wanyu/IteraTeR_full_sent huggingface dataset:
Original:  DESIGN : Retrospective cohort, analysed using a matched nested case-control study design. SETTING : Community-based SARS-CoV-2 testing in England, individually linked with hospitalisation data from the Secondary Uses Service and the Emergency Care Data Set. PARTICIPANTS: 493,053 laboratory-confirmed COVID-19 patients, of whom 19,482 had been hospitalised  , tested between 23 November 2020 and 4 January 2021 and analysed at a laboratory with an available TaqPath assay that enables assessment of S-gene target failure (SGTF).
Revised:  DESIGN : Retrospective cohort, analysed using stratified Cox regression. Setting : Community-based SARS-CoV-2 testing in England, individually linked with hospitalisation data from the Secondary Uses Service and the Emergency Care Data Set. PARTICIPANTS: 493,053 laboratory-confirmed COVID-19 patients, of whom 19,482 had been hospitalised , tested between 23 November 2020 and 4 January 2021 and analysed at a laboratory with an available TaqPath assay that enables assessment of S-gene target failure (SGTF).
Edit intent: clarity
Original:  SETTING : Community-based SARS-CoV-2 testing in England, individually linked with hospitalisation data from the Secondary Uses Service and the Emergency Care Data Set. PARTICIPANTS: 493,053 laboratory-confirmed COVID-19 patients, of whom 19,482 had been hospitalised  , tested between 23 November 2020 and 4 January 2021 and analysed at a laboratory with an available TaqPath assay that enables assessment of S-gene target failure (SGTF).
Revised:  SETTING : Community-based SARS-CoV-2 testing in England, individually linked with hospitalisation data from the Secondary Uses Service and the Emergency Care Data Set. Participants: 839,278 laboratory-confirmed COVID-19 patients, of whom 19,482 had been hospitalised , tested between 23 November 2020 and 4 January 2021 and analysed at a laboratory with an available TaqPath assay that enables assessment of S-gene target failure (SGTF).
Edit intent: meaning-changed
Original:  SETTING : Community-based SARS-CoV-2 testing in England, individually linked with hospitalisation data from the Secondary Uses Service and the Emergency Care Data Set. PARTICIPANTS: 493,053 laboratory-confirmed COVID-19 patients, of whom 19,482 had been hospitalised  , tested between 23 November 2020 and 4 January 2021 and analysed at a laboratory with an available TaqPath assay that enables assessment of S-gene target failure (SGTF).
Revised:  SETTING : Community-based SARS-CoV-2 testing in England, individually linked with hospitalisation data from the Secondary Uses Service and the Emergency Care Data Set. PARTICIPANTS: 493,053 laboratory-confirmed COVID-19 patients, of whom 36,233 had been hospitalised , tested between 23 November 2020 and 4 January 2021 and analysed at a laboratory with an available TaqPath assay that enables assessment of S-gene target failure (SGTF).
Edit intent: meaning-changed
Original:  SETTING : Community-based SARS-CoV-2 testing in England, individually linked with hospitalisation data from the Secondary Uses Service and the Emergency Care Data Set. PARTICIPANTS: 493,053 laboratory-confirmed COVID-19 patients, of whom 19,482 had been hospitalised  , tested between 23 November 2020 and 4 January 2021 and analysed at a laboratory with an available TaqPath assay that enables assessment of S-gene target failure (SGTF).
Revised:  SETTING : Community-based SARS-CoV-2 testing in England, individually linked with hospitalisation data from the Secondary Uses Service and the Emergency Care Data Set. PARTICIPANTS: 493,053 laboratory-confirmed COVID-19 patients, of whom 19,482 had been hospitalised within 14 days , tested between 23 November 2020 and 4 January 2021 and analysed at a laboratory with an available TaqPath assay that enables assessment of S-gene target failure (SGTF).
Edit intent: meaning-changed
Original:  SETTING : Community-based SARS-CoV-2 testing in England, individually linked with hospitalisation data from the Secondary Uses Service and the Emergency Care Data Set. PARTICIPANTS: 493,053 laboratory-confirmed COVID-19 patients, of whom 19,482 had been hospitalised  , tested between 23 November 2020 and 4 January 2021 and analysed at a laboratory with an available TaqPath assay that enables assessment of S-gene target failure (SGTF).
