wanyu/IteraTeR_full_sent huggingface dataset:
Original:  By using the first wave to estimate a stratification tree, we simultaneously select which covariates to use for stratification, how to stratify over these covariates, as well as the assign- ment probabilities within these strata.
Revised:  By using the first wave to estimate a stratification tree, we simultaneously select which covariates to use for stratification, how to stratify over these covariates, as well as the assignment probabilities within these strata.
Edit intent: fluency
Original:  In a simulation study, we find that our method is most effective when the response model exhibits some amount of "sparsity" with respect to the covariates,but can be effective in other contexts as well, as long as the first-wave sample size used to estimate the stratificationtree is not prohibitively small .
Revised:  In a simulation study, we find that our method , paired with an appropriate cross-validation procedure ,can improve on ad-hoc choices of stratification .
Edit intent: clarity
Original: This paper studies the profitability of market making strategies and the impact of latencyon electronic market makers' profits for large-tick assets. By analyzing the optimal market making  problem using Markov Decision Processes , we provide simple conditions to determine when a market maker earns zero or positive profits and discuss economic implications.
Revised: This paper studies optimal market making problem using Markov Decision Processes , we provide simple conditions to determine when a market maker earns zero or positive profits and discuss economic implications.
Edit intent: clarity
Original:  By analyzing the optimal market making  problem using Markov Decision Processes , we provide simple conditions to determine when a market maker earns zero or positive profits and discuss economic implications.
Revised:  By analyzing the optimal market making for large--tick assets in the presence of latency. We formulate the problem using Markov Decision Processes , we provide simple conditions to determine when a market maker earns zero or positive profits and discuss economic implications.
Edit intent: meaning-changed
Original:  By analyzing the optimal market making  problem using Markov Decision Processes , we provide simple conditions to determine when a market maker earns zero or positive profits and discuss economic implications.
Revised:  By analyzing the optimal market making problem using Markov Decision Processes . We provide explicit characterizations of the order value and the structure of the value functions. We use such characterizations to study when a market maker earns zero or positive profits and discuss economic implications.
Edit intent: meaning-changed
Original:  By analyzing the optimal market making  problem using Markov Decision Processes , we provide simple conditions to determine when a market maker earns zero or positive profits and discuss economic implications. We also prove that higher latency leads to reduced profits for market makers, and conduct numerical experiments to illustrate the effect of latency and relative latency on the market maker's expected profit.
Revised:  By analyzing the optimal market making problem using Markov Decision Processes , we provide simple conditions to determine when a market maker can earn a positive net profit and investigate the effect of latency and relative latency on the market maker's expected profit.
Edit intent: clarity
Original:  We also prove that higher latency leads to reduced profits for market makers, and conduct numerical experiments to illustrate the effect of latency and relative latency on the market maker's expected profit. Finally, our work highlights the importance of value of orders in optimal market making .
Revised:  We also prove that higher latency leads to reduced profits for market makers, and conduct numerical experiments to illustrate the effect of latency . Numerical experiments are conducted to illustrate our results .
Edit intent: clarity
Original:  We formulate the problem using Markov Decision Processes . We provide explicit characterizations of the order value and the structure of the value functions. We use such characterizations to study when a market maker can earn a positive net profit and investigate the effect of latency.
Revised:  We formulate the problem using Markov Decision Processes (MDP). We characterize the value of an order and show that it plays the role of one-period reward in the MDP model. Based on this characterization, we provide explicit criteria for assessing the profitability of market making when there is latency. Under our model, we show that a market maker can earn a positive net profit and investigate the effect of latency.
Edit intent: meaning-changed
Original:  We use such characterizations to study when a market maker can earn a positive net profit and investigate the effect of latency. Numerical experiments are conducted to illustrate our results .
Revised:  We use such characterizations to study when a market maker can earn a positive expected profit if there are sufficient uninformed market orders hitting the market maker's limit orders compared with the rate of price jumps, and the trading horizon is sufficiently long. In addition, our theoretical and numerical results suggest that latency can be an additional source of risk and latency impacts negatively the performance of market makers .
Edit intent: meaning-changed
Original: We adapt population annealing , a simulation scheme originating from Monte Carlo simulations , to all-atom molecular dynamics simulations . We demonstrate its excellent performance in computer simulations of biopolymers by investigating the folding of the penta-peptide met-enkephalin, a common test protein .
Revised: We adapt population annealing , a simulation scheme originating from Monte Carlo simulations , to all-atom molecular dynamics simulations and demonstrate its excellent performance in computer simulations of biopolymers by investigating the folding of the penta-peptide met-enkephalin, a common test protein .
