wanyu/IteraTeR_full_sent huggingface dataset:
Original:  In the current paper, we examine whether Tennessee's policy has affected (i) the number of confirmed Covid-19 cases, (ii) the number of trips to work, and (iii) the (unobserved) number of actual Covid-19 cases. To study these effects, we employ standard identifying assumptions in the policy evaluation literature, but this strategy is greatly complicated by the non-random nature of the tests.
Revised:  To understand the effect of the policy, we combine standard identifying assumptions in the policy evaluation literature, but this strategy is greatly complicated by the non-random nature of the tests.
Edit intent: clarity
Original:  We find suggestive evidence that Tennessee's open-testing policy has led to a reduction in the number of confirmed and total cases as well as reduced travel in counties that have experienced relatively large increases in confirmed cases.
Revised:  We find suggestive evidence that Tennessee's open-testing policy has reduced total cases (which are not fully observed), confirmed cases, and trips to work among counties with a fast-growing number of confirmed cases.
Edit intent: meaning-changed
Original: Increased testing for Covid-19 is seen as one of the most important steps to re-open the economy. The current paper considers Tennessee's "open-testing" policy where the state substantially increased testing while removing symptom requirements for individuals to be tested. To understand the effect of the policy, we combine standard identifying assumptions with additional weak assumptions to deal with non-random testing that lead to bounds on policy effects of interest. Our results suggest that Tennessee's open-testing policy has reduced total cases (which are not fully observed), confirmed cases, and trips to work among counties with a fast-growing number of confirmed cases .
Revised: Increased testing for Covid-19 pandemic, national and local governments introduced a large number of policies, particularly non-pharmaceutical interventions, to combat the spread of Covid-19. Understanding the effects that these policies had (both on Covid-19 cases and on other outcomes) is particularly challenging though because (i) Covid-19 testing was not widely available, (ii) the availability of tests varied across locations, and (iii) the tests that were available were generally targeted towards individuals meeting certain eligibility criteria. In this paper, we propose a new approach to evaluate the effect of policies early in the pandemic that accommodates limited and nonrandom testing. Our approach results in (generally informative) bounds on the effect of the policy on actual cases and in point identification of the effect of the policy on other outcomes. We apply our approach to study the effect of Tennessee's open-testing policy has reduced total cases (which are not fully observed), confirmed cases, and trips to work among counties with a fast-growing number of confirmed cases .
Edit intent: coherence
Original:  Our results suggest that Tennessee's open-testing policy has reduced total cases (which are not fully observed), confirmed cases, and trips to work among counties with a fast-growing number of confirmed cases .
Revised:  Our results suggest that Tennessee's open-testing policy during the early stage of the pandemic. For this policy, we find suggestive evidence that the policy decreased the number of Covid-19 cases in the state relative to what they would have been if the policy had not been implemented .
Edit intent: meaning-changed
Original: Drastic difference in the COVID-19  infection and fatality counts , observed between Hubei (Wuhan) and other Mainland China provinces raised public controversies.
Revised: A number of models in mathematical epidemiology have been developed to account for control measures such as vaccination or quarantine. However, COVID-19 infection and fatality counts , observed between Hubei (Wuhan) and other Mainland China provinces raised public controversies.
Edit intent: meaning-changed
Original: Drastic difference in the COVID-19  infection and fatality counts , observed between Hubei (Wuhan) and other Mainland China provinces raised public controversies.
Revised: Drastic difference in the COVID-19 has brought unprecedented social distancing measures, with a challenge on how to include these in a manner that can explain the data, but avoid overfitting in parameter inference. We here develop a simple non-homologous model, where social distancing effects are introduced analogous to course grained models of gene expression control in systems biology. We apply our approach to understand drastic differences in COVID-19 infection and fatality counts , observed between Hubei (Wuhan) and other Mainland China provinces raised public controversies.
Edit intent: meaning-changed
Original: Drastic difference in the COVID-19  infection and fatality counts , observed between Hubei (Wuhan) and other Mainland China provinces raised public controversies. To address if these data can be consistently understood, we develop a model that takes into account all main qualitative features of the infection progression under suppression measures, while remaining simple enough to estimate the key dynamics parameters. We find that social distancing measures had a large effect in the provinces, where moreover higher transmissibility appears to have triggered more effective protection . Hubei is, however, a far outlier with significantly larger transmissibility , but lower protection. Predicted infection attack rate is significantly higher for Hubei, but much lower than necessary for herd immunity.
Revised: Drastic difference in the COVID-19 infection and fatality counts , observed between Hubei (Wuhan) and other Mainland China provinces ), which we explain through interplay of transmissibility and effective protection differences. That is, we obtain a negative feedback (also commonly encountered in systems biology) between transmissibility and effects of social distancing, with Hubei being an outlier with both large transmissibility and lower social distancing effects. While
Edit intent: meaning-changed
Original:  Case Fatality Rate is five times larger in Hubei, but our estimated Infection Fatality Rates  are much more uniform/consistent across all provinces. Therefore, interplay of transmissibility, effective protection, and detection efficiency differences may explain apparently drastic count discrepancies .