Revised:  SETTING : Community-based SARS-CoV-2 testing in England, individually linked with hospitalisation data from the Secondary Uses Service and the Emergency Care Data Set. PARTICIPANTS: 493,053 laboratory-confirmed COVID-19 patients, of whom 19,482 had been hospitalised , tested between 23rd November 2020 and 4 January 2021 and analysed at a laboratory with an available TaqPath assay that enables assessment of S-gene target failure (SGTF).
Edit intent: fluency
Original:  SETTING : Community-based SARS-CoV-2 testing in England, individually linked with hospitalisation data from the Secondary Uses Service and the Emergency Care Data Set. PARTICIPANTS: 493,053 laboratory-confirmed COVID-19 patients, of whom 19,482 had been hospitalised  , tested between 23 November 2020 and 4 January 2021 and analysed at a laboratory with an available TaqPath assay that enables assessment of S-gene target failure (SGTF).
Revised:  SETTING : Community-based SARS-CoV-2 testing in England, individually linked with hospitalisation data from the Secondary Uses Service and the Emergency Care Data Set. PARTICIPANTS: 493,053 laboratory-confirmed COVID-19 patients, of whom 19,482 had been hospitalised , tested between 23 November 2020 and 31st January 2021 and analysed at a laboratory with an available TaqPath assay that enables assessment of S-gene target failure (SGTF).
Edit intent: meaning-changed
Original:  Nested case-control analysis of 18,814 pairs of hospitalised/non-hospitalised patients, one-to-one matched on age, sex, ethnicity, deprivation, region of residence, and date of positive test (matches found for 96.6\%).
Revised:  Patient data were stratified by age, sex, ethnicity, deprivation, region of residence, and date of positive test (matches found for 96.6\%).
Edit intent: clarity
Original:  Nested case-control analysis of 18,814 pairs of hospitalised/non-hospitalised patients, one-to-one matched on age, sex, ethnicity, deprivation, region of residence, and date of positive test (matches found for 96.6\%). MAIN OUTCOME MEASURES : Hospitalisation between 1 and 14 days after the first positive SARS-CoV-2 test.
Revised:  Nested case-control analysis of 18,814 pairs of hospitalised/non-hospitalised patients, one-to-one matched on age, sex, ethnicity, deprivation, region of residence, and date of positive test . Main Outcome Measures : Hospitalisation between 1 and 14 days after the first positive SARS-CoV-2 test.
Edit intent: coherence
Original:  RESULTS: The matching-adjusted conditional odds ratio of  hospitalisation was 1.58 (95\% confidence interval 1.50 to 1.67  ) for COVID-19 patients infected with a SGTF-associated variant , compared to those infected with non-SGTF-associated variants.
Revised:  Results: In total, 27,710 of hospitalisation was 1.58 (95\% confidence interval 1.50 to 1.67 ) for COVID-19 patients infected with a SGTF-associated variant , compared to those infected with non-SGTF-associated variants.
Edit intent: meaning-changed
Original:  RESULTS: The matching-adjusted conditional odds ratio of  hospitalisation was 1.58 (95\% confidence interval 1.50 to 1.67  ) for COVID-19 patients infected with a SGTF-associated variant , compared to those infected with non-SGTF-associated variants.
Revised:  RESULTS: The matching-adjusted conditional odds ratio of 592,409 SGTF patients (4.7\%) and 8,523 of 246,869 non-SGTF patients (3.5\%) had been hospitalised within 1-14 days. The stratum-adjusted hazard ratio (HR) of hospitalisation was 1.58 (95\% confidence interval 1.50 to 1.67 ) for COVID-19 patients infected with a SGTF-associated variant , compared to those infected with non-SGTF-associated variants.
Edit intent: meaning-changed
Original:  RESULTS: The matching-adjusted conditional odds ratio of  hospitalisation was 1.58 (95\% confidence interval 1.50 to 1.67  ) for COVID-19 patients infected with a SGTF-associated variant , compared to those infected with non-SGTF-associated variants.
Revised:  RESULTS: The matching-adjusted conditional odds ratio of hospitalisation was 1.52 (95\% confidence interval 1.50 to 1.67 ) for COVID-19 patients infected with a SGTF-associated variant , compared to those infected with non-SGTF-associated variants.
