wanyu/IteraTeR_full_sent huggingface dataset:
Original:  The estimator's limiting distribution can be represented by a linear combination of normal and non-central \chi^2 random variables . Consistent variance estimators are proposed along with a procedure for constructing uniformly valid confidence intervals . Applying a two-way fixed effects model of wage determination to Italian social security records,
Revised:  The estimator's limiting distribution can be represented by a linear combination of normal and non-central \chi^2 random variables , with normality ensuing under strong identification. Standard error estimators are proposed along with a procedure for constructing uniformly valid confidence intervals . Applying a two-way fixed effects model of wage determination to Italian social security records,
Edit intent: meaning-changed
Original:  The estimator's limiting distribution can be represented by a linear combination of normal and non-central \chi^2 random variables . Consistent variance estimators are proposed along with a procedure for constructing uniformly valid confidence intervals . Applying a two-way fixed effects model of wage determination to Italian social security records,
Revised:  The estimator's limiting distribution can be represented by a linear combination of normal and non-central \chi^2 random variables . Consistent variance estimators are proposed that enable tests of linear restrictions and the construction of uniformly valid confidence intervals . Applying a two-way fixed effects model of wage determination to Italian social security records,
Edit intent: clarity
Original:  The estimator's limiting distribution can be represented by a linear combination of normal and non-central \chi^2 random variables . Consistent variance estimators are proposed along with a procedure for constructing uniformly valid confidence intervals . Applying a two-way fixed effects model of wage determination to Italian social security records,
Revised:  The estimator's limiting distribution can be represented by a linear combination of normal and non-central \chi^2 random variables . Consistent variance estimators are proposed along with a procedure for constructing uniformly valid confidence intervals for quadratic forms of interest. We find in Italian social security records that leave-out estimates of a variance decomposition in a two-way fixed effects model of wage determination to Italian social security records,
Edit intent: meaning-changed
Original:  The estimator's limiting distribution can be represented by a linear combination of normal and non-central \chi^2 random variables . Consistent variance estimators are proposed along with a procedure for constructing uniformly valid confidence intervals . Applying a two-way fixed effects model of wage determination to Italian social security records, we find that ignoring heteroscedasticity substantially biases conclusions regarding the relative contribution of workers, firms, and worker-firm sorting to wage inequality  .
Revised:  The estimator's limiting distribution can be represented by a linear combination of normal and non-central \chi^2 random variables . Consistent variance estimators are proposed along with a procedure for constructing uniformly valid confidence intervals . Applying a two-way fixed effects model of wage determination yield substantially different conclusions regarding the relative contribution of workers, firms, and worker-firm sorting to wage inequality .
Edit intent: clarity
Original:  To answer this question and understand post-acquisition behavior and its related individual differences, we propose a psychological principle that naturally extends the basic associative single-cue Rescorla-Wagner model (which may also be called Hull model) of Pavlovian conditioning to (i) a framework of dynamical models predicting resistance to learning in the first few sessions followed by an over-optimal response peak .
Revised:  To answer this question and understand post-acquisition behavior and its related individual differences, we propose a psychological principle that naturally extends associative models of Pavlovian conditioning to (i) a framework of dynamical models predicting resistance to learning in the first few sessions followed by an over-optimal response peak .
Edit intent: coherence
Original:  To answer this question and understand post-acquisition behavior and its related individual differences, we propose a psychological principle that naturally extends the basic associative single-cue Rescorla-Wagner model (which may also be called Hull model) of Pavlovian conditioning to (i) a framework of dynamical models predicting resistance to learning in the first few sessions followed by an over-optimal response peak .
Revised:  To answer this question and understand post-acquisition behavior and its related individual differences, we propose a psychological principle that naturally extends the basic associative single-cue Rescorla-Wagner model (which may also be called Hull model) of Pavlovian conditioning to a dynamical oscillatory model where subjects have a greater memory capacity than usually postulated, but with greater forecast uncertainty. This results in a greater resistance to learning in the first few sessions followed by an over-optimal response peak .
Edit intent: meaning-changed
Original:  To answer this question and understand post-acquisition behavior and its related individual differences, we propose a psychological principle that naturally extends the basic associative single-cue Rescorla-Wagner model (which may also be called Hull model) of Pavlovian conditioning to (i) a framework of dynamical models predicting resistance to learning in the first few sessions followed by an over-optimal response peak . In turn, (ii) the theory can be further extended to describe response fluctuations by the laws of quantum mechanics. (iii) We also introduce an independent model characterized by the presence of a stochastic noise of cognitive origin .