Revised:  Case Fatality Rate is five times larger in Hubei, but our estimated Infection Fatality Rates are much more uniform/consistent across all the provinces. Even for Wuhan, our inferred Infection Attack Rate is much below the herd immunity threshold, raising a major reemission risk. The obtained results demonstrate applicability of our developed method to extract key infection parameters directly from the data (publicly available for a number of countries), so that it can be applied globally to potential future outbreaks of COVID-19, or other infectious diseases .
Edit intent: meaning-changed
Original:  We apply our approach to understand drastic differences in COVID-19 infection and fatality counts ( observed between Hubei (Wuhan) and other Mainland China provinces ), which we explain through interplay of transmissibility and effective protectiondifferences.
Revised:  We apply our approach to understand drastic differences in COVID-19 infection and fatality counts , observed between Hubei (Wuhan) and other Mainland China provinces ), which we explain through interplay of transmissibility and effective protectiondifferences.
Edit intent: fluency
Original:  We apply our approach to understand drastic differences in COVID-19 infection and fatality counts ( observed between Hubei (Wuhan) and other Mainland China provinces ), which we explain through interplay of transmissibility and effective protectiondifferences. That is, we obtain a negative feedback (also commonly encountered in systems biology) between transmissibility and effects of social distancing, with Hubei being an outlier with both large transmissibility and lower social distancing effects. While Case Fatality Rate is significantly larger for Hubei, we find that Infection Fatality Rates (IFRs) are much more uniform/consistent across all the provinces. Even for Wuhan, our inferred Infection Attack Rate is much below the herd immunity threshold, raising a major reemission risk .
Revised:  We apply our approach to understand drastic differences in COVID-19 infection and fatality counts ( observed between Hubei (Wuhan) and other Mainland China provinces . We find that these unintuitive data may be explained through an interplay of differences in transmissibility, effective protection, and detection efficiencies between Hubei and other provinces. More generally, our results demonstrate that regional differences may drastically shape infection outbursts .
Edit intent: clarity
Original:  The obtained results demonstrate  applicability of our developed method to extract key infection parameters directly from the data (publicly available for a number of countries), so that it can be applied globally to potential future outbreaks of COVID-19 , or other infectious diseases.
Revised:  The obtained results demonstrate the applicability of our developed method to extract key infection parameters directly from the data (publicly available for a number of countries), so that it can be applied globally to potential future outbreaks of COVID-19 , or other infectious diseases.
Edit intent: fluency
Original:  The obtained results demonstrate  applicability of our developed method to extract key infection parameters directly from the data (publicly available for a number of countries), so that it can be applied globally to potential future outbreaks of COVID-19 , or other infectious diseases.
Revised:  The obtained results demonstrate applicability of our developed method to extract key infection parameters directly from publically available data so that it can be applied globally to potential future outbreaks of COVID-19 , or other infectious diseases.
Edit intent: clarity
Original:  The obtained results demonstrate  applicability of our developed method to extract key infection parameters directly from the data (publicly available for a number of countries), so that it can be applied globally to potential future outbreaks of COVID-19 , or other infectious diseases.
Revised:  The obtained results demonstrate applicability of our developed method to extract key infection parameters directly from the data (publicly available for a number of countries), so that it can be globally applied to outbreaks of COVID-19 , or other infectious diseases.
Edit intent: clarity
Original:  The obtained results demonstrate  applicability of our developed method to extract key infection parameters directly from the data (publicly available for a number of countries), so that it can be applied globally to potential future outbreaks of COVID-19 , or other infectious diseases.
Revised:  The obtained results demonstrate applicability of our developed method to extract key infection parameters directly from the data (publicly available for a number of countries), so that it can be applied globally to potential future outbreaks of COVID-19 in a number of countries. Overall, we show that applications of uncommon strategies, such as methods and approaches from molecular systems biology research to mathematical epidemiology, may significantly advance our understanding of COVID-19 and other infectious diseases.
Edit intent: meaning-changed
Original: We investigate the efficacy of three social distancing controls on the spread of SARS-CoV-2 using an agent based SIR model on a small world network structure : 1) Global social distancing with a fixed probability of adherence.
Revised: We investigate phase transitions associated with three control methods for epidemics on small world networks. Motivated by the behavior of SARS-CoV-2 using an agent based SIR model on a small world network structure : 1) Global social distancing with a fixed probability of adherence.