Edit intent: meaning-changed
Original:  RESULTS: The matching-adjusted conditional odds ratio of  hospitalisation was 1.58 (95\% confidence interval 1.50 to 1.67  ) for COVID-19 patients infected with a SGTF-associated variant , compared to those infected with non-SGTF-associated variants.
Revised:  RESULTS: The matching-adjusted conditional odds ratio of hospitalisation was 1.58 (95\% confidence interval CI ) for COVID-19 patients infected with a SGTF-associated variant , compared to those infected with non-SGTF-associated variants.
Edit intent: meaning-changed
Original:  RESULTS: The matching-adjusted conditional odds ratio of  hospitalisation was 1.58 (95\% confidence interval 1.50 to 1.67  ) for COVID-19 patients infected with a SGTF-associated variant , compared to those infected with non-SGTF-associated variants.
Revised:  RESULTS: The matching-adjusted conditional odds ratio of hospitalisation was 1.58 (95\% confidence interval 1.50 to 1.67 1.47 to 1.57 ) for COVID-19 patients infected with a SGTF-associated variant , compared to those infected with non-SGTF-associated variants.
Edit intent: meaning-changed
Original:  RESULTS: The matching-adjusted conditional odds ratio of  hospitalisation was 1.58 (95\% confidence interval 1.50 to 1.67  ) for COVID-19 patients infected with a SGTF-associated variant , compared to those infected with non-SGTF-associated variants.
Revised:  RESULTS: The matching-adjusted conditional odds ratio of hospitalisation was 1.58 (95\% confidence interval 1.50 to 1.67 ) for COVID-19 patients infected with SGTF variants , compared to those infected with non-SGTF-associated variants.
Edit intent: fluency
Original:  RESULTS: The matching-adjusted conditional odds ratio of  hospitalisation was 1.58 (95\% confidence interval 1.50 to 1.67  ) for COVID-19 patients infected with a SGTF-associated variant , compared to those infected with non-SGTF-associated variants. Secondary cohort analyses yielded similar estimates.
Revised:  RESULTS: The matching-adjusted conditional odds ratio of hospitalisation was 1.58 (95\% confidence interval 1.50 to 1.67 ) for COVID-19 patients infected with a SGTF-associated variant , compared to those infected with non-SGTF variants.
Edit intent: clarity
Original:  The effect was modified by age (p<0.001), with ORs of 0.96-1.13 below age 20 years and 1.57-1.67 in age groups 40 years or older.
Revised:  The effect was modified by age (p<0.001), with HRs of 0.93-1.21 for SGTF compared to non-SGTF patients below age 20 years and 1.57-1.67 in age groups 40 years or older.
Edit intent: meaning-changed
Original:  The effect was modified by age (p<0.001), with ORs of 0.96-1.13 below age 20 years and 1.57-1.67 in age groups 40 years or older.
Revised:  The effect was modified by age (p<0.001), with ORs of 0.96-1.13 below age 20 years , 1.29 in those aged 20-29, and 1.45-1.65 in age groups 40 years or older.
Edit intent: meaning-changed
Original:  The effect was modified by age (p<0.001), with ORs of 0.96-1.13 below age 20 years and 1.57-1.67 in age groups 40 years or older.
Revised:  The effect was modified by age (p<0.001), with ORs of 0.96-1.13 below age 20 years and 1.57-1.67 in age groups 30 years or older.
Edit intent: meaning-changed
Original:  CONCLUSIONS : The results suggest that the risk of hospitalisation is higher for individuals infected with the B.1.1.7 variant compared to wildtype SARS-CoV-2, likely reflecting a more severe disease.
Revised:  Conclusions : The results suggest that the risk of hospitalisation is higher for individuals infected with the B.1.1.7 variant compared to wildtype SARS-CoV-2, likely reflecting a more severe disease.
Edit intent: fluency
Original: Bayesian Optimization (BO) is a successful methodology to tune the hyperparametersof machine learning algorithms. The user defines a metric of interest, such as the validation error, and BO finds the optimal hyperparametersthat minimize it.
Revised: Bayesian Optimization (BO) is a successful strategy to find good hyperparameters. BO defines an iterative procedure where a cross-validated metric is evaluated on promising hyperparameters. In practice, however, an improvement of the validation metric of interest, such as the validation error, and BO finds the optimal hyperparametersthat minimize it.