Revised:  To answer this question and understand post-acquisition behavior and its related individual differences, we propose a psychological principle that naturally extends the basic associative single-cue Rescorla-Wagner model (which may also be called Hull model) of Pavlovian conditioning to (i) a framework of dynamical models predicting resistance to learning in the first few sessions followed by an over-optimal response peak and a sequence of progressively damped response oscillations. We detected the first peak and trough of the new learning curve in our data, but their dispersion was too large to also check the presence of a stochastic noise of cognitive origin .
Edit intent: meaning-changed
Original:  There is weak to positive evidence that Hull model is the best nonlinear regression to averaged data only for a minority of the subjects, while its dynamical extension (i) can explain the  totality of data .
Revised:  Using the data of this and another Pavlovian experiment by Harris et al. (2015), as an illustration of the principle we tested the theory against the basic associative single-cue Rescorla-Wagner (RW) model. We found evidence that the RW model is the best nonlinear regression to averaged data only for a minority of the subjects, while its dynamical extension (i) can explain the totality of data .
Edit intent: meaning-changed
Original:  There is weak to positive evidence that Hull model is the best nonlinear regression to averaged data only for a minority of the subjects, while its dynamical extension (i) can explain the  totality of data .
Revised:  There is weak to positive evidence that Hull model is the best nonlinear regression to averaged data only for a minority of the subjects, while its dynamical extension (i) can explain the almost totality of data .
Edit intent: meaning-changed
Original:  There is weak to positive evidence that Hull model is the best nonlinear regression to averaged data only for a minority of the subjects, while its dynamical extension (i) can explain the  totality of data . The noise encountered in all individual responses is white , thus confirming the simplest version of model (iii) but standing in contrast with the colored-noise findings in human performance .
Revised:  There is weak to positive evidence that Hull model is the best nonlinear regression to averaged data only for a minority of the subjects, while its dynamical extension (i) can explain the totality of data with strong to very strong evidence. Finally, an analysis of short-scale fluctuations of individual responses showed that they are described by random white noise, in contrast with the colored-noise findings in human performance .
Edit intent: meaning-changed
Original:  The noise encountered in all individual responses is white , thus confirming the simplest version of model (iii) but standing in contrast with the colored-noise findings in human performance . Finally, data are compatible with the quantum extension (ii), although experimental uncertainties dominate the result. On the other hand, data do not favor models with (iv) a long-memory effect or (v) where response variability is solely described by a random fractal .
Revised:  The noise encountered in all individual responses is white , thus confirming the simplest version of model (iii) but standing in contrast with the colored-noise findings in human performance .
Edit intent: coherence
Original:  A second alternative, the Bayesian bootstrap strategy, does not suffer from the same deficit and is more logically consistent with the type of confidence interval desired  , but must be used with caution nevertheless.
Revised:  A second alternative, the Bayesian bootstrap strategy, does not suffer from the same deficit and is more logically consistent with the type of confidence interval desired . The Bayesian bootstrap provides uncertainty intervals that are more reliable than those from the standard bootstrap method , but must be used with caution nevertheless.
Edit intent: meaning-changed
Original: In this paper , we analyze a genetic toggle switch recently studied in the literature where the expression of the two repressor proteins can be tuned by controlling two different inputs, namely the concentration of two inducer molecules in the growth medium of the cells.
Revised: In this letter , we analyze a genetic toggle switch recently studied in the literature where the expression of the two repressor proteins can be tuned by controlling two different inputs, namely the concentration of two inducer molecules in the growth medium of the cells.
Edit intent: clarity
Original: In this paper , we analyze a genetic toggle switch recently studied in the literature where the expression of the two repressor proteins can be tuned by controlling two different inputs, namely the concentration of two inducer molecules in the growth medium of the cells.
Revised: In this paper , we analyze a genetic toggle switch recently studied in the literature where the expression of two repressor proteins can be tuned by controlling two different inputs, namely the concentration of two inducer molecules in the growth medium of the cells.
Edit intent: fluency
Original: Deep neural network models are usually trained in cluster environments, where the model parameters are iteratively refined by multiple worker machines in parallel. One key challenge in this regard is the presence of stragglers, which significantly degrades the learning performance .
