wanyu/IteraTeR_full_sent huggingface dataset:
Original:  We also study the utility threshold policy in an unbalanced matching market with heavy-tailed matching utilities , and find that the buyers and sellers have the same asymptotically optimal utility threshold  .
Revised:  We also study the utility threshold policy in an unbalanced matching market with heavy-tailed matching utilities , and find that the buyers and sellers have the same asymptotically optimal utility threshold . We derive optimal thresholds when the matching utility distribution is exponential, uniform, Pareto, and correlated Pareto. We find that as the right tail of the matching utility distribution gets heavier, the threshold level of each policy (and hence market thickness) increases, as does the magnitude by which the utility threshold policy outperforms the population threshold policy .
Edit intent: meaning-changed
Original: In a general one-sided limit order book  where the unaffected price process follows a Levy process, we consider the problem for an investor with constant absolute risk aversion to optimally liquidate a given large position of shares.
Revised: In a one-sided limit order book where the unaffected price process follows a Levy process, we consider the problem for an investor with constant absolute risk aversion to optimally liquidate a given large position of shares.
Edit intent: clarity
Original: In a general one-sided limit order book  where the unaffected price process follows a Levy process, we consider the problem for an investor with constant absolute risk aversion to optimally liquidate a given large position of shares.
Revised: In a general one-sided limit order book , satisfying some realistic assumptions, where the unaffected price process follows a Levy process, we consider the problem for an investor with constant absolute risk aversion to optimally liquidate a given large position of shares.
Edit intent: meaning-changed
Original:   Since liquidation normally takes place within a short period of time, modelling the risk as a Levy process should provide a realistic model with good statistical fit to observed market data, thus providing a realistic reflection of the investors market risk.
Revised:  We assume that the agent has constant absolute risk aversion and aims at maximising the expected utility of the cash position at the end of time. The agent is then faced with the problem of balancing the market risk and the cost of a rapid execution. In particular we are interested in how the agent should go about optimally submitting orders. Since liquidation normally takes place within a short period of time, modelling the risk as a Levy process should provide a realistic model with good statistical fit to observed market data, thus providing a realistic reflection of the investors market risk.
Edit intent: meaning-changed
Original:   Since liquidation normally takes place within a short period of time, modelling the risk as a Levy process should provide a realistic model with good statistical fit to observed market data, thus providing a realistic reflection of the investors market risk.
Revised:  Since liquidation normally takes place within a short period of time, modelling the risk as a Levy process should provide a realistic model with good statistical fit to observed market data, and thus the model should provide a realistic reflection of the investors market risk.
Edit intent: coherence
Original:   Since liquidation normally takes place within a short period of time, modelling the risk as a Levy process should provide a realistic model with good statistical fit to observed market data, thus providing a realistic reflection of the investors market risk.
Revised:  Since liquidation normally takes place within a short period of time, modelling the risk as a Levy process should provide a realistic model with good statistical fit to observed market data, thus providing a realistic reflection of the agent's market risk.
Edit intent: clarity
Original: The number of infections and the number of fatalities in the 2019 novel coronavirus epidemics follows a remarkably regular trend. Since the end of January , the ratio of fatalities per infection is about 2\% and remarkably stable. The increase appears to be roughly exponential , but with an e-folding time that gradually increases from just two days at the end of January to about ten days by February 9. Here we show that, since January 20 , the number of fatalities and infections increases quadratically and not exponentially, as widely believed.
Revised: The number of infections and the number of deaths in the COVID-19 epidemic is subexponential. Here we show that a piecewise quadratic law provides an excellent fit during the thirty days after the first three fatalities on January 20 , the number of fatalities and infections increases quadratically and not exponentially, as widely believed.
Edit intent: meaning-changed
Original:  Here we show that, since January 20 , the number of fatalities and infections increases quadratically and not exponentially, as widely believed. At present, no departure from this behavior can be seen, allowing tentative predictions to be made for the next 1-2 months.
Revised:  Here we show that, since January 20 and later since the end of March 2020. There is also a brief intermediate period of exponential growth. During the second quadratic growth phase, the characteristic time of the growth is about eight times shorter than in the beginning, which can be understood as the occurrence of separate hotspots. Quadratic behavior can be seen, allowing tentative predictions to be made for the next 1-2 months.
Edit intent: meaning-changed
Original:  At present, no departure from this behavior can be seen, allowing tentative predictions to be made for the next 1-2 months. Contrary to the usual exponential growth, a quadratic growth is the result of control interventions. It can be explained by spreading on the periphery of a bulk structure, which can be geometrical or sociological nature .