Edit intent: meaning-changed
Original:  We propose the first problem-adaptive and interpretable criterion to early stop BO, reducing overfitting while mitigating the cost of cross-validation. Experimental results on real-world hyperparameter optimization tasks show that our approach can substantially reduce compute time with little to no loss of test accuracy,demonstrating a clear practical advantage over existing techniques .
Revised:  We propose the first problem-adaptive and interpretable criterion to early stop BO, which aims to maintain the solution quality while saving the unnecessary iterations that can lead to overfitting. Experiments on real-world hyperparameter optimization tasks show that our approach can substantially reduce compute time with little to no loss of test accuracy,demonstrating a clear practical advantage over existing techniques .
Edit intent: meaning-changed
Original:  We find the regions of large population or high effective regeneration number should be vaccinated with priority and that frequent travels tend to cause an influence to enhance the spread especially in the up-trend stage of infections and in regions of smaller populations.
Revised:  We find the regions of large population or large effective regeneration number should be vaccinated with priority and that frequent travels tend to cause an influence to enhance the spread especially in the up-trend stage of infections and in regions of smaller populations.
Edit intent: clarity
Original:  We find the regions of large population or high effective regeneration number should be vaccinated with priority and that frequent travels tend to cause an influence to enhance the spread especially in the up-trend stage of infections and in regions of smaller populations.
Revised:  We find the regions of large population or high effective regeneration number should be vaccinated with priority and that frequent travels tend to cause an influence to enhance the spread especially in the uptrend stage of infections and in regions of smaller populations.
Edit intent: fluency
Original:  Moreover, we show it is not expected that vaccination at a slow pace in Japan will  suppress the infection spread sufficiently to extinguish the current  wave within a few months  .
Revised:  Moreover, we show it is not expected that vaccination at a slow pace in Japan will suppress the infection spread sufficiently to extinguish the current wave within a few months . However, a restricted quantity of vaccines can be used efficiently by maximizing a diverted index value ? the conditional entropy ? of vaccination distribution. Furthermore, travel activity across regional borders that accelerated infection spread so far is found to rather sup-press it if vaccination is processed at a high pace .
Edit intent: meaning-changed
Original:  First, we consider the movements of susceptible (S), exposed (E), and recovered (R) individuals who may get infected after movement , as well as infected (I)  . Second, the infectious contacts between people from different regions are dealt with separately from the first factor.
Revised:  First, we consider the movements of susceptible (S), exposed (E), and recovered (R) individuals who may get infected and infect others in the destination region , as well as infected (I) . Second, the infectious contacts between people from different regions are dealt with separately from the first factor.
Edit intent: meaning-changed
Original:  First, we consider the movements of susceptible (S), exposed (E), and recovered (R) individuals who may get infected after movement , as well as infected (I)  . Second, the infectious contacts between people from different regions are dealt with separately from the first factor.
Revised:  First, we consider the movements of susceptible (S), exposed (E), and recovered (R) individuals who may get infected after movement , as well as infected (I) individuals . Second, the infectious contacts between people from different regions are dealt with separately from the first factor.
Edit intent: fluency
Original:  First, we consider the movements of susceptible (S), exposed (E), and recovered (R) individuals who may get infected after movement , as well as infected (I)  . Second, the infectious contacts between people from different regions are dealt with separately from the first factor.
Revised:  First, we consider the movements of susceptible (S), exposed (E), and recovered (R) individuals who may get infected after movement , as well as infected (I) . Second, people living in a region and moving from other regions are dealt with separately from the first factor.
Edit intent: meaning-changed
Original:  First, we consider the movements of susceptible (S), exposed (E), and recovered (R) individuals who may get infected after movement , as well as infected (I)  . Second, the infectious contacts between people from different regions are dealt with separately from the first factor. By this, we can reflect the potential future influence on the infection spread due to the movements of S and E that are hard to detect by the infection test at the time of immigration .
Revised:  First, we consider the movements of susceptible (S), exposed (E), and recovered (R) individuals who may get infected after movement , as well as infected (I) . Second, the infectious contacts between people from different regions are dealt as separate but interacting groups with respect to their states, S, E, R, or I. This enables us to consider the potential influence of movements before individuals become infected, difficult to detect by the infection test at the time of immigration .
