wanyu/IteraTeR_full_sent huggingface dataset:
Original:  We consider the problem of generating an  n-cross field using a higher-order Q-tensor theory that is constructed out of tensored projection matrices.
Revised:  It was recently by other authors that 3-cross fields can be embedded into the set of symmetric 4th-order tensors. Another concurrent work further develops a relaxation of this tensor field via a certain set of varieties. In this paper, we consider the problem of generating an n-cross field using a higher-order Q-tensor theory that is constructed out of tensored projection matrices.
Edit intent: meaning-changed
Original:  It is shown that by a Ginzburg-Landau relaxation  , one can reliably generate an n-cross field on arbitrary Lipschitz domains  .
Revised:  It is shown that by a Ginzburg-Landau relaxation , one can reliably generate an n-cross field on arbitrary Lipschitz domains . Our work provides a rigorous approach that offers several new results including porting the tensor framework to arbitrary dimensions, providing a new relaxation method that embeds the problem into a global steepest descent, and offering a relaxation scheme for aligning the cross field with the boundary. Our approach is designed to fit within the classical Ginzburg-Landau PDE theory, offering a concrete road map for the future careful study of singularities of energy minimizers .
Edit intent: meaning-changed
Original: We study unzipping of a complementary RNA-DNA helix applied to an external force . The force-force correlations are measured, and compared to predictions from an exact solution of a 1-d toy model , as well as field theory based on functional renormalization.
Revised: We study unzipping of a complementary RNA-DNA helix applied to an external force , focusing on the force-force correlations are measured, and compared to predictions from an exact solution of a 1-d toy model , as well as field theory based on functional renormalization.
Edit intent: coherence
Original:  The force-force correlations are measured, and compared to predictions from an exact solution of a 1-d toy model , as well as field theory based on functional renormalization.
Revised:  The force-force correlations . While at the microscopic level these are given by the sequence, the experiment measures effective, macroscopic correlations. The latter are sequence-independent, i.e. universal, and constitute the central object of the underlying field theory of disordered systems. Comparing field-theory predictions and the exact solution of a 1-d toy model , as well as field theory based on functional renormalization.
Edit intent: meaning-changed
Original:  The force-force correlations are measured, and compared to predictions from an exact solution of a 1-d toy model , as well as field theory based on functional renormalization. Within error bars, the agreement is excellent .
Revised:  The force-force correlations are measured, and compared to predictions from an exact solution of a 1-d toy model with the experiment, we find an excellent agreement, confirming fundamental theoretical concepts via a biologically inspired experiments .
Edit intent: meaning-changed
Original: We study the incidental parameter problem in "three-way" Poisson Pseudo-Maximum Likelihood ("PPML") gravity models recently recommended for identifying the effects of trade policies and in other network panel data settings.
Revised: We study the incidental parameter problem for the "three-way" Poisson Pseudo-Maximum Likelihood ("PPML") gravity models recently recommended for identifying the effects of trade policies and in other network panel data settings.
Edit intent: clarity
Original: We study the incidental parameter problem in "three-way" Poisson Pseudo-Maximum Likelihood ("PPML") gravity models recently recommended for identifying the effects of trade policies and in other network panel data settings.
Revised: We study the incidental parameter problem in "three-way" Poisson Pseudo-Maximum Likelihood ("PPML") estimator recently recommended for identifying the effects of trade policies and in other network panel data settings.
Edit intent: clarity
Original: We study the incidental parameter problem in "three-way" Poisson Pseudo-Maximum Likelihood ("PPML") gravity models recently recommended for identifying the effects of trade policies and in other network panel data settings.
Revised: We study the incidental parameter problem in "three-way" Poisson Pseudo-Maximum Likelihood ("PPML") gravity models recently recommended for identifying the effects of trade policies and in other panel data gravity settings.
Edit intent: clarity
Original:  Despite the number and variety of fixed effects this model entails , we confirm it is consistent for small T and we show it is in fact the only estimator among a wide range of PML gravity estimators that is generally consistent in this context when T is small .
Revised:  Despite the number and variety of fixed effects involved , we confirm it is consistent for small T and we show it is in fact the only estimator among a wide range of PML gravity estimators that is generally consistent in this context when T is small .
Edit intent: clarity
Original:  Despite the number and variety of fixed effects this model entails , we confirm it is consistent for small T and we show it is in fact the only estimator among a wide range of PML gravity estimators that is generally consistent in this context when T is small .
Revised:  Despite the number and variety of fixed effects this model entails , we confirm PPML is consistent for small T and we show it is in fact the only estimator among a wide range of PML gravity estimators that is generally consistent in this context when T is small .
Edit intent: meaning-changed
Original:  Despite the number and variety of fixed effects this model entails , we confirm it is consistent for small T and we show it is in fact the only estimator among a wide range of PML gravity estimators that is generally consistent in this context when T is small .
