association for Computational Linguistics (ACL)},
title = {{GoEmotions: A Dataset of Fine-Grained Emotions}},
year = {2020}
}
### Contributions
Thanks to [@joeddav](https://github.com/joeddav) for adding this dataset.
Thanks to [@antoniomenezes](https://github.com/antoniomenezes) for extending this dataset. |
deutsche-telekom/ger-backtrans-paraphrase | 2023-06-12T17:46:57.000Z | [
"task_categories:sentence-similarity",
"multilinguality:monolingual",
"size_categories:10M<n<100M",
"language:de",
"license:cc-by-sa-4.0",
"arxiv:1907.05791",
"arxiv:2004.09813",
"region:us"
] | deutsche-telekom | null | null | null | 7 | 14 | ---
license:
- cc-by-sa-4.0
language:
- de
multilinguality:
- monolingual
size_categories:
- 10M<n<100M
task_categories:
- sentence-similarity
---
# German Backtranslated Paraphrase Dataset
This is a dataset of more than 21 million German paraphrases.
These are text pairs that have the same meaning but are expressed with different words.
The source of the paraphrases are different parallel German / English text corpora.
The English texts were machine translated back into German to obtain the paraphrases.
This dataset can be used for example to train semantic text embeddings.
To do this, for example, [SentenceTransformers](https://www.sbert.net/)
and the [MultipleNegativesRankingLoss](https://www.sbert.net/docs/package_reference/losses.html#multiplenegativesrankingloss)
can be used.
## Maintainers
[](https://www.welove.ai/)
This dataset is open sourced by [Philip May](https://may.la/)
and maintained by the [One Conversation](https://www.welove.ai/)
team of [Deutsche Telekom AG](https://www.telekom.com/).
## Our pre-processing
Apart from the back translation, we have added more columns (for details see below). We have carried out the following pre-processing and filtering:
- We dropped text pairs where one text was longer than 499 characters.
- In the [GlobalVoices v2018q4](https://opus.nlpl.eu/GlobalVoices-v2018q4.php) texts we have removed the `" · Global Voices"` suffix.
## Your post-processing
You probably don't want to use the dataset as it is, but filter it further.
This is what the additional columns of the dataset are for.
For us it has proven useful to delete the following pairs of sentences:
- `min_char_len` less than 15
- `jaccard_similarity` greater than 0.3
- `de_token_count` greater than 30
- `en_de_token_count` greater than 30
- `cos_sim` less than 0.85
## Columns description
- **`uuid`**: a uuid calculated with Python `uuid.uuid4()`
- **`en`**: the original English texts from the corpus
- **`de`**: the original German texts from the corpus
- **`en_de`**: the German texts translated back from English (from `en`)
- **`corpus`**: the name of the corpus
- **`min_char_len`**: the number of characters of the shortest text
- **`jaccard_similarity`**: the [Jaccard similarity coefficient](https://en.wikipedia.org/wiki/Jaccard_index) of both sentences - see below for more details
- **`de_token_count`**: number of tokens of the `de` text, tokenized with [deepset/gbert-large](https://huggingface.co/deepset/gbert-large)
- **`en_de_token_count`**: number of tokens of the `de` text, tokenized with [deepset/gbert-large](https://huggingface.co/deepset/gbert-large)
- **`cos_sim`**: the [cosine similarity](https://en.wikipedia.org/wiki/Cosine_similarity) of both sentences measured with [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2)
## Anomalies in the texts
It is noticeable that the [OpenSubtitles](https://opus.nlpl.eu/OpenSubtitles-v2018.php) texts have weird dash prefixes. This looks like this:
```
- Hast du was draufgetan?
```
To remove them you could apply this function:
```python
import re
def clean_text(text):
text = re.sub("^[-\s]*", "", text)
text = re.sub("[-\s]*$", "", text)
return text
df["de"] = df["de"].apply(clean_text)
df["en_de"] = df["en_de"].apply(clean_text)
```
## Parallel text corpora used
| Corpus name & link | Number of paraphrases |
|-----------------------------------------------------------------------|----------------------:|
| [OpenSubtitles](https://opus.nlpl.eu/OpenSubtitles-v2018.php) | 18,764,810 |
| [WikiMatrix v1](https://opus.nlpl.eu/WikiMatrix-v1.php) | 1,569,231 |
| [Tatoeba v2022-03-03](https://opus.nlpl.eu/Tatoeba-v2022-03-03.php) | 313,105 |
| [TED2020 v1](https://opus.nlpl.eu/TED2020-v1.php) | 289,374 |
| [News-Commentary v16](https://opus.nlpl.eu/News-Commentary-v16.php) | 285,722 |
| [GlobalVoices v2018q4](https://opus.nlpl.eu/GlobalVoices-v2018q4.php) | 70,547 |
| **sum** |. **21,292,789** |
## Back translation
We have made the back translation from English to German with the help of [Fairseq](https://github.com/facebookresearch/fairseq).
We used the `transformer.wmt19.en-de` model for this purpose:
```python
en2de = torch.hub.load(
"pytorch/fairseq",
"transformer.wmt19.en-de",
checkpoint_file="model1.pt:model2.pt:model3.pt:model4.pt",
tokenizer="moses",
bpe="fastbpe",
)
```
## How the Jaccard similarity was calculated
To calculate the [Jaccard similarity coefficient](https://en.wikipedia.org/wiki/Jaccard_index)
we are using the [SoMaJo tokenizer](https://github.com/tsproisl/SoMaJo)
to split the texts into tokens.
We then `lower()` the tokens so that upper and lower case letters no longer make a difference. Below you can find a code snippet with the details:
```python
from somajo import SoMaJo
LANGUAGE = "de_CMC"
somajo_tokenizer = SoMaJo(LANGUAGE)
def get_token_set(text, somajo_tokenizer):
sentences = somajo_tokenizer.tokenize_text([text])
tokens = [t.text.lower() for sentence in sentences for t in sentence]
token_set = set(tokens)
return token_set
def jaccard_similarity(text1, text2, somajo_tokenizer):
token_set1 = get_token_set(text1, somajo_tokenizer=somajo_tokenizer)
token_set2 = get_token_set(text2, somajo_tokenizer=somajo_tokenizer)
intersection = token_set1.intersection(token_set2)
union = token_set1.union(token_set2)
jaccard_similarity = float(len(intersection)) / len(union)
return jaccard_similarity
```
## Load this dataset
### With Hugging Face Datasets
```python
# pip install datasets
from datasets import load_dataset
dataset = load_dataset("deutsche-telekom/ger-backtrans-paraphrase")
train_dataset = dataset["train"]
```
### With Pandas
If you want to download the csv file and then load it with Pandas you can do it like this:
```python
df = pd.read_csv("train.csv")
```
## Citations, Acknowledgements and Licenses
**OpenSubtitles**
- citation: P. Lison and J. Tiedemann, 2016, [OpenSubtitles2016: Extracting Large Parallel Corpora from Movie and TV Subtitles](http://www.lrec-conf.org/proceedings/lrec2016/pdf/947_Paper.pdf). In Proceedings of the 10th International Conference on Language Resources and Evaluation (LREC 2016)
- also see http://www.opensubtitles.org/
- license: no special license has been provided at OPUS for this dataset
**WikiMatrix v1**
- citation: Holger Schwenk, Vishrav Chaudhary, Shuo Sun, Hongyu Gong and Paco Guzman, [WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipedia](https://arxiv.org/abs/1907.05791), arXiv, July 11 2019
- license: [CC-BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/)
**Tatoeba v2022-03-03**
- citation: J. Tiedemann, 2012, [Parallel Data, Tools and Interfaces in OPUS](https://opus.nlpl.eu/Tatoeba-v2022-03-03.php). In Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC 2012)
- license: [CC BY 2.0 FR](https://creativecommons.org/licenses/by/2.0/fr/)
- copyright: https://tatoeba.org/eng/terms_of_use
**TED2020 v1**
- citation: Reimers, Nils and Gurevych, Iryna, [Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation](https://arxiv.org/abs/2004.09813), In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, November 2020
- acknowledgements to [OPUS](https://opus.nlpl.eu/) for this service
- license: please respect the [TED Talks Usage Policy](https://www.ted.com/about/our-organization/our-policies-terms/ted-talks-usage-policy)
**News-Commentary v16**
- citation: J. Tiedemann, 2012, [Parallel Data, Tools and Interfaces in OPUS](https://opus.nlpl.eu/Tatoeba-v2022-03-03.php). In Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC 2012)
- license: no special license has been provided at OPUS for this dataset
**GlobalVoices v2018q4**
- citation: J. Tiedemann, 2012, [Parallel Data, Tools and Interfaces in OPUS](https://opus.nlpl.eu/Tatoeba-v2022-03-03.php). In Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC 2012)
- license: no special license has been provided at OPUS for this dataset
## Citation
```latex
@misc{ger-backtrans-paraphrase,
title={Deutsche-Telekom/ger-backtrans-paraphrase - dataset at Hugging Face},
url={https://huggingface.co/datasets/deutsche-telekom/ger-backtrans-paraphrase},
year={2022},
author={May, Philip}
}
```
## Licensing
Copyright (c) 2022 [Philip May](https://may.la/),
[Deutsche Telekom AG](https://www.telekom.com/)
This work is licensed under [CC-BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/).
|
matchbench/Itunes-Amazon | 2022-12-12T05:40:26.000Z | [
"region:us"
] | matchbench | null | null | null | 0 | 14 | Entry not found |
Brosnan/WIFI_RSSI_Indoor_Positioning_Dataset | 2022-12-02T20:42:32.000Z | [
"task_categories:tabular-classification",
"task_ids:tabular-single-column-regression",
"language_creators:expert-generated",
"size_categories:100K<n<1M",
"license:cc-by-nc-sa-4.0",
"wifi",
"indoor-positioning",
"indoor-localisation",
"wifi-rssi",
"rssi",
"recurrent-neural-networks",
"region:us... | Brosnan | null | null | null | 2 | 14 | ---
license: cc-by-nc-sa-4.0
language_creators:
- expert-generated
pretty_name: WiFi RSSI Indoor Localization
size_categories:
- 100K<n<1M
task_categories:
- tabular-classification
task_ids:
- tabular-single-column-regression
tags:
- wifi
- indoor-positioning
- indoor-localisation
- wifi-rssi
- rssi
- recurrent-neural-networks
---
# WIFI RSSI Indoor Positioning Dataset
A reliable and comprehensive public WiFi fingerprinting database for researchers to implement and compare the indoor localization’s methods.The database contains RSSI information from 6 APs conducted in different days with the support of autonomous robot.
We use an autonomous robot to collect the WiFi fingerprint data. Our 3-wheel robot has multiple sensors including wheel odometer, an inertial measurement unit (IMU), a LIDAR, sonar sensors and a color and depth (RGB-D) camera. The robot can navigate to a target location to collect WiFi fingerprints automatically. The localization accuracy of the robot is 0.07 m ± 0.02 m. The dimension of the area is 21 m × 16 m. It has three long corridors. There are six APs and five of them provide two distinct MAC address for 2.4- and 5-GHz communications channels, respectively, except for one that only operates on 2.4-GHz frequency. There is one router can provide CSI information.
# Data Format
X Position (m), Y Position (m), RSSI Feature 1 (dBm), RSSI Feature 2 (dBm), RSSI Feature 3 (dBm), RSSI Feature 4 (dBm), ...
|
dream-textures/textures-color-normal-1k | 2023-01-13T21:20:22.000Z | [
"task_categories:image-to-image",
"size_categories:1K<n<10K",
"license:cc0-1.0",
"region:us"
] | dream-textures | null | null | null | 5 | 14 | ---
dataset_info:
features:
- name: color
dtype: image
- name: normal
dtype: image
splits:
- name: train
num_bytes: 110631687.194
num_examples: 1426
download_size: 111043422
dataset_size: 110631687.194
license: cc0-1.0
task_categories:
- image-to-image
size_categories:
- 1K<n<10K
---
# textures-color-normal-1k
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
The `textures-color-normal-1k` dataset is an image dataset of 1000+ color and normal map textures in 512x512 resolution.
The dataset was created for use in image to image tasks.
It contains a combination of CC0 procedural and photoscanned PBR materials from [ambientCG](https://ambientcg.com/).
## Dataset Structure
### Data Instances
Each data point contains a 512x512 color texture and the corresponding 512x512 normal map.
### Data Fields
* `color`: the color texture as a PIL image
* `normal`: the normal map as a PIL image
### Data Splits
| | train |
| -- | ----- |
| ambientCG | 1426 |
## Dataset Creation
### Curation Rationale
`textures-color-normal-1k` was created to provide an accesible source of data for automating 3D-asset creation workflows.
The [Dream Textures](https://github.com/carson-katri/dream-textures) add-on is one such tool providing AI automation in Blender.
By training models designed for image to image tasks, this particular use-case can be more accurately automated.
### Source Data
#### Initial Data Collection and Normalization
The data was obtained from [ambientCG](https://ambientcg.com/)'s CC0 textures. Only the color and normal maps were included in this dataset.
## Additional Information
### Dataset Curators
The dataset was created by Carson Katri, with the images being provided by [ambientCG](https://ambientcg.com/).
### Licensing Information
All of the images used in this dataset are CC0.