Revised:  At present, no departure from this behavior can be motivated by peripheral growth when further spreading occurs only on the outskirts of an infected region. We also study numerical solutions of a simple epidemic model, where the spatial extend of the system is taken into account. To model the delayed onset outside China together with the early one in China within a single model with minimal assumptions, we adopt an initial condition of several hotspots, of which one reaches saturation much earlier than the others. At each site, quadratic growth commences when the local number of infections has reached a certain saturation level. The total number of deaths does then indeed follow a piecewise quadratic behavior .
Edit intent: meaning-changed
Original:  It remains elusive how  public discourse is driven by the intricate interplay between identity politics and dueling campaign efforts .
Revised:  It remains elusive how public discourse is driven by the intricate interplay between identity politics and dueling campaign efforts , and social media platforms. We examine this complex socio-political process by integrating observed retweet networks from the 2016 political networks with an agent-based model of political opinion formation and network structure.
Edit intent: meaning-changed
Original:  The dataset consists of gridded data spanning over 21 years ( 1993--2013 ), while the methodological strategy makes use of a spatial dynamic panel data (SDPD) model that accounts for both space and time fixed effects, besides dealing with both space and time dependences.
Revised:  The dataset consists of gridded data spanning over 21 years ( 1993-2013 ), while the methodological strategy makes use of a spatial dynamic panel data (SDPD) model that accounts for both space and time fixed effects, besides dealing with both space and time dependences.
Edit intent: fluency
Original:  In addition to finding  a good support for the existence of an environmental Kuznets curve for fertilizer application, it shows peculiar responses of N-fertilization to deviations from normal  conditions of moisture for each selected region, calling for ad hoc policy interventions.
Revised:  In addition to finding both spatial (in the form of knowledge spillover effects) and temporal dependences as well as a good support for the existence of an environmental Kuznets curve for fertilizer application, it shows peculiar responses of N-fertilization to deviations from normal conditions of moisture for each selected region, calling for ad hoc policy interventions.
Edit intent: meaning-changed
Original:  In addition to finding  a good support for the existence of an environmental Kuznets curve for fertilizer application, it shows peculiar responses of N-fertilization to deviations from normal  conditions of moisture for each selected region, calling for ad hoc policy interventions.
Revised:  In addition to finding a good support for the existence of an environmental Kuznets curve for fertilizer application, the paper shows peculiar responses of N-fertilization to deviations from normal conditions of moisture for each selected region, calling for ad hoc policy interventions.
Edit intent: clarity
Original:  In addition to finding  a good support for the existence of an environmental Kuznets curve for fertilizer application, it shows peculiar responses of N-fertilization to deviations from normal  conditions of moisture for each selected region, calling for ad hoc policy interventions.
Revised:  In addition to finding a good support for the existence of an environmental Kuznets curve for fertilizer application, it shows peculiar responses of N-fertilization to deviations from normal weather conditions of moisture for each selected region, calling for ad hoc policy interventions.
Edit intent: meaning-changed
Original:  Although often used to compare outbreaks and forecast pandemic risk, this single number belies the complexity that two different pathogens can exhibit, even when they have the same R_0. Here, we show how predicting outbreak size requires both an estimate of R_0 and an estimate of the heterogeneity in the number of secondary infections.
Revised:  Although often used to compare outbreaks and forecast pandemic risk, this single number belies the complexity that two different pathogens can exhibit, even when they have the same R_0. Here, we show how to predict outbreak size using estimates of the distribution of secondary infections, leveraging both its average R_0 and an estimate of the heterogeneity in the number of secondary infections.
Edit intent: meaning-changed
Original:  Although often used to compare outbreaks and forecast pandemic risk, this single number belies the complexity that two different pathogens can exhibit, even when they have the same R_0. Here, we show how predicting outbreak size requires both an estimate of R_0 and an estimate of the heterogeneity in the number of secondary infections. To facilitate rapid determination of outbreak risk, we propose a reformulation of a classic result from random network theory that relies on contact tracing data to simultaneously determine the first moment (R_0) and the higher moments (representing the heterogeneity) in the distribution of secondary infections.
Revised:  Although often used to compare outbreaks and forecast pandemic risk, this single number belies the complexity that two different pathogens can exhibit, even when they have the same R_0. Here, we show how predicting outbreak size requires both an estimate of R_0 and the underlying heterogeneity. To do so, we reformulate and extend a classic result from random network theory that relies on contact tracing data to simultaneously determine the first moment (R_0) and the higher moments (representing the heterogeneity) in the distribution of secondary infections.
