SentenceTransformer based on Atipico1/bert-checkpoint-980000

This is a sentence-transformers model finetuned from Atipico1/bert-checkpoint-980000. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

Model Details

Model Description

Model Type: Sentence Transformer
Base model: Atipico1/bert-checkpoint-980000
Maximum Sequence Length: 512 tokens
Output Dimensionality: 768 tokens
Similarity Function: Cosine Similarity

Model Sources

Documentation: Sentence Transformers Documentation
Repository: Sentence Transformers on GitHub
Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("Atipico1/our-bert-no-negative-10000")
# Run inference
sentences = [
    '비정규노조에 따르면 경찰은 어떻게 안 위원장을 납치했는가?',
    '경찰이 현대자동차 비정규직 노조위원장을 체포해 노동계가 거세게 반발하고 있다. 울산 동부 경찰서는 안기호 위원장이 지난달 18일부터 파업을 주도해 자동차 생산에 큰 차질을 빚어 ‘업무 방해’를 했다고 밝혔다. 하지만 경찰의 노조위원장 체포는 그 과정부터 적법성이 문제점으로 드러나고 있다. 비정규노조가 밝힌바, 경비대가 조합원 3명과 점심을 먹으려고 농성장에서 식당으로 가던 안 위원장을 미리 대기하고 있던 차량으로 ‘납치’했고, 문제의 차는 공장 정문으로 이동해 밖에서 대기하던 경찰차로 옮겨졌다고 한다. 결국 경영진과 경찰의 ‘합동작전’으로 파업 중인 노조위원장을 체포한 셈이다. 경영진은 이에 앞서 노조위원장을 비롯해 비정규직 노동자 84명을 경찰에 고발했다. 현재 경영진은 ‘납치’를 부인하며, 안 위원장이 공장 밖으로 스스로 나가다 경찰에 연행됐다고 주장하는 것으로 알려졌다. 하지만 상식으로 판단하더라도, 파업 중인 노조위원장이 제 발로 공장 밖으로 나가리라고 보기 어렵다. 더구나 수많은 사람들의 목격담이 나오고 있다. 안 위원장을 납치해 경찰에 인계하는 과정에서 경비대가 얼굴을 비롯해 온몸을 폭행했다는 가족들의 ‘증언’도 나왔다. 만일 노조의 주장이 사실이라면, 경찰이 먼저 할 일은 안 위원장 체포과정에서 누가 어떤 폭력을 저질렀는지에 대한 진상 규명이다. 더 심각한 문제는 현정부의 비정규직 대책이 원시적 수단을 동원해 탄압하는 수준까지 갔다는 데 있다. 이미 현대차 비정규직노조는 부위원장과 사무국장이 감옥에 갇혀 있는 상황이다. 거기에 더해 위원장 체포는 비정규직 노조를 뿌리뽑겠다는 의도라고 볼 수밖에 없다. 현대자동차 경영진의 불법파견은 모르쇠하는 정부가 비정규직 노조의 파업에 강경책으로 일관하는 것은 누가보더라도 공정하지 못하다. 체포 과정의 진상을 스스로 밝히고, 비정규직 탄압을 지금이라도 중단하길 촉구한다.',
    '‘한국과학기술지주회사, 벤처기업과 손잡다’ - 벤처기업과 함께 출연(연)기술 기반 신사업 발굴 및 강소 스타트업 기업 육성 추진 -\n□ 미래창조과학부(장관 최문기) 산하 17개* 출연(연)이 공동으로 설립한 기술사업화 전문회사인 한국과학기술지주회사(대표 조남훈)가 사단법인 벤처기업협회(회장 남민우)와 2014년 4월 22일 11시, 벤처기업협회에서 강소 스타트업(Start-Up) 기업의 육성·발전과 공공 기술 사업화 촉진을 통해 창조경제 성과 창출을 위한 업무협약(MOU)을 체결했다. * 기초(KIST, 생명연, KISTI, 표준연, 원자력연), 산업(생기연, 국보연, 건설연, 철도연, 식품연, 지자연, 기계연, 재료연, 에기연, 전기연, 화학연, ETRI',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Training Details

Training Dataset

Unnamed Dataset

Size: 473,130 training samples
Columns: anchor and positive
Approximate statistics based on the first 1000 samples:
anchor positive
type string string
details
min: 7 tokens
mean: 18.25 tokens
max: 86 tokens

min: 27 tokens
mean: 304.44 tokens
max: 512 tokens

	anchor	positive
type	string	string
details	min: 7 tokens mean: 18.25 tokens max: 86 tokens	min: 27 tokens mean: 304.44 tokens max: 512 tokens

Samples:

anchor	positive
`국회세종의사당 건립을 위한 법안을 누가 새로 제안했어`	국민의힘 정진석 의원(공주·부여·청양)이 국회세종의사당 설치를 위한 '국회법' 일부개정법률안을 대표발의했다고 21일 밝혔다. 국회와 세종시 정부청사와의 물리적인 거리로 세종시 공무원의 관외 출장비는 3년간 917억원에 달한다. 출장횟수는 87만회에 달하고 있어 업무 불편과 비효율성 심화는 물론 정책 질 저하도 우려되는 실정이다. 개정안은 △서울시에 국회서울의사당을, 세종시에 국회세종의사당을 두도록 하고 △상임위원회는 국회세종의사당에 두는 것으로 하되, 국회운영위원회와 정보위원회 및 세종시로 이전하지 않은 부(部)를 소관하는 상임위원회는 국회서울의사당에 둘 수 있도록 했다. 행복도시법에 따른 이전 제외 대상 부처는 외교부, 통일부, 법무부, 국방부, 여성가족부 등 5곳이다. 또 예산결산특별위원회와 국회예산정책처는 세종시에 두도록하고 국회사무처, 국회도서관, 국회입법조사처는 국회세종의사당에 별도 기관을 둘 수 있도록 했다. 정진석 의원은 "여야 합의로 세종의사당 설계비 147억원이 확정됐고 지난 2월 국회 운영위원회 공청회에서 나온 의견들을 다듬어 법의 완성도를 높인 개정안인 만큼, 여야 합의를 통해 21대 국회 임기 중에 첫 삽을 뜰 있도록 최선을 다하겠다"고 말했다.
`어떤 국가가 과반수의 표를 확보해야 중의원 의장이 될 수 있게 정해 놨어`	3. 일본 가. 의장 선출 □ 중의원 의장은 중의원 선거 후 최초 집회일에 열리는 회의에서 중의원 의원의 무기명 투표에 의해 직선으로 선출됨(「국회법」제6조, 「중의원규칙」제3조, 제8조) ○ 의장 선거는 전체 의원의 3분의 1 이상이 참석해서 사무총장이 의장의 직무대행을 하여 실시함(「국회법」제7조) ○ 의장으로 당선되기 위해서는 과반수 득표를 해야 하므로, 1차 투표에서 총 투표수의 과반수를 획득한 득표자가 없으면, 최다득표자 2인에 대하여 결선투표를 실시함 ○ 결선투표에서 두 후보자의 득표수가 같은 경우 추첨으로 당선인이 결정됨 ○ 중의원 의장의 임기는 4년이나, 임기 중에 해산에 의해서 모든 중의원 의원이 지위를 잃으면, 의장도 그 지위를 상실함 □ 의장 선거절차는 중의원 규칙에서 정하는 것 외에, 제1회 일본 제국의회에서 정한 ‘의장 후보자 선거절차수칙’을 따르고 있음 ○ 의장 선출은 선거 전에 각 회파(교섭단체에 해당) 간의 대화로 미리 후보자가 결정되지만, 공식절차상으로는 각 의원의 본회의에서 선거로 선임함 ○ 1970년대 중반까지는 집권여당이 국회의장과 부의장을 모두 독점하였으나, 제79회 국회(1976년) 이후 중의원 국회의장은 여당 제1당에서, 국회부의장은 야당 제1당(제2당)에서 선출하는 관행이 정착되었음. 그러나 1993년 연립정권 성립 후에는 중의원에서 자민당이 제1당이면서 여당이 아니었기 때문에 여당 간에 의장직을 둘러싸고 다툼이 있었음
`1분기 코로나 예방접종을 약 2000명에게 시행할 건 누구야`	부산 동래구(구청장 김우룡)는 코로나19 예방접종의 차질 없는 추진을 통한 빠른 일상회복을 위해 코로나19 예방접종 계획을 마련하고, 이달 말 1분기 대상자 2000여명을 대상으로 ‘코로나19 예방접종’을 시작한다고 밝혔다. 코로나19 예방접종 추진기간은 인플루엔자 유행시기인 11월 이전까지로, 접종대상은 18세 이상 전 구민이며, 임신부 및 만 18세 미만 소아·청소년, 65세 이상 고령자는 임상시험 결과에 따라 추후 접종 여부 및 시기가 결정된다. 동래구는 △과학적 근거를 기반으로 안전하고 효과적인 접종 추진 △코로나19의 사망예방 및 지역 사회 전파 차단을 위하여 전 구민의 70%인 189천여 명을 목표로 예방접종을 추진할 계획이다 1분기 우선 접종대상자는 △요양병원·요양시설입원·입원자, 종사자 △고위험 의료기관종사자, 코로나 1차 대응요원 △정신요양·재활시설 등 입소자·종사자 등 2000여 명이며, 백신 배송 등 일정을 조율해 26일부터 병원은 자체접종, 시설은 보건소 방문팀·시설별 협약의료기관 또는 계약된 의사가 방문 접종할 계획이다. 단계별 예방접종 기관은 △7월 개소 예정인 예방접종센터(사직실내체육관) △위탁의료기관 △방문접종 △자체접종 △내소접종을 병행하며, 위탁의료기관 정보는 질병관리청 코로나19 백신 및 예방접종 홈페이지에서 확인할 수 있다. 또한 동래구는 지난 4일 코로나19 예방접종 추진단을 운영 중이며, 22일 민·관·군과 병협·의협·간협 및 민간 등으로 구성된 민-관 협력체계인 ‘동래구 코로나19 예방접종 지역협의체’를 발족하여 전 구민의 코로나19 예방접종의 차질 없는 추진을 위해 최선을 다하고 있다. 김우룡 동래구청장은 “코로나19 예방접종은 전 국민 대상의 대규모 사업으로 관의 철저하고 꼼꼼한 계획과 함께 주민과 유관기관의 협조가 반드시 필요하다”며 “안전하고 신속한 예방접종을 추진을 위해 최선을 다하겠다”고 말했다.

Loss: MultipleNegativesRankingLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "cos_sim"
}

Evaluation Dataset

Unnamed Dataset

Size: 10,000 evaluation samples
Columns: anchor and positive
Approximate statistics based on the first 1000 samples:
anchor positive
type string string
details
min: 8 tokens
mean: 18.06 tokens
max: 112 tokens

min: 19 tokens
mean: 301.38 tokens
max: 512 tokens

	anchor	positive
type	string	string
details	min: 8 tokens mean: 18.06 tokens max: 112 tokens	min: 19 tokens mean: 301.38 tokens max: 512 tokens

Samples:

anchor	positive
`릴레이로 이번주 TV와 라디오 방송 출연을 확정한게 누구야?`	▲ 사진=롯데엔터테인먼트 제공 영화 완벽한 타인의 주역 유해진, 조진웅, 이서진, 염정아가 릴레이로 이번주 TV와 라디오 방송 출연을 확정했다. 완벽한 타인은 완벽해 보이는 커플 모임에서 한정된 시간 동안 핸드폰으로 오는 전화, 문자, 카톡을 강제로 공개해야 하는 게임 때문에 벌어지는 예측불허 이야기를 담은 작품이다. 완벽한 타인에서 완벽한 연기를 펼친 배우들은 이번 주 릴레이로 TV와 라디오 방송 출연을 확정하며 열일 행보를 펼친다. 먼저 오는 24일 오후 7시 MBC FM영화음악 한예리입니다에는 유해진과 염정아가 함께 출연한다. 간첩, 전우치에 이어 세 번째로 함께 호흡을 맞춘 두 사람은 이번 라디오 출연에서 영화에 대한 이야기를 나누며 걸출한 입담과 절친 케미스트리를 선보일 것으로 보인다. 이어 이번 영화로 처음 만나 절친이 된 유해진, 조진웅, 이서진이 25일 오후 11시 10분 KBS2 해피투게더4에 출연한다. 세끼 인연 유해진과 이서진, 그리고 조진웅의 예능감이 유감없이 발휘될 예정이다. 마지막으로 26일에는 MBC 배철수의 음악캠프에서 이서진을 만날 수 있다. 완벽한 타인에서 가장 파격적인 연기 변신을 선보인 그는 음악캠프 특별 DJ로 활약했던 인연으로 이번 출연이 성사됐다. 이서진은 거침없는 언변으로 영화 완벽한 타인의 현장 비하인드 스토리를 밝힐 예정이다. 한편 완벽한 타인은 오는 31일 개봉을 앞두고 있다.
`벡터맵 빈 분류 기반의 제안기법은 무엇에 비하여서 압출효율이 높다는 것을 표 4에서 알 수 있어?`	IV. 실험 결과 제안한 빈 분류기반 벡터맵 압축 기법에 대한 성능 평가를 위한 실험을 수행하였다. 실험을 위해 그림 10 과 같이, ( 10 \mathrm{km} \times 10 \mathrm{km} ) 의 국부 영역을 갖는 벡터맵 레이어를 생성하였으며, 이 중 폴리곤으로 구성된 '건물' 레이어와 폴리라인으로 구성된 '일반도로’ 레이어에 대해 각각의 실험을 수행하였다. 또한 TM 좌표계에 의해 표현되는 실측치 ( 1 \mathrm{cm} ) 이내의 오차를 갖도록 식 (1)의 ( c=100 ) 으로 설정하여 정밀 벡터맵 압축에 대해 결과를 도출하였다. 또한 ( 10 \mathrm{km} \times 10 \mathrm{km} ) 영역에서 ( 1 \mathrm{cm} ) 정밀도를 갖는 벡터맵 데이터의 최적의 압축효율을 위해, 실험적으로 dist ( {D B}=10 \mathrm{~m} ) 및 dist ( { }{A B}=0.64 \mathrm{m} ) 로 결정하였다. 제안 기법의 객관적 비교를 위해 일반적인 데이터 압축기법으로서 7-zib 알고리즘, 대표적인 벡터 간소화 알고리즘으로서 Douglas-Peucker 알고리즘[16] 및 기존의 공간 에너지집중 기반에서의 압축 알고리즘등과 압축 결과를 비교하였다. 표 4 에 각각의 알고리즘 에 대한 압축 결과를 나타내었다. 표 4 의 결과로부터 벡터맵의 특성을 고려하지 않은 7-Zip과 비교하였을 때, 각 좌표점들의 오차범위로 ( 0.01 \mathrm{m} ) 미만을 갖는 벡터맵 빈 분류 기반의 제안 기법이 월등히 높은 압축효율을 가짐을 확인하였다. 한편, 벡터 간소화 기법을 사용하는 Douglas-Peucker 알고리즘과 제안 알고리즘은 압축원리가 상이하므로 RMSE(root mean square error) 등의 방법을 통한 직접적인 비교는 어렵다. 또한 제안 기법과의 비교를 위해 Douglas-Peucker 알고리즘의 정밀도 범위 ( \epsilon=0.01 \mathrm{m} ) 로 설정하게 되면, 각 좌표점들의 간소화 조건이 대부분 만족하지 않으므로 실제 간소화를 통한 압축은 거의 이루어지지 않는다. 따라서 그림 10의 벡터맵 레이어에서 시각적으로 용인할 수 있을 것으로 간주되는 적정 임계치 ( \epsilon=1 \mathrm{m} ) 로 설정하여 압축을 수행하였다. 표 4의 실험 결과는 이때의 압축 결과를 나타낸 것이다. 그림 11은 벡터맵을 확대하였을 때, 표 4 의 압축 효율에 대해 제안 알고리즘과 Douglas-Peucker 알고리즘의 시각적 오차를 비교한 것이다. 표 4와 그림 11로부터 제안 기법이 Duglas-Peucker 알고리즘보다 월등히 적은 시각적 오차를 가짐에도 불구하고 보다 높은 압축효율을 나타냄을 확인할 수 있다. 더욱이, 표 4에서 Duglas-Peucker 알고리즘의 특성상 연속한 좌표점들이 급격히 꺽히는 오브젝트들의 집합인 '건물' 레이어에서 압축효율의 저하가 발생한다. 반면. 제안 기법은 Duglas-Peucker 알고리즘에서와 같은 압축효율의 저하는 발생하지 않음을 확인하였다. 공간영역에서의 에너지 집중(SEC)을 이용한 기존방법과의 비교에서 역시 제안 알고리즘이 보다 우수한 압축 효율을 가짐을 알 수 있었다. 또한 에너지 집중 이후 실질적 데이터 압축을 위한 엔트로피 코딩으로써 zlib 또는 7-zip 알고리즘을 이용하는 기존 기법과는 달리, 제안 기법은 압축 과정의 일부로써 정의된 단순한 허프만 테이블을 참조하므로 계산 복잡도에서 큰 이점을 얻을 수 있다.
`대면하지 않고 진료에서 약 배달까지 해주는 처방솔루션은 뭐야`	비대면 진료 서비스에 기반한 브랜드 페어(pare)는 올해 초부터 비대면 진료와 처방을 모바일로 쉽고 빠르게 받을 수 있게 하고, 처방받은 약은 집까지 배송해 주는 서비스를 국내 환자와 재외국민들을 위해 시작했다. 페어는 의사의 처방을 통해서만 구매가 가능한 의사의 솔루션을 페어만의 브랜드 감성을 깃들여 환자에게 노출한다. 이는 기존의 처방약이라는 고질적인 부분을 소비자 감성 브랜드로 승화해 환자와 소비자의 벽을 허무는 국내 최초의 전문처방 솔루션 비즈니스 모델이다. 또한, 플랫폼 내 처방이 필요하지 않은 일반 건강기능식품을 통한 사후관리 서비스도 제공한다. 강신욱 대표는 “페어만의 브랜드 감성과 의사들의 전문성이 실린 솔루션을 집까지 배송해 주는 게 특징”이라며 “처방약뿐만 아니라 진료에 따른 맞춤형 건강관리제품을 추천 혹은 패키지로 받아 볼 수 있는 국내 최초 비대면 진료 기반의 커머스형 처방솔루션”이라고 강조했다.

