wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Many proposed exit strategies have risks and uncertainties which could lead to a second wave of infection 1 , 2 . We demonstrate that our proposed policies have the potential to be a method of virus suppression, while at the same time allowing continued(albeit reduced) economic activity.
Revised:  Many proposed exit strategies have risks and uncertainties which could lead to a second wave of infection repeating waves of infection. We show -- as proof of concept grounded on rigorous mathematical evidence -- that periodic, high-frequency alternation of into, and out-of, lockdown effectively mitigates second-wave effects, while allowing continued, albeit reduced , 2 . We demonstrate that our proposed policies have the potential to be a method of virus suppression, while at the same time allowing continued(albeit reduced) economic activity.
Edit intent: meaning-changed
Original:  Many proposed exit strategies have risks and uncertainties which could lead to a second wave of infection 1 , 2 . We demonstrate that our proposed policies have the potential to be a method of virus suppression, while at the same time allowing continued(albeit reduced) economic activity.
Revised:  Many proposed exit strategies have risks and uncertainties which could lead to a second wave of infection 1 , . We demonstrate that our proposed policies have the potential to be a method of virus suppression, while at the same time allowing continued(albeit reduced) economic activity.
Edit intent: coherence
Original:  Furthermore, these policies, while not eliminating the virus , can nevertheless be sustained over long periods of time, until a vaccine or treatment becomes available .
Revised:  Periodicity confers (i) predictability, which is essential for economic sustainability, and (ii) robustness, since lockdown periods are not activated by uncertain measurements over short time scales. In turn -- while not eliminating the virus , can nevertheless be sustained over long periods of time, until a vaccine or treatment becomes available .
Edit intent: coherence
Original:  Furthermore, these policies, while not eliminating the virus , can nevertheless be sustained over long periods of time, until a vaccine or treatment becomes available . The robustness of these policies stems from the fact that they are open loop methods; namely, lock-down periods are not triggered by measurements -- inevitably uncertain and delayed -- over short time scales such as hospital admissions, but rather are driven by predictable, high-frequency, periodic triggers in- and out- of lock-down. A slow and inherently robust outer supervisory feedback loop, based on measurements  over longer time scales , is used to tune the parameters of the mitigationstrategy.
Revised:  Furthermore, these policies, while not eliminating the virus , can nevertheless be sustained over long periods of time, until a vaccine or treatment becomes available , while alleviating the social costs associated with long lockdowns. Typically, the policy might be in the form of 1-day of work followed by 6-days of lockdown every week (or perhaps 2 days working, 5 days off) and it can be modified at a slow-rate based on measurements over longer time scales , is used to tune the parameters of the mitigationstrategy.
Edit intent: meaning-changed
Original:  A slow and inherently robust outer supervisory feedback loop, based on measurements  over longer time scales , is used to tune the parameters of the mitigationstrategy. These methods can act alone, or can be used in combination with other mitigation strategies, to provide additional levels of effectiveness in their operation .
Revised:  A slow and inherently robust outer supervisory feedback loop, based on measurements over longer time scales . Our results highlight the potential efficacy of high frequency switching interventions in post lockdown mitigation. All code is available on Github URL A software tool has also been developed so that interested parties can explore the proof-of-concept system .
Edit intent: meaning-changed
Original:  Statistical inference of the model generalizes the Newman's modularity to the case of stochastic networks with hidden linear memory. In single cell chromatin interaction networks the optimal partition can be inferred from localization of the non-backtracking walks on the corresponding polymer graph, which responds to the maximum entropy principle.
Revised:  On the basis of this new model we modify the non-backtracking walks on the corresponding polymer graph, which responds to the maximum entropy principle.
Edit intent: clarity
Original:  In single cell chromatin interaction networks the optimal partition can be inferred from localization of the non-backtracking walks on the corresponding polymer graph, which responds to the maximum entropy principle.
Revised:  In single cell chromatin interaction networks the optimal partition can be inferred from localization of the non-backtracking flow operator and suggest the first protocol for annotation of compartmental domains in sparse single cell Hi-C matrices. In particular, we prove that our approach corresponds to the maximum entropy principle.
Edit intent: meaning-changed
Original:  Finally, we show that the polymer non-backtracking walks reveal the compartmental structure in single cell Hi-C maps. The found clusters have similar sizes with the domains from the population data and differ in the gene density  , corroborating biological significance of the partition into active and inactive compartments .
Revised:  We test various operators on real data and conclude that the sizes of the non-backtracking single cell domains are most close to the sizes of compartments from the population data and differ in the gene density , corroborating biological significance of the partition into active and inactive compartments .
