wanyu/IteraTeR_full_sent huggingface dataset:
Original:  The two intervention strategies (SD and TQ) try to reduce the disease reproductive number (R_0 > 1) to a target value ( R_0^{\rm target} < 1 ) , but in distinct ways, which we implement in our model equations.
Revised:  The two intervention strategies (SD and TQ) try to reduce the disease reproductive number (R_0 > 1) to a target value ( R_0^{\rm target} < 1 , but in distinct ways, which we implement in our model equations.
Edit intent: fluency
Original:  However, for TQ to be effective, it has has to be based on contact tracing and the ratio of tests/ per day to the number of new cases/ per day has to be scaled with the mean number of contacts that an infectious person has , which would be high in regions with high population density and low levels of social distancing .
Revised:  However, for TQ to be effective, it has to be based on contact tracing and the ratio of tests/ per day to the number of new cases/ per day has to be scaled with the mean number of contacts that an infectious person has , which would be high in regions with high population density and low levels of social distancing .
Edit intent: fluency
Original:  However, for TQ to be effective, it has has to be based on contact tracing and the ratio of tests/ per day to the number of new cases/ per day has to be scaled with the mean number of contacts that an infectious person has , which would be high in regions with high population density and low levels of social distancing .
Revised:  However, for TQ to be effective, it has has to be based on contact tracing and the ratio of tests/ day to the number of new cases/ per day has to be scaled with the mean number of contacts that an infectious person has , which would be high in regions with high population density and low levels of social distancing .
Edit intent: fluency
Original:  However, for TQ to be effective, it has has to be based on contact tracing and the ratio of tests/ per day to the number of new cases/ per day has to be scaled with the mean number of contacts that an infectious person has , which would be high in regions with high population density and low levels of social distancing .
Revised:  However, for TQ to be effective, it has has to be based on contact tracing and the ratio of tests/ per day to the number of new cases/ day has to be scaled with the mean number of contacts that an infectious person has , which would be high in regions with high population density and low levels of social distancing .
Edit intent: fluency
Original:  However, for TQ to be effective, it has has to be based on contact tracing and the ratio of tests/ per day to the number of new cases/ per day has to be scaled with the mean number of contacts that an infectious person has , which would be high in regions with high population density and low levels of social distancing .
Revised:  However, for TQ to be effective, it has has to be based on contact tracing and the ratio of tests/ per day to the number of new cases/ per day has to be scaled with the mean number of contacts of an infectious person has , which would be high in regions with high population density and low levels of social distancing .
Edit intent: fluency
Original:  However, for TQ to be effective, it has has to be based on contact tracing and the ratio of tests/ per day to the number of new cases/ per day has to be scaled with the mean number of contacts that an infectious person has , which would be high in regions with high population density and low levels of social distancing .
Revised:  However, for TQ to be effective, it has has to be based on contact tracing and the ratio of tests/ per day to the number of new cases/ per day has to be scaled with the mean number of contacts that an infectious person , which would be high in regions with high population density and low levels of social distancing .
Edit intent: clarity
Original:  However, for TQ to be effective, it has has to be based on contact tracing and the ratio of tests/ per day to the number of new cases/ per day has to be scaled with the mean number of contacts that an infectious person has , which would be high in regions with high population density and low levels of social distancing .
Revised:  However, for TQ to be effective, it has has to be based on contact tracing and the ratio of tests/ per day to the number of new cases/ per day has to be scaled with the mean number of contacts that an infectious person has , which would be high in densely populated regions with low levels of social distancing .
Edit intent: clarity
Original:  However, for TQ to be effective, it has has to be based on contact tracing and the ratio of tests/ per day to the number of new cases/ per day has to be scaled with the mean number of contacts that an infectious person has , which would be high in regions with high population density and low levels of social distancing .
Revised:  However, for TQ to be effective, it has has to be based on contact tracing and the ratio of tests/ per day to the number of new cases/ per day has to be scaled with the mean number of contacts that an infectious person has , which would be high in regions with high population density and low levels of SD .
Edit intent: clarity
Original:  We point out that, apart from R_0, an important quantity is the largest eigenvalue of the linearized dynamics which provides a more complete understanding of the disease progression  both pre- and post- intervention  and explains observed data .
Revised:  We point out that, apart from R_0, an important quantity is the largest eigenvalue of the linearised dynamics which provides a more complete understanding of the disease progression both pre- and post- intervention and explains observed data .
Edit intent: fluency
Original:  We point out that, apart from R_0, an important quantity is the largest eigenvalue of the linearized dynamics which provides a more complete understanding of the disease progression  both pre- and post- intervention  and explains observed data .
Revised:  We point out that, apart from R_0, an important quantity is the largest eigenvalue of the linearized dynamics which provides a more complete understanding of the disease progression , both pre- and post- intervention and explains observed data .