Loss: MultipleNegativesRankingLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "cos_sim"
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 32
per_device_eval_batch_size: 32
learning_rate: 1e-05
num_train_epochs: 10
warmup_steps: 500
bf16: True

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 32
per_device_eval_batch_size: 32
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 1
eval_accumulation_steps: None
torch_empty_cache_steps: None
learning_rate: 1e-05
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 10
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.0
warmup_steps: 500
log_level: passive
log_level_replica: warning
log_on_each_node: True
logging_nan_inf_filter: True
save_safetensors: True
save_on_each_node: False
save_only_model: False
restore_callback_states_from_checkpoint: False
no_cuda: False
use_cpu: False
use_mps_device: False
seed: 42
data_seed: None
jit_mode_eval: False
use_ipex: False
bf16: True
fp16: False
fp16_opt_level: O1
half_precision_backend: auto
bf16_full_eval: False
fp16_full_eval: False
tf32: None
local_rank: 0
ddp_backend: None
tpu_num_cores: None
tpu_metrics_debug: False
debug: []
dataloader_drop_last: True
dataloader_num_workers: 0
dataloader_prefetch_factor: None
past_index: -1
disable_tqdm: False
remove_unused_columns: True
label_names: None
load_best_model_at_end: False
ignore_data_skip: False
fsdp: []
fsdp_min_num_params: 0
fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
fsdp_transformer_layer_cls_to_wrap: None
accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
deepspeed: None
label_smoothing_factor: 0.0
optim: adamw_torch
optim_args: None
adafactor: False
group_by_length: False
length_column_name: length
ddp_find_unused_parameters: None
ddp_bucket_cap_mb: None
ddp_broadcast_buffers: False
dataloader_pin_memory: True
dataloader_persistent_workers: False
skip_memory_metrics: True
use_legacy_prediction_loop: False
push_to_hub: False
resume_from_checkpoint: None
hub_model_id: None
hub_strategy: every_save
hub_private_repo: False
hub_always_push: False
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
eval_do_concat_batches: True
fp16_backend: auto
push_to_hub_model_id: None
push_to_hub_organization: None
mp_parameters:
auto_find_batch_size: False
full_determinism: False
torchdynamo: None
ray_scope: last
ddp_timeout: 1800
torch_compile: False
torch_compile_backend: None
torch_compile_mode: None
dispatch_batches: None
split_batches: None
include_tokens_per_second: False
include_num_input_tokens_seen: False
neftune_noise_alpha: None
optim_target_modules: None
batch_eval_metrics: False
eval_on_start: False
eval_use_gather_object: False
batch_sampler: batch_sampler
multi_dataset_batch_sampler: proportional