Edit intent: clarity
Original:  Lastly, we demonstrate that without data on the heterogeneity in secondary infections for emerging pathogens like 2019-nCoV , the uncertainty in outbreak size ranges dramatically , in the case of 2019-nCoV from 5-40%DIFDELCMD < \\%%% %DIF < of susceptible individuals.
Revised:  Lastly, we demonstrate that without data on the heterogeneity in secondary infections for emerging infectious diseases like COVID-19 , the uncertainty in outbreak size ranges dramatically , in the case of 2019-nCoV from 5-40%DIFDELCMD < \\%%% %DIF < of susceptible individuals.
Edit intent: meaning-changed
Original:  Lastly, we demonstrate that without data on the heterogeneity in secondary infections for emerging pathogens like 2019-nCoV , the uncertainty in outbreak size ranges dramatically , in the case of 2019-nCoV from 5-40%DIFDELCMD < \\%%% %DIF < of susceptible individuals.
Revised:  Lastly, we demonstrate that without data on the heterogeneity in secondary infections for emerging pathogens like 2019-nCoV , the uncertainty in outbreak size ranges dramatically %DIFDELCMD < \\%%% %DIF < of susceptible individuals.
Edit intent: coherence
Original:  Taken together, our work highlights the critical need for contact tracing during emerging infectious disease outbreaks and the need to look beyond R_0 when predicting epidemic size.abstract 
Revised:  Taken together, our work highlights the critical need for contact tracing during emerging infectious disease outbreaks and the need to look beyond R_0 when predicting epidemic size.abstract . Taken together, our work highlights the critical need for contact tracing during emerging infectious disease outbreaks and the need to look beyond R_0 when predicting epidemic size.
Edit intent: fluency
Original:  The new goal of HCAI is more likely to produce designs that are Trusted, Reliable Safe (TRS  ). Achieving these goals will dramatically increase human performance, while supporting human self-efficacy, mastery, creativity, and responsibility.
Revised:  The methods of HCAI are more likely to produce designs that are Trusted, Reliable Safe (TRS ). Achieving these goals will dramatically increase human performance, while supporting human self-efficacy, mastery, creativity, and responsibility.
Edit intent: clarity
Original:  The new goal of HCAI is more likely to produce designs that are Trusted, Reliable Safe (TRS  ). Achieving these goals will dramatically increase human performance, while supporting human self-efficacy, mastery, creativity, and responsibility.
Revised:  The new goal of HCAI is more likely to produce designs that are Safe (TRS ). Achieving these goals will dramatically increase human performance, while supporting human self-efficacy, mastery, creativity, and responsibility.
Edit intent: coherence
Original:  The new goal of HCAI is more likely to produce designs that are Trusted, Reliable Safe (TRS  ). Achieving these goals will dramatically increase human performance, while supporting human self-efficacy, mastery, creativity, and responsibility.
Revised:  The new goal of HCAI is more likely to produce designs that are Trusted, Reliable Reliable, Safe ). Achieving these goals will dramatically increase human performance, while supporting human self-efficacy, mastery, creativity, and responsibility.
Edit intent: clarity
Original:  The new goal of HCAI is more likely to produce designs that are Trusted, Reliable Safe (TRS  ). Achieving these goals will dramatically increase human performance, while supporting human self-efficacy, mastery, creativity, and responsibility.
Revised:  The new goal of HCAI is more likely to produce designs that are Trusted, Reliable Safe (TRS Trustworthy (RST ). Achieving these goals will dramatically increase human performance, while supporting human self-efficacy, mastery, creativity, and responsibility.
Edit intent: meaning-changed
Original:  Our experiments show that as a result of the substantial reduction of these biases, models trained on the filtered datasets yield better generalization to out-of-distribution tasks , especially when the benchmarks used for training are over-populated with biased samples.
Revised:  We present extensive supporting evidence that AFLite is broadly applicable for reduction of measurable dataset biases, and that models trained on the filtered datasets yield better generalization to out-of-distribution tasks , especially when the benchmarks used for training are over-populated with biased samples.
Edit intent: meaning-changed
Original:  Our experiments show that as a result of the substantial reduction of these biases, models trained on the filtered datasets yield better generalization to out-of-distribution tasks , especially when the benchmarks used for training are over-populated with biased samples. We show that AFLite is broadly applicable to a variety of both real and synthetic datasets for reduction of measurable dataset biases and provide extensive supporting analyses.
Revised:  Our experiments show that as a result of the substantial reduction of these biases, models trained on the filtered datasets yield better generalization to out-of-distribution tasks .
