wanyu/IteraTeR_full_sent huggingface dataset:
Original:  I find that the provision of Head Start access induces parents to enroll their child into Head Start and also positively impacts test scores , and that these effects heterogeneously depend on the availability of access to an alternative preschool  .
Revised:  I find that the provision of Head Start access induces parents to enroll their child into Head Start and also positively impacts test scores , and that these effects heterogeneously depend on the availability of access to an alternative preschool was available. In addition, I find that the earning benefits associated with the test score gains can outweigh the net costs for various policies providing access to Head Start .
Edit intent: meaning-changed
Original: We show that the Fr\'echet distance of two-dimensional parametrised surfaces in a metric space is computable . This settles a long-standing open question in computational geometry .
Revised: We show that the Fr\'echet distance of two-dimensional parametrised surfaces in a metric space is computable in the bit-model of real computation. An analogous result in the real RAM model for piecewise-linear surfaces has recently been obtained by Nayyeri and Xu (2016) .
Edit intent: meaning-changed
Original:  This paper introduces two classes of semiparametric triangular systems with nonadditively separable unobserved heterogeneity .
Revised: Triangular systems with nonadditively separable unobserved heterogeneity provide a theoretically appealing framework for the modelling of complex structural relationships. However, they are not commonly used in practice due to the need for exogenous variables with large support for identification, the curse of dimensionality in estimation, and the lack of inferential tools. This paper introduces two classes of semiparametric triangular systems with nonadditively separable unobserved heterogeneity .
Edit intent: meaning-changed
Original:  We show that these discrete conformal maps converge to the Riemann mapping uniformly and in H^1  . Additionally,  the discrete conformal maps  are known to be bijective , and we show that the Riemann mapping between two bounded Lipschitz domains can be approximated arbitrarily well by composing the Riemann mapping between each Lipschitz domain and the triangle.
Revised:  We show that these discrete conformal maps converge to the Riemann mapping in H^1 . Additionally, the discrete conformal maps are known to be bijective , and we show that the Riemann mapping between two bounded Lipschitz domains can be approximated arbitrarily well by composing the Riemann mapping between each Lipschitz domain and the triangle.
Edit intent: coherence
Original:  We show that these discrete conformal maps converge to the Riemann mapping uniformly and in H^1  . Additionally,  the discrete conformal maps  are known to be bijective , and we show that the Riemann mapping between two bounded Lipschitz domains can be approximated arbitrarily well by composing the Riemann mapping between each Lipschitz domain and the triangle.
Revised:  We show that these discrete conformal maps converge to the Riemann mapping uniformly and in H^1 , even for non-Delaunay triangulations . Additionally, the discrete conformal maps are known to be bijective , and we show that the Riemann mapping between two bounded Lipschitz domains can be approximated arbitrarily well by composing the Riemann mapping between each Lipschitz domain and the triangle.
Edit intent: meaning-changed
Original:  We show that these discrete conformal maps converge to the Riemann mapping uniformly and in H^1  . Additionally,  the discrete conformal maps  are known to be bijective , and we show that the Riemann mapping between two bounded Lipschitz domains can be approximated arbitrarily well by composing the Riemann mapping between each Lipschitz domain and the triangle.
Revised:  We show that these discrete conformal maps converge to the Riemann mapping uniformly and in H^1 . Additionally, for Delaunay triangulations the discrete conformal maps are known to be bijective , and we show that the Riemann mapping between two bounded Lipschitz domains can be approximated arbitrarily well by composing the Riemann mapping between each Lipschitz domain and the triangle.
Edit intent: meaning-changed
Original:  We show that these discrete conformal maps converge to the Riemann mapping uniformly and in H^1  . Additionally,  the discrete conformal maps  are known to be bijective , and we show that the Riemann mapping between two bounded Lipschitz domains can be approximated arbitrarily well by composing the Riemann mapping between each Lipschitz domain and the triangle.
Revised:  We show that these discrete conformal maps converge to the Riemann mapping uniformly and in H^1 . Additionally, the discrete conformal maps converge uniformly and are known to be bijective , and we show that the Riemann mapping between two bounded Lipschitz domains can be approximated arbitrarily well by composing the Riemann mapping between each Lipschitz domain and the triangle.
Edit intent: coherence
Original:  We show that these discrete conformal maps converge to the Riemann mapping uniformly and in H^1  . Additionally,  the discrete conformal maps  are known to be bijective , and we show that the Riemann mapping between two bounded Lipschitz domains can be approximated arbitrarily well by composing the Riemann mapping between each Lipschitz domain and the triangle.
Revised:  We show that these discrete conformal maps converge to the Riemann mapping uniformly and in H^1 . Additionally, the discrete conformal maps are known to be bijective . As a consequence we show that the Riemann mapping between two bounded Lipschitz domains can be approximated arbitrarily well by composing the Riemann mapping between each Lipschitz domain and the triangle.