### Citation Information
[N/A]
### Contributions
Thanks to [@carson-katri](https://github.com/carson-katri) for adding this dataset. |
metaeval/sts-companion | 2023-02-03T08:36:00.000Z | [
"task_categories:sentence-similarity",
"task_categories:text-classification",
"language:en",
"license:apache-2.0",
"sts",
"region:us"
] | metaeval | null | null | null | 2 | 14 | ---
license: apache-2.0
task_categories:
- sentence-similarity
- text-classification
language:
- en
tags:
- sts
---
https://ixa2.si.ehu.eus/stswiki/index.php/STSbenchmark
The companion datasets to the STS Benchmark comprise the rest of the English datasets used in the STS tasks organized by us in the context of SemEval between 2012 and 2017.
Authors collated two datasets, one with pairs of sentences related to machine translation evaluation. Another one with the rest of datasets, which can be used for domain adaptation studies.
```bib
@inproceedings{cer-etal-2017-semeval,
title = "{S}em{E}val-2017 Task 1: Semantic Textual Similarity Multilingual and Crosslingual Focused Evaluation",
author = "Cer, Daniel and
Diab, Mona and
Agirre, Eneko and
Lopez-Gazpio, I{\~n}igo and
Specia, Lucia",
booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
month = aug,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S17-2001",
doi = "10.18653/v1/S17-2001",
pages = "1--14",
}
``` |
gokuls/glue_augmented_sst2 | 2023-01-30T13:21:43.000Z | [
"license:apache-2.0",
"region:us"
] | gokuls | null | null | null | 1 | 14 | ---
license: apache-2.0
---
# Dataset Card for glue_augmented_sst2
## Dataset Description
Augmented SST-2 dataset
**Reference:** https://huggingface.co/datasets/glue |
TurkuNLP/squad_v2_fi | 2023-10-10T19:55:56.000Z | [
"task_categories:question-answering",
"language:fi",
"license:cc-by-sa-4.0",
"region:us"
] | TurkuNLP | combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions written adversarially by crowdworkers
to look similar to answerable ones. To do well on SQuAD2.0, systems must not only answer questions when possible, but
also determine when no answer is supported by the paragraph and abstain from answering. | null | null | 0 | 14 | ---
license: cc-by-sa-4.0
task_categories:
- question-answering
language:
- fi
---
### Dataset Summary
This is a Finnish SQuAD question answering dataset. It is a DeepL -based machine translation of the English SQuAD2.0 dataset which combines the 100,000 questions in
SQuAD1.1 with over 50,000 unanswerable questions written adversarially by crowdworkers to look similar to answerable ones.
To do well on SQuAD2.0, systems must not only answer questions when possible, but also determine when no answer is supported
by the paragraph and abstain from answering.
### Data Fields
The data fields are the same among all splits.
#### Example Data
```
{
"title": "Victoria_(Australia)",
"paragraphs": [
{
"qas": [
{
"question": "Millainen talous Victoriassa on?",
"id": "570d2417fed7b91900d45c3d",
"answers": [
{
"text": "monipuolinen",
"answer_start": 26,
"texts": [
"monipuolinen"
],
"starts": [
26
]
},
{
"text": "hyvin monipuolinen",
"answer_start": 20,
"texts": [
"hyvin ",
"monipuolinen"
],
"starts": [
20,
26
]
},
{
"text": "hyvin monipuolinen",
"answer_start": 20,
"texts": [
"hyvin ",
"monipuolinen"
],
"starts": [
20,
26
]
}
],
"is_impossible": false
}
],
"context": "Victorian talous on hyvin monipuolinen: palvelualat, kuten rahoitus- ja kiinteistöpalvelut, terveydenhuolto, koulutus, tukkukauppa, vähittäiskauppa, majoitus- ja ravitsemistoiminta ja teollisuus muodostavat suurimman osan työllisyydestä. Victorian osavaltion bruttokansantuote on Australian toiseksi suurin, vaikka Victoria on asukaskohtaisen bruttokansantuotteen osalta neljäntenä, koska sen kaivostoiminta on vähäistä. Kulttuurin alalla Melbournessa on useita museoita, taidegallerioita ja teattereita, ja sitä kutsutaan myös \"Australian urheilupääkaupungiksi\". Melbournen krikettikenttä (Melbourne Cricket Ground) on Australian suurin stadion, ja siellä järjestettiin vuoden 1956 kesäolympialaiset ja vuoden 2006 Kansainyhteisön kisat. Kenttää pidetään myös australialaisen kriketin ja australialaisen jalkapallon \"henkisenä kotina\", ja se isännöi vuosittain Australian jalkapalloliigan (AFL) suurta loppuottelua, johon osallistuu yleensä yli 95 000 ihmistä. Victoriaan kuuluu kahdeksan julkista yliopistoa, joista vanhin, Melbournen yliopisto, on perustettu vuonna 1853."
}
]
}
```
#### squad_v2
- `id`: a `string` feature.
- `title`: a `string` feature.
- `context`: a `string` feature.
- `question`: a `string` feature.
- `answers`: a dictionary feature containing:
- `text`: a `string` feature.
- `answer_start`: a `int32` feature.
- `texts`: a `string` feature.
- `starts`: a `int32` feature.
### Data Splits
| name | train | validation |
| -------- | -----: | ---------: |
| squad_v2 | 130319 | 11873 |
### Evaluation Results
Results from fine-tuning [TurkuNLP/bert-base-finnish-cased-v1](ttps://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1) for extractive question answering.
| dataset | F1 |
| -------------------- | ----: |
| TurkuNLP/squad_v2_fi | 73.66 |
| ilmariky/SQuAD_v2_fi | 61.87 |
### Considerations for Using the Data
Due to DeepL terms and conditions, this dataset **must not be used for any machine translation work**, namely machine translation
system development and evaluation of any kind. In general, we wish you do not pair the original English data with the translations
except when working on research unrelated to machine translation, so as not to infringe on the terms and conditions.
### Licensing Information
Contents of this repository are distributed under the
[Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0)](https://creativecommons.org/licenses/by-sa/4.0/).
Copyright of the dataset contents belongs to the original copyright holders. |
Dahoas/filtered-SHP | 2023-02-24T21:58:31.000Z | [
"region:us"
] | Dahoas | null | null | null | 2 | 14 | Entry not found |
OllieStanley/humaneval-mbpp-testgen-qa | 2023-03-15T15:12:49.000Z | [
"region:us"
] | OllieStanley | null | null | null | 0 | 14 | ---
dataset_info:
features:
- name: INSTRUCTION
dtype: string
- name: RESPONSE
dtype: string
- name: SOURCE
dtype: string
splits:
- name: train
num_bytes: 304315
num_examples: 591
download_size: 0
dataset_size: 304315
---
# Dataset Card for "humaneval-mbpp-testgen-qa"
This dataset contains prompt-reply (question-answer) pairs where the prompt is to create a Python unit tests which tests for the functionality described in a specific docstring. The responses are then the generated unit tests. |
nadlej/reuters15k | 2023-03-01T18:52:02.000Z | [
"task_categories:tabular-classification",
"size_categories:10K<n<100K",
"reuters15k",
"reuters",
"tabular",
"region:us"
] | nadlej | null | null | null | 0 | 14 | ---
task_categories:
- tabular-classification
tags:
- reuters15k
- reuters
- tabular
size_categories:
- 10K<n<100K
--- |
metaeval/mutual | 2023-02-28T13:27:49.000Z | [
"region:us"
] | metaeval | null | null | null | 1 | 14 | ```bib
@inproceedings{mutual,
title = "MuTual: A Dataset for Multi-Turn Dialogue Reasoning",
author = "Cui, Leyang and Wu, Yu and Liu, Shujie and Zhang, Yue and Zhou, Ming" ,
booktitle = "Proceedings of the 58th Conference of the Association for Computational Linguistics",
year = "2020",
publisher = "Association for Computational Linguistics",
}
``` |
SaylorTwift/the_pile_books3_minus_gutenberg | 2023-03-03T19:46:43.000Z | [
"region:us"
] | SaylorTwift | null | null | null | 4 | 14 | ---
dataset_info:
features:
- name: title
dtype: string
- name: text
dtype: string
- name: first_name
dtype: string
- name: last_name
dtype: string
splits:
- name: train
num_bytes: 106199627990.47722
num_examples: 192661
download_size: 63006723975
dataset_size: 106199627990.47722
---
# Dataset Card for "the_pile_books3_minus_gutenberg"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
theblackcat102/instruction_translations | 2023-03-05T06:36:37.000Z | [
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:en",
"license:mit",
"ChatGPT",
"SimpleAI",
"Detection",
"doi:10.57967/hf/0423",
"region:us"
] | theblackcat102 | Translation of Instruction dataset | \ | null | 5 | 14 | ---
task_categories:
- text-generation
language:
- en
tags:
- ChatGPT
- SimpleAI
- Detection
size_categories:
- 10K<n<100K
license: mit
---
# Translations for Instruction dataset
Translations were generated by [M2M 12B](https://huggingface.co/facebook/m2m100-12B-avg-5-ckpt) and the output generations were limited at 512 tokens due to VRAM limit (40G).
|
pythainlp/tlcv2.0_oa | 2023-03-04T19:36:15.000Z | [
"task_categories:text-generation",
"size_categories:n<1K",
"language:th",
"license:mit",
"region:us"
] | pythainlp | null | null | null | 0 | 14 | ---
dataset_info:
features:
- name: TEXT
dtype: string
- name: SOURCE
dtype: string
- name: METADATA
struct:
- name: ch_num
dtype: string
- name: title
dtype: string
splits:
- name: train
num_bytes: 27856275
num_examples: 361
download_size: 11507610
dataset_size: 27856275
license: mit
task_categories:
- text-generation
language:
- th
size_categories:
- n<1K
---
# Dataset Card for "tlcv2.0_oa"
Thai Literature Corpora (TLC): Corpora of machine-ingestible Thai classical literature texts by Jitkapat Sawatphol (Faculty of Arts, Chulalongkorn University).
This project use [Thai Literature Corpora (TLC) v2.0](https://attapol.github.io/tlc.html). All text are from old Thai book that out of copyright (or public domain).
This dataset was build for [Open Assistant](https://github.com/LAION-AI/Open-Assistant/).
## Columns
The dataset was following columns:
1. **TEXT** (string)
2. **SOURCE** (string)
3. **METADATA** (JSON string, optional) |
argilla/news-summary-new | 2023-07-13T11:15:37.000Z | [
"language:en",
"region:us"
] | argilla | null | null | null | 0 | 14 | ---
language: en
dataset_info:
features:
- name: text
dtype: string
- name: target
dtype: string
splits:
- name: train
num_bytes: 252347
num_examples: 114
download_size: 87832
dataset_size: 252347
---
# Dataset Card for "news-summary-new"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
RiniPL/Dementia_Dataset | 2023-03-15T07:48:14.000Z | [
"task_categories:image-classification",
"language:en",
"license:ecl-2.0",
"code",
"region:us"
] | RiniPL | null | null | null | 2 | 14 | ---
license: ecl-2.0
task_categories:
- image-classification
language:
- en
tags:
- code
pretty_name: Dementia
--- |
pythainlp/thaigov-v2-corpus-22032023 | 2023-03-22T08:44:49.000Z | [
"size_categories:10K<n<100K",
"language:th",
"license:cc0-1.0",
"region:us"
] | pythainlp | null | null | null | 2 | 14 | ---
dataset_info:
features:
- name: title
dtype: string
- name: context
dtype: string
- name: url
dtype: string
splits:
- name: train
num_bytes: 252319219
num_examples: 30380
download_size: 85313027
dataset_size: 252319219
license: cc0-1.0
language:
- th
size_categories:
- 10K<n<100K
---
# Dataset Card for "thaigov-v2-corpus-22032023"
This corpus made from Thaigov v2 corpus in 22 Mar 2023. [https://github.com/PyThaiNLP/thaigov-v2-corpus/releases/tag/22032023](https://github.com/PyThaiNLP/thaigov-v2-corpus/releases/tag/22032023)
Corups: [https://github.com/PyThaiNLP/thaigov-v2-corpus](https://github.com/PyThaiNLP/thaigov-v2-corpus)
## English
- Data from Thai government website. https://www.thaigov.go.th
- This part of PyThaiNLP Project.
- Compiled by Mr.Wannaphong Phatthiyaphaibun
- License Dataset is public domain.
## Data format
- 1 file, 1 news, which is extracted from 1 url.
```
topic
(Blank line)
content
content
content
content
content
(Blank line)
ที่มา (URL source) : http://www.thaigov.go.th/news/contents/details/NNN
```
## Thai
- เป็นข้อมูลที่รวบรวมข่าวสารจากเว็บไซต์รัฐบาลไทย https://www.thaigov.go.th
- โครงการนี้เป็นส่วนหนึ่งในแผนพัฒนา [PyThaiNLP](https://github.com/PyThaiNLP/)
- รวบรวมโดย นาย วรรณพงษ์ ภัททิยไพบูลย์
- ข้อมูลที่รวบรวมในคลังข้อความนี้เป็นสาธารณสมบัติ (public domain) ตามพ.ร.บ.ลิขสิทธิ์ พ.ศ. 2537 มาตรา 7 (สิ่งต่อไปนี้ไม่ถือว่าเป็นงานอันมีลิขสิทธิ์ตามพระราชบัญญัตินี้ (1) ข่าวประจำวัน และข้อเท็จจริงต่างๆ ที่มีลักษณะเป็นเพียงข่าวสารอันมิใช่งานในแผนกวรรณคดี แผนกวิทยาศาสตร์ หรือแผนกศิลปะ [...] (3) ระเบียบ ข้อบังคับ ประกาศ คำสั่ง คำชี้แจง และหนังสือตอบโต้ของกระทรวง ทบวง กรม หรือหน่วยงานอื่นใดของรัฐหรือของท้องถิ่น [...])
