wanyu/IteraTeR_full_sent huggingface dataset:
Original:  To evaluate our bias alleviation mechanism, we employed a cross-domain approach in which we use the trained classifiers on the aforementioned datasets to predict the labels of two new datasets from Twitter, AAE-aligned and White-aligned groups, which indicate tweets written in African-American English (AAE) and Standard American English (SAE) , respectively.
Revised:  To evaluate our bias alleviation mechanism, we employed a cross-domain approach in which we use the trained classifiers on the aforementioned datasets to predict the labels of two new datasets from Twitter, AAE-aligned and White-aligned groups, which indicate tweets written in African-American English (AAE) and Standard American English (SAE) respectively.
Edit intent: fluency
Original:  The results show the existence of systematic racial bias in trained classifiers , as they tend to assign tweets written in AAE from AAE-aligned group to negative classes such as racism, sexism, hate, and offensive more often than tweets written in SAE from White-aligned.
Revised:  The results show the existence of systematic racial bias in trained classifiers as they tend to assign tweets written in AAE from AAE-aligned group to negative classes such as racism, sexism, hate, and offensive more often than tweets written in SAE from White-aligned.
Edit intent: fluency
Original: Various intervention methods have been introduced worldwide to slow down the spread of the SARS-CoV-2 virus , by limiting human mobility in different ways. While large scale lockdown strategies are effective in reducing the spread rate, they come at a cost of significantly limited societal functions.
Revised: Various intervention methods have been introduced worldwide to slow down the spread of the SARS-CoV-2 virus . Large scale lockdown of human movements are effective in reducing the spread rate, they come at a cost of significantly limited societal functions.
Edit intent: clarity
Original:  We show that natural human mobility has high diversity and heterogeneity such that a small group of  individuals and gathering venues play an important role in the spread of the disease. We discover that interventions that focus on protecting the most active individuals and most popular venues can significantly reduce the peak infection rate and the total number of infected people while retaining high levels of social activity overall.
Revised:  We show that natural human mobility has high diversity and heterogeneity such that a small group of individuals and gathering venues . We find that interventions focused on these most mobile individuals and popular venues reduce both the peak infection rate and the total number of infected people while retaining high levels of social activity overall.
Edit intent: clarity
Original:  We discover that interventions that focus on protecting the most active individuals and most popular venues can significantly reduce the peak infection rate and the total number of infected people while retaining high levels of social activity overall. This trend is observed universally in multi-agent simulations using three mobility data sets of different scales, resolutions, and modalities (check-ins at seven different cities , WiFi connection events at a university, and GPS traces of electric bikes), and suggests that strategies that exploit the network effect in human mobility provide a better balance between disease control and normal social activities.
Revised:  We discover that interventions that focus on protecting the most active individuals and most popular venues can significantly reduce the peak infection rate and the total number of infected people while retaining high levels of social activity . These trends are seen consistently in simulations with real human mobility data of different scales, resolutions, and modalities (check-ins at seven different cities , WiFi connection events at a university, and GPS traces of electric bikes), and suggests that strategies that exploit the network effect in human mobility provide a better balance between disease control and normal social activities.
Edit intent: clarity
Original:  This trend is observed universally in multi-agent simulations using three mobility data sets of different scales, resolutions, and modalities (check-ins at seven different cities , WiFi connection events at a university, and GPS traces of electric bikes), and suggests that strategies that exploit the network effect in human mobility provide a better balance between disease control and normal social activities.
Revised:  This trend is observed universally in multi-agent simulations using three mobility data sets of different scales, resolutions, and modalities from multiple cities across the world. The observation implies that compared to broad sweeping interventions, more targeted strategies based on the network effects in human mobility provide a better balance between disease control and normal social activities.
Edit intent: clarity
Original:  This trend is observed universally in multi-agent simulations using three mobility data sets of different scales, resolutions, and modalities (check-ins at seven different cities , WiFi connection events at a university, and GPS traces of electric bikes), and suggests that strategies that exploit the network effect in human mobility provide a better balance between disease control and normal social activities.
Revised:  This trend is observed universally in multi-agent simulations using three mobility data sets of different scales, resolutions, and modalities (check-ins at seven different cities , WiFi connection events at a university, and GPS traces of electric bikes), and suggests that strategies that exploit the network effect in human mobility provide a better balance between pandemic control and normal social activities.
Edit intent: clarity
Original: Group testing pools multiple samples together and performs tests on these pooled samples to discern the infected samples. It greatly reduces the number of tests , however, with a sacrifice of increasing false negative rates due to the dilution of the viral load in pooled samples.