Edit intent: coherence
Original:  In contrast to parallel tempering , however, population annealing scales to a nearly arbitrary number of parallel processors and it is thus able to tap into the massively parallel computing power available in petaflop and future exaflop machines .
Revised:  In contrast to other methods , however, population annealing scales to a nearly arbitrary number of parallel processors and it is thus able to tap into the massively parallel computing power available in petaflop and future exaflop machines .
Edit intent: clarity
Original:  In contrast to parallel tempering , however, population annealing scales to a nearly arbitrary number of parallel processors and it is thus able to tap into the massively parallel computing power available in petaflop and future exaflop machines .
Revised:  In contrast to parallel tempering , however, population annealing scales to a nearly arbitrary number of parallel processors and it is thus a unique tool that enables molecular dynamics to tap into the massively parallel computing power available in petaflop and future exaflop machines .
Edit intent: meaning-changed
Original:  In contrast to parallel tempering , however, population annealing scales to a nearly arbitrary number of parallel processors and it is thus able to tap into the massively parallel computing power available in petaflop and future exaflop machines .
Revised:  In contrast to parallel tempering , however, population annealing scales to a nearly arbitrary number of parallel processors and it is thus able to tap into the massively parallel computing power available in supercomputers that is so much needed for a range of difficult computational problems .
Edit intent: meaning-changed
Original:  Based on the frequency decomposition of covariance between indicator functions  , we define the quantile cross-spectral beta of an assetcapturing tail-specific  as well as horizon-, or frequency-specific  risks.
Revised:  Based on the decomposition of covariance between indicator functions , we define the quantile cross-spectral beta of an assetcapturing tail-specific as well as horizon-, or frequency-specific risks.
Edit intent: fluency
Original:  Based on the frequency decomposition of covariance between indicator functions  , we define the quantile cross-spectral beta of an assetcapturing tail-specific  as well as horizon-, or frequency-specific  risks.
Revised:  Based on the frequency decomposition of covariance between indicator functions capturing fluctuations of different parts of return distributions over various frequencies , we define the quantile cross-spectral beta of an assetcapturing tail-specific as well as horizon-, or frequency-specific risks.
Edit intent: meaning-changed
Original:  Based on the frequency decomposition of covariance between indicator functions  , we define the quantile cross-spectral beta of an assetcapturing tail-specific  as well as horizon-, or frequency-specific  risks.
Revised:  Based on the frequency decomposition of covariance between indicator functions , we define the quantile cross-spectral beta of an assetcapturing tail-specificquantile spectral as well as horizon-, or frequency-specific risks.
Edit intent: meaning-changed
Original:  Based on the frequency decomposition of covariance between indicator functions  , we define the quantile cross-spectral beta of an assetcapturing tail-specific  as well as horizon-, or frequency-specific  risks.
Revised:  Based on the frequency decomposition of covariance between indicator functions , we define the quantile cross-spectral beta of an assetcapturing tail-specific beta representation that characterizes asset's risk generally. Nesting the traditional frameworks, the new representation explains as well as horizon-, or frequency-specific risks.
Edit intent: meaning-changed
Original:  Based on the frequency decomposition of covariance between indicator functions  , we define the quantile cross-spectral beta of an assetcapturing tail-specific  as well as horizon-, or frequency-specific  risks.
Revised:  Based on the frequency decomposition of covariance between indicator functions , we define the quantile cross-spectral beta of an assetcapturing tail-specific tail as well as horizon-, or frequency-specific risks.
Edit intent: fluency
Original:  Based on the frequency decomposition of covariance between indicator functions  , we define the quantile cross-spectral beta of an assetcapturing tail-specific  as well as horizon-, or frequency-specific  risks.
Revised:  Based on the frequency decomposition of covariance between indicator functions , we define the quantile cross-spectral beta of an assetcapturing tail-specific -specific as well as horizon-, or frequency-specific risks.
Edit intent: meaning-changed
Original:  Based on the frequency decomposition of covariance between indicator functions  , we define the quantile cross-spectral beta of an assetcapturing tail-specific  as well as horizon-, or frequency-specific  risks.
Revised:  Based on the frequency decomposition of covariance between indicator functions , we define the quantile cross-spectral beta of an assetcapturing tail-specific as well as horizon-, or frequency-specific spectral risks.
Edit intent: clarity
Original: We examine how extreme market risks are priced in the cross-section of asset returns at various horizons. Based on the decomposition of covariance between indicator functions capturing fluctuations of different parts of return distributions over various frequencies, we define aquantile spectral beta representation that characterizes asset's risk generally.
Revised: We examine how extreme market risks are priced in the cross-section of asset returns quantile spectral beta representation that characterizes asset's risk generally.