**สามารถติดตามประวัติการแก้ไขคลังข้อความนี้ได้ผ่านระบบ Git**
### จำนวนข่าว
- วันเริ่มต้นโครงการ 17 ก.ย. 2563
### รูปแบบข้อมูล
- 1 ไฟล์ 1 ข่าว ซึ่งดึงมาจาก 1 url
```
หัวเรื่อง
(บรรทัดว่าง)
เนื้อความ
เนื้อความ
เนื้อความ
เนื้อความ
เนื้อความ
(บรรทัดว่าง)
ที่มา : http://www.thaigov.go.th/news/contents/details/NNN
```
### รายละเอียดชื่อไฟล์
- ชื่อหมวดหมู่_จำนวนที่ของข่าว.txt
### Script
- run.py สำหรับเก็บข้อมูลจากหน้าเว็บ โดยจะดึงหน้าเว็บจาก url ```http://www.thaigov.go.th/news/contents/details/NNN``` โดยที่ NNN คือเลขจำนวนเต็ม
- เปลี่ยนค่าตัวแปร i ในไฟล์เป็นเลขที่ต้องการเริ่มเก็บ
- clean.py สำหรับทำความสะอาดข้อมูลเบื้องต้น โดยจะลบช่องว่างหน้าและท้ายบรรทัด ลบบรรทัดว่าง
- ```clean.py ชื่อไฟล์```
- ```clean.py ชื่อไฟล์1 ชื่อไฟล์2```
- ```clean.py *.txt```
We build Thai NLP.
PyThaiNLP |
jamescalam/langchain-docs | 2023-03-22T11:24:00.000Z | [
"region:us"
] | jamescalam | null | null | null | 11 | 14 | Entry not found |
niv-al/instruct | 2023-03-24T19:12:36.000Z | [
"task_categories:question-answering",
"task_categories:text-generation",
"task_categories:text2text-generation",
"task_categories:table-question-answering",
"size_categories:10M<n<100M",
"language:en",
"license:openrail",
"region:us"
] | niv-al | null | null | null | 9 | 14 | ---
license: openrail
task_categories:
- question-answering
- text-generation
- text2text-generation
- table-question-answering
language:
- en
pretty_name: Instruct
size_categories:
- 10M<n<100M
---
# Dataset Card for Instruct
Based on Alpaca's instruction finetuning.
```
"Below is an instruction that describes a task, paired with an input that provides further context.\n"
"Write a response that appropriately completes the request\n"
"### Instruction:\n{instruction}\n\n### Input:\n{input}\n\n### Response:"
``` |
Francesco/hand-gestures-jps7z | 2023-03-30T09:18:38.000Z | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
] | Francesco | null | null | null | 0 | 14 | ---
dataset_info:
features:
- name: image_id
dtype: int64
- name: image
dtype: image
- name: width
dtype: int32
- name: height
dtype: int32
- name: objects
sequence:
- name: id
dtype: int64
- name: area
dtype: int64
- name: bbox
sequence: float32
length: 4
- name: category
dtype:
class_label:
names:
'0': hand-gestures
'1': 0
'2': 1
'3': 2
'4': 3
'5': 4
'6': 5
'7': 6
'8': 7
'9': 8
'10': 9
'11': 10
'12': 11
'13': 12
'14': 13
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- cc
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- object-detection
task_ids: []
pretty_name: hand-gestures-jps7z
tags:
- rf100
---
# Dataset Card for hand-gestures-jps7z
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/hand-gestures-jps7z
- **Point of Contact:** francesco.zuppichini@gmail.com
### Dataset Summary
hand-gestures-jps7z
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/hand-gestures-jps7z
### Citation Information
```
@misc{ hand-gestures-jps7z,
title = { hand gestures jps7z Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/hand-gestures-jps7z } },
url = { https://universe.roboflow.com/object-detection/hand-gestures-jps7z },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. |
andersonbcdefg/supernatural-instructions-2m | 2023-03-30T20:45:33.000Z | [
"region:us"
] | andersonbcdefg | null | null | null | 9 | 14 | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: response
dtype: string
splits:
- name: train
num_bytes: 1859403487.079275
num_examples: 1990915
download_size: 521457643
dataset_size: 1859403487.079275
---
# Dataset Card for "supernatural-instructions-2m"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
camel-ai/physics | 2023-05-23T21:12:11.000Z | [
"task_categories:text-generation",
"language:en",
"license:cc-by-nc-4.0",
"instruction-finetuning",
"arxiv:2303.17760",
"region:us"
] | camel-ai | null | null | null | 20 | 14 | ---
license: cc-by-nc-4.0
language:
- en
tags:
- instruction-finetuning
pretty_name: CAMEL Physics
task_categories:
- text-generation
arxiv: 2303.17760
extra_gated_prompt: "By using this data, you acknowledge and agree to utilize it solely for research purposes, recognizing that the dataset may contain inaccuracies due to its artificial generation through ChatGPT."
extra_gated_fields:
Name: text
Email: text
I will adhere to the terms and conditions of this dataset: checkbox
---
# **CAMEL: Communicative Agents for “Mind” Exploration of Large Scale Language Model Society**
- **Github:** https://github.com/lightaime/camel
- **Website:** https://www.camel-ai.org/
- **Arxiv Paper:** https://arxiv.org/abs/2303.17760
## Dataset Summary
Physics dataset is composed of 20K problem-solution pairs obtained using gpt-4. The dataset problem-solutions pairs generating from 25 physics topics, 25 subtopics for each topic and 32 problems for each "topic,subtopic" pairs.
We provide the data in `physics.zip`.
## Data Fields
**The data fields for files in `physics.zip` are as follows:**
* `role_1`: assistant role
* `topic`: physics topic
* `sub_topic`: physics subtopic belonging to topic
* `message_1`: refers to the problem the assistant is asked to solve.
* `message_2`: refers to the solution provided by the assistant.
**Download in python**
```
from huggingface_hub import hf_hub_download
hf_hub_download(repo_id="camel-ai/physics", repo_type="dataset", filename="physics.zip",
local_dir="datasets/", local_dir_use_symlinks=False)
```
### Citation
```
@misc{li2023camel,
title={CAMEL: Communicative Agents for "Mind" Exploration of Large Scale Language Model Society},
author={Guohao Li and Hasan Abed Al Kader Hammoud and Hani Itani and Dmitrii Khizbullin and Bernard Ghanem},
year={2023},
eprint={2303.17760},
archivePrefix={arXiv},
primaryClass={cs.AI}
}
```
## Disclaimer:
This data was synthetically generated by GPT4 and might contain incorrect information. The dataset is there only for research purposes.
---
license: cc-by-nc-4.0
---
|
RealTimeData/bbc_news_march_2023 | 2023-04-12T20:59:10.000Z | [
"license:cc-by-2.0",
"region:us"
] | RealTimeData | null | null | null | 0 | 14 | ---
license: cc-by-2.0
---
|
ruanchaves/hatebr_por_Latn_to_spa_Latn | 2023-04-22T19:12:11.000Z | [
"region:us"
] | ruanchaves | null | null | null | 0 | 14 | ---
dataset_info:
features:
- name: instagram_comments
dtype: string
- name: offensive_language
dtype: bool
- name: offensiveness_levels
dtype: int32
- name: antisemitism
dtype: bool
- name: apology_for_the_dictatorship
dtype: bool
- name: fatphobia
dtype: bool
- name: homophobia
dtype: bool
- name: partyism
dtype: bool
- name: racism
dtype: bool
- name: religious_intolerance
dtype: bool
- name: sexism
dtype: bool
- name: xenophobia
dtype: bool
- name: offensive_&_non-hate_speech
dtype: bool
- name: non-offensive
dtype: bool
- name: specialist_1_hate_speech
dtype: bool
- name: specialist_2_hate_speech
dtype: bool
- name: specialist_3_hate_speech
dtype: bool
splits:
- name: train
num_bytes: 426153
num_examples: 4480
- name: validation
num_bytes: 94951
num_examples: 1120
- name: test
num_bytes: 120538
num_examples: 1400
download_size: 0
dataset_size: 641642
---
# Dataset Card for "hatebr_por_Latn_to_spa_Latn"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ruanchaves/rerelem_por_Latn_to_spa_Latn | 2023-04-22T19:12:38.000Z | [
"region:us"
] | ruanchaves | null | null | null | 0 | 14 | ---
dataset_info:
features:
- name: docid
dtype: string
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype: string
- name: same_text
dtype: bool
- name: __language__
dtype: string
splits:
- name: train
num_bytes: 1137478
num_examples: 2226
- name: validation
num_bytes: 379879
num_examples: 701
- name: test
num_bytes: 410261
num_examples: 805
download_size: 0
dataset_size: 1927618
---
# Dataset Card for "rerelem_por_Latn_to_spa_Latn"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ruanchaves/reli-sa_por_Latn_to_spa_Latn | 2023-04-22T19:12:49.000Z | [
"region:us"
] | ruanchaves | null | null | null | 0 | 14 | ---
dataset_info:
features:
- name: source
dtype: string
- name: title
dtype: string
- name: book
dtype: string
- name: review_id
dtype: string
- name: score
dtype: float64
- name: sentence_id
dtype: int64
- name: unique_review_id
dtype: string
- name: sentence
dtype: string
- name: label
dtype: string
splits:
- name: train
num_bytes: 1833644
num_examples: 7875
- name: validation
num_bytes: 323687
num_examples: 1348
- name: test
num_bytes: 673218
num_examples: 3288
download_size: 0
dataset_size: 2830549
---
# Dataset Card for "reli-sa_por_Latn_to_spa_Latn"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ruanchaves/porsimplessent_por_Latn_to_spa_Latn | 2023-04-22T19:13:25.000Z | [
"region:us"
] | ruanchaves | null | null | null | 0 | 14 | ---
dataset_info:
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype: int32
- name: production_id
dtype: int32
- name: level
dtype: string
- name: changed
dtype: string
- name: split
dtype: string
- name: sentence_text_from
dtype: string
- name: sentence_text_to
dtype: string
- name: __language__
dtype: string
splits:
- name: train
num_bytes: 2285502
num_examples: 4976
- name: validation
num_bytes: 652413
num_examples: 1446
- name: test
num_bytes: 776229
num_examples: 1697
download_size: 0
dataset_size: 3714144
---
# Dataset Card for "porsimplessent_por_Latn_to_spa_Latn"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ruanchaves/faquad-nli_por_Latn_to_spa_Latn | 2023-04-22T19:12:01.000Z | [
"region:us"
] | ruanchaves | null | null | null | 0 | 14 | ---
dataset_info:
features:
- name: document_index
dtype: int32
- name: document_title
dtype: string
- name: paragraph_index
dtype: int32
- name: question
dtype: string
- name: answer
dtype: string
- name: label
dtype: int32
- name: __language__
dtype: string
splits:
- name: train
num_bytes: 914711
num_examples: 3128
- name: validation
num_bytes: 197365
num_examples: 731
- name: test
num_bytes: 210232
num_examples: 650
download_size: 0
dataset_size: 1322308
---
# Dataset Card for "faquad-nli_por_Latn_to_spa_Latn"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Papersnake/people_daily_news | 2023-04-19T15:31:16.000Z | [
"license:cc0-1.0",
"region:us"
] | Papersnake | null | null | null | 9 | 14 | ---
license: cc0-1.0
---
# 人民日报（1946-2022）数据集
The dataset is part of CialloCorpus, available at https://github.com/prnake/CialloCorpus
|
ruanchaves/reli-sa_por_Latn_to_cat_Latn | 2023-04-22T19:12:08.000Z | [
"region:us"
] | ruanchaves | null | null | null | 0 | 14 | ---
dataset_info:
features:
- name: source
dtype: string
- name: title
dtype: string
- name: book
dtype: string
- name: review_id
dtype: string
- name: score
dtype: float64
- name: sentence_id
dtype: int64
- name: unique_review_id
dtype: string
- name: sentence
dtype: string
- name: label
dtype: string
splits:
- name: train
num_bytes: 1818917
num_examples: 7875
- name: validation
num_bytes: 321437
num_examples: 1348
- name: test
num_bytes: 669615
num_examples: 3288
download_size: 0
dataset_size: 2809969
---
# Dataset Card for "reli-sa_por_Latn_to_cat_Latn"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ruanchaves/faquad-nli_por_Latn_to_glg_Latn | 2023-04-22T19:12:18.000Z | [
"region:us"
] | ruanchaves | null | null | null | 0 | 14 | ---
dataset_info:
features:
- name: document_index
dtype: int32
- name: document_title
dtype: string
- name: paragraph_index
dtype: int32
- name: question
dtype: string
- name: answer
dtype: string
- name: label
dtype: int32
- name: __language__
dtype: string
splits:
- name: train
num_bytes: 868512
num_examples: 3128
- name: validation
num_bytes: 187993
num_examples: 731
- name: test
num_bytes: 200922
num_examples: 650
download_size: 0
dataset_size: 1257427
---
# Dataset Card for "faquad-nli_por_Latn_to_glg_Latn"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ruanchaves/porsimplessent_por_Latn_to_cat_Latn | 2023-04-22T19:12:28.000Z | [
"region:us"
] | ruanchaves | null | null | null | 0 | 14 | ---
dataset_info:
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype: int32
- name: production_id
dtype: int32
- name: level
dtype: string
- name: changed
dtype: string
- name: split
dtype: string
- name: sentence_text_from
dtype: string
- name: sentence_text_to
dtype: string
- name: __language__
dtype: string
splits:
- name: train
num_bytes: 2257186
num_examples: 4976