Revised: Repeated asymptomatic screening for SARS-CoV-2 promises to control spread of the virus but would require too many resources to implement at scale. Group testing is promising for screening more people with fewer test resources: multiple samples tested together in one pool can be excluded with one negative test result. Existing approaches to group testing design for SARS-CoV-2 asymptomatic screening , however, with a sacrifice of increasing false negative rates due to the dilution of the viral load in pooled samples.
Edit intent: coherence
Original:  It greatly reduces the number of tests , however, with a sacrifice of increasing false negative rates due to the dilution of the viral load in pooled samples. Therefore, it is important to balance the trade-off between number of tests and false negative rate.
Revised:  It greatly reduces the number of tests , however, do not consider dilution effects: that false negatives become more common with larger pools. As a consequence, they may recommend pool sizes that are too large or misestimate the benefits of screening. Modeling dilution effects, we derive closed-form expressions for the expected number of tests and false negative rate.
Edit intent: meaning-changed
Original:  We explore two popular group testing methods , namely linear array (a.k.a. Dorfman's procedure) and square array methods , and analyze the optimal group size of a pooled sample that minimizes the group false negative number under a constraint of testing capacity.
Revised:  We explore two popular group testing methods : the linear and square array methods , and analyze the optimal group size of a pooled sample that minimizes the group false negative number under a constraint of testing capacity.
Edit intent: clarity
Original:  Dorfman's procedure) and square array methods , and analyze the optimal group size of a pooled sample that minimizes the group false negative number under a constraint of testing capacity. Our analysis shows that when there is reasonably large testing capacity, the linear array method yields smaller false negative number and hence is preferred. When the testing capacity is small, square array method is more feasible and preferred. In addition, we consider testing a closed community in a period of time and determine the optimal testing cycle that minimizes the final prevalence rate of infection at the end of the time period.
Revised:  Dorfman's procedure) and square array methods . We find that test error correlation induced by a common viral load across an individual's samples results in many fewer false negatives than would be expected from less realistic but more widely assumed independent errors. This insight also suggests that false positives can be controlled through repeated tests without significantly increasing false negatives. Using these closed-form expressions to trace a Pareto frontier over error rates and tests, we design testing protocols for repeated asymptomatic screening of a large population. We minimize disease prevalence by optimizing a time-varying pool sizes and screening frequency constrained by daily test capacity and a false positive limit. This provides a closed community in a period of time and determine the optimal testing cycle that minimizes the final prevalence rate of infection at the end of the time period.
Edit intent: meaning-changed
Original: Recent studies conclude that the global coronavirus (COVID-19) pandemic decreased power sector CO_2 emissions worldwide. We analyze the statistical significance of CO_2 emissions reductions in the U.S  power sector from March through July 2020 and we present model-informed expectations of the likelihood of sustained reductions in CO_2 emissions.
Revised: Recent studies conclude that the global coronavirus (COVID-19) pandemic decreased power sector CO_2 emissions globally and in the United States. In this paper, we analyze the statistical significance of CO_2 emissions reductions in the U.S power sector from March through July 2020 and we present model-informed expectations of the likelihood of sustained reductions in CO_2 emissions.
Edit intent: meaning-changed
Original:  We analyze the statistical significance of CO_2 emissions reductions in the U.S  power sector from March through July 2020 and we present model-informed expectations of the likelihood of sustained reductions in CO_2 emissions.
Revised:  We analyze the statistical significance of CO2 emissions reductions in the U.S power sector from March through July 2020 and we present model-informed expectations of the likelihood of sustained reductions in CO_2 emissions.
Edit intent: fluency
Original:  We analyze the statistical significance of CO_2 emissions reductions in the U.S  power sector from March through July 2020 and we present model-informed expectations of the likelihood of sustained reductions in CO_2 emissions.
Revised:  We analyze the statistical significance of CO_2 emissions reductions in the U.S . power sector from March through July 2020 and we present model-informed expectations of the likelihood of sustained reductions in CO_2 emissions.
Edit intent: fluency
Original:  We use Gaussian process (GP) regression to assess whether CO_2 emissions reductions would have occurred with reasonable probability in the absence of COVID-19 considering uncertainty due to factors unrelated to the pandemic .
Revised:  We use Gaussian process (GP) regression to assess whether CO2 emissions reductions would have occurred with reasonable probability in the absence of COVID-19 considering uncertainty due to factors unrelated to the pandemic .
Edit intent: fluency
Original:  We use Gaussian process (GP) regression to assess whether CO_2 emissions reductions would have occurred with reasonable probability in the absence of COVID-19 considering uncertainty due to factors unrelated to the pandemic . We show that CO_2 emissions are lower than levels expected in the absence of COVID-19 for each month from March through July 2020 but that those monthly reductions are not statistically significant considering hypothesis tests at 5\% significance levels.