Edit intent: coherence
Original:  We show that these discrete conformal maps converge to the Riemann mapping uniformly and in H^1  . Additionally,  the discrete conformal maps  are known to be bijective , and we show that the Riemann mapping between two bounded Lipschitz domains can be approximated arbitrarily well by composing the Riemann mapping between each Lipschitz domain and the triangle.
Revised:  We show that these discrete conformal maps converge to the Riemann mapping uniformly and in H^1 . Additionally, the discrete conformal maps are known to be bijective , and we show that the Riemann mapping between two bounded Lipschitz domains can be uniformly approximated by composing the Riemann mapping between each Lipschitz domain and the triangle.
Edit intent: clarity
Original:  We show that these discrete conformal maps converge to the Riemann mapping uniformly and in H^1  . Additionally,  the discrete conformal maps  are known to be bijective , and we show that the Riemann mapping between two bounded Lipschitz domains can be approximated arbitrarily well by composing the Riemann mapping between each Lipschitz domain and the triangle.
Revised:  We show that these discrete conformal maps converge to the Riemann mapping uniformly and in H^1 . Additionally, the discrete conformal maps are known to be bijective , and we show that the Riemann mapping between two bounded Lipschitz domains can be approximated arbitrarily well by composing the Riemann mappings between each Lipschitz domain and the triangle.
Edit intent: fluency
Original:  It is shown that consistency and asymptotic normality of the nonparametric spillover effects estimators require a precise relationship between the number of parameters, the total sample size and the probability distribution of the treatment assignments, which has important implications for the design of experiments.
Revised:  I show consistency and asymptotic normality of the nonparametric spillover effects estimators require a precise relationship between the number of parameters, the total sample size and the probability distribution of the treatment assignments, which has important implications for the design of experiments.
Edit intent: clarity
Original:  It is shown that consistency and asymptotic normality of the nonparametric spillover effects estimators require a precise relationship between the number of parameters, the total sample size and the probability distribution of the treatment assignments, which has important implications for the design of experiments.
Revised:  It is shown that consistency and asymptotic normality of the nonparametric spillover effects under a precise relationship between the number of parameters, the total sample size and the probability distribution of the treatment assignments, which has important implications for the design of experiments.
Edit intent: clarity
Original: This paper employs a potential outcomes framework to study causal spillover effects in a setting where units are clustered and their potential outcomes can depend on the treatment assignment of all the units within a cluster. Using this framework, I define parameters of interest and provide conditions under which direct and spillover effects can be nonparametrically identified when a treatment is randomly assigned. In addition, I characterize and discuss the causal interpretation of the estimands that are recovered by two popular estimation approaches in empirical work: a regression of an outcome on a treatment indicator (difference in means) and a regression of an outcome on a treatment indicator and the proportion of treated peers (a reduced-form linear-in-means model). I show consistency and asymptotic normality of the nonparametric spillover effects under a precise relationship between the number of parameters, the total sample size and the probability distribution of the treatment assignments, which has important implications for the design of experiments. The findings are illustrated with data from a conditional cash transfer program and with simulations. The wild bootstrap is shown to be consistent, and simulation evidence suggests a better performance compared to the Gaussian approximation when groups are moderately large relative to the sample size .
Revised: See manuscript for full abstract .
Edit intent: meaning-changed
Original: See manuscript for full abstract .
Revised: I study identification, estimation and inference for spillover effects in experiments where units' outcomes may depend on the treatment assignments of other units within a group. I show that the commonly-used linear-in-means regression identifies a weighted sum of spillover effects with some negative weights, and characterize the estimand that is recovered by a simple difference in means in the presence of spillovers. My results reveal the potential pitfalls of failing to flexibly account for spillover effects in policy evaluation. I then propose nonparametric estimators that overcome these issues and are consistent and asymptotically normal under a precise relationship between the number of parameters of interest, the total sample size and the treatment assignment mechanism. I show that these results can be used to rank assignment mechanisms for experimental design. These findings are illustrated using data from a conditional cash transfer program and with simulations .
Edit intent: meaning-changed
Original:  I show that the commonly-used linear-in-means  regression identifies a weighted sum of spillover effects with some negative weights, and characterize the estimand that is recovered by a simple difference in means in the presence of spillovers.
Revised:  I show that the commonly-used linear-in-means (LIM) regression identifies a weighted sum of spillover effects with some negative weights, and characterize the estimand that is recovered by a simple difference in means in the presence of spillovers.
Edit intent: meaning-changed
Original:  I show that the commonly-used linear-in-means  regression identifies a weighted sum of spillover effects with some negative weights, and characterize the estimand that is recovered by a simple difference in means in the presence of spillovers.
