---
base_model: nreimers/MiniLM-L6-H384-uncased
datasets: []
language: []
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:730454
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: Continuous finite-time control approach for series elastic actuator
sentences:
- Distributed coordination is difficult, especially when the system may suffer intrusions
that corrupt some component processes. We introduce the abstraction of a failure
detector that a process can use to (imperfectly) detect the corruption (Byzantine
failure) of another process. In general, our failure detectors can be unreliable,
both by reporting a correct process to be faulty or by reporting a faulty process
to be correct. However, we show that if these detectors satisfy certain plausible
properties, then the well known distributed consensus problem can be solved. We
also present a randomized protocol using failure detectors that solves the consensus
problem if either the requisite properties of failure detectors hold or if certain
highly probable events eventually occur. This work can be viewed as a generalization
of benign failure detectors popular in the distributed computing literature.
- 'This paper deals with multilevel partial-response class-IV (PRIV) transmission
over unshielded twisted-pair (UTP) cables. Specifically, transmission at a rate
of 155.52 Mb/s over data-grade UTP cables for local-area networking is considered.
As a low-complexity method used to compensate for cable-length dependent signal
distortion, adaptive analog equalization with two controlled parameters is proposed:
one parameter determines a frequency-independent receiver gain, the other parameter
controls the transfer characteristic of a variable analog receive-filter section.
For the stepwise design of the transmit and receive filters, a combination of
analytic techniques and simulated annealing is employed. First, the variable equalizer
section, then the remaining fixed analog receive filter section are developed
and finally the analog transmit filter is determined. The paper also describes
the adjustment of the equalizer section, and the control of the sampling phase
in the receiver front-end. The two equalizer parameters are controlled by an algorithm
that operates on the sampled signals and adjusts these parameters to optimum settings
independently of the sampling phase. The latter is controlled by a decision-directed
phase-locked loop algorithm that becomes effective when equalization has been
achieved. The dynamic behaviour and mean-square error in steady-state obtained
with these control algorithms are investigated.'
- 'In this paper, a practical control approach is suggested for series elastic actuators(SEAs)
to generate the desired torque. Firstly, based on the analysis of a nonlinear
SEA, the generic dynamics for a class of SEAs is summarized. Then the dynamic
equations are transformed into a novel state-space form which is convenient for
controller design. Finally, based on the recently developed finite-time control
technique, a finite time disturbance observer and a continuous terminal sliding-mode
control scheme are introduced to synthesize the control law. The finite-time stability
of the proposed controller is theoretically ensured by Lyapunov analysis. Compared
with most existing methods, the contribution of the paper is two-fold: (i) The
proposed controller is suitable for not only linear, but also a class of nonlinear
SEAs, which means that it is a more generic method for SEA torque control; (ii)
It achieves faster convergence rate and works well even in the presence of unknown
payload parameters and external disturbances. A series of experiments are carried
out on the self-built SEA testbed to demonstrate the superior performance of the
proposed controller by comparing it with the cascade-PID controller.'
- source_sentence: Matrix Methods for Solving Algebraic Systems
sentences:
- We present our public-domain software for the following tasks in sparse (or toric)
elimination theory, given a well-constrained polynomial system. First, C code
for computing the mixed volume of the system. Second, Maple code for defining
an overconstrained system and constructing a Sylvester-type matrix of its sparse
resultant. Third, C code for a Sylvester-type matrix of the sparse resultant and
a superset of all common roots of the initial well-constrained system by computing
the eigen-decomposition of a square matrix obtained from the resultant matrix.
We conclude with experiments in computing molecular conformations.
- 'Design trade-offs between estimation performance, processing delay and communication
cost for a sensor scheduling problem is discussed. We consider a heterogeneous
sensor network with two types of sensors: the first type has low-quality measurements,
small processing delay and a light communication cost, while the second type is
of high quality, but imposes a large processing delay and a high communication
cost. Such a heterogeneous sensor network is common in applications, where for
instance in a localization system the poor sensor can be an ultrasound sensor
while the more powerful sensor can be a camera. Using a time-periodic Kalman filter,
we show how one can find an optimal schedule of the sensor communication. One
can significantly improve estimation quality by only using the expensive sensor
rarely. We also demonstrate how simple sensor switching rules based on the Riccati
equation drives the filter into a stable time-periodic Kalman filter.'
- The Multi-stage Genetic Algorithm, MGA, is introduced to solve a class of compositional
design problems. The problem with complicated constraints is formulated as a set
of local subproblems with simple constraints and a supervising problem. Every
subproblem is solved by GA to generate a set of suboptimal solutions. And in the
supervising problem, the elements of each set are optimally combined by GA to
yield the optimal solution for the original problem. The method is a learning
method where the empirical knowledge obtained by solving the problem is effectively
utilized to solve similar problems efficiently. Extended knapsack problems are
solved to demonstrate the proposed method, and the efficiency of the method is
shown. In addition, the method is successfully applied to optimal realization
of cooperative robot soccer behaviors.
- source_sentence: Low-power partial-parallel Chien search architecture with polynomial
degree reduction
sentences:
- In this paper, we present a novel attentive and immersive user interface based
on gaze and hand gestures for interactive large-scale displays. The combination
of gaze and hand gestures provide more interesting and immersive ways to manipulate
3D information.
- There is significant interest in the synthesis of discrete-state random fields,
particularly those possessing structure over a wide range of scales. However,
given a model on some finest, pixellated scale, it is computationally very difficult
to synthesize both large- and small-scale structures, motivating research into
hierarchical methods. In this paper, we propose a frozen-state approach to hierarchical
modeling, in which simulated annealing is performed on each scale, constrained
by the state estimates at the parent scale. This approach leads to significant
advantages in both modeling flexibility and computational complexity. In particular,
a complex structure can be realized with very simple, local, scale-dependent models,
and by constraining the domain to be annealed at finer scales to only the uncertain
portions of coarser scales; the approach leads to huge improvements in computational
complexity. Results are shown for a synthesis problem in porous media.
- The Chien search for the error locator polynomial root computation in BCH and
Reed-Solomon decoding accounts for a significant part of the overall decoder power
consumption, especially r long codes over finite fields of high order. For serial
Chien search, the power consumption is substantially lowered by a polynomial degree
reduction (PDR) scheme. Every time a root is found, it is factored out of the
error locator polynomial. Only the hardware units associated with the reduced-degree
polynomial coefficients are active. However, this PDR scheme can not be directly
extended to partial-parallel Chien search, which is needed in any systems to achieve
high throughput. By analyzing the formulas of the evaluation values over finite
field elements and available intermediate results of the Chien search, this paper
proposes a partial-parallel Chien search architecture that reduces the error locator
polynomial degree on the fly whenever a root is found without using long division.
For a 122-error-correcting BCH code over GF(215), an 8-parallel Chien search using
the proposed architecture achieves 32% power reduction over existing partial-parallel
architectures for a typical case.
- source_sentence: An efficient network-switch scheduling for real-time applications
sentences:
- Bursts consist of a varying number of asynchronous transfer mode cells corresponding
to a datagram. Here, we generalized weighted fair queueing to a burst-based algorithm
with preemption. The new algorithm enhances the performance of the switch service
for real-time applications, and it preserves the quality of service guarantees.
We study this algorithm theoretically and via simulations.
- Online Social Network (OSN) is one of the hottest innovations in the past years,
and the active users are more than a billion. For OSN, users' behavior is one
of the important factors to study. This demonstration proposal presents Harbinger,
an analyzing and predicting system for OSN users' behavior. In Harbinger, we focus
on tweets' timestamps (when users post or share messages), visualize users' post
behavior as well as message retweet number and build adjustable models to predict
users' behavior. Predictions of users' behavior can be performed with the discovered
behavior models and the results can be applied to many applications such as tweet
crawler and advertisement.