Training Logs

Click to expand

Epoch	Step	Training Loss	loss
0.0005	1	3.3083	-
0.0011	2	3.3167	-
0.0016	3	3.3371	-
0.0022	4	3.3229	-
0.0027	5	3.3023	-
0.0032	6	3.3391	-
0.0038	7	3.3458	-
0.0043	8	3.316	-
0.0049	9	3.2995	-
0.0054	10	3.2891	-
0.0060	11	3.302	-
0.0065	12	3.339	-
0.0070	13	3.2859	-
0.0076	14	3.2979	-
0.0081	15	3.2822	-
0.0087	16	3.2952	-
0.0092	17	3.3016	-
0.0097	18	3.284	-
0.0103	19	3.3112	-
0.0108	20	3.2825	-
0.0114	21	3.3122	-
0.0119	22	3.2824	-
0.0124	23	3.2968	-
0.0130	24	3.2771	-
0.0135	25	3.3069	-
0.0141	26	3.2656	-
0.0146	27	3.2799	-
0.0152	28	3.2727	-
0.0157	29	3.2773	-
0.0162	30	3.2604	-
0.0168	31	3.2837	-
0.0173	32	3.2645	-
0.0179	33	3.2555	-
0.0184	34	3.2479	-
0.0189	35	3.2685	-
0.0195	36	3.2229	-
0.0200	37	3.2475	-
0.0206	38	3.2264	-
0.0211	39	3.2206	-
0.0216	40	3.2359	-
0.0222	41	3.2341	-
0.0227	42	3.223	-
0.0233	43	3.2209	-
0.0238	44	3.2261	-
0.0244	45	3.2131	-
0.0249	46	3.1897	-
0.0254	47	3.1894	-
0.0260	48	3.1939	-
0.0265	49	3.1562	-
0.0271	50	3.1783	-
0.0276	51	3.1751	-
0.0281	52	3.1607	-
0.0287	53	3.1507	-
0.0292	54	3.1468	-
0.0298	55	3.1358	-
0.0303	56	3.1438	-
0.0308	57	3.1281	-
0.0314	58	3.1284	-
0.0319	59	3.1251	-
0.0325	60	3.1179	-
0.0330	61	3.1292	-
0.0335	62	3.1079	-
0.0341	63	3.0789	-
0.0346	64	3.0876	-
0.0352	65	3.0858	-
0.0357	66	3.044	-
0.0363	67	3.0674	-
0.0368	68	3.0461	-
0.0373	69	3.0399	-
0.0379	70	3.0533	-
0.0384	71	3.0263	-
0.0390	72	3.0131	-
0.0395	73	3.0062	-
0.0400	74	2.9679	-
0.0406	75	2.9549	-
0.0411	76	2.9529	-
0.0417	77	2.9382	-
0.0422	78	2.8984	-
0.0427	79	2.9031	-
0.0433	80	2.9108	-
0.0438	81	2.8789	-
0.0444	82	2.8907	-
0.0449	83	2.8611	-
0.0455	84	2.8058	-
0.0460	85	2.8168	-
0.0465	86	2.7397	-
0.0471	87	2.7809	-
0.0476	88	2.7377	-
0.0482	89	2.7353	-
0.0487	90	2.7217	-
0.0492	91	2.6976	-
0.0498	92	2.6548	-
0.0503	93	2.5839	-
0.0509	94	2.5945	-
0.0514	95	2.555	-
0.0519	96	2.4825	-
0.0525	97	2.4743	-
0.0530	98	2.4728	-
0.0536	99	2.4233	-
0.0541	100	2.4008	-
0.0547	101	2.3213	-
0.0552	102	2.2799	-
0.0557	103	2.2258	-
0.0563	104	2.183	-
0.0568	105	2.1662	-
0.0574	106	2.1077	-
0.0579	107	2.0564	-
0.0584	108	1.9469	-
0.0590	109	1.9423	-
0.0595	110	1.8048	-
0.0601	111	1.7313	-
0.0606	112	1.7134	-
0.0611	113	1.6828	-
0.0617	114	1.5218	-
0.0622	115	1.4383	-
0.0628	116	1.405	-
0.0633	117	1.3442	-
0.0639	118	1.2303	-
0.0644	119	1.1665	-
0.0649	120	1.1102	-
0.0655	121	0.9739	-
0.0660	122	0.9238	-
0.0666	123	0.7891	-
0.0671	124	0.818	-
0.0676	125	0.7027	-
0.0682	126	0.6281	-
0.0687	127	0.5882	-
0.0693	128	0.5139	-
0.0698	129	0.5281	-
0.0703	130	0.4401	-
0.0709	131	0.3761	-
0.0714	132	0.4047	-
0.0720	133	0.3737	-
0.0725	134	0.3054	-
0.0731	135	0.2736	-
0.0736	136	0.3193	-
0.0741	137	0.2612	-
0.0747	138	0.3041	-
0.0752	139	0.2253	-
0.0758	140	0.1909	-
0.0763	141	0.2199	-
0.0768	142	0.2051	-
0.0774	143	0.207	-
0.0779	144	0.2474	-
0.0785	145	0.1898	-
0.0790	146	0.2249	-
0.0795	147	0.2177	-
0.0801	148	0.1837	-
0.0806	149	0.2152	-
0.0812	150	0.1632	-
0.0817	151	0.1848	-
0.0823	152	0.1426	-
0.0828	153	0.1686	-
0.0833	154	0.247	-
0.0839	155	0.1992	-
0.0844	156	0.1523	-
0.0850	157	0.189	-
0.0855	158	0.1636	-
0.0860	159	0.1245	-
0.0866	160	0.1323	-
0.0871	161	0.217	-
0.0877	162	0.1377	-
0.0882	163	0.136	-
0.0887	164	0.1872	-
0.0893	165	0.166	-
0.0898	166	0.1841	-
0.0904	167	0.1544	-
0.0909	168	0.1394	-
0.0915	169	0.1515	-
0.0920	170	0.1325	-
0.0925	171	0.1131	-
0.0931	172	0.1993	-
0.0936	173	0.1515	-
0.0942	174	0.1293	-
0.0947	175	0.1804	-
0.0952	176	0.1818	-
0.0958	177	0.1029	-
0.0963	178	0.14	-
0.0969	179	0.0877	-
0.0974	180	0.1204	-
0.0979	181	0.1134	-
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0.1970	364	0.0511	-
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0.1986	367	0.1183	-
0.1991	368	0.0847	-
0.1997	369	0.0873	-
0.2002	370	0.0722	-
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0.9545	1764	0.0751	-
0.9551	1765	0.0286	-
0.9556	1766	0.0338	-
0.9562	1767	0.0354	-
0.9567	1768	0.0232	-
0.9573	1769	0.0316	-
0.9578	1770	0.0639	-
0.9583	1771	0.0427	-
0.9589	1772	0.0428	-
0.9594	1773	0.0553	-
0.9600	1774	0.0638	-
0.9605	1775	0.0309	-
0.9610	1776	0.0292	-
0.9616	1777	0.0241	-
0.9621	1778	0.0424	-
0.9627	1779	0.014	-
0.9632	1780	0.0451	-
0.9637	1781	0.0158	-
0.9643	1782	0.0567	-
0.9648	1783	0.0419	-
0.9654	1784	0.0211	-
0.9659	1785	0.0169	-
0.9665	1786	0.0455	-
0.9670	1787	0.0662	-
0.9675	1788	0.0147	-
0.9681	1789	0.0393	-
0.9686	1790	0.0398	-
0.9692	1791	0.0612	-
0.9697	1792	0.0503	-
0.9702	1793	0.0389	-
0.9708	1794	0.0349	-
0.9713	1795	0.0363	-
0.9719	1796	0.0294	-
0.9724	1797	0.0422	-
0.9729	1798	0.0228	-
0.9735	1799	0.0199	-
0.9740	1800	0.048	-
0.9746	1801	0.0194	-
0.9751	1802	0.0545	-
0.9756	1803	0.0459	-
0.9762	1804	0.0468	-
0.9767	1805	0.0305	-
0.9773	1806	0.0353	-
0.9778	1807	0.0245	-
0.9784	1808	0.0212	-
0.9789	1809	0.0343	-
0.9794	1810	0.0226	-
0.9800	1811	0.0548	-
0.9805	1812	0.0495	-
0.9811	1813	0.0253	-
0.9816	1814	0.0421	-
0.9821	1815	0.0595	-
0.9827	1816	0.031	-
0.9832	1817	0.0274	-
0.9838	1818	0.0292	-
0.9843	1819	0.0444	-
0.9848	1820	0.0299	-
0.9854	1821	0.048	-
0.9859	1822	0.0296	-
0.9865	1823	0.0154	-
0.9870	1824	0.0358	-
0.9876	1825	0.0579	-
0.9881	1826	0.0452	-
0.9886	1827	0.0241	-
0.9892	1828	0.0269	-
0.9897	1829	0.0228	-
0.9903	1830	0.0398	-
0.9908	1831	0.0328	-
0.9913	1832	0.0559	-
0.9919	1833	0.0275	-
0.9924	1834	0.019	-
0.9930	1835	0.0304	-
0.9935	1836	0.0519	-
0.9940	1837	0.013	-
0.9946	1838	0.0511	-
0.9951	1839	0.0659	-
0.9957	1840	0.0157	-
0.9962	1841	0.0404	-
0.9968	1842	0.0471	-
0.9973	1843	0.0264	-
0.9978	1844	0.0537	-
0.9984	1845	0.0399	-
0.9989	1846	0.0407	-
0.9995	1847	0.0536	-
1.0	1848	0.0435	-
1.0005	1849	0.0156	-
1.0011	1850	0.0258	-
1.0016	1851	0.0168	-
1.0022	1852	0.0405	-
1.0027	1853	0.0267	-
1.0032	1854	0.0207	-
1.0038	1855	0.0144	-
1.0043	1856	0.0393	-
1.0049	1857	0.0391	-
1.0054	1858	0.0347	-
1.0060	1859	0.0615	-
1.0065	1860	0.0156	-
1.0070	1861	0.0199	-
1.0076	1862	0.0129	-
1.0081	1863	0.0366	-
1.0087	1864	0.0305	-
1.0092	1865	0.0321	-
1.0097	1866	0.0302	-
1.0103	1867	0.0266	-
1.0108	1868	0.0206	-
1.0114	1869	0.0247	-
1.0119	1870	0.0335	-
1.0124	1871	0.0355	-
1.0130	1872	0.0328	-
1.0135	1873	0.0178	-
1.0141	1874	0.0277	-
1.0146	1875	0.0157	-
1.0152	1876	0.0308	-
1.0157	1877	0.0135	-
1.0162	1878	0.022	-
1.0168	1879	0.0233	-
1.0173	1880	0.031	-
1.0179	1881	0.0285	-
1.0184	1882	0.0364	-
1.0189	1883	0.0245	-
1.0195	1884	0.0328	-
1.0200	1885	0.0228	-
1.0206	1886	0.0217	-
1.0211	1887	0.0264	-
1.0216	1888	0.016	-
1.0222	1889	0.0231	-
1.0227	1890	0.0176	-
1.0233	1891	0.0323	-
1.0238	1892	0.0367	-
1.0244	1893	0.0122	-
1.0249	1894	0.0409	-
1.0254	1895	0.025	-
1.0260	1896	0.0373	-
1.0265	1897	0.0354	-
1.0271	1898	0.0247	-
1.0276	1899	0.0207	-
1.0281	1900	0.0337	-
1.0287	1901	0.0267	-
1.0292	1902	0.0199	-
1.0298	1903	0.0233	-
1.0303	1904	0.039	-
1.0308	1905	0.0173	-
1.0314	1906	0.0197	-
1.0319	1907	0.026	-
1.0325	1908	0.0273	-
1.0330	1909	0.0307	-
1.0335	1910	0.0301	-
1.0341	1911	0.0287	-
1.0346	1912	0.0274	-
1.0352	1913	0.0289	-
1.0357	1914	0.0326	-
1.0363	1915	0.021	-
1.0368	1916	0.0162	-
1.0373	1917	0.0445	-
1.0379	1918	0.0172	-
1.0384	1919	0.0153	-
1.0390	1920	0.0305	-
1.0395	1921	0.0393	-
1.0400	1922	0.0249	-
1.0406	1923	0.0473	-
1.0411	1924	0.0193	-
1.0417	1925	0.0204	-
1.0422	1926	0.0375	-
1.0427	1927	0.024	-
1.0433	1928	0.0233	-
1.0438	1929	0.0312	-
1.0444	1930	0.0153	-
1.0449	1931	0.055	-
1.0455	1932	0.0337	-
1.0460	1933	0.0307	-
1.0465	1934	0.0513	-
1.0471	1935	0.0164	-
1.0476	1936	0.0136	-
1.0482	1937	0.0171	-
1.0487	1938	0.0301	-
1.0492	1939	0.0203	-
1.0498	1940	0.0235	-
1.0503	1941	0.0255	-
1.0509	1942	0.0205	-
1.0514	1943	0.0145	-
1.0519	1944	0.0261	-
1.0525	1945	0.0264	-
1.0530	1946	0.023	-
1.0536	1947	0.0494	-
1.0541	1948	0.0241	-
1.0547	1949	0.0226	-
1.0552	1950	0.0404	-
1.0557	1951	0.0293	-
1.0563	1952	0.0293	-
1.0568	1953	0.0456	-
1.0574	1954	0.0161	-
1.0579	1955	0.0274	-
1.0584	1956	0.0545	-
1.0590	1957	0.0395	-
1.0595	1958	0.0246	-
1.0601	1959	0.0269	-
1.0606	1960	0.0464	-
1.0611	1961	0.0267	-
1.0617	1962	0.0227	-
1.0622	1963	0.0201	-
1.0628	1964	0.0261	-
1.0633	1965	0.0672	-
1.0639	1966	0.0345	-
1.0644	1967	0.0234	-
1.0649	1968	0.0276	-
1.0655	1969	0.0269	-
1.0660	1970	0.0147	-
1.0666	1971	0.0207	-
1.0671	1972	0.0172	-
1.0676	1973	0.0149	-
1.0682	1974	0.0109	-
1.0687	1975	0.0188	-
1.0693	1976	0.0209	-
1.0698	1977	0.0242	-
1.0703	1978	0.0459	-
1.0709	1979	0.0333	-
1.0714	1980	0.0337	-
1.0720	1981	0.048	-
1.0725	1982	0.011	-
1.0731	1983	0.0286	-
1.0736	1984	0.0216	-
1.0741	1985	0.0281	-
1.0747	1986	0.0337	-
1.0752	1987	0.0283	-
1.0758	1988	0.0297	-
1.0763	1989	0.0223	-
1.0768	1990	0.0181	-
1.0774	1991	0.0242	-
1.0779	1992	0.0205	-
1.0785	1993	0.0312	-
1.0790	1994	0.0182	-
1.0795	1995	0.0211	-
1.0801	1996	0.022	-
1.0806	1997	0.0146	-
1.0812	1998	0.0288	-
1.0817	1999	0.0595	-
1.0823	2000	0.0384	0.0256
1.0828	2001	0.0343	-
1.0833	2002	0.02	-
1.0839	2003	0.0211	-
1.0844	2004	0.0353	-
1.0850	2005	0.0501	-
1.0855	2006	0.0248	-
1.0860	2007	0.0408	-
1.0866	2008	0.0178	-
1.0871	2009	0.0236	-
1.0877	2010	0.0147	-
1.0882	2011	0.0135	-
1.0887	2012	0.022	-
1.0893	2013	0.0317	-
1.0898	2014	0.0199	-
1.0904	2015	0.0261	-
1.0909	2016	0.0342	-
1.0915	2017	0.032	-
1.0920	2018	0.0156	-
1.0925	2019	0.0168	-
1.0931	2020	0.0223	-
1.0936	2021	0.0234	-
1.0942	2022	0.0237	-
1.0947	2023	0.0228	-
1.0952	2024	0.0176	-
1.0958	2025	0.0472	-
1.0963	2026	0.0588	-
1.0969	2027	0.0233	-
1.0974	2028	0.0164	-
1.0979	2029	0.0203	-
1.0985	2030	0.0216	-
1.0990	2031	0.0277	-
1.0996	2032	0.0208	-
1.1001	2033	0.037	-
1.1006	2034	0.0108	-
1.1012	2035	0.025	-
1.1017	2036	0.0421	-
1.1023	2037	0.0194	-
1.1028	2038	0.0277	-
1.1034	2039	0.0234	-
1.1039	2040	0.0337	-
1.1044	2041	0.0312	-
1.1050	2042	0.0217	-
1.1055	2043	0.0219	-
1.1061	2044	0.0351	-
1.1066	2045	0.0404	-
1.1071	2046	0.0139	-
1.1077	2047	0.0296	-
1.1082	2048	0.0171	-
1.1088	2049	0.0325	-
1.1093	2050	0.0298	-
1.1098	2051	0.0189	-
1.1104	2052	0.0334	-
1.1109	2053	0.0176	-
1.1115	2054	0.0182	-
1.1120	2055	0.046	-
1.1126	2056	0.0563	-
1.1131	2057	0.0298	-
1.1136	2058	0.0162	-
1.1142	2059	0.0272	-
1.1147	2060	0.0245	-
1.1153	2061	0.0328	-
1.1158	2062	0.0205	-
1.1163	2063	0.027	-
1.1169	2064	0.02	-
1.1174	2065	0.0488	-
1.1180	2066	0.0369	-
1.1185	2067	0.0309	-
1.1190	2068	0.0194	-
1.1196	2069	0.0365	-
1.1201	2070	0.0164	-
1.1207	2071	0.0368	-
1.1212	2072	0.0285	-
1.1218	2073	0.0252	-
1.1223	2074	0.0195	-
1.1228	2075	0.0141	-
1.1234	2076	0.0474	-
1.1239	2077	0.0625	-
1.1245	2078	0.0173	-
1.125	2079	0.0355	-
1.1255	2080	0.0149	-
1.1261	2081	0.0241	-
1.1266	2082	0.0237	-
1.1272	2083	0.0142	-
1.1277	2084	0.0361	-
1.1282	2085	0.0318	-
1.1288	2086	0.0373	-
1.1293	2087	0.0119	-
1.1299	2088	0.0276	-
1.1304	2089	0.023	-
1.1310	2090	0.0328	-
1.1315	2091	0.0161	-
1.1320	2092	0.0602	-
1.1326	2093	0.0306	-
1.1331	2094	0.0582	-
1.1337	2095	0.0218	-
1.1342	2096	0.0406	-
1.1347	2097	0.0489	-
1.1353	2098	0.0306	-
1.1358	2099	0.0267	-
1.1364	2100	0.0421	-
1.1369	2101	0.0469	-
1.1374	2102	0.0269	-
1.1380	2103	0.0242	-
1.1385	2104	0.0227	-
1.1391	2105	0.0179	-
1.1396	2106	0.0166	-
1.1402	2107	0.0296	-
1.1407	2108	0.0378	-
1.1412	2109	0.0299	-
1.1418	2110	0.0372	-
1.1423	2111	0.0323	-
1.1429	2112	0.0446	-
1.1434	2113	0.0209	-
1.1439	2114	0.0199	-
1.1445	2115	0.0172	-
1.1450	2116	0.0187	-
1.1456	2117	0.0371	-
1.1461	2118	0.0259	-
1.1466	2119	0.0283	-
1.1472	2120	0.0135	-
1.1477	2121	0.0397	-
1.1483	2122	0.0297	-
1.1488	2123	0.023	-
1.1494	2124	0.0156	-
1.1499	2125	0.0481	-
1.1504	2126	0.0313	-
1.1510	2127	0.0418	-
1.1515	2128	0.0115	-
1.1521	2129	0.0246	-
1.1526	2130	0.0457	-
1.1531	2131	0.042	-
1.1537	2132	0.0189	-
1.1542	2133	0.0246	-
1.1548	2134	0.0238	-
1.1553	2135	0.0253	-
1.1558	2136	0.0184	-
1.1564	2137	0.022	-
1.1569	2138	0.0224	-
1.1575	2139	0.0286	-
1.1580	2140	0.0262	-
1.1585	2141	0.0341	-
1.1591	2142	0.0571	-
1.1596	2143	0.0272	-
1.1602	2144	0.0316	-
1.1607	2145	0.018	-
1.1613	2146	0.032	-
1.1618	2147	0.0357	-
1.1623	2148	0.0109	-
1.1629	2149	0.0308	-
1.1634	2150	0.0208	-
1.1640	2151	0.028	-
1.1645	2152	0.0319	-
1.1650	2153	0.029	-
1.1656	2154	0.0204	-
1.1661	2155	0.0258	-
1.1667	2156	0.0381	-
1.1672	2157	0.0253	-
1.1677	2158	0.0323	-
1.1683	2159	0.0313	-
1.1688	2160	0.0183	-
1.1694	2161	0.0349	-
1.1699	2162	0.0293	-
1.1705	2163	0.0223	-
1.1710	2164	0.0279	-
1.1715	2165	0.0258	-
1.1721	2166	0.0188	-
1.1726	2167	0.0229	-
1.1732	2168	0.0169	-
1.1737	2169	0.0251	-
1.1742	2170	0.0284	-
1.1748	2171	0.024	-
1.1753	2172	0.0185	-
1.1759	2173	0.0309	-
1.1764	2174	0.0312	-
1.1769	2175	0.0211	-
1.1775	2176	0.0444	-
1.1780	2177	0.0161	-
1.1786	2178	0.0303	-
1.1791	2179	0.0425	-
1.1797	2180	0.0272	-
1.1802	2181	0.0337	-
1.1807	2182	0.0221	-
1.1813	2183	0.0537	-
1.1818	2184	0.0264	-
1.1824	2185	0.0174	-
1.1829	2186	0.0286	-
1.1834	2187	0.0271	-
1.1840	2188	0.0249	-
1.1845	2189	0.0221	-
1.1851	2190	0.0309	-
1.1856	2191	0.02	-
1.1861	2192	0.0182	-
1.1867	2193	0.0366	-
1.1872	2194	0.0299	-
1.1878	2195	0.0123	-
1.1883	2196	0.0472	-
1.1889	2197	0.0271	-
1.1894	2198	0.0247	-
1.1899	2199	0.0165	-
1.1905	2200	0.0297	-
1.1910	2201	0.0182	-
1.1916	2202	0.0147	-
1.1921	2203	0.0202	-
1.1926	2204	0.0304	-
1.1932	2205	0.0384	-
1.1937	2206	0.0208	-
1.1943	2207	0.0374	-
1.1948	2208	0.0236	-
1.1953	2209	0.0163	-
1.1959	2210	0.0348	-
1.1964	2211	0.053	-
1.1970	2212	0.0256	-
1.1975	2213	0.0312	-
1.1981	2214	0.0253	-
1.1986	2215	0.0147	-