Edit intent: clarity
Original:  By this, we can reflect the potential future influence on the infection spread due to the movements of S and E that are hard to detect by the infection test at the time of immigration . As a result , we show the results of simulation where the influence of traveling across regions, which mean prefectures here, and of the choice of regions to vaccinate with priority , on the risk of infection spread.
Revised:  By this, we can reflect the potential future influence on the infection spread due to the movements of S and E that are hard to detect by the infection test at the time of immigration , on the spread of infection. In this paper , we show the results of simulation where the influence of traveling across regions, which mean prefectures here, and of the choice of regions to vaccinate with priority , on the risk of infection spread.
Edit intent: clarity
Original:  As a result , we show the results of simulation where the influence of traveling across regions, which mean prefectures here, and of the choice of regions to vaccinate with priority , on the risk of infection spread.
Revised:  As a result , we show the results of simulation where the influence of traveling across regions, which means prefectures here, and of the choice of regions to vaccinate with priority , on the risk of infection spread.
Edit intent: fluency
Original:  As a result , we show the results of simulation where the influence of traveling across regions, which mean prefectures here, and of the choice of regions to vaccinate with priority , on the risk of infection spread.
Revised:  As a result , we show the results of simulation where the influence of traveling across regions, which mean prefectures here, and the government chooses regions to vaccinate with priority , on the risk of infection spread.
Edit intent: clarity
Original:  As a result , we show the results of simulation where the influence of traveling across regions, which mean prefectures here, and of the choice of regions to vaccinate with priority , on the risk of infection spread. We find the regions of large population or large effective regeneration number should be vaccinated with priority and that frequent travels tend to cause an influence to enhance the spread especially in the uptrend stage of infections and in regions of smaller populations. Moreover, we show vaccination at a slow pace in Japan will not suppress the infection spread sufficiently to extinguish the current 4th wave within a few months. However, a restricted quantity of vaccines can be used efficiently by maximizing a diverted index value?
Revised:  As a result , we show the results of simulation where the influence of traveling across regions, which mean prefectures here, and of the choice of regions to vaccinate with priority . We found a general law that a quantity of vaccines can be used efficiently by maximizing a diverted index value?
Edit intent: coherence
Original:  the conditional entropy ? of vaccination distribution. Furthermore, travel activity across regional borders that accelerated infection spread so far is found to rather sup-press it if vaccination is processed at a high pace .
Revised:  the conditional entropy Hc, when we distribute vaccines to regions. The efficiency of this strategy, which maximizes Hc, was found to outperform that of vaccinating regions with a larger effective re-generation number. This law also explains the surprising result that travel activities across regional borders that accelerated infection spread so far is found to rather sup-press it if vaccination is processed at a high pace .
Edit intent: meaning-changed
Original:  Analyzing the current development of AI , the proposal is that AI should be clearly divided into the following categories: Artificial Human Intelligence (AHI), Artificial Machine Intelligence (AMI), and Artificial Biological Intelligence (ABI), which will also be the main directions of theory and application development for AI.
Revised:  Analyzing the current development of Artificial Intelligence (AI) , the proposal is that AI should be clearly divided into the following categories: Artificial Human Intelligence (AHI), Artificial Machine Intelligence (AMI), and Artificial Biological Intelligence (ABI), which will also be the main directions of theory and application development for AI.
Edit intent: fluency
Original: Bandit algorithms are increasingly used in real world sequential decision making problems, from online advertising to mobile health. As a result, there are more datasets collected using bandit algorithms and with that an increased desire to be able to use these datasets to answer scientific questions like: Did one type of ad increase the click-through rate more or lead to more purchases?
Revised: Bandit algorithms are increasingly used in real-world sequential decision-making problems. Associated with this is an increased desire to be able to use these datasets to answer scientific questions like: Did one type of ad increase the click-through rate more or lead to more purchases?
Edit intent: clarity
Original:  As a result, there are more datasets collected using bandit algorithms and with that an increased desire to be able to use these datasets to answer scientific questions like: Did one type of ad increase the click-through rate more or lead to more purchases?
Revised:  As a result, there are more datasets collected using bandit algorithms and with that an increased desire to be able to use the resulting datasets to answer scientific questions like: Did one type of ad increase the click-through rate more or lead to more purchases?
Edit intent: clarity