Revised:  Despite the number and variety of fixed effects this model entails , we confirm it is consistent for fixed T and we show it is in fact the only estimator among a wide range of PML gravity estimators that is generally consistent in this context when T is small .
Edit intent: clarity
Original:  Despite the number and variety of fixed effects this model entails , we confirm it is consistent for small T and we show it is in fact the only estimator among a wide range of PML gravity estimators that is generally consistent in this context when T is small .
Revised:  Despite the number and variety of fixed effects this model entails , we confirm it is consistent for small T and we show it is in fact the only estimator among a wide range of PML gravity estimators that is generally consistent in this context when T is fixed .
Edit intent: clarity
Original: We describe and examine a test for  shape constraints, such as monotonicity, convexity (or both simultaneously) , U-shape, S-shape and others, in a nonparametric framework using partial sums empirical processes.
Revised: We describe and examine a test for a general class of shape constraints, such as monotonicity, convexity (or both simultaneously) , U-shape, S-shape and others, in a nonparametric framework using partial sums empirical processes.
Edit intent: meaning-changed
Original: We describe and examine a test for  shape constraints, such as monotonicity, convexity (or both simultaneously) , U-shape, S-shape and others, in a nonparametric framework using partial sums empirical processes.
Revised: We describe and examine a test for shape constraints, such as constraints on the signs of derivatives, U-(S-)shape, symmetry, quasi-convexity , U-shape, S-shape and others, in a nonparametric framework using partial sums empirical processes.
Edit intent: meaning-changed
Original: We describe and examine a test for  shape constraints, such as monotonicity, convexity (or both simultaneously) , U-shape, S-shape and others, in a nonparametric framework using partial sums empirical processes.
Revised: We describe and examine a test for shape constraints, such as monotonicity, convexity (or both simultaneously) , log-convexity, r-convexity, among others, in a nonparametric framework using partial sums empirical processes.
Edit intent: meaning-changed
Original:  Assuming that the spatial correlation structure follows a linear factor model, we show that inference ignoring such correlation remains reliable when either (i) the second moment of the difference between the pre- and post-treatment averages of common factors is low, or (ii) the distribution of factor loadings has the same expected values for treated and control groups, and do not exhibit significant spatial correlation.
Revised:  If the spatial correlation structure follows a linear factor model, we show that inference ignoring such correlation remains reliable when either (i) the second moment of the difference between the pre- and post-treatment averages of common factors is low, or (ii) the distribution of factor loadings has the same expected values for treated and control groups, and do not exhibit significant spatial correlation.
Edit intent: coherence
Original:  If the spatial correlation structure follows a linear factor model,  we show that inference ignoring such correlation remains reliable when either (i) the second moment of the difference between the pre- and post-treatment averages of common factors is low, or (ii) the distribution of factor loadings has the same expected values for treated and control groups , and do not exhibit significant spatial correlation . Simulations with real datasets corroborate these conclusions.
Revised:  We consider a setting in which the spatial correlation structure follows a linear factor model, we show that inference ignoring such correlation remains reliable when either (i) the second moment of the difference between the pre- and post-treatment averages of common factors is low, or (ii) the distribution of factor loadings has the same expected values for treated and control groups , and do not exhibit significant spatial correlation . Simulations with real datasets corroborate these conclusions.
Edit intent: clarity
Original:  If the spatial correlation structure follows a linear factor model,  we show that inference ignoring such correlation remains reliable when either (i) the second moment of the difference between the pre- and post-treatment averages of common factors is low, or (ii) the distribution of factor loadings has the same expected values for treated and control groups , and do not exhibit significant spatial correlation . Simulations with real datasets corroborate these conclusions.
Revised:  If the spatial correlation follows a linear factor model, we show that inference ignoring such correlation remains reliable when either (i) the second moment of the difference between the pre- and post-treatment averages of common factors is low, or (ii) the distribution of factor loadings has the same expected values for treated and control groups , and do not exhibit significant spatial correlation . Simulations with real datasets corroborate these conclusions.
Edit intent: clarity
Original:  If the spatial correlation structure follows a linear factor model,  we show that inference ignoring such correlation remains reliable when either (i) the second moment of the difference between the pre- and post-treatment averages of common factors is low, or (ii) the distribution of factor loadings has the same expected values for treated and control groups , and do not exhibit significant spatial correlation . Simulations with real datasets corroborate these conclusions.
Revised:  If the spatial correlation structure follows a linear factor model, which allows for a rich variety of spatial correlation structures. In this setting, we show that inference ignoring such correlation remains reliable when either (i) the second moment of the difference between the pre- and post-treatment averages of common factors is low, or (ii) the distribution of factor loadings has the same expected values for treated and control groups , and do not exhibit significant spatial correlation . Simulations with real datasets corroborate these conclusions.