Revised: Machine learning (ML) models are increasingly trained in clusters with non-dedicated workers possessing heterogeneous resources. In such scenarios, model training efficiency can be negatively affected by stragglers -- workers that run much slower than others. Efficient model training requires eliminating such stragglers, yet for modern ML workloads, existing load balancing strategies are inefficient and even infeasible .
Edit intent: clarity
Original:  In this paper, we propose  to eliminate stragglers by adapting each worker's training load to its processing capability; that is , slower workers receive a smaller batch of data to process. Following this idea, we develop a new synchronization scheme called LB-BSP (Load-balanced BSP).
Revised:  In this paper, we propose to eliminate stragglers of distributed ML workloads. The key insight is that ML workers shall be load-balanced at iteration boundaries, being non-intrusive to intra-iteration execution. We develop LB-BSP (Load-balanced BSP).
Edit intent: meaning-changed
Original:  Following this idea, we develop a new synchronization scheme called LB-BSP (Load-balanced BSP). It works by coordinately setting the batch size of each worker so that they can finish batch processing at around the same time. A prerequisite for deciding the workers' batch sizes is to know their processing speeds before each iteration starts. For the best prediction accuracy, we adopt NARX, an extended recurrent neural network that accounts for both the historical speeds and the driving factors such as CPU and memory in prediction. We have implemented LB-BSP for both TensorFlow and MXNet.
Revised:  Following this idea, we develop a new synchronization scheme called LB-BSP based on such an insight, which is an integrated worker coordination mechanism that adapts workers' load to their instantaneous processing capabilities by right-sizing the sample batches at the synchronization barriers. We have custom-designed the batch sizing algorithm respectively for CPU and GPU clusters based on their own characteristics. LB-BSP for both TensorFlow and MXNet.
Edit intent: meaning-changed
Original: Despite the fact that the Kendall correlation is becoming recognized as an important tool in neuroscience, most standard algorithms to compute the Kendall correlation of large spike trains are computationally very slow.
Revised: The ability to record from increasingly large numbers of neurons, and the increasing attention being paid to large scale neural network simulations, demands computationally fast algorithms to compute the Kendall correlation of large spike trains are computationally very slow.
Edit intent: meaning-changed
Original: Despite the fact that the Kendall correlation is becoming recognized as an important tool in neuroscience, most standard algorithms to compute the Kendall correlation of large spike trains are computationally very slow. Here we present a new, significantly faster method for calculating the Kendall correlation of spike trains that takes advantage of their structure .
Revised: Despite the fact that the Kendall correlation is becoming recognized as an important tool in neuroscience, most standard algorithms to compute relevant statistical measures. We present an O(n) algorithm for calculating the Kendall correlation of spike trains that takes advantage of their structure .
Edit intent: meaning-changed
Original:  We show that our method is more than 300 times faster than traditional approaches for particularly large, sparse spike trains, and still more than 10 times faster for large, but considerably less sparse, spike trains . A MATLAB function executing the method described here was made freely available on-line.
Revised:  We show that our method is around 50 times faster than traditional approaches for particularly large, sparse spike trains, and still more than 10 times faster for large, but considerably less sparse, spike trains . A MATLAB function executing the method described here was made freely available on-line.
Edit intent: meaning-changed
Original:  We show that our method is more than 300 times faster than traditional approaches for particularly large, sparse spike trains, and still more than 10 times faster for large, but considerably less sparse, spike trains . A MATLAB function executing the method described here was made freely available on-line.
Revised:  We show that our method is more than 300 times faster than the O (n ln n) method which is a current standard for quickly computing the Kendall correlation. In addition to providing a faster algorithm, we emphasize the role that taking the specific nature of spike trains had on reducing the run time. We imagine that there are many other useful algorithms that can be even more significantly sped up when taking this into consideration. A MATLAB function executing the method described here was made freely available on-line.
Edit intent: meaning-changed
Original:  We show that our method is more than 300 times faster than traditional approaches for particularly large, sparse spike trains, and still more than 10 times faster for large, but considerably less sparse, spike trains . A MATLAB function executing the method described here was made freely available on-line.
Revised:  We show that our method is more than 300 times faster than traditional approaches for particularly large, sparse spike trains, and still more than 10 times faster for large, but considerably less sparse, spike trains . A MATLAB function executing the method described here has been made freely available on-line.
Edit intent: clarity
Original: We study the problem of estimating heterogeneous treatment effects from observational data, where the treatment policy on the collected data was determined by potentially many confounding observable variables. We propose orthogonal random forest, an algorithm that combines orthogonalization, a technique that effectively removes the confounding effect in two-stage estimation, with generalized random forests  Athey et al., 2017 , a flexible method for estimating treatment effect heterogeneity.