Revised:  I show that the commonly-used linear-in-means regression identifies a weighted sum of spillover effects with some negative weights, and that the difference in means in the presence of spillovers.
Edit intent: clarity
Original:  I show that the commonly-used linear-in-means  regression identifies a weighted sum of spillover effects with some negative weights, and characterize the estimand that is recovered by a simple difference in means in the presence of spillovers. My results reveal the potential pitfalls of failing to flexibly account for spillover effects in policy evaluation. I then propose nonparametric estimators  that overcome these issues and are consistent and asymptotically normal under a precise relationship between the number of parameters of interest, the total sample size and the treatment assignment mechanism.
Revised:  I show that the commonly-used linear-in-means regression identifies a weighted sum of spillover effects with some negative weights, and characterize the estimand that is recovered by a simple difference in means between treated and controls identifies a combination of direct and spillover effects entering with different signs. I propose nonparametric estimators that overcome these issues and are consistent and asymptotically normal under a precise relationship between the number of parameters of interest, the total sample size and the treatment assignment mechanism.
Edit intent: meaning-changed
Original:  I then propose nonparametric estimators  that overcome these issues and are consistent and asymptotically normal under a precise relationship between the number of parameters of interest, the total sample size and the treatment assignment mechanism.
Revised:  I then propose nonparametric estimators for average direct and spillover effects that overcome these issues and are consistent and asymptotically normal under a precise relationship between the number of parameters of interest, the total sample size and the treatment assignment mechanism.
Edit intent: meaning-changed
Original:  These findings are illustrated using data from a conditional cash transfer program and with simulations  .
Revised:  These findings are illustrated using data from a conditional cash transfer program and with simulations . The empirical results reveal the potential pitfalls of failing to flexibly account for spillover effects in policy evaluation: the estimated difference in means and the LIM coefficients are all close to zero and statistically insignificant, whereas the nonparametric estimators I propose reveal large, nonlinear and significant spillover effects .
Edit intent: meaning-changed
Original:  I show that the commonly-used  linear-in-means ( LIM ) regression identifies a weighted sum of spillover effects with some negative weights, and that the difference in means between treated and controls identifies a combination of direct and spillover effects entering with different signs.
Revised:  I show that the commonly-used reduced-form linear-in-means ( LIM ) regression identifies a weighted sum of spillover effects with some negative weights, and that the difference in means between treated and controls identifies a combination of direct and spillover effects entering with different signs.
Edit intent: meaning-changed
Original:  I show that the commonly-used  linear-in-means ( LIM ) regression identifies a weighted sum of spillover effects with some negative weights, and that the difference in means between treated and controls identifies a combination of direct and spillover effects entering with different signs.
Revised:  I show that the commonly-used linear-in-means ( RF-LIM ) regression identifies a weighted sum of spillover effects with some negative weights, and that the difference in means between treated and controls identifies a combination of direct and spillover effects entering with different signs.
Edit intent: meaning-changed
Original:  The empirical results reveal the potential pitfalls of failing to flexibly account for spillover effects in policy evaluation: the estimated difference in means and the LIM coefficients are all close to zero and statistically insignificant, whereas the nonparametric estimators I propose reveal large, nonlinear and significant spillover effects.
Revised:  The empirical results reveal the potential pitfalls of failing to flexibly account for spillover effects in policy evaluation: the estimated difference in means and the RF-LIM coefficients are all close to zero and statistically insignificant, whereas the nonparametric estimators I propose reveal large, nonlinear and significant spillover effects.
Edit intent: meaning-changed
Original: Plasticity is one of the most important features of the nervous systems that enable animals to adjust their behavior to an ever-changing external world. A major mechanism of plasticity is the changes in synaptic efficacy between neurons , and therefore estimation of neural connections is crucial for investigating information processing in the brain.
Revised: Plasticity is one of the most important features of the nervous systems that enable animals to adjust their behavior to an ever-changing external environment. Changes in synaptic efficacy between neurons , and therefore estimation of neural connections is crucial for investigating information processing in the brain.
Edit intent: clarity
Original:  Although many analysis methods have been proposed to this end , most of them suffer from a mathematical difficulty that stems from the fact that activity of only a limited number of neurons are available. That is, many existing methods ignore effects from unobserved neurons. To overcome this difficulty , a novel probabilistic framework is proposed for estimating neural connections from partially observed spikes .
Revised:  Although many analysis methods have been proposed to this end , most of them suffer from one or all the following mathematical difficulties: (1) only partially observed neural activity is available; (2) correlations can include both direct and indirect pseudo-interactions; and (3) biological evidence that a neuron typically has only one type of connection (excitatory or inhibitory) should be considered. To overcome these difficulties , a novel probabilistic framework is proposed for estimating neural connections from partially observed spikes .