1.1991	2216	0.0208	-
1.1997	2217	0.0194	-
1.2002	2218	0.0417	-
1.2008	2219	0.022	-
1.2013	2220	0.0298	-
1.2018	2221	0.0451	-
1.2024	2222	0.0309	-
1.2029	2223	0.0239	-
1.2035	2224	0.0478	-
1.2040	2225	0.0281	-
1.2045	2226	0.019	-
1.2051	2227	0.0218	-
1.2056	2228	0.0245	-
1.2062	2229	0.0438	-
1.2067	2230	0.0274	-
1.2073	2231	0.0338	-
1.2078	2232	0.027	-
1.2083	2233	0.0227	-
1.2089	2234	0.0303	-
1.2094	2235	0.0416	-
1.2100	2236	0.0394	-
1.2105	2237	0.0256	-
1.2110	2238	0.062	-
1.2116	2239	0.0158	-
1.2121	2240	0.0167	-
1.2127	2241	0.0305	-
1.2132	2242	0.0175	-
1.2137	2243	0.0268	-
1.2143	2244	0.0321	-
1.2148	2245	0.031	-
1.2154	2246	0.0382	-
1.2159	2247	0.0176	-
1.2165	2248	0.0322	-
1.2170	2249	0.0382	-
1.2175	2250	0.0272	-
1.2181	2251	0.0401	-
1.2186	2252	0.0422	-
1.2192	2253	0.0259	-
1.2197	2254	0.0181	-
1.2202	2255	0.0107	-
1.2208	2256	0.0243	-
1.2213	2257	0.026	-
1.2219	2258	0.0374	-
1.2224	2259	0.0436	-
1.2229	2260	0.014	-
1.2235	2261	0.027	-
1.2240	2262	0.0202	-
1.2246	2263	0.024	-
1.2251	2264	0.0284	-
1.2256	2265	0.0502	-
1.2262	2266	0.0236	-
1.2267	2267	0.0282	-
1.2273	2268	0.0293	-
1.2278	2269	0.0202	-
1.2284	2270	0.0334	-
1.2289	2271	0.0185	-
1.2294	2272	0.0314	-
1.2300	2273	0.0284	-
1.2305	2274	0.0495	-
1.2311	2275	0.0354	-
1.2316	2276	0.0178	-
1.2321	2277	0.0374	-
1.2327	2278	0.0198	-
1.2332	2279	0.0135	-
1.2338	2280	0.0156	-
1.2343	2281	0.0279	-
1.2348	2282	0.0415	-
1.2354	2283	0.0214	-
1.2359	2284	0.0266	-
1.2365	2285	0.0353	-
1.2370	2286	0.0208	-
1.2376	2287	0.0175	-
1.2381	2288	0.0143	-
1.2386	2289	0.0173	-
1.2392	2290	0.0312	-
1.2397	2291	0.0235	-
1.2403	2292	0.0246	-
1.2408	2293	0.029	-
1.2413	2294	0.0142	-
1.2419	2295	0.017	-
1.2424	2296	0.0188	-
1.2430	2297	0.0204	-
1.2435	2298	0.0388	-
1.2440	2299	0.0212	-
1.2446	2300	0.0324	-
1.2451	2301	0.0329	-
1.2457	2302	0.0148	-
1.2462	2303	0.0416	-
1.2468	2304	0.0178	-
1.2473	2305	0.013	-
1.2478	2306	0.0251	-
1.2484	2307	0.023	-
1.2489	2308	0.0214	-
1.2495	2309	0.0138	-
1.25	2310	0.0423	-
1.2505	2311	0.0292	-
1.2511	2312	0.0128	-
1.2516	2313	0.0502	-
1.2522	2314	0.0175	-
1.2527	2315	0.0205	-
1.2532	2316	0.0377	-
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1.2543	2318	0.0173	-
1.2549	2319	0.0182	-
1.2554	2320	0.0373	-
1.2560	2321	0.0225	-
1.2565	2322	0.0152	-
1.2570	2323	0.0109	-
1.2576	2324	0.0118	-
1.2581	2325	0.0261	-
1.2587	2326	0.025	-
1.2592	2327	0.0115	-
1.2597	2328	0.0256	-
1.2603	2329	0.0236	-
1.2608	2330	0.0291	-
1.2614	2331	0.0236	-
1.2619	2332	0.0192	-
1.2624	2333	0.0223	-
1.2630	2334	0.0241	-
1.2635	2335	0.0371	-
1.2641	2336	0.0321	-
1.2646	2337	0.0142	-
1.2652	2338	0.0272	-
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1.2662	2340	0.0248	-
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1.2679	2343	0.0194	-
1.2684	2344	0.0319	-
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1.2700	2347	0.0153	-
1.2706	2348	0.0305	-
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1.2722	2351	0.024	-
1.2727	2352	0.0481	-
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1.2744	2355	0.02	-
1.2749	2356	0.0311	-
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1.2760	2358	0.0177	-
1.2765	2359	0.036	-
1.2771	2360	0.0279	-
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1.2781	2362	0.0094	-
1.2787	2363	0.0283	-
1.2792	2364	0.0289	-
1.2798	2365	0.022	-
1.2803	2366	0.0315	-
1.2808	2367	0.0209	-
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1.2825	2370	0.0116	-
1.2830	2371	0.042	-
1.2835	2372	0.0288	-
1.2841	2373	0.0543	-
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1.2852	2375	0.0227	-
1.2857	2376	0.0166	-
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1.2868	2378	0.0513	-
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1.2884	2381	0.0289	-
1.2890	2382	0.032	-
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1.2900	2384	0.0178	-
1.2906	2385	0.0402	-
1.2911	2386	0.0187	-
1.2917	2387	0.0221	-
1.2922	2388	0.0136	-
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1.2933	2390	0.0155	-
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1.2944	2392	0.0283	-
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1.2955	2394	0.0111	-
1.2960	2395	0.0212	-
1.2965	2396	0.0225	-
1.2971	2397	0.0124	-
1.2976	2398	0.0249	-
1.2982	2399	0.0141	-
1.2987	2400	0.0359	-
1.2992	2401	0.022	-
1.2998	2402	0.0197	-
1.3003	2403	0.0155	-
1.3009	2404	0.019	-
1.3014	2405	0.0234	-
1.3019	2406	0.008	-
1.3025	2407	0.0256	-
1.3030	2408	0.015	-
1.3036	2409	0.0314	-
1.3041	2410	0.0176	-
1.3047	2411	0.0141	-
1.3052	2412	0.029	-
1.3057	2413	0.0108	-
1.3063	2414	0.0187	-
1.3068	2415	0.0381	-
1.3074	2416	0.0281	-
1.3079	2417	0.019	-
1.3084	2418	0.0198	-
1.3090	2419	0.0429	-
1.3095	2420	0.0227	-
1.3101	2421	0.0214	-
1.3106	2422	0.0423	-
1.3111	2423	0.0419	-
1.3117	2424	0.0534	-
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1.3128	2426	0.0374	-
1.3133	2427	0.021	-
1.3139	2428	0.0147	-
1.3144	2429	0.0208	-
1.3149	2430	0.0377	-
1.3155	2431	0.0468	-
1.3160	2432	0.0205	-
1.3166	2433	0.0216	-
1.3171	2434	0.0232	-
1.3176	2435	0.0097	-
1.3182	2436	0.0249	-
1.3187	2437	0.0179	-
1.3193	2438	0.0248	-
1.3198	2439	0.0259	-
1.3203	2440	0.0313	-
1.3209	2441	0.0275	-
1.3214	2442	0.0311	-
1.3220	2443	0.0133	-
1.3225	2444	0.0353	-
1.3231	2445	0.0275	-
1.3236	2446	0.0281	-
1.3241	2447	0.0134	-
1.3247	2448	0.0226	-
1.3252	2449	0.0226	-
1.3258	2450	0.016	-
1.3263	2451	0.0439	-
1.3268	2452	0.0286	-
1.3274	2453	0.0143	-
1.3279	2454	0.0334	-
1.3285	2455	0.024	-
1.3290	2456	0.0371	-
1.3295	2457	0.0247	-
1.3301	2458	0.0394	-
1.3306	2459	0.0169	-
1.3312	2460	0.0396	-
1.3317	2461	0.0144	-
1.3323	2462	0.0202	-
1.3328	2463	0.0421	-
1.3333	2464	0.0148	-
1.3339	2465	0.0266	-
1.3344	2466	0.0257	-
1.3350	2467	0.0139	-
1.3355	2468	0.0237	-
1.3360	2469	0.0279	-
1.3366	2470	0.0337	-
1.3371	2471	0.0248	-
1.3377	2472	0.0241	-
1.3382	2473	0.0277	-
1.3387	2474	0.0442	-
1.3393	2475	0.0222	-
1.3398	2476	0.0226	-
1.3404	2477	0.0362	-
1.3409	2478	0.0158	-
1.3415	2479	0.0245	-
1.3420	2480	0.0125	-
1.3425	2481	0.0348	-
1.3431	2482	0.0378	-
1.3436	2483	0.0321	-
1.3442	2484	0.0144	-
1.3447	2485	0.0251	-
1.3452	2486	0.0099	-
1.3458	2487	0.0256	-
1.3463	2488	0.0278	-
1.3469	2489	0.0176	-
1.3474	2490	0.0268	-
1.3479	2491	0.0304	-
1.3485	2492	0.0478	-
1.3490	2493	0.026	-
1.3496	2494	0.033	-
1.3501	2495	0.0161	-
1.3506	2496	0.0157	-
1.3512	2497	0.0189	-
1.3517	2498	0.0367	-
1.3523	2499	0.0173	-
1.3528	2500	0.031	-
1.3534	2501	0.0178	-
1.3539	2502	0.0405	-
1.3544	2503	0.0171	-
1.3550	2504	0.0243	-
1.3555	2505	0.0239	-
1.3561	2506	0.0363	-
1.3566	2507	0.0207	-
1.3571	2508	0.0342	-
1.3577	2509	0.0568	-
1.3582	2510	0.0406	-
1.3588	2511	0.0277	-
1.3593	2512	0.0154	-
1.3598	2513	0.0115	-
1.3604	2514	0.0489	-
1.3609	2515	0.0184	-
1.3615	2516	0.0152	-
1.3620	2517	0.0155	-
1.3626	2518	0.0378	-
1.3631	2519	0.0191	-
1.3636	2520	0.0592	-
1.3642	2521	0.0111	-
1.3647	2522	0.0238	-
1.3653	2523	0.0294	-
1.3658	2524	0.0129	-
1.3663	2525	0.0358	-
1.3669	2526	0.017	-
1.3674	2527	0.0387	-
1.3680	2528	0.0135	-
1.3685	2529	0.0208	-
1.3690	2530	0.0214	-
1.3696	2531	0.0169	-
1.3701	2532	0.0392	-
1.3707	2533	0.0308	-
1.3712	2534	0.0478	-
1.3718	2535	0.0199	-
1.3723	2536	0.0314	-
1.3728	2537	0.0198	-
1.3734	2538	0.0356	-
1.3739	2539	0.0225	-
1.3745	2540	0.039	-
1.375	2541	0.0246	-
1.3755	2542	0.0275	-
1.3761	2543	0.025	-
1.3766	2544	0.0694	-
1.3772	2545	0.018	-
1.3777	2546	0.0347	-
1.3782	2547	0.0374	-
1.3788	2548	0.0277	-
1.3793	2549	0.0319	-
1.3799	2550	0.02	-
1.3804	2551	0.0363	-
1.3810	2552	0.0505	-
1.3815	2553	0.0104	-
1.3820	2554	0.0327	-
1.3826	2555	0.0155	-
1.3831	2556	0.0165	-
1.3837	2557	0.0178	-
1.3842	2558	0.0431	-
1.3847	2559	0.0293	-
1.3853	2560	0.0304	-
1.3858	2561	0.0273	-
1.3864	2562	0.0233	-
1.3869	2563	0.0201	-
1.3874	2564	0.0174	-
1.3880	2565	0.0279	-
1.3885	2566	0.0183	-
1.3891	2567	0.0322	-
1.3896	2568	0.0397	-
1.3902	2569	0.0333	-
1.3907	2570	0.0436	-
1.3912	2571	0.0245	-
1.3918	2572	0.0256	-
1.3923	2573	0.0345	-
1.3929	2574	0.036	-
1.3934	2575	0.0281	-
1.3939	2576	0.0251	-
1.3945	2577	0.0216	-
1.3950	2578	0.0287	-
1.3956	2579	0.0203	-
1.3961	2580	0.021	-
1.3966	2581	0.0222	-
1.3972	2582	0.0108	-
1.3977	2583	0.0192	-
1.3983	2584	0.0253	-
1.3988	2585	0.0208	-
1.3994	2586	0.0237	-
1.3999	2587	0.0176	-
1.4004	2588	0.0376	-
1.4010	2589	0.0313	-
1.4015	2590	0.0293	-
1.4021	2591	0.0484	-
1.4026	2592	0.0324	-
1.4031	2593	0.0198	-
1.4037	2594	0.0077	-
1.4042	2595	0.0308	-
1.4048	2596	0.0101	-
1.4053	2597	0.0211	-
1.4058	2598	0.0273	-
1.4064	2599	0.0207	-
1.4069	2600	0.0347	-
1.4075	2601	0.0175	-
1.4080	2602	0.0282	-
1.4085	2603	0.0177	-
1.4091	2604	0.0457	-
1.4096	2605	0.061	-
1.4102	2606	0.0379	-
1.4107	2607	0.0424	-
1.4113	2608	0.0203	-
1.4118	2609	0.0191	-
1.4123	2610	0.0303	-
1.4129	2611	0.0138	-
1.4134	2612	0.0155	-
1.4140	2613	0.0173	-
1.4145	2614	0.0254	-
1.4150	2615	0.0246	-
1.4156	2616	0.0184	-
1.4161	2617	0.0243	-
1.4167	2618	0.0535	-
1.4172	2619	0.0149	-
1.4177	2620	0.0163	-
1.4183	2621	0.0156	-
1.4188	2622	0.0284	-
1.4194	2623	0.0456	-
1.4199	2624	0.0233	-
1.4205	2625	0.0215	-
1.4210	2626	0.0282	-
1.4215	2627	0.0605	-
1.4221	2628	0.026	-
1.4226	2629	0.0158	-
1.4232	2630	0.0204	-
1.4237	2631	0.0103	-
1.4242	2632	0.0398	-
1.4248	2633	0.0376	-
1.4253	2634	0.0178	-
1.4259	2635	0.0313	-
1.4264	2636	0.0285	-
1.4269	2637	0.0252	-
1.4275	2638	0.0385	-
1.4280	2639	0.0139	-
1.4286	2640	0.0393	-
1.4291	2641	0.0128	-
1.4297	2642	0.025	-
1.4302	2643	0.04	-
1.4307	2644	0.0121	-
1.4313	2645	0.0263	-
1.4318	2646	0.0388	-
1.4324	2647	0.0224	-
1.4329	2648	0.0314	-
1.4334	2649	0.0233	-
1.4340	2650	0.0327	-
1.4345	2651	0.035	-
1.4351	2652	0.0264	-
1.4356	2653	0.0659	-
1.4361	2654	0.022	-
1.4367	2655	0.0214	-
1.4372	2656	0.0266	-
1.4378	2657	0.0389	-
1.4383	2658	0.0426	-
1.4389	2659	0.0202	-
1.4394	2660	0.0372	-
1.4399	2661	0.053	-
1.4405	2662	0.0391	-
1.4410	2663	0.0494	-
1.4416	2664	0.0142	-
1.4421	2665	0.0129	-
1.4426	2666	0.014	-
1.4432	2667	0.0268	-
1.4437	2668	0.0199	-
1.4443	2669	0.0095	-
1.4448	2670	0.0342	-
1.4453	2671	0.0173	-
1.4459	2672	0.0144	-
1.4464	2673	0.0167	-
1.4470	2674	0.0221	-
1.4475	2675	0.0244	-
1.4481	2676	0.0301	-
1.4486	2677	0.0261	-
1.4491	2678	0.0323	-
1.4497	2679	0.0486	-
1.4502	2680	0.0304	-
1.4508	2681	0.0152	-
1.4513	2682	0.0219	-
1.4518	2683	0.0154	-
1.4524	2684	0.0173	-
1.4529	2685	0.0257	-
1.4535	2686	0.0221	-
1.4540	2687	0.0168	-
1.4545	2688	0.0167	-
1.4551	2689	0.0242	-
1.4556	2690	0.0394	-
1.4562	2691	0.0204	-
1.4567	2692	0.0211	-
1.4573	2693	0.0227	-
1.4578	2694	0.031	-
1.4583	2695	0.0278	-
1.4589	2696	0.0247	-
1.4594	2697	0.0438	-
1.4600	2698	0.0299	-
1.4605	2699	0.0118	-
1.4610	2700	0.0217	-
1.4616	2701	0.0313	-
1.4621	2702	0.0256	-
1.4627	2703	0.0159	-
1.4632	2704	0.0228	-
1.4637	2705	0.029	-
1.4643	2706	0.0258	-
1.4648	2707	0.0231	-
1.4654	2708	0.0147	-
1.4659	2709	0.0179	-
1.4665	2710	0.0182	-
1.4670	2711	0.0223	-
1.4675	2712	0.0198	-
1.4681	2713	0.0341	-
1.4686	2714	0.0568	-
1.4692	2715	0.0272	-
1.4697	2716	0.0342	-
1.4702	2717	0.031	-
1.4708	2718	0.0101	-
1.4713	2719	0.0291	-
1.4719	2720	0.0199	-
1.4724	2721	0.0238	-
1.4729	2722	0.028	-
1.4735	2723	0.0358	-
1.4740	2724	0.0221	-
1.4746	2725	0.026	-
1.4751	2726	0.0244	-
1.4756	2727	0.0127	-
1.4762	2728	0.0369	-
1.4767	2729	0.0185	-
1.4773	2730	0.0182	-
1.4778	2731	0.079	-
1.4784	2732	0.0164	-
1.4789	2733	0.0192	-
1.4794	2734	0.0316	-
1.4800	2735	0.036	-
1.4805	2736	0.0218	-
1.4811	2737	0.0277	-
1.4816	2738	0.0303	-
1.4821	2739	0.0355	-
1.4827	2740	0.0158	-
1.4832	2741	0.0321	-
1.4838	2742	0.0371	-
1.4843	2743	0.0308	-
1.4848	2744	0.0329	-
1.4854	2745	0.029	-
1.4859	2746	0.0218	-
1.4865	2747	0.0262	-
1.4870	2748	0.0146	-
1.4876	2749	0.0195	-
1.4881	2750	0.0297	-
1.4886	2751	0.0331	-
1.4892	2752	0.0383	-
1.4897	2753	0.0199	-
1.4903	2754	0.0254	-
1.4908	2755	0.0244	-
1.4913	2756	0.0246	-
1.4919	2757	0.0288	-
1.4924	2758	0.0254	-
1.4930	2759	0.0234	-
1.4935	2760	0.0339	-
1.4940	2761	0.0253	-
1.4946	2762	0.0153	-
1.4951	2763	0.0451	-
1.4957	2764	0.021	-
1.4962	2765	0.019	-
1.4968	2766	0.0243	-
1.4973	2767	0.0302	-
1.4978	2768	0.0476	-
1.4984	2769	0.0477	-
1.4989	2770	0.0169	-
1.4995	2771	0.0254	-
1.5	2772	0.0305	-
1.5005	2773	0.0233	-
1.5011	2774	0.0239	-
1.5016	2775	0.0111	-
1.5022	2776	0.0111	-
1.5027	2777	0.0282	-
1.5032	2778	0.0165	-
1.5038	2779	0.0384	-
1.5043	2780	0.0335	-
1.5049	2781	0.0139	-
1.5054	2782	0.0313	-
1.5060	2783	0.0211	-
1.5065	2784	0.0193	-
1.5070	2785	0.0337	-
1.5076	2786	0.0278	-
1.5081	2787	0.0292	-
1.5087	2788	0.0194	-
1.5092	2789	0.0235	-
1.5097	2790	0.0088	-
1.5103	2791	0.0309	-
1.5108	2792	0.0106	-
1.5114	2793	0.0509	-
1.5119	2794	0.0246	-
1.5124	2795	0.0206	-
1.5130	2796	0.0233	-
1.5135	2797	0.0305	-
1.5141	2798	0.0198	-
1.5146	2799	0.0206	-
1.5152	2800	0.0269	-
1.5157	2801	0.0168	-
1.5162	2802	0.0197	-
1.5168	2803	0.0192	-
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1.5184	2806	0.0202	-
1.5189	2807	0.0283	-
1.5195	2808	0.0155	-
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1.5211	2811	0.0274	-
1.5216	2812	0.0446	-
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1.5233	2815	0.0118	-
1.5238	2816	0.023	-
1.5244	2817	0.0473	-
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1.5260	2820	0.0345	-
1.5265	2821	0.0082	-
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1.5298	2827	0.031	-
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1.5314	2830	0.0313	-