- The computation and memory required for kernel machines with N training samples
is at least O(N2). Such a complexity is significant even for moderate size problems
and is prohibitive for large datasets. We present an approximation technique based
on the improved fast Gauss transform to reduce the computation to O(N). We also
give an error bound for the approximation, and provide experimental results on
the UCI datasets.
- source_sentence: Summarizing the Evidence on the International Trade in Illegal
Wildlife
sentences:
- This paper proposes a method to represent classifiers or learned regression functions
using an OWL ontology. Also proposed are methods for finding an appropriate learned
function to answer a simple query. The ontology standardizes variable names and
dependence properties, so that feature values can be given by users or found on
the semantic web.
- The global trade in illegal wildlife is a multi-billion dollar industry that threatens
biodiversity and acts as a potential avenue for invasive species and disease spread.
Despite the broad-sweeping implications of illegal wildlife sales, scientists
have yet to describe the scope and scale of the trade. Here, we provide the most
thorough and current description of the illegal wildlife trade using 12 years
of seizure records compiled by TRAFFIC, the wildlife trade monitoring network.
These records comprise 967 seizures including massive quantities of ivory, tiger
skins, live reptiles, and other endangered wildlife and wildlife products. Most
seizures originate in Southeast Asia, a recently identified hotspot for future
emerging infectious diseases. To date, regulation and enforcement have been insufficient
to effectively control the global trade in illegal wildlife at national and international
scales. Effective control will require a multi-pronged approach including community-scale
education and empowering local people to value wildlife, coordinated international
regulation, and a greater allocation of national resources to on-the-ground enforcement.
- Griffithsin (GRFT) is a red alga-derived lectin with demonstrated broad spectrum
antiviral activity against enveloped viruses, including severe acute respiratory
syndromeāCoronavirus (SARS-CoV), Japanese encephalitis virus (JEV), hepatitis
C virus (HCV), and herpes simplex virus-2 (HSV-2). However, its pharmacokinetic
profile remains largely undefined. Here, Sprague Dawley rats were administered
a single dose of GRFT at 10 or 20 mg/kg by intravenous, oral, and subcutaneous
routes, respectively, and serum GRFT levels were measured at select time points.
In addition, the potential for systemic accumulation after oral dosing was assessed
in rats after 10 daily treatments with GRFT (20 or 40 mg/kg). We found that parenterally-administered
GRFT in rats displayed a complex elimination profile, which varied according to
administration routes. However, GRFT was not orally bioavailable, even after chronic
treatment. Nonetheless, active GRFT capable of neutralizing HIV-Env pseudoviruses
was detected in rat fecal extracts after chronic oral dosing. These findings support
further evaluation of GRFT for pre-exposure prophylaxis against emerging epidemics
for which specific therapeutics are not available, including systemic and enteric
infections caused by susceptible enveloped viruses. In addition, GRFT should be
considered for antiviral therapy and the prevention of rectal transmission of
HIV-1 and other susceptible viruses.
---
# SentenceTransformer based on nreimers/MiniLM-L6-H384-uncased
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [nreimers/MiniLM-L6-H384-uncased](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased). It maps sentences & paragraphs to a 384-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:** [nreimers/MiniLM-L6-H384-uncased](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased)
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 384 tokens
- **Similarity Function:** Cosine Similarity
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, '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:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the š¤ Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'Summarizing the Evidence on the International Trade in Illegal Wildlife',
'The global trade in illegal wildlife is a multi-billion dollar industry that threatens biodiversity and acts as a potential avenue for invasive species and disease spread. Despite the broad-sweeping implications of illegal wildlife sales, scientists have yet to describe the scope and scale of the trade. Here, we provide the most thorough and current description of the illegal wildlife trade using 12 years of seizure records compiled by TRAFFIC, the wildlife trade monitoring network. These records comprise 967 seizures including massive quantities of ivory, tiger skins, live reptiles, and other endangered wildlife and wildlife products. Most seizures originate in Southeast Asia, a recently identified hotspot for future emerging infectious diseases. To date, regulation and enforcement have been insufficient to effectively control the global trade in illegal wildlife at national and international scales. Effective control will require a multi-pronged approach including community-scale education and empowering local people to value wildlife, coordinated international regulation, and a greater allocation of national resources to on-the-ground enforcement.',
