wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Our  model is a time-dependent susceptible-infected-recovered (SIR) model that tracks two time series : (i) the transmission rate at time t and (ii) the recovering rate at time t.
Revised:  Our first model is a time-dependent susceptible-infected-recovered (SIR) model that tracks two time series : (i) the transmission rate at time t and (ii) the recovering rate at time t.
Edit intent: coherence
Original:  Our  model is a time-dependent susceptible-infected-recovered (SIR) model that tracks two time series : (i) the transmission rate at time t and (ii) the recovering rate at time t.
Revised:  Our model is a time-dependent susceptible-infected-recovered (SIR) model that tracks two time series , the transmission rate at time t and (ii) the recovering rate at time t.
Edit intent: fluency
Original:  Our  model is a time-dependent susceptible-infected-recovered (SIR) model that tracks two time series : (i) the transmission rate at time t and (ii) the recovering rate at time t.
Revised:  Our model is a time-dependent susceptible-infected-recovered (SIR) model that tracks two time series : (i) the transmission rate at time t , and the recovering rate at time t.
Edit intent: fluency
Original:  Using the data set provided by the National Health Commission of the People's Republic of China (NHC) [ 2 ], we show that the one-day prediction errors for the numbers of confirmed cases are  less than 3 %DIFDELCMD < \\%%% %DIF < except the day when the definition of the number of confirmed cases is changed.
Revised:  Using the data provided by the National Health Commission of the People's Republic of China (NHC) [ 2 ], we show that the one-day prediction errors for the numbers of confirmed cases are less than 3 %DIFDELCMD < \\%%% %DIF < except the day when the definition of the number of confirmed cases is changed.
Edit intent: clarity
Original:  Using the data set provided by the National Health Commission of the People's Republic of China (NHC) [ 2 ], we show that the one-day prediction errors for the numbers of confirmed cases are  less than 3 %DIFDELCMD < \\%%% %DIF < except the day when the definition of the number of confirmed cases is changed.
Revised:  Using the data set provided by the National Health Commission of the People's Republic of China [ 2 ], we show that the one-day prediction errors for the numbers of confirmed cases are less than 3 %DIFDELCMD < \\%%% %DIF < except the day when the definition of the number of confirmed cases is changed.
Edit intent: clarity
Original:  Using the data set provided by the National Health Commission of the People's Republic of China (NHC) [ 2 ], we show that the one-day prediction errors for the numbers of confirmed cases are  less than 3 %DIFDELCMD < \\%%% %DIF < except the day when the definition of the number of confirmed cases is changed.
Revised:  Using the data set provided by the National Health Commission of the People's Republic of China (NHC) [ 1 ], we show that the one-day prediction errors for the numbers of confirmed cases are less than 3 %DIFDELCMD < \\%%% %DIF < except the day when the definition of the number of confirmed cases is changed.
Edit intent: meaning-changed
Original:  Using the data set provided by the National Health Commission of the People's Republic of China (NHC) [ 2 ], we show that the one-day prediction errors for the numbers of confirmed cases are  less than 3 %DIFDELCMD < \\%%% %DIF < except the day when the definition of the number of confirmed cases is changed.
Revised:  Using the data set provided by the National Health Commission of the People's Republic of China (NHC) [ 2 ], we show that the one-day prediction errors for the numbers of confirmed cases are almost less than 3 %DIFDELCMD < \\%%% %DIF < except the day when the definition of the number of confirmed cases is changed.
Edit intent: clarity
Original:  In that case, the total number of confirmed cases is predicted to be less than 80,000 cases in China under our deterministic model.abstract 
Revised:  In that case, the total number of confirmed cases is predicted to be less than 80,000 cases in China under our deterministic model.abstract \%. Also, the turning point, defined as the day that the transmission rate is less than the recovering rate, is predicted to be Feb. 17, 2020. After that day, the basic reproduction number is less than 1 if the current contagious disease control policies are maintained in China. In that case, the total number of confirmed cases is predicted to be around 80,000 cases in China under our deterministic model. One problem for the first model is that there are asymptomatic infections for COVID-19. To model this, we propose our second SIR model that has two types of infected persons: detectable infected persons (type I) and undetectable infected cases (type II). To analyze whether there is an outbreak in such a model is characterized by the spectral radius of a 2 by 2 matrix that is closely related to the basic reproduction number. Our numerical results show that there are several countries, including South Korea, Italy, and Iran, that are above the percolation threshold curve, and they are on the verge of COVID-19 outbreaks on Mar. 2, 2020.