Edit intent: meaning-changed
Original:  If the spatial correlation structure follows a linear factor model,  we show that inference ignoring such correlation remains reliable when either (i) the second moment of the difference between the pre- and post-treatment averages of common factors is low, or (ii) the distribution of factor loadings has the same expected values for treated and control groups , and do not exhibit significant spatial correlation . Simulations with real datasets corroborate these conclusions.
Revised:  If the spatial correlation structure follows a linear factor model, we show that inference ignoring spatial correlation is more problematic when both (i) the second moment of the difference between the pre- and post-treatment averages of common factors is low, or (ii) the distribution of factor loadings has the same expected values for treated and control groups , and do not exhibit significant spatial correlation . Simulations with real datasets corroborate these conclusions.
Edit intent: clarity
Original:  If the spatial correlation structure follows a linear factor model,  we show that inference ignoring such correlation remains reliable when either (i) the second moment of the difference between the pre- and post-treatment averages of common factors is low, or (ii) the distribution of factor loadings has the same expected values for treated and control groups , and do not exhibit significant spatial correlation . Simulations with real datasets corroborate these conclusions.
Revised:  If the spatial correlation structure follows a linear factor model, we show that inference ignoring such correlation remains reliable when either (i) the second moment of the difference between the pre- and post-treatment averages of common factors is large, and (ii) the distribution of factor loadings has the same expected values for treated and control groups , and do not exhibit significant spatial correlation . Simulations with real datasets corroborate these conclusions.
Edit intent: meaning-changed
Original:  If the spatial correlation structure follows a linear factor model,  we show that inference ignoring such correlation remains reliable when either (i) the second moment of the difference between the pre- and post-treatment averages of common factors is low, or (ii) the distribution of factor loadings has the same expected values for treated and control groups , and do not exhibit significant spatial correlation . Simulations with real datasets corroborate these conclusions.
Revised:  If the spatial correlation structure follows a linear factor model, we show that inference ignoring such correlation remains reliable when either (i) the second moment of the difference between the pre- and post-treatment averages of common factors is low, or (ii) the distribution of factor loadings has different expected values for treated and control groups , and do not exhibit significant spatial correlation . Simulations with real datasets corroborate these conclusions.
Edit intent: clarity
Original:  If the spatial correlation structure follows a linear factor model,  we show that inference ignoring such correlation remains reliable when either (i) the second moment of the difference between the pre- and post-treatment averages of common factors is low, or (ii) the distribution of factor loadings has the same expected values for treated and control groups , and do not exhibit significant spatial correlation . Simulations with real datasets corroborate these conclusions.
Revised:  If the spatial correlation structure follows a linear factor model, we show that inference ignoring such correlation remains reliable when either (i) the second moment of the difference between the pre- and post-treatment averages of common factors is low, or (ii) the distribution of factor loadings has the same expected values for treated and control groups . The choice among different estimators also affect the relevance of spatial correlation for inference . Simulations with real datasets corroborate these conclusions.
Edit intent: meaning-changed
Original:  We consider a setting in which the spatial correlation follows a linear factor model, which allows for a rich variety of spatial correlation structures. In this setting, we show that inference ignoring spatial correlation is more problematic when both (i) the second moment of the difference between the pre- and post-treatment averages of common factors is large, and (ii) the distribution of factor loadings has different expected values for treated and control groups. The choice among different estimators also affect the relevance of spatial correlation for inference  . Simulations with real datasets corroborate these conclusions.
Revised:  We consider a setting in which the spatial correlation follows a linear factor model, which allows for a rich variety of spatial correlation for inference . Simulations with real datasets corroborate these conclusions.
Edit intent: clarity
Original:  The choice among different estimators also affect the relevance of spatial correlation for inference  . Simulations with real datasets corroborate these conclusions.
Revised:  The choice among different estimators also affect the relevance of spatial correlation for inference (when it is ignored) depends on the amount of spatial correlation that remains after we control for the time- and group-invariant unobservables. As a consequence, details such as the time frame used in the estimation, and the choice of the estimator, will be key determinants on the degree of distortions we should expect when spatial correlation is ignored . Simulations with real datasets corroborate these conclusions.
Edit intent: meaning-changed
Original:  A receiver tries to predict a latent characteristic of the sender .
Revised:  An intermediary aggregates multiple features of a sender into a score. A receiver sees this score and takes a decision. The receiver wants his decision to match the sender's latent characteristic. But the sender wants the most favorable decision, and she can distort each of her features at a private cost. I characterize the receiver-optimal scoring rule. This rule underweights some features to deter sender distortion, and overweights other features to keep the score unbiased. The receiver tries to predict a latent characteristic of the sender .