Revised:  We use Gaussian process (GP) regression to assess whether CO_2 emissions reductions would have occurred with reasonable probability in the absence of COVID-19 considering uncertainty due to factors unrelated to the pandemic and adjusting for weather, seasonality, and recent emissions trends. We find that monthly CO2 emissions reductions are only statistically significant in April and May 2020 but that those monthly reductions are not statistically significant considering hypothesis tests at 5\% significance levels.
Edit intent: meaning-changed
Original:  We show that CO_2 emissions are lower than levels expected in the absence of COVID-19 for each month from March through July 2020 but that those monthly reductions are not statistically significant considering hypothesis tests at 5\% significance levels.
Revised:  We show that CO_2 emissions are lower than levels expected in the absence of COVID-19 for each month from March through July 2020 considering hypothesis tests at 5\% significance levels.
Edit intent: clarity
Original:  To make predictions about whether COVID-19-related CO_2 emissions reductions will be sustained in the power sector, we assess the relative impacts of the pandemic on electricity generation (E) and on the carbon intensity of electricity supply (C/E).
Revised:  Separately, we consider the potential impact of COVID-19 on coal-fired power plant retirements through 2022. We find that only a small percentage of U.S. coal power plants are at risk of retirement due to a possible COVID-19-related CO_2 emissions reductions will be sustained in the power sector, we assess the relative impacts of the pandemic on electricity generation (E) and on the carbon intensity of electricity supply (C/E).
Edit intent: meaning-changed
Original:  To make predictions about whether COVID-19-related CO_2 emissions reductions will be sustained in the power sector, we assess the relative impacts of the pandemic on electricity generation (E) and on the carbon intensity of electricity supply (C/E).E is on average 2.9%DIFDELCMD < \\%%% %DIF < lower than what we would expect in the absence of COVID-19 from March through July 2020.
Revised:  To make predictions about whether COVID-19-related %DIFDELCMD < \\%%% %DIF < lower than what we would expect in the absence of COVID-19 from March through July 2020.
Edit intent: clarity
Original:  We conclude that COVID-19 is unlikely to have a material impact on U.S. power sector CO_2 emissions in the long-run.abstract 
Revised:  We conclude that COVID-19 is unlikely to have a material impact on U.S. power sector CO_2 emissions in the long-run.abstract sustained reduction in electricity demand and prices. We observe and anticipate a return to pre-COVID-19 CO2 emissions in the U.S. power sector.
Edit intent: meaning-changed
Original:  In this paper we take a target-centric viewpoint, by analyzing the accumulation of resources in a set of targets due to multiple rounds of search-and-capture events;
Revised:  In this paper we take a target-centric viewpoint, by analyzing the accumulation of resources in each of N small interior targets within a three-dimensional bounded domain. The accumulation of resources is based on multiple rounds of search-and-capture events;
Edit intent: meaning-changed
Original:  We develop the theory by considering diffusive search in two distinct geometric configurations. First, we determine the mean and Fano factor of the distribution of resources between pair of concentric spherical targets, and explore how these depend on the initial location of the searcher, the dimension d, the degradation rate and the distribution of delays. Second, we use asymptotic analysis to determine the mean number of resources in each of N small interior targets within a three-dimensional bounded domain. In particular, we show that the mean number of resources in a target is proportional to its effective `` shape capacitance.
Revised:  We use this to show that the mean number of resources in a target is proportional to its effective `` shape capacitance.
Edit intent: coherence
Original:  '' Finally, we extend the analysis to the case of diffusive search with stochastic resetting before  capture.
Revised:  '' Finally, we extend the analysis to the case of diffusive search with stochastic resetting before capture, where the position of the searcher is reset to its initial position at a random sequence of times that is statistically independent of the ongoing search process, in contrast to the sequence of resetting times after capture.
Edit intent: meaning-changed
Original:  One difficulty in deriving these quantities is that they must be computed on a model-by-model basis, since it is typically not practical to obtain a general reproduction number expressions applicable to a family of related models, especially if some are of different dimensions.
Revised:  One difficulty in deriving these quantities is that they must be computed on a model-by-model basis, since it is typically impractical to obtain general reproduction number expressions applicable to a family of related models, especially if some are of different dimensions.
Edit intent: clarity
Original:  One difficulty in deriving these quantities is that they must be computed on a model-by-model basis, since it is typically not practical to obtain a general reproduction number expressions applicable to a family of related models, especially if some are of different dimensions.
Revised:  One difficulty in deriving these quantities is that they must be computed on a model-by-model basis, since it is typically not practical to obtain a general reproduction number expressions applicable to a family of related models, especially if these are of different dimensions.
Edit intent: clarity
Original:  Here we show how  the next generation operator approach for computing reproduction numbers can be used in conjunction with the generalized linear chain trick (GLCT) to find reproduction numbers expressions that apply generally to such families of models of varying dimensions .