Edit intent: meaning-changed
Original: In this paper, we propose two mathematical models for analyzing and predicting the number of confirmed cases of COVID-19 . Our first model is a time-dependent susceptible-infected-recovered (SIR ) model that tracks two time series, the transmission rate at time t, and the recovering rate at time t.
Revised: In this paper, we conduct mathematical and numerical analyses for COVID-19 . Our first model is a time-dependent susceptible-infected-recovered (SIR ) model that tracks two time series, the transmission rate at time t, and the recovering rate at time t.
Edit intent: clarity
Original: In this paper, we propose two mathematical models for analyzing and predicting the number of confirmed cases of COVID-19 . Our first model is a time-dependent susceptible-infected-recovered (SIR ) model that tracks two time series, the transmission rate at time t, and the recovering rate at time t.
Revised: In this paper, we propose two mathematical models for analyzing and predicting the number of confirmed cases of COVID-19 . To predict the trend of COVID-19, we propose a time-dependent susceptible-infected-recovered (SIR ) model that tracks two time series, the transmission rate at time t, and the recovering rate at time t.
Edit intent: meaning-changed
Original: In this paper, we propose two mathematical models for analyzing and predicting the number of confirmed cases of COVID-19 . Our first model is a time-dependent susceptible-infected-recovered (SIR ) model that tracks two time series, the transmission rate at time t, and the recovering rate at time t.
Revised: In this paper, we propose two mathematical models for analyzing and predicting the number of confirmed cases of COVID-19 . Our first model is a time-dependent SIR model that tracks two time series, the transmission rate at time t, and the recovering rate at time t.
Edit intent: clarity
Original: In this paper, we propose two mathematical models for analyzing and predicting the number of confirmed cases of COVID-19 . Our first model is a time-dependent susceptible-infected-recovered (SIR ) model that tracks two time series, the transmission rate at time t, and the recovering rate at time t.
Revised: In this paper, we propose two mathematical models for analyzing and predicting the number of confirmed cases of COVID-19 . Our first model is a time-dependent susceptible-infected-recovered (SIR ) model that tracks the transmission rate at time t, and the recovering rate at time t.
Edit intent: clarity
Original: In this paper, we propose two mathematical models for analyzing and predicting the number of confirmed cases of COVID-19 . Our first model is a time-dependent susceptible-infected-recovered (SIR ) model that tracks two time series, the transmission rate at time t, and the recovering rate at time t.
Revised: In this paper, we propose two mathematical models for analyzing and predicting the number of confirmed cases of COVID-19 . Our first model is a time-dependent susceptible-infected-recovered (SIR ) model that tracks two time series, the transmission rate and the recovering rate at time t.
Edit intent: fluency
Original:  Using the data provided by the National Health Commission of the People's Republic of China 1 , we show that the one-day prediction errors for the numbers of confirmed cases are almost less than 3 \%.
Revised:  Using the data provided by the National Health Commission of the People's Republic of China China authority , we show that the one-day prediction errors for the numbers of confirmed cases are almost less than 3 \%.
Edit intent: meaning-changed
Original:  Using the data provided by the National Health Commission of the People's Republic of China 1 , we show that the one-day prediction errors for the numbers of confirmed cases are almost less than 3 \%. Also, the turning point, defined as the day that the transmission rate is less than the recovering rate, is predicted to be Feb. 17, 2020. After that day, the basic reproduction number is less than 1 if the current contagious disease control policies are maintained in China. In that case, the total number of confirmed cases is predicted to be around 80,000 cases in China under our deterministic model. One problem for the first model is that there are asymptomatic infections for COVID-19. To model this, we propose our second SIR model that has two types of infected persons: detectable infected persons (type I) and undetectable infected cases (type II). To analyze whether there is an outbreak in such a model is characterized by the spectral radius of a 2 by 2 matrix that is closely related to the basic reproduction number. Our numerical results show that there are several countries, including South Korea, Italy, and Iran, that are above the percolation threshold curve, and they are on the verge of COVID-19 outbreaks on Mar. 2, 2020.