Edit intent: clarity
Original: Automated market makers, first popularized by Hanson's Logarithmic Market Scoring Rule for prediction markets, have become important building blocks (often called 'primitives ') for decentralized finance.
Revised: Automated market makers, first popularized by Hanson's logarithmic market scoring rule (or LMSR) for prediction markets, have become important building blocks (often called 'primitives ') for decentralized finance.
Edit intent: clarity
Original: Automated market makers, first popularized by Hanson's Logarithmic Market Scoring Rule for prediction markets, have become important building blocks (often called 'primitives ') for decentralized finance.
Revised: Automated market makers, first popularized by Hanson's Logarithmic Market Scoring Rule for prediction markets, have become important building blocks , called 'primitives ') for decentralized finance.
Edit intent: coherence
Original: Automated market makers, first popularized by Hanson's Logarithmic Market Scoring Rule for prediction markets, have become important building blocks (often called 'primitives ') for decentralized finance.
Revised: Automated market makers, first popularized by Hanson's Logarithmic Market Scoring Rule for prediction markets, have become important building blocks (often called 'primitives ,' for decentralized finance.
Edit intent: fluency
Original:  In this paper, we focus on the analysis of a very large class of automated market makers, called constant function market makers , which includes popular market makers such as Uniswap and Balancer .
Revised:  In this paper, we focus on the analysis of a very large class of automated market makers, called constant function market makers (or CFMMs) which includes existing popular market makers such as Uniswap and Balancer .
Edit intent: meaning-changed
Original:  In this paper, we focus on the analysis of a very large class of automated market makers, called constant function market makers , which includes popular market makers such as Uniswap and Balancer .
Revised:  In this paper, we focus on the analysis of a very large class of automated market makers, called constant function market makers , which includes popular market makers such as Uniswap , Balancer, and Curve, whose yearly transaction volume totals to billions of dollars .
Edit intent: meaning-changed
Original: Interacting, self-propelled particles such as motile epithelial cells can dynamically URLanize into large-scale patterns. In such living systems, cell number and density can vary dramatically over time due to proliferation, which is not commonly considered in other active matter systems. As a consequence, it remains challenging to determine individual and collective phases over varying populations sizes without a priori information.
Revised: Interacting, self-propelled particles such as motile epithelial cells can dynamically URLanize into complex multicellular patterns, which are challenging to classify without a priori information.
Edit intent: clarity
Original:  Here, we demonstrate an unbiased machine learning approach to analyze multiparticle clusters based on topological structure, which is robust to changes in population size. For a given particle configuration , topological data analysis (TDA) determines the stability of spatial connectivity at varying length scales (i.e. persistent homology) , and can compare different particle configurations based on the "cost" of URLanizing one configuration into another.
Revised:  Classically, different phases and phase transitions have been described based on local ordering, which may not capture structural features at larger length scales. Instead , topological data analysis (TDA) determines the stability of spatial connectivity at varying length scales (i.e. persistent homology) , and can compare different particle configurations based on the "cost" of URLanizing one configuration into another.
Edit intent: meaning-changed
Original:  For a given particle configuration , topological data analysis (TDA) determines the stability of spatial connectivity at varying length scales (i.e. persistent homology) , and can compare different particle configurations based on the "cost" of URLanizing one configuration into another.
Revised:  For a given particle configuration , topological data analysis (TDA) determines the stability of spatial connectivity at varying length scales (i.e. persistent homology) and can compare different particle configurations based on the "cost" of URLanizing one configuration into another.
Edit intent: fluency
Original:   We show that TDA can accurately map out phase diagrams for interacting particles with varying adhesion and self-propulsion , at constant population size as well as when proliferation is permitted.
Revised:  Here, we demonstrate a topology-based machine learning approach for unsupervised profiling of individual and collective phases based on large-scale loops. We show that TDA can accurately map out phase diagrams for interacting particles with varying adhesion and self-propulsion , at constant population size as well as when proliferation is permitted.
Edit intent: meaning-changed
Original:   We show that TDA can accurately map out phase diagrams for interacting particles with varying adhesion and self-propulsion , at constant population size as well as when proliferation is permitted.
Revised:  We show that these topological loops (i.e. dimension 1 homology) are robust to variations in particle number and density, particularly in comparison to connected components (i.e. dimension 0 homology). We use TDA to map out phase diagrams for interacting particles with varying adhesion and self-propulsion , at constant population size as well as when proliferation is permitted.
Edit intent: meaning-changed
Original: In this work we propose a model to forecast the evolution of the number of COVID-19 symptomatic patients after drastic isolation measures using a combination of exponential grow models and the probability distribution of the incubation period.
Revised: We present an empirical algorithm to forecast the evolution of the number of COVID-19 symptomatic patients after drastic isolation measures using a combination of exponential grow models and the probability distribution of the incubation period.