Edit intent: fluency
Original:  We point out that, apart from R_0, an important quantity is the largest eigenvalue of the linearized dynamics which provides a more complete understanding of the disease progression  both pre- and post- intervention  and explains observed data .
Revised:  We point out that, apart from R_0, an important quantity is the largest eigenvalue of the linearized dynamics which provides a more complete understanding of the disease progression both pre- and post- intervention , and explains observed data .
Edit intent: fluency
Original:  We point out that, apart from R_0, an important quantity is the largest eigenvalue of the linearized dynamics which provides a more complete understanding of the disease progression  both pre- and post- intervention  and explains observed data . Weak extended intervention strategies (that reduce R_0 but not to a value less than 1) can reduce the peak values of infections and the asymptotic affected population.
Revised:  We point out that, apart from R_0, an important quantity is the largest eigenvalue of the linearized dynamics which provides a more complete understanding of the disease progression both pre- and post- intervention and explains observed data for many countries. Weak intervention strategies (that reduce R_0 but not to a value less than 1) can reduce the peak values of infections and the asymptotic affected population.
Edit intent: meaning-changed
Original: Modeling accurately the evolution and intervention strategies for the Covid-19 pandemic is a challenging problem. We present here an analysis of an extended Susceptible-Exposed-Infected-Recovered (SEIR ) model that accounts for asymptomatic carriers, and explore the effect of different intervention strategies such as social distancing (SD) and testing-quarantining (TQ).
Revised: The Susceptible-Exposed-Infected-Recovered (SEIR ) model that accounts for asymptomatic carriers, and explore the effect of different intervention strategies such as social distancing (SD) and testing-quarantining (TQ).
Edit intent: coherence
Original:  We present here an analysis of an extended Susceptible-Exposed-Infected-Recovered (SEIR ) model that accounts for asymptomatic carriers, and explore the effect of different intervention strategies such as social distancing (SD) and testing-quarantining (TQ).
Revised:  We present here an analysis of an extended Susceptible-Exposed-Infected-Recovered (SEIR) epidemiological model is one of the standard models of disease spreading. Here we analyse an extended SEIR model that accounts for asymptomatic carriers, and explore the effect of different intervention strategies such as social distancing (SD) and testing-quarantining (TQ).
Edit intent: meaning-changed
Original:  We present here an analysis of an extended Susceptible-Exposed-Infected-Recovered (SEIR ) model that accounts for asymptomatic carriers, and explore the effect of different intervention strategies such as social distancing (SD) and testing-quarantining (TQ).
Revised:  We present here an analysis of an extended Susceptible-Exposed-Infected-Recovered (SEIR ) model that accounts for asymptomatic carriers, believed to play an important role in COVID-19 transmission. For this model we derive a number of analytic results for important quantities such as the peak number of infections, the time taken to reach the peak and the size of the final affected population. We also propose an accurate way of specifying initial conditions for the numerics (from insufficient data) using the fact that the early time exponential growth is well-described by the dominant eigenvector of the linearized equations. Secondly we explore the effect of different intervention strategies such as social distancing (SD) and testing-quarantining (TQ).
Edit intent: meaning-changed
Original:  The two intervention strategies (SD and TQ) try to reduce the disease reproductive number R_0 to a target value R _0^{\rm target < 1, but in distinct ways, which we implement in our model equations.
Revised:  The two intervention strategies (SD and TQ) try to reduce the disease reproductive number , R_0, to a target value R _0^{\rm target < 1, but in distinct ways, which we implement in our model equations.
Edit intent: fluency
Original:  The two intervention strategies (SD and TQ) try to reduce the disease reproductive number R_0 to a target value R _0^{\rm target < 1, but in distinct ways, which we implement in our model equations.
Revised:  The two intervention strategies (SD and TQ) try to reduce the disease reproductive number R_0 to a target value R ^{\rm target < 1, but in distinct ways, which we implement in our model equations.
Edit intent: fluency
Original:  However, for TQ to be effective, it has to be based on contact tracing and the ratio of tests/day to the number of new cases/day has to be scaled with the mean number of contacts of an infectious person, which would be high in densely populated regions with low levels of SD.
Revised:  However, for TQ to be effective, it has to be based on contact tracing and our study quantifies the required ratio of tests-per-day to the number of new cases/day has to be scaled with the mean number of contacts of an infectious person, which would be high in densely populated regions with low levels of SD.
Edit intent: meaning-changed
Original:  However, for TQ to be effective, it has to be based on contact tracing and the ratio of tests/day to the number of new cases/day has to be scaled with the mean number of contacts of an infectious person, which would be high in densely populated regions with low levels of SD. We point out that , apart from R_0, an important quantity is the largest eigenvalue of the linearised dynamics which provides a more complete understanding of the disease progression, both pre- and post- intervention, and explains observed data for many countries.