- name: validation
num_bytes: 643309
num_examples: 1446
- name: test
num_bytes: 762310
num_examples: 1697
download_size: 0
dataset_size: 3662805
---
# Dataset Card for "porsimplessent_por_Latn_to_cat_Latn"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ruanchaves/rerelem_por_Latn_to_cat_Latn | 2023-04-22T19:12:32.000Z | [
"region:us"
] | ruanchaves | null | null | null | 0 | 14 | ---
dataset_info:
features:
- name: docid
dtype: string
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype: string
- name: same_text
dtype: bool
- name: __language__
dtype: string
splits:
- name: train
num_bytes: 1081392
num_examples: 2226
- name: validation
num_bytes: 363260
num_examples: 701
- name: test
num_bytes: 383612
num_examples: 805
download_size: 0
dataset_size: 1828264
---
# Dataset Card for "rerelem_por_Latn_to_cat_Latn"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ruanchaves/hatebr_por_Latn_to_cat_Latn | 2023-04-22T19:12:45.000Z | [
"region:us"
] | ruanchaves | null | null | null | 0 | 14 | ---
dataset_info:
features:
- name: instagram_comments
dtype: string
- name: offensive_language
dtype: bool
- name: offensiveness_levels
dtype: int32
- name: antisemitism
dtype: bool
- name: apology_for_the_dictatorship
dtype: bool
- name: fatphobia
dtype: bool
- name: homophobia
dtype: bool
- name: partyism
dtype: bool
- name: racism
dtype: bool
- name: religious_intolerance
dtype: bool
- name: sexism
dtype: bool
- name: xenophobia
dtype: bool
- name: offensive_&_non-hate_speech
dtype: bool
- name: non-offensive
dtype: bool
- name: specialist_1_hate_speech
dtype: bool
- name: specialist_2_hate_speech
dtype: bool
- name: specialist_3_hate_speech
dtype: bool
splits:
- name: train
num_bytes: 381784
num_examples: 4480
- name: validation
num_bytes: 83822
num_examples: 1120
- name: test
num_bytes: 105619
num_examples: 1400
download_size: 0
dataset_size: 571225
---
# Dataset Card for "hatebr_por_Latn_to_cat_Latn"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ruanchaves/reli-sa_por_Latn_to_glg_Latn | 2023-04-22T19:12:52.000Z | [
"region:us"
] | ruanchaves | null | null | null | 0 | 14 | ---
dataset_info:
features:
- name: source
dtype: string
- name: title
dtype: string
- name: book
dtype: string
- name: review_id
dtype: string
- name: score
dtype: float64
- name: sentence_id
dtype: int64
- name: unique_review_id
dtype: string
- name: sentence
dtype: string
- name: label
dtype: string
splits:
- name: train
num_bytes: 1776947
num_examples: 7875
- name: validation
num_bytes: 313722
num_examples: 1348
- name: test
num_bytes: 652065
num_examples: 3288
download_size: 0
dataset_size: 2742734
---
# Dataset Card for "reli-sa_por_Latn_to_glg_Latn"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ruanchaves/hatebr_por_Latn_to_glg_Latn | 2023-04-22T19:12:59.000Z | [
"region:us"
] | ruanchaves | null | null | null | 0 | 14 | ---
dataset_info:
features:
- name: instagram_comments
dtype: string
- name: offensive_language
dtype: bool
- name: offensiveness_levels
dtype: int32
- name: antisemitism
dtype: bool
- name: apology_for_the_dictatorship
dtype: bool
- name: fatphobia
dtype: bool
- name: homophobia
dtype: bool
- name: partyism
dtype: bool
- name: racism
dtype: bool
- name: religious_intolerance
dtype: bool
- name: sexism
dtype: bool
- name: xenophobia
dtype: bool
- name: offensive_&_non-hate_speech
dtype: bool
- name: non-offensive
dtype: bool
- name: specialist_1_hate_speech
dtype: bool
- name: specialist_2_hate_speech
dtype: bool
- name: specialist_3_hate_speech
dtype: bool
splits:
- name: train
num_bytes: 366154
num_examples: 4480
- name: validation
num_bytes: 82771
num_examples: 1120
- name: test
num_bytes: 98956
num_examples: 1400
download_size: 0
dataset_size: 547881
---
# Dataset Card for "hatebr_por_Latn_to_glg_Latn"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ruanchaves/porsimplessent_por_Latn_to_glg_Latn | 2023-04-22T19:13:10.000Z | [
"region:us"
] | ruanchaves | null | null | null | 0 | 14 | ---
dataset_info:
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype: int32
- name: production_id
dtype: int32
- name: level
dtype: string
- name: changed
dtype: string
- name: split
dtype: string
- name: sentence_text_from
dtype: string
- name: sentence_text_to
dtype: string
- name: __language__
dtype: string
splits:
- name: train
num_bytes: 2219813
num_examples: 4976
- name: validation
num_bytes: 632532
num_examples: 1446
- name: test
num_bytes: 750592
num_examples: 1697
download_size: 0
dataset_size: 3602937
---
# Dataset Card for "porsimplessent_por_Latn_to_glg_Latn"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ruanchaves/faquad-nli_por_Latn_to_cat_Latn | 2023-04-22T19:13:18.000Z | [
"region:us"
] | ruanchaves | null | null | null | 0 | 14 | ---
dataset_info:
features:
- name: document_index
dtype: int32
- name: document_title
dtype: string
- name: paragraph_index
dtype: int32
- name: question
dtype: string
- name: answer
dtype: string
- name: label
dtype: int32
- name: __language__
dtype: string
splits:
- name: train
num_bytes: 885044
num_examples: 3128
- name: validation
num_bytes: 190538
num_examples: 731
- name: test
num_bytes: 204906
num_examples: 650
download_size: 0
dataset_size: 1280488
---
# Dataset Card for "faquad-nli_por_Latn_to_cat_Latn"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ruanchaves/rerelem_por_Latn_to_glg_Latn | 2023-04-22T19:13:28.000Z | [
"region:us"
] | ruanchaves | null | null | null | 0 | 14 | ---
dataset_info:
features:
- name: docid
dtype: string
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype: string
- name: same_text
dtype: bool
- name: __language__
dtype: string
splits:
- name: train
num_bytes: 1053646
num_examples: 2226
- name: validation
num_bytes: 341845
num_examples: 701
- name: test
num_bytes: 378419
num_examples: 805
download_size: 0
dataset_size: 1773910
---
# Dataset Card for "rerelem_por_Latn_to_glg_Latn"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
jlh/uci-adult-income | 2023-04-25T23:19:35.000Z | [
"region:us"
] | jlh | null | null | null | 0 | 14 | ---
dataset_info:
features:
- name: age
dtype: int64
- name: workclass
dtype: string
- name: fnlwgt
dtype: int64
- name: education
dtype: string
- name: education-num
dtype: int64
- name: marital-status
dtype: string
- name: occupation
dtype: string
- name: relationship
dtype: string
- name: race
dtype: string
- name: sex
dtype: string
- name: capital-gain
dtype: int64
- name: capital-loss
dtype: int64
- name: hours-per-week
dtype: int64
- name: native-country
dtype: string
- name: income
dtype:
class_label:
names:
'0': ' <=50K'
'1': ' >50K'
splits:
- name: train
num_bytes: 5552570
num_examples: 32561
download_size: 586658
dataset_size: 5552570
---
# Dataset Card for "uci-adult-income"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Thaweewat/gpteacher-20k-th | 2023-05-09T17:54:22.000Z | [
"task_categories:question-answering",
"task_categories:summarization",
"size_categories:10K<n<100K",
"language:th",
"license:cc-by-sa-3.0",
"instruction-finetuning",
"region:us"
] | Thaweewat | null | null | null | 1 | 14 | ---
license: cc-by-sa-3.0
task_categories:
- question-answering
- summarization
language:
- th
tags:
- instruction-finetuning
size_categories:
- 10K<n<100K
---
# Summary
This is a 🇹🇭 Thai-instructed dataset translated using Google Cloud Translation from [GPTeacher](https://github.com/teknium1/GPTeacher), A collection of modular datasets generated by GPT-4, General-Instruct & Roleplay-Instruct
and is comprised of around 20,000 examples with deduplication. The dataset was asked to include reasoning and thought steps in the example responses where appropriate.
Supported Tasks:
- Training LLMs
- Synthetic Data Generation
- Data Augmentation
Languages: Thai
Version: 1.0
---
|
umarzein/databricks-dolly-15k-en | 2023-05-17T06:30:05.000Z | [
"language:en",
"license:cc-by-sa-3.0",
"region:us"
] | umarzein | null | null | null | 0 | 14 | ---
license: cc-by-sa-3.0
language:
- en
---
This is a checkpoint of the databricks-dolly-15k dataset |
Abrumu/Fashion_controlnet_dataset_V3 | 2023-05-19T09:44:48.000Z | [
"region:us"
] | Abrumu | null | null | null | 10 | 14 | ---
dataset_info:
features:
- name: target
dtype: image
- name: mask
dtype: image
- name: cloth
dtype: image
- name: control
dtype: image
- name: prompt
dtype: string
- name: CLIP_captions
dtype: string
splits:
- name: train
num_bytes: 7964862365.0
num_examples: 11647
download_size: 7944023014
dataset_size: 7964862365.0
---
# Dataset Card for "Fashion_controlnet_dataset_V3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
tasksource/prontoqa | 2023-06-05T07:46:05.000Z | [
"task_categories:question-answering",
"task_categories:text-classification",
"language:en",
"license:apache-2.0",
"region:us"
] | tasksource | null | null | null | 0 | 14 | ---
license: apache-2.0
task_categories:
- question-answering
- text-classification
language:
- en
---
https://github.com/asaparov/prontoqa/
```
@article{saparov2022language,
title={Language models are greedy reasoners: A systematic formal analysis of chain-of-thought},
author={Saparov, Abulhair and He, He},
journal={arXiv preprint arXiv:2210.01240},
year={2022}
}
``` |
ttbui/html_alpaca | 2023-06-12T14:27:09.000Z | [
"region:us"
] | ttbui | null | null | null | 0 | 14 | Entry not found |
AIML-TUDA/v-lol-trains | 2023-06-30T12:22:38.000Z | [
"task_categories:image-classification",
"size_categories:10K<n<100K",
"language:en",
"license:cc-by-4.0",
"vlol",
"v-lol",
"visual logical learning",
"reasoning",
"visual reasoning",
"logical reasoning",
"ILP",
"Symbolic AI",
"logic",
"Inductive logic programming",
"arxiv:2306.07743",
... | AIML-TUDA | null | @misc{helff2023vlol,
title={V-LoL: A Diagnostic Dataset for Visual Logical Learning},
author={Lukas Helff and Wolfgang Stammer and Hikaru Shindo and Devendra Singh Dhami and Kristian Kersting},
journal={Dataset available from https://sites.google.com/view/v-lol},
year={2023},
eprint={2306.07743},
archivePrefix={arXiv},
primaryClass={cs.AI}
} | null | 2 | 14 | ---
license: cc-by-4.0
task_categories:
- image-classification
language:
- en
tags:
- vlol
- v-lol
- visual logical learning
- reasoning
- visual reasoning
- logical reasoning
- ILP
- Symbolic AI
- logic
- Inductive logic programming
pretty_name: 'V-LoL: A Diagnostic Dataset for Visual Logical Learning'
size_categories:
- 10K<n<100K
---
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage** https://sites.google.com/view/v-lol/home
- **Repository** https://github.com/ml-research/vlol-dataset-gen
- **Paper** https://arxiv.org/abs/2306.07743
- **Point of Contact:** lukas_henrik.helff@tu-darmstadt.de
### Dataset Summary
This diagnostic dataset ([website](https://sites.google.com/view/v-lol), [paper](https://doi.org/10.48550/arXiv.2306.07743)) is specifically designed to evaluate the visual logical learning capabilities of machine learning models.
It offers a seamless integration of visual and logical challenges, providing 2D images of complex visual trains,
where the classification is derived from rule-based logic.
The fundamental idea of V-LoL remains to integrate the explicit logical learning tasks of classic symbolic AI benchmarks into visually complex scenes,
creating a unique visual input that retains the challenges and versatility of explicit logic.
In doing so, V-LoL bridges the gap between symbolic AI challenges and contemporary deep learning datasets offering various visual logical learning tasks
that pose challenges for AI models across a wide spectrum of AI research, from symbolic to neural and neuro-symbolic AI.
Moreover, we provide a flexible dataset generator ([GitHub](https://github.com/ml-research/vlol-dataset-gen)) that
empowers researchers to easily exchange or modify the logical rules, thereby enabling the creation of new datasets incorperating novel logical learning challenges.
By combining visual input with logical reasoning, this dataset serves as a comprehensive benchmark for assessing the ability
of machine learning models to learn and apply logical reasoning within a visual context.
### Supported Tasks and Leaderboards
We offer a diverse set of datasets that present challenging AI tasks targeting various reasoning abilities.