Revised:  Using the data provided by the National Health Commission of the People's Republic of China 1 , we show that the one-day prediction errors for the numbers of confirmed cases are almost less than 3 
Edit intent: meaning-changed
Original: In this paper, we conduct mathematical and numerical analyses  for COVID-19 . To predict the trend of COVID-19 , we propose a time-dependent SIR model that tracks  the transmission rate and the recovering rate at time t.
Revised: In this paper, we conduct mathematical and numerical analyses to address the following crucial questions for COVID-19 . To predict the trend of COVID-19 , we propose a time-dependent SIR model that tracks the transmission rate and the recovering rate at time t.
Edit intent: meaning-changed
Original: In this paper, we conduct mathematical and numerical analyses  for COVID-19 . To predict the trend of COVID-19 , we propose a time-dependent SIR model that tracks  the transmission rate and the recovering rate at time t.
Revised: In this paper, we conduct mathematical and numerical analyses for COVID-19 : (Q1) Is it possible to contain COVID-19 , we propose a time-dependent SIR model that tracks the transmission rate and the recovering rate at time t.
Edit intent: meaning-changed
Original: In this paper, we conduct mathematical and numerical analyses  for COVID-19 . To predict the trend of COVID-19 , we propose a time-dependent SIR model that tracks  the transmission rate and the recovering rate at time t.
Revised: In this paper, we conduct mathematical and numerical analyses for COVID-19 . To predict the trend of COVID-19 ? (Q2) If COVID-19 can be contained, when will be the peak and the end of the epidemic? (Q3) How do the asymptomatic infections affect the spread of disease? (Q4) If COVID-19 cannot be contained, what is the ratio of the population that needs to be infected to achieve herd immunity? (Q5) How effective are the social distancing approaches? For (Q1) and (Q2), we propose a time-dependent SIR model that tracks the transmission rate and the recovering rate at time t.
Edit intent: meaning-changed
Original: In this paper, we conduct mathematical and numerical analyses  for COVID-19 . To predict the trend of COVID-19 , we propose a time-dependent SIR model that tracks  the transmission rate and the recovering rate at time t.
Revised: In this paper, we conduct mathematical and numerical analyses for COVID-19 . To predict the trend of COVID-19 , we propose a time-dependent susceptible-infected-recovered (SIR) model that tracks the transmission rate and the recovering rate at time t.
Edit intent: meaning-changed
Original: In this paper, we conduct mathematical and numerical analyses  for COVID-19 . To predict the trend of COVID-19 , we propose a time-dependent SIR model that tracks  the transmission rate and the recovering rate at time t.
Revised: In this paper, we conduct mathematical and numerical analyses for COVID-19 . To predict the trend of COVID-19 , we propose a time-dependent SIR model that tracks 2 time series: (i) the transmission rate and the recovering rate at time t.
Edit intent: meaning-changed
Original: In this paper, we conduct mathematical and numerical analyses  for COVID-19 . To predict the trend of COVID-19 , we propose a time-dependent SIR model that tracks  the transmission rate and the recovering rate at time t.
Revised: In this paper, we conduct mathematical and numerical analyses for COVID-19 . To predict the trend of COVID-19 , we propose a time-dependent SIR model that tracks the transmission rate at time t and (ii) the recovering rate at time t.
Edit intent: meaning-changed
Original:   Using the data provided by China authority , we show our one-day prediction errors are almost less than 3 %DIFDELCMD < \\%%% %DIF < . The turning point and the total number of confirmed cases in China are predicted under our model.
Revised:  Such an approach is more adaptive than traditional static SIR models and more robust than direct estimation methods. Using the data provided by China authority , we show our one-day prediction errors are almost less than 3 %DIFDELCMD < \\%%% %DIF < . The turning point and the total number of confirmed cases in China are predicted under our model.
Edit intent: meaning-changed
Original:   Using the data provided by China authority , we show our one-day prediction errors are almost less than 3 %DIFDELCMD < \\%%% %DIF < . The turning point and the total number of confirmed cases in China are predicted under our model.