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1.5335	2834	0.0247	-
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1.5384	2843	0.0292	-
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1.5400	2846	0.024	-
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1.5422	2850	0.014	-
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1.5460	2857	0.0105	-
1.5465	2858	0.0145	-
1.5471	2859	0.0371	-
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1.5492	2863	0.0212	-
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1.5525	2869	0.0255	-
1.5530	2870	0.0197	-
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1.5563	2876	0.0332	-
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1.5584	2880	0.0435	-
1.5590	2881	0.0245	-
1.5595	2882	0.0211	-
1.5601	2883	0.0176	-
1.5606	2884	0.0185	-
1.5611	2885	0.0343	-
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1.5639	2890	0.0255	-
1.5644	2891	0.0433	-
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1.5655	2893	0.0224	-
1.5660	2894	0.0218	-
1.5666	2895	0.0233	-
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1.5687	2899	0.0207	-
1.5693	2900	0.0152	-
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1.5714	2904	0.0221	-
1.5720	2905	0.0288	-
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1.5731	2907	0.0195	-
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1.5741	2909	0.0187	-
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1.5752	2911	0.0251	-
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1.5768	2914	0.0186	-
1.5774	2915	0.0473	-
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1.5785	2917	0.0295	-
1.5790	2918	0.016	-
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1.5812	2922	0.0262	-
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1.5828	2925	0.0303	-
1.5833	2926	0.029	-
1.5839	2927	0.0277	-
1.5844	2928	0.0489	-
1.5850	2929	0.0248	-
1.5855	2930	0.0144	-
1.5860	2931	0.0272	-
1.5866	2932	0.0486	-
1.5871	2933	0.0356	-
1.5877	2934	0.014	-
1.5882	2935	0.0176	-
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1.5898	2938	0.0303	-
1.5904	2939	0.0275	-
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1.5915	2941	0.0297	-
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1.5925	2943	0.0252	-
1.5931	2944	0.0114	-
1.5936	2945	0.0428	-
1.5942	2946	0.0392	-
1.5947	2947	0.0439	-
1.5952	2948	0.0277	-
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1.5963	2950	0.0296	-
1.5969	2951	0.0396	-
1.5974	2952	0.0387	-
1.5979	2953	0.0211	-
1.5985	2954	0.0407	-
1.5990	2955	0.0226	-
1.5996	2956	0.0282	-
1.6001	2957	0.0222	-
1.6006	2958	0.0239	-
1.6012	2959	0.0197	-
1.6017	2960	0.0251	-
1.6023	2961	0.0275	-
1.6028	2962	0.0387	-
1.6034	2963	0.0139	-
1.6039	2964	0.0177	-
1.6044	2965	0.0324	-
1.6050	2966	0.0199	-
1.6055	2967	0.0173	-
1.6061	2968	0.0278	-
1.6066	2969	0.0331	-
1.6071	2970	0.0175	-
1.6077	2971	0.0185	-
1.6082	2972	0.0094	-
1.6088	2973	0.0178	-
1.6093	2974	0.0543	-
1.6098	2975	0.0217	-
1.6104	2976	0.0245	-
1.6109	2977	0.0212	-
1.6115	2978	0.0243	-
1.6120	2979	0.015	-
1.6126	2980	0.034	-
1.6131	2981	0.0292	-
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1.6142	2983	0.0159	-
1.6147	2984	0.0451	-
1.6153	2985	0.0292	-
1.6158	2986	0.0171	-
1.6163	2987	0.0272	-
1.6169	2988	0.0121	-
1.6174	2989	0.0178	-
1.6180	2990	0.0152	-
1.6185	2991	0.0153	-
1.6190	2992	0.0159	-
1.6196	2993	0.0266	-
1.6201	2994	0.0234	-
1.6207	2995	0.0292	-
1.6212	2996	0.0243	-
1.6218	2997	0.0289	-
1.6223	2998	0.0161	-
1.6228	2999	0.0169	-
1.6234	3000	0.0361	0.0236
1.6239	3001	0.0366	-
1.6245	3002	0.0265	-
1.625	3003	0.011	-
1.6255	3004	0.0113	-
1.6261	3005	0.0239	-
1.6266	3006	0.0414	-
1.6272	3007	0.0249	-
1.6277	3008	0.0528	-
1.6282	3009	0.0142	-
1.6288	3010	0.0216	-
1.6293	3011	0.0296	-
1.6299	3012	0.024	-
1.6304	3013	0.0217	-
1.6310	3014	0.0278	-
1.6315	3015	0.0176	-
1.6320	3016	0.0093	-
1.6326	3017	0.0355	-
1.6331	3018	0.0172	-
1.6337	3019	0.027	-
1.6342	3020	0.0263	-
1.6347	3021	0.0248	-
1.6353	3022	0.0108	-
1.6358	3023	0.0233	-
1.6364	3024	0.0245	-
1.6369	3025	0.0377	-
1.6374	3026	0.0312	-
1.6380	3027	0.0096	-
1.6385	3028	0.0293	-
1.6391	3029	0.0449	-
1.6396	3030	0.0195	-
1.6402	3031	0.0132	-
1.6407	3032	0.0291	-
1.6412	3033	0.0191	-
1.6418	3034	0.0346	-
1.6423	3035	0.0145	-
1.6429	3036	0.0405	-
1.6434	3037	0.0324	-
1.6439	3038	0.0243	-
1.6445	3039	0.0165	-
1.6450	3040	0.0287	-
1.6456	3041	0.0181	-
1.6461	3042	0.0234	-
1.6466	3043	0.0279	-
1.6472	3044	0.0187	-
1.6477	3045	0.0311	-
1.6483	3046	0.016	-
1.6488	3047	0.0362	-
1.6494	3048	0.0249	-
1.6499	3049	0.045	-
1.6504	3050	0.0207	-
1.6510	3051	0.0344	-
1.6515	3052	0.0114	-
1.6521	3053	0.0098	-
1.6526	3054	0.0092	-
1.6531	3055	0.0175	-
1.6537	3056	0.016	-
1.6542	3057	0.0295	-
1.6548	3058	0.0212	-
1.6553	3059	0.0351	-
1.6558	3060	0.0252	-
1.6564	3061	0.0088	-
1.6569	3062	0.0245	-
1.6575	3063	0.0316	-
1.6580	3064	0.0363	-
1.6585	3065	0.0459	-
1.6591	3066	0.0166	-
1.6596	3067	0.0276	-
1.6602	3068	0.0372	-
1.6607	3069	0.0165	-
1.6613	3070	0.0238	-
1.6618	3071	0.0261	-
1.6623	3072	0.0244	-
1.6629	3073	0.0253	-
1.6634	3074	0.0088	-
1.6640	3075	0.0216	-
1.6645	3076	0.009	-
1.6650	3077	0.0168	-
1.6656	3078	0.0226	-
1.6661	3079	0.0194	-
1.6667	3080	0.0221	-
1.6672	3081	0.022	-
1.6677	3082	0.016	-
1.6683	3083	0.0437	-
1.6688	3084	0.0302	-
1.6694	3085	0.0278	-
1.6699	3086	0.0107	-
1.6705	3087	0.0197	-
1.6710	3088	0.0126	-
1.6715	3089	0.0353	-
1.6721	3090	0.0234	-
1.6726	3091	0.0169	-
1.6732	3092	0.0209	-
1.6737	3093	0.0643	-
1.6742	3094	0.0238	-
1.6748	3095	0.0099	-
1.6753	3096	0.0273	-
1.6759	3097	0.0241	-
1.6764	3098	0.0208	-
1.6769	3099	0.0192	-
1.6775	3100	0.0207	-
1.6780	3101	0.0192	-
1.6786	3102	0.0206	-
1.6791	3103	0.0578	-
1.6797	3104	0.0304	-
1.6802	3105	0.0266	-
1.6807	3106	0.0131	-
1.6813	3107	0.0368	-
1.6818	3108	0.0277	-
1.6824	3109	0.0293	-
1.6829	3110	0.0407	-
1.6834	3111	0.0116	-
1.6840	3112	0.0149	-
1.6845	3113	0.0343	-
1.6851	3114	0.0076	-
1.6856	3115	0.037	-
1.6861	3116	0.0121	-
1.6867	3117	0.0331	-
1.6872	3118	0.0369	-
1.6878	3119	0.0227	-
1.6883	3120	0.0576	-
1.6889	3121	0.0178	-
1.6894	3122	0.0617	-
1.6899	3123	0.0144	-
1.6905	3124	0.0221	-
1.6910	3125	0.0188	-
1.6916	3126	0.0183	-
1.6921	3127	0.0204	-
1.6926	3128	0.0275	-
1.6932	3129	0.0322	-
1.6937	3130	0.0321	-
1.6943	3131	0.0258	-
1.6948	3132	0.0316	-
1.6953	3133	0.0344	-
1.6959	3134	0.02	-
1.6964	3135	0.0337	-
1.6970	3136	0.0312	-
1.6975	3137	0.0173	-
1.6981	3138	0.024	-
1.6986	3139	0.0162	-
1.6991	3140	0.0256	-
1.6997	3141	0.0109	-
1.7002	3142	0.0175	-
1.7008	3143	0.0315	-
1.7013	3144	0.0325	-
1.7018	3145	0.0184	-
1.7024	3146	0.0146	-
1.7029	3147	0.0157	-
1.7035	3148	0.0228	-
1.7040	3149	0.0276	-
1.7045	3150	0.0222	-
1.7051	3151	0.0108	-
1.7056	3152	0.017	-
1.7062	3153	0.016	-
1.7067	3154	0.0234	-
1.7073	3155	0.0253	-
1.7078	3156	0.0173	-
1.7083	3157	0.0318	-
1.7089	3158	0.0204	-
1.7094	3159	0.0179	-
1.7100	3160	0.0306	-
1.7105	3161	0.0259	-
1.7110	3162	0.0326	-
1.7116	3163	0.0192	-
1.7121	3164	0.0239	-
1.7127	3165	0.0251	-
1.7132	3166	0.0155	-
1.7137	3167	0.0166	-
1.7143	3168	0.0146	-
1.7148	3169	0.0323	-
1.7154	3170	0.0158	-
1.7159	3171	0.0186	-
1.7165	3172	0.0291	-
1.7170	3173	0.011	-
1.7175	3174	0.0511	-
1.7181	3175	0.0247	-
1.7186	3176	0.0207	-
1.7192	3177	0.0191	-
1.7197	3178	0.0308	-
1.7202	3179	0.0172	-
1.7208	3180	0.0247	-
1.7213	3181	0.0167	-
1.7219	3182	0.0172	-
1.7224	3183	0.0407	-
1.7229	3184	0.0107	-
1.7235	3185	0.0224	-
1.7240	3186	0.0187	-
1.7246	3187	0.021	-
1.7251	3188	0.0148	-
1.7256	3189	0.0387	-
1.7262	3190	0.0154	-
1.7267	3191	0.0231	-
1.7273	3192	0.0192	-
1.7278	3193	0.0212	-
1.7284	3194	0.0176	-
1.7289	3195	0.0392	-
1.7294	3196	0.0143	-
1.7300	3197	0.0141	-
1.7305	3198	0.0131	-
1.7311	3199	0.0176	-
1.7316	3200	0.0246	-
1.7321	3201	0.0247	-
1.7327	3202	0.0209	-
1.7332	3203	0.009	-
1.7338	3204	0.0116	-
1.7343	3205	0.0313	-
1.7348	3206	0.0289	-
1.7354	3207	0.0376	-
1.7359	3208	0.0133	-
1.7365	3209	0.0162	-
1.7370	3210	0.022	-
1.7376	3211	0.0446	-
1.7381	3212	0.0147	-
1.7386	3213	0.0329	-
1.7392	3214	0.0108	-
1.7397	3215	0.0168	-
1.7403	3216	0.0143	-
1.7408	3217	0.0075	-
1.7413	3218	0.0136	-
1.7419	3219	0.0261	-
1.7424	3220	0.0179	-
1.7430	3221	0.0194	-
1.7435	3222	0.0191	-
1.7440	3223	0.0205	-
1.7446	3224	0.0151	-
1.7451	3225	0.0073	-
1.7457	3226	0.0213	-
1.7462	3227	0.0188	-
1.7468	3228	0.0205	-
1.7473	3229	0.0333	-
1.7478	3230	0.0281	-
1.7484	3231	0.0088	-
1.7489	3232	0.0163	-
1.7495	3233	0.0171	-
1.75	3234	0.0375	-
1.7505	3235	0.0263	-
1.7511	3236	0.0213	-
1.7516	3237	0.0252	-
1.7522	3238	0.0133	-
1.7527	3239	0.0436	-
1.7532	3240	0.0204	-
1.7538	3241	0.0379	-
1.7543	3242	0.0365	-
1.7549	3243	0.038	-
1.7554	3244	0.0144	-
1.7560	3245	0.0231	-
1.7565	3246	0.0282	-
1.7570	3247	0.0286	-
1.7576	3248	0.0322	-
1.7581	3249	0.0286	-
1.7587	3250	0.0181	-
1.7592	3251	0.0172	-
1.7597	3252	0.0233	-
1.7603	3253	0.0203	-
1.7608	3254	0.0155	-
1.7614	3255	0.0193	-
1.7619	3256	0.0255	-
1.7624	3257	0.025	-
1.7630	3258	0.0182	-
1.7635	3259	0.0184	-
1.7641	3260	0.0257	-
1.7646	3261	0.0242	-
1.7652	3262	0.0135	-
1.7657	3263	0.012	-
1.7662	3264	0.0154	-
1.7668	3265	0.0444	-
1.7673	3266	0.0238	-
1.7679	3267	0.0133	-
1.7684	3268	0.0281	-
1.7689	3269	0.0164	-
1.7695	3270	0.0211	-
1.7700	3271	0.0187	-
1.7706	3272	0.0257	-
1.7711	3273	0.0206	-
1.7716	3274	0.0392	-
1.7722	3275	0.0309	-
1.7727	3276	0.0161	-
1.7733	3277	0.0159	-
1.7738	3278	0.0155	-
1.7744	3279	0.0216	-
1.7749	3280	0.018	-
1.7754	3281	0.0237	-
1.7760	3282	0.0152	-
1.7765	3283	0.0229	-
1.7771	3284	0.0329	-
1.7776	3285	0.02	-
1.7781	3286	0.0383	-
1.7787	3287	0.0379	-
1.7792	3288	0.0377	-
1.7798	3289	0.0109	-
1.7803	3290	0.0264	-
1.7808	3291	0.0242	-
1.7814	3292	0.0383	-
1.7819	3293	0.0169	-
1.7825	3294	0.0144	-
1.7830	3295	0.032	-
1.7835	3296	0.0424	-
1.7841	3297	0.0246	-
1.7846	3298	0.0197	-
1.7852	3299	0.0488	-
1.7857	3300	0.0251	-
1.7863	3301	0.0223	-
1.7868	3302	0.0174	-
1.7873	3303	0.0243	-
1.7879	3304	0.018	-
1.7884	3305	0.045	-
1.7890	3306	0.0174	-
1.7895	3307	0.021	-
1.7900	3308	0.0289	-
1.7906	3309	0.0157	-
1.7911	3310	0.0215	-
1.7917	3311	0.0386	-
1.7922	3312	0.0169	-
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1.7933	3314	0.0131	-
1.7938	3315	0.0107	-
1.7944	3316	0.0162	-
1.7949	3317	0.0298	-
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1.7960	3319	0.0419	-
1.7965	3320	0.0425	-
1.7971	3321	0.0317	-
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1.7982	3323	0.0252	-
1.7987	3324	0.0103	-
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1.8003	3327	0.018	-
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1.8025	3331	0.0273	-
1.8030	3332	0.0176	-
1.8036	3333	0.0481	-
1.8041	3334	0.0162	-
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1.8225	3368	0.0287	-
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1.8360	3393	0.0201	-
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1.8398	3400	0.02	-
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1.8469	3413	0.0199	-
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1.8512	3421	0.0358	-
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1.8544	3427	0.0125	-
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1.8566	3431	0.0351	-
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1.8593	3436	0.0252	-
1.8598	3437	0.0205	-
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1.8615	3440	0.0291	-
1.8620	3441	0.0161	-
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1.8631	3443	0.0227	-
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1.8680	3452	0.0373	-
1.8685	3453	0.0098	-
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1.8739	3463	0.0278	-
1.8745	3464	0.0228	-
1.875	3465	0.0194	-
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1.8761	3467	0.0359	-
1.8766	3468	0.0178	-
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1.8782	3471	0.0277	-
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1.8793	3473	0.0349	-
1.8799	3474	0.0252	-
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1.8815	3477	0.0229	-
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1.8891	3491	0.0248	-
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1.8912	3495	0.0098	-
1.8918	3496	0.018	-
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1.8939	3500	0.0154	-
1.8945	3501	0.0529	-
1.8950	3502	0.0157	-
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1.8961	3504	0.0139	-
1.8966	3505	0.0204	-
1.8972	3506	0.0226	-
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1.8988	3509	0.0331	-
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1.8999	3511	0.0163	-
1.9004	3512	0.0153	-
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1.9021	3515	0.0388	-
1.9026	3516	0.0172	-
1.9031	3517	0.0183	-
1.9037	3518	0.016	-
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1.9053	3521	0.0291	-
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1.9064	3523	0.0345	-
1.9069	3524	0.0124	-
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1.9232	3554	0.0656	-
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1.9259	3559	0.0178	-
1.9264	3560	0.0117	-
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1.9297	3566	0.0241	-
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1.9313	3569	0.0244	-
1.9318	3570	0.0136	-
1.9324	3571	0.0331	-
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1.9399	3585	0.0102	-
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1.9410	3587	0.0117	-