The following provides an overview of the available V-LoL challenges and corresponding dataset splits.
| V-LoL Challenges | Train set | Validation set | # of train samples | # of validation samples |
| --- | --- | ----------- | --- | ----------- |
| V-LoL-Trains-TheoryX | V-LoL-Trains-TheoryX | V-LoL-Trains-TheoryX | 10000 | 2000 |
| V-LoL-Trains-Numerical | V-LoL-Trains-Numerical | V-LoL-Trains-Numerical | 10000 | 2000 |
| V-LoL-Trains-Complex | V-LoL-Trains-Complex | V-LoL-Trains-Complex | 10000 | 2000 |
| V-LoL-Blocks-TheoryX | V-LoL-Blocks-TheoryX | V-LoL-Blocks-TheoryX | 10000 | 2000 |
| V-LoL-Blocks-Numerical | V-LoL-Blocks-Numerical | V-LoL-Blocks-Numerical | 10000 | 2000 |
| V-LoL-Blocks-Complex | V-LoL-Blocks-Complex | V-LoL-Blocks-Complex | 10000 | 2000 |
| V-LoL-Trains-TheoryX-len7 | V-LoL-Trains-TheoryX | V-LoL-Trains-TheoryX-len7 | 12000 | 2000 |
| V-LoL-Trains-Numerical-len7 | V-LoL-Trains-Numerical | V-LoL-Trains-Numerical-len7 | 12000 | 2000 |
| V-LoL-Trains-Complex-len7 | V-LoL-Trains-Complex | V-LoL-Trains-Complex-len7 | 12000 | 2000 |
| V-LoL-Random-Trains-TheoryX | V-LoL-Trains-TheoryX | V-LoL-Random-Trains-TheoryX | 12000 | 12000 |
| V-LoL-Random-Blocks-TheoryX | V-LoL-Blocks-TheoryX | V-LoL-Random-Blocks-TheoryX | 12000 | 12000 |
The following gives more detailed explanations of the different V-LoL challenges:
Logical complexity:
- Theory X (marked 'TheoryX'): The train has either a short, closed car or a car with a barrel load is somewhere behind a car with a golden vase load. This rule was originally introduced as "Theory X" in the new East-West Challenge.
- Numerical rule (marked 'Numerical'): The train has a car where its car position equals its number of payloads which equals its number of wheel axles.
- Complex rule (marked 'Complex'): Either, there is a car with a car number which is smaller than its number of wheel axles count and smaller than the number of loads, or there is a short and a long car with the same colour where the position number of the short car is smaller than the number of wheel axles of the long car, or the train has three differently coloured cars. We refer to Tab. 3 in the supp. for more insights on required reasoning properties for each rule.
Visual complexity:
- Realistic train representaions. (marked 'Trains')
- Block representation. (marked 'Blocks')
OOD Trains:
- A train carrying 2-4 cars. (default)
- A train carrying 7 cars. (marked 'len7')
Train attribute distributions:
- Michalski attribute distribution. (default)
- Random attribute distribution. (marked 'Random')
### Languages
English
## Dataset Structure
### Data Instances
```
{
'image': <PIL.PngImagePlugin.PngImageFile image mode=RGBA size=480x270 at 0x1351D0EE0>,
'label': 1
}
```
### Data Fields
The data instances have the following fields:
- image: A PIL.Image.Image object containing the image. Note that when accessing the image column: dataset[0]["image"] the image file is automatically decoded
Decoding of a large number of image files might take a significant amount of time.
Thus it is important to first query the sample index before the "image" column, i.e. dataset[0]["image"] should always be preferred over dataset["image"][0].
- label: an int classification label.
Class labels mapping:
| ID | Class |
| --- | ----------- |
| 0 | Westbound |
| 1 | Eastbound |
### Data Splits
See tasks.
## Dataset Creation
### Curation Rationale
Despite the successes of recent developments in visual AI, different shortcomings still exist;
from missing exact logical reasoning, to abstract generalization abilities, to understanding complex and noisy scenes.
Unfortunately, existing benchmarks, were not designed to capture more than a few of these aspects.
Whereas deep learning datasets focus on visually complex data but simple visual reasoning tasks,
inductive logic datasets involve complex logical learning tasks, however, lack the visual component.
To address this, we propose the visual logical learning dataset, V-LoL, that seamlessly combines visual and logical challenges.
Notably, we introduce the first instantiation of V-LoL, V-LoL-Train, -- a visual rendition of a classic benchmark in symbolic AI, the Michalski train problem.
By incorporating intricate visual scenes and flexible logical reasoning tasks within a versatile framework,
V-LoL-Train provides a platform for investigating a wide range of visual logical learning challenges.
To create new V-LoL challenges, we provide a comprehensive guide and resources in our [GitHub repository](https://github.com/ml-research/vlol-dataset-gen).
The repository offers a collection of tools and code that enable researchers and practitioners to easily generate new V-LoL challenges based on their specific requirements. By referring to our GitHub repository, users can access the necessary documentation, code samples, and instructions to create and customize their own V-LoL challenges.
### Source Data
#### Initial Data Collection and Normalization
The individual datasets are generated using the V-LoL-Train generator. See [GitHub repository](https://github.com/ml-research/vlol-dataset-gen).
#### Who are the source language producers?
See [GitHub repository](https://github.com/ml-research/vlol-dataset-gen).
### Annotations
#### Annotation process
The images are generated in two steps: first sampling a valid symbolic representation of a train and then visualizing it within a 3D scene.
#### Who are the annotators?
Annotations are automatically derived using a python, prolog, and blender pipline. See [GitHub repository](https://github.com/ml-research/vlol-dataset-gen).
### Personal and Sensitive Information
The dataset does not contain personal nor sensitive information.
## Considerations for Using the Data
### Social Impact of Dataset
The dataset has no social impact.
### Discussion of Biases
Please refer to our paper.
### Other Known Limitations
Please refer to our paper.
## Additional Information
### Dataset Curators
Lukas Helff
### Licensing Information
MIT License
### Citation Information
@misc{helff2023vlol,
title={V-LoL: A Diagnostic Dataset for Visual Logical Learning},
author={Lukas Helff and Wolfgang Stammer and Hikaru Shindo and Devendra Singh Dhami and Kristian Kersting},
journal={Dataset available from https://sites.google.com/view/v-lol},
year={2023},
eprint={2306.07743},
archivePrefix={arXiv},
primaryClass={cs.AI}
}
### Contributions
Lukas Helff, Wolfgang Stammer, Hikaru Shindo, Devendra Singh Dhami, Kristian Kersting |
graelo/cancre | 2023-06-20T21:44:12.000Z | [
"task_categories:text-generation",
"task_categories:text-classification",
"task_ids:language-modeling",
"annotations_creators:no-annotation",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:n<1K",
"size_categories:1K<n<10K",
"size_categories:10K<n<100K",
"source_d... | graelo | null | null | null | 0 | 14 | ---
annotations_creators:
- no-annotation
language_creators:
- crowdsourced
pretty_name: Cancre (French Grammatical Errors)
paperswithcode_id: null
license:
- cc-by-sa-3.0
task_categories:
- text-generation
- text-classification
task_ids:
- language-modeling
source_datasets:
- original
multilinguality:
- monolingual
size_categories:
- n<1K
- 1K<n<10K
- 10K<n<100K
language:
- fr
dataset_info:
features:
- name: phrase1
dtype: string
- name: phrase2
dtype: string
- name: explication
dtype: string
splits:
- name: train
num_bytes: 1934861
num_examples: 10000
- name: test
num_bytes: 327827
num_examples: 1681
download_size: 2866947
dataset_size: 2262688
---
# French Grammatical Errors
This dataset contains pairs of sentences and an explanation:
- "phrase1" is a french sentence containing a grammatical error
- "phrase2" is the same sentence without any error (please reach out if you think
an error is present -- I could not see any)
- "explication" is some text explaining the grammatical error
## Release Notes
`0.1.0`
- No error category is present, you would have to infer it from the `explication` column
|
KaiLv/UDR_COLA | 2023-06-21T12:27:30.000Z | [
"region:us"
] | KaiLv | null | null | null | 0 | 14 | ---
dataset_info:
features:
- name: idx
dtype: int64
- name: sentence
dtype: string
- name: label
dtype:
class_label:
names:
'0': '0'
'1': '1'
splits:
- name: train
num_bytes: 517945
num_examples: 8532
- name: test
num_bytes: 31522
num_examples: 527
download_size: 272237
dataset_size: 549467
---
# Dataset Card for "UDR_COLA"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
KaiLv/UDR_ComE | 2023-06-21T12:35:45.000Z | [
"region:us"
] | KaiLv | null | null | null | 0 | 14 | ---
dataset_info:
features:
- name: idx
dtype: int64
- name: label
dtype: string
- name: question
dtype: string
- name: choices
dtype: string
- name: len_question
dtype: int64
- name: max_len_choices
dtype: int64
splits:
- name: train
num_bytes: 4855852
num_examples: 9996
- name: test
num_bytes: 468814
num_examples: 1000
- name: debug
num_bytes: 2432484
num_examples: 5000
download_size: 3748196
dataset_size: 7757150
---
# Dataset Card for "UDR_ComE"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
portuguese-benchmark-datasets/BLUEX | 2023-09-19T01:02:52.000Z | [
"arxiv:2307.05410",
"region:us"
] | portuguese-benchmark-datasets | null | null | null | 3 | 14 | ---
dataset_info:
features:
- name: question
dtype: string
- name: number
dtype: int64
- name: id
dtype: string
- name: alternatives
sequence: string
- name: associated_images
sequence: string
- name: answer
dtype: string
- name: has_associated_images
dtype: bool
- name: alternatives_type
dtype: string
- name: subject
sequence: string
- name: TU
dtype: bool
- name: IU
dtype: bool
- name: MR
dtype: bool
- name: ML
dtype: bool
- name: BK
dtype: bool
- name: PRK
dtype: bool
splits:
- name: questions
num_bytes: 54794231
num_examples: 1098
download_size: 49630117
dataset_size: 54794231
---
# BLUEX
There is a repository with the minimal code for using this dataset available [here](https://github.com/Portuguese-Benchmark-Datasets/BLUEX). If you use this dataset for research, please cite the paper:
```bibtex
@misc{almeida2023bluex,
title={BLUEX: A benchmark based on Brazilian Leading Universities Entrance eXams},
author={Thales Sales Almeida and Thiago Laitz and Giovana K. Bonás and Rodrigo Nogueira},
year={2023},
eprint={2307.05410},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
jondurbin/airoboros-gpt4-1.4.1 | 2023-06-26T09:56:34.000Z | [
"license:cc-by-nc-4.0",
"region:us"
] | jondurbin | null | null | null | 34 | 14 | ---
license: cc-by-nc-4.0
---
The same as 1.4, but with coding updates:
- rosettacode instructions were removed, due to a few issues found when spot-checking examples
- limited the coding examples to fewer languages, to test if a more focused dataset would produce better results |
epinnock/intercode-nl2bash-curated | 2023-07-06T02:09:17.000Z | [
"region:us"
] | epinnock | null | null | null | 2 | 14 | ---
dataset_info:
features:
- name: query
dtype: string
- name: gold
dtype: string
splits:
- name: train
num_bytes: 30692
num_examples: 200
download_size: 16521
dataset_size: 30692
---
# Dataset Card for "intercode-nl2bash-curated"
# NL2Bash Data Augmentation
* Reproduction of dataset found here: https://github.com/princeton-nlp/intercode
* Adopted from [nl2bash](https://github.com/TellinaTool/nl2bash) by manual curation
* This dataset consists of 200 natural language to bash command <query, gold> pairs
* MIT License
|
CptNemo/small-shakespear-sonets-1 | 2023-07-06T10:50:10.000Z | [
"license:apache-2.0",
"region:us"
] | CptNemo | null | null | null | 0 | 14 | ---
license: apache-2.0
---
This dataset is collection of Shakespear sonnet's, with a query for LLM. |
ssbuild/alpaca_finance_en | 2023-07-09T03:32:00.000Z | [
"license:apache-2.0",
"region:us"
] | ssbuild | null | null | null | 3 | 14 | ---
license: apache-2.0
---
|
refugee-law-lab/canadian-legal-data | 2023-07-30T22:47:52.000Z | [
"size_categories:100K<n<1M",
"language:en",
"language:fr",
"license:cc-by-nc-4.0",
"arxiv:2207.00220",
"region:us"
] | refugee-law-lab | null | null | null | 0 | 14 | ---
license: cc-by-nc-4.0
language:
- en
- fr
size_categories:
- 100K<n<1M
---
# Refugee Law Lab: Canadian Legal Data
## Dataset Summary
The [Refugee Law Lab](https://refugeelab.ca) supports bulk open-access to Canadian legal data to facilitate research and advocacy.
Bulk open-access helps avoid asymmetrical access-to-justice and amplification of marginalization that
results when commercial actors leverage proprietary
legal datasets for profit -- a particular concern in the border control setting.
The Canadian Legal Data dataset includes the unofficial full text of thousands of court and tribunal
decisions at the federal level. It can be used for legal analytics (i.e. identifying patterns in legal
decision-making), to test ML and NLP tools on a bilingual dataset of Canadian legal materials, and to
pretrain language models for various tasks.