Revised:  Using the data provided by the Chinese government , we show our one-day prediction errors are almost less than 3 %DIFDELCMD < \\%%% %DIF < . The turning point and the total number of confirmed cases in China are predicted under our model.
Edit intent: clarity
Original:   Using the data provided by China authority , we show our one-day prediction errors are almost less than 3 %DIFDELCMD < \\%%% %DIF < . The turning point and the total number of confirmed cases in China are predicted under our model.
Revised:  Using the data provided by China authority , we show that the one-day prediction errors are almost less than 3 %DIFDELCMD < \\%%% %DIF < . The turning point and the total number of confirmed cases in China are predicted under our model.
Edit intent: clarity
Original:   Using the data provided by China authority , we show our one-day prediction errors are almost less than 3 %DIFDELCMD < \\%%% %DIF < . The turning point and the total number of confirmed cases in China are predicted under our model.
Revised:  Using the data provided by China authority , we show our one-day prediction errors for the numbers of confirmed cases are almost in 3 %DIFDELCMD < \\%%% %DIF < . The turning point and the total number of confirmed cases in China are predicted under our model.
Edit intent: clarity
Original:  We show two approaches of social distancing that can lead to a reduction of R_0.\end{abstract} 
Revised:  We show two approaches of social distancing that can lead to a reduction of R_0.\end{abstract} \%, and the total number of confirmed cases is precisely predicted. Also, the turning point, defined as the day that the transmission rate is less than the recovering rate can be accurately predicted. After that day, the basic reproduction number R_0 is less than 1. For (Q3), we extend our SIR model by considering 2 types of infected persons: detectable and undetectable infected persons. Whether there is an outbreak in such a model is characterized by the spectral radius of a 2 by 2 matrix that is closely related to R_0. For (Q4), we show that herd immunity can be achieved after at least 1-1/R_0 fraction of individuals being infected. For (Q5), we analyze the independent cascade (IC) model for disease propagation in a configuration random graph. By relating the propagation probabilities in the IC model to the transmission rates and recovering rates in the SIR model, we show 2 approaches of social distancing that can lead to a reduction of R_0.
Edit intent: meaning-changed
Original: In this paper, we conduct mathematical and numerical analyses to address the following crucial questions for COVID-19: (Q1) Is it possible to contain COVID-19? (Q2) If COVID-19 can be contained, when will be the peak and the end of the epidemic? (Q3) How do the asymptomatic infections affect the spread of disease? (Q4) If COVID-19 cannot be contained, what is the ratio of the population that needs to be infected to achieve herd immunity?
Revised: In this paper, we conduct mathematical and numerical analyses to address the following crucial questions for COVID-19: (Q1) Is it possible to contain COVID-19? (Q2) When will be the peak and the end of the epidemic? (Q3) How do the asymptomatic infections affect the spread of disease? (Q4) If COVID-19 cannot be contained, what is the ratio of the population that needs to be infected to achieve herd immunity?
Edit intent: coherence
Original: In this paper, we conduct mathematical and numerical analyses to address the following crucial questions for COVID-19: (Q1) Is it possible to contain COVID-19? (Q2) If COVID-19 can be contained, when will be the peak and the end of the epidemic? (Q3) How do the asymptomatic infections affect the spread of disease? (Q4) If COVID-19 cannot be contained, what is the ratio of the population that needs to be infected to achieve herd immunity?
Revised: In this paper, we conduct mathematical and numerical analyses to address the following crucial questions for COVID-19: (Q1) Is it possible to contain COVID-19? (Q2) If COVID-19 can be contained, when will be the peak and the end of the epidemic? (Q3) How do the asymptomatic infections affect the spread of disease? (Q4) What is the ratio of the population that needs to be infected to achieve herd immunity?
Edit intent: clarity
Original:   For (Q1) and (Q2), we propose a time-dependent susceptible-infected-recovered (SIR) model that tracks 2 time series: (i) the transmission rate at time t and (ii) the recovering rate at time t.