'This paper proposes a method to represent classifiers or learned regression functions using an OWL ontology. Also proposed are methods for finding an appropriate learned function to answer a simple query. The ontology standardizes variable names and dependence properties, so that feature values can be given by users or found on the semantic web.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 730,454 training samples
* Columns: sentence_0 and sentence_1
* Approximate statistics based on the first 1000 samples:
| | sentence_0 | sentence_1 |
|:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
| type | string | string |
| details | - min: 5 tokens
- mean: 15.55 tokens
- max: 41 tokens
| - min: 21 tokens
- mean: 195.91 tokens
- max: 512 tokens
|
* Samples:
| sentence_0 | sentence_1 |
|:-----------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| A parallel algorithm for constructing independent spanning trees in twisted cubes | A long-standing conjecture mentions that a kk-connected graph GG admits kk independent spanning trees (ISTs for short) rooted at an arbitrary node of GG. An nn-dimensional twisted cube, denoted by TQnTQn, is a variation of hypercube with connectivity nn and has many features superior to those of hypercube. Yang (2010) first proposed an algorithm to construct nn edge-disjoint spanning trees in TQnTQn for any odd integer nā©¾3nā©¾3 and showed that half of them are ISTs. At a later stage, Wang etĀ al.Ā (2012) inferred that the above conjecture in affirmative for TQnTQn by providing an O(NlogN)O(NlogN) time algorithm to construct nn ISTs, where N=2nN=2n is the number of nodes in TQnTQn. However, this algorithm is executed in a recursive fashion and thus is hard to be parallelized. In this paper, we revisit the problem of constructing ISTs in twisted cubes and present a non-recursive algorithm. Our approach can be fully parallelized to make the use of all nodes of TQnTQn as processors for computation in such a way that each node can determine its parent in all spanning trees directly by referring its address and tree indices in O(logN)O(logN) time. |
| A Novel Method for Separating and Locating Multiple Partial Discharge Sources in a Substation | To separate and locate multi-partial discharge (PD) sources in a substation, the use of spectrum differences of ultra-high frequency signals radiated from various sources as characteristic parameters has been previously reported. However, the separation success rate was poor when signal-to-noise ratio was low, and the localization result was a coordinate on two-dimensional plane. In this paper, a novel method is proposed to improve the separation rate and the localization accuracy. A directional measuring platform is built using two directional antennas. The time delay (TD) of the signals captured by the antennas is calculated, and TD sequences are obtained by rotating the platform at different angles. The sequences are separated with the TD distribution feature, and the directions of the multi-PD sources are calculated. The PD sources are located by directions using the error probability method. To verify the method, a simulated model with three PD sources was established by XFdtd. Simulation results show that the separation rate is increased from 71% to 95% compared with the previous method, and an accurate three-dimensional localization result was obtained. A field test with two PD sources was carried out, and the sources were separated and located accurately by the proposed method. |
| Every ternary permutation constraint satisfaction problem parameterized above average has a kernel with a quadratic number of variables | A ternary Permutation-CSP is specified by a subset @P of the symmetric group S"3. An instance of such a problem consists of a set of variables V and a multiset of constraints, which are ordered triples of distinct variables of V. The objective is to find a linear ordering @a of V that maximizes the number of triples whose rearrangement (under @a) follows a permutation in @P. We prove that every ternary Permutation-CSP parameterized above average has a kernel with a quadratic number of variables. |
* Loss: [MultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `num_train_epochs`: 5
- `multi_dataset_batch_sampler`: round_robin
#### All Hyperparameters
Click to expand
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: no
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 8
- `per_device_eval_batch_size`: 8
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `learning_rate`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1
- `num_train_epochs`: 5
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `warmup_steps`: 0
- `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`: False
- `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`: False
- `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
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin
Click to expand
| Epoch | Step | Training Loss |