## Dataset Structure
### Data Instances
#### Court Decisions
- SCC: Full text of Supreme Court of Canada decisions, based on the Refugee Law Lab's
[Supreme Court of Canada Bulk Decisions Dataset](https://refugeelab.ca/bulk-data/scc/) (1877 – 2023)
- FCA: Full text of Federal Court of Appeal (Canada) decisions that have been given a neutral citation, based on
the Refugee Law Lab's [Federal Court of Appeal Bulk Decisions Dataset](https://refugeelab.ca/bulk-data/fca/) (2001-2023)
- FC: Full text of Federal Court (Canada) decisions that have been given a neutral citation, based on
the Refugee Law Lab's [Federal Court Bulk Decisions Dataset](https://refugeelab.ca/bulk-data/fc/) (2001-2023)
- TCC: Full text of Tax Court of Canada decisions that have been given a neutral citation, based on
the Refugee Law Lab's [Tax Court of Canada Bulk Decisions Dataset](https://refugeelab.ca/bulk-data/tcc/) (2003-2023)
#### Tribunal Decisions
- RLLR: Full text of Immigration and Refugee Board, Refugee Protection Division Decisions, as reported in the
[Refugee Law Lab Reporter](https://refugeelab.ca/rllr), based on the Refugee Law Lab's [RLLR Bulk Decisions Dataset](https://refugeelab.ca/bulk-data/rllr/) (2019 – 2022)
### Data Fields
- citation1 (string): Legal citation for the document (neutral citation where available)
- citation2 (string): For some documents multiple citations are available (e.g. for some periods
the Supreme Court of Canada provided both official reported citation and neutral citation)
- dataset (string): Name of the data instance (e.g. "SCC", "FCA", "FC", "TCC", etc)
- year (int32): Year of the document date, which can be useful for filtering
- name (string): Name of the document, typically the style of cause of a case
- language (string): Language of the document, "en" for English, "fr" for French, "" for no language specified
- document_date (string): Date of the document, typically the date of a decision (yyyy-mm-dd)
- source_url (string): URL where the document was scraped and where the official version can be found
- scraped_timestamp (string): Date the document was scraped (yyyy-mm-dd)
- unofficial_text (string): Full text of the document (unofficial version, for official version see source_url)
- other (string): Field for additional metadata in JSON format, currently a blank string for most datasets
### Data Languages
Many documents are available in both English and French. Some are only available in one of the two languages.
### Data Splits
The data has not been split, so all files are in the train split. If splitting for training/validation,
some thought should be given to whether it is necessary to limit to one language or to ensure that both
English and French versions of the same documents (where available) are put into the same split.
### Data Loading
To load all data instances:
```python
from datasets import load_dataset
dataset = load_dataset("refugee-law-lab/canadian-legal-data", split="train")
```
To load only a specific data instance, for example only the SCC data instance:
```python
from datasets import load_dataset
dataset = load_dataset("refugee-law-lab/canadian-legal-data", split="train", data_dir="SCC")
```
## Dataset Creation
### Curation Rationale
The dataset includes all the [Bulk Legal Data](https://refugeelab.ca/bulk-data) made publicly available by
the Refugee Law Lab. The Lab has focused on federal courts (e.g. Supreme Court of Canada, Federal Court of
Appeal, Federal Court) as well as federal administrative tribunals (e.g. Immigration and Refugee Board) because
immigration and refugee law, which is the main area of interest of the Lab, operates mostly at the federal level.
### Source Data
#### Initial Data Collection and Normalization
Details (including links to github repos with code) are available via links on the Refugee Law Lab's
[Bulk Legal Data](https://refugeelab.ca/bulk-data/) page.
### Personal and Sensitive Information
Documents may include personal and sensitive information. All documents have been published online or
otherwise released publicly by the relevant court or tribunal. While the open court principle mandates
that court (and some tribunal) materials be made available to the public, there are privacy risks when these
materials become easily and widely available. These privacy risks are particularly acute for marginalized groups,
including refugees and other non-citizens whose personal and sensitive information is included in some of the
documents in this dataset. For example, imagine a repressive government working with private data aggregators to
collect information that is used to target families of political opponents who have sought asylum abroad.
One mechanism used to try to achieve a balance between the open court principle
and privacy is that in publishing the documents in this dataset, the relevant courts and tribunals prohibit
search engines from indexing the documents. Users of this data are required to do the same.
### Non-Official Versions
Documents included in this dataset are unofficial copies. For official versions published by
the Government of Canada, please see the source URLs.
### Non-Affiliation / Endorsement
The reproduction of documents in this dataset was not done in affiliation with, or with the endorsement of
the Government of Canada.
## Considerations for Using the Data
### Social Impact of Dataset
The Refugee Law Lab recognizes that this dataset -- and further research using the dataset -- raises challenging
questions about how to balance protecting privacy, enhancing government transparency, addressing information
asymmetries, and building technologies that leverage data to advance the rights and interests of
refugees and other displaced people, as well as assisting those working with them (rather than technologies that
[enhance the power of states](https://citizenlab.ca/2018/09/bots-at-the-gate-human-rights-analysis-automated-decision-making-in-canadas-immigration-refugee-system/)
to control the movement of people across borders).
More broadly, the Refugee Law Lab also recognizes that considerations around privacy and data protection are complex
and evolving. When working on migration, refugee law, data, technology and surveillance, we strive to foreground
intersectional understandings of the systemic harms perpetuated against groups historically made marginalized. We
encourage other users to do the same.
We also encourage users to try to avoid participating in building technologies that harm refugees and other
marginalized groups, as well as to connect with [community organizations](https://www.migrationtechmonitor.com/ways-to-help)
working in this space, and to [listen directly](https://www.migrationtechmonitor.com/about-us) and learn from people who are affected by new technologies.
We will review the use these datasets periodically to examine whether continuing to publicly release these datasets achieves
the Refugee Law Lab's goals of advancing the rights and interests of refugees and other marginalized groups without creating
disproportionate risks and harms, including risks related to privacy and human rights.
### Discussion of Biases
The dataset reflects many biases present in legal decision-making, including biases based on race, immigration status, gender, sexual orientation, religion, disability, socio-economic class, and other intersecting categories of discrimination.
### Other Known Limitations
Publicly available court and tribunal decisions are not a representative sample of legal decision-making -- and in some cases may reflect
significantly skewed samples. To give one example, the vast majority of Federal Court judicial reviews of refugee determinations involve negative
first instance decisions even thought most first instance decisions are positive (this occurs because the government seldom applies for judicial
reviews of positive first instance decisions whereas claimants frequently apply for judicial review of negative decisions). As such, generative models
built partly on this dataset risk amplifying negative refugee decision-making (rather than more common positive refugee decision-making). Due to the ways that
legal datasets may be skewed, users of this dataset are encouraged to collaborate with or consult domain experts.
## Additional Information
### Licensing Information
Attribution-NonCommercial 4.0 International ([CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/))
NOTE: Users must also comply with upstream licensing for the [SCC](https://www.scc-csc.ca/terms-avis/notice-enonce-eng.aspx),
[FCA](https://www.fca-caf.gc.ca/en/pages/important-notices) & [FC](https://www.fct-cf.gc.ca/en/pages/important-notices) data instances, as
well as requests on source urls not to allow indexing of the documents by search engines to protect privacy. As a result, users must
not make the data available in formats or locations that can be indexed by search engines.
### Warranties / Representations
We make no warranties or representations that the data included in this dataset is complete or accurate. Data
were obtained through academic research projects, including projects that use automated processes.
While we try to make the data as accurate as possible, our methodologies may result in
inaccurate or outdated data. As such, data should be viewed as preliminary information aimed to prompt
further research and discussion, rather than as definitive information.
### Dataset Curators
[Sean Rehaag](https://www.osgoode.yorku.ca/faculty-and-staff/rehaag-sean), Osgoode Hall Law School Professor & Director of the Refugee Law Lab
### Citation Information
Sean Rehaag, "Refugee Law Lab: Canadian Legal Data" (2023) online: Hugging Face: <https://huggingface.co/datasets/refugee-law-lab/canadian-legal-data>.
### Acknowledgements
This project draws on research supported by the Social Sciences and Humanities Research Council and the Law Foundation of Ontario.
The project was inspired in part by the excellent prior work by [pile-of-law](https://huggingface.co/datasets/pile-of-law/pile-of-law) (Peter Henderson et al, "Pile of Law: Learning
Responsible Data Filtering from the Law and a 256GB Open-Source Legal Dataset" (2022), online: arXiv: https://arxiv.org/abs/2207.00220). |
kongshan/cl_code_search_net | 2023-07-16T06:59:37.000Z | [
"region:us"
] | kongshan | CodeSearchNet corpus contains about 6 million functions from open-source code spanning six programming languages (Go, Java, JavaScript, PHP, Python, and Ruby). The CodeSearchNet Corpus also contains automatically generated query-like natural language for 2 million functions, obtained from mechanically scraping and preprocessing associated function documentation. | @inproceedings{Gao2023repeat,
title={Keeping Pace with Ever-Increasing Data: Towards Continual Learning of Code Intelligence Models},
author={Shuzheng Gao, Hongyu Zhang, Cuiyun Gao, and Chaozheng Wang},
booktitle={ICSE},
year={2023},
publisher={IEEE}
} | null | 0 | 14 | Entry not found |
wbxlala/Epilepsy_seizure_prediction_str | 2023-07-21T09:14:58.000Z | [
"license:cc-by-4.0",
"region:us"
] | wbxlala | null | null | null | 0 | 14 | ---
license: cc-by-4.0
---
|
sam-mosaic/orca-gpt4-chatml | 2023-07-21T23:31:37.000Z | [
"region:us"
] | sam-mosaic | null | null | null | 2 | 14 | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: response
dtype: string
splits:
- name: train
num_bytes: 1868875699
num_examples: 994896
download_size: 1050255655
dataset_size: 1868875699
---
# Dataset Card for "orca-gpt4-chatml"
As of 7/21/23, the [OpenOrca](https://huggingface.co/datasets/Open-Orca/OpenOrca) dataset has something malformed, causing a crash when you try to load it in `dataset`.
The GPT-4 data looks good though, so I preprocess it and push it up here in ChatML format. |
Johnade/consumer_complaints_cfpb | 2023-07-26T13:00:56.000Z | [
"license:wtfpl",
"region:us"
] | Johnade | null | null | null | 1 | 14 | ---
license: wtfpl
---
|
DynamicSuperb/SarcasmDetection_Mustard | 2023-07-26T04:55:38.000Z | [
"region:us"
] | DynamicSuperb | null | null | null | 0 | 14 | ---
dataset_info:
features:
- name: file
dtype: string
- name: audio
dtype: audio
- name: utterance
dtype: string
- name: speaker
dtype: string
- name: context
sequence: string
- name: context_speakers
sequence: string
- name: show
dtype: string
- name: label
dtype: bool
- name: instruction
dtype: string
splits:
- name: test
num_bytes: 115618860.0
num_examples: 690
download_size: 115326889
dataset_size: 115618860.0
---
# Dataset Card for "sarcasm_detection_mustard"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Ankursingh/openwebtext_10K | 2023-07-27T01:47:37.000Z | [
"license:mpl-2.0",
"region:us"
] | Ankursingh | null | null | null | 0 | 14 | ---
license: mpl-2.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: val
path: data/val-*