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1.9426	3590	0.0341	-
1.9432	3591	0.0223	-
1.9437	3592	0.0229	-
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1.9464	3597	0.0212	-
1.9470	3598	0.0158	-
1.9475	3599	0.0126	-
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1.9491	3602	0.0146	-
1.9497	3603	0.0197	-
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1.9513	3606	0.0112	-
1.9518	3607	0.0306	-
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1.9540	3611	0.0318	-
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1.9589	3620	0.0283	-
1.9594	3621	0.0432	-
1.9600	3622	0.0359	-
1.9605	3623	0.0167	-
1.9610	3624	0.0404	-
1.9616	3625	0.025	-
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1.9632	3628	0.0563	-
1.9637	3629	0.0259	-
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1.9665	3634	0.0183	-
1.9670	3635	0.0215	-
1.9675	3636	0.025	-
1.9681	3637	0.0254	-
1.9686	3638	0.0399	-
1.9692	3639	0.0115	-
1.9697	3640	0.0149	-
1.9702	3641	0.0281	-
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1.9713	3643	0.0256	-
1.9719	3644	0.0439	-
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1.9740	3648	0.0234	-
1.9746	3649	0.0258	-
1.9751	3650	0.0182	-
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1.9762	3652	0.0246	-
1.9767	3653	0.0231	-
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1.9784	3656	0.0162	-
1.9789	3657	0.0119	-
1.9794	3658	0.0188	-
1.9800	3659	0.013	-
1.9805	3660	0.0141	-
1.9811	3661	0.0141	-
1.9816	3662	0.0207	-
1.9821	3663	0.0227	-
1.9827	3664	0.0161	-
1.9832	3665	0.017	-
1.9838	3666	0.0184	-
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1.9848	3668	0.0098	-
1.9854	3669	0.0183	-
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1.9865	3671	0.0175	-
1.9870	3672	0.0236	-
1.9876	3673	0.0199	-
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1.9886	3675	0.0223	-
1.9892	3676	0.0263	-
1.9897	3677	0.0384	-
1.9903	3678	0.03	-
1.9908	3679	0.0494	-
1.9913	3680	0.0454	-
1.9919	3681	0.0381	-
1.9924	3682	0.0225	-
1.9930	3683	0.0149	-
1.9935	3684	0.024	-
1.9940	3685	0.0216	-
1.9946	3686	0.0342	-
1.9951	3687	0.0128	-
1.9957	3688	0.0184	-
1.9962	3689	0.0171	-
1.9968	3690	0.0172	-
1.9973	3691	0.0524	-
1.9978	3692	0.0253	-
1.9984	3693	0.013	-
1.9989	3694	0.0196	-
1.9995	3695	0.0192	-
2.0	3696	0.0133	-
2.0005	3697	0.0148	-
2.0011	3698	0.0144	-
2.0016	3699	0.0138	-
2.0022	3700	0.0263	-
2.0027	3701	0.0213	-
2.0032	3702	0.0169	-
2.0038	3703	0.017	-
2.0043	3704	0.0143	-
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2.0070	3709	0.0155	-
2.0076	3710	0.0086	-
2.0081	3711	0.0127	-
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2.0092	3713	0.009	-
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2.0103	3715	0.0158	-
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2.0130	3720	0.0257	-
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2.0179	3729	0.0176	-
2.0184	3730	0.0125	-
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2.0200	3733	0.0087	-
2.0206	3734	0.0108	-
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2.0216	3736	0.0171	-
2.0222	3737	0.0289	-
2.0227	3738	0.0107	-
2.0233	3739	0.0344	-
2.0238	3740	0.0262	-
2.0244	3741	0.0187	-
2.0249	3742	0.0163	-
2.0254	3743	0.0195	-
2.0260	3744	0.0302	-
2.0265	3745	0.0068	-
2.0271	3746	0.0202	-
2.0276	3747	0.0256	-
2.0281	3748	0.0367	-
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2.0292	3750	0.0134	-
2.0298	3751	0.0134	-
2.0303	3752	0.0161	-
2.0308	3753	0.0218	-
2.0314	3754	0.0327	-
2.0319	3755	0.0152	-
2.0325	3756	0.0179	-
2.0330	3757	0.011	-
2.0335	3758	0.0268	-
2.0341	3759	0.0147	-
2.0346	3760	0.0258	-
2.0352	3761	0.0529	-
2.0357	3762	0.0159	-
2.0363	3763	0.0117	-
2.0368	3764	0.019	-
2.0373	3765	0.0186	-
2.0379	3766	0.0113	-
2.0384	3767	0.0308	-
2.0390	3768	0.0164	-
2.0395	3769	0.0124	-
2.0400	3770	0.0106	-
2.0406	3771	0.0137	-
2.0411	3772	0.0129	-
2.0417	3773	0.0176	-
2.0422	3774	0.0087	-
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2.0433	3776	0.0219	-
2.0438	3777	0.0154	-
2.0444	3778	0.0104	-
2.0449	3779	0.0225	-
2.0455	3780	0.0319	-
2.0460	3781	0.0105	-
2.0465	3782	0.0087	-
2.0471	3783	0.0171	-
2.0476	3784	0.0136	-
2.0482	3785	0.0191	-
2.0487	3786	0.0252	-
2.0492	3787	0.0092	-
2.0498	3788	0.0196	-
2.0503	3789	0.0133	-
2.0509	3790	0.0158	-
2.0514	3791	0.0175	-
2.0519	3792	0.018	-
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2.0530	3794	0.0143	-
2.0536	3795	0.0196	-
2.0541	3796	0.0192	-
2.0547	3797	0.011	-
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2.0557	3799	0.0186	-
2.0563	3800	0.0186	-
2.0568	3801	0.0105	-
2.0574	3802	0.0364	-
2.0579	3803	0.0122	-
2.0584	3804	0.0303	-
2.0590	3805	0.0164	-
2.0595	3806	0.0121	-
2.0601	3807	0.0134	-
2.0606	3808	0.0217	-
2.0611	3809	0.0195	-
2.0617	3810	0.0187	-
2.0622	3811	0.0257	-
2.0628	3812	0.0161	-
2.0633	3813	0.016	-
2.0639	3814	0.0131	-
2.0644	3815	0.0267	-
2.0649	3816	0.0172	-
2.0655	3817	0.0235	-
2.0660	3818	0.0257	-
2.0666	3819	0.0388	-
2.0671	3820	0.0325	-
2.0676	3821	0.0107	-
2.0682	3822	0.0252	-
2.0687	3823	0.0104	-
2.0693	3824	0.0063	-
2.0698	3825	0.0166	-
2.0703	3826	0.0235	-
2.0709	3827	0.0219	-
2.0714	3828	0.0135	-
2.0720	3829	0.0172	-
2.0725	3830	0.019	-
2.0731	3831	0.02	-
2.0736	3832	0.0316	-
2.0741	3833	0.0146	-
2.0747	3834	0.0101	-
2.0752	3835	0.0124	-
2.0758	3836	0.018	-
2.0763	3837	0.0343	-
2.0768	3838	0.0299	-
2.0774	3839	0.0189	-
2.0779	3840	0.0216	-
2.0785	3841	0.019	-
2.0790	3842	0.0077	-
2.0795	3843	0.0202	-
2.0801	3844	0.0112	-
2.0806	3845	0.0398	-
2.0812	3846	0.0245	-
2.0817	3847	0.0212	-
2.0823	3848	0.0199	-
2.0828	3849	0.0097	-
2.0833	3850	0.0294	-
2.0839	3851	0.0276	-
2.0844	3852	0.0123	-
2.0850	3853	0.0283	-
2.0855	3854	0.0132	-
2.0860	3855	0.0179	-
2.0866	3856	0.0184	-
2.0871	3857	0.0086	-
2.0877	3858	0.0193	-
2.0882	3859	0.0191	-
2.0887	3860	0.022	-
2.0893	3861	0.0213	-
2.0898	3862	0.0185	-
2.0904	3863	0.016	-
2.0909	3864	0.0222	-
2.0915	3865	0.02	-
2.0920	3866	0.0104	-
2.0925	3867	0.0129	-
2.0931	3868	0.0073	-
2.0936	3869	0.0311	-
2.0942	3870	0.0188	-
2.0947	3871	0.0096	-
2.0952	3872	0.0167	-
2.0958	3873	0.0067	-
2.0963	3874	0.0161	-
2.0969	3875	0.026	-
2.0974	3876	0.0118	-
2.0979	3877	0.034	-
2.0985	3878	0.0098	-
2.0990	3879	0.0386	-
2.0996	3880	0.0155	-
2.1001	3881	0.0203	-
2.1006	3882	0.0241	-
2.1012	3883	0.0165	-
2.1017	3884	0.0282	-
2.1023	3885	0.0235	-
2.1028	3886	0.0307	-
2.1034	3887	0.0253	-
2.1039	3888	0.0082	-
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2.1050	3890	0.0163	-
2.1055	3891	0.0299	-
2.1061	3892	0.0157	-
2.1066	3893	0.0166	-
2.1071	3894	0.01	-
2.1077	3895	0.0098	-
2.1082	3896	0.018	-
2.1088	3897	0.013	-
2.1093	3898	0.0167	-
2.1098	3899	0.0269	-
2.1104	3900	0.0353	-
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2.1115	3902	0.0269	-
2.1120	3903	0.0105	-
2.1126	3904	0.0258	-
2.1131	3905	0.0321	-
2.1136	3906	0.0177	-
2.1142	3907	0.0112	-
2.1147	3908	0.0127	-
2.1153	3909	0.0145	-
2.1158	3910	0.0176	-
2.1163	3911	0.0094	-
2.1169	3912	0.0205	-
2.1174	3913	0.0183	-
2.1180	3914	0.0201	-
2.1185	3915	0.0433	-
2.1190	3916	0.0156	-
2.1196	3917	0.0184	-
2.1201	3918	0.0205	-
2.1207	3919	0.0125	-
2.1212	3920	0.03	-
2.1218	3921	0.0207	-
2.1223	3922	0.0115	-
2.1228	3923	0.0086	-
2.1234	3924	0.0103	-
2.1239	3925	0.0165	-
2.1245	3926	0.0123	-
2.125	3927	0.0106	-
2.1255	3928	0.0326	-
2.1261	3929	0.0162	-
2.1266	3930	0.0202	-
2.1272	3931	0.029	-
2.1277	3932	0.0257	-
2.1282	3933	0.0094	-
2.1288	3934	0.0142	-
2.1293	3935	0.0164	-
2.1299	3936	0.0175	-
2.1304	3937	0.0088	-
2.1310	3938	0.022	-
2.1315	3939	0.0172	-
2.1320	3940	0.0201	-
2.1326	3941	0.0137	-
2.1331	3942	0.0136	-
2.1337	3943	0.0343	-
2.1342	3944	0.0169	-
2.1347	3945	0.0134	-
2.1353	3946	0.0331	-
2.1358	3947	0.0106	-
2.1364	3948	0.0073	-
2.1369	3949	0.0192	-
2.1374	3950	0.0278	-
2.1380	3951	0.0266	-
2.1385	3952	0.0142	-
2.1391	3953	0.0108	-
2.1396	3954	0.0142	-
2.1402	3955	0.0119	-
2.1407	3956	0.0239	-
2.1412	3957	0.0202	-
2.1418	3958	0.0149	-
2.1423	3959	0.018	-
2.1429	3960	0.0156	-
2.1434	3961	0.034	-
2.1439	3962	0.0205	-
2.1445	3963	0.031	-
2.1450	3964	0.0235	-
2.1456	3965	0.0229	-
2.1461	3966	0.0169	-
2.1466	3967	0.0176	-
2.1472	3968	0.0224	-
2.1477	3969	0.0195	-
2.1483	3970	0.0322	-
2.1488	3971	0.0111	-
2.1494	3972	0.0127	-
2.1499	3973	0.0286	-
2.1504	3974	0.0278	-
2.1510	3975	0.0247	-
2.1515	3976	0.0198	-
2.1521	3977	0.0306	-
2.1526	3978	0.0219	-
2.1531	3979	0.0192	-
2.1537	3980	0.013	-
2.1542	3981	0.0259	-
2.1548	3982	0.0253	-
2.1553	3983	0.0228	-
2.1558	3984	0.0263	-
2.1564	3985	0.0072	-
2.1569	3986	0.0145	-
2.1575	3987	0.0279	-
2.1580	3988	0.012	-
2.1585	3989	0.0351	-
2.1591	3990	0.0248	-
2.1596	3991	0.0091	-
2.1602	3992	0.0275	-
2.1607	3993	0.0137	-
2.1613	3994	0.0222	-
2.1618	3995	0.018	-
2.1623	3996	0.0275	-
2.1629	3997	0.0132	-
2.1634	3998	0.016	-
2.1640	3999	0.0205	-
2.1645	4000	0.011	0.0218
2.1650	4001	0.0192	-
2.1656	4002	0.0146	-
2.1661	4003	0.0348	-
2.1667	4004	0.0155	-
2.1672	4005	0.01	-
2.1677	4006	0.0187	-
2.1683	4007	0.0168	-
2.1688	4008	0.0067	-
2.1694	4009	0.0173	-
2.1699	4010	0.01	-
2.1705	4011	0.0129	-
2.1710	4012	0.0199	-
2.1715	4013	0.0202	-
2.1721	4014	0.0155	-
2.1726	4015	0.0138	-
2.1732	4016	0.0188	-
2.1737	4017	0.0172	-
2.1742	4018	0.0204	-
2.1748	4019	0.0144	-
2.1753	4020	0.0297	-
2.1759	4021	0.0218	-
2.1764	4022	0.0208	-
2.1769	4023	0.0194	-
2.1775	4024	0.0267	-
2.1780	4025	0.0216	-
2.1786	4026	0.0174	-
2.1791	4027	0.009	-
2.1797	4028	0.0288	-
2.1802	4029	0.023	-
2.1807	4030	0.0133	-
2.1813	4031	0.0228	-
2.1818	4032	0.0126	-
2.1824	4033	0.0137	-
2.1829	4034	0.0265	-
2.1834	4035	0.0119	-
2.1840	4036	0.0145	-
2.1845	4037	0.0145	-
2.1851	4038	0.0121	-
2.1856	4039	0.0178	-
2.1861	4040	0.0171	-
2.1867	4041	0.0149	-
2.1872	4042	0.0112	-
2.1878	4043	0.0074	-
2.1883	4044	0.0171	-
2.1889	4045	0.0179	-
2.1894	4046	0.0165	-
2.1899	4047	0.018	-
2.1905	4048	0.0122	-
2.1910	4049	0.0214	-
2.1916	4050	0.0127	-
2.1921	4051	0.0154	-
2.1926	4052	0.0151	-
2.1932	4053	0.0146	-
2.1937	4054	0.0162	-
2.1943	4055	0.0139	-
2.1948	4056	0.0187	-
2.1953	4057	0.0124	-
2.1959	4058	0.0371	-
2.1964	4059	0.0311	-
2.1970	4060	0.0163	-
2.1975	4061	0.0222	-
2.1981	4062	0.0156	-
2.1986	4063	0.0183	-
2.1991	4064	0.0221	-
2.1997	4065	0.0252	-
2.2002	4066	0.0146	-
2.2008	4067	0.0167	-
2.2013	4068	0.0184	-
2.2018	4069	0.0145	-
2.2024	4070	0.0237	-
2.2029	4071	0.0267	-
2.2035	4072	0.0141	-
2.2040	4073	0.0291	-
2.2045	4074	0.0063	-
2.2051	4075	0.0235	-
2.2056	4076	0.0204	-
2.2062	4077	0.0118	-
2.2067	4078	0.0132	-
2.2073	4079	0.0121	-
2.2078	4080	0.0253	-
2.2083	4081	0.009	-
2.2089	4082	0.011	-
2.2094	4083	0.0227	-
2.2100	4084	0.0084	-
2.2105	4085	0.0183	-
2.2110	4086	0.0608	-
2.2116	4087	0.0106	-
2.2121	4088	0.027	-
2.2127	4089	0.0312	-
2.2132	4090	0.0111	-
2.2137	4091	0.0172	-
2.2143	4092	0.0257	-
2.2148	4093	0.0304	-
2.2154	4094	0.0178	-
2.2159	4095	0.018	-
2.2165	4096	0.0262	-
2.2170	4097	0.0155	-
2.2175	4098	0.0119	-
2.2181	4099	0.0178	-
2.2186	4100	0.0107	-
2.2192	4101	0.0119	-
2.2197	4102	0.0151	-
2.2202	4103	0.0142	-
2.2208	4104	0.0203	-
2.2213	4105	0.0189	-
2.2219	4106	0.0233	-
2.2224	4107	0.0152	-
2.2229	4108	0.0214	-
2.2235	4109	0.0141	-
2.2240	4110	0.0148	-
2.2246	4111	0.0117	-
2.2251	4112	0.0199	-
2.2256	4113	0.015	-
2.2262	4114	0.0169	-
2.2267	4115	0.0089	-
2.2273	4116	0.0205	-
2.2278	4117	0.0182	-
2.2284	4118	0.0157	-
2.2289	4119	0.009	-
2.2294	4120	0.0089	-
2.2300	4121	0.0183	-
2.2305	4122	0.0147	-
2.2311	4123	0.0206	-
2.2316	4124	0.0193	-
2.2321	4125	0.0189	-
2.2327	4126	0.0209	-
2.2332	4127	0.0126	-
2.2338	4128	0.0094	-
2.2343	4129	0.0117	-
2.2348	4130	0.0117	-
2.2354	4131	0.0162	-
2.2359	4132	0.0167	-
2.2365	4133	0.0085	-
2.2370	4134	0.0259	-
2.2376	4135	0.0222	-
2.2381	4136	0.0292	-
2.2386	4137	0.0208	-
2.2392	4138	0.0309	-
2.2397	4139	0.0383	-
2.2403	4140	0.0211	-
2.2408	4141	0.021	-
2.2413	4142	0.0181	-
2.2419	4143	0.0142	-
2.2424	4144	0.0088	-
2.2430	4145	0.0139	-
2.2435	4146	0.0192	-
2.2440	4147	0.0174	-
2.2446	4148	0.0133	-
2.2451	4149	0.0229	-
2.2457	4150	0.0263	-
2.2462	4151	0.0239	-
2.2468	4152	0.0399	-
2.2473	4153	0.012	-
2.2478	4154	0.01	-
2.2484	4155	0.0119	-
2.2489	4156	0.024	-
2.2495	4157	0.0307	-
2.25	4158	0.0103	-
2.2505	4159	0.0244	-
2.2511	4160	0.023	-
2.2516	4161	0.0137	-
2.2522	4162	0.0413	-
2.2527	4163	0.0111	-
2.2532	4164	0.0177	-
2.2538	4165	0.0205	-
2.2543	4166	0.0222	-
2.2549	4167	0.0202	-
2.2554	4168	0.0162	-
2.2560	4169	0.0243	-
2.2565	4170	0.0223	-
2.2570	4171	0.0232	-
2.2576	4172	0.0182	-
2.2581	4173	0.0144	-
2.2587	4174	0.0168	-
2.2592	4175	0.0139	-
2.2597	4176	0.0216	-
2.2603	4177	0.0176	-
2.2608	4178	0.0245	-
2.2614	4179	0.0105	-
2.2619	4180	0.0254	-
2.2624	4181	0.0201	-
2.2630	4182	0.013	-
2.2635	4183	0.0208	-
2.2641	4184	0.0184	-
2.2646	4185	0.0138	-
2.2652	4186	0.0116	-
2.2657	4187	0.0207	-
2.2662	4188	0.0208	-
2.2668	4189	0.0082	-
2.2673	4190	0.0125	-
2.2679	4191	0.0227	-
2.2684	4192	0.0178	-
2.2689	4193	0.0225	-
2.2695	4194	0.0162	-
2.2700	4195	0.0338	-
2.2706	4196	0.0201	-
2.2711	4197	0.015	-
2.2716	4198	0.0187	-
2.2722	4199	0.0088	-
2.2727	4200	0.0258	-
2.2733	4201	0.0172	-
2.2738	4202	0.0072	-
2.2744	4203	0.0228	-
2.2749	4204	0.0227	-
2.2754	4205	0.0146	-
2.2760	4206	0.0124	-
2.2765	4207	0.0126	-
2.2771	4208	0.0288	-
2.2776	4209	0.0223	-
2.2781	4210	0.0117	-
2.2787	4211	0.0145	-
2.2792	4212	0.0213	-
2.2798	4213	0.0174	-
2.2803	4214	0.017	-
2.2808	4215	0.0111	-
2.2814	4216	0.0113	-