Edit intent: coherence
Original:  Then, we show that, if the human policy is known, we can find the optimal switching policy directly by solving a set of recursive equations using backwards induction. However, in practice, the human policy is often unknown. To overcome this, we develop an algorithm that uses upper confidence bounds on the human policy to find a sequence of switching policies whose total regret with respect to the optimal switching policy is sublinear . Simulation experiments on two important tasks in autonomous driving---lane keeping and obstacle avoidance---demonstrate the effectiveness of the proposed algorithms and illustrate our theoretical findings .
Revised:  Then, we develop an online learning algorithm that uses upper confidence bounds on the human policy to find a sequence of switching policies whose total regret with respect to the optimal switching policy is sublinear . Simulation experiments on two important tasks in autonomous driving---lane keeping and obstacle avoidance---demonstrate the effectiveness of the proposed algorithms and illustrate our theoretical findings .
Edit intent: clarity
Original:  To overcome this, we develop an algorithm that uses upper confidence bounds on the human policy to find a sequence of switching policies whose total regret with respect to the optimal switching policy is sublinear . Simulation experiments on two important tasks in autonomous driving---lane keeping and obstacle avoidance---demonstrate the effectiveness of the proposed algorithms and illustrate our theoretical findings .
Revised:  To overcome this, we develop an algorithm that uses upper confidence bounds on the agents' policies and the environment's transition probabilities to find a sequence of switching policies whose total regret with respect to the optimal switching policy is sublinear . Simulation experiments on two important tasks in autonomous driving---lane keeping and obstacle avoidance---demonstrate the effectiveness of the proposed algorithms and illustrate our theoretical findings .
Edit intent: meaning-changed
Original:  To overcome this, we develop an algorithm that uses upper confidence bounds on the human policy to find a sequence of switching policies whose total regret with respect to the optimal switching policy is sublinear . Simulation experiments on two important tasks in autonomous driving---lane keeping and obstacle avoidance---demonstrate the effectiveness of the proposed algorithms and illustrate our theoretical findings .
Revised:  To overcome this, we develop an algorithm that uses upper confidence bounds on the human policy to find a sequence of switching policies . We prove that the total regret of our algorithm with respect to the optimal switching policy is sublinear . Simulation experiments on two important tasks in autonomous driving---lane keeping and obstacle avoidance---demonstrate the effectiveness of the proposed algorithms and illustrate our theoretical findings .
Edit intent: meaning-changed
Original:  To overcome this, we develop an algorithm that uses upper confidence bounds on the human policy to find a sequence of switching policies whose total regret with respect to the optimal switching policy is sublinear . Simulation experiments on two important tasks in autonomous driving---lane keeping and obstacle avoidance---demonstrate the effectiveness of the proposed algorithms and illustrate our theoretical findings .
Revised:  To overcome this, we develop an algorithm that uses upper confidence bounds on the human policy to find a sequence of switching policies whose total regret with respect to the optimal switching policy is sublinear in the number of learning steps. Moreover, we also show that our algorithm can be used to find multiple sequences of switching policies across several independent teams of agents operating in similar environments, where it greatly benefits from maintaining shared confidence bounds for the environments' transition probabilities. Simulation experiments in obstacle avoidance in a semi-autonomous driving scenario illustrate our theoretical findings and demonstrate that, by exploiting the specific structure of the problem, our proposed algorithm is superior to problem-agnostic algorithms .
Edit intent: meaning-changed
Original: In simultaneous machine translation, the system needs to incrementally generate the output translation before the input sentence ends. This is a coupled decision process consisting of a programmer and interpreter. The programmer's policy decides about when to WRITE the next output or READ the next input, and the interpreter's policy decides what word to write. We present an imitation learning (IL) approach to efficiently learn effective coupled programmer-interpreter policies.
Revised: We present a novel approach to efficiently learn effective coupled programmer-interpreter policies.
Edit intent: coherence
Original:  Experiments show our method outperforms strong baselines in terms of translation quality and delay , when translating from German/Arabic/Czech/Bulgarian/Romanian to English .
Revised:  Next, we perform a coupled scheduled sampling to effectively mitigate the exposure bias when learning both policies jointly with imitation learning. Experiments on six language-pairs show our method outperforms strong baselines in terms of translation quality and delay , when translating from German/Arabic/Czech/Bulgarian/Romanian to English .
Edit intent: meaning-changed
Original:  By comparing analytical approximations and exact numerical simulations, we show that the proposed methods provide more accurate estimates for the evolution of equal-time and delayed covariance structures than classical equations, and consequently outperform previous mean field theories for solving the inverse Ising problem.
Revised:  By comparing analytical approximations and exact numerical simulations, we show that the proposed methods provide more accurate estimates of covariance evolution in the system than classical equations, and consequently outperform previous mean field theories for solving the inverse Ising problem.
Edit intent: clarity