Revised:  However, for TQ to be effective, it has to be based on contact tracing and the ratio of tests/day to the number of new cases-per-day. Our analysis shows that the largest eigenvalue of the linearised dynamics which provides a more complete understanding of the disease progression, both pre- and post- intervention, and explains observed data for many countries.
Edit intent: clarity
Original:  We point out that , apart from R_0, an important quantity is the largest eigenvalue of the linearised dynamics which provides a more complete understanding of the disease progression, both pre- and post- intervention, and explains observed data for many countries.
Revised:  We point out that , apart from R_0, an important quantity is the largest eigenvalue of the linearised dynamics provides a simple understanding of the disease progression, both pre- and post- intervention, and explains observed data for many countries.
Edit intent: meaning-changed
Original:  Weak intervention strategies (that reduce R_0 but not to a value less than 1) can reduce the peak values of infections and the asymptotic affected population. We provide simple analytic expressions for these in terms of the disease parameters and apply them in the Indian context to obtain heuristic projections for the course of the pandemic .
Revised:  We apply our results to the COVID data for India to obtain heuristic projections for the course of the pandemic .
Edit intent: clarity
Original:  We provide simple analytic expressions for these in terms of the disease parameters and apply them in the Indian context to obtain heuristic projections for the course of the pandemic . We find that the predictions strongly depend on the assumed fraction of asymptomatic carriers.
Revised:  We provide simple analytic expressions for these in terms of the disease parameters and apply them in the Indian context to obtain heuristic projections for the course of the pandemic , and note that the predictions strongly depend on the assumed fraction of asymptomatic carriers.
Edit intent: coherence
Original:  the separation of latent codes for intrinsic (i.e. identity) and extrinsic(i.e. pose) geometry .
Revised:  the separation of latent codes for intrinsic (i.e. identity) and extrinsic(i.e. pose) geometry , is a prominent task for generative models of non-Euclidean data such as 3D deformable models. It provides greater interpretability of the latent space, and leads to more control in generation.
Edit intent: meaning-changed
Original:  We further propose CFAN-VAE, a novel architecture that disentangles identity and pose using the CFAN feature and parallel transport convolution. CFAN-VAE achieves this geometric disentanglement in an unsupervised way, as it does not require label information on the identity or pose during training  .
Revised:  We further propose CFAN-VAE, a novel architecture that disentangles identity and pose using the CFAN feature . Requiring no label information on the identity or pose during training .
Edit intent: coherence
Original:  CFAN-VAE achieves this geometric disentanglement in an unsupervised way, as it does not require label information on the identity or pose during training  .
Revised:  CFAN-VAE achieves this geometric disentanglement in an unsupervised way, as it does not require label information on the identity or pose during training , CFAN-VAE achieves geometric disentanglement in an unsupervisedway .
Edit intent: fluency
Original:  Furthermore, a series of ablation studies confirmed that our AI-Interpret algorithm was critical to the discovery of interpretable decision rules and that it is ready to be applied to other reinforcement learning problems .
Revised:  Moreover, another experiment revealed that this approach is significantly more effective than training people by giving them performance feedback. Finally, a series of ablation studies confirmed that our AI-Interpret algorithm was critical to the discovery of interpretable decision rules and that it is ready to be applied to other reinforcement learning problems .
Edit intent: meaning-changed
Original:  Unlike the propagation of chaos in the classical mean field limit for interacting particle systems , the chaos here is imposed at every discrete time.
Revised:  Unlike the propagation of chaos in the classical mean field limit for interacting particle systems where the law of large numbers plays the role and the chaos is propagated to later times, the mean field limit now does not rely on the law of large numbers and chaos is imposed at every discrete time.
Edit intent: meaning-changed
Original:  Despite this, we will not only justify this mean-field limit (discrete in time) but will also show that the limit, as the discrete time interval \tau \to 0, approaches to the solution of a nonlinear Fokker-Planck equation arising as the mean-field limit of the original interacting particle system , in Wasserstein distance ;
Revised:  Despite this, we will not only justify this mean-field limit (discrete in time) but will also show that the limit, as the discrete time interval \tau \to 0, approaches to the solution of a nonlinear Fokker-Planck equation arising as the mean-field limit of the original interacting particle system in Wasserstein distance ;
Edit intent: fluency
Original:  Despite this, we will not only justify this mean-field limit (discrete in time) but will also show that the limit, as the discrete time interval \tau \to 0, approaches to the solution of a nonlinear Fokker-Planck equation arising as the mean-field limit of the original interacting particle system , in Wasserstein distance ; namely, the two limits \lim_{N\to\infty and \tau \to 0 commute} .