2.2819	4217	0.0169	-
2.2825	4218	0.0214	-
2.2830	4219	0.0198	-
2.2835	4220	0.0294	-
2.2841	4221	0.025	-
2.2846	4222	0.0233	-
2.2852	4223	0.0296	-
2.2857	4224	0.0288	-
2.2863	4225	0.0151	-
2.2868	4226	0.0443	-
2.2873	4227	0.0131	-
2.2879	4228	0.0232	-
2.2884	4229	0.0192	-
2.2890	4230	0.012	-
2.2895	4231	0.0153	-
2.2900	4232	0.0378	-
2.2906	4233	0.0152	-
2.2911	4234	0.006	-
2.2917	4235	0.0272	-
2.2922	4236	0.0134	-
2.2927	4237	0.0199	-
2.2933	4238	0.0093	-
2.2938	4239	0.0136	-
2.2944	4240	0.0252	-
2.2949	4241	0.0206	-
2.2955	4242	0.0176	-
2.2960	4243	0.0172	-
2.2965	4244	0.0243	-
2.2971	4245	0.0139	-
2.2976	4246	0.014	-
2.2982	4247	0.0158	-
2.2987	4248	0.0135	-
2.2992	4249	0.0232	-
2.2998	4250	0.025	-
2.3003	4251	0.0156	-
2.3009	4252	0.0209	-
2.3014	4253	0.0277	-
2.3019	4254	0.0159	-
2.3025	4255	0.0206	-
2.3030	4256	0.0155	-
2.3036	4257	0.0158	-
2.3041	4258	0.0154	-
2.3047	4259	0.033	-
2.3052	4260	0.0153	-
2.3057	4261	0.0188	-
2.3063	4262	0.025	-
2.3068	4263	0.0241	-
2.3074	4264	0.0134	-
2.3079	4265	0.0156	-
2.3084	4266	0.0109	-
2.3090	4267	0.0192	-
2.3095	4268	0.0268	-
2.3101	4269	0.012	-
2.3106	4270	0.0158	-
2.3111	4271	0.012	-
2.3117	4272	0.0159	-
2.3122	4273	0.0172	-
2.3128	4274	0.0107	-
2.3133	4275	0.0132	-
2.3139	4276	0.0167	-
2.3144	4277	0.0142	-
2.3149	4278	0.0141	-
2.3155	4279	0.0285	-
2.3160	4280	0.0266	-
2.3166	4281	0.0259	-
2.3171	4282	0.0273	-
2.3176	4283	0.0535	-
2.3182	4284	0.01	-
2.3187	4285	0.0078	-
2.3193	4286	0.0149	-
2.3198	4287	0.0281	-
2.3203	4288	0.0166	-
2.3209	4289	0.0193	-
2.3214	4290	0.0158	-
2.3220	4291	0.0233	-
2.3225	4292	0.0287	-
2.3231	4293	0.0197	-
2.3236	4294	0.023	-
2.3241	4295	0.0261	-
2.3247	4296	0.0112	-
2.3252	4297	0.0158	-
2.3258	4298	0.0221	-
2.3263	4299	0.017	-
2.3268	4300	0.0179	-
2.3274	4301	0.0352	-
2.3279	4302	0.015	-
2.3285	4303	0.0265	-
2.3290	4304	0.0098	-
2.3295	4305	0.0123	-
2.3301	4306	0.0088	-
2.3306	4307	0.0076	-
2.3312	4308	0.0305	-
2.3317	4309	0.0304	-
2.3323	4310	0.0167	-
2.3328	4311	0.0276	-
2.3333	4312	0.0109	-
2.3339	4313	0.0124	-
2.3344	4314	0.0169	-
2.3350	4315	0.0185	-
2.3355	4316	0.0284	-
2.3360	4317	0.0209	-
2.3366	4318	0.0112	-
2.3371	4319	0.0217	-
2.3377	4320	0.0145	-
2.3382	4321	0.0112	-
2.3387	4322	0.0202	-
2.3393	4323	0.0159	-
2.3398	4324	0.0154	-
2.3404	4325	0.0188	-
2.3409	4326	0.0126	-
2.3415	4327	0.0204	-
2.3420	4328	0.0224	-
2.3425	4329	0.0137	-
2.3431	4330	0.0095	-
2.3436	4331	0.024	-
2.3442	4332	0.02	-
2.3447	4333	0.0234	-
2.3452	4334	0.014	-
2.3458	4335	0.0132	-
2.3463	4336	0.0186	-
2.3469	4337	0.0169	-
2.3474	4338	0.0161	-
2.3479	4339	0.0205	-
2.3485	4340	0.008	-
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2.3501	4343	0.0194	-
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2.3512	4345	0.0142	-
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2.4361	4502	0.0177	-
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2.4805	4584	0.0177	-
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2.5276	4671	0.0227	-
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2.5298	4675	0.0187	-
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2.5335	4682	0.0214	-
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2.5433	4700	0.0114	-
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2.5465	4706	0.023	-
2.5471	4707	0.0204	-
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2.5487	4710	0.0124	-
2.5492	4711	0.0184	-
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2.5714	4752	0.017	-
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2.5752	4759	0.0178	-
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2.5768	4762	0.0198	-
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2.5785	4765	0.0159	-
2.5790	4766	0.0287	-
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2.5806	4769	0.0238	-
2.5812	4770	0.0078	-
2.5817	4771	0.0177	-
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2.5828	4773	0.0187	-
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2.5844	4776	0.0274	-
2.5850	4777	0.0125	-
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2.5860	4779	0.0194	-
2.5866	4780	0.0124	-
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2.5904	4787	0.0058	-
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2.5931	4792	0.0256	-
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2.5963	4798	0.0188	-
2.5969	4799	0.0172	-
2.5974	4800	0.0115	-
2.5979	4801	0.0176	-
2.5985	4802	0.0275	-
2.5990	4803	0.0178	-
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2.6001	4805	0.016	-
2.6006	4806	0.0143	-
2.6012	4807	0.0128	-
2.6017	4808	0.0135	-
2.6023	4809	0.018	-
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2.6034	4811	0.0128	-
2.6039	4812	0.0157	-
2.6044	4813	0.0125	-
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2.6066	4817	0.015	-
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2.6088	4821	0.0081	-
2.6093	4822	0.0152	-
2.6098	4823	0.0088	-
2.6104	4824	0.0194	-
2.6109	4825	0.0091	-
2.6115	4826	0.0186	-
2.6120	4827	0.0226	-
2.6126	4828	0.0079	-
2.6131	4829	0.0094	-
2.6136	4830	0.0108	-
2.6142	4831	0.0243	-
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2.6153	4833	0.0273	-
2.6158	4834	0.0131	-
2.6163	4835	0.0051	-
2.6169	4836	0.0139	-
2.6174	4837	0.025	-
2.6180	4838	0.0119	-
2.6185	4839	0.0224	-
2.6190	4840	0.026	-
2.6196	4841	0.0215	-
2.6201	4842	0.0082	-
2.6207	4843	0.0235	-
2.6212	4844	0.02	-
2.6218	4845	0.0166	-
2.6223	4846	0.018	-
2.6228	4847	0.0181	-
2.6234	4848	0.0203	-
2.6239	4849	0.0105	-
2.6245	4850	0.0227	-
2.625	4851	0.0139	-
2.6255	4852	0.023	-
2.6261	4853	0.0197	-
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2.6299	4860	0.0125	-
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2.7056	5000	0.0148	0.0207
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2.7219	5030	0.045	-
2.7224	5031	0.0083	-
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2.7240	5034	0.0101	-
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2.8225	5216	0.018	-
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2.8301	5230	0.0187	-
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2.8360	5241	0.0191	-
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2.8463	5260	0.0201	-
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2.8485	5264	0.021	-
2.8490	5265	0.0292	-
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2.8512	5269	0.0191	-
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2.8566	5279	0.0123	-
2.8571	5280	0.0145	-
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2.8598	5285	0.0289	-
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2.8636	5292	0.0168	-
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2.8669	5298	0.0249	-
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2.8680	5300	0.0125	-
2.8685	5301	0.0191	-
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2.8701	5304	0.0199	-
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2.8712	5306	0.0141	-
2.8718	5307	0.0183	-
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2.8745	5312	0.0158	-
2.875	5313	0.0181	-
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2.8761	5315	0.0235	-
2.8766	5316	0.0099	-
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2.8788	5320	0.0144	-
2.8793	5321	0.014	-
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2.8810	5324	0.0183	-
2.8815	5325	0.0237	-
2.8820	5326	0.0315	-
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2.8891	5339	0.0196	-
2.8896	5340	0.0123	-
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2.8918	5344	0.0137	-
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2.8945	5349	0.0233	-
2.8950	5350	0.027	-
2.8956	5351	0.0131	-
2.8961	5352	0.0146	-
2.8966	5353	0.013	-
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2.8983	5356	0.0141	-
2.8988	5357	0.0119	-
2.8994	5358	0.0175	-
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2.9004	5360	0.0243	-
2.9010	5361	0.0232	-
2.9015	5362	0.0235	-
2.9021	5363	0.0214	-
2.9026	5364	0.0171	-
2.9031	5365	0.0194	-
2.9037	5366	0.016	-
2.9042	5367	0.014	-
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2.9069	5372	0.0099	-
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2.9470	5446	0.0142	-
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2.9594	5469	0.0187	-
2.9600	5470	0.0321	-
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2.9632	5476	0.0201	-
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2.9692	5487	0.0188	-
2.9697	5488	0.016	-
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2.9713	5491	0.0275	-
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2.9740	5496	0.0107	-
2.9746	5497	0.0082	-
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2.9762	5500	0.0083	-
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2.9865	5519	0.0326	-
2.9870	5520	0.0076	-
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2.9978	5540	0.0148	-
2.9984	5541	0.0133	-
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3.0	5544	0.0327	-
3.0005	5545	0.0135	-
3.0011	5546	0.0161	-
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3.0027	5549	0.0176	-
3.0032	5550	0.0082	-
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3.0043	5552	0.0116	-
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3.0060	5555	0.0169	-
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3.0103	5563	0.0111	-
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3.0130	5568	0.0228	-
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3.0146	5571	0.0095	-
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3.0162	5574	0.0081	-
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3.0200	5581	0.0151	-
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3.0222	5585	0.0098	-
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3.0233	5587	0.0102	-
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3.0254	5591	0.0208	-
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3.0271	5594	0.0194	-
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3.0281	5596	0.0163	-
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3.0292	5598	0.0141	-
3.0298	5599	0.018	-
3.0303	5600	0.0085	-
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3.0314	5602	0.0232	-
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3.0325	5604	0.0122	-
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3.0341	5607	0.0111	-
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3.0363	5611	0.0166	-
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3.0373	5613	0.0145	-
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3.0384	5615	0.0098	-
3.0390	5616	0.0152	-
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3.0400	5618	0.02	-
3.0406	5619	0.0121	-
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3.0422	5622	0.0143	-
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3.0438	5625	0.0208	-
3.0444	5626	0.008	-
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3.0460	5629	0.0242	-
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3.0471	5631	0.0255	-
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3.0492	5635	0.01	-
3.0498	5636	0.0195	-
3.0503	5637	0.0145	-
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3.0628	5660	0.0145	-
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3.0644	5663	0.0288	-
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3.0660	5666	0.0187	-
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3.0687	5671	0.0213	-
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3.0747	5682	0.0276	-
3.0752	5683	0.0135	-
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3.0806	5693	0.0263	-
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3.0839	5699	0.0195	-
3.0844	5700	0.0136	-
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3.0871	5705	0.0161	-
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3.0893	5709	0.0229	-
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3.0931	5716	0.012	-
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3.0947	5719	0.0085	-
3.0952	5720	0.0183	-
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3.0979	5725	0.017	-
3.0985	5726	0.0141	-
3.0990	5727	0.0104	-
3.0996	5728	0.013	-
3.1001	5729	0.0059	-
3.1006	5730	0.0135	-
3.1012	5731	0.0078	-
3.1017	5732	0.0059	-
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3.1034	5735	0.0131	-
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3.1050	5738	0.0206	-
3.1055	5739	0.0173	-
3.1061	5740	0.0151	-
3.1066	5741	0.0314	-
3.1071	5742	0.0112	-
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3.1088	5745	0.0145	-
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3.1104	5748	0.0084	-
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3.1115	5750	0.0231	-
3.1120	5751	0.02	-
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3.1190	5764	0.0104	-
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3.1201	5766	0.0143	-
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3.1212	5768	0.0092	-
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3.1228	5771	0.0124	-
3.1234	5772	0.0155	-
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3.125	5775	0.0078	-
3.1255	5776	0.0155	-
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3.1288	5782	0.0231	-
3.1293	5783	0.0096	-
3.1299	5784	0.0125	-
3.1304	5785	0.0177	-
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3.1315	5787	0.0198	-
3.1320	5788	0.0135	-
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3.1331	5790	0.0063	-