|:------:|:------:|:-------------:|
| 0.0055 | 500 | 1.6701 |
| 0.0110 | 1000 | 0.8225 |
| 0.0164 | 1500 | 0.3883 |
| 0.0219 | 2000 | 0.2685 |
| 0.0274 | 2500 | 0.2349 |
| 0.0329 | 3000 | 0.1685 |
| 0.0383 | 3500 | 0.1409 |
| 0.0438 | 4000 | 0.1262 |
| 0.0493 | 4500 | 0.1195 |
| 0.0548 | 5000 | 0.1044 |
| 0.0602 | 5500 | 0.0989 |
| 0.0657 | 6000 | 0.0787 |
| 0.0712 | 6500 | 0.0895 |
| 0.0767 | 7000 | 0.0708 |
| 0.0821 | 7500 | 0.0834 |
| 0.0876 | 8000 | 0.0634 |
| 0.0931 | 8500 | 0.0643 |
| 0.0986 | 9000 | 0.0567 |
| 0.1040 | 9500 | 0.0646 |
| 0.1095 | 10000 | 0.0607 |
| 0.1150 | 10500 | 0.0564 |
| 0.1205 | 11000 | 0.068 |
| 0.1259 | 11500 | 0.0536 |
| 0.1314 | 12000 | 0.0594 |
| 0.1369 | 12500 | 0.057 |
| 0.1424 | 13000 | 0.0555 |
| 0.1479 | 13500 | 0.0485 |
| 0.1533 | 14000 | 0.0528 |
| 0.1588 | 14500 | 0.0478 |
| 0.1643 | 15000 | 0.0586 |
| 0.1698 | 15500 | 0.0539 |
| 0.1752 | 16000 | 0.0432 |
| 0.1807 | 16500 | 0.0542 |
| 0.1862 | 17000 | 0.0536 |
| 0.1917 | 17500 | 0.0492 |
| 0.1971 | 18000 | 0.0427 |
| 0.2026 | 18500 | 0.0489 |
| 0.2081 | 19000 | 0.0502 |
| 0.2136 | 19500 | 0.0432 |
| 0.2190 | 20000 | 0.0459 |
| 0.2245 | 20500 | 0.0376 |
| 0.2300 | 21000 | 0.0489 |
| 0.2355 | 21500 | 0.0515 |
| 0.2409 | 22000 | 0.0429 |
| 0.2464 | 22500 | 0.0417 |
| 0.2519 | 23000 | 0.0478 |
| 0.2574 | 23500 | 0.0359 |
| 0.2628 | 24000 | 0.0452 |
| 0.2683 | 24500 | 0.0443 |
| 0.2738 | 25000 | 0.0409 |
| 0.2793 | 25500 | 0.0421 |
| 0.2848 | 26000 | 0.0393 |
| 0.2902 | 26500 | 0.0409 |
| 0.2957 | 27000 | 0.032 |
| 0.3012 | 27500 | 0.0468 |
| 0.3067 | 28000 | 0.0285 |
| 0.3121 | 28500 | 0.0311 |
| 0.3176 | 29000 | 0.0304 |
| 0.3231 | 29500 | 0.0349 |
| 0.3286 | 30000 | 0.0352 |
| 0.3340 | 30500 | 0.0367 |
| 0.3395 | 31000 | 0.0385 |
| 0.3450 | 31500 | 0.0325 |
| 0.3505 | 32000 | 0.0302 |
| 0.3559 | 32500 | 0.0393 |
| 0.3614 | 33000 | 0.032 |
| 0.3669 | 33500 | 0.0263 |
| 0.3724 | 34000 | 0.0343 |
| 0.3778 | 34500 | 0.0349 |
| 0.3833 | 35000 | 0.0282 |
| 0.3888 | 35500 | 0.034 |
| 0.3943 | 36000 | 0.0376 |
| 0.3998 | 36500 | 0.0265 |
| 0.4052 | 37000 | 0.0267 |
| 0.4107 | 37500 | 0.0241 |
| 0.4162 | 38000 | 0.033 |
| 0.4217 | 38500 | 0.0323 |
| 0.4271 | 39000 | 0.0278 |
| 0.4326 | 39500 | 0.025 |
| 0.4381 | 40000 | 0.0363 |
| 0.4436 | 40500 | 0.0312 |
| 0.4490 | 41000 | 0.0307 |
| 0.4545 | 41500 | 0.0305 |
| 0.4600 | 42000 | 0.028 |
| 0.4655 | 42500 | 0.0279 |
| 0.4709 | 43000 | 0.0265 |
| 0.4764 | 43500 | 0.0262 |
| 0.4819 | 44000 | 0.0308 |
| 0.4874 | 44500 | 0.0282 |
| 0.4928 | 45000 | 0.0243 |
| 0.4983 | 45500 | 0.0236 |
| 0.5038 | 46000 | 0.02 |
| 0.5093 | 46500 | 0.0254 |
| 0.5147 | 47000 | 0.0275 |
| 0.5202 | 47500 | 0.0309 |
| 0.5257 | 48000 | 0.031 |
| 0.5312 | 48500 | 0.0271 |
| 0.5367 | 49000 | 0.0218 |
| 0.5421 | 49500 | 0.0249 |
| 0.5476 | 50000 | 0.0285 |
| 0.5531 | 50500 | 0.03 |
| 0.5586 | 51000 | 0.0284 |
| 0.5640 | 51500 | 0.0258 |
| 0.5695 | 52000 | 0.0228 |
| 0.5750 | 52500 | 0.0305 |
| 0.5805 | 53000 | 0.0234 |
| 0.5859 | 53500 | 0.0209 |
| 0.5914 | 54000 | 0.0341 |
| 0.5969 | 54500 | 0.0269 |
| 0.6024 | 55000 | 0.0267 |
| 0.6078 | 55500 | 0.0245 |
| 0.6133 | 56000 | 0.0263 |
| 0.6188 | 56500 | 0.0195 |
| 0.6243 | 57000 | 0.0209 |
| 0.6297 | 57500 | 0.0313 |
| 0.6352 | 58000 | 0.0247 |
| 0.6407 | 58500 | 0.0285 |
| 0.6462 | 59000 | 0.0301 |
| 0.6516 | 59500 | 0.0227 |
| 0.6571 | 60000 | 0.0235 |
| 0.6626 | 60500 | 0.0272 |
| 0.6681 | 61000 | 0.025 |
| 0.6736 | 61500 | 0.0276 |
| 0.6790 | 62000 | 0.0289 |
| 0.6845 | 62500 | 0.0232 |
| 0.6900 | 63000 | 0.0258 |
| 0.6955 | 63500 | 0.0254 |
| 0.7009 | 64000 | 0.0205 |
| 0.7064 | 64500 | 0.0216 |
| 0.7119 | 65000 | 0.0304 |
| 0.7174 | 65500 | 0.0234 |
| 0.7228 | 66000 | 0.0233 |
| 0.7283 | 66500 | 0.0239 |
| 0.7338 | 67000 | 0.0166 |
| 0.7393 | 67500 | 0.0211 |
| 0.7447 | 68000 | 0.0212 |
| 0.7502 | 68500 | 0.0247 |
| 0.7557 | 69000 | 0.023 |
| 0.7612 | 69500 | 0.0261 |
| 0.7666 | 70000 | 0.0204 |
| 0.7721 | 70500 | 0.026 |
| 0.7776 | 71000 | 0.0299 |
| 0.7831 | 71500 | 0.0183 |
| 0.7885 | 72000 | 0.0228 |
| 0.7940 | 72500 | 0.0181 |
| 0.7995 | 73000 | 0.0237 |
| 0.8050 | 73500 | 0.0237 |
| 0.8105 | 74000 | 0.0158 |
| 0.8159 | 74500 | 0.0222 |
| 0.8214 | 75000 | 0.0196 |
| 0.8269 | 75500 | 0.0242 |
| 0.8324 | 76000 | 0.0218 |
| 0.8378 | 76500 | 0.0201 |
| 0.8433 | 77000 | 0.026 |
| 0.8488 | 77500 | 0.0232 |
| 0.8543 | 78000 | 0.0254 |
| 0.8597 | 78500 | 0.0218 |
| 0.8652 | 79000 | 0.0219 |
| 0.8707 | 79500 | 0.0255 |
| 0.8762 | 80000 | 0.0201 |
| 0.8816 | 80500 | 0.0301 |
| 0.8871 | 81000 | 0.0275 |
| 0.8926 | 81500 | 0.018 |
| 0.8981 | 82000 | 0.028 |
| 0.9035 | 82500 | 0.0223 |
| 0.9090 | 83000 | 0.0201 |
| 0.9145 | 83500 | 0.0299 |
| 0.9200 | 84000 | 0.0251 |
| 0.9254 | 84500 | 0.0203 |
| 0.9309 | 85000 | 0.0209 |
| 0.9364 | 85500 | 0.0236 |
| 0.9419 | 86000 | 0.0191 |
| 0.9474 | 86500 | 0.0168 |
| 0.9528 | 87000 | 0.017 |
| 0.9583 | 87500 | 0.0201 |
| 0.9638 | 88000 | 0.0171 |
| 0.9693 | 88500 | 0.0217 |
| 0.9747 | 89000 | 0.0208 |
| 0.9802 | 89500 | 0.0157 |
| 0.9857 | 90000 | 0.0218 |
| 0.9912 | 90500 | 0.021 |
| 0.9966 | 91000 | 0.0159 |
| 1.0021 | 91500 | 0.0189 |
| 1.0076 | 92000 | 0.0182 |
| 1.0131 | 92500 | 0.0206 |
| 1.0185 | 93000 | 0.0179 |
| 1.0240 | 93500 | 0.0168 |
| 1.0295 | 94000 | 0.019 |