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 49626451.28403377
num_examples: 10000
- name: val
num_bytes: 19885319.02951233
num_examples: 4007
download_size: 41616125
dataset_size: 69511770.31354609
---
|
Elliot4AI/openassistant-guanaco-chinese | 2023-07-27T04:59:21.000Z | [
"task_categories:question-answering",
"task_categories:text-generation",
"task_categories:conversational",
"size_categories:1K<n<10K",
"language:zh",
"license:apache-2.0",
"biology",
"finance",
"art",
"region:us"
] | Elliot4AI | null | null | null | 0 | 14 | ---
license: apache-2.0
task_categories:
- question-answering
- text-generation
- conversational
language:
- zh
tags:
- biology
- finance
- art
pretty_name: fine-turn dataset 中文数据集
size_categories:
- 1K<n<10K
---
### Dataset Summary
🏡🏡🏡🏡Fine-turn Dataset:中文数据集🏡🏡🏡🏡
😀😀😀😀😀😀😀😀 这个数据集是timdettmers/openassistant-guanaco的中文版本，是直接翻译过来，没有经过人为检查语法。 对timdettmers/openassistant-guanaco的描述，请看他的dataset card。 License: Apache 2.0
😀😀😀😀😀😀😀😀 This data set is the Chinese version of timdettmers/openassistant-guanaco, which is directly translated without human-checked grammar. For a description of timdettmers/openassistant-guanaco, see its dataset card. License: Apache 2.0 |
imoxto/prompt_injection_cleaned_dataset | 2023-08-07T15:31:57.000Z | [
"region:us"
] | imoxto | null | null | null | 0 | 14 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: level
dtype: int64
- name: prompt
dtype: string
- name: user_input
dtype: string
- name: completion
dtype: string
- name: model
dtype: string
- name: expected_completion
dtype: string
- name: token_count
dtype: int64
- name: correct
dtype: bool
- name: error
dtype: bool
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 529771818
num_examples: 374573
- name: validation
num_bytes: 115495832
num_examples: 80266
- name: test
num_bytes: 114490591
num_examples: 80266
download_size: 243813448
dataset_size: 759758241
---
# Dataset Card for "prompt_injection_cleaned_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
pie/cdcp | 2023-08-08T10:18:53.000Z | [
"region:us"
] | pie | null | null | null | 0 | 14 | Entry not found |
amitysolution/Pantip_QA_200000_20220220 | 2023-08-09T03:27:57.000Z | [
"region:us"
] | amitysolution | null | null | null | 0 | 14 | Entry not found |
EgilKarlsen/AA_BERT_Baseline | 2023-08-23T03:37:43.000Z | [
"region:us"
] | EgilKarlsen | null | null | null | 0 | 14 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: '0'
dtype: float32
- name: '1'
dtype: float32
- name: '2'
dtype: float32
- name: '3'
dtype: float32
- name: '4'
dtype: float32
- name: '5'
dtype: float32
- name: '6'
dtype: float32
- name: '7'
dtype: float32
- name: '8'
dtype: float32
- name: '9'
dtype: float32
- name: '10'
dtype: float32
- name: '11'
dtype: float32
- name: '12'
dtype: float32
- name: '13'
dtype: float32
- name: '14'
dtype: float32
- name: '15'
dtype: float32
- name: '16'
dtype: float32
- name: '17'
dtype: float32
- name: '18'
dtype: float32
- name: '19'
dtype: float32
- name: '20'
dtype: float32
- name: '21'
dtype: float32
- name: '22'
dtype: float32
- name: '23'
dtype: float32
- name: '24'
dtype: float32
- name: '25'
dtype: float32
- name: '26'
dtype: float32
- name: '27'
dtype: float32
- name: '28'
dtype: float32
- name: '29'
dtype: float32
- name: '30'
dtype: float32
- name: '31'
dtype: float32
- name: '32'
dtype: float32
- name: '33'
dtype: float32
- name: '34'
dtype: float32
- name: '35'
dtype: float32
- name: '36'
dtype: float32
- name: '37'
dtype: float32
- name: '38'
dtype: float32
- name: '39'
dtype: float32
- name: '40'
dtype: float32
- name: '41'
dtype: float32
- name: '42'
dtype: float32
- name: '43'
dtype: float32
- name: '44'
dtype: float32
- name: '45'
dtype: float32
- name: '46'
dtype: float32
- name: '47'
dtype: float32
- name: '48'
dtype: float32
- name: '49'
dtype: float32
- name: '50'
dtype: float32
- name: '51'
dtype: float32
- name: '52'
dtype: float32
- name: '53'
dtype: float32
- name: '54'
dtype: float32
- name: '55'
dtype: float32
- name: '56'
dtype: float32
- name: '57'
dtype: float32
- name: '58'
dtype: float32
- name: '59'
dtype: float32
- name: '60'
dtype: float32
- name: '61'
dtype: float32
- name: '62'
dtype: float32
- name: '63'
dtype: float32
- name: '64'
dtype: float32
- name: '65'
dtype: float32
- name: '66'
dtype: float32
- name: '67'
dtype: float32
- name: '68'
dtype: float32
- name: '69'
dtype: float32
- name: '70'
dtype: float32
- name: '71'
dtype: float32
- name: '72'
dtype: float32
- name: '73'
dtype: float32
- name: '74'
dtype: float32
- name: '75'
dtype: float32
- name: '76'
dtype: float32
- name: '77'
dtype: float32
- name: '78'
dtype: float32
- name: '79'
dtype: float32
- name: '80'
dtype: float32
- name: '81'
dtype: float32
- name: '82'
dtype: float32
- name: '83'
dtype: float32
- name: '84'
dtype: float32
- name: '85'
dtype: float32
- name: '86'
dtype: float32
- name: '87'
dtype: float32
- name: '88'
dtype: float32
- name: '89'
dtype: float32
- name: '90'
dtype: float32
- name: '91'
dtype: float32
- name: '92'
dtype: float32
- name: '93'
dtype: float32
- name: '94'
dtype: float32
- name: '95'
dtype: float32
- name: '96'
dtype: float32
- name: '97'
dtype: float32
- name: '98'
dtype: float32
- name: '99'
dtype: float32
- name: '100'
dtype: float32
- name: '101'
dtype: float32
- name: '102'
dtype: float32
- name: '103'
dtype: float32
- name: '104'
dtype: float32
- name: '105'
dtype: float32
- name: '106'
dtype: float32
- name: '107'
dtype: float32
- name: '108'
dtype: float32
- name: '109'
dtype: float32
- name: '110'
dtype: float32
- name: '111'
dtype: float32
- name: '112'
dtype: float32
- name: '113'
dtype: float32
- name: '114'
dtype: float32
- name: '115'
dtype: float32
- name: '116'
dtype: float32
- name: '117'
dtype: float32
- name: '118'
dtype: float32
- name: '119'
dtype: float32
- name: '120'
dtype: float32
- name: '121'
dtype: float32
- name: '122'
dtype: float32
- name: '123'
dtype: float32
- name: '124'
dtype: float32
- name: '125'
dtype: float32
- name: '126'
dtype: float32
- name: '127'
dtype: float32
- name: '128'
dtype: float32
- name: '129'
dtype: float32
- name: '130'
dtype: float32
- name: '131'
dtype: float32
- name: '132'
dtype: float32
- name: '133'
dtype: float32
- name: '134'
dtype: float32
- name: '135'
dtype: float32
- name: '136'
dtype: float32
- name: '137'
dtype: float32
- name: '138'
dtype: float32
- name: '139'
dtype: float32
- name: '140'
dtype: float32
- name: '141'
dtype: float32
- name: '142'
dtype: float32
- name: '143'
dtype: float32
- name: '144'
dtype: float32
- name: '145'
dtype: float32
- name: '146'
dtype: float32
- name: '147'
dtype: float32
- name: '148'
dtype: float32
- name: '149'
dtype: float32
- name: '150'
dtype: float32
- name: '151'
dtype: float32
- name: '152'
dtype: float32
- name: '153'
dtype: float32
- name: '154'
dtype: float32
- name: '155'
dtype: float32
- name: '156'
dtype: float32
- name: '157'
dtype: float32
- name: '158'
dtype: float32
- name: '159'
dtype: float32
- name: '160'
dtype: float32
- name: '161'
dtype: float32
- name: '162'
dtype: float32
- name: '163'
dtype: float32
- name: '164'
dtype: float32
- name: '165'
dtype: float32
- name: '166'
dtype: float32
- name: '167'
dtype: float32
- name: '168'
dtype: float32
- name: '169'
dtype: float32
- name: '170'
dtype: float32
- name: '171'
dtype: float32
- name: '172'
dtype: float32
- name: '173'
dtype: float32
- name: '174'
dtype: float32
- name: '175'
dtype: float32
- name: '176'
dtype: float32
- name: '177'
dtype: float32
- name: '178'
dtype: float32
- name: '179'
dtype: float32
- name: '180'
dtype: float32
- name: '181'
dtype: float32
- name: '182'
dtype: float32
- name: '183'
dtype: float32
- name: '184'
dtype: float32
- name: '185'
dtype: float32
- name: '186'
dtype: float32
- name: '187'
dtype: float32
- name: '188'
dtype: float32
- name: '189'
dtype: float32
- name: '190'
dtype: float32
- name: '191'
dtype: float32
- name: '192'
dtype: float32
- name: '193'
dtype: float32
- name: '194'
dtype: float32
- name: '195'
dtype: float32
- name: '196'
dtype: float32
- name: '197'
dtype: float32
- name: '198'
dtype: float32
- name: '199'
dtype: float32
- name: '200'
dtype: float32
- name: '201'
dtype: float32
- name: '202'
dtype: float32
- name: '203'
dtype: float32
- name: '204'
dtype: float32
- name: '205'
dtype: float32
- name: '206'
dtype: float32
- name: '207'
dtype: float32
- name: '208'
dtype: float32
- name: '209'
dtype: float32
- name: '210'
dtype: float32
- name: '211'
dtype: float32
- name: '212'
dtype: float32
- name: '213'
dtype: float32
- name: '214'
dtype: float32
- name: '215'
dtype: float32
- name: '216'
dtype: float32
- name: '217'
dtype: float32
- name: '218'
dtype: float32
- name: '219'
dtype: float32
- name: '220'
dtype: float32
- name: '221'
dtype: float32
- name: '222'
dtype: float32
- name: '223'
dtype: float32
- name: '224'
dtype: float32
- name: '225'
dtype: float32
- name: '226'
dtype: float32
- name: '227'
dtype: float32
- name: '228'
dtype: float32
- name: '229'
dtype: float32
- name: '230'
dtype: float32
- name: '231'
dtype: float32
- name: '232'
dtype: float32
- name: '233'
dtype: float32
- name: '234'
dtype: float32
- name: '235'
dtype: float32
- name: '236'
dtype: float32
- name: '237'
dtype: float32
- name: '238'
dtype: float32
- name: '239'
dtype: float32
- name: '240'
dtype: float32
- name: '241'
dtype: float32
- name: '242'
dtype: float32
- name: '243'
dtype: float32
- name: '244'
dtype: float32
- name: '245'
dtype: float32
- name: '246'
dtype: float32
- name: '247'
dtype: float32
- name: '248'
dtype: float32
- name: '249'
dtype: float32
- name: '250'
dtype: float32
- name: '251'
dtype: float32
- name: '252'
dtype: float32
- name: '253'
dtype: float32
- name: '254'
dtype: float32
- name: '255'
dtype: float32
- name: '256'
dtype: float32
- name: '257'
dtype: float32
- name: '258'
dtype: float32
- name: '259'
dtype: float32
- name: '260'
dtype: float32
- name: '261'
dtype: float32
- name: '262'
dtype: float32
- name: '263'
dtype: float32
- name: '264'
dtype: float32
- name: '265'
dtype: float32
- name: '266'
dtype: float32
- name: '267'
dtype: float32
- name: '268'
dtype: float32
- name: '269'
dtype: float32
- name: '270'
dtype: float32
- name: '271'
dtype: float32
- name: '272'
dtype: float32
- name: '273'
dtype: float32
- name: '274'
dtype: float32
- name: '275'
dtype: float32
- name: '276'
dtype: float32
- name: '277'
dtype: float32
- name: '278'
dtype: float32
- name: '279'
dtype: float32
- name: '280'
dtype: float32
- name: '281'
dtype: float32
- name: '282'
dtype: float32
- name: '283'
dtype: float32
- name: '284'
dtype: float32
- name: '285'
dtype: float32
- name: '286'
dtype: float32
- name: '287'
dtype: float32
- name: '288'
dtype: float32
- name: '289'
dtype: float32
- name: '290'
dtype: float32
- name: '291'
dtype: float32
- name: '292'
dtype: float32
- name: '293'
dtype: float32
- name: '294'
dtype: float32
- name: '295'
dtype: float32
- name: '296'
dtype: float32
- name: '297'
dtype: float32
- name: '298'
dtype: float32
- name: '299'
dtype: float32
- name: '300'
dtype: float32
- name: '301'
dtype: float32
- name: '302'
dtype: float32
- name: '303'
dtype: float32
- name: '304'
dtype: float32
- name: '305'
dtype: float32
- name: '306'
dtype: float32
- name: '307'
dtype: float32
- name: '308'
dtype: float32
- name: '309'
dtype: float32
- name: '310'
dtype: float32
- name: '311'
dtype: float32
- name: '312'
dtype: float32
- name: '313'
dtype: float32
- name: '314'
dtype: float32
- name: '315'
dtype: float32
- name: '316'
dtype: float32
- name: '317'
dtype: float32
- name: '318'
dtype: float32
- name: '319'
dtype: float32
- name: '320'
dtype: float32
- name: '321'
dtype: float32
- name: '322'
dtype: float32
- name: '323'
dtype: float32
- name: '324'
dtype: float32
- name: '325'
dtype: float32
- name: '326'
dtype: float32
- name: '327'
dtype: float32
- name: '328'
dtype: float32
- name: '329'
dtype: float32
- name: '330'
dtype: float32
- name: '331'
dtype: float32
- name: '332'
dtype: float32
- name: '333'
dtype: float32
- name: '334'
dtype: float32
- name: '335'
dtype: float32
- name: '336'
dtype: float32
- name: '337'
dtype: float32
- name: '338'
dtype: float32
- name: '339'
dtype: float32
- name: '340'
dtype: float32
- name: '341'
dtype: float32
- name: '342'
dtype: float32
- name: '343'
dtype: float32
- name: '344'
dtype: float32
- name: '345'
dtype: float32
- name: '346'
dtype: float32
- name: '347'
dtype: float32
- name: '348'
dtype: float32
- name: '349'
dtype: float32
- name: '350'
dtype: float32
- name: '351'
dtype: float32
- name: '352'
dtype: float32
- name: '353'
dtype: float32
- name: '354'
dtype: float32
- name: '355'
dtype: float32
- name: '356'
dtype: float32
- name: '357'
dtype: float32
- name: '358'
dtype: float32
- name: '359'
dtype: float32
- name: '360'
dtype: float32
- name: '361'
dtype: float32