Edit intent: meaning-changed
Original:  A receiver tries to predict a latent characteristic of the sender . The sender has a vector of features, which can be distorted at a cost. An intermediary observes the features of the sender and assigns a linear score, which is passed to the receiver. The optimal scoring rule downweights features on which gaming is heterogeneous. Optimal scoring discourages sender manipulation and allows the receiver to make more accurate predictions than full transparency .
Revised:  A receiver prefers this score to seeing the sender's full features because the coarser information mitigates his commitment problem .
Edit intent: coherence
Original:  We aim to investigate the cross-cultural comparability of IADL. METHODS: IADL were measured in 3,571 individuals (67.1 +- {\pm} 9.5 years old, 44.7\% female) in 11 international studies using the Amsterdam IADL Questionnaire (A-IADL-Q).
Revised:  METHODS: 3,571 individuals (67.1 +- {\pm} 9.5 years old, 44.7\% female) in 11 international studies using the Amsterdam IADL Questionnaire (A-IADL-Q).
Edit intent: clarity
Original:  We aim to investigate the cross-cultural comparability of IADL. METHODS: IADL were measured in 3,571 individuals (67.1 +- {\pm} 9.5 years old, 44.7\% female) in 11 international studies using the Amsterdam IADL Questionnaire (A-IADL-Q).
Revised:  We aim to investigate the cross-cultural comparability of IADL. METHODS: IADL were measured in 3,571 individuals (67.1 {\pm} 9.5 years old, 44.7\% female) in 11 international studies using the Amsterdam IADL Questionnaire (A-IADL-Q).
Edit intent: clarity
Original:  We aim to investigate the cross-cultural comparability of IADL. METHODS: IADL were measured in 3,571 individuals (67.1 +- {\pm} 9.5 years old, 44.7\% female) in 11 international studies using the Amsterdam IADL Questionnaire (A-IADL-Q).
Revised:  We aim to investigate the cross-cultural comparability of IADL. METHODS: IADL were measured in 3,571 individuals (67.1 +- {\pm} 9.5 years old, 44.7\% female) from the Netherlands, Spain, France, United States, United Kingdom, Greece, Serbia and Finland were included. Functional impairment was measured using the Amsterdam IADL Questionnaire (A-IADL-Q).
Edit intent: meaning-changed
Original:  We aim to investigate the cross-cultural comparability of IADL. METHODS: IADL were measured in 3,571 individuals (67.1 +- {\pm} 9.5 years old, 44.7\% female) in 11 international studies using the Amsterdam IADL Questionnaire (A-IADL-Q). Cultural, age and gender bias were assessed on item level using differential item functioning (DIF) and on scale level by calculating correlations between IADL and cognitive and functional measures . RESULTS: We did not find meaningful bias on item or on scale level, as indicated by low DIF effect sizes (  R2 range 0-0.03) and correlations in expected directions and of similar magnitude.
Revised:  We aim to investigate the cross-cultural comparability of IADL. METHODS: IADL were measured in 3,571 individuals (67.1 +- {\pm} 9.5 years old, 44.7\% female) in 11 international studies using the Amsterdam IADL Questionnaire . Item bias was assessed using differential item functioning (DIF) and on scale level by calculating correlations between IADL and cognitive and functional measures . RESULTS: We did not find meaningful bias on item or on scale level, as indicated by low DIF effect sizes ( R2 range 0-0.03) and correlations in expected directions and of similar magnitude.
Edit intent: clarity
Original:  Cultural, age and gender bias were assessed on item level using differential item functioning (DIF) and on scale level by calculating correlations between IADL and cognitive and functional measures . RESULTS: We did not find meaningful bias on item or on scale level, as indicated by low DIF effect sizes (  R2 range 0-0.03) and correlations in expected directions and of similar magnitude.
Revised:  Cultural, age and gender bias were assessed on item level using differential item functioning (DIF) analysis . RESULTS: We did not find meaningful bias on item or on scale level, as indicated by low DIF effect sizes ( R2 range 0-0.03) and correlations in expected directions and of similar magnitude.
Edit intent: clarity
Original:  Cultural, age and gender bias were assessed on item level using differential item functioning (DIF) and on scale level by calculating correlations between IADL and cognitive and functional measures . RESULTS: We did not find meaningful bias on item or on scale level, as indicated by low DIF effect sizes (  R2 range 0-0.03) and correlations in expected directions and of similar magnitude.
Revised:  Cultural, age and gender bias were assessed on item level using differential item functioning (DIF) and on scale level by calculating correlations between IADL and cognitive and functional measures . RESULTS: There were some differences in activity endorsement. A few items showed statistically significant DIF. However, there was no evidence of meaningful item bias: effect sizes were low ( R2 range 0-0.03) and correlations in expected directions and of similar magnitude.
Edit intent: meaning-changed