| 1.0350 | 94500 | 0.0173 |
| 1.0404 | 95000 | 0.0172 |
| 1.0459 | 95500 | 0.0187 |
| 1.0514 | 96000 | 0.0199 |
| 1.0569 | 96500 | 0.0202 |
| 1.0624 | 97000 | 0.0198 |
| 1.0678 | 97500 | 0.0157 |
| 1.0733 | 98000 | 0.0178 |
| 1.0788 | 98500 | 0.0147 |
| 1.0843 | 99000 | 0.0152 |
| 1.0897 | 99500 | 0.0152 |
| 1.0952 | 100000 | 0.0126 |
| 1.1007 | 100500 | 0.0115 |
| 1.1062 | 101000 | 0.0122 |
| 1.1116 | 101500 | 0.0097 |
| 1.1171 | 102000 | 0.0149 |
| 1.1226 | 102500 | 0.0151 |
| 1.1281 | 103000 | 0.0134 |
| 1.1335 | 103500 | 0.0157 |
| 1.1390 | 104000 | 0.0141 |
| 1.1445 | 104500 | 0.0139 |
| 1.1500 | 105000 | 0.0149 |
| 1.1554 | 105500 | 0.0103 |
| 1.1609 | 106000 | 0.0138 |
| 1.1664 | 106500 | 0.0116 |
| 1.1719 | 107000 | 0.0146 |
| 1.1773 | 107500 | 0.0168 |
| 1.1828 | 108000 | 0.0166 |
| 1.1883 | 108500 | 0.0136 |
| 1.1938 | 109000 | 0.0103 |
| 1.1993 | 109500 | 0.0128 |
| 1.2047 | 110000 | 0.0112 |
| 1.2102 | 110500 | 0.0103 |
| 1.2157 | 111000 | 0.0133 |
| 1.2212 | 111500 | 0.0118 |
| 1.2266 | 112000 | 0.009 |
| 1.2321 | 112500 | 0.0151 |
| 1.2376 | 113000 | 0.0146 |
| 1.2431 | 113500 | 0.0143 |
| 1.2485 | 114000 | 0.01 |
| 1.2540 | 114500 | 0.0147 |
| 1.2595 | 115000 | 0.011 |
| 1.2650 | 115500 | 0.0121 |
| 1.2704 | 116000 | 0.0117 |
| 1.2759 | 116500 | 0.0151 |
| 1.2814 | 117000 | 0.0143 |
| 1.2869 | 117500 | 0.0163 |
| 1.2923 | 118000 | 0.0135 |
| 1.2978 | 118500 | 0.0118 |
| 1.3033 | 119000 | 0.0129 |
| 1.3088 | 119500 | 0.0062 |
| 1.3142 | 120000 | 0.0127 |
| 1.3197 | 120500 | 0.014 |
| 1.3252 | 121000 | 0.0131 |
| 1.3307 | 121500 | 0.0162 |
| 1.3362 | 122000 | 0.0107 |
| 1.3416 | 122500 | 0.0125 |
| 1.3471 | 123000 | 0.0136 |
| 1.3526 | 123500 | 0.0112 |
| 1.3581 | 124000 | 0.0126 |
| 1.3635 | 124500 | 0.0079 |
| 1.3690 | 125000 | 0.0104 |
| 1.3745 | 125500 | 0.0137 |
| 1.3800 | 126000 | 0.0075 |
| 1.3854 | 126500 | 0.0108 |
| 1.3909 | 127000 | 0.0087 |
| 1.3964 | 127500 | 0.0138 |
| 1.4019 | 128000 | 0.0056 |
| 1.4073 | 128500 | 0.0067 |
| 1.4128 | 129000 | 0.0103 |
| 1.4183 | 129500 | 0.0102 |
| 1.4238 | 130000 | 0.0119 |
| 1.4292 | 130500 | 0.0094 |
| 1.4347 | 131000 | 0.0075 |
| 1.4402 | 131500 | 0.0146 |
| 1.4457 | 132000 | 0.0103 |
| 1.4511 | 132500 | 0.0123 |
| 1.4566 | 133000 | 0.0107 |
| 1.4621 | 133500 | 0.0071 |
| 1.4676 | 134000 | 0.0087 |
| 1.4731 | 134500 | 0.0072 |
| 1.4785 | 135000 | 0.0094 |
| 1.4840 | 135500 | 0.0083 |
| 1.4895 | 136000 | 0.0104 |
| 1.4950 | 136500 | 0.0076 |
| 1.5004 | 137000 | 0.006 |
| 1.5059 | 137500 | 0.0085 |
| 1.5114 | 138000 | 0.0061 |
| 1.5169 | 138500 | 0.0106 |
| 1.5223 | 139000 | 0.0088 |
| 1.5278 | 139500 | 0.0111 |
| 1.5333 | 140000 | 0.0094 |
| 1.5388 | 140500 | 0.0079 |
| 1.5442 | 141000 | 0.0095 |
| 1.5497 | 141500 | 0.0098 |
| 1.5552 | 142000 | 0.0139 |
| 1.5607 | 142500 | 0.0085 |
| 1.5661 | 143000 | 0.0094 |
| 1.5716 | 143500 | 0.0088 |
| 1.5771 | 144000 | 0.0092 |
| 1.5826 | 144500 | 0.0071 |
| 1.5880 | 145000 | 0.0101 |
| 1.5935 | 145500 | 0.011 |
| 1.5990 | 146000 | 0.0097 |
| 1.6045 | 146500 | 0.0071 |
| 1.6100 | 147000 | 0.0114 |
| 1.6154 | 147500 | 0.0087 |
| 1.6209 | 148000 | 0.0075 |
| 1.6264 | 148500 | 0.0039 |
| 1.6319 | 149000 | 0.0091 |
| 1.6373 | 149500 | 0.0117 |
| 1.6428 | 150000 | 0.01 |
| 1.6483 | 150500 | 0.0099 |
| 1.6538 | 151000 | 0.0069 |
| 1.6592 | 151500 | 0.0084 |
| 1.6647 | 152000 | 0.0118 |
| 1.6702 | 152500 | 0.0078 |
| 1.6757 | 153000 | 0.0067 |
| 1.6811 | 153500 | 0.0133 |
| 1.6866 | 154000 | 0.0079 |
| 1.6921 | 154500 | 0.0092 |
| 1.6976 | 155000 | 0.0069 |
| 1.7030 | 155500 | 0.008 |
| 1.7085 | 156000 | 0.0124 |
| 1.7140 | 156500 | 0.0112 |
| 1.7195 | 157000 | 0.0074 |
| 1.7249 | 157500 | 0.0091 |
| 1.7304 | 158000 | 0.0088 |
| 1.7359 | 158500 | 0.0061 |
| 1.7414 | 159000 | 0.0089 |
| 1.7469 | 159500 | 0.0082 |
| 1.7523 | 160000 | 0.0103 |
| 1.7578 | 160500 | 0.0094 |
| 1.7633 | 161000 | 0.0073 |
| 1.7688 | 161500 | 0.0116 |
| 1.7742 | 162000 | 0.0112 |
| 1.7797 | 162500 | 0.0057 |
| 1.7852 | 163000 | 0.0075 |
| 1.7907 | 163500 | 0.0062 |
| 1.7961 | 164000 | 0.0046 |
| 1.8016 | 164500 | 0.0091 |
| 1.8071 | 165000 | 0.0066 |
| 1.8126 | 165500 | 0.0051 |
| 1.8180 | 166000 | 0.0066 |
| 1.8235 | 166500 | 0.0093 |
| 1.8290 | 167000 | 0.0079 |
| 1.8345 | 167500 | 0.0067 |
| 1.8399 | 168000 | 0.007 |
| 1.8454 | 168500 | 0.0133 |
| 1.8509 | 169000 | 0.0071 |
| 1.8564 | 169500 | 0.0091 |
| 1.8619 | 170000 | 0.0067 |
| 1.8673 | 170500 | 0.0091 |
| 1.8728 | 171000 | 0.0103 |
| 1.8783 | 171500 | 0.0058 |
| 1.8838 | 172000 | 0.0116 |
| 1.8892 | 172500 | 0.0089 |
| 1.8947 | 173000 | 0.0137 |
| 1.9002 | 173500 | 0.0065 |
| 1.9057 | 174000 | 0.0098 |
| 1.9111 | 174500 | 0.0083 |
| 1.9166 | 175000 | 0.0115 |
| 1.9221 | 175500 | 0.0083 |
| 1.9276 | 176000 | 0.0084 |
| 1.9330 | 176500 | 0.0091 |
| 1.9385 | 177000 | 0.0092 |
| 1.9440 | 177500 | 0.0054 |
| 1.9495 | 178000 | 0.0049 |
| 1.9549 | 178500 | 0.0072 |
| 1.9604 | 179000 | 0.0052 |
| 1.9659 | 179500 | 0.0063 |
| 1.9714 | 180000 | 0.0107 |
| 1.9768 | 180500 | 0.0061 |
| 1.9823 | 181000 | 0.0059 |
| 1.9878 | 181500 | 0.0067 |
| 1.9933 | 182000 | 0.0078 |
| 1.9988 | 182500 | 0.007 |
| 2.0042 | 183000 | 0.0065 |
| 2.0097 | 183500 | 0.0073 |
| 2.0152 | 184000 | 0.01 |
| 2.0207 | 184500 | 0.0072 |