Edit intent: meaning-changed
Original:  To overcome this difficulty , a novel probabilistic framework is proposed for estimating neural connections from partially observed spikes . In this study, we estimate influence on observed neurons from unobserved neurons based on probabilistic firing model and abstract the connections among observed neurons.
Revised:  To overcome this difficulty , a novel probabilistic framework is proposed for estimating neural connections from partially observed spikes is proposed in this paper. First, based on probabilistic firing model and abstract the connections among observed neurons.
Edit intent: clarity
Original:  Our numerical experiment supports usefulness of our proposed method .
Revised:  Second, the relationship between pseudo-correlations and target connections is modeled by neural propagation in a multiplicative manner. Third, a novel information-theoretic framework is proposed for estimating neuron types. The proposed method was validated using spike data generated by artificial neural networks. In addition, it was applied to multi-unit data recorded from the CA1 area of a rat's hippocampus. The results confirmed that our estimates are consistent with previous reports. These findings indicate that the proposed method is useful for extracting crucial interactions in neural signals as well as in other multi-probed point process data .
Edit intent: coherence
Original: Brillouin microscopy is a new tool to characterize the stiffness of cells and tissues  , however the underlying relationship between stiffness and Brillouin measurements remains unknown.
Revised: Brillouin microscopy is a new tool to characterize the stiffness of cells and tissues . Using hydrogels with tunable properties, we demonstrate that Brillouin microscopy is insensitive to stiffness of hydrated materials , however the underlying relationship between stiffness and Brillouin measurements remains unknown.
Edit intent: meaning-changed
Original: Brillouin microscopy is a new tool to characterize the stiffness of cells and tissues  , however the underlying relationship between stiffness and Brillouin measurements remains unknown. As the stiffness of biological materialsis influenced by hydration, we performed Brillouin microscopy on hydrogels whilst independently examining the role of stiffness and water content. Brillouin measurements were independent of stiffness , but correlated strongly with hydration , indicating that Brillouin microscopy really measures water content .
Revised: Brillouin microscopy is a new tool to characterize the stiffness of cells and tissues , but depends strongly on water content, consistent with a theoretical model of biphasic compressibility. Empirical correlations between Brillouin measurements and stiffness arise due to their mutual dependence on water content, but correlations vanish once hydration is controlled .
Edit intent: clarity
Original:  We propose a new method to correct short reads using de Bruijn graphs, and implement it as a tool called Bcool.
Revised: Motivations Short-read accuracy is important for downstream analyses such as genome assembly and hybrid long-read correction. Despite much work on short-read correction, present-day correctors either do not scale well on large data sets or consider reads as mere suites of k-mers, without taking into account their full-length read information. Results We propose a new method to correct short reads using de Bruijn graphs, and implement it as a tool called Bcool.
Edit intent: meaning-changed
Original:   The implementation is open source and available at github. com/Malfoy/BCOOL ois Flot jflot@ulb.ac.be }& 
Revised:  The implementation is open source and available at URL under the Affero GPL license. Contact Antoine Limasset antoine.limasset@gmail.com ois Flot jflot@ulb.ac.be }& 
Edit intent: meaning-changed
Original:   The implementation is open source and available at github. com/Malfoy/BCOOL ois Flot jflot@ulb.ac.be }& 
Revised:  The implementation is open source and available at github. com/Malfoy/BCOOL ois Flot jflot@ulb.ac.be }& Pierre Peterlongo pierre.peterlongo@inria.fr
Edit intent: meaning-changed
Original:  We show that when the interest rate process is correlated with the price process of the risky asset, the value of the information is  infinite, as is usually the case for initial-enlargement-type problems.
Revised:  Incidentally we show that an affine process conditioned to its future value is still an affine process. When the interest rate process is correlated with the price process of the risky asset, the value of the information is infinite, as is usually the case for initial-enlargement-type problems.
Edit intent: meaning-changed
Original:  We show that when the interest rate process is correlated with the price process of the risky asset, the value of the information is  infinite, as is usually the case for initial-enlargement-type problems.
Revised:  We show that when the interest rate process is correlated with the price process of the risky asset, the value of the information is proved to be infinite, as is usually the case for initial-enlargement-type problems.
Edit intent: clarity
Original:  However since the agent does not know exactly the correlation factor, this may induce an infinite loss instead of an infinite gain. Finally weakening the information own by the agent , and assuming that she only knows a lower-bound  for the terminal value of the interest rate process, we show that the value of the information is finite  .
Revised:  However , weakening the information own by the agent , and assuming that she only knows a lower-bound for the terminal value of the interest rate process, we show that the value of the information is finite .
Edit intent: coherence