Edit intent: clarity
Original:  Based on the decomposition of covariance between indicator functions capturing fluctuations of different parts of return distributions over various frequencies, we define aquantile spectral beta representation that characterizes asset's risk generally. Nesting the traditional frameworks, the new representation explainstail -specific as well as horizon-, or frequency-specificspectral risks.
Revised:  Based on the decomposition of covariance between indicator functions capturing fluctuations of different parts of return distributions over various frequencies, we define aquantile spectral tail -specific as well as horizon-, or frequency-specificspectral risks.
Edit intent: clarity
Original:  Nesting the traditional frameworks, the new representation explainstail -specific as well as horizon-, or frequency-specificspectral risks. Further, we work with two notions of frequency-specific extreme market risks. First, we define tail market risk that captures dependence between extremely low market and asset returns. Second, extreme market volatility risk is characterized by dependence between extremely high increments of market volatility and extremely low asset return.
Revised:  Nesting the traditional frameworks, the new representation explainstail -specific as well as horizon-, or frequency-specificspectral heterogeneously across horizons. Specifically, we find that tail risk is a short-term phenomenon whereas extreme volatility risk is characterized by dependence between extremely high increments of market volatility and extremely low asset return.
Edit intent: meaning-changed
Original:  Second, extreme market volatility risk is characterized by dependence between extremely high increments of market volatility and extremely low asset return. Empirical findings based on the datasets with long enough history, 30 Fama-French Industry portfolios, and 25 Fama-French portfolios sorted on size and book-to-market support our intuition. We reach the same conclusion using stock-level data as well as daily data.
Revised:  Second, extreme market volatility risk is priced by investors in the long-term. These risks stem from a dependence structures in the joint distribution of stochastic discount factor and asset returns at various investment horizons that are more general than usually assumed by traditional covariance-based measures. The risk premium we document suggests that investors care about the transitory as well as daily data.
Edit intent: meaning-changed
Original:  Here we demonstrate that geography /climate variables satisfactorily explain the worldwide economic activity as measured by the per capita Gross Cell Product (GCP-PC) at a high geographical resolution, typically much higher than country average.
Revised:  Here we demonstrate that geography and climate satisfactorily explain worldwide economic activity as measured by the per capita Gross Cell Product (GCP-PC) at a high geographical resolution, typically much higher than country average.
Edit intent: clarity
Original:  Here we demonstrate that geography /climate variables satisfactorily explain the worldwide economic activity as measured by the per capita Gross Cell Product (GCP-PC) at a high geographical resolution, typically much higher than country average.
Revised:  Here we demonstrate that geography /climate variables satisfactorily explain the worldwide economic activity as measured by the per capita Gross Cell Product (GCP-PC) at a fine geographical resolution, typically much higher than country average.
Edit intent: clarity
Original:  While this study confirms that latitude is the most important predictor for GCP-PC when taken in isolation, the accuracy of the GCP-PC prediction is greatly improved when other factors mainly related to variations in climatic variables,  rather than average climatic conditions  , are considered.
Revised:  While this study confirms that latitude is the most important predictor for GCP-PC when taken in isolation, the accuracy of the GCP-PC prediction is greatly improved when other factors mainly related to variations in climatic variables, such as the variability in air pressure, rather than average climatic conditions , are considered.
Edit intent: meaning-changed
Original:  While this study confirms that latitude is the most important predictor for GCP-PC when taken in isolation, the accuracy of the GCP-PC prediction is greatly improved when other factors mainly related to variations in climatic variables,  rather than average climatic conditions  , are considered.
Revised:  While this study confirms that latitude is the most important predictor for GCP-PC when taken in isolation, the accuracy of the GCP-PC prediction is greatly improved when other factors mainly related to variations in climatic variables, rather than average climatic conditions as typically used , are considered.
Edit intent: meaning-changed
Original:  However, latitude diminishes in importance when only the wealthier parts of the globe are considered. This work points to specific features of the climate system which appear key to driving economic activity, such as the variability in air pressure or the seasonal variations of dew point temperature. The implications of these findings range from a better understanding of why socio-economically better-off societies are geographically placed where they are , in the present, past and future, to informing where new economic activities could be established in order to yield favourable economic outcomes based on geography/climate conditions.
Revised:  Implications of these findings include an improved understanding of why socio-economically better-off societies are geographically placed where they are , in the present, past and future, to informing where new economic activities could be established in order to yield favourable economic outcomes based on geography/climate conditions.
Edit intent: clarity
Original:  The implications of these findings range from a better understanding of why socio-economically better-off societies are geographically placed where they are , in the present, past and future, to informing where new economic activities could be established in order to yield favourable economic outcomes based on geography/climate conditions.