| 2.0261 | 185000 | 0.0055 |
| 2.0316 | 185500 | 0.0087 |
| 2.0371 | 186000 | 0.0077 |
| 2.0426 | 186500 | 0.0067 |
| 2.0480 | 187000 | 0.008 |
| 2.0535 | 187500 | 0.0074 |
| 2.0590 | 188000 | 0.0072 |
| 2.0645 | 188500 | 0.0045 |
| 2.0699 | 189000 | 0.0082 |
| 2.0754 | 189500 | 0.0042 |
| 2.0809 | 190000 | 0.0076 |
| 2.0864 | 190500 | 0.0058 |
| 2.0918 | 191000 | 0.005 |
| 2.0973 | 191500 | 0.0047 |
| 2.1028 | 192000 | 0.0045 |
| 2.1083 | 192500 | 0.0043 |
| 2.1137 | 193000 | 0.0049 |
| 2.1192 | 193500 | 0.0058 |
| 2.1247 | 194000 | 0.0081 |
| 2.1302 | 194500 | 0.0057 |
| 2.1357 | 195000 | 0.0047 |
| 2.1411 | 195500 | 0.0073 |
| 2.1466 | 196000 | 0.0056 |
| 2.1521 | 196500 | 0.006 |
| 2.1576 | 197000 | 0.0061 |
| 2.1630 | 197500 | 0.0042 |
| 2.1685 | 198000 | 0.0057 |
| 2.1740 | 198500 | 0.0055 |
| 2.1795 | 199000 | 0.0053 |
| 2.1849 | 199500 | 0.0085 |
| 2.1904 | 200000 | 0.005 |
| 2.1959 | 200500 | 0.0055 |
| 2.2014 | 201000 | 0.0032 |
| 2.2068 | 201500 | 0.0054 |
| 2.2123 | 202000 | 0.0037 |
| 2.2178 | 202500 | 0.0046 |
| 2.2233 | 203000 | 0.0029 |
| 2.2287 | 203500 | 0.0043 |
| 2.2342 | 204000 | 0.0063 |
| 2.2397 | 204500 | 0.0064 |
| 2.2452 | 205000 | 0.0046 |
| 2.2506 | 205500 | 0.0061 |
| 2.2561 | 206000 | 0.0034 |
| 2.2616 | 206500 | 0.0046 |
| 2.2671 | 207000 | 0.0059 |
| 2.2726 | 207500 | 0.0044 |
| 2.2780 | 208000 | 0.0054 |
| 2.2835 | 208500 | 0.0049 |
| 2.2890 | 209000 | 0.0096 |
| 2.2945 | 209500 | 0.0045 |
| 2.2999 | 210000 | 0.0057 |
| 2.3054 | 210500 | 0.0032 |
| 2.3109 | 211000 | 0.0031 |
| 2.3164 | 211500 | 0.0043 |
| 2.3218 | 212000 | 0.0068 |
| 2.3273 | 212500 | 0.0048 |
| 2.3328 | 213000 | 0.0042 |
| 2.3383 | 213500 | 0.0068 |
| 2.3437 | 214000 | 0.0041 |
| 2.3492 | 214500 | 0.0042 |
| 2.3547 | 215000 | 0.0051 |
| 2.3602 | 215500 | 0.0049 |
| 2.3656 | 216000 | 0.0019 |
| 2.3711 | 216500 | 0.0039 |
| 2.3766 | 217000 | 0.0068 |
| 2.3821 | 217500 | 0.0033 |
| 2.3875 | 218000 | 0.0048 |
| 2.3930 | 218500 | 0.0052 |
| 2.3985 | 219000 | 0.0063 |
| 2.4040 | 219500 | 0.003 |
| 2.4095 | 220000 | 0.0036 |
| 2.4149 | 220500 | 0.004 |
| 2.4204 | 221000 | 0.006 |
| 2.4259 | 221500 | 0.0048 |
| 2.4314 | 222000 | 0.0037 |
| 2.4368 | 222500 | 0.0034 |
| 2.4423 | 223000 | 0.0049 |
| 2.4478 | 223500 | 0.0036 |
| 2.4533 | 224000 | 0.0046 |
| 2.4587 | 224500 | 0.0039 |
| 2.4642 | 225000 | 0.0021 |
| 2.4697 | 225500 | 0.0035 |
| 2.4752 | 226000 | 0.0034 |
| 2.4806 | 226500 | 0.003 |
| 2.4861 | 227000 | 0.0032 |
| 2.4916 | 227500 | 0.005 |
| 2.4971 | 228000 | 0.0025 |
| 2.5025 | 228500 | 0.0036 |
| 2.5080 | 229000 | 0.0021 |
| 2.5135 | 229500 | 0.0025 |
| 2.5190 | 230000 | 0.0036 |
| 2.5245 | 230500 | 0.0033 |
| 2.5299 | 231000 | 0.0049 |
| 2.5354 | 231500 | 0.0044 |
| 2.5409 | 232000 | 0.0029 |
| 2.5464 | 232500 | 0.0028 |
| 2.5518 | 233000 | 0.0091 |
| 2.5573 | 233500 | 0.004 |
| 2.5628 | 234000 | 0.0036 |
| 2.5683 | 234500 | 0.0029 |
| 2.5737 | 235000 | 0.0035 |
| 2.5792 | 235500 | 0.0038 |
| 2.5847 | 236000 | 0.0028 |
| 2.5902 | 236500 | 0.0041 |
| 2.5956 | 237000 | 0.0037 |
| 2.6011 | 237500 | 0.0031 |
| 2.6066 | 238000 | 0.0036 |
| 2.6121 | 238500 | 0.0052 |
| 2.6175 | 239000 | 0.0031 |
| 2.6230 | 239500 | 0.0023 |
| 2.6285 | 240000 | 0.0043 |
| 2.6340 | 240500 | 0.0027 |
| 2.6394 | 241000 | 0.0048 |
| 2.6449 | 241500 | 0.0046 |
| 2.6504 | 242000 | 0.0038 |
| 2.6559 | 242500 | 0.0033 |
| 2.6614 | 243000 | 0.003 |
| 2.6668 | 243500 | 0.0057 |
| 2.6723 | 244000 | 0.0044 |
| 2.6778 | 244500 | 0.0058 |
| 2.6833 | 245000 | 0.003 |
| 2.6887 | 245500 | 0.0042 |
| 2.6942 | 246000 | 0.0045 |
| 2.6997 | 246500 | 0.0031 |
| 2.7052 | 247000 | 0.0021 |
| 2.7106 | 247500 | 0.0043 |
| 2.7161 | 248000 | 0.0058 |
| 2.7216 | 248500 | 0.0041 |
| 2.7271 | 249000 | 0.0038 |
| 2.7325 | 249500 | 0.0019 |
| 2.7380 | 250000 | 0.0029 |
| 2.7435 | 250500 | 0.003 |
| 2.7490 | 251000 | 0.0038 |
| 2.7544 | 251500 | 0.004 |
| 2.7599 | 252000 | 0.0049 |
| 2.7654 | 252500 | 0.0039 |
| 2.7709 | 253000 | 0.005 |
| 2.7763 | 253500 | 0.0046 |
| 2.7818 | 254000 | 0.0025 |
| 2.7873 | 254500 | 0.0044 |
| 2.7928 | 255000 | 0.0023 |
| 2.7983 | 255500 | 0.0038 |
| 2.8037 | 256000 | 0.0032 |
| 2.8092 | 256500 | 0.0021 |
| 2.8147 | 257000 | 0.0023 |
| 2.8202 | 257500 | 0.0042 |
| 2.8256 | 258000 | 0.0042 |
| 2.8311 | 258500 | 0.0053 |
| 2.8366 | 259000 | 0.0021 |
| 2.8421 | 259500 | 0.0033 |
| 2.8475 | 260000 | 0.0047 |
| 2.8530 | 260500 | 0.0048 |
| 2.8585 | 261000 | 0.0022 |
| 2.8640 | 261500 | 0.0036 |
| 2.8694 | 262000 | 0.0034 |
| 2.8749 | 262500 | 0.0029 |
| 2.8804 | 263000 | 0.0038 |
| 2.8859 | 263500 | 0.0067 |
| 2.8913 | 264000 | 0.003 |
| 2.8968 | 264500 | 0.0049 |
| 2.9023 | 265000 | 0.0027 |
| 2.9078 | 265500 | 0.004 |
| 2.9132 | 266000 | 0.0042 |
| 2.9187 | 266500 | 0.0042 |
| 2.9242 | 267000 | 0.0038 |
| 2.9297 | 267500 | 0.0029 |
| 2.9352 | 268000 | 0.0039 |
| 2.9406 | 268500 | 0.0039 |
| 2.9461 | 269000 | 0.002 |
| 2.9516 | 269500 | 0.0022 |
| 2.9571 | 270000 | 0.002 |
| 2.9625 | 270500 | 0.003 |
| 2.9680 | 271000 | 0.0019 |
| 2.9735 | 271500 | 0.0044 |
| 2.9790 | 272000 | 0.0028 |
| 2.9844 | 272500 | 0.0031 |
| 2.9899 | 273000 | 0.0025 |
| 2.9954 | 273500 | 0.0021 |
| 3.0009 | 274000 | 0.0025 |
| 3.0063 | 274500 | 0.0038 |
| 3.0118 | 275000 | 0.0045 |