Edit intent: clarity
Original: In this work we propose a model to forecast the evolution of the number of COVID-19 symptomatic patients after drastic isolation measures using a combination of exponential grow models and the probability distribution of the incubation period.
Revised: In this work we propose a model to forecast the evolution of the number of COVID-19 symptomatic patients in the early stages of the pandemic spread and after strict social distancing interventions. The algorithm is based on a low dimensional model for the variation of the exponential growth rate that decreases after the implementation of strict social distancing measures. From the observable data given by the number of tested positive, our model estimates the number of infected hindcast introducing in the model formulation the incubation time. We also use the model to follow the number of infected patients who later die using the registered number of deaths and the distribution time from infection to death. The relationship of the proposed model with the SIR models is studied. Model parameters fitting is done by minimizing a quadratic error between the data and the model forecast. An extended model is also proposed that allows a longer term forecast. An online implementation of the model is avalaible at www.ctim.es/covid19
Edit intent: meaning-changed
Original:  We develop an explicit expression for the matrix of asset holdings which minimizes market inefficiency , and characterize it in terms of two key sufficient statistics: the banks' systemic significances and the statistical moments of asset shocks  .
Revised:  We develop an explicit expression for the matrix of asset holdings which minimizes market inefficiency , and characterize it in terms of two key drivers, the banks' systemic significances and the statistical moments of asset shocks .
Edit intent: clarity
Original:  We develop an explicit expression for the matrix of asset holdings which minimizes market inefficiency , and characterize it in terms of two key sufficient statistics: the banks' systemic significances and the statistical moments of asset shocks  .
Revised:  We develop an explicit expression for the matrix of asset holdings which minimizes market inefficiency , and characterize it in terms of two key sufficient statistics: the banks' systemic significance and the statistical moments of asset shocks .
Edit intent: fluency
Original:  We develop an explicit expression for the matrix of asset holdings which minimizes market inefficiency , and characterize it in terms of two key sufficient statistics: the banks' systemic significances and the statistical moments of asset shocks  .
Revised:  We develop an explicit expression for the matrix of asset holdings which minimizes market inefficiency , and characterize it in terms of two key sufficient statistics: the banks' systemic significances and the statistical moments of asset shocks , and develop an explicit expression for the matrix of asset holdings which minimizes such inefficiency .
Edit intent: clarity
Original:  This model is calibrated to reproduce several characteristics of COVID-19 transmission , accounting for its reproductive number, the length of incubation and generation periods, age-dependent attack rates, and the growth rate of cumulative incidence during a sustained and unmitigated local transmission .
Revised:  This model is calibrated to reproduce key characteristics of COVID-19 transmission , accounting for its reproductive number, the length of incubation and generation periods, age-dependent attack rates, and the growth rate of cumulative incidence during a sustained and unmitigated local transmission .
Edit intent: clarity
Original:  We report  an important transition across the levels of social distancing compliance, in the range between 70\% and 80\% levels . This suggests that a compliance of below 70\% is unlikely to succeed for any duration of social distancing, while a compliance at the 90\% level is likely to control the disease within 13-14 weeks, when coupled with effective case isolation and international travel restrictions.
Revised:  We report an important transition across the levels of social distancing compliance, in the range between 70\% and 80\% levels , with compliance at the 90\% level is likely to control the disease within 13-14 weeks, when coupled with effective case isolation and international travel restrictions.
Edit intent: coherence
Original:  This suggests that a compliance of below 70\% is unlikely to succeed for any duration of social distancing, while a compliance at the 90\% level is likely to control the disease within 13-14 weeks, when coupled with effective case isolation and international travel restrictions.
Revised:  This suggests that a compliance of below 70\% is unlikely to succeed for any duration of social distancing, while a compliance at the 90\% level found to control the disease within 13-14 weeks, when coupled with effective case isolation and international travel restrictions.
Edit intent: clarity
Original:  This suggests that a compliance of below 70\% is unlikely to succeed for any duration of social distancing, while a compliance at the 90\% level is likely to control the disease within 13-14 weeks, when coupled with effective case isolation and international travel restrictions.
Revised:  This suggests that a compliance of below 70\% is unlikely to succeed for any duration of social distancing, while a compliance at the 90\% level is likely to control the disease within 13--14 weeks, when coupled with effective case isolation and international travel restrictions.
Edit intent: fluency
Original: In this article, a biallelic reversible mutation model with directional and quadratic selection is analyzed that reconnects to an approach proposed by Kimura 20 . Kimura starts from a diffusion model and derives its equilibrium distribution up to a constant.