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3.1380	5799	0.0154	-
3.1385	5800	0.0115	-
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3.1402	5803	0.0106	-
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3.1429	5808	0.0214	-
3.1434	5809	0.011	-
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3.1445	5811	0.0293	-
3.1450	5812	0.0129	-
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3.1466	5815	0.0143	-
3.1472	5816	0.0222	-
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3.1488	5819	0.018	-
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3.1504	5822	0.0093	-
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3.1521	5825	0.0125	-
3.1526	5826	0.0172	-
3.1531	5827	0.0184	-
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3.1542	5829	0.0145	-
3.1548	5830	0.008	-
3.1553	5831	0.0156	-
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3.1564	5833	0.0164	-
3.1569	5834	0.0146	-
3.1575	5835	0.0152	-
3.1580	5836	0.0084	-
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3.1602	5840	0.011	-
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3.1634	5846	0.011	-
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3.1672	5853	0.014	-
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3.1688	5856	0.0138	-
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3.1705	5859	0.0097	-
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3.1721	5862	0.0087	-
3.1726	5863	0.0082	-
3.1732	5864	0.0086	-
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3.1748	5867	0.0164	-
3.1753	5868	0.0206	-
3.1759	5869	0.0191	-
3.1764	5870	0.0161	-
3.1769	5871	0.01	-
3.1775	5872	0.0221	-
3.1780	5873	0.0148	-
3.1786	5874	0.0173	-
3.1791	5875	0.0114	-
3.1797	5876	0.0236	-
3.1802	5877	0.0141	-
3.1807	5878	0.0097	-
3.1813	5879	0.0153	-
3.1818	5880	0.0081	-
3.1824	5881	0.0104	-
3.1829	5882	0.0074	-
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3.2002	5914	0.0139	-
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3.2018	5917	0.011	-
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3.2468	6000	0.0203	0.0200
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3.3604	6210	0.0145	-
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3.3994	6282	0.0231	-
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3.4096	6301	0.0163	-
3.4102	6302	0.0193	-
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3.4416	6360	0.0225	-
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3.4481	6372	0.0111	-
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3.4540	6383	0.0164	-
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3.4594	6393	0.009	-
3.4600	6394	0.0094	-
3.4605	6395	0.0317	-
3.4610	6396	0.0105	-
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3.7381	6908	0.016	-
3.7386	6909	0.0192	-
3.7392	6910	0.0148	-
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4.0	7392	0.0133	-
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4.0173	7424	0.0177	-
4.0179	7425	0.0068	-
4.0184	7426	0.0128	-
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4.0281	7444	0.0123	-
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4.0947	7567	0.0101	-
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4.0974	7572	0.0101	-
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4.0985	7574	0.0192	-
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4.1450	7660	0.0209	-
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4.1510	7671	0.0179	-
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4.1640	7695	0.0101	-
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4.1650	7697	0.0174	-
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4.1661	7699	0.006	-
4.1667	7700	0.0163	-
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4.1677	7702	0.017	-
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4.1699	7706	0.0199	-
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4.1715	7709	0.0125	-
4.1721	7710	0.0181	-
4.1726	7711	0.0082	-
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4.1737	7713	0.0147	-
4.1742	7714	0.0093	-
4.1748	7715	0.0051	-
4.1753	7716	0.0101	-
4.1759	7717	0.0113	-
4.1764	7718	0.0084	-
4.1769	7719	0.0121	-
4.1775	7720	0.0165	-
4.1780	7721	0.0111	-
4.1786	7722	0.0126	-
4.1791	7723	0.021	-
4.1797	7724	0.0067	-
4.1802	7725	0.0094	-
4.1807	7726	0.0153	-
4.1813	7727	0.014	-
4.1818	7728	0.0156	-
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4.1829	7730	0.0099	-
4.1834	7731	0.0213	-
4.1840	7732	0.0083	-
4.1845	7733	0.0045	-
4.1851	7734	0.0093	-
4.1856	7735	0.0165	-
4.1861	7736	0.0103	-
4.1867	7737	0.0101	-
4.1872	7738	0.0157	-
4.1878	7739	0.0131	-
4.1883	7740	0.012	-
4.1889	7741	0.0101	-
4.1894	7742	0.0273	-
4.1899	7743	0.0114	-
4.1905	7744	0.009	-
4.1910	7745	0.0095	-
4.1916	7746	0.0083	-
4.1921	7747	0.0087	-
4.1926	7748	0.0088	-
4.1932	7749	0.0048	-
4.1937	7750	0.018	-
4.1943	7751	0.0128	-
4.1948	7752	0.0245	-
4.1953	7753	0.007	-
4.1959	7754	0.0215	-
4.1964	7755	0.0154	-
4.1970	7756	0.0198	-
4.1975	7757	0.0251	-
4.1981	7758	0.0111	-
4.1986	7759	0.0138	-
4.1991	7760	0.0073	-
4.1997	7761	0.0124	-
4.2002	7762	0.022	-
4.2008	7763	0.0136	-
4.2013	7764	0.0168	-
4.2018	7765	0.0155	-
4.2024	7766	0.0185	-
4.2029	7767	0.01	-
4.2035	7768	0.038	-
4.2040	7769	0.0136	-
4.2045	7770	0.0088	-
4.2051	7771	0.0107	-
4.2056	7772	0.028	-
4.2062	7773	0.0133	-
4.2067	7774	0.0166	-
4.2073	7775	0.015	-
4.2078	7776	0.0083	-
4.2083	7777	0.0167	-
4.2089	7778	0.0155	-
4.2094	7779	0.0078	-
4.2100	7780	0.015	-
4.2105	7781	0.0112	-
4.2110	7782	0.0147	-
4.2116	7783	0.0138	-
4.2121	7784	0.0061	-
4.2127	7785	0.0063	-
4.2132	7786	0.0107	-
4.2137	7787	0.0118	-
4.2143	7788	0.0112	-
4.2148	7789	0.0069	-
4.2154	7790	0.0263	-
4.2159	7791	0.0219	-
4.2165	7792	0.012	-
4.2170	7793	0.012	-
4.2175	7794	0.0131	-
4.2181	7795	0.0113	-
4.2186	7796	0.0128	-
4.2192	7797	0.0088	-
4.2197	7798	0.0181	-
4.2202	7799	0.0091	-
4.2208	7800	0.0108	-
4.2213	7801	0.024	-
4.2219	7802	0.0111	-
4.2224	7803	0.012	-
4.2229	7804	0.0179	-
4.2235	7805	0.0109	-
4.2240	7806	0.0097	-
4.2246	7807	0.0078	-
4.2251	7808	0.0161	-
4.2256	7809	0.019	-
4.2262	7810	0.0077	-
4.2267	7811	0.0147	-
4.2273	7812	0.0116	-
4.2278	7813	0.0135	-
4.2284	7814	0.0135	-
4.2289	7815	0.0128	-
4.2294	7816	0.0182	-
4.2300	7817	0.0158	-
4.2305	7818	0.0111	-
4.2311	7819	0.0191	-
4.2316	7820	0.0213	-
4.2321	7821	0.0121	-
4.2327	7822	0.0095	-
4.2332	7823	0.0181	-
4.2338	7824	0.0071	-
4.2343	7825	0.0179	-
4.2348	7826	0.0092	-
4.2354	7827	0.0129	-
4.2359	7828	0.0118	-
4.2365	7829	0.007	-
4.2370	7830	0.0161	-
4.2376	7831	0.0109	-
4.2381	7832	0.0051	-
4.2386	7833	0.0092	-
4.2392	7834	0.0152	-
4.2397	7835	0.01	-
4.2403	7836	0.0105	-
4.2408	7837	0.0105	-
4.2413	7838	0.0092	-
4.2419	7839	0.0215	-
4.2424	7840	0.0063	-
4.2430	7841	0.0142	-
4.2435	7842	0.0267	-
4.2440	7843	0.012	-
4.2446	7844	0.0132	-
4.2451	7845	0.0109	-
4.2457	7846	0.0278	-
4.2462	7847	0.0124	-
4.2468	7848	0.011	-
4.2473	7849	0.0092	-
4.2478	7850	0.0083	-
4.2484	7851	0.0101	-
4.2489	7852	0.0066	-
4.2495	7853	0.0059	-
4.25	7854	0.008	-
4.2505	7855	0.0071	-
4.2511	7856	0.0258	-
4.2516	7857	0.0102	-
4.2522	7858	0.0109	-
4.2527	7859	0.0114	-
4.2532	7860	0.0087	-
4.2538	7861	0.0212	-
4.2543	7862	0.0108	-
4.2549	7863	0.0375	-
4.2554	7864	0.0169	-
4.2560	7865	0.0095	-
4.2565	7866	0.0132	-
4.2570	7867	0.0218	-
4.2576	7868	0.0066	-
4.2581	7869	0.0067	-
4.2587	7870	0.0087	-
4.2592	7871	0.0167	-
4.2597	7872	0.0158	-
4.2603	7873	0.0192	-
4.2608	7874	0.0142	-
4.2614	7875	0.0116	-
4.2619	7876	0.0072	-
4.2624	7877	0.0164	-
4.2630	7878	0.0091	-
4.2635	7879	0.0129	-
4.2641	7880	0.0109	-
4.2646	7881	0.0127	-
4.2652	7882	0.0151	-
4.2657	7883	0.0096	-
4.2662	7884	0.0146	-
4.2668	7885	0.0297	-
4.2673	7886	0.0189	-
4.2679	7887	0.0115	-
4.2684	7888	0.015	-
4.2689	7889	0.0177	-
4.2695	7890	0.0141	-
4.2700	7891	0.0103	-
4.2706	7892	0.0155	-
4.2711	7893	0.0162	-
4.2716	7894	0.0111	-
4.2722	7895	0.0058	-
4.2727	7896	0.0108	-
4.2733	7897	0.0087	-
4.2738	7898	0.0114	-
4.2744	7899	0.0104	-
4.2749	7900	0.0199	-
4.2754	7901	0.0107	-
4.2760	7902	0.0192	-
4.2765	7903	0.013	-
4.2771	7904	0.0099	-
4.2776	7905	0.0198	-
4.2781	7906	0.009	-
4.2787	7907	0.0086	-
4.2792	7908	0.0108	-
4.2798	7909	0.0113	-
4.2803	7910	0.011	-
4.2808	7911	0.013	-
4.2814	7912	0.0178	-
4.2819	7913	0.0103	-
4.2825	7914	0.017	-
4.2830	7915	0.0128	-
4.2835	7916	0.0075	-
4.2841	7917	0.0177	-
4.2846	7918	0.0129	-
4.2852	7919	0.0156	-
4.2857	7920	0.0174	-
4.2863	7921	0.0078	-
4.2868	7922	0.0102	-
4.2873	7923	0.0107	-
4.2879	7924	0.0225	-
4.2884	7925	0.0173	-
4.2890	7926	0.0103	-
4.2895	7927	0.0113	-
4.2900	7928	0.0052	-
4.2906	7929	0.0134	-
4.2911	7930	0.0111	-
4.2917	7931	0.0113	-
4.2922	7932	0.0136	-
4.2927	7933	0.0205	-
4.2933	7934	0.0141	-
4.2938	7935	0.0111	-
4.2944	7936	0.0159	-
4.2949	7937	0.0162	-
4.2955	7938	0.0064	-
4.2960	7939	0.011	-
4.2965	7940	0.0192	-
4.2971	7941	0.0091	-
4.2976	7942	0.0078	-
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4.5741	8453	0.0123	-
4.5747	8454	0.0062	-
4.5752	8455	0.0058	-
4.5758	8456	0.0132	-
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4.7982	8867	0.0101	-
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4.8084	8886	0.0101	-
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4.8101	8889	0.0101	-
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4.8214	8910	0.0123	-
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4.8360	8937	0.0125	-
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4.8398	8944	0.0205	-
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4.8415	8947	0.0125	-
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4.8431	8950	0.0089	-
4.8436	8951	0.0122	-
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4.8447	8953	0.0125	-
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4.8463	8956	0.0193	-
4.8469	8957	0.0157	-
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4.8517	8966	0.0156	-
4.8523	8967	0.0062	-
4.8528	8968	0.0102	-
4.8534	8969	0.0059	-
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5.1320	9484	0.0084	-
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5.1997	9609	0.0133	-
5.2002	9610	0.0101	-
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5.2013	9612	0.0088	-
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5.3398	9868	0.0096	-
5.3404	9869	0.0039	-
5.3409	9870	0.0089	-
5.3415	9871	0.0129	-
5.3420	9872	0.0126	-
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5.3479	9883	0.0124	-
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5.3506	9888	0.0215	-
5.3512	9889	0.0078	-
5.3517	9890	0.011	-
5.3523	9891	0.0141	-
5.3528	9892	0.0051	-
5.3534	9893	0.0102	-
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5.3544	9895	0.007	-
5.3550	9896	0.0071	-
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5.3642	9913	0.0096	-
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5.3658	9916	0.0124	-
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5.3669	9918	0.0094	-
5.3674	9919	0.0115	-
5.3680	9920	0.0085	-
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5.3788	9940	0.0078	-
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5.3804	9943	0.0092	-
5.3810	9944	0.0129	-
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5.3820	9946	0.0241	-
5.3826	9947	0.0233	-
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5.3842	9950	0.0123	-
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5.3869	9955	0.0078	-
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5.3880	9957	0.0196	-
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5.3891	9959	0.0138	-
5.3896	9960	0.0148	-
5.3902	9961	0.0097	-
5.3907	9962	0.0097	-
5.3912	9963	0.0082	-
5.3918	9964	0.0123	-
5.3923	9965	0.0084	-
5.3929	9966	0.007	-
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5.3945	9969	0.006	-
5.3950	9970	0.0126	-
5.3956	9971	0.0126	-
5.3961	9972	0.0129	-
5.3966	9973	0.0159	-
5.3972	9974	0.0121	-
5.3977	9975	0.0099	-
5.3983	9976	0.0195	-
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5.3994	9978	0.0079	-
5.3999	9979	0.0156	-
5.4004	9980	0.0165	-
5.4010	9981	0.0053	-
5.4015	9982	0.0073	-
5.4021	9983	0.0107	-
5.4026	9984	0.0109	-
5.4031	9985	0.012	-
5.4037	9986	0.0147	-
5.4042	9987	0.0098	-
5.4048	9988	0.0057	-
5.4053	9989	0.0098	-
5.4058	9990	0.0071	-
5.4064	9991	0.0081	-
5.4069	9992	0.0081	-
5.4075	9993	0.0215	-
5.4080	9994	0.0133	-
5.4085	9995	0.0118	-
5.4091	9996	0.0085	-
5.4096	9997	0.0144	-
5.4102	9998	0.01	-
5.4107	9999	0.0064	-
5.4113	10000	0.015	0.0170

Framework Versions

Python: 3.10.14
Sentence Transformers: 3.0.1
Transformers: 4.44.2
PyTorch: 2.4.0+cu121
Accelerate: 0.33.0
Datasets: 2.21.0
Tokenizers: 0.19.1

Citation

BibTeX

Sentence Transformers

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}

MultipleNegativesRankingLoss

@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply}, 
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

Atipico1
/

our-bert-no-negative-10000

SentenceTransformer based on Atipico1/bert-checkpoint-980000

Model Details

Model Description

Model Sources

Full Model Architecture

Usage

Direct Usage (Sentence Transformers)

Training Details

Training Dataset

Unnamed Dataset

Evaluation Dataset

Unnamed Dataset

IV. 실험 결과

Training Hyperparameters

Non-Default Hyperparameters

All Hyperparameters

Training Logs

Framework Versions

Citation

BibTeX

Sentence Transformers

MultipleNegativesRankingLoss

Model tree for Atipico1/our-bert-no-negative-10000