Revised:  (Q6) What is the ratio of the population infected in the long run? For (Q1) and (Q2), we propose a time-dependent susceptible-infected-recovered (SIR) model that tracks 2 time series: (i) the transmission rate at time t and (ii) the recovering rate at time t.
Edit intent: meaning-changed
Original:  Herein, we introduce COVID-19 Docking Server, a web server that predicts the binding modes between COVID-19 targets and the ligands including small molecules, peptide and antibody.
Revised:  Herein, we introduce COVID-19 Docking Server, a web server that predicts the binding modes between COVID-19 targets and the ligands including small molecules, peptides and anti-bodies. Results:
Edit intent: clarity
Original: The deep reinforcement learning method for human-robot cooperation (HRC) is promising for its high performance when robots are learning complex tasks. However, the applicability of such an approach in a real-world context is limited due to long training time, additional training difficulty caused by inconsistent human performance and the inherent instability of policy exploration. With this approach, the robot has two dynamics to learn: how to accomplish the given physical task and how to cooperate with the human partner.
Revised: Applying Deep Reinforcement Learning (DRL) to Human-Robot Cooperation (HRC) in dynamic control problems is promising yet challenging as the robot needs to learn the dynamics of the controlled system and dynamics of the human partner.
Edit intent: clarity
Original:  Furthermore, the dynamics of the task and human partner are usually coupled, which means the observable outcomes and behaviors are coupled. It is hard for the robot to efficiently learn from coupled observations. In this paper, we hypothesize that the robot needs to learn the task separately from learning the behavior of the human partner to improve learning efficiency and outcomes.
Revised:  In existing research, the robot powered by DRL adopts coupled observation of the environment and the human partner to learn both dynamics simultaneously. However, such a learning strategy is limited in terms of learning efficiency and outcomes.
Edit intent: meaning-changed
Original:  In this paper, we hypothesize that the robot needs to learn the task separately from learning the behavior of the human partner to improve learning efficiency and outcomes. This leads to a fundamental question: Should the robot learn the task first or learn the human behavior first (Fig. 1)? We develop a novel hierarchical rewards mechanism with a task decomposition method  that enables the robot to efficiently learn a complex hierarchical dynamic task and human behavior for better HRC.
Revised:  In this paper, we hypothesize that the robot needs to learn the task separately from learning the behavior of the human partner to improve learning efficiency and team performance. This work proposes a novel task decomposition method that enables the robot to efficiently learn a complex hierarchical dynamic task and human behavior for better HRC.
Edit intent: clarity
Original:  We develop a novel hierarchical rewards mechanism with a task decomposition method  that enables the robot to efficiently learn a complex hierarchical dynamic task and human behavior for better HRC. The algorithm is validated in a hierarchical control task in a simulated environment with human subject experiments , and we are able to answer the question by analyzing the collected experiment results .
Revised:  We develop a novel hierarchical rewards mechanism with a task decomposition method that enables the robot to learn the hierarchical dynamic control task separately from learning the human partner's behavior. The method is validated with a hierarchical control task in a simulated environment with human subject experiments , and we are able to answer the question by analyzing the collected experiment results .
Edit intent: meaning-changed
Original:  The algorithm is validated in a hierarchical control task in a simulated environment with human subject experiments , and we are able to answer the question by analyzing the collected experiment results .
Revised:  The algorithm is validated in a hierarchical control task in a simulated environment with human subject experiments . Our method also provides insight into the design of the learning strategy for HRC. The results show that the robot should learn the task first to achieve higher team performance and learn the human first to achieve higher learning efficiency .
Edit intent: meaning-changed
Original:   ]  Gawrychowski, Landau, Sung, and Weimann [ICALP'18] devise a greedy algorithm that runs in time O(n^{1.5}\log n) based on micro-macro decomposition .
Revised:  Given a rooted tree t and another tree T, one would like to incorporate compatible information from T to t. This problem is a subproblem in the tree refinement problem called the RF-Optimal Tree Refinement Problem defined by in Christensen, Molloy, Vachaspati and Warnow ] Gawrychowski, Landau, Sung, and Weimann [ICALP'18] devise a greedy algorithm that runs in time O(n^{1.5}\log n) based on micro-macro decomposition .
Edit intent: meaning-changed