Revised: We propose the orthogonal random forest, an algorithm that combines orthogonalization, a technique that effectively removes the confounding effect in two-stage estimation, with generalized random forests Athey et al., 2017 , a flexible method for estimating treatment effect heterogeneity.
Edit intent: coherence
Original:  We propose orthogonal random forest, an algorithm that combines orthogonalization, a technique that effectively removes the confounding effect in two-stage estimation, with generalized random forests  Athey et al., 2017 , a flexible method for estimating treatment effect heterogeneity.
Revised:  We propose orthogonal random forest, an algorithm that incorporates double machine learning--a method of using Neyman-orthogonal moments to reduce sensitivity with respect to nuisance parameters to estimate the target parameter--with generalized random forests Athey et al., 2017 , a flexible method for estimating treatment effect heterogeneity.
Edit intent: meaning-changed
Original:  We propose orthogonal random forest, an algorithm that combines orthogonalization, a technique that effectively removes the confounding effect in two-stage estimation, with generalized random forests  Athey et al., 2017 , a flexible method for estimating treatment effect heterogeneity.
Revised:  We propose orthogonal random forest, an algorithm that combines orthogonalization, a technique that effectively removes the confounding effect in two-stage estimation, with generalized random forests ( Athey et al., 2017 , a flexible method for estimating treatment effect heterogeneity.
Edit intent: fluency
Original:  We propose orthogonal random forest, an algorithm that combines orthogonalization, a technique that effectively removes the confounding effect in two-stage estimation, with generalized random forests  Athey et al., 2017 , a flexible method for estimating treatment effect heterogeneity. We prove a consistency rate result of our estimatorin the partially linear regression model, and en route we provide a consistency analysis for a general framework of performing generalized method of moments (GMM) estimation .
Revised:  We propose orthogonal random forest, an algorithm that combines orthogonalization, a technique that effectively removes the confounding effect in two-stage estimation, with generalized random forests Athey et al., 2017 )--a flexible non-parametric method for statistical estimation of conditional moment models using random forests. We provide a consistency rate result of our estimatorin the partially linear regression model, and en route we provide a consistency analysis for a general framework of performing generalized method of moments (GMM) estimation .
Edit intent: clarity
Original:  We prove a consistency rate result of our estimatorin the partially linear regression model, and en route we provide a consistency analysis for a general framework of performing generalized method of moments (GMM) estimation .
Revised:  We prove a consistency rate and establish asymptotic normality for our estimator. We show that under mild assumption on the consistency rate of the nuisance estimator, we can achieve the same error rate as an oracle with a priori knowledge of these nuisance parameters. We show that when the nuisance functions have a locally sparse parametrization, then a local l1-penalized regression achieves the required rate. We apply our method to estimate heterogeneous treatment effects from observational data with discrete treatments or continuous treatments, and we show that, unlike prior work, our method provably allows to control for a high-dimensional set of variables under standard sparsity conditions .
Edit intent: meaning-changed
Original: We propose the orthogonal random forest, an algorithm that incorporates double machine learning--a method of using Neyman-orthogonal moments to reduce sensitivity with respect to nuisance parameters to estimate the target parameter--with generalized random forests (Athey et al., 2017)--a flexible non-parametric method for statistical estimation of conditional moment models using random forests.
Revised: We propose the orthogonal random forest, an algorithm that combines Neyman-orthogonality to reduce sensitivity with respect to nuisance parameters to estimate the target parameter--with generalized random forests (Athey et al., 2017)--a flexible non-parametric method for statistical estimation of conditional moment models using random forests.
Edit intent: clarity
Original: We propose the orthogonal random forest, an algorithm that incorporates double machine learning--a method of using Neyman-orthogonal moments to reduce sensitivity with respect to nuisance parameters to estimate the target parameter--with generalized random forests (Athey et al., 2017)--a flexible non-parametric method for statistical estimation of conditional moment models using random forests.
Revised: We propose the orthogonal random forest, an algorithm that incorporates double machine learning--a method of using Neyman-orthogonal moments to reduce sensitivity with respect to estimation error of nuisance parameters with generalized random forests (Athey et al., 2017)--a flexible non-parametric method for statistical estimation of conditional moment models using random forests.
Edit intent: clarity