Edit intent: meaning-changed
Original: We investigate the efficacy of three social distancing controls on the spread of SARS-CoV-2 using an agent based SIR model on a small world network structure : 1) Global social distancing with a fixed probability of adherence.
Revised: We investigate the efficacy of three social distancing controls on the spread of SARS-CoV-2 , we construct a theoretical SIR model of a virus that exhibits presymptomatic, asymptomatic, and symptomatic stages in two possible pathways. Using agent-based simulations on small world networks, we observe phase transitions for epidemic spread related to : 1) Global social distancing with a fixed probability of adherence.
Edit intent: meaning-changed
Original:  The combination of social isolation and PPE resulted in very low levels of infection. Our model suggests that widespread application of social distancing through government control can drastically reduce viral spread; even in the absence of widely adopted social distancing protocols, high use of PPE can also dramatically reduce the viral spread while short-duration quarantine following exposure to an infected individual was less effective .
Revised:  For each of these control measures, the total number of infections exhibits a sharp phase transition as the strength of the measure is varied .
Edit intent: meaning-changed
Original: Between February 14, 2019 and March 4, 2019, a terrorist attack in Pulwama, Kashmir followed by retaliatory air strikes led to rising tensions between India and Pakistan, two nuclear-armed countries.
Revised: Between February 14, 2019 and March 4, 2019, a terrorist attack in Pulwama, Kashmir followed by retaliatory airstrikes led to rising tensions between India and Pakistan, two nuclear-armed countries.
Edit intent: fluency
Original:  For subgroup-level balance, we derive measures of cohesiveness ( internal solidarity) and divisiveness ( external antagonism) to capture balance in subgroups using the most fitting partition of nodes into two groups.
Revised:  For subgroup-level balance, we propose measures of cohesiveness ( internal solidarity) and divisiveness ( external antagonism) to capture balance in subgroups using the most fitting partition of nodes into two groups.
Edit intent: clarity
Original:  For subgroup-level balance, we derive measures of cohesiveness ( internal solidarity) and divisiveness ( external antagonism) to capture balance in subgroups using the most fitting partition of nodes into two groups.
Revised:  For subgroup-level balance, we derive measures of cohesiveness ( intra-group solidarity) and divisiveness ( external antagonism) to capture balance in subgroups using the most fitting partition of nodes into two groups.
Edit intent: clarity
Original:  For subgroup-level balance, we derive measures of cohesiveness ( internal solidarity) and divisiveness ( external antagonism) to capture balance in subgroups using the most fitting partition of nodes into two groups.
Revised:  For subgroup-level balance, we derive measures of cohesiveness ( internal solidarity) and divisiveness ( inter-group antagonism) to capture balance in subgroups using the most fitting partition of nodes into two groups.
Edit intent: clarity
Original:  For subgroup-level balance, we derive measures of cohesiveness ( internal solidarity) and divisiveness ( external antagonism) to capture balance in subgroups using the most fitting partition of nodes into two groups.
Revised:  For subgroup-level balance, we derive measures of cohesiveness ( internal solidarity) and divisiveness ( external antagonism) to capture balance within and among subgroups of the network using the most fitting partition of nodes into two groups.
Edit intent: clarity
Original:  Through extensive computational analysis, we document frequently repeated patterns of social structure in triads, subgroups, and the whole network across a range of social settings from college students and Wikipedia users to philosophers and Bitcoin traders.
Revised:  Through extensive computational analysis, we document frequently repeated patterns of social structure in triads, subgroups, and the whole network across a range of social settings from college students and Wikipedia editors to philosophers and Bitcoin traders.
Edit intent: meaning-changed
Original:  We then apply our multilevel framework  to examine balance in temporal and multilayer networks  which demonstrates the generalizability of our approach  and leads to new observations on balance with respect to time and layer dimensions.
Revised:  We then apply our multilevel framework to examine balance in temporal and multilayer networks which demonstrates the generalizability of our approach to evaluating balance, and leads to new observations on balance with respect to time and layer dimensions.
Edit intent: clarity
Original: We present a formulation of the SEIR concept that represents the evolution of an epidemic under the assumption of homogeneity and uniformity of the microscopic parameters: rate of infection, incubation and recovery/removal times. Non-homogeneities in the basic parameters are easily included by integration for arbitrary distributions  of incubation and removal times , recovering the classical SEIR model under the assumption of exponential distributions .
Revised: The paradigm for compartment models in epidemiology assumes exponentially distributed incubation and removal times, which is not realistic in actual populations. Commonly used variations with multiple exponentially distributed variables are more flexible, yet do not allow for arbitrary distributions of incubation and removal times , recovering the classical SEIR model under the assumption of exponential distributions .
Edit intent: meaning-changed
Original:  Non-homogeneities in the basic parameters are easily included by integration for arbitrary distributions  of incubation and removal times , recovering the classical SEIR model under the assumption of exponential distributions .