Revised:  Despite this, we will not only justify this mean-field limit (discrete in time) but will also show that the limit, as the discrete time interval \tau \to 0, approaches to the solution of a nonlinear Fokker-Planck equation arising as the mean-field limit of the original interacting particle system , in Wasserstein distance and \tau \to 0 commute} .
Edit intent: meaning-changed
Original:  We identify three different regimes of our toy economy upon varying the amount of debt that each agent can accumulate before defaulting.
Revised:  The CSP paradigm allows us to propose an alternative price-setting mechanism: given agents' preferences and budgets, what set of prices satisfies the maximum number of agents? Such an approach permits the coupling of production and output within the economy to the allowed level of debt in a simplified framework. Within our model, we identify three different regimes of our toy economy upon varying the amount of debt that each agent can accumulate before defaulting.
Edit intent: meaning-changed
Original: We study Susceptible-Exposed-Asymptomatic-Infectious-Recovered (SEAIR) epidemic spreading model inspired by two characteristics of the infectiousness of COVID-19: delayed start and its appearance before onset of symptoms, or even with total absence of them.
Revised: We study Susceptible-Exposed-Asymptomatic-Infectious-Recovered (SEAIR) epidemic spreading model of COVID-19. It captures two important characteristics of the infectiousness of COVID-19: delayed start and its appearance before onset of symptoms, or even with total absence of them.
Edit intent: clarity
Original:  Its design is based heavily on cadnano, the most widely-used software for designing DNA origami, with three main differences: 1. scadnano runs entirely in the browser, with *no software installation* required.
Revised:  Its design is based heavily on cadnano, the most widely-used software for designing DNA origami, with three main differences: 1. scadnano runs entirely in the browser, with no software installation required.
Edit intent: fluency
Original: Grammatical error correction (GEC) literature has reported on the effectiveness of pretraining a Seq2Seq model with a large amount of pseudo data.
Revised: Grammatical error correction (GEC) literature has reported on the effectiveness of pretraining a Seq2Seq model with a large amount of pseudodata. However, this approach requires time-consuming pretraining for GEC because of the size of the pseudodata.
Edit intent: meaning-changed
Original:  In this study, we explored two generic pretrained encoder-decoder (Enc-Dec) models, including BART, which reported the state-of-the-art (SOTA) results for several Seq2Seq tasks other than GEC.
Revised:  In this study, we explore the utility of bidirectional and auto-regressive transformers (BART) as a generic pretrained encoder-decoder (Enc-Dec) models, including BART, which reported the state-of-the-art (SOTA) results for several Seq2Seq tasks other than GEC.
Edit intent: meaning-changed
Original:  In this study, we explored two generic pretrained encoder-decoder (Enc-Dec) models, including BART, which reported the state-of-the-art (SOTA) results for several Seq2Seq tasks other than GEC. We found that monolingual and multilingual BART models achieve high performance in GEC, including a competitive result compared with the current SOTA result in English GEC.
Revised:  In this study, we explored two generic pretrained encoder-decoder model for GEC. With the use of this generic pretrained model for GEC, the time-consuming pretraining can be eliminated. We find that monolingual and multilingual BART models achieve high performance in GEC, including a competitive result compared with the current SOTA result in English GEC.
Edit intent: coherence
Original:   This study finds that training a regression model on a subset of the outputs from an this pre-trained chest X-ray model predicts our geographic extent score (range 0-8) with 1.14 mean absolute error (MAE) and our lung opacity score (range 0-6) with 0.78 MAE.
Revised:  Results: This study finds that training a regression model on a subset of the outputs from an this pre-trained chest X-ray model predicts our geographic extent score (range 0-8) with 1.14 mean absolute error (MAE) and our lung opacity score (range 0-6) with 0.78 MAE.
Edit intent: fluency
Original:  All code, labels, and data are made available at URL and URL
Revised:  Conclusions: These results indicate that our model's ability to gauge severity of COVID-19 lung infections could be used for escalation or de-escalation of care as well as monitoring treatment efficacy, especially in the intensive care unit (ICU). A proper clinical trial is needed to evaluate efficacy. To enable this we make our code, labels, and data are made available at URL and URL
Edit intent: coherence
Original:  Both methods are straightforward to implement and able to provide pointwise confidence intervals and uniform confidence bands that achieve exact limiting rejection probabilities under the null. We illustrate their finite sample performance using both simulations and a well-known dataset on microfinance.
Revised:  Both methods are straightforward to implement and able to provide pointwise confidence intervals and uniform confidence bands that achieve exact limiting coverage rates. We demonstrate their finite sample performance using both simulations and a well-known dataset on microfinance.
Edit intent: clarity