Revised:  The implications of these findings range from a better understanding of why economically better-off societies are geographically placed where they are , in the present, past and future, to informing where new economic activities could be established in order to yield favourable economic outcomes based on geography/climate conditions.
Edit intent: clarity
Original:  The implications of these findings range from a better understanding of why socio-economically better-off societies are geographically placed where they are , in the present, past and future, to informing where new economic activities could be established in order to yield favourable economic outcomes based on geography/climate conditions.
Revised:  The implications of these findings range from a better understanding of why socio-economically better-off societies are geographically placed where they are 
Edit intent: coherence
Original: The nonuniqueness of solutions of linear rational-expectations models is explainedand resolved. In the stochastic, discrete-time,  constant-coefficients case, the associated free parameters are shown to be the initial values of the impulse responses of expectation terms.
Revised: The nonuniqueness of rational expectations is explained: in the stochastic, discrete-time, constant-coefficients case, the associated free parameters are shown to be the initial values of the impulse responses of expectation terms.
Edit intent: clarity
Original:  In the stochastic, discrete-time,  constant-coefficients case, the associated free parameters are shown to be the initial values of the impulse responses of expectation terms. These values may be completely unconstrained by the requirement of model-consistency  , yet when  appropriately specified, they determine a unique solution .
Revised:  In the stochastic, discrete-time, constant-coefficients case, the associated free parameters are coefficients that determine the public's most immediate reactions to shocks. The requirement of model-consistency , yet when appropriately specified, they determine a unique solution .
Edit intent: clarity
Original:  These values may be completely unconstrained by the requirement of model-consistency  , yet when  appropriately specified, they determine a unique solution . This finding demonstrates the necessity of complementing rational expectations with modelsof the most immediate forecast revisions that result from economic shocks .
Revised:  These values may be completely unconstrained by the requirement of model-consistency , yet when appropriately specified, they determine a unique solution is determined. In a broad class of models, the requirement of least-square forecast errors determines the parameter values, and therefore defines a unique solution. This approach is independent of dynamical stability, and generally does not suppress model dynamics. Application to a standard New Keynesian example shows that the traditional solution suppresses precisely those dynamics that arise from rational expectations. The uncovering of those dynamics reveals their incompatibility with the new I-S equation and the expectational Phillips curve .
Edit intent: meaning-changed
Original:  Due to the nonlinearity of the space of persistence diagrams equipped with their {\em diagram distances}, most of the recent attempts at using persistence diagrams in Machine Learning have been done through kernel methods, i.e., embeddings of persistence diagrams into Reproducing Kernel Hilbert Spaces (RKHS) , in which all computations can be performed easily.
Revised:  Due to the nonlinearity of the space of persistence diagrams equipped with their {\em diagram distances}, most of the recent attempts at using persistence diagrams in machine learning have been done through kernel methods, i.e., embeddings of persistence diagrams into Reproducing Kernel Hilbert Spaces (RKHS) , in which all computations can be performed easily.
Edit intent: fluency
Original:  Due to the nonlinearity of the space of persistence diagrams equipped with their {\em diagram distances}, most of the recent attempts at using persistence diagrams in Machine Learning have been done through kernel methods, i.e., embeddings of persistence diagrams into Reproducing Kernel Hilbert Spaces (RKHS) , in which all computations can be performed easily.
Revised:  Due to the nonlinearity of the space of persistence diagrams equipped with their {\em diagram distances}, most of the recent attempts at using persistence diagrams in Machine Learning have been done through kernel methods, i.e., embeddings of persistence diagrams into Reproducing Kernel Hilbert Spaces , in which all computations can be performed easily.
Edit intent: clarity
Original:  Since persistence diagrams enjoy theoretical stability guarantees for the diagram distances, the {\em metric properties} of a kernel k , i.e., the relationship between the RKHS distance d_k and the diagram distances, are of central interest for understanding if the persistence diagram guarantees carry over to the embedding.
Revised:  Since persistence diagrams enjoy theoretical stability guarantees for the diagram distances, the {\em metric properties} of the feature map , i.e., the relationship between the RKHS distance d_k and the diagram distances, are of central interest for understanding if the persistence diagram guarantees carry over to the embedding.
Edit intent: meaning-changed
Original:  Since persistence diagrams enjoy theoretical stability guarantees for the diagram distances, the {\em metric properties} of a kernel k , i.e., the relationship between the RKHS distance d_k and the diagram distances, are of central interest for understanding if the persistence diagram guarantees carry over to the embedding.
Revised:  Since persistence diagrams enjoy theoretical stability guarantees for the diagram distances, the {\em metric properties} of a kernel k , i.e., the relationship between the Hilbert distance and the diagram distances, are of central interest for understanding if the persistence diagram guarantees carry over to the embedding.
Edit intent: meaning-changed