| 3.0173 | 275500 | 0.002 |
| 3.0228 | 276000 | 0.0035 |
| 3.0282 | 276500 | 0.0046 |
| 3.0337 | 277000 | 0.0033 |
| 3.0392 | 277500 | 0.002 |
| 3.0447 | 278000 | 0.0036 |
| 3.0501 | 278500 | 0.0025 |
| 3.0556 | 279000 | 0.0039 |
| 3.0611 | 279500 | 0.0029 |
| 3.0666 | 280000 | 0.004 |
| 3.0721 | 280500 | 0.0023 |
| 3.0775 | 281000 | 0.0019 |
| 3.0830 | 281500 | 0.0019 |
| 3.0885 | 282000 | 0.0027 |
| 3.0940 | 282500 | 0.0014 |
| 3.0994 | 283000 | 0.0019 |
| 3.1049 | 283500 | 0.0018 |
| 3.1104 | 284000 | 0.0016 |
| 3.1159 | 284500 | 0.0017 |
| 3.1213 | 285000 | 0.0049 |
| 3.1268 | 285500 | 0.0022 |
| 3.1323 | 286000 | 0.0023 |
| 3.1378 | 286500 | 0.0016 |
| 3.1432 | 287000 | 0.002 |
| 3.1487 | 287500 | 0.0025 |
| 3.1542 | 288000 | 0.0012 |
| 3.1597 | 288500 | 0.0021 |
| 3.1651 | 289000 | 0.0017 |
| 3.1706 | 289500 | 0.0019 |
| 3.1761 | 290000 | 0.0019 |
| 3.1816 | 290500 | 0.0042 |
| 3.1871 | 291000 | 0.0027 |
| 3.1925 | 291500 | 0.0011 |
| 3.1980 | 292000 | 0.002 |
| 3.2035 | 292500 | 0.0021 |
| 3.2090 | 293000 | 0.0015 |
| 3.2144 | 293500 | 0.0017 |
| 3.2199 | 294000 | 0.002 |
| 3.2254 | 294500 | 0.0012 |
| 3.2309 | 295000 | 0.0017 |
| 3.2363 | 295500 | 0.0029 |
| 3.2418 | 296000 | 0.0019 |
| 3.2473 | 296500 | 0.0017 |
| 3.2528 | 297000 | 0.0019 |
| 3.2582 | 297500 | 0.0012 |
| 3.2637 | 298000 | 0.0024 |
| 3.2692 | 298500 | 0.0017 |
| 3.2747 | 299000 | 0.0022 |
| 3.2801 | 299500 | 0.002 |
| 3.2856 | 300000 | 0.0028 |
| 3.2911 | 300500 | 0.0036 |
| 3.2966 | 301000 | 0.0015 |
| 3.3020 | 301500 | 0.0024 |
| 3.3075 | 302000 | 0.0015 |
| 3.3130 | 302500 | 0.0012 |
| 3.3185 | 303000 | 0.0022 |
| 3.3240 | 303500 | 0.0015 |
| 3.3294 | 304000 | 0.0023 |
| 3.3349 | 304500 | 0.0017 |
| 3.3404 | 305000 | 0.0021 |
| 3.3459 | 305500 | 0.0017 |
| 3.3513 | 306000 | 0.0015 |
| 3.3568 | 306500 | 0.0023 |
| 3.3623 | 307000 | 0.0014 |
| 3.3678 | 307500 | 0.0019 |
| 3.3732 | 308000 | 0.0017 |
| 3.3787 | 308500 | 0.0027 |
| 3.3842 | 309000 | 0.0016 |
| 3.3897 | 309500 | 0.0019 |
| 3.3951 | 310000 | 0.0037 |
| 3.4006 | 310500 | 0.0016 |
| 3.4061 | 311000 | 0.0012 |
| 3.4116 | 311500 | 0.0024 |
| 3.4170 | 312000 | 0.0016 |
| 3.4225 | 312500 | 0.0022 |
| 3.4280 | 313000 | 0.0015 |
| 3.4335 | 313500 | 0.0017 |
| 3.4389 | 314000 | 0.0015 |
| 3.4444 | 314500 | 0.0018 |
| 3.4499 | 315000 | 0.0015 |
| 3.4554 | 315500 | 0.0019 |
| 3.4609 | 316000 | 0.0009 |
| 3.4663 | 316500 | 0.001 |
| 3.4718 | 317000 | 0.001 |
| 3.4773 | 317500 | 0.0023 |
| 3.4828 | 318000 | 0.0012 |
| 3.4882 | 318500 | 0.0012 |
| 3.4937 | 319000 | 0.0011 |
| 3.4992 | 319500 | 0.0008 |
| 3.5047 | 320000 | 0.0018 |
| 3.5101 | 320500 | 0.0009 |
| 3.5156 | 321000 | 0.0016 |
| 3.5211 | 321500 | 0.0012 |
| 3.5266 | 322000 | 0.0015 |
| 3.5320 | 322500 | 0.0024 |
| 3.5375 | 323000 | 0.0016 |
| 3.5430 | 323500 | 0.0014 |
| 3.5485 | 324000 | 0.0014 |
| 3.5539 | 324500 | 0.0047 |
| 3.5594 | 325000 | 0.0013 |
| 3.5649 | 325500 | 0.0012 |
| 3.5704 | 326000 | 0.0013 |
| 3.5758 | 326500 | 0.0011 |
| 3.5813 | 327000 | 0.0011 |
| 3.5868 | 327500 | 0.0016 |
| 3.5923 | 328000 | 0.0022 |
| 3.5978 | 328500 | 0.0017 |
| 3.6032 | 329000 | 0.0012 |
| 3.6087 | 329500 | 0.002 |
| 3.6142 | 330000 | 0.0016 |
| 3.6197 | 330500 | 0.0009 |
| 3.6251 | 331000 | 0.0011 |
| 3.6306 | 331500 | 0.0019 |
| 3.6361 | 332000 | 0.0011 |
| 3.6416 | 332500 | 0.0021 |
| 3.6470 | 333000 | 0.0029 |
| 3.6525 | 333500 | 0.001 |
| 3.6580 | 334000 | 0.0016 |
| 3.6635 | 334500 | 0.0016 |
| 3.6689 | 335000 | 0.0036 |
| 3.6744 | 335500 | 0.0012 |
| 3.6799 | 336000 | 0.003 |
| 3.6854 | 336500 | 0.0014 |
| 3.6908 | 337000 | 0.0018 |
| 3.6963 | 337500 | 0.001 |
| 3.7018 | 338000 | 0.001 |
| 3.7073 | 338500 | 0.0016 |
| 3.7127 | 339000 | 0.0025 |
| 3.7182 | 339500 | 0.001 |
| 3.7237 | 340000 | 0.0018 |
| 3.7292 | 340500 | 0.0015 |
| 3.7347 | 341000 | 0.001 |
| 3.7401 | 341500 | 0.0009 |
| 3.7456 | 342000 | 0.0013 |
| 3.7511 | 342500 | 0.0014 |
| 3.7566 | 343000 | 0.0013 |
| 3.7620 | 343500 | 0.0011 |
| 3.7675 | 344000 | 0.0026 |
| 3.7730 | 344500 | 0.0014 |
| 3.7785 | 345000 | 0.0021 |
| 3.7839 | 345500 | 0.0015 |
| 3.7894 | 346000 | 0.0013 |
| 3.7949 | 346500 | 0.0013 |
| 3.8004 | 347000 | 0.0019 |
| 3.8058 | 347500 | 0.0009 |
| 3.8113 | 348000 | 0.0009 |
| 3.8168 | 348500 | 0.0014 |
| 3.8223 | 349000 | 0.0012 |
| 3.8277 | 349500 | 0.0032 |
| 3.8332 | 350000 | 0.0015 |
| 3.8387 | 350500 | 0.0011 |
| 3.8442 | 351000 | 0.002 |
| 3.8497 | 351500 | 0.0012 |
| 3.8551 | 352000 | 0.0026 |
| 3.8606 | 352500 | 0.001 |
| 3.8661 | 353000 | 0.0018 |
| 3.8716 | 353500 | 0.0014 |
| 3.8770 | 354000 | 0.001 |
| 3.8825 | 354500 | 0.0018 |
| 3.8880 | 355000 | 0.0027 |
| 3.8935 | 355500 | 0.0027 |
| 3.8989 | 356000 | 0.0011 |
| 3.9044 | 356500 | 0.0024 |
| 3.9099 | 357000 | 0.0012 |
| 3.9154 | 357500 | 0.0018 |
| 3.9208 | 358000 | 0.0012 |
| 3.9263 | 358500 | 0.0015 |
| 3.9318 | 359000 | 0.0015 |
| 3.9373 | 359500 | 0.0018 |
| 3.9427 | 360000 | 0.0017 |
| 3.9482 | 360500 | 0.0009 |
| 3.9537 | 361000 | 0.001 |
| 3.9592 | 361500 | 0.0013 |
| 3.9646 | 362000 | 0.0008 |
| 3.9701 | 362500 | 0.0018 |
| 3.9756 | 363000 | 0.0027 |
| 3.9811 | 363500 | 0.0009 |
| 3.9866 | 364000 | 0.0008 |
| 3.9920 | 364500 | 0.001 |
| 3.9975 | 365000 | 0.0009 |
| 4.0030 | 365500 | 0.0012 |
| 4.0085 | 366000 | 0.0011 |