- name: '362'
dtype: float32
- name: '363'
dtype: float32
- name: '364'
dtype: float32
- name: '365'
dtype: float32
- name: '366'
dtype: float32
- name: '367'
dtype: float32
- name: '368'
dtype: float32
- name: '369'
dtype: float32
- name: '370'
dtype: float32
- name: '371'
dtype: float32
- name: '372'
dtype: float32
- name: '373'
dtype: float32
- name: '374'
dtype: float32
- name: '375'
dtype: float32
- name: '376'
dtype: float32
- name: '377'
dtype: float32
- name: '378'
dtype: float32
- name: '379'
dtype: float32
- name: '380'
dtype: float32
- name: '381'
dtype: float32
- name: '382'
dtype: float32
- name: '383'
dtype: float32
- name: '384'
dtype: float32
- name: '385'
dtype: float32
- name: '386'
dtype: float32
- name: '387'
dtype: float32
- name: '388'
dtype: float32
- name: '389'
dtype: float32
- name: '390'
dtype: float32
- name: '391'
dtype: float32
- name: '392'
dtype: float32
- name: '393'
dtype: float32
- name: '394'
dtype: float32
- name: '395'
dtype: float32
- name: '396'
dtype: float32
- name: '397'
dtype: float32
- name: '398'
dtype: float32
- name: '399'
dtype: float32
- name: '400'
dtype: float32
- name: '401'
dtype: float32
- name: '402'
dtype: float32
- name: '403'
dtype: float32
- name: '404'
dtype: float32
- name: '405'
dtype: float32
- name: '406'
dtype: float32
- name: '407'
dtype: float32
- name: '408'
dtype: float32
- name: '409'
dtype: float32
- name: '410'
dtype: float32
- name: '411'
dtype: float32
- name: '412'
dtype: float32
- name: '413'
dtype: float32
- name: '414'
dtype: float32
- name: '415'
dtype: float32
- name: '416'
dtype: float32
- name: '417'
dtype: float32
- name: '418'
dtype: float32
- name: '419'
dtype: float32
- name: '420'
dtype: float32
- name: '421'
dtype: float32
- name: '422'
dtype: float32
- name: '423'
dtype: float32
- name: '424'
dtype: float32
- name: '425'
dtype: float32
- name: '426'
dtype: float32
- name: '427'
dtype: float32
- name: '428'
dtype: float32
- name: '429'
dtype: float32
- name: '430'
dtype: float32
- name: '431'
dtype: float32
- name: '432'
dtype: float32
- name: '433'
dtype: float32
- name: '434'
dtype: float32
- name: '435'
dtype: float32
- name: '436'
dtype: float32
- name: '437'
dtype: float32
- name: '438'
dtype: float32
- name: '439'
dtype: float32
- name: '440'
dtype: float32
- name: '441'
dtype: float32
- name: '442'
dtype: float32
- name: '443'
dtype: float32
- name: '444'
dtype: float32
- name: '445'
dtype: float32
- name: '446'
dtype: float32
- name: '447'
dtype: float32
- name: '448'
dtype: float32
- name: '449'
dtype: float32
- name: '450'
dtype: float32
- name: '451'
dtype: float32
- name: '452'
dtype: float32
- name: '453'
dtype: float32
- name: '454'
dtype: float32
- name: '455'
dtype: float32
- name: '456'
dtype: float32
- name: '457'
dtype: float32
- name: '458'
dtype: float32
- name: '459'
dtype: float32
- name: '460'
dtype: float32
- name: '461'
dtype: float32
- name: '462'
dtype: float32
- name: '463'
dtype: float32
- name: '464'
dtype: float32
- name: '465'
dtype: float32
- name: '466'
dtype: float32
- name: '467'
dtype: float32
- name: '468'
dtype: float32
- name: '469'
dtype: float32
- name: '470'
dtype: float32
- name: '471'
dtype: float32
- name: '472'
dtype: float32
- name: '473'
dtype: float32
- name: '474'
dtype: float32
- name: '475'
dtype: float32
- name: '476'
dtype: float32
- name: '477'
dtype: float32
- name: '478'
dtype: float32
- name: '479'
dtype: float32
- name: '480'
dtype: float32
- name: '481'
dtype: float32
- name: '482'
dtype: float32
- name: '483'
dtype: float32
- name: '484'
dtype: float32
- name: '485'
dtype: float32
- name: '486'
dtype: float32
- name: '487'
dtype: float32
- name: '488'
dtype: float32
- name: '489'
dtype: float32
- name: '490'
dtype: float32
- name: '491'
dtype: float32
- name: '492'
dtype: float32
- name: '493'
dtype: float32
- name: '494'
dtype: float32
- name: '495'
dtype: float32
- name: '496'
dtype: float32
- name: '497'
dtype: float32
- name: '498'
dtype: float32
- name: '499'
dtype: float32
- name: '500'
dtype: float32
- name: '501'
dtype: float32
- name: '502'
dtype: float32
- name: '503'
dtype: float32
- name: '504'
dtype: float32
- name: '505'
dtype: float32
- name: '506'
dtype: float32
- name: '507'
dtype: float32
- name: '508'
dtype: float32
- name: '509'
dtype: float32
- name: '510'
dtype: float32
- name: '511'
dtype: float32
- name: '512'
dtype: float32
- name: '513'
dtype: float32
- name: '514'
dtype: float32
- name: '515'
dtype: float32
- name: '516'
dtype: float32
- name: '517'
dtype: float32
- name: '518'
dtype: float32
- name: '519'
dtype: float32
- name: '520'
dtype: float32
- name: '521'
dtype: float32
- name: '522'
dtype: float32
- name: '523'
dtype: float32
- name: '524'
dtype: float32
- name: '525'
dtype: float32
- name: '526'
dtype: float32
- name: '527'
dtype: float32
- name: '528'
dtype: float32
- name: '529'
dtype: float32
- name: '530'
dtype: float32
- name: '531'
dtype: float32
- name: '532'
dtype: float32
- name: '533'
dtype: float32
- name: '534'
dtype: float32
- name: '535'
dtype: float32
- name: '536'
dtype: float32
- name: '537'
dtype: float32
- name: '538'
dtype: float32
- name: '539'
dtype: float32
- name: '540'
dtype: float32
- name: '541'
dtype: float32
- name: '542'
dtype: float32
- name: '543'
dtype: float32
- name: '544'
dtype: float32
- name: '545'
dtype: float32
- name: '546'
dtype: float32
- name: '547'
dtype: float32
- name: '548'
dtype: float32
- name: '549'
dtype: float32
- name: '550'
dtype: float32
- name: '551'
dtype: float32
- name: '552'
dtype: float32
- name: '553'
dtype: float32
- name: '554'
dtype: float32
- name: '555'
dtype: float32
- name: '556'
dtype: float32
- name: '557'
dtype: float32
- name: '558'
dtype: float32
- name: '559'
dtype: float32
- name: '560'
dtype: float32
- name: '561'
dtype: float32
- name: '562'
dtype: float32
- name: '563'
dtype: float32
- name: '564'
dtype: float32
- name: '565'
dtype: float32
- name: '566'
dtype: float32
- name: '567'
dtype: float32
- name: '568'
dtype: float32
- name: '569'
dtype: float32
- name: '570'
dtype: float32
- name: '571'
dtype: float32
- name: '572'
dtype: float32
- name: '573'
dtype: float32
- name: '574'
dtype: float32
- name: '575'
dtype: float32
- name: '576'
dtype: float32
- name: '577'
dtype: float32
- name: '578'
dtype: float32
- name: '579'
dtype: float32
- name: '580'
dtype: float32
- name: '581'
dtype: float32
- name: '582'
dtype: float32
- name: '583'
dtype: float32
- name: '584'
dtype: float32
- name: '585'
dtype: float32
- name: '586'
dtype: float32
- name: '587'
dtype: float32
- name: '588'
dtype: float32
- name: '589'
dtype: float32
- name: '590'
dtype: float32
- name: '591'
dtype: float32
- name: '592'
dtype: float32
- name: '593'
dtype: float32
- name: '594'
dtype: float32
- name: '595'
dtype: float32
- name: '596'
dtype: float32
- name: '597'
dtype: float32
- name: '598'
dtype: float32
- name: '599'
dtype: float32
- name: '600'
dtype: float32
- name: '601'
dtype: float32
- name: '602'
dtype: float32
- name: '603'
dtype: float32
- name: '604'
dtype: float32
- name: '605'
dtype: float32
- name: '606'
dtype: float32
- name: '607'
dtype: float32
- name: '608'
dtype: float32
- name: '609'
dtype: float32
- name: '610'
dtype: float32
- name: '611'
dtype: float32
- name: '612'
dtype: float32
- name: '613'
dtype: float32
- name: '614'
dtype: float32
- name: '615'
dtype: float32
- name: '616'
dtype: float32
- name: '617'
dtype: float32
- name: '618'
dtype: float32
- name: '619'
dtype: float32
- name: '620'
dtype: float32
- name: '621'
dtype: float32
- name: '622'
dtype: float32
- name: '623'
dtype: float32
- name: '624'
dtype: float32
- name: '625'
dtype: float32
- name: '626'
dtype: float32
- name: '627'
dtype: float32
- name: '628'
dtype: float32
- name: '629'
dtype: float32
- name: '630'
dtype: float32
- name: '631'
dtype: float32
- name: '632'
dtype: float32
- name: '633'
dtype: float32
- name: '634'
dtype: float32
- name: '635'
dtype: float32
- name: '636'
dtype: float32
- name: '637'
dtype: float32
- name: '638'
dtype: float32
- name: '639'
dtype: float32
- name: '640'
dtype: float32
- name: '641'
dtype: float32
- name: '642'
dtype: float32
- name: '643'
dtype: float32
- name: '644'
dtype: float32
- name: '645'
dtype: float32
- name: '646'
dtype: float32
- name: '647'
dtype: float32
- name: '648'
dtype: float32
- name: '649'
dtype: float32
- name: '650'
dtype: float32
- name: '651'
dtype: float32
- name: '652'
dtype: float32
- name: '653'
dtype: float32
- name: '654'
dtype: float32
- name: '655'
dtype: float32
- name: '656'
dtype: float32
- name: '657'
dtype: float32
- name: '658'
dtype: float32
- name: '659'
dtype: float32
- name: '660'
dtype: float32
- name: '661'
dtype: float32
- name: '662'
dtype: float32
- name: '663'
dtype: float32
- name: '664'
dtype: float32
- name: '665'
dtype: float32
- name: '666'
dtype: float32
- name: '667'
dtype: float32
- name: '668'
dtype: float32
- name: '669'
dtype: float32
- name: '670'
dtype: float32
- name: '671'
dtype: float32
- name: '672'
dtype: float32
- name: '673'
dtype: float32
- name: '674'
dtype: float32
- name: '675'
dtype: float32
- name: '676'
dtype: float32
- name: '677'
dtype: float32
- name: '678'
dtype: float32
- name: '679'
dtype: float32
- name: '680'
dtype: float32
- name: '681'
dtype: float32
- name: '682'
dtype: float32
- name: '683'
dtype: float32
- name: '684'
dtype: float32
- name: '685'
dtype: float32
- name: '686'
dtype: float32
- name: '687'
dtype: float32
- name: '688'
dtype: float32
- name: '689'
dtype: float32
- name: '690'
dtype: float32
- name: '691'
dtype: float32
- name: '692'
dtype: float32
- name: '693'
dtype: float32
- name: '694'
dtype: float32
- name: '695'
dtype: float32
- name: '696'
dtype: float32
- name: '697'
dtype: float32
- name: '698'
dtype: float32
- name: '699'
dtype: float32
- name: '700'
dtype: float32
- name: '701'
dtype: float32
- name: '702'
dtype: float32
- name: '703'
dtype: float32
- name: '704'
dtype: float32
- name: '705'
dtype: float32
- name: '706'
dtype: float32
- name: '707'
dtype: float32
- name: '708'
dtype: float32
- name: '709'
dtype: float32
- name: '710'
dtype: float32
- name: '711'
dtype: float32
- name: '712'
dtype: float32
- name: '713'
dtype: float32
- name: '714'
dtype: float32
- name: '715'
dtype: float32
- name: '716'
dtype: float32
- name: '717'
dtype: float32
- name: '718'
dtype: float32
- name: '719'
dtype: float32
- name: '720'
dtype: float32
- name: '721'
dtype: float32
- name: '722'
dtype: float32
- name: '723'
dtype: float32
- name: '724'
dtype: float32
- name: '725'
dtype: float32
- name: '726'
dtype: float32
- name: '727'
dtype: float32
- name: '728'
dtype: float32
- name: '729'
dtype: float32
- name: '730'
dtype: float32
- name: '731'
dtype: float32
- name: '732'
dtype: float32
- name: '733'
dtype: float32
- name: '734'
dtype: float32
- name: '735'
dtype: float32
- name: '736'
dtype: float32
- name: '737'
dtype: float32
- name: '738'
dtype: float32
- name: '739'
dtype: float32
- name: '740'
dtype: float32
- name: '741'
dtype: float32
- name: '742'
dtype: float32
- name: '743'
dtype: float32
- name: '744'
dtype: float32
- name: '745'
dtype: float32
- name: '746'
dtype: float32
- name: '747'
dtype: float32
- name: '748'
dtype: float32
- name: '749'
dtype: float32
- name: '750'
dtype: float32
- name: '751'
dtype: float32
- name: '752'
dtype: float32
- name: '753'
dtype: float32
- name: '754'
dtype: float32
- name: '755'
dtype: float32
- name: '756'
dtype: float32
- name: '757'
dtype: float32
- name: '758'
dtype: float32
- name: '759'
dtype: float32
- name: '760'
dtype: float32
- name: '761'
dtype: float32
- name: '762'
dtype: float32
- name: '763'
dtype: float32
- name: '764'
dtype: float32
- name: '765'
dtype: float32
- name: '766'
dtype: float32
- name: '767'
dtype: float32
- name: label
dtype: string
splits:
- name: train
num_bytes: 80318780.21618997
num_examples: 26057
- name: test
num_bytes: 26774087.073587257
num_examples: 8686
download_size: 147064679
dataset_size: 107092867.28977722
---
# Dataset Card for "AA_BERT_Baseline"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Norquinal/claude_multiround_chat_1k | 2023-08-11T01:40:28.000Z | [
"region:us"
] | Norquinal | null | null | null | 1 | 14 | This dataset is ~1k random samplings from my [claude_multiround_chat_30k](https://huggingface.co/datasets/Norquinal/claude_multiround_chat_30k) dataset.
The instructions were generated synethically using a method that can be tenatively described as "multi-instruct." These instructions consist of numerous discrete tasks that the AI has to work its way through, thereby hopefully increasing its comprehension and awareness of complex instructions.
The topics of the instruction ranged from STEM, Arts & Humanities, Social Knowledge, and General Knowledge. |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.