Revised:  Here we show how to find general reproduction number expressions for such models families (which vary in their number of state variables) using the next generation operator approach for computing reproduction numbers can be used in conjunction with the generalized linear chain trick (GLCT) to find reproduction numbers expressions that apply generally to such families of models of varying dimensions .
Edit intent: meaning-changed
Original:  Here we show how  the next generation operator approach for computing reproduction numbers can be used in conjunction with the generalized linear chain trick (GLCT) to find reproduction numbers expressions that apply generally to such families of models of varying dimensions .
Revised:  Here we show how the next generation operator approach in conjunction with the generalized linear chain trick (GLCT) to find reproduction numbers expressions that apply generally to such families of models of varying dimensions .
Edit intent: clarity
Original:  We further show how the GLCT enables modelers to draw insights from these results by using theory and intuition from continuous time Markov chains (CTMCs) and their absorption time distributions (i.e., the phase-type probability distributions).
Revised:  We further show how the GLCT enables modelers to draw insights from these results by leveraging theory and intuition from continuous time Markov chains (CTMCs) and their absorption time distributions (i.e., the phase-type probability distributions).
Edit intent: clarity
Original:  We further show how the GLCT enables modelers to draw insights from these results by using theory and intuition from continuous time Markov chains (CTMCs) and their absorption time distributions (i.e., the phase-type probability distributions).
Revised:  We further show how the GLCT enables modelers to draw insights from these results by using theory and intuition from continuous time Markov chains (CTMCs) and their absorption time distributions (i.e., phase-type probability distributions).
Edit intent: fluency
Original:  We then apply this technique to a generalized family of SEIRS models of arbitrary finite dimension, and to a generalized family of  predator-prey (Rosenzweig-MacArthur ) models of arbitrary finite dimension .
Revised:  We then apply this technique to find reproduction numbers for two sets of models: a family of generalized SEIRS models of arbitrary finite dimension, and to a generalized family of predator-prey (Rosenzweig-MacArthur ) models of arbitrary finite dimension .
Edit intent: meaning-changed
Original:  We then apply this technique to a generalized family of SEIRS models of arbitrary finite dimension, and to a generalized family of  predator-prey (Rosenzweig-MacArthur ) models of arbitrary finite dimension .
Revised:  We then apply this technique to a generalized family of SEIRS models of arbitrary finite dimension, and a generalized family of predator-prey (Rosenzweig-MacArthur ) models of arbitrary finite dimension .
Edit intent: fluency
Original:  We then apply this technique to a generalized family of SEIRS models of arbitrary finite dimension, and to a generalized family of  predator-prey (Rosenzweig-MacArthur ) models of arbitrary finite dimension .
Revised:  We then apply this technique to a generalized family of SEIRS models of arbitrary finite dimension, and to a generalized family of finite dimensional predator-prey (Rosenzweig-MacArthur ) models of arbitrary finite dimension .
Edit intent: meaning-changed
Original:  We then apply this technique to a generalized family of SEIRS models of arbitrary finite dimension, and to a generalized family of  predator-prey (Rosenzweig-MacArthur ) models of arbitrary finite dimension .
Revised:  We then apply this technique to a generalized family of SEIRS models of arbitrary finite dimension, and to a generalized family of predator-prey (Rosenzweig-MacArthur type) models .
Edit intent: meaning-changed
Original:  Relying on the established fine-tuning paradigm, we first couple a pretrained transformer with a biaffine parsing head, aiming to infuse explicit syntactic knowledge from Universal Dependencies (UD) treebanks into the transformer.
Revised:  Relying on the established fine-tuning paradigm, we first couple a pretrained transformer with a biaffine parsing head, aiming to infuse explicit syntactic knowledge from Universal Dependencies treebanks into the transformer.
Edit intent: clarity
Original:  Results from both monolingual English and zero-shot language transfer experiments (with intermediate target-language parsing) show that explicit formalized syntax, injected into transformers through intermediate supervised parsing , has very limited and inconsistent effect on downstream LU performance.
Revised:  Results from both monolingual English and zero-shot language transfer experiments (with intermediate target-language parsing) show that explicit formalized syntax, injected into transformers through IPT , has very limited and inconsistent effect on downstream LU performance.
Edit intent: clarity
Original:  Our results, coupled with our analysis of transformers' representation spaces before and after intermediate parsing, make a significant step towards providing answers to an essential question: how (un)availing is supervised parsing for high-level semantic  language understanding in the era of large neural models?
Revised:  Our results, coupled with our analysis of transformers' representation spaces before and after intermediate parsing, make a significant step towards providing answers to an essential question: how (un)availing is supervised parsing for high-level semantic natural language understanding in the era of large neural models?
Edit intent: meaning-changed