| 4.0139 | 366500 | 0.0023 |
| 4.0194 | 367000 | 0.0023 |
| 4.0249 | 367500 | 0.0012 |
| 4.0304 | 368000 | 0.0018 |
| 4.0358 | 368500 | 0.0013 |
| 4.0413 | 369000 | 0.0009 |
| 4.0468 | 369500 | 0.0016 |
| 4.0523 | 370000 | 0.0011 |
| 4.0577 | 370500 | 0.0011 |
| 4.0632 | 371000 | 0.0009 |
| 4.0687 | 371500 | 0.0012 |
| 4.0742 | 372000 | 0.0011 |
| 4.0796 | 372500 | 0.0008 |
| 4.0851 | 373000 | 0.001 |
| 4.0906 | 373500 | 0.0008 |
| 4.0961 | 374000 | 0.0009 |
| 4.1015 | 374500 | 0.0008 |
| 4.1070 | 375000 | 0.0008 |
| 4.1125 | 375500 | 0.0008 |
| 4.1180 | 376000 | 0.0009 |
| 4.1235 | 376500 | 0.0021 |
| 4.1289 | 377000 | 0.0007 |
| 4.1344 | 377500 | 0.0014 |
| 4.1399 | 378000 | 0.0008 |
| 4.1454 | 378500 | 0.0015 |
| 4.1508 | 379000 | 0.0008 |
| 4.1563 | 379500 | 0.0008 |
| 4.1618 | 380000 | 0.0015 |
| 4.1673 | 380500 | 0.0008 |
| 4.1727 | 381000 | 0.0009 |
| 4.1782 | 381500 | 0.0018 |
| 4.1837 | 382000 | 0.0013 |
| 4.1892 | 382500 | 0.0012 |
| 4.1946 | 383000 | 0.0008 |
| 4.2001 | 383500 | 0.0008 |
| 4.2056 | 384000 | 0.0008 |
| 4.2111 | 384500 | 0.0008 |
| 4.2165 | 385000 | 0.001 |
| 4.2220 | 385500 | 0.0008 |
| 4.2275 | 386000 | 0.0008 |
| 4.2330 | 386500 | 0.0009 |
| 4.2384 | 387000 | 0.0008 |
| 4.2439 | 387500 | 0.0008 |
| 4.2494 | 388000 | 0.0011 |
| 4.2549 | 388500 | 0.0009 |
| 4.2604 | 389000 | 0.0007 |
| 4.2658 | 389500 | 0.001 |
| 4.2713 | 390000 | 0.0007 |
| 4.2768 | 390500 | 0.0011 |
| 4.2823 | 391000 | 0.0007 |
| 4.2877 | 391500 | 0.0019 |
| 4.2932 | 392000 | 0.0009 |
| 4.2987 | 392500 | 0.0011 |
| 4.3042 | 393000 | 0.0008 |
| 4.3096 | 393500 | 0.0006 |
| 4.3151 | 394000 | 0.0009 |
| 4.3206 | 394500 | 0.001 |
| 4.3261 | 395000 | 0.0007 |
| 4.3315 | 395500 | 0.0011 |
| 4.3370 | 396000 | 0.0008 |
| 4.3425 | 396500 | 0.0007 |
| 4.3480 | 397000 | 0.0007 |
| 4.3534 | 397500 | 0.0007 |
| 4.3589 | 398000 | 0.001 |
| 4.3644 | 398500 | 0.0008 |
| 4.3699 | 399000 | 0.001 |
| 4.3753 | 399500 | 0.0014 |
| 4.3808 | 400000 | 0.0006 |
| 4.3863 | 400500 | 0.0006 |
| 4.3918 | 401000 | 0.001 |
| 4.3973 | 401500 | 0.002 |
| 4.4027 | 402000 | 0.0006 |
| 4.4082 | 402500 | 0.0007 |
| 4.4137 | 403000 | 0.001 |
| 4.4192 | 403500 | 0.0008 |
| 4.4246 | 404000 | 0.0008 |
| 4.4301 | 404500 | 0.0009 |
| 4.4356 | 405000 | 0.0005 |
| 4.4411 | 405500 | 0.0008 |
| 4.4465 | 406000 | 0.0008 |
| 4.4520 | 406500 | 0.0007 |
| 4.4575 | 407000 | 0.0006 |
| 4.4630 | 407500 | 0.0006 |
| 4.4684 | 408000 | 0.0006 |
| 4.4739 | 408500 | 0.0006 |
| 4.4794 | 409000 | 0.0009 |
| 4.4849 | 409500 | 0.0007 |
| 4.4903 | 410000 | 0.0009 |
| 4.4958 | 410500 | 0.0006 |
| 4.5013 | 411000 | 0.0007 |
| 4.5068 | 411500 | 0.0006 |
| 4.5122 | 412000 | 0.0007 |
| 4.5177 | 412500 | 0.0006 |
| 4.5232 | 413000 | 0.0008 |
| 4.5287 | 413500 | 0.0007 |
| 4.5342 | 414000 | 0.0013 |
| 4.5396 | 414500 | 0.0006 |
| 4.5451 | 415000 | 0.0009 |
| 4.5506 | 415500 | 0.0015 |
| 4.5561 | 416000 | 0.0014 |
| 4.5615 | 416500 | 0.0007 |
| 4.5670 | 417000 | 0.0007 |
| 4.5725 | 417500 | 0.0008 |
| 4.5780 | 418000 | 0.0008 |
| 4.5834 | 418500 | 0.0007 |
| 4.5889 | 419000 | 0.0006 |
| 4.5944 | 419500 | 0.0008 |
| 4.5999 | 420000 | 0.0008 |
| 4.6053 | 420500 | 0.0006 |
| 4.6108 | 421000 | 0.001 |
| 4.6163 | 421500 | 0.0005 |
| 4.6218 | 422000 | 0.0007 |
| 4.6272 | 422500 | 0.0006 |
| 4.6327 | 423000 | 0.0007 |
| 4.6382 | 423500 | 0.0009 |
| 4.6437 | 424000 | 0.0014 |
| 4.6492 | 424500 | 0.0008 |
| 4.6546 | 425000 | 0.0006 |
| 4.6601 | 425500 | 0.0006 |
| 4.6656 | 426000 | 0.0016 |
| 4.6711 | 426500 | 0.0006 |
| 4.6765 | 427000 | 0.0006 |
| 4.6820 | 427500 | 0.0012 |
| 4.6875 | 428000 | 0.0007 |
| 4.6930 | 428500 | 0.0009 |
| 4.6984 | 429000 | 0.0006 |
| 4.7039 | 429500 | 0.0005 |
| 4.7094 | 430000 | 0.0007 |
| 4.7149 | 430500 | 0.0007 |
| 4.7203 | 431000 | 0.0006 |
| 4.7258 | 431500 | 0.0006 |
| 4.7313 | 432000 | 0.0006 |
| 4.7368 | 432500 | 0.0006 |
| 4.7422 | 433000 | 0.0006 |
| 4.7477 | 433500 | 0.0006 |
| 4.7532 | 434000 | 0.0006 |
| 4.7587 | 434500 | 0.0006 |
| 4.7641 | 435000 | 0.0006 |
| 4.7696 | 435500 | 0.0018 |
| 4.7751 | 436000 | 0.0009 |
| 4.7806 | 436500 | 0.0007 |
| 4.7861 | 437000 | 0.0007 |
| 4.7915 | 437500 | 0.0005 |
| 4.7970 | 438000 | 0.0009 |
| 4.8025 | 438500 | 0.0013 |
| 4.8080 | 439000 | 0.0007 |
| 4.8134 | 439500 | 0.0006 |
| 4.8189 | 440000 | 0.0007 |
| 4.8244 | 440500 | 0.001 |
| 4.8299 | 441000 | 0.0019 |
| 4.8353 | 441500 | 0.0006 |
| 4.8408 | 442000 | 0.0006 |
| 4.8463 | 442500 | 0.0009 |
| 4.8518 | 443000 | 0.0006 |
| 4.8572 | 443500 | 0.001 |
| 4.8627 | 444000 | 0.0011 |
| 4.8682 | 444500 | 0.0007 |
| 4.8737 | 445000 | 0.0007 |
| 4.8791 | 445500 | 0.0007 |
| 4.8846 | 446000 | 0.0018 |
| 4.8901 | 446500 | 0.0007 |
| 4.8956 | 447000 | 0.0012 |
| 4.9010 | 447500 | 0.0007 |
| 4.9065 | 448000 | 0.0009 |
| 4.9120 | 448500 | 0.0007 |
| 4.9175 | 449000 | 0.001 |
| 4.9230 | 449500 | 0.0007 |
| 4.9284 | 450000 | 0.0007 |
| 4.9339 | 450500 | 0.0007 |
| 4.9394 | 451000 | 0.0011 |
| 4.9449 | 451500 | 0.0005 |
| 4.9503 | 452000 | 0.0007 |
| 4.9558 | 452500 | 0.0006 |
| 4.9613 | 453000 | 0.0009 |
| 4.9668 | 453500 | 0.0008 |
| 4.9722 | 454000 | 0.0015 |
| 4.9777 | 454500 | 0.0008 |
| 4.9832 | 455000 | 0.0006 |
| 4.9887 | 455500 | 0.0006 |
| 4.9941 | 456000 | 0.0007 |
| 4.9996 | 456500 | 0.0006 |