Revised: In this article, a biallelic reversible mutation model with linear and quadratic selection is analyzed that reconnects to an approach proposed by Kimura 20 . Kimura starts from a diffusion model and derives its equilibrium distribution up to a constant.
Edit intent: meaning-changed
Original: In this article, a biallelic reversible mutation model with directional and quadratic selection is analyzed that reconnects to an approach proposed by Kimura 20 . Kimura starts from a diffusion model and derives its equilibrium distribution up to a constant.
Revised: In this article, a biallelic reversible mutation model with directional and quadratic selection is analyzed . The approach reconnects to one proposed by Kimura 20 . Kimura starts from a diffusion model and derives its equilibrium distribution up to a constant.
Edit intent: clarity
Original: In this article, a biallelic reversible mutation model with directional and quadratic selection is analyzed that reconnects to an approach proposed by Kimura 20 . Kimura starts from a diffusion model and derives its equilibrium distribution up to a constant.
Revised: In this article, a biallelic reversible mutation model with directional and quadratic selection is analyzed that reconnects to an approach proposed by Kimura . Kimura starts from a diffusion model and derives its equilibrium distribution up to a constant.
Edit intent: coherence
Original: In this article, a biallelic reversible mutation model with directional and quadratic selection is analyzed that reconnects to an approach proposed by Kimura 20 . Kimura starts from a diffusion model and derives its equilibrium distribution up to a constant.
Revised: In this article, a biallelic reversible mutation model with directional and quadratic selection is analyzed that reconnects to an approach proposed by Kimura 20 ( Possibility of extensive neutral evolution under stabilizing selection with special reference to nonrandom use of codons (PNAS,1981)), who starts from a diffusion model and derives its equilibrium distribution up to a constant.
Edit intent: meaning-changed
Original:  We use a boundary-mutation Moran model, which approximates general mutation models for small effective mutation rates, and derive its equilibrium distribution for polymorphic and monomorphic variants in small to moderately sized populations.
Revised:  We use a boundary-mutation Moran model, which approximates a general mutation model for small effective mutation rates, and derive its equilibrium distribution for polymorphic and monomorphic variants in small to moderately sized populations.
Edit intent: fluency
Original:  Using this model, we show that biased mutation rates and directional selection alone can cause  polymorphism rates within and substitution rates between populations that are  ascribed to balancing or overdominant selection by the McDonald-Kreitman test 26 .
Revised:  Using this model, we show that biased mutation rates and linear selection alone can cause polymorphism rates within and substitution rates between populations that are ascribed to balancing or overdominant selection by the McDonald-Kreitman test 26 .
Edit intent: meaning-changed
Original:  Using this model, we show that biased mutation rates and directional selection alone can cause  polymorphism rates within and substitution rates between populations that are  ascribed to balancing or overdominant selection by the McDonald-Kreitman test 26 .
Revised:  Using this model, we show that biased mutation rates and directional selection alone can cause patterns of polymorphism rates within and substitution rates between populations that are ascribed to balancing or overdominant selection by the McDonald-Kreitman test 26 .
Edit intent: clarity
Original:  Using this model, we show that biased mutation rates and directional selection alone can cause  polymorphism rates within and substitution rates between populations that are  ascribed to balancing or overdominant selection by the McDonald-Kreitman test 26 .
Revised:  Using this model, we show that biased mutation rates and directional selection alone can cause polymorphism rates within and substitution rates between populations that are usually ascribed to balancing or overdominant selection by the McDonald-Kreitman test 26 .
Edit intent: clarity
Original:  Using this model, we show that biased mutation rates and directional selection alone can cause  polymorphism rates within and substitution rates between populations that are  ascribed to balancing or overdominant selection by the McDonald-Kreitman test 26 .
Revised:  Using this model, we show that biased mutation rates and directional selection alone can cause polymorphism rates within and substitution rates between populations that are ascribed to balancing or overdominant selection 26 .
Edit intent: meaning-changed
Original:  Using this model, we show that biased mutation rates and directional selection alone can cause  polymorphism rates within and substitution rates between populations that are  ascribed to balancing or overdominant selection by the McDonald-Kreitman test 26 .
Revised:  Using this model, we show that biased mutation rates and directional selection alone can cause polymorphism rates within and substitution rates between populations that are ascribed to balancing or overdominant selection by the McDonald-Kreitman test .
Edit intent: meaning-changed
Original: On January 3, 2018 MiFID II regulations came into effect. This paper compares properties of European stocks for 2017 and 2018. The introduced tick size regime impacted the microstructure in accordance with existing literature on tick size changes.
Revised: This paper examines effects of MiFID II on European stock markets. We study the effects of the new tick size regime impacted the microstructure in accordance with existing literature on tick size changes.
Edit intent: clarity