Revised:  Non-homogeneities in the basic parameters are easily included by integration for arbitrary distributions . We present a new formulation, focussing on the SEIR concept that allows to include general distributions of incubation and removal times , recovering the classical SEIR model under the assumption of exponential distributions .
Edit intent: meaning-changed
Original:  We compare the solution to these equations, uSEIR, to two types of agent-based model simulations, a spatially homogeneous one where infection occurs by proximity, and a model on a scale-free network with varying clustering properties, where the infection between any two agents occurs via their link if it exists.
Revised:  We compare the solution to two types of agent-based model simulations, a spatially homogeneous one where infection occurs by proximity, and a model on a scale-free network with varying clustering properties, where the infection between any two agents occurs via their link if it exists.
Edit intent: clarity
Original:  Furthermore a family of asymptotic solutions of the uSEIR equations is found in terms of a logistic curve, which after a non-universal time shift, fits extremely well all the microdynamical simulations.
Revised:  Furthermore a family of asymptotic solutions of the equations is found in terms of a logistic curve, which after a non-universal time shift, fits extremely well all the microdynamical simulations.
Edit intent: clarity
Original:  From these samples, either a random poem was chosen (Human-out-of-the-loop) or the best one was selected (Human-in-the-loop) and in turn matched with a human written poem. Taking part in a new incentivized version of the Turing Test, participants failed to reliably detect the algorithmically-generated poems in the human-in-the-loop treatment, yet succeeded in the Human-out-of-the-loop treatment.
Revised:  From these samples, either a random poem was chosen (Human-out-of-the-loop) or the best one was selected (Human-in-the-loop) and in turn matched with a human-written poem. In a new incentivized version of the Turing Test, participants failed to reliably detect the algorithmically-generated poems in the human-in-the-loop treatment, yet succeeded in the Human-out-of-the-loop treatment.
Edit intent: clarity
Original:  Taking part in a new incentivized version of the Turing Test, participants failed to reliably detect the algorithmically-generated poems in the human-in-the-loop treatment, yet succeeded in the Human-out-of-the-loop treatment.
Revised:  Taking part in a new incentivized version of the Turing Test, participants failed to reliably detect the algorithmically-generated poems in the Human-in-the-loop treatment, yet succeeded in the Human-out-of-the-loop treatment.
Edit intent: fluency
Original:  These bounds significantly improve existing results for indefinite B, reducing the the number of required samples by a factor n or even more, where n is the size of A . Even for an SPD matrix, our work improves an existing result by Roosta-Khorasani and Ascher for Rademacher vectors.
Revised:  These bounds significantly improve existing results for indefinite B, reducing the the number of required samples by a factor n or even more, where n is the size of B . Even for an SPD matrix, our work improves an existing result by Roosta-Khorasani and Ascher for Rademacher vectors.
Edit intent: clarity
Original:  In this work, we  introduce the first fully polynomial time randomized approximation scheme (FPRAS) for counting the number of trees of size n accepted by a tree automaton, where n is given in unary , and the first polynomial time algorithm for sampling uniformly from this set of trees.
Revised: Conjunctive queries are one of the most common classes of queries used in database systems, and the best studied in the literature. A seminal result of Grohe, Schwentick, and Segoufin (STOC 2004) demonstrates that when restricted to graphs, a class of conjunctive queries can be evaluated in polynomial time if and only if it has bounded treewidth. In this work, we introduce the first fully polynomial time randomized approximation scheme (FPRAS) for counting the number of trees of size n accepted by a tree automaton, where n is given in unary , and the first polynomial time algorithm for sampling uniformly from this set of trees.
Edit intent: meaning-changed
Original:  In this work, we  introduce the first fully polynomial time randomized approximation scheme (FPRAS) for counting the number of trees of size n accepted by a tree automaton, where n is given in unary , and the first polynomial time algorithm for sampling uniformly from this set of trees.
Revised:  In this work, we extend this characterization to the counting problem for conjunctive queries. Specifically, for every class C of conjunctive queries with bounded treewidth, we introduce the first fully polynomial time randomized approximation scheme (FPRAS) for counting the number of trees of size n accepted by a tree automaton, where n is given in unary , and the first polynomial time algorithm for sampling uniformly from this set of trees.
Edit intent: meaning-changed
Original:  In this work, we  introduce the first fully polynomial time randomized approximation scheme (FPRAS) for counting the number of trees of size n accepted by a tree automaton, where n is given in unary , and the first polynomial time algorithm for sampling uniformly from this set of trees.
Revised:  In this work, we introduce the first fully polynomial-time randomized approximation scheme (FPRAS) for counting the number of trees of size n accepted by a tree automaton, where n is given in unary , and the first polynomial time algorithm for sampling uniformly from this set of trees.
Edit intent: fluency