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INTRODUCTION
To prolong human life and to alleviate suffering are the ultimate objects of scientific medicine. The two great branches of the healing art Medicine and Surgery are so intimately related that it is impossible to draw a hard-and-fast line between them, but for convenience Surgery may be defined as "the art of treating lesions and malformations of the human body by manual operations, mediate and immediate." To apply his art intelligently and successfully, it is essential that the surgeon should be conversant not only with the normal anatomy and physiology of the body and with the various pathological conditions to which it is liable, but also with the nature of the process by which repair of injured or diseased tissues is effected. Without this knowledge he is unable to recognise such deviations from the normal as result from mal-development, injury, or disease, or rationally to direct his efforts towards the correction or removal of these.
PROCESS OF REPAIR
The process of repair in living tissue d

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Estimate Additional cluster loss for Rural via ITU-R P.452-16

Reference: https://www.itu.int/dms_pubrec/itu-r/rec/p/R-REC-P.452-16-201507-S!!PDF-E.pdf

The additional loss due to protection from local clutter is given by the expression:

$$ A_h = 10.2 F_{fc} .e^{-d_k}(1-tanh(6(h/h_a-0.625)))-0.33 $$

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2023-04-18 20:32:36,678 [INFO] Training data ./data/small_3.txt
2023-04-18 20:32:36,679 [INFO] length of dataset in words: 22,420
2023-04-18 20:32:36,713 [INFO] encoding.input_ids.shape torch.Size([1, 4742])
2023-04-18 20:32:36,713 [INFO] encoding.attention_mask.shape torch.Size([1, 4742])
2023-04-18 20:32:36,713 [INFO] length of dataset in tokens = 4742
2023-04-18 20:32:57,546 [INFO] Over-fit check answer: Formation of Granulation Tissue
2023-04-18 20:32:57,546 [INFO] len_train_data=4742 block_size =256 batch_size= 4
2023-04-18 20:32:57,547 [INFO] Epoch 1 of 50
2023-04-18 20:33:04,405 [INFO] Epoch 0 complete. Loss: 5.974085330963135 saving ./test-gpt2-4/gpt2-epoch-1-2023-04-18 20:32:35.343858
2023-04-18 20:33:06,065 [INFO] Over-fit check answer: Formation of Granulation Tissueation of granulation tissue of granulation tissue of granulation tissue of granulation tissue of granulation tissuea,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,,1P

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Processing Message from input() Question: New York

Generated `

New York City. New Yorkers live within walking distance of the capital, and over 90% are located at or near high-speed Internet access points (h/t to WIRED). NYC is a global cultural center with an important influence on commerce; it constitutes one major city in terms
[a]century's worth [of news content]. With its rich media culture coupled by vibrant online communities that foster collaboration among writers from aroundthe world—from emerging markets like China through Latin America into Europe via Asia —NYC has become perhaps most influential place for new creative expression.[1][2], where innovative ideas can be disseminated quickly across disparate audiences without compromising quality control as well,[3],[4](http://www:washingtonpost.-times/.wp.] NYX provides opportunities both inside your home town hall meeting room full time but also outside when you're not there because many people don't have internet connections yet! It offers unp

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The answer to the question here https://stackoverflow.com/questions/563198/how-do-you-detect-where-two-line-segments-intersect by Gareth Rees is the best explanation.

We need to expand this to fit the co-ordinates

From https://stackoverflow.com/a/565282/429476

$$ \begin{align} t= (q-p) \times \vec s / \vec r \times \vec s\

alexcpn

from transformers import T5Tokenizer, T5ForConditionalGeneration
import numpy as np
import torch

class FlaxDataCollatorForT5MLM:
    """
    From https://github.com/huggingface/transformers/blob/main/examples/flax/language-modeling/run_t5_mlm_flax.py
    """
    def __init__(self,tokenizer,noise_density,mean_noise_span_length) -> None:
        self.tokenizer = tokenizer

alexcpn

from transformers import T5Tokenizer
import numpy as np

class FlaxDataCollatorForT5MLM:
    """
    From https://github.com/huggingface/transformers/blob/main/examples/flax/language-modeling/run_t5_mlm_flax.py
    """
    def __init__(self,tokenizer,noise_density,mean_noise_span_length) -> None:
        self.tokenizer = tokenizer
        self.noise_density = noise_density

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loki:
  auth_enabled: false
  commonConfig:
    path_prefix: /var/loki
    replication_factor: 1
  compactor:
    apply_retention_interval: 1h
    compaction_interval: 5m
    retention_delete_worker_count: 500
    retention_enabled: true

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'''
 Adapted and extended from 
 https://github.com/huggingface/transformers/issues/1950#issuecomment-558679189

'''
import pandas as pd
from transformers import BertTokenizer, BertModel
from sklearn.metrics.pairwise import cosine_similarity
import torch

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    #---------------------------------------------------------------------------------------------
    # Populate the Confusion Matrix
    #---------------------------------------------------------------------------------------------

    for key,val in wrong_per_class.items(): # Key is category and val is a list of wrong classes
        summed_wrong_classes =Counter(val).most_common()
        print(f"**To Predict {categories[key]}")
        for ele in summed_wrong_classes:
            print(f" --Predicted {categories[ele[0]]} count={ele[1]}")
            confusion_matrix[key][ele[0]]=ele[1]